Forecasting Rides.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting Bike Ride Data - Healthy Ride (Pittsburgh)\n",
    "\n",
    "The following data set uses the Healthy Bike Ride dataset which can be found: https://data.wprdc.org/dataset?q=healthy+ride\n",
    "\n",
    "There are two types of files in this repo - daily bike ride information, and station information. This workbook attempts to merge the two datasets together to obtain meaningful insights later on.\n",
    "\n",
    "So far we have implemented ARIMA and XGBoost as a way to forecast general bike ridership throughout the year. We see that XGBoost performs better than ARIMA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import isfile, join\n",
    "import re\n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot\n",
    "\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from sklearn.model_selection import train_test_split, TimeSeriesSplit\n",
    "\n",
    "from pmdarima.arima import ADFTest, auto_arima\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filelist = [f for f in os.listdir(os.getcwd()+\"\\\\data\") if isfile(join(os.getcwd()+\"\\\\data\", f))]\n",
    "rentalfiles = [f for f in filelist if re.search(r'^rentals.*csv$' ,f)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The daily bike ride information contains information around the start and stop time of each bike ride, and which station a bike exits from. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripid</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>fromstationid</th>\n",
       "      <th>fromstationname</th>\n",
       "      <th>tostationid</th>\n",
       "      <th>tostationname</th>\n",
       "      <th>usertype</th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33295341</td>\n",
       "      <td>2017-07-01 00:00:00</td>\n",
       "      <td>2017-07-01 00:06:00</td>\n",
       "      <td>70045</td>\n",
       "      <td>396</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>S 27th St &amp; Sidney St. (Southside Works)</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>S 18th St &amp; Sidney St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33295396</td>\n",
       "      <td>2017-07-01 00:05:00</td>\n",
       "      <td>2017-07-01 00:19:00</td>\n",
       "      <td>70274</td>\n",
       "      <td>850</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>Taylor St &amp; Liberty Ave</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>S Negley Ave &amp; Baum Blvd</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33295429</td>\n",
       "      <td>2017-07-01 00:10:00</td>\n",
       "      <td>2017-07-01 00:23:00</td>\n",
       "      <td>70249</td>\n",
       "      <td>819</td>\n",
       "      <td>1033.0</td>\n",
       "      <td>Ivy St &amp; Walnut St</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>Alder St &amp; S Highland Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33295436</td>\n",
       "      <td>2017-07-01 00:10:00</td>\n",
       "      <td>2017-07-01 00:23:00</td>\n",
       "      <td>70030</td>\n",
       "      <td>760</td>\n",
       "      <td>1033.0</td>\n",
       "      <td>Ivy St &amp; Walnut St</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>Alder St &amp; S Highland Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33295621</td>\n",
       "      <td>2017-07-01 00:35:00</td>\n",
       "      <td>2017-07-01 01:49:00</td>\n",
       "      <td>70009</td>\n",
       "      <td>4460</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>Centre Ave &amp; PPG Paints Arena</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>Centre Ave &amp; PPG Paints Arena</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tripid           starttime            stoptime  bikeid  tripduration  \\\n",
       "0  33295341 2017-07-01 00:00:00 2017-07-01 00:06:00   70045           396   \n",
       "1  33295396 2017-07-01 00:05:00 2017-07-01 00:19:00   70274           850   \n",
       "2  33295429 2017-07-01 00:10:00 2017-07-01 00:23:00   70249           819   \n",
       "3  33295436 2017-07-01 00:10:00 2017-07-01 00:23:00   70030           760   \n",
       "4  33295621 2017-07-01 00:35:00 2017-07-01 01:49:00   70009          4460   \n",
       "\n",
       "   fromstationid                           fromstationname  tostationid  \\\n",
       "0         1045.0  S 27th St & Sidney St. (Southside Works)       1048.0   \n",
       "1         1021.0                   Taylor St & Liberty Ave       1024.0   \n",
       "2         1033.0                     Ivy St & Walnut St          1029.0   \n",
       "3         1033.0                     Ivy St & Walnut St          1029.0   \n",
       "4         1008.0             Centre Ave & PPG Paints Arena       1008.0   \n",
       "\n",
       "                   tostationname  usertype  year  quarter  \n",
       "0          S 18th St & Sidney St  Customer  2017        3  \n",
       "1       S Negley Ave & Baum Blvd  Customer  2017        3  \n",
       "2      Alder St & S Highland Ave  Customer  2017        3  \n",
       "3      Alder St & S Highland Ave  Customer  2017        3  \n",
       "4  Centre Ave & PPG Paints Arena  Customer  2017        3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse_dates = ['Starttime', 'Stoptime']\n",
    "df = pd.concat([pd.read_csv(os.getcwd() + \"\\\\data\\\\\"+f, parse_dates=parse_dates) for f in rentalfiles])\n",
    "df.columns = df.columns.str.replace(' ', '')\n",
    "df.columns = df.columns.str.lower()\n",
    "df['year'] = df['starttime'].dt.year\n",
    "df['quarter'] = df['starttime'].dt.quarter\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Location files hold information around the station name, GPS coordinates, and the number of bike racks available at each station."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "locationfiles = [f for f in filelist if re.search(r'locations.*csv$', f)]\n",
    "\n",
    "df_loc = pd.concat([pd.read_csv(os.getcwd() + \"\\\\data\\\\\"+\n",
    "                                locationfiles[i]).assign(year=re.split('_|\\.', locationfiles[i])[1],\n",
    "                                                                      quarter = re.split('_|\\.', locationfiles[i])[2])\n",
    "                   for i in range(0, len(locationfiles))])\n",
    "\n",
    "df_loc = df_loc.rename(columns={'Station #' : 'station', 'Station Name': 'station_name', '# of Racks':'num_racks'})\n",
    "df_loc = df_loc[-df_loc.station.isna()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>station_name</th>\n",
       "      <th>num_racks</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>Liberty Ave &amp; Stanwix St</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.441326</td>\n",
       "      <td>-80.004679</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>Forbes Ave &amp; Market Square</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.440877</td>\n",
       "      <td>-80.003080</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002.0</td>\n",
       "      <td>Third Ave &amp; Wood St</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.439030</td>\n",
       "      <td>-80.001860</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003.0</td>\n",
       "      <td>First Ave &amp; Smithfield St (Art Institute)</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.437200</td>\n",
       "      <td>-80.000375</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004.0</td>\n",
       "      <td>First Ave &amp; B St (T Station)</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.435887</td>\n",
       "      <td>-79.997102</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   station                               station_name  num_racks   Latitude  \\\n",
       "0   1000.0                   Liberty Ave & Stanwix St       16.0  40.441326   \n",
       "1   1001.0                 Forbes Ave & Market Square       19.0  40.440877   \n",
       "2   1002.0                        Third Ave & Wood St       15.0  40.439030   \n",
       "3   1003.0  First Ave & Smithfield St (Art Institute)       15.0  40.437200   \n",
       "4   1004.0               First Ave & B St (T Station)       15.0  40.435887   \n",
       "\n",
       "   Longitude  year  quarter  \n",
       "0 -80.004679  2017        3  \n",
       "1 -80.003080  2017        3  \n",
       "2 -80.001860  2017        3  \n",
       "3 -80.000375  2017        3  \n",
       "4 -79.997102  2017        3  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc = df_loc[['station', 'station_name', 'num_racks', 'Latitude', 'Longitude', 'year', 'quarter']]\n",
    "df_loc['quarter'] = df_loc['quarter'].str[-1].astype(int)\n",
    "df_loc['year'] = df_loc['year'].astype(int)\n",
    "df_loc.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning and Checks\n",
    "\n",
    "One interesting thing to ask of the data set is to see if the number of racks at each station is constant over time. A quick aggregation shows that the answer to the question is no."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>station_name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th colspan=\"2\" halign=\"left\">num_racks</th>\n",
       "      <th>eq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1005.0</td>\n",
       "      <td>Forbes Ave &amp; Grant St</td>\n",
       "      <td>40.438882</td>\n",
       "      <td>-79.997592</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1006.0</td>\n",
       "      <td>Ross St &amp; Sixth Ave (Steel Plaza T Station)</td>\n",
       "      <td>40.440193</td>\n",
       "      <td>-79.995084</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1007.0</td>\n",
       "      <td>Stevenson St &amp; Forbes Ave</td>\n",
       "      <td>40.437643</td>\n",
       "      <td>-79.986695</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1008.0</td>\n",
       "      <td>Centre Ave &amp; PPG Paints Arena</td>\n",
       "      <td>40.440368</td>\n",
       "      <td>-79.988636</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1012.0</td>\n",
       "      <td>North Shore Trail &amp; Ft Duquesne Bridge</td>\n",
       "      <td>40.445834</td>\n",
       "      <td>-80.008882</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1020.0</td>\n",
       "      <td>42nd St &amp; Penn Ave (Children's Hospital)</td>\n",
       "      <td>40.465893</td>\n",
       "      <td>-79.954417</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1023.0</td>\n",
       "      <td>Liberty Ave &amp; Baum Blvd</td>\n",
       "      <td>40.456505</td>\n",
       "      <td>-79.939362</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1028.0</td>\n",
       "      <td>Penn Ave &amp; Putnam St (Bakery Square)</td>\n",
       "      <td>40.455821</td>\n",
       "      <td>-79.915248</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1035.0</td>\n",
       "      <td>Fifth Ave &amp; S Dithridge St</td>\n",
       "      <td>40.446744</td>\n",
       "      <td>-79.950881</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1039.0</td>\n",
       "      <td>Atwood St &amp; Bates</td>\n",
       "      <td>40.437987</td>\n",
       "      <td>-79.953670</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1050.0</td>\n",
       "      <td>Healthy Ride Hub</td>\n",
       "      <td>40.461817</td>\n",
       "      <td>-79.968002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1061.0</td>\n",
       "      <td>33rd St and Penn Ave</td>\n",
       "      <td>40.462026</td>\n",
       "      <td>-79.968114</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   station                                 station_name   Latitude  Longitude  \\\n",
       "                                                                                \n",
       "5   1005.0                        Forbes Ave & Grant St  40.438882 -79.997592   \n",
       "6   1006.0  Ross St & Sixth Ave (Steel Plaza T Station)  40.440193 -79.995084   \n",
       "7   1007.0                    Stevenson St & Forbes Ave  40.437643 -79.986695   \n",
       "8   1008.0                Centre Ave & PPG Paints Arena  40.440368 -79.988636   \n",
       "12  1012.0       North Shore Trail & Ft Duquesne Bridge  40.445834 -80.008882   \n",
       "22  1020.0     42nd St & Penn Ave (Children's Hospital)  40.465893 -79.954417   \n",
       "25  1023.0                      Liberty Ave & Baum Blvd  40.456505 -79.939362   \n",
       "30  1028.0         Penn Ave & Putnam St (Bakery Square)  40.455821 -79.915248   \n",
       "37  1035.0                   Fifth Ave & S Dithridge St  40.446744 -79.950881   \n",
       "41  1039.0                            Atwood St & Bates  40.437987 -79.953670   \n",
       "54  1050.0                             Healthy Ride Hub  40.461817 -79.968002   \n",
       "63  1061.0                         33rd St and Penn Ave  40.462026 -79.968114   \n",
       "\n",
       "   num_racks           eq  \n",
       "         min   max         \n",
       "5       13.0  19.0  False  \n",
       "6        7.0  21.0  False  \n",
       "7       15.0  19.0  False  \n",
       "8        5.0  35.0  False  \n",
       "12      31.0  35.0  False  \n",
       "22       8.0  15.0  False  \n",
       "25      15.0  19.0  False  \n",
       "30       8.0  19.0  False  \n",
       "37       8.0  19.0  False  \n",
       "41       8.0  19.0  False  \n",
       "54       0.0   2.0  False  \n",
       "63       2.0   6.0  False  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hold = df_loc[['station', \n",
    "        'station_name', \n",
    "        'Latitude', \n",
    "        'Longitude', \n",
    "        'num_racks']].groupby(['station', \n",
    "                               'station_name', \n",
    "                               'Latitude', \n",
    "                               'Longitude']).agg({'num_racks' : ['min', 'max']}).reset_index()\n",
    "\n",
    "df_hold['eq'] = (df_hold[('num_racks', 'min')] == df_hold[('num_racks', 'max')])\n",
    "df_hold[df_hold['eq'] == False]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, from the above aggregation we note that the number of available racks for station number 1050 is dubious, so we are going to replace that with 2. First check that this happens for station number 1050, and then we will replace accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>station_name</th>\n",
       "      <th>num_racks</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1050.0</td>\n",
       "      <td>Healthy Ride Hub</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.461817</td>\n",
       "      <td>-79.968002</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1050.0</td>\n",
       "      <td>Healthy Ride Hub</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.461817</td>\n",
       "      <td>-79.968002</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1050.0</td>\n",
       "      <td>Healthy Ride Hub</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.461817</td>\n",
       "      <td>-79.968002</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1050.0</td>\n",
       "      <td>Healthy Ride Hub</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.461817</td>\n",
       "      <td>-79.968002</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1050.0</td>\n",
       "      <td>Healthy Ride Hub</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.461817</td>\n",
       "      <td>-79.968002</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1050.0</td>\n",
       "      <td>Healthy Ride Hub</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.461817</td>\n",
       "      <td>-79.968002</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station      station_name  num_racks   Latitude  Longitude  year  quarter\n",
       "50   1050.0  Healthy Ride Hub        0.0  40.461817 -79.968002  2018        1\n",
       "50   1050.0  Healthy Ride Hub        0.0  40.461817 -79.968002  2018        2\n",
       "50   1050.0  Healthy Ride Hub        0.0  40.461817 -79.968002  2018        3\n",
       "50   1050.0  Healthy Ride Hub        0.0  40.461817 -79.968002  2018        4\n",
       "50   1050.0  Healthy Ride Hub        0.0  40.461817 -79.968002  2019        1\n",
       "50   1050.0  Healthy Ride Hub        0.0  40.461817 -79.968002  2019        2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_loc[df_loc['num_racks'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_loc['num_racks'] = df_loc['num_racks'].apply(lambda x : x if x > 0 else 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling NULL information\n",
    "\n",
    "Now turning our attention to the bike ridership dataset, we note that sometimes bikes take journeys and do not get immediately reracked at a station. Below we see three consecutive trips for Bike 70604 moving from station Arch St & Jacksonia St to NULL station id. The second trip has no station information, and for the third trip the bike does not start at any particular station but ends up back at Arch St & Jacksonia St.\n",
    "\n",
    "We also note that the start and stop times for these three rides are fairly close together. This leads us to believe that these trips were taken by the same person, and should thus be considered as a single trip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripid</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>fromstationid</th>\n",
       "      <th>fromstationname</th>\n",
       "      <th>tostationid</th>\n",
       "      <th>tostationname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3108</th>\n",
       "      <td>107999252</td>\n",
       "      <td>70604</td>\n",
       "      <td>2020-10-10 10:18:00</td>\n",
       "      <td>2020-10-10 10:31:00</td>\n",
       "      <td>49881.0</td>\n",
       "      <td>Arch St &amp; Jacksonia St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BIKE 70604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3125</th>\n",
       "      <td>108001051</td>\n",
       "      <td>70604</td>\n",
       "      <td>2020-10-10 10:42:00</td>\n",
       "      <td>2020-10-10 10:53:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BIKE 70604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BIKE 70604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3148</th>\n",
       "      <td>108003271</td>\n",
       "      <td>70604</td>\n",
       "      <td>2020-10-10 11:09:00</td>\n",
       "      <td>2020-10-10 11:13:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BIKE 70604</td>\n",
       "      <td>49881.0</td>\n",
       "      <td>Arch St &amp; Jacksonia St</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tripid  bikeid           starttime            stoptime  \\\n",
       "3108  107999252   70604 2020-10-10 10:18:00 2020-10-10 10:31:00   \n",
       "3125  108001051   70604 2020-10-10 10:42:00 2020-10-10 10:53:00   \n",
       "3148  108003271   70604 2020-10-10 11:09:00 2020-10-10 11:13:00   \n",
       "\n",
       "      fromstationid         fromstationname  tostationid  \\\n",
       "3108        49881.0  Arch St & Jacksonia St          NaN   \n",
       "3125            NaN              BIKE 70604          NaN   \n",
       "3148            NaN              BIKE 70604      49881.0   \n",
       "\n",
       "               tostationname  \n",
       "3108              BIKE 70604  \n",
       "3125              BIKE 70604  \n",
       "3148  Arch St & Jacksonia St  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['tripid', 'bikeid', 'starttime', \n",
    "    'stoptime', 'fromstationid', 'fromstationname',\n",
    "   'tostationid', 'tostationname']][(df.bikeid == 70604) & (df.year == 2020) & (df.quarter == 4) & \n",
    "       (df.tripid>=107796326) & (df.tripid<108005204)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To account for this we shall make the appropriate refinements to the datasets to capture the above view of a single bike ride."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>tripid</th>\n",
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       "      <th>stoptime</th>\n",
       "      <th>fromstationid</th>\n",
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       "      <th>tostationid</th>\n",
       "      <th>tostationname</th>\n",
       "      <th>stoptime_adj</th>\n",
       "      <th>tostationid_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>280262</th>\n",
       "      <td>107999252</td>\n",
       "      <td>70604</td>\n",
       "      <td>2020-10-10 10:18:00</td>\n",
       "      <td>2020-10-10 10:31:00</td>\n",
       "      <td>49881.0</td>\n",
       "      <td>Arch St &amp; Jacksonia St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BIKE 70604</td>\n",
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       "      <td>49881.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280263</th>\n",
       "      <td>108001051</td>\n",
       "      <td>70604</td>\n",
       "      <td>2020-10-10 10:42:00</td>\n",
       "      <td>2020-10-10 10:53:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BIKE 70604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BIKE 70604</td>\n",
       "      <td>2020-10-10 11:13:00</td>\n",
       "      <td>49881.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280264</th>\n",
       "      <td>108003271</td>\n",
       "      <td>70604</td>\n",
       "      <td>2020-10-10 11:09:00</td>\n",
       "      <td>2020-10-10 11:13:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BIKE 70604</td>\n",
       "      <td>49881.0</td>\n",
       "      <td>Arch St &amp; Jacksonia St</td>\n",
       "      <td>2020-10-10 11:13:00</td>\n",
       "      <td>49881.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tripid  bikeid           starttime            stoptime  \\\n",
       "280262  107999252   70604 2020-10-10 10:18:00 2020-10-10 10:31:00   \n",
       "280263  108001051   70604 2020-10-10 10:42:00 2020-10-10 10:53:00   \n",
       "280264  108003271   70604 2020-10-10 11:09:00 2020-10-10 11:13:00   \n",
       "\n",
       "        fromstationid         fromstationname  tostationid  \\\n",
       "280262        49881.0  Arch St & Jacksonia St          NaN   \n",
       "280263            NaN              BIKE 70604          NaN   \n",
       "280264            NaN              BIKE 70604      49881.0   \n",
       "\n",
       "                 tostationname        stoptime_adj  tostationid_adj  \n",
       "280262              BIKE 70604 2020-10-10 11:13:00          49881.0  \n",
       "280263              BIKE 70604 2020-10-10 11:13:00          49881.0  \n",
       "280264  Arch St & Jacksonia St 2020-10-10 11:13:00          49881.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stoptime_adj'] = df.apply( lambda x : x['stoptime'] if x['tostationid'] > 0  \n",
    "                                      else x['tostationid'] , axis=1)\n",
    "\n",
    "df['tostationid_adj'] = df['tostationid']\n",
    "df = df.sort_values(['bikeid', 'starttime']).reset_index(drop=True)\n",
    "df['tostationid_adj'] = df['tostationid_adj'].fillna(method='bfill')\n",
    "df['stoptime_adj'] = df['stoptime_adj'].fillna(method='bfill')\n",
    "\n",
    "df[['tripid', 'bikeid', 'starttime', \n",
    "    'stoptime', 'fromstationid', 'fromstationname',\n",
    "   'tostationid', 'tostationname', \n",
    "    'stoptime_adj', 'tostationid_adj']][(df.bikeid == 70604) & (df.year == 2020) & (df.quarter == 4) & \n",
    "       (df.tripid>=107796326) & (df.tripid<108005204)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to merge the datasets, which will then be used for forecasting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.merge(df_loc[['station', 'num_racks', \n",
    "                      'year', 'quarter', 'Latitude', 'Longitude']], how='left', left_on=['year', 'quarter', 'fromstationid'], \n",
    "                                      right_on=['year', 'quarter', 'station'])\n",
    "df = df.rename(columns={'num_racks' : 'from_tot_racks', 'Latitude' : 'from_lat', 'Longitude' : 'from_long'})\n",
    "\n",
    "\n",
    "df = df.merge(df_loc[['station', 'num_racks', \n",
    "                      'year', 'quarter', 'Latitude', 'Longitude']], how='left', left_on=['year', 'quarter', 'tostationid_adj'], \n",
    "                                      right_on=['year', 'quarter', 'station'])\n",
    "\n",
    "df = df.rename(columns={'num_racks' : 'to_tot_racks', \n",
    "                       'Latitude' : 'to_lat', 'Longitude' : 'to_long'})\n",
    "\n",
    "df = df[['tripid', 'starttime', 'stoptime', 'stoptime_adj', 'bikeid', 'tripduration',\n",
    "       'fromstationid', 'fromstationname', 'tostationid', 'tostationid_adj', 'tostationname',\n",
    "       'usertype', 'year', 'quarter', 'from_tot_racks', 'to_tot_racks', 'from_lat', 'from_long',\n",
    "        'to_lat', 'to_long']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33337058</td>\n",
       "      <td>2017-07-01 12:55:00</td>\n",
       "      <td>2017-07-01 13:14:00</td>\n",
       "      <td>2017-07-01 13:14:00</td>\n",
       "      <td>70000</td>\n",
       "      <td>1164</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>Fifth Ave &amp; S Bouquet St</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2017</td>\n",
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       "      <td>-79.957604</td>\n",
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       "      <td>2017-07-01</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33406113</td>\n",
       "      <td>2017-07-02 17:33:00</td>\n",
       "      <td>2017-07-02 18:28:00</td>\n",
       "      <td>2017-07-02 18:28:00</td>\n",
       "      <td>70000</td>\n",
       "      <td>3277</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>S 12th St &amp; E Carson St</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>1049.0</td>\n",
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       "      <td>40.428661</td>\n",
       "      <td>-79.986358</td>\n",
       "      <td>2017-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33431751</td>\n",
       "      <td>2017-07-03 07:28:00</td>\n",
       "      <td>2017-07-03 07:36:00</td>\n",
       "      <td>2017-07-03 07:36:00</td>\n",
       "      <td>70000</td>\n",
       "      <td>513</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>S 12th St &amp; E Carson St</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>40.428661</td>\n",
       "      <td>-79.986358</td>\n",
       "      <td>40.440193</td>\n",
       "      <td>-79.995084</td>\n",
       "      <td>2017-07-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33481665</td>\n",
       "      <td>2017-07-03 15:37:00</td>\n",
       "      <td>2017-07-03 16:51:00</td>\n",
       "      <td>2017-07-03 16:51:00</td>\n",
       "      <td>70000</td>\n",
       "      <td>4409</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>Ross St &amp; Sixth Ave (Steel Plaza T Station)</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>40.440193</td>\n",
       "      <td>-79.995084</td>\n",
       "      <td>40.440193</td>\n",
       "      <td>-79.995084</td>\n",
       "      <td>2017-07-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33625477</td>\n",
       "      <td>2017-07-05 16:49:00</td>\n",
       "      <td>2017-07-05 17:18:00</td>\n",
       "      <td>2017-07-05 17:18:00</td>\n",
       "      <td>70000</td>\n",
       "      <td>1768</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>Ross St &amp; Sixth Ave (Steel Plaza T Station)</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.440193</td>\n",
       "      <td>-79.995084</td>\n",
       "      <td>40.429338</td>\n",
       "      <td>-79.980684</td>\n",
       "      <td>2017-07-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tripid           starttime            stoptime        stoptime_adj  \\\n",
       "2  33337058 2017-07-01 12:55:00 2017-07-01 13:14:00 2017-07-01 13:14:00   \n",
       "3  33406113 2017-07-02 17:33:00 2017-07-02 18:28:00 2017-07-02 18:28:00   \n",
       "4  33431751 2017-07-03 07:28:00 2017-07-03 07:36:00 2017-07-03 07:36:00   \n",
       "5  33481665 2017-07-03 15:37:00 2017-07-03 16:51:00 2017-07-03 16:51:00   \n",
       "6  33625477 2017-07-05 16:49:00 2017-07-05 17:18:00 2017-07-05 17:18:00   \n",
       "\n",
       "   bikeid  tripduration  fromstationid  \\\n",
       "2   70000          1164         1041.0   \n",
       "3   70000          3277         1049.0   \n",
       "4   70000           513         1049.0   \n",
       "5   70000          4409         1006.0   \n",
       "6   70000          1768         1006.0   \n",
       "\n",
       "                               fromstationname  tostationid  tostationid_adj  \\\n",
       "2                     Fifth Ave & S Bouquet St       1049.0           1049.0   \n",
       "3                      S 12th St & E Carson St       1049.0           1049.0   \n",
       "4                      S 12th St & E Carson St       1006.0           1006.0   \n",
       "5  Ross St & Sixth Ave (Steel Plaza T Station)       1006.0           1006.0   \n",
       "6  Ross St & Sixth Ave (Steel Plaza T Station)       1048.0           1048.0   \n",
       "\n",
       "   ...    usertype  year  quarter  from_tot_racks  to_tot_racks   from_lat  \\\n",
       "2  ...    Customer  2017        3            19.0          19.0  40.442325   \n",
       "3  ...    Customer  2017        3            19.0          19.0  40.428661   \n",
       "4  ...  Subscriber  2017        3            19.0          21.0  40.428661   \n",
       "5  ...    Customer  2017        3            21.0          21.0  40.440193   \n",
       "6  ...  Subscriber  2017        3            21.0          16.0  40.440193   \n",
       "\n",
       "   from_long     to_lat    to_long  starttime_date  \n",
       "2 -79.957604  40.428661 -79.986358      2017-07-01  \n",
       "3 -79.986358  40.428661 -79.986358      2017-07-02  \n",
       "4 -79.986358  40.440193 -79.995084      2017-07-03  \n",
       "5 -79.995084  40.440193 -79.995084      2017-07-03  \n",
       "6 -79.995084  40.429338 -79.980684      2017-07-05  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filt = df[(-df['from_tot_racks'].isna()) & (-df['tostationid'].isna()) ] \n",
    "df_filt['starttime_date'] = df_filt['starttime'].dt.date\n",
