Hypothesis testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis testing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we determine if the average travel time among different socio-demographics, in this case strata, are statistically significant. Additionally, we hypothesize that the lower the strata, the longer the travel time. Therefore, we posit the following hypothesis: \n",
    "\n",
    "- $H_0 : \\mu_1 = \\mu_2 $ \n",
    "- $H_0 : \\mu_1 = \\mu_3 $\n",
    "- $H_0 : \\mu_1 = \\mu_4 $\n",
    "- $H_0 : \\mu_1 = \\mu_5 $\n",
    "- $H_0 : \\mu_2 = \\mu_3 $\n",
    "- $H_0 : \\mu_2 = \\mu_4 $\n",
    "- $H_0 : \\mu_2 = \\mu_5 $\n",
    "- $H_0 : \\mu_3 = \\mu_4 $\n",
    "- $H_0 : \\mu_3 = \\mu_5 $\n",
    "- $H_0 : \\mu_4 = \\mu_5 $\n",
    "\n",
    "Where the $\\mu_j$ is the average travel time of strata j. Respectively, the alternative hypothesis are:\n",
    "\n",
    "- $H_1 : \\mu_1 > \\mu_2 $ \n",
    "- $H_1 : \\mu_1 > \\mu_3 $\n",
    "- $H_1 : \\mu_1 > \\mu_4 $\n",
    "- $H_1 : \\mu_1 > \\mu_5 $\n",
    "- $H_1 : \\mu_2 > \\mu_3 $\n",
    "- $H_1 : \\mu_2 > \\mu_4 $\n",
    "- $H_1 : \\mu_2 > \\mu_5 $\n",
    "- $H_1 : \\mu_3 > \\mu_4 $\n",
    "- $H_1 : \\mu_3 > \\mu_5 $\n",
    "- $H_1 : \\mu_4 > \\mu_5 $\n",
    "\n",
    "To test these hypothesis, we will use a t-statistic, and a confidence interval $\\alpha = 0.95$. The t-statistic is define as follows: \n",
    "\n",
    "$ t-statistic = \\frac{\\bar{\\mu_1} - \\bar{\\mu_1}}{\\sqrt{\\frac{\\sigma_1^2}{n_1} + \\frac{\\sigma_2^2}{n_1}}} $\n",
    "\n",
    "We will test travel time in both directions, HOME-to-WORK and WORK-to-HOME. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os; os.chdir('../..')\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas.io.sql as sqlio\n",
    "from datetime import datetime\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import geopandas as gpd\n",
    "from itertools import combinations\n",
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "HW = pd.read_csv(os.getcwd() + '/08_Results/HW_location.csv')\n",
    "tm_skims = pd.read_csv(os.getcwd() + '/07_Notebooks/cp255/data/tm_skims.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging travel times to users table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "skims = pd.melt(tm_skims, id_vars = ['origin_id', 'destination_id'],\n",
    "        value_vars = ['4_7', '7_10', '10_13', '13_16', '16_19','19_22'],\n",
    "        var_name = 'period', value_name = 'travel_time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "HW['HW_period'] = pd.cut(HW.rank1_time, bins = [0,7,10,13,16,19,24], \n",
    "       labels = ['4_7', '7_10', '10_13', '13_16', '16_19', '19_22'])\n",
    "\n",
    "HW['WH_period'] = pd.cut(HW.rank2_time, bins = [0,7,10,13,16,19,24], \n",
    "       labels = ['4_7', '7_10', '10_13', '13_16', '16_19', '19_22'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging travel times for HOME-WORK and WORK-HOME\n",
    "HW = HW.merge(skims, how = 'left', left_on = ['home', 'work', 'HW_period'], \n",
    "         right_on = ['origin_id', 'destination_id', 'period'])\n",
    "\n",
    "HW = HW.merge(skims, how = 'left', left_on = ['home', 'work', 'WH_period'], \n",
    "         right_on = ['origin_id', 'destination_id', 'period'], suffixes=('_HW', '_WH'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "idnumerotarjeta           0\n",
       "rank1                     0\n",
       "rank2                  2128\n",
       "rank1_time                0\n",
       "rank2_time             2128\n",
       "home                      0\n",
       "ESTRATO                   0\n",
       "home_lat                  0\n",
       "home_lon                  0\n",
       "work                   2128\n",
       "work_lat               2128\n",
       "work_lon               2128\n",
       "HW_period                 0\n",
       "WH_period              2128\n",
       "origin_id_HW         263206\n",
       "destination_id_HW    263206\n",
       "period_HW            263206\n",
       "travel_time_HW       263206\n",
       "origin_id_WH         263206\n",
       "destination_id_WH    263206\n",
       "period_WH            263206\n",
       "travel_time_WH       263206\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HW.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are some missing values. Missing values occured because the GTFS did not include the recent cablecar in the south of the city. \n",
    "\n",
    "TO DO: \n",
    "The cable car is located in a estrato 1 region, so we will aproximate the home station to the closest station to the cable car, which in this case is \"Portal Tunal\". In fact, users using the cable car will need to transfer at this location to board the BRT system. However, this will add more travel time for strata 1 users. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "HW = HW.dropna(subset=['destination_id_WH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "idnumerotarjeta      0\n",
       "rank1                0\n",
       "rank2                0\n",
       "rank1_time           0\n",
       "rank2_time           0\n",
       "home                 0\n",
       "ESTRATO              0\n",
       "home_lat             0\n",
       "home_lon             0\n",
       "work                 0\n",
       "work_lat             0\n",
       "work_lon             0\n",
       "HW_period            0\n",
       "WH_period            0\n",
       "origin_id_HW         0\n",
       "destination_id_HW    0\n",
       "period_HW            0\n",
       "travel_time_HW       0\n",
       "origin_id_WH         0\n",
       "destination_id_WH    0\n",
       "period_WH            0\n",
       "travel_time_WH       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HW.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Home - work travel times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Strata</th>\n",
       "      <th>sample_size</th>\n",
       "      <th>Average Travel Time</th>\n",
       "      <th>Standard deviation</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>918421</td>\n",
       "      <td>27.706606</td>\n",
       "      <td>11.949338</td>\n",
       "      <td>142.786521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3021902</td>\n",
       "      <td>22.463491</td>\n",
       "      <td>11.006309</td>\n",
       "      <td>121.138791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2129308</td>\n",
       "      <td>18.421185</td>\n",
       "      <td>10.412604</td>\n",
       "      <td>108.422267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>307540</td>\n",
       "      <td>17.362999</td>\n",
       "      <td>10.402443</td>\n",
       "      <td>108.210473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>30796</td>\n",
       "      <td>17.136014</td>\n",
       "      <td>10.497342</td>\n",
       "      <td>110.190603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Strata  sample_size  Average Travel Time  Standard deviation    Variance\n",
       "0       1       918421            27.706606           11.949338  142.786521\n",
       "1       2      3021902            22.463491           11.006309  121.138791\n",
       "2       3      2129308            18.421185           10.412604  108.422267\n",
       "3       4       307540            17.362999           10.402443  108.210473\n",
       "4       5        30796            17.136014           10.497342  110.190603"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_1 = HW[HW.ESTRATO == 1].travel_time_HW\n",
    "x_2 = HW[HW.ESTRATO == 2].travel_time_HW\n",
    "x_3 = HW[HW.ESTRATO == 3].travel_time_HW\n",
    "x_4 = HW[HW.ESTRATO == 4].travel_time_HW\n",
    "x_5 = HW[HW.ESTRATO == 5].travel_time_HW\n",
    "\n",
    "distributions = [x_1, x_2, x_3, x_4, x_5]\n",
    "summary_table = pd.DataFrame({'Strata': [1,2,3,4,5]})\n",
    "\n",
    "summary_table['sample_size'] = [len(x) for x in distributions]\n",
    "summary_table['Average Travel Time'] = [x.mean() for x in distributions]\n",
    "summary_table['Standard deviation'] = [x.std() for x in distributions]\n",
    "summary_table['Variance'] = [np.var(x) for x in distributions]\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strata 1 and 2 have the highest average travel time with 27.7, and 22.4 minutes respectively. For higher strata, meaning 3,4, and 5, the mean travel time seems to be similar, with a variation of around 1 minute. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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druBPBsFwvk8RfKYPmNn3w89hOqX6PeuJnu6zUQlLDS1FUfe7fc/DIUOR/VuUrCx9+H6EPXQuBj5LkJQ/IjIr2K+AcjO71cxKerudGOl4j5+kq73/Eg5TjvgEXbP9/S994O5PE8xsdjddgY4igtn5vgWsNrMtZpbuIZ0pfbfM7NPA9QSBJifoNXY9Qa6rS+g6h30parVE57ChoCefZc0wJyISUs8mEZEhxMzeQhBQguBHyzmJ8vmYWbf5Oty92cz+QPBD50Qze7u7R/JLXEzXD6Z4vZoAonueXOzu93W3TUnJQO/X6O2lEtwp7OsG3b0J+E8z+zbBcKl3hLfzgMlhO/4PQQL1d8YZwjmU9HSf9aXH1kBeCKyLut/te25mGQRDI2PXTbsw4BTp0TSPoNfY3wHnAicQJGP/F+BMMzstWaLsFPX5PXZ3N7NbgP+PoEfWhcADZpZFMJMpwH6CXkl94u6bgUvNrJBgv7ydYLjaGQRD1eYBfzKzT7t7OnMFJhUG2CI9aJsJZl2LO/TOzN4xUO2KQxfcRUT6mQ60IiJDy7ui7n+rm8TRs1KsMzq59OVx7jtwZ4J1o7c/GMloR6qB3q/7ou7PSViqZ2VSEs6e+Kq7/8zdLycYYvYPBD+6IfihnGy2xKGgp/tsX8yy6GE23fVMG59Si9Kjgq5Z4eamUP5Euv53jH2N/cbdt7r7be7+CXefTfCZ2Rwungt8Jg2b6cl73Eri4VK/pOv9/kT4973ApPD+bekMrIYzvz3u7te7+7vC7UTPKPlfYW6ugXIKwYx8AL9JFGgKpXoOS9VQ/Z6JiByXFGwSERlaJkXdT5isN+xhcH6KdT4DvBne/7CZZZvZxKj1n3X3N+Ovyl+i7g+lWbYiQxUsaamha6D3a3TS2nOTFTSz0UB309r3Whh8ehi4Jurp2PxQ0UNRhsJ73N0+KyIY1gRwIE5y8OheN1NJrrvE22nbN2Hvsw3hwxPN7IRuVomeCTFhIuT+FvbO/ETUU33JLxbR3Xs8l64k1q8kChi5eyXwx/Dhu81sCl2JwZ1g1rp+4+6H3f0bwBPhU0UEAaBo/fn9SukcFvr7FOrrybE+nd8zERHpIwWbRESGlui8IScmKfcxjk4ynZC7R/dcGg+sIOhJEhlKnWgIHeHsdZFhd0vN7EOpbHMARIaw9Hm412AYhP26HtgR3j/XzGJ/fEb7NAOTvHZX1P3YYf3RQ5SGwnv8DjM7Lcnyf6NreNmf4izfGHU/YVDDzN4NzO6mLeneN5HAiAFfTlTIzHKBz0c9Fe91DqRdUffTkRbiA2Y2M8nyL0bd7+61/zz8m0WQJDsSVHnC3Xf2sn09tSvq/kB+v1I6h5nZSQT5m7rTk2N9qt+zxQSzZYqISD9SsElEZGh5Mer+v4f5OI5iZucAP+5hvdEBpcvpGkLXAqzsZt2v0XV1+bbuks6a2Ylm9uN+HroR+cE2y8zG9eN2+tOA7dcw4PiD8GEGcJeZTY6zjXcRNYNWb5nZTWZ2apLlRjCjVsT6mCJlQFt4P2E9A8iAO81s2jELgu/jdeHDNoKZ+GI9Tddsa/9iZvPj1HMy8IsU2hIdrEjHvrmZrkkErjazj8cWMLNsgh45kaF2TySYUCAtzOw7ZvbO8HOSSPQMjrGfn97IB+42s2OS95vZR+n6vNZw9OQMxwiHjkUCH1fT9f92nxKDh205w8y+kiwxevg5fV/4sI2jgzCQ/s9QtHVApNfXZfFyC5rZDOAegtxS3enJsf41uoYov9vM3hlbINw3v2do9JgUERnRlCBcRGRoeYxgCvJ5wEJgc5hwdjtBMu8LgA8Q/IC4m9SuDOPum83sJYKhPhfTlc/i/u4S67r7X8zsywTBilEESWfXAA8Q/BBoBcYBCwgS1L41XPVrqbStl/4cbisLWGVmvyCYhtzD5S+7e0U/br/PBmG//ozg83Imwaxer5vZ/xL8UM8lyBf2EYKgyGMcPWSqpy4HPm1mWwlm6HoNOETwg35W2I6FYdkygtnFjnD3VjN7jqD3wVvN7A6CpMq1UcWeGMCk4vcSfG82hPvsFYJ9di7wUbpm0ro+TNx8FHc/HK73WYIeGqvN7KcE+76AYBjY5QT7/mGCxNJxuftOM9tB0APqIjO7EXiWrpnS2tz9z6m+MHevMLOrgd8SBEV+aWaXAPcBVQTJuD9O8JmBIFfRlanW30v/QPA5LzOzxwj20wGCpODTCGbrjPQ0q6Pnwfd4Iu/xxvB4shEYDbwbuCiq3Ofc/VAK9d0M/DDq8cFwG301Hvge8G0z+wvwPMH5oQEoIZjx8VK6Zry82d2rYup4iuBYacB1YQB7I10B3gPu/kpvGhd+1n9FkLw9H3gh6jvTSfC+fZzgmPcrgllWk0n5WO/unWb2A+B/CD7Lj4Tnz9Xh+suAfyboufknumZ9FRGR/uDuuummm266pXgj+CfZw9vtKZQvjip/b4rbWAyUR60Xe6sHPgx8Ieq5i1Oo9/Nx6npfD177pQQ/NBO1K/pWDuTFqWNduLymj+9DCbA7yfYvjirb7XtA8CMwUqY4He9nqu9POvZrD/ZbMfDXJPXXEfywvjHquSVx6lkStfzGOMurUnw9W4D5Cdp6BkHPu0TrFkeV7dF3IZXPYmydwLe7eS0/6GZ7o7rZ94cIgmtJ931Y1yUEP9zj1VOT7HUkad9lBMeWZK9xG7AgSR0pfY+6Kwu8nOLnZx/wjj58H6L39akEwY9E22oHvtjD71pj1Pr/3dt2xtT7vhT3jRP0RstOUM9Pk6x3b0zZHh23CfJErUlSfyfBjH1JjyNhXSkf68PymQSBpETlGwiOuUm/F6m0LcHnKO53NqpsTVhuXTo+D7rppptuQ/mmYXQiIkOMu79G8I/uDwh6ObUQ9OjYBNxA8M/s73tR9e/oGt4AQYDj4R606y6CXimfAx4i6JHSTNAD5wDwXNi+FcB0d29OUFWfeZCEdynBD5b1BEES76/t9aeB3K8e9GI7E7gqrLcGaCLoGfET4FR3v6/XL6bLXOBDBL2p1hJ81toJXtdu4H6CHgaLPU5PoLCtzxEk8b0jbF9TGtrVa+7+H8B5BDmOygjen3KC4UDnuvuXulm/niCY9EWC4bJ1BK9pC8Hn+BR3fyrFttwd1vUHguT/ff6uufudBLOt/SfBe1ZN0NPlAEFPt6uBk919U1+3lYJ3Au8h+NyvJpg1r41gn+8P2/NZYJ67/zVN2+x0938CPgg8QvDethIMy/oNsMzdb0i1svC7Fj3UMJUhkqnUu4pgBsvPEQRVthIEUDoIzhOvEfSqOt3dr3T3tgRVfZrgO/gEXfs3Ldy9jqA30pcIPuv1BJ/1nQT5A89292sS13BUXT061rt7B8F7+M8Ek2PUEJxDdxDk0jo1POaKiEg/M/dh+b+5iIiIiMiQFM5Ct5tg+NZf3P3swW2RiIjIwFLPJhERERGR9PoEXblRbx7MhoiIiAwG9WwSEREREUmTcNa0LQTJvPcBpUmGs4mIiIxImo1ORERERKQPzOzvCGavm0YwGcP4cNF3FGgSEZHjkXo2iYiIiIj0gZmtA06Jefop4F3u3jkITRIRERlU6tkkIiIiIpIekZnP7gK+r0CTiIgcr9SzSURERERERERE0kaz0YmIiIiIiIiISNoo2CQiIiIiIiIiImmjYJOIiIiIiIiIiKSNgk0iIiIiIiIiIpI2CjaJiIiIiIiIiEjaKNgkIiIiIiIiIiJpo2CTiIiIiIiIiIikjYJNIiIiIiIiIiKSNgo2iYiIiIiIiIhI2ijYJCIiIiIiIiIiaaNgk4iIiIiIiIiIpI2CTSIiIiIiIiIikjYKNomIiIiIiIiISNoo2CQiIiIiIiIiImmjYJOIiIiIiIiIiKSNgk0iIiIiIiIiIpI2CjaJiIiIiIiIiEjaKNgkIiIiIiIiIiJpo2CTiIiIiIiIiIikjYJNIiIiIiIiIiKSNgo2iYiIiIiIiIhI2ijYJCIiIiIiIiIiaaNgk4iIiIiIiIiIpI2CTSIiIiIiIiIikjYKNomIiIiIiIiISNoo2CQiIiIiIiIiImmjYJOIiIiIiIiIiKSNgk0iIiIiIiIiIpI2CjaJiIiIiIiIiEjaKNgkIiIiIiIiIiJpo2CTiIiIiIiIiIikjYJNIiIiIiIiIiKSNgo2iYiIiIiIiIhI2ijYJCIiIiIiIiIiaaNgk4iIiIiIiIiIpI2CTSIiIiIiIiIikjYKNomIiIiIiIiISNoo2CQiIiIiIiIiImmjYJOIiIiIiIiIiKSNgk0iIiIiIiIiIpI2CjaJiIiIiIiIiEjaZA12AwbC+PHjvbS0dLCbISIy5Lz00kuH3H3CYLdjsOk8ISISn84TOkeIiCST6DxxXASbSktLWbt27WA3Q0RkyDGzNwe7DUOBzhMiIvHpPKFzhIhIMonOExpGJyIiIiIiIiIiaaNgk4iIiIiIiIiIpE2/BpvMbIWZbTGz7WZ2bZzluWZ2d7h8jZmVxiyfaWb1ZvblVOsUEREREREREZHB0285m8wsE/gJcD5QBrxoZqvcfWNUsSuBanefY2aXAt8DLolafgPwcA/rFJERoK2tjbKyMpqbmwe7KSNCXl4e06dPJzs7e7CbchQzWwH8EMgEfuHu/xWzPBf4FfA2oBK4xN13RS2fCWwErnP376dSp4iMDDpPpNdQPU+IiPSWzhPp1dPzRH8mCD8N2O7uOwDM7C7gIoIfBREXAdeF91cCN5mZubub2cXADqChh3WKyAhQVlZGUVERpaWlmNlgN2dYc3cqKyspKyvjhBNOGOzmHKGLEiLSFzpPpM9QPU+IiPSFzhPp05vzRH8Oo5sG7Il6XBY+F7eMu7cDh4ESMysEvgp8qxd1AmBmV5nZWjNbe/DgwV6/CBEZHM3NzZSUlOjEkAZmRklJyVC8qnPkAoK7twKRCwjRLgLuCO+vBM6z8EMRdVHi9R7WKSIjgM4T6TOEzxMiIr2m80T69OY80Z/BpnjvqKdY5lvADe5e34s6gyfdb3H3pe6+dMKECd02VkSGHp0Y0meI7ktdlBCRPhmix7ZhSftSREYiHdvSp6f7sj+H0ZUBM6IeTwf2JShTZmZZwBigClgOfNDM/hsoBjrNrBl4KYU6RURkeEjLRYmYE1+PLkoAtwAsXbo0bhkREREREem5/gw2vQjMNbMTgL3ApcBHY8qsAq4AVgMfBJ50dwfOjBQws+uAene/KQxIdVeniIxAv12zO631fXT5zF6td+ONN3LVVVdRUFDQo/Vuv/12LrjgAqZOndqr7Sbzhz/8geuuu45NmzbxwgsvsHTp0rRvo5/oooSIpI3OE4ldc8013H///eTk5HDiiSfyy1/+kuLi4rRvR0RkKNN5IrGvf/3r3HfffWRkZDBx4kRuv/32Pm+n34bRhcMdPgM8CmwCfu/ur5vZ9Wb2vrDYrQTDIbYDXwKu7U2d/fUaZGTYdqCOz/3uFV7cVTXYTZER4MYbb6SxsTHuso6OjoTr3X777ezb1z8xj0WLFvGnP/2Js846q1/q70dHLkqYWQ7BBYRVMWUiFyUg6qKEu5/p7qXuXgrcCHzH3W9KsU4ZRq5b9TrPbTs02M0QSdlQPE+cf/75bNiwgVdffZV58+bx3e9+t1+2I5JOa3dV8ZWV6+nsVOdjGVmG4nnimmuu4dVXX2XdunW85z3v4frrr+9znf2Zswl3f8jd57n7ie7+7fC5b7j7qvB+s7t/yN3nuPtpkVnmYuo4Mp11ojpF4mnv6OQHj2/lH370LKvW72Pl2rLBbpIMIw0NDbz73e/mlFNOYdGiRdx999386Ec/Yt++fZxzzjmcc845AIwaNYpvfOMbLF++nNWrV3P99dezbNkyFi1axFVXXYW7s3LlStauXctll13GkiVLaGpqorS0lEOHgh/Qa9eu5eyzzwbguuuu44orruCCCy6gtLSUP/3pT3zlK19h8eLFrFixgra2tmPaumDBAk466aQB2zfpoosS0p3G1nZu/9su7noxvVciRdJhOJ0nLrjgArKyggENp59+OmVl+p9Ihr4/vryX368t45U91YPdFJFeGU7nidGjRx/V7nTkuurXYJPIYHrg1f386M/b+IfFU1gyo5hN5bWD3SQZRh555BGmTjnGetIAACAASURBVJ3K+vXr2bBhAytWrOBzn/scU6dO5amnnuKpp54CgoPxokWLWLNmDWeccQaf+cxnePHFF9mwYQNNTU088MADfPCDH2Tp0qXceeedrFu3jvz8/KTbfuONN3jwwQe57777uPzyyznnnHN47bXXyM/P58EHHxyIlz9gdFFCkimrbgLgtb2HB7klIscarueJ2267jQsvvDBt+0Gkv2zaH/zv/siG8kFuiUjvDLfzxH/8x38wY8YM7rzzzqHfs0lkMK0vqyEvO4MffHgJy0rHsrm8jvaOzsFulgwTixcv5oknnuCrX/0qzz77LGPGjIlbLjMzkw984ANHHj/11FMsX76cxYsX8+STT/L66z3vVHPhhReSnZ3N4sWL6ejoYMWKFUfatGvXrl69HpHhqKw66GL+ZmUjhxuPvQonMpiG43ni29/+NllZWVx22WU93qbIQOrodDaHF4ofeb2cIK2vyPAy3M4T3/72t9mzZw+XXXYZN910U4+3GUvBJhmxNu2v5aTJo8nMMBZOHU1reyc7DzUMdrNkmJg3bx4vvfQSixcv5mtf+1rC6H5eXh6ZmZkANDc386lPfYqVK1fy2muv8clPfpLm5ua462VlZdHZ2XlkvWi5ubkAZGRkkJ2dfaQba0ZGBu3t7Wl5fSLDwZ6qpiP3N+xT7yYZWobbeeKOO+7ggQce4M4779RU4DLk7apsoLmtk7fNGsueqiY27a8b7CaJ9NhwO09EfPSjH+WPf/xj6i80AQWbZERydzbtr2PhlCIAFkwJxqBu3K+hdJKaffv2UVBQwOWXX86Xv/xlXn75ZQCKioqoq4v/D0/kID9+/Hjq6+tZuXLlkWWx65WWlvLSSy8BpOVgLjISlVU3kp0Z/HOkoXQy1Ayn88QjjzzC9773PVatWtXj2Y9EBsPGfcH/7J8/by5mQe8mkeFmOJ0ntm3bduT+qlWrmD9/fp/qA8jqcw0iQ9D+w80cbmo7EmQ6ccIocjIz2Li/louWTBvk1klv9HZq0d567bXXuOaaa45cDfjZz34GwFVXXcWFF17IlClTjoyzjiguLuaTn/wkixcvprS0lGXLlh1Z9vGPf5yrr76a/Px8Vq9ezTe/+U2uvPJKvvOd77B8+fI+tfWee+7hs5/9LAcPHuTd7343S5Ys4dFHH+1TnSJDwZ6qJmaVFNLS3sFrZQo2SXI6TyT2mc98hpaWFs4//3wgSBL+85//vE91ivSnTftrycowls8ex7LScTy6oZwvnT9vsJslw5zOE4lde+21bNmyhYyMDGbNmpWWc4QdD+Nfly5d6mvXrh3sZsgA+vOmA1x5x1r+cPXbWVY6DoB3/+hZxhXm8Osr+/ZFlIGxadMmFixYMNjNGFHi7VMze8ndlw5Sk4YMnSeGpvf8+FnGj8qlICeT1/Ye5tmvnDvYTZIhROeJ9NN5Ij6dIwbex3/5AuWHm3nkC2dx23M7uf6BjTz5f97J7AmjBrtpMozoPJF+PTlPaBidjEiR2SvmTy468tzCKaM13ltEZBjZU9XE9LH5LJ5WzJ6qJmoaWwe7SSIywMxshZltMbPtZnZtnOW5ZnZ3uHyNmZXGLJ9pZvVm9uVU65TBt3FfLQvDEQrnzp8IwJqdVYPZJBHpIQWbZETatL+OmeMKKMrLPvLcgimjOVTfQkVd/ARrIiIydNQ2t3G4qY0ZYwt4y/Rg9hblbRI5vphZJvAT4EJgIfARM1sYU+xKoNrd5wA3AN+LWX4D8HAP65RBFPy/3sLCqUGwafrYfDIzjL3VTd2sKSJDiYJNMiJt2l/LgilFRz0Xyd+k3k3Dx/EwzHegaF/KcFMWzkQ3fWwBi6YGwaZXlbdJYujYlj5DdF+eBmx39x3u3grcBVwUU+Yi4I7w/krgPAunXTKzi4EdQPS84anUKYMoMkIh8r97VmYGk0fnsa9GwSbpuSF6bBuWerovFWySEaextZ2dlQ1HTlARka64kdktZGjLy8ujsrJSJ4g0cHcqKyvJy8sb7KaIpKysuhGAGePyGVOQzaySAjaoZ5NE0XkifYbweWIasCfqcVn4XNwy7t4OHAZKzKwQ+CrwrV7UiZldZWZrzWztwYMH+/QipGdig00A04rzKVOwSXpI54n06c15QrPRyYizpbwOd44JNo0pyGZacf6RE5gMbdOnT6esrAz9g5ceeXl5TJ8+fbCbIZKyPdVdPZsAFk0do2F0chSdJ9JriJ4nLM5zsb8aE5X5FnCDu9eHHZ16UifufgtwCwQJwlNqraTFxn21TB6dx7jCnCPPTRubzwvK2SQ9pPNEevX0PKFgk4w4kWFyC2OCTQALphSxuVzBpuEgOzubE044YbCbISKDpKy6kcKcTMYWBLn3po/N5/GNB3B3Yn44ynFK54njQhkwI+rxdGBfgjJlZpYFjAGqgOXAB83sv4FioNPMmoGXUqhTBtHm8jrmx6TDmFqcR3ltM+0dnWRlanCOpEbnicGlb6qMOJv211KUm8X0sfnHLJs5rpCy6iZ1pRQRGeKCmegKjgSWJhTl0trRSW1T+yC3TEQG0IvAXDM7wcxygEuBVTFlVgFXhPc/CDzpgTPdvdTdS4Ebge+4+00p1imDaF9NEzPCXq0R04oL6Oh0KupaBqlVItJTCjbJiLOlvI6TJhfFvfI9tTiPxtYODje1DULLREQkVWXVjcwY13XRYEJRLgAH6zWjqMjxIszB9BngUWAT8Ht3f93Mrjez94XFbiXI0bQd+BJwbW/q7K/XID3T1NpBbXM7k8ccnRdmWngRea/yNokMGxpGJyOKu7O1oo4LF02JuzzS26msuonigpy4ZUREZHC5O2XVTZw+u+TIc5FgU0VtC3MmFiVaVURGGHd/CHgo5rlvRN1vBj7UTR3XdVenDA3ltcEFhUmjY4JNxcHjvdVNLCsd6FaJSG+oZ5OMKAfrW6hpbGPepFFxl08tDoJNmjpVRGToOtzURn1L+1HDoSce6dmkIRQiIiNV+eEg2DQ5JtgU+R9ePZtEhg8Fm2RE2XagHoB5k+Jf9VawSURk6NtTdfRMdAATioIfHgeVr0NEZMQ6EPZsmjwm96jnC3KyGFeYo2CTyDCiYXQybP12ze5jnvvbG4cAeG3vYd6sbDxmubuTlWH8eVMFOVmZAHx0+cz+baiIiPRI5MdEdM+m0XlZ5GRlKDmsiMgIlmgYHcC04nz2VivYJDJcqGeTjCgVtS3kZ2dSlBs/jmpmjMnPpkYJwkVEhqxD4VC5SJ4mCI7fE4ty1bNJRGQEKz/cTGFOJkV52ccsm1qcp55NIsOIgk0yohyoa2bi6Ny4M9FFFBdkU9PYOoCtEhGRnogEm8YVHj2RwwQFm0RERrQDtc1MGnNsryaAacUF7K1uwt0HuFUi0hsKNsmI4e5U1LYwsSj+CSqiuCBHPZtERIawyvpWiguyyc48+t+UCaNyqahrHqRWiYhIfyuvbT4mOXjEtLH5NLV1UNOo/+NFhoN+DTaZ2Qoz22Jm283s2jjLc83s7nD5GjMrDZ8/zczWhbf1Zvb+qHV2mdlr4bK1/dl+GV7qW9ppautg0ujcpOWK87Opa26nvaNzgFomIiI9cai+hfGjjj2WTxytnk0iIiPZgcNJgk2akU5kWOm3YJOZZQI/AS4EFgIfMbOFMcWuBKrdfQ5wA/C98PkNwFJ3XwKsAG42s+gkPOe4+xJ3X9pf7Zfh50Bt8AOk+55NwRjw2ub2fm+TiCSnixIST2V9KyUxQ+gAJozKo7qxjdZ2XSwQERlpOjudirqWJMPogmBTmZKEiwwL/dmz6TRgu7vvcPdW4C7gopgyFwF3hPdXAueZmbl7o7tHIgF5gAbmSrciQysmdtOzaUx+8ANGeZtEBpcuSkgihxpaGF907LE8kjA8ktNJRERGjkMNLbR3etJhdKCeTSLDRfwpu9JjGrAn6nEZsDxRGXdvN7PDQAlwyMyWA7cBs4CPRQWfHHjMzBy42d1vibdxM7sKuApg5kxNbX886G4muohIzyblbRIZdEcuSgCYWeSixMaoMhcB14X3VwI3RS5KRJXRRYkR5lBdC+PnHNuzaWIYbDpY18LU8Ap3ujy77SAPbyjnr9sPMWfCKL7/oVMYG6d3lYiI9Mxv1+xOqdzesMfS1gN1cddxd/KzM9mnYJPIsNCfwaZ404HF/hhIWMbd1wAnm9kC4A4ze9jdm4F3uPs+M5sIPG5mm939mWMqCYJQtwAsXbpUP0KOA6nMRAcwJj8MNqlnk8hg00UJOUZreye1ze1xczZNiAo2dSfVHzcA2yvque2vO8nJyqC0pIC/bD3IeT/4C/90+iwmjs7jo8v1+RAR6W+1zcGF4NF52XGXmxnTxuZTVt0Yd7mIDC39OYyuDJgR9Xg6sC9RmXD4wxigKrqAu28CGoBF4eN94d8K4B6CK+NynEt1JjqA7MwMRuVmaSYLkcHX54sS7n4ysAz4mplFDgDvcPdTCYbnfdrMzoq3cXe/xd2XuvvSCRMm9O4VSNpVNgSBpJIECcIBKtKYJLzTnUdfL6c4P5t/v3ABH/+7E/jEGSfQ0t7Jzc/soLmtI23bEhGRxA6How4iF4bjOWF8Idsq6geqSSLSB/0ZbHoRmGtmJ5hZDnApsCqmzCrgivD+B4En3d3DdbIAzGwWcBKwy8wKzawofL4QuIAgb4cc5yobWmlq62BKgoSCsYoLso+c0ERk0OiihByjsj7odVoy6tghbCWFqfdsStWGvYfZW9PEuxZOIicr+LdoZkkhlyydQVNbB7sqG9K2LRERSay2uY0Mg1F5iQffLJgyml2HGmhq1YUAkaGu34JN4XCGzwCPApuA37v762Z2vZm9Lyx2K1BiZtuBLwGRmYjOANab2TqCHwqfcvdDwCTgOTNbD7wAPOjuj/TXa5DhY3N5HQAnTSpKqfyY/Gz1bBIZfLooIceIJP+ON4wuJyuDsQXZHKxvTsu22js7eWzjASaPzmPJjOKjls0qKSArw9h5UMEmEZGBUNvUzqjcLDKSpMRYOKWITg/yOonI0NafOZtw94eAh2Ke+0bU/WbgQ3HW+zXw6zjP7wBOSX9LZbjbvL+WSaNzU07mWpyfzdYDdbgrnZfIYAlzMEUuSmQCt0UuSgBr3X0VwUWJX4cXJaoIAlIQXJS41szagE7CixJmNhu4J8zdlgX8VhclhpdDYc+m8XF6NgFMLMqjojY9PZvW7KiiqqGVK95eesyPm+zMDGaMK2DHIQWbREQGQm1zG6OTDKGDoGcTwKb9tZwSc5FARIaWfg02iQyEptZgmMOZc1PPuVJckENbh6sLrsgg00UJiVWZpGcTBEnCD9b3PdhU09jK45sOMHfiKOZNGhW3zAnjC3lqc0XwAyhBwloREem5+pZ2crMyyM7sGmhT29SW8NgfMWNsAYU5mWzaX9vfTRSRPurPnE0iA2JbRR2dDvMnpzaEDoKcTQDVGkonIjKkHKpvIS87g4KczLjLJxTlpiVn0/2v7sfduWjJtISzmM4eX4gDa3dVxV0eT0VdM9+8bwOr36hU71kRkRj7Dzdx94u7+e5Dm3jg1f1HLUulZ1NGhnHS5CI27dcwOpGhTsEmGfY2l9dRkJPJjHEFKa8zLhxuV9XY2l/NEhGRXqisb2X8qNyEAaCJRblU1LX0KZCzYe9hNu2v5bz5k46cD+KZMS7I2/T8jtSDTbc+t5M7Vr/JR/73ed5301/ZUq4fRCIiAAdqm/nJU9vZVF5HccHRKS1a2ztpbutkTJLk4BELpoxmU3mtAvoiQ5yCTTKsdXQ6W8rrOGlSUdJkgrEiPy4q0zAUQ0RE0udgfQslSYZRTCjKpbW9k9rm9pTr7HSnpa2D8sPNrHxpD3e9uJspY/J4x5zxSdeL5G16fkdlSttp7+jkTy/v5eyTJvCd9y/mzcoGfvjnrSm3U0RkJNtxqIFOh8+eM4cz507gcFMbVQ3Bhd+a8AJwdz2bIAg21TW3s7emqV/bKyJ9o5xNMqy9WdVAU1sH88NkganKzcqkKDeLygb1bBIRGUoq61uZMiYv4fIJRUEg6mBdM2NS+FGyekclD766j87wAnh2pvH22SWcNW8CmRndX6Q4YXwhT29JLW/TX7Ye5GBdCx89bSYXnDyZ1/Ye5v71+2hp7yA3K/6wQBFJzsxWAD8kmEjiF+7+XzHLc4FfAW8DKoFL3H2XmZ0G3BIpBlzn7veE6+wC6oAOoN3dlw7Eazne7a1uojA3i3GFOZw4IciV98bBBkpG5bK1oh4IcjJ1pytJeB3TUygvIoNDPZtk2Gpt7+SB9fspyMlk7sT4yV2TGTcqh8p6BZtERIaSyoaWpAliJ48OAlH7apq7retwUxuPbNjPjHEFXLhoMu9fMo1r/n4+737LVIpSTPg9e3whnQ4v7ux+KN0f1pYxflQO58yfCMD5CydS39LOmh4MwxORLmaWCfwEuBBYCHzEzBbGFLsSqHb3OcANwPfC5zcAS919CbACuNnMoi+0n+PuSxRoGjh7axqZXpyPmTF+VA5FeVnsOBQEmV4rq2HKmDzGFyVPEA5BnlYzlCRcZIhTsEmGrQde3Ud5bTMfXjqDvOyeXzEuKcyhqkHD6EREhorOTqeyvpWSUYnzKJ0YXlzYHl4FT+bR18txhw+9bQZnzp3AshPGMSq3Z526Z4wrIMPg1bLDSctV1rfwxKYDvP+t047MrvR3J44nPzuTJzYd6NE2ReSI04Dt7r7D3VuBu4CLYspcBNwR3l8JnGdm5u6N7h4Zb5sHKMHPIGpt76SitoVpY/MBMDNmjy9kx8EGqhpa2VPdxFumjUmprsLcLGaNK1CwSWSI0zA6GZb+9HIZa9+s5uyTJjBvUuqz0EUbV5hLbXMNTa0d5CeY9UhERAbObX/dSXun82ZlI79dsztuGXenICeThzeUJ73QsLuqkXV7ajh73oSkScC7k52ZwcxxBd0Gt+5dt4/2TudDS2cceS4vO5Mz547niY0H+Nb7Tk6Y9FxEEpoG7Il6XAYsT1TG3dvN7DBQAhwys+XAbcAs4GNRwScHHjMzB25291ti6sTMrgKuApg5c2b6XtFxal9NEw5ML84/8tyJE0axvuwwT26uAGDx9OKU61swZTSbNQGDyJCmnk0y7Lg7P/zzNqaPzee8+ZN6XU/kyvnuqsZ0NU1ERPqgPkz6naz3kZkxoSiXirrkw+ge2bCforws3nnShD63a87EIrZVJP9R89jr5SycMvqYCyDvWjCJfYeb2agr8CK9ES9CG9tDKWEZd1/j7icDy4CvmVkkIdw73P1UguF5nzazs46pwP0Wd1/q7ksnTOj7ceR4F0nmPXVsV7Bpdpi36eXd1Uwfm9+jCwMLpoxmV2UDB2q7H1ItIoNDPZtk2NlWUc+blY1ctGRqSsldEykJT2i7Khs4aXLvekeJiEj61LeGwaZupr6eWJTHhr2Hcfe4vYUaWtrZVdnIuxZMSkti7raOTt6oaODXq9+Me97p6HRe3l3NstJxx/TIqmtuw4AfPL6VW69Y1ue2iBxnyoAZUY+nA/sSlCkLczKNAY5KlObum8ysAVgErHX3feHzFWZ2D8FwvWf65yUIBMGm0XlZR020MLYgm+L8bGqa2lIeQhdx8ZJp3PTUdr7z0CZ+eOlb091cEUkD9WySYefxjUHuiwWTezYDXaySwiAB4e5K9WwSERkKGlo6gCAfRzITi3JpauugobUj7vI3DgZD3ub0YvKIRNvrcKcyQZ6/fTVNtHU4s0oKj1lWlJfNjHEFbNFwD5HeeBGYa2YnmFkOcCmwKqbMKuCK8P4HgSfd3cN1sgDMbBZwErDLzArNrCh8vhC4gCCZuPSjsuqmY2aOM7MjvZsW9TDYNLOkgKvPms196/ax+o3KtLVTRNJHwSYZdh7beIBTpo9hdApTXieTn5NJfnYmuyob0tQyERHpi/rmNiD5MDqAiaODiwUVCYZPvHGwntysDKZF5Qbpi4lFeeH24geb3gyHY88aF38K7tKSQvbXNNPe0ZmW9ogcL8IcS58BHgU2Ab9399fN7Hoze19Y7FagxMy2A18Crg2fPwNYb2brgHuAT7n7IWAS8JyZrQdeAB5090cG7lUdf5rbOjhU35UcPNq58yfykdNmUlzQ89x6/3b2HKaPzeebqzbQNoDH15d3V7NHaThEuqVhdDKsHKhtZv2eGr58wby01FcyKoc31bNJRGRIqG/pwICCbiZtOBL8qWs5clU82hsHG5g9YVSfhlpHmxBOxV1RlyDYVNnA2ILshBdBJo4Oeka9WdXIiXHaKyKJuftDwEMxz30j6n4z8KE46/0a+HWc53cAp6S/pZJIJF9TvAsA4wpzej2JQ35OJt94z0Ku+vVL3PG3XXzizNl9amcqOjqdK257gRljC7j/s2ek7TwjMhKpZ5MMK5Hpo89fODkt9ZUU5qhnk4jIEFHf0k5BbhYZ3czaNjovi9ysjLjBn6qGVqoaWjlxwrFD2norJyuDsQXZcZOSuzu7KxvjDqGLmBgGq7YdSD6jnYjISLS3Ogg2TU9Tb9No5y+cxNknTeDGJ7Yl7O2aThv31VLX3M7G/bX88eWyft+eyHCmYJMMK49vPMDMcQXMm5SeK8PjCnPZV9NEa7uGNoiIDLbDTa2M7iY5OAR5PiYmmJHuSL6mNPcgmliUx8E4wa3qxjbqWtqZmWAIHXT1jNrezYx2IiIj0cb9tUwsyqWgmyHSvWFmXPfek2lt7+S7D2/uVR1/fKmMr6xcT1l196Md1uwM8kPNmTiK7z+6hYaW9l5tU+R4oGCTDBuNre38bXsl5y+cFHf2od4oGZVDp5PSyUVERPpX+eFmJo/O674gYfAnTg6l7RX1jM7LOhLgSZeJRbkcrGuh04+edf3NsHfsrJLEwabcrEyKC7LZVqGeTSJyfNm4r5bdVY0sLR3Xb9soHV/IVWfN5p5X9rJmR8+She+taeI/7n2N368t47z/+Qs/+vM2POY4H23NzipmlRTwvQ+8hYq6Fm5+Zkdfmy8yYinYJMPGrkONtHZ0curMsWmrsyQcI668TSIig6u6oZXa5nYmj0kx2DQ6l7qWdhpbu64qd7qz42A9J04YlbaLEtHba+90qhpaj3r+zapGcrMymNRNkGxiUa6G0YnIcefONW+SlWGcOrO4X7fz6XPmMK04n2tWvsqh+vj59eL59oMbAfjD1W/n3PkT+cHjW3l668G4ZTs7nRd3VXFa6TjeNmss/7B4Mrc9tzNpcErkeKZgkwwbkd5HM8alb7x3yajgyvdA523aXlHPT5/eznWrXtfsRCIiwKbyWoCUezZFei5FD23bW91EQ2tHvyThTjQj3e7KRmaOK+g2z9TEojzeOFhPR6d+lIjI8aG+pZ17X9nLW6aPoSCnf+elys/J5EcfeSsVdc388y9fpL6lHXenorY54XH3uW2HeOi1cj599hyWlY7jhkuWkJ+dyZObKuKW31pRR01jG8tnlwBw+uwS6lva4w6xFhHNRifDSFkkueDYxEMVeqowJ5PReVlsHcCrzdf+8VXuenHPkccXLpp85KQlIjIS7KlqJCPD4s48lMjm/UE+o5R7NkXNSBdJzv3s9kPkZmWwcOroHra4e10z0jWzkKD+uuY2DtQ2s2japBTam0tLeydl1cmTiYuIjBT3rdtLQ2sHp52Qvv9zf7tmd9LlH37bDH6z5k3O/f7TtLZ3UtPUxtTiPP7xrdOZGnVOqm1q43+f3cG4whxG52cfqbd0fCH3r9/H/MlFx/SQXR0O0Ss/3Mxv1+zmjYrgYvXuqkYmpnihROR4op5NMmyUVTdRkJPJ2IL4U0v3hpnxtlljeWFnz8Z398XTWw5yxpzxPP7Fs8jKMJ7ZFr+rbk88sfEAn7jjRT77u1f47kObqFeyQhEZRF/+w3quvP3FHq2zubyWwtwsivJSO8YXF2STnWlsO1CHu3OoroXX9x7m9Nkl5GVn9qbZSeVlZzImP/uoGfA27D2MAyenENyK/BDRUDoROR4cbmrjtud2smDKaGaMTf8sdInMnzKafzx1Os1tHUwbm8+7Fkyktqmdnz69nVXr97Kvponyw8387C9vUNfSzgdOnU52ZtdP4vmTiqhpauNAnN5KOw81MCY/+8hvkXFhOo7dVUrHIRKPejbJsFFW3cj0sflpz8OxfHYJT205yMG6lrQnlI11uKmN8tpmrvi7UuZOKuLUmWP5y9aDXPP383tdp7vznw9upLqxjXGFOdy/fh+ZGcZXViSus6PTycxI734UEYkoq25ib00Tm/bXsmBKar2MNu2vY0oPrgxnmHHGnAk8taWCF3ZVUVbdRGaG8Y4543vb7G5NHp3HzkMNR46hr+49zKTRud3ma4KgZxPAtop63rWw+55QIiLDVUVtM/902wvsrmrk5o+9jfLDAzvM7NSZY4/K8fr22eN55PX9vLizmud3VJFhMCo3i6vOnH1UbyeAeZOLANiyv/aoYd3uzq5DDcyZ2JUTsDgMOu2paurvlyQyLPVrzyYzW2FmW8xsu5ldG2d5rpndHS5fY2al4fOnmdm68LbezN6fap0ycpVVN6V1CF3E8hOC2TFe2FmV9rpjbTsQDBOZNynIJ3LWvPFs2Fvbo0SGsVa/Ucmuykaue99Cnvry2bzvlKn88q+7qKg9dkpwgJrGVlbc+Awfu3UNdc1tvd6uiEg87k5FXXD8ufeVvSmt097RydYDdSkPoYs4b8FE5k0axQPr97Nudw1LS8cxqh+m1o5YfsI4Dje1sX5PDTWNrbxZ2chbpqeW9DYvO5MpY/KOnAdEREaiQ/UtfODnf2N3VSO3XrGMc+cPfnA9PyeT9791Ol+7cD7vPWUqS0vHcfU7Tzwm0AQwJj+bqcV5bI45Vh+sb6G+pZ3SqGHQ2ZkZjM7LUs8mkQT6LdhkZpnAT4ALgYXAR8xsYUyxK4Fqd58D3AB8L3x+A7DU3ZcAK4CbzSwrxTplhIr0bEq3RdPGb7WvIAAAIABJREFUUJCTyZoBGEoXyQ01b1Jw1eSseRMAeLYPQ+l++8JuxuRnc+GiKQB86fx5tHV08uMntx9Ttr2jk8/+7hV2VTaw+o1KLrn5+SM/CkUGgy5KjDzVjW20dThmcN+6fSklxN5V2UhLe2fKycEjMsy4ZOlMxhRk4zhnzu2/Xk0AJ00uYsqYPJ7eepBXyw4D8JZpY1Jef87EUWyr0DA6ERm5/vRyGXuqmvj1lacd+T93qCjIzeLts0u4eMk0igtyEpY7adJodlc20hiVlmLD3uCYP3fS0RNQjC3MYU+1gk0i8fRnz6bTgO3uvsPdW4G7gItiylwE3BHeXwmcZ2bm7o3uHvl25wGR/1RTqVNGoMNNbdQ2t/dLsCk7M4O3zRrL8zsGIthUR0FO5pGkuYumjmFcYQ7PbD3Uq/oO1bfw6Ovl/OOp047kKCkdX8gly2bwuxd2s7vy6JPffz+6hWe3HeLbFy/mF1csZeehBi695XnaupkRb09VIzc+sZVzv/801616vVdtFYmlixIjUySAfeGiyZTXNrMmhWPrpv3hTHQ97NkEwRXrT545m0+eOZuxSX48pIOZ8c55EzhU38KTWyqYWpx3ZFbTVMydWMT2ino6NSOdiIxQj75+gJOnjuZts8YNdlN6bf7kIhzYGl4ccHde2V3DCeMLjznPjCvIYY96NonE1Z/BpmnAnqjHZeFzccuEwaXDQAmAmS03s9eB14Crw+Wp1Em4/lVmttbM1h482PcEzDK49vbDTHTRTp9dwtYD9VQ1tPZL/RHbKuqYO3EUGWG+pIwM48y543lm68Fe/fj440tltHU4Hz1t5lHPf+68uWRlGtesXE9zWwcAd655k1ue2cE/vX0WH142g7NPmsgNl5zCjoMNPPb6gYTbKKtu5IIbnuGHf97GofoWHnu9vMftFElAFyVGoAO1wbDgj5w2k1G5WdyTwlC6zeW1ZGbYkbxGPTUmP3vAZnhbNG0M40fl0NreyVumpTaELmLupFE0tXWwt0b5PURk5KmobealN6tZcfLkwW5Kn0wbm8+Y/GxWv3GITnfKqpuobGjlrTOOPeaPK8yhvLaZlvaOQWipyNDWn8GmeNmHY39NJyzj7mvc/WRgGfA1M8tLsU7C9W9x96XuvnTChKHVhVN6rizsntofPZsATp8dydvUv72btpTXMzccQhdx1twJVDa0sjG8sp8qd+euF/ewrHTsMXVOGp3Hf/3jW1izs4rP/e4V7lu3l/977wbOnT+Rr7+nq5PH+QsnM2NcPr9avSvhdu59ZS9NbR08/Pkz+bez57DvcDM1jf0blJPjhi5KjEAHwnxxs8YVcuGiyTy8ofxI0DuRzfvrOHFCIVmZQ3+S3AwzzlswiZzMDN4yPfUhdNCVr29LufI2icjI8+jG4OLlikXDO9iUYfb/s3fn8XHX94H/X++Z0YxGM7pvSz5kW76wMWCDIYFCQqCQA9IGCiEJya9p0yP0sd3sZpf0V7Jpmm7T7W7bpEm7SUhboEtJCiHrFCgJZziNbYwxvmX5kmTd1+iY+7N/zHdkSZ6RRrJGM5p5Px8PPSzNfGf0GbC/35n3533woY21nB0YZ//ZQfadHcBhEzYnKJsu9zgx5vzGuFLqvHROo2sDlk/6uRHoSHJMm4g4gFJgSpdmY8xhERkFNqf4nCoHtaU5s2lLQxmFBTbebO3nFqv30UIbGA3SOxKY+LARd926WI+RXx7vSXgRS+Zk7ygne0f5zWubEt7/8csbGBgL8ic/O8TPD3Vx5apyvnvPFVPGu9ptwqd3rOTPnznCkc5hNtRNnRpljOEn+9q5qqmCDXUldA7FPkQe6fRx9erKlNeqVBIXvSkBXCIiG4GHROSZFJ8T6/HfB74PsH37dq1rWiA91rjomhIXN22q5V/3tvFe+xDbVyUvqTh8bnjG+7PN1sYyNi8rnfNUz/XWOfZI57BOpFNKZb1Hd52Z0/EPvXaKKq+Tt072s/vUQJpWtTguX1HGm619PHuwk3DUsLG+ZKJlxWQVVlndmf4xVld7L7hfqXyWzi3E3UCziDSJiBO4G9g57ZidwGet7+8AXjDGGOsxDgARWQmsB06l+JwqB7UNjFPktFNujRhdaE5HrG/TrjROpDtmTbWYnoVUU1xIc42XN1vn9rtfa4n1ebpuhjHf/9/7m7j/1g38yrpqHvzslbidF14kf2P7clwOGw+/cfqC+w60D9HaM8qvXR5LDNlkjTA/PMcsLKWSmMumBDNtSgC6KZEluob9lBQ6KCywc7k1enrfmcGkx//yWA8dQ36uWDG3krRMm2ugCWKjtldUFHH4nGY2KaVyy1gwTGvvCJcsK0Vk7ufHbGMT4aOX1jPsDzMWjHB5kmtUuScWbDqrmU1KXSBtwSarnOE+4FngMPBjY8xBEfm6iNxmHfZDoFJEWoAvAfGpQdcC+0XkHeBJ4PeNMb3JnjNdr0Flj/gkunRevK5uquTwuWEG0tS3Kd5kcP20YBPEekbtOdU/a6PuyV5t6aWhzM3KypmzvX73+jU8/JtXUepOHKgr9zi5besynny7naHx0JT7fvJ2O06HjQ9viWV7VRe7qPA4NdikFopuSuSg7uEAtdZUuepiF8sr3Lx9JvEO91gwzP//0wOsrvZw97Tec7lqY32xnkOVUjnnSKePqIFLlpXMfvASsbLSw+XLyyh1F9Bcc+H7d4DiQgdOh02bhCuVQFqbIxhjnjbGrDPGrDHG/Jl121eNMTut7/3GmDuNMWuNMVcZY1qt2x8xxlxijLnMGHOFMeanMz2nyn1tA+NpK6GLe5+VIfRGmqbSHev0UexyUJ9g2tLVqysZC0Y4YI1VnU04EuX1E31cu7ZqQQJwn33fKsZDEb738omJ20KRKD/b38GHNtZMBKpEhI31xRzRfiNqAeimRG7q8vmpKTnf6PuKFeVJM5u+9dxxzvaP899/bUvC8oRctLG+hJN9o4wFw7MfrJTKSwc7hrjmz5+f6Fm6FBzv8lFc6JiYuJwrfv2KRv7wxuak2aw2EZaXuzXYpFQC2d+JUynOZzal09bGUrwux0R52kI71uVjba03YXBoh9WgfFeKpXQH2ofw+cNc25y8hG4uNjeUcse2Rr7/y9aJxrW/ONRF32iQj182tbfyxroSjnb6CM8hC0upZHRTIvd0DweoLT4fVL98eRmdw346pk1ge699iAdfPcndVy7Pqx5wG+tLMGb+TcL/6hfHeOytufVRUUotLf/85mnODfmX1DCBbl+AZaXprULIBLtNcM2yGbK8oogzGmxS6gIabFJZb2g8xLA/nPZgk8Nu46qmCt44kZ7MpuPdI6xLkoJb5XVZfZtS+93xgNj71izcB7Q/+vBGigsd/NGTB/jZ/g7+8EfvsLraww3ra6Yct7G+hEA4yqm+0QX73Uqp3GCModvnp3pyZtPKC/s2RaKGr/zkAOVFTr5y68ZFX2cmne99N/cPkQOjQb77Ygt/89xxolHtaa9ULvKHIvzb/nMA9KeptcNCixpDjy9ATbFr9oNz0AoNNimVkAabVNZrT/Mkusnet6aS1t5Rzg0tbJO/vpEA/aNBmmuTT6mYS9+mV1t62VRfQqV34S7qFR4nf/yRTew9PcAf/Ms+tjSU8vjvvg+nY+ppYkN9LGB2SBvcKqWmGRgLEYqYKZlNG+pKcDlsU/o2/eNrJznQPsTXbttEaZoGP2SrxnI3xS7HvPo2PXuwk0jU0DnsZ9/ZpT3pSalUicgtInJURFpE5P4E97tE5EfW/btEZJV1+1Ui8o71tV9Efi3V58yknx/qwheIldkulWDTwGiQcNRMKaHOJ8vLi/D5wwyNhWY/WKk84sj0ApSaTbxePd2ZTQDvt/o2vdbSxx3bGhfseeO7HSsrPUmPuXp1JY+8eZr32ocmJjglMhYM8/bpQT73/lULtr64X7+igVdbeimwC1+/fXPCHipra7w4bMKRc8PctnXZgq9BKbW0TB6N3TnkB2KZnJNvryst5BeHulhT7WVgNMjfPH+M9bXFDI2F5jxae6kTETbUF3Okc+7BpqcOnKOhzE2PL8BT73aybWVFGlaoVPYQETvwXeAmYtNGd4vITmPMoUmHfR4YMMasFZG7gb8A7gLeA7YbY8IiUk+sz9/PAJPCc2bME3vbWFZaSO9ocMkEm7p9AQCqiy/sS5oPllfENsTP9I+xpag0w6tRKntosEllvTYrs2kxGg6ury2mwuPk9RO9Cxps6hiMfQCb6TXE+za92do/Y7Bp18l+gpHoRGBsJvP5EHflqtg6fvJ2+5Tb79kRmxTlcthZW+PVaUpKqQsM+2O7usWuqW8vVpQX8XprH/2jQf7lrTMIwu2XLcu53h6p2lhfwpNvt2OMSfm/Qd9IgNdP9PG716/maKePZ947xx9/ZCO2JE1rlcoRVwEt8X59IvIYcDswOTB0O/A16/vHge+IiBhjJtc1FRILMqX6nBnRNeznleM9/P4Na3ni7Tb6lkqwaTj2Pjdfy+jik6FP9Y2ypVGDTUrFaRmdynrdvgAFdqHC40z777LZhGvWVPJ6Sx/GLFw/jPbB2Pudhhmys+J9m14/MXOD8ucOdVHktLOjKXM72hvqiufVb0Qpldt8/ljpR4l7amnc8ooiIlHDt58/Ts9IgLuuXE5ZUfrP6dlqY30JvkB4YjMlFc8e7CISNXx4Sz0f3lLPuSE/+84mnvKnVA5pAM5O+rnNui3hMdZE0iGgEkBEdojIQeAA8LvW/ak8JyLyBRHZIyJ7enp6FujlzOyn+9qJGvjEtkbKi5wMZDjYlOowmG5fgJJCR95MFZ2uqcqDTWJZvUqp8zSzSWW9Hl+Aaq9r0XbA37+miqfePUdr7yhrqpP3WJqL9oFxil0OSt0z9yb54IYafvjqSX7wy1Y8rgv/eUaNYef+DlZXeS7IPFpMG+tL+Ok7HQyNhfKu34pSKjlfPLOpcFpmU2URNoESt4NP7VhJbUl+llrEbaiL974bnii/mM1TBzpoqvKwqb6E5RVFOO02njlwjm0rk2fCKpUDEr35m74bmPQYY8wu4BIR2Qg8JCLPpPicGGO+D3wfYPv27YvSkf/wuWEay900VXmo9Dozmtn0i0Nd/OlTh/jCr6yZtbqg2xegJo/P64UFdlZWejjepRuxSk2mmU0q68UmGy3eBeyy5WUAHFnAzJ32wfEZs5ribrtsGeGo4UD7UOLnGRjH5w+zaVnJgq1tPuLpwmcHdPKGUuq8YX+YwgIbBfapby9KCgv44gfW8sUb1uZ9oAlgfV0xInCoI7Vy5P7RIG+c6OMjW+oREUoKC7iuuYpn3utc0CxcpbJQG7B80s+NQEeyY0TEAZQC/ZMPMMYcBkaBzSk+Z0aMBSN4rc3GCo8zoz2bXmvpJRQxPLG3jXA0eYZTvk+ii2uu8XJMg01KTaHBJpX14plNi2VV1fm664XSNjDOshR6Tm2qL6G5xsv+JKURh84NYxNYX5vZYFN8MuBcSkCUUrnP5w9RXJg427G+1I0rT0sspityOri0sYyfH+pK6fg9p/qJGrhhffXEbdevr6Z9cJyu4UC6lqlUNtgNNItIk4g4gbuBndOO2Ql81vr+DuAFY4yxHuMAEJGVwHrgVIrPmRHjoQhuZ+w8melg0/62QYqcdjqH/bx8LHkZ4dB4iGAkSk2eNgePW1dbzKm+MQLhSKaXolTW0GCTynq9IwGqF3G3pMjpoLbERWvPwgWb2gfHU2pwLiJ8/PIGTvePJazTP9gxzOoq78QbkUyJv5Y2zWxSSk0yPB6ipFAr9FNxxxUNHD43zMGOxJmsk+1vG8RuEzY3nG88u9Yq827RHiEqh1k9lu4DngUOAz82xhwUka+LyG3WYT8EKkWkBfgScL91+7XEJtC9AzwJ/L4xpjfZcy7eq0puLBihKB5sKnIyEghnJHgRDEc52DHMFSvKubSxlJeO9NBpNQGfrtsKeOd9ZlOtl0jUcLJ34T4/KLXU6TtCldXCkSh9o8FFDTYBrKr0zCuzKdH0N38ogs8fpscXSG06nFURsb9tkBvW10zc3O3z0zsS4Jo1lXNe10IrKyrA47TTPqiZTUqp83yBME1eT6aXsSR8bOsy/vTfDvPE3nYuWTbz9KL9Z4dYX1s8pfnu2pp4sMnHtc2zTydVaqkyxjwNPD3ttq9O+t4P3JngcY8Aj6T6nNlgLBih3BqeUOGN/TkwGqKudHE3GY91+QiGozSWu1lTXc177UO8e3aQukvqLji225ffk+jimmtivfiOdY2woS6zFQhKZQsNNqms1j8axBgWPdi0utrDzw+mVt4wm8GxWMPcshQbaZd7nKysKOKds4Ncv656