TF_Classification_Recap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/navarra/snap/jupyter/common/lib/python3.7/site-packages/joblib/_multiprocessing_helpers.py:45: UserWarning: [Errno 13] Permission denied.  joblib will operate in serial mode\n",
      "  warnings.warn('%s.  joblib will operate in serial mode' % (e,))\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data and EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('cancer_classification.csv')\n",
    "pd.set_option('display.max_columns', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>radius error</th>\n",
       "      <th>texture error</th>\n",
       "      <th>perimeter error</th>\n",
       "      <th>area error</th>\n",
       "      <th>smoothness error</th>\n",
       "      <th>compactness error</th>\n",
       "      <th>concavity error</th>\n",
       "      <th>concave points error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>fractal dimension error</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>benign_0__mal_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>1.0950</td>\n",
       "      <td>0.9053</td>\n",
       "      <td>8.589</td>\n",
       "      <td>153.40</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.04904</td>\n",
       "      <td>0.05373</td>\n",
       "      <td>0.01587</td>\n",
       "      <td>0.03003</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>0.5435</td>\n",
       "      <td>0.7339</td>\n",
       "      <td>3.398</td>\n",
       "      <td>74.08</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.01308</td>\n",
       "      <td>0.01860</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.01389</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>0.7456</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>4.585</td>\n",
       "      <td>94.03</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.04006</td>\n",
       "      <td>0.03832</td>\n",
       "      <td>0.02058</td>\n",
       "      <td>0.02250</td>\n",
       "      <td>0.004571</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>0.4956</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>3.445</td>\n",
       "      <td>27.23</td>\n",
       "      <td>0.009110</td>\n",
       "      <td>0.07458</td>\n",
       "      <td>0.05661</td>\n",
       "      <td>0.01867</td>\n",
       "      <td>0.05963</td>\n",
       "      <td>0.009208</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>0.7572</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>5.438</td>\n",
       "      <td>94.44</td>\n",
       "      <td>0.011490</td>\n",
       "      <td>0.02461</td>\n",
       "      <td>0.05688</td>\n",
       "      <td>0.01885</td>\n",
       "      <td>0.01756</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  radius error  texture error  perimeter error  \\\n",
       "0                 0.07871        1.0950         0.9053            8.589   \n",
       "1                 0.05667        0.5435         0.7339            3.398   \n",
       "2                 0.05999        0.7456         0.7869            4.585   \n",
       "3                 0.09744        0.4956         1.1560            3.445   \n",
       "4                 0.05883        0.7572         0.7813            5.438   \n",
       "\n",
       "   area error  smoothness error  compactness error  concavity error  \\\n",
       "0      153.40          0.006399            0.04904          0.05373   \n",
       "1       74.08          0.005225            0.01308          0.01860   \n",
       "2       94.03          0.006150            0.04006          0.03832   \n",
       "3       27.23          0.009110            0.07458          0.05661   \n",
       "4       94.44          0.011490            0.02461          0.05688   \n",
       "\n",
       "   concave points error  symmetry error  fractal dimension error  \\\n",
       "0               0.01587         0.03003                 0.006193   \n",
       "1               0.01340         0.01389                 0.003532   \n",
       "2               0.02058         0.02250                 0.004571   \n",
       "3               0.01867         0.05963                 0.009208   \n",
       "4               0.01885         0.01756                 0.005115   \n",
       "\n",
       "   worst radius  worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "0         25.38          17.33           184.60      2019.0            0.1622   \n",
       "1         24.99          23.41           158.80      1956.0            0.1238   \n",
       "2         23.57          25.53           152.50      1709.0            0.1444   \n",
       "3         14.91          26.50            98.87       567.7            0.2098   \n",
       "4         22.54          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   worst fractal dimension  benign_0__mal_1  \n",
       "0                  0.11890                0  \n",
       "1                  0.08902                0  \n",
       "2                  0.08758                0  \n",
       "3                  0.17300                0  \n",
       "4                  0.07678                0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>radius error</th>\n",
       "      <th>texture error</th>\n",
       "      <th>perimeter error</th>\n",
       "      <th>area error</th>\n",
       "      <th>smoothness error</th>\n",
       "      <th>compactness error</th>\n",
       "      <th>concavity error</th>\n",
       "      <th>concave points error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>fractal dimension error</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>benign_0__mal_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>0.405172</td>\n",
       "      <td>1.216853</td>\n",
       "      <td>2.866059</td>\n",
       "      <td>40.337079</td>\n",
       "      <td>0.007041</td>\n",
       "      <td>0.025478</td>\n",
       "      <td>0.031894</td>\n",
       "      <td>0.011796</td>\n",
       "      <td>0.020542</td>\n",
       "      <td>0.003795</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>0.627417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>0.277313</td>\n",
       "      <td>0.551648</td>\n",
       "      <td>2.021855</td>\n",
       "      <td>45.491006</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>0.017908</td>\n",
       "      <td>0.030186</td>\n",
       "      <td>0.006170</td>\n",
       "      <td>0.008266</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>0.483918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>0.111500</td>\n",
       "      <td>0.360200</td>\n",
       "      <td>0.757000</td>\n",
       "      <td>6.802000</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007882</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>0.232400</td>\n",
       "      <td>0.833900</td>\n",
       "      <td>1.606000</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>0.005169</td>\n",
       "      <td>0.013080</td>\n",
       "      <td>0.015090</td>\n",
       "      <td>0.007638</td>\n",
       "      <td>0.015160</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.324200</td>\n",
       "      <td>1.108000</td>\n",
       "      <td>2.287000</td>\n",
       "      <td>24.530000</td>\n",
       "      <td>0.006380</td>\n",
       "      <td>0.020450</td>\n",
       "      <td>0.025890</td>\n",
       "      <td>0.010930</td>\n",
       "      <td>0.018730</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>0.478900</td>\n",
       "      <td>1.474000</td>\n",
       "      <td>3.357000</td>\n",
       "      <td>45.190000</td>\n",
       "      <td>0.008146</td>\n",
       "      <td>0.032450</td>\n",
       "      <td>0.042050</td>\n",
       "      <td>0.014710</td>\n",
       "      <td>0.023480</td>\n",
       "      <td>0.004558</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>2.873000</td>\n",
       "      <td>4.885000</td>\n",
       "      <td>21.980000</td>\n",
       "      <td>542.200000</td>\n",
       "      <td>0.031130</td>\n",
       "      <td>0.135400</td>\n",
       "      <td>0.396000</td>\n",
       "      <td>0.052790</td>\n",
       "      <td>0.078950</td>\n",
       "      <td>0.029840</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean radius  mean texture  mean perimeter    mean area  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       mean symmetry  mean fractal dimension  radius error  texture error  \\\n",
       "count     569.000000              569.000000    569.000000     569.000000   \n",
       "mean        0.181162                0.062798      0.405172       1.216853   \n",
       "std         0.027414                0.007060      0.277313       0.551648   \n",
       "min         0.106000                0.049960      0.111500       0.360200   \n",
       "25%         0.161900                0.057700      0.232400       0.833900   \n",
       "50%         0.179200                0.061540      0.324200       1.108000   \n",
       "75%         0.195700                0.066120      0.478900       1.474000   \n",
       "max         0.304000                0.097440      2.873000       4.885000   \n",
       "\n",
       "       perimeter error  area error  smoothness error  compactness error  \\\n",
       "count       569.000000  569.000000        569.000000         569.000000   \n",
       "mean          2.866059   40.337079          0.007041           0.025478   \n",
       "std           2.021855   45.491006          0.003003           0.017908   \n",
       "min           0.757000    6.802000          0.001713           0.002252   \n",
       "25%           1.606000   17.850000          0.005169           0.013080   \n",
       "50%           2.287000   24.530000          0.006380           0.020450   \n",
       "75%           3.357000   45.190000          0.008146           0.032450   \n",
       "max          21.980000  542.200000          0.031130           0.135400   \n",
       "\n",
       "       concavity error  concave points error  symmetry error  \\\n",
       "count       569.000000            569.000000      569.000000   \n",
       "mean          0.031894              0.011796        0.020542   \n",
       "std           0.030186              0.006170        0.008266   \n",
       "min           0.000000              0.000000        0.007882   \n",
       "25%           0.015090              0.007638        0.015160   \n",
       "50%           0.025890              0.010930        0.018730   \n",
       "75%           0.042050              0.014710        0.023480   \n",
       "max           0.396000              0.052790        0.078950   \n",
       "\n",
       "       fractal dimension error  worst radius  worst texture  worst perimeter  \\\n",
       "count               569.000000    569.000000     569.000000       569.000000   \n",
       "mean                  0.003795     16.269190      25.677223       107.261213   \n",
       "std                   0.002646      4.833242       6.146258        33.602542   \n",
       "min                   0.000895      7.930000      12.020000        50.410000   \n",
       "25%                   0.002248     13.010000      21.080000        84.110000   \n",
       "50%                   0.003187     14.970000      25.410000        97.660000   \n",
       "75%                   0.004558     18.790000      29.720000       125.400000   \n",
       "max                   0.029840     36.040000      49.540000       251.200000   \n",
       "\n",
       "        worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "count   569.000000        569.000000         569.000000       569.000000   \n",
       "mean    880.583128          0.132369           0.254265         0.272188   \n",
       "std     569.356993          0.022832           0.157336         0.208624   \n",
       "min     185.200000          0.071170           0.027290         0.000000   \n",
       "25%     515.300000          0.116600           0.147200         0.114500   \n",
       "50%     686.500000          0.131300           0.211900         0.226700   \n",
       "75%    1084.000000          0.146000           0.339100         0.382900   \n",
       "max    4254.000000          0.222600           1.058000         1.252000   \n",
       "\n",
       "       worst concave points  worst symmetry  worst fractal dimension  \\\n",
       "count            569.000000      569.000000               569.000000   \n",
       "mean               0.114606        0.290076                 0.083946   \n",
       "std                0.065732        0.061867                 0.018061   \n",
       "min                0.000000        0.156500                 0.055040   \n",
       "25%                0.064930        0.250400                 0.071460   \n",
       "50%                0.099930        0.282200                 0.080040   \n",
       "75%                0.161400        0.317900                 0.092080   \n",
       "max                0.291000        0.663800                 0.207500   \n",
       "\n",
       "       benign_0__mal_1  \n",
       "count       569.000000  \n",
       "mean          0.627417  \n",
       "std           0.483918  \n",
       "min           0.000000  \n",
       "25%           0.000000  \n",
       "50%           1.000000  \n",
       "75%           1.000000  \n",
       "max           1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='benign_0__mal_1', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAASPElEQVR4nO3df6xfd33f8ecLOyR0sCWe7zzXduaIemWBrgZu07R0UhoGDelWBwZRmFo8Fs1MMitIVUfgjyZUiwQbNAIKkUwT4rQM6vFjcVEKzUxaBioEuzWJHTeLB8lsy4kNSSCMNa3Ne398z/3wxb62v9f4fL/Xvs+HdPQ9530+53zfV7q6r3t+fM83VYUkSQDPmnQDkqT5w1CQJDWGgiSpMRQkSY2hIElqFk+6gR/F0qVLa/Xq1ZNuQ5LOKjt27PhmVU3Ntu6sDoXVq1ezffv2SbchSWeVJI+eaJ2njyRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEnNWf2JZulc9n9++6cm3YLmoYt/64Fe99/bkUKSC5Lcl+RrSXYneWdXvyPJN5Ls7Ka1XT1J3p9kb5L7k7ykr94kSbPr80jhGeDKqvpukvOALyb5427db1bVJ44Z/ypgTTf9LHBr9ypJGpPejhRq4Lvd4nnddLIvhF4H3Nlt92XgwiTL++pPknS8Xi80J1mUZCdwCLinqr7Srbq5O0V0S5Lzu9oKYN/Q5vu72rH73JBke5Lthw8f7rN9SVpweg2FqjpaVWuBlcBlSV4EvB14AfAzwBLgbXPc56aqmq6q6ampWR8HLkk6TWO5JbWqngLuBa6qqoPdKaJngI8Al3XDDgCrhjZb2dUkSWPS591HU0ku7OafA7wC+KuZ6wRJAlwD7Oo22Qq8obsL6XLg21V1sK/+JEnH6/Puo+XA5iSLGITPlqr6TJLPJ5kCAuwE/n03/m7gamAv8D3gjT32JkmaRW+hUFX3Ay+epX7lCcYXsLGvfiRJp+ZjLiRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJKa3kIhyQVJ7kvytSS7k7yzq1+S5CtJ9ib5wyTP7urnd8t7u/Wr++pNkjS7Po8UngGurKqfBtYCVyW5HHg3cEtV/QTwJHB9N/564Mmufks3TpI0Rr2FQg18t1s8r5sKuBL4RFffDFzTza/rlunWvzxJ+upPknS8Xq8pJFmUZCdwCLgH+N/AU1V1pBuyH1jRza8A9gF0678N/P1Z9rkhyfYk2w8fPtxn+5K04PQaClV1tKrWAiuBy4AXnIF9bqqq6aqanpqa+lF3J0kaMpa7j6rqKeBe4OeAC5Ms7latBA508weAVQDd+r8HfGsc/UmSBvq8+2gqyYXd/HOAVwB7GITDa7th64G7uvmt3TLd+s9XVfXVnyTpeItPPeS0LQc2J1nEIHy2VNVnkjwIfDzJfwL+EritG38b8PtJ9gJPANf12JskaRa9hUJV3Q+8eJb61xlcXzi2/tfA6/rqR5J0an6iWZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKnpLRSSrEpyb5IHk+xO8pauflOSA0l2dtPVQ9u8PcneJA8l+aW+epMkzW5xj/s+AvxGVf1FkucBO5Lc0627pareMzw4yaXAdcALgR8H/keSf1xVR3vsUZI0pLcjhao6WFV/0c0/DewBVpxkk3XAx6vqmar6BrAXuKyv/iRJxxvLNYUkq4EXA1/pSm9Ocn+S25Nc1NVWAPuGNtvPLCGSZEOS7Um2Hz58uM+2JWnB6T0UkjwX+CTw1qr6DnAr8HxgLXAQeO9c9ldVm6pquqqmp6amznS7krSg9RoKSc5jEAgfrapPAVTV41V1tKq+D3yYH5wiOgCsGtp8ZVeTJI1Jn3cfBbgN2FNVvzNUXz407NXArm5+K3BdkvOTXAKsAe7rqz9J0vH6vPvoZcCvAQ8k2dnV3gG8PslaoIBHgDcBVNXuJFuABxncubTRO48kabx6C4Wq+iKQWVbdfZJtbgZu7qsnSdLJ+YlmSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWr6/Oa1s8JLf/POSbegeWjHf3nDpFuQJsIjBUlSYyhIkpqRQiHJtlFqkqSz20lDIckFSZYAS5NclGRJN60GVpxi21VJ7k3yYJLdSd7S1ZckuSfJw93rRV09Sd6fZG+S+5O85Az9jJKkEZ3qSOFNwA7gBd3rzHQX8Lun2PYI8BtVdSlwObAxyaXADcC2qloDbOuWAV4FrOmmDcCtc/5pJEk/kpPefVRV7wPel+Q/VNUH5rLjqjoIHOzmn06yh8HRxTrgim7YZuBPgbd19TurqoAvJ7kwyfJuP5KkMRjpltSq+kCSnwdWD29TVSPdz9mdbnox8BVg2dAf+seAZd38CmDf0Gb7u9oPhUKSDQyOJLj44otHeXtJ0ohGCoUkvw88H9gJHO3KBZwyFJI8F/gk8Naq+k6Stq6qKknNpeGq2gRsApienp7TtpKkkxv1w2vTwKXdqZ2RJTmPQSB8tKo+1ZUfnzktlGQ5cKirHwBWDW2+sqtJksZk1M8p7AL+4Vx2nMEhwW3Anqr6naFVW4H13fx6BhetZ+pv6O5Cuhz4ttcTJGm8Rj1SWAo8mOQ+4JmZYlX9ykm2eRnwa8ADSXZ2tXcA7wK2JLkeeBS4tlt3N3A1sBf4HvDGEXuTJJ0ho4bCTXPdcVV9EcgJVr98lvEFbJzr+0iSzpxR7z76s74bkSRN3qh3Hz3N4G4jgGcD5wH/t6r+bl+NSZLGb9QjhefNzHcXkNcx+JSyJOkcMuenpNbAfwd+6cy3I0mapFFPH71maPFZDD638Ne9dCRJmphR7z76l0PzR4BHGJxCkiSdQ0a9puBnBiRpARj1S3ZWJvl0kkPd9MkkK/tuTpI0XqNeaP4Ig8dQ/Hg3/VFXkySdQ0YNhamq+khVHemmO4CpHvuSJE3AqKHwrSS/mmRRN/0q8K0+G5Mkjd+oofBvGTy47jEGX3rzWuDf9NSTJGlCRr0l9beB9VX1JECSJcB7GISFJOkcMeqRwj+dCQSAqnqCwddrSpLOIaOGwrOSXDSz0B0pjHqUIUk6S4z6h/29wJ8n+W/d8uuAm/tpSZI0KaN+ovnOJNuBK7vSa6rqwf7akiRNwsingLoQMAgk6Rw250dnS5LOXYaCJKnpLRSS3N49PG/XUO2mJAeS7Oymq4fWvT3J3iQPJfELfCRpAvo8UrgDuGqW+i1Vtbab7gZIcilwHfDCbpsPJVnUY2+SpFn0FgpV9QXgiRGHrwM+XlXPVNU3gL3AZX31Jkma3SSuKbw5yf3d6aWZD8StAPYNjdnf1Y6TZEOS7Um2Hz58uO9eJWlBGXco3Ao8H1jL4MF6753rDqpqU1VNV9X01JRP75akM2msoVBVj1fV0ar6PvBhfnCK6ACwamjoyq4mSRqjsYZCkuVDi68GZu5M2gpcl+T8JJcAa4D7xtmbJKnHh9ol+RhwBbA0yX7gRuCKJGuBAh4B3gRQVbuTbGHwiekjwMaqOtpXb5Kk2fUWClX1+lnKt51k/M34kD1Jmig/0SxJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLU9BYKSW5PcijJrqHakiT3JHm4e72oqyfJ+5PsTXJ/kpf01Zck6cT6PFK4A7jqmNoNwLaqWgNs65YBXgWs6aYNwK099iVJOoHeQqGqvgA8cUx5HbC5m98MXDNUv7MGvgxcmGR5X71JkmY37msKy6rqYDf/GLCsm18B7Bsat7+rHSfJhiTbk2w/fPhwf51K0gI0sQvNVVVAncZ2m6pquqqmp6ameuhMkhaucYfC4zOnhbrXQ139ALBqaNzKriZJGqNxh8JWYH03vx64a6j+hu4upMuBbw+dZpIkjcnivnac5GPAFcDSJPuBG4F3AVuSXA88ClzbDb8buBrYC3wPeGNffUmSTqy3UKiq159g1ctnGVvAxr56kSSNxk80S5IaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWLJ/GmSR4BngaOAkeqajrJEuAPgdXAI8C1VfXkJPqTpIVqkkcKv1hVa6tqulu+AdhWVWuAbd2yJGmM5tPpo3XA5m5+M3DN5FqRpIVpUqFQwJ8k2ZFkQ1dbVlUHu/nHgGWzbZhkQ5LtSbYfPnx4HL1K0oIxkWsKwC9U1YEk/wC4J8lfDa+sqkpSs21YVZuATQDT09OzjpEknZ6JHClU1YHu9RDwaeAy4PEkywG610OT6E2SFrKxh0KSv5PkeTPzwCuBXcBWYH03bD1w17h7k6SFbhKnj5YBn04y8/7/tao+m+SrwJYk1wOPAtdOoDdJWtDGHgpV9XXgp2epfwt4+bj7kST9wHy6JVWSNGGGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJauZdKCS5KslDSfYmuWHS/UjSQjKvQiHJIuCDwKuAS4HXJ7l0sl1J0sIxr0IBuAzYW1Vfr6q/AT4OrJtwT5K0YCyedAPHWAHsG1reD/zs8IAkG4AN3eJ3kzw0pt4WgqXANyfdxHyQ96yfdAv6Yf5uzrgxZ2Iv/+hEK+ZbKJxSVW0CNk26j3NRku1VNT3pPqRj+bs5PvPt9NEBYNXQ8squJkkag/kWCl8F1iS5JMmzgeuArRPuSZIWjHl1+qiqjiR5M/A5YBFwe1XtnnBbC4mn5TRf+bs5JqmqSfcgSZon5tvpI0nSBBkKkqTGUJCPFtG8leT2JIeS7Jp0LwuFobDA+WgRzXN3AFdNuomFxFCQjxbRvFVVXwCemHQfC4mhoNkeLbJiQr1ImjBDQZLUGAry0SKSGkNBPlpEUmMoLHBVdQSYebTIHmCLjxbRfJHkY8CfAz+ZZH+S6yfd07nOx1xIkhqPFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQ0Fknyeoz8SjlJNNJ3n8mehra55Ik9yR5uHu96Ezuf4T3vyPJa0+y/s3dI9IrydJx9qazg6GgBauqtlfVr5/h3d4AbKuqNcC2bnk++RLwz4FHJ92I5idDQWerxUk+mmRPkk8k+bEkL03yZ0l2JPlckuUASf40ybuT3JfkfyX5Z139iiSf6eanuv/sdyf5vSSPJlnaHZXsSfLhbt2fJHnOSfpaB2zu5jcD18zlh0pyU5LNSf5n18NrkvznJA8k+WyS87pxv5Xkq0l2JdmUJKPsv6r+sqoemUtPWlgMBZ2tfhL4UFX9E+A7wEbgA8Brq+qlwO3AzUPjF1fVZcBbgRtn2d+NwOer6oXAJ4CLh9atAT7YrXsK+Fcn6WtZVR3s5h8Dls3x5wJ4PnAl8CvAHwD3VtVPAf8P+OVuzO9W1c9U1YuA5wD/4jTeRzrO4kk3IJ2mfVX1pW7+D4B3AC8C7un+aV4EHBwa/6nudQewepb9/QLwaoCq+mySJ4fWfaOqdp5i++NUVSU5nefI/HFV/W2SBxj8HJ/t6g8MvfcvJvmPwI8BS4DdwB+dxntJP8RQ0Nnq2D+2TwO7q+rnTjD+me71KHP/vX9maP4og//MT+TxJMur6mB3+urQHN+rvV9VfT/J39YPHlD2fQanzS4APgRMV9W+JDcBF5zG+0jH8fSRzlYXJ5kJgH8NfBmYmqklOS/JC+ewvy8B13bbvhI43buGtgLru/n1wF2nuZ+TmQmAbyZ5LnDCu42kuTIUdLZ6CNiYZA+DP+AfYPDH8d1JvgbsBH5+Dvt7J/DK7lbX1zG4HvD0afT1LuAVSR5mcJfPu05jHydVVU8BHwZ2MXjk+VdH3TbJryfZz+DLlO5P8ntnuj+d3Xx0tgQkOR84WlVHuqONW6tq7YTbksbOawrSwMXAliTPAv4G+HcT7keaCI8UpNOQ5IPAy44pv6+qPjLL2DcCbzmmvAZ4+Jjal6pq4xnq79PAJceU31ZVnzsT+9e5y1CQJDVeaJYkNYaCJKkxFCRJjaEgSWr+P9s7zTpw4fSzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('benign_0__mal_1', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJYAAATPCAYAAABTFn9MAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzddZhU1f/A8feZmZ3tbrq7Y+kuRUoEA1EBkRAUEEQBsQWRFpQy8KtgoWLS3d29xLKw3TU99/fHDLsz24Tr9+f3vJ5nn2dn7rn3c8+tc+bcc88ViqIgSZIkSZIkSZIkSZIkSXdL9U+vgCRJkiRJkiRJkiRJkvT/k2xYkiRJkiRJkiRJkiRJku6JbFiSJEmSJEmSJEmSJEmS7olsWJIkSZIkSZIkSZIkSZLuiWxYkiRJkiRJkiRJkiRJku6JbFiSJEmSJEmSJEmSJEmS7olsWJIkSZIkSZIkSZIkSfp/TgjxhRAiQQhxtojpQgjxsRAiUghxWgjR7EHElQ1LkiRJkiRJkiRJkiRJ//+tBh4qZvrDQE373yhg2YMIKhuWJEmSJEmSJEmSJEmS/p9TFGU3kFJMkv7AfxSbg4CfECL8fuPKhiVJkiRJkiRJkiRJkqR/v/JAtMPnW/bv7ovmfhcg/fuYkq4pZRkv47nhZRkOAJ+vvizzmFgtZR7yavsJZRqv4hNeZRoPQFWndpnHFNXqlXnMyKfXlHnMT6weZRpvwey6ZRoPYO2rV8s85pA5Vcs8Jr7+ZR5y9vhjZRpvmHdimcYDsFpEmccMamws85g/7rnv+uZd87OUaVWE/seml2k8gLRnXizzmH5ff1rmMTHqyjzkuY7vlWm86r3KPo/qisFlHlPVvGWZx7wxcXOZx7ye4VOm8Z7KPlKm8e5Iz7pa9oVYGSnr37NlSRtcfTS2R9juWKkoysp/an3ukA1LkiRJkiRJkiRJkiRJ/+XsjUj305B0G6jo8LmC/bv7Ih+FkyRJkiRJkiRJkiRJ+vf7DXjW/na41kC6oiix97tQ2WNJkiRJkiRJkiRJkiTp/zkhxLdAZyBICHELeAtwAVAUZTnwF9AbiARygAcyLo1sWJIkSZIkSZIkSZIkSfp/TlGUp0qYrgDjHnRc2bAkSZIkSZIkSZIkSdK/wz/w0qT/dXKMJUmSJEmSJEmSJEmSJOmeyIYlSZIkSZIkSZIkSZIk6Z7IhiVJkiRJkiRJkiRJkiTpnsgxliRJkiRJkiRJkiRJ+ndQrP/0GvzPkT2WJEmSJEmSJEmSJEmSpHsiG5YkSZIkSZIkSZIkSZKkeyIbliRJkiRJkiRJkiRJkqR7IsdYku7bG7MWsHvfYQL8/Vj/zfIHtlyX5hF4jnoJVCr0m/9E/+Nap+muD/fDrc+jYLWg6HRkL5mHJToKlyYt8Bg+CjQuYDaR/fkyzKdP3Pf6/B353HvoGB8uXonFauWxPj0ZOXSw0/SYuARmzl5ESloGvj5efDhzCmEhQQAsWPYluw8cAWD0c0/ycLeOpYrp2aE5ITNGI9Qq0n7cRMrKH52mu7doQOiMUbjWrkrMpA/J3LQPAI9WjQiZ/kJuOm21isRMmkPW1gMlxlTXaIz2keEgVJiPbcO051en6ZqmndD2egZrRgoA5kMbMR/bDoDrs9NRV6iJ5eZFDN/MKVUeAfZFxvLRppNYrQqPNq3KiPZ1C6TZdC6aFbvOgYBaoX58OLA1AAu3nGJPZCyKotC6WihTezVFCFF8vNNXmLN2I1arlUc7NuP5Ph2cpscmp/HGqvVk5uixWq1MGNydDo1rceDsVRb/uBWTxYKLWs2kJ3rQql61UuXRq2Mzwt8cBSoVqT9sJmn5OqfpHi3rEz7zBdzqVCV6wkdkbNiXO63+lV/RX4oCwBSTyM1R75UqJsDjbw2nfpemGHUG/jPlU6LPXS+QZvxX0/EN8UOlVhN55CLfzfwMxarQrHdrHpk4mLAa5ZnTfzo3z1wrMd6+K7F8tPG4bV82q8aIDvUKpNl09iYrdp7N25eD2gKwcPNJ9lyJQVGw7cuHmxW5L1u/+wwVuzbBrDOwe9JKks/eKJAmsGEVOi4cjcZNS/T2kxx882sAAupWot2Hw9F4upEVncjOl5ZhytJR/dG2NBzzSO78AXUrYti4DCU1zjmP93i8HrmewNzNJ3PT3EjK4MPH2tC1TvmSt+vFm3y0fr8tZqs6jOjW1Gn63F/3cyQyBgC90UxKlo69HwzPnZ6lNzLwox/o0qAK0wa2LzHeHb3fepaaXRpj0hn5ZcoKYs/dcJru4qbliU9fxr9yKIrFyqVtx9ky53sA2j7/MM2e7ILVbCEnJYNfpq4i/XZSsfHc27Ug8LWxCLWKjJ83kv75907TfZ99DO+BD6FYLFhT0kl8cz7m2AQAwpZ9gGujuuhPnCV+/JulzqNH+xYETRsDajUZ6zaQ9tkPTtPdmjcgaNoYXGtVI27KLLI3782dFvjK83h0igAgddlasjbuKjGepnFL3J8dDyo1xh1/YvjtW6fp2u59ce0xAKxWFL2OnM/mY70dBWo1HqNeRV2lJqjVGPdsxvDr2sKDlKCNw/mzq4jzp8XUwdQc1B5XX09W1x5Z6mU3ee9Zwrs1xqwzcmTiCtLOFFy2X6MqRCwag9rNhdhtpzg58z9O02uN7k3jt5/m1/qjMaZkUa5Xc+pPHQRWBavFgtAKFGNOgeXuPXycOUu/wGKxMvCR7owcMtBpekxcAm9+9Akp6Rn4ensxe8YEwoKDOHziDB998mVuuus3b/PRm6/QrX2rUucbQNsyAq9x9rrQX3+S853z/nHr0w+P/o+i2OtCmQvnYYmKuqsY/0Qe9x45xZzlX9tiPtyZkU/0c44Zn8ibC1blxZw6lrDgwNzpWdk59B81la5tWjBj/LBS5dO7U1MqvP0CQq0i+bstxH/6k9N0z4h6VHhrJO51q3Bj/DzS/toPgHu9qlT8YAwqbw+wWIlb+iNpv+8tLEQB6votcHtyLEKlwrhnI8aN3xeaTtOsPR5j3yTr/XFYo64gPL1xHzMTdZXamPZvRv/tJ6WKB6Cu3ghtr2dApcJ8Yiemfb87x2rcEW33p7BmpgJgPrIZ84mdCN8gXB+fCEKFUKkxHdmM+di2EuPtu3CTj9bvtZUlresyolszp+lz1+/jSORtAPQmMymZOvbOej53epbeyMA539GlQVWmPeZcdyqKZ4fmhL5hr8v+sInk/HXZlg0Is9dlb0/6kMyNefUfTXgw4bMm4BIeBApEj3wT0+0Ep2V3nG4rM6LXbOfakt+clq3Sami0dBy+japiSs3ixKjF6KITAaj+cn8qDOmCYrFyfsZqknaexrN6OE1XTshbt8ohXPnoR26s3EBY31bUnDKI1Frl6dppICdOnCmQ1zlz36Rnz87k6HS8OHoqp06dc5ru5eXJhs3f5X4uXz6M77/7lWmvvc+I559i5KhnsFgsZGflMOHlGVy6GFmqbfz/nlWOsVTWZMPS/0NCiBtAC0VRkoQQ+xVFaftPrs+A3j0Y8lg/pr8378EtVKXCc+xEMt6YjDUpEd+FKzAd3IclOq+yZNy5FcMG28XepVVbPF4YR+abU7FmpJPxzjSUlGTUlavi8+5cUp8bdN+r9KDzabFYeH/BMlYtfJ+w4ECeeGESXdq1onrVSrlp5n3yOf0e6kb/h7tx6NgpFq34ig9nTmbX/iOcv3yVdV8swWgyMfzlaXRo3QIvT4/ig6pUhL71ItHDZ2CKS6LKT4vI2nYQ49Xo3CTm2ARiX19AwPOPOc2ac+g0N/q/ZFuMrxfVt3xO9t7jJWdUCLR9n0e/+n2UjGTcxszGfPEoSuJtp2TmM/sx/vlFgdlNe3/D7OKKpmX3kmPZWaxWZm84zvKhnQj1cefpz7bSqXY5qgf75qaJSs7ki30XWD28Kz7uWlKy9QCcjE7iZHQSP47uCcDwL3dwNCqRllVCio036+u/WPHqM4QG+DDknVV0blqb6uXz5ln12256RdTn8a4tuXo7gfEL1rBhfi38vD34eOJThPj7cOVWPGPnfcPWRZNLzqRKRbl3xnL92TcwxyVTbf1CMrcewhCZty9NMYncmrqIoJEDC8xu1Ru52uflkuPkU79zU0KqhvFW55ep2rQmT30wko8GzCiQ7rNxC9Fn6QAYtWwyzR9pw9Hf9xNzKZqVY+YxZNaoUsWzWK3M/usoy5/pYtuXq7bQqXZ5qofk25d7z7P6+e62fZll35c37fty7EMADP9iG0dvJNCyamiBOBW6Nsanahg/tp9McLPqtJ09jN/7vl0gXbvZw9k79TMSj1+l59evUqFLI27tOE37uSM5/P5a4g5epOYTHWk45hGOz1vH1V/2c/UX248T/zoV6P7ZJFzyNSrdz/HasmoIP9iP1XSdgb5LNtCmesH8Fbpdf97H8tGPEOrrydOLfqZT/SpUD/PPTfNq/7yi5ds9Z7mYrwHnk41HaFYtvMRYjmp2bkxg1TAWd55MhaY16PvBcFYOeKtAun2r/uL6gfOoXdQMWzOdmp0bc2XnKWLPR7Gi7xuY9EZaDu1Gz2lP8eP4JUUHVKkImjGe2FGvY45Lovx3S8jZcQDTtZu5SQwXIsl4cjyK3oD3430IeGUkCa/OAiBt9Y+o3NzwHty79JlUqQh+Yxy3R07DHJ9Exe+XkL3jIKareTHNsYkkTJ+P33DnssmjYwSu9WoQPXAsQutC+dVzyd5zBCW7YINHLqHCffgEsme9ijU5Ee8PlmM6tt/WcGRn3LcN41bbj0tN87a4P/Mi2R++hkurzqBxIfO150Hris+81Zj2bcOaFF/6/AIVuzbGt2oYP7SfTEiz6rSfPYxfCzl/bm49zrnVW3hiT+nL0bCujfGqFsaGtpMJaFaDZh8OZ/sjBY+Z5h+O4OiUz0g5Hkn7NVMJ69qYuO2nAHAvF0Bo54Zk38o7huP3nCVm0zEAfOtWpMemdzAlOv/gslgsfLB4FSvnvkVYcCBPjplKl7YtqV6lYm6aecu/om/PzvR/qAuHjp9h8ao1zJ4+gYimDVn32QIA0jMy6T10HG1bNCl1vgFQqfB+eSKpUydjTUzE/9MVGA7sc2o4Mmzfiv4PW11I26YtXmPGkT5taqlD/BN5tFisfPDJalbOnkZYUABPvjSTLq2bUb1yhbyYq9bSt3t7+vfoyKGT51j85ffMnvpi7vSl/1lH8wZ1Sp1PVCoqvj+ayKffwhSbTO3f55G+5TD6K45lZhJRkxcTOvpRp1mtOgNRkxZhuBGLJjSAOn/OJ3PXCSwZ2cXHFCrch4wne+HrKKlJeM5YgvnUAayxN53Tubqj7fYo5msXcr9STCYMv36FqnwV1OWqlD6fQqB9eBj6b2ajZKTgNvI9zJeOoyTlq3OdO4hx41dO3ymZqei/eBssZnBxxX3sHCyXjqFkpRUZzlaW7GH5mL62smThT/ayJCA3zasD2uX+/+2eMwXLkg2H764sUakIe/tFbg6z1WWr/rSIzO0HMTrUf8wxCcS8VrAuC1Bu7mSSl31P9r4TCA83sCoFlr1v0Gz0Mcm02zSLhE3HyLqct/0qDOmCOS2LXa0nEj6gDbVnDuHkqMV41SpP+IC27Ok4BdcwfyJ+fINdbSaSfTWWvd1ety9f0O3UMuL+st0YzrwYzfERC1DNeaLQrPbo2Znq1avQtHFXWrRswoJF79Kti3OesrKy6dC2b+7nXXt+5fffNgHw4w+/88XnthsND/fuxqzZM3js0eFI0t9BPgr3X0IIcU+NfP90oxJAiyYN8fXxfqDL1NSqiyXmNta4WDCbMezejktr5zviii6voi3c3MFeLliuXUFJSbb9H3UdXF1tvZfu04PO55kLl6lUPpyK5cJwcXHh4W4d2b73oFOaqzeiiWjWCICIZo3YYZ9+9cZNWjSuj0ajxsPdjVrVq7D30LESY7o1qoUxKgZTdByYzGT8uRuv7m2c0phuJ2C4dKPYln7vh9qTtfsoit5QYkxVhRpYk+NQUhPAYsFyZj+aui1LnO8O67WzKAZdqdMDnL2dQkV/Lyr4e+GiVtOrfiV2XopxSvPz8Ws80aIGPu5aAAI83QAQgNFixWSxYrRYMVutBNqnFRnv2m0qhgZQISQAF42Gh1o1YOeJS86JhCBLZ9teWToDwf62Y6lu5XBC/H0AqFE+BIPJhNFkLjGP7o1rYYiKxRQdj2Iyk/7Hbrx7tHZKY7qdgOHijQd616ZxzxYc/Hk3ANdPXMHD2xOfYL8C6e40Kqk0atQuGhTFdoLGXb1N/LXYUsc7ezuFigHeVAjwwkWjpleDSuy85FxB/vnYVZ5oWTNvX3rZ96UAo9li25dmK2aLlUCvwvdl5Z7NiVxnuwOdePwqWh9P3EOc8+Ue4oeLlzuJx68CELluL5V7tQDAt1oYcQcvAhCz+yxVehc8xqv1b8u13w4W+P5+jldHW87fol2NMNxdSi5Ozt5MoGKgDxUCfWzbtWkNdubrOeRow4lIHmpaI/fz+ehEUjJ1tKlVoch5ClOnZ3NO/rwHgFsnInHz9sAr3/Fj0hu5fuA8ABaThZhzN/Cx/0i5fuA8Jr0RgOgTkfg6/HgpjGvD2phuxmC+FQdmM9kbduHZxbnY1B85lXstM5y+gCY0OG/aoZNYi2vUKYSbY0yTmawNO/Hq6nydNcfEY7x8vcC5qa1RCd3RM2CxougMGC9fx7NDi2LjqWvUwRoXgzUhFixmjAe249KinXMix/LS1Q2UOz+kFNtnlQqhdUUxm5zK1tKq3LM5V+znT0IR58+dabqEtLtadrmHmhP1o+2YSTkeidbHA7d8y3YL8UPj7U7KcVvDUNSPeyj3UPPc6U3eeYbT733rkG+w5OSVX2oP10Jjn7kYSaVyDuV01/bs2HfYKc21G7do1awhABFNGxSYDrB51wHaRzTF3a3wOEXR1KmL+fZtrLH2utCO7bi2zVcXyslXF7pL/0Qez1y6SqVyoVQMD8HFRcPDnVuz44BzHeZa1G1aNa5vi9m4ntP0c1euk5yaTtvmDUudT48mNTHciMN401Zmpv6+B9+eEU5pjLcS0F+MQsl3Xhqux2C4YSu3zPEpmJPS0QT4lBhTXbU21sQYlKQ4sJgxHdmFpknBarvrgOdsPZlMRoeV0WOJPOf8XSmoylfHmhqPkpYIVguWcwfR1G5e8owAVoutUQls9eYSemqDvSwJ8nUuSwrprXjHhhNXCi9Lalcscp783Aupy3p3K6Iuq+S/xlZEqNVk77M9yaDk6J3qsneWrYtKQDFZiF2/n9CHnK/BoQ+14NYPtnpQ3O+HCGpfP/f72PX7sRrN6G4mknM9Dr9mNZzmDerQkOwb8ejtjdzZV2LIvlp0neiRPt359ttfADh65CS+vj6EOpRR+VWvUYWg4ED277M3XGVm5U7z8PTIrYtJ0t/hf7phSQhRRQhxUQixWghxWQixRgjRXQixTwhxRQgRYU/nKYT4QghxWAhxQgjR32H+PUKI4/a/tvbvOwshdgoh1tmXv0YU8uyFPc0iIcRRYIIQoq8Q4pA9xlYhRKg9XaAQYrMQ4pwQ4jNsv33vLCPLIeYfDt8vFUIMs///oRDivBDitBDiAXYr+vuoAoOwJuV1S7UmJaIODCqQzvWRAfh9thaP4WPIXrG4wHRtu06Yr14Gs+lvXd97kZCYTFhIXuEQGhxEQlKyU5raNaqydbett8PW3QfIztGRlp5B7RpV2XvoODq9ntS0dI4cP01cQmKJMV1CAzHH5d0pMscl4RIaWMwchfPp3YmMP0p+PANA+ASgpOflS0lPRngX/DGort8K93FzcX3yFYTP3a+To4RMHWG+eb23Qn3cSch0bpyKSskkKjmT577YxjOfb2VfpK1gb1wxiJaVg+m+4Hd6LPidNtXDqBZcfAUyITWDMIdKZoi/D/GpGU5pxg7ozJ8HTtNj0nzGLVjD60ML9oDYevQ8dSuHoy1Fw4BLWCCm2Lx9bo69u32pctVS/deFVPtpXoEGqeL4hQaQGpN3DKXGJeNXxI/7l/4znbnHVmHI1nH8r4INKqWRkKEjzCffvszIty+T7fvy8608s2oL+6447MsqIXSf9ys95v9KmxrhVHPoBeTII8yf7Ji84zQnNgVPh947AJ5h/mTHpuR+zo5NwcOeJvXyLSr3slXeq/ZphWe5gtukWt9WXPu14KOj93O8Otp0LpqHG1Qq8H1hEtJzCPPzyovp60lCeuF332NSMolJySSiZjkArFaF+b8f4JW+pT9u7vAJDSDdYTtnxKXgk287O3Lz8aB2t2Zc23e2wLTmj3fmys5TxcbThARhjnM4T+ITURdznngPfIicvUeKXWZJ1KGBmBxjxiWhDilYfhXGcPEaHu1bINxcUfn54B7RGE1Y0T8iAFT+QViTHcrL5ERU/gXjaXsMwHvRN7gPGY3uK1svL9OhXSgGPT7LfsJnyXcY/vgBJTuzVOvqyDPMnyyH/ZpdyPlzr9zDAsjJd266hzsv2z3cH11M3rmpi03B3X5dKterObq4FNLP5+slApR7uAW99sylw9evYk67XWB6QlIyYSF5x0tocCDxSSlOaWpVr8LW3bbr27Y9h+zltPM23LhjL727le4RH0fqoCCsiQ77NjERVVDBfevefwCBX6/Fa9QYspYWrAsV55/IY0JyitNjbaFBAcQnpTrHrFaJrfYfx9v2HSU7R09aRiZWq5V5K9cw+YUhpc8koA0LxOhQdhljk++p/uPRuCbCRYMhKq7EtMIvCGtK3rVASU1E5eccU1WpBir/YMxnCjbW3Qvhna/OlZGC8C54LqrrtsR99GxcB01A+OSVV8InAPfRs/GY+DGmfX8U21sJICE9mzA/z9zPoX4llCXJmUTUtD2qbbUqzP9tP6/0a1No+qJowgIxx+btS1NcEppS7kttlQpYMrMp/8kMqv66hJDXRoAq7+dw/mXrYlJwzVfHcQsPQH/bto0VixVTpg6XAG9cwwLQ3c7b9vrYFNzyzRv+aBti7b2YSyM8PJTbt/JuNMXExFGuXFiR6R8b1IdffvrT6buRo4Zy8vR23n3vNaa++m6pY0vS3fqfbliyqwHMB+rY/4YA7YEpwHR7mhnAdkVRIoAuwFwhhCeQAPRQFKUZ8ATwscNymwITgXpANSDf7cNcWkVRWiiKMh/YC7RWFKUp8B1wpy/zW8BeRVHqA78ApfvlgK1RCngUqK8oSiPg/dLO+/+B4c/1pI0cQs6XK3B/4lmnaepKVfAYPprsJfP/obW7f1PGjeDoybMMGvEyR0+eITQ4EJVKRbuIZnRo04KhY1/l1Xfm0rhBHdQqdZmskzrYH9faVcjeW3IPqdIyXzyGbv44dJ+8iiXyNK6PjXtgyy6KxapwMyWLz57rwocDW/PuH0fJ0Bu5mZLJtaRMNk/qw+ZJfThyPYHjUSU32pVkw8Ez9GvXhC0LJ/PJK08zY+XPWB3uikbeTmDRD1uZOaxvMUt5cC51GMHV/pOInjiX8JkvoK1UdEXlXi15dhavRYxGo3WhdtsGD3z5d9j2ZSafDevKh4Pa8O7vh8nQGbmZnMm1pAw2v9KPza/048j1eI5HJZS8wHuwZ/Iq6j7bnf5/vYeLlxvWfL3OgptWx6w3knrp1j0tv6jj9Y7ETB2RCem0qf7g9+Omk1fp3qgqanvl+4f952hfpxKhDg1TfweVWsXgj8dzaPUmUqOdz8FGA9pRrlE19q78o4i5755Xn2641qtF2pc/lpz4b6Lbf5ycPUeosHYhYfOmoT91AcXyYHocGresJ3PiUHRrV+L26DMAqKvXBauVjBcHkTFhCK6PDEYVcnePN/43U7trqfNyP85+tK7Q6TEbjrKpw6vsG7EQtU/RjzsXZ8rY5zh6+hyDX5jM0VPnCAkKQKXOq14nJqdw5dpN2rZsck/LLw3dr+tJfmYIWatW4DH02ZJnuEv/RB6njHqao2cuMPjF6Rw9c4GQIH9UKhXf/b6VDi0bOzVMlRVNiD+VF00iasrHTr3f7pkQuD0+Gv2PK+9/WXfBfPk4uo8nolsxDcu1M7j2H5M7TclIQbdiGrolr6Bp3AE8S+6ZVVqbTkTSvXG1vLJk31na1/37yxJHQqPCo0V9Ej78nOsDJ+BSMRzfgaUfauG+YruoCe3ZnNjf7+1GW2k8NqgP6350HlPrs5Xf0KRRV96aOYdXp/799ev/Fopi/df+/beSYyzBdUVRzgAIIc4B2xRFUYQQZ4Aq9jQ9gX5CiCn2z27YGndigKVCiCaABajlsNzDiqLcsi/3pH1ZhY305ziKXwXgeyFEOKAF7oyI2xEYCKAoyp9CCOfbOsVLB/TA5/YeTYXWwoUQo4BRAJ/Of5+Rzz51FyEePGtyEqqgvEqeKigYS3LRA7Qad2/Dc9wkshfa0wcG4/3G+2TNn4U1LqbI+f5JIcGBTr2M4hOTCAlyriiFBAWy+APb+DU5OTq27tqPj7etAB797BOMftb2TPbUd+ZSuWK5EmOa4pPRhOXd7dSEBWGKTy5mjoJ8Hu5I1pb9YLaUKr2SkYLwzcuX8A1EyXS+G4our6uu+dg2tL2G3tU65Rfi7U5cet7jAfEZOkK8nR8RCPVxp0H5QFzUKsr7e1E5wJubyVkcjUqgUYUAPLS2xyfb1Qjj1K1kmlUuutdAiL8PcSl5PZQSUjMI9XeujP2y+wTLJtvy1bhGRQwmM6lZOQT6eBGfks6kj7/j/VGPUjGk+Ed77jDFJeMSnrdOmvC725dme1pTdDzZB8/gVr86xpuF34Ht9Ewv2j3VDYCoU1fxLxcE2B718w8LJC0updD5AMwGE6e2HKFxj5Zc3FtwUMqShPi4E5eRb1/6FLIvKzjsy0BvbqZkcvRGAo0qBOLhemdfhnMqOplmlW3XFnXNCAZsGgFA0qlreJbLO049wgPIjnO+1GbHpeIZnrd/PMMDyLGnSb8ay8anbYPL+1QNo2K3Jk7zVuvXmmvrCx/o/n6O1wblbeuz+Xw0XeqUx0VduvtFIb4exKXlnXfx6dmE+HoWmnbjiUinwblP3YjnxPU4fth/Dp3BjMliwUPrwoQ+hQ/YG/FMD5o/1QWA26eu4euwnX3CAsiIK7xI6zf7eZKvx3Hgi41O31drV59O4/vzxRPvYzEW/9ioOSHJqcePJjQYSyHniXvrpvi98BQxw6eA6f56uFrik3FxjBkWhCWh+AHGHaWu+JbUFbYxMUI/eh1TVPGNkdbUJFSBDuVlYDDW1KLjmQ5sx+P5iQBo23XDdOowWCwoGWmYL59DXa227bG6EtR7rjt1htj2a+Kpa3iVC+TOyEyehZw/d6Pec92pbz9mUk5dw6NcIHf2mkd4ALpY52XrYlNxd+gl6B4egC4uBc/KoXhWCqbnttm53/fY/AFbH34TQ2J6bvqkgxcRai2o1LbHgexCggKJS8g7XuITkwkNcr5GhwQFsOjd1wDI0enYsvsAPl5559KmHfvp2r4VLpq7r3JbkpJQBTvs2+BgrElF71vDjm14T5jE3fQ5+yfyGBIYQFyiQ8ykFEKD/POl8WfRm5PsMfVs2XsYHy9PTl24wvGzl/j+j63k6PSYzGY83N2Y9PyTxcY0xiWjLZdX/9GGB95Vmanycqf6lzOJnfsNOScul2oeJS0JVUDetUD4B2NNc4jp5o6qXBU8p8y1TfcNwGP8u+QsfRNr1JVSr5tTzMx8dS6fAJTMfOeiY53rxA603QvW+ZWsNKwJ0agr1cFyoejeVCG+nsSl5fVQik8rpiw5Gcm0gXm92k5FxXPiWiw/7DuHzmjCZLbg4erChD7F94Y1xyWjCc/bly5hQbl1mpKY4pLQX7hme4wOyNxyAPcmdUhft7nQZbuXC8CQr46jj03BrXwg+tgUhFqFi7c7ppRMDHEpuJfP2/Zu4QHoHeYN7taE9DM3MDpcewozctRQnhtmq9+fOHaG8hXKAbabueXKhRETU3hdrUGDOmjUGk6eLNjDF+CndX+wYFHpX9QiSXdL9lgCx0FirA6freQ1vAngMUVRmtj/KimKcgGYBMQDjYEW2BqDCluuhaIb8Rz7iy4BliqK0hAYja0Bq7TMOO9PNwBFUcxABLAO6ANsLDgrKIqy0t5zqsU/3agEYL58EXX5CqhCw0CjwbVjV0yH9jmlUZXLe+uRS8s2WGNslW/h6YX32x+Ss3oF5guFX1z/GzSoU4ubt2K4FROHyWRiw7bddMn3JpXUtPTcXi2rvvmRR3v3AGyDbaal2xoyLkVe5/LV67Rt6fwWjsLoz1xGW6UcLhVCwUWDzyMdydp2d3dOfPqU/jE4AOvtq6gCwxF+waBWo27YFvPFo05phJdf7v/qOi2wJt5br4476pcP4GZKFrdTszBZLGw6d5NOtZwb3rrULs/RG7beK6k5BqJSMqng70m4rwfHohIxW23jLB2LSqRaUPF37OpXLcfN+GRuJaZiMpvZeOgsnZrWdkoTHujLofO2N6Bdi0nEaDIT4O1JRraO8QvXMmFwd5rWLHVnRHSnL+Nq35fCRYNvn45kbj1UqnlVPp4Ire2SpPb3waNFPQxXCj4mcseurzcxq/dUZvWeyqnNh2k90PYGwqpNa6LLzCEjMc0pvauHa+64Syq1igZdmxF3teBjJqVRv1wAN5MzbfvSbGHT2Zt0qu38xrMudSrk7ctsA1HJmVTw9yLc15NjNxIxW+7sywSnxxotVw6zvtcM1veaQdTGY9QYZGs8CW5WHVNmToGxYHQJaZiydAQ3qw5AjUHtidpsq+y5BdqXKwRNJvTnwtcOb9IRgqp9W3Htt8Iblu7neL1j49mbPFy/9MdP/Yoh3ExK53Zyhm27noikU/3KBdJdj08lQ2egcZW8AcFnD+3GxplPs+GNp5nUtzV9WtQqslEJ4PDXW1jWezrLek/n4uajNLH/sKjQtAb6TB1Z+Y4fgG6TB+Pq7cGGd792+j6sfmX6zXqeNSPnk52cUWC+/AxnL+FSuTya8rayxPPhTmTvdN4P2jrVCXpzAnEvvYk1peC63C19bkzbddbr4c5k7yjldValQuVrG39NW6sq2tpVydlXfO9Qy9WLqMLKowoOA7UGbZuumI45P2qhCss7ZzRNW2OJs52P1qR4NPXtbwN0dUNToy6WmKKvBY7Of7WVn3vN4OdeM7ix8Rg17edPSLPqGAs5f+7G+a+2sqXHdLb0mM7tDUepPNh2zAQ0q4EpU4c+37L1CWmYM3UE2MczqTy4AzEbj5FxMZrfG77IXxET+StiIrrYFLb0nIEhMR1Ph2Par2EV23gyVucbJg3q1CDqdiy3YuNt5fT2vXRu6zx+Wmp6Rm45/dman3n04W5O0zds30PvbqV/a6Ij88WLaMpXQBVmrwt16Yphv3NdSF0+b99qW7fBcvvuys9/Io8Nalcj6nYct+ISMJnMbNh5kM6tnccBSk3PzIv53W882rMzAHNeH8eWbz5m038WM/mFIfTt1qHERiWAnFNXcK0ajrZiCMJFg3/fDqRvKd3jZ8JFQ7VV00j5eUfum+JKw3LjEqqQ8ogg27np0rIT5lMO1x9dDlmvDCZr2rNkTXsWy7UL99WoBGC9fQ1VQJitzqVSo67fGvNl52uIU52rVnOsSbYbsMI7IG9MUjcP1JVqY00uvpG5fsUQbiamOZclDaoUSHc9PpWMnPxlSXc2vvkMG2YOZVLfNvRpUbvERiUAXSF12cxS1mX1p6+g9vZEbR++wLNNY4yRede8O8t2rxSMcFETPqAt8Zuct1/CpmNUeNxWDwrr24rkvba3tMVvOkb4gLaotBrcKwXjWS2MtON5LwQo92g7Yn9xPn8L89nKb+jQti8d2vbljz8289RTtsHkW7RsQkZGJvHxhfeiHzS4L+vWOfdWqla9Su7/vR7qwrWrN0qML0n3SvZYKp1NwEtCiJfsvZmaKopyAvAFbimKYhVCPAfc77NIvsCdX1/POXy/G9sjeu8LIR4GChu4IAqoJ4RwBdyBbsBeIYQX4KEoyl9CiH1Aye/2vkuvvvUhR06cJi0tg24DhvLi88/wWN9e97dQq4XsZYvweW8eqFQYtvyF5eYN3IeOwHzlIqZD+3HrMxCXJs3BYkbJyiJrge2OpFufR1GXK4/7U8/h/pRtM2a8MQUlPe2/Kp8ajZrpk8YwevKbWKxWHn2kBzWqVmbpZ99Qv05NurRvxZETZ1i08isEguaNG/DGK2MBMJstPDvOdvfQy9ODD2dOQaMpxeFnsRL/7jIqfv4+qFWkr9uMMfImQS8PRX/2ClnbD+HWsCblP5mJ2scLry6tCHp5KNcfscV1KR+CJjyInMN30fPEasX4xxe4PTfD9urb4ztQEm7h0vVxrDFXsVw8hqbNw2jqtECxWiAnC8PPn+bO7vb8O6iCy4PWDfcpyzCuX44lsoRxVVQqXn+4GWPX7MaqKPRvUpUaIb58uuMs9cr507l2edpWD+PA1XgGfroRlUowqXtj/Dxc6V63AoevJzB4+SYEgrbVw+hUu/jeYBq1mmlDezN23tdYrQoDOjSlRvkQPvl5O/WrlqNz0zpMfrIn7375O99sPogA3h05ACEE3207zM34FFb+uouVv9oa7Ja9+gyBPiV0DbdYiXl7OVW+ehehUpH64xYMV24SMvFpdGeukLntMO6NalJp2QzUvl54d4sgZMIQIh8ah2uNipT/YDyKVUGoBEnLf3R6m1xxzu44QYMuzXh318cYdUb+82revpr+10fM6j0VrYcbYz+bikbrgkoluHTgHHvWbAGgca+WPPH2CLwCfBj3xevcunCDJc/OKmbbqni9d3PGfr0Lq2Klf9Nqtn25/Qz1ygXQuU552tYI48DVOAYu/cu2L3s0se3LehU4fD2ewcs2IoC2NcILNErdEb39JBW6Nmbw3vmY9Ub2vJL3aMKATR+wvpet5+D+6avpuGAUajctt3ae4pb9rVPVB7Sh7nO27vQ3Nhzlyve7c+cPa12H7JgUMm8WXhm8n+MV4HZaNnEZOppXKX4sngLbdWB7xq78yxYzojY1wgL4dOMR6lUIprP9h8HGk1d5qEkNChkm8J5c3nGSml2aMHHXAkw6I7+8uiJ32ti/ZrGs93R8wgLo9NIAEiNvM+bPDwA49NVmjn+/k17ThqD1cOOJT22vbk6/ncTaFxYUHdBiJWnWUsKWz0KoVWT+sgnT1Sj8xz2L4dxlcnYeJGDyCwgPd0LnzwRsb8aMf9n21rHw1fPRVq2I8HCn0tY1JL65AN3+Eh4DtlhJ/OATyq2ahVCpyPhlM8bIKALGP4v+3GVydhzEtUEtwj9+E5WPN55dWmMe/yzR/UYhNGoqfGN7fNualUP8a3OgpEfhrFZ0qz/Gc9pHoFJh3LkB660buA0ajvn6JczH9uPa81E0DZuD2Yw1O5OcZR8CYNi8Ho8xr+E91/bKeOOujVhv3n01IXr7SSp2bcwT9vNnl8P5M3DTB/xsP38iZjxJ9QFt0bhreerIx1z6difHF/xc7LLjtp0kvFsTHj6wAIvOyJFJecdMjy2z2NLDNmrB8Wlf0nLRaNRuWuK2n8p9I1xRKjzSksqDO6CYLFj0RsypBRtkNGo1018eyZip79rK6Ye7UaNqJZZ+8S31a1enS7sIjpw8y+JVaxACmjeqx4wJeW+8vB2XQFxiMi3sg1DfNauFzCWL8JszD6FSodvwF5aoG3gOG4Hp0kWMB/bjPmAg2mbNUcy2ulDGnNl3FeKfyKNGrWb6uGGMmT7HFrNnJ2pUqcDSr9ZRv1ZVurRpzpHT51n8xfcIIWjesA4zxg27q3wVYLFya+ZKqn/9NkKtIvn7begvRxP2yhByzkSSseUwHo1qUHXVNNS+Xvh2b0nYK09xsftL+PVph1dEfdR+3gQM6grAzckfozt/vfiYViv6tUvxmDgLIVQY923CGhOFa79nsURdxnyq+MYQr9n/Qbh7gNoFTdO25CycVvCNcvkpVowbVuP29GsgVJhP7kJJvI1L58ewxlzHcvk4moheaGo1s9W59NkYfl0OgAguh1uPp1EUBSEEpgN/oiQUXzewlSUdGLvyD6xWhf4RdWxlyYbD1KsYTOcGVQFbz9eHmj6gssRiJe6dZVT84n2EWkXanbrshKHoz+TVZSt8mleXDX55KNd6jwWrlYQ5n1Ppq9kgBPpzV0j9YWOBZUd8Nx3UKm59u4OsS7eoOXUw6aeukbDpGNFrd9B46Tg6HVyEKS2LE6NtI6FkXbpF7G8H6LBnPorZwrnXv8x945zaw5Wgjg05O2WVU1ZCH25JvVnDUAV68cNPn3Hm9HkGDsh7a9vmTTvp2aszJ09vJ0enZ9yY13Kn7dn/u9Pb4B4d2JtBjz3vtPxRo5+hc5e2mExm0tIyGDP61fvf/pJUBPG/PDq8EKIK8IeiKA3sn1fbP69znCaEcAcWAW2x9Qq6rihKHyFETeAnbO8j2wiMUxTFSwjRGZiiKEof+3KXAkcVRVmdL/5Oe7qj9s/9gYVAKrAdaKkoSmf7OEnfAuWB/dgezWuuKEqSECJLURQv+/wfYRtP6TqQBfyGrVHsV2w9mAQwT1EU5/eL5mNKulamB0XGc2X/2kufr74s85j574SWhavtJ5RpvIpPlN1z8neo6tQuOdEDJqrVK/OYkU+vKfOYn1g9Sk70AC2YXbdM4wGsffVqmcccMqdqmcfE98EMpHw3Zo9/cOOwlcYw7/sfC+1uWS0PpsHtbgQ1vru3RD0IP+4pvEH27+RnKdv6af9j00tO9IClPfNimcf0+/rTkhM9aMa7e7Prg3CuY9k+8lO9V9nnUV2x9DcTHhRV89K/1fdBuTFxc5nHvJ7x4MaWKo2nsu/vpRH3Kj3ratkXYmXEGHPuX9vIoS1X/79yv/1P91hSFOUG0MDh87DCpimKosP2aFr++a8AjRy+es3+/U5gp0O68UXE75zv86/YGoHyp0vG1phU2DK8HP6fSt6A344iCvlOkiRJkiRJkiRJkv5drP+9g1z/W8kxliRJkiRJkiRJkiRJkqR7IhuWJEmSJEmSJEmSJEmSpHsiG5YkSZIkSZIkSZIkSZKke/I/PcaSJEmSJEmSJEmSJEn/IoocY6msyR5LkiRJkiRJkiRJkiRJ0j2RDUuSJEmSJEmSJEmSJEnSPZENS5IkSZIkSZIkSZIkSdI9kWMsSZIkSZIkSZIkSZL072C1/NNr8D9H9liSJEmSJEmSJEmSJEmS7olsWJIkSZIkSZIkSZIkSZLuiWxYkiRJkiRJkiRJkiRJku6JHGNJkiRJkiRJkiRJkqR/B8X6T6/B/xyhKMo/vQ7Sf5nkRzqV6UHh89WXZRkOgIzhw8s8Jv/A9W3XsfJlGq9lxfgyjQfg5mcu85j/RFl15Vxwmce8qnYr03g9q98q03gA7pXVZR5TH132A0pajKLMY568Glqm8WoFppZpPACdTlvmMTWasj9+rmf4lHlML1G2+WzYLrFM4wF4zH6/zGPmzHijzGMq5rL/rXHxYFCZxqtWM7lM4wGoXct+u/4T9Z+bl/zLPKZaVbbb9j3xz/we/znqt7KvHJQR442j/9pGDm2VFv+V+00+CidJkiRJkiRJkiRJkiTdE9mwJEmSJEmSJEmSJEmSJN0T2bAkSZIkSZIkSZIkSZIk3RM5eLckSZIkSZIkSZIkSf8OVjl4d1mTPZYkSZIkSZIkSZIkSZKkeyIbliRJkiRJkiRJkiRJkqR7IhuWJEmSJEmSJEmSJEmSpHsix1iSJEmSJEmSJEmSJOlfQVHkGEtlTfZYkiRJkiRJkiRJkiRJku6JbFiSJEmSJEmSJEmSJEmS7olsWJIkSZIkSZIkSZIkSZLuiRxjSZIkSZIkSZIkSZKkfwerHGOprMmGJalUXJpH4DnqJVCp0G/+E/2Pa52muz7cD7c+j4LVgqLTkb1kHpboKFyatMBj+CjQuIDZRPbnyzCfPnHf6/PGrAXs3neYAH8/1n+z/L6XB+DSLALPF+x53PIn+nX58vhQP9wesedRryN7qS2Pmpp18Bw/xZZICHRrV2M8uKd0MctwuzZ6/1nCujXBojNybMJy0s7cKJDGr1FVmi8ejdpNS9y2k5x+4z8ANHhzCOE9mmE1mcm+Ec+xiSswZeTg37Q6Tec+n5t38+rP0e3cV2C5bm1a4jd5HKhUZP/6F5lffec03WvIILz690axWLCmpZHy7lwscQm504WnB2Hff4Fu1z7S5i4pzaZ14tIiAq8XX0KoVOg2/Inue+ft7NanH+798rZz5sJ5WG5G3VUMbcsIvMbZ9+Vff5LznXMM90GP4977EbDnMWPuHKwJ8QB4vjAa11atAcj+5j8Ydu4oMo5flyZUe284qFXEr9nG7aXrnaYLrYZaS17Cs1E1zKlZXBq9AEN0IsEDO1DuxX656TzrVeZUj6lkn7tBpdefImRwJzR+nhys/kyhcVu++wzluzbBojOwb9JKUs7eKJAmoGEV2i20HT+3t5/kyJtfA9Bx2Xh8qofbtpOPB8aMHP7oOcOWn7oVaTNnBC5e7ni6GEkeNQaMRudtGxGBz8vjQaVG9+efZK9x3rYejw/Go88jucdP+ocfYY23bVuvMaNxbdMaoVJhOHKUzI9Ld/xoGrbE7RnbMWva+ReGP5yPWW3XPmi79werFUWvQ/fFQqwxUaDW4D5iEuqqtUBR0H39CZaLp0qMp42IwHv8eFDb8pizNl8eBw/G/ZG8PGZ85JDH0aNxbd0aVCqMR4+SuaR0eXRt1RLfibaYOb//SdbX3zpN93xyMB59e9uP2XTSZn2EJc4WUx0agu+0KahDQkBRSJn8eu60O8vu+J+JCLWK6DXbubbkN6dlq7QaGi0dh2+jqphSszgxajG66EQAqr/cnwpDuqBYrJyfsZqknacB0Ph40HDBaLzrVAAFTk9aTtrRKwBUfr4XlUZ2R7Faydl1iOT5n+dtu/YtCJo2BtRqMtZtIO2zH5zWxa15A4KmjcG1VjXipswie/Pe3GmBrzyPR6cIAFKXrSVr465SbVuvjs0o99YLoFKR+v0WEpevc5ruEVGfcjNfwK1OFW6+/BEZG/Y7bx8vd2pt/pSMLQeJeWtFifE8OzQn9I3RCLWKtB82kbzyR6fp7i0bEDZjFK61q3J70odkbsy7TmvCgwmfNQGX8CBQIHrkm5huJzgtu+P0sQ90X1Z54WEqDu0KQPSa7dxYuQGAsL6tqDllEF61ynPi4WlknbqGf5cmVH9vOEKtIm7NNqILuebVXvIS3o2qYUrN5MLohRiiE3GtGEyL3YvQXY0BIOPYZSJfW+U0b/2vXsOtcgjHOk8ucttqmkTgMcJ2/TFs+xPDL87nprZnP9weGoBitYJeR/byeVhvRaEKDsNn8VdYYqIBsFw+T87KBUXGcbT32BnmrPoWq1VhYI8OPD+4t9P0mIQk3lz8JakZWfh6eTJr8kjCggIAGPPWQs5cukrTujVZ+taEUsXTNInAY7hDHtfny2OPfHlcYcsjgLpSNTxGT0a4e4CikPH6GDAZCwvjxKVpBB7P28pMw9Y/0f+cr/7Tqx+uDzvUuT7NiwmgCgrB9+Ov0H2/Gv2v3xcZx69LE6q+OwLUKhLWbuP20l+cpguthpofv2wvMzO5PHoBhluJBA3sQPmx/XPTedSrzKmer5Jz7gZBA9pT/uWBoIAxPoUr4xcDyYXG/yfKL0dlUf/Jr6T6kFuffnj0fxTFMWbU3cX06dyUSu+MBLWKpG+3EPfJz07TvVrVo+Lbz+NRtwrXxs0j9c8DudNqfvMmnk1rk3XkPJHDPih1TO9OTanw9gsItYrk77YQ/+lPTtM9I+pR4a2RuNetwo3x80j7y3Zdd69XlYofjEHl7QEWK3FLfyTt972FhSjU82+/QLMuLTDoDCydsohrZ68VmXbaZzMIrRTGxJ4vAfDs9GG06BaB2WQmPiqWJa9+TE5GdqljS9LdkA1LZUQIMQzYrChKzD3OP11RlFkPdq1KSaXCc+xEMt6YjDUpEd+FKzAd3IclOq8QMO7cimGDrbLp0qotHi+MI/PNqVgz0sl4ZxpKSjLqylXxeXcuqc8Nuu9VGtC7B0Me68f09+bd97IAWx7HTCRj5mSsyYn4LliB6VC+PO7aimGjPY8RbfF4fhyZb0/FfPM66ZNGg9WC8A/A7+MvMB7eD1ZLyTHLaLuGdmuCV7UwNrd5Bf9mNWgyZwQ7e79ZIF2TOSM4PvkzUo9H0nbtVEK7NiZ++ykSdp3h3AffoVis1H/jSWq93I9z739HxsVodvR6A8VixS3Ej147P0C35wBYHO4SqFT4T32ZhPFTscQnEvrVp+h2H8B8PS+fpkuRxD87FsVgwPOxvvi9PIrk6e/nTvcdMxzDidPFb89itrP3SxNJe822nf2XrsB4YJ9TxcmwfSv6P2zbWdumLV5jxpE+ferdxXh5IqlTJ2NNTMT/0xUYDuxzqiiZI6+QMnYUGAy49+2P16gxZLz/DtpWrXGpWYuUUSNB64L//MUYDx9CyckpNE612SM59/i7GGNTaLzxQ1I2H0V3+VZuktAh3TCnZXO8zUsE9W9HlTeGcmn0QhJ/3kPiz7YGT486laiz2taoBJCy+SixX2yg+YHCK63luzbGp2oY69tPJqhZdVrNHsaGvm8XSNd69nAOTP2MpONX6fb1q5Tr0oiYHafZPXZpbprmbw7BlGHLm1Cr6PDxWPZOWE7q+Zv0apoBZnOBPPtMmkDqK1OwJCYSuHI5+r35tu2VKyS9MNq2bfv3w3vsaNLffheXBvXRNmxA8nBb42fA0iVomzTBePJkkbvStmIq3J57mew5U1FSEvF691NMxw/YGo7sjPu3Y9z+BwCapm1we3oMOXOnoe3yCABZ019A+PjhOWU2WW+9CIpSdDyVCu8JE0ibYstjwPLlGPY559F05Qo5o+157NcP79GjSX/3XVzq18elQQOSn7fl0X/JElyaNMFUUh5VKnynTCB5wqtYEhIJ/nw5+j37Md9wiHn5CkkjxqAYDHg82g+fF0eT+ua7APjNnEbWV99gOHIM4e4GVqXAsvc8+iH6mGTabZpFwqZjZF2+nZukwpAumNOy2NV6IuED2lB75hBOjlqMV63yhA9oy56OU3AN8yfixzfY1WYiWBXqvf8ciTtOcmLkQoSLGrW7KwAB7eoR+lALbj46Fkwm1AG+TusS/MY4bo+chjk+iYrfLyF7x0FMV2/mJjHHJpIwfT5+w52voR4dI3CtV4PogWMRWhfKr55L9p4jKNmFnJv5tm25d8dw/ZmZmOOSqf7rAjK2HsIQGZ23bW8ncuvVRQS98Gihiwh9ZSjZh88VH8chXtjbL3Jz2AxMcUlU/WkRmdsPYnSIZ45JIOa1BQQ8/1iB2cvNnUzysu/J3ncC4VFwX4a9/SL7Bs1+YPvSq1Z5Kg7tyr6HZqAYzbT8bhoJm4+TcyOezIvRHB+xgAZzX8iNX2P285x5/D0MsSk03Tib5M1HyXG45oUN6Yo5LYsjbV4iuH9bqr4xlIujFwKgj4rjePdXC91sgb0jsGTrS9y2Hi9MIOvdKViTE/GesxzTkX1ODRzGPVsxbraX0y3a4jFsHFnv28oPa3wMmVNGFh8jH4vFyqzla1j53mRCA/156pX36NyqCdUrlctNM/+LH+jbtS39u7Xj0KkLfPzVT8yabNtmwwb2Qm8wsm5D6RpBUanweH4CWe9NwZqSiPfs5ZiO5svj3q0Ytzjk8blxZH0wFVRqPF6eQc6SWViiriK8fMBiLiqSc8xRE8l821bn8vloBcbDzjENu7di2GSP2bItHsPHkfVeXrnsMXwcphOHS4xTbdYLnHviXYyxyTTaMIeUzUecy8ynumFOz+JE2/EE9m9H5Tee4fKYBST9vIckxzLzy9fIOXcD1CqqvjeCE50mYE7JpPIbzxA+/GH4bVmh8cu8/MoX/2+v/xQWs4T6UKExp91dnavS+6O5POQtTLHJ1P1zLmmbD6O/krdfjbeTuPHKx4SOHlBg9rhl61G5uxI8tOddxaz4/mgin7bFrP37PNK3HEZ/xeG6HpNE1OTFhI52vq5bdQaiJi3CcCMWTWgAdf6cT+auE1hK0cDTrEtzwquWY1yn0dRqWptR74/l9QGFX9NaPdQGXY7zNe3UnpN8M+c/WC1Wnnn9OR57cRBff/hV6fMtSXdBjrFUdoYB5UpKVIzpdzuDEOKBNBxqatXFEnMba1wsmM0Ydm/HpXV7pzSKLq+iLdzcwV4vtVy7gpJiu4tjiboOrq62Xjb3qUWThvj6eN/3cu7Q1KyLJfY21niHPLYqIY93GAy5jUhCqy3+B6RjzDLcruV6NefmD7YKUurxSFx8PHAL8XNK4xbih4uXO6nHIwG4+cMeyj3UAoCEXWdQ7I1FqccicQ8PtMXWGXO/V7m55K6fI239Opiib2O5bctnzpYduHdq65TGcOwkisEAgPHMBdQhwbnTXOrURB3gj/7QsSLzVxxNbeftrN+5HW3bfNs5p/DtXOoYdepivn0ba6x9X+7Yjmu+GKaTJ2zHCmC6cB5VsC2PmspVMJ4+ZTuG9HrM16+ibdmq0DjeTWugvx6H4WYCislM4vp9BPRq6ZQmoFdLEn7YCUDSHwfwbd+wwHKCHm1P0vq8HgtZx69gSkgrMn8VezXn6jrb3bWk41fR+nrinu/4cQ/xw8XbnaTjVwG4um4vlezHj6MqfVtx/VfbncNynRqSeiGa1PO2H/lKRkaBrssudetguX0bi33b6rdtx619O6c0xhMn87bt+fOo7dsWRbGdkxoNuLggNBosqSlF5vMOdfU6WONvoyTGgsWM6eAOXJo7H7PoHY4ZV7fcY0ZVvjLm8yfs+UlDycmy9V4qhkudfHncvh3Xds55NJ10zqOqmDxaU0rOo0u9OphvxWCJscXUbd2OW4d82/W4w3l57nzueampUhnUagxHbOekotPnpnNcti4qAcVkIXb9fkLzHQuhD7Xg1g+7AYj7/RBB7evnfh+7fj9WoxndzURyrsfh16wGGm93AtrU5dYaW28+xWTBbG+grPxcD64u+RVMJgAsKem5cdwa1sZ0MwbzrTgwmcnasBOvrm2c1sUcE4/x8vUCx562RiV0R8+AxYqiM2C8fB3PDgWP6fw8GtfEGBWLKToexWQm/ffd+PRwPqdNtxPQX7zh3IhzZ50bVEcT5EfmntL17nVvVAtjVAymaFseM/7cjXc35zyabidguHQDlPx5rIhQq8neZz9mc/Qo+rx9eWfZD3JfetUsT9rxSKz28iNl/wXCHrH1Csu+EkP21djcZXs3rYHuehx6h2teYC/n+IG9WhL/g60RJfGPg/i3b1DiNlN5uFFhdF9uLvqp2HTqGnWwxuXVDUx7t6Nt6Xye4FBO4+ZW6jpAUc5euUal8BAqhAXj4qLhoY4R7DjkfCxcuxlLq0Z1AYhoVIcdh07mTmvduB6e7m6ljpebxwR7HvdtR9uimDy65uVR07gFlqhrWKJs130lq+A1vDCamnWxOtS5jHu3o41wLjMdYwpXd6dJLhHtsSTEYrl5vdg4Xk1roLsRh+Gm7VxM+nVvgTLT/6GI3DIz+Y8D+HYoosz81VZmCiFAgNrDto3V3u4Y41MLjf9PlF+OyqL+UyBmKepDBWLeJc8mNTHciMVo368pv+7Fr6fzNdZ4KwHdhahCr7GZ+05jzdbdVUyPJjUx3IjLjZn6+x58e0YUiKm/GGXr2efAcD0Gww3bdc0cn4I5KR1NgE+p4kb0aMXOn2zl3uUTl/D08cQ/xL9AOjcPN/qN7M+6Jc49ck/tOYnVXk+/fOISgfb6uyT9Hf5nGpaEEFWEEBeFEKuFEJeFEGuEEN2FEPuEEFeEEBH2dJ5CiC+EEIeFECeEEP0d5t8jhDhu/2tr/76zEGKnEGKdfflrhBAiX+xBQAtgjRDipBDCXQjRXAixSwhxTAixSQgRLoTwFUJcEkLUts/3rRDiBSHEh4C7fd419nU567D8KUKIt+3/7xRCLBJCHAUmFBbnbredKjAIa1Jet3hrUiLqwKAC6VwfGYDfZ2vxGD6G7BWLC0zXtuuE+eplMJvudhX+dgXymFxEHnsPwG/lWjyGOedRU6suvp+sxm/Jl2R/uqDk3kqFxfwbt6tbuD+6mLwKiS42Bbdw/4JpYotPA1D5qc7Ebz+Z+9m/aXW67/qI7jvmkPrhQufeSoA6OAhLfGLuZ0t8Iurggvm8w7P/w+j32+9CCoHfxDGkLb73xx1VQUFYEvNt56CC8d36DSDgq7V4jhxD1qcFt3Nx1EFBWB1jJCaiKiRGbqyHe2M8fAgA89VItC0jwNUV4eOLtnFTVA4Na4604QEYY5JyPxtjk3ENDyiQxnAnjcWKOTMHTYBzI2xQ/7YkrS99N2yPMH9yYvK6+efEpuAR5l8wjcPxU1iakFa10SWmk3nd1s3fp1oYCgrd10zlkY3v4/nUkwViq4KCsSQ4HD+JiXmNKoVwf+QRDIdsx4/p3HmMJ04S8svPhPzyE4bDh7FE3Sxy3juEfxBKSl5Ma0oiwr/g/tR274/XvK9xe3IU+q9tvbIsN6/i0qwtqFSI4DDUVWohAkKKjacKDsaa6BAvMTHvx0UReTQetufx/HmMJ08S/PPPBP9kz+PNkvNoOy/zjllLYgnnZZ/e6A/ajllNpQooWVn4z3qH4NUr8Rk3GlR51Yn8y9bFpOAa5nycuoUHoL9tO6YUixVTpg6XAG9cwwLQ3c471vSxKbiFBeBeKQRjcgaNFo+l3dbZNFwwCrWHrceSZ/VwAlrVocJ3iyn/1VxcG+Q15KlDAzHF5W1bc1wS6pCi8+nIcPEaHu1bINxcUfn54B7RGE1Y0fvlDk1YIKbYvPPUFJeMS1gpK/NCED7jeWJnfVG69PZ4Zqd4SWhCSxdPW6UClsxsyn8yg6q/LiHktRFO+zL/sh/Evsy8GE1Aqzq4+HuhctcS3L0JbuULX1/X8AAMDtceQ2wK2nw/jFyLuea5VQqh2ZaPaPTLO/i0qpM7T5XXnuDW8t+x6AwURxUQjDUp37UgsOAx4PrQAHw+WYPHM2PI+eLjvPlDwvCeuwqvdxehqVuwwaIw8clphAblbePQQH8SktOc0tSqWpGtB2wNu9sOHCdbpyctI6tUy89PFRCMNbkUeew1AJ8la/AYmpdHdXhFQMFrxkd4z1mJa7+C1/DCiIAgLPnqXKrC6j8PD8B32VrcnxtDzmf2ctnNHfeBQ9B9X3KvC9ewAIy3HcvMFLT5zkXXMIdy1WLFklFImdmvHUm/2G7OKWYL115bSePtC2hx8jPca1Ukfu22QuP/E+WXc/y/v/6TX2nrQ+79BxD49Vq8Ro0ha+ndxdSGB2B0uC4Z45LR5qsLPWjasMAC9S+XUl5nHXk0rolw0WCIiitV+oCwQJJi8o6h5LhkAgqJ+9Tkp/lt1XoMxVzTuj7eneM7j9/1Ov+/pVj/vX//pf5nGpbsagDzgTr2vyFAe2AKeT2CZgDbFUWJALoAc4UQnkAC0ENRlGbAE8DHDsttCkwE6gHVAKfbEYqirAOOAk8ritIEMANLgEGKojQHvgA+UBQlHRgPrBZCPAn4K4qySlGU1wGdoihNFEV5uhT51CqK0sK+jgXilGpL3QPDn+tJGzmEnC9X4P7Es07T1JWq4DF8NNlL5v9d4cuE4a/1pI0aQs5Xznk0X75A+rhhpL8yBvfBT4OL9sHF/C/arrUn9EcxW4j+Ka+3S+qJq2ztNJUdD72B97AhoL33HmkeD3dHW7cWGV/b7rh4DeqHft9hLAlJJcx5//S/rSfluSFkf7YCjyHPljzDPXLt3gOXWrXJ+cE2Zo/x2FGMhw7i//En+L7xJqbz5wo0zj1IXk1rYtUZyLkYXXLiB6zqgDbc+DVvnAOhVhPSshZ7xn/KxgHv4tqhA9pmze55+W49euBSuzbZ39q2rbp8edSVK5E4aDCJjw1G26wZLo1K9+OuNIxbfyVryjPov1+Fa/+hAJh2bcCakojXu8twf/pFzJHnHmglwK1HDzS1a5P9XV4eNZUqkTR4MEmD7Xls+ODyCODeqzsudWqTtcY+jolajbZxQzKWLifx+TGoy5XDo/dDDzRmfiqNGp+GVYn6agv7uk/DnGOg2ku28U+ERo2Lvxe3npxA0rzPCFsw44HE1O0/Ts6eI1RYu5CwedPQn7qQ20Pz7xL4TG8ydx7FHFf4eC0PmtCo8GhRn4QPP+f6wAm4VAzHd2D3vzVm9pUYri79jYjvpxPx7TQyzkb9LdvVGJ/KoeZjOd5jKtfe+oq6n05A7eWOZ/0quFcJI3lDCY9R3QXDxvVkjHuanK9X4PaYbZw6a2oy6aOfIPPVF9Ct/hTPiTPB3eOBxJs8YjDHzl7m8Qlvc/TsJUIC/VGp/t4qvWHTejJeepqcNXl5RK1GU6ch2R9/QObMl9C26oCmwb1fwwvE3LCe9LFD0P1nBe6DbeWy+xPD0P/2I+jvrsfJvfJqWhOLzkDOJVuZKTRqQp/rxakeUzjaZCQ556Oo8HLhj7TejbIuvxyVVf3Hke7X9SQ/M4SsVSvwGFo2Mf9pmhB/Ki+aRNSUj++7Z6OjKvWqElY5jEObDhaZ5rHxg7GaLez+ZecDiytJ+f2vNSxdVxTljKIoVuAcsE1RFAU4A1Sxp+kJvC6EOAnsBNyASoALsEoIcQb4EVsj0h2HFUW5ZV/uSYdlFaU20ADYYo/zBlABQFGULfb1+QS4uwfz89wZxbDIOPkJIUYJIY4KIY5+dTPWaZo1OQlVUN4dd1VQMJbkon/oG3dvQ9smr9urKjAY7zfeJ2v+LKxx9zTE1N+uQB4DS5HHfI+tAVhuRaHodKgrV737mA94u7o+MoCuW2fRdess9PFpuJfLu5vjHh6APta567Y+NhX38KLTVHqiI2E9mnFk3CeFrl/mlRiUHB0u1Z3zbklMQh2ad4dOHRqMJbFgPl0jmuEzfAhJk2fmPtKibVQPr8f7E/7rGvwmjMazdw98x9/daWFNSkIdnG87JxW9nQ07t6FtV3DfFseSlITKMUZwMNZCYrg0a47nkGdImzk9N48AOWu/IXX0SNKmTgYhsNwqvNHHGJuCtlzenT9teCAGh15Cd9K43kmjVqHx9sCckpk7PXhAO5J+KTjAen5hwx+iz+YP6LP5A3TxaXiUy7tD5hEeQE6c8/GTE5eKh8Pxkz+NUKuo9HBLbvx2KG+e2BQSDl3CkJqFRW/EcPAgmlo1nZZrTUp0ejRSna93T+62aN4cr2eHkjYtb9u6dmiP6dx5FJ0ORafDcOgQ2vr1S8y7kpqECMiLqQoIRkkt+phxelTOakW/ZhlZb4wmZ9GbCA8vrLG3ipwX7Hd0He5iq4KDsRSRR8+hQ0mb7pDH9u0xnc/Lo/HQIVxKkUfbeZl3zKqDCz8vtS2a4fXcUFJem5H3qFlCIqYrV22P0Vms6PfsxaV2zSKX7V4uAEOc83Gqj03J7aUi1CpcvN0xpWRiiEvB3aH3ilt4APq4FHQxyehjUki3P6ob9/shfBtWsS0rJpm4P22NBIYzl8BqReVvG2fJEp+Mi0MvI01Y0F01VKeu+JbogS8SM3IaIDBFFb8vAcxxybaBsO1cwgIxlbKhyKNpHQKf6UPtPZ8RPn0Efo92JXTqcyXG0zjFC8IcX7p4prgk9Beu2R6js1jJ3HIAt/o1ilz2g9iXALfW7mBfz+kcHPAOpvRsp8ffHBliU3B1uPa4hgdgjE0uJE3Ba55iNGNOtfXiyTp9DV1UPO7Vw/FpUQuvxtWIOPIJTX59D/dq5Wj089uFxremJKIKynctSC54bt5h2ufwSJfZZHs0DLBcu4wlLgZ1uYpFzntHaKAf8Ul52zg+OZWQQD+nNCGB/iycPo4fFr/Ny88MBMDH694arawpiagC7y2P1uREzOdPoWSmg9GA6fhB1NVqFjnvHUpKEup8dS5rcfWfvdtwscfU1KqH+3Oj8V3xHa59B+H22FDbIN+FMMSloC3vWGYGYMx3LhriHMpVtQq1j3OZGTSgnVMPX8/6VWzzRdl63ib/vh/vFnm94Rz9E+WXc/y/v/6TX2nrQ7kxd2wr8KhcSYyxKWgdrkvasECM+epCD5oxLrlA/ctUyuss2F7IUP3LmcTO/YacE5eLTfvQs72Z/9ci5v+1iNSEFILK5R1DgWGBpOSLW7tZHao3qsHyvauYte5DwquW493v8voRdBnUlRbdWrJwwv/vm/vSf7//tYYlx/6BVofPVvIGMhfAY/beQU0URamkKMoFYBIQDzTG9libY5cUx+VaKHlQdAGcc4jRUFGUngBCCBVQF8gBCj6HZGPGed/lf5j+zmhwRcbJT1GUlYqitFAUpcVzlZyfljNfvoi6fAVUoWGg0eDasSumQ84/TFXlyuf+79KyDdYYW+VbeHrh/faH5KxegfnCWf5bma9cRF0uXx4P58tjuEMeW+TlURUaBiq17f/gUNQVKmFNKLmL69+9XQ1/rmd79+ls7z6d2I1HqfR4BwD8m9XAlKlDn29MHX1CGqYsHf7NbD8qKj3egZhNtq72oV0aUWtcHw48Nw+LLu9tLx6VghFq26HoXiEIlyoVscQ45914/iIulcqjLmfLp0ePLuh2O78ByaVWDQKmTSJp8kysqXnrlTJzNrF9hxDb/2nSFq8g+68tpC/9rNjtmp/5kn07h9niu3XuivGA83ZWl8/bztpWbbDcLvnHo1OMixfROMRw7dIVw37nGJoaNfGZNJn0mdNQ0vLyiEqF8LE9a6+uVg1NtWoYjx4tNE7myUjcq4XjWikE4aIheEA7UjYfcUqTsvkoIY93BiCoTxvS9zkcH0IQ2K8NiaV4DC7uy4380XMGf/Scwc1Nx6g+yFbxC2pWHVNGDrp8x48uIQ1Tpo6gZtUBqD6oPdGb8sbFCu/QgPTIGKfH5WJ2ncavTkXUblqEWoW2SRMsDoNHA5guXkJdoQLqcPv+69YVwz7n40dTswY+U14hddp0rA7b1pqQgLZJE1Crbb1smjTGXIo3z1iuXUQdVh4RHAZqDS6tu2A6nu+tXaF5x4ymSWsscfbBjLWutjFIAE2D5rY3qsUUH9N0yZbH3GO0a1cM+/PlsUYNvF95hbTp052OH0tCAi4OeXRpXLo8mi5cRFOhfO52de/eFf3efDFr1cDvtVdImTrD6bw0XbiEyssLlZ+t8ca1eVNMjoPx25ftXikY4aImfEBb4h2OBYCETceo8HhHwPYmsOS9toGq4zcdI3xAW1RaDe6VgvGsFkba8UiMienoY5LxtL9dMKhDg9wBpOM3HCWwne0Hl0vl8uDigjXVNs6S/uwlXCqXR1M+FFw0eD3cmewdRd/VdaJSofK1PRKjrVUVbe2q5Owreay3nNNXcK1SDpcKoQgXDb59O5KxtXS9Y6InzedS+xFc6jCS2FlfkPbLduI/Kv6RH92Zy2jt8XDR4PNIRzK3lS6P+tNXUHt7oraP9+HZpjHGyLzHbe4s+0HuSwBtkC2eW/lAwnq3JObnwhu771zz3Byuecmbna+PyZuPEvp4JwCC+7QmzX7Ncwn0yX2sz61SCO5Vw9FHJRD71WYONRnN4ZbjONl/JrprMZwe+Hah8S2Rl1CFV0AVYjtPXNp3xXg037XAsW7QvDWWWNtxKXx8c+OrQsNRh5fHGl/yzbX6NasSFRPPrbhETCYzG3cfpnNEE6c0qemZWO3juHz241882v3eGwMK5LFdIXkMc8hjs7w8mk8dRl2pmu26p1KjqdcEy62Srz/mKxedYmrbd8V0pJg6V/M2uQ30mTNeIn30k6SPfhLD7+vQ//QNhg3Ob3q7I+tkJO5Vw3GtaDt+gvq3J2WT8/GTuulIbpkZ2KcN6XvzlZl92zqNSWiIS8GjVkU0gbZj2LdjI3RXCq8z/BPll6OyqP8UiFmK+pBTzNZ3HzP71BXcqoajte/XgP7tSdvy4HogFibn1BVcHWL69+1AeiljChcN1VZNI+XnHblviivOxv/8xeTeE5nceyKHNx+i82NdAKjVtDY5mTmkJjjf2Nv0zQZGRgxnTPsXmD7odWKvx/Dmk7aeu007NWPAmIHMfv59jPqS39YoSfdDvhWuoE3AS0KIlxRFUYQQTRVFOQH4ArcURbEKIZ4D1He53EzgzkPbl4BgIUQbRVEOCCFcgFqKopzD1oB1AdujeV/a05gAkxDCxf5/PBAihAgEsoA+wMZCYhYXp/SsFrKXLcLnvXm218Ju+QvLzRu4Dx2B+cpFTIf249ZnIC5NmoPFjJKVRdaC2QC49XkUdbnyuD/1HO5P2e66ZrwxBSU97a5WIb9X3/qQIydOk5aWQbcBQ3nx+Wd4rG+ve1+g1UL28kX4vGPP41Z7Hp+25/GwQx7N9jwusuVRU68R7oOG2N5mpShkLV+IkpFeQkDKdLvGbT1JaLcm9Dy4EIvOwLGJea+v7rp1Ftu7254EPfn6FzRfPAa1m5b47aeI33YSgMazhqHSutD++2kApByL5ORrXxAYUZvaL/XDajKDVSF1zsdY0zOcg1uspH60hOCP5yDUKrJ+24D5WhQ+o4dhvHAJ/e4D+E0YhXB3J/BD25vqLHEJtp5LD4LVQtbSRfjOnodQqdBv+gtL1A08nhuB+fJFjAf249Z/INqmtu1szcwi86PZdx0jc8ki/ObMs7/S1xbDc9gITJdsMbxGjUG4u+Pz5ju2WRISSJ85HdQa/BfZ3samZGeTMfuDosfosli5Nv0z6n/7hu3Vyd9uR3fpFpWmPkHWyaukbD5K/Npt1Fr6Ms0OLMGclsUl+9uRAHza1MMYk4zhZoLTYivPHErwox1QubvS4vgK4tduI3pe3gCQt7edpHzXxjy6bz5mnZH9r6zMndZn8wf80dNWgTk0fTVtF45C46bl9o5T3N5+Kjddlf6tnR6DAzCm53B+5QYe+etdFEXBdHwvhoP5fhBbLGQsWoz/vLmgUqH7awPmGzfwGjEc06VLGPbtx3vsWIS7O37v2LatJSGetGkz0O/chbZZU4JWfwGKguHQYQz7ndeh8P1pRfefJXi+OgdUKky7N2C9HYXrwGFYrl/CfOIA2h4D0NRvZjs3s7PQrZwDYHsT3NQ5YLWipCaRs7wUx5LFQubixfjPteVRv2EDlhs38Bw+HPOlSxj278fLnkdfex6t8fGkzZiBYdcutE2bEviFPY+HD2M8UIo8WqykL/iYwIUfgVpFzh8bMF+/gffI4RgvXsKwdz++42zHbMD7b9tmiY8n5bU3wGolY+kyAj+eD0JguniZnN/+KLDsiO+mg1rFrW93kHXpFjWnDib91DUSNh0jeu0OGi8dR6eDizClZXFitO3p8qxLt4j97QAd9sxHMVs49/qXuYOvnpv+JU0+HY/QasiJSuD0BNvYa9Hf7qDRojEE/boCxWQiYfpcp3VJ/OATyq2ahVCpyPhlM8bIKALGP4v+3GVydhzEtUEtwj9+E5WPN55dWmMe/yzR/UYhNGoqfGO7w2vNyiH+tTmle0zVYiXmreVU/c87oFKR+uNWDFduEjLpaXRnrpC59TDujWpSefl01L5eeHdrSejEp7nSa1zJyy4iXtw7y6j4xfsItYq0dZsxRt4kaMJQ9GeukLX9EG4Na1Lh05mofbzw6tKK4JeHcq33WLBaSZjzOZW+mg1CoD93hdQfNhZY9oPel80+fwUXfy/b99O+zB2IPfThltSbNQxtoA/e30wj6+wNIqd/ToNvZyDUKuK+3UHOpVtUnvoEmfZrXtza7dRZ+hItDyzBlJaV+0Y439Z1qTz1CRSTBcVq5crUlZjT7nIcIquFnM8W4zXTdm4at2/AGn0DtyeHY4m8hOnoflwffhSXRs1RzBaU7Eyyl96pGzTG/cnhKGYLKFZyVi5AycosISBo1Gqmj3masW8txGK1MqB7e2pULs8n36ynXs0qdGnVhCNnL/HxVz8hhKBZ/VrMGJs3SsJzr33IjVux5OgNdB82hXdeHka7ZsUMaG61kPP5Yrxm2PO4YwPWWzdwe2I4lqsOeWzYHMViQcnKy6OSnYXhjx/x+XA5KGA6cRDz8VI0alot5KxahPdb9vrPtr+wRN/A/akRmCMvYjqyH7feA9E0yqv/ZH98l+Uy5JaZ9b6diVCriP9uO7rL0VR89UmyTkWSuvko8d9uo+aSl2m6fynmtCwuj3EoM1vfKTPjc78zxacSveAHGvzyHorJguFWIpETl1CpsKHb/onyK992/tvrP4XELKk+5D5gINpmzVHs9emMOXcZ02Ll5sxV1FrzFqjUJH+/Ff3laMpNeYrsU5GkbzmCR+Ma1PjsddS+Xvj1aEG5V57iXLeXAaj90yzcapRH7elGoyOfcWPKUjJ2nSwx5q2ZK6n+9dsItYrk77ehvxxN2CtDyDkTScaWw3g0qkHVVdNQ+3rh270lYa88xcXuL+HXpx1eEfVR+3kTMKgrADcnf4zufPGDzwMc236UZl2a8+nuFRh0BpZOyRuJZf5fi5jce2Kx8498dzQuWg1vfWN7o+vlE5dYMaOQNxhK0gMglAf4jOd/MyFEFeAPRVEa2D+vtn9e5zhNCOEOLALaYusVdF1RlD5CiJrAT9jel7ARGKcoipcQojMwRVGUPvblLgWOKoqyOl/8x4BZgA5og+0xtY+xNVhp7DF3A+uBCEVRMoUQC4BMRVHeEkLMAfoBxxVFeVoI8TIwAbgNXANuKIrythBip319jtrjNskfR1GUVcVtq+RHOpXpQeHz1ZdlGQ6AjOHDyzwm/8BYa7uOlS850QPUsmJ8yYkeMDe/Urza+AH7J8bNu3Ku5EGDH7Sr6tK/WehB6Fn9/u6U3gv3ynd7j+D+6aNLHtz/QbMYRcmJHrCTV0PLNF6twMLfzPR30uke3Hh6paXRlP3xcz2jdG8wepC8RNnms2G7oh//+rt4zH6/zGPmzHijzGMq5rL/rXHxYOkG6X9QqtUsm3HRHKldy367/hP1n5uXinqA4++jVpXttn1P/DO/x3+O+q3sKwdlxHBx17+2kcO1Tqf/yv32P9NjSVGUG9jGG7rzeVhh0xRF0QGjC5n/CtDI4avX7N/vxDYW051044uI/xO2hqk7TgIdC0la12GeVxz+f+1OTPvnj3EeQPzO953zfS4qjiRJkiRJkiRJkiRJ0n35XxtjSZIkSZIkSZIkSZIkSXpAZMOSJEmSJEmSJEmSJEmSdE/+Zx6FkyRJkiRJkiRJkiTpX+6fGBDsf5zssSRJkiRJkiRJkiRJkiTdE9mwJEmSJEmSJEmSJEmSJN0T2bAkSZIkSZIkSZIkSZIk3RM5xpIkSZIkSZIkSZIkSf8OVjnGUlmTPZYkSZIkSZIkSZIkSZKkeyIbliRJkiRJkiRJkiRJkqR7IhuWJEmSJEmSJEmSJEmSpHsix1iSJEmSJEmSJEmSJOnfQZFjLJU12WNJkiRJkiRJkiRJkiRJuieyx5JUgM9XX5ZpvIzhw8s0HoDPl2WbRwCsljIP2aTbuDKNFzioUpnGAxC1apd9zEp1yjxm8NDlZR7zF51LmcYbPL5rmcYD+G7S5TKP+eTCWmUeE0+fMg957MUjZRqvsa+pTOMBuLiV/XXdv6ko85iHNpZ9ddFiKdt8ei75pEzjAaQ/92KZx/T96tMyj4lRV+Yhfbq+XabxPJt5l2k8AHX54DKPKZpGlHlMv5d/KfOYt5LLtszcnHm0TONJ0t9B9liSJEmSJEmSJEmSJEmS7olsWJIkSZIkSZIkSZIkSZLuiXwUTpIkSZIkSZIkSZKkfwerHLy7rMkeS5IkSZIkSZIkSZIkSdI9kQ1LkiRJkiRJkiRJkiRJ0j2RDUuSJEmSJEmSJEmSJEnSPZFjLEmSJEmSJEmSJEmS9K+gKJZ/ehX+58geS5IkSZIkSZIkSZIkSdI9kQ1LkiRJkiRJkiRJkiRJ0j2RDUuSJEmSJEmSJEmSJEnSPZFjLEmSJEmSJEmSJEmS9O+gWP/pNfifI3ssSZIkSZIkSZIkSZIkSfdE9liS7tsbsxawe99hAvz9WP/N8ge2XJdmEXi+8BKoVOi3/Il+3Vqn6a4P9cPtkUfBakHR68heOg9LdBSamnXwHD/FlkgIdGtXYzy4577X5+/I595Dx/hw8UosViuP9enJyKGDnabHxCUwc/YiUtIy8PXx4sOZUwgLCQJgwbIv2X3gCACjn3uSh7t1LFVM93YtCHxtLEKtIuPnjaR//r3TdN9nH8N74EMoFgvWlHQS35yPOTYBgLBlH+DaqC76E2eJH/9mqfOpqtoAbbchoFJhPrUb86G/nKarG7RD2+UJlMxUAEzHt2E5vRsA4R2A9uHhCJ8AUBQMPy5EyUguMea+S7f46PdDWBWFR1vWYkTnRk7T5/5+iCPX4gDQm8ykZOnZ+/bTXIxJZtb6A2TpTahVgpFdGtGrcbWS4526xJz//IbVqvBol5Y836+L0/TYpFTeWP4Dmdl6rFYrE558mA5N63A7MYVHp8ynSrlgABrWqMTM5weWGA/Ao31zQqaPBZWK9HUbSf3sB6fp7i0aEDxtDK61qhI7eTZZm/fmTgua8jyenSJACHL2nyBx1rJSxQTo/9Zz1O3SBKPOyPdTlnH73A2n6S5uWp79dCKBlUOwWhTObzvGX3O+y53e+JHW9Jz4GIoCMReiWDthabHx9l2J4aM/j9r2ZfMajOhYv0CaTWeiWLHjNCCoFebHh4+3B2DRphPsuXwbgFGdG9CrYZViY7V69xkqdG2CWWdg76SVJJ+9USBNYMMqdFg4GrWbllvbT3Loza8BCKhfiTYfjkDt6oJitnBg+mqSTl6jwZhHqDawLQAqtQq3WuXR/zYPjPp/JI+5MS/c5KP1e23HbOu6jOjWzGn63PX7OBJpW67eZCYlU8feWc/nTs/SGxk45zu6NKjKtMc6lCpmfr3efpYaXRpj0hn5bcoK4vJtb42blkHLXsa/UiiK1crlrcfZPuf7whdWBNfWLfGdOB6hVpH9219kff2t03SvJwfh0a83WCxY0tJJ+2Aulrh4AMrt3YLp6nUALPEJpEx9o1Qx3dq2JGDKi6BWkfXLBjJWf+c03fvpx/B61B4zNY3kd+ZhiU1AHR5C8Lx3ECoBGg2Z360n66c/SoynbtACt6deRAgVxj0bMG4ofBtpmrfH48W3yHp3HNaoywhPb9xffBN1ldqY9m1Gv7b4cxEgIt85klLEOdLe4Rw5XMg5YjVbOGg/R6o92pYGL/ZBCIEpW8eBaaup/0RHQrs1waIzcnLCMtLPFIzj26gqTRaPQe2mJX7bSc698RUALn6eNF8xAfeKQeiikzg2ajGm9GwC29al5eop5Ny0lWmxfx3hyoKfbfOE1ERRrIACCpiTrgGw9/Bx5iz9AovFysBHujNyiPP1OSYugTc/+oSU9Ax8vb2YPWMCYcFBHD5xho8++TI33fWbt/nozVfo1r5VidvYkUuLCLxefAmhUqHb8Ce6753rQm59+uHez14X0unIXDgPy82ou4rxT+Rx75FTzFn+tS3mw50Z+UQ/55jxiby5YFVezKljCQsOzJ2elZ1D/1FT6dqmBTPGDytVPr06NiP8zVGgUpH6w2aSlq9zmu7Rsj7hM1/ArU5Void8RMaGfbnT6l/5Ff0l23Y1xSRyc9R7pYqprtMMt4EvgFBhOrgF47Z1habTNGqL+4hpZM+fhDU6EgBVeBXcnhgHrh6gWMlZ8AqYTSXGVFWx17mEwHx6D+bD+epc9duh7fw4SpZDneuMrZ4svAPQPjQM4R0AChh+KrnOte/cDT5atxOr1cqj7RowomeE0/S563Zy5PItAPQmk60smfciRy5HM3fdrtx0N+JT+HBEb7o2rlFiHj3atyBo2hhQq8lYt4G0fPUft+YNCJo2Btda1YibMotsh/pP4CvP49HJto6py9aStXEXRQno0oQa7w9HqFXErtnGzSXrnaYLrYa6S1/Cu1E1TKmZnB+1EH10Im4Vg2m5ZxG6qzEAZBy7zOWpqwCoOu0pQgd3xMXPiz3Vnikxr3PnvUXPXp3R5egZPXoKp06ec5ru5eXJ5q15+S9fLozvvlvPa1PzjtH+/R9izbfL6NC+HyeOnykxpiTdC9mw9A8QQpQDPlYUZdADWNYA4LKiKOfve8Xu0YDePRjyWD+mvzfvwS1UpcJzzEQyZk7GmpyI74IVmA7twxKdV1ky7tqKYeNvALhEtMXj+XFkvj0V883rpE8aDVYLwj8Av4+/wHh4P1jv77WTDzqfFouF9xcsY9XC9wkLDuSJFybRpV0rqletlJtm3ief0++hbvR/uBuHjp1i0Yqv+HDmZHbtP8L5y1dZ98USjCYTw1+eRofWLfDy9Cg+qEpF0IzxxI56HXNcEuW/W0LOjgOYrt3MTWK4EEnGk+NR9Aa8H+9DwCsjSXh1FgBpq39E5eaG9+Depc+oEGh7PIPh+3komSm4PfcmlsiTKMkxTsnMFw5j2vpNgdm1fV7AdOB3rDfOg4srKEqJIS1WK7N/Pcjy53sR6uvB00t/p1PdSlQP9ctN82rfvIrvt/vOczEmBQB3Fw3vPd6BykG+JGTkMGTJb7SpVR4fd9di4836cj0rpo0kNNCXIW8spXOzelSvEJqbZtUv2+nVqhGP92jD1VvxjP/oSzY0fR2ACqGB/DB7Yon5cqJSETJzHLefn44pPonKP3xM9o6DGK/m7UtTTCJx0+YTMOIxp1ndmtTFvWk9ovqPBaDimvm4t2yE7sjpEsPW6dyE4KphfNh5EpWa1uCxD57n4wEzC6TbueoPrh44j9pFzeg1b1Cnc2Mu7jxFUJUwur7Yn6WPvY0uIxuvQJ9i41msVmb/foTlw7oS6uPB08s30qlOBaqH+OamiUrO4Ivd51j9Qk983F1JybI12Oy+dJsLsSl8/2JvTBYrz3+xhXY1y+Pl5lJorApdG+NTNYyf2k8muFl12swexh993y6Qrs3s4eyb+hmJx6/S4+tXKd+lEbd3nKbFjKc4ueBnbu84TYWujWkx4yk2Dv6As8v/5OzyP23bukdTuswZ6NSoVJZ5dIr58x6Wj+lLqK8nTy/8iU71q1A9LCA3zasD2uX+/+2eM1y8neS0jE82HKZZtfBi4xSnRpfGBFQN45NOkynftAa93x/OFwPeKpDuwMq/iDpwHpWLmmfWTqd658Zc3XmqdEFUKvwmTyBpwqtYEhIJ+WIZ+j37Md9wKEsuR5I9fCyKwYDno/3wGTeK1Jm2CrliMJL43Ki7y5hKRcBrL5Hw4muY4xMJ/+YTdLv2Y7qed24aL0USN/RFFL0Br0F98Z8wiqTX38eSmELcsJfBZEK4u1Hux8/Q7TqAJamYH3ZChfvTL5E9/zWU1CQ8Zy7FfPIA1tibzunc3NF2fxTz1Qu5XykmE4ZfVqMqXxV1+SolZq28/Rz52eEc+bOQc6T17OHst58j3R3OkeYO50h5h3MkMzqRjYPex5ieQ/kujei8bDyGmwlsbzMJv2Y1aDjnefb2LnidaThnBKcmryLteCSt1r5GSNfGJGw/RY2X+pO05yyRS3+jxvh+1HipHxfetzUophy6yOFn5haaP3PyDae6gsVi4YPFq1g59y3CggN5csxUurRtSfUqFXPTzFv+FX17dqb/Q104dPwMi1etYfb0CUQ0bci6zxYAkJ6RSe+h42jbokmJ29iJSoX3SxNJe20y1qRE/JeuwHhgn1PDkWH7VvR/2OpC2jZt8RozjvTpU0sd4p/Io8Vi5YNPVrNy9jTCggJ48qWZdGndjOqVK+TFXLWWvt3b079HRw6dPMfiL79n9tQXc6cv/c86mjeoU+p8olJR7p2xXH/2DcxxyVRbv5DMrYcwREbnJjHFJHJr6iKCRha8uWPVG7na5+XSxwMQKtwGjSFn2UyUtGQ8XlmA+ewhrPHRzulc3XHp1BfLjYtO6+v2zCvov1mANeYGeHiDpRT1WCHQ9hiK4Yf5tjrXM29iuVpIneviYUzb1hSYXdt7JKaDf2CNKl2dy2K1MvuH7Sx/aSChft48/dFaOjWsTvXwvEbAVwd1zv3/250nuBidCEDLWhX5YfpQANKz9fR9+wva1K1cch5VKoLfGMftkdMwxydR8fslZO84iMmh/mOOTSRh+nz8hjv/1PLoGIFrvRpEDxyL0LpQfvVcsvccQcnOKTROzQ+f59Tj72GISaH5ptkkbTpKjr2RDCB8SFfMaVkcav0SIQPaUm3mUM6PWgiAPiqOo91eLbDY5M1Huf35BlodXFJiVnv26kz1GlVo3LALLVs2YdHi9+nS6VGnNFlZ2bRt/Uju5z37fuO3Xzflfvby8uTFccM5fPhEifEk6X7IR+HKmBBCoyhKzINoVLIbANS723V4QLEBaNGkIb4+3g9ykWhq1sUSextrfCyYzRh2b8elVXunNIourxAQbu55EwyG3Iqh0GpL1RBRGg86n2cuXKZS+XAqlgvDxcWFh7t1ZPveg05prt6IJqKZradNRLNG7LBPv3rjJi0a10ejUePh7kat6lXYe+hYiTFdG9bGdDMG8604MJvJ3rALzy5tndLoj5xC0RsAMJy+gCY0OG/aoZNYCyt8i6EKr4aSloCSnghWC+YLh1HXbFqqeUVgOVCpbI1KACYDmI0lznc2OomKgd5UCPTGRaOmV+Nq7Dx/s8j0G05d46EmVQGoHOxL5SDbD/oQHw8CPN1IzdYXOS/A2choKoYGUiE0EBeNhofaNGbnsXxtvQKydLbtmpWjJ9j//o4lt0a1Md2MxXQrDkxmMv7ahWfXNk5pzDHxGC9fR7EWPAeEqxbhokFoXRAaNZbk1FLFrd+zOUd/tt3ZvHkiEjdvD7yD/ZzSmPRGrh6w5d9isnD73HV8w2yVzFZPdmXffzajy8gGICs5o9h4Z28l2/ZlgH1fNqzMzgvOlfKfj0byRKtauY1/AV5uAFxLTKd55RA0ahXuWg21Qv3ZdyWmQIw7KvVqTuQ6213NxONX0fp64h7inDf3ED9cvN1JPH4VgMh1e6n8UAvbREVB6227Frl4e5ATX3CbVu3fBkv02X8sj7kxbyZQMciXCoE+tphNa7CzkJ4nd2w4cYWHmubdRT4fnUhKpo42tSsWOU9JavVozumfbMfS7RORuPl44JVve5v1RqLsx5LVZCH27A18HBq/SqKtVwfzrdtYYmxlSc7W7bh1dL7mGY+fRDHYzk3jufOoQ4ILW1TpYzaojflWDObbtpjZm3bi3rmdUxrDUYfr7JkLqO09UTGbwWTrkSC0WhAlV9PU1WpjTYhBSYoDixnT4Z1omrYtkM51wDBbTyaTwzXUqMcSea5U11WwnSNXS3GOaB3Okavr9lLJ4RxxsZ8jWodzJPHoFYzpOfblRuIRHkD0D7ZjI+14JC4+Hrjmi+Ma4oeLlztpx229O6J/2EOYPU5Yr+ZE/7Db/v3u3O/v1pmLkVQq51BOd23Pjn2HndJcu3GLVs0aAhDRtEGB6QCbdx2gfURT3N2KvkFRGE3tulhibmONsx1L+p3b0bbNVxfKyVcXussqzz+RxzOXrlKpXCgVw0NwcdHwcOfW7DjgXIe5FnWbVo1tPTcjGtdzmn7uynWSU9Np27xhqfPp3rgWhqhYTNHxKCYz6X/sxrtHa6c0ptsJGC7eAOuDGZtFVbkm1qRYlOR4sJgxn9iNpmHB3lyuvZ/GuO0nFIfeSOraTbHG3LA1KgHkZJZqzBhVeDWUVIc618VDqGs0KdX62upcalujEpSqznX2RhwVg/2oEORnK0ua12bn6atFpt9w9BIPtahd4PstJy7Trl5V3LXF3xQBcHOsy5rMZG3YiVcR9Z/8+1JboxK6o2fAYkXRGTBevo5nh8KvDz7NaqC7Hoc+KgHFZCZh/T6C8l1Lgh5qSdwPth5Pib8fxL99gxLXP+PYFYwJaSWmA+jTpwffrrH1qjxy5CS+vj6EhhVdRtWoUZXg4ED2OZyjM998hQULlmOwlzn/M6zWf+/ff6n/lw1LQogqQoiLQojVQojLQog1QojuQoh9QogrQogIezpPIcQXQojDQogTQoj+DvPvEUIct/+1tX/fWQixUwixzr78NUIIUUj8nUKIxUKIk0KIs6WIN0wI8ZsQYjuwzR7/rMO09UKILUKIG0KI8UKIV+zzHxRCBNjTVRdCbBRCHLOvex37evcD5trXpXph6ezzrxZCLBdCHAI++rv30f1SBQZhTUrI/WxNTkQdGFQgnWvvAfitXIvHsDFkr1ic+72mVl18P1mN35Ivyf50wX33Vvo7JCQmE+bwAyY0OIiEfHela9eoytbd+wHYuvsA2Tk60tIzqF2jKnsPHUen15Oals6R46eJS0gsMaYmJAhzXF46c3wi6tDAItN7D3yInL1H7jZrToS3P0pGSu5nJTMF4eVfcN1qN8dt+LtoB7xo64INqAJCQZ+DdsB43Ia9jUvnx6HgKVlAQkYOYb6euZ9DfT1IsDdk5BeTmkVMahYR1Qv2vDgTnYjJYqViQPG9ahJS0wkL9Mv9HBLgS3xKulOasY/14M99J+gx/gPGffQlrz/XP3fa7cQUHp+2mBHvLuf4xesl5g9AExKYb18m4VLMvnSkP3mBnEOnqLZ7LdV2ryV77zGM16JLnhHwDQ0gLSbvOE2PS8G3mB/6bj4e1OvWjCv7bI0pwdXCCK4azrh1b/PSL+9Su1PjYuMlZOgI883riRfq60FCps4pTVRSJlHJGTy3ahPPrNiY27BSK8yffZEx6IxmUrP1HLkeT3xG0Q2jHmH+ZDvkLTs2BY8w/wJpcmLzjucchzSH3vqGFm88xeNHFtNy5lMcm+38OJLaTUuFzo2w3Lrg9H1Z5jE3Zno2YX4O54ifJwnpRZwjKZnEJGcSUbM8AFarwvzf9vNKvzaFpi8t77AAMhy2d0ZcCt6hBa8Nd7j6eFCrezOu7ztbZJr8VMFBWBLyyhJLQhLq4KIr5R59e2M4kFchF1otwV8sI3jVUtw6tityPkea4CDMcY4xE1GHFH1ueg14CP2+vOusOjSY8O9XUv6vtaR/9V3xvZUA4ReENSXvWqCkJqHycy4vVZVqoAoIxny6YIPA3SjtOZLtcI44pjlsP0cGH1lMi0LOEYCaT3bGkJaN3iGOLjYFt3Dn64xbeAA6hzj62OTcNK7BvhjsP9wMCWm4Buf1/vNvXpOO2z6k1drX8KpdwWmZmoDKaIKqofKwrW9CUjJhDvsuNDiQ+KQUp3lqVa/C1t22mz7b9hyyl9OZTmk27thL7253/7ioKigIS6JDXSgpEXVQwbqQW78BBHy1Fs+RY8j6dHGB6cX5J/KYkJzi9FhbaFAA8UnODfG1qlViq/282LbvKNk5etIyMrFarcxbuYbJLwwpfSYBl7BATLEOZWZs6ctMAJWrluq/LqTaT/MKNEgVOY9vINbUvJ6e1rRkhK9zTFWF6gi/YCznjzp/H1IeFHAf8w4ekxeh7Vq6R+SFlx9KpmOdK7XwOlet5rgNewdtvxcR3rbpKv9QMOSg7T8Ot2ffwqXT4BLrXAlpWYQ53CgL9fMiIS2r0LQxyRnEJKcTUcgNiU3HLvNwIQ1OhVGHBmJyrP/EJeU1zpfAcPEaHu1bINxcUfn54B7RGE0RDTWuYQEYHK5DhpgUXMOc959reAAGe29exWLFnJmDS4Bte7hVCqH51o9o8ss7+La6i951DsLLhXLrVmzu55jbsZQrF1Zk+kGD+/DTuj9zPzduUp8KFcLZtHHHPcWXpLvx//lRuBrAYGAEcAQYArTH1tAyHVtPnhnAdkVRRggh/IDDQoitQALQQ1EUvRCiJvAtcKcJuilQH4gB9gHtgLwHc/N4KIrSRAjREfgCaFBMPIBmQCNFUVKEEFXyLauBPa4bEAm8pihKUyHEQuBZYBGwEhijKMoVIUQr4FNFUboKIX4D/lAUZR2AEGJb/nRAV3ucCkBbRVH++1pZ7pHhr/UY/lqPtlN33J94luxFswEwX75A+rhhqCtUxmvSNIzHDjnfpf1/Ysq4EXywcDm/bthG88b1CQ0ORKVS0S6iGWcvXmHo2Ffx9/OlcYM6qFXqBxrbq083XOvVImb4lAe63MJYIk+iu3AILGY0jTujfWQkhu8+ApUaVcVa6L98GyUjGW3/sagbtsdy+v7HzLpj06lrdG9QBbXKuZ09MSOHN77fzXuPd0ClKrkxqyQb9p+kX8fmPPdIR05djmLGsu/5ac4kgv182PTxNPy8PTl/7RYTF/yHnz96BS8Pt/uOWRSXSuFoq1fiWhdbF/QKn88mZ+9RdMfOlTDn3VGpVQz9+CX2rt5ESnSC/Ts1QVXDWPbke/iFBfDiD28x76Gp6EvRGFIUi9XKzeRMPhvRg4SMHEZ8toUfxz9C2xrhnLuVzHOrNuPv6UqjikGoStEwea/qPNuNw2+vIeqvI1Tp24r2819g05Mf5k6v1LMp8UcvE2wsvgdcYf7JPG46EUn3xtVyz5Ef9p2lfd1KhPp5PdA4xRFqFQOXjOfwl5tIiy65Ef1euPfqjrZOLRJfnJT7XdzAp7AmJqEuF07Q0vmYrl7HcrvkHmGl5dm7G671ahM38pXc7yzxicQ+MQp1UCDBC94hZ+turClp9x5ECNyeGIPui8If/ypLtZ/txhGHc6Td/BfY7HCOhLWtS82nOpFuH9vrQVHsPZfTT99ga4uXsOQYCOnWhJZfvsKOtrZtb0q6DlYzqNRoAqugmEt3d3/K2OeY9fEqft20g+aN6hESFIBKnVeeJCancOXaTdq2bPJA8+RI/9t69L+tx7VLdzyGPEvm3NkPdPn/RB6njHqaWZ+s5tctu2nesA4hQf6oVCq++30rHVo2dmqYKguXOozAHJ+MS8VQqq6ZheHSDYw34+5voULgOuB59GsXFZymUqOuVo+cBa+gGA14jHsfS3QklislP7JeEsvVk+gu3qlzdUL78EgMP8wFlQpVhZrov3rHVufqNwZ1g/a54y/dr03HLtG9aa2C9a30LCJjkmhTrxSPwd0n3f7j5DSsTYW1C7GkpKM/dQHF8uB7gBjiUznQbCzm1Cy8GlWjwepXOdLxFSxZupJnvg+DBvVlpL08EULw4YdvMHrU31+PlyT4/92wdF1RlDMAQohzwDZFURQhxBmgij1NT6CfEOLOGeUGVMLWaLRUCNEEsAC1HJZ7WFGUW/blnrQvq7CGpW8BFEXZLYTwsTckFRUPYIuiKCkFFwPADkVRMoFMIUQ68Lv9+zNAIyGEF9AW+NGhA1WBvsalSPdjUY1KQohRwCiAT+e/z8hnnypiVcuGNTkJVVBI7mdVYDCW5KQi0xt3b8Nz7CTy32+33IpC0elQV66KJfLS37S29yYkONCpl1F8YhIhQc4VpZCgQBZ/MAOAnBwdW3ftx8fb9mNu9LNPMPrZJwCY+s5cKlcsV2JMc0KS050ZTWgwlviCd8PdWzfF74WnbI1KppIHiiyOkplqG3jbTngH5A4YmUuft+fMp3fh0mWwfd4UrPE3bV26AcuV46jKVcdC8ZWcEB8P4hx6X8Sn5xDi41lo2o2nrjNtgPPdxyy9kZdWb2F8r+Y0qhRS6HxO8fx9iUtOy/2ckJJOaICvU5pfdh5h2eu2gY8b16qMwWgmNTOHQF8vtC62S3G9ahWoGBpIVFwS9as530nPz5yQnG9fBmEqZF8Wxqt7O/SnLqLk2Bo4svccwa1J3SIblto+04NWT9nap6NPXcOvXN5x6hsWQHpc4Ze2QbNfIPF6HHu+2JD7XXpcCjdPRmI1W0i5lUji9ViCq4QRffpaocsI8XEnLj2v0Sk+PYcQb3enNKG+HjSoEISLWkV5fy8qB3lzMzmTBhUCeaFzA17obOua/voPe6kc5PwIYp3nulPradtA60knr+HpkDfP8ABy4pyP1Zy4VDwcek54OKSpMbhD7kDeN34/RLu5I53mrdqvDdfXHyC4U9nmsTAhvp7EpTmcI2nZhPgWcY6cjGTawLyeCKei4jlxLZYf9p1DZzRhMlvwcHVhQp+S7+K3eLYHTZ+0be+Y09fwcdjePmEBZBby+CBAnw+fJ+V6HIe/2FhiDEfWxCTUIXnnsDokCEtiwYYp15bN8B72NEkvTnK65lkTbeWOJSYWw/GTuNSqUWLDkjkxCU2YY8xgLAkFz023iGb4Pj+EuJGTC73OWpKSMV29gVvThuRsK/qap6QloQrIuxYI/yCsaQ7lpZs7qvJV8JxqGx9Q+Abg8X/s3XWYFdX/wPH3zI3du8V20t3doLSEKKGgggiihAIiAiqpooAtJmBiYGEr3Uh3d8d2191bM78/5rJ77/YCLn79ndfz8LD3zpn5zJk898w5Z56aQ/Z7s1EunS42LwCGLvdy7zPawMqlPUe8Xc4R73znyG6Xc6S9yznS/LlBNBjbh4xL8WRdTcLTJY4pIpCcGPfrTE5MMiaXOJ4RQblpLAlpeIT6a62VQv2xJmpdbu0uP+ri1x+k0asjMQb6Yk3O0CqVwPlCkHQkg4nQ4CBiXfZdXEISYcHuLadCgwNZMOc5Le9mM2u37MDPJ+9cWr1xO107tsGgL3uRW0lMRBfiUhYKDsGRWHRZyLJpPT4TJ0EZ6hBvRx5DgwKJTXCJmZhMWHBAvjQBLJg9yRkzh7Vbd+Pn482hE2fYf/QUP/y1jmxzDja7HS+TJ5Mee7DYmLbYJAwRLvfMiNLfMwHszrS2K3Fk7TyCZ4MaJVYsKWlJGALyWtLI/kGoaS4xPUzI4VXwGq+NYyn5BmB6fCbmT19BTU3Ece4oapbz2D2+F7lijRIrltTM1NxW39eXWXyZa4vWMgmtvKbEX3Epcx3QylzFVCyF+vsQm5LXei0uNZPQIh46rNp3imkPdC3w/Zr9p+nSpAYGXekekDrikjC4ln/Cg3HEF31e5Jey+DtSFmtjroW9/jy2S1cLTWeJTcbD5TrkERmIJdb9mLHEJOMRFYwlJhlJJ6P39cKWrG0Pu1VruZV5+Dw5F+PwqhFBxqHCyzquRo8ZxohHteN5377DVKyY16I+MiqC6OjCj7uGjeqh0+s5eEBr1evr60P9+rVZuVp7eURYWAg/LvuEwYNGiQG8hX/E/2RXOCfXR0mKy2eFvAozCbhPVdWmzn+VVVU9AUwC4oAmaC2VjEUs10HRlW/5e7GrxcQDCtR5lCUvMpDqstymqqrWK2Q5JaUrch1UVf1YVdWWqqq2vN2VSgD2MyfRRVZEDgsHvR6PO7ti273NLY0cEZX7t6FlO5Ro7cYgh4WDs/WOHBKGrmJllPibfKr0D2hYtzaXr0ZzNToWm83GyvVb6JLvTSopqWkozr60n3yzjAF9egDaYJupaVph49TZC5w+d4H2rdzf6FQYy9FTGKpEoY/Stqt3705kbdrhlsZYtwbBsycSO2H2zT0ld1JiLiAFhCJVCNaeBNdrjeNsvgEEvfMqYXQ1m6EkxeTN6+kFJu2Hsq5KPdTEklsLNKgYzOWkdK4lZ2CzO1h96Dyd6hdsen0hPpV0s5UmLpVHNruDZ77eQN/mNelRyrdrNahRkcuxSVyNT8Zmt7NqxyE6tXA/RSOC/dl1VBsL5Py1OKw2G4F+3iSnZ+Jw7uOrcUlcik2kYmjJY8jkHDmFoUok+qgwMOjx69OJrI07S5wPwBYTj6lVI9DJoNfh1bIR1nNFd4Xb/vVa3ukzjXf6TOPYmr20dFYyVG5Wk5yMbDISUgvM02vyYDx9Tfwx5yu374+u2UuNttqwcF4BvoRUiyDpcnyB+a9rEBXE5aQMrqVkavvyyCU61XWvdOtSrxJ7L2hv8UrJyuFSYgYVA31wKAqp2drl9HRsCmfiUmmXr8vjyS/X8cddM/jjrhlcXr2Pmvdr45eENK+BNT0bc75xEMzxqdgyzIQ0rwFAzfs7cnm1NvZHdlwK4e20/R7RsQHpF/KuOwZfE+Ft63J59f5yz2Oh27VSKJcTUrmWlK7FPHCWTg2rFkh3IS6F9GwLTarmDUQ//+HurJo9jJWzHmbSPe3o27JOqSqVAPZ+tZZP+kznkz7TObVmL42db5OLalaTnAwzmYWMO9F5yiA8fL1Y/dLXpYrhynriJPpKUegitGueV/eu5Pztfs0z1K6J/7PPkDR1JkpKXnzJ1wcM2ngfcgU/PBo3xH6h5LdtWY+dQl8pCn2k8zrbszPmzdvdY9apSeCMp4l/erZbTF1oMJKHViSRfX3waNqwyB891zkunEIOi0IKDgedHkPrztgPuuTRnE3m0/eT+dwwMp8bhuPciVJXKgHYNv7hdo7UKMU5YnU5R2qU4hzxjgyi6j1tWTVoHr91fo7Lq/dRabB2bPg3r4ktIzu3a9t1lvhUbJlm/JtrY39VGnwHsc44sWv2UWnwnc7v78z93rVLnH+zGkiShDU5A52XR954VpKE5OGDarfQsG5NLl2L4WpMnHaf3rCVzu1bua1HSlp67n3606W/MKB3N7fpKzf8TZ9u7uMilZb91El0URWRw7VjybNzV6w73MtCuqi8spCxTTsc14o/XvK7HXlsWKc6l67FcjU2HpvNzspNO+nctkW+mBl5Mb//gwF3dQbgtefHsfab91j91btMHjWEe7rdUWKlEoD58Gk8qkZiqBiGZNBToe+dZKzbVar1lf28kYzazwFdgB9eLetjOVP0uI3XKZfPIAdHIgWGgU6Pvtmd2I+6dEfNySZr5lCy5jxO1pzHcVw6hfnTV1CunMV+cj9yRFVtAG1ZRlejYcFBvwuLGXMBKSAsr8xVtw2OswfdExVV5oq9gOThUuaqXK/AoN/5NagSzuX4FK4lpmn3kn2n6NSo4Jt0L8Qma/eSagXvT6v2nqJ3y9J3FcvJLctq5R+f3p1LXf5BlpEraPkz1q6GsU41srcVPkZpxoGzmKpH4Fk5FMmgJ7R/BxJXu3dZTFy9l/DB2tOikHvakrJVq9QxBPmBs2WWZ5VQTNUjMF8quqzj6uPFX9O+7d20b3s3f/25hoeGat0gW7VqSnp6BnGxhbfaHTToHn5a9kfu5/T0DKpUbkGDenfQoN4d7Nl9QFQqCf+o/+UWS6WxGpggSdIEZ2umZqqqHgAqAFdVVVUkSRoO3EgfogeAjZIkdQTSVFVNkySpqHg3RVXVdEmSLkiSNEhV1WXOcZ8aq6p6CMgAfEuR7h8z9YVX2XPgMKmp6XTr/zBPPjaM++7peXMLVRxkLVqA30tvgixjWbcCx+WLmIaOxH7mJLbd2/HsOxBD0xZgt6NmZpLp7Aanr98Y0/1DtEFQVZXMRe+gpqeVELD886nX65g+aSxjJs/GoSgMuLsHNatV4YNPv6FB3Vp06diGPQeOsODjL5GQaNGkITOf0d7iZbc7eGSc9vTQx9uLV2dNQa8vxWHsUEic9wHhi+Yh6WQyfl2N7dwlAsY9guXYabI37SRw8igkLxNhb2lv4LHHxBP3lPampoglb2GsVgnJy0TldUtJmP025u0lDBquKljXLsVj8GSQZOxH/kZNjMbQsT9K7EUcZw9iaNEDXa2mztclZ2Fd/qlzXhXrxh/wfHAqSBJK7EXsh4p+LWzuttXJPH9vW574fA2KotKvZS1qhgXw0Zr91K8YTOf6WkPCVYcu0KtJNVyHUltz5CL7L8SSmm3hj31aRdCcQR2p6/LUqmA8HdNG9OOJVz9DURT6d25FzYrhfLhsDQ2qV6Rzi/pMHtqXOZ/+zDcrtyJJMGfsYCRJYv/JC3y4bA0GvQ5Jkpg5cgAVfEp4ux+AQyHhlY+o+OlckGXSf1mD9ewlgiYMI+foGbI27sSjYW0i35+Fzs8Xny5tCJowjEv3jCFz9Va82jSlyu+LQFXJ3rqPrE2lK2Cf2HiAul2a8vzmBdjMFn6Yujh32qQV83mnzzQqhAfSfcIA4s5e4+nl2pPYbV+uYfcPGzm1+RC172jE1LVvoDgU/pq/lOwixmPQtq3M831b8sSXG7R92bwGNcP8+Wj9IepHBtG5XkXa14xgx9kYBr73J7IkMalnM/y9PLDYHIz8dA0A3h4G5t7fHr2u6OcpV9cfpGLXJty37S0cZit/P/Nx7rR718zlj7u01oM7pi/hjndGo/M0cm3jIa5u0C6x26Z+Rps5w5D1Mo4cG9uf/Sx3/iq9W3JtyxHs5oLda8ozj24xB97BEx//pcVsXZea4YF8tHI39SuF0LmhNpj9qgNn6dWspts5cquc3XCQml2aMm7L29jNVv6YkncsjVoxj0/6TMc3PJA7JvQn8ew1Ri2fC8Cer9Zw8PtNpQviUEh9632CF7wGso6sv1Ziv3AR31EjsJ04Tc7W7fiNH4Pk5UngXO0654iLJ/nZmRiqVsH/uUmgqCBLZHz9ndvb5IqLmfza+4R++CrIMpl/rMJ2/hIVxg7Hevw05i07CHh6NLKXiZDXndfZ2HgSJs3GUK0yAc+M1V46IUmkf70M29kSxl1TFHKWfoDXpPlIsox162qU6Et49BuO4+Jp7Id2FDu7z2tfI5m8QGdA36w92W8/X/CNck5X1x8kqmsTBjrPka1FnCM7py+ho8s5cs15jmyf+hmtXc6RHc5zpMmkAXgE+NBu3ggtS3YHGfvP0nXnAhxmCwefzjs27lw3ny3dpwFw5PkvaPruWHSeRuI3HCR+/UEAzr7/By0+nkilIZ0xX01k32ht3KGIe9pQdXgPFLsDJcfKvrHvAeARXAF9cLW8TWpOQ7VkotfpmP7U44x9do52n+7djZrVKvPB59/RoE4NunRozZ6DR3n3k6VIErRoXJ8ZE/PeIngtNp7YhCRaOgehLjPFQeYHC6gw/00kWSZn9Qocly7iNXwk9tMnse7Yjme/gRibtQCHHSUjk4zXy9YN7nbkUa/TMX3cCMZOf02LeVcnalatyAdf/kSD2tXo0q4Few4f593Pf0CSJFo0qsuMcSPKlK8CHArRLy6i6pdzkGSZlGVrsZy5TOjTQzEfOUPG+t2YGtei8sIZ6Cr44NutNaETh3C21zg8alYiau54VEVFkiUSFy1ze5tckRSFnJ8X4TX2JZBlbLvWocRexth7KI7LZ3AcK2bMM3MW1k2/4fXM24CK4/jeAuMwFUpVsK77Bo/7nwFZxn5kK2pSNIYOzjLXuYMYmnfXBvRWFNScTKwrnfcqVcW66Qc8H5gCSChxJZe59DqZ5wd35YkPf9HuJe0aUDMymI/+2k79ymF0bqxVMK/ad4peLWoXuJdcS0ojNiWDFjWLb6XtxqGQMPdDIj+ZhyTLpP+qlX8Cxz9CzrHTZDvLPxHvzUb288W7S1vs4x/hyr2jkfQ6Kn7zFgBKZjZxz70GRXSFUx0KZ6Z9RuPvZyDpZGK+20j2qatUffYBMg6dI2n1XmK/3UDdDybQZuf72FIzOT5GeyNchbb1qPbsA6h2B6qicPrZj7E7yzrVZz1M2MCOyCYj7Q4sImbpenih8H27etVGevbswuGjmzBnmxk7Nu+Nj9t3Lnd7G9zA++7mvgGPln47/teVYrB74daS1Fv0xqzy5Byj6C9VVRs6Py9xfv7JdZokSSa08Ynao7XmuaCqal/nuEo/o7UyWgWMU1XVR5KkzsAUVVX7Opf7AbBXVdUl+eJvAg4CnQADMFJV1d3FxBsBtFRVdXz+9S9k2kXn50TXaZIkVQMWAhHOmN+rqjpHkqQOwCdorZzuR2vlVFi63G1U0va1JZ4v14Mi/dHyvwj6ffFFuce8HQOIX+k2rlzjhT8cVXKiW0yqXbrBHm9pzMo3Ngjjzbj88KJyj/mxueiBlP8JL79eWEPMf9b3k0rXauNWevCd2iUnutW8ix+A/p/w5pM3N/B/WY2MjCk50S1mt5R/w++AZv/cOGFF+WVVWMmJbrGg0rxa/RbquX9mucYDSBv+ZLnHrPDlR+UeE+s/O65MYU53fbFc41W5t/zPS13Uzb3B8kZIzVqXe8xrT/1a7jGvJpXvPbNvRikqDf8BmdkXyv/ALSc5+37736vkKCXPFv3/lfvtf7LFkqqqF9EGvL7+eURh01RVNQNjCpn/DNDY5avnnN9vAja5pBtfzGp8o6rq0/mWW1S8JcCSItYx/7Sqhc2nquoFoFchy94G1M/3dWHpRhSdFUEQBEEQBEEQBEEQhLL7Xx5jSRAEQRAEQRAEQRAEQbiN/idbLN1uqqp2vt3rIAiCIAiCIAiCIAhCPrdhCJL/70SLJUEQBEEQBEEQBEEQBOGGiIolQRAEQRAEQRAEQRAE4YaIiiVBEARBEARBEARBEAThhogxlgRBEARBEARBEARB+G9Qldu9Bv/viBZLgiAIgiAIgiAIgiAIwg0RFUuCIAiCIAiCIAiCIAjCDREVS4IgCIIgCIIgCIIgCMINEWMsCYIgCIIgCIIgCILw36CIMZbKm2ixJAiCIAiCIAiCIAiCINwQUbEkCIIgCIIgCIIgCIIg3BDRFU4oSHGUc7zyDafFLOc8Asi6cg9ps5VvTDU9s1zjAUhWS7nHxGou/5CW8r9c21DLN6CnV/nGAwzlnEUAPDzLP6avf7mHNKlSucazZpf/NVZVyjePAEq2rdxj3o6nkA7Kedvehm4Tir3cQ96e7iF6j3IPabOX7/VASSr/8o/sl13uMaWs9HKPaTYbyz1mVjn/RPbUG8o1niD8E0SLJUEQBEEQBEEQBEEQBOGGiBZLgiAIgiAIgiAIgiD8N6hi8O7yJlosCYIgCIIgCIIgCIIgCDdEVCwJgiAIgiAIgiAIgiAIN0RULAmCIAiCIAiCIAiCIAg3RIyxJAiCIAiCIAiCIAjCf8PteAPm/3OixZIgCIIgCIIgCIIgCIJwQ0TFkiAIgiAIgiAIgiAIgnBDRMWSIAiCIAiCIAiCIAiCcEPEGEuCIAiCIAiCIAiCIPw3iDGWyp1osSQIgiAIgiAIgiAIgiDcEFGxJAiCIAiCIAiCIAiC8D9OkqRekiSdkiTprCRJzxcyvbIkSRslSTogSdJhSZL63Iq4oiucUCpbd+3j1Xc/xqEo3Nf3Lh5/eJDb9OjYeGbNX0ByajoV/Hx4ddYUwkODAXh74Rds2bEHgDHDH6R3tztLFdPQojXeoyeALJOzZjk5y751m+7R+148+w4AxYFqNpP1/ps4rlzC0LQlXo+OBr0B7DayPluI/fCBf2UeSzJz3tts2babwAB/fvtm0S1ZplfHFoTNGAuyTNpPq0j+ZJnbdFPLhoROG4NHnWpET36VzNVbc6eFTBmJd6fWIEtkbz9A/Nyyr5OuVlOMdz8Ksox973psW35zm65v1hlj72Eo6ckA2HeuxL53Q5njbDsTzesr9qGoKgOa12DknQ0KpFl99BKLNx4BJGqH+/PqoA4ALFhzgL9PRwMwulNDejaqUnK8w2d47dtVKIrCgDub81jfO9ymxySlMvOT38jIzkFRFCYO6s4dTWqz4+g53l22DpvDgUGnY9IDPWhTv3qp8uhzZ3MiZo8GWSblxzUkLvrJbbpXqwZEzBqFZ91qXJn4Oukrt+VOa3Dmd3JOXQLAFp3A5dEvlyomwMAXhlO/SzNsZgtLpyzk6rGLbtMNnkYe/ehpgquEoTgUjq3fz5+vfQdAQGQQQ996EpOfF7Is8+dr33F808Fi4207eYXX/9iBoqgMaF2HkV2buk1/448d7Dmr7a8cm53kzBy2vjyc6JQMnvlyLYqiYlcUHurQgEHt6hcZp+XLw4jq2hS72cKOSR+TfORigTSBjarSbsEY9J5Grm04yN5ZX+dOqzOyB7VH9EB1KFxbf5ADr3wPgH+9SrR5bSQGXxOqosLZH0FxuOfx1FVe/30niqpoeezSJF8ed7LnXIx7HucMc+ZxPYrqzGP7+gxqV6/Y7VmYbUfP8fr367Tj946mjOzdzm16TFIas774i4xsC4qi8NR9nbmjUc0yxwHo+tIwqnXRtvPKyR8Tf/RigTQdpw6i/n0d8azgzXv1Hs/9vvPsoVR27kO9yYhXkB8fNBpTbDxT+5YEPvckkiyT8etK0j7/wW2637D78B3QGxwOHClpJL7wJvaYeADCPpqHR6N6WA4eJW7CrFLn0dShJUHPPYGkk0n/ZRVpn7nHrPDIffgO7IXqcKAkp5Ew+63cmOEL5+LRuB45B44SN352qeLpm7TC9Mh4kHVYNy7H8sd3btON3e/Bo0d/UBTUHDPZn76Fcu0Shg7d8ez7QG46uXJ1MqePxnHpXJGxWs3RzhOH2cK2SR+TXMj+C2xUlQ7vjEHnPE/2zNbOkzsXjsevRoS2Tn5eWNOz+euuGUTc0ZDm0x9ANuhRbHb2vfId2VuOFhq/0SuPENatKQ6zlf0TF5FWyHlaoXE1mr+rxY9bf5AjM78CoMHsIYT3aI5is5N1MY4DTy/Glp5d3KZl6+4DvPbhFzgUhYF9uvH4QwPcpkfHJTD7jQ9zywbzp00kPCQIgJi4BF54ayGxCUlISHw0fzpR4aHFxgMwtmqN7/gJoJMxL19O9nfu5R+vQYMx9blbO37SUkl//TWUuDgAfEaPwaNtWwAyv/4Ky8aNJca7HXm8HTF9OzWj4oujkHQySd+vJe6jn92me7euT8UXHsdUryoXx79J6ortAJjqV6PS3LHIvl7gUIj9YBmpf24tLEQB+kat8Bw2DmQZ26YVWP763m26sWtfjN375Z6b5s/fQYm+BDo9ppGT0FWrDaqK+esPcZw8VKqYuppNMPYZDpKMff8GbH//4b5OTTth7Dk0r5y1azX2/dpx4jHseXQVa+G4fArL0tdLFe9G718no5OY98s2Mi02dJLE412b0rNp6cs/kS+M0so/P6wlIX/5p3UDImeNwrNuVS4/9TrpK7e7TZd9TNRe8xHpa3cS/cLiAsvuNGsMkk7mytKNnHvfffvJRj1NPniSCo2rYU3J5MDodzFfSQSgxlP9qDSkM6pD4diML0ncdBjZw0C732cjGw1IOh0xf+3izBva+rb7/QV0Pp5sVMyEhASyf98RHhnypFu8ea/PpPtdnTBnm5nwxPMcPnTcfX19vPlzVd41IjIqnGU//M7M5+fRrn1L5r46g/oN6zDq0Un8+fvqUm1f4X+XJEk64EOgB3AV2CNJ0h+qqroeODOBH1VVXShJUn1gBVD1ZmOLiiWhRA6Hg1feXsgn77xCeEgQD4yaRJcObahRrXJumjc//Ix7e3WjX+9u7Np3iAWLv+TVWZPZvH0Px0+f46fP38dqs/HoU9O4o21LfLy9ig8qy3g/8TTpMyejJCZQ4Z3F2HZuw3HlUm4S66Z1WFZqF3tDm/Z4jRpHxuxnUdLTSH9pGmpyEroq1fCb8wYpw+//9+WxFPr36cGQ++5l+stv3vSyAJBlwmaP4+rI6djiEqmy7F0yN+zCeu5ybhJbTDyx094iYOR9brN6NquHqXl9LvbTbniVv30TU+tGmHcfKX18ScZ4z2PkfPEyanoynk/Mx35iL2rCVbdk9iPbsf752Q1n06EozP9rL4uGdyXMz8TQxavpVLciNUIr5Ka5lJTO51uOs+Txu/AzGUnOzAFgy6lrnIhO4YcnemNzKDz2+To61IrEx9NQbLx5X69g8dRhhAX6MeSlT+jcrA41ovIKup/8sYWerRswuGsrzl2LZ/zbS1n5Vm38fb147+mHCA3w48zVOJ548xvWLZhcciZlmciXnuDCIzOxxyZR/bd3yFi3C8vZK7lJbNEJXH12AcGPDywwu5Jj5Vzfp0qzOd3U79yUkGoRvNL5aao0q8mguY/zTv+ZBdJt+OQvzu44js6gY9zSWdTr3JQTmw5y1/iBHFi+k23frCWsZhRjljzPnI4TioznUBTm/7qNRaP7EFbBm6Hv/UanBlWoERaQm2bqvXmVIN9tPcrJ6CQAQny9+Gp8P4x6HdkWG/e99ROd6lchtIJ3gTiRXZvgWy2c3ztMJrh5DVrPH8Gqvi8WSNf61UfZNfVTEvefo8s3U4ns0pjojYcJa1+Pij1bsLz7dBSrHY8gPwAknUyH959g21OLSD1+GWOAD/fMq1FIHrezaFQvLY/v/0Gn+pXz5bFtXh63HePkNdc83pOXx7d/oVP9yoXmsdht/O0aFk16kLAAP4bOXUKnJrWoERmcm+aT5du5q2U9BnduzrnoRMa/9yMrXy17xVK1Lk0IqBrOZ3dOJqJZDXrMHcHSfi8WSHdu3X4OfLmWxza7X/s2zVma+3ezET0IbVC1+ICyTND0CcSOeQ57XCKR335A9qYd2M7nXfOsJ88SPWQcao4F30F9CZg0ioRn5wKQtmQZkskDv/vvLn0mZZngGeOJGf089thEor5/n+yN7jEtJ86S/uB4LebgvgQ+8zjxU+cBkLpkGbKnJ76DSvkAUZIxPTqRrHlTUZIS8J27CNu+7SjXXO6X29ZjXfcnAPoW7TENe5KsV5/Dtm0dtm3rtNWuVA3vyS8XW6kU1bUJftXC+a2jdp60mT+Clfe8WCBd2/mPsuNZ7Tzp9nXeebLliQ9y07SYPSS3UseSnMGGEW9hjkvFv05Fui99ljXNxhdYbli3pvhUD2ddu2cIaF6TJq+NZEufgpVvTV8bycHJn5Ky/yztvn2W0K5NiN9wiPjNRzg+93tUh0L9mQ9S66l7Of7K9wXmv87hcDD3vU/5+PXZhIcE8uCTz9OlXUtqVK2Um+bNRV9yT4/O9OvZmV0HjvDup0uZP027tk5/7X1GDbmP9i2bkG02I0ml6Cggy/hOfJrUqZNxJCQQuGgxlu3bcFzK25+2M2fIHjsaLBZM9/bDd8xY0ua8hLFtW/S1apP0+ONgNBD4zrtYd+1CzS668ux25PF2bddKr4zh7NAXsMUkUefPN0lbu5ucM673zEQuTX6XsDHulVyK2cKlSQuwXIxBHxZI3eVvkbH5AI70rOJjSjKew58i67VnUZMT8JnzEbb9O7SKIyfr9g1YN/wFgL5ZOzyHjiX7jWkYu2jXnMzpo5D8/PGeMp/MF54EVS0hpoSx70hyvpyLmp6E55h52E/uQ0245pbMfnQH1uVfFJjdtu0v7AYj+pbdi4/jdDP3L5NBz8sPdKJKSAXi07IY8t7vtKsThZ/Jo/igskzknLFcGDYLe2wSNX5/m/T85Z9rCVyduoDgUQMKXUTYMw+TtftYkcveNuhVcqKT6Lh6LnGr95F5Om/7VRrSBVtqFpvaTiKifzvqzhrCgdHv4VM7isj+7dhy51Q8wgNos2wGm9pNQrHY2DnwFRzZFiS9jnZ/vkjChoOk7jvLjn4vATDcvI8vvn6flSvWu61O97s6Ub1GVVo37UGLVk14452X6NnV/cF3ZmYWXTr2y/28fvMvLP9jDQBXr8Yw/onnGffUY8Vv0/8gVXWUnOi/qTVwVlXV8wCSJH0P9ANcK5ZUwM/5dwUg+lYEFl3h/gMkSfpHKwiPnDhN5agIKkWGYzAY6N3tTjZs3emW5tzFK7Ru3hiA1s0bs9E5/dzFy7Rs0gC9XoeXyZPaNaqydde+EmPqa9fDEX0NJTYG7HYsWzZgaNvRLY1qzisoSZ4m7RQBHOfPoCZrNy3HpQvg4aG1XvqX5bE0WjZtRAU/31uyLADPxrWxXY7GdjUWbHYyVmzGp1tbtzT2a/FYTl8sWHhRVSQPI5JBj2Q0IOl1OBJTyxRfrlgTJTkWNSUeHHYch7ehr9fy5jJViKNXk6gU6EPFQB8Meh09G1Vh00n3yqtf9p7jgTa18DMZAQj08QTgfEIaLaqGoNfJmIx6aof7s+1s8dfbo+evUSkskIqhgRj0enq1acimA6fcE0kSmWYLAJlmCyEB2n6tVyWC0ADt2l4zKhSLzYbVZi8xj6YmtbFcisF2JQ7VZiftry349nDfl7Zr8VhOXrylAxg2vKsle37ZAsClA2cx+XrhF+LvHjfHytkd2v3LYXNw9dgF/MMDAVBR8fQxaXnw8yI9LqXYeEcvJ1Ap2I+KQX7avmxag03HLhWZfuXBc/RqqlXcGPQ6jHodAFa7A7WYAnmlni248JP2BDpx/zmMFbwxhbrnyxTqj8HXROJ+7Yf3hZ+2UqmXdvzWfqQ7xz74E8Wq7TtLUjoAEZ0akXriCqnHtUoFa0omuRer63m8ki+PTaqz6dhlirLy4PkbymNRjl6IplJIABVDArT4reqx6eBptzSSBFm5x28OIf4+ZY4DUPOuFhz7WdvOMQfO4eHnjXe+7Xx9WlZ8arHLqntvO07+saPYNB4N62C7Eo39WizY7WSt2oRX5/ZuaXL2HELN0fJmOXICfWhI3rTdB1Czim/RUiBmozrYLkdjv+qMuXIz3l2KiXn4BPowl5i7DqKUIaauZl2U2GiU+Bhw2LHu2IChZQf3RK73Sw/PQn+cGtt3w7a9+NYtlXq24FwZz5NzP22lcq+C1/mq97Thwu/a/ks+dglzXCoAqaeuovM0IhsLFm3Ce7bg8o9/A5Cy/ywGPy888sX3CPVH72MiZf9ZAC7/+DcRzvgJm4+gOrTrYcq+s5gigorN75GTZ6kcFU6lyDCtbNClAxu373FLc/7SVdo0awhA66YNc6efu3gFh0OhfUut9YaXyYTJs4QfzIChrlb+ccRo5Z+cDRvw6OBe/rEdPAAW7fixHT+OHKIdP/oqVbEdPqS1iMzJwX7+HMbWbf51ebwdMb2a1sJyMRbrZe2emfLn31S4q7VbGuvVeHJOXkLNd8+0XIjGclFrcWOPS8aemIY+0I+S6GrURYm7hpqgnZu2nRsxtHC/FpCT/9zU/pajqmA/rrW2V9NTUbMztdZLJXAvZzlwHNmOvm7py1nK+aOolpxSp7+Z+1eVkApUCdEe+IVW8CbQx0RKZsmxvZrUwupa/vlzC3493I9z27V4ck5eBKXgtc6zYQ30wf5k/F2wN8P1ZZsvxaPaHET/toOwfNevsF4tuPqjVg6K/XMXwR0bOr9vSfRvO1CsdsyXE8i+EIt/c+0BjCNbO18lgw5ZrytwDfbx9eaOO9uy4q+1bt/37tONH7/7FYB9ew5RoYIvYS73i/xq1KxKcEgQO7bvBeDK5WscP3YKRQxk/f9JFHDF5fNV53euXgQeliTpKlprpaKf8JaBqFgqgSRJVSVJOilJ0hJJkk5LkrRUkqTukiRtkyTpjCRJrZ3pvCVJ+lySpN3O/or9XOb/W5Kk/c5/7Z3fd5YkaZMkST85l79UkiSpkPijJEnaI0nSIUmSfpYkycv5/RJJkhZJkrQLeF2SpBqSJK2SJGmfM15dZ7p7JEna5VyndZIkhZV1G8QnJBHuUtAOCwkmPjHJLU2dmtVYt0VrZrpuyw6yss2kpqVTp2Y1tu7ajzknh5TUNPbsP0xsfEKJMeWgYJTE+NzPSmICuqDgAuk87u6P/6ff4vXoWLIWv1tgurFDJ+znToPd9q/L4+2gDwvGFpO3bvbYRPRhxResr8s5eJLsXYep8fdSavy9lKyt+7Gev1LyjC4kv0DUtLztqqYnI1UoGF/XoA2mCW/i8dDkQqeXJD7DTLhLi40wPy/i83V3uJSUwaXEDIZ/soZhH69m2xmt8qh2eADbzsRgttpJycphz4U44tKK/5EXn5JOuEshMzTAj7iUdLc0T/TvzPIdh+kx6S3Gvb2U5x8u2Bph3d7j1KsSgdFQcl2xITzIfV/GJGIo5b4EkD2M1Pj9Har//GaBCqni+IcFkhqdtw/TYpOp4Kw0KozJz4sG3ZpzepvWrWXVOz/Rsn9HXtrxIWO+eI6fXij4xNRVfHoW4S6VGGEVvIlPK/wpcXRKBtHJGbSuGZn7XWxqJoPe+plec79lROcmRbbkMYUHkOWSr6zoZEzhAQXSZMckF5rGt0Y4oW3q0OuvF+nx8wyCmmjN+f2qh6OqKl2/fZY+q1+h/pMFW77Ep2W7H68VvIgv4kl4Xh4j3PP49i/0mvc9Izo3LlNrJYD41Ey34zcswJf41Ay3NGPvuYPlu45x19QPGP/eMp5/qEeZYlznEx5ARkzeds6ITcYn33YuDb+oICpUDuXytkKeOLvQhQbjiM07TxzxiejDCt5LrvMd0Bvztt1lXh9X+tBg7C4x7XEJ6Io5N30H9iJ7654ip5dEDghGSXK5XyYlIAcUzKOxR398F3yDacgYzF++X2C6oV1nrNvXF/jelVd4ANku50l2TDJe+fafV77zpLA0oW3qYE5II+NCXIEYle9uRfLRi7mVtK5MEQGYo/OWnROTjCkioGCamOLTAFR5qDNxGw4WkVNNfGIy4SF52zIsJIi4xGS3NLVrVGXd37sAWL91l7NskMHFqzH4envx9AuvM2jMFN5a/BUOR8lP0OXgYJR4l/2ZkIAuuOhj1tSnD9ZdWnz7ubMYW7cGDw8kvwoYmjZDF1L0j9DblcfbEdMYHoQ1OjH3szUmqUz3zOu8mtRCMuixXIotMa0UEIyanHctUJITkAo7N7v3w+fNr/F8cDQ5X2ut+hyXz2Fo3h5kGSkkHF3V2kiBJXf3k3wLKWf5FbxH6+q3xvTka3g8MAnJr+zb4bqbvX9dd+RyAjaHg0pBJVfY6cODsMXk7UtbbBKG8FLmQZKImPEYMfM+L9Wyc6KT8Mx3/fKMCCTH2epKdSjYMrIxBPriGR6Q+z1o157ceWWJjuvn0+PYYhI3HyF1v3vL0D59e7Bl8w4yM9y3XURkGNeu5h1r0dfiiIgs+qfcgPvu5rdfVhSzAYT/AkmSRkuStNfl3+gyLuIhYImqqhWBPsDXUqmafhZPVCyVTk3gLaCu898QoCMwBZjuTDMD2KCqamugC/CGJEneQDzQQ1XV5sADwHsuy20GPA3UB6oD+R4xAvCLqqqtVFVtApwAXNsyVgTaq6r6DPAxMEFV1RbO9frImWYr0FZV1WbA98CzN7oRijNl3Ej2HjzK/SOfYu/BI4SFBCHLMh1aN+eOdi15+ImpTH3pDZo0rItO1t2yuJblv5H6+BCyv1iM6YFH3KbpKlfF69ExZL3/1i2Jdbvy+G9hqByBsXolznUexrlOD+PVtgmmFgXHLbpZ9pN7Mb/xJOb3p+A4ewiP+wp2h7gVHIrC5eQMPh3ZnVcHdWDO77tJN1tpXzOCjrUjGf7pGp5ftp3GlYKRC9b5ltnKnUe4t0NT1r4zmQ+fGcqMj39xe4J09lo8C35cx6wR99x0rNI4dcdIzvWbxJWn3yBi1iiMlcNveQxZJ/PIe0+xZckqkq5oP5Sa39ue3T9t5oV241j86GsMe2cchdSp35DVB8/RvXE1dHLerS3c34dlk+/jj+ce4M99Z0jKKFvrk9KSdTJGfx9W9X2R/S9/xx2LteNW0usIbV2bbeM/YnX/OVTq1RI5uHIJSyva6oPn6d6okDw+M5A/nh3kzKP5pvOT36rdx7m3fSPWvDGeD54axMzP/kQp5Elweal7bztOL9+tjVl1i3jf3Q1j/dqkLllWcuJbxKdvNzzq1yb1i38+pnXtb2Q8/TDmbz/Gc8Awt2m6GvXAYkG5evEfXw+Aav3bcfH3gq3NKtSOosX0B9nxXOE/+m6V2hP7odgdXP15W8mJSzBlzCPsPXyMQWOmsPfQcUKDA5F1Mg6Hg/1HTzJ5zHC+++g1rsbE8fvqTTe/8i48u/dAX6cOWT9o3fmse/di3bmTwA8+pMKs2diOH7slLVZvRx5v53Ytij40gCoLJnFpynsld0krA+u638mcMoycHz7Bo9/DANg2r0RJTsBnzkJMQ5/EfvYYqLem1Yn91D7Mb0/A/NFzOM4dxmPgE7dkuSUp7P4FkJCezczvN/PSoDuR5VtTHihK0LA+ZGzaiz02qeTEt5KisrXbNNY3HYd/8xr41K3oNnng/X355ae/bjrMgPvuviXLEf7dVFX9WFXVli7/PnaZfA2o5PK5ovM7V48BPzqXtQPwBIp+glFKYoyl0rmgquoRAEmSjgHrVVVVJUk6Qt5AV3cB90qSNMX52ROojNZn8QNJkpoCDsC1HetuVVWvOpd70Lms/KMBNpQk6RXAH/ABXEddW6aqqkOSJB+gPbDM5Qfa9XbBFYEfJEmKAIzAhcIy6KzpHA3w0RtzePyRB3OnhYYEubXAiUtIJDTY/clAaHAQ786dAUB2tpl1m7fj56u1MhjzyAOMeUQbHPTZl96gSqVISqIkJSIH5z2ZkYNDcCQlFpneumU93uMmkfWOM31QCL4zXyHzrXkosSV3G70debwd7HGJGCLynl7qw4Oxx5Xu5urTvT05h06iZmvNlLO27MXUtB7mfcW3GHCVv4VS/hZMAJgz89Z37waMvdx/AJVGqK+JWJdWLXHp2YT6uY95FebnRcOKQRh0MlEBPlQJ8uVycgYNo4IY1akhozppTZufX7aNKsHFd0cMDfAjNjmvhVJ8SjphAe5P3X7dcoCFk7UCY5OalbDY7KRkZhPk50NcchqT3vueV0YPoFJo0a1/XNlik9z3ZUQwtlLuSyB3v9uuxJG18wieDWpgvVz4E9iOw+6i3UNdAbh86Bz+kXn7sEJ4IGmxyYXO98D8USRciGHz5ytzv2v7QBcWDX8VgIv7z6D3MOAd6EtmUnqhywj18yY2Ne+YiEvLKrJFzqqD55k2oLD6ea2Zfc3wAPZfiKVHY601ka5qU/qs1cYqSDp4Hu/IIK5fBbwjAzHHunfTM8em4BWRt39c02THpHBlxZ7cZamKikegL9kxycTtPIUlWctD9IZD+N8djpKY11UgtIKX+/Galk2oXxF5PHSeaf3bFzottII3NcOu57FaoWkKnc/fx+34jUvJINTf/Zj/deshPnpau8Y1qVERi81BamY2gUWsp6umj3Sn8UNdAIg9fB5fl+5HvuGBZMYW3x2yMHXuacv6WV+WmM4Rn4guPO880YUGY48reC/xbNMM/8eHEPPYZLAV38K1JPb4RPQuMfVhITgKOTdNbZvhP+ohoh+dclMxlZRE5CCX+2VQCEpK0fdL244NeD32tNt3hvZdsG4v/CUJxh796TuzP6Ad214u579XRCDZ+fZfdr7zJH8aSSdTuXcrlvd2HwzdKyKQLp89zdaJi8i8FJ87+EO1R3tQdah2/KQcPI8pMm/ZnhGBmGPynacxKZgiik5T+YE7Ce/RnG2D5haaX1ehwYHEJuRty7iEJMKCAwukWfCS9swu22xm7d878fPxJiwkiDo1qlLJ2bqga4fWHDp+moF0KzamkpiIHOqyP0NCcCQW3J/G5i3wfngYyU8/5Xb8ZC39hqyl3wDgN3MW9qvFtyy+HXm8HTGtsUkYXcaNM0YElemeKfuYqPHFLGLe+IbsA6dLngFQUxKRAvOuBXJgCGpx5+bOjZhGTMQMoCjkLF2YO8179nsoMVeLnDc3ZkYh5az0fPdo13LWvg0Y7xpacmaKcLP3r8wcKxM+X8P4Xi1oXKXkFlkA9tgkDBF5+9IQHoStlBVFXs3q4tWqAUEP90H2MiEZ9Diycoh7/ctCl+0ZGUROvmtcTkwynlFB5MQkI+lkDL5e2JIzyIlNwTMqb9t7RgQWmNeenk3i1uOEdmlCpnOIBkOgL81bNGK4c9DukaOGMmz4YAAO7j9CVMW8B3+RUWHERBds6QnQoGFd9Hodhw6Wvlwu/CftAWpJklQNrULpQbRGMa4uA92AJZIk1UOrt7jp7jaixVLpWFz+Vlw+K+RVzknAfaqqNnX+q6yq6glgEhAHNAFaolXuFLZcB4VX9C0Bxquq2gh4CW3HX3f9Si4DqS6xm6qqev21QO8DHzjnH5Nv/lyuNZ+ulUoADevW5vLVaK5Gx2Kz2Vi5fgtdOrr3ZU5JTcttffHJN8sY0EfrJuFwOEhN036wnDp7gdPnLtC+VfPCVsGN/fRJdFEVkcPCQa/H486u2Ha5P1mUI/O6ixpatUOJ1i7QkrcPvi++SvaSxdhPFP5WmfxuRx5vh5wjpzFUicQQFQYGPb59OpG5YWfJMwL2mARMrRqBTga9DlOrRljK2BVOuXYWOSgCKSAUdHp0jTtgP7nXLY3k65/7t65eS5T4kgtS+TWICuJycgbXUjKx2R2sPnKJTnXduxd3qVeRvRe1VjQpWTlcSsqgYoAPDkUh1dkX/nRsCmfiUmlXo2Czbbd41SK5HJfE1YQUbHY7q3YdpVOzOm5pIoIqsOv4eQDORydgtdkJ9PUmPcvM+He+ZeKg7jSrVfqWLObDp/GoGomhYhiSQU+FvneSsW5XqeaV/byRnOOX6AL88GpZH8uZosdE2Pr1Gt7o8zxv9HmeI2v20mqg9tbDKs1qkpORTXpCaoF5+kwejMnXi1/nfOX2fUp0ErU7OMcjqBGJwcNQZKUSQINKIVxOTOdacrq2Lw+eo1P9gtvpQnwq6WYLTVwKpnGpmeQ4x6tKz7Zw4EIsVV3Gg3JcPMiKHjNY0WMGV1fto9r92jgmwc1rYE3PxpxvjB9zfCq2DDPBzbXxIard35Erq7Xx1K6s2ktYB+1tZb7Vw5GNeizJGcRsOkxAvUroTEYknUxou7ooGe4/LBpUvJ7HDC2Ph84Xk0drvjxmuefxYhxVQyoUmLc4DapGcjk+hWsJqVr8PSfo1KSWW5qIID92nbgIwPmYRKw2OwG+pXtBwcGv1vFV7xl81XsGZ1fvo8F92naOaFYDS0Z2iWMp5RdYIwLPCt5E7ztTYlrLsVMYKkehj9LuJd69OpO92b2ljLFuDYJnPU3cxNkoyWVbl0JjHj2FoYpLzN6dyNpUSMzZE4mdcPMxHedOIodHIYeEg06PsV1XbPvyvQEpPO/6p2/WFkesy8NLScLYtjO2HYVXLFnX/sZfd83gr7tmcHn1Pmq4nCe2UpwnNVzOE4CIOxqSdjbarbucwc+Lrl9NZv+8H0jY675fL3yxlo3dp7Ox+3RiVu2l8mDtjZsBzWtizzBjyRffEp+KPdNMgHNsk8qD7yDWGT+0S2NqjuvLzuFv4jBbC82vq4Z1a3LpWgxXY+K0ssHGbXRu38otTUpaem7Z4NNvf2VAL60SvmGdGmRkZpGcmgbArgNHqVHFvZVCYWwnneWfcO348ezaFct29/KPvmYtfJ+ZTOqMaaipLvmXZSQ/rUpOX706hurVse5xv8f+G/J4O2JmHzqDR7UIjJVCkQx6Au65g7S1pev2Khn0VP9kGsm/bMx9U1xpOM6fRBceheQ8Nw1tu2Dbn+/cDHM5N5u6nJtGD/DQiuz6hi3A4XAb9LsoyrVzyIHhSP4hoNOha9Qe+0n3cT8lH//cv3V1W6Ik5G/MUHo3c/+y2R0889U6+raoWaaHIdmHz7iXf+65k/R1pduXVya9xamOIzl1x+PEzPuc1F835FYquS7bVDkEyaAjsn874la7b7+41fuoOFgrB4Xf04bErcdyv4/s3w7ZqMdUOQTv6uGk7j+LMcgXvfPhpuxpIKRTIzJdxu6M6NuGNas2YbFo16TPP1lKl4796NKxHyuWr2Ow842JLVo1IT09k7i4wn//a62elpdqO/y/oCj/3X/FUFXVDoxHa4xyAu3tb8ckSZojSdK9zmSTgVGSJB0CvgNGqDcySGc+osXSrbMamCBJ0gRna6ZmqqoeQBtp/aqqqookScOBsvaR8gViJEkyAEMp2JQNVVXTJUm6IEnSIFVVlznHamqsquohZ/zr8wy/kYzp9TqmTxrLmMmzcSgKA+7uQc1qVfjg029oULcWXTq2Yc+BIyz4+EskJFo0acjMZ7RmtXa7g0fGPQeAj7cXr86agl5fik2gOMhauAC/l98EWcaydgWOyxcxPTwS+5mT2HZtx7PvQAxNW4DDjpqZSebb8wHw7DsAXWQUpoeGY3pIy3L6zCmoaan/rjyWwtQXXmXPgcOkpqbTrf/DPPnYMO67p+eNL9ChEP/yQip+9grIOtJ+XoP17GWCJgwj5+hpsjbuwrNhbSI/mIXOzwefLm2wj3+Yi/eMJWP1VrzaNqHqHwtBhayte8naWLqKjFyKgvXPz/AcMcP5GtyNqPFXMXR7AOXaORwn96Jv1wd93ZaoigPMmVh+/rDM2dTrZJ6/uyVPfLURRVHp17w6NUP9+Wj9YepHBdK5bkXa14xgx9kYBr7/F7IkMalnU/y9PLDYHIz8TBs80dvDwNz72qPXFV8Hr9fpmPZwH55482sURaX/Hc2oGRXKh79soEG1SDo3q8vkB+9izhd/8s2anUjAnMf7I0kS36/fzeW4ZD7+fTMf/74ZgIVThxHkV8LgyA6F6BcXUfXLOUiyTMqytVjOXCb06aGYj5whY/1uTI1rUXnhDHQVfPDt1prQiUM422scHjUrETV3PKqiIskSiYuWub1NpTjHNx6gfpemzNr8LlazhW+nLsqdNnXFq7zR53kqhAfSc8JAYs9eY8py7bz8+8vV7PxhI7+98jUPvjqazo/1QVVVlk5ZVFQo57aVeb5/e574ZKW2L1vXoWZ4IB+t3kv9iiF0blAFgFXOQbtdu9Wdj0/l7T93IUlar4VHOjWmVkThLcKurT9IZLcm9Nv+FnazlR2T8loV91k7lxU9tNaKu6ctof2C0eg8jURvPET0Bu31z+e+30y7t0fTd8N8FJuD7RO11xdb07I5sXglvVfMAVXl2oZD+EedL5jHfu144tNVWh5b1aZmeAAfrd5H/YrBLnk8T68m1Qvm8a9dSJKEqqo8cmejIvNY7DYe0oMnFnyPoqr069CYmlEhfPT7FupXiaBz01o8M6gbc75awdJ1Wquslx69+4a6MJ7fcJBqXZrw+N9vYTNbWTUlbzs/snIuX/XWtvOd0x+kXr/2GExGxux6jyPfb2L7O78AzkG7/yxdhTgOhaT5HxC+cD7IMhm/rcZ27hL+Tw7Heuw02Zt3EDhpNLKXidA3tBY09th44idqbxqL+OJtDFUrIXmZqLTmWxJffBvz9uJ/qONQSJz3AeGL5iHpZDJ+1WIGjHsEy7HTZG/aSeDkUUheJsLecsaMiSfuqRe0mEvewlhNi1l53VISZr+NeXsxL4RQFMxL3sN72usgy1g3rUS5ehHP+x/FfuEU9n3b8bhrAPpGLcBuR8nKIHvhq7mz6+s2RklK0Ab/LsG19QeJ6tqEAdu082T7M3n7r++aufx1l7b/dk1fQvt3RqP3NHJt4yGubch7TXrVfm0LdIOr+2gPfKuG0XjSABpP0n5E7XxgPtZE90rnuHUHCevWlB4738FutnDg6bzXhHdZN4+N3bXRCQ49/znN3x2LztNI3IZDxK0/CEDjeSOQjQY6/DANgOR9ZzlUTLc7vU7H9AmPM/a5V7SyQe+u1KxaiQ+++J4GdWrQpX0r9hw8xrufLdXKBo3rM+OpxwHQ6XRMHvMIj095CRWoX6s6999dijdtKQ4y3ltAwOta+Sdn5QocFy/i/ehI7KdOYtm+HZ+xY5FMJiq8qL1RSomLJ3XmdNDpCXxXGz9Lyc4ibe5cbSDvYtyOPN6W7epQuDrrY2p8/SKSTibph/XknL5C+DNDyD5ylvS1u/FqXJNqn0xDV8GHCt1bEf7MQ5zsPgH/vh3wad0Anb8vgfc7W+9Ofg/z8UI7AeRRFMxfvY/31NdAlrFtWYly7RIeA0fguHAK+4EdGHv0R9+guVaWzcrE/PFrANqb4J59DRQFNSWR7EXzS86jM6Z1+Rd4PjIdZGc5K+Eqhq6DUK6dx3FqH/q2vdDXbaENUm7OxPJrXssoz8deRA6OBKMnpskfYv19MY6zh4sMdzP3rzWHL7D/fCypWRb+cFYoz3ngTupGBhUaK5dDIfqFRVT76iWQZVKWrdPKP5Oc5Z91WvmnyqLpzvJPK8KeHsqZnuNK3n7OZbf+fhqSTubqd5vIPHWV2s/eT+qhC8Sv3seVbzfR9IMn6bzzHWypmewfo51zmaeuEvPHTu78+01Uu4Ojz38BiopHWABN3nsCSScjyRLRv+8kfm3ewOGR/dvx8luFD9uxdvUmut/ViT2H1mHONvPUk9Nyp23c+rvb2+D6DejNg/ePcpu/WfNGfLn0Qyr4+9Gzdxeem/4UHduU4S2nwv8kVVVXoA3K7frdbJe/j1P4EDw3RboFlVP/aZIkVQX+UlW1ofPzEufnn1ynSZJkAhagdUmT0brP9ZUkqRbwM9p7HlYB41RV9ZEkqTMwRVXVvs7lfgDsVVV1Sb74T6CNi5QA7AJ8VVUd4boeznTVgIVABGAAvldVdY5zEPF3gBRgA9BKVdXOxeXZFn+mXA+K9EcfL89wAPh98Wm5x+Q2jLt0vmMpbqK3UMX7buxNUTdDbtyw3GNKlUt+M8utdnbo0pIT3WKfKOW7P19979a/IbAkP489WO4x71vYuNxjElC6Lga30ocPryvXePcFFN494J+kKv/seCCFCWx4c131bsQfW/K/UOaf5+co37cY9dk7reREt1jKsH9mDMHiBDgHh/6vO9Z+ZrnGq94ps+REt5i+WvEDs/8T5Bbl3yL/7NN/l3vMy9nlW/4Zbr41b5Muq8T00+V/Eysn5k2f/2crOUydR/4r95tosVQCVVUvAg1dPo8obJqqqma0rmb55z8DuP6KeM75/SZgk0u6QksXqqouRKswyv/9iHyfLwC9Ckn3O/B7YcsWBEEQBEEQBEEQBEG4GaJiSRAEQRAEQRAEQRCE/4Zb9BZFofTE4N2CIAiCIAiCIAiCIAjCDREVS4IgCIIgCIIgCIIgCMINERVLgiAIgiAIgiAIgiAIwg0RYywJgiAIgiAIgiAIgvDfoIgxlsqbaLEkCIIgCIIgCIIgCIIg3BBRsSQIgiAIgiAIgiAIgiDcEFGxJAiCIAiCIAiCIAiCINwQMcaSIAiCIAiCIAiCIAj/DaoYY6m8iRZLgiAIgiAIgiAIgiAIwg0RFUuCIAiCIAiCIAiCIAjCDREVS4IgCIIgCIIgCIIgCMINkVRVvd3rIPzLnKzdp1wPiuPp/uUZDoCmIYnlHtNm05V7zOpbPyzXePYVn5RrPADl/MVyjymZPMs95m3hYSzXcFnLT5ZrPIDTR0LKPWatBgnlHtOaWf7XH1Ooo1zjqfZyDQdATnL5b1efmuUekqRDhnKP6RNsKdd42Snln8fzsYHlHrN25dtQ/skp//Mk8qNh5RrP/vtP5RoPQM22lntMOci33GPaLyeVe0x9xfI9N59cenvGA/ry4s/SbQlcDszrFv1nKzlM3cf+K/ebGLxbEARBEARBEARBEIT/BkUM3l3eRFc4QRAEQRAEQRAEQRAE4YaIiiVBEARBEARBEARBEAThhoiKJUEQBEEQBEEQBEEQBOGGiDGWBEEQBEEQBEEQBEH4b1DFGEvlTbRYEgRBEARBEARBEARBEG6IqFgSBEEQBEEQBEEQBEEQboioWBIEQRAEQRAEQRAEQRBuiBhjSRAEQRAEQRAEQRCE/wZFjLFU3kSLJUEQBEEQBEEQBEEQBOGGiIolQRAEQRAEQRAEQRAE4YaIiiVBEARBEARBEARBEAThhogxloRS8b6jBaEzxiDpZFKXrSb542Vu000tGxI2YzQedaoRPelVMlZvA8CrTWNCp4/KTWesXonoSa+RuW5HkbEav/II4d2a4jBb2TdxEalHLhZI49+4Gi3eHYPO00js+oMcnvkVAA1nDyGiR3MUm52si3Hse3oxtvRsAprVoNkbj2kzSxI5Hy8he8M29zx0aEnQc08g6WTSf1lF2mc/uE2v8Mh9+A7shepwoCSnkTD7Lewx8QCEL5yLR+N65Bw4Stz42aXbqIBXxxaEzRgLskzaT6tI/qTgdg2dNkbbrpNfJXP11txpIVNG4t2pNcgS2dsPED93UanjFmXmvLfZsm03gQH+/PbNzS/vum3nYnl9zWEUVWVA06qMbF+nQJrVx6+y+O8TANQOq8Cr/VsDEJOWzUvL9xOXbkaS4P0H2hPl711iTF3NJhh7PQKyjH3/Rmxb/3Cbrm96J8YeQ1EykgGw716Dff9G5PAqGO8eieThBaqCdcuvOI7tLDGeXK0hxm5DtHiHtmDftcJ9fRp2wNjlAdSMFABs+9fjOLwFANPUz1ATrgKgpCdh/eW9EuPdbEzJNxBj70eR/AJBVbEsewc1PankmFUaYOw0WIt5dCv2vavdY9Zvh7HjfahZqVrMgxtxHNPONUPHgeiqNQJJwnHpBLbNP+RffIkMLVvj8+QEJFnGvHI55h++dZvu2fdeTPcOAMWBajaT8c6bOC5fKnG5/l2aUv3lR0EnE7d0Pdc++M1tumTUU/v9CXg3ro49JZNTY97GciUBAK96Vajxxmj0vl6oisKhXs+jWmwE9+9AxYkDQQVrbDKnx7+HPTmjVPk0tmqNz7gJIMvkrFhO9vcF8+nVbwCqaz4vlZxPVx5tW+H/zHgkWSbrjxVkfPWd23Sfh+7Hu18fVLsDJTWNlFfewBEbB0DU9rXYzl0AwBEbT9LUmaWKaWjeGu9RznytXU7OT+758uh1L553O/dfjpmsD97EceUS+lp18R4/RUskSZi/XYJ159+li9miNd5jtWMmZ9VyzMvybcs+9+LZNy9m5nvaMWNo1hLvR0eD3gB2G1mfLcR26ECpYpb3ttU3boVp2HiQZaybVmD50z2esds9ePToB4qCmmMm+7O3Ua5dAp0Or8enoKtWC2Qd1q1rsPzxXRFRimbq0JLg58ci6XSk/7yS1M9+dJte4ZGB+N2n3T8dyWkkzHo79/5ZFsbWrfEdPx50OszLl5P9rfu+9Bo0CNPdd2v36dRU0l9/HSVO264+Y8bg0batto327iXj/fdLl7f2LQl87kkkWSbj15Wkfe5+3fIbdh++A3qDw4EjJY3EF97MzVvYR/PwaFQPy8GjxE2YVWSMwC5NqP3KCCSdTPTSDVx6/3e36ZJRT4MPxuHbuDq2lAyOjn6XnCsJ+DWrQd03RzsTSVx4YxkJK/cAoPfzot7bY/CuWwlUOD5pIel7zxQa37NdK/wnjwNZJuv3FWR8+b3bdJ8h9+PTr0/udk2e8waO2Lz9J3l7Ef7D55g3byP1jVJu19tQ5tp29ByvfbcaRVEZcEdTHuvTwW16TFIaMz//g4zsHBRFZeJ9XbmjcU1SM7OZvPBnjl2M5t72TZg+tFepY+rqNMOj3yiQZWy71mLb+HPh6Rq1wzT8ebIXTEa5ehZ9s04YOvfPnS5HVMW84BmU6Aslx6zfAs9BY0GSsW1fhXXNskLT6Zt2wDR6JlmvPoVy+Qxyldp4DnlKmyhJWJcvxX5oe6nzmhu/ZhOMdz8Kkox933psf7sfz/pmnTD2HIaS7ix/7VqFfd+GMsXQN2qF51DtmLVtXoFlufsxa+zSF2M35zXPYsb8xTso0ZdAp8f06CR0VWuDqmJe+iGOk4fKnsdaTbU8yjL2veuxbfktXx47Y+ztksedK7HvLVserxv6wkiadGmO1Wzlkynvc+lYwWNg8pcz8Q8NQKfTcWrPcb6a9Smqc4yh7sN70+2R3qgOhYMb9vHjq1/f0Hr8zxFjLJU7UbF0G0iS9DTwsaqq2c7Pmaqq+tzetSqGLBP2wpNceXQGtthEqv68gMz1O7Geu5KbxB4TT8zzbxP42H1us2bvOszFfhO0xVTwocbaz8jaur/IUGHdmuJTPZw17Z4hoHlNmr42kk19ChYamr42kv2TPyVl/1naf/ssYV2bELfhEPGbj3Bs7veoDoUGMx+k9lP3cuyV70k/eYWNPWeiOhQ8Q/3puXEelzbvAIeSm8fgGeOJGf089thEor5/n+yNO7Cdv5wb03LiLOkPjkfNseA7uC+BzzxO/NR5AKQuWYbs6YnvoD5l266zx3F15HRscYlUWfYumRt2YT2XF9MWE0/stLcIGOm+XT2b1cPUvD4X+z0JQOVv38TUuhHm3UdKH78Q/fv0YMh99zL95TdvajmuHIrK/FWHWDSkI2F+JoZ+vpFOtSKoEeKXm+ZSciafbz/Fkkc64WcykpyVkztt5h97ebxDHdpVDyPbakeSShFUkjD2eZScr+ehpifhOWou9lP7UBOuuSWzH9uBdcUSt+9UmwXLrwtRk2ORfAPwHD0X87nDkJNdfLwew7D88CZqRjKew2fjOHsQNSnaPd6J3djWfVNwfruVnCUvlCJjty6mse8obDv+RLl4HAweoKqli9nlISy/LEDNTMHzoWk4zh9GTY5xj3l6L7ZN7oU8OaI6cmQNcr6ZA4DH4GeRK9ZGuXq69HmWZXwnPE3qc5NREhMI+GAx1h3b3CqOLBvWkfOXVolobNcen7HjSJv+bInLrT7/cY4NnoM1Jpkmq14lec1ezKev5iYJG9INe2oW+9tNILhfB6rOfJhTY94BnUztD5/i9Pj3yD5+CX2AD6rNATqZaq+M5MCdT2NPzqDKrIeJGNmbK2/+WMyKuOTzqadJeXYySkICAR8txrJjm1vFUaH5nFZCPvPFCJg6kYQJU3HEJxC6ZCHmv7djv5AXw3b6LPHDn0C1WPAeeC8Vxo8meebLAKgWK/HDRpc+njOm99inSZ81GSUpgQpvL8a2axuOK3kxrZvXYVml5cvQuj1ej40j48VnsV++QNqkMaA4kAIC8X/vc6y7t4PiKDGmz7inSZuuHTP+7y7GuivfMbNpHTkrnNuyTXu8R40jfdazKOlppL84DSU5CV2Vavi98gYpw+4vVT7LddtKMqYRE8maPxUlOQHflxdi279dqzhysm5fj3X9nwDom7fHNPQJsl5/HkObTmAwkPH842D0wO/1L7Bt34CSGFf6+LJMyMxxRI+ahj02kYo/vE/Wxp357p/nuPrABNQcC34P9CVo8uPETZlX+hjOOL4TJ5I6ZQqOhAQCFy3Css39vLCdOUP2mDFgsWC69158x4whbc4cDA0aYGjYkKTHtAdMAe+/j6FpU2wHD5YYM2j6BGLHPIc9LpHIbz8ge5N72cB68izRQ8ZpZYNBfQmYNIqEZ+cCkLZkGZLJA7/77y4mhkSdV0dyYPBcLNFJtFo9n8TVe8k6nXe/ihzSFVtqFjvaTiSsf3tqzhrC0dHvknnyCnvumobqUDCG+tNm4+skrtmH6lCo/coIkjYe4sjj7yAZdOhMHkXmMeDZp4gf/yyOuATCvvwI85Yd7sfrqbPEPeI8Xu+7B/+nRpM0/ZXc6RXGPorlwOHit2W+mOVd5nIoCvOWrmTxM0MJC/BjyCuf0blpbWpEhuSm+WT5Vnq2rM/gLi04F53A+He/Z2XjCRgNesb178TZawmcvZZQ+nxKMh4DxmD++AXUtCRME9/Efnw3atwV93QeJox33IPj0qncr+wHNmM/sFnbXOFV8BwxrVSVSkgyng+MI/u96aipiXg99y72w7tQYi+7p/MwYejSD8eFk7lfKdGXyH7tKVAUJL8AvGZ8hP3IzrL9QJckjPc8Rs6SV7Ty19j52E/uLVj+OrId6/LPS7/c/Hl85CmyXn8WNTkBnxc/wnZgh1Zx5GTdsQHrxr8A0Ddrh+dDY8l+axrGztq5mDlzFJKvP95T5pP54pOlKwO5xDfe8xg5X7yMmp6M5xPzsZ/Ym/uA0C2Pf352Y3l0aty5OeHVIni283hqNKvF8LmjmdN/WoF0H457i5xMMwDjF06l9d3t2PXnNuq2a0jzHq2Z1fsZ7FY7vkF+BeYVhFtFdIW7PZ4GvG73SpSWZ+PaWC9FY7sSCzY76cu34NO9nVsa27V4LKcuFnvz8e3Vkcwte1FzLEWmiezZgss/ak+hU/afxeDnhWeov/v6hPpj8DGRsv8sAJd//JvIXi0BiN98BNVZWZSy7yymiCAAHGZr7veypwEV9xuIR6M62C5HY78aC3Y7WSs3492lvVuanD2HctfdcvgE+rC8wkjOroMoWcVUPBTCs3FtbJejsV3VtmvGis34dGvrlsZ+LR7L6YsFb3iqiuRhRDLokYwGJL0OR2JqmeIXpmXTRlTw873p5bg6Gp1MpUBvKgZ4Y9DJ9KxfkU2n3SsifjlwgQdaVMfPZAQg0NsTgHMJ6TgUlXbVwwDwMuoxGUquD5ejaqIkx6KmxGtPkY/uQF+nZanWV02KRU2O1f7OSEHNSkfyKv5GLEdUR02NR01LAMWB/cRudLWalSrejbqZmFJQJMiyVqkEYLOA3VpyzPBqqGnxqOmJWszTe9HVaFLqdZZ0BpD1oNODrEPNSi/1vAD6OvVwRF9DiY0Bu52cTRswtu/olkbNzjsPJU8TlKKs6NusJjkXYrFcjke12Un4bRuBPVu5pQns2Yr4HzcBkPjXDip0bARAQOcmZB2/RPZxrUBrT8nUCuWShCSBzkv7Maf38cIam1y6fNath/3aNZQYLZ+WjRvwKCmfZWSsXxf71Ws4orUY5rUbMN3pfs2z7DuIatGuedajx9GFhhS2qFLT16qHI+YaSpwzX1s2YGiTL1/mIvJlseRWIklGY6l/BOhrux8zls0bMLYtelvicsw4zp1BSdZa8TkuXUDy8ACDocSY5b1tdTXqosRdQ0mIAYcd684NGFq4x8N1u3p4kptJFSQPE8gyktED1W5z2welkf/+mblyE95d3csIrvfPnEMn0IUFlzmfhrp1cVy7hsN5XuRs2IBHB/cWJ7aDB7VjBbAdP44c4tyuqqodN3o9GAxIej1Kcsnno0fDOtiuRGO/5iwbrNqEV+diygZHTqB32Zc5uw+gllA28GteE/OFOHIuxaPaHMT9tp3gXu7Xn5BeLYn5UatkiP9zJwEdGwKg5CvbXD8vdL4m/NvVI3qp1kJCtTmwpxe+HsYGdbFduYbjmrZds9duxNSpmOP1yAm349VQtxa6wABydu0rNp+ubkeZ6+iFaCqFBlIxJACDXkev1g3YdLDgg41MZ8xMs4UQf6085OVhpHmtyniUovzhSq5cCyUpFjU5Dhx27Af/Rt+gdYF0xp5DsG78ucj7sL7ZHdgPbi10WoGYVWujJESjJsVqMfdtRt+kbYF0Hvc8gnXtMlSbS0ybJa8cbyj9ddYtfsWaWp6vl7+ObEdfr1XJM5aBrrp2zVOd1zzbro0Ymue75uXkv+Y51y+yCvbjWstTNSMVNSsTXbXaZYovV3QtY9pxHN6Gvl7pyphl1fyuVmz7RTv3zx04g5evNxVC/Auku16ppNPr0Bv0qM59121oT/5a+Ct2qx2AjKSylbsEoSz+NS2WJEmqCqwCdgLtgT3AF8BLQCgwVFXV3ZIkeQPvAw0BA/Ciqqq/O+f/GrjeR2a8qqrbJUnqDLwIJDrn2Qc8rKruV0tJkp4CxgJ24Liqqg9KkvQiUA2oDlQGJgFtgd7ANeAeVVVtkiR1A95E2557gCdUVbUU9j0wBogENkqSlKiqahdn/LlAX8AM9FNVNU6SpCVAOtASCAeeVVX1J2f6qcBgwAP4VVXVF5zb5kegIqADXlZV9QdJkl4F7nXmbY2qqlPKsm8MYUHYYxNzP9tjEzE1KdiVqSR+fTqR/MWvxabxjAjAHJ1X2DPHJOMZEUBOfKp7mpiCafKr8lBnrv6e1+UuoFkNWiwYg1fFYBKnv5bXWgnQhwZjj817CmWPS8Cjcd0i19N3YC+yt+4pNi8l0YcFY4txiRmbiGcpt2vOwZNk7zpMjb+XgiSRuvRPrOevlDzjbRCfkUO4b94PxDA/E0euuRfoLyVnAjD8y00oisrYO+vRoUY4l5Iz8fU08MxPO7mWmkWbaqFM7NIQnVx8syXJL8CtW5eanoRcsWaBdLp6rTFVqYeSFIN11Veo6e7rJUfVQNLpUVOKf3ov+Qa4zatmJCNH1CiQTl+nBbpKtVFSYrGt/x7V2Q0PvQGPR2aDqmDfuRzHmZK729xMTDkwDHKyMfYfj+wfjOPicWybl5VYiJS8/XO71WkxU5DDqxWMWas5uqhaKKlx2DYvQ81MQYk5j+PqKUyjXwck7Ic2oqbElphPV3JwMI6EvO4XSmIChrr1CqTzvLc/XvcNBr2BtGefLnG5xohArNF51zhrTBK+zWsVSGO5nsahYM/IRh/oi2f1SFBV6n83E0OQH4m/b+Pah7+j2h2ce+4Tmm58GyXbgvl8DOemfVqqfOqCg1Fc85mQgL5ewXya+vXH634tn6lTSs6nW4zQYBxxeTEc8YkYGxSMcZ33vX3I2bE797NkNBK6ZCGqw0HGl9+Rs2VbkfNeJwcFoyS65CspAUPtgjE9+vTH1F/LV/qMp3O/19euh/fE59CFhJH59rySWyuhHTNKvmNGX6eQY6Zvf0wDncfM808XmG7s2An72dNgs5UYs7y3rRwYjJLkksfkRPQ1CsYz9uiHR+9BSHo9mXMnA2DbvRlDi/b4ffgTktED8zcfoWaVrrvmdfrQoHz3z0Q8GxV9//Qb2Ivsv8t+/5RDQlAS8uIoCQkY6tcvMr3p7rux7ta2q+34cawHDxLyyy8AZP/6K47Ll4uc9zpdaDAOl7w54hPxKCZvvgN6Y962u8jphfEMDyQnOu9+ZYlOwq+5+/3KIyIQyzUtjeq8/hgCfbElZ+DXvCb13hmLZ6UQjo/7ANWhYKocijUpnXrvPoFvgyqkH77A6ZlLULILPtjThQTjiHPJY1wCxobFHK/9epOz3ZlHScL/6bEkz56PR+sWpc7z7ShzxadkEB6Q95AoNMCXI+fdW/g+ce+djH3nW77bsAezxcbHzwy9qZhShSDU1Lx7i5qahFzFvRJDjqqO7B+M9cQ+6Dyg0OXom3QkZ0npWvjJ/sEoKS7nSUoiuqruZUu5Ug2kgGAcR/dAd/dWmHLVOng+PAk5MJScL98sc3ciyS8QNc2l/JWWhFyxVoF0ugZtMFV1lr9WfFmqrvi5MQKCUZNd8picgK6wa163fhh73Y+k05P1mvbTx3HlHIZm7bHt3IAUGIquam2kwFA4f6rA/KXOY3oycqVi8pgYg3XFErd5SisgLJAkl/JJcmwSAeFBpCWkFkg75atZVG9Sk8ObDrBnhTaEQ1j1COq0rsf9Ux/CZrHx/dwvuXD4XJnXQxBK49/WYqkm8BZQ1/lvCNARmAJMd6aZAWxQVbU10AV4w1mhEg/0UFW1OfAA4DpISTO0VkL10SqJ3B9xaZ4Hmqmq2hitgum6GkBXtIqZb4CNqqo2QqsAuluSJE9gCfCA83s98ERR36uq+h4QDXS5XqmEVhm2U1XVJsAWIG9QIohwboO+wKsAkiTdBdQCWgNNgRaSJN0J9AKiVVVtoqpqQ2CVJElBwACggTNvr3Ab6EIC8KhTlaytpX+idTPqTOyHandw5ee8AnnKgXOs6/QsG3vNxP/xB5CMJT95LoxP32541K9N6heF91kvD4bKERirV+Jc52Gc6/QwXm2bYGrR4Latz81yKCqXkzP59OE7eXVAa+YsP0B6jhWHonLgSiLPdGvE0pFduJaSxR+HyzaOTFHsp/ZjXvAU5oXP4Th3BI8BT7pNl3z88RjwJJbfF93QU7v8HGcPYl40lZwvZqNcOI7x7sdzp+UsnILlqzlY/1iModsQJP+baxlSYkxZh1ypNraNP5Dz5Rwk/xB0jToWv7DSxjx/GPPn08lZ+jLK5RMYe44AQKoQghwYgfnT5zF/+hxypbrIkQUr+26FnD9+I3n4ELI+XYzXkEf+kRjXSXodfm3qcnrcuxzpN5PA3q2p0LERkl5H+PCeHOo+lT1NRpF94hIVnyr8R8ONMv/+G0nDhpD5yWK8Hv7n8unVqzuGerXJ+CZvDJSY/g8RP+IJkmfNxX/SOHRRkbcsnmXFb6SOHkL2l4sxPZCXL/vpE6SNG0HaM2MxDRqqPVG/RXL++o2UkUPI/nwxXg+5b0td5ap4jxxD5vtv3bJ415XntrWu/Z2MZx7G/P3HePZ/GNBaO6EopI8fRPqkoXj0GYwcEnFL4hXGp29XPBrUIvWLn/6xGACePXqgr1OHrO+1Lrm6qCj0lSuTOGgQiYMGYWzeHEOjRrc0pvfd3TDWr03qkvItG6TvP8uuTlPY03M6VSb2R/bQWjH7NqrGtS/Xsrv78yjZOVSd0O+mY3n17o6xXm3Sv9a69Prcfy8523bjiE8sYc4bV55lrpW7j3Fv+yasfWMiH058kBmf/Y6i3Pz9v0iShMe9I7H8+UWRSeTKtVFtloJd2W4m5n2jsfz8SaGTlYunyH5lLNmvT8TYU6tsv9XsJ/dhfmsc5g+n4jh7GI/7xt3yGADW9b+TOXUYOT9+gse92jXPtmUlSkoCPi8uxDT0Sexnj/0jY/HYT+7F/MaTmN+fguPsITzuG3/LY+T35iMvM7H14+iNBuq311o16nQ6vCv4MKf/NH6Y9xXjPpz8j6+H8P/Xv61i6YKqqkdUVVWAY8B6Z8uiI0BVZ5q7gOclSToIbAI80VoTGYBPJEk6AixDq0S6breqqledyz3osixXh4GlkiQ9jNay57qVqqranOugQ2tVhcs61XGu9/X2tF8CdxbzfWGswF/Ov/flW7/fVFVVVFU9DoS5bIO7gAPAfrRKuFrOdeohSdJrkiTdoapqGpAG5ACfSZI0ECi07bAkSaMlSdorSdLeH9Pcb162uCT04XnN1vXhwdjiylbr7tf7TjLXbgd7wSfM1R/tQdd18+i6bh45camYIgNzp5kiAsmJSXFLnxOTgimi6DSVH7iT8B7N2TPuw0LXJeNMNGp2DoaaVXO/s8cnog/P+zGvDwvBUUgeTW2b4T/qIWKfeqFUT66LY49LxBDhEjM8GHspt6tP9/bkHDqJmp2Dmp1D1pa9mJoW/YTxdgr19SQ2w5z7OS7dTKhLCyaAMF8TnWpHYNDJRPl7UyXIh8vJmYT5magT5k/FAG/0skyXOpGciE0tMaaanoLkF5T7WfILQk13P44wZ4JDO9Xt+zcgR7i0vPEw4TH0WawbfkC5erbkeBkp2iDY1+P5BqJm5ouXk5UX7/Bm5PAqefNnanlS0xJQLp9EDqtCSW4mppqRjBJ3WetGpyo4zuwvXcysVCTfvNaBkm9A7iDdhcY8uhU5VFuurmYzHDHntab2NguOi0eRI6qXGNOVkpiILiQ097McHIIjsegfNJZN6zF2KLnCzBqTjDEy7xpnjAjCEpNcII3H9TQ6Gb2vF/bkDKzRSaTvPIE9OQPFbCVl/QF8GlfDu2FVAHIuaa3dEv/Yjm+r0rVIdCQmIrvmMyQEpbh8blxfoKtciTHiE9GF5cXQhQbjcGkNcp1Hq+b4jhhK0pSZbtc8JUFbH0d0DJb9BzHWKbmSUElKRA52yVdQCI6kovNl3bK+QLc1AMfVS6hmM7oqBVvLFYiZf1sGh6AUE9OyeT3Gdh3d0vvNeoWMN+ehxEQXOZ/b+pXztlWSE5GDXPIY6N5iIT/bjo0YWmrP14ztu2E7vAccDtT0VOynj6KrXrZuIfb4pHz3z2DshVQ0mNo2I2D0Q8ROuLH7p5KQkNe1De28KGy7Glu0wPvhh0mdPj03jkfHjtiOH0c1m1HNZqy7dmFoUPLDGEd8IjqXvOlCg7HHFcybZ5tm+D8+hLiJs8uct5zYZDwj8+5XHpFBWGLdr+WWmGQ8orQ0kvP6Y8v3IoDsM9dwZOXgXbcSlugkLNFJpDuHDIj/cxe+jQo/XxwJiehcupnpwkJwJBTMo0fr5vg9OoTEybNy82hsXB+fwf2I+H0p/hPH4N2nBxXGP15g3vxuR5krNMCX2JS8bkDxKRmEBbh3/f9160F6ttLKUk1qVMRis5OSWbYud67UtCQk/7x7i+Qf5N5qxcOEHF4F0xOv4DX9Y+TKdfB8dIZbC2t90zuwHyjdiwoAlNRE5ACX8yQguGDMyCp4TXod75eXoKtWF9PYF5Aru7e4UWKvoFrMyJFVS59htNY7UgWX8leFoLzW2de5lr/2rUeOLFtZQE1JRAp0yWNgCGpK0dd1t65yikLOtwvJnD2G7HdnI3n5oMReLXLeQuPnz2O+FkyAex73bkCOKn0euw3rxZwVbzJnxZukxqcQ5FI+CQwPIiW26N8KNouNA2t307yH1uUyOTaJvat3AXD+0FlURcU38P/JOEuq8t/99y/1b6tYcm2jq7h8VsjrticB96mq2tT5r7KqqifQuqnFAU3Quo65Psp0Xa6DwrsA3g18CDQH9kiSdD2NBcBZKWVz6ULnuk43y3W5+dfPdd0ll//nu2yDmqqqfuasxGqOVsH0iiRJs1VVtaO1bPoJrdXTKgqhqurHqqq2VFW15eAKld2m5Rw5jbFqJIaKYWDQ43f3nWSuL/ktWa78+nYi/a/NhU47/8VaNnSfzobu04lZtZfKg+8AIKB5TWwZZrducAA58anYMs0EOJuKVx58B9GrtZZQYV0aU3tcX3YMfxOHOa/fuFflECSddribKgZjqFYJe3Re9ybL0VMYqkShjwoHvR7v3p3I2uT+5jpj3RoEz55I7ITZKMnu63Qjco6cxlAlEkOUtl19+3Qic0Pptqs9JgFTq0agk0Gvw9SqEZZ/aVe4BpEBXE7O5FpqFjaHwurjV+lU2/2JeJc6Eey9pBUKUrItXErKpKK/Nw0iAsjIsZGcpZ0Guy/GUz245DGglOhzyEHhWssfnQ5dw3bYT7m3lpN8/HP/1tVpgZLoHFhSp8PzgWewH/obx/HSdWlQYi4gBYQiVQgGWYe+XmscZ/N1Z/OukBevZjOUJOc4Ux5e2phDACYf5KhaKIkl/4C9mZhKzAUkTy8wadtSV6Ueamlixl5E8g/VKu1kHfraLXGcy/c2FZfxqHTVm6A4B/ZWM5LRVawNkgyyjC6qdu5YVqVlP3USXVRF5HDtPPXs3BXrDvduQrqoqNy/jW3a4bhWcoEx4+BZTNUj8KgcimTQE9K/A8lr3LtdJK/ZS+jgzgAE921H2rajAKRsOohX3crIJiPoZCq0q0/26atYY5Lxql0RvXOgTP87m2A+U7rCq/3kSfQu+fTo0hXL9mLy2bZ0+XRlPXESfaUodBFaDFOPrpi3uF/zDLVrEvD8MyRNnYmSkpr7veTrkzvWkFzBD2OThtgulNyS0H7mJLrIishhznzd2RXbbvd8yRF5+TK0bIcSreVLDgsHWaf9HRKGrmJllPiSjx/76XwxO3XFujNfzEiXbdk6b1tK3j74vfQqWV8sxn78aImxrivvbes4fxI5PAo5JBx0eoxtu2Lb5x5PDsvLo75pWxyx2vVOSYxHX985NpuHpzYOVnTZ7iWWo6cwVI5CHxUGej0+vTuTtdH9XmasW4OQF54idvwLOJLTyrT862ynTqGr6HL+d+2KZbv7G6v0NWvi+8wzpE6fjpqamvu9Iz4eQ9OmoNOBToehSRPspXiLouXY9bw5ywa9OpO9uZCywayniZt4Y2WDjAPn8KoejmflECSDjrD+7UlcvdctTeLqvUQM7gRA6D1tSdl6DECbx1m28awYjHfNSHKuJGBNSMMSnYRXDe1eG3BHQ7JOF36NsB4/iaFyFLpILY9ePbpg3uK+XQ21axI4bRKJk2e5Ha/Js+YTc88QYvoNJfXdxWStWEvaByV3+b0dZa4GVSO5HJfM1YQUbHYHq3Yfo1MT90rUiMAK7DpxEYDz0YlYbXYCfW98SFTlyhnk4Aitq5VOj77pHTiOuZQrcrLJemEY2fNGkz1vNMrlU+R8MTfvgZYkoW/SAfvBMlQsXTqNHBqJFBSmxWzRCfthl/MxJ5usZx8ka9YIsmaNwHHhJOZFL6FcPqPNI2vHkxQYihxWCTWpDAP5A8q1c8hBEXnlr0btsZ90P57dyl91W6IklO3+5bhwEl1YFFKwds0ztOmC7YD7Met2zWvSFkecs4xn9ACjNuaSvkELUBxug36XhnLtrJbHAG2/6hp3KJhHX//cv3X1WqLElz6P679exew+U5jdZwr71+ymw0Dt3K/RrBbmjOwC3eA8vDxzx12SdTJNurYg5pyW3/1rdlOvrdZ6KaxaBDqDnoxkMc6S8M/414yxVAargQmSJE1QVVWVJKmZqqoHgArAVVVVFUmShqO1LioVSZJkoJKqqhslSdoKPAiU9i1tp4CqkiTVVFX1LDAM2FzM9wAZgC/auE83YjXwsiRJS1VVzZQkKQqwoe3PZFVVv5EkKRV4XJIkH8BLVdUVkiRtA86XOZpDIW7OQip99groZNJ+WoP17GWCn3qYnKNnyNywC89GtYj6cBY6Px98urQh+KmHuXD3EwAYokLRRwSTXYo3lsWuO0hYt6bctfMdHGYL+55enDut67p5bOiu9Yg8+PzntHh3LDpPI3EbDhG3/iAATeaNQDYa6PiD9saE5H1nOfjc5wS1rkOdCfei2OygqCTOfR8l1eXC6lBInPcB4YvmIelkMn5dje3cJQLGPYLl2GmyN+0kcPIoJC8TYW9prwy2x8QT95T2Jq+IJW9hrFYJyctE5XVLSZj9NubtJXT7cyjEv7yQip+9ArKOtJ+17Ro0YRg5R0+TtXEXng1rE/lB3na1j3+Yi/eMJWP1VrzaNqHqHwtBhayte8nauKtUu7M4U194lT0HDpOamk63/g/z5GPDuO+enje1TL0s83zPpjzx3TYURaVfkyrUDPHjo83HqR/hT+fakbSvHsaO8/EMXLwWWZKY1K0h/s5Bjyd1a8iYb/9GVaFehD/3NSu5lQKKgnXFEjyHTdNed3tgE2rCVQxd7keJvoDj1D70bXqhr9MCVXGAORPLb4sA0DVoh1ylLnovH/RNtUaG1t8WocQWU/BQFaxrl+IxeLIW78jfqInRGDr2R4m9iOPsQQwteqCr1VR7pbk5C+tyrfAtB0di7DlcewIhydh2LS/wZrdbHRNVxbrxBzwfnAqShBJ7Efuhwit+C8Tc+D0eAyZqMY9tQ02OwdD2HpT4SzjOH8bQrCu66k2cr27PxrpmCQCOM/uQK9XBc9hsUFUcl47juFCGtwgBKA4yP1hAhflvaq+OX70Cx6WLeA0fif30Saw7tuPZbyDGZi3AYUfJyCTj9fklL9ehcH76pzT4biboZOK/24D51FUqP/sAmQfPkbxmL3Hfrqf2B0/RfMf72FMztTfCAY60LKIX/0mTVa+hqiop6/eTsk578+WVt5bR6Nc5qHYHlqsJnJn4QanzmfH+Avxf0/JpXqnl03vESGyntHya+g/E2LwFqt2OmplJ+mulyGe+PKe++T7B772GJOvI+nMl9gsX8Rs9AuuJ0+T8vZ0KE8YgeXkSOE+7zjli40maOhND1SoEPD8JVVWRJImML79ze4NUcfnKWrQAv5feBFnGsm4FjssXMQ0dif3MSWy7t+PZdyCGpi3Ama/MBVq+9PUbY7p/CNjtoKpkLnoHNb0UFRSKg8yFC6jwypugk8lZo8X0GuY8ZnZtx3TPQAzNtJhKZiaZb2kxPe8ZgC4yCq8hw/EaMhyAtBlTUNNS/13bVlEwL3kf7+deA1mHdfNKlGsX8bxvBPYLp7Hv347HXf3RN3SeF1kZZC96DQDL2t/wGvMcvq99DhJYN69GuVLGYoJDIXHeh0Qs1u6f6b+uKXD/DLp+/3x7JqDdP2MnvFjGOA4y3n2XgDfeAFkmZ+VKHBcv4v3oo9hPncKyfTs+TzyBZDJR4aWXtE0TF0fqjBlYNm/G2KwZQZ9/DqqKZfdurDt2lBBQy1vS/A8IXzgfZJmM37Sygf+Tw7EeO0325h0EThqN7GUi9A1n2SA2nviJ2ttsI754G0NVrWxQac23JL74Nubt7j9AVYfCqWmf0+z76aCTifluE1mnrlL92UGkHzpP4up9RH+7kfofjKfdznexpWZydMy7APi3rkuVCVrXf1VROfn8Z7ktmU5N/4IGH01AMurJuRTP8YkLi8xjyuvvE/Lea0g6mcw/VmI/fwm/MSOwnjhFzpYd+E8cjWQyEfSqli9HbLzWculG3YYyl14nM21IL55Y8B2KotC/Q1NqRoXw4W+baFA1ks5NazN5cHfmfLmcb9buQpIk5oy8B8n5Ktrez71PptmCzeFg48FTLJo0xO2NcoVSFCy/foxp1Iva/X3PepS4Kxh7DsFx5WyJD6901RugpiZqg3+XlqKQ88NCvMZrZUvbjjUoMZcx9h2G49JpHEeKLivqajTAeNdgraWNqmL54cMyv2QDRcH61+d4Dp8Bsox9/0bU+KsYug5GiT6H4+Q+9O16o6/bUit/ZWdi+eWjMscwf/0+3lNfA1nWurddu4THgBE4Lp7CfmAHxu790Tdort1LsjMxf6Jd8yQ/f7ynvAaqgpqSSPbiMt47r+fxz8/wHDFDKwtdz2O3B1CuncNxci/6dn3y8mjOxPJz4b0oSnJo434ad2nOG5s/xGK28OnUvOXMWfEms/tMwcPLg6c/nYbBaECSJU7sOMqGpasB2PLjBh5//Unmrn4Hu83OJ5Pfv6H1EITSkNRbMHbIreAcfPsv59hAOAeu/ktV1Z9cp0mSZAIWoA3wLaN1N+srSVIt4Ge0V52sAsapqurjHLx7iqqqfZ3L/QDYq6rqEpfYBmAjWuWUBHyjquqrzsG7M1VVfdOZLlNVVR/n37nTyjJ4t/P7CcB4tPGQuuRb7v1AX1VVR7hug0LiTwSutzfOBB5GG6PqDbTWVDa0wcKvAb+jdRmUgDdVVf2yuH1xsnafcj0ojqf7l2c4AJqG/HPjARTFZit1XectU33rjd3IbpR9ReF99v9JyvmL5R5TMnmWnOi/wOPWjWFTGlnLT5ac6BY7feTWjGdVFrUalOF11beINbP8rz+m0JIH176VVHvJaW61nOTy364+/8zQZMVKOnTrx1kpiU9w0W+Q/Sdkp5R/Hs/HBpac6BarXfk2lH9yyv88ifxoWLnGs//+z44bVhg1u+S3ud5qctCtfXNwadgvl33Q65ulr1i+5+aTS29P96YvL/5c/Ntw/oeZ/3r731HJ8Q8w9X3mX7nf/jUtllRVvYj21rbrn0cUNk1VVTPam9Xyz38GaOzy1XPO7zehjcV0PV2B0dOcYygVGMhBVdUX8332KWyaqqrr0QYIzz9/Ud+/j/Zmu8KW+xNatzW3bVBIuneBd/Mt+hxaa6b8Cr7bVBAEQRAEQRAEQRD+a/6BQdmF4v3bxlgSBEEQBEEQBEEQBEEQ/keIiiVBEARBEARBEARBEAThhoiKJUEQBEEQBEEQBEEQBOGG/GvGWBIEQRAEQRAEQRAEQbgpqhhjqbyJFkuCIAiCIAiCIAiCIAjCDREVS4IgCIIgCIIgCIIgCMINERVLgiAIgiAIgiAIgiAIwg0RYywJgiAIgiAIgiAIgvDfoIgxlsqbaLEkCIIgCIIgCIIgCIIg3BBRsSQIgiAIgiAIgiAIgiDcEFGxJAiCIAiCIAiCIAiCINwQUbEkCIIgCIIgCIIgCIIg3BAxeLdQQKUHfMo1nvequHKNBxB0f+Vyj6mmZ5Z7TPuKT8o1nr7PqHKNB6DEniv3mGpmcrnHVPb9Xe4xyc4u13BerUPLNR6A5bBU7jG9u1ct95imuPI/Zi/8Vr7xan3QpXwDAl4nj5d7TCnAv9xjGk4dKPeYPneU7/XAx8NYrvEADH9Gl3vMwCG1yz2mmpRa7jGVnRvLNZ7+4fIv/5CVVv4xb4fVf5Z7SLlBvXKNt/fTFeUa7/8FVQzeXd5EiyVBEARBEARBEARBEAThhoiKJUEQBEEQBEEQBEEQBOGGiIolQRAEQRAEQRAEQRAE4YaIMZYEQRAEQRAEQRAEQfhvUMQYS+VNtFgSBEEQBEEQBEEQBEEQboioWBIEQRAEQRAEQRAEQRBuiKhYEgRBEARBEARBEARBEG6IGGNJEARBEARBEARBEIT/BjHGUrkTLZYEQRAEQRAEQRAEQRCEGyIqlgRBEARBEARBEARBEIQbIiqWBEEQBEEQBEEQBEEQhBsixlgSBEEQBEEQBEEQBOG/QVVv9xr8vyMqloRS0dVsgvHuR0GSse9bj+3v392m65t1wthzGEp6MgD2Xauw79sAgMcj09FVrIXj8kks37xW6pie7VrhP3kcyDJZv68g48vv3ab7DLkfn359UB0OlNRUkue8gSM2Pne65O1F+A+fY968jdQ33i8xnlytIcZuQ0CWsR/agn3XCvdt0LADxi4PoGakAGDbvx7H4S1aLN9AjL0fRfILBFXFsuwd1PSkUucVQFerqbaNZRn73vXYtvzmNl3frDPG3i7beOdK7Hs3lCkGwLZzsby+5jCKqjKgaVVGtq9TIM3q41dZ/PcJAGqHVeDV/q0BiEnL5qXl+4lLNyNJ8P4D7Yny9y7zOriaOe9ttmzbTWCAP799s+imluVq64HjvPb5TyiKwsBu7Xls4F1u02MSkpn5/tdkZJtxOBSefrgfd7RogM3u4MWFSzlx/goOh8I9nVvz+MCeJcbbdvgMry1dgaKoDOjUnMf63ukeLymVmR//QkZ2DoqiMnFwD+5oUpsj567y8pI/AFBVlbH9u9CtZf0y53fbxUTe2HIKRVXp3yCKkS2rFUiz5nQsi3adR5KgdrAv83s1KnMcuarzPJEk7If/xr4733nSoAPGzoNRM13OkyN/I1eqi7Hrg7nppMAIrH8uwnH2QJni/1PnSWCXJtR65VEknUzM0vVcet/9GicZ9dT/YDy+jatjS8ng2OgF5FxJyJ3uERVEm7/f4cIby7iy8E9tXfy8qPv2WLzrVgJV5cSkhaTvPVN4vqo3wth9qJavg5ux7Vzunq9GHTF2fQDFef2x71uP/dBmALye+wIl4QoAanoylp8WlJhfAF3d5ngOHAWSjG3nWqzrfyo0nb5xe0wjp5H11iSUK2cBkCOq4vnAOPDwAlUh++1nwG4rMaZPp+ZEzR4FOpnkH9aSsNA9pnfrBkTOHoVn3apcnvA6aSu3u02XfUzUXvsR6Wt2Ev3C4lLlc9vJy7z+23bt3GxTl5HdmrlNf+P37ew5Gw1AjtVOcqaZrXMfzZ2emWNl4Os/0qVhVaYN7FiqmHLVBhi7PqTdM4/8jX33SrfpugbtMXYalHeeHNjoPE/qYOzyQG46KTAC61+LcZw9WHwez8fz+vqj2nW9cWVGtq1VIM3qk9Es3nYKgNqhFXj1nuZ5ebTYGPjZJrrUCmdaj9JdFzzbtSJgivMe/dsK0vPdo32HutyjU1JJct6jdeGhhLw5ByQJ9Hoyf/yVzJ//KlXM21EW0VVvjLHnMC3mwU3Ytv/pHrPxHRi7PZR3bu5di/3gprwERhOmsa/hOLUX6+qvShXTo20r/J8ZjyTLZP2xgoyvvnOb7vPQ/Xj364Nqd6CkppHyyhs4YuMAiNq+Ftu5CwA4YuNJmjqzxHhylfoYOw3W8nhsG/a9q923Qb12GDsORM1KBcB2aBOOY9sAMHQYgK5qQ+373StwnNlXqjzqajbB2Ge4FnP/Bmx//+E2Xd+0E8aeQ1325Wrs+zdq22fY8859eQrL0tdLFQ9uz/1r28GTvLbkNxRFYUDXNjzWv5vb9JjEFGZ++B0Z2WatbDDkbu5oVo/lf+/jyz835aY7fTmG71+dRN2qUSXHPHKW175djaIqDLijGY/d7X7diklKY+Znv5GRbUFRFCbe3407Gtdix7FzvPvTBmx2Bwa9jkmDu9OmXsGyxO2OB7fn+Nl2JprXV+zTrrPNazDyzgYF0qw+eonFG48AErXD/Xl1UAcAFqw5wN+ntfvM6E4N6dmoSqnjupo29xnu7NYeszmHGU+9zIkjpwqkMRj0zJg/lVbtm6MoCu/NX8Ta5RtvKJ4glIWoWPqXkSRpBLBGVdXo270uuSQJ4z2PkbPkFdT0JDzHzsd+ci9qwjW3ZPYj27Eu/7zA7Latf2A3eKBv1b30MWWZgGefIn78szjiEgj78iPMW3Zgv3Apb7mnzhL3yBOoFgve992D/1OjSZr+Su70CmMfxXLgcOnz2GMYlh/eRM1IxnP4bBxnD6Imue8G+4nd2NZ9U2B2Y99R2Hb8iXLxOBg8yl5LLsnaNv7iZdT0ZDyfmI/9xF7UhKvu8Y9sx/rnZ2VbtguHojJ/1SEWDelImJ+JoZ9vpFOtCGqE+OWmuZScyefbT7HkkU74mYwkZ+XkTpv5x14e71CHdtXDyLbakaQbXpVc/fv0YMh99zL95TdvfmFODofCvE9+5OPZ4wkL8ueh596gc6tG1KgUkZvm459WcVf75jzQ6w7OXYlh3NyFrGoxhzU79mOz2fnlnRmYLVYGTHyF3h1bEhUaVHQ8RWHeV3+x+NnhhAX6MeTFxXRuVpcaUaG5aT75fTM9WzdkcLfWnLsWz/i3v2HlW89Qs2Io3744Br1OR0JqBoNmfkSnZnXQ63Slz6+i8uqmkywc0JwwH0+G/rCLTtVCqBHkk5vmUmoWn++9yJJBrfDzNJCcbS3jVsV5njyM5ce3tPNk2Gwc5wo5T07uxrZ+qdt3ypWT5Hz5ovbB0xvT4/NxXDxWxvj/0HkiS9R59TEODH4FS3QSLVfPJ2H1XrJP513jIod0xZ6axc62TxHavz01Zg3l2OgFudNrvTSc5PXuPzJqvfIoSRsPcvTxt5EMOnQmjyLyJWG86xFyvn9dy9eIF7GfOVDo9ce65uuC89ut5Hw+u/T5BZBkPO8fS/bCWaipSXg98zb2o7tQ4q64p/MwYeh0D46LJ/O+k2U8hz1Dzjdvo0RfBC9fcDhKjinLRM0Zy4WHZ2GLTaLmH2+TvnYXlrN5Ma3RCVyZsoCQUQMKXUT45IfJ2l3648ahKMz/ZRuLxtxNWAVvhi74hU4NqlIjPCA3zdR+7XP//u7vo5y8lui2jA9X7aF59QhKTZIwdh+KZdnbqBkpeD4803mexLgls5/ag239t27fKVdOkfPVHO2Dpzemx+bhuHi8hDyqzF93hEWD2xLma2LoV3/TqWY4NYJ9c9NcSs7k851nWDK0A36eRpKzLO553HqK5pWKvsYVIMsEPPcU8eO0e3T4Vx+Rne8ebT15ltiftHu0j8s92pGYTOyjE8BmQzJ5EvHDZ5g378CRWMLDmNtRFpEkjL2Hk7P0Ve3cfGwO9tP7UBPznZvHdxZZaWTsfD/K5ZOFTiuULBMwdSIJE6biiE8gdMlCzH9vdy//nD5L/HBn+WfgvVQYP5rkmS8DoFqsxA8bXbY8dn4Iy6/vomam4PngNBznD6Mm5ztez+zDtsm98lCu2hA5tDI5384FnR6P+5/BcekYWHMoliRh7DuSnC/navtyzDzsJ/cV3JdHd2Bd/kWB2W3b/sJuMKJvWcZ9Wc73L4eiMO/zX1g8YwxhQRUYMm0BnVs2oEbF8Nw0n/yyjp7tmjL4rvacuxrL+Fc/ZeUHM7n7jhbcfUcLAM5cjuHpN78oVaWSQ1GY981KFk9+WCuPzPmUzk3rUCMqJC/mn3/Ts1UDBndpyblrCYxf8C0r35iIv48X7z31IKEBvpy5Gs8Tby9l3duT/lXxgNty/DgUhfl/7WXR8K5a+XnxajrVrUiN0Aq5aS4lpfP5luMsefwurfycqZ0HW05d40R0Cj880RubQ+Gxz9fRoVYkPp6GUscHuKNbe6pUq0TvtvfTuEVDZr/+LA/1fqxAutFPP0pyYjJ3tx+EJElUCPArZGmCcOuJMZb+fUYAkbd7JVzJFWuiJMWipsSDw4HjyHb09VqVen7l/FFUi7lMMY0N6mK7cg3HtRiw28leuxFTp/ZuaSz7DqJatMKx9cgJdKF5NzFD3VroAgPI2VW6J2dyRHXU1HjUtARQHNhP7EZXq1nJMwJSUCTIslapBGCzgL1sP9rlijVRkq9vYzuOw9vQ12tZpmWUxtHoZCoFelMxwBuDTqZn/YpsOu1eePzlwAUeaFEdP5MRgEBvTwDOJaTjUFTaVQ8DwMuox2S4+brplk0bUcHPt+SEZXD07EUqhwdTMTwYg0FPr47N2bjHvZJRkiSyzNpNPzPbTEigVjiQkMjOsWJ3OLBYrRj0OnxMnsXHO3+VSmGBVAwNxKDX06tNIzbtz/dDQpLIzNGO10xzDiH+Wp5NHsbcSiSL7cYq647GpVHJ34uKFby0/VornE3nE9zS/Hr0GoMbV8TPWZAJ9DKWOY4cUR01xeU8ObkLXc2mZV6OrnZLHBeO/GvOE7/mNcm+EEvOpXhUm4P437YT0sv9GhfcqyUxP24CIOHPnQR0bJg3rXcrzJfjyTqVV8GlY/1x4AABAABJREFU8zXh364eMUu11hKqzYE9PbvwfEVWR0mJQ03VtqvjxC70tZsXmvZWkavUQkmMQU2KA4cd+4Et6Bu1KZDOo89QrOt/RnVpjaSr0wwl+qJWqQSQnQFqya/19WpaC+ulGKxX4lBtdlL/3ILfXe4xbVfjyTl5EbWQynlTwxrog/3J/Lv0rdyOXo6nUpAfFYP8MOh19GxWk03HLhaZ/v/Yu+/wKIr/gePvubv03jsBEkLvTYqEKohSLSiIICjFrlgRG2LvHSxYERULNpo06b33npCE9N7vbuf3xx5JLgkpiOGrv3k9j49cdnY/O3O3u7OzM7NLd59gSMfoss+HzqaRmVdEj5jwOsc0BDexHSfptuNkG8aoDnVe/zxjTOc6HScHzmUR4e1GuLftvN4ylLUnku3S/LQvnjEdG+PpfP68Xt7IeSg5m8yCEno0DqCuHFu3wFLxGr1iDa41XKNLDhzGFGTbvsUCZv33JBwdwVC3k97lqIsYQqPQMiscmwe3YIrpXPf1gxsj3Dyxntpf53UcW7XAkpCINUkv26I/V+PSp4b6z4FDdvWf+jIENUbmpCJzbb/XY9sxNm1Xt3V9Q7AmHtePf0spMj0RY2TVXhxV1rM7l9u+yxZ1P5fr32UtjVeVY16G69eBE/FEBPkRHuSn1w16dmTt9qoNUvlldZFiAqppBFi6cTdDetZtXw+cSiQi0IfwQB8cTEaGdG/N2j2VerUIyC+qWh9pGRlCoI/+7+iwAErMZkrNlv+peHB5fj8HEjKI8HUn3Nddv5a0jWTtEfuHWj/tOMmY7s3K68/uet3xVFoOnRsHYDIacHE0ERPszcYT9e8/0H9IH35dpPd+3bfzAB6eHvhX89Bz1M3D+PidLwC9J3x2Zk69YynKxbgsPZaEEI2BZcAWoCewHfgMeBYIBMZJKbcJIdyAd4E2gAPwjJTyF9v6XwHnx+DcLaXcJIToCzwDpNvW2QncIivVUIUQ0cBcIACwAjcAp4BXgKsBCcyRUn5n2+azQDbQFvge2A/cB7gAI6WUJ4UQnwPFQBfAE3hQSvn7hfbVth+PArcAGrAU2GFbf4EQogjoARwGvgCG2crgBinlkRrKprWtLB3RGw6vA5Js+x0OGIHnpJTf1fI1lZeXpy8yp/xJoszJwBBetZu9sXV3XBq3RMs4R+mSL+o9FMxuWwH+WFPKb4ytKWk4tml5wfRuI66meNM22w4LvO+fRuZTL+LUrW4VQOHhg7R1lwWQeZkYQqKqpDM174wxIgYtKxnzqm/1dL5BUFyI48i7MXj7Yz1zCPNfi+rVa6lKGedmYoiooYzTz1G65HO7deoiNa+YYA+Xss9Bni7sT8y0SxOXmQ/AhC/WommSaX1a0isqmLjMfDycHXjwhy0kZhfQvUkg9/Vrg7GONwUNKSUzhyD/8h4JQb4+7D9+xi7N9DFDmTr7Pb5Z8hdFJSV8/PQ9AAzq0ZG12/cx4Ha9x9IjE0fj5VHzcL/UrDyCfcufWgX6erL/pH2FY/qofkx79QsW/rmVopJSPnpkYtmyfSfP8vQnizmXkcPzU0bXq7cSQGp+CUHu5TeKQe5OHEjJtUsTl603akxctA1Ng6ndm9KrsX+94gh3b2RexeMkC0NI0yrpTDG24yQzBfOahWXDR8uWt+iGeceKesWGf+44cQr2pSSpPE1JUgaeney36xTiS0minkZaNax5hTj4eqAVlxJ59wj23PAcje4cXpbepVEg5oxcWr59J+6tI8nbd4pjsz5HK7TvKQIg3Ks5/4RWPf8Ym3fBJaI5WmYypSu/Kf8uTA44T3wGNCvmzX9gPb6rxvwCGLz80LLKe+Zo2RkYI2Ps04RHIbwDsB7aAf1Hl/89MAwkuEx7FuHmhWX3OkpX/1RrTIcgP8xJ5THN5zJw7RBTwxoVCEHIrMmcvf913Ht3qNs6QGpOIcHe5T33grzc2B+fWm3apMw8kjLz6NZMf7ajaZLXf9vMC2P7s+VYYrXrVLurHj52v3mZf4HjpFknjOG268ma76o5Trpi3vFnrfFS8yud1z2c2Z+UbZem7Ly+YIN+Xu/VnF5NA9Gk5PU1h3jh2o5sOWPfU6smxkD7a7QlNQ2nGq7R7iOupuj8NRowBgUQ8NYLmCJCyX77o9p7K3F56iLV1g2qOzZbdMOlUQv92Pzza9s6AsdB4yhZ/CHGJrU3tpRtK9Afa0r5b9Samo5j6xrqP8OHUry5vGyFoyOBn3+ItFrJ+2Ihxes21pxH98q/12wMwVWHI5miO2IMi0bLSsW8bhEyPwstPQGH7tdi2fUnmBwxhMegVeqZV21Mj2rO5eHRVdIZW3XDJbIFWkYypUu//Hvf5WW4fqVm5hDs5132OdDPi/0n4u3STL9hMNOen8fCZRv0usGsqVW2s3zzHt566LYqf682Znal+oiPJ/tP2Z+/po+IZdrrC1i4ahtFJWY+euiWKttZufMwLRuF4FjLA8SGjgeX5/eTmldEsFd5XTDI05X9CfbnzLiMPAAmfLwCTUqm9WtLr2ahxAT7MG/Nfsb3bEmx2cL20yk0DfCivgJDAkhOTCn7nHIulaCQANJTy/Pl4alf7+55dCpde3bi7JlEnp/5GhlpmVW2pyiX2uUcCheN3qAzCb1haSzQGxgOzARGAk8Aq6WUk4QQ3sA2IcRKIBUYJKUsFkI0AxaiN8gAdARaozembAR6ARsqxV4AvCSl/FkI4YzeADMa6AC0B/yB7UKIdbb07YGWQCZ6A9QnUspuQoj7gHuA+23pGgPdgChgja0Bq9p9FUJcDYwAukspC4UQvlLKTCHE3cBDUsodoPeqANKllJ2EEHcCDwG311A204C3pZQLhBCO6A1JQ4EkKeU1tm3W/2xWC8uRnVj2bQSrBVOXgThddxfFn82+1GGq5Xr1QBxbxpA69UEA3K8fTvHGbVhT615JrgvriT0UHd6q57F9XxyvuZ2Sb18BgxFDRAzFnz2DzM3AccR0jG17Y923/pLGtxzZgWXfBj1+14E4XXc3xfOfvaQxQB9WEZ+Zzye39CE1r4hJX65j0ZQBWDXJ7rPpfDt5AMFeLjz60zZ+3RfHqA6NL/k+NISl63cwot8VTBg+gL1HTzHznS/56c2ZHDhxBoPBwMqPnye3oJCJs97kinYtCA+uXyNMlXhb9jG8d0cmXN2LvSfieeKjH/nx+bswGAy0i4rg5xfv4VRSGrM++one7Zrh5Fi/LtK1sWqS+OxCPh7dhdT8Eib/uJ1F43rg4XSJ45zcQ9GR88dJLI5X307J96+WJ3DzwhAQjnbmwCWNe15DHSfnNXn4Rs7O+wNrpQYjYTLi3rYJx2bOJ3fXCZrNmUjkPSM5/XKd2/TtWE7sxnJoi56vDn1xuvYOihfq88QUvT8DmZ+F8A7A+eZHKU5LQGZX33hSZ0LgNHIyxd+8VXWZwYixaSsK33gQWVqC611zsJ49gfV4HYceXwS/8UPJW7MDc/LF3xjUZvmekwxs1wSjQe/M/f2mg/Ru0YigCg1Tl4r15F6KjmzTv892fXC8ehIl379ensDNC4N/OFp9h4teKJ4mic8q4JObepKaV8ykhRtZdFtflhxKoHfTQIIqNExdauev0SlTHizfn5Q0km++A6O/H/6vz6Zw1Tq0zKwatlI3l6MuYjm+G8vBzXrMTv1xGj6V4q9fxNRloD6kPu+fu6FzHTIQh5YxZE8rHz50buTNaGnpGENDCHj/ddJOnsaa+PdmV7Ce3kfRse16HttcieNVEyj56S20+MNYgxrjfOMjyKJ8tHOn69R7sS4sR3di2X/+uxyA0+jpFH8+p/YV/4bLcf1aunE3w2O7MmFYX/YeO8MT7y3kx9cewmA7D+07HoezowPNGtVjOG5tMbceYHiv9kwY0oO9J87yxMeL+fG56RhsDwpPJKby1qJVzJ0x7l8ZDy7T70fTiM/M45NJA0nNLWTSpytZdNdQekaHcDAxgwmfrMDH1Zl2Ef4YLsVcEtUwmoyEhAWxZ/t+Xnn6bSZMvZmHnr6Xx+9+5h+J9z9NuzTnIqXuLudQuNNSyv1SSg04CKyy9Szaj95AA3AV8JgQYg+wFnAGGqH30PlYCLEfWARUnOl2m5QywbbdPRW2BYAQwgMIk1L+DCClLJZSFqI3ai2UUlqllCnAX8D5PtbbpZTnpJQlwEng/KOK/ZW2/72UUpNSHkdvgGpRw74OBD6zxUZKWVPN4/yj4J11KJvNwExbb6hIKWWRbT8HCSFeFkJcKaWs0idSCDFFCLFDCLFj/q5TdstkbibCq7yrpfDyq1pRKsoHq9591bJzFYbQqk+B6sOalo4xqLxrtzEoAGta1YYip26d8LxtLOkznizrWu/YrhXuN44g5JcFeN83Fbehg/C6+/Ya48m8LH3ibRvh4Vs2eWOZ4oLyPO77C0NwpG3dTLSUeL17tdSwHt+FIah+k/JVKeNKT2YB+zLesRpDWP3LONDDmeS88qEAKblFBFa6oQjycCE2JgQHo4Ewbzci/dyJz8wnyNOF5kHehPu4YTIY6Nc8lMPJ2fXeh4YQ5OtFSnr595eSmUWgn3176s+rNjO4pz7cqH3zppSUmsnKK2DJ+h306tAKB5MRPy8POrZoysGT9k8YKwv08SC5Qlfj1Mxcgip1Z//5r10M7qYPn2of3YgSs4WsfPuhUU1DA3B1duREYv0aBQLdnUjJL2/YSMkvIcDNqUqa2KYB+vfq5UKktxvx2dUPzboQmZ+N8Kh4nPjUcpysKztOzjM176r3qNHqMCdP5fj/0HFSkpyJU2j5dp1C/ShJtj/HlZzLxClMTyOMBowerpgz8/DsFE3Uk+Posf09wqcMpfF9owibNJiSpAxKkjLI3aVPdp362xY82lY/KanMr+b8U+kpOUUVynXvXxiCG9utDyCz07DGH8EQ1KjWPGs5GRh8yhtLDd5+9mXp5IIhOBLXu1/A7alPMEY2x+X2WRgiopHZ6VhPHkAW5IK5BMuhHRjCq/biqMyckoFDaHlMhxA/zCl1ayhy7dQCv1uvpcWGTwiZOQmf0f0JfnRCresFermSnJ1f9jklp4BAr+p7IC6rNAxu75kUvtt4kKvnLODN37bw+45jvP371lpjyrwshEd5j8nKPUIA++Nk//oq1wxT8y51Pk4C3Sud1/OKCfSwH74b5OFCbHSQ7bzuSqSPO/FZBexNzOK7Xae5eu5K3lx7kN8PJvD2X4drjWlNtb9GmwIDqn2Y49StE16TxpL2YPk12m476RmYT57GqWPtE4ZfjrpItXWDKsdmhZi715T19jGGR2PqMgiXu9/EceBYTO2uxKHCxOwXopdt+fx8xkB/rGlpVdI5de2Ex8RxZDw0y65sNVtdyZp0jpJde3BsXrUnh10e8yv/Xr1rPq8f3IAhsPz3atm+lOJvnqfk57dBgFaHRm2ZV825PLem73L13/8uL8P1K9DXi+SM7LLPqRk5BPlUqous2crgHu0BaB/TmBKzXhc5b/mmPVzdq25TMwAEeleqj2TlEuRjP93Az+v3MLibfjvSPjrCrj6SkpnLA+99z5zbRxAR6EttGjoeXJ7fT6CHC8k55d9LSm4hgZ6udmmCPF2JbR6mn2d93In08yA+U+/FdEdsG76/cyjzJvZHSoj0ty+jC7n5tuv5cdVX/LjqK9JT0gkOCyqPFxJIyjn7c0N2Zg6FhUVlk3Uv/20VrdpWfUmPovwTLmfDUsXHu1qFzxrlPakEcJ2UsoPtv0ZSysPAA0AKek+iLujDvqrbrpVL0yurLvsK+hA6Kn2uaV/rG79ifqotGynlN+i9voqAJUKI/lLKY0An9AamOUKIKrO8Sik/klJ2kVJ2mdTJ/uSrJZ7E4BeC8A4AoxFj255YjuywSyPcvcv+bWzRBa3SZLr1VXroCA6NwjCGBoPJhOugfhSts387kENMNL6PP0D6jCfRsrLL/p755IucGzaWcyPGkf32PAqW/EnOe5/UGE87dxrhE4jw8geDEVPLblXf9uFWXhkwRncs6+6tnTuNcHYFF/0iYYxsWWViz9poiSf0MvYJBKMJY7teVcvYw7s8fssuaKn1L+PWoT7EZ+aTmF2A2aqx/FACsTH2T8H6NQ9hR5xeMc0qLCEuI59wbzdah/iQV2wum/R125lUmtbxwtjQWkdHEncujYSUdMxmC8s27KJvF/s5I4IDfNm6T58H4FRCMqVmM76e7oT4+7LtgP73wuIS9h07Q5MKF/Jq4zUJIz4lk4S0LMwWC8u27ie2Ywu7NCF+Xmw9pDfankpKo9RswdfDjYS0LCy2iY+T0rM5cy6dUH/v+uU3yJP47EISc4r07/V4Mn2b2s+50a9pIDsS9Ep0VlEpcdkFhHnWr5eCfpwElR8nLbpXfVvVBY6Tsr+17I7lcO0359XG/4eOk7zdJ3FtGoJzowCEg5HAkT1JX26/3fTlOwm5sS8AAcOuIGuD3pNk14in2dz1bjZ3vZuEj5Zw5u2fSZy/nNK0HEqSMnCN0o8v3yvbUnCs+n3Rkk5jqFCuxpbdsRy3P/+IiuXarBPa+QlnnV3BaLssuLhjDG+GVofzjxZ/HIN/KMI3CIwmTB37YDlQPpyG4kIKZo2jYPbtFMy+HWvcUYo+mYN29gSWI7swhDTWX1RgMGCMalN10u9qFO49jmPjUBzCgxAOJryH9SH3z221rgdw9v7XOdJrEkd63865F+aT9dNqkl/+otb1WkcEEp+eQ2JGLmaLleW7TxDbumrD/+mULHKLSmjfuPxYf/GWASx7chxLZ43jgWFXcG2XGO67tuo8VJVpyWcqHSfdsJ7ca5+o4vcZ1aHqcdKiG5YjdSub1iHexGcVkJhdqB//h5OIjQ62S9OvWTA74vVGvKzCEuKy8gn3duXFYZ1YNn0QS6cN5IG+rbm2dTj3xV542NV5pYeO4BBR4Rp9VTXX6ObR+M58gLQH7a/RxkB/hJNeFRIe7ji1b4vlTO2/n8tRF9GSTmHwDdZjGowYW1+B5Zj9UFO7mDGdy46/ksUfUvTu/RS99wClK7/Bsm895jW191gsPXwEU0QYxhC9bF0G9ado3Wa7NA4x0fg89iAZD8+yK1vh4Q4Oei9Ug5cnju3bYK4w6Xe1eUyJQ3gHIjz99N9rTFespyr1PnQtf1BibNoe7fzE3kKAs95QK/zDMPiFocXVPNk82L7L8+Va9l3az4lZ9bus+3DUamNehutX66gI4pPTSUjN0OsGm3YT28V+WGSIvw9bD+hvCz2VkKLXDWzDmTRNY/nmPQzpWfeGJfv6iJVlWw8SW2nIcYivJ1sP6W8OLK+PuJJbWMzdby3kvusH0LFZ7Q8oLkc8uDy/n9ZhfsRn5pGYla9fS/bHEdvCfjL1fi3D2XFGb1jNKigmLiOPcB93rJpGtq1X87HkLI6nZNMjqm490BZ+9gPXDRjPdQPGs2rpOobfcDUA7Tq3IT8v324Y3HlrV2ygWy/9wekVV3bl5LHTF51vRamP//W3wi0H7hFC3COllEKIjlLK3YAXkCCl1IQQE9CHe9WJlDJPCJEghBgppVwshHCyrb8emCqE+ALwBfoAD6P3OqqrG2zrNwGaAkdr2Nc/gaeEEAsqDoUD8oC63K1XWzZCiKbAKSnlO0KIRkA7IcQRIFNK+bUQIht9KF3daRqlv8/HecIT+quwd61Bpibg0P9GtKSTWI/sxNTjakwtuiA1KxTmU/LTB2WrO09+FkNAGDg64/LQh5Qunov1xN4aAgJWjaxX3iXgnZcRRgP5vy7FcioOz6kTKT18lOJ1m/G+bwrCxQW/l/R2Mmtyqt5z6WJIjdI/F+B044yy10PL9CQceo9ESz6D9cQeHDoPwtisA2hWZFEBpX/YGqukpHTNdzjf9DAIgZZ8puw14HWmaZT+9inOE5+wvTrVVsYDxqAlnsR6ZAemHkPLy7gon5If3693Nk0GA48N7sD0hRvRNMmI9pFEB3jywV+HaBXiTd+YUHo2DWLzqVRGz/sTgxA8MKAN3q5675cHBrRh6jfrkRJahnhzXce6vRa2Jg8//RLbd+8jOzuXASNv4c7J47lu2OC/tU2T0cjM229k+nPvY9UkI/tfQXSjEN5f+DutohvRr2s7Hpowimc/XMhXv69BCHju7vEIIbhpSB+efP9rRt03BwmM6HcFMbW8icVkNPL4+GuY/uqXaJrGyD6diA4P5P2fVtG6cRh9O7Vgxs1DmD3/F75evgkhBLNvH4UQgt3H4pj/+3ocTEaEEMy89Vp8apnTqUp8g4FH+zbnzl926d9r61Ci/Nz5YMsJWgV60rdpID0j/dgcn8HorzZhNAju7x2Dt0s927mlRunKr3G6/kH9XLB/AzIjCYdetuPk5B4cOg3UJ0TVNGRxPqVLy9/OJjz9EB6+aGervh63Tv6h40RaNY49Pp8O3z6BMBpIWriGgqMJNHnkRvL2niR9+U7OfbOaVu/dzRVb3sGSnc+BqW/Vut1jM+fT6oN7MTiaKIpL5fB9H1SfUGqU/vmV7RxiwLJvHTI9EYcrR6GdO4P1xG5MXa7C1Kyjnq/iAkp+188/Br9QnIZMREqJEALz5j+qvOXoQmVZ/ONcXKc9CwYD5q0r0ZLjcbx6HNb441gP1tCoUVRA6drFuD74BiCxHtqhz8NUG6tG0lNzafrls2A0kPX9SkqOxxP0wDiK9h8nd+U2XNo1I3LeTExe7ngO6ErQA+M4dtVdtW/7AkxGA4+N7s30j5agScmIbs2JDvblg2XbaRUeQN82jQFYtuckQzpEnx9+/vdIjdJV3+B03f2242Sj7TgZYTtO9uLQaQDGqPa246SA0mXlby4qP06O1S2PBgOPDWzD9EVb9Dy2jSDa34MP1h+hVbA3fZsF07NJAJvPpDH60zX6eb1vq/of/xVZNTJffZfAd18Go4GCX5diPhWHl+0aXbRuMz73TsHg4oK/7RptSUkl/cEncWgSiff90/R5CIUg9+vvMZ+sw43P5aiLSI3SZV/gfPMjesw9f+nHZux1aEmnsR7fhanrVZhiOtnOOQWU/Dbv4ssVwKqR/dq7+L/zMsJgpOC3pVhOn8FzykRKDx+jeP0mvO6ZinB1xveFp/VVklPJeHgWDo0j8XnsgbLzQd4XC+3eJnfBPK79DqeR9+rnn0ObkJnncLhiGFpKHNbT+3Do0F+f0Pv87/VPW6OuwYjz9Q/pmyktomT5Z3UbCqdplP7xGc63ziz/LtMScOh/A1riKaxHd2K6YgimFp2Rmqafy3/+sGx158nPYPAP1b/LGe9T+ss8rCdqGYp7Ga5fJqORxyeNZvoLH6FpkpF9uxEdEcz73y+jddNw+nZpw4zxw5g9bxFf/7FOrxtMv6nsPLTz8CmC/bwJD6r7GxtNRgOP33I1099YoMfs3YHosEDe/3kNrRuH0rdjc2aMuYrZX/zG1yu2IgTMnjwCIQTfrtpGfGomH/26jo9+1WcD+XDGLfh5XrhO0tDxgMvy+zEZDTx2TRemf7lGr2d1akp0oDcfrNpHqzBf+rYIp2d0CJtPnGP0u7/r59nBHfB2daLEbGXSp/p8eW5ODjx/XU9Mxvr37Vi3ciN9BvRk6dYfKS4qZtZ9z5Ut+3HVV1w3YDwAbzz3Hi+99wyPPvcAWRnZdukU5Z8kqnvzyj8eVJ/Q+ncpZRvb589tn3+ouEwI4QK8hT7BtwF9+Ny1trmKfkTvEbQMuEtK6W6baPshKeW1tu2+B+yQUn5eKX4zYB76XEpm9LmeTnPhybsrbnOt7fOOistqmLy72n21besx4FagFFgipZwphLgOeAG9x9H5ybu7SCnThRBdgNeklH1rKJvHgPG2fCWjz13VFXgVvYeVGZh+fg6n6hQ8eWOD/igyl/1zc2ZciN/1dX8ycqnI3PzaE11ihlq6wF9qpqF3NGg8AC35ZIPHlPkNPwmitvPSztlVJ4X1Gyr3d8nsvAaNB7D1k4affL77/f/cfDYXoqU0/G/29OKGjdfsvX4NGxCQR2rvnXGpCR/vBo+Z9mHd38R3qfhdXfeb6UvC6W80tF2krN/+3txHF8N3bB0nzL+EZIXhYA1FeLjWnugSMgwaXXuiS63g/8fbvqzLf2vwmIYaJsz/J3S5d0mDxjvvYMrW/7038FwiRQuebPhGjgbiMu65/8nv7bL0WJJSnkF/m9n5zxOrW2abH6jK6xFscxhVHNfyqO3va9HnGzqf7u4LxD8O9K9m0cO2/yqmrbzNvhdaBqyUUk6ry77alr0EvFQp/Y/oDVHnNa6wbAfQ1/bvC5VNlW2i925aXjmtoiiKoiiKoiiKoijK33E551hSFEVRFEVRFEVRFEVR/sX+1+dY+teo2OtKURRFURRFURRFURTl/wPVsKQoiqIoiqIoiqIoyn9DXV4koFxSaiicoiiKoiiKoiiKoiiKclFUw5KiKIqiKIqiKIqiKIpyUVTDkqIoiqIoiqIoiqIoinJR1BxLiqIoiqIoiqIoiqL8N2hqjqWGpnosKYqiKIqiKIqiKIqiKBdFNSwpiqIoiqIoiqIoiqIoF0U1LCmKoiiKoiiKoiiKoigXRTUsKYqiKIqiKIqiKIqiKBdFTd6tKIqiKIqiKIqiKMp/g5SXew/+31ENS0oVhhbNGzSe85Z1DRoPQMQ0bB4BRGlJg8fUDh9p2HjJJxs0HoAhOKrBY2pZbg0eUyalNHjMkgOpDRrPZVjnBo0H4GyIa/CYhrDQBo8pfLwbPGaJOb5hA2ZnNGw8QERENHjMy/Gmm6J8xwaPqeUUNmg8U6/WDRoPwMnzbIPHFE2iGz6mf06Dx7Rs3d2g8UTKmQaNB2AIa/i6rLQ0fF3Wmtjw53Y43KDRUouzGzSeovwT1FA4RVEURVEURVEURVEU5aKohiVFURRFURRFURRFURTloqihcIqiKIqiKIqiKIqi/DdchmHj/9+pHkuKoiiKoiiKoiiKoijKRVENS4qiKIqiKIqiKIqiKMpFUQ1LiqIoiqIoiqIoiqIoykVRcywpiqIoiqIoiqIoivLfoOZYanCqx5KiKIqiKIqiKIqiKIpyUVTDkqIoiqIoiqIoiqIoinJRVMOSoiiKoiiKoiiKoiiKclHUHEuKoiiKoiiKoiiKovw3SDXHUkNTPZYURVEURVEURVEURVGUi6J6LCl1svHEOV5ZvgdNk4zq2IRJvVtWSbP84Fnm/XUQBMQEefPS6CsAePPPvaw/cQ4pJVc0DeKRwR0RQtQrvkOXbrjfeQ/CYKBo6R8UffeN3XLna4fjMnwUaFZkURF5b76GNT6ufnk8msArv21Fk5JRXWOY1Led3fJXf9vK9lPJABSbLWTmF7PhmXEcScrghcWbyS82YzQIbu/XjsHtm9Yt5vEkXlmyU4/ZKYpJfVpXSbP8QBzz1uwHBDHB3rx0Qy8A3lqxm/XHkgCYEtuGwW0j6xTTGN0exyG3gsGAZdcazBt+tVtu6tAHx0Hj0PIyAbBsW4Fl1xoMwZE4XjMJ4eQKUqN03c9YD26pU8wNuw/x8vwf0DSN0QN6Mnn0VXbLz6VlMuvdr8grLMJq1bj/lhFc2bk1ZouVZz5cwOFTZ7FaNYb17cbtowfXKWZNZr3wBus2bsPXx5vFX8/929sD2LBzPy9/9I2ex6v6MPmGa+yWJ6Wm89Rb88nKzcPL3Y0XHppCsL8vR07FM+f9LykoKsJgMHDHjdcypE/3esc3NuuA4zW36d/rjlWY1y22W27q2BfHq8ej5dq+1y1LsexYXe84Dh274XrHPWAwUPLnHxT/aH8sOg0ZjtPVtmOxuIiCD15DO1t+LBr8A/F67wuKvv2c4sXf1SnmxtOpvLLqkH6ctItgUvfoKmmWH0li3qbjAMQEevLStR1JyinkwcU70SRYNI2bOzXmhg7VHyfe/TrQZPYkMBpI/WYVie/9bLdcOJpo9s69uLVriiUrj2NT36AkIQ3/0VcSNn1EWTrXVpHsvephCg+eQTiYaPLC7Xj1aI2UkviXviHzj+qPmY0nk3llxT49jx0aM6ln86p5PJTAvPWH9TwGefHSyG4AnMsp5Nk/dpGSW4QQ8O6YnoR5u9VermfSeHXtYTQNRrYJZ1K3quetFUfPMXfLCQSCmAAPXhzanqOpuTy/+iAFJVaMBpjcLYrBzUMuGMezb0cazZ6MMBhIW7iS5Pd/qlK2Td++D9e2UViy8jg5/TVKE9IQDiYiX56GW7tokBrxT31K3uaDAPgO70XIPdcjjAayV+4g4YWvLpzPE8n69Uvarl+9WlQt24NnmbfuECD0sh3dne1nUnl1xd6yNGfS83hpdHf6twirrWgb/Ny+8fg5Xlm2S79Gd2rKpCtbVRMvnnlrD5Rfo6/vCcCbK/aw/ngSUqJfo6/uVKdrtGvvzgQ9MQ0MBnJ+WEbmx4vslrt0aUPg41Nxat6EpBkvkb98Q9mygIcm4RbbDQyCwk27SX2+budhY8tOOI+eAgYD5s0rKF35Q7XpTO174jJ5JgWv3o929gTCNxC3mR+ipSYCYD1zlJLv369TzMtxna6oQeo/R87yyq+b9d9Pt+ZM6t/Bbvmrv25m+wk9H2X1n+cmkJSVx4Nf/ImmSf0c26s1N/So+turNuZlqFdelt/PoThe+Wmdns8erZg0qIvd8ld/Ws/24wkAFJdayMwvZMPLUwHodN97RIf6ARDi48HbU66tU8yGrnNt3HOElz9fjKZpjOrfnckjB9jHS89i1vsLySssQtMk9429his76t/3sbgknvv4B/KLijEIwTcv3I+To0OtMY1tuuB8850IYaB0/VJKl1ZfnzB17o3rnU+TP/sutLhjCDcPXO58CmPj5pg3rqD4m/dqjVUWM7o9jkMngDBg2bUa8/rK9edYHAePK69nbV2OZdcaAJzGP4YxvBnW+KOULHilzjEre+HlJxh4VSyFhcXce+dj7Nt7yG65m7sbvy9dUPY5JCyYH777lVmPv3DRMRWlrlTD0n+AEKILcKuU8l4hRF+gVEq56VJt36ppvLh0F3NviSXI04Vxn6wktnkoUQFeZWniMvKYv/Ewn9/WH08XRzILigHYczadPWfTWTRVv6jd9tkadsSl0bVxYN13wGDA4577yX50Blp6Gj7vzaN080a7ilPJ6pUU/66f4B179MR92l3kzHykfnn8ZQtzJw8myMuVce/9RmzLRkQFeZeleXhY+c3+wo2HOJKkXzhcHEw8d+OVRPp7kZpbyNh3f6VHTBieLk61x/x9B3Mn9NfLdd5yYluEExVYsVxzmb/uEJ/ffpVervl6ua47msjhpCy+m341ZqvG5Pkr6dUsFHfnWi7GQuA49DaKv3oBmZuB8x3PYzm6E5mWaJfMcnAzpUs+t/ubNJdQ8vOHyMxkhIcPzlOep+jkPigurDmfVo0XPv6ej566myA/b25+9FX6dm1LVET5jehHPyzjqp6dGDPkSk6ePcddz3/Iss6zWbF5F2azhZ/efIKiklJG3TeHq3t3ISzQr+Z81mLk0EGMvW44M5977W9t5zyrVeOFD7/iozkPEeTny80PzKZv9w5ENSq/AX390+8YNqAnIwb0ZuveQ7zzxQ+8MGMKzk6OPP/g7USGBZOakcVN9z9Lz05t8XR3rfsOCAOOwyZT/NlzyNxMnKe/iOXwDmRagl0yy/5NlP726cVn1GDAder95D09Ay0jDc/X5lG6baNdw1HJXyspWaYfiw7deuI66S7yny0/Fl0n34V517Y6h7Rqkhf/PMjcG7sT5OHMuK82EBsVRJS/R1mauKwC5m89yedje+Lp7EBmQQkAAe7OfDmuJ44mI4WlFq77bB2x0UEEujtXyVfTF+7g4JjZlJ7LoN3Sl8lcsZ2iY+XlF3TzACw5+ezueTd+I3oROWs8x6a9QfpP60n/ab2etxaNaPHZoxQePANA+H3XYU7PYXfve0AITD7uF87jsr3MHdtbPxfMX0NssxCiAjzL85iZz/xNR/n81li7cyzArF93cHuv5vRoGkRhqYW6tNtbNclLqw/x4eiuerl+s5nYqECi/Mr3MS6rgPnbT/H5mCv0ci3Uy9XZwchzg9sR6eNGan4x4xZspmekPx7VnX8MBiKfn8Kxm5+h9FwGrZa8QvaKbRQfLy9b/5sHYskpYH/vO/Ed3puIJ27l5PTXCRg7CICDA+/H5OdFzNdPcmjowxi93QmfNYFDQx7CkplLk7fuxaN3W8B6gbLdzdxxVxLk6cq4T1YRGxNqX7YZeczfeJTPJ/azK9uujQP5foq+DzlFpQx7byk9ooLqULYNe263ahovLtnB3PH99Hgf/0ls87BK8fKYv+EQn08eaBdvT7ztGj19CAC3zV/FjjOpdG1SSz4NBoKeuouESTMxp6QTueht8ldvpfRkfFkS87lUkh9/HZ9J19mt6tyxJS6dWnFmxJ0ANPrmNVy6taVo2/6aYwoDzjdMp/D9WcjsDFwfehPLga1oyWft0zm54BA7HOuZI3Z/1tKTKXzl3ppjVHJZrtMVNVT95+eNzJ0ylCAvN8a9s5jY1pFEBfmUpXl4eI+yfy/ccIAjSRkABHi48uXdI/RzbImZ617/gdhWkQR61dywfVnqlZfr97NoLXPvGkmQtzvjXvuO2DZNiQrxLUvz8Ogry/698K+9HElIK98VBxPfP3pz/WI2cJ3Lqmm8MP8n5j0xlSA/L8Y+/hZ9u7QmKjy4LM3HP61kcI8O3HhVT04mJHP3S5+w9L1ZWKxWZr73Dc/fNZbmjUPJzivAZDLWnklhwGXcPRS8/igyKx23J9/Dsmcz2rl4+3TOLjgOHIXl5OGyP0mzmZKfP8cQ1gRjWOM6lytC4HjtJIq/eF6vP099AcuRaurPBzZT+sdnVVY3b/wdi4Mjpi4D6x6zkoGD+tA0qjHdOl5F5y7teeWNZxgy4Ea7NAX5BfS7cmTZ55V//cgfv6246JiKUh9qKNx/gJRyh5Ty/NWuL9DzUm7/QGImET7uhPu442A0Mrh1I9YeTbJL89OuU4zpEo2niyMAvm76jZsASq0aZqtGqVXDomn4uTlXDlEjU/OWWJMS0ZLPgcVC8drVOPbsbZdGFpY3bghnF5D1zOPZdCL8PAj388DBZGRw+6asPRR/wfRL955iSIcmAEQGeBHpr1eGAj1d8XVzJqvCTd8FYyZkEOHrTrivux6zbSRrj9g3BPy04yRjujcrL1fbDfGptBw6Nw7AZDTg4mgiJtibjSeSqsSozBAWjZaZjMxKBasV64HNmJp3qXU9AJmRjMzUe2zJvCxkQS7C1bOWteDAiTM0CvYnPNgfBwcTQ3p3Ys32fXZphBAUFOllll9YRICvXp4CQWFxKRarlZLSUhxMRtxd6vf7qU6XDm3x8vSoPWEdHTh2ikYhgYQHB+p57NONNVt226U5dTaJ7u30J3Td2rUsW944LJjIML0CFujng6+XJ1k5ufWKbwiv+L1asO7biKll3b7X+jA1a4mWnIiWoh+LpetX49jN/likqMKx6GR/LDp074015RzW+NN1jnngXDYRPq6Ee7viYDQwuEUoa0+k2KX5aW88YzpG4mm7YfN10xt1HYwGHG2V1FKrhpTVnxjcO0ZTdCaZkvgUpNlC+i8b8B3c1S6Nz5BupH6/FoCM3zfjdWXbKtvxH9Wb9F82ln0OvKk/ie/YeudIiSUzr/o8JmUS4etGuI+bnsdW4aw9ds4+j7tPM6Zz0yrn2JNpuVg1SY+mekOAq6MJF4fanxkdSM4mwrtCuTYPZu1J+3L9eX8CN7ZvVF6urnq5Rvq4Eemj3zgGujvj4+pIZlFptXHcOjaj5My5srLN/GUDPoO72aXxuaob6Yv0p7qZf2zCo7feW9Q5JoK8jXpjgyUjB2tuAW7to3FuFETJ6XNYMvXjJHf9XnyH9qA6B5IqXr8MDG4dUfX6tfs0Y7pGVSnbiv48nECv6OC6lW0Dn9sPJGYS4etRHq9NI9Yetb/Z+WnnScZ0rRpPCCi1WPVrtEXDYtXwq9zwWg3ndjGY45MwJySD2ULekr9wH3CFXRpLYiolx85A5eNOSoSTI8LBhHB0QJiMWNOza41piIxBSzuHzEgBqwXLrnWY2l5RJZ3TNbdQuvIHpNlc6zZrczmu0xU1SP0nPo0If0/C/Tz1PHaIYu3BC/d4WrrnJEM6RAHgYDKWn2Mt1gueY6vEvAz1ysvy+4lLISLAm3B/L71sO8Wwdv+pC6ZfuvMYQzrH/L2YDVznOnAinoggP8KD/HAwmRjSsyNrtx+ski6/LF4xAT563XHzvmM0axRC88ahAHh7uGE01H5ramzaHC01CZmeDFYL5m1rMXWseuvjNHKi3pPJXOH6VFqM9cRBsFR/zboQ+3qWFev+TZha1L2epZ06gCyp/d6gJkOuGcB3CxcDsHPHXry8PAkKCrhg+qZRjfH392Pzph1/K66i1NW/tseSEKIxsAzYgt6Qsh34DHgWCATGSSm3CSHcgHeBNoAD8IyU8hfb+l8B5x+r3C2l3GTr8fMMkG5bZydwi6x0tRRCRANzgQD0x6Q3AKeAV4Cr0S/tc6SU39W0TSFEV+Bt236UAAMAvwvs27fAV1LKP2z78Dnwu227DwF3A9MAqxDiFuAe4EsgRkppFkJ4AnvPf65rWafmFRHsVd57IsjThf2JmXZp4mw3TBPmr0KTkmmxrekVHUL7CH+6RgYw8I3fABjTNZqmAbU3RlRk8PfHmpZa9llLT8OhRdUu087DR+J63Y1gciDnkfvrFSM1t5DgCk/Ygrxc2X82rdq0SVn5JGXl0y2q6tCP/WfTMFs1Inxrz6NerhVierqyPyHdLk1chq1cP16hl2u/tvRqFkpMsA/z1uxnfM+WFJstbD+dQtMKT/ouRHj6IHMzyj7L3AwM4VWHFRlbdsMlsiVaxjlKl32JzLX/vg1hUQijCZmVUmXdylIycwjyL3/yGeTrw/7jZ+zSTB8zlKmz3+ObJX9RVFLCx0/fA8CgHh1Zu30fA27Xn549MnE0Xh61D/FpaCkZWQQFlD+BDPL3Zf/Rk3ZpYppEsHLTTm4ZcRWrNu+koKiY7Nx8vD3Le4jsP3oKs8VCREg9evQBwtMXmVPxe83EENGsSjpj6+64NG6Jln6O0iWf261Tpzh+/ljTKxyLGWmYYqoei05DR+I8/EZwcCBv1v36H51dcBk9ltynZ+AyckydY6bmFxPs4VL2OcjDmf3nsu3SxGUVADBhwSb9OOnVjF5N9DJMzi3inh+3cza7gPtjW1btrQQ4BftSmlh+7JWey8S9Y7OqaZJsaawa1txCTL4edo1F/sN7cWTiSwAYPfVzZqNHb8azZ2uKzyRzeuYnmNNzquYxr1Ieqz3H5ut5/GItmiaZ1qclvaKCicvMx8PZgQd/2EJidgHdmwRyX782GA01d1tKzS8hqGJMd2cOJNvvW1y2Xq4Tv92CJiVTe0TTq7F9JfZAcjYWTSPCu/oedo4Vyw0oPZeBW0f7GyeHYL+qZevjQdGh03hf1Y2MxetxDPXHtW0UjqF+5G7cj3NUKI7hAZSey8B7cHcMjiao5nSUmltEsGctZZthK9vP1ui/nz6t6BUdbJdm+cGzjO9e9ZiqTkOf2/U8VrpGJ1TOoy3epyv130/fNvRqZrtGNw5k4Gu/ADCmW7M6XUtMQf6Yz5VfIy3J6Ti3rzp8szrFe45QuHUfUesXgBBkL/iN0lNna13P4O2Hll0eU8tOxxhpH9MQHoXw9sd6aAcMsO8pZfALwvWRt5HFhZT+/jXWU1Vvfiu7HNdpu31ukPpPAcHe5dehIC839senVps2KSuPpMw8ukWHlv0tOTufez5dztmMHO6/pnutvZXg8tQrL8vvJ7tS2Xq7sz8uudq0SZm5JGXm0i0mvOxvpRYLY1/9DqNBcNugzvRvF1VrzIauc6Vm5hDs5132OdDPi/0n7B/MTr9hMNOen8fCZRsoKinlo1n6UL+4pDSEEEx7fh5ZuQUM6dmB20b0rzWPwtsfLbP8u5RZ6Rib2A9xNjSKxuAbQMm+bTgOvrHyJupNeFRTz6qu/tyqGy6RLdAykild+qVdnfvvCgkJIimx/PeTlJRMcGgQKSnV36+Muu4aFv+85JLF/7eRWj1b2ZW/7V/bsGQTjd6gMwm9YWks0BsYDswERgJPAKullJOEEN7ANiHESiAVGCSlLBZCNAMWAuebnjsCrYEkYCPQCyifHEC3AHhJSvmzEMIZvffXaKAD0B7wB7YLIdZdaJtCiG3Ad8AYKeV2W8NPUQ379h1wI/CHEMIRvRFqOtAdQEp5RggxF8iXUr4GIIRYC1wDLAZuAn6qT6NSXVk1SXxmPp9M6EdqbiGTvljDommDyS4s4VR6Hise0MeFT/tqHbvi0ugUeeEW9otV/Otiin9djFO/gbiOvZW8V1+85DEAlu89xcA2jas8VUnLLWTWd+t47sYrMdRyU1dXVk0jPjOPTyYN1Mv105UsumsoPaNDOJiYwYRPVuDj6ky7CH8M9Zy36kIsR3dh2b8JrBZMnQfgNOpOir+YU7ZcuHvjNOpOShZ/WPVJ9EVaun4HI/pdwYThA9h79BQz3/mSn96cyYETZzAYDKz8+HlyCwqZOOtNrmjXgvBg/0sStyHNmDSGF+d+za+rNtKpdQyBfj4YKvyG0jKzmfnGx8x54Ha7v18qliM7sOzboH+vXQfidN3dFM9/9pLHAShZspiSJYtx7DMQlxtvpeDtF3G5aSLFvy6C4qJLHs+qSeKzCvjkpitIzStm0rebWTSxD57ODgR7urDotj6k5hfzwM87GNQ8BD+3moepXgz3js2wFpVQeFS/QRYmI05h/uRtP8KZZz4nZOowIp+ewIl73rmo7ZedY2/pQ2peEZO+XMeiKQOwapLdZ9P5dvIAgr1cePSnbfy6L45RHRr/7TxZNUl8dgEf39CN1PxiJn+/jUXje5UNeUvLL2bWsn3MHtzukp1/Kkr7dhXOzcJpvfQ1ShLSyN9xBGnVsOYUcObxeUR9+BBISf6OIzhFBte+wQuwSk0v21tjSc0tYtKXa1k0dRCeznpPibS8Ik6k5tAj6uJjVInZwOd2/feTxycT++vxPlvFoulX267Ruax4cDgA075ay664VDpF1q9xuz4cGoXg2DSCk33HAxAx/wVcOremaGftN+o1EgKnUbdTvODNKotkbib5T98GhXkYIqJwuX0WBS/eeUnOR5fjOl1Zg9V/9pxkYLsmdvWfYG93Fs24jtScAh744k8GtWuCn0c9hnJfQIPXKy/T7+e85TuPM7BDtF3ZLnlmIkHe7iSk53DHez/TLMSfiHo2TlanoetcSzfuZnhsVyYM68veY2d44r2F/PjaQ1g1K7uPnOabF+7D2cmRKc/NpVXTcLq3/Xu9thAC5zHTKJr/6t/bTj1Zju7Esn+jXs/qMgCn0dMp/nxO7Sv+Q0ZdN5Q7p9Z9WKyi/F3/9qFwp6WU+6WUGnAQWGXrWbQfaGxLcxXwmBBiD7AWcAYaofde+lgIsR9YBFScbXCblDLBtt09FbYFgBDCAwiTUv4MIKUsllIWojdqLZRSWqWUKcBfQNcattkcOCel3G7bTq6U0lLDvi0F+gkhnNB7Ra2TUtZ2VfsEuM3279vQe3VVIYSYIoTYIYTY8enqXXbLAj1cSM4p72qdkltEYIUn3aA/bYptHoqD0UCYjzuRvh7EZ+Sz+kgi7cJ9cXV0wNXRgV7RwexNqF/rvZaejjGgvJJr8A/Amp5+wfQla1fh2Kv3BZdXJ9DTleScgrLPKTmFBHpW/5Rm2d7TZcPgzssvLuWez//k7sGdadeobhVyvVwrxMwtJNDTvjIW5OlKbPOw8nL18yDe9hTvjtg2fH/nUOZN7I+UEOlf+9AumZuF8CwfKy88/ZC5WfaJivLBagHAsms1hpAKeXVywWncI5Su/g4t4USd8hnk60VKenmMlMwsAv3sK0Y/r9rM4J6dAGjfvCklpWay8gpYsn4HvTq0wsFkxM/Lg44tmnLw5IWHKF4uQX4+pKSVP21NSc8k0M/HLk2gnw9vPnEP37/zLPfeqj8JPT+PUn5hEXc9+yb3jB9N+xa1P5GsTOZmIrwqfq++VXsjVfxed6zGEFa3Cebt4mSkY/SvcCz6BaBlXPhYLF2/Cofu+rFoimmFy4SpeH30LU7Drsf5+ltwGjqq1piB7s4k55Wf5lLyiqv0OgrycCY2Okg/TrxdifRxIz6roMp2ov092FWpJwdASXImjmHlFWfHEF9KkzOqpgm1pTEaMHq62vdWGtmL9MXlzx8smXlYC4vJWLIVgIzfNuHetvoyD/SolMfqzrEeLsTGhNjy6EaknzvxmfkEebrQPMibcB83TAYD/ZqHcjg5u9o49uXhRErFmPnFBLg7VUrjTGxUoB7Ty5VIH1fis/VrQX6JhXt/2cVdPWNoF+J9wTilFcsNcAzxw1ypbM3JGVXLNisPrBpnn/mMg1c9yIlJL2LycqP4lD5cJufPHRwe9iiHhz9G8cnEsr9XyaenC8m5tZWtK7Ex569fbkT66mV73opDCfSznYfroqHP7XoeK12jPau7RleNt/pIAu3C/XB1csDVyYFe0SHsPVv7NdqSko5DSPmNvCnYH0tK3a7t7gN7Urz3CLKwGFlYTMG6Hbh0qNoLpzItOwODd3lMg7e//XnOyQVDSCNc73kRt6c/xdi4OS5TnsQQEQ0WCxTq5audPYmWnowhoPZJ2C/Hddouzw1S/3EjObv8956SU3DBXkfL9pxiSIeqvTQAAr3ciA72Ydfp6nvk2KW9DPXKy/L78a5Uttn5BHpVP9fesl3HGNLJvkElyNbbKdzfiy7RYXbzL11IQ9e5An29SM7ILvucmpFDkE+leGu2MrhHez1eTGNKzHq8QF9vOrdsio+nOy5OjvTu2JLDp+2H8VZHZqdj8C3/LoWPP1p2hePC2QVDWGPcHnkN95e/whjVEtd7Z2OIvPgGK5lXTT2rUm9+u3rWztUYQutfz6ps0u1jWbN+MWvWLyYlJY3QsPIHHKGhwSQnVT9yoHWb5phMRvbt+ZsN9opSD//2hqWSCv/WKnzWKO+NJYDrpJQdbP81klIeBh5A7zjfHr03kOMFtmvl0vTsqs82q903KWUxeuPYYGAMeg+mGkkpNwKNbcPxjFLKAxdI95GUsouUssvk/p3slrUO8yU+M5/ErHzMVivLD8YTGxNql6Zf8zB2nNG7TmcVlhCXmUe4jxshXq7sjEvDounj4XfGpdHUv35D4SxHj2AMC8cQHAwmE859+1O6eaNdGmNY+QXesXsPrIkJlTdTo9bh/sRn5JKYmYfZYmX53lPEtoqoku50aja5RaW0r9B4ZLZYefCr1VzbKZpBbRvXPWaYH/GZeXq5Wqws3x9HbKW3DfVrGV5ergXFxGXkEe7jjlXTyLZNpHssOYvjKdn0qGZoXmVa0kkMfsEI7wAwGjG26YHl6E67NMLdu+zfxuad0dJtF3mjEecxD2LZux7robpPvtw6OpK4c2kkpKRjNltYtmEXfbvYv3EvOMCXrfuOAnAqIZlSsxlfT3dC/H3ZdkD/e2FxCfuOnaFJWO2T5za01jFNiEtKJSE5Tc/jum307d7RLk1WTh6apgHwyaI/GDVIn7DTbLZw/5x3Gda/F1f17lpl23WhJZ7A4BeC8AkEowlju15YjtiPqRce3mX/NrbsgpZav2MEwHL8CIaQcAyB+rHoeGV/zNvsj0VDSPlv2KFLD7Rzepy8mfeQM+UmcqbcRMlvP1D8w9eULLF/81p1Wod4EZ9VQGJ2IWarxvIjScRG2/8G+jULYoftZjirsJS4rALCvV1JySui2KxP6JxbbGZ3YhaNfaveMOXvOYFLkxCcIgIRDib8R/Qmc7l9+WUt307gjX0B8Lu2BzkbKpxKhcBvWE/SF9uXRdaKHXj21N8g5dW7HYXHqh/u0zrURz/HZhfoeTyUQGyM/fHcr3kIO+LSbXksIS4jn3BvN1qH+JBXbC6bsHzbmVSa1uHmtXWwF/FZhSTm2Mr1aDJ9m9o3iveLDmTHWb3inFVUSlxWIWFeLpitGjN+28W1LUMZFFNzL56CPcdxahKCo61sfUf0JmvFdrs02Su2439DPwB8r+lZNq+SwdkRg+0lCJ5XtkdarGWTfptsN0pGLzcCJ1xN2sKV1efzfNlm2cr24NlqyjaUHXH6DZt+/dLL9rxlB89ydZuq14MLaehze+tQX+IzKsQ7EE9s80rxWlSMV1IWL8TLjZ1n0rBYz1+jU+s0rKh4/zEcIkNxCAsCBxMeQ2PJX123t4RazqXh0rUtGA1gMuLStS0ldRgKp8UfwxAQivANAqMJU6c+WPZvrbBThRTMHEfBs5MpeHYy1jNHKfroOf2tXu6eIPQqr/ALwhAQipZRewPI5bhOV9Qg9Z+IAOLTc0nMzNXzuOcksa0aVUmn139KaF+hN1tKdj7FZv1GOrewhN2nk2kc4F17zMtQr7wsv59GQcSnZZOYkaOX7a5jxLZtUiXd6ZRMvWyblJ9PcwuLKbVdv7Lyi9hz+hxNg32rrFslZgPXuVpHRRCfnE5CagZmi4Vlm3YT28X+zYkh/j5sPXDcFi+FUrMFX093erVvzvH4cxSV6PM67Tx0kqbhdXhBwumjGILCEP7BYDTh0K0vlj2byxMUFZJ///XkPzqe/EfHYz15mMJ3nkKLO1brti9ESzyJwbdC/bltTyxHaqg/t+iCllZ7I1lt5n/yDf2uHEm/K0ey9PeVjLl5JACdu7QnNzfvgsPgRl9/LT/98Mffjq8o9fFvHwpXF8uBe4QQ99jmNOoopdwNeAEJUkpNCDEBqMNrCHRSyjwhRIIQYqSUcrGtB5ERWA9MFUJ8AfgCfYCHgarvNtYdBUKEEF1tQ+E80IfC1bRv3wG3ozc4Taxmm3lA5Svsl8A3wHN1zWNFJoOBx67uxPQF69CkZESHJkQHevHBmgO0CvWhb/MwekYFs/lkCqM/WIbBIHhgYHu8XZ0Y2DKcbadTuWHucgSCnlHBxDYPrT1oRZqV/PfewuvF1xAGA8XLl2CNO4PrhElYjh2hdPMmnEeMxrFjZ7Ba0PLyyXulft3ATUYDjw2/gunzV6BpkhFdmhEd5MMHK3bRKtyfvrZK1rK9pxnSvonda21X7D/DrtPJZBeW8OtOvRfP7Bt60yK05jeXmYwGHrumC9O/XKPH7NSU6EBvPli1j1ZhvvRtEU7P6BA2nzjH6Hd/xyAEDwzugLerEyVmK5M+/RMANycHnr+uJ6a6PE3XNEqXfI7z+Mf116XuXotMS8Ch3/VoSaexHt2JqfsQTM07IzUrFOVTslh/DbSxdQ8MkS0wubpj6tAHgNLFc9GSa36tscloZObtNzL9ufexapKR/a8gulEI7y/8nVbRjejXtR0PTRjFsx8u5Kvf1yAEPHf3eIQQ3DSkD0++/zWj7puDBEb0u4KYxrU/JazNw0+/xPbd+8jOzmXAyFu4c/J4rhtW+yt1a8zjtHFMf+p1rJrGyEFXEh0Zxvtf/0yrZo3p170j2/cf4Z0vfkAIQac2MTwxXR8GsnzDNnYdPEZOXj6/rtR7vDz3wO20aFq1Yn9Bmkbpb5/iPPEJ22tw1yBTE3AYMAYt8STWIzsw9RiKqUWX8u/1x7q9Ktk+jpXCj97C45nXwGCgZNUSrGfP4DJ2EpYTRzBv24TzNaMxte8MFguyIJ+Ct/7ekAyTwcBjA9sw/Ydt+nHSNpxofw8+2HCUVsHe9I0OomfjADafTmf0/L/04yS2Jd4ujmw+k8Ybaw4jhD5q89auTWlW3U2zVePUzE9otfBJhNFAyrerKTp2loiHbyJ/7wmyVuwgZeEqmr17Lx03vYclO59j08qHS3he0YrSpAxK4u2fHMY9/zXR796LafYkzBk5nHig+jI3GQw8NrgD0xdu1PPYPpLoAE8++OsQrUK86RsTSs+mQWw+lcroeX/qeRzQBm/bZNoPDGjD1G/WIyW0DPHmuo5Vb1yqi/lo/1bc+dMO/bzeOpwofw8+2HScVkFe9I0KpGekP5vj0hn9xXqMQnB/n+Z4uzjyx+EkdiVmkV1s5tdDesV59lVtaR5YfdnGz/qY5t88DQYD6d+tovjYWUIfupnCvSfI/nM7ad+upOk799N2wwdYsvM5defr+j76exHzzdOgSUqTMzh179tlm200ezKurRoDkPTm95ScSgK8qy/bIR2Y/s16PZ/tG+vXr7UHaRXiQ9/mofSMCmLzqRRGf7jcVrbtyso2MbuA5NxCOtdjmE1Dn9tNRgOPDe3M9K/+QpMaIzo21fO4ej+tQn3p2yKMntHBbD6ZzOj3lujX6EF6vIGtwtl2OoUbPlyGAHpGh1RplKqWVSP1uQ8J/3QOGIzk/LiC0hPx+N0znuIDxyhYsxXnNjGEvvckRk933Pt1x3L3LZwZNo285RtwvaI9jX/9ECQUbNhBwZqttcfUNIp/mIvrnbP118Vv+RMtOR7HoeOwxh/HeuDCDzyMUW1wHDoOrFaQGsXfvw+F+RdMb1e2DX2dtstzA9V/RvZk+sdL9Tx2a050sC8fLN9Bq/AA+raOBGCZbdLuivWfU6nZvPHb1vJzbGw7moXU3vhxWeqVl+v3c30s0z/4FU3TGHFFK6JD/Pjgjy20ahRIX1sv1mU7jzOkUzP7sk3OYs53azAI0CRMGtjZ7m1yF47ZsHUuk9HI45NGM/2Fj9A0yci+3YiOCOb975fRumk4fbu0Ycb4Ycyet4iv/1iHEILZ029CCIGnuyvjr41l7My3EAiu7NiCPp1a1RgP0L/LBe/h+sCLCIOB0g3L0ZLicBoxAeuZY1j2bq5xdfeXv0K4uILRAVPHnhS+8VjVN8pVE7P0j89wvnUmGGz1rLQEHPrfgJZ4Sq8/XzEEU4vOSE3T61k/f1i2uvPkZzD4h4KjMy4z3qf0l3lYT+yrIWBVf674i4FXxbJtz58UFRZx710zy5atWb/Y7m1ww0ddzc3XT6nX9v9zbA9zlYYj6voGh/81tsm3f5dStrF9/tz2+YeKy4QQLsBb6BN8G9CHz11rm7voR/RJtpcBd0kp3W09ex6SUl5r2+57wA4p5eeV4jcD5qHPpWRGn+vpNBeevLvabdom734XcEFvVBoIhFS3b7Z1HdB7M/0ipbzN9rey7QshYoAf0Htt3SOlXC+ECLbtW4iUMru2si1a8GSD/ijyP19Xe6JLzP3Oqxs8JqUltae5xLTDR2pPdAmZrp/UoPEADMH1Hzr2d2lZtT+pvNQsn1ed9+GfVnKg+glc/ykuwzo3aDyA3U/W3DD6T+j48t+cP+IiVHxzVEM5+FzDDltt83IdbkguNQfH2tNcapehshz/bN17qV4qoVfV+XnfJWHqdXE9Rv+O/E9WN3hM93uGNXhM8qq+tOCfZtm6u/ZEl5DpmiENGg/AEFa3CfMvJWlp+Lps6btv157oEjOG1fxw+FJr/HbD/l7PS8s5+s9M/PY/oHDuff/ORo46cJ329v/k9/av7bEkpTyD/oa1858nVrfMNgfR1GrWPw5U7Bv6qO3va9GHm51Pd/cF4h8Hqnt1wcO2/yqmveA2bfMrVX7fabX7ZktvRu8NVe32pZTHKq0L+txPP9SlUUlRFEVRFEVRFEVRFKWu/rUNS0rdCCHeRe9BNfRy74uiKIqiKIqiKIqiKP8tqmHpP05Kec/l3gdFURRFURRFURRFaRBSzbHU0P7tb4VTFEVRFEVRFEVRFEVRLhPVsKQoiqIoiqIoiqIoiqJcFNWwpCiKoiiKoiiKoiiKolwUNceSoiiKoiiKoiiKoij/DZq83Hvw/47qsaQoiqIoiqIoiqIoivIvJ4QYIoQ4KoQ4IYR47AJpbhRCHBJCHBRCfHMp4qoeS4qiKIqiKIqiKIqiKP9iQggj8D4wCEgAtgshfpVSHqqQphnwONBLSpklhAi8FLFVjyVFURRFURRFURRFUZR/t27ACSnlKSllKfAtMKJSmjuA96WUWQBSytRLEVg1LCmKoiiKoiiKoiiKovyPE0JMEULsqPDflAqLw4CzFT4n2P5WUQwQI4TYKITYIoQYcin2Sw2FUxRFURRFURRFURTlv0HTLvce/GOklB8BH/2NTZiAZkBfIBxYJ4RoK6XM/jv7pXosKYqiKIqiKIqiKIqi/LslAhEVPofb/lZRAvCrlNIspTwNHENvaPpbVI8lpQrRtFWDxpPaugaNByAatWjwmJQWNXhIceZMg8aT+ZkNGg9Ay3Jr8JgGn+AGjymLSho8Zurhhi3byHHeDRoPIF9LavCYuHs0eEjh5NzgMbMtyQ0az3rwSIPGA5A5hQ0e09gupsFjZue6NHhMjx1ZDRrPd2zD1wuktrrBY4rwhv/9UJDT8DG37m7YeGnnGjYeoF2GHhmGqE4NHlMrtDR8zKMpDRovp7igQeMp/2nbgWZCiCboDUo3AWMrpVkM3Ax8JoTwRx8ad+rvBlY9lhRFURRFURRFURRFUf7FpJQW4G5gOXAY+F5KeVAIMVsIMdyWbDmQIYQ4BKwBHpZSZvzd2KrHkqIoiqIoiqIoiqIo/w3/4TmWaiOlXAIsqfS3pyr8WwIP2v67ZFSPJUVRFEVRFEVRFEVRFOWiqIYlRVEURVEURVEURVEU5aKohiVFURRFURRFURRFURTloqg5lhRFURRFURRFURRF+W+Q8nLvwf87qseSoiiKoiiKoiiKoiiKclFUw5KiKIqiKIqiKIqiKIpyUVTDkqIoiqIoiqIoiqIoinJR1BxLiqIoiqIoiqIoiqL8N2ja5d6D/3dUjyVFURRFURRFURRFURTloqiGJUVRFEVRFEVRFEVRFOWiqKFwSp1s3Hecl79ZhqZpjOrTicnXXmm3/FxGNrM+XkxeYTGapnHfDQO5sn0Mmw+c5O1FKzFbrTgYjTwwZhDdWzWtU0zHrt1wv+seMBgoXvIHhd9+Y7fc5fobcRl6DVitaNnZ5L76MlpqCgBud0zFqfsVABR8/SUla9fUnse9R3n5y1/RNMmofl2ZPLyffR7Ts5g193vyCmx5vOlqruzYgsS0TEY99DqNQwMAaBvdiCcnj65THi9HuRqatMFxwFgwGLDsXYdl6xK75cY2vXDsNwaZlwWAedcqrPvWAeDy8KfItAQAtNwMSn96p+75XLBEL9vYTky+tk/VfH70ky2fkvtuHMSV7WPYfzKB5z7/FQApJdNG9mNAl1Z1irlh535e/ugbNE1j9FV9mHzDNXbLk1LTeeqt+WTl5uHl7sYLD00h2N+XI6fimfP+lxQUFWEwGLjjxmsZ0qd7nWLWZNYLb7Bu4zZ8fbxZ/PXcv709AGOLTjiNvB0MRsxbVmBe/WP16dr1wGXi4xS+8SBawgkADCGNcbrhTnB2BalR9OYMsJhrjel2ZWcCn5iKMBrIXrSczI8W2S136dKGoCem4NS8CUkPvETe8o0AuHZvR+DMO8rSOTaNIOmBl8lfubleed54PIlX/tiBJiWjOkczqU9ru+WvLtnJ9tP6eaDYbCGzoJgNT9xY63b9+rWnxZwJCKOBhAWrOfPur3bLhaOJtu/dhWe7Jpiz8tk75W2Kz6bh26ctMbNuRjiakKUWjs1eQOaGgwBEPz6G0Bv6YPJ2Y3XTiTXn62gir/y+TT9GujZjUt+29vn6fRvbTyXr+Sq1kllQxIanxwJw5/w/2Xc2jY6RQbw7cUCteS2LeTyJV5bs1MuyU1SVsgRYfiCOeWv2A4KYYG9euqEXAG+t2M36Y0kATIltw+C2kReM49evPc3nTEQYDSQuWM2Zd3+xWy4cTbR57y482zXFnJXHvgpl22zW2Apl+zVZtrINHtWTxveNAikpSc7iwF3vAVnVxjfGdMDp2klgMGDevgrzXz9Xn671Fbjc8jCF7z2ClngSjCacRk7FEB4FUlL623yspw/WVqz6tlp1xvmGaSAMmDcto3TFomrTmTr0wmXKLApeuhct/nh5mfgE4PbkPEqWLMC8svrjuqKNZ9J4de1hNA1GtglnUreq14MVR88xd8sJBIKYAA9eHNqeo6m5PL/6IAUlVowGmNwtisHNQy4Yx6tvRyKfm4QwGEhduJJz79mXpXA0EfXOfbi1bYolK4/j016nNCEN4WCiySvTcGsXhdQkcU99St5mvSxb/jAbhyAftOJSAI7cNBtLRk618Z26d8Xr/rvBaKTwtz/I/2qh3XK3m27AddhQW70gh+wXXsGarJ8PjEGBeD3+EMbAQJCSzBmPlS2rsWwvw/Wrwes/e47Y6j8ao/p1Y/KI/vZ5TM9i1offkVdQpNdFbh7KlR1bAnAsLonnPv2R/MISDAbBN3PuxcnRofaYB07y8sLlerle2YHJQ3vZx8zIYdb8X8vL9br+XNkumuz8QmZ8+CMHzyQxvGd7Zo4bUmus84wtO+E8eop+Lti8gtKVP1SbztS+Jy6TZ1Lw6v1oZ08gfANxm/khWmoiANYzRyn5/v06xdx4IplXlu/Rz7MdmzCpV4sqaZYfPMu8dYcAQUyQFy+N7s72M6m8umJvWZoz6Xm8NLo7/VuE1R7zcByv/LQBTWqMuqIVkwZ2tlv+6s8b2H5cr8sVmy1k5hWx4SX9+tzpgQ+IDvEFIMTHg7fvsK87VWfDzv28/PFCNE0yetCVTL5hqN3ypNR0nnr7M7Jy8/X61ozbCfbXY0x7+k32Hz1Jx5bNeO/p+2qNdZ6pfVdcbr0bDEZK1/xBya/25wLHgcNwGjQSNA1ZXEThJ6+jJcaB0YjrlIcxNm4GRiOl61dQ8ss31QepLuZEW8zVf1DySzUxB1eI+ZEe06H3QJyHjSlLZ2jUlPzHpmCNO1mnuG+8MZshQ/pTVFjE5NsfYM+eA3bL3d3dWLP6p7LPYWEhfLPwJx566BkiIkL59JO38Pb2xGg08sSsF1m2bHWd4ipKfamGpX8ZIcQnwBtSykM1pBkJHKspTX1YNY0XvlrCvIfHE+TrydhnP6Zvx+ZEhQWWpfn413UM7taaG/t35WRiKne/sYClr8fg7eHKO/ffTKCPJ8cTUpj+2tesfGtG7UENBjzuvZ+sR2agpaXh88E8SjZvxBoXV5bEcuI4mdOnQEkJLsNG4D5lGrlznsWx+xU4NIshc8rt4OiAz+tvU7ptK7KwsOY8fraYeY/fTpCfF2NnvUffTq2ICg8qz+PPqxncvR03DurByYQU7n7lM5Z2fAyA8CA/vn/x/v/9chUCx0HjKfnuNWReJs4TnsJ6Yg8yI8kumeXwNswrv666vqWU4s+frn8+v/ydeY9M0PP5zDz6dmxhn89f/mJwtzbcOKCbLZ9fs/T1B4kOD+SbZ6ZiMhpJy87jhlkfENuxOSajseaYVo0XPvyKj+Y8RJCfLzc/MJu+3TsQ1ai8Qvb6p98xbEBPRgzozda9h3jnix94YcYUnJ0cef7B24kMCyY1I4ub7n+Wnp3a4unuWq98VzZy6CDGXjecmc+99re2U0YYcBo9laK5TyFzMnB54HUsB7chU87ap3NywfHK4Vjjjpb/zWDAadyDlHzzBlrSGXD1AKu19pgGA0FP38nZ257AnJxO4x/fIn/VFkpPlse0nEvl3GNv4Dv5OrtVC7fu48yIe/TNeLkT9eenFGzYVa8sWzWNF3/bztyJ/QnydGXc3GXEtggnKtCrLM3DQ8sr0Au3HOXIucw65EvQ8qVJ7LzxeYqTMrhi+QukLd9JwbHEsiThY/thzs5nwxX3EzyyBzFPjmXflLcxZ+axe/yrlKRk4d4inE7fzmRdhzsBSFuxk/hPl9N7y1u15+vXLcydfJWer/f/ILZlBFFB3uX5urZbeb42HeZIUnm+JvRpQ7HZwg9bj9We14oxf9/B3An9CfJ0Ydy85VXKMi4jl/nrDvH57Vfh6eJIZn4xAOuOJnI4KYvvpl+N2aoxef5KejULxd25mptJg6DFS5PYZSvb7stfJG35DruyDRvbH0t2ARuvuI+gkT1p9uRY9tvKds/4VyhJycKtRQSdvp3J+g7TEUYDzedMZNOVMzBn5tHsyXFETBoMmd9WjS8MOA2/g6JPZyNzM3C562Ush7cjUxPs0zk649jrGqzx5WXo0HUgAEVvP4hw88T5tlkUvf8oSFlz4QoDzmPuovCdmcjsdFwffRvLvq1oyfH26ZxccOg3AuvpI1U24XTdFCyHdtQcx8aqSV5afYgPR3clyMOZcd9sJjYqkCg/97I0cVkFzN9+is/HXIGnswOZhSUAODsYeW5wOyJ93EjNL2bcgs30jPTHo9rv0kDjF+7gyE3PUnoug9ZLXiF7+XaKjpeXZcDNA7Fk57O31134juhFo1m3cmLa6wSO08ty/4AHMPl50WLBLA5c/UhZWZ686y0K9tVyk2Uw4PXQfWTc9zDW1DQCPp1L8fpNWM6U1wvMx46TPmkasqQE11HD8bxzKllPzQbA+8nHyf/ia0q270S4OINWy/fI5bl+XZ76z8/MmzlFr/888Q59O7euVP9ZxeAr2nHjoJ56/eflT1n6bkssVisz31/I83fdTPPIULLzCjCZasnf+ZgLljLvwXEE+Xgyds6n9O0QQ5TtAR3Ax39sYHCXVtzYrzMnk9K4++1vWdruHhwdTNw1MpYTiWmcSEyrNVYZYcD5hukUvj8LmZ2B60NvYjmwFS256jXTIXY41jP2x6WWnkzhK/fWPR76sfnist3MHXelfm7/ZBWxMaFEBXiWpYnLyGP+xqN8PrGffp4t0M+zXRsH8v2UQQDkFJUy7L2l9IgKqjaOfUyNF39Yx9zpwwnydmfcG4uIbdOEqGDfsjQPj+pd9u+F6/ZxJKG8HJ0cjHz/yE11z6NV44W5C/jouRkE+flw84PP2epboWVpXp//PcP692TEgF5s3XuYd774kRdm6A1ZE0cPpriklB+W/lXnmAgDLrfdR8ELD6NlpOHx/FzMOzfpDUc2pRtXUbryNwBMnXviMv5OCl56FIfufcHkQN6jk8HRCc/XPse8cRVaei2NzMKAy6T7KHjeFvPFuZh31BLz1jspePFRzBtWYt6wEgBDRBPcHnquzo1KQ4b0Jzq6Ca1a9aZbt0689+6L9L5ymF2a/PwCunYbXPZ5y+YlLF68FIDHH7+PH378jY8++oqWLZrxyy9fEtO8R51iK0p9qaFw/zJSytvr0GA0EqjbY7E6OHAqkYggX8IDfXEwmRjSvQ1rdx+1TyQE+UV6RTW/qIQAHw8AWkaGEOijX0CjwwIpMZspNVtqjWlq0RJLYiLauXNgsVCyZjVOPXvbpTHv2Q0lekzz4UMYAvQKiSmyMaX79oJmheJiLKdP4ti15h4nB06cJSLIj/AgPz2PPdqzdmelYhaU57GwuCyPF+tylKshpCkyOxWZkwaaFcvhbRibdfxb+ajNgVMJlfLZlrW7Kt1ICUF+8fl8FhPgrefTxcmxrBJeYrYgRB1jHjtFo5BAwoMDcXAwMaRPN9Zs2W2X5tTZJLq305+4dmvXsmx547BgIsOCAQj088HXy5OsnNyLyntFXTq0xcvz7/1mKjI0aoaWfg6ZmQJWC5bd6zG1qfo7d7x6HKWrfwRzadnfjM07op07ozcqARTmgax9kkPndjGUxiVhPpsMZgu5f6zDfaB9BcWcmErJ0TM1TproMaQ3+et2IG3feV0dSMggws+DcF8PHExGBreNZO3hsxdMv3TfGYa0bVzrdr06RVN4OpmiuFSk2Ury4k0EDulilyZgSBeSvtd77qX8thXf3nrvnrwDZyhJ0XvK5B9JwOjsiHDUn9nk7DxBaWp27fk6m06En2d5vto3qTlfe08zpH2Tss/do0Nwdaq9h4BdzIQMInzdCfd1Ly/LI/aNLT/tOMmY7s3wdHEEwNfdGYBTaTl0bhyAyWjAxdFETLA3G08kVYkB58s2xa5sA4Z0tUujl61+U5H62xZ8e7cB7Mu24MjZ8rIVAhAYXZ0AMHm4lKWrzBARjZaRjMyyHSd7N2Bq2bVKOserbqb0r5/BUn6ciMBwrKf0J8OyIBdZVIAhLOrChXo+ZuMYtLQkZEayHnPnX5jaX1ElndOwWyn9cxGywrEJYGrfA5mRjHYurso61TmQnE2Etyvh3q44GA0Mbh7M2pP2N0k/70/gxvaN8LQ1GPnayi7Sx41IHzcAAt2d8XF1JLPIfn/Oc+8YTfGZc5TEpyDNFjJ/2YDP4G52aXwGdyV9kd5DJvP3zXj21nveucREkLthPwCWjBwsOQW4ta+9LCtyaNUCS0IS1iS9XlC0cjXOV9r3cindtQdpqxeUHjyEMdBWL2gcCUYjJdt3AiCLisvS1eRyXL8avv4TT0Swf4X6TwfW7qjUM8+u/lNEgK3+sXnfMZo1CqF5pN6I4O3hhtFQ+63FgdNJRAT6Eh7gg4PJyJBurVm7p2rDeHm5lpSVq6uTI52aNcLJoX7Pxg2RMWhp55AZtnPBrnWY2lZzXF5zC6Urf0Caa+/BW5sDSZlE+LgT7uOuH5utI1h71P5c+dPu04zpGlV+nnVzrrKdPw8n0Cs6GJc65PlAXCoR/l6E+3vp5/aOzVi7//QF0y/ddZwhnWPqmbMK8Y6fr28FlNe3tlaqb8Wfq1DfasGarXvKll3RvhVuLlXzXBNjdAu05CS01HNgtVC6eTUOXezPBRSVN6YKJ+cKDwSk/tlgQDg6IS1mZNGFG17tYqZUiLlpNQ5d6xqznGOvAZg31d6L8Lxhw65iwdd6z7pt23bh7e1JcHDgBdM3a9aEgAB/NmzYCui9JT099GPH08uDc+dq76X5n6HJ/+5//6MuacOSEKKxEOKIEOJzIcQxIcQCIcRAIcRGIcRxIUQ3Wzo3IcR8IcQ2IcRuIcSICuuvF0Lssv3X0/b3vkKItUKIH2zbXyBE1Uu0ECJaCLFSCLHXtn6U0L0qhDgghNgvhBhT2zaFEF2FEJts29kmhPCoYd++FUJcU2EfPhdCXC+EMNribhdC7BNCTK2hvBYIIQ7b9sXVtmyArWz228rKyfb3tUKILrZ/5wshnrft5xYhRJBtv4YDrwoh9tjK4F4hxCHbflTzSLdmqVm5BPuWP10J9PEkJcv+Rnv6yL78sXkfgx54nbveWMBjtwytvBlW7jhEy8gQHOtwYTT6+6OlpZZ91tLSMPj7XzC989VDKd2mn0QtJ0/g2LUbODkhPL1wbN8RQ2DABdfV85hDsJ93eR59vUjJtO+KP/26QfyxcTeD7n6eu175jMcmjChblpiWyY2Pv82k2XPZdeTCF3D7mA1frsLDB5lb3tNB5mUi3H2qpDM174zzbbNxHHknwsO3wgIHnG59Cqfxs+rcIJWalUewb3kviEDfavI5qh9/bNrLoPtf467Xv+axW8q7Xu87eZZRj7/L9U+8z6wJw2p/2gukZGQRFFC+30H+vqRm2N94xjSJYOUm/SZj1eadFBQVk52bb5dm/9FTmC0WIkIufBG/XISXHzI7veyzzE5HePnZpTGENcXg7Y/1sH3PB0NAGEiJ85RncHnwTRz61W3opkOQH5bk8piW5HQcgvxqWKN6nkNjyf29Hk8nbVJziwj2Ku85FuTlSmpeUbVpk7LzScrKp1vT2p/wOgf7UpyUUfa5OCkTpwpPdwGcQ3wpTtTTSKuGJa8IB1/7hsKga7uTu/80srT2Rt6KUnMLCfZyK9+OpyupOQXVpk3KsuUrKrheMarEzCuqGjPXvnIdl5FHXHoeEz5ewfiPlrPxuH5DFBPsw8bj5ygqtZBVUMz20ymk5FRfMXcK9qWkQtmWJGXgFGx/zqlatoVVyjawQtlKi5XDj35Cj7Wv0mffXNxiwklcUH3XfuHpi8ypcJzkZlY9TkKbYPDyx3rUvgeddi4OU8su+g2ITyDGsCiE14WvQ2Xb8/ZHyyrvAaBlVXNsRkQhfPyxHthuv7KTM46DbqBkyYJa45yXml9CkIdL2ecgd2fS8u0bTeKyC4jPKmTit1u4deFmNp6p2tPjQHI2Fk0jwrv63pmOwX6UVvguS89l4BDie+E0Vg1rbiEmXw8KDp7B+6quYDTgFBGIW7soHEPLy7Lpm3fT5s/XCb3/hgvm0xjgjzWlvF5gTUvDGHDh78Pt2qEUb9HrBaZG4cj8fHxeeJaAzz/C866pUIcGkMtx/Wr4+k+uff3Hz4uUrMr1n6v4Y8MuBt01h7temc9jE0cCEHcuHSEE0178mDGPv8Vnv9bthjk1K49gn4r1Hw9SsvLsYw7vwx9b9jPo4be56+1veezmwZU3Uy8Gbz+07ArHZXXXzPAohLc/1mp6Cxr8gnB95G1c7n0RY9Oqw4ark5pbRLBnhWPT06XKNSsuI5+4jDwmfLaG8fNXs/FEcpXtLD94lqtbR9QtZk4+wT7lvRWDvN0vfD3JzCUpM5duzcp7c5darIx9/XvGv/kDq/edqjVeSkY2Qf4V6lt+PqRmZNuliWkSwcrN5+tbu6qtb9WHwccfLaPCMZKRhsGn6jHiOGgkHm99jcvYqRR98S4A5q1/IUuK8fzwRzzf/ZaS379HFuRVWbdKTN86xrxqJB5vf43LuKkUff5uleUOPfpSumlVnfIJEBoazNmE8sbIhMRzhIZe+Pp/4w0jWPRD+VD+5557g7FjR3Pq5HZ+/eVL7n/gyTrHVpT6+ieGwkUDNwCTgO3AWKA3emPHTPTeNE8Aq6WUk4QQ3sA2IcRKIBUYJKUsFkI0AxYC5x8bdwRaA0nARqAXsKFS7AXAS1LKn4UQzugNZ6OBDkB7wB/YLoRYd6FtCiG2Ad8BY6SU24UQnkBRDfv2HXAj8IcQwhEYAEwHJgM5UsqutkahjUKIFVLKyq0OzYHJUsqNQoj5wJ1CiPeAz4EBUspjQogvbdt8q9K6bsAWKeUTQohXgDuklHOEEL8Cv0spfwAQQjwGNJFSltjK+5JbumU/w3t1YMLVPdl74ixPfPQTP865E4Ot4nYiMZW3vl/J3IfHX/LYTgMH4RDTnKwH9bHZpTt3YGreAp933kfm5GA+dBCsf/+Vk0s37WF4n85MuKYPe4/F8cSH3/Hjyw8Q4O3J8ncex9vDjUOnErj/jS/56ZUHcXet3xOYamNehnK1nthD0eGtYLVgat8Xx2tup+TbVwAo/vAhZH42wisAp5sfQUtLQGbXoyv6BSzdso/hvTsy4epe7D0RzxMf/ciPz9+FwWCgXVQEP794D6eS0pj10U/0btesTvM31GbGpDG8OPdrfl21kU6tYwj08ykrV4C0zGxmvvExcx643e7v/xpC4DRiMsUL3666zGDA2KQVhW89CKUluEyfg5ZwAuvxff/4bhkDfHBq3piCDTv/0TjL98UxsHWjOj09vxTcmofT7Mmx7LzxhX80zvJ9pxnYJrJB8mXVNOIz8/hk0kBScwuZ9OlKFt01lJ7RIRxMzGDCJyvwcXWmXYQ/hrp2x7gI58t2l61shclI+MRBbBnwGEVxKTR/4Taa3DcKcr6v/8aFwOmaiRQveq/KIsvOVRgCw3C56xVkdhrW+KN16tlXp5jXTaH4y9erLHK65hZKV/8MJcV/P04FVk0Sn13Axzd0IzW/mMnfb2PR+F5lQ97S8ouZtWwfswe3+0e+y7RvV+HSLJw2y16lNCGN/B1Hyno1nrj7LczJmRjcnIn55BFKr+9L+g9r/1Y8l8EDcWjRnJy77tf/YDTi2L4taROnYE1JwWf207gOHULh70tq3E5dXI7r13kNV//ZzfA+XZhwbSx7j53hiQ8W8uMrM7BqVnYfPc03c+7D2cmBKc/Po1XTcLq3afb3Y247yPCe7Zkw+Ar2nkzgiU9/4cdnp2Iw/EPnGiFwGnU7xQverLJI5maS//RtUJiHISIKl9tnUfDinVBc/YON+rBKjfjMfD65NZbU3CImfbmWRVMH4ems92BKyyviRGoOPf7mw4TqLN91goHto+yuJ0ueupUgb3cS0nO44/1faBbqR4S/Vw1bqd2MSTfw4rxvLljf+qeU/rmY0j8X49BzAM6jxlP44UsYo1qCppF75/UINw/cn34by4Gdek+kSxFzxWJKVyzGodcAnEePp/CDl8qWGaNbQmkJ2tkzlyRWdW68cTgTbyufq2rMmBF8+dX3vPXWR3Tv3onPP3ubDh0HIGsb0q0oF+GfOKpPSyn3Syk14CCwSuq/3v1AY1uaq4DHhBB7gLWAM9AIcAA+FkLsBxZhP5xrm5QywbbdPRW2BYAQwgMIk1L+DCClLJZSFqI3ai2UUlqllCnAX0DXGrbZHDgnpdxu206ulNJSw74tBfrZGo+uBtZJKYtsebzVlsetgB9Q3ZX2rJRyo+3fX9v2t7mtHM/3C/4C6FPNuqXA77Z/76xcJhXsAxYIIW4Bqn2ULoSYIoTYIYTY8eli+5b0QB9PkjPLn9ClZuUSVOFJE8DP63YzuJv+BKd9dAQlZgtZ+foT7JTMHB5451vmTBlFRKD9080LsaanYwgo7yViCAhAS0+vks6hU2fcxo4n+8mZUKHbcuE3X5M19XayH5kBQmBNuPCwEj2PXiRXeMKSmplDkK/9hfTntdsZfEU7PY8xkZSUWsjKK8TRwYS3h/7kv1XTcCKC/IhLrrqvVWM2fLnKvCyEZ3la4eGLzK80hKS4AKz6z8Sy7y8MweWT8sr8bP3/OWlo8UcwBF14wt7yfHqQXKH3V2pmNfn8axeDu7Wx5bORXT7PaxoagKuzIycSU6lNkJ8PKWnlPbNS0jMJ9LPvJRHo58ObT9zD9+88y7236vMBnZ9HKb+wiLuefZN7xo+mfYv6DddoKDInA+Fd/rRMePsjc8p7E+DkgiE4Epe7nsd11scYIpvjPPkJDOHRyOwMrKcOQkEemEuxHN6pT1BcC3NKBqbg8pimYH/MKRk1rFGV59V9yP9zE1jqMKdTJYGeLiRX6BmTklNIYIWeGhUt2x/HkHaN67Td4uRMnEPLn1w7h/pSkmw/N1PxuUycw/Q0wmjA5OGCOVN/yukU4kuHz2Zw4O73KYqrfzfzQE9Xkis8UU7JLSSwQm+iipbtPWM3DO5iBXq4VI3pad9TJcjTldjmYTgYDYT5uBPp50G8Lc93xLbh+zuHMm9if6SESP/qh3mWJGfiVKFsnUL9KEm2P+dULVtXu7Jt/9kMDtz9QVnZerRpDFD2OeXXLXh3qX4oh95DqcJx4ulrf5w4umAIaoTLlNm4PvIhhogYnG99TB/ypmmU/vE5Re8+RPFXLyOcXdHSqx/yV5GWnY7Bp7yXiMGnmmMzNBLXB17B7bnPMTZpgcu0pzE0aoahcXOcRk3G7bnPcew3EqfBY3CIHVZNlHKB7k6kVOgFkZJfTIC7U6U0zsRGBerfpZcrkT6uxGfrx1J+iYV7f9nFXT1jaBfifcE4pckZOFb4Lh1D/DBXmsPMLo3RgNHTFUtmHlg14p/5jAODZnDstpcwerlRfFIvS7PtWNMKikn/eT1uHaOrjW9NS8cYVF4vMAYEYE2req117NIJ9wm3kPnoE2X1AmtqGubjJ/VhdFaN4vUbcGhee+PH5bh+NXz9x9O+/pORQ5BPpfrPmu0M7tFez2NMYz2PeYUE+nrTuUVTfDzdcHFypHeHFhw+nUhtAn08SM6qWP/JI6jS9AI/b9jD4K768Kn2UeHVlmt9aNkZGLwrHJfVXTNDGuF6z4u4Pf0pxsbNcZnyJIaIaLBY9CHjgHb2JFp6st7zt7Z8erqQnFvh2MwtqnLNCvJwJTYm1HaedSPS1534zPLePCsOJdDPdh6ui0Avd5KzytdPyc6/8PVk93GGdLI/DoK89d5O4f5edIkOs5t/qTpBft6kpFeob2VkEVihBxzY6lsz7+L7t5/h3vF6D+m/M2+llpWOwa/CMeIXgJZ14Xq3ucJQOcdeAzDv3QZWKzI3G8uxgxibNq89ZmY9Y1YzVM6hZz9KN9Y+cfa0aRPYvm0527ctJ/lcKhHh5fNVhYeFkJRUtVcbQLu2LTGZTOzevb/sb7dNvIkfftDnfdq6dRdOzk74+9ftnkFR6uufaFiq2P9aq/BZo7yHlACuk1J2sP3XSEp5GHgASEHvXdQFcLzAdq1cmt5W9dlmtfsmpSxGbxwbDIxB78EEeh7vqZDHJlLKFdVst3KTcX2akM2yvMm5pv2/Bngf6ITeY6tKOinlR1LKLlLKLpNH2r9VqHWTUOJTMkhIy8JssbBs6wFiO9qfhEP8vNh6SO8yeyopjVKzBV8PN3ILirj7zW+474aBdGzWqM4Zsxw5giksHENwMJhMOPXrT8mmjXZpTNHN8HxgBjlPPo7Mzi5fYDAgPPWKn7FpU0xNm1K6o+ZJUFtHhROfnEFCaqaex817ie3c0j6P/t5sPaC/UetUYgqlZjO+nm5k5uZjtT15TUjJIC45nfA6NPRcjnLVzp1G+ATqN1oGI6aW3bCesB8Lj1t5hdIY3REtw/YUx8kVjLafjos7hrBmdbrJat0kjPiUzAr53E9sR/u3olwonwlpWVhsk0onpWdz5lw6of7etceMaUJcUioJyWmYzRaWrdtG3+72Q/eycvLQbN/bJ4v+YNQg/Y18ZrOF++e8y7D+vbiqd9W5WP5XaGePYwgIRfgGgdGEqeOVWA9sLU9QXEjBU7dQOOcOCufcgRZ3lOJPn0dLOIHl6C4MIZHg4Kj3XopqXXUC02oU7z+GY+NQHMKDwMGE5zV9yF+1pV777XntxQ2DA2gd5kd8Rh6JWfmYLVaW748jtkV4lXSn03LILS6lfUTtw5YAcnefxLVpMC6NAhAORoJH9iR1uX2PqrTlOwm9UW/fDxrWvezNbyZPVzoteJTjc74he3vdJ8+2y1e4P/HpuSRm5un52nua2JbV5Cs1h9yiEto3qnloS51ihvkRn1m5LO1vlPq1DGfHGf1GOKugmLiMPMJ93LFqGtm2yZ+PJWdxPCWbHlHVv0nsfNk6VyjbtOX25+O05TsIvTEWgMBhV9iVbccFj3FizkJytpfPP1dyLhP3mHAc/PQbUb/YthQcr/5mVks4gcE/BOETqB8n7XvbDw0tKaRgzm0UvjKdwlemo509RvGXL+lvhXNwBAe9gcYY3U5/00/lSb+rixl3DENgKMLPdmx2jsWyr8JxUlxIwSM3UfDkRAqenIj19BGK5j6LFn+cojceLvt76ZrFlCz/DvNfv9UYr3WwF/FZhSTmFGK2aiw/mkzfpvbDd/tFB7LjrH7zl1VUSlxWIWFeLpitGjN+28W1LUMZFFNzj4j8PSdwbhKCU0QgwsGE74jeZK2wH8qXvWI7/jfob1T1vbZH2bxKBhdHDC56WXr2aY+0WPVJv40GTLZhj8JkxGdgF4qOVJrk3MZ8+Aim8DCMIXq9wGVgf4o3bLJLY4qJxvvRB8l85Am0rOwK6x7F4O6OwVu/vjl17oj5dO1zWF2O61fD138iiE9Or1D/2UNsZ/tpOvX6j/7WwlOJKZSWWvD1dKNXuxiOn02mqKQUi9XKzsOnaBpW+/Dj1o1DK5SrlWXbDhLb3r5xOMTXi62Hz+gxk9Jt5fo3GiPij9lfMzv1wbK/0jVz5jgKnp1MwbOTsZ45StFHz+lvhXP3BKHfMgm/IAwBoWgZ1d/c2+Uz1If4zHwSswr0Y/PgWWJj7M+V/ZqHsiNOb7zJKiwhLjOfcO/yhqBlB89ydZu6DYMDaN0okPj0HBIzcvVz++7jxNoa4ys6nZJFbmEJ7RuXH/e5hcWU2h76ZOUXsef0OZoG11yfbd2sCXFJKfb1rW4d7NLY17eWMGpg72q2VHfWk0cwBIdhCAgGownHHv0x77Q/FxiCy69npo5XYE3WrxFaegqm1rb6oJMzpuiWWJOqP+fUGLNnf8w7aol5rsJ1SQgce/TFvKn2hqW5c7+ga7fBdO02mF9/W8a4W64HoFu3TuTk5JGcXH0D9ZgxI/nuO/u3rsafTaJfP728W7SIxtnJibS0+j0Q/NeS2n/3v/9Rl+utcMuBe4QQ90gppRCio5RyN+AFJEgpNSHEBKD2weg2Uso8IUSCEGKklHKxrQeREVgPTBVCfAH4ovf8eRio+r5P3VEgRAjR1TYUzgN9KFxN+/YdcDt6g9PECnmcLoRYLaU0CyFigEQpZeWBzo2EED2klJvRhw1usO1DYyFEtJTyBDAevadVXeUBHgBCCAMQIaVcI4TYANwEuAPZdd2YyWjk8VuGMv21r9A0ycgrOxIdFsj7P62mdZNQ+nZswYybrmL2Z7/x9YotCGD27SMRQvDtqm3Ep2Ty0S9/8dEvehY+fHg8fp7uNQfVrOS9+xbeL7+GMBgoWroEa9wZ3CZOwnz0CKWbN+E+ZRrCxQXPp57VV0lNJefJmWA04fOWPq5ZFhSQ++Lz+kSWteVx4gimv/QpmqYxsm9XosODeX/RClo3Dadv51bMGHctsz/5ka+XbkAImD3tRoQQ7DpymvcXrcDBZEQIwaxJo/Cqw5OYy1KuUqP0zwU43TgDhAHL/vXI9CQceo9ESz6D9cQeHDoPwtisA2hWZFEBpX98AoDBPxTHwRP0E5owYN76R5W3yV0wn+OvYfqrX+pl26cT0eGBvP/TKlo3DqNvpxbMuHkIs+f/wtfLNyGEYPbtoxBCsPtYHPN/X19WtjNvvRYfj+qfvFWOOXPaOKY/9TpWTWPkoCuJjgzj/a9/plWzxvTr3pHt+4/wzhc/IISgU5sYnpiuDydcvmEbuw4eIycvn19X6qNtn3vgdlo0rXsDXnUefvoltu/eR3Z2LgNG3sKdk8dz3bC/MWeEplHy0zxcpjyjvzp520q0lLM4DhmL9ewJrAe3XXjdogLMf/2CywNvgJRYD++sMg9TtawaKbM/JOLTOWA0kPPDCkpPxON/7y0UHzhO/uqtOLdtRtj7T2L0dMe9X3f8772F09dMB8AhLBBTiD+F2/bXEqh6JqOBx67twvQvVqNpkhGdoogO8uaDVXtpFepHX1tjzLL9cQxpG0k1U/FVS1o1jjz+GZ2+nYkwGkhcuIaCowlEPXIDuXtPkbZ8J4nfrKHNe3fRe8tbmLPz2Tf1HQAiJg/GtUkQTWdcR9MZes+3XWNeoDQ9l2ZPjiVkdC+MLo702f0+iQvWcPK1qq+3NhkNPDa8O9Pnr0STGiO6NCM6yIcP/txNqzA/+rbSf3vL9umTdlfO123zlnImLYfCEgtXvbiIZ67rSc+Ymp+mm4wGHrumC9O/XGMry6ZEB3rzwap9tArzpW+LcHpGh7D5xDlGv/s7BiF4YHAHvF2dKDFbmfTpnwC4OTnw/HU9MV3gabq0ahx9fH5Z2SYtXFulbJO+WUOb9+6m15a3MWfns3/q27ayHVKlbHeOeZ6SlCxOvfYDXRY/i7RYKE5I5+C9H+A3qpod0DRKfv0El0lP6uetHavRUs/iOPAmrIknavzdCzcvfT0p0XIzKf7+nRrLtGLM4u8+xPXuOWAwYt68Au1cPI7XjscadwxrxZvZS8BkMPBo/1bc+dMONCkZ0TqcKH8PPth0nFZBXvSNCqRnpD+b49IZ/cV6jEJwf5/meLs48sfhJHYlZpFdbObXQ/pN0Oyr2tI80LNqIKvGmSc+ofk3TyGMBtK+XUXRsbOEPXwTBXtPkr1iO6kLVxH1zn203/g+lux8Tkx/Q99HPy9aLHwKNElpcgYn79HL0uDoQItvnkKYjGA0kLt+H6kLVlafUatGzhvv4PfmK2A0UPj7Uiynz+Bx+22UHjlKyYZNeN2l1wt85zyjr5KSQuajs/ShL+99iN87r4MQmI8co/DX36uPU7FsL8P16/LUf0Yy/cWPbfWfbkRHBPP+ouW0bhJO3y6tmXHLMGZ/vIivl6zX6z/T9fqPp7sr44deydgn3kEIuLJDC/p0alljPD2mgcfHDmH6Wwv1mL06EB0WwPuL19K6cSh9O8Qw48aBzP7iD77+c6terpOGlZ37rn70XfKLSjBbrazZc5S5D4y1e6Nc9eWqUfzDXFzvnK1fM7f8iZYcj+PQcVjjj2M9cOFrpjGqDY5Dx+lvT5Uaxd+/D4W1zxFkMhh4bEgHpn+zXj822zcmOtCLD9YepFWID32bh9IzKojNp1IY/eFy/Tw7oB3etsn1E7MLSM4tpHNk3R8mmIwGHrvuSqbP/VU/t3dvSXSIHx8s2UqrRoH0baP3eF22S++tVPF6cioliznfr8UgBJqUTBrYye5tctXHs9W3nn5Tr28N7G2rby221bc6sP3AUd754ke9vtU6hiemjytbf8KjL3Em4RyFxSUMnPgQz947kV6d2tScSU2j6PN3cHv8FTAYKF27FC3hDM7X34bl9FEsOzfhdNUoTG07g8WCVpBH4Yf6kLSSFYtxnfYoHq9+BkDpX8vQ4mufSwpNo2j+O7jNrBTzhtuwnLLFHGyLabXFrDAMztSyHVpGWr2H3C1dupohQ/pz+PAGigqLuf2OB8uWbd+23O5tcNddfy0jRtxqt/6jj8zmww9f4b5770BKabe+olxq4lKOsRRCNEaf26eN7fPnts8/VFwmhHBBny+oJ3qvqdNSymttcxf9iN5rZxlwl5TSXQjRF3hISnmtbbvvATuklJ9Xit8MmIc+l5IZfa6n08Ar6MPUJDBHSvldTdsUQnQF3gVc0BuVBgIh1e2bbV0H9N5Mv0gpb7P9zQDMAYah915KA0ZKKcv6VNvKZBmwA+gMHALGSykLhRADgNfQG/+2A9NtcyStte33DiFEfoV9uB64Vko5UQjRC/gYvUfWTcCn6A1jAvhaSll+pqtG8eaFDTrwNnfW3IYMB4DnKw80eExK//5Y/PrS1lXXSe6fY4gd0qDxAIRf3Z/kXSoGn0s/10FtSl64v8FjJvxev0mo/67IOVXf0PNPW39PbS/ZvPSu/LBdg8fkErzhqL423NuwZdtjUsP+XgHkBSYt/ycZ2138G5wu1v7ZtfdovNQiGlf/pr9/iu8b0xo0HkDuEx80eEzPV2c0eEwKcmpPc4mZF31Xe6JLyNStfYPGA8Cv9t5hl5ohqlODxyx6+jJMON3AnUICFx9v2IA2pSUJ/9wEiZdZ4auT/rMTSbk+PP9/8nu7pD2WpJRngDYVPk+sbpltDqIqb0mTUh4HKta4H7X9fS36cLPz6e6+QPzjQP9qFj1s+69i2gtu0za/UuU7nGr3zZbejN4bquL2NfTJymdWt68VWKSUt1T+o5RyFfrk4pX/3rfCv90r/PsH4AfbvzdiPz/V3+tzqiiKoiiKoiiKoiiKUo3LNRROURRFURRFURRFURTl0tL+sx2W/mephqXLqHIPL0VRFEVRFEVRFEVRlH+Tf+KtcIqiKIqiKIqiKIqiKMr/A6phSVEURVEURVEURVEURbkoaiicoiiKoiiKoiiKoij/CVJr4Ff7KarHkqIoiqIoiqIoiqIoinJxVMOSoiiKoiiKoiiKoiiKclFUw5KiKIqiKIqiKIqiKIpyUVTDkqIoiqIoiqIoiqIoinJR1OTdiqIoiqIoiqIoiqL8N2jycu/B/zuqx5KiKIqiKIqiKIqiKIpyUVTDkqIoiqIoiqIoiqIoinJRhJSqm5hi70DTaxv0R5FT6NyQ4QAI8Mlv8JilJQ0/8rTp1ICGDeju1rDxAJmU0vAxi0oaPKbTzLcaPGbJyzMaNF7RnswGjQeQeMyrwWOGt8pp8JgFKQ4NHtNcbGzQeIFXNmg4ACypxQ0e0+jZ8NcSg5dLg8csPZ3XoPEyTjR8HrNyXBs8ZlBgw5YrQElxw/9mQ/s27P2NcHdq0HgAsrC0wWNejuFFpoG9GzymZfXGBo334BL3Bo133sdnFonLErgBFDx/63+2kcPtiS//J783NceSoiiKoiiKoiiKoij/DVK73Hvw/44aCqcoiqIoiqIoiqIoiqJcFNWwpCiKoiiKoiiKoiiKolwU1bCkKIqiKIqiKIqiKIqiXBQ1x5KiKIqiKIqiKIqiKP8Nl2Gi+f/vVI8lRVEURVEURVEURVEU5aKohiVFURRFURRFURRFURTloqiGJUVRFEVRFEVRFEVRFOWiqDmWFEVRFEVRFEVRFEX5b9C0y70H/++oHkuKoiiKoiiKoiiKoijKRVENS4qiKIqiKIqiKIqiKMpFUQ1LiqIoiqIoiqIoiqIoykVRcywpdeLepxMhT00Bg4Gs71eQPvcHu+WuXVsT8uQdOLdowtn7XiF36cayZa2P/0Lx0TgAzElpxE95rtoY3v060PS528BoIGXBKhLfW2y3XDiaiHn3HtzaNcWSlc/RqW9QcjaNgNFXEnrn8LJ0bq0i2TvoEQoOnqHRYzcTeEMsJm83tkSNrzGPrr07EzhzOhgM5PywjKxPvrdb7tKlDQGPT8MppgnnZrxI/ooNZcv8H5qMW2w3EILCTbtJe+HDGmOd1xDlWpmhSRscB4wFgwHL3nVYti6xW25s0wvHfmOQeVn6tnetwrpvHQDCwxfHq29DePqClJQsehOZm1GnuOdtPJPOq+uOoknJyNZhTOrSpEqaFceSmbv1FEJAjL8HLw5pW68YlRmbdcDxmtv0PO9YhXndYrvlpo59cbx6PFpuJgCWLUux7FhdvxgtOuE08nYwGDFvWYF59Y/Vp2vXA5eJj1P4xoNoCSf+j73zDpOi2PrwWz1hdzbnQM45R0GUJIIYwKwgiqCAIiqKCUQxgjkHjBgwe8WE5CQ555x2gc05Te7+/uhhd2bjLHDXe+9X7/PwsNNdXb8+1V2nqqtPVQOgJDYh4MZ7ITAINBXrGw+Dy1lrO8vz5Iuvs2bdZqIiI1jw9YfnnR+AoXVXAq4ZD4qCc/MynCv/VXm6jhdhuf0xSt6ahnr6GCgGAm6cjFK/GUIx4Ny2sspjq8PUoxch905BKArWv/7E+v03PvsDr7oGyzXXgupGs1opfONV3MlJtdYJ7d+N+k/fhTAYyP5uCRkf+F7P4F7tqf/0XVjaNOHklFfIX7heP7/6sTT9aDpCCDAZyZr3B9nzF9VsV/deBE/S7bIt+hPrj+XsGn4NgVd57LJZKXpbt0uEhhE641lMrVpjW7qI4g/e8tvGwL49iZp2LxgUin75i4J53/mWwejrCbl2OLjduHPzyH7mVdypGZhaNSd6+gOI4CBQVfI//YaSJav80gzq14OYJyaBwUDBT3+RV87PBnbvQMwTkwho1Yy0aS9S7OVnox8aT1D/XgDkfvANRYtW+6VpaN+DwJsmIRQDjrV/4Vj8Q6XpjF37ETRpJkUv3oeadARD224EXjsOjEZwubD9/DHuQ7v80jR17UXQ+CmgKNiX/YntX77XM2DoNQRcUXY9i99/FfV02X2qxMQR/vYXWL+fh+3X72vUM3bsSeDoyXq9XL0Q+5++19I88CrMg0eAqqLZrVg/fwM1JQkMRix3TsXQpBVoGtb57+E+6J+NhrbdCLxOb7+cG5bgWPZTpemMnftiGT+d4lceRD11FBEVR/D0D1AzzgDgPnkI+w/v+aVp6taL4Lv1crUt/RPbT+XKddg1BF7pVa7vvor7VBLGlm0Ivm+ankgIrN/Mw7Hxb780z6tv8PA4vW8AZH/wDUV/ralUI3xAVxo/Nw6hKGR8u4zUd3/x2S/MRpq//QDBHZvhyi3kyKTXcJzORJiMNH15EsGdmqOpGklPfUrhhn0ANHhsFDE3DsAYHszWlqOrtdHStwdRj94Liu4L8j/3vefCbruekGuv8PiCfLJm6b7A3Lo5UdPvRwkJArdK3iffULLEv3oZ1K878TMmlZZrzsc/VijXuCcmEtC6KSkPz6FocVm5xk7zlKui97kyXvCvfTO0607gTfq1dK5bVI0vuBjLxJkUvzgFNfkISpNWBI5+QN8pBI4/vsa1c71/mq27EjDibl1z01KcK6voG3Tsg+WOxyl582HU00cxdu2PacDI0v1KYhOsbz6EmnLCPztvnARCwbl+EY4lP1aaztjlYiwTnqR4zv26nY1bETjq/jI7/5yPa1fNdv4T5erNusNnePnPraiqxrU9WjCufwef/a/8uYUtx9MBsDld5BTbWDvzllrrnGu5nkVExhI8cy72hfNxLqv8PqiMW56+k44Du+Gw2vl82nsk76t4DzzwxQzC4yIwGAwc2XKA+TM/RVNVRjx0M12G9ETTNAqy8vl82nvkZ+TW2naJxB/kwNJ/OUKIJkBfTdO+qSntOaMo1HvmHk7c/iSutGyaLXiDwmWbsB89VZrEmZLJ6UffJOau6yocrtocHLvq/ho1ms2+i303PYsjNYfOi+aQs2Qr1sOnS5PEjxqMK6+Y7X2mEDPiYpo8eRuHJr5B5r/+JvNfegcxqE0j2szTB5UAcpZsJfWzv+i+4Z0a9eNmTubM+Ok407No/MPbFK/ciONYso+NaU+8RtS4630ODezSFkvXdiSNuAeAhvNfw9KzE9Ytu2vU/LeXa3mEwDxkDPbvX0UrzCHwjqdwH92Jlp3ik8x1YDPOZV9XONx81d04N/yOenI/mAJA02ol71Y15qw6yAfXdiM+JJDR32+if9NYmkeHlKZJyivms60nmXdjT8ICTeSUOGpnY3mEgvnq8dg+fw6tIIfAe2bjOrAVLfO0TzLXnvU4fv/0nDUCrpuI9cOn0PKzsUx9Dde+zWjpp3zTBVgwX3IN7qRDZdsUhYDRD2H/5nXUlJMQFApu97mdRzlGDh/CqOuvYfpzr16Q/BAKAddOwPrRLN3O+1/W7czwLUsCAjH3u8rHTmOnvmA0Yn39QTCZCZr2Dq6df6PlZvqvryiETnmQvMceRs3KJPLduTg2rPMZOLKvWIbtj98AMPfpS8ikyeRPf7R2dioKDZ6byLHRT+FMy6bVb6+Rv2wz9iO+dTP54beImzDS51BXRi5Hrn0EzeFCCQqkzZJ3yF+6GVdGTrV6IZMfJH+6blfEW3NxbCpn16pl2BZ67Ordl+C7J1Mw81E0h4OSrz7F2LgphsYVB2mr04x6bAoZ9z6GKz2TxK/fw7p6Pc4TZT7Pcegoabfdi2azE3LD1UQ+MIGsx59Hs9nImvkSrlNnMMREkzD/fazrt6AVFdeoGfvkZM7c9QSu9Cwafv8OxSs34vTys67UTDKmv0bEnTf4HBp0aS8C2rXg1HX3IMwm6s97heK/t6AVl1SvKRQst06m+M0n0HKzCH7iHVy7N6KmJvumC7BgHjwS1/EDpZu0onxK3nsKLT8HpV5jgu5/kaLHq39AP2tn0IQHKZz1MGp2JmEvz8WxeZ3PwJF9zTLsi/XraerZl6A7J1P0XNl9GnTnZJw7Ntes5bEx8Pb7KX75UbScTEJmvY9zxwZ94MiDY8MKHCv/AMDYtQ+Bt06i5LUnMA+4EoCiJ+9GhEYQPG02RbPurdm3C4XAG++h5L0n0fKyCZr2Bq69m1DTKvo8U/9rcJ886LNZzUqj5OVatl+KQvCkBymYqZdr+OtzcW5ah/uUl52rl2Ff5CnXXn0JGj+ZwlmP4ko+Qf7UiaC6EZFRRLz9GY7N60GtwdeeR98guL9+zyZdey/CbKLhF69QsmYravl7VlFo8uLdHLzlGRyp2bRf+DJ5i7dgPVLmV2NvvQxXXhG7Lp5M1IiLafTk7Ryd9Bpxoy8DYM/gqRijw2kz/0n2XvEoaBp5S7eS/vlfdF73bo02Rj0xhfRJj+FKz6Le/HcpWb0B53EvX3DwKKmjJ6PZ7ITeeBVRD95N5mMvoFptZM18GVfyGQyx0SR+8x62DVtRC2v2BfFPTeb0OE+5/vgWRSs2+ZZragZpT7xGZPk+V9e2WLq14+SIewFo9M2rWHp1xLp5T/WaQiHw1smUvDUdLTeLoCfertIXmAaNxO3lC9QzSZTMngKqigiLIujJ93Ht3ljz4sBCIeDaiVg/elpvMx94Fdf+qvoGV/u0ma4dq3Ht0AfplITGBI59wq9BJYRC4M2TKXl7OlpeFkGPvYVr9ybUtErsHDgC94myuqmmJFHy0v0eOyMJmvE+rj012PlPlKsXblVl9u+b+fDOy4gPC2L0B3/Rv20DmsdFlKZ55MqepX9/u+EgB1OqaY+rs/Mcy7V01/UTcO3fWivZDgO6Etc0kRkDptCsa0tGv3A3s0dOr5Bu7uTXsRVZAZj0wcP0uPIitvy+nsUf/cavr+sDxYPGXsHVD9zA1zM+rtU5/Nei1u4ZRXL+yKlw//00AUZVtkMIcUEGDi2dW2FPSsV5Kh3N6SL/jzWEDrnIJ43zTAb2gyfPeQX+0K4tsJ1Iw56cgeZ0kblgHVFDe/qkiRrak4wfVgGQ9ccGwvtVjGKJubYfWQvKonqKth/BmZFXo35gp9Y4k1Nxnk4Dp4uChasJHtTHJ40rJR3H4RNolTgqEWBGmIwIswlhNODOrvltQF2Ua3mUxGZoeRlo+ZmgunEd2IyhZVe/jhXR9UBR9EElAKcdXLUb9Nmbnk/DiCAahAdhMigMbZnAquO+Awu/7D3DTZ0aEBZoAiAqyFwrjfIoDVqg5qSh5WaA24V79zqMbXucV54VNBq1RM1KRctJB7cL146/MXboXSGd+YrROFb8DM6ycjO07oqaelIfVAIoKQTtwlzvHl06Eh4WekHygkrs3LkWY/teFdKZh47CsfKXclFXGsIcCIqiD0q6XWg2a630ja3b4k45g5qWqkeQrFqBuW8/nzRaSdlDmwi0wDn0K4K6tMR+MhWHp27m/v434UN8r6fjdAa2gycrdFw0pwvN4dL1zSbd3prsauVrl331CswXVW0X3nbZbbj27UFz1K4umju0xnU6BdcZXbN48SosAy72SWPfugvNZtf/3nMAQ1wMAK7kM7hOeaJNsrJRc/MwREbUqBnYsTXO5BRcHj9b9NcqQqrws+V9nrlFI6xb94BbRbPacRw+QfAlNddjQ9PWqBkpaFlp4Hbh3LoKY+c+FdIFjLgDx6IffOqmeuoYWr7+AKKmJCHMAWA01ahpbNkWNfUMarpeto61KzD38r2eWL3u0wCLzy5Tr364M1JxJ/vxEAkYmrVBTT+Dlpmq27hpJaZufX0T2bz1Akv/Vuo1xrV/BwBaYR5acRGGpq1q1FQat0LNTEXL9viC7WswdryoQrqAK2/DsewnNOf5R2AaW7bF7VWu9jUrMPUuV0+s5er/Wez20kEkYTb7/VLkfPoG5uaNsG7dW3rP2g+fIOiS7hU0Qrq2wHYyFXuy7m9yfl1L5FBfvxo5tCdZP64EIOePDYR5+j+WVg0pWKsPqLiy83HlFxPcuTkARdsP4/QjMiGgQ2tcp1JwnUkr9QVBA3zvH5u3L9h9AEN8rK6ZfAZXsscXZGaj5uSh+OMLOrXCmZxSWq6FC1cTMtj3/nGdycB++GTFa6VpFftcWXk1aipNWqNmpJb6AteW1Rg7VeILrrkdx+If0bzbL6e9zCeZTPjbsCiNWqJmp3m1mX9X02b+XGWfytj1Elw711a6r4Jmk1aomSlo2R47t63G2LmSunn17TiW/ojm5fN87fSvnvwT5erN3tPZNIwKpUFUKCajgaGdGrPqwKkq0/+1+yTDOjeptc55lStg7NwHLTsNNbV2EdRdLu/Jxn/pA4zHdxwhKDSY8NiICunODioZjAaMJmPppTu7HSAgKKC274MlklrxXzewJIRoIoQ4KISYJ4Q4LISYL4S4TAixTghxRAjRy5MuWAjxmRBisxBihxBihNfxfwshtnv+9fVsHyCEWCWE+MmT/3whhKhE/34hxH4hxG4hxHdCCMWjG+vZrwghjgohYj3n+IEQYqMQ4rhH4zMhxAEhxDyvPIuEEK8IIfYJIZYJIXp5zuW4EOIaTxqDJ80Wj/ZEz+FzgEuEEDuFEFOFEGOFEL8JIVYAy4UQXwohRnppzT9bFv5iSojGmVr28O9KzcIUH+338UqAmea/vkGzn1+tMHByFnNiFI6UrNLfjtRsAhKjKqSxn03jVnEVlmCM8n1ojhnRl6wF/jW+3hjjonGledmY7r+Ntp0HKNm0i2ZrvqHZmm8oXrsNx/GqG7Wz1EW5lkeERqIVlL2p0QpzECGRFdIZW3cn8M5nMY+8FxGqXwclKh5sJZhH3kfg2FmYBtwEFatItWQU2YkPCSj9HR8SQGax3SdNUl4JyXkljP1xM7d/v5l1J7PKZ1MrRFgUWn7ZdD2tIAcRXrGcDe17Y5nyKgG3Plzp/mo1wqPR8srOU8vLqpCHUr8ZSkQM7gO+b6uU2PqgaQROmIXloTcwDawYnfafggiL8rUzP7tqOw9u89nu2r0BzWEjeOZnBM/4CMfqBWAtqpW+EhODOzOj9LealYkhJqZCusBrRhL1xTcE3zWJovf9nxp2Fr1ultnpTM3ClOD/PWFKjKH1ordpv/EzMj78ufpoJXS71HJ2KdGV2HXVSCI/+4bg8ZMo+rD2dnljjI3BlVam6c7IxBBXtY0hI4dhW7elwnZz+9YIkxHX6ZRKjvLFEB+N09vPpmWVDlbVhP3gcYL69UAEBqBEhGHp1RljQmyNx4mIaFSvqDgtNwslwldTadgCJTIW196qI4SM3frhTj7q1xRVERWDO8vremZXfj0DrhhJ+AffYLljEiWfeK5noAXLdaOwfv9FjTqlepExaDllNqo5mYjIinrmwSMIeeUrAm+agO1rPYrFfeoYpq59QVEQMQkYmrRCRMXVqKlERKPmeWlW5vMaNEdExOCu5A29Eh1P0KNvYbl/NoZm7f2yU4mOQS1XrobKynX4SCI++oagsZMonltWT4yt2hL+3jwi3vmc4vdfrzlaifPrG9gPHie4X3eve7YTpkruWXNCNI6UsjbKkZqNqXz/xzuNW8VdoPd/ivedJOLynmBQCGgYR3Cn5pjr+VenzmKIi6lgY3X1MuTaK7CurVhXzB1ag8mE61TNvsAYH+Pb/0nLwuh3n+sgJZt20/zv+TT/ez7Fa7f71edSIn19gZqXhYgsd882bIGIjMVdiS9QmrQm6Km5BM/8EPs37/j1wq9i36CaNvPAtvKHl2Ls3A/XzsqnUVY4z4gYXztzK6mbDZsjImNw763o05UmrQl68kOCZ3yA/dt3a7TznyhXbzIKSkgIDy79HR8WTEZ+5S+tUnKLSMkpolezhFppwHmWa0Ag5iE3Yl84v9a6kfFR5Hj5h9y0bCISoipN++CXM3ht2yfYim1sW7ixdPvIabfy0voP6D3iktLoJYnk38F/3cCShxbAa0Abz79RQD9gGnA2PnAGsELTtF7AQOAVIUQwkAEM0TStG3Az8LZXvl2BB4F2QDPA9/WtzuNAV03TOgGTNE1Tga+Bs/HxlwG7NE07630igT7AVOA34A2gPdBRCNHFkybYc67tgULgeWAIcC3wrCfNeCBf07SeQE/gbiFEU8/5/K1pWhdN097wpO0G3KBpWn/gU2AsgBAiHOgL/FlVwf47OHTJOI6NmMqpB18hcebdmBvV3qH7Q0jXlqhWOyUHa+5gXEhMjRIxN2/E8YG3cXzAaIIu6oKlu38d5fPh31Wu7qM7sX74CLbPn0I9sR/zlXfpOxQDSsNWOFd+j+2LZxERsRg69qs+s3PRVzWS80r4+LoezB7WkedW7KfQfv5vu6vDdXAr1lfuxfrONNxHdxFw/X0XVkAIAkaMx/7rZxX3KQqGpu2wzX8N6zuPYex4EYaWnS6sfl0hBAFX34n9988r7FIatQRVpfi58ZS8OAnzpSMQUfH/ltOw/baAnDtGUfzJXIJG3f5v0agOZ2oWh4bdz/5LJxJ5/SCMMREXJF/bHwvIHTeKks/mEnRr3dkVPHwwAe1ak/+l77oZhpgoYp57nKxZr9Z6Wmxtsa7fTsnfW2jwzRskvPoEtl0H0NwXILJPCAJvnIDtp4+qTKIkNibwuvFYvz6/wbzy2P9aQP49o7B+ORfLjfr1tNw8FttvP0Ito/n8wbH8V4oeGYPth48JuOY2AJxr/kLNzSRk1gdYRt+L6+i+CxMhKwQB196FfUHF6cVaQQ5FT99JycsPYP/lEwLvmKZH4V0g7AsXkDdhFCVfzMVyc1k9cR0+QP7kseQ/NAnLjaP1iIx/IyXrt1O8ZgsNv3mdxNcex7bzANoFij4+S+Z3y3GkZtNh0Ss0fnYcRVsPXrAI58rQfUEr8r/wXVvGEBNF7POPkf30v98XmBolYm7WkGMDxnCs/20EXdT5wvS5hCDgxgnYf658mpB68hAlz06kZM79mIfd7Ff0ol+a14yrtM08i9KoFZrTXnHK1floXl+Dnc9PouTlBzAPven87fwnyrUKFu85yWUdGmHwI5K41lRTrgFX3oZjxS9gt114XS/evP0FpvWagNFspE3fsnWmFrz6LY/1vYdNv/7NoDuG/VvPQfL/m//WgaUTmqbt8Qzq7AOWa5qmAXvQp4YBXA48LoTYCawCAoFGgAn4WAixB/gRfRDpLJs1TTvtyXenV17e7AbmCyFuA1yebZ8BZ3sv4wDvFuJ3r3NLL3feZ/N3AGdXd90DrNY0zVmJPbd77NkERAMtqyifpZqm5QBomrYaaOmJqLoV+FnTNFf5A4QQE4QQW4UQW38s8G28nGnZmBLL3rIZE2Nwpvu/YLPLk9Z5Kp3ijXsIbN+8QhpHao7PWzZzYjT21JwKaQLOpjEoGEODcOUUlu6PHXkxWb+s41xwZWT7vP02xvtvY8hlF2PbdRCtxIZWYqP47y0Edmlb43F1Ua7l0Qpz9YW3PYjQKLSicqHytmJw67eIa/dqlITGnmNzUNOT9Wl0mor7yHaU+MZ+ny9AXEgA6UVlEUrpRXZigwMqpOnfLBaTQaF+uIXGEcEk59Wwjko1lI9QKh/BBOiRM2dt3roCpX6z2mnkZyO8oiBERIyvRoAFJaExlskvEPTkxyiNWxM4fgZKgxZoedm4j++D4kJwOnAd2IbSoOZr+U+gFeT42hkeXYmdjbBMep6gJ+aiNGpF4NjpKA2aY+x6Ke5DO/RFdYvzcZ88iKGWdqpZWRhiy6IplJhY3FlVR7TZVy3HfHHtBz/1ullmpykxBmda7RapB3Bl5GA7nExwr3bVplOzslDK2aVmV2PX6uWY+5zfoK4rMwtjQpmmIS4Wd0ZFGwN7dSN8/CgyHpwJXtOZRHAQsW+9QN57n+HYc6DCcZXhTs/2idgwJsTgzvA/IjF37recuu5eUu56AhA4k07XeIyWl40SWaYpImNQvSIICLCg1G9C8EMvE/LCFxiatSXo3mdQGuvNq4iIwXLPU1g/fwUtK9Wv89RysjDEeF3P6Oqvp2PtckyeqXLGVu2w3DGR8LnfEXD1DQRef5u+yHd1erlZiKgyG5WoWLTcqvV8psqpKrZvPqDoqYmUvPUUIigENa3mclXzslEivDQr83mJjQiaMpvgpz/F0KQ1lgkzURq2AJdLn/KLPt1QzUrTIzdr0szOQilXru7qynXN8gpTSgHcp5PQrFa/1iQ7n74BQM7c70i+Tl+jCSFwnjxT8TzTsjHXK2ujzInROMv3f7zTGBQMYZ7+j1sledbn7B3yMIfvnIMhPBjbsZojhrxxZ2RVsLGyehnYuyvhd40i/YGnKviCuHeeJ/fdz7H76Qtc6Vm+/Z+EmNI+TU2EXNbXt8+1ZisWP/pcaq6vL1AiYtByy92z9RoT9NDLBL/wBYambbDcO0t/KeKdT9opNJsVpV6TGjUr9g0qazMbY7nneYKmf4TSqDWBd+p9g7MYu1yCa4d/C82DHjHkY2dkJXWzXmOCpr5M8HPzdDsnPV25nfaa7fwnytWbuLAg0vLL1vRKLygmLrzygepFu08yrFPt8i89v/MoV6VJawKuHU/wc/MwDxxJwNCbMfW/ukqtAWOG8tTCV3hq4SvkZeQS5eUfIhOiyUurOgraZXeya+kWugzpWWHfpgVr6Tas4jIN/7No6v/uv/9Q/lsHlrznzqhev1XKFiQXwPWeSJ4umqY10jTtAHrkUDrQGegBeL+y8s7X7ZWXN1cC76FHBW0RQhg1TTsFpAshBgG9gL8qydP7PMufq9Mz+OSTzjMA5W3PFC97mmqatqSS8wMov2ril8BtwJ3og2AV0DTtI03Temia1uPGsEY++6y7DxPQpB6mBvEIk5Hwqy6lcNmmKqR9UcKCEWbdBENkGEE92mE/UvGtS+HOo1iaJRLQKA5hMhI78mJylviGkuYs2UrcTQMAiLmqD/nr9pbtFILoa/qQeQ7T4ABsew5halwPY/14MBkJG96f4pUbaz4QfYFJS8+OYFDAaCCoR0ccx2qOmqqLci2PmnoCERmHCI8BxYCxbS/cR3f4JgoOL/3T0KIranZq2bGBQWDRpx8aGrdFy6pdB7Z9fBjJeSWcybfidKssPpLGgGa+UwMGNotj62l9sCvX6iApr5j6Yef+Nls9cxQlOhERGQcGI4ZOF+M66Ds1Q4RGlP5taNsDtfxi1DVpnDqCEltPj8AxGDF2vQT3Xq9raSuh+KnbKHn+bkqevxs16RC2T19APX0U16HtKImN9bfnioKhefuKC+D+h6CeOoISU1aWxi79cO/3qqe2Eopn3UHJ7ImUzJ6ImnwY27wXUU8fQ8vNxNDCsy6aKQBD41aomRUfsqrDdegghvoNUBISwGgkcMAgHBt8B5MN9cseUM29++A+U7trCVCy6wgBTethbqjXzcirL6FgqX9105QQjQjQmxVDWDDBPdpiP1a9na7DBzHUa4ASr9sV0H8Qjo2+din1vOzqdW52eePYdwhjw/oY6+mawUMHYF3t+zUeU+sWRM14kIwHn0LNzSvbYTQS+9osiv9cSsly/x94bHsPYWpcv9TPhlwxwG8/i6KghOu+x9yqKebWTSlZV/XUkbO4Tx5CiauPiNbrpqnHAFy7vDRtJRQ9fBNFM+6gaMYduI8foOT9p1GTjoAlmKD7nsP+y2e4j+33207XkYMoiQ1Q4vSyNfcbhHNLueuZWHY9Td37oKbq17NwxhTyJ95C/sRbsP/+E7afv8b+l+8XwirYeOIghvj6iJgE3cbeA3Hu8L2WSnyZnrHzRbjTPfekOQDM+ppLxvbdQXX7LPpdFWryYV+f1+1SXHvK+bzpoyl+ZjzFz4zHffIQ1o+e078KFxIGQu9+iuh4lNh6qNlpNWq6jpSrJ5cOwrm5mnLt0Qc15bTH/gRQDPrfsfEYGjRCzahZ83z6BigKSkTZPRvQuinFldyzRTuPEtg0kYCGev8nakQ/csv1f/KWbCHmxoEARF3Vp3RdJcViRrHoL2fCLu2M5nL7LPrtD/Z9hzA28vUFJas3+KQxt25O9JOV+4K412dR/MdSSpbVwhfsOYypcT1MnnINHd6fohX+lasrNdOnz2Xp2RG7H1Ph1KRDKHH1Sn2BsWd/faHo0pMqoXjazRTPuIPiGXfgPnEQ6/uzUJOP6Md4olxEVBxKQkN9fbGaNM+2mVFn28xLcO/zmg5mK6H46TGUvDiBkhcnoCYfwvb5C6VfjEUIjJ0vxrWzFgNLSYd97exeiZ2P3kLxzLEUzxyr2/nhM5XbGV+znf9EuXrTvn40ydmFnMkpxOlys3h3Ev3bNKyQ7kRmPgVWB50b1TyFunI7z71cra8/UrrdsXIB9sXf41z9e5Vaq75azLPDH+HZ4Y+wc8kWLrquPwDNurbEWlhCfmaeT/qAoMDSdZcUg0LHQd1J8/Q74pqUzWboMqQHabUceJZIasP/8lfhFgNThBBTNE3ThBBdNU3bAYQDpzVNU4UQdwAGfzMUQihAQ03TVgoh1gK3ACFAHvAJ+pS4rzRNuzCfdPJlMXCPEGKFpmlOIUQr4Az61LmaVuedB2wG0jRN8793fBa3SsqsD2nyxbMIRSH3x6XYjyQT9+BorHuOULh8M5ZOLWn0wQwM4SGEDu5F3AOjODpsMgEtGlL/hfvQVA2hCLI+/NHnq2feGsenf0L7b58Eg0LGtyuwHjpNo0dvpmjnMXKWbCX9m+W0evd+um14B1deEYcmvlF6eFifdjhSsrEnZ/hk23jmbcReewmKJYAe2+eS/s1yTr1ayWdQ3SqZz79Pg09eAEWh4F9LcBxNInrKGGx7j1C8ciMBHVpR752ZGMJCCRnYm+gpY0i6eiJFi9cS1LsLjX/9EDSNkrXbKF7lx0NoXZRreTQVx9L5BNz0MAgF156/0bJSMPUbiZp2EvfRnZi6D8HQsovnc+3FOP78xHOshmPl9wTe8ggIgZp2Etcu/z4rfBajovDYgNbc++t2VFVjRPt6NI8O4f2NR2kXF8aAZnH0bRzNhuRsrvtqPQZF8GC/VkRYzmPKgqri+P1TAsfO0G3evhIt4zSmwTejnjmG++BWjH2GY2zTA011g7UI+8/+ffraW8P+r7lYJszSP7e7eRlq+inMw0bhPnXUtyNZHmsxztW/Ypn6Omga7gPbKqzDdK488vQctuzYTV5eAYNH3sa948dw/dVDzz1DVcW+4GMsdz/tsXO5buflt+I+fdR3kKkczvV/EXjTFCwPv4UQAueWFbVexBLVTdG7bxI++1WEomBbvBB30kmC7hiH6/BBHBvWEzjiOsxdu4PbhVpYROHLs2tvp1vl9FNzafblLIRBIeeHZdiOnCLhoVGU7D5KwbLNWDq1oOlH0zGEhxB2WU8Spo7i0JD7CGjRkGZPjtOngwhB5kcLsB2qwU7VTdEHbxL+/KtgULAtWYg7+SRBYzx2bVqP5errMHXtDi4XalERRa+V2RU57ztEUDDCaMTctx8FM6b5fFGuKhtzXnqHuPfm6J8Y/20RzuNJhE+6A8f+w1jXbCDywQkoQRZiX54JgCstg8ypTxF8eX8Cu3bCEB5GyNWXA5D19Cs4Dx+rUTPzhfeo9/GLCEWh4Bfdz0bddzu2fYcp8fjZxLefQgkLJXjgRbjuu51T10xAGA00+Po1vbiKSkh/7CXwZyqcqmL77j2CHtA1HeuWoKYmEXD17biTDvs+GJTDPPAalLh6BFw5moAr9dnuJW89gVaYX4Omm5KP3yT06VdBUbAvX4j71Ekst47DdfQgzi3rCRx+HcZO+n2qFRVR/PY53KdeNlq/eofgR17S6+Wav1DPJBFw7VjcJw/h2rEB82UjMbbvBi4XWkkR1o9fAkCERRA87SXQVLTcLErm+nkeqortpw8JuvdZXXPjUtS0ZMzDR+NOPlLpWipnMTTvgHn4aP3rl5qK7Yf3oMSP9dZUN8UfvknYM55yXabXE8vocbiOHMS5eT2BV12HqYteT7SiIore1O0xtuuE5YZRerSUplH04RtoBTVcRzivvoEwGmj4lf5FTrW4hLRHX678nnWrnJzxCa2/eQphUMj8bjnWw6eo/8gtFO86Rt6SLWR8u5zmbz9A53Xv4cor4ug9r+t2RYfT5tunQNVwpGVzbErZ6g4NnxxDzMhLUSwBdN36MRnfLuPMa5Wsr+JWyZnzLvEfzNZ9wa+LcR5LIuKeO7DvP4x19QYip+q+IO4Vjy9IzSDjQY8v6NYRQ0QYIdfobUvWU6/gOFSzL8h47gMafPo8KAbyf16C42iyp1wPU7xyE4EdWlHv3ZkYwkIIGdgb1323cfLqSRQuXkvQRZ1p8tsHoEHx2q0Ur/Sjz6Wq2L5/n6D79WvpXK/7AvPVY3AnHcFdjS8wtOigTwtz6/eP/dt30YoL/NK0//IRlrtn6Z+o3+JpM4d6+gb7q//yo6FZe7S8LH3xb39RVWzff0DQfXrZOjcsQU1NxnzVGNxJh3HvqbqsDM3bY77cy87v36vZzn+iXL0wGhQev7oX98xbjqppjOjWghbxEby/bCft6kczoK0+yHQ2WqmS5XP94zzK9XzYs3I7HQd25YXV7+CwOpj3SFn/9KmFr/Ds8EcwBwVw3yePYTSbEIrg0IZ9rJ6vxx5c99hoEprVQ1M1ss9k/v/5IpzkH0Fo/2XLwwshmgB/aJrWwfN7nuf3T977hBAW4E30NYUU9OlzVwkhWgI/o396YBEwWdO0ECHEAGCapmlXefJ9F9iqado8L20TsBJ9cEoAX2uaNsdrXzbQS9O0g9WdWyX7ijRNC/FsnwUUaZr2qud3kef8FPS1l672aGcCI4ES9EGnaPQBpFygh6ZpPovECCEWAQs0TfuwpjLe2+yqOr0p8ksCa050gYmNrN2iwRcCh73ux3GbTTy3NzPnTEhwzWkuMFpK7d5uXRBNq73mRBeYgOlv1rmm/aWH61TPuvMcPgF8npw5HF5zogtMg3Z+PNBeYIrT/33rVlSF0+b3e5sLQtwldSoHgCvj37tmRmUYwuq+LVGqmFry78RxorDmRBeQ7KN1b2NuflCda8bH1W25AthtdX/P1htQt883wuvDJHWFVlK7r4FeEP6BT7gbL7vw63nWhGvFuS2tca48tDCkTvXO8vHJH89xpO0/n+KZN/13DXLUguDnfviPvG7/dRFLmqadBDp4/R5b2T5N06zARMqhadoRwHtl3Mc821ehr8V0Nl2F1Xs96x5V5d06oy/afbCmc6tkX4jX37PKaYZ4/lfRFyafTkUGlfs9z/uHECIIfT2mb6s4d4lEIpFIJBKJRCKRSP77+QcGQf+/89+6xtJ/FEKIx9GjoJ74p8+lPEKIy4ADwDuaptX9a3KJRCKRSCQSiUQikUgk/7P810Us/SfimQ43558+j8rQNG0ZULtPd0kkEolEIpFIJBKJRCKR+IGMWJJIJBKJRCKRSCQSiUQikZwTMmJJIpFIJBKJRCKRSCQSyf8EmurHl2MlFxQZsSSRSCQSiUQikUgkEolEIjkn5MCSRCKRSCQSiUQikUgkEonknJADSxKJRCKRSCQSiUQikUgkknNCrrEkkUgkEolEIpFIJBKJ5H8DVfunz+D/HTJiSSKRSCQSiUQikUgkEolEck7IgSWJRCKRSCQSiUQikUgkEsk5IQeWJBKJRCKRSCQSiUQikUgk54QcWJJIJBKJRCKRSCQSiUQikZwTcvFuSQXeU4PqVK+PwVynegC/WE11rumk7heRmx1Qx2VbUlK3eoB9b0ada2YcCK5zzQaGh+tcM+Cx1+pUb3X7GXWqB9AsLL/ONXdtT6hzzWRj3fu86y5Pq1M9Y4tGdaoHIALq3v84kwrrXNNdUPeaQbcNqlO9lx87WKd6AF3+gfe7i3Mtda7pEHXf/3ki1lWnemqhtU71AJypdd/nyjxQt88IADFnlta5ZtCUm+tU7/qf19Sp3v8L5OLddY6MWJJIJBKJRCKRSCQSiUQikZwTcmBJIpFIJBKJRCKRSCQSiURyTsiBJYlEIpFIJBKJRCKRSCQSyTkh11iSSCQSiUQikUgkEolE8r+Bpv7TZ/D/DhmxJJFIJBKJRCKRSCQSiUQiOSfkwJJEIpFIJBKJRCKRSCQSieSckANLEolEIpFIJBKJRCKRSCSSc0KusSSRSCQSiUQikUgkEonkfwNV+6fP4P8dMmJJIpFIJBKJRCKRSCQSiURyTsiBJYlEIpFIJBKJRCKRSCQSyTkhB5YkEolEIpFIJBKJRCKRSCTnhFxjSSKRSCQSiUQikUgkEsn/BJpcY6nOkQNLEr+56ek7aT+wKw6rnS+nvc+pfScqpLnvi+mEx0WgGAwc3XKQ72Z+gqZqdBt+EVc+eCMJLerz0ojpJO85XqlGz2fHUH9QF9xWO+umfkTO3pMV0kR1bMLFb0zEEGjmzIqdbHnqKwAu/eA+wponAmAOC8JRUMIfl88AIKJtQ/q8NA5TiAVN1dg/cgYuu7NC3iOevoO2A7vgsDr4ftoHnNnnq28KNHP7+w8S3TgO1a2xf/k2Fr70Xen+zldexOUPXo+mQcqBJL554N0ay/W6p++g3cCuOK125k/7gNOVaN75/oPENI5HdavsW76d31/6FoDIetGMfu1eLGFBKIrC7y99y/5VO6vVUxq3x9z/JlAUXHvX4tq62Ge/oV0fzP2uRyvOA8C5cyXufev0c+l3HYamHUEI3EkHcK7+vkb7AJQmHTAPHgVC4Nr9N67NC30121+MecBNaEW5uub25bj3/I3SsA3mQbeUphNRiTh+/xD30R01apq69iLo7imgKNiX/ont52989gcMu4aAK64F1Y1ms1L8/quop5LKzjkmjvB3v8D63TxsC2q2M/iS7sTNmIgwKOT9uJicj3702W/p0YH4GRMIaN2UlKlzKFysl2lQ707ETb+7NJ25WUNSpr5E0bINNWoaWncl4JrxoCg4Ny/DufJflafreBGW2x+j5K1pqKePgWIg4MbJKPWbIRQDzm0rqzy2Njz54uusWbeZqMgIFnz9YY3phcmCITgahEC1FVTYr5iNdH73XsI7NcWRW8SOCW9hPZUFQPP7R9Bw1AA0t8q+GV+QtWo3ALEDO9Pu+dsRBoVT81dy7J3fAGg87nKaTriC4KYJLGk7AWdOIQDmZg1InDOVwPYtyHz9C3I+1csh+JLuxD/puZ4/LCa7/PXs2YEEz/U8M3UOhYvWle4zJsaS+OIDmBJjQINTdz2F80xGtWURNbAzrZ4fizAopMxfQdI7v/qWldlI+3cnE9qpGc7cQvZOeAvbqczS/QH1o7no79c58cqPJH/wR41lf5bez46hwaAuuKx21k79iOxKfG50xyZc4vG5p1fsZJPH50a2a0TfOXdiCgqk8HQma+77AGeR1W9tY4eeBI66V79/1/yFfeF3lafrfgnB9z1N0TP34j552O/8z3Ku/gdAhEZhHjYWERoFGth/fgOtILtGTUPbbgReN0G3bcMSHMt+qty2zn2xjJ9O8SsPop46ioiKI3j6B6gZZwBwnzyE/Yf3atSra39Xqjneo7nsT2z/Kqc5tBLN00kosQmEv/Ml7pRkAFyH91Py4et+aa47ksLLC7ehahrXdmvOuEvbV0izeG8Sc1fuAQStEiKYc+PFALy5ZAd/H04BYEL/Dgzt2NgvTYBrnr6D1gO74LQ6+GHaB6RU0k6P9vQNNE/fYJGnb9D9hksZ/sRoCtJzAFj/xRK2fL+ygka3526n3qDOuK0ONk6dS+6ekxXSRHZswkVvTsIQaCJlxS62z/wSgA4PX0fzUQOxe/zartnfk7piF+bIEPp99ABRXZpx4oc1bJvxRZU2Dp11Oy0HdsZpdfDrtLmklfMFxkAzN35wP5GN4lFVlSPLtrP8Jf1eadSrDUOfvo34No34ecq7HFi42a9yvfLp22nlKdefp31IaiXlesv7DxDl6f8cWr6dJZ5y7Tl6ML3HDEFTVRzFdhY88QmZR8/4pQtgaNEZ85V3glBwbVuO829fn2vs2h/z0DGoBfp1c21ahGvbCr/zL9Vp043A6+4GoeDcuBTH8ip8Qae+WMY9QfFrU1FPHQVASWxC4M2TISAINJWS1x8CV8W+a3nqum4G9etO/IxJoCjk/7SInI8r9n/inpio938enkPR4rWl+2KnjSO4fy9QBCXrd5DxQs19BwBTt14Ee3yebemf2H6q6PMCr/Sy8d1XcZ9KwtiyDcH3TdMTCYH1m3k4Nv7tl+a6/Sd5+efVqKrGtX3aM+7ynj77X/l5NVuOnAbA5nCRU1TC2pfvYcvhU7zyrzWl6U6m5zJn7BUM6ty8Up2YgZ1p+/wdYFA4PX8FJzz9mLMIs5FO704mrFNTnLlF7JrwFtZTmZgiQ+jy6VTCuzTnzHerOTD989Jjun/7OAHxkQiDQu6mg+x//DO5uLXkgvMfPbAkhGgDfAdowA2aph07j7y6APU0TVtYQ7oBwDRN066qId0qT7qtQoiFwChN0/LO9fzOBSHEs8AaTdOW/bu12g/oSlzTBJ4ecD9Nu7bk1hfu4uWRMyqk+2TyG9g8DxYTPniY7lf2Yevv60k5dIqPJr3KqBcnVKlRf1BnwpomsKDfw8R0a07v2WP56+pZFdJdNPtONjz6CVnbjzH4q0eoN7ATKSt3s+aeskGc7k+NwllQAoAwKFzy9j2sfeBDcvcnExAZgtvpqpBvmwFdiG2awJwBU2nUtQXXvzCet0fOrJBu1cd/cGzDfgwmAxPnP0mbAZ05uGoXMU0SGHTvCN69fhbWgmJCosNqLNd2A7oQ2zSR5wc8SOOuLbjxhbt4Y+STFdKt+PgPjno0J8+fSdsBXTiwaieX33cdO/7cyLqvlxLfoj4T5z3Os/2mVC0oBOaBt2L/15toRbkE3voE7uO70XJSfZK5Dm/Fucr3IU9JbIZSrzm2r58FIOCmR1EatEI9XcODnhCYh9yG/YfX0ApzCBzzFO5jO9GyU3w1D27GuXy+zzb11EFsX8zSfwQGY7lrNu6T+6rXA1AUgiY+SOHTD6NmZxL26lwcm9f5PEjZVy/DvkhvrE29+hI0bjJFzzxauj9o/GSc2/3rHKMoxD99L6funIEzLYsmP79J0fKNOI6dKrMvNYPUx18navz1PoeWbNrNyRH6NVPCQ2i+9FOK126vWVMoBFw7AetHs9Dys7Hc/zKufZvRMk77pgsIxNzvKtxJh0o3GTv1BaMR6+sPgslM0LR3cO38Gy03k/Nh5PAhjLr+GqY/96pf6Q0hMbjyU0F1YYyoT0ir+hQdLnswaDhqIM68YlZdNJXEkX1oM3MUOya8TUir+tQb2Yc1lz5CQEIkvX+cwao+UwFoP+dONt30IraUbPotfoH0xdsoOnyG3M2HyVi6nYv+9ZTPObjzCkl/7kNCL+tTtlFRSJh1L8lj9evZ9Oc3KVyxEcdRr+uZkkHKYxWvJ0C9Vx4m+4PvKV63AxEUWHPnTRG0njOOHTe9gD0lm56LZ5O1eCvFXmVRb9QgnHnFbLjoAeJH9qXFzFHsnfBW6f5Wz9xO9vKd/hR7KQ08Pvfnfg8T2605fWaP5Y9KfG6f2Xey7tFPyNx+jCFfPUL9gZ04s3I3F79yF1ue+4b0jQdpefOldLjnSna8UvlDUwWEQuCYKRS/+hhaTiYhT72Hc+d6VM8DTSmBFgKGXIvr2IFa2Vamc+7+B8A8/C6cG/9ATdoPpgDQ/OiIC4XAG++h5L0n0fKyCZr2Bq69m1DTTvmmC7Bg6n8N7pMHfTarWWmUvHy//zbWtb87qznhQQpneTRf9mie9tJcswz7Yo9mz74E3TmZoud0TXf6GQoeust/PcCtqsz+Yysf3jGI+DALo+cupn+bBjSPCy9Nk5RdwGdr9jPvrssJs5jJKbIBsObQGQ6k5PL9PVfgdKuM/2wZF7esR0igqUbd1gO6ENM0gVc8fYNrXxjPe5X0DdZ8/AfHPe303fOfpPWAzhxatQuA3X9s4Nen51WpkTioM6FNE/jj4oeJ7taCHrPvZOlVT1dI13POODY/8gnZ24/S/+tHSRzYmdSVusahj//i4Ie+XVy3zcnuV34konVDwts0qFK/xcDORDdN4N3+D1O/awuufP5OPh1ZUX/DRws5uWE/isnA7d9Mp8WAzhxdtYv8lCx+fXgufSZcWaVGeVoN6EJ00wTeGPAQDbq24JoXxjF35FMV0q39+E9OeMr1zvkzaDmgM0dW7WL3r+vZMn85AG0u68YVM2/jyzte8k9cCMxXj8c273m0gmwCJ83GdXArWqbvwJRrz3ocf37mt00VdRQCb5hEyQczdV/w0Ou6L0ivzBdc7esLFIXAMQ9h+/p11JSTEBQKbnfNmnVdNxWF+Kcmc3rcdJzpWTT+8S2KVmzCcazMlztTM0h74jUix/m2l4Fd22Lp1o6TI+4FoNE3r2Lp1RHr5j01agZPepCCmbqN4a/PxblpHW4vn+co7/PGT6Zw1qO4kk+QP3UiqG5EZBQRb3+GY/N6UKsvW7eqMvvHVXw4+VriI0IY/cp39O/YjOaJ0aVpHrm+f+nf367eycHTep+qZ6uG/PD4aADyi21c/ew8+rRtVIVtgnZzxrHlphewpWTTZ/GLZCze5tMfaDBqIM68Iv6+6EESRvah1cxR7JrwFqrdyZE5PxDapiEhbRr6ZLvz7rdwe57Punw6lYRrLiJtQc0vMCWS2vCfvsbSSOAnTdO6eg8qCZ3annsXYPgFPLdSNE0bXteDSh7dp+piUAmg8+U92OgZbT+x4whBocGExUZUSHd2UEkxGjCYjGieTnjasTOkH0+tkN6bhkO7c+wn/S1G1vZjmMODscT5aljiIjCFWsjart8Ox35aS6NhPSrk1eTq3pz4VXeY9fp3JPfAKXL3642cPbeo0vDI9pd3Z+u/9LcWyTuOEhgaRGg5G502B8c27AfA7XRzZt8JwhP0RqX3LYNY9+USrAXFABRlV4zAKE+Hy3uwxVOuSTuOYgkNqlCuTpuDo16ap/edICIhCgANjcAQi142YUEUpOdWq6ckNEXLz0AryALVjevwVgzNO9d4nmcRBhMoRjAYQTGgFddso5LYDC03Ay0/U9c8uAlDiy5+a57F0KoH7hN7wOWoMa2xZVvUtDOo6angcuH4ewXmXv18E1lLSv8UARZ9+NqDqXc/3OmpuJMrRuVVRmCnVjiSUnCeSgOni4I/1xDiPVABOM9kYD90ElS1ynxCh/WjaM1WNJu9Rk2lUUvUrFS0nHRwu3DtXIuxfa8K6cxDR+FY+Uu5t5wawhwIiqI/LLtdaDb/I02qokeXjoSHhfqVVhgD0NxOUPVBXtVeTHy5uhw/rDunf9DrR9rvm4jp18GzvQcpCzagOlxYkzMpOZFGRLcWRHRrQcmJNKxJGWhONykLNpTmWbD3ZGm0kzfunHxse46guco6lZZKrmfo4Cqup+Z7Pc0tGiIMBorX6VF1WomtxusZ1q0F1hPp2Dznnb5gPTHDfN+Exg7rQeoPqwHI+H0jkZ6yAIi5ogfW5AyKD5V7WKmBRkO7c9TjczNr8LmZHp979Ke1NPaUaXizBNI36g9CKX/vpclw33OuDkOz1qgZKWiZqeB24dy8ClPXiyukC7x2LPaF34Oz5npfGefjf0R0PVAM+qASgNPul/9RGrdCzUxFy/bUze1rMHa8qEK6gCtvw7HsJzRnzREI1VHX/q5UM9VLc60fmufJ3tPZNIwKoUFUCCajgaEdG7PqoO9A+r+2HuPm3i0Js5gBiAoJBOB4Zj7dm8RiNChYzEZaJUSw7mhKBY3KaH95d7Z59Q0sVfQNjlfRN/CHBkO7c/InXSN7+1HM4UEElquLgZ66mL1dj2Y5+dPfNBjWvdp83VY7WZsP464kQtub1kO6s+tnXf/MjqMEhAURUk7fZXNw0mOj6nSTuvckoZ6+SP7pLDIOnqrV1JO2l3dnp6dcT3v6XCGVlOsJr3JN2XeScI+m3Ss60hzk56CvB6VBC9TsNLTcDHC7ce9Zj7Gt//7Lb53Gnnb6rC/YsQZjx94V0gUMH41j+c9oXu20oXVX1JST+qASQElhhfamMuq6bgZ2aoUzOQXnab29LFy4mpDBvv7OdSYD++GTFa+RpiECzAiTEWE2IYwG3Fl5NWoaW7bF7WWjfc0KTL19bdS8bQz0stFuLx1EEmaz3/fN3qR0GsaE0yAmXPc/3VuxqorZFwB/bTvMsO6tKmxfuvMIF7drgsVc+aB2+X5M2oL1lfSNepDi6Rul/76J6H565Ka7xE7e5kOoldT3s4NKwmhAMRt92gCJ5EJR7eCMEKKJEOKgEGKeEOKwEGK+EOIyIcQ6IcQRIUQvT7pgIcRnQojNQogdQogRXsf/LYTY7vnX17N9gBBilRDiJ0/+84UQopz2cOBB4B4hxEpPXoeEEF8Ce4GGQogPhBBbhRD7hBDPeB3bUwixXgixy3NO4cCzwM1CiJ1CiJuFEL2EEBs857teCNG6hrKwCCG+E0IcEEL8Ali89p0UQsRcgPIaK4T4lxBikSf9y57tBk+ee4UQe4QQUz3b5wkhbvD8PdiT1x5P3gFe5/aMp/z3eKLAak1EfBS5KWUPZblp2aWDG+WZ8uV0Xtn2MfZiK9sXbvRbIyghkpKUsikGJak5BCVEVkyTmlNtmrjerbFm5lN4Ih2AsGYJaGhcNv9Rrlz0PO3vqfyNWnh8FHle+vlpOaUdmMoIDAui3eBuHFm3F4DYZgnENk1k8k+zmPLLs7TuX/OATUQtNS1hQbQf3I3DHs1Fb/xEj5H9eGbDe0z8/DF+evrzKo8FEMERaIVlg09aYS4iOKJCOmPLbgSOnon5ygmIEL181dTjuE8fwjLhZSx3v4KatA8tN61GG0VIBFph2TXTCnNL8/TRbNWdwLHPYL7mXkRoJfvb9MJ1wL836iI6BndW2dQjNTsTJTqmQrqA4SMJ//AbLGMnUfKxJ/oj0ILlulFYv6t6ykB5TPHRuNLK6ocrLQtTvP8PFWcJG96fgj9W+5VWhEWh5ZVpavnZiHBfTaV+M5SIGNwHt/lsd+3egOawETzzM4JnfIRj9QKwFtX6fM8LxVg6qASgqS4Cy9XlwMQobGf0+qG5VZyFJZiiQglMiCzdDmBLzSEwIZLAhEisXvXJlpJdIU9/MCZE40otK1tnWhZGP6+nuUkD3IXF1H9vBk1/fYe4x8bpA3jVEJgQhc3rvO0p2QSUO++AxCjsXmXh8pSFISiAJveN4MSrfkYKeRGUEEmxl25xLX1u3uHTNBqqP9g2uao3wfWq9l3lEZExaDledTQnExFZ7v5t3AIlKg7X7k3+G1Ve5zz8jxIZD/YSzCMmE3j705j63wi+XZVKUSKiUfPKov/UvKyKdbNBc0REDO79WyseHx1P0KNvYbl/NoZmFad6VbCxjv0dgIjyU/OKkYR/8A2WOyZR8klZhJ0hLpGw1z4h9Pm3MLbt5JdmRqGVhPDg0t/xYUFkFJT4pEnKLiQpq5A7Pl7CmI8Ws+6IPnjUKiGSdUdSsTpc5Bbb2HIinfR832OrIiw+ivxy7XRYDX2DtoO7cdTTTgN0uKIXD/71Ere9/yDhiRWPtSRE+dTFkhQ/6mJKDhav82h55+VcsWw2vV+/G1N4kF+2nSU0IYoCL/3CtBxC46v2nQFhQbS6rBsnvGysLaHxkeSnlNlTkJZDWDX+OjAsiDaDu3FsXVnUcu8xQ3ho9RsMfXwUf8760m9tERaFll9mr5afrU93LYehfW8sk18h4JaHEGG1b9OV8GjU3LK2RM2rpJ1u0BwREVvBFyhx9UEDy6RnCHr4TcyDrvNLs67rpjE+Bmdqmb9z1aK9tO08SMmm3TT/ez7N/55P8drtOI7X/IJEiY5BLWejoQqfF/HRNwSNnUTx3DIbja3aEv7ePCLe+Zzi91+vMVoJICOviITIshdn8REhZORV3m9KySkgJTufXq0aVti3eNthrqhkwKn0nBOiyvVjcggo528CEqOw+vQHrJiian6p1+O7Jxi0by6uIhtpv/v/fCaR+Is/UT8tgNeANp5/o4B+wDRguifNDGCFpmm9gIHAK0KIYCADGKJpWjfgZuBtr3y7og8ctQOaAT6vKj1T1j4E3tA0baBnc0vgfU3T2mualgTM0DStB9AJ6C+E6CSEMAPfAw9omtYZuAwoBp4Cvtc0rYumad8DB4FLNE3r6tn3Yg3lcA9QomlaW+BpoKrXROdTXqBHVt0MdEQfCGvo2VZf07QOmqZ1BHxGD4QQgcA84GbPfqPnfM+S5bkGH3jO49/KO7e/yGO9JmI0m2jdt0PNB1xgmo7sw8lfy8I7hcFAXM9W/H3f+ywa+SyNruhBi741d9arQzEo3Pb2FNbOW0zOqQzPNgMxTRP44JbnmD/lHW6cfTeBYbXr3NWkefvb97Nm3iKyPZrdrunL5p9W83Sfycy98yXGvDEZ4ceDT3W4j+/G+tl0bPOfQ00+gHnoWABEeCxKVCLWTx7H+sljKA3boNRrcb5m6ZrHdmL96FFs855GTdqH+YpyYdjB4SixDVBPnnsntjLsCxeQP2kU1i/mYrnpdgAst4zF9tuPcAEieGqDITaSgNZNKF67rebE/iAEAVffif33ioONSqOWoKoUPzeekhcnYb50BCIq/sLo/j9HGBWCerQnY86nnLjuAUwNEwm/7rJ/m17TR24kee6fuEtqjnK70Kx96GPa3HEZV//1HKbgwEqnGJ8zQmC55R6s3/m33sb5UKX/URSUBi1xrvoB21fPISJiMXToV31m/iAEAdfehX3BpxV2aQU5FD19JyUvP4D9l08IvGMaBJ5/tA/8M/7O/tcC8u8ZhfXLuVhu1DXV3GzyJtxEwcN3UfLZewQ/NBMsF6atdKsqyTmFfDLuMubceDHP/rqZAquDvi0S6deqHnd8soTHf1xPp4YxKOfZVlaGYlAY9fYU1nv1DQ4s286cfvfz5hWPcWTtHm567d4Lrnv0i2X80Wcqfw2ZjjU9j25Pj77gGmcRBoXr37mPzZ8vJu/U+U2f9hfFoHDT2/exYd4ick+VDShs+mopr/efyuI53zJgysgLquk6uA3ra5OxvvcI7qO7Cbh+8gXNH9B9wcjx2H+t6AtQDBiatcP21WuUvP0Yxk59MLT0bxDWH+q6blaGqVEi5mYNOTZgDMf630bQRZ2xdD+//rk39oULyJswipIv5mK5+fbS7a7DB8ifPJb8hyZhuXE0mMwXTBP0waPLurTEUO6lUmZ+MUdTs+nT1v/13S4kW2+ZzcpO96CYjUT3q/vnszpH1f53//2H4s8aSyc0TdsDIITYByzXNE0TQuwBmnjSXA5cI4Q4O2gRCDQCUoB3PesbuQHvIdrNmqad9uS705PXWqonSdM07yHWm4QQEzx2JKIPUmlAqqZpWwA0TSvwaJTPKxz4QgjR0nNMTRPtL8UzMKZp2m4hxO4q0p1PeeFJn+85fj/QGNgHNBNCvAP8CSwpp9nao3t2sZsvgMnAm57fZ1fl3QZU+srDU44TAC6N6k670Gb0HzOUi28dDEDSrmNE1osB9HVaIhOiyUvLqSwrAFx2J7uWbqHzkJ4cXFv1XOn+Y4Zy+S36Q1f2zuME1St7yxGUGEVJmu/UrpK0XIK83vaVTyMMCo2u6MmfV5Stf1CSmkPGpkPYc/U3C6dX7KJBh6YcXb+PvmOG0PvWQQCc2nWcCC/98IQo8quw8YbZd5N5Io2/P/urdFt+Wg7JO4+iutzknM4k80QqsU0SOLXbN1S235jL6ePRTN51zG/Nm2ffTeaJVFZ7aV5080A+vGMOACe3H8EYYCI4KrTKaXhacZ5PNJAIjSxdpLsUW3Hpn669azH10+fEG1p0xZ16XJ8OArhP7kVJbIaacrRSrVLNojyfN4EiNLJ0kdxKNXev0SMDvDC27on7yHa/3ioBaNlZGGLiSn8r0bGo2RWnQZ3F8fdygibpa/QYW7XD3Lc/ljsmIoJDQNPQHA7sC3+p8nhnejbGhLK3ZcaEGJzpNS/w603YFZdStHQ9uPy0sSAHEVGmKcKjfd7AEmBBSWiEZdLz+v7QCALHTsc270WMXS/FfWiHvqhlcT7ukwcxNGiOKye9Vud8XqguPWrp7PkrRmzl6rstNYfA+tHYUnMQBgVTaBDOnEJsabkE1i+rN4GJUaXHWrzqU2C96Ap5+oMrLRtjYlnZmhJicPl5PZ1pWdgOHNen0QGFSzdg6dKG/J/Ku+0ybGk5BHqdd0C9aOzlztuemkNA/WjsnrIwesoivFsL4q7qTYuZozGGB4OqodqdnP5scXkZANrccRmtRuvvarJ2HifYSze4lj43/1gqS0bpa5qENUugweAuNRVPKVpuFiLKq45GxaLlepVxYBBK/SaEPP4aACI8iqD7n6Xk7adqtYD3+fgfrTAXNeOUPo0OcB/ZgVKveenC3lWh5mVjiogtsy0ipmLdTGxE0JTZ+jmFRWKZMBPrR8/pi/a69AWY1VPHULPSUGLrly7mW6mNdezvALScWmquXU7QRF0TlxOtUJ+q4T5+GDXtDIZ6DXEfO1Tl8QBxoRbS8suuVXpBCXHlXt7EhwXRoUE0JoNC/cgQGkeHkpxTSIf60dzdvwN399cfph7/cR2NY6p+w99nzBB6edrp07uOE16unS6oop2+bvbdZJ1IY61XO13iFdWw+bsVDH98VKlG/1v0Pla2py6eLcGgen7UxXpRWD3nYcsqa/OPzV/JpV/W/A6xx+1D6HaL7gtSdh8nzMvG0IQoCquYWn/VnPFkn0hj02eLatQoT+8xQ+hxq655Ztdxwr2iHMMSoiiowl+PmH0X2SfS2FCF5p7fN3DN8+P8Pg+tIMcnckiER/tENgI+UbyubcsxD73N7/zPouZnY4osa0uUiMra6cYE3ae/2xahkVjuehLrJ8+j5WXhPra3dMkB1/6tKA2a4z5S1eOHx7Y6rpuu9CxMiWX+zliL9jLksr7Ydh1EK9HXQitesxVLl7ZYt1W/lqaanYVSzkZ3dTauWU7wPVMpLrfdfToJzWrF0Lgp7qM1+J+IENJyC0t/p+cVERcRUmnaRdsP88SNAypsX7LjMAM7NcdkMFSpY0/LKdePicJezt/YU3Ow+PQHLKUfJKkJ1e4kY9FW4ob1IHtNDWtZSSS1xJ+IJe/XoKrXb5WygSkBXO+JBuqiaVojTdMOAFOBdKAz0APwHhL2zteNf4NcpT5BCNEUPfpmsKZpndAHXAL9yOMszwErNU3rAFxdy2Or43zKq/zxbsCoaVouehmuAiYBn5zjOVVZzpqmfaRpWg9N03q0C20GwOqvFvPi8Ed5cfij7FqymYuuuxSApl1bYi0soSAzzyePgKCA0vWBFINCh0HdSDtW/Rc6Vn+1mD8un8Efl88gefE2mt+gvw2O6dYcZ0EJ1gxfDWtGHs5CKzHd9C8pNL+hH6cWl0V4JF7SgfyjKT7h4imrdxPRpiGGQDPCoJBwURvSj+jntf6rpbwx/AneGP4E+5Zspcd1lwDQqGsLbIUlFJazEWDYwzcRGGrht2d9w673LtlK84vaARAUGUps00Sykyt+CWrtV0t4ZfjjvDL8cfYs2UpPT7k29miWL1eA4Q/fhCU0iF/KaeamZNPqYs+6M83rYQowVbu2k5p2EhERp4d0KwaMrXrgPrbLN1FQ2aLjhmadUT0Le2uFORgatAKhgKJgqN8KLafmqXBq6glEZDwiPEbXbNMb99GdvomCyxZfNbToiprtux6XoW1vXAf8nw7jOnIQJbEBSlwCGI2YLxmEc/M6nzRKYv3Sv009+qCm6mt1FE6fQv6EW8ifcAv233/C9tPXNT5k2fYcxtykHqYG8WAyEnblpRQtr12YcdhV/k+DA1BPHUGJSURExoHBiLFLP9z7t3idVAnFs+6gZPZESmZPRE0+jG3ei6inj6HlZmJo0dFjfACGxq1QM6uvqxcazWUvW7MLUAKCSV/sG62VvngbDW7S60fC1b3JWruvdHu9kX1QzEYsjWIJbpZA3vaj5O84RnCzBCyNYhEmA/VG9qmQpz9YK7mehX5eT9vuIxhCgzFE6fUouE9nHEeTqz2mcMcxgpolEOg57/iRfcla7DstImvxVhJv0hcGjbv6InI9ZbFtxCzW95zC+p5TOPXRQk6+9UuVg0oAB79Yxm+Xz+A3j89t4fG5sd2a46jG58Z6fG6LG/qR7CnTwLMfKBCCzg+M4NBXy/0oIR33iUMY4uojYhLAYMTUawDOHeu9hIspvP96Ch+5jcJHbsN97ECtB5Xg/PyPmnYCERAEFn0AwtCobYVFvyvVTD6MEltPjwI0GDF2uxTXHi//ZSuhePpoip8ZT/Ez43GfPFQ6qCRCwnQfC4joeJTYeqjZ1fvZuvZ3lWr2G4RzSzWa3cs0RVh46fRQJT4RQ2ID1PSay7V9/WiScwo5k1uE0+Vm8Z4k+rep75NmYNsGbD2pt7u5xTaSsgtpEBmCW1XJ80T1HU7L5Uh6Hn08X5GtjA1fLeWt4U/wlqdv0N2PvsHlnr7B7+Xaae/1mNoN6U6Gp1+04aulLBoynUVDpnNm0Vaa3KBrRHdrgbPAiq1cXbR56mJ0Nz1SuMkNl3D6bF30Wg+pwRU9yD9U7iMOlbD1y6V8NHw6Hw2fzqElW+l8va5fv2sL7IVWisrpAwycdiOBoUEsfuarGvOvjE1fLeW94dN5b/h09i/ZShdPuTY4q1lJuV72sK658FlfzegmCaV/txrUleyTNfdHzqKeOYYSnYiIiAWDAUPHvrgO+vpcERJR+rehTQ/UzJrLtIJO8hGUGC9f0PVSXHu9pvTbSih+cjTFz95F8bN34U46hPWT51FPHcV1cDtKYhN9HURFwdC8Q8VFvyuhruumbc9hTI3rYaqvt5ehw/tTtMK/9tKVmomlZ0cwKGA0YOnZEbsfU+FcRw5iqNcAJV63MeBSP3xeymmPXQmg6AM7Smw8hgaNUDNqvnfaN4onOTOPM1n5uv/Zdpj+HZtVSHciLYeCEhudm1b0L4tqmAYHkO/pD5ztxySM7EtGuX5MxuJt1PP0jeKv7k322uoH4gxBAQR4fIQwKMQO6Uaxn2vMSSS14UJ9FW4xMEUIMcUTndNV07Qd6FFBpzVNU4UQdwBVD9HWnjD0gaZ8IUQ8cAX6wMshIFEI0VPTtC1CiFDAChQC3q+nwoGzT1Jj/dBbgz6tbYUQogP69LtzparyqhQhRAzg0DTtZyHEIeDrckkOAU2EEC00TTsKjAH8f0L1g70rd9BhYDeeXf02DquDLx95v3Tf9IUv8+LwRzEHBXLPJ49iNJtQFMGhDfv4e/5SADoP7cnNs8YREhXG5M8e5/SBk7xzu+/swzPLd1J/UGeuXfcaLquD9Q99VLrvqiUv8Mfl+lfoNk2fR983JmAMNHNm5S7OrCgbGGky4iKfaXAAjvwS9n/0F1cufBZN0zizYhcHVlYs7gMrd9BmYBceX/0mTqud7x+ZW7pv6sLZvDH8CcITorhsyrWkHz3Dg3/q57/uiyVs/n4lh1bvotUlHXlk6SuobpU/Zs/3eVNZGftX7qDdwC7MXP0WDqudbx4pm/bxyMI5vDL8ccITohg65TrSjp5h2p/6G+6/v1jMxu9XsuD5r7hlzgQGjB+OpmnMn1bDtBFNxbHyOwKufUD/xO6+dWg5qZguuho1Iwn38d2Yug7C0Kyz5xOtJTiWzAPAfWQbSsPWBI55CjQNd9J+3Ceqf3NWqrnsawJueAgUBdeetWjZKZguHomadhL3sZ2Yul2mL6irqmi2Ihx/lYWFi7BoRGgU6qnq3yb5oLop+ehNQme9qn9ud/lC3KdOYhk1DtfRgzg3ryfwyuswdu4OLhdacRHFb872P//yuFXSn/2Ahp8+DwaF/J+W4DiaTMz9t2Hbe4SiFZsI7NiS+u/NxBAWQsjA3sTcfxsnrtRnrJrqx2FMjKGkpi+h+NioYl/wMZa7n9Y/ab55OWr6KcyX34r79FHfQaZyONf/ReBNU7A8/BZCCJxbVqCmJlWZ3l8eeXoOW3bsJi+vgMEjb+Pe8WO4/uqhVaZ3F2VhDE8ABKqtkKJDp2n16A3k7TpBxuJtnPpmFV3evZcBG9/AmVfE9onvAFB06DSpv23k0r9fRXO52fv456BqaGjsfWIevb57AmFQOP3tKoo8D1hN7hpKs8lXExAXwaUrXyJj+Q72PPQxhphImv7yFkpIEKgqUWNHcvyKiaQ98wENP3seYVDIO3s9H7gN256y69ng/bLrGXv/bRwffg+oKhkvfUqjL2aDENj2HSH3h+rf7GtulUNPfEbX76aDQSH121UUHzpNs0dvpGDXcbIWbyPlm5W0e/c++mx8C2deEXsnvlVtnv5wevlOGgzqzPXrXsNtdfC3l8+9ZskL/ObxuRumz+OSNyZg8Pjc0x6f22xkH9qM1SNOkxZu5cj3ayqKVIWqYp3/DsEPz9Hv378XoaYkETDyDtwnD+PaeYG+VnM+/kfTcKz6nsCbpwECNf0krl1+NK2qiu2nDwm691ndto1LUdOSMQ8fjTv5CG7vB8tyGJp3wDx8tP71J03F9sN7UFLD+md17e/Oan78JqFPl9O81aO5ZT2Bw6/D2Km7/nGAoiKK39Y1je06Y7l1HLhdoGoUf/g6WlHNb9uNBoXHr+zBPV+uRFU1RnRrRou4CN5fvpt29aMY0KYBfVsksuFoKte98weKEEwd2oWIoADsTjfjPtX7I8EBJl64vi9Gg3/fgDm4cgetB3bh0dVv4rDa+dGrb/DAwtm85ekbDJ5yLRlHz3C/p2+w/oslbPl+JRffOYx2l3XH7XZjzSvih0ra6ZTlO0kc3IWr1r+O2+pg09QyjWFLX2TREH0lha1PfE7vNydiCDSTunIXqZ662OXJW4ls3xg0jaLTmWx5tOxLZldvehNTiAXFbKTB0B7Mu30OWUd8XyQcWbGTFgO7cN+a13FaHfw2rUx/wsIX+Wj4dEITorhkykgyj55hwp8vALDlyyXs+G4V9To146aPphIYHkSry7rSf+r1fDjksWrL9fDKnbQa2IWHVr+Bw2rnX17lOnnhi7w3fDphCVEM8JTrvR7NjV8sYdv3q+h9x+U0v7gDqsuFNb+Ynx/+oFo9H1QVxx+fEXjHDN0vbF+JlnEa06CbUFOO4T64DWOfKzC26YGmuqGkCPu/3q8530p0bD9/SNCkZ3RfsGmZ7guu8PiCfdWsG2ktxrFqAUEPvQ5ouPdvrXRNtoqadVw33SoZz31Ag0+fB8VA/s96exk9ZQy2vYcpXrmJwA6tqPduWXvpuu82Tl49icLFawm6qDNNfvsANCheu5XilX68RFTdFH/4JmHPeGxcthB38kkso8fhOuLxeVddh6mLx+cVFVH05lkbO2G5YRS4XHp9+fANtIL8GiWNBoXHbxzAPe8vQNU0RlzUjhaJ0bz/5wbaNYpngGeQadH2wwzr1qrCTJkz2QWk5RbSvUXVX2cEvT+w/4nP6fHddE8/ZiVFh07T4tEbyd91nMzF2zj9zUo6vTuZSza+iTOviF0Ty1aa6b/lHQyhen2Pv6IHW25+EWduEd2+fAQlwAiKQs66fZz6YmnN5SyR1BKhVbMavhCiCfCHJ6oHIcQ8z++fvPcJISzo0676okdBndA07SrPNLOf0aeaLQIma5oWIoQYAEzTNO0qT77vAls1TZtXTn8WUKRp2qvlz8XrfPoCp4B84DdN0+YJIXoC76AvsG1FX2fJjD6gYwJmA8noU8aK0aOdbtM0rUn5c/PSsqCvbdQZOADU99izVQhxEj0iK+Q8y2ss0EPTtPs8x/8BvArkerTP9oKe0DTtr3L5D/akNQJbgHs0TbOfPTdN07KEED2AVzVNG0A13NPkpjqdvNnHeWHnNvvDbpN/040uJM5/4BMMsx8MrznRheQ8v250LtjWHqs50QUm40BwzYkuMA1GBNS5ZsBjr9Wp3pL2M+pUD6BZWM0dygtNSoF/X867kCQba/6s+oXmusv9jyC4EJjaVfH55n8j7lMVo1L/3TiT/JvycEH5B74hbLllUJ3qzXrsYM2JLjBdHHVfsEfq3hXgEHXf/3lizAVc880P1MK6XZcRwJlU85d5LzSZB/59ay5VRUxL/xbav5AETbm5TvVWj6nFi5kLyLD07y78YnP/IRTeN/w/dzGi8yT03YX/kdet2oglTdNOAh28fo+tbJ+maVZgYiXHH8E3sucxz/ZV6NFFZ9PdV4X+rKrOpfz5lNu+Baj4bV8o/y1R73jEJys7N688rcAtVeg18fyZxfmV1zz0RbjP/vYe3OpWSXrv/JejL4he1bmhadpWYEBlNkgkEolEIpFIJBKJRCKR1JZ/4B2URCKRSCQSiUQikUgkEonkfwE5sCSRSCQSiUQikUgkEolEIjknLtTi3RKJRCKRSCQSiUQikUgk/yzq/+wSS/+xyIgliUQikUgkEolEIpFIJBLJOSEHliQSiUQikUgkEolEIpFIJOeEHFiSSCQSiUQikUgkEolEIpGcE3KNJYlEIpFIJBKJRCKRSCT/G8g1luocGbEkkUgkEolEIpFIJBKJRCI5J+TAkkQikUgkEolEIpFIJBKJ5JyQA0sSiUQikUgkEolEIpFIJJJzQg4sSSQSiUQikUgkEolEIpFIzgm5eLdEIpFIJBKJRCKRSCSS/wk0TS7eXdcIWeiS8li/fbpOb4r8D1bXpRwA4fcNqnNNAoPqXLLond/rVC+oV1yd6gEoTRvWuSZhEXUuWfTx8jrX3Ly7Xp3qXb7vhTrVAzg9eGKdazZY9Hada2ouR51rHhv0RJ3qJXQpqVM9ANVW932owK6Jda6Z81dmnWuqblGnenEzL6tTPYCUmSvrXLPecwPrXPOf6P8cvXdpnerFNSmoUz2AgHqGOtc0ta3bfgHA3vfr3rdb3XVbtlNFSp3qnWVH2rq6dbR1SMHEof+zgxxhcxf/R143ORVOIpFIJBKJRCKRSCQSiURyTsiBJYlEIpFIJBKJRCKRSCQSyTkh11iSSCQSiUQikUgkEolE8r+B+j87E+4/FhmxJJFIJBKJRCKRSCQSiUQiOSfkwJJEIpFIJBKJRCKRSCQSieSckANLEolEIpFIJBKJRCKRSCSSc0KusSSRSCQSiUQikUgkEonkfwO5xlKdIyOWJBKJRCKRSCQSiUQikUgk54QcWJJIJBKJRCKRSCQSiUQikZwTcmBJIpFIJBKJRCKRSCQSieS/HCHEMCHEISHEUSHE49Wku14IoQkhelwIXbnGkkQikUgkEolEIpFIJJL/CbT/p2ssCSEMwHvAEOA0sEUI8ZumafvLpQsFHgA2XShtGbEkkUgkEolEIpFIJBKJRPLfTS/gqKZpxzVNcwDfASMqSfcc8BJgu1DCMmJJ4hfrjqTy8qLtqKrGtd2aMe6SdhXSLN6bzNxVe0FAq/gI5tzQF4A3luzk7yMpaBpc1CyeR6/ohhCiRk1zr16E3X8fKAasf/5J8fxvfPYH3XQjQVddieZ2o+blkT/nZdT0dABCJk0koM9FCEXBvmUrhW+/44eNKbz851ZUTePa7i0Yd2n7ijbuSWLuyt2AoFVCBHNu6gfAm4t38PfhMwBMGNCBoR2b1KgHsO7gKV7+bYNerr1aM25QF5/9r/y2gS1HUwCwOV3kFNlY+9wdpOQW8tAXS1FVDZeqcuvF7bmxT8VrUhOmHr0IuXcKQlGw/vUn1u99yzjwqmuwXHMtqG40q5XCN17FnZxUax1vDC27YL7yTlAUXFuX41yzwGe/sesAzFeMQS3IAcC18S9cW1fUWmfdiQxeXr5fv56dGjKud4sKaRYfTGHu+iMAtIoLY85VXUnJL+GhBdtQNfSy7daEG7s0rr1+DffTKwu3seWEfr/anC5yim2snXFTrXXOciGuZfk82L3KZ79iNtL53XsJ79QUR24ROya8hfVUFgDN7x9Bw1ED0Nwq+2Z8Qdaq3QDEDuxMu+dvRxgUTs1fybF3fgOg8bjLaTrhCoKbJrCk7QScOYW6iMGEISQWYQxALclBteZXa/eTL77OmnWbiYqMYMHXH55r8VXA0rcHUY/di1AUCn/5i/zPvvfZHzbmekKvvQLcbty5+WQ9/Squ1AwA4t9/kYCObbHv3Ev6lJl+6a3dtJ05736C261y/ZVDuGv09T77U9IymPnyO+TkFRAeGsKcGVNJiIth8449vPTup6XpTiSf4ZWnHmbwJRfVrLl5Jy+9/zluVeW6KwZz160jfTXTM3nq1Q9KNWc/MYWE2GgAOl9+My2bNgIgMS6Gd557zC87gy/pTvyTExEGhbwfFpP90Y8++y09O5AwYwIBrZtyZuocChetK91nTIwl8cUHMCXGgAan7noK55mMGjWNXXoRdKfeltiX/4l9gW/dMA+5hsBhI9FUFWxWiue+inparxuGRs0ImvgwwhIEmkbB45PA6ahR09StF8F3TwFFwbb0T2w/+WoGDLuGwCs99dFmpfjdV3GfSsLYsg3B903TEwmB9Zt5ODb+XaOeoUVnzMPvAKHg2r4C59+/lSuD/piHji7zq5sW49q+Uj+XMY9jaNASd/Ih7PNfrlHrLIF9ehLx8GRQFIp/XUjhF9/57A8ZdQMhI4aXttE5z76CO63seongIBK+/wzr6nXkvVJzGw0Q2LcnUdPuBYNC0S9/UTDPVzN09PWEXDvcUy/zyH7mVdypGRgS44h99RmEIsBopPC7BRT9/IdfmusOnebl3zfpvrxnK8YN6OSz/5XfN7HleBrg1U7PGs3BlGxeXLCBIpsTgyK4a2AnhnZu5pem5eIexDw+CWEwUPDzX+R9+oNvOXTvQMxjkzC3akb6Iy9SvHRt6b6oqeMJvrQXADlzv6F40er/SBvXHUji5X+tRdVUrr2oHeMu6+6r+ctathw5XaZZaGXtnLsB6Db1fVokRgGQGBnKW3df6ZdmyKXdqPf03aAo5H6/lMwPf/LZH9SrPfVm3k1gmyYk3/8yBX+t99mvhFhoteR9CpZuJOXpuX5pmnv2IvS+KWBQsP75JyXfluvL3ngTluGevmx+HgUvv1TWl50wkYCLdF9e9NWX2Feu9EvT2KknljH3gaLgWLUQ++/f+p7T4KsJGDICVBXNZqXk09dRzySBwUDQXdMwNG0JigHH2iXYf/u2CpUyDM06YR46Rvc/O1fhXP97ufO5BPPgW1ELcwFwbV2Ka+cqRHg0ATdMBSEQBgPOLUtwba+6vxc+oCuNnxuHUBQyvl1G6ru/+OwXZiPN336A4I7NcOUWcmTSazhOZyJMRpq+PIngTs3RVI2kpz6lcMM+ANr+9Cym+EhUm+7XD97yLK7syvsdUQO70OL5OxEGhdT5y0l+Z0EF/bbvTiG0UzOcuYXsn/AGtlOZBDaMpeffb2I9pvfnC7Yd5vCjH9dYrmd59PkHuXhwH2xWG08/8AIH9xyukObjf71DTFwMdpsdgHtueZDcrDwefuZ+el7cDYBASwBRMZFc2nqY39qS/0rqA6e8fp8GensnEEJ0AxpqmvanEOKRCyUsB5YuAEKIAcA0TdOuEkJcA7TTNG3OP3tWFw63qjJ74VY+HDOQ+DALoz9eSv/W9WkeF16aJim7kM/W7mfe+MsIs5jJKdIHP3cmZ7HzVBY/3qM7sTs/W87Wkxn0bBpfvaiiEDb1AXIfmoY7M5Pojz7EtnYd7qSyB2HXkSNk3T0R7HYsI64h9J6J5M96FlOH9pg7diD7zvEARL37DuYuXXDs3Fm9jb9v4cOxg4gPC2L0h4vo36ZBORsL+GzNPubdfTlhloBSG9ccOsOB1By+v3c4TrfK+M+WcnHL+oQEmmou11/W8eGE4cSHBzP67QX0b9+Y5vGRpWkeuaZP6d/frt3LwZRsAGJDg/jyvhGYjQZK7E6uf+0n+rdrTFx4cPXlWq6MQ6c8SN5jD6NmZRL57lwcG9b5DDbYVyzD9of+gGLu05eQSZPJn/6o/xrlEQrmq8dj+/w5tIIcAu+ZjevAVrTM0z7JXHvW4/j90yoyqRm3qjF76T4+vKk38aGBjP5qLf2bx9M8JrQ0TVJuMZ9tOsa8UX0JCzSRU6w3xrEhgXw5uq9etg4X13++hv4t4okLCayFfs330yPDyzrR3248xMHUnHO294Jcy0ryCGl1hCLPgClAw1EDceYVs+qiqSSO7EObmaPYMeFtQlrVp97IPqy59BECEiLp/eMMVvWZCkD7OXey6aYXsaVk02/xC6Qv3kbR4TPkbj5MxtLtXPSvp3xtUd2oxdkIc5Bfpo8cPoRR11/D9OdePcfCqwRFIXr6FNImPoYrPYt637xLyaoNOI8nlyZxHDxKyqjJaDY7oTdeReTUu8l89AUA8uf9iLAEEHaDfw87breb59+ay8evPkNCbDQ3T3qEgRf3onmThqVpXv1gHtdcPpARwwaxaftu3vz4K+bMmEqvrh35+dM3dd2CQq4YfQ99e3b1Q1PlhXc+5aOXniQhNppbJj/BwL49aN64QZnm3K+4esiljLh8AJt27OWtT79h9uNTAAgwm/lp7it+2VeKopAw616Sx87AmZZF05/fpHDFRhxHy/o/rpQMUh57najx11c4vN4rD5P9wfcUr9uBCAr071PCikLQ+Acoem4aak4mobM/xLl1XenAEYBj7TIcS/W6YerRl6A7JlP0wqOgGAi6fwYl77yIO+kYIiQM3C6/NIMnPUjBzIdRszMJf30uzk3rcJ/y0ly9DPsij2avvgSNn0zhrEdxJZ8gf+pEUN2IyCgi3v4Mx+b1oLqr1hMC81XjsH3xAlpBNoETX8R1cBta5hmfZK69G3D8+XmFw53r/sBlMmPscVnNtnnZGPno/WTc9yju9Eziv3gf65oNuE6U2eg8dJT02+9Bs9sJvv5qIu6fQPb050v3h0+6E/uO3bXSjHpsChn3PoYrPZPEr9/Duno9zhNe9fLQUdJuuxfNZifkhquJfGACWY8/jzszh7Sx94PTibAEUu/HT7Cu3oA7K7taSbeqMvvXjXw4fijx4UGMfvd3+rdtRPP4iNI0j1xd1l//dt1+DqbovtxiMvLcTZfQOCacjIISRr3zG31a1SfMElCjnbFPTibl7idwpWXR4Pt3KF650cf/uFIzyXjyNSLG3uBzaNClvQho14JTN9yDMJuo9/krlPy9Ba245D/KRreqMvunNXx4zzXER4Qw+vUf6d+hKc0Toso0r+1XprlmNwdPZ5b+DjAZ+OHRW6rVqICiUO/ZSZwYMxNXWjbNf32dgmWbsHv5H+eZTE4/8iYxd19baRbxD91G8eZ9tdIMfeBB8h55GHdmJlEfzsW+3rcv6zxyhJJJE/S+7DUjCJ04ifxnn8F80UUYW7Yi+667wGwi6o23cGzahFZS9bUEQChYxj5A8exHdJ/33Ac4t6/XB448ONYvx7FcH/wxduuLZfQ9FL/8OKbe/cFkovDxu8AcQNjLn+NcvwI1K70aPYH5ijuwzZ+j9+vGP4vr8Da0rBSfZK79G3Es/tJnm1aYh23eLN2vmgKwTJyD+/B2tKK8SsuyyYt3c/CWZ3CkZtN+4cvkLd6C9UhZ/zH21stw5RWx6+LJRI24mEZP3s7RSa8RN1r3bXsGT8UYHU6b+U+y94pHQdPbkGOT36R497Hqy1VRaDlnPLtueg57Sg7dF88ma/FWSg6X6SeOGoQrr4hNF00hbmRfms28jf0T3gDAlpTG1sG1f37vN7gPjZo1YESfm+nYrT3TX5rG7cMnVJp2xuRn2L/roM+2155+u/TvW8bfQOsOLWt9DpL/PIQQEwDvG+EjTdM+8vNYBXgdGHuhz0tOhasCoVPr8tE07be6GlQSQhir+13NcYba6Ow9k0PDqFAaRIVgMhoY2qERqw75dlj/te0YN/dsSZjFDECU5yFcCHC43DjdKg6XisutEu3HA7qpbRvcZ87gTk0Flwvb8hUE9rvYJ41jx06w64MBzv37McTG6js0DWE2g9EIJhPCaMSdW/1D+97T2TSMDqVBVKhuY8fGrDpwyifNv7Ye5eberUo7TGdtPJ6ZT/fGcRgNChazkVbxkaw7klJBo4JmciYNY8JoEB2ma3Zpzqp9VUcD/bXzGMO6NNfLx2jAbNQvo8PlRtNqP4/Y2Lot7pQzqGmeMl61AnPffj5pvDswItAC5zldWWnQAjUnDS03A9wu3LvXYWx7QdaL82Fvah4NI4NoEBGEyaAwtE09Vh317Rj9a1cyN3dtTJhnADAqWL+uJoNSVrZu9ZzK1p/7yZu/dp9kmJ9RbpVxIa5lZXnED/O9NvHDunP6hzUApP2+iZh+HTzbe5CyYAOqw4U1OZOSE2lEdGtBRLcWlJxIw5qUgeZ0k7JgQ2meBXtPlkY7+Z6oiuay+217jy4dCQ8LrTlhLQjo0BrnqRRcZ9LA5aJ40SqCBvT1SWPbsgvN82bQvucAxrjYsn2bd1T7IFeePQeP0Kh+Ig3rJWAymbhiUD9WrPOd8n4s6RS9unUEoFfXjqxct7lCPktWr+eS3t2wBNbw4ArsOXSURvUSaFgvHpPJyBUD+rJy3RafNMeTTtO7i36Ne3Vpz8r1W/22qTIsnVrhSErBeSoNnC4K/lxD6OA+PmmcZzKwHzoJmuqz3dyiIcJgoHjdDgC0Eltp+VeHoUUb1LQzqBn6fe1ctwJzD9+2BKvXtQoILH3YMHbugTvpOO4k/YFDKyoA1fe8KsPYsi3u1DOo6bqmfc0KTL3L1Udrufp4Fru9dBBJmM2l51Idvn7VjXvPeoxt/Per6vG9aPbaRcKb27fBeeoM7jO6jSVLV2Lp71tH7Nt2onnaaMeeAxi86oipTUsMUZHYNm3zX7NDa1ynU3B5NIsXr8IywPda2rf61ktDXIy+w+UCpxPwlKuf3bu9p7J0Xx7t8eWdm7Fqf3KV6f/adZxhXZoC0Dg2nMYx+suEuLAgooIDyS2uuZwDOrbGmZyC67Tuf4r+WkXwIN964kpJx3H4hB5l54W5eSNsW/eAW0Wz2nEcPkFQv+rvhX/Cxr1JGTSMCadBTLiu2bUlq/acqFpz+xGGdW9VY77VEdS5JY6kVJyn0tGcLvJ/X0PYEJ+X+DjPZGA7eLLSQevADs0xxkRQ+PcOvzVNbfR2tbQvu2IFARf7+gLnzh0+fVnF05c1Nm6Cc/cu3R/YbLiOH8Pcq3cFjfIYmrdBTT+DmpkKbheOjSswdfetm94+TwQEUtoh0EAEWEBREOYANJfTx1dVhlKvOWpOOlpeJqhu3Ps2YmzVvdpjSlHdZYP1RpP+0FAFIV1bYDuZij1Zv345v64lcmgvnzSRQ3uS9aMe1ZXzxwbC+ultpqVVQwrW7gHAlZ2PK7+Y4M7N/TtHD2HdWmA9kYYtKQPN6SJjwTpiyvWRYob1JO0HPUIw8/eNRHr6SOdD/6H9+OOHRQDs2b6P0LBQYuKizymvYSMvY9Evy877nCT/PJqmfaRpWg+vf96DSmeAhl6/G3i2nSUU6ACsEkKcBC4CfrsQC3jLgSUvhBBNPCuofwnsBRoKIT4QQmwVQuwTQjzjlXaYEOKgEGI7cJ3X9rFCiHc9f88TQtzgta/I83+iEGKNEGKnEGKvEOKSSs6luxBitRBimxBisRAi0bN9lRDiTSHEVuCBSn4PFkLsEELsEUJ8JoQI8Bx3Ugjxkud8b6xNuWQUWEkIK4seiA+zkFFg9UmTlF1IUnYhd3y6jDEfL2XdkVQAOjeMoWeTOC579VeGvPYrfVok0iw2nJpQYmJxZ5S9nXJnZpY2tpVhufJK7Jv0By3nvv04duwk7pd/EffLz9g3b8adVHUnqdTGcC8bw4PIKCxnY1YhSdkF3PHxYsbMXVQ6eNQqIZJ1R1OwOlzkFtvYciKd9IKaHyozCopJiAjx0gwmI7+40rQpuYWk5BTSq0W90m1peUXc+NrPDHvhG8YO6Fy7aCVAiYnBnVk2LUHNysQQE1MhXeA1I4n64huC75pE0ftv1UqjPCIsCi2/7A2xVpCDCK/YQBra98Yy5VUCbn240v01kVFkIyG07GEtPjSQjCLfzm5SbjFJOcXcMX89Y75ex7oTZWWRVmDlxs/XMOzD5Yzt1bxW0Urg3/10lpS8IlJyi+jVrIYovmq4ENeysjwCEyJ9j0+MwnZGv36aW8VZWIIpKpTAhMjS7QC21BwCEyIJTIjEmuK1PSW7Qp7/iRjiYnCnefmfjCyM8RXL8yyh116BtZKBHn/JyMwhIbYs//jYaDIyfQfDWzdvwrI1GwFY9vdGikus5OUX+KT5a8VarhhUoTmpXDMrhwSvzml8bDTp2b6arZo1Ztla3a7lazd7NPUpiw6Hk5vvfZzR981guZ+2GxOicaWWDSY607IwxvtXv81NGuAuLKb+ezNo+us7xD02DpSauzBKVCxqdtm1VHMyEdEV25KAoSMJe2c+QbdNouQz/Q2vIbEhoBEy42VCX/qIgGv8i5BQomNQs7zqUnYmhuiK90/A8JFEfPQNQWMnUTy3rD4aW7Ul/L15RLzzOcXvv159tBIgQivxq2FRFdIZ2vXCcu9LBNw8FRF2bg8mpXnFxuBO96oj6ZkYYquuI8EjrsC23nOfCEHEg5PIe6t2U1eNsTG4vKbSuTMyMVTzgBUychg2r8FSQ3wsid9/RP2F35D/xXc1RisBZBSUkODVtsaHB5FRUFU77fHlzRMr7NtzKhOnW6VhVFiNmsa4aFxe/seVnoUxruqy9cZ+6DiWfj0QgQEoEWFYenbGmFB13wn+GRsz8otIiPTq/0SEVN3/ySkgJaeAXi3rl25zuNyMeu0HxrzxEyt2H69RD3T/4/TxP9mYEvysB0KQOGM8qS9+5l96D0pMDGqGly/IrLxtPotl+HAcm/SXCq5jRzH36gUBAYiwcExdupa9QK1OMyoGNdtLMycLJbLiceYhIwh9/Wsst07A+sW7ADg3r0azWwl77yfC3voW+58/oBUXVqsnQiPRCsraDq0wBxFasZ03tOmF5e4XCbj+fh//JMKisNz9IkH3v4Vz/R+VRysB5oRoHF79CUdqNqbEqKrTuFXcBSUYo0Ip3neSiMt7gkEhoGEcwZ2aY65Xdh2avXEfHZa+Rr0Hq348CkiIwu6lb0/JIaDc/ROQGIX9jH6PaW4Vl6ePBBDYKI7uy16myy/PEN67TZU65YlLjCUtpex6pqdmEJdY+X0w683pfLdsHndPHVthX2KDeOo1SmTLWv8H8//rUbX/3X/VswVoKYRoKoQwA7cApfPjNU3L1zQtRtO0JpqmNQE2AtdomnZ+bw+RU+EqoyVwh6ZpGwGEEDM0TcvxRPksF0J0Ag4DHwODgKPA91XmVjmjgMWapr3gyddnzocQwgS8A4zQNC1TCHEz8AIwzpPErGlaD0/aq8/+FkIEAkeAwZqmHfYMkN0DvOk5LlvTtG61PFe/cKsayTmFfDJ2EBkFJYz7fDk/3nMFeSV2jmcVsOShawCY9NUqtidl0K1x3AXTDhwyBFPr1uTc/wAAhvr1MTRuROYNegMR+dqrmDptxrl7z3npuFWV5OxCPhk3RLfxk6X8eN+V9G2RyL7T2dzx8RIigwPo1DAGxY81pGrD4p3HuKxTUwxeD1IJESH8+PD1ZOQXM/WLpQzp1JToUP+mD9UG228LsP22gICBlxE06nYKX5l9wTW8cR3cimv3WnC7MPa8jIDr78P22TM1H1hL3KpGcm4xn9xyERmFNsZ9t4Efx15KWKCJhDALP955KRlFNqb+spUhrROJDq45CuRcWLw7icvaN/K5tv8u6vpa/n8g+MrBmNu1InXcw/9WnWn33MkLb33Er4tW0L1ze+JjolG87pnM7ByOHE/i4l41T4PzW3PiGF589zN+XbyK7p3aEhcThWLQNRd/8z7xMVGcSknnrkeepVXTRjSsl3DBtMsjjApBPdpzYsQUnCkZ1H/rCcKvu4z8n5ZckPztixdgX7wAU7/BBF4/hpL35oDBgLFNRwofn4RmtxH69Ou4jx/GtXf7hdFcuAD7wgWY+1+G5ebbKX5Tr4+uwwfInzwWQ4PGhEx9Ase2TX6t61QdrkPbcO1Zp/vVHoMJuO4ebPOer/nAC0DQFZdhbtuKjIkPARBywzXY1m3GnVFJxOIFInj4YALatSbtrodKt7nTM0m9eQKGmGhiX3+GkmVrUHPyLpjm4l3HuaxDkwq+PLOghCe/X8NzN12ColzYvkF5rOu3E9ChNfW/fgN3bj62XQfAXXOUnb/8EzYu3n6Uyzo399Fc+NTtxEeEcDorn7vf+5WW9aJpGFPzS8tzJXrMcApXbcWVVvNg5LkSeNkQjK1bk/ug3pd1bN2KqXUbot59DzUvH+f+fX5FTPqLY+mvOJb+iqnvIAJH3kbJ3JcwNG8DqkrBfTcigkMJmfkWrr3b9ein88B1ZAeufRt0/9NtEAHXTMT2te7vtIIcrB9PR4REEHDjVFwHN0NxQQ051o7M75ZjadmADotewXE6k6KtB0vL8uh9b+JMy0EJDqTVJ4/iuGEAWT+tuqD69vRcNnS7B1duESGdmtFh3iNsufQh3EWVv3A8F6bf+wyZaVkEBQfx6qcvcNWNw/jjx0Wl+4eOvIzlf6xCvYD3kOQ/E03TXEKI+4DFgAH4TNO0fUKIZ4Gtmqb9Vn0O546MWKpI0tlBJQ83eaJ8dgDtgXZAG+CEpmlHNH2ezNe11NgC3CmEmAV01DSt/OuA1ughakuFEDuBJ9HD2M5SfiDre6/jTmiadnZVty+AS6s5rhQhxARPZNbWT5f7jmbHhVlI84rASS+wEhdm8UkTH2ahf+v6mAwK9SNDaBwdSnJOISsOnqZTg2iCAkwEBZi4uEUiu07V3DCrWZk+YfOG2FjUzMwK6czduxNy+23kPTG9NMw94JJ+OPftR7Na0axW7Js2YW5fcSHuCjbme9mYX0JcaDkbw4Po36ZBmY0xoSRn65fu7gEd+GHycOaOHYymaTSOqXlqTlxYMGl5RV6axVVGHS3aeZxhXSouPg0QFx5Mi4RItp9Iq1HTGzUrC0Ns2QCfEhOLO6vqjr591XLM5UK4a0v5CKXyEUwAWItKQ6NdW1eg1PdvIVBv4kICSfOKEEovtFWIOooPDaR/i3j9ekYE0TgymOTc4gr5tIgJZfvp2q1/5M/9dJZFe5IY1qlJrfIvz4W4lpXlYUvL9UljS80hsL5+/YRBwRQahDOnEFtabul28EQ2peViS8vFUs9re73oCnn+J+LOyMLg9ZbfEBeDK71ieQb27krEXaNIf+CpUv9zLsTFRpGWWZZ/emY2cbG+b2LjYqJ467nH+emTN3hg/GgAwkLL3vgvWrmOwZf0xmT0731RXEwUaRlldS89M5v46Iqab86axo9zX+b+cbfqmiG6j4qP0dM2rBdPj87tOHD0ZI2arrRsjIllb4lNCTG40v17UHOmZWE7cFyfRudWKVy6gcD2lftEb9ScTBSvCCUlKhYtu2JbUqqzbgXmXnrdULMzce3fhVaYDw47zu0bMTSreX0KNTsLJcarLkXH4s6uuj461izHfFFF3+o+nYRmtWJo3LRaPa2wEr9aUM5nefvVbStQ6tXer/qcW2YWhnivOhIfizuzoo0BvboRducosh6eWVpHzJ3aEXLTCBJ/nU/EAxMJHj6E8PvuqlHTlZmFMaGsXA1xsbgzKt4/gb26ET5+FBkPzqy0XrqzsnEeO0lg1441asaFBZHmFUmTnl9CXFgV7fSuE6VTxM5SZHMwZd5S7hvanU6N/Huh5srI9okyMsbH4KrFIFzeR99y+oZ7Sb37CRACZ9LpatP/EzbGhYeQluvV/8krqrr/s+MIw7r51rt4T7R3g5hwerSo77P+UlW40rL1hf89mBKicfo5UBTUtQ3RY66i9d+fkDh9HBHXDiL+0TtqPE7NykKJ8/IFsZW3zeZu3Qm+bQx5M6b73LPF878m5+67yHvkYRAC1+mqp9SXauZkoUR7aUbFoOZW4/M2rMTkmR5s7jsY5+4t4HajFeThOrwXQ7PqpyBqhbm+EUihUWiF5dp5b/+zYyVKQkWfphXloWaextCwdaU6jrRszF79CXNiNM5ya1P6pDEoGMKCcOUUglsledbn7B3yMIfvnIMhPBibZyFtZ5qeh1psI+uXvwnuWnm7Yk/LIcBLP6BeFPZy9489NYeA+vo9JgwKRk8fSXO4cHnu96Ldx7GdTCeokqi/s9x053V8t2we3y2bR1Z6Ngn1yq5nfGIcGakVr2dmmn5flRSX8NcvS2nf1feDPkNHXMaiX5ZWqSn530LTtIWaprXSNK25pmkveLY9VdmgkqZpAy5EtBLIgaXKKG1dhRBNgWnoEUCdgD+B2syJceEpY896TWYATdPWoA/4nAHmCSFuL3ecAPZpmtbF86+jpmmXV3aOVfyuiirTec/VHD/Yd250+3pRJGcXcia3CKfLzeK9yfRvXd8nzcA2Ddh6Ug/VzC22k5RdSIPIEBLDg9l2MhOXW8XpVtmWlEGz2JrDpJ0HD2Fo0ABDYgIYjQQOHoR9ne/XOYwtWxA27SFyn5iOmpdXul3NyMDcpQsYDGAwYO7SGVdS1WsXAbSvH+1r454k+rdp4JNmYNuGbPV8xSu32J43NjwAAQAASURBVEZSViENokJwqyp5Jfr8+MNpuRxJz6NPNQ1GqWbDWJKzCjiTU6Br7jxG/3aNKqQ7kZFHgdVOZ68or/S8ImxOvZEuKLGz40QaTWIjatT0xnXoIIb6DVASPGU8YBCODet80hjql11nc+8+uM9U30GtCfXMUZToRERkHBiMGDpdjOugry8ToRFl+m17oGbUXrN9YjjJucWcySvB6VZZfDCF/i18p5oNbBnPVs8gZ26Jg6TcYhpEBJFeaMXm1KeeFNic7DiTS5Oo2k0z9Od+AjiRmU+BzUHnhv5Nc6iKC3EtK8sjfbHvIHP64m00uEkfq064ujdZa/eVbq83sg+K2YilUSzBzRLI236U/B3HCG6WgKVRLMJkoN7IPhXy/E/Evu8Qpkb1MdbXyyJ42ABKVm/wSWNu05yYmQ+S/sBT5x310KF1S5JPp3I6NR2n08lfK9YysK/v2hG5eQWlbxo//uZnrh0+2Gf/X8v/ZvjgS/GXDq2bk3QmldOpGTidLv5atZ4BfX2n1+fml2l+8u0vXDtsIAD5hUU4HM7SNDv3HfJZ9LsqrHsOY25SD1ODeDAZCbvyUgqXb6zxOADb7iMYQoMxeKbZBPfpjONo9VOcAdxHD6EkNkCJ06+l6eJBOLaW+9JTQlndMHW7CHeqvhSBa9dmDI2agTkAFAPGdl1wn66+LQFwHTmIoV4DlHhdM+DSQTg3+9ZHJdFLs0cf1BS9PirxCaDoa7wpsfEYGjRCzaj+pYF65hhKVAIiIhYMBgwd++I66FvPREhE6d+GNj1Qyy3sXVsc+w9ialQfQz3dxqAhA7Gu8S1XU6sWRD0xlayHZ6Lm5pVuz5k5m9SrR5E6YjR5b82leOFS8t/9pGbNfYcwNqyP0aMZPHQA1tXlNFu3IGrGg2Q8+JSPpiEuBhGgrwGphIYQ0KVDjQMuAO0bxJCcXcCZnELdl+86Tv92DSuk09tpB529BlacLjcPfbWCq7q1YEgt1tCz7z3rf+LBaCTkigEUr/SvnqAoKOH6iy1zq6YEtGpKyfrqfe4/YWP7RnEkZ+VzJtvT/9lxhP4dKh5/Ij2XghI7nZuURUMWlNhwuPQ2OrfIys4Tqf/H3n3HR1H0Dxz/7JX03kNC7713kF6sFOVRQaWodERAkCKIWABBLKCCBbGBFRFBepPee29JIL0nl+Tq7u+PPZJcOgHj8/OZ9+vlS3I7u9+bLbNzszOz1AjxK7RuQdlnruJsL38kvQ7vRx8gY3vZhvHemvQelzuN4HLnF4h9ZyVpv+0k/t2vS13PcqnAfbV7d0wHHMsCXa3aeE6eQtqsGSj56rJoNEheanmnq1EDfY0amI+W/tvPduMSmpAwNIEhoNXh1K47luOO9y9NcF75o2vWDlucWh7ISQnoGth7vTq7qPPFxZTcmCXH3MgrfzRatA3bYb3i2KvTofyp0xLZPrG35Omnzq0E4OKGtnId5OSie0cZTl3DpXoozpWDkPQ6/Pp1InWr47yAaVuPEjBIvU/5PdI+d14ljasTGvv8qF4PNEWx2tRJv7UadPahapJOi2/PVuRcKvq+knnyGq41QnGposYP6t+RpC2OxyNpyzFC/tMFgMBH25G67xwAen+v3KHbLlWDcK0RSk5k8W80/emrtTzVcxhP9RzGrs1/8ch/1BcgNW7REEOmgaQCDeparRYfP7XHnk6n5YFeHbh+KW+IaLVaVfDy8eT0sXPFxhSE+0EMhSuZF2pjTLokScHAg8Bu4BJQTZKkmoqiXAeeLmb9CKAl8BPwGKAHkCSpKnBbUZTP7XMgtQDyvyrhMhAoSVJ7RVEO2ofG1VEUpbRXUVy2f69aiqJcA54FSn/PbCl0Wg3TH2rJmG/3ICsy/ZrXoFaQN5/sPEuDSn50rRdGh1ohHLwex8Blf6LRSEzq1QwfN2d6NgjnyM14Bn26GQnoUCu0UKNUkWw2Mj74EN/Fi0CjIefPTVgjIvAYMRzL5cuY9h/Ac8wYJFdXfN5Qh0nZEuJJmzEL4+49OLVoTsCqlaAomA4fwXTgYInhdFoN0x9pxZivdyLLCv1a1KRWsA+f7DhNg0r+dK0fTodaoRy8FsvAj/5AI0lM6tMcHzdnTBYbI75Qh2O4O+t5+4kO6LSlt9nqtBqm9+/AmM83qTHb1KVWiB+fbDlGg/BAujZUX3G/2T5pt5RveN2NhDSW/HEYSVLndn2uSxNqh5ZesXIg2zAs+wDv+YuRNBqMW/7EFhmB29ARWK9cwnzwAC79BuLUvCXYrMiZBjLfvcehU7KM+Y8vcRk2y/5a7F0oCbfR93gSOfo6tkvH0LV/CF29ViiyDXIMmH79+K7D6DQapvdsxJhfjqj7tnE4tQI8+WTfZRqE+NC1VjAdqgVy8GYSA1fuUY9nl/r4uDpxMCKRJbsu5u3b1jWoXYbGUIf4ZTifwN5bqXFVh2NbLvfjWBaxDcPl29SZ9gRpp2+SsOU4t1bvptmysXQ99D6WNAMnRqmvCDdcvk3s+kM8sHcxitXGuelfgaygoHBuxira/DADSavh9prdGC6rP+aqvdCHGuMexTnIhwd2LSRhh31CVEmLzifMPrmugsbFG2varWInMZ76+gKOnjxDWloGPfo/w9jnn+XxR/vc2/60ySTPX0bIp/NBoyFz3RYs1yPxGTsU8/krZO85iN+kkWjcXAlaNBsAa1wCCRPVN9yFfrUEfbXKSG6uVN66mqS5S8gpYeJrnU7LzIkvMmrqG9hkGwMe7Emt6lVYtnI1DevWolvHNhw9dY4PPv8WSZJo2aQBr708Knf96Nh44hKTaNW05J6ZDjG1WmZOGMHo6W9jk2UG9O1GrWqVWbbqRxrWqUm3Dq04evoCH365GgmJlk3qM2uC+qbNm1HRvPH+Z2g0GmRZ5vmn+pepYQmbTNwbn1J55VtIWg1pv2zFfC2KgInPYDx7FcPOw7g0rk34J7PRenng0a0tgS89w42HxoAsk7DwS6p8PR8kCeP5q6T+tLn0mLKN7C8/xGOWei8x79qEfDsClyeHY7t+GcuxAzg/OAB945YoNhuKIZOsZfYhGlkGTBt+xmvBclDAcvIQ1hNl+IEv28ha/gFebywGjQbT9j+xRUXgOmQE1quXsBw5gMsjA9E3awlWK4rBgME+DE7XoAmuTwxWJ5tWFAzL30fJKPrV13nxZMwbv8LluZmgsZeribfRdx+EHH0D2+Xj6Nr1RVevpTrZc44B02+f5q7u8vxcNAGVwMkF1ykfY/59BbZrpbytzSaT+u5SAj9aiKTVYFi/CeuNSLxGDcN88TLGvw7iM3Ekkqsr/gvU68IWl6D2XCovm0zKwqUEfbwANBoM6zdjuRGJ9+ihmC9cIeevg/i+rF6Xge/mXZeJk+agr14F38mj1XJEksj49mcs14qfLPoOnVbD9MfaMWblVrUsb1WbWsG+fLL1BA3CA+hqfxi0+fRN+jat7lCWbz0bwYmbcaRlm1h//BoA8wZ1ol6+Xg/F5TPpnY8JXfEOklZDxm9bsVyPxHfcc5jOXyF79yGcG9Uh5IM5aLw8ce/aDr9xz3Gr/0gknZawb94DQDZkEz99YalD4f6JPOq0GqY/3pkxy9erMdvWp1aoP5/8eZgGVYLo2kjt0bL5hNpbyaH+E5/KWz/tRiNJyIrCiJ4tHN4mV9J+jXl9OdW/eQM0GlJ/3o7pahRBk4aQc/YqmduP4NqkNlWXz0Tr7YFnj9YEvzyEq33Glb7t4sg2Mj/6AN931bLAuOlPbBERuA8fgfXyJUwHDuAxejSSqyvec9W6rByfQNprM0Grw+9D9R4rZ2eR/vbbpc63piaWyVm1FPdXF4JGi3nPJuToCFweH4b15hWsJw7g3Ls/ukb2+kBWJtnLFwJg2rYOt1Gv4rlwJUhg3rMF+VYpc1gpMubNX+Py9DS1/Dm1ByUpGn2Xx5FjbmK7egJd697o6rSw1+uyMP2xAgApoBIuPQejoCAhYTn0Z6G3BOeyyUTM+oK6q+cgaTUk/rCDnCu3CJv6FFmnr5O29SgJa3ZQ86OJNN3/MdY0A9fGLAFQ3wS3Zg7ICua4ZK5PUOfR0zjpqbd6DpJOC1oNGXvPkPB90ZNbKzaZqzO+pMkPs5C0GmLX7CL78m2qTXuSzNPXSd5yjLjVO6m3bAJtDy3Fkmbgwij1jXDe7epTfdqTKFYbiixzZdpnWPONWCjJvu0H6dSjPesP/YQxx8jcl9/JXfbD9lU81XMYemc9H69Zgk6vQ6vVcvivo6z9Lq9jSp/+Pdmy7n9w0m4x6q/CSeV549G/lSRJ1YANiqI0yvfZKqADcAtIB9YrirJKkqS+qHMXZQN7gZqKojwiSdIwoJWiKOPtjVG/A67AZmCcoigekiQNBaYCFsAAPKcoikMNR5KkZsBHgDdqA+AH9oao3cArd7qsFfF3D2CxfZ2jwBhFUUz2Wd9bKYpSan/qnDWvV+hJkf7pPbd93TXv8d0rPCYu93/+o9IYlv5RofHc2ty/ubPKSlO98JPVv52XT4WHNHy+o8JjHjlTqfRE91Hv829XaDyA2z1GlZ7oPgvf/FHpie4zxXpvc/SUx/XuMyo0Xkizsr+J736RjRVfh3JpXnqP2PstZVPpQ43uN9n2985HVFDQ7J4VGg8gZvauCo9Z6c1uFR7zn6j/XBtbscN+gqrd33mBysK50l295Pm+0Nev2HoBwLlPKr5sz7FV7L6dJJX+Num/w8m4/RVb0Fag9Gd7/GsbOby/3fFfedxEj6V8FEWJQJ3bKP9nw4pJuxl1rqWCn68CVtn/HY/6Cr87XrV//jXq/EclfZdTOM6PdOfzrqX8vQMoNHurfdZ3QRAEQRAEQRAEQRCE+0bMsSQIgiAIgiAIgiAIgiCUi+ixJAiCIAiCIAiCIAjCv4Ii/2tHwv3XEj2WBEEQBEEQBEEQBEEQhHIRDUuCIAiCIAiCIAiCIAhCuYiGJUEQBEEQBEEQBEEQBKFcxBxLgiAIgiAIgiAIgiD8O4g5liqc6LEkCIIgCIIgCIIgCIIglItoWBIEQRAEQRAEQRAEQRDKRTQsCYIgCIIgCIIgCIIgCOUiGpYEQRAEQRAEQRAEQRCEchGTdwuCIAiCIAiCIAiC8O8g/9Nf4H+P6LEkCIIgCIIgCIIgCIIglIukKOJVfIKjL8OfqdCT4olusRUZDoC1O0MrPKb+H7jUqsvGCo1nUiq+rdpFY6vwmAZZX+Exg1yyKzyms5O1QuO5uFkqNB5A+I4VFR4zpvfICo+ZnOhe4TFrdjdUaLz08xUaDgCLseI7futdKva6BPBprq3wmDlXzRUab9218AqNB/B0n/gKj/njluAKj/lP1H8qujZS8TWRf2a/Giq+KKDiSzwItFbszvWU/4kzCB6K/0H6RwJXgLQnu/1rGzl8ftz1X3ncRI8lQRAEQRAEQRAEQRAEoVzEHEuCIAiCIAiCIAiCIPwrKPK/tsPSfy3RY0kQBEEQBEEQBEEQBEEoF9GwJAiCIAiCIAiCIAiCIJSLaFgSBEEQBEEQBEEQBEEQykXMsSQIgiAIgiAIgiAIwr+D/E9/gf89oseSIAiCIAiCIAiCIAiCUC6iYUkQBEEQBEEQBEEQBEEoF9GwJAiCIAiCIAiCIAiCIJSLmGNJEARBEARBEARBEIR/BUVW/umv8D9H9FgSBEEQBEEQBEEQBEEQykU0LAmCIAiCIAiCIAiCIAjlIhqWBEEQBEEQBEEQBEEQhHIRcywJJWo371kqd2+GNcfEX5M+I/lcRKE0/o2r8cD7o9C5OHFr5ykOzfkWAL/6Vei4YDg6dxcMtxLZPeFTLIYcag7oQOPRD+eu71W/MobZo5GjrjtsV9e4NS7PjgONBsvuPzFt+MFhuVP3R3Dq2Q9kGcWYQ87K95FjIkGrw3XEJLTV64CikPPtx9gunS42j23nPUu4PY/7Sshj5/dHoXVx4vbOUxy+k8eGVWi/YARaZz2K1cbBmatIOnWDRqMfpsbADgBotBq8a4fxS+MxmNOyAGj15rOE2WMenPQZKWcLx/RrXI32H6j7NXrnKY7N/jZ3Wd0RvagzrBeKTSZ6xylOvqXuG5/6lWm7cAR6T1cUWeFi32koJgs+3ZpR483hoNUQ//0Oopetc4glOemos3QC7k1qYE01cHnUEky3EgFwq1+VmotGovN0Q5FlTvedjmKyENC/I+ETB4IC5rgUroz/CFNyVt7379aU2m8NR9JqiP1+B5FLfy8Us8Gy8Xg2qYElNZPzIz/AaI8J4BzmT9u973Nz0c/c+vQP9ZzwcqPektG416sMisLFSZ9iPnFJzXu3ZlSfNwK0GhJW7yB62W+F4tX+6CV7HjO5MmoJptuJBAzsTNiYfrnp3BpU5XTvqWSfj0DS66j+zgt4t2+IoihELVhNysZDeedFt6bUe2soklbD7e93ErF0faGYjZeNw6tJdSypBk6P/BDjrUT8HmhMndeeRnLSoZitXJn3PSn7zgNQa8aTVBr0ADofd3bWGFbovMjPs0sLwl5/AUmrJfmHrSR8+qvDcvc2DQl7/QVc61UjYsIi0v88AIA+LJDqn81EkiTQ60hatYHk7zeXGCt3m51bEvzaKCSthrSftpD82c8Oy11bNyJk1kic61YnetICMjfvz12mCw0k9J2J6EMDQIFbL8zBEp1QakzXDq3we3UskkZD5m+bSF/5o8Nyr2cfx3PAg2CzYUtNJ+n1xVhj1e0Gf/IOzo3rYzp1jvgJs8uUx9K89s4S/tp/BD9fH9Z9t/y+bNOlQ2v8XhkLWg2G3zaRscqxvPMc8jgeAx6y5zGN5DcWY4tNQF+nJv4zJyK5u4Esk/7larK37i5TTK+uzQmf+yJoNSSv2Ub8J47nj0fbBoS//gKu9atxc9xi0uznD0DNb1/HvXkdso5e5Prwt8qVZ13j1rgMsZfxe/7EtPGHotO16oz7hLkYXh+DLeLKXcdxad8a31fUOFnr/iTj64L79gk8+j2EYrMhp6aRPG8RtrgEtCFBBC6eB5IEOh2Gn37D8OuGMsV07diKgOmjkbRaMn7dRNqXPzl+p5aNCHh1NE51ahA/9R2ytu3LXeY36XncH2gDQMqK1WRt3vNfmUdtw1a4PDUGSaPBvHcz5s0/FplO16ITbmPmYHhrHHLkVSR3T1xHz0ZbrS6WA1sxrvm4TPEAnNq0wXP8eNBqydm4kezVqx2Wuw0ahOvDD6v5TEsj4913kePjAfAYNQrndu1Ao8F87BiZS5eWOW6Hec9SxX7P3j3pM5KKqCe0njaIOk90wtnbnZV1X8j9PLRtXdrPfRb/+pXZPm4ZNzceLTWetlErXJ4eiyRpMO/dhHlTMfu2ZSfcxr6OYd445Mgr6r4dO0fdt/u3Yly9rMx5LKh9vvrfnmLqRq2mDaK2Pc+r8uW5JG0K1LlSiqlzdcpX5zpSRJ1Ltto4ZK9zVe7dguZTnwBFQbbaOPL6dyQdzSsrWs9T61y2HBP7i4np17gaHe0xo3ee4qg95gOfjserZigATl5umDOy2dB7Vu567pX8eWz3Qk69t5ZzK/7M/fxe6pa+DarQYcFw9G4uZN5O5K/xav25UudGtJz5JFq9DpvFysk31xC//4J6LP6GeqVGr6Xtu8/j16Q6yDLH5nyH4cjFIo9rxzfyrpFdk4u+RtpMG0Sdx9Xz5ct6eedLkxcfpN5TXVFsNnKSM9n9ymcYopOLjJNf5zeepao95o7Jn5FYRMx20wZR1x7zs3wxGz7TnSZDeyHbZCxZRnZN/5LUqzFFxmn+5nOE9miKLcfMkZdXkFrEvvVtUo02H4xG66IndsdpTs7+xmF53VEP0WzuEH5rOApzigGAwPb1aT7vWTR6LZLNhsZJj6TVcOv7ndwoUJ/UOOlosmwc3vb65MmRH5JjrzPXfKkf4YO7odhkLsxaRdLuMwBUG/UQlQd3AyDzYhRnJi5HNllo/P4ovJvWAAmAX4BhgKG0/S0Ipfmf7rEkSZKPJElj72H9ZpIkPXQ/v9N/k/DuTfGqHsLPnaaw79Uv6TB/WJHpOs4fzr5pX/Bzpyl4VQ8hvFsTADoteoGj83/kt54ziNh8LLcx6fpvB1jXZxbr+sxiz8RPkRPjCjUqIWlwGfoSWYtmYHh1BPr23dFUquqQxHxgJ4aZL2J4bRSmjT/iMmQ0AE7d1DiGmS+StXAaroNHq5XmEvL4a6cpHHj1S9oXk8f284ezf9oX/GrPY5g9j61mPc2pJWtZ33sWJxf/SqtZTwNwbvlG1veexfreszi+4CfiD13MbVSq1L0pntVD+L3jFA5P+5I2xcRss2A4h6d+we8dp+BZPYRK9pjBHeoT3qclG3vOZEO36Vz4VK3ISFoNHZeO4fD0r9jQbTrbnngbxWIDjYYa81/g/OC3OfnAJAIHdMK1TrhDrODBPbCmZXGi/QRiVmyg2mvPqAu0Gup8/BLXp33GyS6TODfwdXWbWg3V3xrBucfncqr7FLIuRhI64sG8DWok6i54ntOD3+Fw50kEDeiIW50wh5iVBnfHmpbFoXYvcWvFRmrOHuKwvPYbQ0nZcdLxs7eGk7zrFIc7TeJI96lkX4m2x9NQ450XuTDkbU51eZmA/kXk8ekeWNMNnOwwnpjPNlD1tWcBSFq7l9O9XuF0r1e4OuEjTFEJZJ+PACB84uNYktI52WkCpx6YSMbB8w55rL9gBCcGL2B/5ymEDuiIe4E8hg/uhiXNwL52LxO5YiN1Zg8GwJKSyclnF3Gw6zTOvfQJjZaNy10ncetxDvWdRak0GsLfHMWNoW9wqec4fB97AOfalR2SWGISiZryIam/O/4wtSakcnXAVC4/9DJX+71C8JjH0QX5lSlmyNyx3HphDtcfHI3XI11wquUY0xqTQMyrS0j/Y3eh1SstmkLKF79yo+9obj7+Mtbk9DLF9J85gfixM7k94AXc+3ZDX6OKQxLzpWvEDB5H9KBRZG37C99JL+YuS1/1M4mvLSw9zl3o/1Avli8pX2NKkTQa/F6dQMKEmcQ8/ryax+oF8nj5GnHPjCX2yZFkb9+L78SRAChGI0mzFxI76AUSxs3Ad8oYJA/3MsWs/NYorj33Bhe7j8e3X2dcCpw/5ugkIid/SMq6vwqtnrD8NyJf/qDcWUbS4PLcS2S9NwPDjBHo2xUu4wFwccW590Cs1y6UL45Gg++rL5Hw0gxiB43ArU93dNUL3EsuXSPu2THEPf0i2Tv+wucldd/aklKIGz6BuCGjiB82Dq+hT6MN8C9TzMDXxhE75jWiHnsRj4cKn7PW2EQSXnsPw5+7HD53e6ANzg1qceuJMdwe/BI+wx5XGw3/2/IoaXAdPJ7sD2dhmPMi+jZd0YRWKZzO2RWnHgOw3sj7MapYLJh+/xrjL5+VHqdAPj0nTiTt1VdJHjoUl+7d0VZ1zKfl6lWSR40i5fnnMe3Zg+eoUQDoGzZE36gRyc8/T/Lw4ejq1UPfrFmZwlbu3hTv6iH80GkKf736JZ2KuWdHbj/Bb4+8XujzzOhkdk9ewbV1B4pYqwiSBtchE8h+fyaG2S+gb9ut6H3r4opTzwFYrxfYt7+twvjTXe7bAu7k+Sd7/a+4PEdtP8G6IvJcnDB7nWttpykcLKHO1W7+cA5M+4K1BepcLYupc8XuO8/6XjNZ33sW+6d8TsfFeQ0Id2Kus8dsW0LMg9O+YJ095p06119jlrGh9yw29J5F5J9HifrTsWGw1dwhRO9yfHh5r3XLjote4Ng7P7Ku5wyiNh2j0Ri1XmtMyWT7sPdY13MGe19eQceP1Hrv31WvrDVEbZTY2GMG259aSIvXBxdZn67STT1f1nSewp5Xv6TzO0XHj9h2grWPFj5fks5FsPbh2fzceyY3/jxCO/txLUnVbk3xqR7Cd52nsOvVL+lSTMyb207wcxExr6w7yJpeM/ix7yxOLN9IpznPFLl+aPemeNYI4c8OUzg29UtaLhheZLqWC0Zw7JUv+LPDFDxrhBDSvWnuMtdKfgR3bUzW7aTcz/RebrRcMJx9w95jS/fp6DzcODp4AX91nkKlAR3xKKI+aU0zsKfdy9xcsZG69vqkR50wQvt3YO8Dr3D06fk0XPg8aCScQ3yp9kJf9veZyd4uU5E0GkL7qw+8L87+hn3dX2Vft1cBooDxxezm/9/kf/F//6X+pxuWAB+g3A1LQDPgrhqWJNV92++SJGlL+rus6xWlau+WXPtFfYqaeOI6Tl7uuAb5OKRxDfJB7+FK4gm1YejaL/uo2qcVAN41Qog7pPYmifnrHNUeal0oRo1+HbAc2lXoc23Nesjx0SiJsWCzYjm0C33LDo6JjNl5+XF2Afvk/5qwqlgvqA0SSkYaSrZB7b1UhCp9CuTRu5g8ehbIY181jygKTp6uAOg93ciOTy0Uo3q/9txYdzD378p9WnLTHjOplJhJ9pg3f9lHZXvMOs/15PyyP5DNVgBMyRkAhHZpTNrFW6RdiALAnGoAWcazeS2MN+MwRSWgWKwkrtuPXx/HY+HXpzUJP+1Wv9OGg3h3agyAb9emZF2IJPtCJABW+zYlSUKSQOvmDIDOww1zXEru9rxa1CL7ZhzGyAQUi42EdQcI7OsYM6BvK2LtMRP/OIRvp0Z5yx5sTU5UAlmXb+d+pvV0xad9fWK/36nueosNa4Z6Dng0r0VORBymqHgUi5Wk3/cVyqNv3za5eUzecBDvzo0pKGBAJ5J+z+thE/RUd6I/Wqv+oShYUzJzl3nb85hjz2PcugME3Tkv7AL7tiLmJ/VHefwfh/Hr1BCAzHMRmOzniuHSbbQuTkhOagfS9OPXMCekFfpuBbk1q40pIhbzLTXPqX/sxbtXW4c05tsJGC9FQIE3YygWK4r9/JGc9KApW5Hk2qQO5sgYLLfiwGIlY+NfePZo75DGEp2A6XIEKI53PqdalZG0WrL226/NbCOK0VRqTOdGdbHcisEaHQdWK1mbd+PW1bEsMB49nbst09mL6IIC85YdOYmSlc391KpZY7y9PO/b9pwa1cV6OwZrdKyaxy27ce3a0SGN6ZhjHrVBAQBYo6Kx3lIbWG1JycipaWh9fUqN6d6sNqaIOMz2ayZ1/V68e7dxSGO+nUDOpchCxxIgc/8ZbIac8mQXAG2NAmX84V3oW3QolM5l4HC1J5PFXK44Tg3rYb0Vjc2+b7O37sKti2Mc0/FTKCb7vj13EV2w/fyxWsFiAUBycgJN0Q8oCnJuXBdLVAzW2+o5a9i0G/fujteJNSYe85WbKHKB66RmFYzHzoJNRskxYb5yE7dOjuXKf0MetdXrIifGoCTFqcfv6B50zQofP+f+Q9WeTPmPn9mI7dr5uz6m+nr1sEVHY4tV82ncuRPnjo7XieXUKbDn03LhAppAez4VRc2fTgd6PZJOh5ySQllU692SK/Z7dsKJ6zh7ueNW4J59Z1l2EWW34XYSKRdvlfkNRdoadZET8u3bI7vRNS9q3w5TezIVtW+t5bte7qjauyVX8+W5qPrfnWU5Zbhf3VGlT0uul6HO5ZSvznX9l31UyVfn0tvrXE756lzW7Lx7ic7NGUXJ29eV88Usa53LIWY+1R5ty83fHetzhqhE0i9HF8rnvdQtvWuEEH+n/rw3r/6ccj6SnPg0ANIuq3UHjZPub6tXetcJI87em9qUnIE5PZugptUL75feLbnyq/18OVnCNXKy6Gsk5uBFrEb1nI0/cQ2PkNIfdFXv3ZJL9pjxJcSMLyamJd/9S+/mDErR12dY35ZE/LwXgOQT19B7ueFSII6Lfd8mn7gGQMTPewnv2zJ3efM3nuXMm2scYlQd0IHbfx4lOzoZv+Y1yboek1ufjF13gOAC519w31bcttcn4/44TIC9PhnctxWx6w4gm63kRCWSfTMOnxa1AJC0WrV+qdWgdXPGFGe/Xhzv3a7k/oIShHvzv96wtACoKUnSKUmSFgFIkjRVkqSjkiSdkSTpDftnAyRJ2mFvFAqVJOmKJElVgHnAk/b1n5Qkaa4kSa/c2bgkSeckSapm/++yJEnfAOeAykXFKUiSpN6SJB2UJOmEJEk/S5LkYf88QpKkhZIknQAGFfH305IknbXHX5hvewZJkt6TJOk00L6omPm5hfiSFZPXFTU7NgX3EF+HNO4hvmTF5lXOsmJTcLOnSb1ym6p91IK1+iNtca9U+EZR49G2WA7tLJx33wCUlLxhUXJKIpJvQKF0Tj374bH4W1yeGonxW7XLty3quvoDRaNBCgxBW60Okl9QmfKY//vnT5OdL4/Z+dIcfv07Wr32NP85+iGtZz/N8fmO3dW1Lk6Ed21CRL4nXK4FY8ak4FogpmuBmPnTeNYMIahtXfpumEuvX2fh37QGAF41QlAUhe6rp/HQlrdoMFZ9wuUU6oc5Ju8piTk2GedQx2PhFOqH6U4am4w1MxudnycuNSqBotBgzWs03fouYePUIWOK1cb1Vz+n2a4ltD79Oa51wolfnXccnUP8MOXLoykmGecCFQXnUD9M9q7Oik3GlpmN3s8TrZszVcf3I2JxgSFWVYKwJGdQ/8OxtN6+kHpLRqGxN2w5h/hhjs6fxxScQhyfujuH5NsPNhlbhprH/AIe60jSb2oFQuul9hKo8urTNNm6iDqfTUEf4J2b1iXED2O+PBpjUgrl0SXUD2O+PFozc9AXiBn8SFsyzt7MbegpK32IP5bYvDxbYpPQh5Shp8Gd9UMDqLv5IxoeWknC8l+xJpT+I0sX4o81f8y4JHTBZYvpVC0cW2YWYR/PovrvSwl6dUSZGrS0QQHY4vLKAltCErrgwmXBHZ4DHiRn/5Eyfaf/FrrAAKxxeUMCbQmJaIOK368e/fti3F94OI1Tw7pIeh3W20V3589PH+LvUC5YYpPv6vy5V2Up4zVVa6PxC8R6+nC542iDArDF58WxJiTmNsoVxaPfg+QcyDt/tMGBhKz5nEob15Dx9Y/YkkofnqEL8sea75y1xiehKyFmfqbLN3Dt1ArJxRmNjxeurZuiCwkscZ1/Io+STwByvuOnpCai8XE8fzRVaqHxDcR69v5cj5rAQOTEfOdMYiLawOL3jevDD2M+osa2XLiA+dQpAteuJfDXXzEdOYItKqpMcd3LUE+4nwrv2yQ0PgWujSq11GvjzN9T1rmH+GIokOeC9b/yKGudq7h65RF7nWvQ0Q9pVaDOVaVvKwbseZeeX7/C/imfO2wvu0Bd9m7qeXcEta1LTmI6mTfVoZU6N2cajXuE00vWljufxcVMu3KbKvb6c7Vi6s9VH25NyrkIZLP1b6tXpp6PIrx3CyStBvfKgfg3qYZ7aOH7RMHzxXAP50v9p7oQtbv46Svu8Cgipsddxmw8tCfP7nuPDjOf4q853xSZxjXEz+H8yYlNwTW0wL4N9SU7xvFYutrrg5X6tCQnLiX3oe8dnjVDcPJ2p9uvs2j78Vg0zvq8GGWoT1rs9UnnED9y8g0bNMam4BLihykulZufbqDbiY/pfmY5loxskvacyU3X5IPR9Di3HKAeUPZxwYJQgv/1hqXpwHVFUZopijJVkqTeQG2gDWpvpJaSJD2gKMpvQCwwDvgceF1RlChgDvCjff2iB8DnqQ18oihKQ6BuUXHyJ5YkKQB4DeipKEoL4BgwOV+SZEVRWiiK8kP+v4G/gIVAd/u2W0uS1N+exh04rChKU0VR9uXbFpIkjZQk6ZgkScf2ZF0tJStls3fK59R/rif9/nwTvYcLssXxh3Ng85pYjWbk2xHljmHe/juGV57F+OPnOPdTu7Fa9mxCTknEY96nuA4Zi/Xa+SKfuN8P9Z7rwZG53/NT64kceeN7Or33osPyKr2bE3/sSu4wuPtBo9Xg5OPB5kfmcuLNNXReofZglXRagtrUYf/4T9jSfx6V+7bK7XlUXpJOi1fbelwZ9yFn+72G34Nt8O7UGEmnJWRoH073nMrRpi+SfTGS8JcG3I/sUX3qf7i1YiO2bMfeLJJOi0fj6kR/vZWjPV/Flm2i6oT+9yUmgEfz2thyTGRfvpUbzzksgMyjlzjTeyqZx69Q9fWh9y0egHvdcGrPHsyFV764r9stC0tsEpf7vsSFB0bh+3h3dAE+f2s8SafBrVVDEhZ8yc2BE9FXDsV7YM/7GsP94R44NahD2qqfS0/8/5T7Qz1wblCX9G8c5+zRBvgR8OZ0kuYuLvbJ6/8rkoTr06PJ+eH+zGFVFm4P9sSpfh0y8u1bW3wicU+/SGz/53B/pDcav7+vQQEg58AJsvceJey79wleNAPj6Ytgu3/3rwrLoyTh8p9RGH++tyFZ5eXSqxe6unXJ+kGtImnDwtBVqULSoEEkDRqEU4sW6Bvf2/3xHyNJuDw5GuOPK/7pb1Lh6j7Xg6Nzv+fn1hM5+sb3dMxX54rafIzfukxj5/Pvq/Mt3WfV+7cnIl9vpaZTBnLh880OvaXul32TP6fe0J48uulN9O4u2ArUn33qhNFq5lMcnrbyvsQrrl55/Yc9ZMem8ODmN2k17xkSj10t1Mvyfqo9oCOBTWpwavnGvy1Gfme/3s63naZwcP4PtH6p/33fvtbViQYvPca5d38ptEzSavFrUp2/nlnMhQ/W4dWoGu41Qu9bbJ23O0F9W7K79QR2Nh2D1s2ZSo93yl1+5uXl7GgyBuAi8OR9Cyz8TxOTdzvqbf/vzsQuHqgNQH8BE1B7Gx1SFGVNObYdqSjKnVl/S4pzRzugAbBfUsczOwEH8y0v2JB15+/WwG5FURIBJEn6HngAWAfYgF8pgqIonwGfAeOSz0e2BEg6fQP3SnlPJtxC/ciKcxzqlRWXinu+3i/uoX5k29OkX49l8xC1w5RX9RAq92jmsG6Nx9pxY91Bahf1fVKTkPzynkRq/AJRUpOKSKmyHNqF67CJ5ADIMsbvP837TnM+Qo7NG1Ll1LMfj01Xe94knXLMY/7vf0d2XCpu+fLoli9NrUGdcydbjPjjMB0XOU5eWf2x9txcd5B6Q3tS1z5WPdke887zSPdKfuQUiJlTIGb+NNmxqdyy94BKPnUDRVZw9vMkOzaF+EOXMdknBYzZeRrvJtXJOHwJp0p5TzudQv0xxTr2TjHHpuBcKQBzbApoNeg83bCmZGKOSSbj0MXcIWCpO07i0aQ6NoM6tMgYqT65S1p/gLAJeQ1LprgUnPPtV+dK/pjiHGOaYlNwDlO/i6TVoPV0w5KSiVeLWgQ+0paas4eg83YHWUE2mUn84xCmmGQy7F2NE/44RNUJ/Ym1x3MKy59HP8xxjk/dTXEpOOXLo9bLzWFoW0D/jiSty2tvtaZkYss2kvyn2lsi+Y8DBD/dI3e5MS4Fl3x5dKnkVyiPxtgUXPLlUefpisUe0znUj2ZfTeHc+I/Jse/Hu2GJS1YnwbbThwZgiSu9p0FB1oQUjFeicG/TIHdy72LTxiWjyx8zJABrfNliWuKSMF68oQ6jAzK3HcS1WT3Sf9la4nq2hCS0+XpsaIMCsMYXLgtc2jbH54XBxD4/JXdoz/8X1sQkdCF5vSq1QYHYEgrvV5c2LfB+fjBxLzjmUXJ3I/DDt0n7eCXms0VPrFqQJS7ZoVzQh/qX6/wpr1LLeBc3NOHV8Zi+BADJ2w+3l98k+4PZdzWBty0hCW1wXhxdUCC2hMLnj3ObFniPGEz8yMlFnj+2pGQs12/i3LwxOTsKzzmVnzUh2aGXkS44AGsRMYuT9tka0j5TqxlBC6djibxdYvp/Io9KWhKafMdP8g1ETst3/ri4oqlUDfdXFqnLvf1wGz+P7GVzkCPL9/BKTkzMG9qG2oPJlq8H0x1OLVvi/swzpEycmJtP506dsFy4gJKjDgExHz6MvmFDLGfPFhmr4dCe1LNPept4uvR6wv1UeN8GIKflvzZc0YRVw33aYnW5tx9uL80j+6M5yJF3P7n9HQ0K5Nmjkj937kzuRdT/7ma79e3bLWudq7h6Za1BnXMn8o744zAdCtS5AOIPX8avQVUe3TEfxSaTfOoGbgXqsndTzwN1DssqD7Zm44N5L4AIaF6Lqg+3oeWsp3DyckPS62jwYl+MKZn3XLdMvx7L1sH2+nONEMLz1Z/dQv3o/uXLRG05zgNfTAT+vnqlKSWT43O/z12nz/o5pN+IBdRrpP7TjufLHR7lOF/COjWkxYTH+H3Q27lD8gpqPLQnDewxE4qIaSjnOXrl90N0eTtv7qTGQ3vS5Ck1Tsppx/PHNdSPnNgC+zY2FbdKjscyJy4Fj6rBuFcJpM+O+bnr9t76NtsfnEN2bAqmVAO2HBOZV2OwZhnxbFiFrBuxuJZQnzTa65N6e33SFJeCa1i+umioH8a4FAIeaEROVCLmZLXOGb/xCL6t6xDza75+Berw3B+AacBX5dp5/8X+pj4FQgn+13ssFSQB8+09kJopilJLUZQv7cvCUafLCi5hjiQrjvvUJd+/83dZKSlO/jTb8qVpoCjK88Vsr6i/i2JUFMVWSpqP70ysHbn5OLWeUFu3A1vUxJKZXWgsfU5CGhZDDoEtagJQ64lORG49DoCLv5c9JxLNJvbj4rc78uVOovqjbbmx/iBFsd24hDYkDCkwBLQ69O26YTnh+KNXE5w3sZ2uWTtscfYx7k7O4Kzuel2jlmCzqW+LszNv/z13Yu2oLY55NGcUk8dMxzxGbVHzmB2fSkj7+gCEdmpIxs243PX0nq6EtKtH1JYTXPp6O3/2msWfvWZxe/NxqttjBpQSM8Aes/oTnbhlj3lr8zGCOzYAwLNGCBonHaaUTGJ3n8G3fmW0rup46qD29ci+cpvMU9dwrRGKc5UgJL2OwP4dSdnqOIwmZesxgv7TVf1Oj7Qnff85AFJ3n8KtXhU0rk6g1eDdvgHZV25jjk3BrU44Ovsx9nmgKTlX8378ZJ68jluNUFyqBCLptQT170DSlmMOMZO2HCfUHjPw0Xak2sfxn+j3Ogdbj+dg6/Hc/uxPIj78jeiVWzAnpmOKScbN/nYWv86NybqixjScuoZr9VCcK6t5DOjXiZQC8VK3HM3No/8j7Unfdy5voSTh/2gHktbtd1xn6zG8Oqjj2L07NSH7yq3cZRknr+NWIwRXex5D+ncgwX6M7kjccpxK/1E7IwY/2jb3zW86LzdafP8qV99aTdrR8v0QyD59FefqlXCqHIyk1+H7aGcytpVtyJA+xB/J2QkArZc77q3qY7oeXcpakHP2Ck7VKqEPDwa9Dq+HHyBzx6FS1wMwnrmK1tMdrZ96zri3b4r5WulDUUznL6OvEoYuLAR0Otz7diV7j2O54VSvJgGzXyZ+4hzklLQyfZ//Jubzl9FVDkNXyZ7HPl3J2eNY3unr1sJv1sskvDwHOTUtb4FOR+B7c8nauI3sHXvLHDPr9FWcq4XiZL9mfB/rTPq2ihtCaLt5CW1wGFKAvYxv2w3LyXx5zskic/xAMl8ZQuYrQ7Bdv3DXjUoA5guX0FcOQ2vft269u5HzVxH7duYkEifPdti32qCA3OtE8vTAuWljrBG3KI3p3J1zNhh0Ojwe7ErWrrJdJ2g0aLzV4bJOdarjXKc62QeOl7jKP5FHW8RlNEH5jl/rLlhP57suc7IxTB6EYcZzGGY8h+3GxXtqVAKwXL6MNjwcTYiaT5fu3TEdcMynrlYtPCdPJm3mTJS0vHzaEhLUybq1WtBq0TdtijUykuKc/3o7v/aZxa99ZhGx+Th17PfsoBY1MWdmFzlny/1iu3kZTf5ro01XrKcK7NuXn8Dw6rMYXn0W2/WL99yoBHDh6+2s7TOLtfY81y6Q57uZS6ngdvPXuWqWoc5lzlfnqlmGOpdnteDc9f0aVcNmtvBHjxlsKBAzoEVNLGWoc9XMV+cCCO3ciPRrMQ5DybYMfJO17Saxtt0kLn6xhVOLf+WnVi/dl7pl/vpz04n9uGyvPzt5udHrmykcf+dHjs5b/bfXK7WuTmhd1SkHQh5ohGyVc9+cdv7r7fzSdxa/9J3FzS3HqWPvDRPU/O6vEf+GVXlgwQg2j1iC0T6/U1HOfr2dH/vO4se+s7ix5Tj17DGDyxHTO985U61HM9Ij8urvZ7/eztZeM9naaybRm45RbVBn9Xu2qIUlMwdjgThG+771t89tVG1QZ6I3Hyf90i1+bzyWDW1eZkObl8mJTWFr71kYE9OJ3nKcwDZ1kLQa0i/fxtnfC2tmDpJeS2j/DsQXqE8mbDlOuL0+GfJoW5Lt9cn4LccJ7d8BjZMO1yqBuNcIIe3ENXKik/FpUUutvwP+nRthuKrW89zy5R14DLhU5h0nCCX4X++xlAnkn/BkC/CmJEnfK4pikCQpDLAAKcBK4GlgKOqQtMVFrB8BPAIgSVILoPAMdyXEURQl/3u3DwEfS5JUS1GUa5IkuQNhiqKUVnM4AnxkH0qXav/O5Ro7e2vnKcK7N2XQvvewGs3snZzXpb3/lrdZ10d9c9WBmat4YMlI9XWpu09ze6c6Nrpm//bUH6oOdYnYdIyrP+Y9AQ1pV4+smBQyoxKhZhHBZZmcb5biPnWh+irqvzYhR0fiPHAYtpuXsZ48iFOv/ugatgCbFSXLQM5n6tMdycsH92kLQZZRUpPIXj6/2Dze3qHm8fH972HLcczjY1vfZr39lbIHZ66i8/tqHqN35eVx/9QvaTvvWTQ6DTajhQPT8toHqz7Yiui/zmLNcewmHb3jFJV6NKXfgfew5pg5OCkv5kPb3ubPXmrMIzNW0eEDNWbMrtPE2GNe/2EP7ZeM5JGd85EtNg5MVLvDm9OzubhiEw/+OQ8Uheidp0nbfgKAGzO/oOGa10CrIWHNTnIu36bKtCcxnLpOytZjxK/eQZ1lL9Hi4FKsaQYuj3ofAFt6FjEr/qDp5oUoikLqjhOk2rd5672fafzbPBSrDdPtRK5OzHutsWKTuTJjJc1+mIWk1RCzZhdZl29Tfdp/yDx9naQtx4ldvZMGy8bT7tBHWNMMnBv1QbHH6Y4rM1fS4JOX0DjpyIlM4OLET9RCzCZzY+YXNFgzG0mrIf6HneRcuUXlqU9hOH2N1K3HiF+zg9pLX6L5gWVY0wxcGf1+7na92jXAHJOMKcqx51Dk299Ra+lL6OaNwJKczrVJea/FVmwyl2Z8RYsfZiJpNUTb81hz2iAyTt8gcctxolfvotGycXQ69AGWNANnRn0EQOXn++BWPZgaUx6nxpTHATjx5DuYkzKoPXswoQM7onV14oGTHxP9/S6uLy7cjRqbzO05K6jxzVwkrYaUn7ZjvHqLkMmDyT5zjYztR3BtUovqn81E6+2BV8/WhEwazOVe43GuVZkar41Qh0xJEomfrcN4ufgfWfljxr3xKZVXvoWk1ZD2y1bM16IImPgMxrNXMew8jEvj2oR/Mhutlwce3doS+NIz3HhoDMgyCQu/pMrX80GSMJ6/SupPm8sUM3n+MkI+nQ8aDZnrtmC5HonP2KGYz18he89B/CaNROPmStAi9WmyNS6BhIlzAAj9agn6apWR3FypvHU1SXOXkHPgWEkRSzX19QUcPXmGtLQMevR/hrHPP8vjj/Yp/wZtMikLlxL08QLQaDCs34zlRiTeo4divnCFnL8O4vuymsfAd/PymDhpDu69u+DSvAlaby88Hu0NQNLri7BcuV5SRLDJ3Jr9GbW+U8+f5B93YLxyi9Ap6vmTvu0Ibk1rUePzGWi9PfDu2ZrQyU9zsecEAOr8+g7ONcPRurvQ6MiXRE5dRuaekyXHzE+Wyfm2iDJ+wDBsEWoZf1/YZFIWLSVo6ULQashav0ndt6OGYb54Wd23L41E4+pKwAL1nLHGJ5A0eTb66lXxeXl07nWS8d1PWK7fLFPMpHc+JnTFO0haDRm/bcVyPRLfcc9hOn+F7N2HcG5Uh5AP5qDx8sS9azv8xj3Hrf4jkXRawr55T91Fhmzipy8sfSjcP5FHWca4ehluL7+DJGkw79+CHBOJ82PPYYu8gvV0yQ1pHvO/QXJ1A60eXfMOZL8/Azm2lIZmm43MDz/Ed9Ei0GgwbtqELSIC9+HDsV6+jOnAATzGjEFydcX7DXXqSjk+nrRZszDt2YNT8+b4r1wJioLpyBHMB8t2jkXtPEWV7k15yl4X2p2vnvD4lrf51V4XajvrKWr174DO1YkhRz/i0prdHF+ylsCmNej9xcs4e7tRtVdzWk1+nJ97TC95336/DLdJ85E0Gsz77Pu231BsEVccG/CK2rcLv3Xct0uml75vC7i18xSVuzflSXue9+TL88Atb7PWnuc2s56ipj3PTx/9iMtrdnOiiDmH7ri94xRh3Zsy0F7n2ldMnevQzFV0ylfnirbXfw5M/ZI2+epcB+11rqoPtabmE51QrDb1+47Jq49E22MO2K/WuQ7ki/nI1rfZYI95eOYqOrw/El2BmADV+rVzGAZXmnutW9bo3556w9T6c+SfefXn+sN74VktmKaTBtB00gAkYMdTC/+2eqWLvxc91ryKIstkx6VyYELeaID87lwjT+9T4++ekhf/ic1v84v9TbftZuZdI88cUa+RY++vpf2sp9G7udBr+UsAGGKS2TxiSYn7OHLnKap2b8qz9pg78sV8cvPb/GiP2WHmU9Tp3wG9qxPDjnzEhTW7OfL+WpoM6014p4bIVhum9Cy2Typ6aGnsjlOE9mjGwweXYM0xcyRfut7b3mFrr5kAHJ/xFW0/GIXWxYnYnaeJ3VnyPFGZV2OI3XWGPjsXgCxz+4fdNHxnOGg13F6zC8Pl29SeNoj00zdI2HKcW6t30XTZOLrY65Mn7fVJw+XbxK4/SOe976FYbZyf/hXICuknrhG34TCdtqk99zLORnDr2x0gSTRZOladBF8dERMKjCnxywpCGUnKv2EuhnsgSdJqoAmwyT7P0kTgTt9aA/AMMATwURRlsiRJnsBRYAAQj9pIpAfmA+uB34Ew4DDqBNl33sG+QVGU3NdeFRVHURSHXwKSJHVHnS/J2f7Ra4qirJckKQJopShKkj1dwb+fBmai9nraqCjKq/bPDYqieJS2T74Mf6ZCT4onusVWZDgA1u68f+OYy0r/D1xq1WVjhcYzKRXfCdJFU1onvPvPIOtLT3SfBbnc3zeblYWz091NKH6vXNwqfghb+I6Kn6ckpvfICo+ZnOhe4TFrdjdUaLz08xUaDgCLseKfz+ldKva6BPBpXqYXzt5XOVfv7c1md2vdtfAKjQfwdJ+7HwZ9r37cElx6ovvsn6j/VHRtpOJrIv/MfjVUfFFAxZd4EGit2J3rKf8TZxA8FP9D2V4F+v9Q8qNd/rWNHP5/7PmvPG7/6z2WUBRlcIG/PwQ+LJBsXr7lmagz6N/RukDa3sWEapT/j2LiFPxuO4vYPoqiVCvl7zVAoXmgytKoJAiCIAiCIAiCIAj/b4k5liqcmGNJEARBEARBEARBEARBKBfRsCQIgiAIgiAIgiAIgiCUi2hYEgRBEARBEARBEARBEMrlf36OJUEQBEEQBEEQBEEQ/h0UMcdShRM9lgRBEARBEARBEARBEIRyEQ1LgiAIgiAIgiAIgiAIQrmIhiVBEARBEARBEARBEAShXETDkiAIgiAIgiAIgiAIglAuYvJuQRAEQRAEQRAEQRD+HcTk3RVO9FgSBEEQBEEQBEEQBEEQykU0LAmCIAiCIAiCIAiCIAjlIobCCYUMXli9QuNlfH67QuMBPPV+nQqPibNLhYfM/GhjhcZz71mtQuMBaMIqVXhMPDwrPGTW51sqPObpEyEVGu+Bv+ZWaDyAmN4jKzxmpa2fVXjMUENKhce80n1uhcar0sNcofEA5IyKj+nUolqFx0z/I6LCY9osFfvs87klFV8viJmXWOExn/m4SYXHRKr459iXJ/xVofGqtEiv0HgAuqCKr1fqalR8nevC0tQKj2mt4HN2rCahQuPd8dA/ElX4txINS4IgCIIgCIIgCIIg/CsoYo6lCieGwgmCIAiCIAiCIAiCIAjlIhqWBEEQBEEQBEEQBEEQhHIRDUuCIAiCIAiCIAiCIAhCuYg5lgRBEARBEARBEARB+FcQcyxVPNFjSRAEQRAEQRAEQRAEQSgX0bAkCIIgCIIgCIIgCIIglItoWBIEQRAEQRAEQRAEQRDKRcyxJAiCIAiCIAiCIAjCv4KYY6niiR5LgiAIgiAIgiAIgiAIQrmIhiVBEARBEARBEARBEAShXETDkiAIgiAIgiAIgiAIglAuomFJEARBEARBEARBEARBKBcxebdQJvuvxfLullPIssKA5tUZ0al+oTRbzt9ixZ7zIEGdYB8WDGzH0ZsJLNp6KjdNRFIGCx5vT/d6YaXGdGrTBs/x40GrJWfjRrJXr3ZY7jZoEK4PP4xisyGnpZHx7rvI8fEAeIwahXO7dqDRYD52jMylS0vP49UY3t14DFlRGNCyFiMeaFg4j2cjWbHrDCBRJ8SHBf/pBMAHW06y90o0ACO7NqJP42qlxgPYf/k27/5+CFmRGdCmLiO6NXVYvmj9IY5ejwXAaLGSYjCyb96zxKRmMvnrHciKglWWebpDAwa1L3xMSuPUug0e4yaARoPxz41k/+C4j10eeQy3fgNQZBtKTg6Z7y/GFhl513G0NRrj1HMIaDRYT+3Bcmijw3Jd4044dX8SOTMVAOvxHVhP7wHA7dWvkBNvAaBkpGD65YMyxdx/PY53t55Rj2ezaozoULdQmi0XbrNi70UA6gR7s6B/GwBi07N5Y+MJ4jNykCRY+mQHwnzcS453OZp3NxxRr5HWtRnRtbHD8kUbjnD0RhwARrONlKwc9r0+GICxK7dx5lYizasGs3RYjzLlD0Dfsg3uoycgaTQYN28k5+cCx++hx3B5ZADINhRjDoaPFmOLikTy9MJz1jz0depi3LaZrE8/LHNMv25NqfPWMCSthpjvdxK59HeH5ZKTjobLxuHZpAaW1EzOjfwQ463E3OXOYf6027uEm4t+JurTDWWKue/wCRYs+wKbTebxh3vxwpDHHZbHxCUw+92lpKRl4O3pwYJZkwgJCuDIybMsXPZlbrqbUdEsmjOFHp3blRrTpUNr/F4ZC1oNht82kbHqB4flnkMex2PAQ2CzYUtNI/mNxdhiE9DXqYn/zIlI7m4gy6R/uZrsrbvLlM+SvPbOEv7afwQ/Xx/Wfbf8nrcHsO/oaRYu/xabTWbgg1154cnHHJbHxCcyZ8nnpKSr+3X+tDGEBPrnLjdkZdNv5DS6t2/FrPHDyhTT44EWhM4ZCRoNqT9tJWn5Lw7L3Vo3JHT2i7jUq86tie+SsWl/7rKGV3/HeFktfywxiUSNfLNMMbUNW+Hy1BgkjQbz3s2YN/9YZDpdi064jZmD4a1xyJFX0dZvgcvjz4NWBzYrxl8+x3bpVJli6pq2xnXYeNBoMe/ciOn3NQ7LnXo+inOf/iDLKMYcsj97DzlazZumSg3cXpyM5OoOikzmzNFgsZQYT1O9EU49Bqtl7Om/sB7+03EfNOqIU7cnUexlrOXEDmxn/gJA8vTD6cHhSF5+oCiYfn4fJSO51Dw6t22N10Q1j9kbNpL1nWMe3Z8chOsj6jUip6WTPv9dbPHxODVvhtdL4/L2VZUqpM6dh2nv/oIhCsds1xqfyeORNBqy1v9J5jeOMT2efgL3fg+hWNWYqW8twhYXn7tccncj+IevMO7ZT9rij0qNV1Bp9YRFfx7n6E01ntFiJSXLyL5Z/7nrOPm5dmxFwPTRSFotGb9uIu3LnxyWez83EK/H+6LYbNhS0kmcvQRrbMLd5aucdZFLMcm8s3Y/BpMFrSTxQvdm9GlWo2wxL93i3fUH1Xtmm7qM6N6sQMyDHL0W4xjzzaH2+s82ZNle/+nYkEHtG5QppmeXFoS9/gKSVkvyD1tJ+PRXh+XubRoS9voLuNarRsSERaT/eQAAfVgg1T+biSRJoNeRtGoDyd9vLlNMffM2uD2v1rNM2zdiXOt4n3bu8xjOD+bdp7M+WYx8O6+epQkIwvujr8n5cRXG34suuwrSNmyFy39GI2m0mPdtwrzlpyLT6Zp3wm30bAzvjM8r8waMAJ0OrFaMv36O7fLpUuPdS/njOvVLlMTbAMgZyZjXlu269OranCpvvABaDUlrthH38VqH5R5tG1B57vO41a/GjXGLSd14MHdZ7e/m4N68LoajF7g27O0S43h3bU61N0cgaTQkrNlOzLLfHJZLTjpqfTQR98Y1sKZmcnX0e5huJyLptNRYPBb3xjWQdFoSf95NzDL1OzY/vBybIQdFllGsNs49OK3E7zD1zYl06tEeY46R119+h0tnrxRK89mvSwkI8sdkNAEw9qlJpCanAdDr0e6MemU4igJXzl9j1rg3Soz3r6FI//Q3+J8jGpbsJEkaDWQrivLNfdjWTEVR3rkPX+u/gk2Wmb/pBMuf6UKwlytDvthOl7qVqBnonZsmMjmTlfsvsmp4d7xcnUjJMgLQunoQP43qDUB6jolHl26ifc3g0oNqNHhOnEjaK69gS0zEb/lyTPv3OzRqWK5eJXvUKDCZcH3sMTxHjSJ93jz0DRuib9SI5OefB8B36VL0zZphOXWq5Dz+cZTlw7oT7OXGkOWb6VIvnJpB+fOYwcq/zrPqxd54uTqTYlDz+NflaC7GpvDj2Iew2GSeX7mNjrXD8HDRl75ffzvA8hf7EuztzpCl6+nSoAo1g31z00x9LO8H8Jr957kUrVb2Az3d+Gb8ozjptGSbLDy+ZC1dGlQhyLvkxo9C+/ill0mdNgU5MRHfT1ZgOui4j007t2PcsB4Ap/Yd8Bg9jvQZJd8AC5EknHo/h/GHd1EyUnAZNhfr1ZMoyTEOyawXj2De+m3h9a1mjCvn3FVIm6wwf/Nplg/upJ6zK3fRpXYoNQO9ctNEphhYeeAyq57r4nDOAry2/hgvdKxL+xrBZJutSKXcm2yyzPz1h1j+fG/1/Pl4I13qV6ZmsE9umqmPtMn995oDF7kUk5L799AHGmG0WPnlcOHKQrE0GjzGvUz6zCnISYn4fLgC8+H92KLyHb/d2zH+aT9+bTvg/uI4MmZPQzGbyf72S3RVq6OtWv0uYkrUXTCCk/95G1NMMq23zCdpyzGy7I2qAJUGd8eSlsXBdhMJ7t+BWrMHc25kXsNVnTeeI3nHqTKHtNlsvPXhCj5f/AYhgf48OXoq3Tq2oWa1yrlpFn+6isd6d6Nf3+4cPnGGDz7/lgWzJtGmeWN+/fIDANIzMnlwyBg6tG5ehnxq8Ht1AgljX8Uan0jodx+Ts+cAlptRuUnMl68R98xYFKMJjycexXfiSJKmv4ViNJI0eyHWW9FoA/wJ+f4Tcg4cRTFklTnPRen/UC8GP/4YM99cfE/bucNmk3n741V8Nn8GIQF+PDVhNt3ataBm1fDcNIs/X82jPTvRr9cDHD51ng+/+pH508bmLl/2zS+0bFSv7EE1Giq9MYabz72GNS6ZGuveJ3P7YUzXbuUmscQkcnvaBwS8MLDQ6rLRzPVHXrq7jEoaXAePJ+v96SipSbjPWor19EHk2CjHdM6uOPUYgPXGxdyPFEM62Utno6SnoKlUDbeX38EwbXDZYo6YSNbbU5GTE/GcvxzLsQO5DUcA5v07MG//AwBdyw64PjeWrPmvgkaD+/iZZH08HznyOpKHF1htpcSTcOr1LKYfF6NkpuAydA62a6eKLGMt278rtLrTIy9iOfgHcsQF0DuDopSeR40Gr8kTSZk0FVtCIgFfLMe07wDWiHz36CtXyXphNJhMuPV/DM+xo0h7fR7mk6dIGv6i+tU9PQn68TtMR46VKabv1IkkTlBjBq36lJy9B7DezB/zGglDx6CYTLgPfAzv8SNJeS2vAdJr1HDMJ8+UHqsIZaknTH2oZe6/1xy6zKXYlKI2VXYaDYGvjSPmxRlY45II/3EpWbsOYbmRd/6aLl7n9pMTUIwmvJ58BP8pLxD/StmroPdSF3HV63jzyS5UDfQmIT2LwR/9Tvu6YXi5Opch5n6Wj3xIjfnROro0rFogZvu8mPvOcSkmf/2nX179571f6NKgaun1H42G8DdHcX3IHCxxydRZ/x7p249guupY/kRN+ZCgkf0dVrUmpHJ1wFQUsxWNmwv1ti4lfdsRrAmlHF+NBreRL5M5dwpyciJe767AfGS/Q8OR6a/tmLao92l96w64DR+H4c28epbb8HFYTh4pOU5+kgbXp8eR9cEMtcybsRTrmUPFlHn9C5d5H8+xl3lVcXvpHQzTh5QS797KH6xmjKteL3v+ADQaqrw1iiuDX8cSm0z9jYtI23oE49XbuUnM0UlETP6I4FH9C60e9+k6NK7OBD7Tu9Q41d95kYtPvYE5NplGf75L6paj5OSLE/R0T6xpBk51HId/v45Uee05ro5+D79HOyA56znTYxIaVyea7v6I5HV7Md1WH7RdGDQHa0pmqVnt2L0dVWpUpl+Hp2jcoiEzFrzC0IdHFpl21vg3uHj6ssNnlauHM3zCMwx/bCyZ6Zn4+vuUGlMQyksMhQMkSdIpirL8fjQq2c0sx3fQlmMdXUl/l3W90pyLTqGyrwfhvh7otVr6NKzC7suON4y1J27wZKtaeLk6AeDn7lJoO9su3KZjrRBc9aWH19erhy06GltsrPrUZOdOnDt2dEhjOXUKTGrLvOXCBTSBgeoCRUFyclKfuOj1SDodckrJN/9zt5Op7O9JuJ8nep2WPo2rsvviLYc0a49d48m2dXIrTH4eah5vJKbTsmoQOq0GVycddYJ92X81plCMQjFvJVI5wItwfy81ZtMa7D4fVWz6Tadu0LdZTXX/6LQ46dRTxmy1oZTlh0ABunr1sUZHI9v3sWnXTpw7dHJIo2Rn5/5bcnG96xgAmko1kFPjUdISQbZhu3gYXZ0W5dpWWZ2LSaGynzvhvu7otRr6NAhn95VYhzRrT97kyZY1Cp2z1xMzsMkK7WuoDaBuTrpSz9lzt5Ko7O+Vd/40rV7o/Mlv0+mb9G2a16DTtlYobs4lN0QWpKtTH1tMNHKc/fjt2YlTu+KPHy6ucOc0MRmxnj+LYjbfVUyvFrXIuRmPMTIBxWIjft0BAvq2dkgT2LcVsT+pvc0S/jiEb6dGucsCHmxFTlQCWZeL3zcFnb10lSphoVSuFIJer+fB7p3Yuf+wQ5rrkbdo00LtIdameWN27S9cCd+65wCd27bA1aXkHzwATo3qYr0dgzVa3bdZW3bj2tWx/DEdO41ifzJoOnsRbVAAANaoaKy31IY2W1IycmoaWl+fMue3OK2aNcbby/Oet3PH2cvXqVIpmMqhQej1Oh7s2o5dB487pLkRGU3bpmqPjDZNGzgsP3/1Jsmp6XRo6dgzrySuTetgiozFcisexWIlfcNfePZy7D1miU7AdCkC5PvznmBt9brIiTEoSXFgs2I5ugddsw6F0jn3H6r2ZLLkXRPyreso6eq9Q46JsN9XSr9OtbXqIcfHICfEgs2K+cBO9K0dzx9y8pWtzi65jTm6Jq2xRd1AjrwOgGLIKPWdyZrQGihpCSjpahlrvXgEbe0yNKACkn8l0GjURiUAiwmspZcL+vr1sN2OwRajXiM523fi3Mkxj+aTp3Lv0ebzF9DeuUfn49KtC6ZDR3LTlcSpQT2st6PzYm7biesDjsfSdPwUyp2Y5y6gDcqLqa9XG62fL8bDZWjEKkJZ6gn5bToTQd8y9l4ujnPjuliiYrDejgOrFcOm3bh3b++Qxng0rywynr6INjjgrmLcS12kaqA3Ve0PGYO83fHzcCXVYCx23dyYUQViNqvJ7vPF94bedOr6Pdd/3JrVxhQRi9le/qT+sRfvXm0d0phvJ2C8FAGy4zYVixXFbAVActKDpmw/n3S16yPHRiPHq+esed9OnNo43qcdywLHepa+TSdsCbHYom6WKR7Yy7yEfGXesd3omrYvlM6531DMm38qocyLRHJyLrXMu5fyp7zc7xzLKPVYpvy+D5/ehY9lzsXIQscSIHP/GeSsnFLjeDSvhTEiFpM9TvLv+/Dt08YhjW+f1iT+vAuA5A0H8epkvycqClo3Z9Bq0Lg4IZutWA2lxyyoa9/ObPhZ7R139sR5PL08CAjyL2WtPAOHPMpPq9aSma42Yt3pxSQIf4d/RcOSJEnVJEm6JEnS95IkXZQk6RdJktzsy1pKkrRHkqTjkiRtkSQp1P75bkmSPpAk6RgwUZKkuZIkvZJv2fuSJB2zb6+1JElrJUm6KknSW/niPiNJ0hFJkk5JkrRCkiStJEkLAFf7Z98Xl87+uUGSpPckSToNtC+Qp5qSJG22f++9kiTVs3++SpKk5ZIkHQbeLeLvZpIkHZIk6YwkSb9JkuRbVH7vZv8mZOYQ4u2W+3ewlysJmY6FY2RKJpHJmQxduYNnv9zO/muxBTfDlvO3eLBRlTLF1AQGIifmDZ+RExOLrJTe4frww5iPqD8mLRcuYD51isC1awn89VdMR45giyq+kgSQkFEgj95uhfOYlElkcgZDP9/Csys25zYe1QnxZf+1GHLMVlKzjBy9GU98RjalSUjPJiTfE7ZgbzcSMoru1RCTmklMSiZtaoXmfhaXZmDQkrX0fecHhnVtcne9lQBtQAByYl53eTkxEU1A4Qqpa7/++H+7Go+RozEsK/uQqTskD1+UjLyGPSUzBcnTt1A6bd1WuD7/Fs4DxiN5+uUt0OlxGTYXl+dmo61dtgaphEwjIZ55FbSiz1kDkSkGhn69m2e/2sX+63G5n3u66Jn8yyGe/GIHS3acxVZExcQhXkaBY+nlRkJ6ccfSQEyqgTY1Q8qUl+JoCh6/pEQ0/oWPn8sj/fFduRr350djWH73x89hWyF+GGPyhsiYYpJxDnE8ls6hfpjsT7MVm4w1Mxu9nydaN2eqje/HzcWOQ59Kk5CYQkhgXr6CA/1JSHRsKK5bsxrb/zoEwPa9h8jKziEtPcMhzaad+3iwe+cyxdQFBmCNy9u3toREtCVU5Dz698W4/2ihz50a1kXS67DeLr2huaIlJKc4DGsLDvAjPinVIU2dGlXYbs/Xjv3HyMo2kpaRiSzLLP7se6a8WIbeO/noQ/yxxOaV69bYJPTBZa8ga5ydqPn7+9T4dXGhBqniSD4ByCl5MZXURDQ+jjE1VWqh8Q3Eerb4XgG6Fp2xRV4Da8lD0gA0fgHIyfmuzeRENL6Fr02n3v3x/PA7XIeMImeVOlxbWykcFAX3me/isWAFzo89VXoePYsoYz0Kl7G6ui1xGT4Pp/5jc8tYjV8wGLNx6j8el2Fz0Xf9D6V20QS0gQHYEhzvH9rA4hs03B55CNPhw4U+d+3RjZztO0qNB6ANCsAWn/+6TCqxXuD+2EMYD9qPqSTh89IY0j8q/zDSstQT7ohJs5fzNcrQQ7sEuiB/rHH5rpn4JHRBxe9nr4F9yd5buCwqyb3WRe44G5WIxWajsr9XEWsWiJmRRYiPR76Y7iXcM+/ErJT7WVyagUHv/Urft1czrGvTMtV/1PInKfdvS2wS+pCylz/60ADqbv6IhodWkrD819J7KwGSXwC2pAJlQRH3aecH++P96Wpch44m+wv7fdrFFdeBg8n58esyf0cAyccfOTV/mZeExscxpqayvcw7V1KZ1wlbVOll3r2UP+oCPc7PzcH52dfK3CDlFOqHOd+xNMcl4xTqV8Ia5eMU4o85X53HHFs4jkMam4wtIxudnycpGw5iyzbR8tSXND/6GbHLf8eWZgBAURTqr3mdRpsXETSkV4nfISgkgPiYvHMoITaBwNCiy4C5789kzbaveGHS0NzPqtSsTNUalVn5+yd8vWEFHbq1LXJdQbgf/hUNS3Z1gU8URakPZABjJUnSA0uBJxRFaQmsBPIPpnVSFKWVoijvFbE9s6IorYDlwO/AOKARMEySJH9JkuoDTwIdFUVpBtiAIYqiTAdyFEVppijKkOLS2WO4A4cVRWmqKMq+AvE/AybYv/crwCf5loUDHRRFmVzE398AryqK0gQ4C+TvX1psfiVJGmlvSDv25c4TReyOktlkhagUA18M7caCge2Yt+EYGca8pyCJmTlcS0in/T3+mC6KS69e6OrWJesHdQ4UbVgYuipVSBo0iKRBg3Bq0QJ947I/VS+OTZaJSs7kixG9WPCfTsxbd5iMHDMdaoXSqXYYQz/fyvSf99OkcgCaMlTM78aWUzfo2bg62nxPyUJ8PPh58kDWTxvEH8evklxMBfde5fy+juRnB2P4fAVuzzz3t8SwXjtJzidTyPnyNWw3z+H8yIt58T+egnHVXEzrl+PUczCST9B9iZl7zj7zAAsGtGHexpNkGM3YZIWTt5KY3KMx34/oRnRqFuvP3P28UsXZcuYmPRtVdTiWfyfjhnWkjhhM9soVuD399xy/sqg+dRBRKzZiyy69Z8LdemXMcI6dPs8TL0zi2OnzBAf4o8m3fxOTU7h6I5KObe7/U1T3h3rg3KAu6d84zmGhDfAj4M3pJM1dXLahRf+FXhk5hGNnLzJo7EyOnb1IUIAvGo2GH/7YTufWTR0apirC5c4juN5vErdeXkTo7BdxqnIf7ieShMt/RmH8+bNik2gqVcXl8efJ+e7eGmYLMm9dR+bEZ8hZ/RkuA5+1B9OirdeY7KVvYZjzEvrWndA1uvcenrZrp8hZPhXjV3OQb17A6eEXcuNpKtfBsutHjF/PQ/IJRNu4U8kbu0uuvXuir1cXw2rH+WE0/n7oatTAdPjuGkLKwq1vT/T165D5nRrT/fF+5Bw4jC0hqZQ1748tZyLp2bBKhZXzAB6PdMe5YW3Svrq7xvu7UVRdBCAxI5vXftjDG4MeQKO53/Wf6/RsUkT9Z8rjrH/1SXv9p/SHeffKEpvE5b4vceGBUfg+3h1dgM9927Zp0zrSxwwm55sVuA5S79OuTw7DuP5nMN7nup0k4TJoJMZfSijzQqviMvD+lXnFlj+A8dNXMH0zD/P6Feh7DEbyKb6x+P8T9+a1wSZzovkLnGw7htDRj+FcRW1oPt9/Fmf7vMKlIW8RPOxBPNuWbY6wkswa9wZPdh/K8/3H0rxtUx4e1BcAnVZL5RqVGfn4BGaMmctri6bh4eVRytb+HRT53/vff6t/0xxLtxRFuTPz43fAS8Bm1MagbZL6Q18L5O9KU9IseOvt/z8LnFcUJRZAkqQbQGWgE9ASOGrftitQ1GyJPUpIZwN+LbiCJEkeQAfgZymvgSL/+I2fFUWxFfxbkiRvwEdRlD32z78Gfi5LfhVF+Qy1MYuc72c7/AoK8nQlLj3vph2fkUOQp2N33WAvVxqF+aPXagjz9aCqnydRyQYahakt+1sv3KJbvTD02rJVsuTExLyhbag9mGz5ejDd4dSyJe7PPEPKxIm5k5s6d+qE5cIFlBz1Zmw+fBh9w4ZYzp4tNl6QV4E8pmcXzqO3G43CA/LyGOBJVHImjcL9ebFrI17sqg75mf7TPqoGlD5kJcjbjbh8T+ji07MJ8ir6qdvm0zeY0b/w8A11O+7UCvblxM04ejUp+3w5tqQkNIF5jTSawEDkpOIr3aZdO/CcOInSR4Q7Ugyp6qSwdpKnX+4Ejrly8vaD9fQenLo96bA+gJKWiC3qEprgKtjSSp6YNMjThbh8DW1FnrOerjQK81WPp487Vf09iEoxEOzlSt1gH8J91WPRrW4lzkSnMKCkeF4FjmVGdrFPUDefjmBGv3t/YiQXPH4BgcjJJRy/PTtwHz/pnmIa41JwqZTXmOBcyR9TnOOxNMWm4Bzmjyk2BUmrQefphiUlE+8WtQh6pC21Zg9B5+0OsoJssnB75ZYSYwYF+hGXmJev+MRkggIdnxgGBfjx4ZvTAcjOzmH7noN4eeZVnDbv2k+Pzm3R68p2y7MmJqELydu32qBAbAmFJzN2adMC7+cHE/fCFIfJlSV3NwI/fJu0j1diPnux0Hr/DYL8/YhLzMtTfFIKwQG+BdL48sEc9ZzJzjGybd8RvDzcOX3xKifOXebHDdvJzjFisVpxc3Vh0vMl966xxCWjD80r13WhAVjiS58k+g6rPa3lVjxZh87i0rAm5qi4EtdR0pLQ+OXFlHwDkdPyxXRxRVOpGu6vLFKXe/vhNn4e2cvmIEdeRfINwHXs6+SsfBclsXBP3KLIKUlo/PNdm/6ByKnFX5uWAztxe+Fl+7qJ2C6eQclUe9xZTh5GW7021nPFP/BRMosoYw0FylhjvjL2zB703QbZ101Bjo9Sh7EAtqsn0FSqiY29JebRlpiENsjx/mFLLJxHp1Yt8HjuGZLHv1xoAnKX7t0w7d0HtlLmkLoTMyEJbXD+6zKgyHqBc+sWeA4bQuKYSXn1gsYNcGrWGI/H+yG5uSLpdcjZOWR88nmZYkPZ6gl3bD4byYxHWxe57G5YE5LRheS7ZoIDsBbROObarjm+I58mZtgrpU70XtC91kUMRjMTVm5lfN+WNKlatgc/QV7uxNl7b6gxs4q/Z566wYwBHYtcFuTtTq2QO/WfkicNV8ufvJ4e+tAALHFlL3/usCakYLwShXubBrmTexdHSUlCG1CgLCjhPm3etwO3UWqZq6vTAKcOXXAdOgrJ3QNkBcVsxrTpt2LXB1DSktH45i/zApDT8sV0dkUTVg33ye+qy739cBv7BtmfvK6WeT4BuI6ZQ85Xi1CSSi/z7qX8AVAMaer/0xORoy6hCa6KLa3wdZ2fOTYFp3zH0inEH/O9zmdWVJy4ZJzy1XmcQgvHuZPGHJsMWg1aLzesKZkEvNKZtF0nUaw2rMnpZB69hHvTmpii4rHEqduwJqeTuvkwHs1rw6Hdudv8z7CBDBjyKADnT18kuFLeORQUGkRibOFzKDFO/Sw7K4fNa7fRqFl9Nv68mfjYRM6duIDVaiPmVixRN25RpXo4F05fum/7SRDu+Df1WCr4SFgBJNRGoWb2/xoripJ/praSZlO981hdzvfvO3/r7Nv+Ot+26yqKMreI7ZSUzliggegODZCWb51m9p5YxX3vss4KW67ZYxuG+RGVYiA61YDFZmPL+Si61KnkkKZb3TCORag/9lOzTUSmZOb+MAfYfC6KBxuWbRgcgOXyZbTh4WhCQkCnw6V7d0wHHG/gulq18Jw8mbSZM1HS0nI/tyUkoG/WDLRa0GrRN22KtZQ3mTUM8ycqOVPNo9XGlrORdKkX7pCmW/3KHLO/7SU1y0hkUibhfh7YZJk0ey+MK3GpXI1Po33Nwt3EC8UMDyQqKYPolEw15ukbdGlQeB/dTEgjI8dM03wVtvi0LIwWdbx/RraJkxHxVMs3mXpZWC9dQheWt4+du3XHdMDxrTzasLy39zm1a48t+nbBzZRKjrmJxjcYyTtAfRpfvy3Wqycd0kjued9dW7sF8p1JH13c1DcyAbh6oA2vjZxU+rCihpV81XM2LQuLTWbLhdt0qeN4TLrVDeVYpHojTs02EZlsINzHnYahvmQaLaRkqcf0SEQCNUppKGwYHlDgWN6kS/3wQuluJqSTkWOiaZV7fyJnvXIJbaVwNMH249elO+ZDjsdPUynf8WtTvuOXX+bJ67jVCMGlSiCSXktw/w4kbXGcryRpyzFC/9MFgKBH25G67zwAx/vN5UDrCRxoPYFbn/1JxIe/ldqoBNCobm2ibsdyOzYei8XCpp376NbBcY6D1LQMZPucPJ+v/pUBDzm+WW/Tjr081OOBMufTfP4yusph6Cqp+9a9T1dy9jiWP/q6tfCb9TIJL89BTk3LW6DTEfjeXLI2biN7R8k/zv9JjerWIDI6jttxCVgsVjbtPkTXdi0d0qSmZ+bu1y9+WM+A3l0BWDh9HNu++4gt33zIlBcH82iPzqU2KgHknLmCc7VK6MODkfQ6vB95gMzthYdHFUXj5Y7kpJYFWl8v3Fo1wHS15CHOALaIy2iCwpACQkCrQ9+6C9bTeW8HIicbw+RBGGY8h2HGc9huXMxtVMLVHbcJb2L69Uts1y+U6XsC2K5fQhMShiZQjenUoTuWY47njyYk79rUNW+HLVadl8t6+iiaKtXByRk0GnQNmmK7XfL9S469ieQblFvG6uq3wXbNsYwlfxlbqzlycmzeui5u4KqWcdqq9VHKUMZaLl1CWzkMbah6jbj27I5pf4F7dO1aeE+dTMr0Wcj57tF3uPbsTs62sg2DAzBfvIQuf8xe3cn566BDGn2dWvhOn0zy1NccrsuU198hrt/TxA0YTPpHy8n+c9tdNSpB2eoJADcT08kwmmla+e7mOiqK6dxl9FXC0IUFg06Hx4Ndydp1yCGNU72aBL7+EnHjX8eWkn7XMe6lLmKx2pj8zXYeaVnrrh5sNax8J2aGGvPU9RJimgrUfwyO9Z+bcVQL9Ck1ZvbpqzhXr4RTZbX88X20Mxnbylb+6EP8kZzV+Ri1Xu64t6qP6Xp0KWuB9eolNKHhaILUc9apU3csRwvcp0PzygJ9y/bIsep9OnPWBNJHPUX6qKcw/fELxl+/K7VRCfKVef7BapnXqivW0/nOGWM2hin/wTBrKIZZQ9Uyz96ohKs7buPfxPTbyjKXefdS/uDsWMfThJWtjpd1+iou1UNxqhyEpNfh168TadvuYoLzMjKcuoZL9VCc7XH8+3UidatjD8vUrUcJHNQNAP9H2pOxT32IbY5Oyp1vSePqjEeLOhivRaNxdUZjn9NT4+qMd5emZF9yvJf9tGotT/caztO9hrN7014esfc+atyiIYZMA0kFHnRptVp8/NR9rNNp6dyrA9cu3wBg9+a9tOyg9tb28fOmSo3KREf99w3PF/4d/k09lqpIktReUZSDwGBgH3AZCLzzuX1oXB1FUc7fh3g7gN8lSXpfUZQESZL8AE9FUSIBiyRJekVRLKWkK5KiKBmSJN2UJGmQoig/S2q3pSaKopT4zk9FUdIlSUqVJKmzoih7gWeBPSWtUxY6jYbpD7ZgzPd/ISsK/ZpVp1aQN5/sOkeDSr50rRtGh5ohHLwez8BPNqPRSEzq2RQfN7WTVXRaFnEZObSsdhc/pm02Mj/8EN9Fi0CjwbhpE7aICNyHD8d6+TKmAwfwGDMGydUV7zfU12bK8fGkzZqFac8enJo3x3/lSvXVyUeOYD54sMRwOq2G6Y+0YszXO5FlhX4talIr2IdPdpymQSV/utYPp0OtUA5ei2XgR3+gkSQm9WmOj5szJouNEV9sBcDdWc/bT3RAV4aeWTqthun92jPmi81qzNZ1qBXiyydbjtMgPICuDasC6tO6vk1rkK/3GjcS0liy4TCSJKEoCs890Jjadzu+XLaRufQDfBYuRtJoyNn0J7bICNyHjcBy+RLmgwdw7T8QpxYtUaxWFIOBjIXz7y4GgCJj3vYtLk9NBUmD9cxfKEnR6DsPQI6NwHbtJLpWvdHVbo4i28CYhWnDFwBo/Cvh3HcYiqIgSRKWgxsLvWmkKDqNhul9mjFmzX513zatSq1ALz7Zc4EGoT50rVOJDjWCOXgjgYErtqnHs0ej3HN2Uo9GjFq9F0WB+qE+PN685AqzTqth+mNtGbNyO7Ii069VbWoF+/LJtpM0CPOnq73CvPmMOmm3VGCo5PAVm4hITCfbZKX3/J+Z+3gHOtQJKypUHtmG4dMP8H5rMWg1GLf+iS0qArdnR2C9cgnz4QO4PjoQffOWYLUiGwwY3ss7fr6rfkByc0fS6XDq0ImMWa84vFGuKIpN5vKMlTT/YSZoNcSu2U3W5dvUmDaIjNM3SNpynJjVu2iwbDztD32IJc3AuVH31pVep9Myc+KLjJr6BjbZxoAHe1KrehWWrVxNw7q16NaxDUdPneODz79FkiRaNmnAay+Pyl0/OjaeuMQkWjVtWEKUAmwyKQuXEvTxAtBoMKzfjOVGJN6jh2K+cIWcvw7i+/JING6uBL47GwBrXAKJk+bg3rsLLs2boPX2wuNR9TlG0uuLsFy5fk/7YerrCzh68gxpaRn06P8MY59/lscf7VPu7em0WmaOG8bomQuxyTIDenehVrVwln39Cw3rVKdb+5YcPXOBD1f+qO7XxvWYNW7YPeUBm0zM3OVU+3oekkZD6s/bMF2NIujlIeScvUrmjiO4NqlNlU9nofX2wLNHG4ImDuZa33E416pM2NvjUWQFSSORtPxnh7fJFUuWMa5ehtvL7yBJGsz7tyDHROL82HPYIq84/uAqwKl7PzRBYTg/+gzOjz4DQPb7M1Ay00qNmbPyI9xnvgsaDebdm5BvR+AyaDjWG5exHj+Ac58B6Bq3BJsVOSuT7E8WAKBkGTBt+BnPd5YDCpaTh7GeLP47qivJmLd9j/N/pqhl7Nm9KEkx6Dv1R46LwHbtFPqWvdDWbqa+0jwnC/PGL+zrKph3/WgvnyXkuAisp8tQbbDJZCz5CL8lah5zNm7CejMCj+eHY7l0GdP+A3iNG43k6orvm3PVVeLjSZ3+GgDakGC0QYGYT5X+KvP8MdMWLyXgo4VIGi1Zf6gxvUYOw3zxCsa9B/CeMArJzQW/d9RZAGxxCSRPfa3sMUpQlnoCqL2V+jauWqicLxebTNI7HxO64h0krYaM37ZiuR6J77jnMJ2/QvbuQ/hPeRHJzZXgJWo+rbEJxE2Ye3f5KmddZOuZm5y4EUdalon1x64CMO/JB6hXqeRhsjqthun9OzDm801qzDZ1qRXixydbjtEgPDBfTHXS7kL1nz8OI0nqKOPnujQpW/3HJnN7zgpqfDMXSash5aftGK/eImTyYLLPXCNj+xFcm9Si+mcz0Xp74NWzNSGTBnO513ica1Wmxmsj1ICSROJn6zBeLsMQedlG9ucf4Pn6YtBoMO34E9utCFyfHoH12iUsRw/g8tBAdE3UskAxGMj6qBz1LIeYMsYfPsZt4jtIGg3m/VuRYyNxftRe5p0poczr9hiaoEo4PzwE54fVWTuyP5yBkllCg+U9lD+agEo49Rmqju2RNFgOl62Oh00mavbn1Pn+ddBoSf5xO8Yrt6j0ytNknb5G+rajuDWtRa0vpqP19sCnVysqTX6a8z3Ut4rW/fUdXGqFoXV3ocnRL4h4ZRkZe04VGSdi1hfUWz0HSash4Ycd5Fy5RfjUp8g6fZ3UrUdJWLODWh9NpNn+j7GmGbg6ZgkAcV9toub742my6wP1nPlxJ9kXI3GuEkydL18FQNJpSPptL+m7TxaObbdvx0E69WjP7wd/xJhjZO6kvDc+rtn2FU/3Go7eSc/Ha5ag02nRaLUc3nuM375T3zp6YNdh2nVpzS97vsVmk/ngzU9IT80oLpwg3BOpPG+T+m8jSVI11GFvx1CHnV0AnlUUJVuSpGbAR4A3akPaB4qifC5J0m7gFUVRjtm3MRcwKIqyOP8ySZK62v/9iD1d/mVPAjNQexhZgHGKohySJGkh8Bhwwj7PUnHpDIqiFDnQVZKk6sCnQCigB35QFGWeJEmrgA2KovxiT1fw72ao80K5ATeA4YqipBbMb0kKDoX7u2V8XvFP9b3Gdq/wmDgXflPe3y3zo40VGs+9Z7UKjQegCatUeqL7zeP+vZ2rrLI+L713z/12+sT9nxOtJA+cmluh8QBiHp5Q4TErbS1+bou/i2K4/8MESnOl+9wKjVelx9295fB+kDPubtjR/eDUolqFx0z/I6LCY9os93euntL4T+laofEAYubtLz3RfVbp7R6lJ7rfpIofIHF5wl8VGq9Ki7vvLXavdEEVX6/U1aj4OteFpamlJ7rPrHLFnrNjlZKnePi7nIjdV7EFbQWK7dTt/38jRzFC9+36rzxu/6YeS1ZFUZ4p+KGiKKeAQmMgFEXpWuDvuUUtUxRlN7C7mGU/UsS8RYqivAq8WoZ0xc6epijKTaBvEZ8PK+XvU0Ch1+UUzK8gCIIgCIIgCIIgCMK9+jfNsSQIgiAIgiAIgiAIgiBUoH9FjyVFUSJQ3/4mCIIgCIIgCIIgCIIgVJB/RcOSIAiCIAiCIAiCIAiCIv/T3+B/jxgKJwiCIAiCIAiCIAiCIJSLaFgSBEEQBEEQBEEQBEEQykU0LAmCIAiCIAiCIAiCIAjlIhqWBEEQBEEQBEEQBEEQhHIRk3cLgiAIgiAIgiAIgvCvoCjSP/0V/ueIHkuCIAiCIAiCIAiCIAhCuYiGJUEQBEEQBEEQBEEQBKFcRMOSIAiCIAiCIAiCIAiCUC5ijiWhMG/fCg1nM/8DY2DdvSo+pqdPhYc0G7QVGs81PqVC4wFIvj4VH9PZpcJjZsXrKzxmlK5iYypWc4XGA0hOdK/wmKGGf+A68fCr8Jjp2RV7neRcNVRoPADZWvH3L41XdIXHTLztUeEx5QqeH8Pfu+KvkX+Ei1uFh5Q8KrZe+U+wpFZ8WaD1sFZ4TMVqq/CYJmvF/1zNVCo2plYr+nrcb4r8T3+D/z3iLBYEQRAEQRAEQRAEQRDKRTQsCYIgCIIgCIIgCIIgCOUiGpYEQRAEQRAEQRAEQRCEchFzLAmCIAiCIAiCIAiC8K+gyP/AHL7/40SPJUEQBEEQBEEQBEEQBKFcRMOSIAiCIAiCIAiCIAiCUC6iYUkQBEEQBEEQBEEQBEEoFzHHkiAIgiAIgiAIgiAI/wqK8k9/g/89oseSIAiCIAiCIAiCIAiCUC6iYUkQBEEQBEEQBEEQBEEoF9GwJAiCIAiCIAiCIAiCIJSLaFgSBEEQBEEQBEEQBEEQykVM3i0IgiAIgiAIgiAIwr+CIkv/9Ff4nyMaloQy2X8pinfXHUCWFQa0rceIHs0dli/6/QBHr8UAYDRbSTHksO/t4bnLDUYzA9/9iW6NqjFjYKcyxXRu2xrvl8eDVkv2HxsxfLvGYbn7U4Nwe/QhsNmQ09JJe+ddbHHxAGiDg/Ce8QraoCBQFFKmTM9dVmweL0bx7rp9ah7b1WdEjxaOeVy3n6PXotU8WqykZOaw753nHfO48Ae6NarOjMc7lymPDvHPXefdH7YjyzIDOjdjxIPtHZbHJqcz+6sNZGabkGWZlx7vSufGte46jnO71vhMHo+k0ZC1/k8yv3Hcrx5PP4F7v4dQrOp+TX1rUe6+CzuwDcv1mwDY4hJInvpamWJq67XAZeCLIGmwHNqGeccvRabTNemA64gZZL03CfnWNQA0odVweXIcOLuBIpO9ZDJYLaXG3B+RyKLdF5Fl6N8onBFtahRKs/VyLMsPXUNCok6gJ/MfasrlhAze3nmeLJMNrQaeb1OTPnVDS493NYZ3/zyOrCgMaFGTEQ80LJRmy7lIVuw6C0jUCfFhwaCOAHyw9SR7r6jXz8gujejTuGqp8QBcOrTG75WxoNVg+G0TGat+cFjuOeRxPAao14gtNY3kNxZji01AX6cm/jMnIrm7gSyT/uVqsrfuLlNMgLbzniW8ezOsOSb2TfqM5HMRhdL4N65G5/dHoXVx4vbOUxye8y0Avg2q0GHBcPRuLmTeTuSv8Z9iMeSUGG/fkVMs/OQrbLLMwAd78MLT/R2Wx8QnMmfxp6SkZeDt6cH8GRMICfQHoGnvJ6ldvQoAoUEBLH3z1TLl0atrc8LnvghaDclrthH/ya8Oyz3aNiD89RdwrV+Nm+MWk/bngdxlNb99Hffmdcg6epHrw98qU7x9R0+zcPm32GwyAx/sygtPPlY4j0s+JyXdnsdpY3LzCGDIyqbfyGl0b9+KWeOHlSlmaV57Zwl/7T+Cn68P675bXu7t+HRrRo03h4NWQ/z3O4hets5hueSko87SCbg3qYE11cDlUUsw3UokcGBnKo3N2w/uDapyutc0ss5H0GjtGzgF+WAzmgG48NSbQFKR8Z3atMFzvHovydm4kezVqx2Wuw0ahOvDD6PYbMhpaWS8+y5yvFrmeYwahXO7dqDRYD52jMylS8uUZ+e2rfGaOB40WrI3bCTruwL3rycH4fpI3v0rff672OLjcWreDK+XxuWm01WpQurceZj27i8xnrZhK1yeGoOk0WDeuxnz5h+LTKdr0Qm3MXMwvDUOOfIq2votcHn8edDqwGbF+Mvn2C6dKlMePbq0IGyOeo2k/LiNxE8dy3X3Ng2pNOdFXOpVI2rCu6RvUq8RfVgg1VbMAo2EpNOR9PUfpHy/uUwxPbu0IOz1F5C0WpJ/2ErCp47XpXubhoS9/gKu9aoRMWER6X/mxaz+2UwkSQK9jqRVG0guY8z9FyJ5d+1fat2gfQNG9GrlsHzR2r0cvXobuFP/yWbfwlEAxKZk8saaHcSnGZCApaMfI8zfq0xx73Dt2IqA6aORtFoyft1E2pc/OSz3fm4gXo/3RbHZsKWkkzh7CdbYhLuK8U/kcf/ZayxcvQVZkRnQuTnPP+xYN4xNTue1L9fl1nkmPtGDzk1qk2bIZsonP3P+ZgyPdWzGzGceLHM+/4nz558of3SNW+Py7DjQaLDs/hPTBse6gVP3R3Dq2Q9kGcWYQ87K95FjIkGrw3XEJLTV64CikPPtx9gunS41nrZGY5x6PwOSBuupPVgObnD8Pk064dT9KWRDKgDWY9uxntqT7wu54DpqAbYrxzFv+bbYOH/HveSO+l+/inPVYE51neywzYBuTan/1lDQarj9/U5uLl1fKGaTZePwalIdS6qB0yM/JOdWInpfD5p9OQnvZjWJ/mEPF2d+lbtO6IAO1JjYHxQFY1wqZ8Z9DOnF798pb75Ex+7tMOaYeGPSfC6fvVIozfJfPiQg2B+T0QTA+KemkJqcxiP/6ctLs8eSGJcIwE9freX31RuLDyYI90A0LAmlssky89fuZ/mohwn2dmfIB2vp0rAaNUN8c9NM7dch999r9p7jUrRjBf/jzUdpUaP0H+e5NBq8X5lI8sSp2BISCfxyOca9B7BGROYmsVy5StKI0SgmE24DHsNr7ChS58wDwGf2DAxff4fp6HEkVxeQS37npJrHvSwf/aiax/d/tefRLy+P/Tvmy+PZwnncdOTu8lgw/uqtLJ/0FMG+Xgx5exVdmtamZqWA3DSfbzxA71b1+U/XFlyPSWL8Rz+xacFdNixpNPhOnUjiBHW/Bq36lJy9B7DezL9fr5EwdAyKyYT7wMfwHj+SlNfeBEAxmUl4duTdxZQ0uDwxmuxPZ6OkJeM2eQnWc4eR4285pnN2Rd/lUWwRlxy+r8uzkzF+twQ5JgLcPMFmKzWkTVZYsPMCnw5sTbCnC0NWH6RLzSBq+nvkpolMzWLl0RuserIdXi56UrLVm7GLXsubfZpQ1dedBIORId8fpEPVADxd9CXEk5m/4RjLh3Yn2MuVISu20KVeODWDvPPiJWew8q8LrHqhN16uTqQYjAD8dTmaizGp/DjmQSw2medXbqdj7Up4lBDvzr7xe3UCCWNfxRqfSOh3H5Oz5wCWm1G5ScyXrxH3zFgUowmPJx7Fd+JIkqa/hWI0kjR7IdZb0WgD/An5/hNyDhxFMWSVum/DuzfFq3oIv3aaQmCLmrSfP4wNj84tlK79/OHsn/YFiSeu0+vbqYR1a0L0rjN0XPQCR99cTfyhS9R+8gEajXmYk4uKbmgEsNlk3l76JZ8tfI2QQH+eGjeDbh1aUbNqeG6axSu+5dFeD9Cvd1cOnzzHh1+uZv70CQA4Oznxy4pFpebLgUZD5bdGcXXw61hik6m7YTHp245gvJp3zpqjk4ic/CFBowYUWj1h+W9oXJ0JGNKnTOFsNpm3P17FZ/NnEBLgx1MTZtOtXQvHPH6+mkd7dqJfrwc4fOo8H371I/Onjc1dvuybX2jZqN7d5bMU/R/qxeDHH2Pmm4vLvxGNhhrzX+D8f+Zhjk2h6eYFpGw9Rs6V27lJggf3wJqWxYn2Ewjo15Fqrz3D5VHvk7h2L4lr9wLgVq8K9VY5/hC4Mu4jDKev58XKKy4d4ntOnEjaK69gS0zEb/lyTPv3Y4vMV+ZdvUr2qFFgMuH62GN4jhpF+rx56Bs2RN+oEcnPqw8QfJcuRd+sGZZTp0rNs9fkiaRMUsvZgC+WY9pX+P6V9cJoMJlw6/8YnmNHkfb6PMwnT5E0/EUAJE9Pgn78DtORYyXHkzS4Dh5P1vvTUVKTcJ+1FOvpg8ixUY7pnF1x6jEA642LuR8phnSyl85GSU9BU6kabi+/g2Ha4JLj2fMYNm80N5+ZjSUumVrrl5Cx7TCma/mukZhEbr3yAYEvOl4j1oRUrg18BcVsRePmQp2ty8jYdgRrQkqpMcPfHMX1IXOwxCVTZ/17pG8/ginfdWmJSSRqyocEjexfKObVAVNzY9bbupT0MsS0yTLzf97N8nH9CfbxYMjiH+nSqAY1Q/PVDQbmPUhas+c0l24n5v792nfbeKF3K9rXq0K2yaw2TNwNjYbA18YR8+IMrHFJhP+4lKxdh7DcyDu2povXuf3kBBSjCa8nH8F/ygvEv/JOmUP8E3m0yTLvfLeJFVOeIdjPi8HzvqBrs7rUDAvMTfP5H3vp07oh/+nWiuvRiYz/YDWbFk3ESa9jXP9uXItO4Fp0YglRCvgHzp9/pPyRNLgMfYmshdNQUhLxmPcJlhMH1YYjO/OBnZh3qo0/uubtcRkymuxFM3Dq9jAAhpkvInn54P7KfAyvjy35ve2ShFPf5zCufhclIwWXEW9gvXoCJSnGcR9ePFxso5FTl8eRoy6XnK+/8V7i91BbbFnGImJKNFgwgqP/eRtjTDLtt7xDwpbjZF2Jzk0SPrgbljQDe9u9TEj/9tSZPZjTIz9ENlm4uuAnPOtVxqNe5bzdpdVQ762h7Ov8CpaUTOrMHkzVEX3g/aLz36F7O6pUD2dgx8E0atGA6fMnM/yR0UWmnT3uTS6eKbydbet3smjWByXtXUG4L8QcS38TSZK0FRBDV9LfZV2vNOeiEqjs70W4vxd6nZY+zWuxO1+BXNCmk9fo2zyvwePCrURSMnNoXye82HUK0jeoh/V2DLaYWLBaydm+E5fOHR3SmE+cQjGpjQHm8xfQBqkVEl21qqDVYjp6HAAlx5ibrsQ8Bng75rGIXhh5ebxadB7rVi52nRLj34yhcqAv4YG+avzW9dl9yvGJhCRBVo6aD0OOkUAfj6I2VSKnBvWw3o7O26/bduL6QAeHNKbj+fbrubz9Wl6aqrWRk2JRkuPBZsV68i90jdsWSuf80BDMO35FydcbSVu3OXJMhNqoBJCdCYpcasxzcWlU9nEj3McNvVZDn7oh7L7u2GPtt7O3+U/TKnjZG3D83JwBqOrrTlVfdwCCPFzwdXMiJcdccrzbyVT28yDcz0M9fo2rsvvSbYc0a49d58m2tfFydVLjebgAcCMxnZbVAtFpNbg66agT4sP+azGFYhTk1Kgu1tsxWKPVY5m1ZTeuXR2vEdOx0yj2p1emsxfRBqm/vK1R0VhvqRUjW1IycmoaWl+fUmMCVOnTkmu/7AMg8cR1nLzdcQ1yXNc1yAe9pyuJJ9Qf/dd+2UfVvuoTcO8aIcQfUhsPY/aeo9pDrUuMd/byNapUCqFypWD0eh0Pdu3Arv1HHdLciLxN22aNAGjTrCG7DpTyQ7wU7s1qY4qIwxwVj2Kxkrp+L9692zikMd9OIOdSZJHnY+b+M9hK6YWV39nL16lSKZjKoUH2PLZj18HjDmluREbTtqnaC65N0wYOy89fvUlyajodWja+m2yWqlWzxnh7ed7TNjyb18J4Mw5TVAKKxUriuv349XE85n59WpPw024AkjYcxLtT4XwEDOhE0rqSe+0URV+vHrboaGyx6nVi3LkT546O14nl1Cmwl3mWCxfQBNrLPEVBcnICnQ70eiSdDjmllB+SgL5+PWwF7l/OnQrcv07mxTSfv4A2sHA569KtC6ZDR3LTFUdbvS5yYgxKUhzYrFiO7kHXrEOhdM79h6o9mSx55Zl86zpKuponOSbCnt9SGrUBt2a1MUfGYr6lXiNpf/yFV2/Hct1yOwHjpQiUAj9KFYsVxWwFQHLSg1S2qqhbs9qYIvJipv6xF+9ejjHN9pgFHyYViqkpW8xzkfFUDvQhPMBbLdtb1GH32RvFpt90/Ap9W9YB4HpsCjZZpn09tcekm7MTrk6l79v8nBvXxRIVg/V2HFitGDbtxr27Y29m49G8ct54+iLa4KJaWIv3T+Tx3I1oKgf5Eh6k1nn6tm3I7lMFfhBLYHCo83jmxmhRpwrO+rt7Nv5PnD//RPmjrVkPOT4aJTFWLQ8O7ULfskB5YMzO/afk7AL27GrCqmK9cFINn5GGkm1Qey+VQFOpJnJKAkpaIsg2bBcOoavTosR1HNYPqYbk7o3t5tkS0/1d9xKNmwthox7h1ge/Fkrr06IW2TfjyIlMQLHYiFt3gOC+jr35gvu2IuanvwCI/+Mw/p3U+7Qt20TakcvIpgI97CUJCQmtvc6p83TFGJ9abL679OnExl+2AHDuxAU8vT3wD/IvNr0g/JNEw1I5SJK0TpKk45IknZckaWS+zw2SJL0nSdJpoL0kSc9IknREkqRTkiStuNPYJEnSp5IkHbOv/0YxMWpKkrTZHmevJEn17J+vkiRpuSRJh4F3i/i7mSRJhyRJOiNJ0m+SJPna19stSdIHkiQdAybeTX4T0rMJydeIEeztTkJ60T0bYlIyiUnJpE3tSgDIssJ7fxxk8qPt7iYk2sAAbPF5XbltiYloA4uvLLk/8hDGQ4cB0FUJRzEY8H3nDQJXfYbXuFGlVgIS0rMI8XHP/TvYp5Q8JmfSpnYYYM/j+gNMfqx9kenLIiHNQIhfXtfxYF9PEtIyHdKMfrQzGw+fp/fUZYz/6GemP93rruNogwrs14SkIn/Q3OH+2EMYDx7J/VtyciJo1acEfrkMlwc6Frtefhpvf+TUvN5dcloykrfjTVETXhPJJxDbBccGAU1QGCjgOvoN3KZ8gFP3gWWKmWAwEezpmvt3sIcLiQbHH2eRaVlEpWYz7IdDPLfmIPsjCj/5PBeXhlWWqezjVnK8zBxCvPOdP15uJGRkO6SJTM4kMimToZ9v5dnPtrD/qtp4VCfEl/1XY8kxW0nNMnL0Zjzx6Y7rFkUXGIA1Lv+xTERbQmXDo39fjAUaZACcGtZF0uuw3i69MQvALcSXrJjk3L+zYlNwy9d78U6a7Ni8CnB2vjRpV25TpU9LAKo90hb3Sn6UJCEphZB8+QoO9Cc+2bFyXadGVbbvU8/THfuOkJWdQ1q6ev2YzRaeHDudIeNnsWP/EcpCH+KPOSbvnLXEJqMP+fsqcgnJKQ7D2oID/IhPcqxo1qlRhe3247dj/zGyso2kZWQiyzKLP/ueKS+WoZfJP8Ap1M9hX5pjk3EO9SuUxnQnjU3GmpmNzs+xQSugXweS1u1z+KzWB2Npun0R4ZOeKDa+JjAQOTHv2pYTE0ss81wffhjzEfU8sVy4gPnUKQLXriXw118xHTmCLSqq2HX/j727jpOq+h8//jpTu7PdRcPS3S2NioCIYoAioYSEEgap8jGxARVMLOyW7k7p7l1gu2t28v7+uMPuzPYirn79nefjwePBzD33vufcOPfsueece502NAR7UuG16Sjn/uU1cADmvXuL/5Y+vTBt2FhuPBEQgiOtMI9KejKagCJlbM1oNIGh2I6Vfg3o2nTHHnO+QkON9eHBWIteI+EVv0b0kSHUX72Ixrs/JXnpD+X3/EC9Lq3xrjFTKnVd6iNDaLhmEU33fELS0h8rFDMpI9e9/hPgQ1JmTolp49KyiEvLooPzIVpMcjq+Rg+mf7SS+179mjd/2YHdUf6DEVe6sGBsCYXH1paYgi6s9HPJb+ht5G0vXs6X5Z/IY1JGNhFBhT16wwL9SEx3r/NMvLMHK3cfo9+Mt5j09tc8M+K2SuWrqH/i/Pknyh8RGILiUh440pIRgcXPGUPfO/F5/Qs87x9H/hdLALDHXkDfpgtoNIjQCLS1GyCCwsqO5xuIkl1YJ1Cy0hC+gcXSaRu1x/jIC3gMnYzwvX4PEBj6PoBl49fF0hf7vX/TvaTW0/dzbenvOEzFG/A9IoIwudR38uPS8Ihwj+kRGYTpmppGsTuwZZvQB5X+QEax2Tnx9Md027KQnkffx6dBda5+tanU9KERISTGFd5PkuKSCYsouQyY/9Ysvlr/MWOfGOn2fe8BPVix4VNe+WAB4VFlH8//EsUh/rP//q1kw9KNGaMoSlugHTBVCHH9zuQN7FUUpSWQCtwHdFUUpRVgB0Y4081RFKUd0ALoIYRoUUKMD4ApzjgzgfdcllUHuiiKMr2Ez58DTyuK0gI4Bjzrsp5BUZR2iqK8UTSYEGKcs7HrwMdrdldub7hYe/gCfVvUQetsyPlu1wm6NapJ+A30rqko46190TdqSM5XzjkltFoMLZuTtWQpyWMnoI2KwmvAX6uQuFp76Dx9W9YtzOPO43Rr/PfmEWDNvpMM7tKcda9NZsnUYcz9+Hcc5Qzx+yu8buuLvnEDsr8snKsjfsgDJI2aSNq8FwmYNglttai/HkgIPIaMxfzrx8WXabRo6zYh/4s3yFv0NLoWndHWL+lyqTy7QyE2I5cPh3Xg5QEt+d/6E2TnF/5BlZyTz9w1R3muf3M0lR2+UGI8B7Fp2Xw0pi+vDOvKgl/3kWWy0CU6km4Nonj4o3U88/0uWtQIuSnxXHkP6INHk4Zkfu4+N4c2JIiQ/z1DynOvl93V/SbaMf1DGj3cl0Gr/4fe2xO71faXtzlz/EMcOHqSYeOf4sDRk4SFBKHRqtfn2hXv8e17r/DK7KksfO8zrsQl/OV4/4SZ40Zw4Ngphj02mwPHThEWEohGo+Gb3zfQvX1Lt4ap/xqf1vVxmMzknS4csnL2sXc43GsGx++ch1/HxoQO6/GX43j264euYUNyv1HnI9FWq4auZk1Shg0jZdgwDG3aoG9+c3uFGfs7718r3OdE0gQHoatbF/PeyjUSlEgIPO8dT/73H5SaRBNVC8+7x2L68p2/Hq8CrPEpnLt9Kqd7jCPw7j7oQgKqJOaZ26Zy8pbxBN7d+6bHXPvnOfq2ii6oG9jtCocuxDF9SDe+mnkf11Kz+G3vqXK2cuN8BvbGo2l9Mj4tfWjxX1WVeVy99ziDu7Zk/RvTePeJB5jz4S9/a52nPH/3+VPV5Y9lw6/kzHyI/G8/xOPOBwGwbl2NIy0ZnwXvYxzxGLbzJyrUS7w8tnOHMS2ZjumjudgvncBjsPpMXteuD/bzR1CyS++xczMVvZd4N62NZ+1w0lZX7KHTzSB0WmqO6sfOPrPY0mIi2Sdj1fmW/qJ5k//HA31G8eiQybTq2JIB96hD8bev38XgjvcyvO9o9m47wLNvz/7LsSSpNHKOpRszVQhxfdKAGkB91IYkO3C9L2UfoC2w3zne3Ahcb3K+19nTSQdEAk2Ao9c3LoTwAboA37uMVfdwif+9oij2op+FEP5AgKIo12fE+wz43iVdybN5AoqifIDamIXpjzfd7txh/l4kZBQ+vUrMzCXMpXeGqzWHzrtNzn3kciKHLiXw3a4TmMw2rHY7XgY9jw8sPhTKlT05BW14Yau6NjQUe3LxiVkN7drg8/CDpE56Aqxqo4A9KRnruQvqMAQgf/sODE2bwB/FVnfJozcJGYU9lBIzysjj4fPMcplv4EhMIocuxvPdzhOYLFasNjteHnoeH1jxXlphAT4kpGUVxk/PJizA/YnHzzuO8N4T9wHQsl51zFY7GTl5BPmV/DtLYk8qsl/DQrAnF++p49G+Db6jRpA8cVrBfgVwOI+BPS4e88HDGBpGY7pWdk8XR2YqepenZZqAYJTMwidAeBjRRNTCa7I6J4TwDcT4yFxMH72AkpGC/cJxlFx139hOHkBTvR72c0cpS5iPB4nZhUOREnPyCfXxKJLGk+aR/ui1Gqr5e1Er0IvYjDyaRviTY7Yx9deDTOrSgBaRAWXGAgjzNZLg0sMtMSuPMD/3Xk7hfl40qx6sxgv0oVawL7Fp2TSrFsyjPZrxaA91KNcz3++kVkj5w49sySnoIlyPZSj2pNRi6Tw7tMF/7HASHpnhdiyFtxeh77xIxrufYDlW9h8CjR7uS4MRvQBIOXwR76jCRgzvyCDyEtwrhXkJ6Xi5PEn0ckmTeSGedcNfBcCvbgTV+7QqM3ZYSBAJLvlKTE4lPDioWJq3n5upxjbls377Xvx81OsiPERNWyMqnHYtm3Dq/GVqREWUGdOakIrBZX4zfWQw1oTi+/ZmCQsOIiHZJY8paYSHBBZJE8jb86cBzjzu2IefjzdHTp3j4PEzfPvHBvJM+VhtNryMnkwbe//f9nsrwxKf5rYvDZHBmOPTiqXxiArBEp8GWg06Xy9saYW9F0KHdCXlZ/dhcJYEdRv23HxSft6Ob+to9VFKEY7k5MKhJag9CEoq8wxt2+L94IOkPf54wXXi0a0b1pMnUUxqWWLZuxd906ZYj5U9ZMOenKK+OMItZin3r5EPkjr5CbdrE8Czdy/M23dUaE45JSMFTVBhHkVgKI4Ml/PV04gmqjbeM9W5xoR/EF6TF5C3ZD6OmHOIwBCMjz2L6ZOF6vCZCrAmpqIveo0kVv4asSWlkX82Bu/2TQom9y41ZkIq+kjXmCE3dF2qMWPx7tCkYHLm0oQFeLvXfzJyCPMv+SHSmoNnmTWsZ8Hn8AAfGlYLoXqI2jOnV/O6HL2cwF2V6NxsS0pFF1F4bHXhIdiSip9Lxk6tCRz3AHGjZhY7l8rzT+QxLMCXhLTC2YqT0rMIDyxS59l+mPenqz0xW0bXwGy1kZ6TR3Al6jyu/onz558of5T0FIRLeaAJCkVJL37OXGfdsxnjqMcxATgc5H/1fsEy7/mLcMRfLXVdACU7HeFbWCcQfkHFG4pMheeX7fAWDL3Vuqy2WjSaGg3Rte2DMHiCVodiMWPd7P4QDP6ee4lvuwb4tKxH2/3vIbRa9CF+NPvpeY4PVZ/JmxPSMLrUdzyjgjAnuMc0x6dhrKb+FqHVoPM1Yk1z733nyreZ+nIWU4w6PUPCb7upO+VOtzTDRt3FkBEDATh5+LRbL6OwqFCSEoofz2Tnd3m5Jtb+vJ6mrRuz6oe1ZKYX/m3x64o/mDq35PmZJOlmkD2WKkkI0RPoC3R29kw6BHg6F+e7NPgI4DNFUVo5/zVUFOU5IUQd1B5IfZy9ila6rH+dBshwWbeVoiiNXZYXHaNV/oy7lUvnpmmNMGJTMrmWmoXVZmftofP0aFr8rVWXEtPJMplpWTu84LuXH+zDmnkjWD13BNMGdWJguwblNioBWE+dRle9GtrICNDpMPbtTf4O9xu4rkE0AU9PJ+2pOTjSM1zWPYPGxwdNgFrR8WjbGqvL5NSl5jE5wz2PzWqXnMe8onnsy5r5D7F63oNMG9SZge0aVqpRCaBp7Shik9K5lpyhxt9/ih4t67uliQz2Y++pywBcjE/BYrUR6Fv2EK2iLKdOo6vhsl/79ca0zb2Hmr5BNIHPTCf1yblu+1X4+oBenTtB4++HoWWzcvcrgCP2HJqQKERQOGh16Frfgu24y9Oh/Dxy544gd8Ej5C54BHvMGUwfvYDjynlspw+iiawNeg/QaNDWa1Z80u8SNI3wJzY9j2uZeVjtDtaeSaBnXffuv72iwzhwRa0gpJssxKTnUc3fiNXuYMbvBxnYOIp+DcpugCiIVy2Y2LRsrqXnqMfvWAw9GlVzj9e4Ogcuq23L6bn5xKRmUz3QB7vDQYZz4vCzCemcS8ygc73yJ4G3nDiDrkY1dFHqsfS+tSemre7XiL5hNEFzniDpifluxxKdjtA3niN35XryNm4vN9bpzzbwW/85/NZ/DrFr/yT6HrXxOLRNPSxZeZiSMtzSm5IysGabCG1TD4Doe7oRu1adE8jz+tuChKDl43dy5ouyh/o0a1iPmGvxXI1Pwmq1sXrLLnp2cZ/jID0zC4dz+MVHX//MXbepjWCZ2TlYLNaCNIdPnHGbELs0uUfO4VE7EkONMIReR+Dg7mSu//ueaDZrWJeYawlcTbiexz307NTWLU16ZnZhHr/5jbv69wTg1Wcmsf7LRaz9/B1mPDqcQX26/2salQCyD5/HWDcSj5rqvgwd0pW0de69cNLWHSDs3p4AhAzsTObO44ULhSB4cGeSXYfBaTUFwxuETktgv7ZuvZlcWc+cQVu9OpoI9Trx7N0b864i95LoaHynTydj9myUjIyC7+1JSehbtQKtFrRa9C1bYospv8yznj6Ntob7/cu8s0jM+tH4PzmdtGfm4HCJeZ2xb29M68sfBgdgv3wGTVg1REgEaHXo2/fAdsSlXDflkTN9GDmzRpIzayT2i6cKGpUweuM15X+Yf/wY+4WTFYoHkHfkHIbaUeirhyP0OgIG3UJWBa8RfUQwwkOda07r5413uyaYL14rZy01pkedKAw11JiBg7qTtb74EMKKxWyM+UL5MZvWDHfWDTLVsv3gWXo0r1Ms3aXENLX+U6fwntG0VhjZJjNpzocc+85dpW5E2UN/izIfP4O+ZjV01cJBp8Pn9p7kbt7jlsbQqB6hz04lYfKz2F0aayrqn8hj0zrViE1M42pyOlabnTV7T9CjlftcPpFBfuw9qb6F9mJcMharjaBK1nlc/RPnzz9R/tgvnkYbUQ0R6iwPOvXCetA9pia8sH6ia9UJe4IzLwYP8FD/JNE1a6u+tTKu7JiOuItogsIR/iFqL/MmnbCdPeSWRvgUDnvUNmiDI1V9IGn+dSmmJdMwvTsDy8avsR3bUWKjEvw995KEz9axv9U4/mz/GMfunIvpYnxBoxJA5qELeNWNwFgzFKHXEjGkC0lr3ec/TFr7J1H33gJA+KCOpO44Ueb+Msen492gGvpg9R4W3KMFOefcz6Xvl//MiH5jGdFvLFvWbOcOZ++jZm2akJOVS2qRh4harRZ/59BSrU5Lt75duHBanSfNdT6mW/p35dK58s8hSbpRssdS5fkD6Yqi5DnnPSqtBWEj8KsQ4i1FUZKEEEGAL+CH2sCTKYQIB24HtriuqChKlhDikhBimKIo3wu121ILRVHKfOenoiiZQoh0IUR3RVG2Aw8BW8tapyJ0Wg3PDO3GxA9W4VAU7uzQkOiIIN5bs58m1UPp6WyAWXP4Are1iq78W09KYneQ+eYigt9aCFoNeX+sxnbpMr6PjMZy+gzmHbvwnzQBYTQS9MJz6iqJiaQ9PRccDrKWvE/wojdACKynz5L3WxndlQry2J2JH/yBw6FwZ4dGah5X76NJjVB6NlMrWWucE5PflDwWjT+8HxPf/kbdx11bEF0tlPd+3UaTWpH0bFWf6cP6sODzVXy1Qb2RPj/6jsr/DruDjNcXE7LoVYRGS+7v6n71GzcKy6mz5G/fhf+U8QgvT4JeUm+u9oQkUp+ci752LQKfmYaiKAghyP7sa7e3yZXK4SD/x6V4TXheffXt3g04EmIx3D4Ce+w57CfK+GPElItlyy94TX8TULCfPFBsHqaS6DQanu7dhMd+OqDuz6bVqRfiy3u7ztEk3J+e9cLoUiuE3TEpDP1sO1oheOKWhgQYDaw8FcfBa+lk5Fv57aR6s1/QvzkNw0p/fbJOq+GZO9ox8fPN6vnTpi7RYQG8t/EoTaoF0bNRdbpER7L7fDxDF/+BRgim3dqKAC8PzFY7Yz5eD4C3h54X7+6CTluBNn+7g7RXFxP27iug0ZDz2xqsF2Pwn/AwlpNnMW3bTeAT49B4GQldOA8AW0ISydPm492/B56tW6D198NnUH8AUp59DevZC2VFBODqxsNU792Su3e+gd1kYfv0wuE1g9e9yG/95wCwe/Zyur81Dq2ngWubj3B1k1p81R3SmUaj+gIQs+oA577dVmY8nVbL7CljmPDMi9gdDu66rRfRtWuwZPm3NG1Qj15d2rH/yEne+XgFAkHbFo2ZM0V9i86l2Gs8/9YHaDQaHA4HY+8fUqGGJewOrsz7gOgvn0NoNaR+u5H8s1eInDGcvKPnyVy/D6+W0dT9cBZafx/8+7YncvoDnOqrvomuwY8v4VGvOlpvT5rt+5iYJ5eQvfVQqeF0Wi2zJ41iwuxX1Tz270F07eos+ewHmjaoQ6/Obdl/9CTvfPItQgjaNm/EnEmjys/HX/Tks6+w/9BRMjKy6DPkQR4b+xB3D6rYm+4K2B1cnP0RTb+eC1oNSV9vwnTmKjWfuo+cwxdIW3eAxBUbabBkKm12L8aWkcOZ8W8VrO7XuQmWuFTMsYVzTGg89DT9ei5Cr0NoNWRsO0rClxuILmmOWbud7HfeIfC110CjIX/1auyXL+M9ejS2M2cw79qFz8SJCKMR/+fVKQ8diYlkzJmDeetWDK1bE/zJJ6AomPftw7K7AkPF7Q6y3lxE0JsLQaPBtFItZ33GjsZ6+gzmnbvwc96/Av/3nLpKYiLpz8wFQBsRjjYsFMvh8l/zrf5gB/krluD1xEsIocGycy2OuBg8Bo/EHnMW25E9pa5q6H0nmrBqeAx6EI9B6nCYvLdmoWRnlJvHuPlLqfv586DVkP7dBsznYgmfNgLTsXNkbdiHsUV9ai2bjc7fB78+7QmfNoKz/SfhEV2D2nPGFGwq+cOfyT9TgXuJ3cHV+cuo+7l6XaZ9t4H8c1eImK5el2rMaOp8MButvw9+fdsTMW04Z/pNxiO6BnXnjlGH/ApB8ge/VCimTqvhmXt6MPG933A4HNzZqQnRkcG8t3IPTWqG0bN5XQDW/HmO29rUd7snazUapg3pxvh3f0ZRoHGNUO7u0rT8fBbJc8pL7xK57CWEVkPWz+uwXoghcNJIzCfOkrdlD8EzHkV4GQl/Uz1/bPFJJEx5rsIh/ok86rQaZj14OxPf/AqHQ2FIt1ZEVwvj3Z8307R2FD1bN2TGff1Z8NnvfLluL0LAgrF3FsS+/cl3yMk3Y7XZ2XzoNEunP+j2RrnS9mVVnz//SPnjcGD6fDHeT76q1rm2rcZxLQaPoaOwXzqD7dBuDP2GoGvaBuw2lNwcTB+ovYiFXwDeT70KDgdKegp5S18uP57iwLL2czwfeAo0AtuRbSgp19DfMhRH/CXs5w6ha9cfXYPWKA4HmHIw//5h+dstti9v/r2k3KzZHZyc9SntvpmN0Gq4+vVmcs5cJfqpYWQeuUjy2j+5umIzLZZMovuet7Fm5HBk/KKC9XvsX4zW14jGoCP89nbsv+8lcs9e48LrP9Lxl+dQbDZMV1M4NvX9Un/Dzo176NqnMz/v+pp8k5kF0wqPyVfrP2ZEv7HoDXoWr3gdnU6HVqth3/Y/+eUr9e+e+8fezS39u2Kz2cnKyOL5aRU4pv8RVTTDg+RCFH1bh1Q2IYQH8AtQGzgDBADPKYqyRQiRoyiKj0va+4BZqD2QrMAkRVH2CCGWow51uwJkAr8pirK8SJw6wPuoQ+X0wDeKoixwrvuHoig/ONMV/dwKWAp4AReB0YqipAshtgAzFUUp96/yokPh/m7pL/1eleEACJxzZ/mJbjbfgCoPmfrk51Uaz7/zjXVR/yu0jepWeUwRWLmnzjdD0qvl9yy62TYlVqzH1s0yYs+TVRoP4HiX+VUes9n2eVUeU/hU/Tm7r9lTVRovulHpwz3+Lg5b1U+i6d24cm8Wuxkur/coP9FN5lCqdt82/KB/lcYDiJtR9fWfqDcGVXlM4VN8Mue/2+nhpc7+8LeIrJNVfqKbzLPG3/4C6mJ0RXpjV4XDi8t/ocnNlq1Ubd+LedqKvUDlZtsft+3fOxP0X3SpZb//bCNHnSPr/5XHTfZYqiRFUcyovYxKWuZT5PO3lDCvkaIooyoQ5xJQbMbpouuW8PkwJfSiUhSlZ3kxJUmSJEmSJEmSJEmSKkPOsSRJkiRJkiRJkiRJkiTdENljSZIkSZIkSZIkSZKk/wTF8a8cLfafJnssSZIkSZIkSZIkSZIk/R8nhLhNCHFGCHFeCPFMCcunCyFOCiGOCiE2CiGKv+79BsiGJUmSJEmSJEmSJEmSpP/DhBBa4F3UOaGbAA8IIZoUSXYIaKcoSgvgB2DhzYgtG5YkSZIkSZIkSZIkSZL+b+sAnFcU5aKiKBbgG8DtdeiKomxWFOX66xb3ANVvRmDZsCRJkiRJkiRJkiRJkvR/WzXgisvnq87vSjMWWH0zAsvJuyVJkiRJkiRJkiRJ+k9QlP/u5N1CiHHAOJevPlAU5YMb2M6DQDugx834XbJhSZIkSZIkSZIkSZIk6V/O2YhUWkPSNaCGy+fqzu/cCCH6AnOAHoqimG/G75JD4SRJkiRJkiRJkiRJkv5v2w/UF0LUEUIYgPuB31wTCCFaA8uAwYqiJN2swLJhSZIkSZIkSZIkSZIk6f8wRVFswGRgLXAK+E5RlBNCiAVCiMHOZK8BPsD3QojDQojfStlcpcihcJIkSZIkSZIkSZIk/Scojn/6F/xzFEVZBawq8t18l//3/TviCkVR/o7tSv+Hza89okpPik4me1WGA+BPT22VxzT+A5PIjW55pfxEN9G1Y35VGg/AbK369vEMm6HKY9YJzqjymKEdq/bajNtR9fs1qstNGVZeKVe2G6s8ZmaeZ5XH7HB8YZXGyx47ukrjAZhTq77jtyGw6mvL+giPKo8ptFW7b19dE1Sl8QAaWKv+/Lmoq/p6vydVX/95pEnV1n/WH7kpb+uuFMc/sF/z/oGxLv9E+0AbJbdK4+Xbq/7vEoDuCT/8Z2e4Pt/k1v9sI0f0ybX/yuMmh8JJkiRJkiRJkiRJkiRJN0Q2LEmSJEmSJEmSJEmSJEk3RM6xJEmSJEmSJEmSJEnSf4LjH5iC5P93sseSJEmSJEmSJEmSJEmSdENkw5IkSZIkSZIkSZIkSZJ0Q2TDkiRJkiRJkiRJkiRJknRD5BxLkiRJkiRJkiRJkiT9JyhyjqUqJ3ssSZIkSZIkSZIkSZIkSTdENixJkiRJkiRJkiRJkiRJN0Q2LEmSJEmSJEmSJEmSJEk3RDYsSZIkSZIkSZIkSZIkSTdETt4tSZIkSZIkSZIkSdJ/guKQk3dXNdljSZIkSZIkSZIkSZIkSbohsseSVGEDnh1J/V4tsZos/DxzGfEnLrst13sauO+9qQTWCkexOziz8SDrX/0WgC5jb6fN/b1w2OzkpWXx81MfknkthegeLbhl3kMIrYYrX23i4uLf3LapMehosWQS/i3qYE3P4dC4dzBdSQag3tQ7qT68F4rdwck5y0nZchQAnZ8Xzd8cj2+j6qDA0WlLyThwDoBaY2+l1uj+tFcUzm06zMaXvy4zz7c+N5JoZ55/m7mMhOPuedZ5Grjn/akE1gxHcTg4u+Egm5x5rqjezz9EnV6tsJnMrJ7xAUlFYgB0e3IYTe7uhqe/N4saP1Lwfc/5I6jZuYn6W4wGvIL9WNJ8fJnx9G064P3oFNBoyF+/kvwfVrgt97htMJ533AUOO0q+idwlr2O/EoOufiO8J89UEwmBacVyLHu2VyiPPj3aUG3+o6DVkPbtepLf/8FtuXeHpkTNfxTPRrWJnbKQzNW73JZrfIw0WP8eWev2EPfsshJj+PVsTc0FYxEaDclfbyDh3Z/clguDjrrvPI5X83rY0rO5MPF1LFeTEXodtV6dgHeLaFAcxM7/mOzdJwAIGtyVyCn3ILQaMjYc4OpLX7htM7hXSxq+MAqh1XDtq01cXvxrsZjNlkzCr0VdrOnZHB33DvlXkgm6pTn15w5HGHQoFhtnF3xJ+g41ZsRdXaj9+F2gKJgT0jk+aQnWtOwS8+zVrR0hsyaAVkvWD6vJ+Og7t+WebZsRMmsCHg3qkjDzJXLX7Sj87dPH4tWjAwDp768gZ83WEmOURdesPZ7DHwONBuu21ZhXfVNyurbd8Z78LDnPP4b98tlKx/Hu3pbwuePV4/DdWlI/+N5tubF9MyLmjMOjYR2uTXuF7DU7C2NHhhL50uPoI0NAgSuPzMd6Laly+WzeHs8Rk9R8bl2FeWUp+WzXHe8pz5Hz7MRK59PnljZEzh8HGg3p360jZan7NeLVvimR8x7Fs1Edrjy+kKzVhXlseu5X8s/EAGCNSyZ23P9KjRPQqxV1/zcatBoSv9rItSW/uC0XBh0NFk/Bu0VdbOk5nBn/JuYryYQO7U7UY4ML0nk3qcWRfk+Re+IyzX56HkNYAPZ8CwAn7/8f1pSsSuUfYO5Lb7Jt5z6CAgP45cullV6/NPrWHfBylnnm9SvJ/7F4medxu0uZ997rOK7EFCzXhIThv+QzTN8sJ/+XipXvHh3b4/f4ZNBoyftjJblfut9rvO8bhnHgALDbcWRkkvnyQuyJiRhat8Jv6qSCdLqaNUl/bgHm7TuLhnDP4z9QrmubtsPz/okIjQbL9jVY1pS8b3RtuuE1cT45L0zCEXMO4e2LccI8tLUbYt21jvyv361QPABtk7Z43jtRvRZ3rsGy9rsS0+lad8U4fh65L03BEXsOTe0GeI54vCCflj++xHZ4V4nrluSOZ0fSoFcrrCYLP85cWmL95/73HieoVjgOZ/1n3atqOdF+RB86PtQPxeHAkmvml1kfkXz+WrEY7Rc8RLXerbCbzOyc9gFpJdQFgprXputb49F6Gri26TD756v3pFven4xfvUgADH5eWLLy+KP/HLyrh3DnloVkXYwHIPngeS7O/aTEPN7+XGEd75eZy4gvEl/vaWDY+1MJqhmOw1nf2eCs73R+pLCOl5uWxa9PqnW88vR97iHq9WqF1WRm5cwPSCwhz7c8OYxmQ9X6z5tNCus/flHB3PHmeDz9vBAaDVte/ZaLm4+UGU/ftgPeE6YgNBry16zE9L37deI5YDCeAwuvk5xFr2OPjUH4+uE7ZwH6Bg3JX7+G3PffKTdvbf43kqjeLbGbLOyZtoz0Y8XzFti8Np3enoDWU0/cpiMcnPc5AM1mDKXe8F6Ynff+Iy9/S/wmNW8BjWvQ/tWx6H2NKA6F1QPm4zBbAWj7P/UcspnM7J72QYkxg5rXpvPbhefQn/PUc6j5jKFED+9JfkHM74jbdASh09Lp9UcIal4bodNw5ocdHHn393Lz33nBQ9Rw/pat0z4gtYRj2+6pYdS/pxse/t4sb/hI8Y2Uo8uCh6jpjLFl2geklBCj/VPDaOCM8YlLjMiODen83EMEN67BhklLuLRyf6Vil1fn9OnYhJrPj8GrcW0uPPYG6St3V3jbgc77tNBqSPhqI1dLuE83XDwFnxZ1sabncNp5nwbwalyL+q+NQ+vrBQ4Hh257Bo1OS4tfC+sFHpHBJP24jYvzl1cqz5JUHtmwBAghagNdFEVZ4fw8CminKMrkf/J3/ZvU79mS4DoRvNNzBtVbRzPoxdF8MOTZYul2friKS7tPotVrGfXVbOr3bMm5LUeIPxnDskFzseZbaP9gH/rPeoAfpi5h4IJR7B/2IvlxqXRd+xJJa/8k52xhhav68F7YMnLY2ukJIod0puG84Rwe9w4+DaoROaQL22+ZiUdEIB2+n8vWzk+AQ6HJCw+TvPkwhx55C6HXojV6ABDUtQnht7VjR++n2a9R8Ar2KzPP0b1aElQngnd7zKBa62gGvDCaT0rI8+4PVhGz+yQavZaHVsymXs+WXNhSduXmujq9WhJYO4KPb5lBZOt69HtxFF/d+VyxdBc2HOTQZ+sZu/V1t++3LPiq4P+tR/UjrGntsgNqNHhPeIKseTNwpCbj/+YyrHt3Ynf5I8qydQPmNWoDn75DF7zGTiL7uaewxV4ic9p4cNgRgUEELPoEy75d4LCXG7PagglcenAe1oRUon97k6z1ezGfv1IYMy6ZKzPfJvTRu0rcRMSMB8ndd6LMGLVeHMfZB57DEp9Kk1ULyVi3j/xzVwuShDzQF1tmLse6PUbQ4G7UmDOSCxPfIHR4PwBO9H0CXbA/Db6cx8kBT6IN8KH63Ic5edtMbGlZ1Hl7Kr7dmpO945gzpqDRK2M4eK96/nZc+zLJaw+Q63L+VhveG1tGLjs7PU74kC7UnzecY+PewZqWzeGHFmJOTMe7UQ3afDOb7a0mIrQaGr4wil3dZ2BNy6b+vBHUGHMrF193b2S4nufQuZO49sgsbIkp1Ph2Mbmb92C9EFuQxBafTNLsNwgYfY/bql63dMCjSTRXhk5EGPRUW/4audv3o+Tmlb6PixIaPB+aQu7rT6OkJeMz/12sh3fhiIt1T+dpxKPfXdgunKr4tovkM+K5x4gdNQdrQgp1fnyb7E17sLicP7a4JOKefpOgsXcXWz3qtRmkvv8tuTsPIbw8waFULr7Q4DlyKrkLn1Lz+dx7WA/txhEX457O04hH/6HYzp+8oTxGPT+RSyPnYktIpe4vb5G9wf0ascYlc/Wptwl5ZGix1R35Fi4MnFqhOHVffoQT9y7AEp9GyzWvkLbuAKazhddJ+PA+2DJyOdh5CiF3dqX23Ac5M/4tkn/aTvJPaoODV6OaNFquNipdd3bSInKOXKh83l0MGdCP4XcPZvb/Xi8/cUVpNHiNf4LsZ9Uyz+/1ZVj27XRrODIXLfPGTCLn+acKlnuNnYT14L5KxfSb/jhp057EnpRMyEdLMe/Yhe1yYUzr2XPkPjIBzGa8hgzG97HxZDy7AMuhw6SMfhQA4etL2LdfYt53oNx4VV6uCw3G4ZPJfesZlPQUvOcsxnZkN474Ite/hxFDn7uwXSy8/hWrFfOvn6GpVhttVO3y96dLTM8HJpH3zmyU9BS8Zi3CdnRPiTH1vYdgd4npuBZD3stTwOFA+AXhNfc9bEf3gMNRbtgGPVsRXCeCt3pOp3rraAa/OIZlQ+YXS7fjw5UF9Z/RX80pqP8c/XUX+7/aCECjvm24fd6DfP7wq27rVuvdEr86EfzSbQYhberR8eVRrB70XLEYnV4eze6nPiLl4AX6fPEkUb1aELf5KNsmLilI03b+cKxZhWV5dkwif/SfU7iREmr+9Z31nUU91DreHS+M5qMS6ju7PljFZWceR66YTXTPlpzfcoT4EzF8MFCt47V7sA/9Zj3AD5MXl7lf6/ZqSWCdCJb1mEFU63rc+sIoPh9SPM/nNxzkz8/WM36Le7nQZcqdnP5jL4e+3Ehw/Sju/fRJ3u82rfSAGg0+k54gc/YMHCnJBLyzDMvendhjXcqCLRvIX6VeJ4aOXfB+dBJZ855CsVjI++JjdLXqoK1Vp8x8AUT2bolvnQj+6DqD4DbRtHt5NOsHFt+f7V8Zw74nPyL14Hl6fPkUkb1aEu9sHDvz4WpOL13lll5oNXRe/Bi7p75PxslYdIG+KFYbAFHOc+i3rjMIblOPDi+PYu3A4vuz/Suj2fPkR6QevECvLwvPIYDTH67hVJGYtQZ1QOOhY2WfWWiNBu7Y8ioXft1NztXSGw5r9G6Jf50Ivus2g7A29ej28ih+LeF8jt1wkBPL13Pf9sqX+ddjfOMS45cSYsQ4Y9xfJEb2tVS2TF9Gy/EDKh27InVOy7VkLk1bTMSEOyu97XovP8Lxexdgjk+jlfM+nedyn45w3qcPdJ5C6J1dqTP3QU6Pfwu0Ghq9O5UzkxeRezIGXaAPitWO3WzlUN8nC9ZvtfZVUlbtrXy+JakcciicqjYw/J/+EZUlhNCV9bmM9bSVjdWof1sOO/+ouHroPJ6+XviEBrilseZbuLRb/aPKbrUTd+IyfhFBAFzafRKr82n2lUPn8Y8IonqreqTFJGKKSUKx2on/ZRfht7Vz22b4be24+t02ABJ+30tIt6YF38f/sguHxYYpNpm8SwkEtIlG52skqHNjrn61GQDFasfmrGDVergfFxb/isOi3oTzUst+ot6gX1uO/qjm+dqh83j6eeET5p5nW76FGGeeHVY78ccL81wR0f3bcuJHtRdJ/KELePh5410kxvVluUkZZW6r0eDOnP6t7CciuvqNscdfw5EYDzYb5m2b0Hfs5pZGMRVWSIWnsXCB2Vzwx4YwGECp2B/oXq3qY4mJx3IlEcVqI+P3bfj17+iWxno1ifzTl1FK2KaxWT10IQHkbD9Uagzv1vUxX47HHKvGSPt1B4G3dnBLE9i/Aynfq+dF2spd+HZrAYBngxpk71Qbi2ypmdizcvFuGY1nzXDMl+KxpannSdb2IwQN6FywPf820eRdKjx/E37ZReht7d1iht7Wjrjv1J5ASb/vIahbMwCyj1/GnJgOQO7pK2g9DQiDDoQABFovtTFU52ssSFeUZ/OGWGPjsF1NAKuNnNVb8Ond2S2NLS4Ry9lLxf54MkTXxHTgGNgdKCYzlrOX8O7ufu2VR1u3IY6kOJTkeLDbsO7bgr511+K/865RmFd9C1ZLpbZ/nbFFAywxcVivqPnMWrkN3z7u+bReS8J85jIoRfNZA6HVkrtTPXeUvHyUfHOl4mvrNsKReK0wn3s3o2/TpVg6z6Gj1Z5MN5BPY8sGmGPisTqvkcw/tuHbr5NbGuu1JMynL1foD+HS+LaOJv9SAubYJBSrjeRfdhJ0q/s5G3Rre5K+2wJAyh+78e/WvNh2Qu7qRsovZfeguRHtWjXH38/3pm5TV78xjoTCMs+yfROGDu5lHq5lnocRXIohfcdu2BPjscdeqnBMfeNG2K/GYY9TY5o2bMKjm/u1YTl0WC1TAcuJk2hDQ4ttx7NXD8x79hWkKyuPVV2ua+s0xJEch5KSoF4X+7eia1X8uvAY8rDak8n1urDkYz9/otLXiqZ2QxxJ8QUxbfu3omvRuVg6j8Ejsaz9HsVmLfzSai68dvR63A5yORrfYP3H31kXMOeYCtIZvDxK3Mc1bm3LhR/UukDKwQsY/L0xFqkLGMMC0PsaSTmoNuBe+GEHNW8rXm7XHtSRS79WvHcEQMN+bTnyo0seS6jvWPMtXHbJo2t957JLHe/qofP4RZZfD6rfry3HnfWfuDLqP3Gl1H8UBQw+6rns4etFdlLJ98rrdA0aY4+7hiPBeZ1s3YShU5HrJM/l4YqnS1lgzsd24hiKpWLnbPVb23L5B3V/ph48j8HfC88iefN0Hs/Ug+cBuPzDdqrf1rbM7Ub0aE7GqVgyTqqNqZb0HBTnw5Lqt7blovMcSnWeQ6XHVM+hiz/soHoJ55ArRQGdlwdCq0HracBhtWF1OadLUqt/W845f0vSwQsY/Iqfz9eXmcqp25amdv+2nHWJ4eHnjVcpMfJKiJFzNYW0U1cK9l9lVKTOabmajOlUTKUfZl2/T+eXcZ8OvrU9ic77dPIfuwlw3qcDe7Yk92QMuSfVxlJbek6xOoOxbiSGEH+y9tzgw77/QxTlv/vv3+pf27AkhPAWQqwUQhwRQhwXQtzn/P6yEOJlIcRhIcQBIUQbIcRaIcQFIcQEZxohhHjNud4xl3VL/B54Beju3Ob1xx1RQog1QohzQoiFLr8rRwjxovN37RFChDu/DxVC/CiE2O/819X5fQ/ndg8LIQ4JIXyFEJFCiG3O744LIbqXkP+2QoitQog/nfmLdH6/RQjxthDiAPB4CZ/7OOMcE0J8IoTwcNlvrwohDgLDKns8/MKDyIxLLficlZCGX0Rgqek9/bxo2KcNF3ceL7as7b09ObflCL5FtmmKS8OjSKOMZ2QQ+dfUNIrdgTXbhD7IF4+IIEzXCtfNj0/DMyIIY80wLKlZtHhnIl03vEzzN8cV/JHuXS+SoI6N6LL6BUZ+O5fIFnXLzLNvRBBZRfLsG156nj38vGjQtw2XSshzaXwiAsmOL4yRnZCGTxn7tTR+1YLxrxlG7M4yevUAmuAQHCmFw4Acqclog0OKpfMYMISAD1bgNWoCucsKu3zrGjTG/93lBCz+lNz33iz/qTagDw/GGlf4ZMsan4o+PLgi2QIhiJw7lvgXS+66f50hIgiLSwxLfCr6CPcY+ojgwjR2B/asPHSBvphOXiKgfwfQajDUCMOreT0MUcHkX47Hs14UhuqhoNUQcGtHDFGF+8ojIgizy/lhjkvFo8ixK3r+2rLz0Ae5/+EcNrAjWccuoVhsKDY7p57+iM5bXuOWo0vxblCda19tKjHP2vBgrAnJBZ9tCSlow4ofy5KYT1/Eq1s7hKcHmgA/jB1aooso/odtWURgCEqay7mUlowIdN/nmlrRaILCsB298SdjuohgbPEu509CCroKnj+G2tWxZ+dS7d051Pl1MWFPjwFN5W57aj4L97OaT/f9rKlVH01QKLYjN5ZPfUQw1niXYxmfUvFrBNB4GKj361vU/fH1Yg1SrgyRxa8TjyJ/ABoigzC7XCe27Dx0Rc7ZkDu7kPLLDrfvot9+jJYbXqP6NPfecf80ERyCvUiZpymlzPNfugLjqAnkfegs8zyNGIcOx/TNZ5WKqQ0NwZ7kEjM5GW1o6dem18ABmPcWP3eMfXph2rCx3Hj/RLkuAkJwuFwXSnoymoAi13/NaDSBodiOVaK3Vxk0gcE40l2uxYyU4mVOjWhEYCj248Vjamo3xGv+MrznLcW8YnGFG2l9wwPJjEsr+FyR+k+jPm244HI/7vhQP6ZvfYtbnxnOyuc+L7aOV0QgeS73k7z4NLyKxPCKCCQvPq3MNGEdG2JKziT7UmLBdz41Qxm49gX6/zCHsA4NS/zNfiXUd/zKqO94+nnRsJT6Tpv7enK+Ar22fSMCyY5zr/+UVccqasfbP9H0rq48tmcR9y5/kvXzi+9XV5qQEBzJLtdJSsllgefAIQR+sgLvsRPIWVr+kLeSGCOCyHU9nnEVOJ5xaRhd6sD1R/fn9g0v0/HNR9H7ewHgVzcSRYGeK57m1rUv0OSxO9y3dwMxXdM0GN2PARteotObj2Jwxoz9Yx+2PDNDDy/hrv1vc3TZKswZuWXm3zsikByX35Ibn4b3DdRty4uRWyRG0fz+XSpS57xRHq73YCp3nzbWjUJRFJp9PZfW6xZSfVLx3lKhQ7qS/FvFhwFLUmX8axuWgNuAOEVRWiqK0gxY47IsVlGUVsB2YDlwD9AJeN65fCjQCmgJ9AVeczbMlPb9M8B2RVFaKYrylnMbrYD7gObAfUKIGs7vvYE9iqK0BLYBjzq/fwd4S1GU9sDdwEfO72cCk5y/tztgQu0dtdb5XUvgsGvGhRB6YDFwj6IobYFPgBddkhgURWmnKMobrp+Bd5374z5FUZqjdnie6LJeqqIobRRFKXmCkJtEo9UwbNFk9i5fS/qVZLdlLYZ0JapFXXZ88MffE1unxa95HWI+W8/OvrOw5ZmpO0UtWIVOiz7Qh123z2XDSyu4+70pNy2u0GoYungy+z5dS0aRPFeFRoM7c3blvht68lIS86pfyBg3nLzPlmG8b2TB97azp8icNIrM6RMwDhsBesNNiVea4IcGkL35ANaE1PIT36DkbzZiiU+h6erXqfn8WHIOnEaxO7Bn5nJ51jLqvT+Txj+/hOVqEor9xnuLlMS7YXXqzxvOqZkfAuo5Wn1UP/b0eYZtLSaQfTKGOo+XPDzwrzDtOkje9v1UX/EWEa/PIv/IqZueN4TAeP9ETN/cvLlyKv0TdBq82jUl6ZWPuTT0cfQ1IvEf2vcmBxEYH5jwj+bzTPcxXLhzGleeeI3IeY9iqBnxt8XyaV0fh8lM3unCYXpnH3uHw71mcPzOefh1bEzosB5/W/y/i3nVL2ROGI7ps2UY71XLPOP9o8j/7XvIL/vp/F9h7N8XfaOG5Kxwn59IExyErm5dzHsrN+9HWaq0XBcCz3vHk//9B399W5WI6TFsHOYfPyxxsePyGfIWjCfvlakYbrsPdPqb/hM0Wg33LprM7uVrSL9S2Iix94v1vNljGmtf+ZqeU4bc9LjX1RnSmcsuvZVMSRn81OEJ/rh1Lgee/4ru7z6Gh4+xjC2UT6PVcPfiyez9tIQ63l1diWpel53L/p46nqsmgztz/IdtvNdpKt+Neo1Bb0909vr9a/L/+IX0McPJ+2QZXg+MLH+Fv8H5zzbwR+dprO43G1NiBm2eHQGo97TQDg3YNfldNgxZQPXb2hHu7Mn/V537bAO/dZ7Oqn5z3GKGtK6LYnfwU+sp/NJxOs3HDcC3ZuUeRElVQ+i0+HdsxOlJ73DkzrkE396hoDfTdaFDupL8845StiBJf82/eY6lY8AbQohXgT8URXGdTfI3lzQ+iqJkA9lCCLMQIgDoBnytKIodSBRCbAXal/F9SWOiNiqKkgkghDgJ1AKuABbg+h3zT6Cf8/99gSai8KbmJ4TwAXYCbwohvgJ+UhTlqhBiP/CJswHpF0VRDheJ3RBoBqx3bk8LxLssLzpD5rcu611SFOX6rLGfAZOAt0tZr4AQYhwwDuCOoA608Y2mw0P9aPtALwCuHbmIf1Rha7xfRBBZCSV3Ox788lhSLyWw+5M1bt/X7dqUHpPv5JP7XsBusZGdmIZ/VDDXn6sZo4IwJ6S5rZMfn4ZntWDy49MQWg16XyPWtGzMCWkYqxX+Hs/IIPIT0jDFpZIfl0ams2txwu97qTdFnXA2Py6VhJXqU8y4IxdRHApeQb7kuUyM3G5kP1rfr+Y57uhF/IrkObuUYUkDXxlL2qUE9hXJc0lajexLC+d+TTh6Ed/Iwhi+EUHklLJfy9JwUCc2ziv/qbojNQVNSFjBZ01wKPbU0sfJW7ZtxHviNIo+m7JfjUExmdDWqoP9/JkyY1oTU9G79PTRRwZjTaxYQ5FXm0Z4t29K8EMD0HgZEXodjrx8El51z6slIc2tN5EhMrhYY5Q1IRVDVAjW+FTQatD6eWFLV4/9lec+LUjX+NeXyb8YB0Dm+gNkrlfnNwkd0c+t8cWckIaHy/nhERWMucixu37+mp3nr87Xq2Aibo/IIFp+OoPjk9/DFKNeBb7NagMUfE78bQ91ppQ8Pt+emIrepZeRLiIEe1L5k6Vel77sa9KXqRMKhy98BmvM1XLWcKekpyCCXM6loFCUdJd97umFplptfJ5R27+FfxBeUxeQt2h+pSa2tiWkoot0OX8iQrBV8PyxJqSQf+qiOowOyF6/G2OrRmT+sK7C8dV8Fu5nNZ8u+9nTC031Ovg88ybgzOcT/yPv7XkVzqc1IRV9pMuxjAyp8DUCFOwP65VEcvccw7NpPSyxCcXSWeKLXyfm+LRiaTyiQrDEp4HznLW5lJGhQ7qS8rP7MDiLs9y25+aT8vN2fFtHk/x95SeD/zsoqSloi5R5jrLKvO0b8ZqgdlzWNWiCoUsPjA+PR3j7gKKgWCyYV/1cZkx7cgraMJeYoaHYk4vHNLRrg8/IB0md/ARYrW7LPHv3wrx9B9jL7z30T5TrSkYKGpfrQgSG4shwvf6NaKJq4z3zNXW5fxBekxeQt2Q+jphz5eapJI70VPSBLtdiQIh7meNhRBNVC6/paidz4ReI8bHnML33HI7YwpiOhCso+SY0UbXdvnfV8aF+tHOr/xT2GCir/nPny4+UWP+57tjvuxn8wpiCGF3v7w1A6uGLeLncT7wig8grEiMvIR0vl54LRdMIrYaat7dn5e3zCvNqsWG25ACQduwy2ZeTCK4TQdyxS7Qf2Y+2zvrOtRLqO1ml1HcGOes7e0qo43WffCfL71XreCVpM7IvLZ0x449exDfKvf5TWh2rJC3u68F3I9VjHXfwPDoPvVqnK2WaA0dKCppQl+skpOyywLx1I96Ty5izqYj6o/pRb4Sat9TDF/GOCub61r2iKnA8o4IwOcvSfJeXH1z4ajO3fK5OsJ8Xn0byntNY0tRjak7PocuiCeSnZpFW9ByqYMzraVxjnv9qMz0/nwFA7bu6EL/5KIrNjjk1i8T9