    "df_filt.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's do a visual inspection to see if there are differences between types of bike users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='starttime_date'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rjtbuv8jzRmRK0GDq6scW4Yx7PmmWAZfTW5b13RMdjKiZfSKlO4dRKZcHNutZKXysAI+2WWSl0P0NYcCZiSIlBd8FSS7IMM4+YQzjKICJAP7BOT8SQA1M2YQPqpbi+eUYY9cwxhYxxhbt27cv1Mk2B3e8vRbbDtQ6llnTI4r2pmqC1C6JMDS1A2tqpy42f3FJKcqzXAGK6JgENbmFWw4AaJ7UakkyjIkM0JhkGEvtRW6bSV1vtjzG7jbqF/SXLl2bnIlCpbNXQQ6P7BPGMC4FUMo5X2C+fwGGobyHMdYPAMz/e6X1B0nbDwSw071TzvlDnPNJnPNJvXr1auz5NwtB0mGnxthcRlN9RAia2oE11kAQXg2dc2zcV42fPLcMNz67tGknQ3QKggZzov9LNsOTOSVpjKl/JdIRMS2ZlEtHLL92f5YN5PgNdyYVGVtKofYkC2SPsfw66BYjiVz2SWsYc853A9jOGBtpLpoBYDWAVwFcYS67AsAr5utXAVzEGMtljA0DMALAwqyedSsiN+wgzzJBuGlqB9bUTl3n3JIH7amsb9K+iM5B0ANZ9H/NYRiH1VcSBCB5jN2aXYfHuDkMYzuPcUIezPmcR7qS0EmX9ENeb/7G/Xji862ec6D7I/tEQ673IwBPMcZyAGwCcBUMo/o5xtjVALYB+AYAcM5XMcaeg2E8JwFcxzn3inXbAUFV7jg4ZaUgMqKpHVhjt2fSdB8N4ohMCPLWMsYAzpFKNb5R7a2KIy8WQXGes3Rv2Ih8ggCAqKlp0N1SCh9ZRbaQ+9agwZxow+62HFZKoXOOi//5OQDgsmOGgHOOzWU1GN6rC2mMm4FQhjHnfCmASYqPZvisfxuA2xp/Wq1LoLlrNXDPIoIIpC14jAkiE4KajO0x1v1XSsOU295Ht4IYlv5mlmM5eYyJTIiYhnFS547BnNznNovHWFS+49yVYlBtAOvc6VlO6RzbD9RiT2Uck4aWuILv/HX2L325Az95bhmevPpoHNa3i+/5cc6x/UAdBvcoyPi7dWao8l0A6YLvyF9MZEJrP+DJLiYyxc7C409TpRTltQnPMod3j9otkQZNs+Mo/ILvUinuKcncVOw4IyCRcnp4ZcT7lM6xZped4pBzjjP+9gkueGC+YVy7ggX99re8tAIAsG5PVWC//uKSHZh2x4f4bGNZRt+rs0OGsYIgiYSumBKhfpsIQ7Y9vimdY29V+mpgojWr9PFEx2NvZTxr8oMgm1foOptbY5yN4LstZTUYOvt1fLaBDISOiJBSuAPsvFkpsttW7awU3GHI+mWp4Jxj5Y5KaTlQVW+kh91f0+BI0RYUfKdJ2uag7/Tl9oMAgI17q8N9IQIAGcZKAtO1mcu4vCJBhKCpfbK7c/y/d9Ziym3vY19VuEA6VUYVomOxbk8VpvzpfTz22Zas7C/QkDDbUFM0xn6k9OxWvvt8034AwMtLdzR9Z0SbQx6kyc3FbSRnvcCHlfHHbci6s0/AWk+uj5DiHHkxwwzbuLc6MPjOcVzJIA8u/uHagAgFGcYZYgXfOTTG5DMm0pNtj/H7a4wMiWXVaQxjpp5mJDoeW8pqAACfbtiflf1x13+ZbGiM/XCmrWrc/jeX1eCFxaXGPkwLIaLRI68jEvELvnNlpch26j/bE+zUDvtln3CndeOco09xHgBgR3mds90HBKDKZq475ZuM2EzL0C5eUVqBgzWdN9c99RImKuNW1dzsmuecat8RGdFUo5S77IOINH0YBnk1ciB0TLI97uHSA92NaEONjfYPcigkdbWBAAAVdQms3FGRdv9n3vMJfvb8MgD2+UcUT7zPN+3Hba+vDnPKRBvF6gs5HDeB7jKSs636EfvjcOqD/Src6dybkk20zWSKuz7zN7T9NNVuxD2Wqa1y1n3zcN4/Pstom44EGcYmjobnqn/uQNwIFBtCZEi2PcYRqXMMA0f2PSZE20I0hWwNfFQxFQIxfZ1opJSiPunvCXaX8pW57JEF+Nq989LO1NU2SFPWwmOsuDAXPfQ5/vnJ5lDnTLRNHB5jqNtOc2Sl8PMYuw9jSTA59xi8QoqUcmW2cKdrk5HlnkHfqbEeY8CYcemskGFs4gxM8m9F8tSi1ceSrUGEoKnNxL29lqHHTueS4USzHR2abP26lkdM0cTEMRrrMa5P+BvGsjwj4ZJSiIj8hpASCy5NX5OUon2TSOlK6YDoz9xln3XOpZm17JeEFudipGsL0BhLWSncBm/CfJ/U3R5jeT3XgaVsGEGGsfiMZggzg3oJk7AjSftGkEZtZBkTIWgtj7HtXaCWSmSGaFtvrtyNBpeHlzHbGGkM9Un/uk9uT5+KeIBhLaNzyWMc8MQTffu+qnrsrkif7YVoeUb88k189/FFnuW24emc3dB1bjkQkimelXigzzaU4eFPNhnH85FI+OcxdpWO1u33us59S0J7NcYif3KactFifbKMM4IMYxNVxL6qvUlNlUZhREZku8CHldQ+5FS23gxTiUQHR2ouD3y00fFRkz3GAVIKuU27PcYCObo/CNlLF+QxFutMvu09HPPn90Ptm2h53v9qr2eZaC26a/Cf4twuF52lrBSXPLwAf3x9jXE8q9Szu8CHcxvZoeYuXiPat+Exdhb/sNdzGcaWjRJs7Fse41DfzNwnPSPIMBY4DGNrof96zqwUzXVWREeiqdN47nbGrGwTwduJTtSQUtDUWsemeTSUgBH0JiPaUOM1xuE8xn6Gd1jDWPbSRQPEls1RMphoGbjlMXZnpXCmcgtyDFTFEzjpzrlYsu0gAOCI372DGf83N/C4osm4JRLe0s+2Ae1o29zpMZa3cgbzOY9r509O0/9bGuPwHT7dBmQYW2QSwAQIKQVZF0R4gtLqhNre1UajGWal4DAeFETHJfvBd/Zrt1HJWGbtz02QFCIoK4WgTmEYP79ou6eIB5ekFFqQYUwejnYLlwxUYZRqzK0xdmalcBuvCzYdwOayGtz7/noAxkBw477gADTRZpI6d5Z69jWMvRIJ8T6pO72/qYDKd3Ip6mxrjGlWkQxji9CGsfAYO0QVBJGeprYTt30gOvxESI2nHISUrp/knOOe99djV0VdpqdJdCDkfjHiMYyN/43XGPtvV2NWAwOchoRshKsM65teWI5LHl7gWJaSjAdVVgprvWYoVEK0DHJwm/gVo5qGlM6lduo0PHVuVImsM7OX7DULJfUqyvXsf+js1/Hrl1d6j6vbx3UE37mapuxZlu8pXedW/x1Uzto5Q82lAh9pslKY/zPzGNN9QIaxiSrHqypUSdxYnDf/dHRZdT2+2HKgeQ9CtBhN7XA8ARgiXVaAgQE4S0Jb+0jTeL/aXYW73l2HHz39ZaPOlegYyE3OYxib/xuvMfaXQsiyDWcKK/t1XUN4KYXwOkcj/u2+OQqVEC2D5TGWjEtNMwxPZ1YK5yBryp/et/L1igqivYvyHPsW7fuJz7e6jinlIHbpg72V77i1TVLXrdmXpCT9SKacHm13dg35uzJpnaDHih6uu3eda/h1OypkGJvIRocc8elZz/Xfb71scPZ9n+IbD8xvnp0TaXn5yx0YOvt1VNQm0q8cgqa2E/f2EckTEgadh58uFt4P1XQ10fbJlsxL7hfdhnFz5jGWDeOE7jRmBPEAw1pG17llmAR5zkhK0X4Rv1xKymMc1TToUvBdUncbnsabNbsqAQD7qo1MJD265Dj27dbWC1I6t6RpKV133AfupiRLKVI6R07UML3ke0Ce2ZC3EdvJ6wlLlyNYSmHHlJDHOBPIMDYJ6/SQE3rbQU3N05B2lNM0dmvy4MdGSp7tB2uzsr/sp2szbl+/qH3V9pYHIeQxKUivfZHtnkjuF2OuXGdNrXwXlMe4vC6BvJhm7t9eT94mHtpjLGmMA9ozBd+1XxxSCvNnjGgsMCuF3J82JHUcMEsgu7X0B3xKIydS7qp1AR5jbi9Pprh1L9U22JKhlK77BvW7DWZngQ/l6Tn2kUk3ToYxGcYWYVOUKDXG1JA6JMLgFKP7ptLU565XY2z8d+eX9YPz8AGAon1TgGn7IvvBd3Z7kb2tuyrqUFZtGAyNlSAEPYAr6hLolm947r79mJ23Vp7BcHuM/QxbOWOAexXhLQTCpz0k2iCS4SnaVSzCoOtw5DH288LGkyk7O4SrGRys9TGMJWlGSucOj7FYvnDzATQk7fXEIE0YxlVx2TB25WDmahuDc/te5Ai+j6x+nKQUGRFt7RNoK6g1xl6440XTorKJto1lGAdVBciAbOcxlqcIgxDTaI7guzQdZbYNLKJ94peV4mrJWG1s/xeUhq0hqaNbQQy7K+OezwR1Dc4nuJ9mWW737nvozRW70p4P0fYJ8hhb+d513ZX1QXodULXO12Oc1K19JN3Bd9wYdF344HxcNXWotc+y6nq8+OUOFOREAAC7pEIyKV132BxBemMmuYwD8xg3wsgljzF5jC0cGmMp4tNvPQ448sO21LkRLYcIasskojeIpmuM3VIKofEMXwEsfFpCor2worQCt72+upn6CXufcuBaueRFa6yn1R2dLxCazm4FMc82Do+xS//ul/7tYG3CCpxyG78HpfgB0hi3X2SNsUBkpbClFM5ndYNL3yt+fvfzvNzHYyznRTb0xvb+OOeWQb12d5XHQK01ZUBy1h/j/Pw0xk6j2666m67yndoLHoSfp7oz0akN46Xby63Xqoajzkoh/nNHtH9z0knbZqvTkBKDoOz8AE3PSuF8LwzjdFIKuZ2GT0sopBREW+fCB+fjn59sdhiNWZNSSE1LDr6LSEZy2OBPN3JTlFMOitiKvsV57k0cmSjcUgq/gh8b9lZbr92GcbkUWEUe4/aL6NcSKd3hMQak4h+u/q9BzjscUJxDHnDJsxINSd26P5K67uvh5dx/0LWz3OUxdmynln24K+IFyeN0yV4Ji3yujb232zud2jA+5++fWq9VBoPaY2z+T7NeNiFPRuvQYHaC2eobsq0xDpsVQHxqeIyN12nzGIsXpKVo80Tk1E/NWPlONoyjUmnlVAbztS8uKUW1maNYfsDLXueNpiE7ok+RZ/sayTBOJJ3f1S/LhRxL5e7ns+H5Jlof8bPWS3peEbwpsproruA7h8fYJ2cw4JyRk9tYUufWs1kuLAK4AuTAfZ0XB2oaEIswdC+IKbJSyN/PabjbBm+44LtMnDJ+QX+diU5tGMs4G4BzpCnjqHyX5awUy7aXY9If38VBl6apszbO1kYYnNmaTsp2VgpLY5xGSmGdP+ehvWKNiWYmWodYxM5nbf9u2fnlHLEXYFi05QDW76lyGMlhvUpLt5fjJ88tswolyO3ZYRjvq0EswjCopMCzj1qp8IdbQuTnMZY96XL7f/iTTfhkfZnyM6J9Ifq4+qSt082LGTpe0U7cBT78DGN3P5v0GcAlU7YR7pcKTnUsN8V5MUQjmqf9BUkpLIM8jcZYfJaJ1tidM7kz0qkNY7lzlxtAUBoia6SmWNZU/v7hBpRVN2DBZmdRj87aOFubpFWRKDv7a+rv6CkLarbTdBpjp8fYlEik9QSL9TI9S6KliZrBoQ3NUO/bEazEOS54YD5OuftjRyBeWE9rtRmBv7fKmD6W27MspdhTGUef4jzkKIpxVDfVMDaPWdeQwh9fXwMAKCnMcXxGtD/EL1efSFltVhjGwijVXcarv2Hs3LdjZkNqpw0p3VH5LsigVN2bfzl/nHk8jqjGvFXxfKQZcqEm93dyw6VtwhI0SOgsdGrDOOpjGAuUumNJsyO8Ms3deDpr42xtsu0xbup+3JuLdtGQxjCRc2hahnGaY2Wa75hoPWIhteaNwS8QpzEeY7EvMdMhbyYb15V1CXTNj0HV+molKYVbOuEXfCfrkoUhs2pnhbWshzCMKU9Vu0WWUrg9xuK+8GqM7XaRDDAG5QGY02PMId4mU4ocyVLzVWVMmTi4u7mucU/IVfCM7+TnMbZn/twBe27kSr1hkdftrLMondowlhPWqxqXUncspBTysmYwXB2pZDpp42wrNIfGuDFtxq/MaNqsFMK7wAH3qmt3V2H9nirPJuIBkEnFJKJ1iJl5th2GcZZ+NtnTJQf5yE6FsAal8MgyqeCCQG7DlfEEivNiymIcv311lXIbwBmMJ5+rwzA2F6+XAvL6djWC/P763nrsrnCmhyPaDoFpyWQphblavqkxTjq8uvY2crEYndvqfPdxZGPY3WYdaeIkq0A+zuebDmD7AW+xLiEV+tmswxCNMI/3188DreuSlMJV5tqN7BQJi5/XujPRqQ1jOf2Qzjk++GoPkindUVXGjbWMN2e6NncVnWzvn8iE5shK0ZixlNdjbPxPN3CSI5PdEcyn/vVjnHL3x77nSmZx20cYqbK3LFvEE+p+qDEeY2FwiG5XNkBkuUNlXRJFedG0gzK3YSwbOnuqbANXpTGulLJR9DGzX3yyvgzXPb3Esc/fvLISzyzcFngeRPPBOcd7q/eAcx7YZ6o8rfmmx1jgNl7rfTzBnuA76VmcSOlSmky7P/VmpeBpdf55sQi2zDkTlx07FBHTY+w2SsVtJvfbunQt3F5mN7aUwrWcc9w/dwP2VnoHgk114HQEOrdhLEVWz127D99+bBHu/WCDrTFWeYzF1AScDTXbOG/Uztk42wrZmmH1C6ZozPaA3T7TVR6TZznCHlcYO+QwbvvEFBrjbP1s8hSw3HacWSnCtSlxeprCY1wVTzheF+fH0n6HRIrjJ88uxQm3f+A519tM/TDgNIzFd6iUjtevq50Wrqy63l5X53h8/lbMfnFFuq9GNBPPLdqO7zy+CM98sT2NZ9Tr5cxTGcbSLkJrjF0e44hVWEmXJA1Oo3bBpgOONpaOiMaM7V3GtbjP3Ea333HdWFpk1zqrdlbi9rfW4sZnl3q2CboWnYVOXfkuJnmM95gjp9KDdcjPEY1RJaUw/zsaavbPLUmNs83QHAOfxvym7m3kwA83n24oAwNw3KE9LcM+JaUXSmfwhi0dTbQ+lmHcDBpj2WMsB7fJHmO/dIF1DSms3FmByUNLANjtlCk0xpVSadzKeNKQUqRx2zQkdbwuVa7z+/5xpcfYPl5vKV/y1v211uvOmsO1LSHy/O6uiHvki34zCmKA5DaM3Tl/3QU+5PVk5HaQSJmV9FJCSmFvL2/2wEcbUZQb3ryKaMxTsppzGPdAyilX4txux+5sGG7kmCgZoc+XNfv2NmrpSGeiU3uMVZ17RLNTHQUF5MmBTNkyIuT7POmTfJxoebJlFzs6vUZMers7N6tzVBgmlz68AJc8vMA8lr2+HVTnfKi425jlMSYxRZtHLvSS7dkl2QsrP0RlGZqfxvi3r67ENx6Yj637awBIUgrzqSPfD1WmYZxM6aiuT6I4P5q27bkLNPgZ6OK8c6Oa0mMsgu/cUNBz6+M0hu3X7kGL/FuJ39ttGLuNSKfHWPc1IhOuduaQUgitb8qbNq1KyqACOHX5boTHWN6Dbnqnc6KaQ/YhZ6XwZLLwuS4qKQUApY6fpBSd3DCWS/2K6eiII6gkQErBG5c8OyxBlXiCWLa9HJ+s35f18+nMZE1jLNkPQT9pPJHC8tJy7/Zuj7HwVqQZODk6UZ91y2sbsH5PFX7x0gqzvKllQRNtnBzT0pQf4I0NmtxbGcdzi7Zb7+MJHUcO7gbAaRiH0RiLinP7qgx5Qsp6GHuz+Tz40UYAQE29cYyivFjgrEZUY47vW17bYBXkAeyy0oAdfFeQE3FojAd0y8ffLpqAIT28+ZKDvhfR8jDmSu/n0pfL/an4vd0aY10Pqnwnyw6cz1zZgE7oumVMVtYlAj23XVweY7ehLhPRvBrjFDfKWedFNYd+Xp75c+dmdss//WwUcd4RhWVMUopObhjLHa+40eSGEiilgOw9zv65JUI2Tl3nGH/r23j2CyNA5Oy/f4rLHlmY/RPqxDT2903pHE/M32J53RydXsBOf/PKSnz9vk+t0rj2eai9Aeke4HKghp/Be6CmAd9/agmeXrANm/ZVU/BdO0J4b7Mhpfjek4tx8wvLcf1/vgTnHPXJFAZ0ywcA1EgesGgaBwIAFOQYhkFNQwp1DSlrPVW6tlU7K7GnMo76lO3dlY37U8f0ce074jCONuytxq3/W229PyhVtHtn9R4AhqEkNqmMJzGkRwHOnjDAoZeWoZm6NoCPB8E9OyD/VEJTLirfCdy/Z4Ojip2d3YHDWQgpnlR7jG96YbkjK4W7f+5TnOt4H9SXRjSG3RVx7Dho9/m6zsGYYVDHE87MKqIduzNZ+BUJ8XOqaIrRZ1NjYToCndowLs6LWa8bzPKiESYbxoqNrOkW+02zpGuTBf8B+69P6qiMJ/GbV1b5rkM0jcZ2Di99uQO/fmUV7v/Q8IYFTQXKLNtu5FiVI+fd2wN2J5jWYwyxnu5r8FbGk5bnMZ7QKfiuHSEX+BBtpLE/2wGz6uary3aiuj6JeEJHQY7h6arNQGO8o7zOepj/3ztrcfhv3sL+amPfmuYNvgOAo//0vuUZi2hOIcXRw3o41i3IiToMGzmNGwAcrHHeOzkRDZGIMV29o7wOm8tqrP5f5TUzvpe9/1eW7sBxf36ftPctjLjaDMyVsszlGZVm9fykFO48xg5jU3c6uuRnrpzuL5HSzRzbBmIApvIYB1WycxNhDF/trsL9czday5I6h6Yx5MY0R85uLkkpkq50be5jcmkbANhcVoPq+qS1jcowlk+zsw4OO7VhnEjZ0yK2lMLWoakMXrsRcmtqPNujKs6dKWKCOmPRIZAB03w09ucVhq2Y1pU776Ayzn6/pbs96lLnGIRoPu6pOtfeLQ9LPJmy2lwYjfH6PVXpcykTzYaoEGeka8ussW4pq8G4W9/G9gNG0FluNCJ9Vot9VfWW57fW4TGWs1J4f/upcz7Aoq0HAQDLS42B3n/MtGeqdG2CvabsIqIxx0O7MNdp5BTkRhyFbcqqGxyfl9c634MZRsCX2w5i6pwPcKCmAT265FjHUiEbTre8uAI7K+Kobkgq1yWaF7eUwk8yANi6eFW6NqdhrPYYG0F63v2JfRRLhvHKHZXWcveDYosUyGns1//7+UkaDCmFymMse6rtbdzxJu6sFCfdORcXP/S59V55XFcAYGcktGHMGIswxr5kjL1mvi9hjL3LGFtv/u8urXsLY2wDY2wtY+zU5jjxbJBI6daDQHjIIlqwdlMsMzKrqKcpsoFTYxxuPaJ5aOyMgHsrRwemc7y1chcqav1T+rgP650OC+kx5upOVCal2x6W+gw8xjvK63DK3R870mMRLYswUhuk4gZheXbRdlTFk3h12U4Azunns+6bBwDIjWlgzJBECBqTx1gU1bDTtRnLhVQDsL1zEcYcbU822AGgMCeKRNJ2bNS6DNYaV7R9Q1JHhDGHsTKwu6Et9guKko0RsU5VnAzjxqLrHH956yuUHqxNv7KJ3J7lViY7iz5cuxf/XVxqvU+Y3tW8HJVhbL93p/Gzg++cRris7xWZIyaauntruSuPsfK7BAxa/Q1jlZSCO5wd8vPJ7SSxNcb2shU7KuwgbGXwHUkpMvEY3wBAfvrNBvA+53wEgPfN92CMjQZwEYAxAE4DcD9jzF913ookUhy5ojqO2UtrGnOI8N2IZRzy1Etw4zlQ04A/v7HGOkYipeOMv32Cj9apg+Q4nNN4Qfu3UiCRGrTZaOzYQ7QjKy+2tKPtB2rxvSeX4If/WaLaFIC3k/MLoGiUxtiFzjlyzQpq9ZLHOB0Hzan3BZsPhFqfyD5y5bugB54KYUSI1JW5igAhBsN7K08py/r3TAfnmsawYW+15Zm6+bSR1mfV9cZAMRpxGsZyFgzA1hgLg0KVdkp1XJn+3Yw0bX4e47oG+/7LMa9xVQa5aQknq3dV4h9zN+JH//ky420Z/D3GV/3rC2wqq7He7zSrF+bHIp6YIUdRGWkwJadK06XgNsBZUTGR0qHrQElhLnp2sTXE6fIJG8e3X7/2o+Mdn/kZxowx5EadUgr5WO7j+muMXctden8Zv1LUnYlQhjFjbCCAMwE8LC0+G8C/zdf/BnCOtPwZznk953wzgA0ApmTlbLNMQ1K3jAExBRFhTBo5+ksp5Eo8fm1n+4FacM7xx9dW48GPN+G9NUYQyO6KOFbvqsQvX3ImjhfGbcplwARpjMlj3PwEdQ4HahrSPizF7yoXDxDeii37a5TbAN5gKm/nZvxP6RwVdQkMnf06nlqw1bMfsVVKqtTk7g91zi2PcSYaY9Ghk/ay9YiJdG2p4PKwKsTvLLzOqsj5iroENOb0sC02ZRKAOl1gEKt3VmLmXR/hnvfXgzE7DzNg5xc2NMZ245PXAQzDeP3eat8UbW4MaYZzWaEpEZGNEtl7LH9fcXzyGDce0TQzkV3JXlZHjEbA7/7Qx5sAGLO/YvADeAt8uD3GcqCa3J/FEylrJkUYohoDDu1daJ9PwGycIM+0NaYMLcHYAV0dn6kM46SPx7iiLoH9pkMimXLKPhKukxDvuOs72TPkKoPcfk2GcTB/BXAzALlF9+Gc7wIA839vc/kAANul9UrNZQ4YY9cwxhYxxhbt29c66cVkKUVCaih+oyx5GYd/VRkAWLz1AE64/UM8+8V2a99C0xRPqIMDBO6cnEFT+X6GMRnM2SOob5j4h3cx/Y65gds/+ulm7K2MY/sBr5ctFdDBuw1jr7TC9BinuKURffLzbc4Rv+RRkDtv9wwD55KUIpmyC4GkmYkQHWvQ4I1oXuSsFNYsRchthZFieYyj3kdCeW0DNMaUuvieXXLTatzdOAqFMOZ4OIv8wm4pRczjMfYvnuBeFwC6F8RQLRm13QtiOHFkL+NY0vG/dcwQ67XaMCaPcUtiBZMyeKrCpYOBYXCJnYrPHWNR59AY232jOytFfVK3+saEaVxrjDmC993yCxXfP/FQAGpjVCXnsdK1xTTHjMilDy/Ax+Zsszug0P08sWa1XV5wEReQPitF4FfqsKQ1jBljXwOwl3O+OOQ+VX2y5/Jyzh/inE/inE/q1atXyF1nl4aUbo0ELSkFY5LG2LuNU2NsoLohRA7PxVsPWh4d8RASBrLqISSOETaXoF8ls+aogtVZSacxFpH83u3s108u2IbtkrZOTI2pZBAiTVW9O1en6zaSS0KL/eVGNcc+V++qtKp5pXTd2sa9L1lKEU/ovp5lv+9IHuPWQy6xnOnPYBvGtkfMu47xgHZ7Z2MRhj7FuRkPwuUHdFLnDkNWBKy6g++imoZFv5qJ168/Hj8/bRS6FthGiZtCM3+svN8jB3e3vGwAcOPMw6xsHrJRcu6Rtg9Hlo6IfZHHuHVgjCFIMqDeCBjU3TaM3anN5N83JWl1OXe20XgihTzTgSYy+2iaV94T9Mw998gB6GIGkObGvM99lYGaMu+73GjEVyrk9oJ75He67QWXr5m4l9UFPkhKEcZjPBXA1xljWwA8A+BkxtiTAPYwxvoBgPl/r7l+KYBB0vYDAezM2hlnkURKt7RjlpTCoTH2NgpLPiF9nm5mSNxAwmCJ+5SsFKR07vDOuM9jc1kNrnh0oSM3qBsyjLNHY20+RzWm+iR2HKxDYY6QKxhtIKiD90gpXD+p6PSSOrfWzXHp0X718krrddLV+cukdFtKUZdIhQ6oEoYVeYxbD3Hp3Q/JMIh+L2ql6kth0pDuDmPxD+eMgcZUmnfDqMy0EIa7XUekDBeiNHRE82qMe3bJxZj+XfH9Ew+xUguqENkI8qSAvbu/OcFxX3STDGvZg9enOA9/v2QiAKdnWwwcKskwblG4z+swBpvGGLoV2FUNUy6vrrtUuBwzJDf1eEJHvtlvJ1KG55Ux5sl/HSQR0RizHB55Ue9z321kA0Z/zZhhSPsZxm4vuPteTOm2LeM0jAM8xrr6dWcirWHMOb+Fcz6Qcz4URlDdB5zzbwF4FcAV5mpXAHjFfP0qgIsYY7mMsWEARgBokxUnEikuSSns/Jm2Lsd789keY1ljrPD6ibLSsB86SctjrE5AbgVpce5o4G7j6Q+vrcZH6/bh0w1lUvCdEznFDJE5sgc0TCd83dNLvNWYpNc1DUlU1iXQs8gI2BCRzkqPsfk/rcZYMojEYCs3qjm2kw0IeSbC/ZU4t2cw6hqSkseYmctS+MfcjZ7pdHH+JN1pPawBujStGrbynS0hM94besqI9aA+64j+6Nc1H5rm9RhzbhQ78EsRJVOUZ0sf3O1aNsJlj3HvIju4yT275hcwB9hGhuyZk6uQffeEYThrfH/rvZzWKxZh1rWQpRTCgbLjYB2Gzn4db67Y5Xt8Ins4slI4PMbpt2Vwti3dNXDcuK9a2p+sMXbKDuqTKav9icFnhDGP/CHIMI5othGq8hiriszolpQi4ggUlFm2vRyzX7Rjldz3omyvyM8a2RGoOq7qdWeiKXmM5wA4hTG2HsAp5ntwzlcBeA7AagBvAbiOc97mrDTOOX540qGYemhPAM6G4lctxljmbTTKxiPr46Ta6oAtpXCPHOXsAbJ3xq9tMiZlpXA9COvJY9wk5E7Er2uQjefXl+/Cml2VvvuriidR3ZC0PBh1jfEYK7y8gNF2RVWynIjTMI5FnWm1Uj5t1pgetCP8k64B19/eX4+/vPWVldbLPgfjWCSlaD1kHWEmqQVTOsf/zN9TdDfxhBGQHDMf1AWm0ajSGBseY83jSVa1abkoQqBhHLcN4yE9ClFkGrRj+nf13caN8O66U7wJrpo6zJGhIioNHqMRzfJgy1Pt4rKu2FEOAHh43mbf4xPZx8hjbL8PMxBnzJ1W0NDgCyN3V0XcKl7j9Bi7g+9sjXHSDHDVmNfLGxQQKM+KqCSUqmUi+C43qnnSD/phxCfplvNNOPw8UgqhMSbDWElGhjHnfC7n/Gvm6/2c8xmc8xHm/wPSerdxzg/hnI/knL+Z7ZPOBowx/PiUw3C8aRhbJaFljXGQlIKrcwSq1rc8xmZjFDk3/aQUuiv4zt04nSNn8hg3B06Nt/oHlj1KgNoLK9hXVQ/OgRJzCjeUlMLtgVYYs4DwRhttKhbRHL+922PMfdq2LrXnukRK8jway/ZWxq31ZEQ7Jbu49bArG8rBlel5Q/J6inYYTzo9xmIK2ZBSeH/kaIR52rAqU4TssXVr52UDo9QsiSsMmrd/PA2fzT7Z01eqHugC0eZVnjnAaaSrtlV5jFfsqAAA1NTbMzNE8+PISoH0fbIMA3OVLjf6qRzpt5txuFFqPCU90Dn3prW0YpGsrBTMMaACvP21TESzbQzVgC1H0Z503fYYhyWh6/jmg/Mx6tdvGedr9c8uKUVSSCm8+6DgO8A/tLeTYFe+sw2BoMp3cvCSXeDD8NT88D9foiqexOPfnmI9mDi41fGLvJjCE+HuXOV8t2GC7xjz13bKVX2IzHF67NXX2K37cq8ldzC7TcOyewiPsWgH9S7D289jvHV/LX5hpv7LjTk9xvJMQlLnWLa9XLkvOV1RQ1L3eD/E+brbrJVdo5N6FtoC8mAnEw+PvKbo/+oTOnJjttdUGMYRjSmniiMaQ13CeUyVgSAbxkEa4692VwGwp5b7S8U/ZMJ4jFVaTgCWl1C9rR30F094nQvCqUGGceNpbFchbxfGY6wxICINukQflxuNoArG7ygcFfKg0h18B9hyG5EejWUqpWDMV0IJqI3lpK6DMVhxKWFI6RxLtpXb+5BiQOT2bKVrS6cx7qT9eqc3jIXh4GjU1tSkd33baLY/59wwNF5fbntgZINEpFCpTRg3o5gWUSXTB+zpEPm9jHjHIOn73FkpqERvkwgTgFDnNowDOpE9pmEspBRxS2Ps/zs1pHTHPlWZJNxU1iVwyt0fW+/l9FK19Uks216tPFfO7RzHDUnd82AQhrFboiPaKUkpWg8rWDjD4DvZuPvFSyuwuawa9abHWCCkFEyRlQIwDFR3/6RK69Ylz/9RozJyA2LrzM+DDGN/LScQrL+OaHZQ1Ydr93o+Fx5jlRdv6/4adCvICfRIE43HYRiHaegMLo8xd8RSAED3whzrM2dJaLfH2K6Qy4WUQhOSHSPg+e1Ve8CY2vCPaJqUjcrbdlRtNWlmpQhKTajaRkbEEDz+2VY8+NEme3lKOAKDpRSNrfra3un0w17RLuQph8A8xlLwkjyFKRuyjsbEbeOh1uxU6ywphU+6Nte0h9eIESfvP6KrJ49xk3B4jH3WEQMdQZBtKDrFkkKnlEK1jWiTciUzACirqsdus6qT37buaopV8SRyohpOGtnL0iEb2zo3FtOMgFkoQkrzA9iDAHfpXfIYtz6y9Mv6XUNoKdwe0X9+stnQU0YjVp8lSylURDTNYzCrDOjC3ADDWBGRH1EEIzk+VzzQjxjYFedM6J/WYxwEYwzi0Ct3eGMGgjzG0++Yi6/d+0nGx+xshK3KCMDqfBmYU0oRRmMM5mhHwviVZQslpmFsVL4z982B/y13BlcK49TQGBuae1Xub7+ZjIhmyxtVz31VlpVFWw9CYwyFueHbsdvRIgapbtmfWK4agDrT4oU+dIei0xvGYtosKYnU5ShvN9Z0C7jjgeTOImFLKYAG4TG2jAvjv99NZKRr85dSqKZA3XuKk8a4SYTRGHtT6NjrVcYTDiNWYHuM/X8f0bcZhrG9z1v/txrH/Pl9aypaNZ3oXlRdn0S3/BjyYhHUypWe3Knf3FIKKZoZAOLmMYXHTCCMINW5LNx8AOfd/6nHmCayi1weNpPxiSoFlKjyJfYpZrVUhihg9GFrdlViq1TBUTWl3CXA66X0GKexnsQU+eCSAlw0eRAmDu6GV354PP560ZF28J3LADl9bF9MGNQtcL8AcGjvLg7ph4w12+djdMtFfIjs0djgO5XHWM5vLaRtt72xxg4k5hz3vL8eADCqbxEA4JTRRv0yS2Os2bMW8rPfbyZD01jGHmPxHTLyGLscan6pFMU9qirgJG/SWaUUZBgrPcbGMlWbcBT4sCQXzrzD7sYoDBkxYrRTXDn3LWuMkyEMMwb/kTNNbTcNdwejwi2lKKtuwFxz+vXkO+fiic+3erZxa4zVxzbbS0pXtsHb3/oKQLhOq7o+aWpGmeN8VVkpZMNYNrYAuz3JXmf5XMXnx//lA5x5j+E1e+jjTViyrRxvrtid9jyJxiN+SXk6OKhi4U+eXYqhs19XGsZJ3chnLX7PXNPI9JMfiJLmP/rPl9YypWEcKKXwPoaCpBLGNsbnXfNjmHP+eLz4g6nWZ8LwcXuM//Gto/DydVOholg6v95FeZg0tLtnna75Masv95vtI7KL3Es5gs5DBd8525GYUZFz93aX8hyX1ybM49j7uO6kQ/HolZNw6pi+RmpC3fAYMyn4Tr6PggaQ4vmvMoJjPjMkmXqM5cp3SZeDTUZILNTOv/S2R0en09/d4iYRmlzZ4FV75EzDGPaNmnRlkZAfUID9oHB7+jzGifk8cadrcxu5cgdhBw06b0iyi5uGU8qiXsdtWFz7xGJc+a8vkNI5yqrV1fCE/jDIYyzaidtjLFgqAuikczxqSHeMHVDsWbemPoncaMTsmP1TAHJppqQhpTsGivL51qSRUpQerMOqncYU9LgBXQEA767e4/tdiabDrUGM875/a+VufPiVVyf74pc7AMA3N2peTLN+TzHt7KdsEP2mqqqWjJ8HFlAbwekMY1VhAoEwWApy7cDBdLz3k+l46QfHWe9V5yt7HzPJFEA4aYytxVzbqUIz3HIETWOePMZCBiGQC72ICqYvmfcHYARqnjyqD5hZuvzjdWU4WNtgaozDt9sIY47qpJ7v59NENS1Tj7F9YYyZP7UWIhkQGxLm2dfR6fSGsUdjrNu5QFWeD8tQ4VJJaJchK1ej4dzWH7sfIh6dpxREEySlEHC4ClGE8DIT4QiTFcQvJV5QdLJtGPuvI+e7Vv2MqmpzPzr5UKVOLZHiZgoq52dqj7Hxuj6pS/mJjWXCw13rI6VQXSOhCfxo3T6awWhGrJkrzh06zO89uRhXPfaF73a1DSnlAzk3GrF+T2EYy56wo4eVWK9tD6ptKKraf1GQx1ipMQ7nMVYhPik0DYoQdjF6F+fhyMG2l3j6Yb0AAEN62CWF5YGwO72WXz9MNA1H8LEspVB0jDFXO3J7jEXgnNzm5SIyKtmBnBYwmdKxYkcFUrpI12Z8dvXxw9Czi7Efdwo3eT9nHWEUlTl6WA/v5z6WscaY1Y7DIH+H+qSuHKQCkgROcR0zzf7REen0hnGQxlhVJMOpMbYbV9LlMRYN9OWlO/HmSmMqWTxE/IL7dMn75vAY+xi5unQcxuyk3UDnjSbNFmGkLH7GbVCJXGEgBEkpRJGD+kTKk4kCkHW99rL8WMTSVrrJjWkOQyInonk6xJTuzEpRZUombI+xqTH2eIz9PQ8JKfAjEZB9g2gaTdEYd8mJeozjvJhm9R/CuyU/uH9++ijHPgBn9TiVYRwYfKdwRwcZvgAQMdu66v4QD3PhMQ7yLvtxwVED8eHPTsQxphHjNtTdM3Ry+66qJ029Cndu9DBYsaQMaYPvvL+Rc9Al5GJyexDBd374BbFrjFlypajGcN7EAcpzEEQ1humH9cKWOWdiaM/CtOdufwdmteMwyIZs0DNGOHVUjyqSUpBhbN2kCYXG2J1v8/NN+y2d5hdbDmK/OV3u1gQndV050hIPDLGue5WU9IALY5ildGfpyjBeTiIcquC7fVX1WLWzwlru6zH2qTqoMfth7SelSKZ0VMWNB2s8mVL+jkldx13vrkNZdb21LD8nokwSDxjGjZzPMzeqBUop6pM6Kky9nWhfIqey+7zttD/e6+EoUkN2cbPhyEphvqmuTwRsYVBZl0B+TsRjhObFIlb7F21KNmbkmYnquDfDjspLFVjCuRFSiqDgPKGfFAF/YaQUbhhjGNaz0PIYRpgzK4Lb8SA7RjpjcaVfvLQCR//pvcB1mmJkMTBHX6h6vnqLvjizUiwvrcD2g3WOGQQ/D6/AHVNhHcvV/oT0xu8rBhWkCfpcY8jIYyzfe7UBAzTxjCEphZpObxi7Gzjndvcnd3ClB2tx0UOfY0e5HXUsqiF9sr4Mb620A4x0XV0e0vIYuwKaBJZe0yOlcK4na6Blsb1b50w0HpU265S7P8KZ98yz3vt5E/ykFAU5USvIQjYwf/b8Mpx3/6cAgPI626CJJ9Qa40TKjpoW5McilsEyuKQAC385w/pMaIyt97FIoJSiIZlChXkeYpnwPriNHrvyIrO2sc9TlheRZdxcyEHA4vfaWe7NiOLmYG0DCnIiHsPRIaUw25S8jqyRFPKw3DQe435d83zPI5JlKYWYxSgwjZV0GS6CEHaTpvlnRTj7vnm4+YXl1vvOmCrz6QXbsKeyPnCdoKB2P+RV0wXfuX9llQ54w95qj7f/rRtP8D1+ta9h7BwsCsPYz+GRrg36NWctY4+x3faCykiL76WyExxa7k5qGZNh7GqQOrdvwC+2HLQyAIgRlh9/MdcD/D3G9WmC72TDOO5KrTX7v8tx6cOfA5DyJ3PbY8yg9nISjUN1LUXUssAvJV5lXO2ty8+xS+3K01wvLC7Fkm3l2H6gFgdr7KC9eCIVqDGWyZOkFMX5UYdXLzeqOQwNkY7reimTgM7tgVpDSrcMdJFxRcxguI9tpf1hsLzMqvOkgVrzIQcBi7a6vzrYSAGAXRVxFOfHPFIG2fubo5BSqCQ7Mal9qdpnj8JcbPrTGcrzUEXkp/UYm5+r7g/hVBAV7ppgF1sGjWbM5VukOEdFbQI3PPMllpVW4HWpvDYVV/Ij8z7AKaWwUXk63U4uETDnXc/5fmSfIt82Ig/o5CO6PbxpDeNGzoBoLDjVoZuwHmMx05MuK0Vn7bc7vWHszebgzChx/9yNAIxpx7C4pRCCBlfQVEoHbn11FS755+fWduK/rF/VOcczX2zHpxv2A3B6jEUHwRhzpIwju7hpOAxjn+ecn2do5l0fK5cX5EQsI0SlTz7h9g9xUDIu6xIppVZcZXjkRjXETCOmKDfmMF5yYy6PcVSDrgOvLttpLZPb/f7qBmzYW20tj0vSEPdMiGjnGmPWNqp1g3TXRNOwYhZ0e7ZLeIvcAUkyX+2uwuF9iz0PbTmQzpZSSIax5DF+4XvHAnDGY6ikFDlRzXe6uDFZKYI+F9dD5e3OFPG93VIKXed48OONeGXpTs82ndFjHIamdgFpPcaun5lBPbPAGMPMw/vg21OHWe/zFVlGph7aA7PG9FWei3wsDvs+8UsjlzbLSoDGOJ0MQ0Z+bgV5jAOlFA6NcehDdyg6vWHsHmUanjPveuUZGMZJnSvLorrL53LO8dhnW/DZxv3msW1PsOyNDNIYywU+wuiSiXCE8b6rgjODyI/ZHmM/z8LBWsNj3K9rniml8K7jzgwBGNPfOZLHWDaMi/OiDq1dXiziMa65JKWQo+9TOnfkP3Z7w1KWYQw8t2g7ANtTJ69LkfrNh7iyKW5n1BFFVRIpjndW+eeRHjOg2ONBkw1jO/jOeB9xpcCaNLQEYwcUO+4FVd+nypgiaFSBjwBjQfSDJV2MwKpvTBoUuK8wx9E05ppiVhdIAchj7Edj+gCnrttenk4CABjGq3LQxRgevmISfnPWaGuZ6LNkxvbv6nteRvCdOC63jFdfjXFaKYW/x1jmzHH9cP3Jh3rWu2rqUABOG8BPHw0A5XXGc0ZZ3Vda9L0nF/vuoyNDhrGr4XFXyiOBe5o4CF+PcdIZfOceXVrLUy4phWtXYjN56hRAqBRvRDgcBT5cn1nV4AKiflUUSIFOftuKmYk+xXmoT6SU2lw56v3CSQNxxwXj0bUgZnXuRXkxh6ewW0HMYXyM7d8Ve6ucU+3u3NsCnTvbovt8bCkFwwHzHhFGOXmMWwYr+E76DWWv7TVPLMYn6/epNrXSTMk4pBQRZy7giGJ6Oi8awQdf7cUVjy40zkPRjoRH7bPZJ3s+U3nEVLpjmWiAISKMpu4FOVj9+1Mx+7RR3pVCYn1vjXmmmP0qOtZn2C90FkQ7bYy0hTFn8J1uBpr+4bXV1jJ3U2BgStmPKj2gKi91kIzA3WTl/vXNG2zN8oBu+QC8gfxuvja+n89xnAe6+5sT0LvYqde//YLx+NmskQCcg9IFmw/4Hk/Eg6iyuVFGKzKMPRWiONRGpRxYFOT9AAyDQHVTedO1SceVIspTLmNEDgKUG62c75gxhErxRoTDGZnrvJbC6MjUY1yQEzWmxph6uvnQ3l0sL1SPwhzEE6m007IDuxdYHrE+xYaRkxPVHFPf3QtyHMbM6eO804P/WbgdH69zGk/TDusFnTvbXyKp1sUDQJWprRbGskpjXF7bgL2V6QPDiPBwqz/hvgPiyx5ZqFwe1ZjHoJArxokqXaI9ucvsyut8ZLYfVd8njJGgIDz3eQURNL0s7q1YhKEgJ5rRVLTnOJLG2KFx5dx3qpo8xmpEO80o+E5+Rkq/QEoHlu+owCPzNvtuK3uMpwwrCZTWqDzGQWN5jTHLwOfcacAe3s8utPT1CUbu4nSZSvJiEfzl/HHK48jEIsxzf2nSYFV2QPxn4Tbf4wkpX7qsFJ0VMozdwXc69xhCZ//9UxyotYOivjFpoFKTJEjnMVYF38UTuuVpSaR0xBO6dYyXv7R1bPVJ3eogkq50bQ0OjTE17qYQlPrOKtmcYVom0fn69TuJlG7lCS4pzEFdIpXW+JY7+d5FhtHhLlXdNd/pMR7RpwinjunjWGftnipUSgGmXfNjKM6LQpf07nkxzZOP2M6pzC3dmhisyYbxgx8bWv0pt72PKX96P/A7EcC2/bW46911oe5jOeYg09te5VHLdXiMvVIKVRYLGVVGHs0yrMMZqemmnsNkpVDlR84UKysF80br+wU3kcZYTVOeSAzO67+rvA6fbihz7t/V+GXDWNe51a5VbSdfEeA2oHu+//lIeYwBf2mPGGSGcaLIcrerjxf6Z+9x3baFxtSzdGEQz7mP1+3D0Nmv46vdlYoZ6s5nS3R6w9jtTdC513BZtr0cZdLUc0QLrl++s6LOkTZF4C7MII/WquoTVqOOJ1KIJ1LWMT6SPHmyrk3nsmeaoabeX35BZEZQLkfLY5zhA1DllXDsN6mjtj6FiMbQNT+GeEK3jG+/ICq5k+9iFg9xa8u6FeQ4jJ1YhGF0P3/9HAArEb7OubW/4ryYR5uclAJKRaRzQ0o3Kz7aF+7Jz7dZnxHpueaJRbjn/fXYdqBW+fmkP76HCx+cD8AeYH++6QC27q/J6Dgqw1ieEXNXvoswFpjFgnPvbNmtZ41OW0gBsGc8gBAFPgI+F8VE/PJ6Z4Iw0N3He/LzbfhwrVqekulMUmehSXmMmXP7/3t3He54e61jHZWUQvx+OueWXEKVuzg/5lzWrSCGK48b6ty/dABZA8/h315FvxvOMLZfx6wBqbHfB741EZcdMwQAPLYFY/bANaiohwrhWHvLjEP4YstBT4xAZ7QlyDD2pGtTay1lg1RjDPkBRs61Tyz2jOoYsw0Gq1qYdJyqeNJ6oMQTOuKJlPIYNfVJW2PskmzImjeSUjSNoEBGYRxm6jFW6dhkGkyPcUFOBPk5EcSTKctbW5QXU24jP7D7mtPU/bs5PR3dCmLoUWgbHbmRSODATt53inPsrDBydw8uKfDNStGQ1FGXSFlGVVLnHiM6aGqPcCKkVH4PpbLqeiw0NYRy83xzpX+gnUB+iEcjzDPwiyoMY0tSkMZj3CCl9gOMNnSlGf0fxJJfn4KnvnOMY7sggj7/+yUTMfv0URgqlXNuLLKUIiwNKdIYq2iMgSXLL9I90lTBd6KZpLg9gFMZsQUuj/Eph/cJbGPuj9wOth7mQFAEr4bRncttLCdiS5cA4LSx/fCHc8YCcHqWAWMAwJgRFJtp3IvHyw5v1qPOmIOeDGNXh/fYZ1vw5bZyz3pyKVzGgIJYcG5B93S2mFJpSOmW4F1uf1XxpKOEbl1CVx5jzltfWaJ62WPMGJwe4844zMsiqYDy2slGaozdHqwbZoxwvK9rSOG15btQkBNBXiwCzoFN+4wUaH6GrNx5TxzcHQ9ddhRmn+4MNiopzEGvItswjkUNj3Qgpm5O14Gt+2vBGDCsZyESKR2z/7scryzdAcDbiQrPYCKle4zoW15cEXxMwkJIDsJMY2Y6CJYf4qqZCNljLIwI0U26s1IAzoIftfUpx70T9txKCnOsXLDG8cIZxqq99++Wj+9NPyS0bCPMcTKBpBRq5LawdncVhs5+HV9uOxhq2zCtiHOOa6cPt97LUgrOufUMVv2mbieUKkBPRtOYQ+bgvifeuOEEPHftsdagMZzHWL4vjXtK9Ri/9OjB+N70QzDzcEMOJ84jGmEeuyMdwn6Qb1N3oGAntIvJMA7b7cnT05E0HmMA2O0KMBKj1Yak7kjXJqiOJ6Up+hTqEylHxZsi86Hx+nI7kXxSd3qM5XPs6HZxPJGygr2aA6fH2PmZ5THO8AHonrYeP9ApZ6hpSGFfVT32VNZbnd7fP9wAwL8sqLtDnjWmr8czPaBbviP7QCyiWUEhfnAY04A7yuvw7uo96Fech8LcKBIpI6f2Dc8sBeDt8LsLwzjJSTbRBMSvGuY+DmsXv7VyN55esM3pMVbocKMRZnm8mEtKoDGvd0xu1zUNyUAZUhBBOZfdNKWaXSY0xjBuC+1+w17D8FwS0vDMFoEOGemjhZuNFKXPLSoN3J/YhPvM5LrXlTOQaMyWUqR0W0qhkg+5Y4bS/e5yv+cOvgOMrEJThpWgt+mQ6B5CSiTflyJXuEqSmReLYPbpoxwSJmN7zeHAC4NbkswYUO9OydkJZ5/JMA7Zwda4csceNaR74Pq7K5yGsbjxGpJ2VTy5wZVV11sd6rLSCmwqq3EYQ30V0dwpyTBm6FxSivPu/wzjbn2n2fYfnJXCNIwzfAC6O+Q+xf4R+iP7FkFjwJb9hsbUT5/snlZTkReLoGeR3TFHNYbcaATnTxwYuF1JgbHNqp2V6NM1D7EI83iI3VN3Pc3csQldR1LXm1RcoTMjuqV09/HqnZUoC1HlDjBykv7ipRUOY1XlGYtqDK9dfzyeuHqKtUxVAU8g/8a1DalGp+aLZaAJTufRyxZBEgr3wFbQFjzGc03982vLdqVZM7sEGVFyW+5lBgrvqwrOUCM2CSOlADee5yLzCQMkjbHtnAqTlSJd4GZlXQJfG98ffYpz8a1jBvu2xxmH98bfLpqAG2eOUH4uo6ouGRRMZ0UXmdtFI8xToffnp40K7OftgYx9HE/moQwD+joCnd4wDuulkI1ODuDyY4cErr+rwu0xlqUURkPTuX1D7iiv8xgdsmfG1zCWeouaBmfw3dfvm4ebnl8WeJ7tldW7Kpt1/8FZKbwe/zAI3di0w3oBAHp0yZE+896K8nFFQJG7vaYLUhIII7dbQczqSIM25Zw7vByFOVFEI5qno3Z7jE8c2RuAMXhIJLlv9hZVEQjCRvxGqiqHMmfc8wk2lWUWcCfberGI5mhDORHNNC7yccKIXtZyIZdQGRXy9jX1yUanewrK9ONGDAibO2JedY+Ia+A3WG0LHmNxWVrIsW4R9NvLH+VEjRPbUxluUMcRzmMMyBlQAGHf6g6PsUJKkaHHuDKeQN+ueVjwi5kY3quL7wCKMYazJwzwZG5REXHch7an249e5ixgkRl0HdWYZxZ13ICuuOI4f1vFvX8G5tHId0aPcfgi3B0UvxvgjHF9kdI53l61BwBQXe/MBiFSY/lR4aqUV2xqOqulIDvOuXX80oN1SLiMjHIpRdxAReqYN1fuxuF9i6z3cvogzjmWl1ZgeWkF7vjGEYHnSngJCr5zp90Li/ACPPCtidhZHnd0xoW5ETTU+j9Qhf4yLxpBImX/zkE5Wv9y/jh0yTXaXTSi4c0bTsDgEjsgKcgbxgGUFNo65PycCGIa8zz0ZcP40N5d0M1s54kkR0LXkRfToHJo1id1ZXQ4YSB+mUzTL2VKVGMO6Y2f50s82JUeY2mbJnmMIxoevXISlipiPDzHbEUpRW5UQ21Dylfe1JYKfLT0fE2Q8aoqRrU3nccYthMprcPY3L/t7GUQTVPn3GrDqt/UHf+RzuFQWef0zIZ1UASh0hgnAu6l2aePwuh+xTjRdLRENIYlrnsnP0fDuAHqmQ3A/k3kn62iLmkEXouZ7Y6uy1TQ6Z9MKr0RYKSmkjMBOGQKOk+rMXYjgpLK6xKOBic8QqUHax3prSYM6obfSiUrB5V4I6zX7KrEi18aQVCMGR7jsFOwRDBBOslkIzsMMVVckBPFob27ONqeSLUGQNmRCY9xbsw95effIX9z8mCcKVVUOrxfsbUfINibxLmR5k1QkBNR3iuylILB/o4NKR2JlO7rKaGUVsGI3yadx7ipxCKaQ6vo1x9apaGlj2cebswOxKSF1Rl6jAe4MqicPKoPfmJW8QoiCymKQ6EaCAgjSr6XZDpz2w767WXvvuhDa+pTgdl9MpFSWNIC2DNiYuYlxbnVhlUyiVyXYRzGY5zJ+mGQ9yHsi6BBVl4sggsnD7K+Y229d93caASMMaz942n44UneUtJuO+Ffn27G/5btdPyOZBh3QvwMi8LcqCNyv7ahaTmCu5tGRnltQqp8Z+d63XGwzuGNu+K4IRjWs4v1/oyx6pKRAgaGmvqk5VnshG05qzhLQqs1xk0tpiC/l71PL/3gOM+2hWZHmWkHHkQ6fb38aW5UU3p4ZSNAY8yaArSkFD4DyEzTCnU2NEtKkf0bWX7QGYZxRHrv4zEW+kzzvL76w2l48LJJxjKpDdYn9dAe43V/PB1zbzoxo3MXuCuWNhd+HmPAP1NMWzAkRJ/V0lKKoAwGcn8p2kh1fRIjf/WWlX3Hd1t4C2/57V/+yeysFPaARvXMdw/g03mAq11a3kzS+fkhz4IIeURGwXSKU8i3nhsR/HTWYZhznl1dT2Petrp+r/d36IxSik5vGPsZFgU5Efxs1khcO2245zPRUP797SlWA1Yhyx++c8IwAIbEQjRGOQ/xVlci/y65MccNPrRnYeD3SJklSossw7jzNeZsojukFM7PhGGcaYfhnq6TjRC5HakM0KZqjFUEaozBPZ5rldEkezSMcsHmFKDpMfbTjXZmr1omNIfHWL720QhzGcZ+HmNTSmE2mrxYxOo75TaYTOmhU0XmRDXf47UVVFIlcS38ZkMaKyXpCPg9d3ZV1OG9NXut9+5sC2t3Vym3s7NShJBSmGsII5VDymOsc7sktKIfy425HQ7+7fKIQd1w9zcnOJZlIxhUtkXEbLU76D8I1aWX+1/GmGOWIxbRrGdb0KOMgu86IX5es6imIT8ngklDSzyfiY5/+mG9cORgIzuFKktFsdm4rz/5UKvowlMLtlrG8FdmZ9C3OM8zcsuLaSjMjaKkMMdK7B2ErhslSsUNRXmMm0aQxljo49zXuMhnalXQxeVhktue37Ss+3P3QK5pHmP/zzgHenTJxW3n2m3PbcRs21/riYIWUopESkdFXcKTpugfl04EkHlxlM6GeLg3d3L9mOaUUvhrjJ0eYxnZ0Fi/txr3mSkGOwKq20tcC7cxJdZvEx7jVjqFW/+3CgdrGjzLz77vU/x3iZ2azT0Tks7hyjlP+0xzBxxyzjGwewHOntAf9186MdBj7A5+DjJ077hgPEZKsT1AdjzGUYWjpNqn7LgKlUfdnbrTEeAX1UK1VfIYExai/ahuItlQEo3xmmnDMe/nJznWE41b05hlNH25rdxKryQa/TCFNzg3amg6l/z6FKsUZBCGxzhpaVXbQN/cbvjgqz2oqHVqxpwFPpydjvDiuQ3mD352Ipb+5hSHZ3Xm4b2x6nen4qZTR+LrRwzwPQe/ynYC0XG7jdOmeCrSBd8BrtyarmNPu+ND7Civs94zxqzvfqAmgaTOrchpgVUitQ2ktGrLiJ+mIem9kbOZiSEaYQ6vUjqPsQpZY/zgRxuzdm5B2MZP8x4nak3F2wcSBlauTz7cTAYztQ1JnPbXj5st33A2ipxkwitLd2LOm195lu+tckbgeg0y9XmKyx4q+M78L6doi2gMf7voSIwd0FUyjBVl0DOQqKXLzNJY5HtPPA/cxTaCUD3z3TN28mnmRGzD2C0XlGkLA72WhgxjH8QUmuomUM1uRjXmueGEYRxhzDElV1btHFFPHNLNen3p0YMx/bBevjky/UjpHLUNKeuYJKUIx76qenz7sUX4wdOLHcsdGmPOHe+v/vciTPrju46R9Oh+xehVlItuBTkOg/P4Q3uiMDeK6046NLCzLQ6Q5AC2IVDsMqCb4qnIdFu/zlPshsE24PeYBW7k/MkAAitB7a6IZ/Qg6MiIa6oysrL5nIpGmCOgM+YzhSwGNCqZgNyuw5zb0989Gg9ddlSGZ9o6CINJ/t7it3EHwgKGpjOZ4qgzi/WkY3lpBb7aXYU5b3iNyabQkr2/e6BWFyJ+wN2O/LsiY7273l2HZ9KVlHd5jN3PwKhC+iPIJCuF6rNsBN85DePME4ap+md3TIrc55cU5jgyX/lBhjFhIRqQ6ibgDo+x8T+iMc/NIaa/hVH8o5O9UaEAMLB7gWUYjR/YFf/+9hTPFAgA/OrMw33PV9c5auqT1jGbO81TW6Gp3jPh/d2415kLVtZV6dyr9SyrbvANNBFbXjt9OK44bmio8xAeAtUD4s/njbM8P8WuUs7pEtE3GsVl3eKTL7fAbKuM2frovaZh7PEYmx21W0qRSOk45s/vY/Z/lzfptDsKIrhMpTHOprwipmlWij3AKBeuIleSyLjJdNbiuEN6YtaYvhlt01rkRIy2LRtyom9V5R7PjUZQm0hh5l0fYfJt71ml0/0Q19PvujcWS1YQYl1d53hn1e5G96VuIzdM+3TnMXef50fr9nnKG7+8dGfgPj0aY9fXEc9hlW7c3Y9m6jHOtmHcxScVYBCqn8/9XeXz7F2ci5qGFOoaUsEaYzKMCYFoo6oG75BSSDeje92CHKcuVJT5dROLaFbJ3qBglO+cMBw/nnmY8rOEzlFTn0JhTgQaaxtJ5luCpt6z9rSbc0eyN1jn3FMNCAAOKLR0MoNLCkJPZYqKcaoOqk9xrjUF1jXf7TEOtXslwVIKM6od9kNGLvggIx6MjNkBeiJxf68ip2EspD7uPN/lppTl3dV7MvoOHRXxu6raXboHldv7FXgcjeHnp9tldNNJKVSGcWtUNxzeqxBjBxTj92ePadbjiAFBMqXj3ouPxLTDelnt3RuwxZAT1fD68l2WxEiUTvdDGNnNNsAN8dM89tkWXPPEYry6LNjwVHH3u+sw4pdvOpaFccoEteHNZTW44tGF+Pl/l2cklbE1xuo+XTRTlVY5xzUwCfYYe3+rbNwD8kArKD+9H2GulbzfPmYthv01wTMbpQdrsWpnRcbn055JezcyxgYxxj5kjK1hjK1ijN1gLi9hjL3LGFtv/u8ubXMLY2wDY2wtY+zU5vwCzcVxh/QE4PSG3DDDKOso31d2ihjmuRFF6ioRsNKtQK0jzY9FAuu4y/hJJBqSRrBTYW4UGmNtKsl8c5It75m7r0zp3KFjVAWL+U4ZmvvKRHd2/IieKM6LKst3RjXNMmI91baaZBj7fyaamZD5nDG+H6Yd1gs9XMF0gC2LMNK1Ge13t5BSuDzGosDI1v3OLCwVdcYgIxOjrkMj0rUp2ne6rAeqYkBBFOfFrDgGPymFCNBTpY/z26Y5yY1G8NqPTsDRw3s063FkKcVZR/TH49+eYnk73R7jWMTrHEmHcGBkOztHkGbUza4Kw4gX8qdM+Nv76z3LwngYvVIK+7qJVI5rdlVmJAm0NcbGf/etI57DqmAyb+yG/+/RbB5jl3E+8/A+oQLvBeJa/V9AQS85eLZXsdE3769uCGwt1zyxGGfeMy/0eXQEwtyNSQA/5ZwfDuAYANcxxkYDmA3gfc75CADvm+9hfnYRgDEATgNwP2Mss2oYrczs00dhrJmqSvaqHdLbyCucUkgpGAO65cdwaG8797BVelcThrHXqACAvl1zrQdPug4yqNOpSxjVmDTGOk06rKbaxeK3dE8jJnWOmKaBMeOzuE+wmCqFmeigMnnYFeZEseTXp+DOb4z3fBbVmNXZZ/LAS0eQV0Ic5dDeRdgy50ycZJZ6DnoAMNjfecPeakQ0hkHdnYVpCnKi6Fuch/kb9zuWC49xW0/f1VKIq+yuhgmkT5+USWllgfhZ/aUUbctj3FLkKjTG4rUn/aKm+UrvPt1QppQqWFKKLKT7UhEm3zPzkR40lqBqbYIgKYW4Fg0pvVEzgqIPcfeVor9T7TMTKYVSY5yFIEd33/fwFZNCBd4LxNcKGhjL9kw6j/FjV00OfeyORtqnEOd8F+d8ifm6CsAaAAMAnA3g3+Zq/wZwjvn6bADPcM7rOeebAWwAMCXL592syA1fvmEiVgfivbMYjFHmez+ZjmlmiUb3veKXzqtf13wrEXe6DjKdtyg/JwLGOk+e2KamkhFGhkdKoXNompgJ8E8vpppW83twBpEbMwpoqKQX0YhmNSbP123C1w9qaX56Q/dD4ZjhJfj118wKjYxZ6dp2lNdhSI8CRyowQe/iXMzbUOaYnhOGMXmMDezgO4UxlWY0qIpPSIcwGvym9G1Jgfd8spHDta2SI0kpBCJA1D17E4tqSoPq8flbcenDCzDsljdw8wvLwDnH55v2o6I2YQ24s+4xbkVZqDtHsYqg4DsxK1Gf0DPzGJvr3nvxkfjO8cMwtr8zgF0LeH5nIqVQ5UFujPTBTVPbgOWQiRql1f8sFfMQyLe3kLkdqEko20uzyXvaARl9c8bYUABHAlgAoA/nfBdgGM8AepurDQCwXdqs1FzWbpAbqNzRycnCBSoP3mNXTsaG20733IiaxjB5aHfP+r2Lci0vT7ppqHSdztb9NabH2GnIpXSOcbe+jecXbffZsn3S1OTjwrB2X/ZkiiOqaWAwOpx0HmPVWQSluHITZMxENNvvo3M4qhc1hSD9s99VdT8Uzjqiv9XBMgAx6X4Z3rOL0lD47glG0Rw5O0t5ndMwfmPFLjw+f4v1eX0y5fEydWRE36GKFVD1EaOkvKqN8eCK4/lqjGN2qW83HdtjbNyX8iUXQWHu3ONGZiLvtfho3T7r9XOLSvHK0p246KHP8Y+PNqLOrGzWXIOLTByZ2bKlw1RrDHrOicFXQ0rPTGNs/h9UUoBffW20b+CZ6tjudh8YfKdyXmRZY9wYxLXKiWg4eVQfXDxlsGcd+dyFtLPaVd4aAAaV5CvLrn/38UX4+QsdP0A69C/BGOsC4L8AbuScVwatqljmaYmMsWsYY4sYY4v27dun2KT1kG8SucOy6q5LzwbrxpW+taYxw/tnvpdHvT8/zQ50sY+hWal/0qW6UXmQ5Bvqm5MHQWPwGHLV8SSq4kn8/n+rA/ff3miyx1hXe4x1zhHRGDTGwOHvMRYaWlVS+0y8n0HT37EIk6bAOC6aMhjHZkFb2Zi+3P1QiGrMWmYE39nfuUdhjsPrIB4eQ3sYebsbkjo+21iGybe9h+1m5UfRln/w1BL85pVV1rYjf/UWLntkYeYn3E4Rv43KQ6taJqcOa8wj2pJS+BhoQhuuMio6smdJdQ/XJtSGcSyi9hhvcJXZFYWddpTXocY0sptqFPkRpi1YPUuWLOMwA9igAh9i8NWQzMxjnA45v7EbdxsuUcRSCJovK0V2BkdBnmf5PLvlG9/RXaRpYPd8/O+Hxyvv63dX78GzHcy5piLU3cgYi8Ewip/inL9oLt7DGOtnft4PgKj3WApgkLT5QACecFfO+UOc80mc80m9eqmj3VsL2RhWeYwd6drM/yotlyoJvdsz+MTVhspEGEbxNIax6sFk5dWMajhqSAk0zesxFgZ3R5uqbmoqGSvBudtjrOuIagyMBXuMR/QxNOW7FYErmTzs0nVmfgElTfn26Srf+Z2LjJGNxdwf7Mp3gOFllPtW4cERbbAhqeNv763Hvqp6zNtQpty/zPxN+30/62iI66/S9KrafErX8cTVU3DDjBGNym2dzmMsDGMV2fCWtVXceWAB+/p38RjGTBm0tb/aqeEUBZ7+t2ynlQIx21538YyKJ3Q8s3BbcCq2LMcvpMuIxHnwzKdo8/XJVEYa44I0EiKrDw2QUhTlRfHydVN9M/AAzZfHOFttIMjAlr3oeTENuVEN1fVJx29/eL9idCvIgbspZ7OwUFsnTFYKBuARAGs453dJH70K4Arz9RUAXpGWX8QYy2WMDQMwAkC7cvXIDUu+CUTDVd1YqmeRaoTqNoxH9S0GYBT2AOxsGH7ku7MSwO6IRCduZKVwdjw15pRdRwtuEg+pvZVxpdc27PZqjbHpMQ7QGLuDy2SyNQiJRTT4pZVrCo0xoNyddzTCrJkUufIdYLRHlUbfMoxTKfQw09TtNNNbqQaGnUlCIRABTKpp6bjZFo8/tKelc02mOE4Y0Qs/PuUwZV/kfljO+/lJWPP706z34jf0m9JnjGHKsBJ8c9Igz2cqzWVHQWUYC1QeY5XRFHfFe4gsEADwzBeG9y3buWJFN/Hop5sx+8UV+GLLQd91wwToZUKYdG3umU+5WxOGcSLFfYxY9W/SJU1RjCApxbCeXXD+xIF47tpjMWFQN+X23U3pgdJjnIXgu2xVKQx6xst9vqYxFOXFUBlPKp137ueDO8UmYPTXW/er89u3Z8JkkZ4K4DIAKxhjS81lvwAwB8BzjLGrAWwD8A0A4JyvYow9B2A1jIwW13HO21XuMF+NsSqqNagPUIzE3cFIQrt35ODu2DLnzLTndsOMEeiaH8Pzi0qx2fQ2iEYtGjTnHIu2OjvC2voO6jE2v/yUP72PgpwIVksP+1Db+xjGhsbY9Bjr/h7jaETDD048BOMGdPV8FvRQzYSIxqy2JNreyaN6Y/6m/Rmn5pJpTEcccU2vacyWUmjMmborLxZx3D8RhcdYVPLbVWF43GsbvF1FpTTVxzkPdd5DZ7+OK48bilu/3rx5bpsL4VFze4y/2l2J0/76CQDgkqMH4+rjh+Gqx75wGBqqAU9EYw4je6BrQCd+pqBZjueuPVa5vGN7jP29kF1yonj821NQFU/iuqeXIOojpXAbYmtNKYVMtvPOux9LYfwh2Rpzp/sujHmN58c+24Ku+TFMGlriaPPKQLmIpqyQ6R6ouAlyLkQ0hv+78IjA7V++bioWbDqg7H+yEXyXLQJnH6Vzj2oMRXlRVNc7pRTiueWWUqhmRb//5GJ8uHYfNv3pjDZ1DZpKWsOYcz4P/lKlGT7b3AbgtiacV6vi1EXar8f0d3p3AWDaYT2xcMsBDOjmNVBUFXjcWtJMjaeCnCh+cOKh+GjtPsswBowgqKuPHwbAaUgIROPvcIax1MGqjKq02/sE36X0cBpjjQE3K3TjQBY9xlIeYzHI+s4Jw3DuxAGePMGZENSNnTm+n3K52wiKarYxwOAsfW54jBWGsdlxxxM61riMBJXGXvZUVMaTniInfjz22ZZ2axgLw0H2ll/yz8/xmZTmLqIx6yEoG1+qcYPRj/kbLOmkFEFk2+PYlgi6hwtzI5h2WC9UmsFLORFmB0BF1cYbYASdDi4pwLYDdi7vMAFrmeA2xoOyFIUdH6/eWQnGjKn2IFTyHzduj/En68vwyfoybJlzJhqkojYqxUVOVAMUGcb8sj4JxGxrY/vlIT0KMcSMj2jLBEsppNeMoTA3gv8t24mZh/e2llszz67LdKDaOyP74VojPiypc+R0JsO4MxLz0Rj3LsrzeHV/cOKh+MakQehTnOfZj0qT7JZSNDbo4t6Lj8Tzi0txx9trccOMEfjxKeqKeILvPr4oo+PtLK/D7so4Jg7u3qjzaylSnPs+gEJtb/a8bs9EivNQGuMgXViYa/2nc8dh3R6vB8lxjAjzBMgwxppkFAP+UopFv5ppeXI923givaUHq2t3ebEINI3hjetPwBn3fOLxGD8+fws27nNOwx2oacBXu+3YXs45ymvtDrmmPr1hrKps1d4Qhk2DZDB95sr9HNVs6YpsjCgD5NLIHcTP2pjsCFmaAW6TBBlRQk8s7vNoRLN+h5mH98YbK3b7bjtrdB/8Z+E2K/hOla+6KWRkGIfYX1U8gTPuMWYq0s1sqqo1ugmSR8ltWSml8OlX00kpLjhqIHaV1+Ha6YekPb/2TCygzbpn8FbuMPra99bstZYLG8X9bAtKDJBNiV9bgAxjBY6sFGlGQZrGlEYxYHtSgjTGjdUV9S7Ow3UnHYrrTjo01PrCYxx008jcP3cD3l29Bwt+MbNR59dSpHRuBbM0bnvjvyddm+Qx3rSvBnPXqjOnBOl03SVjVVwizT74ETNLzQLZLQTgbtq3nTsWY/p3DTS4p43oiWXby633EYfH2InwPIhy1+JaieVuo9gopgLHtU6kuJXKDWicfrE9kUjpmLe+zDJsggyIaESz7mfZEFJOFafpZ1gTPMYdmTABUcJQi0VsuUpemlSNBblR47cThnGWpRTubD3umJNMWLj5AC58cL69b7Nv9CPddzGC7zg0ps4Q4TSMvZ/7DVYKc4LNmVhEw09mjQxcpyMQ5JBxSym6F8RwsNapHbalFM7fuCZgRrY997kqqBdUIHukmhJYYnuM7WURjeGz2Sc3ep+NQX5o5oT8PhV1SRys8Yrt2xopnWNvVVMMY5/gu5TR+TNm5CGVZSsyQYZxtlIwRTSG08f2xfemH4JfnjE6K/sEvJ6+S48e4ht4Irhx5mE4YqCtp45o9jVw70/oM8WVLcw1pzLN6+IuLz2wez40ZniFBfXJFDZK6a7SFbcAsh/IlAmJlO7wcGfK3e+uw1WPfYFNZnsLMjIiUglueSpe9fXTGXji88YEZHZgh7GS08f2dbzXzPzFsYhmDWRy02RIKMyJOGR6si73nvfX44XFpU06R6/H2N+oET+530yLbBQDkGJb1OuH0Usnde6r306rMW5k8F1nIWhwKzviNI3h1R8e71nHL/hO5NxW0dR6Am0NMowVyDkMmxRY4iP2798tHy/+4Dj8roX0j3JHkxPVsLsinnb6vq4hiYaUnjZ9XGuT0rmVDqkxznf/dG0cEUWJ10JXVpAg2zdbGuNoxKiKN/v0UehaEE5fG4bD+hSlX8lFRGMOnZ3cebq1psJj3rsoFzfMGIF/X2WkJhRyjP1mFpH+XY0Zl5yIhsIcZzBIPKHj1WV2tscwRm+yqXXCm8BPn1uGCb9/t9Gpjba4IryDyusmUrqkMQ42JsL2Y43q7wI2kbWLHYV/fOsoj5wgFtEMw9j8vY4c3C1wHwU5EYeTQu6j73p3HX72/DIAwHOLtmPjvmrP9ulwz6yEqYQappQzYKeb8/Mgyt9lT2XcUajHOj9dV1bF5Jw75EOq2Q8/w+/7HVwiEZbA4iSa02PcXxEbJTzG7v0ExfC0Zp/bHNAQS4FIIQU0LbegSmMsmDi4e4vod/NjEadhHNFwzJ/fBxCsFRM3QVU82agSsy2FzrllSKWbvlThVyBENzXG7k7YrbENisTNmmHcTEENs8b0xds3TsOpf/04o+2cnasm6Z6d6wmPEGMsUAM/aWgJXl22E7GIhoLcCKrjTo9xeW0CBTkR1DakQk05t6bHWBjx8YSuTK2YKUJ7qupDKuMJDIkY2SXkqUzV90838yW2yUZk+Us/OA650QhG9w8O0movPPWdowPTMgLGvR7VmGWQdi/Iwe3nj8fN/1VXCSvIiTpyHguPv+y1jSdSuPmF5ejZJQeLfnVKRufsNijrAxwcqkDPIGpNz+E+n5k6efbimscXYVlpheNzkZXCeK44ZyVrG1IOvbXKEanqVx+67CiMaMRAvyNx+wXj8dinWwLXibg8xhGNoSg3iirJGRHWMJb7JHefs3V/DaIRTZmUoD1AhrGCAkmr1JSqTnYmgdYjL6Y5vAeqc/l43T50L8jBOGmK3DaME1bJ37ZIUpcM4xCaXjd+ieaFxthtGLvtk5aQUjRXuVgAGNm3CF8/on9gpSc3zlyYklHluhZhfw9x7JyohsLcKA5KUoT6pI66RApFeVHUNqTavMZY6KSr4omsGMZJneOT9fvwwVd7PZ/J7VO+LqpLlK4fE79hYwZh7pmCI9t4wG6mTD00OLc8YLTdWFSzpD65US3wvi3MjTjiBcSAT87AInJ7lymyAQCGIbustAJHDfFeb7cHL8hjLO4X1aDTXZwEAGrM1J9+hnFK59DNPPCqcxcaY1VGpnjCOfhVBSXmKvrVsLEzHZkLJw3ChYo84zJyNyDu9aI8p2HsF3xX65JSVNbZ790Opul3zAWQPlCzrUKGMYDjDunhifgWiLYxeWjmnb1oVq0ZsalzZ4en6vwuf9SovyI34jrJY9yWSenc0qQG5Rz1395vue6I+he4PXdBQU3ZStje3CV377n4yIzWjzg6Vw2JlNFW/DzG6SgytYGxiIYuuVHHw7Q+Ych5ehflYg/qQ03ZhTGem4uYmWO1Mp5E7yw4TBMp3VMK+8zx/XDM8B44Y2w/7DMNF9ljI9qoHNwkG7wq21c82JpafevkUR1POhGG3KiG3IjthJBzeBfmRDyyg4KcqGPQLTLr7JMM0a1mOje/cvF/eesr/POTzXj7xmkY2dfpLXX3a4GGsVRQw82pZs5smdqGJPZX1+OBjzb67jOh68jVIr6SotqGFPIVwXKJFHc8o2oVnm6Vx7i5SmpnwviBXXHiYU2r4tslN9qsjhBnJV/jdXF+DDsr7BzFQgLn7gvEgAgw+pjtB+10g63Z5zYHZBgDeOLqo5HUdZxy18eO3JKAYdy8deMJjZoSuHLqUGwuq8E101pP+6TrHOv2SMFLIRtwbcIwNtu6YWxIKRpfvMRv2j2ZMjweOS7jzr12cyY1P2pIdyzeejDr5WKbiuwZVj2PohozgmtCeoxFad1kSkdhThSl5fY9WBVPIJ5IoYuZPi6Z4li/pwoDuxf4emRbU+8mig+4k+Y3FtVANjeq4bJjhgCw9ZZyUKJo0xGNQU95DV7VgE3eJlNGm3ltH7tqMk4c2TkN4z+cMxZ9i/Pw+WNfADBmS8RvU5wfUxjGEYdhLH5n2Qu7fHuFta4KUSLd7ckDvDNhQVIKoS1W3TeqjD819Snc+OxSfLK+zH+fKY7cqDoQlANYs7sSJ4/qjTW7Kl3b6Q6NcY3iPlL1820hm4oqkC1TvvxNZpKZTNFcWSkAeFJzCoeG2+lTJ7XhRIpjR7ldwbE15WvNQeu3pjZARGPIjUbw3k+m46s/eCunjepbjCKfvK5BFOXFcNc3J4QuSNAcVNUn8a1HFljv3Q9avxF9nSSlaMskU7bHOOjmrIon8IuXVngyBvhtk9INjbE7i4dXStGIkw7Jv66ajNevb3pnm23kwUBE06w2JAyuQ3t3ARC+kpaIJq9P6ijMjToSyX/zoc+RSHEreX9tIoVT7v4YP/rPEt/9tWYnLWYYsnHfMKYeyMrSBXE8+VoL7apzACMHSXppipSia0EMW+ac2WmNYgA4aWRvHN6v2DIyZY+xygOYnxNxLBdyBtkwXr3LMIz31zQ4jBKBkF2oWrtbTuT2GNfUJ3HWvfPw2cYyK6NAuvzDP55pxAkYHuPgzCu/eHEFAPVsaemBWpTXJpQSkERKdzyjVIaxKmVlc3pZWxIRxNlcqPqEglznwEtI4Nx9j+y9T6R0S+oDULq2Dk1OVGvTgWbZwKFF1LkjabdsJNe2EymFzm3DOCgw6+1Ve/D0gm34y1tfOZb7Bd+luFpj7O7oVR62t248AX+/ZGKo8w+iOC+GMf27pl+xhZE9CRGzMiBgG1y3nHE4AGBoj+CAJYHwGNcndXTJ9U47y+scNDNZyAnp3bRmJy3aS3UW7psB3fKRSOmB08Sqh2hPU7N90WRbbzjCHKwAzuBigZ9OnMgMIUvIi9oaYtVvpDFn3yK0tLJhvF5KU/jIvE2efVSY+WfjiRRW76zE3z/cAMCQZby4ZIe1Xk5UswzjlTsqcN8H67G5rAYrdlTgkn8uwHOLtxvnYHqM1++pwl3vrnM8D/p3zcMPTz4UORENNQ2ptPEDry7bieueXqJMpSn0rKqMCIkUd+iKd1V4yxC7Z/GAtiGlaA+oZo7cWm/hMe5dlIsLjhqIb08dBgColQYpiZTu6OM6WoEPak0dmCuOHeJZJhuPDUndYfgOu+UN/PaVleDcNpgr27rHWAq+S+ocFXUJvLt6j2c9cfN/ua3csdwv+M7yGLs6jTDBd6P6FvuWVO4IuKsn2R5jY9n0w3phy5wz0SNkZT6hMa5PpFAolXWV5UtinQM16XMEt67H2GgvYQaUzy/ablWkVNGnOA+JFLfKgAvkJqcyCIThO6pfMTb/+Qws+fUpGNnXFjw/f+1xnm30LGmMOztJy2OsIWLGBsSkGIGzJ/QHYBiFsuezIcWRTOn45ye2AbxJKoCjak+V5rL6hI7z/vEp7nh7LZIpHZ+sdxYjKs6LWnmMz/77p7jznXUOLbPo04TT5DuPL8I97693aP2PHNIdEY2hIDeC2np1pqIjB3fDtdOHW+9fX77Le4EkVMF3bo+xClWbbwtSivaAKibGPdAQgx5NY7jzG0dggpl6UM5K0ZDSHR5k2eHWESqPUmvqwLgTnufFNEfy9Ze+3GF5GQT/nr8V9Und6iz/+PoaLNi0H3e9uw4frvX30rUWus6tGzaR0vHDp5fgu48vwt4qp6dBPFjc5aPlPlhOV5RMGXmMPVkpXEZKZ/SwyV/ZMIzN5Rns472fTLNed8k1pEb1Sd0RsPen88bZ65ht+WCI4hmtGQgiBlJhBpQ3vbBcOYgTlBTmoD6Z8kxpytdZpXEXA5L91fVgjKGkMMeSSJw9oT8GKzz5wqBrTs18Z0C0vdxYBDGFlOKcCQOwZc6Z6JofcwTVJlI63li52/KwiuI3xXlRRDUWKBWIJ1JWv1abSOF9VwaT/JwI6hM6DtY0WIPGLYqCRcIgFevI+mKx/8IcI4OByqh97KopGNU3fMo0lXHdkNLTFgjJz1EZxtRuw6CK43b/lu5nnug7ZE1xIsUd8h7ZGRFUOrq9QMF3HRi30VaQE3UYDb94aYVnm+4FMU++wm8+9Ln1uq2lX0np3NJzpnSO1TuNYA63ASwMFbcHWPYY769psMp7x5Mp5EQZ3GNH9zR9Z3RURFzBd0JDP7gknHQCAA7tbT9AhUwinkg5fh856EhojA+EqMbYmh5jYVeqNKF+1CdTygwe+bEI9lWGq+p4zTTbU9fPLJgiP6C6m4aWu/yrQG+CxpiwKciJoC6RQm5UkzTGdpuO+bw2JDP2te9dnIf9NQ3Ii0XQkNIDg6Z3lNdZQW7vrzEkYzIlhbnYsr8Gv35lpbVMVclTNn4BOGQQwku7o7wOLy7ZodQHxyIsoww6KuM6meJpi5GoSj+TxzgcKkeO+3dwp18U7VhOTJBI6o6gTzlws7bBqUVuj78NGcYdGPdNoHPuybrhpm/XfHzbjKxuDyR1jg2mFi+Z4ojDuCndhkmlGaji9rzIzom9lfXoU5wHXefYcbAOM0b1xs5yp+dZeEcjGkNK553SYxxxBd9NGlqCBy87CtMbmaqoSAq+kw1juWCL8Bj7lVuuiifw6Lwt+MFJh7RqVgphoGTiNamKJ5Hbxfiu8sA1FtEc+UUF7ibnHqx+c/Ig7CyP41qpEpgYtOySvD4yYsAXlH6QSM+z1x6LD7/a6/CGygZvzOd1IqVbfdGFkwZa2trcmIZYQrO8ubUNSSR17sgk8MfX11iv5633ph2dOao3/u/ddY65LiHT+Nr4fnjNlDysNauhimCsvZVxDOlRgK37a3GrWaV1/MCuWF5aoWxHUU3LaFCqGgwmUnpaw1iVjaY9Gl+tgUoqJX6Hq48fhklDujvqGQDqPiGR0h2xIPLvLlfLrUuk2uVv0/7OmAiN+yYo9/EWydQ1JLF0e3kznVH22V1Rh4O1CRTnRZHQdcTFlKLbMDY9xu7Iazn4Tsgv9lbVoz6pY3CPQsfDS+4fhJezMxrGzBV8BwCnjunb6MBVYRgnXUn/c2Oa5f0UWWH8NMb3frABd7+3Di9/ucPRST+1YCsAYP7G/XhrpWEAfLW70jEt6Afn3BEMdbCmIW11MPFQ8DOMOee45cXlDllSlaPSn73/xk4P50YjmH36KIfxNKjE0Gvv9PnewmNMGuOmcWjvLviu6b1PWl54u037eY8TKW61netOOhS9TDlMXjSCnIhtGE+7/UOMv/UdAOqMOOtM41ZmaE+jhLvsad1cVoOcqIYpw0qsZaUH67CjvM6aqfntq6sQT6RwwVEDrSJP//eNIwDAkffW/j4sowGhKoCvIaWjPqG+x4TXWpXXmaQU4VA9r2JRY1mX3ChOH+eNjVFVzfy/d9b5SinkZ28mM2dtCTKMOzCNecbtTxPc5E7vtq+qHl/trvRZu/nZaHo+RvUttioqAV7D2NIYmw8Y3QzUS0mGzs6KOP4xdyPWmN9nSEmB4+ElX05hGHdGQ0J2AKQrNRxETkTDhEHdHAF3uS6P8RBTDyuanawxlo1U8bsfrG1wyF3ufd/Q0F/8z8/xvSeX4EBNA0776yeYOueDwHObu3Yvvv/kEky+7T1s21+LeCKFI//wLv7w2urA7YRh+69Pt+B/ZnloXeeYt74MnHMs2VaO/yzcjl+/bE9ry6ndRJAUkF0vWJ8iY4Bx1NAS5edJMoyzjjBm5apssjHonr2qabALFfU0DdHcmFFBTwzo5YA41W+lGjiKe6q8rsEyIHeU16FrfsyTSnTBpv0QEy61DSnsqax3nLO7Cqqc+YQx5vAWpkPpMU7qjntA5t2fTMPVxw/DMNPQl6HKd+FQtRlhLPvJqOTlIrvNvup6h5TCaRgnpddkGBNtjDCBNNMP6+VI5ZQumt7tETjtrx/jNEV1pOZENs6FJ7hrgbODr0vY32NXRR0Wbz0IwNDRNSR1XPTPz3HE795BhVTW8oG5G/GXt77CnW+vBWBoNeWpfdlTWmhON3ZCh7EnXVtjWfvH0/DSD46zDMCLpwxyXO/cmIYrjxsKABjey3gYyg9+2bsqBirV8aSjk3Y/yK+SZEJB0e9X/usLvLVqNwBgy/4aK2fsS1/u8N0GcE4j/vkNY4r7Hx9txLceWYBP1pdZszGH9bE11v4eY+NaFLmCaGeN7ht4Dio0jeGjm07EPy5VpxFsSoEPQo3QBceka3qo1Ne6syuIdpAb1dDTzCySSHLEIhrqEilPbmy3FhQA9td4NeliJudgTQL9uuZbfVZRbtTyvg7olo/ivCi+2HIQNQ1JHC+VwZYlTW5D+s9SgCzgX4xEhcpjnNQNjbHKSBvSoxC//tpoFKg0xs1cHbSjENEYbpgxAq/9yM6Pn+6Ol/uEAd3zccroPqipT6KuIWXFfsjOCNlLrCo+I/jeE4vbrGyTWlMHxm20PHz5JM86J43shSPNdCxhcHskhId5w17vFJ6bt1buwsNSOiIZXeehvQ2yjE0EExXlOjtLuXzlefd/htKDxhRyQ0rH719bhYWbDwCwk+QDdtStMP5LCnMcXju5rxaGWEer+BMGZ4GPxhtSjDFrsLHhttPxp3PHOYyFvGgEp43th3V/PB0jTUPSUS5aMiLFQKWmIeXopIVh3NOcml4myYR+++oq5Xn9Z6EzeGlHeV2ogh2cO9uwaJtvmwZ2dX3S+lxuN/K+hRY+N6pZ3r2B3e2gxg23nY6Zo/ukPRcVQ3oUOrzzMmQYZ5+jhnRHQU4EPzjJ1nrLXlLhMRaXXMh2cmOa1V6r65OIRTS8umwnxpkSCsDoLxMKLX1cIUMQHuMDNQ0oyouim2ncFuRGLJnFLWeMQr+u+ThQU4+a+iS65scsI1euYOmumuh+f8FRg0JnpvDTGDck9UCJGmWlaBo/PuUwjB1g64jT+TZkKVBOREOPwhx8tbsKy0orrNgPEfj+4Vd7HV5it3zzuUXbrWwnb63ajQ++anuZrgAyjDs07s5FeN1k6hK6Q/cm8/R3j/YsK/OpeDTzro99q+gJvvfkEkegiMwtL67AqF+/Fbi9QE4m/vE6I2en26smj1pFIEtUYyivbcCTn9uGT1wxbVdWVQ/GgG4FOU6PMWSPcSc2jH0qqjWFaEQDY8zxEBavc6KaMl2VaiBVU590JKKPagy6znGwtgFHuIJK5HK08UTKar+3vOjM1iJ7jIN+7aTOHYM2McDaU2m0v/LahGXMy549kY92f3U9Nu6rwYWTBuKz2SdbgzI5n7PfvdpUhNa+M2rmm4uSwhys/v1pOGpICeb9/CR8fssMx+cxl2b2sc+2ADClFKZhXBlPKI2+ynjCt7Lk6H7F+OimE633ueb+6xIpFORErAwlhTlRHNanCKt/fyq+Nr4/8nIiqEvoqKk31hOe5jyXAfujkw91vL/5tJE43CwLHtEYvn38sMDrAhjGmOp7NSTN4LuAZqiKZaABXeMRzzW/vk2+ttEIc8waFEmG8Rn3fIKrHvvCkd9YTvm3bX8tbn5hOa7/z5dZPPvmgQzjDoxbSqHSLJYUxhxTfTKDunvTb5Ue9M9qsXGfNwVQGBqSOp5dZFZfShPcBKiNUXfOZjGF89/FpRjYPR9dcqO4/NihnrRHomqebFhXxpPolh8zK9/Z10ZEZsvrd0bD2F3gI5vkROyHnqw3VrVd2WMsAnbKaxP4/lNGuei8mIaahqShJdc5hvSwB4YlhTnYsKcanHNUxhMY9eu38I+PNirP6flFpdgTIm2a34yHMGAO1NSj3lxntxS8JB4ey0rLARhetx5dcq3BwIBueXjzhhPwyBXeGZ9scfIoo5zziD5d0qxJNIaB3QvQ1wwkFQiPab5LGhDRmDXTUV2fhKqL+WjdPu9C61j5jrYuSxYKcqIoKTANY3NwL6QJ+TEN8UQKNfVJFOZGLTlDrkvycOoYp5TnByceijdvOMF6rwqOc5MXjXi8zYAhP6lPpnBYQDtU7V+1LyIc4tL5DbTkS6sx5sjRLoKinVIK2zEhlw4X24Up0tTakGHcgXEPyGMRDddLo/2zjuiPbxw1yNcLlZ8TwWezT8Yp0tTtlrIaX9nE2t3p5RRuXlm6A4f96k3rfRixvqr8pCgSYe0nkcLGfdX46fPLUHqwDqeM7qPUtAnDWI7gBwzDCbANtSuOHYJLjh4sHc+rreosNIfHWOCn6VZpDi96aL4VgCeM5HfX2AUzuuXnoDqetAzPgd1tz+tRQ7qjqj6JfdX12G6mMLz9LUNbLuskh/csxIGaBszf6E2DJXhqwVZ8uHavb5opUZlxX1U9Vu6sAOCcebn9rbXYVVGH3RXO8xzdrxi9inLx9Qn9cXi/Ysw4vHESijBcNHkQlt86C4f0IsO4pRA64gbFrJXwGHNu91F9im29/A3PLPXdr0hn9snNJ+GTm09ySBYKciJW3+aW1eTFIqiOJ1FVn0S3gph1z7kNUVW6NMfnIQxjYWz/+9tTHMuTupGV4rA+RfjZrMPU+89Ax0ykR/Ss7uJVArlEd1Rj6NfV7ke7FwjDWJ3HWJ4ZEzNouY3MXtSSkGHcARHGhdtjHNEYfjJrJLbMORNLfn0K7r34SGgBVZUKciLo3y0fw6Uo4DvfWYeZd32MvZXedD1hU/XIXuEvthxw7iOEYbz9gDfllCjUcd7EAWAMWLe7yuGVK86Leop7AMB7awyNkztopEeh8RASnueIK7hDPGw6p8dYep1lT40q6b9xTO9x9lTW45WlRuYHlXa3W0EMy0or8LmpJ5e1ukJWVFGbwC4pV3V9MuXo2I8e3gMA8NnGMt9z/uVLK3HVv75Qeowr4wlrf/+evxWfblAb2Pd/uNFKFyiMollj+uKLX87EUUPUmSSyCWPMMzgkmhdRuluVq1oYr4AdxDf79FF48mqvvM2NMEwHlRRgUEmB12NsSSkinu2E7Kdbfsw6r97FTk93ugA7dx5cwX2XHImTRhq5zkUsgTv3uZBS5EYjnkA/gVvaQTQv8oBf0xi+J+VH727OPjz+2VZr2UHTI1ycF3V4jCtMvXFeO8gg0vbPkMiYpb85Bat+d6pHLyiPCOWO1y+iN6gDKq9LgHOOWIRZnowgw1hOwVMjPQjcnV9QFKvgrPvmeZYJD9zhfYuRF43g5aU7cenDC6zPi/JiSsNY4NYady80zkvk0T33yAGOz4Wh1hkNY7ldZTsY3O838psq/enzy/CH11YrvbWiupdIjTZA8hgLI7miLuHIaewOFjmkVyGK86KWTCiZ4paXWte5Q1evOoetZcEFdQR9inOxt6oeJYU5ge2U6DiIwbdqCjuiMeTFNHxvul2wJj8WtdIXBuHW4Lo9xsLQdcuT8mMRK5i6e2GOlSVD1riL9YLoU5yHG2aMwHVS0KHYjxho+vWaf3x9Dcqq65Eb1TC6f7FyncbmSyd8MPtWPymFvDiqMeRENRwz3BisCztioeTg2ldtxOgM7VloBb0Dhs0AeH8/vQ0+Q6kH7oAU5ERRmBt1eNlOGtnL6ojd+HmMhcf5qqnDMHlod0fwUmVdAnWJFBIpjvMnDgQAxAO8vbKuSK6YI2ePAIB73l/vu4+9lXF8599feMo9A7AioacMK1FKLfJimic9kswuV4W7EvNafeuYIVjwixkeL0i0ExvGcrsKuqaNIYxR+MdzxjrePzJvs2OwJZg42Fm2VpZSDOhmGAcVdQnsrLA77/s/NPIeiwFbfo4dCAUYg7873l4LzjnOuf9Tx5S2ymO8eX+w7v7np40CYBj+eyvj6F2kvkeJjofsnFDx1R9Ox+zTR1lT2fk5EQwqKcDfLpoQuJ1bEyx7jIvyohhqGtdurac8xd2twD43d6xJGCnDj085DDedOsqznSVBSxNLkhvTcNSQEk/AotjPtdOHK7YiGoNw/vRxzQwITji0p1U5U8hvhHJC1Yb3VtajIBbB6H7FmLehzOqbK6SMO45KeT55q1sTMow7MMJ+OaRXIf511RRfPajbc9C9IIbnv3es9b5v1zw8/73jMKqvPYJ/fcUu3GqmuxrT3zAag9Ktrd5pZwCQjRi3QfPy0p34bIN62vqReZst6YObb04ehHk/PwlHDOqmHPmWHqyzjK6oxhwp6v7z3WMsrfBoM7q6h3nDRyOao8MQxpW4lp1RYyxPpWY76MVPSiEzYVA3z7I3zKp2Mn8+bxxumDHCet+/q2wY2x7jSimX9b/nG1OCQv8Z1ZjV+fcpzsX0w3rh6YXbsGRbOZaXVuBVs4gHYHubfzbrMMwwg9m2mF5rOVe4zNXHD0NUY7jj7bV4b81eR55bomNzWJ8iHHdID/x4plpLKxB9jLjvzp4wIGh1lFW5DN6obPDG0Nfsz4R0RyB7grsXxHDcIYZ3tzjfqUXOZDC86Fczrdd50YgVtJzO65xrHsMdsCi46jg788U3Jw1SrkOE45uTBuGBb010FGuR0TSG934yHdeddAh+corRVkUWm+7yzLPpYFuy7SDyc6JWSjhRFKnCLM4Ui2oOW6EtFgEhw7gDY1e0Cf6Z3YFNPbrkYrKiQpZcLOFfn27Bc4tKcfrYvjhjXF9EteByoIvMAhuAHYwE2NWeZESAkhu3HlmGMWZNj5853i5rOaBbPqYMLcHVxw+zOvRYRMO9Fx9prXPsIT1wwVEDEdWYZfh29/HmvHXjNCz61UxJSpE+i0ZHwy8XbjYQhrFb/yg4b+IApfGsyt9akBvBGGk6VvacCc9sRV3C0R4Fw3oaBipjtmHcNT+GKcNKUBVP4sttBz3biCC+o4aU4FpTh2cZxj5R9jlRzTG4+s3XRivXIzoeOVENT3/3GBw9PFhDLtpHmKA2ANjqmqWQHSLd8nOs1GoXHDXQsZ58f3TLz8EjV0zGgl/MSJu7OAhZppefE7F07AVp+pDdihgWGfmR9ZcLxoc+H8KLpjGcNrZfYEGwnKiGm04dZWWhEB5fuX7AUDMTysHaBMqq662ZZJGfW8h0GpK6w1Zoi2WjyTDuwIgOMV3mAHdWCr+SnA2u6a9YhOFXXxsNxhjyY5FAw1jWGtWa8gnOObYd8GowhectpXPMXbvXKpywYodtMAcldJ9zvl2N6eXrpuK57x2LEX2KLM94LMI8Oqc7v3EE1t92uhVM4Lf/LrlR9OySi4unDEb/rnk4d+JA5XodmUJF5alsIWxEd8APAGyZcybuunCCsjCA4L2fTLcemlFNcwzm5Ad6sSmVqKhLKGUYwkumSYZxcV7MMqg/VcxqiJRueTHN8oxtMg3jQ3unL3rw1HeOVn5vomMj9zXfnurNASyMEFnC4M7bDhh90xGDumH26aM8nwmK82PoXpiDLXPOxDcnD/Zdr2/XPOTnRHyn1wEoSzO7kUvG58Ui1ndQDa7FLA3grVjpJkxVV6L5EHJFebZZyC0E+TkRTBlWYjke9poGcjyRchjDYYP2W5Lme8IRrY7QSbo9CG7cRqDK+wYAVxw3FPFECo+b082rfneaJU/IjUV8twOA8toG5MU0xBO6lc/wsc+2YOUOQ2LRs0uuXRFn5W6MG9AVq3dV4t4PNuDp7xyNiMaQSHGcNLIXPly7DyeM6IVbTh+FU+7+2HOs3GgExw7vgfmb9luSCMDWr+ZENd9cmGIqW5U3V2ZQSQE+U+jfOgOZlH3NlEHdCzC8VyFuO2ec7zqRgEHRob27ID8WQU1DChHGrHyut57l9MRGNIbivCgO1DR4PMZXHz/MqkYnSymK82OWobBg8wEU50Wt4hwAsMecms6NRqwHuygB7X5oTDusFy492mmYkL64cyJm9AZ0y8dvzvKfMZDvu+K8mKOUOGCk3XrluqmBx+pW4J95RFRq/MPZY9Jq/T+dfTKKFca5G3k2Mj8WsYz87orzmPfzk7H9QC0iGnMEyqqgQjStyy/OOByz/7scYwbYM3JHDy9BWXU9lpXaDqzivBh2lNfhD6+txrurjVSa9S6PcVuUUpBh3IGZZqbC+XoaTZromIXh6hdEN6BbPn5/9lg88flWnHJ4H0fnmZ+jeTTGO8rr8NHafbh4yiAcrG3AuAFd8cWWg9aU8wuLS61137j+eEz50/sADC/b959aYkVDV8YTlsf5m5MH4cO1+7CnMo4RfYpwxbFDHMaJ4OErJmFvVb3DsyCM3ZyI5hvZfNXUoSjMjeIbR3U+T3BYmlNKkZ8TwQc/PTFwHfFAvv7kQ1FW04CnFzhLOE8f2QtvrNiNSISha24M6/54uvJBP6xnITbuq0Z13MjbWl6bwBEDu+LXXxuN/dX1iEU0nD6ur2U4V8eT6F0s0vilMPXQHo70ayKFYZ5U0lcg9JqCx135W4H0XjKiYyJmLPp3C54tKIjZ913X/JgjmwoQLogpyDA+YOacLfEJ0pZxZ6rwQzaMc6IapgwrwWXHDHGUyRbkxSIY0SdcOWmx1+YcpBP+HDO8B+bedJLDqTCsZxe88sPjsXR7uTUAKs6PYt6GakeV0fpECmVVdn7jtihHJMO4AxOLaPjqD6elDWgSQvoehbnYUV6XtoNd+4fTPbrk/FjEmh55a+Uu9O+WjzvfWYeP1+3DhEHdUF6bwGFDi7BpXw22mB5s2ZBWJf0WOrMl28rx0MebEIswTDK1z0In97uzx3q2AwzjbZjLgDu0dyGG9yzEb84abclL3J7jaERzFPIgvDSnYRyGorwY1t9mtEHGmGUYv2x6y+66cAJumFFrRcD7eb9G9CnCGyt2IS8WQWFOFOW1CWvA1KNLLm471/BaHzGwGwBjwNa7yDZe3MaBkFKo2nL/bvn46g+nBZY998vbSnRsRvcvRtf8GG4+zV8CAQB5OXY7VrUVd1VPmcP7FWPNrsrANnbyqN54e9UejPfJQ9wY3DK+WETDH85R99mZIDJvqKQnRMsh57EXUhg5OLprfswzkxxP6Hhq4TZ0K4jho5tOapP9HhnGHZwwOR9FwNmNM0fgpheWB3awgNrQyItFEE+msGTbQXzvySUY3qsQvUyv2VurdqO8NoFuBTkY0qMAW8pqUZ9MWfri2aePQtf8GP7vG0fgp88vs/YpRp0PfbwJgNHx9+ySi9d+dHyjKnQd2rsIH/zsROv9/ZdOdARnEeHwC4xrSVRSF9Eh58UiGNk3vedp8tDueGFxKWobUjjvyAHIiWq47qRDPeuN6mfs6+tH9Ef3ghhiEUPW08PlFRYaeJHA/tUfTsWqnZU4/tCe1nkB/p5hKmvbOelWkINlv52Vdj05G4Q7UwQAXDjJf5briaun4O1Vux1Vy7zbD8LXjxiQ1cpyYdr0L884PFR+ZpmCnGgopw/RvMgDH1W/JhcNOnN8P6zeWYl4MoVVOypw3CE92qRRDJBhTAA48bBeWPbbWSjOi+KmF5bjvInB0gsVebEIahtSuP/DjQCMKTQhcfho7V5Um2VGh/YoxPxN+7FyRwUSKY6/XzLRyiJx/lEDcfvbX1meNz9EGpimcsa4fulXIjykiyhvy3x800mIRY3O/MJJg7B0ewX+s3AbdlfG8fR3j1FuE4toWPW7U5EXi4AxhtxoBIlUEj0Kc/DBT6cjNxbB1DkfWOsLj/H4gd0w3vQ2C9798TRP7s+/XzIRO8rDFQIhOh83nzYSj87b7DAyr5k2HG+v2oOi3Ciq6pN4+bqpGBfQL/bskotLjx4SeBzGWKuUW/7utMblJKZCH62P7CRzy8cAO8g5J6Lh75dMxOz/Lsdry3ehpiGZVuLZmjTbE44xdhqAvwGIAHiYcz6nuY5FNA3GmDVyW3HrrNBpgWTyYxF8tG6f9b4qnrSySwgxfklhDjgHXvxyB87/x3wA8Ezb/e9Hx+NATQOemL8VT7m0o/19cloSLUtj2kdbYbDkmWKM4QcnHoL/LNyGZJpZElk+Ikqa9+ySi+GumYuoxgI96ioNpZxekCDc/ODEQ/GDE50zGUcNKTEySzw4Hws2H0BhTiRt9qHW4pppw3HCiJ5N2gd5htsuP5t1GF5bvks5UDnMTFMpMlqNG9gVz3yxHQAwMqSevDVoFsOYMRYB8HcApwAoBfAFY+xVzvnq5jgekT2K8ho3tXHJ0YMtw/iqqUPxr0+3ADCmt0Vk/iG9uqBLrjM/5UBX9HHvojz0LspDFzPAKqoxXDt9OE4b088qPU20LuIBPPPw3q18JgZPfefoRhdaGdg9H78/ewxOGhn+uwzpUYB1e6pxrBRQt+y3s7BxXzWGlBSQJIJoMe695Ej8b9muNl0Y5hdnHN6k7Vf//lQw0D3VVvnhySPww5NHKD877pCeGNKjAJcdY8xWnDmuH3750koAwMQh3VrqFDOmuTzGUwBs4JxvAgDG2DMAzgZAhnEH5dQxfVGQE0FOVHNMr5w0srdlGB/Wpwv6dc1Dl9woquuTGFSS72tEHDu8Bx78aBMe+NZRmDm6T0t8BSIDvvz1Ka0ehCeYemjjvVGMMVx+7NCMtnnkisk4WNvgyPHaNT/mKUFNEM1N76I8XH18xw5AK2jGvOlE8xLRGD666STrfbeCHIzo3QXr91YH6t1bm+ZqcQMAbJfelwI4upmORbQR5pt5fXdXxPHgR0bA3IzDe+Pu99YBMCL9e3TJxYpbZ6GiLhE49XfiyN74dPbJodMCES2LX2XAzsCgkgIMKsksWIggCIIwsge5U7u2NZrLMFZZPI65TsbYNQCuAYDBgyk9VkdA6JS75sew4tZZmL9xP8YO6IqnvnO0Y6qPMYZuBekNKzKKCYIgCKLjUJgbbTOzjX40l6K9FMAg6f1AADvlFTjnD3HOJ3HOJ/Xq1auZToNoLYryYpg1pi8AY6o7qLQoQRAEQRBEW6C5DOMvAIxgjA1jjOUAuAjAq810LIIgCIIgCIJoMs3iz+acJxljPwTwNox0bY9yzlc1x7EIgiAIgiAIIhs0m9CDc/4GgDeaa/8EQRAEQRAEkU0oazZBEARBEARBgAxjgiAIgiAIggBAhjFBEARBEARBACDDmCAIgiAIgiAAAIxznn6t5j4JxvYB2NpKh+8JoKyVjt3RoWvbPNB1bV7o+jYPdF2bF7q+zQNd1+alta7vEM65sohGmzCMWxPG2CLO+aTWPo+OCF3b5oGua/NC17d5oOvavND1bR7oujYvbfH6kpSCIAiCIAiCIECGMUEQBEEQBEEAIMMYAB5q7RPowNC1bR7oujYvdH2bB7quzQtd3+aBrmvz0uaub6fXGBMEQRAEQRAEQB5jgiAIgiAIggDQDg1jxtggxtiHjLE1jLFVjLEbzOUljLF3GWPrzf/dzeU9zPWrGWP3SfspYowtlf7KGGN/9TnmUYyxFYyxDYyxexhjzFx+t7T9OsZYefNfgeYjW9fW/Oxi85otZ4y9xRjr6XNMv2s7jTG2hDGWZIxd0NzfvTlpY9e1Q7VZIOvX95vmtV3FGLs94JjUblv2ulK7ZewUxthi8/osZoydLO1Led0Ux6R227LXldpt8PW9jTG2nTFWneaYLdtuOeft6g9APwATzddFANYBGA3gdgCzzeWzAfzFfF0I4HgA3wNwX8B+FwOY5vPZQgDHAmAA3gRwumKdHwF4tLWvT1u4tgCiAPYC6Gm+vx3ArZlcWwBDAYwH8DiAC1r72nSU69rR2myWr28PANsA9DLf/xvADGq3rX9dqd1yADgSQH/z9VgAOzK5btRuW/66UrtNe32PMfdXneaYLdpu253HmHO+i3O+xHxdBWANgAEAzobR4cL8f465Tg3nfB6AuN8+GWMjAPQG8Inis34Aijnn87nxSzwu9u3iYgD/ady3ahtk8doy86/QHNkVA9jpPl7QteWcb+GcLwegZ/M7tgZt6bq6aPdtFsjq9R0OYB3nfJ/5/j0A57uPR+225a+ri87abr/knIv7fRWAPMZYbtjrRu225a+rC2q30vU1P/ucc74r6Hit0W7bnWEswxgbCmM0sgBAH3GBzf+9M9jVxQCeNS+6mwEASqX3peYy+TyGABgG4IMMjtmmacq15ZwnAHwfwAoYhttoAI8oVk17bTsabeW6dsQ2CzS5T9gAYBRjbChjLAqj8x2kWI/abStdV2q3FucD+JJzXo/w7ZHabStdV2q3FvL1DUuLt9t2axgzxroA+C+AGznnlU3c3UXwH8mpNEVuA/oiAC9wzlNNPI82QVOvLWMsBsOAOxJAfwDLAdyiWlWxrMOmSWlj17VDtVmg6deXc34QxvV9Fsbs0RYASdWhVJtnerz2Qhu7rp2+3TLGxgD4C4BrxSLFaqr2SO02eP3mvK7Ubr3XN/ShFMuatd22S8PYNBD+C+ApzvmL5uI9pstduN73htzXEQCinPPF5vuIJJb/PYzRyUBpk4HwTl8HGdbtiixd2wkAwDnfaHrhnwNwXCOvbYegDV7XDtNmgez1CZzz/3HOj+acHwtgLYD11G7b1HXt1O2WMTYQwEsALuecbzQXK68btds2dV2p3Xqvr9++W73dtjvD2NRWPgJgDef8LumjVwFcYb6+AsArIXfp0P1wzlOc8wnm32/MKYEqxtgx5rEvl/fNGBsJoDuA+Y3+Um2ELF7bHQBGM8Z6me9PMfeZ0bXtKLS169qR2iyQ3T6BMdbb/N8dwA8APEzttm1c187ebhlj3QC8DuAWzvmnYmW/60bttm1cV2q36uvrR5tot7wNRDlm8gcj6pnDmEZeav6dASPy+X0A683/JdI2WwAcAFANY/QxWvpsE4BRaY45CcBKABsB3AcYhVHMz24FMKe1r0tbu7YwItPXmPv6H4AemVxbAJPN/dUA2A9gVWtfn45wXTtam22G6/sfAKvNv4sCjknttgWvK7VbDgC/MtvVUumvd7rrRu229a4rtdu01/d2s93p5v9b20K7pcp3BEEQBEEQBIF2KKUgCIIgCIIgiOaADGOCIAiCIAiCABnGBEEQBEEQBAGADGOCIAiCIAiCAECGMUEQBEEQBEEAIMOYIAiCIAiCIACQYUwQBNEkGGM3MsYKGrHdlYyx/n77YYy9YSbHb3YYY3MZY5PSrNOo70kQBNGeIMOYIAiiadwIICODkTEWAXAlgP7SYsd+OOdncM7Lm3x22eNGZPg9CYIg2htkGBMEQYSEMVbIGHudMbaMMbaSMfZbGMbth4yxD811/sEYW8QYW8UY+5207RbG2G8YY/NglKKfBOApxthSxtgNiv1sYYz1ZIwNZYx9xRh72DzmU4yxmYyxTxlj6xljU6Rze5Qx9gVj7EvG2NkB3yOfMfYMY2w5Y+xZAPnSZ57zZ4xdrzi/WYyx+YyxJYyx5xljXbJ5rQmCIFoDqnxHEAQREsbY+QBO45x/13zfFcAyAJM452XmshLO+QHTK/w+gOs558sZY1sA3M85v91cby6An3HOF5nvt7j2swWG8dwFwAYARwJYBeAL85hXA/g6gKs45+cwxv4EYDXn/ElTgrEQwJGc8xrF9/gJgLGc828zxsYDWALgGM75ojTnP4lzXsYY6wngRQCnc85rGGM/B5DLOf99dq40QRBE60AeY4IgiPCsADCTMfYXxtgJnPMKxToXMsaWAPgSwBgAo6XPnm3kcTdzzldwznUYxvH73PBqrAAw1FxnFoDZjLGlAOYCyAMw2Gd/0wA8CQCc8+UAloc8f8Ex5vJPzeNdAWBII78bQRBEmyHa2idAEATRXuCcr2OMHQXgDAB/Zoy9I3/OGBsG4GcAJnPODzLGHoNhoAo83tuQ1Euvdem9DrsfZwDO55yvDblPz3RhiPO3VgXwLuf84pDHIgiCaBeQx5ggCCIkZhaJWs75kwDuBDARQBWAInOVYhjGbwVjrA+A0wN2J2+nep8pbwP4EWOMmed6ZMC6HwO41FxvLIDx5vKg85fP73MAUxljh5r7KGCMHdaEcycIgmgTkMeYIAgiPOMA3MEY0wEkAHwfwLEA3mSM7eKcn8QY+xKG3GETgE8D9vUYgAcYY3XmPh6S99OIc/sDgL8CWG4ax1sAfM1n3X8A+BdjbDmApTD0yOCcLws4f8f5McauBPAfxliu+fmvAKxrxHkTBEG0GSj4jiAIgiAIgiBAUgqCIAiCIAiCAEBSCoIgiA4LY+xUAH9xLd7MOT+3Nc6HIAiirUNSCoIgCIIgCIIASSkIgiAIgiAIAgAZxgRBEARBEAQBgAxjgiAIgiAIggBAhjFBEARBEARBACDDmCAIgiAIgiAAtJF0bT179uRDhw5t7dMgCIIgCIIgOjiLFy8u45z3Un0WyjBmjP0YwHcAcAArAFwFoADAswCGwig9eiHn/KC5/i0ArgaQAnA95/ztoP0PHToUixYtCnMqBEEQBEEQBNFoGGNb/T5LK6VgjA0AcD2ASZzzsQAiAC4CMBvA+5zzEQDeN9+DMTba/HwMgNMA3M8YizT1SxAEQRAEQRBEcxJWYxwFkM8Yi8LwFO8EcDaAf5uf/xvAOebrswE8wzmv55xvBrABwJSsnTFBEARBEARBNANpDWPO+Q4AdwLYBmAXgArO+TsA+nDOd5nr7ALQ29xkAIDt0i5KzWUEQRAEQRAE0WZJqzFmjHWH4QUeBqAcwPOMsW8FbaJY5qk7zRi7BsA1ADB48OAw50oQBEEQBBGKRCKB0tJSxOPx1j4VopXIy8vDwIEDEYvFQm8TJvhuJoDNnPN9AMAYexHAcQD2MMb6cc53Mcb6Adhrrl8KYJC0/UAY0gsHnPOHADwEAJMmTfIYzgRBEARBEI2ltLQURUVFGDp0KBhT+eyIjgznHPv370dpaSmGDRsWerswGuNtAI5hjBUwo2XNALAGwKsArjDXuQLAK+brVwFcxBjLZYwNAzACwMLQZ0QQBEEQBNFE4vE4evToQUZxJ4Uxhh49emQ8YxBGY7wAwAsAlsBI1abB8PTOAXAKY2w9gFPM9+CcrwLwHIDVAN4CcB3nPJXRWbVhdJ3jiflbEE90mK9EEARBEB0SMoo7N435/UNlpeCc/5ZzPopzPpZzfpmZcWI/53wG53yE+f+AtP5tnPNDOOcjOedvZnxWbZi3Vu3Gr19Zhf97Z21rnwpBEARBEG0Mxhh++tOfWu/vvPNO3Hrrra13Qo2kvLwc999/f2ufRotDJaEzpDqeBAAcrE208pkQBEEQBNHWyM3NxYsvvoiysrLWPpVGk0qlyDAmMoMmZwiCIAiCcBONRnHNNdfg7rvv9ny2detWzJgxA+PHj8eMGTOwbds2AMCVV16J66+/HscddxyGDx+OF154wdrmjjvuwOTJkzF+/Hj89re/VR6zS5cu1usXXngBV155JQDg+eefx9ixY3HEEUdg2rRpAAyj96abbrL2+eCDDwIA5s6di5NOOgmXXHIJxo0bh9mzZ2Pjxo2YMGECbrrpJlx22WV45ZVXrONceumlePXVV/HYY4/h7LPPxmmnnYaRI0fid7/7nbXOk08+iSlTpmDChAm49tprkUq1fRkqGcYEQRAEQRBZ5LrrrsNTTz2FiooKx/If/vCHuPzyy7F8+XJceumluP76663Pdu3ahXnz5uG1117D7NmzAQDvvPMO1q9fj4ULF2Lp0qVYvHgxPv7449Dn8fvf/x5vv/02li1bhldffRUA8Mgjj6Br16744osv8MUXX+Cf//wnNm/eDABYuHAhbrvtNqxevRpz5szBIYccgqVLl+KOO+7Ad77zHfzrX/8CAFRUVOCzzz7DGWecYW331FNPYenSpXj++eexaNEirFmzBs8++yw+/fRTLF26FJFIBE899VTjL2oLESZdG0EQBEEQBBGS4uJiXH755bjnnnuQn59vLZ8/fz5efPFFAMBll12Gm2++2frsnHPOgaZpGD16NPbs2QPAMIzfeecdHHnkkQCA6upqrF+/3vL+pmPq1Km48sorceGFF+K8886z9rl8+XLLK11RUYH169cjJycHU6ZM8U1tNn36dFx33XXYu3cvXnzxRZx//vmIRg0z8pRTTkGPHj0AAOeddx7mzZuHaDSKxYsXY/LkyQCAuro69O7dW7nvtgQZxhnCvbVKCIIgCIIgHNx4442YOHEirrrqKt915KwJubm51mvOufX/lltuwbXXXht4LHk/cnqyBx54AAsWLMDrr7+OCRMmYOnSpeCc495778Wpp57q2MfcuXNRWFgYeJzLLrsMTz31FJ555hk8+uijyuOL95xzXHHFFfjzn/8cuM+2BkkpGgllgCEIgiAIwo+SkhJceOGFeOSRR6xlxx13HJ555hkAwFNPPYXjjz8+cB+nnnoqHn30UVRXVwMAduzYgb17jXpqM2bMwI4dOwAAffr0wZo1a6DrOl566SVr+40bN+Loo4/G73//e/Ts2RPbt2/Hqaeein/84x9IJIwkAuvWrUNNTY3n2EVFRaiqqnIsu/LKK/HXv/4VADBmzBhr+bvvvosDBw6grq4OL7/8MqZOnYoZM2bghRdesM73wIED2Lp1a/oL18qQxzhDODmMCYIgCIIIwU9/+lPcd9991vt77rkH3/72t3HHHXegV69elmbXj1mzZmHNmjU49thjARhBdk8++SR69uyJDRs2oKSkBAAwZ84cfO1rX8OgQYMwduxYy5C+6aabsH79enDOMWPGDBxxxBEYP348tmzZgokTJ4Jzjl69euHll1/2HLtHjx6YOnUqxo4di9NPPx133HEH+vTpg8MPPxznnHOOY93jjz8el112GTZs2IBLLrkEkyZNAgD88Y9/xKxZs6DrOmKxGP7+979jyJAhjb2cLQLjbcDSmzRpEl+0aFFrn0Yonlm4DbNfXIFvThqEv1wwvrVPhyAIgiAIBWvWrMHhhx/e2qfRLKxcuRKPPvoo7rrrrhY9bm1tLcaNG4clS5aga9euAIDHHnsMixYtcgwA2hKqdsAYW8w5n6Ran6QUGdL6wwiCIAiCIDozY8eObXGj+L333sOoUaPwox/9yDKKOyIkpWgkpDEmCIIgCKKzMHPmTCvvssyVV15p5U3uCJDHmCAIgiAIgiBAhjFBEARBEARBACDDOGPaQKwiQRAEQRAE0QyQYdxISGNMEARBEATRsUhrGDPGRjLGlkp/lYyxGxljJYyxdxlj683/3aVtbmGMbWCMrWWMnRq0f4IgCIIgiI5IXV0dpk+fjlQqhS1btuDpp5/2XXfnzp244IIL0u7zuOOOUy6/8sorrTLPQTz++OMYO3YsxowZg9GjR+POO+9Mu42b8vJy3H///Rlv11RWrFjR7IF+aQ1jzvlazvkEzvkEAEcBqAXwEoDZAN7nnI8A8L75Hoyx0QAuAjAGwGkA7meMRZrn9AmCIAiCINomjz76KM477zxEIpFAwziZTKJ///6hDNvPPvus0efz5ptv4q9//SveeecdrFq1ypGPOBNawzBOJpMYN24cSktLldkxskWmUooZADZyzrcCOBvAv83l/wZwjvn6bADPcM7rOeebAWwAMCUL59om4FYmY9JSEARBEAThz1NPPYWzzz4bADB79mx88sknmDBhAu6++2489thj+MY3voGzzjoLs2bNwpYtWzB27FgARtGMs88+G6eddhpGjhyJ3/3ud9Y+u3TpAgDgnOOHP/whRo8ejTPPPNMqvRzEn//8Z9x5553o378/ACAvLw/f/e53AQAnnngiRLG1srIyDB06FACwatUqTJkyBRMmTMD48eOxfv16zJ49Gxs3bsSECRNw0003gXOOm266CWPHjsW4cePw7LPPAgDmzp2L6dOn48ILL8Rhhx2G2bNn46mnnsKUKVMwbtw4bNy4EQCwb98+nH/++Zg8eTImT56MTz/9FABw66234pprrsGsWbNw+eWXAwDOOussq6x2c5BpHuOLAPzHfN2Hc74LADjnuxhjvc3lAwB8Lm1Tai7rEIjgO9IYEwRBEAThR0NDAzZt2mQZmHPmzMGdd96J1157DYBh/M6fPx/Lly9HSUkJtmzZ4th+4cKFWLlyJQoKCjB58mSceeaZVqllAHjppZewdu1arFixAnv27MHo0aPx7W9/O/CcVq5ciaOOOiqj7/HAAw/ghhtuwKWXXoqGhgakUinMmTMHK1euxNKlSwEA//3vf7F06VIsW7YMZWVlmDx5MqZNmwYAWLZsGdasWYOSkhIMHz4c3/nOd7Bw4UL87W9/w7333ou//vWvuOGGG/DjH/8Yxx9/PLZt24ZTTz0Va9asAQAsXrwY8+bNQ35+PgBg0qRJmDNnDm6++eaMvkdYQhvGjLEcAF8HcEu6VRXLPLkcGGPXALgGAAYPHhz2NAiCIAiCIDLid/9bhdU7K7O6z9H9i/Hbs8b4fl5WVoZu3boF7uOUU05BSUmJ72c9evQAAJx33nmYN2+ewzD++OOPcfHFFyMSiaB///44+eSTM/8SITj22GNx2223obS0FOeddx5GjBjhWWfevHnWufTp0wfTp0/HF198geLiYkyePBn9+vUDABxyyCGYNWsWAGDcuHH48MMPARhV9VavXm3tr7KyElVVVQCAr3/965ZRDAC9e/fGzp07m+W7AplJKU4HsIRzvsd8v4cx1g8AzP/Ch18KYJC03UAAnm/AOX+Icz6Jcz6pV69emZ85QRAEQRBEGyU/Px/xeDxwncLCQt/PmGtq2v3eb1kQY8aMweLFi5WfRaNR6LoOAI7zvuSSS/Dqq68iPz8fp556Kj744APPtjwgl21ubq71WtM0672maUgmkwAAXdcxf/58LF26FEuXLsWOHTtQVFQEwHuN4vG4w1DONplIKS6GLaMAgFcBXAFgjvn/FWn504yxuwD0BzACwMKmn2rbgNIYEwRBEET7Isiz21x0794dqVQK8XgceXl5KCoqsrygYXj33Xdx4MAB5Ofn4+WXX8ajjz7q+HzatGl48MEHcfnll2Pv3r348MMPcckllwAAbrnlFkyZMgXnnnuuY5tbbrkFN998M1577TX07dsX9fX1ePDBB3H99ddj6NChWLx4MaZMmeIIAty0aROGDx+O66+/Hps2bcLy5ctxxBFHOL6LOJcrrrgCBw4cwMcff4w77rgDX331VajvOmvWLNx333246aabAABLly7FhAkTlOuuW7fO0mI3B6E8xoyxAgCnAHhRWjwHwCmMsfXmZ3MAgHO+CsBzAFYDeAvAdZzzVDZPui1AEmOCIAiCIIKYNWsW5s2bBwAYP348otEojjjiCNx9991ptz3++ONx2WWXYcKECTj//PMdMgoAOPfcczFixAiMGzcO3//+9zF9+nTrsxUrVqBv376efZ5xxhm47rrrMHPmTIwZMwZHHXWU5bX92c9+hn/84x847rjjUFZWZm3z7LPPYuzYsZgwYQK++uorXH755ejRowemTp2KsWPH4qabbsK5556L8ePH44gjjsDJJ5+M22+/XXl8P+655x4sWrQI48ePx+jRo/HAAw/4rvvhhx/izDPPDL3vTGFB7u+WYtKkSVxEQrZ1nvh8K3798kpcevRg3HbuuNY+HYIgCIIgFKxZswaHH354q57Dl19+ibvuugtPPPFERts99thjWLRoEe67775GHffUU0/F22+/3aht2zL19fWYPn065s2bh2g0nOhB1Q4YY4s555NU61PlO4IgCIIgiGbgyCOPxP+zd91xchNn+xlJW6763LuxMaYYjA02vdh0CEkgtBB6SQgtBEjofEBCCaEnIbSEGnqHQDDYxgaMDZhiXMC99/P5+m2TNN8fo5Fm1HbvvOezfXp+P/t2tdKsdnc0euaZ533fww47DIaxZRfOt0dSDAArVqzA3XffXTApbgvar+XtFVuBwh4hQoQIESJE2DaQL4WaH84777x2r/C2LWLYsGG+WTGKiUgxbiOiPMYRIkSIECFChAjbFyJi3EpEenGECBEiRIgQIcL2iYgYtxEkyksRIUKECBEiRIiwXSEixq1EZDGOECFChAgRIkTYPhER4zYi8hhHiBAhQvth8vwN+O/37Vf2NUKELYFUKoWxY8fCMAwsW7YML774YuC+a9aswSmnnJK3zQMPPNB3+3nnnScV5vDD/PnzMW7cOIwaNQq77bYbLrroorzvV15ennef1uLXv/61XQJ6c9t/7733cOuttxbjtABExDhChAgRImyFOP/pGfjdS9919GlEiLBZeOqpp3DSSSdBVdVQYqzrOvr165eX2ALAtGnT2nw+V1xxBa666irMnDkTP/74I373u9+1ua22wjAM/Pvf/8bw4cM3uy1d13H88cfj3XffRUtLSxHOLiLGESJEiBAhQoQI7YIXXngBJ5xwAgDg+uuvx2effYZRo0bhwQcfxDPPPINTTz0VP/vZz3D00Udj2bJldqnjZ555BieccAKOPfZY7LLLLvjTn/5kt8kVVkopLr/8cgwfPhzHH388NmzYkPd81q5diwEDBtjPR4wYYb/f5Zdfbm//6U9/iilTptjP//CHP2DvvffGEUccgerqagCsWt3w4cOx55574vTTTwcANDU14fzzz8eIESOw55574o033rDP+ZZbbsF+++2H6dOnY9y4cRALu/m1v3jxYhx77LEYPXo0DjnkELu89HnnnYerr74ahx12GK677joQQjBu3Di89957hfwkeRER41Zia6gUGCFChAgRIkTwx3PTl+HbFbUdfRrIZrNYsmQJBg8eDAC4++67ccghh2DmzJm46qqrAADTp0/Hs88+i48//thz/FdffYUXXngBM2fOxGuvvQZ3heC33noL8+fPx+zZs/Gvf/2rICX5qquuwuGHH47jjjsODz74IOrq6vIe09zcjL333hvffvstxo4da5P0u+++G9999x1mzZpll3C+/fbb0aVLF8yePRuzZs3C4Ycfbrexxx574Msvv8TBBx9cUPsXXXQR/vGPf+Cbb77Bfffdh0svvdQ+ZsGCBZg4cSLuv/9+AMCYMWPw2Wef5f0shSAq8NFGRBbjCG3B5HkbsGPPMuzQvayjTyVChAgRtkvc8s5cAMAH5+7obPzgemDd7OK+UZ8RwHF3B768ceNGVFVVhTZx1FFHoVu3boGvde/eHQBw0kknYerUqRgzxqli/Omnn+JXv/oVVFVFv379bBIahvPPPx/HHHMMxo8fj3feeQePP/44vv/++9BjFEXBL3/5SwDAWWedhZNOOgkAsOeee+LMM8/EiSeeiBNPPBEAMHHiRLz88sv2sV27dgUAqKqKk08+ueD2m5qaMG3aNJx66qn2fplMxn586qmnQlVV+3mvXr2wZk1xYhIixThChC2I85+ZgbH3Tuno04gQIUKECO2MkpISpNPp0H3KyoJFEuKK8nc/D9qWD/369cMFF1yAd955B5qmYc6cOdA0DaZp2vuEnTd/z/fffx+XXXYZvvnmG4wePRq6roNS6ntOyWRSIrJhIITANE1UVVVh5syZ9r8ff/zR3sf9vaXTaZSUlBTUfj5EinErERkpIkSI0FmwqTmLw+6bgucu2BcjB1Z19OlEiNB2hCi77YWuXbvCMAyk02kkk0lUVFSgsbGx4OMnTJiATZs2oaSkBG+//Taeeuop6fVDDz0Ujz/+OM455xxs2LABkydPxhlnnAEAuOGGG7DvvvviF7/4hXTM+PHjccQRRyAWi2HdunWoqalB//79kUql8Mgjj8A0TaxevRpfffWVfYxpmnj99ddx+umn48UXX8TBBx8M0zSxcuVKHHbYYTj44IPx4osvoqmpCUcffTQefvhhPPTQQwCA2tpaWzUOgl/7lZWVGDJkCF577TWceuqpoJRi1qxZGDlypG8bCxYssP3Zm4tIMW4luMW4LbO0CBEiRNiWMH1xDepTOTz2yeKOPpUIEbZJHH300Zg6dSoAZj3QNA0jR47Egw8+mPfYgw8+GGeffTZGjRqFk08+WbJRAMAvfvELDBs2DCNGjMAll1yCsWPH2q/Nnj0bffr08bT50UcfYY899sDIkSNxzDHH4N5770WfPn1w0EEHYciQIRgxYgT++Mc/Yu+997aPKSsrw9y5czF69Gh8/PHHuOWWW2AYBs466yyMGDECe+21F6666ipUVVXh5ptvRm1trf0ekydPzvs5/doHWODik08+iZEjR2L33XfHO++8E9jG5MmTcfzxx+d9r0JQkGJMCKkC8G8Ae4CJphcAmA/gFQCDASwDcBqltNba/wYAFwIwAFxBKf2wKGcbIUKECBG2GKL5f4RtGemc0dGngMsvvxwPPPAAjjzySMRiMUyaNEl6/bzzzrMfDx48GHPmzLGf9+rVCw8//LCnzaamJgBMoPN7HQByuRwOOOAAz/YHHngADzzwgGc7IQQvvPCCb1v8/W6//XZpOyf8IsrLy/Hss88GtsEhZrwIan/IkCEYP368p61nnnlGer5+/XqkUik7w8bmolDF+G8AxlNKdwUwEsCPAK4HMIlSOgzAJOs5CCHDAZwOYHcAxwJ4hBBSmLEkQoQIESJsdYiS8UTYFrGxKdvRp4C99toLhx12GAxjy5L0Dz/sPHrkihUr7OwUxUBexZgQUgngUADnAQClNAsgSwg5AcA4a7dnAUwBcB2AEwC8TCnNAFhKCFkEYF8A04t21hEiRIgQIUKECCHQlK1jyeP8889HOmeipBUS4XnnnSepyRGCsc8++xS1vUIU4x0BVAN4mhDyHSHk34SQMgC9KaVrAcD628vavz+AlcLxq6xt2wUi4SRChAidBVsHrYgQoW1QtxJiXNeSw8INjWhI5Tr6VCIUgEKIsQZgbwCPUkr3AtAMyzYRAL+e6OGThJCLCCFfE0K+5lVOtiVE3rsIESJ0FtBIEoiwDUIhZKsoypXWDelvhC2Htvz+hRDjVQBWUUq/tJ6/DkaU1xNC+gKA9XeDsP9A4fgBADxZlymlT1BKx1BKx/Ts2bPVJx4hQoQIEdoXkQAQYVtGk05QU1PT4eSYX0ZbAUfvVKCUoqamBslkslXH5fUYU0rXEUJWEkJ2oZTOB3AEgB+sf+cCuNv6y/NovAvgRULIAwD6ARgG4CtvyxEiRIgQYVtAdEOPsC0hrinI6ia+3ADs0qcRHb0q3ZDKoSGtI5XUsKkkVvBx6ZyBprSO7uWJaJLaRiSTSQwYMKBVxxRa4ON3AF4ghMQBLAFwPpja/Coh5EIAKwCcCgCU0rmEkFfBiLMO4DJK6XazftDRM88I2y46qu80pHP4askmHDm8d4e8f4RtGexuHI16EbYpWB02pbOUXyLembkayZiKY3b35vhtL/xj0kLcP2EBLj9sJ/zxmF0KPm74LePRkjUw90/HoCwR1WPbUijom6aUzgQwxuelIwL2vxPAnW0/ra0fJApLidBKmB3ELq58eSY+nrcBU687DAO6lnbMSUSIECHCFgL3xJs+YsTvX54JAFh2d3GKQRQCxQoC1Ft5E8gZrERzpBZvWUSV79qIqKNGaC38BuktgWUbmwEA6ZzZIe8fYdsFH+eihbII2xI4/zS2kiGPp41r7T0gZ3CCX/RTihCCiBhHiLCF0GHkwp7ERaNrhNYhmv9H2BbBbWtuItpRdjaeNi7XRqbeUaJKZ0VEjFuJqH9GaCs6anCLyE2ECBE6E/hIa7ik1lQHlYjmirH7fAoF3UqU786CiBi3ERHZiNBadPSkqqPfP0KECBG2BPhYZ7gGvdqWjimwoamMarXWY8zh/hwR2hcRMY4QYQuhwxRjEmUWiLC52LK9x4xMlRGKANOkSGUNbGzKAABqm7Mdch7cSmEYbevXkZViyyIixhEibCG0dXC76a3ZGHHbh21+32h1I0JbYU+qtvB9ua3KWoQIoo/YpBSnPDYNY+6YCIClruwIqKRtWSk4ImK8ZRER41ZCLI2qGyamLtzYgWcTYVtCW4e2F75cgca0vvnvH42tEVqJjppUtdWLGSGCOM4ZJjB3TYP93Owory7h59O2E4jG7i2LiBi3EryDEgI8NHEhznryS3yxpKZjTyrCNoGOCqCIUgtG2Fxs6fuy3mEMJsK2DrGvupXWjlJeuYodKcbbBiJi3EqI3XOplR+W+5ciRAhDRw9uNHIZR2glOmpSFSnGEdoK0Urh7kcdNQY7eZXbSoyLeDIR8iIixq2E33UVTeYiFIKOS9fWMT7RCNsPtnT+14gYR2gNvl62CYOvfx9fL9sUqhh31Bhobq5iHF0PWxQRMW4luOpGCLF9Q1GXjVAIOqy+R2SliNBGRIpxhG0BUxexWJ9PF1S7PMbOE0pp0cWJxnQON701Gy3Z8BgQTmzbrhhH18OWRESMWwnbY4wo2j9C69DRg1s0tkZoK7a8xzjqrBEKB8/6YFAqWcbEMTdn0KKPgY9OWYwXvlyB56YvD92Pd+e2e4zDX6eUdlhVv+0RnZ4Yz1ldj5e/WiFtm7+uEWPvnYy6Fm/OQ7/OF3XICIUg6iYRIhSGSDGO0BoodmU5eZwVYzh10yy6OBFUYc8N/r48K8WkH9fj5w9PLbif5zvvITf8D9e+PqugtiLkR6cnxj/9x1Rc/+ZsadvDkxdheU0LPllQ7dlf7J8kWqOO0Ap0uGIcmX4itBId1WUjxThCa8ALaDDl1NkuVozTTVr0IDbOAPKJY7ZibBX4uPLlmZi1qh5NmcLScBYivr32zaqC2oqQHwURY0LIMkLIbELITELI19a2boSQCYSQhdbfrsL+NxBCFhFC5hNCjmmvk28vhNFdsXtGtDhCa9BR93o+gYsyYEVoLXif3dIEua35XiN0Tih2nmDZSiEqsrpRfLsB18byNcvf13T9VQokEdE8ccuiNYrxYZTSUZTSMdbz6wFMopQOAzDJeg5CyHAApwPYHcCxAB4hhKhFPOd2R1hnj7JSRGgrOspyw8fejlasI2x76Kg+a0S8OEIroCqMyuimrBiLY55umO2gGFtKtfX8she+xSszVnj24+fB97cnnAW+TzR2b1lsjpXiBADPWo+fBXCisP1lSmmGUroUwCIA+27G+2xxhBEJexsRCHS0RB2hAHTU2Mb7aTS4RmgtWnsDLxaiAh8RWgPVFrPku7GoGOdcanIxoLhEtPdnr8V1b8z27Oee6HGLR6FFnyLP/ZZFocSYAviIEPINIeQia1tvSulaALD+9rK29wewUjh2lbVtm0GYd5gKDyIrRYTWoMPyGNvEuEPePsI2DesGvoX7bjSHi9Aa2MF3ruwM4pjXHooxH1zzje2ioAZ4rRX5EF0PWxZagfsdRCldQwjpBWACIWReyL5+fNHzs1oE+yIAGDRoUIGnsWXgGOp9XvTNStGupxNhO0FHE9Moe0qE1qKjCyJEiFAIVDsrhawJi4Uxcu3hMbb+ulullEoCm/t97Up4IecjE/zoetiSKEgxppSusf5uAPAWmDViPSGkLwBYfzdYu68CMFA4fACANT5tPkEpHUMpHdOzZ8+2f4L2QEjhDir85R0/6rMRCkFHV77raGIeYdtDR/WZqK9GaA1UIcA4KCuFYRa/wIciBCSJRDaVM6T93P3ZHYTnB/GYsOshEjyKj7zEmBBSRgip4I8BHA1gDoB3AZxr7XYugHesx+8COJ0QkiCEDAEwDMBXxT7xLQG/Dsc7MqU0slJEaBUij3GEbQ0dFT8ReSojtAaKUOBD7LKyYmwWPTOPaFOTq+zJ+wWVpg4bksX2wsbuaFgvPgqxUvQG8JaljmoAXqSUjieEzADwKiHkQgArAJwKAJTSuYSQVwH8AEAHcBml1PBveuuEO9JUhF+HjvplhELQ0TP7iBhHaC06Kl1bR18rEbYtcI+x6U7XRik0hUA3KctYUez3FQLw9RAiawZEsYZNAMU2wq6HaFwvPvISY0rpEgAjfbbXADgi4Jg7Ady52WfXQSBBxiHIVgq3kT5ChDB0WB5j62/UTSO0Fh01tkWCcYTWQLXWvg13gQ+TMtJsUiv4rsgeY8FOKSu88n78uYcw+5zPxqYMNIUgpjoL+mHpC6NLpfjo9JXv/ODw4uBAO0odZTlChELQ0TP7jn7/CNsetpbgO90wpWXxCBFE2FYKlypMqeM/bo/gO/t9IFdr9Abb+XuK/U5nzB0TsdftEyR/dLgXObouio2IGFsQO3J4gQ/LYyxcflG3jFAINncAa/OgTqLguwhtAx/ntrTX2E2Cd7rpA5z06LQteg4Rth0oQto0cZw1TCplrCh6gQ8hfqMwxVjeHmSloFS+BiKP8ZZFRIwtiJ0r1GMs7B+S7rgo+GZ5LR6csKB93yTCFsPmDmBt5sXW30hZiNBahHWZWavqcM/4sMydbYcfX5i5sq5d3ivC9gPDVfnOoA4xzpnFt1I4WSnkojReZZhKf4P2ExEWzFdoGxHahogYWzB9FGM/SMqyvbF9zunkR6fhb5MWtk/jEbY4NpsYb/b7RwNohNYhLPjuhH9+jkemLG4Xi0N0s4/gh/dmrcG8dQ2e7by3mFRe3TAFxVg3aN4xOKMb9jj56YJqfLV0U+j+ouigGyHBd/ZKM1zbg9uOslJ0HCJibEHsW+FWCv5XtFJEPTNCfnSUlcJe7ouq7EZoJcL6HH8prEhBW9EebUbY9nH5i9/h2Ic+82y3K8kxZgwA0BQiKca6YYb25zV1Kexy83i8PIMV7j3nqa9w2uPTQ89H5AphCi8Pnisk+M4+RvIYB59DNIksPiJibEHuXNxK4e1wYgdtbytFhO0LmzuAba4wFw2gEVqLsHyrPFVVe+QcjlY3IrQGvLuIKdkUhTCPMQ++y+MxnruGKdETflhf8PtyK4U7+C6IALvFiUKtFIUWAolQHETE2EKYOixts4NRBC9y1DEjFIDNtkK0sQVnuW8zTyBCp0NY8B1PVaW3h5VCIBARSY6QD7x/mkK6Nk0hLCuFHXwX7jFuSOUAAJXJQso7yGDBd6LH2HV+AVkpwlbxxNdC7UrR5VF0RMTYghR8F1YS2kdBifplhEKwuTd47/IcxcamTN7jnFybUU+N0DqE3Y/5hKs9FGO5uEHb2sjqJmqbs0U6owhbM0zBqsBJsmopxorFcnKGVzH+etkmrKtPAwDqLWJcVRov/H3toLp86dpg7+d3vB8iK0XHISLGFkRFxAmqC/HXgUZWigitwubyB3d3fGDCfIy5YyKqG/OT42K8f4TOB97nvliyCZtcJFPMHVtsSMS4jW1c8dJ32Ov2CcU5oQhbNXgfEbNScI+xlOPYNYie8th0jL13MgCgjivGJbGC31csvCEG3wUR4FZ5jAu2UkQDe7EREWMLft5hv+4mzhDDgvSKiUjp2z6wudH77uXsST9uAIC8xDhK1xahrRD73N/dGXKsjqW3Q1SneKm0dfwbP3dd2978icOAh0a07dgIHQIn+M65b6uKAtOk0vhHffpVRmf9l1spuuQhxle/MhODr3/fbpO3FUZkg4lx8Pu4S0JvbMpg0YZGz37RqF58RMTYgpyGLdg7bFspxG3t3DUjPrN9YPOD5+TnmlqYYicmoQcYkW4PlS9Cx8MwKf7ywY+oKcBiUwjEbsK9mhw24WiHbCfFUIzbjDXfAnUrtvS7RtgMiBlS+L2cK8bcSmaaNNSiw60UmhK+FPzmd6vtx1zsMKk7j7F8DH/Jzw4XBDn9G3DoPZNx5AOfevaLBI/io1MT429X1NqPfRVjnw5nB6NQAFuoJHTU7bcPbO4Eyt0fVcs8V6hiRymwoTGNfe6ciAcmzN+sc4mwdWLK/A14/JMl+L935hSnQaHPuQmDYgfftbdivJltRZPA7R58bBWtFKoVfCd64cWu4FZk0znDflwobO8wCstjHFT4gyNIdTYpRUvW8D2HiBcXH52aGJ/0iFBiVCTGIcc4nZBGVortHBndwDfLa/PvWCA2+wbvVoyVAhVjOOVSNzYynyi3YUTYvsD7QlYvzpgRqhi3Y7q2QsvhFtRWNH5u93BKLjvEOKFx4cAizdRV/EPoFqmc4bs9//s6irGcx9jfMhFUKppDnGSK7YVdY1H/Lj46NTEWIVe+Cy4J7Rdd2t7dMhI8Oga3vTsXJz86Dcs2Nhelvc0ewFyH26VOjcLajfpRhNaCFqQYt71j/frZr3Hvh96y0sW82UfFQrZ/2FYK08lKkYipAFh2EraPq1y00G9zQlU8v773xZIarK1Pud7TsWaYJnXlMXafn79ibJgU9S057HPnRHyzvFZSncV+G9aF83Xv9Q3p8B0ieFAwMSaEqISQ7wgh71nPuxFCJhBCFlp/uwr73kAIWUQImU8IOaY9TrzY8Otb/h3OsVJsqaQUUWW9jsGsVfUAgMa0XpT2NjsrhasfFKoYQ/AYR5lUIrQGYs/i1h0Ox2Pc9o498cf1+OfkxZ7tYVaKJz5djB1veL/g94h48fYPPytFSYz114xu2K+JfTUrpJQI68OUUpz+xBcYe+8UaTtL/8aO083Cgu/cfZFSii+W1qC6MYNHpyyW7RhFyErxzszV2O+uSXlLW0eQ0RrF+PcAfhSeXw9gEqV0GIBJ1nMQQoYDOB3A7gCOBfAIIUQtzum2H5ZUN2Hw9e/j43lO1Zt8ZNkmGe088kYDe8eAj0vFIpPFrnynqYV5jJ1uWpgCESECh9jneLAnB78u2qfAh9BXXSPxXf+b51m6Dm0r6uzbPXhXyOqm3VuSlmLMV9QMKnuMuZIMhFeta8roTtvCaznDtNO1GW7F2DUkc77rl5VCLCySC7BShNb3CHltxjJGiOetawjeKYIHBRFjQsgAAMcD+Lew+QQAz1qPnwVworD9ZUpphlK6FMAiAPsW5WzbETOWMS/pe7PWhgffcSsFxIG7OMjoBsbPWRv4nhG2LPjvrxSLGW+uYuwOvmulxzOyUkRoLcQ+5/UYb6E8xgHNp3PeYKRXZ6zE54s2StuiDCydAFYnSeUMu89yYswVY3dWipyoGNNgErpBSIfZLATA5QyHKDPF2NveR3PXIZU1AhVjg1J7RbIiqcm+YlfwXZ6PHqGIKFQxfgjAtQDEeVBvSulaALD+9rK29wewUthvlbVtqwbv1CohdrCSH6Q8xjyoqUgD7z3j5+Pi57/F9MU1vu8ZYcuCf+1KkZz4xVaMnawU4e2607VFiFAoxC6jErfHmP1tH2IsnIPrtZg1I/Qjxte+MQtn/vvLwLYibJ/gv3EqZ9j9pcSlGJtU7ksiMZYD5+S2RY+uIVgdsrppH+f2GFMKzF5Vj4v+8w3+9N+5kseYugivQ4xjMlkvsCx6IeN65KBrHfLe8gkhPwWwgVL6TYFt+v0Gnl+OEHIRIeRrQsjX1dXVBTbdfuAXj6qQ0GwTVPjrEI7inMPKTS0AgPqUXGEqGtc7BnzACZsota69zTs+yGOsFxp8Z0Ye4witg9jnghTjdrFSuFJpiYhZFqK0XmiawmgE3d7Bf+OWrOFkpYjJ9MZd+S6IGLuJplhASVRxs4Zpj+m6aXrStfG8yMtrWoSS1bK3mVKKxjTbryKpSW2E5UUWEanJxUchWthBAH5OCFkG4GUAhxNCngewnhDSFwCsvzz/0yoAA4XjBwBY426UUvoEpXQMpXRMz549N+MjFAf8wlCUcBpkWymEDtfeSlw0sHcM+O+aJ997q9trMzwe48LyyHIriKzCRX0qQgA+/xuwZiaAPB5j6297WynczdvE2Ecx9kNkpdj+wX9h5htmz7hizGFS2UohpjQ0qZPNwn2/5QQXkMfarG7a7bk9xu4eJ1bIS2dFQu4Ed8dUxUWGBVtFWHBg4CsCIkWkVchLjCmlN1BKB1BKB4MF1X1MKT0LwLsAzrV2OxfAO9bjdwGcTghJEEKGABgG4Kuin3mRkROtFHZJaB+PMb94QIWgpuKcQ5BSHY3rHQP+O5AiDSqbO8EJymOczaOciVaKQtXvhnQOg69/H+/MXJ1/5wjbFybcAjwxFoDLSrElPcYhXorWEuNo/Nw+IAcPyz+qXxaTpC8xdp4XaqWQUqi5UrzJWSlkUsvHXULkAh9yzmRqB/eZVCbXRoFWitDXAl+JEIbNcU/eDeAoQshCAEdZz0EpnQvgVQA/ABgP4DJKaWEjWAfCkKwUXoWNw+6DFMJ+xel+gaQl6t0dAlP8sYuAzReM5Qa4x7g1eYwL7asrapit5/FPlrTiDCN0BB74aD4GX/9+uxBUsc958xizv+3tMXbnIY7bHuPCrBSRt37bRUY38NDEBUjnDGn8dI95IjnMWEJB0mOlkPtCsJVCPoegohuhinEAcTepXEyEUmq3b1IaSMLd5zRt8UYMvv59/LCmoaCJX6QXtw5aa3amlE4BMMV6XAPgiID97gRw52ae2xYF79RiBgK/AZV3ePGVYt8XgpZhImxZ8G+9WL/v5rYTrBiHzzv5hEtMSF8oop639eNfny0FULiCGgrqJhzOY/fKiZOurT1KQgcvI8esimaZghXjqBdvq3jm82V4aOJCxDUFFx2yo709Z5iIaw7xFX/iZkuB9bNSiD1YXGkLywAhq7gUmkKgmxRZV7o2M4TISoqxkNmCkXXncXCmDLnBiT8w5+r0JTU4aKfuCELU9duGqPKdBd4hVUVYevZhMraGWGAqldYgyEoR9e2OQVB9+81tD2ibrcKTrk1tbeU7J/l9NGBuP4jZ/aAIBNWUyabU51x9hk+42ttj7CHGdvBdcawUzRkdS4tU3TJCcdFikch0zpS6nzvgWFzZaM4yYuy2UhiudG1Zl2LsjI0uYiwFxFGh4qicrk3sZ27uIFbMdVspxIwVbhLuPp6fn3UJYMSSf6N8ceHFbgrF0o3NtsWjMyIixhbk4LsCrBRAaL7jtiDIyhopHh0DO5K4SIJYWEBRIXB3A1sxzkOIxOwpUV/a/hDX5NK3mwWX6y0sYLOjrBS83xdspTAp6lqyOPahT7G4usnz+nlPf4XD7pvS5nON0P4gkMc/d58QnzZlWB8uiefzGAvjsaDcunuzXLiD2hMzMV2bm3S7LwmR/KZdxNhRjGmgYkxdE0XFugb2XfIwBky4GMXGYfdNwRn/+qLo7W4riIixBd751TxWCju6VNpW3HNx34AiLtMxCKpvXwy0pU33Idz2U6hSaFJaMIkp9qQvQvuBe24zxSDGHsVYeMmtGG+hAh+GSx3kS+iFB99RfPTDesxb14hHfMpP8+JOEbY+yPfZ4FUE8WmKK8aaixiHFPgwXMqtCDGozq0Yi6TWrerK5+dvpRDHZMOUx+cwv7E7p3g+tCV+fNaq+tYftJ0gIsYWdNtKIRDjUCuFUOCjvdO1RWaKDkGx7/dhnrFCENQPClUKKS38MxUrd3OE9oftuW0HxVjsc0F9tr1LQkv+T0GxK1QxNkxaUG8uVqGmCEWE9du7iZ27L4r9lCvBbsXYEKxkbD/ZSuGQV5nYugkqty65g+9ke6V8vnYhELeVwhRfk4mxEdCeSaknQ0wwoj7dFkTE2IIYfEeFC8QDn+WWYo2nQWQkEu06BkX3GEvpd9pyPvJz3k/zKcZ29hRh8I661PYDbi3ISJ7bNv7Cpuwr9EuDxcErQhZ6fVBK8fDHC1HTlLGfB56GuGzuIgthle/ytRUG9/J8hK0HBES2Urh+VPE1LnJpCpEyUxim3Oek4DtB8aVUbt/t+1UFC5uYrk1eXXEpxiZv25uujY/fhrsNSSWWHyutVYwjoaNViIixBV1I18a7oG9WCiEJuJi0u5jwBN9F43WHwOkHxWmv0ITtgefjWZ5jf1uXrq3VbxthKwdXUItjpXC1ERIwym/OhVZe/GZ5Le77aAGufX0We6uQw4JWVwxBMS708xY6Phf6OSK0HePnrLMrvRWCQq0UksJrcpWZoCIZk/YJ8hgbJpUC68X9dEO2UmiK0/8cK4Xp6bNit+OTVkrh8Rjz8zWobPUwAq49k3pzigehLdwhss91YmLsiTzlHmOF2DM1PwXBnvmJ24rVkezCIq73jDpqh6DYHuMwRaGg413PC52Y8SHUXfkpwvYB7rmVLTVtVIhCgu88HmPrb6GTPB4kyqPdJd9kiGc0SL3zLKcH9G2JYIQo6e2Rdi6Cg8XVTbj4+W/siVFrQEj4PVe2SFjEGKzMMoc7QM4d6CaO9+J+bsW4PMHabEzrDlfweIzlc+S2H5NS6To1qUO8zZDMFu7robXEuDUCcySedGJiDADTrj/cfswHRYUQyUfsht3ZqUicWve+OcPEif/8HFMXbpS2B/XdqJ92DPgNuVgzaNmv2YbjAxTjfG1JSohr5yXVTVjmk6oqItDbDoqrGAd7jN39zw6+a2NfkUiKi5SaLjKS7zFrL+B9zMKqV0alo9sXLVa2iJW1LQUfEyQmiL9VRjfQkhNzA7O+RAgkxdjwKMb+HmM3qXXvV5Zg3uX6VE7yGIvH/OuzJVIKQJ5a0KSubBiU2s/dBUiMgEkgbYOVojWIxv5OTIwJIfYNBXA6oaoI3lKfgdLJSkEFYhLekb5ZXouhN/4P1Y3MW7euPo2ZK+tw/Zv+M2cPAcozYD8wYQHmrWsAANzyzhyc89RWX4F7m4DoOStme0DbAn3c5xG2siEf50zgnM/EHhx+/ycYd9+UwPeKxsitH3HVTzFuI1qjGNsFPgrrJG6fo7u8rvy+rSfGQcRWGp9DTrU9gggjFAfudG3ib3rE/Z/g0SlOtpGnP1/GjiFApaAYUyrfW7OurBR2ek4q9/WMy4vM0ZDK2c91V/DdtMU1uPXdufZz0T4hKdWmXPmOBlxvnqwUBTI3PrFtDY2OJoidmBgDsGd+gNNZWfAd2+ZrpRAIg+M39ra9vKYZg69/H9MX1+CpqUthmBRfLKkB4Fwk7gTkhagabqRzBv4+aSFOfmQaAOC56cvx6YLqVrcTwYu2rggEIcg/Vvjx/u3lU7T5y62xUkSBSNsONLWYwXdh6drkNrlqZbSysAhvRexjuqsNiRS4SLIYxS8f469uuyueBSEiBFsfJOuLRBSdx6tqU9IxNc1ZAGwi5rZSiF1GCr4zxKwUcnYIcT/ddHzFTDH2b9sNkVyL16moIHszWwjXh2tC2J6KcTT0d3JiXBrXcPsJuwMQFWOnw4VZKdjs09rmM6BOX8xI8FvfrbJvXHxmmLKJcWFffyEdNRcN6kWHSCiLgaAoYzfW1afxz8mLPITX7Y+0Vyzy8BIxDVGhnyVaTtt2UFTF2M5KYZURDyHYfKTMp7SOn7MOpz0+3ZtySyQfHmJMffcTiXGYYix7kUNPz/eYCFsHRI9sq4OXCVCRcFspnOPEdH8GlXt6WPYK/lpdS9ZlpfA/jZxhoq7FCTjM5GRvs52Vgnp9yuL7iudWeLq21iMa+zs5MQaAPl1KAHh9P+JfEX5Li34XBN9EQOwoVv4e/IJ013KXjg+YOXr3c71hhKKh2OnaxD7WkjGw2/+Nxwez13r2+91L3+LeD+djwXq5Spf7NHgfyW+lcPYP+iytte9E2Hrg7zFua/Cd1YbFYt2K8a+e+AK3vjNH3MW3T53+xHTcZi0lX/z8N/hq6SZP1oegYgZAsMdYF6L/3f1efO72hRYCt2odSWdbD4gQ+wMUNia7g+/cwW1SdghTDr4zAiZtIgEO8xiL4AVk/CqVGia1+757fOaPVYW4AgULz0rB0RqBOVotjIixp6ypqAT7DahOHlgKzkZ907oJM12ed5NfAKkgKwWc95VmiCHnH3Xi9kOxPcZi1Pua+hRSOQN/+WCeZ78WqzKSvDQevHSc7yZBhX7ql1UFABrS/vlr8xWXyRkmLn3hG9vjHmHLI+ablaKNsK0UnBjLyuv0JTV4dvpyAGLlRW8f+WLJJjwzbRk7P2v8s/O38vE1gMjy9+KQC3wIBRHcwXeiZ1mXFTb7sedMHXgUY1qE7zOCjbYUqgpScQuZ7CiE2NcGIGeeACBVoBPVWnfp6EzOtImoSIDrWoI9xn7gBUcyOdMmyZQ694WgstIxlXhyeRdc3qMN9y6x23dWgaTTE2N3AInow/TrE75WCuvB/HWNeG76MvY6N70TeKwUQR5jDsOkoWmJ/M4nQvEh/tbFgEggnByw3puvZimA7hu1+zwcK0X4CTo+uOD+Umv58jgKVdnmr2vE/2avw1WvfF/Q/hGKj5jgMd7srmoF31FCbHVMVbzqsXRInguEK9opV0EO0QLkzTBBfffTTRP8MnJfH0Eqn0kLU8w8fZ6aePu71fh62ab8B0dodwRNloJACBATlFX3+CdnsgjOSpExTCQ0Z0zm57F8U4t9Lw+zUnCUWsQ4K7Tnzkrh5+mPq4rMB1zCWSFozT1M8vR3Un6RlxgTQpKEkK8IId8TQuYSQv5kbe9GCJlACFlo/e0qHHMDIWQRIWQ+IeSY9vwAmwviQ1B4V/Ab8EXFza3YHfPQp7jlnbnWsfY7+FgpgoLvYLcne4qCz5/a59M5O3B7otgeY78CAn7ecE1QJ/zOhyNsAicf6OwftG9dSk66X2iKOj7pM6IcsB0G7jHO5Ezhd2tjn7UU45xB8cqMlaAUUHnlxIA+kS/2jvfnVFa3zswiAiEKYFCgqmE6KQfDch8HWSnC+rXn+qQmrnxlJk55bHrgMREKR1uqr4k/l5TusgBiSEBskQGwFGPhdVExFsdGdyxGJmfY92pRdTZMinnrGu3H+e7BCc1SjHUDCas9g1InjzGV2+DvE9cUmZ/Qwn3zfLfW8Oig4jqdCYUoxhkAh1NKRwIYBeBYQsj+AK4HMIlSOgzAJOs5CCHDAZwOYHcAxwJ4hBASbKbtYLiT1Ls7vhv+irG3Xb5JtlJYwXfWBZnU5K/fORdZDQkbzINmdJ11CaSYEC0IxYBopbCX4PwUY8W/opg3+K6w8xN9cIFFEEyKdM7A3DX11jHWe1p/0zkD//p0ied87XON+luHQRF+g83uqlwxhoLqxgxMSu3Sz4UU0PADL0DSlHErxs5x7v4jB1rJQVJ28F2AtQiQrx1KQxS2FV/gHu1xAD77RFaKrQaEwJWVojDFWJUUY1kYSOUc+5huOGOju79kDdOefOpW9oqhPcsAsCIffm37gU/WMjlHMTYMirQuWClEG4PVnlsxdgcRFoLW7B+0WtOZkJcYUwYeBRSz/lEAJwB41tr+LIATrccnAHiZUpqhlC4FsAjAvsU86WLC9slZPSAv4bW30UBlmQqNEHiXxrmHNNBK4VaMQ84/aLnfHeUdofUotsdYtFLw38dPRXZbbzg8irGdd9Pbxp3v/4Db3/uBHWdtMyh1yISP+nzrO3Nx/N+nYl192kM6Hpm8CHf+70e89d1qabtqsaYo1VXHwf5JpRWBNgbfmZwYC8E/PoF44vN8k3BupWjOuH3sYYqx81i8RnQhrZZ7iHP7MMXHdgyJ++Se+SlO0z5BT9R5K9+5UtdF2Dy0zWMsqpfO9oKsFHBEKXY86zt8Mr9oQ5P0mrhCKDaf1U0kYs44Z1Kge3lCei/dVRLaD3yleNK8DTYXuH/CAmmi56fWxjTFU+yjUKIrjg2FQibnnXNcL8hjTAhRCSEzAWwAMIFS+iWA3pTStQBg/e1l7d4fwErh8FXWtq0S3L5gGFx9C+9Mfj4k93hKqUsxVniQSni6Nm7rcHuICvEYu/eIiPHmY7OzUpgmMPNFwGA2BVFt5Td7d8UvALb1xhOpH6CQ+c3q//XZUjw5dSkASEpIEIcxTYo5llrMlUIR9ZbVotEVpMfb9iP4EbYMxOwkm30js8igCQXUWj3jqpu77wRlh3CDE+MmFzEOSq8GBGflEeMv3IQ8qPiHaVIMXPUeSpD2npzJ+vVuygqMfHonYN7/hJOIxtD2QKssFdQ5RrZSFPRG9lgKOKvBXK1d35BB70pGcEX/MaUuv7puIqk51geTUnQrjUtvZdL8E0Qxawwv9iW14Vrx4c1pCvFZAfF/j6BzaI1wEVkpCiTGlFKDUjoKwAAA+xJC9gjZ3a/Xe75dQshFhJCvCSFfV1d3XEEKRzF2SFDYgC8RUSpv4zCEGafoc+Kdu8Xy2qmK/9efz2NsmBQzV9axfQMukKJEqHdyOBOkNjYw53Xg7UuAqQ8BkH2PuTDFWOHBVC7F2LWf6ep/c1bXezJZiPuFWSlMCpTFWWqjpozuGWD5pC3oHCLFuOMgrnBt9q9AHcWYR+o7xNg7zgGFKMbseE6Mqc+4GZaTWCQBrEKZPD6LHk0O8XHFhq9w0KwbcJP2QuA5HqLMhkJ1YPJdzsaIGLcL2hoPI3a/whRjIinG3KqQEFZqd+tbabcX6DEWFGNuV4prCsoTTio4wD87i4jjR/QNfd2TlcKkUAibWMo2PHZ+BN7+GURkWzM8y5PKwo/bntCqrBSU0joAU8C8w+sJIX0BwPq7wdptFYCBwmEDAKzxaesJSukYSumYnj17tv7MiwS3xxgQlp59PcbWPtS5vP2UFE5ACHEuaK4O2nkLAwYIporIZnsRf5u4ACf+83PMWlUXeCG4SVWEtqPNs+YWVuTl1U+/w5q6lBRoZxNjv+A730pmAZYd6/zW1afx039Mxc1vzfG05/Tn4M9CKbUrQTZndKefW6/zlRVPvmNOTiJi3GEQVzaKFXxHQSyi7VTZcrfoR3D9EGSlkAhviJXCU/lOIOQL1zdip5s+wPg56wIV41iKXYc9SIM8ljbX2A/7ECvzhCmcY0SMi4o2Bd/xY10FPvwmY+7UvsxjLCrGrD8nhNiebmVxuz15ZU1WjB1PMLNMKAToUuIUDwG8KQdFHLlbL+w5oIu07YAdu0vP3cGBprVao7oUY9OaHCo+13jQONyae5isWnfOcb2QrBQ9CSFV1uMSAEcCmAfgXQDnWrudC+Ad6/G7AE4nhCQIIUMADAPwVZHPu2hwZ6UwTSrdaNxw8hiLSy+uUo6mcEHDCR7JCiZ7dpzrXPjxNNxKMXcNyxm7rj4tnYOISDEuHvJxPt0wsbym2fuC9Zs0ZQy89d1qXyuFH/jyX0Y3pd/V3V/ErBRNGbYk/O2K2sDzCMtjbFKgzFJAmrN6oALc7AqgcvJ4Rv2to2BP0IsafEfsggiEMNIRNCnK59pyE2M/4cHdf4JIkG7IwXffr2L2n4/mrgtWoA22bJ2BTGTwxgX2wySsrCxU6N8RMS4qNidzEoFLMfYVFLzB7JroMbb6s0iMq0ridnumdG932snohp1RwrDUZIUQVJXK/SlsTFcVYnMNjpcu2h//udAJv/LLY0wIYVYKj8cYvsTYk8kI/lwDAJbXNPsGf0dWisIU474AJhNCZgGYAeYxfg/A3QCOIoQsBHCU9RyU0rkAXgXwA4DxAC6jlG61UQxOijT2l9r/hSvGgNPZ3MFybvM+Vzi4ihvkkePM2J2VIuicWRv++0TEuHjIF7hw74fzMfbeKVhdl3Ifaf3vzTIRpi6ogpVCfGs/Msv+Usfq4HOqot0hMIsJpbaVoiVreD4zz6TSkM55jgMcj36ELQ9xguTcyNoafMfJK7GsN6wtQojXSiHYz8LAl7Pd/vTQ4LsA/zFXy9zbKWSCLrat6Oy6zNCYfA0tmWI/tP3HGxc4r0fEuKgQ7YWFH+Pft/zGsZhLMibEZaWg3GPsWCm6WuR2zpp6aQVEvp87ZNowTZgma7ury2ecb0xXfJJp97Uq77Lzc/MLpkxrqiv4zlr5U3ysFIUqxqvrUhh77xTc+9F8z75RHmNAy7cDpXQWgL