ojF6vLfHpvrMX8BEhIZyt5bRKaUmGGPw+cMXNAdX503fcFhX6+Wx3WdYVVWEw5Y42TxqDHtO97OloWzK4z951XKKCx209Ghmk1K5YjwYnshsqrSmMPeNBqgrXdwStf1tsXYOjeVFeFwOyoqSNyvv9gXwuhwUJRhsk09WV1sT6bRvk1ITtIxOZbVuXyw1dzF7NkEss6lvNMjQeOiin2twLHZxnsuY78tWlNHtC7DrZKy/ZZdVL99U5Zl1ot1iaShz067BJqWUZTwYIRI1SXs2qQtdsbKcsWCEY53JA0b9I0H8oSjLpw2ZEBHW1ng50a0lG0rliilldJ7Ye99M9G169+wQ5UUFlFsbpZUz9I/qHvbnfVYTxCbSrar0cLxLNwCUitNgk8pqPSNWsGmxy+iqYmUgpxag7npgfG6ZTQDbV1awoa6Yn+3v4K2T/Tzy5mkK7Dbu3NZ40etZKI3lRVpGp5SaMBIMA+DJ893tuWiuKcbrcvD2mYGkx7QNxnrjJZpouqbaq5lNSuWQ6Q3CITPBpv1tg2xpLJvIrq/wOOkbvXAYgTGGnkXurZrNmmu9HOu+MLMpFEk+zU+pXKbBJpXVejLUdLApHmxagIl0g2NB7DaZGGWbCrtNuPvKFayoKOKn77QzPB7i01evnFN2VLo1lLsZGg/h81989pdSaukbD8aa2BZleIDBUmK3CVesKONI5zC9vsRT5c4OjFNgl4STntbWeOnxBRYkC1cplXnjUzKbMhNsGguGOdbl47LG873kKj1O/KEoY9amQpwvEMYfimpmk2VdbTGnp02ka+ke4dKv/ZzffngPZ/t1sI7KLxpsUlktU5lNKyqKEGFBJkoMjoUocxdgs3aHUuV02Lj3mlVcsqyE39i+nBUVRRe9loXUaO2ya3aTUgpiO/IA7gINNs3F+9dWYbcJzx1J3CewfSA2zdRuu/AaEp9Id0Kzm5Ra8oLhKOGomTiHxt47phZsenJfG3f8/etEo+ai13GwY5iogUsbyyZui5f09Y1MXUs8SF6dIBiej5pri4lEzZSJ1t9+/jgGw2stvXzor17mp/vaM7hCpRaX5rqrrNbjC1Bc6JgygWcxFBbYWVbqXqBgU5DSOZTQTeZ22vnUjpUXvYZ0aCiLBZva+sd16oZSSjOb5qm4sID3ranil8d6uGGdf0oj4EjU0DE4ztWrE08hPT+RboQrVpQvynqVUgvv0V1nJs6hh8/5JoYBuAvs7Grtn3Wa8f/ZdZqDHcP8zfPH+dJN6y5qLfvPxpqDX7q8lOcOdQPnJ+P1jwZZPmnzM1Obwtmq2TonH+vysbG+hONdPn72bge/8ytr+Oz7VvK5f9jND189yccvb8jwSpVaHJrZpLJajy9zdeBNVZ4F6dk0OB6i3J095W8LpbE89mZDM5uUUhBrbAua2TQf1zVX4XTYeO7w1OymrmE/4aiZyCSdbnlFEU67jRPdmtmk1FIXtPr6OO3nP555XA5Gp5WuJdJhvRdbiPetu0/1U19aOKV0t6IoPhlvamZTjy+A026jRKeQArGJdHabcKQz1rfp2y+04C6w89vXNVFf6uba5iqOd/sWJANNqaVAg00qq/X4Aos+iS5uVVURJ3tHMWb+F4RwJIrPH55Tc/ClosrrxOWw0Tag9edKqfNldIWa2TRnRU4H1zZXcejcMGcm9Qo8a51f48H96ew2oanKQ4sGm5Ra8oLhWLCpwDEt2BSIJHsIEOuxNDAW69t2sRn5vzzWw7MHu7ht67IptzsdsYDS9JK++KawzLFVRK5yOexsaSjl7186wZ3/+3X+7d0O7r1mFZXWZ5l1tV78oejEuV2pXKfBJpXVMjnhYlWlh2F/+KIaMw5NTKLLvcwmEaGhzK2ZTUopIFZGV1hgm3N/OhVz7ZoqSt0FPLGvnVAkyngwwstHe6j0OCfGjyeytsarPZuUygGJMpuKnPZZM5s6Bv0AlBQ6ONU3/03SobEQ/+Xxd1lb4+U/JijFSzSRTifRXejBz27ny7+6nq7hAKXuAn77uqaJ+9bVFgNwrEvP2So/aLBJZbVMltGtrr74iXTxnaZczGyC2ES69gENNimlYplNRU4tpZgvV4GdX7u8gR5fgBeOdPOTfW0M+0P8xvblM2YNrKnxcqZ/DH9o5uwHpVR2i2c2OS/IbJot2BR7H3b16kp8/jCn++aeNWOM4b/tfI/ekQB//RuXJeyVWuFxTdmADYajDI6FqMpQBUK2qvK6+OIH1vLSf76BN79y40RWE8QaiEOsp5NS+SCtwSYRuUVEjopIi4jcn+B+l4j8yLp/l4issm6/SUT2isgB688PTnrMS9ZzvmN91aTzNajMGQuGGQmEE457XgyrKmPBppO98091HRyLXZTLczCzCWIT6do02KSUInbO1n5NF2ddbTFXrCjn5WM9HOwY5uZNdVOa8SaytsZL1FzcxohSKvNCE5lN54PLHqeD8WCE6AzZSh1D45QVFbBpWWxYy66TfXP+3d95oYWfvtPBH3ywmS2NpQmPqfA48fnDE0GxXm0OPiObTS4I2nldDhrK3BpsUnkjbVuQImIHvgvcBLQBu0VkpzHm0KTDPg8MGGPWisjdwF8AdwG9wMeMMR0ishl4Fpjctv9Txpg96Vq7yg49vsW5iCWb8BGJGmwC/7a/Y+LCOld9o0FsAiXu3Nztbywvom80yHgwglv7tKh5EJFbgG8BduBBY8w3p93vAh4GtgF9wF3GmFMichPwTcAJBIEvG2NesB7zElAPxCOhNxtjuhfh5eQ1PQ8sjI9sqae1Z4SaEhfXNlfNevwaKwu3pXtEJ4MqtYQl7tlkxxA7v3pcid9LdgyOs6zUTbXXhcdpZ9fJfu66ckXKv/eHr57kf/3iGL9+RQN/8MG1SY+rjE+kGwtSV1J4fhKdZjbNybpaL0c7Ndik8kM6M5uuAlqMMa3GmCDwGHD7tGNuBx6yvn8cuFFExBizzxjTYd1+ECi0PnCoPLJYwaZk7DahrMh5weSNuTg3NE5NcSEOW25WrDaUxSYktQ9qo0M1d5M2JW4FNgGfFJFN0w6b2JQA/prYpgSc35TYAnwWeGTa4z5ljLnM+tJA0yIYD0U0s2kBuJ12/vBD67j3mlUp9b9aXRUbtX2yRzOblFrKEk6js0qTk5XS+UMRekeCLCsrRERYVeXhrZP9Kf/Ol45286f/dohbN9fxPz5xKTZb8nNOpccKNllBpl5fAOF8EEqlZl1dMa09o4Qj89vIVmopSecn4Abg7KSf25ianTTlGGNMGBgCKqcd8wlgnzFmcke6f7RK6B6QJI0MROQLIrJHRPb09PRczOtQGTIRbMrgjklFkZOBsfkHmzqH/NSVZqYMcDHEx3FrKZ2aJ92UyCFjwQhFmtm0IJyO1Butu512lpUWXvQUKqVUZiXr2QQwGkzck+3cUKw5+DJr86+pykPbwHjKw1ue3NdOpcfJ39x9GQ77zB8LK6xgU3wTtmckQLnHScEsj1NTraspJhiJcmoevbWUWmrSeXZI9C5pesHxjMeIyCXEdrF/Z9L9n7J2sq+zvj6T6JcbY75vjNlujNleXV09p4Wr7NCTBbXg5Z6CeU+jGw2EGfaHqc/hYFODFWw6q8EmNT+6KZEjjDH4NbMpY5qqPbRqsEmpJS2UKLPJFTunJstsijcHjweb4v1Gd6eQ3RSJGl4+1sP166txOWY/dxc5HRQW2CbeF/f4AlpCNw/r62JNwo9r3yaVB9IZbGoDlk/6uRHoSHaMiDiAUqDf+rkReBK41xhzIv4AY0y79acPeJTYzrjKQT2+ADY5v5OSCRVFTsaCEQLzmPIT323K5cym2uJCipx2TnTrCFc1L7opkSMC4ShRg/ZsypCmKg+tPSPzHnmulMq8eBndlJ5N8TK6YPJgU7HLQUlhbOpxXWkhxYUOdqUQbNrfNsjgWIgb1qc+a6nS46JnJEDUGHpHMjcxeilbU+1FBI5qsEnlgXQGm3YDzSLSJCJO4G5g57RjdhLrtQFwB/CCMcaISBnwFPAVY8xr8YNFxCEiVdb3BcBHgffS+BpUBvX4AlR5XdhnqB9Pt3LP+WaIc9U5FNttqi91L+iasonNJjTXFutUDTVfuimRI8atEg8to8uM1VVehv3heWfiKqUyLxSO4rDJlBLaoonMpuRldPVl5zc1bSJcuaqCt1KYSPfSkW5sAr+SwiCCuJWVRbT2jPLwG6cIRYxmNs2D22lnZUURx7t0o1blvrQFm6xyh/uITZI7DPzYGHNQRL4uIrdZh/0QqBSRFuBLwP3W7fcBa4EHrDKId0SkBnABz4rIu8A7QDvwg3S9BpVZ3b7M75jEs6oGRkNzfuy5IT/FLgfeJNNDcsX6Wq8Gm9R86aZEjhizsj/dBbl9vstWTdZEOu3bpNTSFYxEp/RrAnDYbLgctqSZTQNjQSo9U98rX9VUwYme0Ynep8m8eLSHK1aUU1aUegXBrZvr+dDGWk50x841VZrZNC/NtcWa2aTyQlrfFRpjngaennbbVyd97wfuTPC4bwDfSPK02xZyjSp7dfv8VGV4x6Si6CIym4Zzuzl43LraYn68p43ekUDG/3+ppcUYExaR+KaEHfiH+KYEsMcYs5PYpsQj1qZEP7H/7V1gAAAgAElEQVSAFEzdlHjAuu1mYJTYpkSB9ZzPoZsSaRfPbNIyusxYXRULNrX2jrJ9VUWGV6OUmo9g2CRstu11ORjxXxhsCoajBMJRigunfpzb0RQ7B+w+1c+Ht9Qn/F09vgAH2of4zzevm9Ma7Tbhgxtq2FhfzKFzw6yoKJrT43PVo7vOzOn4UCRKa88ID79x6oKJ1ffsWLGAK1Mqs3QLUmWtzqEAm5eVZnQNbqcdl8PGwBxLEyJRQ7cvQHONN00ryx7xRofHunwabFJzppsSuWE8pMGmTGooc1NgF1p7NLNJ5R4RuQX4FrENhAeNMd+cdr8LeJjYub8PuMsYc0pEbgK+CTiBIPBlY8wL1mNeAuqB+ISTm40x3YvwcpIKRqJTmoPHeQsdjCRoEO7zx7Lu4/2a4jY3lOIusPPWyeTBppePxYZizKVf02T1pe6cbhORblUeF1EDg2Mhfe+scprOqlRZKRiO0jsSyPiFTESo8DgZmGNmU48vQCRqqMuDC/H62vhUDa09VypfjVklHkU6jS4jHHYbKyqKONmr52GVW0TEDnwXuBXYBHxSRDZNO+zzwIAxZi3w18SGRgD0Ah+zBkZ8Fnhk2uM+ZYy5zPrKaKAJYj2bppfRgZXZlCDYNGxlO03PbCqw29i2snzGJuHPH+6iptjFJctKLnLVaj4mesJqnz2V4zTYpLJS13Bsklt9FpShlRc553wxODfRHDzz60+36mIXpe4CrT1XKo9pGV3mNVV5tWeTykVXAS3GmFZjTBB4DLh92jG3Aw9Z3z8O3CgiYozZZ4yJD504CBRaWVBZKRiJzqmMLp7ZVDwtswlifZuOdA4zNHZhz9HD54Z59mAnt1+2DJHMDeHJZ+VFsf9nc93MVmqp0TI6lZU6rWBTNvQ8qvA4Od7twxiT8kW5c8iPwyY5lRo7Uz16eVEBrx3vTalmXWvRlco948EIDpsk/KCkFsfqag+/PN5DJGoyOsVVqQXWAJyd9HMbsCPZMVYvwCGgklhmU9wngH3GmMlds/9RRCLAE8A3jDFm8pOKyBeALwCsWJH+9y7BcBSP68KAvbfQwXgocsG/bV+SzCaIBZuMgT2n+7lxY+3E7cYY/uypw5S4C7jvA81peBUqFSXuAuwic27TodRSo+8KVVbqGMyezKDyogJCEZMwhTmZtsFxakpcefOGv7akkC6fn2nv05RSeWI8FKFIs5oyanWVh2A4OnH9VCpHJHojNf3NxozHiMglxErrfmfS/Z+yyuuus74+c8ETGPN9Y8x2Y8z26urqOS98rpL1bCp2xbJgpr8P9fnD2EUSnnsvW16G027jxaNTqwNfONLNqy29/IcbmyktujAjSi0OmwhlRQX0J8g8UyqXpBRsEpEnROQjIqLBKbUoOoeyK7MJSHn34a2T/ZzsHeWSDDc3X0y1JYX4Q9GJ/gEq/+h1Ir+NBSNaQpdhTZMm0imVjeZ5nWgDlk/6uRHoSHaMiDiAUmLTSxGRRuBJ4F5jzIn4A4wx7dafPuBRYuV6GTVTzybgglI6nz+Et9CRMOu+sMDOrVvq+Oc3z/BHTx5gLBjm9RO9fOOpw6yu8vDpq1em50WolJV7nJrZpHJeqif7vwfuAY6LyDdFZEMa16QU54b8eF2OhHXoi628yGril0Jd9em+UX62v4N1tV6uX5f+XbBsUVsSCwrGe22pvKTXiTw2HorgLtDK/Exqqo4Fm072aJNwlbXmc53YDTSLSJOIOIG7gZ3TjtlJrAE4wB3AC8YYIyJlwFPAV4wxr8UPFhGHiFRZ3xcAHwXeu5gXthCS9myyyuRGAlOzYHyBcMISurj/dedWfu+GNTy66wxb/+Tn3PODXXQN+/n67Zu15DkLVBTNfQCRUktNSu8MjTHPAc+JSCnwSeAXInIW+AHwz8YYzQFUC6pzyJ8VJXQweWJE8r/m4WiUA21DPPNeJ6VFBdy1fQW2PGq6WFsc603VNexnnTWdTuUXvU7kt/FgZOJcqTKj2uvC63Jok3CVteZznbB6MN0HPAvYgX8wxhwUka8De4wxO4EfAo+ISAuxjKa7rYffB6wFHhCRB6zbbgZGgWetQJMdeM5aQ0YFZ8tsuqCMLkSFJ3lvUIfdxn+9ZQNXrirnF4e6ua65ihvWV1Pk1I2BbFDucTIWjBAIRXDpJFeVo1I+24hIJfBpYjXN+4D/A1xLbCfhhnQsTuWvc8P+rCihg9gI2eJCR9JU11O9ozy2+wzD/jA1xS4+edWKvCsnKXI5KC50aGZTntPrRP4aD0Vo0DfLGSUiNFV5ONGjwSaVveZznTDGPA08Pe22r0763g/cmeBx3wC+kWQp2+a++vSJGkM4ahL2bEpWRjc8HmZlpeeC4xMNa9nSUMrgWIif7ptegagyJT6Rrn8sSH2pO8OrUSo9Ugo2ichPgA3AI8DHjDHnrLt+JCJ70rU4lb/ODY6zfn32lKFVFDmTltH9+8FORITPXrOKdbXevB0jW1dSSLs2ps1bep3Ib2PBcN4F2bNRc42X1070zn6gUhmg14nkQpEoQMLMJqfDhtNhm5LZFI5EGQ9FZiyjU9ntfE/YkAabVM5K9Qz1oLWrMEFEXMaYgDFmexrWpfJYKBKlZyRAXRadeCs8zoRNV9sHxjnTP8ZHttSzvi6/y8fWVHv594OdDI4FKSvScpo8pNeJPBWKRAlFjE6jywLNtcX8ZF87Q+MhSt2Z73mo1DR6nUgiGI4Fm5L1UvK6HPgmBZvi35e49N/5UhXvCat9m1QuS7U7XKIU1DcWciFKxXX7AhhD1vRsgthUvKHx0AUXhDda+3DabWxbWZ6hlWWPeLDtWJc2p81Tep3IU+OhCBCbfqQya12tF4DjXb4Mr0SphPQ6kUQoYoDEmU0AxS7HlDI6n/W9ZjYtXUVOO06HLaUBREotVTOeoUSkDmgA3CJyORCvDyoBitK8NpWjEtWST3a6L5ZBdOScb9ZjF8vG+hKeea+Tw+eGed+aKiDWqPHdtkG2rSzXD1lATbGLsqICjnYOc1VTRaaXoxaJXifUeDAWbNLMpsyLD2g41jXC9lV6HlbZQa8Ts4tnNiXq2QSxiXQ9vsDEzz5/rJd6sWYwLlkiEptIl6QnrFK5YLZw+K8CnwMagb+adLsP+KM0rUnluaHx2AW0tCh7LqBVXhfVxS4OTQo27TnVTzhquHp1ZYZXlx1EhPW1xbx9ZoBQkvG9KifpdSLPxYNN2rMp8xrK3BQ57RzTzCaVXfQ6MYtgZPYyusmTJjWzKTeUFxVoZpPKaTOeoYwxDwEPicgnjDFPLNKaVJ6bCDYVZk+wCWBTfQmvHO9hPBjBZouV0K2p9lBbkj3lfpm2oa6YXSf7OdU7SnNtfvewyhd6nVDxMrqiAv3Qk2k2m9Bc4+V4twabVPbQ68TsJjKbkpTReV0OxoIRIlGD3Sb4/CGE85Pq1NJU7nFyomcUY0zeDhhSuW22MrpPG2P+GVglIl+afr8x5q8SPEypizI8HsJpt1FYkF2ZMRvrS3j5WA9Hu4bp8QXw+cPcc9WKTC8rq6yu9uKwCUe6fBpsyhN6nVBjmtmUVZpri3n5WE+ml6HUBL1OzG5iGt0MZXQAo4EwJe4CfP4wXpcDmwYolrTyIifBSJTRYEQDhyonzfa32mP96U33QpSKGxoPUeIuyLoIf2O5m2KXg12t/bQPjnNpYykrKz2zPzCPFNhtrKn2cqzTB5dmejVqkeh1Is+NB2PlHG7tXZcV1tV6eXxvm04GVdlErxOzmJhG50j83jceiBiZFGzSErqlr8JjTaQbDWqwSeWk2crovmf9+SeLsxylsEY2Z98J1ybChvpidp8aoMAu3HJJXaaXlJXW1xVzdL+PzmE/dVpimPP0OqEGxkK4HNmXjZqvmic1CddhDSob6HVidsHZMpusQES8V5PPH6I4y9pNqLkrtzYEBsaCLK/QXvkq96T0zlBE/oeIlIhIgYg8LyK9IvLpdC9O5adhf5hSd3buxm6qLwXguuZq3TFOYnNDKTaBfWcGMr0UtYj0OpG/ekcCVHqdWZeNmq/iE+mOapNwlWX0OpHcbD2b4oGlkUA82KSZTbmgwuNEgK7hwKzHKrUUpboNebMxZhj4KNAGrAO+nLZVqbwViRqGszSzCWLlCZ+5eiU3rK/O9FKyltflYENdCe+cGSQSNZlejlo8ep3IU70jAaq8rkwvQ1mWlRbidTk4rsEmlX30OpHErD2bJpXRRY1hJKDBplzgdNioLSmkfXAs00tRKi1SDTbF8zQ/DPyLMaY/TetReW4kEMYAJe7sTA0WETbWl+CwabnITK5YUY4vENaJSPlFrxN5KBCOMDgW0mBTFhER1tZ4OabBJpV99DqRRDAcxSZgtyXOEHU6bDjtNkb8oYn3ylpGlxsayty0D4xjjG7QqtyTakj8ZyJyBBgHfl9EqgH/bA8SkVuAbwF24EFjzDen3e8CHga2AX3AXcaYUyJyE/BNwAkEgS8bY16wHrMN+CfADTwN/Aej/zpzRu9ILI003jBPLU3r64rxOO28fXqADXUlmV6OWhzzuk6ope1s/xgGqPLqOXuxPbrrTNL7HDbhQNvQjMdMds8OnayqFoVeJ5IIRaIU2G0zliN7Cx2MBMJ0DsX+k2lmU25oKHez98wAQ+MhbdGhck5K6RnGmPuBa4DtxpgQMArcPtNjRMQOfBe4FdgEfFJENk077PPAgDFmLfDXwF9Yt/cCHzPGbAE+Czwy6TF/D3wBaLa+bknlNailodsXCzbVFmtj6aXMbhMuW17G4U4fY1Z/AZXb5nOdgNimhIgcFZEWEbk/wf0uEfmRdf8uEVll3X6TiOwVkQPWnx+c9Jht1u0tIvJt0WZCadPaMwpApUczm7JJXWkho8EIZ/q1NENlj/leJ/JBMBJN2q8pzuty0DUc4Im9bZQXFbCmWof75YKGMjcAbQPjGV6JUgtvLrVAG4G7RORe4A7g5lmOvwpoMca0GmOCwGNceEG5HXjI+v5x4EYREWPMPmNMh3X7QaDQ+sBRD5QYY96wspkeBj4+h9egslz3sJ/CApvu1uSAK1aWE4kadp/SLPk8MqfrhG5KLH0ne2PBJi2jyy7bVpRTUujgp/vatXeeyjZz/TyRF4LhaNJ+TXFel4POYT+BSJTPXLOKwgL7Iq1OpVNdaSE2gfZBDTap3JPSJ3oReQRYA7wDRKyb48GeZBqAs5N+bgN2JDvGGBMWkSGgktiHiLhPAPuMMQERabCeZ/JzNiRZ8xeIfdhgxQpND18qun0BaooLdapRDqgvdbOhrpjnj3SzaVkp1cX6YTSXzfM6MbEpYT1HfFPi0KRjbge+Zn3/OPCd+KbEpGMmNiWACqxNCes545sSz8z/1alkTvWN4nHacTv1Q082cRXYuW3rMv551xlea+llXW0x/3agg4oiJ79+RWOml6fy1DyvE3khGDGzZjaVuB0IcNf25dSVaAVAriiw26grKdRgk8pJqaaPbAc2zbE3UqJowfTHz3iMiFxCbBf75lSOn3KjMd8Hvg+wfft23dZbIrp9ATbWFWd6GWqBfPzyBr713HEe33uW37l+DTYNIuay+VwndFNiiWvtGaVSs5qy0qZlpWyqL+G5w138/FAnxsBJRrl+XbX+P1OZMp/rRF4IhWM9m2Zy/boaLllWquVzOaih3M177cPaJFzlnFTL6N4D6ub43G3A8kk/NwIdyY4REQdQCvRbPzcCTwL3GmNOTDp+8pZcoudUS9RoIMxoIEyNZsDkjJLCAm7buoyzA+O8crx39geopWw+14mF3JT4nTk8Z+xGY75vjNlujNleXV2dwnLVdCd7R7WELot9bOsyigsdXL6inPs+uBYReEtLm1XmzOc6kReCkdnL6Erd2qcpVzWUFTEeijAwFsr0UpRaUKlmNlUBh0TkLSAQv9EYc9sMj9kNNItIE9AO3A3cM+2YncR6bbxBrG77BWOMEZEy4CngK8aY1yb9vnMi4hORq4FdwL3A36b4GlSWizcHr9HU4JxyaWMp73UM8dzhLtZr1loum891Yi6bEm26KZFdRgNhun0BLltelumlqCRK3QV8+Vc3TPy8sb6EvacH+NDG2oRZFM8e7OSh10/xP+/cyjKraa1SC2g+14m8EAxH8bi0X2m+aiiPNwnXoQ4qt6R6VvvaXJ/YKne4D3gWsAP/YIw5KCJfB/YYY3YCPwQeEZEWYh8e7rYefh+wFnhARB6wbrvZGNMN/B7wT4CbWA8O7cORI7p9sVGumtmUW0SE2y9r4ORzx3hibxt/8MG1s6aKqyXpa/N4jG5KpNmp3lEOnxumtXeUWzfXsXoBd8W1OfjSs6OpkoMdw7zXPsTlK8onbjfG8INXWvnzZ45gDPzXJ97l4d+8SvsnqoX2tUwvIFvFMpv031u+qi1xYbcJ7TqRTuWYlD7xGWNeBk4BBdb3u4G3U3jc08aYdcaYNcaYP7Nu+6oVaMIY4zfG3GmMWWuMuSreJNYY8w1jjMcYc9mkr27rvj3GmM3Wc96ndd+5o9sXwGm3UeouyPRS1ALzuhx8/LIG2gfH+fuXTsz+ALXkzOc6YYwJE9tceBY4DPw4vikhIvGd7h8CldamxJeA+63bJ29KvGN91Vj3/R7wINACnCDLNiWGxkLc84M3OdblS+vvea2llxv+50v83v95m7989ijffv74gj5/PNhU6XUu6POq9FlT7aHK6+TN1r4pt/+vnx/jvz99hA9vruePP7KRV4738tjus0meRan5me/nCRG5RUSOikiLiNyf4H6XiPzIun+XiKyybr9JRPaKyAHrzw9Oesw26/YWEfm2ZDiyGgpHZ20QrnKXw2ajvrSQVuu6qlSuSOmsJiK/TWwK0PesmxqAn6ZrUSo/9QwHqC526U5qjtrcUMqljaV8+/njdOjEjZwz3+tEPm5KPH+ki9dP9PHy0Z60/p5XjvdSYBf+7xffz82batl1sn/G5qN9IwGOdA6n/PwTwSaPZjYtFSLCjqZKzg6Mc7BjCID2gXH+7qUW7tjWyN9+8nJ+8/1NvG9NJX/21GEt6VALaj7XCRGxA98FbgU2AZ8UkU3TDvs8MGCMWQv8NbE+fhAbJPExY8wWYhmyj0x6zN8TGxDRbH3dMs+XtSBS6dmkctvly8toHxxnj/bVUzkk1bPaF4H3A8MAxpjjQM2Mj1Bqjrp9fi2hy3HXr6smHDXs1gtpLtLrRIpetIJM6d7B3HOqny0NpWxdXsZ1zVWcG/Jztj9xoPdEzwgf+9tXue07r9E/Gkzp+U/1jrKstFB345eYHasraChz85O32xkYC/LTd9qp8Lh44KObsNkEm034i09cSjAS5R9fO5Xp5arcMp/rxFVAizGm1RgTBB4Dbp92zO3AQ9b3jwM3iogYY/YZY+I9+w4ChVYWVD1QYox5w9qMeBj4+MW+uIsRisw+jU7ltm0rK3AX2PneL1szvRSlFkyqZ7WAdYIHJibHZdVOsVra/KEIw/6wNgfPcTXFhbgL7LxzdjDTS1ELT68TKQhHorx8tBuA1p6RtP0efyjCu21DXLmqAoCrV1cCXFA+BfBe+xC/8b/fYDQYIRiO8pO322Z9/jdO9PFKSy9N1Z6FXbhKO4fNxl1XLidiDH/30gnaB8d54KMbp5SwL68o4rLlZew5PZDBlaocNJ/rRAMwuaazzbot4TFWefYQUDntmE8A+4wxAev4ySe6RM+JiHxBRPaIyJ6envRlooYiUaIGCjRwn9ecDhvXrKnkF4e6aOlO3/sDpRZTqme1l0XkjwC3iNwE/Cvws/QtS6UqHInyr3vOsn+Jf3ifmESnmU05zW4TtjSULvm/ryohvU6kYN/ZQYb9YSo8zokytHQ40D5EMBJluxVsWlvjpdJzYa+eSNTwWw/tobDAzpO//z6uWFHGo2+dSVpuFwhH+NKP3+GTP3gTl8PGf7p5fdpeg0qfKq+L27YuYzQQZk21h9u2LrvgmO0ryznYPsR4MJKBFaocNZ/rRKLeCtNPUDMeIyKXECut+505PCfGmO8bY7YbY7ZXV1fPssz5Gw/F/o0V2LSNRL67enUlLoeNB1/R7CaVG1INNt0P9AAHiJ2onwb+OF2LUqnZe7qfj/7tq3z58Xf5s6cPZ3o5F6V7WCfR5Yuty0t5r2OYUCSa6aWohaXXiRS8cKQbh02468rldPsC+PyhtPyeeKnqtpWxiWMiwtWrK3mztW9KIGnfmQE6h/3cf+sGVld7uWfHSlp7Rtl1MnGp61/++1F+8nY7X/zAGn7xH6/nikkTzdTScvnyMj5z9UruunJFwl6J21aWE44a3m3TzQG1YOZznWgDlk/6uRHoSHaMlS1VSmzKNSLSCDwJ3GuMOTHp+MZZnnPR+OPBJs1syntel4M7tjXyk7fbtWeeygmpTqOLEmvg9/vGmDuMMT/Itoar+eZEzwi/8b03GRoPsaOpgv1nB5f0h/e2gXGcDhvlHp1qlOu2Li8jGI5ytDO9k7jU4tLrRGpePNLN9lXlbG0sA+BUb3reTO45NcDaGi8Vk86pV6+uoGPIT9uk0co/P9RFgV24YX1s1/4jW+opLnTwL2+dueA5Xznew4OvnuTea1by5V/dgNtpT8va1eIQETbWl+B1ORLeHw8kaimdWijzvE7sBppFpElEnMDdwM5px+wk1gAc4A7gBWOMEZEy4CngK8aY1yat4xzgE5GrrSl09wL/96Jf4DwFQrH37wU2DTYp+OIH1mK3CV//2aFML0WpizbjWU1iviYivcAR4KiI9IjIVxdneSqZ11p6iUQNP/rCNdx7zSoC4SgHO1KfIpRNjDEc7/axptqLTSfR5bz4h+x9WkqXE/Q6kbpzQ+Mc6fTxgfU1rLZ6HbX2LnxfhmjUsOdUP1eumpp1tMPq2/SGVUpnjOHnBzu5Zk0VxYWxfj1up51PXNHIMwc6pzQK7x8N8p9+vJ+1NV7+6MMbF3zNKvuUe5ysqfbwtgab1EW6mOuE1YPpPuBZ4DDwY2PMQRH5uojcZh32Q6BSRFqALxHLoMJ63FrgARF5x/qKNyT/PeBBoAU4ATyzMK927uJldA67vgdWsKzMzR/cuJafH+rixSPdmV6OUhdlthD6HxKbGnGlMabSGFMB7ADeLyL/Me2rU0ntPjVAfWkhyyvcbLc+UCzVUZm9I0EGxkKsq/VmeilqETSWu6n0OLVvU+7Q60SKXrKm0H1wQw0rK4sQgdaehe/bdLx7hGF/mO0rK6bc3mxlOsX7NrV0j3Cqb4ybNtVOOe5TO1YQjkb5m+eOTdz2wE/fY2AsyLfuvozCAs1oyhfbV1aw98wA0agmKaqLclHXCWPM08aYdcaYNcaYP7Nu+6oxZqf1vd8Yc6cxZq0x5ipjTKt1+zeMMR5jzGWTvrqt+/YYYzZbz3lfJjNx42V0Tp1Gpyy/de1qVld7+NrPDk78/VBqKZrtrHYv8EljzMn4DdYJ/NPWfSoDjDHsPtnP9lUViAi1JYU0lrt5+8zS3H083h0rp2quKc7wStRiEBG2Li/TYFPu0OtEik72juJy2Fhb48XlsNNY7k5Lk/B4v6b4JLo4EeGa1ZW8cKSb032j/PxQFwA3bZwabGquLebea1bxyJuneefsIE8fOMdTB87xhx9axyXLShd8vSp7bVtZzuBYiNY0NrNXeUGvEzPwW2V0Dg02KYvTYeNPb9/M6b4x/vnN05lejlLzlrhQ/7wCY0zv9BuNMT0iUpDoASr92gbG6Rz2TymR2LaynDdOxBq/Jmr0mc2Odfmo8jqn9BZRuW1rYxkvHu3G5w9NlO+oJUuvEykaHg9R6i6YOEc3VXnTUka3+1Q/1cUulle4L7jvDz/UzGsnevnUg7sIhqM0lrt5IUGa/oqKIopdDn73kb34/CEaytyUFBbw6K4Lezmp3HWF1WB+7+l+1tZo9rGaN71OzGBiGp2W0alJ3r+2istXlPH43jY+f23Tkvt8pxTMntkUnOd9Ko32nI7tWk8ukdi+spxuX2BK49elIBSJcrJ3lOZazWrKJ1uXl2JMbDy7WvL0OpGiofEQJe7zn6tWV3k42TPKQlZvGGPY1drPjqaKhG9Mm2uLefg3r2JwLES3L8Cm+pKEz1NYYOcjly6jc9iPPxTlE9sasetY7ryzptpDWVEBe7Vvk7o4ep2YwcQ0Os1sUtP8+hWNHOn0cejc0uzLq9RsZ7WtIjKc4MsHbFmMBaoLvXVygOJCB+vrzgdotlmBp3ggaqk41TdKKGJYpzumeSXeJHz/WQ025QC9TqRo2B/LbIpbXe1hNBih2xdYsN9xum+MzmE/V1vNwBO5tLGMf/jclTRVebhseVnS4zYvK+FXmqv5tcsbqCspXLA1qqVDRNi2olyDTepi6XViBhpsUsl87NJ6nHYbT+xtz/RSlJqXGc9qxhi7MaYkwVexMSbv014zZc+pfratLJ+yy7y+rhivy7Hk3hAe7xrBYROaqjTYlE/KPU6qi12c0j4gS55eJ1I3NB6ipPB89fpq67y3kE3C482/Zwo2AVzVVMFvX7easqLk5csiwi2b6yZKqVR+urSxjNbeUUYC4UwvRS1Rep2YmV/L6FQSZUVObtxYw8797YQi0UwvR6k50xD6EjMwGuR498gFjV/tNuHyFWXsObV0gk1RYzh0bphVlR6cDv2rmG8ayty0DY5lehlKLZqh8amZTU3VHoAF7dv0ZmsfVV4Xa6znVupibW4owRg4rGUcSqWFNghXM/nEFY30jgT55bGeTC9FqTmbrUG4yjLxzKXtCXaaL19Rzt++cJzxYAS3M/tHU+8/O0j/aJBbLqnL9FJUBjSUuzmoPZtUHhkeD08JNtWXFFJYYOPkAmU2GWN4s7Wfq1cn7tekVDIzNX4fHg8B8NDrpzjeNXtg9J4dKxZsXUrlg3hmk1ODTSqB69dXU+lx8sTbbdw4bXqsUtlOz2pLzN4zAxTYY6Pjp2uu8WIMaRmlvdAiUcMLR7ORnNUAACAASURBVLqpLy1k07LEDWpVbmssd9Mx6CcaXbjmyEplq2jUMOyf2iDcZhNWVXoWbKz8mf7Z+zUpNVfFhQ68Lgcdg0trAIlSS0V8Gp1Dy+hUAgV2Gx+9tJ7nD3czquXMaonRzKYl5minjzXVXgoLLsxcWj2pJCPbAzjvnB2gbzTIZ65eiU134PNSY5mbYCRK70iAGm0+rHLYo7vO4A9FMAZOdI9MySKx24QDbUMzZpakas+p2ICIXl9gQZ5PKYj17lpWVkjHoD/TS1EqJ/lDUew20ffDKqmPXLqMh944zfNHurlt67JML0eplGlm0xJztNNHc21xwvuaqqxg0wI2m02HcDTKC0e6aShzs6Eu8WtRua+h3A3A2QHdLVe5bzwY27meXuJc4XHSPxYkai4+w6+1dxSPy0F1seuin0upyZaVuen2+bVBrVJp4A9FtDm4mtH2leXUFLt4+t1zmV6KUnOiwaYlZCQQpn1wnPW1iSe3FTkdLCstzPoyurdPDzIwFuJDG2u1r0geaywvAqBdSzNUHoiXSUzPSq3wOIlEzURfnPkyxtDaM8LqKo+eV9WCW1bqJmqgc0izm5RaaLFgk34kU8nZbMKtm+t48aiW0qmlRc9sS8jxLh9A0swmgNXVXlp7Fm6y0UILhCO8eLSbFRVFrEsSNFP5oaEsltnUrplNKg/Eg03uacGmSk8sC6lvNHhRz9/aO8qwP8z6Ga4PSs1X/HzdMaTna6UWmgabVCo+cukyAuEozx/pzvRSlEqZntmWkPgUmJk+TKyu9tDaM4pZgJKMdPjR7rMMjWtWkwKPy0FZUQFtA2OZXopSaZesjK7S4wSgf+Tigk1vtvbhLrCzpbH0op5HqUTKigpwF9i1SbhSaeAPRbWMTs1KS+nUUpTWYJOI3CIiR0WkRUTuT3C/S0R+ZN2/S0RWWbdXisiLIjIiIt+Z9piXrOd8x/qqSedryCZHu3y4HDaWVxQlPWZ1lQdfIEzPSGARV5YafyjCd15oYVVlEWusZuYqvzWWu7WMTuUFf5IyutKiAuwiF5XZNDgW5PC5Ya5cVa674yottEm4UukzrplNKgWTS+l8/osrvVdqsaRtGp2I2IHvAjcBbcBuEdlpjDk06bDPAwPGmLUicjfwF8BdgB94ANhsfU33KWPMnnStPVsd6/LRXOvFbku++7G6Olaa1tozSk1xdk34euytM3T7AvzWdU2a1aSAWGlGtje0V+klIrcA3wLswIPGmG9Ou98FPAxsA/qAu4wxp0SkEngcuBL4J2PMfZMe8xJQD8QjmTcbYzKad56sjM4mQrmngP7R+W8QvHWqH2NgR1PlRa1RqZksK3Pz+ok+wtEoDpt+MFZqofhDEf03pSbMNE3W43IQCEf50o/384H1s+db3LNjxUIuTak5S1uwCbgKaDHGtAKIyGPA7cDkYNPtwNes7x8HviMiYowZBV4VkbVpXF9WS3Si2X92kDXV3hlPQgNjsd3xx946S2vPaFadZP51bxuXNpayukp7NamYhrIifnmsF2OMBiDzUD5tSoyHIgjgdFz4gaLC45x3ZlM4EmX3yX421BVTbpXkKZUODWVuIlHDuUH/jBnWSqm58YejOB36HkjNrrG8iA11xbxyvIermyovKM1XKtukM4zeAJyd9HObdVvCY4wxYWAISGVr9h+tEroHJMknVBH5gojsEZE9PT09c199lhkPRhj2h6kpmTlbqdRdQIFd6M2yMrqW7hEOdgxz+2XT/wqofNZQ7mY8FGFgTNOB89TEpoQxJgjENyUmux14yPr+ceDG+KaEMeZVYkGnrOcPRSgssGNLcMmq9LjoHw3Oq9feW6f6GQ1GuHq1ZjWp9FpT7cUmcLBjKNNLUSqn+IOa2aRSd9OmWvyhKK8cX/qfb1XuS+eZLVEQaPo76VSOme5TxpgtwHXW12cSHWSM+b4xZrsxZnt1dfWsi8123b7Y56naEteMx9lEqPS46PGlP9i0+1Q/N/zli+w9PTDrsTv3dyACH7u0Pu3rUktHY7lOpMtzebMpMR6MJN2BrPA4CYSjjFpNxFPV4wvw7MFOmmu8rK3RjFGVXh6Xg7U1Xt5tGyKapUNIlFqK/OGINghXKasvdbOloZTXT/QxEgin9JhQJMpT755jaFw3d9XiSmewqQ1YPunnRqAj2TEi4gBKgf6ZntQY02796QMeJbYznvM6h61gUwp9mKqKXWnPbIpGDf/t/x7kVN8Y9z36Nn0z/D5jDD/b38E1qytnzcxS+SU+Tlsn0uWtvNmUGA9FLujXFFfpjU+kS/28HYka/nXvWRw2G5+4olHLUNWi2NpYxuB4iLP9es5WaqH4tUG4mqMPbawlFIny2FtnJgaQzORvX2jhi4++zfV/+SIPvtJKIDy3zS2l5iudPZt2A80i0gS0A3cD90w7ZifwWeAN4A7gBTNDHYEVkCozxvSKSAHwUeC5dCw+23QPB3A6bJQWFcx6bLXXyaGOIcLR6IL9/ul9ot4+M8Chc8P8SnM1r5/o5a7vvcnn3r8qYYlI28AYJ3tHuXx52Yz9plT+mchs0ol0+WoumxJt89mUEJH4psTDC7Xo+fCHokmDTRVWr6W+0SArKlOb1PnSsW7aBsb55FUrKHHPfl1QaiFsqi/BYRP2tw2xMsW/q0pdrFwfJDEe1GCTmpvqYhd3bGvkibfb+N4vT/DZa1ZRVpS4b+Phc8P83Yst3LihhmAkyjeeOkzbwDhfu+2SRV61ykdpO7NZ5Q73Ac8Ch4EfG2MOisjXReQ267AfApUi0gJ8Cbg//ngROQX8FfA5EWkTkU2AC3hWRN4F3iEWxPpBul5DNuka9lNT7EoYzJmuyusiaqD/IkZpzyQUifKLQ100lLm5+ZJabtu6jJaeEV45lrgMZf/ZQewiXLKsNC3rUUtXqbsAr8tBm5bR5auJTQkRcRLblNg57Zj4pgSkuCkhIlXW9/FNifcWfOVzNB6MUJisjK7IiUDKTcKHxkO8fLSHLQ2lbGnQ86paPK4COxvqijnQPkQkqqV0Kv0mDZK4FdgEfNL6TDDZxCAJ4K+JDZKA84Mk/nOSp/+UMeYy6ytjE0v94aiW0ak5u3xFOZ97XxODYyG+9fxxnj5wjsGxqe8jwpEo/+Xxdyl1F/A/79zKI5/fwa9eUssz752bV59IpeYqnZlNGGOeBp6edttXJ33vB+5M8thVSZ5220Ktbynp8QVori1O6djq4lhfp9409W1640QfQ+Mh7tzeiE2E7asqONzp46VjPVy5qoIi1/m/VqFIlP1tQ6yr9erEBHUBEaGhzK3Bpjz1/9i77zg5z+ru/58zs7O9d0m7qy6ruMmSJXdwNxAwxcbGNAcTA4GEkvKY5/mFHyQhgRQMzw9C4mATsCk2psQxLmBjcJctWbIsWZLVpZV2V9v77LTr98fMyitpu2Z2yn7fr9e+tHPPPfecWxrNmTn3dZ3LORcys+GLEl7gnuGLEsBG59xDRC9K3Bu7KNFBtCAFHL8oUQxkm9m7gWuAg0QvSvhix3yCFLgoEZ1GN/r1nSyvh5I836QvEDyxowUHXLuqNo4RikzO2XWlbDvaw762PpZWT+5zichpyOjVrSMRRyAU0cgmmZYl1YX86VuX8LudLTy/t43n97ZxyZJKrlhegz8U5q9/vpXXjnTz7VtWH1+x9qoVNTy+vYXtR3s4UxesJMESWmyS+BgKhekdClFZOLllrauLcvEYHOlKzCJNu1p6mVeax6LKNxvSXrOyhv/7ZA9/2N3K2858swn4hn3R5nUXL61MSCyS/uaV5Wka3Sw2Wy5K+Mfp2QTRqXQn97770YaDtPQMcdnSSs5tKCXL46Glx88rBzu5aHHF8el3IjPpjNoicn0efr+rNbZCnUZkSEKNtpDE+rH2iV3EGF5Iom2CY3/fzMLAz4G/P3nUrJndDtwO0NDQMO0TGI8/1jtHxSaZrqqiHG46v4FrVwV4cucxnt7dxpbDXQzEFh35xFsW8Y6z3vxu9tYzqjGDp3YeU7FJEk7vbGlg+Gr3ZL9YZGd5qCnO5XACmi4752jp8TOn5MRG3zXFuZxbX3p81BNEi2R/eKOVJVWFJxSmREaqL8ujsWNAw3klYwXDEUIRR+44xaaKwuwTRjYd7hhg+9Ee/MEwv9h8hK8/tosfbTjIg5sayfF5uPyM6pkIXeQUPq+Hd5w1l/1t/fx+l5beloRL2kISM7GIhD8Y7a+apWl0cppK87N533l13H7pIioLczi7roQn/+ItfPFtK05YRKSqKIez60p5cmfSZo7KLKJiUxpo74t+AakozJn0Y+rL8zncMUAkzj0VeodCDATC1IyyqtyVK2pwDh7d1sRQMMyLe9vpD4S5amVNXGOQzFJfnk/vUEjLsUrGGoytFDPeVOKqwhz6A2H2t/UD8Ic3Wsn1efjC1cv444sWsKSqgKZuP0e6BrnijOoTpiuLzLTzGko5p66E3+1s4WB7f7LDkcyW0atbD68klq2RTRInCyoL+Pili7hhTf2YCzlccUY1rzZ2jbuauEg86NNqGhi+2l0xhSkTDWX5vLS/g72tfZPu9TQZLT3RqXm1JacWm8oLsrl4SQVP725jZ1MvAGfUFNFQnh+355f0d/KKhAdiX67venofdWWTf63csj4xQ9pF4m0wNpR9vGl0axeUs2F/B/e/fIj3r63n9aYerlheTa7Py9KaouPv4wE1kpUUYGZcf+48DncO8rNNjXz+qmV4PXpdSkJk9OrWwxcjslRskhl0xfJq7nziDX6/q5X3ralLdjiSwfTOlgba+4fIz/aOOwXjZHXl0SXlNx/uimssLd3RYtNoI5sg2rD2k29ZzNl1JRTkeLlmlUY1yfiGGxZ2Dmhkk2Sm4SvX472H5/q8fGBdAwOBMN9/7gDZXg8XLao4Zb/sLM8Jw+FFkiXX5+VtZ9bS0R9gZ3NPssORDJXpq1sP5wddRJCZtGpuMdVFOfxOU+kkwTSyKQ209wemNKoJoLIwh1yfh82Hunj/2vqJHzBJzT1DFOZkUTjGFA4zo6E8X6OZZNLK86Ov7cmuxCWSbo5Po5vggsHc0jz+6Oy5/GrLES5cWKGpcpLyltcWU5rn44W97ayaq0azkhiZvJDEcM8mNQiXmeTxGJefUc0j25oIhSMaWScJo1dWGujoC0ypXxOAx4z6snw2H+qMaywtPX5qxxjVJDIdOT4v+dleFZskYx2fRjdOz6Zh5y8o4+OXLuQa9bqTNOD1GOsXVbCvrZ/mnsSsgCuSyd4c2aSvZDKzLllaSa8/xGtHupMdimQwXTZNcaFwhO7B4LSWuK4vz+f3u47RPxSiIA5XyCOxlejWLyw/7WOJjFRekE2nik2SoSYzjW6YmWn1Tkkr588v48kdLby4t513r56X7HBE0oqm0UkindwndaS+oRAA3/39Xt46iRVu1StVpkNl9BTXMRDAMbXm4MPqy/KJONjaGJ+KdUdfgFDEjdocXOR0lBdk0zGgYpNkpslOoxNJR/k5WZxTX8rmw53HR/GJyOQMT6PTNCaZaYU5WdQW57KntS/ZoUgG0ztbiuvom/pKdMPqy4abhMdnKt3wEPmxmoOLTFdZfjZdAwHCkTEXjxFJW4OBMNlej1brkox1waIKgmHHNk3HEJmS4YsR2So2SRIsqS7kUPsAwXAk2aFIhtI7W4prj00tKp9izyaIXm1cWFnAM2+0xSWW5h4/BlQXqdgk8VVekE3EQc+gVqSTzOMPRibVr0kkXc0tyaWyMJutR+K7Aq5IphueRpelaXSSBIurCghFHAfbB5IdimQoFZtSXHt/gJwsDwXT/KLygXX1vLCvnef2nH7BqaXHT3lBNtlZetlIfA33JNNUOslEg8GwptBJRjMzzppXwr7Wftr6hpIdjkjaON6zyaPP1jLzFlQW4DHYq6l0kiB6Z0txHf1DVBRkYza9Kx4fuXAB80rz+IdHdhA5zSlKLT1+TaGThCjPjxWb1CRcMtBgMEyuT+lWMttZ80pxwGPbmpMdikjaOF5sytLIJpl5OVle6svyVWyShNGn3xTX3heY1hS6Ybk+L3917RlsP9rDQ68enfZxQuEI7X0BFZskIYrzfHgMrUgnGcmvkU0yC9QU51BVmMOvtzYlOxSRtOEPRvAYeKd5UVnkdC2uLuRI56AWeJCEULEphYUjjs6BwLSag4/0rnPmsmpuMf/8+C4GAqFpHaOjP7oqXmXh6cUiMhqvxyjN14p0kpmiI5tUbJLMZmacVVfChv3tHOv1j7vv83vbuPfFgzMUmUjq8sfyw3RnMIicriVVhTjghX3tx7d1DgR45WDntL83igxTsSmFdQ8GibjprUQ3ksdj/L/vXMXR7kH+/tc7pnWM4UbllacxykpkPOX52ZpGJxnJr2KTzBJnzSsh4uDR10afStc1EOAvf/Yqt/znBv7mV9to7VV/J5nd1NNPkm1+RT5n15Xw5I4Wdjb1cLRrkO/+fi8PvtLIPz6ykx9tOEivXwv4yPSo2JTC2vujH8LKT7PYBLBuYTl/cukifrzhEE/uaJny44cbfp5u4UtkLGUFKjZJ5nHOMRSMqGeTzAo1xbksry3iJy8dwrkT+0RGIo6b73qRX20+wtUrawDY1dybjDBFUoY/GNHFCEkqM+O9q+uYU5rL/RsP85/P7MPrMT58wXwuWFTOruZeHt+uXnwyPfr0m0DP7G7lsW3T713Q3hf94l0Rp9FEf3HNMpbXFvG/fr51yqvFtPcHyPN5yc/JikssIicrz/cxEAgzFNSccckc/YEwDvRlQmaN2y5ZyM7mXp7efeIquE/vbmVncy//+N6z+Np7zwJgZ3NPMkIUSRn+UJgcXYyQJMvO8vCh9fPJ9noozvXxicsWsWJOMe84ey4XLKpg86Eu9hzTxQGZOr27Jci2I918/Acb+d+/3HbK1b3J6ugPkOUxinLjU+DJyfLyzZvPpaM/wA+ePzClx7b3DVGhfk2SQGWxUXPtkxzdtOlgJ//y+C4CoUgiwxI5LcNDz3OzVGyS2eH6c+dRU5zDXU/vPWH7Pc8doKooh+vPnUdFYQ5VRTns1MgmmeX8AU2jk9RQmp/N569exp9dsYTS/De/8122rApfloc7f7s7idFJulKxKQE6+wN84t5NBMMROvoD7Dk2veUk2/sDlBdk44lj08DltcVcsKiCh7c2TakI1t4XUL8mSai5pXkAHO4cmHDfvqEQn7h3E99+ag+f/elmgmEVnCQ19fqjzTV15Vpmi+wsD3988UKe29POtiPdAOxu6eXpN1r5yAXzyc6K/l9YXlukkU0y6/lD6uknqSPX5yXLe+LnlcKcLC5eXMmvX2s6/p4uMln69Btnzjk+d/8WWnuHuPOmcwHYsL9jWsdq7xtKSI+kd54zl/1t/Ww/OrkPef5gmO7BoPo1SUJVFGRTkudj7wTFWeccv9p8hJ7BIB+7eCGPbmvm8/dvIaSCk6Sg4yOb9GVCZpFb1jdQmJPFN5/YTWd/gO8/f4DsLA+3rG84vs/y2iLeaOnTe7fMan719JM0cOnSSkryfHzzCY1ukqlJ6LubmV1nZrvMbI+Z3THK/Tlmdn/s/g1mtiC2vcLMnjKzPjP79kmPWWNmr8Ue838txdYKbesL8Ic3Wvn05Ut41zlzqSnOmVaxKeIcnQOBuDQHP9l1q2rJ8hj/s/XopPY/3DGAA02jk4QyMxZXFbCvrZ/IOKPuNh3s5PWmHv7y2mV86Z0r+V/XLefhrU08uk3NCyX19MRGNqnYJLNJca6Pj128gCd2tHDe3/+W+18+zHti0+eGnVFbTCAU4UD7xKNZRTLVoKbRSRrI9Xn56EULeHJnCwfa+pMdjqSRhBWbzMwLfAd4G7AS+ICZrTxpt9uATufcEuBO4Oux7X7gb4C/HOXQ3wVuB5bGfq6Lf/TTd6gj+h/wrLpizIx1Cyt4aX/7lPs29fpDBMMubs3BRyoryObiJZU8/OrkptLtj72pVBRoGp0k1uKqQgYCYZq7/aPe3z8U4pFtTSysLODjlywC4E8uXUhhThYb9rfPZKgSJ5l+UWJ4Gl1ulq5cy+zy+auX8cs/vYjPXbmMy8+o5k8vX3zC/ctriwA1CZfZLdogXMUmSX0fuqABn8fD95/bn+xQJI0k8tPvOmCPc26fcy4A/BS4/qR9rgd+EPv9QeBKMzPnXL9z7lmiRafjzGwOUOyce8FFqyQ/BN6dwHOYskMd0St0DeUFAKxfWE5LzxAHp3jlbngJ+ERNXXvnOXM50jXI5sNdE+57oD1abFLPJkm0RVWFAOxtHX0q3ZM7jzEUjPCuc+bi8UTrB1leD6sbStl4oHPG4pT4mA0XJTSNTmYrM2N1QxmfvWop3/voWuZXFJxw/5LqQrweY5eahMssNhSMaAEJSQvVRbm885y5/GxTI92DwWSHI2kikcWmecDhEbcbY9tG3cc5FwK6gYoJjtk4wTEBMLPbzWyjmW1sbW2dYujTd6h9EIC6smiz4/ULywF4aYpT6dr7hgASMo0O4JpVNWR7PTz8atOE++5vGyA/20tetpKhJFZJno/KwpxRi02tvUO8tL+d8xeUU1Oce8J95y8oZ1dLL90DSn5pJuMvSvRqGp3IqHJ9XhZWFrCjScUmmb0Gg2H1bJK08bFLFjAQCHP/y4eSHYqkiUS+u402beHkOVuT2Wda+zvn7nLOrXXOra2qqhrnkPF1qGOA2uLc418sllQXUl6QPeW+Te39ATzGCUtPxlNxro9Lllbyu50tE+57sL1fzcFlxiyuKuBA2wChyIlNYx/b3kyW18OVK6pPeczaBWU4B68c0uimNJPxFyV6/UE8Bj5vSrUXFEkJy2uL2NWiaXQyvkyebu0PqmeTpI9Vc0u4YFE5P3j+oBZ3kElJZLGpEagfcbsOOLkj9fF9zCwLKAHGq8o0xo4z3jGT6lBHPw3l+cdvmxnrFpRPuZ9MR3+AsvxsvJ7E5b6LFldwoH2Apu7Bcfc70NafkN5RIqNZXFVIIBzhSOebr8vNhzrZ0dTDW5dVUZTrO+Ux59aXkuUxXj4wvZUfJWky/qJErz9ETpaXFFvLQiQlLK8t4nDH4PHppiIny+Tp1s45/MGwRr5KWvnYxQs50jXI49snHrAgkshi08vAUjNbaGbZwM3AQyft8xDw0djvNwC/c+N0rHbONQG9ZnZB7ArER4D/jn/o03eoY4CGivwTtq1fVE5j5yBHu8Yv6ozU3j+UsCl0wy5YFB0c8OK+sQth/mCYo91+rUQnM2ZRVQEGPLO7jb6hEDuaevj5K40sqizgkiWVoz4mPzuLVfNK1Lcp/WT8RYlef0hTJETGsLy2GIA3WjSVTsaUsdOtA+EIEYdyhKSVK1fUML8in3vUKFwmIStRB3bOhczsM8DjgBe4xzm33cz+FtjonHsIuBu418z2EP3ycPPw483sAFAMZJvZu4FrnHOvA58C/gvIAx6N/aQEfzBMS8/QCSObIDrqAuC1I93MLc2b8DjOOdr7AjQ05E+47+lYOaeYkjwfL+xt5z2r60bdZ7ixeaVWopMZkp+dxVuWVfH07lb+5fE+Is4xtzSPD18wnyzv2B/Izp9fxg9fPMhQKEyOmm2mi+MXJYAjRHPALSftM3xR4gUmeVHCzHrN7AJgA9GLEv9fIoKfjF5/UFetZVb58YbJ9/IYXgzlvhcPsav5xF59t6xviGtckrZGm269fqx9Yt8/hqdbt41zzAmnW5vZ7URHP9HQEP/Xoz8YnYakHCHpxOsxbr1oAV/5n9fZcrjr+PdckdEktJTunHvEObfMObfYOffV2LYvxQpNOOf8zrkbnXNLnHPrnHP7Rjx2gXOu3DlX6JyrixWacM5tdM6dGTvmZ8b70jHTGjuHV6I7sUi0vLYYj8H2I92TOk7nQJChUITyBBd4PB5j/cJyXtw39iCB/W3RD38a2SQz6ZpVtXzuymWcUVtEXVket164YMKlgdcuKCcQirBtkv/PJPliPZiGL0rsAB4YvihhZu+K7XY3UBG7KPEF4Hi/jthFiW8At5pZ44ipFZ8CvgfsAfaSxIsSPf6QvkiIjKEkz4fH3iw6iYwiadOtEz3VeigYBlRskvRz49p6inKyuOdZjW6S8SVsZNNs9MPnDwKws6mHHwfCJ9xXWZjDb15vobZk4pFNhzqiRat4NOWe6Aqjz+vhUMcA//bUnlGbkT++vRmPccrqXyKJVlmUwwfWTf5K4vkLygB4+UAna+aXJyosiTPn3CPAIydt+9KI3/3AjWM8dsEY2zcCZ8Yvyunr9YfIzdIUCZHReD1GaX62ik0ynqlMt25Mp+nWgyOKTYGQmi1L+ijMyeLmdfV8/7kDfPHty5kzie+3MjvpE3AcdQxEPyyVj9JMe25p3qR7NnX0DwHxKTZNZFFVAQD72vpHvf9wxwBzSvLwjTN9SSQVVBTmsKiygE0H1bdJUoem0YmMr6JAxSYZV8b2gB2eRqfV6CQdfeTCBTjg33+/N9mhSApTBSGOOvoDZHs9FGSfmjTmluTS4w/RNxSa8DjtfQEMKJuBYlNNcS752V72tZ5abApHHI2dg9SXJ7Z3lEi8rJxbzM5mLaMtqaPXH5pwCqjIbFamYpOMI5OnW/uPj2zS1zFJP/Xl+dx0fj0/fukQh2I9fkVOpml0cdTRH6C8IHvUJa7nxBqDN3UNsrSmaNzjtPYNUZLvm5HRRB4zFlYWsK+t75T7jvX6CYQjp/SgEklVK+YU8/DWJnr9QYpyfckOR2Y55xx9Q1qNTmQ85fnZDAbDDAbC5I1ysU4kU6dbD6pnk6S5z165lF+80si//nYX37p5dbLDkRSkT8Bx1NEfGHM00tzYXNaj3f5R7x+pqcs/o3NfF1UV0jUQpK136ITtw72jVGySdLFiTrSQu6tZy2hL8g0EwoQjjlytjigypvLY56bOAY1uktnFr2KTpLma4lw+dvFC/nvLUS3QI6NSsSlOnHN0K27daAAAIABJREFU9AfG7LOUl+2lLN83Yd+mQChCW98Qc0pmriH3yjnFGLD1pDeJwx0DFORkUZavESKSHpbXFgOwo0lT6ST5ev3RadP6IiEytuFik6bSyWyjaXSSCT7xlsWU5Pn4h0d2kEKLxEuK0LtbnLT2DhGKuHH7LM0pmbhJeEuPH0e0x9NMKcnzMb+igK2NXSe8SRzqGKChLG/UaYEiqWhOSS7FuVns0MgmSQG9/iCgLxIi41GxSWarnsHoBYliTfuXNFaS5+Mvr1nG83vb+dWWI8kOR1KMPgHHyfCUs/L8sYtNc0vzaO8PMBS7kjGao93RYtRMLyF5dl0Jx3qHaOmJTqUbGArR1hfQFDpJK2bGijnF7NTIJkkBPRrZJDKhXJ+X/Gyvik0y63QPRi9IlGgGgaS5D66fz3kNpfzdwzv0Xi4nUIPwOBkuNpUVjJ0w5pZGRys1dftZUFkw6j5N3X5yfR5KZzjxnDmvhIe3HmVrYxe1JbUc7oyej1aik3SzYk4xD2w8TCTi8Hg0Kk+S5/jIpixd1xEZT3lBNh3q2SSzTI8/iBkUZuvrmKS+H284NO79lyytYsvh3Xz8By9zw5r6Mfe7ZX1DvEOTFKZPwHHSFGv8XZo3zsim403Cx55K19wdbQ4+01PXCnOyWFxVyNYj3TjnONA+gAF1ZSo2SXpZXlvEQCB8vGAqkizDPZtyNLJJZFxl+dm6Gi6zTvdgkOJcny6MSUaoLc7lsqVVvHKoiyOd47eNkdlDxaY4aeoeJM/nJXucK9hFuVmU5vnY19o/6v0R52LFppnr1zTS2XUldPQH+M5Te/jDG63Mr8gf93xEUtGKOWoSLqlBDcJFJqeiIJuugQDhiJrLyuzRPRikJE9T6CRzXLasipwsD0/vbk12KJIiVEmIk+Zu/4QJw8xYVlPEntY+QpHIKfd39AUIhCMz3q9p2Mo5JeT5vATCEd52Zi0fumB+UuIQOR3LaorwGOxoUpNwSS41CBeZnPKCbCIOemI9bERmgx4VmyTD5Pq8rF9YwbYj3bT3DSU7HEkB+gQcJ809forzJp5zvaymiEAowsH2U6f4vNkcPDkjm/KyvdzxtuV8/qplXLq0inzNIZc0lJftZUFlATubNbJJkqvXH8LrMbK9SrUi4xleybddU+lkFukeDE7qu4NIOrloSQUej/HMnrZkhyIpQJ+A42QyI5sAFlcX4DXjjZZTR100dfvxGFQX5SQixEnxeT0z3i9KJN5W1BZrZJMkXa8/SGFOlt5TRSZQHis2darYJLOIptFJJirO9XFeQymvHOw8PsJbZi8Vm+JgKBSmrS9A8SQSRk6WlwWV+exqHq3YNEh1US5ZugouclpWzCniUMfA8WWFRZKh1x+iKFdXrUUmUpLnw2umFelkVukeDKnYJBnp0iVVhCOOF/a2JzsUSTJVNeLgWE90TmpJ7uQSxrKaIo71DtE14kNVOOI40jmYtCl0IpnknPpSALY2diU5EpnNevwhiiaZF0RmM48Zpfk+9fiQWaXHH5zUhWqRdFNZlMPKucW8uL+doWA42eFIEqnYFAfNPX6ASSeMZTVFAOwaMZVuy+Eu+gNhzppXEv8ARWaZc+pLMYMth1RskuTp9Qc1sklkkuaW5nGoYwDntCKdZD5/MEwgFKFYFyQkQ71lWRX+YISXDnQkOxRJIhWb4qCpO1psmuxQ2OqiHErzfcen0kWc4/e7jjG3JJczaosSFqfIbFGc62NJVSGbD6vYJMnT6w9RrGKTyKQsqSqkxx+iVaObZBYYnuavaXSSqerK8llUWcBze9pGXYVdZgcVm+KgObaK3GSvTpgZ59SVsrO5l2d3t7K1sZv2/gBvPaNajWRF4mR1QymbD3XqKrkkTe9QUNPoRCZpcXUhAHuP9SU5EpHEU7FJZoPLllXR4w/xqi7+zlq65BoHzd1D5Gd7yfVNvnZ31Yoa2vsDPLKtmTyfl5ri6NxWEZmcH284NO79gZCjcyDIt3+3h4rC8Vd4vGV9QzxDEwHUIFxkKsoLsikvyGZPaz8XLq5MdjgiCdWjYpPMAkurC5lbkstj21uYU5LH3NK8ZIckM0wjm+KguWeQ2pLcKY1K8nqMm9bWc+a8EgaDYS4/oxqPRjWJxE19eTShHeoYSHIkMhs551RsEpmixVWF7GvtIxzRiFTJbMMjm9QgXDKZmfH+8+vJ8hj/+cw+9o7z/t7RH+C3r7fwL4/vYtuR7hmOVBIloZ+Czew64FuAF/iec+5rJ92fA/wQWAO0Azc55w7E7vsicBsQBv7cOfd4bPsBoDe2PeScW5vIc5iMpm7/tFaRGy44Xba0knmq9IrEVU1xLtleD4c7B1ndUJbscGQMmZonBoNhwhGnaXQiU7CkupCXD3RwpGsw2aGIJJSm0clsUV2UyycuW8R/PX+Au5/dz93P7ifb62F+RT5n1ZXg83jYeLCDva39xx/z+PZmHv3spWR5NS4m3SWs2GRmXuA7wNVAI/CymT3knHt9xG63AZ3OuSVmdjPwdeAmM1sJ3AysAuYCT5jZMufc8NqJlzvn2hIV+1S1dPu5YHHFtB7r9Rh1ZflxjkhEPGbUleVxWCObUlYm54lefwhAI5tEpmBRZQEG7FHfJslwKjbJbFKan80nLlvMK4c6WVZTxEAwxJ6WPp7Z3UYwHGFNQxnvW1PH+QvKOdo1yGd/uoWfbWrkA+vU5iLdJfJT8Dpgj3NuH4CZ/RS4Hhj5JeJ64Mux3x8Evm3RuWjXAz91zg0B+81sT+x4LyQw3mkJRxwtvUPTGtkkIolVX57PM7tbCYYj+HR1JBVlbJ7o9Ue/SBTl+uiLFZ5EZHwFOVnMKc1lz7E+/MEwA4EwhzoG2Nfax7KaIs6cV5LsECUJMnEEbM9gNC9oxVKZLfKyvVy8pHLCPqnOOe578SD/+ps3eNc5cynI0f+RdJbIb1/zgMMjbjfGto26j3MuBHQDFRM81gG/MbNNZnb7WE9uZreb2UYz29ja2npaJzKetr4hwhFHbYmmwYmkmobyfCIOjmpKRqrK2DzRORAtNpXqqrXIlCypKuRAez/L/+Yxzvu73/Lu7zzHFx54lY//YCPBsJbPnm1GjIB9G7AS+EBsZOtIx0fAAncSHQHLSSNgrwP+LXa8YZc7585NxlTr7sEgBdleTRMSOYmZ8b/fvoK2viH+4w97kx2OnKZEvsON1u365I5gY+0z3mMvds6dRzTpfNrMLhvtyZ1zdznn1jrn1lZVVU025ilr7vYDMKdYI5tEUk19eX50SkarpmSkqIzNE8O5oVajXkWm5KLFlVy5opq/uvYMvvRHK7nrw2v46nvOpLnHz+Pbm5Mdnsy84yNgnXMBYHgE7EjXAz+I/f4gcOXJI2Cdc/uB4RGwSdc9GNQUOpExrG4o4x1nzeGe5w7gD4YnfoCkrEQWmxqB+hG364CjY+1jZllACdAx3mOdc8N/HgN+SZKTRpO+UIikrMKcLOrK8tjZ1JvsUGR0GZsnWnqiuaFGFyJEpqQ4z8eVy2v49OVL+NglC7lmVS03n99AQ3k+P3j+QLLDk5mXtBGwiRz92j0Y1Ep0IuP4wLoG+oZCPLXzWLJDkdOQyGLTy8BSM1toZtlEh7E+dNI+DwEfjf1+A/A755yLbb/ZzHLMbCGwFHjJzArMrAjAzAqAa4BtCTyHCTV3R6fnqNgkkppWzCnmSNfg8WacklIyNk80d/vJ83nVj0MkDrwe4yMXzuflA51aEnv2SdoI2ESOfu3xa2STyHguXFxBZWEOD7168jVISScJ+xTsnAuZ2WeAx4k29LvHObfdzP4W2Oicewi4G7g31ti1g+gXDWL7PUC0SWwI+LRzLmxmNcAvoyNjyQJ+7Jx7LFHnMBlNPX6yvR7K87OTGYaIjGHFnGJ+83oLO5t7WL9weqtGSmJkcp5o7vFTW5JLLA4ROU03rq3nX3/zBj94/gD/fOM5yQ5HZs5URsA2TmcErJkNj4B9OhEnMJqewSAN5VqNWmafH284NOl9l1YX8tvXW7jn2f3k+ryn3D9Rs3FJvoRecnXOPQI8ctK2L4343Q/cOMZjvwp89aRt+4CU+oTR0u2nujgHj0dfKERSUXVRDuUF2exs6lWxKQVlap5o6fFTU5yT7DBEMkZJno/3rZnHAxsb+T/vWEGpLvLNFsdHwAJHiF5wuOWkfYZHwL7AiBGwZvYQ8GMz+wYwlxEjYAGPc653xAjYv52Z04nSNDqRiZ1TV8IL+9p5vamH8xrKkh2OTIOWQDhNR7oGmauV6ERSlpmxoraIva19DIXUZFBmRnOPn1r1axKJq/evrScQivCb7S3JDkVmSKwH0/AI2B3AA8MjYM3sXbHd7gYqYiNgvwDcEXvsdmB4BOxjxEbAAjXAs2b2KvAS8OuZHgGrBuEiE6svz6c038fWxq5khyLTpGYSp2lfaz9Xr6xJdhgiMo4Vc4p5bm87e471sWpuSbLDkQznnKOle4ga9fITiauz5pVQV5bHI9uaeP/59RM/QDJCpo2ADYYjDATCKjaJTMDMOHteKc/uaaVvKERhjkoX6UYjm05DZ3+A9v4Ai6sKkx2KiIxjfkUBeT4vrx/tSXYoMgt09AcIhCMa2SQSZ2bGO86aw3N72uge0KIPkp56YguWqNgkMrFz60uJODS6KU2p2HQa9rb2AbCkWsUmkVTm9RhnzivhtSPd9A+Fkh2OZLjmHj+Aik0iCfD2s+YQDDt+83pzskMRmZbh1XGL8zRKQ2QitSW5zC3NZdPBzmSHItOgYtNpGC42aWSTSOq7aHEFoYhjw/6OZIciGa4lVmzSNDqR+Du7roR5pXk8um36xSbnHF/6721c9Y0/8Pj2ZpxzcYxQZHzdGtkkMiVr5pfT1O3naNdgskORKVJJ/TTsbe0nO8vDvDI1CBdJdTXFuSytLmTDvnYuW1pJlle1dkmM5u4hQCObRE7HeMtjL6ws4A+7Wrn7mf3kZZ+6HPbJRi6P7Zzj7x7ewQ9fOEhVUQ6fuHcTly6t5NsfOI+SfH35l8Tr8UdHWKvYJDI559SV8OhrTWw62MncUn3vTif6tnUa9h7rY1FlAV6PJTsUEZmES5ZU0jsUYuuR7mSHIhmsucePGVQV5SQ7FJGMdNa8EsLOsf3o1N/Lv/nEbu55bj9/fPECnr/jCr78zpU8v7edO594Y0rH6ewP8Ml7N7HpoEbLytRoZJPI1ORnZ7FiTjFbDncRCkeSHY5MgYpNp2FPa5+m0ImkkSXVhVQX5fDcnjZNm5CEaen2U1mYg0+j50QSoq4sj9riXH7/RiuhyOS/eDzyWhPfenI3N66p40t/tBKf18OtFy/kA+vquffFg+w51jfpY33ryd08tr2ZT9y7iaZuTe2QyTvesylXxSaRyVo7v4zBYJgdzb3JDkWmQJ+Ep8kfDHO4Y4DFVQXJDkVEJsnMuGxZFU3dfl7Y157scCRDNff4NYVOJIHMjGtX1dDRH2Djgck1jd3b2sdf/exVVjeU8tX3nIXZm6PSP3fVMvJ9Xv7xkR2TOtb+tn7ue/EgVyyvZjAQ5pP3vYI/GJ7Wucjs03O8QbiKTSKTtbi6kNJ8H8/sbtUF4zSiYtM0HWwfIOKiL3wRSR+r60tZXlvEo9uadTVaEqKlx0+Nik0iCbWspogFFQX8bucxhkLjF3q6B4J86r5N5Pi8fOeW88jOOvHjb2VhDp++YglP7jzGs7vbJnzuf3psJ9lZHr72vrP4xk3n8urhLv7psV2ndT4ye/QMBsnJ8pDrm7jfmIhEecy44oxqGjsH2dHUk+xwZJJUbJomrUQnkp7MjPeeV0d+tpefvnSYQEhzvyW+mnv81JaoX5NIIpkZ151ZS99QiGfGKRC90dLLtd98mr2t/Xzr5nPHbC77xxcvoL48j6/8z3aC4/QE2XSwg0e3NfOJyxZTXZTLtatqee9583hg42EGAxrdJBPrHgyqX5PINKxuKKOyMIffvN5CRKOb0oJWo5umvbF5/Ys0jU4k7RTmZHHjmnq+/9x+7n3xADesraMwR2+Hcvr8wTBdA0FNoxOZAQ3l+Zw1ryQ6uikY5roz5+D1GM459rf38+LedrYd7YmuOnfZIg53DI67yt1bl1Vz74sH+fOfbObSpVWj7vPb15upLMzmTy5beHzbDWvq+MUrR/jtjhbedc7cuJ+nZJbW3iHK8rOTHYZI2vF6jKtX1vCTlw6x5XAXH7pgfrJDkgno29U07WntY15pHvnZ+isUSUdLqgt535o6fvFKI7f854t8/9bzqSjUaBQ5PS09fgBNoxOZITeuiV4seG5vO7uP9ZHr89I9GKR7MEiez8tbz6ji8jOqJ9Wwf3ltEWfUFPHkzmOcU1d6Sk+dYz1+ntrVyheuXnbC578LFlYwtySXX77SqGKTjMs5x9Yj3Vy6pDLZoYikpVVzi5lbkstvtjfTeNVS6sry43LccMSx6WAnG/a182pjFx+7ZCEXLdb/09OlSsk07W3t06gmkTR3XkMZeT4vD2w8zI3/8QL33raeeWNMsUgk5xxtfQHK8n1kaQWztNbcHS021Zao2CQyE7K8Ht55zlzmV+Tz7J42srzG/Ip8llYXcta80lP6M43HzPijs+fwzSd388i2Jm4+v+GE+5/d00ZOlueUq+kej3H96nnc9fQ+WnuHqCrShQsZ3ZGuQVp7h1jdUJrsUETSkseM96yu4+7n9nHTf7zIT/7kAhoq3iw4RSKO15t6eGZ3G5sOdgKOvOws8nwe8nxeSvJ8LK4uZEl1IfNK8yjJ8/HUrmN8/dFd7GrpxQwKs7PYsK+DBz91EWfUFiXvZDOAik3TEIk49h7r5+Z15ckORURO04o5xdz38fV87L9e5n3/9jz33raOpTUzk1he3NfON37zBjuae+j1h1hSXcj3PrKWBZUqZKer5tjIJk2jE5lZZ9eVcnbd6X+BryjM4a3Lqnhy5zHml7dxYezKdq8/yJbDXbz//HrKC06dAvXe1fP47u/38vDWo/zxxQtPuV8EYPOhLiDae0ZEpmdeWR63XbyIH710kPf/xwu897x5LK0p5I2WPh7acpQjXdEFgBZVFZCb5cUfDDMY++n1hwhH3uz35PMawbBjfkU+d950DpefUc1AIMy7v/McH/uvl/nlpy+iukif6aZLxaZp2NXSy2AwzBkz9IVURBLr/AXlPPCJC/nIPS9x43+8wPdvPX9aHwTH6wUyUt9QiN9sb2bjwU7K8n2smltMca6PZ3a38bZvPcPdt67V0N00NTyNTiObRNLX5curOdo1yMNbmygvyGZRVSFPv9FKKOK47ZLRC0lLa4o4c14xv9x8RMUmGdPmQ13kZHk0WkLkNM0ry+PDF8zn56808u9/2EvEgcdibTLOq2NpTSHFuac24g9FIrT1BWjtHaJ7MEivP0hlQQ7/dOPZx6dbl+bD3R89n/f/xwt86r5XeOATF+L12EyfYkZQsWkafrXlyPEGZSKSGVbMKebnn7yID9+zgQ9+bwP//qE1XLZs9AaxUxFxjr6hEL3+EN0DQV470sW2Iz04HJctreKK5dXHp3mcNa+EH754kFvviV5JWTW35LSfX2bW7pY+inKzKBrlA46IpAePGe8/v567nt7HjzYcwhHt57FqbjEb9nWwYV/HqI9bUFHAw1ub+Ptfv86iyvFXK75lfcO490tm2nK4k7PrSibVQ0xExjenJI/PXL70eAGpMCdrwgV/sjweaotzTxmBfvL/ybPqSvjqe87kCw+8yvef28/HL10U9/hnAxWbpigScTy05ShvWValZsIiGaahIp+fffJCPnrPy9z2g5f562uXc+vFCyb9oXAoFGbvsT4OdgxwpDPal6FvKMTIxVlzfR7WLSrngoUVp/T1qCjM4fZLF/G9Z/fxZz/ZzMN/dokWIUgjQ6Ewj29v5qoVuhAhku5ysrx85MIFPLz1KOUF2SyoKGBJ9fgFpPMXlPPM7jYe29bMJ9+yGI/pSri8aSgUZtvRHm69aEGyQxHJKMMFpHh7z+p5PPJaE//8+C6uXFHDQrW5mDJ9i5miDfs7aOr288W3r0h2KCISJydPf7txTR0PbDzMVx/ZwV1P7+PKFdUsqyki1+c95bE9g0F2NPews6mXva19hCIOr8eYU5LLspoiivOio1yGR7vUFueO27C2ICeLO286lw9+bwNffmg7/3TDOXE/X0mMp3a20uMPcf25Wo1KJBOU5Pn44PrJL63t83q4ekUND77SyLYj3XHpISWZY0dTL4FQhHPr9boQSQdmxlffcxZXf+MP/PWDr/KTP7lAC/lMkYpNU/SrzUcoyPZyta5ci2SsXJ+XD18wn13Nvfz6tSZ++vJhPAZ1ZfkU5WaR6/PS5w/R2jdER38AgPKCbNYvLGfFnGIayvNPKxldtLiST791Cd9+ag9r55fz/vPr43VqkkAPvXqEysJsLtGS1iKz1rkNpTy3t43Htzezck5xQr+YNHf72bC/nbcsq6I0/9Sm5ZJaNh/qBNBKdCIpaLy+q9euquVnmxp517ef4+bz6yd8X9c06Tep2DQF/mCYR15r4roz55CXfeoIBxHJHGbG8jnFLK0p4mBHP28093Gwo59jvUP4g2EKc7KYV5rH+fPLWD6nmOqiHCyOUyY+d9VSXm3s4ou/fI2qohwuX14dt2NL/PX4gzyx4xi3rGvQVS+RWcxjxnWravn+8wf40YZDvPe8eWP2cOseCLK/vZ/uwSCDgTCVhdlUFeVQXZQ77ufMDfvaufvZ/Ty58xjhiKMoJ4uPX7qI2y5dOGG/EkmeLYe7qC3OZU5JXrJDEZEpWN1QxmAwzMNbm/jhiwe5ZV3DqLMd5FQJzUhmdh3wLcALfM8597WT7s8BfgisAdqBm5xzB2L3fRG4DQgDf+6ce3wyx0yk+148SO9QiPesnjdTTykiSeb1GIsqCyds9hpvWV4P3/3QGm6+6wX+9EevcN/H17FmfvmMxjATMiVPPLatmUAooil0IsLSmiLeefYcHt3WzLee3M1bllVRW5xLQU4WbX1DtPT4+fc/7OVwx8AJPf1GysnyUFGQTUNFAfVleeT6vIQjjpcOdLDnWB8F2V4uWVLJkupCGjsHuPOJN/jl5ka+88HzMm5xiUzIE5GI45VDnZpCJ5KmLlpcSW6Wl5+/0sjXH9vJmvllrJpbQl62F5/HGApFGAyG8Zix7Ug3JXk+qotzyMma3UWphBWbzMwLfAe4GmgEXjazh5xzr4/Y7Tag0zm3xMxuBr4O3GRmK4GbgVXAXOAJM1sWe8xEx4y7UDjCPz66k7uf3c+lSyu5cHFFIp9ORASAwpwsvn/rOt733ee58d9f4KbzG/jC1ctOaSyerjIlTxzpGuS+Fw8yvyJfXyREBIALF1eyqKqQBzc18ui25hPuM6LLdl++vJq60rzolxWvh/7YyqW9/iA9QyGO9fjZdLCDF/e9WZIqyPby9jNrWb+o4vjiFX/zRyt5aX8Hf/6Tzbzn357njuuW8741dZTkpf+qmJmQJ7oHgnz+gS0c7hjkT9+6JBFPISIz4Lz5ZdQU5/Lc3jY27Ovg+b3to+73n8/sO/57Wb6PmuJcaopzKc7zkefzkOvzkufzkhP7M9fnIcsTnR0x/G7vRlyJKM33saiykPryaL7I9nroD4Tp7A/QNRCkYyDAYCBEXnbW8RX5CnOzKMzOoiDHm9QR94kc2bQO2OOc2wdgZj8FrgdGvpFfD3w59vuDwLctOg/leuCnzrkhYL+Z7Ykdj0kcMy5ae4f4wfMH2HOsj9ebejjUMcCtFy3g/3nHCrwerS4iIjOjqiiHhz5zMd96cjf3vnCQn208zJLqQlbOKaa8IJv8bC8NFQXcsKYu2aFOR1rniQNt/XzziTf4n61NGPDV95wZ16mUIpLeaopz+dO3LqZvKNrjr38oTEVBdKrcZFc5DUcc7X1DBCOOSMSNeaV83cJyfv3nl/C5+7fwtw+/ztce3clFSypoKM+nJM/HmfNKuHZVbbxPcSakdZ7YdqSbT963iZYeP1951ypuVg9GkbQ2ryyP96+t521n1tLU7WcoFCEYjpCb5SHH58U5CIQiDARC9AxfPBgMsudYH/5gmFDEEQhFCEUiBMNjjW2Nr1yf53gRqiDnxILUyNvnLyiP+6CaRBab5gGHR9xuBNaPtY9zLmRm3UBFbPuLJz12eO7aRMcEwMxuB26P3ewzs13TOIcTfCX2A1QCbad7vCRK5/jTOXZQ/MmUNrF/cPTNx+PfCzwev6eb/FJL8ZdReeLmf4Sb0+h1Ng6dQ2rQOaSGlD+HMXIGu9/89XTPYVbmiXjniFv/AW6N/pryr6kJpHv8kP7nkO7xQ/qfQ7rHD/E9h1HzRCKLTaNd3j25fDfWPmNtH+0y0KglQefcXcBd4wU4XWa20Tm3NhHHngnpHH86xw6KP5nSOXZI//jHkHF5IhP+nXQOqUHnkBp0DkmXtDyRqO8Saf7vkfbxQ/qfQ7rHD+l/DukeP8zMOSRyAl8jMHKsaB1wdKx9zCwLKAE6xnnsZI4pIiLpQXlCRETGozwhIpKmEllsehlYamYLzSybaIO+h07a5yHgo7HfbwB+55xzse03m1mOmS0ElgIvTfKYIiKSHpQnRERkPMoTIiJpKmHT6GJzpj9DtLWIF7jHObfdzP4W2Oicewi4G7g31rCvg+ibPbH9HiDaqC8EfNo5FwYY7ZiJOodxJGR63gxK5/jTOXZQ/MmUzrFD+sd/igzNE5nw76RzSA06h9Sgc0gi5YmUlO7xQ/qfQ7rHD+l/DukeP8zAOZhzM9MFXUREREREREREMl8ip9GJiIiIiIiIiMgso2KTiIiIiIiIiIjEjYpNU2Bm15nZLjPbY2Z3JDueiZjZPWZ2zMy2jdhWbma/NbPdsT/LkhnjeMys3syeMrMdZrbdzD4b254W52BmuWb2kpm9Gov/K7HtC81sQyz++2PNKVOSmXnNbLOZPRy7nU4ymB93AAAgAElEQVSxHzCz18xsi5ltjG1Li9cOgJmVmtmDZrYz9n/gwnSKf7ZJt/wA6Z8jIP3zBGRGrhiWzjkD0j9vgHJHKlOemHnKEalD+SG5kpUbVGyaJDPzAt8B3gasBD5gZiuTG9WE/gu47qRtdwBPOueWAk/GbqeqEPAXzrkVwAXAp2N/5+lyDkPAFc65c4BzgevM7ALg68Cdsfg7gduSGONEPgvsGHE7nWIHuNw5d65zbm3sdrq8dgC+BTzmnFsOnEP03yGd4p810jQ/QPrnCEj/PAGZkSuGpXvOgPTOG6DckZKUJ5JGOSJ1KD8kV3Jyg3NOP5P4AS4EHh9x+4vAF5Md1yTiXgBsG3F7FzAn9vscYFeyY5zCufw3cHU6ngOQD7wCrAfagKzY9hNeV6n0A9TF3niuAB4GLF1ij8V3AKg8aVtavHaAYmA/sUUc0i3+2faTrvkhFmvG5IhYzGmbJ2Kxpl2uGBF7WueMWIxpmzdi8Sl3pOiP8kRq/ChHJC1u5Yfkxp603KCRTZM3Dzg84nZjbFu6qXHONQHE/qxOcjyTYmYLgNXABtLoHGJDRrcAx4DfAnuBLudcKLZLKr+Ovgn8NRCJ3a4gfWIHcMBvzGyTmd0e25Yur51FQCvw/diQ4++ZWQHpE/9skyn5AdL4NZaueQLSPlcMS/ecAemdN0C5I5UpTySZckRSKT8kV9Jyg4pNk2ejbHMzHsUsZGaFwM+BzznnepIdz1Q458LOuXOJVvTXAStG221mo5qYmf0RcMw5t2nk5lF2TbnYR7jYOXce0SHrnzazy5Id0BRkAecB33XOrQb6Se2hubNduv3fyDjpnCcgfXPFsAzJGZDeeQOUO1JZOv5/yBjKEcmj/JASkpYbVGyavEagfsTtOuBokmI5HS1mNgcg9uexJMczLjPzEU0OP3LO/SK2Oa3OAcA51wX8nuh88VIzy4rdlaqvo4uBd5nZAeCnRIe9fpP0iB0A59zR2J/HgF8STc7p8tppBBqdcxtitx8kmiTSJf7ZJlPyA6ThayxT8gSkZa4YlvY5A9I+b4ByRypTnkgS5YikU35IvqTlBhWbJu9lYGmsc342cDPwUJJjmo6HgI/Gfv8o0bnLKcnMDLgb2OGc+8aIu9LiHMysysxKY7/nAVcRbcb2FHBDbLeUjN8590XnXJ1zbgHR1/rvnHMfJA1iBzCzAjMrGv4duAbYRpq8dpxzzcBhMzsjtulK4HXSJP5ZKFPyA6TZayzd8wSkd64Ylu45A9I/b4ByR4pTnkgC5YjkU35IvqTmhkQ2o8q0H+DtwBtE58n+n2THM4l4fwI0AUGiFc3biM6RfRLYHfuzPNlxjhP/JUSHVG4FtsR+3p4u5wCcDWyOxb8N+FJs+yLgJWAP8DMgJ9mxTnAebwUeTqfYY3G+GvvZPvz/NV1eO7FYzwU2xl4/vwLK0in+2faTbvkhFnNa54jYOaR1noidQ0bkihHnk3Y5Y0SsaZ03YvEqd6Toj/JEUuJXjkihH+WHpJ5DUnKDxZ5cRERERERERETktGkanYiIiIiIiIiIxI2KTSIiIiIiIiIiEjcqNomIiIiIiIiISNyo2CQiIiIiIiIiInGjYpOIiIiIiIiIiMSNik0iIiIiIiIiIhI3KjZJyjKzCjPbEvtpNrMjI25nJ+g5nzWzc0fZ/gUzyx1x+3EzK0rA89eZ2X9P8TFfNbPLp/l85Wb2yek8Nvb4n5nZouk+XkTkdChPTPoxyhMiMusoR0z6McoRkhDmnEt2DCITMrMvA33OuX85absRfR1H4vQ8zwKfcc5tOWl7I3Cmc64rHs8zzvPfCTzhnPt1Ip9nxPMtAR50zp2SFCf5+CuBG5xzn4pvZCIiU6M8kbDnU54QkbSnHJGw51OOkDFpZJOkHTNbYmbbzOzfgVeAOWZ2l5ltNLPtZval2H7vNLMfj3jcVWb2y9jvbzOzF8zsFTO738wKxnm+zwPVwDNm9kRsW6OZlY6I5Z7Yc//QzK41s+fN7A0zWxvbv9DM/svMXjKzzWb2zlGex4B3A7+N3f64mf3CzB42s/1m9ikz+6vY4583s9LYfveZ2btHxPXl2D5bzWxZbPvfm9nnRjzXTjOrA74GnBG7wvO12H13xOLcOuLvssjMHjWzV2Pne0PsUL8HrjMz7xT/GUVEEkZ5QnlCRGQsyhHKETIzVGySdLUSuNs5t9o5dwS4wzm3FjgHuNrMVgKPA5eaWV7sMTcB95tZNXAHcKVz7jxgK/DZsZ7IOXcncAy41Dl31Si7nAH8C3AWcDbR6vxFwBdjzwPwJeAx59w64ArgX23EUNqYJcAx51xgxLZVsbgvAL4OdDrnVgObgA+NEXJLbJ/vAV8Y67xi7gB2OefOdc7dYWZvBxqA9cC5wEVmdhHwduCAc+4c59yZxJKYcy4MHADOnOB5RERmmvKE8oSIyFiUI5QjJMFUbJJ0tdc59/KI2x8ws1eIXp1YAayMvdH+FniHmfmAa4H/AS4immCeN7MtwAeBBacRyx7n3Oux4bevA0/Etr824rjXAP8n9nxPAblE34hHmgO0nrTtd865fudcC9AXi//kY5/sF7E/N42zz1iuAd4GbCb6d7kEWEY0iV5nZl8zs4udc90jHnMMmDvF5xERSTTlCeUJEZGxKEcoR0iCZSU7AJFp6h/+xcyWEr2asM4512Vm9xF9Awa4H7gNGABecM71x4aYPuac+3CcYhka8XtkxO0Ib/4fM+Ddzrm94xxncETcUzn2WPGER+wT4sTi8snPM8yAv3fO3X3KHdFhvG8H/tnMHnbO/cOIYw2OcTwRkWRRnlCeEBEZi3KEcoQkmEY2SSYoBnqBHjObQ/Sqw7AniQ7jvI1osgB4HniLxVY+MLOCWJIZTy9wOitGPA78+fANM1s9yj67gIWn8RzjOQCsiT33OqA+tv3k83ocuG143rlFV7SoNLN5RJsq3gt8AzhvxGOWAtsTFLeISDwoT0zsAMoTIjI7KUdM7ADKETJFGtkkmeAVokNOtwH7gOeG73DOhczsUeAWokNccc61mNltROdcDy97+r+B3eM8x13AE2Z2eIy51hP5CvBNM3uNaJF3D3D9yB2ccz1mdtjMFjrn9k/jOcbzM+BDZrYZeIno39Pw38XGWFy/js21Xg68GL1oQy/Rv7uVwNfMLAIEgE8CmNlcoNs5d/KQXRGRVKI8MTHlCRGZrZQjJqYcIVNmzrlkxyAiMWZ2I7DKOfflZMcyGWb2V0QbEf4g2bGIiMwGyhMiIjIW5QhJJRrZJJJaHgRKkh3EFLQD9yU7CBGRWUR5QkRExqIcISlDI5tERERERERERCRu1CBcRERERERERETiRsUmERERERERERGJGxWbREREREREREQkblRsEhERERERERGRuFGxSURERERERERE4kbFJhERERERERERiRsVm0REREREREREJG5UbBIRERERERERkbhRsUlEREREREREROJGxSYREREREREREYkbFZtERERERERERCRuVGwSEREREREREZG4UbFJRERERERERETiRsUmERERERERERGJGxWbREREREREREQkbhJabDKz68xsl5ntMbM7Rrk/x8zuj92/wcwWnHR/g5n1mdlfTvaYIiIiIiIiIiKSPFmJOrCZeYHvAFcDjcDLZvaQc+71EbvdBnQ655aY2c3A14GbRtx/J/DoFI95isrKSrdgwYI4nJWIiIikok2bNrU556qSHYe8SZ+/REREMt9Yn8ESVmwC1gF7nHP7AMzsp8D1wMjC0PXAl2O/Pwh828zMOefM7N3APqB/isc8xYIFC9i4cePpn5GIiIikJDM7mOwY5ET6/CUiIpL5xvoMlshpdPOAwyNuN8a2jbqPcy4EdAMVZlYA/C/gK9M4JgBmdruZbTSzja2trdM+CRERERERERERmbxEFptslG1ukvt8BbjTOdc3jWNGNzp3l3NurXNubVWVRtWLiIiIiIiIiMyERE6jawTqR9yuA46OsU+jmWUBJUAHsB64wcz+CSgFImbmBzZN4pgiIiIiIiIiIpIkiSw2vQwsNbOFwBHgZuCWk/Z5CPgo8AJwA/A755wDLh3ewcy+DPQ5574dK0hNdEwREZGUEQwGaWxs5P9n777j46ru/P+/zox6r1Zv7k22sWUbjIEYApieAqGEEBIWUpYkSxYSyG9JWL7ZtN0UsikLSQgJCQECIRgwEDA21RX3KsuybMnqvZfRnN8fGjnCyLbazGis9/Px0IPRnXPv/Yww+OhzP+dzOjs7/R3KGSEsLIzMzEyCg4P9HYqIiIiMY5qDja3hzsG8lmyy1rqMMXcCrwJO4FFr7R5jzIPAFmvtKuB3wOPGmCL6KppuGMk1vfUZRERERqusrIzo6Ghyc3MxZrDV4DJU1lrq6uooKysjLy/P3+GIiIjIOKY52NgZyRzMm5VNWGtXA6tPOPbtAa87getOc40HTndNERGR8aqzs1OTnDFijCExMRFt/CEiIiKnoznY2BnJHMybDcJFREQENMkZQ/pZioiIyFBp3jB2hvuzVLJJRERERERERETGjFeX0Yl4Q31bN3vKm3Aag8NhCHYaZqfFEh7i9HdoIiKn9cTGo2N6vZuWZo/ovJ/97GfccccdREREDOu8xx57jEsuuYT09PQR3Xco/ud//od77rmHmpoakpKSvHYfERmeY40dRIUGERuuBv0iEng0Bzu5Bx54gN/85jckJycD8L3vfY/LL798VNdUskkCRn1bN4+8Vcwf15fQ3t37gfdyEiP46fULWJgd75/gREQCzM9+9jNuvvnmQSc6vb29OJ2DJ/Afe+wx5s6d67VkU2lpKa+99hrZ2SObwInI2Ot1Wx55q5ifvHaABVlxPP2Fc7Q0RURkhMbrHOyuu+7i7rvvHrPrKdkk415rl4tfryvisXdLaO/p5ap56dywOAunw9BrLfVt3Xx/9X6u+7/13LliKl+5cCpBTv+tEB3rjPnpjDSjLiITQ1tbG5/61KcoKyujt7eX+++/n6qqKsrLy1mxYgVJSUmsXbuWqKgovv71r/Pqq6/y4x//mDfeeIMXXniBjo4Oli1bxsMPP8yzzz7Lli1b+PSnP014eDjr169n1qxZbNmyhaSkJLZs2cLdd9/NunXreOCBBzh8+DAVFRUUFhbyk5/8hA0bNvDyyy+TkZHBCy+8MOjWuXfddRc/+tGPuOaaa/zw0xKRE5XWt/PvT+9gU0k9M1Oj2VzSwCu7K7ksP83foYmIjGuBNgcba+rZJOOWtZYXd5Zz0Y/X8cu1h/jIzEn849/O5+c3nsWyqUksnZzIsilJXDkvnZf/7TyumZ/OQ2sOcu3/raehrdvf4YuIjAuvvPIK6enp7Nixg927d7Ny5Uq++tWvkp6eztq1a1m7di3QNyGaO3cuGzduZPny5dx5551s3ryZ3bt309HRwYsvvsi1115LQUEBf/7zn9m+fTvh4eGnvPehQ4d46aWXeP7557n55ptZsWIFu3btIjw8nJdeeulD41etWkVGRgbz58/3ys9CwBiz0hhzwBhTZIy5d5D3Q40xT3ne32iMyfUczzXGdBhjtnu+/s/XsYvvbSmp5/KH3mZvRTM/vm4+L35lOdNTovjBK/vpdrn9HZ6IyLgWSHMwgF/84hfMmzePz3/+8zQ0NIz686uySXxuKJU/1S2dvLizgqLqVtJjw/jSBVPISohgc0kDm0sG/4NfkJtASJCDZ94v4+O/eo/PL88lyOE4Yyp/3NYC4FDZuogMQ35+PnfffTff/OY3ufLKKznvvPMGHed0OvnkJz95/Pu1a9fyox/9iPb2durr65kzZw5XXXXVsO592WWXERwcTH5+Pr29vaxcufJ4TCUlJR8Y297ezn/913/xj3/8Y3gfUIbMGOMEfglcDJQBm40xq6y1ewcMuw1osNZONcbcAPwQuN7z3iFr7QKfBi1+9at1hwgPcfLsl5aRldC33ONbl8/i1t9v5vENR7hteZ6fIxQRGb8CZQ4G8KUvfYn7778fYwz3338///7v/86jjz46rHueSJVNMm5YazlY1cIf15fw0OsHKa1v56p5aXx5xdTjE5zTmZcZxycWZlJS18bz28uxngRNoNtX0cwPX97P89uP+TsUEQkw06dP5/333yc/P5/77ruPBx98cNBxYWFhx3sEdHZ28uUvf5lnnnmGXbt2cfvtt9PZ2TnoeUFBQbjd7uPnDRQaGgqAw+EgODj4eI8Xh8OBy+X6wNhDhw5x+PBh5s+fT25uLmVlZSxcuJDKysqRf3g50RKgyFpbbK3tBp4ETlyveA3wB8/rZ4CLjJrzTEj1bd28VVjDxxdmfGAedsH0ZM6blsTP1xyksV2V5CIiJxMoczCAlJQUnE4nDoeD22+/nU2bNo3sQw+gZJP4Va/bcqSujdf3VfGz1w/y+/dKKG3oYMXMSXz94umcMyVp2JU8C7LiWDFjEu8faeDtg7Veitw3Orp7+euWUh7fcIROVy/vH2mgqaPH32GJSAApLy8nIiKCm2++mbvvvputW7cCEB0dTUtLy6Dn9E9YkpKSaG1t5Zlnnjn+3onn5ebm8v777wPw7LPPjjjO/Px8qqurKSkpoaSkhMzMTLZu3UpqauqIrykfkgGUDvi+zHNs0DHWWhfQBCR63sszxmwzxrxpjBn08awx5g5jzBZjzJaampqxjV586qVdFbjclmvmf/CPiDGGb10+i+bOHn7xRpGfohMRGf8CZQ4GUFFRcfz1c889x9y5c0d1PdAyOvGTkto23imq5VBNK10uNwbISojgukWZ5GfEjrrB90WzJlHb2sWreyp5dU8ll84JvF9WjjV28Pj6Elq7XKyYMYmzsuL46euFrD9Ux8q5gfd5RKSPr5f27tq1i3vuuef4k61f//rXANxxxx1cdtllpKWlHe8Z0C8uLo7bb7+d/Px8cnNzWbx48fH3br31Vr74xS8eb075ne98h9tuu43vfe97LF261KefTYZtsKc3J5YAn2xMBZBtra0zxiwC/m6MmWOtbf7AQGsfAR4BKCgoODPKiyeoVduPMT0lillp0R96b1ZaDNctyuQP60u49dxcMuOHt323iIg/aA52ct/4xjfYvn07xhhyc3N5+OGHR3U9AHOmLDM6lYKCArtlyxZ/hzHhWWt5t6iO+5/fzeHaNiJDnMzJiGVqchRTkqMIDxl8i8eR6ul185u3i2ls7+Glry4nJzFyTK9/MmOxG53bWn61tojWLhc3n51zfBL3xKajFFW38M2VMwkN6vt5nSk9qUTOVPv27WPWrFn+DuOMMtjP1BjzvrW2wE8hBQRjzDnAA9baSz3f3wdgrf3+gDGvesasN8YEAZVAsj1hwmiMWQfcba096QRL86/AVdbQzvIfruWeS2fwryumDjrmaF075//3Wv7z6jl8dlmubwMUERkCzcHG3nDmYFpGJz5hreXfntrOzb/bSF1rF1fOS+OeS2fysQUZzM2IHfNEE0Cw08GNS7JxGPjqX7YF1K4pO0obKW/qZOXc1A88LVw+NYnOHjdbj4x+dwAREZlwNgPTjDF5xpgQ4AZg1QljVgGf9by+FnjDWmuNMcmeBuMYYyYD04BiH8UtPvb89nIArp6fftIx2YkRZMSFs6G4zldhiYhIANEyOvGJ57Yd4/nt5Xzxgimkx4aNepncUMVHhHDlvHSe2HSU2x7bzGX5aT6572j09Lr5x94q0uPCmJcZ94H3shMiyIoP591DdSydnKid6UREZMistS5jzJ3Aq4ATeNRau8cY8yCwxVq7Cvgd8Lgxpgiopy8hBXA+8KAxxgX0Al+01tb7/lOIL6zaXk5BTvxpN2g5e3Iiaw9U43ZbHA7NSURE5J9U2SReV9nUyQOr9lCQE889l87wWaKp39yMWJbmJfB2US2FVYM3YhsNay1VzZ0cqGzh/SP1rDtQzebD9fT0jqyS6r1DdTR19HDZ3LRBk0nLpyVT39bN/ormQc4WkfFoIixZ9xX9LEfHWrvaWjvdWjvFWvtfnmPf9iSasNZ2Wmuvs9ZOtdYusdYWe44/a62dY62db61daK19wZ+fQ7xnX0UzB6pauGbByaua+p09OYH6tm4OVrf6IDIRkeHTvGHsDPdnqcom8SprLff+bSfdvW7++7r5OP301Ovy/DSO1LXz1y2lfOWiacSEBY/JdbtcvTy/vZztpY0feu/1fVWcNy2JJXmJhAQNLcHW2uVi3YFqZqZGMyU5atAxs9NiiIsI5p2iWmanx44qfhHxvrCwMOrq6khMTEQ7yI+OtZa6ujrCwsL8HYrIGev57eUEOQxXzBtKsqlvo8INxXXMSP1wI3EREX/SHGzsjGQOpmSTeNVft5Sx7kAND1w1m7wk3zToHkyw08H1i7P41bointpcyufPzRt14ququZMnNh2ltqWLFTOSmZESTXRYMFFhQZQ2tLN2fzWrd1eyrrCGK/LTOCs7/rTXXLu/mm6X+5S75zkdhmVTkli9q4KKpo5RfQYR8b7MzEzKysrQNvBjIywsjMzMTH+HIXJGcrstq7Yf47xpSSREhpx2fFZCX9+mjYfr1CRcRMYdzcHG1nDnYEo2iddUNHXw4It7OXtyAreck+vvcEiJCeOa+Rk8s7WMNfuruGT2yRM6p7O9tJHntpUREuTkc+fmMXXSB6uQJidFMXl5FEfq2nhldyV/fb+Mo/XtXDEvjSDH4FVO+yqa2Xi4jsW5CaTEnDpjPC8zltW7KiiuaRvxZxAR3wgODiYvL8/fYYiInNbeimbKmzr590tmDPmc/r5N1lpVDojIuKI5mH+pZ5N4zVObS2nrdvGjT84fN00jF+bEsygnnnUHajhQObL+TTvLGvnrllIy4iL4yoVTP5RoGignMZJ/OW8y501NYuPhen7zVjFNHT0fGrftaAN/3niE9LjwU1Y19YsJCyY2PJiyhvYRfQYRERGRE2063Nfz/ZwpiUM+R32bRERkMEo2ides2VfNWVlxZCeeeicTX7t6fjqpMWE8vaWUxvbuYZ17qKaVv75fRnZiBJ87N3dIvZ+cDsNl+WncuCSbqpYufr7mIKt2lFNS24bbWtYfquWv75eRmxjJbefmER7iHFIsmfHhlDVoGZ2IiIiMjU2H68lKCCc9LnzI5wzs2yQiItJPySbxiqrmTnYda+KiWSn+DuVDgp0Oblqajdta/rLpKN2uoe0aV9HUwZ82HCExMoRbzs4leJi76uVnxPLlC6YwOTmSLSX1PPJ2MT94eT8v7KxgVloMn12WS2jw0BJNAJnxEdS1dQ87YSYiIiJyImstm0rqWZI79Kom6Hv4lREXrmSTiIh8gJJN4hVr9lUD8NFxmGwCSIoK5dpFmZQ1dPD7dw/T0d17yvH1bd089l4JoUEObl2WO+TqoxNNignj00tz+P8un8WnCjLJTojgvKlJ3LQke9jJq8z4vqeOO8qaRhSLiIiISL9DNa3Ut3WzJO/0G5oMZIxh6eQENhTXa4txERE5Tskm8Yo1+6rIjA9nesrJ+xn525z0WG5ckk1ZQwe/faeY1i7XoOP2VzTzy7VF9PS6ufXcPOIiTr87y+mEBjtZkBXPzWfncFl+2oh2xsuIC8cAO0obRx2PiIiITGwbPf2aluQNr7IJ+pbSqW+TiIgM5NVkkzFmpTHmgDGmyBhz7yDvhxpjnvK8v9EYk+s5vsQYs93ztcMY8/EB55QYY3Z53tvizfhlZDq6e3mnqJaPzkoZ97uSzM2I5TPn5FDb2sVv3iqmsqmTXnffU7let+XlXRX8ccMR4iKC+fJHppJ6ml3ifCks2ElydKiSTSIiIjJqmw7XkxwdSu4Iem2e4+nbtFFL6URExCPIWxc2xjiBXwIXA2XAZmPMKmvt3gHDbgMarLVTjTE3AD8Ergd2AwXWWpcxJg3YYYx5wVrbX3qywlpb663YZXTeLaqly+XmolmT/B3KkExPiebWZXn8cX0JP3/jIE5jSIgMwQK1rV0szUvg8vy0YS9z84XM+Ah2lDVqu2EREREZMWstmw7XsyQvYUTziX/2barnM+fkjn2AIiIScLyWbAKWAEXW2mIAY8yTwDXAwGTTNcADntfPAL8wxhhr7cD93MMALQAPIGv2VxEVGsTSEZRh+0teUiRfvXAaxbVt1LZ2UdvaRUunixuXZJOfEevv8E4qMz6crUcbONbYQWb8+Nr1T0RERAJDWUMHFU2dLM1LGNH5xhiW5iXw1kE9CxYRkT7eTDZlAKUDvi8Dlp5sjKeKqQlIBGqNMUuBR4Ec4DMDqpos8A9jjAUettY+MtjNjTF3AHcAZGdnj80nktNyuy1r9lVz/vQkQoLGXyXQqcRHhrAocvT9mHwpy5Ng2lHapGSTiIiIjMim4/2aRpZsAsjPjOVv245R3dLJpOjx03ZARET8w5vZgMFqcE+sUDrpGGvtRmvtHGAxcJ8xpv9vrXOttQuBy4B/NcacP9jNrbWPWGsLrLUFycnJI/sEMmx7ypupbuniwpnjcxe6M01KbCghQQ52lKlvk4iIiIzMpsP1xIYHM31S9IivMTstBoC95c1jFZaIiAQwbyabyoCsAd9nAuUnG2OMCQJigfqBA6y1+4A2YK7n+3LPP6uB5+hbrifjxOv7qjAGVsxQgs8XghwO5qTHsF1NwkVERGSENpXUszg3HscIdsftN7M/2VShZJOIiHg32bQZmGaMyTPGhAA3AKtOGLMK+Kzn9bXAG9Za6zknCMAYkwPMAEqMMZHGmGjP8UjgEvqaics4sWZ/FQuz40mMCvV3KBPG/Mw4dpU14ep1+zsUERERCTDVzZ0crm0b1RI6gNjwYDLjw1XZJCIigBeTTZ4eS3cCrwL7gKettXuMMQ8aY672DPsdkGiMKQK+DtzrOb6cvh3ottNXvfRlz+5zKcA7xpgdwCbgJWvtK976DDI8Te097D7WzEemq6rJlxZkxdHR00tRTau/QxEREW4gU6QAACAASURBVJEAs6mkv1/T6Dd2mZ0Wo8omEREBvNsgHGvtamD1Cce+PeB1J3DdIOc9Djw+yPFiYP7YRypjYU9FE9DXIFJ8Z35WHAA7ShuZmRrj52hEREQkkGw6XE9EiJM56aOfQ8xOj+G1fVW0d7uICPHqrxkiIjLOBdZ2YTKu9ZdNz0lXssmXchMjiAkLYntpk79DERERkQCzuaSBs7LjCHaO/teC2WkxWAv7K1vGIDIREQlkSjbJmNlT3kxKTCjJ0erX5EvGGOZnxalJuIiIiAxLW5eLA5XNLMqOH5PrzfZUR+3TUjoRkQlPySYZM3vKm1TV5CfzM+MorGqhs6fX36GIiIhIgNhR1ojbwlljlGzKiAsnJixITcJFRMS7PZtk4ujo7qWoupVL56T6O5QJaW5GDL1uy/7KFhZ4ejiJiIiInMq2o31V0QPnDk9sPDqqayZGhfJWYc1pr3PT0uxR3UdERMY3VTbJmNhf2YzbMibNJWX45mb0VZTtOqa+TSIiIjI02442MjkpkvjIkDG7ZlpsGJXNnbitHbNriohI4FGyScbEHjUH96uMuHDiI4LZXaZkk4iIiJyetZZtRxvGbAldv7TYcHp6LXWt3WN6XRERCSxKNsmY2FPeTGx4MJnx4f4OZUIyxjA3I1aVTSIiIjIkpfUd1LV1c1b22C6/T4sNA6CiqWNMrysiIoFFySYZE3vLm5idFoMxxt+hTFj5GbEUVrXQ5VKTcBERETm1rUcbAFg4xpVNk2JCcRpDRVPnmF5XREQCi5JNMmo9vW72VbaoX5Ofzc2IxeW2HKhs8XcoIiIiMs5tO9pARIiT6SlRY3rdIIeD5OhQVTaJiExwSjbJqB2qaaXb5T7epFr8I19NwkVERGSItpU2Mj8zjiDn2P86kBYbpsomEZEJTskmGbU9x/qbg6uyyZ8y48OJDQ9mt5JNIiIicgqdPb3sLW8e835N/dLiwmnpdNHS2eOV64uIyPinZJOM2p7yZsKCHUxOHtsybBkeYwz5ahIuIiIip7HrWBMutx3zfk39+puEV6q6SURkwlKySUZtd3kTM1NjcDrUHNzf5mbEcqCyhW6X29+hiIiIyDi19Uhfc/AF3qps8iSbypVsEhGZsJRsklFxuy37ypuZm6EldOPB3IwYenothVVqEi4iIiKD23a0keyECJKiQr1y/YiQIGLDg6lUk3ARkQlLySYZldKGdlq6XMxJV3Pw8UBNwkVERORUrLVsPdrAQi9VNfVLjQmjqrnLq/cQEZHxS8kmGZU95WoOPp5kJ0QQExakZJOIiIgMqrypk+qWLs7yUr+mfqmxYVS3dOJya2m/iMhEpGSTjMqe8iacDsP0lGh/hyL0NQmfmxHLHiWbREREZBD9/Zq8tRNdv9TYMNwWalpU3SQiMhEp2SSjsq+ihanJUYQFO/0dinjkZ8Syr7KFnl49SRQREZEP2lJST0SIk9lp3q1KT43RjnQiIhOZkk0yKgcqW5iRqqqm8WRORizdLreahIuIiMiHbCpp4KzsOIKc3v01ICkqFKfDUNmsZJOIyESkZJOMWEtnD8caO5RsGmf6m4Tv1lI6ERERGaC5s4f9lc0szk3w+r2cDkNKdKgqm0REJiglm2TEDla3Aqhf0ziTkxBBbHgw73t6MoiIiPQzxqw0xhwwxhQZY+4d5P1QY8xTnvc3GmNyT3g/2xjTaoy521cxy9h5/0gD1sISHySboK9vkyqbREQmJq8mm0Y6oTHGLDHGbPd87TDGfHyo1xTfKazsW6Y1Q8mmccXhMCyfmsRbhbVYa/0djoiIjBPGGCfwS+AyYDZwozFm9gnDbgMarLVTgZ8CPzzh/Z8CL3s7VvGOzYfrCXIYr+9E1y81JoyWThetXS6f3E9ERMYPryWbRjmh2Q0UWGsXACuBh40xQUO8pvjIgaoWwoOdZMaH+zsUOcEF05OpbO6ksKrV36GIiMj4sQQostYWW2u7gSeBa04Ycw3wB8/rZ4CLjDEGwBjzMaAY2OOjeGWMbS6pZ25GLOEhvtnYJTW2b45YpeomEZEJJ8iL1z4+oQEwxvRPaPYOGHMN8IDn9TPAL4wxxlrbPmBMGNBfnjGUa4qPFFa1MD0lCofD+DsUOcF505MAeLOwWj21RESkXwZQOuD7MmDpycZYa13GmCYg0RjTAXwTuBg46RI6Y8wdwB0A2dnZYxe58MTGo6M6v6fXzdajjSybnDjqaw1Vauw/d6Sbkhzlk3uKiMj44M1ldINNaDJONsZa6wKagEQAY8xSY8weYBfwRc/7Q7kmnvPvMMZsMcZsqampGYOPIyc6UNmqfk3jVFpsODNSonmzUH/2RUTkuMGeDp243vpkY/4T+Km19pQls9baR6y1BdbaguTk5BGGKd5Q1tBBr9uSkxjps3tGhQYRFRqkJuEiIhOQN5NNo5nQYK3daK2dAywG7jPGhA3xmnjO12THi+pau6ht7VLVzDh2wYxkNh9uoL1bfRJERAToe0iXNeD7TKD8ZGOMMUFALFBPXwXUj4wxJcC/Ad8yxtzp7YBl7BypawMgNzHCp/dVk3ARkYnJm8vohjOhKTthQnOctXafMaYNmDvEa54RfFXe3O+mpcMrde/vBaTKpvHrgunJPPJWMRuK67hwZoq/wxEREf/bDEwzxuQBx4AbgJtOGLMK+CywHrgWeMP27TZxXv8AY8wDQKu19he+CFrGRkldG5OiQ4kI9eb0/8NSY8LYUFxHr9viVOsFEZEJw5uVTccnNMaYEPomNKtOGNM/oYEBExrPOUEAxpgcYAZQMsRrig8UVnl2olNl07hVkBtPeLCTNw9oKZ2IiBxvWXAn8CqwD3jaWrvHGPOgMeZqz7Df0dejqQj4OqCdf88Abms5UtdOrg+X0PVLjQ3D5bbUtXX5/N4iIuI/Xnu04Wkq2T+hcQKP9k9ogC3W2lX0TWge90xo6ulLHgEsB+41xvQAbuDL1tpagMGu6a3PICd3oKqF2PBgJkWH+jsUOYnQICfnTElU3yYRETnOWrsaWH3CsW8PeN0JXHeaazzgleDEayqbOulyuclN8u0SOuirbOqPYVJ0mM/vLyIi/uHVOtqRTmistY8Djw/1muJ7hZUtzEiJxrMbsoxTF0xP5o391ZTUtpGb5PunmSIiIuJ/Jcf7Nfl+LjApOhSHgcrmTub5/O4iIuIv3lxGJ2coay0HqlqYnqotbMe7C6b3Ncd/66Cqm0RERCaqkrp2YsODiYsI8fm9g5wOkqJCtSOdiMgE49sOgTJuDacheVNHDy2dLpo7XD5vZC7Dk5sUSXZCBG8eqOGWc3L9HY6IiIj4mLWWI3VtTPZjhXNqbBhH69v9dn8REfE9VTbJsFV5tq9NidG6+0BwwfRk1hfX0eXq9XcoIiIi4mPVLV20dLqYnOS/ivS0mDAa23vo7NFcRERkolCySYbteLJJzcEDwsWzU2jv7uVvW4/5OxQRERHxsQOVfTsIT/fjDsKpseEAlDd2+C0GERHxLSWbZNiqmjuJDgsiIlSrMAPBedOSWJQTz09fK6S92+XvcERERMSHDlS1kBYbRmx4sN9iyIjvSzaVNSjZJCIyUSjZJMNW1dylJXQBxBjDfZfNpLqli0ffOezvcERERMRHOnt6OVLXxvQU/1U1AUSFBhEXEUyZKptERCYMJZtkWNzWUt3SSaqSTQGlIDeBS+ek8H9vFlPb2uXvcERERMQHiqpbcVv8nmwCyIwL51iDmoSLiEwUSjbJsDS0ddPTa0mJUb+mQPONlTPp6Onlf9cc9HcoIiIi4gMHqloIC3aQnRDh71DIjI+gob2H1i4t6RcRmQiUbJJhKW/STnSBakpyFDcszuLPG49SUtvm73BERETEi6y1FFa1MG1SNE6H8Xc4x/s2HVPfJhGRCUHJJhmWsvp2nA5DaqySTYHoax+dRkiQg289t4umjh5/hyMiIiJeUtHUSUunixnjYAkdQEZcOAYoa9RSOhGRiUDJJhmW0oYO0mPDCHLoj04gmhQdxneums2mw/Vc/tDbbC6p93dIIiIi4gWFVS0ATEuJ8nMkfcKCnSRFhaqySURkglDGIEC5raWutYuj9b57OtTrthxrbCdzHKz7l5G7fnE2z3xpGU6H4fqH1/PT1wrp7On1d1giIiIyhg5UtpARF050WLC/QzkuMz6csoYOrLX+DkVERLwsyN8ByPCsPVDN/opmqpq76O51A/CZs3OYlRbj9XtXt3TS02vJ8qy5l8C1ICuOl766nO88v4eH1hzkkbeKOXdqIitmTuLCmZNIi9W/YxERkUDV3u3iaH07K2ZO8ncoH5ARH8620kYt5RcRmQCUbAog7V0uXttbRUpMKAW58aTGhLGusIbX91UxIzUah/Fu88ey+r6y56x4VTadCaLDgvnJ9Qv45KJMXt1TyRv7q3l9XzXGwHnTkrlpSTYfnTWJIKcKIEVERALJwepWLIybfk39Mj1zyDItpRMROeMp2RRADtf17SD2sQUZ5CRGAuB0GP76fhl7ypvJz4j16v1LG9oJD3aSEBni1fuIb507NYlzpybxn1dbDtW08cKOcp7aXMoX//Q+k6JD+dcVU7nlnByMl5OZIiIiMjb2VTQTEeI8vgPceJEWG4bDwLFGJZtERM50KlkIIMW1bQQ7zQcmDvOz4kiOCmXNvircXl7/XtbQQVZCuJIOZyhjDFMnRXHXxdN555sr+M0tBUxJjuI7q/bwlb9so63L5e8QRURE5DQ6unvZW97MvMxYr1e9D1ew00FqTBhlDdqRTkTkTKdkUwA5XNNGTkLkB3aCcxjDRbMmUd3Sxc6yJq/du8vVS1Vz5/HyZzmzBTkdXDw7hSduX8o3V85k9a4KPv6rdymuafV3aCIiInIK20sbcLktBTkJ/g5lUBnxERxr7MDtVpNwEZEzmZbRBYj2LheVzZ1cPDvlQ+/NzYgl9UANa/ZVkZ8Ri9Mx9k+xjjV2YEHNwcehJzYe9er1Y8ODuXVZHk9uPsplD73No7cu5typSV69p4iIiIzMliMNpMeFkR43PudsmfHhbC6pp6SujcnJUf4OR0REvESVTQGiv1/T5KTID73nMIaPzppEXVs320sbvXL//ubgGapsmpCmTorizhVTiYsI5nOPbWbt/mp/hyQiIiInONbYQUVT57itaoK+ZBPg1Yp8ERHxPyWbAsRg/ZoGmpUWQ0ZcOOsOVHuld1NpQzsJkSFEhaoYbqKKiwjh9uWTmZ4SxR2Pb+GV3ZX+DklEREQG2FJST5DDMD8zzt+hnNSk6DCCnYYdZd55QCoiIuODkk0BYrB+TQMZYzhvWhJ1bd0cqGwZ8/uXNXQcfxIlE1dEaBB//pezyc+I5V+f2Mrz24/5OyQREREBul1udpQ1MjcjlvAQp7/DOSmnw5AeG+61anwRERkflGwKAP39mvKSP7yEbqA56bHEhgfz3qHaMb1/c0cPTR09ZGkJndDXw+mPty2lICeerz25nW8/v5vOnl5/hyUiIjKh7SlvorPHTUFuvL9DOa285Eh2ljXR1NHj71BERMRLvLomyhizEngIcAK/tdb+4IT3Q4E/AouAOuB6a22JMeZi4AdACNAN3GOtfcNzzjogDejwXOYSa+0Z3UCm5BT9mgZyOgxn5yXw6t4qKps6SY0NG5P7929Pq8omgX82JL8iP41gp4M/rj/CK7sruX5xFmmxY/tn5Kal2WN6PRERkTPVliMNJEaGkJd46vnieDB9UjTrDtTwXlEtl+Wn+TscERHxAq9VNhljnMAvgcuA2cCNxpjZJwy7DWiw1k4Ffgr80HO8FrjKWpsPfBZ4/ITzPm2tXeD5OqMTTXD6fk0DLc5NINhpxrS6qbShA4dh3O5qIv4R5HRweX4anzs3l46eXn617hAv7CjnSF2bV/qGiYiIyOBqWro4XNvGopx4jBn7XYnHWlZCBNGhQbxZWOPvUERExEu8uYxuCVBkrS221nYDTwLXnDDmGuAPntfPABcZY4y1dpu1ttxzfA8Q5qmCmpAO1566X9NAEaFBLMiKZ3tpI21drjG5f2lDO2mx4QQ7tepSPmzapGi+euE08jNi2VxSz8NvFfPfrx7gpZ3lFFa10O1y+ztEERGRM9oreyoJCXKwKGf8L6GDvmr8c6cm8WZhDVYPqEREzkjezB5kAKUDvi/zHBt0jLXWBTQBiSeM+SSwzVrbNeDY740x240x95uTPL4xxtxhjNlijNlSUxO4T03au11UNp2+X9NAy6Yk4nJbNpfUj/r+nT29lNa3k52gfk1ycpGhQXyqIItvXT6LTxVkkR4XzsbD9Tz2Xgn/78W9PPJWMe8W1dLTq8STiIjIWDpY3cK+imZWzJhEdFiwv8MZsgtmJFPR1MnB6lZ/hyIiIl7gzZ5NgyWBTnx0ccoxxpg59C2tu2TA+5+21h4zxkQDzwKfoa/v0wcvYu0jwCMABQUFAfvIpKS2Dcvp+zUNlBITxtTkKDYU13HetGScjpGXU+861kRPr2VB1vjdQlfGj7BgJwuy4liQFUe3y82R+jYOVbdSVNPKS7sqePtgDRfNTGFhTvyo/lyKiIgI9LotL+6sICEyhHOnnPi8dnw7f3oyAG8V1jA9JdrP0YiIyFjzZmVTGZA14PtMoPxkY4wxQUAsUO/5PhN4DrjFWnuo/wRr7THPP1uAJ+hbrnfGKqlrJ8gxtH5NAy2bmkhzp4udZaPbVnbrkQaSo0LVHFyGLSTIwbRJ0aycm8adK6Zx+3mTiYsI4bntx3hoTSEltW3+DlFERCSgbSiuo6aliyvy0wgKsHYHGXHhTJsUpb5NIiJnKG9WNm0Gphlj8oBjwA3ATSeMWUVfA/D1wLXAG9Zaa4yJA14C7rPWvts/2JOQirPW1hpjgoErgde9+Bn8rq61i8SokCH1axpoeko06bFhvLqnktnpMYQGOYd979rWLo7Ut3PpnNSAaDYp41teUiRfOH8y+ytbWL2rgkffPcx1BVnkZ8T6OzQREZGA09rlYs3+KqZNimJmauBVBj2x8SiTokNZf6iOx94tISTIO8ky7WwrIuIfXnsE4unBdCfwKrAPeNpau8cY86Ax5mrPsN8BicaYIuDrwL2e43cCU4H7Pb2ZthtjJgGhwKvGmJ3AdvqSWL/x1mcYDxo7eoiPCBn2eQ5juGp+Os2dLtYdGNkTo61HGzDAWVpCJ2PEGMOstBi+9JEpZMSF8+Smo7xbNHY7J4qIiEwU/9hTSbfLzRX5aQH7UHB6SjQut+Wwqp1FRM443qxswlq7Glh9wrFvD3jdCVw3yHnfBb57kssuGssYx7uG9m5yEkfWnDsnMZKF2XG8c7CWhdnxJEcPfUM/t7VsO9rItJQoYsIDp9mkBIaIkCA+vzyPp7eU8tKuChrbu7k8gCfLIiIivrRmXxVbjjRw3tQkJsWE+TucEctNiiTYaSisbmHGOK3Oautysf5QHcFBDhIjQ0iODiUxMiTgli2KiPiaV5NNMjod3b109rhHVNnU79I5qewpb+bFneXcuix3yL/MH6pppamjh8vmpo743iKnEux0cOOSbF7cWcG7h+owxnDZXC3ZFBERORlrLWv2V/PG/moWZsdxaYDP04KdDvKSIjlY1eLvUD6gs6eXdQeqeWFHBWv2V9HZ88HddJOiQvjZ9WexfFqSnyIUERn/lGwaxxo7ugGIG0WyKTosmI/OSuGlXRXsq2hmdvrQ+uNsPdJAWLCDWWkxI763yOk4jOGqeWkAvFNUS1iwkwtnTvJzVCIiIuOPtZbX9lWx7kANi7Lj+fjCDBxnwAOaaZOieWlXBfVt3SREjnzOOxZcvW6eeb+Mn7xWSHVLF0lRIXyqIIuVc1MJcTqobe2ipqWLP64/wi2PbuQbK2fyhfMn60GZiMgglGwaxxrbewCIjxjdMrazJyey5Ug9L+6sICshguiwU1+vs6eXPeXNLMqJJ1glwuJlxhiunJdGV08vr++rIizYwbIpelIoIiLS7/0j9fxpwxH2VbZQkBPPx846MxJN0Ne3qf+h6LlTx/7v/yc2Hj3tGGst+ytbeGVPJTUtXWQnRHDrslymJEfhdBhKatuPj3U6HNy0NJtntx7jBy/v54Ud5Vy7MJPQYKeakYuIDKBk0zjW0D76yiYAp8Pw8bMy+d07xTzyVjGfPzeP+FM8OdpQXIfLbVmUEz+q+4oMlcMYPrEwky6Xmxd3VhAa5NSfPxERmdDq27p571Atj75zmK1HGwkPdvLRWSl8ZEbyGZNogr4laVnx4WworuOcKYk+/2x1rV08v6OcoupWkqJC+PTSbGanxZyyWik0yMmNi7N4Jz6cV3ZX8mRvKbeck+PDqEVExj8lm8axxvYegp2GyBDnqK+VnRDBbefm8dj6Eh5+6xCfOzePlBMaSlpreX1fNWsPVDMjJZqMuPBR31dkqJwOw/WLs3h8/RGe3VpGR0+vnhCKiMi4N5TKmaHo7OllZ1kTh2paKWtop8FT4Z4QGcJV89JYlJNASNCZV3FujOHcqUk8ubmU/RUtzE73TQsHV6+bNw/W8OaBGpyOvirrpXmJOB1DS3YZYzhvWjIGWL27kr0Vzd4NWEQkwJx5f2OdQRrau4mLCBmzdeDZiZHccd4UrIVH3ipm97Em2rtcALjcfWvU1x6oZlF2PDefnaP15+JzwU4HnzknhznpMazeVcH3V+/D7bb+DktE5IxhjFlpjDlgjCkyxtw7yPuhxpinPO9vNMbkeo4vMcZs93ztMMZ83Nexn4mstRyubeOvW0r5/sv7+Pv2YxytbycjLpyVc1L5l/Py+PrF0zlnStIZmWjqNyc9lriIYN4pqvXJ/Y7WtfHzN4pYs6+aWWkx3PXR6SybkjTkRNNA50xJIjUmjBd3VtDe7fJCtCIigUmVTeNYY3vPqPs1nSg1NowvXDCFR989zBOb+p7EJUaGEOx0UNncyUdnTWLFjElKNInf9O9S98KOch5+q5iali6+94l8woJHX+EnIjKRGWOcwC+Bi4EyYLMxZpW1du+AYbcBDdbaqcaYG4AfAtcDu4ECa63LGJMG7DDGvGCt1W/XI2StZdWOcjYeric0yMFZWfEU5MaTERc+4eZhTodh2ZQkVu+qoKyhncz4CK/cp6fXzev7qnjnYC2x4cHcuiyX6SnRo7qm02G4ZkE6D79VzM/XFHHvZTPHKFoRkcA2pGSTMeZZ4FHgZWut+3TjZWw0tHeTET+03eOGIyEyhK9dNI3S+nZK69s52tBBXWsX1y3K5Kxs9ckR/3MYw9Xz0zl3ahI/ea2Q1/dV8fGzMrhxaTYzU/vK691uS3NnD10uN9aCxRIa5PT7TjYiIr4wwrnZEqDIWlvsucaTwDXAwGTTNcADntfPAL8wxhhrbfuAMWGAyk5H6dU9VWw8XM+5UxK5eHbqGV25NBQFOfGs2VfFO0W13LB47JfRl9a388z7ZdS0drE4N4HL5qaO2YOsnMRIFmXH89u3i/nkwgymjTKBJSJyJhhqZdOvgc8BPzfG/BV4zFq733thSZerl/buXuLDx7ayqV+w08Hk5CgmJ0d55foio2WM4asXTePsyYn8eeMR/rKplD+sP0JGXDgdPb00tncz2Aq7T5yVwX9cOVtJJxE5041kbpYBlA74vgxYerIxniqmJiARqDXGLKUvwZUDfGawqiZjzB3AHQDZ2eq7dzJvHqjmrYM1LMlL4PL8tAlXyTSYsGAni3MTeO9QLY1zuke9QU4/V6+bNfureauwhpjwYD63LNcryaBL56ZSVNPK/c/v5i+3n61/pyIy4Q0p2WStfR143RgTC9wIvGaMKQV+A/zJWtvjxRgnpEZPU8g4/cIsE9ySvASW5CXwwFXd/G3bMbYdbSA2PJiEyBDiIkIIC3ZgMBgDxTWt/P7dEtYV1nD/lbP42IIMTfZE5Iw0wrnZYP9DPDFtf9Ix1tqNwBxjzCzgD8aYl621nSfE9QjwCEBBQYGqnwaxobiOV/dWMT8zlqvnp+vvqQHOmZLIe4dqWV9cx2Vz00Z9vbKGvmqm6pYuCnLiuTw/zWvL8qNCg7jn0hn8x99388b+ai6aleKV+4iIBIoh92wyxiQCNwOfAbYBfwaWA58FPuKN4CayhvZuAK9VNokEmvjIEG5bngfknXLcJxdlcu+zu7jrqR28sruSX3160YgafoqIjHcjmJuVAVkDvs8Eyk8ypswYEwTEAvUDB1hr9xlj2oC5wJZRf5AJpKalixd2lDMzNZprF2XhUKLpA+IjQpiTHsumw/WcnZdI/Agfurrcbtbur+bNwhqiQoP47Dm5zEj1/tK26xdn8fM1B/nThiNKNonIhDekxeHGmL8BbwMRwFXW2quttU9Za78CaB2WF6iySWRkZqbG8OyXlvGNlTN4dU8V//vGQX+HJCIy5kY4N9sMTDPG5BljQoAbgFUnjFlFX7IK4FrgDWut9ZwT5Ll3DjADKBnTDzUBvFNUg9Nh+MTCTD0IOYmLZ6VgDPz+vZLjuyYPR3ljB79ae4i1B2pYkBXH1y6a7pNEE/S1qbhhSTbrCmsorW8//QkiImewoXYi/K21dra19vvW2gro2xoXwFpb4LXoJrCG9m6CHIaoUG0YKDJcTofhyx+ZyicWZvDQmoO8d8g3WymLiPjQsOdmnh5LdwKvAvuAp621e4wxDxpjrvYM+x2QaIwpAr4O3Os5vpy+Hei2A88BX7bW6n+uw9DS2cO2o40szI7X/O4UkqJDueXsXBrbu/njhiN0u4bW/77L1ctreyv51boi2rpcfObsHK5dlEV4iG93s71xSRYGju/6LCIyUQ012fTdQY6tH8tA5IMa23uIDQ9WebXIKPy/a+aSlxTJvz25ndrWLn+HIyIylkY0N7PWrrbWTrfWTrHW/pfn2Lettas8rzuttddZa6daa5f071xnrX3cWjvHWrvAWrvQWvv3Mf00E8D64jp63Zbl05L8Hcq4l5sUyacKsiitb+epzUfpNI3qJAAAIABJREFUHWxHEI9et2VDcR0//kchaw/UMC8zjq99dBqz0mJ8GPE/pcWGc9GsFJ7eXEqXq9cvMYiIjAenfKxijEmlb1eScGPMWfyzaWQMfWXb4iUN7d0jXqcucqZ4YuPonwpekZ/Gr9cd4sZHNvDZZbmnTODetFQ7J4nI+Ka5WWDqcvWysbie2ekxJEWF+jucgDA3I5Yr56fzwo5yfvt2MfMyY5mZFkN8RAiuXjfHGjs4UtfOliP11LZ2k5MYwc1Ls8lOjPR36Nx8dg6v7a3ild2VXLMgw9/hiIj4xelqeC8FbqWvgeRPBhxvAb7lpZiEvsqmmalh/g5DJOClxYZzxbw0nt9eznuH6lg+VU+URSSgaW4WgLaUNNDR08t505L9HUpAOWdyIljL+uI6XthZwQs7K0iMDKGpoweXp9opLTaMz5ydw8zU6HGzs995U5PISYzgzxuOKtkkIhPWKZNN1to/0Le17Settc/6KKYJr7Onl9YulyqbRMbIktwE9le08Pq+KuZlxBKjXR5FJEBpbhZ4et2Wdw/VkpMYQXaCis+G65wpSZwzJYmali72VzZzuLaNWWkx5CRGkJMYOS77XzkchpuWZPP9l/dzoLLFZw3KRUTGk9Mto7vZWvsnINcY8/UT37fW/mSQ02SUjjV2ABCnX4hFxoQxhqvmp/Oz1wtZvbuCGxZruZyIBCbNzQLP7mNNNLb3cNW8dH+HEtCSo0NJjk4OmOqw6wqy+PFrhfx54xEevGauv8MREfG50zUI71/0HAVED/IlXlDW0Jdsio9QZZPIWEmIDOH86cnsLGviUE2rv8MRERkpzc0CzKaSehIjQ1TdMsEkRIZw+dxUntt2TI3CRWRCOt0yuoc9//xP34QjAMf6k01aRicypi6Ynsy2ow28sKOcr1w4DadjfPR2EBEZKs3NAktnTy9H6tpYPjVZOwxPQNeclcHft5fzdmEtH52d4u9wRER86nSVTQAYY35kjIkxxgQbY9YYY2qNMTd7O7iJqqyhHYeB6LDxtwZdJJAFOx1cOS+d6pYu3jtU6+9wRERGTHOzwFBU3YrbwvTUKH+HIn5w7pQkYsODeWlXhb9DERHxuSElm4BLrLXNwJVAGTAduMdrUU1wxxo7iIsI0RMwES+YlRbDjJRo1uyvprmjx9/hiIiMlOZmAaCwqoXQIAc5CZGnHyxnnJAgByvnpPLa3io6e7SU7nSqmjv5/buHeWLjUV7cWc6bhTXUtnb5OywRGaGhJpv6O1VfDvzFWls/lJOMMSuNMQeMMUXGmHsHeT/UGPOU5/2Nxphcz/GLjTHvG2N2ef554YBzFnmOFxljfm7Gyx6nY6isoUPNwUW86Mp5abjdltW79aRRRALWiOZm4jvWWgqrWpg6KUrLtiewK+al0drl4s3CGn+HMm65et08+s5hLvrxm/znC3v51nO7uPOJbXz20U1c/JM32VHa6O8QRWQEhppsesEYsx8oANYYY5KBzlOdYIxxAr8ELgNmAzcaY2afMOw2oMFaOxX4KfBDz/Fa4CprbT7wWeDxAef8GrgDmOb5WjnEzxAwyhra1RxcxIsSo0KPNwsvVrNwEQlMw56biW9VtXTR3Olieooag09k50xJJD4imJd26gHXYLYebeCqX7zLgy/uZVFOPK/ddT4b7ruIf9x1Pn/+l6VEhQVx0282qP2BSAAaUlMga+29xpgfAs3W2l5jTBtwzWlOWwIUWWuLAYwxT3rO2TtgzDXAA57XzwC/MMYYa+22AWP2AGHGmFAgAYix1q73XPOPwMeAl4fyOQJBl6uX6pYu5qbH+jsUkTNaf7PwVWoWLiIBaIRzM/GhwsoWACWbJrhgp4OVc9N4fvsxOnt6CQt2+jukceOFHeXc9dR2IkODuGlJNnPSY9hc0vCBMZ9eksOj7x7mlt9t4obF2cxOjxnx/W5amj3akEVkGIZa2QQwC7jeGHMLcC1wyWnGZwClA74v8xwbdIy11gU0AYknjPkksM1a2+UZX3aaawJgjLnDGLPFGLOlpiZwylYrGjuxFlU2iXjZwGbh6/W0TEQC03DnZuJDhVUtpMaEEavWCBPelfPSaO/uZd2Ban+HMm48vaWUrz25jYXZ8XztomnMzYhlsO4oMeHB3HH+ZNJiw3hi0xEKq1r8EK2IjMRQd6N7HPgfYDmw2PNVcLrTBjlmhzPGGDOHvqV1XxjGNfsOWvuItbbAWluQnJx8mlDHj4qmvgr42AhNTES8bWZqtM+ahde3datBqIiMmRHOzcRHunp6OVLXzvQU7UInsDQvgcTIEF7QUjoA/ri+hG88s5Nzpybxh88vOW21V0RIEJ9fnkdiVCirdpTj6nX7JlARGZUhLaOjb/Iy21o7aGLnJMqArAHfZwLlJxlTZowJAmKBegBjTCbwHHCLtfbQgPGZp7lmQKvx7LgQHTrUfzUiMlLGGK6cl8ZDaw7y4q4KvnDB5EGfqo1GdXMnv3m7mD9tOEpHTy95SZH84BP5LJ18YhGniMiwjGRuJj5yqKaVXmuZpiV0AgQ5HVyWn8qz7x+jvdtFRMjEnec/8tYhvrd6PxfPTuEXN51FaNDQlhWGBjm5Mj+N379XwnuH6jh/euAUE4hMVENdRrcbSB3mtTcD04wxecaYEOAGYNUJY1bR1wAc+sq/37DWWmNMHPAScJ+19t3+wdbaCqDFGHO2Zxe6W4DnhxnXuFbd3FfZFB2myiYRX0iMCuXCmZPYfayJpzaXnv6EIep1W76/eh/Lf7SW371zmJVzU3nohgX0ui3XP7KBbz23i+ZO71ZTicgZbSRzM/GRA1WthAQ5yEmM8HcoMk5ckZ9OR08va/cHTnuPsdafaLpiXhq/+vTCISea+k1LiWZmajRrD1TTojmUyLg31LR6ErDXGLMJ6Oo/aK29+mQnWGtdxpg7gVcBJ/CotXaPMeZBYIu1dhXwO+BxY0wRfRVNN3hOvxOYCtxvjLnfc+wSa2018CXgMSCcvsbgZ0xzcICali5CghyEBQ+nnZaIjMb505Mprmnj26v2MC8zblTNJwHcbst9f9vJ01vK+MTCDL564TRykyIBuHh2Cj/5RyGPvnuYrUcaePErywly6r93ERm2Yc/NxDestRRWtTA1OYogh/7/Ln2W5CWQHB3KizvLuWJemr/D8bnfvl18PNH00PULRjz3uXxuX0X6a3ur+MTCzNOfICJ+M9Rk0wMjubi1djWw+oRj3x7wuhO4bpDzvgt89yTX3ALMHUk8gaCmpYtJ0aFjvpRHRE7OYQyfWpzFb98u5l+f2MqqO88dcXWhtZb/eH43T28p46sXTuXrl8z4wPsRIUH8x5Wzyc+M5WtPbudvW4/xqcVZJ7maiMhJPeDvAGRw1S1dNHX0sGLGJH+HIuOI02G4fG4qT24upa3LReQEapnx27eL+e5L+7gif3SJJoCk6FDOmZLIu0W1LJ2cSEZc+BhGKiJjaUj/pVtr3wRKgGDP683AVi/GNWFVt3SRHB3q7zBEJpyo0CD+98azOFLXxn1/28VI2qBYa3lg1R6e2HiUL31kCnddPP2kY6+en878rDh++nqhmoaLyLBpbjZ+Ha5tA2DqJDUHlw+6Yl46XS43a/ZPjF3prLX875qDfPelfVyen8rPbhhdoqnfihmTiAhx8uLO8hHN10TEN4a6G93twDPAw55DGcDfvRXURNZf2SQivrd0ciL/fskMXtxZwY//UTisCYyr181//H03f1h/hNvPy+Mbl844ZYWiMYZvrvz/2bvv+LiuMv/jn2dGXVbvtmRLLnJvsWOnOHF6I4WF9EJgAwEWdmEX2A28frD8+C0sWZaFQCgJpENI2JCAE0IK6Y4T9xZbLrIs27JkNUsa9Xp+f2icCMdFlmZ0Vb7v12temnvnlmduMuMzzz3nOdOpaGjj0XdKBx+8iIwpapsNX2V1LcRHR5CimYXlKIsnpZCVGM1zm0fV/EbH1N3j+Oaf3uOHL+/iYwsncM+NC4kMUdmA2Cg/F87MYl9tC3uqm0NyTBEJvf5+4r8AnA0EAJxzuwH1DQ6DqsY29WwS8dDnl0/h+sW53PtaMV/5/WY6uk4+vW5zexefeXQdv129n88tn8I3rpjZr6GwZ01J59zCDH722h4aWlXoUkROidpmw9SBw63kpcSqJIJ8iM9nXDE3h9d3VY/qAtdtnd184bcb+M27+/ns8sn88Pr5IUs0HbFoUgrjoiNYWTx2C66LDHf9/dS3O+c6jiyYWQSgPosh1tHVQ11LJ5kJMV6HIjJm+XzG3R+fx1cuLuTpjQe5/cE1J0wEVQbauP6+d3hzdw3f/bs53HX5jFP6gfGvl06nobWT+97YE4rwRWTsUNtsGGrr7Ka6qZ3cFM1CJ8d25bwcOrp6eKVodA6lq25s57YHVvPCtkN888pZfP3y/t2AO1WRfh9nTkljV2UThxraQn58ERm8/iab3jCzbwCxZnYx8L/As+ELa2yqaeqdTEY9m0S8ZWb844XT+NEN81m37zBX/XQl9766m12VjTjncM7x3sEG/uelnVx970pKa5r59e2LuWXppFM+15wJSVw9fzwPvr2XyoAaSyLSb2qbDUNlda0A5KWqaLEc28K8FHKSYnhuy+gbSrfpQD1X/XQlWw828NObFnLHsoKwnm9pQSpRfh9v7VbvJpHhqL/TINwF3AFsBT5L7wxzvw5XUGNVVWNvsikzIZrKQPtJthaRcPu7hbnkJMXy/b/s4L9f2sV/v7SLgvR42ju7KW9ow2dwen4q37pqFrPHJw34PF+5pJA/b63gwZV7+foVM0P4DkRkFFPbbBgqq2sBIDdZPZvk2Hw+4yNzc3j0nX00tHaSFDs6ans9uXY/3/zjNjITo/nD588aVLuov+KiIliUn8LqkloumZ09aq6lyGjRr2STc67HzP4I/NE5p9RxmFS/n2yKUbJJxAOPr95/zPXXL87jstnZFB0KUFQRIDYyijOnpDMjO4H46Ag2H2hg84GGUz7fzUsnAjApLZ7zp2fw9MaDfO3S6SGZqUVERje1zYanA3WtpI+LIjbK73UoMox9ZF4Ov165l79ur+Tji3K9DmdQOrp6+L/PbuO3q/dzzrR0fnLjQlLio4bs/MumpPPunlpWFddw+dycITuviJzcCX/RWK9vm1kNsAPYaWbVZvatoQlvbKlq7B1Co2F0IsNPYmwkSwvS+ORZBdx6xiQWTUohPrq/nUNP7tpFuVQ3tvNWcU3Ijikio4/aZsOXc46ywy3kqV6TnMSCvGQmJMfy560VXocyKJWBNm761bvvT5Dy8KeWDGmiCSAlPoo5E5JYU3qYts7uIT23iJzYyW6ff5nemU5Od86lOedSgaXA2Wb2z2GPboypCrRjBmnjhvZLWkS8d8GMLFLiInlqfZnXoYjI8Ka22TDV0NpJY3sXuSmq1yQnZmZcOS+Ht3ZXU9/ScfIdhqH1+w5z5U9XUlQR4N6bF3LX5TPw+7yZgfHcaRm0d/WwtvSwJ+cXkWM72W35TwAXO+fev9XunCsxs1uBl4AfhTO4saa6qZ3UuKiQTw0qIsNfVISPaxZM4PE1+2lo6SQpTnUHROSY1DYbpg68XxxcPZvk5K6aP5773izh2S0V3HbGqU8w4qUVm8v5yu83MT45lt/csZTp2QmexjMhJZbJGfGsLK7hzMlpxy1HcLxyCeFwpFSCyFh2smRTZN/GzBHOuWoz0y+hEKsKtGsIncgY9vHTcnl4VSnPbinn1hHW8BSRIaO22TBVVteC32dkJ8Z4HYp45FSTGTlJMdz3xh78dmo9grxMZDy4ci/feW47SwpS+dVti4fNzbHzCjN58O29bNhfz5KCVK/DERFOPozuRP06R2afz2GsuknJJpGxbM6ERKZnJWgonYiciNpmw9SBw62MT4rRJA/Sb6dNTKGsrpXKQJvXoZyUc467X9jBd57bzmWzs3n075cMm0QTwJSMeHJTYnlzdzXdPc7rcESEkyeb5ptZ4BiPRmDuUAQ4llQH2shM0N0wkbHKzLh2US6bDtRTXNXkdTgiMjypbTYMdfc4Dta3kKvi4HIK5ucl4zPYsK/O61BOqKfH8Y1ntvKL1/dw89KJ/OyW04iJHF4zLpoZywszONzcwXsHT32GYBEJvRMmm5xzfudc4jEeCc654ZPKHgWcc1Q3tZOZqJ5NImPZNQvH4/cZf9ig3k0i8mFqmw1PVY1tdHY78lJVHFz6b1x0BDOyE9l4oH7Y9sZxzvHd54v43ZoDfOH8KXz3o3M8KwR+MjNzEslIiOaNXdU4Nzyvp8hYErp5u2VQ6ls66ex2ZIxTsklkLMtMiGF5YQbPbDjIVy+ZPmwbdCIi8oGyYHFw9WySU7VoUgrbKwLsqmxkZk6i1+H8jcdX7+fVHZX8taiKM6ekMT4plt+tOeB1WMflM+O8wgz+d30ZOw81MmOYXU+RsUaDyoeJqsZ2APVsEhE+flouhwJtrC6p9ToUERHph7K6FmIj/aTFR3kdiowwhVkJxEdHsGH/8BtKt2pPDX8tqmJhXjIfmZuDnWIhcy/My00mOS6S19W7ScRz6tk0TFQHk03q2SQiF87MJD7Kz4rN5Zw1Nd3rcERE5CQOHG4lNyV2RPwYl+HF7zMW5iXzzp5amtu7iI8eHj/PnttSznNbKpiZk8jHTsvFN0L+3/b7jHOnZbBiczm7q5oozEoY8LGa2rt4paiS5vYu/D7D7/ORHBfJedMziPCpz4bIyehTMkxUNfbOQpGp6XJFxryYSD+XzM7m+a0VtHd1ex2OiIicQGd3D1WNbeSmqF6TDMxpE1Podo5NB+q9DgWArWUNfOX3m5mUFseNp+eNuCH9iyalkD4ummc2HqStc2DtqJKaJu59dTfr9tVR1dhOWV0re6qbeHVHFSs2lavXlEg/KNk0TLzfsylBPZtEBK6eP55AWxdv7qrxOhQRETmBqkA7PQ5ykpRskoHJTophQnIsa0sP0+NxEqMq0MZnHl1H+rhoblk6iUj/yPu5GOn3cd2iXAKtnTy/teKU9u1xjld3VPLAW3uJ9Pv4/PIpfPmiQr5yyXT+7bIZnFeYwbp9dbyjUgciJzXyvj1GqarGduKi/IwbJl1nRcRby6alkxIXyYrN5V6HIiIiJ1DR0FscPCdJvdNl4JZNTaeqsZ3t5QHPYmjr7ObOx9bT0NrJ/Z9YNKJ/l+SlxnFuMDG081Bjv/Zp6+zm0XdK+WtRFfPzkvni+VMZn/y3SeSLZmUxMyeRP2+pYHdl/44rMlYp2TRMVDW2k6leTSISFOn3cfncHP66vZKWji6vwxGRUcLMLjOznWZWbGZ3HeP1aDN7Mvj6ajPLD66/2MzWm9nW4N8Lhjr24aoi0EaU30eKioPLIMzNTSJ9XBSv7azyZIiWc45vPLOVTQfq+dEN85k9PmnIYwi1C2dkkpkQzTMby2jtOPFwuvqWDu5/s4TiqiauWTCe6xblEh3p/9B2PjOuX5xLVmIMv1u7n5rg6BQR+bCwJpsG0aBJM7PXzKzJzO49ap/Xg8fcFHxkhvM9DJXqxjYNoRORv3HN/PG0dnbz8vZKr0MRkVHAzPzAz4DLgVnATWY266jN7gDqnHNTgR8BdwfX1wBXOefmArcDjw1N1MPfoYY2shKjR0wBZRmefGacV5hJRUMbO/rZEyeUfvVWCU9vOMg/X1TIZXNyhvz84RDh93Hdojya2rt4ZmPZcetglte38ss39lDX0sHtZ+WztCDthMX+oyP83HbGJHxm/GFjWbjCFxnxwpZsGmSDpg34JvDV4xz+FufcguCjKvTRD73enk3qfi0iHzg9P5WcpBie1VA6EQmNJUCxc67EOdcBPAFcc9Q21wCPBJ8/BVxoZuac2+icO/JltA2IMbMxf5fMOUdFQ6vqNUlIzM9LJiUucsh7N722o4r//MsOPjI3h3+6cOqQnXcoTEiJ5aKZWbxXHuB/XtrFmr2H6e5xOOc4cLiFZ7eUc/+bJZgZn10+hWmZ/Zu9LiU+ivMKM9hX28KhhrYwvwuRkSmcPZsG06Bpds6tpDfpNCZUN7arZ5OI/A2fz7hyXg5v7KqmvqXD63BEZOSbABzos1wWXHfMbZxzXUADkHbUNh8HNjrnPjR+xMzuNLN1Zrauuro6ZIEPV+UNbbR19pCtek0SAn5fb++msrpWdlc1Dck5i6sa+affbWRmdiI/uG7eCXv0jFTnTc/kc+dOJjU+ij9uOsg9r+zmf17exS/e2MPavYeZnp3A58+bQvYpzgp+2sQUInzG6r0qFi5yLOFMNoWqQXMsDwWH0H3TjvONOJIaO22d3TS2dSnZJCIfcvX8CXR2O15475DXoYjIyHesNtPR3SdOuI2Zzaa3J/pnj3UC59z9zrnFzrnFGRkZAw50pCgKFnNWcXAJlYWTkkmKjeS1HeHv3VTf0sGnH1lHdKSPX92+mLiokVsQ/GQmpsVz57mTuXXpRKIjfCTFRfKxhRP4xhUzuWnJRBJjIk/5mHHREcydkMSmA/XHHaInMpaFM9k06AbNcdwSrBdwTvBx27E2GkmNnapA741BJZtE5GhzJiRSkB7PMxsPeh2KiIx8ZUBen+Vc4Ohxuu9vY2YRQBJwOLicCzwDfMI5tyfs0Y4ARRW9yaZT7REhcjwRPh/nFmaw73ALxWHs3dTW2c3nfrOe8vo27rttEROSR/9QUDNj1vgkvnD+VD69bDKL81OJOUYR8FOxtCCV9q4eNh9oCFGUIqNHOJNNg2rQHI9z7mDwbyPwOL3D9Ua06qbe0YKajU5EjmZmXLsol9V7D7O3ptnrcERkZFsLTDOzAjOLAm4EVhy1zQp6C4ADXAu86pxzZpYM/Bn4unPu7SGLeJgrOhQgNT7qmLNWiQzU4kkppMVH8fTGgyedRW0gunscX3piI++WHOYH181j0aTUkJ9jrMhLjSMnKYY1e2s9mUVQZDgLZ7JpwA2a4x3QzCLMLD34PBK4Engv5JEPsSM9m1QgXESO5bpFufh9xpNrD5x8YxGR4wiWLPgi8CJQBPzeObfNzL5jZlcHN3sASDOzYuBfgCOzCX8RmAp8c7TNCDwYRRWNGkInIRfp93HD6Xk0tnXy9MaykCYxnHP8nz9u5cVtlfz7VbO4ZsHRVU7kVJgZSwpSKW9oo6yu1etwRIaVsCWbBtmgwcxKgf8BPmlmZcGZ7KKBF81sC7AJOAj8KlzvYahUN2kYnYgcX2ZiDBfMyOSp9WV0dvd4HY6IjGDOueedc4XOuSnOue8G133LObci+LzNOXedc26qc26Jc64kuP4/nHPxfWYDHjUzAg9US0cXpbXNKg4uYZGbEscls7LZVh5gXWldyI77w5d28bs1B/ji+VP51NkFITvuWLYgN5moCB+r955wgI7ImBPWKnDOueeB549a960+z9uA646zb/5xDrsoVPENF1WBdvw+IzU+yutQRGQIPb56f7+3zUmK4eWmdv79T9uYMyFpQOe7eenEAe0nIiIftuNQI85BTuLor3Uj3lg2LZ3i6iae21rOxLQ4sgZRG6y7x/FfL+7gvjdKuGlJHl+5pDCEkY5t0ZF+FuQls2FfHVfMzR7VhdZFTkU4h9FJP1U1tpEWH4XfN/qmGhWR0CjMSiAxJoJ1+3TXTERkONhR0QhoJjoJH1+wbmOU38cTa/fT2NY5oOM0t3fx2cfWc98bJdx6xkT+46NzOc6E3jJAS/JT6epxbD2oQuEiRyjZNAxUN7aTmaghdCJyfD4zFk1KZXdlE3UtHV6HIyIy5hVVBEiIjiA57tSnTBfpr8SYSK4/PY/DzR387LVi9tWe2mQhB+tb+fgvVvHaziq+c81s/uOjc3WDOwxykmJIjY9i56FGr0MRGTaUbBoGqhrbyRinZJOInNji/BQA1u8LXe0GEREZmKKKADNyEtRDRMJuWmYCn1s+hQi/j1+9VcIjq0pPWjS8obWTH728i8t+/CYH61t56JOn84kz84cm4DHIzJienUBxVRMdXaqvKQJKNg0LVY3tgxqDLSJjQ0pcFFMzx7F+Xx09ml5XRMQzPT2OHYcamZmT6HUoMkbkJMXyhfOmUpiVwL+v2MZ1v3yH+9/cQ3FV4/uJp4bWTraXB/jRy7tYdver3PPKbs6eks6fvnA25xZmePwORr8Z2Ql09ThKapq8DkVkWFD1Mo91dfdQ09ROppJNItIPp+en8via/ezUjxwREc+U1bXS1N7FzJxElPuXoRIb5efWMybR1tnNU+vL+N7zO/je8zvISoympaObxrau97e9bHY2/3ThNGaNV1thqBSkxRMV4WOHhtKJAEo2ea66qR3nIFvJJhHph5k5iaTGR/HXokqmZyfg0/ANEZEht70iAPR+J28vD3gcjYwlPjPioiL4xJn51Ld0sLOykdKaZmKjIkiJiyQ5LoqcxBjSE6LZdKCeTQfqvQ55zIjw+5iWOY6dh3p7m2mIrYx1SjZ5rDLQDkCWCoSLSD/4fcZFM7P4/boDbC1rYH5estchiYiMOUUVAXwG07MSlGwSzyTHRbG0II2lBWlehyJBM7IT2FYeoKiiUb3KZMxTzSaPVQbaAFSzSUT6bV5uEjlJMbxcVElXj4pQiogMtaKKAPnp8cRG+b0ORUSGkcKsBABe3VHpcSQi3lOyyWNVwWRTpno2iUg/+cy4ZFY2h5s7WFeqmelERIZa0aGA6uaJyIckxESSmxLLKzuqvA5FxHNKNnmsMtCO32ekxSvZJCL9V5g1jvy0eF7dUaUpdkVEhlBjWycHDrcyS8kmETmG6dkJbDpQT21Tu9ehiHhKySaPHQq0kZkQjd+nAnIi0n9mxmWzs2hq7+LtPTWDPt7h5g7e2l1NW2d3CKITERm9jsw0NTMnweNIRGQ4mpHdO0vl6zurvQ5FxFMqEO6xykAbmarXJCIDMDEtnpk5ibyxq5oZ2QnkJMWe0v7OOdbsPczja/bzl62H6OjuISUukhuXTOTWMyYMWcHiAAAgAElEQVQxIfnUjiciMhYU9ZmJTkTkaOOTYshKjObVHVV8fFGu1+GIeEY9mzxWFWgnK0FD6ERkYK6al0NspJ+H3i49pe7a7x1s4IqfrOSG+9/l1R1V3Lx0Ir+89TSWFqRx3xt7OOfuV/n2im0458IYvYjIyFNUESA5LpJs3SwUkWMwMy6Ykcmbu6rp7FapAxm71LPJY5WNbSwpSPU6DBEZoZLjovjUWfnc/1YJD60q5c5zJ5MYE3nc7bu6e/jF63u455XdpI2L4r+uncdV88a/P6PSZXNyOFjfyr2vFvPwqlJykmL47PIpQ/V2RESGvaKKRmZmJ2KmEggicmznTsvgd2sOsPlAPYvz9VtPxib1bPJQW2c39S2dZGkmOhEZhMzEGG4/M5+mti4efruU1o4P111yzrGlrJ5rf/kOP3x5F1fMzeGlLy/n+sV5H5q6e0JyLN/7uzl8ZG4Od7+wgzd2qeaAiAhAd49j56FGZqhek4icwFlT0jGDlcWDr6spMlKpZ5OHqgK9Q16y1A1bRAYpLzWOW86YyKOr9vH9F4qYmpnAzOwEclPj2HWokQ3766hqbCcpNpKf3rSQq+aPP+HxzIwfXDePkppm/vHxDfzpi8soSI8foncjIjI87attprWzW/WaROSEkuIimTchibeLa/jyRYVehyPiCfVs8lBlYxugZJOIhMa0zATuPHcyp01Moby+lac3HuQnr+zmhW2HiIn0892/m8Ob/3r+SRNNR8RFRXD/bYvw+4zPPLqOxrbOML8DEZHhraiidya6WUo2ichJnD01nY3762lq7/I6FBFPqGeThyoDSjaJSGjlpcaRlxrH1c5xKNBG2eFWJmfEkzYumpuXThzQ8X5+yyJufWA1316xnR9ePz8MUYuIjAxFFQH8PmNq5jivQxGRYW7ZtHR+/voeVpfUcuHMLK/DERly6tnkocr3h9GpZpOIhJaZkZMUy+kFqaSNG9x3zJlT0viH86bwhw1lvFJUGaIIRURGnqKKAFMy4omJ9J98YxEZ006bmEJMpE91m2TMUrLJQ5WBNqIifCTFHn/mKBGR4eAfL5jGjOwEvv70VupbOrwOR0TEE0UVAdVrEpF+iYn0c3p+Kit3K9kkY5OG0XmoMtBGdmKMps4VkSHx+Or9g9r/wplZ/OL1Yj750FquX5x30u0HMmxPRGS4qm/poLyhTckmEem3c6al873nd1AZaFPpFBlz1LPJQ71fOhpCJyIjw4TkWM6bnsmmA/VsLw94HY6IyJA6UhxcySYR6a+zp6YD8LaG0skYFNZkk5ldZmY7zazYzO46xuvRZvZk8PXVZpYfXJ9mZq+ZWZOZ3XvUPovMbGtwn5/YCO4WVBVoJ1MZbhEZQc6bnkFOUgx/3HSQgGanE5ExpKiiN8k+MyfB40hEZKSYmZ1IWnyUhtLJmBS2ZJOZ+YGfAZcDs4CbzGzWUZvdAdQ556YCPwLuDq5vA74JfPUYh/4FcCcwLfi4LPTRD43KQBtZCUo2icjIEeHzcd2iPNq7unl89X66unu8DklEZEgUVQRIHxdFptpuItJPPp9x1tR0VhbX4JzzOhyRIRXOnk1LgGLnXIlzrgN4ArjmqG2uAR4JPn8KuNDMzDnX7JxbSW/S6X1mlgMkOufecb2f1keBj4bxPYRNY1snzR3dGkYnIiNOdlIM1y7KY//hFlZsLlfjSUTGhKJDKg4uIqdu2dQ0qhrbKa5q8joUkSEVzmTTBOBAn+Wy4LpjbuOc6wIagLSTHLPsJMcEwMzuNLN1Zrauurr6FEMPv8pAO9D7o01EZKSZOyGJ5YUZrNtXx5rSw16HIyISVl3dPeyqbGJGtobQicipOVK36S0NpZMxJpzJpmPVUjr69nd/thnQ9s65+51zi51zizMyMk5wSG9UBXo7bakrtoiMVBfPymJ6VgLPbi5nb02z1+GIiIRNSU0zHV096tkkIqcsNyWOgvR43to9/DpAiIRTOJNNZUDfubFzgfLjbWNmEUAScKJb5GXB45zomCNCZWNvsknD6ERkpPKZcf3iPFLjo3j0nVJKqtU9XERGp/cONgAwe3ySx5GIyEi0vDCDd0pqaevs9joUkSETzmTTWmCamRWYWRRwI7DiqG1WALcHn18LvOpOUPzDOVcBNJrZGcFZ6D4B/Cn0oYffkWF0WZqNTkRGsNgoP3csm0xSbCQPryplR3C2JhGR0WTrwQZiI/1MzRzndSgiMgItL8ygrbOHtSo9IGNI2JJNwRpMXwReBIqA3zvntpnZd8zs6uBmDwBpZlYM/Atw15H9zawU+B/gk2ZW1mcmu88DvwaKgT3AX8L1HsLpUEMbCdERxEdHeB2KiMigJMVGcuc5k8lKjOE3q/ex+UC91yGJiITU1rIGZo9PxO87VkUHEZETO2NyGlERPl7fqaF0MnaENdPhnHseeP6odd/q87wNuO44++YfZ/06YE7oovRGVWMbmRpCJyKjRFx0BHcsK+Cxd/fx+3UHaO7o4ualE70OS0Rk0Lp7HNvKA9y4JO/kG4uIHENslJ+lBam8sauab3odjMgQCecwOjmBykC7htCJyKgSE+nnk2flMzMnkee2VPC954vo6TnRnA8iIsPfnuomWju7mTtB9ZpEZOCWF2ZQXNVEWV2L16GIDAklmzxSGWhTsklERp1Iv4+bl07kjMmp3P9mCf/0xEbau1QMU0RGri1lvcXB5+Uq2SQiA3fe9N4Z0t/YpaF0MjYo2eQB5xxVgXYNoxORUclnxlXzxvP1y2fw3JYKbn9wDc3tXV6HJSIyIO8dbCAuyk9BuoqDi8jATckYx4TkWN5Q3SYZI5Rs8kBdSycd3T1kq2eTiIxSZsZnl0/hxzcsYG1pHZ96eK0STiIyIm0pq2fO+CQVBxeRQTEzlk/PYNWeWjq6erwORyTslGzyQGWgDUDD6ERk1Pvowgn8+IYFrCs9rISTiIw4Xd09bK8IMEf1mkQkBJYXZtDU3sX6fXVehyISdko2eeCDZJOG0YnI6HfV/PHcc+NCJZxEZMQprm6irbNH9ZpEJCTOmpJGhM9Ut0nGBCWbPFAVaAcgM0E9m0RkbLhq/nh+HEw43fnYOnUfF/GImV1mZjvNrNjM7jrG69Fm9mTw9dVmlh9cn2Zmr5lZk5ndO9Rxe+VIcfC5SjaJSAgkxESyOD9FySYZE5Rs8kB5QytmqEC4iIwpV88fz90fn8fbxbV87anN9PQ4r0MSGVPMzA/8DLgcmAXcZGazjtrsDqDOOTcV+BFwd3B9G/BN4KtDFO6w8N7BBsZFR1CQFu91KCIySiwvzKSoIvD+aBeR0UrJJg+U1jQzPimW6Ai/16GIiAyp6xbn8bVLp/OnTeV8/4UdXocjMtYsAYqdcyXOuQ7gCeCao7a5Bngk+Pwp4EIzM+dcs3NuJb1JpzFjS1kDs8cn4lNxcBEJkQtmZALw0rZDHkciEl5KNnlgb20L+elxXochIuKJfzhvCredMYn73yzhgZV7vQ5HZCyZABzos1wWXHfMbZxzXUADkNbfE5jZnWa2zszWVVeP7GEind09FFUEmKvi4CISQtOzEyjMGseKzeVehyISVko2eaC0ppl8dccWkTHKzPj21bO5bHY2/++57TyrxpbIUDlW95yjx7P2Z5vjcs7d75xb7JxbnJGRcUrBDTe7K5to7+pRvSYRCbmr5o1nbWkdB+tbvQ5FJGyUbBpidc0dNLR2UpCuZJOIjF1+n/HjGxewJD+Vr/x+M6uKa7wOSWQsKAPy+iznAkdne9/fxswigCTg8JBEN8xsPVgPoJ5NIhJyV80fD8BzuuEmo5iSTUOstLYZQD2bRGTMi4n086tPLCY/PY47H1vP9vKA1yGJjHZrgWlmVmBmUcCNwIqjtlkB3B58fi3wqnNuTFbz33qwgYToCLXZRCTk8tPjmZ+bxLNblGyS0SvC6wDGmveTTerZJCKj3OOr9/dru48umMB9b5Zww/3v8Llzp5ASH3XK57p56cRT3kdkrHHOdZnZF4EXAT/woHNum5l9B1jnnFsBPAA8ZmbF9PZouvHI/mZWCiQCUWb2UeAS59z2oX4fQ2VrWQOzJ6g4uIiEx1Xzx/Mffy6ipLqJyRnjvA5HJOTUs2mI7a1pwWeQlxrrdSgiIsNCclwUnzwrn87uHh5aVUpLe5fXIYmMWs65551zhc65Kc657wbXfSuYaMI51+acu845N9U5t8Q5V9Jn33znXKpzbpxzLnc0J5qa27t4rzzAaRNTvA5FREapK+eNxwwVCpdRS8mmIVZa08z45FiiI/xehyIiMmxkJcZw2xn51Ld08Oi7++js7vE6JBEZw9btq6O7x3HG5H5PxCcickqyk2JYkp/Ks5vLGaOjlWWUU7JpiJXWNqs4uIjIMRSkx3P94jwOHG7hibUH6O5Rw0tEvLG6pJYIn7Fokno2iUj4XL1gPHuqm9leobqVMvoo2TSEnHPsrWlWoUkRkeOYMyGJK+flUFQR0J0+EfHMuyW1zMtNIj5a5U1FJHwun5NDhM80lE5GJSWbhtDh5g4a27pUHFxE5ATOnJLO8sIM1pQe5rWd1V6HIyJjTEtHF1vKGliqIXQiEmap8VEsm5bOnzaW097V7XU4IiGlZNMQKq1tAaAgPc7jSEREhrdLZmWxMC+ZvxZVsq70sNfhiMgYsn5fHV2q1yQiQ+RTZxdwKNDGU+vLvA5FJKTUN3gIldY0AzBJw+hERE7IzPjYabk0tXfxx00HSYiJYHp2otdhicgY8G5JLX6fsVj1mkRkCJw7LZ2FE5P5+Wt7uG5RHlER4e8PUhVoY2VxDXtrmimpaWZfbTNp8dEsmpTCokkpzM9LZpyGEcsghfX/IDO7DLgH8AO/ds59/6jXo4FHgUVALXCDc640+NrXgTuAbuCfnHMvBteXAo3B9V3OucXhfA+hVFrbjM8gL0U9m0RETsbvM25eMpFfrSzh8TX7+fSyyeSl6vtTRMLr3ZLDqtckIkPGzPjShdP45ENreWp9GTcvndjvfR9fvf+UzlUZaGPl7ho2Hain2zl8BilxUaTGR1FUEeCNXb3lC/w+45yp6Zw3PZOoCN8pxSRyRNj+FTUzP/Az4GKgDFhrZiucc9v7bHYHUOecm2pmNwJ3AzeY2SzgRmA2MB74q5kVOueODGQ93zlXE67Yw2VvTTO5KXFDkq0WERkNoiP93H5mPr98Yw+PvFPKHcsKyEmK9TosERmlWjq62Hygnk+fM9nrUERkDFlemMGCvGR+9lox1y7KDfnvxdqmdv68tYIdhxqJ9BunF6Rwen4qGQnRRPg+OFdrRzcH6lrYdKCe13dVs6msnqvmjQ9pLDJ2hDPrsQQods6VOOc6gCeAa47a5hrgkeDzp4ALzcyC659wzrU75/YCxcHjjWiltc0qDi4icooSYiL51NkFRPp9/OqtEg4cbvE6JBEZpTbsqw/Wa0r1OhQRGUPMjC9fNI2D9a0hrd3U4xwri2v4yau7Ka1t5sIZmfzrpTO4ev4EcpJi/ybRBBAb5acwK4HrF+fx6WUFRPl9PPbuPr74+AbaOlXAXE5NOJNNE4ADfZbLguuOuY1zrgtoANJOsq8DXjKz9WZ25/FObmZ3mtk6M1tXXe39bEbOOUprWihI0xAQEZFTlT4umjvPmUxcVAQPvL2Xkuomr0MSkVHo/XpN+Uo2icjQ6tu7qaOrZ9DHq2ps4/43S3h+awVTMsbxpQsLuXBmVr+HCE/OGMc/XjCNi2dl8dyWCj710Fqa2rsGHZeMHeFMNtkx1rl+bnOifc92zp0GXA58wczOPdbJnXP3O+cWO+cWZ2Rk9DfmsKlp6qCpvUvFwUVEBiglPoo7z5lMcmwkD68qZXt5g9chicgo825JLXMmJKkwrogMub69mx5etXfAx3HO8W5JLfe+Wkx1YzvXLcrltjMmkRQbecrH8vuM86dn8qMb5rOm9DC3/Opd6po7BhybjC3hTDaVAXl9lnOB8uNtY2YRQBJw+ET7OueO/K0CnmGEDK/bV9s7E12BhtGJiAxYYmwknzlnMlmJMfxm9X7+sKFM3bpFJCRaO7rZXFavIXQi4pnlhRlcNDOL//zLDv6yteKU929q7+Kxd/exYnM5BenxfOmiaSycmEJvpZqB+7uFudx36yJ2HGrk+vveoTLQNqjjydgQzmTTWmCamRWYWRS9Bb9XHLXNCuD24PNrgVedcy64/kYzizazAmAasMbM4s0sAcDM4oFLgPfC+B5CZm9Nb7JJNZtERAYnPjqCO8+dzPLCDDbsq+OeV3bz5i7vh0uLyMi2YX8dnd2OMyaneR2KiIxRZsZPb1rIwrxkvvTEJt7ZU9uv/ZxzvHewgZ+8spviqiaunJfD7Wflkxhz6r2ZjueiWVk88vdLKK9v5fYH1xBo6wzZsWV0ClsfYedcl5l9EXgR8AMPOue2mdl3gHXOuRXAA8BjZlZMb4+mG4P7bjOz3wPbgS7gC865bjPLAp4JZmYjgMedcy+E6z2EUmltM36fkZuiWZRERAYr0u/j0tnZzMpJ5Kn1ZXziwTUsyU/lusW5XDE3R1OWi8gpe3N3NRE+Y/GkFK9DEZER7vHV+we1/xVzcyira+WTD63hM+dMZnzy8X9DHgq08dyWckqqm8lOjOHvzy4gOylmUOc/njMmp/GLWxfx9w+v5R9+s4GHPnU6kX7NtC7HFtbWuHPueeD5o9Z9q8/zNuC64+z7XeC7R60rAeaHPtLwK61pIS8lVh9GEZEQykuN44sXTKWts5sn1x7ga09t4dsrtnHpnGyWTU3nzClp5CQpyS8iJ+ac489bKjh7ajoJIewJICIyEHFREXzq7AJ++cYeHnx7L6fnpzJnfBLjk2MwM9o6u9lX28L2igDr9x0mOsLP1fPHc3p+Kn7f4IbMncy5hRl872Nz+dentvD1p7fyg2vnDXqYnoxOuvU7RPbWNGsInYhIGET6fdx+Vj53njuZDfvreHLtAV7aXsnTGw4CkJ8Wx/kzMrlq/ngW5iWrQSQiH7LpQD1lda186cJpXociIgJAUmwknzo7n+e2VPDW7mre2FVNSlwksZF+KhracIDfjNPzU7l4ZhZxQ9ir+/rFeRysa+WeV3YzITmWf764cMjOLSOHkk1DwDlHaW0zSwpUcFJEJFzMjEWTUlk0KZXv9ziKDgV4Z08t7+yp5ber9/PQ26VMSI7l6gXj+dRZ+WQmhqeLuYiMPM9tqSDK7+OS2dlehyIi8r7MhN5hcS3tXRQdCrCtPEBHdw/nz8ikID2evJQ4oiK8GTlzZOa8e17ZzeSMeK5ZMMGTOGT4UrJpCFQ0tNHS0a2Z6EREhojPZ8wen8Ts8Ul8+pzJBNo6eXlbJc9uKef+N0t4+O1SPn1OAXeeO1lDZkTGuJ6e3iF05xamD2hqcBGRcIuLjnj/htpwYWZ87+/msv9wC197aguT0uJZkJfsdVgyjKiA0BB4t6R3FoHF+So4KSLihcSYSD6+KJeHP7WEV7+ynItmZfHTV4tZ/oPXeWRVKd09zusQRcQj6/fXcSjQxpXzxnsdiojIiBIV4eOXty4iMyGaOx9dx6GGNq9DkmFEyaYhsLK4htT4KGZmJ3odiojImDcpLZ6f3rSQZ7+4jJk5Cfz7im1c98tVFFc1eh2aiHjguc3lREf4uGhWltehiIiMOKnxUTxw++k0t3dx52PraO3o9jokGSY0jC7MnHOsKq7lzMlp+MI8M4CIyFg10CmGr5iTw4TkWJ7dXMGlP36LC2dkcs60jJPO5HLz0okDOp+IDC/dPY7n3zvE+dMzGTeExXVFREaT6dkJ/PjGhdz52Dq++r+b+elNC/XbV9SzKdxKapo5FGjjrKlpXociIiJHMTMW5KXw5YumMTMnkZe2V/KL14spr2/1OjQRGQKr99ZS3djOlfNzvA5FRGREu3hWFl+/fAZ/3lrBf/6lyOtwZBjQLZwwW1VcA8CyqekeRyIiIseTEBPJzUsm8t7BBlZsLufnrxezvDCD86dnEuHXfRmR0eq5LRXERvq5YEam16GIiAxb/e1BHh8VwRmTU/nVW3upaGjjrCmn9htYPcdHFyWbwmxlcQ0TkmOZmBrndSgiInIScyYkMTkjnj9vqeC1ndW8Vx7g8jnZTM9KwEzdwUVGk87uHl547xAXzswkLkpNYhGRwTIzrpw3nobWLv68pYKk2Ehmj0/yOizxiG7XhlF3j+OdPbWcPTVNP1JEREaIuKgIrlucx+1n5tPd43j0nX386q0S9tU2ex2aiITQMxsOcri5g4+flut1KCIio4bPjBsW55GbEsuTaw9QUtPkdUjiESWbwmhbeQOBti7O1hA6EZERZ3p2Av98USHXLBhPbVMH971ZwkNv76WoIkB3j/M6PBEZhI6uHu55ZTfzc5M4b3qG1+GIiIwqURE+bjszn5S4KB5ZVUpxlRJOY5GSTWH0dnEtwCmPVRURkeHB7zOWFqTxlUumc+nsbCoDbTz27j7O/a/X+NlrxZTVtXgdoogMwJPrDnCwvpV/uWS6ep+LiITBuOgIPnPuZNLio3n0nVJ2Hmr0OiQZYko2hdHbxTVMz0ogIyHa61BERGQQoiJ8LC/M4GuXzuDmJRPJT4/jBy/uZNndr3H1vSv5+evF7Kluwjn1eBIZ7to6u7n31d0snpTCudN0Q1BEJFzGRUfw6WUFZCZG85vV+9heHvA6JBlCqoYYJm2d3awtPayK+iIio4jfZ8yZkMT3PjaXfbXN/OW9Q/xlawX/9cJO/uuFnYxPiuHMKemcPTWNs6akk50U43XIInKU367eT2WgnR/fsFC9mkREwiwuOoI7zp7MQ6v28tvV+7h4VhbnFmbg0/fvqKdkU5hs2F9He1cPy1SvSURkVJqUFs/nlk/hc8uncLC+lVd3VPHOnhpe2VHJHzaUATA5I56zpqRx9pR0lk1LJyEm0uOoRca2lo4ufvF6MWdNSePMKWlehyMiMibERvm5Y1kBz2w8yEvbK9lX28J1i3M1E+gop/+6YfJ2cQ1+n7GkINXrUEREJMwmJMdy2xmTuO2MSfT0OLZXBHhnTy1v76nh6Q0H+c27+4ny+zhrahqXzMrm4llZGmIt4oGHV5VS09TBfbcVeh2KiMiYEh3h54bFeUxKi+f5LRXc+1rx+8syOinZFAYtHV38cWM5iyam6C62iMgo9Pjq/SfdJj46gktmZXPhjCz2H26hqCLAlrIGXt9Zzf/541Zm5iRyen4qUzPHnbQruYZkiwzemr2H+fHLu7loZhaLJulmoIjIUDMzzpycRm5yLL9bs5/73ixhXm4Sl83OJjkuyuvwJMSUbAqDe/66m4P1rdxz4wKvQxEREY/5fUZBejwF6fFcPiebQ4E2Nh2oZ/2+OraVB0iJi2RpQRqn56cSG+X3OlyRUWlvTTN3PraO3NRYfnjdfK/DEREZ0/JS4/jSRdN4c1c1b+2uoagiwLKpGVw2J5vUeCWdRgslm0KsqCLAr1fu5cbT81icr7tmIiLyATMjJymWnKRYLp6ZxfaKAKv3HuaFbYd4ZUclp01M4awp6RpiJxJCdc0d/P3Da/GZ8dAnTycpTr3ORUS8Fh3h5+JZ2SzOT+XFbYd4bWcVZ/znK1w5N4dbzpjEaROTNYnDCKdkUwj19Di+8cxWkmIj+bfLZngdjoiIDGMRfh/zcpOZl5tMRUMrq/bUsn5fHav3HiY/LZ5Fk1KYMyGR6Aj1dhIZqPaubj77m/UcrGvl8c8sVW0QEZFhJiUuihtPn8h509uoa+7gmY0HeXrjQaZljuOCmZlcMD2TRZNSiPD7vA5VTpGSTSH0u7X72bi/nh9eN58Udf8TEZF+ykmK5eOn5XLp7GzWlR5m/b46/rChjGc3+5iRk0C3cyzJT2Va5jh8Pt3lE+mP9fsOc9cftrK7qol7blygHuciIsNYdmIM/3JxIf92+Qz+tOkgf95SwQNv7eW+N0pIjIlg0aSU4E26JOZOSCIjIVo9n4Y5JZtCpKS6ibv/soMzJ6fxsdMmeB2OiIiMQOOiIzhveibLCzPYf7iF9fvq2FXZyDf/+B4ASbGRTEqLI31cNBnjokmJjyIqwkeU34j0+3offZZT4qJIjY8ibVwUafHRqgklY0JjWyc/eHEnj727j5zEGB785GIumJHldVgiItIP46IjuGXpJG5ZOonGtk5W7q7h9Z3VbDxQxxu7qulxH2w3KS2O/LR4clNiyUiIJiMhmvRx0STERBAXFUFclJ/4qAjiov1EqmfUkAtrssnMLgPuAfzAr51z3z/q9WjgUWARUAvc4JwrDb72deAOoBv4J+fci/055lCrDLTxk1d28+TaA8RE+vl/H52jDKuIiAyKmTEpLZ5JafE451g2LZ01ew+zYX8d5fVtVAbaeO9gA3UtHXR2u34fNzbSH0w8RZE2LrpPIqo3GZU6Lor04N+0+ChiIpWcGm3C0TYbDjq6eli1p4YXt1Xy0rZDHG7p4PYz8/nqpdMZF617qyIiI1FCTCSXz83h8rk5ADS3d7GtPMD28gZKa1sorW1mW3kDLxdV0tHVc8JjRfl9xEb5iYrw0d7Zjc8Mn88wwGeGGcF1weX31x95rbd95jMwPtjegusjfRa84ecj0m9E+X2cMSWN2Eg/42IiSIyJJCk2ksTY3r8JMRGjPgEWtn99zcwP/Ay4GCgD1prZCufc9j6b3QHUOeemmtmNwN3ADWY2C7gRmA2MB/5qZoXBfU52zCHR3N7Fva8V89Dbe+nqdty0ZCL/eMFUMhNjhjoUEREZxcyMt4trAZg7IZm5R3Wedc7R7RzdPY6eHujq6aG7x9HV42jp6Ka5veuDR0c3TcHn1RUBmoOvd/UcO2EVH+UndVwUqfHRpMUHe0kF/x5JVCXGRBId4Scm0kd0hJ/oSB/RET5iIv1E+IyuHkdnd29Mfp+REKPizF4JR9vMOdc9tO+it+D3hv117K1pZl/wx8am/fU0tto+N0sAAAzFSURBVHcRF+Xn/OmZ3HFOAadNTBnq0EREZBAeX72/X9tFRfgpzEqgMCsB6G0LtXf10NjWRWN7Jx2dPbR399DRFXwEn7d3dfe2l1zvPj0OetxRyz0OxwevueBrXT3Q43pwgOvz2pG/XT295+js7m33OODF7ZUnfB/xUf73k0+JMb2JqMTYiPeX+yanEmMiiI+OICoi2JM9mNA60qs9wme9sfaNveeD5bgo/5DfRAznrZ4lQLFzrgTAzJ4ArgH6NmiuAb4dfP4UcK/1dgu6BnjCOdcO7DWz4uDx6Mcxh4TfZ/xx40Eum53NP19cqIKTIiLiCTMjwoyI92+OnVpD4kgDrW9Cas6ERGqaOjjc3EFtUzu1zR1UBtooqghQ29xx0ruHx/OReTn87ObTBrSvhEQ42mbvDFHs73uvvIE7HlkHQEJMBAXp8VwxN4dLZmdx9tR09cgTERljzIyYyN5kynCY0bc3OeW4ev54Wjp7b+w1tHYSaO3s87eLQFvn36w/WN9KUUXvcmN7V0hj+v7H5nLjkokhPebJhDPZNAE40Ge5DFh6vG2cc11m1gCkBde/e9S+R+7lnuyYAJjZncCdwcUmM9s5gPdwUu/S2xf9FKUDNaGOZYzStQwtXc/Q0vUMHV3L0PLkev4c+PktYTv8pLAdefQIV9vsfUPV/urrPeBZ4L9Cczh91wyeruHg6RoOnq7h4OkaDt6wuYY33Q03he/wx2yDhTPZdKzCRUf30z/eNsdbf6xBjcfs+++cux+4/0QBesXM1jnnFnsdx2igaxlaup6hpesZOrqWoaXrOWaFo232tyuGcfurP/TZGDxdw8HTNRw8XcPB0zUcvLF+DcNZkaoMyOuznAuUH28bM4sAkoDDJ9i3P8cUERERkQ8LR9tMRERE5EPCmWxaC0wzswIzi6K3qOSKo7ZZAdwefH4t8KpzzgXX32hm0WZWAEwD1vTzmCIiIiLyYeFom4mIiIh8SNiG0QXH+X8ReJHeaqUPOue2mdl3gHXOuRXAA8BjwSKTh+lt9BDc7vf0FqzsAr5wZLaTYx0zXO8hjEZs9/JhSNcytHQ9Q0vXM3R0LUNL13MMClfbbJTRZ2PwdA0HT9dw8HQNB0/XcPDG9DW03ptVIiIiIiIiIiIigxfOYXQiIiIiIiIiIjLGKNkkIiIiIiIiIiIho2TTEDKzy8xsp5kVm9ldXscz0pjZg2ZWZWbv9VmXamYvm9nu4N8UL2McKcwsz8xeM7MiM9tmZl8Krtf1HAAzizGzNWa2OXg9/29wfYGZrQ5ezyeDBXmlH8zMb2Ybzey54LKu5QCZWamZbTWzTWa2LrhOn3WRPtRGGxi1zQZH7bHBUxssdNT2Ghy1tz5MyaYhYmZ+4GfA5cAs4CYzm+VtVCPOw8BlR627C3jFOTcNeCW4LCfXBXzFOTcTOAP4QvD/R13PgWkHLnDOzQcWAJeZ2RnA3cCPgtezDrjDwxhHmi8BRX2WdS0H53zn3ALn3OLgsj7rIkFqow3Kw6htNhhqjw2e2mCho7bX4Km91YeSTUNnCVDsnCtxznUATwDXeBzTiOKce5PemXH6ugZ4JPj8EeCjQxrUCOWcq3DObQg+b6T3H5YJ6HoOiOvVFFyMDD4ccAHwVHC9rmc/mVku8BHg18FlQ9cy1PRZF/mA2mgDpLbZ4Kg9Nnhqg4WG2l5hM6Y/y0o2DZ0JwIE+y2XBdTI4Wc65Cuj9BxvI9DieEcfM8oGFwGp0PQcs2PV4E1AFvAzsAeqdc13BTfSZ778fA/8K9ASX09C1HAwHvGRm683szuA6fdZFPqA2Wmjp+2UA1B4bOLXBQkJtr8FTe+soEV4HMIbYMda5IY9CpA8zGwf8Afiycy7QexNDBsI51w0sMLNk4Blg5rE2G9qoRh4zuxKocs6tN7Pzjqw+xqa6lv13tnOu3MwygZfNbIfXAYkMM/qOEU+pPTY4aoMNjtpeIaP21lHUs2nolAF5fZZzgXKPYhlNKs0sByD4t8rjeEYMM4ukt2HzW+fc08HVup6D5JyrB16nt/ZCspkdSerrM98/ZwNXm1kpvUNZLqD3bpuu5QA558qDf6vobYQvQZ91kb7URgstfb+cArXHQkdtsAFT2ysE1N76MCWbhs5aYFqwqn8UcCOwwuOYRoMVwO3B57cDf/IwlhEjOA77AaDIOfc/fV7S9RwAM8sI3k3DzGKBi+itu/AacG1wM13PfnDOfd05l+ucy6f3e/JV59wt6FoOiJnFm1nCkefAJcB76LMu0pfaaKGl75d+Unts8NQGGzy1vQZP7a1jM+fUG26omNkV9GaJ/cCDzrnvehzSiGJmvwPOA9KBSuDfgT8CvwcmAvuB65xzRxeqlKOY2TLgLWArH4zN/ga9dQJ0PU+Rmc2jt+ifn94k/u+dc98xs8n03iFKBTYCtzrn2r2LdGQJduX+qnPuSl3LgQlet2eCixHA486575pZGvqsi7xPbbSBUdtscNQeGzy1wUJLba+BUXvr2JRsEhERERERERGRkNEwOhERERERERERCRklm0REREREREREJGSUbBIRERERERERkZBRsklEREREREREREJGySYREREREREREQkZJZtExjAzSzOzTcHHITM72Gc5KkznXGlmC46x/l/MLKbP8otmlhCG8+ea2Z9OcZ/vmtn5Azxfqpl9biD7Bvf/3+B0qiIiIjJKqA3W733UBhMZocw553UMIjIMmNm3gSbn3H8ftd7o/a7oCdF5VgJfdM5tOmp9GTDHOVcfivOc4Pw/Av7qnPtzOM/T53xTgaeccx9q3PVz/wuBa51znw9tZCIiIjIcqA0WtvOpDSbiIfVsEpEPMbOpZvaemf0S2ADkmNn9ZrbOzLaZ2beC211lZo/32e8iM3sm+PxyM3vHzDaY2ZNmFn+C8/0zkAm8ZWZ/Da4rM7PkPrE8GDz3o2Z2qZmtMrNdZrY4uP04M3vYzNaY2UYzu+oY5zHgo8DLweVPm9nTZvacme01s8+b2deC+68ys+Tgdr8xs4/2ievbwW22mFlhcP1/mNmX+5xrh5nlAt8HpgfvVH4/+NpdwTi39LmWCWb2FzPbHHy/1wYP9TpwmZn5T/E/o4iIiIwwaoOpDSYyWijZJCLHMwt4wDm30Dl3ELjLObcYmA9cbGazgBeBc8wsNrjPDcCTZpYJ3AVc6Jw7DdgCfOl4J3LO/QioAs5xzl10jE2mA/8NzAXm0XuX6Szg68HzAHwLeME5twS4APih9ekSHjQVqHLOdfRZNzsY9xnA3UCdc24hsB649TghVwa3+TXwL8d7X0F3ATudcwucc3eZ2RXARGApsAA4y8zOAq4ASp1z851zcwg2xpxz3UApMOck5xEREZHRQW0wtcFERjwlm0TkePY459b2Wb7JzDbQe5dtJjAr2GB4GfiImUUClwLPAmfR21BaZWab/n979w9qdRnHcfz9gYYgqMVFI0hQEZtKcGlok3KxxcU/012c3FxsUQgRhGgOGiKXKJwUFUynUqJyMAVJS3CyCNLrTQq734bnd+jXqXvuvd5j517v+7Wc8/v3PN/zDIcvz+/L8wB7gJeXEMvNqrrelZFfB85356/22t0OvNP1dxF4lpZQ9K0Ffh46d6GqZqrqLvCgi3+47WEnu89vRtwzl+3AW8AV2lhuADbRksE3kxxL8npV3es98xOwbpH9SJKklckczBxMWvGemXQAkpatmcGXJBtpb8W2VdWvSU7QEgmAT4Ap4DfgUlXNdKXSZ6tq35hi+b33fbZ3PMvf/2MB3q6qWyPaediLezFtzxXPn717HvHPCfzhfgYCvFtVH/7rQitH3wEcT3Kqqo722no4R3uSJOnpYg5mDiateFY2SVqI54Fp4H6StbS3ZwOf08qRp2hJD8CXwBvpdvBI8lyXLI0yDSxl55NzwIHBQZJX/+OeG8D6JfQxym1ga9f3NuCl7vzw7zoHTA3WT0jbmWVNkhdpi4N+DLwHvNZ7ZiNw7QnFLUmSli9zsPndxhxMWnasbJK0EN/SSqe/A34AvhhcqKpHSc4Au2ml2lTV3SRTtLUDBtv3HgK+H9HHB8D5JHfmWDNgPkeA95NcpU2k3wR29m+oqvtJ7iRZX1U/PkYfo3wK7E1yBfiKNk6Dsfi6i+t0t2bAZuBye/nING3stgDHkswCfwD7AZKsA+5V1XDpuSRJevqZg83PHExahlJVk45Bkv43SXYBr1TV4UnHshBJDtIW1Pxo0rFIkiQ9LnMwaXWxsknSavMZ8MKkg1iEX4ATkw5CkiRpiczBpFXEyiZJkiRJkiSNjQuES5IkSZIkaWycbJIkSZIkSdLYONkkSZIkSZKksXGySZIkSZIkSWPjZJMkSZIkSZLG5i8aLg+gZPc/ZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting travel time differences among estrata\n",
    "fig = plt.figure(figsize = (20,10))\n",
    "\n",
    "ax1 = fig.add_subplot(231)\n",
    "ax2 = fig.add_subplot(232)\n",
    "ax3 = fig.add_subplot(233)\n",
    "ax4 = fig.add_subplot(223)\n",
    "ax5 = fig.add_subplot(224)\n",
    "\n",
    "axes = [ax1, ax2, ax3, ax4, ax5]\n",
    "distributions = [x_1, x_2, x_3, x_4, x_5]\n",
    "\n",
    "for x in range(len(axes)):\n",
    "    label = \"stratum \" + str(x+1)\n",
    "    sns.distplot(distributions[x], bins=15, ax = axes[x], label = label)\n",
    "    axes[x].legend()\n",
    "    axes[x].set_ylabel('Density')\n",
    "    axes[x].set_xlabel('Travel time (minutes)')\n",
    "\n",
    "fig.suptitle('Travel time distributions by stratum', fontsize=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the travel time distributions, we cannot assume that they are normally distributed. The standard deviation seems to be wider for a normal distribution, and it tends to be sweked to the left, with a sharp cut at zero (no travel time can be less than zero). The best way to "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Home-work tt hypothesis testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>sample_size_x1</th>\n",
       "      <th>Average Travel Time_x1</th>\n",
       "      <th>Standard deviation_x1</th>\n",
       "      <th>sample_size_x2</th>\n",
       "      <th>Average Travel Time_x2</th>\n",
       "      <th>Standard deviation_x2</th>\n",
       "      <th>mean_diff</th>\n",
       "      <th>var_x1</th>\n",
       "      <th>var_x2</th>\n",
       "      <th>t-test</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>918421</td>\n",
       "      <td>27.707</td>\n",
       "      <td>11.949</td>\n",
       "      <td>3021902</td>\n",
       "      <td>22.463</td>\n",
       "      <td>11.006</td>\n",
       "      <td>5.243</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>374.933</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>918421</td>\n",
       "      <td>27.707</td>\n",
       "      <td>11.949</td>\n",
       "      <td>2129308</td>\n",
       "      <td>18.421</td>\n",
       "      <td>10.413</td>\n",
       "      <td>9.285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>646.337</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3021902</td>\n",
       "      <td>22.463</td>\n",
       "      <td>11.006</td>\n",
       "      <td>2129308</td>\n",
       "      <td>18.421</td>\n",
       "      <td>10.413</td>\n",
       "      <td>4.042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>423.735</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>918421</td>\n",
       "      <td>27.707</td>\n",
       "      <td>11.949</td>\n",
       "      <td>307540</td>\n",
       "      <td>17.363</td>\n",
       "      <td>10.402</td>\n",
       "      <td>10.344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>459.227</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3021902</td>\n",
       "      <td>22.463</td>\n",
       "      <td>11.006</td>\n",
       "      <td>307540</td>\n",
       "      <td>17.363</td>\n",
       "      <td>10.402</td>\n",
       "      <td>5.100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>257.632</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2129308</td>\n",
       "      <td>18.421</td>\n",
       "      <td>10.413</td>\n",
       "      <td>307540</td>\n",
       "      <td>17.363</td>\n",
       "      <td>10.402</td>\n",
       "      <td>1.058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>52.727</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>918421</td>\n",
       "      <td>27.707</td>\n",
       "      <td>11.949</td>\n",
       "      <td>30796</td>\n",
       "      <td>17.136</td>\n",
       "      <td>10.497</td>\n",
       "      <td>10.571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>172.997</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3021902</td>\n",
       "      <td>22.463</td>\n",
       "      <td>11.006</td>\n",
       "      <td>30796</td>\n",
       "      <td>17.136</td>\n",
       "      <td>10.497</td>\n",
       "      <td>5.327</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>88.568</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2129308</td>\n",
       "      <td>18.421</td>\n",
       "      <td>10.413</td>\n",
       "      <td>30796</td>\n",
       "      <td>17.136</td>\n",
       "      <td>10.497</td>\n",
       "      <td>1.285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>21.334</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>307540</td>\n",
       "      <td>17.363</td>\n",
       "      <td>10.402</td>\n",
       "      <td>30796</td>\n",
       "      <td>17.136</td>\n",
       "      <td>10.497</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>3.621</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x1  x2  sample_size_x1  Average Travel Time_x1  Standard deviation_x1  \\\n",
       "0   1   2          918421                  27.707                 11.949   \n",
       "1   1   3          918421                  27.707                 11.949   \n",
       "2   2   3         3021902                  22.463                 11.006   \n",
       "3   1   4          918421                  27.707                 11.949   \n",
       "4   2   4         3021902                  22.463                 11.006   \n",
       "5   3   4         2129308                  18.421                 10.413   \n",
       "6   1   5          918421                  27.707                 11.949   \n",
       "7   2   5         3021902                  22.463                 11.006   \n",
       "8   3   5         2129308                  18.421                 10.413   \n",
       "9   4   5          307540                  17.363                 10.402   \n",
       "\n",
       "   sample_size_x2  Average Travel Time_x2  Standard deviation_x2  mean_diff  \\\n",
       "0         3021902                  22.463                 11.006      5.243   \n",
       "1         2129308                  18.421                 10.413      9.285   \n",
       "2         2129308                  18.421                 10.413      4.042   \n",
       "3          307540                  17.363                 10.402     10.344   \n",
       "4          307540                  17.363                 10.402      5.100   \n",
       "5          307540                  17.363                 10.402      1.058   \n",
       "6           30796                  17.136                 10.497     10.571   \n",
       "7           30796                  17.136                 10.497      5.327   \n",
       "8           30796                  17.136                 10.497      1.285   \n",
       "9           30796                  17.136                 10.497      0.227   \n",
       "\n",
       "   var_x1  var_x2   t-test  p-value  \n",
       "0     0.0   0.000  374.933      0.0  \n",
       "1     0.0   0.000  646.337      0.0  \n",
       "2     0.0   0.000  423.735      0.0  \n",
       "3     0.0   0.000  459.227      0.0  \n",
       "4     0.0   0.000  257.632      0.0  \n",
       "5     0.0   0.000   52.727      0.0  \n",
       "6     0.0   0.004  172.997      0.0  \n",
       "7     0.0   0.004   88.568      0.0  \n",
       "8     0.0   0.004   21.334      0.0  \n",
       "9     0.0   0.004    3.621      0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hypothesis one \n",
    "h = list(combinations(summary_table.Strata, 2))\n",
    "hypothesis = pd.DataFrame(h, columns = ['x1', 'x2'])\n",
    "hypothesis= hypothesis.merge(summary_table, how = 'inner', left_on = 'x1', right_on = 'Strata')\n",
    "hypothesis = hypothesis.merge(summary_table, how = 'inner', left_on = 'x2', right_on = 'Strata',\n",
    "                             suffixes=('_x1', '_x2'))\n",
    "\n",
    "hypothesis['mean_diff'] =  hypothesis['Average Travel Time_x1'] - hypothesis['Average Travel Time_x2']\n",
    "hypothesis['var_x1'] = hypothesis['Variance_x1']/hypothesis['sample_size_x1']\n",
    "hypothesis['var_x2'] = hypothesis['Variance_x2']/hypothesis['sample_size_x2']\n",
    "hypothesis['t-test'] = hypothesis['mean_diff']/(np.sqrt(hypothesis['var_x1']+hypothesis['var_x2']))\n",
    "hypothesis['p-value'] = st.norm.sf(abs(hypothesis['t-test']))\n",
    "\n",
    "hypothesis[['x1', 'x2', 'sample_size_x1', 'Average Travel Time_x1',\n",
    "       'Standard deviation_x1', 'sample_size_x2',\n",
    "       'Average Travel Time_x2', 'Standard deviation_x2', \n",
    "       'mean_diff', 'var_x1', 'var_x2', 't-test', 'p-value']].round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the p-values are less than 0.95. This means that we reject all null hypothesis. Even small differences in travel time (such as those between strata 4 and 5) are statistically significant. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Work-Home travel times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "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>Strata</th>\n",
       "      <th>sample_size</th>\n",
       "      <th>Average Travel Time</th>\n",
       "      <th>Standard deviation</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>918421</td>\n",
       "      <td>28.440505</td>\n",
       "      <td>12.256253</td>\n",
       "      <td>150.215573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3021902</td>\n",
       "      <td>23.547812</td>\n",
       "      <td>11.512400</td>\n",
       "      <td>132.535313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2129308</td>\n",
       "      <td>18.620950</td>\n",
       "      <td>10.504063</td>\n",
       "      <td>110.335278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>307540</td>\n",
       "      <td>17.039316</td>\n",
       "      <td>10.152666</td>\n",
       "      <td>103.076285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>30796</td>\n",
       "      <td>16.855643</td>\n",
       "      <td>10.161819</td>\n",
       "      <td>103.259209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Strata  sample_size  Average Travel Time  Standard deviation    Variance\n",
       "0       1       918421            28.440505           12.256253  150.215573\n",