ZwltUZfsWPdGxSYP96XRcDX/yUcu4uNSv/eODCL3Buq2RTUtEsPbJb/eJVwzf5eK1DlvlNl5D3bddkXv05a4VDL3nCq4Z6dtPIR3izpkOOcG9W5SC7Raco5evCm/VZKK+tf2WHI2jrRBbTx6QQjhOlPi9QkHHC7/v/75ZjWWuW7X7rJdq1I4EYzr9xqgk7PXUytFUaopipKjKMorwCOAEdgphGikKMo24BbgGrBcCFH0kYgATrhsq7miKP1dlhetD5bdJ7UC6RRF+cDZC6pdG99oAPZ9sZ73B8zm/QGzOb3uAK2GqiP2qreOJj/bRE5yRrHt9JkxDA9fL1YvcH97VkTTWgx+aSxfPfIGuc6b/rUjFwmqHYGxZihCryVySBcS1/7ptl7S2j+pfu8t6jYGdSTV+easxLV/EjmkCxqDDmPNULzrRpBx8DyW5Ezy41Lxdr4dJaR7s4LJwBNXHyC4q/pkJ6hOBFq9zq1RCeDA5+v5cMBsPhwwmzPrDtDibjXP1a7nuYRx2j1nqnle+/wXxZaV5PDnG/j89jl8fvsczq/9k6Z3q2P8I1vXw5ydV+5cSkUF1YvE09+buD/Lr6zbzp1GG1UdTXgE6HR43NIb6z73PxI1kdUK/q9v1xlHnNrgoAmPAI06PZcmNBxt9Zo4kor/8VpU3pFzGGpHoa8ejtDrCBh0C1nrKzY04soTb3C66xhOd3uE+Jc+If2nTcUalQByD5/Do04khhphCL2OoDu7kb7O/Ul/xrr9hAxTKyRBd3QpmFdJ42lA45zg3a97SxSbvWACRl2wPwBaf2/CHr6d5K83FGwv69AFvOpG4Ok8fyOGdCF5rfsku8lrDxB1r9p7I2xQJ9Kc56/Oz4vWXz3D+Re+JnN/4R9w5vg0fBpURx+sDj0K7tGc3HPF3x4EkH/8DPpa1dBVCwe9Dp/be5K7eU9FditoNGj81RiGBnUwNKxD3s4/y1nJnf3SGbRh1RAhEaDVoe/QE+shl+7Vplyyp95N9pMPkv3kg9gvnKp0oxKA6djZgvMHvQ6/O24he2PF8pl/9BxaX2+0QepE/d6dW2I5H1vOWu7sl06jDXfJZ8dexfM5eSjZM0eQPXME9gsnK9WoBGA6ehYPl2vEf+AtZG+o2LA6jZ+3Oj8XoA30w6tdE8znSs5j9uHzGOtG4lFTvU5Ch3Qlrch1krbuAGH39gQgZGBnMl2HughB8ODOJLsOg9NqCobKCZ2WwH5t3Xoz/dNs506jiayOJkwt8wzdK1DmxavXf/bsKWSOu5/Mcfdj/v0H8n/4stxGJQDr6dNoa1RDG6nGNPbtjXmn+9ADXf1o/J+cTtozc3BkZBTbhrFvb0zryx8Gdz2PVV2u2y+fQeN6/bfvge2Iy7w+pjxypg8jZ9ZIcmaNxH7x1F9qVAJwxJxBExaFCA4HrQ5d+x7qBNzX5eeRO/M+cuc8TO6ch7FfOl3QqCSCwwuGwYqgMDQRNVBSE0uJpPYwenfAbN4dMJuTReo/5lLqP31nDMPT14tVReo/wbULexA26N2a1MsJBTH+6D+HP/rPIXbtn9S7R60LhLSphzUrr9jcM6akDKzZJkLa1AOg3j3duOJSZ4rs3ozM83FuQ508gnwRGvWBp0/NUPzqhJMeq/ak2v/5epYOmM1SZx2v5d1F8lhCXaS3s76z5vnidbyBL4/l67GFdbySHPx8A58OmMOnA+Zwbt2fNHPWf6JuoP6TFZdKbWedLjg6Cq2Hvsy5M21ni1wnPXpj2VPkOokqvE4MHTpjv1bxBy7nlq9nTb/ZrOk3m2trDlD7HnV/BreJxpplIr9I3vKdxzO4jVrnrn1Pd646j6fr3EjVb29H5hn1d8RvOYp/4xpojQbnnEd69j75Mav7zeHKmj+p6zyHgtvUw5KVV0ZM9Ryqe0+3EmPWuL0dGc6YuddSC3pFaY0ehLWJJuNCXLH8n/xsAz/dOoefbp3D5TV/Ut/5W8La1MOSXfx8vhEnPtvAj7fO4UdnjAZFYpQ0l9LfoSJ1zhuVffg8nuXcp1PXHSDceZ8OHdiZDOd9On3LYbwb1URjNIBWg3/nJm6Tfofe1c39/v0fpzjEf/bfv9W/tseSECIKSFMU5UshRAZq40xFbQfGCyE+A4JQG3GeRM1vSd9XA/7qjKHrgCnAa87f30pRlMNCiHqKohwDjgkh2gONhBAm4KqiKB8650BqA7gODj8DhAohOiuKstvZs6mBoihlT6CjrldbCBGtKMp54CHgpjw2Prv5MPV7teKJrW9iNVn4+cnCV75PXPUS7w+YjV9EED2mDCH5/DUmrFRH7u39bB0Hv93CrbOGY/Dy5L731Nf9Zl5LYcWjb7Jy/nKGfjMbtBqufr2ZnDNXqf/UMDKPXCRp7Z9cWbGZlksm0WPP21gzcjg0fhEAOWeuEv/bbrpvfwPFZufEM58WTJB3YvantHpvMsKgIy8miaOPq0NUrny9mRZvT6D71tdobbPx24yyh66c33SY6F6tmLTtTWwmC7/NLMzzo6te4sMBs/GNCKL7lCGknL/Go8487/98HYe/2VKh/Xpx02Hq9GrJI9vfwGqysGZm4bCBkatf5PPb1Te53DL7fhrf2QW90cD4vYs49s0Wdr2lvtq00eDOnP69gg0KDju5S9/G7/nX1Vdvb1iFPfYyxhFjsJ07jXXfLjwHDkXfqi3YbCg5OeS8/TIAuiYtMN4zHGw2UBRylr6FkpVZfky7g7j5S6n7+fOg1ZD+3QbM52IJnzYC07FzZG3Yh7FFfWotm43O3we/Pu0JnzaCs/0nlb9tlxixcz+k4YpnQaMh5duN5J+9QtTMB8g7cp6M9ftJ/mYDdRc9QfMd72HLyOHiY2pPGl2IPw1WPAsOBUtCKhenFs6pUHPBWLya1AYg7q3vMF8srEwpdgdnZn1Cm29mI7Qa4r7eQu6Zq9R7ahhZRy6SvPZP4lZsptmSyXTd8w7WjByOjVe3XWPsbXjVCafujLupO0N9k9mf972IOTGdi6//QLtfnkex2ci/msKJqe+VmufkF98l6sOXEBoNWT+vw3I+hqDJI8k/cZa8zXvwaNaAyEXz0fj54t2rE7bJI7kyeJw65O5LNf+OnDwSn34VKjsUzuHA9NVivGe8or76e/saHHExeAx5GPvls9gOV27y2FLZHSQ8/z41PnkBodWQ8cM6LOdjCXn8QfKPnSNn0148m9en+nvz0Pr54NOrI6FTH+TigIngcJD06sfU/OxlEIL8E+dI/678XoXF8vnFYryffFXN57bVOK7F4HHXKOyXz2A7dBPyaXcQ99xSan+2AKHRkP79esznYgl7Qr1Gsjeq10jN9+eg9ffBt08Hwh4fzvnbJuERXYNqL05GcSgIjSBl6fdub5MrGufi7I9o+vVc0GpI+noTpjNXqfnUfeQcvkDaugMkrthIgyVTabN7MbaMHM6Mf6tgdb/OTbDEpWKOLRzio/HQ0/TruQi9Tj0+246S8OWGkqKX68lnX2H/oaNkZGTRZ8iDPDb2Ie4edOsNbauAw07eB2/j+5yzzNu4CvuVyxiHj8F23lnm3TEUXUtnmZebQ66zzLthdgdZby4i6M2FoNFgWrka26XL+IwdjfX0Gcw7d+E3aQLCaCTwf8+pqyQmkv7MXAC0EeFow0KxHK7Y20X/kXLd4SB/xRK8nngJITRYdq5Vr//BI7HHnMV2pOx7ks/LnyOMXqDVo2vdhby3ZhV/u1tJMb99D6+pL6rX4q51OOJjMAx6CHvMOexHS4+pjW6G4dZ7wa7m0/z1EpTcsl/gcd3ZzYdp0KsV07e+hcVk5ieX+s+kVS/xrrP+03PKXSSdv8ZjzrrAns/W8ee3W+j4cH/qdW2Gw2bDlJnLjzPeLxbj2sbDVOvdkrt2voHNZGHX9MK6wMB1Lxa81W3v7OV0eWscOk8D1zYf4dqmwnOk9p2d3IbBAYR3akSrmXfjsNlRHAp7Zn2KKbP4M8Zzm9Q63tRtah3vV5f6zoRVL7HUmcdbnHW88c487vt8HQe/2UL/2Wod797rdby4FL5+5M0y9+uFTYep26sl47ep9Z9VLvWf0ate5NMBap57zrqfJs76z2N7FnH0my3sePsnNr3wFbe/8gjtx96GosDKGctKC6Vy2Ml5/238X3gdtBry16nXiddDY7CdPY1l7y6Mg4aib61eJ46cHHLeKCwLApd/g/DyRuh0GLp0I2vOTLc3yrmK23iYyD6tGLjrTewmC3unFf6229a/xJp+swE4MOtTOr49Hq2ngfjNR4h3Hs9Wcx8gsGkt9Zq8msz+p9R5Jq2ZeZxZtppbV/0PRVGI23SEuI2HC2JW69OSwbvewG6ysHta4f68ff2LrO6n7s/9s5bT+e1xaD0NxG0+QpwzZpu59xPYtBaKopB7NYW9zphnP11Pp7fGccfmVxBCcPq7baSdKvvhwZVNh6nRuyX37XgDW76FrS7n89C1L/LTrepv6TDnfuoN6YLOaOCB/Ys48/UWDr75U9nH0Sl202Fq9m7J/c4YW1xi3L32RX50xug4536inTFG7F/E6a+38OebPxHasi79P3oCD38vavVrTbvpd/N9n2cqFLsidU7vltFEf/w0Wn8fAvq1p9qM+zne+/EKbfvC7I9o9vVchFZD4tebyDtzlVpP3Ue28z6dsGIjDZdMpZ3zPn3aeZ+2ZeZyddnvtFrzKigKaRsPkr7hYMGmQwZ34cSIF0uLLEl/mSjpLUz/BkKIW1EbaRyAFZioKMoBIcRloJ2iKClCiFHO/092rnMZaAekAguB21Hf6fCCoijfCnVcWUnf64G1QDDqHEXpRbb7B/C6oihbhBA5iqL4OL+/BxioKMooIUQI6hxHjVEbsLYpijJBCLEY6OXMxwlgFHA/aoOWFcgBRiqK4vbKGSFEK2AR4O/c3tvOhqgtwExFUQ440xX93Ad43bnOfud+M7vut/L2/fzaI6r0pOhkKr+r/832p2elX4z3lxmVqm9hHt2yansOXDvmV6XxAMzWqm8fz7D9vXNLlaROcEaVxwztWLXXZtyOqt+vUV0q95a4m+HK9r82x8mNyMzzrPKYHY4vLD/RTZQ9dnSVxgMwp1Z9x29D4E2eD60C9BEeVR5TaKt23766puJvdL1ZGlir/vy5qKv6er8nVV//eaRJ1dZ/1h+pXqXxABz/wH7N+wfGulR9iQdtlIoOBrk58u1V/3cJQPeEH/693V/+opP17vh3NnLcBE0urPxXHrd/bY8lRVHWojb2FP2+tsv/l6M2BBVbhtpw82SRdZVSvrcCvYuEct3uQJf/+7j8/wfgB+f/U1An+y76e0uaIfoz579SOedduqWE73uW83kj0LqE9WqXFU+SJEmSJEmSJEmSJKmy/rUNS5IkSZIkSZIkSZIkSZXh+AdGivz/7l87ebckSZIkSZIkSZIkSZL07yYbliRJkiRJkiRJkiRJkqQbIhuWJEmSJEmSJEmSJEmSpBsiG5YkSZIkSZIkSZIkSZKkGyIn75YkSZIkSZIkSZIk6T9BkZN3VznZY0mSJEmSJEmSJEmSJEm6IbJhSZIkSZIkSZIkSZIkSbohsmFJkiRJkiRJkiRJkiRJuiFyjiVJkiRJkiRJkiRJkv4TFOWf/gX//5E9liRJkiRJkiRJkiRJkqQbIhuWJEmSJEmSJEmSJEmSpBsih8JJxYzyTa7SeA6vqn8dZEt/a5XHtORpqzymYqvaePWX9KragAAZqVUe0n7idJXHdKRVeUh00TWrNF5EzsUqjQeQeaLqr8uafSxVHtN0LqfKY2aPHV2l8Xw//rRK4wEYT26r8phkZ1R5SMt3v1d5TP3Y4VUab8zOFVUaDyA3x6PKY/aNrPqywJRlqPKYuYn6Ko035I3oKo0HQF5ulYe0n636+3TO7qqvAPnd3ahK4z3zVnqVxruu+z8SVfqvkg1LkiRJkiRJkiRJkiT9JziUqu+48P87ORROkiRJkiRJkiRJkiRJuiGyYUmSJEmSJEmSJEmSJEm6IbJhSZIkSZIkSZIkSZIkSbohco4lSZIkSZIkSZIkSZL+ExQ5x1KVkz2WJEmSJEmSJEmSJEmSpBsiG5YkSZIkSZIkSZIkSZKkGyIbliRJkiRJkiRJkiRJkqQbIhuWJEmSJEmSJEmSJEmSpBsiJ++WJEmSJEmSJEmSJOk/QVH+6V/w/x/ZY0mSJEmSJEmSJEmSJEm6IbJhSZIkSZIkSZIkSZIkSbohciicVCHGru0IfnoiQqsh66c1ZH78rdty/5F34zv0NhS7HUdaJsnz38AWnwRAxPsv4tGiMfmHjpM4eX6FY3p1a0fIrAmg1ZL1w2oyPvrObbln22aEzJqAR4O6JMx8idx1OwqWBU8fi1ePDgCkv7+CnDVby43n0ak9/k9MRmg15P62ipwvvnZb7nP/PXgNHgB2O/aMTDJefA17QiIAUTvWY71wCQB7YhJpT82tUB6NXdoR9PRjCI2G7J9Xk/mJ+371e+hufO+6XY2ZnknKs68X7Nfw917Co3ljzIePkzhlXoXiAejbdsB7whSERkP+mpWYvl/httxzwGA8B94FDjtKvomcRa9jj41B37od3qPHgU4PNiu5H7+P9cihCsXceTqWhb/swuFQuKtjI8b0ae22/LVfd7H/fBwA+RYbaTkmdrw4umB5Tr6FoQu/o1ez2swa2q1iMc8nsHDtYRyKwl2t6zCma6NiadaeuMKybScBQYNwf14Z2pH9l5N4bd2RgjSXU7J5ZWhHejeqVmY8bYNWeAwcAxoN1v0bsW79ueR0TTthfPBJ8pY8hePaBdDq8BgyHk31eqAoWH7/BPulExXKo7ZpOzzvnYDQaLHsWI1l7XclptO17obXhHnkvDQZR8w5tI3b4HnXGNDpwGYj/8cPsZ85UuK6RWlqN8PQZzgIge3odmz7VhX5TV0x9LwXJScdAOvBjdiPbQdA+AZhuG0UwjcIFDD/+BZKVmq5MXWtOuA1ejJotJg3rsT8i/s5a+g3GM/bhqA4HJBvInfZ6ziuxqi/p2ZdvMbPQBi9QFHIemYCWC3lxvTs3J7AmZNAoyH3l1VkffaN23LfEffgc+cAtcxLzyB1wWvYE5LQRoQR+voCEAJ0OnK++5mcH/8oN562aTs875+I0GiwbF+DZc23JabTtemG18T55LwwqfBY3j0WtDqw28j/4UPspw+XGw/A0KEDvpMng1aLaeVK8la471evYcMw3nGHmseMDLIWLsSRqJZ5PuPH49GpE2g0WA4cIHvx4grF1LfugNejU0Cjwbx+Jfk/usf0uG0wHrcXlj+5772O40pMwXJNSBj+Sz7D9M1y8n8peR9VxtyX3mTbzn0EBQbwy5dL//L2SrLz+AUWfrMBh8PBXd1bMeb2zm7L41MzmffpH2TnmXE4HEy9uyfdm0dXLsapWBb+skMtYzs1ZkyfNm7LX/tlJ/vPXwMg32ojLdvEjpfGFizPybcw9NVv6NWsDrPu7l6hmLrm7fEcoV4j1q2rMK90v0YMvQZi6HMnOBwoZhOmT9/CERcDWh3G0dPQ1m4AioLpq3exn65Y+eOW5yrYr0X9lbpQZfjc0oaoZx8FjYb0b9eTvPQHt+VeHZoSNe9RPBvVJnbqQrJW73JbrvEx0mDde2St30Pcs8vKjefRUa3/oNWS9/vKYvUf7/uH4TVIrf84MjLJeGlhQf1HGx6G/6yZaMPCQFFIm/FMwbKKMnZtR8gzExBaLVk/ribjY/d7mf/Iofjdre5Xe1omyfPevKH96tmlPUEzHwOthpyfV5O1vGi5fjc+dznreekZpD7/Ovb4JLSRYYS+/jxCo5br2d/8UqFyHWDnuXgWrjmoXptt6jKme5NiadYej2XZluMgoEF4AK/c0wWAt9YdZvu5OBQFOtUN56nb2yCEKD/mxSQWbjyu1n9a1GRMp/rFY56OY9nOMwA0CPPnlUGFZUaO2crQj7fQq34Es/o1LzeetkFrPAaPAaHBun8D1i2l1H+adcL40FPkLXpSrf9otHjc8xiaqLoIrRbrn1uwbvmp3Hig3r/8pqr1AtPKleR+VeT+de8wvAYW3r8yX3G5f00Yj0fnTgiNBvP+A2Qvqtj9S1OrCYYe94LQYDuxE9uBte75a9wZQ7ehKLkZAFiPbMF+YicA+q53oa3dTP1+3yrs5/6sUEyAu58dRZNerbGYzHw1832unrjktlzvaWDMe9MIqRWOw+7g+MY/+f1V9RoOrBbC8IUT8AnyIy8zhy+eWEJGQlqFY0tSZciGpXIIIQKA4YqivPdP/5Z/jEZDyJzJxI97BltCCtW+WUze5t1YL8YWJDGfOk/W/ZNR8s343juQoOmPkPTkSwBkLP8ejacnvsMGVCpm6NxJXHtkFrbEFGp8u5jczXuwXiiMaYtPJmn2GwSMvsdtVa9bOuDRJJorQyciDHqqLX+N3O37UXLzyowXMONxUh5/EntSMmGfvE/+9l3YLhf+QWM5e57c0RNRzGa87xqM36RxpM/7HwCK2ULyw+Mqnj9nzODZU0gY/zS2xBSiViwhb4v7frWcPk/c8Enqfh02kMBpj5L81IsAZC7/HmH0wO+eOyoV02fSE2TOnoEjJZmAd5Zh2bsTe2xhPs1bNpC/6jcADB274P3oJLLmPYUjK5Os52bhSEtFW6sOfi+8RvpD95QWqYDd4eDln3aydPwdhPt7M+Ltn+jRtDb1IgIL0jx5Z5eC/3+9/Tinr6W4bePdNftpUzeywtm0OxReXnOIpSO6E+7nxYiPNtKjQRT1Qv0K0sSkZvPJzjMsH9ULP6OBtNx8ANrXDuO7cf0AyDRZGLRkNZ3rhZcdUGjwGPwopo8XoGSlYpz0KrZT+1GSrrqnM3hi6HoH9tizBV/p2/cFwPTOdIS3H56j52J69+nyB4cLDcYHJpH79iyU9BS8Zy3GdnQPjvhY93QeRgx9hmC7eKrgKyUnk7x356NkpqGJqoXX1JfIeWZE2fEAhMDQ70HM372Bkp2G50PzsV84jJIa55bMdnof1o1fFVvdMOARrHv+wBFzEvQeFRsAr9HgNfZxcv43E0daMr4vL8V6YGdBwxGAZccGLOvVc1bfrgteD08i58WnQKPFa+oc8ha/hD3mAsLHD+y2CsUMfHoqSZOewp6YTMTn75G3bTe2Sy4xT58n4Qe1PPC5exABU8eROvsF7ClpJIyeAlYrwuhJ5LcfY9q6G3tKGQ1oQoNx+GRy33pGPZZzFmM7sruUY3lX8WO5eJ7zWNbG64mXyHlqeIXy6Pv442TMnIk9OZmgpUsx79yJPaYwj9Zz58gbPx7MZoyDB+M7fjyZCxagb9oUfbNmpI5VGyYCFy9G36oV1sOHy43pNf4Jsp+dgSM1Gb/Xl2HZt9Ot4ci8dQPmNc5j2aELXmMmkfP8UwXLvcZOwnpwX/n5q6AhA/ox/O7BzP7f6zdtm67sDgcvr1jH0mn3Ex7ox4gXl9OjZX3qRYUUpPlw5S76t2vMvT3bcCEuhcmLvmP1KxVvAFHL2O0snTBILWPf+tFZxgYVpHlySNeC/3+9/VjxMnb1vkqVsQgNniOnkrvwKZS0ZHyeew/rod1qw5GTZfcmLJvVP751rTvj+cAE8t6YhaGnes/KmfsowjcA75kvk/PcY5WaEKMq9msxf7EuVJk4UQsmcOmhedgSUqn365tkbdiL+fyVgiTWa8lcffJtQh69q8RNhE9/kNx9FXtAgUaD/8zHSXXWf0I/Xlqs/mM9e46UMRNQzGa87hqM32PjSZ+/AICAebPI+exLzPv/RBg9wVHJiU2c9b24R2dhS0ih+vX6ntt+vcDV+6ag5Jvxu28gwTMeIXFm5fdr0NNTSHrsaWyJyUR++S6mrbuwXnKpc505T8KDj6Hkm/G5ZxCBj48j5ZkXsCenkTBqakG5HvX9R+WX6zjP01UHWPpQL8L9jIz4cD09GlajXph/QZqY1Gw+2XGS5WP7qnWRHLUucjg2hcNXUvh+4m0AjP5kIwcuJ9G+Ttn1EbtD4eUNx1h6byfCfY2M+Hw7PaIjqBfiWxgzLYdP9pxj+Yiu+HkaSMs1u23j3R1naFMjuGL7VWjwGPIopo+eR8lMxTh5IbaTFav/6Fp0AZ0e09vTQG/Aa/oibEe2o6Qnlx1To8Fv2uOkT1fvX8EfLCV/h/v9y3buHCmPOu9fdw7Gd+J4Mp9bgL5ZUwzNm5E6Wr1/BS1ZjKFVKyzl3b+EwNDzAcw/v4OSk47n/bOwXzyKkhbvlsx27k+sW9wbLDW1m6EJq0n+ihfVh4n3TMcecwIs+WXHBJr0bEVonQj+1/Nxareuz70vjuXNIcUfXm/68A/O7T6BVq9l8lfzaNyzFae2HGbI7IfY/9M29v24jfqdmzLoqQf4Yvq75cb9L3Ao5TfCSjeXHApXvgDgsX/6R5RGCKEt63Mp6wghRIWPvUfzhlhj47BdTQCbjdzVW/Hu1cUtTf7+Iyj56o3JfPQUuvDQwmV7D+Moq1GnBJ6uMa02clZvwae3+9NIW1wilrOXwOFw+94QXRPTgWNgd6CYzFjOXsK7e7sy4xmaNMJ29Rr2uHiw2cjbsAnPW9zzaDl4GMWs5tFy4iTasNCSNlVhHs0aYr0Sh+2ac7+u2YJXzzL267FT6Fxi5u87VHZjWQl0DRpjj7uGI0HNp3nrJgyd3HsAKXku2/Q0grOOaL9wDkeaWomyx1xCeHiAXl9uzOOxSdQI9qN6sB96nZZbW0ez5cTlUtOvPnSe21oXVv5PXkkmLdtE5wbVK5zP43Fp1Aj0oXqgD3qthlub1mDLGffGj58OXeK+9vXwMxoACPL2LLad9aeu0jU6AqO+7DZ4TY1oHKkJKOmJYLdhO7IDXeP2xdIZ+j+AZevPYCvsMSPCqmO/eBwAJTcLxZSLplq9cvOordMQR1IcSkoC2G1YD2xB17JzsXQedz6MZc13br10HFcuoGSqT6wccTEIg4faE60cmsi6KOlJKJnJ4LBjO70XbXSrctcDEMFRoNGqjUoAVrPbfig1n9GNcCRcw5GknrPWnZswtOvqnsjkcs56eBb8gapr2Q57zEXsMRcAUHKyipUXJTE0bYTtyjXs15zlwbrNePVwvzbNfxaWB+bjLmWezQZWq5pngwE05VdstHUa4kh2OZb7t6Jr1aVYOo8hD6s9mUo9lpfVmBU4lvpGjbBfu4Y9Xs1j/qZNeHR136/Ww4fBmUfryZNoQp15VBRnHB3o9QidDkda+U9AdfUbq8cyUY1p2b4JQ4ciPRBdjqXwKCx/APQdu2FPjMce6/609q9o16o5/n6+5Se8QccvxVEjNJDqoYFq+de+MVsOn3VLIwTkmtT9nGPKJzTAp3IxYpOoEeLvXsYev1xq+tWHzpVcxjasUeGY2rqNcCReQ0mOV8/ZvZvRtylyzua7HsvC8lUTVQvbSbW3q5KdgZKbg7ZOgwrHhqrZr0X91bpQRXm1rI8lJh7rlUQUq43M37fh16+jWxrrtSTyT18usRHHs1k9dCEBZG+vWI9ifZNG2K7GFdR/TBs24dndvSworf6jq10LtFrM+9XeF4opvyBdRRXdrzmrt+BdpL7nul/zj5xCGx5S0qbKZGjWENvVOGzOcj137RaMPd3zaT7gXufShjnjFC3XK1iFPn4tjRpBvlQP8lHP02Y12XLmmluan/68wH3t6xfWRXzUa0UIsNjsWO0OLDYHNruDYJ/i9ZRiMePTqRHgTfUAb7X+0ziKLecT3GMejeW+1rXx87xe//EoWHYyIYO0XDOda1fs3FXrP/EoaS71nyYdiqUz3Docy9Zf3HsMKwpC7wEaDegNYLeh5JvKjalvXOT+tXETnt2KnLOHDrvdv7Rl3L/s6eXfvzThtVEyk1CyUtT6z9n9aOu2KHc9AE1QJPZr50BxgM2CknINba2mFVq3ef/27PtpGwCXD53D6OuNX2iAWxprvoVzu9WGZLvVzpUTlwhwPliIqF+Ns7vUZed2n6B5v7L/HpKkv+Jf07AkhBgphDgqhDgihPjC+V1tIcQm5/cbhRA1nd8vF0K8L4TYI4S4KIToKYT4RAhxSgix3GWbOUKIt4QQJ5zrhzq/f1QIsd8Z60chhJfz+3AhxM/O748IIboArwD1hBCHhRCvOWNtEUL8IIQ4LYT4Sjj7pQoh2gohtgoh/hRCrBVCRDq/nyqEOOnMxzfO73o4t3lYCHFICFGsZiuEeFAIsc+ZZtn1RiNnvt4QQhwBOpfweboQ4rjz3xMu+/KMEOJz4DhQ4RqkLiwEW0Lh0wNbYjLa8NKfZPgOvY28HfsruvkSacODsbrGTEgpvMGXw3z6Il7d2iE8PdAE+GHs0BJdRNk3SE1oCPakwm7V9qSUwptQCbwGDcC8u/CpuTAYCP3kfUI/XILnLV1LXc+VNiwEu0se7Ukp6MqoLPnedTumnX/tSb0mJARHcmE+HSnJaIKLx/QcOITAT1bgPXYCOUvfKbbc0K0HtvNnCypaZUnKzCPCpUIf7u9NUmZuiWnj0rKJS8umQ/0o9fc5FN74fTfTB3UqN45bzCwTEX7Gwph+RpKy3SsrMak5xKRm8/Cnm3nok03sLFLxAnWo3O1Ny79UhF8QSmZhDwAlKw3h736NaKLqoPEPwX7moNv3jvgYdI3bgUaDCAxDW60ewr/8c10EBONweaqnpKegCXBfT1MjGk1gKLbjpZ83ujbdsMeeB1v5x1L4BKBkF1bAlOx0hE9gsXS6Bm3xHPU8hsGPIXzV5ZrAcDDnYbhzEp4jn0XfY5hacy6HJigUR2phPh1pyYjg4temx61D8Fv8FV4PTiDvk0UAaCNrAAo+cxbi++oHeAy+v9x44Lw2E13Kn6TkMssfnztvx7SrcB9rw0OJ+PpDolZ+TdZn35b7VFsEhOBIcz2WyWgCipw/NZ3H8lhZx7I79piKHUtNaCiOZJf9mpxcZplnvOMOLPvU2NaTJ7EcPkzoTz8R+uOPmPftwx4bW+q614ngEOwpLuVPasnlj8eAIfgvXYFx1ATyPnSWP55GjEOHY/rms3Lj/JskZeQQEVTYUzI80JekjGy3NBMGdWfl3hP0f3IJkxd9zzMP9KtcjMxcIgK8C2MElFPGpmbTob46tNfhUHjjt11MH1y8UbosIjAEJa3IdRlY/Fga+tyJz2tf4HnvOPK/XAKA/coF9K27qGVeSATa2g0QQWGVil8V+7WoqqoL6SKCscYX3k+sCanoIyrae0QQOWcs8S99UuF42tAQ7Iku9Z/kZLShpZd33gMHkL9nr/pba1ZHyckh8KXnCV3+AX6TxquNBJWgCwsusl9T0JVR3voNvY287TewX0NDsCW41vOS0YaVvl99htxG/s7CONrwUCK//YBqq1aQ+dk35ZbrcL0u4lXwOdzPSFJW0bpItloX+XgDD324np3n1B4wLWuE0L52GH1f/5V+b/xK5+hI6ob6U56knHwifF3qP76eJGW794yJScshJj2Xh7/awUNfbGfnRXW/OBSFNzafZHqv4sP1SiP8g1EyCveFkpmK8A9yS6OJqovGPxj7affhX7Zju1GsZrznfIz3rA+wbPsVTDnlxtSEhGJPcqk/JycXPvgogfGOOzDvdd6/TpzEcugwYT//RNjPzvtXTAXuXz6BKNnphfnMySi5/hPdGs8RczEMGFew3JFyVW1I0unB0xtN9QYlrlsS//BAMuIK929GQir+EUGlpjf6edGsT1vO7lQfWF47FUPL29SGvha3dsDT1wuvv9jILkml+Vc0LAkhmgJzgd6KorQEHncuWgx8pihKC+ArYJHLaoFAZ2Aa8BvwFtAUaC6EaOVM4w0cUBSlKbAVeNb5/U+KorR3xjoFXJ9oYBGw1fl9G+AE8AxwQVGUVoqiPOlM1xp4AmgC1AW6CiH0zt97j6IobYFPgBed6Z8BWjvzMcH53UxgkqIorYDugNudRgjRGLgP6OpMYweuj1XxBvYqitJSUZQdrp+d2xkNdAQ6AY8KIa5PaFMfeE9RlKaKohT2F72JfAb2waNJAzI+/f7v2HyFmHYdJG/7fqqveIuI12eRf+QUir38XgoVZby1L4ZGDcj+qnBuhYShD5A8ZiJpz76I/xOT0FaLumnxALzv6IOhSQMyllfNfs3/4xfSxwwn75NleD0w0m2ZtmZtvMeMJ2fxGzc97trDF+jbog5aZ8X0u10n6NaoJuF/w03QrjiITcvho5E9eOWujixY+SdZ+YVP0pKzTZxPyqRzvYi/HkwIPO4YhXnl8mKLbH9uxJGZinHSQjwGjsYee0Z9qnUTYnoOG0f+Dx+UmkQTWQvPoWMxfVm88fBG2S8cxvTBU+QvfxZHzAkMtz/iDKZBU70+1i3fkf/F/xABoWibVWy+rIowr/2FrCkjyPtqGZ53P6R+qdWia9Sc3EUvkj1vCoaO3dE1a1P2hirJ6/a+GBo3IOvzwjlB7InJJDzwKPFDRuI9sD+aoIpVIEslBJ73jif/+zKOZVQtPO++ucfyOs9+/dA1bEjuN2r3fm21auhq1iRl2DBShg3D0KYN+ublz8NRUeZVv5A5YTimz5ZhvFctf4z3jyL/t++hAk+z/69Zs+8kg7s0Z91rk1kydRhzP/4dR2WHE1XQ2kPn6duybmEZu/M43Rr/PWUsgGXjr+Q8+RD5332Ix+AHAbBuW40jPRmf597HOOIxbOdPVKgnYWVV5X4t6p+qCwU/NIDsLQewJZTf6HEjjLf2Rd+oITnX6z9aLYaWzclaspTksRPQRkXhNeC2vyU2gM/A3ng0rU/Gpz+Un/gv8B7QB48mDcksUq7H3zeOuDsfxmdgfzRBATcllt2hEJuWzUejevPKPZ1Z8Ps+skwWYlOzuZiSxbrpg1k3fTD7LyVyMKby80qVGjM9l4/u78Irg9qyYO0RsvKtfHfoMt3qhhHu0jD1lwmBx8CS6z+aGvXB4SD3xUfIe2UihlsGI4LKmXqgkjz79UPfsCG5Xxfev7S1apJ8zzCS73bev1rcnPuX/dJRTJ/OIf+rF3DEnsLQ/2EAHLGnsF8+jue9T+Fx+yM44i/dnHpeERqthocXTWXb8jWkXlHPlV9e/JLojk14auUrRHdqTEZ8qjofpST9Df4tcyz1Br5XFCUFQFGU64/EOwNDnf//Aljoss7viqIoQohjQKKiKMcAhBAngNrAYcABXP/r/0vg+oxwzYQQL6AOc/MBrs++1hsY6fwNdiBTCFHSXwT7FEW56ox32BkvA2gGrHd2YNIC1wfeHgW+EkL8Avzi/G4n8KYQ4ivUhq4ig5HpA7QF9ju3ZwSu31HswI8uaV0/dwN+VhQl1/n7fkJtuPoNiFEUZU8J+UEIMQ4YB/BCVGMeCCocdmRLSnHr8aMLD8WeWLzSYuzUmoBHHyBu9MwK9WQpiz0xFb1rzIgQ7EkpZazhLn3Z16QvUyeuC1/4DNaYorvXnSM5RZ140kkbFoLd5Wn+dR7t2+A7agQpj01zy6MjWf1t9rh4zAcPo28Qjf1aXLH1XdmTUtC65FEbFoItsXgePTu2JuCR4cSPnfGX96sjJQVNaGE+NSGhOFJL36/mrRvxnjzNLb3fvBfIfv0lHPFl5++6MH8vEjIKn0AlZuYS5u9dYto1h867Tc595HIihy4l8N2uE5jMNqx2O14GPY8P7Fji+gUx/YwkuDwVTMwyEVakohTu60WzakHotRqqBXpTK8iH2LQcmkWpT4LWnbxKr4bV0GvLb39XeygVPmFVezC5XCMGI5rwmhjHqXNSCJ8APEc+Q/7nr+C4dgGLS4XLOOFFHCnl71slIxVNYOH5IwJDcGS4HEsPI5pqtfGerhabwj8Ir8eeJ++9Z3HEnEMEhGCcOB/Tp6+hpMQX3XzJMXMy1Im3r8f0DSyYpLtAfmFPCdvRbWrPJNTeTY6kK+owOsB+7hCaqHoFE3uXxpGWjMalh5ImKBQltfi1eZ115ya8H51G3rtqjxjbySMo2ZnqsoN70Natj+34wVLXB+e16TKERRcWWmL549GhDf5jhpM4bnqJ16Y9JRXrhUt4tG6OaeO2UuMpGSloglyPZSgOlyfAeBrRRNXGe+Zr6nL/ILwmLyBvyXz1WAaGYHzsWUyfLFSHJlWAo8gTXk1oaIllnqFtW7wffJC0xx8vyKNHt25YT55EManXmGXvXvRNm2I9dqzMmEpqCtoQl/InuOzyx7J9I14T1PJH16AJhi49MD48HuHtA4qCYrFgXlXyJLH/FmEBPiSkZRV8TkzPJizAvYPyzzuO8N4T9wHQsl51zFY7GTl5BPmVXE4Wi+HvTUJG4XWXmFFGGXv4PLOGFk7OfSQmkUMX4/lu5wlMFitWmx0vDz2PDyy7l6iSnoIIKnJdppd+LK17N2N8+HH1CZrDQf6K9wuWec9dhCOh7Ht0UVWxX4uqqrqQLSEVfWTh/UQfEYy1gg1FXq0b4dW+KcEPDkDjZUToddhz80lcWHpPP3tyCtpwl/pPaCj25OLH0tCuDT4PP0jqpCcK8mVPSsZ67oI6jA7I374DQ9MmULF5rQGwJaUW2a8h2Eoob42dWhM47gHiRt3gfk1OQRfhWs8LxZ5UfL96dmiD/9jhJDxScp1LLdcv49m6OXkby75/qXWRwiGhiVkmwvyK1EX8jDSrHuysi/hQK9iX2LRsDlxOokX1YLw81KHNXaMjOXIllTa1yu7dF+bjSYJLD+3E7HzCfN2H0IX7GmkWFaDGDPCiVqAPsem5HLmWzqGrqXx36DImqw2rXcHLoOPxHo1LjadkpiJcetgK/+CC4dmAWheJqIlxnDonqfANwHPULPKXv4yuVXfsZw6pL2vIzcR++TTa6vWwpZU9+bsjJdltOgptkR641xnatsVn5IOkTXG5f3XvhvVE4f3LvHcvhqZNsR4t5/6Vk17QAxucPbjLqv+c2IG+29DCz/tXY9u/Wv1dt43BkVF6I2H3h/rT+YE+AMQeuUBAVOH+DYgIJrOUybfvf3kcyZcS2PJJ4UtVspLS+XiC+iDY4OVBq9s6YnI5J//LFDnHUpX7V/RYukHXB3E7XP5//XNpDWbXH1UtByYritIceB4of9ByybFBbdTRAQI44ezZ1EpRlOaKovR3prkDeBe1F9R+IYROUZRXgEdQG4x2CiGKvrJKoPbWur69hoqiPOdclu9s+KKUz6UpuW88oCjKB4qitFMUpZ1roxKA+fgZ9LWqoasWATod3rf3IHfLbrc0hkb1CJn/OAlT5uNIy6jATylbfkHMcNDr8Lm9J7mbS2wTK06jQeOvVjANDepgaFiHvJ1lv33Bcuo0uhrV0EaqefTq25v87e551DeIJuCp6aQ+ORdHekbB98LXp2CuIY2/Hx4tmrlN8lsa84kz6Gu67NfbepK3tYT9Ou8JEh+/OfvVdvY02qjqaMLVmB49emPZs9MtjSaq8O1nhg6dsV9TK/zC2we/518h99Nl2E4er3DMpjXCiE3J5FpqFlabnbWHztOjaa1i6S4lppNlMtOyduHTqpcf7MOaeSNYPXcE0wZ1YmC7BuU2KgE0jQokNi2Ha+m5WO0O1p64Qo8G7hPT9moYxYEYtSKSnmcmJi2H6i7DSdacuMLtzSo2YtRx9TyakEhEYBhodehadsN+6kBhAnMeuS+MJm/hRPIWTsRx5WxBoxJ6gzqRNaCNbqG+QanopJclsF8+gyasGiI4HLQ69O16Yjvico3k55Ez415y5jxMzpyHsV88VdCohNEbr8n/w/zzJ9gvnKxQHgEc8ZcQgeFqI5pGi65RR+znD7sn8i7sqq+Nbo0jVf2Dw5FwCeHhBUb12tTWbFxs0u8S83n+DJrI6mjC1HNW37U3lgNF3oAUUXjO6tt0wh6vzmFhO7IPbc26YPBQf2+TVtivln9tWk6eRl+jGtooZ3nQvxembe4x9Q2jCZo9jeTp89zKA21YCMJDnbdC+Prg0bI5tstXKEvBsQyJUI9l+x7YjriUBaY8cqYPI2fWSHJmjVSPpbNRCaM3XlP+h/nHjyt1LK1nzqCtXh1NhJpHz969Me9yz6MuOhrf6dPJmD0bJaMwj/akJPStWoFWC1ot+pYtscWUv19t5067HUtD995Y9xUpfyJdjmW7zjji1Wshe/YUMsfdT+a4+zH//gP5P3z5r29UAmhaO4rYpHSuJWeo5d/+U/Ro6f6GpshgP/aeugzAxfgULFYbgb5eJWytlBg1wohNznAvY5vVLpbuUmI6WXlFy9i+rJn/EKvnPci0QZ0Z2K5huY1KAPZLp9GGu5yzHXthPVTkugwvPJa6lp2wJzrnljF4gEGtdumatgWH3W3S7wrluQr2a1FVVRfKO3oOj9pR6KuHI/Q6/AfdQtaGig2DvzLtDc50G8OZ7o8Q/9InZPy8qcxGJQDrqdPoqhfWf4x9e5O/o0hZ0CCagKenk/bUHLfyznrqDBofHzQBarnv0bY11grUf1yZj1+vC4WDruT6nqFRPUKfnUrC5Gexp2VWavvXWU6cQVejGjpnue59a09MW0so1+c8QdIT80st1zW+Pni0albuA0uAplFBxKZmcy09Rz1Pj8fSo6H7G2Z7NarOgctqI0N6rpmY1GyqB/oQ6e/Nn5eTsdkdWO0O/oxJoq7LC0hKjRkZQGx6Ltcy8tT6z6k4ekS797zuVT+CA7Fqo1p6npmY9ByqB3jx8qA2rJnYj9UT+jKtZ1MGNq1eZqMSOOs/wUXrPy5DFfPzyF0wirxXJ5D36gQcsWfJX/4yjmsXUDJS0EY7ewvpPdDWbIAj6VrJgVxYT6v3r+vnrGef3ph3Fjln60fjN3M66bNm43C5fzmSkjC43L8MrSp2/3IkxiACwhB+wWp9okF77BePuifyKjw+2rotcVyf2FsI8FTrlyKkGprgaoXzTZZg+xfrWDjgaRYOeJqj6/bTYegtANRuXZ/87DyykjOKrXPHjPvw9PXipwXu17t3oG/BmwT7PTaEPd9tLjevknSj/i09ljYBPwsh3lQUJVUIEeTstbQLuB+1t9IIoOxHA8VpgHuAb4DhwPX30fsC8c7hayOA66XYRmAi8LZzPiMfINuZvjxngFAhRGdFUXY7t90AdahdDUVRNgshdjjz4yOECHb2sjomhGgPNAJOu2xvI/CrEOItRVGShBBBgG8FhrBtB5YLIV5BbZy6C3ioAr+/dHYHKS8tIWLpSwithuyf12K9EEPgpJGYT5wlb8segmY8ivAyEv6G+tp7W3wSiVPVkYeRy9/AUKcGwstIzQ1fkTz/TUy7ynnNpt1B8ovvEvXhSwiNhqyf12E5H0PQ5JHknzhL3uY9eDRrQOSi+Wj8fPHu1Qnb5JFcGTwOodNS/Uu1dd6Rk0fi069CeUPh7A4y3lhMyNuvgkZL7h+rsV26jO+jo7CeOkv+jl34TR6P8PIk6EU1X/bEJNKemou+di0Cnp6mTqKpEWR/8bXb21TKipn68hIi3n8ZNBqyf1H3a8BjD2M5cZa8rbsJmjYOjZeRsNec+zUhiaTH56v79dM30ddW92uNdStIee5NTLsOlBURHHZy3n8b/xdeB62G/HWrsMdexuuhMdjOnsaydxfGQUPRt24LNhuOnBxy3ngZAM9Bd6GNqobX8IfxGq52782cMxMlM6PMkDqthmeGdmPiB6twKAp3dmhIdEQQ763ZT5PqofR0/gG05vAFbmsVXaFX6ZZHp9HwzG2tmLhiuxqzZW2iw/x5b8sJmkQG0rNhFF3qhbP7YiJD31+LRgim9WlBgJfawHMtI5eErDza1qrgxKsOB+bfPsI4Zp76ut0Dm3AkXcHQ937s1867NzIVIbz91fUUBUdWGvnfLSo1bdGY+d+8i9fj6jVi2bkOR3wMHoNGYo85i+1o6Q2xhl6D0YRF4XHHCDzuUEfY5r0zq6BnT6kUB5YNX+Jxz3TQaLAd24GSGoe+6xAcCZexXziMvk1fdUJvhwMlPwfL6o+d6ypYtnyL530zAYEj8TK2I1srkE87eR+/g8+c19RX229ejePqZTzvG439whmsB3bhcftd6Ju3RbHbUXKyyV2inrNKbg7mP77H75WloID10B5sByvQQG13kPbaYsIWvwpaDbm/rcZ6MQb/8aOwnDqDadtuAqeOQ2M0EvKKej3aEpNImT4PfZ1aBDwxQZ1AXAiyvvwO64VyJpt2OMhfsQSvJ15CCA2WnWtxxMXgMdh5LI+UcSx734kmrBoegx7EY5A61CjvrVko2Rnl5NFO9jvvEPiaul/zV6/Gfvky3qNHYztzBvOuXfhMnIgwGvF//nn1ZyYmkjFnDuatWzG0bk3wJ5+AomDetw/L7t1lxwP1WH7wNr7PvQ4aDeaNq7BfuYxx+Bhs509j3bcLzzuGomuplj9Kbg65b79c/nb/gieffYX9h46SkZFFnyEP8tjYh7h70K03bfs6rYZnhvdj4tvfqGVR1xZEVwvlvV+30aRWJD1b1Wf6sD4s+HwVX21Q/xh7fvQdlSoH1TK2OxM/+AOHQ+HODo3UMnb1PprUCKVnszqA2iP0ttY3p4zF4cD0xWK8n3wVNBp1eNu1GDzuGoX98hlsh3Zj6DsEXdM26rHMy8H04asACL8AvGe+CooDJT2FvGWVP8ZVsV+L+Yt1ocrEiXt2KXU+fx40GtK/34D5XCxh00ZgOnaO7A37MLaoT62ls9H6++Dbpz3hT4zg3K2TbjhfmW8uIvithaDVkHe9/vPIaCynz2DesQv/SRMQRiNBLzynrpKYSNrTc8HhIGvJ+wQvegOEwHr6LHm/VaK7kjN+ykvvErlM3a9ZP68rtl+Dr+/XN9W3Ydnik0iY8lyl46S9upiwd18BjYac39ao5fqEh7GcPKuW60+oda7QhYV1ruRp89HXqUngdJdy/YvvsZ4v/yUCOq2GZwa0ZeIXW3EoDu5sXVeti2w6RpOoIHo2qkaX6Ah2X0hg6JJVaDSCaf1aEeDlQd8m1dl3KZFh769BAF2iI4s1SpUYU6Phmb7NmPj9HvXaaF6D6BBf3tt+miYRAfSsH0GXOqHsvpzM0I83q/Wfnk0IcE4eXmkOB+ZfP8I4dr5aFuzfiCPxCoZ+92O/esG9kakI6+7VeA6bjHH62wiEWndKqEj92U7W2+8Q+Lp6/zKtWo3t8mV8xozGeuYM5p278HXevwKc9y97UiIZs+aQv2UrhjatCVnuvH/t3Yd5VwXuX4oDy5Zv8RgyFYQG28ldKGnx6DsNwpEYg/3SUfSteqsTejscKPm5WNY7G3k0WjzvmaluxmLCvPbTCg+FO7n5EE17tWb+1newmCx89WRhb8+nVr3KwgFPExARxK1ThpJw/hpPrnwFgO2frWX3t5uo36kJA596ABSFC/tO8/38jysUV5JuhFAq8XrXv5MQ4mHgSdQeQIcURRklhKgFfAqEAMnAaEVRYp0TdP+hKMoPQojazv83c27HdVkO8AHQH3UY2X2KoiQLISYCTzm3uRe1wWaUECLcmb6u83dMdDYSrQBaAKuBlcBMRVEGOuMtQZ3HablzbqdFgD9qo93bqL2jNju/E8CXiqK8IoRYDPRC7WF1AhilKIrbqzSEEPcBs1AbyKyoczLtEULkKIri45Ku6OfpwBjnx48URXm76H4qy8Xm/av0pHDYq76rotH/rw0puxGWvHJf2HfT+UaV/9atm8l70p1VGg+AjOJd2f9u9hOny090kznSyp/Q8mbTRZdfib2ZzPsuVmk8gOxLVX9dBrSu+pimc1VbFgDo/au2fuH78adVGg/AdrL0IY5/m/IaDf8Glu9+r/KYhrF/7ZlYZcVPWlGl8QByczzKT3STBUdW/b3ElHWDjRZ/gc5QkY78N0/YkxV7cctNlVfqQIS/jf1s1d+nc3aX/9a2m83v7qIDSf5ez7yVXn6iv8Giy9/+Z8eL7a9217+jkeNv0P7az//K4/Zv6bGEoiifAZ8V+S4Gdd6jomlHufz/MurcRsWWOT9PL2H994H3S/g+ESj2l7GiKMOLfLXFZdlkl/8fBm4puj7qvEdFtzmlhHRF03xL4RxRrt/7lPP5TeDNIt9dxmU/SZIkSZIkSZIkSdJ/jUPOsVTl/i/PsSRJkiRJkiRJkiRJkiT9g/7TDUtFe/JIkiRJkiRJkiRJkiRJN89/umFJkiRJkiRJkiRJkiRJ+vvIhiVJkiRJkiRJkiRJkiTphvxrJu+WJEmSJEmSJEmSJEn6K/6zr4T7F5M9liRJkiRJkiQr3GH1AAEAAElEQVRJkiRJkqQbIhuWJEmSJEmSJEmSJEmSpBsiG5YkSZIkSZIkSZIkSZKkGyLnWJIkSZIkSZIkSZIk6T/BoYh/+if8f0f2WJIkSZIkSZIkSZIkSZJuiGxYkiRJkiRJkiRJkiRJkm6IbFiSJEmSJEmSJEmSJEmSboicY0kqxmGv2jGpJpOhSuMB6D3tVR5TcVT9WN/8NG2VxvM6fbJK4wGIGjWqPKaSmVflMW1J+VUeU3gkVWk8R75SpfEArPlVfxt0ZFmqPqat6ssfc2rVxjSe3Fal8QB0TW6p8pi2w+uqPKY1wVblMQ0ZKVUaz2ar+met+ZaqL3+s+VVbLwBQHFUeElNO1dYt7YePVWk8AG3ThlUeU8mt+rpIbppHlcf0PhNTpfGMBFRpvP8fKHKOpSoneyxJkiRJkiRJkiRJkiRJN0Q2LEmSJEmSJEmSJEmSJEk3RDYsSZIkSZIkSZIkSZIkSTdEzrEkSZIkSZIkSZIkSdJ/wj8wtdv/92SPJUmSJEmSJEmSJEmSJOmGyIYlSZIkSZIkSZIkSZIk6YbIhiVJkiRJkiRJkiRJkiTphsiGJUmSJEmSJEmSJEmSJOmGyMm7JUmSJEmSJEmSJEn6T1AQ//RP+P+O7LEkSZIkSZIkSZIkSZIk3RDZsCRJkiRJkiRJkiRJkiTdENmwJEmSJEmSJEmSJEmSJN0QOceSVCFe3doRMmsCaLVk/bCajI++c1vu2bYZIbMm4NGgLgkzXyJ33Y6CZcHTx+LVowMA6e+vIGfN1grF9LmlDVHPPgoaDenfrid56Q/uv6lDU6LmPYpno9rETl1I1updbss1PkYarHuPrPV7iHt2WbnxPLu0J2jmY6DVkPPzarKWf+O23HfE3fjcNQDsduzpGaQ+/zr2+CS0kWGEvv48QiNApyP7m1/I+fGPCuXR2LUdwU9PRGg1ZP20hsyPv3Vb7j/ybnyH3oZit+NIyyR5/hvY4pMAiHj/RTxaNCb/0HESJ8+vUDwAj07tCZg+GaHRkPvbKrI//9ptuc8D9+B95wAUmx1HRibpL7yGPSERgGq71mO9cAkAe0ISqU/OrVBMTe2mGHo/AEKD7dh2bPtWuy3XNu2CoccwlJx0AKyHNmM/th1NjYYYet1XkE4ERWL5Yxn284fLjbnzXBwLV/2JQ1G4q009xtzStFiatcdjWLb5GCBoEBHAK8O6AvD2ukNsPxsHwLgezbi1ea1y42mbtMVz2AQQGqy71mBZ932J6XStumIcN5fcV6biiD1XmLfAULznLcO86iusG34sNx6AvnUHvMZOAY0G84aV5P+0wm25x62D8bj9LnDYUfJN5L73Oo6rMQXLNSFh+C/6DNO3y8n/9duimy85n43b4Dl0HGg0WHevw7LhhxLT6Vp2wTh2NrmvPYHjynlEUBjes9/HkXQNAPvlM5i/e7di+WzTAe9H1Xzmr19J/g9F8nnbYDzvcMnnktexX4lBV78R3pNnqomEwLRiOZY92ysU09i1HSHPTEBotWT9uJqMj0so856egKFBXRKffInc9YVlXtC0sXjfopZ5actWkFuBMk/Xsj3GUZNBo8WyaSXmX92vS0PfQXjcOgQcDpR8E3kfvIHjmnosNTXr4vXodITRGxQH2bMngNVabkyPju3xe1yNmffHSnK/dI/pfd8wjAPVMs+RkUnmywuxJyZiaN0Kv6mTCn97zZqkP7cA8/ad/8qYrnYev8DCbzbgcDi4q3srxtze2W15fGom8z79g+w8Mw6Hg6l396R78+hKxSjP3JfeZNvOfQQFBvDLl0tvyjZ3nrjMwh+2qPnq2owx/Tu4LX/thy3sP3sVgHyrlbRsEztef4z9Z6/w2g+F5+flxDReGTOA3i3Lz7O+XQe8J0xBaDXkr16J6Tv369LzjsF4DnJelyYTOe+8jj02BuHrh++8BegbNCR//Rpy332n4vk8fYWFv+3G4VC4q0NDxvRu5Z7P33az/3ycM5820nLy2fG/h4lLz2b6Z+txOBRsDgcPdG3KsM5NKhTTq1tbwmZPBI2GzB/WkF6k/mNs14zQWRPwaFCH+Bkvk+NS/wmZMQZvZ/0n9f0V5KzeVuG8XufXszU1F4xFaDQkf72BhHd/clvu07EJNZ8fg1fj2lx47A3SV+6udIz/x955R0lRdH34qZ6wO5tzIOeck4IoUVEMIIqJIEEBRRRUUMGcUMyYMItZ8VWMSA6Sc85hd4HNOU3u/v7oYXdmM8Hlff3qOWfP2emurl9XVfet27erqi+G/3Mx/MrAy7sSM2siwqCQu2Ax2R/69tOWbu2InTUBv5aNSZ72EgWLdfsScEkHYmbeXZLO3KQ+ydNepnBZ9XVtaNEZvxvG6b7BlmU4V/1ccbp2l2IZNYPiudNRTx8DxYDfzfei1GmCMBhwbluFc9VPFR5blnXHUpmzZLfu/3RqxLheLculWbz/FB/8fQCAFrGhvDRUr8+UvGKe+WM7aflWhIC3b+1F3bDAqsvYuiv+N0/U/YL1i3EsrcL/uWsWRXMeKO//PD5P93+W16yM3lTXX4eOHkbITbov7c7OI+OJ10t86bPhYvh5ADc8dSct+3XCaXXww8Pvk7wvwWe/yd/MiPemEtkwBs2tsX/5Nv56Wb+Hu958BYMfG0F+WjYA6+cvYcv3K8+y5P+bqNrFPoP/f8jA0j+IEOJZYI2macuEEFOBDzVNK77Ip3X2KArRj0/m9F2P4UrLpP73b1O0ciPOY0klSVwpGaTPfI2wsTf7HBpwRQ/82jTj5LB7EGYTdT9/haK/t6AVVVMNikKdZydxYtQTuFKzaPrL6+Qv24T96MmSJM7TGZya/iZRd99YYRaxD46kaPO+Gpcx4pEppN/7CK60DOK/ehfr6vU4T5SW0XHoKKkj70Wz2Qm6+XrCH5hA5qPP487IJnXM/eB0Iiz+1FnwMdbVG3BnZlWrGTXrPlImPIorNZO6371N8coNOI+XatoPHCX/tvvQbHaCb7mOiAfvIn36iwDkfr4Axd+f4OGDa1ZGj2b49AfImDIdd3oGMZ+/j/Xv9bhOlAYbnIePkn7nPWh2O4HDbiD0vglkP/4cAJrdQfqoCTXXAxAC88AR2Be8jlaQg//Ix3Ef24mWleKTzHVoC87lvg8m6slD2L54Vv/hH4hl/Iu4E/ZXK+lWVWb/vpV5d/YnNsTCiA8W06dVPZrGhJakSczK59M1+/n8rqsIsZjJLrQBsObQaQ4k5/D9PdfgdKuM/3QZlzWvQ5C/qYoyKvjfOpniuTPRcjMJeOQtXLs3oaYm+abzs2DqNwT3iYPlsvC7aQKu/VurLVsJikLAhKkUPP0QalYGIXM+wLF5nU/gyL5mGfbFvwJg6t6LgLGTKXxuRsn+gLGTce7YXHNNoeA//B6K330cLTeLgIffwLV3E2rqSd90fhZMfW7AneBbTjUzleI599dcz1POwElTyX9CL2fo6x/g3LQO98nScjpWL8P+l6ecPXoRMH4yBU/PwJV0grxpE0F1I8IjCJv7KY7N60F1V6sZ/fhkku9+DFdqJvXO2LzjZWze468RNqYSm3ezbvPqfPYKxdXZPKFgGfcARS9MR83KIHj2PJxb15cEjgAc65bjWPYbAMauvbCMvpei2Y/o9XPfTIrenY2aeAwRFAKuasrnKWPIgw+QPU23BVEfz8O+dj2uBG9bcISiuyaB3U7A0BsIvnciuU89i2PHTjLH6g9ZIjiYmO+/wr65BtfuxdD0wq2qzP5mCfOm3UZseAgjXvicPh2b07ROVEmaj/5Yz1XdWnNL3y4cS87kvrk/sOilCxtYGjr4Su646QZmPvfqBcnPrarM/mEF86YMIzYsmBFzvqFP+6Y0jY8sSTP95r4l/3+7agcHT2YA0L1FfX6YORKAvCIb1z/9KT1bVx9IR1EImjyVvMceQs3MIOztD3BsXIc7ycv+rFyG7Q/9vjRf2ovAiZPJnzUDzeGgeP4nGBs1xtCo8dmV8+d1zJswmNjQQEbMXUiftg1pGhteWs4bSgOF367dy8FkvR+ODg7gi/uGYDYaKLY7uem1H+nTpiExoVU/MKMoxDwxmdPjZ+JMy6ThD3MpWrkRh5f/40zOIPWx14gYd5PPoYF9dFuQeOO9CLOJ+vNfoXjNVtTq/J8y+g1fmMDh25/GkZJFmz/nkLtkM7Yjp0qSOE5ncGLa28RNGlLzfMtoXAz/52L4lbFP3cvJsbNwpmbS6D9vUrh8I45jpf2XKyWdlEdfJ2K8b1sWb9pNwpApejahQTRd+glFa7dXrQcgFPyG3o3142fQ8rKw3DcH1/4taOmnfNOZ/TFfdi3upMMlm4wdeoHRhPXNaWAyE/DgXFy7/kbLyahS0q1qzP5rF/Pu6K37P5+upE/zeJpGh5SkScwu5NP1h/h8dB/d/ymylex7/Net3HVZS3o2iaXY4UJUt/6xUPC/5V6K35ml+z/T38S1Z2PFfkHfSvyfYXfj2nd29ryEGvTX9gPHOHXrFDSbnZBbryPyobtIe/jFs9O5GH4e0LJvJ6Iax/FK32k06NyMG18Yz7tDnyiXbs1Hv3N8w34MJgN3f/04Lft25NCqXQDs/n0Dvzz1+VnpSiTngpwK9w+iadqTmqYt8/ycCgRcaA0hhKGq3zU9rir827fEmZSM61QqOF0ULlpFUH/ft7yu5DQch0+AqvpsNzdrgHXrHnCraFY7jsMnCLy8W7WaAR2b40hMwXkyDc3pIu+3NYRceYlPGufpdGwHEyoMSfu3a4oxKoyCv3fUqIzmdi1xnUrGdToFXC6KFq/C0vcynzT2rbvQbHb9/z0HMMR4HkZcrpLRAcJsBlGz28rPu15dLooWrSawXy+fNLYtXpq7D2CMjS7dt2nn2TmogLlNK1ynTuNO1stpXboCyxW+mvZtO9HsuqZj734MMdEVZVVjlLjGaDnpaHmZoLpxHdyMoWmns87H0KIr7hN7wOWoNu3eU1nUjwiiXkQQJqOBQe0bsuqgryP309Zj3HpJc0IsZgAigvwBOJ6RR9dG0RgNChazkRZxYazzvAGvtIyNWqBmJKNlpYLbhWvbaowdLy2Xzu/60TiWLkBz+pbB2LEnWlYqakpiuWMqw9i8NWrKadQ0vS0da1dg7tHbN5G19PoQfhafXaYevXGnp+BOOlFjTaVhC9SMFLSsNL2c29dgbF9BOa8diWPZj2g1GDVTHcbmrXF7ldO+ZgWmS3zLqXmX09+rnHZ7SRBJmM2g1ez1Vdl7s3DRKgIrsXlaWZvXtAG2MjYvoHfVNs/QrBVqWjJqegq4XTjWr8DU3df++Lalf0lZjB264046jpp4TK+LwnzQfM+pIkytW+E+lVxqC5atwK+3r6Zjx069DgHHvv0YosvbAv9+fbBv3FyS7r9N05u9J5KpHx1Ovehw3S50b82qnYd90ggBRVY930KrjeiwoLPSqAndOrUnNCT4guW3NyGV+tFh1IsK08vVtSWrdh+rNP2irYe4ulv5EQxLdxzmsjaNsZirCKJ7MLZsjTv5NGqq575ctQJzzzL3ZbFX/+RvgTO3n92Ga98eNEf1ttybvUkZ1I8KoV5kiF7OTk1Zta9ym7lo5zGu7tQUAJPRgNmouz0OlxuthrbAv0NLnEkpOD3+T/6fq6uwBb55mps2wLp1b4ktsB8+QcDlXc+myAR2bo49IQV7ku4LZf+ylvBBvqPRHKcysB5IPOfX8xfD/7kYfqV/hxY4EpNxnvS05R9rCBroq+k8nY79UEI5TW+Cr+5N4ZqtJfVRFUr9ZqhZKWjZnj5z11qMbXqUS2cedAeO1QvB2zfQNITJDxQFTGZwu9Bs1mo19yZnUz8ikHrhgZgMCoPa1GPVYd8XeT/tOMGtXZuU+j+Buv9zLCMft6rRs0ksAAFmIxZT1WMQlEYtUDO9/J/tazB26Fkund91o3T/p4wPZ+zg8X/KBmhqSE36a29f2rbrAIbYqIqyqpKL4ecBtL2qK9t+0kdaJ+04iiU4gODoMJ80TpuD4xv0l65up5vT+04QGhdZNiuJ5B/nXxdYEkKMFkLsFkLsEkJ86dnWSAixwrN9uRCigWf750KIuUKI9UKI40KIm73yeUQIsceTz0uebXcLIbZ4tv1HCBEghAgVQiQKofemQohAIcRJIYTJk//NQoj7gTrASiHESiHEOCHEm15adwsh3qigLFcJITYIIbYLIRYIIYI82xOEEC8LIbYDwyv4fbvn3PcKIV72yq9QCPGaEGIXUN7qV4IhNhJnaukbEldqZqlTUQ32g8cJ6N0N4e+HEhaCpUdHjHHVByqMcZE4UzJLfjtTszDV1EgKQfys8aS8+GnN0gPG6ChcqaXDYt3pGRhiKtcLGno1tnVbSn4bYqOJ//5D6v75DXnzv6v+bR1gjInC5V2vaRkYYivXDB52NcVrt1S6vyYYYqJwp3mXM7PCB7czBN4wGNuG0hEtwmwm5vP3if7kHfyvuKzS47wRweFoBTklv7XCHERweLl0xuZd8L/zacw3TKp4f6vuuA7WbHRNeoGVOK830bEhAaTn+wbhErMKSMws4M6PljDqw8WsO6IHj1rEhbPuSApWh4ucIhtbTqSRlld1AE8Ji0L1eouo5mQiQn3bUqnfFBEehXtvmTb088d85XDsf35do7KdQURE4c4sbUs1KwMlsvx96XfNUELf/wbLnZMo/tgz5cTfgmXYHVi/n39WmkpYJGquVzlzKyhnvaaIsCjcFbyVUyJjCZjxFpb7Z2NoUn5qYoWakVGoZcppqKicg4cS9uE3BIyZRNEHpVNrjC1aE/ru54S9/RlF771e/WglwBgTWebezMRYU5t36DgWb5vXvXqbp0REoWaVacvw8nrmq4YS/NZXWEZMxPr52wAY6tQDTSNw5hyCXvoAvxtuq9F5GqKjcKd7aWZkYIiuvIwB1w3GvmlTue2WAf2wLlv+X6vpTXpuIXERpW/tY8ODSc8t8Ekz6frL+WPTPq6a/g73zV3Ao7dfedY6tU16biFx4aWBqtiwINJzCytMm5yVT3JWHj1a1i+3b/G2w1xTQcCpIpTIKNQMr7bMzECJKt+W/tcPJfyzbwi8axKF79V8yltFpOcXEecV6IsNDSQ9r6jCtMk5BSRnF9CjWZ2Sbam5hQx/7T9c/cI3jOnbsfrRSlRsC0xV9NPe2A8eJ7B3Vy//pwOmGvg/3pjjInAkl/pCjpSz8IVqyMXwfy6GX2mKjcSVWlqXrtSat6U3IYP7kP97zabeidBItNzS+tDyshChET5plDpNUEIjcR/c5rPdtWcDmtNO4KxPCHzsQxxrfgFrxfe1N+kFNuKCS1+wxIZYSC/wDUglZheSmF3InfNXMeqzlaw7llqyPdjfxIM/buTWj5fz+vI9uKsJWCqhkag5pfVaof9TrykiPBr3vjL+j9kf85U3Y//Td7T62XC2/XXIsKsp/vvsfemL4ecBhMRGkJdceg3lpWYTEhdRaXr/kABaD+jC0XV7S7a1u6YHUxe9zMj3phIaX/mxEsn58q+aCieEaAs8DvTSNC1TCHHm7nkbmK9p2nwhxDhgLjDUsy8e6A20An4FfhRCXAMMAS7RNK3YK5+fNE37yKP1PDBe07S3hRA7gT7ASuA6YLGmaU7hGT+qadpcIcSDQD/PeQUBs4QQ0zVNcwJjgYllyhLlKctATdOKhBCPAA8CnnlBZGma1sWT9qUzv4UQdYCNQFcgB1gihBiqadpCIBDYpGnaQ+dRzWeFdf12itu3pN43b+DOzsO26wCau/q36edD5KjBFKzaiiu1eufmXAgcPAC/Ni1JvevBkm3utAxSbp2AISqS6NefoXjZGtTs3AumGXTdAPzatCB57MMXLM/qCLh6IKbWLcidNK1kW8rQ21EzMjHUiSf63dfIOHYC9+mqR/PUBPexXVgPbga3C2OHKzBfMw77D6+VJggMRYmqh5pQw6mNNdFUVZKyC/h43EDS84sZ98kyFkweTK9m8ew7ncWdHy8hPMCfDvWjUKodC14NQuB30wRsX7xWbpfftSNxrPgZ7LYKDjx/7IsWYl+0EPPlA7EMH03R3NlYbh2D7dcFUIO3n2eFEPjdeBe2r8vFydHysyl8aiwUF6DUb4rlrscpmn3vBTsH+58Lsf+5EHOfgVhuHU3Rm7MBcB0+QN7kMRjqNSRo2mM4tm3yfSt8gbGu345fu5bU/eoN3Dm6zeMC2TzHkoU4lizEdNkA/IeNovi9l0AxYGjVnsKZk9DsdoKeeA338cO49tZgmkYNsVw1EFOrlmTdN9VnuxIZgbFJE+ybzi/g/d+iCfDX5v3c0Ks9o6+6hF3HTvH4J7/x49N3oyjnaQP+S1i87RADO7fAoPi+V8zIK+RociY929RgGtxZYPttIbbfFuLXbyABd4ym8NXZFzT/yli88xgDOzT2KWdcWBALHrqJ9Lwips1fypUdGhMZfMEHkpdQvH47/u1bUP+b13VbsPNAuRGO/2tcDP+nLBfDrzyDITocv5aNKFq7rfrENUEI/K4bg23B2+V2KfWbg6pS9MJdCEsQlnuex310tz766TxxqxpJ2YV8PPIK0gusjPtiDQsmDMCtauw4mcl34wcQF2rhkZ828+vuRG7s1OjcxYTA76a7sX35erldfteOwLFiITj+Gf+nLEHX9cevbXMyx0y/8JlfRD/vDIpB4Y65U1j/+WKyT+rB4gPLtrPz1/W4HS4uuWMAt7x2Lx/d8fw/eh7/Laj8O/rt/yX+VYEloD+wQNO0TABN07I923sCwzz/fwnM8TpmoaZpKrBfCBHr2TYQ+OzMekhe+bTzBJTCgCBgsWf798Ct6IGl24D3qjpJTdMKhRArgOuEEAcAk6Zpe8okuxRoA6zzBKjMgPcqgWVX2j3zuzuwStO0DAAhxNfAFcBCwA1UuFqcEGICMAHgubg23BZer2SfOy3L5y2bMS4Kd3pmuTwqI+eDb8n5QF+gNXbOozgTT1VzBLhSszDFl75xMMVF4qxhoCigcysCurclcuRglAALwmTEXWQjbU7lozNcGZkY42JKfhtionGnl9fz79GF0PF3kHrXQxUujuvOzMJ5LAH/zu0pXl71IsGu9Eyft2zG2GjcaeU1LZd2Juzu2/Wg0nlOLXKnZ2KI9S5nFO6M8vP1/bp3IXjMCDLumeajqWbo7e5OTsG+fSfmls2wVhNY0gp8RyiJIN8RTADYSt86u/b8jamP75oKxpbdcB/ZXqPRJgAxwRZSvd5kp+UXExPi+wARGxJAu3qRmAwKdcODaBgZTFJ2Ae3qRnJ3n3bc3acdAI8uWEfDqKqnrai5mZjCS9tSCY9Cy/NqSz8LSp2GBEzTTY8ICccy6Sms855BadQSY+fe+N043rP4sgZOB87Vv1WpqWVnYogqbUslMho1q/L70rF2OQET9SChsUUbzL36YLlzIiIwCFQNzeHAvqjiRUVLy5mFKcyrnGEVlDO+AQFTZpeWc8ITWD98DvXkUXDpo0PUk8dQM1NRouvq26vSzMpEKVNOd1XlXLOcwHumUXYcg/tUIprViqFhY9xHD1Wp6UrPKnNvRuE6C5uX++G35H6o27yYl6u3eWp2JkpkmbbMqVzPuX4FAXdN9RybgfvAbrSCfH3fjk0YGjevNrDkzsjEEOOlGR2NO6O8prlbF4JGj9QDPGXsj3//ftj/Xgvumt2XF0PTm5iwIFKz80t+p+UUEBPme2//vHYX703VPxjQsWk97E43uYXFRIRUP7rlYhETFkRqTunIq7TcQmIqmcL317ZDPHZr/3Lbl2w/TL+OTTEZajZLXs3KRIn2asuoaNTMyq9Z+6rlBE6ZVun+mhATEkiq10istLyiSkcd/bXzOI/dWPGo2pjQQJrFhbP9RCpXdmhSpWZFtsBZQT9dGdkffEf2B/oiunGvPIIz4XSNjwVwpGZj9loDzBxfc1+oplwM/+di+JXOtCyMcaV1aYw7u7YECLnmCgqXrq/ZOnZ4RiiFlY5qEaGRaHnZpQn8LChxDbBM0NexFMFh+I95DNvnszF2uhz3oR364vdFebgTDmKo1xRXNYGlmGB/Ur1GKKXlW4nxGsEEEBtsoV3dcN3/CQukYWQQSdmFxIZYaBkbRr1w/b7q17IOu09nU/FKpjpqXhYmrxG2Ffo/8Q0JeECfQCFCwrFMfBLrB8+iNGyJsVNv/IaO8/V/1tRsAXioeX9tubQz4RNuJ3nMufnStenn9Rx1JT1u1+30qV3HCa1Teg2FxkWQn5pd4XHDZt9N5olU1n5a+oGcYi+bufm7FQx+9I6zLrtEUlP+dVPhzgHvSdLVhTY/B+7TNK098Azg79n+K3C1Z2RTV2BFDXQ/Bsagj1b6rIL9AliqaVonz18bTdPGe+0v+9xU8XhwX2yaplXYG2qa9qGmad00TevmHVQCsO09hKlhXYx1Y8FkJOiavhSt3FgDOUBRUEJ1x93cojHmlo0pXlf9m57i3Ufwa1QHU71YhMlI6PVXkL+sZtOgTk57jUO9x3Ho8rtIefFTcn9eUWVQCcCx7xDG+nUx1okDo5HAQX2xrvb9ypypZTMiZk0lfeqTqDm5JdsNMVEIP32euhIchF+ndjVycuwl9erRvKYPRat8vzBibtWUqCcfIHXKkxfkDaDjwEGM9etiiNc1LVf2x7rGV9PUohnhjz5I1vTHfcopgoPApK+9oYSGYO7YDueJ6ueKq6kJiPBYRGgUKAaMrXrgPrbLN1Fg6aLahqadUMss7G1o1aPG0+AA2taNJCm7gNM5hThdbhbvSaRPq7o+afq1rsfWBP2NTk6RjcSsAuqFB+FWVXKLdbNwODWHI2m59GwaX3UZEw+jxNRBRMaCwYixax9cu73uEVsxRTNuo+iJMRQ9MQb3iYNY5z2DmnQE6+vTS7Y7Vi7Evvj7aoNKAK4jB1Hi66HE6G1p7t0f5xbfL2Qp8aVlNnXtiZqiX5cFs6aQN/E28ibehv23H7H956tqg0oAatJhlOg6iAhPObtcgWuP11QlWzFFM0dQ9Mx4ip4ZjzvhUElQSQSFlKy/ISJjUaLroGal1qichjr1UGL1cvpd0R/n5irK2a0narJeTiU2DhT9QVmJjsVQrwFqevWa9r2HMDXw2Dzj+dk8vxaNKV5ftc1zHzuIElcXJToODEbMvfrj3FrmK5dxpWU0dr4Ud4r+gOratQWlQWMw62txGNt0xH2q+vvSefAgBm9bMLA/9nW+msbmzQid/iDZj85Czc0tl4dlYH+sS2s+Je1iaHrTtlEdktJzOJ2Rq9uFLQfo07G5T5r4yBA2HUgA4HhKJg6ni/B/cFTLhaBtwzi9XJl5erm2HaJP+/IBkxOp2eQX2+nYuLw9+2vrIa7p1qrGmq5DBzHU9bov+/bHsbHMfVmn9Jo19+iJ+3T1/WJVtK0fTVJmPqez8/Vy7jxGnzYNyqU7kZ5LvtVOx4alwZK03EJsThcA+cV2dpxIpVGZdUoqwrbnEKaGdUr8n5DBfc7OFoR52YKWjSmqgf/jTdHOI/g1jsdcPwZhMhIxpDc5Sy7saL2L4f9cDL/StucwZo9ficlIyLVXULi8hpoeQq6r+TQ4APXUUZTIeER4jN5nduyN+4BX+9mKKXp2DMUvT6L45UmoSYexfT4b9fQxtNxMDM3a6+lMfhgatCj5qmpVtK0TTlJ2Iadzi3C6VRbvP0WfFr73fL+W8WxN1IMvOcV2ErMKqRcWSNv4cApsTrKLdB9oc0I6Tap7sZbo8QsivfyCsv7Po7dT9NRYip4aizvhINYPntX9nzdnlGx3rPoF+5LvzyqoBDXrr82tmhL91P2k3vcU7uy8s8rfp5y15Odt+HIpbw1+jLcGP8a+JVvpOuxyABp0boatoJiCjNxyx1z10C34B1v47dkvfLZ7r8fU5squpB87u+C2RHI2/NtGLK0AfhZCvK5pWpYQIsIz2mg9+kiiL4ERQHXfm14KPCmE+PrMVDhPPsFAihDC5MnnNJSMQNoCvAX8XknwpsBz/JnRVJuEEPWBLkCHCtJvBN4VQjTTNO2oECIQqKtp2uEK0nqzGZjrmUqXA9yOPhXw3HGrZLzwLnU+ehGhKOT/vATH0UQi7huNbd9hilduxK9dC+LnPokSEkxgv0tx3TeakzdMQBgN1PtKHxaqFhaT9sjLNZsW4lZJfmoejb94BhSFnAXLsB9JImbaCKx7jlCwbDOWDs1pOG8mhtAgggd0J3bqCI4Mmlx93pXoZb/8NjHvvgSKQuGvf+E8nkjopDtx7D+Mdc0GwqdOQAmwED1H/xqDKzWdjGlPYmrcgPAHJ+lvIIQg/8sFOI/WYEFkt0rmi+8QN+9FhEGh4OfFOI8lEj55NPZ9hyletZGIh+5GBFiIfc2jmZJO2v1PARD/+WuYG9dHBFhosOxrMp58HWs1D7C4VXJffZuouS8jFANFvy3CdSKBkAljcBw4jO3v9YROmYgI8CfiRV3HnZpO1vTHMTVqSPij09A0DSEEBfO/9fmaXKVoKo7l3+B301RQFFx71qFlJWO6bAhqagLuY7swdRmAoWlHz6fUi3D8VRprFSGRiOAI1JPVXfqlGA0Kj17bjXu+WImqagzp0oRmMWG8t3w3bepG0LdVPXo1i2fD0RSGvf07ihBMG9SJsAA/7E434z5ZCkCgn4kXbuqF0VBNDF5VsX3/PgH3PQ+KAeeGJagpSZivG4U78TDuPeXXiTlvVDfFH71J8FOvgqJgX/4n7pMJWG4fh+voQZxb1uM/eBjGDl31RT8LCymae57TUFQV24/zCLj3Wf2zwhuXoqYmYR48AnfSEdx7Kw/+GZq2wzx4hD7SRFOx/fAuFFe/XgSqm6J5bxLyjKecy/7EnZSAZcQ4XEcO4ty8Hv/rhmHq1BVcejkLPdPgjG06YLn5Dn2BWU2jcN4baPk1cCrdKpkvvkv8B/q9mf/zknL3pl+7FsS96bF5fS8lYvJoTg7VbV7dL7xs3qM1sHmqivXTuQTOnAOKgmPVItRTCfgPH4vr+CFc29bjN+hGjO31tlSLCvRpcIBWVIj99wUEvzgP0HDu2IRrRw0eltwq+a/PJeJ1XdP6h24LgsaPxXnwEPZ16wmZPAlhsRD+3NP6IWlp5Dz6OACGuFgMMdE4du6qQuS/QNMLo0Hh0Tuu5J43v0PVNIZc1oFmdaN575c1tGkYT99OzXlw+ACe/eJPvl6mP/w9M/ZaxPlOhS3D9KdeYsuO3eTm5jNg6EjuHT+Km64fdM75GQ0Kj97Sn3ve/Um3dz3b0qxOFO/9vp42DWLp20FfwPqvbYe4umuLcuU5nZVHak4BXZvVqyj7ilHdFL77JqEv6velbcmfuBMTCBg9Dtfhgzg2rsdywzBMXfT7Ui0s9JkGFz7/O0RgIMJoxNyzN/kzH/b5olyl5Rzai3s+WqSXs0dLmsVF8N7irbSpF03ftvo0vr88i3Z7l/N4ei6v/7YJIfSuenSfDjSvyVojbpWM59+j3scvgKKQ/5Pu/0ROGYVt7xGKPP5PnbefwBASTFC/S4icMorE6ycijAbqf6l/+U8tKiZ1xpyznxbrVkl6/CNafvMUKAqZ3y/HdvgkdR6+neJdR8lduoXAjs1o9skjGEKDCLuyO3Ufuo29/R84K42L4f9cDL8y7dn3qf/J82BQyPtxCY6jSUTdPxLb3iMUrtiEf/vm1H33CQwhQQT1u4So+0dy4tp7ADDVjcEYH0Xx5rITDKpAVbH/8jGW8U/qfeaW5ahpJzFfeRvuU8d8g0xlcG5YhP/w+7A8+CYCgXPrCtTU6n0uo6Lw6KBO3PPtOv0+6diQZtEhvLd6P23iw+jbog69msSy4Xg6wz5Yqvs/A9oRFuAHwLQB7Zj4zd9oGrSOD+OmztV8uVFVsf3wPgGTnweh4Ny4RPcLrh2p+wX/hP/jTQ3668gzvvTrel/iSkkndcrTZ6dzMfw84ODKHbTs14kZq9/EYbWzYPoHJfse+HM2bw1+jNC4CAZMuZH0o6e5/w/9a3fr5y9hy/cruWzs1bQZ2BW32401t5AfHp73j5ynRAIgavpljP8VhBB3AtPRp33t0DRtjBCiIfqooCggAxiraVqSEOJz9EDQj55jCzVNO7NA9qPAaMAB/Klp2kwhxD3ADE8em4BgTdPGeNLfDCwA+mqattqzrSR/IcQU4D4gWdO0fl4anTRNq3C1VSFEf+BlwM+z6XFN034VQiQA3c5M+avg9+3ATPRRT39omvZI2fJVxdE2g2r1orBazbUpB0BI+AVeW6YGuJ21P0DQHOCqVb2I4VVPK/gnEPXLL0L7T+NafXZvOS8EzpSz+wLghcBU/8J9uaomOE4UVJ/oApN7wr/6RBeYyLb/3BpPlWH9f/CSMuzFO2td09jmilrXdO1cUuuaRa98W+uagZOvr1W9k48sqz7RBSYv31J9ogtMTGzt21mnrcYfIr5guFy1q1nvxtrvSwxta7bY/oXEtfncAvvnQ/qqs5/6fL7EXFG7Pvvzf4bVqt4ZXk749l+7ENHy2Fv/XUEOLwakff9f2W7/thFLaJo2H5hfZlsi+vpLZdOOKfM7yOv/l4CXyux/H3i/Et0fKTOVzjt/TdPepvzIod5A+VVuS49Zgb5mUtntjar5/S1QzgusSVBJIpFIJBKJRCKRSCQSiaSmyDWWLgJCiDAhxGHAqmnauS0WIZFIJBKJRCKRSCQSiURykfnXjVj6X0DTtFygxcU+D4lEIpFIJBKJRCKRSCSS80GOWJJIJBKJRCKRSCQSiUQikZwTcsSSRCKRSCQSiUQikUgkkn8FZ/kNTskFQI5YkkgkEolEIpFIJBKJRCKRnBMysCSRSCQSiUQikUgkEolEIjknZGBJIpFIJBKJRCKRSCQSiURyTsg1liQSiUQikUgkEolEIpH8K9AQF/sU/t8hRyxJJBKJRCKRSCQSiUQikUjOCRlYkkgkEolEIpFIJBKJRCKRnBMysCSRSCQSiUQikUgkEolEIjkn5BpLEolEIpFIJBKJRCKRSP4VqBf7BP4fIgNLknJEdXTUql7KVkOt6gGEd679Bd3UYmeta6LUbjlFeFit6gGg1n7XYejQotY11aK9ta7pTCyoVT3/zvG1qgdgSsmodU1zl0a1rqmEnK51TWeqvXYFC3JrVw9w7VxS65rGTlfVuiZ8W+uK2unEWtWLbmerVT2A7HUBta4Z1l6rdU13fu3Xbf4Jc63qadbanwSink6udU1D6ya1rln8R+3aAgD7sfxa1TummWpVTyL5J5BT4SQSiUQikUgkEolEIpFIJOeEDCxJJBKJRCKRSCQSiUQikUjOCTkVTiKRSCQSiUQikUgkEsm/ArnGUu0jRyxJJBKJRCKRSCQSiUQikUjOCRlYkkgkEolEIpFIJBKJRCKRnBMysCSRSCQSiUQikUgkEolE8j+OEOJqIcQhIcRRIcSjFez3E0J879m/SQjR6ELoysCSRCKRSCQSiUQikUgkEsn/MEIIA/AucA3QBrhdCNGmTLLxQI6mac2AN4CXL4S2DCxJJBKJRCKRSCQSiUQi+VegIf61f9XQAziqadpxTdMcwHfAkDJphgDzPf//CAwQQlSbcXXIwJJEIpFIJBKJRCKRSCQSyf82dYGTXr9PebZVmEbTNBeQB0Ser7AMLEkkEolEIpFIJBKJRCKR/JcjhJgghNjq9TfhYp8TgPFin4BEIpFIJBKJRCKRSCQSiaRqNE37EPiwkt2ngfpev+t5tlWU5pQQwgiEAlnne14ysCSRSCQSiUQikUgkEonkX4F63isG/c+yBWguhGiMHkC6DbijTJpfgTuBDcDNwApN07TzFZaBJUmNMHbsjmX0faAYcKz8A/uv3/rsNw+8Hr8rh4KqotmsFH/8GurpRDAYCJgwHUOj5mAw4Ph7CfZfvqmRZuDlXYl9fCLCoJD7w2KyPlzgs9/SvR1xsybg17Ixp6e9RMFf60rPNz6a+BcfwBQfBRqcvOtJnKfTq9QztOuG/+33IoSC4+9FOBZ9X3FddO1NwL1PUfjsZNTEw4jAYCz3PomhUUuc65Zg++adGpUPzr1eTZcNxP+6W0vSKQ2aUDhzAu7EY9VrduiOZdR9oCg4Vv2J/bcymgOux+/KIaWan7xe2pZ3PYyhcXP9fNcuKXe+lbHueDpzlu9F1TRu7NCAcZc2L5dm8cFkPlh3CIAWMaG8dH2Xkn2FdifDPllFv+ZxPHZl+5ppHklhzl/bUVWNG7s0YdzlZT+IAIv3JvHBqr0goEVsGC/d3AuAN5bs5O8jyWgaXNoklhnXdKG6Ne3WJWTwyqoDqCoMbVePcT2alEuz5FAK8zYeRSBoER3M7MEdOZSezwsr9lFkd2NQYHyPpgxqGV+jMhrbd8d/xGRQFJyr/8T+x3c++839rsM8wNOWdivWz95ATU4EgxHL2GkYGrUATcP69bu4D+6qkaapcw8C7p4CioJ96R/Y/uN7P/tdfQN+19wIqhvNZqXovVdRTyaW7FeiYgh9Zz7W7z7HtrDie6wshmYdMQ++E4SCa/sKnH//6lsPnfpgHjQCNT8bANemxbi2r9TPZ9SjGOo1x510CPvXc2qkB+DfszvhD+t1W7TwT/Ln+9Zt8IibCRoyGM3tRs3JJevZV3CnpmOIiyH61WdBCDAaKfzhZwr/83u1ekrjdpgH3AGKgmvXGlyb/vStg3aXYe53K1pBDgDO7ctx714DgAiOwHzNWERIBGga9gVvoOVX/+LJ0LYb/rfdg1AUHH//heOvSmxel94E3PMkhc9PRk08gqF1F/xvGg8GI7hd2H78CPfBndXqAZi69CDQc/3Ylv6B7cfy14//tV7Xzzuv4j6ZiLF5KwLve1hPJATWbz7HsfHvGmmuO5DEnIVrdVtwaWvGDejis/+VhevYclR/mWdzusgusLL2xfEl+wttDoa9/B392jXmsZsur5nmvgTm/LgKVVW58bJ2jLuqh6/mj6vYcviUR9Opa756L1sOn+SVH1eXpEtIy+alcYPp37FZjXQr4/EXX2fNus1EhIex8Kt555XXGUzdehA4aQrCoGBb9AfWH3zb0v/aG/C/3tOWViuFb72KOykRERxC8BPPYmrREtvSvyh6960aa65LyOSV1QdRVU23s90bl0uz5HAq8zYeQ4BuZ6/pAMDkn7exOyWPznXDmDukS7njKsPYqQcBY/V+2r78D+wLfctpvvIG/K8eiqaqYLNS9MGrqKd0m2do0ISAiQ8hLAGgaeQ/OgmcjnIaoX070+i5cQhFIf3bZSS/87PPfmE20mzuAwS2b4Irp4Ajk17DfioDYTTQ5NV7CWzfBGE0kLFgFcnv/IR/0zo0n/dQyfF+DWI59cp3pH5csS26aH3JeE9fsuwPbD+VsQWDKuhLTpXpS+bOx/r959h+qVlfYunVjYgZ94KiUPjzIvI+8z0uZORNBN14DbjduHPyyHz6Vdwp6ZhbNiVi5v0oQQHgVsn9+BuKl6yuRMUXQ+su+A+boNfthiU4lv1YYTpjx15Yxs+k6JWpqCePIiJiCJz5Pmq6bpvcCYew//BuzTSbtMc8cITen+xcjXPjH75a7Xtj7n8rqqc/cW1bjmuXXp6ARz5DzdCXZ9Hys7H/+GaNNL1Zl5DJK2sOoWoaQ9vWZVy3Su7TTccRAlpEBTP76pr5dmcIuqILdZ66GxSFnO+XkjHPt14DerSlzhN349+qEUn3zyF/0Xqf/UqQhRZL3iN/6UaSn/qgRpoXw+YBjHv6bjr364bDauedh9/kxN7jlaZ95ONZxDaI48GrpgDQc/Bl3DLtduo2q8djNzzMsT1Hz0pb8r+HpmkuIcR9wGLAAHyqado+IcSzwFZN034FPgG+FEIcBbLRg0/njQwsnSdCiDrAXE3Tbq4m3UxN016spdO6sAgFy9gHKHpxOmpWBsEvzMO5bb0ebPDgWLccx7LfADB27YVl1L0UvfQIpkv6gtFEwSPjwexHyKuf41y3HDUzrWpNRSHu6XtJGjMLZ2omjf/zJgUrNuI4WroWmSs5neRHXidi/E3lDq/zykNkvf89Ret2IAL8Qa0mCCsULCOmUPTaI2g5mQQ+8Q6unRtQU5J80/lbMA+8EdexAyWbNKcT+8+fo9RtjKFuo6p1ymqeY7061y3DuW6ZXlX1GxP40HM1CiohFCxjHqBo9nTU7AyCn3sf5/YymuuX41ju0ezSC8uIeyia8yimS/qAyUTBo3fpbTnnM5zrV1Tblm5VY/ayPcy75VJigy2M+OJv+jSLo2lUcEmaxOxCPt14hM9HXEaIv5nsIrtPHu+uPUSX+jVfU86tqsz+cyvzRvUjNsTCiI+W0qdlXZrGhJZqZhXw6dr9fD5+ICEWM9mFNgB2JmWy82QmC+65GoCxny5na0I63RvHVlnGl1bs5/1h3YkN9mfENxvo0zSGppFBpXo5RXy65Tif33opIf4msov1MvqbDDw3qAMNwwNJL7Qx4usN9GoYRbC/qepCCgX/0fdTNGcGWnYGQU+/h3PHBt3Z9+DYsALHSv1Bwti5J/63T6L4tccw970WgMLH70YEhxH48GwKn74XqntZoSgETJxKwVMPoWZlEPLqBzg2r/MJHNlXL8P+lx74MfXoRcC4yRQ+M6Nkf8D4yTi3b65ax6ecAvN147DNfwEtPwv/iS/iOrgNLcN3VK9r7wYcf3xW7nDnut9xmcwYuw2suaaiEP7I/aRPnoE7LYO4L96jeM0GXCe86vbgUVJ/vAfNbifopusJu38CWTOfx52ZTerYKeB0Iiz+xH//CdbVG3BnVhHoEQLzlaOwf/8qWkE2/nc+ifvoTrSsZN8yHtiMc9lX5Q43X3c3zg2/oSbsB5Nf9e0Iui244z6K3nhUt3mz3sa1qwKb52fBPOBGXMe9bF5hHsVvP4GWl41SpxEBU1+kcEbZl2EVoCgETppK/hP69RP6+gc4N63D7XX9OMpeP+MnU/D0DFxJJ8ibNhFUNyI8grC5n+LYvB5Ud5WSblVl9k9/M2/S9cSGBjLijf/Qp20jmsZFlKSZPvSykv+//XsPB09n+uTx7qLNdGlSs2BvieYPK5g3ZRixYcGMmPMNfdo3pWl8qQ2bfnPfUs1VOzh4MgOA7i3q88PMkQDkFdm4/ulP6dm6YY21K2Po4Cu546YbmPncq+edFwCKQtDkqeQ99hBqZgZhb3+AY+M63EletmDlMmx/6G1pvrQXgRMnkz9rBprDQfH8TzA2aoyhUfkHzspwqxovrTzA+8O6Ehvkz4hvN9KnSXQFdvYEn9/Sw8fOAozu2giby81/9pw6q3IGjH+Awuce1vvM2fNwbl3nE+BwrF2GY6nnmu3Wi4A7J1P4wgxQDATcP4vit1/EnXgMERQCbleFGo1fvJsDtz2DIyWLdn/OIWfxFqxHSs8z5vaBuHIL2XnZZCKHXEaDx0dzZNJrRFzfC+FnYveAaSgWMx1XzSVr4d/YjiWz58qHSvLvsv0jshdtqriMF6svmTCVgqc9fckcT1/iVa/2NcuwL/bUa/deBIydTOFzXn3J2Mk4d5xFX6IoRDw2hbRJj+BKy6TO1+9QvHoDzuOlNs9x8CgpIyaj2ewED7+OiKl3k/HIC6hWG5lPzMGVdBpDdCTx37yLbcNW1IKiqjWFgv/weyh+93G03CwCHn4D195NqKknfdP5WTD1uQF3wkGfzWpmKsVz7q95GUHvT64aje27OWj52fiPeRrXkR0V9ieOJV+WP97lwPbpk2en6YVb1Xhp1UHev7GLfp9+v4k+jcvcp7lFfLo1gc+Hd/fcp+WDrVWiKNR5dhInRj2BKzWLpr+8Tv6yTdi9nhGcpzM4Nf1Nou6+scIsYh8cSdHmfWelWds2D6Bzv67EN67DlD4Tad65JROev4fHhk6vMO0lV/fEVmzz2ZZ0OJFXJs5m4ov3npWu5H8bTdP+BP4ss+1Jr/9twPALrSsX7z5PNE1Lri6o5GHmhdb2zIms9HdNj6sOQ7NWqKnJqOkp4Hbh2LACU7fLfBNZi0vz9/P3cio0/beiIMx+aC4nmlfayrB0aIEjMRnnyVRwusj/Yw3BA3r6pHGeTsd+KAE01We7uVl9hMFA0bod+hkU29BsvoGKcmVs0hI1PRktMxXcLpybV2Hs3KtcOr+hY/SRTN5vHB023Ef3gevsOsbzq9dSzL0G4Fy/smaaTVuhpp1GzfBoblyBqWuZcpbVxKOpgfCznHVb7k3JoX5YIPXCAjEZFAa1rsOqo6k+aX7ancStnRsR4m8GICLQr2Tf/tRcsovs9GwUXaMyAuw9nU39iGDqRQRhMhoY1K4Bqw75BiJ+2naMW7s3J8Ti0Qzy18sswOFy43SrOFwqLrdKpGdfpXqpudQPC6BeWIBexpZxrDrmG3D7ec8pbunYgBBPwCgiQC9jw/BAGoYHAhAT5E94gJlsa/XXkqGJ3paapy2dm1Zi6lKmLW1l21JHqdMQ137P/VGQi1ZUiKFxi2o1jc1bo6aeRk1LAZcLx98rMPfo7ZvI5/qxlFw+AKZLeuNOS8GddKJarZJzrdcMNTsVLSddf4u8Zz3GVt1qfLx6fC+a3VZ9Qi/MbVvhOnka92m9nMVLVhLQx7du7dt2otl1u2LfewBjrOf6dLnA6QRAmM2gVD8WW4lvgpabjpaXAaob14HNGJp3rtG5isg6oCh6UAnAaa+RLTI0boma4WXztqzG2Kkim3enPpLJy+apJ4+h5emjw9TkBL2cxmoCoejXjzul9Pqxr1mB6RLf68fbpgh/S+kOu70kiCTM5poFz4C9SenUjwqlXmSIbgs6N2PV3oRK0y/acYSrO5eODtp/MoPsAis9W9av9Jhymgmp1I8Oo15UmK7ZtSWrdlce+F+09RBXd2tZbvvSHYe5rE1jLObq67Y6unVqT2hIcPUJa4ixZWvcyadRUz1tuWoF5p5l2rLYq3/w97IFdhuufXvQHGfXZ+5NzaN+aAD1Qj12tkUcq475jkT+ee9pbulYv5ydBbikQSSBprN7n6r306f1ftrlwrluBeYq+mm8+mljx264E4+XvPTRCvNB9fVXAII6N8OWkII9KQ3N6SLrl7WED/Id4RY+qDsZC/R+Puv3DYT09ozu0DQMAX5gUFD8zagOF65Cq8+xoZe3x56YhuN0RsVlvFh9iZctcKytQV/ihalHb9zpZ9eX+LVrietkMq7TqeByUbR4FQF9fctp27qrxF+07z6AwWPXXUmncSV5Rg5lZKFm56KEh1WrqTRsgZqRgpaVBm4Xru1rMLa/tPy5XTsSx7If0Tx9x/mg1GmCmpOGlqv3J+4DmzC2qPkIvfNlb1qe7g+duU+bx7HquO+19/Pe09zSoZ7XfWo+K42Ajs1xJKbgPKnfM3m/rSHkykt80jhPp2M7mFDhi2X/dk0xRoVR8PeOGmteDJsH0P3KS1j1H/3eP7LjEAEhgYTFhJcvU4A/1901hP+8/YPP9tNHT5F8vOzyOhLJP8M/PmJJCDEaeBj99tqtadooIUQj4FMgCsgAxmqaliSE+BzIB7oBccAMTdN+9OTzCDASUIFFmqY9KoS4G5gAmIGjwCjABOwGGmuapgohAoGDQBOgAfAuEA0UA3drmubzekAI8TTQFGjmOb85mqZ9JPR5MHOAazxleV7TtO89Zfld07R2QogxwA1AgCePnzVNmyGEeAmwCCF2Avs85/wD+mJaBuA5TdN8xuMKIZpWdK6eOrIBnYF1QoiIMr+/AOZ5zuEYME7TtBwhxCpgJ9Ab+BZ4rdrG86CER6FmlTpvalYGxmaty6UzXzkUv2tvRhhNFD7/IADOTasxdb2MkPf/gzD7Yf3yPbSigmo1jXGRuFJK3xo7UzOxdCzveFeEuVE93AVF1H13FuZ6cRSt30H6K59X6NCdQYRFoWaXdnxaTiaGxq180igNmqFERGPfvRnzoFtqdC5VcT716o2pZ1+KXn28ZpoRZTSzMzE2rUhzCH7XDEcYjRS+oL/1dG5ejalrL0Le/VFvy69q1pbphTbigkudwthgf/Yk5/qkScwuBODOr/XpKpMua8llTWJQNY3XVu7nxes6szHBdxRBlZr5VuJCAko1QyzsOZXtq5mln/udnyzTNfu247Lm8XSsH0X3RjEMfPUXAG7t0Zwm0aFURXqhnVjvMgb5szc1z1cvV3+zOea7jaiaxsSezbisTLBsb2ouLlWlflgA1SHCo9C8rlk1OwNDRW05YAjmq29GGIwUvaxPJXKfPIapcy+cG1cgImIwNGqBiIiB44eq1oyMwp1Z5pptUV7Tb/BQ/G+4RR/h9vhUfaO/BcuwO8h/6iEsQ28td0ylmsERaHmlo320/GyUeuWnBRna9MDSsBVqViqORV/UaCpYZRhionCnldatKz0Dv3bly3mGoCHXYF1f+ubcEBtN9JsvYqxfh9y3Pqx6tBIggsPR8kuvT60gGyW+abl0xpZdMdRvgZqTinP5d3q6iFiwFWMeeh9KWBTuhP04Vy+oNvBS3uZlVGzzwqOx79mMeVDFL7aMXS7HnXgUXNU/ECmRUahlrh9TJdePZegtYDSRP2tqqVaL1gQ+8AiG6FgKX3+x2tFKAOl5RcSFBZb8jg0LZE9ixdOik7MLSM4qoEdz/au8qqrx2q/reXHEADYervkol/TcQuLCS4M4sWFB7ElIrTBtclY+yVl59KggcLV422FG9a+9B8KzQYmMQs3wasvMDIytyrel//VDsQzTbUHejKnnpZleZCM2uDSoERtcgZ3N8djZ7zfrdvbSplzWKOqcNZWIaNSsMna2eflp1X6DhuJ33XCE0UTBM9MAMMTXBzSCZs1BhIThWLcC+6/flTvWHBeJI7nURjhSsgjq0rzyNG4Vd34xxohgsn/fQPigHnTd+QmKxY/Epz7DnVvoc2zkkN5kLqx82uhF6UsiatiXXOPpS4wmCp6cqm8805c8/RCWITXvSwwxUbhSvex6WiZ+7VtVmj7oxmuwri0/IsrcriWYTLhOJldwlC9KWCRqrlfd5mZiaOjryyr1miLConDv3woDfEfgK5GxBMx4C81WjOP3r3Afr36EjQiqoD+pU74/MbTshqV+S9TsVBzLvkEr8BxjNOE/5mlQ3Tg3/IH7yPZqNb1JL7QTG1Qa0I0N8mNvWr5PmsRcPQAzZsFmVBUmXtLkrO5TY1wkTp9nhCwCOlUf0ARACOJnjefktNcIuqxTjTUvhs0DiIyLJCu59BrKTs0iMjaS3PQcn3S3PTSC3z5aiN1a9Yv0/0+o/P9dZOli8Y+OWBJCtAUeB/prmtYReMCz621gvqZpHYCvgbleh8WjBz+uA17y5HMNMAS4xJPPmYUyftI0rbtn2wFgvKZpeegBlD6eNNcBizVNc6Kvnj5F07Su6MGu9yo59Q5Af6An8KRnutswoBPQERgIvCKEqGhsfCfgVqA9cKsQor6maY8CVk3TOmmaNgK4GkjWNK2jpmntgL8qyKeqc60H9NI07cEKfn8BPOKp2z3AU17HmTVN66ZpWo2DSmeDY+lCCqaOxPrNh/jfOApAd05Ulfx7byb/gTvwu3Y4SkzNpxScC8KoENCtLekvfcKJYQ9gqh9P6LCzmAZTYaYC/1snYfu+ZvOwLyQV1esZDE1bg92OeirhAmv+QsGDI7F+9yH+Q0d6tFrpbXnfcPKnjcBv8C0o0RemLd2qRlJOER/f1ouXru/Ks4t3kW9z8sOOBHo3ifEJ2lwo3KpGUnYBH4/pz0s39+TZ3zaTb3WQlFXA8cx8ljx4A0sevIEtJ9LYXsmD6Fnr5Rbx0fAezB7ckeeW7qPAVvownlFo4/G/dvP0Ve1RqlnP6WxwLP+FwumjsP3wEX436G3pXLMINSeDoKffxzLiXlxH91UZeD1b7H8uJG/SHVjnf4DlltEAWG4bg+3XBWCzVnP02eM6tA3r61OwvvcI7mO78Rt2zwXXqIyAawZibt2C/C9K3xK60zJIvf1uUoaOJvC6q1Aiyr9dPFvcR3dinTcd22dPop7Yj/nau/QdigGlfgucK7/HNv9ZRFg0hva9q86sJgiB/y0TsS2o7KMj+mgF/5vGY/3q7NaLqA77nwvJnXAHxfM/wHLr6JLtrsMHyJs8hrwHJ2EZPgJMZ/eWuzoW7zjKwI5NMCi6a/TDur30bt2A2LCgao48D81thxjYuUWJ5hky8go5mpxJzzbnPw3uYmL7bSE5Y++g+JMPCLhjdPUHnCduTSMpt5iPbu7G7Gva89wyXzv7T2FfvJD8KSMo/voD/G/y9NMGA8ZW7Sma+wIFT0zBfMnlGNtd2EBhYOfm4FbZ3vkudlxyD/GTbsCvQem0bWEyEn5Vd7J/W19FLjXjovQlixaSd88dWL/4AMtwT19y6z/Xl5whcPAA/Nq0IG++79qehqgIop9/hKynXq3xqMkqEQK/G+/CvvCTcru0/GwKnxpL8ZwHsP/8Mf53PqyPgrkAuI7uwPreQ1g/eRz3ib34XXd3yT7ruw9h+/xp7L/OwzzwDkRYzAXR9Eb3h4r5aFg3Zl/dnudW7KfA/s/fpwCRowZTsGorrtTz/gBWhdS2zQNo1KYxsQ3j2Lx4Y63oSSSV8U+PWOoPLNA0LRNA07QzIfSe6IEagC8pDRQBLNQ0TQX2CyHO9I4Dgc80TSsuk087IcTzQBgQhL5IFcD36MGdleiLUb0nhAgCegELvBbhLQ2p+/KLpmlWwCqEWAn0wDPSR9M0N5AmhFgNdEcfHeXNck9wCyHEfqAhUGYyNXuA14QQL6OPdvJ5lVSDc13gOQ+f30KIUCBM07QzKwrOB7x7xUpXNxRCTEAfScUb3Vowplmdkn1qTiZKZGnHokRGo+ZUPnrEuWEFAeOnAmC+bADOXZvB7UbLz8V1eJ9n2llKpccDuFKzMMaXvr0wxUXhSqtZJ+BMzcR24Lg+jQ4oWLoBS6dW5P24pNJjtNxMlIjS0SMiPAo116uM/haUuo0InKGvTyFCIwi4/1mK5z6Jmni4RudVlvOp1zOYevXDsX5FzTWzy2hGRKHmVDw8XtdcScDYqfCBZ8rd7i1ebbkXQxN9mHdVxAT5k1pQ6gCmFdiICfadWhYbbKFdnTBMBoW6YQE0DA8iKaeIXadz2HEqix92JGB1unC6NQLMRh7oU/noEYCYEAup+aVDktPyrcSE+DpksSEW2tWL1DXDg2gYGUxSdgFbE9LpUC+SAD99iPZlzeLZdTKLLg0rd65igvxI8y5joY3oIL8yafxpHx+q64UG0DA8gKTcYtrGhVJod3H/L9uZ3KsFHeLDqizbGbScTITXNatERKNVdf1sWonlzgewAqgqtm/eL9kX+Phc1NTqR2RoWZkYospcs1mVazr+Xk7AJP3tvbFFG8y9+mC5cyIiMAg0Dc3hwP7nz5UeD/rbVhFaujaNCInweRsLgLX0Db1r2wrMV42otixV4U7PLJkCAWCMicadXr6cfj26EDruDtImPFgy/c0nn8wsnMdO4Ne5PdblayrV0wpy9IW3PYjgCLRC37eR2ErX8nDtXo2p33DPsdmoaUn6NDrAfWQ7Sp2muKl6YevyNi8aNdfLxvpbUOo0IvDhV/T9oREE3Pcsxe88iZp4BBEeheXep7B+OkefQlMD1KxMlDLXj7uq62fNcgLvmUbZVUzcpxLRrFYMDRvjPlr1yIiY0EBSc0tzSMstIiY0sMK0f+08ymPDShfn3pWYxo7jKfywbh9WhxOny02An4kHris/lcVHMyyI1JzS0ZxpuYXEVBKc+mvbIR67tX+57Uu2H6Zfx6aYDIYqtS4WalYmSrRXW0ZFo2ZW3pb2VcsJnDLtvDRjAv1JKyid1ppWYCM6sAI7G+dtZwNL7Oy5oGZnoESWsbNZVfSZ61YQePc0it/VR+G49u9CK9BHVTm3b8TQpDmuvb4jQBypWZjrlNo4c3wkjpTsCtM4UrLAoGAICcCVXUDUw5eTu3IHmsuNKyuPgi0HCezYFHuSPhU7rH9nivYcx5npO7LLm4vSl2SfZV+ydjkBEyvpS1RPX7Ko6r7EnZ6JMc7LrsdGVWjX/S/pTOhdd5A6/iEfuy4CA4h5+3ly3vkM+54D5Y6rCDU3C1OYV92GRfmMwMXPghLfgIAps3WNkHAsE57A+uFzqCePgku3I+rJY6iZqSjRdfXtVaAVVtCfFJTpT6xe/cmu1Zj7lY78OtP3aLkZuJMOosQ2wJ1b85drMUF+pBWWjppJK7RXcJ/6ed2nFhqGee7T2Jrdp67ULP3jPB5McZE4axgoCujcioDubYkcORglwIIwGXEX2UibM7/K42rT5l09ejADbrsKgGO7jxBZJxp9/ARExEWSVeZ5qEWXVjTt0Iz31n6EwWggJDKUZ757gadum3VO+hLJufLfuMaS9xi+6l7bfw7cp2lae+AZ4MzT6q/A1Z5pYl2BFehlzfWMGjrzV9nTadnXEGfzWsL7/N1UELzTNO0w0AU9wPS8EKLsKnnVnWtZP7ua1QOrT6dp2oee0UzdvINKAO5jB1Hi6qJEx4HBiLlnf5zbynxdIa5uyf/GzpfiTtXn86qZaRjbetYK8fPH2Kw17uQyi8NWgHXPYcyN6mCqFwsmIyHXXkHB8ppF4m27j2AIDsQQEQJAYM+OOI5Wrek+cQglti4iSi+jqUdfXDs3eJ1QMYVTb6bwkVEUPjIK97ED5xVUgvOrV0BfoPHSvjg31Dyw5D5eRvPS/ji3bfBJo8R6aXbybst0jG282rJ5a9zJZWOm5WkbH0ZSThGnc4txulUWH0imT7M4nzT9msexNUnvKHOK7STmFFIvLIDZ13fhr3uuZNGkgUzr25br2tarNqgE0LZOBElZBZzOKcTpcrN4bxJ9Wtb1SdOvVT22JujOUk6RncSsAuqFBxEfGsi2hAxcbhWnW2VbYjpNokOq1osLJSmnmNN5njIeSqVvE99AVL9mMWw9qT8s5FgdJOYUUzfUgtOt8tBv27mudR2ubBFXUfYV4j5xEIP3NXtJP5w7ylw/3m3Z8VLcaZ7rx+wHZt1cGtt2BdXts1BrZbiOHESJr4cSEwdGI+bL++PcvM4njRJfqmnq1hM1RX/IKJg5hbwJt5E34Tbsv/2I7cevqg0qAainj6FExCHCosFgwNC+F66D23zSiKCwkv8NrbqhllnY+2xx7D+IqX5dDHX0cgZc1Q/rGt+6NbVsRsTMaWQ8+ARqTm6pfkwUwk8fSSOCg/Dr2B5XQtX3iZpyAhEegwiNAsWAsXUP3EfLrP0QWOpwG5p1Rs1KKT3WPwAs+tQrQ8PWaJnVT9FwJxxCifG6frr3wbWrjM17cDiFj42m8LHRuI8fKAkqYQkkYMpz2P/zCe5j+6vVOoPryEEMdeqhxOr16ndFDa6fZP36UWLjQNGDLEp0LIZ6DVDTK55e5k3b+jEkZeRyOitftwU7jtKnXaNy6U6k5ZBfbKdjo9LRHrNHDuSvJ0ex6ImRTLu+J9d1a1ltUAmgbcM4ktJzOJ2Zp2tuO0Sf9uW/EnkiNVvXbFx+5OdfWw9xTbfKp+lcbFyHDmKo69WWffvj2FimLeuUtqW5R0/cp89i0ewKaBsXQlKul509nErfpmXsbNMYtp7ytrNF1A0991Ee7qOHfGye6bL+OLZW3k+bulyKO0W3P65dmzE0aKLbW8WAsU0n3KfK29nCnUfxbxyPX/0YhMlI5JDe5CzZ4pMmZ8kWoof3AyDyup7kr90DgON0Zsl6S4rFj6AuLbAdLbV/kUMvJ2vh2qrL+N/Ql/Tuj3NLFbagq1dfMmsKeRNvI2+ipy/5z1fVBpUA7PsOYWxQF6PHrgcO6kvxal//x9yyKZGPTyV96pM+dh2jkZjXn6bo96UUL6vZ1ygB1KTDKNF1EBGxYDBi7HIFrj1ei6jbiimaOYKiZ8ZT9Mx43AmHSoJKIigEhP6YJiJjUaLroGZVb/PU5BMo4bEl/Ymh9SW4jvj2J8K7P2neBfXMwt7+AfrXPgEsQRjqNUetQX/iTdvYM/epVb9Pj6TSt4nvtP9+TWLYekoPYOVYHSTmFlE3pOb3afHuI/h5nhGEyUjo9VeQv6xmC7mfnPYah3qP49Dld5Hy4qfk/ryi2qAS1K7N++uLP5k+eCrTB09l85JN9L1Jv/ebd25JcUFxuWlwS75axIQeY7m39908fvOjpJxIlkElyUXhnx6xtAL4WQjxuqZpWUKICM9oo/XoI4m+BEZANa9WYSn6lLSvNU0r9sonGEgRQpg8+ZwG0DStUAixBXgLfUSQG8gXQpwQQgzXNG2BZ82kDpqmVfRd1CFCiNlAINAXeBR9LaSJQoj5QARwBTCd0mBWdTiFECZN05yeqXXZmqZ9JYTIBe7yTqhp2tmcq/dxeUKIHCHE5Z5RUKOAmn0PtSpUFevncwl8bI7nE/WLUE8l4H/zWFwnDuHath6/q27E2L4ruFyoRQUUv/8SAPYlCwmY9AjBr+hfa3Ks/gs1qfLPZJbgVkl95n3qf/o8wqCQ++MSHEeTiHpgJLY9RyhcsQn/9s2p994TGEKCCOp3CdH3j+T44HtAVUl/+RMazJ8NQmDbd4ScHyqabehbRtvX7xAwbbb+6e21i1GTE/EbcifuhMO+D1wVEPTyl/rnhA0mjJ17Ufz6o+W/rnQB6xXA2KoDalZGtaO/ymu+TeAjL4NiwLF6EerpBPxvGoPrxGFc29fjd9VQjO26gtujOe9lAOxLFxIw8RGCX/4UBDhWL0Y9WX1bGhWFRwe2454F+tpCQ9rXp1lUMO/9fZA2cWH0bR5Hr8bRbEjIYNgnK1GEYFrfNoRZzn2ai9Gg8Ojgrtzz5WpUTWVI5yY0iwnlvRV7aFMngr6t6tKrWRwbjqUy7J0/URTBtCs7ERbgx8A29dh8Io3h7/+FAHo1iy8XlKqojI/0b8O9P23Vy9i2Hk2jgnlv/RHaxIbSt2kMvRpGsSExk2Hz/8YgBFOvaEmYxcwfB5LZfjqHXJuTX/frzvqzV7WnZUzVwSxUFeuXbxM4/WX9M8ZrFqGeTsTvxjG4Ew7h2rEB88ChGNt2AZcLrbgQ60d6W4qQMAIffhk0FS0nk+IPZtesYlU3xR++SfDTr+qfiF7+J+6TCVjuGIfr6EGcm9fjf+0wjB31a1YrKqTozRrmXUU5HX98hv/omfqnk7evRMs4han/cNTTx3Ef2obx0qsxtuqqf+7bWoj959I36P7jn0aJqgNmfywPvYvjlw9wHy070LQMbpXsV94m5u2XwaBQ9OsinMcTCZ04BseBQ1jXbCD8/gkoFgtRL+nvBVxp6WQ++ASmxg0JmzpJnyYhBPlf/YDzWDULzGoqjqVf43fLQyAUXHv+RstMxtR7KGpqAu6jOzF1vRJD806ezxgX4fjjY8+xGo6V3+N/23QQAjU1oeSz0dXVq+2bdwiY+iJCKDjWeWzeDaNxJx7GtavyQL65/xCUmLr4XT8Sv+v1KTHFbzyGVpBbjaabonlvEvKM5/pZ9ifupAQsI8bhOuK5fq4bhqmT5/opLKTQc/0Y23TAcvMd+uLomkbhvDfQ8isfhXEGo0Hh0WGXc8+Hv6OqGkN6tKJZXATvLdpMm/rR9G2nf6Hnrx1HubpzM8QFmIZqNCg8ekt/7nn3J12zZ1ua1Ynivd/X06ZBLH076Oud/LXtEFd3bVFO83RWHqk5BXRtVu+8z+UM0596iS07dpObm8+AoSO5d/wobrp+0LlnqLopfPdNQl/U29K25E/ciQkEjB6H6/BBHBvXY7lhGKYunv6rsJDCV0ttQfj87xCBgQijEXPP3uTPfNjn60oVYVQUHunXint/3u6xs3VpGhnEexuO0iYmxGNnI9mQmMWwL9bpdvbyFiV9ybgfNnMipwirw82gj1fz1MC29KpuXRfVTfEnbxE06xW9n17p6advHYv72CGcW9fjd82NmNp3RXO70QoLKHpHL6dWVIj99wWEvDQPNHDu2IhrewX3lVslYdbHtPrmSYRBIf275VgPn6Te9Nso2nWMnCVbSP92Oc3mPkCnde/iyi3kyD2vA5D62SKavnEfHVa+CUKQ8f0Kig/o9ahY/Ai9vCMnZsyrpowXqS/56E2CnyrTl9zu6Uu2rMd/8DCMHXRfRCsspGjuefYlbpXsl94h9v3ZoCgU/rIY57FEwu65E/v+w1hXbyB82gSUAAsxrzwBgCslnfSpTxJ4VR/8u7THEBZC0A36fZP55Cs4DlXzNV5VxfbjPALufVav241LUVOTMA8egTvpCO69lQdDDE3bYR48Atxu0FRsP7wLxYWVpi9BU3Es/dLTJyi4dq9ByzyN6fIbUVMScB/dgbHbVRibd0ZT3WArwv673p8okXXwu3oMmqYhhMC54Y9yX5OrDqOi8Ejfltz7y3bd/rWto9+nGz33aRPPfZqUxbAv12NQBFN7tzg7n8+tkvzUPBp/8QwoCjkLlmE/kkTMtBFY9xyhYNlmLB2a03DeTAyhQQQP6E7s1BEcGTT5rMriw0WweQDbV2ylS7+uvLPmA+xWO+89XLp6zCt/vsn0wVOrPL7HoEsZ/8wEQiJCeeyzJ0nYf5znRz99rrXwP8UFmKwqOUuEdiHmCFclIMSd6AEYN7BD07QxQoiGwGdUvHj3714Ldhdqmhbk+f9RYDTgAP7UNG2mEOIeYIYnj01AsKZpYzzpb0afBtb3zNQwIURj4H30dZxMwHeapj1b5nyfRl/ouznntnh3N03T7vPk9TvwqqZpqzzT3m4AtqOvg/QK+kLkTuAeTdO2ljmPCs+1gjoq+7sTpYt3H/fU7ZnFux8uq1MRubf3q9V7MWXrhV8/pzrqXlE7c7m9UYsr+MzwP00Nvkh1IfG7GAvNBlQ8reWfRMvLrXVN54a9ta7pzq3da9av4z+7/lpFZP1Z+bSWf4qo4Q1qXdN19PxGcZ0LztTaXUQ0YFLFn5X+R/Gr6bulC4ex01W1rpk3YmytawYM7VCrevYVe2pVD+DQuojqE11gWl9ZfUD2QuPOr33/J//EhV2DrToir6hsdY1/DiU+svpEFxhxAb8uWVOOvVp9AOZCE98iv/pEF5BJB89tuu758mPir//aFa4Xxt3xr40tDU395r+y3f7xr8JpmjYffa0f722J6OsvlU07pszvIK//X8KzmLfXtvfRgy8V6f5Imal0mqadQF84uzp2a5rms+Kapkfgpnv+vLcnAO08/3+OPj3vzL7rvP5/BHjE69DFVEFl51pBHZX9vRMoN1Zf07S+VelJJBKJRCKRSCQSiUQikZwt/41rLEkkEolEIpFIJBKJRCKRSP4H+MdHLP2voWna0xf7HCQSiUQikUgkEolEIpFI/heQgSWJRCKRSCQSiUQikUgk/wrUi30C/w+RU+EkEolEIpFIJBKJRCKRSCTnhAwsSSQSiUQikUgkEolEIpFIzgkZWJJIJBKJRCKRSCQSiUQikZwTco0liUQikUgkEolEIpFIJP8KVCEu9in8v0OOWJJIJBKJRCKRSCQSiUQikZwTMrAkkUgkEolEIpFIJBKJRCI5J2RgSSKRSCQSiUQikUgkEolEck7INZYkEolEIpFIJBKJRCKR/CvQLvYJ/D9EjliSSCQSiUQikUgkEolEIpGcE0LTZDxP4stH9UbW6kVR1+mqTTkA0o21P1jvYkRxLwvPqFU9k3/tt6W10Fzrmrn5llrXbHOzo9Y1nScLalWv6FTt35ehnQy1rll0wFnrmhmngmpds9GV9lrV0xzuWtUDcKbWvs27GIR+/VmtaxZNHl+rel+tr1uregAhaq1LYr8IH0q6CMUkzlW7quHUvl13arXvWeYotd9PXzkwtdY19y0Lq1W9XNVUq3pnGJz23b/202kL4kf8a4Mcw1O+/q9sNzliSSKRSCQSiUQikUgkEolEck7INZYkEolEIpFIJBKJRCKR/Cu4GCMl/78jRyxJJBKJRCKRSCQSiUQikUjOCRlYkkgkEolEIpFIJBKJRCKRnBMysCSRSCQSiUQikUgkEolEIjknZGBJIpFIJBKJRCKRSCQSiURyTsjFuyUSiUQikUgkEolEIpH8K1DFxT6D/3/IEUsSiUQikUgkEolEIpFIJJJzQgaWJBKJRCKRSCQSiUQikUgk54QMLEkkEolEIpFIJBKJRCKRSM4JucaSRCKRSCQSiUQikUgkkn8FKnKRpdpGjliSSCQSiUQikUgkEolEIpGcE3LEkuSc6PnsKOr374TLamf1tA/J2ptQLk23GcNpfnNv/EID+bzlXRXmU69vBy5/eiTCoHDy6xUcf/tXn/2K2UiHdyYT2qExzpxCdkx4C+vJDACa3j+Eenf0Q3Or7J/1OZmrdgPQ6O5rqD+yPwAnv15BwoeLAIi7/hKaP3wzQS3q8vu1T5G1+wQAPZ4dRT1PWdZO+5DsCsoS2b4Rvd+YiMHfzKkVO9n85JcARLRtQM+XxmHwM6G63Gyc+TmZO4/T5MZetLv3OoQQOIusbHjsc/L2J5Xk1/3ZUdTt3wm31c66SjQj2jfiMo/m6RU72eLRvOL9+whpGg+AOSQAR34xv181i/jL29Fl5q0oJiOq08W257+F/asrrPczWC7rRtSjkxAGA/n/WUTuJz/47A8dPYyQm65Gc7txZ+eR8cTruFLSq8yzIvx7dif84cmgKBQt/JP8+d/57A8ecTNBQwajud2oOblkPfsK7tR0DHExRL/6LAgBRiOFP/xM4X9+r5FmQO+uxM6aBIpC3o9/kf3RAt+yd2tHzGMT8WvZmOSHXqJw8dqSfdEPjyOwTw9QBMXrd5D+wrwKNUL7dqbhc+MQikL6t8tIeednn/3CbKTp3AcIbN8EV04BRya9huNUBsJkpPGcSQR2aIqmaiQ++QkFG/YB0PrHZzHFhqPaHAAcvO1ZXFl5FeobWnfBf9gEUBScG5bgWPZjhemMHXthGT+Tolemop48ioiIIXDm+6jppwFwJxzC/sO7NahVMHXuQcD4KaAo2Jf9ge2nb3z2+w26Ab9rbgTVjWazUvTeq6inElGi4wh9+wvcyfp94Dq8n+J5r9dI079nd8Ie8lw/v/xJQZnrJ+gOr+snN5dsz/VzBhEYQNz3n2JdvY7cV96ukaahbTf8b7sHoSg4/v4Lx1/fV5jO2KU3Afc8SeHzk1ETjyACg7FMegJDo5Y41y/B9m3N6tXvku6EPHAfKAaKf/+Doq++9dkfeOtwLNcNBrcbNTePvNlzcKelYe7ciZD7J5eeT4MG5Dz9LPa/11WrGdSnC3WfvBsMCtnfLyXjfd/rJ7BHW+o8eTf+rRqRNGUOeYvWA2CqG02jD2aBIhBGI5nzfyP7679qVM7arlcAY/vu+I/Qrx/n6j+x/+F7/Zj7XYd5wBBQVTS7Fetnb6AmJ4LBiGXsNAyNWoCmYf36XdwHd9VI09StB4GTpiAMCrZFf2D9wfc+8b/2Bvyv99wnViuFb72KOykRERxC8BPPYmrREtvSvyh6963/Sr2a8PiLr7Nm3WYiwsNY+FXFNvRsMXbsjmWMfp84VvyB/Rff+8Q88Hr8Bg3V29JmpfjD11BPJ2LqPRD/628tSac0aELhoxNwJx6rke7lz4yiocdPWP7gh2RU0GdfOmM4LW/SfZ4PW5X6PG1H9qfDnVeiulWcRTZWPvoJOUeSyx3/T/gFjW/sRdt7ri05Prx1fX66+nGyvfyRyrhQfl5V9Hp2FA08GqumfUhmBRrdZwynhUfjUy+N+Eta0vPpUUS2rs+yye9w4o8tleq0f340MQM64bY62PHAPPL2lNcJ7dCYLm9NRPE3k758J3se/wKAOtdfQsuHbyK4eR3WXPMEubt03zGsc1M6vTJeP1gITr36A1mLNpfLN7xfJ5o+NxZhUEj9ejkn31nos1+YjbR8ewrBHZrgzCngwMQ3sHv83MDWDWj+ykQMwRZQNbZf/Sia3VlOI6JfR1o8PwZhUEj+egWJb/9STqPtO5NLNPZOeAvbyQxCOjel1asTSspw4pUFZCzaQkDTeNp9OLXkeEvDGI7PWcDJD/+stI47Pjea+AEdcVkdbJ36AbkV1HFYh0Z0f3MSBn8TKct3sesJvY7bzriZ+EFdQdWwZ+Wz5YF52NJyK9UCj10f5bHrq/7E/nsZu97/OswDh5TYAuunXnZ93DQMjT12/cuq7XpYv040fnYcGBTSv1nO6Qr8vOZz7yewg+7nHZ74OvZTGUQNu5y69wwpSRfQpiG7rppO8b4Eoob2pu79w0ADR1o26d+toMP02y/os5AxJID2r08kuFU90GD3tHnkbj1C84dvpv7I/jiy8s9kPxiovGElkrNABpb+AYQQQ4HDmqbtv9jn8k9Qv39HQhvH8UPvh4jp0pTes8fwy/VPl0uXtGw7+z5fyq1/v1phPkIRXPb8nWy55QVsyVlctvhF0hdvo/Dw6ZI09e7ohyu3kNWXTiV+aE9aPnEHOye8RVCLusQP7cXfVzyMX1w4PRY8zuqeUwlqUZf6I/uz7upZaA4X3b97jPQl2ylOSKPg4Em2j3uddq/cXZJ/3f4dCWkcx0+9HyK6S1N6zh7DHxWU5dLZY1k/42Myth9j4JfTqduvA6dX7qbrrNvZ+fpPnF65m7r9O9Jt1u38NfwFCk5m8NfNz+PIK6Zuvw70enkcizz5ntFc2Pshoro05ZLZY0r2ldXcMONjMrcfY8CX06nTrwPJK3ez5p53StJ0ffIOnPnFANizC1gx5jWsabmEtazHwK9nkHJlFYElRSH68ckk3/0YrtRM6n3/NkUrN+I8Xupw2g8c49StU9BsdkJuvY7Ih+4i7eEXK8+zEp3wR+4nffIM3GkZxH3xHsVrNuA6kViSxHHwKKk/3oNmtxN00/WE3T+BrJnP487MJnXsFHA6ERZ/4r//BOvqDbgzs6rVjH1yMqfGzcSZlknDBW9RuGITjmOlZXOmpJP62GuEj7vJ51D/zq2xdGlDwpB7AWjwzatYerTHunlPOY1GL97NwduewZGSRds/55C7eAvWI6dKkkTfPhBXbiG7LptMxJDLaPD4aI5Oeo2YEQMB2DNgGsbIUFp9/Th7r5kBmgbAsclvUrS7mgceoeA//B6K330cLTeLgIffwLV3E2rqSd90fhZMfW7AnXDQZ7OamUrxnPur1iiLohAwYSoFTz+EmpVByJwPcGxeh3qqtC3ta5ZhX6w7RabuvQgYO5nC52YA4E47Tf6DZ/nwoSiEz7if9Pv06yd2/ntYy1w/zkNHSRutXz+BXtfPGUInjcW+Y3fNNYWC5Y77KHrjUbScTAJnvY1r1wbUlDIPY34WzANuxHX8QMkmzenE/st8lLqNMNRpVOMyhjz4ANnTpuNOzyDq43nY167HleBVxsNHKLprEtjtBAy9geB7J5L71LM4duwkc6xu00RwMDHff4V989YaadZ9dhInRj6BMzWLZr++Tv7STdiPll4/juQMTj78JtF33+hzqCs9h6PDHkZzuFAC/Gmx5B3yl27GlZ5dtWZt16tH03/0/RTNmYGWnUHQ0+/h3LFBf8A4U84NK3Cs1APWxs498b99EsWvPYa5r/5AXvj43YjgMAIfnk3h0/eW3KeVoigETZ5K3mMPoWZmEPb2Bzg2rsOd5HWfrFyG7Q/9PjFf2ovAiZPJnzUDzeGgeP4nGBs1xtCocc3KWNt6NWTo4Cu546YbmPlcxT7AWSMULOMeoOiF6ahZGQTPnodz63rU015tuW45jmW/AWDs2gvL6Hspmv0IzrXLcK5dBoBSvzGBDz9X46BSw34dCWscx1eXP0Rs56b0eXEMP97wdLl0J5ZuZ/fnSxm5xre8hxduYN9XKwBodGUXej85kt9GzfFJ80/5BSd+Xs+Jn/WAcFirevT7ZFqNgkoXys+ricZ3XhoLK9BI9GjcVkaj4HQWqx78gI4TB1epEzOgE4FN4lje80HCuzSj48vjWDP4yXLpOr48jp0PfUzO9qNc+s0MYvp3JH3FLvIPnmTLuDfoeCaIdEb/4ElWD3ocza3iFxNG/xWzyVqyFdxqaSJFodns8ey55TnsKdl0/ktPU3y41E+Iu6M/rtxCtvScQvSQXjR+fCQHJ74BBoWW797Pofvepmh/IsbwIDSnu3wBFUHLl8ax45YXsCdn0X3xbDIXb6XIy5euc0d/nLlFbLj0AWKH9qLZE3ewd8JbFB48yZarHkNzq5hjwrhk5Rwyl2yj+FgKmwc8UpJ/713zyPizfNCspAz9OxLcJI6/ej1ERJdmdHlpLCuufapcui4vjWPbwx+Tvf0ovb+eQVz/jqSu2MWh9/5g3xz9xUaz8YNo/eAwdjzyaaV6CAX/O++n6GWPXX/2PZzby9j19StwrPCy6yMmUfzKY5j7eez6zLsRIR67/lQldl1RaPLi3ey79VkcKVl0WPQy2Uu2YPVqv9jbB+DKK2RHr/uIHHIZDR8fxeFJr5P5099k/vQ3AAGtGtDqs0co3pcABoXGz41jR58HcGUX0PCJUTR9eSJ/XzXzgj0LoWq0ef5OMlbuZMddbyBMBgwWv5L8TnzwJyfe1+tmcNp3MqgkuWDIqXD/DEOBNhXtEEJcsGBe2bxqmvf5nkPDq7py5Ed9dEf69mOYQwKxxISVS5e+/RjW9NxK84nu1JT8hDSsieloTjcpC9cTe3U3nzSxV3fj1A9rAEj9bRNRvduWbE9ZuB7V4cKalEHxiVTCujQjqHldcrcfRbU60Nwq2esPEHdtDwCKjiRTdCzFJ/8Gg7pyzFOWjO3HMIeWL4slJgxzsIWM7boTeuzHtTQ4c56ahinYAoA5OIDitBw9r61HcOQVe/I9SkB8REl+9b00M6vQNAVbyKxI04tG11/CiV82AJC9LxGr5w1P7qFTGPzNYDJVWPcAfu1b4kxKxnUqFVwuChetIrB/T580ti270Gx2/f9dBzDERlWaX2WY27bCdfI07tMp4HJRvGQlAX16+aSxb9uJZtd17HsPYIyN1ne4XODU384JsxmUms2X9u/QAmdSMs5TqeB0UfDnaoIGXOqTxnU6HfvhhPLOhKYh/MwIkxFhNiGMBtyZueU0gjo3w5aQgj0pDc3pIvuXtYQP6uGTJnxQdzIXrAQg+/cNhPRuD4ClRX3y1+qBKldWHq68IgI7Nq1R2c6gNGyBmpGClpUGbheu7Wswtr+0XDq/a0fiWPYjmrP8W86zxdi8NWrKadQ0vS0da1dg7tHbN5G1uORf4Wc5b01z21Y4va+fpSuxVHH9OPYcwBATXbLP1Ko5hohwbJu21VjT0LglakYyWmYquF04t6zG2KlXuXR+Q+/UR9w4HaUbHTbcR/f5bqsGU+tWuE8l407Wy2hdtgK/3pf5pHHs2AlnyrhvP4bo6HL5+Pfrg33j5pJ0VRHQqTmOxBQcJ/XrN/e3NYRcdYlPGuepdGwHE9DK3COa04XmcAEgzCYQNXMlarteAQxNWqGmnUbLSNE1N63E1KWMps37mvUv+V+p0xDX/h0AaAW5aEWF+lvuajC2bI07+TRqqt6e9lUrMPf0vU+04lJN/C1wportNlz79qA5al7O2tarKd06tSc0JPiC5Wdo1go1LRk1XW9Lx/oVmLr73ie+9se/wodF82UDcK5fWWPdxld15eB/9D47bccx/EICCajA50nbcYziCnweZ6G15H9TgF+F5/RP+QU+5RjaixO/bqy0nN5cKD+vKhpd1ZXDXhqV1Wv69orrtfBUJtkHTqKpVQd64wd15eQP+gN+zvajmEIC8Cuj4xcThjHIQs72owCc/OFv4j11W3gkmcIyviOA2+NnAhj8TRW2a3DnZlhPpGJLSkdzushYuI7IQb5tFjmoO2k/6C8BM37fSHjvdgCE9+1I0f5EivbrwRJXTiGoKmUJ6dIM64k0bB5fOm3heqKu7u6TJvrqbqR4NNJ/K9VQvcqgVFKGiMvbY01Iw3Yqs9y+M9S5uiuJC/Q6zvbUsX+ZOvaPCcMYbCHbU8eJC/6mztVd9bJ53SOGSu4RbwxNy9j1jSsxda3GrnuyVOp62fX8XLTiyu16UOdmWBNSS/y8zF/WEjHIt27Dr+5B+g+rAMj6fQOhl7cvl0/Ujb3J/EUfRSyEAAGGAL2v8W8UhyMt54I+CxmDLUT0bM2pr3U7pznduPKL+f+G9i/++2/lfz6wJIQIFEL8IYTYJYTYK4S4VQjRXwix0CvNlUKInz3/FwohXhFC7BNCLBNC9BBCrBJCHBdC3OBJM0YIsVAIsVQIkSCEuE8I8aAQYocQYqMQIsKTrqkQ4i8hxDYhxN9CiFZCiF7ADcArQoidnjSrhBBvCiG2ArOEECeEECZPHiHev73OOVoI8R8hxBbP32We7U8LIb4UQqwDvqzgdyMhxAohxG4hxHIhRAPPcZ8LIeYJITYBvq/KzpLAuHAKk0tHjBSlZBMYF372+cSHU5hS+obbmpyNX1yETxr/+Ahsp3Utza3iLLBiigjGLy4C6+nSc7ClZOMfF0HBwZNEXNIKU3gQisVM9MBO+NeNrPQcAuLCKSpTloAyZQmIC6fI6zy902x+6iu6PX47w7e8Rbcnbmfb7PLTOprf1pfTK3f75FfspVlciWaxl2ZFaWIuaYk1I4+CE2nlNBtc210fRl9FMMEYE4krNaPktystE2NM5YGjkGFXU/x35UPNK8MQE4U7zUsnPQNDFTpBQ67Bur70zZghNpq4bz+izh/fkj//++pHKwHG2CicKV6aqZkYYyu/Dryx7TxI8abdNP37a5r+/TVFa7fjOH6yXDpzXCQOr3Z0pGRhio+oPI1bxZ1fjDEimKJ9CYRd1R0MCn71Ywjs0BRzndI6afLGfbRb+hp1pg6v9DyVsEjU3NIyqrmZiFDfMir1miLConDvLz+CRYmMJWDGW1jun42hSduqK8WDiIjCnVk6xUzNykCJLN+WftcMJfT9b7DcOYnij0un1hhi4gl57WOCn38LY+sONdI0RPteP+60DAzRlV8/gUOuwXbm+hGCsKmTyH3r7KbhiLAo1OxSTS0nAyWsTN02aIYSHo1rT+VvcWuKIToKd7pXvWZUXcaA6wZj37Sp3HbLgH5Yly2vkaYpNhJncumDgjMlC1MN7xEAU3wUzRfNpfWGz8iY92P1o5Wo/XoFEOFRaF6aanYGIrx83ZoHDCHolS/xv2UCtq/00R/uk8cwde4FioKIisPQqAUiIqZaTSUyCjXDqz0zM1Ciymv6Xz+U8M++IfCuSRS+d+5T0Gpb72KhREShZpWxPxW15VVDCX7rKywjJmL9vPzUV1PPvjjW1+w+AQgq4/MUpmQTdJY+T/s7BzJq7Wv0mnkba578otz+2vALGl1/CQkLywecKuJC+XnVaVTnf10I/OPDsSZ7+Zop2VjifXUs8eHYUnzT+MdXfy7hnZvSb/Uc+q18mSMzPvIdrQT4xUdg9yqjPSUbc3xkBWk8ttit4irQ/YSAJvGgQbtvZ9F5ycvUm3xDxeWLi8DmrZGchV+ZevSLj8Du5Uu7CooxRehB35Auzbhk9atcsupVDk7/uCTQdIbYG3uR9nPVU6stcRE+129ldVyuHbx8/raPDmfw1rk0GNaLfa9UPK3/DDW26wOHEPTql/jfNgHblx67nnRMf7mgKIjoqu26X1wEjtOl/aQjJRtzXGT5NF7td8bP8ybqhsvI/FkPvGkuN8cf+ZCOK16n286P8W9Wl4Jth0vr5QI8C1kaxODIyqfDW/dw2bLZtH99gh6w89Bw3CB6r3yZ9m9OBLjwN53k/y3/84El4GogWdO0jpqmtQP+AlYCrYQQZ17pjgXOjKkMBFZomtYWKACeB64EbgSe9cq3HTAM6A68ABRrmtYZ2ACM9qT5EJiiaVpX4GHgPU3T1gO/AtM1TeukadqZsdZmTdO6aZr2DLAKODPp/TbgJ03TykYA3gLe0DStO3AT8LHXvjbAQE3Tbq/g99vAfE3TOgBfA3O9jqsH9NI07cHKKvN/naIjyRx751d6fD+THt8+Rv7exHKd5IWk5egBbHn6axZ0f4Atz3zNZa/d7bM/rldrmt/eh20vfldJDudO46E9SajgrWRoi7p0nXkbG6oaRnyWBF3XH7+2zcn9rOrO/nwJuGYg5tYtyP+idK0nd1oGqbffTcrQ0QRedxVKxD/bB5oaxGNuUp9jfUdxrM9IAi7tiKVrzQIvNSXju+U4UrJo99crNHx2HIVbD5a8iTx635vsGTCN/UNnEXJJa6Ju7ntuIkLgd+Nd2Bd+Um6Xlp9N4VNjKZ7zAPafP8b/zof1UQwXCPuiheTdcwfWLz7AMlw3l2pOFrkTbiH/obso/vRdAh98AiwBF0wTvK6fL/XrJ+jmG7Ct24w7vfI3reeEEPjfMhHbgg8vbL41wHLVQEytWlL4jW8QW4mMwNikCfZNZx/8PRecKZkcueZ+DvaZQPhNAzBGhZ1/phexXh3Lf6Fw+ihsP3yE3w0jAXCuWYSak0HQ0+9jGXEvrqP7KhwxcK7YfltIztg7KP7kAwLuGF39Af9jehcLx5KFFDwwEus3H+I/bJTPPkOz1uCwo55MqNVz2jN/GV/2fogNs7+j+/1D/zGdyvyCqM5NcVkd5B46VcFRknMlZ8cxVvaZweqrH6f+/Tci/CofJX62CKOB0EtacXDyXHYNeYKoay4hzDPS6EKSv/0om/o8zJZBM2n4wFAUrzIIk4Goq7qS/lvNRrqdD/teWsCf3e4n6af1NBt71QXJ07HsFwofHoXt+4/wG+Kx66sXoWZnEPSsl13X/rnnhKDOzXFb7RQf0l9QCqOB2DsHsevKh9na6S4cpzIJbNvwgmoqRgMh7RuTOH8p6wY+hqvYTpMp+npPifOXsuqS+1nb/1Hs+iyH1y6ouOT/Nf+GNZb2AK8JIV4Gftc07W8AIcSXwEghxGdAT0qDQQ704NOZY+2apjmFEHuARl75rtQ0rQAoEELkAb95HdNBCBEE9AIWCFEyPcePyvF+CvgYmAEsRA963V1B+oFAG6+8QzyaAL9qmmb1Suv9uyd6QAzgS3xHJy3QNK2CCdoghJgATAAYGdaDKwKb++xvc+dAWt3RD4CMXccJqhPJmfdhgfERFKXmVJRtlRSl5BDkNcLDUicCe6rvW29bSjb+dSOxpWQjDAqmYAvO7ALsqdlYvEYi+cdHYPMce+qblZz6Rh/+2WLmbT5vcgD84sK54p17cdscZO48TmCd0nwC4yMoLlOW4tQcAr3O0ztNs+GXlyzknfDbJnq9Urp+THjr+vR65S6WjXqFxjdcSosRev1l7TxOgJdmQCWa3tPnyqYRBoUG13Tnj2ue8DkuID6Cfp9MZe0D8yhMTK/yPYQrPQtjXOl0GmNsFK4KHsAtl3YmfMLtJI95uMoRUJXhTs/EEOulExNd4YO+X48uhI67g7QJD1ao487MwnnsBH6d22NdvqZKTVdaJqZ4L824KFxp1Y90Agga2AvbroNoxTYAitZsxdKpNdZt+3zSOVKzMHu1ozk+EmdKdoVpHClZYFAwhATgyi4AIOnpz0rStfn1RWzH9MVcnZ7rWC2ykfnz3wR2bkbmj6vKnaeam4UprLSMSlgUWp5XGf0sKPENCJgyGwAREo5lwhNYP3wO9eRRcOnnoZ48hpqZihJdV99eBVp2Joao0jd7SmQ0alblQRvH2uUETJym/3A50Qr0dnUfP4yaehpDnfq4jx2qUtOd4Xv9GGKjcWdUfP2EjL2D9Iml14+5Qxv8OrUn6OYbEAEWhNGIZrWS987H5Y73KWduJkpEqaYIj0bN9apbfwtKnUYEPvyKvj80goD7nqX4nSdRE49UmXelZYzxqtfoisto7taFoNEjybpvarl7xL9/P+x/rwV3hSa+HM60LExeo+RM8ZE4a3iPeONKz8Z2OJHA7m1KFveujNquVwAtJxPhpalERKPlVH7NOjetxHLnA1gBVBXbN++X7At8fC5qavUP5mpWJkq0V3tGRaNmVq5pX7WcwCnTqs33v0XvYqFmZ6JElrE/VbXl+hUE3DXVZ5upVz8c61ZUq9X+zoG0uV3vs9M9Ps8ZguIjKDwHnwfg8C8b6fPC2BKN9rf/834BQKMhl1Y4Pc6bf8LPK0vbMhrV+V/no9P+9r4A5Ow8jqWOl68ZH4E1xVfHmpKDf7xvGltKzc+l8EgyapGNwFb1Kdx1vGS7PSUbP68y+sVH6P6AF3qaKBwp2WBQMAbrfoI9OYu8jftLfIbs5dsJ6tCE3LV7fY63pWbj761RJxJ7mXq0p2TjVzcSu8eXNgYH4PTke4biI6dxe8pQ4ClD5IDOFOw5gSOj/AdEmo65ksYevzZ7l379nilZZXVcrh1Sy490TfppHb2/ms7+V/9Tbt8Zztqub1yJZYyXXf/ay64/ORc1pWK7bk/Nxly3tJ80x0fgSM0qn8ar/bz9PICooZeRubD04zCBbRvpxyXqd1f2ki00mH5byf4L8SxkTc7ClpxNnmfaYepvm2g6RR/x5t2WJ79aQbOpN/qu4SCRnAf/8yOWNE07DHRBD/g8L4Q4syLfZ8BI4Hb0gIrLs92plS4YoQJ2Tz4qvoE270UqVK/fZ9IpQK5nVNKZv9ZVnGqR1zmvAxoJIfoCBk3T9laQXgEu9cq7rqZphWXzquR3tedQFk3TPvSMqOpWNqgEsH/+Mn4aNIufBs0i4a9tNL9ZX78hpktTHAXF5zTHPmPXcUIax2FpEI0wGYgf2ou0xb7roKQv3ka9W64A9K+6Za3VH+7TFm8jfmgvFLMRS4NoApvEkesxoOaoEAD860YSN7g7yT/5DuG1p+aw5r73+PWqWSQt3kZTT1miuzTFkV++LNb0XBwFVqK76GvgNL25N0me8yxOyyGup97s8b3bkn8iFYDAOpH0+2gqfz8wj/zjqRycv4zfr5rF72U0o7o0xVmJprPASpSX5kmvuom/vB15R5N9hsWbQgLo/8VDbH/xezK2Vv8QZt97CFODuhjrxoLRSNA1fSla6ftWytyqKdFP3U/qfU/hzq74y2TV4dh/EFP9uhjqxIHRSMBV/bCu8X0ANbVsRsTMaWQ8+ARqTm7JdkNMFMLPDIAIDsKvY3tcCeWnpZXFtucwpoZ1MNWNBZOR4MF9KFxRszdurpQMLN3bg0EBowFL9/bYK5gKV7jzKP6N4/GrH4MwGYkY0pucJb6jRXKXbCFquO54RVzXs2RdJcViRvEspBhyRUc0l1tf9NuglAyhFkYD4QO7YT1Y8UKratJhlOg6iIhYMBgxdrkC1x6v6VG2YopmjqDomfEUPTMed8KhkqCSCAopWRdHRMaiRNdBzUqtvm6OHESJr4cSo7eluXd/nFt87y8lvm7J/6auPUscNhESCoquqcTGY4ivh5pW/stIZXHsP4ipgdf1c2UF10+LZkQ8No3Mh3yvn+wnZpNy/R2kDBlB7lsfUPTn0mqDSqB/JU+JqYuIigODEVP3Prh2eT2UWYspfHA4hY+NpvCx0biPHziv4Ifz4EEM9etiiNfLaBnYH/s63zIamzcjdPqDZD86CzU3t1weloH9sS6t+fSe4l1HMDeqg6leLMJkJOz6K8hfWrPpZ6a4yJL70hASSGC3NtiPn67mqNqvVwD3iYMYYr00L+mHc4dv3SqxpdesseOluNM8ZTH7gVlfB8PYtiuobp/FYSvDdegghrr1UGL19vTr2x/HxjL3SZ1STXOPnrhPn/tIktrWu1i4jx1EiauLEq23pblXf5xby7RlnFdbdr4Ud4rXdSkE5p59ca6vPrC0Z/4yvr96Ft9fPYvji7fR6ia9z47trPs8Fa35UxmhjWJL/m80oBN5CaklGv+0X3Cm3I2uu6TCkUze/BN+Xln2zV/GfwbN4j8ejRZlNM6mXqvTWTVwJqsGziT1r63Uv+VyAMK7NMNZYMVeRseenour0Ep4l2YA1L/lclLK+KNlCWgQjTDofZqlXhSWZnWwnczwSVOw8yiWJvH4N9D9hOihl+kLfHuRtWQrsbf0ASD6ukvJXac/EuSs2kVAqwYoFjMYFEJ7tvFZ9LtEY8cxAprE4e/xpWOH9iJzsa9G5uKtxHs0Yq6/lByPL+3vVQb/elEElilD3I2XkfZzxS8Mjn2+lGVXzmTZlTNJXrSVhsP1Oo7w1LGtTB3b0nNxFViJ8NRxw+GXk/yXXsdBjUvvkTqDulJwtPyaVt64jx/EEFcX4bEFpkv74dxehV3vdCnuVC+77llLz9iuq/6l1UrseuHOo1i8/LyoIb3JLlO3OYu3EHNLXwAir+tJnnfgTwgir+9F5sJSe2xPzSagRX2MkfqzijkuAmEyXNBnIUdGHrbkLAI9X4qMurxdyWLg3uuLxQ7uDlDRM+i/AlX8e//+W/mfH7EkhKgDZGua9pUQIhe4C0DTtGQhRDLwOPronwuKpmn5nrWRhmuatkDoQ4s6aJq2C32KXXUrVn4BfAM8V8n+JcAU4BUAIUQnTdN21uDU1qNPr/sSGAH8XYNjzoqTK3ZSv39Hbl37Gi6bg9UPlk5bGLb4BX4aNAuAHrNuo+nQXhgtZm7fMpdD365i++s/laTV3Crrn5jP5d/NBIPCqW9XUnjoFM1nDCdv13HSF2/j5Dcr6fjOZPpsfBNnbiE7Juoz+woPnSLl1w1c/vdraC43+x79DDwLOHb55EFM4UH69sc+K1mwLvaa7rR5cQzmyBAGfvEw2fsSWTpiDnX7d2TYutdwWx2s9SrLDUte4Ner9LJsnPk5vd+YoH/id+UuTq/QP026fvon9Hh2FIpRwW1zsmGGPu2o47Qb8QsPoueLYwBQXW7+9HyF5PTyndTt35Eb172Gy+pgvZfmdUte4HeP5qaZn9PrjQkYy2iC/vaxrJPYauyVBDeKpcO0G+kwTf+KU/490ysPCLlVMl98l/gPXkQYFPJ/XoLzWCLhk0dj33eY4lUbiXzobkSAhdjXHwfAlZJO6pSnK784KtHJfuVtYt5+GQwKRb8uwnk8kdCJY3AcOIR1zQbC75+AYrEQ9ZJeR660dDIffAJT44aETZ2kL+QoBPlf/YDz2IkaaaY/9z71PnkeFAN5/1mC42gSkVNGYdt7mKKVm/Bv14I67zyBISSIoH6X4LpvJAnXT6Jg8VoCLu1Io1/fBw2K1m6laGX59WxwqyTM+piW3zyJMChkfLcc6+GT1J1+G0W7jpG7ZAvp3y6n6dwH6LjuXVy5hRy953UA/Utw3z4JqoYjNYtjU/TrWjGbaPXNkwijAQwK+X/vJv3rZRWXUVWx/TiPgHuf1T+3u3EpamoS5sEjcCcdwb238iCBoWk7zINH6KNbNBXbD+9CcWGl6Us13RR/9CbBT70KioJ9+Z+4TyZguX0crqMHcW5Zj//gYRg7dAW3C62wkKK5+ogpY5uOWG4fB24XqBpF815HKyyoRlCv55w5bxM992WEQaHw10W4jicS4rl+bGs2EPbABITFQqTn+nGnppP5UPm39jVGVbF98w4BU19ECAXHusWoyYn43TAad+JhXLuqDlIGzf4CYQkAgwlj514Uv/FY+S+flSlj/utziXh9DigK1j8W4TqRQND4sTgPHsK+bj0hkychLBbCn3taPyQtjZxH9fvSEBeLISYax85dlWtUoJn85DyafPEMGBRyfliG/UgSsdNGYN1zhPxlm7F0aE7DD2ZiDA0iZEB3YqeN4PBVk/FrVp9Gs8aVZJXx0c/YDlUfcKn1evVoWr98m8DpL+v3yZpFqKcT8btxDO6EQ7h2bMA8cCjGtl3A5UIrLsT60csAni8GvQyaipaTSfEHs6svI4DqpvDdNwl9Ub9PbEv+xJ2YQMDocbgOH8SxcT2WG4Zh6tIVXC7UwkIKXy3NO3z+d4jAQITRiLlnb/JnPuzzhbeLrldDpj/1Elt27CY3N58BQ0dy7/hR3HT9oHPPUFWxfjqXwJn6feJYtQj1VAL+w8fiOn4I17b1+A26EWN73f6oRQUUv/dSyeHG1h1QszL0xb/PgsQVO2nYvyOj1up99vKHSvvsW/96ge+v1vvsXjNvo8XQXpgsZsZsnsv+b1ex+Y2f6DDmKur1bovqcmPPK2LZtA/KafxTfgFA7KWtKErJpjApo9y+yrhQfl5VJK3YSYP+HbnNo7HKS+OmxS/wH4/GJbNuo5lHY8SWuRz8dhXbXv+J6I5NuOrjqfiFBtDwys50e/AmFgx4tJxO2rKdxA7oxMCNb+C22tkxtbT++y57kVUDZwKw+9FP6fzWJAz+ZtJW7CJ9+U4A4q/pRvsX7sQcGcIlX80gf28iG25/iYgeLWk+5Qb9YwaqxtFHP/YZqQKAW+XozE9o9+0shEEh9duVFB86RcMZt1Kw8xjZS7aS+s0KWr0zhe4b3saZW6h/EQ5w5RVx+oPf6fzXS6BpZC/fQfay7eXKp7lVDj32KZ09vnTKt6soOnSKJjOGk7/rOJmLt5H8zUravHMfPTe+hTO3kL0T9TXWwnq0ouGUIWguN5r6f+zdd5jUxP/A8ffs7u313ulwR++9Kr2qFBUbKghKEQHpCNgVBLuiIjbsvRekd5AqvbcDrve6d7ub5PdHlrvd6+B5+vM7r+fh4XYzySeTZJLZyWSicWLee4U9mQxe7gRd35Ljsyp+NDlh/QEi+rZh0M6XUCxW9jod4/3WLmJdf30b//nIB3R4ZQJGDzMJGw6S4Dh+Wyy4A9+oSDRVI+9yCvsrGspBVbF8VMp5/eYxKOcd5/X+jvO6YkfLzcGywum8PmcJqI7z+vJyzuuKyrn579Ls80cRRgOJX2zAcuoStWffQc7BM6Sv2Uvi5+tp+PpU2u5Yhj0jh1MTXy6c3a9LM6xxqRRcLBrzzJaYzqWXvqLF90+j2RQKLidzds5yOlXxb6Gj8z+gzZsPIcwm8mKSODRNH2uyyWOj8GtRF03TsOiNiP//uq9K/1qi+Nte/r8RQgxEb3xRARswSdO0vY5pdwAPa5rWxSl9jqZpPo6/nwByNE17wXmaEGIM0EHTtIcc319wfE5xniaEqA+8BUQCbsAXmqY95Rho+x30Xk63Au8Bs66sl2OZEcB5IFLTtIxS8hUCvAE0RW8A3KJp2sRS1rn457rovbVCgGTgPk3TLgohVqI/KljhIDnv1Lq7Wg+KmjZ7xYmqWJKp+ttU/4nugd0DK1+RrApuHtW/Ly055mqPmZFVdeMRVVazW6v+rU0VsV2qRMNPFcq9XP3l0r+Nsdpj5h7/62/ou1rJl30qTlTF6vWv+O10VUmzVu4xwKpkS6j+c94/wf/TDypOVMVyJ4+rOFEV+mRHzYoTVTG/v29olzIV/AN3u/+BbBJhr96ogVT/ed2mVX/NMt1Q/dfp/v0q7lFd1Y6uC6jWeBlq1Y3PdTWGJH7xL+7/8td8VLN6f89Wp3tjP/lX7rf/9z2WNE1bDawuY3IP9AYe5/Q+Tn8/Udo0TdNWAiudvq/n9HfhNE3TzqMPHl58nbajD6h9Ra8y1u2b0hqVHMtIAW4v5fvi61z8cwzQp5T5xpQWR5IkSZIkSZIkSZIk6Vr9v29YKosQYh/6mEIz/+l1KU4I8TowGBjyT6+LJEmSJEmSJEmSJP1X/BM9Jf/X/WcbljRNa/9Pr0NZNE2b8k+vgyRJkiRJkiRJkiRJ0l/1//6tcJIkSZIkSZIkSZIkSdI/QzYsSZIkSZIkSZIkSZIkSddENixJkiRJkiRJkiRJkiRJ1+Q/O8aSJEmSJEmSJEmSJEn/W7R/egX+B8keS5IkSZIkSZIkSZIkSdI1kQ1LkiRJkiRJkiRJkiRJ0jWRDUuSJEmSJEmSJEmSJEnSNZFjLEmSJEmSJEmSJEmS9J+gin96Df73yB5LkiRJkiRJkiRJkiRJ0jWRDUuSJEmSJEmSJEmSJEnSNZGPwkklBCjV+4JGH6FUazwARan+/pEK1R/TJ6SgeuNdF1at8QDUzLxqj+m7N73aY1rPq9Ue0+vuPtUaL3vJ1mqNB2A5ba32mIqt+u/pqFr1n3+EsXrz6TburmqNB2DOSKn2mFpsTLXHzJ08rtpjer/xXrXGC2/5aLXGA/DQqv+8bvgH3sFtE9V//mnsl1Gt8SI7V299C0BTq39nph8xVnvMs5t8qj1mm3mh1Rrvm6U51RpPkv4OsmFJkiRJkiRJkiRJkqT/hOpvtpfko3CSJEmSJEmSJEmSJEnSNZENS5IkSZIkSZIkSZIkSdI1kQ1LkiRJkiRJkiRJkiRJ0jWRYyxJkiRJkiRJkiRJkvSfIMdYqn6yx5IkSZIkSZIkSZIkSZJ0TWTDkiRJkiRJkiRJkiRJknRNZMOSJEmSJEmSJEmSJEmSdE1kw5IkSZIkSZIkSZIkSZJ0TeTg3ZIkSZIkSZIkSZIk/Sdo4p9eg/89sseSJEmSJEmSJEmSJEmSdE1kw5IkSZIkSZIkSZIkSZJ0TeSjcFK52jx9L5F9W2O3WNnz8NtkHL5QIk1Aq3p0emUiRg834tcf5MCjH7lMbzRhCK2fGMWPzSdgTcuhxsD2NJ9zK6gaqqJw8dEPyNp9gsDebYh6+j6E0UDCp+u5tOwHl+UIs4nGr0/Bt1UDbOnZHJ/wMgWXknGvHUqHLa9gORsHQNa+U5yZ+47LvM0/nItH3TD29Zqpf35mNOF926BYrByY9haZpeTLv1V92rw6EaOHmcT1Bzi68EMA3AK8af/2NDxrh2C5lMK+8a9iy8wluFtTOq6cRd7FJADif9vD6Ze+A6Dvntew5VhAUVEVlc0DFxbGafnMvYXrsn/a8jLXpd2rEwrX5fBCfRs3f+wuIvq3Q7XZyb2QyJ8Pv40tK6+Mvakzd+qE70MPgdGI5ddfyfvsM5fpXiNH4nnDDWiKgpqRQdbSpaiJiQD4TJiAe5cuYDBg3buX7NdfLzfWFcbo1phvuA+EAfu+9di2/ugy3dS2J+aB96BmpQFg3/U79n0bAHC/dz7GWg1RLp6g4JMllYoHYGzaDo+bx4PBgG3nGqzrvik1nal1NzzHzSf3+YdRL51BBIXhPf8t1KRYAJQLJyn46o0K47l37oj/w/p2zfv5V3I+/txluvcdI/G6aQgoCmpGJhmLlqIk6NvVGB6G/yOzMIaFgaaRNnNe4bTyuLXrhPcDU8BgIH/tr+R/47ov3QcNxeOGEaAqaPkWcpe9gHIpBlPDJng/NEtPJASWz1Zi/WNrhfEAtp+OY+lv+1A1jRHtohh7ffMSaVYfieHtjYcBQaOIAJ4b2R2AV9b8ydZTejkd37MFA1vWrVRMj24dCZr1IBgN5Hy/iqyVX7hM9x11Cz4j9G2rpGeQ+uQLKPFJGCPDCH3hSYRBgMlE9hc/kPPtL5WKWd3lxL1LRwJmPIQwGMj96TeyP3I9fnzuvBXvYUPQ7Prxk/7M8y7HiPD2IvyLD8jfvJ2MF16rVB59e7aj5uP3I4xGUr9YQ9Jb37pM9+7UnJqP349nk3pcmPI8mb/tAMCtZij1V8xHCAFuJlJW/kLqp79XKqaxWXs8bpukl8vtv2Nd/VWp6Uxtu+M54VFyF01BvXgaQ71GeIya5siswPrLJ9gP7KhUTGfbj5xl6RfrUFWVEde1Yezgri7T41MzefSDX8jOK0BVVabe0ovrWkZffZwTl1j6005UVWNEp8aM7dPGZfrzP+1kzxm9LOTb7KTl5LPt6dHEpWcz48O1qKqGXVW5s3tzRnZtVnG8Cyk8v/kEqqoxvEUtxnasXyLNmlMJLP/jLAJoFOrL4sGtAJj8/T4OxWfStmYArw1rV+k8mlp3xHPMQ2AwYt3wKwU/uh6z5n434T5wOKgqWr6FvBUvosbG4NajHx433V6YzlCnATnzxqPEnK107NIsXPQSW7bvJigwgB8+Wf6XlgV/rf7TfM6t1BjYHlSN/NQs9kxbTn5iBo0m3UDdm/XzoTAZ8GtUi7xLySi5+VVWFwkf2J4mc29DU1U0ReXoox+Rtvskfs3r0nLJWNx8vNBUlTOvfE9gp8aEOeofB6e+RVYp8f1a1af1a3r8pPUHOLagKH7bFdPwqh1C3qUU9j/wKvbMXLyja9D61Qn4tazPqcVfcu6tX12W93fUeQLaRtH2+XH6zEJw4oVvYfu6Esv1vq49YQsmIIwGMr5eTdqKr12me3ZoQfiC8bg3rk/c9OfIXr0dAK/OrQib/0BhOnOD2sRNX0LOup0lYhRnatkRj3sm6+e8Tb9R8Ivr9cvc50bM/YYVlhPL+y+jxsWA0YTn2OkY6zcCTcPy8RsoJw5WGA/A1Kojnvc8pF+DNv1Gwc/Fymbfm3DvXxQz772XUGNjwGjE6/5ZGOs31Mv1tjUU/PR5GVGKeHbrQNDcBxEGA9nfryLz/S9dpvvdcwu+IwY7rtGZpDz+Avb4JMyNowheMBWDjxcoKhnvfkbu6s2VyqNfr7bUeuIBMBpI/XwtiW+6Xr98Ojej1uP349m0Hucnv0DGb0XXi6iPH8e7bSNy9xzn7H3PVCoegKFuM8w9b9Prske3Y9+72mW6sWlXzD1uRsvNAMB2cBPKUf0Ycus+AmO9Fvr3u39DOb2v3Fgdn7qHmn3aoFgK2D59BWlHLpRIE9SyHt1f1stJ7IYD7HnsYwCuf+sh/KIiATD7eWHNyuOXAQsQJiPdXrifoBb1ECYD577ZVum8S1JlXHXDkhBiKjAJ2K9p2qi/ElwIMQZYo2laXAXpVgK/aJpW+i9DPU09R5oWQogOwL2apk39K+t3LYQQOzRN61bdcf8OEX1a49MgglXdZhLULpp2z93HhhseL5Gu/XNj2TvrXdL2n6HHp3OI6NOahA36xc+zRhDhvVqSezmlMH3i1iPErdZPqP5Na9Pj7Sns7TmD6MXjOHzb0xTEp9H298WkrtlL3qnLRetzVx/sGTns6TqF0GHdqL/wbk5MeBmA/JgE9vebXWo+god0QsnNL/wc2LctPg0i2NB1OgHtomm5ZBzbhjxaYr6WS8ZycOY7ZOw/Q+fP5hLWpzVJGw4SPWUYKVuPcGbZT0Q/NJToKUM5/ox+4U3bdYLd9zxf6npsv+VZrGnZLt+F922DT4MI1nWdQWC7aFovGcuWIY+VmLfNkrEcmPku6fvP0PWzOYXrkrT5MMee/QJNUWm28A4aTh3KsWe+KDF/IYMB32nTyJg1CyU5maDlyynYvh0lJqYwie30afImTICCAjyHDsV3wgQyn3oKt+bNcWvRgtRxeuUt8PXXcWvTBtuBA2XHAxAC803jyF/5DFpWKh4TF2M/sRctOdYlmf3wDqy/vl9idtu2n7C7uWPq2K/8OC4xDXiMnETeGwvRMlLxmvUy9iO7UBMuuaZz98St51CUCydcvlZTEshbehWnD4MB/1nTSJ02GyUpmdD3lpO/dQf2C07b9dRpUsZORCsowGvEUPwenED6Y08BEPDoI+R8+AkFe/YhPD1A1SoV03viw2Q9OhM1NRn/l97Gtms7yqWimNbN6yj4/ScA3Dp1w2vcZLKfmIP94nkyp08AVUEEBhHw2vtYd+8AVSk3pKKqLP5lL8tH9yHcz5NRb6+mZ5NaRIX5F6aJSc3i/S3HWHn/APw8zaTl6GVvy8lYjsel8+WkwdgUlXHvr6N7wxr4eLhVmM+guVNIenAu9sRkIj95A8vmHdjOXyzK58kzJNz9IFp+AT633kTgtPGkzHsGJTmNhDFTwWZDeHpQ4+t3sWzeiZKSWmHMai0nBgOBs6eRPEU/fsJWvoVl6w7s552PnzMkjZ6EVlCA981D8X9oPGkLny6c7jfhPqx/Hio/X8Vi1np6AmdHPYYtIZVGP71I5rrdFJwuKiO2uGQuznyVsPHDXWa1J6VzesRsNKsdg5cHTda8Tuba3diT0sqPKQx43DmZvFfno6Wn4PXIa9gP/YEaf9E1nbsnbn2Go5w7XviVGhtD3uIpoKoIvyC8Fr6J/dAfoKqVzrKiqiz+bA3Lp99BeKAfo55dSc/WDYmqEVKY5p1fdzCgQ1Nu69WOs3EpPPTaV6x67uoalhRVZfH321k+fgjh/t6Meu0HejavS1R4YGGa2UOLGrQ+33aEE3H6MRnq68VHDw3DbDKSV2Djlhe/oWezuoT5e5cTT+O5jcd56+b2hPt4MOrzP+jZIJSoYJ/CNDHpuby/5zwrb+uEn4cbaXkFhdPubV+PfLvCt4cvl7b40gkDnmOnkfvsbNTUZHwXL8e2d4f+49TBun091nU/A2Bq3w3Pex8kd/FcbNvWYdum/+g31K6P96yn/3KjEsDwIf2565ahzH/6hb+8rL9a/zn55q8cXapXWaPHDaTZjJvZP/d9Tr31K6ccDS3NZt5Mw7EBbOg8rUrrIilbj7DZUcfybVqHDiumsvG6WSiWAg5MeQvLuQTcwwO5fvMSMg9dYFOX6QS0j6bF0nHsGFxK/KVjOTzzHTL2naHjZ3MJ7dOa5A0HiZoyjNStR9j9+k9ETdHjn3jmc2wZORxd8CERgzuUWNbfVefJPnGJTQMXoikq7mEB9NmwmPPXbQDF6fxgMBD++INcum8BtoQU6n37Cjnr/8B6tuicZ49PIn7eSwSNu8VlffJ2HeLCsCn6Yvx9iFr7Hrnb9pdY7xKEAY/RU8ldMgctLRmfp97Etn+n3nDkYN2xAesG/YaHqW1XPEZNJO/5RzD3vgGAnPkPIPwC8J61mJzHHwStgvqBMOA5Zhq5i2ejpiXj+/Rb2PYXK5s71mNd7yib7brhOWoSuUvn4da5J7i5kT3vfjC747f0A2w7NqCmlHOjy2AgeP4UEibMxZ6YQo3PlpG3aSe2c07X6BNniLtrMlp+Ab4jbyRw+gMkz3kWNT+f5IVLsV+MxRgaTI3P38CyYy9qdm75eTQYqP3MBE7f9Ti2+FQa//ICmWt3k+90/bLGphAz41XCJowoMXvS8u8xeLoTMmpg+XGcCYG5150UfP8qWk46Hnc8gnLuEFpavEsy++l92Da51sMN9VpgCKtD/mfPgtGE+60zUGKOgjWf0tTs0xq/+hH80GMmIe2i6Lx4DKtueqJEui6L72PnnHdJ2X+Wvh/PpkbvVsRtPMSWScsK07R/7K7CG871buyEwWzi536PYPQwM2zTEoB6wIXKb4j/PypfO5CqyrU8Cvcg0L94o5IQ4lp6P40BalzDfOXSNG3vP9Go5IhdbY1Kxbd5ZfdBZdPVGNSemK/1Xgxp+89g9vPCIyzAJY1HWAAmX0/S9p8BIObrrdQY1L5wepsn7+HQ05+7XAgVpwqt0csdNA3fttFYzieQfzEJzWYn+YftBA90rZQED+xI4lf6nYzkX/4gsEeLCvNg8PKg1oSbuPhK0Z2MkIEdufSVnq+M/Wdw8/PCvVi+3MMCcPPxJMORr0tfbSVikL4+EQPbc+mrLY7vtxR+fy0iBrbnomNd0stZF5OPJ+mOdbn41VYiHTGTNx9Gc1Sc0vedwTMyuNx4bk2aoMTGosTHg91O/oYNuHfv7pLGduAAFOj7yHbsGIbQUH2CpiHMZjCZwM0NYTKhplXwQxIw1IpGTU1AS0/S71Yd3oGpaccK57tCPXcErcBS6fQAhrqNUJPj0VITQbFj378FU8suJdK533A31nXfoNlsV7X84tyaNcF+OQ4lTt+ulnUb8LjOdbta9x9Ac2xX69FjGMP07WqqVxeMRgr26D8ENEt+YbrymBo2RYmPRU3UYxZs2YBb5x4uaTRLUe814eFZNKGgoLARSZjNFVdUHY5cTqV2kA+1gnxwMxkZ2LIum064/hj9bu9Zbu/cED9PMwBBPh4AnEvOpH29UExGA55mE40iAth+ptx7CgCYWzTGfjkOe6yez9zVm/Ds5bptC/YeRMvXt1nB4eMYwxwNBXY7OPatMJtBVO6SV93lxNysCfbLsUXHz9oNeF7veikp2Od0/BwpOn709W2IMSiQ/F17K5U/AK82DSm4EI/1UiKazU76z1vx79/ZJY31chL5Jy6UaOjUbHY0qx0AYXYDQ+W2q6FeY9SkeLSUBL1c7tmMqVXXEunch96LdfXXaHancmkrKGpEcnMDKnfMOjtyPo7aoYHUCg3Uj9+OTdl04JRLGiEg16Jv5xxLPqEBPqUtqvw4F5OpHeJHrWA/PU6bKDYdjSkz/aoDZxnUJgoAN5MRs8kIgNWuoFWibB5JyKS2vxe1/L1wMxoY2CiCTWeTXNJ8fySW21rXxs/RkBvk5V44rXOdYLzdrq4KZ4xugpoYh5oUD4od644NuHV0LSM4n3/cPUo9z5i798W2Y+NVxS5LhzYt8ffzrZJl/dX6jz2n6JplctRziqtzS3fif9sDVG1dxLmOZfJyLwydey6B3PMJABQkpoMGSev+1OPvK7/+kbFPjx/79VbCHQ1G4YPac/lLPf7lL7cUfm9NySLzwDlUW8kbFX9XnUexWAu/N3q4lXpJ82jVCGtMHLZLCWCzk/XrFnz6uZ5/bLFJFJy8UG6Dte+gHuRs2Vt4zSmPMaoJamIsWrJeTmx/bMStfbGfCfnFy4n+t6FmXezH9P2jZWWg5eXovZcqGVN1xLT+saFkzOJl80pQDYS7JxgMCLM7mt3mUo8ojXuLxtguxWGPTdCv0b9vwquXa7z8Pa7XaJPj+mWPicV+0dE7PDkVJS0DQ2BAhXn0btOQggsJWC86rl8/bcV/QCeXNNbLSVhOxIBWcl9mbz+EknOV9crwemiZSWhZKaAq2E/twdigVeXmDYpEiT2tr4vdipYSi7Fuyd7eV9Qe2J6zjt5EKfvPYvb3xrNYOfEMC8DN15OU/Xqj/NlvtlGnlN8j9W7qzPkf9Z51mqafE4TRgMnTjGqzA2RVKhOSVAlXVZMQQiwHGgCrhBDvA/5AlOO7i0KIR4CPgSu31h7SNG2HY965wN3oDYirgL1AB+BTIYQF6ArMBm4CPIEdwAStnFqVEKI9cKWLwxqn73sBszRNu1EI8QRQ37GOdYDpQBdgMBAL3KRpms2xrJcAHyAFGKNpWrwQYhOwC+gNBADjNE3bKoRoDnwAmNEb6G7RNO20ECJH0zQfIYQAljriaMAzmqZ96Vi3JxwxWgD7gLuL51MIEQW8AYQCecADmqadcPTeygfaAtuFEEHFPn8ELAe8gLPAWE3T0h35OAD0AD4HXixru17hGRFEXlzRnf28+DQ8IwPJT8ooShMZiCWu6EeTJT4Nz4ggAGoMbI8lIY3MY8XuRgM1Bneg5fzb8Qj24+jdi3GPDKLAKVZBfBq+7Rq6zKOncfR8UlTs2XmYgvRKpEedMNqtXYo9x8KF5z4na5feA6Xe3Nu5vPxnFEtRBcAcGUS+UyxLfBoekUEUOOXLIzIIS3xRvvLjU/GI1PPlHupfmLYgKQP30KLeGoHtG3L9+ucoSEzn6JOfknPS8aNb0+j2xTzQ4PzH64n5ZEOp2y/fsY0Lim/j+JJpiqt7Zy8u/1h+t2xDaChqcnLhZzU5GbdmZT9m4XnDDVh37wb0H8/WAwcI/U5/vC/v++9RLpbct8UJvyC0zKLtrWWmYqjVsEQ6Y/POeNZripoaj/W3D9GyKuhVUg5DQDBqhlM+M1Iw1m3smqZWFCIgBOXYXujrenfSEByO15xX0fLzsP7yCcq5o+XGM4aGoCQW/ZBTkpMxN2taZnrvG4eQ/8cuAEx1aqHl5BC46ElMNSIp2LOPrLfeqbAnhiE4BDWlKKaamoxbo5Ix3YcMx3P4bWByI2vBw4Xfmxo1xXvaXIyh4eS8tKjC3koASdkWIpx6ToT7eXHYqTciQEyq3itv9DtrUDWNib1b0r1hDRpFBPL2xsPc060p+TY7e84n0sCp7JTFFBqCPcFp2yYlY27RpMz0PsMHkb99T+FnY3goYa89i6lWDdJfXVFxbyWqv5wYw4odP0kpmJuXc/wMHUL+Tj0eQhAwdRJpTyzCvWP7Mucpzi0iGFt80b6zxafg1bZxOXMUmz8yhAYfPIZ7vUjiFn1QcW8lwBAYjJperFzWL1Yua0cjAkNRjuyGAbe6TqvXGI97Z2AICiN/5fNX1VsJICkjh4ggv8LP4YG+HD7v2rg58abrmPTKF3y+YR8Wq423Z9xxVTEAkrJyiXBqkAr39+bwxaRS08alZxOXlk2n6KJ7bAkZOUx5bzWXUjN5+IbO5fZWAkjKzSfc16Monq8HRxIyXdLEpOs9AMZ8uRtV05jQJYru9UK4VoagENRU1/OPKbrkMWseMBz3G25FmNzIeXpGieluXXuR+8LCEt//0/5q/QegxbyR1L31OmzZeWy69VmX5Rs9zXjXDOHob7td5q+qukjE4A40nX8H5hB/dt+9tET+/NtGYTC7kXnwvNOyS4+f7xTfEle5+GX5O+s8gW2jaPvKBLxqhbDvoTfxV1zPD27hwdgTis559oQUPFtX/px3hd+QnqR98H2l0orAELQ0p3NeWjLGqFLKSb9hmAfdijCZyF2sP6auXDyLW7tu2HZuQASHYazXCBEUBudOlhuzRNlMS8FUWsz+w3AfPBJhMpHzrD5MhG33Ztzad8PvjW8QZncsn7yJlptdYl5nxrAQlISiPCpJKbi3LPsa7TtiMJbtu0t8b27RGOHmhv1SxTec3CKCscY5X79S8WpbcaPbXyF8AtGy0ws/azkZGCJKPnJsim6LsWY0anoSti1fo+Wko6Zcxq3zjdj3rwWTGUOtRqip8SXmvcIrIrDE+ccrIhCLUznxiggkz6mcXEnjLKxzYyzJmWSf13ucxfy6m9oD2zHyz2UYPc3sfeJTui4dV/HFW5Iq6ap6LGmaNhGIA3prmvay4+tmQD9N0+4EktB7M7UDbgdeAxBCDAaGAZ01TWsNLHU81rYXGKVpWhtN0yzAMk3TOmqa1gK9cenGClbpA2CKY5nliQL6AEOBT4CNmqa1BCzADUIIN+B14FZN0640VjnXAkyapnUCHgau9IWeCLyqaVob9Aay4n3IbwbaAK2BfsDzQohIx7S2jmU1Q2/wKnabD4AVjry1B2YBbzpNqwV00zRtRimfPwLmaprWCjjstL4AZk3TOmiaVmGj0l9l9DTTZOpQjiwt/enFuFV7WX3dbLaPfZl6c28vNU1lWRPT2dV+Evv7z+Hc4x/S9M1pGH088W5eD896EaSuKnkBq0pX2gQzD11gXYcpbOk7j/PvrabjB0WV6O1Dn2DTgAXsGLWEBvf1J7hL2Rfda9Fo2jBUu8Llb7dX2TI9+vfH1LgxuV/oXXqNNWtiqlOHlJEjSRk5EnO7dri1bFklsewn9mF5cTKWN2ajnDmE+y2Tq2S5ZRIC9xH3U/DDeyUmaVlp5Dx+H3lLp1Hw/bt4jJ4Fzr19/iLPgf1wa9KYnE8dYxAYjZhbtyRr2XKSx03EWKMGXkMGVVm8gt9+IGP8XeR9+Daet99b+L391HEyJ48hc8ZEPEeOAjdzlcRTVJWLadm8O7Yfz43szlM/7ibLYqVbdCQ9GtVg9LtrmPf1DlrVDsEgqvZdsN5D+uLerDGZHxWN26MkJhN/+3jiho3G58YBGIICqjRmdZYTAK9B/XBr2ojsT/Tjx/uWYVh27EJJSqlgzqpli0/h5KCpHLt+AoG39MEUEvDXFyoE7iPHU/DtO6VOVi+cJO+pCeQ9NxXzoNvBVMFjlNfg993HGNqtJWuef4hlU0ey8L2fUSvzaOo1Wn3gLP1a1cfo1OsrIsCHr2fewk9zb+fnfadJzS6/x0BlKJrGxYw83rm1A4sHt+TpdUfJzv9rPTUrw7rmB7Kn3Y3lsxV43HyPyzRjdFOwFqBeuvC3r8c/4chzX/Nrh6lc/G4H0fcNcJkW2b8d1sxclJzSH4W5Fs73JxNW7WXjdbPYc9+LNJ470iWde1gAbZY9SNaxmEr3Vq3kClTdsipQWp0n/c+zbOg5h02DFtJo6jC9N2UVM4YG4t64Hrnbyh8f52pZ1/1Izqx7yP/yHdyH3Q2AbfMq1LRkfJ56C89RD2I/c7TU3jfXHHPtj2TPuBvLFyvwGK7HNEY1AVUl66GRZE0fhfuQ2zCERlawpMrzvqEv5maNyFjpOraVMSSI0GfnkvLYC9V6HFU15fwhLB8sIP/TZ1AvHsc8YDQA6sXjKBeO4HHbHNwH348af75K92VZ6g/vygWnxteQNg3QFJWv203h+y4zaDZhCOi/QyWpSlTFW+F+cjQKAbgB7wghDgNfozecgN6w8oGmaXkAmqaV1TraWwixyzF/H6DMfoJCiAAgQNO0LY6vPi5nHVdpmmZDb2gxAldGGT2M/mxpY/TeQ2uFEAeAheiNNVd85/h/nyM9wE5gvqMnVl2nbXBFD+BzTdMUTdMSgc3Aled/dmuadlnTNBW9F1E95xmFED5AN+Brx/q8DTif2b/WNE0p/lkI4e/YJldGvvsQuN4pnetoeq4xxwsh9j7yyCMxXVY/Tv+1i8hPysCrRtGjVV6RQVji013ms8Sn41mj6A6dZ2QQloQ0vOuG410nlAHrFzNk9yt4RgbRf82zJe5opfxxAo+64dhz83F3iuUeGYQ13rVnQUF8Gu5XxsIwGjD5emFPy0az2rGn5wCQc+gclphEPKMi8evQCJ/WDei05w3arV2KV6PadD/7EdbEdDycYnkWuyMHV+6QFeXLIzK4ME1BcmZh1233sACsKXovUnuOpbALetL6AxjcTJgdParyE/TtZk3JwhKfRoflD9F73SLyEzNctp9HWds4suw0dW6/noj+7dg3ueIBptXk5KJHdtB7ZihOPTOuMLdvj/fdd5Mxf37ho0TuPXpgO3YMzWJBs1iw7tqFW/Oyu/JeoWWlIfyLtrfwD0bLLnYKsOSAoj9aY9+3HkONv3adUzNSMQQ45TMgxKXXFO6eGCLr4DVlMd6Pv4exXmM8xz+KoXa0/vhUnn6HTr10FjUlAUNozXLjKckpGMPDCj8bQ0NRkkv+0Dd3aIfP6LtJm7ugcLsqScnYTp/VH4NSVPK3bsOtcckeXSXymJqCIaQopiE4FCW17MYF65b1mLv0KPG9cjkGzWLBWLfknbfiwnw9ScgsGvsgMSuPMD8vlzThfl70bFwTN6OBmoE+1A325aJjbLEHerbgqweH8PaYPmga1A2p+LEVe3IKpginbRsWipJUsteRR6d2+I+7i6SHHy3cti75TEnFdvYCHm0rbuSp7nKiJBU7fsJCSo3n3rEdvmNGkTprYVG8ls3wGTmciO8/w3/qRLyG9MfvwQdKzFucLSEVt8iiHitukSHYEq6+l6A9KY38Uxfx7lTxANNqeiqGwGLlMr1YuaxRF68ZS/F+9kOM9Zvg+eATGOq4lgc14RJavgVDjXpXta5hAT4kpBX1+k9MzyYswPUY/H7bQQZ00O/ut46qRYFNISPn6hp2wvy8ScjIKYqTmVtmr6PfD5xjUJvSx3AK8/cmOiKQ/Y7Hl8qM5+1BYnZRA0Vidj6h3u6uaXw86NkgVC+X/l7UDfTmYsa1N1ipaSkYgl3PP2p62ecfWymPyrl16411+4ZrXoeqFjWmP/3XLvrL9Z/iYr7bTq0bOrrEaP/8ODJPXv5b6iLO0v44gVfdsMK6SIOJN9B79ysYzCZyTsXiWTPYadmlx/dwiu9Zo+z4BaXEB6h7X396r1tUbXWenNNxep2yUT2X722JqZgiis55pogQbIlXd87zG3w9OWt3gL3iHr4AWnoKIsjpnBcUilZeOXF+VE5Vyf/0LXIWTiDvlccQXj6o8RWPg1aibAaFuPQULRFz50bcOuhl09ytL7ZDe0BR0LIysJ86grFB+T2BlKQUjBFFeTSGhWBPLJlHj85tCbj/LhKnPeZyjRbeXoQve4b01z+g4PDxEvOVus4JqZhrOF+/gq/p+nU1tJx0hG9RjyDhE4CW43rskp9bVJc9ug1DWNELSux7VpH/2bMUfP8qCFAzXHuxNh7djxvXPMuNa57Fkljy/JOX4BorLyEdL6dyUjyNMBqoM7gjF37aVfhd/RHdiNt0CM2ukJ+aRfKeU6B3jvhPUv/D//6tqqJhyXmEtelAInovnQ7oj4lVihDCA71Xzq2O3kTvAB7lz1VpBQCOhhyb02NnKvrjgAI46ug51UbTtJaapg0oPj+gONKjadpn6D2gLMBvQog+V7s+xZfpxABkOK1PG03TnPuxFh/VroJR7ipOp2naCk3TOixevLjuHwOfZG3/+cSu2kvdkdcBENQuGlu2xaUbOEB+Ugb2bAtB7fSKcd2R1xH3+z6yTlzi55YP8lunh/mt08NY4tNYO2ABBcmZeNcLL5w/oGU9DGY3MrYcwrNBJB51whBuJkKHdyd1jet4Ialr9hJ+W08AQm/sQsb2IwC4BfsVjvHhUScMz/qR5MckEf/hGna1mcDujpPZ338OeacusT3qXlJ/30Pt2/R8BbSLxpad59ING/Ru3bYcCwGOfNW+7ToSHINhJqzZR+3brnd8f33h986NZgFtoxBCYE3LxujljtFbP5SNXu64+Xvz5/QVbOw3n/jf91LHsS6B7aKxZ1tKXRd7joVAx7rUcVqXsN6tiJ58I3+MfgHFYi1r9xaynTyJsVYtDBERYDLh0acPBTtc36xkio7Gd8YMMubPR8soWhclKQm3Nm3AaASjEbfWrbHHlD1uyBVq7FkMwZGIgFAwGjG27Ib9hOu+FT4BhX8bm3RATb6KQWRLi3nxFIbQGoigcDCaMLW7Hvvhoosr+Xnkzh9F7pPjyH1yHMqFk1hWPK2/Fc7Hr3AsHhEcjiG0Bmpq+T/sbMdPYKpVE2Okvl09+/Uhf1ux7doomoC5M0ibswA1PcNp3pMYfHwwBOjHj3v7ttjOV7xd7adPYKxRC0O4HtP9+j7Ydrv2WDNEFjWIuXXoihqnb1dDeAQY9HFcDKHhGGvVQU0qP48AzWsGczEtm9j0HGx2hdWHY+jZxLXRrXfTWuy9oFeY0nPziUnNplagD4qqkuFoeD2VkM7pxAy6RlV8J9R69CSm2jUx1dDz6T2wF5bNrtvWrXE0QQseJunhx1y2rTEsBOGuX4YMvj64t2mBLabiY6u6y4n1+AlMtZ2On/59sGxxfazVrVE0gfNmkDp7oUse0x5fRMKwO0kYcReZry0n77e1ZL1Zeo8fZ3kHT+Nevwbm2uEINxOBN11H1tpdFc4H+mMIV7ar0c8b7w5NKTgbW8FcoMacxBBWAxHsKJcde+oDcF+Rn0furNvJXTCa3AWjUc6fwPLmE6gXT+vzOM7zIigMQ0RtfQy1q9C8Xg0uJqUTm5yhH797jtOztWujVWSwH7uOXwDgXHwKVpudQF+vUpZWTpzaoVxMySI2LUuPc+AsPZvVKZHufFIGWZYCWtct+hGYmJFDvj7uBVl5Bfx5PoF6oQHlx4vw42JGHrGZedgUldWnEugVFeaSpndUGHsv6w0C6RYrMem51PS/9p6YytkTGCJqYgiNAKMJc7c+2Pa6lhFDRNG5wdS2C0q80zEiBOauvbDt+Pc0LJ1duZa1/ef/5foPgE/9onpOzYHtyT4TXxhj481Pg6Jy9oO1f0tdxMupjuXvqGNZ07IRbkbC+rbhxKIv2dhxGomr9lLTkceA9tHYy4hvz7EQ0F6PX3PkdSQ68pi4eh+1btfj17r9+sLvi4v5YC0b+83/W+s8XnVCEUb9/OBZKwSf6BrYYl3PD/mHT2GuVwO3WuHgZsLvhuvJWf8HV8Pvxp5k/VK5t5YBKOdOYIyoiXCUE7cuvbHtL1ZOwp3KSZsuKAmOcmJ2B3e97mhq0V5/o2xcxXUD5VyxstmlD7Z9rteTsmKqKUmYmrXVJ7h76GM5xhV76UkxBUdP4lanJqaajmv0oF7kbXaNZ24SRcijD5M47THUtIyiCSYT4S8/Qc7Pa8lbV7k30wLkHjyNe71IzLX13wyBQ68jc+3f+3SCmhiDCAhD+AWDwYipUUeUc8VemOFV9Ki1sUFr1CsDewsBHvrNBRFSE0NwTdSYYy6znvxwHb8MWMAvAxZwcfU+om7VbwSGtIvClpXn8hgcgCUpA1u2hZB2+vh8Ubf24NLqojIYeV0LMs/EuTwulxubSkR3/UaXydOdEL2Mub69RpL+gmsZcLs8/sBlTdNUIcRo9N5BAGuBx4QQn2qalieECHL0WsoGrtwuvNKIlOLosXMrUOZb4DRNyxBCZAghemiatg34K2+oOwmECiG6apq20/FoXCNN08ocWEUI0QA4p2naa0KIOkArwLmGtBWYIIT4EAhC7zk0G6jwGShN07KEEOeFECM1TfvaMV5TK03Tyn3PqKZpmUKIdCHEdZqmbQXuQe8pdU0S1h8gsm8bBu98CcViZc/0twun9V+7iLX95wOw/5EP6PiK/rrLhA0HC98IV5ZaN3Sk7sjr0GwKSr6V4xNeBkXlzPz3aPH5AoTRQMLnG8k7eZm6c24n+8BZ0tbsJeGzDTRZNoWOO1/HlpFT+EY4/y5NqTvndjSbgqaqnJ6zArvT3eLi0tbtx7tve/r88Yr+lpSHi/J1/brFbOn3CACH531Q+IrfpA0HSFp/AIAzr/9E+xXTqH1XLyyX9Vf8AkTe1Jl6o/uj2hXUfCv7Juqv/HYP8aeD47E4YTJy+bvtJG3UL0aJ6w4Q3rcN/f94GbulgD+d1qX3ukVs7Kdv44Pz3qfdldcNbzhIomNdWi0ag8HsRvcv9XVO23eGg3Pfp0yKQvarrxL4/PP6K+pXrUK5cAHv++7DfvIkBTt24DNpEsLTE/8nnwRATUwkY8ECCjZvxty2LcHvvw+aRsHu3Vh3VvyqXVQV6y/v4zF6ARgM2PdvREu6jFuf21DjzqKc2Iep62BMTTqgqQrk5VDwXdGTnx7jntR7DJk98Jz1FtYflqOcqeCVu6pK/jfL8XrwKf0Vv3+sRU24iHnIKJSLp/XxW8pgjGqBecgoUBTQVPK/egPyyj6e9O2qkvnSawS/vBSMBvJ+WYX9/AV8778P64mTFGzbgf/kiQhPT4KeeUKfJTGRtLkL9W7ny94i+LUXQQhsJ06R99MvldiuCrnLX8HvyRfAYKBg3W8oFy/gOWos9tMnsO3egceNN+PWpj3Y7Wg5OeS8shgAU7NWeN56l947S9PIWf4yWlZmBQHBZDQw74YOTPpoI6qqMaxdA6LDAnhz/SGa1QyiV5NadIuOZOeZeG5+/RcMQjB9YBsCvNwpsCmMfW8tAN7ubjx7SzdMxkrc21BU0pa8Ttgbz4HBQM5Pv2M7F4P/xNFYj53CsmUngQ+Px+DlSehS/Y1G9oQkkqc/hlv9OgTOmKh3rReCrI+/xnbmfAUBqf5yoqhkvPA6Ia8tQRiM5P6sHz9+48dgPX6K/K078J8yAeHlQdAi/elmJSGJ1Nl/YWwaReXyY2/T4KMnEEYDaV+tI//0JSJm3EXeoTNkrduNZ6to6q+Yj9HfB79+HYmYfhcn+z+Ee3RtGiwcW7hdk1f8QP7Jin/woKrkf/kmXlOf1cvljjWo8TGYb7oHJeY0yqGyf+QZo1tgHnibfjdY0yj4fBla7tWNOWoyGph3V38mvfIFqqYxrHsromuG8uaPW2hWN5JebRoyY2RfnvroNz5dp4/T9eR9NyCu8pFNk9HAvOHdmPTOKr2cdGpMdEQQb67eS7NaofRqrt/F/t0xaLfz8s8lZfDSz7sQQt+89/ZsRUOnu9KlxjMYmNu7CQ9+v1/PV/OaRAX78ObOMzQL86NXVBjd6gazMyaVmz/ajlEIHr6uEQGOAfbHfrWb8+m5WKwKA9/dzOP9mtOtovGXVBXL+6/hPX+p45Xmq1AvX8Bj5H3Yz53Evm8H7gNHYGrZHhQ7am42eW8+V7TOTVuhpibrg39XkdmPP8eePw+RkZFF3+F38+C4e7jlpqt465OTv1r/abngDnyjItFUjbzLKexzui7XHNyRhM2Hif1tD5E9mlV9XeTGTtQeeT2qzY6Sb2X/BL0uUmNoV4K7NMEc6FPYIJR7Jp5eu/T4h6YVxe+xfjHb+urxj8z9gNavTcTgYSZ5/QGSHfHPvv4T7d4pir//AT2+e6g/3dc8i8nXE1SNeuMHs/76OdhzLH9bnSe4U2MaThmqv1hA1Tg07wP804udHxSVxKfeovZ7z4DRQOY3a7CeuUjI1LvJP3KanA278GjZkJpvPIrRzwef3p0JmXo352+YBIBbzTBMkSHk7T5cuYMI9HLy0et4z16in/O2rEKNjcH95jEo509i/3Mn5v7DMTVvB4odLTcHy4olAPqb4OYsAVVFS08hb/niysdc+Trec5eAwYh18yrU2At43DIG+/lT2PfvwH3AcEdjlaNsLtdjFqz9Aa8Jc/Fd8j4IsG5ejXrpXPnxFJXUxcuIeGsxGAxk/7Aa29kYAh4cjfXoKfI27yRoun6NDnu+6BqdNO0xvAf2xKNdSwz+fvgM1ctqymPPYz1ZwVsiFZVLj64g+hP9+pX65XryT10icqZ+/cpcuxuv1tE0eOcRjP4++PfrSOSMOzneT3+zX6NvF+EeVQujtwctdr9HzOxlZG/+s/yYmop105e4D58KwoD92A60tHjcutyEmhiDcv4Qbm366AN6qypafi7WtR/q8xqMeNyqj52lWS0UrP6g3EfhYtcfoGaf1ozY/iJ2i5UdM1YUTrtxzbP8MmABALvmr6Tby+MxeZiJ3XiQWKffX/WGdXF5DA7g5Mq1dHt5PEM3PAdCcPbLLbR/9M6reJ2sJJVPVOaNIy4zCHEB6KBpWopjYOwcTdNecExrCHyLPlj178BkTdN8HNPmAfcCVuA3TdPmCyFuARah9/rpCiwA7gQSgFNAjKZpTzgGrP7FMS6T87pcGQ9JQx+8e4imaS1KGbzbeR1znNapcJoQog36mFD+6A1ur2ia9o5