18ttcAOCLgmDsB3LnZZ7cFwDurOMiL6a3ckNK1WdsohSedin17Is5sj0el5ss/Kw7+4v4OnKXNoJtSRIyLh3yK8fQlbEm2ujGD/lXOQMdvrPxwyUoR0qjtMc4ZocuHfmnY3P2BWX4chU1cLnS3VSpYKcRSqoBDaupd+Y75xCyyUnQc7Am6mT9lVF6YTp81LZVAsRRj8b4/ef4GrKpN2e8bhrYE34lP3SQ5yNscZKV474sfsHsMyPLb3azXgFIr7X5lfzTXb0Qp8QZDScS4ZROw4Qdg8MEBnzJCPmyu+pjPSuEuk+yxUljXR0xz9quyrBRvfrsaQ3qUWe/jPVfZY8yuCV7JjiNsTFcVxWP1AICdepXjyXPH4K/j50l2Dv55FUJ8SkJTy2OcXzF24qDkzRsa2ETwi8U1cCNslbKzIKp8x1VaH8LrN+CLaX/EID3Jo2w6nYsQR03T7Qo3/iSGz6Tdle88lgvibA/OSrHVivTbHPItAbonV86B1u8MHnwpBN+FTFwkxVg6DxniJM2unuTqLBnddNK6hRAnSmEHkzSJHmOrPa4UNwYpxhEx7jCIk53WpGXyb0z0GFNLHbMm+EL/venN2fZjd59zn4PHSkG9fTWU5ErKsmkTdMN0SuO6faFV81/Bj4nzoMJAV8KKMNhLzxP+D7Pf+CvSiCO72y+gQ0MpfIixmK7txV8CzxwP6D77RSgIzqps2/qo3yqsCMVNjCGryCblKqxAjAWfcIM16aeC5YyjJMbGRj6GKi41GsgzphP4KsYAcMRuvdGlJCYVr+Hny9/HL12br8c4QLX2ZjRif932DiDc+99ZkFcx3t7BrxvH9+uQBz8VjIqEgc/GXEvU4mMCYj+3lY4A1c4+3gz3GItdOQq+a3/kGxuCovYdKwWDOOsP8+Q6wXdybkzP4CYQhJztkZfbSmUNaaIXHLXs3DBasoaHqHArhSd7gNVe5DHuONhWGcnC1bob2j3j56F/1xKcWSkTYx58RyD3XzGy37v64D4/tqE1BT5SrnK9AJBAFmUbZ9sxG+5gJbFvDvryz1BJFmVIozthMRklJINSvQ5oXIsWswtUYqBFJzCgoMwvlZuoGK+dyf7mUoCW8O4boQC0nWS5g+/8CJubeBIiV77jKyoiGRR9wvU2MQae/nyp1Ba3XOjWGKoocio4gHmRFeJ/v1AVJbQsObcqSSsllNqqty5M0pjVA1ALsFLwZ0GB234qduQxjhRjALLaRyFH+7shbrOXqE0qeSyDvHiGRzGW2xYrrbn9c9IZE+cVR9mT94mIcfGQT4VT8yjGFASUUimdT1hqH95O1uUx9ivKwf9y0uI+15acIfRn/yVI/hrfL2d4k9Xz/hjkBe2kwsJWAf7Vm7TtatwjUxbjprfmQErXxokEGOkQl4rFAKawVGuAs1LmDr5Dqg7XaS9BgzfYk6e0ZJ+LvXar9iz2m3gyepkb7PfhmVsoZEGCWtdkAln0Qi0AoAxp9E0vtR/HiAGDaNChocTHSiGFxvC2c+44ggiFwlYpW+UxFh5LbXn7ubtVQhxbGj+GZ5Tg4MF3gBD7QynedBUyqiqNsdVfgVxzAYMrx5PnV3vIMoeqBCvGALuHuCd61FKMWfCdaKVgY7VfurYghZfPaU9+dBpenbHS/i38zkniOJ10XO/0xNg9YxI7p1+Uqbh8LVYekwJETEe5UYhAKuxlZ/aap8SvcGGG5THmA4topfCcZ8RUigb+HU9btBGPTFnkeZ2PhZ7v3FacvJXswgI1+EvpnBEafOesXjhE271PKqtLQSXOcqYM8TV3gRl2TrwPu48L/BgRthDsCZJkldncyndWujaLBChEXiqWibHchIcYm3JhI46B3/wFl2j/xU+UL6XxszGVxfD1/0UZZA/zcGUFAOBDXIYz1YkwKJDOCYRBfF/Czq+EZNGbMGJcggz6ZBYDACrBssjoRIUO1VcxNqUPxolxi2e/CIWhLfckfgQBkdOI+Qyfbo7nzkoxb10jvlleK5HBbuVyAB3gTwYrS2LQFB6QauUXtgb+uKBKu33OzvZwqqUKbXNwEu8u8MHHZz8rhfu+4hb5vllei2vfmCUoxj7EOLJSRMTY7bGh1NuZ3K8DjNQ6Cq97+cF5TohAeF3eYpMC6xvSWLax2X4OsBuNTIz9OydXdfzQSftzu4APFGf8+0vcM16O4tUNeSYvw/qdwSsfOuVIw4mxk9Yv1EohWHRsK4VbMc4a0spGWB5jMSex089hbwPCvaBupLIGZq2qC3w9QnEgjlebvfRpV74jdjAQ9xiLfTYuEGPPClnAipkbRM8CADQYdp8HgKv//jxuMf6Je+L/AgA8OIFlimiiSXufG7UXYJrUDmim1J2Lnl1npcjYxLiMpNEvzYhxF8LGXAMx6FBRQrKe8zP8SkI3rAZu6wLMfMn3M0UIxubck4grXSAPJH7Kqu7J9vFaKdxEtSVrSFPGvpVJuDFj2SbPtspkDAoh9hiqCIqxuDqrBRJjtnrH8ZtDhkivKwqxU8FxiO/jDu43aaHp2qy/QXanyErhi05PjL2KsUN+/TzGUlYKm8hST8flNwcxK4WjGPM2KPa7axLG3TdFapsp0EKBD9c5iMF3Qf22s3bo9kDYd7nTTR/giyVsIPUEhNhWCitzieEkinevRiza0IgF6xutdtg23UVk3c37WSncXTarm44STIOD79hkztnP/ix2H/cn3mE2k+vfnIWfP/y5b/nTCMWDMw4VYenTtlIodnYdRoxl+4+Y8sqzuuDq20G2IXE9RZwnrqljquzuynIAQIOVEaUZTsaXGloJ3TSRsciGOOY6rQJdSSO6ENZeCTLon2VEim/jirEf1I9uRAIWYeZtr/2e/f3mGd9jIgSjLYGhgVYKk2Lumgb8+b0f7G0eKwUcm5sIUUVWFIIz9xskvV7T7J0kVZZo0BSCxz9dgtqWnBUU52Sq4FDVAGJMiB24/NuxO+Km44e7XvcGRxsmJ/eKZwU5KPjOfa2Jtk0p2wS4Yuw9VykotpPSiE5PjN1+JwqhwIcvMbb2cxMJ11KHeKy9DO0iyO7lIDHCX85bKJ+HTYwRrgBGKA4K/SYNz/oeJ8bcSkFtpc09gB35wKc4+sFPpXZ0Q85KERRZbFIgqzuTLfmcnOW58JLQgmJsePtVkMc4LM/lbKv4gjv3cYTiQgzmLV7lO8cipikKFEKQDVCMPQqx8Pzmt2dLxwHAdyvqsKEhbV8XhFDp2klaZLQ76qXjmkRijC4wTceekcnqSG5iyvKU+FWIZesAAL0tfzHAfMX9c8ukNnVoMIKI8aKP8Et1MrqiwZ4wIGW1p3mX4COEY7PnbCJppNTTr9xggWs+xFhRcOWRw3D7CbsDAK4+aue8712eiEnv5/Yvc/gRcYBZKRpSbIJXmYz5vE68K4Qmt2y4PcY8K4XX6uOtIOmIJ36lnv2sFG47R2dERIxd/UJUYcMUY2tvAN7CCaLnmMBLjN2WCvs4IctAaPCdQObzme0jbD4KnWR4vnNbMXasFFxpC7dSCERW2M19FuIELkgxNkzHyS5OuLzqs9Mv+VKd9HpAnw2zUnDyFOXUbl/YijH1ht7d/cE8PDd9me9xL3y5HH+ftNDVmFz5Lp0zkNAUSzF2fkfxhuq+PsSb+PNfrMCiDU2e9777g3nSceJ4x3MKV0D28+rUIbAbaSUMSm2P8ej6Cdj7/eMwVvkeg5X19n4VljJsQMUgpRpJmga6DnbahKwYr+gxVnrPHqQe98Uedzak6thfzbsEHyEcbSFZYo92k8Z8LnoCYqdw4zmKAUZCrzxyZ5x9wGAAQIUPUeUos3IVs7zBgtIsZLy48OAhtvIa7DF2qpO6c8Tz9kwqFwbTTSePsbjCaNs5BGLM1WPdNFHdmLGvOScOSp6w5qwX/Hi8OKRHxLiTwkuMHVLgd9OXPMbCfoYralTMcuFRjIXlDRHi7C60wIeUx9j/c3XWDt0eKHQ5yaMY89/TSsWeM0whUXx+Yuy2UhSSlcKksiospvAKs1K4+717lUIP6LNh3YwT4yhDSvtCXDlwfjc2SDz2yWLc8s5c3+NuemsOHpiwQN7oCr7L6CYSMRUKkW/OYvVdb6aS/Ofc1ahxFGPIapY7p/A92uO4RH0XScEHXEMrYQge4wEZ9jl2ISukYystcp1WSu1ttJezjK1DhU4cYpxT5FRsPVGPciJkorAV4yhlW2uxeR5jOfhufUMGXy2VvcAeAYk4Cm55QkPSGnvdSm9cUzw5iQHgtDEDMPmacfjtoTtizA5dpdcU4mSjKEtouPBg5hkOC74b2JX1waE9y31e9wbfGaZpV75z1zUwTNljzFO36SbFuHsn48gHPmFtCKtJ4jXGA2n9FGM/ZbmzIcpj7OoYrE/4L0uw1x3FTXwseYwFxZc9lv2ZfnmMxWUUt2LsvuKJsDlsaTxC2xFeedAfWY+X0iEpbo8xtz74wSHGwVaK9Q1p1DbnrO0Q8hhTT18UrRRheYzF4Dt34Rj3pM59rhzVjRkoBOhenrCjtSPFuH1hB9+ZwROfwhuT059lcgaSGqvaJS4liwTAk5Uiz/WyO1mG/1two/38JGUqRkx5BxgzC4glUSIQ493JUpymsZv8h8YY1JTthETTKphQYFJqWylyJjsf1TVYcsU4pZSizLQ8/Ikq+8aXgwZTUIx1FzEeRDZgI610NtjEOFKMW4s2KcZciKJUUo8fnuzNDuTXPO+nhklRGteQzmV9yWtJTEXOkCsz9qlMoldFEjf8ZDfP/gohdrAfAVAa1+zHflAV4NeH7Ii9d+iKfQZ387anECzc0ITnv3AmdrpJoSjEzobB4WTGcLZx9Vg3KJqzTpCfGJgr3hfsFe086do6K4+IFOMQK4XfAC8SBps0UHd6NUdxE5ev3T5NsdOlcoak/MgR1sDXyzZhwg/OEiF/n6Cl7E7an4uGsAAEkWiK8FQ+EvoKyxzheIyl4MoAP69uuIIwLQKcM0zsd9ckrK5jSpZpUpuUMwLunnDxzxQykTJlguVNueXts37P97lzIkbfMRGAU0aV55qN0D7gv4A7D2oYRFuE1CfsrBQKKKW2YgzIWSnEZWWPvSYkRzcAVFoZITgOUH9AeWY9zMb1yOiGVJ55pLLEflyCDLJKEg0ohQpTKmzD+2sJkdOu8bRsLcRZSjcTDtHNCVYKncTsKpUc/chGVNMqZwMnxmqkGLcWmxv3kv9weQfRY2xSihKrKI3mow5zYivCM+4L7RNCJIGKVw0NWh1TFQWqQnxJMeCkfFvX4PRfnpVCVYh0b+F+YTGPMVeMA9NsmvJ1ya8b3wIfwkeIiHEnhVcxlpVbN+xNIoF2EQlDmJ2JpnfHw+lqCyyhvegZlfIiU4pTHpuO3zz3NQB5lhdZKdoH0m8f4Kt1Ez6PPYLyohusnG7WMFFqlRZ1k1cRonLrroC4312TsN9dkzz787RxrO8558E8xs4KRWAOber45MV0be5zzEeMRfDBPpWNiHF7QhyvCg2+Wy/cgKX8wqZccY57jBUi91lOAOKa4r0Z5xl7gtyhyt/3xPCb30eJlVM4R1XsRFbbj0tIBlnEYVAVGjGwW3Y2Dql9GwBsNfgK7W2pzUpLMW4hTuCeqTm2Cp06xNhQ4nZqRY6+ZBMa4OxvtFjL95GVotXYnFuSKFiF7fPQL0fZzwmISzFmv7NfTuHShDcAM6wfK0QQ1ShFmUWM0zn/sS4oKI/Dz8qhG9S2bIirNSZl//ysFDnXPcgR21yKsXUt5yvwsdkrUNsoOj0x9uuuvC/4FfjgNx4pewV1F/iQiYQ7XZtfIFMqa0hZK8Sl/ACLcZ4sA76bIxQImZDKr/HfUywuAARbKShYyr50zkCJNTiLA507yJP3O92kUh+kADY1Z7HJlU5IzGPsPXfZCy/af6QzpcIgarpTX8nKg7wdgeDquLuwQ4TiwvGQF04+WoTJCo+WBwAYrG8ZVIFhMhsMI8ayYpw1TPSqSGD3fpU+AZnh1hkVwf2hC5ptjzFRYxhK1gAAYsRAX7IJGZKADgUaDNzXdAPO2vQPxKCj0qz3bY97jJsFcqurDknOUs0O6jNIDAaVb4lJkkMPITtGS101e+BnpdCzwMaF3u0RAMjjKKUUXyypwYYGn1LckPdjx1LPZN4PJ+7VHz2soh2KUG3OoA4xjvnIpPw1EZ6cwMJTVSFOoS0AJXH/bEPO/uHnLWZ5Ed9fsdK1ycSYjeOqFHxnCSMuVVi0bYqfh4snfvxHvJ6jAh+dFG6PjVQBzNdKwf5Kq4/UGyzHO55YrIP3bb/MAC1ZQyIwbsVYPmdnv6BZbaQYbx7Cvn/+3K0O6G6WKFgpcoaJTM5EmaVM6CHE2Okvpifowg/MYiEOiOIAKBNje2LnaksM2pM8xq7VE3dQXlg/43k+g1SUCMWBFLRr/x7hChWfcCWQhfb5/YjDSqnXxOxaacRtK0UypoK4PMY03YQRWGRH04vIlxFH9Slly5FEFmWWlUKBiR7EIaUDyEZkEIcODSoMGNbtaxeyAt1Mb1EGAHbAXpOo+moiMVaQE6wUhs8tsTepA7QkKAgqeCCeGgNWf8OKfXxyD5BtAf77e+DhMU7miggSxH4ya1U9Tn/iC5z/zIyCjqXIL/bYPd/2/jqKsWlSW5Tw8xj7WSnCSCEhxL4Pm5TaKrT7WuCBfqGV77ItvmTdoKy4TlxTvHyDUiuk23ofa7IprhZmdFMYG9yVV6n9OdyQc+d3Th6RlxgTQgYSQiYTQn4khMwlhPze2t6NEDKBELLQ+ttVOOYGQsgiQsh8Qsgx7fkBNhf+HmOHILgheYxtwuElUvzmIPo17Ypmwo2MoyVruJbQ/Qt8pHOGpBgHXbudtUMXC/JER37NUYxlwpczTFBK8cBH87G8phn8lzOhIKtbVgprAJaIrMuXxvtHznD1g5BJkBjg5heowffjL7ltHyaFkNrHLyuFf4GPMGLMU9O1RFaKomPO6nosqZbToIVlHXGD/25nqRPQ46t7cIH6AXuhgSm0BBTDUjNxVvYVVKHRk5Xiok1/xZO561BJm3z86OHMWAkhxl1JEy7W/svOgeqoIk2ooRX262nELcXYxEJlRwDAT9Sv0AfVaC7p52mPB/LVmY7CayjO4xwcxVgncV9iXIIMoGjsHwc1gYUT2OPJdwJv/gZYYH2HUdloX/BxiBCgKcNWKeauaQg/xvrrzrYT1j7nmIQ4qdH2HFBlj73+HmOvYiymeBPPhb+H4KSwCbBJKV797QH2fkcN7w3ApRjXLgMm3c4ObNkE3NUXh254wfP+XDHm3mgOHoMkWikUISsFRzrnrEKzMd1pgwszvunahP0ixTgYOoA/UEp3A7A/gMsIIcMBXA9gEqV0GIBJ1nNYr50OYHcAxwJ4hBDin0F9K4Cfx9hemvTpFGLVOtET7CYjIgkuJI+xaKXQDbdS6DxuSOfsWZ5Bg7MMRHmMNw9B3z/gLFe5rRSNaR1TFlTj7x8vwiXPf+t4jAE0WzcCPgCLy9ItLoIt9pecy0rhB2JkcdXn++CX6mRPwJ5p6hhiLLfO28RhS+7FcLLMEyQieYwNL8FyF6cRjwuC20oxd019FIhXJPz0H1Nx+P0sW4PkMS4w/I7377ilNHUlzKNrNjBPrwKK69f/EZeYL+Pmucd78hjvlGOWgTK0eAsV+Yw9YoqsMMV4INlgPybURHc0YAN1UmWlEIcBFXHFRC26AAB+rk5DNzSioXSgp71ucfZeNTnHE5wTrBQ5qiJr2Sd0JSZ5jPWSHgDAykUrKqibGIuq/OpvmGoMOH8jSBBXW8PyuPuB0nyueTEHkJMtokd5Au/97mDcc8qedkYgP8W4zKUYD+xW4qmIJ0IRFGMKMcgP2HeIE2DHybikGL92HvDZfUD1fKCJ9feRG9/zvIdDjGWaNnNFHb5aukmaYPoF36VdAf1+inFU+c4feYkxpXQtpfRb63EjgB8B9AdwAoBnrd2eBXCi9fgEAC9TSjOU0qUAFgHYt8jnXTS4+wWFE5jEbx6Lq5vw8lcsjQrvM5JiTL1BUmLwnTsHrF9O2FTOsVIwpVAkZs5+oh/QXWEvqlhTPAR9/4AzcKRdJO8fHy/C+U+zpUGTLT3YrzXZxNgKvhPa56TZ/d66Ycq5JwNuJpWUkZo/aq8ga5iSsrf77Pvwgn4VBpANKKeNGL3+NTwfvwvpnCGXCBUmWWJ/5nsEWSnC7m88oCSdNbCpOYvj/z4Vx/99avABEdoEfr+Tu1z49c9/zzRYcQNeba5+HZtEuVVdkRj3RB36khoAjDS6J0t+ijEPTgIALYQYc+tEQ/lQdl4kh/UiMaZMMe5F6rCrycj5ALKRnXvSS4yTlmJcozsqsegxzgjBdzpkj7FhEeOkn2Js6rLcZuQAw8qmkZOzbkRgELtJoUqkfb8tSDFmf0XFGAD26N8FyZhqT9Q1H1tDiUsx3n9Id1+bAQcLvnNUYr8qeKxdRTonAIBu9ZMV01k/AqBSr2Cgm8xK4T63pz5fig2NGUkx1ogVfOcKkOXjtSePsRmcx9h9X+iMaJXHmBAyGMBeAL4E0JtSuhZg5BlAL2u3/gBWCoetsrZtlXB3DOpSjGubszji/k9w/Zuz2Tax08AhC+5gLVMgEu4sF355jHXBKG+YZjAxTudsMq+7rBTulHER2g53CU4RNqkI8c6yFFfWrBwUjWk2AJb5KMYiMRYHMN2Ug+puf+8H3/cqNdmNOGdlZxUJ++CVbwEAypGGYjIfaRw60jnT02f9vMjcViEqDyL8VlU4ePupnGF/Rr8qaBE2D3wcCgvGBYD56xrxTyv/q51ZBSxQKWl5jJM6I6aitxeAFXzHjnk4/nd7eylNBWZVEVEuEOMYCT5H/r65eBd7m0iMW0ymGO+BxaxUs4D6WA9PewnKSIhYTjqnOiS5JmVK6doMQSrZtOdFAIASZAFFkxVjU4ckq5hC2fNIMfaF2C/8bIp+cDLqFJKVgu3gEFr53s5jHvw9xjL59LNbiNeW6DEGhV1hzw3fSqeK9V7vXQk0rmPnBB1uLKluhkIIkoKV4hrtZfwrdh9rRiDGfAIoWu/SOcNXoAPyZaVwHufLMLO9omBiTAgpB/AGgCsppWHGoLBED2J7FxFCviaEfF1dXV3oaRQdfh5j0et7wbNOcIBIctl+bPuSjc249V2nupRU4IM6ndBtqRD7XM6U02W5FWiOhlTO/obdBRvEDt1Zl0CKBTmXo/yaTSpywcpXQlPsH1gBtRXjEpsYi4qxQ2TF396dBnBjk5yNgqPMZGQzS5n6J3p64zpTk89SJ0CzFK2YtXyeceXGFCdwTgYVeXAshASx45z2WrJGwTfCCK2HM2kRJ8reYfiUR6fh3g/n47Z35+K0x6cDcDJEJEkWKgzbk1tpBZktMJmmkYCOSn0THog9gh3JWrvNUqRAKcu4cvUrM7G2PuWbzUckHpXJ4NtOd4vs6nHHV7weVfbjFhqTSjiLaKLeTBFxToypQIyJs9+aBh05y2OcE4Lvpg+5DOmqE31eugAAdcZJREFUnQAAJYQpxpSIxNiQv2JDIMZbgWK8uLoJg69/H9+vrOvoUwHAJv/LNrLvhbhS/81bF+4zBloXfMcFYfe93VGMfQp8uIhxUAU7+70otUklDWgTgFDQSSTGQj+qs1ZoqJcYb2zKQFEgEePLtHdxlPotO0chu0uJteIje4xN7JSeg1Fkke1L5vAEigsIy8jUWVAQMSaExMBI8QuU0jetzesJIX2t1/sC4OawVQDENa0BANa426SUPkEpHUMpHdOzZ8+2nv9mw33xSOmtKMWPa52LNmeajk/K/o9hxaYWqQ1+4U+etwE/WG14S0K7FWPrfdweY+H8Mrppe6j8smH4PY4QjJasjsHXv48nPl0sbd9sxVhTbI+xQkybGPMlZXFgas46g2JOsE+4+4E/KO437mL7W4pxrqnOs9dZ2iQMMlcBADRLnRAJtClM9MSgTt10K8uu7yJg2S2jO5aOVM4IHYgjbCYkYhzcX/hE6Jlpy+xt3EKRQA5lSHmOWUu7A2Be4t/hZZykTkVPQU0uoykYlOLdmWvw5ner8eCEBXaf2J0sxTOxvyKJjGSliCvB59iNWGOlSIyp49msyaielGoczaY3t7BmehXjjBB8t6o+B8Mi2jk4HmNqmjAUNtFkirHLY/zN08DHdzjPja1LMZ48j92O3565uoPPhOH8p2fgfqH8uDi+HvvQZ4EEngp9O+8qqPUyJ6xuqsrzqvupwVzZ5fCzW4hoTOtC8B31EOlxu/SU2pViOsSQq9plAACV6uiPagwha3G19irOt4Jh/YLvAOC/8RvxbuL/7OflikWMRStFzsB9jdfi7cQtntoIORcXERHxiMKyUhAATwL4kVL6gPDSuwDOtR6fC+AdYfvphJAEIWQIgGEAvireKRcXbh+RuGRjmO5ITufiTGX1wEAX03QCqpqEZXJ3NgqxT+qGQ3J1j8dYXgLhpxxmpeisM73WoraF3dCe/nyZtD3MlmIT45BAsoSmOsQYJprSwcF3opUiZzhqrWFSX/VNRBWaUAWmGOegYQhZi/1eHYVT1E88+8YtNVAlvA/LpUPt/ieovSyYL0QxFp6L6bwyugGDUpyoTMWNC05HzvqurtdeBF4+M/QzRWgdxPEkzGPsZ5k8chirApdEFhW+xJiR0jK0wK+KeaVZjwq9FrUt7MZckYzZfeRK7Q2MU7/HOOV7DFJrcZjyHbqhAXES5jFmxNiMO9XpRCvFhrQSqBg3mjHPNu6VboJDhptp3H68vD5np2vLkRh0TrpNA7o10eRZKcywGHLRStEJs1KsqGnBjGX+KfMA4CvhNUrhGdfW1st9b119Gqbp3GG/WV6LXz7xRUHnEqT18pgHv9RpSVeAm58CLJ5xbUvWCb6jXiL9xNljMOu2o+3qn7Zi3LAGWP210NAyAEwx/jz5e0xO/AFXaG/j1th/2GchxKNmA8AIZZn0vGuc3UPEsVok45rRjIqlH9jPuVARlnkLCLfKbc8oRDE+CMDZAA4nhMy0/v0EwN0AjiKELARwlPUclNK5AF4F8AOA8QAuo9THWb6VwN3/TSp79sSb/f9mr7WXwJfVtAQm8zYpRUvGuzRiumZpYgdkarRDTMSBQ8vUoT+qrdecuGlxudrdXuQxLgz89w9XQuVj+GCSyoZYKWKKHVihgKLRFXwn9p0NjU4J3JwQPKeb1DeQScRA4tiQstAwmDDP2iXqu1LJUAAoMeUbdktOmLSZcpYV3ldzhteLLEJ8Li4XpnMs8OO+2GPooa+FmWOJ/C/W3gPmeSOwI7QdfpXv/C5/P2JcrlgWH5JBuWWfaBBsB1wxrkDKUy4ZAM6peRAv1Z+NumZ2bNdShxjPoyyqf6SyGNduuhlPx+/Ft8mLfRXjf+vHAYBdTCOIGLN0bf4EdW61POYuNvvaj1sEm0WLQKCbdVVSjO0uTB3FOEYMr8c4DNmOt1JwkECaWFwceu9knPrY9IL3Dwu+W1efxv5/mYQHJiyw+/FnCzfmbdO2UggWBxGcEPuR3qRLMVZ9VGURdS05qcCHWzGOawoqkzHbSmGT1PeulhvixNj08gWA3Z/8FGM3qjSLGBviGOzQrl+sfxhDP74Yu5OlAJz7j2+tBhMYTNaiDClc+OzXntc7AwrJSjGVUkoopXtSSkdZ//5HKa2hlB5BKR1m/d0kHHMnpXQopXQXSukHYe13NNwDB6to57/vNa/PAgD0qUwiq5tYVeuvDBiU+uZudWej4H0ygSx2n/8IVJP7hExkDef4Qz76CT5P/h4A69BOgQ/37E44h04602stFDuyWN4uTkzc/qygAh8ietEaYN0c9h6ggJ7BIcos3+C72audpel0zmA+cnhXDvwgprfKQbWDl4Yqa1EOuapUOZUD37xWCvZee2W/xkmLbgRAPRlSgmwlAECm/xNXa6/an8OgTrS0kQuvcBWh7eC/AKWOwra2Pm37iDn8Am3ilP0uJcig3FKMG+Dkb63qOwQAU4z9cvzabW9gfb2yJGb3kWaLjO5JlqDScPp4zIcY/2DugGaaQHdLMaaJAGJM4zaRdWNFk/P5/ogr7cBCQLZSNAqWi2YkEY+z/XIkhpxpfUZqwIBAhBUNJimQGHdCxbg1IMRPqXR+u3pr/Bs/d12rBB4xT7L4nIPnEvajvO7Kc0GeYY66VM6lGPvvH7eJsTXWujVCu1KizwoPTKiEeNRsP1TFHMWYn4q4oqk0sbiAXqTOOh9vejcOk1JMSfwBz8f/kvd9t1d0+sp37v7MrBThF+OOPdmNIygYilKKlmxhirFCgAvU8Ri15DEcn2aJ7XVDLtiQyDjLULph2oQ6nt4I6M4SVJR/sPXwyxACeImwSGQ5aXZbKS5R38UeZAkA4NaFpwArpgEALtXexYLkufhP/G5UpZYBkInxHIEYz1i2CY0ZHVWlMSsrRfgP2c9KmwWwoLqeqLOf76v8KO1bTmUlS7RSxLK1iBmsL12b/ht2q52MoWQNDqLfQPueLetpCgkMvhunfIfyT27FFdrbAORUQQBgZiNi3F6wveGCx3j++kZ8tVRe2vYnxmwMK0XGrurWSJ0qcakkK1BQHqAYc9Qt/BKAvEwes7zsB6lzEaPOWOlHjHVoaEESVYT1UVMgxvUCUU9ZBT780EIdwtuYhbSf9JrhKMZNKIGWZO3rVEGap74zTejE2Y8qauHEmHuMF08GZjxZ2DHbMNoiwrhXwsSuyT3ALA9vK4ix9TdI7AhSkgE/YuztY+KpJGMqfj6yHwZ2K8E5B+wQGKzn8RhrLh+8df8m1LsyOJoswFlNTyOpERCYuEHzFgHhaGp04pj45xQLR23Msr7MJ7/cvuf721np5PZSFgW+3/aOTk+M3dPH9Q1prG/I+O9roWeFN8hDxPg569DsoxiL3lGAE2OC7jyYxRosxmSmo7JpqU/LVPIVX/L1sTh46vn2q5FpvvVwB0Ta2122FD+frVzgg+K62Mt4L3Fz6PtVpFgcqqhI1wgTrIXrmao7amCVJ/AN8A7gpZZveIIxGklk0ZvU2q/tROSY1wpBMY5Bl4jxr6cdgT+tvADnqB+iG60DAByufId/a/eg8qOr8Ub8VsRUJbDy3TPxe+1tCkyp6hIAmDmXf9Xcat1V2xRyhmnfsU3noaff1AmeSBExK2tDKUkLirFDjDMlLAtnEplQYlyBFvt8+HtrRIiatzKnmJT4eoxzUKWsEqJinIFDUNMIVozTgkKsQ4UhEFlRMW4W7MAmFCglzEetmRlkLCM1oQZ0kQiTPB5j6cNYE9D/nAi8f3XortsDaprD75d+CIudsIUHoUBFIeB9XyzVLIITRj8ymGiFYnzOATvg/lNHoldlEp9dezgG9yjzDegDgOP26IMjdu2FPxy9C9ug+nMHv6wUryf+jBObX0V5/UIMJWvwW+39wHOKmUx40A0Tu5AVLMBaCALlgdlVhF2HXJUXA8mfnbYMpkmh5lwVNTuhytbpibHqultMW1yD1XXeIBQR3crioa+/+vUqiXRwuL3FJrWiTq3I8CZL1bgjdRd+9+MZnuMrkMK+s27BuGpn5ti9bpb9OKjYR4RgOGWQ5e1uX604++akOSNYKThBzYeKzHoAToJ1ANLqAp/Jlyc0GFYVuyQyeCN+K3YnSz35NuMkB50qaEQJSkkGvUkdMpQNgjfEXpL2LYejGI9R5mNTi7zi0d3YgGssKwQAnK1OsB+PVhYirinyIPnlExi87kNPoF9f1Fj+aJEYuxTjbJTPuBiQq1vJNrDrtZdwqcpiosfeO8WzhFyJJvRbzl4vhegxZgpqhsagJFh2iDjNhVopSgjrSznDicuIQ0eGxtAoeJYVQlFdW+85PmcpxhwkWSm86px5moYoxnBIhwFVSq+WEl6rTckkRKtgPuqEmULacLJS6GijYmzkgO9fLmzfdgKx1dH2vw/kCxD2g5ucin2Tr6alsq1VjK1JDff+ug4NslgATsYKjjCP8dn77+ARx4KIdFlCw5Pn7YP+VdY1oArc4YhbAt9DRAltCS2KA7Dg2ZhKUJpag/dj1+Em7QU8O8XhBvz4npaVIt3SiOdjd2Jghlk5Hv54EW59dy7e+X41FBcx7oypNjs9MY5prf8KqkrivuqLiGaXlUIhfnmM2d+kdVPJmN5zOVf90H58mPIddl7zNn5W/TiGEm8anshK0Xq4M4VwiIM9hWyl4EnURY9xGQqzCpSk1gKguAIvY1eywnpv5/UmK6dxWVyDblJoTeswWlmA0cpC3Bd7DLfg36iEM3DFoSOLGFI0gRJk0JvUYgnt5/veFYKVYhhZhS/ffBinqlOkfahwixLVZwDooTQ7fczQgQ+uwdE/3ID7Yo9L+/UnGz2pw7qu+lg+mUxEjMOwdGMz/jp+Xt4JbjpnChNtOaXVxdp/cW3sFQBMIXJn4NlDiGwvQcZWfblinEEM8QQjAHGSC1WMeYEBXQgejUFHFpqd9cHel3qvFR0qmi1inKExkBgjElkqH5tGIlAxFgPsdKgwhWC5rOAXfnSKnJqxshuzi8RpylaM4VKMKdGQMQoMZDNywFu/LWzfdsKWFEbaYqXIuawUos3HJsZCNdhC4FaM3ccGWSyA1nmM/WwTfpkufKEJxHjMBQUdksjW+aZSFFFCMlAVgkS2DgCwrzIPqSZnAspjT3hw625NX+BgdS5Oa3wOAFCXYhykMa1DyzXax8Wgd8p4pYgYF9qhAexKViAGlqatS4mjJsSRwy4WyeFwX5QlMdU3XZtBqZ1UXzOzti8PYBf4n2LP2s93UhwyPClxjef8lE2LsSx5BvYiC7d7K8X8dY12rs7NgV+xFcBtS5FTkfGHopWijIQPXADQTBOItaxHDzTgUvVtvBi/w7PPl0s24qvEpThp/d+gmGkcP/Fw/Fl7BgCwm7ISJ5kf4jjVKToTRw5ZaEghjhJk0JPUYbGLGH+OkQCYD5hjMFmP++OP4d7YE/LnFohxgsiTu53Uteymcvcg4ANv/+MoJynoOR0PrTjZOfb7eyC5+yLFOBQXPfc1Hp2yGMtrvIFcomqfFsiDYYbrg+77uS6QzgTR0ZU0waTEtjRkoCGeZI97m9V2GWg/8FWvnOlMIjUY0KEiCzmN2qg+3hW3LDQ7WC+NGBSNHbMBXaX90ojZBTncEBVjHapdSIGlYAsmOj16MmJsppuwKcUmpsQVfGcSFUs2+ceUeGAUsN+fugGf3ldYe1s52qIoGi6VWZyzORkT2mYJdAiwO/jOfztQmMc47DX3ynMgiHBsSVfgt58CJ/wz/Jj6VXgi/kDoLqXIQFMUUJ31PQpiW6MA2LEnlaQFAEW/DLNqrlRZyQlRZdcExbgE6byZkbZHRMQ4T1oWjv6oxvjE9bhRewE0l8Yf1NdsleRO7Ul8mLge3eFdIuQoiat2YJ+7shknxnGaRomgPB6nfSu1McRKxRWEsq/ZBXawMnu7V4yPeehTnP/MjPw75oEZpBi7gu9EBZkPFGLw3aje3hyqIp7Rj8ZGVEExMhiostRD3Sy/VwJZdLfyUH6QOhO9SB32q34dMas4wVBlrdRWf1KN9+I3ohJNFjGOoQUJlJM0eqMWK2gvOx9rhmp4SPMqE7sLaqGY1k0JoVZdSIqRsnQ98PVTgfuVIY2KNVNRacrXAy8mwU4sIsZh4OTS7yYurgyJAUqUOqR5J7LKcxwnDCcpn+LvsX8gZk18PjD2AcAi1ptQYivDWcSQiDPl9tTUqzhBnRZ4viWCYsyLB8ShIwcNOSpbEA4bUuo5XqeOYpxCApqVa7tW6Sbtd+UJB9lZJNwQs1DoVHGIcYDCzNGnJyslXYq0bRdZuK4eXyzbZNuSWnQEWjg8EIt9+J5oE8tO8PHthbW3GdgS6dqMNhCnXMgNSlyd89st6J5tB9/xxCIBVgpfxdhlpQjyDAP+Not86d1suC1lfUcCpd5S5rW03Hny8Z3oThql191VHs/uMhNJRUfMqnSqwcCfY8/Yr3MLxU/VL3Gn9hR+bk4EANu+JNpMRI9xOdKRYtwZka/0IwdPc7K3shC7r3sHZ+dexcUayyJxiDobgFU6NAD7YS5mJy6E0VLnsVRwlThuZlAmeFUfUWVFYXAeYqxtnA8AqEaV71La5HkbUNPU+kCJ7Rl6QD5HcbCn1DVYWw+7Ni/GhPg12JWsQAUJt1JkEGNR7kYW98cek177JHEVPlSuAAA7MwAATI1f4dvWFdrb2ENZhqfj92KsOgtZaEhb/vQYMbCedrWDjV40jgBRvURiP2UeALZULRJWpij4I6noMANSkpvUuY5KSAalNXM8+3SDMLhHinEowpZ9DUkxlis07rppEsqQwsTEtZ7juJXigfhj+Lk63R536i1PcS/UolEgxjmqIZEs8bTjBz72sTzcjmKcg2rbGBp4tgufdGY6VNsKkaJxaFbqytrynaT9frb/7jhp9A6+5yBaNgyogJWHOIdgb/BLv9kfffuxfMsz6U42MVZA8dHc9faxTbn8BNt58zyKcfPmr3RtTWiTYhySlUImxt62Y6o/bUlY2/cexFYZxFVdwFF1fdt0KcZhvMDPZpEvvZsN3bpP/FqwlsXLPLttok7lR2S8gtsBmYftx8/rRyDWvA47K6tttXdXZSVGKY5lqFK4r5ypTUIfyybHg2/tKn6QFeNSko48xp0Rbt9dEEot4pNBDMQatCstT14CTCGQFDEL+5EfMYbMwwXma6ggKZirv7NvdoMyC6FBZwnkwRTj0hCCNZis991+ojIVAKBY0dAJ5DyzvHTOwPnPzMA5T221RQg7BH5VCAHHLgEwFU60UnDFeJfGLzFMWY3/0/6DUkHp/zT+e8/7ZBGDQWKAnsWOcCwxXdCEPqQWPYQ0axzlecj2aGUh+pMaZGhMWkZOqRUor6wCYKlo7hRBAupQYS+D58N+5kwsSZzl+9oadMfqyr0AMMU43uz1wE9LCkS/boXndanspN9rnWhJzy+yfmNTBqtqW2RirDPFOIEspjSfiFOW3Ix7XJ5vDve9u9Jaaq0DU6d6kjo00RLbTpOFhpKkf9951zgAM8yd7ee2lcJwrBQxoiNHNZsYJ7tYyphPAYwsNFsxTiMBffBY4Ni7sct5D+O4Pfqg8aIZwDXWjd5SglM0jj9kLxZacT6gDhVQOTH2EtoNSi9gxGk4YGh3oLQbfqU9gBtyv7Y99qqVWcUmxtkiEuMmixjHvMp5sdCq4LvV3wC3dQE2/Jh/Xx/oBsXRygxcrb1acAYDN9kSlW0xRaWfwBNIjK18vzcfPxzvX3EwBnWXv197sllAVoowi6W/xzgPj2iqBl49h/32fUYAA0Y7r/kRY1R4tnEsHHIWGoXMMd9Y1+EwrMRB1a+En4cLMVPuq/FcPUYse8Z+XhYpxhFEnLnfIOyXXIFeYDOr7pbalaExe4DcrVcJDt25J+IWMR7VW1bmVIXglcTteD3xZ6QsBY9mm2CYFH1Qg382XYk/ac8ibik3CZrxzW7wcfdfAZDVRBEPxR9BDDqIpcRUohkHrXgMC1dX20VI+EC0uDpS6kS4AyGd7fJyXk4q+MEed8uydGgGFCSp89sMUpxqdBwZahHjnEwKHo793X6cKJCgupGFhmEDetnPu3aphJZkA2saMRCBGL+oHyYdS0B9J3R+ODz7ceBrZUjby38lyCDhQ4wlvHu5QxAAFsz3pyrgY6/vGgBwe0/g5V8VdJ7bAzixEfOZj7ljIg7+62SJVHCPsajG70ZWYLnJ+sNGyrI77EPmoTfkvMY9LdWIZ6HoTWotK4WVBxUaShOab4qpt4yDMcscaj+3rRSmibLGpRgfvw49UcfSplnjZbyiJ9vZIsYTjL3t43WJGMehaSqw/yXo3b07Hj1rNCr67QyUWcRaYe09axyNdS4PstOeCqJaBBre879xh5eAk/9lP5+jD0AGccwydwQAfG7ugZas0UbFOGer1QC8qQmbLIFDk5fDOwxz32Z/57etFpdhUjwRfxBXaG8XrC66M1n8/uXv8NJXbLLstzonIpAYWzmD45qC3ft18byuWOTV8CHbbmIchjYpxlP+AvzwDrB8qvd3V702vBrqPX8Ow5UdZTHtB6rGcQj9FoNSP4Sfhwu2YmyNN4fMuRk9Wpz8xWWRYhxBRPeyOF7B9ZicYHkoea7hLJySpwcMrkBZXLUV46OHlUttVAlLOWnCLgYz0wSDUpRaS49Hq1/bS5qlJIMySyX8Ir6/fWy9mX8ATSJrk67faP/DQWuexluP3IiD/zoZgFMqckuVCN1WYAQoxrph4pX4n3Gu+qEnKwUf1PvkmI+zlGQ85ZbdyCAGU40BGUZgaqylskNUx3LQHQ1t+gxZxPDT0Q5JOXjXAfZgm6ZxkJjTf/5Pl/3GceRCLUAi6lx+TxFlSKHOiCNHVZSSDJLNawP3tbHhB+ZXnv06kLJI21QryOSD64AXTnX2NXPAgvEFnef2AA0G+qPaKQwgQFRwhk84G6enX5VWmspI2iaDacTxz9hDeC3xZ5yXfk5qh9vDeAGNbqQJTbQEVPAYl8Y13xWHFBKONQJOujbdoNhr+VPYVVmJg9W5yEGDCosYWvmCkWsBJSp+k/sj5piDrc+ro8lK60aRJyjaIsYZxNCji6OslSccwqBDhcIJR6wEO/aQVbmqUpmM8PSai7Rh2DP9L7xv7o/6VM5WuxtbQ4wXfMD6Kwf3HL94OvC/a4HG9fZ5bRXgVquAssT54I7HkF4zTN/0p24VsiGt44Y3mSUxn5UiHuDnzVchjnNXv0WpQd3KMHJAF4zZgU20VgZUtQXaqBhnBBuZmxj32h0Yez1mjr7L3iRZKVxwf3dZxIA9f4kjzanh5+CDOJXH/soWeSWvDGlPoKRzIjmg3hvLsD0gIsbwLjECQIXCLuYyizR0s8zvJhTEDOui+fY5JPRGu+zt8B7yYNtFGHztKkqZZpimo9L1JPU2MU4ig64a276K9LWP3aTnJ8bXay8hlmZR45yoM8M+69T5Kqh1VrirEXJUbJqF/ZR5+FPsWY/HuPeyd4CpD6HEZBORMqTsFFSP6j+z93tRPxwpym46WcRgkpgddCZWF+PYQfG3yuRDFhpKy528r4fs1t8efNOIQxGIjZjqaqY5FDEYBSvGOcVLkJZU7gsAiBMDdVmCFEkwK0VmY2A764ZfaDWYBu7dCXjjQracCwC8AtSXjwELP7JOWiAZ23m2FY6L00/h8+TvYTZ5v0cxSrz7hum4MPu8FIHem9RhV2UlABa/MFpZaD8WwYlxnRDo0wjZSlEaV+XcqxZSNI5ZdEf7+V7KIlyuvsVKiFPntpKDihgnxqUWMc422+R2HmX+XgrFXh6OQQ8PZrKClQaSauQE9Wz2bUfbjw0oIFZmi349umHC1WOlJqpc/lNO7iZcfahUEjttXb+NWVo4MXaDk+QFHwBfPQ5ssiwhWgL44jHg7h2AtbOCj28DpBWwfMGAfAKRbz8/ZBoRr5lnP/3tf77BGoEI3/7eDzjobu9KU1imA8lK4fO6O4MERzIW/vt0L2fjl1+Brrim4J3LD8YNP9kVADCwW7DNxS8rRVgWCzSuZyIAh3tCpCjAYTcgW9bf3sStFFKf2+c37K9l1VlOBgBgqzVkrDemAACOyDiFl5aZvT2vx23F2PochmzfKw3LSvHBtcCDuzNxYztDRIzhP9v72UBnkOiFWuxk5Q0uRRox3bEjDE05A1q5wjrZQcpslCAtmf/LLD+ymaqDQeXla27FKEEWXVT2eCntY79eCDE+U5tkP+bR0xdqH+DF2J0AZGJXEBrWAmtmtu6YbRBB/qnSenbzWmD2h+EqzTxyxrXAxFuhWtWKypC2S97yQCYAmGHuAsXK+JBBjBEMSzkQq4txvBRnv1VNiFrgh32G9kEsIShiWtIefNOIQ9X8M2bU0zLEkctLjDeWDAYA31yaWcUZ5GvTQE4pQTlSiGeD1e9szPp8P77r+DHXzXZ2cJfRTdU6j1tkO0AQzvjXF/jv92vy77iVYj+dZVyh4me3wPssHzcAoAvx+nYBi2RaxNSdcaSX5WuXSi7ThEOMaTAxTiKHr81dpG1/jL2GitRqjKj+r70tBw2aTYy7WxtbQCyf8M2589H0s39hLh1sTxZjMMIV49HnoqliKN4xDkJWyEQhxovkoEKzcsaSWIlnjHcrxhwVSXk7/26yVJFyfItIH3pj8LkCQO0y4B9jnOfVLEgamSZg0QQgXQes2vwMO344YP3LwO09gLqVwTvxfM9mG4jxU8dhxLvH2k8/X7AWf/nAIcqTfFJqEoQXBRHvVa3yGOexQ/xsz7742+mjcNGhOwbuM3qHbvjs2sNw6ugBgfu0WjG+f2dgvRCMHGChoYLnPENZP6wnXPAgQC9G2jXrvvNElyuwgVZhMe0LVA2yj11Hmeq9wOyPpdQR2O7QvfEhPPNR9/RKfBC/DuUZWZy5THsn2EqxwBIuRDV8O0FEjOENwLvyyGHoYzodZJiyCvsrLDChjKSQ0J2bkCqUclT0FHYny/BC/C+4RnsVlcIgu7PG2iv77A6oVJeWrxOEDUhdSRO6WIrxt03OsnVdTvUkug+DmIj/QJXNVEun349RpBW1zx/ZD3hibP79Ohibm8jez28GwI6cpyAwKJUq33Eo1g2/jKShWSRFLD2rQ7VzAWdoDFSN2dkY/BRjjk20MvC1QIgBHFrCDuxJ0Tg+W+Sff7YBpdCImTfI74V+NwEASkwvMc4IxDgHDaZWit6kVkoB50ZWs4hxg0Bc1wmK2TxX6VORDOf8CaAb0xbX4HcvfZd/x60MlFKsEHIXG1mfYhgWqeDBvwArquKHGAwnhsGaADVbGUz4CoU4mctBswlgDpplpZCJMR24P2bRIUzhvX4l5vU90X5t3Ca54psO1SkNLSrGVnnlNBLAHicBcCaLMejhRCPZBV8c9wE+MUdijdLXd5d6Wg4tZp03V+h67Q6jpBv2GlSF0/YZ6HucaMdg7bDvxqAqiI9+maMqmoedEHyuADDj30DNQud5jTUOZxqdSYcefg22FoQQDCfLcPQqK4YhbMl7cxTj9bOlpyXI2tU7Af8FHgqm0PvFvRsmtW1/gH/GiyBinE8xJoTghFH9A4/nGNitNDQo3/YTr58LrJwhbwPYSljY9x0v990sWt7G/exsfG7sjnvVC50drL6yU/c4rjhiGI489hfYN/MIGiC3N90cDoDZnUQuMNEcjWtyF9nPm5HEQfRb0JdOx6HrnsZuinfytLOyGps+uhfPTlsW+HE+Wxi8OritIiLG8CbnjmsK0OKQiZFkCbpaOWcrkELcaLZn2SWGM1tScy0YozA1oCepQ3+tzn6td3qp/fgo5Rt0jzsBGf0Iu/H3JptQqbCb1zLTUYwbsnKOThHTdnQyIHwy5Eqspd0kJQkAQCmqvrwXbyesEpRrvwcWfIhQ8OWRrXTpugqN2IGs2+x8zUGrREqOkUBCrNysPop7mVVJrhxpaDQHqmhMGbYgVtvKIgZTidvEeMgApwjH4/rxUrtfmLu17kOkG+TlOUExtgfGg67EmdkbpMP4Tb8SXrK5Gj3tx2vT7DPFfXzUIjHOQgNJlPuStJWm015GtQZycQlOVIx7CZ/f0B3/MVDQzbvQyPitEU9/vgyH3jvZrsBGfdQY7rvsQpyVqyB/ehy6bWXgKwNpaywZYP1OomKchSZYKWJMMbZIbB0tw5+VS0Eu/BApXr45WQk97kzkSnT5fLNUcywcXDHONDnJZuHkpeWeZQ1G3vzyPM9shvirbw0oQ8wmxtYk9NJpUK9bircuPQi9KvyPcxPyZsK+Gx2Kd1y1EFRwhOOTH12BqA1r2DnpKWDRRKuR8BiF1oJSKhdk0UMKEPFAwSCP8ezXWTGSXBqY9jC7JgFA9640/VSdjiaBGAcV6NANE0nN+701pnPICmpy1keQCKpWm48Yby6uPZatkPAgPjx6IPDkkcDKGbId862LmMXACPg+eRCpC0Qcw/uOxJm5m7BQ5/YHagfBqlTH1UftzK5NHyw22b1FvBdxZIWc4k2WGk3mf4DSXJ3/uQLYf/HfcOu7c63GPwaWT7dOmH3oG974brvLXJGXGBNCniKEbCCEzBG2dSOETCCELLT+dhVeu4EQsogQMp8Qckx7nXgxIQ6GO5I1+Nms37EoUgunDXJuOuUkhaTZBHRhikMX3SEBipHCAQpTaHWouGuJEDwEFiG+2OyL67WXfKs/9SObsCsYgRbTtTTqBBninzapRXVuSnVGKbJU81Qs8wx4jx8KvHiab3sebKX+oYmJa/BJ4urNrsoTpBgTnd2oFEKgG3K6No7ulC1zJ0gOCZoG1Li9BAbI/rAMYqDCknS/3o7fS4zuf0UfZ0fnF4zUJjn1k6AYJ0kWE646FDjqT7jmkotx5ZHD7N369GaTr4u09zxNnok77ccbWgiyVIVGvcSgRThXHSqURBkGEDkrx2mZ/8M/DUdVS3PFWOxbtcucxyL5zTbKxUQKqCrmLje7LeHLpYzM2BUIM2zsEW88XEUTFePuhO13auYWqT0NuhPDYAXIuTPf1AmKsQ5VUIxVlMRV2/f9T/0E/E870nPOekyw/qTr5NegoneZdTPuZi1hZxttsg04gXbcY6wRI28aTUn1O+054EQnN/gyleU55uWsC8n+8PrFB+Dm470T0myMja8ZxHFYb39ymTHDb6N1ja6JJzWccsC8P7sLP2wmTEodbzfgmyLPBrdSBF1bb1wIfHw7pjzzf8BHNwEzn2fbW7wT4L/EnkQq7fSvQGJsUt9guaxLhPAjxm0NvmsTKAWa2TV56bidsOzu4737PHmkPcEZTNay7BMA0BRQdyCQGDv9tCzBro/6nEBubWWf/U5BRUjWW5la1tOueOvSA6XXxCqUKc3JfDGwyetx/3PubPuxPbn9zy+Ap7l1hr1/HHrrrZpbOQrpSc8AONa17XoAkyilwwBMsp6DEDIcwOkAdreOeYQQ0r7TuCJA/FE/TvwRAzdNAxqtqPqKvugH5pNqpMw/2SO7Gug1HCAquuoCCcg2YX+LGB+ieAscpBHH68ZY7KBsQIUpKzw8sfwRmYnIUVWa7enQoKtsNvmafqhEvho1J11Rna75zhLx/UuFfA3+ENW6rQg9iIswzHgS+OHdVrcTVLVJsRSWBHLImdTXE6cKdoEKky2Lit+/WFgggxiomMIp6UxoaoWlsDRigasDgWipdVkpHMU4iSyG9WbEZdTAKlx5pJN7NqOxcxghVMHjqKFV9uN1LUDOCh7NURXTjeF2Zb0m6kzYclSDliz3TMyakZSUihlrrRu2RfpQbk0S1DhQ2V++ia/9nv3jKIAYb8vqBecRtp/VUoybs853yj+f6CvuYWXNWUidAJ4M1aASaudJTyKLE5SpdgYJDjEnag4aMlbAmQKTVQSziHEacV+LgxF3iPF+hlytMwfN8a6WdgcqrJUSRcOkP4zFZ9ceZitwjVZWijgClDYBfOmaEAIMPwEYZaXyu2kdLi9/EAAQK7HOi+S/zY0Z3A2/PsTrPa3U2He3gvZCaSOL2L8iexneNA4GwFZkfv2Cf+CcoTKi4w56BMCuV0WwbRTZSmGYsgcd2RBFmhftybMaU7LyU/aA+96b/ZfQ+6Uc24gfLyZgfdhP4dUN2UqRynkLCuVL11ZUfPEIcO+O3rzruiuTT6oWGnRMSfzB2Va/2ldV96t0BwCKIG6UxVnfqDdEYmzdF6zfSQ3w4X9s7IUHcqfgltx5kjXoV/sOxKghjiBjCJaOhM9qoJgbvwpNLO5IhDUUxKBv02OuH/KOGJTSTwG42dEJAJ61Hj8L4ERh+8uU0gyldCmARQD2Lc6pth92Mhbb5Z096DoYWj27KGppOcpIBn1zK4G+ewIlVeyxBbV2CaqsmxW/UYmoKCtD2iJO5aa85LgYjtmfpVqSk9UnS9hFswkVuEf/pf1arep4kTfl4sgSH1L17u/8P1shaPEG/2xNsAfR968GXj07cL9NzVlc+MwMbGqWB6qgiS4nxiUkI1gpKM5WP5KPtyq+VZgNgJqQZuQiMc7SmBzElHRm67VCsF0acTsSvmBkG4GE4EvWksDIMwCwfKxByIhKn4CVZk9J6VnTRKHDqSKmQ7EzsTQLqQRz0BArYYNtVi1HDaoAsP4sfi8z1llferqeKdv8hqCVMMIgBpw9dwKwcYGzDO93o3FhW87A4r6/EMt605R2yJWeZWOVqBj3IZuQpjEpZ6975SGJHK6NuQoAKDFQKHb2lBxUe7Vq124KI55WX0gjbqtUl44bimcvYEO7GeCZ5O3ZS8qxUqDbEOt9VQztWS5F/zdb/vwl1N83LEIL8onGSpC2JmGkwmon0+C/bwHoqbBxfC3tDux0OADgAxyIN41DAAA1qMDaRn8ir2vss/1E9SmqpCWk/NB1DW0/Rz+YlNqxKwDCK01yQuxnpfjvlfZDXi3T/j19FGMA6JNdJpyH/1vmDH9inDNMyUoh+pU5gojxoJBMEm3CmpnAh1ZgZaNL/f3wJvm5nsaFqisP9FNHA3/pDw8CFONEqSNulCV8cnC7Vj6Ccic3ohR/N05CLSpZO+e8C1w0BX85aU8cvItj4WsqG+x7PAB8Z+6EFkH06EKaAKuyrgNHMQ4LptwW0da1h96U0rUAYP3l1QX6AxAd3KusbVsv6lbi/cRNmJc8Hycrn3pfrxoEpYWpwlI1ml67AS012DXrKMPxpSxn8HdV/g6SRLLEVgPLDJkYf6CPwWKTDeTuG1qOqlDj7KLPIiapkk2mMxjUZDXoAjH+0tzVcw6KGBS14ENvoJMbW6lizGFk03KhiAA8M20ZJs3bgP9MXy4fHzByq5aVooRmsHvDZzji8zOxJ1mC24X68wBQDTYxGZpbAGgxVJQ5JEH0HjYjAYUI7yUQYzFnZQYxjN4pPzGQQFQg7rJSDBiNwekXsYT2CzyMugo3TDOG4zNzBE7J3ird0JoMBbriECcx5VuD6fS3LDS7sMjK7gcibdl/dKEsMACszXDlI8uUbe6t0xIsMMVvCZIr4kYWWDYVSNUFfq4OVS8MveDMGX5wB5NyYtxi5dk9QJmLXZ7bE8coMyTLyj7KAtSjTJqM8bzAHElk8YmxJwDgZX0c22ipuXxMyVHNTt82tJLL1+y9MzRmK8bXHrsrxu7MfONU8Bi7ISnG8XKgu2Ub8llIPGD4ECw66mksGPdoYHscYR7kE0exPl/W0wqw2ww72MIubCl6tjmE2TX+sACaqtk2qY20S2DJ6ZxS4rsdACPFQlDjN4uFQNRUHZAL8QQXAN2gsvIeZKVYNBGY8zp77FaMU7XAN097j+G/Z4BiXJFZj7/8jwWr+wVHUwDVTRnfLBI5l5WiOVuYYvyrfQfi8sN38mzfLKz+2nn87M+BOW+yxw1rgB//K+/bsgl9iM9177fCFaAYlwnEmPuHbataSVdgx3HA/pcBx98PwOuH/0AdB0C2S5TFNWDHsUA/VpVUzEn+3YgbcXX2YtRW7S6180DuFPwqe5NEyqvQZKcadSMGfZu2r/mh2KYcv9HK9y5FCLmIEPI1IeTr6mpvpbAtBqFu/c/U6fJrWhKocEhKVQ+BsJR0A/o5lZvqex9g1zTfc//Dfd9KiSVtG0SJy0qRoRrmUTaQq8lyPHzGXvZrOlRo8YS1n4sYC+pidUa1CQzAUi+5kRRTbr14GvDyGb7n6jSydSvGRrpRToUTAO5Vi2lyFw3ywKlWPscEMrh4w+3oWT/LdxVgoTIYANCFNoBkW/D3s/azXxNvmC1IIkGFQVJQeOtEKwWN48i9HLtDPqza5Tzgt64JnUV4+1QmseeA4ApKVJGV6TNyN+Pc3A1Yj25SKVkdqu2PzlqKMUeDIVgpoIFYBDalVeA15ScAGHkQB+vVaeF9Y6WOEhJLMgLMCyCI4Mv12SbgmeOB508O/Fx6R/rdPr4duGdIKHEPg7s/Kjl2M+L99wL1A6i5Zvxaex97KkvkfWFKUehNrpSAScJC69bQbviODpNe4301B82ZqPEbIeUpB+OeQGUAoDFvSVsOHZpDuOKlQB9GzG2rmoUf/3wsHjlzb+x00Ek4+/C9kQ+cFPjdcC47bCfM+dMxKO9urcK18bcAgBndT8Tu6SdZMGosCVT0RlxT7NSFNbRSjiUQLEPZgMBAAB7FOMELLdSvBv66A/BfK6h68l/aVJHOoNRlpbCIcS7t2AI2/MiuI25VEtO1ZZqAvw72b9xkntLaav90iP3IRjz+KeubfuPr4g1N+H5lnZTOlCNnyBmA/BRjP0L9i70G5M020XoIvUtPAa+fzx7/Y4x38j7xVpyvBQS0dxkI3FwNHHYTW8nrN8p3t4oSZ1zkn9GEAhz/AHDhREDVgGPvAipYbIibGN+hXobd0k9J20oT8gRUESZjZSUleNM8FE0JOb/xCtoLaSQkK8Xz8b94Vh2oZVGKk05opQjAekJYBQrrL2eXqwCIeXAGAPC9eiilT1BKx1BKx/Ts2dNvly2DpQ6p8HjBqMlmahYGD9zBeS1ZCZzwT/up3sPJ6akGLJWosaStnJnNm+zlS4DdkFooG0h7deuKn+7pKH06NMQ0dlwGMXs/AGgynIF4VTORVMCUj1e1Qlh+tTHlbmDWq77nvDnq15YAzTSxtDmA41/0AVch3PlRgy5ozbJSaDBslb0K3hmz2dUJnEO2SZqRu9W7mEiMBcVYnOhkYPlsXUjH/Uvfrt31PKCPyy5hlcL94sYj8O7lB3sP6sUUgrjgBb4yeyn7PFwklL4WYpPoHDRJMd5kyOnaFEu5TisVeFH5KX7e4320ICl9xgZTmLBJinEJUxWbfVYAEhZZ49liRDXHhQ4tYcoLlfzwdpsONykwlKzGUIURR9Uixrz/9rSKclSiBXsrC7HAdPpKwjV+NQqpAzNUQwJZxEkOWRqTUrQBTgaVHFTH8561VrUO+j1aYt2xkPb3vV6UEG9nM004WUZipUCfEewxlZXAkrgabI/wQVg6N0II81Zy73qX4Jy0fnj7soMwrBf7DjRNtS0eHHFNsWNC5tLB9mMALitVyOdR45JizEvzYrFVDGPWK2wl7JO7gZdODw+eA5gXff1c5iV+/4+4+vN9cJTyjfM6T3P46jnAQyNYOp5H9pfb4BMISoH6kLzHRg53vv8jXpj8re/LPMvSso3NqG3x+pa5Cuzn6c4ZpnT9tvgqxt7fPl8WExsb5gG3dQE+/3vwPqbJJnPujDBlvZi9gn+Xgw8B9gieoNsYcwH7rcdeC/ziUbu4jRvlSed+IQWf7nMh0MOrhruvgYypONliLLgnC0S4P3H/cdYV2N+MJJ45fx/cdtI+9rYkybF7rdQYt1LkOmXwnR/eBXCu9fhcAO8I208nhCQIIUMADAPgY7DaSpBtASbeZj8tI67lKyMr+3p4Hk6A3ajLe9lPu+8gkJNEpW9SfCWWsAlChdkgWSZy0JwcuC7PXg4qVIsYZxHDMurM8D5f7hDdulwMNUmHvPtVapqiXu7Zhil/Ad78DTDlr8DKr+Q0M1u5lcJMNzrEOKTEas4wUYI0kpCDXIIUY81w+gInxl2JlxgnkyXIDjzIakyXiPHxo5yk6y1IyOU3heA7UZkYtWMf6UbOU6yt6nMkLs1egand2ED8D/1EXIPfY+dd5GWwgvDricC1S1kmDQAv6YfhbVMm0O5vxbT6c47KivFKoyt+ALvBqTBArMjpjJJkkecWaRIDRikUJxBRIMbVaaDRjDvLjz+5zzmBhHVNCGkUg+A72cmlnBvd2llyFoxigntoRU/ivPeBef/z7vvtf4CXfiVtogAOVObaz4kV5MOzonSxUuvtRFajF6nDJNNRV93pxEQrRSNKkUQWCeSQQcxTYIZXq8tBQz2vhMdvhPtfglfHTcIS2s+3RDUJue4aUQqc8w5w9tuMEHQbGrhva8BL24cmryjvCZzxGnDyv1vV9qiBVfjVvuza3cHHtxpXFUwxR+GOkmvwkH4yxOtXCsjN+Kx8cGhJSTGO0yzLifuuMD4vn+Y8Xi2QXD+89CuWPuzDG4EZ/wLg5LBHssoh1gstVdPPH7x+LiPFn93nJc0i9Aw+XViN7miwg3A5GmipvbI27r4poaccpBj79TERfpXvQtXiGU8CL1pxOfx7nPB/clCviP+cyIqiTLxV3l7aXc7tn6gETpEVWgDA4f8HHOdUnLNtDHngDh4cs0NXXHes1w7J4fYYBwWSi1BiTp/jPmZ3xis1WYlxu/SSA7oBmC5izG+dMR+PcXNGt8usb4soJF3bSwCmA9iFELKKEHIhgLsBHEUIWQjgKOs5KKVzAbwK4AcA4wFcRinder8dV2qhKuumkykRlhbE5PY8AAgAEl2YnYKjt0CMY0mJpNWVsEGWaAnc+HN2kXQza6Sgqxw0Z+kiJg/GVx0zHMR6rwxiUnDK8nqHxDYjiR96/1Rqs1WYchfw2f3ykknLJuC184FXggPbOhI00whssnJE51JA9QLgg+s9CYpzhokfkxfg9E9km0ugYmx6o8S5WidBVREfa0UiU1OaSFVVOANLM5IuxbjK931/NnqopBjz1QFDK8X/zP2hWyVwG2kJ7r3tz+hS2spAPYAtaZd2s5dvpQAPC6I38Kojd0aPLqyvuhXj5hzFO3GWwqi70gxiFbzJURX1qZxdfjXr6ou8bzabcfs7W95AMW2l8L0LqzX2ZLEAYuyrXjx+KPAXa8Lx+CHA30bmbceDH94FmvLYvnhwoHgNvXwG8PKvvPu+ezkw/3+SPE8ptYPqamgFYtl6YMIt2P2tozGIrLczUaiWX/0jw6moNtkcJTUvKsZNKEWcGOhbShFLJD2KMVc9dWiOYrz/pfbrpVaUvF/awpaee2OuuYNnO2DlJi7vBQw9jG0IWE1rN+x8tCxoFIjzDhyMF369H47do4/nNbbMTfBj9yOZVUSASIxLjZCAOi0uiSdxmgXWuYjagvHO43xVSJdNZX8b5JzJJhS2OrVsqjwmrvzS20bLRnb8dy+Ev1e2CQQsReAK6ohDL+vj8Km5J0qCAtldCPIYZ/RwysD7ooigMtEAWGA2/y5VgYx/+x9go0/Rq6WfOI+FlT27lDcHD1Y8/SXg4Kuc7Yf+EdjvIuCkfwGjzmLe4Dbg9UsOxCXjgieSbsWY38te/PV+frsDkBVjnhIu4xr/dYt/KAkhWw1VZcWYOma7GAxP2tTdb/0Q+901MfA8tnYUkpXiV5TSvpTSGKV0AKX0SUppDaX0CErpMOvvJmH/OymlQymlu1BKW2+O2pJIywPXYGU9WmgC1fsKdcdFxVgc1BMVUpJ6aTlbK7GDS94xDsSPfX7GtlOKwb3ZIN0HNdiECjurQRYamrlFQpVn0sftOdAm5QMqNHu55HtTXopKIYHSSucmoLeiWp4NasrJ5mf8C5j7Jivf++zPWt9eO8PMNjtLW3oKeO1c4MtHgU2y/zJrFUyIGfKqgEiMRUKVcBUqAIAdFB9SpMTkmbVwszttP+f3oVDk0suV/YCfPoT90g/L7WlJNrEq7Y6HjFPtVQTDinDnHOr3R/gEmoiTswKwohtTid+wIuxFUArgV68AR9yC3x85DGo56/ssK4XTr2ozFJ+XHYUbchfiefwEMNlNrVln323vStZXs640gimDXTspJOy0Vmkak4MIRWLspxgHqP38N92dLHNUz40L2N98S9IiNvwIvHQGW1lK17OsJy/9MvwYfu2EpccKOgbs3LuQZrTQBJpoCUbUTgA+/xtK6xfiGGUGKtFi+1hbuu6G3518lH3sVblLpWYbBPJLrBWKLmjGjr27Ybmw6nTW/oPsyY5BNOjQMDj9IjD2GnufEisYyC+vrKIo+Lt+kv38B4EkN7pLn+fJT1woqH/oStGgKAQH7dTDV4nkJIz3bREXKH/G0/oxeEE/As91vzKwfeqyUsRphi3Vi9jwg5Nqjk+0TMO+xvDDO8wW0LjeGXdcfmoFJjBwX9b/a50iU4EK9Nrvpbgajky8ynmSrgchBN1II9bQ7rZqrENFM02ilBRGjIPStYUpxkPJat+Vu7iqeAMWb+sCTPyT89w05QwlM/7FchB//jfg9QuApZ9535CowGE3s+/EHUjHifGuPwF2+7n32D1PA078Z9H6vBtagC2wS2kMJ+89ALv28WYdUuNOn42pChKaYhf8sdvR2Firqs4EJEYMmGKGKj0NKSuFj8DUIGTS2dbQuSvf+UQrz6ZDkOgnLE+LlgjRq8YHtZ8+CJz5uuOBBBixsS6Gb8xhTnQyNSSiXUsrbKUmSzXsO8xSCl1LGFBjNjEe3ZMNGqPSj+OX2f+TdssghgqRGPtYKfLC1IMjogU/9taCXEs9aurYYKdnWpwbiauSVFAhEJEYp3nOTNNA19xarCTyDeI4xUdlUWOu4hrCwCMsWz1y5t6oqhKInhoHxpyP9XCpWfz4a5fgEfNkO2WOYeWx5oSgzEc14RaJQrHrCJa5Yi4d4nnNpBTY5VjgEEsN34FF53chTVKAV5Zq0CnBS8YRyKpl9s2iPsPOs3elv2Js2zHiZZi23FqpQdz2KAMASqqcxzz4rlkgxgF5V3MGRXfU4/3EjcD7f5BffOIw32MkfHwHS8f07hXA/PeZ2sYn0TWLw4/l+Wj9KpkFVZEUAlxNSlGJZjSg1JOTfDBZD42YWEaZitk4cBxKy5yxomsXOdBSrGi3KcHGliGZHwEtgSd+fRjmDT4b2Pe3uOPEEehRaY1Rind5G3Ci5P2IMSFARvh9f5a9w86IE1b6fHNQkWTnuUe/4ODSYiCMGPPVEBFL4rvgT/q5uEm/EIuFnNJuNOQU6MT5rhM04yVeG+ax+4qacPLmPn4oC+4EgGn/YH9rFjliSrPP5J2TNrHsuosYt1CrfPCamcyCImLQAfh+hwuc5/Pew7+aLsdAsgGbUGlnTuCrnqXwrrb5IVAxzgUT40mJa3DBbO/qZUndfODOPsDct9kGbgec+oDQeLPXN5yqBSbcAsx5A3jlTGDFl4AYTJraxCaIe5/jPRkxvZ2YLnMLwZ3GmJPThKbi/tNGYvyVh3qOIYJ9R1UIkjEVWVeBmuaElW2moi9Wmj0xzWAlpjP1gj0sl7JzrcdJLkrXtl3BJ79lIy1BZZVgmRAV40qfII4xFwDDjpK3xUrBZ1MpJED4Dbyyn6QSbKIVtlKTg4YuGStOsddwuT0lZnsXB/RgF2Blt95IW0sgt5bcgFf0cQAIDh/lLL3k2kKM9Wx4qiB3PscOxrQfViCVYsRKMzOOx9f12/rd0AG58p2dTL5uBTSqY5HiJYweKJo8kRH9lsKk6icj+oKc+CiLTP71JJfHWIBQ/YiCogaVyJX0xNDdR2OvQVXYe5B/EJ793q1YNt53SDf88Gf/1IKeYW4AC8ToRzZJvkKdKuhezj5nTFVse9HaLPse+nSxbpqWykkHHoCKpIbuhN2g1gw4Dovr2PeeRgxNYkJ7ccLhpxgHlNE1TIr9FZYuyrP8KebiDOrLn94LTH/Y8devn+1MovOpP/za8csb61PeGYCk8pkUqCQtaKClkpKTjXfBUIWND4utTCi9Rv8cSSHF03u/Oxgf/N5R/xeazni1plSo6kYpDtypB3Y972HgJ/ewTdYKF/GJjQAcEuhXcIFALlk/6Y9HYNQg1g88ijEAXDkH+J1/4Fah6F9VgjcuORB3nTRis9rJh7gPMeZDRmXSO4koEVRQPWTVYH0L8OUKZ/UiTrPOpOqyGUypNDKMcGkCMV4/x+mLdn+iTrEQvxRq/DoSA6zXzWEq6DgWw6BDRXPpAEay3RPOrkPQYMifdYi5Ar1JHWpoJZosYqxDRQqJgq0Ufopx1sdKMZQwewjPU1yRlb3bt2rPovfz1oR3vFX2PutzrWUa2T/FR1QA2Pf61NGMQI+5UH5tiOAt7j+a/S13Vl0kYWwLwa0Yc2IcVgFQ9BjHVAUlMRXueQi1bGtaLIFDsn/Df80DAABZgRg3NDbaxDjWSSvfbb/gA8xOTpnTZpQgUS6QC8GT47Y4BEJzFOM0jdvpjjD0CIlo7z5siB1Nm0UM87pbBHvEKXJ7agwY+Svg+Acw8CfXYOlffoL+VQ4Bm1l2MK7TLwIA9K0Sy7sW4DEeex3QdbD9lGab8OB4S1mo8vEN8iVpjrlvsyWrB9v3BhUEJdckWRTsKGgxzdzKr7Bz3VT5wFwKWPMdTFExzlq/k/UZl6j5iTFRXVYKyVbh6i8VvVlk8oAxcMMORitz1BqTAmkksPz8mSjd46d469KDUFVR7t92G+H26+1iVcnziJtdnGQzosf4uJEDcclYZuuIqQpw8JW4g/wGD29kXnq+3LwaPXFd7jcgZ7yEn490soesLB9pT/AyiKNByLIyXSAOnqwUgHcCN/8DYOUMGJkm7KVY1be6DGBLqETxBn69cxlCwQnr6m+F1aV8xNgiQz/+F/j2OfmL5Gpe4zrZ7yn2VQpLMS6zFWPaYxc0lg6yCcL0+IHAxVNBdjgQ5SXOeNK9PIHd+joTru+pY+XZWCrYrgwvceHEGKr/mDGiP1NmxfZtEEiVDQf3KEMixp4bfreYqoFOPuPNwOgduvqSq2LCr+wuJ26VSfm7MrsNRUI4n9ose/1p/RjcljsH9+ZOs1+77u350ipKAhmbGK9oicEos0hXooLdg4yM56I0UqxPmqkGZxLuRwj5ZLtOyDSR2sRIt2UP1GCwSmjZZrY6ssNBQPed7OPrdf9+UUMr7TgIbqWIE8O/2p8Lfooxt1JwVf4Y5StMSlyDY5QZuOEYbxYLAHKatNQmYMKt/itDD+zGrkuRxJ78JHDpl7JHGHDIL8fgg5zH574H/PwfwPFicHCRiPEfF7J/BSDIYxxWAVAkxkwxVpDR5X7FrynePl+17L7RyQR0xqMfg8snZUh7YnUOVb7HfuTHbTaNW+cmxlxV/Pk/7JzEzTTpqiKWkB//7lvgNx+HtxsrsW+CNajEt31PA057Dtj9F1J7I3fbjamcYAnk11buCdxWD1QNkttTNBbRvc+FQCwJQgiO2d2ZrfL0N251o6Dgu5KuQH+HqOVaGvDVglXsyQkPe/evlQtk4DUrOUn9CubJbAuWT2+1Es19lnGjBUlkbRVz1SaLTIlk48mjcNXGW+yn6+rT+PGxc4AnxkFL14DAxNXaq9CrFwIf3QzM/x8MqPgxHuzZ5e9HVE1WNkVFUY0B5X3kpbkAkF88Bhx3j+QT5hkzpBvIwVexoKgxF7ib2Gy88Ov98NolB/i/WCmmD3TOZ+d+3VASZ881lQBaAm+rx4Ba++zQ3flu3sThQElX3H6C8xlfnZexq0FmaAwNOafP/v71ec77+wXfCYpxfSrH0lo9eSRG/Wc4TlYtv2C6ngXZUhPo7crgsfhjb45bkXzwyP0133kCdYOQbhGU4vE3yOS9eSMw+3Xg/l1YP+NwWyksxZhn8ljZBHxbE0dPqwx6i1Zppz0rS2iYaQ7FbeZvPOci2hhMtQR35M5kT3zKD9uKsTjeCSCE4Ptbj8ZrF/v3D7eHnFu/lCJN4DoKflYKnkKsUsyqcO1SKBdPldS6z3M7Y9LIB/AX/Qw8YxyLfxon2q9lEZO+M6YYswn+Tx+dgXkN7LVGWgJDiTPFeMUX0nlkmusAAH9+/Qs0G8K4Ey/HG/2dOJk/vG0RrYZVcsajRLk9dmnQoasl7JrK1FtKtUWotRKb5LuxCZV2dqWcpRgDQImvnYLiKu019EUNSpBG+ae3eWwXLPjOtK07I61c3TuR1Thu5wLsCnoa+Pwh2U8tYuMCZss64Z/MAjniFKDXrt57btUglh/+UsE+99tP2YpfvJRZK8TAvJDMLK1CeS8p21UYgirfhSnGvB7Cc/pR0BSCZTUtWFQtr25xYszb9w3OzrTYlrqbYy8gJ6j8Wd3Ec/G/4pXE7WhMh5cZ31rRuYkx9w0mu9gDhBkvlzNRiNXB1ARTOtyzSTe0JLDfxcio5ZhuDkeOJIDhJzDSJFozdjkO2PN0pGNVWI9uqArKMOBzczn3wMF47Cx2HjwtyrXH7iLtE2ilOPZu53GsRDonI92IBFdgYz7LoGu/B2a95t8uT5vWGpgG8PSxwL8Od7JLtAJxM4UksrafcgSxls6/fY5VKPLBNa9/jypr9nvAD7djd7IMV2hvY8eXD2W+vW+ewdLEzmjWgm0LPN0VcVspRKhx4Kq5wPXL/V8XMeIUYL/fSsSaczSJGCcqgGP/UryBWMBufStRYaXwufwwV3Cf0AdFxTgei0Hh+SwtEsGVhlNHD0DPclmhAFhgE8fijSm7BHYFaUGdoEylBeLw3LcWIRbTBwqk8/7x8qTMDtBZ/DGwhFWkFFVv9B/NyLK7aqLbAqEmWKQ+Xynxs1I0rLXJ7foaYUKWbZLbS9c7xRVWCKm4OAGnFP1zy9AVTahHmW1PWN2sopo6pCCjOo/LEhpOzN6OZ3NehawFCVvtIbGEk83Gp6w2J8YVpcH9qktJzM59KoKASMUAAADHP4CN+9+A+6+51LP/tgS//Liralm/G9xduO5LuwHxUslK0ZTR8V3pwd5JA1jflhVjx0qRQcyuPvjNOh3rW8BSDL7oKM4wDXu1y0jXY2OLMKHb5Th82dXJTjRrvfV7p2qZJY+vDsTL7XEkTgwWC8MV40SFo0LHktiYZtf2OiqPiTW00rb85KiTWanUx06xC1mJ32tv4R/xf+A89SPEvvwnHon9DcuSZ6An6lgbhomsbtqfLWGlIMxCkwJnS5CWKj+2CokKYK+zZAukmxj3Gg70HclIM0ffkWzFzw/tFGAXhqBc3mGKsaqq2CX9DG7Vz/XkDZ9r7oBTM7c4xFh1AqQ5VlG2wrArWQEl69gVTeG3ack6qwV+eaw5Lnn+G/z62RmBr3ckOjkxrmNqbKzUJsNnHepSldpqpTjur8j+cQl+sddA/OZQYQnIHcx30uPQrluCO07cA+cc4J/yyC8ghhCCYb3Z4NmQymHZ3cd7EqYHZqXY85eOfUIrwevfO+RAzTWjhBNjV212ACyS981fA9XuuukI9HwCYIPaS7/yFhLhRKFhNfD3UcAXj/pXPgOAZZ+z6GFDR8IqTjGyZTo0YnqX7lbNAJ47EXj5TE8zLRkdxFoGGlw9GQN9BthapTt01fn8/9GPxAGJN+3nPEesQbTAhO1QVLY0vZmqWdhAVwyUJzTEVIJuZXEQQrDs7uPxx2N28e543D24r/I6KagznkjYxJgvO/PqaL0rk9BUBddYbflVTVvfkMY0k11z88xBqMkKVcQE3+rU5T5967GDWJCNnoWS8i9PC4D1GQDoIgRDcQXcnafb7dEcYnl2l0xhf1tqnH2mPsgmUg/sCvydWUeSxEU6RV9xpsEhyuLKy7LP2d9vnsHfNl2CgUo1VtKetpWiBQlUo8ppJuYs2/IgTL+4vt0G9MRGi1ATRXPyGvtYKbIm+226VrY+WE5ViJRCEgBQ2g09jr0ePSuLP4HbkiAhhGdY73IcMqyHtHonWjsMk+LThdWSgpfSmMqYgyYRZg2G3Tey0FBnTfSbUIIWU2U+90wDMMhS7D++wx7DKpCSA621JETLpzjBRFlPp+8nKiTxI0sSTr7vZKUzrmklyFmFnr43h4IKHt0aWmGvbOhCkaoyIivBBCYOGcLuV13QjK4KI1LjVJaibpTC0qaxPMaGrcbz3NwZxKSSxM/F78bUxO8RUFg3HGJQL4c7E0dZd+8++dBtR+AAnzoB7YQgYhxW7ERTFGQQB4ViK8L8G5xojsYMuqs9+eV2FrEQ2UbK+u/98ccQaxZWeUVi3FhnP17fEByI+cGcdZj4o08xp60AnZsYN1WzFDmEOD5g0Ss0cD+vlSIMJzzClCjLFF9RksADvxwlJzL3IZuapuKs/XcIrvwUQLy4zzjh8tndUXEzLsn+PlgxjpcDQ6yI1ZYaNOSc942bLU7wRJgqmarzBmmEBe2t+prlbH3TteTrDo4afz3w9sX+bbx3JYseFtS2AbllAICXjcO9+2+cD8x7z7P5bzW/RR/iKHv7KF6S34RSmKrz+dOIS8VAuEoi3ZB2dVSaQnH1UTvjyN16h+6TCFkaKwam3XA4Zt5ydP4d9/stDjnxt5JvNB5L2GIJX3bmQSC8HCn3FIsDee6kp3BR9irkDIrZGIp90v/E48bP0CgoxmJWhixiUrldG88cD/x1B1TmvAMsT4VoQ8wqwwNp3ZUd3XmSdziQ+ZM5MQZY2sJsMysOxC0RlmJcAjcxFoJAF05gecIBh5D3HwMsmsAIvtBXl5p97bGiBQn7hgQA2ZijGPPv9OS9vYHBIwd1w6BBQ+zzsgPhfBTjtLUU37M8pIxxAMbs0BUXjxuG5jGXS7mPt1ccsStb6i6Na/jPhfvh8bMdKxpXjLuWsr47a1U9+nVxxpGVPdhEK0c15CzhopFPWKrng6qMtNRZOe4baYmzatl/NEsBBgBTH0CZlRbtMu1tu1IiACBWKo9VVLhvlXa386SnSAmocD/KKElGztN1LIjWuicurs3h3YZdsJL0w736aXhu1Ev2MZtQaV+nOai2YjyYyNa4e7QncPMaRho16ChX5HsHv1fpJstKsd+Q7hizQ1fELcHjcGWmtPqyj8JWcO7SnkSr4ZfWsttQVhH0pw+y4Oi24IrvgGPubNuxbYBbaHj/ioNx0092C53MiWMwf8wnzM1WP+lpBVOXJzT8+5wx+N1YR03nIoYbNNvC7v+pWuQ2rbC3L3PZNLYVdHJivN7x8/Acknw2fHM1cN7/XIpxnmIKe52Z33/MB6IdC0gbxRHQ0ZMxFfecsidevkiuUjQjeSA+MPezSZvRY1f8PHO7/XqOaMDo8wEALX1Ge9JC7W0FLv325R/kNxSV60wD0GgNxrzKT1iO2PVznMem40fytU+4fZ8c1m+UnesluytpT7yme9PT+GGAIZc7tTMYCGgmJdA154b2jbmzpMLwFEUGXw69eQPzkbcSVxwxDP8+1xuMJ8IvMr6YqEzG7CpI+bDfjt2xz47sd0jTGOKJOHJWqh4+seN5SLmaaSvJwqCsjTgJH5ks00VcVVCNrshBc/nZCDZZS8oZxBzPvKjurPwCyLWge9Zrm7k291vgCMdbLhNjQTE2TeCN3wD/OcnrJS7rJUefA2y1ZMV0z/vphomkewm5fpXzeNbLnmOw99nMYvHoAcAiJyH+MtoH3a2iKi00KRFjMyZXxpx/x7G495Q9PU1TSoEDGRlpLhvgEGMfxTgRZ9f2sF6tV3gVheC6Y3dF2U/vZDaf7RxPnDMG8+841vc1rtZ1FyxEvSqdx9/seRvoBR9iLbrDtAI57UIs894DsVK29a5iv9U62g21WWu/WA8nbaGAMnfe4FgShknx19zpuDt3OlLCysuS5oS9cvLa7Dr8Y6YzqKWRZKWgqcmUZSsd2dMzNqAaVbiy15NYRAfg8WkOCd9IK21LCAFFLMmU7ifj9+MoxQnWOlVzUn3GiIEBirwyo1i65S3vzMXquhQSMQWX9JqNX2pTAFjK8gwvCT5Dc91vBx3o2cfGblYefl6WXEQsCVw6jcVu+ARHb41QXIrx7v26yKvTPhB9yTFFQdfSGJ4zjsY9udPwjMH6tNh3jxzeG1qvnQGwIFJW6dEHmSbg8bHAXwdDr3Xur6s3OKuxpmHi9afvx5Jlywr6fB2JiBjzmx4v28gVIy1uLYMLN+pi+IgUhQXw/eql/Pte8CFw6DWhu5w2ZiB26iXfKOesZtHKPCuFrpZiFh2Kq0vuwMP6CVi4vgmfNg/EkktXYXXpcI8H7iyNzZinr3SWryeNfQOmmMvxhVOAh6wBptuOAEi4lUIMzBN9nX7phYImIBZpWf3dh96XEMdGtC2n6XBFWNa2JkbNtAQQrBQTzb0lFYYXY8nxS0hLBFsq2oi3LzsIF48d6hkAOxrE+o5akEBSU+1UPXGLFPCofR5Aw5UNVUgvJKoanEj3rkwglpCX8pdbOXsvOngw6nhFNrHwh4Vhzd95tm1ChRNZD0ge4w2KVaynZRPw/YvA7FeBxZO8k7JEuVPxsstA4MjbWD7yFd6c1s2NdYgTA/fkfoklB1o+/vU/ePazocRYphrAk+3lB7oDBvVi2XFakEC1QIzdK0QJTfXtIyYFi224rR7Z0j6OMql7iXG/ruy73anHtm19aC/sPajKfqwqJNDexD2VO/V0xmQx53hpaSnIoP3x6TWHYXAv9pvG4E2Bt3OMjZHz6CBkLavC+GWmk7YwDLFSGCbFo8bP8Zjxcynt39vz02gqZRNEHSoe+KoFnxh74hn9aKSF0sDL0qV2DA6flI3dmWXMEYl2A8ow3tgXALCBViFX6gSO3R97FHcf2d1TqjwGHbtQWRB5Kn4f7tJYKWsCE4dtfAlHzHb5eb9/MfgzjzgN+NnfvJUVuVjRdyRw0r9Zmfk9AshdJ4CkGKsEk/84DuVlZXjEONHmAe44AqO8HwanX8Sf9HM9XGE9rQIAkGyjnQqTCmJATbUziVo97wucsvzPWPLCFUX9TO2BTk6MNziK8ZgLmD9oP9cyvp/PdnPRfWhhwVOD9gcOvzn/fi7wFCl8eWr+eracsbHnfrhP/yV+8vfPcM5TX+HwBz7FUQ9+6r9EDebx/NbcCTpVcOGHGTw7O8Aq0aU/C0ALq/YlJp4Xg+LE7BEc9asYYU7VOpH92Ra7mt0QY5n3XGncHsBrqWuicOTzweflQqNFeBtQipgQ9KZDg2FSnFX+L/w8czvSxFKM3cv1AHDMXcBQH2tHKzFqYBWuP27X/DtuYagWKUgjjmRMsZO7cysFz0Rmq9DWV9TLpyACxysX7Y/3fncIDhkuB8F8bIwCAPSoqsDZ2RuwsdveeCHlzYzQN808iq8f8CZSSTbZradlUnltlDo3zbl1cTYJSm1CZvlXzj7u7CjxcoeIJ6ucNkRrhQV9LQs+rUU5mrk6u4Ft02NepS9b0hNPz3SsFg2/fBtraDf8aA5CBnGUlLAxogUJqVKdX6YEP4jV4XpWJOwqihh9nmdfYqVpU2l4Od7OiO9vORov/mb//DvC8VTu0d+xu4h6Ck+POKh7KZbvwNJyzjTl1HUlMRWNB1yHOeZgTDX3sJXlFbkuclaJANDmjXh/tkNIxLSdm1CBm6YyotqXMBHo3Nz1uE0/z66oCgDXf7gWpmUz2kCrcORuvXHxWHae7lWd/5oHYr/0w3jXPBBaz2H2K5UkhWMW34FukHPK9yZ16I0aYMSp0vYzNBYkuxdZhKNW/zPv55Qw9jrWr4+/HzjwCpYLetyNTopGPctU4X1/I1fX7GSQVu0UgqrSOHbrK49N7vLaYspCd7pGXlp+j++dFemyVU4Fwa61s533XsXGWZ7/+DrtJdykPS+lTN1a0HmJsWkyssYV41iS+YPcBRK0PPaJrRjcSsHT8w7o6k/G3VYKcfsp2duwW+YZAMD8Jv8B5dMNcUb0cyFWiuaNdiAQ6p2lFrOlBkZJd9QfeKOzb8MqlqVi4p+ALx5huSfnvc8U6Z39lzDTiNnkIYU4qsc4Fc9+MAf5HuPGY/rPwOuALG9S0asyiYxaZhVPYam01im9MYsORcaqZqj4pL7CAZcBZ79V0Htui4jH2TWRoTEkY45izK0UXSx/JVeMe1UkcfuJe+Cp8/bxbe9vp4/Cfjt2R8+KBCoq5EH6YeNEfHrQszAGHoCltC/GrPkjHq4e5WljSJops+u0AbY3shYVQFchF7WgWGeUBEul17AGqQbHZ/z+J8y/3sTLs8fLHMU42cV5vHampxCPYa2K1NFy3D3FIibrf0Am2QMrM95r58fGJP403lHO6kgFxmYews+ydwAAKjOsjdW0J6rRFedlr8Vl2St887+KeH3cRIzL3A/xfnPozj1hQMWpPd8Bjrrde9DRd7K4g8EHh7bdGdGlNFZwvuTqRqbG79zbP69tWdxpR+k3Eruln8Krxjhpn9P3HYjKnQ7AT7N3oRkl6ELYuLoBXfHW0uDffrHJLEarcvJ7iyuKdbQcCylTjEX1r6o0JhHejbQSSqbeft8hPUptwiTeLw63/Nbr0Q03/GQ4/nqqnLGprGW1XczHA3cRDbBsEyOVPNUl/cCtUeW9gKNvB3ruDIy7zrFCuqsKdlJoLmIMAF1d2bCG9JCzLIkT8a6lMYwf8SC+M9lK3HorS0mXBmfFq+9qZ0X3msa/AvP+x55Us/SbLaQchklxifZf/Eb7H5qFLBZbCzovMQYFzn4TGHl6+G5qsMq1tYMTY35/HNjVn9j6pRNiIDChYJ+hjHCuoUKk7u++RaaKqQPnPD8PNFbqBN+tncVU33QDy1EMAC0b8Z1pqQk1TN3D30ZCmfM6Zjd3waWfu86tbjmwgS9DE7ZMQ1SpJv1Jmdvsx2nE8a3Vfj+yCbMGnmW/9t0G04nItyCq5EdkH8DV2o24W/+V/V3VGUmcc8AO+PfBn9rFU0yTMt8mAMPyH8fNkIDD7RTxmEWMEUcypmLEAKbUX2RlRelexl4Xi4ecvf8OdhU8N04Y5ai6iUp2o31BZxYDCgUrK/eSlvfWoSte0I/At+ZOuBG/k9pqzlHkNEYEmmgSX64H89MPP0Har4VUsJWbmkXINTurFsdn3keOxPCGwYKkDKI5k2WRGBtZoNduUpuKlamlDuVODuGahVC7DfG1+bCsAs6NaqNZgRw0HDCsD/55xt52DuBPDOYfnmKOwvvm/hjSI1w1NMv6YBntK3nTe5Qn8PjZo/HAr/b11pIFGJE4978gifw5tyME47rjdkVZXMUe/cXf2/mNRS9/78okUkh68sRefdTOUlW9KrAVvw20Cld95LPCZqERJfjm5xPxlwYnkPaNSw60LRAAU4x/oIPx++yluDV3rnNecc1O7cffi2OHHYbgiiPY2PrVjUfg/IMchfvukx2/7kWHDkWX0hi+PepVnJD5M140jkAsW4fupB6+6Ov1xY9SFmO0ssBnZwHnvOvdFqQCdxkAgACH3ej/eieDaGHj6nE3a7z+7dgdMfHqQ119VybT/9/encdHVZ0NHP89k32ZkIRsJCwBwhYQAgQCgoCyg4q4oChK9fWVWqW1Vq28UqXaqqBvF2urVdxaUMGiLajVVyuopaLsYZU1IHuAIJCEJJM57x/3TjKTTEKADEng+X4++SRz751z7zw5uTn33HOfExrsYGPMQJa5rXPfh+6cinXfe+VN3+WuHFJTsOyvsO1TUrdZw0djKKSopPJCpbC4bjMlnk91e+LmQuQIgnZDTr+dJ9WWn6vbxs6TOucbt3U7Pq2GHmNPA/qEicApdkPvtoXwknVCthrUR/jOeCUeb96ergd+QSxWb4YrKIKQ0kLYtwZeGmzNJhjbGla8avWeFh5ht+nCARNHQv5WgstdUJAHwDHj5FBZKNXyiH9nj+MszLeGX0QnW7PH2VaZjhU/Z3XpzBcbwlhierGwrC+t8110NXGkSAFvrdhHVPxPGBWWy8HDh1nl7sAr5WOZF/o4q+LHsu9IGpHNOsHJ79luUomXLbgRWsZF+pwUyo2hsMS61dylTSpsh/4tm+6F09kK8/QYE0KzEAcJ0WHkPT22Yr3nRHvKz/TB3kKDHLSvMj4+OjaRvqf+yPGQeNbNGM7vP93K+J5pHC+u7FUwOHjEZf09RoU6eNLxh4p1xaXlLMz8DQf+8yb5xPLQglw+f/B37CkoYm3ufjxH+dBn35NzSRppO98kEeuJbM9DTCGmjCdct/IfdzeGHkjhhoh4q2kT5vQdw1glxVPC+tkAFBgnhV6VObjHBOLKPoZ833/4LcQ3I8beEuvvc/rYTDqlOCHjf7lldSf2UtmweenW3hW9dDW5pmca2/JPcu8VvrmoR3ZNqfV94D+lnqq7CdmtmJBtjWVfcHd/rnvhK67q0YJPN1kpKL17khPsh5xOGd+OCWd4iM+MYRU9xiYWEKYFP8hTrmeq7TtVjjJgQX7Fw7DrZozAGR7CsaJSsJ+Da9uqFd3T2/Pi51ZP8rOjOnOyxMVzn21l2UHhWmCrOw0TFsvXzuHknPiEOVMGVTSokmLCmXpFBqy0yvNcfEV69YRn9h3GkKK29Dl+HFn3L/4SOrPasboRHKFRcO3LPpmKbgpaQj/HJg46u5HcZQBk3Wz9P/HWbjDHJywgZr49VvjqP1CjkAiYcazm9Rcxz+/Uk0nFGRZMRpKfIV+e26hYv+/iUhevuK5hubsT69xteSrEeihyk2TQj1wOORK5puwplob+jEjXMb7bsZm4vMpx3dEUcuroHjx7KjmyC+L8Z7toKBdxj3EdiVgZKsY8e/ptG5lP3L2ZWPoIz7huBKBNvP+r6gGXWHlm3y/3GkfXbjBX9UilVXwEEfZJb6+pbBS43QYXwRU9YevzXbhLiyrzFB/bXTkO8+s/Q+kJjhgn292puPO3+MxMVExoxcNsfn08DdbMhYhYXOEJfjdJbGPd1p4Z/zjvugfx7YETjCp5mhEl1kl5X9oIPunwKPeU3ccr5WO5Oac1N5Y+yswDvemWFkOiPf51muM+Fpf34Gt3F6LDgunVJq6icfzaD/pywB5D6OxtpU0K6XAG2UUuEMFR1u2zUMr8PoT06/GXMKxLEn3S46ut87bx8ZEsuneAz7K4qFAOEUdcZBjO8BCmX5lJZGgw0V7T7z56ZeUQhsJSt8/7T5a4mP5lMc+XjweExOgw3vpmNwNnLuaeN1dVbOcimEUHK+uSdw+ZZ/3H7j489O46HvjCapQf3PINS/Z7nTKjk30nDbEVmGjfi8geNxIRWXkB8F659ZmXVkl9tKvAapi39vydRsaz1G31yM2f0p8XJ/ViRNeUmtM62kKDHfzPmC4+vY51VVNuVHXmereJZ/MTo3zuiER4NSA9d1AS42Mr39SiB1D5ewh2CM3EenbjkH3bOnPYrax1V88+UGCiKxrF86f0x2n//lvFR7LCbXUiOKKac10v63hGdk1mWGYy1/RMIyIkiAXFvfhV2S08UDaFeVP6k/PT+TA9v1r6r7ioUI52v4vjV/6ZmPAQru6Ryht39K1YHx4SxE+Hd6RDD/8ZIj4rz2J6899ZLzzp5wCTfSfjgpaSIMdJGnwnjJkFqVm+b7bTLJrY9Mpl3g+Fq9N6ZXI2N+dUDi/01LWahvoWeXVwhAQ7uPfyDhQSwWfuXhXZmQB2pV3JZ+5e3FT8cwrKI9j3X6t52zWETo49PuVFmUKCciuTD7iO5tXDp6pfAesxFpFRwO+BIGC2Mebp07yl8Wpi44x/f1MWJWVu2iVGcf2Llf8cW1S5lZ0WG0FW61gOpHRg5NqnOWqcTLQfgAD4w0QrU8fmA8d5/T95FUMuvnZ3Zt1S36eKi00o7PwSdtjpc1wllZN3bPkIgHxi2WFa0O/I19ZEHsB6dzpvlV9RMa1ooQnj4G1f0m5Ov8rc0h6HNvLsvw/zMNV7WUrE+h11T2vGpv3HWbmrgGM4aZmaBnuP06JZBE67cdUuMYrJ/dN582vr+AZkJHDwuNUo6Z7ZldtX/byi3D7p8Wx7ckzF62YRIXxfXEZSlwHW9N0XodBEa9xugnzvd/rRtglRzJ7sfzyxN38NPE/vRdVhP5FeYzxTY/1fRB00saze7XurecWuAlbsqlz2UXmfigkP/ngkmx/a1agAJ38qG8cIx0qOGN9ek0UlPflpWAKzjo9h4ZyN5Hl2H53EyVs+YNJv/87fwyrTwhXgBISxJU8SL8f58YFyEoc8wjuvHOUXrts5RRj/67qB/CqN8Z2Hi0hyhvk0nh67KpMXlmynb9vaLzLqS205UNWZ84xNXv7IsGoXHQnRYSx5YAiRpw7CbNjfcRItbq586Oz12/vQLiEa13MOgsXNhMFZfLm9gKt7pFL0TULFw8gAvym7nnfKrZ7VDknRPvUlLTaCG0of4ArHamKapdEh2cmn9w+ijdfMfY+M6cLNs79mdvlYRndLsR7IEgGH//998ddW9lg/Z/+fqMaTK99j0rsU7VjGnYuz+OvoHJ9VO93JtB37LJw8YKWt63Zt9fLu31Qxy2iE0/p8+c37eN1PUXUxtEsyQ71y53vqZXkNLeNLvIZWhAY5aBYZwrisVP6xZp/PjJeJHfsig25nx2vWbHYZLeLZkNKRsMNLfMqLKD+Je+P7rHR3YHLpz3k1sT++j582vIA0jEUkCPgjMBzYAywXkYXGmFpyF6n64umhcHkl3u2WFkNcVCjz7urH5gMnmHxpesW6Oct28a1pXW3eeo/OKTHsfGoM989fS/fVL1NCCCUfVKZfG98zjX3rE3B4fr2pPWGflT7roLMrySesJ/N3ulOIdhQTVHYCVli3X64rnWHPcGZ4pmwCH7pzeEoSaHfpVFj6+4p9bHS3Ya6M5e01RVwR2ok/uawxo3PazWJSrwS+2271qmQkRZPVKpY13x0D4M6B7bhv3hrSYiMqTgBhwUHERVU2rG/JacN7q62rWs8/spo6zt6fOpCCotKLugERnmTdok+U47jreVa+zilO7rm8PZP6+c4C6Z2OLKWZ/yFBz3Wdz/ZVlen/ruqRyqK1VgaUEZnJ/PnW3rSdZq375dVdeWzhBh5M/gPPHJ1KUtBJ3ikdwt/cQ3xmkQt2CLf078DApc9V299LG4Moyi9hjcngsbLJ/DLkDXa7E8lKT+abvKPsi+jAhqIyvnzxK964oy8Puioz3owemMPLX+5kUMdELtv6W2IoZsOqPQzI8J1x6/YBbbl9QNuqu1ZNTGINGVnSE6KAdvCzb2lRJV/2kE7WXYexpU+Q7djCjFGZPGifd5olJYHXSJyP3dnspzkv35ZN3yp3aqLCgiEijveKL2N+d+shtaq3zC/NSKBragwb9h3njoFt6+f8FhQC92+2ZoYEyBhKZMZQdlSZS+iRTh/SKrEZPxSBG163shGF+0m96XnADgh1xsOkd0lsefoLcFU7z8OZ6Qn+7ygnRIfx5PhL+J/31lUMnXn62u78Y80+jNegg/adLqFNiu8d3ZNxXcA+Jb/qGkX/sJ20dO3GeaKYxRHXsuyR8X6nmW9ogTqivsA2Y8wOABF5GxgHaMP4PAoOcvDp/YNoFhFacWLOadecnHa+/3w9abS6tE6BQ2CiEql6WhQRfntjFgdHdybnycqZgeZP6U/3ls3ot/oWrg/6gsKo1uQnXk663TCeX9CBqcF2w9i0INL4DrQvIZT05pHkHSni786J7D1WzAtLtrO7xMEE4MmyibxTPpgCKtPETCh9jL/fM4AXU5wVDdnhYYd546tdjOiazA3ZLfnpvDXkHSliXFYqaXERZLWK5W8rrcZvQnQosRGVPSGJzjBGdW3Bkx9uZnS3FO4e3L7GW8qt4iNpVcOQlItFZHPrdubn5d0ZXM+33h0O4cGR/lPUPTexJ52SncREWKetsGAHJS43N5VOJ4hyHhnYhfWHcglyCK3iI5kyqD2ucjf/XH+Am3NaIyI8NKoTsRGhjMtK5bGFG/hwXyTPhMOptP5M79SFPunxjPvjUl67vQ/OsGCy7UZGl5QYMpKjeeL9jWCn4Z61NhQXW4kJD+aNUyP5xt2ZfaY5n03qhTM8hKOFpfR7yvpbaZ8YRVpsBHuPWWP4p43uws9GdCIkyMHby5N5fNFGxnVL4b5hHat/8AbgeYBSnSfOmsd/PzB5Ap9sOujbWG03BDa/z4qE8WQffo/9pjk/HtqB4Zn+Z9F8879zKCgsq/XOwz2XZ/CjuauqZSU4JzEteLf1I7TocinVkyxafj3RazhVUEhlCtXTyRh6zofX1DnDgxmY4X94YV1d3SOV1NgIsttUzw/vcWWPFnyy8QA/GWY9hBkRGkRsZAjHisoYXfYsl4bvZHqy1a5Y+vAVFSnYTib3AWu+MELGzuTYyZV0WXoHADkDhzXKRjGAGFPDwJJzKVTkemCUMeZO+/WtQI4xxu9E4tnZ2WbFihX+VqnzwO02bDl0go5JThxbP7JmBvKeJayK33yyhfAQB2O6tbB7PODuOStZvt56Kn9o0CpmhljJ2u8NfYLnS38BwJyRa9lxoIDgFX8mFBdL3D3o0XcIPxzcnstmLebWfm346zJrso0gyrk+6AsWlF/mk4ezY3I070+9rFquxZo+l3dP4/zl3/HQglx+dU03JvVrw9jnvmRcVip3DWpsN3Iav5cXfU6Pju3o26n6GNvzYeHafQzMSODTjQcpcZUTGuzgxj7+0/IZY/z2gH2Qu59/b8unhfsgd181kJCwiFq396z76G+zWbFmNbmtJjG4YyKXZiRwz9xV7P/euuOy5VejK+pn+sMfAFQ8nPinJdu4JK0Zl3XwvQFcta42pMWbD9EpxUlqrE720WgZA0e24Yprz56C4orz8AVp9Vxr0qfuN5x+W3XeXP7sEnYeLmTZtKEkRIf6HRq39eAJvnj/r4wfPoT41pm43YafTZ9GJ8ce7prxGo7gM38Oor6IyEpjjN9pDgPVML4BGFmlYdzXGDPVa5u7gLsAWrdu3XvXrl1+y1JNQ1GpixOnXMxZtotWUYbue+fSKc7Bnl4PcvSDGfS4fAK06ktZuZvPv83nwPFThAY7uLpHKuEhQRwtLCUmPJh3V+/lu6NFTMhuxWtL8xiemcy+Y8V0TYshIzH6tA8e1cZV7uazzYcYnpl8UQ+FUOemuLScZ//vW6YMakdSjDXgeO+xYtbtOUbvNvE+t82355+k1OWulhhfKaWast1HiliUu48fDWl/Rv9P8w4XUlBUSs/WNfdQnw8N0TDuD8wwxoy0X08DMMY85W977TFWSimllFLnQ20N40Cla1sOdBCRtiISCtwE+MnKrZRSSimlVOMQkJHPxhiXiNwLfIyVru1VY8yGQOxLKaWUUkqp+hCwRwKNMR8CHwaqfKWUUkoppeqTznynlFJKKaUU2jBWSimllFIK0IaxUkoppZRSgDaMlVJKKaWUAgKUx/iMD0IkH2ioGT4SqJjNW9UzjW1gaFwDS+MbGBrXwNL4BobGNbAaKr5tjDGJ/lY0ioZxQxKRFTUleVbnRmMbGBrXwNL4BobGNbA0voGhcQ2sxhhfHUqhlFJKKaUU2jBWSimllFIK0IYxwEsNfQAXMI1tYGhcA0vjGxga18DS+AaGxjWwGl18L/oxxkoppZRSSoH2GCullFJKKQU0wYaxiLQSkcUisklENojIT+zl8SLyiYhstb/H2cub29ufFJHnvcpxisgar6/DIvK7GvbZW0TWicg2EXlORMRe/luv928RkWOBj0Dg1Fds7XUT7ZjlishHIpJQwz5riu0gEVklIi4RuT7Qnz2QGllcL6g6C/Ue3xvt2G4QkVm17FPr7fmNq9ZbkeEistKOz0oRucKrLL9x87NPrbfnN65ab2uP769F5DsROXmafZ7femuMaVJfQAugl/2zE9gCZAKzgIft5Q8DM+2fo4CBwA+B52spdyUwqIZ13wD9AQH+CYz2s81U4NWGjk9jiC0QDBwCEuzXs4AZZxJbIB3oDvwFuL6hY3OhxPVCq7P1HN/mwG4g0X79BjBU623Dx1XrrQHoCaTaP3cD9p5J3LTenv+4ar09bXz72eWdPM0+z2u9bXI9xsaY/caYVfbPJ4BNQBowDuuEi/39GnubQmPMv4FTNZUpIh2AJOBLP+taADHGmK+M9Zv4i6fsKiYCb53dp2oc6jG2Yn9F2Vd2McC+qvurLbbGmDxjTC7grs/P2BAaU1yraPJ1Fuo1vu2ALcaYfPv1p8B1Vfen9fb8x7WKi7XerjbGeP7eNwDhIhJW17hpvT3/ca1C661XfO11y4wx+2vbX0PU2ybXMPYmIulYVyNfA8meANvfk86gqInAPDvoVaUBe7xe77GXeR9HG6At8NkZ7LNRO5fYGmPKgLuBdVgNt0zgFT+bnja2F5rGEtcLsc7COZ8TtgGdRSRdRIKxTr6t/Gyn9baB4qr1tsJ1wGpjTAl1r49abxsorlpvK3jHt67Oe71tsg1jEYkGFgD3GWOOn2NxN1HzlZy/MUVVG9A3AX8zxpSf43E0CucaWxEJwWrA9QRSgVxgmr9N/Sy7YNOkNLK4XlB1Fs49vsaYAqz4zsO6e5QHuPztyt/bz3R/TUUji+tFX29FpCswE5jiWeRnM3/1Uett7dsHMq5ab6vHt8678rMsoPW2STaM7QbCAmCuMeZde/FBu8vd0/V+qI5l9QCCjTEr7ddBXoPlH8e6Omnp9ZaWVL99XVvDukmpp9hmARhjttu98POBS88ytheERhjXC6bOQv2dE4wxi4wxOcaY/sC3wFatt40qrhd1vRWRlsB7wG3GmO32Yr9x03rbqOKq9bZ6fGsqu8HrbZNrGNtjK18BNhljfuO1aiEw2f55MvCPOhbpM+7HGFNujMmyvx61bwmcEJF+9r5v8y5bRDoBccBXZ/2hGol6jO1eIFNEEu3Xw+0yzyi2F4rGFtcLqc5C/Z4TRCTJ/h4H/AiYrfW2ccT1Yq+3IhILfABMM8Ys9WxcU9y03jaOuGq99R/fmjSKemsawVOOZ/KF9dSzwbqNvMb+GoP15PO/gK3293iv9+QBR4GTWFcfmV7rdgCdT7PPbGA9sB14HqyJUex1M4CnGzoujS22WE+mb7LLWgQ0P5PYAn3s8gqBI8CGho7PhRDXC63OBiC+bwEb7a+batmn1tvzGFettwZgul2v1nh9JZ0ublpvGy6uWm9PG99Zdr1z299nNIZ6qzPfKaWUUkopRRMcSqGUUkoppVQgaMNYKaWUUkoptGGslFJKKaUUoA1jpZRSSimlAG0YK6WUUkopBWjDWCmllFJKKUAbxkopdU5E5D4RiTyL9/1ARFJrKkdEPrST4weciCwRkezTbHNWn1MppZoSbRgrpdS5uQ84owajiAQBPwBSvRb7lGOMGWOMOXbOR1d/7uMMP6dSSjU12jBWSqk6EpEoEflARNaKyHoReQyrcbtYRBbb27wgIitEZIOI/NLrvXki8qiI/BtrKvpsYK6IrBGRn/gpJ09EEkQkXUQ2i8hse59zRWSYiCwVka0i0tfr2F4VkeUislpExtXyOSJE5G0RyRWReUCE17pqxy8iP/ZzfCNE5CsRWSUi74hIdH3GWimlGoLOfKeUUnUkItcBo4wx/22/bgasBbKNMYftZfHGmKN2r/C/gB8bY3JFJA/4kzFmlr3dEuABY8wK+3VelXLysBrP0cA2oCewAVhu7/O/gKuB240x14jIk8BGY8wcewjGN0BPY0yhn89xP9DNGHOHiHQHVgH9jDErTnP82caYwyKSALwLjDbGFIrIz4EwY8zj9RNppZRqGNpjrJRSdbcOGCYiM0XkMmPM9362mSAiq4DVQFcg02vdvLPc705jzDpjjBurcfwvY/VqrAPS7W1GAA+LyBpgCRAOtK6hvEHAHABjTC6QW8fj9+hnL19q728y0OYsP5tSSjUawQ19AEop1VQYY7aISG9gDPCUiPyf93oRaQs8APQxxhSIyOtYDVSPar23dVTi9bPb67WbyvO4ANcZY76tY5nVbhfW4fgrNgU+McZMrOO+lFKqSdAeY6WUqiM7i0SRMWYO8CzQCzgBOO1NYrAav9+LSDIwupbivN/n7/WZ+hiYKiJiH2vPWrb9ArjF3q4b0N1eXtvxex/fMmCAiGTYZUSKSMdzOHallGoUtMdYKaXq7hLgGRFxA2XA3UB/4J8ist8Yc7mIrMYa7rADWFpLWa8DL4pIsV3GS97lnMWxPQH8Dsi1G8d5wJU1bPsC8JqI5AJrsMYjY4xZW8vx+xyfiPwAeEtEwuz104EtZ3HcSinVaOjDd0oppZRSSqFDKZRSSimllAJ0KIVSSl2wRGQkMLPK4p3GmPENcTxKKdXY6VAKpZRSSiml0KEUSimllFJKAdowVkoppZRSCtCGsVJKKaWUUoA2jJVSSimllAK0YayUUkoppRQA/w/ap1QnJGcm3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pyplot.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "df_filt[['starttime_date', 'tripid']].groupby('starttime_date').count().plot(ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "df_filt[['starttime_date', 'usertype', 'tripid']].groupby(['starttime_date', 'usertype']).count().unstack(1).plot(ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that there is more variability with one-off Customers compared to Subscribers. But since the seasonality is similar, we will attempt to forecast on the entire dataset.\n",
    "\n",
    "Now we split the data into a training set and a test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "srs = df_filt[['starttime_date', 'tripid']].groupby('starttime_date').count()\n",
    "srs = srs.reset_index()\n",
    "test_size = 90\n",
    "srs_train = srs[:-test_size]\n",
    "srs_holdout = srs[-test_size:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement ARIMA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -1.862305\n",
      "p-value: 0.349996\n",
      "Critical Values:\n",
      "\t1%: -3.436\n",
      "\t5%: -2.864\n",
      "\t10%: -2.568\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "X = srs_train.tripid\n",
    "result = adfuller(X)\n",
    "print('ADF Statistic: %f' % result[0])\n",
    "print('p-value: %f' % result[1])\n",
    "print('Critical Values:')\n",
    "for key, value in result[4].items():\n",
    "    print('\\t%s: %.3f' % (key, value))\n",
    "    \n",
    "# Insufficient evidence to reject null hypothesis. Let's try d=1 max."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "fig = pyplot.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(srs_train.tripid, lags=200, ax=ax1)  \n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(srs_train.tripid, lags=40, ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(0,1,0)(2,1,2)[7]             : AIC=14238.045, Time=1.54 sec\n",
      " ARIMA(0,1,0)(0,1,0)[7]             : AIC=14840.833, Time=0.04 sec\n",
      " ARIMA(1,1,0)(1,1,0)[7]             : AIC=14363.546, Time=0.30 sec\n",
      " ARIMA(0,1,1)(0,1,1)[7]             : AIC=13873.994, Time=0.36 sec\n",
      " ARIMA(0,1,1)(0,1,0)[7]             : AIC=inf, Time=0.21 sec\n",
      " ARIMA(0,1,1)(1,1,1)[7]             : AIC=13873.340, Time=0.89 sec\n",
      " ARIMA(0,1,1)(1,1,0)[7]             : AIC=14173.337, Time=0.32 sec\n",
      " ARIMA(0,1,1)(2,1,1)[7]             : AIC=13875.332, Time=1.09 sec\n",
      " ARIMA(0,1,1)(1,1,2)[7]             : AIC=13875.338, Time=1.20 sec\n",
      " ARIMA(0,1,1)(0,1,2)[7]             : AIC=13873.385, Time=1.02 sec\n",
      " ARIMA(0,1,1)(2,1,0)[7]             : AIC=14017.712, Time=0.66 sec\n",
      " ARIMA(0,1,1)(2,1,2)[7]             : AIC=13874.118, Time=4.01 sec\n",
      " ARIMA(0,1,0)(1,1,1)[7]             : AIC=14237.766, Time=0.35 sec\n",
      " ARIMA(1,1,1)(1,1,1)[7]             : AIC=13823.419, Time=1.34 sec\n",
      " ARIMA(1,1,1)(0,1,1)[7]             : AIC=13821.438, Time=0.90 sec\n",
      " ARIMA(1,1,1)(0,1,0)[7]             : AIC=inf, Time=0.35 sec\n",
      " ARIMA(1,1,1)(0,1,2)[7]             : AIC=13823.420, Time=1.82 sec\n",
      " ARIMA(1,1,1)(1,1,0)[7]             : AIC=inf, Time=0.98 sec\n",
      " ARIMA(1,1,1)(1,1,2)[7]             : AIC=13823.310, Time=2.54 sec\n",
      " ARIMA(1,1,0)(0,1,1)[7]             : AIC=14079.427, Time=0.48 sec\n",
      " ARIMA(2,1,1)(0,1,1)[7]             : AIC=13823.297, Time=1.26 sec\n",
      " ARIMA(1,1,2)(0,1,1)[7]             : AIC=13823.243, Time=1.16 sec\n",
      " ARIMA(0,1,0)(0,1,1)[7]             : AIC=14236.246, Time=0.24 sec\n",
      " ARIMA(0,1,2)(0,1,1)[7]             : AIC=13822.796, Time=0.64 sec\n",
      " ARIMA(2,1,0)(0,1,1)[7]             : AIC=13979.534, Time=0.68 sec\n",
      " ARIMA(2,1,2)(0,1,1)[7]             : AIC=13825.329, Time=1.28 sec\n",
      " ARIMA(1,1,1)(0,1,1)[7] intercept   : AIC=13823.437, Time=2.24 sec\n",
      "\n",
      "Best model:  ARIMA(1,1,1)(0,1,1)[7]          \n",
      "Total fit time: 27.907 seconds\n"
     ]
    }
   ],
   "source": [
    "autoarima_model = auto_arima(X, start_p = 0, d=1, start_q = 0,\n",
    "                           max_p = 20, max_d = 2, max_q = 20,\n",
    "                            start_P = 2, D=1, start_Q = 2,\n",
    "                             max_P = 20, max_D = 10, max_Q = 20,\n",
    "                             max_order = 20,\n",
    "                           m = 7, seasonal = True,\n",
    "                           error_action = 'warn', trace = True,\n",
    "                           suppress_warnings = True,\n",
    "                           stepwise = True,\n",
    "                           random_state = 20, n_fits = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                 <td>y</td>               <th>  No. Observations:  </th>   <td>1183</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)x(0, 1, 1, 7)</td> <th>  Log Likelihood     </th> <td>-6906.719</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                  <td>Fri, 02 Apr 2021</td>        <th>  AIC                </th> <td>13821.438</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                      <td>10:58:30</td>            <th>  BIC                </th> <td>13841.714</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                        <td>0</td>               <th>  HQIC               </th> <td>13829.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td> - 1183</td>            <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>              <td>opg</td>              <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>   <td>    0.2667</td> <td>    0.027</td> <td>   10.012</td> <td> 0.000</td> <td>    0.215</td> <td>    0.319</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>   <td>   -0.8991</td> <td>    0.015</td> <td>  -61.667</td> <td> 0.000</td> <td>   -0.928</td> <td>   -0.871</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L7</th> <td>   -0.8414</td> <td>    0.013</td> <td>  -64.723</td> <td> 0.000</td> <td>   -0.867</td> <td>   -0.816</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>  <td> 7400.0900</td> <td>  187.378</td> <td>   39.493</td> <td> 0.000</td> <td> 7032.837</td> <td> 7767.343</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>620.08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.93</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.10</td> <th>  Skew:              </th>  <td>0.37</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.33</td> <th>  Kurtosis:          </th>  <td>6.48</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                     SARIMAX Results                                     \n",
       "=========================================================================================\n",
       "Dep. Variable:                                 y   No. Observations:                 1183\n",
       "Model:             SARIMAX(1, 1, 1)x(0, 1, 1, 7)   Log Likelihood               -6906.719\n",
       "Date:                           Fri, 02 Apr 2021   AIC                          13821.438\n",
       "Time:                                   10:58:30   BIC                          13841.714\n",
       "Sample:                                        0   HQIC                         13829.083\n",
       "                                          - 1183                                         \n",
       "Covariance Type:                             opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.2667      0.027     10.012      0.000       0.215       0.319\n",
       "ma.L1         -0.8991      0.015    -61.667      0.000      -0.928      -0.871\n",
       "ma.S.L7       -0.8414      0.013    -64.723      0.000      -0.867      -0.816\n",
       "sigma2      7400.0900    187.378     39.493      0.000    7032.837    7767.343\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):               620.08\n",
       "Prob(Q):                              0.93   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.10   Skew:                             0.37\n",
       "Prob(H) (two-sided):                  0.33   Kurtosis:                         6.48\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoarima_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c6565488c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_pred = autoarima_model.predict(test_size)\n",
    "\n",
    "fig = pyplot.figure(figsize=(12,8))\n",
    "\n",
    "pyplot.plot(srs_holdout['starttime_date'], srs_holdout['tripid'], label ='Actuals')\n",
    "pyplot.plot(srs_holdout['starttime_date'], x_pred, label = 'ARIMA Forecast')\n",
    "pyplot.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE Error:  3.0842426850376654\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "mape = 1/test_size * np.sum(np.abs((srs_holdout['tripid'] - x_pred ) / srs_holdout['tripid']))\n",
    "print(\"MAPE Error: \", mape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement XGBoost Time Series with Hyperopt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "from hyperopt.pyll.stochastic import sample\n",
    "from hyperopt import tpe, hp, fmin, Trials, STATUS_OK, STATUS_FAIL\n",
    "from functools import partial\n",
    "from hyperopt.pyll import scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_features(df, label=None):\n",
    "    \"\"\"\n",
    "    Creates time series features from datetime index\n",
    "    \"\"\"\n",
    "   # df['date'] = df.index\n",
    "    df['dayofweek'] = df['date'].dt.dayofweek\n",
    "    df['quarter'] = df['date'].dt.quarter\n",
    "    df['month'] = df['date'].dt.month\n",
    "    df['year'] = df['date'].dt.year\n",
    "    df['dayofyear'] = df['date'].dt.dayofyear\n",
    "    df['dayofmonth'] = df['date'].dt.day\n",
    "    df['weekofyear'] = df['date'].dt.weekofyear\n",
    "    \n",
    "    X = df[['dayofweek','quarter','month','year',\n",
    "           'dayofyear','dayofmonth','weekofyear']]\n",
    "    if label:\n",
    "        y = df[label]\n",
    "        return X, y\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:12: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "srs_train['date'] = pd.to_datetime(srs_train['starttime_date'])\n",
    "x_train, y_train = create_features(srs_train, label='tripid')\n",
    "\n",
    "srs_holdout['date'] = pd.to_datetime(srs_holdout['starttime_date'])\n",
    "x_test, y_test = create_features(srs_holdout, label='tripid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(x_train, y_train)\n",
    "dtest = xgb.DMatrix(x_test)\n",
    "\n",
    "space ={\n",
    "    'eval_metric' : 'rmse',\n",
    "    'objective'   : 'reg:squarederror',\n",
    "    #'max_depth'   : hp.choice(\"max_depth\", np.arange(2, 7, dtype=int)),   # something wrong with hyperopt and maxdepth  \n",
    "                                                                            # will use default value for now\n",
    "    'alpha'       : hp.uniform('alpha', 1e-6, 1e-4 ),\n",
    "    'lambda'      : hp.uniform('lambda', 1e-6, 1e-4 ),\n",
    "    'gamma': hp.quniform('gamma', 0.5, 1, 0.05),\n",
    "    'learning_rate': hp.quniform(\"learning_rate\", 0.001, 0.5, 0.01),\n",
    "    'eta': hp.quniform('eta', 0.025, 0.5, 0.025),  \n",
    "    'seed'             : hp.randint('seed',2000000)\n",
    "   }\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(space,\n",
    "            xgb_train = dtrain,\n",
    "            tcsv = TimeSeriesSplit(n_splits = 3)):\n",
    "    \n",
    "    xgb_params = sample(space)\n",
    "    mdl = xgb.cv(\n",
    "                        xgb_params,\n",
    "                        xgb_train,\n",
    "                        num_boost_round = 750,##change to 750,\n",
    "                        folds = tcsv\n",
    "                       \n",
    "                        )\n",
    "    \n",
    "    return{'loss' : mdl['test-rmse-mean'].min(), 'status' : STATUS_OK}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 3.561383538529097e-05, 'eta': 0.125, 'gamma': 1.0, 'lambda': 5.9406290352885717e-05, 'learning_rate': 0.37, 'seed': 55661}\n"
     ]
    }
   ],
   "source": [
    "trials = Trials()\n",
    "best_params = fmin(fn=objective,\n",
    "                     space=space,\n",
    "                     algo=tpe.suggest,\n",
    "                     max_evals=100,\n",
    "                     trials=trials,\n",
    "                     verbose=0)\n",
    "print(best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xgb.train(best_params, dtrain)\n",
    "xgb_pred = model.predict(dtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c602426dc8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pyplot.figure(figsize=(12,8))\n",
    "pyplot.plot(srs_holdout['starttime_date'], srs_holdout['tripid'], label = 'Actuals')\n",
    "pyplot.plot(srs_holdout['starttime_date'], xgb_pred, label = 'XGB Forecast')\n",
    "pyplot.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE Error:  0.6908268476132706\n"
     ]
    }
   ],
   "source": [
    "mape = 1/test_size * np.sum(np.abs((srs_holdout['tripid'] - xgb_pred ) / srs_holdout['tripid']))\n",
    "print(\"MAPE Error: \", mape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Decay in Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adelynyeoh\\Anaconda3\\envs\\working_python\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "srs_holdout['forecast_xgb'] = xgb_pred\n",
    "srs_holdout['forecast_arima'] = x_pred\n",
    "srs_holdout['forecast_lead'] = [x for x in range(0, len(srs_holdout))]\n",
    "\n",
    "srs_holdout['difference_xgb'] = np.abs(srs_holdout['tripid'] - srs_holdout['forecast_xgb']) / srs_holdout['tripid']\n",
    "srs_holdout['difference_arima'] = np.abs(srs_holdout['tripid'] - srs_holdout['forecast_arima']) / srs_holdout['tripid']\n",
    "\n",
    "srs_holdout['mape_xgb'] = srs_holdout['difference_xgb'].cumsum() / srs_holdout['forecast_lead']\n",
    "srs_holdout['mape_arima'] = srs_holdout['difference_arima'].cumsum() / srs_holdout['forecast_lead']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c602eac3c8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pyplot.figure(figsize=(12,8))\n",
    "\n",
    "pyplot.plot(srs_holdout['starttime_date'], srs_holdout['mape_xgb'], label = 'XGB MAPE Error')\n",
    "pyplot.plot(srs_holdout['starttime_date'], srs_holdout['mape_arima'], label = 'ARIMA MAPE Error')\n",
    "pyplot.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecasting For Each Station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "srs_stat = df_filt[['starttime_date', 'fromstationid', 'tripid']].groupby(['starttime_date', 'fromstationid']).count()\n",
    "srs_stat = srs_stat.reset_index()\n",
    "test_size = 90\n",
    "srs_stat_train = srs_stat[:-test_size]\n",
    "srs_stat_holdout = srs_stat[-test_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Stations:  114\n"
     ]
    },
    {
     "data": {