       "1       2      3021902            23.547812           11.512400  132.535313\n",
       "2       3      2129308            18.620950           10.504063  110.335278\n",
       "3       4       307540            17.039316           10.152666  103.076285\n",
       "4       5        30796            16.855643           10.161819  103.259209"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_1 = HW[HW.ESTRATO == 1].travel_time_WH\n",
    "x_2 = HW[HW.ESTRATO == 2].travel_time_WH\n",
    "x_3 = HW[HW.ESTRATO == 3].travel_time_WH\n",
    "x_4 = HW[HW.ESTRATO == 4].travel_time_WH\n",
    "x_5 = HW[HW.ESTRATO == 5].travel_time_WH\n",
    "\n",
    "distributions = [x_1, x_2, x_3, x_4, x_5]\n",
    "summary_table = pd.DataFrame({'Strata': [1,2,3,4,5]})\n",
    "\n",
    "summary_table['sample_size'] = [len(x) for x in distributions]\n",
    "summary_table['Average Travel Time'] = [x.mean() for x in distributions]\n",
    "summary_table['Standard deviation'] = [x.std() for x in distributions]\n",
    "summary_table['Variance'] = [np.var(x) for x in distributions]\n",
    "summary_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are similar to those obtain in the Home-to-work travel times, however, it is important to notice that the difference among low and high strata is higher, 12 minutes instead of ten mins difference in the morning trip. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C+Ilz7nCCsv81hPqjRYb6HWdmI51G4ccu80M4f+acSzRcJ5wY+q1xS+UYMyvFB37AByFviVfWOfcSA8PDKvBB3ET+J8Fzf8b/ToDY+/XIMM55I5hQ+dsuztBA55zDD5WNiPUZfy/4a/hhh7F8KHT/B2m30AsPcR3ypBfD8L8ZrOtvwd9yRue9pOpu51x1rCecH/63N/RQrKF+Ecl+z4jIMChnk0juSTQjXeT/p51znaHH1+ADM4YPLv0awMyOx189hzj5mjj6JOnBRA1zzrUGOaJehx9qcQKDc1NEPB7n8bjM7FR8V/3p+GFVH3TO/TTdekZJDfCRFMuGc5/Ekq3PJNksaOEcRU/FLXV0uUnhJ4JgVyRQcgg/O1ySxR6ZTW6xmRWmcJKfSSlvp+ZnmnsLPk/EqfhhGhOIPbPrRDMrd861RT3+GL6nwVzgnWb2iRjv9wr8dxn8ld5o54TuV5rZW5I0PTxz41KG8N0cZc3OuS0Jnk91uw2XzcN/X8LBipFer2eE7v8pSdm/44PK5WkuI+whfD6jZcBD5mfpejjqWJIta7JQZ7J19iQD6+yMJGVzyakMBCEfdYNzv0V7EHhfcH8VvjdPLM3OuXh5f3DOdZhZA3543qQYRR7C966eBvzZ/AyZf3DOtSRp33Ak+4zDz8f6jB/CJ/w+BviAmX0xfBHFzIoYuDiyF//7YyjW4C8CFgM3mJ9B8mfOuQ1DrC8du51ze5MX88zsFfhjzFn4nnEVDGxv0ebih7rnolR+z8wL7g/p94yIDJ+CTSI5xjm3zcz24WdfWmZmU5xz9cHTkV5Kf456TYOZbcJfhTqXINjE0cmbH4uzyPAJ09YUmriVgQSgs4gf2NiXQl1hefj3VYHvEfMO59y9adYRUxAASTRFdKtzLqWpthNoc879LsX2TE1SJFufSX2cxyPCV5ATlQ2XK4l6rgrf0wN8V/a7kiwz2iSGlkx3qFLaTs1sPj4J66lp1F3JQK8zAJxPXPsLfAL2qfgeDL+Pet0Vwd82Yp84Lgzd/3oa7YGx+WM6U9ttdNnobXdh6P5IrNfwNPPbExUMtpuX8IGiofoU8Er8BYhzg1uXmT2N7x32J1ILbgxFuseDVKSzzqabWX6C3pq5ZCj7/1ivjZbsuwED34/o7wbAjfhE+yfg94O/AnrNbD1++3kUeCDDwctkn/EhM2vFB/1nx3jemdkP8DnWZgGX4ocoRrwVvx8G37uwnyFwztWa2b/iexgV4hO7X2tmB/E9hdbgA3ODJiXIgFSPYcX4XEaXp1F35ZBaNDJS3df3x7nQGl0OYm/3IjIMGkYnkpsiwaRIT6XIrHLTg8djDYeLPBbuDRW+/1icZYW7w0f3woglPONQoq70QxkykR/8LSCzB/18fNAj3u178V86KrL1maTzQ3pIP7oZmNFnqIqSF8mopNtpcPX7QQYCTbX4PEGfxAeG3o4/aXkrRweC84kt7lA6MzuNga78d8XoGQXDW8cjvX4zYSS2Wxj59TohdD+VmeZS2RfE5ZzbAawA/g8/Ax34nhhnA5/Gb+PVZvbxJMNxhyIbM4imu84mxC2VW3Jh/z+Ic64B33PqqwxcECjAz0D3L/hg/EEzuz7GENUhLjKloZeRdRTv870FnxsNjh4yF/6/Hx+IGTLn3HeA8/FBNxc8PAN/XPhvYIuZrTGzlcNZTgypfre+x0CgqQt/IeNz+F5xlzFwDAsPOYt3DMsFqW7PLnkREckW9WwSyU1/ZuBHwWvwAZFI4Cgy/Xq0v+CHcZ1oZtOcc7Wh18TL1wR+ppuIVIZohH/QZbL7fD8+h8nv8Yl6f2lmOOdWZ3AZY0WufCZDET7xedg5l8lpsEfLlfhZz8APs3h7kP9nEDN7X6zHw5xzG4PeACuAN5pZpXOuObSsiNsGvxo4eh3Pdc5lo9fIy9FIr9fw8spSKD+cIXQAOOcOAh8zs0/ghx29Eh9sei0+UDEDPwvlUvxMprks3XXWGrdUciN5cTZn9//BfupzZvYF/P7rVQxsP1PxPWG+hN+2Lh3m4szMSlMIOEXWUczPN+j9dBfwLuBiM5vjnNtnZscE7QbfI2vPMNuLc+5R4NGg9/I5wCvwv8POwF88PBv4q5ld4JzLxtDSmMxsMQNDLXfjJ5jZFafsgpFqVwzqBCEyzuhLLZKbHgvdf03U32ecc7F+VIWDSa8OEm1HupXHy9cEEE4+fGwKbQuX2Z9C+ZQ55x7BJ8duxwfDf2lml2Wg3l7nnCW4LRnuMjIsZz6TIWhkoMdBLicXTUc46e4n4gWaAqn+UI/kYirF94yK5IR6d/B4DfHzlYSDIONlHeeCkV6v4e9qwn2QmeXh86tkhHOuxzn3N+fc151zb8Hn4fkQAz1APmJmSzO1vCxJZ50dijGELjJ8Jj/47iWSbOhzJuX8/t851++ce8Y597/OuXfhh2ZeBkSS3F9iZhfFryFlyT7j6QwE2xK990jv5XzgA8H9qxjIjTfUxOAxOefqnHN3Oec+5ZxbhR9SHknkXkRmEv6n43wG3utX4wWaApkONoWHqSXrATqS3zMRGQEKNonkIOfcVgZ+cJ5sZpMYCDbF7KHknNsP7Aj+PZfU8jXB0YkTE/ZCMbNy/JVw8MMwMp5/ILgyeClHB5zekenl5Lic+kzSEeS8iCRKnmVm8WZly6Rwd/pMD/8B39sjYke8QmY2Czg5xTp/AUROfiO9mc5nIOfKLxLklwkP1cuVWbay/RmMhJFer+Hv+WvjlvLOIgM9m+JxznU5536EH2IX8aqoYrn2GSdbZ2cysM5izcIZnv0vbq6jINnz4iTLyuS6eZaBSRPOSyEQFp7FM1mC/KxwzvU5534LXB96+Ow4xdOR7DMOPx9zplUA59xjDBwbrwqGRr8/+L8GyEh+yATLr8YnIq8NHjozaENYZBsatWNY4PVJnk93Ww9/zwbl1TpSkZ8h8/QU6hORMUTBJpHcFTnxycNfcZ4Z9XgskW7ZryG1fE3gf2RFrmZ/IEny6o8xcBXxrmDq4YwLfhhegs/FUAD84mUWcMq5zyRN4RkEvxr0MMiaIMAVGWqRjRPycE+mRCednyPFHBfOuQMM9Fw618zmkNoQOvDbR0Nw/wNmdkIqy8yycG/LrAVFsmyk1+u9DAQV/tHMEuWM+tcstyViV+h+dKqFXPuM3xcEguL5ZOh+rET74ZnZEgU1riH57+WMrZtg2Ngfg39nAO+NVzYY8vSu4N8W/Oxro2lX6H4mUnV8LEZQBjgy8ccnQg/Fm4Uv4vvB34XANxkIfNySpaT4RwlmHY30njQGHysi21CemWU6UXVKxzAzezsDOQPjSXdbT/V7dgUwOYX6RGQMUbBJJHc9FrofOdEI9xqJJdLraRl+lqvIa+Lla8I5V8NAcGAycIeZDUoyamYXAv8e/NsLfCNBO4bNOfdnfA+ncMDpndlcZq7I1c8kDb8CngnunwfcZmZxk/OaWamZfWCYn+9Lwd8Tg1l3Mil8xfzLsYJnZvZRfOAvHZGhdHnABxnoTfOCc259vBcFU43/R/BvMfBHM0s4S56ZrTKz/0yzfemoZeAkJJ0Z+3LGSK/X4Ht+e/DvdPz3ZNBJppn9C/C2oSwjVMdcM7spUT6WoJdkOLARPeX5S6H7pw2nPRkyCX9cGJS7ycyuASIXKPbj90nRwtPcfz5WsM/M3ogPIieT6XXzXwwkNr45mK4+um2TgdX4obgA3wm24Ywzs3wz+14wUUm8MgUcnYA7evsZimOB7wd1h5dlwNfwCcvB9wZ7NEldt+JnuoWBfGQO+NFwG2lm/2Bm7zOz0gRlXsVAz9etMXJRZfP7FT6GfcrMJkYXCLaxH6ZQV7r7+gcZ6A31cTObF2PZq4CbU6hLRMYYJQgXyV2Phe5HZqF73jl3OEbZiEhQyRgY+54oX1PEtfgrTouDv5vM7CfAZnwS1guBdzLQZfrzzrmNqbyJ4XDO/dnMLgH+gL+CdnuQNPzX2V52DsjJzyQVwZTjbwOewA9PuRy4yMzuwAehDuPfw3z8TEYX4D/fzw5jsX/CX5GtAH5vZj/DT40cOWH7e5LvTiI/xs/WVYbPS7LWzG4HqvE9Dt+OTwa7H//5nJ9ivXcC38W/988xkM8iUa8mAJxzNwezGl2Bv1K/1sz+CDyCv3pu+Dw8y/HrdxF+GMlnUmxbWoIpxh/FJ/k/zsx+hZ+dKrzOH3XOdcWsIEeMwnq9Dv9dno1fdxvM7Bb8lO9T8NvW+fihL+3BcoeiJFjWtWb2NL4X7Bb851OFn87+cgZ6ezzqnHsiqo7n8N+pKcD7zaweP2wrctLcNpJJj/Hb11uAjWb2Y/w6m4QPzEWGH/cDV8fKs+ac+6uZPYFP4nwc8KyZfQ9/0j8FuBh4E/473UPiIbJ/wQf8C4BPB8GQDQz0UK1zzq1N9Y055x43s6/jP7NK4C9m9gv874JO/AWlDzLw22A9Rw9hyzQD/gn4JzPbGLRjI74nYDlwDPAeBnIsbcFPbDIcffgeXu8HTjeznwJ78fvcyxkINHUCVyXr1eucazSz3+CHs0X8yTm3c5jtBD+BxOeB75jZQ/jgzh785z8d39P8zQxc5P9KjDr+xEAQ7BYzuzmoIzKceusw2roGv42swH9Wm83s+8BWfLDyfPzvCfBDvC+PVQmkv693zu0Njv3vwf8uXWtm/4fv8VSB/33zLvy+5TGOTgEhImOdc0433XTL0Rs+b5ML3f4nhddUR73mmykuaxb+xMEluHUDn0pQxwWhsl9IcbmPB+V7E5Q5Bz9EwOF/0L9ztD+boF1LQu93exqv+2DodT/Klc8E39MmUnZugnIFoXIPJyg3m4FpoJPdeoH3D+OzmAfUJaj/7GFup2/Dn9TEq38PPt9ESuswVO9tUfX0pfK64LWGn/kpUbvCt0GfVaqfZYrtOR0feIi3/Lmhskk/Awb2ZQm/W5nexjOxXtNcbyfgZ4iKV/9ufJAp4b6So/crV0Y9tzjF9+KAh4FJcZbx0QSv2x5VNt3vQtJtMarOxcDdCdrTBXwgyTIX47+78erYFJRJ5Th1U6rbSBrb4Y34fWOiz+uReJ9XOt+jRGXxQ75S3X6eBeYP4/twZF3je/b+LcGymoCL0qj7lVGvz8hvCeCGFNdNF3Btgu0/0Xv9QlTZtPY/+IBq9O/D8K0DP2Nd3P1IqK6U9/VB+an4YHW88vvwF54Sfi9SaVus7ShJufDvuLi/x3TTTbeh3TSMTiS3RednijscLiT6yvJjqSzI+Rwyq/BXn36HP/h3A834K5jfBE50zt2USn2Z5PzV8kvwXbfz8UMn3pX4VWNfLn8mqXDO7XfOnYe/cvkD/InbYXxApTn4/1f4K+ZznXO3DmNZe/FDD74FvIDfVtxw2h9V/534H9iRq+s9+Cux64AvACucc+uGUHV0L6Y/O59MNpU2Oefcv+OvVH8J/90/GLStI2jnQ/gToVXOuQvi1ZUJwftfie8JtpWj84SMGSO9Xp1zW/Cz3/0b/oSsFR9c34AfJnuac27DMJexAz/L1D/it7mN+BP1PvxQ5a34Hg1vcM5d4OL0hnXO/R9+X3w3/sR1NHuqdeF7Nr0XH3A5hN8/7sFvg6c4525JVEGwXlYA/4nvwdSB3zc9g+/NeEZQJinn3KfwPeIeYGB7GbJgO/w8cArw7aB9Lfj3vRc/hO6tzrnXxvu8MsX5yQoiPYp+hA8oNeK3nw58rqY78ceqlc65PRlabgO+V9A1+J6y9fj3vx34H/zx7/74NQzydwaGgNXhj6uZ8O/4HnKfx3/+u/HB6l58768n8dvYic65r8eqwPm8Uefje7n+nYH1mxHOTzyzAj/8cHPQvhZ8L7T/BU51zv00fg1H1ZXWvt45V4dfP5/D97BqC24vAF/Gf1dT7vknImOHOZex3+IiIiIiIiI5x8wuYiD5+jecc9eOZntERMY79WwSEREREZHx7iPBX4fvbSsiIlmkYJOIiIiIiIxbQeL/Nwb/PhAMKxMRkSzSbHQiIiIiIjJuBDMCvh6f5/E44FP4pOsOn2NJRESyTDmbRERERERk3DCzAmInaf9v59y/jnR7RERejtSzSURERERExqtW/Kxp3wESzlAoIiKZo55NIiIiIiIiIiKSMUoQLiIiIi2rAjoAACAASURBVCIiIiIiGaNgk4iIiIiIiIiIZIyCTSIiIiIiIiIikjEKNomIiIiIiIiISMYo2CQiIiIiIiIiIhmjYJOIiIiIiIiIiGSMgk0iIiIiIiIiIpIxCjaJiIiIiIiIiEjGKNgkIiIiIiIiIiIZo2CTiIiIiIiIiIhkjIJNIiIiIiIiIiKSMQo2iYiIiIiIiIhIxijYJCIiIiIiIiIiGaNgk4iIiIiIiIiIZIyCTSIiIiIiIiIikjEKNomIiIiIiIiISMYo2CQiIiIiIiIiIhmjYJOIiIiIiIiIiGSMgk0iIiIiIiIiIpIxCjaJiIiIiIiIiEjGKNgkIiIiIiIiIiIZo2CTiIiIiIiIiIhkjIJNIiIiIiIiIiKSMQo2iYiIiIiIiIhIxijYJCIiIiIiIiIiGaNgk4iIiIiIiIiIZIyCTSIiIiIiIiIikjEKNomIiIiIiIiISMYo2CQiIiIiIiIiIhmjYJOIiIiIiIiIiGSMgk0iIiIiIiIiIpIxCjaJiIiIiIiIiEjGKNgkIiIiIiIiIiIZo2CTiIiIiIiIiIhkjIJNIiIiIiIiIiKSMQo2iYiIiIiIiIhIxijYJCIiIiIiIiIiGaNgk4iIiIiIiIiIZIyCTSIiIiIiIiIikjEKNomIiIiIiIiISMYo2CQiIiIiIiIiIhlTMNoNGAlTp051CxcuHO1miIjknHXr1tU556aNdjtGm44TIiKx6TihY4SISCLxjhMvi2DTwoULWbt27Wg3Q0Qk55jZ7tFuQy7QcUJEJDYdJ3SMEBFJJN5xQsPoREREREREREQkYxRsEhERERERERGRjFGwSUREREREREREMuZlkbNJRMaenp4eqqur6ezsHO2mjAslJSXMnTuXwsLC0W6KiEhG6DiRWTpOiMh4o+NEZqV7nFCwSURyUnV1NRUVFSxcuBAzG+3mjGnOOerr66murmbRokWj3RwRkYzQcSJzdJwQkfFIx4nMGcpxQsPoRCQndXZ2MmXKFB0YMsDMmDJliq7qiMi4ouNE5ug4ISLjkY4TmTOU44SCTSKSs3RgyBytSxEZj7RvyxytSxEZj7Rvy5x016WCTSIiIiIiIiIikjHK2SQiY8IvntyT0fouXzV/SK+7+eabufrqqykrK0vrdbfeeisXXnghs2fPHtJyE/nNb37D9ddfz+bNm3nqqadYuXJlxpchIpLrdJyI77rrruP3v/89RUVFLF68mFtuuYWJEydmfDkiIrlMx4n4/u3f/o27776bvLw8pk+fzq233jrs5ahnk8g4t6uuja/+YTMfu/0ZrvjR39ld3zbaTRrTbr75Ztrb22M+19fXF/d1t956K/v3789Km5YtW8add97Jq1/96qzUn01mdpGZvWhm283sMzGeLzazO4LnnzSzhcHjZ5rZ+uD2nJm9NfSaXWa2IXhu7ci9G8mG6+95gTXbake7GSIpy8XjxOte9zo2btzI888/z3HHHcdXv/rVrCxHJJPW7mrg2t88R29f/2g3RSSjcvE4cd111/H888+zfv163vCGN3DDDTcMu04Fm0TGsc6ePv7x1qf5yV9fYvOBZtbtbuSm+18c7WaNCW1tbVx66aWccsopLFu2jDvuuINvfetb7N+/n/POO4/zzjsPgAkTJvDFL36RVatW8cQTT3DDDTdwxhlnsGzZMq6++mqcc6xevZq1a9dyxRVXsGLFCjo6Oli4cCF1dXUArF27lnPPPReA66+/nve9731ceOGFLFy4kDvvvJNPfepTLF++nIsuuoienp5BbV26dCnHH3/8iK2bTDGzfOA7wMXAicB7zOzEqGJXAY3OuSXAN4GvBY9vBFY651YAFwHfN7Nwb93znHMrnHPq5jWGNbR1c+vfdvHzv+8e7aaIDDKWjhMXXnghBQV+F3nWWWdRXV09MitJZBj+sKGG1euquff5A6PdFJEhGUvHicrKyqPanYlcVwo2iYxjX3/gRXbWtXHL+8/kkWvP5epXL+a+DQfYUN002k3Leffffz+zZ8/mueeeY+PGjVx00UX88z//M7Nnz+bRRx/l0UcfBfzOeNmyZTz55JOcffbZXHPNNTz99NNs3LiRjo4O7r33Xi677DJWrlzJ7bffzvr16yktLU247B07dnDfffdx9913c+WVV3LeeeexYcMGSktLue+++0bi7Y+UM4Htzrmdzrlu4FfAm6PKvBn4aXB/NXC+mZlzrt051xs8XgK4EWmxjKgtNc0ArN3ViHP6iCW3jNXjxE9+8hMuvvjijK0HkWzZ0+B7fnzn0e309+sYIGPPWDtOfP7zn2fevHncfvvt6tkkIvGt3dXAj//6Elesms/Zx04F4EPnLGJSWSE3PbBllFuX+5YvX87DDz/Mpz/9adasWUNVVVXMcvn5+bz97W8/8v+jjz7KqlWrWL58OY888ggvvPBC2su++OKLKSwsZPny5fT19XHRRRcdadOuXbuG9H5y1Bxgb+j/6uCxmGWC4FITMAXAzFaZ2QvABuDDoeCTAx40s3VmdnW8hZvZ1Wa21szW1tZqmFYu2nygBYD6tm521mkIsOSWsXicuPHGGykoKOCKK65Ie5kiI21vQzsTigvYdqiVhzYfHO3miKRtrB0nbrzxRvbu3csVV1zBt7/97bSXGS2rwSbl4hAZHc45PnfXBuZMLOWzlyw98nhFSSEfPXcJa7bV8bcddaPYwtx33HHHsW7dOpYvX85nP/vZuNH9kpIS8vPzAejs7OSjH/0oq1evZsOGDXzoQx+is7Mz5usKCgro7+8/8rqw4uJiAPLy8igsLDzSjTUvL4/e3l7GkVj9c6MvXcYt45x70jl3EnAG8FkzKwmef5Vz7jT88LyPmVnMZFbOuR8451Y651ZOmzZtaO9AsmrLgWYK8/0m8PRLDaPcGpGjjbXjxE9/+lPuvfdebr/9dk0FLjnPOceehnbeftocFkwp4zuPblcPVxlzxtpxIuLyyy/nt7/9bepvNI6sBZuUi0Nk9GypaWHrwVY+dt4SJhQfPenkP7xiAVMnFHP73zM7G8N4s3//fsrKyrjyyiu59tpreeaZZwCoqKigpaUl5msiO/mpU6fS2trK6tWrjzwX/bqFCxeybt06gIzszMeoamBe6P+5QHTWwyNlguNAFXBU1ME5txloA5YF/+8P/h4C7sIP15MxaEtNC6sWTWFyeRFP72oc7eaIHGUsHSfuv/9+vva1r3HPPfekPfvRaBvqxevQ8/PNrNXMrk21Thl99W3ddPT0sWhqOR9+zWKer27i7zt10UHGlrF0nNi2bduR+/fccw8nnHDCsOoDKEheZMiO5OIAMLNILo5NoTJvBq4P7q8Gvh3JxREqo1wcIml68IWDmMEFS2cMeq6kMJ9Viybz/L7Do9CyoRvq1KJDtWHDBq677rojVwO++93vAnD11Vdz8cUXM2vWrCPjrCMmTpzIhz70IZYvX87ChQs544wzjjz3/ve/nw9/+MOUlpbyxBNP8KUvfYmrrrqKr3zlK6xatWpYbb3rrrv4+Mc/Tm1tLZdeeikrVqzggQceGFadI+Rp4FgzWwTsA94NXB5V5h7gfcATwGXAI845F7xmr3Ou18wWAMcDu8ysHMhzzrUE9y8Ehj/oXEZcb18/Ww+28N5XLKCsKJ+nd43MSca63Q0U5eezfG7sru6Su3SciO+aa66hq6uL173udYBPEv69731vWHWOhNDF69fhLz48bWb3OOfC5xNHLl6b2bvxF6/fFXr+m8Af06xTRlkkX9P8KWWcNn8Sn71zAy/sb+IVi6eMcstkLNNxIr7PfOYzvPjii+Tl5bFgwYKMHCMsW90Rzewy4CLn3AeD//8BWOWcuyZUZmNQpjr4f0dQps7MVgE/ARYA/+Ccuyso8xLQiA9Afd8594M4y78auBpg/vz5p+/erZls5OXjkv9ZQ1lRPqs/8sqYz3/3sR187f4trP/i65hYVjTCrUvN5s2bWbp0afKCkrJY69TM1o1mL1EzuwS4GcgHfuKcu9HMbgDWOufuCYbG3Qaciu/R9G7n3M7gmPIZoAfoB25wzv3OzI7B92YCf0HlF865G5O1Y+XKlW7tWo3MziXbD7VywX//mW+84xQa2rq58Q+befJz5zOjsiT5i4eopbOHc256lLmTSrn34+dkbTmSGTpOZF6uHSfM7BXA9c651wf/fxbAOffVUJkHgjJPBD1ga4BpwYWJtwCvwvd+bXXOfT2VOqPpGDHy7l6/j3/51Xoe/uSrWTxtAid+8QEuXzWff3tD9EAZkfh0nMi8dI4T2ezZNOxcHMBJZrYU+KmZ/dE514nPxbHfzKYDD5nZFufcXwZV4oNQPwB/gBjOG5Gxrb/f8YW7N/LmU2az6pjxfzVkb0M7mw4087lL4nd9XD7HX7HfsK+Jc45VrhoZPc65PwB/iHrsi6H7ncA7YrzuNnwQKvrxncApmW+pjLTITHQnzKqgp88fxp/e1cAbTp6dtWX+7IndHG7voaWzl7auXsqLs/kzSURSEGsiiejL90dNJGFmTcAUM+sAPo3vwXRtrPIJ6oy+cD28dyFp21PvezbNnVSGmTFrYgkHmjpGuVUiko5sJghXLg7JCev2NPKLJ/fwf4/tGO2mjIiHNvnZOi48cWbcMuFgk4hILtpyoIX8PGPJ9AmcNLuS0sJ81mYxb1NrVy8/XLOTaRXF9PU7nts7toYai4xTw7l4/e/AN51zrUOoU5NIjLI9De3MqCympNAnTZ5dVcr+w7GTJItIbspmsOlILg4zK8Ln4rgnqkwkFwdE5eKIJASPzsVhZhXB45FcHBuz+B5kHPjds/sAeHx7HfWtXaPcmux7cFMNx8+oYOHU8rhlqsoKmT+5jI05HmzSrCOZo3UpY82WmmYWTyunuCCfwvw8Tp0/Mat5m372xC4Ot/fw3+/0HePW7VZC8rFA+7bMydF1OZyL16uAm8xsF/AJ4HNmdk2Kdcoo29PQzvzJA8nsZ1WpZ5MMTY7u28akdNdl1oJNzrle4BrgAWAz8Gvn3AtmdoOZvSko9mN8N9ftwCfx+TcAzgaeM7P1+N5LH3XO1QEzgMfN7DngKeA+59z92XoPMvZ19/Zz34YDnDirkr5+xx831ox2k7Kqoa2bp15q4MKTBicGj7Z8ThXPV+dusKmkpIT6+nodIDLAOUd9fT0lJdnLdSOSaZsPtHDCzMoj/y+dVcnO2ras7BM6e/r44V92cu7x0zjn2GkcN2MC6/Yo2JTrdJzInBw+Tgz54rVz7hzn3ELn3EJ8bsCvOOe+nWKdMsr2NrQzb1Io2DSxlEMtXfT09Y9iq2Ss0XEic4ZynMhqMgLl4pDR9pettRxu7+Eb7ziFr/5xC79/bj9XnrVgtJuVNX/bUUe/g9eeMD1p2eVzq7hvwwEa27qZVJ57ScLnzp1LdXU1tbW1o92UcaGkpIS5c+eOdjNEUtLc2cO+wx1ccdZAnpTZE0vp6OnjcHtPxvdZz+xppLG9h/e+wh8fTl8wifueP0B/vyMvL9aIm8G+dv8WntxZzy0fOJOq0sKMtk9i03Eis3LxOBHkYIpcvI5MJPFCeCIJ/MXr24KL1w344FHadWb1jUhaunv7OdDcybxQz6bZVSU4BwebO5kbCkKJJKLjRGale5xQ5ksZ1363fh+Tygp59XHT2LivmZv/tJWapk5mVuXclbuMWL/nMMUFeZw0O/mU3ZG8TRv352aS8MLCQhYtWjTazRCRUfBiTQvgezNFzJno99v7DndkPNj07B6fn+n0+ZP93wWT+eVTe9le28pxMypSquPhTQfZdqiVq259mtuuWkVpUX5G2yiD6Tjx8jDUi9dR5a9PVqfkjn2HO3COo4fRTSwF4ECTgk2SOh0nRlc2czaJjKrWrl4e3nyQS0+eRWF+Hm84ZRbOwX0bDox207Lm2b2HWTaniqKC5F/tZUFAKpeH0onIy9PeBj8L0YLQicacif7+vsOZz9nxzO5GFk8rp6rM90g6fcEkIPW8TR3dfeyobeW0+RN5Zk8j//TzdfRqqIeIyJDsCY4B86cc3bMJYH8WjgEikh0KNsm4tWZrLZ09/bzplDkALJ7mZzS67/nxmQOyu7efDfuaOHXexJTKV5UVsmBK7icJF5GXn4a2bgCmVhQfeWz2xOycaDjneHbvYU6bP+nIYwunlDG5vCjlYNOLB1vod3D1q4/h+jedxF+21vLYi+qyLyIyFEeCTXF6NonI2KBgk4xbL9W3AbBszsAwjLOXTGXjvuZxmVxwS00z3b39rJifWrAJYNmcKjYo2CQiOaautZvCfKOieGC0/+TyIkoK89jXmNlg0676dhraujltwUCwycw4bf4knkkx2PTCfr8fPWl2Fe8+Yz6VJQX8YRz3ohURyaa9De0UF+QxbcLABYcJxQVUlBRwQD2bRMYMBZtk3NrX2MGkskLKigZOVpbOqqS7r5+dtW2j2LLsiOQcOTV0dT6ZE2dVUt3YQUtnT7aaJSKStvrWLqaUF2M2kJzbzJg9sZT9GZ76OhJQOi1q33nagonsrGujqSP5/vGF/c1UlBQwd1IpRQV5vP6kmTy06SBdvX0ZbauIyMvBnvp25k0uGzRBw6yqEvarZ5PImKFgk4xb1Y0dgxIInjjb93LadGD89eZZv/cw0yuKj4xpT8WS6RMAxmXwTUTGrvq2bqZMGJwEfM7EUvYdzuyJxjN7GqkoLuDYYH8YERm+UZPCic2m/c2cOKvySHDs0pNn0dLVy5qtdRltq4jIy8HexnbmTSod9PisqlIOZPiCg4hkj4JNMm7tO9zBnIlHH6iOmVpOUUEemw+0jFKrsufZPY2smDfxqJ4AySye5k+uth9qzVazRETS5oNNxYMenzOxNOPD6J7Zc5gV8ycOuoIeGb5xqCVxsKmv37GlpvmoWUBftWQqVaWF43pCChGRbDnY3BVz5ujZE0tSugAgIrlBwSYZl5xz7GvsYE7UVZGC/DyOn1HBpv3No9Sy7Ghs62ZXfXtaQ+gAFkwpoyDP2FGrYJOI5A4/jG5wz6bZE0upa+2isyczw9Nau3p5saY55r5zeqU/0TnU3JWwjp21rXT29HPS7IH8gIX5eVwUDKXLVFtFRF4O+vodDW1dR+VriphVVUpda7eGKIuMEQXJi4iMPQ1t3XT09DE3RhfcpbMq+NPmQzjn0uoFlMvW7/X5mlakOBNdRGF+HgumlKUcbHLO0e8gP298rDcRyU31rd0xg01zQrMRLZpanrCOXzy5J+lydtS20u+guaNnUPnIycyDmw7y9tPnxq1j0wF/8eLEULAJ/FC6O9buZc22Ol534oykbREREWhs76bfHT0bacSsoLdTTVMnC6YkPgaIyOhTsEnGpX3BTBXRw+jAJ8X+9dpqDrV0MaMy9fxGuezZPY3kGZw8t+qox1M52SouyOeZ3YeTlp0/uYxP//Z5zlg4iZvffeqw2isiEk97dy8dPX0xh9HNDvbp+w93JA02pWJvML32vKj8fuD3jUUFeUknUHhhfzNFBXlHcuBFvGLxFCaXF/HbddUKNomIpKi2xfcmnZrwGKBgk8hYoGCTjEuRnB7Rw+jAz0gH/mr0eAk2bdzfzJLpEygvTv8rPa2imC01zfT1u5g9lpxz3LvhAE/sqAegd2f/sNsrIhJPfWs3QMKeTfsyNPX1gaZOJpUVUlqUH/P5iuICWjp7E9bxwv4mjp9RQWH+0ZkJCvPzuOz0ufzk8Zc41Nx5ZFieiIjEV9fqg03TEvRsUpJwkbFBOZtkXKoOgk1zJw6+Wr00MiPdOMrbtOVA85EgWrqmVRTT7/zQw1gONHXyxI563rVyHv/vguM42NzF4fbYZUVEhqs+2BfFmo1uZlUJZmQsSXhNcyczqwZflIioKClM2LPJOXdkJrpY3n3GPHr7Hb9ZVz3stoqIvBwk6tk0q2pgKLWI5D4Fm2Rc2ne4g4riAipLB/f0qSwpZO6kUjYfGB/Bpqb2HvY3dXLCzCEGm4KDeW2cGZf2BMNMrnntEk6Z54fpbakZf7P5iUhuqA+uascaRldUkMf0imL2Z6BnU09fP3UtXcxM0OOooiRxz6amjh4a23s4dsaEmM8fM20CrzhmCr98ag/9/W7YbQb4xoMv8tO/7cpIXSIiuSbSs2lqjAsOpUX5TCorzMgxQESyT8EmGZeqg5no4iUAP3FW5ZGkrmPdlhr/Pk6YVTGk10e6KUeuJEWrbmynvCifuZNKjwS0th5UsElEsuNIz6YYw+jA5+zIxDC6Q81dOAaGZcRSmSTYFLm6PitB76jLV82nurGDv2yrHXJbI9btbuR/H9nOl+55gYc2HRx2fSIiuaautZuSwjwmxEkNkaljgIhkn4JNMi5VN7bHTA4esXRWJS/VtdHenTgXx1gQ6WUUbxhHMiWF+VSWFHAoTrBpb0MH8yaXYWbMqCymqrRQPZtEJGuO5GyKcVUbfN6mTFzVrmn2dcxMEGyqKCmku6+f1q7Yx4qaINg0s2pwL6yI1580kynlRSlN2JCIc46v3b+FqROKWTankk/+ej2769uGVaeISK6pbeli6oTiuBeMF00tZ2et9n0iY4GCTTIu7TvcwdwYycEjTpxdiXOw+cDYD5psqWlmUlkh02MkUkzV1IpialsHB5s6uvuobe1ibjBTk5lx/IwKXlSwSUSypL61i9LCfMqKYl/VnjOxlP1NncMellbT1ElhvjE5Tg8q8MPoAA41xx5mXNMcCTbFP94UFeRx6cmzWLOtjr5htPnPW2t56qUG/vn8JXz3itPJM+OfbluXdLY8EZGxpK61K2a+pohjp1ewt7Gdju6+EWyViAyFgk0y7jR19NDS2RtzJrqIU+dPBODZPY0j1ays2XyghRNmVsa9ApSK6RXF1LZ04dzRJ0LVh4NpwScPrMvjZ1awtaZlUFkRkUyob+uO26sJ/Cyj3b391LXF7o2ZqgNNncyoLCEvwb6zoqQQIG7PzwNNnZiRNNi/Yt5EOnr62FHbOqS29vc7brr/ReZNLuXdZ8xn3uQyvvWeU9l2qJWrf7aOzh6ddInI+FDb0hVzJrqIY2dMwDmGvD8VkZGjYJOMO5FZiubEmIkuYnpFCfMnl7F219gONvX3O16saRlyvqaIaROK6ertH5SbZG/D4Fn9jp9ZQUtXL/s1E4iIZEF9W3fcfE0As4NeRMOZkc45R01zZ8J8TRDq2RQn2FTT1MHUCcUU5if+OXXyXD+5wvPVTUNoLWw60MymA81cc94Sigr8sl5z3DS+/o6TeWJnPf/8y2fp7esfUt0iIrkkec8mPyHD9kMKNonkuth91EXGsEjSwETD6ABOXzCJx7fX4ZwbVq+g0bSnoZ2Onj6WDnEmuohpFf6E61BLF5WlhUcer25sZ9qEYkqL8o88dsJMH9h6saY5YV4sEZGhqG/tYkaCGeLmTfbB7z0N7Zw6f9KQltHS2Ut7d1/CmegglWF0XUkDVgCLpk6gvCifDdWHuez0uWm396U6n59kxbyj3+9bT51LY1sPN9y7iV+vrebyVfPTrltEJNtSzVnX7xz1rd3UNHXGfc1lp8+lIM/YdkgpHURynXo2ybizr9EP/Uo0jA7gtAWTqG3ponoYV8dH23BnoouYXVWCAbtCyWadc+xtaD9qCB3AcUGwSUnCRSQb6lsT92yaHwk21bcPeRkHmpLnWgIoLcynIM/iztZZ09SRNGAFkJ9nnDSniuf3Da1nUyQReOS9h33gVQs5bsYEfvtM9ZDqFhHJFW1dvTgGAv2xFBXksXBqOdsOqmeTSK5TsEnGnerGDkoK8xKerACcHlwRX7d77A6l23yghTyD42YML9hUVlzAvMllRyX+bmzvoa2770hy8IjKkkJmV5UoSbhkhJldZGYvmtl2M/tMjOeLzeyO4PknzWxh8PiZZrY+uD1nZm9NtU7JXc45Gtq6mZJgCEVpUT7TK4rZ3TD0YNORxN5JAkVmxoQEs3XWNHUmnM0ubPmcKjbtb6ZnCMPddte3M6Py6F6m4Ta+9dS5rNvdqNnpRGRMi8z8OaE48eCb42ZMYJuG0YnkPA2jk3Fn3+EO5kwsTTo07viZFUwoLmDd7kbecuqcEWpdZm2paWbR1HJKCgefgKTrhJkVPLjpIM2dPVSWFLK3MZIcfPCV9ONnakY6GT4zywe+A7wOqAaeNrN7nHObQsWuAhqdc0vM7N3A14B3ARuBlc65XjObBTxnZr8HXAp1So5q6eqlu68/6cWCBVPKhtWzqaapg4mlhTGDN9Eqigt4vvrwoCEdXb19NHf2cjDBcI+wls5eunr72XawlRNnpzf0eXd9Owsml8d9/s0rZnPTA1u469l9fOKC49KqWyTXmdlFwP8A+cCPnHP/GfV8MfAz4HSgHniXc26XmZ0J/CBSDLjeOXdX8JpdQAvQB/Q651aOxHuRxFqD3KGJejYBLJlewf0ba+js6cvIb2ARyQ4Fm2TMivfj/vnqJsqK8lP68T+zqoS1Y7hn05aaFpbNqcpIXccHwaatNS2sXDiZZ3Y3Ul6UH/PK//EzK3l8ex09ff1JE+OKJHAmsN05txPAzH4FvBkIB4beDFwf3F8NfNvMzDkXjjSU4INMqdYpOaq+tRsg4Wx0APMnl/P49tohL6emOfUeSRUlhdS1Du7Z1NzhT4rCee4SmRvkuNu4ryn9YFNDG68+dlrc52dPLOWsRVP43bP7+Jfzjx2zeQhFomXjooRzLjIbynnOubqRezeSTEuKPZuOnT6Bfufz2S2dNby8pSKSPTpLlHGnsb2biWWJT1Qi5k8u48WaZlo6e7Lcqszr6O5jT0M7x00f3hC6iJmVJVSVFrKlpoXqxna2HWrl7CVTyc8bfNJy7PQJ9PQ59gxjGIsIMAfYG/q/OngsZpngBKEJmAJgZqvM7AVgA/Dh4PlU6iR4/dVmttbM1tbWDj1wIZlTHwR1Eg2jA9+z6WBzF509fWkvo7evn9qWrpRyLYG/wh49UydATtkGfgAAIABJREFUc3DcSDXYNHlCke8lte9w6o3F7+sPNnexYEr8GVYB3nraHHbVt/Ps3vTqF8lxRy4gOOe6gcgFhLA3Az8N7q8Gzo9clAgFlsIXJSRHRXo2JQ02zfAz0mkonUhuy2qwSbk4ZKR19/bT3t3HpLLUfvwvmFxGv4Pn9g4taeto2lnXinOwJJgCdrjMjONnVLC9tpU/bT5ESWEeq46ZErPsoml+OMeuOuUHkWGJ1f0i+mQgbhnn3JPOuZOAM4DPmllJinUSvP4HzrmVzrmV06bF7zUiI6e+LejZlMIwOmBIAe9DLV30O9Lo2VRAR0/foFxLTR0+2FSVYrApz4xlc6rYUJ3e8SbyHhdMiT+MDuDiZTMpLsjjd8/uS6t+kRyXjYsS4I8LD5rZOjO7OtaCdUFi5LV29VKYbxQVJD5FXTS1nDyD7QeV0kEkl2VtGJ1ycchoONzuT1RS7dk0b3IZZvD3nfWcfezUbDYt43bU+kBPpoJN4IfSPbWrgRcPtvDaE6bHHQe/KDjpeUnBJhmeamBe6P+5wP44ZarNrACoAhrCBZxzm82sDViWYp2So/64oQaANdvqeD5BUGZvEIC57YndaQ+hOJIcPI1hdOCvuE8KBcGag2BTZUlqwSaAk+dWcctfd9Hd25/0ZCoiMktosp5NFSWFvHLxFJ7c2ZCwnMgYM+yLEsBJZrYU+KmZ/dE51wm8yjm338ymAw+Z2Rbn3F+OqsC5HxDkfFq5cqV6RY2A1q5eJhQXJB0KXFyQz8Ip5erZJJLjstmzKRvdXlOpU17GGtv9j/9UezaVFOZzzrHTuGPtXrp60x+OMZp2HGolz5KfgKRj8bQJFOT5K0qvXBy7VxPApPIiqkoLFWyS4XoaONbMFplZEfBu4J6oMvcA7wvuXwY84pxzwWsKAMxsAXA8sCvFOiVHRWYiKk+SuHtyEPSJ9IRKR01TJwV5xpTyxEP1IiKJaqOHWzd19FBamJ9y0Ahg2Zwquvv62ZrG1fjIDHOJEoRHnDi7kh21rWPueCaSQDoXJUh0UQKIXJTAObc/+HsIuAt/jiGjrLWzN+kQuogl0zM3I91tT+zin3/5LN296c8WKiLxZTPYpFwcMuIOd6TXswngQ+csorali3vWj63ODztqW5k3uSyjs3AUFeTx2hOmc+myWZQVJT7YL5xafuSKu8hQBPv1a4AHgM3Ar51zL5jZDWb2pqDYj4EpZrYd+CQQGT59Nr7X63r8icJHnXN18eocuXclw9HW3UtJYR4FSSYeKCvKp7ggj4a2wYm7k6lp6mRGZUnMfHSxRHo2NUflbWru6El5CF3E0lk+x156waZ2JpYVUpXCRZSlsyrp7XdsO6ir/TJuZPyihJmVm1lF8Hg5cCF+VIWMoP2HOwYF8Vu6eo7sc5M5dsYEdtW10TiEiw5hbV293PTAi9zz3H6+8LsNOKdObCKZks1gk3JxyIg73N5DvlnSKVPDzl4ylRNmVvDjx18aUweY7YdaWTwtc0PoIs49fjpnLJqctNwxU8vZVacE4TI8zrk/OOeOc84tds7dGDz2RefcPcH9TufcO5xzS5xzZ0ZmmXPO3eacO8k5t8I5d5pz7neJ6pSxobWzl/IkgW7wOeamlBfRMISTjAPNnSknBweojNOzqbmzl8rS9LIRLJhSTkGepXU1fnd9e9J8TRGRIYWbDzSn1S6RXJWNixLADOBxM3sOeAq4zzl3/8i9K3lmdyPfeXQ7v3/u6Au96fRsunjZLPLyjH+6bd2wenPe+Uw1LZ29XHjiDH69tpqf/HXXkOsSkaNlM9iUjW6vysUhCTW2d1NVVkheGtM+mxlXnb2ILTUtrNk2NmbA7et3vFTXltF8TelaOKWc/U0dQ5oNSkQklsPt3UxMcRj05AnF1LemF2xq6eyhras35XxNAOXFBRTl51EXFdhq6uhJK18TQGF+HoumlqfV82h3QxsLJqc2XHrhlHJKCvPYpGCTjCOZvigRpOM4JbidpIsSI+vJl+pZ/Uw1eWbsrGujP7jQ29fvaO/uY0KKF4yXzaniG+84had2NfCp1c8P6YJxf7/jlr/t4uS5VXzvytO56KSZ3HjfJnbUqneoSCZkM9ikXBwy4g639zAxzWENAG9aMZtpFcX8cM3OLLQq8/Yf7qCrt5/F01K72p0NC6eW4Zy/6i4ikgn1bd1MTjGX0pTyIg639xw5UUlFTVN6ycHBzyI3taKIupaBIXu9/f20dvWmPYwO/NCPVE9kunv72dfYkXJuvvw84/iZlerZJCI56UBTB3ev388JMyt4wymzaO/uozbYt7Z19+Ig5Z5NAG88ZTafuuh47l6/n2/9aXva7fnLtlp21rbxgVctJC/P+OwlJ9Dv0EQLIhmStWCTcnHIaDjc3s2kNPI1RRQX5PPesxawZlsdO8fA1YztQRuzMYwuVYumakY6Ecmcls4e2rv7jiT/TmZyeRF9ztHU3pO8cODITHRpDKMDmDahmNrWgWBTS5C/qXIIwaYl0yvYXd+WUq/QfYc76HekPIwO4MRZFWw+0DKmhoWLyMvDruAC5ZtOmc2x030Ou8jvyEjQKd0g/kdes5i3nzaXbz68lQdfqEnrtbf8dRfTKoq5dPlsAOZPLmNSWSHr9zamVY+IxJbNnk3KxSEjqrevn+bO3pSHYER71xnzKMgzfvnUngy3LPN2HBr9YNPCINikJOEikgl7GvxJSDrBJkhvRrqapk4qSwooT+PKOcC0imIOt/ccmamoucMHuIbUs2n6BPpdaoH6yP41nVlHl86qpKmjhwNBLy4RkVyx/3AH5UX5VJUWMqmskMqSgiP7wuf2HqYoPy/t37Zmxo1vXcYpc6v4f3esZ1uKEzDsqW/nz1truWLV/COzipoZp8ybyHN7m9J7YyISU1aDTSIjqSn48Z/OTHRh0ytLuPCkGfxmXXXO5yHaUdvK5PIiJqV4UpYNlSWFTJ1QxC71bBKRDNhTn16waUpQLp0k4TXNnWkNoYuYVuFfUxf0bjocHG/SzdkEHMm1l0qS8Mg6STfYBEoSLiK5Z//hDmZPLMXMMDMWTS1nV10bPX39bNzfxEmzK48EftJRUpjP9/7hdEqLCvjX3zyXUs/O36zbixm8c+W8ox4/Ze5Eth5qobWrN84rRSRVCjbJuNEYDKWYNMSeTQBXrlrA4fYe/rjxQKaalRU7DrWxZBR7NUUsnFLOTgWbRCQDIj2bpqQYbKosLSQ/z2ho+//s3Xl02+d54Pvvix0gCALcKS4SJWq1ZMu2bHmJ4zipE7uTxNMmmdhO4zbNTDqZ5J729qb3JHMnOWnGPU07cyd3pul04jZN3GTcpHU2p7HrxHXseLckS5Zs7Rv3nQAIgtjx3j8AUFwAEgRJYXs+5+iIAn4AX8oWXuB5nyWy8sWk+iyNTUdoddlXvbZGZ2pNmVK6IW8Io0HR4Fx9wL+7sQaDgvN5nL73Tc1iNxtpcubXxwpgV2uqNEWCTUKIUhJPJBmdDrPJfeU1uLvRSSAS56XzE4RjSfZ3uQt+/rY6O3949w6OD/g5dHn5MrhEUvP4kQHeub1pwXoA9ne50RreGsyd3fTMyVFODslrrBArWV0euRAlzDebOt0uNLMJ4NZtDWxtrOG7r/bxG9d3rNfS1t2F8Rnee01LsZfBlsYafnV2vNjLEEJUgN6pWRwWIzazMa/rDUrR6LQwFsgv2DQ2HSGhdUGZTY1OKwrmmoRfngzS4bZjNhZ2Ar+5oSavzKYB7yztnlQWQL5qbWa66h2cGs6vlEQIIa6GkekwSc2iYFOqJcMvz4zhspnW3B7iN65v58+fPs3fvniJm7vrc173q3PjDPvDvGtnM4+9trB9RjCd0fTtly5zcXzpgWpSa77yTycxGxSfuatn7nPHgwe71rR2ISqRZDaJiuGdjaEorIdGhlKKBw92caTXy/mx0nyj7g1GmQxGi9qvKaO7sYaxQGRuYxZCiEL1T83mXUKX0eqyzU2YW0mmL8iWVZSkZZiNBtwOM+MzkdSEOF9oVU27F+tpduYVbBr0hWh3rz4Ta3dbrWQ2CSFKypAv9Vo9/zWt0WnBaTURS2iu63BjWEVgPRu7xciDN3fx85Mj9E/lnpb8D4f6qa+xsLutdsl9NVYT9TUW+r3ZHz86HSYaTxKMJnjs9T5iieSa1ixEJZNgk6gYvtnoXFnFWrxndypj6I1e33osa91dKIFJdBlbGqRJuBBiffROFhBsqrPjC8UIRVfus3dxIkh9jaXg7NemWivjgQgD3lmSGrY0rj5olbG92cnlieBcw/FcBr0h2j2FBJtcXJoMSs8RIUTJGPKFsJkNC9pdKKXmBs6spYRuvodu3YJBKb798uWs90/ORHjm1Ci/cX07JkP2j8IdHjsD3lDW+/qnUrffu7eVAW+Ifzpe2q03hCgmKaMTFcM7Gyt4Et18m+sdOCxGTpboqXApBZsy6c+XJoJcs6muyKsRQpSreCKVLbS1cXXZQq2uVEncyHR47vUom6TWXJqYYe8aXqeanFYuTQS5NBFEAZvrC89s2t7iJJ7U9E4G2d6y9GQdYDYaxzsbKyiz6cbNHrSGQ5enuGtnc8HrFEKI9TLkv9IcfL539DTSXGulrS6/17rFZW/ZXLPJxXdf7aXObqZxXs87rTXfO9RPPKFxLjOVtNPj4PiAn+lwbMkgiH5vqpfeO3oamZyJcujyFB+8blNeaxei2kiwSVQMXyg6l2mzFgaDYmdr6ZYgXBgPYjUZCjrtXotsm3s0nsSoFN97vZ/pUH4n6FLTLoRYbMgXJpHUBWQ2pYJNoysEm0b8YcKxJFubCt8jGmutxBKaNwf8tLhs2C359ZbKZntzKsB0bmwmZ7BpMH2q3lHAa/2BzfVYjAZePj8hwSYhRNElkpoRf5hbtzYsua+r3kFXfeGZotnctauZc2MzfOP5C/zObd1z75lfODfBiUE/77umlRZX7v59mdfdQW8IV9uiYNPULJ31qaBZh8fO65evTMQWQiwkwSZRERJJzXQohnsN/Zrm293m4p/eHEJrvarGrOtluVObX50dx+Ow8P1D/VdxRdlZTAa6m2o4MxLg1/e1FXs5QogylZlEV7/K6W4umwm72bhi36aL6YzQ7sbCM0KbalOn4xMzEQ4u03h2OZnX9mg8iQJ+cGQA32z2DylnRlJ9A08OTROMLC0TXC5wb7cYuWGzm5fOTxa0TiGEWE9jgTDxpF4y+W2jNNfa+L13buNbL1/ir1+8yLXtdVhNBl6+MMneTS7eub1x2cdvctsxqNTetLvNNXd7OJZgPBBhX3sqS9aTPiDxpocUCSEWkp5NoiKM+FMTLpqXOaVYjd2ttUyH4wzl2Xj2ahoLROY+9JSCXa21jM9EmJzJbyKUEEIs1juV6vtWv8p+SkopWutsjEyvEGyaCNJQY1nTAImmeaUYW1ZZ7reYxWTAU2NZdt2+0NomrN6+rZGTw9NMBeVDkBCiuIZ8qUzNqxVsgtQBwe+9cxub6x2cGQ3w8oVJ2j12PnRjx4oHyWajgQ6PY+6gImPAG0IDnelMLE/69dkrr7NCZCXBJlERLk1kTq3XXkYHzJ1inC6xUrpYIok3GC2pYNPOdAnImdHSnN4nhCh9fZOzWIwGXAUEg1pdtvRIbZ31/qTWXJ4MrqmEDsBpNWEzp942rUfJdludjeFlDjR8szEMCmpthSWh39aTOrl/5YJkNwkhiqt3charyUDDKrNX16rObuYTt3fzhXt385X79vLpO7dhNeVXAr2tycmAN0Q4diWzdCA9oa7T45h7foOSzCYhcpFgk6gIl9bh1Hq+Xelg00b3bdI5PhzlMhmMoll4wl5sDU4rTU4rp0ck2CSEKEzf1Cwd9faCxl631tmIxpM5y9GGfel+TWsooYNUFlWT04rHYV6XvWaT285UMLrgg8x83tlo+oNMYaXc13XU4bSaeOnCxFqWKYQQaxKOJXhryM/uNlfBr2frwWhQq2qN0dPsRAMXx69MXO6fmqXRaZnr2Wc0KFx2M94c+48Q1U6CTaLspU6tZ9ctqwlSJ9hd9Q5ODW9cAOWx1/q47avPzp2S5GM8kCpVK6XMJkiV0l0aDxLJ8aFJCCGW0zs5y+YCG8TOTaTLkSWUmeDZvcbMJoBf39fGb97QsebnAdiUbm6eK7vJNxsruIQOwGQ0cLC7npfPS7BJCFE8z5waJRxLckOXp9hLWZXOejtmo5rbQ7TW9HlDc1lNGR6HRTKbhMhBgk2i7I1OhwnFEusabALY3bZxE+kujM/wxz99m2F/mP/69Jm8H5cJNjWWUGYTwM62WhJac35RbbsQQixnyBfi8SMDXJoIFjyNqNllRQEj06El92mteaPPS6fHvmR8dSE2N9SwrWltGVIZbeneJZleJov5ZqN4HGtb8209jVyenGUwx/cQQoiN9oMjA9TZzWsuZb7aTAYD3Y01c+9tvbMxgpH4XL+mDI/DIj2bhMhBgk2i7GXSW9c/2OTi0mSQ2Wh8XZ83nkjyuX98E5vZyEcPdPLjY0McH/Dl9djxQBi3w4zFVFr/dDfX12AzGzi9gZlgQojK8ujLl7ntq8/yuX98E7vFyLt3txT0PFaTkfoaS9bMpr6pWcYCEW7aUtj0uI3ksplxWk0M+5cGguLJJIFwfE2ZTQC396TGjL94bnxNzyOEuLouTQT5V//jBUZXGH5Q6samwzx/dpz9ne6iltAValuTk/FABH8oxi9OjmBQsL154YGDx2EmEI4TiUt2vxCLldYnViEKcGkiiMdhXvOb8sV2tbrQ+sr46fXyNy9e4mifj6/cdw3/6f27aaix8Cc/O5VX/6bxmUhJ9WvKMBoUu1tdvDXkl1I6IcSKXjg3zh//9G3evauZf/6DOzj8//wad+5oKvj5WnM02z50eQqrycC1He61LHfDbHLbGPItXfd0KI4G3GvsDbWzpZa2OhvPnh5b0/MIIa6uHx8d5O2haU4OldagmtX68bFBkpqyK6HL6EkHln52fIg3B/zctauZhkXvwz01FjSp/oBCiIUKG3EiRInITBna1epa9+fek5lINxLg+nXcJB97rY/bexr44HWbUErxB3fv4Is/fovnzoxz167mnI9Las14IMKWLaWZhnzL1gaO9vt4o9/HrVsbir0cIUSJujwR5LOPHWV7cy1/8cD11FjX/laku7GGt4emGfKF5kZrh6IJjg/4uWGzp+SyQTPa6uycHxsnnkhiMl5ZY6b/x3KHKI+91pfX9+isd/DL0+M8+vJlzMbsfw8PHuxaxaqFEBvtl2dSAeKJmUhR15Hv60w2wUicbzx/kU6PveR6jearxWXDYTHy1tA0bXU23rVj6ft0T/p1ut87y5YsVRbT4Ri/87ev84d37+Qd2xs3fM1ClJLSfPclRJ7GAhFmo+vfrwmgw2PHaTVxeh37No1Oh+mbmuWunc1zEzEeuKkTj8PMT98cWvax06EYsYQu2Q27s95Bh8fOKxcmc44gF0KIh392CqXgrx86sC6BJoDrOz2YjYpXL07O3Xas30s8qbm5BEvoMtrqbCQ1jE4v/ECZmay31p5NALtbXUQTyQUTlYQoJ0qpe5RSZ5RS55VSn89yv1Up9f30/a8ppbakb79ZKXUs/etNpdRv5PucxTQWCHN8wA/AVJn2AhoLhPmr5y/gD8V4T4El0qXAoBQ9zU4MCj50QwdGw9JSwMzr9IA3e2+8I5e9vNHn4w++f5SxgGQ/ieoiwSZRli6Mz/CTY4M88qsLGBQb0nTQYFBsbarh4sT6vUF//dIUwIL+ISajgTt3NPHc2XGSydxBmlKdRDffbdsamJiJcH5MGoULIZbSWnOkd4r37Wmlq6GwhuDZ2C1Grutw8+aAj1A0QSSe4OULk7S77XOZTqUos7bFfZt86cymujWW0UFqf7QYDZweKe9yHFGdlFJG4C+Be4E9wANKqT2LLvsk4NVa9wBfA/4sfftbwAGt9X7gHuAbSilTns9ZNM+dudJjrRyDTROBCP/r+QtE4kn+7R1b2dFSW+wlrcmv723jU3dszbmXuOxmDIqc06WP9fswKJiJxPm//uHNZd/rC1FpJNgkys6IP8y/+V+vcKTXy86WWv7dHVvnUljX25aGGi6tY7Dp0OUpHBYj12xaWPb3rp3NTAWjHB/053zseDqVuhR7NmXsba+j1mrilQuTK18shKg6A94Q3tkY+zrq1v25b9naQCyhOdw7xT8eHmAqGOV917Su+/dZT/U1FqwmA0NLgk0xaq2mBaV1hTIbDfQ0Ozk9EsirN6AQJeZm4LzW+qLWOgp8D7hv0TX3AY+mv34ceI9SSmmtZ7XWmSkvNiDzDyCf5yyaX54eo9Vlo63OxsRM8YJNL56b4E+fPMV0OLaqx/3yzBiJpObTd24reMpoKXHZzXQ15D7UNiiF22Ghfyp7ZtObAz52tNTypfdfwwvnJvjWy5c3aKVClB4JNomyEksk+cxjbxCKJfjsXT189KYuNi+zAazVlsYahnyhdZsw8fqlKW7o8iz5APHOHU0oBc+dyd3EdTwQwWY24FynspONYDIYuKm7njOjAfyh1b05EUJUvhPpgPp1G9Cwe5PbTle9g5+fHOXk8DT37muba+5aqgxK0Vq3tEm4NxTFvQ4ldBm722rxh2JZm6gLUeLagf55fx5I35b1mnRwyQ80ACilDiql3gZOAP8+fX8+z4lS6lNKqcNKqcPj41dnomM0nuSFcxPctauJBqeFqWDxejY9c2qUQCTOm/35TUwG8AajvDng4+Yt9dTXbMxBcCnyOMxZM5u01rzZ7+O6DjcP3NzJdR11PHViuAgrFKI4JNgkyspXnzrNkV4vX/3QtTS7bBv+/bY21pDU0D+VPTV2NfyhGGdGA1lHcNfXWNjf6eaXZ3K/men3ztJWZ5/r9VSqdqbTpXOlEwsx3xp6cdytlDqilDqR/v3d8x7zXPo5M706cnfeF1fV8QE/FqOBHa0bEwS6ZWsDiaTm+k43t28rj0EFHW47Q74Q0XgSSH04mZqJruuE1Z2tLhRwSkrpRPnJ9qZncYpezmu01q9pra8BbgK+oJSy5fmcaK0f0Vof0FofaGoqfFrmahy+PMVMJM5dO5tpqLEyWcQyuiO9XiBVBpavF86Po1C8Y/vV+fsqFR6Hhf4sPZv6p1LZvNd1ulFKsb2lNmdvJyEqUemmSAixyIg/zDdfvMTHDnbxwes2rWlCRr4yUyUujgfpaV5bzfkbvV60hpu6s0+2e9eOZv6/fznLZJbJI6FogmFfmHcvM62uVLTW2TAoGPSFuGbT+pfKiMoxr2/G3aROlg8ppZ7QWp+cd9lcLw6l1P2kenF8FJgAPqC1HlJK7QWeZuHJ9Me01oevyg8i8n49/sXJEZpqrfzgyOCGrOPajjpqLEa6G2tKPjCfsbvNxUsXJjkzGmBfex3D/jC+UIx3Na1fQM5pNdHhsXNudIb37CrfZr2iKg0AnfP+3AEsnqiSuWZAKWUC6oCp+RdorU8ppYLA3jyfsyiePzuOxWjg9p5G/vmtkaL1wJyNxjk5PE2t1cSwP8zodJiWFQ55A+EYhy972d/lXpd+c+XE7bBwuNdLOJbAZjbO3f7mQCpQd226dLzDY2c0ECYST2A1GbM+lxCVRIJNomycHE6VX/zG9UsynddkuQ9JoWiqfO5HRwfzqptfbnz065enMBkU13dmDzbdtauJrz1zll+dW5rd1DsZRAPdG9AIfb2ZjQaaa20M+eTkRqxorm8GgFIq0zdjfrDpPuDL6a8fB76e7sVxdN41bwM2pZRVa13cOdEiJ601g74Q125ACV2GIX1yXE62NNZQYzHy1qCffe11HB/wY1As6e23Vu0eB2/0edFal00gTgjgELBdKdUNDAL3Aw8uuuYJ4LeBV4APA89qrXX6Mf1a67hSajOwE7gM+PJ4zqIY9ofZ5LZRYzVRX2MpWoPw4wN+EknN3Xta+PGxQY71+1bsgffapSkSSc07qyyrCaC+JhVcG/SF2DbvoODNfh9Wk4Gdral9qcPjQGsY9oXnDrSFqGRSRifKxqnhAAA7Wq/eBwm7xYjDYlyXBo2HLk2xt70OuyX7ScbeTXU0Oi08e3ppsOniRBCTQdHpKY9Gi5vcdga9IWlGK1aypl4c83wIOLoo0PStdAndF1WOT9bF6MdRzaaCUcKxJB0lPB2uGAxKcc2mOs6MBIjGkxwf9NHT7KRmnfvztbisRONJfNJPT5SR9Ov+Z0llr54C/kFr/bZS6itKqQ+mL/sm0KCUOg/8IZApyX4H8KZS6hjwI+A/aK0ncj3n1fupcpuNJrBbUv/2G5xWQrEEs9H4Co9af2/0pUro9rS56Gl28ma/j+QK7+nOj83QVe8o6anJGyUzqKhvcmELiTcHfOxtr8Oc7tXa4Untf1JKJ6rFhgabpBeHWE9nRgK0u+24bFc3NbehxpK1tG01wrEExwf83Ny9tF9ThsGgeM+uFp49NbqkIfmliSCd9Y65zarUtbttBKMJpsNX/w2SKCtr6sUBoJS6hlRp3e/Nu/9jWut9wB3pXx/P9s2L0Y+jmg2ksx3bPRJsWmxvex3RRJJnT4/hm41tSPZXS22qBGZ0WpqEi/KitX5Sa71Da71Na/0n6du+pLV+Iv11WGv9Ea11j9b65ky2rNb6O1rra7TW+7XWN2itf7zcc5aCUCyO3Zx6r9eQbrA9WYSJdG/0etnaVIPDamJ/pxtfKEbvZO5enImkZsgXorMCps8VosVlQyl4a95U6XgiyYlB/4KBGFeCTdLXVFSHDfvkOq8Xx73AHuABpdSeRZfN9eIAvkbqAwNc6cWxj1Ra7HcWPe5j6Y1jv9Y69/guUVFOj0yz6ypmNWU0OtfeoPHk8DTRRJIburKX0GV85EAHwWiCtwavNHENRRMM+UJ0l1G67aZ05sKgnNyI5a2mFweLe3EopTpInVY/pLW+kHmA1now/XsAeIxUuZ56YBDbAAAgAElEQVQoskFvCJNB0Vy78cMdyk13Yw12s5EXzo1jMij2tK1vCR0w129lbFoqTYUoVaFoAsdcZlMq2HS1S+m01rzR5+PG9HvWPW11KODCeO7+USPTYeJJPRdMqTY2s5FtTc4FzdTPjs4QjiW5rvNK/9JWlw2jQUlmk6gaG5kmMdeLQ2sdBTK9OOa7D3g0/fXjwHsyvTi01pkPHHO9ODZwraLEReNJLo4H52qer6YGpxV/KDY3KagQmZOOTIPAXG7c7GFrYw1Heq/0tcz0a9paRsGmtjo7ilTtuhDLmOvFoZSykOqb8cSiazK9OGBhLw438DPgC1rrlzIXK6VMSqnG9Ndm4P3AWxv8c4g8DHhDtNWl3miLhYwGxZ5NLjSwo6V2QYPZ9WK3GHHZTJLZJEQJS5XRpf7912cym4JXN0B8eXKWqWCUGzangk0WkwGX3Yx3maBXZmpzubR72Aj7O90c6/fNtZDINAefn9lkMhpoq7NJZpOoGhsZbJJeHGLdXBifIZ7U7NqA096VNDrXvtmfGPDTUGOhrW75E32lFB8+0MHlyVkm0qV7lzL9msooNdliMtBUa5Um4WJZa+zF8VmgB/jiorJqK/C0Uuo4cIxU89e/vno/lcgmkdQM+UO0V/EHkZVc2546jLiuc+MaqLe4bBJsEqKEhWIJ7Olgc0NN6pz9apfRHelN9Wu6cfOVbHyPw4x3Nne/twFviBqLEbejuqbQzXd9l5vJYHQua+mFc+M011rZ3LBw3+vw2CWzSVSNjQw2SS8OsW5Oj6TKyopRRtfgXPtmf2LQz972urwmAH3ohg4UqXr5pNZcGJ8pq35NGe1uuwSbxIrW0IvjYa11zbyS6v1a6zGtdVBrfaPW+tp0r47f11onlluDWH+haIJI7Mpf+9E+L9F4ku3NzmUeVd22t9TymXf1sHedp9DN1+KyMRaIrNjoVwhRHKkyunSwqUhldG/0eam1meiZN1XN47DgC+Vex4B3lg6Po6onXe5PHxS80eclEk/w/Jlxfm1Py5K/kw6PQ4JNomqs76iThVbTi2OgkF4cSqlML46/26gfQpSG0yMBLEZDUfoWNc41aFw5s+mx1/qW3BZLJDk7GqC1zpb1/mx2tNTy6qVJDvd6mYnEuWeFcbOlaJPbztF+H9OhGC579Z50CVFttNb87UuXCEbjfOZdPVhMBp45NUqnx16UA4NystHN01tcNuJJzdRMlMYqnBglRKkLzSujc1iMWE2GvPqGnhsN8OqlKT5+y+Y1ff9oPMnP3x7llq0NGOaVPLsdZqYHYiSSekkpdDiWYDwQYd8KrSIq3c6WWuxmI8f6fbjsZoLRBHfvbllyXYfHzmggTCSewGpa/5JpIUrJRgab5npxkCpjuB94cNE1mV4cr5BnLw7ArbWemNeL45kN/BlEiTg9HGBbs7Mo2T1WsxGn1cREgSdLI/4wSZ3K9MnXbT0N9L0+y+YGB/va69jbXn4beObnHfSFJNgkRBUZ8Ibm+rV971Af25qcTIfj/JubOqv61LsUtLhSAabRQFiCTUKUkMde60NrTTAa59J4cO5w0mY2cvjy1IqHlT98YyB1QBmO8+l3bSt4HU+/PcLETIQHD3YtuN3jsJDUMB2K4UkfwmYM+kJoqrtfE6T6Me1rr+NYv49IPInDYuTWbYu7w6Qym7SGYV+YLWXUj1WIQmzYJ3fpxSHW05mRALuLeCLe6LTkldmUTeZD12qCTduba/ni+/fwsYObubbDjaEMP6C1uVP9qYb9kiosRDU5dHkKs1Hx/mvbuDAe5OcnR9nZUsvWRimhK7bmWhsKpG+TECUokdQkNZhNVz6eOa0mgpGVK8GH/al/0+dGA2taw3de7aWz3s6d2xe2IHE7UgEm7+zSg9eBdHPwap1EN9/+LjdvD07zi5Oj3LmjKeuwh8zfk5TSiWqwkZlNaK2fBJ5cdNuX5n0dBj6S5XEPAw/neNob13ONovT5ZqOMTIeLMokuo8Fp5fRIYRv4YLppYl2VZfdYTamfeeIqN7YUQhRPJJbg+ICfazvc3LatkYmZKIcuT/G+MiwFrkQWkwFPjYXR6as73UoIsbJYItVLzTIvi7/GamQmEl/2cYmkngsgn11DsOnMSIDXL03xhXt3LSihg1SDcABflibh/d4QDTUWHJYN/VhZFq7vdPNIIsl4IMLde5aW0MH8YJNMpBOVr7w6DouqlAnyFDPY1FZnIxiJ4w/lnsSRy6AvRLvHXpXlI41Oy9xUPSFE5XtzwE80keSmLfUAfODaNr5wzy5aV5jEKa4emUgnRGmKJpLAomCTxUQwunywaTwQIZ7UOCxGzo/PEE8/z2p999VeLCYD/+ZA55L76hxmFDkym7yzktWUtr8r1STcaFC8e1dz1mtaXTaMBiWZTaIqSLBJlLzMKc2u1o2b0LOSrvpUHXrf1OpOIWKJJGOBMJtWUUJXSRqdViZmImiZfCREVTh0eYoWl5XO9AcPpRQOq5x2l5IWV+p1OdcH0kI/qAoh1iYaT/3bm19GV2M1EVwhs2ko3a7g1m0NhGNJjvX7Vv29ZyJxfvjGAB+4dtOSnkwAJoOBWpsJ76LMpplInOlwfFWtIipZW52dtjobN23xzJUeLmYyGmirs0lmk6gKEmwSJa9/aharyTDX2LQYWutsmAyKvsngqh5XSHPwStLotBKOJQlGZfK8EJXONxtl0Bfixi5PVWZylot2t52khsuTSz/ofPPFS1z3xz/nhXPjRViZENUtNpfZdOX1s8ZqIpbQc4GobIZ9IcxGxa3dDRgUPH929f9+f3R0kGA0wcdvzT3NzuOwLMlsGkn3imqtq873udn89UMH+PMPXbfsNR0eu2Q2iaogwSZR8ob8qcygYn54MRkMtLvt9K9yYxgooDl4JWlwpk51Cm2uLoQoH5n+bNWayVkudrTUYjUZONrnXXD7D98Y4D//00liCc1n/vcbXByfKdIKhahO2TKbnNZUg+nlspuG/GFaXTYcVhOdHseqg01aa777Si/72uu4riP39GNPjQXfomBTZghMm5RKz9nbXkdXw/KT+To8Dgk2iaogwSZR8oZ8ITa5i7+JddU7GPSFVlVicHkiSJ3dXHXNwTManalsNGkSLkTlmwymgsoNzuJloYqVmdPjud8emp77cPvLM2P80ePHuW1bA0/9wR2YjAb+7aOH8WdpBiyE2Bi5ejYBOZuEa60Z9odoSwf5t7fUcmLQv6pDvkOXvZwZDfDxWzYve7Drtpvxh2IkkldaI4z4w7hsJmqkXHpVOjx2RgNhInHJ/BeVTV4ZRMkb9oV5x/bGYi+DznoHifMTDPnDcz2clqO15uJEkB3NzqotKfE4LBgU0iRciCowORPFZFDU2uStRam7vsvD4V4vJ4f9bGty8uffP82Ollq+8fEbqbWZ+cbHb+T+R17lr1+4yOfet7PYyxWiKmTK6MzGhT2bgJxNwr2zMcKx5Fxm0Y4WJ8+cGuXF8xPct789r+/7nVd7cdlMfOC6Tcte53FYSGoIhGNz/YiG/WEZAJH22Gt9eV87MBVCa3jk+YtLDmgePNi13ksTomgks0mUtLkG2yWwkWUCTP15NgkfC0QIRuJ0N9Zs5LJKmtGgqK+xSBmdEFVgMhilwWnBUKXB9XKyucGB22HmaJ+PHx0dJBRN8BcPXE+tLZWFe9OWena01HJ80F/klQpRPTKZhhZTlmBTJHsGzFC6XcOmdM+kTW47TquJ1y9N5fU9xwJh/vmtYT5yoBO7xbjste6a1OtDpkl4PJlkPBCh1SWl06u1+O9SiEolwSZR0kanUw22S6EHiMtuxm035z2RLtPvYmuTcyOXVfIaaqxSRidEFZicidBQIyV05cCgFPs73Zwbm+H0SID/+55d9DQv3Kv2tLk4NTxdpBUKUX2yltGt0LNp2B/CoJjLLjIoxYEtnryDTU8cGyKW0Hll03jS2UyZvk3jgQgJraVfUwEyf5eLG64LUWkk2CRK2nB6ykVbCQSbIFVKl3ewaSKI22GmPssI2WrS6LQwGYyQ1Hrli4UQZSmpNVPBKA1V/npXTq7v9ADQ3VjDJ27bsuT+PZtcjAcijAckM1WIqyGWJbPJYjRgMqhlgk1hGp3WBaV3N3fXc25sJq+s8pfOT7C1qYZteRyMZvqPZgIkVybRSbBptVw2MwYlwSZR+STYJEra0Nw0t9LYyLrqHfhDMfyh5dNek1pzaSLI1sbqzmoCaKy1Ektoplf4OxNClK/pUIx4UlPvlGBTuWiqtfLQLZt54OYuDIalpY+722oBJLtJiKskmkgdys0PHCmlcNpMORuEj0yHl2QWHeyuB1KNv5cTSyR5/dIUt21ryGt9ZqOBWptprvRr2B/GZFBzw2BE/owGRZ3djE/K6ESFk2CTKGlDvnRmU11pZDZtaUj1Xzo+4Fv2utHpMLPRBFuruF9TRuZNyGRQTm+EqFSZf99SRldedrW5cOaYIrWnzQXASQk2CXFVxBJJjEphXBT8dVqzB5uSWhMIxeeadWfsa3djNRlWLKU7PuAjGE1w+7b8h/B4HBa8wSuZTS0u25L1ivy45/1dClGpJNgkStqQL0Sd3VwyI1U3uW3saHHy7OkxAuHcpxEXx4MAbG2SYFOmrEYm0glRuabSfdkaJLOpYrgdFtrddk4OSbBJiKshGk9iNi0N3OQKNs1GEyS0XjIB1GIycEOXh9cvTy77/V46P4lScMvW/DKbADo8di5NBDkx6GfYH6LVVRqVB+XI47BIGZ2oeBJsEiVt2B8qqcaDSinev28T8YTm6bdHc153cXyG+hrLktOmauSymzEbFRPS90OIijUZjMyVBYjKsbutVsroRNEppe5RSp1RSp1XSn0+y/1WpdT30/e/ppTakr79bqXUEaXUifTv7573mOfSz3ks/av56v1E2cUSyQXNwTNqbSYC4aXBpsyhp8u29HX35u56Tg5NM73MwejLFybY0+bCs4pee++7ppWuBgffP9RHMJqQfk1r4HGYCYTjxNON4YWoRBJsEiVt0BemvUSag2c01lq5vaeBN/q8WZuFD/lCnB4JcE26BKHaGZSSiXRCVLjJYJR6hwWDknKKSrKnzcWF8RnCsexj14XYaEopI/CXwL3AHuABpdSeRZd9EvBqrXuArwF/lr59AviA1nof8NvAdxY97mNa6/3pX2Mb9kPkKZpILujXlOG0mghG4ksGrWR6YbqyBPkPdteT1HCkN3vfplA0wRu9Pm7vyb+EDlJ9mx66Zctci4RSOhAuN26HBQ0r9oEVopxJsEmUtGF/iLYSaQ4+3107m3HZTHz31V4GvaG527XW/NPxIewWI+/aWfRDspKRmUgnhKhMkzNRKaGrQHs2uUhqODMSKPZSRPW6GTivtb6otY4C3wPuW3TNfcCj6a8fB96jlFJa66Na66H07W8DNqVUyTaWi8aTCybRZTitJjSpsrn5ptPZTi7b0lYT13d5MBkUr17MXkp3uHeKaCLJrXk2B5/PbjHyu7d3c/eeFjY3SLuIQnkcmel+EmwSlUuCTaJkzUbj+GZjbCqxzCYAq9nIJ27vxmRUPPLCBU4M+kkkNScG/VyenOW9e1qxW4zFXmbJaHBamQpGSST1yhcLIcqK1prJYGSuP5uoHLvTGbpSSieKqB3on/fngfRtWa/RWscBP7A4ivIh4KjWev7J17fSJXRfVGppWqZS6lNKqcNKqcPj4+Nr/TlWFM1RRudMl8kt7hWaKZFzZgk22S1G3rG9ke++0sv5sZkl9798YRKTQXHzlvqC1uqym7lrZ7M0B18DT7rVhvRtEpVMgk2iZGUm0W0qkUl0i7W4bHz6zm001Vr5+9f7ePhnJ/nxsUE21dk4sMVT7OWVlEanlaSWDVWISjQWiBBLaBpk/HXF6fQ4cFpNMpFOFFO2aMbik6tlr1FKXUOqtO735t3/sXR53R3pXx9f8gRaP6K1PqC1PtDU1LTqha9WLJ7EnCOzCVjSJDwQilNjMWIyZP8496e/uQ+r2ch/+N9HmI1eeezETIQfHx3khi5PyQzgqUYuuxmDkvfGorJJsEmUrGF/qjytlOvBa21mfu+d27j/pk6u7XDTUGPlvv3t0rdkkUanTKQTolJdnkhN35TMpspjMChpEi6KbQDonPfnDmAo1zVKKRNQB0yl/9wB/Ah4SGt9IfMArfVg+vcA8Bipcr2iiiV09gbhmWDToibh0+FY1n5NGW11dv77/fs5NzbDHz1+nJlInEg8wb//zhGmglH+0/t3r+8PIFbFaFC47GZ8UkYnKlhe4Wyl1A+AvwWe0lpLy3xxVQz5UsGmUiyjm89sNHBth5trO9zFXkrJyjSSnJQm4RVL9onq1TuZGpQgmU2VqafZyc+Xmb4qRL4K3CcOAduVUt3AIHA/8OCia54g1QD8FeDDwLNaa62UcgM/A76gtX5p3jpMgFtrPaGUMgPvB55Zw4+2LqKJHD2bbDkym8JxarOU0M13x/YmPvfenfyXp8/wwtlxdrTUcrjXy188cL28by0BHocFb1DeG4vKlW9m01+RemE/p5T6qlJq1wauSQggVUanFDJWtQI4LEZsZoNkNlU22Seq1LmxACaDom6ZE3ZRvlpddiaDUSJxmUgn1mzV+0S6B9NngaeBU8A/aK3fVkp9RSn1wfRl3wQalFLngT8EPp++/bNAD/DFdG+mY0qpZsAKPK2UOg4cIxXE+uv1+zELE4tnn0ZnNRkwGdTSzKZQDJdt5dfdz9zVw08+czu3bmvgcK+X33/Pdj5w3aZ1W7conMdhljI6UdHyymzSWj8DPKOUqgMeAH6hlOon9cL8Xa215P+JdTfkC9Fca8268YryopSi0WmVYFMFk32iep0aDtDsskqj2AqVKWUfm47QWe8o8mpEOSt0n9BaPwk8uei2L837Ogx8JMvjHgYezrGcGwv6ITZQqkH40tdRpRROm2lBZlMiqZmJxLOW0T32Wl/W579zRzMHuxuwmY05rxFXl9thIRCOE08kMcnnHVGB8v6/WinVAPwO8G+Bo8B/B24AfrEhKxNVb9gfpq1Em4OL1Wt0WqWMrsIVsk8ope5RSp1RSp1XSn0+y/1WpdT30/e/ppTakr79bqXUEaXUifTv7573mBvTt59XSv2PbFOGxPo5PTJNm0teqytVSzrYNDIdLvJKRCWQzxPZaa2J5mgQDqm+TYF5waaZSBwNK5bRLWYzy6TkUuJxWNCAPyTncaIy5duz6YfALuA7wAe01sPpu76vlDq8UYsTlSnf05RTw9O0ue1y+lIhGp0WjvX7CMcS8manAhWyTyiljMBfAneTavB6SCn1hNb65LzLPgl4tdY9Sqn7SU0U+igwkf4+Q0qpvaRKLDLjsP8K+BTwKqnT8HuAp9bvpxUZY4EwEzNRDnYvnjIuKkUms2nEL8EmsTbyeSK3RFKjIWuDcEhNpPPOayQdCKe+zqeMTpQujyP13887G5O+h6Ii5ZvZ9Dda6z1a6z/NbAxKKSuA1vpArgfJibUoVFJrfKEY9Q7ZRCtFZhO9PBks8krEBilkn7gZOK+1vqi1jgLfA+5bdM19wKPprx8H3qOUUlrro1rrzESitwFbek9pA1xa61e01hr4O+Bfr9tPKRY4NRwASntqqFibFpcEm8S6KejzRDWIJlL90nO1jlhcRjcdSn0twaby5nGkprhOBqXNhKhM+QabstU7v7LcA+adWN8L7AEeUErtWXTZ3Ik18DVSJ9Zw5cR6H6npEt+Z95jMifX29K978vwZRBkJhOMkkhq3Q0ZpV4rMRLpL4xJsqlCr3idIZSL1z/vzAFeyk5Zck24U6wcWp9F8CDiqtY6krx9Y4TkBUEp9Sil1WCl1eHx8fIWlimxODU8DMsihkrlsJhwWI8MSbBJrV8g+URWi8VSwKds0OgCn1UwwEiepNQDT6cymWvvqyuhEaXE7zNRYTXNTXYWoNMu+QimlWkm9Sbcrpa4HMllELmClLpFzJ9bp58qcWM8vj7gP+HL668eBr2dOrOddM3diDdSTPrFOP2fmxFrKIyqMLz2Zob5Ggk2VojH93/KSZDZVlDXuE9kyU/VqrlFKXUPqoOK9q3jO1I1aPwI8AnDgwIGs14jlnRqepq3OhsMiH3gqlVKK1jobo9KzSRRojftEVchkNuUso7OZ0EAwEqfWZiYQjmFQqfI6Ub6UUmxrquHC+Axaa6RgR1SalV6h3keqiV8H8N/m3R4A/uMKj812Yn0w1zVa67hSKnNiPTHvmrkTa6XUqk6sSWVA0dXVtcJSRanJjAF1SxldxbCajdTaTJLZVHnWsk8MAJ3z/twBDOW4ZkApZQLqgCkApVQH8CPgIa31hXnXd6zwnGKdnB4OsLvNVexliA3W6rIx7A8VexmifK1ln6gKsUTqvCN3ZlPqI9tMOtg0HYrjtJowSHCi7G1rdHJ8wM/4TITmWskSFpVl2WCT1vpR4FGl1Ie01j9Y5XPLibUo2FQwlR7skTK6itJQY+XShASbKska94lDwHalVDcwCNwPPLjomidIlVO/AnwYeFZrrZVSbuBnwBe01i/NW8+wUiqglLoFeA14CPiLQn42sbxIPMGF8Rl+bU9zsZciNlhrnY3XLk4VexmiTK1xn6gKmTK6XD2bajPBpnAc6lJldC67HMhWgq1NNQBcHA9KsElUnJXK6H5La/1dYItS6g8X36+1/m9ZHpYhJ9aiYL7ZKLVWU85NV5SnRqdFgk0VZi37RDqj9bOkJskZgb/VWr+tlPoKcFhr/QTwTeA7SqnzpPaH+9MP/yzQA3xRKfXF9G3v1VqPAZ8Gvg3YSZVZS6n1GmmtiSaSWE1XJkmeG50hntTsanURCMeXebQod62uVBldMqkxGCSTQqzOGj9PVIXYXBld9n9fTtuVzCZI9Tb1SKuJilBfY8FtN3NhfIZbtspkV1FZViqjq0n/7izgueXEWhRsajYqJXQVqNFp5XCvF38oRp2cyFWKtewTaK2fBJ5cdNuX5n0dBj6S5XEPk73ZLFrrw8DeQtZzNcQSSb72i7N84vZummpLf9RxMBLn9793jMO9Uzz+72+lp7kWuNIcfHebi9cvSdZLJWursxFPaiaCUuYhCrKmfaIazGU2rVBGlwnsT4djbG6QdleVQCnF1iYnp4an5xrAC1EpViqj+0b69z9e7RPLibVYC99sjA6PvdjLEOssM5Hu8kSQ6zrdRV6NWA9r2Seq1fEBP//zuQs0Oq387ju6i72cZY1Oh/ndbx/i1PA0tTYzv/vtw/zkM7fjqbFweiSA1WSgu7FGgk0VrsWVCjCN+MMSbBKrJvvEymIrNAi3mgyYjYqZSJx4IslsNEGtTQ7tKsW2phre6PMyIlM/RYXJq0ZJKfXnSimXUsqslPoXpdSEUuq3Vnqc1vpJrfUOrfU2rfWfpG/7UjrQhNY6rLX+iNa6R2t9c2Zyndb6Ya11jdZ6/7xfY+n7Dmut96af87NaSwi40iS1xjcblX5NFajBmZ5IJ6V0FafQfaIaZf7/PzsaKPJKlqe15qFvvs7liSDf/J2b+NYnbmJkOsynvnOY//aLszx1YpidrbUYpayq4rXVpQ5/5IOQWAvZJ3LLTKPLldmklMJpNTETic9lN7lsMomuUmxtSiX9XRifKfJKhFhf+TbEea/Wehp4P6m+STuAP9qwVYmqNh2KkdTSHLwSNdRYUEqCTRVK9ok8XUy/mTw9UtrBplcuTHJmNMAf37eXu3Y2c0OXh//y4Ws53Ovl68+ew2kz8du3bin2MsVV0FKXykodmZZgk1gT2SdyyJTR5cpsglQpXSAc4/lz4wA0OEu/DFvkp85uptFpLfn3BUKsVr4h8Uye5q8Df6+1nlIyalNsEO9sehJdjaQHVxqT0UCHxy7Bpsok+0SeLo5fyWwq5YbLj73eR53dzPuvbZu77b797dze04jDYsRhkVP1SvHYa33L3p/UGoOCfzk1hsmQ+8Pwgwe71ntporLIPpFDpoxuucE4TpuZU8PTXBgP8s7tjWyRnk0V5aYtHp56a4Tnzozxrp0y5VVUhnwzm36qlDoNHAD+RSnVBMjxltgQ3mAUkMymStXd6JRgU2WSfSJPlyaCKAWz0QSDvtCGfq9CK80nZiI8/fYIv3lDOzazccF9jU6rBJqqjEEpXHYz06FYsZciypvsEzlE4xqjQS1bllybLpt7965m3ndNKxKoqyy3bmugocbCwz87NRd8FKLc5RVs0lp/HrgVOKC1jgFB4L6NXJioXt7ZKApwy7SyitTd4ODSRLDgD8GiNMk+kZ9EUnNpMsiBzR5gY0vpXr4wwbVf/jknBvyrfuzjRwaIJTQfk0wVkeaymfFLsEmsgewTuUUTyWVL6ADu6GnkwZu7+LXdLRJoqkAmg4Ff39fG+bEZ/vervcVejhDrIt/MJoDdwEeVUg8BHwbeuzFLEtXOOxuj1mbCtMKmK8pTd2MNM5E4EzPRYi9FrD/ZJ1Yw5AsRjSd53zWtwMY2Cf/6s+cJROJ88SdvkUzmH9xNJjV//3ofN2+pp6e5dsPWJ8pLnd3MdFiCTWLNZJ/IIpZIYsnRHDyjwWllb3vdVVqRKIZdrbW8o6eRrz1zToL7oiLkO43uO8B/Bd4B3JT+dWAD1yWqmFcm0VW07vTEDSmlqyyyT+QnM2nm2g437W77hmU2vTXo5+ULk9y42cOxfh8/eGMg78e+0eeld3KWBw52bsjaRHly2Uz4QzHJShUFk30it2g8uWy/JlEdlFJ8/t5d+EMxvivZTaIC5Nt04QCwR8s7DHEVeGejbGmoKfYyxAbZ2pj6b3tpYoabu+uLvBqxjmSfyEMmyLq1qYZdrbWcGZnekO/zyK8u4rSa+NvfuYlPfOt1/uyfT/Pea1qpy6M8+bkz4xgNinfvatmQtYnyVGc3E0towrEkdotx5QcIsZTsEznEEkksRimNE7C3vY47dzTxrZcu8cl3dC/pm/RnsjYAACAASURBVChEOck3hP4W0LqRCxECUv1M/LMxPA7p11SpNrntWIwGLkpmU6WRfSIPF8eD1NpMNNRY2Nlay8Xx4NzI6/Uy4J3lZyeGeeDmTursZr5y314mg1G+9dKlvB7//Nlxbuhy5xWYEtXDlf7/wS+ldKJwsk/kEE0kMa9QRieqx6fftY2JmSj/eCT/rGQhSlG+r2qNwEml1NNKqScyvzZyYSI/8USSr/z0JD9cRYlEKfPNRtHIJLpKZjQouhocXJZgU6WRfSIPFydm2NrkRCnFztZa4knNxYmZdf0ef/dKLwr4xO3dQOqUdH+nmxfOTaz42ImZCCcG/bxze9O6rkmUv0zwUSbSiTUoaJ9QSt2jlDqjlDqvlPp8lvutSqnvp+9/TSm1JX373UqpI0qpE+nf3z3vMTembz+vlPofqsgdt2PxlRuEi+pxsLue67vcPPKrC8RlMp0oY/mW0X15IxchCqO15vM/PMHjRwbY2ljDb97QUewlrVlmDHhbnb3IKxEbqbuxRno2VZ4vF3sB5eDSeJBbtjYAsLM11Xz7zEiAXa2udfsevzo7zq3bGtjkvvI6erC7gW++eJFQNLFsCdSL6YDUnTsl2CQWctkk2CTW7MurfYBSygj8JXA3MAAcUko9obU+Oe+yTwJerXWPUup+4M+AjwITwAe01kNKqb3A00B7+jF/BXwKeBV4ErgHeKqgn2odRBNJam2STSpSlFJ8+s5tfOo7R3jqrRE+cN2mYi9JiILkFULXWj8PXAbM6a8PAW9s4LpEHv70qdM8fmSA3W0uLk4E6Z0s/w/v/VOzmAyK1jpbsZciNtDWxhouT86uakKWKG2yT6xsNhpnyB+mO923bGujE5NBcWYdm4T7QzHOjAY4sHlhP7SDW+uJJTRv9HmXffzzZ8epr7Gwd5NMPBIL1dpT55MyIUkUqsB94mbgvNb6otY6CnwPuG/RNfcBj6a/fhx4j1JKaa2Paq2H0re/DdjSWVBtgEtr/Uq6f9TfAf96rT/fWkTjK0+jE9Xl13a3sKnOxk+ODa18sRAlKt9pdP+O1Iv3N9I3tQM/3qhFiZW9NejnkV9d5Ldu6eKvPnYDkGrqWu76vSHa3XaMBmmSWMm2NNYQjScZ8oeKvRSxTmSfWNmV5uCpiYwWk4GtTTWcHV2/MrqjfV60hgNbPAtuP7DZg0HBaxcncz42mdS8cG6cO7Y3YpDXYLGIyWDAaTUxLT2bRIEK3Cfagf55fx7gSnbSkmu01nHADzQsuuZDwFGtdSR9/fz+E9meE6XUp5RSh5VSh8fHN/Y9diyhpYxOLGAwKN63t5VfnRtnJhIv9nKEKEi+r2qfAW4HpgG01ueA5o1alFjZq+kPDP/Hu7ezpbGGLQ0OnjszVuRVrU08mWTIF6Kz3lHspYgN1j03ka78s/HEHNknVjB/El1Gp8cxVz68Ho70ejEaFPs73Qtur7WZ2dtex6uXpnI+9uTwNBMzUe7cISV0IjuX3SSZTWItCtknskW+F6dFL3uNUuoaUqV1v7eK50Rr/YjW+oDW+kBT08a+LkYTktkklrp3bxvReJJnT5f3ZzxRvfLt2RTRWkczvfOUUiayvCiLq+fwZS8dHjstrlS52bt2NvO9Q32EY4myHZE54g8TT2oJNlWBrfOCTXdII+JKIfvECjJN8bc0XAk2bXLbObJCadtqHL7sZXdbLTXWpdv7we56Hn2ld26feOy1vgX3Zw4sxgORJfcJAVBnM+OdlWCTKFgh+8QA0Dnvzx3A4rqizDUD6eesA6bS36MD+BHwkNb6wrzr5zc6zfacV43Wmlg8ickoGaVioRs3e2iqtfLUiWE+KH2bRBnKN4T+vFLqPwJ2pdTdwD8CP924ZYnlaK053Ovlpi1XenLcuaOJcCzJa8ucWpe6fm/qdL/TI83BK11TrRWHxSiZTZVF9okVeGdjOCzGBQ2629w2fLMxZqNrT5GPJZIc6/ct6deUcbC7gWg8dU02Z0dn2FRnkya1IieX3SyZTWItCtknDgHblVLdSikLcD+weILdE8Bvp7/+MPCs1lorpdzAz4AvaK1fylystR4GAkqpW9JT6B4CfrLWH65QsYRGg5TRiSWMBsX7rmnhuTPjhKKJYi9HiFXL91Xt88A4cIJUCuqTwH/aqEWJ5fVNzTIxE+HGzVd6ctyytQGLyVDWpXT9U7PUWk1z45VF5VJK0elx0D8lPZsqiOwTK5gJx6m1Lcw4ak9PjBvyhdf8/KeHA4RiiQV7w3w3ddejFLx2cemhRDiWoG8qyPaW2jWvQ1SuOruZUCxBTEZxi8Ksep9I92D6LKlJcqeAf9Bav62U+opS6oPpy74JNCilzgN/mP4+pB/XA3xRKXUs/StTtvdp4G+A88AFijiJLhRLBRFMEmwSWdy7t41QLMHzZ8v3M56oXnmV0Wmtk0qpHwM/1lqXfxfqMnf4cqrkYn4DWLvFyC1bG3j+zDh8oFgrW5v+qVk66x1k0qtFZeusdzDgnS32MsQ6kX1iZYFIbEnWUFtdJtgUoqfZuabnP9ybCiItbg6eUWc3s7vVxWuXJoHtC+67MD5DUsP2lrWtQVQ2V/r/3+lQjAantcirEeWm0H1Ca/0kqcDU/Nu+NO/rMPCRLI97GHg4x3MeBvbmu4aNFEkHm8xSRieyONhdj8dh5skTI9yzt63YyxFiVZYNoauULyulJoDTwBml1LhS6kvLPU5srMO9U9TaTOxoXngCffu2Bi5OBPEGo0VaWeFmI3Emg1EpoasinfV2+qZmSU0dFuVK9on8BcJxnIt6KW1yp/ruDa1Dk/DDvV7a3fa5AFY2t/c0cPiyd8lEsXOjM1hNBrqkZ55YhiudeeyXiXRiFWSfWF44lsoUNEtmk8jCZDTw3j2tPHt6jEhcSulEeVnpVe0PSE2NuElr3aC1rgcOArcrpf7PDV+dyOrwZS83bvYsGU29I13+cH58/cZoXy1z/Zrkg07V6PQ4mI0mmCrD4KhYQPaJPAWylNG1uGwoBUP+tZXRaa05ctnLDTlK6DLu3ddGNJHkmZOjCx57dizA1iYnJoN82BG5Zcrcp6Vvk1gd2SeWEZrLbJLXX5HdPftamYnEefHcRLGXIsSqrFRG9xBwt9Z67v9srfVFpdRvAT8HvraRixNL+WajnBub4b79SycSZEowzo3OLGgeXg7eGvJjUNAumU1VI5NB0e8NSTlGeZN9Ig+PvdbHoDdES51tyaS3WquJF89N0JqeLrqcBw92Zb390kSQkekwN3cv/9p/faebdrednx0f5j27WwAYn4ngm41x5w6ZDCmW57Kn3jb6Q2tvaC+qiuwTywhLGZ1Ywe3bGqm1mXjyxMjc3i1EOVgp2GSevzFkaK3HlVLSxbkIjvRm+jUt/UDR7rZjNxs5P1ZemU0Xxmc40uvljp5GrCbjyg8QFSGTxdY3Ncv+TneRVyPWQPaJPIXjCWympSfXbocFfyi/DL/FgaqMVy6k/hP4Z2M5r8nobqzhuTPj3LatEbvFyLnR1J6xvVmag4vlWU1GbGaDZDaJ1ZJ9YhmS2SRWYjEZuHt3C8+cGiWWSMr/K6JsrPR/6nLvfqX2pQiO9vkwGRTXdSz9cG4wKLY113BuLFCElRUmGk/yo6ODNNRYJFJfZTrSWWz9U9IkvMzJPpGnSCyJzbw0oF5nN+ObXduH93NjM9TXWKivsax47b72OhJac2pkGoCzowEanfk9VgiXzYx/lcGmQV+IZFL681Ux2SeWEZZgk8jDvfva8IdivHJhsthLESJvK72qXaeUms7yKwDsuxoLFAudGQ3Q3ViD3ZI9A2h7cy0XyiSzKak1PzsxzFQwym/e0IEly4m/qFw1VhONTotMpCt/a9onlFL3KKXOKKXOK6U+n+V+q1Lq++n7X1NKbUnf3qCU+qVSakYp9fVFj3ku/ZyLR10XTSKpiSaSWM1ZMpvsqQ/vhTbLjyeTXBwPsj3PaXYdHjtuu5k3+3389M0hzo3NsHdTXUHfW1SfOrt5SYP55Tx5Ypjbv/os/+ovXuTZ06MyFKI6yeeJZVxpEC5ldCK3O7Y3UmMx8tRbw8VeihB5W/bTvdbaqLV2ZflVq7Wu+rTXYjg/NrPseOyeZidD/jAzkdLup+CdjfI3L1zi0OUp7uhppLuxpthLEkXQ4XHQJ5lNZW0t+4RSygj8JXAvsAd4QCm1Z9FlnwS8WuseUn09/ix9exj4IvC5HE//Ma31/vSvsUJ/vvWSmSBjy1IqXOcwE09qgtHCpsz0Tc4STSTzLoNTSrGvvY5zYzO8cnGSd/Q0SmapyJvLZs67jG7YH+ILPzzBjhYns9E4v/vtw/yXp89s8ApFqZHPE8uby2ySAQ1iGTazkbt2NfPzt0dJSKaoKBMr9WxaE6XUPcB/B4zA32itv7rofivwd8CNwCTwUa31ZaVUA/A4cBPwba31Z+c95jmgDcjMiX5vKXyQWG/Zem7EE0kuTwTZ0uDI2ZNjOD0++3/+8jwdHkfOZrLFNBWM8vVfnkNr+PCNHVwv/XqqVme9gzf7fcVehiiem4HzWuuLAEqp7wH3ASfnXXMf8OX0148DX1dKKa11EHhRKdVzFddbsEj65NqWI7MJUv2WnNbVb8vnxmYwKNjalH/Q/obNHt4enuaunc3cuMIEOyHmc9nNBMJxEkmN0ZA7EyOZ1HzuH98kGk/yjY8foMNj55OPHuYnx4b4o/ftRCnJ4hAC5gWbJMNfrOBf7Wvjn44P8/KFCe7YLkM9ROnbsGDTvBPru4EB4JBS6gmt9fwPEXMn1kqp+0mdWH+UKyfWe9O/FvuY1vrwRq29VE0Eo2iguTb3xKKm9H1j0xE6PI6rtLLV+dXZcWIJze+/ezuNtTKFrJp11dt56sQw8UQSk/QqqEbtQP+8Pw+QGoed9RqtdVwp5QcagJXm/35LKZUAfgA8rLPU7iilPgV8CqCra2MD8+F0ZlO2IQh19lSvJH8oWtBEznNjAbrqHVn7QeXS4rLxuffuXPX3EsJlN6GBmUicOnvupJS/P9THS+cn+epv7pvLXr57dzO/OjtO39Qsmxsko1kImJ/ZJAFYkXsQCEAskcRmNvD//vws/VOhnNdllGLSgaguG/npbu7EWmsdBTIn1vPdBzya/vpx4D2ZE2ut9Yukgk4ibWw69dfRtEyApr7GglEpxgKRq7WsVRmbDnOkz8uNXR4JNAk6PQ7iSc2wX/6pV6ls76wXB4XyuWaxj2mt9wF3pH99PNtFWutHtNYHtNYHmpo29oQwPJfZlL2MDsBXwISvmUicIV+Y7S0ySU5cHZkA00qldD8+Osiu1lo+elPn3G239TQC8NJ5aXArREYovT/IoZtYidlo4Np2N2///+zdeXxbZ5no8d8ryZK8yJu8b7FjO83SNGuT7ntLS6HtlBbaMkyBQmcYOgyXyzAw9wPMcIdhuTMwW4ehQ4EWKKUbNKUb3UvbpM2+b47jJN7iXZYXWdt7/5DsOo5ly7Zk6cjP9/Pxx/LR0fGjRNar85znfd5WFyO+2U29F2I+xfNdbbIr1uWR9tFa+4HRK9bT+Vm46evXVYQ6bKXUvUqpbUqpbZ2dnTOPPgl1ukdQTJ1sMpsUziwrHe7kPHl/8O3jBIOaS+sLEh2KSAKV+aHqu1PSJHyhagYqx/1cAbRG2kcpZQFygJ6pDqq1bgl/dwOPELr4kVCjHwonm0aXaTVjMSlcs1iR7sjp0Oqj0TYHF2KuRpNNU61I1zvoZfuJXq5bXnzGdLnFBZkUZ9t459h0hYlCLByjlU0WaRAuorCmKhdfQLOvtT/RoQgxrXgmmxbMFev50uEeIS/TOu3SqEXZdjqTsLLJNezjV1tOsrIiB2eWVDUJqAonm5qjKAUWKWkrUK+UqlFKWYE7gE0T9tkE3B2+fRvw6mRT4kYppSxKqYLw7TTgQ8C+mEc+Q1NNo1NKkZOeNqvKpj3NfeRmpFGWO/Ppd0LMRrZ9+mTTG0c6CWq4akLjeaUUF9UWsPlYt6xKJ0SYxx/AYlKYpI+ZiEJVfgbOTCs7T/YmOhQhphXPZNOCuWI9XzrdIxRGkaQpctjoGfTiCwTnIaro/fq9kwyM+LlMGtqJsNIcO2aTkhXpFqhwRet9wIvAQeAxrfV+pdS3lFI3hXd7EHAqpRqALwFfHX28UqoJ+AHwSaVUc3glOxvwolJqD7ALaAH+Z76eUySeKRqEA+RmpE158j6ZgRE/DR0DrKrIlZMUMW8yRivxpni9vnKog4IsG+eV55x134W1TroHvRwOV+UJsdB5vIFpLyQLMUopxZqqXBq7Bukd8iY6HCGmFM/V6MauWBP6sH8HcNeEfUavWG8myivWQK7WumvcFeuX4xF8sglqTdfASFRTJYocNjTQNZBc1U1P72pl3aI8uQIvxljMJkpz7DKNbgHTWj8HPDdh2zfG3fYAt0d4bHWEw66LVXyx8v40usmbeOekW2nomNnJ957mPoIaVsmKnmIeKaUodNgiTtf3BYK8friDG84twTRJw+OLakPdEt5p6GZpSXZcYxXCCDy+IGkyhU7MwJrKPF4+2MH2E71cM6GCVIhkErc0+kK6Yj0fege9+IOaouxoKptCK9Kd7k+eZNPxrkEOtvXzwZWliQ5FJJmq/AxOSWWTSHEefxCTAkuE1YZyM0LLyftnUJG6+1QfJdl2SrIjr1AqRDyU5qTT2jd5smlbUy9uj5+rlk5+AlSRl8EiZ4b0bRIibNgnlU1iZvIyrSwtcbClsRuvP7lmsggxXjwrmxbMFev5MLq6XKFj+pOKQocNi0nR2pc8fXCe29sGwA3nlvD64dRo2C5mZ+KSrl5/kEPt7imXep2MLOcqjMTjC2BPM5/RLHm88tx0NKHEfDQry3UPjHCqd5jrV5TEOFIhpleaY2fHyV7cHh+OcA+nUa8eOo3VbJpyIZCLap38fncbwaCetPpJiIXEI8kmMQtXLCnkv99sZGtTDxfXycJLIjnJO5tBjCabiqZYiW6U2aQozbHTHOepSR1uD3/z+G7+6/UGtjR2EwhGbvb53N421lTlyhQ6cZZCh40hb4ChEX+iQxEibkb8wYhT6ADqirKwmk3sb4tudZndzX0AnFdxdk8cIeJtdCyfrLrplYMdXFDrJNMW+XrmueU5uEf8tPUn58q5Qswnj1+m0YmZq3JmUlOQyR+PduIPSnWTSE6SbDKITreHbLtlypOV8SryMmjt80yZAJqrh95p4vHtzXz/hcPc8cAW/uaJ3ZPud6J7kP2t/dwoU+jEJEYTqJ1J1mNMiFjy+ALYLJGH3DSziSXFWRxs7Sc4zSpdQa3ZcbKPmoJMcjOssQ5ViGmV5oSqrNtcZ1ZQN3YO0Ng1yNVLi6Z8fLUzE4ATXYPxCVAIA/F4A1iksknMwuVLCun3+Nl1si/RoQgxKXlnM4gO9wiFUVQ1jarIS8cbCNLQMRCXePyBII9va+aqpUXs/Pq13HNJDU/taOHVQ6fP2vfZ0Sl0kmwSkxidGjpavSdEKvL4pq5sAlheFqr2mK6HWWPnID2DXs6vzotliEJEzZ5mJj/TSqvrzMqkVw91AHDVdMmmglCyqalb+vUJ4fEHpLJJzEp9URZluXZeO9zBiD+Q6HCEOIskmwyia2CEgqyZJJsygPenWsTaG0c66XCP8LHzK8nLtPK31y9lSXEWf/fUPvo97y+HHAhqNu1qZVVlLuUyhU5MIjcjDYtJ0SnJJpHCRvwB7FNUNgEsLXFgVooDraGpdCe6BznZfXblx3tNPaSnmVlRJlPoROKU5thpm9Ab8tVDHSwpzqIyP2Pqx2bbsVpMnJjk9S3EQiM9m8RsKaW4cWUZfUM+XtjXnuhwhDiLvLMZwLA3gMcXxJkZ/XQJZ5YVm8XEnjglmx7deoqCLNvY1UurxcT3b1tFh9vDt545gA5PA3ngzUYOtbv51EXVcYlDGJ9JKQqybJJsEinN4wtgm6ayyZ5mprYok/1t/Wxp7OaBNxt54I+h5p+j3B4fB1pdrK3KlZMTkVClOel0D3oZ8YWupnt8Ad473sPVUSzDbTIpqvIzaJJkk4iCUup6pdRhpVSDUuqrk9xvU0r9Jnz/u0qp6vB2p1LqNaXUgFLqPyc85vXwMXeFv6Yux4sjWY1OzEVNQSYX1xXw7vEejp52JzocIc4g72wG0DPoBULLXEbLpBTleensPuWKeTwd/R5ePdTBbesqzhgcV1fm8rkranliezN/++Qedp/q4wcvHeaDK0u4eXVZzOMQqaPQYZOeTSKlhabRTT/kLi/NoWfQy6bdrZxT4qCuKIvf7mzhhX1tDI342Xmyj6CG86vz5yFqISIrG+vbFJpKd+S0G39QT9uvaVS1M4OmLplGJ6amlDID9wM3AMuBO5VSyyfsdg/Qq7WuA34IfC+83QN8HfhyhMN/XGu9OvzVEfvoo+PxBSXZJObk2uXFFDpsPLmjmWGvTKcTyUPe2QygZyiUbMqfQbIJoCI3g0Pt/TGfw/vEjmYCQc3Hzq88674vX3cOX7iqjse2NXPbf79DXoaVb9+yMuJy30JAqEl476AXX0BW0xCpR2sdnkY3/QIPy8uycdgtXFzr5E8vWMQnLqhm/aI83jzaxT89f5BXD3VQ7cygKNs+D5ELEVlpeGr8aJPww+1u8jLSWFMVXS+xamcmJ3oGCcZxIROREjYADVrrRq21F3gUuHnCPjcDD4VvPwFcrZRSWutBrfVbhJJOScvjlZ5NYm7SzCZuX1fBwIif3+5sHpthMpFryMd3nj/Ia4cSllsVC0zkdWlF0hirbJrhqkMVeen4ApqDbW5WV+bGLJ63G7pYUZZNTbjB53hKKb503TmU5KTz/RcP8c+3r5pRRZZYmAodNjSh3mSlOdLbS6QWjy9IUDPtNDqALJuFr16/9P0EvYJb11ZwwWIne1tcHO1wc8U5CZvtIcSYbLuFDKuZVpeHoNYcPu3mnGIHv9l6KqrHd7hH8PiC/PjNRnLS0866/66NVbEOWRhTOTD+RdUMbIy0j9bar5RyAU6ga5pj/0wpFQCeBP5RTzhDV0rdC9wLUFUVv9djqEG4XP8Xc1ORl8F1y0t4YX87W5t62VDzfgW01ppn97bx95sO0DUwwq+2nOSFL1461uNXiHiRdzYD6Bn0kmE1T7uS0UQVeaGT9lj2bdI6lLxaUZY95X53baxi59ev5bIlhTH73SJ1ja60KH2bRCpyhxdNiGYaHTBpJWhZbjofWFHCfVfWs6TYEdP4hJgNpRRluensb3Xx3ecPMeQNsLR06s8G4zmzQheiugflfV9MabKSn4llG9HsM9HHtdYrgUvDX5846wBaP6C1Xq+1Xl9YGJ/Ps/5AEF9AS2WTiIlL6guoK8ri93taaekNVZ0Gg5pv/f4A9z2yk9IcOw98Yh1aa778+O6IlaWH29387O3jESukhIiWVDYZQO+Qd8ZT6ABy0tMoyLKy82Qff3bh3GJ45N2TAPR7fPQMehnyBsa2CTFXBVk2FKEr3UKkmn6PHyCqaXRCGMnK8hz6hnxU5KVTW5jFudNciBrPmRm6yNA94GVxQbwiFCmgGRjft6ECaI2wT7NSygLkAD1MQWvdEv7uVko9Qmi63sOxCjpaHn+ofYBUNolYMCnF7esq+M9XG/jRGw0MjPjoGvTy7J42Pn1xDf/nxmWYTYpvfngFX3lyDz99+zifuXTxGccYHPHzmYe3cqpnmPMqcli3SHpEitmTZJMB9Ax6Kc+d+dQipRRXLS1i0+5W/u6Dy8aqR+aiPdwItET6hYgYSjObyMu0SmWTSEkDI6Fkky3KyiYhjOL86vxZN6vPzUjDrBTdA94YRyVSzFagXilVA7QAdwB3TdhnE3A3sBm4DXh14pS48cIJqVytdZdSKg34EPByPIKfjie8mqNFkk0iRhz2NP7q6npeOXiaX2w5QVDDDeeWUFeUNTbNWWvN0hIH333+EL6APmMq89O7WmjuGSbNrPj7TQe4c0NoCqlMbRazIe9sSS4Q1PTNsrIJ4HNX1OH1B/nJHxtjEs9YsilHkk0itgqzbJJsEilpbBqdVDYJMcakFHmZVplGJ6aktfYD9wEvAgeBx7TW+5VS31JK3RTe7UHAqZRqAL4EfHX08UqpJuAHwCeVUs3hlexswItKqT3ALkJJrP+Zr+c03miyySrT6EQMZdks3Ly6nC9cXc9nL13MpfVnTgNVSvHh88rQwCsHT49tb+gY4N3jPVxU62RjjZP9rS5cw755jl6kEqlsSnL9wz6CeuYr0Y2qKcjkw6vK+MWWE/z55bWzPs6o9n4POelpZFjlpSNiq9Bh41jnAEGtMcnqhSKFuD1S2STEZJyZ1rFFUISIRGv9HPDchG3fGHfbA9we4bHVEQ67LlbxzYVUNol4KnLYIUKbx7xMKxtr8tl8rJtL6goIavjNtlMUZNm4bkUJbo+ftxu6ePd4N9ctL5nfwEXKkHe2JNczFPoQNpck0X1X1jHsC/DgW3Ovbmp3eWQKnYiLIocNf1DTG+HEQ5oUCqMaGO3ZNMNFHoRIdc4sK90DXnl/FwuWxxfu2WSSUzIx/644pwirxcRTO1v4nz82YlbwpxdUkWY2kZ9pZWlpNu8d78EXCCY6VGFQ8s6W5EZPvPMyZp9sqi92cMO5JTz0zgla+oZnfRx/MEiH2yNT6ERclIdXTzzaMXDWfce7Bvnmpv387O3j7GtxEYiweoYQyahfptEJMSlnphVvIIg73NdMiIVmOFzZlGaRim4x/7JsFi6tL+RkzxD2NBP3XlYbqoYKu3CxkyFvgINt/QmMUhiZJJuSXM+gF5PijMZts/GVDywF4PO/2oHXP7vsdKd7hKCWfk0iPkpz0inNsbPtxJkLyHj9QZ7c0UyG1UyHe4RH3jvJ7/dMXIhGiOQlDcKFmJwz6/0V6YRYiEan0Ullk0iUS+oK+MDyYu697Ox2K4sLM7FZTDR2DiYoOmF08s6W5HqGvORmTQe6/gAAIABJREFUWDGb5nbFo7ogk+/fdh67TvXx3ecPzeoYshKdiLf1i/Jo7fPQOq4C76UD7fQMevno+kr+5gPncF5FDrtO9Y19QBMi2bk9fqwWk/QiE2ICZ/jEpkeahIsFamwanfRsEglitZi4/JyiSQsbTEqxyJnB8W5JNonZkXe2JNcz6CV/DlPoxvvgylI+eVE1P337OG8e6Zzx49tdHiwmRUH4SqQQsbaqMheLSbHtRC8Qmj73zrFuNtbks7gwC5NSrFuUx4g/yGuHOhIcrRDRcXt82C0y3AoxUW6GFZOSyiaxcI1No5PV6ESSqnFm0ukeoXtALgqImZNPv0mud9BLXubcptCN93cfXEahw8Yvt5yY8WPb+j0UZdvmXGUlRCQZVgvLy7LZfaqPPc19PPROE3mZVq5f8f4qGIsLssiyWdi0W6bSCWMYGPFjk+bgQpzFbFLkZVjplhXpxAI1No1OKptEkqouyARga1NvgiMRRiTvbElsxBdg0BuIWWUThEolb1pVxmuHO+gbmtmHu3aXh9Ls9JjFIsRk1i3KY9gX4NGtpyjKtnHvZYvPOFE3mxQry3N45VDHWONlIZKZ2+OXyiYhIgitSCdXzMXCNBJONlmkskkkqfK8dCwmxXvHe6bfWYgJ5NNvHO082cvrh2c/1acnnAzKy4xdsgngT9aU4wtont3bFvVjBkf8DIz4Kc6WKXQivmoLs6jKz2B1ZS6fvXQx2fazK/tWVebi9Qf5w/7TCYhQiJnp9/ixS2WTEJNyZtroHvSitawyKhae0Wl0VqlsEknKYjJRlZ/Be03diQ5FGJC8s8WJ1pq/eWIPX/zNLvyB2a3+1hsuK5+4MsBcrSjLpr4oi9/tbIn6MR3u0FXHImkOLuLMpBR/cXktH11fGbGsvDIvncr8dJ7eFf1rWIhEGfD4ZBqdEBE4s6yM+IMMemXRB7HwjDYIt0iySSSx6oJMDrT2y4wCMWPyzhYne1tcNHQM0Dfkm/Uc156h0B90LKfRASiluGVNOVubejnVMxTVYzrDyaZCh1Q2icRTSnHjyjLeOdbNkNef6HCEmJJMoxMistEV6WQqnViIPL4AaWYl/VBFUqspyCSoYfsJ6dskZkY+/cbJUztasJpNWC0mXjowu6k+PYNebBYT6dbYXxG/eXUZQNTVTR1uD2lmNemymEIkwoaaPAJBzd5mV6JDEXOglLpeKXVYKdWglPrqJPfblFK/Cd//rlKqOrzdqZR6TSk1oJT6zwmPWaeU2ht+zL8rpRL6KX5gRKbRCRGJM7zCrTQJFwvRsC+A3SLjg0hulXkZ0rdJzEpck00L4SRiMr5AkGd2t3L1siIurSvgDwfaZ9WLoHfQS36mlXg8xYq8DM6vzuOF/e1R7d/pHqHIYceUfP/cYoFaVZELwM5TfQmORMyWUsoM3A/cACwH7lRKLZ+w2z1Ar9a6Dvgh8L3wdg/wdeDLkxz6R8C9QH346/rYRx8dfyDIkDeALU2u7QgxmdyMNExKKpvEwuTxBWWatUh6VouJ1ZW5/PFoZ6JDEQYTt0+/C+EkIpI/Hu2ke9DLn6wp59rlxTT3DnOo3T3j4/SEk03xcnFdAQfaopt/2+EekSl0Iqk4s2wscmaw86SU9BrYBqBBa92otfYCjwI3T9jnZuCh8O0ngKuVUkprPai1fovQeDFGKVUKZGutN+tQlv9h4Ja4PospDIyEpnnKlWshJmcxmcjNsEplk1iQPL4A6Va5GCGS3zXLi9nX0k9r33CiQxEGEs93t5Q/iYjkqR0t5GakccU5RVy9rBilmPGqWUGt6R3ykhfjfk3jbajOR2vYPk1PqYERP65hH0WSbBJJZk1lLjtP9skqRsZVDpwa93NzeNuk+2it/YALcE5zzOZpjgmAUupepdQ2pdS2zs74XK1ze8LJJrlyLUREzkwr3QOSbBILj0em0QmDuG55McCs28OIhSmeyaaUP4mYjNcf5KUDp7lxZSlWi4lCh421VXm8dDC66WqjBjx+/EEd18qmNVV5WEyKd6eZf3usYwBAkk0i6ayuzKXDPUKbyzP9ziIZTTYvd2LmMJp9ZrW/1voBrfV6rfX6wsLCKQ45e6PJJps0CBciovxMK92DI3LhQCw4Hl9ALkYIQ1hcmEVdURZ/ODCzc1qxsMXz02/Kn0RMps01zIg/yOrK3LFt14bLDttncELcEy4nj2eyKd1q5ryKHLY2TZ1sOhpONhU67HGLRYjZWFOVB8DOk9K3yaCagcpxP1cArZH2UUpZgBxgqjet5vBxpjrmvHGHpynLyYQQkRVk2fD4ggx7A4kORYh5NewLkC7jgzCI65YXs6WxB9fQ9C1YhID4JptS/iRiMi29oXms5bnpY9s21OQDsKc5+hPinqFwsimO0+gANtQ42dPcN+UHvIaOAcxKxTXxJcRsLCvNxmoxseuU9G0yqK1AvVKqRillBe4ANk3YZxNwd/j2bcCreoryB611G+BWSl0QXkDiz4CnYx96dMZ6NkmDcCEicoY/X3RJ3yaxwIQahMv4IIzhuhUlBIKaVw7JVDoRnXi+u6X8ScRkWsJN08rz3k82LS1xoBQcaOuP+jg9g14UoVVa4mlDTR6+gGbnFCfrDR1unFlWzCZZiU4kF6vFxLll2VLZZFDh6dP3AS8CB4HHtNb7lVLfUkrdFN7tQcCplGoAvgSMrWyqlGoCfgB8UinVPG4Ris8BPwEagGPA8/PxfCYz1rNJenIIEVF+VijZJCvSiYVGptEJIzmvPIfibNuMexGLhcsSrwNrrf1KqdGTCDPw09GTCGCb1noToZOIX4RPInoIJaSAsZOIbMCqlLoFuE5rfYDQScTPgXRCJxAJO4mYzGiyqSTn/SlnGVYLNc5MDs4g2dQ76CU7PQ2LOb5XO9YtykcpeO94DxfVFky6T0PHgPRrEklrTVUev9xyAl8gSFqc/15E7GmtnwOem7DtG+Nue4DbIzy2OsL2bcC5sYty9kan0cmVayEiy8+wokBWpBMLjkem0QkDMZkU1y4v5sntLQx5/WRY45ZKECkirq+QVD+JmExL7zBFDhu2CVexl5Vlz3gaXSxWonvk3ZPT7lOSbWfT7laKJunJ5AsEOdE9xBXnFM05FiHiYXVlLg++dZxDbW5WVuQkOhwhzuAekdXohJiOxWwiNyNNKpvEpJRS1wP/Ruji9U+01t+dcL+N0ArV64Bu4GNa6yallJPQatfnAz/XWt837jHreP/i9XPAX081uyJePL6gTLMWhvLh88r45ZaTvLCvnVvXVkz/ALGgybtbjLW6hikb169p1PLSbE71DNPvia6hWu+gd956JFUXZHKqZwh/MHjWfV0DI2igKFsqm0RyGm3Gv6dFptKJ5OP2+EkzKywyDVmIKRVk2eiUZJOYQCllBu4HbgCWA3eOmzI96h6gV2tdB/wQ+F54uwf4OvDlSQ79I+BeoD78dX3so5/esEyjEwazoSafamcGj207Nf3OYsGTZFOMtfQOn9GvadTy0mwADrW5pz2Gxxeg3+MnPzO+/ZpGLS7IxBfQnOwZOuu+0RX0ZBqdSFYVeek4bJYZTVMVYr4MePw47GmE2gwKISIpzrbT0T9CcP6LS0Ry2wA0aK0btdZe4FHg5gn73Aw8FL79BHC1UkpprQe11m8RSjqNUUqVAtla683haqaHgVvi+iwikGl0wmiUUty+vpItjT2c6B5MdDgiyUmyKYaCQU2ry0PFJJVNy8LJpmhOiJt7Q0mfWEyji0ZtYRYmBUfaz06EHescJD3NTHH22VPshEgGSimWlWZzMIpErhDzze3xkWWTngZCTKc4244/qOkZkL5N4gzlwPgSiubwtkn3CS884QKc0xyzeZpjopS6Vym1TSm1rbOzcxahTy0Y1Iz4g9gk2SQM5ta15ZgUPLG9efqdxYImn4BjqGtwBK8/SGvf8Fm9krTWZFjNPLO7ddomxofbQwmp+ZpGZ08zU+3M5PBpN9efWzq2XWtNQ4eb2qIsTHJVXiSxZaUOHt/eTDCoMcl0JZFE3B4/DrsMtUJMpyR8Uau930OBVFOL9002qE8sf4tmnxnvr7V+AHgAYP369TEvuRvxh9pXSM8mYTSlOelcWl/IE9ub+eI1S2TFchGRvLvFUGtfqEo3d5KKJKUUpTl22lyes+6bqCe8Gst8JZsAzilxcLp/hL6h968odrhH6Pf4qS/Kmrc4hJiNZaXZDHkDk04FFSKR3COSbBIiGoUOGwo43T/95ySxoDQDleN+rgBaI+2jlLIAOYRWuZ7qmOM7G092zLjz+AIAMo1OGNJH11fS5vLwVkNXokMRSUySTTHU0jsMQG7G5L2WSnPSOd3vIRCc+uJIz6CXNLOa16kXS4odABw5PTC2raEjdLtOkk0iyc1kmqoQ88nt8ZNlm5/+e0IYmdViIj/TSrskm8SZtgL1SqkapZQVuAPYNGGfTcDd4du3Aa9OtbKc1roNcCulLlChhnp/Bjwd+9Cn5vGHkk3SIFwY0TXLi8jNSJNG4WJKkmyKoda+cLIpffKKpNKcUD+CrmlWW2nr91DksM9rQ9kih43c9DQOn36/783RDjcFWdZ56x0lxGydU+LApCTZJJKP2+MjWyqbhIhKcbad0/2yIp14X7gH033Ai8BB4DGt9X6l1LeUUjeFd3sQcCqlGoAvAV8dfbxSqgn4AfBJpVTzuJXsPgf8BGgAjgHPz8fzGW/YK5VNwrhsFjO3rC7npf2nz5gZI8R48gk4hlr6hrFZTBHnXpfkhPoRtLk8ERtua61p7RtmZXlu3OKcjFKKJSUOdp3qwx8IzSE/3jXIukX58xqHELNhTzOzuDCLA9IkXCSZAZlGJ0TUirPtHGzrxxf+HCIEgNb6OeC5Cdu+Me62B7g9wmOrI2zfBpwbuyhnzuN7v2fTUDjxJISRfHR9JT9/p4mnd7Vy90XViQ5HJCGpbIqhlr5hcjMiL3Fd5LBjMamxCqjJ9A758PiClOXO/+pv5xQ78PqD7Gvt50TPEL6Aln5NwjBCK9JJZZNIHlrr0DQ6STYJEZWSHDsa6HRLdZNIfcPhnk2yGp0wquVl2Zxbni1T6UREkmyKoZbe4YhT6ADMJkVFXgbHuwYj7jOaiCrPTY95fNOpLcwi227hsW2n+OWWE5gULC7InPc4hJiNZaUOWvqGcQ35Eh2KEEDoRCIQ1Djs0rNJiGgUh1ehkybhYiEYkQbhIgV8dH0l+1v72dfiSnQoIgnJ5dYYaukbZmmJY8p9agoyeP1wJyO+wKRXMlr7hjEpIk6ziyerxcQXr1nC7uY+dpzopdBhl6stwjDGmoS393PBYmeCoxECBjx+gHld7EEII3Nm2TCblDQJFwuCNAgXRvLIuycn3e71B7GYFP/47EFuWlU27XHu2lgV69BEEpPKphgZGPHjGvaRmz71Feyagiw0cCLCEu2trmGKHHbSzIn5r7GnmdlY4+RzV9Rx27qK6R8gRJJYISvSiSTTH042Sc8mIaJjNimKHDapbBILwuBIKNmUYZVkkzCuDKuFleU5bD/Rg9sjswvEmSTZFCNjK9FNs3JbVX4GJsWkU+m01rT0DidkCp0QRlfosOHMtEqySSSNgZFQsilbptEJETVZkU4sFK7h0Il5zjQXqoVIdleeU4Q/oHnzSGeiQxFJRpJNMdIylmyaesCwWkyU56bTNEmyqd/jZ9AbSEhzcCGMTikVbhIuK9KJ5DB6hU8ahAsRvZJsO65hHx1uqW4SqU2STSJVFDhsrK3K493jPWOvayFAkk0xM1rZFM2AUVOQSXPvMF7/mUv7jh6jTCqbhJiVZaUODp9245dls0UScMs0OiFmbElxqPfli/vaExyJEPHVP+zDajFJzyaREq5aWoTW8NrhjkSHIpKIJJtipN3lwaSIatWh6oJMAlpzqvfMvk2tfcMooDRHkk1CzMbysmy8/iCNU6z4KMR8GRhLNslVayGiVZxto9Bh45k9bYkORYi4cg37pKpJpIy8TCvrqvPY1tQz5crrYmGRZFOMtLs8FDpCq6hMp9qZieLsvk0tfcMUOGxYLfLfIsRsLJMm4SKJ9I9Oo5PV6ISImlKK88pz2NrUI43CRUqTZJNINdctK8aZaePhzU1jM3bEwiZZjRhp7/dQEmVFkj3NTGmOncbO95NN/kCQU9IcXIg5qS3Mwmo2cUCSTSIJjE6jk2STEDOzsiIHreG5vVLdJFKXJJtEqsmwWfjUxdXY08z8/J0muWAgJNkUK20uD6XZ0Tf2XlGeQ1P3IIfaQyfFbx7tZHDEz5qq3HiFKETKSzObqCvKkibhIikMjPjJslmiqngVQryvyGFnaYmD38tUOpHCJNkkUlFuhpVPXVyNBu5/rYHNjd1orRMdlkgQSTbFyGmXh5Kc6JNNl9YVUOSw8budLbT0DvP64U5WludQX+SIY5RCpL7lZdkcaJXKJpF4bo9PqpqEmKUPrypj+4lemYohUpYkm0SqKnLY+cJVdSwuzOSZ3a08tLlprLWAWFgk2RQDAyN+3CP+GSWbLGYTt62rwO3x8+M3j2E2KW5cWRrHKIVYGJaVZtM1MEKneyTRoYgFzu3xy0p0QszSjStLUQq+8fQ+RvyBRIcjRMxJskmkMoc9jbsvrOamVWU0dg7y768c5UCrK9FhiXkmyaYYaHeF5qOWzGAaHUBFXgaX1hfiD2quWVZMtgw4QszZstJQdaA0CReJNjAiySYhZqu6IJNv3bSClw928Be/2I7HJwknkToCQY3b45fP/iKlKaW4YLGT+66qIzcjjV++e5KH3mlKdFhiHkmyKQbGkk0zqGwade3yYu65pIYLa52xDkuIBWm5rEgnkkS/x0+WXU4khJitT1xYzT/9yUpeO9zJ3z65J9HhCBEz7vCUIqlsEgtBkcPOX1xey/LSbL65aT+/2HIi0SGJeSLJphhoc4X6CZTOItlkNilqC7MwKWkgK0Qs5GZYKc2xS7LJIJRS1yulDiulGpRSX53kfptS6jfh+99VSlWPu+9r4e2HlVIfGLe9SSm1Vym1Sym1bX6eydkGPD6pbBJiju7aWMXnrqhl0+5WjnUOJDocIWLCNSzJJrGwWEwm7thQyTXLivj67/bx6/dOJjokMQ/immxK5ZOI8UaXdSye4TQ6IUR8LC/NlhXpDEApZQbuB24AlgN3KqWWT9jtHqBXa10H/BD4Xvixy4E7gBXA9cB/hY836kqt9Wqt9fo4P42I3B4/2ZJsEmLO7rmkBqvZxE/+2JjoUISICUk2iYXIYjJx/8fXcuU5hXztqb08tvVUokMScRa3ZFOqn0SM1+bykJeRhj3NPP3OQoi4W1aaTUPnAMNe6fGR5DYADVrrRq21F3gUuHnCPjcDD4VvPwFcrZRS4e2Paq1HtNbHgYbw8ZKG2+OX1eiEiIGCLBsfWVfBkztaZPEHkRIk2SQWKpvFzI/+dB2XLSnkb5/aw7+/cpTNx7o53e9Ba53o8ESMxbOyKaVPIsY73e+RqiYhksi6RXkEgpqdJ3sTHYqYWjkw/rJWc3jbpPtorf2AC3BO81gN/EEptV0pdW+kX66UulcptU0pta2zs3NOT2QifyDIsC+AQ3o2CRETn7mkBl8gyMObmxIdihBzJskmsZDZ08w88Il1XFZfyA9eOsKd/7OFjf/0Cud+80U+9B9/5I9HY/uZTCROPC+5TnYisDHSPlprv1Jq/EnElgmPnXgSoYEfa60fmOyXh08w7gWoqqqa2zOZRpvLM6t+TUKI2Xvk3chzvT2+AAr4yVvHaeoemvZYd22M73uEiGiyZnUTL2tF2meqx16stW5VShUBLymlDmmt3zxr59D48QDA+vXrY3o5bWDEDyCVTULEyOLCLK5dVszDm0/wl1fUkW6VanJhXJJsEgudPc3Mzz91Pq0uD42dAxzvGqSxc5CXD57mK0/s4bUvXyGzhlJAPCub4nkSsZbQ9LzPK6Uum+yXa60f0Fqv11qvLywsjDbmWWl3eSjJSY/r7xBCRM+eZqYsN53jXYOJDkVMrRmoHPdzBdAaaR+llAXIAXqmeqzWevR7B/BbElAZ6/aEkk3SIFyI2PmzC6txDft444hc9V5IUrEHrCSbhAClFOW56VxaX8ifXVjN39+0gv932yraXB4eeqcp0eGJGIhnsillTyLGG/EH6B70UiLT6IRIKjUFmZzqGcIXCCY6FBHZVqBeKVWjlLIS6tW3acI+m4C7w7dvA17VoUn9m4A7wicZNUA98J5SKlMp5QBQSmUC1wH75uG5nKE/vKy1TKMTInY2Ls4nNyONF/e3JzoUMU9StQesa9iH1WzCniYLgwsx3oW1Tq48p5D7X2ugb8ib6HDEHMXzHS5lTyLG6+gPNaqUaXRCJJeagkz8QU1z73CiQxERhHsw3Qe8CBwEHtNa71dKfUspdVN4twcBp1KqAfgS8NXwY/cDjwEHgBeAz2utA0Ax8JZSajfwHvCs1vqF+XxewFgT40KHdb5/tRApK81s4uqlxbxy8DRev1xIWCBSsgds/7CP7PQ0QmEKIcb7yvVLcY/4+a/XjyU6FDFHcavvD/dgGj2JMAM/HT2JALZprTcROon4RfgkoodQQorwfqMnEX7CJxFKqWLgt+E3ZgvwSCJOIsZr7/cAUCzJJiGSSrUzEwUc7xqgpiAz0eGICLTWzwHPTdj2jXG3PcDtER77beDbE7Y1AqtiH+nMnA6PDTLFWojYuv7cEp7c0cyWxm4uWzLzNgk7TvaypbGbg21u8jLS+NoNy6T/U3JLWA/YePZ/7R/2k5Mu06yFmMyy0mxuXlXGL7ec4IvX1JNhlb8Vo4rr/1yqnkSM1+YKnVBIZZMQySXdaqYkxy59m0RCtLk8KAVFDluiQxHCkCItAuELBLGaTfznaw1RV67etbGK3kEv//fZAzy1owWA8tx0Wl3DHGjt5yd3ryc3Q6oQk1TCFpKI5yISrmGf9GsSYgp3bqjid7taeenAaW5ePXGhYmEUMlF4jtpdoQ86JZJsEiLpVBdkcrJnCH9QpluI+dXu8lCQZSPNLMOsELGUZjaxpDiLg639BHV05/8v7m/nmh+8waZdrfzVVXXs/sZ1vP3Vq7j/rrXsaXbx0R9vpndQeoMkqZTsASvJJiGmdn51PmU5dn63syXRoYg5kJq0OWp3jZBhNeOQ5a2FSDo1zkw2H+umpXeYRU6ZSifmT5vLIxWvQsTJ8rIc9rX2c6pnaMr3dq8/yLN729ja1MO55dn86rMbWVqSPXb/B1eWkpuexid++h7/+vIR/uHmc6OOweML8Ps9bVx/bglZ8hkwnsZ6wAIthFpu3DVhn9EesJsZ1wNWKbUJeEQp9QOgjHE9YAGT1to9rgfst+bn6YS4hn3UFsrnErHwRKpanUxdkYM3jnTywJuNk77P3rUxttNbRezJJdc5au8fpiTHLg3+hEhCiwszMSnY39qf6FDEAtPu8sgqpULEydISBxaTYltTb8R9AkHNQ5ub2NbUw59fvpinPnfxGYmmURfVFXDXhip++e5JGjoGovr9jZ0D/Ml/vcOXH9/Nv79ydLZPQ0QhVReSkMomIaa3ujKXoIa9La5EhyJmSZJNc3SqZ5jyXGkAK0QyyrBaWFqSzc6TvQSCMW23IMSU2vulskmIeLGnmTm/Op+dp3rpjbA09rN72zjeNcht6yr42g3LsFoif+QNNaA1853nDk77u9873sOH/+Mt2lzDrKnK5VdbTuAa8s36uYjpaa2f01ov0VrXhnu6orX+RnixIbTWHq317VrrOq31hnCP19HHfjv8uHO01s+HtzVqrVeFv1aMHnO+BIOafk9oNTohRGQlOXZKsu3sPtWX6FDELEmyaQ6CQU1DxwB1RVmJDkUIEcH6RXkMegMcbpfqJjE/hrx+XMM+WaVUiDi6tL4AheLNI51n3betqYctjd1cUlfAmqq8aY/lzLLxV1fV8cqhDt462jXlvv/2yhGy09N47guX8p1bVzLoDfDQ5qZZPguxELlH/GiNVDYJEYVVlbmc7Bmie2Ak0aGIWZBk0xy09A0z7AtQX+RIdChCiAjqix04bBa2nYg83UKIWGqXVUqFiLvcDCtrF+Wy/UQv/cPvVxYdOe3m6V2t1BVm8YEVJVEf7+6LqqnIS+dfXjqMjtB4/FTPEG83dHPH+VWU5aaztCSbq5cW8bO3jzPk9c/5OYmFYfT1KpVNQkxvdWUuJgVbm3oSHYqYBeloOAcNnaG5/fXFUtkkRLIymxRrqvJ4q6EzVLZulw93Ir5Gk00l2TLFWoh4unxJEdtP9PL8vjZuWFlK94CXX717gqJsG3duqMJsCvXTjLYh7dqqPDbtbuWfnjtETcHZzZvbXcMoBbevrxjb9pdX1vKRH23m0fdO8elLamLzxERKc4WTTVLZJMT0ctLTWFaazbYTvVy9rFhW+TUYSTbNQcPpULKprlCSTUIks3WL8njzaCc7T/Zx+ZLCuP2emaywMRVZXcPY2qSySYh5kZ9p5YLFTt451s2eZhdmkyI3w8qnLq4h3Wqe8fHWLcrjlYOnefNI51nJpqDWPL69mcvqCykb16tz3aJ81i3K49GtJyXZJKIiySYhZuaCxU72t/azp9nFukXTT40WyUNSg3PQ0DFAQZaVvExrokMRQkyh0GGjpiCTdxq68PgC8/I7/cEgAyP+s6ZjaK0ZGPHT5hqmw+05Y/qHSA3t/eHKJkk2CRF3HzqvjC9eXc+VS4uoL8ri0xdXT7pEdjTSzCYuqivg8Gk3ba7hM+47enqANpeHj51fOUkMpRw5PUBjZ3Sr2YmFTZJNQszM4oJMCh02tjR2JzoUMUNS2TQHRzvc0hxcCIO44dwSfvT6MV491MEHV5bG5XdordnS2M3+1n5O9Q7hC2gsJoXDbsFiMqEUuD1+hickvNZW5fHhVaXYLDO/Ei+ST7vLQ25GGvY0+f8UYj4UZdu5Jjs2yd0Lapy8caSTN450csddQ5vRAAAgAElEQVT571eZvne8m/xMK9csKz7rMR9YUcI/PHOAF/a385dX1MUkDpG6JNkkxMwopbigJp9n9rRxqmeIyvyMRIckoiSVTbOkteaorEQnhGFU5GWwblEe7xzr4nS48iSW/IEgT+1o4Zk9bQz7Apxfnc+NK0u5sNbJImcmxTl2Ch02zqvI4caVpdy5oYqPra/k0roCdp7s5f7Xjo1VxAhja3N5KInRia8QYn6lW81cUJPPnmYXu5tDy21vP9HDwXY3d19YjdVy9kfnstx0VlXm8uK+9vkOVxiQJJuEmLk1VXlYLSZePniaYIRFHETykcqmWep0j+D2+GUlOiEM5LoVJexrdfHMnlbuubgGpVRMjuvxBbjvkR1sP9nL1UuLuGppUdTHXlWZy5ISB49tPcUvt5zgr6+uj0lMInHa+4elX5MQBnbNsmJO9Azx5PZmhrwBnt/bRm1hJp+/sjbiY244t4TvPn+Ilr5hynNlcQARmWvYh8WkyJhFXzEhFip7mpnrV5SwaXcr7zR0cUl9/HqwitiRyqZZOtoRbg4ulU1CGEaWzcIHVpTQ2DnIK4c6YnJMrTVfeWIPLx/s4KZVZVy9rHjGSazawixuX19Jz6CXN450xiQukTjtLg8lOXKyKYRRWcwmPrFxETnpaTyzu5Usm4WPnV+FZYpVkD6wogRAqpvEtFzDPnLS02J2wUuIhWJjTT7LS7N5cf9pWnqHp3/ALAx7A7x5pJOBEX9cjr/QSGXTLB097QagXpJNQhjKhup8mnuHefVQB85MK2uq5raqxb+/0sCm3a185fpzyE2f/WIBdUVZrK7M5Y0jnRzrHKBWVrk0pBF/gK4Br0yjE8LgMmwW7r6ommf3tHHN8mKybJZpVxwtybbz8OamqPq1yaqjC1fPgJecDJlCJ8RMKaW4dW05//FqAw9vbsKRbuFj6ytjsljXzpO9/OSPx3n1UAfDvgCrKnP5xT0byLbL3+pcSLJplho6B8i2Wyh02BIdihBiBpRS3Ly6jN5BL0/tbCF9Dk2cn9zezA9fPsKta8v53OW1/Pq9U3OK7YZzSzjU3s/Xf7ePX31mo1z1NKCO/hEAmUYnRAooyLJx90XVUe+/ojybVw920DPoJV9WKhYR7Gt1cV5FTqLDEMKQMqwWPnHBIp7d28Z3nz/Ev/zhMOW56ZTk2CnNSac0x05pjp2SnHScWVY6+kdo6h7kRPcgx7sG6R/2U1OQSW1RFmU5dpxZNn6/p5Wnd7WSl5HGrWvLWVyYxXefP8gnHnxPEk5zJMmmWTp6OtQcXE4GhTAei8nExzcu4sG3GvnFlhPkZ1n5wlX1mEzR/T0Hg5p/eekw9792jAsXO/nOrStj8l7gsKdx7fISntndyptHu7h8icxHN5rRJu8lkmwSYsFZvyif1w518M6xLj50XlmiwxFJqMPtobl3mE/OIIkphDhTWW46n710Me39Hnaf6qNn0Etbn4dD7W76h30EJ+kfnmk148yykZ5mZktjN8/tbWN0N4tJcd+VdfzFFbVk2ULpkcq8dD7/yA7ufXgbj3zmgqjPEcSZJNk0S8c6B7h66dnL3wohjCHdaubey2p5elcL//ryUd5p6OYvr6zl8iWFERNHQ14/rx/u5FfvnuDthm7uOL+Sb9187qSrE83W+dV57DjRyw9eOsJl9QWS0DaYNlco2SSVTUIsPDnpaayqyGXbiV6uXlpMujSAFhPsPBla4XBNVW6CIxHC+Eqy7ZSE++WNCmrN4Igf17CPAY8fhz0NZ5b1rOnN/kCQgRE/bo+f3Iw0/vzyMxeAuG5FCd++ZSVfeXIPD21u4lMX18T76aQkSTbNQptrmK4BL/XF0lNFCCOzWkzctq6Cj55fyb++fIRP/mwrNQWZnFueQ7UzA4D+YR+nwyW4x7sGGfEHcWZa+b83r+BPL1gU82SQxWTivqvq+NpTe3n9cCdXLi2K6fFFfLW7Qg0rpbJJiIXpkvoCdp7qY2tTD5dJdaqYYOfJPtLMihVlMo1OiHgwKYXDnoZjmqlvFrOJ3AwruRmRpzzfvr6C5/e18b0XDnHlOUVUF2TGOtyUJ8mmWfjdzlYArl4mlU1CGJ1Sijs3VPGRtRU8s7uVZ/a0svtUH8/uCf2dO+xpFGRZqXZmcml9AVctLWZDTT7mOJbT3raugv96vYEfvnyEK86JXGklks/h9lA/v+k+5AghUlNpTjq1hZm8c6yLi+qcWEyy8LN4346TvSwvy4mqibwQIrGUUnzn1vO49odv8JUn9/Drz14Q18//qUiSTTOktebJHc2sX5RHjWQ3hUgJ41cYum55qBw3ENQoFbpCMt7xrlCFUzylmU381ZX1fOXJPbywr50bVpbG9feJ2PD4AvxhfzsfOLdk+p2FECnrkrpCHtrcxBtHOqXlghjjDwTZ09zHnRtkJUIhjKIkx843P7yCLz++m++/eIiv3bAs0SEZiiSbZmhPs4uGjgG+c+vKRIcihIijRF+5uHVtOT97p4m/f2Y/F9UVkJMulTLJ7tVDHbhH/NyyujzRoQghEmhJcRbnVeTwysEO0kymSafTaa3Z39rPgdZ+XMM+Alpz9dIi6osdCYhYzIdD7W48viBrqvISHYoQYoLxF54ns7Emnx+/0Uj3gJe10/wN37VREsqjJNk0Q0/uaMZmMXHjeVJpIISIH4vZxPc+spJb7n+b7z5/kO/cel6iQxLT+N3OFoocNi6sdSY6FCFEAimluH1dJQAv7G+ne9DLirJsirPtdPR7ONk7xE/fPk5Dx8AZj/vu84dYVprNJy5YxEfWlWOzRJ5q5Rr2sflYF0dPD3Dz6nKqwn0GRfLaebIXgLXSHFwIw/nQeWV0Dozw250t5KSnUVsovZujIcmmGRjxB3h6VyvXrSghW/pxCCHi7LyKXD5z6WIeeLORD68q46LagkSHJCLoG/Ly2uEO7r6wOuFVcUKIxDObQgknm8XEjhO9bG3qOeP+Rc4MblldTl1RFhlWM75AkL0tLnac7OXvfruX7z5/kAtrC1hTmUv2uMrWlr5hXj14mkPt7rFlu//j1QY+fUkNn7+yVvrFJbEdJ/sodNgoz01PdChCiBkymxR3bajix2828vO3m7hlTRnrFuUnOqykF9dkk1LqeuDfADPwE631dyfcbwMeBtYB3cDHtNZN4fu+BtwDBIAvaK1fjOaY8fSLzSdwDfv4yFqZIiGEmB//65ol/GF/O3/+i+38x51ruOKc1FqdLlXGief2tuMLaG5ZI+ODECLEbFL8yZoKblxZxomeQbrcIxRl2ynLSSfdembVkj3NzEW1BVy42MmxzkFeP9zBi/vb+cP+dhY5M0gzm/D4ApzqHcaeFpqad06xg5z0NBq7BvnvN47x3N427r9rLSsrUmuls1QYJ7TW7DjZy9qqXFn0QwiDyrBa+IvLavn1eyd5ckcLJ3uGuXxJIfmZkVe0W+jilmxSSpmB+4FrgWZgq1Jqk9b6wLjd7gF6tdZ1Sqk7gO8BH1NKLQfuAFYAZcDLSqkl4cdMd8yY8wWCfPvZg/z8nSYurS/gkjqpLhBCzI90q5lffmYjn314O5/++Va+dO0SPr5xEXkpMLClyjjR0OHm4c1N1BZmsqIsO16/RghhUFaLifoiB/VF0/djUkpRV5RFXVEWXe4Rdp7q5WjHAIFgALPJxDXLiriotuCM1cz+5ao67txQyRd+vZOP/Ogd/te1S/jY+ZUpcQKUCuOEa8jH/358Nye6h7j3ssXx+BVCiHmSbjVz90XVvLCvjc2N3Wxr6qG+OIsih51suwWUontghCFfgGFvgBF/kEyrGYc9DYfdQnZ66LvDbiHbnkZ2eLtS4PEF8fgCePwBfH5NdroFZ5aNTKvZsEnqeFY2bQAatNaNAEqpR4GbgfFv5DcDfx++/QTwnyr0L3kz8KjWegQ4rpRqCB+PKI4ZE53uEX729nGOnB7gYFs/LX3D3HNJDV+7YSkWsyxjK4SYPxV5GTz5uQv58uO7+ec/HOEHLx1hTVUe1c5M8jPTqCvK4mPnG7IZoaHHieNdg/zzi4d5bl8bdouZ7992nmE/DAghkk+Bw8a1y0u4dvn0+66vzufZL1zKlx/fzfdeOMQ//+EwFy52UuXMIC8jjZXluVxvzJUyDT1O7G128ZePbKetz8M3P7ycu2QlOiEMz2xS3HheGZfUF/Le8R72tvTR2DmIPxia3Pzc3jbMSpFmUZhNJnz+IN5AcNa/z2Yx4cy04syy4cyykp9hxWxSaEBr0GjQENR63LZQRaWGsfsgdPEjw2rGnmYOfbeYMYXbP6ytyot539F4JpvKgVPjfm4GNkbaR2vtV0q5AGd4+5YJjx2dmzDdMQFQSt0L3Bv+cUApdXgWz+EM3/gefCN0swDomuvxEsjI8Rs5dpD4E8kwsX988s1nxH98wp13zP7XLZr9Q+cspcaJm/4RMNDrbAryHJKD0Z+D0eMHgzyHCGMGjaFvsXgOC3KciPUY8envwKdDNw3xupqC0eMH4z8Ho8cPxn8ORo8fYvscJh0n4plsmuzyro5yn0jbJyspmnjM0EatHwAemCrA2VJKbdNar4/HseeDkeM3cuwg8SeSkWMH48cfQcqNE6nw/yTPITkY/TkYPX6Q55AkEjZOxOtcwuj/J0aPH4z/HIwePxj/ORg9fpif5xDP+WDNQOW4nyuA1kj7KKUsQA7QM8VjozmmEEIIY5BxQgghxFRknBBCCIOKZ7JpK1CvlKpRSlkJzfLYNGGfTcDd4du3Aa9qrXV4+x1KKZtSqgaoB96L8phCCCGMQcYJIYQQU5FxQgghDCpu0+jCc6bvA14ktKzoT7XW+5VS3wK2aa03AQ8Cvwg37Osh3HYkvN9jhBr1+YHPa60DAJMdM17PYQpxmZ43j4wcv5FjB4k/kYwcOxg//rOk6DiRCv9P8hySg9Gfg9HjB3kOCSfjRFIyevxg/Odg9PjB+M/B6PHDPDwHpfWkrSyEEEIIIYQQQgghhJixeE6jE0IIIYQQQgghhBALjCSbhBBCCCGEEEIIIUTMSLJphpRS1yulDiulGpRSX010PNNRSv1UKdWhlNo3blu+UuolpdTR8Pe8RMYYiVKqUin1mlLqoFJqv1Lqr8PbjRK/XSn1nlJqdzj+fwhvr1FKvRuO/zfh5pRJSSllVkrtVEr9PvyzkWJvUkrtVUrtUkptC28zymsnVyn1hFLqUPj1f6FRYl/IjDY+gLHHCDD+OAGpMVaMMvKYAcYeN0bJ+JG8ZIyYfzJGJA8ZHxIrUWODJJtmQCllBu4HbgCWA3cqpZYnNqpp/Ry4fsK2rwKvaK3rgVfCPycjP/C/tdbLgAuAz4f/vY0S/whwldZ6FbAauF4pdQHwPeCH4fh7gXsSGON0/ho4OO5nI8UOcKXWerXWen34Z6O8dv4NeEFrvRRYRej/wCixL0gGHR/A2GMEGH+cgNQYK0YZfcwA444bo2T8SEIyRiSMjBHJQ8aHxErM2KC1lq8ov4ALgRfH/fw14GuJjiuKuKuBfeN+PgyUhm+XAocTHWOUz+Np4Fojxg9kADuAjUAXYJnsNZVMX0BF+I3nKuD3gDJK7OH4moCCCduS/rUDZAPHCS/gYKTYF/KXUceHcKwpMUaE4zXsOBGO1XBjxbjYDT1mhGM05LgxLlYZP5L0S8aI5PiSMSJhccv4kNjYEzY2SGXTzJQDp8b93BzeZjTFWus2gPD3ogTHMy2lVDWwBngXA8UfLhndBXQALwHHgD6ttT+8SzK/hv4V+AoQDP/sxDixA2jgD0qp7Uqpe8PbjPDaWQx0Aj8Llxv/RCmViTFiX8hSZXwAg77WjDpOgOHHilFGHzPAuOPGKBk/kpeMEQkmY0RCyfiQWAkbGyTZNDNqkm163qNYYJRSWcCTwBe11v2JjmcmtNYBrfVqQhn9DcCyyXab36imp5T6ENChtd4+fvMkuyZd7ONcrLVeS6hk/fNKqcsSHVCULMBa4Eda6zXAIMldlitCjPb3kVKMPE6AcceKUSkyZoBxx41RMn4kLyP+PaQMGSMSR8aHpJCwsUGSTTPTDFSO+7kCaE1QLHNxWilVChD+3pHgeCJSSqURGhx+pbV+KrzZMPGP0lr3Aa8Tmi+eq5SyhO9K1tfQxcBNSqkm4FFCZa//ijFiB0Br3Rr+3gH8ltDgbITXTjPQrLV+N/zzE4QGCCPEvpClyvgABnutpco4AYYcK0YZfswAQ48bo2T8SF4yRiSIjBEJJ+ND4iVsbJBk08xsBerD3fOtwB3ApgTHNBubgLvDt+8mNH856SilFPAgcFBr/YNxdxkl/kKlVG74djpwDaFmbK8Bt4V3S8r4tdZf01pXaK2rCb3OX9VafxwDxA6glMpUSjlGbwPXAfswwGtHa90OnFJKnRPedDVwAAPEvsClyvgABnqtGX2cAGOPFaOMPmaAsceNUTJ+JDUZIxJAxojEk/Eh8RI6NsSzGVUqfgEfBI4Qmiv7fxIdTxTx/hpoA3yEspr3EJon+wpwNPw9P9FxRoj9EkIllXuAXeGvDxoo/vOAneH49wHfCG9fDLwHNACPA7ZExzrN87gC+L2RYg/HuTv8tX/0b9VAr53VwLbwa+d3QJ5RYl/IX0YbH8IxG3aMCMdv6HEi/BxSYqz4/+zdd3ycV53v8c9vRr1a3Vax5V7lEst2igMOaU5CCpeEVBKWbEK5XO7CUsKWEPICFrjskmXDLgkJSwiEBJyEOKSR4oADjnvvXZJlFcvqXZpz/9DIq8iSrTKjUfm+Xy+9rHnmPM/5jeL4Ofo95/xOl88z4u4ZXWIdsfeNLp9D949h+qV7REji1z1iGH3p/hDSzxCSe4P5OxcRERERERERERk0LaMTEREREREREZGAUbJJREREREREREQCRskmEREREREREREJGCWbREREREREREQkYJRsEhERERERERGRgFGySUREREREREREAkbJJhm2zCzFzLb5v0rM7ESX1xFB6vM9M1vYw/Evm1lUl9dvmFl8EPrPNrOX+nnOd8zssgH2l2xmnx3Iuf7zf2dmUwZ6vojIYOg+0edzdJ8QkTFH94g+n6N7hASFOedCHYPIeZnZQ0Cdc+6H3Y4bHX+PfQHq5z3gC865bd2OFwHznHNVgejnHP3/CHjLOfdKMPvp0t80YJVz7qybYh/Pvxy42Tn3ucBGJiLSP7pPBK0/3SdEZMTTPSJo/ekeIb3SzCYZccxsmpntMrOfAluACWb2uJltMrPdZvagv931ZvZMl/OuMLMX/d9fY2brzGyLmT1nZrHn6O9LQDqw1sze8h8rMrNxXWL5ub/vX5rZ1Wb2VzM7YGb5/vZxZvYLM9tgZlvN7Poe+jHgJuBN/+u/NbMXzOwPZnbUzD5nZl/1n/9XMxvnb/crM7upS1wP+dvsMLMZ/uPfNrO/69LXPjPLBr4HzPQ/4fme/70H/HHu6PKzjDez18xsu//z3uy/1LvASjPz9vM/o4hI0Og+ofuEiEhvdI/QPUKGhpJNMlLNAZ50zi1yzp0AHnDO5QMLgCvNbA7wBnCpmUX7z7kVeM7M0oEHgMudcxcAO4D/21tHzrkfAWXApc65K3poMhP4IZAHzKcjO38x8A1/PwAPAq8755YCHwH+1bpMpfWbBpQ551q6HJvrj/tC4PtApXNuEbAZuKuXkEv9bZ4Avtzb5/J7ANjvnFvonHvAzK4FJgLLgIXAxWZ2MXAtcMw5t8A5Nw//Tcw51w4cA+adpx8RkaGm+4TuEyIivdE9QvcICTIlm2SkOuyc29jl9e1mtoWOpxOzgTn+f2jfBK4zs3DgauBl4GI6bjB/NbNtwJ1A7iBiOeSc2+OffrsHeMt/fGeX614F/KO/vzVAFB3/EHc1ASjvduwd51y9c64UqPPH3/3a3b3g/3PzOdr05irgGmArHT/LacAMOm6iK83se2Z2iXOuuss5ZUBmP/sREQk23Sd0nxAR6Y3uEbpHSJCFhToAkQGq7/zGzKbT8TRhqXOuysx+Rcc/wADPAfcCDcA651y9f4rp6865TwYoluYu3/u6vPbxP/+PGXCTc+7wOa7T2CXu/ly7t3jau7Rp44PJ5e79dDLg2865J896o2Ma77XA/zOzPzjnvtvlWo29XE9EJFR0n9B9QkSkN7pH6B4hQaaZTTIaJAC1QI2ZTaDjqUOnt+mYxnkvHTcLgL8CHzb/zgdmFuu/yZxLLTCYHSPeAL7Y+cLMFvXQZj8weRB9nMsxYLG/76VAjv9498/1BnBv57pz69jRItXMsugoqvg08G/ABV3OmQ7sDlLcIiKBoPvE+R1D9wkRGZt0jzi/Y+geIf2kmU0yGmyhY8rpLuAI8JfON5xzbWb2GnAHHVNccc6Vmtm9dKy57tz29B+Ag+fo43HgLTMr7GWt9fl8C3jEzHbSkeQ9BNzYtYFzrsbMCs1ssnPu6AD6OJffAXeZ2VZgAx0/p86fxSZ/XK/411rPAt7veGhDLR0/uznA98zMB7QAnwUws0yg2jnXfcquiMhwovvE+ek+ISJjle4R56d7hPSbOedCHYOI+JnZLcBc59xDoY6lL8zsq3QUInwq1LGIiIwFuk+IiEhvdI+Q4UQzm0SGl1VAYqiD6IcK4FehDkJEZAzRfUJERHqje4QMG5rZJCIiIiIiIiIiAaMC4SIiIiIiIiIiEjBKNomIiIiIiIiISMAo2SQiIiIiIiIiIgGjZJOIiIiIiIiIiASMkk0iIiIiIiIiIhIwSjaJiIiIiIiIiEjAKNkkIiIiIiIiIiIBo2STiIiIiIiIiIgEjJJNIiIiIiIiIiISMEo2iYiIiIiIiIhIwCjZJCIiIiIiIiIiAaNkk4iIiIiIiIiIBIySTSIiIiIiIiIiEjBKNomIiIiIiIiISMAo2SQiIiIiIiIiIgGjZJOIiIiIiIiIiARMWKgDGAqpqakuNzc31GGIiIhIkGzevPmUcy4t1HHI/9D4S0REZPTrbQw2JpJNubm5bNq0KdRhiIiISJCY2fFQxyAfpPGXiIjI6NfbGEzL6EREREREREREJGCUbBIRERERERERkYBRsklERERERERERAJmTNRsEhERCZXW1laKiopoamoKdSijQlRUFNnZ2YSHh4c6FBERERnGNAYLrP6OwZRsEhERCaKioiLi4+PJzc3FzEIdzojmnKOiooKioiImT54c6nBERERkGNMYLHAGMgbTMjoREZEgampqIiUlRYOcADAzUlJS9IRSREREzktjsMAZyBhMySYREZEg0yAncPSzFBERkb7SuCFw+vuzVLJJREREREREREQCRjWbRAag8HQD4V4P4xOjQh2KiIwwz6wvCOj17lg2cUDnPfLII9x///3ExMT067xf/OIXXHXVVWRmZg6o37744Q9/yFe/+lXKy8tJTU0NWj8i0nfOOfaX1jIhIZrEGBXoF5GRR2Ow3j300EP87Gc/Iy0tDYDvfve7XHvttYO6pmY2ifRTbVMr1/14LRf+y9vc8tO/8ou/HKW0RvVDRGRkeeSRR2hoaOjxvfb29l7P+8UvfkFxcXGwwqKwsJA333yTiRMHNoATkcBp9zne2F3C11ftYNl332blI2v58m+3hTosEZERbbiOwb70pS+xbds2tm3bNuhEEyjZJNJvz6wvoKapjU9fMpnapjYeenkPl/5gDbtOVIc6NBGRs9TX13PdddexYMEC5s2bx3PPPcePf/xjiouLueyyy7jssssAiIuL48EHH2TZsmWsW7eOhx9+mCVLljBv3jzuv/9+nHOsWrWKTZs2ceedd7Jw4UIaGxvJzc3l1KlTAGzatIkVK1YAHU/I7rnnHq666ipyc3N54YUX+NrXvkZeXh4rV66ktbW1x3i/9KUv8YMf/EA1FkSGgZ+sOcRnnt7MqztPkp+bxBWzM1izv4ySaj1kExE5n5E2Bgs0JZtE+qG5rZ0n3zvKJdNSePD6Obz+dx/izS99iEivh8f+fCTU4YmInOX1118nMzOT7du3s2vXLlauXMkXv/hFMjMzWbNmDWvWrAE6BkTz5s1j/fr1LF++nC984Qts3LiRXbt20djYyB/+8Aduvvlm8vPz+fWvf822bduIjo4+Z9+HDx/mlVde4aWXXuKuu+7isssuY+fOnURHR/PKK6+c1X716tVkZWWxYMGCoPwsRKTvnHOs2lzERVNS2PLglfznnYv5p+tm43PwwtaiUIcnIjLsjaQxGMCjjz7K/Pnz+fSnP01lZeWgP7+STSL98NLWYspqm/nMh6aeOTY9I57blubw6s6TFFc1hjA6EZGz5eXl8dZbb/H1r3+dtWvXkpiY2GM7r9fLxz/+8TOv16xZw7Jly8jLy+Odd95h9+7d/e77mmuuITw8nLy8PNrb21m5cuWZmI4dO/aBtg0NDXznO9/h4Ycf7nc/IhJ424uqKTjdwMcuyCLc2/ErQ25qLEtzk/ndpiKccyGOUERkeBspYzCAz33ucxw+fJht27YxYcIE/v7v/77ffXanZJNIH/l8jp/++TBzMxO4dPoHC9bec3Euzjme+uux0AQnItKLGTNmsHnzZvLy8vjGN77RazInKioKr9cLQFNTE5///OdZtWoVO3fu5L777qOpqedlM2FhYfh8vjPndRUZGQmAx+MhPDz8zNI4j8dDW1vbB9oePnyYo0ePsmDBAnJzcykqKuKCCy6gpKRk4B9eRAZs9bZiIrwerp47/gPHb8nP5uipejYdH/xTbxGR0WykjMEAMjIy8Hq9eDwe7rvvPjZs2DCwD92Fkk0iffTm3lKOlNfzmQ9PPauWSHZSDNfkTeCZDQXUN5/9P6+ISKgUFxcTExPDXXfdxVe+8hW2bNkCQHx8PLW1tT2e0zlgSU1Npa6ujlWrVp15r/t5ubm5bN68GYDnn39+wHHm5eVRVlbGsWPHOHbsGNnZ2WzZsoXx48ef/2QRCah2n+MPO4pZMTONxOgP7jx3bd4EYiO8/G5TYUMss5kAACAASURBVIiiExEZGUbKGAzg5MmTZ75/8cUXmTdv3qCuBxA26CuIjAHOOX76p8PkJEdz7byef/G5d/lkXtlxklWbi7jn4tyhDVBERoyBbpM7UDt37uSrX/3qmSdb//Vf/wXA/fffzzXXXMOECRPO1AzoNG7cOO677z7y8vLIzc1lyZIlZ9771Kc+xWc/+1mio6NZt24d3/zmN7n33nv57ne/y7Jly4b0s0n/mdlK4N8BL/CEc+573d6PBH4JLAYqgFudc8fMLBfYC+z3N33fOffZoYpbhtb6oxWU1TZz/YKzt9eOjQzjo/MzeXlHMd+8fi6xkfp1QkRGBo3Beve1r32Nbdu2YWbk5uby2GOPDep6ADYW1lvn5+e7TZs2hToMGcE2HD3NJx5bx8M3zuXui3J7bfex//wLlfUtvP33K/B6tJOSiMDevXuZPXt2qMMYVXr6mZrZZudcfohCGhHMzAscAK4EioCNwO3OuT1d2nwemO+c+6yZ3QZ8zDl3qz/Z9AfnXJ8fdWr8NXJ944UdvLStmM3/dCXREd6z3t907DQ3/3Qd/+/m+dySnxOCCEVEzk9jsMDrzxgsqMvozGylme03s0Nm9kAP70ea2XP+99f7BzJd359oZnVm9pW+XlMkGH67qZCEqDBuWXzuAdW9yydzrKKBt/eWDlFkIiIifbYUOOScO+KcawGeBW7s1uZG4Cn/96uAy6372nEZ1VrafLy2q4Qr52T0mGgCWDwpiSmpsfxuk3alExGRngVt3qv/6dlP6PL0zMxWd316BtwLVDrnpvmfnn0fuLXL+z8CXuvnNUU+4Jn1BYM63+ccr+08ybT0OF7ceuKcbdt9jqxx0fz8L0e5aq7qjIiIyLCSBXQttFMEdJ93f6aNc67NzKqBFP97k81sK1AD/JNzbm33DszsfuB+gIkTh3a5ggTGe4fKqWpo5YYeltB1MjNuyc/h+6/v49ipenJTY4cwQhERGQmCObNpUE/PzOwm4AjQdZ+/vlxTJKBOVDZS39LOrPEJ523r9Ri3L83h/SOnOVHVOATRichIMBaWrA8V/SwHpacZSt1/oL21OQlMdM4tAr4MPGNmZ90YnXOPO+fynXP5aWlpgw5Yht7qbcUkRodz6fRz//db6a9h+f6RiqEIS0RkQDRuCJz+/iyDmWzq6elZVm9tnHNtQDWQYmaxwNeBbw3gmkDHkzUz22Rmm8rLywf8IUT2ldRgwPSMuD617yym+cqO4iBGJSIjRVRUFBUVFRrsBIBzjoqKCqKiokIdykhVBHRdD54NdL9ZnWljZmFAInDaOdfsnKsAcM5tBg4DM4IesQypptZ23txTyrV544kIO/evCbkpMcRHhbHzRPUQRSci0j8agwXOQMZgwdw+YjBPz74F/Mg5V9etTEBfrtlx0LnHgceho0DleaMV6cW+klompcQQE9G3/10mpcSyIDuR1duLuf9DU4McnYgMd9nZ2RQVFaEHH4ERFRVFdnZ2qMMYqTYC081sMnACuA24o1ub1cA9wDrgZuAd55wzszQ6kk7tZjYFmE7HDHQZRbYVVlHf0s4VszPO29bMmJeZyK7imiGITESk/zQGC6z+jsGCmWzqz9Ozoq5Pz+ioH3Czmf0AGAf4zKwJ2NyHa4oETHVjKyerm1jZz/pL1y/I5Nuv7OVIeR1T0vo2I0pERqfw8HAmT54c6jBEOmswfQF4A/ACP3fO7Tazh4FNzrnVwJPA02Z2iI4x2W3+0z8EPGxmbUA78Fnn3Omh/xQSTFsLqgC4YGJSn9rPy0rgqXXHaW33Ee4N6r5DIiL9pjFYaAUz2TTgp2fApZ0NzOwhoM4596g/IXW+a4oEzL6Sjqd1M8fH9+u8j87P5Duv7uXl7Sf5v1dMD0ZoIiIi/eacexV4tduxB7t83wTc0sN5zwPPBz1ACamtBZVMTo0lKTaiT+3nZSXS0ubjYGkdczLPX9tSRETGjqA9gvDXYOp8erYX+G3n0zMzu8Hf7Ek6ajQdoqPY5AMDuWawPoPI/pJakmLCSY+P7Nd54xOjWJqbzOrtJ7RGWERERIY95xxbC6tYlDOuz+fkZSUCsEt1m0REpJtgzmwa8NOzbu0fOt81RYKhtd3H4fI68icl0612WJ/csDCTf3xxF3tO1jA3MzEIEYqIiIgExomqRsprm1k0se/JptyUWOIiw9hVXM0nPlDpQkRExrqgJptEutpw9DRv7C6hsbWdptZ22tod1y/I5IrZ6QNK5gTbkfI6Wtsds/q5hK7TNfMm8M2XdvPy9pNKNomIiMiwtsVfr2lRH+s1AXg8xpzMBO1IJyIiZ1GySYZE4ekGPvXfG2jzORKiwogK99LU6mP19mIunJLMP103h3lZwyshs6+klgivh8mpsQM6Pzk2guXTU3l5ezFfXzlzWCbURERERKCjXlNUuKffdSrzshL59frjtLX7CFORcBER8VOySYLOOcc3XtiJAWu+soKscdFAxzK132wo4JG3DnL9o+9xw4JMPpGfw4VTUvB6zk7MOOc4Xd/CkVP1OAcXTBwXtEGNc459JbVMS48bVB83LMjky7/dzpaCShZPSg5ghCIiIiKBs7WgivlZ4/q9q1xeViJNrT4Oldcxa7yKhIuISAclmyTonttYyHuHTvHtm+adSTQBhHs93H1RLjctyuInaw7xq3XHeWlbMalxkXx0/gQmJEZRUtNEaU0TxVVNHD1VT3Vj65nzk2LCuXJOBtfMm8Cl01MDmngqrmqiurGVy2elD+o6V87JICLMw8vbTyrZJCIiIsNSc1s7e4pr+Jvluf0+d15WR4JpZ1G1kk0iInKGkk0SVCerG/nOK3u5cEoydyydCMAz6wvOajcpOZavrZzFvpJadhRV8av3j9Pmc0SEeUiICicxOoyZ4+NJi4skNS6SlnYfe4qreWlbMb/dVMTk1FjuWjaJ6AhvQOLeXFBJmMcGXWspPiqcFTPSeG3XSR786Bw8PczYEhEREQml3cU1tLT7WJTT93pNnSanxhET4WV3cc25d/0REZExRckmCRrnHP/wwk7afI7vf3z+eRMt4V4PeVmJ5GUl0tLmw+ccUeG9J4/yshJpa/extbCK1duKeXztYT518WQSo8MHFXdbu4/thVXMnpAQkOTVdfMn8Mc9pWwuqGRJrmY3iYiIyPCy9Uxx8L7vRNfJ6zHmqki4iIh0oyp+EjRv7y1jzf5yvnr1TCal9K/IdkSY55yJpk5hXg9LcpO55+Jcqhpa+emfDlNa0zTQkAHYW1JLY2s7iyf1/+leTy6fnUFkmIdXdpwMyPVEREREAmlrQSVZ46LJSIga0PnzshLZU1xDu88FODIRERmplGySoHl9dwmJ0eHcfdGkoPc1LT2O+y6dgs/nePzPRyipHnjCafPx0yRGhzMtPS4gscVFhrFiZhqv7jyJT4MwERERGWa2FlSxcACzmjrNy0yksbWdw+V1AYxKRERGMiWbJCh8PsefDpQHvHD3uWSOi+YzH55KuNd4at2xDxQT76uaxlYOltaxKGccHgtcfaXr5mdSVtvMpuOVAbumiIiIyGCV1TRxoqqRRTkDTzblZXfUuNylpXQiIuKnZJMExZ6TNZTXNrNi5uB2c+uv5NgI7r4ol8bWdn657hjNre39On9rYRUOuCBAS+g6XT4r3b+Urjig1xUREREZjK2FnfWaBj72mZoWR3S4V3WbRETkDCWbJCj+dKAcgA/PSBvyvjPHRXPH0omU1jTxm40Ffa4f4Jxj8/FKJiXHkBoXGdCYYiPDuGxmOq/tKlE9AxERERk2thZUEe7tKPI9UF6PMSczQTObRETkDCWbJCje3V/GvKwE0uIDm7TpqxkZ8dy4IIsDpXX8ftsJfO78CZ7C0w2cqmsOWGHw7q6bP6FjKd2x00G5voiIiEh/bS2oZE5mYp82ZjmXuZkJ7D1Zi+vDmEtEREY/JZsk4KobW9lSUMWKGUO7hK67JZOTuWxmOpuPV/L7redPOG04Vkm418jLSgxKPB/xL6V7dad2pRMREZHQ8/kce4prmB+Asc+09DjqmtsorWkOQGQiIjLSKdkkAffewVO0+xwrZg79ErrurpidzmUz09h0vJIXz5FwWnuwnC0FleRPSiZykE/2ehMbGcZHZqXzqpbSiYiIyDBQWNlAbXPboJbQdZqW1rGL76Ey7UgnIiJKNkkQvLu/jISoMBYOYleTQDEzrpidwUdmdcxwemFL0VkJp/cOlvParhLmZSVybd6EoMZz3fwJlNc2s1FL6URERCTEdp2oAWBegGY2ARwqqx30tUREZOQLC3UAMro45/jTgXIunZFGmHd45DI7E04GvL2vjAOldczNTGBeViLFVY1nEk235ufg9VhQY/nIrHSiwjuW0l04JSWofYmIiIicy67iasI8xvSMuEFfKy0+kvioMA6Va2aTiIgo2SQBtudkDWW1zawIwS5053P57AwmJEazrbCSLQWVrD/aMbtoqBJNADER/qV0O0v45vVzh6RPERERkZ7sLq5hRkY8kWGDLyFgZkxLj9MyOhERAZRskgB7d385AB8eBvWaejInM4E5mQm0tPk4UFpLZUMLF09NHdKkz3V5mby6s4SNx05rdpOIiIiEhHOO3SequXx24DZ0mZYWxxr/WFBERMY2JZskoN7dX8bczATS46NCHco5RYR5AlKfYCAum5VGVLiHV3ZoKZ2IiIiERmlNMxX1LczN/J/x0DPrCwZ1zdqmNk7VNfPk2qNER5x7ttQdyyYOqi8RERnegppsMrOVwL8DXuAJ59z3ur0fCfwSWAxUALc6546Z2VLg8c5mwEPOuRf95xwDaoF2oM05lx/MzyB919jSzpaCKu7/0JRQhxJSfRmoTUuP58WtJ5g5Ph6PDW5WlQZrIiIi0l+7TlQDMC9r8DvRdUqPjwSgvLaJiSmxAbuuiIiMPEGr4GxmXuAnwDXAHOB2M5vTrdm9QKVzbhrwI+D7/uO7gHzn3EJgJfCYmXVNjF3mnFuoRNPwsrekhnafY0F26HehG+7yshKpa27j2Kn6UIciIiIiY9Cu4mrMYPaEwCWb0vzJprLa5oBdU0RERqZgbhe2FDjknDvinGsBngVu7NbmRuAp//ergMvNzJxzDc65Nv/xKMAhw97u4o7tc+dmBm7QMlrNzIgn3Gvs9D9VFBERERlKu4trmJIaS0xE4BY6JMVGEOYxypVsEhEZ84KZbMoCCru8LvIf67GNP7lUDaQAmNkyM9sN7AQ+2yX55IA/mtlmM7u/t87N7H4z22Rmm8rLVahwKOwpriYxOpzspOhQhzLsRYR5mDU+gV3FNficcqkiIiIytHafqA54/UqPGalxkZrZJCIiQU029VSIpvtv1b22cc6td87NBZYA3zCzzorTlzjnLqBjed7/NrMP9dS5c+5x51y+cy4/LW147ow22uwurmHOhARskDWIxop5WYnUN7dxVEvpREREZAidrm+huLopKLPR0+IjKa9TsklEZKwLZrKpCMjp8jobKO6tjb8mUyJwumsD59xeoB6Y539d7P+zDHiRjuV6EmJt7T72ldRqCV0/nFlKV6SldCIiIjJ0dhf7i4NnBn5n3rT4SCrrW2ht9wX82iIiMnIEM9m0EZhuZpPNLAK4DVjdrc1q4B7/9zcD7zjnnP+cMAAzmwTMBI6ZWayZxfuPxwJX0VFMXELscHk9LW0+5gZwR5PRLiLMw9zMRLYXVdHc2h7qcERERGSM2HWis85m4JNN6fGROOCUZjeJiIxpQUs2+WssfQF4A9gL/NY5t9vMHjazG/zNngRSzOwQ8GXgAf/x5cB2M9tGx+ylzzvnTgEZwHtmth3YALzinHs9WJ9B+q7zCVkwBi2j2UVTUmhu87G5oDLUoYiIiMgYsbu4muykaBJjwgN+7c4d6VQkXERkbAvc9hM9cM69Crza7diDXb5vAm7p4byngad7OH4EWBD4SGWwdhfXEBnmYUpqbKhDGVFykmOYmBzDusMVXDglBY/qXYmIiEiQ7S6uCcoSOoDUuEgMVCRcRGSMC+YyOhlDdhdXM2tCAmFe/ZXqr4unplBR38KBktpQhyIiIiKjXG1TK0dP1TMvSKUPwr0ekmIjNLNJRGSMU2ZABs05x57iGhUHH6C5mYkkRofz18MVoQ5FRERERrm9JzsebgWz9EFaXKSSTSIiY5ySTTJoRZWN1DS1Kdk0QF6PceHkZA6V11FS0xTqcERERGQU23nCX2cziJu6pMdHcqquGZ9zQetDRESGNyWbZNBUHHzwluQmE+411h0+FepQREREZBTbUVTFhMQo0uOjgtZHWnwkbT5HZX1L0PoQEZHhTckmGbTdxTV4Pcas8fGhDmXEiokMY2FOElsLqqhvbgt1OCIiIjJK7SiqZn52cB8Qpvt3pFORcBGRsUvJJhm03cU1TE2LJSrcG+pQRrSLp6bQ7nP8ZmMBza3toQ5HRERERpnqxo7i4POzxwW1nzT/rCnVbRIRGbuUbJJB211cHbTtc8eSjIQobl6czbFT9Tz5l6M0aIaTiIgEmJmtNLP9ZnbIzB7o4f1IM3vO//56M8vt9v5EM6szs68MVcwSODuLOkofLAhysik6wkt8ZJhmNomIjGFKNsmgnKprprSmmTkqDh4QiyYmceeySZRUN/H42iPUNLaGOiQRERklzMwL/AS4BpgD3G5mc7o1uxeodM5NA34EfL/b+z8CXgt2rBIc24uqAMgL8jI6gPSESMpqtfGJiMhYFRbqAGRk211cA6g4eCDNnpDAPRfn8vT7x/m3tw4QHxlGZLiHyDAvWeOiWTwpiYyE4BX1FBGRUWspcMg5dwTAzJ4FbgT2dGlzI/CQ//tVwKNmZs45Z2Y3AUeA+qELWQCeWV8QkOu8suMkKbERvLLjZECudy7p8VFsPl6Jcw4zC3p/IiIyvCjZJIPSuROdZjYF1tS0OO6/dAobjp6mqa2d5lYfTa3t/PXwKd47dIqJyTEsyU1i0cSkUIcqIiIjRxZQ2OV1EbCstzbOuTYzqwZSzKwR+DpwJdDrEjozux+4H2DixImBi1wCoqiygcmpsUPSV3pCJC3tPqoaW0mKiRiSPkVEZPhQskkGZU9xDdlJ0SRGh4c6lFEnc1w0Ny3K+sCxuuY2thZUsul4Jc9vOUHB6UbuXDZRTwxFRKQverpZuD62+RbwI+dc3bnuOc65x4HHAfLz87tfW0KopqmVmqY2spNihqS/dH+R8LKaZiWbRETGINVskkE5UFrLrPGa1TRU4iLDuHR6Gn93+XQ+PCONjcdO88hbB0MdloiIjAxFQE6X19lAcW9tzCwMSARO0zED6gdmdgz4O+AfzOwLwQ5YAudEZSMA2UnRQ9JfRnwkgOo2iYiMUZrZJAPW0ubjSHk9V87JCHUoY46ZcdWcDOqa2vj3tw+SFh/JXRdOCnVYIiIyvG0EppvZZOAEcBtwR7c2q4F7gHXAzcA7zjkHXNrZwMweAuqcc48ORdASGEWVDXgMJiQOTbIpJjKMuMgwymq0I52IyFikZJMM2OHyOtp8jpma2RQSZsZNi7JIjAnnwZd2kRoXycp540MdloiIDFP+GkxfAN4AvMDPnXO7zexhYJNzbjXwJPC0mR2iY0bTbaGLWAKpqLKR9PgoIsKGbmFDekIkpZrZJCIyJmkZnQzYgdJaAGZmxIc4krHL6zEevWMR87PH8cVnt7Lh6OlQhyQiIsOYc+5V59wM59xU59x3/Mce9CeacM41Oeducc5Nc84t7dy5rts1HnLO/XCoY5eBc85RVNk4ZEvoOqXHR1FW20zH5DgRERlLlGySAdtXUku415iSNjS7mkjPYiLC+PmnlpCdFM3fPrWR/SW1oQ5JREREhpHKhlYaW9vJGuJkU0ZCJC1tPqobW4e0XxERCT0lm2TADpTUMjUtjnCv/hqFWnJsBL/89FKiI7zc8/MNnKhqDHVIIiIiMkwUVTYADNlOdJ3O7EhXq7pNIiJjjbIEMmD7SmqZoSV0w0Z2UgxPfXop9S1t3P3keu3+IiIiIkBHvaYwjzE+IWpI++3cka60RmMSEZGxRgXCZUBqm1o5UdXIHcsmhjoU6WLW+ASeuDufT/58A5d87x2umJ3BzYuz+fCMNBxwqq6Zsppm6lva8Jrh8RhejzF7fALREd5Qhy8iIiJBUFTZyITEKLweG9J+YyLDiI0M08wmEZExKKjJJjNbCfw7HTuePOGc+1639yOBXwKLgQrgVufcMTNbCjze2Qx4yDn3Yl+uKUPjQGkdoOLgw9GyKSm8+sXl/GZDIb/feoLXdpUQFe6huc1Hb/U5YyK8XD47g4/On8CHZ6QRFa7Ek4iIyGjQ7nMUVzVywaSkkPSfER9JmWY2iYiMOUFLNpmZF/gJcCVQBGw0s9XOuT1dmt0LVDrnppnZbcD3gVuBXUC+f4veCcB2M3sZcH24pgyBziLUM8cr2TQcTUuP558/OocHrpnFu/vL+cuhUyRGh5OeEEl6fBRxkWE4HM5BQ0s7a/aX8fquEl7eXkxSTDjfuHY2tyzOxmxon4CKiIhIYJ2sbqSl3UduytDWa+qUnhDJ1oIqnHMaV4iIjCHBnNm0FDjUuWWumT0L3Ah0TQzdCDzk/34V8KiZmXOuoUubKDqSTH29pgyB/SU1xEZ4h3wLXemfcK+HK+dkcOWcjHO2u3JOBt+6YS7rDlfw6DuH+NqqHbywpYjvfiyPKWlxQxStiIiIBNrh8noAJqeGZvfg9Pgomv070o2LiQhJDCIiMvSCWSA8Cyjs8rrIf6zHNs65NqAaSAEws2VmthvYCXzW/35fron//PvNbJOZbSovLw/Ax5Gu9pfWMmN8vJ5QjSLhXg8fmpHGs/dfyL/8rzz2FNew8pG1PLH2CK639XciIiIyrB0pryM9PpL4qPCQ9J+e0FEkXHWbRETGlmAmm3rKQnT/jbXXNs659c65ucAS4BtmFtXHa+I//3HnXL5zLj8tLa0fYcv5OOfYX1LLLC2hG5U8HuP2pRN56+8/zIqZaXz7lb3880u7aPcp4SQiIjKStPl8HK9oYEpaaGY1AWTEd+yAp2STiMjYEsxldEVATpfX2UBxL22KzCwMSAROd23gnNtrZvXAvD5eU/rpmfUF/Wpf29RKZUMrtU1t/T5XRo70+Ch+etdivv/GPh770xFKqpv5j9sXadc6ERGREeJEZUe9pimpoVsSHxsZRmyEV0XCRUTGmGAmmzYC081sMnACuA24o1ub1cA9wDrgZuAd55zzn1PoLxA+CZgJHAOq+nDNEW+4J3BK/IOFjISoEEciwebxGN+4ZjZZ46J5aPVubvvZ+/z3p5aQHKuaCyIiIsPd4fJ6DJgSonpNndITojSzSURkjAnaMjp/jaUvAG8Ae4HfOud2m9nDZnaDv9mTQIqZHQK+DDzgP76cjh3otgEvAp93zp3q7ZrB+gzSs9JqJZvGmrsvyuWndy1m38ka7vjZ+1TWt4Q6JBERETmPI6fqGJ8YRUxkMJ8vn196fCSlNU2qASkiMoYE9c7jnHsVeLXbsQe7fN8E3NLDeU8DT/f1mjK0SmuaiYsMIy7EAxcZWlfNHc8T9+Rz71ObuPOJ9Txz3zLtKiMiIjJMtbb7KKhoYNnk5FCHQkZCx450NU1tJEaHplC5iIgMrWAWCJdRqqSmifGa1TQmXTo9jZ/dnc+h8jrufGI9VQ2a4SQiIjIcFZ5uoM3nmJIWunpNnSYkdowbT1Q2hjgSEREZKpqaIv3ic46y2iaW5ob+KZl0GOoaX3csm8hjn1zMZ365mU8+uYFf3buMxBg9pRQRERlOjpzqqNc0OcT1mgAyx0XjMSisbGBOZkKowxERkSGgmU3SL5X1LbS2O9VrGuMum5nOY59czP6SWj758/VUN7aGOiQRERHp4kh5HZnjookKD/0usuFeDxMSoyk83RDqUEREZIgo2ST9UuwvDj4+Ucmmse6yWen8110XsPdkDXf/fAM1TUo4iYiIDActbT4KTzcyJS30s5o65SRHU1TViE9FwkVExgQlm6Rfik434PWYkk0CwOWzM/jPOxezp7iau5/cQK0STiIiIiF3/HQ97c4xJTX09Zo65STF0NLmo6ymOdShiIjIEFCySfqlsLKRzMQowjz6qyMdrpyTwaN3XMCuE9Xc+tj7nKxW8U8REZFQOlJej8cgNyUm1KGckZPcEUthpZbSiYiMBSoQLn3W7nOcqGogf5KKg49lvRUkv+vCSfxmQwFX/+jPfPLCXLKSogfd1x3LJg76GiIiImOJc47dxTVMSoklchjUa+qUEhtBdLiXwtMNLNFGMyIio56mp0ifldU20druyEkefBJBRp8ZGfF85kNT8Zjx+NrD7CmuDnVIIiIiY86JqkZO1TWzMGdcqEP5ADMjJzlaM5tERMYIJZtGmK0FlWwrrKSlzTfkfRdVdiyPyk4aPlOyZXgZnxjF51ZMJSMhil+vL2DtwXKcCoGKiIgMme2FVXg9xrzMxFCHcpacpBjKapppbm0PdSgiIhJkWkY3gtQ2tbJqcxEOiPAWMyczgfxJSUxJG5rij0WVDUSHe0mJjRiS/mRkio8K575Lp/C7zUW8tquEU3XN3LAgC6/HQh2aiIjIqNbuc2wvqmbW+HiiI4bPErpOOckxOKCoSvUdRURGO81sGkH2nqzFAf9rURYLchLZV1LDE+8d5VBZ3ZD0X3i6keykaMyUNJBzC/d6uG1JDitmpLHxWCW/+OtRGlv0FFNERCSYDpfXUdfcNuyW0HXK9tdzLDytpXQiIqOdZjaNILuLq0mOjWDxpCTyc5O5Li+Tf3tzP+8eKGNaenBnN7W0+SitaWL2hPSg9iOjh8eMq+aOJyUukt9vPcFjfz7MvcsnEx8VHurQRERERqXthVVEhXuYmREf6lB6FBMRRmpcxJAmm3rb2CTQtKmJiMgHaWbTCNHY0s6R8nrmZiacmVkUEebhkmmpHCmvD/pNI1z66wAAIABJREFU+0RVIw5UHFz6bfGkJO65OJfKhhaefO8odc1toQ5JRERk1Glp87G7uIa8rETCvMN3iJ+TFENhZeOwqenonMPn3LCJR0RktNDMphFif2kN7c4xt1uxx6W5yby7v5x3D5TzyQsnBa3/Iv/OISoOLgMxLT2Ouy/K5ZfrjvHE2iP87aVTiIvUPz8iIiKBsudkDS3tPhYM0yV0nXKSY9haWEVRZSM5ycEfV7b7HMcr6jlQWkdJTSN1zW3UNbVR39JOu++DCaaocA/R4d6OGqVxkUxLj2NaehxJMapXKiLSX/ptb4TYXVxDfFTYmbXunSLDvVw0NYV39pVRWtNERkJUUPovrGwkKSZcCQIZsKlpSjiJiIgEy/bCKhKjw8lNiQ11KOeU439wua2wKqjJpl0nqvnJmkO8s6+M5jYfHoPxCVHER4UzPiGauEjvmRlgBvgcNLW109TSTmNrO8cr6tl5ohqA1LgIrpidwfzs4Z3IExEZTvSb3gjQ0ubjQGktF0xMwtNDce6Lp6Tw3sFT/PlAObfk5wQlhqLTDUPy9ElGt64Jp1+9f5y/XT55WE/1FxERGQnqmts4WFbLpdPTehwrDifjE6MI8xjPbiigtinwS+sbmtv4455SNh47TXSEl/nZ45iZEcfUtDgiw/u+Q59zjrLaZg6V1bG1sJJnNxZyoLSW6+dn9us6IiJjlZJNI8Chslpa289eQtcpJjKMJblJrDtSweWzM0iODexU39qmVqoaW7k4SfWaZPCmpsVx8+IcfrOhgN9vK+bjF2Rph0MREZFBeHd/Gc7BomG+hA7A6zGyxkVzrCLw9UY3Hz/NqztLaG5r5+KpKVw+O4OoASaGzIyMhCgyEqK4cEoKa/aXsWZfGccqGvhEfg4T9RBWROScNKVgBNhdXEN0uJfJqb1Pi14+PQ3DWHuwPOD9F1U2AmhmkwRMXlYiH5mVzpaCSt47dCrU4YiIiIxYpTVNvH+kgiW5yaQHqZxCoM2ekMCJqkZO1TYH5HrOOf64u4Tnt5xgfGIU/+cj07lufuaAE03deT3GFbMzuP9DU3DO8cTaIxRXNQbk2iIio5WSTcNcu8+xt6SG2RPi8Xp6n/2RGB3Owonj2Hy8kvoA7/ZVWNmAx2BComY2SeB8ZFY6czMTeH1XCftLakMdjoiIyIjjnOMPO4qJCPNw5ZyMUIfTZwtzxmHAlsLKQV/L5xwvbSvm3QPlLMlN5t7lk4NWw3RSSiyfWzGNmAgvz24spLmtPSj9iIiMBkFNNpnZSjPbb2aHzOyBHt6PNLPn/O+vN7Nc//ErzWyzme30//mRLue867/mNv9XejA/Q6gdOVVHU6uv1yV0XS2flkqbz7H+aEVAYyiqbCQjIYqIMOUmJXA8ZtyyOIfxiVE8u7GAirrAPN0UEREZK/acrOFweT1Xzs4gdgRtupEQHc609Di2FVThc+78J/SizefjuY2FbDh2mg/PSOOmhZlBr1kVFxnGJ/JzqKhr5uXtxUHtS0RkJAta9sDMvMBPgGuAOcDtZjanW7N7gUrn3DTgR8D3/cdPAdc75/KAe4Cnu513p3Nuof+rLFifYTg4UFJLuNeYlh533rYZCVHMyIhj3ZHTtLb7AtJ/S5uPgooGrUuXoIgI83DXskmYwXObCs/aglhERER61tru49WdJ8lIiGTp5JRQh9NvF0xMoqqxlaOn6gd0vs85nttYyM4T1VwzbzxXzx0/ZDUgp6TFcdmsdLYUVLG1YPCzs0RERqNgTlVZChxyzh1xzrUAzwI3dmtzI/CU//tVwOVmZs65rc65zkcFu4EoM4sMYqzDVnldM2lxkYT3cceu5dPSqG9uY3thVUD631dSQ0u7j7ys88+sEhmIpNgIblqYRVFlI2/vLQ11OCIiIiPC2oPlVDa08tH5mecstTBczclMIDLMw5bj/U/WOOd4ZedJdhfXcG3eBC6dnhaECM/tspnp5KbE8NK24oDVnhIRGU2COd82Cyjs8roIWNZbG+dcm5lVAyl0zGzq9HFgq3Ou67/i/21m7cDzwLedO3v+rZndD9wPMHHixEF+lNCpqGthwri+10qamhbLhMQo1h46xQWTkgY9lXhbYRUJUWHknqM4uchgzc8ex8GyOv50oJyp6R3bE4uIiAx3z6wvGPI+nXP85dAp3t5bxrzMhBF7zwz3esjLSmRHUTU3tLUTGdb3Yt7vHTrFusMVLJ+WyvJpqUGMsndej3Hrkon8+O2DvLT9BF+8YnpI4hARGa76NF3GzJ43s+vMrD8zoXrKcnRPCp2zjZnNpWNp3We6vH+nf3ndpf6vT/bUuXPucedcvnMuPy1t6J92BEK7z1HV0EpKbESfzzEzlk9Lpby2mYOlgyu63NDcxoHSWhZkjwv6+neRj86fQEpcBL/bVEhDgIvci4iMNgMcm8kI19ruY9XmIl7dVcKczAQ+vjg71CENygUTk2hp97H7RE2fz9leWMVru0rIy0pk5bzxQYzu/BKjw1kxM43D5fVsHsAMLRGR0ayvA5T/Au4ADprZ98xsVh/OKQJyurzOBrpX0TvTxszCgETgtP91NvAicLdz7nDnCc65E/4/a4Fn6FiuNypVN7bS7ly/kk3QMUskISqMtYPcUn5ncTU+Bwtyxg3qOiJ9ERnm5dYlE6lvbueFrSfoYcKiiIj8j4GMzQazecvSLpuzbDezjwXyw4xVzW3tHCit5VBZHccr6jlR1UhVQ8tZRbMbW9o5eqqeJ9YeYWthFZfPTuf2pRP7NRtoOJqUEkNybARb+lj3aH9JLas2F5GbEsvNi7OHxcPQpZOTiYnw8h/vHAx1KCIiw0qfltE5594C3jKzROB24E0zKwR+BvzKOdfaw2kbgelmNhk4AdxGx6Coq9V0FABfB9wMvOOcc2Y2DngF+IZz7i+djf0JqXHOuVNmFg58FHir7x93ZKmo71g5mBzXv2ST12NcPDWV13eXUFzVSGY/luF1tb2wirT4SCYkBmf7WJHussZFc9XcDF7bVcKGY6e588JJoQ5JRGRYGsjYrMvmLVfS8cBvo5mtds7t6dLszOYtZnYbHTPMbwV2Afn+sgcTgO1m9rJzTlNRB8DnHNsKqnhjTwm1TWf/CL0eIykmnPiocCobWqhq6PjPGeH1cOeyiX3apXgkMDMWTRzH23vLqGxoISmm9zHvgdJafr3+OBkJkXzywkl9rmcabJFhXpZPS+WPe0rZUVTF/Gw9pBURgX4UCDezFOBTwN8CW4F/By4A3uypvX/w8QXgDWAv8Fvn3G4ze9jMbvA3exJIMbNDwJeBzidsXwCmAf/c5SlaOhAJvGFmO4BtdCSxftaPzzuinK5vASAltv+10ZfkJhMV7uEPO4oHtKVsZUMLxyoaWJgzbsh29hABuGRaKtPT43h158lBLwUVERnN+js2Y3CbtzR0SSxFcXZpBOmjgop6fvqnw6zaUsS46HDuuSiX+y6dwt9cnMtdyyZx08IsLpmawviEKNrafUxMjuHqueO556Jcvnb1zFGTaOp0QU4SBvxuUxGNLe09tjlYWsuv3j9OWnwkn14+meiI4TWj68IpKSREhfEf7xwKdSgiIsNGn2Y2mdkLwCzgaeB659xJ/1vPmdmm3s5zzr0KvNrt2INdvm8CbunhvG8D3+7lsov7EvNoUFHXQpjHiI/qfx336Agv1+Vl8vyWIjYcPc2FU/q3Je4O/252C/R0RoaYx4ybF2fz47cP8n9+s5Xf/+9LiAofXoNKEZFQG+DYbFCbt5jZMuDnwCTgkz3NahotG7QEy8nqRh5fe4S4yDBuWZzNghzVxUyKjeAT+Tms2lzE42sP86mLJ5MYHQ50zADbe7KG5zYWkhYfyb2XTCYmIpj7Gw1MVLiXTy+fzCNvHWRPcQ1zMhNCHZKISMj1dWbTE865Oc65f+kczJhZJIBzLj9o0Y1xp+tbSI6NGPAg5IKJ45ieHsfru0uobGjp17nbiqqYmNyxjl5kqMVHhXPz4mz2ldTyvdf2hTocEZHhaCBjs0Ft3uKcW++cmwssAb5hZmetsx8NG7QEi3OOl7cXExXu5Ysfmc6iiYPfNXi0WJAzjnsuzqWqoZWf/ukwB0treWtvKf/6x/38en0BqXGRfPqSycREDr9EU6e/uXgycZFhPLpGtZtERKDvyaaeZhmtC2QgcraK+uZ+Fwfvysy4aWEWOPh9Pwoul1Q3UVrTrMLgElIzxyfwN5fk8ou/HuOdfaWhDkdEZLgZyNhsUJu3dHLO7QXqgXn9iHfM215UzbGKBq6eM35YJ01CZVp6HPddOgWfz/Hffz3Gmn1lpMRGcsvibD63Yiqxw/xnlhgTzj0XT+K1XSUcKqsLdTgiIiF3zmSTmY03s8VAtJktMrML/F8rgJghiXCMcs5xur6FlLj+12vqKik2gqvnZnCwrI4tBVXnbe9zjjf3luIxyMsaXTUBZOT5+spZzJ6QwFd+t4OymqZQhyMiEnKDHJud2bzFzCLo2Lxldbc2nZu3wAc3b5nsTz5hZpOAmcCxwHyq0a+ptZ3Xdp0ka1w0i3OTQh3OsJU5LprPrpjK9fMn8NWrZ/Lp5ZNZNDFp2BQDP5+/uWQyYR7jmfUFoQ5FRCTkzveI4Go6Ck9mA//W5Xgt8A9BikmA2qY2WttdQJaxLZuSwo4T1byys5jE6HCmpcf12M45x+ptxew9WcN1eROIG+ZPkGT0iwr38h+3L+L6/3iPL/12G09/ehkej5YciMiYNuCxmb8GU+fmLV7g552btwCbnHOr6di85Wn/5i2n6UhIASwHHjCzVsAHfN45dypwH2t0W7OvjNqmNu5aNklL584jKSaCi6amhjqMAUmNi+SqOeN5YWsRX1s5UzUnRWRMO2c2wTn3FPCUmX3cOff8EMUkQMWZnegGn2zymPGJxTn8Yt0x/vsvR7l8dgYrZqadNdh5e18ZG46d5sMz0rhk2si8ycvoMy09jodumMPXn9/JY38+wudWTA11SCIiITPYsdkgNm95mo5i5NJPZTVN/OXwKfInJZGTrIUBo91tS3N4ZedJ3thdwo0Ls0IdjohIyJwz2WRmdznnfgXkmtmXu7/vnPu3Hk6TADhd3wwQsALdSbERfH7FVF7aVsxbe0spOF3PVXPGE+YxzIz9pbW8s6+MxZOSuGpORkD6FAmUT+Tn8OeDp/jXP+7nwinJLJqoJQgiMjZpbDbyvLm3lIgwD1fNHR/qUGQIXDI1leykaJ7dUKhkk4iMaedbJxXr/7PndVcSNBV1LXgMxsUEbje4yDAvtyzOZlJKDH/YcZJH1xz6wPuzxsdz08IsTNO7ZZgxM777sTy2FVTxxWe38soXLyUhKjzUYYmIhILGZiNIc1s7+0tqWZKbrPIEY4THY9yan8O/vnmAY6fqyU2NPf9JIiKj0PmW0T3m//NbQxOOdKqobyEpJgJvgOvTmBnLJqcwNTWOkzVNOOfwOQjzGDPHxwe8P5FASYwO58e3L+QTj73PP764ix/ftlCJUREZczQ2G1n2l9TS5nPMzUoIdSgyhG7Jz+FHbx3guU2FfH3lrFCHIyISEn3a2sHMfmBmCWYWbmZvm9kpM7sr2MGNZafrWwK2hK4nqfGR5GUlMj97HAtzxjEvK3HE7PQhY9fiScl86YrpvLy9mFWbi0IdjohIyGhsNjLsLq4hNjKM3BTNbhlLxidG8ZFZ6fxuUxGt7b5QhyMiEhJ9zS5c5ZyrAT4KFAEzgK8GLaoxzjlHRX0zKXHBSzaJjFSfWzGNC6ck883VuzlcXhfqcEREQkVjs2Gutd3H/pJa5kxI0A50Y9BtSyZyqq6Zt/eWhToUEZGQ6GuyqbM4yrXAb5xzp4MUjwCNLe00tfpIjo0MdSgiw47XYzxy6yIiwjx88TdbaW5rD3VIIiKhoLHZMHewtI6Wdh/zMrWEbixaMTONjIRInt1YEOpQRERCoq/JppfNbB+QD7xtZmlAU/DCGtsq6lsASAniMjqRkez/s3ff4XFddf7H32eaeu9dsuXu2I5rYqeQkOKQ4pBCGimQJbAQYCn727BsgGWXXbLswlISNoFACoQQQgIOqaQ4zYl777ItybKK1ayR1aU5vz80No4j27I1ozuSPq/nmUd37px75zv3UTn63nO+Jzspmh9cN5Mt1X7+84XtTocjIuIE9c0i3JbqFmK8bsZlqJb7WORxu7huTj5v7ayntiUyfzRbOnpYv+8gu+sPcaC1k84e3cATkdAZ1LIY1tp7jDH3AX5rbZ8xpg1YEt7Qxi4lm0RO7uKpWXx6UQm/encv03ITuX5ugdMhiYgMG/XNIltvIMC2Wj9TcxK1+MoYdt2cAu5/YzfPrtvP339kvNPhHGGtZemGav71ua00Bf/vOOzqWbn861XTSYrVqr8iMjSnsgbrFKDYGHP0MY+FOB4BGtu6MECKkk0iJ/TPH5vM9lo/33x2M6WZ8ZxZmOJ0SCIiw0l9swi1p76Nzp4A03KTnA5FHFSSHsfcohSeXrOPz50/LiJW0a1p6eBfnt3Ma9sPMKsgmf/4+HS6egP4O3vZfeAQj79fwft7mvjB9TM4d0KG0+GKyAg2qGSTMeZxYDywHjg8vtKiDk1YNB3qJjHGq9XhRE7C43Zx/82zuer+d/js42t47ovnkJUY7XRYIiJhp75ZZNtS3YLP46I0U1Poxrrr5uRzzzObWL/voOM3xdZWNnP7wyvpCQT4l8un8KlFJR8aeXfN7Dy+8vv13PrwSu5YWMy9V0zV6DwROS2DHdk0F5hqrbXhDEb6NbZ1k6pRTSKDkhLn4xe3zeWaB5bz2cfX8ORdZxHtdTsdlohIuKlvFqEC1rK12s+krATdOBQ+NiOH7zy3hT+urXI02dRwqIvP/2YtKXE+Hr9zPkVpcQO2m5GfzPNfOpfvv7idR5aX0xewfHfJtIgYlSUiI8tg/wJuBrLDGYj8TWNbt+o1iZyCydmJ/M/1M1m/7yCffmQVbV29TockIhJu6ptFqIrGdtq6+5iepyl0AonRXhZPy2bp+mrHCnD39gX44hPraG7v5v8+Oee4iabDor1uvnPVNO46bxyPv1/BQ2/tGaZIRWQ0GWyyKR3Yaox52Riz9PAjnIGNVYe6emnr6iUtPsrpUERGlMvOyOF/rp/J+3sa+eTDK2hp73E6JBGRcFLfLELtbTiEAUq1Cp0EXTenAH9nL69uq3Pk/X/wyg7e29PIf3z8DKbmJg76uHsWT+aKGTn854vbWbqhOowRishoNNhpdN8JZxDyN5WN7QCaRicCPLGi8pSPuWl+IU+u2sfiH7/FpxaVEB81+HUQbl5QeMrvJyLikO84HYAMrLKpnczEKGJ8mtIt/c4en0ZuUjRPr6niihm5w/reL22u5cE393DLgkKunZN/Sse6XIb/vn4mB/xdfP2pDWQmRHHWuLQwRSoio82g/guz1r5pjCkCJlhrXzXGxAIn/QtqjFkM/DjY9pfW2u8f83oU/YUs5wCNwA3W2nJjzMXA9wEf0A38o7X29eAxc4BHgBjgBeDLo6leQa2/A4DkGC03KnI6puUmcdtZLn6zooIHlpVx5YxcpuQM/i6eiMhIcLp9MwmvgLVUNrVzhqbQyVHcLsM1s/N5YFkZdf7OIS9mMtibcYe6evnhX3eQnxLDpKyEU76Jd/OCQqK9bh66bQ7X/Hw5dz+xlhe+fC6ZCVqMRUROblDT6IwxnwGeBh4M7soD/nSSY9zA/cBlwFTgJmPM1GOa3Qk0W2tLgR8B9wX3NwBXWmvPAG4HHj/qmJ8DdwETgo/Fg/kMI0WdvwuAhOjBj8YQkQ+akJXA350zDp/bxePvV/Do8nIaD3U5HZaISMicTt9Mwq++tYvOngCFqSeuiSNjz7Vz8glY+OPaqmF7z5e31NLdG+C6Ofl4hlCsPjnWx/99cg6tnb187akNBAKj5j6/iITRYH/rfAFYBPgBrLW7gMyTHDMfKLPW7rHWdgNPAkuOabMEeDS4/TTwUWOMsdaus9Yenhi8BYg2xkQZY3KARGvte8HRTI8BVw/yM4wItS2dGCAhWiObRIaiIDWWL144gY9Nz6a8sY3/fW0Xz22sprm92+nQRERC4XT6ZhJmlU395RAKU2MdjkQiTUl6HAtKUnliRSV9w5Cs2dfUzpqKZhaVpodkJNLErATuvWIqb+9q4OF39oYgQhEZ7QabbOoKJowAMMZ4gJP9lswD9h31vCq4b8A21tpeoAU4diLwtcA6a21XsP3RtwMGOufhGO8yxqw2xqyur68/SaiR40BrJ3FRHtwuLS8qMlRul+GcCRl85eKJzMpPZsWeRv7nlR08tXofNS0dTocnIjIUp9M3kzCrbGonxusmPV61N+XDbju7mKrmDpbtOBDW9wlYy3Mbq0mI9nDhpNDloG9ZUMil07L4r5e3s7HqYMjOKyKj02CTTW8aY/4ZiAnWU/oD8NxJjhkoW3JsJ+iEbYwx0+ifWvfZUzhn/05rH7LWzrXWzs3IyDhJqJGjzt9FoqbQiYRUYrSXa+fk8/VLJrFwfDpba/z89PUynl5TRWunVq0TkRHpdPpmEmaVje0UpcVijG4ayoddMi2LrMQoHn2vIqzvs7aimarmDi6bnk2UN3Sl3Iwx3HftDNLjo/jS79ZxqKs3ZOcWkdFnsMmme4B6YBP9iZ8XgH85yTFVQMFRz/OBY9fMPNImeEcuCWgKPs8HngVus9buPqr90csoDHTOEa3O36kpdCJhkhzr42Nn5PBPl07mvAkZbNh3kB/+dSfvljUMy5B2EZEQOp2+mYRRe3cv9Ye6NIVOjsvrdnHz/CLe2lnP3oa2sLxHR3cfL22ppSgtlpn5ySE/f3Ksj/+9YRaVTe18+89bQn5+ERk9BpVsstYG6C86+Xlr7XXW2l8MYgW4VcAEY0yJMcYH3AgsPabNUvoLgANcB7xurbXGmGTgeeAb1tp3j4qjBmg1xpxl+m8Z3Qb8eTCfYaSo83eSqJXoRMIqxudm8fRsvvzRCRSmxvL8phoeWFZG2YFWp0MTERmU0+ybSRjtU70mGYSb5hfgcRl+8354Rje9uq2Oju4+rpyRG7YRdgvGpXH3hRP449oq/rx+f1jeQ0RGvhMmm0y/7xhjGoDtwA5jTL0x5lsnO3GwBtPdwMvANuApa+0WY8x3jTFXBZs9DKQZY8qAr9J/l47gcaXAvcaY9cHH4QnHfw/8EigDdgMvnsoHjmQ9fQEaDnVrGp3IMElPiOKOhcXcPL+Qlo4ervjpO/xuZSX6f01EItVQ+mYSXhVN7bgM5Kco2STHl5kYzWVn5PDU6n20d4d2GlpNSwfv72lkfkkquckxIT33sb50YSlzi1L45rObqWxsD+t7icjIdLKRTf9A/0on86y1adbaVGABsMgY85WTndxa+4K1dqK1dry19nvBfd+y1i4Nbndaa6+31pZaa+dba/cE9/+7tTbOWjvrqMeB4GurrbXTg+e8ezTdxatv7V+aPVHT6ESGjTGG6XlJfOnCCcwpSuEbz2zi879dS1ObVq0TkYg0pL6ZhE9lUzvZSdH4PKe/xLyMDbedXURrZy9/Xh+6aiDWWp7bUEOMz83FU7NCdt7j8bhd/O+Ns3AZ+OKT6+jpC4T9PUVkZDnZX8PbgJustUfWtwwmhD4ZfE1CqNbfCUBCjEY2iQy3xBgvj396AfdcNpm/bq3jgv9exuPvlauWk4hEGvXNIlBfwFLV1EFhapzTocgIMLcohcnZCTy6vDxko6k3VrVQ3tjGJVOzifUNz/8S+SmxfP/aGUdqYIqIHO1kySavtbbh2J3W2npAw29C7EAw2aSRTSLOcLkMnzt/PC98+Vym5iRy75+3cOVP32H57gYCSjqJSGRQ3ywC1fk76e4LUKR6TTIIxhhuX1jM9tpWlu2oH/L5unr7eHFzDbnJ0cwtTglBhIP3sTNyuGl+IT9ftpu/bBxV6zaJyBCdLO19onkkmmMSYnX+4DQ6FQgXcdTErASe+MwCnt9Uw/ee38bNv1hBSqyXhaXpnFOaTkFKLN19fXT3BujqDdB4qJvGti4aD/X/WpxVkMycohTGZ8Tjcmn5axEJKfXNIlClioPLKbp2dj4PvbWHf39+K+dMSMfrPv3pl29sr8ff2cvN8wtxhako+Il8+8qp7Kpr5atPbSAnKYY5RcOb8BKRyHSyZNNMY4x/gP0GiA5DPGNanb8Tr9sQ63M7HYrImGeM4YoZuVw4OZOXNtfyTlkD75Y18PzGmgHbu12GtDgf3X0Bnly1D4CkGC+fPX8cf3/++LCtCCMiY476ZhGosqmdhGgPybG6YSiD4/O4+OePTeEzj63miRWV3L6w+LTOU+vv5N2yBmYXJlOY5sw0zmivm4dum8vHH3iXux5bzbOfX0RhmhKvImPdCZNN1lplPYZRrb+TzIRoR+5IiMjAYn0erpmdzzWz87HWsru+jaa2bnweF163IcrjJjXOR3KMF5fLYK1lb0MbayqaeWlzLf/10g4O+Lv41hVTNcpJRIZMfbPIVNnUTmFqrG4syCm5aEomC8en8aNXd3L1rDySTjFZ2d0b4MmVlcT43CyenhOmKAcnNc7Hr+6YxzUPLOdTj6zkmc8vImmIszV6+wLsP9hBTUsnNS0d1Ld2kZccyxl5SRSkxujnTSTCqRJ1BDng7yIzMcrpMETkOIwxlGbGn7TNuIx4xmXEc+3sfL73wjYefmcvLR09/Nd1M4Y0TF5ERCJPR3cfTW3dzCtOdToUGWGMMdx7xVQu/8nb/Pi1XXzryqmndPwLm2s40NrFpxYWEx/l/L914zPiefDWOdz68ApuePA9HrhlNuMyTtxvOpa1li3Vfp5Zu5+lG/bTcGjg2cGJ0R5mF6Vw/ZwCLp6apVUgRSKQ87+V5Ig6fyfjT/EXsoiEzhMrKkN+znHpcVw8NYtn1+1nW42fmxcU4nH1d4huXlAY8vcTEZHhVdPSAUBOkmYxjmVD6UPMKUpwwDi9AAAgAElEQVThkeV7SY7xkp4wuBvPm/e3sHJvE+dOSGdCVsJpv3eonTUujV/cNpd/+P16rvrZu3z/2jO4YkbuSY871NXLM2ureGJFJdtrW/G5XXx0SiYXTMokNzmGnORo0uOjqGxsZ9P+FjZXt/Dmjnq+8MRa0uOjuGFePjcvKCIvOWYYPqWIDIaSTRGkzt/JwvFpTochIiFkjOGCSZlEe908t6Ga5WWNnDcxw+mwREQkRGpa+lcTVrJJTtdFU7LYWNXCs+v3c8fC4pOOgj7Y3s0z66rIT4nh4qlZwxTl4H1kUibPf+lcvvjEWu5+Yh3v7W7kjoXFlGbGf2DqW1/Asr3Wz1Or9vHHtfs51NXLjPwk/u3q6Vw5I4fkWN+Hzn1GfhJn5CcdOf6tnfX8dkUFP1+2m/97cw+Xn5HDZ84dd6TNscJxY3EguqEoomRTxOjo7sPf2UuWOioio9LZ49Ioq2vl9R0HmFWQrFUnRURGiZqWDhKiPCRE6/e6nJ6EaC9Xzszlj2uqeGR5ObeeVUS0d+DybE1t3fx2RQXWwg1zC46Mlo40eckx/P6zZ/ODl3fw0Ft7+O2KStLifMwvSSU9Poot1S1sq2mlo6cPn9vFFTNyuG1hMbMKkgf9Hm6X4YLJmVwwOZOq5nYeXV7O71buY+mGas4al8rNC4q4ZGrWca+liISXkk0Ros7ff1csKyGart6Aw9GISDh87Iwc/ve1Xby8pZbr5xY4HY6IiIRATUsnOcm6WShDM7swBbcx/GHNPh5+Zy+fWlhM7DF1mDbvb+GZdVUA3DivkLT4yK716nX3r7h361lFvLe7kff3NrJiTxMtHT1MzUnkxvkFTM9NorGtm/goD1ur/WytHmixzcEpSY/nqxdPZFV5E+/tbuRLv1tHtNfFjLxkzixMpiA1VgsxiQwjJZsixJFkU2I0lU3tDkcjIuGQFh/FuaXpLNtZz/wSFZIVERnpegMBDvi7mJAZOTVzZOSaWZCMz+PidysreejtPZw1Lo1or5sYr4uddYd4b08j+Skx3DivkNS4D08xC7VQTzmbW5TK3KIP9n+6egMhLW4e7XVz7oQMFpWms7ehjbUVzazb18zK8ibifG4mZScwKTuRiVnxRHk04kkknJRsihC1R5JNUUo2iYxi50/KYG1lM3/ZWMP/WzwZt0t32ERERqr61i76rNXIJgmZKTmJ3LGwmN+sqGDphuoPvLZofBqXTs+O2KlzkcRlDOMz4hmfEc+VPbnsqGtle42fbTWtrK08iNdtmJydyMz8JCZmJeDRasEiIadkU4Q44O8CUM0mkVEuyuPmsuk5/H71Pv6weh83zlcBSRGRkarmoIqDS+iNy4jnnz82hY7uPjp7AnT29OH1uMhO1PfZ6Yj2upmZn8zM/GT6ApaKpjY2VbWwaX//I8br5qNTMllQkqabgCIhpBRuhKjzdxLjdZMQwmGkIhKZZuQnUZQWy3+/spPOnj6nwxGRMcQYs9gYs8MYU2aMuWeA16OMMb8Pvr7CGFMc3H+xMWaNMWZT8OuFwx17JKpp6cDrNqRHeO0cGXk8LhcJ0V4yEqIoSI1VoilE3C7DuPR4lszK4xuXTeGOhcXkpcTwl401PLCsjIrGNqdDFBk1lGyKEHWtXWQlRn1gOVARGZ2MMVw0JYuGQ138YU2V0+GIyBhhjHED9wOXAVOBm4wxU49pdifQbK0tBX4E3Bfc3wBcaa09A7gdeHx4oo5sNS2dZCVGq+iwyAjkdhkmZiXwqYXF3Dy/kPbuPh58aw/PbagmYK3T4YmMeEo2RYi6lk4ydcdCZMwYlx7HrIJkHnprN719WoFSRIbFfKDMWrvHWtsNPAksOabNEuDR4PbTwEeNMcZau85ae7iAzBYg2hgzpofzWGv7V6JLinE6FBEZAmMM0/OS+IeLJnD2uDTe29PI8xtrsEo4iQyJkk0Roq61U8NjRcYQYwyf/8h49jV18PymGqfDEZGxIQ/Yd9TzquC+AdtYa3uBFiDtmDbXAuustV3HvoEx5i5jzGpjzOr6+vqQBR6Jqls66ejpU70mkVEiyuPmihk5nFOaznt7Gnl12wGnQxIZ0ZRsigDWWur8nWQljukbhCJjzkVTspiQGc/Pl+3W3TMRGQ4DzfU69pfPCdsYY6bRP7XuswO9gbX2IWvtXGvt3IyMjNMOdCTYWu0HIFfJJpFRwxjDZdOzmVuUwhs7DvDOrtGdNBcJJyWbIoC/s5fOngBZGtkkMqa4XIbPnT+e7bWtvL5dd89EJOyqgIKjnucD1cdrY4zxAElAU/B5PvAscJu1dnfYo41w22r8GLSSsMhoY4zh6jPzmJ6XxAuba9m0v8XpkERGJCWbIkCdv3/ZXNVsEhl7rpqVS15yDA9odJOIhN8qYIIxpsQY4wNuBJYe02Yp/QXAAa4DXrfWWmNMMvA88A1r7bvDFnEE21rtJzXOR5TH7XQoIhJiLmP4xNx88lNi+MuGaq0eLHIawppsGsLyumnGmDeMMYeMMT875phlwXOuDz4yw/kZhsPhZJNqNomMPV63i7vOG8eaimZW7m1yOhwRGcWCNZjuBl4GtgFPWWu3GGO+a4y5KtjsYSDNGFMGfBU43H+7GygF7h1NfbCh2FrjJydZxcFFRiuPy8VVM3M51NXLa9vqnA5HZMQJW7JpiMvrdgL3Al8/zulvsdbOCj5G/NyTOn9/fU3VbBIZm26YV0B6vI+fvVHmdCgiMspZa1+w1k601o631n4vuO9b1tqlwe1Oa+311tpSa+18a+2e4P5/t9bGHdX/GhV9sNPV2tlDZVO76jWJjHL5KbHMK07lvT2N1LZ0Oh2OyIgSzpFNQ1let81a+w79SadR7/DIJtVsEhmbor1u7jpvHG/vamBNRbPT4YiIyElsr20FIFvJJpFR75KpWUR73SzdsF8lD0ROQTiTTaFaXncgvw4O377XGDPQqikjaundOn8nSTFeor2a8y8yVt2yoIjUOB8/fX2X06GIiMhJHF6JLidJ0+hERrvYKA+XTs2mvLGdDVUHnQ5HZMQIZ7JpyMvrHsct1tozgHODj1sHajSSlt6t83dqCp3IGBcX5eHvzi1h2Y56NuxTR0ZEJJJtrfaTEuslMdrjdCgiMgzmFKeQnxLDi5tq6e4NOB2OyIgQzmTTkJbXPR5r7f7g11bgCfqn641odf4uTaETEW47u5jkWK9GN4mIRLjtda1MyUnkOAPsRWSUcRnDx6bn0NrVy9pKlTwQGYxwJptOe3nd453QGOMxxqQHt73AFcDmkEc+zOr8nWQmKNkkMtbFR3m4c1EJr247wOb9LU6HIyIiA+gLWHbWtjIpO8HpUERkGBWlxZKfEsO7ZQ0EVLtJ5KTClmwa4vK6GGPKgR8CdxhjqoIr2UUBLxtjNgLrgf3AL8L1GYZDX8ByoLWL7CRNoxMRuH1RMQnRHo1uEhGJUJVN7XT09DElO9HpUERkGBljWFSaTmNbNzuDiwSIyPGFdaK5tfYF4IVj9n3rqO1O4PrjHFt8nNPOCVV8kaDhUBd9AUu2CkyKCJAY7eXTi0r48Wu7WL/vILMKkp0OSUREjrKjtr84+OScBDbv9zscjYgMp+m5SbwUU8s7uxuYnKOEs8iJhHManQxCbUsnANmq2SQiQX93bgkZCVF8e+kWAgEN0xYRiSTbaloxBiZkahqdyFjjdhnOHpfGnvo2alo6nA5HJKIp2eSwWr+STSLyQQnRXr5x2WQ27DvIH9dWOR2OiIgcZXutn5K0OGJ8bqdDEREHzCtOxes2vFvW6HQoIhFNySaH1QWTTVmq2SQiR7l6Vh6zC5O576Ud+Dt7nA5HRESCdqg4uMiYFuNzM6cohQ1VB2lVH03kuJRsclhtSycelyE9TskmEfkbl8vwnaum0djWxU9fU7FwEZFI0N7dS0VTO5NVHFxkTFs4Pp1AwPL+nianQxGJWEo2Oay2pZOsxGhcLuN0KCISYWbkJ3PD3AJ+/W45ZQcOOR2OiMiYt7PuENb2FwcXkbErPT6KiVkJrKloImBVX1NkIEo2OazW30lWokY1icjA/vHSScT43PzLnzapWLiIiMO21wRXotM0OpExb3ZRCv7OXnbrhqDIgJRsclitv5PsJBUHF5GBpcVHce/lU3l/TxO/fGeP0+GIiIxp22tbifW5KUiJdToUEXHY5OwEor0u1u076HQoIhFJySaH1QWn0YmIHM/1c/O5bHo2P3h5B5v3tzgdjojImLW91s+k7ASVPxARvG4XM/KT2VLdQmdPn9PhiEQcj9MBjGWtnT20dfeRrWSTyJj0xIrKQbedU5TCu2UNfOrXq/jCBaX4PKd+r+DmBYWnfIyIiPSz1rK9tpXLpmc7HYqIRIjZBcms3NvEluoW5hSlOh2OSETRyCYH1fk7ATSNTkROKtbn4bo5BdQf6uKFzTVOhyMiMubU+bs42N6jlehE5IiC1FjS4nysrdRUOpFjKdnkoJqWYLJJI5tEZBBKM+M5tzSdlXub2FClTo2IyHDaXttfHHySioOLSJAxhtlFKextaKOprdvpcEQiipJNDqpt0cgmETk1F0/NoigtlqfXVLG7XqufiIgMl+21rYBWohORD5pVkAzA+n3NDkciElmUbHLQ4Wl0KhAuIoPlcbu47axi0uJ8/Ob9CmpaOpwOSURkTNhe4ycnKZrkWJ/ToYhIBEmJ9TEuPY51lQex1jodjkjEULLJQbX+TpJjvUR73U6HIiIjSIzPzR0Li4nyuHh0eTkH2zVsW0Qk3LbXtmoKnYgM6MzCFBrbuqlsanc6FJGIoWSTg2pbulSvSUROS3KsjzsWldDdF+BX7+6lvrXL6ZBEREatnr4Au+sPqTi4iAxoem4iXrdh/T7V1BQ5TMkmB9X5OzWFTkROW3ZiNLefXUx7dx/3Lytjo4qGi4iExe76Q/T0WdVrEpEBRXndTMpOZPP+FvoCmkonAko2OaqmpZMcFQcXkSEoSovj7gtKyU6M5slV+3huYzW9gYDTYYmIjCpbq/tXopuaq5FNIjKwmflJtHX3sUcLuIgASjY5pqcvQGNbl0Y2iciQJcf6+LtzS1g0Po33djfy41d3sbq8SUknEZEQ2bzfT7TXxbj0OKdDEZEINTErgSiPiw1VLU6HIhIRlGxyyIHWLqyFbI1sEpEQ8LhcXD4jl9vPLiLK6+KZdfv5n1d2snx3A129fU6HJyIyom2pbmFydiIet7rOIjIwr9vFtNwktlS30NmjvpdIWP9iGmMWG2N2GGPKjDH3DPB6lDHm98HXVxhjioP704wxbxhjDhljfnbMMXOMMZuCx/zEGGPC+RnCpbalE0AFwkUkpCZlJ/KFj5Ryx8JikmO9/GVjDd9/cTvPbaim7ICGdYuInKpAwLK12s/0PE2hE5ETm5mfRFdvgDd31jsdiojjwpZsMsa4gfuBy4CpwE3GmKnHNLsTaLbWlgI/Au4L7u8E7gW+PsCpfw7cBUwIPhaHPvrwq/P3J5s0jU5EQs0Yw8SsBD573ng+d/54puQksrK8iYt++Ca3PryC1eVNTocoIjJiVDa109rVy/TcJKdDEZEINy4jnjifm6Ubqp0ORcRx4RzZNB8os9busdZ2A08CS45pswR4NLj9NPBRY4yx1rZZa9+hP+l0hDEmB0i01r5nrbXAY8DVYfwMYXNkZJOm0YlIGBWmxvKJuQX80+LJ/OOlk9hW4+e6/3tPSScRkUHaEiwOPk3JJhE5CbfLMD0vide21dHW1et0OCKOCmeyKQ/Yd9TzquC+AdtYa3uBFiDtJOesOsk5ATDG3GWMWW2MWV1fH3nDGGv9nfg8LlJivU6HIiJjQHyUhy9cUMpb/+8CvvmxKR9IOq2pUNJJROR4Nle34HEZJmbHOx2KiIwAM/OT6ewJ8Oq2OqdDEXFUOJNNA9VSsqfR5rTaW2sfstbOtdbOzcjIOMEpnVHb0kl2YjQjtOSUiIxQsT4Pnzlv3AeSTtf+XEknEZHj2VLtZ0JWAlEet9OhiMgIUJgWS25SNEvXayqdjG3hTDZVAQVHPc8Hjv2JO9LGGOMBkoAT/bdTFTzPic45ItT6O1UcXEQco6STiMjJWWvZsr+F6bkqDi4ig+Myhitn5vLmznqa2rqdDkfEMeFMNq0CJhhjSowxPuBGYOkxbZYCtwe3rwNeD9ZiGpC1tgZoNcacFVyF7jbgz6EPPfzq/J1kqV6TiDjsREmn5WUNBAInGmwqIjK61fm7aGzrZpqSTSJyCq4+M4/egOUvG0fkuAiRkAhbsilYg+lu4GVgG/CUtXaLMea7xpirgs0eBtKMMWXAV4F7Dh9vjCkHfgjcYYypOmolu78HfgmUAbuBF8P1GcLFWhucRhfldCgiIsDASaebf7mCRfe9zn++uI3ttX6nQxQRGXab97cAMD1PxcFFZPCm5CQyOTuBZ9budzoUEcd4wnlya+0LwAvH7PvWUdudwPXHObb4OPtXA9NDF+XwO9jeQ1dvgCxNoxORCHM46XTr2UX8dWsdf1q3n4ff3suDb+5hcnYCS2blsWRWLrnJMU6HKiISdluq/RjT/4+jiMipuGZ2Hv/xwnb21B9iXIYWGJCxJ5zT6OQ4av2dAOQk6Z81EYlM0V43V87M5eE75rHymxfxb0umEetzc99L21l03+vc8OB7PLmykpaOHqdDFREJm83VLZSkxxEXFdb7syIyCi2ZlYfLwLPrNLpJxib95XTA4WRTdpKm0YnI8HliReVpH+t2ubhuTgEXTMpkQ9VB1u87yD3PbOKbf9rM5OwE5hSmMDE7AddRK2zevKAwFGGLSAgZYxYDPwbcwC+ttd8/5vUo4DFgDtAI3GCtLTfGpAFPA/OAR6y1dw9v5M7YWu1nTlGK02GIyAiUlRjNotJ0nl23n69cNBGXS6uQy9iikU0OqGvpTzZpGp2IjDRp8VFcODmLr1w0kc9/ZDxnlaRS0djOY+9X8D+v7OCtnfW0d/U6HaaIDMAY4wbuBy4DpgI3HVUT87A7gWZrbSnwI+C+4P5O4F7g68MUruOa2rrZf7BDxcFF5LRdMzuPquYOVlc0Ox2KyLBTsskB1Qc7cBnITFCySURGJmMM+SmxXD4jl39aPJmb5heSHOvjpS21fP+l7fxxTdWRwroiEjHmA2XW2j3W2m7gSWDJMW2WAI8Gt58GPmqMMdbaNmvtO/QnncaELdUqDi4iQ3PptGxifW6eWVvldCgiw07JJgfsaWgjPyUWn0eXX0RGPrfLcEZeEp85dxxfunACswtT2Lj/IFf89B0+/sC7PLehmkDAOh2miEAesO+o51XBfQO2Ca4s3AKkDfYNjDF3GWNWG2NW19fXDzFcZ22p7l+FUyObROR0xfo8LJ6WzfObaujs6XM6HJFhpWyHA8ob2yhJj3M6DBGRkMtOiubqM/O4Z/EUvnXFVA629/DF363j4w+8y8q9TU6HJzLWDVQw5NhM8GDaHJe19iFr7Vxr7dyMjIxTCi7SbN7fQl5yDMmxPqdDEZER7OOz82jt7OW1bQecDkVkWCnZNMysteytV7JJREa3GJ+bT59TwmtfPZ8ffmImdf4uPvHge3z28dVUNbc7HZ7IWFUFFBz1PB+oPl4bY4wHSALGZKZ48/4WjWoSkSFbOD6d7MRonlx1+gu1iIxESjYNs/rWLtq6+5RsEpExweUyXDM7nze+/hG+dvFE3t7VwNX3L1c9JxFnrAImGGNKjDE+4EZg6TFtlgK3B7evA1631o65ebD1rV2UN7ZrJToRGTK3y3DzgkLe3tVA2YFDTocjMmyUbBpmexvaAJRsEpExJcbn5osfncDSuxcR5XFxw4Pv8dbOkV3PRWSkCdZguht4GdgGPGWt3WKM+a4x5qpgs4eBNGNMGfBV4J7DxxtjyoEfAncYY6oGWMlu1Fhd3j+Ya25xqsORiMhocNP8QnxuF4+9V+50KCLDRsmmYaZkk4iMZaWZCTzz+YUUpsXx6UdWaXUWkWFmrX3BWjvRWjveWvu94L5vWWuXBrc7rbXXW2tLrbXzrbV7jjq22Fqbaq2Nt9bmW2u3OvU5wm1VeTNRHhdnaCU6EQmBjIQorpiZw9NrqvB39jgdjsiwULJpmO1tbMPndpGbHON0KCIijshKjOb3nz2L+SWpfPWpDTy/scbpkEREPmB1RROzCpK1crCIhMynFpbQ3t3HH1brRpuMDfoLOsz21rdRmBaL2zXQYi8iImNDYrSXX39qHrMLk/nHpzewq67V6ZBERABo6+plS7WfeZpCJyIhdEZ+EnOLUnh0eTl9gTFXCk/GICWbhtneBq1EJyICEOVx88Atc4j1efjs42s0rFxEIsK6yoP0BSzzSpRsEpHQumNRMZVN7SzbccDpUETCTsmmYdQXsFQ0tSvZJCISlJ0UzQO3zKayqZ2vPbWBgO70iYjDVpU34TIwuzDZ6VBEZJS5dFo22YnRPLK83OlQRMLO43QAY0n1wQ66ewNKNonImPDEispBt108PZu/bKzhs79ZwwWTMk/r/W5eUHhax4mIHG11RROTsxNJiPY6HYqIjDJet4tbzy7iBy/vYFuNnyk5iU6HJBI2Gtk0jLQSnYjIwM4el8bM/CRe3VpHefB3pYjIcOvpC7C24iDzilOcDkVERqlbFhSSFOPl35/firUa0S2jl5JNw6i8UckmEZGBGGO4elYeKXE+nlq9j47uPqdDEpExaGu1n46ePtVrEpGwSY718dWLJ/JuWSOvbK1zOhyRsFGyaRjtqW8j1ucmMyHK6VBERCJOlNfNDXML8Hf28Kf1+3W3T0SG3aryJgDmFinZJCLhc8uCQiZmxfO957fR2aMbbDI6qWbTMCpvbKM4LQ5jjNOhiIhEpILUWC6aksUrW+uYlJXA7CJNZRGR4bOqvImC1Biyk6KdDkVERjGP28W3r5zGLb9cwcPv7OULF5QOewz+zh4qG9upaGxnX3M7XreLrMQoshKjyUuOITc5ZthjktElrMkmY8xi4MeAG/iltfb7x7weBTwGzAEagRusteXB174B3An0AV+y1r4c3F8OtAb391pr54bzM4TS3oY2puclOR2GiEhEO29iBrsOHGLphmoK02JJj9doUBEJP2stq8ubOX9ShtOhiMgYsKg0nUumZnH/G2VcOzs/7EnuQMCyvuogf91ax1+31lF24NAJ259ZmMwNcwu4YmYu8VEaoyKnLmzfNcYYN3A/cDFQBawyxiy11m49qtmdQLO1ttQYcyNwH3CDMWYqcCMwDcgFXjXGTLTWHh5jeIG1tiFcsYdDd2+AfU3tXDUz1+lQREQimssYrp+Tz09fL+Px9yr43PnjifG5nQ5LREa5vQ1tNLZ1M69YU+hEZHj8y+VTueiHb/K9F7bxkxtnnfIMmMGs/NvS0cOq8iZWlzfh7+zFZaA4PY5Lp2aRFh9FWryP1Fgffdbi7+yltaOHWn8nayqaueeZTXzrz1v45FlFfP3SicT6lHSSwQvnd8t8oMxauwfAGPMksAQ4Otm0BPhOcPtp4Gem/ydsCfCktbYL2GuMKQue770wxhtW+5rbCVgoTlNxcBGRk0mO9XHLWYX8+p1ynlhZwR0LS3C7NAVZRMLncL0mrUQnIsOlMC2WL1xQyo9e3UlJWixfvWRSyM5d0djGu7sb2VrdgrUwMSuBxdOTmJSVeNybeLE+D9mJ0UzISuCc0nT2NXewcm8jv16+lzd2HOBHN8xiVkFyyGKU0S2cyaY8YN9Rz6uABcdrY63tNca0AGnB/e8fc2xecNsCrxhjLPCgtfahgd7cGHMXcBdAYWHh0D5JCOytD65El6Fkk4jIYIxLj+fqM/P449oqlm7Yz9Wz8lTzTkTC5vXtB8hKjGJ8RrzToYjIGPKlj5ZSfbCDn7xeRkK0l8+cN+60z2WtZUdtK2/urKeiqZ0Yr5tF49NZMC6N1DjfKZ3LGENhaiyFqbH846WT+dpT67n258v54oWl3H1BKR631hqTEwtnsmmg/wiOXVroeG1OdOwia221MSYT+KsxZru19q0PNe5PQj0EMHfuXMeXNCpvDCabNLJJRGTQ5hSl0Hioi2U760mPj+LcCaqlIiKh197dy5s76/nE3AIltUVkWBlj+I9rzuBQdy/fe2Eb8dEebpp/aoMlevoCbKxq4Z2yeur8XSTHeLliRg5zi1LxeYaeFDp7fBov/sN5fGfpFv731V1s3t/C/bfMJsqjMgdyfOFMNlUBBUc9zweqj9OmyhjjAZKAphMda609/PWAMeZZ+qfXfSjZFGn2NLSRHOsl5RQzyiIiY91FU7NoONTFi5tr6ekLcMGkTP0zKCIh9dbOejp7Aiyelu10KCIyBrldhh99YhZtXb3887ObqGnp5FMLi0/6v6O/s4cVe5pYubeRtu4+MhOiuH5OPjPyk0NefiApxsuPbpjF7MJk7v3zFj7z2BoeunUO0V4lnGRg4Uw2rQImGGNKgP30F/y++Zg2S4Hb6a/FdB3wurXWGmOWAk8YY35If4HwCcBKY0wc4LLWtga3LwG+G8bPEDJ769soSdeoJhGRU+UyhuvnFuBdt59Xtx2gtqWT6+YUhOROnYgIwEuba0mO9TK/RMXBRcQZPo+Ln98yh6/9YT0/eW0Xv3x7DzfOK+T2hUWkxUfhMv19orIDh3hj+wGW7axnbUUzAJOyE1g4Pp3xGXFhvyF369nFRHnc/NMzG/nUr1fx8B1zVThcBhS274pgDaa7gZcBN/Ara+0WY8x3gdXW2qXAw8DjwQLgTfQnpAi2e4r+YuK9wBestX3GmCzg2eAPkAd4wlr7Urg+QyiVN7Zx9rg0p8MQERmRvG4X183pXxb4pc21NLbt5pMLijRaVESGrLs3wGvbDrB4erZqkIiIo2J8bh64ZQ7ba/08+OYeHn2vnF+9u3fAtjPyk/jIpExmFyaTFh81rHF+Yl7/Tb+vPrWe23+1ksc+vUArB8uHhDUFaa19AXjhmH3fOkwYXKMAABelSURBVGq7E7j+OMd+D/jeMfv2ADNDH2l4tXf3UtPSqZFNIiJDYIzh3AkZZCVG8+SqSv73tZ0sGp/OeRMzNIRbRE7b8t0NtHb1sni6ptCJSGSYnJ3Ij26YxdcumcirW+vo6bMErCVgISsxivMmZpAeH8UTKyodi/HqM/PwuA1f/N06vvzkOn7+yTlaOVg+QOPdhsH6yoMATM1NdDgSEZGRb2JWAl+8YAKvbK1l2c56VpY3ccGkTK4+M1fDuEXklL28pZY4n5tFpelOhyIio0Qok0A+j5ujuzedPQFe2VIXsvMPxRUzcmlo7eI7z23l35/fyrevnOZ0SBJB1CsfBu+UNeBxGRZoGp2ISEikxPm4YV4h50zo4KXNNTy/qYbXtx/g0mlZLDkzj3NL0zUdRkROqi9g+evWOi6YnKkRkiIip+GORSVUNnXwq3f3UpASy6fPKXE6JIkQSjYNg3fKGjizMJn4KF1uEZFQykuO4dOLSqhsaqe1q5fnN9bwp/XVJER5mJKTyJScBKbkJFKQGktKrI/UOB8pcV4t1SsiAKypaKbhULem0ImIDME3L5/C/oPt/NvzW8lLieFSrewpKNkUdgfbu9m0v4Uvf3SC06GIiIxKxhiK0uK4eUEh375yKm/uqOfNnfVsq/HzhzVVtHf3feiYnKRoxmXEUZIex/TcJBZPzyY5VsXGRcaalzbX4vO4+MikTKdDERGJWIOZFnj2uHS2VPu5+4m1/N054yhIjT2t97p5QeFpHSeRR8mmMHtvdyPWwjmqAyAiEnZRHjeXTMvmkuAdtUDAsq+5ndqWTprbu2lq66G+tYuKxjb2NLSxdH01v3m/knv/vJnzJ2ayZFYuF0/N0nQakTHAWsvLW2o5b0K6Rp+LiAyRz+PitrOL+fmyMh57v4K/P388qVo1eEzTX9Ywe7usgfgoDzMLkp0ORURkzHG5+kc9FaUNvBqotZYt1X7+vH4/SzdU8+q2OjITovjMueO4eUEhcfoHVGTUeqesgf0HO/jqxROdDkVEZFSIj/Jw+8JiHnxzD48sL+dz54/T4i1jmKqnhtm7ZQ2cNS4VrwrViohEHGMM0/OS+OblU1l+z0d5/M75TMiK53svbGPRfa/z41d3cbC92+kwRSQMfvp6GdmJ0VwxM8fpUERERo3MhGg+eVYRze3d/HZFJb19AadDEocozRhG+5raqWhs546FxU6HIiIy6oVqmeHLz8hlRl4yy3Yc4Eev7uT+ZWWcVZLKotJ0EqK9R9qppoDIyLVybxMr9zbx7SunasEAEZEQK0mP49rZ+Ty1eh9PrtrHTfMLcbuM02HJMFOyKYzeKWsA4NwJqtckIjKSFKTGcuvZxdS2dLJs5wHe3tXA8t2NzCxIZkZ+EuPS450OUUSG4GdvlJEe7+PGeUoai4iEw6yCZNq7e/nLxhqeXrOP6+cW4DJKOI0lSjaF0TtlDWQlRjE+Q/+UiIiMRNlJ0dw4r5CLp3Tx1q56NlS1sKaimTifm+21fi6cnMmCcWkqLiwygmzYd5C3dtbzT4snE+PTqCYRkXBZOD6d7t4Ar2ytw+dxcfWsPIwSTmOGesdhEghYlpc1cMHkTP1AiYiMcGnxUXz8zHyumJHLjtpWNu1v4Zm1+/ntiko8LsPMgmQWjU9jYWk6ZxYma1qOSAT72RtlJMV4+eRZGtUkIhJuH5mUSXdfgGU76vG4XFw+I0cjnMYIJZvCZGuNn+b2Hk2hExEZRbxuF9Pzkpiel8Q1s/NYW9HMu7sbeKeskZ+9UcZPXi8jxutmXkkqi8ansag0nak5ibhUp0AkImyv9fPXrXX8w0UTPlCDTUREwufiKVn09lneKWvA39nD9XMK8Hm0gNZop2RTmByu17RovJJNIiKjUbTXzcLSdBaWpvOPl0JLRw8r9jTyblkD7+5u5D9f3A5ASqyXhePTuXR6NhdNydQSwCIOsdbyg5d2EOdza/EWEZFhZIzhsunZJMZ4eXFTDQ+17ebWs4tJilHSfzRTjzcM+gKWpeurmZSVQGZitNPhiIhIGBxv9btJ2YlMyk7E39HD7vpD7K4/xFu76nl+Uw1et2FKTiKz8pOZkJUw6JVZtPKdyNA9sGw3r20/wL9cPoXkWJ/T4YiIjCnGGM4pTScj3seTq/bxwBtl3Di/kJL0OKdDkzBRsikMHl1eztYaPz+56UynQxEREYckxng5szCFMwtTCFhLeUMbG6ta2LS/hY1VLSRGe5hTlMLcolRS4vSPr0g4vb69jv9+ZQdXzczlznNKnA5HRGTMmpSdyOfOH8/j71fwi7f3cGZBMounZ2tq8yikZFOI7T/YwX+/soOPTMrgyhk5TocjIiIRwGUM4zLiGZcRz5Uzc9le62dVeRPLdtSzbEc94zPjmVecypScBDwu1TAQCaXd9Yf48u/WMzUnkfuunaGFW0REHJaVGM2XLpzAsh0HeLusga01fj46OZP5JWlOhyYhpGRTCFlrufdPm7EW/m3JdHVmRETkQ9wuw7TcJKblJnGwvZvVFc2sqWjmdysrifO5mV2YwsyCZHKSovV3RGSImtu6+cxjq/F6XDx46xxifFopUkQkEvg8Li6Zls3sohT+srGaFzbX8ur2A2yv9fOJeQWcWZCsftAIp2RTCL2wqZbXg7UAClJjnQ5HREQiXHKsj4umZHHh5Ex21R1iVXkT7+5u4O2yBtLjo5iRn8TUnET6AnbQ9Z1EpN+rW+v452c30dzezeN3LiA/RX0zEZFIkx4fxe1nF1PR2M7qimb+vL6aJ1ftIz8lhvnFqcwuSmFucQrj0uO1gt0Io2RTiBxo7eQ7z21hel6iVjgREZFT4jKGSdkJTMpOoK2rly3VfjZWHeSN7Qd4ffsBHl1ezqzCZOYUpVCcFkdGQhSZCVGkxvnweVx43f0PJaREoKW9h3/9yxaeWbufydkJ/OqOeUzPS3I6LBEROQ5jDMXpcRSnx3HVrFz+sqGaN3Yc4K1d9Tyzbn+wDWQnRpOfEkNucgzJMV4SY7wkxXiJ9Xnwug0+jwtfsE/k9bj69wWfH+4vRXlcRHldRHvdRHvceN1GI6jCJKzJJmPMYuDHgBv4pbX2+8e8HgU8BswBGoEbrLXlwde+AdwJ9AFfsta+PJhzDreD7d089NYeHlleTm+f5dd3zMPjVsZVREROT1yUh/klqcwvScXf2cPuA4fweVysqWjmx6/twtrjH2sM/R0sl8HrcZEQ7SE5xkdSjJek2P4OWXKwY5YcfJ4UfP3w81ifW52uUSwcfbNI0N0bYPnuBl7aXMtLW2pp7ezlixeW8sULJ+hOuIjICBIf5eHG+YXcOL8Qay37mjpYW9nM3oY2qpo7qGpuZ21lMy3tPbR29Z6wXzQYLkN/4snrJtrTn4SK8rqJ9rqI9bmJ8XqIi3IT6/MQ63MT53MT4+vfF+N1ExflIcbnJi74eqzvb/tive4xnRsIW7LJGOMG7gcuBqqAVcaYpdbarUc1uxNottaWGmNuBO4DbjDGTAVuBKYBucCrxpiJwWNOds5h0d7dy0Nv7eHht/dyqLuXK2fk8uWLJjA+I364QxERkVEqMbp/RTuAablJdPX24e/opbWzh9bOXtq6e+kL2P6HtQQObwcsvQFLV2+A9u5eKhq76ajto6O7j46ePgIn6Ji5jSEl7nAiyktyrO/I9tFJqcNf46I8R+4aRnn+dufQ53HhcRl6+iy9gQA9ff1TAeOjNKjaKeHom1lr+4b3U/SPXFq3r5nyhjbKG9spb2xjbUUz/s5e4qM8XDg5k7vOG6fRTCIiI9ATKyoH3J+VGE1WYjRzilKO7AtYS1dPgO6+wN/6Q0ceAXqP6Rcd/trTF6C3L0DPke3+rz19f3uttbOXpkPddPcF6O4NPoLbp5Lf8nlcxPn+lqyKPXo7ykOs101sVH+yKiaYzIr1eYiNOqatz0O014XH1T+S3es2uF0Gj8uFx21wG0PAWgKW4NejtgP2SEJtOIWzxzcfKLPW7gEwxjwJLAGO7tAsAb4T3H4a+Jnpv526BHjSWtsF7DXGlAXPxyDOOWx+u6KSRaXpfOXiiUzKTnAiBBERGUOiPG4yEtxkJESd9jmstXT3Bmjv+Vvy6divuckx+Dt6ONjRzYHWTnbWtdLS0Z/gGorLZ+Rw/82zh3QOGZJw9M3eG6bYj9hQdZA7fr0KgDifm6K0OC6Zls1l07NZVJo+7J1pERFxhssYYnxuYhi+3/vWWnr67N+SUEcno3oDdPf10d1r6e7to7svwPjMeDq6+2jr6qO9u5f27v6vtf6eI9vtXX20dfee8GbgUH3/mjO4cX5h+N5gAOFMNuUB+456XgUsOF4ba22vMaYFSAvuf/+YY/OC2yc7JwDGmLuAu4JPDxljdpzGZzip1cCDp3ZIOtAQjljGKF3P0NL1DC1dz9DRtQwtR67nA8ADt4Tt9EVhO/PoEa6+2RHD1f862lbgReB/hn4q/Z4ZOl3D0NB1DA1dx6HTNQwNx6/jTffBTeE7/YB9sHAmmwYq+HBsru54bY63f6AJjwPm/6y1DwEPnShAJxhjVltr5zodx2ih6xlaup6hpesZOrqWoaXrOWaFo2/2wR0R2v8aDP1cDJ2uYWjoOoaGruPQ6RqGxli9juGsVlUFFBz1PB+oPl4bY4wHSAKaTnDsYM4pIiIiIh8Wjr6ZiIiIyIeEM9m0CphgjCkxxvjoLyq59Jg2S4Hbg9vXAa9ba21w/43GmChjTAkwAVg5yHOKiIiIyIeFo28mIiIi8iFhm0YXnOd/N/Ay/cvr/spau8UY811gtbV2KfAw8HiwyGQT/Z0egu2eon8afi/whcOrnQx0znB9hjAZkUPLI5iuZ2jpeoaWrmfo6FqGlq7nGBSuvtkoop+LodM1DA1dx9DQdRw6XcPQGJPX0fTfrBIRERERERERERm6cE6jExERERERERGRMUbJJhERERERERERCRklm4aRMWaxMWaHMabMGHOP0/GMNMaYXxljDhhjNh+1L9UY81djzK7g1xQnYxwpjDEFxpg3jDHbjDFbjDFfDu7X9TwNxphoY8xKY8yG4PX81+D+EmPMiuD1/H2wIK8MkjHGbYxZZ4z5S/C5rudpMsaUG2M2GWPWG2NWB/fp510E9c9Ol/plQ6f+WGioHxY66nsNnfpcf6Nk0zAxxriB+4HLgKnATcaYqc5GNeI8Aiw+Zt89wGvW2gnAa8HncnK9wNestVOAs4AvBL8fdT1PTxdwobV2JjALWGyMOQu4D/hR8Ho2A3c6GONI9GVg21HPdT2H5gJr7Sxr7dzgc/28y5in/tmQPIL6ZUOl/lhoqB8WOup7hYb6XCjZNJzmA2XW2j3W2m7gSWCJwzGNKNbat+hfGedoS4BHg9uPAlcPa1AjlLW2xlq7NrjdSv8flTx0PU+L7Xco+NQbfFjgQuDp4H5dz1NgjMkHLgd+GXxu0PUMNf28i6h/dtrULxs69cdCQ/2w0FDfK6zG5M+0kk3DJw/Yd9TzquA+GZosa20N9P/BBjIdjmfEMcYUA2cCK9D1PG3BYcfr4f+3d68xdlVlGMf/T7gEAmgDSFIsBEwBKQgFSTVFIgJyKaB8gHgBw4caA4lRJGIKJogGTI0IfDREiBfEFBAEIVBbgQQsBmiL3AQFaeQSO8ZYbhII9PXD3hMOw8x0Lrs9M9P/L2nm7H3WPus9qzk7T9ZeZx8GgBXAs8CGqnq7beJnfnyuAr4DbGy3d8PxnIwC/pBkdZKvtfv8vEvms655Xpkg89jkmMM6YfbqhpmrtW2/C9iKZJh9tcWrkHok2Rn4LXBeVb3SXMDQRFTVO8D8JLOAW4ADh2u2ZauanpKcAgxU1eokRw/uHqap4zl2R1bVS0n2AFYkearfBUlThOcW9Z15bPLMYZNj9uqUmavlyqYt5wVgr57tOcBLfaplJlmfZDZA+3egz/VMG0m2owk2v66qm9vdjuckVdUG4F6aey/MSjI4qe9nfuyOBD6XZB3NV1qOobna5nhOUFW91P4doAnhC/DzLoH5rGueV8bJPNYtc9iEmb06YuZ6l5NNW85DwH7tHf23B74I3NbnmmaC24Cz28dnA7f2sZZpo/0O9jXAX6vqip6nHM8JSPKh9koaSXYEjqO578I9wOltM8dzjKrqwqqaU1X70Jwr766qM3E8JyTJTkl2GXwMHA88jp93CcxnXfO8Mg7msW6YwybP7NUNM9d7pcqVcFtKkkU0M8TbANdW1WV9LmlaSfIb4Ghgd2A98D3gd8ANwN7AP4EzqmrozSo1RJJPAfcBj/Hu97IvorlPgOM5TkkOobnZ3zY0k/g3VNUPknyE5urQrsBa4KyqerN/lU4/7VLub1fVKY7nxLTjdku7uS1wfVVdlmQ3/LxL5rMJMpdNnnmsG+awbpm9Js7M9V5ONkmSJEmSJKkzfo1OkiRJkiRJnXGySZIkSZIkSZ1xskmSJEmSJEmdcbJJkiRJkiRJnXGySZIkSZIkSZ1xsknaiiXZLckj7b9/JXmxZ3v7zdTn/UnmD7P//CQ79GwvT7LLZuh/TpJbx3nMZUk+M8H+dk1yzkSObY+/sf0ZVUmSNEOYwcZ8jBlMmqZSVf2uQdIUkOQS4LWqunzI/tCcKzZ21M/9wNer6pEh+18ADq6qDV30M0r/VwIrq+qOzdlPT39zgZuq6n3hbozHHwucXlXndluZJEmaCsxgm60/M5jUR65skvQ+SeYmeTzJT4E1wOwkVyd5OMkTSS5u252a5Pqe445Lckv7+KQkDyRZk2RZkp1G6e9bwB7AfUlWtvteSDKrp5Zr275/meSEJKuS/C3JEW37nZP8PMmDSdYmOXWYfgKcBqxot7+a5OYktyd5Lsm5SS5oj1+VZFbb7rokp/XUdUnb5tEk+7f7L01yXk9fTyWZAywFDmivVC5tn1vS1vloz1jukuTOJH9p3+/p7UvdC5yYZJtx/jdKkqRpxgxmBpNmCiebJI1kHnBNVR1WVS8CS6rqCOBQ4LNJ5gHLgaOS7Nge8wVgWZI9gCXAsVV1OPAo8M2ROqqqK4EB4KiqOm6YJgcAlwMfAw6hucq0ELiw7QfgYuCuqloAHAP8JD1LwltzgYGqeqtn30Ft3Z8EfgT8t6oOA1YDZ41Q8vq2zc+A80d6X60lwNNVNb+qliRZBOwNfAKYDyxMshBYBKyrqkOr6mDaMFZV7wDrgIM30Y8kSZoZzGBmMGnac7JJ0kieraqHera/lGQNzVW2A4F5bWBYAZycZDvgBOD3wEKaoLQqySPAmcA+k6jlmap6sl1G/iSwst3/WM/rHg98t+3vHmAHmkDRazbw7yH77q6q16tqPfBaW//Q1x7q5vbv6lHajOR44CRgLc1YzgX2pwmDJyZZmuTIqnq555gBYM9x9iNJkqYnM5gZTJr2tu13AZKmrNcHHyTZj+aq2IKq2pDkOpogAbAMWAz8D3igql5vl0rfVVVf6aiWN3seb+zZ3si757EAp1XVs6O8zhs9dY/ntUeq552eNm/z3gn8of0MCnBpVV3zviea5eiLgB8nub2qftjzWm+M8HqSJGlmMYOZwaRpz5VNksbiA8CrwCtJZtNcPRv0R5rlyItpQg/AKuDTaX/BI8lObVgazavAZH75ZDnwjcGNJIcN0+ZpYN9J9DGadcDH274XAHu1+4e+r+XA4sH7J6T5ZZbdk3yY5uagvwKuAA7vOWY/4InNVLckSZq6zGCbtg4zmDTluLJJ0lisoVk6/TjwD+BPg09U1dtJ7gS+TLNUm6pan2Qxzb0DBn++9yLg76P0cTWwMsnzI9wzYFO+D1yV5DGaifRngM/3NqiqV5I8n2TfqnpuAn2M5kbgrCRrgQdpxmlwLB5u67qjvWfAR4E/NxcfeZVm7OYBS5NsBN4CzgFIsifwclUNXXouSZJmPjPYppnBpCkoVdXvGiRpi0lyBnBQVV3S71rGIskFNDfU/EW/a5EkSZooM5i0dXFlk6StzU3AB/tdxDj8B7iu30VIkiRNkhlM2oq4skmSJEmSJEmd8QbhkiRJkiRJ6oyTTZIkSZIkSeqMk02SJEmSJEnqjJNNkiRJkiRJ6oyTTZIkSZIkSerM/wEHKQFAIpPMjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting travel time differences among estrata\n",
    "fig = plt.figure(figsize = (20,10))\n",
    "\n",
    "ax1 = fig.add_subplot(231)\n",
    "ax2 = fig.add_subplot(232)\n",
    "ax3 = fig.add_subplot(233)\n",
    "ax4 = fig.add_subplot(223)\n",
    "ax5 = fig.add_subplot(224)\n",
    "\n",
    "axes = [ax1, ax2, ax3, ax4, ax5]\n",
    "distributions = [x_1, x_2, x_3, x_4, x_5]\n",
    "\n",
    "for x in range(len(axes)):\n",
    "    label = \"stratum \" + str(x+1)\n",
    "    sns.distplot(distributions[x], bins=15, ax = axes[x], label = label)\n",
    "    axes[x].legend()\n",
    "    axes[x].set_ylabel('Density')\n",
    "    axes[x].set_xlabel('Travel time (minutes)')\n",
    "\n",
    "fig.suptitle('Work - Home travel time distributions by stratum', fontsize=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>sample_size_x1</th>\n",
       "      <th>Average Travel Time_x1</th>\n",
       "      <th>Standard deviation_x1</th>\n",
       "      <th>sample_size_x2</th>\n",
       "      <th>Average Travel Time_x2</th>\n",
       "      <th>Standard deviation_x2</th>\n",
       "      <th>mean_diff</th>\n",
       "      <th>var_x1</th>\n",
       "      <th>var_x2</th>\n",
       "      <th>t-test</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>918421</td>\n",
       "      <td>28.441</td>\n",
       "      <td>12.256</td>\n",
       "      <td>3021902</td>\n",
       "      <td>23.548</td>\n",
       "      <td>11.512</td>\n",
       "      <td>4.893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>339.724</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>918421</td>\n",
       "      <td>28.441</td>\n",
       "      <td>12.256</td>\n",
       "      <td>2129308</td>\n",
       "      <td>18.621</td>\n",
       "      <td>10.504</td>\n",
       "      <td>9.820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>669.103</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3021902</td>\n",
       "      <td>23.548</td>\n",
       "      <td>11.512</td>\n",
       "      <td>2129308</td>\n",
       "      <td>18.621</td>\n",
       "      <td>10.504</td>\n",
       "      <td>4.927</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>503.697</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>918421</td>\n",
       "      <td>28.441</td>\n",
       "      <td>12.256</td>\n",
       "      <td>307540</td>\n",
       "      <td>17.039</td>\n",
       "      <td>10.153</td>\n",
       "      <td>11.401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>510.529</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3021902</td>\n",
       "      <td>23.548</td>\n",
       "      <td>11.512</td>\n",
       "      <td>307540</td>\n",
       "      <td>17.039</td>\n",
       "      <td>10.153</td>\n",
       "      <td>6.508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>334.309</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2129308</td>\n",
       "      <td>18.621</td>\n",
       "      <td>10.504</td>\n",
       "      <td>307540</td>\n",
       "      <td>17.039</td>\n",
       "      <td>10.153</td>\n",
       "      <td>1.582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>80.401</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>918421</td>\n",
       "      <td>28.441</td>\n",
       "      <td>12.256</td>\n",
       "      <td>30796</td>\n",
       "      <td>16.856</td>\n",
       "      <td>10.162</td>\n",
       "      <td>11.585</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>195.358</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3021902</td>\n",
       "      <td>23.548</td>\n",
       "      <td>11.512</td>\n",
       "      <td>30796</td>\n",
       "      <td>16.856</td>\n",
       "      <td>10.162</td>\n",
       "      <td>6.692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>114.823</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2129308</td>\n",
       "      <td>18.621</td>\n",
       "      <td>10.504</td>\n",
       "      <td>30796</td>\n",
       "      <td>16.856</td>\n",
       "      <td>10.162</td>\n",
       "      <td>1.765</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>30.253</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>307540</td>\n",
       "      <td>17.039</td>\n",
       "      <td>10.153</td>\n",
       "      <td>30796</td>\n",
       "      <td>16.856</td>\n",
       "      <td>10.162</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>3.024</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x1  x2  sample_size_x1  Average Travel Time_x1  Standard deviation_x1  \\\n",
       "0   1   2          918421                  28.441                 12.256   \n",
       "1   1   3          918421                  28.441                 12.256   \n",
       "2   2   3         3021902                  23.548                 11.512   \n",
       "3   1   4          918421                  28.441                 12.256   \n",
       "4   2   4         3021902                  23.548                 11.512   \n",
       "5   3   4         2129308                  18.621                 10.504   \n",
       "6   1   5          918421                  28.441                 12.256   \n",
       "7   2   5         3021902                  23.548                 11.512   \n",
       "8   3   5         2129308                  18.621                 10.504   \n",
       "9   4   5          307540                  17.039                 10.153   \n",
       "\n",
       "   sample_size_x2  Average Travel Time_x2  Standard deviation_x2  mean_diff  \\\n",
       "0         3021902                  23.548                 11.512      4.893   \n",
       "1         2129308                  18.621                 10.504      9.820   \n",
       "2         2129308                  18.621                 10.504      4.927   \n",
       "3          307540                  17.039                 10.153     11.401   \n",
       "4          307540                  17.039                 10.153      6.508   \n",
       "5          307540                  17.039                 10.153      1.582   \n",
       "6           30796                  16.856                 10.162     11.585   \n",
       "7           30796                  16.856                 10.162      6.692   \n",
       "8           30796                  16.856                 10.162      1.765   \n",
       "9           30796                  16.856                 10.162      0.184   \n",
       "\n",
       "   var_x1  var_x2   t-test  p-value  \n",
       "0     0.0   0.000  339.724    0.000  \n",
       "1     0.0   0.000  669.103    0.000  \n",
       "2     0.0   0.000  503.697    0.000  \n",
       "3     0.0   0.000  510.529    0.000  \n",
       "4     0.0   0.000  334.309    0.000  \n",
       "5     0.0   0.000   80.401    0.000  \n",
       "6     0.0   0.003  195.358    0.000  \n",
       "7     0.0   0.003  114.823    0.000  \n",
       "8     0.0   0.003   30.253    0.000  \n",
       "9     0.0   0.003    3.024    0.001  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hypothesis one \n",
    "h = list(combinations(summary_table.Strata, 2))\n",
    "hypothesis = pd.DataFrame(h, columns = ['x1', 'x2'])\n",
    "hypothesis= hypothesis.merge(summary_table, how = 'inner', left_on = 'x1', right_on = 'Strata')\n",
    "hypothesis = hypothesis.merge(summary_table, how = 'inner', left_on = 'x2', right_on = 'Strata',\n",
    "                             suffixes=('_x1', '_x2'))\n",
    "\n",
    "hypothesis['mean_diff'] =  hypothesis['Average Travel Time_x1'] - hypothesis['Average Travel Time_x2']\n",
    "hypothesis['var_x1'] = hypothesis['Variance_x1']/hypothesis['sample_size_x1']\n",
    "hypothesis['var_x2'] = hypothesis['Variance_x2']/hypothesis['sample_size_x2']\n",
    "hypothesis['t-test'] = hypothesis['mean_diff']/(np.sqrt(hypothesis['var_x1']+hypothesis['var_x2']))\n",
    "hypothesis['p-value'] = st.norm.sf(abs(hypothesis['t-test']))\n",
    "\n",
    "hypothesis[['x1', 'x2', 'sample_size_x1', 'Average Travel Time_x1',\n",
    "       'Standard deviation_x1', 'sample_size_x2',\n",
    "       'Average Travel Time_x2', 'Standard deviation_x2', \n",
    "       'mean_diff', 'var_x1', 'var_x2', 't-test', 'p-value']].round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>Average Travel Time_x1</th>\n",
       "      <th>Standard deviation_x1</th>\n",
       "      <th>Average Travel Time_x2</th>\n",
       "      <th>Standard deviation_x2</th>\n",
       "      <th>mean_diff</th>\n",
       "      <th>t-test</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>28.441</td>\n",
       "      <td>12.256</td>\n",
       "      <td>23.548</td>\n",
       "      <td>11.512</td>\n",
       "      <td>4.893</td>\n",
       "      <td>339.724</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>28.441</td>\n",
       "      <td>12.256</td>\n",
       "      <td>18.621</td>\n",
       "      <td>10.504</td>\n",
       "      <td>9.820</td>\n",
       "      <td>669.103</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>23.548</td>\n",
       "      <td>11.512</td>\n",
       "      <td>18.621</td>\n",
       "      <td>10.504</td>\n",
       "      <td>4.927</td>\n",
       "      <td>503.697</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>28.441</td>\n",
       "      <td>12.256</td>\n",
       "      <td>17.039</td>\n",
       "      <td>10.153</td>\n",
       "      <td>11.401</td>\n",
       "      <td>510.529</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>23.548</td>\n",
       "      <td>11.512</td>\n",
       "      <td>17.039</td>\n",
       "      <td>10.153</td>\n",
       "      <td>6.508</td>\n",
       "      <td>334.309</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>18.621</td>\n",
       "      <td>10.504</td>\n",
       "      <td>17.039</td>\n",
       "      <td>10.153</td>\n",
       "      <td>1.582</td>\n",
       "      <td>80.401</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>28.441</td>\n",
       "      <td>12.256</td>\n",
       "      <td>16.856</td>\n",
       "      <td>10.162</td>\n",
       "      <td>11.585</td>\n",
       "      <td>195.358</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>23.548</td>\n",
       "      <td>11.512</td>\n",
       "      <td>16.856</td>\n",
       "      <td>10.162</td>\n",
       "      <td>6.692</td>\n",
       "      <td>114.823</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>18.621</td>\n",
       "      <td>10.504</td>\n",
       "      <td>16.856</td>\n",
       "      <td>10.162</td>\n",
       "      <td>1.765</td>\n",
       "      <td>30.253</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>17.039</td>\n",
       "      <td>10.153</td>\n",
       "      <td>16.856</td>\n",
       "      <td>10.162</td>\n",
       "      <td>0.184</td>\n",
       "      <td>3.024</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x1  x2  Average Travel Time_x1  Standard deviation_x1  \\\n",
       "0   1   2                  28.441                 12.256   \n",
       "1   1   3                  28.441                 12.256   \n",
       "2   2   3                  23.548                 11.512   \n",
       "3   1   4                  28.441                 12.256   \n",
       "4   2   4                  23.548                 11.512   \n",
       "5   3   4                  18.621                 10.504   \n",
       "6   1   5                  28.441                 12.256   \n",
       "7   2   5                  23.548                 11.512   \n",
       "8   3   5                  18.621                 10.504   \n",
       "9   4   5                  17.039                 10.153   \n",
       "\n",
       "   Average Travel Time_x2  Standard deviation_x2  mean_diff   t-test  p-value  \n",
       "0                  23.548                 11.512      4.893  339.724    0.000  \n",
       "1                  18.621                 10.504      9.820  669.103    0.000  \n",
       "2                  18.621                 10.504      4.927  503.697    0.000  \n",
       "3                  17.039                 10.153     11.401  510.529    0.000  \n",
       "4                  17.039                 10.153      6.508  334.309    0.000  \n",
       "5                  17.039                 10.153      1.582   80.401    0.000  \n",
       "6                  16.856                 10.162     11.585  195.358    0.000  \n",
       "7                  16.856                 10.162      6.692  114.823    0.000  \n",
       "8                  16.856                 10.162      1.765   30.253    0.000  \n",
       "9                  16.856                 10.162      0.184    3.024    0.001  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypothesis[['x1', 'x2', 'Average Travel Time_x1',\n",
    "       'Standard deviation_x1',\n",
    "       'Average Travel Time_x2', 'Standard deviation_x2', \n",
    "       'mean_diff', 't-test', 'p-value']].round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the home-to-work trips, we fail to reject the null hypothesis. Therefore, the differences in travel times among strata are significally differente\\. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the p-values are significantly inferior that 0.95, our significance level. Thus, we reject the null hypothesis. This suggest that travel time in transit does increase when we lower the strata. On average, a strata 1 user spends 21 more minutes in the BRT than a strata 5 user (10 and 11 more minutes, in the morning and the afternoon respectively). In average, Colombia has 244 working days a year. That is a strata 1 person spends around 85,4 hours more in public transit a year. "
   ]
  }
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