j0OtZmqbtFUKEAHs1TavnyM89gM2xvndpmpZWycG7Z2madqNjHZY5lrmyWN7qA2+hPwLnBnyhadpTxbdFKZ/bUDR49zngPqfBu2dpmlbha4O+jhxVrQ84h2sV97SpaplV3qZaMYWqHU+mMro2qXgQxKrkc11YxYmqmJr518cfuVrZeyvbSbDquAdXf+dXr7uvpiPmX5e0pPJ3LKuKh5+92mPaLFXRWfjqpCRc/dvN/qqowdV7bjfddfUDbf9lGdU7thWAFluJRrwqZt10pNpjer9Rcgy8v9MPLR+t1ngAHtUwzkpxhn9gCBtbFY+nVxlN/DKqNV5k54rfElfVtL9x/LeypB8xVpyoqmOmXV2v0arQZFbVjS1VGd8sreAG5t/k3thPqr9wVpNlte/+/ztgVwUeuvTv3G9X/eta07R6Tn8/UWzaafSeO1fMdZr2HPBcsfTfojdEXbHQ8a94zDFlrMs+9Mfurpjj+H4TsKmMdfRx+vsJp78P4Doe0ZXvezn9nYJjPKTS8uO8fMfjdrMd/5ynF66b4/NDZeTtPFBiBN/i26KUzwfQ33pXZj4kSZIkSZIkSZIk6b/oP9uq9C9W/bdNJUmSJEmSJEmSJEmSpP8E2bAkSZIkSZIkSZIkSZIkXRPZsCRJkiRJkiRJkiRJkiRdE9mwJEmSJEmSJEmSJEmSJF2T6n81liRJkiRJkiRJkiRJ0t9A/Ve+N+2/TfZYkiRJkiRJkiRJkiRJkq6JbFiSJEmSJEmSJEmSJEmSrolsWJIkSZIkSZIkSZIkSZKuiRxjSZIkSZIkSZIkSZKk/wT1n16B/0Gyx5IkSZIkSZIkSZIkSZJ0TWTDkiRJkiRJkiRJkiRJknRNZMOSJEmSJEmSJEmSJEmSdE2Epmn/9DpI/zLWuKPVelDkTplcneEA8H79jWqPiVr9T/vG3jCjWuOFjQyv1ngAhoYNqz2mqNuk2mNevO+Dao/5niWwWuM98XLrao0H8NGUI9Ue896XGlV7TPyDqj3ksxN2VGu8sb4p1RoPwG6v/vtzoS3yqz3mp3/UqvaY4fbqrZ8OP/x0tcYDyJk0ttpj+rz1frXHRLFVe8jYgQ9Va7yQ/j7VGg/AEBlS7TFF287VHjN13mfVHjP2UkC1xrsudXe1xrvCWnBZ/COBq8GLde7+zzZyzLz4yb9yv8keS5IkSZIkSZIkSZIkSdI1kQ1LkiRJkiRJkiRJkiRJ0jWRDUuSJEmSJEmSJEmSJEnSNTH90ysgSZIkSZIkSZIkSZJUFf6zAyz9i8keS5IkSZIkSZIkSZIkSdI1kQ1LkiRJkiRJkiRJkiRJ0jWRDUuSJEmSJEmSJEmSJEnSNZENS5IkSZIkSZIkSZIkSdI1kYN3S5IkSZIkSZIkSZL0n6CKf3oN/vfIHkuSJEmSJEmSJEmSJEnSNZENS5IkSZIkSZIkSZIkSdI1kQ1LkiRJkiRJkiRJkiRJ0jWRYyxJlbJt936WLHsfRVG5+YZ+3H/XzS7T4xKSeGzpG6RlZuHv68PiBdOICA1h95+HWfrGB4Xpzl+MZeljM+jbo3OFMU1tOuE19iEwGClY/ysF33/mMt08YCgeg4ajqSrkW8hd/gLq5RgMoRH4vfohStwlAJRTx8hb8dK/Mo/bdv/Jkjc+QFFVbh7Sl/vvHOEaMzGZx55/g7SMLPz9fFj8yDQiQoMBiE9M5vEX3yIhORWB4M3F86kZEVZhTM9uHQia+yDCYCD7+1Vkvv+ly3S/e27Bd8RgUBSU9ExSHn8Be3wSAOFvLsK9ZVMKDhwhccqjFca6wtigFeaB94AwYD+wCduOn12mm1pdh7nvnajZ6QDY967FfmBTUQKzJ54Tl6Cc3It19UeVirn9dBxLf9uHqmmMaBfF2Oubl0iz+kgMb288DAgaRQTw3MjuALyy5k+2nooDYHzPFgxsWbfieIdOs+TT31BVjRE92zHuxutdpsenZrBwxXdk5+WjqhrTbuvPda0bcfjsZZ5e+RMAmqYxcXhv+nZoVqk8evVoT9j8SWAwkPnN76S/+5XLdM8OLQh9ZCLujeoTP3MxOWu2FU4LmTkW756dAEh96zNyVm2pVEyAoY+PpnHvNtgsVr6a9RZxRy+4THfzMDPqzYcJrhuGpmgcW7+P35d8AUD7W69nyCOjyEpMA2DHh2vY8+XGcuNtP3mZpT/v0vdlx0aM7dXKZfrzP+9iz7kEAPJtdtJy8tn2xChOxKWy6Ied5OTbMBoE9/duxcDWDSqdz25P3UOdPm2wWwrYNH0FKUculEjTcc5IGt3aA3d/b95vfH/h95GdG9P1iXsIblqbdZOXcf7XPZWOC47j99e9ep7bR5c4fp//bR97zicW5Tk3n20Lbru6GMdiWPrdFv2Y7dqMsf07uMb4bit7Tl/WY1jtpOXksW3JBADi07J58vP1JGbkIIDXJw6lZrBfpeLe8Pi9NHIcP9/OWk58KcfPHW9OI6huOKqicnL9ftY4jp+Oo/rS+Z7+aKqKNbeAHx55l+QzsZXOs2f3DgTPnYQwGsj67ncy33M9//nfewu+Nw9CUxTUtEySH3ux8Px3Naq7bJradMLrPqfr5Q/Frpf9i10v39avlwDGOg3wmjAT4ekFmkbWvIlgs1Yqn9c9eQ91HWVk/YwVJJdSRrrMGUnjW/QysqJJURlpfncfWo3uj6qo2HLz2TjvPdJPx5Uap83T9xLZtzV2i5U9D79NxuGScQJa1aPTKxMxergRv/4gBx7VrxXN59xKjYHtQdXIT81iz7Tl5Cdm0GjSDdS9WT/nC5MBU3At7GkxoKmVyjvAwkUvsWX7boICA/jhk+WVnq8i1b0/t/2xl+deWY6iqtxy0yDuv8f1XBKXkMiji14mLSMTfz9fnntsNhFhoQC89OZ7bNmhn98mjLmTwf16ViqP23bt47nX3kVRFW65YQD3331rsZhJPPrca0UxF84gIiwEgBff+oAtO/eiqhpdO7bhkakPIETFA6p4dutA0JwHwWAg5/tVZH5QrP5z9y34ONd/nngBJT4Jc+MoguZPxeDjBYpKxrufkbdmc6XyaWzcFveh48BgwLZ7HbaN35WermUXPO+dS96rs1AvnwWDEfeRkzHUbIAwGLHt21jmvMUZ6jXH3OsOMBiwH96Kfc/vrrGadcN8/a1oORkA2A5sQDmyDUPtxph73l6YTgRFYP11BcrZA+XG2370PEu/Wq9fS7q3Yuwg1/rv819tYM+pi4DjWpKdx7aXpwLwyneb2XrkHADjh3RlYIcmlcqje5eOBMx4CGEwkPvTb2R/9LnLdJ87b8V72BA0u4KakUn6M8+jJCQW5c3bi/AvPiB/83YyXnitzDj+vdpS9+mxCIOBpM/XEb/se5fpwmwi6rVpeLdsgD09m9MTX8R6ORnhZqL+0ol4t4pCUzViHnuP7J1HAWj86aO4hQUiTAaydx3nwvx3ys3rSy89xaBBfbDkWRh3/3QOHDjimlcfbzZuKDo2ataM5LPPv2PWrCeoXbsG7737CgEBfhiNRhYsXMzvv28of+P+R1T+TC5VFdmw9DcTQgwHTmmadqwKl7kJmKVp2l4hxG/AXZqmZVTV8otTFIVnX32HFc8/TkRoMHdMnEPvbh2Jqle7MM0Lyz/kpgG9GDaoN7v2H+bVdz5l8fxpdGrbkm/e1Rt1MrOyGXL3ZLp1aFNxUIMBrwemkfPULNTUZHyXLMe2Z3thxQnAunUd1jX6D3K3Dt3wGjOZnGfmAKAmxpE96/5SF/1vyaOiKDz72rusWPoYEaFB3PHgPHp37VAyZv9eDBvYi11/HubVdz9l8SP6xXj+ktd54K5b6NahNXkWC0JUogOiwUDw/CkkTJiLPTGFGp8tI2/TTmznLhYmsZ44Q9xdk9HyC/AdeSOB0x8gec6zev5Wfo3wdMfv1hsqs1l1QmAePJr8T59Dy0rDY9xT2E/tQ0tx/QFhP/ZHmY1G5l63ol48UemQiqqy+Je9LB/dh3A/T0a9vZqeTWoRFeZfmCYmNYv3txxj5f0D8PM0k5aTD8CWk7Ecj0vny0mDsSkq495fR/eGNfDxcCs33qKPfuHtOaMJD/LjrifeplfbJkTVLGroe+fHzQzs1ILb+nbibGwSD730CatenEF0rTA+e2ICJqOR5IxsRi58k55tG2MyGsvPpMFA2KOTiR03H1tiCnW/eo3cjX9gPVu0L21xySQ88iJBY29xmdW7Zyfcm0UTM+JBhNmN2h8+T96Wvai5eRVu28a92hBSP4Lne02nTttoRjw7jjeGl2xk3PLOL5zbeQyjm5EHPl1I416tObnpIACHftnJj4+vrDAWOPblj3+wfNxAwv29GLXsZ3o2rUNUeEBhmtk3FVViP99+jBNxeqOVp5uJp2+7jroh/iRl5XHX6z/RtVFN/DzdK4xbu09r/OtH8EWPmYS1i6LH4jH8cNMTJdLFrNvP0ZVruWPrCy7fZ8emsmnG27SeMKRS+SyR55/3sHxMH8L9vBi1/PcSx+/sIe2L8vzHSU7Ep119jK83sXzycMIDfBj1wpf0bNGAqMigohg3X1cUY/NBTlxOLvy88JO13D+gA12b1CGvwFqpH3UAjXq1Ibh+BC/3mkGtttEMfXYsbw9/rES6be/8ynnH8XPfpwto2Ks1pzcd5NCPO9jz6XoAmvRrx+BH7+aj0Usql2mDgZAFDxE/fh72hBRqfvE6eRtdz38Fx8+QdcdD+vnvthsJmnE/SbMXVW75TnGqtWwaDHiNm0bO07NQ05LxXbwc295i18tt67Cudbpejp5MzrNzwGDEa+oC8l5fhBJzFuHjB4q9Utms27s1AfUj+OS6mYS3jaLnojF8M/SJEunOr93PoZVruXuLaxk59cNOjn6i/8Cp178dPR67m5/vWVpi/og+rfFpEMGqbjMJahdNu+fuY8MNj5dI1/65seyd9S5p+8/Q49M5RPRpTcKGg5x881eOLv0GgOhxA2k242b2z32fU2/9yqm3fgUgsn9bur07+aoalQCGD+nPXbcMZf7TL1ScuLKqeX8qisIzL77BO68sIiIshNvvn0bvHp2Jql90Q+WFZe8ydFBfhg3pz659B3hl+Uqee2w2m3fs5tjJs3yz8g2sNhv3PTSH67p2wMfbu+KYL7/NOy89RURoMLePn0nvHp2IqlenKOab7zN0YG+GDe7Lrn0HeWXFRzy3cAZ/Hj7On4eP890HemPAvQ/NY8+BI3Rq27LC7Rr0yBQSJzrqP58uI29zyfpP/Kii+k/Qww+QPPdZVEs+KY8uxX4xFmNoMJGfvUH+zr2o2bnlxxQG3EeMx7LiCbTMVDynLsV+dDda0mXXdO4emHvciBJzsvArU6tuYDJheelhcDPjNet17Ae2oqUnUy4hMPe5i4JvX0bLTsdj1AKUswfR0uJdktlP7cG2wbUxRr10kvxPntI/eHjhOXYRSkz5P18UVWXx52tZPu02wgN9GbX4Y3q2iiKqRkhhmtm39Sn8+/ON+zlxSW/g2XL4LMcvJvLlgtHY7HbGvfQl3ZvXx6eia7TBQODsaSRPmY2SlEzYyrewbN2B/XxRGbGdOkPS6EloBQV43zwU/4fGk7bw6cLpfhPuw/rnoQrj1Fv0ACfueBJrfCrNf1tKxuo9WE4X7b/QO/thz8jhYPfJBA3rTp2F93Jm4ouEjeoHwOG+0zEF+9Pk04UcGTwHNI0zE15AybEA0PCd2QTd1BXe/6PUVRg0qA/R0fVp1qwHnTq1Y9nri+lx3U0uaXJycunYaWDh5z92/sYPP6wC4JFHpvHNtz+zYsXHNG3SkB9//IhGjbuWn29JukbyUbgqIoQo61fgcKDC7gdCiGtq5NM0bcjf2agEcPjEGerUiKR2jQjc3NwY3KcHG7fvdklz7sJlOrfTL+qd2rYoMR1gzead9OjUFk+Pin/UGaOboCbEoibGg92ObdsGzB27uyayOFW0PTxA064+cw7/RB4PnzhDnZoR1K4Rrsfs3Z2NO1x7NZyLuUznti30mG1aFE4/e+ESiqLSrUNrALw8PSsV071FY2yX4rDHJoDdTu7vm/Dq1c0lTf6eg2j5BQAUHD6OyXFXEiB/959olWh8cGaoEYWaloiWkQyqgnL0D0yN2lc845X5I+ohvP1Qzh2u9DxHLqdSO8iHWkE+uJmMDGxZl00nXCty3+09y+2dG+LnaQYgyMcDgHPJmbSvF4rJaMDTbKJRRADbz5R+F70w3rnL1A4PolZYEG4mE4M6t2TT/mINYUKQ49iuOZZ8QgN8AfB0Nxc2IhXY7FTy9zkerRpjuxiP7XIC2Oxk/bYZ7z6ulQV7XCLWU+fRVNeyYY6qg2XvEVBUNEsBBafO43Vd5fZJ8wHt2ffdVgAu/nkGT18vfEMDXNLY8q2c26lXRhWbQuzR8/hHBFcuY8UcuZRC7WBfagX76vuydQM2HbtYZvpVB88xqE19AOqG+lM3RG+MCfPzIsjbg/Tc/ErFrTegPae+0XuRJO0/i7ufN15hASXSJe0/S15SRonvcy6nkHb8UoltXxlHLqfqeQ7yLTp+j18qM/2qQxcY1LLe1cWISaR2aAC1Qvz1GO0asenwubJj7DvFoPaNADgbn4aiqnRtov8I9HI342kuu+HVWdMB7TngOH4u/3kGD18vfEo5fs47HT9xRy/gH6E3eBU4KuMAZi/3qzrvu7dsjO1iHPbLjvPfqs149y7n/HfoOKbw0NIWVa7qLpuF18skx/Vy+wbMHcq5XroXXS9NrTugxJxDiTkLgJaTBWrlGlfqD2jPiW/1MpL4Z9llJPHP0suIzWlfupWzL2sMak/M1/oxk7b/DGY/LzyKxfEIC8Dk60na/jMAxHy9lRqD9O1md4pjKiNOneHdUAtyys5sGTq0aYm/n+9Vz1ee6t6fh4+fok6tGtSuGanXRfr2ZMNW1x+5Z89fpFP7NgB0ateajVt3Fn7foU0LTCYjXp4eNIquz7Y/9lWYx8PHT1OnplOdq+91bNi2yzXmhUt0atfKEbMVGx3ThRBYrTZsdjtWmx2bXSE4MKDCmO4tGmN3rv+sLqX+s9e1/Bsd5d9+MRb7Rb1npJKcipqWgaESMQ11GqKmxKOlJYJix35gG6bmnUqkMw+8C+vG78Fuc/pWQ5g9wGAAN3dQ7Gj5lhLzlogZUR8tIxktMwVUBfuJPRij2lQ4X3HGhu1Rzh8Be/m93Y5ciKd2WCC1QgP0a0nHJmw6dKbM9Kv2HGdQh6YAnItPpX3DWnp9y91Mo5qhbD96vsJ1Mzdrgv1yLEqcXkYsazfgeb3rvizYdwCtQN+X1iPHMDrVZd2aNMQYFEj+rr3lxvFpG03+hXgKLiai2eyk/biNwIGu+y9wYEdSvtZ7Xaf9shO/HvrvBM9Gtcnaptdb7amZ2DNz8W4dBVDYqCRMRoTZBOVcxm66aQCffqI3jO/evZ+AAD8iynk6oWHD+oSGhrDNUV40TcPPVz9H+fn7Eh+fWOa8kvRX/c83LAkhZgshpjr+flkIscHxdx8hxKeOv+8UQhwWQhwRQixxmjdHCPGiEOIg0FUI8ZwQ4pgQ4pAQ4gUhRDdgKPC8EOKAECKqWOyVQojlQohdwFIhRCchxE4hxJ9CiB1CiMaOdJ5CiC+EEMeFEN8Dnk7LuCCECBFC1BNCHHH6fpYQ4gnH31Od1uuLq91GSSmpRIQV/SgMDw0mMcX17nijqHqs26JXRNZv3UVunoWMzGyXNL9v3MaQvtdRGYagUNSUorsyaloyIrhkBd990HD83vgUr3smkvd+UVdWQ1gEvs+/g89Tr2BqWsFdrH8oj0kpaUSEFt3RKTPmVv3isH5bUcwLl+Px9fbi4ceXMnLCLF58+yMURakwpjEsBCWhaLsqSSmYwkPKTO87YjCWUhrQrobwDUTLKsqXlp2G8A0suW5NOuH5wCLcb5mK8LvSa0Jg7j8K67rPS6QvT1K2hQj/ojum4X5eJGW5NojFpGYTk5LN6HfWcM+K1Wx3PILRKCKQ7afjsVjtpOfms+d8IomZ5TemJaVnExFU1JskLMiPxPQslzSTRvTm1x0H6f/wC0x+8RPm3V3U6+vQ2UuMeOR1bl3wBgtH31RxbyXAFBaM3Wlf2hNTcAuvXONNwYlzePdoj/BwxxDgh2enVrhFVO4HtF94EJlxqYWfMxPS8IsIKjO9h58XTfu248z2oq7bLQZ34uFVS7j7zYfxjyx7XoCkrDzXfenvRVJW6XeJ49JziEvPoVNUZIlphy8lY1NUagdV7nEt74hAcp3ymRufhldEyeP275CUZSHC36vwc7i/F0nZpf+giMtw5LlB+NXFyMglIsCnKEaAD0mZpf+wjkvLIi4ti06NagEQk5yOr6c7M979lduXfM5LP2xDqWRjhG94IJlxReeDrIQ0/MrZrh5+XjTp246z248Wftf5nv7M2PwyA+fdxa9PVO7RWABTWEixMpOMsZwy43vzIPK2Xd0jjHqc6i2bhqBQ1NRKXC8HDsfv9U/xurvoemmMrA1o+CxYiu+SFbgPvaNS6wngExFIjlMZyYlPw+cqy0jL0f24Z9uLdJt/B1seK31fekYEkecUJy8+Dc9I1ziekYFYnI4rS3wank7npRbzRnLD3teoc3M3jjz/jcu8Rk8zEb1boVkr6H1STap7fyYlpxQ+1gYQHhZCUnKqS5rGDRuwbvN2ANZt3uGoi2TROLo+23btw5KfT3pGJnv2HyIhqYIeNVypcznXf0qJGV2fdVv0Bqx1W3YWxmzTogkd27ak94gx9B4xmu6d2rr09C6LsUT5T8EYVnb9x2fEYCzbStZ/zC0ag5sb9kvl33ACEH5BaBkphZ+1zFSEv+u5wFCzAYaAEJQTrg1y9kM70az5eD/6Pt4LVmDd/ANYKm78FD4BaNlOda6cdIRvQIl0puh2eNzzOOYbJyJ8SpZbU+NO2E9UXP9LSs8hIrCocTU8wJek9DKuJamZxKVk0slxU6JRLb0hyWK1kZ6Tx55TF0lMzy51XmfGsBCUxKJHlJWkFIyhZZ8nvYcOIX+nIy9CEDB1EpmvVfzoqjkiGKvTuccan4pbsTqLSxpFRcnKwxTkS+7RCwQM6AhGA+61w/BuFYXZqRdX488epd2hD1ByLKT9srPMdahRI4JLl4uOtcux8dSoEVFm+ttGDuPrb34q/Pz00y9x1103c+7sHn768SMenl75YSwk6Wr9zzcsAVuBKy0BHQAfIYSb47stQogawBKgD9AG6Oh4vA3AG9ilaVpr4DgwAmiuaVor4BlN03YAPwGzNU1ro2na2VLi1wK6aZo2AzgBXKdpWlvgMeBK//tJQJ6maU2Bx4HKd/fQzQPaOtZr4lXOWymzJo1m76GjjHxgJnsPHiUsJAiDsejwSk5N4/S5i3Tr2KZK4xb8/gNZk0eR9/HbeNxyDwBqeiqZE24ne/YDWFa+iffDj4KnVwVLqtg/kcdZE+7VY06Yxd6DxwpjKorC/iMnmDlhNJ+/uYTL8Yn8uHpTlcUF8L6hL+ZmjchY+XWVLrc09tN/Yln2MJZ35qOcP4L7UH0cF1OHfihnDrhUkqqKoqpcTMvm3bH9eG5kd576cTdZFivdoiPp0agGo99dw7yvd9CqdgiGynYjKseqPw4xtEdb1r4yizdm3s2CFd+iOn6Mt4qqzfeLp/DZExN475etFFhtFSztr8nbsZ/cLXuo/dlLRL44j/wDx/WxOqqYwWjgrtemsGPlatIu6ZXA4+v281yPqbwyeC6ntx3mthcfrLJ4qw+eo1+LehgNrpe25Kw8Fn65hSdH9sBg+Ov78t9k9aEY+jWvUyLPVRpj32n6tYkujKEoGn+ejWPG8B58Out2YlOz+GnX8SqPazAauO21h9i58nfSLxX9iNj18Vpe6jmd1c99Tq8pw6s8LoDPjX1xb9aIjA/+/vOfs7+zbBas/oGsKaPI+7ToeonRiKlJS3Jfe5bsR6dg7nwdphbtqiReZRz+cB0f95jJzsVf0HHq8L8tzpHnvubXDlO5+N0Oou8b4DItsn87UvacuurH4P5p1bk/Z02+n71/HubWMZPZe+Aw4aHBGAwGunduz3VdO3D3hJnMfnwJrZs3qbJz0awH72PvgSPcOm4aew8cLYx58XIc52Ius/6b99nw7Qfs3n+IfQePVrzAq+A9RC//mR+6ln9jSBChz8wl9fEX/lIv+UJC4H7TfRT8/EGJSYY6DUFVyX16HHmLJmK+fhgi6OpuIJRFOXcQy3uPkP/xk6gxxzAPGuuawNsfQ0hN1Jiq3a6r956gX7tGhcdIt2b16dGiAaOXfsq8d3+hVf0aVX6N9hrUD7emjcj+RB9Py/uWYVh27EJJSqlgzr8m+Yv1WONTafH789R9aiw5e0+49B48edfT7G87DoPZrbCXU1W47bahfPnlj4Wfb799GB99/BUNojoydNi9rPzg1Uo/uv7/nfYf/vdvJcdYgn1AeyGEH1AA7EdvYLoOmAp0BDZpmpYM4OjFdD3wA6AA3zqWkwnkA+8JIX4Bfqlk/K81TbvS1cQf+FAI0RD9uLnybMH1wGsAmqYdEkJU8FBwCYeAT4UQPzjWuwQhxHhgPMAbSx7n/rtHFk4LCwkmIamoxT4xOZXwENcW+7CQIF55ai4AeRYLa7fsxM+nqJfB6o076NOjM26myh1yaloyhpCiuw+GoFC01LLvgtm2b8B7/HTyAOw2tBz9h7ly7hRKQhzGGrVRzp4sc/5/Io9hIUEkJBdd2MqM+eScophb/8DPx5vw0GAaR9Wjdg29ktGneycOHjvFzfQtN6aSlILR6e63MSwEe2LJi6tH57YE3H8X8eNmgu2vNXJo2elOPZBA+AahOQbpLuR0F87+50bMffS7rMZa0RhqN8bUvp/eJdxoQrMWYNvoOuBmcWG+niRkFt19TszKI8zPtXEx3M+LFrWCcTMaqBnoQ91gXy6mZdOiZjAP9GzBAz31RxDnfb2duiHlP+oQFuhLQlpm4eektCzCA117xny/eT9vzboXgNbRdSiw2UnPySPYr6jXSIMaoXh5mDkTm0Tz+jXLjWlPSsXktC9N4SHYElPLmcNV2ttfkPa23oEx4vm52C6UPQBy13v60+lOfYyEywfP4V+j6I6rf0QQWQmlN/zdvPgBUs4nsO39VYXf5WUU7evdX2xgyLy7yl3PMD8v132ZmUeYX+njd/x+8DyPDO/i8l1OvpUpK9fy0MD2tKpT/uD2zUf3o8ldvQFIPngOb6d8ekcGkZeQXtasVSrMz5MEp15yiZl5hPl6lpr298MxPHJTx6uPEeBNgtO+SMzIIczfp9S0v+8/xSMjexV+Dg/woXHNEGo5HjPs3bIBhy4kMKKMYRs639OfDnfq2zX24Dn8axSdD/wigsgqY7sOW3w/qecT2Pn+76VOP/zzToY+M7bUaaWxJ6UUKzOhKKWUGc8ubQl44E7i7pt1Tee/6iyb4LheBl/l9fKB6eS9AWpqMvZjB9Gy9fOXbf8fGBs0xH5kf6nzthzdj2aOfZl08Bw+TmXEJzKInGssI6d+/IOez97nEqf1HXqctIPn8KoRzJUt6BUZhCXeNY4lPh1Pp+PKMzIISynnpZjvtnPdJ7M59sK3hd/VGd6Fiz/sJLTd4Gta96pWnfsTICw0xKWXUWJSCmGhwcXSBPPqYr2nQ16ehXWbtuHnq58vJoy+kwmj7wRgzhNLqFu7/GsXXKlzOdd/SokZEsyrz84virllB36+PnzzyxpaN2+El5d+TuzRuT0Hj56gfeuSL+hwppQo/yGlNi54dG6L//13kVCs/iO8vQh7/RnSl31AweHKNaRrWWmIgKJeKsI/GC3T6Vzg7okhog6eE5/Rp/sG4DFmPvkrF2Fqez3KyT9BVdByM1EunMBYKwp7WvmPMmk5GQhfpzqXTyBadoZrovyia6r9yFbcrncd683UqAPKGT12RcICfUhw6mWUmJFNWGAZ15K9J3jkjn4u3z0wpCsPDNEvHvPe+4W6YeX3YgZHXTa86HpuDAtBSS5ZRtw7tsN3zCiSJ00v3JfuLZthbtMSn1uGIbw8EW4m1DwLWW+WHEDbmpCK2ekcZ44MxlZsPMMraazxqWA0YPTzwp6mb4+LTxQ1GDb7aRH5Z117uWkFNtJX7yFwYEf4vij+xImjGTdWrxvt3XuQ2rVqFE6rVTOSuLiEUrdLq5ZNMZlM/Pln0dAR9425gxtvuhuAXbv24+7hTkhIEMnJlb8mSVJl/c/3WNI0zQacB8YAO9B7MPUGotF7IZUn/0qjkKZpdqAT8A1wI1B6Tbgk577XTwMbNU1rAdwEeFRyGQB2XPen87w3AG8A7YA9pY3npGnaCk3TOmia1sG5UQmgRZNoYmLjuRyfiM1mY9WGbfTq5vpDJj0zq7D3xbuffseIwa4NHKs2bGVI3x6Vzoxy5iSGyFoYwiLAZMKtRx+se3e4pDFEFlVe3Np3QYnXK9/Cz19/Jh0whEdijKyJmlh+l+V/Io8lYm7cXn7Mz75nxCD9x32LxlFk5+SSlqFXHnf9eYSourUqjFlw9CRudWpiqqlvV+9Bvcjb7NoF19wkipBHHyZx2mOoaRmVzk9Z1LhzGIIiEAGhYDBibN4F+ynXSq7wCSj829ioPapjYO+CH97C8vrDWJZNx7ruM+yHtlbYqATQvGYwF9OyiU3PwWZXWH04hp5NXCu7vZvWYu8FvRdEem4+ManZ1Ar0QVFVMvL05/JPJaRzOjGDrqU8WuUSr35NLiamcTk5HZvdzu+7DtOzreubTSKD/dl1TB/D5lxcMlabnSBfby4np2N3PMYYl5LBhfgUaoQEFA9RQv7hk7jVrYGpZji4mfAb0pPcjaUP/liCwYDBMcaTuVF93BvXJ3d72eNi7Px4La8OeYRXhzzC0TV7ae8Y1LlO22jys/PITs4oMc+Ambfh4evJz0+5Pt7iPB5Ts/7tSTpb/o/m5rVCuJiaRWxatr4vD56jZ7OSjz2cT8ogy2KltVPjkc2uMOPjDdzYLpr+lRiD6OiH6/h24AK+HbiAC7/vo9GtenkOaxeFNTuv1HFi/g7NawZzMbX48VuyfJ9PziQr30rr2mU/zlFmjDrhXEzOIDY1U4+x/xQ9W9YvGSMxjSxLAa3rF3W9b143jGxLAWmOx/N2n75Mg3Ieh9z18VreGDKfN4bM59iavbRxHD+12kZTkG0hp5Tjp9/MkXj4evHbUx+7fB9cr2g9GvVpS+qF0ivYpSk4chK3uk7nv8E9yd1UyvnvsWkkTLn28191lk0o5XrZvZTrZYTT9bJd0fXSfnA3xjoNwOwOBiOmZm1QnAaJLu7wh+v4ctACvhy0gHOr99HkFr2MhLe9+jLiX6+o90W9vm3IdNqXhz9cx9r+81nbfz6xq/ZSd6R+zAS1i8aWbSG/WJz8pAzs2RaC2kUDUHfkdcT9rm83n/pFcWoObE/2maJBjE2+noR2aVqY9t+gOvcnQIsmjbh4OY7LcQl6XWT9Znr3cG2gT8/ILKyLvPPxl4y4Qe/1pSgKGZn6Y98nz5zn1JnzdOtUcaf6Fk0aFou5ld7dXd8klp5RVP9559NvGDFEb5CIDAtl74Gj2O0KNrudvQeO0KBuxY/CFRw9ialOTUw1HOV/YCn1n8ZRBC98mKSHH0NNzyiaYDIR9tIT5P6ylrx1WyuMdYV66TSGkEhEYBgYTZja9EA55vR4bX4euU+MJm/xBPIWT0C9eIr8lYtQL59FS0/GGO3oyeLmjrFuI9Tkit+AqSZcQASEIfxC9GOgSUeUcwddE3kXPbpvjGqDmuZ6HjU26YT9ZOWGQWheN5KLSenEpmTo15I9J+jZKrpEuvMJqWTl5tO6QVEjiaKqZDjGGzp1OYnTscl0bVavwpjW4ycw1a6JMVLfl579+2DZ4rov3RpFEzhvBqmzF7rsy7THF5Ew7E4SRtxF5mvLyfttbamNSgA5B87gUT8S99phCDcTQcN6kL7G9fHojDV7CBmpN4IH3di1cFwlg6cZg2MQcr/rW6PZFSynL2Pw8sAtzPHoodFAQL/25Bd7s+ny5R/SsdNAOnYayE8//84oxxsTO3VqR2ZmNgkJpb+p9Pbbh7v0VgK4eCmO3r3183STJtF4uLvLRiXpbyN7LOm2ArOAscBh4CVgn6ZpmhBiN/CaECIESAfuBF4vvgAhhA/gpWnab0KI7cCVUVCzgcqO7OgPXDm7jHH6fgtwF7BBCNECaEVJiUCYECIYyMHRuCX0V4XV1jRtoxBiG3AH4ANkVHKdMBmNzJ96PxPnPIWiqowY3Jfo+nVY9v7nNG8cRe/undhz4AivvvMpQkD7Vs1YMG184fyxCUkkJKfSoYK7SS5Uhbx3X8Xn0efBYMC6YRXqpQt43HEfypmT2PbuwH3wCNxatUezK2i52eQuW6yvb7PWeN5xH5pdAU0lb8VLaDnlP7P9T+TRZDQyf8r9TJz7jCNmH6Lr1WbZB1/oMbt1ZM+Bo7z63qcIhB5zqv6mO6PRyMwJ93L/rCfRgGYNG3DrDf3KDwigqKQuXkbEW4vBYCD7h9XYzsYQ8OBorEdPkbd5J0HTx2Pw8iTsef3upD0hiaRp+lubIj94Cbd6tRFentRe8xkpT7yEZUf5gx+iqVh//xCPO+for749sBktJRa3nregxp1HOb0fU8cBmBq1Q1MVsORS8PPbld6OpTEZDcy7oQOTPtqIqmoMa9eA6LAA3lx/iGY1g+jVpBbdoiPZeSaem1//BYMQTB/YhgAvdwpsCmPfWwuAt7sbz97SDZOx/DZ4k9HII/fcwKTnP0JVVYZf347oWmG88d16mterSa92TZh55yCeev9HPlm9AyEET90/AiEEf56K4f1ftuJmMiKEYP69NxLoW/4bdQBQVJKfeZNa7z4LBgNZ363BeiaG4Cn3kH/kNLkb/8C9RSNqvP4oRj9ffHp3JnjKPcTcNAFhMlL7Y/0tRmpuHglzloJSucdATmz8k8a92zBn8ytYLQV8PbtoX037bTGvDnkE/4gg+k4ZQdKZWKb+qj/Ru+PDNez5ciPd7xtEs37tURQFS0YOX80qf5wDk9HAvKFdmPT+Gn1fdmhIdHggb67ZT7NaIfRqpo/V8PvB8wxqXd+li/eawxfYfz6BjLwCftqnDyb61MgeNKlR8Xg3FzccoE6f1tyx7UXs+VY2zVhROO2W1c/y7cAFAHRecAfRw7th8jQzas9rnPh8E/te+o7Q1g0Y8O7DuPt7Ubd/WzrMuIWv+86r1DY2GQ3Mu7EDkz7c4Dh+o4gOD+DN9QdpViOYXk31RqbfD8cwqGXda+rWbjIamHdrTya9+ROqqjKsSzOiI4N589c/aFYnjF4tG+gx9p1mULuGLjGMBgPTh/dgwhvfo2nQtHYot3Sr3Lnv1MYDNOrdhhmbX8ZqKeA7p+Nn8m+LeGPIfPwigujlOH4e/FV/I+UfH65h35eb6Dx6AFHdW6Da7Vgyc/l25luVz7SikrJoGRHLFyGMBrK/189/gZPvpeDoKfI2/UHQzAcQXp6Ev+g4/8UnkTi15BvIKopTrWVTVch771V8FjiulxtXoV6+gMft96GcdbpetmyPpihoOUXXSy03h4JfvsbvueWgge3PP7Dvr1wjWMyGA9Tt05p7tr2I3WJl/cyiMnL778/y5SC9jHSbfweNhnfDzdPMmN2vcezzTex++TtajRlArR7NUe0KBZm5rJte+nk/Yf0BIvu2YfDOl1AsVvY4peu/dhFr++u9WvY/8gEdX5mA0cNMwoaDJGzQf0y3XHAHvlGRaKpG3uUU9s19v3D+moM7krD5MIqloFJ5Lm7248+x589DZGRk0Xf43Tw47h5uuWlgxTOWp5r3p8lkZP70SUyYsRBFURhx4wCiG9Rl2Tsf0bxJI3pf14U9fx7ileUrEULQvnULFs7UH2G22xXufXAWAD5eXjz32GxMpkqMEWgyMv/hCUyY9YRe/xnST69zvfcpzRtH07tHZ/YcOMwrb3/kiNmchdP1kRwG9OrG7v2HGDFmCkIIenRuR6/uJQfELkFRSXtuGeGO+k/Oj476z6TRFBw7hWXzTgKL13/ik0h6+DG8B/TEo11LjAF++AzV92/KY89jPVnaCBdOVJWCH97B84HHwWDAtns9auIlzAPuRLl8xrWRqRjbjlV43DYFz5n6o0u2PRtQ48tvJAT0OtfGz3C/5WEQAvuR7Wipcbh1G4qaEINy7iBubftgbNAGNAUtPxfr70U9a4RfMMI3EPXSqYpj4biW3N6PSa99o19LurUkukYIb/60jWZ1I+jVWm9k+n3PCQZ1bOJyLbErKmNf0MfQ9PY08+x9QyqsbwGgqGS88Dohry1BGIzk/rwK+/kL+I0fg/X4KfK37sB/ygSElwdBi/Tzt5KQROrshZXKk3OcCwvepfFnjyGMBpK/WI/l1CVqzr6D3INnyVizh6TP1xP12jRab38De0YOZybpb4k2BfvT5PPHQNWwJqRydoo+DprBy51GKx/BYDbp14cdR0j8aHWZq7Bq1QYGDerD8ePbsOTlc/8DMwqn7dm92uVtcLfceiPDht3rMv/cOU/x1ltLmTb1ATRNc5lfkqqa0KriGeH/54QQfdF7GAVompYrhDgFLNc07SXH9DuB+YAAftU0ba7j+xxN03wcf0cCP6L3FBLAC5qmfSiE6A68g/6Y3a3O4ywJIVYCv2ia9o3jc1fgQ/ReTL8Cd2uaVk8I4Ql8AFwZy6kmMFnTtL1CiAtAB03TUhyDkE9Db5w6B1wAngU2ojdaCeATTdOeK297WOOOVutBkTtlcnWGA8D79TeqPWZl37RTlWJvqN4LSNjIqnn+/2oYGjas9piibpOKE1Wxi/eVHIPh7/aepXoGq77iiZdbV2s8gI+mHKk4URW796VG1R4T/4ofL6hqz07YUXGiKjTW9+8dM6M0dnv1d/wObVG5NxtWpU//qLhHbFULt1dv/XT44acrTlTFciZV/lHOquLz1vsVJ6pqyt87bmBpYgc+VK3xQvqX/vjX38kQefW9Vf8q0bZzxYmqWOq8z6o9ZuylgGqNd13qX3tRzrWyFlz+zw649GzdUf/ZRo4FMZ/+K/eb7LEEaJq2nqLxjNA0rVGx6Z8DJV5LdaVRyfF3PPqjcMXTbAealRF3TLHPOwHn2Asd31vQexqVtox6Tn+/hmMspmIq/3yWJEmSJEmSJEmSJElSJf3Pj7EkSZIkSZIkSZIkSZL0XyaECBJCrBVCnHb8X+LxBCFEGyHETiHEUSHEISHE7ZVZtmxYkiRJkiRJkiRJkiRJ+m+bB6zXNK0hsN7xubg84F5N05oDg4BXhBABFS1YNixJkiRJkiRJkiRJkiT9tw1DH9MZx//DiyfQNO2UpmmnHX/HAUlAaEULlmMsSZIkSZIkSZIkSZL0n1D9r0yqPkKI8cB4p69WaJq2oqz0xYQ7xoYGSADKffOSEKITYAYqeA2mbFiSJEmSJEmSJEmSJEn613M0IpXZkCSEWAdElDJpQbHlaEKIMt+e53jr/cfAaE3TKmyrkw1LkiRJkiRJkiRJkiRJ/89pmtavrGlCiEQhRKSmafGOhqOkMtL5Ab8CCzRN+6MyceUYS5IkSZIkSZIkSZIkSf9tPwGjHX+PBn4snkAIYQa+Bz7SNO2byi5YNixJkiRJkiRJkiRJkvSfoP2H//1FzwH9hRCngX6OzwghOggh3nWkuQ24HhgjhDjg+NemogXLR+EkSZIkSZIkSZIkSZL+wzRNSwX6lvL9XuB+x9+fAJ9c7bJljyVJkiRJkiRJkiRJkiTpmsiGJUmSJEmSJEmSJEmSJOmayEfhJEmSJEmSJEmSJEn6T1D/6RX4HyQblqQSMu55sFrj+b/1bLXGA8gcXb15BFDt1R6ScwkR1RrP7ee4ao0H4O53qdpjauqGao+Znhlc7THbVHOn1rhHN1ZrPIA7B1Z/1SPuqeRqj/lPaGQLrdZ4uTnu1RoPIN9a/dWotO1e1R7TT1R7SDy06i2bOZPGVms8AJ+33q/2mLmTx1V7TNVa/edZYXCr1ni2C+nVGg9AXMyo9pjaztPVHlNVjNUeMzwsu1rj9TW2rNZ4kvR3kI/CSZIkSZIkSZIkSZIkSddENixJkiRJkiRJkiRJkiRJ10Q+CidJkiRJkiRJkiRJ0n+C+g88wv2/TvZYkiRJkiRJkiRJkiRJkq6JbFiSJEmSJEmSJEmSJEmSrolsWJIkSZIkSZIkSZIkSZKuiWxYkiRJkiRJkiRJkiRJkq6JHLxbkiRJkiRJkiRJkqT/BBXtn16F/zmyx5IkSZIkSZIkSZIkSZJ0TWTDkiRJkiRJkiRJkiRJknRNZMOSJEmSJEmSJEmSJEmSdE3kGEuSJEmSJEmSJEmSJP0nyBGWqp9sWJKumrljJ3wmTwGDgfzffiXvi89cpnvcOBSvYSPQVAXNYiH75RdQYmKuOs62fYdZ8s7nqKrGzf2vY9zIIS7T45JSeOzVD0jPysHfx5tFM+8nIiQIgImPv8zhk2dp27Qhyx6fdk35dOvQCZ8HpyAMBiyrfsXyZcl8eg4dAc75vHh1+TR37ITvQ1PAaMDy66/kfe4aw2vkbXgOuQFNUVAzM8haugQ1MREAn/ETcO/SBYCcjz+iYOPGMuME9W5No2fGIIwG4j7dQMzrP7pMF2YTzZdNxrdVA2zp2RwZ/yr5l5LxaxtFkxfGOxIJzj//Ncmr9gBg8vOi6UsT8G5SGzQ4Nv0tSEkuNb57l44EzHgIYTCQ+9NvZH/0uct0nztvxXvYEDS7gpqRSfozz6Mk6PmsuWMttrPnAVASkkidvbAym9ZFde3L8sqF5636vkRRUDMyyHp+CWqSnkfvBybg3lnfl7mffETBprL3pX+vttR9eizCYCDp83XEL/veZbowm4h6bRreLRtgT8/m9MQXsV5ORriZqL90It6totBUjZjH3iN751EAas29i5CRvTD5e7O34ahS47Z7+l5q9GmNYrHyx/S3ST98oUSawJb16PLKRIwebsRtOMj+Rz8CoMXMm4m6qzcFadkAHFz8JfEbDmIO9KHHimkEtWnA+a+2wIsvlhrbs3sHQuZNRBiNZH27ioz3vnKZ7tG+BSFzJ2Ju1IDE2YvIXbutcFrQ9HF4X98JgLS3PyP3981lbltnxhYd8LjzQYQwYN26CuuqL0tNZ2rfA68HHyfnqcmoMacQ3r54PvgYxnqNsW1fQ/5nyyoV72rz7H/vzfjdMghNUVDSMkl+9CXs8UnXFKs64nV86h5q9mmDYilg+/QVpB25UCJNUMt6dH95AkYPM7EbDrDnsY8BuP6th/CLigTA7OeFNSuPXwYswLtWCMM2LSXrXDwA2oFjxC18s8Ryfa5vR43HHwCDgfQv15K8/BuX6V6dmlPj0QfwaFKPi1OXkrVqh8t0g48njda8SdbaP4h7/O1K5/kKv15tqfPUOITBQPLn60h44zvX9evcjDpPjsWraT3OPvgi6b/urNRy/Xu1pZ7TuSCulHNBdLFzQcHlZITJSIMXHsS7ZQOEyUjy15uIW/YdHlE1aLh8ZuH87nXCufz8FyS8+4vLcv+OfVl/RDeaT7qhcP7AprWJ/WEngW2jUCxWDkx7i8xSzjn+rerT5tWJGD3MJK4/wNGFHwLgFuBN+7en4Vk7BMulFPaNfxVbZi7hA9vTZO5taKqKpqgcffQj0nafxK95XXyfXYjw9EJTVfK/+wTbDv08bGrTCa/7HgKDkYL1v1Lwg+u53dx/KB6DhqOpKuRbyH37BdTL+vXDWKcBXhNmIjy9QNPImjcRbNbK7N4yLVz0Elu27yYoMIAfPln+l5Z1hal1RzzH6Hm0bviVgh9dr9HmfjfhPnA4qCpavoW8FS+ixsbg1qMfHjfdXpjOUKcBOfPGo8ScrTCmW9tOeI3Tr5kF634l/zvX7eo+cCjugx3X5XwLuW/q29UQGoH/6x+hxF0EwH7qGHnLX6pUPj26dSRo1oNgNJDz/SqyVn7hMt131C34jBgCioKSnkHqky+gxCdhjAwj9IUnEQYBJhPZX/xAzre/lBHF1T9x/FT3/vwn8ujRtSOBsyaDwUDuD7+R9WHxfXkrPsOG6PXn9AxSn3oeJSEJY0QYoS88BULflzlffV/pfenZrQNBcx4Eg378ZH7gWi/wu/sWfEYMdhw/maQ8oR8/5sZRBM2fisHHCxSVjHc/I29N5eoiABOfnEjHPh0psBTw4owXOXuk7P3x+PuPE1Engkn9JhV+N3TMUG4cfSOqorJ7w27eX/R+pWNL0tWQDUtVSAgxHDiladqxq5lWyWU/DKzQNC3vr6zjX2Yw4Dv1YdLnzERNTibwzbcp2LndpeGoYMM68n/5CQBz1274TJxM5iNzriqMoqgsWv4pK56eSXhwIHfOeJpendsQVadGYZoX3/+Km/p0Y1jf7uw6eJzXPvyWRTMfAGDMzQPJL7DyzarKn7hL5HPKw2TMnYmakkzgsrex7tzu0thQaj7nX0U+DQZ8pz1MxuyZKMnJBC1/m4IdrtvSdvo0eRPHQ0EBnkOH4TthIplPPYm5SxdMDRuRev/9YHYj6OVXse7ahZZXyuFhEDR+bix/3vYsBXGpdFy9mJTVe8k9FVuYpMZdfbBl5LKzyzTCh3cj+tG7ODL+VXJOXGLPgEfQFBVzWACdNy4lZc0+NEWl0TNjSN14kMP3v4xwM2L0dIeg0vMZOHsayVNmoyQlE7byLSxbd2A/75TPU2dIGj0JraAA75uH4v/QeNIWPg2AVmAl6Z7xld+upW3n6tiXFZQL+5nTpE1y7MubhuEzfiJZzzyJuXMX3Bo2Im28vi8DX3wV6+6y9qWBeose4MQdT2KNT6X5b0vJWL0Hy+nLhUlC7+yHPSOHg90nEzSsO3UW3suZiS8SNqofAIf7TscU7E+TTxdyZPAc0DQy1u4l8YNVtN5eeiNIZJ/W+NaP4JfuMwluF02Hxfex9sbHS6Tr+NxYds9+l9T9Z+j5yRwie7cmfuNBAE6+s4oTy39zSa/k2zj0/NcENK6Nf5NaZW7b0IWTiXvgEewJKdT68nVyN/6B7dzFom0bn0zSwhcJGHOry6xe13fCvVk0l26dhDC7UeOD58nbugctt4LTqDDgOWoKuS/ORUtPwfvRZdgP7ESNv+iazsMTc78R2M8eL/xKs9ko+H4lhpr1MdasV36cslQizwXHz3L59ilo+QX43X4jwTPvJ3HWon9lvJp9WuNXP4IfeswkpF0UnRePYdVNT5RI12Xxfeyc8y4p+8/S9+PZ1OjdiriNh9gyqei4bP/YXdiyivZfdkwivwxYAEBbc2apeavx1ETO3/Mo9oRUon58iax1uyg4c6kwiS02mcuzXyHkgRGlrn/4jLvJ3X20UnktLX7dZ8dz6s4nsMan0uy3pWSs2U2+U5m1xiZzfvrrREwcdlXLrb/oAY47zgUtfltKerFzQZjjXHCg+2SCHeeC0xNfJOimbgh3Nw71nY7B00zrTa+R+sNW8s/Gcbj/zMLlt9v/DmmrdrmE/bv25fnvd3D+e71BL6BJLfp9NhdzgDcbuk4noF00LZeMY9uQR0vEablkLAdnvkPG/jN0/mwuYX1ak7ThINFThpGy9Qhnlv1E9ENDiZ4ylOPPfE7K1iNsXr0PAN+mdeiwYiobr5uFYikg9/VFqAmxiMBg/JasIOvAHrT8PLzGTSPn6Vmoacn4Ll6Obe/2wh/FANZt67Cu1a8fbh264TV6MjnPzgGDEa+pC8h7fRFKzFmEjx8o9srv4zIMH9Kfu24ZyvynX/jLywL0893YaeQ+Oxs19Uoed6DGOuVx+3qs634GwNS+G573Pkju4rnYtq3Dtm0dAIba9fGe9XSlGpUwGPAa/zDZT8xETU3Gb+nbWHe7bteCLesoWO3Yrh274XXfZHKe1q/LSmIsWTPuv7p8GgwEzZ1C0oNzsScmE/nJG1g278B2vug8Zz15hoS7H0TLL8Dn1psInDaelHnPoCSnkTBmKthsCE8Panz9LpbNO1FSUivOZ3UfP9W9P/+JPBoMBM6dStLkOSiJyUR89CZ5W3a61CutJ86Q8I1er/S55SYCpo4ndf4zKClpJNw3pXBfRn75XqX3ZdAjU0icOBd7Ygo1Pl1G3uadLtdJ64kzxI+ajJZfgO/IGwl6+AGS5z6Laskn5dGl2C/GYgwNJvKzN8jfuRc1O7fCrHbs3ZEa9Wsw7rpxNGnbhIcWPcT0odNLTdttUDcsuRaX71p1bUWXAV2YPHAyNqsN/2D/CmNK0rWSYyxdAyGEsYxJw4Fm1zCtMh4GvK5mBiFElTccmpo0xR4bixofD3Y7BRs34N6th0sa5x/EwsPzmuIcOX2OOpFh1IoIxc3NxKDrO7Fx158uac5djKdzq6YAdGrVhI27DhRO69K6Gd6eHtcUG8DUuClKXCxqgp7P/E0bMFeUz6vsc+nWRI+hOLZl/oYNuHd3jWE78CcUFOh/HzuGITRUX7+69bAdOgiqAvn52M+dxdypc6lx/NpFYzmfSH5MEppNIfGHHYQM6uiSJnRQB+K/0hvhkn7+g8AeLQBQLVY0RQXA4OEGmp5Jo68nAV2bEvfpBn1b2BTsWaX/WDc3a4L9cixKnJ5Py9oNeF7fzSVNwb4DaI58Wo8cwxgWWoktWDnVsS8rUy5c9uVx131pdd6X589i7lj6vvRpG03+hXgKLiai2eyk/biNwIGdXNIEDuxIytf6nfa0X3bi16MlAJ6NapO17TAA9tRM7Jm5eLeOAiBn/ylsSell5q/WwPZc+GYrAKn7z2D298IjLMAljUdYAG6+nqTuPwPAhW+2UmtQ+3K3m2IpIGX3KZQCW5lp3Fs2xnYxDvvlBLDbyVm1Ce8+XV3S2OMSsZ46r98NdWKOqkP+3sOgqGiWAqynzuPVo0O56wRgbNAYNSkOLSUBFDu23Zswte1WIp378DF6Tybnu6vWfJQzR8F+7b0SKpPn/D0H0fL14yn/4HGM4SH/2ni1B7bn7Dd6L7KU/Wcx+3vjWez48XQcvvZLLgABAABJREFUPyn79R8xZ7/ZRp1BJfdVvZs6c/7HyvXoAfBq3RBrTDy2S3qZyfx5C379XcuXLTaJ/BMXQC1Z8D1aRGEKCSB7658lplWGd9uGFFRQZq2Xk7Ecjyk1flmKnwtSyzgXJDvOBalO5wI0DaOXOxgNGDzMqFY79hzXHyP+17WkICYRa6xrL9Tq2Jf1h3fDkpTJpa/0c07G/jO4+XnhXiyOe1gAbj6eZDjOOZe+2kqEI07EwPZc+mqL4/sthd8reQWF85u83K9c0sg9l4CaoN9s0dJTUTPTEX7+GKOboCbEoibp53bb9g2YO3R3XWGL07XP3aPwOmlq3QEl5lzhD3MtJwuKnaOuRYc2LfH38/3Ly7nCGN0ENTFOz6Nix7pjA24dy86jcMqjM3P3voW9vCpiatgUNT4WNVHfrtZtGzB3cr1musa8trqky/q1aIz9chz2WD1m7upNePZyzWfB3qLzXMHh4xjDHOc5ux1s+nVKmM0gKvfz6Z84fqp7f/4TeTQ3b4L9UiyKY1/mrdmIV8+y65UFR45jCnfUK4vvS4OoMB6Ae4vG2C/FYY9NKDx+vHq5xsx3Pn4OHcfoiGm/GIv9on5+UZJTUdMyMAQGVCpulwFdWP/tegBO/HkCHz8fAsMCS6Tz8PLg5gdu5ovXXHtu3XDPDXz15lfYrHqeM1NLuQEjSVXkf6phSQgxWwgx1fH3y0KIDY6/+wghPnX8facQ4rAQ4ogQYonTvDlCiBeFEAeBrkKI54QQx4QQh4QQLwghugFDgeeFEAeEEFFO85aY5vj3uxBinxBiqxCiiRDCJITYI4To5ZhvsRDiWcc61wA2CiE2Xlkfp+XfKoRY6fh7pRBiuRBiF7C0tDh/ZRsaQ0JQk4sef1CTkzGElPyB4TlsOMEff4bP+InkLHv1quMkpmYQHlLU/SU8OJCk1AyXNI3q12bdTv3O4/qd+8m15JORlUNVMISEoDjnMyUZYyn59Bg6nKAPP8P7/onkvHl1+TSEhKAmuW7L0mJc4TlkCNZd+t1j+9kzmDt1And3hJ8/bm3aYgwtvTHGIyKI/LiiOzEFcam4R7helNwjgyiI1dNoioo9Ow+3IL3i6tcums6bX6Dzphc4MftdNEXFs04Y1tQsmr46iU7rnqPJSxMweLmXGt8YFoKSWJRPJSmlzHUF8B46hPyduws/C7OZsJVvEfreMjyu717mfGWpjn1Z2XJRGGvwEKy7nfZlx6J9aW7dFkMZDWvmiGCsTvvSGp+KW2RQ2WkUFSUrD1OQL7lHLxAwoCMYDbjXDsO7VRTmGpVrHPCMCCLXKW5eXBpexY4hr4hA8uLTXNJ4RhStW8P7BjB43WI6v/QAbv6VbyM3hQVjTyj6cWtPTMEUVrn1Ljh5Ds8eHRAe7hgC/PDs2BpTRMWNliIgBDWtKKaWnoIhwDWmoU40hqBQ7Id2F5/9L7vaPPvdPIi8rXv+tfG8IgLJcz5+4itx/JSSJqxzYyzJmWSfTyz8zqdOKDeufoYB3yzAq2PJezemiGBs8SmFn20JqbhFBFduxYUgcsE44v/CYwPmiCCscUXxrfFXEb/c5ZY8F5greS5I+2UnSl4B7Q+8R9s9K4hf/iNKhuu1M3hYD1J+2Foi7t+5L6+od1NnbDkWl+uWJT4Nj2L584gMwuIUJz8+tTCNe6g/BUkZABQkZeAeWnSXPmJwB3pvfYFOn8zh4PSSjzYao5sgTG6oiXEYgkJRU4vKhpqWjAgueQ5xHzgcv9c/xevuieS9/5q+nMjagIbPgqX4LlmB+9A7Ssz3b2AICkFNdbp+pSZjCCxZ/s0DhuP76id4jpqAZeXrJaa7de2Fdcf6SsUUQSEoKcViBpeM6T54OP5vfYbn6InkvVt0XTaGReL34rv4PvMqpqatKhXTFBqCPcG5LpKMMazssugzfBD524vOc8bwUCK/XEHN3z4j88MvKu7hAv/I8VPd+/OfyKNer3S6ZiUlFzUClsJn2GAsO4qu1cbwUCI+f4cav35O1odfVmpfGsNCSlwny405YjCWbSXrB+YWjcHNDfuluApjAgRHBJPidA1JiU8hJKJk3Htn38t373xHviXf5fuaDWrSolMLXv7pZZZ+vZRGrRtVKu5/gfof/vdv9T/VsARsBa5z/N0B8BFCuDm+2yKEqAEsAfoAbYCOjkfYALyBXZqmtQaOAyOA5pqmtQKe0TRtB/ATMFvTtDaaphX2HS1j2gpgiqZp7YFZwJuaptmBMcBbQoh+wCDgSU3TXgPigN6apvWuRD5rAd00TZtRWpyr22TXxvLjD6Tecxc577yN1933/i0xZo4dyb4jp7ht2hPsPXKSsOBADIbqPaTzf/qBtNF3kfvu23jd9ffkE8CjX39MjRuT+6V+J8K6dy/WP/4gaNkb+D/6GLZjR6vkTmhpsvafYVfPWewZOJ+604ZjcHdDmIz4tqxP7Idr2d1vHmpePvWmXMVjHGXwGtQPt6aNyP6k6Ln1+OF3kjRmEmmPPkvA9MkYa9YoZwnXrrr2pXu//rg1akzeV459uW8v1l1/EPjaG/gvdOxLper3ZfIX6/VHZn5/nrpPjSVn74m/7Zgp7syH6/il63RW9Z+PJTGDdo+XPo5TVbPs2E/e1j3U/ORlwp9/hPyDx6tm2wqBx+3/x959h0dR/A8cf+/dpffe6AlFWkJVmvQq0lFpgnQERLoUG4oUBQFBUFBBBQtWLPTee2jSO+m9J1d2f3/skeRSD8Tgz++8noeH3O3cfm52Z3fnZmdmR5P93YPPt/OoOXdtg12tqiR/8UPpif8fxsuvco8m3MrXwyUrNpmfGr/K7x1nc+Lt9ZRfMgWN89/v2XCf16AupO05gTG69B8d/5841asKJplT9YZz+skxBIzuhl0Fv9zlko0Ojw6NSPztUAlr+XsK7sv7vOsFY8zSY8zILuJTD0/J1yMjevMJdreYwvGXFlF9el+LdJK7J07jZ5Lx8YIie3EUJ2frL6SOH0Dm+k+w7z1IfVOrRVejDhnL5pL2+nhsn2yBrnb9R5Kfx0G/7RfSJgwka8On2PcaZLFMG/IE6HOQ7956pDFzNv9Cypj+ZH35CQ591euynJRA8sjnSJ08nMzPV+A06XVweKAO/aVy6tIWu5rVSfkyb645U0wcUc+PJLL7YJy7dkDj6f7I4j2O8lPW+/NxHSOOndth+0Q1Ugvsy+h+I4jq8SJOXTug8SzcA+jvUMtPNVLWbbR4X+vtic+700l484MHOr+UpkrNKgRUDODQlsLnbK1Oi4u7CxO7TWTN3DXM+HjGI4srCAX9rzUsnQQaSJLkCuQAh1EbmFqgNjo1AvYoihJnbuRZDzxt/qwJ+NH8dwqQDXwmSVIv4IHmPZIkyRloCmyUJCkc+AQIAFAU5QLwFfA7MFRRlIcZT7FRURRTSXGK+E4jJUk6IUnSiS8joopdsSk+Ho2Pb+5rjY8Pcnx8selzdu8sNCTIGn5e7sTE592JjElIwtfL3SKNr5cHH84cy/dL3+KVQb0AcHV+NJULOT4ebf58evtgKimfe3Zi2+zB8inHx6PxtdyWRcWwrd8Ap4GDSJ41M7f7LkDG+q9JHDGc5KmTQZIw3rtb6LMA2dGJ2Afm3ZWzC/QiJ9py2FNOVCJ2QWoaSatB5+KIwTzR8n2ZVyMwZWTjVKM8OZEJ5EQmkGoeghD721Fc6lQuMr4pNh6tX14+tb7emOIKT/Jt16g+LkMGkDBltkU+5Th1m5gio8g5FY5t9ZAi4xSnLPaltceFTf0GOPUfRPLrlvsyc8PXJI0aTvI0dV+aitmX+ugEbPPtS9sALwz57tgXSqPVoHV1xJiYBiaZO299wfn2k7ny0ny0bk5kXy/+jpnfkE502v4enba/R1ZsMk754joGepJZoAxlRifhmK9HgWOgJ1nR6nfLjk9FkRVQFK6v341nWDDWMsYmWPQy0vl5Y4wtfv8VlPzpN9zr8zJRI2aAJGG4fa/UzyjJ8Wg882JKHt7Iyfli2jugCaqE07QPcF7wFdrgJ3B8ZQ6aio/mTqC1eXZ4qh4eI/sRPf5Ni/L0b4jn+sKzdN02l67b5pIVk4xj/vITYEX5KZBG0mqo0LkRtzblzfkj643kJKk9bRLP3UJ/Jxq7ykGWeYtOwCYg7+6ujb8XBisbihzr1cBrUFeq719DwMyhuPdsg9+0wVZ99j59dKJFz0DbAOvjl7zewucCvZXnAu+eLUjefRrFaMKYkELa8Uu5w2IB3NvUI+PcDQzx6pAJvyGd/vF9CVB9cDvarJuMnaczWTHJFtcthwBPsgvkLzsqEYd8cewDvHLT5MSl5A6ds/N1Rx+fWmgbJh65hGNFX2zNvXNxcMR5xnyyvvkM01V1Okw5MQ5Nvt4XGk8flITC16/7DAfzhnTJCXEY/zqDkpYC+hwMp46grVK12M8+LnJiPBqvfNcvLx/kpOLPsYYihlbZNG2N/uAuq2MqifFovQvETCg+pv7ATmzuD5UzGlDS1P1punEFOToCbWD5UmMa4+LR+eevi/hgii18LNo3ro/bsP7Evvp6kec5U3wChuu3sK9Xp9SYj6P8lPX+fBx5VOuV+a5Zvj6Yirhm2TWuj9vQ/sRNKmlf3sTOin1pio0vdJ0sKqb9k/VwG96fmAlvWMSUnBzx/ehdkpZ/Qc65i4U+l1/XwV1ZvmU5y7csJzE2Ee981xDvAG/ioy3jPtHgCarWrcraQ2tZ9NMigioHseB7ddBNfFQ8BzcfBOBK+BUURcHNU8yzJPwz/qcalhRFMQA3UXsFHUJtTGoNhKD2QipJtqIoJvN6jEBj4AegK7DlAb+KBkg29166/++JfMvrAMmAb5GfNmcn398FJxO6PxtcaXHyVqYonyqK0lBRlIYvBhXZ9gSA8dIldEHl0Pj7g06HXes25Bw6aJFGG5RXsbd9qgmmiNJ/yBVUq2plbkfGcC86DoPByJZ9x2jVOMwiTVJKGrK5x8WajX/Ss92DN2AVx3j5Etp8+bRv1Qb94RLy+eSD59NwqUCMNoW3pS6kKi6TJpM8awZKcnLeAo0GydVVTVOlCjZVqqA/fqLIOGmnr+NYxR/7Cj5INlr8ejQlfqtl2vitJwh4riUAvs8+RdIBdaJa+wo+SFr1NGFfzhunkECy78ahj0shJzIBR/PTfTxa1CbjStH511+8hK58ENoANZ8O7duQtc/yTrVNtRA8XptEwtTZyEl5+ZRcnMHGRs2ymyu2obUx5Juc0RplsS+tOS50IVVxnTiZlNeL35faKlXQVamC/kTR+zI9/Br2lQOwK++LZKPDs3tzkrZZDklK3nYc775qx0bPrk1y51XSONiicVCHK7o+HYpiNFlM9FtQzNotbGk/ky3tZxKx5QSV+qidPb3qh2BIzSI7NtkifXZsMoa0LLzqqw1/lfq04J55ktz88zGV69yQlMvWb9+c85exqRCELsgPdDqcO7ciY/cR6z6s0aBxU3802larjF21ymQeOlnqx0w3L6PxC0Ly9getDpvGrTCG5yuzWZmkv9qH9OmDSJ8+CNP1i2QuewP59hWr81USa/JsWyMYnzdfIXrcm5gS/96cCf9EvNRvf+P3DrP4vcMs7mw9SXAf9fzsXT8YQ2omWQXKT5a5/HjXVxs4gvs05+7WvH0V0KI2KdciLYZY2Xm6qE9pQh0SZ1cpEP2daIv1Zp69il2lQGzK+SHZ6HB79mlSd1g3fPHuxEVcbj6Uyy2GE/Xe5yT/vIuYheus+ux9GeFXsascgG0Jx+zDKHgu8CpivUnbjuNjPhd45TsX6CPic+db0jjY4Vy/GtnX8h7m4NWjBQm/5D1ZMWbtln98XwJc/nInst7Ilh5zuLP1JOWfU8857vVDMKRl5g5tuy8nNhlDehbu5nNO+edaEG2OE73tJOWfe9r8/tO57ztWyuuZ5VanEhpbG/SJaUg2WpynvoN+7zYMR/Ie+mG6dhlNQDk0vuq53aZZG/QnCjw10D/v+mFT/ylMUeq2NJ45hrZCFbC1A40WXc0wTPce7PpVFkzXL6HxD0Ljo57vbJu2wVBCHnX18vIIgCRh26QVhkPWNywZr16y2K62zdtgOG55zdQE5NuuDZogR6nXDcnVDcy90zV+AWgDyiHHlD6sSH/hMrryQegC1ZhOHVuRtdcynzbVQ/Cc9Sqxr75hURfR+noj2dmqMV2csQurbdVNisdRfsp6fz6OPOr/uoRN+SC05n3p2KE1WfuK2JczJxI36fVi96Xk4oxdaB2Mt4q+mZdfzoXL6CpYlp/MvZZ1WdvqwXjNLlx+0OnwXfwWGb9vJ3NH4SHGBf2+7nfGdRrHuE7jOLz1MG17twWgRr0aZKRlkFRgTsw/vvqDgQ0HMqTpECb3mkzEzQimPzcdgMNbDxPaNBSAoMpB6Gx0pPzNOoMgFOd/8alw+1GHhA0FzgGLgZOKoiiSJB0DlkmS5A0kAf2AQgOPzT2BHBVF+VOSpIPADfOiNKC4GRVzlymKkipJ0k1JkvoqirJRkiQJqKsoyhlzDyhP1J5Sv0uS1FhRlOR8n7/fTB0jSdITwGXUYXlpBeKVGOdBNpgF2UTaR0twX/CB+dHtf2K6fQunIUMxXL6E/vAhHHr0wrZ+AxSjESU9ndQF8x44jE6rZeboAYx580NMskyPds0JqRjEiq9/oWbVSrR+Mozj5y+zbN2PSJJE/VrVmDUmb3jN4OnzuXUviszsHNoNmcLbrwyhWf3aD5TP9OVLcJun5jN7q5pPx8FDMV5R82nfvRe29RqAyYiclk7awgfMp2wibdkSPBZ+oD6ifvOfmG7dwumloRgvXyLn0CGcR49GcnDA7a231Y/ExJI8eyZodXguVYumnJlByty56uTPRVBMMpdnfE69b2eCVkPUN3vIuHyPKtP6knrmBvFbTxK5YTc1l4+jyZGlGJLTOT9KncvAvXENKo7vjmI0ocgKl177LLcn0+WZX1Dr4/FItjqyb8fy14SVBBfVm9gkk/zBR3gvW4Ck0ZLx22aMN2/hOnII+otXyN5/CLfxo5Ac7fF8T33SmCk6loSps7GpVBGP1yaiKAqSJJG27huLp35Yu53LZF+Wclw4j1T3pesb5n0ZG0vK6+q+9Fii7kslI4PUecXvS0wyt2atofqGN5C0GuK+3UnWlbsETX2BjDPXSd52nNhvdhK8bAKhB1dgTE7n2hj1Mcw6LzdqfPMGyAr66ASuj1+Wu9ryswfh3eNpNA521DuxmthvdhCxKG84YuTOcALahtH10GJMWXqO5puXpNP299jSfiYAJ2Z8wZNL1EeMR+0+Q9Qu9VQTNrsfHrUqgqKQfi+O49Py5qx59ugSbJwd0NjqkDrXI3LkTIunrGCSiX9vBQGfvIek1ZD68zYM12/jMfZFci5cIXPPEexqV8N/yRtoXF1wavUUnmNf5G6PkUg6LUFfLlK3d3omMa8tsG4onCyTvX45jhPnIWk06A9sRY68jV33wZhuXcF4puTJo50XfKU+Ollrg65eUzIXv1b4iXIlsSLPXpNHIDk64Ld4NgDGqFiix79lfYwyjBexM5ygNqH0PLgIY5aeQ5M+zV3Wddvc3Ke6HZ25lqYfjkRnb0vE7jNE7Mq7VFXq/lShoVN+T9UgbEpvZPP5KWL2CkwpBebZM8lEvrmKyl++DRoNSRt3kHP1Dr4TB5B17ippO47hULcqFVfNROvmjEvbRvi9OoCrHcc+xIYsgknmzuzVVN/wJmg0xH+3k+wrdwmc0o/MM9dI3n4cp9AQQj6bjtbNGff2jQia/ALn20wodb23Zq2hhvlcEGs+F5QznwuSzOeCkGUTCDOfC66azwXRX2wm+MNx1N29BCSJuO92kXlRPa9qHOxwaxHKzWlFP8b+n9qXoO7PjKhE0u/EkX4njiqt69LmyBJMWTmEv5p3znl6xzz2tVOHc5x77QvClo5Ga29L7K5wYneGA3Dto000+HQC5fu3IutePCdHqte0gK6NKd/3aWSDEVO2nlOj1PNgYLcm6J4IRXJxw7Z1JwAyV8zHdOsamZ8txXnW+6DRoN+9GfneLeyffwnT9csYThzCrnNPbOo0QDGZUNLTyFiuXj+UjHRyft+I6/xVoIDh9BGMp6xsFC/B1Dfnc/z0WZKTU2nbYyAvDxtE72c7PvwKZZmsz5fhNHOhmsc95jz2fQnjjcsYTx7CrmNPdHXM18iMNDI/np/7cd0TdZET4tTJm62OaSJz9RJc3lTrPzk7/8R09xYO/YZivHYJw/FD2Hfpha6uGlNJTydjmbpddTVDceg3VH16mKyQsWoxSnqhKnBhJpnEBR/hu2K++rj4TVsw3LiN2+jB6P+6Qta+w3i8OhKNowM+C9UnEBqjY4mb+AY2lSvgMWm0OnxJkkj9aiOGazety2dZl5+y3p+PI48mmcT3P8L3owWg1ZCxabO6L0cNQX/xsrovXxmJxsEB7/lvAGCMiSV+0uvYVK6I+6v59uXX32O4bsW+NMkkzl+O38p5avn5dSuG67dxHzOYnL+ukLX3MB4T1fLj+765/ETFEvvqGzh1aIl9/Tpo3V1x7qYeq/FvvI/+culPUDy+6ziN2jTi8wOfk52VzYeTP8xdtnzLcsZ1Glfi57d9t42JH0xk5Y6VGPVGFk1cVHpe/yPkB30Sj/C3ScojHOP5/4EkSW1Rexi5K4qSIUnSFWCVoiiLzcv7ATMBCfhDUZTp5vfTFUVxNv8dAPyK2lNIAj5QFGWdJEnNgNWow+z65J9nqeAy1Lm3VqIOTbMBvkWd/+gQ0FZRlLvmSbsbKIoyWJKk8cA4IFJRlNaSJPVBnQ8qDjgBOCuKMsQ8iffviqL8YI5buWAcRVHmlLSNYtu2LNNC4bZyblmGAyB17Kwyjyn//acMP7Dzf/mXabzqlYrv/vxPsXMtphHmH6Q8hpnzbl78+xP+PqgbPLr5aqzR2NP6IW6Pis+TZb8z447+b3QWPpj06J7uaI16tmV/FzZbX/b350yKdU8xepSuSI92DhtreJjK9tzevMUDNIw8Is4rH35i+IeVMXZYmceU9WV/nk27bVOm8VyDy76SJ1n5RLNHSXmAp1c+Kmk3i3sY9z9HNpbtdXpMwuPp67H57uayL0RlZHqlfv/ZRo4Ft775V+63/7keS4qi7ERtYLn/ulqB5d8A3xTxOed8f0ehDoUrmOYgUPixNMUv61RE0tzvY560+/7fH5Gv95S54ajQ7KmKogwp8PpmMXEEQRAEQRAEQRAEQRD+lv+N26aCIAiCIAiCIAiCIAjCIycalgRBEARBEARBEARBEISH8j83FE4QBEEQBEEQBEEQhP+m/+wES/9ioseSIAiCIAiCIAiCIAiC8FBEw5IgCIIgCIIgCIIgCILwUETDkiAIgiAIgiAIgiAIgvBQxBxLgiAIgiAIgiAIgiD8J8iP+wv8DxI9lgRBEARBEARBEARBEISHIhqWBEEQBEEQBEEQBEEQhIciGpYEQRAEQRAEQRAEQRCEhyLmWBIEQRAEQRAEQRAE4T9BRnncX+F/juixJAiCIAiCIAiCIAiCIDwU0WNJKMT9q4/LNF7GhLFlGg/AbV3Z5hEAueyfT1Ct56tlGs+zf7UyjQcgVQ4p+5jlyj6ffkM/KvOYW5McyjRej3dal2k8gK/Hny/zmANX1C3zmNg7lnnIGyMPl2m8dgHpZRoPwJCtLfOY7nXK/i7suZ1lX340ZZxN55Wfl21AIGPssDKP6bTiszKPiSGnzENmdBtXpvFs6wSVaTwAyde7zGNq6jQu85jZ09eUecz4iLI95+2IOVWm8QThnyB6LAmCIAiCIAiCIAiCIAgPRfRYEgRBEARBEARBEAThP0HMsFT2RI8lQRAEQRAEQRAEQRAE4aGIhiVBEARBEARBEARBEAThoYiGJUEQBEEQBEEQBEEQBOGhiIYlQRAEQRAEQRAEQRAE4aGIybsFQRAEQRAEQRAEQfhPkB/3F/gfJHosCYIgCIIgCIIgCIIgCA9FNCwJgiAIgiAIgiAIgiAID0U0LAmCIAiCIAiCIAiCIAgPRcyxJAiCIAiCIAiCIAjCf4KC8ri/wv8c0WNJEARBEARBEARBEARBeCiix5JglQPHTrFg+eeYTDK9nmnH8P69LJZHRsfyxsIVJKak4ubizLxZE/D38ebY6XMsXPFFbrqbdyJY+MYk2jZ/stSYurDGOL40DjRacnb+Qc4vGyyW27bvhn2nHiiyDNlZZHzyAfK92wBoK1TBcdRkJAdHUBRSXxsNBv2/Lo8Hjp1mwYovMMkyvbq0ZXi/npYxY+J44/0VJCan4ubqzLwZE/D38QIgKiaONxetJDouAQmJj+fNJMjft9SY9k0a4T55LGg0ZPz6J2nrvrVY7ty/D87du6CYTMjJySTOeR9TdGzucsnJEf/vPidr70GS3/+o1HgAmoo1sW35HEgajBcOYjyx1WK59okm2DbvhZKRDIDhzB5MFw4CYNOsJ9pKtdX3j/2J6epJq2IevHSXhZsOI8sKPRtXZ2ibMIvl7286zPFrkQBkG4wkpmdz4J3BRCalMWnddmRZwSjL9GtWi75NapYeL/wSC77chCzL9GzdmGHd21gsj4pPYvbK70jLyEKWZSb060KLek8AcOV2JO989iPpmTloNBIb3n0FO1ubUmM6NG2I57SXQaMh/efNpHzxncVy14G9ce7ZGUwmTEkpxL/1AaaoWGyrB+M58xU0zo5gkkles4HMbXtLjXdfx7depGrrUAxZen6d8gnR529ZLNfZ29J35St4VPBDlmWu7jjFzgXqd6vQuAYd3xyIX40K/Dh+ORf/PFZqvIOX77Hwt6PIikLPRtUY2qquxfL3fzvK8RvRQL59+dYALkUm8N4vh0nPNqDVSAxvXZeOoVWszmd+TeYMonybMIxZOeyd+CkJBfIM0HBaX6r2aY6dmxNrqw9/oPUfvHyPhb8eQVZktby2DrXM46YjHL8eZZnHOYPUPP50kPQcA1pJYnibMDqGWZfHg3/dZuFP+9RjpElNhrZvaBnzp/0cv3pPjak3kpieyYEFowCISkzj7W92EpOcjgR8NLobQV6uVsXtnK/8/DLlE6IKbEsbc/nxNJefKztOscNcfpoM70z9F1ojG01kJKby69TVpETElxjP7slGuL06DrRaMn/7g/SvvrFY7vRCXxyf7QImE3JyCsnvLcQUHQOA1s8XtxlT0Pr6gqKQOPm13GUlsW/aCM8pL4NWPTZT11qeZ10G9Ma5ZxfzsZlMwtvqsakN8MXng7eRNBLodKR9+wvpP/5eajxdnUbYD1DP64a9f5Lzh2U829ZdsW3bHWQZJSeLrC8+RI68DVodDi9NRFupGigKWetXYLp0ptR4Rfknj5Gacwfj2zYMU5aeM6+sJPVc4XW71q1M6LLRaO1tid0Zzl+z1gFg4+5EvU8n4Fjem8y78ZwasRRjSgZOIYGELh2Fa53KXJn3XaH13XfgyAnmL1mFSZbp/Wwnhg96zmJ5ZHQMr7/3IYnJKbi5ujD/jan4+/oAsPjjz9h36DgAo4b0o3O7llblVxfaCIchav1Hv+sPcn61LLO27Z7FrmMPdX9mZ5H56SLkiNvYNG+H/bPP56bTVKhC+msjMd2+blXc4sx+bzH7Dh7D08OdX75e9bfWdd+Bo6eYv3wNJpNM72faM3xAb4vlkdGxvL7wI7X+4+LM/FkT8ff1BmDRqrXsO3ISWVZo0jCUGeOHI0lSqTEfR/1HG1wX246DQKPBeHoPhoO/WSzXhT6Nbbt+yGlJABiPb8N4eg+Smzd2z70KkgZJo8VwfBvGkzutinnwVhzv77mILEOP2uUY2rjwNWHb5ShWHbmGhEQ1HxfmdQnlcmwqc3ddICPHhFYDwxoH07F6QOnxzl1jwYat6vWrRT2GPdPcYnlUQgqzP/uFtMwctf7Tpy0t6lbl8IXrLP1hFwajCRudlonPtePJJypblUe7JxvhOkE9RjJ//4OMrwuc15/vi0PXvPN6yryFmGJisK0XhusrY3PT6SpUIOmtOeTsP1hqTOen6xP45gjQaEj6bjtxq36wWO7YuBaBr4/AvkYl7ryykNTNhyyWa5wdqLbtY1K3HyHyzU+syifAh4vn0KlTG7Kyshg2bCKnw89bfi9nJ/bs/jn3dVBQABs2/MTkKW9Svnwgn3+2FHd3V7RaDTNnzWPLll1WxxaEByEaloohSVIP4IqiKH/9Q+s/pChK00ewnlaAXlGUQ6UkfWgmk4m5S1fz6ftv4u/jxQujp9G6aSOCK5XPTfPBqnU826EV3Tu15uipcyxdvZ55MyfQuF4dflizGICU1DS6DBxL04ZhpQfVaHAcNoH0d6YgJ8bhMm8VhhMHcxuOAPQHdqDfvgkAm4ZNcRw8lvS500CjxfGVWWR+9B6m29eRnF3BZPzX5dFkMjF32Ro+XfgG/j6evPDya7Ru0rBwzPat6N6xFUdPn2PpmvXMm/EKADMXfMSI/r1p2jCUzKwsJMmKDogaDR7TXiF23DRMMXH4rfuYrH2HMd7M266Gy9eIeXEMSk4OTr2fxf2VkSTMfDd3udvol8g5fbb0WPdJErat+pHz81KU9CTsX5iB6cZZlMQoi2TGqycx7LGs5Gkq1UbjW4HsDXNBq8OuzyRMty+APrvEkCZZZt7PB1k1sgt+bk4MWPYLLWtVJNjPIzfN1G5Ncv/+5sB5LkUmAODj4siX47pjq9OSmWOg96IfaFmzIr5uTiXGe++Ln/lk5kj8vNzoP2sZrRrUIricX26a1T/vpONTdXmufVOu34th3ILP2PzRExhNJmau+Ia5Y/tRvWIgyWkZ6HTa0rerRoPnjPHEjJ6OMSaewPXLydx7GMONO7lJ9JeuETVgLEp2Di59u+L56gjips9Fzsom/vWFGO9EoPXxImDDCrIPn0BOyyg1bEjrULwq+7O85WSC6oXwzLsv8VmPNwulO/zpn9w6/BcaGy0vbphJSKtQru05Q0pkPL9O/oQmI58pPY/mbTvv1yOsGtYRPzdHBiz/jZZPVCDYzz03zdRn8xpxvzn4F5ciEwFwsNHxznMtqOjtRmxqJv0/2kSTakG4OthZFfu+8m1Ccavsz/fNJ+NbP5jm84bw67NvFUp3Z8cpLqzdzvP7P3ig9avl9RCrRnRSy+tHm2hZs0KB8vpUvjxe4FJEQl4en29JRR83YlMy6L/sV5pULz2PJllm3sY9rBrbAz93ZwZ88B0ta1chOMAzL2avFnkx957h0r243Nezv97O8A4NaVKjApk5eqt+1AFUbR2KZ2V/lrWcTDlz+VlTRPk5ZC4/2gLlJ+rCbT7tOhtDtp6GA9vSfkY/fhhXwg88jQa3KRNImDAVU2wcPp+tInv/IYy38p3zrlwlfuholJwcHHt2w/XlUSS9MQcA99dnkL7ua3KOn0RysAfZiq71Gg2e08cT+/J0jDFxBHy9gqy9hzDczHdsXr5G9MCXUbJzcO7zLB4TRhL/2ruY4hKJHvIKGAxIDvYEblxD1t7DmOITio8nabB/8RUyFk5DSYzD+a2PMZw+rDYc3Y93eBf63WoDla5eE+z7jSZz0QxsW6nHYfrsEUgu7jhNmUf6Wy+D8mBDCP7JY8SnbRhOlf3Z89RE3BuEUHvhMA51fr1QujoLh3Ju8mqST16j0Ybp+LQJJW7XGYLHdydh/3mOfbSJ4PHdCBnfjUvvfoMhOZ0Ls9bh37lhEVFVJpOJdxetYPWS9/D39eb54RNo3fxJgitXzE3zwfI1dOvUlu5d2nP0ZDhLVq1l/htT2XvoGH9dvs4Pa1egNxh4adw0WjRpiLNT8dcSACQNDkMnkDF3KnLC/frPIeSIfPvz4E70O9QGCl2Dpji8+DIZ86ZjOLADw4EdAGjKV8Zpyjt/u1EJoEeX9vTv3Y2Z7zzYua04JpOJd5d+wuoP3sbfx4vnR0+ldbPGlvWflWvp1qE13Tu14eipsyxZ/RXzZ03k9PlLnD5/iZ8+WwLAi+Nncjz8PI3r1Sk56OOq/3QeQvbX81BSE7Ef/g7Gy6dQ4iMskhkvHEG/ZZ3Fe0paEtmfv6XWX23scBizANPlkyjpySWGNMkK83f9xcpejfBzsWfAhsO0DPYl2Ms5N83tpAw+P36Dtc8/hau9DYmZOQDY22h5p2NdKno4EZuezYD1h2la0RsX++JvdJlkmfe+3swnkwfi5+lK/zlraBVWneAgn9w0q3/bT8dGtXiudUOuR8QxbskGNr8/AXdnR5a98gK+Hi5cvRfLmMXr2bF4YunbVaPBddIEEieq53XvNavIOVD4vJ4xfDTk5ODYoxsuL48i+c056E+HE//SCAAkFxd8v/uanGMnrIoZOGc0Nwe9jjE6geBfF5O64yg51+7mxYyI497UJXiP6FnkKvwmDSTj2IXSY+XTqVMbQkIq80TN5jzZuD7Ll8+jWfNnLdKkp2fQsFGH3NdHj2zm51/+BGDmjAn88MNvfPLplzzxRFU2/foVVas9hSD8E/7nh8JJklTcL7geQOndFB48ng7gUTQqmbUCHmhd97+Dtc5dukaFwADKB/pjY2ND5zbN2X3QspfBjVv3eLK+elFvXK92oeUA2/YepnnjejjYl/6jThtSAzk6Ajk2CoxGDAd3YduwmWWirMy8v+3scyvCutCGmG7fyK1MKempIMv/ujyeu3SNCkH+lA/0U2O2bsZu853N3Ji37/FkPbW3TuOw2rnLr9+6i8kk07Sh2qvB0cHBqpi2tWpguBuBKULdrpnbd+PQ0rL45JwMR8lRKxn6cxfR+uZVDmxqVEXr6UH2Uet6DQFo/CqhpMSipMaDbMJ45TjaKnVL/yCg8QzAFHEVFBmMepT4CLQVa5X6ufN34ijv7Uo5L1dsdFo6hgWz58LtYtNvDr9Op7BgAGx0WmzNDTt6ownFih9Y56/doby/N+X8vLDR6ejUJIw9JwpUHiRIz1K3a3pmFj4eau+Ow2evULVCANUrBgLg7uKEVlP6qdmudnWMdyMxRkSD0UjG1j04trLcl9knzqBkqzFzzl5E66fuS+OdCIx31IqtKS4BOTEZjYd7qTEBqrdvwJkf9wMQcfoadq6OOPtaftaYrefWYbVNXjaYiDp/Cxd/tcEi5V48sZfuoljzAx04fzee8l4ulPNyUfdlaBX2/HWn2PSbz9ygU5h6x7OijxsVvd0A8HV1xNPJnqSMkhsli1KxQwOu/nAAgNhT17F1dcKhQJ7vL8uKTX7g9Z+/W6C8hlZhz4US8hh+I7e8VvRxo6KPOY9uTng6O5CUXnoez9+OobyPO+W83dSY9aux59yN4mOevEKnBtUAuB6ViEmWaVKjAgCOdrY4WNHDDizLz73T17AvovwY8pUfk7n8uJrLz63Df2HI1ud+3jVfQ1hRbGrWwHgvElOkes7L2rEL+xaW1xL9qXznvAt/5Z7zdJUqglZLznH1fKdkZeemK4lt7eoY70ViNJ9nM7buwaGVZcyc/MfmuYtozT0xMBrBYABAsrUFK24YaKvUQI6JQImLApMRw9Hd2NQvUC3IzrteSnb2uX9rAiti/Ou0mr+0ZJSMdLSVq5Uas6B/8hjx69SAiI1qmUk+eQ0bV0fsCqzbztcdnbMDySevARCxcT9+5gYjv04NuPfdPgDufbcv9319fCop4TeQDaZiY5+7eIUK5QIpHxSgXqfbtmTX/iMWaa7fvEPjBmEANK4fyu79h3PfbxhWG51Oi6ODPdVCKnPgSOnXTm1IDeSYSLX+YzKiP7QLm0bF13+kfPWf/GybtcVwaHep8azRMKwObq4uj2RdAOcuXaVCkGWda9fBoxZprt++S+PcOled3DqXJIFer8dgNKI3GDEYjXh5upca87HUf4KCkZNiUJLjQDZhunAEXfUG1n1YNuXdFNXZqBm3wvnoZMq7O1LO3REbrYaO1f3Zc92yl+XP5+7xXGgFXM0NRp6Oav2xoocTFT3Uhk9fZ3s8HG1JzCq5t//5GxGU9/WgnK8HNjotnZ6sxZ7wy5aJ8td/srLxcVfL0hMVA/D1UP8OCfIhx2BAbyj5RjCAzRM1MBU4r9s1L3BePx0O+c/rPj6F1mPfuiU5R47lpiuJY2hV9LejMNyNQTEYSfltH67tLUcmGCJiyb50q8gbEPa1g9F5u5O2/3SpsfLr9mxHvl6v9ow6euwUbu5u+JcwOqFq1Sr4+Hhz4IB6PCkKuLiqjYpurq5ERZXe4/a/Qv4P//u3+n/bsCRJ0lRJkl4x//2hJEm7zH+3kSRpvfnvfpIknZMk6bwkSQvyfTZdkqRFkiSdAZpIkjRfkqS/JEk6K0nSB5IkNQW6Ae9LkhQuSVJwgdhrJUlaJUnSCUmSrkiS1NX8vlaSpPclSTpuXtco8/utJEnaL0nSJuCv+98h37K9kiT9KknSDfN3GSBJ0jHzdw82p/ORJOlH87qPS5LUTJKkSsBoYKL5e7YoKp35829JkvSVJEkHga8eZFvHxifg7+uV+9rPx4uY+ESLNNWCK7Fjn1rZ2rn/KBmZWSSnpFmk2bL7AF3atsAaGk8f5IS8u+NyYhySV+GLgl3HHrh+tB7HgaPJ/HwZANqA8oCC86yFuCz4FLtuL/wr8xgbn4i/j3fpMferF4edB/Ji3roXhYuTI6++uZC+o6aw6JMvMZmKrxzfp/XxxhSTt11NMXFo832Hgpy6dyb7kLkBTZJwf3U0yUsfrBu85OyBYu7iDaCkJyM5exRKpwuph/2A2dh2GZm7XI6/pzYk6WzA3glNuWpFfrag2NQM/N3z7s75uTkRm1J0b5zIpDQiE9NoHBKY+150cjp9F/1Ip7kbGNIqtMTeSgCxSan4e7nnvvb1ciMmKcUizZjeHfjjwCnaj32XsQs/57UhPQC4HRWPJEmMnrea52cs4YtN1v0Q0Pp6Y4zO25fGmPi8H6dFcO7ZmawDhRtDbWtXBxsbjHcjrYrr4u9JamRe74m06ERc/IrfJ3aujlRrV5+bB88Xm6YksamZ+Ofb/n5ujsSmFrcv04lMSqdxcOGu++fuxmEwyZT3tG64Vn5O/h6k58tzRlQiTv6ll0NrxaY8SB7vl9ci8ngnDoPJRHkrhqTFJhc4RtydiU1JLzpmYiqRiak0rlYOgNtxSbg42DFpzR88v+AbFv9yAFMpjff3uRYoP6nRibiWUH7sXR2pXkz5qf98K67tKXnYlnrOyxvKYoor5ZzXtQvZR9Rzrq5COZT0dDzeexuftZ/iOnYUWNHoq/Pxxphv+IwpNg5tvutLQc49OpF9MO+mgtbPh4DvPiXozw2krPu25N5KgOThjZJY4HrpUTiPtm274/z+V9g/N5Lsr5er3+3udWzqNQWNBsnbH22lakiepQ+pLuifPEbsAzzJishbd3ZUIvYFGhTtAzzJjsq7fmZFJuSmsfNxI8fcmJUTm4yduSHWGrFx8bnD2gD8fL2JjbPcH9WrVmHHXnUYzY69h8zX6VSqh1TmwNGTZGVnk5ScwvFTZ4mOjaM0Gk9v5IS88iMnxKEpan926IHL0q9xGDCKrLWFe+3ZNGmF/pB1Q6fKWmxc4fpPbJxl/ad6vjrXjv1HcrdrWK0aNAqrQ+teL9G690s0a1yP4IrlKc1jqf+4eKKk5JUXJTURyaXwcaF9ohEOo+Zh12cCkmte2ZZcPXEYNQ/HV5dhOPh7qb2VAGLTc/Bzcch97edsT1y6ZcPJ7eQM7iRlMuTbI7z4zWEO3ipcLs9HJ2OUZcq7O5YcLzkNf8+8Y8rXw5WYJMu68ZjuLfnj8DnaT/6QsUu+4bUBnQqtZ8fJizxRIQBbm9LvfWt9vDHF5jtGSjmvO3btQs7Ro4Xed2jbmqwd1h0jOn8vDFF5w64N0QnY+Bd/XrcgSQTMGkbUe59blz6fwEB/7uWrn0XciyIo0L/Y9M89142NGzflvp7zziIG9O/FzRsn2LTpS159dfYDfwdBsNb/24YlYD9w/xd8Q8BZkiQb83v7JEkKBBYAbYAwoJF5eBuAE3BUUZRQ4CLQE6ilKEpd4F3zsLJNwFRFUcIURSmqH3EloDHwDLBKkiR7YBiQoihKI6ARMEKSpPuDhesDExRFKepWYChqA9ETwCCgmqIojYE1wHhzmqXAh+Z19wbWKIpyC1hlfj9MUZT9RaXLF6cm0E5RlH7FbdSHNWXMYE6cvUDfEZM5ceYCvt6eaLR5xSsuIZGrN+7QtFHYI42bs/UXUscPIHP9J9j3HqS+qdWiq1GHjGVzSXt9PLZPtkBXu/7fjvU48jhl1ItqzFFTOHHmr9yYJpOJU+cvMXnUYL75eAH3omL4deueRxYXwLFzO2yfqEbqV98D4NynG9kHj2GKLXk+k4dhunmWrC9mkb3+XeQ7F7HtMBgA+c5FTLfOY//cNOw6D0eOuqn2XnqEtoZfp13dyha9hPzdndk4uTebpj/PbyevkpCWWcIarLP50Gm6Pd2Q7Stms2LaUGZ9/A2yLGOSTZy+fJN5Y/uz9q2X2XXiPEfPX/3b8fJz6tIWu5rVSFm30eJ9rbcnPu9OJ+HNDx546Is1JK2G3h+N49gXW0m+W/oPqr9r65kbtKtdqVCPr7jUTGZ/t4+3+zZHo7Huru+/1dbwG7SrU7noPH67l7f7Pv3I87j15FXahYXkxjSZFE5fj2RSj+asn/I8EQmpbDp68ZHGBNCYy8/RL7aSVKD81O3ZjMA6VTj4SenzD1nLoWM7bGpUJ329eb4drRbb0DqkLl9F3LDRaAMDcexS+AfR36Eem9VJ+fL73PdMMXFEPT+SyO6Dce7aAY0VvTGsod/5K+lTB5H9/Wrsug0EwLBvM3JSHM5vrcRhwMsYr10otYfv/3uP+Fw3ZexwTpw+R58hYzkRfg4/Hy80Gg3NnmxAiyYNGThqMlPfXEBorRpW9Ua1ln7bL6RNGEjWhk+x7zXIYpk25AnQ5yDfvfXI4pW1KWNe4sSZC/QZPpETZy7g561u1zv3orhx5x47N37Gro2fcezUOU6efbDhRaUpy/qP8copspa9StYnMzDdOIdd99G5y5TURLI+mUHWR5PQhbYApwe/MVIUk6xwJzmD1X0bM69LKO9sv0BatiF3eVx6NrO3nOWtDnXQWNlTqiSbj56nW7NQti+ayIpX+zFr9S/I+Xr1XIuIZcnGnbw+2Loh8g/CoYP5vL7Bch41jZcnuipVyDl6vJhPPjpeg7qQtucExuiSbxI8Cs89153vvvsl9/ULz/dg3ZcbqVylId26vcgXa5dZPXRdEB7U/+c5lk4CDSRJcgVygFOoDUwtgFdQG3b2KIoSB2DuxfQ08AtgAn40rycFyAY+kyTpd8DaWur3iqLIwFVJkm4ANYAOQF1JkvqY07gBVQE9cExRlJvFrOu4oihR5u95Hdhmfv8c0Nr8dzugZr6TgaskSc4UVlK6TYqiZBX1BSRJGgmMBFix4E2GD+ybu8zX24vo2LyTYUxcAn7elncLfb09WTJnOgCZWVls33cYV+e8O/Bbdx+iTfMnsdFZV+TkxDg0+XooaTx9UBKK/2FqOLgLpxETyVyh3t0z/nUGJU3tMWI4dQRtlaoYz58q9vOPI4++3p5Ex+VVUoqN+fa0vJj7j+Dq7ISfjxfVgytRPlCdw6dNs8ac+esKvWhbYkxTXHzucChQ74yb4gpXlOwa18f1pf7EjpqUOyzDtm5N7MLq4NynG5KjA5JOh5KVRcryNYU+n5+SnmRxh05ydkdJT7JMlJ3XO8N44QA2zfMmTjce34zx+Gb1O3QaipwcS2l8XZ2ITs7rfRGTklFsr6Mt4TeY0bNZkct83ZwI8ffg1M1o2tctfkJkXw9XohOSc1/HJqTg52F5V/zn3cdZOUOdrDa0WiVyDEaS0jLx9XSnQY0qeLiq3695WA0u3ozgydpVS8yjKTYenX/evtT5eRdZ6bV/sh5uw/sTPWxy7r4EdRJS34/eJWn5F+ScK7lRoOGL7an/gnoqijx7A9fAvLt0Lv6epMUkFfm5rvOHkXAzmqOfbylx/SXxdXUkOl9vs5iUTHxdi9mXZ24yo4fl3AHp2XrGr93OuI4NqFvB+p4YNQe3o0Z/Nc9xZ27gHOjF/Q7kTgGeZEQXneeH4ev2IHm8wYwelsM30rP1jP98G+M6NaBuRevy6Ote4BhJTsfXrahLCmw5dYUZfVvlvvZzd6Z6kDflzMMMW9epwtlb0fRsUuTHafRiexqYy09EgfLj6u9JajHl59n5w0i8Gc2RAuWnSrNatBjXnbXPvYtJX8r8eXHxaP3ytonWp+hznm3D+jgPHkjC2FdzjxNTbByGq9fV4RZA9v4D2NaqWWotwRgXjy7fUAWtrw+m2MI/KOwb18dtWH+ih1sem7nfPT4Bw/Vb2NerQ+bO/cXGU5LikTwLXC+Tiv8BbDi6G4fBE8gCkGWyN6zMXeY0exly9L2SM2j2Tx4jNQe3o3a/VgCkhN/AIciL+2sr2DsJCvdicgj0yk2TE5eCna+72lvJ152c+FSrv4evj7dFL6OY2Hh8fbwKpPFi6Tx1zqfMzCx27DmAq4t6LI0a3I9Rg9V7edPeWkDF8kGlxpQT49F45ZUfjZcPckn789AuHIe/avGeTdPW6A/+eyfo9fUpXP/x9Slc/1n6zmuAebvuPYyrizM//LGd0JrVcHRUe+U0f7I+Zy5cpkHdkofJP5b6T1oiklteeZFcPS16cAOQlXceNp7ejW27wvd+lfRk5Ni7aCvUwHSx5Ade+DrbEZOWV9WPSc/Gx9muQBp76gS4YaPVEOTmSEUPR+4kZ1LL3430HCOv/HqKsU2rUTfAvcRYAL7uLkQn5vXQjk1Kxc/Dctjkz/vDWTmpPwChIeXV+k96Jl6uTsQkpjJx+fe8O7w75X1LHtp8nykuXn2ggpmmpPP6iwNJGPdqoXOsfZvW5Ow/AFb09gcwRidgE5DXK8rG3wuDlQ1FjvVq4NioFl4Du6BxdECy0WHKyCZm4boi048ZPZhhwwYAcOJEOOXK5/WoDyoXQERkdJGfq1u3JjqdjlOnz+W+N+SlF+jaVb2RcOToSezt7PD29iQu7p9v5BL+9/y/7bGkKIoBuAkMAQ6h9mBqDYSg9kIqSbaiKCbzeoyoPY9+ALoC1v4CKnjLSwEkYLy591CYoiiVFUW530hU0qy4+fuoyvley+Q1/mmAp/KtO0hRlKLGLZSUrtjvoCjKp4qiNFQUpWH+RiWA2jVCuB0Rxb2oGAwGA5t3HaBV00YWaZJSUpHNdznXrP+Jnp0tGzg279pPl7aWT4koienaZTQB5dD4+oNOh02zNuhPFHi6gn9eBc2m/lOYotQ5Y4xnjqGtUAVs7UCjRVczDFO+Sb+L8jjyWCjm7oMlx9zwMz07qU8aq109mLT0DBKT1Yv50dPnCa5YrtSY+r8uYVMhCG2gul0d27cma5/ldrWpFoLnjInET34dOSk59/3E1+cR9Wx/oroPIHnpJ2T8ub3UShWAHHMbyd0XydVL3R/VGmG6UWDyS8e8u3DaKqHI9yf2liSwV39gS95BaLyCkG+XPp9+rfI+3IlPJSIxFYPRxNbw67SsWaFQupuxyaRm5RCa78d4THI62eYx/qmZOZy+GU0lH/eS4wWX5050PPdiEzEYjWw5HE7LBpZTtAV4u+f2RLoREYNeb8TT1Ylmdatx9W40WTl6jCYTJy/eoEqQX1FhLORcuIyuQhA687506tiKzL2HLdLYVg/Ga/arxL76hsW+RKfDd/FbZPy+ncwdxf9gve/El9v5tMtMPu0yk8vbThDaW+0sGlQvhJy0LNKLmDOl9ZS+2Ls4svXtBxp5W0itct7cSUglIjFN3ZdnbtCyZuFhD+q+1BOar/HIYDQx6atddK0fQvs6lR4o7l/rdvBTx1n81HEWt7acpGof9dj2rR+MPi3zoeZSKk6tcvfLa/48Flde9Rbl1WA0MenLHXRtEEL7utY9TQegVgU/7sQlE5GQosY8dYWWdQp//mZMonqMVM7rel+roi9pWTkkmn+8HLt6jyr+xf8gOP7ldlZ1mcmqLjO5lK/8lCuh/LSZ0hc7F0e2FCg//rUq0nXeML4ZtoiMhNIbCAwXL6ErF4Q2QD1OHNq1IfuA5TlPVy0E9+mTSJw2y+I4MVy8jMbZGY272oBm16AehpslX0sA9BcuoytveWxm7S1wnq0egueswsem1tcbyc4WAI2LM3ZhtTHcLrmhx3TzElq/ICRvf9DqsHmyNYbTBa6XfnnXS13oU5hizJMH29qBrTrnkq5WA5BNFpN+l+SfPEb+WreDA21ncKDtDGI2nyCor1pm3BuEYEzLzB3adl9ObDLG9CzcG4QAENS3BTFb1LlwYraepNzzTwNQ7vmnc9+3Ru0a1bhzL5J7kdHqdXrnXlo3t2y8TkpOyb1Or/7qO3o+o06iazKZSE5Ry+jlaze5cu0mTRuXPr+O6folNP5BaHzU/WnbtA2GEuo/unp59R9AnTC6SSsMh/69DUu1q1flzj3LOlfrpo0t0iQl59V/Vm/4kZ5d1DpXgK8PJ8IvYDSaMBiNnDhznir/1vpPxA00nv5I7j6g0aKt9RTGK5blT3J2z/1bW60Bcrw67Ely8VSnAQCwd0RboTpyguVDT4pSy9+NO0mZRKRkYjDJbL0cTasqljccWof4cuKu2vCalKXndlImQW4OGEwyk387RdcnAmlfrfjhVhbxKgdxJyaRe3FJGIwmthy9QMswy8EZAZ6uHP1Lvbd+IzIOvcGIp4sjqZnZjFvyDRP6tKVe1cLXvOIYLl1CW97yvJ5zsMB5vWoIblMnkfjaLOTk5ELrcGjXhqzt1g8VzTx7FbtKgdiU80Oy0eH27NOk7ij9qbYAdycu4nLzoVxuMZyo9z4n+eddxTYqAaxctY6GjTrQsFEHft20lYED1P4KTzauT2pKKtHRRd9gff55y95KAHfvRNCmtXpurlEjBHt7u/+ZRiUZ5T/779/q/3OPJVAbk6YAQ1F79ywGTiqKokiSdAxYJkmSN5AE9AMKDUI39+ZxVBTlT/P8Q/dnL00DSpqpsK8kSeuAykAV4DKwFRgjSdIuRVEMkiRVAyJKWMeD2IY6LO598/cOUxQl3Pw9Xa1I99B0Wi0zXxnO6GlzMMkyPTu3JaRyBZZ//g21qgfTulljjoefZ+nq9UgSNKhbk1kTRuZ+PiI6lui4BBqGlj7pci7ZROZnS3Ge9T5oNOh3b0a+dwv751/CdP0yhhOHsOvcE5s6DVBMJpT0NDKWzwNAyUgn5/eNuM5fBQoYTh/BeOpIieEeRx51Wi0zxw9n9PR3zTHbEFKpPMu/+FaN2bQRx8MvsPSz9UhIasxX1B4vWq2WyaNeZPiUt1GAmlWr0OeZdqUHNckkLfwIn2ULkLQa0jdtxnjjNq6jhqC/eJnsfYdxnzASycEBr/lvqB+JjiV+cuEn8FhNkdHv+Q67Hq+ApMH41yGUxChsnnoWOeY2pptnsQlro07oLcso2Rnot5svuBot9n2mqKvRZ5Gz9QurhsLptBpe69GUMas3I8sK3RtXJ8Tfk4+3nqBmOR9a1VKf6LPFPGl3/m7BN2KTWfzbUSRJHTHxYsu6VC1lgmCdVsuMIT0YM281sizTo1VjQsr7s2LjVmpVLkerhrWYPPBZ5qzeyNd/7keSYM6Y55AkCVdnRwZ1aUH/WcuQJGgRVoOn6z9R+nY1ySTOX47fynmg0ZD+61YM12/jPmYwOX9dIWvvYTwmjkTj6IDv++r+M0bFEvvqGzh1aIl9/Tpo3V1x7tYRgPg33kd/ufSnB13dFU5I6zDG7VuMIUvPpil5j8wd+ed7fNplJi7+nrQY34O4axGM/GMuAMe/3Mbpb/cQWLcKz306EXs3R6q1q0fLib1Z1X56CdtWw2vdnmLM59vUfdmwKiF+Hny87RQ1y3nTytwAs+XMTTqFVrbYl9vO3eLUzWiSM3PYZJ7Ud07f5tQItHJeBLO7u8Ip3yaU5w8swpitZ++kT3OX9do6l586zgKg8awXCO7RFJ2DLf2OL+PyN3s4tfinUtev02p4rXsTxqzZouaxUTVC/D34eOtJNY+55fUGnUKrWObx7E1O3YgmOSOHTSfUhss5zz9dah51Wg2v9WnJmI83Icsy3Z+qSUiAFx//cYSaFXxpVUftobfl5FU61a9qEVOr0TCxR3NGrfgZRYEnyvvQu6l1576ru8Kp2jqMV8zl59d85Wf0n++xqstMXP09edpcfkaZy8+xL7dx6ts9dJjZH1tHe577eAIAKZHxfDN8cfEBTTIpi5fh9eFC0GrI/H0zxpu3cBn+EvpLl8k5cAi3saORHBzwfPct9SMxMSROnw2yTOrylXgtWwSShOHSFTI3WdGp2SSTuOAjfFfMV4/NTVsw3LiN2+jB6P+6Qta+w3i8qh6bPgvNx2Z0LHET38CmcgU8Jo1WTz6SROpXGzFcK66zs5ksk/XVRzhNXQAajTq8LeI2dj2HYLp1GePpw9i264GuVn0wGlEy08larU47Kbm64zRlASgySlI8mZ/MKz1/Rfgnj5HYHafxaRtGq6NLMGXlcHZCXplpvnMeB9rOAOD89C8IXTYajb0tcTvDidsZDsD1jzZRf/UEyvdvRda9eE6NWAqocy812zYXnYsDyAo6ZzuMyXcthsrpdFpmThzDqEmzMZlM9OzagZAqFVm++ktq1ahG6xZPcfz0WZasWoskSTQIrc3syS8DYDSaePFl9frl7OjI/DemWvfET1km6/NlOM1cqNZ/9pjrP31fwnjjMsaTh7Dr2BNdnQZgMiJnpJH58fy87/xEXeSEOHXy70dk6pvzOX76LMnJqbTtMZCXhw2i97MdH3p9Op2WmRNGMGrq25hkEz07tzPXuTZQq3pIbp1ryeqv1O1atyazXx0FQIeWTTh2+iw9h05AkqB54/q0KtAoVaTHVf/ZvBb7AdPV+k/4XpS4CGxa9UaOvInpyil0jTuiq1YfRTZBdgY5v6rzOEk+gdi3H4CiKEiShOHwHyixd0sJCDqNhultavLyTyeQFYXutcoR7O3Cx4euUtPPjVbBvjSt6M3h2/H0WrcfrSTx6tPVcXew5Y+LkZyKSCI528Cmv9SfL3M61KG6b/FD8HRaDTMGdmbM4vXIskKP5mGEBPmy4ufd1KoUSKt61Zn8fAfmrPuNr7epdas5w7ojSRLf7jzGndhEPt20j083qRPsr5w8EK9ieuzmMsmkLl6G52L1GMn6Qz2vOw97CcOly+QcPISr+bzu8c5b6kdiYkh6TZ1fSOvvh9bXB314yXP0FYwZ+eYqKn/5Nmg0JG3cQc7VO/hOHEDWuauk7TiGQ92qVFw1E62bMy5tG+H36gCudhxrfYwibN68k86d2nDp4kGysrIYPnxS7rITx7dZPA2uT+9n6dbdcljstOlzWLXyfSZMGIGiKAwbbsVT9wThIUnWPPHo30qSpLaoPYzcFUXJkCTpCrBKUZTF5uX9gJmoPYn+UBRluvn9dEVRnM1/BwC/AvbmdB8oirLOPOn1atTeQ33yz7MkSdJa1OFzDVEbdSYpivK7pD7v/V3gWfO64lCfLlcPmKIoStd860hXFMVZkqRW+ZdJkrTH/PpE/mXmBrIVqPMw6YB9iqKMNjde/YDau2k8am+totK9BaQrilLqs2L1kRfKtFBkTPh7J92H4bR0RZnHfBzzVsT0fLVM43kNLHn41j9BqhxS9jHLPfhTk/6u6KElPFL9H/Jl0oNP4vt3TF1SyuOi/wFfj3+4ScX/joErrHsq4iNlX/IErP+EBSMPl57oERoZZN0E9I+SIduKhoJHzL1O2dfbvt9ZeLL4f1pgCU9r+yd0uDC3TOMBZIwdVuYxnVZ8VuYxMZT+1K1HLbrbuDKN5/msdT18HiWphAd2/FM0daxoxHvEkqaX3jvsUYuPKHpo+D+lfkTx03X8kwz6iP/shEsvV3ru/28jRyk+vvX9v3K//b/usaQoyk7AJt/ragWWfwN8U8TnnPP9HYU6FK5gmoOok10XZ4eiKKPzv2Gec2mm+V9+e8z/Cn0HRVEslimK0irf37nLFEWJB54v4nteAQr+Sikq3VvF5kQQBEEQBEEQBEEQBOEh/L+dY0kQBEEQBEEQBEEQBEF4vP5f91h6XBRFGfK4v4MgCIIgCIIgCIIgCJb+s+Pg/sVEjyVBEARBEARBEARBEAThoYiGJUEQBEEQBEEQBEEQBOGhiIYlQRAEQRAEQRAEQRAE4aGIOZYEQRAEQRAEQRAEQfhPkMUsS2VO9FgSBEEQBEEQBEEQBEEQHopoWBIEQRAEQRAEQRAEQRAeimhYEgRBEARBEARBEARBEB6KmGNJEARBEARBEARBEIT/BPlxf4H/QaLHkiAIgiAIgiAIgiAIgvBQRMOSIAiCIAiCIAiCIAiC8FDEUDihMH1WmYZTjI/hcZBlnEcAdHZlHtKQrS3TeEpCcpnGA5C8U8o8JhllHzMnu+xP13qpjI9Ne8eyjQfYPI6n0Uplf09HcvYo85j2SGUaLyvVtkzjASiPoa+9KTW7zGM+jiEFBqlsyw8mQ9nGA2T9Y9iyhpyyj2lT9vUfo75sz7NydGKZxgPQaMu2jgegJMeVeczstLKv/6Rll+31xOExHCOC8KiJhiVBEARBEARBEARBEP4TFB7HncP/bWIonCAIgiAIgiAIgiAIgvBQRMOSIAiCIAiCIAiCIAiC8FBEw5IgCIIgCIIgCIIgCILwUETDkiAIgiAIgiAIgiAIgvBQxOTdgiAIgiAIgiAIgiD8JzyOp5n+rxM9lgRBEARBEARBEARBEISHIhqWBEEQBEEQBEEQBEEQhIciGpYEQRAEQRAEQRAEQRCEhyLmWBIEQRAEQRAEQRAE4T9BQXncX+F/juixJAiCIAiCIAiCIAiCIDwU0bAkCIIgCIIgCIIgCIIgPBQxFE6wyoHjZ1iw6itMJplenVsx/PluFssjY+J4Y/FqElNScXNxZt60Mfj7eOUuT8/IpPvIabRp0pBZ44ZYFdOmXmMch40HjYacHX+Q/dMGi+V2Hbth17knyCaU7CwyPv4A+d7t3OUab1/clq0j67u1ZP/63b8yjweOnWbBii8wyTK9urRleL+ehWO+v4LE5FTcXJ2ZN2NCbsyomDjeXLSS6LgEJCQ+njeTIH/fUmM6NGuI1/QxSFoNqT9tIeUzy23j9mJvXHp1QjGZkBNTiHtjEcaoWAD8V87Fru4TZJ8+T8y4N6zKI4A2JBTbLoNB0mA8tQvD/k0Wy3VhLbHtOAA5NREA49GtGE/tBsBu0Gtoy1XFdOcyOesXWh3z4LUoFm4NR5YVetarzNDmTxRKs/XCXT7ZewEkqObnzvxeTwHw4fYz7L8WhaIoPFXFj2kd6yFJUsnxzl9nwTdb1XgtwhjWpZnF8qiEFGZ/vom0zGxkWWFC7za0qBtCcnomk1f+yIVbkXRrGsrMAZ2szqNj8wb4zRoNGg0pP2whcfVGi+UODWvjO2MUdtUrEzl5PulbD+Qu85kyFKeWjUEjkXnoNLFzV1kd95k3X6Ra6zAMWXp+nLKKqAu3LJbb2NvywscT8Kzoh2ySubzzFNsWfAtAowFteXJQexRZRp+Rwy8z1hB3LaLEeAcv3mbhTweQFZmeT9VkaLsGFsvf//kAx6/eAyDbYCQxLYsD80cAUH/ix4QEeAIQ4OHC0hHPlBir8ZxBlGsThjErhwMTPyXx/K1CabzqVKL5h6PQ2ttyb1c4x974CgDPWhVoMn8oWjsbZKOJIzPXEh9+g/Id6lNvah9QFGSjCU3aUeREyzwfvHSXhZsOq+WncXWGtgmzzOOmwxy/FpmXx/RsDrwzmMikNCat244sKxhlmX7NatG3Sc0S85gb89w1FmzYqm7XFvUY9kxzi+VRCSnM/uwX0jJzkGWZCX3a0qJuVbXMfryRCzcj6dYsjJkDO1sV7752bw0iuHUYhqwc/pjyKTFFbOOnp/aldq/m2Ls5sbjm8Nz3XQO9eGbxKOxdHZE0GvYs+I4bu89YHduhWUO8XxuNpNWS+uNmkj/73mK524u9cO2tnv9MiSnEvb449/z3IBybN8R7xmjQakn9YTPJayzj2DeojfeM0dhVq0L0lPfI2JZ3bHpNGoZjy8YAJK3cQPqWvaXGK+vr5X1N5wyigvl42TPxU+KL2JeNpvWlWp/m2Lk58Xn1vH0Z8GR1mrw1CK8nyrNj7HJu/nG82Dh13n0Rv7ZhmLL0nJqwipRzheO41a1M/aXqcRmzM5xzs78EoNYb/fFvXx/ZYCTjVgynX/0EQ2om7vWCqff+MPXDkoRkp0PJSS+03gNHTzJ/2RpMsonez3Rg+MA+Fssjo2N5ff4yEpNTcHN1Yf7sSfj7egOwaOUX7Dt8AllWaNIojBmvjCj1WgIPvz81Pv64ffQlpsg7ABiv/EXmqsWlxjtw9BTzl6/BZJLp/Ux7hg/oXTiPCz9S6yIuzsyfNTEvj6vWsu/ISTWPDUOZMX64VXkszez3FrPv4DE8Pdz55Wvrr08lcWjaEM/pLyNpNKT9vJmUzy3Luuug3rj07AwmE6akFOLf/CD3+Pf7+D3s6jxBTvh5Ysa/bnVMbc0G2PcdDZIGw6Et6LdtLDKdLqwZDiNnkzH/FeQ7V3Pflzx8cHr9E3L+XI9hx4/WxaxSB9sOA9U6V/heDId/t4xVtzm2bV5ATk8CwHhiB8bwfOcZW3scRs3HdOUk+q1flRrv4MU7LPzlgHr9euoJhratb7H8/V8Octx8nc+9Rr+nHnv1J6/Kd412ZumwLlbl8XHsS/fWYVSeMxS0GmI37CRi+c8WyyVbHVWXvYJT3SoYk9K4MmoxOffikHRagheNwalOFSSdlriNe4j46OdiohS28P036NCxFZlZ2YwZNZUz4Rcsljs7O7Fle17+gwL9+e67X3lt2jsMHdafEaMGYTKZyEjP5JXxM7l86ZrVsQXhQYiGpX8BSZJ6AFcURfnrcX+XophMMnNXrOXTeTPw9/bkhfGv0/qp+gRXLJeb5oPVG3i2XXO6t3+ao+EXWPrFd8yb9nLu8uVf/kCD2jWsD6rR4DjyVdLemoycEIfrwk/QHztoURHO2beDnK1qA4VNo6Y4vjSW9Hem5S53fGkshtPH/rV5NJlMzF22hk8XvoG/jycvvPwarZs0JLhS+byYq9bxbPtWdO/YiqOnz7F0zXrmzXgFgJkLPmJE/940bRhKZlYWkmRFB0SNBu9Z44ga+RrG6HiCvv2IzN2HMdy4k5sk5+I1Ul8Yh5Kdg8tzXfGcNJzYqe8BkLx2Ixp7e1z6WnfhB0CSsO06lOx1c1FSE7Af9R7GSydR4ix/WBvPH0b/xxeFPm44+DtGG1t0DdtZHdIky8zbfIpVA1vi5+rAgDU7aFk9kGAft9w0txPS+PzgRda+1AZXB1sSM7IBCL8bT/jdeDaO6gDAS1/s5sTtOBpVKr7RziTLvLd+M59MGoCfhyv93/2MVmHVCA70yU2z+o8DdGxYk+daN+B6ZBzjln7L5rrjsbXRMbZHS65FxHEtIs7qPKLR4PfGWO4NnYkhJp6KG5eSvuso+ut5+9IQFUv0jEV4DLX8kWBf7wkc6tfkVne1/FbY8AEOjeuQdexcqWGrtQrDq7I/H7aaRLl6IXSbO5RPehRuZDyw+g9uHv4LrY2Wl9bPomqrUK7uOcPZXw9xfP1OAGq0q0/n1wfy5eAFxcYzyTLzftjHqjHd8HN3ZsDijbSsXZlgf8/cNFN75jWIfLPvLJfu5W1HOxst3097odR8AQS1CcW1sj8/NZ+MT/1gmswbwh/PvlUo3VPzXuLQtDXEnbpOu6+mEtS6LhG7z9JgVj/CF/9ExO6zBLUJpeGsfmzpO5eoAxe4u+0UAB5PlOfZn6eQszuvrJtkmXk/H2TVyC74uTkxYNkvtKxVkWA/j7w8dmuSl8cD57kUmQCAj4sjX47rjq1OS2aOgd6LfqBlzYr4ujmVmFeTLPPe15v5ZPJA/Dxd6T9nDa3CqhMclK/M/rafjo1q8VzrhlyPiGPckg1sfn+Cucy25lpE7IOVWaBK61A8KvvzScvJBNYLpuO7Q/iyR+FtfG3HKU6u286oPR9YvN90fHcu/X6U01/vxKtqIM99MZWVzSdaF1yjwWf2WCJHzMAYHU+57z4iY/eRAue/69x7fjxKdg6uz3fFa/JwYqa890B5vB8nYvgMjDHxlL8fJ9+xaYyKI3bmItxfsmykcHy6MXY1Q7jbawySrQ1Ba98nY/9xlIzMEuOV5fXyvvJtQnGr7M+3zSfjWz+Y5vOG8EsRx8vtHae4sHY7L+y33JdpEQnsmfQJoaNKvp74tQ3DuYo/O5pMwqN+CKELhrKvS+FzTtiCoYRPXkPSqWs02TAN3zahxO46Q+zec/w191sUk0zN2S9Q9ZVu/PXut6RdusuejrNRTDJ2vu50Cl+GMc6yYclkMvHuh5+wevEc/H28eH7kZFo3b0xwpQq5aT74+HO6dWxN985tOXryDEs+/ZL5sydx+txFTp+7yE9fLAPgxXGvcTz8PI3r1Sl5w/7N/WmKiSB10vAiV10Uk8nEu0s/YfUHb6t5HD2V1s0aW9ZFVq6lW4fWdO/UhqOnzrJk9VfMnzWR0+cvcfr8JX76bImax/EzrcujFXp0aU//3t2Y+c4HpSe2hkaD18zxRI+ajjEmnsANy8ncY1n/0V+6RmT/sWr9p29XPCaOIG7aXABS1m5EcrDDtU/JNycsSBrsnx9L5rKZKMnxOE5fivHsUeToO5bp7Bywad0d081LhVZh13skxr9OPEBMCdtOL5K9YSFKaiL2Q9/GePUUSnykRTLjxaPFNhrZtuyNfOeyVeFMssy8n/azavSz6vXrwx9pWauS5TW6R96Ntm/2n+NSRHxe/my0fD/lOevzB49nX2o0VHlvBBeen4M+KoG6mxeQuO04WVfu5Sbx69cWY0o6p5uOw6t7MyrOHsSV0YvxerYJGlsbzrSZhMbBlrC9S4n/+QA590q/fnbo2IrgkEqE1W1Do0ZhfLjkHdq06mWRJj09g+ZNuua+3nvgVzb9ugWAjd9v4vPP1Ibpzl3aMm/+LHr1eMn6fP8/Jj/uL/A/SAyFK0OSJGmLWdQDsO4Wc9Hr/UcbCM9dvk6FQD/KB/hiY6Ojc6un2H34pEWaG7cjeDK0FgCNQ2taLL9w9SYJSSk0bWB9RUNX9QnkqAjkmCgwGtEf2IVtY8u76WTlVbQlOweLRTaNm2OKjcJ05+a/No/nLl2jQpA/5QP9sLGxoXPrZuw+ZHnH9sbtezxZr7YaM6x27vLrt+5iMsk0bRgKgKODAw72dqXGtKtTHcOdSIz3osFoJGPzXpxaN7VIk338DEp2DgA5Zy+i88v7oZl9NBy5pB84RdCUC0FOjEZJilXvHJ07hK5GQ6s/L984j5KT/UAxz0ckUt7DmXIezthotXSsVYE9ly0rVT+dusHzDUNwdbAFwNPJHgAJ0JtkDCYZvUnGKMt4mZcVG+9mJOV9PSnn44GNTkunxrXYE36lULp083ZNz8rBx90FAEc7W+pXrYCdzYMdxvZ1q2G4E4nhXjQYjKT9uRfntk9ZpDFGxJJz5RYoBSYwVBQkO1skGx2SrQ2STospPtmquE90aED4T/sBuHf6GvYujjj7uFukMWTruXlYbSc3GUxEXriFm7mSmZOelZvO1tGu8Hcr4PztWMp7u1HO2w0bnZaO9aqy51zxx/XmU1fp1KCaVXkpqELHBlz/Qe05EnfqOrZuTjj4ulukcfB1x9bFgbhT1wG4/sMBKnQyl2dFwcZFPRfZujiSGWO+G5yZk/t5nWPh4/T8nTjKe7tSzstVzWNYMHsu3C6ULjeP4dfpFBYMgI1Oi61OvbTojSaUUrZnbswbEZT39aCcr7nMPlmLPeEFfkhIalkFSM/Ktiyz1R68zAJUbd+A8z+q2zjy9HXsXJ1wKrCN7y/LiE0u9L6igK2zuo3tXBxJi02yOnbB81/65j04tWlikSb/+S/7zEW0ft5Wr/8++/xxDGoc5wJxjJEx6K/cBNmy6msbUoGsE+fAJKNk5aC/chOnFiWfL8v6enlfpQ4NuGI+XmJPqfvSsYh9GXvqOplF7Mv0e/EkXryLIpdcZv07NuDO9+o5J+nUNWxcHbErEMfO1x2dswNJp9Q78Xe+30+A+biM23sOxaRu56ST13AIUHv9mrL0ue9r7W0oap7XcxevUiEogPKB/up1um0Ldh04apHm+q27NK5fF4DG9euy27xckiT0egMGoxG9wYjBaMLLo/D2Kejv7s8Hde5SgTy2ac6ugwXyePsujeur9ZvG9eqw++Axcx5Br9fny6MRL8/S82iNhmF1cHN1eSTrArCrXR3D3UiMEeb6z5Y9OLYqof5z7iI633z1n2OnS27gLYKmUjXkuEiUhGgwGTGe3Isu9KlC6eyefRH99o0oBr3F+7rQJigJ0chRxV8PCsUMDEZOjEVJjgPZhOmvI+iq1S/9g/c/718JyckN083SbzQBnL9jvkbfv37VC2FPET0X79t8+iqd6oVY/X2K8jj2pXO9ELJuRZNzJwbFYCT+1wN4dmxkkcajU2Niv98DQMLvh3FrYf5NoIDG0R60GjT2tih6I6Z8daGSdHmmHd9sUHs3HT8ejpubK37+PsWmDwmpjI+PF4cOqr8X0tLyGsudnBytriMIwsMQDUtWkCRpqiRJr5j//lCSpF3mv9tIkrTe/Hc/SZLOSZJ0XpKkBfk+my5J0iJJks4ATSRJmi9J0l+SJJ2VJOkDSZKaAt2A9yVJCpckKbhA7GclSToqSdJpSZJ2SJLkZ37/LUmSvpIk6SDwlSRJPpIk/ShJ0nHzv2bmdI0lSTps/vwhSZKqP2j+YxMSLYZ8+Xl7EhNvWZmvVqUCO8wnsZ0HT5CRmU1yahqyLPPBp+uZPKL/A8WUPL0xxecNP5AT4tB4Fa7g23XugdvKDTgMHk3mmqXqm/YOOPTqT9Z366yO9zjyGBufiL9PXp78fLyIiU+0jBlciR371QrezgNHycjMIjkljVv3onBxcuTVNxfSd9QUFn3yJSaTqdSYOl9vjNF5d0iMMXFo/byKTe/SqxOZB4ofnmANycUTJSUh97WSmojk6lkonbZmYxxeXoDd8xORXIv/TtaITcvC380x97WfqwOxaZYX8duJadxOSGPw5zsZ9NkODl6LAiC0vDeNKvrQbvFvtF/8G02C/ani41pyvKQ0/D3y0vh6uBCTlGaRZky3p/njyDnaT13K2KXf8lq/jn8rjzo/bwxR+fZldDy6EvZlftnhl8g8epbg/esJ3r+ejAOn0N+4a9VnXfw8SInMK6ep0Ym4+nsUm97e1ZEabetz/WBe1+0nB7Vn0t4P6fhaf/5468sS48WmpOPv4Zz72s/dmdiUjCLTRiamEpmYSuOqQbnv6Y0m+i/6nkEf/sCuszdKjOXo70FGZF5ZzYhKxLFA3hz9PciISiwyzbE3v6bh7H70Pb6Uhq/34+S8vK7pFTo1pOfehbRbNwVD+BbLPKZm4O+eL49uTsXnMSmNyMQ0GocE5r4XnZxO30U/0mnuBoa0Ci21txJAbHIa/p55Pfh8PVwLl9nuLfnj8DnaT/6QsUu+4bUHGKZZHBd/D9LybeO06ERc/IovPwUdWPITtXo24+Ujy3hu7VS2v1Fy+clP5+tV4PwXj863+IYj116dyNz/4Oc/rZ8XhmjLY1NbQpz8ci7dwLF5QyR7OzTurjg0DkVXwo8IKPvr5X1OVhwvj4JDgAdZ+c452VGJOAR4FE4TVXIagIr9WhGzKzz3tUe9YNrsXUib3QswpcUUSh8bn5A75AvAz8eb2LgEizTVQyqzY99hAHbsO2y+TqcSVrsGjerVoXXPIbTuOZhmjetZ9AIqzt/an4DWNwDXRWtweXcpuifqlhovNq5wXSQ2zrIuUj24Ejv2HVHzuP9IXh5r1aBRWB1a93qJ1r1fUvNYsfQ8Pg5aX29M+Y5LU2w8uhIajl16dibr4IP14itI4+6NnJQXU06KR3KzvE5rygcjeXhjOl/gXGNnj237vuT8uf6BYkouHihpBepcLoWPBW2NRjgMfxe7XuOQXO7XySRs2/VDv/Mbq+PFpmTg7553zfFzL+H6lZhGZEJa4Wv04h8YtORHdpVw08jiuz+GfWnn74k+X08rfVQitv5ehdNEmtOYZEypmeg8XUj4/TByZjaNzqyhwYlPiFy1CWNy4WG3RQkM9Ofevajc1xGR0QQG+Bebvnefrvz04x8W740YOYgz53Yz593pTJsyx6q4gvAwRMOSdfYDLcx/NwScJUmyMb+3T5KkQGAB0AYIAxqZh7cBOAFHFUUJBS4CPYFaiqLUBd5VFOUQsAmYqihKmKIo1wvEPgA8pShKPeBbYFq+ZTWBdoqi9AOWAh8qitII6A2sMae5BLQwf/4N4AH79FtnysgBnDh3kb4vz+TEuYv4enug0Wj49rcdtGgUatFo8yjlbP6FlDH9yfryExz6vgiAw/NDyN60EbKtuxtgrceRxymjXuTE2Qv0HTWFE2f+wtfbE41Wg8lk4tT5S0weNZhvPl7AvagYft2655HGdu7aFrua1Uj+ouj5AB4l4+WTZC0eT9bH0zFdP4tdrzH/eEyTrHAnMZ01g1szv9dTzPn9BKnZeu4kpnEjPo1tE7uybWJXjt+M5dTtBxvuU5TNxy7QrWko29+fwIoJLzDrs1+RS7lT/0+xqRCAbZXyXG81iOstB+L4VCgODWo98jgarYbnlo3j8NotJN3N+6F09KvtLG45ka3zv6HV+B6PLN7WU9doFxqMVpN3afvzjRfZMPk55g1qz/s/H+BufMoji1dQ9Rfbcvyt9WxsNIHjb6+n2aIRucvubDnBzy2nsWvYh+hqNC9hLSXbGn6ddnUrW+TR392ZjZN7s2n68/x28ioJaQ92J7Y4m4+ep1uzULYvmsiKV/sxa/Uvj63M3lezWxPO/7CPj596he+HvM+zS8ao3SYeMeeubbCrVZXkL3545OsuSdahU2TuP065DR/i/8EMss9czO1V83eV5fXy36jahO7IRhP3fjyY+17S6evsajmNPZ1mo3HyRO2z+mCmvPwSJ8LP02fYBE6EX8DPxwuNRsOde5HcuH2PnT98zq4fv+DYqbOcPHOh9BVaqaj9KSclkDzyOVInDyfz8xU4TXodHBxLWVPppox5iRNnLtBn+EROnLmAn/f9PEZx4849dm78jF0bP+PYqXOcPPvo8vi4OD3TFtua1Uhe+w/XfyQJu94jyflxdaFFds8MRL/rZ3jAHtvWMF4NJ2v5JLLWzMZ08wJ23UYCoGvYFtO1Myhp1vcEfRBbT1+jXWgVy2v07IFsmNRHvUb/cvCRX6PLbF+WwLleCIoscyJsBKcajyFw1LPYVfD7R2L17tOVH77/zeK91Z9+RWid1rz5+kKmTh/7j8QVBBBzLFnrJNBAkiRXIAc4hdrA1AJ4BWgE7FEUJQ7A3IvpaeAXwATcn20vBcgGPpMk6XfAcja9opUDvpMkKQCwBfI3529SFOV+bbAdUDPfhImukiQ5A27AOkmSqqJ29LYpKogkSSOBkQAr5s5geP+88bu+Xp5E57tDFxOfiJ+35d0PXy8PlryhznORmZXN9gPHcHV24szFq5w6f5nvft9BZlY2BqMRRwd7Jg4rec4TJTEerXfenDYaLx/khPhi0+sP7MRxlBpfV60mtk1b4jB4FJKTM8gKil5PzubiJ8p7HHn09fYkOi4vTzFxCfh5exZKs+TtaeaYWWzffwRXZyf8fLyoHlyJ8oHqhalNs8ac+esKvWhbYkxjbLzF3W+dnw+mmIRC6Ryeqof7iH5EvjQFDIYS11kaJS3R4g6d5OqJkmp5N5SsvDs3xpO7sO0w4G/F9HVxIDol78d1TGoWvi6WwwX8XB2oHeSFjVZDkIczFT1duJOQzonbsdQt54mjrXqoNAvx58y9BOpXLL7XgK+HC9FJqbmvY5PS8POw7ML/84FwVr7aD4DQ4HLkGIwkpWfi5Vp675KiGGPisQnIty/9vTEWsS+L4tyuKdlnLqFkqhXWjH0ncAh7gqyTRf8geHJQexr2aw1AxJkbuAXmlVNXf09So4uuhHafN5yEm9Ec/nxLkcvP/XaYbu8OLfG7+ro5E52UVz5iktOL7ZGz5fRVZvR52uI9P3NPoHLebjQMCeLSvTjKe+f11KkxuB3VBqh5iw+/gVNgXll1CvAks0DeMqOTcArwLDJNSN8WuRN53/rtKE3fLzzPSczRy0iObmDrAHr19O3r6kR0vruXMSkZxecx/AYzejYrcpmvmxMh/h6cuhlN+7pVikyTm9bdhejEvAp8bFJq4TK7P5yVk9SemKEh5R+6zNZ/sR2hL6jbOOrsDVzybWMXf0/SYqz/EVP3+ZZ8/6I6iX/kqWvo7Gxw9HQhMyG1lE+CMTahwPnPG2Ns4euKw1P18BjZj8ghD3f+M8UkYONveWyaiohTnKRPviHpE7XHgN/C1zDcvldi+rK8XtYa3I4a/dV9GXem9OPlYdUa3I46/VoBkBR+A4d85xz7AE+yoizjZEUl4RBQfJoKzz+Nf/v6HOw7t8h46VcjQZGRdLYoxryhq77eXkTH5r9Ox+Nb4CaSr7cXS+fOBCAzM4sd+w7h6uLMD79vI7RWNRwd1WtP8ycbcObCJRqEltyI/3f2J0YDSppaZk03riBHR6ANLI/pevHz5fj6FK6L+PoUrossfee1vDzuPazm8Y/thNbMn8f6nLlwmQZ1H/2Nir/LFBuPNt9xqfX1xhhTeLvaP1kP9+H9iRo2+W/Xf+TkeGw88mJqPLwtenBj54AmsCKOE9VzmuTqgcPoN8la9TaaStXR1WuOXc9hSA5O6jhggx7D3t8KhrGgpCUhuRSocxVsKMpf5wrfg22b5wHQBoWgKV8dXYO2SLb2oNWh6HMw7LZ8+EB+vm5ORCfn9VCKSS7p+nWNGb1aWLyXe432cqVhSCCXIuItrtFFeRz7Mic6EdugvF5RtgGe6KMTCqcJ9EYflQhaDVpXR4yJaXhPaUHy7nAUowlDQiqpxy/hHBpMzp3CvSRB7WE0+CV1n5w6eZZy5QJylwUF+hMZFV3k52rXqYFOpyM8/HyRy3/Y+BuLl7wDTH2QrP+/JYthf2VO9FiygqIoBtQGnSHAIdQeTK2BENReSCXJVhTFZF6PEWgM/AB0BYr+tWXpI2C5oih1gFFA/sle8vc11aD2bAoz/wtSFCUdeAfYrShKbeDZAp/Pn8dPFUVpqChKw/yNSgC1q1fhdkQ096JjMRiMbN5zhFZPWT6VKSlFHRIGsObbTfTs0AqABa+NZfvXy9j65VImj+jPs21blNrgAmC8eglNQDk0vv6g02HbvA2G4wct0mgC8rrS2jRoghylVr7TZo0nZdQLpIx6gZzffiD7x69LbFR6XHmsXSOE2xFR3IuKwWAwsHn3QVo1tRyvnZSSmhdzw8/07NTG/H2DSUvPIDFZ/WF49PR5i4nGi5Nz/jI2FYPQBanb1alzSzL2HLZIY1sjGO83JhA9/g3kxORS11kaOeI6Gk9/JHcf0GrR1mmK8ZLl/FWSs3vu39oaDZELTOz9oGoFeXInMZ2IpHQMJhNbL9yhZbVAizStqwdx4pbaiyYpM4fbiWmU83AiwM2Rk7fjMMrqPEsnb8dRxbvkoXC1KgVyJyaRe3FJGIwmthy7QMtQy3l+AjzdOHrxFgA3IuPRG4x4ujz8neTsc1ewqRiITZAf2Ohw6dKS9F1HrPqsMSoOh0Z1QKsBnRaHRnXIKWEo3NGvtrOiy0xWdJnJX9tOEGauGJarF0JOWhbpccmFPtNucl/sXRz5c47l5KBelfK6cFdrU4+EW0VXkO6rVcGXO/EpRCSkYjCa2Hr6Ki1rVyqU7mZMEqmZOYTmW39qZjZ6ozpENCk9i/CbUVTxt/zBdGndDjZ1mMWmDrO4s/UkwX3U3kQ+9YPRp2aSVWBumKzYZPRpWfjUV0ctB/dpzp2tannOjEnCv4n69MGA5rVIvanmzaVS3p1Jz9qVkDTa3EYlgFrlfbgTn0pEojmP4ddpWTNvcuDcPMYmk5qVQ2jFvB+dMcnpZBuM5vzmcPpmNJUKzHlVlFqVgyzL7NELtAwrWGZdOfqXei/jRmTcQ5fZU1/u4Isus/iiyyyubjtJ7d7qNg6sF0xOWmaRcykVJzUygUrN1B+tXiGBaO1srGpUAvP5r0IQuiA/0Olw7tyKjN2Wx4xtjWB83nyF6HFvYkp8uDvn2bnnWfXYLCpOsTQaNG5qA59ttcrYVq9M5sGTJX6kLK+XF9bt4MeOs/ix4yxubTlJNfPx4ls/GH1aZpFzKT2MC+t2sLvdTHa3m0nUlhNUeE4953jUD8GYlkVOgTg5sckY07PwqK/O3VLhuRZEm49L39Z1CRnblSODP8CUlTeHjWMFHyStWg12KOeNpLVDMRkt1lu7RlXu3IvkXmS0ep3euZ/WzZ60SJOUnHedXr3+B3p2UR80EeDrw4nwCxiNJgxGIyfCz1PFimFif2d/Sq5uYO4NovELQBtQDjnGcn7BgmpXr8qde/nqIrsO0Lpp4+LzuOFHenZpW3Qez5ynihV1kcch58L9499c/+nUisy9RdR/Xn+VmAmPqP5z+woa30AkLz/Q6tA1aInxbL5zQXYmGdNeIOP1IWS8PgTTzUtkrXob+c5VshZPzX1fv/sXcrZ+V2qjEoAceQONpx+SmzdotGhrPoXxymmLNJJzXsONtlp95AS1jOT8uoqs5RPJWjEZ/c5vMJ47UGKjEkCt8r7ciUvOd42+Vso1Ou96mJqZU+AaHU0VK4ZGP459mR5+DYfKAdiV90Wy0eHdvTmJWy0nVU/aehzf51oB4NW1CSkH1AYefUQ8bs3U+VI1Dna4NKhGVglPw1396Vc0b9KV5k268sdv2+nXX31idKNGYaSmphETXXQv+j59u/HDRssyEhxcKffvjp1ac/36rQfJtiA8ENFjyXr7gSnAUOAcsBg4qSiKIknSMWCZJEneQBLQD7VByIK5B5Gjoih/mudGuj/hRxpQ3AyFbsD9s8/gEr7fNmA88L45VpiiKOEFPj+k9GwWptNqmTl2CKNnLsAky/Ts0JKQSuVYvu4HalWrTOsmDTh+9i+Wfv4dkiTRoE4NZo19qFB5ZBOZq5fg8uYH6uN2d/6J6e4tHPoNxXjtEobjh7Dv0gtd3QZgMqKkp5OxbN5Dh3scedRptcwcP5zR099VY3ZuQ0il8iz/4ltqVQ+mddNGHA+/wNLP1iMh0aBuTWa9ovaA0Gq1TB71IsOnvI0C1KxahT7PWPHUNJNM/HvL8V/1HpJWQ9rPWzFcv43H2BfJuXCFzD1H8Jw8AsnRAb9F6iNYjVGxxLzyJgABaxdhW7k8kqMDFXasJ+6NxWQdKvlHD7KM/o8vsH9xJmg0GE/tRom7h02bvsgRNzBdPonuqU7oajRAkWXISifn55W5H7cf9hYa70D18beTV6D/9RNM186WvG01Gl7rXJ8x6/chKwrdwyoT4uvGx7vPUzPQg1bVg2ga7M/h6zH0+ngLGo3ExHahuDva0e6Jchy7GUvfVVuRkGga7E/L6oElx9NqmNG/E2OWfIMsy/RoFkZIkA8rftlDrUqBtAqrxuTn2jFn3R98vf0okiQxZ+izuY9k7jz9I9KzcjCYTOwOv8yqif0tnihX3L6MfWcl5T57FzRaUn7chv7aHbzGDyL7/BUydh/FvnY1Ape/jtbVGefWT2IcN5Bbz44mbesBHJ8KpdKmlaBAxoETZOw+WnI8syu7w6nWOoxJez9En5XDT1M/yV029s/3WNFlJq7+nrQa35PYaxG8/IfaQ+DIum2c/G4PTw7uQHCz2shGI1kpGfw4eWVxoXK37Wu9WzBm1SZkWaH7k08QEuDFx38epWYFX1rVrgzAllNX6VS/qsVjrm/EJPHu93vQSBKyojC0XX2LJ9UUdG9nOEFtQul1cBGmLD0HJn2au6zbtrls6jBLzcvMtTT/cCRae1sidp8hYpf6uPtDUz+j8ZxBaHQaTNkGDk/7DICKXRoR3Kc5itGEMVuP/mSBRz9rNbzWoyljVm9W89i4OiH+nny89QQ1y/nQqlZFNY/mSbst8hibzOLfjiJJ6g3tF1vWpWpA8XnMH3PGwM6MWbweWVbo0TyMkCBfVvy8Wy2z9aoz+fkOzFn3G19vU9c/Z1j3vDI7dSnp2TkYjCZ2n77EqkkDLZ4oV5zru8Kp0jqUUfsWYcjS8+eUvG380p9z+aKLuo1bzXiBmt2bYuNgy8tHlnH22z0cWPITu95dT+f5w2k0rBOKAn9M/qS4UIWZZOLfW0HAJ+r5L/XnbYXOf173z3+LZwPq+S96/FvWxzDHiZu7gsDV7yFp1Dj6a7fxHPci2ReukLn7CHa1qxGw7A00ri44tX4K47gXudttJJJOS7mvFwEgp2cSM30BlDYUroyvl/fd2RVOhTahvHBgEcZsPXvyHS+9t87lx47qvnxy1guE9GiKzsGWAceXcembPZxc/BM+oVXosOZV7Nwcqdi+Hg0n9WZj29cKxYnZEY5f2zDaH/kQY1YOp1/N2+etd7zH7nZqb6Ezr31O/aWj0drbErPrDDE7wwGo+94QNLY2NPtuBgCJJ69xZvrneDWuTtXx3VAMRhRZUedYUiznKtTptMx8dRSjprylXqe7tCOkcgWWf7aeWtVDaN38SY6Hn2PJJ1+qdYPQWsyeOBqADq2acuzUWXoOGY8kSTR/sj6tmlk22BTpb+xPXc1QHPoNBZMRZIWMVYtR0tNKDKfTaZk5YQSjpr6NSTbRs7M5j59vUPPYrDHHw8+zZPVXah7r1mT2q6PUPLZswrHTZ+k5dAKSBM0b16dVUyvyaIWpb87n+OmzJCen0rbHQF4eNojez/6NuQlNMgnzluO/ch5oNKT9otZ/3F8ejP7CFTL3HsZz4kg0jg74vm+u/0THEjtBfQJhwBeLsamk1n/Kb9tA/FuLyTpUytPaZJns71biOE69ThsOb0OOuoNt10GYbl/BdM666+4DUWT0W7/Evt800EgYz+xDiY/A5uleyFE3MV09ja5hB3TV6uXVuX4rPBTPWjqthtd6tWDMp7+br1811OvX5mPULO+Td40+fY1O9UIKX6M37s27RrepV+I1Otfj2JcmmRsz11Dzm9eRtBpivt1F1pW7lJ/6AulnrpG07QQx3+yk6kevUO/QcozJ6VwZ/SEAUV9sIWTJWML2LAEJYr/dTeZF6yZk37p1Nx06tuLMud1kZmXz8qi8GVEOHP7d4mlwPXt1oU8vy17gI0cPolWrZhiMRpKTUhg9copVcQXhYUhidnjrSJLUFrWHkbuiKBmSJF0BVimKsti8vB8wE3WA/h+Kokw3v5+uKIqz+e8A4FfUXkMS8IGiKOvME22vRh1m1yf/PEuSJHUHPkRtsNoFNFIUpZUkSW8B6YqifGBO5w2sAJ5AbTDcpyjKaEmSmgDrUHs3/QEMVBSlUkl51d86UaaFIn3i5LIMB4Dzh4vKPCa60p/a9qjd61y229avpxUVgkdMU71qmceUKjzcU8f+jtsjNpR5zK+yH/1EvCWZtbLw03L+ad8N/3sTej6M51eGlXlMySug9ESP2JL+m8s0Xl+Xvz8X2oNSHsPzjD2rP/o5V0qz8XjZT8jsZyzbjfvMmTfLNB5A2uiRZR7T5eNVZR4Tm8dQ/2k7qkzjeTcr+3v1Gisa8x95zDDrnzD3qETP/KP0RI9YVGzJPdUftU6pp0tP9A9Izbjx6Ccp/JcYVLHXf7aR46vbP/0r95vosWQlRVF2km9+IkVRqhVY/g1Q6DEK9xuVzH9HoQ6FK5jmIOpE3EXF/RW1Marg+28VeB0PPF9EusNA/u86u6g4giAIgiAIgiAIgiAID0o0LAmCIAiCIAiCIAiC8J/wn+2u9C8mJu8WBEEQBEEQBEEQBEEQHopoWBIEQRAEQRAEQRAEQRAeimhYEgRBEARBEARBEARBEB6KmGNJEARBEARBEARBEIT/BFnMslTmRI8lQRAEQRAEQRAEQRAE4aGIhiVBEARBEARBEARBEAThoYiGJUEQBEEQBEEQBEEQBOGhiDmWBEEQBEEQBEEQBEH4T1DEHEtlTvRYEgRBEARBEARBEARBEB6KaFgSBEEQBEEQBEEQBEEQHopoWBIEQRAEQRAEQRAEQRAeiqQoYvyhYOl0he5lWiiyDGU/1ZerQ06ZxzQYtWUe84n1fco0nnxkd5nGAzDdjS3zmI+F3ljmITU+rmUa78bXWWUaD+C03q3MY9bVpZZ5zMehXM2UMo2XEWNTpvEAstJtyzymvaOhzGOeifYp85jVXZPLNN7j2K6Spuzr4Bpt2cc06sv+Pna5nZ+UaTzD2vfKNB6Akp5R5jGxK/tznpKaXuYxNRXLlWm8Ae/dKNN49/185zfpsQQuA89X7PGfbeT47vYv/8r9JnosCYIgCIIgCIIgCIIgCA9FNCwJgiAIgiAIgiAIgiAID0U0LAmCIAiCIAiCIAiCIAgPRTQsCYIgCIIgCIIgCIIg/IdJkuQpSdJ2SZKumv/3KCGtqyRJ9yRJWm7NukXDkiAIgiAIgiAIgiAI/wkyyn/239/0GrBTUZSqwE7z6+K8A+yzdsWiYUkQBEEQBEEQBEEQBOG/rTuwzvz3OqBHUYkkSWoA+AHbrF2xaFgSBEEQBEEQBEEQBEH4b/NTFCXK/Hc0auORBUmSNMAiYMqDrFj397+bIAiCIAiCIAiCIAiC8E+SJGkkMDLfW58qivJpvuU7AP8iPjor/wtFURRJkooaW/cy8KeiKPckSbL6e4mGJUEQBEEQBEEQBEEQ/hOUvz8X0b+WuRHp0xKWtytumSRJMZIkBSiKEiVJUgAQW0SyJkALSZJeBpwBW0mS0hVFKWk+JtGwJAiCIAiCIAiCIAiC8B+3CRgMzDf//2vBBIqiDLj/tyRJQ4CGpTUqgZhjSRAEQRAEQRAEQRAE4b9uPtBekqSrQDvzayRJaihJ0pq/s2LRY0kQBEEQBEEQBEEQBOE/TFGUBKBtEe+fAIYX8f5aYK016xYNS4JVXFrWo9xbI5C0GhK+3U7Mxz9aLHdqXJNybw7H4YlK3Br3Acl/HgLAoWZlys8djcbFEUwy0cs3kvzbgSJjuLcOo/KcoaDVELthJxHLf7ZYLtnqqLrsFZzqVsGYlMaVUYvJuReHd68WBI3pnpvOsWZFznSYSuaFW3j3aE7QK71AAX1MIlfHLcWYmFZkfOen6xPwxkjQaEj6fhvxq36wWO7YqBYBr4/AvkZl7k5YSOrmg7nLal39lezLtwEwRMZxZ+Q7/5rtWtDB89dZ8M1WZFmhZ4swhnVpZrE8KiGF2Z9vIi0zG1lWmNC7DS3qhpCcnsnklT9y4VYk3ZqGMnNAJ6viAWgq1ca2bX+QJIxn92M89qfFcm2tZti2eg4lPQkAw6mdmM7tR1O+BrZtXshNJ3kGoP9tFaZrp0uNqX2iPva91P1pOLwN/Y4fikynC22Kw7CZZLz/KvLda0ievjjNXIkcGwGA6dZlcr5f8a+LB6Ct2QD758aoMQ9uQb/1+6Jj1muGw6jXyXhvPPKdq2gqVcN+wAR1oSSh//1rjOGHrIppET8kFNtnXgJJg/HkTgz7LXvT6uq1xLbjIOTURACMR7dgPLnrgeM4P12fwDdHqMfmd9uJK3hsNq5F4OsjsK9RiTuvLCR1s2VeNM4OVNv2ManbjxD55iclxmo0ZxBBbcIwZeVwcOKnJJ6/VSiNZ51KNPtwFFp7WyJ2hXP8ja8AeHrlOFyDAwCwdXVEn5rJ7x3y5kl0CvSi254FxC7ZQNynv1is06VlfYLeHI6k1ZLw7TZiVxY8F9Qi6M3hONSoxK3x75NiPhfYBPlQ+dOZSJIENjri1/5OwvotJebxcca0adAYp9HjkTQasrf8QdbGDRbL7bt0w75rT5BNKNlZpC/7ANOd20gurrjMmoNNtepkb99CxsqlVsUDsG/aCM8pL4NWQ/rPm0ld+63ldhjQG+eeXcBkwpSUTMLbH2CKikUb4IvPB28jaSTQ6Uj79hfSf/zdqphOLRrgO2sUklZD8satJH660WK5Q8Pa+M0aiV31ykROnE/aVvV64vhkXXxnjshNZ1ulPJETF5C+43CJ8RyaNsRz2sugUfOY8sV3FstdB/bGuWdncx5TiH9LzaNt9WA8Z76Cxlm9liSv2UDmtr0lxqrz7ov4tg3DlKXn9IRVpJy7VSiNW93K1F86Co29LbE7wzk3+0sAAp99kupTeuNSNZB9nV8n+cxNANzrBRP2/jD1w5LE5Q9+hIM7Hvt2hbLdtveVdZm1b9II98ljQaMh49c/SVtnGc+5fx+cu3dBMZmQk5NJnPM+pui8aTkkJ0f8v/ucrL0HSX7/I6vy6NC0IZ7TX0bSaEj7eTMpnxfYroN645J/u775AcYoNabfx+9hV+cJcsLPEzP+davilWb2e4vZd/AYnh7u/PL1qkeyTgBNpVrYtumnXifP7cd4bLPFcm2tpti27JtX/zm921z/qY5t6+dz00meAeh//wTTtfBSY2pDQrHt9CJoNBhP7cZwYJPFcl3Y09i2H4CcZr42H9uG8dRuNP4VsX1mKJKdIygy+n0/Y7pwpPR4Vepg226AGi98L4Yjf1jGq9Mc2zbPI6epeTSe3InxjHosOE7/AjnuLgBKaiI5PywpNR6AtloYdl2HqvWf4zsx7P256HS1nsJh4FQyl09DjrgOWh12PUahKRcMioL+t88x3bxgVcyDt+J4f89FZBl61C7H0MZVCqXZdjmKVUeuISFRzceFeV1CuRybytxdF8jIMaHVwLDGwXSsHmBVTIBhb4+kQesG5GTl8NHkpdw4f73YtDM+m41/BX8mtB8HwOCZL9GwXWOMBgPRt6P5aMpSMlMzrI79/5n8uL/A/6D/qYYlSZJ6AFcURfnrMX+PIcA2RVEiza9voY5djH+c36tYGg3l3x3FtQFvYohKoPpvH5Cy/RjZV+/mJjFExnN78lL8RvW0+KiclcPtiUvIuRWFzs+TGn8sIm3vaUwFT2oaDVXeG8GF5+egj0qg7uYFJG47TtaVe7lJ/Pq1xZiSzumm4/Dq3oyKswdxZfRi4n/aT/xP+wFwrFGBGl9MJ/PCLdBqqPzOUE63nIAxMY2KswcR8FJn7i4q4se3RkPg22O4+eJsjNEJVPnlQ9J2HCXnWv48xnFv2hK8h/cq9HE5W8/1rq/8+7ZrASZZ5r31m/lk0gD8PFzp/+5ntAqrRnCgT26a1X8coGPDmjzXugHXI+MYt/RbNtcdj62NjrE9WnItIo5rEXHW51OSsG0/kJzvF6GkJWI/6A1M18NREiItkhkvHcOwc71lPu9eInvdW+oLeycchs/DdMuKCoCkwb7vGDJXzEZJTsBxyocYzx9Fjr5rmc7OAZuW3TDdumQZNz6azIUPsD/LOt79mP3Gkrl0JkpSPI4zlmE8ewQ56k7hmG16YLpxMS9exG0y540HWUZy9cRx9scYzx4B+QEuw5KE7bPDyF77LkpqAvaj52G8dAIlLsIimfHcIfR/fP5gectPoyFwzmhuDnodY3QCwb8uJrXgsRkRx72pS/Ae0bPIVfhNGkjGsdLLTVCbUFwr+/NL88l41w/myXlD2PzsW4XSPTXvJQ5PW0P8qeu0/Woqga3rErn7LPvGLM9N0+CN/hhSMy0+1/CtAUTsPoNNEXks984org94A0N0AtU2LSJlxzFyrlqef+5MXorvyB4WHzXGJnG151QUvRGNoz01tn1EyvZjGGMTS87sY4rpPPZVUmZORo6Pw33pJ+iPHsR053Zukpw9O8j+U/0RZPtkU5xGjCX19Wkoej2ZX32GrmJltBUrlxynQEzP6eOJfXk6xpg4Ar5eQdbeQxhu5h0n+svXiB74Mkp2Ds59nsVjwkjiX3sXU1wi0UNeAYMBycGewI1ryNp7GFN8Qqkx/d58mbsvzcIQHU+lH5eQvvMI+ut529YYFUvUa4vxHNbb4qOZR89yq/t4dTVuzgRv/4yMA6dKz+OM8cSMno4xJp7A9cvJ3HsYw418ebx0jagBY1Gyc3Dp2xXPV0cQN30uclY28a8vxHgnAq2PFwEbVpB9+ARyWtHXEt+2YThV8Wdnk0l41A8hdMFQ9nV5o1C60AVDCZ+8hqRT13hqwzR824QSu+sMqZfucnzoh4Teb0QyS7t0l70dZ6OYZOx83Wm9ax43W+wCU75zUllv1zLethYxy7LMajR4THuF2HHTMMXE4bfuY7L2HcZ4M++4NFy+RsyLY1BycnDq/Szur4wkYea7ucvdRr9EzumzpW/PfDG9Zo4nepR5u25YTuaewts1sn/edvWYOIK4aXMBSFm7EcnBDtc+z1gfsxQ9urSnf+9uzHzng0e2TiQJ23YDyNm4GCUtCfuBs831nyiLZMbLxzHstGxkl+9eJvvLOeoLeycchr2H6ZYVP18kCdsuL5H91XvqtXnEXIyXTxa+Nl84jP7PtRbvKYYccn5eiZIYjeTigf3IuWRdPwvZlteyQvE6vEj2twtRUhOxH/IWxqunC9fxLh5Dv+2rwp836sn+vPA5pOQ8arDrNoKsz+agpCbgMHYBxovHUWLvWaaztce22TOY7lzJfcumkTqncdbSSUhOrti/NJusFdNBKXmiZ5OsMH/XX6zs1Qg/F3sGbDhMy2Bfgr2cc9PcTsrg8+M3WPv8U7ja25CYmQOAvY2WdzrWpaKHE7Hp2QxYf5imFb1xsS9UGyikfusGBFYK5OWnR1GtXnVGzR3D9O5FPwH+qU5NyM7ItngvfH84Xy1Yh2ySGTRjML3H9uGreetKjSsID+M/OceSJEnaYhb1AGqW4VcpzhAg8HF/CWs5hv0fe2cdXcXVPeznXIu7By0J7lq0eIEK0JYaVCi0BapAFWlLqUBdaSl1d2+xosXdJXgCxI3YzbU53x9zSXLjUBr6e7/zrJW1cmfOzJ69Z47t2WdPU2wnUrEnpSEdTnJ+X0PQ5d08ythPpVN8MBFZbmJqO56M7YTegTrTsnFmnsEUGlhBhn/HeKwnUrG5ZWT+upbQIV09yoQM7Ub6d6sAyPpjA0F92lY4T/g1vcn8VX9DKYQAAUZfbwCMAT7Y03Iq1dGnfTNsiSk4Turyz/zxNwGDu3uUcZxOx3bwxLlNvquhLuxanr3Hk2kQGUr9iBDMJiNDu7Vm1c5DFcoVFOudYYHVRkRwgH69XhY6NW2Il/nc/NGGmCbInHTkmQzQXDgPbsIY3+GczgFgbNYF1/E94LTXLLNRM7SMFGRWGricOLf/jalt9wrlvK68BfuyH5AOxzlfz8WUB2Bo3BwtPQWZmarL3LIaU7seFWUOvw37ku+RzjIyHbbS59hshvP4coahfjxaVioyJ11/o7xnPaaWXWs+8Bzxbd8Ue9m6+fvfBA6+1KOM43Q6xQdPgFZRD+82cZjCg8lfU3OUW4MhnTn6gx75l7n9KJYgP3wigz3K+EQGYw7wIXO7/sbw6A9raTi0S4VzNb76Uo7/WhoN0WBIZwqSMjiTcLpCWb0tSMF+skxbUE5HvS2oqKN0OJF2JwDCYgZD7br1iyHT1KwlruTTaKkp4HRiW70CS/fenucuKjOB8fYpfTRtxTj37UHaa67/ZbG0aY7zVDLO07rMwiWr8OnnGaVp27oL6W7zbHsOYIwM13c4neCuq8JiAVE7Pb3bNcOemIzjZCo4nOT9+Tf+gzzrpuN0OraEE9X2JwFDe1Pw99aSa6sKrzbNcZ5Mxnk6tURH3349PcoUl9Vx9wGMUfrLBGfSaZxJ7mjJjCy07FwMIcFVyooZ0pmT3+kvcnK2H8Ec6ItXuTriFRmMyd+HnO1HADj53Rpi3HWk4HAyBUc9J9UALqsd6XYiGb3Nlc7x6tquULe2PUtdP7OW1i1wnDyNyy2v6K+V+PT11NG2bSfSpsuz7zmAMbL0ZZS5RVOMoSEUb9pWo6yzeLVpjqOsXRdXYtctnjqaysgs3rwDWViNs+M86NKhLUGBARf0nIboS9zjn0z3+GczxrgO53weY7POtR//1ItHyy7TN+/dgKl5xT6qMmRWKjI7Vf8/PwdZmIfwrX5saYhtgpaThszVx3iuA5swNetUK3nni6HB2fGHe8y1a22l4w/L5TdjX/2zh91EZH1cx/YCIAvzkNZCDPXiapS5NzWXBsG+1A/2xWw0MKR5NKuOpnmU+XnPKW5o35BAt8Mo1NcLgEYhfjQK8QMg0t+bEF8L2dba9WXdLu/Oyh/1SO9DOxLwC/QjJDKkQjlvX2+G3zWS79/yjPzbtWYHmrttPbQ9gbDo8FrJVSjOh/+UY0kI8YgQ4gH3/68JIVa4/x8ghPjS/f/NQog9Qoi9QogXyhxbIIR4RQixC+ghhJgrhNgvhNgthHhZCNETGA68JITYKYSIKyf7evc5dwkh/nZvGyuE+EUI8ZcQ4oQQ4j4hxFQhxA4hxEYhRKi7XAf3791CiJ+FECFVbRdCjAK6AF+6r8PHfQn3CyG2u3Vr4T5+lhDiIyHEKiHEsbO2ce+7RQix2X2O94QQRvffJ2499gghprjLPlDGFp7xzbXAEh2GPbk0mMqekoU5KuxcT4Nv+6YIswlbYmqFfV7RodhPl5WRjSU6rGKZs9fh0nDlFWEK9RwEhA/vRebP+qBXOl0ce2wB7Ve8SpedH+DTrAFpXy2v9NrM0WE4UkqjcJwpmeeko8HLQtyvr9Hkx5crOKSqoi7sWp70nHyiQ0oHCZEhAaTleC4NnDT8Mv7cuIfBj7zBvW98w+M3DznnayqL8A9G5pdGMsj8HIR/xU7R1Kwz3mOfxjL8HkRAJftbdMN5YHOtZBqCw9ByS++nlpuJCPK0raF+HCI4HNf+rRWPD4vC99E38HlgDsYmrf9z8gAMIWFoOeVkhpST2SAeERKBa29FuxkaN8f3yffwe2I+tq/eOmeHqQgMRZ4pfQsuz2QhAkIrlDO2vhSfe1/C66apiMBzf75N0WE4UkrriSM1C3N0Lc8jBDEzxpPyfO0ipnyjQyhKLtWpKCUb3+iQimVSsqstE3lpc6wZZ8g/rg86Tb5etLn3Kna9+lOlcs3ldUzJrL2OgDkmnOaL36T1xo9In/9jzZFDF0mmITwcLaN0+YyWmYEhrOIg1/uqkYR89BV+4ydSML/2S94qwxQRjrPMkh1XegbGyKr19B85lOJ1W0p+G6MiiPl2AfUWfsWZT7+pOVoJMEeF4Uwtta0z9dz6k7MEXtGXvD9qXjpljAzHmVqm/0rLLHU0VIL/NcOwrq3YJljaNAezGefJ5EqO0vGOCcGaXHqvrSnZ+MR4Pv8+MSEUp3iW8Y6p2KaXJ6RjHP1Xv0j/lS+w+9EPPaOVqHu7Qt3a9ix1/cwaI8JxpZXq6ErLwBhRtY5+I4ZRvN6toxAET55I7hvntnTMGBmOq4xdXemZmKKqlhlwzTCs62rX//+XEAEhyPzSF5qyIKfy8U3TTnjfPgvL8IlVjH+64jxYO/1FYAgyr0zfnJeFCKx4TmPLbvhMegGvGyYjAiv23YZ6cQijSXfeVCfPPwSZV3aMl12pDsbmXfAZ/yxe19znOVYwmfEeOwvv257A2LR2Dil9/FHaFsi87IpjrthLMASF40rwjEzUUhIxtewCBgMiJBJjvThEUM3OlvQCG1EBPiW/o/y9ySjwdE4n5haSlFPE2G82ctvXG1h3omJ0/97UXJyaRoNg31rpGhYdRlaZvjorNYvQSvrqmx++hV8X/IzNWrXDfOCNg9mxqvYOYIXiXPlPOZaANUAf9/9dAH8hhNm97W8hRCzwAjAA6AB0dS9vA/ADNkkp2wMHgGuA1lLKdsCzUsr16J/Xe0RK2UFKWX6B6pPAEPfxw8tsbwNcC3QFngOKpJQdgQ3Abe4ynwGPuWXtAZ6qaruU8gdgKzDGfR1Wd9lMKWUn4F2gbIxjC2AI0A14SghhFkK0BG4EekkpOwAuYIzbJvWklG2klG2Bj93neBzo6L6OiZUZ/t/GFBlCo9enkPjwmzWGm54v/h2b4rLaKErQw+KFyUjU7UPYNfhhtna4k6L9idR/oPKlMv+UhD7jODpiCicnv0TME3dhaRj9r8gpz79h10Wb9zG8Z3v+eulB5j14EzM+/BWtkkiQC4nr6E6sCx6l+JOn0BL3YRlWLnecXxCGiPpoJ/ZeGIFC4HXNndh++bDCLpmXTcFTd1D04oPYfv4A79sf1iMn/i/JOyvz+rux/fh+pbu1EwkUzZ5A0dwHsAy9EUw1h2SfK86D27C+ci/WeY/gOrIbr+vuveAyqiPs1ivIX7UVZ2rNzoALySUje3CiTLRS+4euZf/7i3EW1RwhcT44UjJJGPoA+y+bQMh1AzCFB/8rcupKZvEfv5AzbjRFH72H78231XzABcLvioF4tWrOmc9Kl0u70jJIufFukkfcjv9Vl2MIDa6TazFGhODVvDGFay/sJEDXsRlnPvXMTWQMDyXi2cfIeurlf62PromcHUdZ2fdRVg+dSdMHRujRcBeYf8uucHFsW9fPrO+wQVhaNiPvc12e/6jhFK/bjCv938vk4HflQCytmpH7yfc1F/4/iOvoLqzvP07xp7PQTuzHMmycZwG/IAzh9dFqkwagljgTtmN9/QGs7z6G6+gevK65x2O/8A/G65p7sP06/4I8s84jO7C+8xDWD2fiOr4Xr6tK851Z5z1E8SezsP02H8ug0YjgyH8sDyHwunIstj8/qXgt25ajncnC594X8brqDlxJCSAvzEoElyZJyi3k/eu7MeeK9jzz1z7yi0ujxTMKipm5eDezLm+LQYgLIhOgcatLiG4UzaYlVefDGnXfDbicLlb/vOqCyf2vI6X8n/37r/Jfy7G0DegshAgEbMB2dAdTH+ABdOfOKillBoA7iuky4Bd058rZzKNngGLgQyHEH0Btsm2uAz4RQnwHlH2tvFJKmQ/kCyHOAL+7t+8B2gkhgoBgKeXZ11+fAt9Xtb0a+WdlbkN3ZJ3lTymlDbAJIdKBKPRM7p2BLUJvmHyAdPe1NRFCvAX8CSx1n2M3eoTUL+i2qoAQ4m7gboAZIe24zr9xyT57ahaW2FJvviUmDEda7SdqBn8f4j5+gpSXvqBoR8VlVwC21Gws9crKCMVebjJoS83GEhuOPSUbjAaMgb4eibjDR/Yi85fSBNZ+rXUdbIn625as39dT777KHUuO1CzMMaVh1qaY8HPS0eku6ziZRuHGPXi3jsOeVH0EUV3YtTyRIQGk5uSV/E7PyScqxDPq6+e1O3l38s0AtI+rj83hJKegiLBAv1pfW1lkQa7H2ykREFKSpLKE4tKcE87df2Pue73HblPzrrgObwfNVSuZWm4W5uDS+2kIDveIrsHLB0NMQ3zvn6NfU2AIPnc/gXXBM2gnj4BTf660k0fRMlMxRNTTt/9H5AFoOVmYQ8rJzCknM7YRvlNfLJV5zyys78xCSzpcep7Uk8hiK4bYxh7ba6L8G0IRFOYRmQaAtaDkX+e25ViG3FLr85ccl5qFOaa0npijw3DU0lHk27EFvl1bE3bLFRh8fRBmE67CYtJeLM0v0Pz2QTQd0x+ArJ3H8I0t1ck3JpSiVM9ntSg1B9+Y0CrLCKOBhsO68uew0oSy4R3jaXRlNzrPuAlLoC8GqSFtDjI/1ROcOsrrGBNeax3L4kzPpvhQEn7dWpUk2q6KiyFTy8zEEFE6aTCER6BlVT0hta1ejt99U875mjyuLyMTU3SpTGNkBK70inp6d+tE0PjRpN75UMlSorK4MrNwHD2Bd8e2FC1fU61MR1oWpjLLDUzR59afAAQOu4yCv9aDs+Y2z5WeiSm6TP8VFV7pRN/70o4E3Tma1PGeOgo/XyLfepactz/GtudAheMCbhxOv6uvBiBn5zF8Ykuff5+YUKwpnnXEmpKDd4xnmeKUypehV0bB4WSchcV4NWtM8d7SNqmu7Qr/vm0ro66fWVdGZsnyPdAjnlwZFXX06taJwDtGkz5haok8S7tWeHVoi/+o4QhfH4TJhLRaOfN29V+sdqVnYixjV2NkOM60yu0afOdoUsZXruN/HZnvGaEk/D0jmADP8c+eNZj7jvLYbWre5ZzGPzIvxyM6WASGIfPKySzbN29fgWXw6NJ9Xj54jXkU+4pv0U5VPw4BdxRWYNkxXmhFHa1ldNy12iMp+dnxoMzNwJV0EENUQ1y56VSHPv4obQvKR1Bj8cEQ1RCfu/UcVcI/GO/bHqf4s7lop49iL+Nw8pn4HFpmzZGEkf5epOVbS36nFRQT4e9Vrow3bWOCMBsN1AvypVGIL0m5RbSODqLA5uSBX7dzb89mtIsJrlbWsNuuYLB7tcCR3YcJK9NXh0WHkV2ur27eqQXx7eJ5b90HGExGgsKCeObb53nixukA9B81kC4Du/LkzTNr1FOh+Cf8pyKWpJQO4Dh6DqL16BFM/YF49Cik6iiWUrrc53GiR/j8AFwF1Pi5GinlRGAm0ADYJoQ42yqXfcWslfmtcWEdc2fP6yp33rLyz+4TwKfuiKcOUsrmUspZUsocoD2wCj0y6WzPfiUwD+iE7oyqcN1SygVSyi5Syi5lnUoARbsO43VJDJYGkQiziZCr+3Dmr1qG5JpNNHl/Gtk/rSz5olllFOw8gs8lMXi5ZYSP6E32Es8lQzlLthB5Qz8Awq7qwZm1ZaJXhCDs6p5k/lL6pTZbaja+zRpgCtOXfgVd1g7r4XKJ/dxYdx/Cq3Es5vpRCLOJoKsuI3/ZplrpaAj0Q1h0kxpDAvHt0grb4aQajqobu5andeNYktKyOZWRg8PpYvHmffRt38yjTExoEJsOnADgWHImdoeT0IDahexWhpZyHBESpQ8CDEZMLS6t+FUTv6CSf43xHdHKJbY0trwU54Ha3Q8ALekQhohYRGgUGE2YOl2Gc0+Z44uLKJw+hsKnx1P49HhcJxJKnDzCP7AkL4UIi8IQEYuWVb2TsK7lAWiJCRgiYxFhbpld++oJuMvKfPhGCmfcTuGM23EdP1jiVBJhUSV5cURoJIboBnp+qHNAO30UQ1gMIjgCjEaMbXviPOhZZ4V/cMn/xhZd0DIqr3/VUbT7sGfdvPoy8pbVrp6cnPIKCb3HkdDnTlKe/4jcn1d4OJUAEj5dxh+Xz+CPy2eQtGQbcaP0nD/hneJw5BVhTc/1KG9Nz8WRbyW8k76aOm5Ub04uKY1+iOnThjNHkj2Wyy259hl+6j6Fn7pP4cAHS0ib932JUwnOtgWxWBpElbQFeX/V7nk3R4chvCwAGAP98OvSEtvRinmcynMxZDoPHcQYWx9DVDSYTHj1HYB94zqPMobYeiX/W7r1wHX63J+Zstj3JWBqUA9TrC7Tb0g/rKs920xz83hCZ0wmffKTaDm5JduNkeElehoC/PHq0AZHYs3XU7znEBb3M4vZROCVl1GwvOYvK5Ul8KraL9ey7UvA1NBTx6LVnl87szSPI2xmRR0xmYh8dRaFf/xF0bLKnQ/53/7GqkHTWTVoOqmLt9LgBj2wPKRTPI58K7ZydcSWnouzwEpIp3gAGtzQh5QydaQyfBtGIIx6m+RTP5yA+Fgcpz3bpLq2K/z7tq2Mun5m7fsPYm5YD6Nbnu/g/lj/LievWTyh06aQ+dATHvKyn5hDytWjSRkxhtw33qNw4V81OpVAt6u5YT1M9dw6Dq3Eri3iCH9iMmkPPomWnVv5if7jaKknyo1/uuE6usuzUNnxT1yHiuOfFt1qvQwOQEs+iiEsurRvbtMDZ4Jn/fPom5t3Rst0t99GI943TsW5aw2u/bWTqSUfx1BGR2PLS3Ee9sxpKMrq2LQT2tnE3t6+YHRPSXz8MdZvWisnj3bqCIbwGERIpD7+ad8b14Ey4w9bEYXP3kHRi5MoenES2slDJU4lzBYw6w4hY3w70LSKSb8roXV0EEk5RZw+U4TDpbEkIZV+TTyjq/rHR7L1pN7351jtJOYUUS/IB4dL46Hft3NVy1gGN6t5RcOizxYyddiDTB32IJuWbKT/dQMAaNaxOUX5ReSkezrulnyxiPFdxzKh151Mv+4xUo4nlziVOvbtxDWTruX58c9gr0VeOYXin/Bfi1gC3Zn0MDAOPSroVWCblFIKITYDbwohwoEc4GagwndNhRD+gK+UcqEQYh1wzL0rH6g0M58QIk5KuQnYJIQYhu5gqhEp5RkhRI4Qoo+Ucg1wK7C6qu01XUctWQ78KoR4TUqZ7s71FAAUAnYp5Y9CiATgCyGEAWggpVwphFgL3AT4A7m1lubSOPXEAuI+n4UwGsj6djnFh04SPXU0RXuOkPfXZnzbxXPJ+9MwBvkTNKgr0VNv5uCg+wm+qhf+3VpjDA4gdJTeMCY99CbW/ccryDg2/QNaff0Ewmgg7ZsVWA+dpMEjN1Gw6wg5S7eS9vVymr71AB3Xv40zt4BDE18rOTyweyvsyVnYkkoHoo60HE6++h1tfn4G6XBhO5XBkclVfAbXpZE8az6NP52NMBjI+f4vbIeTiJw8Buuew+Qv34xPu6Y0fHcGxiB/AgZ2I/LB0RwZei9e8Q2o99x9SE0iDILM+d97fLHqotq1HCajgWmjhzLp9a/RNI2RvToQXy+Ceb+sonXjWPp1aMZDNwxi9qd/8sVfmxBCMHvc1bgj4xj22FsUWG04XC5W7kxg/pTRHl+UqxSpYV/2BV6jpuqfot2zFpmVjLnXSLTUE7iO7sTcaZCe0FvTkMUF2BeVLhkTgWGIgFC0kwk12/QsmkbxD/PxvWe2/inajX+hpSZhuWIMrqTDleYcOosxrg2WK8aAywVSo/i7eVBUUGX5iyLvrMxv38H3ged0meuXoqUkYrn6VlyJh3HtrnrCZYxvg2XIDeBygpTYvn4bWZhXZfmq5Nv/+Ajv22eUfNJYpp/CPOAGtOSjuA5uw9RjGKYWXZCaC4oKsP30zrnJAL1uPjWfSz57GgwGcr5fptfNKe66uUyvm43mT3fXza5ETR7D4SHnvuzu9PKd1BvQnmvWvYLTamf91AUl+65a+hx/XD4DgE3TP6Hna3dj8rZweuUuTq8onSg0HtHdYxlcbXU89eR7NPlMbwuyv1tG8WF3W7D7CHnLNuPTLp5LFug6Bg7qSvSU0SQMvg+v+AY0mTlOX64gBBkLfqE4IbFGkRdFpuai4N3XCXr2ZTAaKF66EFfSCXxvHYfz0EHsm9bjc/W1mDt2BqcTraCAglfmlBwe8sk3CF8/hMmEpWdv8mY87PFFuar0zH7hLSLnzdU/F//bYhzHEgmaeDv2/Yew/r2BkMl3Y/D1IeJFPcrMmZpOxpQnMV/SkJCpE0v0zPv8exxHqm9jz8pMm/0uDT58FowGzvywFPuRJMIfuIXivYcpWLEJ77ZNqTfvCYyB/vj3v5TwB27h+JWTADDXi8QUE07R5j01yzqr49y3iXp3jq7jr0twHE0keNLt2PYfwrp6AyFTdB0jX3LrmJJO+uQn8bu8L96d2mIMDsR/uP6GPPPJl7AnVP4567RlO4ka2IFBG1/DZbWxY/J7Jfv6LXueVYP0yczuxz+i4xsTMXpbSFuxi/TlOwGIGdaFts/djiUskEu/eJS8vYlsuHkuod2a0/T+4XpieE2y6/GPCcop1ybVtV3r2LYeMuvymXVp5Lz4FhFvvoAwGij4bRHOY4kEThiL/UACxX9vIPjBuxE+PoTN1b/e5UpNJ/OhJ6o/bw0ys+a8TbTbrvm/uO16z+3Y9x2iaPUGQsvbNTWd9Ad1+TEfv4q5cQOErw8Nln5F5qxXsa6vmL/wXHjkqbls2bGb3Nw8Bo68hXvG38p1V/+zHJNIDfvyr/C6brJ7/LPOPf4Z4R7/7MLcaSDGuPbu8U8h9sUflxxeOv6pXVQ6oPfNCz/B+9ZpIAw4d6xCZpzC3H8UWvJxXAnbMF06FFPzznrfbC3A9oueI8vYugeGRi0w+fpj6nAZAPZf5qOlVtPOSg37X5/jfdMjurzdfyMzT2Pucw1ayglcR3Zg6nI5pqYddXnFhdj+0J2PhrBYvIaORUqJEALHhj8rfE2uKh1tv32Az7gnQBhwbF2Bln4Sy6CbcJ0+4ulkKofwC9KPkxItL5vi796slVlNBgOPDWjFPT9tRZOSEa3rExcewDvrD9MqKoh+cZH0bBTOhsRMrv10DUYhmHxZc4J9LPx5IJntp3PILXbw237diTf78rY0j6z5ozvbVmylc/8uvLtmATarjbceLs07+OqiN5g67MFqj7/rmQmYLWZmffkMoCcAnz/9PMZiCkUtEP+1dXpCiIHoEUbBUspCIcQhYL6U8lX3/puB6ehRO39KKR9zby+QUvq7/48BfgW83eVellJ+KoToBbyPHgU0qmyeJSHET0BTd/nlwGTgdqCLlPI+d5kT7t+ZQoixZ/cJIToA8wFfdCfWHVLKnGq2Xwc8D1iBHujRWGfP28V9vf2EELOAAinly275e4GrpJQnhBA3AtPQo84cwL3u831MaSTaNGAZsBIIcuv2hZRybnX3YEfDEXX6UFgdde/fDPSpe6+9w1nVxwr/PVp+OarmQhcQbePKOpUH4DpZfcj0/wzur3DVJYaImgc9F5JjX1hrLnSB2WEPqrnQBaad6RydeP9Hqd/qTJ3KK0y78Dl5asJaYKlzmd6+db8kaFdqDS8Q/gWaB+bWqbyLYVdhqPsxuMFY9zKd9rpfIFF/+Xs1F7qAOD55vk7lAciCwpoLXWi86r7Nk3m1eNl2gTE0ql+n8sY8f6zmQv8CPyf9fuGSPf3HuKbh1f8tJ8cF5L963/5zEUtSyuWAuczvZuX2fw18Xclx/mX+T0FfCle+zDqgVRVyr61k8yfuv7NlGpf5v2SflHInUOFTYNVs/5HSfFAAZc+7Fejn/n9WuePalPn/W8Dzm5I6lX1SoXcl2xQKhUKhUCgUCoVCofifQuN/1q/0n+U/lWNJoVAoFAqFQqFQKBQKhULxfwflWFIoFAqFQqFQKBQKhUKhUJwXyrGkUCgUCoVCoVAoFAqFQqE4L/5zOZYUCoVCoVAoFAqFQqFQKM4H7WJfwP+HqIglhUKhUCgUCoVCoVAoFArFeaEcSwqFQqFQKBQKhUKhUCgUivNCOZYUCoVCoVAoFAqFQqFQKBTnhcqxpFAoFAqFQqFQKBQKheJ/Aom82Jfw/x0qYkmhUCgUCoVCoVAoFAqFQnFeKMeSQqFQKBQKhUKhUCgUCoXivFCOJYVCoVAoFAqFQqFQKBQKxXmhciwpFAqFQqFQKBQKhUKh+J9AUzmW6hzlWFJUIG6ItU7lFSXY61QegF+ngDqXqWUV1LlM568/1Kk80y131ak8AJF2os5lkpFS5yJdO/fWuUwtv27bgsjGeXUqD8B1KKjOZTbsdKbOZTpyRJ3L/GtX/TqVN/KV+DqVB+DauafOZUpr3Qebh3znqHOZMZfa6lSeMTqwTuUBOE7k1LlMS9t6dS5TS82uc5mOT56vU3nmsdPrVB6Alna87mVmnaxzmXLftjqXaWjdpU7lHbJtrFN5CsW/gVoKp1AoFAqFQqFQKBQKhUKhOC+UY0mhUCgUCoVCoVAoFAqFQnFeKMeSQqFQKBQKhUKhUCgUCoXivFA5lhQKhUKhUCgUCoVCoVD8TyClSt5d16iIJYVCoVAoFAqFQqFQKBQKxXmhHEsKhUKhUCgUCoVCoVAoFIrzQjmWFAqFQqFQKBQKhUKhUCgU54XKsaRQKBQKhUKhUCgUCoXifwLtYl/A/4eoiCWFQqFQKBQKhUKhUCgUCsV5oRxLCoVCoVAoFAqFQqFQKBSK80I5lhQKhUKhUCgUCoVCoVAoFOeFyrGkUCgUCoVCoVAoFAqF4n8CibzYl/D/HSpiSaFQKBQKhUKhUCgUCoVCcV6oiCVFrTC27oL3TZMQBgP2NYuxL/620nKmTr3xnfQkBc/ei5Z4GOEXgM/EJzA2bo5j/VKKv55Xa5mWbt0IfOA+MBix/vknhV9+5bHf94br8b3qSqTLhZaby5m5L6KlpQHgP3ECXj26IwwGbFu2kv/mWzXr2KIT3tfeBcKAY+Nf2Jf/ULmO7XriM24aha9MQTt5BABDTGO8b7wXvHxBahS9OhWcjhplmtp2xfvWe8FgwLFqIbY/vvG0wYCrsAwaAZqGLLZi/eg1tOREMJrwGTcF4yXNQEqsn8/DdXBXjfIAjM074jXiLl3mpr9wrPyx8nJte+Bz++MUvf4Q2qkjmDr2xdxvZMl+Q0xjrK9PRUs+XqPMdTsP8sInv6BpGtcMuJTxIwd67E/JzGHmvK/JL7KiaZIHR19Jn44t+XPNNj79fVVJuUNJKXwzdwotGterWeb+RF786W80TXJNj1aMG9zFY/9LP61hy+FTABTbnWQXFLH2hQkAdHrwbeJjwwCICQngjbuvqlnekVReXLITTUqu6XgJ43q1qFBmyb6TvPf3fkDQLCqIuddeypYT6by0tPTencjMZ+61lzKgRc06Xox7eTHqiaVrNwLuux+MBqx//knR1+XagutvwOcKd1twJpe8F18obQvunoBX9+4AFHz+GbaVK6uUc+nsW6k/oANOq421UxaQtfdEhTJhbRvT57UJGL0tnFqxk01Pfg5ASKuG9Jx7B2Zfb/JPZfD3fe/iKLAS26cNnaffiNFswuVwsvXZr8FxssJ5zR274Tv+fjAYsC37k+KfPHX0GjIcr2HXgOZCFlspfOdltFOJJfsN4ZEEvfkp1m8/ofjXytvnCnbt1o2A++4Do97GFn1V3q7X43NlaRub92KZNnaC264GA/atW8l/q/o2ttMztxE7oD0uq52NU94jZ09F24a0bUz31ydi9DaTvGIX25/4DIA2D11L3Oj+2LLzAdg151tSVuh1JrhlA7q+MB5zgA9eUWZsSxaA5vQ477rDKby4eLveFnRqwrg+rSrIXrI3ifdW7QUBzaKCmTuqJwCvLd3JmsPJSAndm0Tx6LBOCCFqtK2xWUe8ho/T68mWZThW/Vx5uTbd8bn1UYrefATt9FEwGPEadQ+G2CYIoxHHtlU4Vv1Us7yWnfC+9m69LdiwFPuyKupl+574jJ9O4UuT0U4eQYRG4jf9XbT00wC4TiRg+652/XRI/w7EPXMHwmgg9cvlnHz7F4/9wmKi+Vv3E9CuCY6cfA5MeA3byQwA/Fo2pOlLEzAG+IAm2T70caTtP9xnDh+vy9y8DMfKyu+HsW13fG57jKI3HkY75b6X19+LoV4ThMGIY9vKKo+toGeHbvjeoY9/bMv/xPaLZ920DB6O99CRSE2DYiuF75W2B8aGTfCd8BDCxxekJO/xieCwV69jXDssQ24FgwHnjlU41v3ueT3tL8My6Ga0/BwAnFuW4tyxChEUjtcNk0EYdB23LMW5bXmtdDS26oz39RP1OrJ+Mfal31dhi1743D2TwrkPoCUdLtkuQiLwe+I9bAu/xLGs8r6vPIbGrbEMuBmEAeeeNTg3L/K8ptY9sfS9Hlmg6+nYsRLXnjUYGjTH0v/GUtmhMdj/eA/XkZ21klsVM59/lb/XbSY0JJhfvpj/j85VlrXb9/HCR9+jaZJrB/Vk/LVDPPanZGQz861PyS+04tI0Jt8ykj6d2+BwOJk9/yv2HU3CIASPjb+erm2anbP8dXuP8uI3y/QxX58OjBvWw1N+1hme+PgP8otsaJrGA9f1o0/b+HOTcSKTl1YfRNMkI9vUZ1zXSyqUWXoolfkbjyKAZhEBzBnWDoB7f97G7pQzdKwXzJsjOp2TXi98vUTvS/p0YPwVvSroNfOj38gvKtbHstcNoE+7eHILinjo3R/ZdyKZ4T3bM33M0HPSddpzU7lsYE+s1mJmPPAMB/YkVCjz8U/vEBEVjq3YBsBdNz5AdmYOMfWjefb1mYSEBXMmJ4/H751FWkr6OclXKGrL/6RjSQgxEjgkpdx/sa/lXPjPXrcw4DP6PgpfexyZk4nfjLdw7tqAlpLkWc7LB8vAa3AeO1CySToc2H79FEO9xhhjG9depsFA4JQHyZn6MK6MDMIWzKd47TpciaUTKefhw2TeNQFsNnxGDCdg0gTOzJqNuU1rLG3bkHXHeABC334LS4cO2HfurFZH71ETKXr3CWRuFr5TX8W5dxNaWrkJoJcP5r5X4zpx0ONavW+dSvEXr6IlnwDfAHC5atZRGPC+/QEKX3gUmZ2B/+x3cGzfoA+C3djXr8C+4g8ATB174D1mIkUvTcPS/0oACqbfhQgMxu/hORQ8dQ/IGsI+hQGvayZgXfAU8kwWPg++jHP/ZmQlelr6XI0rsbTzcu5YjXPHal3l6EZ4j51WK0eES9N4/qOfeG/GBKLCghg97XX6dWlNXP3okjLv/7SMIT06cMPlPTl6KpX75n7AordncmWfzlzZpzMAh5NSmPzyx7VyKrk0jTnfr2L+vSOJCvZnzMvf0rdNE+JiQkvKPHJtn5L/v169i4OnMkrVN5v47rGba5RTKk8yZ/EO5o/pQ1SgL2M+WE7fZrHERQSWlEnMyuejdQl8MrY/gT4WsguLAejaOJLv7h4MwBmrnavfXkSPuKiahV6Ee3lR6onBQMCDk8l95CFcGRmEzn8P23rPtsBx+DBFE+/W24LhIwiYMJEzs5/G0r07pqbNyLrzTrCYCX3tDeybNiGLiiqIqT+gPYGXRPNj74eI6BRHjzlj+ePqWRXK9ZhzB+se/YCM7UcZ/Pkj1OvfjtMrd9PrpTvZ8sxXpG08SNMbL6PNpCvZ8dIPFGfns2zsK1jTcgluXp/Lv3wU2z1LKujoe/dk8mc9hJaVQeCL72HfvM7DcWT7exm2Jb8BYO7aE9877qXgmUdL9vvecS+OHZtrtqeHXR8k9+GH3Xadj21dJXad4G5jhw8nYMIEzsyejbl1a8xt2pA1Xm9jQ956C3OHDjiqaGNjBrQn4JJo/uj1EGGd4uky5w7+uuqpCuW6zh3H5kc+IGv7Efp+8Sgx/duTslKf/Ce8v4iD8xd6lBdGAz3euocND7xL7v4kRr7VFqTnM+XSNOYs3Mr8W/sTFejDmPf/om/zesRFBpWUSczK56O1+/lk/CC9bhbodXNnUiY7T2by/SR9EnDHR8vZeiKdrpfUUD+FAa+Rd2H94Gm9bt73Is79W5DppzzLWbyx9LoSV9Khkk2mdj3BZMb6+hQwW/Cd+ibOXWuQORlUiTDgff0kiubN1Ovlw6/p9TK1sno53LNeAlpmKkUvPlC9TuUxGIifM549NzyDLSWbjovnkLV0K0WHSnWMHj0AZ24BW3rcT8SInlwy8xYOTngNjAaaz3uAhPveonB/IqYQf6Tjv9xn3o11wSz9Xj7wIs59myveSy9vLL2v8mhn9XtpwvrqZP1ePvwWzp013Eu3bX3HP0jBMw+jZWcQMGc+jq2e7YF97TLsf7nbgy498b39XgqeexQMRnwfmEHRW8/jSjyK8A8El7MqSW4dBZZhYyn+Yg4yLxvvO5/BmbAdmXnao5hz30bsiz/12Cbzcyj+aJYuw+yFz6QXcCVsQxbk1iDTgPeN91L05nRkbia+j72Bc/cmtNSK40pz/xG4jh+scAqv6+7GuX9r9XLK6zloDLbvX0Xm5+B9y0xcR3cis1I89UzYgmO5pyNPO5lA8Wez9R/efviMfx7XiX8+VB95xWBGXzec6c+8/I/PdRaXS+P5979lwVMPEBUWzM2PvkC/ru2IaxBTUmbBD4u4vGdnbhx6GUdPpnDvs/NY/N6z/LhsHQA/vT6TrNx87nn2bb5+8TEMhtovcHFpGnO+Wsr8KTcRFRLImOc+oW/7psTFhpeUef/P9VzepSU39OvE0eRM7nvzOxbNrb1jyaVJ5q48wLvXdibK35sxX2+kb5MI4sL8S8ok5hTy0ZbjfHJDNwK9zWQX2Ur23da5McVOFz/uOVXZ6avU6/kvF/He1DFEhQQy+tkP6dehGXGxEWX0WsuQLq24oX9njiZncN8b37Co3f1YzCbuHdmXI6czOHK6hvpfjj4De9LokgYM6z6Kdp3b8OSLj3LzsPGVln3snifZt8uzrjzy1AP89t1Cfv1uIZf27szkGfcw7b5Z53QNCkVt+T+9FE4IYaxi10ig4ivJ/z4j+Q9et/GS5mgZycjMVHA5cWxZjalDzwrlvEberkcylX0zZi/GdWRfjW/LymNu2QLX6dO4UlLA6aR4+Qq8e3u+GbDv2Ak2vaNw7N+PMcLduEuJsFjAZAKzGWEy4crJrlaeoVFTtMwUZFYauJw4d/yNqe2lFXW8Ygz25T8iy0RZGJt3REs+oU+WAYryQWo16miMa4GWdhqZkaLbdeNKzJ3L2bW4dAIsvLw5u1zYUK8Rzv07dHXzcpFFBfqb2BowNGyKlpWKzHbruXMNptbdKpSzDBmNfeWP4Kz8vpk69sG5c22N8gD2HkmiQVQY9aPCMJtMDO3ZkVVb9lUoV2DVJ3MFRcVEhARW2L9o3Q6G9uxQO5mJaTSICKZ+eBBmk5EhnZqxas+xKssv2naIoZ3P/a1cibzkbBqE+FM/xB+z0cCQ1g1YlZDsUeanHce5sWscgT4WAEL9vCuc568Dp+gVH42PuWaf/8W4lxejnphbtMSVXKYtWLECr169Pco4du7waAsM7rbA1Kgxjt27QHNBcTHOY0exdKt4vQANh3TmyA+6HTK2H8US5IdPZLBHGZ/IYMwBPmRsPwrAkR/W0mioHgkX1CSatI36gC55zV4aX9EVgOx9iVjTcgHITTiFydsCJrPHeU1NW6KlnEZL03W0r12BpZunjljLtgU+njbq1htXegqupFo4B88e06JcG7tiBV69PNtYx86dldq1sjZWy666ja0/pDMnflgDQNb2I1iCfPEuZ1tvt22ztuvRbSd+WEP9oZ2r1SG6b1tyDySRu989GbVbKzgK9p7OpkFoAPVD/fW2oE1DViV4Tph/2naUG7s2La2b/nrdFALsThcOl4bdqeF0aYT5V6y35TE0iEfLSimtm7vWYmpVRd1c/Ytn/yglwuwFBgOYLeByIout1ctr1Awto0y93P43prbdK5TzuvIW7Mt+QDpqjgyqiYCO8ViPp1KclI50OMn4ZR1hQzyjQsOGdCXtO92BnfHHRkJ6twEgpF97CvcnUrhfd5Q4cwpA+w/3mZll7uXOtdW0sz+Xi8CUCIu3+1561epeAhjjW6ClnkZL1+umY90KLF0862bZ9gAv75Ln3tS+C67EY7gS9TZKFuTVaFtDvTi0nDRkbgZoLlz7NmJqXn3dK0FzlTquTGa90tQCQ+Nm+rgySx9XOretxtS+kmf26tuw//U9stwY0tS+BzIrFS0lscIxVcqMvgSZk448kwmaC+fBzRjjOtT6+LMYm3XGdXxPlX3qudClQ1uCAgP+8XnKsvfICRrGRFA/Ohyz2cTQ3p1ZudkzOk8gKCw6O+ayEhGqO9qPnkyhW9vmAIQFBxDg58u+o+WcfTXJP55Mg4gQ6keE6G1u15as2nnIo4wQUGjV+5YCazERwf6VnapqGalnaBDkS/0gX33M1SyaVUc9o3B+3nuaG9o3INBb729Dfb1K9l3aMAy/WoyzKugVGVqi19BurSvoBVBQfFYvGxHB+r319bLQqWlDvM5RJsCAoZfx2/d6ZN3ubXsJCAwgPDKs1sfHNbuETWt1B+ymtdsYMPSyc76G/6toyP/Zv/8qF8WxJIR4RAjxgPv/14QQK9z/DxBCfOn+/2YhxB4hxF4hxAtlji0QQrwihNgF9BBCzBVC7BdC7BZCvCyE6AkMB14SQuwUQsSVkx0lhPhZCLHL/dfTvX2qW9ZeIcRk97bGQoiDQohPhBCHhBBfCiEGCSHWCSEOCyG6ucvNEkJ8LoTY4N5+l3u7vxBiuRBiu1uXEWWu4zb3Ne9yH1vhuoUQq4QQLwghNrvl93EfaxRCvCSE2OI+xwT39hghxN/u4/cKIfq4y37i/r1HCDHlnO9XcDhadqmHXeZkYAj2bNQMDeMxhETg3HMOb8yrwRAegSu9VKYrI6N0UlMJPldeiW2TLtuxbz/2HTuJ/PknIn/+EdvmzbgSq+8YDUFhaDmZJb+13CxEUDkd68chgiNwlXtDZoisBxJ8Jj6N70OvYxlwba10FCHhyDJ21bIzECHhFcpZBo3A/+XP8b7pboo/fxsAV9JRzJ16gsGAiIjG2LgZIjSyZplBYcjcUj1lZXrWa4IhOBzXgW1VnsfUvjfOnX/XKA8gPfsM0WHBJb8jw4JIyznjUWbS9UP4c802Bk+azb1zP+DxO66pcJ4lG3YytGfH2snMLSS6zEAlKtif9DMFlZZNzs4jOTuPbs3ql2yzO52Mfulbbn3lO1bsPlqzvDwr0YGlk/2oQB/S8z0nEIlZBSRm5XP7xyu59aMVrDuSWuE8S/adZFjrBjXKg4tzLy9GPTGEh6Ollw4YtYwMjOEV68lZfK64AvumTQA4jx7B0q0beHkhAoMwd+hY6oAuh290CIXJWSW/C1Oy8Y0OqVCmKKXUgVJUpkzuoVM0HKJPxhpfdSl+saGUp9GVXfXldeWW/4nQcFyZZXTMysAQVlFHr2EjCXr3K3xun0jRB2/oG7198Ll2NNZvP61QvjoMERFoGWXan4yMKm0Dehtr3+xuY/fvx75zJxE//UTEj+42NqnqNtYnOtTDtkXJtbBtcjY+0aU2bHrH5QxbNodLX70Lc5AvAIFNYpAS+n31GEOWPIupZbnJN2frpm/J76hAH9LzytfNfL1ufriMW9//i3WH9QiG9g3C6do4kkEv/8rgV36lR3wMTSKCqAm9bpbqK89kIYI8nwdDbBMMQWG4DnrWTeeeDUiHDb8ZH+I3bQH2v38Fa+VtV8m5gsPQcsvcy9zMKupleIV6CWAIi8L30TfweWAOxiata9QPwCsmFFuZe2pLycYSE1ZJGXd74dJw5hdhCg3At0kMSGjz9Qw6Ln2B+vcOr5XMi9JnBoZ6trNnqmlny9/L3RuQ9mL8nvgIvxkLdCdiDfcSwBAagZZVTs+winXTa8hIAt/6Et9bJlL00ZsAGGMaABL/GS8S8MICvIbfVLOOAaHIM2We17xsREBIhXLGll3xmTAHr1EPIgJLn2cRGIrPhDn4Tn4Tx7o/ao5WAgzB4WhlIre0nEqe2QZxiJBwXHu3lFPcG8vg67Et/LJGOWURASFI91I+AFmQU6mepqad8L59FpbhEyvf36IrzoMXZqz7b5CWlUtUWOl1R4WFkJ5dbsx145X88fdmBt05nXuence0O/Vlfs0b12fVlt04XS5OpWVy4GgSqZk5nAvpuQVEh5a+HIwKCSA9N9+jzMSr+/Dnpn1c/sjb3Pfm9zx+8+Bzk1FYTFRAqZM/KsCbjEKbR5nEnEKScooY++1mbvtmE+tOZJY/zbnJzMknusxLz8iQANJyPPWaNPwy/ty4h8GPvMG9b3zD4zcPKX+acyYyJoLU02klv9NS0omKqbyvfvaNJ/hx+edMnDKuZFvC/sMMurI/AIOu6Id/gB9Blby8VSguBBdrKdwa4CHgTaAL4CWEMAN9gL+FELHAC0BnIAdYKoQYKaX8BfADNkkpHxJChAEfAi2klFIIESylzBVC/Ab8IaWsLMnAm8BqKeU17ognfyFEZ+AO4FJAAJuEEKvdsuOB64FxwBZgNNAb3Qk0HT3KCKAd0N19fTuEEH8C6cA1Uso8IUQ4sNF9ba2AmUBPKWWmECJUSpld/rrdeRxMUspuQogrgKeAQcB44IyUsqsQwgtYJ4RYClwLLJFSPufWzRfoANSTUrZxnzO4shsihLgbuBvg9d4tuaNF/cqKVY4QeN8wAevHFy6U91zwHjwYc/PmZD/wIADGevUwNmpIxqjrAQh55WXM7Tbj2L3n/IUIgdfI8RR/9XrFfQYjxiatKHp1KtJuw/feZ3GdPILr8O7zl1cG+7JfsS/7FXOPAXiNuAXrghdwrF6EMbYh/rPfRctMw3lkX62iP2pECLyGj6P4mzerLGJo2AzpsFUMWf8HLFq3g+F9u3L71f3YdegEM97+mh9ffrgk/Hr34US8LWaaNoyp4UznzpJthxnUIR5jmVDvhbPGEhXsz6nMM9z19s80jQmnQS0mlNXhkhpJ2QV8cFtf0vOsjPtsFd9PGEygtx4lkZFv5Uj6GXrERddwplpyMe7lRawnAN6DBmNq3pycyXpbYN+6FXPzFoS+PQ8t9wyO/ftqFRlxPqyd+j6XPnMb7SeP5OTS7bgcnstPgpvVo8v0m1g6+gUG1GKlY2XYFv2CbdEvWPoMwuf62yh8cw4+N46l+LfvoRaREOeL92C3XR8sbWNNDRuSeb3exga//DL2zZtx7PkHbWw1HPl0Gfte+xkpod2jo+j01Bg2TX0fYTIQ0a0ZS654ApfVzqhNs9Gyk9HSah+5BfqyiqTsfD4YO4D0vCLGfbyc7ycNI7fIxrHMPJZO1R0fEz9fxfbEdDo1qtkhUS1C4HXVWIq/r5iXytCgKWgahc/difDxx2fSs7iO7NYjZv6JvGvupPjL1yrsknnZFDx1BxTlY2gQh8+dMymcc8+/+jwJk5GgS1uwfejjaFYb7b5/ioJdx8hdu/eCnL/O+8yr76D424rtrKGh+14+M16/l/c8h+vwP7yXZbAt+QXbkl8w9x6I93W3UjRvLhiNmFq0Jf/xiUhbMQFPvYrr2CGce7f/I1nOQ9tx7l0PLiemTgPwGjGR4s+fB/RnyPreNIR/MF43TsV5YBMU5v0z5YTA67q7Kf7slQq7vK68BfuKn8FW/M9kVILr6C6sBzfrera7DMuwcdi+K3MNfkEYwuujnagYdf1/iUVrtzKif3duHzGIXQnHmP7GJ/z0+kxGDuzBsVOp3PzIC8REhNK+RROMhtpFoZ0LizfvZ3jPttx2+aXsOnqKmR/+zg+z7sJwAWW5pCQpt4j3R3UhvaCY8d9v4ftbehLgba754PNk0eZ9DO/ZntuHdGfX0VPM+PBXfnx6wgXVqyoeu+cp0lMz8PXz5fWP5jL8+mH89v0iXpr1JjPmPMzIG69k68adpCano7n+nXGQQnGxHEvbgM5CiEDABmxHdzD1AR4AugKrpJQZAO4opsuAXwAXcDZL3xmgGPhQCPEH8EctZA8AbgOQUrqAM0KI3sDPUspCt7yf3NfyG3BcSrnHvX0fsNztxNoDNC5z3l+llFbAKoRYCXQD/gSeF0JcBmhAPSDKfQ3fSykz3ddR3Tqts5ket5WRdznQTggxyv07CGiK7vj6yO2k+0VKuVMIcQxoIoR4y309SysTIqVcACwAyLvrco8YO5mbiSG01DsuQiLQyryNxdsHQ2xj/B5+Sd8fFIrvfbMpevtJtMTDnA9aZgbGyFKZxnJv189i6dwZ/9tuIfv+B8Ed3u/VpzeOffuRVn1gbNu0CUvr1tU6lrQzWZjLvPk0BId5vMHDywdDdCN879MHUiIgBJ87Z2L94Flkbiauo3uR7oGUc/9WDPXjapwwy5xMRBm7GkIjkDlVv1FxbFyJz9gHsQJoGsVfvluyz+/JN9FSal4rLs9kIYJL9RRV6Okz6dkSPb3vmEHxx8+hndKXqJg69MG5Y02Nss4SGRpEalZuye/0rDNEhXg6aX5euYl3p90FQPtmjbE5HOTkFxIWpIcRL1m/k2G9ahetBBAZ7Edqbumb4bTcAiKDKg+1Xrz9ENOu7+exLcod7VQ/PIgu8fU4eCqjWsdSZKAPqWWiINLyrEQGeC5XigrwpU29UMxGA/VC/GgU6k9SdgFt3JEtS/efon/zepiNtQskvRj38mLUEy0zE0Nk6UTeEBGBK7NiPbF06ozfLbeSPfmBkrYAoPDLLyj88gsAAmc+gfNUad4Zn5EjGT5Ij5zK3HkMv9jSN+Z+MaEUpXq+qS1KzcG3TJ4u3zJlzhxNYeloPbg2sEk09Qd28Cg34MPJrHlwPvmJ6XovUAaZnYkxvIyOYRFoWVW3Bfa1y/GdoAefmpq1wtKzLz63T0D4+YMmkXY7tkWVJ4s+i1YuCtQQEYGrijbW75ZbyH6wTBvbuzeO/aVtrH3TJsytW3s4lnxGjmToYD3yMMtt27Ma+cbWwraxoVhT9a6xOLN0knr0y5Vc9tnD+jEp2WRsPIg9W6/rruTDGEJiPRxLet0sXTaUlmclMrBc3Qz0oU39MHfd9KdRWABJ2flsPZFOu/ph+HrpE5Fe8THsOplVo2NJr5ulz5IICkOeKdPNe/lgiG6Iz93P6PsDgvEeO43iT+Zg6tAHV8IOPUl74RlcJw5irB+HsxpnhJabhTm4zL0MDq9YL2Ma4nv/HF1eYAg+dz+BdcEzemJ9p/7WXTt5FC0zFUNEvZKE+1VhS8nGq0x98YoJxZ6SVUmZcOwp2WA0YArwxZmdjy05izMb9+N0J2PPXr4d/3ZNanQsXZQ+My/bs50NqqzNa4jPxLPtbDDeY6dT/MnzmDpeds73EvQIJUNYOT2zKtbNEj3XrcDvrikUzdOjHZ37dyHz9QgVx/aNGJs0rdaxJPOzPaKFRGCoR2QP4BFp5dyxEsugijkIZUEuWvpJjA1b4DpQfUSPlpuJOaSMjiGVPLOxjfCd8qL7mkLwmfgU1vlPY2jcHFPH3nhdMx7h46cvA3TYcaz+vbyYcnp6RigJ/5CKehYXluq5Zw3mvqM8dpuad8F1eLu+BPA/SlRYMGlZpXqlZeUQGVpuzLV8Pe8+cS8A7Zs30cdceYWEBQfw6LhSnW+d9hKNYs/tTUhksD+p2aVtdlpOPpHBnsv9fl67i3cm61FS7ePqY3O4yC0oIjTQr3Yy/LxJyy91LKblFxPh5+VZxt+bttFBerse5EujED+ScotoHX1+LwkjQwJIzSnVKz0nn6iQ8nrt5N3JN5fRy0lOQRFhtdTrLDffMYpRt+iLXPbu3E90vdJ7EBUTSVpKxfYgPVXfVlRYxMKfltC2Y2t++34RGWmZTB73OAC+vj4MvrI/+Xk1R04qFOfDRVkKJ6V0AMeBscB69Aim/ujRQQeqPhKAYrdDCCmlE92B8wNwFbD4X7jcsrGVWpnfGp6OufILHiUwBogAOkspOwBpQM0JGiqX7yojTwD3Syk7uP8ukVIulVL+je6AOw18IoS4TUqZA7QHVgETgQ/OUT6uEwkYIushwqPBaMLctS/OXRtKC1iLKJh6PQXTbqNg2m24jh34R04lAMfBBIz162OMiQaTCe+BA7CtW+9RxtQ0nsCHp5IzbTpabm7Jdi09HUuHDmA0gtGIpUN7nGUS0laGlnQYQ3gsIjQKjCZMHS/DubfMwKi4iMKZYyicfSeFs+/ElZiA9YNn0U4ewXlwO4aYxnoOBYMBY1ybismMK8F17CDG6HqICLddu/fHsd1TR0NUaaJqU4fuuFLduUEsXnpeBcDUpjO4XB4JTKvU8+RhDOEx+hIAo0mfxOwrp+dTt1L0/N0UPX83WlKChyMCITC174VzZ+2dEa3jGpCUmsmp9CwcTieL1++gbxfP5RYx4SFs2qs/L8dOpWF3OAkN1J07mqad0zI4gNYNo0jKyOV01hkcThdLth+ib9uKXww5npZNntVG+0tKo4TyioqxuxPJ5hRY2Xk8hSZlluRUKi82hKTsAk7nFOJwaSzZd5K+zTyjq/o3j2Vrot7x5xTZSMwuoH5w6WBj8b6TDGtTu2VwcHHu5cWoJ46DBzHWq48h2t0WDBiAbf06jzKm+KYETH2I3BnTkGXaAgwGRKAe8m1q0gRzkybYt5QuBbL+8gu/XT6D3y6fQdKSbcSP0vMaRXSKw55XhDW9zLkAa3oujnwrEZ30Fdbxo3qTtERf/uId5g4tF4L2D44g4XP9y0iWQF8Gf/YQ257/lvStlbeJzsMHMcTUxxCp62jpPQDHFk8dDTGlbYG5c4+SSXH+jPs5M+Emzky4CdvvP1D84xc1OpUAHAl6G+tp13JtbHw8AVOnkjt9uoddXenpmMu0seb2FdtY6y+/sHjwdBYPns7pxVtpPEpPlh/WKR5HnpXicrYtdts2rJOewLXxqD6cOmvbMvmY6g/rwpkEXfeUVbsJatkAo48FYTRgiGyElueZZ6N1bChJWfmczinQ24K9SfRt7vkBgP4t6rP1hH5cTqGNxKx86of4ExPkx7YTGThdGg6XxrbEdJpE1LyEQDt1BENYDCLEXTfb98Z1oMxynuIiCmePpeiFiRS9MBEt6RDFn8xBO30UmZuJMb6tXs7shbFhs5IvtlUpL+kQhogy9bLTZTj3bPKUN30MhU+Pp/Dp8bhOJJQ4lYR/IAh9KCjCojBExKJlVVymW578nUfwaRKDd8NIhNlExMheZC31XGaXtXQrUTf0BSDiqu7krtMdRzmrduHboiEGHwsYDQT1aOWR9LsqLmqfefZeduiNa3+5eznrdormTKBozgT3vXwe7dRRZE6G571s1Awto/p7CeA6kuDRHph7DcC+tZye0WXag07dcaXo53Xu2oyxYRNdX4MRU6sOuE7VMP45fQxDaDQiOEKPLG3dHechz2V9wj+45H9js85omXoOQREQWpozztsXY8PmaOWSYVcqM/EQhshYRJj7me3cF+fujaUFiosofPQmCp8YS+ETY3EdP4h1/tNoSYexvvpIyXb7yl+wLfm2RqcSgJZ6AhEShQgK123Tohuuo+W+DOhX6nQwxnWooIuxRbf/9DI4gNbxjUhMSedUWiYOh5PFa7fRr2s7jzLR4SFs2q0nmj92KgW73UlokD9Wm50id46gDTsPYDQaPZJ+10p+41iS0nM4nZGrt7lbDtC3fVOPMjFhgWw6cEKXn5KJ3eEkJMC3krNVISM6kKTcIk6fKdLHXIdS6Rfn6fDvHxfJ1lO6Qz/Haicxp5B6QT6Vna72eqVlcyojB4fTxeLN++jb3jNPW0xoUKleybpeoeeg11m+/vgHrht4K9cNvJXli/5m+PXDAGjXuQ0F+QVkpns68Y1GI8Fu56HJZKTv4N4cPqincQgODSr5kumdD97Oz1/XXFcUivPlYn4Vbg3wMPoSsz3Aq8A2dzTQZuBN9/KxHOBmoELMuBDCH/CVUi4UQqwDzmbnzQeqyoa3HJgEvH52KZz7Wj4RQsxFd9pcA9x6jvqMEELMQV8K1w94HH0JXbqU0iGE6A80cpddAfwshHhVSpl1dilcDdddliXAJCHECve5m6E7k8KBU1LK991L5DoJIRYCdinlj0KIBOCLc9RLf9P31dv4Tn4eIQzY1y1BS07Ea/htuBIP4dy1sdrD/ed8pn/21mjG1LEnRa9Nq/hFufK4XOS9/gYhL78EBgPWhYtwnjiB/7g7cCQkYFu3noBJkxA+PgQ//bR+SHoaudNmULxqNZZOHQn/5COQEtumzdjWb6henqZR/ON8fCc+7f50+zK01CQsw8bgSjrsOWEvj7UQ+6pf8J36KiBx7d9aaR6LymRaP3sLv0de0GX+vQjtdCJe147FdTwB544NWAaPxNS6k570s7AA6wI9IkIEBuP36Av6J5VzMimaP6dmeW6Ztp8X4HPXLPdnsJejpZ3EMmS0vixpf/UDJmOT1sjczHMK5TcZjUwbdy2Tnl+gfxa2XzfiG0Qz77vFtG5Sn35d2vDQrVcz+73v+eLPvxFCMHvSTSUd4bYDx4gOC6Z+VO2TFZqMBh4f1ZdJ7/yGpmmM6N6K+Jgw3vlzI60aRtKvbRMAFm87zNBOTT0+H34sNYdnv12JQYAmYdygzh5fk6tUnsHA40M7MOmrNWhSMqJ9Y+Ijg3hn1T5axYTQr3ksPeOi2HAsjWvfXYJBCKYMbEewO5nk6dxCUvOK6Nyo6hw3FbgI9/Li1BMX+W++TsiLL4PBQPGihbhOnMDvjnE4Ew5iW78e/4kTET4+BM3S2wItLZ3cmdPBaCL0Db3r0IoKOfPcc1W+aT61fCf1B7TnunWv4LLaWTN1Qcm+4Uuf47fLZwCwYfon9HntbozeFk6v3MUp92fvm4zsQYuxgwBIXLiVw9/qeata3jGYgMZRtJ9yDe2n6BE84qUHkGdyPXQsev91Ap7SdbQtX4jr5Al8bh6H88hBHFvW433FtZjaddbbgoICCt+sZZ2vCpeL/DfeIOSll9x2XeS26x04ExJ0u7rb2KCnz9o1jdwZM7CtXo2lY0fCPnK3sZs3Y99QdRubvHwnMQM7cNX6V3FZ7Wya8l7JvqF/Pc/iwdMB2DrtYy59fQJGbwspK3eR4rZth5k3E9K6EUhJwakMtjz6EQCOM0UkvLeIIQufQUqJlpOIluzpvDMZDTx+RWcmfb4aTWqM6NhEr5sr9tAqNpR+LerRMz6aDUdTufbthRgMgimDOxDs68WgVvXZfDyN699djAB6xsdUcEpViqZh+/UDfMY/qdeTs3Vz8E24Th31dDKVw7FhEd7X34fP1NcRCBxbV6Cl1uAA0TSKf5iP7z2zdXkb/9Lr5RXuerm36nppjGuD5Yox+hcapUbxd/OgqBZvs10aR6Z/SJuvZyCMBlK/XklRwikaPXoj+TuPkr10K6lfraDF2/fTdcNbOHIL9C/CAc4zhZx+7w86Lp4LUpK9fAfZy2qxVOti9Zm/vI/PXU/pMje77+XlN+M6dcTTyVQOx/pFeN9wPz4PvYEQAseWFbVLNq25KPrwDfxn6HXTvnIR2qkTeN94B66jCTi2rsdr2DWY23ZGulzIgnwK39b1kYUF2P74nsC580GCY8dGnNurH6MhNeyLPsF7zGMgDDh3rkZmnMbc7zq05OO4Dm3H1G0IpmadkJoLigux/Tpft2tELN6DxyCl1HXc8CcyveYXBmgaxd++i+99z4LBiGPDUrSUJCxX3Yor8RCuso7RC4XUsC//Cq/rJoPBgHPPOmRWMuZeI9BST+A6ugtzp4EY49rrz0lxIfbFH5ccLgLDEAGhaCcrJmw+Xx55ai5bduwmNzePgSNv4Z7xt3Ld1f8sL4/JaGT6nTcyafbbuDSNkQN7EN8wlnlf/06ruEb079aOh8dex9PvfMnnv69ACMEz99+KEILsM/lMnP0WBiGIDAvm+QduPw/5Bh4fPZhJr3+jj4d6tSO+XgTv/Po3rRrF0K9DU6ZeP5DZny3ky2V6/Xn6jis9xmE1yjAYeKx/C+75ebsuo3U94sL8eWfDEVpFBtIvLpKejcLYkJjFtZ+twygEk/s0I9j9gYZx323meE4hVruLIR+s5qlBrenZuOrcjWf1mjZ6KJNe/xpN0xjZqwPx9SKY98sqWjeOpV+HZjx0wyBmf/onX/y1SR/Ljru6RK9hj71FgdWGw+Vi5c4E5k8Z7fFFuar4e9k6LhvYk0WbfqTYWszMB58p2ffj8s+5buCtWLzMLPjmTUxmI0aDkQ1rtvDDF78C0K2n/iU4KSVbN+7g2cdfqrWd/68ja/rqp+KCIy6W0YUQA9EjjIKllIVCiEPAfCnlq+79N6PnMBLAn1LKx9zbC6SU/u7/Y4Bf0aOABPCylPJTIUQv4H30aJ9RUsqjZeRGoS/5aoIeBTRJSrlBCDEV3ckF8IGU8nUhRGP0nEdn8xN94v79Q9l9QohZ7vM1RXfuvOh27oQDv6M7r7ai52AaJqU8IYS4HXjEfQ07pJRjy183ev6oh6WUW93n2iqlbCyEMADPAle79c5Az/U00n1OB1CAvuQvEPiY0ui0aVLKRdXdm/JL4f5tihL++Zc1zhW/Thf2Kxy1Qcuq+Inzfxtj1Ll9aeOfYrrlrjqVByDTTtS5TDJqfiN7oXHtvDD5R84F6azbdfhFu/5hXo7zYOGh2keJXShGdKvFxOsC48j593M8lGfFoVo4YS4gI1+p/eeqLxSunf9OXqnqkNa67zN3fHf+b/rPl/aDq/+a64XGGF33CW0dJ84tMfKFwNK2buslgJZat/cSwNikbvU0j51ep/KAc84nd0FkZtV9/yX3Vf0Bkn8LQ5uKX3/8N+k86u06lXeWfWmb6n5wUEcMrF+389m6ZPmppf/J+3bRIpaklMsBc5nfzcrt/xr4upLj/Mv8n4K+FK58mXXoCbIrk5sGjKhk+6voUVNlt50A2pT5PbaqfcBuKeVt5Y7PBHpUcR2fAp+W21b+uvuVO1dj9/8autOtfC9W4ZxuOlV2DQqFQqFQKBQKhUKhUCgU/4SLkmNJoVAoFAqFQqFQKBQKhULxf5+LmWPpfwYp5ayLfQ0KhUKhUCgUCoVCoVD8/45W4btain8bFbGkUCgUCoVCoVAoFAqFQqE4L5RjSaFQKBQKhUKhUCgUCoVCcV4ox5JCoVAoFAqFQqFQKBQKheK8UDmWFAqFQqFQKBQKhUKhUPxPIFWOpTpHRSwpFAqFQqFQKBQKhUKhUCjOC+VYUigUCoVCoVAoFAqFQqFQnBfKsaRQKBQKhUKhUCgUCoVCoTgvVI4lhUKhUCgUCoVCoVAoFP8TaFLlWKprVMSSQqFQKBQKhUKhUCgUCoXivFARS4oKGBtE1K28E6fqVB6AsV7d6ghgCCyqc5la5pm6FVhYx/IAQ73mdS5T07Q6lymLtte5TEdK3T6zXrHGOpUHYE6oc5GYIr3rXKbR31nnMrVDom4FFhXWrTzA2PoitD+nk+tcpkMW17lMqdXt22ZDTHidygMQSbl1LzOy7vU0GOu+bZcFddseaGnH61QegCHqkjqXKW11385qWTl1LlMmH6tTefmOup8jKBQXGhWxpFAoFAqFQqFQKBQKhUKhOC+UY0mhUCgUCoVCoVAoFAqFQnFeqKVwCoVCoVAoFAqFQqFQKP4nUKm76x4VsaRQKBQKhUKhUCgUCoVCoTgvlGNJoVAoFAqFQqFQKBQKhUJxXijHkkKhUCgUCoVCoVAoFAqF4rxQOZYUCoVCoVAoFAqFQqFQ/E+gqSxLdY6KWFIoFAqFQqFQKBQKhUKhUJwXyrGkUCgUCoVCoVAoFAqFQqE4L5RjSaFQKBQKhUKhUCgUCoVCcV6oHEsKhUKhUCgUCoVCoVAo/idQOZbqHhWxpFAoFAqFQqFQKBQKhUKhOC+UY0mhUCgUCoVCoVAoFAqFQnFeqKVwilphjGuHZcitYDDg3LEKx7rfPfab2l+GZdDNaPk5ADi3LMW5YxUiKByvGyaDMCAMRhxbluLctvyc5Zu7dMP/nvsRBgPWRX9i/fYrj/3eVw3HZ/g1oLmQViv5r72MKynxnGQYGrfBMnA0CIFz9xqcmxd62qB1Lyz9bkAW6Do6ti/HtWcNACIgFMvQsYiAUJBg+/E1ZF5WjTKN8e2xXHE7CAPO7StwrPnNY7+pQ18sQ8ag5WUD4Ny0BOf2lQB43fo4xvpNcSUlYPvyxVrraWzVGe/rJ4Iw4Fi/GPvS7ystZ+rQC5+7Z1I49wG0pMMYGjXDe/QD+k4hsP/5Jc5d62slc92eI7zw1RI0qXFNn46Mv7K3x/6UrDPM/PAX8otsaJrGg6MG0qddUzbsO8obP6zA4XRhNhmZcsMgLm15Sa1krt2xnxc++gFN07h2YE/GX3u5p8yMbGa+9Tn5RVZcLo3Jt4ygT+fWOJwuZr37JQeOncTl0ri6XzfuvHZIzToeSOTFn9bqOnZvxbhBnT32v/TzWrYcPgVAscNJdr6VtXPvAqDTlHeIjwkFICYkgDfuurJWOl6Me2nu2A3f8feDwYBt2Z8U/+RZF72GDMdrmLsuFlspfOdltFOJGCKiCXrrM1zJSQA4D+2naP6rtZJpatcVn1vvA4MB+6qF2H7/2mO/ZeDVeA0eAZqGLLZS9OGraKcTwWjE986HMV7SFAxG7GuXYvvt6yqkQJdnbqXegA44rTY2TFlA9p4TFcqEtm1Mj9cnYPK2cHrFTrY+8XnJvubjBtNs7GCkS+P08p3sePYbDGYjl744ntB2l4CmsfXJL4CMCuc1tu6C9w0TEQYj9rWLsC/5rnJbdOyN78QnKHj+PrTEwxhbdsL7mnFgMoHTSfGP7+NK2FU7u7btivet94LBgGPVQmx/fONp1wFXYRlUalfrR6+hJSeC0YTPuCkYL2kGUmL9fB6ug9XL7FzOtjnV2Nbotu02t23bPnQt8aP7UZydD8CuOd+RvGIXwmSk+8t3Etq2McJkwJS7C+fuFRXOu+5YOi8u34smJde0a8i47k0rlFlyMJn31iUA0CwyiLlXdyrZV2BzcO2Hq+jfNJppg9tWb9SzMo+m8uLS3brMDo0Z17N5RZn7T/HemgO6zKgg5o7sBkDKmSKe/nM7aXlWhIC3buxJvWC/auUZm7TFMmiM3kfvXI1j458e+01te2MZcGNpH71tOc5dqwHwfexjtIyTAMi8bGw/vF6lnND+7Wn27FiE0UDylytIfOtXj/3CYqL12/cS0K4Jjpx89t79BsUnMwjsGEeLl+92FxIcf+l7MhZtwTcuhjYLJpcc79MokmMvfs/JBZ79b4keddQWlMXQuDWWfjfptt2zBueWxR77ja16YrlsFLIgFwDHzhW49q7F0KA5lr43ltomNBr7nwtwHd1Zo0xT+674jL1Pv9YVf2L7tZyeg67Ga8jIUj0XvIJ2OhFz70F4X10q09CwCQWP340r8Wi18tadyOClVQfQNBjZpj7jujWpUGZpQgrzNx5BIGgWEcCcK9qTkJ7Hcyv2UWhzYTTA+G5xDGkeU6N+4H5mL79FH//sXI1jwx+eNmjXG8uAm9DcYy7n1mU4d64uYwRvfCbMxXVoG/Yln1MbjPHtsQy9Tb+X21fiWFt+zHUZlsFj0PLdY67NS3FuX4khuhGWK8chvHxBatj//hnXvo21krl2+z5e+Oh7NE1y7aCejC83ntDHIp+SX2jFpWlMvmUkfTq3weFwMnv+V+w7moRBCB4bfz1d2zSrlczqmPn8q/y9bjOhIcH88sX8f3w+gHU7D/LCZ7+haRrX9O/G+BEDPPanZOYw891vyS+06mO8m6+gT8eWABxKTOaZD3+koMiGwSD46tkH8LKYa5RpbOKelwgDzp2rcKwvNy9p1wfLwDLzkq1/4dy5ChEUhteoKSAEwuiel2yv2G9UqufhZF5cuE1v1zvFMe6y1hXKLNmbyHsr9wCCZtHBzL2+FwCvL93BmkPJANzdtw1D2jaqlUyAp+c8Tv/BfbBai3no3pns3X2gQhmz2cQzL86ge68uaJrkpefeZNHvywC4auQQpjw2CSkl+/ce4oG7H6u1bIXiXFCOpWoQQowEDkkp91/sa6kOIUQs8KaUcpQQogMQK6WsfFR2fgKwDBtL8RdzkHnZeN/5DM6E7cjM0x7FnPs2Yl/8qcc2mZ9D8UezwOUEsxc+k17AlbCtZABWKwwGAu6fTO5jD6FlZhDy9nvYN6zzcBzZViyj+A99gGDp0RP/ifdyZvqj56bj4FuwffcKMj8b71ufxHV0JzIr2VPHg5txLP+ywuGWK+7EsfEPtMT9YPYCWYt1vUJguWocxZ8+h8zLwnvC8zgPbkNmlLPr3g3Y//y4wuGOdX/gNFswdRl0Dnoa8L7xXorenI7MzcT3sTdw7t6ElprkWc7LB3P/EbiOHyzZpCUnUvTCA6BpiMAQfGe8g3PPRtC0akW6NI3nv1jEew/dQlRoIKNnf0C/Ds2JqxdRUub939cwpGtrbujfhaOnM7jv9a9Y9NKDBPv78uYDNxEZEsDhU+lMevVLlr06pUY1XS6N59//jgVP3kdUWDA3P/YS/bq2Ja5B6YB3wQ+LubxnJ24c2oejJ1O497l3Wdx5Nks3bMfhcPLTazOw2uxc8+CzDOvdhXqRYdXqOOeHv5k/aThRwf6MefV7+ra5hLjo0JIyj1xT6kz7+u/dHDxV6lzwMhv57tGbatTLg4twLzEY8L17MvmzHkLLyiDwxfewb16HdqpMXfx7GbYlel00d+2J7x33UvCMXhddaafJm3rnOevpM/ZBCuc8gpadQcAz7+LYvl6fLLqxr1+Ofbk+qDR16onPmEkUvvg45kv7gtlM/uN3gsWLwBc/xrF+BVpmWgUxsQPaE3BJNL/2eojwTnF0mzOWxVfNqlCu29w72PTIB2RuP0r/Lx4htn87klfuJqpnS+oP6cyfg6aj2Z14hQUCED+mPwB/DpyGV1ggA758BN5Z49lGCAM+N99L4evTkDmZ+E17C+fujWgpFe+lZeBInMdKB5Wy4AxF855EnsnGENsI3weep+DxMbWyq/ftD1D4wqPI7Az8Z7+DY/sG3XFUYtcV2Ffokz1Txx54j5lI0UvTsPTXHZ8F0+9CBAbj9/AcCp66p8p2L3ZAewIviea3Xg8R5rbtkkps23XuHWx85AOyytkW4OD7izkw37Nba3R1NwxeJv4cOA2jj4Ubdr6M69iOEuc/gEuTzFm2h/k3dCcqwIcxn62hb3w0ceEBJWUSswv4aONhPhnTi0BvC9mFNg8589Ym0KlB1fW/PC5NMmfxLuaP7k1UoA9jPlpJ36YxxEUEespcn8Ant/Ul0MdCdmFxyb6Zv23lzl7N6dEkiiK7EyFqECgElstvo/ibF/U+euwsnId3VOy/DmzGvrSSCbjTTvFHT9asmEHQfO44dtzwHLbkLLoumUPmkq0UHirts2JHD8CRW8iG7g8SNbIn8U+MZu/db1Bw8CRbLp+GdGlYIoO5dOWLZC7dRtHRFDYPfKzk/L13zSdj4eYq9KybtqCCbQeM1l8W5efgPWYGrqO7kNkpniY8tAXHCk/nj3YygeIvZus/vH3xGfc8rsRaDCeFAZ9xD1L43CNoWRkEzJmPY2s5Pdctx77MrWfnnvjcdg+Fcx7DsXYZjrX6ZNLQ4BL8Hn6mRqeSS5PMXbGfd6/tSlSAN2O+2kDfuEjiwvxLyiTmFPLRlmN8cmN3Ar3NZBfpdcTbbOSZIe1oFOJHekExY77cQM9G4QR41+AYEALL0Nso/sr9zI57Gufh7cjM8s/spiqdRpa+16ElJVQvp7zMK+6g+PPn9THXXc/hTKhkzLVvA/aFn3hskw4btp/fRWanIgJC8L77OaxHd0NxUbUi9bHItyx46gF9LPLoC/Tr2q7cWGQRl/fszI1DL9PHIs/OY/F7z/LjsnUA/PT6TLJy87nn2bf5+sXHMBj+2WKTkVcMZvR1w5n+zMv/6DxncWkaz3/8M+9Nv5uosCBGz3iTfp1bE1c/qqTM+z8vZ0j3dtwwuCdHT6Vx3wsfsuitljhdLqbP+5rn7r2Z5o1iyc0vxGQy1ixUCCzDbqf4y7n68zN+Ns5D2yo+P/s3Yl/ymcc2mZ9L8SezSuclE+biOrS9xnmJS9OY88dW5t8+QG/X31tC3xb1iYsMKimTmJXHR3/v55M7L9fb9QK9Xf874TQHknP4dtIwHC6N8R8to1fTWPxrqidA/0F9aBzXiMu6XEnHLu147pWZjBhcsY+//6G7yczIpl+3qxFCEByiX1fjJg25Z/J4rh16G2fO5BEWHlrh2P9VZG3mYooLiloKBwghqmrFRgKt6vBSzgspZbKUcpT7Zwfgigt5fkO9OLScNGRuBmguXPs2YmreueYDATSX3ngDmMzUPEKuiKl5S1zJp9FSU/Q38qtWYOnpGfEii0o7d+Htw7nmazPENEHmpCPP6Do6D27CGN+hVseKsFgwGHWnEoDDBk57zTLrx6NlpyJz0sHlwrVnPaYWXWp9zdqxvUhbcc0Fy8ps3AwtIxmZlQouJ85tqzG1716hnNfVt2H/63uko4weDlup48FsqZ3zDNh77DQNIkOoHxmC2WRk6KWtWbWz3GBQQIFVH6gWWIuJCNYnfS0bxRAZov8fXy8Cm8OB3eGsWeaREzSMDqd+dDhms4mhvTuxcstuT5FCUGjV7VdQZCUiNMh9KYKiYjtOlwub3Y7ZZMTfx7t6eYnpNAgPon54EGaTkSEdm7Jqz/Eqyy/afpihnf/Zm8eLcS9NTVuipZxGS9Pron3tCizdPOsi1jJ10cvnnPUqjzGuBVraabSMFHA5sW9cgblzz2pkelPSAEj3NRgMCIsX0ulAWiufCDQY0pnjP6wFIHP7USxBfvhEBnuU8YkMxhzgQ+Z2faJ2/Ie1NBiq19lmtw1i39u/o9n159OWlQdAULN6pK7dV7LNfqYIQyPPe2+8pDlaejIyU7+Xjq2rMLXvUeEavUbcjn3xd1DmXmonjyLP6G/XteREhMVLb2tr4Kxdpduujo0rK9q1uJxd3WY11GuEc/8OAGReLrKoQI9eqoL6QzpzzG3bLLdtvcvZ1ttt2yy3bY/9sJb6Q6tvD6UEk68XwmjA6G3Ro+Tsnm3i3pQcGgT7UT/YD7PRwJCWsaw6kupR5qfdSdzYsTGB3hYAQv28SvbtT80lu9BGj8YR1Ja9ydk0CPWjfohbZqv6rDrk6Yj4acdxbuzchECfszL1NuZoRh4uTdKjiT4p87WY8DFX/w7QENvEs48+sAlTs07VHnM+BHaKx3o8jeLEdKTDRdov6wkf2tWjTMTQLqR8p0eVpP++kZDebQDQrHakS29zDN7mStuc0D5tsZ5Io/hUZqXy66otKIsh+hJkbgbyTKZ7bLAFY1yHGo+rcO1NO+M6vrdWYwNjfAu0tGS0dLee61dg7trLs1B5PSuxp6XXQBzrV9Yob29qLg2Cfakf7Ks/r82jWXXU0+H2855T3NC+IYHuiXCor15HGoX40ShEj6aL9PcmxNdCtrUW45/YOLTs9NJndv/Gc3pmDdGNEX5BuI7vqf0x9cqNufZuwNS8dmMumZWKzNbbDZmfgyzMQ/gG1nCUeywSE1FmLNKZlZs9ozsFgsKiimORoydT6NZWj3QMCw4gwM+XfUfLvWw4D7p0aEtQYEDNBWvJ3iNJNIgOp35UGGaTiaE9OrBq6z7PQmXHeEVWIkJ0223YfYimDWNo3igWgOAAP4y1cJzpz0+5eUmzf3desvdUFg1C/akf6q+P89o2YtXBUx5lftp6lBsvbVrarvvr7fqxjDN0bhyByWjAx2KiWXQw644kV5BRGZdf0Z8fv9Ff1u3YupvAwAAio8IrlLthzDXMe/0DQHeo5GTnAjD6tuv47MNvOHNGH5NkZWbXSq5CcT78n45YEkI8AtiklG8KIV4D2kspBwghBgDjpZRjhBA3A9MBAfwppXzMfWwB8B4wCLhXCHEVMBxwAkuBn9y/+wohZgLXSSmPlpEdBcwHzsYLT5JSrhdCTAXGubd9IKV8XQjRGFgErAV6AqeBEVJKqxAi3n2eCMAFXA+kAb8CIYAZmCml/FUIMRc4KaWc576GWUAB8APwB9AJmA34CCF6A3OAZ4GeUsoMIYQBOAT0kFJWXItRlZ0DQpFnSpd1ybxsDPXiKpQztuyKT6MWaFmp2Jd+jnQv3xKBoXjf/AgiNAr7X1+fW7QSYAgPx5WRXvJby8zA3KJlhXLew0fie90NYDJz5tHJ5yRD+Acj80sbW5mfgyGmYii4qVlnjA2aoWWn4Vj5tV4uJApsRVhG3IshKBxX4n4cf/9Q42S9UrvWj69QztiqW6ldF31WqyV2VWEIDkfLKb31Wk4mxsaeSzQMDeIQIeG49m6BQaM89zVujvctUzCERlL86cs1R7gA6bn5RIeWvtGJDAlkzzHPN4STRvRl4itf8vXyzVhtDhY8fEuF8yzbdoCWDWOw1DDJAkjLPkNUeEjJ76jQEPYcPuEp88YrmDD7bb5auBqrzcb7T90PwOAeHVm1ZTcD79Qjlh4dey1BAdUvQ0k/U0B0SOnb3ahgf/YkVv4mPDk7j+TsPLo1rVeyze50MfqV7zAaDNwxsBMD2lV89spzMe6lCA3HlVmmLmZlYGpWsS56DRuJ93C9LuY/OblkuzEyhsBXPkBaC7F++SHOA7srHFtBz9BwtKwyMrMzMcVVlGkZPAKvYdcjTCYKnnsIAMfm1Zg79yRw3g8IixfWL95BFuZXKscnOoTC5NK6VZicjU90CNb0XI8yRSnZFcoABMRFE3lpczo8dj0um4Pts78ma9cxcvYlUf/yTpz4ZQO+sWGEtWuMc18E2olS56oIDvO4lzInE+MlLTzt0CAeQ0gEtr2bsVzueS/PYurUG1fSEXA6Kt1fFhESjswu8/xkZ2CszK6DRmAZOgphMlE452EAXElHMXfqiWPDCkRYJMbGzRChkXCs8ugB3+gQisrYtig5G9/oEIrL2Na3nG3PljlLszsGc8mo3mTvPs72p7/EfqaIpD82U39IJ67d+TYmHwvO7b+D3eohO72gmOiAUgdnVIA3e5JzPcokZhcAcPuXa9E0ycRezenVJBJNSl5ZuZ/nr+rIxhOVOzsqIz2/nMxAH/ac9hzMl8j8dJUu87KW9IqLJjG7gABvM1N/2Mjp3EIuvSSSB/u3wWioevIj/ENK+lsAmZ+NIbaSPrp5F3waNEfLTsW+7KvSPs9kxnvsLNBcODb8ievw9krleEeHUlzmPtqSswjs5NlnecWEYjutl5EuDWd+EebQABzZ+QR2iqflaxPxbhDB/nvfLnE0ldjpmp6k/byuSj3rqi0oS4WxQUEOhpiKy7FN8Z0w1muGlpOGY9W3HlFzAKbm3XBs+6tGeZXqmZWBKb4SPS8fideVoxAmMwXPTK2w39yjH4Uvz6xRXnqBjaiyz6u/N3tTz3iUScwtBGDsNxvRpGRCj3h6lXO27k3NxalpNAj2rVGmCAhB5tdiXNmia+kz+9fZZ1ZgGXQztl/nY7yk4lKkKmUGhniMn2ReVuVjrpbd8GnUEi0rBfvizzzqFugvW4XRhMypIdoNSMvKJSqszFgkrLKxyJVMmP0WXy1cpY9FZj0IQPPG9Vm1ZTfD+nQhNTOHA0eTSM3MoW3TxrXWuS5Iz8kjOiy45HdkWBB7jng6wCZddzkT57zP10vWYbXZWTBdXxabmJKJEIKJc94nJ6+QoT3ac8fw/jXKFAG1bPNadMOnYQv38/OF57zkxof1ecmy2s1L0vOtRAeVjgWjAn3ZU84Jnpiltym3v78UTUom9m9Lr6axNIsO4b2Ve7i1Z0uKHU62HE+jSUQQtSE6JpKU06UvQ1KT04iOiSQ9rVR2oNtR+PD0++jeqwtJJ07xxKPPk5mRxSVxjQH4adFnGIwGXnvhXVYvr7qdVSj+Cf/XI5bWAH3c/3cB/IUQZve2v91LxF4ABqBH8nR1L28D8AM2SSnbAweAa4DWUsp2wLNSyvXAb8AjUsoOZZ1Kbt4EVruP7wTsE0J0Bu4ALgW6A3cJITq6yzcF5kkpWwO5wHXu7V+6t7dHdzqlAMXANVLKTkB/4BUhhAC+BW4ocw03uLcBIKW0A08C37qv+VvgC+BszOQgYFdlTiUhxN1CiK1CiK0fbT1Sma2rxXloO9Y3J2N9bxquY3vwGjGxZJ/My8b63jSsb03F1L4P+NX8lud8KP7tF7JvH03hB+/hO/q2C35+19GdWBc8SvEnT6El7sMyzL2kx2DAUL8pjlXfUfz5M4jgCIxteld/slriTNiG9dX7sb7zGK6ju/G6dtIFOW+VCIHXdXdj+/H9SndrJxIoenYiRS8+iGXIDbWKjKgNizbtZXiv9vz1yhTmTb6ZGe//gqaVOuaOnE7n9e+X88Tttcs9VCuZa7Yyon93lr3/LO/MmMT0Nz9D0zT2HjmBwWBg2fvPsejdp/n09xWcSq39pLImlmw/wqD2cR5v5RY+eRtfPXQDc24dzEs/r+Vk5plqzlBLLtK9BLAt+oUzk0Zj/ew9fK7X66KWk0Xu3TeQ99CdFH00D7+pT4BPzROQ2mL/61fyp96C9ZsFeI/UHZPGuBagaeTddz15U8bgdcUNGCJql//jXDEYDViC/Vl81Sy2P/M1fd67D4Cj36ymKCWbYYufocvsW8jYelh/Y3ouCIH39XdT/MOCquXHNML72vFYv3jjn6hRAfuyXyl4+FaKv30frxG6XR2rF6FlZ+A/+118xtyD88g+kDU7Js+Xw58u47ceU1k4eAbWtFw6PaV3aeEdmyBdGj91vJ9fLp2KqU1fPc/dOeLSJEk5hXxwU0/mXt2Z2Ut2kVfs4LsdJ+jdJNJj0n2hcGmSpOwCPrjlMuZe043Zf+4gr9iOS5PsOJnJ1IFt+XJcf07nFPLb7nPLF1gZziM7sL7zENYPZ+I6vhevq+4q2Wed9xDFn8zC9tt8LINGI4Ij/7G8ysjbfoRNfR9my5DpNHpwJAav0jZHmI2EX96Z9N9rl7emOuq6LXAd24X1w2kUf/40WuJ+LEPHeRbwC8IQXg8tcV/lJzhP7Et/If/BW7B+tQDva2/12GeMbwl2G9rJExdElkuTJOUW8v713ZhzRXue+Wsf+cWlDuyMgmJmLt7NrMvbYjiPyPTKcB7eifXtqVg/mInr+D68huvOCFOXgbiO7ELm59RwhvOQmbAd6+sPYH33MVxH9+B1zT0e+4V/MF7X3IPt1/m1jvStiUVr3WORD57nnZn3Mv2NT9A0jZEDexAVFsLNj7zAix/9QPsWTap1MP+XWbR+B8Mv68Jf82Yy79FxzHjnazRNw6W52JFwnDn3juaTWfewYuteNu09fEFkOg/vwPr2ZKzvT9fbvOETSvbJvGys70/HOu8hTO0u3LzEpWkkZefzwbhBzL2+F7N/3Uye1U7P+Bh6N4vl9g+W8vj362nXIPyC1RMAo8lIbL1otm3eyZX9b2Tbll3MnK071U0mI42bNOKGq8dx/52P8cLrs0ocUQrFheb/umNpG9BZCBEI2IAN6A6mPuhOp67AKillhpTSie7Eucx9rAv40f3/GXRnzodCiGuBmuOjdWfVuwBSSpeU8gzQG/hZSlkopSxAj3o66/g6LqXcWea6GwshAoB6Usqf3ecpllIWoUdXPS+E2A0sA+oBUVLKHUCkECJWCNEeyJFSnqzhOj8CznpZxgEVk/XoshdIKbtIKbuM6+L5BkfmZyOCSvNLiMDQih26taAktNS5Y2Wlb/RkQS5a+kmMDVtU2FcdWmYmxojSga4hPAJXZtUTfduq5Vh6nZtjRxbkekxIREBIhTeOFBeW6rj7bwzReuI9mZ+Dln5SX0YnNVyHd2CIqjkpX6V2LfdmzMOu21ZgiK05kqU6tNxMDCGlbxkNIeEeUVN4+eh5Wqa8iN8zn2C8pAU+E5/C0NAz2a2WehJps2KIbVyjzMjgAFKzSx0l6Tl5RIV4dmo/r9nJkG76qtP28Q2wOZzkFOjVMC07jylvf8ezd46gQWTtJo1RoUGkZZbev7TsHCLDPN8O/bx8A0N66qH37Zs3wWZ3kJNfyMI1W+nVoRVmk5GwoAA6tmhSY/h5ZJA/qTkFpfJyC4gMqjzKafGOwwzt5GnPqGA92ql+eBBd4ut55F+qiotxL2V2JsbwMnUxLAItq+q6aF+7HPPZpXJOBzJfD8V2HTuElnoaY2yDmvXMzsQQVkZmqGekVnkcG1Zi7qIvG7H0HIhj9xZwuZB5uTgP7cXYpHTJlmXwCK746zmu+Os5rOm5+MWW1ke/2FCsqZ5tgDU1B9+Y0ErLFKXkcHLhFgCydh5DahKv0ACkS2PbrC9ZOHgGq+94DUuQL1q6Z8SezM3yuJciJBwtt4xdvXww1GuM39QX8X/uU4xNWuJ7z9MYGun3UgSH4zPpSawfv4TM9FxyVRUyJxMRWub5CY1A5lR9Lz2WymkaxV++S8HMCRS9/iTC1x8txXNJgGXQCIb99RzD3Lb1LWNb39hQisrZtqicbcuWKc7MQ2oSpOTIlysJ66C3g42v6UnKyt1IpwtbVh5a+gkM4Z7PVKS/N6n5pVFMafnFRAZ4Lm2NCvChb3wUZqOBesG+NArxJymnkF2nc/h2+3GGzV/Ga6v28ce+U7yxumLS1PJEBpSTmWclspxzKirAh77NYtwy/WgU5k9SdgFRgT40jwqmfogfJoOB/s1jOZCaW608WZCDCCzbf1XWR5fpv3atxhDd2ON4AJmbgSvpIIaohpXKKU7NxrvMffSKDcNW7j7aUrLxqqeXEUYDpgBfHNmekUFFh0/jKizGr0XpvQob2JH8PcexZ1TtVP8324KqqDA28A9B5ud6Fio7Nti7poL9TM264Dqyo9YO5Qp6hkWgVVc3K1kqZ+7ZH/u62iUkjvT3Iq3s81pQTIS/V7ky3vSNi9Sf1yBfGoX4kpSr99EFNicP/Lqde3s2o11McK1kyvwcRMA5jCt3rip5Zo314jF1GYzPva9gGXgzpra9Mfe/gZqQeTmIwLIyw5B51cjcvsJzLOvlg9eYR7Gv+BbtVO1ewEaFBZOWVWYskpVDZGj5sch6hvQqMxZxOMjJK8RkNPLouFF8/+p03pw2kfzCIhrFRvFfIzIkkNSs3JLf6VlniAopp+PKLQzp0R6A9s0a62O8/CIiQ4Pp3KIJIYF++HhZ6N2hBQeOe/aPlSHza9PmlZuXRFcxL8k4hbFBxY8rVNAzwIfUM4Ulv9PyiogM9Hw5FhXoS9/m9fR6EuJPo7AAktzt31192/DdPVfw3tgBSAmNwqt27tw2/iYWrf6eRau/Jz0tg5h60SX7omOjSE1J9yifk51LUWFRSbLuP39dQpv2epRjSnIafy1ehdPp5GTSaY4fOUHjuMrbeIXin/J/2rEkpXQAx4GxwHp0Z1J/IB49Cqk6iqWULvd5nEA39CVlVwGLqzvwPCmbDdRF9csQx6AvjesspeyAvjTu7Ej4e2AUcCNlopWqwu14SnMvD+yGviTvnNBOH8MQGo0IjgCDEWPr7jgPbfMoI/yDS/43NuuM5k6gJwJCSyMhvH0xNmyOllW7ic9ZnAkHMdarjyE6GkwmvPsNwL7BM4zTWK90WZHl0h64Tp8qf5pq0VKOI0KiEEHhYDBianEpriM7PQv5lXaUxviOJXpoqcf1L4X46J2EsWHLCklTK5V5+mipXY1GjG174jxYjV1bdEHLqLnDrVZm4iEMkbGIsCgwmjB17otzd5k3xMVFFD56E4VPjKXwibG4jh/EOv9ptKTD+jHuKBsRGokhqgEyq+ZQ8NaX1CMpLZtTGTk4nC4Wb9pH3w6eA/qY0EA27ddzEh1LzsDucBIa4EteUTH3vf41D44aSMemte8IW8c3IjElg1NpmTgcThav3U6/Lu08ykRHhLJpt75059ipVOwOB6GB/sSEh7J5r769qNjG7kMnuKRe9YO51g0jSco8w+msPBxOF0t2HKZvm8YVyh1PyyGvyEb7xqWDhLyiYuxOfcKRU2Bl5/EUmkTX7EC7GPfSefgghpj6GCL1umjpPQDHFs+6aIgprYvmzj1KHA4iMKhEpiEqBmNMfbS0muuJ69hBDNH1MEREg9GEpfsAHNs2eMqMKpVp6tAdV6peT7TMdEyt3EGjXt6YmrbElVzqi7f/9SsLB89g4eAZnFq8jUtG6U6w8E5x2POKPJbBAVjTc3HkWwnvpIfcXzKqNyeX6HX25OKtRPXSnaMBTaIxWEzYsvMx+lgw+uiTtOjL2qA5tQpJuV0nEjBE1iu5l+Yu/XDu8ryXBQ/dQMGM2ymYcTuuYwcoeucptMTD4OOH733PYPv5I1xHa/+dCdexgxij6yHcdjV3749ju+eXAauyKxYv8NK7JFObzuByeST9Bj3SadHgGSwaPIOTi7fRxG3bMLdti8vZttht2zC3bZuM6s0pt23L5mNqMKwLuQn6M1V4Oouo3vpSGKOPF4aIRmi5ngPu1jHBJOUUcjq3CIdLY8mBZPrGR3uU6d80mq1JulM2p8hGYk4B9YN9mXN1JxZPGsyiiYOY0q81V7Wuz4N9Ky5JKk/r2BCSstE9q9AAAQAASURBVAs4nVuoy9x/ir7NPKNj+jePYWtiZqnMrALqB/vROiaE/GJHSQLxzSfSaVLNBARASz6OoUz/ZWx5Kc7DOzzKiLL9V9NOaGf7KG9fMLqHIz7+GOs3Lem/y5O/4yi+TaLxbhiBMBuJGtmTzCVbPcpkLtlKzA19AYi8ujs57vxi3g0jEEa9/nvXD8cvPpbik6VOoehrepH2c/Vfpvw324Kq0FJPIIIjEYFnxwZdcR0r9wXEsraN64CW7ZnDy9iiG86EKhKSV6bn0XJ69hyAY2u5uhldRs+O3XGllBkbCIGlRz8c62vnWGodHURSThGnz7jrSEIq/Zp4Rq31j49k60n9xVeO1U5iThH1gnxwuDQe+n07V7WMZXCz6MpOXyla8jEMoWWe2VbdcR4q98z6l7Frs9Jn1vbrfKxvT8E67yHsy7/GuWctjpWVf0XTU+ZRDGFlxlxteuBMqGbM1bwz2tmP1BiNeN84FeeuNbj21/5e6mOR9DJjkW3061puLBIeUmYskoLd7iQ0yB+rzU5Rsd4ObNh5AKPR6JH0+79C67gGJKVmcio9G4fTyeINO+nb2TM9bUx4cEkk0rHTabqOgX70ateMwydTsdr0vJbbDhyjSQ3jLTj7/JSfl3gu4a31vKRBs1rNS1rXCyMpO5/TOQX6OG9PIn1b1PMo079lfbae0PugnMJiErPyqR/ij0vTyHUnvD+UmsPhtFx6xFV9Lz/78BuG9b2eYX2vZ8mfK7jupuEAdOzSjvy8Ao9lcGdZtmQ1PXrrOe96XdadwwnHAFiycAU9eum5xEJCg7kkvjFJJ85tjvR/FQ35P/v3X+X/dI4lN2uAh9GjcfYArwLbpJRSCLEZeFMIEQ7kADcDb5U/gRDCH/CVUi4UQqwDjrl35QNVjeiWA5OA193Jv/3d1/KJOxeSQF9ed2sVxyOlzBdCnBJCjJRS/iKE8AKMQBCQLqV0CCH6A2XDX74F3gfCgb6VnLaya/4AfUnc52edaeeE1LAv+gTvMY+VfBZWZpzG3O86tOTjuA5tx9RtCKZmnZCaC4oL9TBhQETE4j14DFJKhBA4NvyJTK95MOeB5qLg7dcJmvMywmCgeMlCXIkn8L19HM5DB7FvWI/3iGuxdOwMLidafgH5L845dx2XfYHXqKnuTwqvRWYlY+41Ei31BK6jOzF3GqQn9NY0ZHEB9kUfuo+V2Fd9i/eNDwMCLe1EyWecq9dLw/7nx3jfNr3k07cy4xTmAdejnT6GK2Ebpu5DMbXojNQ0sBZg+/ndksO9x8/CEB6rf3L3oXnYf30P15EactZoGsXfvovvfc+CwYhjw1K0lCQsV92KK/EQrj2bqjzUGNcay+U36G+ApMT27TxkYV6NapqMBqbdMoxJr36JpklG9u5AfL1I5v28ktaNY+nXsTkP3Xg5sz/9nS+WbkIImD1+BEIIvlm+maT0bBb89jcLfvsbgHcfuoWwwOpzHpmMRqbfeQOTnpmHS5OMHNCd+IYxzPv6D1rFN6R/13Y8fPs1PP3u13z+x0qEgGfuuxUhBDcNvYwn5n3BNQ8+iwRG9O9Os8b1apBn4PHr+jBp/m9ommTEpS2JjwnjnYWbaNUwkn5t9Ddli7fr0UqiTAj0sbQcnv1uFQYh0KRk3KBOHl+Tq5KLcC/RXBS9/zoBT70MBgO25QtxnTyBz83jcB45iGPLeryvuBZTO70uyoICCt/U66KpVXt8bh6ny9QkhfNfRRbUnOMETcP6yVv4PfaC/unt1YvQTp/A+7qxOI8fwrl9PV6Xj3Q7OJxohfkUzX8BANtfv+A74TECXvgIBNhXL0E7eaxSMaeX7yR2YHtGrH8Fp9XOhimly86u+Os5Fg6eAcDmaZ/Q8/W7MXpbSF65i+QV+iTz6Der6fHq3Vy1Yg6aw8X6B98DwDsskIFfP4bUNIpSc1h//7sMLJ+jVtMo/mYevg8+jzAYsK9bipaSiNfVt+FKPOTpMCyHpf9wDJGxeF05Bq8r9SViRW9MQ+bXsJxS07B+9hZ+j7wABgOOvxehnU7E69qxuI4n4NyxAcvgkZhad9LvZWEB1gW6XUVgMH6PvqC3hTmZFM2vvr1NXr6TegPbM3z9K7jK2XbYX8+xyG3bLdM+oUcltu008yZCWjdCSknhqUw2PfoRAIc+/ovur93NlSvnIoTAdXgLMsdzgmAyGHh8UBsmfa/nhhnRtgHx4QG8s+YgraKD6dc0mp6XRLDhRAbXfrgSgxBM6deKYHfy1fPBZDDw+JAOTPp6nd4etG9EfEQg76zeT6uYYPo1i6Vnkyg2HEvn2vf+0mUObEOwOyHylIFtmPDVGqSEljHBXNex4pt2D6SG/a/P8b7pEb2P3v03MvM05j7XoKWcwHVkB6Yul2Nq2rG0j/5DT/JqCIvFa+hYzz66ihcj0qWRMO0jOn4zHYwGUr5eRWHCKZo8ej15u46RuWQbyV+tpNXb99Fj4xs4cgvYO0FfmhncrQWN7h+BdLqQmuTg4x+WRDIZfL0IvawtBx6ueqknUGdtQQXbrvwKr+smgxA4967TxwY9h6OlJuI6tgtzxwEYm3QA6UIWF2JfXBoYLgLDEAEhaCcP1SyrrJ4fvYnf9BfBYMC+ahHaqRN4X38HzmMJOLetx2vINZjaltHznbklh5tatkPLytCTf9cCk8HAYwNacc9PW/U60ro+ceEBvLP+MK2igugXF0nPRuFsSMzk2k/XYBSCyZc1J9jHwp8Hktl+OofcYge/7dedMLMvb0vzyBqWFkkN+5LP8L75UTAInLvcz+xl16KlHMd12P3MNutYOv75vfJl3bVG07Av/ATvW6fp9WTHKn3M1X+UPpZN2Ibp0qGYmnfW64m1ANsv+ljW2LoHhkYtMPn6Y+qgL3yw/zIfLbX6Zar6WORGJs1+G5d7eVt8w1jmff07reIa0b9bOx4eex1Pv/Mln/++AiEEz9yvj0Wyz+QzcfZbGIQgMiyY5x+4/Z/p7+aRp+ayZcducnPzGDjyFu4ZfyvXXT3kvM9nMhqZNnYkk+a8ry/h69eN+AbRzPt+Ca0vqU+/Lq156Jarmf3+93yxcI0+xpt0A0IIAv19ufWKPoye8SZCQJ8OLbisU83Oe6SGffGn7ufHPS/JPI25r3tecng7pq6Xl85LrIXYftf7ZBEei/eg/8fefcdHUfQPHP/M3eXSe0hC771XpUkvNqoNEEVQAVEUsCOKKEVE7IodfUTsYKX33nvvgfReL9d2fn/skeTSgxifx9+8ffHysju73525LXOzs7MjkUgEAvvOP5GJZTe0mIwGnr21AxO/2qCf19vVo0F4EB+sO0yz6iH0bFKDLg2qsuNsLMPe/V0/rw9oQ5CPJ1a7k7Gf6WOs+Xp6MHt4F0zG8vXtWL9mC7363cSWfX9iseTy5KP546at2PQDN/e4E4C5M9/krUVzeWnOM6QkpTDt0RkAbFq3jZt6dWHdjuU4nRqzX3qDtNTrMNyCohRD/K+/ik8I0Qe9h1GQlDJbCHEaWCSlXOiaX+Lg3VJKP9fnquiDZXu50i2QUn4phOiK3ohjBe4oZvDuj9EH73aiD969o5TBu3+XUrZwLfsk4CelnCmEaIg+iHgYYEcfvDsD+A29sWov+nhNN0spL7qWPwIkSSl7uf7OW78QIgRYhT7o91wp5XeucaeSgU5Syvx3jpcge9aoSt0pcrZUfsu5b/+ig/z93WRmeZ6wvL606zFWTwV4jCo66PbfTQT+PeOBlEa7XPbjMNeb47cKdzb8y+yxlbvPGnzK8Zrh6+z3DdUqPeag28v97oTrRuaU/TbF6+33dZV7d33YK+XvMXHdeFy/scnKS4su39uErqedb1XsDaTXQ/u+lXucmNtXfr3AtqviY1r+VeZercpOdJ3J5NSyE11v9rJfYnA9me58sFLjARgiymh0/hs4o45Wfsw/y+6Rdr0ZGjYsO9F11Hjij5Ua76qolCP/mwN3lUOnaj3+txs5SrE7ZtN/5ff2P99jSUq5Dr0R5erfjQrNXwosLWY5vwKfY9EfEyucZhvQrPB017x4YHAx0xei95oqOO0i0KLA3wsKfD6DPl5TYUXfNZ2/TMuS1i+lTEEfW6qg1uiDdpfZqKQoiqIoiqIoiqIoilJe//MNS0rphBDPoj+yN6qstIqiKIqiKIqiKIryv0z+F49F9G/1Pz14t1I2KeU8KWVtKeXWf3pbFEVRFEVRFEVRFEX5d1ENS4qiKIqiKIqiKIqiKMo1UQ1LiqIoiqIoiqIoiqIoyjVRYywpiqIoiqIoiqIoivKvIKUaY6myqR5LiqIoiqIoiqIoiqIoyjVRDUuKoiiKoiiKoiiKoijKNVENS4qiKIqiKIqiKIqiKMo1UWMsKYqiKIqiKIqiKIryr6ChxliqbKrHkqIoiqIoiqIoiqIoinJNVMOSoiiKoiiKoiiKoiiKck3Uo3BKEYb2HSs1ntx0pVLjAYi2nSo/ZnZGpcdk797Kj1nJpMNa6TEN9dtVeky0Pys9ZOIJn0qNV+Pe0EqNB5C1udJDYqpXrdJjSoez0mPmbNAqNZ7z9PlKjQcgs3MrPaaxab1Kj5lqcFR+zKPGSo0X+eANlRoPQO44U+kxDS0rv/4j0xIrPaZ2+EDlxku+XKnxAKQ1u9JjGmu1qPSYTst/Kj2mduxEpcaLyUqp1HiK8ndQPZYURVEURVEURVEURVGUa6J6LCmKoiiKoiiKoiiK8q8gpRq8u7KpHkuKoiiKoiiKoiiKoijKNVENS4qiKIqiKIqiKIqiKMo1UQ1LiqIoiqIoiqIoiqIoyjVRYywpiqIoiqIoiqIoivKvoKHGWKpsqseSoiiKoiiKoiiKoiiKck1Uw5KiKIqiKIqiKIqiKIpyTVTDkqIoiqIoiqIoiqIoinJN1BhLiqIoiqIoiqIoiqL8K0g1xlKlUz2WFEVRFEVRFEVRFEVRlGuiGpYURVEURVEURVEURVGUa6IalhRFURRFURRFURRFUZRrosZYUspl24ko5i/fiqZJht7YlLF92rnNf335NvacjQYg1+4gJdPC1jnj8uZn5doY9tq39GpRl+eGd69wfHPHTvhNegwMBnL//IOcb79xm+912yB8Bg9Fak6kxULmmwtwXrpUsTweu8j8HzeiaRpDu7ZgbP9O7nn8cSN7Tl9x5dGu53HBI+w5fZnXf9yUl+5ifArzxt5C79YNyo556grzf9mJJjWGdmrM2F6t3WP+upM952JdMR2kZOWyddZoTsYkM+fnbWRZ7RiF4MHebRjQpl6F8gtgbNAa860PgDDg2LcO+5Zf3Oab2vbAPGA0WkYKAI5dK3HsW1/hONuOnOW1b1bp+ezelnG3dnObH5uczgufLSczx4qmaTx+Rx+6t2rIjmPnePvH9dgdTjxMRqbc1ZcbmtYtX8yDJ3lt8XL9++x9A+OG9HGPmZTKC+8vJTPHgqZJHh95K93bNgXg9KUYXvnkR7IsuRiE4Js5T+Bp9ig13tZ9R3jtk6VommRYv+6Mu/MWt/kxCUm8+PYXpGZkEejny5xpDxIZFgLAhJfe5Mipc7Rt2pD3Xnq8XPkDMDZrj9ddE8FgwL5tJbZV3xebztS2K97jZ5A95zG0qDMY6jTCa5QrjhDYfv8ax8Ht5Yrp0609EdMngMFA+o8rSfnkB7f53h1aEP7ceDwb1yVm2jyyVm3Nm1flybH49ugEBkHO9gMkzF5UvnzWa4V5wGh9Pz24Efv239zz16o75j4j0DJTAXDsXYPj4EZEYCied0wBIRBGI/Y9q3HsL//+2/Xl0dTq3QaHxcqGqR+TdPRikTSdnr6TRsO74Rnoy2dNHsyb3uqhm2lyT0+k04klOZONT35MVnRyibEMdVtg7jMSDAYchzbj2PWnexm06Iq5191IVx7t+9fhPLwZAO+nPkMm6ucmLSMZ28/vlCt/xnotMfe/11Wum7Dv+N1tvqlVN8y970HLulqua3EczD/XYfbCe/w8nKf3YVv1n3LFLKzzrNHUdJXxpikfk1xMGXd4+k4a3qGX8eLGDxZdSRmMjdriOWgsCAP2PWuxb1xWfLoWN+I9+mly3nkKLfocGIx43vEIhmr19P1n30bsG38uX8ym7fG6Y7x+bG5fhW3ND8WmM7XpiveD08me/zha1Jm86SK4Cr4vLML65xLs68oXs6BtF5N4ffMpNCkZ0rw6YzsUPW+uPh3Hol3nEQIahfkzd2DLCsdp/cp9VO3TGofFxt4nPiLtyMUiaYJa1aHjWxMwenkQu+4Qh2Z8BUDzp++g6oD2oEmsyRnseXwRufFppcbz7tKBkGceQRgMZC5bQfrn37nNDxg9HP+hN4PTiTM1naSXFuCITcDcuD6h0ydj8PMBp0bap9+QvWpTCVHcbTt2gfnfr9PrP11bMXbgDW7zX/9+PXtORwGQa3OQkpnD1jcnA/DWz5vYcvQ8AA/f0pkBHZqUK6apTSd8HngUDEas6/7Auty9zmPuNwivgUOQmga5FrI/WoB2Ra/zGGvVw2f8NIS3D0hJxrMTwG4rPY//xDX6L9Qr201bRIOq+vWzarAfb49zv96WxFivJea+o/Tz7MFN2Hf+4Tbf1LIb5t53519L9q3DcUjfT3ye+QIt8TIAMiMF649vlSumW56PnmP+t2v1ekn3Noy9ubPb/NjkdGZ88XteOU8e3pPuLcuuSxaJc/Akr331qx6nVyfGDe7tHicplRc+/I7MbIv+fY64xb3+89lPZOVYMRgE37w6ucz6T1lemLOQzdt2ExIcxPKvy3fdrwhjwzZ6XdZgwLF3HfbNy93mm9r2xHxzgbrszhU49la8Lmts0BrzLffr18z967Fv+dU9TpsemAeMKlBnXoVj/wYAPEc/i7FGQ5xRp7AumV+huG8unMXNA3uTY7EwbtwUDhw86jbfz8+XjRvyr2s1qldlyTc/M+3Jl6hVqzqffryQsCohpKakcd+YyURHx1Y47/+LNKnGWKps/3MNS0KIIcBpKeXxf3pb/glCiFnAZinl2lLS9ARsUsry/VIsg1PTmPvzFhZNuJ2IQF9GvfkTPZrXoX5kSF6ap4Z0zfu8dMsRTkYnua3j/RW7aVev6rVtgMGA/+QnSH16GlpiIsEffIR1xza3hiPr+rXk/q6f4M2du+A3YRLpzz1dsTx+v55Fjw0jIsifUfO/oUfL+tSvGpqfxzt65udx4wFOXk4EoGOjmnz//L0ApGfncvvMz+nctHb5Yi7bzqKHBurl+u6v9GhWi/oRwfkxB92YH3PbMU66fph6e5h45e4e1K4SSEJ6NiPf+YXOjasT4O1Z7jwjBObbx5G7+FVkRjJeE+biOLkXmRjtlsxxZDu2Pz4v/3qLyeecr1fw0bR7iQgJYOSsT+nZpjH1q1fJS/PJb1sY0LE5d/XqwLnoRB596xtWvP44QX4+vDP5HsKD/TlzJYGJC5ewduGU8sX8/Gc+mj6eiNBARj73Fj07NKd+jcj8mD+vZUDnNtzVvwvnrsTx6LxPWfHeCzicTp5/7xtmTxpJ4zrVSMvMxmQylh7PqTFn0RI+fmUaEaHBjJj6Cj1vaEP9WtXy0rzx+ffc3rsLg/t0ZdehE7zz5U/MmfYQAGOGDSDXauPHFeX7oQOAMOA1YhI5bz+PTE3C57l3cBzeiRYb5Z7O0xuP3kNwnj+RN0mLvkTO3MdA0xABIfi88AGOwztB00qPaTAQ8eIkrox9Hnt8ErV/eJus9buwncuPaY9NIO65NwgeO9xtUa+2TfFu14yLgx8BoNY3C/Du1BLL7iNl5FNgvvl+cpfMQ2ak4DVuFo7T+5BJMW7JHMd3Ylv1lds0mZlG7uKZ4HSAh6erEWQ/Miut9JhArV6tCawbydLu0whvW5/uc8awbNDMIukurtnP0cVrGLF5gdv0pKMX+fnWGThybTQb3Ycbp49g7SPvlZzHfqOxfrcAmZmC1/0v4jx7EJlcKI8ndmNf+3XR5R02che/VGaeisQceB+538zXy3XsyzjO7C9arid2ldhoZO4xHC3qVMXiFlCzt17G33ebRni7+nSbO4Zfbp9ZJF3U2v0cW7yGu7csKLqSsggDnkMewvLpy8j0ZLwfnY/j+B5kwhX3dGYvzF1vxRl1Om+SqVUXMHlgeWsKeJjxmfoOjkNbkKmJZcb0uusRct6bjkxLwuept3Ac2YkWd9k9nac3Hj0H47xwssgqPIc9hOPY3ornF3BqknkbT/Lh0HZE+Hkx6rtd9KhbhfqhfnlpLqVl8/neiyy+syMBXh6k5JTe8FCcyN6t8a8Xycou0whp14B28x5g/a1F98N288ay78lPSdl/lm5Lniayd2vi1h/i1Ad/cGz+jwA0GDeAplOHceCZUq41BgOhzz9G3PhncMQnUe2b98jZuAP7+fzzj+3kWWJGTkLmWvG/8zaCpzxE4tOz0XJzSXxhPo6oaIxVQqm29H0s2/eiZWaXmkenpjF36RoWPX4XEcH+jJr7H3q0qk/9amF5aZ66K/9H+9IN+zl5OR6AzUfOcSIqnu+m34/d4WDcwu/o2rwufmVdpw0GfMY9TtYrT6KlJOI/dxH2vdvyGo4AbFvXYluj13k8OnTB5/5JZM1+GgxGfCZPJ+fdOTgvnUP4BejnvzLy+E9co/9KvdLTw8j3T95VZhw3QmDufx+537rOeWNm4jhzoNjzrG11Mec8h43cz1+sWMwCnJrG3G9Ws2jKPUQEBzBq9mJ6tG7oti998sd2+ndoyl0923EuJolH3/meFfMq1rDk1DTmfLGMj55/WK//TH+Hnu2bU79GRH6cZesYcGMr7urXhXNX4nn0tc9Y8W5Tvf7z/lJmTxpB49rlq/+Ux5Bb+jFy+CCef+UazuFlEQa9LvvFK/r3OnEujhN78262XOU4sh3bb5/9hTgC821jyf1ytl5nHj8Hx8l9RevMR3dg++OLIovbt/2Ow8OMqUPfCoW9eWBvGjaoS5Nm3bihUzvef28uXbrd7pYmKyubDh375/29a+cKli/Xb07Nf+1F/rPkR/7znx/o1bMrs199jjEPTK7QNihKef3XPgonhCjpTDYEaFaJm/JfRUr5YmmNSi49gS7XK+bRqARqhgVSIzQAD5ORAW0bsLGYu8pXrThwhoFt8y+Exy8nkpJpoXPjmtcU39SkKY7oaLTYWHA4sG5Yj2cX97tpMicn77Pw8q5wjKMX46hZJYgaYUF6Hts3ZuPhcyWmX7H3FAM7NC4yfc2B03RtVhfvctzdOXo5kZphAfnl2roeG49FlZh+xcHzDGxTH4DaVQKpXSUQgPBAX0L8vEnNyi0zZkGGGg3QkuOQqQn63d0j2zE17VihdZTH0fPR1AwPpkZ4MB4mIwNvaM7Gg4V+jArIslgByLLkUiXIH4CmtasSHqx/blC9Cla7HZu99EoywNGzUdSMCKVGRCgeJhMDu7Rl455jRdJlWfQyy8rJpUpwAAA7Dp+mYa2qNK6jNwoF+ftiNJR+qjx65jy1qoZTI7IKHh4mBt7UiQ27DrilOR8Vyw2t9DuCnVo1YcOug3nzbmzdDF9vrzLzVZChTmO0hFhkUhw4HTj2bMLUqnORdJ6D7sO26gekw54/0W7Nb0Ty8IByvjnDq1Uj7FEx2K/Egd1B5p+b8Otzo1saR3QC1tMXofCdIikRnmaEhwlh9kCYjDiT0srOZ7X6aCnxyLRE0Jw4j+3E1Kh9ubYXzZn/o8rkAUKUbzmgTv/2nP5J722VcOAcngG++IQHFUmXcOAcOQlpRabH7DiBI1f/sR6//yx+BX4wFWaoWg+ZloBM1/PoOLEbY8O25d7Wa6GXa0J+uR7fialRu7IXvLp8ZB2EbyDOC2U0DJaidv/2nPnRVcb7z2EO8MW7uDLefw5LMWVcru2s2QAtORaZEq8fJ4e2YmrWqUg684CR2DYtd+/ZISXCwxMMBvAwg9OBzLWUHbNOI7SkGGSy69jcv7n4Y/O20djW/IB0uDfqmFp1RibHocWVfD0ozdH4dGoG+VAj0AcPo4EBDSPZeN69MWzZ0WjualWDAC/9WhXiY65wnGoD23Pphy0ApOw/i0eAD16Fvj+v8CBM/t6k7D8LwKUftlBtoH78OrLyy9Lo41n0nFGIZ4vG2C/H4IiOA4eD7JUb8enpXtXJ3XMImatfS6xHTmAK1xtHHJeicUTpPwCdick4U9IwBLtva3GOXozVr19VXHWDjk3YePhsielX7DnBwA76ef58bDLtG9bAZDTg7WmmUfUqbDt2ocyYxgZN0OKi0RL0Oo9923rMHbq6J7Lk13nw9MorO1PrDjgvncd5Sa+/yKyMMm8Y/CPX6L9Yr7wWhmr10FILXEtO7KrQOe+vOnohhppVgqlRJdi1LzVl48HTbmmEgGy3cvYrblWlxzkbRc3IsPz6T+c2bNxbqP5T8PvMsRSt/9Quf/2nPDq0aUlggP9fXk9xDDUaoKVcrcs6cB7ehqlph785jqvO3KT8cbTzR5HWitXTAW6/fQD/WaI3wO/avZ/AoEAiI8NLTN+wYT3Cq4SxZesuAJo2bciGDdsA2LBxG4Nu71/isoryV133hiUhxFNCiMmuz28KIda7PvcWQixxfR4hhDgihDgqhHitwLJZQog3hBCHgM5CiHlCiONCiMNCiAVCiC7AIOB1IcRBIUT9QrEjhBDLhBCHXP+6uKZPdcU6KoR4wjWtjhDihBDiEyHEMSHEaiGEt2teAyHEWtc69gsh6gsh/IQQ61x/HxFCDHalnSeEmFRgG2YKIZ4sUBZ7XNv/cgnlleUqp2Ou9VdxTW8jhNjpWnaZECLYNX2xEOIO1+eLQoiXC2xTEyFEHWACMMVVRt2FEHe68n5ICLG5ot9pQno2kUG+eX9HBPmSkF78Xb6YlExikjPp1LA6AJomeePX7UwdVLRSXV7GsDC0xIS8v7XERAxhYUXSeQ8eQuh/vsHv4Qlkvfd2hWIkpGURGZx/0YsI8iMhLavYtDHJGcQkp9OpmIayVftOc3MxDU7FxkzPITKwQLkG+pCQUUK5pmYSk5JJpwZFe30diUrE7nRSMzSgXHGvEgEhyPT8R3NkejLCv+iPX2PzG/Ce9Dqe90xFBIQWmV+WhLRMIkMC8/4ODw4gPjXTLc3EwT34Y8cR+k17k0lvLeXZUQOLrGftvhM0rVUVs0fZHS0TUtKJDA3KjxkaSHxqunvMOwfwx5Z99Js4i0nzPuXZB4YCcCkmESEEE2Z/xN3PLOSLX8ruLh2fnEZEWH7ZRYQGk5Cc5pamUd2arN2xD4B1O/aTbcklLaP4faw8DMGhaAV6TmhpSYhg9+/HULMBIrgKzqO7iy5fpzE+L36E74xFWL95t+zeSoApIgx7bH5MR1wSpojy7RO5B0+Ss+sw9bcsof6WJWRv3Y/t/OUylxP+wUhXt3IAmZmC8A8uks7YpBPeD83Bc/hkRED+dyECQvB+aA4+k9/Gvv33cvVWAvCNDCYrJv/4yIpNwTeyaNzyaHpPD6I2HipxfrF59Csay9S4PV4PzMI85BH3Y9Xkged9L+I5+oVyN0gJ/2BkZoHjP6Okcu2I94Ov4jns0QIxBea+I7CtW1quWCUpXMbZf6GMSyICQ5Fphc5zge7nOUO1ehgCQ3Ge3Oc23XFkB9JuxXf6Z/g+9zG2zb+Apexj1hAYipaa37tCS01CBBY6NmvU14/NY3vcFzZ7Ye53B9Y/3R99qoiELCsRfvm9YiL8PEnMtrqluZSWQ1RaDmN+2M193+1m28Wkwqspk3dkCDkFvj9LbAreVd2/P++qwVhiUtzTFGhkbf7sndyy9x1qDevCsdd/LDWeMTwMZ1z++ceZkIQpomhd4Cr/oTdj2Vb03Gdu0Rjh4YHjckwxS7lLSC1cN/AnIbWkukE6MUnpdGpSC4BGNfSGJIvNTmpWDntORxW59hXHEFIFLbnAuT0lERFapUg6zwFDCHh3CT73TiDnc/3xV2PVmoDEb/p8/F/7GM9B95Sdx3/iGv0X6pUANoeTkQt/ZPRbP7H+SNmNdQDCr5zXksYd8B73Kp5DHy1ynvUaMxOv+2ZgbFjxBqmEtCwiQ/LraRHB/iSkuZfzhNu788euY/R/6j0efecHnh3Rr+JxUjPKrv8M788fW/fTb9KrTJr/Oc+OGQLApdgkvf4z9xPufu4tvvh1Q4XjV7YiddmMlCLnW3DVZR9bgOeIacXOLzOOfzFxAoqpMzfrhPcjr+F595RrqjMXVr1aJFcKnKuir8RSvVpkienvvmsQP/yQ/4je4cPHGTrkZgCGDLmZgAB/QkKu73VWUa76O3osbQGuDqLTAfATQni4pm0WQlQDXgN6A22Ajq7H2wB8gV1SytbACWAo0FxK2Qp41fVo16/AU1LKNlLKwl1K3gE2uZZvBxwTQrQHHgBuAG4EHhJCXK15NwTel1I2B9KAq89uLHFNb43e8ycWyAWGSinbAb2AN4QQAvgOKNgf9y7gOyFEf9f6O7ny2V4IcVMx5eUL7HVtwybgaj/yr4BnXHk/UmB6YUmubfoQeFJKeRFYBLzpKqMtwIvAAFd+BhW3EiHEw0KIvUKIvZ+tvPYn6FYdOEvf1vXy7nB8v+0o3ZrWIuIa7rpUlOWX5SSPHknWJx/hc+99f1ucVftO0bdtoyJ3cRLTszgbk0TnZmU/BlfhmAfP07dl3aIxM3J44dtNvHznTRgM5e+NUV6Ok/uwvDEJy/tP4Tx7GM/hk8pe6Bqs2HWUQV1bs+aNKbz/xAimf7IcTcu/e302OoG3fljHjPtvvX4xtx1gUI+OrPnwRd5/9kGmv7cUTdNwak4OnLzA3MdGsXjWo6zfc5RdR06XvcIyTBt7J/uOnuaux2ey9+gpwkODMVyHO4ElEgLPOx/G+tMnxc7WLp4iZ9Z4cuZNxjzwbr1Hz9/Io1ZVzPVqcq7naM71uBefG1vj3b75dVm348wBLO89geWT53FeOIrnoPF582RGCpZPnsfy/jRMrbqDb8UaYP+qhkO7UqVVPQ4u+qPsxKVwnj2IZdFT5H7xItqF45hvzR9rKPfDJ7F+NQvbrx/h0WckIqjoj9Br4ThzEMt7U7F8+gLOC8fwHPQwAKYOfXCePZQ33tP/NCHwvG0M1j8WF5llqNkQNI3s2Q+SM28i5psGIUIiiq7jWmIOfwjrz0WPTc9bR2FbvxxsFb+zXRFOTRKVlsMnwzowd2BLXll/nEyrvewFr7Nj837gzw6Tifp5Ow0euH530n1v7YO5WSPSFruPbWUMC6HK7GdIenFBmT2kKmrV3pP0bZdfN+jSrC7dWtTj/vlLePbT32lVt9p1vU5bVy0n47FR5Cz5CK/ho/WJRiOmJi3Jfmc2mTMew3xDd0wt/nqvnH/iGn1V4XolwJ8v3Ms3U+9g7uh+vL58G5eT0ktZQ/k5zh7A8sE0LJ+9oF9Lbnsob57l/WnkLp6J9ddFmPuORASV3GvkWq3cfZxBXVqy+vVHeW/ynbzw2W9u5Xy9rNh+gEE3dWDN+y/w/tNjmf5BgfrPqQvMnTSSxTMfYf3eo+w6eqbsFf6Xc5zci+X1R7C8+yTOs4fwHP7o3xPn1D4sCx/D8sEzOM8dxnPYxL8lTmnuumsw3363PO/vp595hZtuupE9u1dxU/cbuXIlFqfTWenbpfz/8Hf8qtmH3ogSAFiBHegNTN3RG506AhullIlSSgd6I87VBhcn8JPrczp6Y85nQohhQIF+vyXqjd7AgpTSKaVMB7oBy6SU2VLKLOBn8hu+LkgpDxbY7jpCCH+gupRymWs9uVLKHEAAc4QQh4G1QHUgQkp5AAgXQlQTQrQGUqWUl4H+rn8HgP1AE/SGpsI09MYpgK+BbkKIQCBISnl1wJUvC5RRYVdH9dwH1CkhzTZgsRDiIaDYRwyllB9LKTtIKTuMG+jetTw80Je4tPw7SfFp2YQX6GlT0MqDZxnYNj+bhy7F893Wo9z8yte8+dsOft97ird/31nCZhbPmZSEoUr+BdxQpQpaUsl3V60b1hV5VK4s4UF+xBW4QxeflkV4CY1hK/cV/xjc6v2n6dW6Ph7G8j2PHh7oQ1yBO3Tx6TmEB5RQrofOM7DQ4NxZuTYe+3w1jw5sT6vaFa/gFL6rIwJDkZkp7oksWXmPEjn2rcNQreIDhIcH+ROXkl/pS0jNICLYvUv0si0HGdBJf8K1dYOaWO0OUrP0Qz4+JYMp733Pqw8OpmZ4yY8TucUMCSSuQI+hhOR0IoID3dIs27CLAZ31wdJbN6qD1W4nNTOb8JAg2jetR3CAH96eZrq1bcqJC+7P0BcWERpEfFJ+2cUnpxJe4I4hQHhoMG8+P4nv357J5NHDAAjw8ylXfoqjpSZjCM5vQDAEhSFTCwwO7emNoVptfKbOx3f2lxjrNsH7kZkYarmfhrS4y8hcC4ZqdcqM6YhPwqNqfkxTZBiO+JIHpC7Ir28Xcg+dRObkInNyyd68F+82TctcTmamuvdA8g8p2qBRcD89sAFDZNHBY2VWGlriFYw1S+5R2Pz+vtyxcjZ3rJxNTkIaftXyjw+/qiFkx1WsIaV6t+a0e2wQK8YuRLOV/HhIsXnMKhQrNzs/j4c3YYjMb8C+2gtLpieiRZ3EEFF247bMTEX4Fzj+A8oo14MbMUTWAcBYvQGmDv3wnvQG5j4jMLXshkev8o130uz+vgxbNZthq4qWse81lHFZZHoyIqjQeS69wHnO0xtDZC28H34Fn2cWYajVCK8xz2GoXh9Tm+44Tx0AzYnMTsd58STGGvWLieJOS0/GEJzfk8YQHOZ2pxtPbwxVa+Pz+Gv4vvwFxjpN8B7/IoZaDTHUboznkLH4vvwF5p6D8ex/Nx433VahPIf7eRKfld9DKT7LShVfzyJpetSrgofRQPVAb2oH+RKVVnY1q/6YfvRdM4e+a+ZgSUjDp8D35101BEus+/dniU3Fu1qIe5q4QtcZIOrnbVS/tfRHsZ0JSRgj888/xvAwHPFF6wJeN7Ql6MGRxD/+ItjzG8uErw8R771K6rtfYD1yoshyxQkPLlw3yCQ8uIS6wd6TDOzofk576JbOfP/CGD564i4kULsc1zAtJRFDgR5KhpAqyOTEEtPbt63H3Emv82jJiTiOH0JmpoPNin3/Toz1iqt65vtHrtF/oV4J5N2srBEaQIcG1YqM61kcmVWea0mB8+yhTXnnvKvLA8i0RJxRJzFE1CozZkHhQX7EpWTk/R2fmkl4UKFy3nqI/q5HKVvXr4HV7iQtqzw/fwrECQ4oR/1nT6H6j4PUzBy9/tOkHsEBvnr9p02TMus//7QiddlCPZgA92vZ3vUYqle8Liszi4mTUVqdef011ZkBJk64n717VrN3z2pi4+KpUTN/vM7qNaoSHRNX7HKtWjXDZDKx/0D+I+qxsfHceddDdOw0gBkv6g8JpadnFLv8v438F//33+q6NyxJKe3ABWAMsB29MakX0AC9F1JpcqWUTtd6HOi9fX4EbgNWXu9tRW/4uspJ6YOZjwKqAO2llG2AeODqoCg/AHcAd5PfSCSAua5eQ22klA2klOUZNa6ie8vVPJS4/VLKCcALQE1gnxCiQn0zm9cMJyoxjejkDOwOJ6sOnKVHizpF0l2ITyUjx0rrOvl3dOfe25eVL45mxYx7mXJ7Z27r0JjHb7uxyLKlcZw8ial6DQyRkWAy4dmrN9bt29zSGKvnd5E239gZZ/SVwqspPY+1I4lKSCU6KV3P475T9GhZ9IJwIS5Fz2Pdoo+krdx7ipvL+cYXgOY1qhCVlEF0SqYe89B5ejQrWlG5kJBGhsVG6wKNR3aHk6lfreW29g3o16p8b2ApTIs+hyG0qt67wWjE2LILjpPug8UKv6C8z8YmHdASK1auAM3rVicqPoUrianYHU5W7jpGjzaN3NJUDQlg13G9O/v5mERsdgch/j5k5OTy6FtLefyOPrRtWP5KXPP6NYmKS+JKQjJ2h4OV2w/Qo4N775iqYcF5d+LOX4nXYwb40bV1Y85ExWKx2nA4new7fo56NUrvpdC8YV0uxcRzJS4Ru93Bys276dmpjVua1PRMNNfjZp/+8CdD+1as8bMw7dIpDOHVEKERYDRh6thDH4D7qtwcsp+8m+zp95M9/X6cF05i+WAmWtQZfRnX3V8REo4hsiYyOb7MmLlHTuNRuxoe1SPAw4T/LT3IWl++hmJHbCLeHVuC0QAmI94dW2Itx6NwWsx5DCGR+n5qMGJsfiOO0/vd0rjtp43ao7kGoBb+Ifk9sbx8MNZshJZc8ttQjn25lh8HTufHgdO5sGofjYbr31F42/rYMnOKHUupJKHNa3PTvLGsHLuQ3OTSK3Fa7AVEcDgiMAwMRkxNO+E86z5GF775PwyMDdrm58PTB4yuU7+3H4bqDfPyX2rMmPMYQiLyYhqb3YjjtHtM4VcgZqN2aK5Bbq2/LMLy3hQs70/Dtm4pjiNbsW8o/o2EhR3/ci0/D5jOzwOmc3HlPhre4SrjdnoZX+tYSiXRrpzVz3PB4fpx0robzhMFHj/LzSF71hhyXptAzmsT0KJOk7t4Llr0OWRaEsYGrjeleXhirNUILaHsH1napdMYqhQ4NtvdVPTYfHYE2S89QPZLD+C8eBLLR7PQos5geevpvOm2jb9gXf0d9s2/lxysGM0jAohKyyE63YLdqbHqTBw967n3YutVL5y9V/QfyqkWG5fSsqkeUPb4hOcWr2Ftv+dZ2+95Ylbspfad+n26kHYNsGdayC30/eUmpOHItBDSTh8fp/ad3YlZqT9y6Fc3/7xabUB7Ms+W/qYi67FTeNSqjqm6XhfwHdiTnE073NKYm9QnbMYTxD/+IlpKgW0xmYh4cyZZv60hZ+2WMvN5VfPaVV11gzT9Or3nJD1aFR3r50JcMhnZubSul/8D0KlppLnGkTp9JYEz0Yl0blanzJjOs6cwVK2BIVzPp0fX3tj2uvckN0Tm13k82t2IM1bfLx2HdmOsVQ/Mnvq5pFkbnAUG/S42j//ENfov1CszcqzYHHqPi9QsCwcvxFEvouxHe7SYCxiCC5zzmt6A40yhc17B82zD/HMeXu7nWWON8p1n3fJcp5q+LyVe3ZdO0KO1e4NZ1dAAdp24CMD52CRsdgfB/hW7AZVf/0nR6z87DtKjvfvQtFXDgvLrP9Hx2GwOQgJ86dqqEWcux+XXf06cp17169BL82+kRbuf442tuhaty/oH5X02Nu2AVvjlDeWKcy6/LpJXZ3Z/fLponfnaGuU+XPQlHTr2p0PH/vz66ypGj7oDgBs6tSMjPYO4uIRil7vn7sF8V6C3EkBoaDDCNb7ks888xuIvv72mbVL+PYQQIUKINUKIM67/F3sCFULUcg0VdMI1NFGdstb9d70VbgvwJDAW/TGuhcA+KaUUQuwG3hFChAGpwAjg3cIrEEL4AT5Syj+FENuA865ZmUBJI8CtAyYCb7kG//ZzbctiIcQ89MaeocDokjZcSpkphLgihBgipVwuhPBE7+UTCCRIKe1CiF5AwVvC3wGfAGFAD9e0VcArQoglUsosIUR1wC6lLHw2MKA3Sn0LjAS2SinThRCpQojurkfZRqM/JldemUDe8x5CiPpSyl3ALiHEzegNTOXrZgCYjAaeHdadiR//jqZJBndqQoPIED5YsZtmNavQs4XesLHywFkGtm2QdwK7bjQnme++RdBrCxAGA5YVf+K8dBHfMWOxnzqJbcd2vIcMw9yuPdLhQGZlkfHa3AqFMBkNPHtXbya+/7Oex87NaVAtjA9+306zWhH0bKXfpV657xQD2zcqksfo5HTiUjNp36BGxWIO7szET1fqMTs2okFkMB+s2kezGmH0bK7vYisPnmdg63puMVcfvsD+83GkZVv5da9eOZh19000qVaBNkNNw/b753jdP11/Rev+DciEK3j0vgst5hzOk/swdb4ZU5MOSM0JOVlYf/6g/OsvkM/n7r2ZiQuXoGmSId3a0KB6OO8v20DzOtXo2bYx0+7uz6wvf+Pr1bsQAmaNG4wQgm/X7SYqIYWPf93Mx7/qw4N9OO1eQkvo2ZUf08hzY4cxcc7HesyenWhQM5L3v19J83o16NmhBdNG386sj37g6z82I4Rg1sR7EEIQ4OfD6Nt6MPL5txAIurdtwk3tSn9fgMlo5PkJo5j40ps4NY0hfbvRoHZ13v96Oc0a1qHXDW3Yc/QU73z5E0II2jVvxPSJo/KWv/+ZeVy8EktOrpW+Y57k5clj6NquRekFq2nkfvcBPpNnu15pvhot9hLm20fjvHQG5+GSG3yMDVpgHnCXfmdNSqxL30Nml+MOllMj4ZUPqfHZq2Awkv7Tamxnowh9bDS5R0+TvWEXXi0aUe29GRgD/PDrdQOOR+/l4u0TyFy1FZ8bW1Pn1w9BQvbWvWRv2FV2TKlhW/klXiOezntFtEyKxqPHcLSYCzjP7MfUsT+mRu30/dSSjfW3jwAQYdXw6jsSiUQgsO/8s8ibYkoStf4gtXq3ZsTWN3BYbGyc9nHevDtWzubHgdMBuPH5e2gwpAsmbzP37n6Hk0s3svfNn+k8fQQePl70W6S/fSUrJpmVYxeWnMc1S/C8a5r+GuMjW5BJMXh0G4IWdxHn2YN4tO+HsWEbvfeMJRvbH58CYAirhnnA/SA1EAbsu/4o8pajEmOu+spVrgLHoc16ud40DC32As4zBzB16I+pUVv9leaWLKy/Ff9Y5bW6vP4gNXu35u6tb+DItbFpan4ZD1s1m58H6GXcafo91HeV8Yg973Bq6Ub2L/y5pNW60zSsv3yK97gX9eNkzzq0+MuY+92D88o590amQuw7VuB156N4T9XPBfa969HiSv+RfjVm7vcf4jPpVf072bkaLS4K86334ow6g/NIOfb7v8BkMPBMz8Y88st+/drSvBr1Q/34YOdZmoUH0LNeOF1qh7IjKplh/9mO0SB4olsjgrwrNoB33LqDRPZpw8AdC3FabOyd8lHevL5r5rC23/MAHHjuCzq8NR6jl5m49YeIW6+PN9Zi+j3416+K1CQ5V5LYX9ob4QCcGslz3yPyw7lgMJC5fBX2c5cIeuR+bMdOk7NpByFTHsbg40346zMAcMQlkPD4i/gO6IFXu5YYAgPwGzQAgKQXX8d2quSXdIDrOn13Xya+8yOapjG4S0u9bvDrVprVjqRna72RaeWekwzs2MTtOu1waoxdoI9D5uttZvYDt2AyluN+ruYk57O38Zv+OhgM2DasQLtyEa+7H8B57hT2vdvxvHkoHi3bI51OZFYm2e/pdR6ZnYX19x8ImLcIJNgP7MSxv/TG/3/mGn3t9crz8am8+sMmDEKgScnY3m3d3iZXIqlhW/MfvO55Sj/PHnad87oPRYu9iPOs65zXsK1+LcnNxvq76zwbWg3PgWOQUiKEwL6jnOfZwnke2Y+Jb32LJiWDu7aiQfUqfPDLZprVrkrPNg2ZemcfZn31J0vW6uellx+4tcJ1apPRyHNjhjBx7idompZf//lhFc3r1qBnh+ZMu/d2Zn3yA1//uUX/PifelV//uaU7I6e/gxDQvU0TbmpXds/isjz10jz2HDhMWloGfYbcyyPjRjP89gF/eb2AXpf97TO8xkzXv9erddk+d6NFn8N5ci+mzrfk12UtWVh/ev/a4vzxBV73PZ9fZ068gkfvO9Giz+M8tQ/TjQMxNWmff81c9mHe4l7jZmIIqwZmL7ynvY/tl49wnj1cZtg/V6xj4MDenDqxjRyLhQcfnJo3b++e1W5vg7tj+O3cPtj9J26PHl2Y/cpzSCRbtuzkscnTK5535d/mWWCdlHKeEOJZ19/PFJPuK2C2lHKNq12mzMFYhbzOz5gDCCH6oPcwCpJSZgshTgOLpJQLXfNHAM+jN/T8IaV8xjU9S0rp5/pcFfgFvVeQABZIKb8UQnRFb8SxAncUHGdJCBEBfAzUQ+/BM1FKuUMIMRW9kQvgUynlW65Wt9+llC1cyz4J+EkpZwohGgIfoTcU2YE7gQzgN/TGqr3o4zXd7BrTCCHEEfTxjnoV2J7HgasDYWQB9xYeF0oIkeXa5v5AAnC3lDJRCNEGfawkH/RGtQeklKlCiMWu7f5RCHER6CClTBJCdHCVUU8hRCP0nl4a8BgwBf0xPIHe+PaELOWLt/zxVqX2sctcuKwywwHg/9yISo9JeX64X2fa3mt7XfW1Mg4cXKnxALeeHJVF+ARVekzbwlcrPWbMxus/bldpatz71we6rKj/fFR2muvtvkf/3vGsiiMdlT+mwjcflj0g/PU0YlTFHhm5HmT23zsWUnGMTa/t8Yq/4s9XK388rfbhxd+V/7tEvnV3pcYDyP3g60qP6f3EQ2Unus5kWsmP8v1dtMMHyk50HRm69io70XUm/Mr36OH1ZKxVxk2vv4HtzeJ+8/7NyjmsxfUSOK/8PSmvJ4ctunIrepWoecQN/73PjP1Fx+J3XfP3JoQ4BfSUUsa62ls2SikbF0rTDPhYSlmhxyv+lh5LUsp1gEeBvxsVmr8UKPI6mauNSq7PseiPwhVOsw0otvuAlDIeKPLL1tWgtbDQtItAiwJ/Lyjw+Qz6eE2FlfhqMylly2KmvQ2U+XoyKeXUYqYdRG+8Kjx9TIHPdQp83gv0dH0+DbQqsNg/c7ZSFEVRFEVRFEVRlEqk/Q2dZ/4lIlztLABxQHHPuzYC0oQQPwN10ceXfvbqkEUl+bsehVMURVEURVEURVEURVGuEyHEw8DDBSZ9LKX8uMD8tUBkMYu6PQvpGqaouBY4E/rLztoCUejD/owBSh0vWjUs/cMK9tJSFEVRFEVRFEVRFEUpjqsR6eNS5vctaZ4QIl4IUbXAo3DFPXd+BTgopTzvWmY5+pNUpTYsXfe3wimKoiiKoiiKoiiKoij/VX4F7nd9vh99TOvC9gBBQoirr5TtDRwva8WqYUlRFEVRFEVRFEVRlH8F+S/+7y+aB/QTQpwB+rr+RgjRQQjxKYBrLKUngXWuF5QJ9JenlUo9CqcoiqIoiqIoiqIoivIvJqVMBvoUM30v+W+zR0q5BveXgZVJ9VhSFEVRFEVRFEVRFEVRrolqWFIURVEURVEURVEURVGuiXoUTlEURVEURVEURVGUfwVN/uWxiJQKUj2WFEVRFEVRFEVRFEVRlGuiGpYURVEURVEURVEURVGUa6IalhRFURRFURRFURRFUZRrIqR6/lAp5ETDWyp1p8i2mCszHABBAZZKj2n5B/JZ66acSo1nql2lUuMBOKOTKz2mluOo9Jiew4u8GfRvl/PlmkqNd3ZfaKXGA9hj9Kn0mDfKrEqPaXVU/pCKR01elRpvcL0rlRoPIDvFs9Jj5mT/A9eSbpV7LQE4t9GvUuOFV8us1HgAmlNUekwPL63SY+ZmVv75p8otAZUaz1CrWqXGA5DJqZUeE4u10kOap7xW6TFtH82s1Hj3fvQPfJfAsqjfKv8kVEkaVenwr23kOJ2497/ye1ODdyuKoiiKoiiKoiiK8q8g+de2K/3XUo/CKYqiKIqiKIqiKIqiKNdENSwpiqIoiqIoiqIoiqIo10Q1LCmKoiiKoiiKoiiKoijXRI2xpCiKoiiKoiiKoijKv4KmXlBW6VSPJUVRFEVRFEVRFEVRFOWaqIYlRVEURVEURVEURVEU5ZqohiVFURRFURRFURRFURTlmqgxlhRFURRFURRFURRF+VeQqDGWKpvqsaQoiqIoiqIoiqIoiqJcE9WwpCiKoiiKoiiKoiiKolwT1bCkKIqiKIqiKIqiKIqiXBM1xpKiKIqiKIqiKIqiKP8KUmr/9Cb8v6MalpRy8e3enogXxiOMBtK+X0Xyxz+4zffu2ILI6Q/j2bgu0VPmkblyW948U9UqVJ3zOB5Vw0DC5QdfxB6dUGbMgJ5tqfXyg2A0kLR0DXHv/+w23++GZtScOQ6fpnU4P2kBqX/syJvX8OsX8W3bmKw9xzk7Zna58ujTrQNhz00Ao5GMH1eQ9un3bvO92rcg7LkJeDaqR9yTc8hevTVvXujUcfj06ARA6offkLVyU7li+t3UjmovPQQGA6nfrSFx0Y/u29SpOdVmPIRXkzpETZ5PxortbvMNft40Wv0BGWt2EvPSR+WKaWrZEa9Rk8BgwL7pT6x/fOs239zrNsx9BoOmIa0WLF+8iRZzCYwmvB+YgrFOI5ASy5L3cZ48VK6YxgatMd9yPwgDjv3rsW/51X2b2vTAPGAUWkYKAI5dq3Ds3wCA5+hnMdZoiDPqFNYl88sVD8DYogNeIx5BCAO2LSuwrfiu+PJo3w2fR14ia9YktEunEb7+eD/yIsY6jbFvW03uN++VK56pdUe873sUDEZsG/7A+utSt/nmvrfj2W+IXq65FnI+fQMt+hIYjfg8/BTGOg3BaMS2ZTXWX74pdz6v2nY6mvl/7EXTJEM7NGBsjxZu81//Yw97zscDkGt3kJKdy9YZ91Q4jke7Tvg+9BgYDOSu+YPcH9231XPgILxuHQqaE5lrIfu9BTgvX8LUsAm+jz6pJxICyzeLse3cUmKcwJ5tqf3KWITBQMLStcS+t8xtvjCbqP/O4/i2rIcjNZMzE97AdiUR4WGi7vwJ+Laqj9Qkl178jMwdxwBo+uMsPCKC0XJtAJy8Zxak2Uvchu4vj6Z27zY4LFbWTf2YxKMXi6S58ek7aTy8G56Bvnzc5MG86c3v7U2r+/uhOTXs2blsePYzUs/ElBirss53Qb3aUO+VB8BoIH7JOqLfW+42X5hNNHr3MXxb1cORmsWp8QuxXk6kyrDuVHtkUF4632a1OdTvabKP5ZdJ0y+fwbN2BAd7Ti11G7rMGk0tV7lunPIxScWUa8en76TRHXq5ft44v1yr3tCYzjNHE9q0JmsnvceFP/aUGgvA3KkTAZP1Y9Pyxx9kL3HfZ33uuhOf225FOp1oaWmkz5uPFq8fK34TxuPZ+UaEwYB1z14y33m3zHiFeXftQNizExBGIxk/rSDtM/drS+B9wwgYPhDpdOJMSSdxxkIcsWVfIwv6x64lo13Xko1/Yv290LWk922Y+w7OO+dZPi9wLRk7BWNd17XkP+W/lgT0bEuNmQ+B0UDy0jXEf/CTeznc0IwaLz2Id9M6XJi0gLQ/8/NZ/z8v4du2Edl7TnDugVfLFQ/A88aOBE19FGEwkP3rn2R+5X5u9xtxB76Db0E6nGhp6aS++jrOuPi8+cLXh4hvvyB30zbSFrxTrphenTsS/KRettnL/yTjS/ey9R91B36Db9H32dQ0kme9jjMuAWNkOFUWzAIhwGQi6/tlZP30e9l5vKEjAY/rx0jO73+Q/bV7Hn3vvhPv224Bp57H9LnzccbHY27bhoDJk/LSmWrVInXmLKxbthUOUYR3lw6EPPMIwmAgc9kK0j93v0YHjB6O/9CbwenEmZpO0ksL8o6LiA/m4NmyKdaDR4l/bEaZsa4yNmqD521j9X12zzrsm5YVn675jXjf+xQ57z2NFn0OjCY8h4zHUKM+SIntt89xXjhWrpjbLibx+qaTaJpkSIsajO1Yt0ia1afjWLTzHAJoVMWfuTe3AmDSsn0cjk2nbfUg3hncrvz5rNcK84DRep3r4Ebs239zm29q1R1znxFomakAOPauwXFwIyIwFM87poAQCKMR+57VOPavL3fcvPgN22C+9QEwGHDsXYd983L3+G17Yr55dH6db+cKHHsrHqc0L8xZyOZtuwkJDmL514uuyzqN9Vpi7n+vq1w3Yd/hfmyZWnXD3PsetKyr5boWx8ECvwfMXniPn4fz9D5sq/5T7rjjXn6Y9r3aY7VYeXfa25w/eq7EtM999gKRtSJ5vN+jANz//AN06NsJh91O3KU43n3ybXIysiuQa0UpP9WwVAwhxBDgtJTy+D+9LWURQgQBI6WUH/xtQQwGImc+QtSY6djjkqj701tkrt+J7ezlvCSOmARinllIyLjhRRav9vo0kj/8juxtBxA+XqCVY5R+g4Far47n9MiXsMcm0/SP10lbvZvcM1fyktiik7g49R0ixg8psnjch8sxeHtS5d7+5c5jlRcmEf3gczjik6j53btkb9iJ/VxUfh5jE0l4/g2CHrjDbVGfmzrh2awBl4dNRJg9qL74dbK37EFm55QZs9qsCVwYPQNHXDL1f1lIxtpdWAuUqz06kStPvUXYQ0OLXUXE1HvJ3l2+yg0AwoDXfZPJnv80MiURv5kfYD+wQ6/su9h2rMe2Qb9Ymtp2xmvEBHLeeA5zz1sByHrhIYR/EL5PziVr5iMgy/g+hcB821hyv5yNzEjGa/wcHCf3IROj3ZI5ju7A9scXRRa3b/sdh4cZU4e+Fcqn96jHyH7jGWRqEr4z3sNxcAdabJR7Oi9vzH2H4jh3Im+StNuxLluMoXpdjNXrlD/eA4+TPecptORE/Gcvwr5vu95w5GLbtg7bWr1yZ2rfBe/Rj5A97xk8bugJJg8ynxkHZk8CFizGvm0dWlJ8CcGKcmoac3/bzaIH+hIR4MOoD1fQo2kN6ocH5aV56taOeZ+X7jjJyZiUcq8/j8GA74QnyJgxDS05kcCFH2HftQ3n5QL53LQW60q94dCjUxd8xk0ic+bTOKIukD5lPGhORHAIQe98jm33dtCcxcapM+chTt7zMrbYZJr/OZ+0VXuwFDj+q4zoiyMti0NdJxEyuCu1XriPsxPeIHyUvp8c6TMFU2ggTZa8wNGbn87bT89NeovswwUqZUafYrNau1drgupG8nX3aUS0rU+POWP4cdDMIukurNnP4cVruHfzArfpp5fv4NjXekW5Tr92dHvxXn4bXULDaGWd7wwG6s19kGN3zcIWm0LrlfNIWb0Xy+n8OBEj++BIy2Z/58cIG9yVOi/cy6nxb5L48xYSf9YbAn2a1KLJYvdGpZBbbsCZnVt6fKBm79YE1o3k227TCG9Xn25zx7D89plF0l1au59ji9dwzxb3cs2MTmbj1I9oPf6WMmNdzXPAlMdJnfokzsREQj9eRO7WbTgv5e+zjjNnSHpoPFiteA8ehP/E8aTPnIVHi+aYW7Yg+YFxeh7fexdzmzbYDh4sX2xX/CovTCLmoedwxCVR4+q15Xz+uch64hxX7n4MmWsl4O7bCJ32IPFPzqlQjH/kWnL/ZLJfc11LZn2AfX+ha8n29djWF7iWjJpAzuvPYe7lupY8/xAiwHUteakc1xKDgZqvjueM6zhp/PsC0tfsJvdMfj5t0Ulcmvo24eOL5jNh0TIM3p6EjRpQ/nwaDAQ/9TiJjz2FMyGR8MUfYtmyHceF/HzaT58l4f6JSKsV32GDCHz0YVJeeCVvfsD4B7AdOFyxmM9MJmHS0zjjE4n86gNyNu9wi2k7eZa4H/WYfsNvJ2jywyQ//yrOpBTiHngM7HaEtxdVv/sMy6YdOJOSS40XMPVxUqboeQz7dBHWrdtxXCyYxzNkPzgBrFZ8hgzC/5HxpL00C9uBgyQ98BAAwt+f8O++xrp7b7nyGPr8Y8SNfwZHfBLVvnmPnI073I4L28mzxIychMy14n/nbQRPeYjEp/WG8/TFPyC8PQm449byl6sw4DnoISyfzUJmJOM96TUcJ/YgE664pzN7Ye56K86o03mTPDrq1xXL21MRvgF4PfAClvefKXOfdWqSeRtO8OGw9kT4eTFq6U561KtC/VC/vDSXUrP5fM8FFt/ViQAvD1JyrHnz7mtfh1yHk5+OXClu9SXkU2C++X5yl8xDZqTgNW4WjtP7kEnuNzYcx3diW/WV2zSZmUbu4pngdICHp6sRZD8yK60C8Q2Ybx9H7hev6PEnzsVxYi8y0T0PjiPbsf32WfnXW0FDbunHyOGDeP6VBWUnLg8hMA+8j9xv5uv5GvsyjjP7i5briV0lNhqZewxHizpVobDterWnWp1qPHLTeBq1bcz42RN5ZvCTxaa9cWBncgtdhw9uOch/XvsSzakx+rn7GT7pDv4z98sKbYOilNf/6zGWhBDGEmYNAZpV4qb8FUHAI8XNEEJcl4ZD71aNsF2KwX45DuwOMv7YjH+fzm5p7NEJWE9dhELdDs0NaiKMRrK3HQBA5uQic62UxbdNQ6wXY7FFxSPtDlJ+2UpQ/xvc0tiuJGA5canYhqrMbYfRsi3lzqNXy8bYo2JwXNHzmLViI3693fPoiInHdvoCaIXzWAvL3iPg1JAWK7bTF/Dt3qHMmD6tG2K7FIv9sp7H9N82E9DPPY/26ARyT14sNo9eLepjCgsic8uBcufTWK8JWnw0MjEWnA7suzbg0a6Le6Lc/AYx4emV99lQrTaO467vMTMNmZ2l33Eug6FGA7SUOGRqgn7n8ch2TE3KLp+rtPNHkdayf7AWZKzXGC0hBpkUp+dz90ZMbbsUSec5ZIzek8luy59oy8V59hg4bEXSlxivQRO0uBi0BL1cbTvW49Ghq3siS6FyzauQSv1vgwFh9kQ67EhLGY2ShRy9kkzNEH9qhPjjYTIyoFVtNp64XGL6FYcvMrB1nQrFADA1bIozNhotPhYcDqyb1+NxQze3NAW3XXh558+wWvMakYTZXGqF3K9tA3IvxmItcPwHD+jkliZ4QEeSftB7taX8voOAbi0B8G5Uk4ytRwBwJKfjSM/Gt3X9Cue1bv/2nPxJ75UYf+AcngG++BRoqLsq/sA5chLSiky3Z+Wffzx8PEvNb2Wd7/zbNiD3QhzWqASk3UHi8m2EDOjoliZkQEcSvt8IQNLvOwh0lWtBYUO7kbQ8v0eCwceL6uNv4/JbPxVJW1id/u05/aNergn7Sy7XhP3Fl2vWlSRSTlxGlucGBeDRtAnO6Gicsfo+m7tuPV7d3I9N24GD+v4J2I8fx1ilij5DSn1fNZnAwwNhMuFMrViDrGfBa4tDv7b4Frq25O45lHddzD10AmNEWIVi/CPXkvqFriU7N+DRvoxriSu0oXqBa0lGGjKnfNcS/TiJyztOUn/dQmB/9/OC7UoClpOXitRFQD9OnFnlrxcAmJs1wXElGmeMvv9Y1qzH+yb3fFr3HUS69h/b0eMYw6vkzfNo0hBjSDC5u8rR2HI1ZvMmOC5H44zWY+as3oBPj5JjWo+ewBThiulwgF3vhSnMZjCIMuN5NG2C80pMfh7XrsezlGPEdqzAMVKAV68eWHfuzktXGs8WjbFfjsERrR8X2Ss34tPTPY8FjwvrkROYCpRr7u4DZd+8K8RQswFachwyNR6cDhyHtmJq2rFIOnP/Edg2LXOrA4jwGjjPHwVAZmcgLdkYqpd9XTkal07NQB9qBPrgYTQwoFEkG8+590ZcdjSau1rXJMDLA4AQH8+8eTfUCsXXo2JVeUO1+mgp8ci0RNCcOI/txNSoffkW1px6oxKAyUPv+VZB7nU+B87D2zA1LX+d73rp0KYlgQH+1219erkm5Jfr8Z2YGpW/F5khsg7CNxDnhSMVitup/41s+Em/SXX6wCl8A3wJDg8uks7Lx4tBDw3hh3fde/4d2nIAzamfD0/vP0VoZMWuL4pSEf+TDUtCiKeEEJNdn98UQqx3fe4thFji+jxCCHFECHFUCPFagWWzhBBvCCEOAZ2FEPOEEMeFEIeFEAuEEF2AQcDrQoiDQoj6hWLf6VrnISHEZte0zUKINgXSbBVCtBZCzBRCfCmE2CKEuCSEGCaEmO/arpVCCA9X+otCiLmueHuFEO2EEKuEEOeEEBMK5XuPa1tfdk2eB9R3Lfu6EKKnK96vwHEhxCwhxBMF1jFbCPF4RcrbFBmKIzYp7297XBKmiNByLWuuUwNnZjbV359O3V/eJfwZvQtymctVDcFWIKYtLhlz1ZCKbHaFGCNCsccl5v3tiEvCGF6+k6/15Hl8unVAeHliCArAu1NrTJFFK12FmSJDsbuVazIekeUrV4Sg6vRxxM75vHzpry4WHIZMyc+nlpKICC6aT3Ofwfi9/h+87nqY3K/1R8Gcl8/h0baL3gASFomxTiNESHjZMf1DkOn5d0tlRgoioOh3aWzWCe9HXsPz7imIgHKWQ0kxg8LQCuRTpiZhCHLPp6FWAwwhVXAc3v2XYgEYgsPQkvMri1pyIobiyrXfEPzf+hrvkeOxfKk/UmPftQlpzSXgw58IePdbrL9/j8zOrFD8hIwcIgN98/6OCPAlIb34H1AxqVnEpGTRqV5khWIAGELD0JLc82kMLZpPz1uGEPTxN/iMmUD2R2/nTTc1akrg+4sJevcLsj9YWHxvJcAcGYotJn+fscUm41Ho+HdL49RwZuRgCvEn+9hFgvp3BKMBz5rh+Laqj7la/jbWe/NRWqx5g2pP3FlqXv0ig8kqsA1ZsSn4RRatzJWm5f19Gb31Dbo8fw+bX/yqxHSVdb4zVw3BFlMgTmwynoXLtWoI1qtpnBqOTL1cCwob3IWk5fmPAtd+5h6iF/2GZinHTYPIYLILlGt2bAo+FSzXijCEVcGZkH8ucCYmYijmR/FV3rfeinWXfk6wHzuO7cBBwpf9TPiyn7Du3o3zUlSJyxbHFB6Ko+C1JT4JUynXloBhA8nZUvbjfW4x/puvJX0H47fgP3jd8zC5/3FdS6LO6Tc0DAZElfJfSzwiQ932X3tsBfJ5jYzhYTjj8895zoSkYhtVrvIddAu5O1zXFCEImjyR9Hcq9hiOHrPAPpOQWGp9xG/wzVi251/HjBFViFz6CdX+WErGl9+V3lsJMFYJw5lQ4LyemIixSsnxfG67BeuuXUWme/fphWXtulJj5cUMD8NZ4LhwJiRhKqVB1X/ozVi2/bVrtQgIQabn7z8yIwUR6L7/GKrVxRAYhvPUfrfpWuwlvXHEYEAEh2OsXh8RWHYdMSE7lwj//Bt0Ef5eJGa7nycvpWYTlZrDmO92c9+3u9h2ManwaipE+AcjM/IbwGVmCsK/6DnW2KQT3g/NwXP4ZLc6mQgIwfuhOfhMfhv79t8r1luJq+VcqM4XWPQ4NTa/Ae/HFuA5Ylqx8//bCP9gZGahfBVbrh3xfvBVPIc9ivC/Wq4Cc98R2NYtLZK+LKGRoSQXOL8nxyUTUsx5b8ST9/LLx8uwlnId7nN3Pw5s3FfhbfhfpSH/tf/+W/1PNiwBW4Durs8dAD9XI013YLMQohrwGtAbaAN0dD3eBuAL7JJStgZOAEOB5lLKVsCrUsrtwK/AU1LKNlLKwg+yvggMcC1/dcCJz4AxAEKIRoCXlPLqgAH1XdsxCPga2CClbAlYgIJ9eKOklG1ceVsM3AHcCLzsWm9/oCHQyZWn9kKIm4BngXOubX3Kta52wONSykbA58B9rnUYgHtc2+FGCPGwq1Fr7/fpFas4l0aYDPh0aE7CvM+4MOxxPGpWJXBYBR5p+h9g2b6fnC17qPHNm0QueI7cQyeQzr93wLjQ0beQuXEvjrjSK4zXyrbuF7KeGk3u95/gOeheAOybV6ClJuI380O8Rz2C4+yxIr23rpXj1D4sCx/D8sEzOM8dxnPYxOuy3hIJgdfdE8j9rnxjiVwvtjXLyXziXizffIzX0NEAGOs3BU0j45E7yHh8JJ633okhvOrftg2rjlykb4taGMvRwHutrH8uJ+3hkeR8+RHed9+XN91x+gTpk8aQPnUC3neOAg/zdY+d+O06bLHJtFj5OrVnjSVr78m8/fTso29xpM8Ujg+ZTsANTQm7o+d1j1/QkS/X8p9u09gx91s6Th7yt8aqLH5tG6JZrOSc1HvE+Tavg1edCFJW/PUG2n+aV79+eDRuTPZSfTwbY/XqGGvXIvGOO0kcfifmdu3waFW0B9f14ndbbzybNyTtix/LTnyd/O3XkrW/kPXkaHK/+wTPwa5ryaYVaCmJ+M0qcC35Fwyy6jOwLx5NG5H5td5jwHf4YCzbd+FM+GsNBaXGvLkv5qaNyPgqf9wuZ3wicSMeInbIffje1h9DyPVruPXu3xePJo3J+sa9V4QhNARTvXpYd1WsUbQ8fG/tg7lZI9IW/1B24r9CCDxvHYP1j8VFZjn2rUNLT8Z70nw8b3sAZ9Sp67bPOqUkKi2HT+7owNybW/LK2mNk5pY89t/14DhzAMt7T2D55HmcF47iOWh83jyZkYLlk+exvD8NU6vu4Btw/eOf3Ivl9UewvPskzrOH8Bz+6HWP8U9wnDmI5b2pWD59AeeFY3gOehgAU4c+OM8eQrrGtLre6jSrS2TtSHat2llimjsevQunw8mmZRv/lm1QFPjfHWNpH3rDSgBgBfajNzB1ByYDHYGNUspEAFcvppuA5YATuNpfPx3IBT4TQvwOlD3CIWwDFgshvgeujq76AzBDCPEUMBa9YeiqFVJKuxDiCGAEVrqmHwHqFEj3a4HpflLKTCBTCGF1jaPU3/Xval91P/SGpuJagXZLKS8ASCkvCiGShRBtgQjggJSySA1SSvkx8DHAiYa3uDWFOuKSMVXNvzPjERmGI758lVB7XBK5J87rj9EBmWt24N2mCek/ri51OVtsCuYCMc2Rodhir2FMmHJyxifjUaCXkSkyrEKVwdSPlpL6kX4nImL+s9gvlf08vCMuWR/Q3MUjMhR7OSv3Pm2b4NOxOaH33oLBxxvhYcKZnUv8/NKfm5apSYiQ/HwaQqogU0vOp33XBrzvfxwLgKaR+82HefN8X3gHLa7sfMpM97tVIiDE7W4aAJasvI+Ofesx9x9V5npLjZmWhKFAPkVwGFpagXx6eWOoXgffp/Vn70VgCD6TZ5Hzzotol04XXl2ZtNQkDKH5d9wNoVXQSivXHevxGfcEAOaufbAf2g1OJzIjDcfpY65H+WLLHT88wIe49PzBGOMzsgkP9C427crDF3nu9k7FziuLlpyEIcw9n87kkvNp27wO34lTKDxMpPPKJaTFgrF2XZxni443YItLxlwtf58xVw3FXuj4v5rGFpsMRgPGAB8cKXpPr6iZ+WN1Nft1Drnn9DEQ7HH6OrTsXJKWbcG3bQNYlt8g0vL+vjQb0QuAhEPn8SuwDX5VQ8iKu7ZK4elfdtJj9gMlzq+s850tNsWt95a5aijWwuUam4JntTA9vtGAyT+/XAGqDOlK0rL8x+D8OzTCr3V92u/5AGE04hEWQIufX+bosJfy0jS/vy9NRurlmnjoPL4FytW3agg511iu5aElJbo9mmSsUgUtMbFIOnP79vjddy8pjz2e9yiRZ/du2I8dR1r03n/WXbswN2+O/XD5H2VwJCS79WA1RYThKOba4n1jW4IfHkHMmCfz4pc7xv/CtWTnBrzHFLiWLClwLXnxHbTYsq8l9rhkt/3Xo2r583mtnAlJGCPyz3nG8DCcxew/nh3b4T9mFIkTp+TvPy2bYW7TEr/hgxGustVyLGR88Ek5YhbYZ8KrFFsf8ezUjsCxI4l/eGqx+4wzKRn7uQt4tm2JZd3mkuMlJmEML3Ber1IFZ2LReOYO7fC7716SH32iSDyv3r2wbtkKzuJ7oRaJmZCEscBxYQwPwxFfNKbXDW0JenAkseOmVfi4KEzvOZO//xTuWYPZG0NELbwfnqXP9wvC675nyf1qHlr0OWwFGpy8J8xGKzS2TnHCfb2Iz8x/lD8+M5cqvp7uafy8aBkZiIfRQPVAH2oH+xKVlkPzyMBry2dmqnsPJP+Qog0aBetcBzZg7l30RR4yKw0t8QrGmo1xnix/g2HhHkpFyrlw/L3rMQ8cXe71/1NkZirCv1C+SivXgxsx974bAGP1BhhqNsbUvg/C7AVGE9Jmxb7B/UUOV9183y30G6GPBXf28BlCC5zfQyNDSSl03mvcrgkNWjXgo22fYjAZCQwN5JXv5jDj7ucB6HVHHzr06ciLI1649gJQlHL4n+yxJKW0AxfQewltR+/l0wtogN4LqTS5Ukqnaz0O9B5APwK3kd/oU1rsCcALQE1gnxAiVEqZA6wBBgN3AUsKLGJ1LacBdinzBtrQcG/YsxaYXrAf49V0Apjr6pnURkrZQEpZ0qh3hX/HfYpeVg+g92CqEMuR05jrVMOjRgR4mAi49SYy15XcKl5Q7uEzGP19MYbodzx8O7fGdrbsHlHZh87gVbcq5prhCA8TIYO7kbbm77sjnnv0FB61q2OqrufR7+aeZG8oXx4xGDAE6o+KmBvVxdy4Ljnbyu5qmnP4DJ6uchUeJgJvv4mMteXL4+Upb3Cq21hOdX+Q2Dmfk7ZsfZk/BACcF05ijKiOCIsEowmPG3phP1Do7UAR1fM+m1rfiDPeNci22RPMepduU/P2oDndBmotiRZ9DkNIJCKoChiNGFt2wXHSvXyEX1DeZ2OTDmiFBvauKOeFUxgK5rNTTxwH89+ihSWHrCfuIOuZ0WQ9MxrnuRPX3KgE4Dx3EkNkdQxV9Hjmzr2x7ytUrpEFyrXtjTjj9DxqSfGYmrfVZ3h6YWrQFGdMxXoNNq8eSlRyJtEpmdgdTlYdvkSPJjWLpLuQmE6GxUbrWmU/qlkcx5mTGKvVwBARCSYTnjf1xr7b/e0/hqr5+fTo0BktRv/BaIiIBIM+rJ2hSgTGGrXQEuKKjZN18CxedaviWeD4T13tXrFNW72HsDv1xoqQ2zrnjatk8DZj8NYr7gE3tUY6nPqg30ZD3iNdwmQkuG8HLCfdy/nIl2v5buB0vhs4nfOr9tFkuD5+VETb+tgyc4od86ckgXUi8j7X6dOG9IvF5xUq73yXefAs3vWq4llLj1NlSFdSCpVryuq9hN/VE4Cw2zqTvu1o/kwhCB3UmcQCj8HFfbmaPW0eZl/HRzgy+AUs52PdGpUAjn25lp8GTOenAdO5uHIfje7QyzW8XcXLtaLsJ09hrFEDY1V9n/Xq0xvrNvdj09SwAQFPTiX1uefR0vK3RUtIwNymDRiNYDRibtMax6Wyz3kFWY+ewqOW69piKv7aYm5SnyovTSbu0ZdwpqRXOI//yLXk/EmMkdURrnOex429sO8v5VrSJv+ch9kTXOP3mVq01980Vo5rSfahM3jWyT9Oggd1J/1vrBcA2E6cxFSzet7+492vN5bNO9zSeDRqQPCzU0l+6gW01LS86SkvzSFu8Ajiho4k/Z1F5Py5psxGJQDb8ZN41KyOsZoe06d/Lyyb3cvWo3EDQp6fQuLUGW4xjeFhCE+9J6jw98OzdUscF0sebw/AfvIkxoJ57Fv8MRL41FRSnp3udoxc5d23N5Y15XsMDsB67Opxocf0HdiTnE3u5WpuUp+wGU8Q//iLaClFY1aUduUshrCqiOBwMJowte6G80SBsa+sOWS/+gA58yeSM38i2uXTeY1KeJjBQ7+uGBu00t90WHjQ72I0jwwgKi2H6PQc7E6NVafj6Fnf/bHPXvXD2XtFb+BPtdi4lJpN9RJuDJUrnzHn8+tcBiPG5jfiOO3+aJ9bnatR+7xGMuEfoo+tBODlg7FmI7Tk8t/kAtCiz2IIzS9nY6uuOE66jzEm/AvEb9oBrRxl+U/TyzVCb5w0GDE2uxHHafdx6YRffmOgsVE7tGS9XK2/LMLy3hQs70/Dtm4pjiNbS2xUAljx1Z9Mvflxpt78OLtW7aTX8N4ANGrbmJzMHFIT3Bu0Vn29gnEdxzC+64M8P/wZYi/E5DUqte3RjqEThzFn3CvYyjHGraL8Ff+rPZZAb0x6Er2H0BFgIbBPSimFELuBd4QQYUAqMAIo8o5gIYQf4COl/FMIsQ0475qVCRQ74psQor6UchewSwhxM3oDUzJ6481vwBYp5d9x+3UV8IoQYomUMksIUR2wl7atBSwDZgEewMgKR3ZqxL38ITU/fxVhNJD242psZ6MIe/xeco+cIWv9LrxaNqTGBzMwBvjh1+sGqky+l/O3TARNI+G1z6j15VwQgtxjZ0j9vsz2O3BqRM34hEZLXgKDkeTv1pJ7+jLVnhxB9qGzpK/Zg0/rBjT49FmMgX4E9etAtakjONZnMgCNf5qDV4PqGH29aLXnUy4++R4Zmw6WGi9x9vtU+2QOwmAgY9lqbGcvEfLofeQeO03Ohp14tmhE1XdexBDgj2+vG3E8eh+XBz2MMBmp8fUbAGhZOcQ/8xqU51E4p0bMS4uo+9XL+iuif1iL9UwU4VNGYTlyhsy1u/Fu1ZDai57HGOiHf5+ORDwxijMDJpW97pJoGpb/vIvvU6/pr9vdvAIt+hKeQ8fgvHgKx4EdmPsOwdS8HTgcyJwsLJ/oQ5Tpb+95DaSGTE0i56O55Y5p++MLvO57Xn/17P4NyMQrePS+Ey36PM5T+zDdOBBTk/ZITQNLFtZl+XezvcbNxBBWTX9N67T3sf3yEc6zZbxhR9PIXfIePlPmIgwGbFtXocVcwnPw/TgvnsZxaEepi/u99h+Etw8YPTC17ULOwmeLvlGucLkufgff5+aDwYBt4wq0KxfxuuMBHBdO4di3Hc/+QzG1bA8OB1p2JjkfzgPAuno5PhOewf91vZeNbdNKtKjzJccqhslo4NnbOzFx8To0KRncrgENIoL4YO1BmlUPpWdTvZFp5eGLDGxVB3ENA3Lq+XSSvegtAl5eAAYD1rV/4oy6iPeosTjOnMS+eztetw3Do42eT5mVRdZb+n5iatYK7ztG6oPLSknWojeRGSX8iHZqXJz+KY2/eRFhNJD47Tospy9T/al7yD50jrTVe0hYuo767zxO623v40jL4uzEhXqc0ECaLH0RNIktLplzj+mv9zaYPWjyzYsIkxGMBjK2HCZhyVoQXsVuwqX1B6nduzWjt76Bw2Jj3bSP8+bdvXI23w2cDkCX5++h0ZAueHibGbP7HY4v3cjuN3+m1Zj+1OjWHM3hxJqezdoppTx2WVnnO6fG+ec/pfnSF8BoIGHpeiynrlDr6bvJOniOlNV7if9mHY3em0y7He/iSMvi1Pg38xYP6NwMW0wy1qiEousup6j1B6nVuzX3bH0DR66NjVPzy3X4qtn8NEAv1xum30ODIV0weZsZtecdTi7dyL6FP1OldT36f/oEnoE+1O7Xlg5Th/NDn2dLKVsnGW+9TfCC18FgwPLnChwXL+I39gHsp05h3bYd/4kTEd7eBL2sD13oTIgn7bnp5G7chLldW8IWfw5SYt21G+v20s8dReNrJM15n6ofzUEY9WuL/dwlgifdh/XYaXI27iR02kMIH28iFup3kx2xCcQ9NrNCMf6Ra8lXxVxLho3BecF1LennupY4HcjsLCwfF7iWPP2a/uM8NYmcReW8ljg1Ls/4mAZfz0QYDSR/t47c05epOm0kOYfPkr5mNz6tG1Dvk+cwBvoR2LcjVaeO4ETfxwBo9NMcPOvXwOjrRYvdn3HpqffI3FTGgOVOjbQF7xL2zmsIg5Hs31bguHCRgIfHYDtxmtwt2wl8bDzCx4uQOXqDqjMugeSn/kLPAKdGyuvvEv7ua2A0kP3rCuznLxE4fgy2E6ewbN5B8OSHMXh7EzbvRQAc8QkkTZ2BR93aBD0xQX9ZgBBkfP099nMXyoyXsfAdQhbq1y/LH3oe/cY9gP2kfowETJqA8PYm+JWZ+iLx8aQ+q+fRGBmBMbwKtoOHSglSNGby3PeI/HAuGAxkLl+F/dwlgh65H9ux0+Rs2kHIlIcx+HgT/voMPY9xCSQ8rue36hcL8ahTE+HjTc3V35A0cyGW7WUMkK5pWH/9FO+xM0AYsO9dj5ZwGXPfe3BGn3VvZCpE+Abqy0mJlpFC7vfvlCubJoOBZ3o14ZFl+/Vrc/Pq1A/144MdZ2kWHkDP+uF0qR3KjkvJDPtqG0YheKJ7I4K89cbBsd/v5kJqNhabkwGfbuKlvs3pUqeMsZ2khm3ll3iNeFqvcx3chEyKxqPHcLSYCzjP7MfUsT+mRu2QmhMs2Vh/069PIqwaXn1HIpEIBPadfxZ5m1uZNA3bb5/hNWY6CFedL+EKHn3uRos+h/PkXkydb8HUpIMrfhbWn96vWIxyeOqleew5cJi0tAz6DLmXR8aNZvjtFXgjZGFSw7bqK1e5ChyHNuvletMwtNgLOM8cwNShP6ZGbfPrsr+V3ZBcln3r99K+Vwc+3PIxVouVd5/MH7dy4Yq3mXpz6cPmPvTKeDzMHsxcor+p8vSBUyx6/u97kbjy/5uQZb3e9b+UEKIPeg+jICllthDiNLBISrnQNX8E8Dx6T58/pJTPuKZnSSn9XJ+rAr8AXq50C6SUXwohugKfoPccuqPgOEtCiJ/RH0ETwDrgiau9kIQQJ11/r3T9PRPIklIuKCZ23jwhxEWgg5QySQgxxvX5UVe6gvMeBx50bUoWcK+U8pwQ4hugFbAC+AN4Ukp5W6HyWgSkSSlLqYHrCj8K93fLtlz/cVbKEhRQsTfDXA+WfyCftW6q2FtT/ipT7WvrCfNXOKP/3kchiqPlOCo9pufwPpUeM+fLNZUa7+y+yh/Ac4/Rp9Jj3iizyk50nVkdlX8f6aip+Ea7v8vgepV/1zs7xbPsRNdZTvY/cC3pVrnXEoBzG/3KTnQdhVer2EsTrgfNeY2N/H+Bh1flj2eVm1n5558qt1z/sYFKY6hVrVLjAcjkv+8x4hKV4yUN15t5ymtlJ7rObB/NrNR49370D3yXwLKo3yr/JFRJaoW0/N9s5CiHqJQj/5Xf2/9sjyUp5Tr0HjhX/25UaP5SoMjw+1cbdlyfY9EfhSucZhvQrIS4w4qb7how3ACsLpB2ZimxZxb4XKfA58UUGKOp0Ly3gfym6vzphXshbSy0bQb0gcBLfw2SoiiKoiiKoiiKoihKBfxPjrH030YIcR+wC5juGkvpv4YQohlwFlgnpTzzT2+PoiiKoiiKoiiKoij/Hv+zPZb+m0gpvwK++qe3ozhSyuNAvX96OxRFURRFURRFURRF+fdRDUuKoiiKoiiKoiiKovwraPxrh1j6r6UehVMURVEURVEURVEURVGuiWpYUhRFURRFURRFURRFUa6JalhSFEVRFEVRFEVRFEVRrokaY0lRFEVRFEVRFEVRlH8FKdUYS5VN9VhSFEVRFEVRFEVRFEVRrolqWFIURVEURVEURVEURVGuiWpYUhRFURRFURRFURRFUa6JGmNJURRFURRFURRFUZR/BU2NsVTpVI8lRVEURVEURVEURVEU5ZqoHktKERcyAio1XlVPS6XGA7iSXLl5BMj+Bw63ejW8KjWeoXnTSo2nO1HpEbVT8ZUe07F+W6XH9Hns7kqNZxm9oVLjAVT5B+5oOUTl39PJlJV//mknsys1XsDwJpUaD8D31KVKj2k9l1HpMY+tDav0mG2erVKp8Y68ZqzUeAAR4ZmVHjMp2qfSY2bmmis9ZkTtGpUaz9C8Q6XGA5Ax5ys9pnas8utcto9mVnpM8/jKjRn+yTOVGk9R/g6qx5KiKIqiKIqiKIqiKIpyTVTDkqIoiqIoiqIoiqIoinJN1KNwiqIoiqIoiqIoiqL8K0jU4N2VTfVYUhRFURRFURRFURRFUa6JalhSFEVRFEVRFEVRFEVRrolqWFIURVEURVEURVEURVGuiRpjSVEURVEURVEURVGUfwUp1RhLlU31WFIURVEURVEURVEURVGuiWpYUhRFURRFURRFURRFUa6JalhSFEVRFEVRFEVRFEVRrokaY0lRFEVRFEVRFEVRlH8FDTXGUmVTPZYURVEURVEURVEURVGUa6J6LCkFDQTe7rHzLS4vWc/5d391m2kwm2j13iQCW9XFnprFgYffxnI5EYD6kwdTY2QvpFPj+PTFJG08jG/9qrT9+PG85b1rh3Nm/g9c/HgFkbffQMMn78CvUXVODXoKy+GzRTbGv0dbasx8CGE0kPztGuI/+Mltvm+nZtR46UG8m9bh4qMLSPtzux6nWV1qzp6Awd8HnBpx7/1A2m9bi81wSK82NHj1AYTRQOySdUS9u9xtvjCbaPreY/i3qoc9NZPjD79J7uVEvGpWoeOWt7CciwEgY99pTj/9CQB1nxtBxJ034RHkx5Z6o/PidHDFubxkA+eKKdvW7z1CYKu62PLKNimvbGuO7Il0ahyb/iVJGw9j8PSg8y8vYjB7IIxGYn/fxZnXfwSg8y8vYfTz0suiVgDalbNYl7xebP6NDdtgvvUBMBhw7F2HfbN7/k1te2K+eTRaRgoAjp0rcOxdX+y6SrPtTAzz/9yHJiVD29Vn7E3Ni6RZdfQSH204AggaRQYx786uALy1+gBbTuvl/HCPFgxoWbtcMY0NWmO+5X4QBhz712Pf4l7mpjY9MA8YlZ+3Xatw7N8AgOfoZzHWaIgz6hTWJfPLFc/UuiPeYx4FgxHb+j+w/rLUbb657+14DhgCmobMtZDz8Rto0Zfw6NYXr9vvzktnqFWPrGcfxnnpXNl5bNYerzsngDBg374S2+ofit+2Nl3xfvgFsudNRos6kzddBFfBd8ZHWP9cgn3tT8UuW9i24xeZ/9MmNE0ytHNzxvbv6Db/9Z82sefMFQBybQ5SsnLYOn8ie05f5vWfN+eluxifyrwxN9O7df0yY/4dx2lJ2r5yH1X7tMZpsbH7iY9IPXKxSJrgVnXo9NYEjF4exK47xIEZX7nNbzz+FtrMHMWy5uOxpWQBUKVzU9rOGo3Bw4hISef48BkE9mxLnVfGIgwGEpauJea9ZUXy1eCdx/FtWQ9HaiZnJryB9UoiwmSk3oJH8G1ZD2EykvjDRmLe+1nf/l2LcGZZkJqGdDg5evPTbusM69Wapq/eD0YDV5as50Khc5FwnecDXOf5Q67zvEewH20+m0Jgm/pEf7uJE89/kbdM1aFdqPf4EJCS3LhUDk96H3tKZqnlDBDQsy21Zo1DGAwkLl1L3Ps/u833u6EZtV4ei0/TOpx75A1S/9hR5jqLY6jdDHOPu/RzwbFtOPaucptvbNoZc7dhyOw0AOyHNuI8tg0Aj65DMdZpoU/f/SfOM/vKFbOyj02PDp3wnfAYwmggd8UfWL7/xm2+162D8Lp9KGhOpMVC1tsLcEZdQvgH4D9jFh6NGpO7ZiXZ779dapygXm2oO2ssGA0kfLOO6GL22YbvTMa3lb7Pnh6/EOuVRMKGdaf6xMF56Xya1eZQ/6fIOXaRsCHdqD55GEiwxadw5tG3cZSw/1TWdxnYsy21CxybscXks36hY9N2JRHhYaLu/An4tqqP1CSXXvyMzB3HAGi8ZAYe4cEIk4HMXSe4+HzJ5yLvLh0IefoRMBjIWraC9C++c5sfcO9w/IbeDE4nztR0kmYuwBmbgLlxfUKen4zBT6//pH36DTmrN5UY5yq/m9pR7aWHwGAg9bs1JC760W2+T6fmVJvxEF5N6hA1eT4ZK7a7zTf4edNo9QdkrNlJzEsflRkPrn1fEiYj9d+Y6Hb+i353WQlR3G27mMjrG0+gaTCkRQ3GdqpXJM3qU7Es2nkWgaBRFX/m3tKaUwkZzF5/jGyrE6MBxnWqz4DGVcsX8+g5Xlu6Sr9mdm/DuFu6us2PTU7nhc9/JTMnF02TPD68N91bNSAtK4dpH/7EsYsxDOrSmudHDSxXPKj8Oldl17cAjPVaYu5/rx7z4CbsO353j9mqG+be96Blpeox967FcbDAsWD2wnv8PJyn92Fb9Z9yxy3JC3MWsnnbbkKCg1j+9aK/vL6C7n7pAVr0aofNYmXxk+9z+diFImkmfzmdgPAgjEYjZ/acYOmMz5CaxvDnRtOqb3scNgeJUfF8+dT7WDJyruv2KcpV/3jDkhBiCHBaSnm8mHlVgN8BMzBZSrnlL8SpA3SRUn5TjnS/SylblJFusSvdj0KIT4GFxeXh7ySEmADkSCm/KjNx2YzA+0C/zd2nneu6ag4Jq/aRdTo6L0GNkb1wpGWx6cYnqDqkM41njOTgw2/j16g6VYd0YctNT+IZGUynH15gU+cnyD4Xy9Y+z+oLGwR9Dn1I3J97AMg8eZn9YxfS4vWHit8ag4Gar47n7KiXsMcm0/i3BaSv2U3umct5SewxSVya9jYR44e6LapZrFya8hbWi7GYIkJo8scbZG46gDMju0iMhvPGceiuV7DGpNB+1VySVu0l5/SVvCRVR/bGkZbFrhsfI3xIF+rNuJfjD78JQO6lOPb2earIpiev3kv0Zyu4Yee7bnF23DWX3Jhkuq2aTXyhsq05shf2tGw23jiFqkM602TGSA48/A5+japTbUhnNt/0FJ6Rwdzww3Q2dp6CZrWzc9irOHOsCJORzr/NJHH9QdL2nWXH4Jfz1jtg3eM4T+wpvoyFAfPt48j94hVkRgpeE+fiOLEXmXjFLZnjyHZsv31W/DrKwalpzP19L4vu701EgDejPlpFjyY1qB8emJfmUnIGn28+zuIH+xPgbSYlKxeAzaeiORGTyncTb8bu1Bj3+Vq6NqyGn5dH6UGFwHzbWHK/nI3MSMZr/BwcJ/chE6PdkjmO7sD2xxdFFrdv+x2HhxlTh77ly6Qw4D32cbJnP4WWnIj/3EXY925Hi76Ul8S2bR22tb8BYGrfBe/7HiF77jPYt67FvnUtAIaadfF98pVyNSohDHjdPYmcd55HpiXh88zbOA7vQouLck/n6Y1Hr8E4L5wssgrP4Q/jOL63fHnE9V3+sJFFk4YSEeTHqNe/pUfLetSvGpqX5qnhPfI+L910kJNX9Mbnjo1q8v2zowBIz87l9lmL6dy0VtlB/6bjtDhVe7fGv14kf3aZRmi7BrSf9wBrb32pSLr288ay98lPSd5/lpuWPE1k79bErT8EgHe1ECJ6tiT7SlJeeo8AH9rPe4DNI18jJzqZ+mFmMBioO+chTtzzMrbYZFr8OZ/UVXuwnMnPV/iIvjjSsjjYdRKhg7tS64X7ODPhDUJu74Lw9OBwnykYvM203vgOycu3YHWV9fE7Xyz+h7lB0GzeWPbcNZvcmGQ6u87z2YXO8/a0LLbc+ASRQzrTaMZIDj38NprVzpl53+PfpCZ+TWrmpRdGA01evZ+t3Z/EnpJJoxkjqT12AGcX/Fg0vtu2GKg9+2FOj5iJLTaZZn/OJ231bnIL5N8WnciFKe8SOWFwKSsqgxCYe47AuuxtZFYqXvc8h/P8YWRKrFsyx5l92Dd+676JdVpgCK9F7jezwWjC846pOC8dA1tuGTEr+dg0GPCb9ATpz01DS0ok6N2PsO3chjMq//xj3bCW3D/0H3vmG7vgO34SGdOfRtps5Hz5GaY6dTHWqVtmnHpzHuLY3bOwxSbTasVrpKzeg6XAsRgxog+O9CwOdHmU0MFdqf3CaE5PWEjSz1tI+lmvwvk0qUWTL54h59hFMBqo+8pYDvR4HEdKJrVfGE3VB27m8hvfF1OulfRdGgzUmfMQJ13HZvM/55NW6Nis4jo2D3WdRIjr2Dw74Q3CR+nXjCN9pmAKDaTJkhf0xl0pOTt+Ac4sCwANP3mKkNs7w46VxcYPee4x4ic8gyM+iWpL3iNn0w7s5/P3H9vJs8SOmoTMteJ/522EPPEQic/MRrPkkjRjPo6oaIxVQqn6zfvk7tiLlpldNE6BeNVmTeDC6Bk44pKp/8tCMtbuwnq2QH0rOpErT71F2ENDi11FxNR7yd59rOQYxcS81n0p9PbOGMweHOo9FYO3mTab3iZp2da8819JnJpk3vrjfDisIxH+Xoz6Zgc96odTP9QvL82l1Gw+33OexXffSICXByk5VgC8PIy8MqAVtYN9ScjKZdSSHXSpHYZ/GXURp6YxZ8kKPpo6iojgAEa++hk92zSifrUqeWk++WMrAzo0465e7TkXk8ijb3/LilaPYfYwMWlID85GJ3I2uvS8FY5ZqXWuyq5vXY058D5yv5mv11/HvozjzH5kUox7zBO7Smw0MvcYjhZ1qvwxyzDkln6MHD6I519ZcN3WCdCiZ1vC61ZlRs/HqNu2IaNmP8S8Ic8XSffxpIXkus4v4z+cRvtbb2Tvb9s5vvUQy+YvQXNqDHt2FDc/MpSf5y25rtuoKFdV2qNwQghjCbOGAM1KmNcHOCKlbFu4UamU9ZWkDjCygsuUi5TywcpuVHLFXXSdGpUAOgFngfPS7iR2+XYiBnZwSxAxsANXvtd7HMT9touwbs3zpscu345mc2CJSiTnQhxB7Rq4LRvWvSXZF+PJdf3Yyj4TQ/Y598pgQT5tGmK9GIctKh5pd5D62xYC+3dyS2O7kkDuyUtITXObbr0Qg/Wivm5HfAqOpHRMIQFFYgS0a4DlQhy5lxKQdgcJy7cRVijPYQM7Eve9focj8bedBHcrtb0RgIx9Z7AlpBWJY7mUgLQ7iVm+o5iybV+obFu4pncgZvmOYsvW6arwCA8jBpMRpPuzxCY/b4z1W+AooWHJUKMBWkocMjUBnA6ch7dhatqh2LR/xdErydQM8aNGiB8eJiMDWtZm40n3xquf957j7hsaEuBtBiDE1ePqfGI67etUwWQ04G020SgyiG1nY4rEKD1vTpxHtmNqUv68aeePIq1l/HgswNigCVp8DFpCLDgd2Lavx6Oj+11JLPl3iISnV5HvC8DctQ/27RvKFdNQpxFaYgwyOQ6cDhz7NmFqfWORdJ6334dtzQ9Iu81tuql1Z2RyHFrspSLLlOTopXhqhgVSIyxQ/y7bN2LjkfMlpl+x7zQD2zcqMn3NwTN0bVYHb3MZDYT8fcdpcaoPbM/FH/RLTfL+s3gE+OAVHuSWxis8CA9/b5L3670sL/6whRoD2+fNb/vyaA6/stTt+609tAtX/txDTnQyAI7kdPzaNiD3YixW1zku+ZetBA9wP8cFD+hI4g/6/pD8+w4CurXUZ0iJ0ccTjAYMXmY0mwOHq0JZmqB2DcgpcC6KK+E8H+M6F8X/totQ13nemWMlbfcpNKvdfaVCIBD69gAmf29y41PL3Bbftg2xFsh/SjH5t11JxHLiEmjXPk6CIaIOMj0BmZEEmhPH6T0Y67Uq37IhVXFGnwGpgcOGTIrGWLvonf8iy1XysWlq3BRnTDRaXCw4HFg3rsfcuZtbGplT4A61lzd5Q09Yc3EcO4K0uW9DcfzaNsByMS7vO0v6ZSshA9x7LAYP7ETC9xsBfZ8N7N6yyHrChnYj6Re9F5EQAgQYffRzvtHfG1sJ+09lfZeFj83i9s3gAR1Jch2bKQWOTe9GNcnYegTQj3NHeja+rl6ZVxuVhMmIMJsoafgPzxaNcVyOwREdBw4H2as24tOzi1ua3L2HkLl6HcB6+ATGCL2hwhEVjSNK/0HvTExGS0nDEBxUatn4tG6I7VIs9st6ftN/20xAvxvc0tijE8g9ebHYY9GrRX1MYUFkbjlQapyC/tK+JMHg45V3/pM2R17ZluZoXBo1g3yoEeSDh9HAgMaRbDwX75Zm2ZEr3NW6FgGuhpQQ13mtdrAvtYN9AQj38yLYx0yKpexj5uiFGGqGh1CjSjAeJiMDOzVn48HTRdJlub7LLIuVKkH+APh4mmnXsBaeHhXrA1DZda7Krm8BGKrVR0tJQKYlgubEeXwnpkbtyr98ZB2EbyDOC0cqFLc0Hdq0JDDA/7qt76rW/Tuy82e9fnPhwBm8/X0JqBJUJN3VRiWDyYjJI//8cmLLYTSn/jvp/IEzBEWGFln230pK+a/999+qzIYlIcRTQojJrs9vCiHWuz73FkIscX0eIYQ4IoQ4KoR4rcCyWUKIN4QQh4DOQoh5QojjQojDQogFQoguwCDgdSHEQSFE/QLLtgHmA4Nd87yLWd+LQog9rrgfCyGEa9kGQoi1QohDQoj9rvXOA7q71jVFCFFHCLHFNX+/a1tKKwchhHhPCHFKCLEWCC8wb6MQokOBPL8uhDjm2oZOrvnnhRCDXGmMrjR7XGUx3jW9pyvtj0KIk0KIJQXy5FZ2rmkzhRBPXi0vIcRO1/xlQojgAtv2mhBitxDitBCiewlZrA7k3Z6yxKTgGRnilsCragi5rh9G0qlhz7TgEeKPZ2QIFtd0gNzYFLwKLVt1aGdil7l3nS6NOTIUW0z+HX9bbDIeERU/Gfq0bojwMGG9FFdknmdkCNaY/O22xqTgWeiE61k1BGu0vh3SqeHIzMEjRL9weNUKp/3a+bRZ9jKBNzQpcRsKx8mNScYrMtgtTdGy1eN4RQbnTYerZeta1iDotm4u/Y59RNKmI6Ttd+/lEnFzB5znjoK1+AqXCAhBpuevW2akIAKLlrGx+Q14P7YAzxHTip1floRMC5GBvvnbFeBDQqFuuJeSM7mUlMn9n6xm9Mer2HZGr8g0igxm25lYLDYHqdm57LkQT3x62V14hX8xeQsIKZLO2KwT3o+8hufdUxAB136xNYSEoSUn5P2tJSdiCA4rks7cfwj+b3+N96jxWBa/W2S+R+ee2LavK1/MoDC01Py7mFpqUpHvx1CzPiI4DOfRQo2Lnl6Y+92J9c+K3bVKSMsiMji/4hQR5EdCWlaxaWNSMohJTqdTo5pF5q3ad5qbi2lwKk5lHacA3pEh5BSIZYlNwbuq+7HqXTWYnJiUvL9zYlPwdp3vqg1ojyUuhbTj7j1T/OtHYg70pddP0+m36lXC7ujpOsflx7LFJmOu6r6PuqVxajgzcjCF+JPy+w6cOVbaH/yMtns+JnbRLzhd34OUkqZLX6LFytcJH9WvSFla3M5FRc/znlXzz+d6WVryyrI40uHk2DOf0W3jfHoe/hC/RjW4sqTsx2XNkSFFz/F/Q4VX+AUjM/MbKmRWGsIvuEg6U4O2eI16AfMtD+fN15Ku6I0PJg/w8sVQo1GxyxZW2cemITQMLbHA+ScpEUNY0fOP1+1DCP7iG3wfnEDWB6U/8lYcz8gQbNEFv7MUzIWPxYLfa4F9tqCwQV1JWqY34EqHk/PPfEzr9QvpcPBTvBvVJP6b4s+BlfVdFndsepTz2Mw+dpGg/h3BaMCzZji+repjrpb/XTT+ZgbtDn+BM8tCyu/FP9ppDA/DEZe//zjikzCGF/0+r/IbejOWrbuL5qNFY/DwwHG59IYBU2Qo9tj879UeV4FjUQiqTh9H7JzPy5fe5a/sS8m/70DLyaXjoU9pv/cjYhb9iqOE61BBCVlWIvy98/6O8PMiMcvqluZSWjZRqTmM+XYn9y3dwbaLRXsKHY1Lw6Fp1AzyKTtmaiaRwfk3NsOD/YlPde9NOnHQTfyx8wj9nnqbSW9/y7MjBpS53lJjVnKdq7LrW3rMYGRmoZj+RY9nY5OOeD/4Kp7DHkX4X90mgbnvCGzrlhZJ/98oKCKElALno7S4ZIIji5YvwOSvprNg36fkZuey78+dReZ3vbMXxzaWvwFYUSqqPM3gW4BpwDtAB8BTCOEBdAc2CyGqAa8B7YFUYLUQYoiUcjngC+ySUk4TQoQCnwFNpJRSCBEkpUwTQvyK65GygkGllAeFEC8CHaSUjwIIIfLW5/r7uJRyluvzf4DbgN+AJcA8KeUyIYQXegPas8CTUsrbXOl9gH5SylwhRENgqSt/JRkKNEbvXRUBHAeKu5L6AuullE8JIZYBrwL9XMt9CfwKjAPSpZQdhRCewDYhxGrX8m2B5kAMsA3oKoQ44YqfV3bFxP0KeExKuUkIMQt4CXjCNc8kpewkhLjFNb0C/U3/OuFhJKJ/e07N/rbsxNeRKTyY2m9N4dLUt4rtHfJXWONT2dFuIo7ULPxa1aPF4qfYc9PUct01u240ydY+z2EK8KHD4qn4NalBVoG7UtWGdsFxeO1fCuE4uRfH4a3gdGDq2BfP4Y+S+/nLZS9YQU5NIyolk0/H9iUhI4exn63lh0m30KVBVY5FJ3P/p6sJ9vGiVc0wDHpb61/mOLUPx5Ftet469MFz2ERyF796XdZdEtvq5dhWL8ejax+8ho0m54N5efOMDZqCzYp2+eL1CSYEnsMfJverN4rM8rz1Xmzrl0EF7xJWxKp9p+nbpiFGg/v9i8T0bM7GJtO5afnGyvorKvM4NXqbaTZ5EJvumVdknjAaCWlVlw13zsHo7cHA314i7vM/rzmWb9uG4NTY3/ZBjIF+NF/+KulbDmONiufYkOnY41IwhQbS9NuXsJyNJnPX39ehVpiM1BrTj219nsNyKZ6mcx6g3uNDOP9m+cY8+W/gvHAYy+k9+rmgRXfM/e/H+vNbaFEncEbUweuup5GWLLTYC3qPl7/qHzo2c39bTu5vy/Hs1RefkfeRtWDudY9RFr+2DXFarOSc0u9jCZORiPsHcKjfk1gvxVN39oPUmDyUK2+Vb8y3wir9uywk8dt1eDesQYuVr2O7kkjW3pNQoFf1qZGvIDw9aPDeE3ovp5PFj/9YXr639MGzWSNix01zm24MC6HKq8+QNOP1617/KSh09C1kbtyLIy657MTXiV/bBkhNY2+bhzAF+tJi+aukbdbPf3+VU5NEpWXzyZ2dSMjKZdz3u/lhdNe8R94Ss3J5YeVhZg1odd3qIit2H2NQl9bcP+BGDp27wvTPfuGnl8djMFyf9Rensutc/0R9y3HmII5jO/WYbXvhOehhcpfMw9ShD86zh9waqf8t3rlvNiZPD8a9NZkmXVpwYuvhvHk3TxqG06mxa/k1jyqjKGUqz6Nw+4D2QogAwArsQG+A6Y7e6NQR2CilTJRSOtAbdW5yLesErtYO0oFc4DMhxDDgWkYOK7g+gF5CiF1CiCNAb6C5EMIfqC6lXAYgpcyVUhYXywP4xLXsD5T8ON5VNwFLpZROKWUMUNItWRtw9aH5I8AmKaXd9bmOa3p/4D4hxEFgFxAKNHTN2y2lvCKl1ICDrmVKLTshRCAQJKW8Oirdl+R/BwBXR0TdV2Ab3EyaNKnD1q1b7xRC7F1hOYd3tRCscSluaXJjU/Cqrt9lEEYDHv7e2FMyscal4F09/+6DV9UQcgssW6VPG9KPXMSWmF5c6GLZ4pLd7vKZq4Zijy9/xcXg5039L2YQ+/rX5Bwo2u0YwBqXgme1/O32rBaCtVDlyBqbgmd1fTuE0YDJ3wd7SibS5sCRqt8hyzp8ntyL8fjUL34gx8JxvKqFkhvnfkErWrZ6nNy41LzpcLVs3Zd1ZOSQtPU44b1a503zCPEnqG19nKf2F19AFO2hVLgHEwCWLHA69Dh712OoXnSgy7KE+3sTl54/vkN8Rg7hAe53+iICfOjRuDoeRgPVg/2oHepPlGuMmId6tOD7R27hozG9kRJqh5Xd1VhmFpO3DPf92S1v+9ZjqFbxvF2lpSRhCM3rxIghtApaalKJ6e3FPCrn0aUXtm3lHxhdS0vCEJw/ToMhOMz9+/P0xlCtNj5T5uP7ymKMdZvgPeElDLUaYqjTGM+h4/B9ZTHmXkPwHHA3Hj1uLzNmeJAfcQXutsanZREe5Fds2pX7i38MbvWB0/RqVR8PY/meZv67j9MGY/rRf80c+q+ZgyUhDZ8CsbyrhmCJdT/eLLGp+FTLv1voUzUES1wKfrUj8K1VhQHr5nLb7rfwrhpC/9Wz8aoSSE5sCrEbD+O0WLGlZJG56zhGP2/MBWKZq4Zii3XfR/XzoCuN0YAxwAdHSiZhQ7uTtuEA0uHEkZxO5p6TeY/b2F3nXkdyOqkrd+HXtmHe+qxxKXi7nYuKnuetsfnnc70svUsdiNu/hd5AaLmk/6iL+3UHwR3K7o1mi0speo7/G36cyqxUtzvZwi8ImVXoB0Vudv654NhWDOH5jZ6OPSvI/WY21mVvgwAtLYGy/B975x0fRdWF4eckofceQJEiiKh0kKb0Kk1AFBEVGypKURRBFMTeG/aC2AUsFEFAmoBIb4LSLfTeA6Sc7487m2ySTULJnfWD+/jLD3e2vDtlZ+6ce857/P5tJuzdQ0SRoPNP4SIk7En7/HNi1nSy1muQ5vNpvm/HPrKWDN5nBTmZ8rcYvF+DjtkAhTvWZ88PScGUXJeVNu/zjp+9E34lT83QmYV+7ctkvzu8Y/MUf5vEJ/DPsJH83vxB1vV8jsh8uTi+MXnGkJ6IZf+URRRIUfoVIH7XHqKik46fqGKFid+Ven9mv7Ia+e64kZ19H4fYpBJVyZWTom8+xf4RIzmx6o+QGsHE7dhLluJJ+zVL9Kn/FnNWq0ihHm25ZM6HFB98G/mvbUKxh2/J8H1ncyyZ899yNC6e2L2HOLToT3KfQhOIormzsfNw0qTCziPHKZI7W4rXZKdhuaJmLJIvJxcVyMk/B8yQ+8iJOPqMW0rvehWoXDx/hnpgMpR27D+U+HjX/sMUK5B8DPP93OW0rHUpAFXKXcCJ2Dj2Hzlzc2W/x1x+j7eM5n4kTwrNlIGiYM3ls4iILg1AZMmLiarZnBy9XyZr025EXdGALI27ntX3yWwa9WjJkEkvMmTSixzctZ+CQeej/NGF2J/iuh1M3IlYVkxbRJXmSeeXul0aUblpDT7qe/qZqg7H6ZBhYMkLimwGbgV+xQSTGgMXAxldsY6rarz3OXEYH5+xmMyiEI6FGZL4eV4m0ttAF1W9AvgAyH4an9Uf2AlUwQTKsp7B9wlFrCYVPyZggnF4gaJAhphgsouqen9lVDWQsRSclxuPyTY6220X+Mx40shSe+utt4Y0aNBgr6pe1yZvBYp3rMfOKck7puyasoQLupp4VXS7K9k71xg17pyyhOId6xGRNYocpYqQq2w0B5YmdXkrcW19tn8/77S+8LEV68lWpjhZLyyKZImiQLurODgtdap3KCRLFGU/GMS+72YmdooLxeFlG8hRtjjZSxmNoh3rs2dKcrPUPVMWE93VmBEXaVeH/XN/ByBLobzgZWJkv6goOcoWJ+bv0IPUgE6OUkWQLJGU6Fg31bbdmWLb7gnatiU61k21bbMWykOUN1CIyJ6FIg2v4EhQHXzxtleya9oyiEvhhxJEwtYNRBQqjhQoCpFRRFauT9yfyddf8uRP/P/IS2uSsGsLp8tlJQvxz77DbN1/hNi4eKas+puGFUsme03jSy9g8V9m++0/epy/9x7mggK5iU9I4IDnJbVux37W7zxA3TQCeMnXbSMRBaOR/EUgMpLIK+oR92fybS65g9atYk0SUhhNng7xG/8kIrokEUWiITKKrPWaELs4Rdec6KR1jqpWh/jtQXoiZK3biNhfTyOw9Pc6IoqWQAoVg8goomo0JG5lUOrz8WMcffgGjj52K0cfu5X4zX8S8+4TJPyznphXHkpcfnLmD5yY8g2xsydkqHlZqWL8s/sAW/ccNPtyyToaXpF6gLh5xz4OHTtOlTKp99VPp1EGB/Z/pxs+mcbU5oOZ2nwwWycvpvR1plq4UPWLiT0cw/EgvzSA47sOEHs4hkKe11np665i609LOPjnv4y74l4m1u7HxNr9iNm+j6ktHuX47oNsnbKEIrUrIJERRObISu5qFdg/bQnZyxQnm3eOK9ShAfunJi+L2j91EUWua2y+T9u6id4tJ7fuSfR0iciRjdzVK3B8w1YicmQjIlf2xOX5Glbh2J9JZXkHl20kZ9noxHNRdMd67Apxni/hnYuKBZ3n0+LE9v3kqlCSLIXMzUehhpU5sj7j39LR5cnP8QVDrH9mkLDzbyR/UVN6ERFJVIVaxG9amfxFOZNKVSLLViEhYAYtAtlNSYkULklEoZIk/J1x9pffv824tX8SWfICIopFQ1QU2Ro14eRvya+5ESWSzj9Za9clfuvpn8uPLN9AjqBjtnCHBuxL8VvcP2URRbs2Aswxe9D7LQIgQqF29djzQ9J3O7FjHzkrXEhUIbMP8l1dOZlJdjB+7csjyzck+22GOjYPTF1EYe+3WTDotxmRIysROUywIu/VVdC4eGLWbyEiZ3ayFPWCYpER5G9Wg+MbQv9OTqxeS1SpkkSVMPszV8tGHJudvGwu6yXlKDSkH7v6PU7C/gNJT0RFUfSVYRydOI1jP59aZsKxlevJVroEWS4ohmSJIl+7qzn086mNt/7t/zJrG9zG2qvuYPszH3Pg+xnsfGFUhu87m2Pp5NY95KtvfPQicmQjT40KxKSxLYO5LDof/+w/xtaDx4iNT2DK2h00Kls02WsaX1yUxf+am/b9MSf5e/8xSubLQWx8Ag9OWErbS0vQvEJ0hlqJmqVL8M/OfWzZvZ/YuHh+WriahlWSX/+KF8zHgj/+AmDTtj2cjI2jYJ6My+zS1PR5zOX3eAsgYdsmIgoWQ/IVhohIIivVIW5d8hIvyZ1kVh5ZoToJe80Y+cS4d4kZ0Z+Ytx7k5PSviFs1l9iZIZoFhJFZn03hqTYP8VSbh1g+dRF1OpnxTZlq5Yk5fIxDuw8ke322nNkTfZciIiO4okkNdmw02/iyhlVp0asDb93xPLHHM/YFczjOhlN1hJsDDABuw2TevAIs8cqyFgJviEhhTClcNyCVeYiI5AZyquokEZkHBBxfDwNn4nYWCCLt8T67CzBWVQ+LyJZAOZ5XahYZQicfsEVVE0TkFu816fEL0EtERmH8lRoD6XaYS4cpwD0iMkNVY0WkApDmWTadbQeAqh4Ukf0icpVnct4DyLi/bHLigPuAKVfPfYUtX83kyNotlH/4Og6u2MSuKUv498uZVBnRm4a/vUbsgSMs6/UGAEfWbmH7+PlcNedl47fxyMhEg8fInNkofPUV/D4geVvdYq1rUemZW8laKC95Rz5GzJrNbOwxLOkF8Qlseex9yn02DImMYO830zm+7l+iH7iRY6s2cGjaQnJWvpgyHwwiMl9u8jWrRfQD3fiz2f3kb1uf3LUvIzJ/Hgp2aQLAPw++Qcya5O05NT6B9YM+ovLXj5o25l/N5NjaLZR++HoOr9jI3imL2fHlDCqOuJ8rf3uT2ANHWNPLdJrKV+dSyjx8PRoXjyYksO7h9xNr/Ms+dhPFOjUgIkdW6i57l+1fTGf9oI+o/fUgJDKCLV/N4sjaLVR4uAsHVmz2tu0sqo64l0a/vUrsgSMs7fVm0Lb9javnvGRah3vbNluxAlR54x4kMgKJELaN+80EkjxKdKzLxjfHUyC9SbyEBE5O+Ijstz7qtYidie7aQpam15OwdSPxfy4mqm4boirWRBPiIeYIJ7596zQPK4iKjOCRa2pyz6czSUhQOlQvy8VF8/P29JVUKlmQRhUvoN7FxZm/YTud3pxIhAj9W1Ylf85snIiN57aPpgGQK1sWnu5cj6jIU0i0TEjg5I8jyX7zYIjw1m33FrI0uY6ErZuIX7uEqDqtiKpYw5i/xxzhxPfvJL49++3DiChcwrSjffAtTo57j/gNK9PVi/n4DXINfgEiIjg5azIJW/4i+3U9idu0lrglv5Kt5bVEXVED4uNIOHo4WRlc1KWVSdi725h/nyoJCRz/5h1y3vcUREQSO38qCdv/IWvbHsT/vY74VQtO/bNOkajICB65rhH3vP0DCap0qFOJi4sX4u0f51OpVDEaeUGmn5auo1X1CkiKFPqtew+xY/9halx8wSlr2vqdhmL79OUUb1qVa+a/QlzMSRb2T2qb3WLaM0xtbjqxLBk0kitf60Vk9qxsn7GC7V5HuLQ4vH4b22eupOWM5yAhgV1fTiPmj7/469EPqfjl40hkBLu+nk7Mun+54KEbOLpiI/unLmLXV9O5+I2+VJ33FnEHjrD+nlcA2DFyMuVevY/KM18DEXZ/M4Njf/xNtlLFqPDRQAAkKoI938/hYJCfgsYnsGbQSGp+Pdg7F5nz/MXeeX73lCVs+XImlUf05irvPL/CO88DNFz0JpF5chCRNYpirWuy6PpnOLpuKxtf+pYrfxiGxsURs2UPq/q8Q4bEJ/DPkA+45MuhEBHBHu8cX2JAN46t2MCBaYvIVeViLv5oIJH5cpO/eS1KPngDvzfpm/FnB6MJnJz1Ddk69jHnuTW/ovu2k6VOOxJ2/k385pVkqdrEmEAnJKDHj3JymndjHBFJ9i4DzMecjOHElJGnVj7l928zIZ4jb71GvmdegogIjk+dRPzff5Hz5tuIW/cnJ3/7lRztO5Gleg2IiyPhyJFkZXAFRn2N5MqFREWRtW4DDg0ekKyjXCLxCWwa/CGVvnoMiYxg59cziFn3Lxc+dANHVmxg/9TF7PxqOuXf7EO1X0cQd+AI6+5+NfHteetU4uS2vclKlmJ37uffV0Zz+fdPorHxnNiymw39UvvPAf7ty/gE/nr0Qy7xfpu7vd9mSe+3ecD7bZZ7oy9VvN/mBu+3GVUoHxW/ehwSlJM79rLxfvP7iciZjQqfDCIiaxRERHDo19/Z+ekUQlpHxiew77kRFHvnWYiI4Mi4KcRu/Jv899zCiTXriJk9nwL97yIiZw6KvvgYAHHbd7Gr3+PkatGQ7NWvIDJ/XnK3N149ex5/kZNr0+k0Gp/AtqHvUubTJyAigv1jfubE+n8o2r87MavWc/jnheSoXJ6L3h1MZL7c5Glai2L9urO+Ze+0PzMjzuJY2j7yJy5+rTdVZ70GAru+nsmxPzI2uo+KiGBgk0rc+91ic/267ALKFc7D27+up1KxfDQqV5R6FxVm/t976DRqDpEi9Lv6EvLnyMqPf2xj6db9HDgey/g1Zrg+vMUVXFI0dWOYZJqREQy6sRX3vPYVCQkJdKxflYtLFuGtH2ZxWekSNKpagQe7NmP4qB/5fNoCRITht7VLvHa2HvgmR2JOEBsfz8zla3m3/43JOsqlpenrmMvv8RaYc8GUT8ne7WGIEOJW/ILu2UqWqzuRsH0z8euXEVWzBVEVqiVpTvgg/c88Sx4a+hyLlq3kwIFDNO14E/fe3oPO7c7OLwvg95lLuaJxNZ6a/SYnY04y6qGkcfiQSS/yVJuHyJozG70/HEhU1ixIhLBu/mp++cLkK9zwxO1EZY2i3+fmXLFp2Tq+fNTutvivkPAfNrk+V5FTcRYXkaaYLJn8qnpURNYB76rqK97z3YDBmEycH1V1oLf8iKrm9v6/ODAOExAS4CVVHSUi9THZRicw2Ucbg3RvJbnHUuLneY+fwgSydgDrgL9VdZjnmfQeUBiIBa7DGFNPwZSdfQJMxJTVqbduvVU1t4iUxng+JWsr5Jlov4nxS/rH+9yPVXWsiMzC+DctTrHOw4Ajqhow2z7iaURgvJfaedtiN6Y7XjWS+0CNABZ73zvUtkv8fDFm5+8COTGBp56quj/FdysMLFbV0mnubGBSsRt8/SUWz+ajL5HHwROZlaB26hw95Thu5tHojjhf9SIqn1k3rrMhYXXGqf6ZTezas/dyOF0iC51OQmbmENUx4/K4zGRBj1PriJeZ7Izw/1xwkfh/zjsQ7/96Foo6kfGLMpHLHi7mqx5A/NpT76qYWZzYeOZlMmfKupVpm0fbouoj6d9AZzarns+4zDGzKVY07TJTWxw+6P+15PBx/88/VYee+gRGZhBxWeZ31c0I3ZZ2V1ZbhGPMRTb/j5+svYb5qndfzYG+6gV4768x9sy8wkzBPOXP2cjSvsPr/5P77ZTudFV1OsaTKPC4Qornv8KYX6d8X+6g/9+OKedK+Zp5pOFvpKqfYIJAqT7PezwEGBLifesxnkspSbksuE/tQO+9fwGp7o698rb70viejUJ9R1UdluJ1ub1/EzCBuMEpPmqW9xd4fbBeqG03LOj/lwOp+hmn+G57SMNjyeFwOBwOh8PhcDgcDofjdDkV826Hw+FwOBwOh8PhcDgcDocjFf7X5jgcDofD4XA4HA6Hw+FwWOBU7H4cmYvLWHI4HA6Hw+FwOBwOh8PhcJwRLrDkcDgcDofD4XA4HA6Hw+E4I1xgyeFwOBwOh8PhcDgcDofDcUY4jyWHw+FwOBwOh8PhcDgc5wQJOI8lv3EZSw6Hw+FwOBwOh8PhcDgcjjPCBZYcDofD4XA4HA6Hw+FwOBxnhAssORwOh8PhcDgcDofD4XA4zgjnseRwOBwOh8PhcDgcDofjnEDVeSz5jctYcjgcDofD4XA4HA6Hw+FwnBEusORwOBwOh8PhcDgcDofD4TgjxKWJOVKSL3c5Xw+KpoUu81MOgKm7V/mumT0qi++a1xS8wle9xTFbfNUD2HX8gO+aB48f9V3zluJ1fdfsHBPpq94g+ddXPYCn9ULfNYdE+P87iRT/55FeSSjqq96Y7P4erwA5wjA/t1H9P//cdjyn75p7ovx1a7hjzyxf9QCaFvX3Gg3w886VvmvmyJLNd02/x5brTuzyVQ/gcOwx3zW3Hdnnu2a76Oq+axaNyO6r3ojFz/uqFyBL4bISFmEfyJur7Dkb5Dh0dNN/cr+5jCWHw+FwOBwOh8PhcDgcDscZ4cy7HQ6Hw+FwOBwOh8PhcJwTJLiqLN9xGUsOh8PhcDgcDofD4XA4HI4zwgWWHA6Hw+FwOBwOh8PhcDgcZ4QLLDkcDofD4XA4HA6Hw+FwOM4I57HkcDgcDofD4XA4HA6H45xAcR5LfuMylhwOh8PhcDgcDofD4XA4HGeECyw5HA6Hw+FwOBwOh8PhcDjOCBdYcjgcDofD4XA4HA6Hw+FwnBHOY8nhcDgcDofD4XA4HA7HOUGCOo8lv3EZSw6Hw+FwOBwOh8PhcDgcjjPCBZYcDofD4XA4HA6Hw+FwOBxnhCuFc5wyz7/4OC1aNOJYTAz39nqYFStWJ3s+d+5cTJ76deLjkiWj+ebrcQwa+BS33d6NO+7qQXx8PEePHKNvn0dZ++eGDDVvH3Yn1RvX5ETMCUYMeI1Nv29K87WDPnyUYqWi6dfifgBuHnwrNZvWJi42jp1/b+fNh97g2KGj6eq9+NJQWrRsRMyx4/TqNYAVy1Ov49SfRyetY4lovv76BwY+/GTisg4dWvHFV+9wVYP2LFu6KsN1fOaFITRr0ZCYYzHcf88jrFyxJpXmhJ++THxcomQ0Y74Zx5BHnqFuvZo8/dyjVLr8Eu7s2Z8J46ZkqAfQfehtVGlcnZMxJ/lgwJv8vXpzqtc8OGoI+YsWIDIykrWL1vDpYx+iCQkANLulNU1vbo3GJ7B8xhJGP/fZKekGGPT0A1zdtB4xMcd5tM+T/LFqbarXZMkSxaPPPkStetVJSEjgjWffZdqPM09LJ5hnnn+UZi0acuzYcfrcm3o758qdi4mTv0h8XLxkNGO/Gc+QQc+cssYrrwynVasmxByL4fY7+rN8+e/Jns+dOxczZ3yX+LhkyeJ8+dV3DBgwjAsvLMFHH75G/vx5iYyM5NEhz/LTTzMy1LxhaE+uaFydkzEnGDngLf4JsS/7jnqUfEXzExkZyfpFf/DFYx+hCQl0eOB6qjavhapyaM9BRg54i4O79qd6f+HGVbj0qVsgMoItX8xg85vjkz0vWaOoPKI3eSuXIXb/EVbc9Tox/+4mS4HcVP2oP/mqlmPr17P5Y/DIxPfU+OoRshUrgERGsH/Bn6x55GPS6wz78FP9qN+0LsdjjjO079P8uWpdqtd88N2bFC5amBPHTwBwzw392L/nAA8+0Yda9asDkD1HNgoWLsDVl7SiXuMrufrJAUhkBP9+MYNNKdYrwluvfN56LfPWC6Bcnw5ccGNjND6BNY9+wp5ZKwEo3asNF97YGIDDf/zDyr7vknAilite7UW+KmVB4IVNmxja9xlijsUk03voyb40CKxjv2dCruP7375J4aKFEtfx3hv6s3/vAQCat2tCrwE9UYV1qzfwaO8n0t6gHg8+2Yf6TepwPOYET/R/lrUhNN8d+zqFiyVp3nfDg+zfe4C2XVvR57F72b3DbJPRI79j3Jc/pnp/gcZVKftkTyQygh1fTGfLiB+SPS9Zo7jkzfvJXbkssfuP8GevVzjhbeecl15E+RfvIjJPTkhIYFmrR4iIiqTyuKRzbrbihdj17S/wTNrnoc5Db6VS42qcjDnBFwPeYUuK30mW7Fm57e3+FL6oGAnxCfw+fQkTnv/KfP+ShbnxhbvJXTAvxw4e4bN+IziwY1+G27b90Fu4pHFVYmNOMnrAO2xb/Vcqze5v96PQRUXReGXN9CX89Ly5htbocjVtBnXn0E6j8+uoqSz6Jv3z323D7qRa45qc9K6Xm9O5Xg70rpcPeNfLum3q07V/N0pefAGD2g9g46qk63PVhtW5+vG7MvV3EpU3J1e80os8FS8AhZX93+XA4vWUH9CFC29qwsm9h4gTYdlzo9k6YwW1hvegZJOqxMecYF7/99n3e/JtCVDwitLUf7UXkdmzsnXGchY9bo6Hq9+5j7zligOQNW9OTh46xsQWjyJRkdR76Q4KXl4aiYrg4VGf8sKLb4XcXuE4t9/9xN3UalKLEzEnePmBl9n4+8Y0Xzv046FEl4rmnmb3JC5rf2t72t7SloT4BBbOWMjHz3ycrt6rgXWMieH22/uzLMQ6zpr5ffJ1/PI7HhwwlAsvLMHHH73urWMEgx89tXV84cXHadGyEcdijnNPr4dCjrl+mvZNkmaJaL75ZhyPPPwkt91+I3f2ShpX9rl/8KmNK5+4ixqNa3Ai5gRvPvg6m9LZroM+GkJ0qWj6Nr8PgFsG96Rms9rExcay4+8dvDng9QzHlXBqY56R371NkWJJ1687r+/Dvj37KX5BNE+9NoQChfJzcP8hHuk9jJ3bd2Wo+cSzj9C4+VXExBznwd5D+H3lH6lekyVLFE++8Ch16tckIUF58ek3mDzhZwDadmxJ/4H3oKqs+X0dfe4amK7eq68Mp3WrJhw7xePngpLF+cI7fkqVKsmH779C4SIF2b/vADff2oetW7dnuI7h2JfXD+3J5d6Y65MBb/FviDFXn1GPkjdozPWVN+bqPKgHlZvVIO5kHLv/2cmoh94i5tCxDDXTYsgzr/DLvIUULJCfHz5/94w/x+HILFxgyXFKNG/RiHLlSlOtShNq1qrKK68Np2njzslec+TIUa6q1y7x8ew545gw3gQ6xoyewMcfmUF66zZNeebZR+l8bc90Nas3rkHxMiXo3bAXFapdwl1P3cMjHR8K+dorW9Ul5tjxZMtWzFnO589/SkJ8Aj0euYXO93bhs+dGpanXomUjyl1cmipXNKZWraq89vpTNG54bap1rFfnmsTHc+aNZ3xQMCd37lzc27snCxcuS3fdAjRr0ZCy5UpTu2pzatSqwouvPkHLJtel0mzcoEPi4+mzv+PH8VMB2LJlO/fd8wi9+9x+SnoAlRtVJ7pMcR5udB/lqpXnlqfvYnjHQale91bvlzl+xNz43vfOQ9S+pi4LJsyjYt3Lqd68No+1foC4k3HkKZT3lLUBrmpaj4vKXEjrOl2oXONyHn/hYbq1Tv397+rXk3179nFNvesQEfIVOD2dYJo1v9ps52otqFGzCi+8MoxWTbsme83RI0dpfFXHxMc/z/6WHydMPWWNVq2acPHFZahUqQG1a1dnxJvP0uCqdslec+TIUWrVbpn4+Lf5k/jhh8kADBrUl7HfTuD99z/j0orlGTfuUypcUjddzcsbVaNomeI82uh+ylYrT/en7+TZjoNTve693q8k7su733mQmtfUYdGEX5ny/njGvWIG7U1ubU27vl34/NEPkr85Qqj03G0s6vo0x7ftpe6UZ9g1ZQlH121NfMkFNzYm9sAR5tTpR3THulR47EZW3PU6CSdiWf/caPJUvJDcFS9M9rHL73ydeO87Vf2oP9Ht68C4f0KuZ4OmdSlV9gI61L2eK6pfxuDnB3Bzm7tCvvbR3k+wZsWfyZa9PPSNxP+/4fYuXHJ5eSIiInjk2QdZ1PU5jm/bS31vvY6kWK+4A0eYXacfxTvW5ZLHbmT5Xa+Tu0JJinesx5yrB5AtugC1xwxhdt1+ZCuan9J3tOKXqx4k4Xgs1d7vS/GO9dj6zWz+eOxT4rz13Te8I9ff1plPRnyeqFW/SR1Klb2QDvVu4IrqlzHouQHcck0a63jfE/yxIvmNyYVlLqDn/TfRs/29HD54mAKF8od8bzD1mtShVJkL6FT/Ri6vXolHnn2Anm3vDvnax3o/yR8rU98MTRs/gxcffS1tkYgIyj17B793Hc6J7fuo+tNz7Ju6mGPrtiS+JPrGpsQdOMriuvdTpEN9ygy5iT97vQqREVR8qw9r73uDo2v+JqpAbjQ2nvgTsSxrlnQtqDrlefZMWpDmV6jUqCpFykTzZKO+lK5Wnq5P384rHYeket2MDyayfv5qIrNEct8Xj3Fpo6r8MWs5HQf3YNF3v7Dw218oX/cy2j3cjc8eCB2ACHBJo6oULhPNi436U6raxVz79O281fGxVK/75YOJbJq/hsgskdz5xRAuaVSFtbNWALBy4nzGDf0kXZ0A1bzr5f0Ne1Heu14OSud6eTzF9fKfdX/zYq9n6fXMvcmWR0REcMeTvVh03bOZ9jshQan01C3snrmcZXe8imSJJDJHtsTP2/zeJDa/M5E9UWaIWrJJFfKWieaHBg9SuHo5rnz2Via3G5Zqveo825P5D3/InqUbafrZQ5RoXJltM1fyyz0jEl9T4/EbifVu5Eq3rU1E1igmNBtEZPas3DH9Cb4ZPY6//96S7HPDcW6v1bgWJcqU4ParbqditYrc98x99G/fP+Rr67WqR8zR5EHqynUrU6dFHXq37E3syVjyFcqXrl5gHS+t1IAra1dnxIhnqd8g9TrWrNUi8fGC3ybz/Q+TABg8qC9jx07gvfc/5dJLyzN+3GeUr1AnXc3AmKtq5SbUqlWVV197kiaNOqXSbFC3beLj2XPHMX7cTwCMGT2ejz8yk26t2zTl2ecepVPHjMeVJUqX4N6rzbiy19P3MLDDgJCvrdOqLsePJv+dLJ+znM+eH2XGlYNuoXPvLnz2bNrjSjj1MQ/AwHsfZ3WK69dDQ/swfvQkxo2exJUNatDv0XsZdN+wdDUbN7uK0uUu4uqa11CtZmWefnkIHZp3T/W6+x+8iz2799GodjtEhPwFzHFSumwp7u13O51a3czBg4coVLhgunqtWzWh/MVlqOgdP2+NeJZ6p3D8/OAdPy88/ziffTGWzz4bQ+NG9Xn6qUHc2rNPuprh2JeBMddjje6njDfmei7EmOv9oDFXr3cepMY1dVg84VfWzF3B9y98QUJ8Ap0e6U7re6/lu+e+SPX+U6Vjm+bc2Lk9g5986Yw/41xGnceS7/xflMKJSGkR+T3jV2b4OTVF5I2MX3lan1lQRKaJyHrv3wKZ+fmnoP+JiHRJ5/n7RGSDiKiIFD5TnWvaNuOrr8xMw+JFy8mXLy/FihVJ8/XlLi5N4SKF+HXeIgAOHz6S+FzOXDlP6cdeu/mVzPrWzNCuW7aWXHlzUaBo6s2bPWd22t/RgbFvjk62fMWc5STEJyS+v1DxQunqtW3bnK++MDOOiwLrGJ32Ol58cRmKFCnEvHkLE5c99vgDvPLKu4kzThnRuk1TRnvbdcmiFeTLl+eUtuv8XxcD8O8/W1mzei0JXibRqVC9RS3mfTcbgI3L1pMzTy7yFcmf6nWBi2JkVCRRWaIS91nT7i2Z+M73xJ2MA+Dw3kOnrA3QpNXVjB9jBtwrl/xOnrx5KFw09b65tls7PnjDXORVlQP7Dp6WTjCtrmnKN1/9AMCSxSsyPH7LlitN4cJJ2/lUaNeuBV98PhaAhQuXkj9/XqKji6b5+vLly1CkSGHmzjU3xKpK3jx5AMibLw/bt+/MULNqi1r85u3LTae1L5MvB8iWMxuhfpb5q1/Msc07iPl7Fxobz44ffqVYq5rJXlOsVU22jf4FgJ0TFlCowWUAxB87wYGFa0k4EZvqcwNBJYmKJCJrVLrZSg1bNmDiaHMzsWrp6jSPmVOhVcdm/PT9z1xe7VL+3bwlcb22p7FeW7z12jFhAYW99SrWqibbf/iVhJNxxPyzm2Obd5C/+sVmfSIjicyeFYmMIDJnNk7sMBlgccHbOnu2VOfARq2uYuKY4HXMfVrr2Kl7O0Z/8h2HDx4GSMxiSo+GLRvw41gTGP996Rry5MtNoTPcrmmRp9rFHN+8g+P/7EJj49j9wzwKtqyV7DWFWtZi5+hZAOyeOJ/8Da4AoECjKhxd8zdH1/wNQNz+I5DiXJejbHGyFs7Hod9Sz8YHuKJFLRZ+Z/bjX8vWkyNPLvKm+J3EHj/J+vkmWyI+Np5/V28mf7S5mYouX5J1v5rn1s9fzRXNkx8nobisRQ2WfDcHgH+WbSBHnpzkCaG5af6aRM2tqzeTL/rMtn+toOvl+mVryZk3F/nTuF62vaMD36a4Xm7dsIVtm7amev3FVcuz46/tmfo7icqTg4J1L2XLF+b7amw8cenM2l/YsgYbx84FYM/SjWTNl4scRfMne02OovnJkicHe5aajIWNY+dSqlXq/VS63ZVsHjff6CpE5cyGREYQlSMrsbGxHDp0JNV7wnFur9OiDtO/nQ7An8v+JHfe3GmOfzrd2Ymv3/g62fJrelzD6LdHE3vSnHsP7k3/+tm+XUs+/8Ks44KFS8mXP18G61g2xTpCnry5AciXN+8prWOba5rx1Zdm/HM6Y65Q48pcpzqubFGHmd+aTKoMx5V3dmTMm98kW75izrKkceXStRSKznhYfapjnrQoV6EMC+aasciCuUto0urqDN/Tok1jvv3aZBYuW7ySvHnzULRY6u/atfu1vPXah4A5TvfvOwDAjTd35tOPvubgQTO+27sn/QzNdu1a8tlpHj9FixRmjnf8XHppeWbOnAfAzFnzaN+uRZrvDRCOfVklaMy1OY1rCSSNrSK8MVdgfPPHnJWJmpuWrSf/GZ7vA9SsegX58uY5q89wODKT/4vAUmahqotVNf0Q+OnzCDBdVcsD073H/yXmAc2Av8/mQ4oXL8bWLdsSH2/btoMSJaLTfH3nLm35/tvkJRF33HUTy1fOYPiTA3n4oeEZahaMLsSebbsTH+/dsZeCxVKfhLs92J3xH/zAiZi0gzlNujZj6ayl6eoVL1GMLVuSUm+3bd2e7jp2ua4t345NWscqVS/jgguKM+WnUy/XKl6iGFu37AjS3EnxEsXSfP21na/hh+8mnfLnh6JAsYLs3bYn8fG+HXspkMbFbcCnj/Hmko85fjSGRZN+A6BY2eJcUvtSHv/hWQZ9M5wylcudln7R4kXYsTVp0Llz+y6KFU8+mAwMUO8f2Isx00bxygfPUKhI+jNm6VG8eDG2bQ3aztt2EJ3Rdv7+9LZziRLR/Bv0G9mSwfHT9boOjBmbVFLy5JOvcOONndi0cRHjx31Kv/6psxtSUqBYQfZt25v4eP+OvYk3wynp9+mjvLzkQ44fPc4Sb18CdBzQjed/fYcrO1yVmL0UTLbogsQEaRzfto9sKTSyFS9IzFbzGo1PIO5wDFkKZjzYqfn1IJqsfo+4I8fZMeG3NF9XtHgRdmxLSv3fuX0XRYuHvgEZ9tpgvv75E+7sf2uq54pfUIwSpYqzaO4SihYvws6gz4wJsV7ZixfkeNB6xXrrlS06aX0Bjm/fR/bogpzYsZ/N70yk8dK3aLLyXWIPHWPP7JWJr6v82t00/f1dSl98Ed98PDb5OkYXTvZ9dm3fRZHioQe6w14dzFfTRnJH/1sSl5UqdyEXlb2Qj8e9zaiJ71Gv8ZUh3xtMkZSa23ZTNI3B9eOvDuKLaR9xe7+bky1v0qYhX/48kufeH06xEqlvJLIVL8iJoPPNye17yVY8+XbOGvya+ATiDh8jqmAecpQtgapy+VdDqDb1BS7o3YGUFOlYn93jf013PfMVK8CBoGP4wI695EvjdwKQI29OLm9ag3XzzHzW1j/+pkqr2gBUblmb7HlykjN/7nQ18xYryMEgzYM79pE3Hc3seXNyadPqbJiXNId2eeva9Jv8PDe93Y98xdM//xWKLsTeoOvlvh17KRTiennDg92ZkMH1MpiC0YXYsz1p/2XG7yRHqaKc3HuIyq/fQ/2fn+WKV+4iMmdSxtJFt7WkwcznqffynWTNl5Oc0QU4FrQtj23fR87o5DeQOaMLcGz7vnRfU/TKS4jZfZDDm8315+8fFxJ37ATXLRtBp4Wv8cqr77F//4FU2yAc5/ZC0YXYE/S72bN9D4VD/DZvfuhmvvvgO47HJM/GKFm2JJfXvpxXx7/KC2NeoEKVCunqlSgRzZZ/k9Zx65btlExvHbu2Z8yYpHUc/uTLdL+xE5s3LWb8+E/p1y91RmBIzaAx19ZtOyhRPP1x5XcpxpV33tWDFatmMvypgTw8IONxZaHoQuwNOp737thLwRDjn24DbmLc+9+n+ztpen1zls1akqHmqYx5Ajz1+mN8O/0z7u5/W+KytWvW0+waU17drE0jcufJlWEGd3TxomwPGvPs2LaT6OLJz895vYDEgMH38ePMb3hn5MsULmK2RZlypSlb7iK+m/wpP0z9nIZN66erV/I0j5/rUxw/K1eu4dqOrQHo2LE1efPmoWDB9Ofpw7Ev86cYcx3YsZcCaZzX+3z6KC+FGHMFqH9dY1bPOrXqBofj/4X/p8BSlIh8ISJ/iMhYEckpIjVEZLaILBGRKSJSHEBEZonI8yKyUETWichV3vJGIjLR+/8iXobRahH5UET+FpHCXnbUHyLygffcVBHJkc736gAEcidHAR1PZ6VEZJiIjBKROd536CQiL4jIKhH5SUSyeK97XEQWicjvIvK+iMipfL6qLlPVv07nO2UGnbu0ZeyYCcmWffj+51St3IShjz3PQw/3zhSd0pXKEH1RNAumpH1T2vm+60iIi+eX72dlimaALl3aJV4YRYTnnhvCoEeezlSNlFzb+Rq+GzvRqkYwL938JH1r30FU1ixUqnc5AJGRkeTKl5vhHQfxzTOf0vutBzNdNzIqkuIli7F80Squa34LKxavYsDQzI4Jp821ndvw3dgfM37hWdC1a3u++WZc4uPrr+/Ap5+Npmy5WrTvcDOfjHydU/yZnxKv3fw0A2rfRVTWKCp6+xLgh5e+YmC9e1gwbg5NbmmVaXqnwuIbnmVm5XuIyBpFoQaXZ/yGDBh87xN0bXwzt3W4l2pXVqHtdcnXp2XHZkyfOOu0MvxOh6h8uSjaqgazat3PjCr3EJkzGyU6N0h8fmW/d5le+R42r/+bFu2bnpHGo72f4Pomt3B7R7OO13jrGBUZyYVlL+Suzvcz6J5hDHnxYXLnTT/4cao8dt+TdGt6K3d2vI+qV1ahTRdT8jNn2q+0v7IrNzbryYJfFjP0tdQlAWeDREWS78qK/Nn7dVZ0GEKh1rUTs5kCFOlYn93fz800zYjICG55ow+/fPITe/81Qbcfnv6ci6+sxMM/PsfFdS7lwPa9iX5zmaV54xv38+snU9jnaf7x81Kea9CH11oPZP3cVXR9+d4MPiVjSlcqQ7GLolmYzvXSDyKiIsl7RRn+HjWNec0GEXfsBGXvN0HDv0dNY9aVfZjb5BGO7TpAzcdTl++cKWU61uUvL1sJoHDVsmh8AmOq38/3dR6gf7+7KFOm1Fnr+HVuL1upLMUvKs6vP6UOrEZGRZInfx76t+/Ph09/yKC3U5e7nw1du3bgm29+SHx8w/UdGfXpGMqUrUn79jcz8pM3MvX6Bd64cnTyceUH739GlSsaM/SxF3hooH/jyi73dSU+Lp7ZmTiuHHjvUK5t1J0e7XtRvU5V2l9nAi0vDnuDmnWrMfbnT6lZrzo7tu1KzHo5GyKjIilRMpolC5dzTePrWbJoBUOGm/FcVFQkpcteRNd2t3H/HQN5/rVhiYGozKBr1w58HXT8PDzwSa6+ug6LFk7h6qvqsGXLduLj489aJ1z7EuCNm5/m4RBjLoDWvTsRH5/Agh/mZKqmwxFu/p88li4BblfVeSLyMdAbuBbooKq7ReR64GkgEOaPUtXaItIGGIrJ2glmKDBDVZ8VkVZAcMFzeaCbqt4pIqOBzsDnhKaYqgamXHYAaadBpE05oDFQCZgPdFbVh0Xke+Aa4AdghKoOBxCRz4C2wITQH3f6iMhdwF0A2bMWJmuWvNxx103ccuv1ACxbsoqSF5QATES/RIlotm3bEfKzLr+8IlGRUanMLQN8O3Yir7z2ZMjnWt3chuY3mBTYDSvXU7hEEcCUOBSKLsS+nXuTvf6S6hUpV/li3p37AZFRkeQtlI/hXz/N4zc8CkDjLk2o2bQWQ7uFnj27q1cPbu15AwBLlqzkgguKJz5XomTxtNfxikuJjIpi+TKzjnny5KZSpQpMnmLS0YsVK8LoMR/Q9bo7Uxl433Znd3rcYvx9li9dRckLkmZ1SpQsxvZtoVPIL7u8IlFRkanMLU+Fpj1a0bCb+QlsXrGBQiUKs957rmB0Ifbv2Jvme2NPxLJs2kKqN6/N6rkr2bdjL4unmPTlTSs2oAlKnoJ5Obwv7ZK4bj270OUmc+Pw+/I1RJdM+pkUK16Undt3J3v9gX0HOXYsJtGse8qE6XS6sf1prfNtd9yYuJ2XLVtFiZJB27lENDvS3M6XEBUVycpT2M53330Lt992IwCLF6/gwgtKJD53QTrHT+UrLiUqKoply5KOjZ633kDbdjcBsGDBUrJlz0bhwgXZvTv5vmnUoyVXB+3LgiWSZugKRBdK11Q47kQsK6YtomrzWvwxd2Wy5xb8MJc+Iwcx/tXkJTInduwjR5BG9hIFOZFC48T2feQoWYgT2/eZspI8OYjddzjN7xFMwolYdv20mKKtasKcpCyxrj070am72eerl/9BdFA2TLHiRdmV4pgB2L3DzF4eO3qMyd9P47JqlRLLywBadmjGc4NeBmDX9t0UK1GUA95zOUKs1/Ht+8heshDHvfXK4q3XiR1mfRO3SfGCHN+xj8JXX07MP7s5udes+84fF1KgVgW2fRsU+EhQpo77mVvuvZHsObJzbXfjQ7F6xR/JMn6KFi/K7qDZ2NTrGMNP303j8qqX8uOYn9i5fTe/L11DXFw82/7dzj+b/qVUmQtS+U1dd+u1dOxuPEvWLP8zuWaJIuzakb7mlO+ncVm1S5k0dgoH9yf95sd9OZE+Q1L7M53Yvo9sJZIyLbIWN8dJMCe915zcvg8iI4jKk5O4fYc5uW0vB3/7gzjvWNo3fRm5KpfhwFzzu8lV6SKIjOTIytQm1Vf1aEHdbiZ498+KjeQPOobzRxfiYBq/kxuevYvdm3cw6+OkY/HQrv18dLc5brLmzEbVVleGNFyt26M5tbs1AWDLik3kC9LMF12QQ2lodnr2TvZs3sHcjycnLjt2IKnMZ+HXM2jzyI2p3tfq5jY09a6XG1eup1DQ9bJgdCH2prheVvCul28HXS+f+PpphnrXy1Ds27GXwsULE8hFyIzfScy2vRzfto+DS43R8o4JCyh3v/mtn9xtSrYu6tmCkq1qkrtUETZ/O4+cQdsyZ/GCHNuRvMnAsR37yRmU1ZXyNRIZQanWtfixdVK2UJlr67Ft1ko0Lp7jew/x66+LqFG9Mps3/xOWc3vbW9rSqpsJFK9bsY7CQb+bwsULsyfFb/PSGpdSvnJ5Pvn1EyKjIslXKB/Pj36egV0Hsmf7HuZNNqVF65avQ1XJVzAfB4NKyu+5+xZuv727t47LueDCpHUseUFxtqa1jpUrERUVxdKgdby15w20bWvW8bcFS8ieLfQ63nlXD27pacaVS1OMuUqWiGbb9rTGXBWJikp7XDl2zARvXJnaV6z1zW1o3s0EwzesXE+hoEzQQtGF2Lcj9bjy4soX8968D4nwtuuT3zzDY9ebwHnjLk2p2bQWj6cxroTTH/MA7PKaIBw7eoxJ303himqXMX7MZHbv3EO/20wxRM6cOWh+TWMOhyjZvPn2G+h2s/E+Xbnsd4oHjXmiSxRjRwrD7/37DphrpWfW/eO4Kdxwk/EV3b5tJ8uWrCIuLo5//9nK5g1/UbpcKVYuSxoXZebxs337Tq7reidgyho7XXtNYhleMOHYl416tKSBN+b6yxtzBSzC80cXYv8pjLmqBI256nZpROWmNXjlxoybazgc/2/8P2Us/auq87z//xxoCVwOTBOR5cAQ4IKg1wfacywBSof4vAbA1wCq+hMQPErZrKrLM3h/KtQUeJ+JU9hkVY0FVgGRQOBOaFWQdmMRWSAiq4AmwGVnoJMmqvq+qtZU1ZpZs5gU2w/f/5yr6rXjqnrtmDhxKt26mQtOzVpVOXToMDt3pr4wAnS5rh1jxyaPeZUtVzrx/1u2asymjX+FfO9Pn07iwTb9eLBNPxZOXUCjzib9t0K1Szh2+Bj7U3SsmvL5ZO6o3ZO7G9zJ4C6PsH3ztsSgUrWG1el4dyeevf0pTh4/GVLv/fc+o16da6hX5xomTphKt+7GOLJWYB13hF7H665rx9igNN5Dhw5zUakaXHbpVVx26VUsWrgsZFAJ4OMPvqBxgw40btCBST/+TFdvu9aoVYVDh46kuV07dWl7xlk00z/7icfbDODxNgNYOnUh9Ts1BKBctfLEHD7Gwd0Hkr0+W87siV49EZERVGlSg+0bjf/G0qkLubSOmX0pVqY4kVmi0g0qAXw1ciydm/agc9MeTJ/8S+JMXOUal3Pk8BH27Eod2Jo1dS61vU5eda6qxcZ1qTtvpMfHH35J46s60viqjkye+DPXd+sIQI2aVdI9fk9nO7/77ihq1W5JrdotGT/hJ7rfZOzOateuzsGDh9mxI3Tnluuv75hsRhvgn3+30bixyW6pWPFismfLlmpQDjDrsykMb/MQw9s8xPKpi6jj7cuyp7gvr2hSgx3evixaOmngWbV5TXZs3EZKDi7bSM6y0eQoVQTJEkl0x3rsmpI8ZXzXlCWU6Gp8H4q1u5K9c9MPykXmzEY2zyNFIiMo0rw6Rzck1x498jtuaHYrNzS7lZk//ULbruaG64rql4U8ZiIjI8lf0BiPRkVFcnXzemz8MyngUPriUuTNn4cVi82Nyerlf1Kq7AWJ61W8Yz12hlivC7z1ig5ar51TllC8Yz0iskaRo1QRcpWN5sDSDcRs3Uv+6hcTkSMrAIWuupwj6822zlk66cbi6hYN2LzhH0Z/8h3dmvekW/OezJo8JzHD6lTX8arm9diw1qzjrJ/mUKNeNQDyF8xHqbIXsvWf1PtzzCff07357XRvfjuzfprDNV720eXVK3Hk0FH2htDM52lGRkXSoFnSdg32Y7q6RX02r09dcX14+Qayly1OtlJFkSxRFOlYn31TFyV7zd6piynWtREARdrW5YBXDrZ/1nJyVSxltmdkBPnqVkpm+l3k2gbs/iF0ttKcz6byQpuBvNBmICunLqJ2J7MfS1crz/HDxziU4ncCcM2D15M9T06+G57cwDVXgTyJ2RfN7+3Ib6NDlzvP/2war7cZxOttBrF66mJqdLoKgFLVLub44WMcDqHZ4sGuZM+TgwnDP022PNiPqVLzGuzamNr/6KdPJ/FQm348lOJ6Wd67Xh5Icb2c+vlk7qrdk3sb3MkQ73qZXlAJYMOK9RQvUyJTfycndx/k+La95PI6tRW+6vJEM/DAeeHvkVNZ/+VM/p2yhH+mLKFcF3NuLFy9HLGHjhGzK/m2jNl1gNjDMRSubkqzy3VpwL9B37P4VZdzcMO2ZOVyR7fuJbq+GUpF5cjGlVdWZ+1ac8sYjnP7xFETua/VfdzX6j7mT5lP084mMFqxWkWOHj6aavzz42c/clPNm7i13q082OlBtm7eysCupnPX/CnzqVKvCgAly5QkKktUsqASwDvvjqJmrRbUrNWCceOncFN3s45X1q7OoYOH0lnH5NlKYPwemwSvY/bQ6/jB+5/RoG5bGtRty48TptHtRjP+yWjM1eW69qmy4MulGFduTGNcOfnTSTzQui8PtO7Lgim/0bizCf6mN668vdat9Kp/B4M7D2T75m2JgYhqDatz7T2deOb2JzmZjpfm6Y55Up7bGzZvwPo/zbGYv2C+xPPPHX1v4fuvQs8pf/rR17RueB2tG17HlB9n0PkGE6ytVrMyhw8dYdfO1JMGP0+ZTd0GxvOu/tV1WO9dT6ZMmkHd+sajrEDB/JS5uDT//JXc1D74+Bk/fgo9TvH4uSHE8VOoUIHEdXxk4P18MurrEO8Mz76c9dkUnmrzEE+lGHOV8cZcKa8l2XJmT/RdSjnmuqxhVVr06sBbdzxPbBr3JY7MQ8/h//6r/D9lLKXcioeB1aqaVmuNwFkintNfz+AzTDyQXincThEprqrbvVK8jHuApqGnqgkiEqtJDoQJmBLA7MDbQE1V/VdEhgHZz0DnjJk6ZRYtWjZi+coZHIs5Tu+7k9qOzvl1QrJucNd2akOXzsk7XtzVqweNGtcjNjaOAwcOcXev0N1qglkyYzHVG9fg7V/e40TMCUYMSPJdf3nSazzYpl+6779jeC+yZI1i6Oem7n7dsrW89+g7ab5+yk8zadmyMSt/n0XMsRjuvvvhxOd+/e3HZN3gOnW+JsOudqfCtCmzaNaiIYtW/EzMsRj63JuUrj5z7rhk3eA6XNuaG7rcmez91apfwagv3iJf/ry0bN2YgYP70ODKa0iPFTOXUrlxdV6c/RYnYk7w4UNJ3Y2GT3qJx9sMIFvObPT7cBBZsmZBIoQ/5v/OjC+Mye8vo2dwxwv38vSUV4mLjeODB988rXX+5ed5XN20HpMXfMvxmOMM6ZuUvfbt9M/o3LQHAK88OYLnRgxj4JOmnXrw606XaVNn06xFQxYun2a2c++kkp2Zc35I1g2u/bWt6dYldDeu9Jg8eQatWjXhjz/mEnPsOHfc+UDic4sWTknWMahzl7Z06JDcq2bgw8N5550X6NvnTlQ12fvTYtXMpVzRuBpPz36TkzEn+SRoXz4+6UWGt3mIrDmzcd+HA4ny9uXa+auZ/YXpdtdpYHeiy5ZAE5S9W3en7giH8UxZM2gkNb8ejERGsOWrmRxZu4WLH76Ogys2sXvKErZ8OZPKI3pz1W+vEXvgCCt6Jf1WGy56k8g8OYjIGkWx1jVZdP0zxO4/QvVPHyIiWxRERLBv3mr+HTUtzfWc+/N8GjSty/jfRnM85jjD+j2T+NzXP3/CDc1uJUu2LLz11StEZYkiMjKSBb8s4rvPk4K/LTs2Y8oPPyc+jo+P5/nBr/Lc14MhaL3Ke+u1a8oS/v1yJlVG9Kaht17LvPU6snYL28fP56o5L6Nx8ax+ZCQkKAeXbmDHxAU0mPYsGp/AoVV/8e9n00GEym/eS5Y8OUCEOWv+4NmBybu4zJ1u1nHc/G/MOvZPWsevpo2kW/OeZMnqrWNUJBGRkSyYs5jvPzc3Gb/OXECdhrUYO/sz4uMTeO3Jt5NlFIVi3vTfqN+0Lt//+hXHY04wvP+zic99Me0juje/nSxZs/Dmly8RFRVFZGQEC+cs4YcvTDnuDbd35uoW9YmLi+fQgUM8EfT+pA2dwMbBH3L5V0OQyAh2fjWDY2u3cNHD13N4+Ub2TV3Mji+nc8mIPtSc/yZxB46YjnBA3MGjbHlvAlV/eh5U2Td9Kft/TvLKK9y+Hqu7Z1x+vGbmMi5rXI3HZ7/OyZiTfPFQ0nXg4UnP80KbgeSPLkjL+zuxY8NWHvrxOQDmjJrC/G9mUL5OJdo+3A1U2bjwT8Y8/lGGmn/OXMYljavy8OzXOBlzgjEPvZf4XN9Jz/J6m0Hkiy5I0/uvZdeGrfT50ezvX0dNZdE3M6nfsxWVmtUgPj6emANHGD0g/VbSS73r5Qjvevl20PXyxUmv8VAG18vaLetw+xN3kbdgPgaNfJy/1mziqZuHkRCfwIePv0e/TPydAKwePJKqb9+HZI3i2N+7WNnXrF/Fx7uT9/KLUFUObtnLbwM/JmbXAUo2qcK1814mLuYkvz7wfuL3bjv1aSa2MMGxBYM/od6rdxGVPStbZ65g64wVia8r3aFOsjI4gLWfTKPeq3fRfsZzIMJzI0ey6vfUJvDhOLcvmrGIWk1q8fHcjzkec5xXH3w18bkRP43gvlb3pfv+qd9Mpf9L/Xnn53eIOxnHy/1fTvf1kydPp3WrJvz5xzxiYmK4446k77h40dRk3by6dG5H+w49kr3/4YHDefedF+nb16zj7XeE7mAXzJQpM2nRshErVs3kWMxx7u2VNOaaO39ism5w13ZqQ5dOtyV7/11396BRo/rExsVxYP9B7r4rdEewYJbMWEyNxjV5Z877pkX9gNcTn3tl8us80Lpvuu+/88leZMmahWFfmPHIumVreXfw2+m+51TGPFmzZeH9r98gKkskkRGRzJ+ziLGfmwBl7XqmE5yqsvi3ZTz1yIsZrueMaXNo3Pxq5iyZREzMcQbcl5SRM3n2GFo3NN2Hnx32Kq+9+yxDnxnIvj37ePA+k9E3e/o8rm5cj+nzfyA+PoGnh77Mgf1pG8BPmjydVq2asPaPeRw7heOnXYrjp2HDejz95CAUZc6c37i/T/oBbwjPvvzdG3M95Y25RgWNuYZMepGnvDFX76Ax17r5q/nFG3Pd8MTtRGWNot/nZjtvWraOL0OMu06Vh4Y+x6JlKzlw4BBNO97Evbf3oHO7lhm/0eGwhPw/tOITkdLAZqCeqs4XkQ+B9cCdQA9vWRaggqquFpFZwABVXex1QlusqqVFpJG3vK2IvAX8o6rPi0gLYApQBMgNTFTVyz3tAUBuVR2Wxnd7Edirqs+JyCNAQVV9ONRr03j/MOCIqr7kPT6iqrmDnwM+BNZispcigd+Asao6TEQ+8b7v2FQfnlznL0xgKvWURQry5S7n60HRtFCmJl+dElN3p84ksk32qCy+a15T8IqMX5SJLI7ZkvGLMpldxw/4rnnw+FHfNW8pnn57aht0jon0VW+Q/OurHsDTeqHvmkMi/P+dRIr/CcqvJKTdFcgGY7L7e7wC5AhD4vdG9f/8c9vxnL5r7onyd+7zjj2zfNUDaFrU32s0wM87V2b8okwmR5ZsGb8ok/F7bLnuxJnMK58dh2PT7qZoi21H0u8QZ4N20dV91ywa4ev8PSMWP++rXoAshctmrhnaf4hs2S/87wc5zpATx//9T+63/6dSuLVAbxH5AygAvAl0AZ4XkRXAcqDeaXzeE0ALEfkduA7jj3RqpiDJeQ5oLiLrMT5Oz53BZ6SLqh4APgB+xwTAFqX7hiBEpI+IbMGUCa70gnIOh8PhcDgcDofD4XA4HGfN/0UpnNfVrGKIp5YDV4d4faOg/9+D51OkqrOAWd5TB4GWqhonInWBWqp6AvgL490UeH/yeoXUWnuBM2vvY94/LMXj3KGeU9UhGB+plO+/NYPPfwN4I73XOBwOh8PhcDgcDofDcS7w/1CVda7xfxFYskQpYLSIRAAnMWV1DofD4XA4HA6Hw+FwOByOU+S8DSyp6nqg2qm+3vNkqp9i8euqOjLEa3sCKV3jykNih/cA81S196l+hwy+3/dAmRSLB6rqlMz4fIfD4XA4HA6Hw+FwOByOlJy3gaXT5XQCQF6wKVXAySaqeq2feg6Hw+FwOBwOh8PhcDgcLrDkcDgcDofD4XA4HA6H45zAeSz5z/9TVziHw+FwOBwOh8PhcDgcDsd/CBdYcjgcDofD4XA4HA6Hw+FwnBEusORwOBwOh8PhcDgcDofD4TgjnMeSw+FwOBwOh8PhcDgcjnMC57DkPy5jyeFwOBwOh8PhcDgcDofDcUa4wJLD4XA4HA6Hw+FwOBwOh+OMcIElh8PhcDgcDofD4XA4HA7HGSGqrgLRkTmIyF2q+r7T/P/XPB/W0WmeO3pO89zSPB/W0WmeO3pO89zSPB/W0WmeO3rh0nQ4QuEylhyZyV1O85zRPB/W0WmeO3pO89zSPB/W0WmeO3pO89zSPB/W0WmeO3rh0nQ4UuECSw6Hw+FwOBwOh8PhcDgcjjPCBZYcDofD4XA4HA6Hw+FwOBxnhAssOTKTcNT3Os1zQ89pnlua58M6Os1zR89pnlua58M6Os1zR89pnlua58M6OhwhcebdDofD4XA4HA6Hw+FwOByOM8JlLDkcDofD4XA4HA6Hw+FwOM4IF1hyOBwOh8PhcDgcDofD4XCcES6w5HCkg4hEishL4f4ejsxBRCLDrB8hInl90GknIufs+d3bjl3DoFnPZ81IEel/LmuGaV+KiFzos+b5dPz4ds0MxzqG+A6+nNf9xtu2X4T7e/jJubovAUSkUJj1C4hIZR907heRArZ1HA5Has7ZGw+HfUTkBRHJKyJZRGS6iOwWkZssaxYTkY9EZLL3uJKI3G5LT1XjgQa2Pj8tRKSIiLwkIpNEZEbgz6JeOPbldSKSx/v/ISLynYhUt6kJrBeRF0WkkmWdRETkS2/b5gJ+B9aIyEOWZa/HrOsLIlLRshYiUkFEPhCRqX4cr6qaADxs6/PT0XzLZ814oNu5rBmmfanAJJ81z6fjx7drZjjWEcJzXvfOs9NF5HfvcWURGWJLz9u2F4lIVlsaoRCR8iIyVkTWiMimwJ9FvXDsy76epnhj2qUi0sKmJvCbiIwRkTYiIpa1ABCRWd56FgSWAh+IyCuWZYsBi0RktIi0sr2uIlJfRKaJyDrvWN1s83h1OP7LuMCS42xooaqHgLbAX8DFgO0b5k+AKUAJ7/E6oJ9lzWUiMl5EeohIp8CfZc0vgD+AMsATmO27yKJeOPblY6p6WEQaAM2Aj4B3LGtWwRwzH4rIbyJylw+zk5W8bdsRmIzZpz1sCqrqTUA1YCPwiYjM99Y1jyXJMZhB4xDMcRP4s8nPIjJARC4UkYKBP8ua00Wks1+Dco95IjJCRK4SkeqBv3NMMxz7cqmI1LKskZLz5fjx+5oZjnX0/bwOfAAMAmIBVHUlcINlzU2Y7fuYiDwQ+LOsORIzFogDGgOfAp9b1AvHvrzN02wBFPD0nrOsWQHTPawHZuLpGRGpYFkzn7eenYBPVfVKzHjPGqo6BCiPGVPeStK6lrMk+RHwCiagXguo6f0bFkTEdYhzhI2ocH8Bx/81gePnGmCMqh70YbxcWFVHi8ggAFWNE5F4y5rZgb1Ak6BlCnxnUbOQqn4kIn1VdTYwW0RsBpbCsS8D++0a4H1V/VFEnrIpqKqHMYPzD0SkIfAl8KqIjAWeVNUNFmSziEgWzKB1hKrG+nFfqaqHvPXKgQm+Xgs8JCJvqOqbmSwXp6q2g4Ipud77t3fQMgXKWtTsBTwAxItIDCCYBBibwcmq3r/Dg5Ypyc9H/++a4diXVwLdReRv4ChJ+9Jmqcb5cvz4fc2s6v3r5zqGOq/bbrOcU1UXprh+xFnW3Oj9RQC2JiZSkkNVp4uIqOrfwDARWQI8bkkvHPsysBPbAJ+p6mrbAWcvU3MaME1EGmOCdfeKyArgEVWdb0E2SkSKA12BRy18fkhUVUVkB7AD8xspAIwVkWmqmtkZsgdVdXImf2a6pDPxIphjyuEICy6w5DgbJorIn0AMcI+IFAGOW9Y8KqZOXAFEpA5w0Kagqva0+flpEOv9u11ErgG2ATZn8MOxL7eKyHtAc+B5EcmG5SxKMR5L1wA9gdLAy5jssKswpTE2Zu/ew2SBrQB+EZGLsHzMikgHzEzdxZjZ3tqquktEcgJrgMwOLE0QkXuB74ETgYWqui+TdRJR1TK2PjsdTb9urII1G5/rmuHYl0BLvwXPo+PH12tmONaR0Of1Q5Y193hZF4HxTxdgu01BVX3C08qpqsdsagVxQoxH4HoRuQ/YCuS2qBeOfblERKZisqMGednECTYFvbHzTZiMpZ3A/cB4TGB2jPddMpsnMFUGc1V1kYiUBdZb0ElERPoCNwN7gA+Bh7xgYYSnndmBpZki8iImcB48/lmayTrB7Ab+JilACea8IEBRi7oOR7qICWA7HGeGFzU/qKrx3k1rXlXdYVGvOuam+HJMLXwRoIuXEm5LswImLbuYql4uxnywvapay64RkbbAHOBCzPrmBZ5Q1fEWNf3elzmBVsAqVV3vzWpdoapTLWpuAmYCH6nqrymee0NV+1jQLKOqm4MeC3CxqlobXInIJ8DHqvpLiOeaqur0TNbbHGKxqqq1jBNvhvke4Gpv0SzgPVWNTfNNmaPbPlhTVSda1ssHDA3SnA0MV1VrwUm/NcO4L6tggsoAc1R1hU09T/N8OH4uwFy36nuL5gB9VXWLJT3f1zGN7xGlqtYyiLyb8veBesB+YDPQ3cvqsaVZF1Pqk1tVS3m/mV6qeq9FzVoYK4D8wJOY8c+LqvqbLc0Q38H2vozABHQ2qeoBb/x1geWx7DrgM2Bkyt+iiAxU1ectaNZX1XkZLctkzWGYdUz1uxCRS1X1j0zWmxlisaqqtYxJEVkPNFXVf0I896+q+tqcwuEI4AJLjjNGRG4OtVxVP7WkFwn0wQxYL8FE5tf6cPMxG+MX856qVvOW/a6ql9vU9RMRuQ74yfM8GgJUB56yOePizbxuUdUTItIIqIypwT9gUbOBqs5Nscz2IGepqlZPsWyJqtawpBcJ/BymWXzfEJEPgSzAKG9RDyBeVe+wqPkcxjsh0CmpG7BYVQdZ1PwWE0QPXs8qqmrNs8ZvzTDty77AnSSVZ12LKcnN7Gy+YM3z5fiZhikz/sxbdBMmANLckl441jFkWZaqDg+1PBP0IoHnVXWAGJPpCK+02yoisgDoAoz3e/zjV5aUdy4YCRzGZLhUw5SG2Zzkqg8sV9WjYhqlVAdetxwk7Kqqo1Msu05Vx1jUDDX+SbUsE/UigdWqar1pSTgRkd6YLLBUkyEicr/N65jDkR4usOQ4Y0Qk+MSVHWgKLFXVLhY1F6pqbVufn4bmIlWtJSLLggZWy1W1qkVNX7OkRGSlqlYWY6T9FPAi8Lgao0UriMhyjMlhaUwZ2jjgMlW1Vh/u5yBHTDe2y4AXSG5knReTmn1ZZmsGaU8HOvk1Yx+OjBMRWaGqVTJalsmaK4Gqajp8BQaxy9SiL0+oc40P5x9fNcO4L+uq6lHvcS5gvuV96Y6fc0DP+/wHgx5mxzS++ENVb7Oo+Zuq1rH1+WloLlDVK1OMf2z/Nn3Nkgqsj4i0xPigPYbxPbJmAO+dC6pgJtQ+wQS0uqpqQ4uafo5/6mIy6/oBrwY9lRe41vLxMw64P1Q2jyW9/0TGZChEpLmqTgv393CcPziPJccZo6r3Bz8WkfzA15Zl54nICOAbjOFq4LvYrGX23dcAYzD9EKb2H1VdKSJfYoI+NvDdSBtIUGO+3gl4U1XfFJFlNoSCBjlFJHlHm7xApA1NTFZdW0w6f7ug5YcxmRI2OQKs8jIHgn8nmV7q5/EOJuPkbe9xD2+ZtYwTjAFyOVXdCIllIraN/MHsz4B3VD4f9GKCM+28me6Yc0wzHPtSUmjEe8tsk59z//jZ62VhfOU97oYx87aF7+uoqi8HPxaRlzBeMjZZJiLjMX44wed1m41E/hWReoB6Ewh9MWVqNnkN44E2HkBVV4jI1em+4+zw3Ugb0/BCxfghjlDTrOV2G0Ii0hqzbiVF5I2gp/Jiz/w9K8YXK4rkpu+HMBlwNikArBaRhST/nbS3pPcxJmOyq/e4ByYDznb36FPheYxhu8PhCy6w5MhMjmLH/C+Yqt6/fnZ/6Y3xNagoIlvxfA0s6oH/3V98N9IGYkWkG8ZkMRB4yWJJy/dBjqqOA8aJSF21020lPb4jdQcmm+mptVLMQM4Q02nGJgMwppmbMDcGF2FM2W3yDObmbqaneTXwiGXNu4FPvVlRMN4qt5xjmuHYlyOBBSLyvfe4IyZLwibny/FzG6Zk/VXMeedX7O7PcKxjSnICF1jWCEeH2ruB14GSGBPtqYA1f6UAqvpvivGPzUCz70bawGEx3Y17AFeJ8VyyNf7ZBiwG2gNLgr8D0N+GoCZ1M/7EZnlfGjzms145Ve0c9PgJLyP/v4AfkyUORyIusOQ4Y0RkAkk3qxFAJWB02u84e8LkG6Oq2kyCfA1ExHYAze8sqa4YI+2X1BhJFid5+ZYNemIGrU+r6mZvm36WwXvOiDAPcjaIyGBMyV/iOddmyQSQX1VfD14gxkfCFr5mnHglRFWA8pjMMDB+ayfSftdZa0ZgbjbqYHxyAAaqXYP7SKCHV6aRF0BVrXYr8lszjPvyN0zJZgNvcU9VtZIxGaR5vhw/z1jMDgil5+s6erqrSBr/RGIaiVjxVwqg4elQe4mqJptI8zLCrPkS4n+W1O0kGWkfE9M9zfa2vh64EbhNVXeISCmMBUGm4/nwrBCRL9SiIXkaZBOR90k9/rE5GdxGVQcGLxCR5zElajYIR1boqeL8bhy+4jyWHGeMiATXgscBf6ulri9Bmr4aZnqavpove58fju4vDYDyqjpSRIpg/A1CdfvKTM0cQClVXWtZ5zVV7ZciGJqIzZsgEfkV0xVpCUHBFlX91qJmqGM20SPDgl5TTAZIsowTVQ3VLSWzNMPht7ZYVWv6rBkOXxVfNcO0L639HtLRPF+On7lAE1U96ZNeONbxoqCHccBO2zftIjKS0Ncvm75Ovpove59fGJMl1QxzPZmK6SpopZzSK3vrDpRV1eFekCdaVRfa0AvSvQgz5vpZTJfcSLVgyC4io1W1a4pgaCKWPd5WAO+SevyzJM03nb1mqGN2pa31FJGqmMYB+TDH6z7gVvWhy2hG2P6tOhwpcRlLjjPGywLxm6NB/59omGlDSJLMl/OJ8QEKkNfTtoI3A3tvyiwpW3qe5lCMkfYlmABBFuBzktpF29BsB7yEKVMr412ch1sK8gQyoV6y8NkZkTPl7JktvNLCGzHbc3zQU3lI8nXJdFR1uoj4lnHiEQ6/tZ9FZEAITWvblvD4qvitGY59OV1EOgPfqX8zbOfL8bMJs0/Hp9B8xZKeb+soInm9jKiU1+S8ImJ7X04M+v/smE6G22wISXh8CQPjn9dTZklZ5m1MNmETTNbZYeBbkjILMx0RuRO4CygIlMOUGr6LaYKT2QSyldta+OyMiFPVd/wQEpF7MGWaZcWYowfIgynHtYKqLgd8zZg8Df4K9xdwnF+4jCXHaSMic1W1gYgcJvnsh2DKxvL6+F2yAVNUtZGFz+6A8d1oj2ci6XEY+FpVrV2owpAxsBzTYnepJnV+sTbD433+EsxAbpb61MbYCxD+6EPQI1jzKeBXVZ3kg9ZFGJ+IZ0nu3XIYWJnZM+oi0kRVZ6QIvCZi8+ZVjE9NCEl7KfYiEiqDT1W1rEXNkWlo2sxS8FUzTPvyMJALk21yHB+uX+fR8TM01HJVfcKSnm/rKCITVbWtty+V5B4mVvdliO8SgWk5Xs/CZzcEGmHK1d8NeuowMEFV12e2ZpC23xlvS1W1uvjb+W45UBtYEKS5SlWvsKj5AGbsaiUYmYbmMGAX8D2QOO6yEYAV47FWgBDjH0t6N6nq5ykCr4lYDKST1pgrSNvmxIHDkSYuY8lx2qhqA+/fPBm91gesGWZqeM2X/Z5lPqmqKiIBT6dclnSCiVXVg5LcoNO2YWY74FUR+QWTNfCT7fIFzGzhYBE5AcRi8QbWK5X8G6ibIs0+B5CD1LPsZ0tDYAbJu94lfh0smcp6s9rjVfXVDF+ceZoRwCOq+o2PmpHAXlUdcK5qhnFftlJVmz4xoTTPl+Ongl8ZJ36vo6q29f617bN4KpQHitr4YE3hSygiOVX1mA2tEPid8RbrHUeB8U8R7I9FTqjqycD4R0SiwLofTh5gmojsw4x/xqjqTsuaARP9YM9OBTI9AKuqB4GDQDdJbu1QWETKaOZbOwTGyeG4Fwo15gpg29Df4UgTl7HkOG1EpGB6z9tMBZfQhplPquqbFjUrYFqnF1PVy0WkMtBeVZ+yqOl3xsAAzCC1OWa25zbgS8vb9SNgOmZmqTPQB8iiqnfb0vR0swCtMeaZDYBpqnqHTU2/CU6zV9VyXpnau6pqI80+LMj547E0X1XrnsuaYdqXidkJPmqeL8eP3xknvq2jiKTrV2KzfDNElvgOYJDa9euri+mWmFtVS4lIFaCXqlrrDBeGjLfumPFAdYxXThdgiKqOsaHnab4AHMB0xb0fU8K1RlUftaUZpF0Zs76dgS2q2sy2pp9IkLWDqlYQkRKYIJo1aweHw2FwgSXHaZMiBbwUxlxagPzAPzZn8iQ8hpmzMbMt7/lVshUORKQ50AKzL6eo6jTLejmBR4M1MUHC4zZ1Pe0smC54PYGrVbWwBY2KqvpnWjcilm9AluNjmr2YjnMjMRlRH2AG6I+o6lQbep7mqxgvMN98eUTkOWBPCE2bwfR3MP4bvnnk+K0Zpn35EjAfHz2WzqPj51PgUkwJufWMEz/XMahsMzvm5nUF5vpVGVjsdxDPNiKyABNoGX+Oj38qYvyNBJiuqja70AUyGG8n+fjnQz/ORSISDVwH3ADkUQuWB2Euk1+Oj9YOXpDwKUwnuJ8w54L+qvq5Db0Q+tdg/GATvV/VYkMjhyM9XCmc47QJBI5E5APge/W8Y0SkNcaTyCZPqWqP4AUi8lnKZZlMTlVdmKJky3YwKztm0JHyYmHNF8MLJFkNJqXQO4YJLFmfoQvgHaPXY7wjZgEfAl0tyT2AyRp6OcRzivGXsoXfafa3qerrItISKAT0wBimWwssYdpDQ/IW37a36/Xev71TaNr0VckO7CX5etlOdfdbs6r3r5/7shfmNxovIjH44xF4vhw/G72/CPwpE/FtHVW1MYCIfAdUV9VV3uPLgWGZrReMiExPmXUaallmo6r/phj/xKf12szAK0V7mNTjH5vng/XAIbz7IhEppar/2BJT1QTMJMwHtjRSIiL3YsY7RTBB2DtVdY0lubCUyXv4be3QQlUfFpFrMWbZnYBfMA1wrCIi72IsQRpjxrNdAKvdDB2O9HCBJcfZUEdV7ww8UNXJXuTeJpcFP/BumGtY1twjIuVIqr/vAmy3rPkZ8CfQEnOz1R1L3e8g0QjweYxfg+CPkW0FYABQmqBzkeXB482YbIFeatnAW1Xv8v5tbFMnDWaLyGAgh5eJdi8wwaJe4K6jDfCpqq6WFHcimU04tms4fFVUtee5rhmmfem7L8Z5dPw8ASYr1Q9fnnCsI6bMZlXQd/hdRC61IeRNNOUECotIAZLOt3kxmVo2+VdE6gHqZfr2xeJYxOMLzHW6LcY8/BZgty0xEbkfGArsxATNBDPes9m8pD4mEHkRZvwTGHPZDDJfCPRT08XMKqo61Ps3HL/N0SLyHpDfswW4DbsBvMD49RpMyd1By8OfYOqpamUvI+sJEXkZmOyXuMORElcK5zhjRGQKMIekqHx3TFlRSwtag4DBGAPiwEBVgJPAB6r6SFrvzQTtssD7mNa7+4HNwE2q+pdFzWWqWi2QvusN6OaopU5xIrIBaGc7/TuF5gpMt5klBM2AquoSv76DH3j77h7gam/RLExZZaxFTV/T7D1PsJKYjnRVMP5ns1TVWtBXRIoBzwAlVLW1iFQC6qrqRxY1c2KyXEqp6l2ed9Ulqjoxg7eejWY4PN581QzTvhTMNauMqj4pIhcCxVXV2mzveXT8+OrLE6Z1/ApTdhc8/smtqt0saPUF+gElgK0kBZYOYcY/IzJbM0i7MPA60MzTnQr0VdW9FjWXqGqN4PIlEVmkqrUs6W0ArrS5TiE0/wT6k3r8Y/07iEhRkmeCWcvMEtOpbShJ45/ZwHA1RtvW8NPawStx7ogphauNsQWZqKpX2tIM0l6gqleKyG+YTKm9wGpVvdi2tsMRChdYcpwxYky8gy8YvwBPWPaLeFZVB9n6/Ay0cwERqprZnbVCaS1U1dpiupfdizHpXGhrNktE5qnPxoaBwaPPmnWANzH+H1kxAZCjljOzPsT4x4zyFvUA4vUcMgz3AllVgU2qekBECgElVXWlRc3JGF+nR1W1ipe9uEzttmv+BnMjcLN3A5sT+FVVq1rU9N3jzW/NMO3LdzCdn5qo6qVeJshUWzevnub5cvz46ssTpnXMTvIJg1+Ad9SiR6CI3K8WG2r8VxCR31S1jjd5+QawDRirquUs6c0Emqv9DrHBmgv8CDyk0GwHvIIJUO7CZEv9oaqXpfvGs9P8Fvid5OOfKqoa0nspk7Xzkjwb3ua9SUHgoKrGe+f1vKq6w5ZekO5jmDFtU+AtTKbdh6r6mG1thyMUrhTOccZ4J+m+PstuCH4gpkXsELXULcTTyI8poSoNRAVSXFW1jy1N4H3vRmcIxgA1N2DzQrHYu+n5AUgsEVOLBovABK/m//sUmtYu/sAIjGHlGIzx6s1ABYt6ALVUtUrQ4xletpY1RKQt8CSp0+ytBNBUNUFEdgKVvKCAHxRW1dFeNiOqGiciVr0/gHKqer2IdPM0j9ku+SMMHm9h0AzHvrxSVauLyDJPc7+IZLWseb4cP3778vi+jl4A6VXvzy+KikikqsZD4o3z6zbLjUSkDKZrWWmS36S3t6UJPOVlujyIuWnOi8nuscUmYJaI/EjysYgVs3mPmSLyIsZrKFjTWsMCjMF0HeBnLyO+MXCTRT0w57zOQY+fEGOubQ0R6QU8ARzHTB4EShttlhlWBEqnGP98alEPAFV90vvfb0VkIpDddjaYw5EeLrDkOGMkPAaLTUWkM6bMpxBmlnu2RT2AScBvwCrMRco6qvqh97+/YPdiGCAvpsSwRfDXwK7B4i3evw+l0LS6vqq6IWhwPtK7sbSZBRcvIuVUdSMkllbavml+DZMWvcpW+VswIvI8xph4DUnrppjj1xZHvcyogPdZHcD2gOqkiOQI0ixH0E2BJcLh8ea3Zjj2Zaw3MRHQLIL98/v5cvz47cvj+zqKKWN8FqhE8vGPzetXJLBQRHoCxTATJbYzmH7AlDVOwL/xT6A09CDGlNg2/3h/Wb0/PwhkK9UMWma7YUGsqu4VkQgRiVDVmSLymkU9gBgRaaCqcyHRWyrGsuYA4HJV3WNZBzANhIBywHKSj3+sB5a8a9g1BAV+RcR2UNThSBMXWHKcDb4aLAKo6o0icj0myHMUuFFV59nUxMwAPGBZI6zYnPFMh0tTlg145QU2OeZlJSwXYzS/HdO5yCYPYWYnN2Fmzi4CbG/vf4Hf/QgqeXTEeMXYvkkO5gFMNl85EZmH6XTTxbLmUEw74QtF5AugPnCrZc3eGI+3iiKyFePx1v0c0wzHvnwDky1ZVESe9vSGWNY8X46fuzG+PCUxnkBTSd4JL7MJxzqOxOzPVzHBj55Yvpao6mARmQ4swPg9Xq2qGzJ429lyXFXfsKwRVmxmvKfD7aq6KXiBN+lkkwMikhsz4fOFiOzCjKNtcg8wystAE2AfSZOKtthIkherH9QEKvk43gpmAiYzy7eJb4cjPZzHkuOMEZ8NFr3PL4+p1V6F8clZAzygFjvPiEh/4AgwEf9KtnzFm62/k9Tp7rdZ1FyqqtUzWpbJmhdhOr9kxaTW5wPetj04F5FswCXew7W2AzAiUgtTCjcbH1L7xXjkXKeqR2x8fjq6UZjtKpjtas0QPUizEKacQIDffJwV9c3jLRyaYdqXFTHeFAJMVx+aF5xPx4/f+Hy8BsY/qwJeYGLZN1BErsaYlH8OXAEUwAQotlnUvBEojwkO+lWy5SsShg61aYx/bB8/uTDZQhGYwGs+4Av1xzA8L4CqHvJBqxom8LuA5MesFfsKERkD9FFV25mgobQT778cjv8CLmPJcTYEBv7bReQajMFiQcuaE4D7VPVnz5viAWARphzPFieBF4FH8VLt8aFky2fGYTr8/YzlMi0RicbMZOfwBgDBrZNz2tQG9gAnvUypJ7w04mw2Bb0srHuBBpjjZo6IvGvT5BV4GhMMzY4/qf3HMFlg0/FhIBf0+XHAapsaITT3Aj/6qenp2p5ZDqtmmPbln8CfPmueN8eP3/i8jifENC1YLyL3YTKzclvWfAkTwF8DICKdgBkYfxdbXIExXG5CUkaE1ZItESmjqpszWpaJjMF0qP0Q++Ofipjxaj5v/wXIS1BJpSWKAtu9sccoryy3GKaTmBW8QPpQvPGPiMzFdIWzGcx6D/O78CuLpzCwRkQWknz8Y9OHLMBkEWmhqlN90HI4MsRlLDnOGDEGwXOAC0kyWHxCVcdb1MybcsZDRCqo6jqLmpuA2n7NLAfp1iP1DJqVmm0RWa4WOxOl0LoFU/5RExMUDG6dPMqmYbiYlqzNApk1Xlr4VFWtZ1FzNHCYpLbUNwL5VfU6i5pWOyKF0AuZ2q6qo0Itdzgcjv9nvKzQPzCtxZ/EjH9eVNXfLGomGncHLStk8yZdRDZgynxO2tIIoelrNo/tTKEUWh0wpePtMeW/AQ4DX6vqrxa1FwP1AvvSswWYZ7nKYBqm9C4w/ukONFLVZhY1l6nXHdIPRKRhqOWqatv/FRG5FrNtIzCT/VYbtTgcGeEylhxnhJfpUd4zWfTLYBFMlsurmFbmrUSkElAXsBZYwnSi87NeOxxmgBNFpI2qTrL0+Yl4wYZRItJZVb+1rZeC7MHlWqp6RExrWJtcrqqVgh7PFJE1ljUn+TmLpaqB2c9SqrrWD83zAS8joo7Nmw2HI7MJFQA5l/DGP9er6gBMZqhfHoXlROQdoJiqXi4ilTEBiqcsav6OCZ7tsqgB+J/NI6ZFPPjYoVZVxwHjRKSuqs7P7M/PgKjgAKGqnhT7nTCLa1LnMjAd/663rDlZRO7CVDhYt69Q1dmezUJ5r5oiJ8Zo3w9ewdwD+dKoxeHICBdYcpwRqhovpmWyn612AT7B1E4/6j1ehzEQ/8ii5lFMmc9M/Cvz8cUMUEQOYwJWAgwWkRP4N+tRQ0Smq+oB77sUAB5UVZsGukdFpHrAH0JEamC/Q8lSEakTmMkWkSuBxZY17wEG+LU/RaQdpkwjK1BGRKpi0t2tpYKL6S6zXFWPishNQHVM6+2/bWl6ug0wA8iRnjdZblslGqqaICJvAb7NvgYQkctJ3fHKVsak7/sy4DfibeMKmHKiyTa9nUTkZeBjVfWt5E9ErgN+UtXDIjIEs22fsuyRs15EvgVGBsq2bONnhq83/mlg47Mz4ANMM4j3vO+xUkS+xG5gKT/wp4gswn6ZzyWYZjD5gXZByw9jPCAzmyUkjX/A3w61d4vIHynGPy+rRV9LYLeItA9UFXjZU7Yz8aeKyA3AaO9xF2CKZc1u3r/B3X6t7U8RuRO4C2MFUg5j9fAuxr/PNn43anE40sWVwjnOGC9zKAsmsJPobWBzwCqeOXhwqqvtMq5wlPmE0wzQL0KlK4dKgc9kzVrA1xg/MAGiMTPPSyxq/oEZMP/jLSoFrAXiMMGe/3vjRRFZgvHcmBX0u7RajiciK4EqQGVMwPlDoKuqhkxLzyTNoZig7yWqWkFESgBjVLW+Rc2XgPnAd34NHr31bIQJLE0CWgNzVdVKp7Yw7cslwFUYA+R5mLLck6pqrZuYiNyByW6JwkyQfKWqB23peZorVbWyFwh5CuMX+LiqXpnBW89GMw9wA0md0j7GlPlYMe5NK8PX5uSPlzlUEuPPEzz+sVnKHY7xj+9lPn5n84hIdg3RoTblskzWDDX+sVrCJSLlMN2cS2DGP/8CN6vF5iXe5GUukn6XkST9Xs6Jki0RWQ7UBhYE/S4TTf0ta3+CCZhNxodGLQ5HRriMJcfZUNX7d3jQMqumjpiMk0KeDiJSB1OKZw2bAaR08NUM0MscaprRskwmUkSyqdchzSulsmqkraqLvHT74A5ttrtPtbL8+SHxyiRKk3wG39ZNT6yqHhSR4GW2TTPjVFW9WdcRqvqRiNxuWfNaTPbQUgBV3ebdRNukF6ZJQbyIxOBPNmEXTKBnmar2FJFiJHlk2CAc+1JU9Zin87aqvuDdIFhDVT8EPhSRSzBBl5UiMg/4QFVnWpIN3NBdA7yvqj+KiM0MF9R0ZfsA+MALTHwJvCoiY4EnLdzIhqPdd3aM6XHweEcBa4ElYI8XHAiMf7oAViefbAaQ0uFaEVmNySb+CRNw7q+qts5Bv2Iy+TJalplEiEgBVd0PiWV5Vu/JVHUjUEeMtyTqQxdXVbV9fUyFV6p6DanHP7aCLSfUlBUG9KNIavRjm83eX1b8adTicKSLCyw5zhhV9ctXKZgHMIaH5bwBeRHAigmyiIxW1a4isooQFwnLmSbDLH52ImI6luUCCnup2MEd2kpalv8CmC4iI73HPQE/gni1SBpwVBcRayUTALZLs0IhIh9jBuOrSd7Jx9ZNz2oxbakjRaQ80AczMLfJYREZBNwEXC3GjyiLZc2TXgAkcGOXy7JeWAbmJJWIxYlpE70L06TBFuHYlyIidTFmsoEglnVfDO+mp6L3twdYATwgIr1U9QYLkltF5D2gOfC8iGTDZBFZI+jGrifmXPsy5nx/FSYDrkImS/6OyT71LcNXVf3yVQqmN/A+UFFEtmJuKK1k2InIXFVtIEnl8olPYT+w3UJVHxZjTPwX0InkBtCZgoS3Q+3LwHwvO10wwfynbQp6v/3OeOOfQCBEVYen87b/RyYAx/GvK9xsERmMOY6aY7oAT/BBF1V9Ir3nReRNVb3fj+/icIALLDn+/1gNNMRknAimpMjWILmv929bS5+fJj7OEvYC+mFSo4NLGA8BI2wKq+rzXglMICvqSVW1WnufVskE9kzRw0UdTW4Ybpv7Mb5nJ4CvMB4KT6b7jrPnekyHvdtVdYeIlMKU+dhktHeTnt/zVbgNk5lhDTGj/+5AGVV9UkQuxBiiLrQou1hE8mPWbQnGoNhmaUo49mU/jAfH96q6WkTKArayhoDE8vG2mFbYzwTtw+dFxJbpfVdM1uRLqnpARIqT3EvGBusx2/JFTW48P1ZErragF852336iqtrMC2hHqPHNKmNJqIH3bzgC24Gg8jWYUuOU2bCZRUtMh9oLMCbIAQ4Dg20IBlDVT71y3MAEbSe170c2DpPhv4Sg38k5yAU+Www8gpmcWIUZU0/ClHP/F7BWpu9whMJ5LDn+rwjlwWPTl8ebef3Z7+wsr8TvTeBSTHprJHDU1iyhiNyvqm/a+Oz/EmL8jvwumfAdEfkIYwTqi3Hu+YAX4LkAk2nSAhPYnqKq0yzrvoOZdW2iqpd6mYVT1WKL6BT6pYG8qrrSokYu4LgaU2RfjLRT6EdgTNiteAAF6fQERqvq0RDP5bPhtyQin6lqj4yWZaJeJPCon1kQ4fABCgdpjH+WqGoNS3qRwGpVrWjj89PRfRZTdhyD8a7JD0y05Qsm4elQG9AuSvIGCf+k8/Kz1bLqe/hfQUSeB6arT11x/8vYvD9yOEJhNR3a4cgsRCRaTAevHCJSTUSqe3+NsJiyrKZlcoKI5LOlkQYjMJ0t1gM5gDuAtyzqvScifURkrPd3n4hYLUURkToiskhEjojISRGJFxGrN3YklUz4iohcJCLNvP/PIfZ9eT7FpNmvFZGVIrLKyw6zgojUFJHvRGSpp7fSpp6neVhEDnl/x73jx5rfmheMnKSq01T1IVUdYDuo5HGlqvbGpPbjeXJY9VIQw00i8riq/gUcEJHaFiV/AbKJSElgKtADY+JtDRH5UkTyekGt3zEZL7YzeW5KGVQSkekANoJKHpel0IsErAQiIPGa6WuWrxdA+hPI4/39YTuoFCpTyFb2kIhUFJHOQD4R6RT0dytBQYnMxtuXa70MQl/wgrwTgHpATS+4fAzoYFF2uoi8IiKLvb+XbY/5RKS9iKzHlDPOxpT8TbapCfwqItYNpVMiIg28oDoiUsTW7ySI34DvRSTGGx8ctjm2FJG2IrJMRPb5oedw/JdxpXCOs0L8a/EbnLL8Mkm18IewnLKMKQNZJSLTSN79xVrHGe/zN4hIpDe4Gykiy0jePjUzeRuTfv6297gH8A4moGWLEZjOQWMw5qs3k/neGynxvWRCUreivQD7rWg/wuxDvzwGvsCU1/ill6xEw8sm6gDUsSy7VERqqeoiyzrBxHrBgICvUxHsb+O3PY0mmOYMh4FvMf5kNghlpL3CklaASqp6SES6Y27oHsGUiGR6CZ4YL7uc+OhlJ8azKuD7EbjJEeAkxqfHJvNEZAQ+dYwVka6Y/TYLs45vishDqjrWhp7Ht6Q2dx6LnaDdJZhgXX6gXdDyw8CdFvSCKYDx0FtI8n1p5Zrpebu9pUHd0bxgbKosv0zkI0xwuav3uAema2Mni5pPYq5XP6tqNRFpjPGYs0kD4FYR2YwZ/wT8sqyVjUlQJ1XMNs2C8cqyWaL1ClAXWOVTdvprmGPFLz2H4z+LCyw5zhjx0a9GTWe2UWFKWf4Ou51eQnFMRLICy0XkBYwpqc0Mw1qqWiXo8Qwfbuz8Dp6BT6boKeiN14oWQFXXe+nvNtmtquMta4RTLxneYO4HbxD7iEWpK4HuIvI35kbH+sAceAP4HigqIk9jTF4fs6gHJkuquvd7RFX3e+cjW4ikNtK2nVGdxcvK7IjpRBcrnim7BXz3slPVZ4FnReRZVbV5Tg1FVe9fvzrGPoq5hu2CxODrz5hAT6YipqvoZXjZQ0FP5cVS9pCqjgPGiUhdVbXpdRYK2+eaUEz3MrS+8+lGvZyqdg56/IRY7hCJ6aS6V0QiRCRCVWeKyGuWNVtb/vxQhKOT6r/A7z4GefzWOx3Kh/sLOM4vXGDJcTb43uI3HHXwqjpKRHIApVTVlsFqSnpgbqzuA/pjOjJ1TvcdZ0e8iJRT044WMUa28Rm852zxO3iGqs4W0zo9kHmxMHAzYpFwtKJdJiJfYkoKgjOzbAVIh4rIh8B0n/RIcVMXgTkfHbel59HS8uenQlW/EGPy2hQTyOqoqn9YlvU7S6ofPhtpA+9hyk9WAL+IyEWYQE+mo6qvA69LGLzsVHWQV2J4Eckzi3+xKHu7qm4KXuDtU1tEpDiP78XetSRs2UNhCCoFrpkXAeVV9WcRyYn97om9MB2A40UkBvud6GJEpIGqzgUQkfoYfyebHBCR3MAc4AsR2YXdrCxU9W8RqYLpzggwR1VtTyD63kkV2ATMEpHJJB+PvJL2W86Kh4FJIjLbJ73TYUO4v4Dj/MKZdzvOGDFtUvuoqm8tfsOBiLQDXgKyqmoZEakKDLdZPuXp+hbMEpGmmDTlTZhB3EVAT1W1dnPnDVZ3Yvxi+gP5MGUw1i6EIUomrgKslkx4QbMDmFK/+zGtaNeo6qMWNUeGWKyqepslvc8xhsurSQpAWNPzNIPXMQ4TJPjAZqBQ0vAasWy46qv5svf53TGd2qoDozBZUkNUdYwtTU83p6oes6mRgX6UqsZZ+NwmqjojRTA0EcsB2OcwJcdrCMostlz+67fJ9ItAZUxHSjDH7kpVHWhDz9MMR/aQ7wSXcqtqOREpD7yrqjZLuX3FG9ONwoxBBNgH3KI+NCzw9Lp72l+o6l6Lmn0xwc/A+eZa4H2bwW4RGYDJmmkOPIvppPqlZc2hoZar6hOW9KbiWWYQNAFjS+90CHUudjhs4gJLjjNGRGZiUt7P6Ra/XrZAE2BWoO5fLHfXCEcwS0SyYWZjAdaqqvV2tF7GUkVMZsRaVT1pWW8F0DxlyUSKMsDM1ozAlPckdhIDPrSZ6ScihWwOUEPorVXVSzJ+5f83IrIKc6wKpuylDOa4vSzdN56dZrKBoZdJtEpVK1nSi8B4f+wjKUtqus0sKa8M7iNMZ7ZS3qx6L1W916JmMeAZoISqthaRSkBdVf3IgtYTqjrU74Cvp70WqOzT+TxQJvYCxnMtQF5MAN/m76QzSb4tc1T1e1tant4LwFOYzJafMIGt/qr6uUXNMqq6OaNlmay5HK+UO2j8s0pVrZpAi0h74Grv4SxVnWhTz9PMC6CWu0MG6UVjtq0Ci1R1h2W9lZhz3FHvcS5gvuVSbkSkOf52Ur1CVVfZ1Eih95/ttucCSw6/caVwjrNhWDhExT/D8ACxqnowUMrkYds8dxhmwDELQFWXi8VOGmK8RnoRNJATkffUYrtvEbkGY2K9ETPgKCMivVTVZmcUP0smAnQEPlXVDyzrBPObd0MwEtO23fYMwq8iUklV11jWQUQeVmPu/CYhSgrVoql+ypspEamOyUDLdCS1+XLgBGTVfFmTm+f+aUsnBa9hygzHe99hhYhcne47zp5PML+PQObgOozZdKYHllQ1MIN+hxo/OT/ZhDHMtR5YIrxlYt9iDLX9ooWqPiwi12KyJTthuhtaCyzhr2F4AN9Lub0su1qYphC7y/VoAAA4H0lEQVQAfUWkvlryChPTAW4o3vjHK2karvY6NSIidwCPAzNIMpwfrqof29L0dILPP/EkXVfsCIo8AHxjO5iUgre9idJPMFlg1vajxyQRaaGqUy3rnAlW96/DkRIXWHKcMWq5nW8oxEfD8CBWi8iNQKSXBt4H+NWiHoQOZtkczL2D/13hXgYaB0rfRKQc8CN2W+7+JCJTSF4yYbvFbzvgVRH5BXPj+pONcpsUVACaYdLO3xCR0cAnqrrOkl4djFeWH91mApkziy189mmhqktF5EpLnx1O82W/zXNR1X9TnO9sB2AKq+poL4CHqsaJiG3NzSLyE+Y8MMOnbXsM89tM6X+W6QFY9dlkWkTmqmoDETlM8uujbU8eMNdLgGuAMSGu15mGhMEwPIjZIhIIcDfHBNInWNZsA1RV1QQAERkF2Gzs8TH+d4V7CKgWyCwWkUKYcaXNwNJIYIGIBLL5OmIhkJ6CPMBUEdmHOe+NUdWdNgVV9SoRqQD0BJaI6Wj4icXAzz3AABE5AcTiz/nnVHk93F/AcX7hSuEcZ4yI1AHeBC7F+OREAkdtnkxF5A98NgwXY1b5KMlLmZ5UVWtGwSLyEcYI+RGMaXcfIIuq3m1Jb0XKcrBQyzJZc5Gq1gp6LBgzbVstzQM6nTBtd8GHkglPMwumI8v1nvY0VbUZtAvWboyZRc+FMSp+JLNv+sT4ZaVCVf/OTJ1w482+BojAZA8UUlVrpt5eadqNQBlVfVJELgSKq+pCi5qHMcdLHEk+INYGyiIyFtMiegSm815foKaq3mBDz9OchTm3TlPTAa8O8LyqNrSomROT0XMD5tiZCHytnmmwJc1bQi1X02nVlmYFzMREMVW9XEQqA+1V9Slbmn4jIs9iPGpiMNnF+YGJqprpgWYR6YAJArTHy+rzOIw5fqxNdIWplHsl0EhV93mPC2LK4ayUbInIclWtmtGyTNb8FbOOJ73HWTHrWM+WpqdTneTjn2U29YJ0K2PGP52BLarazAfNSMzv5g1MYwYBBqtFTzu/8LLsBmHWrygmsL4LGAc8p6oHwvblHOc1LrDkOGNEZDFmgDwG05HpZqCCzdl1CaNhuFd/r6p62ActX4NZIrIUuE6Td4Uba7M2W0TewZiEj8ZcFK8D/sG0ibZiaOuVE24PbEcxBunFVPWvzNYKoZ0FaIWZRbtaVQtb1CoE3ISZed2JmZUcj/FEG6OqmVJWKSJ5VfWQN/BPReDGIDMRkQmkk72ndn3Igk1BA4bh31oOMr+DKb1toqqXikgBYKrtAKyfiEhhzMxqM8z5birQV+0a2VbHTIxcjslWKAJ0UYuGvSn0C2DWubuq2u6y5SteKdFDwHvqgy+hl+26RVVPiEgjjN/Rp7ZuriTJh+xP4KCqxovxq8mjFn1y/MoEC6HrtxdiN+A5TGdIwZSoPaKq31jSm4/xAAvuCveSqta1oedpfApcgQkEKNABWOn9Weko5gXPVwfGsN6Y9lJVXZDZWiG0ozFjvBswvxNrvk5eEKsnJptwGvCRl11cAuMpFXIi7Ax0Kqrqn961JBWqujQzdNLQnoIpoxwVOOd42/gWoKmqtrCl7XCkhwssOc4YEVmsqjVFZGXgIiEiywIDSUuavhuGi0gtTHpyHm/RQeA2VV1iS9NvJDxd4UIZ2QZQtWBo6wVD66WYJZxn8yZdRAKZSo0wnlmjMYEBa+VwIrIO+AwYqapbUjw3UFWfzySdiara1iuBC5haB1BVzfQW4yISyCjpBEST5GnSDdipqv0zWzNI+zpN0Rkt1LJM1lzqZdQknlttZxN6GgUw3XwSS23Ubot63xHjF3MJ5rhdqxY95YI0G2LOB60w5ZzfqPEHymyd0araVZIM55Nh+cZukarWSnHMWssAEeMnVxPjvTgJc7N+maq2saHnaVod66ShGQ7D8FReiBhjfasl5CJSHOOzBCaT2WbAriqpu8LdqqorLGqG7FwWQC10FBORZUD1QLaZFyBdbHkC8V5MiWERzCT0aLXsxegFtj/ETI7GpHiuh6p+lkk6H6jqnd59SUpUVZtkhk4a2mk2TEnvOYfDNi6w5DhjxHjGNMOcwHcA2zEXY5vlUyHLFNSi35OXlt1bVed4jxsAb1semNfEGPeWJrlJuU1N37vC+U0aKe+2S/6+wngLTPZrm4qIpBg85lafOt34RSCwndGyTNYM1UbdatcVEVkA1MN0DaouppPhVMsB/Dsw5WgXYPzs6mBmeq0MlL11upPU5ztr3dI8XV8bQYjIXxivmNHAePW6M1nSKq6q2yUMZaoiMhm4D5MdWV1EugC3q2prS3qB4OtDwHFVfdOHSa6XgPn46EMWuH6JMQxvCzwA/GL5+vUn0FZTeCGqakWLmp8DszGlWn41EAhk8PjWFc5v0hj/JE4MW9J8FhM8X25LIwP9AsCFfmWi+oWITMVk949Sz7NKTKfTWzGdj62XGjocoXDm3Y6zoQfGZ+Q+oD9wIaZ+2ho2A0jpEB8IKnnfYa6I2DZf/gJTSrAK+x3oArXoLUm6yWomIlbSsYM0ywD3k/rGzlr2GbBbRNqr6njvO3QA9ljUQ1W72fz8NPhCRO7GGCAvAvKKyOuq+mJmiqSVAh7AZio4kEtEyqrqJu+7lMH4AmU6XtZZG6CkiLwR9FReTEmcTd4AvgeKisjTQBdgiGXNvphsgd9UtbEY8+BnLOqNA+ZgBsq+dE2T8DSCqOzXTat65eKq+rd3wxGc/bEr7XdmCr0xnQsrishWYDOmNNcWsV751C0kdaPLks7rM4NemMBOvIjEwLllGB7E4UBQyWMTxtvJJh8BV2E6pZXDBGN/UVUrRsQikh9j5VAaiApsU7XYYdSbPHwUkx3uy+QhsElE+mD8z8AYsW+yqId61hgiUpTk2a//2NIU45/XHrNdlwC7RGSeqj6Q7htPXyddc3e16+V0PcaDdba3bcHYHownyYTe4fAdl7HkOCvEeNSUUtW1PumFwzD8NSAHppOYYk7ox/FKcGzcPIvX7SazPzcdvUmYdUoWyLKRjh2kuQIzgEypaTP7rBwmaFfCW7QF6KGet5QlzXAcs4GZ7e4Yk+BHgCWZPWgNSgHPjilFWYG5waqMSbG36VHRCnPzGly+2UtVp1jQqoIpwR2OaREd4DAwU1X3Z7ZmCv2KQFPMek5X1T8yeMvZ6gVKmZYDV6rxrlmtqpdZ0rNqlJuGZjgaQWTHGCFfRvKbLGuZWSLSFXgRU4YrmBv2h1R1rC3NIO1cQIRa9iUUkUrA3Zisuq+8IHNXzaSS3/8K4qNheJCm716Inm4kJhjaGLNvY2xlSYkx0v6N1GMRmwb3awkxeWg5k7AoZqKiCWZfTgf62Qw0i0g7TGOGEhhz6YuAP2xdSzzNZapazcu8vVBVh9rIzJIkO4eimKziGd7jxsCvqto2M/XOBBG5xeZx7HCkxAWWHGeMd8F4CciqqmW8OvXhNjNOJDyG4en5DFmpoxbjedQNc+EP9pKyNYizmg6dhuYCmwPiDLRzA6jqER+0wnHMrsYEQr4ERqjqbJslfyLyHTBUVVd5jy8HhqlqFxt6QbrZMKayAH/aLjUUkQs0tWfVJbYD64F0fpLPbNs0Bv0eY37aD3MTsh/TldKKZ42IPIUZiE+y8flpaPreCMLT/BPT5W840B1zk9XXouYKTGnELu9xEeBny+VT+QnKAAkst5kBEg5EpD3GWBpMR6+JFrXCZRgeDi/E6Zjs0/mYTMa5loMfVsuZ09D0dfIwXHjnnyaYc041MV1qb1LV2y1qrsI0vhkFPKqqi2yOcb2ytFsC1xIx/mCfqMVusadKOI5tx/mNK4VznA3DMLNmswBUdbk3U2gVVd0gIpGqGg+MFGNIaO0mXVUb2/rsdOiJuWHOQtJslgK2Umsni0gLVZ1q6fND8boYA8upJA+e2SyfCmhYDyil0PP1mAXew3QsWwH8IsZnxWYJziWBoBKAqv4uIpda1AtQHuMLlh2o4pVv2ixlmi4ij6nqaAAReRCTgVLJlqCIPInxTdhIkgmzYgbrVlDVa73/HeYF1vNhzIJt0RcYLCIngFj8KSsqDKwREd8aQQAXq+p1ItJBVUeJyJeYG2ebRKS4Kd+LKWG3ySRCZIDYQkwXr2EklRUFjp9Mbx4QpPkcJqPmC29RXxGpb2vCQFUTROQtDfKNUuPRZc2ny9PoafPz02AlUAPTsfEgcEBE5msKI+ZM5DMRuROYSPJzQaZ3NQ1iqIh8iE+Th2EkVlX3ikiEiESo6kyvCsAmwzGdlOd6QaWywHqLehemmKDYCZSyqHc6WK+VdTiCcYElx9kQG6LG33YK3DExnbyWi+mQsh37g+RwUEv97erwG/C9Nyvq143dFRifriYkD55Zu2EOE74fs6r6BiblHQAR+QeTnm2Lld4gOdCdqDte22RbeEHJRpigziSgNTAXux45jYD3ReQ6oBjwBya4bpOuQDm13OI7LWyWpgZp5Mn4VZnOsDBoBrrOHfCy+nZgyihs8pOY1tRfeY+vx/xebJJdM9nPJAM+wvg8LsEnjy6M51pVVU0AEJFRGC8gmxMG00WkMz4ahocD9Tp7ikgeTFB9JKYDaDZLkicx5aKPkjx4by0wif+Th+HigJch/gvG+3EX9oOhYzAZ4oHHm7Dr/zo9xDn2Z4t6p8M5e55w/DdxpXCOM0ZEPsLMtjyCOWn3wZRL3G1R8yLMbEBWzEAyH6ZD24Z03/h/hpd+/qJabssapLcZ6ACs8mvAKiIbMB4nYblh9ovz4Zj1vGPuIaks5BfgHVU9blFzFVAFWKaqVcQYFH+uqs1taXq6vTE3jwnADar6q2W9b4F7bJaC/BcQkZKkNrL9xaLe86o6MKNlmax5B/AtJqj+CZAbeFxV37Wl6el2AgJlN3NU9XvLev2BI/iUARKOsmox3WIbBdZJRApiyuFsdtg6jCkRi8f4LPkxAeQ7InIfxgusBibzdg7muJ2R3vvOQm8TUFtVrTbySKHpe0t4ESmjqpszWpbJmrkwx2oEZsIpH/CFqu61pRkOxHRqTBz/2D7HnipiuTumw5ESF1hynDEikhMzw9MCM8CZAjxp82bS0/XVMDwciDGWLYfppnOCpAGkrRrxXzCDZOtlC0GaPwB3+X3DLP63GM+FMR4NzGxHAtlU9ZgtzfMBEVmoqrVFZAkmG+swxq/GZhvsn4FtmCD6hZhMiV9UdYBFzZqYrmm/41/Jlq+IyPOYWd41BHVos7mOobwnwuE15wciEg1ciQmGLrLpyePp9QaeBg4QlAFiqzTNK0uLxGR7+FJWLaYL3XPATMz1+WrgEVX9xpZmOAhTMGIAJpi0RFVtd90MeOR09POa7PfkoacZ6py3RFVrWNQsA2wP3Bd44/diqvqXLU1HEiIyQlXvC/f3cJw/uMCS4/8KCYNhuKfrdzDiolDL1VLHEBH5BJP2PZnkA/NXbOh5mrMw3cMW4dMNs6TRYlztthX+DWgW8HXy0sKnqmo9W5rnAyLyNjAYY4z+ICZDYrlNTxAR6aiqPwQ9jgQGq+qTFjVXYzyzfOue6DdiOiRVVsvm657WPZg22+WA4KzBPBgD8e4WtZ8BXlDVA97jAsCDqjrEouYdmE6GMzABkIaYa+bHFjV9zQCR0A02VC001kihWxzjswSw0HbAztP0zTDc0/M9GOE3YpoVXIYJEgaPRWyOC3ybPBTTVfQy4AVMJ7oAeTEdIm12aFsM1Atkpnu2APNUtVb67zwrTd+DoeFCTAOTzqS+Pxkeru/kOL9xHkuOM8abSR9M6hOazRnfYfhsGJ5WMAKLXi62AkjpsNn7y+r9+cFQn3SCqYnPLcYxfiOJZuGqesTL9rOK38FQPxFj7Pasd4P+roj8BORVVau+Tqr6g4g0AMqr6kigAEm+UrY45nlmnctswniNWA8sYTolTgaexZRxBzhsq1QriNaqOjjwQFX3i0gbwFpgCXMjWS1QeiIihYBfAWuBJUzAzrfsDw1Dgw0R+RyYjSnR+tMnTd8Mw4OCEfm8UsoAeTHNEs4lfvD+/KSVj1qXAG2B/EC7oOWHgTsta0cF2x2o6kkvuGSTb4GUndDGYkorzzXGYQzul+DP9dPhSBcXWHKcDV9gBq2+dH7xCIdheDiCEb6iqk8E/t8z8M6tqja7iKGqsz1fnOAZX9tlcb9jTEB9azEOHBWR6oGyDBGpgfEcsEY4gqF+oqoqIpMwXjX4lVbvGYbXxAzUR2KCsJ8D9S3KzhGRZ4Hx+Nw90TYi8ibmuDyGMbdP2SEp0zMGVPUgcFBEXgf2qeph77vkFZErVXVBZmsGESki2QKZWV5ZiC1D4gB7MTeQAQ57y2xyFLM/fckA8a4jzwAlVLW1iFQC6qrqRzb0PD7C+AC9KSLlMMbdv6jq6xY1/TQMD2cwwldUdVTg/70swgt9mKT4W0SqYI4hMAHKFZa0xgHjRKSuqs63oZEOu0WkvaqOBxCRDoCVTMbzLBga4AJV9TNI6XCkiwssOc6G3YGLhY+sFpEbMQP08hivE6vmuYQnGOErYtpe340JQiwC8orI66r6okXNrphOLLMwaeBvishDqjrWlibhaTHeDxgjItsw6xmN8ZOxiS/BUBGZQDqBXcvbdamI1FLVRRY1UnItUA1YCqCq28R0LrJJwHizTtCyc6V74mLv3yWYwFkwtgP575B8VvtIiGWZzReYDkIjvcc9gVHpvD4z2AAsEJFxmG3aAdPF8QGwVu78A/5mgHyCCfQ+6j1eB3yDCf5YQU3b9F8wEyONMdfPywCbgSUwgZ5AZl0+WyJhDkb4ileW3x5zT7QE2CUi89RiZ0MR6YsJ0AW6wH0uIu+r6pu2NIFrvdLqGOAnjBVBf1W1mXV7N6Yb3AjM+Odf4GZLWr4GQ8U0EAl1nbLqiZqCX0XkClVd5YOWw5EhzmPJccaISFOgG6YzXPBNurV2qRIGw3Bv1rUq4GcwwldEZLmqVhWR7pgbq0cwxpk2O9ysAJoHspREpAjws6pWsajZMNRy2341IpIFM+gBWKuqsem9PhP0xgB9VNVqMDSt7RnA5nYVkT+Bi4G/MRkS1gdzkmQYvlRVq4sxZp/v0wDynEVE+qbM9Ai1LJM1l6tq1RTLrJt3i0hroKn3cJqqTrGsl27JcXC26v8rIrJIVWtJUAekUPs3kzWnYzq0zccYTc+1nXErYTAMF5EXgKfwNxjhK4HjxvMju1BVh9o+F4jpKlhXVY96j61fS4LGeddiAjAPYLLsrI25grRzg7EC8EHLl2CopOGFGsAPSwsRWYMZB/nS6MfhyAiXseQ4G3oCFTHeGIFSOCVpBibTUdO141GSZib9YJiPWuEiixf86AiMUNVYEbEddY5IMRDfi2lJaw3bAaR0qEWS31F1EbHtd+RLZlYYtydAyzBojhaR94D8InIncBvwoQ0hEblJVT8PZJakxFKmSbi4hdSZHreGWJaZbBKRPpgsJTCG3pss6gGgqpMxHk++EI7AkYi0BZ4ELsKc8wI3O3ktSR71vKPU06+D8R2xyUqMZ8vlntYBEZmvqtbKnFX1Ky+7JlA+PlDtG4a3UNWHvWDEX0An4Bfse8v5SZQYI/au+De2FJLK1PH+X9J4bWaRxfv3GmBMCFuJTEdSmEsH9NSuubQvmVl+BI5Ogdbh/gIORzAusOQ4G2qp6iUZvyzzkDAYhof55tkv3sMMGlcAv3gzMVY9loCfRGQK8JX3+Hos33B5NxxvApdi/HEigaMWb3jC5Xc0zOJnp8IrS30WqESQl4Faai/ufbbvgzpVfUlEmmN+G5cAj6vqNEtyubx/bZfahQ0vA+NGoIyIBJfC5SWp3McWdwNvYIyzFZN5e5dNQc/343mgKOYm0nbAJVy8hglArPLJm/ABTCllORGZBxQButgUVNX+AF4p7K2YUrxoLHpmSRgMwwlDMCIMDMdkv89V1UUiUhZYb1lzJKZE9XvvcUcslm56jPcyfWOAe7wscWvZ/h7hMJf2NRgajnFlEIdPcZnD4QuuFM5xxng+ES+q6hofNdcSwjDc5k1mmC8aYUHMyDFSVeMs63QCGngP56jq9+m9PhP0FmPa04/B+BDdDFRQC111gjT/4Bw3fxeRuZguf69ivA16YjLSHg/rF8tkROR5VR2Y0bJM1IvElDS+auPzw40XwC5DiA5twErb5x+/EZENQDtV/SPc38UmXvl404DJtE+aUZhgr+BPufF9GOPlGpib1zmYa9gMi5qNPc2rMJMV1g3DxTQOuBYTjKiN8a+ZqKpX2tI8XxCR6iQf/yyzqBWB8en7EzioqvFe+V0em1lvIvK7ql5u6/PT0FytqpeJyIfAWFX9SURW2Cr5C8e4Mkj7L+BCYD/m3Jcf2AHsBO5U1SW2v4PDEYwLLDnOGO+GuRw+1vaKyFxVbZDxKzNVM2wXjXMZESkDbA/4Y4npkFRMLXb4EpHFqloz2D8h2JfDkqYvfkcpNH0NhorIElWtISKrVPWK4GU29MJFwFspxTLbXhwLVbW2rc//ryA+d4gUkezA7RjD5eAsu9ssas5TVZsdBP8TiEgtTCncbJKX4lop3/QCsNeQOpPZWrmoiAzABJOW+BkA9dY12DA8RlUrWtIKSzDifMC7Rq/WoK6UwKVqsSul7bFOGprvA2+qj+bSfgdDwzGuDNL+ABM8m+I9boEpPRwJvO4CwA6/caVwjrMhHC0uh3qzEL4Zhnufv0FEIlU1HhgpIrZa/J5PjAHqBT2O95bVCv3yTOGYiGTFtMJ+AdPpz6qvE+HpRDeCEMFQi3onvJuQ9d5M/lYgt0U9XxGRezAePGU909UAeYB5luXniemo8w3GpBwAVV1qWdc3ROQ64CX87RD5GeaGuSWmFKY7YDuTaLGIfPO/9u493M6quvf490cqNyEEFQWOikCVECCkJNEgYAHBIy0XEWjlogU9VjjFIKd4joqtCF4ocqkH7wIpQlCIUht4TNAnCgESigkJoUHS2gMoCtUiYEwClvA7f8y5yMrOTraSNefMXnt8nidP8r5774wZ2Hut9x3vHGOQJqbVGnjxOlIfqVfY3lvSeOBo258oFRP4JGnK3pakxHZpN5FKetbayVyS7YtrxOmmdRuGTy6ZgLX9nKTPd98gOzWbXrGBLwu/mxZTKedIOg64seIO6gOBUyVVeQCdr0NuIk0c7iRDV5KmYZbS4rqyY4rt5yfe2f6upIttvy/3twqhqtixFIaV3GNgLLCUrobhhZ8yzwUOIzXpfYz0pnFqqW21I4UGn8pUbLty/vt3IW0R3hw4mzSu+Qu2f1wwZvVJdLWfoOUdCj8iPRm8gNQj56KST19rkrQdsD2DlGzZLtoLKJcVDWTbh5aMW5PaTIhc5DQJaont8UrDC263PaVgzGmDnC79/nUbqXz8y14zMa1oeUrt8pfSuwY3FZIuI5XePUNKaM8lTRIr1jBc0sWkRFbNZERVkna1/eBQ53ocs/pUSknLSYnJ1aTdPMV7vGk9k9MKt6+oujNrPdeVn7f97xVif5f0oP0b+dSfA4eTHvz/cOAO6xBKix1LYbip3jAceCfp6cOZpDeNV5G2mvYVSW9k3VKCkg2mfynpaNszc/xjgP8sGA/bD+eSu51caVqS7dtql/lQ/wnaa2z/kPTU9TR4fhdKXySWbD9FakB6YoPYh9SO2UD1CZFApwfPk5L2Jj00eHnJgLZPK/n3r8fWtu/W2g2XS5dufUfSW2x/t3CcjlmV4zXhBg3DgfeRmqOvllQlGdHAt1h3p9A3SUm8UqpPpbRdfRBEvubal9QXDFLy/t7CYWvvzHqbU5+zp4GPA0g6i7JTTTtOIvW3/HY+vjOfG0WachhCVbFjKQwratAwPMfdCni17WU149ai9Uwusz21YMzdgenAzvnUI8A7Sz7lkXQUqeRmc9u7SpoAnF+yLE3Sn5G2Zd9Kuig/CCha5lN7Z9Z6eg+tcy78/nJS8lPAzraPkDQO2N926QlC1Uj6DGkkdPeEyCUu1BQ9x/wfpBvK8aQb9G2Av7H95YIxX0nqfdbps3Q7cJbtRwrGnEV6KDLD9n6SjgfeY7vYmOqunRHPkBJ4RZMRStOfriUlI4vHa0UNGob3M0ljST3WLiLt6usYTXqP3qtg7JeTplIeypqplB8o/dBJ0tHAm/LhrbZvLhzvLOC9QKfc91jgK7YvLxiz6s6s9Vz/VN01FcKmIhJLYVhRm4bh1ZMRtanh5DJJ2wDY/k2FWAtJF3K3dpWFPN9wulDM6mU+OU7xZKikI4A/IT0Zu77rQ6NJ309933S6tJwYmAaca3tfpelXi0p+z7agyhMiW5D0PeA6Un8ngFOAk20fXjDmbsBXSP3sniC9d55cshSltty/5Rjgvn4t14KmDcOrJiNqybuk3wYcDczs+tBy4Bu257VYVymSLiTtnJ6eT50ILHDZqbhLSA9CVuTjF5PKN4d96aqkE0m7gw4k/Vx2jAZW235zwdh/b/sDkm4iJSbX0k/3J2F4iVK4MNy0aBh+HmmyxK0AthcrTTTrJ/9C2lJfbXJZR42EUpf/sv3UgLKQ0jci1ct8upOhQMlk6M+BBaQL8+6xtstJO6XCxnuZ7RskfRjA9rOSVg/1RcPQnaTdJgbuLh1M0ktJr+0H5Ji3AxfYfrxg2B1sd/dZ+gdJHygYD+Bh24flG7rNnKdQlSTpW8CVwGzbNZpp/xT4l35OKkGzhuEDkxFnSTqgZDKiFtv/BPyTpP1tz2+9ngr+BJjQ+ZmUdDVQehCNWLMLnvxnredzexe0TjJ0Huma+WXAJV3nlwNLBv2K3uk8nKj+mhDChkRiKQwrjZ6ytkhG1NZiclkLSyWdBIyS9FpgKunioKTZkm5h7TKfWYVjnkeFZGjulXCvpH8EVjhNTeyMxI6JJL2xIidBDM+PqX6q7ZJ6a5By0RpT4b5Ban7c6Zd3MmnX3WEFYz4u6RTWvBacSEo0l/SgpNmkf1utkqkvknqtXS5pBjCtcBn5/wNuzbv7ut+/Li0Yc6RokYyo7VhJS0llU7NJ5bFn27627bKKGAN0Bk5sVyHeNOCf8zUCpB1iRcu4ayVD8/3Iw5JOBn5u++kcfyvglaRy1SJsL8y/FxsCE8ILEaVwIQxB0pWk+vcPkW5CpgIvsn1604X1kBpMLmtB0tbAucBbSDewt5B2KTxdOG7VMh9Jd9me0l3nr4ITZyTdBRzW2X2Wyxu/a/uNJeKNJJL2I/Xl2Zu0s3AH4HjbpZ+IVtOiXFSDTC6rUBa7C+n/5f6kROE8YKrtnxSMuTVwJPAOUoPim0llPneUitkVeztS8uxc0q6irwLX2v6vDX7h7x/nY4Odd6UBDf0slzId7Dz9UtJLSDtAhn0pU4fyhLbcq+tIUrPyuYVff1pMojsRuBD4Aen6503Ah2xfv8Ev3Pi4+7H29c+iwvGWsHYydBSpfLzU9c8C4I22f5uPNwfutD15w1/Zk9gHkB4k7kLaLNJpD7Jb6dghDCYSSyEMoVUyYiRQ/Ul01eWdQo8OeJr1CtsPFYxZNRmqwUcnr3MuvDC5r9IepNefZb2+MW9tYEJH0mbAvYWTPJeSSu5uyKeOB15v+5xSMVuTtD1pUtHJtkcVjvVSUg+pd5JKZqeTbi73sX1wydihd1olI2qStNT2XpKuAL5pe7akewsnlgZr+LzQdslJdEjaibUn1D5WON4UYGmnBFfSaGBP28UmxtZOhq7n+qfo909XnAdIbQcW0lVyWLikO4T1ilK4EIZgeyUpsXRu67WUkt/8Lwf2JPXlGUUqbSo2VUfrmUQHFEssSZoEfIR1k1kln77OIDXO7Vidz5V8mvV+0vfrM6Sym1uACwrGWyFpP9v3AEiaSCorCBtJ0pakUdQHknsBSfpSnyW2W5SLvhf4AGmaGKS+ZyskvY9CE4RyGdFZtp/Mx9sDl9h+d69jDYj7x6T/pm8l9UQrOoY6l73sQeoDcpTtTu++6/PT/V7FiQa2hdn+uqRbWfN+9X9KJyMamJlv0FcBZ+Qdk0VeX7VmEt12eSdzx2hgyxIxu2JfC9xG2jX0QMlYXb5I2inZ8ZtBzvXap4FFktZKhhaM90tJR9ueCc83hf/PgvG6PWW79HtlCL+z2LEUwhAaJSOqyhf77yAlPCYB7wJeV3haSPVJdJKWkcYK3wc831S2ZO+ulk+zapE0mdSz5uekC7kdgT/v9AEIL5ykG0jNQDsJkJOAMbZPaLeq3pN0HKmRNvTvVLjnS1M3dK7HMR8i9cS5AZjpPJ2pJEmH2P5BhTgTbS8cKaXcLTRKRlSTd0dOAR4g3aSvVmp0v22JBJoaTqKTdAhwUP61O+l1Ya7tzxaMOdj1T7Gy/K4Y1XZmSdqdtCNzZ9L1z0+Bd9n+camYXbEvJD0IvpG1+8vdUzp2CIOJxFIIQ2iRjKhN0gLbk7rf8Cvc8Mwg9RepNolO0h22Dxz6M3sa83vA5QOeZk112VG01ZOhkl5E2qUAfViu1Yqk+22PG+pcP8hlEt3fr7/awKf3It541v0ZubFgvHtJJRpP5OOXALcVLvkbbfvXpf7+9cQ8gTQRbrmkj5J2J3wibnaGnxbJiNpKX+usJ2aTSXS539Bk4BDgdGCV7bEF491IGsrwxXzqfwKH2H5bwZhNkqG5t2TVScd5V9ZAtn1orTWE0C1K4UIY2i87SYE+tjI3HFws6SLSCNXNCsdsMYnuY7mPwpwBMYvdTJIu3qZL+lw+foTUd6Sk6QySDC1sD2AcaTv/fpL6rl9WI/dImmL7LgBJbyCVM/WNXH72cVL5yXPkBqRAsQakkq4iTX9aypqfEZOe/JZyCTA/J9UBTgA+WTAewG8l/RWp/Ob5UpvC5Xd/Y3uGpANJU/Y+Q7qxfEMvg0i6jw1MaO2nXcWt2P6BpLmsnYzYi9Srq1/MyTsmb6y4g/pYVZ5EJ2kO8GJgPnA7MNl5YEJBpwP/F/go6Wd1DvCXhWNeSUqEXp53E9XYmfWn5NdY5QnSts8vFa/D9iGlY4Tw+4gdSyEMQdKbSZNtaiYjqlKaVvQfpP5KZ5PG0H6h5FbeFuUL+UnWWAbcTJbucZJjV3uaVXtnltJUpoNJiaXvAEcAd9g+vtYa+lUuGd0D6EwOezWwDHiW9L077G+eJf0bsL/tWn0pmu36kjQO6DxN/r7t+wvHm0Eq8zkJOB84GfiR7bMKxlxk+48kfRq4z/Z1JXaF5PctgL/Kv1+Tfz+F9LNRsq/KiDBIMuKOCsmIqiQtJ/0bV5MSPZ3JWiV7TLaYRHcZMJF0HXsnMBeYb7vv+iHW3Jkl6UvA1jnWFaRBEHfbfk+JeANivwL4FLCz7SPy+8v+tq8sHTuEwURiKYQhtExG1KQ0rezVtpe1XkspkpbZ3mPozxzeaidD886BfUkjfffNFzvX2j68RLyRpOvmeVD9UJIraTbwdqdBCbViXklqnF00sdNaV5Jnie3xuWT1dttTCsa8GfgZcDipDG4V6UaryE3zenpXrTN1K/z+RlIyoiY1mETXFXtb4FTgHGBH21uUjllT7WRo12tr5/dtgFm2DyoVsyv2LGAacG6+9voD0nVYsfLqEDYkSuFCGNrkfk9GSDoKuJi0Y2lXSROA80uWpanBJDpgnqRx/X4zCZxGSoa+iDplPqtsPyfp2dwn5xfAqwrFGlH6IXH0O/gw6Wfzn1k7ETq1YMyvkcrSHssxO7sUhv0OsAE6vc6elLQ38Bjw8sIx/4w0ge5i20/mRrofLBhPkg6wfWc+eCPlS7lHBNtnw1rJiGmk4Qz9low4mjQ9DNJo+psLh6w2ia5D0pmkErGJwEPAVaTES79ZQvo37g08RXrtK5kM7fx/WylpZ+BxYKdCsQZ6me0bJH0YwPazklYP9UUhlBKJpRCGNhKSEecBryc1WcT2Ykm7Fo75OQaZRFc45hRSH6kH6e+bydrJ0AWSxgBfBRaSRgpXb0wahq0vA9+nbk+wK0m9zmrGbOErkrYn9TiZCWwD/G3JgHnn2Y1dx4+S+vaV8h7gKknb5eMngb7aUdzKSEhGKE3WmkzqTQhwVk5UFpmKqzSJ7iZS77HOJLqVwDEl4nXZErgUWGj72cKxAJC0q+0HhzrXSw2SoTfl65/PAPeQHuJ9tVCsgVZIemmO2Xlg+1Sl2CGsI0rhQhhC7nGyO9C3yQhJd9me0l1SoMIjYdVmEt2gZUWld4XkJ+ivYe3pU8UaW0uaBnymRjJUqVPlK23/NB+/Bhhte0np2KE/lP65X0/M+bb3rxkzlNVJLNmOG6sekXQOKZFULRlRm6QlwATbz+XjUaRyopLXP9Vf81oYrCRV0kLbEwvGHJgMvZ1U/vv9ArE2A6bYnpePtwC2rPUaJGk/0s7/vUjtOnYAjo/rr9BK7FgKYWhvbb2ACpZKOgkYJem1wFRgXuGY1SfRtSgrknQNKTG5mNQcFNLTpZIT06rtzLJtSd8B9snHD/U6Ruh7syT9Jekpfncp3K8Kxlwk6bpBYvbNUAYASZ8CLrL9ZD7eHvhr2x9turACIqHUe7Yvbr2GSsYAndeb7Tbweb3SYhJdNZLGkpId20l6e9eHRtM1nbKQajuzcguAzwN/lI+foev9pIL7gX8EVgLLgW8D/1oxfghriR1LIQQkbQ2cC7yFlIS4BbjAdrGafzWYRNdC3vE2rubFY+2dWZKuBj5n+4cl/v7Q33ICdCDb3q1gzGnridlXJVTR2DqEDZN0InAh8APS9c+bgA/Zvr5gzOqT6GqSdAzwNuBoUglux3LgG50dPv1A0sWk0v/qSUJJNwC/Zk0Z50nAGNsn1FxHCB2RWAohNDNCJtHNAKbmPiN9KTch/UPgYWAFfVguGsJwlMt8Jucn6Z3X3AW292q7shA2HbnB/OR8eLftx1qup19I2t92X/db7EoSPktq5F0tSSjpftvjhjoXQi1RChdCQNIk4COs2weoZI+B6pPoGnkZcL+ku1m75GbY/zu7mnD+99ZrCcOXpBOA2baXS/ooaUT9BbYXFYz5SlJvigPyqduBs2w/UipmI9NJZTedHVqnAVc3XE/PDCixWUe/lTWGMiRdC9xG6sPzQMW4tSfRtXCspKWkXVmzgfHA2bavbbusjdc1iXKHkrv7h3CPpCm278pregOwoNFaQogdSyEEkLSMNA56rQlJJXsSSVoIHEq6oOo0DL/P9j6lYrYg6Y8HO2/7ttpr6bVOE05Jc2y/ufV6wvDUaeAv6UDgE6TpOn9r+w0FY34PuA64Jp86BTjZ9uGlYrYi6Qig8/P5Pdu3tFxPr6ynnLGj78oaQxmSDiE1ez6I1A9xETDX9mcLxhw4ie5E0k7CIpPoWpG02PYESccCRwL/i/Tfdt/GS9toXdc/1UuLJd1H6tX5ImAP4Cf5eBfggdixFFqJxFIIAUl32D6wcszqk+hCb0laBMwAzgAuG/hx25dWX1QYdjqvAZI+Ddxn+7oKEyIX254w1LkQQv/Lk+AmA4cApwOrbI8tGK/6JLoWJC21vZekK4Bv2p4t6d4+SSzdBSwh9ZL6xsCP255aMPagfTS7YlcfVBMCRClcCCH5WH7jn0O9CUktJtFVJ2kKqeRmT1LZ3yhgRZ806XwH6aLqD4Bt2y4lDGM/k/Rl4HDg7/LI5qITIoHHJZ0CfD0fnwg8Xjhmdblc7O+Al5N6f/RVk+AOSX9KmkL1/MQp2+e3W1EYLiTNIfXImU8qiZ1s+xcVQo+h7iS6FmbmHoyrgDMk7UDqQ9QPjgQOI7UCWFgzcCSOwqYqdiyFEDo9BsYCS1lTCle0lKDFJLoWJC0gJWBmAJOAdwGv66ct75KOsD2r9TrC8JRfC95K2q30b7mR7j62v1sw5i6khO/+pBKCeaQm+z8pFbMFST8GjrL9o9ZrKUXSl4CtSbtNrgCOJzVgfk/ThYVhQdJlwETSQ7U7gbnAfNurCsasPomuNkmbAVOAB4CnbK+W9GJg235qji5pX9v3tl5HCJuCSCyFEJC0zPYerdfRjyQtsD2pu8yvdJlPCCEASLrT9gFDf+bw1dWjq/P7NsAs2we1XlsYPiRtC5wKnAPsaHuLwvH6fhJdXOuEMLJEKVwIAWCepHG2768VsMUkukZWStocWCzpIuBRypf5hBA2QNLVpClwT+bj7YFL+rDh8wJJ1wPfpl6Zc22dnSUrJe1MKmncqeF6wjAi6UxS4+6JwEPAVaSSuJIxm0yia2COpOOAGx07GULoe5FYCiFA2q68WNKDpJuPTh+Okkme6Qwyia4PvZOUSDoTOBt4FXBc0xWFEMZ3kkoAtp+Q1I9P1kcDK0klxx0G+imxdLOkMaRpgveQ/n1XNF1RGE62BC4FFtp+tlLMK0nJrMslVZlE18j7SJPgVktaRR/2eJO0q+0HhzoXwkgQpXAhhPVOmCjZILDFJLpWJG0FvNr2stZrKUXSG1l399nXmi0ohA2QdC9wsO0n8vFLgNts79N2ZeH3JWkL2890/kxKFDzdORfCpqj2JLpQhqR7bO834NxC2xNbrSmEVmLHUgih1YSJFpPoqpN0FHAxaSLcrpImAOfbPrrpwnpI0jXA7sBiYHU+bSASS2FTdQkwX9KMfHwC8MmG6ylC0itJTco7fZZuJ5UAPtJuVT03H9gPICeTnpF0T+dcCJuahpPoqpN0NKk5OcCttm9uuZ5ekTSWNIlyuzx9s2M0XdMpQxhJIrEUQmjlNNIkuhfRNYmO/irRADgPeD1wK4DtxZJ2bbmgAiYB46KHQhgubH8tT2w8NJ96e80ecxVNA64jJc4ATsnnDm+2oh6RtCPw34Ctchmj8odGk6bEhbCpWkLq6bQ38BTwpKSik+hakHQhaVfW9HzqLEkH9MlU3D2AI4ExwFFd55cD722xoBBai1K4EEITI2USnaS7bE/pno7SPSGuH+RdH1NtP9p6LSGENSQttj1hqHPDkaS/IE3xmgT8kDWJpV8DV/fb7tfQf2pPoqtN0hJggu3n8vEoYFGfXf/sb3t+63WEsCmIHUshhFaqT6JrZKmkk4BRkl4LTAXmNV5Tr70MuF/S3axd1tg35X4hDFOPSzoF+Ho+PpE0NW3Ys301cLWk42x/q/V6QvhdtZhE19AY4Ff5z9s1XEcpx0paSppOORsYD5xt+9q2ywqhvkgshRBaaTGJroX3A+eS/o1fB24BLmi6ot47r/UCQgiDejepx9JlpFLjeaQy5H4yUdKczpQ/SdsDf237o22XFcJ6tZhE18KngUWSfkC6xnsT8KG2S+q5t9j+35KOJSUJ3w7MBSKxFEacKIULITTRYhJdCCGE/tJdZtx1bp1JTSGE+iTtROqzBHC37cdarqfXJC21vVceRvNN27Ml3Wt739ZrC6G2zVovIIQwMtl+eLBfrdfVa5ImSbpR0j2SlnR+tV5XL0maIumHkn4j6beSVkv6det1hTDSSbpa0piu4+0lXdVwSSWMkvR8bxpJWwF91asmhOFI0rWkBtf/antmvyWVspmSHiCVNc6RtAPwdOM1hdBElMKFEEJZ04EPAvexZvpdv/kc8A5gBqmR7ruA1zVdUQgBYHynRAzA9hN5glo/mU66oZuWj08Drm64nhBCciWpl9TlknYHFgFzbX+27bJ6Q9JmwE3AZ4CnbK+WtBI4pu3KQmgjSuFCCKEgSXfYPrD1OkqStMD2pO5pd4OVp4QQ6pJ0L3Cw7Sfy8UuA22zv03ZlvSXpCODN+fB7tm9puZ4QQpInwU0GDgFOB1bZHtt2Vb0T1zohrBE7lkIIoayP5dr7Oaw9Ma2fRmGvlLQ5qRn7RcCjRKl1CJuCS4D5kmbk4xOATzZcTxG2ZwGzWq8jhLCGpDnAi4H5pKl3k23/ou2qem6OpOOAGx27NcIIFzuWQgihoNxjYCywlDWlcLb97nar6q3ciP0/gM2Bs0kjhb9g+8dNFxZCQNI44NB8+H3b97dcT69JmkKafLcn6TVoFLDC9uimCwthhJN0Gan30DPAnaRpafNtr2q6sB6StJyUPFsNrGLNhON4/QkjTiSWQgihIEnLbO/Reh2l5Ya5r7a9rPVaQggjh6QFDNLjzfaHmy4shACApG2BU4FzgB1tR3P9EPpQlCqEEEJZ8/KOgb4l6ShgMTA7H0+QNLPpokIII0beHTnK9mrb04C3tl5TCCOdpDMlXU9q2n0McBVwRNtV9Z6koyVdnH8d2Xo9IbQSPZZCCKGsKaTeQw+StoN3tkmPb7usnjoPeD1wK4DtxZJ2bbmgEMKIET3eQtg0bQlcCiy0/WzrxZQg6UJSc/Lp+dRZkg6IHZNhJIpSuBBCKCj3H1qH7Ydrr6UUSXfZntI9HaV7QlwIIZQSPd5CCK1IWgJMsP1cPh4FLIrrnzASxY6lEEIoqJ8SSBuwVNJJwChJrwWmAvMarymE0OfyTdynbJ8MPA18vPGSQggjzxjgV/nP2zVcRwhNxVbhEEIIG+v9wF6kUr+vA78GPtByQSGE/md7NbBLLoULIYTaPg0skvQPkq4GFgKfbLymEJqIUrgQQgghhDAsSfoasCcwE1jROW/70maLCiGMGJJ2IvVZArjb9mMt1xNCK1EKF0IIYaNImgR8BHgNXe8r0WMghFDBv+dfmwHbNl5LCGEEkXQtcBtwu+0HWq8nhJYisRRCCGFjTQc+CNwHPNd4LSGEEUDSNbbfCTxp+7Ot1xNCGJGuBA4CLpe0O7AImBuvSWEkilK4EEIIG0XSHbYPbL2OEMLIIel+4DBgFnAwoO6P2/7VIF8WQgg9lYcITAYOAU4HVtke23ZVIdQXiaUQQggbRdKbgROBOaQG3gDYvrHZokIIfU3SVOAMYDfgZ6ydWLLt3ZosLIQwYkiaA7wYmA/cDtxh+xdtVxVCG5FYCiGEsFFyj4GxwFLWlMLZ9rvbrSqEMBJI+qLtM1qvI4Qw8ki6DJhIeqh2JzAXmG97VdOFhdBAJJZCCCFsFEnLbO/Reh0hhBBCCLVJ2hY4FTgH2NH2Fm1XFEJ90bw7hBDCxponaZzt+1svJIQQQgihBklnkpp3TwQeAq4ilcSFMOJEYimEEMLGmgIslvQgaTu4SKVw49suK4QQQgihmC2BS4GFtp9tvZgQWopSuBBCCBtF0i6Dnbf9cO21hBBCCCGEEOqKxFIIIYQQQgghhBBCeEE2a72AEEIIIYQQQgghhDA8RWIphBBCCCGEEEIIIbwgkVgKIYQQQgghhBBCCC9IJJZCCCGEEEIIIYQQwgsSiaUQQgghhBBCCCGE8IL8fxnwIo0kMU5DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 20))\n",
    "sns.heatmap(df.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will drop highly correlated features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_list = ['worst perimeter', 'worst area', 'perimeter error', 'area error', 'mean perimeter', 'mean area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(drop_list, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 25)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoothness error           0.067016\n",
       "mean fractal dimension     0.012838\n",
       "texture error              0.008303\n",
       "symmetry error             0.006522\n",
       "fractal dimension error   -0.077972\n",
       "concavity error           -0.253730\n",
       "compactness error         -0.292999\n",
       "worst fractal dimension   -0.323872\n",
       "mean symmetry             -0.330499\n",
       "mean smoothness           -0.358560\n",
       "concave points error      -0.408042\n",
       "mean texture              -0.415185\n",
       "worst symmetry            -0.416294\n",
       "worst smoothness          -0.421465\n",
       "worst texture             -0.456903\n",
       "radius error              -0.567134\n",
       "worst compactness         -0.590998\n",
       "mean compactness          -0.596534\n",
       "worst concavity           -0.659610\n",
       "mean concavity            -0.696360\n",
       "mean radius               -0.730029\n",
       "worst radius              -0.776454\n",
       "mean concave points       -0.776614\n",
       "worst concave points      -0.793566\n",
       "Name: benign_0__mal_1, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['benign_0__mal_1'][:-1].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split and Scale Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('benign_0__mal_1', axis = 1).values\n",
    "y = df['benign_0__mal_1'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426, 24)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(24, activation = 'relu'))\n",
    "model.add(Dense(24, activation = 'relu'))\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "\n",
    "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "14/14 [==============================] - 0s 21ms/step - loss: 0.6788 - accuracy: 0.6127 - val_loss: 0.6580 - val_accuracy: 0.6224\n",
      "Epoch 2/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.6504 - accuracy: 0.6197 - val_loss: 0.6296 - val_accuracy: 0.6434\n",
      "Epoch 3/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.6184 - accuracy: 0.6643 - val_loss: 0.5948 - val_accuracy: 0.7273\n",
      "Epoch 4/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.5789 - accuracy: 0.7488 - val_loss: 0.5536 - val_accuracy: 0.7692\n",
      "Epoch 5/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.5317 - accuracy: 0.8568 - val_loss: 0.5027 - val_accuracy: 0.8811\n",
      "Epoch 6/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.4764 - accuracy: 0.8873 - val_loss: 0.4442 - val_accuracy: 0.9301\n",
      "Epoch 7/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.4159 - accuracy: 0.8944 - val_loss: 0.3879 - val_accuracy: 0.9231\n",
      "Epoch 8/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.3602 - accuracy: 0.8991 - val_loss: 0.3364 - val_accuracy: 0.9231\n",
      "Epoch 9/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.3150 - accuracy: 0.9131 - val_loss: 0.2947 - val_accuracy: 0.9301\n",
      "Epoch 10/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.2766 - accuracy: 0.9155 - val_loss: 0.2592 - val_accuracy: 0.9371\n",
      "Epoch 11/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.2453 - accuracy: 0.9178 - val_loss: 0.2324 - val_accuracy: 0.9371\n",
      "Epoch 12/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.2215 - accuracy: 0.9178 - val_loss: 0.2105 - val_accuracy: 0.9301\n",
      "Epoch 13/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.2035 - accuracy: 0.9319 - val_loss: 0.1917 - val_accuracy: 0.9441\n",
      "Epoch 14/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.1878 - accuracy: 0.9319 - val_loss: 0.1789 - val_accuracy: 0.9371\n",
      "Epoch 15/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1769 - accuracy: 0.9390 - val_loss: 0.1688 - val_accuracy: 0.9371\n",
      "Epoch 16/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.1677 - accuracy: 0.9413 - val_loss: 0.1600 - val_accuracy: 0.9510\n",
      "Epoch 17/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.1611 - accuracy: 0.9413 - val_loss: 0.1508 - val_accuracy: 0.9441\n",
      "Epoch 18/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.1530 - accuracy: 0.9460 - val_loss: 0.1469 - val_accuracy: 0.9580\n",
      "Epoch 19/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.1464 - accuracy: 0.9460 - val_loss: 0.1392 - val_accuracy: 0.9580\n",
      "Epoch 20/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1389 - accuracy: 0.9507 - val_loss: 0.1310 - val_accuracy: 0.9441\n",
      "Epoch 21/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.1351 - accuracy: 0.9507 - val_loss: 0.1282 - val_accuracy: 0.9580\n",
      "Epoch 22/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.1294 - accuracy: 0.9554 - val_loss: 0.1206 - val_accuracy: 0.9580\n",
      "Epoch 23/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1237 - accuracy: 0.9554 - val_loss: 0.1232 - val_accuracy: 0.9510\n",
      "Epoch 24/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.1238 - accuracy: 0.9554 - val_loss: 0.1121 - val_accuracy: 0.9580\n",
      "Epoch 25/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1180 - accuracy: 0.9577 - val_loss: 0.1086 - val_accuracy: 0.9580\n",
      "Epoch 26/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1151 - accuracy: 0.9671 - val_loss: 0.1050 - val_accuracy: 0.9650\n",
      "Epoch 27/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1177 - accuracy: 0.9531 - val_loss: 0.1032 - val_accuracy: 0.9720\n",
      "Epoch 28/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.1110 - accuracy: 0.9624 - val_loss: 0.1029 - val_accuracy: 0.9790\n",
      "Epoch 29/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.1044 - accuracy: 0.9671 - val_loss: 0.0981 - val_accuracy: 0.9720\n",
      "Epoch 30/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.1010 - accuracy: 0.9718 - val_loss: 0.0945 - val_accuracy: 0.9720\n",
      "Epoch 31/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0989 - accuracy: 0.9624 - val_loss: 0.0960 - val_accuracy: 0.9790\n",
      "Epoch 32/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0972 - accuracy: 0.9742 - val_loss: 0.0933 - val_accuracy: 0.9790\n",
      "Epoch 33/600\n",
      "14/14 [==============================] - 0s 11ms/step - loss: 0.0972 - accuracy: 0.9695 - val_loss: 0.0911 - val_accuracy: 0.9790\n",
      "Epoch 34/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0929 - accuracy: 0.9765 - val_loss: 0.0889 - val_accuracy: 0.9790\n",
      "Epoch 35/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0908 - accuracy: 0.9789 - val_loss: 0.0854 - val_accuracy: 0.9790\n",
      "Epoch 36/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0894 - accuracy: 0.9718 - val_loss: 0.0892 - val_accuracy: 0.9790\n",
      "Epoch 37/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0917 - accuracy: 0.9742 - val_loss: 0.0819 - val_accuracy: 0.9790\n",
      "Epoch 38/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0875 - accuracy: 0.9765 - val_loss: 0.0819 - val_accuracy: 0.9790\n",
      "Epoch 39/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0845 - accuracy: 0.9765 - val_loss: 0.0796 - val_accuracy: 0.9790\n",
      "Epoch 40/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0836 - accuracy: 0.9765 - val_loss: 0.0807 - val_accuracy: 0.9790\n",
      "Epoch 41/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0824 - accuracy: 0.9789 - val_loss: 0.0775 - val_accuracy: 0.9790\n",
      "Epoch 42/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0824 - accuracy: 0.9695 - val_loss: 0.0809 - val_accuracy: 0.9720\n",
      "Epoch 43/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0798 - accuracy: 0.9789 - val_loss: 0.0763 - val_accuracy: 0.9790\n",
      "Epoch 44/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0788 - accuracy: 0.9765 - val_loss: 0.0755 - val_accuracy: 0.9790\n",
      "Epoch 45/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0783 - accuracy: 0.9812 - val_loss: 0.0733 - val_accuracy: 0.9790\n",
      "Epoch 46/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0793 - accuracy: 0.9718 - val_loss: 0.0787 - val_accuracy: 0.9790\n",
      "Epoch 47/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0751 - accuracy: 0.9789 - val_loss: 0.0719 - val_accuracy: 0.9790\n",
      "Epoch 48/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0795 - accuracy: 0.9695 - val_loss: 0.0802 - val_accuracy: 0.9790\n",
      "Epoch 49/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0720 - accuracy: 0.9812 - val_loss: 0.0699 - val_accuracy: 0.9790\n",
      "Epoch 50/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0740 - accuracy: 0.9742 - val_loss: 0.0782 - val_accuracy: 0.9790\n",
      "Epoch 51/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0737 - accuracy: 0.9789 - val_loss: 0.0718 - val_accuracy: 0.9720\n",
      "Epoch 52/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0710 - accuracy: 0.9789 - val_loss: 0.0690 - val_accuracy: 0.9790\n",
      "Epoch 53/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0702 - accuracy: 0.9812 - val_loss: 0.0700 - val_accuracy: 0.9720\n",
      "Epoch 54/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0702 - accuracy: 0.9765 - val_loss: 0.0687 - val_accuracy: 0.9790\n",
      "Epoch 55/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0696 - accuracy: 0.9836 - val_loss: 0.0673 - val_accuracy: 0.9790\n",
      "Epoch 56/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0705 - accuracy: 0.9718 - val_loss: 0.0713 - val_accuracy: 0.9790\n",
      "Epoch 57/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0687 - accuracy: 0.9812 - val_loss: 0.0673 - val_accuracy: 0.9790\n",
      "Epoch 58/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0683 - accuracy: 0.9812 - val_loss: 0.0655 - val_accuracy: 0.9790\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 59/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0666 - accuracy: 0.9812 - val_loss: 0.0671 - val_accuracy: 0.9720\n",
      "Epoch 60/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0652 - accuracy: 0.9812 - val_loss: 0.0665 - val_accuracy: 0.9720\n",
      "Epoch 61/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0654 - accuracy: 0.9859 - val_loss: 0.0657 - val_accuracy: 0.9790\n",
      "Epoch 62/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0647 - accuracy: 0.9742 - val_loss: 0.0673 - val_accuracy: 0.9790\n",
      "Epoch 63/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0636 - accuracy: 0.9789 - val_loss: 0.0657 - val_accuracy: 0.9720\n",
      "Epoch 64/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0624 - accuracy: 0.9812 - val_loss: 0.0650 - val_accuracy: 0.9720\n",
      "Epoch 65/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0628 - accuracy: 0.9836 - val_loss: 0.0634 - val_accuracy: 0.9790\n",
      "Epoch 66/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0624 - accuracy: 0.9765 - val_loss: 0.0647 - val_accuracy: 0.9790\n",
      "Epoch 67/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0625 - accuracy: 0.9812 - val_loss: 0.0633 - val_accuracy: 0.9720\n",
      "Epoch 68/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0746 - accuracy: 0.9695 - val_loss: 0.0693 - val_accuracy: 0.9720\n",
      "Epoch 69/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0670 - accuracy: 0.9836 - val_loss: 0.0616 - val_accuracy: 0.9790\n",
      "Epoch 70/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0600 - accuracy: 0.9789 - val_loss: 0.0614 - val_accuracy: 0.9790\n",
      "Epoch 71/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0589 - accuracy: 0.9812 - val_loss: 0.0645 - val_accuracy: 0.9790\n",
      "Epoch 72/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0613 - accuracy: 0.9765 - val_loss: 0.0633 - val_accuracy: 0.9790\n",
      "Epoch 73/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0594 - accuracy: 0.9859 - val_loss: 0.0618 - val_accuracy: 0.9720\n",
      "Epoch 74/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0571 - accuracy: 0.9859 - val_loss: 0.0623 - val_accuracy: 0.9790\n",
      "Epoch 75/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0579 - accuracy: 0.9812 - val_loss: 0.0617 - val_accuracy: 0.9790\n",
      "Epoch 76/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0567 - accuracy: 0.9812 - val_loss: 0.0610 - val_accuracy: 0.9790\n",
      "Epoch 77/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0563 - accuracy: 0.9836 - val_loss: 0.0615 - val_accuracy: 0.9790\n",
      "Epoch 78/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0550 - accuracy: 0.9859 - val_loss: 0.0611 - val_accuracy: 0.9790\n",
      "Epoch 79/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0546 - accuracy: 0.9859 - val_loss: 0.0605 - val_accuracy: 0.9790\n",
      "Epoch 80/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0578 - accuracy: 0.9789 - val_loss: 0.0631 - val_accuracy: 0.9790\n",
      "Epoch 81/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0570 - accuracy: 0.9836 - val_loss: 0.0593 - val_accuracy: 0.9790\n",
      "Epoch 82/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0571 - accuracy: 0.9812 - val_loss: 0.0598 - val_accuracy: 0.9790\n",
      "Epoch 83/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0553 - accuracy: 0.9859 - val_loss: 0.0608 - val_accuracy: 0.9790\n",
      "Epoch 84/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0533 - accuracy: 0.9883 - val_loss: 0.0603 - val_accuracy: 0.9790\n",
      "Epoch 85/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0577 - accuracy: 0.9765 - val_loss: 0.0640 - val_accuracy: 0.9720\n",
      "Epoch 86/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0549 - accuracy: 0.9883 - val_loss: 0.0585 - val_accuracy: 0.9720\n",
      "Epoch 87/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0553 - accuracy: 0.9789 - val_loss: 0.0613 - val_accuracy: 0.9790\n",
      "Epoch 88/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0548 - accuracy: 0.9836 - val_loss: 0.0580 - val_accuracy: 0.9790\n",
      "Epoch 89/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0560 - accuracy: 0.9765 - val_loss: 0.0641 - val_accuracy: 0.9720\n",
      "Epoch 90/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0517 - accuracy: 0.9883 - val_loss: 0.0602 - val_accuracy: 0.9790\n",
      "Epoch 91/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0518 - accuracy: 0.9836 - val_loss: 0.0610 - val_accuracy: 0.9790\n",
      "Epoch 92/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0511 - accuracy: 0.9883 - val_loss: 0.0611 - val_accuracy: 0.9790\n",
      "Epoch 93/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0516 - accuracy: 0.9836 - val_loss: 0.0622 - val_accuracy: 0.9720\n",
      "Epoch 94/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0507 - accuracy: 0.9883 - val_loss: 0.0578 - val_accuracy: 0.9790\n",
      "Epoch 95/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0510 - accuracy: 0.9836 - val_loss: 0.0598 - val_accuracy: 0.9790\n",
      "Epoch 96/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0498 - accuracy: 0.9883 - val_loss: 0.0591 - val_accuracy: 0.9790\n",
      "Epoch 97/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0540 - accuracy: 0.9789 - val_loss: 0.0582 - val_accuracy: 0.9790\n",
      "Epoch 98/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0500 - accuracy: 0.9883 - val_loss: 0.0595 - val_accuracy: 0.9790\n",
      "Epoch 99/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0494 - accuracy: 0.9859 - val_loss: 0.0566 - val_accuracy: 0.9720\n",
      "Epoch 100/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0492 - accuracy: 0.9859 - val_loss: 0.0578 - val_accuracy: 0.9790\n",
      "Epoch 101/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0565 - accuracy: 0.9742 - val_loss: 0.0614 - val_accuracy: 0.9720\n",
      "Epoch 102/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0496 - accuracy: 0.9883 - val_loss: 0.0567 - val_accuracy: 0.9720\n",
      "Epoch 103/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0489 - accuracy: 0.9883 - val_loss: 0.0613 - val_accuracy: 0.9720\n",
      "Epoch 104/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0483 - accuracy: 0.9883 - val_loss: 0.0583 - val_accuracy: 0.9790\n",
      "Epoch 105/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0476 - accuracy: 0.9859 - val_loss: 0.0599 - val_accuracy: 0.9720\n",
      "Epoch 106/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0479 - accuracy: 0.9883 - val_loss: 0.0621 - val_accuracy: 0.9720\n",
      "Epoch 107/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0466 - accuracy: 0.9883 - val_loss: 0.0570 - val_accuracy: 0.9790\n",
      "Epoch 108/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0493 - accuracy: 0.9812 - val_loss: 0.0628 - val_accuracy: 0.9720\n",
      "Epoch 109/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0463 - accuracy: 0.9883 - val_loss: 0.0577 - val_accuracy: 0.9790\n",
      "Epoch 110/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0464 - accuracy: 0.9883 - val_loss: 0.0582 - val_accuracy: 0.9790\n",
      "Epoch 111/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0463 - accuracy: 0.9836 - val_loss: 0.0570 - val_accuracy: 0.9790\n",
      "Epoch 112/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0457 - accuracy: 0.9883 - val_loss: 0.0654 - val_accuracy: 0.9720\n",
      "Epoch 113/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0475 - accuracy: 0.9859 - val_loss: 0.0540 - val_accuracy: 0.9860\n",
      "Epoch 114/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0467 - accuracy: 0.9883 - val_loss: 0.0580 - val_accuracy: 0.9790\n",
      "Epoch 115/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0455 - accuracy: 0.9859 - val_loss: 0.0569 - val_accuracy: 0.9790\n",
      "Epoch 116/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0468 - accuracy: 0.9883 - val_loss: 0.0549 - val_accuracy: 0.9790\n",
      "Epoch 117/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0474 - accuracy: 0.9836 - val_loss: 0.0632 - val_accuracy: 0.9720\n",
      "Epoch 118/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0447 - accuracy: 0.9883 - val_loss: 0.0543 - val_accuracy: 0.9790\n",
      "Epoch 119/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0448 - accuracy: 0.9836 - val_loss: 0.0593 - val_accuracy: 0.9720\n",
      "Epoch 120/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0458 - accuracy: 0.9883 - val_loss: 0.0530 - val_accuracy: 0.9860\n",
      "Epoch 121/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0462 - accuracy: 0.9883 - val_loss: 0.0569 - val_accuracy: 0.9790\n",
      "Epoch 122/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0445 - accuracy: 0.9859 - val_loss: 0.0579 - val_accuracy: 0.9790\n",
      "Epoch 123/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0467 - accuracy: 0.9883 - val_loss: 0.0547 - val_accuracy: 0.9790\n",
      "Epoch 124/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0461 - accuracy: 0.9859 - val_loss: 0.0680 - val_accuracy: 0.9720\n",
      "Epoch 125/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0478 - accuracy: 0.9859 - val_loss: 0.0524 - val_accuracy: 0.9860\n",
      "Epoch 126/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0463 - accuracy: 0.9859 - val_loss: 0.0573 - val_accuracy: 0.9790\n",
      "Epoch 127/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0438 - accuracy: 0.9883 - val_loss: 0.0544 - val_accuracy: 0.9790\n",
      "Epoch 128/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0427 - accuracy: 0.9859 - val_loss: 0.0577 - val_accuracy: 0.9720\n",
      "Epoch 129/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0431 - accuracy: 0.9859 - val_loss: 0.0574 - val_accuracy: 0.9790\n",
      "Epoch 130/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0433 - accuracy: 0.9859 - val_loss: 0.0572 - val_accuracy: 0.9790\n",
      "Epoch 131/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0480 - accuracy: 0.9836 - val_loss: 0.0517 - val_accuracy: 0.9860\n",
      "Epoch 132/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0433 - accuracy: 0.9883 - val_loss: 0.0624 - val_accuracy: 0.9720\n",
      "Epoch 133/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0435 - accuracy: 0.9859 - val_loss: 0.0554 - val_accuracy: 0.9790\n",
      "Epoch 134/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0445 - accuracy: 0.9836 - val_loss: 0.0604 - val_accuracy: 0.9720\n",
      "Epoch 135/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0629 - accuracy: 0.9765 - val_loss: 0.0541 - val_accuracy: 0.9860\n",
      "Epoch 136/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0499 - accuracy: 0.9812 - val_loss: 0.0630 - val_accuracy: 0.9720\n",
      "Epoch 137/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0431 - accuracy: 0.9883 - val_loss: 0.0561 - val_accuracy: 0.9790\n",
      "Epoch 138/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0429 - accuracy: 0.9859 - val_loss: 0.0578 - val_accuracy: 0.9790\n",
      "Epoch 139/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0413 - accuracy: 0.9883 - val_loss: 0.0550 - val_accuracy: 0.9790\n",
      "Epoch 140/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0419 - accuracy: 0.9883 - val_loss: 0.0542 - val_accuracy: 0.9790\n",
      "Epoch 141/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0426 - accuracy: 0.9859 - val_loss: 0.0542 - val_accuracy: 0.9790\n",
      "Epoch 142/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0438 - accuracy: 0.9859 - val_loss: 0.0545 - val_accuracy: 0.9790\n",
      "Epoch 143/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0422 - accuracy: 0.9883 - val_loss: 0.0599 - val_accuracy: 0.9720\n",
      "Epoch 144/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0417 - accuracy: 0.9836 - val_loss: 0.0513 - val_accuracy: 0.9930\n",
      "Epoch 145/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0424 - accuracy: 0.9859 - val_loss: 0.0626 - val_accuracy: 0.9720\n",
      "Epoch 146/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0400 - accuracy: 0.9883 - val_loss: 0.0516 - val_accuracy: 0.9860\n",
      "Epoch 147/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0443 - accuracy: 0.9836 - val_loss: 0.0634 - val_accuracy: 0.9720\n",
      "Epoch 148/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0434 - accuracy: 0.9883 - val_loss: 0.0572 - val_accuracy: 0.9790\n",
      "Epoch 149/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0403 - accuracy: 0.9883 - val_loss: 0.0553 - val_accuracy: 0.9790\n",
      "Epoch 150/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0411 - accuracy: 0.9859 - val_loss: 0.0574 - val_accuracy: 0.9790\n",
      "Epoch 151/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0410 - accuracy: 0.9883 - val_loss: 0.0545 - val_accuracy: 0.9790\n",
      "Epoch 152/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0432 - accuracy: 0.9836 - val_loss: 0.0574 - val_accuracy: 0.9790\n",
      "Epoch 153/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0405 - accuracy: 0.9906 - val_loss: 0.0580 - val_accuracy: 0.9720\n",
      "Epoch 154/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0429 - accuracy: 0.9883 - val_loss: 0.0607 - val_accuracy: 0.9720\n",
      "Epoch 155/600\n",
      "14/14 [==============================] - 0s 11ms/step - loss: 0.0451 - accuracy: 0.9859 - val_loss: 0.0513 - val_accuracy: 0.9860\n",
      "Epoch 156/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0455 - accuracy: 0.9812 - val_loss: 0.0616 - val_accuracy: 0.9720\n",
      "Epoch 157/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0429 - accuracy: 0.9883 - val_loss: 0.0558 - val_accuracy: 0.9790\n",
      "Epoch 158/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0409 - accuracy: 0.9859 - val_loss: 0.0555 - val_accuracy: 0.9790\n",
      "Epoch 159/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0410 - accuracy: 0.9883 - val_loss: 0.0571 - val_accuracy: 0.9790\n",
      "Epoch 160/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0393 - accuracy: 0.9883 - val_loss: 0.0539 - val_accuracy: 0.9790\n",
      "Epoch 161/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0392 - accuracy: 0.9883 - val_loss: 0.0617 - val_accuracy: 0.9720\n",
      "Epoch 162/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0392 - accuracy: 0.9883 - val_loss: 0.0537 - val_accuracy: 0.9790\n",
      "Epoch 163/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0394 - accuracy: 0.9883 - val_loss: 0.0564 - val_accuracy: 0.9790\n",
      "Epoch 164/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0397 - accuracy: 0.9859 - val_loss: 0.0548 - val_accuracy: 0.9790\n",
      "Epoch 165/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0409 - accuracy: 0.9859 - val_loss: 0.0554 - val_accuracy: 0.9790\n",
      "Epoch 166/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0384 - accuracy: 0.9859 - val_loss: 0.0528 - val_accuracy: 0.9860\n",
      "Epoch 167/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0440 - accuracy: 0.9836 - val_loss: 0.0580 - val_accuracy: 0.9790\n",
      "Epoch 168/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0467 - accuracy: 0.9836 - val_loss: 0.0536 - val_accuracy: 0.9860\n",
      "Epoch 169/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0402 - accuracy: 0.9836 - val_loss: 0.0600 - val_accuracy: 0.9790\n",
      "Epoch 170/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0406 - accuracy: 0.9906 - val_loss: 0.0551 - val_accuracy: 0.9790\n",
      "Epoch 171/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0444 - accuracy: 0.9789 - val_loss: 0.0598 - val_accuracy: 0.9720\n",
      "Epoch 172/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0414 - accuracy: 0.9883 - val_loss: 0.0524 - val_accuracy: 0.9860\n",
      "Epoch 173/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0408 - accuracy: 0.9859 - val_loss: 0.0559 - val_accuracy: 0.9790\n",
      "Epoch 174/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0387 - accuracy: 0.9836 - val_loss: 0.0598 - val_accuracy: 0.9720\n",
      "Epoch 175/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0377 - accuracy: 0.9883 - val_loss: 0.0552 - val_accuracy: 0.9790\n",
      "Epoch 176/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0384 - accuracy: 0.9859 - val_loss: 0.0553 - val_accuracy: 0.9790\n",
      "Epoch 177/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0377 - accuracy: 0.9883 - val_loss: 0.0549 - val_accuracy: 0.9790\n",
      "Epoch 178/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0376 - accuracy: 0.9859 - val_loss: 0.0586 - val_accuracy: 0.9790\n",
      "Epoch 179/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0381 - accuracy: 0.9906 - val_loss: 0.0543 - val_accuracy: 0.9790\n",
      "Epoch 180/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0382 - accuracy: 0.9859 - val_loss: 0.0588 - val_accuracy: 0.9790\n",
      "Epoch 181/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0396 - accuracy: 0.9883 - val_loss: 0.0510 - val_accuracy: 0.9930\n",
      "Epoch 182/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0423 - accuracy: 0.9836 - val_loss: 0.0571 - val_accuracy: 0.9790\n",
      "Epoch 183/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0381 - accuracy: 0.9883 - val_loss: 0.0559 - val_accuracy: 0.9790\n",
      "Epoch 184/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0385 - accuracy: 0.9883 - val_loss: 0.0531 - val_accuracy: 0.9930\n",
      "Epoch 185/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0406 - accuracy: 0.9859 - val_loss: 0.0541 - val_accuracy: 0.9790\n",
      "Epoch 186/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0371 - accuracy: 0.9883 - val_loss: 0.0543 - val_accuracy: 0.9790\n",
      "Epoch 187/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0362 - accuracy: 0.9859 - val_loss: 0.0562 - val_accuracy: 0.9790\n",
      "Epoch 188/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0375 - accuracy: 0.9883 - val_loss: 0.0523 - val_accuracy: 0.9930\n",
      "Epoch 189/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0365 - accuracy: 0.9906 - val_loss: 0.0582 - val_accuracy: 0.9790\n",
      "Epoch 190/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0379 - accuracy: 0.9859 - val_loss: 0.0564 - val_accuracy: 0.9790\n",
      "Epoch 191/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0368 - accuracy: 0.9906 - val_loss: 0.0594 - val_accuracy: 0.9790\n",
      "Epoch 192/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0375 - accuracy: 0.9883 - val_loss: 0.0538 - val_accuracy: 0.9860\n",
      "Epoch 193/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0377 - accuracy: 0.9883 - val_loss: 0.0539 - val_accuracy: 0.9860\n",
      "Epoch 194/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0383 - accuracy: 0.9859 - val_loss: 0.0612 - val_accuracy: 0.9720\n",
      "Epoch 195/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0354 - accuracy: 0.9906 - val_loss: 0.0523 - val_accuracy: 0.9930\n",
      "Epoch 196/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0362 - accuracy: 0.9859 - val_loss: 0.0566 - val_accuracy: 0.9790\n",
      "Epoch 197/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0391 - accuracy: 0.9906 - val_loss: 0.0511 - val_accuracy: 0.9860\n",
      "Epoch 198/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0431 - accuracy: 0.9812 - val_loss: 0.0537 - val_accuracy: 0.9790\n",
      "Epoch 199/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0383 - accuracy: 0.9883 - val_loss: 0.0577 - val_accuracy: 0.9790\n",
      "Epoch 200/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0378 - accuracy: 0.9859 - val_loss: 0.0574 - val_accuracy: 0.9790\n",
      "Epoch 201/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0366 - accuracy: 0.9883 - val_loss: 0.0543 - val_accuracy: 0.9790\n",
      "Epoch 202/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0353 - accuracy: 0.9883 - val_loss: 0.0521 - val_accuracy: 0.9930\n",
      "Epoch 203/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0370 - accuracy: 0.9836 - val_loss: 0.0536 - val_accuracy: 0.9930\n",
      "Epoch 204/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0366 - accuracy: 0.9859 - val_loss: 0.0546 - val_accuracy: 0.9860\n",
      "Epoch 205/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0347 - accuracy: 0.9883 - val_loss: 0.0546 - val_accuracy: 0.9860\n",
      "Epoch 206/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0367 - accuracy: 0.9883 - val_loss: 0.0583 - val_accuracy: 0.9790\n",
      "Epoch 207/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0360 - accuracy: 0.9883 - val_loss: 0.0516 - val_accuracy: 0.9930\n",
      "Epoch 208/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0484 - accuracy: 0.9812 - val_loss: 0.0550 - val_accuracy: 0.9860\n",
      "Epoch 209/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0568 - accuracy: 0.9812 - val_loss: 0.0494 - val_accuracy: 0.9860\n",
      "Epoch 210/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0445 - accuracy: 0.9812 - val_loss: 0.0505 - val_accuracy: 0.9860\n",
      "Epoch 211/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0370 - accuracy: 0.9906 - val_loss: 0.0542 - val_accuracy: 0.9790\n",
      "Epoch 212/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0341 - accuracy: 0.9859 - val_loss: 0.0502 - val_accuracy: 0.9930\n",
      "Epoch 213/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0340 - accuracy: 0.9906 - val_loss: 0.0583 - val_accuracy: 0.9790\n",
      "Epoch 214/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0346 - accuracy: 0.9883 - val_loss: 0.0515 - val_accuracy: 0.9930\n",
      "Epoch 215/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0339 - accuracy: 0.9883 - val_loss: 0.0549 - val_accuracy: 0.9790\n",
      "Epoch 216/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0340 - accuracy: 0.9906 - val_loss: 0.0501 - val_accuracy: 0.9930\n",
      "Epoch 217/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0359 - accuracy: 0.9859 - val_loss: 0.0554 - val_accuracy: 0.9790\n",
      "Epoch 218/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0364 - accuracy: 0.9859 - val_loss: 0.0514 - val_accuracy: 0.9930\n",
      "Epoch 219/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0350 - accuracy: 0.9883 - val_loss: 0.0613 - val_accuracy: 0.9790\n",
      "Epoch 220/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0333 - accuracy: 0.9906 - val_loss: 0.0493 - val_accuracy: 0.9930\n",
      "Epoch 221/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0373 - accuracy: 0.9859 - val_loss: 0.0538 - val_accuracy: 0.9860\n",
      "Epoch 222/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0337 - accuracy: 0.9906 - val_loss: 0.0590 - val_accuracy: 0.9790\n",
      "Epoch 223/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0337 - accuracy: 0.9859 - val_loss: 0.0545 - val_accuracy: 0.9860\n",
      "Epoch 224/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0351 - accuracy: 0.9883 - val_loss: 0.0519 - val_accuracy: 0.9930\n",
      "Epoch 225/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0346 - accuracy: 0.9859 - val_loss: 0.0561 - val_accuracy: 0.9790\n",
      "Epoch 226/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0367 - accuracy: 0.9883 - val_loss: 0.0525 - val_accuracy: 0.9930\n",
      "Epoch 227/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0392 - accuracy: 0.9906 - val_loss: 0.0603 - val_accuracy: 0.9720\n",
      "Epoch 228/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0353 - accuracy: 0.9883 - val_loss: 0.0521 - val_accuracy: 0.9860\n",
      "Epoch 229/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0340 - accuracy: 0.9883 - val_loss: 0.0735 - val_accuracy: 0.9580\n",
      "Epoch 230/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 11ms/step - loss: 0.0317 - accuracy: 0.9906 - val_loss: 0.0519 - val_accuracy: 0.9860\n",
      "Epoch 231/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0336 - accuracy: 0.9883 - val_loss: 0.0543 - val_accuracy: 0.9930\n",
      "Epoch 232/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0329 - accuracy: 0.9859 - val_loss: 0.0583 - val_accuracy: 0.9790\n",
      "Epoch 233/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0356 - accuracy: 0.9883 - val_loss: 0.0531 - val_accuracy: 0.9930\n",
      "Epoch 234/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0328 - accuracy: 0.9883 - val_loss: 0.0546 - val_accuracy: 0.9930\n",
      "Epoch 235/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 0.9836 - val_loss: 0.0562 - val_accuracy: 0.9930\n",
      "Epoch 236/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0316 - accuracy: 0.9883 - val_loss: 0.0614 - val_accuracy: 0.9720\n",
      "Epoch 237/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0312 - accuracy: 0.9906 - val_loss: 0.0536 - val_accuracy: 0.9930\n",
      "Epoch 238/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0365 - accuracy: 0.9859 - val_loss: 0.0648 - val_accuracy: 0.9650\n",
      "Epoch 239/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0356 - accuracy: 0.9883 - val_loss: 0.0528 - val_accuracy: 0.9930\n",
      "Epoch 240/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0348 - accuracy: 0.9859 - val_loss: 0.0546 - val_accuracy: 0.9860\n",
      "Epoch 241/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0319 - accuracy: 0.9906 - val_loss: 0.0583 - val_accuracy: 0.9790\n",
      "Epoch 242/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0317 - accuracy: 0.9906 - val_loss: 0.0544 - val_accuracy: 0.9930\n",
      "Epoch 243/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0330 - accuracy: 0.9859 - val_loss: 0.0558 - val_accuracy: 0.9930\n",
      "Epoch 244/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0355 - accuracy: 0.9883 - val_loss: 0.0546 - val_accuracy: 0.9930\n",
      "Epoch 245/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0330 - accuracy: 0.9883 - val_loss: 0.0529 - val_accuracy: 0.9930\n",
      "Epoch 246/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0337 - accuracy: 0.9883 - val_loss: 0.0646 - val_accuracy: 0.9720\n",
      "Epoch 247/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0318 - accuracy: 0.9883 - val_loss: 0.0556 - val_accuracy: 0.9930\n",
      "Epoch 248/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0326 - accuracy: 0.9906 - val_loss: 0.0583 - val_accuracy: 0.9790\n",
      "Epoch 249/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0335 - accuracy: 0.9883 - val_loss: 0.0544 - val_accuracy: 0.9860\n",
      "Epoch 250/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0312 - accuracy: 0.9906 - val_loss: 0.0576 - val_accuracy: 0.9860\n",
      "Epoch 251/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0317 - accuracy: 0.9883 - val_loss: 0.0556 - val_accuracy: 0.9860\n",
      "Epoch 252/600\n",
      "14/14 [==============================] - 0s 13ms/step - loss: 0.0311 - accuracy: 0.9883 - val_loss: 0.0537 - val_accuracy: 0.9930\n",
      "Epoch 253/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0302 - accuracy: 0.9883 - val_loss: 0.0569 - val_accuracy: 0.9860\n",
      "Epoch 254/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0306 - accuracy: 0.9906 - val_loss: 0.0554 - val_accuracy: 0.9930\n",
      "Epoch 255/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0296 - accuracy: 0.9906 - val_loss: 0.0540 - val_accuracy: 0.9930\n",
      "Epoch 256/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0311 - accuracy: 0.9883 - val_loss: 0.0546 - val_accuracy: 0.9930\n",
      "Epoch 257/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0304 - accuracy: 0.9883 - val_loss: 0.0561 - val_accuracy: 0.9930\n",
      "Epoch 258/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0300 - accuracy: 0.9883 - val_loss: 0.0545 - val_accuracy: 0.9930\n",
      "Epoch 259/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0295 - accuracy: 0.9906 - val_loss: 0.0550 - val_accuracy: 0.9930\n",
      "Epoch 260/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0296 - accuracy: 0.9906 - val_loss: 0.0573 - val_accuracy: 0.9860\n",
      "Epoch 261/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0302 - accuracy: 0.9906 - val_loss: 0.0546 - val_accuracy: 0.9930\n",
      "Epoch 262/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0317 - accuracy: 0.9883 - val_loss: 0.0566 - val_accuracy: 0.9860\n",
      "Epoch 263/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0361 - accuracy: 0.9906 - val_loss: 0.0551 - val_accuracy: 0.9860\n",
      "Epoch 264/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0335 - accuracy: 0.9836 - val_loss: 0.0611 - val_accuracy: 0.9720\n",
      "Epoch 265/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0300 - accuracy: 0.9906 - val_loss: 0.0551 - val_accuracy: 0.9930\n",
      "Epoch 266/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0296 - accuracy: 0.9906 - val_loss: 0.0569 - val_accuracy: 0.9860\n",
      "Epoch 267/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0292 - accuracy: 0.9906 - val_loss: 0.0536 - val_accuracy: 0.9930\n",
      "Epoch 268/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0296 - accuracy: 0.9883 - val_loss: 0.0550 - val_accuracy: 0.9930\n",
      "Epoch 269/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0301 - accuracy: 0.9883 - val_loss: 0.0608 - val_accuracy: 0.9650\n",
      "Epoch 270/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0299 - accuracy: 0.9906 - val_loss: 0.0540 - val_accuracy: 0.9930\n",
      "Epoch 271/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0327 - accuracy: 0.9906 - val_loss: 0.0531 - val_accuracy: 0.9930\n",
      "Epoch 272/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0385 - accuracy: 0.9836 - val_loss: 0.0792 - val_accuracy: 0.9580\n",
      "Epoch 273/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0322 - accuracy: 0.9930 - val_loss: 0.0520 - val_accuracy: 0.9860\n",
      "Epoch 274/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0314 - accuracy: 0.9859 - val_loss: 0.0586 - val_accuracy: 0.9860\n",
      "Epoch 275/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0305 - accuracy: 0.9906 - val_loss: 0.0605 - val_accuracy: 0.9790\n",
      "Epoch 276/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0288 - accuracy: 0.9883 - val_loss: 0.0554 - val_accuracy: 0.9860\n",
      "Epoch 277/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0286 - accuracy: 0.9906 - val_loss: 0.0545 - val_accuracy: 0.9860\n",
      "Epoch 278/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0286 - accuracy: 0.9906 - val_loss: 0.0543 - val_accuracy: 0.9930\n",
      "Epoch 279/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0288 - accuracy: 0.9859 - val_loss: 0.0557 - val_accuracy: 0.9860\n",
      "Epoch 280/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0278 - accuracy: 0.9906 - val_loss: 0.0534 - val_accuracy: 0.9930\n",
      "Epoch 281/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0283 - accuracy: 0.9906 - val_loss: 0.0519 - val_accuracy: 0.9930\n",
      "Epoch 282/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0298 - accuracy: 0.9859 - val_loss: 0.0525 - val_accuracy: 0.9930\n",
      "Epoch 283/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0275 - accuracy: 0.9883 - val_loss: 0.0572 - val_accuracy: 0.9790\n",
      "Epoch 284/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0275 - accuracy: 0.9883 - val_loss: 0.0545 - val_accuracy: 0.9860\n",
      "Epoch 285/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0296 - accuracy: 0.9883 - val_loss: 0.0594 - val_accuracy: 0.9790\n",
      "Epoch 286/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0299 - accuracy: 0.9859 - val_loss: 0.0532 - val_accuracy: 0.9860\n",
      "Epoch 287/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 14ms/step - loss: 0.0286 - accuracy: 0.9906 - val_loss: 0.0582 - val_accuracy: 0.9860\n",
      "Epoch 288/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0275 - accuracy: 0.9883 - val_loss: 0.0570 - val_accuracy: 0.9860\n",
      "Epoch 289/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0270 - accuracy: 0.9883 - val_loss: 0.0582 - val_accuracy: 0.9860\n",
      "Epoch 290/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0286 - accuracy: 0.9906 - val_loss: 0.0592 - val_accuracy: 0.9860\n",