      "image/png": 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jmYSFccvwlAbDMAzDzHkyFuOZuq6uE8jyCgCckt6YLfsmM5OChTHDMAzDMAuDogQfcy6Occn6sI/xjMLCmGEYhmGYBcjcdKVIlt6VrA8v859ZWBgzDMMwDLPwmC2LselKIXIsxqWFOluMZxIWxgzDMAzDLBDm3uI7MypFWp2SPtCz5SvdobAwbhkeuTEMwzDMnGe2fHNzriuMv6XtxexjPKOwMG4VYm8fhmEYhpmTFCX4mDHDVl5UCvOwcvVhYTyzsDBmGIZhGGbhYbogzJoFOaxH1pWiZH1KxjtmpgYWxi3DDZRhGIZh5jwZ4TlLi++i95nFdyUFL4F9jGcSFsaTQbBbBcMwDMPMHQriGFPBwrxJX7agPGkXtlRSqLMnxczCwphh5gvcOzIMw7RAQZ85ncI4x1KddaUoWfQkqsW0DgvjluEmyrQJC1uGYZjppTDzHcrta+/COa+RWIw58938gIUxw8wXuHNkZpharYZzzz0Xvb29s10VhmmdwjjGM9efUmIxNneUFcbsYzyTsDBmmJmChS0zz9i8eTP27NmD3/3ud7NdFYYpSZHFuKQ1ua3LNk8s0m6CDw7XNrOwMGaYGWOynRt3jgzDMOUpsBhPudgssEZPMlwbC+OZhYUxwzAMwzALjyKL8VQbGgpEdxquDdFfaqkGHK5tZmFhzDAzxWRH/Ww1YBiGKSbuJ4VjSXw3ja4U+oWMtzmL7zjBx5yEhXHLcANl2oXbDsMwzIwgnCZRKabRlSInXJtjWH7LWoLLxjtmpgYWxgwzb+DOkWEYphTCgcVkrLycxsV3mX3htZw2M9+xxXhmYWHcMpztjmkTdoVgGIaZGYRbnPluyg0NRYvvwveuaE+Ms8V4ZmFhzDAzBvsYMwzDTCuaj/EMhmuz1cG4VuxjnCy+K53fg/v+mYSFcYtY2yc3WoZhGIaZOwinOMHHdKaEzgnL5mZ8ijlc21yEhTHDzBST7ty4c2QYhimFEDOcMa4owUfsY2wsvitbP858N6OwMLax7U7gCyuA4d2ZXZ4fAAAkO8MzDMMwzBwjdqUQyBoTClwpbvwS8O3nTuKyzX2MOcHH/ICFsY17fxD+3Xp7ZtdoPRTGfsANlWkV9jFmGIaZEWw+xkWuFLf+JzCwZZoqk+NKUd7JeIrrwxTBwthKHHnC6lA8kxVhFhLcuTEMw0wv2uK7GQzXVuBKQTnh2spGm2CL8czCwtiGyA/JxsHamNmDO0eGYZgy+CSKF99NZ0roHFcK08e4vLGE+/6ZZFLCWAixUghxqRDiCSHE40KIFwshDhJCXC+E2Bj9XTVVlWWY+Q13bgzDMDPBgXEfckbDtZVZfNemjzGvaZpRJmsx/jaAXxPRMwA8B8DjAD4N4EYiOhHAjdH7+UnTRsv2Y6YFJjsdxtNpDMMwTQj7SQlRHPUhrz+VwZTVwbyWufiubJ/OCT5mlraFsRBiOYBXAPgRABBRg4gGAbwFwHnRYecBOGtyVZwNWPAyDMMwzHxFok1XisBr74KFIldP8NGsCtmiOVzbTDIZi/HxAPoA/EQI8YAQ4odCiCUADiGiPQAQ/T14Cuo5s4iixXcM0y4cx5iZX/CiH2be8NBFwPorEoEq0WbmO9mmMC50pYgtxtD+siV4bjIZYVwB8DwA3yWi0wCMoQW3CSHEh4QQ64QQ6/r6+iZRjekgarZFDwXBDZppERYZDMMw08PlHwIu/vPkLVkX36mvp9hiXERu388+xnORyQjjnQB2EtHd0ftLEQrlfUKIwwAg+ttrO5mIvk9EpxPR6WvXrp1ENWYJYncLZoZhYc3MMKIgQg/DzE1SH+O2LMZBo83LNl98l0nwUVrwct8/k7QtjIloL4AdQoiTok1nAlgP4EoA74u2vQ/AFZOq4azCjZGZSrg9MQzDzAQStsx3KtNpMc5ffKe6J5V3peBnx0wy2agUfwXgfCHEwwCeC+ArAL4K4LVCiI0AXhu9n1+0bCXhRsuUoE2L78MnL8ONr1iD2Wxnt23cj2M/fQ3W7x6etTowDMOUhSAgihbfZfrj6Lk/HRZjpe/W1/+VXFTHrhQzSmUyJxPRgwBOt+w6czLlzhk4XSMzB+hb2z3bVcD16/cCAO7Z0o9TDl8+y7VhGIbJIXoek9ViXCBe3WooiqU/DXVKXSkIqUtFeXsxa4yZhDPfWSkRlYJd75iW4TjGDMMwM4GE0yTTnNGfOtXwb7sWYxSUjdSVQhZalnNK5r5/RmFhbINdKZjpgDs3hinNgH8E+rxjZ7sazLxDWXwH5Ith043BjSbQp2XxXRqurQ1d3NzlYuttwPCekoUxzZiUK8WCh4UMM6fg9sh0Dhfs/28AwEdnuR7M/ETGdj+i1NhVpEqdWBi360pRYDFWhK1qMRamRTuv5GZa5KdvBpasBf5uU6nymGLYYmylwGJsa58soBmGWYDwFC4z7yDTYpwjPs3tk3alyNbBfJ8J1zaVrhRjcy0fxPyFhXEhTRojZ8hjGIZhmDmHtK4VKrDqupEwbjfzXZF4TRbf6Rbj8sqBNcZMwsLYRnI/Wc3DM1aNWzf24XO/emTGrsdMM5O1vrH1jplhOMEHM++45m8BxFEpoFuGNVcK02Icu1JMQUrovMV3ggyXZ858NxdhYWylxYfBNAmWWzfuxyXrdk5L2cxswJ0bwzDMtLLjLgBlFt/lWIynOfNd6agURRE0mGmFhXEhTRrjNFvwpOTohYwKtwaGYZgyULL4LsdinAnXNjmLMRUuvrO7T1BRtIm2wlcwUwELYxtzJFybpGkrmpkN5nHnNn9rzkwJfn22a8AwLWH3MVZowZUikISBsWJLsubuULD4LlenZ0tUjiuZIY+ZElgYFzHLQobtxQsN9jFm5ikbrgMe/eVs14JhSmOPStGeK8W/XPUYTvvS9Rir54dyK7QYKwk+CJSJTmEvUK1r88OZqYOFcYtYbcnTJFiIWBwzcwNegsVg+52zXQOGKY0Wxzim0D0h38J8zSNh8ozxRpB/PVnkFqHGMVa3Fz3fVfcL1gEzCQtjK2XCsNG0h2uTRGwkXEjwj8nMZwQ/Lpj5gzUqRaFVN96c308XCdTiaBNq5jtV8JbzMWZXipmFe7q+DdkpQluWHBMSkEEFI/5bpk3vSBZSC4z5+3vO35ozk0FPLMDzBsz8QZLFcFUUri3dYdmWtv0n9g7j149m0y+XWXwXRqVQN5ezGLNRZWbhlNDnvCD8+6w/UTaWewAM7XgBxvyno7J7LxY9f+qrJokFCaPAnSPTKXBbZyZJy+HaCren297wrVsBAFu/+mbjtHLh2lQBLUqGa+MMlDMLW4wngfS7AAAUTI8lhYj4hlhIzOPfkm2FnYmW4IOTfTDzCKuPsYaxfbKukbLAYqy5UjTJIWYtY/4+O+YjLIwLKdsYp8nHmN2KFhiTbSfcOTKzyQwK43k8iGTmBi1nvrMdk9C87auW4MAsWrUYa8WXbOd8P8woLIxtFPoYl902eSTxWlSGYeYIM2ox5p6PmRzN4xiXtCQX7dq3HvjCCqB/s7YmyA+M6BXJPj3zXeETPicpCDP9sDC2UvQAmLmHgyQeKC4oJvtjzmJj4GbIMMycxdI3Tq3FOC7T4MHzw79PXKNFjshEkVD3qdtlUc/Ki+9mCxbGNkpYRkoF6J4k7F/MMMycYSYtxtz3Ma0gs/GFmyb4aOMZno3CFpXtuMbzOi8qBSBl6xZjvh9mFhbGKnseKuHYO3MNlMO1LTTmho/xE3uH0fBbc2DnZVfMzLYC7vuYFiCbMG6W4KN1i7EkwnKM4iixL9oQXdep6AbeAh/jstfSEnwUxTFmnTDlsDCO2XYHcO4rgLvOSbeVbXDT1DDjgSVbjhcIc+B33D04gTd861Z88arHZrsqzHxjtqJSzIH7hpnjWIRjKozLCuB8n+S46Usi/Lr707i1+6+jDVGKaOHorhQ5Azth+BgX+zNzu58tWBjHDGwL/+59BIWWkSRo+PQ/JOIbiO8PBsCUNITBcQ8AcN+2gdYuPekrM/ORWUvwwZ0e0wpFrhS57hM54doKrLNEwOHigHKRSBgbrhQZY1ZiMTaM1s2y6+aVZ1aKmVJYGCfEjUs0iWc4c1EpuL0vNOb/Dyo4li0zI7B/JdMCVovxNCy+y/gYp64U+q48H2PDYlzk0VYqS5/lWsykYWFsIgRgzZhTwDS1y8RiPD3FMzMNEe4/cDh2Tyxrt4AprU5bNWCR0lHEC4UCqrbtSrF+9zDOuXnTVFaLYXQsPsbUNPNdbmG5l5GmVThak/TYnjF9UV1WQQOILMZ6ibnX4sHh7MHCOKblhjf9DTV1peCbYqFw876n4cKtz53tarQM24k7k77tIwCA/d5xaLcVvOWc2/C13zzZWj/GfR7TCjMUrk0VxoGkxJXih7dvK44ioYVyI8T6oXjtHfsizxYsjBMUV4rMNhvTLxW4vS80Zj+OcbspY7gpdijxAmDNxaw1vKCdtRKTC6vFdBiFPsZ5lF98l5RJxuvIUh1AX3xn8bmIrtBe5rvyqaOZqYCFsYkofgCIGU7wAXCzXzDMoZFOu77C7GPcWehNdnK/PYefZKaNwnBtOXGM27AYq+dLokSQO6aHsVl2EgaWjJweJS3BhT7GzFRTme0KzBlsq69L3iDTJXiI2rG0MAuNwx7+Syzf+yLg9NlvCOzW06lQ2xZjIcI+LCAq/8Dh5AZMK1iEo9XHuFS7soRri/6qolZ1paiIQPMxzisz9DFWfJFLZr7jqBQzC1uMM5SNSjFz4dqYhUJ7v+fyvS+a4nq0DtuJOx1lUXKLOFF/2r4rBcM0weZKQRYf40lajNVnsiRKynAgoS6ky3pSKFEpZHZ783qUNNIxUwIL44S517hSV4q5VzemDSY70JnFgRK3QKZti3H0t/2BPrc+pglFCT5yZ3fLW4yTMqXxOhLkLqQRx1ivj0AqjAmUvC9u2SUtxsyUw8I4Jm54Aqm/UllXimnquDnBBzPXYB/jToOUv/pvf2CsgTs39zctIbYYB4XTxuZludNjWsDqY9zMYkzAxCCw+abwvch3oTxVPoGDMaAZqaSUyXVdyGLf4ZwEH+W1A7tSzCQsjDMIYMe94cv+jZm9rj8e/rXciFMNt3dGhxsEM4sYg6J3/+AuvOsHdzW3ZiXpdFu5GPsYMy1QGK4tp/2QBC56L/CztwLjB9QdmUN/FHwWN3R/SisqkEHiY+xCNvFfzolKUdaVorSRjpkKWBgnKI2rHsbuRGMsc5SIR36l/X/ah32MFxgL4PfkKb1ORhfGT+4L+8lmluBkxQa3HWa6KArXlhtGjYC9j2TLymmny8WE7mMsfc2VAporhVlmWgctSUjpBB8clWImYWEcEzc8NVzbLHfk7ErBaMxiQ2AHCsa0GMcuEn4TYcyuFMy0U9bH2BSbgRe+Fo79GLNMZZcMpCKMA93H2BS8yuI7Lf5VyZTQ5ROBMFMBC+OY5MZqNSi4fdNUkPcc2dw3ivf88C6MN/zpuTAzTcxOB7Zv6zCuPuchyEC23Ydy19upiJzXgBO9bSZ4ncm6UnDrY2J23AtccHbWQlzWx9h0TwgalmPyL0+mxVjzMVbL1s8Tqm8yUTqLYrnGN67fgPPu2KrvbdeVYmgn8JM3AxMDBeczJiyMY6xxjG3DOduDYno67iSOsVH+V655HLdv6scdm5ovfGEWEu21sxt+sh7bHunHUN9Esq1dCzAvvmNi4rYQNBltiSRcGwtcZpJc+hfAhuuA4V36dosrhTUltCk2pZe+bjXzXZC6T4Th2gqErGoxbrL47js3bsTnr3xMtxi3qzFu/Qaw7TbgkUvbO79DYWEck0SlUON1FjVG0WT/5EnCtfHzZGGwAH5IFjcdjDEocmNhHDQTxuHfZgJagxN8MK1Q5EqR23zyXCzy21vGYuy4AIAKJEjmCHDlfSiMFZlbOolYgc8F3x9TDgvjhLhxCXW1SIvnTi2Jj/G0lM7MPJP8JdvsAKdCzLKdmMn6GId/m/kY27KGNYd7PaYFSrtSqOfYhWcmnbNapnpKkF7TFXq4NrPPFWq4NrUKkS/y4Hgj209rFuMi+F6ZalgYx9hcKWzMoELIe5DwbbAAsEQ8mS4GvLADH/LbDzHIbY4xKbuozokUdHHKXIMZiPrDzGMKRGSMtM785lhhSaIehO/v26aGbjMvk54fyAJXigKLsdQVNLb3j+O5/3I9fnz7Vus51uKYaYWFcYwalSLdWPbkqa5NWGoSlcJePrt7zjPU39GbyD8uv4C2LjsaTF4Yx7CPcaeRbzCIBW9TH+PoL4efZCZPTv9j9TGOXSkKwrUp2+uRAWFL36hRkLpwTr2mn/j+VhDoFl5zEKiFa9OdNbcdCI0kNz/Rm3vdJisC8/cxbcHCOEaNSlEUrs26bXrEQnwpbvYLBeWXjHzTWjqbO0BmNslxpWjmYxxblltzpVDgds80w+pjbHuO51uM431OJmudIng1H+NUDDuQ2gK5vMVysY+xWp385p3uEIVxjPn+mGpYGMdoDa9o8Z1NBE+vj3HmavygmP+INm69ORDkndteh6H93MbiOyeOY1zcLmM93ZLFuOjYga28yp7RaSVcW9z3mj7Gyftia29MEASJP7JrLr4r8DF2JvpRFUGm7MxkXI4gz8B98pRTme0KzBniG0ttndYGp2wTlm1TSLOoFDyrPc+YQyvtue0wLWM0GpFYgpu15SkO1/b9Pwjjsp76tqkpj5n/FFmMM0GFnUgI26NIZLpGdWGeWpZMy3AhjXV9OT7GgvCMWz9mLy/DFNwv3NG3BVuMY9SGPEcy3xH7UiwwJrugaOoaQrv9JfsYdzLtRaVIE4G0cq2Ce4WTFXQuecYoy7M6N46xsGTEIwkgtuqWsxjLIM12F1qM8+8D1RWia2K/sr2kJbjdBB9MW7AwjpGKj3GCpcHNnCcFL1ZZyLT123J7YOYOcRxjv7SP8RS5UjBMhiJhbAjMXFeK8KUQBcJYWeRH0tddKUoI2VB0q77I5Xr1TIppbSffK1MNC+MYzZWiyGI8c40wcaUwrsm3wfyEmvhiNj1/iurBMKVRZgj2jTaMXeXCtbXlY6ySdx4LAibG0hbe7/4GADBy/2U45TOX45YNfQgtxtHCZ8P3OG7pRa4Uwq8lryWli+9cBNAt0Hk+xuZqu/Ytxh/46b34/BWP5p/PtA0L4wiyulJYhIy1HU/v4rtcH2NOuzDPmKSP8RxYfMd0Lg9sH9TeO9HTo1m4tsRi3LYrRd4hfD8wMdn2slKEYdCefPhOjFMXvn3jRt1inOmPIz/gjCuFYiUOVIux1F0ptGATZtvMEcNlnwOWw258ohfn3bmtyXk8eGwHFsYRROVcKQpc9KccbtMLjDmQtIDbFNMaaoPJSQldMg5b+64UeRZjFsadR+vrf8h8VjeJSlFkMdZiGgdBss8RqY+ytX5K2cWL9PSap8exK8VM0tHCeLzhp2+0BB8FFmMbzRqmlMC+x1quX15KaL4P5idlfNCaFGDdvHX/mN6Wi4pghwymBawzaRFOWR/j6Ckz5WsmuCNkYgraQrwnio2CXlqB/bRcP6d/Y3Jk1mKsiGHVeqzEMa5Aahnt4lOe3DuCYKQPyxq9adnGc8AUxwdjAAdhWDtOgICJQWBwe+7nnFIa48D+TTNzrTlIRwvjD563Ln1TVgS3Yyi+49vAd18C7LqvpdNSV4pcXwpmPpETHqiFAqxb/+A/f6e35aISWEswLaC6a5mWt1gnN00J3c7iuzLTzWwxZhKaty0hABDhhcNfxen17+nt5xfvTt4XRaWAzHelMNvpxn0jeP23boH79ROSMm2L79L6hffJPT0fxf09f6kfRwSc80LgW6daPtk0dOqXvB/47+cDQTmDy0Kjo4XxHZv70ze2qBRTpSJ2PxD+bXG0N8m1WswcQ2tNU2gxBoy2XLYObcAJPjoLbYYhx2JcPiV0KxdmH2OmCFO8Fh2Z4x4pddEnoveFcYyJEFDsM6+4UmTiGEvsGarBxCy7dLg2EDC6r8RxU8TmG6OyO/Me62hhrELWBB+2RmFrhE0aprDkbC9VJ7srBTNPmaSPsbXltdgpsrBl2kEo/8fEme+CppnvYiEx1W2P23LHkZtjoITFGEI/z5/Q9juyER2XbzEmKSFj2SR9xZUizYIX18dWI9OVonSCj5nutztUEMewMI7RfOks25LjCsrY8zBwx39ZdrSXMKRZ5ruEW78B9D7RUtnMLDDpzHeW7E5lDGuYwNjSrSBKu+F2I5pwgo/OhICMuUuU9DFOXC7adaXgxXeMSc7ithInpi893aIrAi/8WyiMg8QCLaVMhK1juFIQUc5AsIV6l00JPR0DxLz02B0CC+MYNSpF0hayjYKsuiA67tyXA7/9XHZ3m5n00sV3Bed5NeDGLwI/fl1LZTMzD5V52LdIGb9Nr/sRjC/djtHhQfYxZiaBaTEO/5b1MW5NF7MrBWOhDcGWe6SvC+PYYpwtQHWlSIUxFFeKVWI04yqX3zfrz4HmywbN18Y9Ny2delmr3MKEhXGE5kpRZDG2ntxkvy1uYglamnn0Jpofw8wu6pTzFMUxLrWgSYTnhf93ZkfHTA4BwDHaTus+xm22PU7wwZi04UoBYZxnPDNFCVcKSAmpWIzj8o4SfRmrru35LcyqGhPVuZGLjLIb/kwNCjvzHmNhHGP9/YumQlqYUm7TxzgdtRXUiqe25w163zY1HU5r2oBYSzBtk1k4VDKOsdNivOMQthgzRZR3SSBtmlf1MTYtxrErhVmA2s5SYRz6GIf7DhaDEKrQtoRhC8sm5CpjmGI6/zhdGE9jp96hD4yOFsaaprRmuctu8xGmkwxEk69u/ybgCyuAzTe1JozPfzvw+68BUFNC2wlHn5095TG/mKTF2GgJl224DC+44LmAU262gKhTx/9Mu7j9+/HOX1yENXt2ZSxp0dq70j7G7btSsMWYicjNStu8LYixvmKLcWC3GF/z0M70KoEERbJJktTEb/fojuT1l69Zj+/+fnO2Dma4NqMN57pImBbjYLLPkrJ05j3W2cJYfWOLSmFpFI0oWr0X51vPY/sd4d9HL0uvVEYY9z4eBRufRNxPZm4ySYux2Yle9ORFAACnq2yoNrYYM61R3bkVAHDY9q2Zfa1agqc8wQf3eZ1LC4vvEp/gkb36Dr+uvU0sxkIv61f3p8JYUmoxJhkAIPTRCgBApXYgOU6AMinUw+3ICl7lrcwbEBYJ40ImeY906AOjs4WxIoKtqz4t2+weF00aTysWYyU1ZbyqNdsHWG6eDm3A8wrtZ5u8j3FPpQcAIIRX+vIcro1pCZHzGkpK6CZtatKuFJzgg8nQuo+xEMXh2pIkHEZ7qziqcpVIbgQpAZIYoKUAALcxrJSVU4eCegthCGMtrJtOy64UbbtcdubzoqOFsaMZh8sJYxCwur8BQeFN0ePc2/w8cy5xbD8eufM3GKlZBE00ClUPz1u3mrnRmamlMR66wkwRhKn1C+t2u8MXTklhTC0u6CzL0E5g94PTUDAz66hpaYnw+J5h7DgwDiBN9ew38zFOUkK3d10AYSjMgW3GMSyMO5Y2nnuZUJNeNgEHkBW1rtZXB5BCsRgTYRChMK7UB5OjznAex3KMWco2XSn0/bonRf7g0JspV4oO1RcdLYy1GyVJ9VjsSrFyYi+e+9gwjjiwHYu9q7Gm64twxo2c4hlhrFuMgx+8Bqf+5h346AUPZCulWoxLNUoy/jJTxtV/DfzsrcD+jVNT3qRD7Ojn9LhtWIyno51885nA91859eUycwohgDd++1a8/D9uBqBYgptM68Yxsyc1W3Huy4FvP1vf1qEP7c4mCRmlby4VnUfo5/n2tRmmVddVLMYkSXelIMJQYjEeSo57T+VG/KDr6/baF/jPB36ajc86MxzBUSmml84WxsrrJGuNEOkei0WiKwhHmd1+DQ71haf4I8ZReRbjsDx3cAsAYMNe8zxEvs6xMI7rVvAh+OEwffRFSVPqw8XHlYQK3rVD7EpR1mIsFX82DmbCtEp28V3sSlF8Xjwz136Cj7xD2GLcsbSx+A5woI3hci3GhiuFehUK0sV3URzjYSwGAFTrQ9p5zxDbS5RPmrFC+mks5Ty3CgCoc1SKaaWzhbG++i57QOk4xobK0Bo0kDfKtYoTi8U4rxZ69TuzAc8MU6UiJ2cxpmRWIyQRxk0txvHArFPH/8xUkA3XFv5tlhI6PrBtVwr2MWZM2ll8JwQ89bCSFmNHtRgTJeVREBqxfHIxTIs0i7F2XWOrVjrJJLy9ACADTzvW/noGXSk69InR0cLYWaSEU1Ez37VMk4UAiSuFIYytRcnk/HLtnV0p5g0FAuInt2/BD299Knyz9xHgoj8DAl3wmm4QsY+xKOtjDEubuvN/gLu+2/xcbl4difqzZ3wvI1Ow18RkHFuMba4U37phAy69b2dmu4YxIEwrx8K4cym/+C4WqEIIBL7SV5a2GCt+9kpK6CBypZAQGMYSVBuDTWttJvh4oLENl277r+S9Koyl8rww753YlcLJZAyZYjq04680P6QYIYQLYB2AXUT0h0KIgwBcBOBYAFsBvIOIBiZ7nenAPeJcYEv0RnOlMLblEo0cm/k75Qljm8lYWizGRY2zQxvufES3FOi/2xevWg8A+ODLjwd++SGgdz2wfwNwyDNzz2nZx1ha2upvPhP+fdFHSpXBdC6mYEgThBb3QfFxtqgU37oh9N9/2/OP1HdQmali7vs6jryg2CWeg0II+MO96YbAngI6OwBULcZpHGOKXCkIQI264AT6YjubxViAtPvom+O/BcYB4MXhOYowLvIxju+litPRts1pYyq+1U8AeFx5/2kANxLRiQBujN7PeYoaIZrtyWwsXnynXNRSVmoxToVx0Wn8cJg3lHrYq4cUH1Pexzh2yWk/jjH7JHcoSnvJhpqKDmnqY9yGK4VqJc4zULDFuHNpISV0skc48NxF6Q7p2w7PlKVFpZABKFl0Gq8HEuGCvBLtMZv5Tkf6dlcKM4ScZuDgqBRTzqSEsRDiSABvBvBDZfNbAJwXvT4PwFmTucaMYXOlsDR0/eGQoxaaRKWIcYUt216QnL+ShrESI+GUemO83LWYOUtR+B2dvJXXenupOlUAgCPslo/M9bMlMkwL6P1dWSeueFDVfsKiPB9jbs2dS+u/vRACvuhKNwR2g4L5VK9kfIxVi3HoSiHhhCFcFRajBgF9W5F9QQihW4yVkaQ50zdzxrHOvMcmazH+FoC/h5brFocQ0R4AiP4ePMlrTB+Ufnxhi0rRvIDoj230qpSRk8bSsV0mshiTDHB/z19iXfdHcMglfwh85bBMccz8gmRJi3HeD2zq5GjDqyr3NLly6vLDCT6YtqD2LcbthGvbO6QsjMrzzee23Lm0u/hOqiFa84Rxvo8xKEgHhFHOAYocJATpvvDdwscnKr/Mll1UV9/uSiHM9SYlm/72KOZ4/LdlOvQea1sYCyH+EEAvEd3X5vkfEkKsE0Ks6+vra7cakyT9+GS1GDe1hdg3k2Gby7EYO7YHTTwKjR4GFSHR1ftwtvj0XZM6Mu0zxd+tZjEukwWx2GIcd5xHi3L3j5F9tCU6tH9k1OlckSOMm7SqNHpF+asOjtkXRukXZleKziNnNq3M4jsI+GoEqRxXCrOdu+p7mVqMZbQeKBbGtvb4h85d1hrlITUf47Q8R5oLsdU3+eXFA8zdgyXuJyud2fFPxmL8UgB/LITYCuAXAF4thPg5gH1CiMMAIPrbazuZiL5PRKcT0elr166dRDUmgWIxtlslyi2+y96jeecV3HDGubJZCCTE8co7s+HOKFNlom/ZlcI43XgvKWe1fmEdiq5Q4nRubx2L2V0lmTlL+xiXbzsNJdFB9gL5ceaZDqHMep3sSQjM0KoWTH9eR2n4pFiMZWQxlpGPsXkeYFuwWlzPsovvyq6JyhtGlKZD+/u2hTERfYaIjiSiYwGcDeAmInovgCsBvC867H0Arph0LacNiz+xFpXCcgoBo/6bQFRF7oFmeKHccG22Jw1FFuM2RA8zO4wfAO7/WdPD8lwpHtk5lD3YOCZ8a3aO0nZULpKoqXUvD5EzBmQWNkmTs3iYlfUx9kQ/KssebmlQ5flq/2cXwP2jJaxgD10EjOwtfV1mntCmYPNKSJ6Mj7EmjNM4xlJKCJKgRBg3f2arZV8TnAHprdD25wrjzOK7FmlfGbd74rxmOmJ9fBXAa4UQGwG8Nno/R1E+vvYESMaE2TNqp2DQ/38Ixt5SUK5xXu7iO+O0eD/JwofIjK1I7XTKfreXfxi48mPAvvVNDrQHZf+j/75NPyzPqGGOv6LpNVnS/EvF7m2ly2A6E3MgTyVNxk9V/x2LjrwAfgu+FPWGMnWcIwr+6VePFBcyfgC4/EPA+W8rfV1mvlDex1g9xpdlJE++xRiK+I0X3wEIhbFNL2S2hZ0wEfBR7xMY3/rRZI+AKYzz7xfdYDyNnXKHdviTjmMMAET0OwC/i173AzhzKsqddlRXClu0CUubELI72rck3WY2HimhqZtcH2Oj4SdWYtJWpOYh2p4QZ1qjyfc8ui/8G9QLDys7/aWcYGwwfIxzVlXnFmcrsuy5ndk/MskCY0vIKNKOyMUTAwCAoAXXh1BE54S5FAIgwkAZP2QAGNxR+rrMPKGFDkmx98Ir8czMWoyVfZrFOJzhlXAsZ9nLiuMYy3hBqr9cr6ufMyDMWKNb7JDblgqd2fF3eHToPGFctE0Yfw0LLpBtxDlByTOuq4nFuBVXis5suHOKsp20OthpK1ybUVy0eESW7PUKF9+V/Azc2joYw8k47veaNp3Ir9NvwSe+4eWlxlWq06yQRLmzL/KCI/ObFs2whs95gXIW48ziO1VayyAVtSQjoZzfB6uzLJJEMh/tw7XXVct8l55rhoJr2VDRbsfdoRYRFsYxLWe+I+RGsMhYOOLrGFM0hgWwvn0Eu2oXIfC7Ict25h3acOcmxY/qlsO1ZSx0hL+/9CF86pKHovJCYRyUNQeQabVWyA12b1apoN7cFhceUbpnIbLzU6nFuNnvHvZ/QZM2puIFEt74TWiMXq33p0o892YLmRIDBa/XWICUd6WIRasAwS9jMY7L2vsI8NWjsdLfr1wmbe1SCdcmc6RUHHlqkzwcz6r/CPuwCov9IQSJMNbbJmn3COGq4EV4ae3bmayR2rtp7Xc7s0/vaGEstNAtlnBthZD+sijiQJ4rhWEyHrl1DwhL0Bg7XBstZq6s3xUl68tMP8W/Rcu+4RaryMXrduLS+3ZGu2NhXO42lkUSxi92AykFW+YWHKIWtjHH4nWXl5kzUwZatxh7vo+g/iCkt0G/wOg+xH10pVnUoFgQtyDImXlCS5nv0hk4WSYqRfziru8BtSGcMnZHuo+CpLzQ0JFGpbCWFVmfLwr+AOPowbXBGQAALxbGinVaCICCtK0SET7jfRC7sBYNzaZCRoSX5s+S9tfedaa+6GhhrH98izWvpQe90oByo1IYwjhvOohaSMTQoQ13TlE2nFtQ0mIck7F06efErhRlrCAAQLIgJXRQnD2vXDQkFsYLDuV3zy6+C/82Ww4hEotxK8I4JyW08rrpbRcL4nbCGjJzE5HnZlbGYlxSIMZi1QnbbUVdcKe0PzUqBTXpg2PLcVyX1GKs95m6tx0llmhhGFXKPvbFpEONdqa+YGEcE7e0pg1J8Tci5SbNHcEpLheenn0mm+CDkv+pRBxjm/1vYnQEowMHmp7LzAYt+hgbD3RzFiEOBp83jRfjIMBBGFBal4UmwjimsNo8ZT17HNgCeFG2uCd/DQStW0kpIHh9UR914CnAq0FtLwKAqAwDTnhMaVeKqE8tcqUwp4q9vDjGymtrHHgVYovxgqUdi3FJQZm4UohQvLrwlX1SsxinThp23ZC6S+rC2AscHDa2X7EYx72zrh2SvcYscfJ2unMZdKjhrbOFMVl8jCGQ0xrT/fbCLGXFp0Tn3PNDbbOTiZifvizyMS5ypfifD74b5/7ln+eey7TC1HYKeqSREj7GptA0B0tRG2nmSvFG8Vt8HD9FV71fiUpotOOSrhSFIogtxrND4APfeS5w6f8BHrkMuPCdbYUoG7puC/Z9/T74+0eA75wGXPYBPbgOCEtP/AqWnvgVAEpbKGkx9gvaR8PX9+lxjI37Jmq7brML80Bt4dJCX6O5UpQ4PukanVAYmxbjRBiT4kqR46KRFJUI33DLyN09+PH1X0V30u9GwlmqFuk0AgYFpsXY/izxWkkvWQoWxh2HsFmMwzfGXxukBw8oXHwXHehPaJuzX37qSlEm8x2Z14XlPTN5pijznRZppJRrgrEwI+NKEVqMmy2+OxbbAABVbzj/sk0sxjnrAXVYGM8OsUV00w3Avii2b+9jLRdT3zwYFhcnzth8k9USJpzwemXDtYkSi+9MYewHOcKYWnBHYmG8gGnTlaKUxTheiB+2W9ViDEMYhxbk/KgUscXYFMbe3rDsqlT7XZFJ6hHPBpIarL6gn81mlyycJ2Ry6GhhrHX61qgUZZsTQTf3mg0356YR0jgqFeRFwb3Vq3KTnz94rfoYZ9KeGv5o0YO/Wbi2dCpRso/xQiQeQAl7CKiWUZuT8sObPsbp4rvm0hgo9jFuGJYuz8tzpUhjxJd2pWAWEHk5Bkq4UpjP6eIrpK4USjsKhXBs3U3FbrOoFKmPsX6cSDRAVJYyq6gv1tY/UV6Cj/xbsU3jToca2jpaGOcm+CDKbrOdTspfrQGVi7FoNtXk4UAo5WOsVYKZeuKYklMk+Hz1Ny3jY2y0gczkQGMUABCI4ts4Pu3AaA1EEn9duQRrg31G5cq6UhTtZCEyK8SCU9hcw1qgyEtGADXoCWUKPc4UYouxF3i484d/jf17twMAxh/pwxnRIiRTGKtZ8kjaF+Jl12gYsMV4AaP/9hNBA99YtRI1y+xeYjEmyi4UdaqZ4+MB4JYD4QyvY7pSUOxjHGjJOorqKRJhHB0r0jqpx6kulCRT67Q0F99l1jEV075K6Ex90dHC2HVSC4ue3lR9nQfBi/x+xuoetAZkdsg5xZg+xhv3jQAARuueZUrEUgMWxdPLYPgAR9+TU1Kc55e0GCd+C4HWBs3BEu15sHlZSC0mNz/Ri57x3fhE5XJ8fuif9YNKiojiW4Lb46wQuyg4brgIDwBqI20UlG8QIAKuwGb9siVdKeLHzPDWdXjxzh9jz0/fDwA4cP4T+DrCDKJmumgpiyzGUallo1IwCw9j4Pez/ffiJyuX42fLl+mHkW4xzrRVN18Y//bxPgCAQ+riO0KcMAQk4UAWWozTdGCx8DUabWwxtsWJV7LskeFClBuO3kwiZj+sPB3ap3e0MBaKxTjNLKNOt1gaRdywFVGbsRiXzMrjGo0uGS1SNgKBDXalmGaUTIRTQesWY8PH2DilrCtFXJ6ARCDCB8FRgZkmt/gzJlqdF9/NPRKLsVB+g9bbbLO1xoE5E9ZiHGMvqlt3MJY5JtPdaRv0cG3ErhSM0ei86B7wzTXFcJP2ImCJY1xgMY7FrktqmubUdhvPZFBBHOPExzgSwLGonkCXdi2Awj5W+VySFGu04TrRuisF0wqdK4yLwr0UzBFm8z/ZzjfnvHNcKYyOXUsyXcaVIuPCwcxlgpZ9jAPj99XPid819zEOcUD5YqGJqNUmVPLgqevZYYp9jFVfYvX3zmS+S/42a8tRVIpY0CLbTjKWLnX6WrttlDjGZRN8MAuHvDjGOW3Qh6NlvitjMU5LjBLJyNTNTEvNHL0mKkjwEf014xgnVmxjnZEWN0NZfGcm9LA/CabBTNah+qJzhTGAU4MnkteJEFVdKazNTN2nxjFWG3RxYoaYTLi2xIUjO22uH9WafxEzSeLO+KpPAl9Ymd1fsvPQF9qXEcZhOKD0FLM7NPzW8oqJ9juQ+dc1tg83hnHqeafiV5t+1byeWn2ZGSd2GWjia96UppZf47JFFuP/PAm48N0AgPu2PICPDQyiEQtjyro4ZNzCKD8qRQPAqccdjR2LHy2uMAvj+c/ANuBrJ6QuQjFl+1zFYmxNnFXgSlGJBnBdUokmpcUxTi3GeXGM47LMqBRxLQQF+Ne+fjzR9cHM56K8vr8gAdiUu1d2qDtS5wpjIeCo0yrqFGSheUwYf0u4UuQ0VvPL1xaDW904LMKoQ0d0M4Px3d73k+w2jWKBGsiSFuO8OMam601SUjlhLMwBnHGUys6RMO30+Y+fX1i2XgQL41lB9TGeFFEbEPZ2avZXhV3P6F7gyWuStx8eHIYXW+Aswth0pdCyghphDofd8HNuWfpwQQXArhQLgcFtwFhf+FelZF/jw9UMB5m+ssCVohqFaavKmnLd1HA2UQ8j+cjCBB8UlSmTY+OrhPsl3jI6hh4RL/RWL6UsxNNsYVLvrTUxnUO7OqFDB5edK4wBOEpjFtYbragxGRZjw6qhh32z38RmuDa1DGlrkKYwZk08r9DCVZXxMSbDwmtmvktWLBeTWozDd/aDyjWmQosEC5HZwRaVoh2KJsrCC+iXpfhvSetd1A+WcaUgLRKA3rcGUTWcJgPCTrV2LSji3z7zPCznShGQm0SRyPjmAoBbyb10LIy7VGGM7ICtaPEdEmGM5Fi1ttk063YrsZ7wS7EYk1ra1GiCvOQhnURHC2PYLMZNo1JYOuNmFuOSi++069quvW+9pVqd2XBnhJJfbYl1koDfAHofL1d4jsU4k+BDea+Ow7b3j2NTbxqVIBXGRb7retkCAqdP1NAV1aFMgg+SAW7qH+ZoKTONnCIf4/RpbSXvIZ75uXPTUYf1tLlSmM1SNVRoyY5IJukW8td7xCfyQG3+YwrjnI4op8sJ4KBGS5ODMoe5XZlzTFcKbR8Fyf7YCkzId2dbLqKQb4YrRYyjXCNce6cKb1VTKCcpAr8RSPQO15Rdk+97Tet0J9LRwljrWG1RKSyNLEj8OgtGrCVdKcx7aSwavDZAkLaHy7kvzRbLImTW2T0Udn67BifyD7rh8zjkyUvS96V8jAMYc2va7jhLqFnSK752M17zjVuS9/FNvpQk6sM5fplG2ZWhnfjJ3l58eJttMGbnur5BvPvhp/Cz3f3ND2amjqnyMY7JeRiKnLS3Gblx+zdzCg7LrcDmSmEMzJQ6aItWScJPYsA2+bwd+lBfUCSW0XIWY1N4BnBw50jo695qVIqqyLZToaz7cBNh7DRd5+EYrhSxhTmzgDTHxzjQrC/6Hff+n9xjO92gvE7Q7W6deQ91tjDWLMaqtTbZaDkrvgGN6QZtyttc5GRYWhQLnkqQDIYlqMQ0ICn/M9NBExNaRM0LO4/xRoGFav9Go9MuI4yNdpTxMY5aQNNgleEBXQCCxmDexbR3TiMMqXX0xGjBUTr99XD19taJ4ix6zBST+BhP1pXC4ktRMDuVu/huQPEH1aaDw/vD5mNslqFGpdCmkUkm0S3YlaIDiH/7zHqL8oLNR3d6WmbxXdaVIm5VXZYBHIiSSK2uYjFu9oxQE3zcvOpPlaQjBbPLeX2/4RLS4lOlKabrUifS4cJYfWdZcGIZfqlSSZgbtbLyBU08L+2aKaGj4wQJUJAvsvRnFQvjuUORe4RjuO4U/W55cYxNYdz0qtH+dKFHrpjI1EdZsFd0mEI1OtbnNjmz0NT4GKf2ALvVKhOuLWfGQlvprwrj2JWihI+xWqoZrk2W9jFmV4r5T47FuGQfI6A+sy2uFBaLcXyGzZVCbYypMM4P15bWIxbGDiQJkIj75PzPJbXXZg3thjfzGWEmGCkD64sOF8Zax1o2wYeCloPdHGUpFujhmt2ClmmsIr1uKYuxpdGevLwXLzjITN7ATI7idpC0gkKta65cLhLRSuY7dUFmYD+nbNflggpueLPnFehvfAoIjkw2/V/3apzUew3yqEbtt9GhnemsMdU+xrFPrzeepAo/ROzAskBPCf20BuFv0ZNt945qhVN3xovvmrtSqNnGpJES2k+sbdnW/J0bN+Kqh3ZHx6bnjXvj+OTNn8S+sX2Zc+Yyg1duRm3z4GxXY/ZI2mSBK8VDv4DYcXeJsghmFzpqZgRBaqCqWtqpiIxeVwUvwi3y1LBqJYSxOjusPgdUi7GqPOL6xgRE+HVwOr7hvS1jMdZOybl+dh2AB1z6AaD3ieJjO9RinL8kswNw2nKlEJlX4apXc/ohfX/Hpv14g1ICRd7NmXBtynXLJPiw1e5NR0xN+mJGYUqEnjA6nBJlSiMldGbxXby9mNRiHEBQjngyLQ0DLibkH8AZOybZ9tnqBcAGAPiktYjEYlxqNSIzZWg+xk39apqjtrnhUGQuxQheNejhwrWphe1zgy4AF+eYD09VGGvtN9+VImMxVn2MjRX5XmJty37Wb1y/AQDwR885XHOl+M3W3+DG7TdiaXUp/vVl/5o5b64yesdujN6xG0d+9eWzXZVZIn4uF6zbufzDwMoVwKIVRSUAyPZNj+0bxxnG8Wm4NrvFWIDwV97HlfKLolKEpAk+HK3qjpZBV49PrL8G/tL7GwDA3xjh2vIS8tg+U8Keh4FHLwUObAY+9Dttl/6c6Uxh3NkWY00YWwLKF7hS6H4YNouxmqHJJM4AZbhSJNdFSYuxvY7MVJHTKbeDMIdCJVwpjOtSoL9vVRi7UcTNvKOK3uYXnh5Yie4Jj9vkzKLFMZ7Ed0/5/R4KSi5rMU58jK2uFOYGxZKmdc0ySfvb3JWiMx/qC4rccG0tFKEUZTYzj5qHazPrk3EvQ/O7TvUxVq3LjhaWEMbNlOcioesNbR4yt+/N82XO3kNaER1q5OhoYWyftqDcB0OWvDjGehlOzqhLZNqkciMUdQSU+4aZSgpnDmyHFblHOPqKaKIwrJVXsxyrCmPdGrAYSmgepTiBcLrY1jGqPsb2eN3ZupsL+gLyoE+kxwem5XVhcsJ4ohFAdmhHPCnUOMbZTqU8qoKw7s7zTzfe51iMZU6ca4eyv3uV0nauLb6TfpIoxGn2+JrM4rv6aPNjphnieyHtXzI+xm0Meijbhn1kZ9AKo1JYnuUEJ3FSq1EVgWU1dFymKavVd5IIednuAuPZUdK8UZBBm4wD8ujMwWVHC2P14wdRR7hn04PYE4wDACZa6VgzUSmiBiWExUpnj0qh7qXcWKDaRdliPBM0sTwtj3dPWKVjDgT87Czgy4cUXDeA7yu+lr1PYH3P/8EfOA+EJSiDfuGO4IwLzsD/rv/f3OJcSD1OplkfDaH8D1wz/H/wyqOPRAalvGrUiXptPNADSTj5n3+Nz1/5WMvndjxTnBKa/FbaMbJ9kJZmt7kwfueuS3XD1MA2vGriJ+l56v0nJfzIFUM0a2btJpzZcgvwb0cAm29u7/ypgoUxkvaTDP5KBFS3lwDbBKtXJIytcYxtwhiJ0eMZ9fPwYe+vM8eo4dqI0vUmQrkGAVp/qgpjYbgT6VEpmrtStGJA0y3GnbmAtaOFsdaxeqEYFge2YL8MF5zUbD5GCaS/1BquGl4oX7yawliot3BRp95638BMiuIvenH0czsTBYMZc/EdAdh6a2G5Uvr41VXXpu/7NwIAXuxEsYXj4BUAnJ7QF/S2XbdZap+6Uuihr4w2rNXXqAt8jLiW7kLpOCuJxbh1K4MfiZ+L7uWFoy0zVZnvEhHSqjA23rdoMV7jHdBnOvo3YVs1LUOzJks/iSXvNrUYt/lQ3xrdQ9vvau/8qYI7eMW9p1kcYzsChDTxXdZE5VmWWcVdn3Wml7LuaGZUihvk6ZnT1AQfEmpUCiWRjemTod47Uu+3NdGsLezLM7ZlTMbJHpO8iBedREcLY/XDC2VqIXnd3HEI6YH5rhSZGyn2kcuYPJT3QYG4KONKwZ3qFJDXKduxTbOpe0mbYiv6fcLjNm/cgEfXp9nykhS8UctVryaqAwCAw5cenlueA933XRcOee2ooJqA9t0kwridWU5uru0zxRbjVgVl5qdr4mNsQ89foD+aA185T/qJj3HzzHdtulLEn9+Z3bXpHRoQwMCwGCebW7EYx+4Y2Qx1nnUxcli2dUbX4mNcJiqFfqwSRs2wEJP2Xo9YodYv15WirMW4wJVCtxh3ZiPsaGHsWgSmKBot9W0AkhBBqhA2EzEEWq+WvcHsrhSqxbjoIdIta3iHe3M4asy7E6IwS8wkaLIYyWTZnhuAwe32ncLJtQbEdKMBrPtJ4oqwfdsWnP685yb7xxrhgz6IbtueYAleNfRCEAiiOggAOHTJodmPkViMCaS2qzzrMaCo7iafXXlgxUkZ2rEYMy2y9fZwZTmA/tEw42Jdlns455H80lJ/IF93zBmoOVXkRbzIJk1IXSmueGBn8lrmDBxXVNfoUSmMvk8qApcCP41K0Syzja19lxnkDu8K/042YcpkYVcK5Xczo1KU62N21k5PZ+pqwxCGwC5ypXhcHo1H5HEAUl9km/FjH63CLlpTWI9YOBNEGMc4uZYqhKH1w+p9NQG931abRilzSxuuJ3HNO5GOFsbqh3ejznepPwAvstYG5mjpnBdosYaDqEn6NotxiekIc+Qp1L8Fwvb9I9/Df1R/gIN6784tG75lURfTGvFDtGQnfPDmHwLnvsK+U9jWMuv8TeUS4OpPYs/u0J1gxcpVeONrX53s37Y//E3jTvasvv+Lv9/9fqz21sBxw0x1PW5P9mNEfx0hsbjeJBJLxP6hfdHWJh2qRWi3s/gum+CBKeSnbwLODUN43bOpFwCwf3ySmd6Sri39Lba5S/Gd096O21ecmt8WzNtDsbR++pcPp8XniNI3HPkB/feXgfawl8rsmZR+Ybg2vV7K9fY8GP192HqoxkMXhn9n3WLM90Q2813earIsAVVw1/D/S4uCAyfQn6u+xWIc99R/5/8l/qjxZQCpy4WwuFL8Rr4Q5wRvLf4YUb0DisxhTpjbwMmE47TrhnXdY8rmAleK0gGOC1wpilzsOoSOFsaHLe1W3oU3oINAeUDYzhLG36hhFoRry/oqRR17nisFiUI/vxUynDZ3/fECizEL4ymjFQvoxEDODoFmme/WiCHsxKEIIiFw1LFPg+uoHVd4u8YroJcFqwAAFUqteYFFfGjh2vwcVwqjPuqiv0LUjj16kPttGBlYF0+CaDFabtSI8gWFfxSDQBwBYsLpRukYyYqg1K1Z+Q3DTCqgz+Yq6aGlTIJoua24UnihVV200i9ONmHKZImF8RSEpp6/5Fj6S3QYlPSXMVmXB98mgSxl+4owbkcskmoxRpqDT4tKYU48K2/qWrXzK5CtehNXS5srhfaGLcYdh6vdMukIKo0nXHAHCDL0c75rhTn9kobPsgtjAcpM+djJb7SyMVHifKYUU6HahICedy5bZgPduABvSbruzFo3kVod1DJIsUZL66ppNW62Or1sSYOevCvZIWriOhQijTY6U7YYt0/cVxCcybXVpBEpv2nU5urW1LnxecY1nVRQatY1UdBfGYYF9XEdKJZTCjzFYtwE6yCxBWbZYpx8r04HK+PcOMatt/MwrJr+XQaW7Im2sqVwIlFdFAu+6Nq6K0ViHDMtxrmLox1t++SjUhRZjNU3LIw7DifHIpw2lewBupE3DrlisRgr5+5yxmDD/PL9KHbnuKAwZWMOyc2dcdlICRrjueczLVLkl3jxn2Op2Ny8DHNhlNGDdcPDFufpIAj0xC3D1zPfyaiMILGEqGWEbUIVxs/70vXYMzSR7HMh9cUUlG8xlkG8rxVXishi3JYrRfSigzVAuxw6EroHSDiYiGYlbL6TJvuGa3j+l67Hpt6RcEP0Gzyw/UByTPyz1J2u/ILMn1sR0WWFcSJ+vRpw8Z9hSC7DD49+P/q6Vmsr8mWQLr5zm/kYFywuvfupfrzoKzdirF4wM+LMrsU4uZU7+p7QLcaj0e/lB80NR+bEb7hszlh8Z5FANuF7Y3AaXl3/Ol7ddz6Oc/Ym27dVyg2e4uumsjo1gp3vvxovqX0HIzVfn4HTjGv63EueW1PpqBRFcYwp903H0NHCuKI2iqSh2JwlUtRtagrnrMU47cxv7t5nFBILav1BMUrhA2mnIwtdKdLRJ2UETYxfZ2E8ZRT1DeuvKFmIMAK/p4W6CPCaro3w0IV34wosk6GfsJhoGGXE0Sj0KULNH1NpdwfGGrj2kb3JcS6CxKobFmAO5lJqXpAp2wplLdCNNnwjk4dAZ/bDk+LUfWkb3B2EYm6syMIb8dvH9qJ/rIH/vXNbtCX88i+9d2tyTNzX1J2qnqBGoXBiTXtXZDGOXkSLVx/1noUJdxEeWPEcSCWmO2kJPloRxlF9ouv8x2+exN7hGh7fM5x//iwL4/hLEZNJ2jLfMSzGe4ZD49FEo4SrF8XPyRi7xfhT3ofhq9F8LA36S433YCsdikEs0bZftmxp83oodZCILM+xBiDCVfLF2I01GK/7Wm31OMZGgo8mHhLJeZkamO9tFmM9bngn0tHC2M21bER/Cy1flBwozOgQhitFJdP4wn1ZH2OFglBDSZMutBizK8XUMVUpodXOLQ34/srqU1gjxnCM3ISjsCe9HgXQOspEGEcdvtJ+4vZr+hg7QnWlID1QfIFFrTj0nHpaNrxQOxZj9qRoH3WathUyLqzRe1uwtdCVouyPZHNRQ6HFOBEBrinohf6gDoLElaKpbC1YXFqK2Xal4MV3af8S95fRWzenqWteANCFsZpYI8aHg0uDV8I76MRS1bE/yZujGjPUAaY6a03GojrdWGFajNU96jMih7wO1hquTT2WhXHHUbEurhDJl1L2MVP1+hEM9qUbSPcVquZOb+RAokkMTuWGz2nwQd3uvsG0QauqbWinZaPxa/sNEICb8GIc7Q7ibv9orMJAfMHwDGl3pcjGzFTsAkZdHSFQF8CNixeFrhSahdfiSjG0E2iMlf/MFqGdWIz9BnBgS6lipDJjw7SGVRCX+Pni79y0SKoDdjcKE1V3qvp1lPax68BYEsnH3KdXpITF2A1dNtQJ5yBQ7gEZJK4UTR9eah9KmRfJO/IC+IO21Oyz7EpRNN3daSR+9CG26AvHNsxZVtNinHWSCJJW5OhHjR+AjVaHKlvkoVp4NoqW4qf22vSuosgFM6AVkLTE+Iz5FuOiqBREwGZ5mOWD5H8SXZx35uCso4Wxk2MRcGScctSW+UZ9ETbWNe6DcM57rXKQ2vQJFWMKMl18ZyzKU0LBoUxKaMo/LqhzVIqpYnDcCJ3XrLP45jMzm8jIfEd+DTV04zW4A4/6h+KJ4JC0g4sta8ZUMJHdlUK6bvLON85xHIErVwT45CFrsbW7H5VAcbGxLb775jOB89+B/NTR5gfLCp/EYnzd3wHfeW7uQ0aFjWPtM1mLcRz5JP7Zqopl92SxDgBQc7qgyYIgdfNZv2cEX9BSeatSRKFghiwdGGUfSVLxJ5XK4rumi6AKZ0RS9p+3Hnu/em/2/LliMe7kp3RO/Gky+rnjGh6u2rUHxyviOLkv4iU5cJK+MyaIwrWR0Be34T+O047La2tFgYY2y8PwqsY38G3/T3QfY+Uk85YgAvbUz8fu+kX69c3Fdzn1MfvRm8aOw5mNr+PR0WVmzeOSLWXkLdDuHDr5lksydWkIkQhW282Ql21JG9QbcVcqOf13NsFHeg1BJRbfgXJ9kf0ywpopxUTd8PUtM4o2jglID9e2bedOLEIouNf5RxonR+1P6q4USVQKY/GdFEh62IwwFsAfDlVxjOfhytUbUJOKi41yLBGFFl4A2HYbSk+hFfmjbb4p/Fsbal5Mh1ompgRhWsbKEX/npiuF2i9VRdi/NJwuvXwl7JkAcNum/WrByr5yrhSJMM48iIUmgogk/LIDAO0Bn39YfdOgfcdcWXzX0dMo0Q9nWIzNH/SgaP9aZRBlDhSzOeuUvlR5gNtFsHGvWI/Q9+6lgwAA99JJ2IvV4cdISo8Gd9pEC2n3Tn6faFqMs/WM2eSFddhdN+LbF4VrU9V1h/bLHS2MXTVrl/JKX1RnINQ9OY2G1ClwgewymOJwQwSU8jE2XTZUZFFKaaYlLF1H85OMqCKSdK/ddQ88krmCOuEWnqQPsGJrR5BpOSI5x0xK4wiBbhL4175+DLl1XN6tZOZT2o4fSKA2GBXnKp1yk8+qlJGZYYktbiVCD3Zm9zs1mL6Upc+LhbCjDLQBVCxWsLqo6kMlJQGRA+P5qbaJkq4UVNDnSi0kYIDAVPK5hU7Wx3iOLL7r5Kd0svjOt26OkckzVd2hz6wRsuI1iD3VNbeZbFvJm43RW3e+bK6hOylHvQu0+hqW4KLFdyrlwrWZFLhSaCE9W0sPv1Do5FsOK2ppgg+1mXiBi9t7j7E3siLRnOyiRKB4gcyEFSII/HjFMoyJQWvhAtmpIvP85PhkEZdOUCKcDVOSTDrSEr1PZFGbmOjHvef/EbzGOFSJvWTxoswpmRXEZhuIptMq8JOjlrjX4UhvJOlE/egB4i59Au7SJyLJLPDcegMvHzkEt3f14fZFkfVA6fQCKYHex8M3PStA0cBKWjJDaVWyLL5LSIRx89kLjmPcPpqLDgF3j7wL9WBJwRkhMsdi7KppatWoFAJ4/egYTp+oJQkztPOVs2z7qMBinMx0WAZkerg2mWQcbepKYcaILT4km2luthUpJ/iAufiuGqd3MfrGuIUURYq3hWsLkkGlahorj2ZcLThTvc6eIWUGkpS8CaBoQX38VhWo+ifTQ7mpe+xkalbkv66F9OzMfrmjhfGLdz47eZ00aiGwftdRuKv/aGwbPqjgbGH3QQYAknhybzh9/Miuocziuz5X4JsHrcKve66zl0yAyAvXRoRkUQEB9egBVTcaeFA2cxnTlGzfUV4YP3TVR/CCjbdgyZOXa33Mc5+b9UNOTo0seA2j8z+KwrB/r3RSa/Oq6jn49OD9qStF1CYXH/VTLD7qp3AU3+bXDwKHBj345zUHYdgRWgcoAwnc8PnwTaUH/lg4aBzzDy38mNv7R9M3ppCPrTAlrA7sYzwJYgFHwL6BxVg39g7sqj+7+Bwousto4KrFOLaC1ZwuEBH+s68fP9nbq/kYZ24PmWcxzp+i3Tr2qHZMXCUCQEp5FKQW4+bpyrP9c+KuFpdRtNBolkUBL74DTCPBUSJa5F4f0Y6S8YJM7ec01mJYhXGOj3GG1EfYdr56HfunSBdOE0TiVecgbZNkRpnSCtQtxnn9ZfkEH5ZytXrEdObMc0cLY5dsrhRAIKObxdJokhFdUag1osSVQZLMpC6Ng4r7MKeHUveL3OlnY8V33iKpgH2MpwxnEhZjXxvgpO1gUZctYULctiIfY0NQxk0u9oEnrf3YXSnUZ2o3gD+fOBr9rot/P2iV7kohJSAiC69bTarSbEFXl2bIiOsVEU9Fl3ClkPG0cdMjGZP0N5LJA1NaM3rpxBbjJMNi/F57iKYWY63VK7+pA1Ok2q1ZukOl3k7j3z/erqfK1ad2VS/SQiwpzwvFdKYrneXRGluM07426gursPcl8U+nO0ToLkZxOmaVdL1GcVbSnBatzaiZC/u06yQxlfU6qBZiM1ybnn1UtwtrPsZCPSdnZqTEFrUeCRzHuMMggkAqWtSU0KQckyE1NSC3cZFMVIxAdvFdOu2j32Tq4jsnb/qZZPogJEo6DLMmQWAmh2Dax+wcSjwwvUgYKyvb1bP8wg4najtGuLY0/FA0lSy8zDmBIUJdJ7WSOCAc43fjA4PDuHLZUty89+7kuCCQwKIV0WWyycrzcNRFouYAIrFklvAxToQ40y5qXHNC84gK6eI7beGEFi0nflV3DEliRgkg+5tci3Gmf1P7VaOealQK6SOI++Ai4wRgtRinV2tmKbPXZUZJTfqzW49ZRbcYx6EETVdDu49xVhhn4xjHFuNmi+/066Tv7a5mZjIcPfOd0idnHi2KyNX8NFSLttQEsD6IzK26/TrWtmWJVtRhdK4whiksQjK31Z3/A3xhRbqYKmnwBC+vvyIJt9aDnbWrUa0fnll8FxeRG+ECAlQgjFfKMPxVtTGYdBBm85V+flQLphx+7AlALYZrAxKL8dBYOuWnWiVsPuTZxXdmgg99kZSAOviJXCmicv939178dPc+OErH54bz0vjLwSE8vd7Avzz2fQw60YI+KYFK7HssIbySMw41JXNYxsc4thg3762bToszuSQDJZJJn1LGYpwsvjO6IVdrc2E5ocVYFbYBlrmX4MieP4RjWLDUdnBn90fTzULi79euxmuOOjzXf972IH7m+q9rZSctU7nog70P4tTzToXT1Zts++7YRpx63NHasXuH9DCWuhU8fPfBQw/GOw4/1FqXmST+GjtbF0e/wbofAf2blR2GMBbAzvHluPahF2GgEfZjNotx5jkZ98nCxQFahmNrF+Bm75Tc6kgITfQGVFH2pfdcI2dgKuHgre5tihku7feP9LdrgzOpDTB1V4q8R1BuP5o5Qa2BsUfmvekcOlcYCwGBNKarsDQUIQDc/JXwjWdmkiPU8ywWRHDHwvAsiyaeEQoSBT+Jw5k5Mb12npWNAhwa7AEALBnfmWuNI/YxnjReJRaNQ8aeMsI4FNP1fbYkA0asyEToGgcaqcVjkZLOLCiCPdroR+3htHoDz6/XIUTatkU0kVcF8OX9/RhsjOArq1eF50n1WgTU7AOrzHShGn3DbIstLb7TPgbTAmm7CJIHKzXPC4fAsBrFb139GQwAqDtdiR8nAED6WFE9LzzenFFR2mxF7SMF4bqlS7CvUslanE2LsebKo1w4CFKLsXLd67aE6zXcJRuSbf8z8ZR2BQB6IhKT6Jp3L+rB491dbDGeC6j95PY709fGYDsA8NjgIQCAHWPRzJfRm4TzKXr/5SeuFAJPyjBs5mX1M/KrA6FFBcrzMfYMYRzv8+Himc62ZLvqSvF0f6NWiv6MUBfYytwnUF6TzQwJErljmznhxXedK4wBOEJZOGTzmoDiLpHkNlfOzy050EK+VYzGFTc7YZagGgTzfITVm4IkKMgRMDnbmdYR7Uyx+hO4//77gdrB6Wlq56b5aJp+lUqCDzWGq9HRu5HFOIzrGrtSGD7G6vFKeL9nNDx8+PizcN3SJbh+8aLQjzNZAZ78l4GMlfrazIbpYxwvvstbSKrAUSnaJ5mBIpm01WbRRMJjwr+pxTjcUMrHmGSSqKBi+n3mWJk0n0ljsCQMYazbHNJWLEk28TEuMjcUHpaNSjFnFt/NajVmGeU3qCqRfIzno1R6z3ThZmwxjgaLlLUYqwk+YpGbGegpZUnShXGej7EZUjN2wfCT4yNXCqVCARytzenJJFVhTNpxTaOzwNaUCyzGnOCjw4UxUmGcalKRut+Asi1KpH9yG6TSwQpBmUQe8U0iAOwenMCm3nC6XZCPbuchCDiFES/iSggiyBwfY8mL76aQfItYHru2PYWrrroKXjWdaVB/I/X3MTtioQpjygrjuN05iPyY4aAa+csHhiUuIFKO16NofuCYN+KUeh1fWnMQ+hujSVsPDEs1ABzleTjS8zMWF1Xgj08Yfu2xK0WJQRon+JgMcbuQSSPL833UiL5zRw0BAWBNkpo8bXN10aW30vpIct0qpBHdIWe2S1l8d9emPqMuqSvFftfBQCXn0SRTi7EqtGPxc3qXh+O79RketYc0ZcBITR3Y5U03p9y37QDG6jPUt0bPkWCgDlmbG/05BYRaXkKUabmgKowXK5ttfbDeP2Yz39miUqSL7+LXNmGsXsNX7i0f9jUkHlzjWuHrGjkY6OtW3ClJOSc1cABAoOoIbXAYLrp/qfMIzDaalSvxd2EO+vL9dPS+uDP75c4WxmIMkhZb9qiuDlHDGNimbAu3O7ltJn1AgQjLggF9r4gXCgi85Ks34TXfuAUA8MzgJqzt+iyWyV4IKmkxzvHfJBbGU0fGYtxcGN9128045JBDUF+2WzlPaT1K5rB4WVPiIkGhwJRBPdcNgYjgxBZj4eAFzhMAgIavi5K6Z+RaUj5LFQ6+3HcAo46D/zpwQyJy9w9n04lfu3MPrtu5GxXSxa/a/q5/bJd+UmxdLuFKwbq4fXQf42hbWxbjkDfSHcnrOP5q3e1CoP5GN38F8eOjakTXyY+9l27/2Pnr7KcQ4U1HHo4N1qgtYXuLr6YO8uKH+Qef80t89pX/pJetX1rjwz9bZxyoXkw/6cBYA3/63TvxiV88aC9sqlG+x7F79s7MNZswfNN27P/hI6g/NThDV1RcC1zFYkzmjEMq/dIJkOwCuPyoFOlCPFfkLxaWhjBWLcaq0cBHRbMax3V72WP3YO+Nq3HYyFhYV6WOj/f42rNFKr+/ZjGWhOfuuQTnd/0bXu+s08R1vo+x+b7IpYjsrzuIzhbGGIUkM4e4sLxCktZWKHdf7gwXqRM7Eq7UxUTiSmHcuMtlmFa1i2q5gleNShFaiNJkD/pFWBhPGZk0S82/20VVB+9+97shhGrVUopULK1uIoxjy25UPvmaK4U6VUcEOJGPcQAHS0XoLz/W0K2zNS9dwOfACD1HEid4Hj46MIg7JzbjOgoX0vkkmy74TyulWr6DpG7hBctbjDmOcfuowjimjMU4fogKw2KsovpQqguKqH8z4sfHoswi5pzButKoXHPxlLJEasLR4/VoljcpFR/jco0mPD4tX7uupgGKLcbhvQQ8tttcczA9aNnW54jF2O8L+5lgZIaiHqmL0VxlGbtl8XIsHrOuFOn7XIuxcBLBW4HM1YMSTimLsU+u0V+H1z166IBWnqC03x9x9EV1eYvviCRW1nYAAA4T/donyuSoyWn3aeOy+RSxMO5sYSxGIbFU26a5VwqkDSMzwpLIdaUwstIY+jd5JJhNMhW8gFMQx1hdqEJ505YsjCdHXgYioFSUhZe96HQsX7489wGvhqCKp+4SYRzHclV8ggEk/r0CBEkENxLGvhJebayhP7BqfpBc1QEZ6jysw/uHRvD06iH4MvWiz3WMx0mxm4M6gKuYgoh9jGcEEupAOfanLLH4zpxNTRbuqQ/h9BHhKSvw4TjJcT2ioTerEj7G5nR1Eqs4Oje5qhC6pdpXfIxL+j9KZNuwKY/CA1ufGZpW1NBdfmf6eqq/gepaYApjdeiTcaWIi4LQLK+AajFOrckuZG56Z4LuP6xGpVDvGw9usrDPrINWb+15rrtSaMJYqbckQrrg1nSlMN/nmO8KXSmC7HEdRkcLY4ExSFpq2a6u/s8RxnkNLjpWyDpWVf8TLkYyu6USlWIRavhm9RxgtFeJpUgFCT5SH2OQzF9kVyKpAlOAZlk1v8vmncXyRdmpYLVTlBaLcUzi+mAsvlNdfH5425bEYrxaTuCl4zW8cvPZcAd016C9dR9xGxYgrJrYolRIRtcH/mLJ81EH4YtrVkf71A46/3Oqi+8qeVEpSlmMWRi3y0AcM50UH2ObMCYCrvsHYOd90VvDxzghfa+KgDqlFrvw1PC4LnjGODJsV548Ege8j6fiWrEYO0Z66HitRLr4LhY3euxY6adRfQDChD+Bz9z6GeyfCGfbesfX4EePvkeLPuELJDNrhf6jZsSK2W6T6lS6NzcFSn3rEAavear5gVNA0aKwUFLq7fh/VrvYvWyTZjM106er7x/rCsO8uZCZxXNxWzctxgHUcG1pWT5cqyuFOSRzlDpICL3fzfExhnKOAz0IXf6sR/j3oR2D+MKVjykzljZhbDu/s+hcYSwDNOQz4dGx2mYSQCV6ADikrBK1WmbzLMYSy7xHsMT9HQ7C9ZnDgkTgCLzVvR1vdW8HbvrXdBRIlM22ppStLsJqRL7EGZ3OPsaTQ5vHNH77MoOOSAxqCy+VlzVVUBo+xkmUCpLQF9+lfPW6J+CItIyzR0dxcu+Lcfrjr9CqcdPEuOKiAYyR4qenfI7V1IWPyVX4/eJFuH6pi4qSICYoUMbqw8oU+K0tvmt6CJPDrT3xb5UmhLFm4QoawN3fA378egBpe0oGJZbfQPWb1FwpRLj8GAAWi4bu2xi1iQPeP2A8eB08OiYqKz3GjGRBfvwZspXQekJJyZkEwq+3/BpXP3U1bth+AwDgx4++G3fsPgP3bRtQzhcQQ6H/+wqMZT9kbGHM9Jmz2yi1KBn+HLtBour0fe9hjN66q/jYSV1HEYrKLJvVYhwdGmeCu/AgF1c+679yXSn01w5+uGIlgLAfy94/aT0CZdAZqINFQxhrrhRK767VmygR1GQuvsudkNaXnWpWYyOxV7Iv+vP2c+/ET+/YCi/+LpstvutQA1vnCmPHxX7vXzAenJnZpU8/JCaY6H2yRKqJK4U5sZPiJ6M9gUrsT+qm4ZAcIH+6Xlt8R4nl0bxG7uI9phxFwtiwbjU9P96kFZGWWVEe9YCaeUxq106tBpTuz1xBr9MipZ2mUsb4HAgt2GfLpXherYb/WdODQTeNplFozdUs32a4tnjxHQvj6SRZBKzFvC6x+E7GbhfmHsVCqzw4PVJTFTmIHx/dwliQmTeoF+ogynRPqscnGzUwBIKUCJIV/TIThUWdVYnLCL3sixpYtC8w7/M5ZDHuVFcK1bVAcwg3f/fUJSFvLpecit4HIxWsBACR4HUgM8JYter6miuFGq5NF8a+5R60u1Ik74yZOvXzqrONSmSqzAVy8hqYLwp9jLMx9juNzhXGCBtYIyN6kv+gNdSowaW+PgWuFCBlIUxWQCcdOym5351qUiYBpcK1mQkg9MM6c6Q3ZZTxMS5c2Rsfo/726srirDBOBazdYpytooSviRVS/g9ZrFghHJDebNX6e2EM3H/tOwAJ4OKVDyZiIu6gA3IzGdVsnyMhshiT7zV9sMfX6ORcBu0Se4ULZdFk4koReMnskTQG28nwypgRIHKSdqX+3r7mSkFANKjvNqJS5EbK0RbfGVPhQSyMIx9jJZuIKjgCSQjU90aGxqTnFukwUgoR+uQTwbG2s6idZ5IizbYwTl/OtjCmgECBnPkbVF1QqvpcywB1XzUapDMauc4EpM+kaBZdkQ707K4U6Tl5rhRq5zpGPXZXCmNq10F6jxEJ7d4hxbVHGM8OddZYK9GqZ7QX0dvEvA4TKnr2dQgdLYx/2/trXLz1l9o2gkgar1RFsjHCcshFj1xuL1gGcKIpDYcCrN5u7I7KXCpHUjHhVrTGnmsxloG22MaLUj+bI9HclNJMOYp8jKN9jz++vqgAPLxzEKNKBjnNx1gp04xzHb8XJK3tIBbQ28aeiXP3XYyaXBadJ+ELIDgy9TN2iTC0aHG030BdOFcnSClxlO/jHcNj2LCoF9etvB0AJYar7+27FBfu/45eRi3rEpJWNHyADNx7MHZ97vbM59CqMtvWuXnMs8ZPiF5ZLMZfWgN89yUAgH+7dn20LxroxHFyje9+/cRrce6+i6OECPbFd05jGI4IxWwPGjl+iYbgLrAYiyTtenjOGXfF1xWagPlV48nQZxjA/937l3jpz46ATtQ3CiQiWEalXrtjN756YA+yRN+DKYzJPpCYqaaqCpTZFsZ7v3Zv4T08bXHINZ/btH/ZvG8Yr/vmLcn7J7xn4FNrPoOdPYdnFpyl2lDPGKe/ThfmVayuFOk5mjAmu4/xu7x/wpe99xR/NoTGsdRqrVuMVw/cp1xYFca+Nh+tGd6M50VVhlFEuoPR6BpkPU6FM991uDAemngEgPnbk9IBqhZjXRibK/d1CG40NehQgIMMF6z49l4ix1K/TCcVxg5EocVY6SoQ5PgSU4npa6YA5fvPhC6jADt27MCvfnlZ4fkP7hg0FkY0sxhH10uEsW+Icn36bE/taQCAsWBVXCoAgjwk9SP2iDAWx4QVLlSrhh4zU2IiajPPqU/gxNpa/PDgy9BbPaCJ1sHAECITWVeKhMhiPL5jJZrB4dra5+j6YdErSsfx6uK7/U8CAC66Z5t2Xvy7mt/9jsZp4XbDT9JDajF2/NTVpgu+LjJyZqvUSWPHaCtJfxXVadFwOvtBivV4F40mFuMTak/PXoPSeyQ+yxcAQeLwIAw+Z1rI4/st8IpdKWY8Cc0ccqUIBuvFBvRp+2rU/jN9XWs0sHswbYNPeicBAHb2HAFpZLjLS/ChvXa6EMshx7AYn1n/WhK2UCLNkAcAwZpnWcsDgP1YafkUhisFdEGtPmuWjW61niUDP9dtxPST7wrC76jb133r08gTTVwp2GLcuUjtZsk7yGxIBVNKZOS8MQ5NSxKJKwU56pQMNRHGyZ2ujaK16rLFeJKoHbL+HfcPDOCCCy7AiuXZiCbpSVKbzg1RV0Tnh2tLj5bZqTFLHbW41gaBMuuR8XZXP5eUGI7ibfdI4O37nwdA4HtHXATf9L3UylDDteVEpSgFK+PJQ0isnxb/RlOM+pHwyhOK0ojFqvsYp3QJPSpF1hoVmRGUp36mrST9VTwoTI9VXSccSl3RbKSuFKnIkBCQSjv1cqxlQaadm8I4/7rTAc2DqBQJ0/Xd5C6+k0ZmuGh9gwjDrtm8ZKVFGMfvA6cLiY+xCDRNsJmOSGZpQ4ux4mPctSp5nRfiTcd4IkjdYqwKY9/psp8nU2HsKPd8WEZeqMTob2LnKwrXlmdX7xxYGAO4racnee0FQm9Eia9o7GMc70z+y0JS2WdZojcGPG+jhENAJcqyE1AFamzCSm2FtejHdg+i7qU+qDLP2Z4X300OkojXFJm/4C+vvBZCCLzzHe/AzvHl2F+zZE8kCUcYLr3abkVQinByboV8vmZVDlOS6350aklCaWNAuBp7rbMLLxl9MDmyHsjUNaPAH+3AeAMHGmHGu24SOMhfjA/1/ikeW7oZv9x0SfbzJVVRBH50rwRJp6uLM693HLXNg9Zi2GLcnCf3juCeLQeyO2IXBULy5LPFMT5YDCSvb9nQhyBa1faLe7fjzs39aXGxMIarPezzhPGRYi/OkA8l7/Mtq+n2zOxC3F/ZFq0q7cgloQllADhhxxK4STOM74VwbceRno8u0hM2+AFBWO5MyrhSEHY+OYADe0Jr20y4+5AvMXbv3vA7nEM+xhkys2jlvpsrHtyFoYlWZjNVVwrFVYgkJKVtzRGpkYDILowJWavuUozjTc5dkTBOLcbZxXfxX91irM5mEARW1Efwsl0PwSQTsSdiEakxk4XWETZEt1IBfbZRksD68TMhiLCYXIwHLw93GoM+M6bzK8X9OAJ9+QtkYfyU7ErRuXzy4INBCEdn1DjB2BubHfQMc4FoluAjFS3mmOzwX3Xh05eGt8PTolu4vmU0tfwRYdHwsdaiP3b+fcrNnR+VolPDrEwZJCGGopF8Xb9NRsfG8e53vxurV63ERdueg/O2PN9yPsEV5sLLtCW4ykJ+FxLvQhcOCd6Kcfny9OhMHGOzFNNiTPjI0n/Gpdv+ITni8T3DSR1MX2ZVhOw8MIY9Y6EAqFJomXv94Evw3JFn4NxHvoPtlRzrr9IRxxbJwfjBZzT8fd+4D/t/8EhOMZ3ZAbfC6791C95x7p2WPeljO14cbPOR/H7lawCAIJD48x/fk1hO94828K4f3JUcF8sKCRdDSjGqK4XKO51LcY78F6U2eQ/dbFtJd8XCOHuW+mx2kLUYv+yRNXjuxpXasSI69oqdu3FoILU6+Zm2Fr73/azF+IpvPoALv3h3WMUZaKLDN2zHwGUbMfHofv2DzxVhPIm1dxv3jeATv3gQn7okKxxzyVl8Fw/Ik7TmSkScjCtF8tcUzAIfq/wK/9P1nai82DAV5MYxJgCeMug8YmBftFeCSOBf7vwRPnvvz7CkMaGdTf4h1o+3KggSg5g6sAUAX9jTogdBgH37TsLNwx+D2zgG76RTcMD7B/i0FmaqbPOG+lH1a7iu+zPp92q1GLMrBQtjAI4yGnOoK2mb2tRIIozzbIAo2K6/d2uxkBHoiR9CDdOeZy87HM2GCJIIDMGeVLcgmgFTAi2Woz7N+oevfSWOPPLI4k6DJBzHZpeKy9QXIh0RZ2BSFnOEmcxUlwvdUmxaA2yuFHBEuhLftJcoZVeUmMhOVKaAwId3vR2uU8E/rF2ttDzlYyjfU2Z6vAUhwbp4Mug2MUBvRzFHIX6IR+4WOV96Es2BKmjkhmsrIGnbevldalsWRhxj0l0ptOKUx5QLZCzGALCoHosV1WIMxNJCbae+mcgj/s7M9RqGtWwmLMbBaDhiplqgu1LMtTjGJiW+m9F6+P32DtfaKld1GxQynp1KHSWiPaErhTDNB+E+dTEpQWC1GInOdpLZOgd+Rhinsyi6xVhEAxYHYbi4Q8cORO8NtyUKrb+ZfAOU6gyCk8y6AbowVp8kgfThe2F5DlWxEuGMN1FX4aK6mOViXJmxbOZjPMfb3TTR4cI46kTVtqQ4+eh+c350RglhrLhSEPIj3IQBYmKLb7owSmQqpdZYSSGpWIwzVejQkd6Uof/4uO6665J3Jx4TLUBrIoxNi7E+Q6X65kosjX3GRLpIIvQxVqxsmesZrhQ5lro01TTpPbPSdqoilRtqnQ/yV+Cjp34Kj/Z045HDfpct2+IrnVavfBssjjPLFBM7DlLSbm0WY9foh7KW0xDVYuwp55QWxjm/e48ycMq3GOt1CqfG088iSCRRKayXjo+DsWBJaV95FmPZJPNdPG0/I21VnXQE5pyPcTueFIl112nB7KwkB9KidERtLB6sxC1ECgHDCyX5vYyvVHOtIKcKzcfYDEuJeCZGj2McW49d6BEvzAXbeV+PUIQxILQDPaHcb1ocY0/Lkktqq8+NY2x850WuFNr9Mbfa3UzBwhixxTi+eaQlDAHSGzRZ4ioQVEIRc/mOU3D/gcPTY0lr6rl3hSCBniitb5j5KZ0Sry17ynpOOB2eulxoK8AnBtMqsMV4cpBMBjT7JupYt25dsuu8Xfvw8ce3FXYu9w6O4EcTw4YVVRWlaYfvIEiEscB4WgWSuG9rX/I+sRgL4DvV/7JYjC0NTQAnjT+V7B93U396KAs0qwjQE4XfcgAIxXfuD454A146PoF7jr4GAz379PIV4f5MsVXfl/f97H4gs6lDDRP5bLoR+O8XAn6j+bHaZHH0ADd8jM/9/ebMWX4g8ZnK+fhS5cfJtvE7voXlO2+PyqjAa8dinPndowVOlXqyxfS5JMPHWG3L6nPaAfCK8Qn8fPde66V7JqLzhZL4BKmFEQC8QOLjA1/GR9wr46uH9bO4UqjM+KzGHIpKEZMrab/7MmDLLXl7AaTCNpuCvAAlk5vqYxwPyGOLsQOJjz14Gd6w6X5ICFy+85T0uslfgUVjNfzvb76EteMDkWtFdL+IChBZghdJT5stfumiG1ChdMZYtRhX62E/7oowxFvGIqxcW/2r7tG2EfDvmMCPUMOa0SesZfmBn3yo+D75K4zhkuB0S0SY6Jkx7mHT616fbi1afNehYliFhTH0DhQgUDTNpzUPi2W2sWQ3AOCp0dW4ed/TlCKMUVweRFgthsKjxnq1Y4eP+L31FEe9kUjqPsbb7kirW2JKhSkibRR94x6e9aw0LM8Fu/bj4r0DhcL49oFh3O81UNFW0KQvpZ8KYxcSS5LfPhWrAhI/vGWTdlzMH7t3QkSzCkmIKsssAwFYFYxE5RFqbjo9p8a6dkFYidHkOKlaKIjw9/0DqARduPmE8+Er56mW724Yi2ryvp/ffTWzKUnwMRknxoXENX8Thlkb3lni4Owsl7n47t+uyz5kJzyJD1euwZ9Vbki2Bb3rsfbxXwKILMbK8R7ZfR6z1VFdO1IWKUJPTWcOAMKISqHF/FYeUw6Af+4fwHPq9gGDm7fmWPG9DCThxRO34B+qvwivHV/TFMYZi3FO2dPFHIpj3JQDW4Bf/2PhIYmIbeUW99PBlBaVIl7omyRsIbx565145Y71kCSwfXxlemj8uARw3NY9WDsxhNdsX4fveX+IWuRsE4hKYjHuIT1c2z+5v9DEtRrHuNqIhDH5KGoeufsMi7Eg4Cp4+AkaWmQWtV9Uw7U5UZTuBxDgH4N3WBbfhbj7RuBtTxMqFIdrUx9Uc7zdTRMdLoxDhGIxzpuOzrhSZEJxKZDUG1fOgS6JxJUiXS4SRyPIOUedsjGFcSVdxcqjvkmiiLqeCuGss85K3juJ+sj/jlW/3hjVCuGoUSkQIA38pkwbg7B2iZJUIflNzcYRtxtLfRSLgAN9EEjKNKVQhnIOpYu4ACAgibVBgJdveTt6l23DTx/7qVpIWoapHPK+H0sowVgzsUtFRGG6cbsrALS2lp8SOv5lJxrFkWskufDbsBjnxTHuVurtGm0jeVBbPrcaqSX0MS64dmIx1+893WKc40rhm9ESTIsx5VVxWtDWms0VYRy3h8yX4ABOsZwI2rEYK8KYLBZjqViMY8LFd1kXNoJIXBBckjhfnpkYAKRwk8FkGO4tL8GHLoy96LUr9EgWuQvzc8oEonau/Mx54d9IBsmHcoQ5mMub9TBdKWKTs00Y577pGDpcGIeNYnF9qXIjkd5QY5KHeTr8JCIMNpSp6RgiNJLwMXZd3FgOVD2CSBo2aX5EebJbIA3vIiDTh5AAoFoD2ZViUuzr25/0HQctkahULAK1IPJHfExeB7m8nrokHOX0YXns1ynStidIYu3S9LqxxbiaJASRWCTSjnWJkw3lJbTXpG2oDfWm+0TaUh0IVBSLYyBDD/in9Z+G4/ufi3MePAcbBjaEO9VEKKYLUpATlsnyvXHmO5OCGafM4hhFGBeEazMZbxT3ERkf45yoFJnq5fyWPURYOl4BCFgKPeFAj9eLvWN7kViMkwe2QN0Dgkbq9uZZHuZx04uvLCWhR7W4qYuaMlaw2GJsWKGNdjqTbZSU3xIAEJDh+9k+/mB9eoS2KG5z8dfutuRjrFiMlQG16UqhGgVCAQzlfbpdiijtc+Tu2KAKdtHqUNRG90zoLpFKowG5HGPRAjcJgYZQ4hhLN7o+aQM2dT2IGi85M5wgSgR16AilDm6VhYLKBwpdKQir3FH0oKaXKgOM1X30jaTfW/otqDNL8ay4xZWCJPbRStSoWmj8Wci0LYyFEEcJIW4WQjwuhHhMCPGJaPtBQojrhRAbo7+rpq66U03YKM567OOpHVak1lo9KkV26uHx3pX40eYXZErdWx/A+ige17jTDd/RO4ygG7jhuKfhBRuWY6Q7dKUI3S9iwSs0i52K7mMsdeuMIow7tUFPBcPDwzj/l9clg2nHcFFIrF0lLMbaDRYt6HCFxNG1R5PN/1r9KY6M/B3TKeQKBAire9QYruH1TnNCf9Hju1bhdSuqkLQcAPCONX+XqcfBzqBaAa0PXXxb6tKg1vMQ70gc4R2WvA9k+JgQEHj5U2/H8q7l+Nxtn4MnPU1wZCzGfs7qc9ugLfGZY1cKDatFRzUlpmEjlaW7hRbjmAlFGL/GuS+zX1IFDaVlVIJlpaqct2Bn1YTA2353BJ6zaQW+0vVjbd/lzj147aWvxXiUUS++DxZXlqPruho2XH4ogNBi3MizOFJ6XkASX+lTYjMrYtjPsRiT19CnjmfBlUIIi6KLyUTTaB3yAuz96j0YuGzjJAsy3zqAKJYT8cCiJWGsrMVQQzoKw5Wi0ThKuY7QxpOqMA6ceIAVPt2/EbwdL63/F8YDN+mfTWH8rvFvII5mIUlg09HpfdCIor8E5OKB7h6o63/U+hS5UtgdJmAJGRd/DwEOpuV4xbJVOHvRHdqyP6IAb/z2rXjBl28w6qF/58PjoT55Yu+I5QIBzqj/D/7C+/uZmx6ZY0zGYuwD+FsiOhnAiwB8VAhxCoBPA7iRiE4EcGP0fo6i+OqmtoZ0typ8pLH4DgL7xyzWYgAHvBGlGZqxbIGgO9w7NrQsGemG4zl19bW9xqqPsSCpW4YVkcxRKdqjVqvh/PPPR03xYTSn95Pfs0gY20KbRX8rFl/gLidM25tmWKpCQKLi6C4XKqvcMPUzUb5gWSTSz+EU5GYSlH7KRcFiqB1pICXcOFWvvxT//KJ/xuMHHscPH/6hllHMTDgCX4/lmcAW4+YUOiyafVQ6O5UO6vOFcZwMQbUYnyy2ZY5TLcZhMiF7f5epXs7iu5VRPPBD+3sgjYY4Hk3D16Kp87iP64oXi8ahtAjwch5bDkRyrSAAzqilVjM1ekpeNA7yPMPNR/8cM95GjXpORWSKuIyJx/ubHNkqIkkBn0eyjqAVVwotqYcaUlJquwP/oPQU44mrOhrFz1g3GlDeIZ8JAKhJN2ljZoY8FYLAeHc6c+Ip7hf7tFnFtAYB3LS8zGcnxTIsNJuBkT833S4DLEd8XxyitVJBEtsPjCOLPls4MhE+F/ZnLMupNflO+UwWxq1CRHuI6P7o9QiAxwEcAeAtAM6LDjsPwFmTrOP0kTRSiUAN05b8tbhSiNSqaxM44YmU3hjW+yvuIEjxtVOnspH7YHQUCS8oSBbZEaCt4KVCTzzGhu/7uPjii9HX14d3vPlV6Q6jc3CjjiMT91QhHqnrP3/cdmw/bixuYqtFNfJ0y2aWM8nzhwN0q0N+dx+2RX2f0rFLPXLsmceciTcf/2Z8/+HvYzOl7huZsr0ci7FVGOdUrOOx/WK6xTjpT5Tp0jKuFBOe6h+evY5E6mPcLRuol7BCp3VSCdtgT5JUIL/NxrGy1QVTekn6VLaKaoIIjHvWURpYNo5xvKOh9aHm55jpNppx2Z+KWMYi54ttlcz5orkrReJj3Mp1FGGsxX4Py0oW36kuCCS09OP5FmNKIkyQEIm9Nux1832MAyWUW3K+2dNrKc0zqZWU45RzCajJtGyZI4z1/lgiUC3Gee59wnwb6wa7K0VKZxrYpsTHWAhxLIDTANwN4BAi2gOE4hnAwVNxjelFKjeS7huUMBhZVKIemyZej0rOCFkM7UgXX5HUrL9EhN0r1YD16m2b3iB5rhSnO0/iSBGG8Aqz7cTT+kJfqMAW45YgIlx11VV46qmn8Ed/9Ec44Rg1/J4pjKNpvMxinRQHEhXp423jabi1ZKxkmQ5wUMPyys+Tg2JhrA5w1AgXu8eXJQ8DwiEY9d+Y7AuogrtHzoZHXQiEg9pABbvuWAkaz7cYO6BEjAghMeHFljsg8Oow00V85oWfwaqeVfiWeACxlBAgdE08iGo9zG43Mt6NB8bekr2YdfEdK2MdMv6qu3SfQvUYovThXoSAxP/xL1beO1jk3KwdIymNSlFGGF/50O74RL26sfU3aa+UnbsgYOXEwdj4oKOdg27dF1kA8HOEsUNO6kphrs5X+sPs4rvY7Ojp4fEywngG2ygh68oxVxbgAZm6NeRJGB95Vs7BIfF4xBUiHDTf9GXAy5lViqh7Pm7vOxqedEBKVAo3sRhnhTHlWowFKPYxljLy703vF6FYjIuEsRoK0Ut6RmEkCkt/qyJhDKTRXg7UTsKtE2mGvDwfY0k+hIzvpUA3gUkJuaKK4LBFUa1SA51mrJb5wlho0T86s1+etDAWQiwFcBmATxLRcAvnfUgIsU4Isa6vr6/5CdNCajFW5bD1mfTgBdEZaUM6uOsV1lKdfY9qWcoqSkrhxo4RfPmPq8m+dOSWWnuEyAzwEv6xeiFWRUkghAySldTdDUdbqCB48V1L3HzzzXjooYfwqle9CqeddprxUDRNN5Ew9rLTUDEOSbxz33WalEhC7FiEcY/7AJZXfoEuRPFZKfQxlsr0oWoxPnzxSNLpuTgbg/5Hk32Pjb8W68beiftH/xQBEWqDVYzsXITa1RNY9MiQdc2g0D4m4anhNLRPYHGJWNG9Al94yRewTYzge6tWhGUQYUXf17Fy4D8BANc+dTbuGHl/9mKWttmh/W9zbF9MjisFlD6kmcX4lc5D+IR7SfLegYPVXV/Xjgmoglqyit9DvUlUio9f+AAANVSk7lrTowhmaUwpCwj86cOfwv036+4acpUeN9shaAkW4nPjq8T3mO/nC+Ps4ruotr6v9aFmqKq8RYVTiiZepkEYT9FnML+Lfu+fcWDPHxeek4RrcwRw/3nALf8B3PbNwnNufmgAd+0/BvcdOEJzG4xdKfwkKoVuMbY9QAlpu3NJ4mivJxGfta5VSTsi5EelkBAIFKtufE8QAFeRDrorRTpgM799IQkjwRoAgCeX4tyRk7RrJcepdfAlAi9cm7RrfKmeWoQCNF50MLxnh64lMnbFcwK9EFJmms3PqPh1+5kU053BpISxEKKKUBSfT0S/jDbvE0IcFu0/DECv7Vwi+j4RnU5Ep69du3Yy1ZgEqY9xMjkhVIlcPOfjimza1WRfMsVj2EYIqTtGZnoj+muEy8pDUJCIDEFCmwa0xbRl7Nx333245ZZb8LznPQ+veEU02DEy36m4UWcRePmdhgNCt9QtyrZOM0MyaVCBgNSFcZNprdiqEETRAzzqhhCElcdN4Pg39cI91MHS+4ew5dcHY3RPt3Zu1saiuH3kJJl4xZGvwGuCI/CjFcvxSFdXxkXEkzlxby3KfEZEx3zEMvOjxSjXhDFQdvGd2ZYcy6NAkpv0i12ygey8QaayAIAA5n0Rlt2tuEmYwkNAoCq7k2JSX3u9H3QAeJkp+9gFLW2BjYbeZjULnjTbevQ+8JtYjDGzmOPxqfAxLpiIKEVqS2rj2tFAXoh0od5YsVEszkbYkK7mJhAviA5sFmPSA1emj1KBILquE903icVYmaUtcqWQEAiUQWdDsRirRrOyrhRFqG1ffR1QkMxIB0SaKwUM974897h0ZtLiSqFYjDs1H8JkolIIAD8C8DgRfUPZdSWA90Wv3wfgivarN93Ena9MrBBqBESyiVN1uiRvhRyAimIx1gSwSMvQfD4pnQAqimOs196ISqF16iw0yrBhwwZcffXVOOGEE/DmN785XRiipR/Vv8vY7zcI8rOSZdM3I/nd3YJ2Y/oYq0Ht3aa/aRw6KCZtYd6i5Vj8ii4MnBl6Nu34/WrsuHUVGqNpuCG1FmqT9fMW0QH4oHcS1gYBPrt2NRqwf08ZCnyMW1mXs6BJM3VkdgXq90epK4WgtN8wM9+ZHUpgCGerMIaLIPo9qrLRNI5x3Ia8zIxA5KesWvWMI1w17XNmiKXUs8Bi7FDa3oO6vgBJjRuuCuNueMmsHQLdYmze93KGlbF5/am1GE/ys7TxfElTQgPoDiPpoG6JiqDgRj91QI4Wri6WmklsadVCSwKBUEVl+le1GJNI12CQ9JI+jwAtwZFK6EqR7mugmmxXu3VdGLtItYbpi4zcbHlxxAvtQwDwpFT8tKXRy+rvVFcK3exc0JbUZDgdOvM8GYvxSwH8GYBXCyEejP69CcBXAbxWCLERwGuj93OOsNNJh7/xSDKMHynS1xFD3vuwq3aJNirMW0RAQqBeF/j64y/HA3UgAOGHm07Hr3efCChXdbSb1xDGEvj64y/XU00biMBJOk8C8I0B9cEV4DXf+D3+KpreXGj84+WP4Iyv3ND8wAJ2796NSy65BH3BIjjHvxium4qFh564DdsqZ+CcvZejEejW1UTwXfDO3LIFCAc1BtHnHYdz9l6Ofu/ojCvFbjoocx5FguUnvV/F3SPvxBn70+nuSm7w9rRmw8Fa3D7yF9FeASEewwX+Wfj+gf/BgWAV/CO7cNwberH2OcMY29eNJ687GD/ecihcP8C+A6vx9cdfDk+SplDfvXtIv8xXjwGuC4PNLJMS/7L/ALZ0VXH+Mr078S2+xGHF8qNS8HjOwDLACoyQYrp/ZfzXxZ9+N82EaVqITYvYhyu/zlynATex7HZJT7GOhfxkxTKcetzRyfstPe+F3HSznqEMQNzjLZUVvPO4f8DTKs+wulLEfOLAkBLHOOQDr/kHvMr5BFwA0qiHEAKHiSNx6cb/TIVxbQLrRv8U5+y9HADwgl+lC0FVYbwCYxiNO/Ig0GNvR9/90V0COz9965RYbFvCtBi3IYxrmwex89O3wh+o6WVO9j7LGSTs/fq63FMCIrxuvIoT7xkBuqOURuuvBL6wAhjalRw3NLAfx376Glx19eWJMJYkdItxPDshCb8e/BSGx1+U7KvJxQiUZCPxeptABvCD1GIskM5cSD9AxQ/bQW20Wpjgg2T6nBinJdH2OCpKiGv4GOf9coJk7k9Rg5qwKy37ukf3JNciCnSLcUFsfd0+l9+WVCsxW4xbhIhuIyJBRM8moudG/64lon4iOpOIToz+Hmhe2sxDpPidEcFPRmeEIFkSnTalkeDtICwyOhR7kyYAg5Elbu9AFUIQhrxFeGzoUAiRjiwdbaioJoYk+H54/u19x+R+Btfv0RYy/WpMf2Bs6h3FVfGCmAXGBXdvx77hfB/fZgwMDOD8889Hd88i3Nh4Ov7791u1/d6jv8QDY38CAJio6wI29t+mRk7UBYQd74kT27Cp9hIAwJb6CzFUCTvRWBgXLZDy0YUHxs9Cl0zd9pu6UsDF7sYzlfcCjnM7Lj9sAwgS9w++BxXHg+MCa04exdPe1Iu7TnLw4rsdHHHlVow9vhIgwpjvKP52Fut3bRC4+7thnbxxvGSihrcPj+FXS0xLXp7FOCuYWQ+bxBbj7G+uxeElmQzgVT9xSS7u2zaQHGaG+jOFMVncJELBGLaDKjVQN1JCf+OgVeG1lG1y3U/gG+00nnlbLcMFQadVz8hazpT37xsay+zfvXQttoyfEQkC874ReJbzbM2Q4XsN3D363vQIV7HgKaKuS/gYjtRXEHggNQVxdNzTu6NZlbFQNM9YW50Ci/HYXXsAAI3tUT8yRVbvvAGs35c/uyQl4TmNCrrqMo1gEVvod6WCeuvWME779+/phxu7TJDQFpTHUjOQhM21l2q/yWiwUht4yci1x5MePC9s5y5JVCjtg4kkFtcjYTxULVx8R0Fa9phclmwH0sx6eYk67GXav8wJKAm7lPthtN5IZjYJQKWqzLaYfWsSIg+Gj3FqUDMJlP6FhXGH4TgClWTaLM2NHvp1Ro3OYhEWxrjLhkQqfjKuFACcOIin4Xychvgi+I2w4+hyCkaAJLUpkaoyBVI0Iux0xsfH8fOf/xxBEOBNb307JlDNPC98t1vpjAx/zDhrUIEfeBxvOo0cIdAQYUeXCGPL+aaY9Eq0N/Wqpl3AgUS9awC3HH8xxhrHYe/AK5JPVF0s8Z23uPj8e1zIqoNTNx/AC57aAzmi+0a7GZ/RlPhr++v+URxsNNU8VwrKWBTTaWN2pYgoSDuuuVLIwOp4YC6+qxq/YfZh7WbEzqgyJdZtsRgn9VFe+5LgJzMCcYGR1VmZKTCjUqhGAlKiS5g4pFvmAKVPFkFynq/4/xMJCCUXuhrHOMwkGr5vBA1I1ZXCaKczPpth/vRtWKxjMS0qsQCcbKXiglsvSPPtLpiiFyIOgQbFYuxoFuO4zaczTbrrW6BELkmEp6IOXRmgora5wE98jEnkR6WQEJCKxVhNma62Sy3Bh+40qX9WypPFwHiOxRhKaDmCgFtR6mr0F3ndaTzIsO4P2JWiY4Wxigz2IYhuRilEYjG2hjJRXuf1DUtlGseYCEBlRbpzYgBxHx2NMaPjdFcKzwt/mmqBMCbRAEhi78RSNKSLpUgFTSYLGQMA8DwPF154IQYHB/Gud70Lq9eECz/NUEzFwji1YqjU5WL0escDABYFEou8bgzVJkAUO8rEi4Si8623nzHlrYWmMtujYVHKiBuRLNLYvOYBrF5yN/YMvgK7GydrU9WPHy3Q++ajsOHolVg5XsfgTWNYu+URkFcDyObCka3CEhL4m4Gw/cVTksiN821ZUMbt1Y7tu1Li8PaP92HUr+DxkZMKrVTmbMMJ2KlfBm7GfDTuKhZj2YCPSqbNA9CSdQxPeAhyEnxUITG+cgOOrw5AKiv7na51mo/zXreSLkI1rre01zNm2oDF7jKsFAchdFqLhLGfPtwlXC0pW2D4qo6443hk8U40pAeprNMwP4bZRgcG7prWsJgZH+MWMt8REe7c3J8RxmnKcKC+dah1K3TcbxS5qObUUxPGmVkjRVQ6qYh3Yy+XyGL8AJ6GX3W/Ca6s4BSxFT177kV3bQArR/em1wcMYayKyrAODpEmjJcPPZkKYyc/KgVBaG039nc3fYyfMbAdi6I47gE5WO+sspZX9JyeoFQYq/U5XOxXDHQCfiZ8Y8TgDlQiw9lAFzC4FHhMHoNBWpI0btug2lMWjWfv5c6go4Vx3JCD2t3JSlPPrSYBvK1tVs1ZPmJv1Mf4Prqc8KaouHX0Hp7GmMUVH00jTjiptUcdNwoieF5YnyJhPLL2SUg/wPlbT8P1e07EIUitHSuDlbnndSpSSlx++eXYsWMH3vrWt+KYY45J+3njpwzcKtLOWt+ZuFIYD+hrBj6HS/q/DiKB1+1t4NCta/Do/v0I6g+AKI1Y7Ra4UpiW/mN8WxajPHSLMZGAUNZEH73qEnRXBnDD4CdRi3zjkus6ArsPXoHfP+ModB9VwUG7NmDsxn8Cdt6HroJFhkG0+l+igud4AU7eugx+hXDPnnvgVXIiI1im5zp0xq6AIleKVFScfc07cdmOg3HtzoPRkBVlfURFa7ZVw3r/r10/Na6W/a1GlObZJcPfuYZspBHV+nvP1gPwcxJ8LHYbGHn6F3Fo9z9juf/0ZO8h4hs4sZH2XfvdSq6F7RlXjcAZ0/e98agP4kT3pMhiHNXJU1IJw9EsxoERD/fzTh0fGV+OuvRB2uK76G/8Xukk9vf/Dvc/8B5s3/Fjaz0nDSEbrs0rP3j8zWN78a4f3IV9ByLXhkQYRwf4En3fexhD121pXhXLg7AoioycsM8w9dUUt74iX1gnzSbnOsrsmpR4a+1L+MTEn+Om8U/h2u5/xInXvB0vvetz+Is706VMkkhzpUjdqnUfYLXFH73rmmSERyITSyotGwKkCePUiKZGkvrEg5fic/ecBwDYhbUYED1J2RoOQVTt7ic1zZUi5WR3O9x0pRJ8Tfcr3/230rjSFx3bhQ99qIo3N/4Nf9L4otKvZH/HQPGzl2wx7kSU6TXlqwjMfKUKmsXYzz+uh0JhLITUxA6N7E0sxhAytSwri+8AJMK4y8lXDAKU+AD11ZaiW7kpqk1DK3Uev/3tb7F+/Xq87nWvw7OepQejz6xAd7p0i7GyP858Fxid5x7v5OhoFwBh1AvFBMmBxAcNKPYxNoVxd5FpxoDU1KMIO+sKpbV0nBpOXH0ZRuVq3Dr0IW3gF7Y9gUa1gmXP68H255wJ0bMSzoM/x+fP+QFqA/b2FE83EzkQRHjekyuxbKyCf7rjn+C7eZnvsp0xW4xzaBKuTUiJuhcu45DkaO1YbZ2VAneY+AjzGTnmpKXEwngC+kJUIGs8NKdf4zbZLXx0NcKjq1DddQQOUqyMnhbeKtvHVnLGaUK1GCtZ/SRVNBcddXGgAGHHeJhUwZO+FmnGtAarTbReC313x8c22yvTLtoDRt/VinV350Aotrx6FM4z6cr0Qr09egIVK1YDUevCeNxXIlCY7Vp1SRCpYSruIQMSye8mGhK9OCZ8bXMjAvTFd8rf+DIOSbjqMx9OMhMhC1wpTItxoAhj9S8AnNr/lLUMFUcSKGdmzVOkOyn1kRBKWm1Hu7NFbjZWARm5Rj1Fh6cWY8vP6CnWfNnCLMVCoqOFsdoHJT7GBMVibBG+JeILA0CPTC0P2prUruXJyFIItWGmIllzpRBNRmxRwxWCUFGmQGSz8zqMO++8E3fddRfOOOMMvPjFL062x/27KcykUK1WgfEgkNE59rYQUCUSuLpIjY9PhbHdx9ja7qK9hWRCdAltdTSBsKK6Ay9Y+gtsnng5NtRemVOyRH35aix+5Wcgn/0OHL6vD1t+uxZP33ARKp4RBispP4wiWw0cvOzh1dgzuge/O+yWnHpmO1uWxTlYLDa6K4B6rD5joLaGSrP+gLKxVseUjAVVCvsWm8U4MJplrisFyZwmrG8kuIXO5nkRmknxMVZjjIfl5VuMY+pBAFL6UDJEpN5HzIAz/BQsvnPjDGnJOnOjzDI3nm3BXkFV8oSxuqirOHpCFO8aaX1DH2NlDU1BxYmMzIqWl+Hiu3SHVAwXocW4KPNdnjDW20S14DPGOFp0LB1fMW6pn8dHBSJx4XC0qBSCcrKxGs8UyiThSZGqGxJbjDsQpU34iqiQRT7GWg52ff8tvcdiz0QYhiZeBb64UcdJi25OD1pyKGIjsABhz9ASPHDgsKgysTAGPL+5K4WgdEQnADjKjUhNLUSdw/r16/Gb3/wGJ598Ml7/+tcro+30YZfp+51KJDSiX0VdER1Z5tUOUiUMJ5UpUHmVCuPfD/yxdpRAULCKudnD2HClgMBSmU66CRCEdDAaHIQ11U34/fCHsKy2OtkXs887CgICQjjA0S/C5//ug1j5tHEcsftWvOieL2Jg82LlgRo/xNLrHDLQg7ee9B48tOYx7FjxRFJul3gIS93LlE455ImxCVxaG2VxbMMWxzgSdiP+WXjO2KlIHujkAAQc330nnrX4Wvypc2tyjulKMRGcpl/G4mM85qQP7cSVgrLCeNRw28ou2BHwAFRUwRz1owNHX4/9q0XyoA/rkj+VDQBOTkMRCJK+e92W/ZD+bjwVPIhz/Dfj0Oen0V3Uh/3HxZ7ktUeBNo2sCmMC4fLeH8HpCX2zRWTV3Drc3BUhZvTO3Zh4dH/p482fvi1hnI78o0IyV2leD5swLhKmOcLYrxOuff5i9C91MNFooCaX4cbBv4Inu6H5GCvrc+KQjwEJkGINdUV+RCIC8MTxaXQeZX4lWWjpksT6SgUfcy/Hi53HQotxOsYvTvChLL5Lw7wCx1gSPn3mnv/FOzbcqNRNb9fCFPEKE1L1MU6P8YJnY1k1DOO6TyzDd+rqdEiOMDaIZ0NsqUfUbHeP7BooVZ4Nf7COA5dumFupzEvS0cJYbY6JKwVRGsLFMs8gFAFt9g339h+FC7aeBpPDup5Mz+lZpfki3bnjaNy07wQQSU2cNCJXikqBKwWQTmULECqkWkk6c6Rnsn37dlx22WU46qij8Cd/8idwHL3Jy+S5YVhSnNRiHD5sFSsdNbcYRxN3aXnq4jvFlWKdkRcynA5uTxibrhSAwKtGTkmNZYIw4R+M9RNvDLPigXDmxj+DI/V1/jcPv0e7lreoikOeP4J7T/80xhcfgr33rsTW69dg4uGHkwFD7EoR8/oT3ofVtZX43dMuxKgTWpkPqn4HK6s/SWY5Yt7z0FO4xasDPW6TT9hBFEalCLcN+R/EX+35q2S7JBdusAhvXPUfeOXyH+CL1Z8l+5ZC92Nc5D5glGpGNAHG3HTWo0vmW4y73SGlFLJajD0hUNUEc/j5ep9xPh59lqtZtNRMYTb55eaKubT8kbEaGiO/wGWrT8J/SX3wqVrETpMvS157RJCKVTIWhATAEz5uGLgMi4/5XvKZAOCB3vtz6pJl8IrN6P/54yWPpqzFuI2oFI5pKc6o7TJVsbgrFIR9y6vniC9w3wk9eOrQKm7fsA/3jJ6NJ2qvxuMTr9aOS9ZwAOgPQlcPCSfZDgCOyBeARMCTx5+Svlf+jiiZ77ZUHXyqegku7PpyWH5Ji3EQZPcRHCy2fOxX7H4Yf7H+uty6OkRaJj2VXqxOXjeUeZLAf0fy+lvV07FRcf10grzvxehZCyzGgTIA2dJfwtUmh/qmAYyv2wf/QH5Y07lKRwtjlSCKY6xnC7W5Uqh+P83JHFNZovgYZ6fngSjtaWQxtq0CjxFESQclBCXTnWHV598obarZv38/LrzwQqxYsQJnn302qtVs5i6ZY0lxpTq6F5pAiaf68hdoVGDONUolBFWSzhTZsFQCfmj5s2C2pWygH9fYLxB6bKbHx03OFQH+YPl3cejocXjertdDz7ZI6UMChKr0QBAYXXok7n/uX+PwFw3AH3ex9R3vRP2OXfBrDkhLyAsM+4Q/2vkKjHcN4fuHXJp8uvDLaB7+jWkujFMi9wGpi1s1+90qUZxhLGzj+pZxNx2sVwsW36ldVFfFtQhjBw0hUIVMjs22e92HssiVIs9irLpSFLmOBNJws1Cuq7pSqJnu/KQ8of2dtoEcITuNNQlXilyLcSlXCvX4PMtz9hATP9ruucKeGdQogKAskCehCW7Hye9DfAgIi3AniKTtuMb1Q+Gd+hgXLr7LtSa3LqecKGKRjVgwu9At2PoSKNMCne9jrP4uabi2Yotxfg7K5lC0nmDmc6lPno4WxqqFqyEr8APRdBEQURWI3BQyK0zVspPjjWOEq/kYp6QPIQGBhh9l5MkRSclZ0UPSEYSKJjg622I8OjqKn//853AcB+9973uxZMkSeJaFBEThwiTzYV6BD0kAURB+lZorRTaOcaC54mSFseb7qfgYm52iAz+/4yWnsI8JLca6q49M1k0DEmGs7EAEkABOXHQ7nlx7N56383UYrx+R1pFI629Da2G0QQisOHYCx7+5Fwf9xV/A3zyIzdccjKENTtIBOpDYX5/AkROr8dxdr8H1K+/C3UsfSUVIh/qttUI6RikO1wYg6UjCSBTp76+uu1+JYmEsqZKsrYiZcHw4FGe+y3elUBehEhECmNYogYYIXSnU1ql9BOU1KQNG8/4I9aK93xVCJsd3GRZFtcnp358SYYAIdSXBR13xRfZE3LdSOLs3AwG3p8SVIj4l8TU2C7UISCIt5JrmUpII7BaVMRHID79PrxLOjOWeLlOLcTyHJEloLjDCybcY++TA9fzk+R4LyZqTtl1TmOuL7xx4ZF9s7KESCXx9BtEhmet+oWOE/pQy0ghpave0ThU4MkCFpJbCPVAMamYrJE//XvU7Ub1PJTxyNbe2+PkYaAv42he1shH9jiyM5y93bzgc397wMmzconQCtpidWIYje85Cj3MHBirZFdoJtuWeCBtJavHQzNOojAyG1/C9xGJclERCUNrhOxBwVWf/DrYY1+t1nH/++RgbG8O73/1uHHTQQbhnywGc+NnrcNdT/dqxVB/Fpp4/x1/iMm27SwEmahtQH/w2Gp7QOvvYYqxa87+379LkdYAKvMV7tJGPhJO0p9iVgpD18AoTDthvyxv3noBvPfEy6774ys9afANOXnR9smVJPe3kGhIYcjz84EV/g3sPCaeAbzvuMox092Nn/xtBScSB1GIMhIljTLHuVgmHTHwZ7mFV7F96Cg48JDB4TQ9ODfbir0++Hdj3KIQIcPrON+DY2uH49mHn44BYFZae4wenO590NhPR7zZWz4ZgyM1GRQJSpserFuODmliMrx18L+4Y+4vkfQXDWNy9NfnVixbfqbU5hPZn6kdw4EGEi+8iAqm3p9cPvjQ93rIQMOa8k9+Il/R8A3WrcEmXIb3W1V0cnrjkcAw+FWbeo5wZi58tfQivefy/kvd/MHE19i/eCQLgR8JYOD6es+ybmHaLMZDRJK0I47i7MhffldE5w7/dhl2fvT210Grxh1P/36YXV7nq43jvhv8AEFmMzYqofeXEUFQMQUQDs4AcNJTFe0XC+GtHfhjXfenL+JvLw/oPumFbqbnd6Bdh3HrXaKOeTC3GNy17Pv7e/7C17E8F78cDh16JrtW/T7ZdftU/4ns3fq2UMHYXb9E3kIBfGcWykz+L6go9nbaPCq658h/w1/ddqEXNUmteddZq56x4UH8+/GrZkuQ6amsdrfk4sf4z3N04FgBw0xP7cOJnr8Oju4a0BEICUps5aQWKhDFbjOcZtim57WPZjl+ly9kIAFjk3FN4nGYB0TwmKM18px0jURkbDOvl19EoIYwBAFFn4QhoPsadkuDDXGUdBAEuvfRS7N27F29729twxBFHAABu3xQuerlzsy6MRX0AAPA2cZO+PQgwUQvD7XgNNLUYq4x3VeEt2p9OQxEhb/Gd7cmaZzEe9bs1C1pWVIf1enrPrdFlHSyppdNhdeliIDIhPbJ6ffjZ3DpuPPFnaATLQD2viUoyrOfSy42Usbd+Mh589sew83lngjzgqEfHsOuOlVi85VE4ILhUwd/ufh+G3VF8bW1ojTPbZvJMZFWcMBFZ10bGs8LYt2QOBELx4KnrcJRV7SsxWni9kWAVBoIjk/dVDGHYcRBnUk7DtRVHpViLA0lK6HQaVsAXQosAIY0+8LTxk5LXpLpSGI38muNeHNUja5RQw7VJi9/m8PZQGEvt+0vrsXnxbpj0Lwm3pRZjYMvS7Zg2YawGVs/EMW7d2FFJHGzL+xiPRmmkY4ufdk4sTovEjq2a9/8vPBG2R98VRlZMXbTJ+nBat+jaAQlADbNX4EoRc8aTaR2X+GH791GNrqjXv0FVuCUemaISllNdeW+yrUv6OGq0LzR+NJlJEK6+aFAQwenaBwCoLH9Y2xdE9+8rdz4AX3P5ycccLtS0zjWt2/6oGr/3w7ClNz/RBwC4b9uA5moEEII2tUTsSsEW43lH9gfTlodMQa9H5i0YSG0xVIpMBIMjRCqMC7peQUic6AUIrupflGOxXmhoWUaJcM0112Djxo1485vfjJNOOin/xOSc8G/Gl0oGiRgWQvcxTsO12W8fz6lGX78++xB3mrHFOF5kZIrOPB/jLOY0c+pNnJJOdEshAZG1tPUu24YjD7oZqJ4Mp+tk6BZjQhd5uX5w9Z5VgBCQRy3Dmj8ax6KTahjZuQhP//IvsGbjIxDSwwn1o/Cu/W/Eb5bVccPiRSU/W2eTTANbQj6ZFtnEH5xc7bbXXClEsTAOUEGdliXvXeFj1EmzecVJXmyuFGYflaTpTbY7CEToYpPWT7/fpGIBdKxr5UPUFOsmWri23KBuAPnqYEOJhmG576pBKMBVYSyQRqWYrrEcKaIw2daKxTjTn8XC2LxO9ptOxiRediqcTJ9l27VzhJQnQlHqVQBRmFEtWu+DdKZOktDC7JURxior/FBsJ89Y06BCAm6ZBz7Z+tiojDZ9jNNnj36+r8yKaHkWCsR3veDO0QSN0Ye4TrxOgXSXFUF6xsIWSCzGQXvnzyYdLoyzqO4R+fFky7NrfDnu7U8tMbWRYxJfJnXhRzXoSnKtCwD1SBiPB8vwxPAa1IP02AkhcPWSxeHUs7L47oBltexCR/UJv/XWW3H//ffjZS97GU4//XTtuLxbM+4ezJS5giTiyB7CiErhygB/dv+4sSYl3R9QBUvG61hZXa9c30k6JjUqBcGBhIs+77gknbQquH+9+qU40CXwW3ioC3NxnUm4X4g4hJpAlxjXfIxXyEH9c0rgaTuX4OiVNwL+TlQXvxpmhIJlfh17a0shfSOEBoBa90oAwKLKATgVwuHPGcbxb+pF/9OPxCFPrMML7/0K/H2P4Z3734CT6i6+tOYgPLroYCD2Y6sN43V7bsyUO2k2Xg8MZy2A84VEnNjCteU8qAIjjrFqNX2G2FF4PUkV1BRh7MDDqJMmau6iosx3oX55cuKV8GUFE6N6SDIHg1hFEk/VXoSgEQ6MAiHQUPyAB6oj2LIqspgRtMxlNn6/aBEGXdM9JHWlsGXyi9k29vucPdlrHil6sMRJXSnMY+MzRmoernpoN27bdRv2ju3FpCFDFwuA/GKBMfFYP4JRe/aTJMJGGetf7LOeLJ5S9uWGfVMvZt+sWYxJJgc+2t2l1UsqcYzjywVRHOMVGMXrnXuAzO+Rz6Fj/Thl/1YAqW+xaQgJlMV3NtwlGyGqB6CsHsocIwsGdDHmJRySqdtjtPMZB7bh2KE9AA5Oy6blynVSzBrXAYgxD06/KZF1i3Hsxx3XOBbGkghSGYAJyLaTLxH7GM9Psqv6dfx6e18PUSqqx4Mu3NZ3XLJvbPDZ6PJD8T1UPzHZXg26lGgVgBeJ3L76Elyz62T8dk967NcPWonPHLwGj4gKRCMMheUIwpp6OsLsFFeKWCQ89NBDuOmmm/DsZz8bZ555Zu7xmedtPbSkLYIRF1P64cI7AJAu1ID0z9l1GI7dWDMsxso0MSro8TycsuKRZJtq4UqEMYWL7yS5uLj/G7ik/+vh9sj/sl4B3v+sr+Ajz6jiXzCBS1ecmv9FILV4qVa1452+pF05fgODkV6Il+Sd+tRyvPzhNegfqAAT1wFEEO4qQKSf7azdLi7ZdjIaIz/PXNOrhD5sIlq13+X46Foa4J4PvA7bTn8NBICJO78N7+5z8bkdEsOOg08feihoYjAsYP2v8JWH/wkH1/sLIxG0zPlvA37w6ubHzXGENcGHXaQROYBrj7jwIvcJFBHARV0uTd478DDiOMlgPfYxrtuEsQD2es/ADUOfxIaxt2CJ3xfVKnroikGs9CV+M/j3eHL9RwAAPgg/W3N1UsYXDr0Zv3nGj1CrjKHq9aSfyZAwscX5s2vX4gtHfVf/FpTFd1Lofbdq1X5g9PzktZbdzmIxPqG7AlcINAyLcdxW4yb72csfxV9d+AA+csNHcPbVZ2fKaRnSlbGouoUWY1n30f+z9dj/08eiOhr3Up6Pse0xEQ/KLD6ipQR2jhDyI2Ec+hinn+WqpUuwpZYOumNXF4JI6iejzHfndn0T53Z9C8sc3SWuiJ9c/2/423W/jD5aZEgy6u8r4dpsLD76R1jytK+nGywzskWzu3k4JJPfOW6737zlv/Ddm78OXRgfkbxWXZfMKzZA6LqtF13r9utrVUg/2onWeQhDGAeStJTuNAmLseSoFPOZ/MacN33cjIbMj7QJAK6MQ7Gl1umwgcZT7JSKrshSM+j1JMf2uuH5Y3CSwaYAoXseTllMFiJg8+bNuOKKK3Dcccfhj//4j+0rxnM6clEPrU5WV4q485b64rtFXtjB16XqFqAIY8vCoNAyHNYrtk5LCJDQp7wBJAHk4xSevYvDtrC9utIo0/ycujAGBCpKvXwBjIquZB8ALK5FD6sAAI3AG78BQnRhAEdE1wBWFvg2xteS5GoPGz/wMXrwUbj7Bf+IrlPeCr93Pbp/6eGr109gr78bv34qEkVeGOOyRzamfl56ZE/zY+Y4lONKoc9mKX61yua8+KiZaxAgUYWHRZDRrIQr/FAYRz9pNXqQWn2MIeBR2D/V5XKLQIhDUgnU6wcldT5QHUqO6K2E8VI9pw6H9LtRb+exxdLB7mqfcR0fqaA2hLHSJ2i+pNL0ddWRUQfrGRZKkdxrUf1H0lit/bXyoi0XIk20iqpTGK4t9j8OBsIBPoH0XyovmoStX4y+K1m3WPziZ0wbFmM/sRjD8DEGxtRFo4owJtWVggIcKyJrfAsWY5XYhcLs72XBgs+YcPCfzkmYtOdKIZMZPnNg5qv9qbZ2IJ860jY5DPX5pLtSVIIwrnmcsCwWxr7UI5IIkOl1URqbK858oaOFcdhOCnzR2iy3FlQKRXW6+E49RiJu8voILR6hK6M95Yx44OqAtBSUokOiUuzdtxcXXXQR1qxZg3e+852oVOxhdmIyVoFGzmp9KROLsSQBNd6TY33GKK4UMOtAUBN8CM2VQl8kBQAklYx7yosRtyAKCoDElSLpOvUFT75wUE+OiY6I24+g8ExvA6QcxSCOQL8fTnc6sE/PhlWLRVlFe9gEgQ+QA3Kq6H76G7HkNV9Cz1Eujr6vC9/+AeHaS/8LfeN9QOS7WiVv6oTxQpotsWa+k7D1WxK6j3GRO4FWniKh/Mri8FxnAp5I7xZXSrgI7D7GIrwaELY9i5RPrFd5Ybq6IxEfOB6ETP3xCbqoVV9lEzClrhTmYFMKAYr63Yras6vC2GIxjkPP+Upc5PBW0RtrT9VFsWRpDVL9CBAK4yKLMcWC1U3rtVidOk8y4JknZstKfYyT7CDp4ZPwMY5THHuugDDUVk1LxZ26UsRFBeQAQZD0Z4FlrYRSgdxdcR8V+vYqfSNclOuADGGsuYA0l1NmzUKhHm/Vz1dTPWuBWJtYjOOzttMhyhH6kZUgWgidzOpE/bgkLU66mMziuzr7GM9fRP7DY5TWtFVkTVZy+w2fZCKsVJFGkLhx4mk4d9tHsLl+cuY8bSo+3kZpZAQhCD0TdWy+di1qg8XicKGwGHVcdvEv0N3djfe85z3o6enJP3jcxweHu4Fx3dLgeKGlyrQgjI82kCzFJCDwfFy8/2vYUX924pe1X8uip3aQ2TZF5GDAfx4ANY6xAwEHklx447fCGw99H/3u8PxkLJSzUMVsYkP+/9U+C0XCOK7lwxOvweDEc3DmrjOxohH6rJ0YueH6TioXKDiAKuq4byyAT0B1pR76SquDcODX7sfmvQPad+jLQIsY6CxahVWv7MbRr9qP5Q7w/y4ex/3vOQu1XeEUapV8/Hv1+9rDqm0KF/bMD+LvUl1w9BH3Sny18n2QlIbojQcnwLgj8ZYjDsMTXVVrZAYbviJ2vWoojD03dNFKYq4D6IaHIbkyOfbgiYPxwt4XhnFdY50gAlyxLHTJWOzGkV4IUgisP/gOXHlMNFMgCATC/65/J3699dVoROf7jgfxlOLWREIXxtFDur7lo1DvgPrjV6L+wBOQCPsAb28NG5Y8Ldk/1L0U7372P+Ghfcfgkz8W8CccbBtbif2+6g8d3imnDJyCZ/c/G4DdYhwOcVOL8b/c+S/oc68FFPE8ds9ebP/Yf2DX3/09WiK550kTmKLLKY5KEYlm4cZuNcBidX9bPsaRxU8VNqV8jO0708V3wlh8J1D7/X8A634SXiKOY0wCXrT4MYCAlAQ36pN9J1+65Nl+37Pj+0kWujBNR3rcx+mTWL/khIIPFVeVtL9qopDAkj0y5qMPXYa/2vB1nLFBr1u1ewcWHXlB+MZwQFYXwWkWY8slFh31Y1SW3486AFoU3vc7KA3ltsZbiX/f+rfJeycIZzhiC7oTu1IoScOiqxW7Ujz1O+Di91l/c07wMQ+RMg42bxeRwj0Eo0pKRht5P3e9wGI8FFDq5C90YfxUoxuj44/i9vG3ZuurWozj6SWlEg4Iq7fvRWO4iv2PLiuo3cKgCz5e27URjXoD73nPe7BixYrC451t41glHThb9BSXTiMWxjo7t6ZWUpIOxgbr6PNPwM1DH02mYu/tUTLpqR2k1ZVCQEahgmIfYwGCEBVIuAjq9yKo3wcAkNU4JXnhR7JeIyw4FcZdaCQia0iuQb1xPFY2VuLEkXCh31HRbHSX5ssa4BA8hRoBG+td6F76WP5FhQN/4ncYGhvWp/Glj0rGOOhgySENPPM1g3jybc/CmqcO4KkvXIp9Dy7HkvExPNPdhjUYbO1D21hAwlgNwP8P1V/g7MrvIotO2nWnC/UcbFg0hKe6qjhn5YrMTEQemjCOfMYb1TGjbIEeNNDrH5Ucu6a2BkeNHRUOBKM2N+GkTgxLK9dGn0WC4OCWp12EJ1duivaGx/9+50txyYaz0NcVhdNyPCx9sKb1n6q/cPy9+MFyrU9sPHk1Gk/ekgjj2tYx/Obg1yX77zjsWdi19GCcM34W1g4IDG9fhN/sPhFrF6ULo+Mb7uTBk3HicLimI14I5mVSEKcDhks2XILtdBlUc+zALzdi7IafYPiqq9AW6QRieJ0mPsaxaBZu+l1pFuNcVwpLYaaPsToLkZcopFmZCAffAOC5iHyM0wMbsgFc/UkAgFTu35HakdE2B75MLcZFizPNGMUx771vQ/JakC2KfHPSgXt4breSgtlMkKPyh1vuxJvWZ127XGdceWe4/5Bq0VYNL9l6V5ZuwKIjLg7n9qLB0QgWIf4xnzd6Co5oHJ5eN1qnEA80VIuxVONFQ1/gnmHr7cD6XwG1ocwuya4U8w/HcdCoLoKwWIwPHRwFRAXtJsmoB5XciBYENY6x0ti1fI3Z4OVq09ICxiSRDiSqsXVJTL275lzC9328qroJK0QNb3rLn+DQQw9tv7BG+EA2LcbditcCESWuFIQ03aj+G6sdl93HOD4m7pBjy5Ppkxz7qkld51rKtJN03iSwGPWkLUgEyfRzYh1OxmhpalIBiUViDCf1OOjzK9jXmyZfyNTBIlqA0Opj+nkm1kcHOPGt78YPP3sabn+mgwNPLMU/fe07oM0SR8L0G20D5WE1390qgtxwbdl+S/UpdmCfubChCmM/sRhHPoiJxZjQIxoIHGWaNdrXEG7SxhwEcDPfudTEbZpcOdtLeVGcV1X4qOdqgllmU7zHFtFsxjx9sRxRVsjY4ocHVotxeHT6Ot4xhRkdM4vvmrhSxPtyXCnyFt9ZLchObDG2WPwSH+MCV4q8xXdRe/Rd02KsxttF4lcvo1YcU5dp3Icio4FbIrNmKIzbeL4nRo3w3KqaQrwdH2OtCkabVSMdKdvVTJM2V4r0dUX5vR3N6iyS+yGyfEdVD4g0LULNLMaRKxzG9md2cYKP+YoAbBZjJ5qELo61GNLlLEJF6B10TVZyRYsvR5J4idqKaG1YbhHGSk8QP+6CegV+FL1CCEIl6hDGnCoECGeIp7AG42ZR8xoiwhVXXIHD3BHc5h2LI44+dlLlxRZj84nRXSUscj1URAAix76gTYbd9Ii7NDnfB6FfLtPK6hJDkEG6OCe2GDuQEMLNCJhxuSosPo57XLL/3l8ZRoBQxNTdcYy7HnqQXpdEgIlq7FMdTdVHVhxHvQiFpTy928EKJ8DjW94H4aQW+XG/Ci/2g1ZEy1gtFVgyqGeExkSjmvSRo4HEZ17/b/jBm11c9A4fw8uXouvmCXzwpktR27ABk8JPPzMOPDW5smaBYMwDooW5SXpc9SEpJfy62m/F/YlIFr45AHrdJeWuZ7EYH3ABUoQ2gdCDhma5InIxTlX4qKAqwu98sTOIrFzV2/iIow3tNXwnnqlRZ9M0z+LkdaVegazrawRkIpx0yeA7rlbWOBx4pquJRW0NVUZQE3XNxzgsR8L1JbqVhUrLAh9r23FjG9qJZIWTqjuVr0d05VuMg5EGZOQiJlwBWQ9QbUht6VVuHONaADluPG+iryFJ6WtLD12kddQoFr4Pb2+4YM4Tuo/xfqSub+NwsZeifk+mRogeZwmqTngv1BQf46IIEBXLYNLE1eIHNyP9/AdNDMMNKBHIXUr6ZGe89YFRPPu7eoggpJ6qWrUYBwJYMzEIh2ShnFdjK9VE6jctIIwFfA7WjA9CSML4+Djc6J70JWmZ7gI4xcI4jpAz1gt/sK7HvI4TfLCP8fyCQFYf4/CGcUr5O771mI/jDUd+QNs24VdzLcYP938vSXOpjgP1UVoTi3F0bN/tz8Sj+8KQLg7CDqF/SQ+uXnkSBvoCXNT9OdxU/X7TzzCfuPHGG/HII4/8f/b+O9Cuqk7/x19rl9NvvzfJTU8gQAKE3gRRUJqKXRHEsY2OOvY2zqhjGdRx7GPvqCBKEZHeey9JICG9t9vb6bus9ftj93POTYJ+vqP+8P0H5J6z+1l7rWc963k/b56057BF9j5nj8XGX0U4pdafo3jfIY/wT4ufQqqIIVAI+ixv2Sg/JVjRfhSXzn8LIz6wvCZv8a560q7pfYsv5+zuHxIyxsEsHQWa1qQFvW3gcwDI/bydcbA0rk/xpoN/ybe6OxEofnniv/O5o/5AGitknC29xmPzb/Dvw4vhXk9LqSGphw9BIhAIITgsbSOExMyfR9Bd/HDjyVyx7SjvODFgfPnG5Tw97rH3qfKuJmB8y5bDeXh4AQBTjsv8Z2/kwyMjXHNQhq+/8yjSR7+FvqkSuz7wgX3f+P7CiQ0POx/9y471V4i9//UI5ZpXPhe/CliikI2UbLlldtN+XmZ9tMT/mQUXHdD5nJg7jm3msIXOT+X51Pa8Hs3HLrnlNhls6rGkp02Tp3Jl/WjqyqDP3MKc1DOck/8tx1RrDcfvRXNnhn8XNY260RqY2JoHdMMVDVprjAHef62kfPPH4rtTFdXE/uG1zvRAV9X3g/1pewdWk+Sp+YX7/qzf897Fl2A3FpSQDi96aIz3rohWOL75+D18/4c1srUWesvp+qnx7fCtw+HerzbukNQYmxrKbn2MoR+uYvLmrd4fusbgN57g7DsHWibfNbK57mSdPV98pOHc/v9qLRi/A5BSbB+NyJjB//4qm158Bq4lIrs2Q/B4qYuPpk5lo6+3uqZyASfXv89ExQrlQzbdnNp+Ea9d8GEA6q4KgfG+KszFGePpxmFNHYjrcHgUAAxH8Zs//YT33BTJQFKx1akjbtpMbwtJwb5Cl9A/qvjhD1xetXIAMyHNiE26qlP85tZLeNuzN7VIvouzxNG/6/GEQpUExm6lwG9uu4QXr1nP//zP/4SMsZd8F8ciYt9jrM8YOwOjDPz3Y0zducPbT6l/MMZ/v6FotSQpwFvvPcCllrzRkfh7ys5MO6Mdr2mhlEKp1sC4tZSiOfkucc1CYSqHqaw3yI1VvP/ntG0Hcgt/F/H444/zwAMPcMyxx/KM2w/sR/8Ui+m2ijTGDVv4L3NXqgYqxtwhQo2D7mjs9DWKRb8Z7TBbtxlNxP8tw/+3YozDS4gl4uwvirp3H/dms4kJnakiEyGroRypf5bwWsLWGJMQZXTF0XN/hmbMxsicHH4+XPcSrFSDX+zGoqfLd9FoleW/vdKJAOquA9vu503FEidWawza9zF+8DJS80/DGWwuJPKcwo3d545Hpt/ubzhk4GHq+2zHBytXShDNz1aiNbjXHFiVQSfhSpHH9dlVp3h4CIyNOS4ZUace44NH694kxyIFKLoNb1Bc4DRYmwkLITvQ/JnelKZho9HKR97SvHasElKK1kvHHaVmbjqECg3AyfYtLgMthabAaFwtnEYjOpAaTUgpNBTKtZp6+BnDKwBItXISm+4VDiwFt9ztbxdjZGP7aPuQUsiyjb3X78d0gTvlgZWklGL/EojwUv3zOBPeBEe1AMb7Ok65Fo1fU7fe4u3mxoCxDptr3gRl0PAm4Wvr3gS9WHOQIUOdfMJ1JUNNrDvN+ApgqOlddILQlTxwKYXfH+r+5qc+G/04qSZP8ecWmoRZ496xjthdTBwvDowL/srI8YPrWyxsxOQmsU+tBOnnlWUPwrE8ydThe73VOc3/zpWgYsjbVeKApBTuqDchqG+a8E8gownWP4Dx31u0Zoy1kDH+8xr9pL1vW62g8h2xEs6JBRLVfN5E8l2LYwpUYgkpmg3//SciAaxfv56bbrqJJUuWcNY55xF0mn+u+XgQmuNn3zd+EUvgUEokgbEfQopI/3hAAN1niuNSCvSWvsew7wSTxghqlCnRCPLNSGOsOaG2LLK68pN2YkJmoWRC/zm//WHc+hr0zIkt7ijZGot+29diFl3hcWP8hu16cg0N+OKI5/v6rdmXgZFC1Woo9y/Qa8ZL/v4dMsaJ8Bmk+ARQSpnot8L5k4wJfpTCPMAlzKQrRT6J4cIcBshghXZ/8ahjogsX3e8vGzXGwis7gCH9wkZ+Rb34u6R87+5IShFFY7GOIGp6s3VcxKG11hgH/9elQms67vTDYVPlO7f5OkWQ0NTqevfXT8UBcfB33K4tpYMjWzPP8e1iM/BsS2C878sAIk/kMZ/5jw9NUSm6afePL8Wriq9VJ5JSKE00XU5CO65k4rsgalJgiCD5bvrrNw5APvjnMMaB2syIZb3HGeM/JzRFmKTs6FoimS/+zkdloFVCb+yReNEPlNAYCz2c7AkEduyZOZoHjFN+XxmvfOfGf+/9Msb+e1GdjF0QUXEP76DT7/83Gs9rYKzwFKONEUgpZmWalyv3F4bQmLQzyH28ub1lr3hCvHLa9lLUeOzaQ4lrfOSY07nx5Fdx28RSfpk+m6HJ09EacIPma4wD/Kyh2DjVw55KpOP62W9XsWXz2HO+p7927N69m6uvvpr+/n5e//rX4yp4zcidfHDnbw4Mj7oO86ZaW45pVtCJtmaMwRujlCNxak8i3QoCxfH5K32NccBAtb6QLbmFib9zRjvplGcDqKFAaE2McUkoHjOLWPZaoDkHVKosRefV9Kd+SUH/A6DYqQLtHRDzHXaUyaHrc5y062AcYYerFXpjEo2QsUcgY1lKAt0FWb0fE8vbXsQmfg0gYMIu4CiTUzeWWiYzBYVs6q4TnmOO47JYP4lV+fWsa/MYNFl5jtr4ehHu/ZrXUcdBy/A6qPz9tfkgglWlePOSrgRfE5jT7qYv7ckt3NhQrwHpA2CzygMpSmORFtk2cy2T3YSANC4WBk5VY3RdbB+/j+kxPBBUUL1snZ9FAQX9DyEwTvnWW1O6xkI5RNxyTvnFcsbbCtxy8unhilvSbTY56avpKVwEn0itpmKkwy2gmTEWqWAJ3rs3TUWTyXAbKXj9g9F+JxeXc0TFs/AaNaIl8l2mwe7HH6AxNJ/QMFpooA6UNYtcH0j86MJ3qqmuak5OTYBlI5ggO5y1+FKkVk+e/0AYYx8YO2PTM8b7up/A1eCmZ/biViNpixP7vRtbZqiNl7afXJrsketaipucU5nclqU2biS07o1hish5aM5I6+105dJZm2J0XT72SBSpnnsQeim5cbDCFz9UoDGWSdnQc40EMNYEZkyzHLdri3saSwFVHb6/0OAx4kVHGhhjOZdeu8u/3GTynS29dzbtJFcRy844a1V0/yvzDs88tYEHrm+d9/HsjrmMOXNRDcA4lFEQ0xgrBQ9/HyZ3tzzW31I8r4Gx1+22ALAKEBrLu5oZsqbtGqJzqsSUnaE83orN8KKr6i+TxpZ87h2KAXTpDXRG7hzGOnu5/6SzeeyI0/jmwW/n30/+NNe+4CN0F5PHF4CunAioCcmfdi/jqh2LAKiULS55ehdv+tnfF4M2NjbG5ZdfTj6f56KLLiKdTuNKxdziBpRTOjDGePXVLJh8vOVX0zLG8apDVY2BbRtwqvdSnLqFOeZjnNR2BUJFjPF0y3I3zjwv8ffpM9/A5nSHv49CiGaN8Z/yFvfmDcad9S2POem8DUMM0mPeQqf5C0yxlU/iDUASEbYfAKlMMvbpzLWPQmgwY9K73pljAbcQJP0kOJvovRAeiF7SfiSntOVBgJmLSm43snmuUqwqn88RT61qYu2EXzBCAFaDpdICDua40jLu6/as4Z4zML7jC3D3JZ51UENnz67Wv/3fQ0h/oHQTjLGL8J97d+obvHmhN5FWaOFkQwDZFpKsxthxTy9Da73BM0UJ28jHVipEKM1QGlScGdQx2PVwN0MrOyIQ/pTXnuekPJDgzO1gy8I8g30pr31qu33G2OuzysLzY1Fx6YLrDdQPHPdavvrW91LMxhI5Y561cY1xzUjx0Owjediaz88Pf4X/DFozxp2Lg8E+YIxpciRaujvNG++Lntnndr2Hr23/KAA3dd6f2PZbu7c2Pkp0/3nPbOU3PC0gbeh5YoxxfBdhetc69rsWfULc1s13pZiYcx/GwY8wuvgGj+4Pl7WnuYzgtFKFnsiy7D+LVsl3+wLG/nfvu/ypqJCHhFJsZawik7kd4QTMKicKfARxb88LWcsS9jzSxdZbZ+yz6IQeSzj++i9aTw41pfjgE9cwtLKD+qSfgJzdQXrGLaT7r2m8I3+f6JOggFY+t2La6ziQ0GTAQHvezKl4oZNEIp7/fJRCAr+fn+I3ftXQuBuKHXtqwjmTYypBTYRkaZ2ar/kPgHHwOJ+s/JA1sYnBQ+0uJ9xTYeGDgy2v/+5nT+L3I9+E2kR4HkgC47CtlEfg1v+A1Y3P928vnvfAuGesuaPxJoMHrjGOR75u4yqNkjs9MA5eeaX2Pds00ocnrLrig0LKbrQa8vwbA8ZYb7j2oLOa+jvyeC2Xy1x22WUopbj44ospFDxdqx1bxjugqjx2NWSaGtUJwpkGgMUPK0FZPvCUk2EGvpCxanYH+FzTejZcDPOkFFqTvVslsFQTUWcYXYpAqgyaiLLxNTEFMRu2uCzCUQZ2ytNiSs1F98GILlWY3914w8mldG9bR9p0GxozDdBTh6GlliauMR6OSuMVcGjsXkR4bZaUiR8jo1w+tPfN2D4z5pYbWJv9RdAxSzfJGGvG37ecIrCuircB1watVREZEep2BZBxD0wKJjWvr8qLMRwzhyui4hUqbgepoI6BU0+e2yKQz3hMnas7/nHjyV9mmHRsBZ6psfahGi0LYxOu+HHira1mpMLtSmbGP07AmDZMyvRkP6FJTywXD8PVp+Uh61pyklHRvb8b3xWItKiJONBuNy6paKh8t999iKQU0vD6KFe3QDtwxjjUMRsaXm5FQ8EHNxi7pj+GbCGzUEo0MMbxyU60DiBdK2SM41HXkuOpw/TXYcYmhMY0z11Xkrxd9S/Ovw6fXRcNv3VoaZY4luKs+XeTaSGpeS6hqei4jpaUUsRZ4jiolcBufRvCn4DFy2s7DdvFWgZObFyQDRUsg/7FVuWYOQAIuY/KwDKgVszQxzhcaLRaTKZsf6ytR8TN32o8z4ExaK1mvkr46sf9gK4WZHO+7jXsKbWPKmwBiNoPMAawYyWOc9VoiSjlNABjJdCVm8hKj4f8O9P52LbNFVdcweTkJBdeeCG9vVEVwkTW7HPQ9jb+E0APGePkF/HCClIIrFr07LXA7D9RlWs6EKKa/p2KA+MGKYVSMagaAuPoCB5Dm1w+0yiGC86uSP72jjLDTR3NjYrLKG+MC4BUw90T+VwKdFdRc737X2CCtHdh5l7CpDOLVuZBCu8imnyMYypjq8FSKS0d+pwuXlQ+BYAbVl/ddNx9RiAb0PQkYzxrOez4+wXGyge3iXmXa7XMjfBKl0fA+EAYYwCpeROnvBjzGeO4lMIPoUghqRPzaPf/Vze9QVaI6Vl+RRrNXxmpC2/dIOEj3ACMg/dKqcZVibiUIh1WHgsSBlWgY228x6T7O1oLxlgJgau3BgKtEgWh9Vuv/wVSioTGOE6KpFoP1aphu0BKESz/K99d5kA1xoGMQsvo0XXE+9sDYoxbMebgxp53/N9CaWFfJx0r7NPjR2nMV7DD22l+LsYBtHtduiHhELXnYFxuPKb/TBqAsRCSjPOXeVdrEky/Ebma1iCliMFav78UeL1/wYqwgy7ikgvC39iNt9pGV4rY+6YrLTyXQqDHwPC+gLEbTwZt0hi3YIwDG83aP4Dx33io1tpQJUDoLe2EgrgJi116c9Z3zgfGe2ifZs8DANzhduDqrROzXrzCK/e47qAj+Np7LuGh9hPprLclCjbEQ9o22zIXcaF++wGc+68bUkquueYadu3axete9zrmz5+f+N6OLe25EjYMFpn9x8d40e1Ptzze5ESRe3cOIp09Td85NYvvD1zL+uoZic/jfpIoje8ORsxAMEMXUgs75jXaDr7WWW06fpy5dyp3AhEw1lEg9ISUYnOpF1f5Fl0BKxgHxmH7iQFjUUowxm5RcOVXHBbtVbiYYTuek9+CFiRjKIUtUozP7ePZQ17C/UP/hO4vdaOiRDkAa6o//HdOTGKVbwYluX3yw7ixdnZM90t4/YKPYdCGaAGMQfB0OsWYM5cvZk5mXTFihYMkluWWl53+x6d/x9bJ5uXq6WK85D37kYpMMsbzT4bdTyaLfvyNxDUbruE9d7xnn9uMb9sIgCxH2tLzn/rnJm03wJrq2QzXPU2sAArTzNW+1dXBGfPmhH9L3QPGOZ8xljF9eafj51noMJlvY1LoTWaSdcNrNxpB+4/AOeEnJprSkHY7nx/6Md+zT+Kp3Mboe5V0mCh2dEbXF2eMY+/CeK6Nz//LhwFYPS/ZTz5yaHJAr4oqbUs/xUiv16au6vsAtzRI5SQaewo9XFo7gSFZCD8/PueQ9lHRJ548mZ/8b/RgnRYrJieNH9n0WRO1KV34fAes/VOwQWK7q/aMMVqPnnQgpWiKBgOHKPnOB8ZKeVKK58gYC78sPbJhUhZojF3Fnv+KHF/29P4nm457Jy6KVrj4x+LlXLNzEXe/90JeeselIdBVeCm4AaHj2hbletCXTX+tLgpHGS2B8bdu/cM+7xE8xjj85fzTFKwyV37F4bc/XMu37v3faGMh+cl3HC64L7qxd9/icOHl95DZV5nuA4g5Y/CuW31NtyYSrhRxjbEeAmPFFVMl7h3oDK9bw+EH33d45SMyMVFzIwUNJmayrcaS+VMY9Ew8w7bMRZwytpD3DV3ofd5zNyrXXLgjiGuuu5qvdVZR1GPAuFlKEWqMA4b+H4zx335oLZbAPVCwfwD7pNnZ9Fna2ffypdB793tcgFT7W71/tL8y/Mw2mu2JRro9rdBAbz9IK5x5N/6wrs94ftz47X7P/dcMpRS33nor69at49xzz2XZsmVN28QZY6kU20cryI4U66dZN9uxw3u5nXozcK7UvWf6ROmC5HUkeneNtB3N0EWQoa4IO/Rn2o9oee5GSQuAGQDrwJUixhg/MLwgPGarbHwPtEjifIpGkWCxTSKoDXhA5fQ1ElvqYXOzEIjwXApLZdCzBvP0SdYaM+J3jxZjdLbWTgwz+DVhgCpiV+5g0D6UeiZi8ufmD0XXDFKiOzhK4toFUNVE6MJxixaBs5T0WV7TW5Zvcw0+88BncA7QDmn3qCctWTdcSYLgeSeCU4W9rSdNf81YO7aWJwae2Oc2xe3bAdCGn0183qpiJ0pSVR6gEwpy0yzz/qKzgxEjxt75jHGOMRwjF5buFbH2IoRia34QlEnj9M/yfzPhr6S0JhQ8dxS34uU8/F5fjoiXApFJYJuxgt9dNEgpomPv7eoLB+JSJvk81sxPHm/CHAdgfNZmAAbT81lZOCh5iUpjZd8hAGx0o3Z9bnvUno5sezmd5Qj0t2qd8yozmz9s7AYC9uyxpM98gFuLjksxxkZOK6Xwd8if3J/4WwXsp3RAE1FS34EyximfgZcq4UChnIgxDjXIQPHYHbg93uK/dJv7vG8Yb2Je1Xv2FzxwN06s/WpxKYVjM1aMQHN43oYJiIuiJtv+rGpz3jlVeMzAnmzu1AQAGUdx2PiO2MklnRU445noil66UjFrpEr6LwTG8XC1ZHGS+GpoHKe4wJAVte+CU6Z3Ci6+WyaSEpPrijQwxtHzTyuDQ/d6E7RXjJ4cFslJz7g1ORNtiP9d4Vu16SUI9c7+tbdypfgHY/z3EQpPP9n8xYEBY60FW2C06BSiSIFy9zkTDo+tewkx0oyqqNlms245AE+OridsqhoZY9cH7K1m2H9L8fDDD/Poo49y8sknc/LJJ7fcxomBVleqBIPcKgIrGlqUCtXs1uDBbZA3GE6cMQ6AcSz5bpoypK2S8kx/fy/5LgKKACU7hfQHjWC5Makx1n2Ncpwxngw7MCkiMlGT4LpGCCZqmggz8YUCS0ZyHyuuB4UQGHv/jmzegv9LewOHZe/ESc9AGB7ArToeONUp+CWmG9qaECjhHQ/AjIHetPSfr+6BrDfNfzVPjzzNpWsubXp+rSJg8ZUwkj7G8/w2tPNvz8/YkQ51t96y7HMQwW/nGg1V7LRW73G0kiBQZA5AqgWexljDISM81sc2opWwwNZPEwpdSEDDbgDl9dA2LbiP5v5NKSNMWAo3iYHhRsY4DoTclveKB67C7K2kPlg1JLQG/KBQzX12dEmRBj5uj6kQoQ+5oyXvrRVj3JhvoBqKdXgfNlC9obbYf940JHtNJ6Xwxy+jM43Rm42AbyClUMpjkUPkuR/G2G5kjFWSJNgP8yyZXrYXaLqVUGG/KfClFP7zlq4V2lkmrqtBHOj6levUNN7T+wsNFYJN6QNjR2993Vl3+vfoL5VSJEIkAXA8+U5vqIzobwBAfzly3UkwxkSsuyRp8xZfpUxhRsl9JPXM+4rQOq/FHi2T7/7BGP+9hMIUGdrN3obPA2Cc7ODGkYwmGoHOMw2cQTHVwajZRUbPUzA6Gw5r4DU7SVVLM2p2hV9NtHWhdxjkjTq2MBjQJbZQbIkdYrSzL3G4wUwfu3o9mYGrGzzdOzs0sm9kbeo7VzNUy0eFR6b2wKg3g7dHqtTHyuzZsK75Ef0fxurVq7nttttYtmwZZ5999rTbOQ1WavsDxlo4sLbYzmcqGzteR0aTkHZ9ioIdLftXHMXOcgcuRmj/lHNb6ytP0pqfadCh6EiUENztHsnm3CIkggqpsHxtqMOLSymERsk2EiZWmjYS3pskAjOGhNJ4TBIhRCyxQuFKM/Zd1BUYaJjp2exklJTYw6B9CBOZGn/IPcuYEXVqL2z7GSiLVO48EOlQb2xpBRDNukCBwHQEe8dh/o6nYKzME+2HA4TZ2MK33VqeP4SzFpzFD1b+gI3jG9lfBNWulNDBtRiu5ai7OrT3Q+f8/7MEvKmazbN7Dqzjt/17ru1j4BUCijUbqaUou11MOF5lwXq6u2lbhYqALJAzor5pT31p0/ZBSM3EEBZpvDZemx20fREeb1N1Iaby3hW7IRHKMgxGdI3tZjB59PbZHsuPUBjEfQezrs7CgWhwbivpLNobLG24DOY7wiOpeHUcI3oZ4gDdQOOpxREDPH80ppPUS1SCwiENPWM8i39BaTbVbN7bKrM9BBWaGwmLXOGyoT+N5jOnjoB+q49uuyM8TspJ9iW7dm1l556kLEhuW4klF4PQqIyYMUsr73+enVwU00sp/B2ECLIlvcOEwFj6Q1l0fHdiO8pJtrnd9+/yvrb9/Id0jDGWClkdT8h5pA8ISwZsLMQSJYGUVaL6TLO9VyBblMKzFpxZHqW9Oo5QIpywuLYVAkKFB+ydkY2eS2TcpcH//53Vw1s/lwOIQJ8eMMZSTwI8YY4hjEkK9vTa+b9ESqEaJgCGK9HjNQxikw+jkXhRiiM3emPLzPJ4+LEb2y9On9jAxnjdhJh2uFfkGMBCelONJpibIPLGt3slzAEjkOtoUW5TSylFE2M8yd96PM+BMRx+xHs4r6GkM2gtNXyvp8TPY1YwN6Q7eC8VVsXA8Ude9HF+O/dNvGr++3n5vH8BwAiKFQif7VOK3895A7+dG5UO/umbP8aHZ9/Ne5Y8xh19Z/KbtjpXdVisGpgCpdCkZMfc5NLflf2vZ3ibx+7snrmAt13wMa4++3XeHSQytiR3//wb/GbrsTgBE/LNpfDdYwEYu2Iduy59lN/95yepTP11Gu327du59tprmT9/Pq95zWtiYLY5nLgrhZQJl4pWoetJ3V0YSuFOeeDErTckd8SWec+Zey/vcSOLmcu2HsKVO5bjCB3X11eGjGdD/Cr11abPIimFYsTI8mXjhdw081z2ZPp54ISXYvngUDc8E/axVDTQSqGxqdRNHC1bHStDNk4Clp+Ao7tgPDAWk1LooSuFUIrZZuQnacWYr1O7lvKRVIoj0//CIfmPsqn6Ar5z0t38dMH3+Jdl/x1ul9JqGNUtoBUwcy8JQXdF7+Cx/lb6P8GM8Qy3jc7kgpv+wB3iOF5xzA94qu0wUsHz8+9dVSt8+qRPUzALfPqBT4cgcroQ/sAhlATH4tdbj+OqHb7Wc97JXgLecywf/ufEP/38MV72v/fvf0MIZSJVp1mbHoRC8a5fP4GSDpcO/4LLR34IwLql/0QzMyuJu1Ksmhs9/zumPjTtOVzdRBcWKd9VovjOaKANuotvbfsg2bL3G1lBOTz/O8tMcc7cObxufvAbedfw084O1qaC98hAaW640+zBMofvjaQ0F92t89VLvd8w/cAQK/NLvCMpLSEpkl3RO2wJI3wE9cpiPrP8veF3i+ZG95db9L8MZraH93P38qitD2aiKovHFs5D61+O0f40gwuu4o4Ob5VhxkTkX2ELh00z5rJwi3dfDoJfbP4Cl2/6Snic+bWklOJld72KVz72hsRnI5euZsj6X+pFne139DFwp9/vBoyxamCMp5NSBKBDEx4oCYFywDB6q1Khg0C1QuWeL1F9/GeJw6gbt7L1+s0tGWNcRfnWf6N8+6fD7ad8Ten7js9x4anRaoYElgxKRi8f5AWqcWLsr1r6vNOlt3+FD9/7BRYMCTI1Hxg7Vug0pABn9+NUH/gap+xdGwJZ8KQUq63ZvFf7aOvncgARsLPBYTWR7MMLB/8PhSVfoWBPP3FN238+Y9y4eqFLGa1GkmSPdd/aNZjWvXzrQ3zj3u9z0t419FajibiSTvhOuMD2rPcbXI/NZdWon4l3hS+oL+Peaif3cLL3zBsWQQJphZIKvrPcK2FODBijhSs0wTAigwmDEWuT/2CM/z6i2jWfTKan6XNPW6cll/7wzLNbaUYHYwPUhJlcErxAe5qOim8Dg+Yv5yuKhieR0FLNGtrdGU8vtjs4V82lvVRMbBOcUav6M/e8d7wd/fMAkhnfdpnhqgc47MYsU6VQdRcmXZSS1J+rTdb/gxgeHuaKK66gs7OTN73pTZhms5Y6HnHG2HVtnP0wxlHRIDe5pOnUQ0CsmpZYk6BjLs3G+hCTsiQqke37egJgrCEpxtg3W5gM9s4OT63ruebzIXxGJcYmyBphsg0CVw8s2YLr8ba1hBYC48bwgLG3XU+qwF4UBREMCNPfj5AlnNpD6KnDGPalGZ0qx1DB86lNsF7hf7wY6vXYz8FUb6Qx9oGxWy7Tk+3hs6d8lrVja/nZM8mBvDH0UJ4iUb62drDmy5DmnwSlAZjYvs9j/L+IlTsnAPbbJiEGjO34gJVsd0IIntg2jmwssww0/y4qZAoFkJFx+U8r9xAvpGZiEDDGKlF4KP5a1KQHgOpa8v2sG2ZCihO1RRjVA0mOEeleAdNyKOciyYYromOKGAunENNWgLQ0cx+TnYit1sxoIBZKsLMvtoqiR8/IBXIig2Z6lRh3pTzvViEjxtjxHQByFX/5vcW1pVu4UiTCsbCUl5Mg/WqApa1+MY0A56KSwNhofcywPo8voQoNj4KJspKJ5Dtpee+ZO7a56Vj1wWoEjFNxKUWL8/rbPdvR4OwB4JMK/Q3vgAjlTiRKyc8Zi9VpdZ3QNlkBsuzlh/SXxiI5Ad57MqzankP1uuYIGWyfMZ6ulHTBmn7imtlPTtFzCVNK4qmt8XsL+regtS0oem2zrzqRsHhT0gllwRJF2Wj97jRWs5tjzWA3Xn9sN+ziBq4XDRIZQwR5LQpokELVXU8aktKbk+/+oTH+2w7HbO34QJh00vzStUoskft4ObPKiWGBZqutVucIkrGCebhWdeloAKxWw3Jmow7PiXXOKqZBshu0d5SGUFJh1P069vWG4gj/H0exWOSyyy5D13UuvvhicrlmMNgYcdChHJv6fnReUTKITL7bVslb4m0RB9rdBktIjoiOY+wHGBtB6Vwk1dh+jeWltQAYN0gpVINDZcqSIUMkhZfEATHfzaBfElrClSIe9jRld5suIBauEkgc3NrjSHsXm+0CZVfRThYNjzFO9rHT6zuDbGyh6ZBKofwCH2ctOIvzFp3HT1b9hLWja6fdX4sxxsppGODmneT9///Qtq3u7B8Yu/41V+Je2g27CaHQtKgkeRAeDm1cAZFIEdk6xVcxWk3WgiFdail0X0ohNCehr40X8NR9bbjVAIxtPfpbQoJQiHbXE8AYkjpiaxr3HaVIFviIfVfXp59A23Kavl0J7FgXGNdaSsCWVYJhUcbsu4J+3wnAnY9aG0GEt+N+fns7sn5Urs88l5IaUtEgpZh2AtDAGKuQwQykE24CGCvLZx5bTjZUlHyXkFK0SFBvpBWjI8TuLfkeBuBOCtBjS+2dJUE41XNs4nJa4SeC6lImGGMlJVITLZPnDzQapRT6dMDYnh4YZ/8fAmNDSrQ4ME4wxsnzBN+5Qku85x5jHEgppvfVUg3SDEe4oc/0tIxxw8qsEeiXhRuNozEphUjpoMcY42BlrF78P1m9+0vieQ2M60LwIcrcGWuMac3mqMN2UJtZZqUe6cJqgd6sxQDjAjMyCxo+87a/IX8KPz/4IjblFvHoUSeyvX8OXXq8uo3b1HjrfgKS9DsnUXVoL3udaZc9ydfX/w/3nnhW8nz+4FH3wf6w6uLO3hfhCJ2dX/wKA72zue/Es7Ab2YzxbdgVGx2DtJbFrtU8K6GbPgEPfAse+SHfe3Ar//vAFgBu/dM6Hn0qWdLxzrWD/PrhbU3PZX9Rr9f57W9/S6VS4c1vfjNdXV373wmPMV6QH+fY7t1Iq4asTfDeJx/lojUr2TRU4r9ueDbBvLluwGC5HF6/AR7/Gdz4cahNJpn1+DnUdJMmsIXBnb0vAlPj88YvSWPhiiCTX3KJ8YuW+2047pU82ptCDxhjoajEJji6lqHD7Aln/P15L0M+3kBu7zuTUoP+PWUrRDh7B9kAjIPu0UZDa/CgDaLuF2UYTvXwfVXjJBGB0FM7fpTY9tCOE5mRWcBvt55HVStRmzmXunUnAniy4qIU5LRuZm1/kPk7bqO+5g9eEpDfac7LL2Vh4YhwKVGgSAXeowpENkdtxyhPfucpakN7+cTAOLrK8qE7Pow9je1aoDHWlIO0GpY+ZyyDdPv/aQKe1QCMn3zySZ591nOWGP7BD6g8+WRrKcXUQPJAAo5WGu4z3vFmm4Ky81JmGBHDH8TO7M2s7L+Zd93ikiuqxID5SP/DPJZJJ7bfUOxlR3c7bsAYqxJHt/0hZIwFwmeMvXMbuMxilLnHjrNxwazwOPqiiAEqaYJ6mLAHMquxYXGehwpdzJh4EZ2l4LYUZTNijOuaQfH4NzY9R4mGk4831uie67o57WJG/P2Vdntsd4OFc6Odbuq4l+z8H6NldmEJh/sW3kB6xi3+uQNwKcJVpy15b99x0+Ec51/Yesd8VEyDetVpgqdHr8Od2MFwW5atvZH2OIh1l76Pn864BoXCaQTw0yTfxX/q0qNe2fSf3LeZz1z7jPehJjz3iaCcs1+kokadsfoYrnSxB8oUbw+Y4mmAbZNdm2rpwazFcJWQkvYrdfQhYNdj2Nu9ctmLgNwZn/OK7BBnjBVaOZocCNcIVyZcp46sF8JbDgBcCi0JFB0XpSzev/IPLe/jQELzJ5sBMDZIAuMzVklOeVbyhk13THuM+db0dmbPNXSpEgBY1yNC621rvGsQoo7Z9XBMr62FVpcAynXDtuIJqxTd1Uk+sPJqzNh2I2oicW5XuGHCeSPUD1ZJhh7ZzOd6uymFzlfe564moUFedY0scXuv7dl2hlIKv1+WdqQ3/huN5zUwlrrkSVz2xErgnty7k8UzBlieW8XeWMOc8FtbKymFBM7ofxN6KkoECBrX5/rewY5cPzfPPJd7T3ohV77yHby0P2YNljoEN9fa6quc84FxxaXgM2if2vozLh64keXtyQHUblhq+03qdTzbtowN+YOZevgJfvuad/PosS/CCrK/g8zz6jh2zWvgeaMTu16DkY2ejdAdn4dbPsUl1iRftr3B7/CHhplz5ZbEud75qyf4z+vWtLyH6cJ1Xa666ioGBgZ4wxvewOzZsw94X8dVvH7+as6YuQW7XuPQbb+kd9MSDlo9n3/+1eP8/IGt7ByLwEZoOK8kL6l9G278GDz+U9h6H1K2ZqhVbXp2c33hEJ5tW8bhcyZ5g3Efr9IfDKUU3RS5wLin5X7XHX8CHzuxO0y+05BUY+zb3MLhZI3IhSSauUcD01B6JremexNcgACyeEy/BNwgua+hqVoi0hg3JhtaHetASa6d9UquEBa/T/9X+N0xuTsT2x7WcRILCssYqhextc3Y3TOpzJ7Boakq465ifU2SFgUWbL6VRVuuw9p4C8jI2eIFM17JSX0vJx3zATfjGuJclvraQWbuLTP8+y/R9+yVDPX+M3tre/jhqh+2fLZxxthtZIw1HeYeDzsfa7nv/xfRyBhff/31XHnllQCM/uSnTN10cwiM44yxuvnfE/ulpMk33Rxqlde+TsgbjDsfZmFaB5KrRreePMihO8uctUJx3L2pBDC+Z979vLM/qX29ZddSVs/rQ+omOhYmZY5v/33CYzbOAOrC5cvmzzm+Ywu1JamQHTJjwHhK01iXjny6rSUZds7N8s6TX8q9x7+W8x6PnstQIZKxWYbJ7LkvbXqOrtQpvbAVAx0w162ZpzjgVG70jguls+z46Lnc3Hk/Rn4ruQU/YiQ1wM6eSGIQB8bBmb+61PsdtqeW8cIVk8zeW8Pe8XD4vK56oc6n3zBF5f6v8vji2ayd05jYDR9IreQPPXcyYkwwIBtY74AAVqArhTmvjcKps0nNj/qF0v0eMfHlm9Zx57O+3MNPvguA8fa0t83TuV1M2BMMFAcY/vlqZM1fEWzJGIswaUpL+4VSpIqq4cUiXsRkwcBuCvfodP/UQD75c2orfg3Aa9K96B1zMGYd7R0zYIw1qNoR/HqsK0oql6VRzOIh4aNQPlubUlqCMdZdh60THZyz489/pwN2VoaMcRISvvcmyUeuk/QXy037BtFm/WWV7+JhSBmy6gCZnrvDfy+Y8qR8wpgiM+u68FlIkfQ+dlQ10hj7P/FF62/nZdse4YxdK8Ltxs3I018phYPHGCtUE2Ns+xPdyx/9NX9oK3B5h9cWlZ87JYVE6f76tt+uPt2u+NQRHeBWmxlj+JuXUzy/gbEPIFwie6o44Iiz/RFj3NwRB5NnM39O+NmBvi6ulkF2ntP6y2BZreqQrSfF96JBv2WlkmyQG9prgUqlQ53elOYPEkHxCLceHjNvdnqM8dSuA7z6Py+UUtxwww1s2rSJV7ziFRxyyCHPaf+4XVutXqUeW/oNEvHiyhI3JqVIhFVCTrPk6kxTAcsL/zfw/xKA4wNcc9oKeIRMsR6za6toyWXoeNSCjrqhyekNUgqAlPCBsRCh7WgqmQuFpiIpBSK5zCbdAl6h1eknBEFoQsfUmq0DZ5mS2YbDhrrElskVlFZCxXjVPDM2KIlsLmQUXN1j3KzcsdTyp/GL1b9g9cjq5msKExpdbKuFHGjeyTC45v/zjOjAGrCRMY6HchxkudyaMW60D3yOPXS4IKAgvZ+ExSACjbGGQgqBlJGVlhJRO9dxI2Ig9nvG2dmiFtlcagJvKTV+O0GfBjgx+Y5lNLcngLqbDp12gIQNpacxbn1PCSlF7Pr21bqretJ9QCYqoQWqbp8lE4IZ1QnvmGkPKNjxrqTFUnFknRVci4AmYOw/O+UZcKQXttN5/kEIXaP9LO+dcktx7XjwD4HQImAbLjsJiS0cNNdfYQhYw2l021HynX9kqZC1ZimB4cbKO/syGGGL1u3VH2uCinRKJEs160qE/Y4ro14h/gR15ZCKSR106bIr5ur054QeJPn5CNKcRgLTssR38J21//7yQMOQKpl856SbtgnOFiYOCkEqXi0vJqWQymunUykPtPZVxmNHij1dJdHQcHw5RBMw1oIV16htQjSeOShUi3wYCHTvDYwx/M0n4D1vgbFSCum3eIdmz1WBIlYbJ/SimE5K0Rh2qx67RalLF0F1Go/KYKYlpmz0QDsWHDau95EK20y+REEteoFCpTNoUoJSTGk5pJS4aEgJyq6H11UwOqjXqqixrXguPck3pHWpz+euFbrvvvtYsWIFp59+Oscdd9xz3t+J6aDr9Rq1wHNYKUy7DEqipPTLpSqk4/jgP/qlJEC9GLOtEUjp+Y26jh0msLUKLTZbb4yUmB4YmzIAxkHynaIS0xg37mn51x9vSgJJyktLSWybFoHtXDQpylgBgPf/r0QIjKUQifYtlQ4q0vE1nyEKBRgi5V9bbCIpBAenquQ1GK+9IuGHi3QRROd0Yw4KEGmMPfo7h3RqSCVxzWgputR1MT3ZHj79wKepu0nwq4VSChe7FYsz70Tvync97l2rUshG/2Ap968P3U/ofpuYTveulALHQVYqodNGAhg36DL3VX2z5fETwDimMW74rcLt8Qp86Fge46g12OyJaHlXx4muJ3YoR0VVQqfiwLhFHxIAII+ZisCVpU8HjFPYRiRTqrsxi0HNaEoICq8pAYzjk1wRewzx31qjpiUBoETiKukDJ4FQCsO18LhkQU8wyfKvvWoQPeMWz7qGZ0UWfGNjo9zkBFwpUFYJTUoMSdKqTve05qrm4jjBqlN4+d5k1wqSLwNg7GJrHjAWmkDJuIwvun83SJgMjuszxtKRWBPjsX2CPkWgfLekULbmQMsKwj5wDnzLlVAYTvR84t2oa9fD5xN/gpp06ZAx+YWU2BT4SyJwlJCuQCgZyrEaI7WP+aVZ/38IjF3pvYdBOM1gM5jvRBpjPeEHL2PlsF3haeBHs14fGiTsgff8wlAuGZmiLg0UCrthXKsHeQuBREoFq+fe/10UiAwtQwCuL8eJyyf+wRj/bYZSytPGAJdi8dDBZ2F3REt753