      "Epoch 291/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0266 - accuracy: 0.9906 - val_loss: 0.0541 - val_accuracy: 0.9860\n",
      "Epoch 292/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0286 - accuracy: 0.9906 - val_loss: 0.0522 - val_accuracy: 0.9930\n",
      "Epoch 293/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0279 - accuracy: 0.9883 - val_loss: 0.0549 - val_accuracy: 0.9860\n",
      "Epoch 294/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0322 - accuracy: 0.9836 - val_loss: 0.0696 - val_accuracy: 0.9650\n",
      "Epoch 295/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0342 - accuracy: 0.9836 - val_loss: 0.0556 - val_accuracy: 0.9860\n",
      "Epoch 296/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0304 - accuracy: 0.9883 - val_loss: 0.0642 - val_accuracy: 0.9790\n",
      "Epoch 297/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0281 - accuracy: 0.9883 - val_loss: 0.0575 - val_accuracy: 0.9790\n",
      "Epoch 298/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0281 - accuracy: 0.9883 - val_loss: 0.0579 - val_accuracy: 0.9790\n",
      "Epoch 299/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0297 - accuracy: 0.9906 - val_loss: 0.0583 - val_accuracy: 0.9860\n",
      "Epoch 300/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0267 - accuracy: 0.9906 - val_loss: 0.0549 - val_accuracy: 0.9790\n",
      "Epoch 301/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0295 - accuracy: 0.9883 - val_loss: 0.0589 - val_accuracy: 0.9860\n",
      "Epoch 302/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0306 - accuracy: 0.9883 - val_loss: 0.0582 - val_accuracy: 0.9790\n",
      "Epoch 303/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0271 - accuracy: 0.9883 - val_loss: 0.0639 - val_accuracy: 0.9860\n",
      "Epoch 304/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0283 - accuracy: 0.9883 - val_loss: 0.0586 - val_accuracy: 0.9860\n",
      "Epoch 305/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0283 - accuracy: 0.9883 - val_loss: 0.0564 - val_accuracy: 0.9860\n",
      "Epoch 306/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0265 - accuracy: 0.9883 - val_loss: 0.0589 - val_accuracy: 0.9860\n",
      "Epoch 307/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0255 - accuracy: 0.9883 - val_loss: 0.0556 - val_accuracy: 0.9790\n",
      "Epoch 308/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0261 - accuracy: 0.9883 - val_loss: 0.0647 - val_accuracy: 0.9790\n",
      "Epoch 309/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0271 - accuracy: 0.9883 - val_loss: 0.0549 - val_accuracy: 0.9860\n",
      "Epoch 310/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0312 - accuracy: 0.9859 - val_loss: 0.0588 - val_accuracy: 0.9860\n",
      "Epoch 311/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0285 - accuracy: 0.9906 - val_loss: 0.0570 - val_accuracy: 0.9860\n",
      "Epoch 312/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0266 - accuracy: 0.9883 - val_loss: 0.0550 - val_accuracy: 0.9860\n",
      "Epoch 313/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0256 - accuracy: 0.9930 - val_loss: 0.0630 - val_accuracy: 0.9860\n",
      "Epoch 314/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0272 - accuracy: 0.9883 - val_loss: 0.0549 - val_accuracy: 0.9860\n",
      "Epoch 315/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0267 - accuracy: 0.9883 - val_loss: 0.0597 - val_accuracy: 0.9790\n",
      "Epoch 316/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0265 - accuracy: 0.9906 - val_loss: 0.0544 - val_accuracy: 0.9860\n",
      "Epoch 317/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0296 - accuracy: 0.9859 - val_loss: 0.0704 - val_accuracy: 0.9720\n",
      "Epoch 318/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0336 - accuracy: 0.9906 - val_loss: 0.0525 - val_accuracy: 0.9860\n",
      "Epoch 319/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0323 - accuracy: 0.9859 - val_loss: 0.0681 - val_accuracy: 0.9790\n",
      "Epoch 320/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0313 - accuracy: 0.9930 - val_loss: 0.0562 - val_accuracy: 0.9860\n",
      "Epoch 321/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0337 - accuracy: 0.9836 - val_loss: 0.0568 - val_accuracy: 0.9860\n",
      "Epoch 322/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0463 - accuracy: 0.9789 - val_loss: 0.0486 - val_accuracy: 0.9860\n",
      "Epoch 323/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0311 - accuracy: 0.9859 - val_loss: 0.0519 - val_accuracy: 0.9860\n",
      "Epoch 324/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0324 - accuracy: 0.9859 - val_loss: 0.0610 - val_accuracy: 0.9790\n",
      "Epoch 325/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0250 - accuracy: 0.9906 - val_loss: 0.0570 - val_accuracy: 0.9790\n",
      "Epoch 326/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0265 - accuracy: 0.9883 - val_loss: 0.0585 - val_accuracy: 0.9790\n",
      "Epoch 327/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0254 - accuracy: 0.9906 - val_loss: 0.0578 - val_accuracy: 0.9790\n",
      "Epoch 328/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0283 - accuracy: 0.9859 - val_loss: 0.0530 - val_accuracy: 0.9860\n",
      "Epoch 329/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0269 - accuracy: 0.9906 - val_loss: 0.0545 - val_accuracy: 0.9860\n",
      "Epoch 330/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0265 - accuracy: 0.9883 - val_loss: 0.0557 - val_accuracy: 0.9860\n",
      "Epoch 331/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0248 - accuracy: 0.9906 - val_loss: 0.0548 - val_accuracy: 0.9860\n",
      "Epoch 332/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0242 - accuracy: 0.9883 - val_loss: 0.0558 - val_accuracy: 0.9860\n",
      "Epoch 333/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0262 - accuracy: 0.9906 - val_loss: 0.0580 - val_accuracy: 0.9860\n",
      "Epoch 334/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0241 - accuracy: 0.9906 - val_loss: 0.0528 - val_accuracy: 0.9860\n",
      "Epoch 335/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0242 - accuracy: 0.9883 - val_loss: 0.0555 - val_accuracy: 0.9860\n",
      "Epoch 336/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0253 - accuracy: 0.9883 - val_loss: 0.0542 - val_accuracy: 0.9860\n",
      "Epoch 337/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0239 - accuracy: 0.9906 - val_loss: 0.0591 - val_accuracy: 0.9860\n",
      "Epoch 338/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0257 - accuracy: 0.9883 - val_loss: 0.0552 - val_accuracy: 0.9860\n",
      "Epoch 339/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0243 - accuracy: 0.9906 - val_loss: 0.0587 - val_accuracy: 0.9860\n",
      "Epoch 340/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0240 - accuracy: 0.9906 - val_loss: 0.0537 - val_accuracy: 0.9860\n",
      "Epoch 341/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0251 - accuracy: 0.9883 - val_loss: 0.0572 - val_accuracy: 0.9860\n",
      "Epoch 342/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0295 - accuracy: 0.9906 - val_loss: 0.0565 - val_accuracy: 0.9860\n",
      "Epoch 343/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0229 - accuracy: 0.9906 - val_loss: 0.0541 - val_accuracy: 0.9860\n",
      "Epoch 344/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0237 - accuracy: 0.9906 - val_loss: 0.0572 - val_accuracy: 0.9860\n",
      "Epoch 345/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0246 - accuracy: 0.9906 - val_loss: 0.0554 - val_accuracy: 0.9860\n",
      "Epoch 346/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0225 - accuracy: 0.9906 - val_loss: 0.0548 - val_accuracy: 0.9860\n",
      "Epoch 347/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0238 - accuracy: 0.9906 - val_loss: 0.0557 - val_accuracy: 0.9860\n",
      "Epoch 348/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0256 - accuracy: 0.9906 - val_loss: 0.0602 - val_accuracy: 0.9860\n",
      "Epoch 349/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0249 - accuracy: 0.9906 - val_loss: 0.0544 - val_accuracy: 0.9860\n",
      "Epoch 350/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0260 - accuracy: 0.9883 - val_loss: 0.0634 - val_accuracy: 0.9790\n",
      "Epoch 351/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0248 - accuracy: 0.9906 - val_loss: 0.0527 - val_accuracy: 0.9860\n",
      "Epoch 352/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0241 - accuracy: 0.9859 - val_loss: 0.0596 - val_accuracy: 0.9860\n",
      "Epoch 353/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0279 - accuracy: 0.9836 - val_loss: 0.0639 - val_accuracy: 0.9790\n",
      "Epoch 354/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0271 - accuracy: 0.9883 - val_loss: 0.0649 - val_accuracy: 0.9790\n",
      "Epoch 355/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0270 - accuracy: 0.9836 - val_loss: 0.0727 - val_accuracy: 0.9790\n",
      "Epoch 356/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0262 - accuracy: 0.9930 - val_loss: 0.0573 - val_accuracy: 0.9790\n",
      "Epoch 357/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0260 - accuracy: 0.9859 - val_loss: 0.0562 - val_accuracy: 0.9860\n",
      "Epoch 358/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0226 - accuracy: 0.9906 - val_loss: 0.0560 - val_accuracy: 0.9860\n",
      "Epoch 359/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0246 - accuracy: 0.9859 - val_loss: 0.0550 - val_accuracy: 0.9860\n",
      "Epoch 360/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0213 - accuracy: 0.9883 - val_loss: 0.0578 - val_accuracy: 0.9860\n",
      "Epoch 361/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0227 - accuracy: 0.9906 - val_loss: 0.0556 - val_accuracy: 0.9860\n",
      "Epoch 362/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0234 - accuracy: 0.9883 - val_loss: 0.0567 - val_accuracy: 0.9860\n",
      "Epoch 363/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0221 - accuracy: 0.9883 - val_loss: 0.0586 - val_accuracy: 0.9860\n",
      "Epoch 364/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0258 - accuracy: 0.9883 - val_loss: 0.0559 - val_accuracy: 0.9860\n",
      "Epoch 365/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0216 - accuracy: 0.9883 - val_loss: 0.0603 - val_accuracy: 0.9860\n",
      "Epoch 366/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0208 - accuracy: 0.9906 - val_loss: 0.0545 - val_accuracy: 0.9860\n",
      "Epoch 367/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0202 - accuracy: 0.9906 - val_loss: 0.0580 - val_accuracy: 0.9860\n",
      "Epoch 368/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0248 - accuracy: 0.9883 - val_loss: 0.0581 - val_accuracy: 0.9860\n",
      "Epoch 369/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0235 - accuracy: 0.9906 - val_loss: 0.0634 - val_accuracy: 0.9790\n",
      "Epoch 370/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0221 - accuracy: 0.9930 - val_loss: 0.0573 - val_accuracy: 0.9860\n",
      "Epoch 371/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0272 - accuracy: 0.9883 - val_loss: 0.0624 - val_accuracy: 0.9790\n",
      "Epoch 372/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0244 - accuracy: 0.9906 - val_loss: 0.0553 - val_accuracy: 0.9860\n",
      "Epoch 373/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0205 - accuracy: 0.9906 - val_loss: 0.0554 - val_accuracy: 0.9860\n",
      "Epoch 374/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0210 - accuracy: 0.9883 - val_loss: 0.0567 - val_accuracy: 0.9860\n",
      "Epoch 375/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0214 - accuracy: 0.9906 - val_loss: 0.0575 - val_accuracy: 0.9860\n",
      "Epoch 376/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0256 - accuracy: 0.9953 - val_loss: 0.0548 - val_accuracy: 0.9790\n",
      "Epoch 377/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0287 - accuracy: 0.9883 - val_loss: 0.0823 - val_accuracy: 0.9580\n",
      "Epoch 378/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0240 - accuracy: 0.9930 - val_loss: 0.0572 - val_accuracy: 0.9790\n",
      "Epoch 379/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0440 - accuracy: 0.9812 - val_loss: 0.0629 - val_accuracy: 0.9790\n",
      "Epoch 380/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0305 - accuracy: 0.9930 - val_loss: 0.0610 - val_accuracy: 0.9790\n",
      "Epoch 381/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0460 - accuracy: 0.9765 - val_loss: 0.0607 - val_accuracy: 0.9790\n",
      "Epoch 382/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0296 - accuracy: 0.9906 - val_loss: 0.0539 - val_accuracy: 0.9860\n",
      "Epoch 383/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0416 - accuracy: 0.9859 - val_loss: 0.0573 - val_accuracy: 0.9860\n",
      "Epoch 384/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0301 - accuracy: 0.9906 - val_loss: 0.0692 - val_accuracy: 0.9790\n",
      "Epoch 385/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0271 - accuracy: 0.9836 - val_loss: 0.0598 - val_accuracy: 0.9790\n",
      "Epoch 386/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0237 - accuracy: 0.9930 - val_loss: 0.0625 - val_accuracy: 0.9790\n",
      "Epoch 387/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0220 - accuracy: 0.9906 - val_loss: 0.0576 - val_accuracy: 0.9860\n",
      "Epoch 388/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0213 - accuracy: 0.9883 - val_loss: 0.0558 - val_accuracy: 0.9860\n",
      "Epoch 389/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0207 - accuracy: 0.9883 - val_loss: 0.0557 - val_accuracy: 0.9860\n",
      "Epoch 390/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0236 - accuracy: 0.9883 - val_loss: 0.0589 - val_accuracy: 0.9860\n",
      "Epoch 391/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0212 - accuracy: 0.9906 - val_loss: 0.0590 - val_accuracy: 0.9860\n",
      "Epoch 392/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0200 - accuracy: 0.9906 - val_loss: 0.0565 - val_accuracy: 0.9860\n",
      "Epoch 393/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0199 - accuracy: 0.9883 - val_loss: 0.0573 - val_accuracy: 0.9860\n",
      "Epoch 394/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0186 - accuracy: 0.9906 - val_loss: 0.0568 - val_accuracy: 0.9860\n",
      "Epoch 395/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0196 - accuracy: 0.9883 - val_loss: 0.0567 - val_accuracy: 0.9860\n",
      "Epoch 396/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0198 - accuracy: 0.9906 - val_loss: 0.0620 - val_accuracy: 0.9860\n",
      "Epoch 397/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0189 - accuracy: 0.9906 - val_loss: 0.0563 - val_accuracy: 0.9860\n",
      "Epoch 398/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0197 - accuracy: 0.9930 - val_loss: 0.0625 - val_accuracy: 0.9860\n",
      "Epoch 399/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0240 - accuracy: 0.9906 - val_loss: 0.0557 - val_accuracy: 0.9860\n",
      "Epoch 400/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0184 - accuracy: 0.9906 - val_loss: 0.0578 - val_accuracy: 0.9860\n",
      "Epoch 401/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0214 - accuracy: 0.9930 - val_loss: 0.0552 - val_accuracy: 0.9860\n",
      "Epoch 402/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0201 - accuracy: 0.9930 - val_loss: 0.0632 - val_accuracy: 0.9790\n",
      "Epoch 403/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0220 - accuracy: 0.9930 - val_loss: 0.0567 - val_accuracy: 0.9860\n",
      "Epoch 404/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0204 - accuracy: 0.9906 - val_loss: 0.0547 - val_accuracy: 0.9860\n",
      "Epoch 405/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0187 - accuracy: 0.9883 - val_loss: 0.0540 - val_accuracy: 0.9860\n",
      "Epoch 406/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0190 - accuracy: 0.9930 - val_loss: 0.0633 - val_accuracy: 0.9860\n",
      "Epoch 407/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0237 - accuracy: 0.9953 - val_loss: 0.0560 - val_accuracy: 0.9860\n",
      "Epoch 408/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0181 - accuracy: 0.9906 - val_loss: 0.0561 - val_accuracy: 0.9860\n",
      "Epoch 409/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0176 - accuracy: 0.9906 - val_loss: 0.0557 - val_accuracy: 0.9860\n",
      "Epoch 410/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0185 - accuracy: 0.9906 - val_loss: 0.0560 - val_accuracy: 0.9860\n",
      "Epoch 411/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0195 - accuracy: 0.9906 - val_loss: 0.0581 - val_accuracy: 0.9860\n",
      "Epoch 412/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0218 - accuracy: 0.9953 - val_loss: 0.0549 - val_accuracy: 0.9860\n",
      "Epoch 413/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0214 - accuracy: 0.9906 - val_loss: 0.0580 - val_accuracy: 0.9860\n",
      "Epoch 414/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0228 - accuracy: 0.9930 - val_loss: 0.0538 - val_accuracy: 0.9860\n",
      "Epoch 415/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0184 - accuracy: 0.9930 - val_loss: 0.0545 - val_accuracy: 0.9860\n",
      "Epoch 416/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0175 - accuracy: 0.9906 - val_loss: 0.0568 - val_accuracy: 0.9860\n",
      "Epoch 417/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0166 - accuracy: 0.9906 - val_loss: 0.0541 - val_accuracy: 0.9860\n",
      "Epoch 418/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0209 - accuracy: 0.9883 - val_loss: 0.0555 - val_accuracy: 0.9860\n",
      "Epoch 419/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0198 - accuracy: 0.9906 - val_loss: 0.0566 - val_accuracy: 0.9860\n",
      "Epoch 420/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0179 - accuracy: 0.9953 - val_loss: 0.0559 - val_accuracy: 0.9860\n",
      "Epoch 421/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0164 - accuracy: 0.9930 - val_loss: 0.0557 - val_accuracy: 0.9860\n",
      "Epoch 422/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0159 - accuracy: 0.9906 - val_loss: 0.0572 - val_accuracy: 0.9860\n",
      "Epoch 423/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0164 - accuracy: 0.9930 - val_loss: 0.0554 - val_accuracy: 0.9860\n",
      "Epoch 424/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0176 - accuracy: 0.9930 - val_loss: 0.0607 - val_accuracy: 0.9860\n",
      "Epoch 425/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0185 - accuracy: 0.9953 - val_loss: 0.0549 - val_accuracy: 0.9860\n",
      "Epoch 426/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0166 - accuracy: 0.9930 - val_loss: 0.0569 - val_accuracy: 0.9860\n",
      "Epoch 427/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0171 - accuracy: 0.9953 - val_loss: 0.0559 - val_accuracy: 0.9860\n",
      "Epoch 428/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0165 - accuracy: 0.9930 - val_loss: 0.0558 - val_accuracy: 0.9860\n",
      "Epoch 429/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0158 - accuracy: 0.9930 - val_loss: 0.0582 - val_accuracy: 0.9860\n",
      "Epoch 430/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0172 - accuracy: 0.9930 - val_loss: 0.0554 - val_accuracy: 0.9860\n",
      "Epoch 431/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0180 - accuracy: 0.9906 - val_loss: 0.0569 - val_accuracy: 0.9860\n",
      "Epoch 432/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0167 - accuracy: 0.9953 - val_loss: 0.0626 - val_accuracy: 0.9860\n",
      "Epoch 433/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0164 - accuracy: 0.9930 - val_loss: 0.0560 - val_accuracy: 0.9860\n",
      "Epoch 434/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0174 - accuracy: 0.9930 - val_loss: 0.0611 - val_accuracy: 0.9860\n",
      "Epoch 435/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0183 - accuracy: 0.9953 - val_loss: 0.0553 - val_accuracy: 0.9860\n",
      "Epoch 436/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0229 - accuracy: 0.9883 - val_loss: 0.1010 - val_accuracy: 0.9510\n",
      "Epoch 437/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0271 - accuracy: 0.9906 - val_loss: 0.0745 - val_accuracy: 0.9720\n",
      "Epoch 438/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0281 - accuracy: 0.9836 - val_loss: 0.0673 - val_accuracy: 0.9790\n",
      "Epoch 439/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0207 - accuracy: 0.9883 - val_loss: 0.0545 - val_accuracy: 0.9860\n",
      "Epoch 440/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0185 - accuracy: 0.9906 - val_loss: 0.0602 - val_accuracy: 0.9860\n",
      "Epoch 441/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0170 - accuracy: 0.9953 - val_loss: 0.0569 - val_accuracy: 0.9860\n",
      "Epoch 442/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0162 - accuracy: 0.9906 - val_loss: 0.0551 - val_accuracy: 0.9860\n",
      "Epoch 443/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0156 - accuracy: 0.9953 - val_loss: 0.0549 - val_accuracy: 0.9860\n",
      "Epoch 444/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0157 - accuracy: 0.9930 - val_loss: 0.0554 - val_accuracy: 0.9860\n",
      "Epoch 445/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0144 - accuracy: 0.9953 - val_loss: 0.0592 - val_accuracy: 0.9860\n",
      "Epoch 446/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0171 - accuracy: 0.9953 - val_loss: 0.0565 - val_accuracy: 0.9860\n",
      "Epoch 447/600\n",
      "14/14 [==============================] - 0s 14ms/step - loss: 0.0154 - accuracy: 0.9906 - val_loss: 0.0563 - val_accuracy: 0.9860\n",
      "Epoch 448/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0163 - accuracy: 0.9930 - val_loss: 0.0632 - val_accuracy: 0.9860\n",
      "Epoch 449/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0193 - accuracy: 0.9930 - val_loss: 0.0565 - val_accuracy: 0.9860\n",
      "Epoch 450/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0188 - accuracy: 0.9906 - val_loss: 0.0567 - val_accuracy: 0.9860\n",
      "Epoch 451/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0199 - accuracy: 0.9906 - val_loss: 0.0633 - val_accuracy: 0.9860\n",
      "Epoch 452/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0167 - accuracy: 0.9930 - val_loss: 0.0565 - val_accuracy: 0.9860\n",
      "Epoch 453/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0168 - accuracy: 0.9930 - val_loss: 0.0576 - val_accuracy: 0.9860\n",
      "Epoch 454/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0150 - accuracy: 0.9953 - val_loss: 0.0560 - val_accuracy: 0.9860\n",
      "Epoch 455/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0151 - accuracy: 0.9930 - val_loss: 0.0569 - val_accuracy: 0.9860\n",
      "Epoch 456/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0153 - accuracy: 0.9930 - val_loss: 0.0640 - val_accuracy: 0.9860\n",
      "Epoch 457/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0156 - accuracy: 0.9953 - val_loss: 0.0609 - val_accuracy: 0.9860\n",
      "Epoch 458/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0142 - accuracy: 0.9953 - val_loss: 0.0579 - val_accuracy: 0.9860\n",
      "Epoch 459/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0150 - accuracy: 0.9977 - val_loss: 0.0613 - val_accuracy: 0.9860\n",
      "Epoch 460/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0141 - accuracy: 0.9977 - val_loss: 0.0570 - val_accuracy: 0.9860\n",
      "Epoch 461/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0154 - accuracy: 0.9953 - val_loss: 0.0585 - val_accuracy: 0.9860\n",
      "Epoch 462/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 0.0595 - val_accuracy: 0.9860\n",
      "Epoch 463/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0142 - accuracy: 0.9953 - val_loss: 0.0600 - val_accuracy: 0.9860\n",
      "Epoch 464/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0145 - accuracy: 0.9953 - val_loss: 0.0567 - val_accuracy: 0.9860\n",
      "Epoch 465/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0173 - accuracy: 0.9930 - val_loss: 0.0647 - val_accuracy: 0.9790\n",
      "Epoch 466/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0166 - accuracy: 0.9930 - val_loss: 0.0589 - val_accuracy: 0.9860\n",
      "Epoch 467/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0145 - accuracy: 0.9953 - val_loss: 0.0570 - val_accuracy: 0.9860\n",
      "Epoch 468/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0132 - accuracy: 0.9977 - val_loss: 0.0598 - val_accuracy: 0.9860\n",
      "Epoch 469/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0152 - accuracy: 0.9977 - val_loss: 0.0562 - val_accuracy: 0.9860\n",
      "Epoch 470/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.0655 - val_accuracy: 0.9860\n",
      "Epoch 471/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0167 - accuracy: 0.9906 - val_loss: 0.0572 - val_accuracy: 0.9860\n",
      "Epoch 472/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0164 - accuracy: 0.9930 - val_loss: 0.0571 - val_accuracy: 0.9860\n",
      "Epoch 473/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0149 - accuracy: 0.9977 - val_loss: 0.0646 - val_accuracy: 0.9860\n",
      "Epoch 474/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0141 - accuracy: 0.9977 - val_loss: 0.0567 - val_accuracy: 0.9860\n",
      "Epoch 475/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0155 - accuracy: 0.9953 - val_loss: 0.0587 - val_accuracy: 0.9860\n",
      "Epoch 476/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0138 - accuracy: 0.9953 - val_loss: 0.0589 - val_accuracy: 0.9860\n",
      "Epoch 477/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0145 - accuracy: 0.9953 - val_loss: 0.0606 - val_accuracy: 0.9860\n",
      "Epoch 478/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0175 - accuracy: 0.9930 - val_loss: 0.0588 - val_accuracy: 0.9860\n",
      "Epoch 479/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0188 - accuracy: 0.9906 - val_loss: 0.0640 - val_accuracy: 0.9860\n",
      "Epoch 480/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0155 - accuracy: 0.9930 - val_loss: 0.0580 - val_accuracy: 0.9860\n",
      "Epoch 481/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0135 - accuracy: 0.9977 - val_loss: 0.0591 - val_accuracy: 0.9860\n",
      "Epoch 482/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0597 - val_accuracy: 0.9860\n",
      "Epoch 483/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0129 - accuracy: 0.9977 - val_loss: 0.0596 - val_accuracy: 0.9860\n",
      "Epoch 484/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0125 - accuracy: 0.9977 - val_loss: 0.0587 - val_accuracy: 0.9860\n",
      "Epoch 485/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0133 - accuracy: 0.9953 - val_loss: 0.0591 - val_accuracy: 0.9860\n",
      "Epoch 486/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0123 - accuracy: 0.9977 - val_loss: 0.0608 - val_accuracy: 0.9860\n",
      "Epoch 487/600\n",
      "14/14 [==============================] - 0s 13ms/step - loss: 0.0134 - accuracy: 0.9953 - val_loss: 0.0604 - val_accuracy: 0.9860\n",
      "Epoch 488/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0151 - accuracy: 0.9953 - val_loss: 0.0601 - val_accuracy: 0.9860\n",
      "Epoch 489/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0143 - accuracy: 0.9930 - val_loss: 0.0650 - val_accuracy: 0.9790\n",
      "Epoch 490/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0141 - accuracy: 0.9953 - val_loss: 0.0573 - val_accuracy: 0.9860\n",
      "Epoch 491/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0156 - accuracy: 0.9953 - val_loss: 0.0658 - val_accuracy: 0.9790\n",
      "Epoch 492/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0154 - accuracy: 0.9977 - val_loss: 0.0564 - val_accuracy: 0.9860\n",
      "Epoch 493/600\n",
      "14/14 [==============================] - 0s 14ms/step - loss: 0.0128 - accuracy: 0.9977 - val_loss: 0.0574 - val_accuracy: 0.9860\n",
      "Epoch 494/600\n",
      "14/14 [==============================] - 0s 13ms/step - loss: 0.0136 - accuracy: 0.9953 - val_loss: 0.0577 - val_accuracy: 0.9860\n",
      "Epoch 495/600\n",
      "14/14 [==============================] - 0s 11ms/step - loss: 0.0161 - accuracy: 0.9953 - val_loss: 0.0622 - val_accuracy: 0.9860\n",
      "Epoch 496/600\n",
      "14/14 [==============================] - 0s 11ms/step - loss: 0.0194 - accuracy: 0.9930 - val_loss: 0.0628 - val_accuracy: 0.9790\n",
      "Epoch 497/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0141 - accuracy: 0.9953 - val_loss: 0.0568 - val_accuracy: 0.9860\n",
      "Epoch 498/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0121 - accuracy: 0.9953 - val_loss: 0.0597 - val_accuracy: 0.9860\n",
      "Epoch 499/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0123 - accuracy: 0.9977 - val_loss: 0.0569 - val_accuracy: 0.9860\n",
      "Epoch 500/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0128 - accuracy: 0.9953 - val_loss: 0.0563 - val_accuracy: 0.9860\n",
      "Epoch 501/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.0573 - val_accuracy: 0.9860\n",
      "Epoch 502/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0129 - accuracy: 0.9953 - val_loss: 0.0589 - val_accuracy: 0.9860\n",
      "Epoch 503/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0147 - accuracy: 0.9930 - val_loss: 0.0573 - val_accuracy: 0.9860\n",
      "Epoch 504/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0122 - accuracy: 0.9977 - val_loss: 0.0582 - val_accuracy: 0.9860\n",
      "Epoch 505/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0132 - accuracy: 0.9953 - val_loss: 0.0562 - val_accuracy: 0.9860\n",
      "Epoch 506/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0132 - accuracy: 0.9953 - val_loss: 0.0619 - val_accuracy: 0.9860\n",
      "Epoch 507/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0134 - accuracy: 0.9953 - val_loss: 0.0564 - val_accuracy: 0.9860\n",
      "Epoch 508/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0112 - accuracy: 0.9977 - val_loss: 0.0582 - val_accuracy: 0.9860\n",
      "Epoch 509/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0121 - accuracy: 0.9953 - val_loss: 0.0605 - val_accuracy: 0.9860\n",
      "Epoch 510/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0112 - accuracy: 0.9977 - val_loss: 0.0585 - val_accuracy: 0.9860\n",
      "Epoch 511/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0153 - accuracy: 0.9930 - val_loss: 0.0829 - val_accuracy: 0.9580\n",
      "Epoch 512/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0273 - accuracy: 0.9812 - val_loss: 0.0565 - val_accuracy: 0.9860\n",
      "Epoch 513/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0365 - accuracy: 0.9859 - val_loss: 0.0649 - val_accuracy: 0.9790\n",
      "Epoch 514/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0262 - accuracy: 0.9836 - val_loss: 0.0603 - val_accuracy: 0.9860\n",
      "Epoch 515/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0135 - accuracy: 0.9953 - val_loss: 0.0592 - val_accuracy: 0.9860\n",
      "Epoch 516/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0149 - accuracy: 0.9977 - val_loss: 0.0581 - val_accuracy: 0.9860\n",
      "Epoch 517/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0112 - accuracy: 0.9977 - val_loss: 0.0600 - val_accuracy: 0.9860\n",
      "Epoch 518/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0115 - accuracy: 0.9977 - val_loss: 0.0580 - val_accuracy: 0.9860\n",
      "Epoch 519/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0114 - accuracy: 0.9953 - val_loss: 0.0609 - val_accuracy: 0.9860\n",
      "Epoch 520/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0124 - accuracy: 0.9977 - val_loss: 0.0585 - val_accuracy: 0.9860\n",
      "Epoch 521/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0112 - accuracy: 0.9977 - val_loss: 0.0589 - val_accuracy: 0.9860\n",
      "Epoch 522/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0108 - accuracy: 0.9977 - val_loss: 0.0592 - val_accuracy: 0.9860\n",
      "Epoch 523/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0114 - accuracy: 0.9977 - val_loss: 0.0586 - val_accuracy: 0.9860\n",
      "Epoch 524/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0106 - accuracy: 1.0000 - val_loss: 0.0599 - val_accuracy: 0.9860\n",
      "Epoch 525/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0100 - accuracy: 0.9977 - val_loss: 0.0604 - val_accuracy: 0.9860\n",
      "Epoch 526/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0103 - accuracy: 0.9977 - val_loss: 0.0637 - val_accuracy: 0.9860\n",
      "Epoch 527/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0103 - accuracy: 0.9977 - val_loss: 0.0599 - val_accuracy: 0.9860\n",
      "Epoch 528/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0105 - accuracy: 0.9977 - val_loss: 0.0600 - val_accuracy: 0.9860\n",
      "Epoch 529/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0120 - accuracy: 0.9953 - val_loss: 0.0589 - val_accuracy: 0.9860\n",
      "Epoch 530/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0103 - accuracy: 1.0000 - val_loss: 0.0618 - val_accuracy: 0.9860\n",
      "Epoch 531/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0119 - accuracy: 0.9977 - val_loss: 0.0602 - val_accuracy: 0.9860\n",
      "Epoch 532/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0117 - accuracy: 0.9977 - val_loss: 0.0587 - val_accuracy: 0.9860\n",
      "Epoch 533/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0107 - accuracy: 0.9977 - val_loss: 0.0633 - val_accuracy: 0.9790\n",
      "Epoch 534/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.0590 - val_accuracy: 0.9860\n",
      "Epoch 535/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0130 - accuracy: 0.9953 - val_loss: 0.0679 - val_accuracy: 0.9790\n",
      "Epoch 536/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0114 - accuracy: 0.9977 - val_loss: 0.0578 - val_accuracy: 0.9860\n",
      "Epoch 537/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0108 - accuracy: 0.9977 - val_loss: 0.0595 - val_accuracy: 0.9860\n",
      "Epoch 538/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0111 - accuracy: 0.9977 - val_loss: 0.0600 - val_accuracy: 0.9860\n",
      "Epoch 539/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.0591 - val_accuracy: 0.9860\n",
      "Epoch 540/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.0590 - val_accuracy: 0.9860\n",
      "Epoch 541/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0097 - accuracy: 0.9977 - val_loss: 0.0595 - val_accuracy: 0.9860\n",
      "Epoch 542/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0105 - accuracy: 0.9977 - val_loss: 0.0692 - val_accuracy: 0.9790\n",
      "Epoch 543/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0120 - accuracy: 0.9953 - val_loss: 0.0587 - val_accuracy: 0.9860\n",
      "Epoch 544/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0100 - accuracy: 0.9977 - val_loss: 0.0604 - val_accuracy: 0.9860\n",
      "Epoch 545/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0102 - accuracy: 0.9977 - val_loss: 0.0588 - val_accuracy: 0.9860\n",
      "Epoch 546/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0098 - accuracy: 0.9977 - val_loss: 0.0616 - val_accuracy: 0.9860\n",
      "Epoch 547/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0112 - accuracy: 0.9977 - val_loss: 0.0612 - val_accuracy: 0.9860\n",
      "Epoch 548/600\n",
      "14/14 [==============================] - 0s 15ms/step - loss: 0.0101 - accuracy: 0.9977 - val_loss: 0.0613 - val_accuracy: 0.9860\n",
      "Epoch 549/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0105 - accuracy: 1.0000 - val_loss: 0.0597 - val_accuracy: 0.9860\n",
      "Epoch 550/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.0673 - val_accuracy: 0.9790\n",
      "Epoch 551/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0107 - accuracy: 0.9977 - val_loss: 0.0598 - val_accuracy: 0.9860\n",
      "Epoch 552/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0118 - accuracy: 0.9977 - val_loss: 0.0684 - val_accuracy: 0.9790\n",
      "Epoch 553/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0128 - accuracy: 0.9953 - val_loss: 0.0629 - val_accuracy: 0.9860\n",
      "Epoch 554/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0118 - accuracy: 0.9977 - val_loss: 0.0633 - val_accuracy: 0.9860\n",
      "Epoch 555/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0103 - accuracy: 0.9977 - val_loss: 0.0613 - val_accuracy: 0.9860\n",
      "Epoch 556/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0101 - accuracy: 0.9977 - val_loss: 0.0602 - val_accuracy: 0.9860\n",
      "Epoch 557/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0111 - accuracy: 0.9953 - val_loss: 0.0620 - val_accuracy: 0.9860\n",
      "Epoch 558/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.0626 - val_accuracy: 0.9860\n",
      "Epoch 559/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0092 - accuracy: 0.9977 - val_loss: 0.0634 - val_accuracy: 0.9860\n",
      "Epoch 560/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0092 - accuracy: 0.9977 - val_loss: 0.0614 - val_accuracy: 0.9860\n",
      "Epoch 561/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0083 - accuracy: 1.0000 - val_loss: 0.0620 - val_accuracy: 0.9860\n",
      "Epoch 562/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.0603 - val_accuracy: 0.9860\n",
      "Epoch 563/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0090 - accuracy: 1.0000 - val_loss: 0.0607 - val_accuracy: 0.9860\n",
      "Epoch 564/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.0611 - val_accuracy: 0.9860\n",
      "Epoch 565/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0102 - accuracy: 1.0000 - val_loss: 0.0614 - val_accuracy: 0.9860\n",
      "Epoch 566/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0107 - accuracy: 0.9953 - val_loss: 0.0608 - val_accuracy: 0.9860\n",
      "Epoch 567/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0121 - accuracy: 0.9977 - val_loss: 0.0656 - val_accuracy: 0.9790\n",
      "Epoch 568/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0113 - accuracy: 0.9953 - val_loss: 0.0604 - val_accuracy: 0.9860\n",
      "Epoch 569/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0114 - accuracy: 0.9977 - val_loss: 0.0595 - val_accuracy: 0.9860\n",
      "Epoch 570/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0096 - accuracy: 0.9977 - val_loss: 0.0681 - val_accuracy: 0.9790\n",
      "Epoch 571/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0136 - accuracy: 0.9977 - val_loss: 0.0609 - val_accuracy: 0.9860\n",
      "Epoch 572/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0130 - accuracy: 0.9953 - val_loss: 0.0596 - val_accuracy: 0.9860\n",
      "Epoch 573/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0086 - accuracy: 1.0000 - val_loss: 0.0619 - val_accuracy: 0.9860\n",
      "Epoch 574/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0108 - accuracy: 0.9977 - val_loss: 0.0611 - val_accuracy: 0.9860\n",
      "Epoch 575/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0151 - accuracy: 0.9953 - val_loss: 0.0674 - val_accuracy: 0.9790\n",
      "Epoch 576/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0103 - accuracy: 0.9953 - val_loss: 0.0588 - val_accuracy: 0.9860\n",
      "Epoch 577/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0102 - accuracy: 0.9953 - val_loss: 0.0627 - val_accuracy: 0.9860\n",
      "Epoch 578/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.0596 - val_accuracy: 0.9860\n",
      "Epoch 579/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0084 - accuracy: 0.9977 - val_loss: 0.0621 - val_accuracy: 0.9860\n",
      "Epoch 580/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0083 - accuracy: 1.0000 - val_loss: 0.0624 - val_accuracy: 0.9860\n",
      "Epoch 581/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.0610 - val_accuracy: 0.9860\n",
      "Epoch 582/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0082 - accuracy: 1.0000 - val_loss: 0.0609 - val_accuracy: 0.9860\n",
      "Epoch 583/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0087 - accuracy: 0.9977 - val_loss: 0.0665 - val_accuracy: 0.9790\n",
      "Epoch 584/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0111 - accuracy: 0.9977 - val_loss: 0.0611 - val_accuracy: 0.9860\n",
      "Epoch 585/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0085 - accuracy: 0.9977 - val_loss: 0.0604 - val_accuracy: 0.9860\n",
      "Epoch 586/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0086 - accuracy: 0.9977 - val_loss: 0.0656 - val_accuracy: 0.9790\n",
      "Epoch 587/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0084 - accuracy: 0.9977 - val_loss: 0.0620 - val_accuracy: 0.9860\n",
      "Epoch 588/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.0620 - val_accuracy: 0.9860\n",
      "Epoch 589/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0079 - accuracy: 0.9977 - val_loss: 0.0633 - val_accuracy: 0.9860\n",
      "Epoch 590/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.0625 - val_accuracy: 0.9860\n",
      "Epoch 591/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.0691 - val_accuracy: 0.9790\n",
      "Epoch 592/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.0638 - val_accuracy: 0.9860\n",
      "Epoch 593/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0093 - accuracy: 0.9977 - val_loss: 0.0673 - val_accuracy: 0.9790\n",
      "Epoch 594/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0102 - accuracy: 0.9977 - val_loss: 0.0606 - val_accuracy: 0.9860\n",
      "Epoch 595/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0089 - accuracy: 0.9977 - val_loss: 0.0738 - val_accuracy: 0.9720\n",
      "Epoch 596/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0639 - val_accuracy: 0.9790\n",
      "Epoch 597/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0085 - accuracy: 0.9977 - val_loss: 0.0666 - val_accuracy: 0.9790\n",
      "Epoch 598/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 0.0621 - val_accuracy: 0.9860\n",
      "Epoch 599/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0090 - accuracy: 1.0000 - val_loss: 0.0666 - val_accuracy: 0.9860\n",
      "Epoch 600/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0081 - accuracy: 0.9977 - val_loss: 0.0621 - val_accuracy: 0.9860\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fc3a6e5f630>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x = X_train, y = y_train, validation_data = (X_test, y_test), epochs = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Early Stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(24, activation = 'relu'))\n",
    "model.add(Dense(24, activation = 'relu'))\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "\n",
    "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor = 'val_loss', mode = 'min', verbose = 1, patience = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.6909 - accuracy: 0.5915 - val_loss: 0.6692 - val_accuracy: 0.6294\n",
      "Epoch 2/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.6558 - accuracy: 0.6315 - val_loss: 0.6401 - val_accuracy: 0.6434\n",
      "Epoch 3/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.6243 - accuracy: 0.6714 - val_loss: 0.6091 - val_accuracy: 0.6923\n",
      "Epoch 4/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.5914 - accuracy: 0.7230 - val_loss: 0.5755 - val_accuracy: 0.7832\n",
      "Epoch 5/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.5555 - accuracy: 0.8216 - val_loss: 0.5410 - val_accuracy: 0.8252\n",
      "Epoch 6/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.5165 - accuracy: 0.8357 - val_loss: 0.5005 - val_accuracy: 0.8392\n",
      "Epoch 7/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.4752 - accuracy: 0.8592 - val_loss: 0.4608 - val_accuracy: 0.8881\n",
      "Epoch 8/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.4337 - accuracy: 0.8826 - val_loss: 0.4200 - val_accuracy: 0.8951\n",
      "Epoch 9/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3923 - accuracy: 0.8897 - val_loss: 0.3810 - val_accuracy: 0.8881\n",
      "Epoch 10/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3544 - accuracy: 0.8873 - val_loss: 0.3433 - val_accuracy: 0.8951\n",
      "Epoch 11/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.3211 - accuracy: 0.9061 - val_loss: 0.3097 - val_accuracy: 0.8951\n",
      "Epoch 12/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.2891 - accuracy: 0.8991 - val_loss: 0.2810 - val_accuracy: 0.9021\n",
      "Epoch 13/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.2629 - accuracy: 0.9131 - val_loss: 0.2582 - val_accuracy: 0.9021\n",
      "Epoch 14/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2436 - accuracy: 0.9108 - val_loss: 0.2387 - val_accuracy: 0.9161\n",
      "Epoch 15/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.2245 - accuracy: 0.9131 - val_loss: 0.2242 - val_accuracy: 0.9301\n",
      "Epoch 16/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.2104 - accuracy: 0.9319 - val_loss: 0.2078 - val_accuracy: 0.9161\n",
      "Epoch 17/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.1978 - accuracy: 0.9319 - val_loss: 0.1960 - val_accuracy: 0.9231\n",
      "Epoch 18/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.1848 - accuracy: 0.9390 - val_loss: 0.1831 - val_accuracy: 0.9231\n",
      "Epoch 19/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1752 - accuracy: 0.9413 - val_loss: 0.1743 - val_accuracy: 0.9301\n",
      "Epoch 20/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1683 - accuracy: 0.9390 - val_loss: 0.1633 - val_accuracy: 0.9231\n",
      "Epoch 21/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.1601 - accuracy: 0.9484 - val_loss: 0.1589 - val_accuracy: 0.9371\n",
      "Epoch 22/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1542 - accuracy: 0.9437 - val_loss: 0.1475 - val_accuracy: 0.9301\n",
      "Epoch 23/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1464 - accuracy: 0.9577 - val_loss: 0.1434 - val_accuracy: 0.9441\n",
      "Epoch 24/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1468 - accuracy: 0.9460 - val_loss: 0.1359 - val_accuracy: 0.9580\n",
      "Epoch 25/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1353 - accuracy: 0.9554 - val_loss: 0.1299 - val_accuracy: 0.9441\n",
      "Epoch 26/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.1277 - accuracy: 0.9648 - val_loss: 0.1326 - val_accuracy: 0.9441\n",
      "Epoch 27/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1252 - accuracy: 0.9531 - val_loss: 0.1201 - val_accuracy: 0.9510\n",
      "Epoch 28/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.1229 - accuracy: 0.9577 - val_loss: 0.1164 - val_accuracy: 0.9720\n",
      "Epoch 29/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1170 - accuracy: 0.9601 - val_loss: 0.1170 - val_accuracy: 0.9650\n",
      "Epoch 30/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1166 - accuracy: 0.9554 - val_loss: 0.1087 - val_accuracy: 0.9720\n",
      "Epoch 31/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1135 - accuracy: 0.9577 - val_loss: 0.1129 - val_accuracy: 0.9580\n",
      "Epoch 32/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1106 - accuracy: 0.9577 - val_loss: 0.1035 - val_accuracy: 0.9720\n",
      "Epoch 33/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1211 - accuracy: 0.9554 - val_loss: 0.1099 - val_accuracy: 0.9580\n",
      "Epoch 34/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1092 - accuracy: 0.9624 - val_loss: 0.0986 - val_accuracy: 0.9720\n",
      "Epoch 35/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1064 - accuracy: 0.9577 - val_loss: 0.1012 - val_accuracy: 0.9720\n",
      "Epoch 36/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0987 - accuracy: 0.9648 - val_loss: 0.0947 - val_accuracy: 0.9790\n",
      "Epoch 37/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0953 - accuracy: 0.9671 - val_loss: 0.0939 - val_accuracy: 0.9860\n",
      "Epoch 38/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0931 - accuracy: 0.9671 - val_loss: 0.0915 - val_accuracy: 0.9860\n",
      "Epoch 39/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0917 - accuracy: 0.9671 - val_loss: 0.0901 - val_accuracy: 0.9860\n",
      "Epoch 40/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0918 - accuracy: 0.9695 - val_loss: 0.0874 - val_accuracy: 0.9860\n",
      "Epoch 41/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0903 - accuracy: 0.9695 - val_loss: 0.0876 - val_accuracy: 0.9860\n",
      "Epoch 42/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0871 - accuracy: 0.9671 - val_loss: 0.0854 - val_accuracy: 0.9860\n",
      "Epoch 43/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0872 - accuracy: 0.9742 - val_loss: 0.0849 - val_accuracy: 0.9860\n",
      "Epoch 44/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0824 - accuracy: 0.9718 - val_loss: 0.0820 - val_accuracy: 0.9860\n",
      "Epoch 45/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0823 - accuracy: 0.9718 - val_loss: 0.0822 - val_accuracy: 0.9860\n",
      "Epoch 46/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0821 - accuracy: 0.9718 - val_loss: 0.0828 - val_accuracy: 0.9790\n",
      "Epoch 47/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0794 - accuracy: 0.9718 - val_loss: 0.0797 - val_accuracy: 0.9860\n",
      "Epoch 48/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0791 - accuracy: 0.9718 - val_loss: 0.0811 - val_accuracy: 0.9790\n",
      "Epoch 49/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0781 - accuracy: 0.9765 - val_loss: 0.0791 - val_accuracy: 0.9860\n",
      "Epoch 50/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0765 - accuracy: 0.9742 - val_loss: 0.0780 - val_accuracy: 0.9860\n",
      "Epoch 51/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0751 - accuracy: 0.9742 - val_loss: 0.0782 - val_accuracy: 0.9790\n",
      "Epoch 52/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0744 - accuracy: 0.9765 - val_loss: 0.0752 - val_accuracy: 0.9860\n",
      "Epoch 53/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0767 - accuracy: 0.9718 - val_loss: 0.0795 - val_accuracy: 0.9720\n",
      "Epoch 54/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0729 - accuracy: 0.9742 - val_loss: 0.0745 - val_accuracy: 0.9860\n",
      "Epoch 55/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0711 - accuracy: 0.9718 - val_loss: 0.0746 - val_accuracy: 0.9790\n",
      "Epoch 56/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0729 - accuracy: 0.9718 - val_loss: 0.0723 - val_accuracy: 0.9860\n",
      "Epoch 57/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0688 - accuracy: 0.9742 - val_loss: 0.0755 - val_accuracy: 0.9790\n",
      "Epoch 58/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0689 - accuracy: 0.9742 - val_loss: 0.0721 - val_accuracy: 0.9860\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 59/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0688 - accuracy: 0.9812 - val_loss: 0.0709 - val_accuracy: 0.9860\n",
      "Epoch 60/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0690 - accuracy: 0.9789 - val_loss: 0.0730 - val_accuracy: 0.9790\n",
      "Epoch 61/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0668 - accuracy: 0.9765 - val_loss: 0.0703 - val_accuracy: 0.9790\n",
      "Epoch 62/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0659 - accuracy: 0.9765 - val_loss: 0.0688 - val_accuracy: 0.9860\n",
      "Epoch 63/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0654 - accuracy: 0.9765 - val_loss: 0.0697 - val_accuracy: 0.9790\n",
      "Epoch 64/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0650 - accuracy: 0.9765 - val_loss: 0.0738 - val_accuracy: 0.9720\n",
      "Epoch 65/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0648 - accuracy: 0.9765 - val_loss: 0.0668 - val_accuracy: 0.9860\n",
      "Epoch 66/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0629 - accuracy: 0.9789 - val_loss: 0.0679 - val_accuracy: 0.9790\n",
      "Epoch 67/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0633 - accuracy: 0.9789 - val_loss: 0.0689 - val_accuracy: 0.9790\n",
      "Epoch 68/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0638 - accuracy: 0.9765 - val_loss: 0.0674 - val_accuracy: 0.9790\n",
      "Epoch 69/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0624 - accuracy: 0.9789 - val_loss: 0.0652 - val_accuracy: 0.9860\n",
      "Epoch 70/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0623 - accuracy: 0.9789 - val_loss: 0.0645 - val_accuracy: 0.9860\n",
      "Epoch 71/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0612 - accuracy: 0.9812 - val_loss: 0.0676 - val_accuracy: 0.9790\n",
      "Epoch 72/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0602 - accuracy: 0.9789 - val_loss: 0.0644 - val_accuracy: 0.9860\n",
      "Epoch 73/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0595 - accuracy: 0.9765 - val_loss: 0.0636 - val_accuracy: 0.9860\n",
      "Epoch 74/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0605 - accuracy: 0.9789 - val_loss: 0.0670 - val_accuracy: 0.9720\n",
      "Epoch 75/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0583 - accuracy: 0.9789 - val_loss: 0.0620 - val_accuracy: 0.9860\n",
      "Epoch 76/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0613 - accuracy: 0.9789 - val_loss: 0.0703 - val_accuracy: 0.9720\n",
      "Epoch 77/600\n",
      "14/14 [==============================] - 0s 11ms/step - loss: 0.0607 - accuracy: 0.9836 - val_loss: 0.0620 - val_accuracy: 0.9860\n",
      "Epoch 78/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0672 - accuracy: 0.9742 - val_loss: 0.0940 - val_accuracy: 0.9371\n",
      "Epoch 79/600\n",
      "14/14 [==============================] - 0s 12ms/step - loss: 0.0706 - accuracy: 0.9765 - val_loss: 0.0608 - val_accuracy: 0.9860\n",
      "Epoch 80/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0583 - accuracy: 0.9883 - val_loss: 0.0684 - val_accuracy: 0.9790\n",
      "Epoch 81/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0607 - accuracy: 0.9812 - val_loss: 0.0604 - val_accuracy: 0.9860\n",
      "Epoch 82/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0609 - accuracy: 0.9789 - val_loss: 0.0644 - val_accuracy: 0.9790\n",
      "Epoch 83/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0632 - accuracy: 0.9789 - val_loss: 0.0601 - val_accuracy: 0.9860\n",
      "Epoch 84/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0580 - accuracy: 0.9836 - val_loss: 0.0604 - val_accuracy: 0.9860\n",
      "Epoch 85/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0559 - accuracy: 0.9812 - val_loss: 0.0625 - val_accuracy: 0.9790\n",
      "Epoch 86/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0560 - accuracy: 0.9812 - val_loss: 0.0601 - val_accuracy: 0.9860\n",
      "Epoch 87/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0553 - accuracy: 0.9836 - val_loss: 0.0605 - val_accuracy: 0.9790\n",
      "Epoch 88/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0544 - accuracy: 0.9812 - val_loss: 0.0598 - val_accuracy: 0.9790\n",
      "Epoch 89/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0560 - accuracy: 0.9812 - val_loss: 0.0632 - val_accuracy: 0.9790\n",
      "Epoch 90/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0537 - accuracy: 0.9836 - val_loss: 0.0586 - val_accuracy: 0.9860\n",
      "Epoch 91/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0531 - accuracy: 0.9836 - val_loss: 0.0617 - val_accuracy: 0.9790\n",
      "Epoch 92/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0535 - accuracy: 0.9836 - val_loss: 0.0590 - val_accuracy: 0.9860\n",
      "Epoch 93/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0549 - accuracy: 0.9789 - val_loss: 0.0598 - val_accuracy: 0.9860\n",
      "Epoch 94/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0522 - accuracy: 0.9836 - val_loss: 0.0578 - val_accuracy: 0.9860\n",
      "Epoch 95/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0535 - accuracy: 0.9812 - val_loss: 0.0594 - val_accuracy: 0.9790\n",
      "Epoch 96/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0542 - accuracy: 0.9836 - val_loss: 0.0579 - val_accuracy: 0.9860\n",
      "Epoch 97/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0517 - accuracy: 0.9812 - val_loss: 0.0629 - val_accuracy: 0.9790\n",
      "Epoch 98/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0522 - accuracy: 0.9859 - val_loss: 0.0593 - val_accuracy: 0.9860\n",
      "Epoch 99/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0511 - accuracy: 0.9836 - val_loss: 0.0587 - val_accuracy: 0.9860\n",
      "Epoch 100/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0502 - accuracy: 0.9836 - val_loss: 0.0574 - val_accuracy: 0.9860\n",
      "Epoch 101/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0516 - accuracy: 0.9836 - val_loss: 0.0579 - val_accuracy: 0.9860\n",
      "Epoch 102/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0513 - accuracy: 0.9883 - val_loss: 0.0570 - val_accuracy: 0.9860\n",
      "Epoch 103/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0522 - accuracy: 0.9812 - val_loss: 0.0612 - val_accuracy: 0.9790\n",
      "Epoch 104/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0524 - accuracy: 0.9812 - val_loss: 0.0565 - val_accuracy: 0.9860\n",
      "Epoch 105/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0573 - accuracy: 0.9765 - val_loss: 0.0587 - val_accuracy: 0.9860\n",
      "Epoch 106/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0507 - accuracy: 0.9859 - val_loss: 0.0576 - val_accuracy: 0.9860\n",
      "Epoch 107/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0490 - accuracy: 0.9836 - val_loss: 0.0558 - val_accuracy: 0.9860\n",
      "Epoch 108/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0504 - accuracy: 0.9812 - val_loss: 0.0561 - val_accuracy: 0.9930\n",
      "Epoch 109/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0510 - accuracy: 0.9836 - val_loss: 0.0562 - val_accuracy: 0.9930\n",
      "Epoch 110/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0480 - accuracy: 0.9836 - val_loss: 0.0613 - val_accuracy: 0.9790\n",
      "Epoch 111/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0498 - accuracy: 0.9859 - val_loss: 0.0561 - val_accuracy: 0.9930\n",
      "Epoch 112/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0496 - accuracy: 0.9859 - val_loss: 0.0571 - val_accuracy: 0.9860\n",
      "Epoch 113/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0504 - accuracy: 0.9812 - val_loss: 0.0554 - val_accuracy: 0.9930\n",
      "Epoch 114/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0492 - accuracy: 0.9836 - val_loss: 0.0618 - val_accuracy: 0.9790\n",
      "Epoch 115/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0517 - accuracy: 0.9836 - val_loss: 0.0574 - val_accuracy: 0.9860\n",
      "Epoch 116/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0492 - accuracy: 0.9812 - val_loss: 0.0565 - val_accuracy: 0.9930\n",
      "Epoch 117/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0493 - accuracy: 0.9859 - val_loss: 0.0565 - val_accuracy: 0.9860\n",
      "Epoch 118/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0476 - accuracy: 0.9836 - val_loss: 0.0586 - val_accuracy: 0.9790\n",
      "Epoch 119/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0469 - accuracy: 0.9859 - val_loss: 0.0581 - val_accuracy: 0.9790\n",
      "Epoch 120/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0468 - accuracy: 0.9859 - val_loss: 0.0554 - val_accuracy: 0.9930\n",
      "Epoch 121/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0478 - accuracy: 0.9859 - val_loss: 0.0588 - val_accuracy: 0.9790\n",
      "Epoch 122/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0485 - accuracy: 0.9812 - val_loss: 0.0611 - val_accuracy: 0.9790\n",
      "Epoch 123/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0486 - accuracy: 0.9859 - val_loss: 0.0541 - val_accuracy: 0.9860\n",
      "Epoch 124/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0521 - accuracy: 0.9859 - val_loss: 0.0601 - val_accuracy: 0.9790\n",
      "Epoch 125/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0468 - accuracy: 0.9859 - val_loss: 0.0569 - val_accuracy: 0.9860\n",
      "Epoch 126/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0475 - accuracy: 0.9836 - val_loss: 0.0578 - val_accuracy: 0.9790\n",
      "Epoch 127/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0488 - accuracy: 0.9812 - val_loss: 0.0558 - val_accuracy: 0.9860\n",
      "Epoch 128/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0481 - accuracy: 0.9836 - val_loss: 0.0583 - val_accuracy: 0.9790\n",
      "Epoch 129/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0465 - accuracy: 0.9859 - val_loss: 0.0548 - val_accuracy: 0.9860\n",
      "Epoch 130/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0468 - accuracy: 0.9859 - val_loss: 0.0580 - val_accuracy: 0.9860\n",
      "Epoch 131/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0523 - accuracy: 0.9789 - val_loss: 0.0634 - val_accuracy: 0.9720\n",
      "Epoch 132/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0537 - accuracy: 0.9812 - val_loss: 0.0567 - val_accuracy: 0.9860\n",
      "Epoch 133/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0467 - accuracy: 0.9836 - val_loss: 0.0592 - val_accuracy: 0.9790\n",
      "Epoch 134/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0445 - accuracy: 0.9883 - val_loss: 0.0548 - val_accuracy: 0.9930\n",
      "Epoch 135/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0456 - accuracy: 0.9836 - val_loss: 0.0573 - val_accuracy: 0.9860\n",
      "Epoch 136/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0450 - accuracy: 0.9859 - val_loss: 0.0579 - val_accuracy: 0.9860\n",
      "Epoch 137/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0446 - accuracy: 0.9859 - val_loss: 0.0553 - val_accuracy: 0.9930\n",
      "Epoch 138/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0441 - accuracy: 0.9883 - val_loss: 0.0621 - val_accuracy: 0.9720\n",
      "Epoch 00138: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fc3906c4da0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x = X_train, y = y_train, validation_data = (X_test, y_test), epochs = 600, callbacks = [early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = pd.DataFrame(model.history.history)\n",
    "\n",
    "losses.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropout Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(24, activation = 'relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(24, activation = 'relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "\n",
    "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "14/14 [==============================] - 0s 11ms/step - loss: 0.6817 - accuracy: 0.5634 - val_loss: 0.6547 - val_accuracy: 0.7902\n",
      "Epoch 2/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.6459 - accuracy: 0.6948 - val_loss: 0.6290 - val_accuracy: 0.8741\n",
      "Epoch 3/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.6237 - accuracy: 0.7254 - val_loss: 0.5971 - val_accuracy: 0.9021\n",
      "Epoch 4/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.5934 - accuracy: 0.7934 - val_loss: 0.5627 - val_accuracy: 0.8951\n",
      "Epoch 5/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.5623 - accuracy: 0.8028 - val_loss: 0.5202 - val_accuracy: 0.8881\n",
      "Epoch 6/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.5258 - accuracy: 0.8404 - val_loss: 0.4701 - val_accuracy: 0.9021\n",
      "Epoch 7/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.4715 - accuracy: 0.8685 - val_loss: 0.4264 - val_accuracy: 0.9161\n",
      "Epoch 8/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.4567 - accuracy: 0.8568 - val_loss: 0.3841 - val_accuracy: 0.9161\n",
      "Epoch 9/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.4184 - accuracy: 0.8662 - val_loss: 0.3506 - val_accuracy: 0.9091\n",
      "Epoch 10/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.3866 - accuracy: 0.8615 - val_loss: 0.3203 - val_accuracy: 0.9301\n",
      "Epoch 11/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.3603 - accuracy: 0.8709 - val_loss: 0.2923 - val_accuracy: 0.8951\n",
      "Epoch 12/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.3424 - accuracy: 0.8826 - val_loss: 0.2684 - val_accuracy: 0.9091\n",
      "Epoch 13/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.2953 - accuracy: 0.8897 - val_loss: 0.2482 - val_accuracy: 0.9371\n",
      "Epoch 14/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.3111 - accuracy: 0.8873 - val_loss: 0.2356 - val_accuracy: 0.9091\n",
      "Epoch 15/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.2876 - accuracy: 0.9038 - val_loss: 0.2196 - val_accuracy: 0.9091\n",
      "Epoch 16/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.2818 - accuracy: 0.8897 - val_loss: 0.2088 - val_accuracy: 0.9371\n",
      "Epoch 17/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.2556 - accuracy: 0.9108 - val_loss: 0.2006 - val_accuracy: 0.9231\n",
      "Epoch 18/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.2555 - accuracy: 0.9085 - val_loss: 0.1913 - val_accuracy: 0.9301\n",
      "Epoch 19/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.2422 - accuracy: 0.9178 - val_loss: 0.1863 - val_accuracy: 0.9301\n",
      "Epoch 20/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2537 - accuracy: 0.9178 - val_loss: 0.1787 - val_accuracy: 0.9371\n",
      "Epoch 21/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.2222 - accuracy: 0.9178 - val_loss: 0.1716 - val_accuracy: 0.9441\n",
      "Epoch 22/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.2045 - accuracy: 0.9296 - val_loss: 0.1633 - val_accuracy: 0.9441\n",
      "Epoch 23/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.2039 - accuracy: 0.9319 - val_loss: 0.1562 - val_accuracy: 0.9441\n",
      "Epoch 24/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.2014 - accuracy: 0.9249 - val_loss: 0.1505 - val_accuracy: 0.9441\n",
      "Epoch 25/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.2169 - accuracy: 0.9178 - val_loss: 0.1464 - val_accuracy: 0.9441\n",
      "Epoch 26/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1996 - accuracy: 0.9225 - val_loss: 0.1470 - val_accuracy: 0.9510\n",
      "Epoch 27/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.2159 - accuracy: 0.9155 - val_loss: 0.1428 - val_accuracy: 0.9510\n",
      "Epoch 28/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.1725 - accuracy: 0.9390 - val_loss: 0.1338 - val_accuracy: 0.9441\n",
      "Epoch 29/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1914 - accuracy: 0.9249 - val_loss: 0.1306 - val_accuracy: 0.9580\n",
      "Epoch 30/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.1905 - accuracy: 0.9366 - val_loss: 0.1281 - val_accuracy: 0.9580\n",
      "Epoch 31/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.1692 - accuracy: 0.9413 - val_loss: 0.1220 - val_accuracy: 0.9510\n",
      "Epoch 32/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1782 - accuracy: 0.9437 - val_loss: 0.1190 - val_accuracy: 0.9510\n",
      "Epoch 33/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1543 - accuracy: 0.9554 - val_loss: 0.1161 - val_accuracy: 0.9510\n",
      "Epoch 34/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.1686 - accuracy: 0.9366 - val_loss: 0.1142 - val_accuracy: 0.9510\n",
      "Epoch 35/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1515 - accuracy: 0.9460 - val_loss: 0.1081 - val_accuracy: 0.9580\n",
      "Epoch 36/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1780 - accuracy: 0.9366 - val_loss: 0.1176 - val_accuracy: 0.9650\n",
      "Epoch 37/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1514 - accuracy: 0.9507 - val_loss: 0.1025 - val_accuracy: 0.9650\n",
      "Epoch 38/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1408 - accuracy: 0.9601 - val_loss: 0.0999 - val_accuracy: 0.9650\n",
      "Epoch 39/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1503 - accuracy: 0.9484 - val_loss: 0.0977 - val_accuracy: 0.9650\n",
      "Epoch 40/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1505 - accuracy: 0.9437 - val_loss: 0.0964 - val_accuracy: 0.9650\n",
      "Epoch 41/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1246 - accuracy: 0.9577 - val_loss: 0.1052 - val_accuracy: 0.9720\n",
      "Epoch 42/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1495 - accuracy: 0.9437 - val_loss: 0.0934 - val_accuracy: 0.9720\n",
      "Epoch 43/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1180 - accuracy: 0.9742 - val_loss: 0.0918 - val_accuracy: 0.9720\n",
      "Epoch 44/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1351 - accuracy: 0.9460 - val_loss: 0.0920 - val_accuracy: 0.9790\n",
      "Epoch 45/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1210 - accuracy: 0.9601 - val_loss: 0.0872 - val_accuracy: 0.9720\n",
      "Epoch 46/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1383 - accuracy: 0.9507 - val_loss: 0.0859 - val_accuracy: 0.9860\n",
      "Epoch 47/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1312 - accuracy: 0.9507 - val_loss: 0.0887 - val_accuracy: 0.9790\n",
      "Epoch 48/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1409 - accuracy: 0.9577 - val_loss: 0.0834 - val_accuracy: 0.9790\n",
      "Epoch 49/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1369 - accuracy: 0.9577 - val_loss: 0.0824 - val_accuracy: 0.9650\n",
      "Epoch 50/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1227 - accuracy: 0.9507 - val_loss: 0.0859 - val_accuracy: 0.9720\n",
      "Epoch 51/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1159 - accuracy: 0.9648 - val_loss: 0.0831 - val_accuracy: 0.9790\n",
      "Epoch 52/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1126 - accuracy: 0.9671 - val_loss: 0.0771 - val_accuracy: 0.9790\n",
      "Epoch 53/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1337 - accuracy: 0.9484 - val_loss: 0.0786 - val_accuracy: 0.9790\n",
      "Epoch 54/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1321 - accuracy: 0.9577 - val_loss: 0.0765 - val_accuracy: 0.9860\n",
      "Epoch 55/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1261 - accuracy: 0.9507 - val_loss: 0.0795 - val_accuracy: 0.9790\n",
      "Epoch 56/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1012 - accuracy: 0.9695 - val_loss: 0.0744 - val_accuracy: 0.9860\n",
      "Epoch 57/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0973 - accuracy: 0.9742 - val_loss: 0.0772 - val_accuracy: 0.9790\n",
      "Epoch 58/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1478 - accuracy: 0.9413 - val_loss: 0.0761 - val_accuracy: 0.9790\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 59/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1144 - accuracy: 0.9577 - val_loss: 0.0721 - val_accuracy: 0.9790\n",
      "Epoch 60/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1165 - accuracy: 0.9577 - val_loss: 0.0805 - val_accuracy: 0.9720\n",
      "Epoch 61/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1169 - accuracy: 0.9554 - val_loss: 0.0694 - val_accuracy: 0.9790\n",
      "Epoch 62/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1052 - accuracy: 0.9718 - val_loss: 0.0786 - val_accuracy: 0.9720\n",
      "Epoch 63/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0957 - accuracy: 0.9718 - val_loss: 0.0667 - val_accuracy: 0.9860\n",
      "Epoch 64/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1077 - accuracy: 0.9648 - val_loss: 0.0674 - val_accuracy: 0.9790\n",
      "Epoch 65/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1195 - accuracy: 0.9554 - val_loss: 0.0710 - val_accuracy: 0.9790\n",
      "Epoch 66/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1109 - accuracy: 0.9624 - val_loss: 0.0670 - val_accuracy: 0.9790\n",
      "Epoch 67/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1121 - accuracy: 0.9648 - val_loss: 0.0717 - val_accuracy: 0.9790\n",
      "Epoch 68/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0968 - accuracy: 0.9718 - val_loss: 0.0657 - val_accuracy: 0.9790\n",
      "Epoch 69/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.1009 - accuracy: 0.9648 - val_loss: 0.0647 - val_accuracy: 0.9790\n",
      "Epoch 70/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0790 - accuracy: 0.9742 - val_loss: 0.0631 - val_accuracy: 0.9860\n",
      "Epoch 71/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.1046 - accuracy: 0.9695 - val_loss: 0.0704 - val_accuracy: 0.9790\n",
      "Epoch 72/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.1082 - accuracy: 0.9624 - val_loss: 0.0626 - val_accuracy: 0.9790\n",
      "Epoch 73/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0959 - accuracy: 0.9695 - val_loss: 0.0614 - val_accuracy: 0.9860\n",
      "Epoch 74/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0861 - accuracy: 0.9765 - val_loss: 0.0624 - val_accuracy: 0.9860\n",
      "Epoch 75/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0738 - accuracy: 0.9742 - val_loss: 0.0610 - val_accuracy: 0.9860\n",
      "Epoch 76/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.1058 - accuracy: 0.9601 - val_loss: 0.0693 - val_accuracy: 0.9790\n",
      "Epoch 77/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0966 - accuracy: 0.9624 - val_loss: 0.0608 - val_accuracy: 0.9860\n",
      "Epoch 78/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0864 - accuracy: 0.9695 - val_loss: 0.0622 - val_accuracy: 0.9790\n",
      "Epoch 79/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0798 - accuracy: 0.9718 - val_loss: 0.0631 - val_accuracy: 0.9790\n",
      "Epoch 80/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0962 - accuracy: 0.9718 - val_loss: 0.0592 - val_accuracy: 0.9860\n",
      "Epoch 81/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0917 - accuracy: 0.9648 - val_loss: 0.0622 - val_accuracy: 0.9790\n",
      "Epoch 82/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0873 - accuracy: 0.9742 - val_loss: 0.0585 - val_accuracy: 0.9860\n",
      "Epoch 83/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0904 - accuracy: 0.9718 - val_loss: 0.0579 - val_accuracy: 0.9860\n",
      "Epoch 84/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0698 - accuracy: 0.9859 - val_loss: 0.0590 - val_accuracy: 0.9790\n",
      "Epoch 85/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0913 - accuracy: 0.9648 - val_loss: 0.0599 - val_accuracy: 0.9790\n",
      "Epoch 86/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0996 - accuracy: 0.9648 - val_loss: 0.0596 - val_accuracy: 0.9860\n",
      "Epoch 87/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0821 - accuracy: 0.9789 - val_loss: 0.0613 - val_accuracy: 0.9790\n",
      "Epoch 88/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0919 - accuracy: 0.9671 - val_loss: 0.0576 - val_accuracy: 0.9860\n",
      "Epoch 89/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0666 - accuracy: 0.9789 - val_loss: 0.0573 - val_accuracy: 0.9860\n",
      "Epoch 90/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0813 - accuracy: 0.9742 - val_loss: 0.0592 - val_accuracy: 0.9790\n",
      "Epoch 91/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0748 - accuracy: 0.9789 - val_loss: 0.0558 - val_accuracy: 0.9860\n",
      "Epoch 92/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0818 - accuracy: 0.9718 - val_loss: 0.0634 - val_accuracy: 0.9790\n",
      "Epoch 93/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0824 - accuracy: 0.9742 - val_loss: 0.0601 - val_accuracy: 0.9790\n",
      "Epoch 94/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0775 - accuracy: 0.9812 - val_loss: 0.0552 - val_accuracy: 0.9860\n",
      "Epoch 95/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0800 - accuracy: 0.9812 - val_loss: 0.0535 - val_accuracy: 0.9860\n",
      "Epoch 96/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0779 - accuracy: 0.9789 - val_loss: 0.0553 - val_accuracy: 0.9790\n",
      "Epoch 97/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0659 - accuracy: 0.9812 - val_loss: 0.0561 - val_accuracy: 0.9790\n",
      "Epoch 98/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0825 - accuracy: 0.9695 - val_loss: 0.0541 - val_accuracy: 0.9930\n",
      "Epoch 99/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0738 - accuracy: 0.9742 - val_loss: 0.0542 - val_accuracy: 0.9790\n",
      "Epoch 100/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0847 - accuracy: 0.9765 - val_loss: 0.0712 - val_accuracy: 0.9720\n",
      "Epoch 101/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0791 - accuracy: 0.9742 - val_loss: 0.0540 - val_accuracy: 0.9860\n",
      "Epoch 102/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0658 - accuracy: 0.9789 - val_loss: 0.0519 - val_accuracy: 0.9860\n",
      "Epoch 103/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0678 - accuracy: 0.9789 - val_loss: 0.0508 - val_accuracy: 0.9860\n",
      "Epoch 104/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0698 - accuracy: 0.9718 - val_loss: 0.0534 - val_accuracy: 0.9790\n",
      "Epoch 105/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0867 - accuracy: 0.9695 - val_loss: 0.0600 - val_accuracy: 0.9790\n",
      "Epoch 106/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0818 - accuracy: 0.9695 - val_loss: 0.0531 - val_accuracy: 0.9860\n",
      "Epoch 107/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0599 - accuracy: 0.9836 - val_loss: 0.0570 - val_accuracy: 0.9790\n",
      "Epoch 108/600\n",
      "14/14 [==============================] - 0s 10ms/step - loss: 0.0576 - accuracy: 0.9859 - val_loss: 0.0531 - val_accuracy: 0.9790\n",
      "Epoch 109/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0704 - accuracy: 0.9812 - val_loss: 0.0501 - val_accuracy: 0.9860\n",
      "Epoch 110/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0980 - accuracy: 0.9624 - val_loss: 0.0616 - val_accuracy: 0.9790\n",
      "Epoch 111/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0732 - accuracy: 0.9765 - val_loss: 0.0558 - val_accuracy: 0.9790\n",
      "Epoch 112/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0648 - accuracy: 0.9789 - val_loss: 0.0518 - val_accuracy: 0.9860\n",
      "Epoch 113/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0614 - accuracy: 0.9836 - val_loss: 0.0602 - val_accuracy: 0.9790\n",
      "Epoch 114/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0783 - accuracy: 0.9765 - val_loss: 0.0516 - val_accuracy: 0.9790\n",
      "Epoch 115/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0559 - accuracy: 0.9859 - val_loss: 0.0503 - val_accuracy: 0.9790\n",
      "Epoch 116/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0616 - accuracy: 0.9836 - val_loss: 0.0603 - val_accuracy: 0.9790\n",
      "Epoch 117/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0694 - accuracy: 0.9718 - val_loss: 0.0516 - val_accuracy: 0.9790\n",
      "Epoch 118/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0673 - accuracy: 0.9765 - val_loss: 0.0501 - val_accuracy: 0.9790\n",
      "Epoch 119/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0734 - accuracy: 0.9765 - val_loss: 0.0525 - val_accuracy: 0.9790\n",
      "Epoch 120/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0648 - accuracy: 0.9718 - val_loss: 0.0542 - val_accuracy: 0.9790\n",
      "Epoch 121/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0701 - accuracy: 0.9765 - val_loss: 0.0523 - val_accuracy: 0.9790\n",
      "Epoch 122/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0660 - accuracy: 0.9765 - val_loss: 0.0576 - val_accuracy: 0.9790\n",
      "Epoch 123/600\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.0676 - accuracy: 0.9812 - val_loss: 0.0594 - val_accuracy: 0.9790\n",
      "Epoch 124/600\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.0818 - accuracy: 0.9648 - val_loss: 0.0495 - val_accuracy: 0.9860\n",
      "Epoch 125/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0669 - accuracy: 0.9765 - val_loss: 0.0502 - val_accuracy: 0.9930\n",
      "Epoch 126/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0585 - accuracy: 0.9883 - val_loss: 0.0494 - val_accuracy: 0.9930\n",
      "Epoch 127/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0655 - accuracy: 0.9859 - val_loss: 0.0617 - val_accuracy: 0.9790\n",
      "Epoch 128/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0644 - accuracy: 0.9765 - val_loss: 0.0522 - val_accuracy: 0.9790\n",
      "Epoch 129/600\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.0881 - accuracy: 0.9718 - val_loss: 0.0492 - val_accuracy: 0.9790\n",
      "Epoch 130/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0637 - accuracy: 0.9836 - val_loss: 0.0582 - val_accuracy: 0.9790\n",
      "Epoch 131/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0682 - accuracy: 0.9812 - val_loss: 0.0513 - val_accuracy: 0.9790\n",
      "Epoch 132/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0520 - accuracy: 0.9859 - val_loss: 0.0594 - val_accuracy: 0.9790\n",
      "Epoch 133/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0627 - accuracy: 0.9812 - val_loss: 0.0537 - val_accuracy: 0.9790\n",
      "Epoch 134/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0608 - accuracy: 0.9812 - val_loss: 0.0526 - val_accuracy: 0.9860\n",
      "Epoch 135/600\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.0646 - accuracy: 0.9742 - val_loss: 0.0545 - val_accuracy: 0.9790\n",
      "Epoch 136/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0636 - accuracy: 0.9765 - val_loss: 0.0557 - val_accuracy: 0.9790\n",
      "Epoch 137/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0621 - accuracy: 0.9789 - val_loss: 0.0541 - val_accuracy: 0.9790\n",
      "Epoch 138/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0748 - accuracy: 0.9765 - val_loss: 0.0602 - val_accuracy: 0.9790\n",
      "Epoch 139/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0555 - accuracy: 0.9883 - val_loss: 0.0494 - val_accuracy: 0.9930\n",
      "Epoch 140/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0607 - accuracy: 0.9836 - val_loss: 0.0564 - val_accuracy: 0.9790\n",
      "Epoch 141/600\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 0.0559 - accuracy: 0.9765 - val_loss: 0.0514 - val_accuracy: 0.9790\n",
      "Epoch 142/600\n",
      "14/14 [==============================] - 0s 9ms/step - loss: 0.0615 - accuracy: 0.9765 - val_loss: 0.0553 - val_accuracy: 0.9790\n",
      "Epoch 143/600\n",
      "14/14 [==============================] - 0s 8ms/step - loss: 0.0564 - accuracy: 0.9836 - val_loss: 0.0492 - val_accuracy: 0.9860\n",
      "Epoch 144/600\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 0.0747 - accuracy: 0.9742 - val_loss: 0.0569 - val_accuracy: 0.9790\n",
      "Epoch 00144: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fc39c24e588>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x = X_train, y = y_train, validation_data = (X_test, y_test), epochs = 600,\n",
    "         callbacks = [early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = pd.DataFrame(model.history.history)\n",
    "\n",
    "losses.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-37-bc83193b8b59>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
      "Instructions for updating:\n",
      "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [1]], dtype=int32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.98      0.97        53\n",
      "           1       0.99      0.98      0.98        90\n",
      "\n",
      "    accuracy                           0.98       143\n",
      "   macro avg       0.98      0.98      0.98       143\n",
      "weighted avg       0.98      0.98      0.98       143\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[52  1]\n",
      " [ 2 88]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 25)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean radius                17.990000\n",
       "mean texture               10.380000\n",
       "mean smoothness             0.118400\n",
       "mean compactness            0.277600\n",
       "mean concavity              0.300100\n",
       "mean concave points         0.147100\n",
       "mean symmetry               0.241900\n",
       "mean fractal dimension      0.078710\n",
       "radius error                1.095000\n",
       "texture error               0.905300\n",
       "smoothness error            0.006399\n",
       "compactness error           0.049040\n",
       "concavity error             0.053730\n",
       "concave points error        0.015870\n",
       "symmetry error              0.030030\n",
       "fractal dimension error     0.006193\n",
       "worst radius               25.380000\n",
       "worst texture              17.330000\n",
       "worst smoothness            0.162200\n",
       "worst compactness           0.665600\n",
       "worst concavity             0.711900\n",
       "worst concave points        0.265400\n",
       "worst symmetry              0.460100\n",
       "worst fractal dimension     0.118900\n",
       "benign_0__mal_1             0.000000\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = df.iloc[0][:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24,)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426, 24)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = scaler.transform(new_data.values.reshape(-1, 24))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 24)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0]], dtype=int32)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_classes(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}