AfvURSiuI+GePmz1oxxpnb9kDN5cRzomUxKXRqKdFkCu8d2D9n2eH+4nyMNeMYuzyRnogB48ztexIDxMrZR7Mmu9DbTimcdArzziGMZyeokOXSX/2Klxf/g8OrP+fH374Cw4f9ebOT6657hgeK3+bt2sdYXL880b8vuiVaignjdxe1uNPpY8WKFdx9990cddRRnHHGGc9pXwDXraLteXH4t1UvUveb8VEjv+KHl3+SD3MHpVNPYOhrX4ffvIajB3/CRw57gHiBj6MWzWekMoLtS0sUAlcp/umX78H64qLQSzgZQcfg/VYVn32P//r7YoyNEGBErhQlzQg75FwDA3LjRA1jfdSBHCm2sDVzMcv0DQ1nhZTf4iRwf8YbqLMNjfCwHe1oPkhwDCNRxERqkkXDk/zhhs+Ss6v8W18PRy2a3/I+htKfpaSlOXz3CKdu9JZsJ/M2FxzxaVAWx+V0XJVn/ZI3RYOcchnXMnx/0Xv4ZWYDr1j6fnTd0+cpBEdNBuWoBSqbZbS6nSu3f52Ht0SdudLyfP6Ij7JlcgvfX/H95P1Lf8m1Popjt2CM5x7v0Rd+At6KW67nWxe+imox1kH/+pXwpVnN+z6HCFYq4lKKpBOKv5xcLofJd3FgXAlBiL+KY1TJaA9R0K/lhW0/o8f8IjNS75/2/EGynJARMHaB3/+3y4evS/Yx0kxROvRYahlvQBbCnzDFwOwH+C2bsm8DoFB2maP7YDDuDKP0cCApalrI3g9PLuOD9/83dcf/bWsuvzjMKzdbPVGRVt59C01RM5vZMYCqm+GS+Z8DYGvmYr7q/jL8rmjkSK2eaLmfHWNi40Ug8kZX5LohYkBIaVT1BmAsJC8SJb7x1NsRynuG1//btzht/kbm5aAnKIPrT7A6avCRP0rENGB97NYPULruPXT5Te6Smb/i6VxytczevZ7STR/ltFt+gange/dupuJXAXRHByj96b3Yux6jNOH12XqwMqh5bG2QfBcx6xJHuGhSgK6FjLGqTVK6zitHviK3jlcsfT+bidm1pTQst8b3PnohP3vs12xdtNA/XNS/WWuv485/fTOXfeFjAEyV27n2Da9ld6MsznccCRhjKSAbA5S/nbOYUsrrS92ak2CMLX9sqilJBxEwtquwQh7d8jkfaAQMsbQFN133Sf7p4Z0tt0vtI7fbtATl1k33uV+PKJHpfDD8uxVjHH4XJjIK0jHGWMttj2mMPfbX9VdmXrjnafK+w8ZJekz6KCWzRw+jqHVQos73DmlI7A+L3nh/PzXqeSEGq0cTshMlvH1ufjYp8RQaVJ8ZYfd/PICsxQak+v+3K3d/aTxvgbEQkCtHhQM2uH0eMI5NlvqJPC6nDkBKEY/p4JFWSk4/XaExkddaL3s22qPsii31NVyGqEVXsanj4OhzFFZWD/evaGl2bN/OOjWfqp6lXLEJmkHB6MCQU1iixr1Vj8l1VPRy1rMtwOL6m6a50+bYtGkT119/PYsXL+b888+fJjN632E7k8RyJalbJao+wJux50kAeic9u6Xxyy6DLXcDASsRHwgVe2rDITAGr4qesbuDrFakVcVdXSTbgO3LIIK7mIPg9f6/K6qNxghM2o2YtdiYZjLDH2xMo9Bk12VsL4UOEidqXkLcstRaGvncuJQiKOAUAOPgiHNGcqHGWAotzEIGjx2bP+qN2F21IjcXkt6k8ThFf5ZJLcWC0QhUOoZXXMJRNl2GRlfqcYZmHs/AzBP9g7hM6R6rcGvOe++q8mnAS2ZMxc+SyaI7jnffspZo6qdkD+N1S17HpWsuZeXQyvDzoPhEtjrk6eQbI90GM48IE/BW3307AFMjMRu+bfcnq+b9GaGFjHFMahDLXFcxYJytV1hWr9O39UF48H/hxo+TmlrLzNS/MjvtJaId3bWT3tSX6TR/zmHZu9DFEK6Kl+8OwmdiWzDGgUf1C9Ym25ZMZUDTsDLSy8gXCqkRs2sTLErtDbdvm4i3hKSUInCFivth12szqTo5yj7zpZVjchkUlp9DoWcVw5nOFvcEtpu0ZDtLf6rldo0RZ4xFDABrQrQExkrp1BqlFH7Le6btYPSYPUfhLp1cf42c47eVGFg8ZZ3yWb0WjLGPN9qL3rG2tG9hbWFh8pxTXt/ljngFMhwUw0XvPM6oBzrsrfcwNe618XAAF8KrohmU4g1t+zwphZA+eG5IXh3uS/NAu0d4bNQ2ecDY0EDXqEvveaTGh9m6aJF/gdH+1pa7EscypYMQil1z5yY+F6b33gfljqVQ05ZStmvRmqwLOH5faShJp4qAsSHdhOb4Lwmr6LWVuRN/3rv/5MGCK0977lDqv96kccuxUbvSZTLhcl+OG4GtmxQCMwaMdWmH74uL8hxAYitj7Zb3DA92Ywn0ymXUmABgimpT060GkiL/Uqv+RDcAxiXaUKFdW8POscciq7Hx92+cMZ4+9f7/z0MIjVR9gGA1RiGadFdxvWVpH64UrUCwNd0SaIPliSsEE3kdUzUDY+EqVEZPgN7YxSW3rbqonGegEu8wPGActc6SlmuxyufbwxmdGHrSL7km/7LlqiD27t3LlVdeSV9fH2984xsxjD+v6SnpElcrWLUKjkzTbWzHFDVcUmGWrmjwQ06LCDBllULYlYRdkyNVmCDWqka8RlCMuTWg70LjEL99rHKP5hQjWeihkTEOOpbZymUvYEmHujX9bx3IfRSN9bsgFTOnD37fbDBu++3aMiXCv18lRANjrEJQFZ/8KVpbUmV9CYhqeGcC3WyXsQY11MOGJRfQMbmFvIrOVna9gVLiDbyOZiCIBiWVzWLIwAnBxo7JTXAcPnHCJ3h4z8N85sHPcNX5V5E1siHrbtiTOG5MwhGP+SfDisvBddACf113H3TQnxGBlMKyXSgNw8QOrKEoWVVd8WYA5LYn+ekqnw3f47Og6XY0bGzVT00up824nvXFfjpTn8JRM7lxMsXLOlpLDjRjPrAtGliVCH2Mp5NBKsO3CtNAd70McqnFgGNDR1GoRH2UkCocKB1peFJilfT1NaT3O7gtfH0FKrQ4NPMug7nO1heJaOozDyQSUoqYTZzUnMh1o4ExbgLGsf10N7ovYYEwFNkQGDdY6SlaaurrfncUFOBBNcNn5TQQJ0C57rPAGf+9qU6EjHFCSqEJCFwlRCSjCjXGDVIKgIHeNPUJr52kVApluwhTQ2iel793UTK0klSujUhJT3PTYPFoSBchZFPfpHz3kbjGeDqJkF1NlvYOkvx06dIeY4xN9f8OGNen/jIYJEzFvUdqvPGB57bfVE6wYQ6c+5R3x4ab1DNr+1h9DL7TlCIV68MMaSUYY0kSDwSex/FXe4u5I2xHEpoa5WT4nkQSKYjGLwcVypX0xmuOS4EcG4yMJ6n4h8b4bzOklIx1RvpFL51JJBpFOcYm3uLP0FoB41EU92In4JINbG7FJTd08tvmdDCalnRuGmreViqU3jiq+X83DFr6Tt8HSdMTM0SUYmNPxDD9tvflTMUyH9bNOAzQEFToSa0mpUlWDR/OOdrjHGXZPJ56PcePrqarPBGe80kcdk9M7+3YGBMTE1x++eVkTJ03v+hQMn4Hb1kWa9bElnTGt8M+rHEAHhobQcQZ48oUWyYclmVvDx+J6QTlhZPPriMVdaztUlIf3Et7ZpysNgECqiuH0JWGq4yWuSlaupu+zLyoVHNDpMxJ5upelvTWBsNziBLM9DBRzLvgef5A7SCx6s1tRhuts0Ts4h3Gzd7fmonlt8OHM2l+3d7GVNu2cPvgObQF7jj+zehmJhzspKYlgLGjRTrPnpiVjwPUleC01cklYuGz5WNZk11WIfTHflyvMSqKpPUjWLb2VwgleXbZ23DKw+TLI8wQRYp+IRAbrw3trHWimOJa8Qx79SolC3Q/YXKe5mDHij+o4iD5bQ/xX6f+F9untvOdp77j3ZsPjE2rmGSMS7EytnNORFl1HrvxIYT/G4Y6YyvuJeyyaucE6weSk8REKAWlIdj1BKy+xrM2vOEj/EB8hdtTn+DEKw6Hrx8MPzsT609RdS41vtt//m3c2HUS/9Y7l8tO/xf4t+3w7zsZcxby5PhrmLDfzebJ4yjtzmCrg1AUwJ1ouoyHjBwPlBbRnV1Kp9OG7sAzBy3AFgYjziTrTRNLtkLGis6FYwjhInWBFjDGAjK2w4u1lU2rF7lyTJOajiZijjKYNSY47zEd14natWlnw+9bRZDso+cUQ+lOHsSGFu0f58CBcZYa52mPhsl3wphAz0bJxI5Rouavgh0jonLtmllkdfc96DGR7OOFDZidj4BWoVKPsd22wNYcjECPr1wv6Sn4HrA7gL4Mu2YtCD+fyKUppU1c/x00lEHJrLJmvvf7FJ/czZ3ZFQnvaBeo3n8fztgY+Mygqk1QnfJAeZghUZuE6mjsGvzl7zGb0co6RK2K0KBEcgk7W9lJXdRZvDtP965hBrZvQRq+LAPJeHs3u2bOZ7a5AITuMc6qir37CYTQmOhuY8Mh/f79eIxxI7ByMt7qUzrl/Q5SU5CKZAsv2rWCglXh5L2rGR/pS+w+GEyepEt7TGPcUS5x/OA6/tKY7JG4tX0lWvv3sA+k5BqKlq/Y/o6pR57zAIsH4RWPRe+Y1iJfJThNwS/fvnzqcWZPRL+p6dohp/O4thOl70gA4yPS9zPD2JaADxYWGrDZ7aaq0miDVTrqRY7Z5O23GZtVOAg0zpg8McxTKVDlXO0xHBGRNinsRL8RnzupugsF3xmnNsXIriLDO/bRx/4V43kLjF1bMtAXaaFUPP3aj0kVMU/PBCxfsEQV2/ZqLD5NFXMyelFtFG+NzXCDEA3A+NYje9k4VMLY3qLinFTJGZd3pcEFx/9CH6x5GcSZbJiIBDAwcy7fO+788O/Nu/r4g31K+Pft+TM8lwFxE33p/6ajchS/XfEmLhHXcXalnYteeBFP9BzBeL4zbCwfosLZ37wXgD20WtaNolqtcvnll2PbNm9uf4z2G/45vPabbrqJq666it27/WWdR34Il78RigMtjyWV4tPrNycY4z3bB9DLKeakV6J8diHtuxIIKzkIFPTo9yhIyZZymUNnr+T8ri8Cgvq1mzm6fCiPl96YLKntxz39r+DM/otCzVZjfNX4Aoca13j3TbM+zJSulyDoz6rT/v/7/c5fIrCt5omXPlDlS+bPmSs8Te5EJs2Y8LK3390/k6/1dDHeFi1512WSVaynPFnHsdnTCGrRKSESUgqQFHVvdeC7+veifYVgcmuOD14vOe+J6JnUNIWla2zsLdC+ez0rqp7l4NdyHVybfoxr5eFk6uMcuuEKptoXsXb3BgaWdPOy9DqCVquU1+afnJrHwOhOvnT2C3jvcRlGtzgYPghfrruJSmv6XR+Ay1/PiTOO5cLDLuTytZfz+MDjoUeqYRdx4q4UMQ38gH0YU86FzH5I0OZXeZJBktzEjtgDm+LV37+fi799nWfx9szVcP834foPw29eC9893tMif30J/OwlcPU7PGvDNdfSyySb1Gx2Ln4TnPtVeNMVWG/4XXTsnPe+SNfkqL2f5YzRj7Mj3wnZTu9ZjC7i0eEbmLCGGXkgRW5lHeUzk275Ghrjmj3LWDHxKUrGMr607UN0lbvYWTDY0jOPH7Xv5vVz+7GbqjrCQWnJkpOfZfHiJ3F12KVLL4NcE7x222ouTf1PM/sjRAi4uk6KlmKl0vnUVYK331mn56lM2GU5PmNclS0YfAG2D4y1jMDVdP6NKulHmitMihYOH6qpX/Tii8al/DD1HRa43nHyi7+Z+N4xK/xOXQzA9zLfTnznanaiXx83Rsn0/5H8Qd9ocibYG8s/QTq48ZLwCu5YuJivve4zXPHqd4Uf7+yay32HzWebn8qiKY3b5tzGF97svYdX3PYrvnHoo9xxdLR6qZw6uc99gp3venckg5AO9aJ3vrAn2nADYm8sB8Tvv37eqfHdQ+9GVKZA1lmdShbJOX7nLynsKXP6ql56brkZfvAhdjkOCIFUkp9d9FF+f/7bOSlzMqnDzkdJm+pTl1F7/Ccoq8Rn35bmNyd6pI4pZUvG2EnnUFoNU/eAuxKQSUU2a5964nJ+e/MX+Nyjl6JPpBN73+GvLBrSoY1o8nr+Iw/y1rW3JM4zMfO5r/48uGj/oBigsg8dsTRgf8UOW4WrNe/XHYMBjfZxEC1kHiK8/uplGzbQUauH16fHVgTyi79Nqu9bCWA82PskLPppIo8g6xpsIMP99kH8xulDH63zlQd/zL9fJdFdxdWyxiVUOap8KJ/c8zaOwZNZvlx/mB+lvo0mSkh/ZpDGDlctEhcMXpnovG/NV5/i95c8zpVffvzAHtb/cTxvpRRm2qAaW2r3lseTnW2r5Z4ww7/Fd+mJ1bDAYwimTWRtLBWrFEgwW80OJbQY0xJRP3s2+u4K5poJRN31SgbHeveamWGgdzbpmF5atABfmv99RtQwyGC1GMz02C2X/SX/esxLVUqvSlcQjuPwu9/9jtHRUd7yxtcw8/df8xLgRjZA36GMj3uJRlbgImCVvO9XXg4v/FjT+WtSkaKeWNs3HBtLM8loZcaUp2VM+a4VQkv+RnqMajYUZITHWHYbEShqlyZjznyEeLrp/IP+rzodY7xERWChSvOSt6EcMtJCF4opsrSLKmksOnx22UVQn8YVYbaI2CBHKAxchqaxlLOkSVxPLQK7JD0dMsZKCOpVE4KfWUTZ8j2ySFB3sSYEjl82u7sUPc+xdJl5fjvrtCcpBX7QehWlYMxfNp05/BSje5exbcE5mNbT2KmpUNspfcbY1XT22p0ADHZ14+ppdKUQSiGwE5XWRHG79w+3zoeP/TAP7H6Azz74Wa5EkQJMp4RLTN898Ez4z4qYiVBzvGchTPJGHX3gKXDWJlcqfvJi1qV3kxY2/Dz2YHM90DEPZiyFQ86BzgXQOR8653mfZ9p5yyW3M1Kz+N6Rx7BwuTfxru+I2pca9nxype9LPtvq40EnkloMV71nXHWLZKv+OyttII0umrvrjpKJyHpJm3OsmaR9/V/C8SFGSw3V/xv4QThty2SLKM2lplyE7iVG9frsudFQqa4eW3lqN+MaY42M70urV6JiGBW/DymqQI4VY5JQuOheeeDYyk5L2ZijiFfuUykNZ2EBc0Pzcuwcf/KY9YGqiJV89q5AsVV5elmnkQkBlqqFPCM2Jz7TjDJGw2XF+1DLqaDJCLR5qpLpO+664T0HXelhSWoJlDRv4j7QJULyQPM9rmvr1tF2dnQRdilYwfRjaicQET2iaWJvItwqUjY8D2mj+R8Ftng1XSB0z5cpHlq2E1wbd2x7+NlgeoLe+EqSaO6/3FQWIezQok0Jmp6nGZA50k5Ukg10ySlp06amX6W86lTB3CMqnPLj6N239H0nzgHs7vE1QPuJcgba/dN//J06+cGT+cINXqKcAL42NAx07/c48XA1jzWeLvYlpWgEBpUU5Oqgy+Z94qW0U7aiariJ9KVhfYTt0nsnpvwWtcgvNGQ6MIZDCkWH673HGd+B4mjNyxWRejlUE6WwkdXY6kmsPSgbyOYg1fYPjfHfctTMyGJEBhrjWHtrBX4DXU0rScVwtj3893TAWDQsCwqpwBSkqtPsMQ0zgutrQoVAZf1lw4qLTGsJNqWeyjZZ0bU+jceAZPQ6qRYDMHjek40OHPFnZEtJWguWqCV//OMf2b59O6997WtZ5G6KXCG2Pwh9hzYn3wV2Lk/9Gk79SNKMGKhJSQorwRinbImVhrQoo1Qb4JKq+1KKBmAcZ4ENFDmCjtZnZ1CY1KjLAprR/NsbwgHVDIxbFX2pqVbA2CXnuyfspZd2dtJOmTbRg5f+JrCnyWhPx1qUUJIUNlumKWneWPZbF56FluXWEHoEjEtjGZgTHNRFhTrCaF9LEwSOUnHWbNSMd2wK6XPRQqtSVwbZWGd8yKarmOw8GHfiMMZ7nwyBsfIzzqWuMyiid8f1Kz/qUiJVDSsmpfBKKSmwa+TyPVxy6iW87Za38e3uNv5zdAzDKSFVDYFO3rC8m3n6SpjYTs/6daSMlaT4FeeIIfQlLtzXokBA12J+OXwEu1Uv//XWl3ngt2MepPevtw+T72LJRVbMPs61HUBH2bZXvlWHil85TUqXMb8AQ9UtkQ503E4dkSpgiuY25VUyDBh4DSOQAsTerWS1S7+t+wyPEBIpHKRro+kKGes74g4rOi6l7q7wb+Wo8BSOMkIg5moRqxwk3ZWlD1YaXhNHGCBA7cMaETzG2IzL0qRqygcJIpAptFjw8S9BMaU60ai01M4L1RqpGA3XblajD4ruKAUZ08i3PnV0Dv//cdlGLe35+UKSnTSCcsSumyixLH29d+hKMbkTiAq4NAFUV4BTRjUAY1w3tFAMrquuA5rHGDeFtFH15OqmHvtpWkkpMDOg2yEYlqL5eUbX4yR2DxKWM45Fm6q03gcPtFYbVvL2B4oBqq0l+00R/00cHVIxs19dKYxphun9xb4kGEaLvKMgGvOEqmmgCFqLJP64DZ3p/9ONl33HYVJ572JeKKZi36Ucr3evAsKXJ1nYMLqZ2ZqfKKpVwzEjLZLAON4YlK28wmKZ9n9ojP9Wo1QuYcUsgrzl8f237iAxqZU7xX1LF4f/HqH1S2xsLmI+GSsj6Svk0/uYHb5Zv4MvGz9NXoflhqBZ+dndqSdGKK6qsXlGZAG0szCHVPx8LUKiMITH3qap803zh/yiegw3mlsT2/33+m+hmX8K/77mTxu4o/vk8G83BuruvPNOVq9ezUte8hKWL18OG2/nl+oVfMZ9F2z3KkWVfd3e3l+/k0t/+h2e3OxLKMa3wS3/Bvf+T+L8dSlJU09ojNvtKivtfpbUf8Vli1/JXUe/hFEMdh+do+245G+gx0ZLU8Fcn41RQnF5r0nJeZixVJFdWTvBuNR8aYKGw7mFKjvakjZmrSzaSLWdMgAAYcpJREFUai0YY9O2yLkeGNwrPHbhXcZNnJD+KRrwS81mYncZBZwo1vJD81vhBCwOjJVwSWOzO5bAmLY0XnPPbBbvzoMSbJrRycaZXf51e+3k8d4sdx7jy2qERnk8WhV4rX4fncKXNugZPn2Fy/v/5HqMsd9LxAfAcWMKRxPMyx/Ga2a9mReICd5z/3I+ueURNMtBpnaR6baYc+oYhqxx+LO/pHvRwyxdtJq3+0ttyp+YdIyv4KHZx/KyZx5Gdxym2r1l8NWFI7hiscvQHDhxxSP84dH3Ux5IsfuhTm9wntzF8qEhXrfT4Kr2Ag9lMhRqAyyuPMmHDnuQf1nymOcL/Id3wV2XUBi8GyVcbHUwE+7LuGvgUHYc8zl4z0PJH+pFn0RDkafGjXdfy+9+uYeH7nm06fecvG0bk7dtg9s/5yX1KcWXS7/l65UNdA5F21fHIiDhxP2NnTrIbGjXNrhlE64PRmpOCen/5gEgMrVoIh+EoXRmGhon573p2YXZ9/HGhZ+kLTObw8cO5+WbzuSK9Ou4/X+WMvUCrzWYtuKffy5pv0Kjb3yIx5elmWmciS1M/s19R3jsOBhNYbPD6GBEeisJN1XeGvaArtSp+r63mlDM87Ww1aoPjP2VHJTiDfo9/LtxOZs52CuiIwSO8PrhM7Wn+IWZfOcBjK0ljPgKiFItRy5hjvDBxVO8bvYsao6B0dZcCKai19HGvkFu8df5XXuzc0x578ymz6CZ4ZyUUd9YWP8A1U23h383JqQG7PFjS8dYs2gqZAm12E1sXOpS1Lx2UsoQAn8zZuXn1KM+oDxR40M7d3Nx0R/DyoMEIOSmzgf4bnmCTbXo+NYd32C0+gg99UOppTvDz9e6mzh0/i6uP3UvxYxEGim0wfXsfOcFVFdf1fAUhNcW/UINEg3dVYkJ82VzNnDdCV4F2K3p3Vx88L/z46W7EcIJn2Erxjg8g+tlT5y39WE+sPLqkDHOuBaFfQBjXUaV3p5L1A8YGMeApAAtJjrWFZjTzcT2EbpMaowbI9U4icFLfHv1Q5Lc7mQOSwDwNSk5Z9ujfOSp34ffZdqjvujEDYpPXO1yYy6a5M+YXMCkT/YcNLGLr90fWWH+x5VumLxqK4PHjU3s6O5m5OrLAHj6wV767CuwXItdx34Ds20nxe17+Kcbfs3F132Y+3fezrai9x66tuK6J2z21rtxixan58ocJDYdyKP6P4/nLTAu5AvUzQgY2JoOeh6hRz6mje+ZThwYt8g6jhHwI9r0ZST1kZgG0qukQWo6jlkpvmT+gouMu5PHcNwoDy+jR44CJQetGLPUKelopX0syQCTGhhiAoCsVuZ4bQM/z7yMZ/NJT9cLRq5jnh4B9IGbdvG9eZGGM6g699hjj/Hggw9y/PHHc9ppp3lFEzbezhfqF3GZfYbHGCtFyQfG/WqQ1PZ7mCwWUX1LIdMJa66FVb9LnL/mKswGxthwFCtdzx7oN4tOpaa2sSWXpWdGmRmzkr+BHmNRTKXI+r+XUrBNwHjtZobMCUZzU2ixbUvKAwMmLqWSzS5zTuK4xgEC446xcfKuN9AN+MD43caNHGLcSSIXO6XxKv0hztMfpz3nDULpmDO2EhZpbCZijLoCOiomaUtDaLChv4eNs7xzCJ9J+dHxS9ncOxNXeHKbuhW11+P1dczyGYA/ae0ctU1x+hqFJQSuPwGLD4ATxiSOLpifX0pXqoc55h6G5y5hYPEiUmkY6LuO3AyL9nk1UgWX9uJ2+o/7LT0LnkArzUQogfLvacnOKm10MH9skP6pUayM137H8rM4eNFqJuc6/HLyEl5Qe4ZU3qZveRG+ewx863CMay7g39wtLLRs/rOvmxKSKTfNE6NzuH3vwSB0Ki++md21q9mWvZIn+Txj9r9Rdf6FEet0JtuPgLwv+gwY89ok7zZu5FPm7/jUjNdw2niK+Xc3/57Fu3ZSvGsnrLgMNt4KpUHWj1/MoHUUncNPhttNPRvJmGKVcFFODaWMEBjveGaV95yFQdEZRwYN3dcYt6o2aCqTkwsZZpoaroJOsxchBId3vojDJg8jo3dx9fLXcnhhFaWLLRQaCwdh3m4wxjWcxZJD5GruWzqXmmlgPhKRBXoMjJo4rJUdOH7izWBHD0EHJB2DsTYPtHeooAANVAJgHDjbSMVLtBW8Tb+NATXb8woX4PgrAr9IfZ0z9ZWJth4/f/jcJC0Z41TPfTgabEinqKcmyc69LPasfc9xczeGuxNhTnJXrlku1pCWED2Lhu6+0SM8uymaXGUaVpSUD/yfXVTk8aXj4ZtuxJwzrjxdY0r3gHElQ1hoxoiVCy+PR/3Z+FiVVNvB8RI9odzk+q57GJSSp6vRdajyCGlrFdk9Q2TqE+HnxVc4PDO/xmiHxROHOri5Lsyh9dg7t+GMRXam4XFiS+Aakt7Jhmfj7uDpOdtRSvF0bgOj5iQPFH4HwkaPESf6dGyuU0cCH1x1DS/b9kiYSJ5xLDJhma1kPLUwzZPL5/KOiSn+8MqoFsF3XzE9vPnOKzW+eKFGrTlHumVMxWptuBo8OWdBiABm2i5GrP/+3/M1fvlSjf/4J50nDm6N1vuOnGKga9/AOB1z6gnCcOGie5uxRwDcDWnz4ZVXcfaOSLtr6NFvdsbTihM2KvK16HpTToopP0H2zU9dwxGj0aRv8QAEE666NFhlbMes1XCG7sZxNcydKd5/8yC3du6h3PsMavYK1j60mfMeuosXrB7kPrWCHWVP116uKTbtsdlaLFAb76Er1cmSQkQm/i3F8xYYAzhGjIFRIEQK3YhATyMTeAGpEBC3tm2LHudkbDliXn36JZEAGE9bGGKaiaghI8YYIXAXxZZ5Y8u4olWWd0PsNcDUvJcn0N0mrm8fEX/tHVeybt06br75Zg455BDOO+88Ty4xsArKMdeNqd0wsSORvZoSDhksVKYDjnoTlEeakvBqPmMcX2HSVesmnNabn6ehRc/FUIpGJZEeQ9xx2UXRH9hbAWCAVAv3kWrM/zmIPiZDxnhA60181x1L1FQpjSM0r3MqLPC8SNOxa9OVxBCSSmzZW/qyEV2KRLKFglCyEvgh140Unno3GjhtRbik2lWO7r1OjDG2cuExpsxJXA20aRIRhaqFUhYzn3xuhamDObi4BICTxh9JfPfvm3+C4zPhl+lfCT/vEcXwhqojKdTS18Mrvs1U//vZfUMPXxoZZVjX+UZPJ+uKM3lgeBFPT/SDcpHp+SgyuIpE4Za0lvOqKAa2Wy/4IABWNWJ4W1U3bIrKCDiWp533w43Z7jmx9hEnjIOkuoq/XL5j9SoyRoG80clobXd4bhUC4+Y2ZWCEv2/8VY3rkZOKopgTSa/3RV6V6JZjbHNn8ub1EfMZr+IYrGiFMgAVTcwd2wjlE0rzGFOpBGXft7oSJDFLyFEjLWyWyF2ebZoQKH+CV/XlR+0tkpYTUgrlnacxlBMxwHpDt6Vq83Frs/zdBXq9j7FWGv1pHDQaGc60tY9+sZExFg2/m/+1K9zQH1kKQmBsGyL0C07F3FJqE9G/O1UghQlOGZhzeZIwgHLDEJVSddqnttMYpn8MW1eQKqAcX/cs4rDbO7KsJFcflZimZLKSlGIFU4QWMcaaIkyubQzhJn3LzRhjnJlGY/ybExdyZHkeBaVYc1iGij9/3NU7/bv74OEaqxdq1MwDeL+BicjWHSnAFAZXvNhrhEvqdgIYP3CExs0naGyaI7j3yNbH7z285CU4TieVJLL3jIc5DUyopoN9YoNjUKGuxe+Tj2FuVzoUg0TYxmqgECY2B8RfCos+9xmerh8ebnNLwXMccbu3QmWCvok6+RpodZeSMwGA7b8zJTuNVfTGv/oBlq3/v47nLTCW0sXVY4yB8myppmJ9mJkYGCx6KMaAcfOL7cT0aXHiwdyHREObsBBSJUsJH8C7aionsV1cpxfP4m7UNLeKHSkH0y+Hampe59vPKAUqdFYnpt1PALmaRY4anRTZvmMHV199NbNnz+b1r389ejDwbLy9+aZ2PBwO5AqBrmwm3BkU7TQc+1ZAgV2GehElJVMjw9RtSZdTSwi4TbuZXapqGdJa8wtuxHxLsw5odgNdoDk4KZ8djW07SgcAKR8c5KglPTUPkDHutcfJ+YzxHq0n8d184U0cepjkUGMnh/lZxwc7u+gU4wl22PSz0+OaOtdHP5oSXueW1dBMl7qhY5p1NM3B9BM/64aJEILh7EHh/o7SqGiCmqHTVQSR66XWOZO1tWxoRKhLDSUEe3WdKX0KqQkESXDR6ZQwHJBaiUoqzbCTZ/f8GdQNnXTdZUx1oYCTt7wG4Wq8ef13Evs7NREC4x/VIjeVmm+dOPBUBxvWLGFV1/nc1X02VuZQSm6K5XWLt09OcW2hwLOFAprP2rlKxylWUE4NqzyIKSwsbRfKqZM38tj1epjtb6c6qToGGyfHqcsclswwqzyCKz1Q7hYtVAtP3ZoseG4iIxtwqFPWBxm3fQu+yTql2GBluyBSbaAZ4NToQaNYtbHrdXavW0NbupusnqXkTIRL8rLmyZwCKUWpHk0YswnS07s2WZ3wtcdetFUixsgWLrN0Hfo1ZDaDUe2ktzhK99gqBrZ00W1F9knxFZbGFS2jbmPi0Msk0jYhncY2ctiaB5ZqTibUrG+rLICqg5CKrF+I5sj6Jmzb0zW24SWhBgmrM8U4XSQ1iPF3TEACfPYzCiiUG5EDmpZk2xQQeK1KujCdTMJzOYiOaVbuGoFxI2Mcj5TSIw9gIO2kaSv3NG2nCQvDR/hmHaYMr0/RpUT5cpSZE15Cr8hqDE14oNTtVPTZZVJiCsOvRLgmu5DRhnrMg7WI5hxtA61WJVvzjmEZiqGOFHucHIZPAlim5vUd/kSsFsthsIwqlYLkWWcLNT0a/Uy3tSxCKolUnsVjZ0nRVZsi7T9aXU7PGGuOlSiLHkkpbFSrwj0Abhu9wnsOFnbo9HAgThEHKqUYb4uTEDBblkJ9cEoqDG3/42yr2JcNXGe5eVxJTQeM/fvoLkfjUtBGWwHjuSPR9Y4rGymEZ23YAhibPngtI7AsySx3FB2HJ2pHh/ewLreHCUfgFEZRUxvQlQe+XU1RcaZQSmH7N1uydGoVj4DUtQOk7P+P43kLjJVSuEbUkSrgt+IINsY6xjhY/YX5NT6WeXuYaNUq4SrOGI/HgGBPenqvF2NHGVGXyYEnPotMvG+x5DHXTloWxf2On4PvJ8DtMyMYX2eECb3AA+kPsjrzz6x74tXT7ndQWuPYlS7/Y/6Ey1Jf4cqrrqOtrY0LL7yQVCrW42y8Dbc/ml2S6YDtD4aMsYvOD93X8z75bn56t4SZyyB4YYoDbHj0QX7+wX9GXb2C920peenGfrjliKUL4mcL3k4l05woFe+8zljVQW5L8nc5unMDl1jfZMqx6UpHiUYbhGcxE7BWD6Q/yNOZd4VtwBTNg2mthY9xrzMRAuO9epIx7tc8Xe01qc9ze+1jIUN86c7P07/wG7xwQVRNKuuzztXY76+ExxTpEjC7Oeota1j+jg3UUgbLXnQ5y4+6NSyBahkmhpmj76AIeF6rH8bT7Z3cs3Q+nZUM4uzPcd25L0ZbezLiaW+ANXwR39nz57AttRNXE02M8YRRQOkauoRLD3sL75n9Rd705u9w//J5nPLYBLePXMTvTjepdGxiztZ38cnJrzNudob7b98Itu8W87C1LPw84z9jxzL4yie+xXnGPC7aPsx167ex4nCPCXzf+CTHTfazN3U6R8w5D4B7pt7D2L0DlG79FOYVH0cc+T22nvkZSjt+xZFd4FhWWO3unoe2c+nuE3nD+GJ+NnQ5lw7/nP/o/yzbl3sOKXu/9CgTf/Q0cdauCEBeP/afPLT1ZOTgelb0/pgrj/5vPjLz1Vi7igx85TF+rCIG2sIgd+Z/klpyDso/74s3H8fPrrkb13E4resmulOepCIYeOtP/gLwGGNncDXc9bXweIedH9n0dRgayq6hZTsxStEKzVlbVkQ/kDyUf81cSPqMzzEn/X0WXPMuvjrwTr5TXcqjh57O9S97ebhpHIwGJEGgvc+uH+PL8qc8kXkvytIo9p/FyuX/yqSuITVBxYkQ++Z1c8ncNwhSkfeXh5eXNlAZNEETrFxzAVelvkAFD/j/KfVZVmTeQzyaXHv8tn+42MbDmQ/wJv1u4tl9m2Y9mdjcczrwpTLlDkzZmhm+pPPKlp83gou2fdi4t8sCJ/SeG/592rNL2LL0203b9TBJu+8icPwATPrA+G13KnBrKCU5buM9aKbkmVM/wNrcS6gfJBn8so059FHeNfOtvKjzw5SosWLoGG7d/gRKRYV6dsXYuPe+32BPcSL8+7/fCe9/n+R/9sLDvk5gd/didMcmWx6nrmusmh+5kOxeegUTL76BX5/xUi55yxGhBCDltAZeRVPjTaPncvHdkp981+WyP/yK8570J+9yeo2xrSxOeOTH4d9hERVgxiOtd3qLeJi3Gt5Kx2yZDq9NCnh2XvP2O2Jdb/0AcdlYbDiRGvyv+gHb/cdT6LQxD8DZIohy2jvpSdXaPsH7ax+O7r3W4fu0T/PcAsb4nJWRZ7evwkN3oaYn2/th0WaMODsxOp6ksOS/0VvINwJgvKM+wP3l2eTVAEWyPOV6Tha24eXpPFb2yQjbk07ka2AbElc51NwSlu+YU6oJXNdzUDGmcXj6a8fzFhhrmk62FMtIV15lz7jLghljDU/1a4sHYKiVY4WttHAw2R1bkjOmoYBlrMRyIrkq3oZjcoN4Aoqh3ASAPmV9pClq9EreXxTq0fJYKj1KQVYTiWqacpvTYIGCJhgVDmdpT3KI2Il0XS6++GIKhVgvUh6FXU9Qn3dk9Nn8k2H7wyh/yd9FY4PyTOKtYAk4YHM2383w9m1I12XimZVIzWJdNUoubJygBFnpmRaUREJKgUGuxxuITOGiIUkHv3e9Gi4fAYz4jLHhD87dfpJaMFinWjDGrXyMe+1xcv6y+YCRZJDa/WMu1Aab9tuZkQ3beqz+477tFADCm51rUoDZHj4+1/9HW9sYpk/R100TS7iMahG4S9e6cDQDqWmYWh8Vn9nbO2cuuXKgX4tO52b24moCQzSPLK6QZGxv7XGkq496KoOZUehKcXBlB2NtXrsfsTzWoBaXCGg6jr/6ER8Ug1Apk4f6ohdkqFLz3CeAFHD+ZAYdnVv6Pbu9zbVTSLkCfOcHK7sbNHCMIaDf8zz2zzM+WaVS0ZlV8kvv+klj9uyIwSo/6X1X3xYxmuPuXEq1PNXBTQy17aGekrQVx3FG/VWYWMnWipiFlulAy/Wi/CSmkypL2LTyKYRSzM1NUTC9dhlfylbSJaWlcce3Ea9Xrqnk8w/K76b1OHuafBc6Bk4lVfHeN1UNmDZv0LLyEaCNt+vGNm7gcp5fzKa7WKLqzsVKdZCSglpGo2znaApLkvV1okdo27zz+7e4XNsaOrkEMqa41rhpVcYfueYIb0J5lha5nQCU00nnBIFEqcB1JUfabQ2MU40l2f2/jYY+dV/A2NUcFhSiSV0p15zQp0nPLq7PBx0TBcGU4bXRGeOAkih/tc5scxhyvD7Pnun99vWl3svYK8aZ0Mpkx3dgyRoSF6eF9SfAZExit7o7ajdj/pJ9xQChXIRdZayQxTYicqOeKVHrG+KozasZ6I45lzQAY6EUKMV4WsfSbE5a3zxuaGp6YOxKi/7hiOwo2NMn3AUxK5bofprVFgJjJeDLF+i891+j9+XLb5/Hf12oc+Gk1/c1aoxrJvz87GZYtLMvmXw3y3F4erHGh96t07ugkqBB3j4Ss83zb3/nYsHs81x4tcOml3lt43uDw3x4IkYYTROffYvO6tdWuPm46ZeSW7lrFAJgrKBmTg9AJ4SOUfBqMBgt7N4COcugM0VVz3CGvpJHOILAfMLRBW3l+TxSNpAKdOlN1jN2VD2y5ExScTxyp1iRgM6kNfIPYPy3FkIINCcykw+aXCuWJB4dwhu0GqUUBi4OepiBvJs4MG4dqitqzZnYIJBY4YudJoMVntckKaUotAARBxp5KwLGx2qPoBAMqIgxnV0fDqu2xSOjwWLtKdLCJiVc5juz6OlpWDLcfBegsGLAWM05EUY3Yjres4xXYKvrWVyrFi2nrbmWySEPjAxWtiE1iyudl4XbNxbiqPsgK/id4qHHGOOMYZPpKFHzgV02NlMuZ/OIWDsYVR4wbhycg4mK2UJjXG8ALJMq5zHGPjCeShUoqUjj3jaNi0k8gqsPZBxTDaBUap7GWIsD2LgEw2eMc6ITgJ1a5I2ct3IhEHMzPaFOUYslfOkN3kKuENMuhc0oex3+lF/1Spjedcy0RkMrqbLPNieau6bhaM3AODi122hRpwtyRjSp7PFZia2pAbbPrCT8bwGE38lLrYKt5uNUo6XbStkbSeaN+XRKi8lgcDh7dwS8bJXFdnUqwzuZbPOupW1ilKrfXsxYmd+q5lNYZi6yJ0TQPbiOzkqNlO5S8IF+wt3ArXuMsXAhppMWsvVacPyuG4FxPOL2XY2yT6PBlcK7Ul+3iGTS97qeUZ7AttuRuknW0bBNjYrdnNgmLEnOn3AtE9vRkIl3qlF+NFdE/XPTOxbo5v0/O0QZMQ0gBDCEHUoplMySmcYnK9Pwm/f65XYbgVxbZXrywRHJjS2z+VmYEmwhCN7qoZwHjE1bkQsqTZe9+7c6oj7VDqzv8rEcCKqM+fIaVzrUYuXh41HbT7FSR3NAumh2ndFCFiu26mdpLuiStsIkmsiFgC/lJF0pDNfLL5g0BZawaaUw0KQ3yS63WEiNW9IBtNn7uWggH2vtWWkj/f5DamCZgtH26LcemjuDyYKg2+8HGhljV4M9LeyIGzXGBb+d7O0RaIIEYzynUaIHZKVgqnAUh6THSKe9680oxUx3+jYbxGAn1FPC87eeJqrp5u+CBDtNgqO1yoryoqjpCF8Prrfw0Q8Y44qmc7A+wAwxwQPqGHRfi+7qkJo8ljFX48mKjmaNh/savnyvbE9Ql97Ka82ycZXDUG07uhCo/eQx/TXieQuMnVoVV7RgFeODQYuOtgtvptnIVOpCMaryYTWxuMH7YLa5sS0T23itfW/4dzpefCLe08Q66pfrj4YstoFMMMYZfTr4vf/otEYTf39m7nt4RkbZogv2bKMQy6wPQkuVeIEeJU91Og3ehKObcW/4NCrVjt3ex7GFTby5eh0DW6G0N03ankIoi19XzgwBzCPdJ3H9dT8KD6F2PsLkgKezW6WVGd/6DEcbUUEEvcGvs+Z74LZK4MnHBqj5uQmEprg9dxLg6YbDc7p6WMwDYFR5Hrtv1W9LHC9g0YwW7cht0N5uUbPpq47Sttvziy6nsiGwAGjzgXyjB3E8LP/nDkC/HUPAp6yVmI7HGMdLNztHviL8d8AYKyNLu8wyppXY7bazx21nyWgHwzMWYXXNoJ5tD032hZ4iN7MOx8zBmHUc/3yL5NBdvgRG95K8ntabE3pmFA8GoJjxmEO3w3uGFXcP+alH2NE1g4r/jKx0nh7GOW78CdqKHTgCnjzqSA4tRO1prNMbbEptfYnzDJWnuLV3MUVH40O5Y1lneix6X20mo7OWUjQ9lCGBwdk9BPbSthzj/k2388wTG/jyo5v4n4OWUp6xnt2ZfjJjOv399zGz78HwPArF2Ka7qZR8jebuJCNpSYPx0jh5K8drH57PWdtvYNWazwKQct1Q8ztsLGSjtpeJnMmwU+XpTbfSNTpEujZGwfTaXN6wMHM2esGhePg5FA+bxUMHreCJ04b5/nEL2Pqa6LyjT3vruev03UyJaHJVN6JJxXTAuNK5ntUnRG260gBMzX0wxvPFUFiJsU+bYM7OR6hlqgy3e6sAA2NJS0PwEoGz1BlTBXKizjfMHzJTRYNoY1GceSLqcxoTX986fj1zxTCfMDxbqg7KCca4MTrEFD1+Gd20q3M+K1tu18gYd7mS7inF+Y8dOGM8d+MUl6vvY9S3kKo8xmhhnPz4ZYltFiiFLaIiI9sKWaRQdPnNaqxNMNn2B778drjlhMPoWnIHRnacdT1p1lQ1yCv+2NbNZ+a9nKHieuqywoplJ3CHsYFJvbUWt1wXOH7/WGhRTdDWJUhJ6TDJjaefhWWmOXTbZs54/CHut87lx0Pv4P65I9TF1ohIcpJ6YcOF7voAe80yljVBbwu72kBKUW52H6TuJK89fwDAuC02FqelFbrotIJbgZQtPF8LYNyVfLWB5LW2cpKIA+Mq0UpcHK5uEAJdOLQRnUAxzv5CKPhNRxvWPob4VpX58jUvSfQlq1TLFe4gRnSB7ucYGS00xoa0MZWiInROLt/B4+MFnraOoa3q3acyBKXiEeQ0xYMTefSJWKK4f7xbDm5ne17H6pqBNEwmrR1ce9BMfrPQRNkHYDj9fxzPW2CspzMYDbevSMonTK0ZGAder3Gf23YdnI7WzI3SBasPb56C3pT+D75T/HYICONSik4nAnV5N+oovmr+lFn+9gZeVviMqXFevP4pujr2zzgmrivmSN5rj+IiuLH3hXx13ru5quNs9qromudu3k5+d7OPkV2r8WJtFQ+53tJQViYBtnroB+jWINaYxUStyoX2LXQP7OHm6+9kz4ou0m6JJ6zZ/F57UViEZH1+CZ/etCA8hlCSyb07MfIF/mXWnbzEXMHXzJ9E38d6Hk0pLC1NGivUpMajLxNp5mbmvGf80MShACGTBVCudIZA4GF3GduUtxR6hr6Kg0RU3S4ADjpuEzPpNrStnaqPXmeStrLXfmpGlikVAeMOyui4Yfv7rPkOGiNItusV3mjjxIDx4r2KjK3QpUgk/iyef1T4b8NnA2qGyUyfBb/dPpTb7EOp93RRnb+E+qz5aKlcyHppRor+EyboXzLA+gV9LCot5o33+b6WuqBo2DxmNntRFia24AqNUtqfjHR77ekH+SHq1uXcdOQpBMNGedYiTuUJlty5kTNW34tEsWnpMoxF0e+1fa53nHsOOj1xnnXyMG5IncW3ZB93zRzhytne6DGzNJMX7Hgduwre7zUwaxad+eGwxxOGzdGr/4DdtpArujIcN2eYmSeN8Gjnieypp+k99nZmH/lweJ6N9UnM1VfgPPJdpOViDyXftwo6m40UZz3WS/u44LiXPYaadQ+2gLTjUPBXBwayB3Fv6lmumzXIwxObWLT6Gnatux4BrO85HvCA8cKzd6H1mMxe8jrEgnOpLrmF7888k7tOPoTCUVGjL23rwsHlAXMdN6UiLfFADK1MB4x3nvgVjOXRalFRpnFiSXvxvrCRJHi3cWP47x5zivk776BS2M5T807wjj1wCI0h6i55aqyQniPJa/QH+SdujZ6hSiKlWSICDXFg3n2Iy1f2fo8H0h/iYG0PgJfUtw/GeLG2h6OFN4FbJCdYJCPwUh8+M/x3RikunozQXE5KPnqtyxHbm4FxYwGhID7xB8mr/7SarsHP0THyXXZ33USueGtimwWVKpYQ1PyJ8FDaY9+6/K5fd1L85NANrJxl8MvFq5h5zO/pWPgQ31o+zk9HMoh2yRd6e7iyzeSZNkktleGO01/Fz49cjqO1ZoyrwGS7R3j0tQAjllnFFQ6fffW/csvpr6Q6I8+PvvoZ/vMX3+VXM05k5a5h1rfvoGqOhExwyklKrAwX5la3UsbBeOqaltehKU9+4WiwamGS6azbFk6M8JlRmaC6H+eIgoozxnWE7k3G31iM5ETrTrJom1vluzvuZpbj8MpSmXNLZT40McGmBREj7xiwanF0PlfAladpzIwVyejxwd67JiY53vZldnFHGGLgO/xcsFfzrqubCaJPvTHTzkhGmm21Ac8qbkzXsfcBjHtbtP1CDU5e511AT6U2bVL/SMdD6LrXn+ktxk29TSOnHCpC55y7dlO4tZ3dehczq96YqXQYlXmOzEj22hrmiAo9pQ0pSWk5frF8Kdcf1Ed91nzszj6G6tu4/dBFrOzUka2qXf6V43kLjD2Lo4ZlVkDPRa0nFVs/C2qLd/nA2PBBs8wZnNTpTutqVn9JP7IQteiv1pJM5gz/JYlLKTqsaEY5s54EpMuChC8c0ASvXXEvhw3sQDNbTHP3FUb00890xhg1uvhW/2c59anFvPL63ybYzA5RbuktqrLryIk6v3O9gcVUJWTsQahNdwNgVzS+N96NO24hUIwbOqvaZmC6tcR5gqhOGqHMoK4JKpU6fSecSZ/vtZy4Bk2EFmI55VLT0i3ZYgARk0t0GDX2qi7GlNcb5WLfadLAxGGP6uZC+zOUiZjmC/R7wn8Hqwsp5dBGkomQDa/WiOqgR0zRIcpUVYqeWiX5jCmH1/Dt9Ov56Qve2vxcGrLonRjgqWQEulTekm9MYmFno8lK0NqrhkFbi5LfQWSVEVqM9ekTpAouhr88W+7oDZdRFQKrRQlYAF3UKWay4cxFb+i4RTW6dhfBInag/KzltF/Va0yP2r6d8r7baLhou31QqhTb22aiZIqyrx9tq3mMvCm99qOEy71tT2C4LtbiWNv0GZYOmUP3qxGmhWIy00ObKuNkR5BG9JsWlXctwi5j7xxPjDEaIN0Um1Mmmr/07voJolMpScp1yPma80zs/cjZ3u89ZTo4wuD8zhcBUDAsjKyLbXg64Jw+l5u08ymJNmyRSlRn04WB5YPGWuwdtWJgYV9SiqA6HUBJpSnFbAaTjHFSShGPGcYkIDCcMaxUDsvKhEVx4pGu19CFYr2KMqIS711b8p3piL3Hemf0XX93Mw2pIfcppXARtPkTwzYpmeNE29oTJ0T3qeCFw7N5bdHrTwtK0V2kKdoqKlEd8+KP6zywLPl+6vvI9ejFxRICS0/us3AgWP6WTAauJP47ZuajScxoBhzqCFmmkstST3ntXdDMDr5s/IWA58Aw2eE50eRbXJuVLrJt2SyGTG8S6xYa9Ot6M/nSmHxnOjClaxTTaZiG7Q36KdvoZ9eRpyS+S7sVDKlYvcC7dw3FjcfvW4faHlvlzbk1lO61vYPtuXx0zJtcrT3OZe5p48x1XG7fuYfZjsvXhkd552SRZ848lWtf5DuW6IrJvODRQ7zzf+98jatfqHHLrj3hOX494MlWPjg+ya2LPVcdM7bS0Nli/NGVYBxv1azHH/MnjALt0mtn+ZTLxAUTTfs9+kIH128j+wLGH5pq3jdXh8UD0XU1Wk9e7tvN6VoZZUR2mE3XPsMkpRzMWE6KDWR9j20dbyzoIEtdt9BHYGevd+yUC7lMH1LT2N7u9S0ynWVlxmE8n+H0YQe3tn85yf91PG+BMdC6EcQZ45iu1vKVwoGUwggAgVI4uqefaxlCJBwj0kJPDDiBTVc64YYRsxVr8BUO6qcbuAkNop5q7rT2YZGIil3TTDnOYKqbvO2iayZTRjuTMTaznQqZFrq1Lv0hRlQ7t8nj/HsrUp7wBzqnjpjcCsBEppurqnMpTmrMy09wTH43O3o7sEZLLRd4JBo3uF5Fvbpv+eZmWqy7Aa5uhM/BQVDX0i31xQBCJHXYu9SMMBM+rjHOuRJDuNh+FqQdU4m/Tr8v/HfWfyamcGhvOKdsmJ6PqA7aRJWZYpxJ8piaYkpFoKSdcngNVkyGEW9VgQvFLuWlVcelQKWMx5inbA1l5EPG2klH1l7BIFw3TdpUNiGhtWJgKyd1bH+wOVjzkzL8a6t39JL1/SgFGhW9tb2VrusUM1EbMrXkdmIiak/dTCTsubK+1nfKmIiuz9coH4SD+ew4YsqCmkvFzIJMEVxGwS9XG2hvuyv9fH/W79GdOtZBEjXlS5183NZV18go753WBJREin5jGKU5yNiSdEn33lOlGVjbI+1rsJ/rpNiUzkbV4Ore9U5liqQch7QyMJWOiAHWYAI8nkvRm5lLu//7BMmEbspz28ib/TykXoyhbCyRApkExnWf5YlXUou/V62AsfLbTlwLXGwAxkmN8fSD12xG2T7/bBwfKJZK3VTd5rXdQF8/7K9WAORjE8p0g1Va/D2Oe7ouqO2lMdqpwD4mAK6Adl/vmNJqzI1XWom9bwIYoz2c/OVbaC7BZ4xjVKllCrqLyd4sM42lW05KskpiC4GlaYny6QuG/Em+ZTNrcxIQmvmo3W1xAt/ZCqV8G9Wsx47qbrOMos31+plqCqb8qp1WC6s6J1ViYHH023RVYseSsiUj35h8Z7owpWlM5DLIaUp9B8l3UtPp1pKzjq6yN7Ed7PT+Hm/v4MEj9q1BLcQ6srxbDd2aRrSu8Dcv72Mw1KRC+U4Rwe/u7AOLZ1UcbHobxjFrl2omqXQ0JmlHAd1qAoApo0CHv3IhhFeNtXnH6MN9SSlarV4UqopFgzEyoOERBImHhiihfKKvleuF6TqYssaizKrwM0cpsrZ37abvgmU6nWTqilRZscWvDZaSoGe98Wok5421bjrL4/0zEEpx2rCLU/3bA8Z/vjD17zyUXcMSnrZ0VfpdvN/+ALfKE5ODQSxDM1iaLwivs1imtnFv6sNcoH+em485nNSjw2hVrydsM4sUbY+JPLK4gdufehcvFN9ip5rBkW3/xcdllLH8dfNHvMj6NtmY7+bvzc+FzgM/caNEM4BRbS8d+FWpgmVhJchnWoi5otyO5vAH4SP1zbxYX8Vd9rFsWDvJvfmF9GX7meSecNMTtXV83IzKg2pIrkt9hkO0bfzWOZMaaaoqRS69jXt/cz3rH/oVA0e8kK/5sG5Ld44v//pK3rzwST5pv4k5qTQTw1tQI5PIgncdr9Qe5K3Gbbze+hxCCX5ovYPl7nry/uBz7dZeXtuib3NS0QBc03TMmXnuSH8S8CYz8QG9U0uWiN2hZlDxgcAf0p8PP39l7y28nAfZLD3GLq77DQtNECXMmTgJdgsIteZBjPhswUFiD5MqT3fPTiZ3xzXGJTLGKE9k0mzOjJKf+D0fndHLtliyWVVofKmniysLeXSRxRYRo1rOwOTcxVTnL+R7x76EpU8fw1n1J/ho9Zu8fe8vuXrjx/lN/oM4mskHzM/xO1nlx+JbPC2+wAq1hKJK0SOqSAVf6TuJM3C4+tgX887VvwMLbN0CCfVcG7sOPZ0P3DXO6hceAdrm5h8FKPbPYSoTAf+UHqGEN9/l8qKFb2V3Rx8fn/x3nplzC4eXX4981Ss4M72Oh448hleteYTHq4ezecVi3nPEr5C6wzfH3sJZGYvj7Cxrb55EScXGXJ3r0zlk22xgmAWVLrpGjqOW93yge6od1FUFpTTsxQqxIQtHl5h6nctH2z/Jt+74NW+Z1QUvgLSAi9XNbDjUc7QopUuht8hj2i6OBUR1jPc/ejs9RpYjV/+Jk9dt4vKXfIJOtwIZkzlCQyhJ4R6N9m0GN5/4MEfPXUW3MNi58Vg2mjs5evmtPP3MS3DPPpObXYuiW6LTzfDIrpu55djXcejR9zK1uoDMeSDGECbLKiUONp7kfQ+fQKf9ee431pIjTUrPUvfbuCUcrks9zoiYQs9EL8tRR0fL+NtP+iKjT1zEjvxmlkLCPaKk0qydvwh8tdAnjajy5BHaVn6W+gajqnm9dzZD/PD4Y/mi/imu5mV8yfgio7MU2kAt4aOed2tgEk5GAV7H/eG/T6ysSRw3WPk5QmzhitoXw89/sO6SpmsoiBqasBGqGQAA9Dku8/0kyJeKVXTFwOgbxf3cFNt2l+qlz/Usp2Y7DrpsBvltVdDSyc61lkp2uD/8vss/fbx5iH3BBsnZf8qx+VzFvcsFS+sOa9PeTK3WOQvYSzUNZzwueNW9DjeeoPExsiw2d4bHKO02uPIHDu95zwQLlj3LqUfcwWuvfYDO21y2zII18wVnrlKUMzBj8kbyxwquOn0Gk7lfYrzcxGlVwljA8sE5fOfLn+I/3urStTNyTLj7X9/M3g6TTXNdvnt+BHjfc3Ny4vClX7k8uEyQ1X7B6vxWjm8+C71TcNguxbZZgi49CSI7pzzw7yWaKW4+6Wimcg+0OEoUcfydk1WmOmcza2CEbWaW5f7vXNH2wQFKcNMZiOWZBOxsHCiWslCoQrbFoBpvcl2qDH5/H4SmBA4GRQqhA9GE0cYs3btfI+uGDijxULqiT7oMa/p+gfFUFtpjPFpbFRbGTI5cTSXup+aTA6aoABqfv8xpCYw/cOXvOHgAnjgkeoZ1JDnfMUT33/HcrnOZwa8A2DoLeMZ7fnqug2M3VnjJqnGmqo8yODvH40tPoW98kt9VV/LxNWnyC09oOu9fM56/jLHSmOnkmSeGMIXLxwwP+GVjWdopNb2Lexs1FmhDnNjvsaLWUd1Yy7tYcuQuPv+Cr/KaRTdx0KwpLhrwtHhnaivpZ4yDtK2+56YXgWZ5vhgLP2uPJdG8SotKjULEsJi44TJ1jhSLOrfz9sMv42XH3hXd4jSo+JjFa3BneIPTS9u95Lk/Vl9AtS75EXWe1XQyMb3f6fozif3bKXOkto1JcnzfeRUAk+RpY5Cdq+8B4NhJb8ArOyZzxkeoFdKYwuVtxsOU6KA8a75nkedf4gu1ZzhO28gysZ3XYbBdM/h5/X10u5Nsyi3m6Vkt7J+AbCZIKBOksfis+Zvwuy3pOS33CWKHnNHSVu1s5T2Tuj/DjTPGYyrSrS3WPOaqjWoTSz2ioo7x5Ud/nz2qFwXMMHbydFZD6I9yc0+Jj/X18IbZs/iXRQ7jS37G2/tn8lDXBrJTN7PRNOl3XA6re+3wM4vfxXX5WbhWLwvHF2KNvIjK9neS2f0KPlUcw7DqdNdtXE1jw8BB3DpxKjqScwfvYx7DHFXeyHHFZzm0uIZ23ZskXGzc4d2r76IxqvLs1EzuEwYzxSjzrL0Mm12klYUCKmmdwZkz2bNwKbYusadJeLJTJlPZCPjn09HE7dS1ivoRku4jBzFym9ibLVMefi1VkeKa085hb+8sXCHYUT6YlcNHMjIyH02X1PKCylSNAUuQ1TQqosocx8QVGmabx3TNrCzCcPJ0+MUNFDnedFcnbbkaKgf64KwQOR2jryFfHOCYzd7oIYTHbuTafc1hKrq3xbloQvSWG64mP/gQvWPe0vaOtpk83bmIEcMJJ0TZjZBer5Heu5eOjkEys9YghcRRAs2w6egYYr0qMaVZKDPFeEby4Pw59Mzx+hPNkOSNzvCcF089xBHjOp0+qbpHG2OLNkhaz2HFmLxhbQolwAnca8zkilOtYwvrOlbh5LwBuTMzyXkLb6egV6hhYMdQ5VItAmJBO4lPDAF+47yUblHiVP06esQE89nDVCZDqmaRazh30K9NqRzvsj7K/qJDlKmf1MdJ87ftc7vHpadnNkU94TZx0WR0rZeMjHHBVInPjIzxhmIJAbxi72w+MlLmq/rl3LhzDy+ues45Pz38QnZwAW8dNrhgqsQtx0XDZPkwDzzl66CbihlnTnHf67zPfnOuYsvcmPON7ZWsb4ylOxSaKzh4j/fdQjtiytOlqJ/be9yZ9E7BW++ULExLNsWq7c15zLvPkzaWqNXmkrkfOm/z2uviATj/MUW+DjP8ufO5Tykmc6MoAa4Pih1jPm412UeWBzawaGCUnskiuWqSbOmftHnhGjVt9TXwgNmpzyrm7ni6iXW98ajD2NQ7L0zIG5vRT48WTe5dATN9i7p18wSPnpHiqhcfnUgs+8HLNYaTmBMhFEU9x8bsfHJujetf+1L6TxpHF4L706d69xX3fG9IcBaokHHPKMVhdSu89rgbyZxzhhk+o0SQnnO1eh3ve+LppmcQZ36DswarOSW839dBY1d6Jqk2l9knj9NxSj0hxwiPpSm+rCY42LKwYnlBvzmjAboJ+NTbk/d18B5FJrYI02jEEpS5DlY2lvmv+9MLBR96t86uI70vDvYXHY/fEE2CXOlGwNhVGEhqo0dz8Ih30K2zfIcjUSVVLfLyp2qkLZf2sk7bqEElv4AjdqaYW1yOPm9W033/teN5C4xFKkW30960RJh3Y5KGFp5+jbFc+oxZRkf251D9GTrTU5yz8C4W5JNFJo7UPDeFDlHBVjpPdM2jQBWBTGiM4zGzQVeb9wcXQ0SMcU6lyWRKnDbnMWb0Rgkr01WzPfbgNeAvTQfSkAecI8Lvn8XlkBba3yBm+Nf0Vf3N7MZzCZhSOVLKYdLwANYSYw+DbgfjVpa0cKhr3gQgYIE63B7cfDuu/7bO17yl6lO11bxCZHiBVFxvHsSgM5t7ZpyBapFFDdATlALSBRfpdzJLi57XVq1/2nsALyGu0gIYB0lHQQW2ODD+lXNOtJ2/utAmKiFjrIAxTWMyO8Z1hTz/29XBBvseVi+4nVMWzOUlC+bwxdmwpbSSJ7pGWJdO0eu6vKxYZ8bASfxoYIgLh05gZN7PuX73Xr4/OMxnR71J0/pcP1WtilVaxlOD76U+fB5uZQnDU6exreswMuUipnTpn/J0xZvUHEbMTi4qJd00Xmo/TGME8o290ht1droG5w/dg4vGH2ecSUo5lNIZJg3bK+F7AKWSi5kcKZ+ly6WmqAtPm9lTUrhdoHKweNbdtLnxSo0y3DeIkl8+1EzV2avlGXcVs00oZidIO1kO0YZC323H9l0rrFiiZW0GziK/YuVAVwiMDx/1QGimGk1KBYIZvkYxbnDUHwP2ZcOhc2qYWirZdsaqS3H9pVXTfw2PeWoDJjXKpa5wO8dJ0dnVLAcwYuOanpIUjE62ZmxqmuToqQpzpqL+pC4cJkUFQ89Sn6ZaG0Bn50DTZ4XCGOmM9z7OzI3w+kOuZ2Z6lDoGHVaxaXto7fKyVs7nYX/16yJxMwAdTFJJZ8lWasw0k57cwTsyRY775ZHsLzooozpTHG5vSXy+OTuXFe2Lwr9/754BBMA42u6VJa+vnOk4dEiJAVxQLIWesweVunjjVBkLk/mOwxnSl/D0dnJZ4XxeMJmhXUquPTUaJpfNjZ6PlpL0zChRmeH1A+fJMjedknwv+ppzlpkx4bXVTr/sWq/rorkmJtA95a9COTAakyKdmHMS0rkBHyTl6y4VkeZRt9kruVW4xixSPnvqsJD68DmJ79W4h47yNUjXW+et5Or70fxWvetqBMY3LXsNo7lIqrFx4Tx6Y1KKQD4BMJGD+45NU01HGluAe5ZrDHY29D0a/Kn9DO7oOYWcW8PNGnQuqpJxHMZ9v+6KiMuMktppTSomc53+d/D2yamwIl0cGM/O2Jw+0+sHnuQI1rKQuVPNK7WVfEya42Nd4UPkmj/e7EnPZNL0Vl86FlZ5KH1Ci5JQkNIlXSZcPFkMWezJHFx/cjN0G+kQPHFw9GwWN1jiNxYTGfdXawsN6puHDxPs7REcNHf6vCWp18j60ijNhR7XYg+SQ0a9Pmp3D9QMQdZy2GY8C4BS3on2+MW+DtntYZ5qx/QF0P5a8byVUuBYzNEVymf6DtF2c472OMe60XJ7Tk5TgtKPYbOLI4sbOby0kYMr3tJth5qgLeuwIn0YI10WRGMuh/um9gCb1WzSbWX0cUWeWpO+broo+IzxydpaDne3Mp9dpPL95AsTACwYG+F87SEqpLlHHIP3SqrQPk4TLh1TWRbae9hNnpzPik/k2wnGPhfoa+HNG8QrdY/FnqpmUBkdUXOZJE+nKpGuDZI3u1miP8ngZBd6qkZBq6P51k4dokx37zY0YZHL1mibqGOrqdCz9DRtNZXaGEdP3csz3WfzIetjfLb0M75rNSejAaQzFpS9Clj/ZvyOjWoOS3zniO1y34OFJ6VorV0G6HC8jsGOySJG6EABk5rGdtNgtdnBFqPGFv1hLkjPZKdhUtQ18vyIz9CDrhSmtRXL6eAN9c0sdGyU0c7GRQchH1nIf/kMt6M0LnGynGrW2NBbSRRxyPm0uqyvQRcSZTe7nNg5gx43x6hucYi7nodZDAjuTR3N68r3JLY9Sz3G0yxMfNajD+JokjGZ4SixmRQ2r9l7Ow9lj2So4oHlNjEKdR2jVsEwF1DzWdQ+O8ew2axxHyunaZsaZSo7yVOuzZR1BK+ctNl9VA+a5pn4n5DbgthyLpcBaDXmbtjCwPwFVMsRCJTb57O22MGc4QqOsRRlQWFqAwfnd1NkHl2qSNvgbN5cmUdBFtjStxEqaRZ1rSQ9djLz+uvk+2ukdkGxZwRZBrcNjixuZDxdgHwVbMFW5xAKvRvJuSXWsow51i5MHxBvXVzhxne9ldNLaxnd65ArFNneNgdpm2hIekWZpRuhkCrTPdfhMS1N5+IsPU6V9N2HMui7amiajcChq30n2+RJpLQyha7t7Bw7hIM7NiMFbOQQeuUgeaOD7YZNR2aAjl2nMyM/zgO5PcyrmSFLXG3LM6g3WymaZpW+ziE6+pqt9Dq79qL7ouxJOljNcrSsS7Vm0me3BsapFv1BhTQ7VDQBcZVGpyhx1vCD6LVxhjozHCR2M6w6mKIQ6vAnVb5lyfTGCLaf6yTB/d50H4Y/0a6odGin2KONIRShAdbW3AKgNq2azMagIGrUfUjy2qE7AahrKYSq4aIl9KTgscTRv/0CRT5gLUjJzq6knGLGhGLWuGJvt0BTsHBQMWvEB7U1DXDpHRD0zdOwMgb9k97Vpx3YrkXSkp4g0U8pDt0FdX/kLtQU7po72bVg/+PHqWskk+0mdlZnfa/kxPVDHLm7wtaSpGbC04t0use83z9fV2RjE8Z4zB0zYRoiJ4h8vd7kYdwvhkNf9ZoJhUyFHjHJmM+iDnUKZo97O03mYZI6mts8IRvqBHZEfwsBfU6RAb2XnKwxp+a9D0J3MKV37Dhj7GhekmW4v1SMZ70+VSGoCxGC+ukqzSll4grJDn2k6TuZbs03ZrNTSNcFC3ZmZlHUo4lPiRw9LRjjjCaxTUGf64Qge7y5qGtITTdaydV1jbRPPDRKjEpZ71nMH1LMHM+Bn+cRWNiZmmpRBy84XZWcX6BIuN5YOeW4LN5uUUl70r56yqCt6vLwMVu4cOftaFaZB2ZJzN5NXLTtj5w6WcRSHdx3zwAXvPWiac7014nnLTB2XYdFHeMUd0cv3o9T33pOx7huxhlctPcmrln1YTqdmOG/pnGifIB/4QF+Mfs1gFfF6STz2bDPXK/msSTtLcN0UKYtPcEBYmMA+sUYt5S8UrX3Ht2Ng0au4vD21ffy9tS9APyz83Fu149jiTHCxrrHur14zoO8e9W1fFBdwUJ+S05aVEWa+sJOzDUTAOSADYWdnOv3fc/KBSzTogH2/cZ1gCefKHSkKNeqDKlOFmSG6DTn8NIZL6DDvI7vtb2ak1nDMe4GjBlVsLwkmbkHreSwDSWWVYd5R/pONst++hnDVjonaOt5w6Eux+/p4oT6Ns7Rb+OCvgeYV22emQPMtD1A/Q31AzLC5iMzPsrVQ/+OqWy2xVjDVrFTzaAYc5xojJw9gpbZiZsa5ge5Du7R57LRfJq7U3MpxoRtmlLknTEW2pLl9TJ73SXcXDqXX6d/xtGVIY544Y9Rz0zw1vQq5opxVvUUkPoET/l6zVGjgx5nkk8ZVwAwa7E3w/5N/yt4y94b6PGdDsyK56srWwDjUnuGRe3HMJTezQu6buNhvJK6j04s4XXmPeF26835HGrv4EWal0ihfCH6N/g+u90ejtIO5w2Gn2DowA+rr0CfkmDC52s/4ZuVV6MGRmlfuDCQojJX9TIcH6n8qG6xELYi23c/jzgpDlnscsy8STa0LeUwNoALJxht/GrW0bAFemb/mret/hCTG4qkxu7g1oXvAKXQRk7l/9femcfHUZx5/1vdPbdG0ug+Lcm2LN83BmyDOZYzgVwkQLKEzb4b8u6RY3Oz2d0QdrNLsp/NTRLItYQkLCQsBHghhHAsJmAwxqd8SLYkW5Z1a0aa++iu949u3eIKyBamvp/PfGamu6eq5zfPdD9V9dRTMuxn0fNf4fkNGxAyS8HzP6B83llE68E9YvLOlhSBdR9BuiQt538OkfUg9Qz0t1F7IMzC3DC0Afnb6W7NY/9aL976HI+HGtn6Z1vopI5ufwXvK3mYJ/LWExZl6J4c8zlMEwcwXCmqkibP5lbzTMECLtf3cl/JaiiBhVo/m90dlKbjVFx+lPuBXYt0NgTCfLAoQ/n+FRzrCoJnhIaGneQXDCGzGudlm9nkeZRkzOA7VRqDftjWdSFL7utmaWs/gQsLaNy/lagvj6LSWnyxAAc92zk4wad80ds5TXcAv3+EBUuennFfebndAysTRXT5K/m++BSGP4IxEqfgZRzjmUhID20ldaRHDFK42SMXcI7Yy537/wHc8DXtSr7geYCD2jwuTdwyFm4U8RdAfOYRh5R0jc3nKHZGsxrMybbVHSygWKZgyG7cRpzwpvnacYalxgmnV25P3hJgJzNxwJo3FvYytVMiI1wYVhILbWwofPsKnTP2mhjecU9Jd7yrtOMYe6Vks4gD46Mdl+2QrD0iGcqznYXaARj1YgIxaOiRnP0/PoJnDPP7TZKKEZ3RocCP/3q8pzyYKADSrDss+cJvLI470T51fZIFu9vZP+/VR3A++YAF2A3Sf7pO5x/vmTzn4p4t7rERnkAK8hLDmGLS/C8ALjgwfs/c3ihw5WB1++SDAimJNzP5nG4b/hr3anaGjJ4QfFb7CVmpjznGUUe2hNuOfw2bWRoSLYxOOYyFnJ52f5aprkss20hCs7f965HvAlBRdYhjXRsBuCQ+3nDXtDQT23malLSWzAeeZtdSg02ZDPfOF7xzu6SlStCUnt4IMCbEyYf9HkIJ24U8UO2hwj+9R10gaVq8FV/3IPRAp6+ULo99f4qQTxZj2uIyAD4sMi6N+hxEnPD+P6ye7ni7A3ZDudu5NcS8dk/wodISVvZMbziDPZkv5oVz9ktWHx2/v6bdcG4iOW3xrLjXtguAqmQXxYnRic6Cd4oTnPnYb8jPJmgvB4TA40vSGNG4qyDORcPfp1/X+ey8ajR5jF3tv6V9dyk5w8vdRe+d8fxOJW/fUAqXl+XdF8yYweDCdT8ee33j/E9wbeZLk/b/ReNXuGvTMvbkNeG3UhTmYtxrXMyNtX/Ldcv/fWyFMbATjgMEGjTWGK1j2w9ZteSc/IwFIo7PHafVqmZT6tuT4lNH2baucOz109bySft8Th7AoUOLAfjbzCdISxfnsIc8K8VG2vnX1d/ku+d9nsZoFq8zpGGQI2jmkJaPv+z8Hd/eciM3LryHz2dSWAGLR88u513+L/HezE1jdX01O96yG5YBrhxJ8K5AMw+ZZ1MmIzx/fgPNhfbElV1WIxGRT87jYjSJhyEs0s+tYGl4fIb1Aq0bTUges9bhExlcIfuGeKnraa42ngLgHHMPgSlZJTJSp0b0o2HRQDcPmGezvGcel7v/k7XpHzKQnq7jKGnpoo9CkppkKf/C50Ob+EFhPjeWFnNNZSWb51VzwbxKAg234q2+h+8XFtLs1UmZ+QRH5vO5wTCfGTb4YEcTz7V38e1jGv8ajXPWYoOqzCKuzcb435GPcpv8FLdaf8W/rPsP0s5y1wmPgddMj6Vr09329/KKLP+w8JPsPbIZgC80fpqHi88hZFm8Z2TcTq1MiKm0FdSQyEYJxGvwRe3fV6/TafPVTjruv9PnkpIuLneW89WwxnrYq8Ug79XHJ0N9cOG/ceeZH8BfPn6Z2Oi38PgW4TE8+KWH94wsxy8mNy7Wmn24PTE+uepHfObMW5GVF3F9Rzk9O2rQ9RwN818CINsRwl88SH3U1qXQuVsVWNpYurjK+CCLW+xJYEl/Gf5EkLzoUe7fciFeJyf0R3b8AXOwmegTX2HbwEMASFcaNInXm8Cf+Sw96R/w3PpC+otcBDMZEvi4e9UH+MEVn+CF/DOpSg1gWDmeK1hPWCvjPeHfclHuUUzTxSNcwf1F1/KzTe/guQ1riS0t4vHSDfiMJBvCL7ApfoAoBt3nJPlG2sPulE6RDPJC3ODWlvP498FS7qCah9KL+NaRa3joiatY+myOLZ5H6JFFBLMZHtt7HgW/MPjC7f/Nu3c9xc7SJh4+/iOez7XwUsSO7y2UAZZ3v7Z85cnUeLfS4dYNDPTbE/lkbtyrzuZclOGMt7oEMgeFr8MxjpYWMrS6hkuX3s7FBd/lSXPVpP0f1ezwisWWM5rmDEn1n1HP5y78/thxbSXjDdgkHpZmf8qtuSupFIP8xPogoWSMziovT20s4qkzyigtCXOwegGb1tzBDQv/iT/rtHt6s0JMWqCj02cvQTu65bu149euto05rqj+w4zf68f8OZfJB8Yyu5xhfJgvX/9dmq7qRvdIQgvt75F22V1rHW6708EtJZ8djlBx0YSVRI/YtRfFbKc43JRh3uW9BGuS+NJ2vDFA6sQSWoJfQUtpzLRc3In+NTx59Dj/x/7bUuNkYVx2zI5lHq3ntss0rBk+f/fmID+5aPx/fO1T456ht9z+r7zn+XEHcOOIl0AKBqdcQnW3xcZddvlfu0rjP9+rccsHNG764GRXQpeQHzfoKB+PH/XoWWqkfd0fdeQn5soezZJwsFaAEPQbOktHdhGwLJref4KCK3UGqm7jMibb6M3y7ygdOZu1nfsnbff4YrgtN+9/YT03RMYdP82EJzcXk3ZG5fIO72Pj809w3Wd0fnpBDcsyWW7Tumh6Xzf/XB/mrhP2iMWCjQ9zc73d4VDmtq89bleMXWvy2HpmiHf/x3f4u8/fxpE8O3zDAP7WSWmmIfH5RshEbIf6mKeS22vez9eNG/ghH8IUgqrc9NBNry5J9C3mzprr+EJ6kA9/Wuf36yZrXXveIK6AfYO9a4vGwaujfPQTOp+6QWfrewrHjpNT7CJjjIdXBCe4Qe+PFPPt3n6MKS2i0uIkj2xZDcBH9tqdb6MTMd8X+Tn5TszxkBOi0VxbTckJg6jU+FzqEr6EnY7Sp13Nrb2Xkh52EYtJLrx8cjjPXOBt6xhrmkapWTwtmwDAIf94/NqDpefTKyc7IkeLywnpg+wJjiexP5JrIJbN47HijezJaxzb3hS34xgXJY/iyVpjAfAHZS05fdwxDupx0rjoonTSqmmjabTiE1qhP869Y9L5+FLWpOdnrOVstxZxlrYfLznyC/qoLGunr2sx6QnOYpUYJE9myOFHz+gkImU0zHuBjB7H641huEyKAkNjcVEAjzmp2cCOFSy1UoTMBM9YKwnLIFf1PEBr6BgZqVMtGik0YxhY5FLjZXxAPMgOc/E03X9tbsGUgnOiO6mkh/doj/GUuYoLC76HOcNU83ZZiVuYLBfteEWW7VYThZk0MisYJjiWozgmBM1uF48E/PywMJ8vlRTxoaoK/I1fJdj0FfSGH/FIYSc/KCxgh9dD0srj4niCzw6GSXR+mPiRTxM7dDPxI18k2flXFPdt5sMjUQz3EmSmBL+wOENroTfoB12SC/swhMRIhHBZfpLDRVQVd47lF417DMKRyrGUeJYzyWvQVcAvKt9JbNB2FCyhE3bZv9eS9PgNRGYLp2lx2FfPoEhQbBaSCNtxfKlgkON6yaTj9mbn85S1eux9PnE2u8YnV+oTegmerDobGXSTnBDvW+s6wFDtBcT1DPnSh18LYEyZzBL0D7Nq1aMsyz9A0QFJ0OunOOUjOWTS3zsfjyeBZWm0Jlei6yah8j8ijAjBtDOcKSVpw3bg1vS3EAofAmAkWE8iUM3RMh0rpGE4Nx1T93HkBo3f1RQw4N0x6VwS+R18N9RBZ+PTJPwGQwE/3myOv5e3ckfJ+1g5rPONXV3cse0wpckY3VYRK7r2U9HST2P/bpa+1MyZW59j44HfUbP9INl9cYyuOJERkwR+Xght4A7fufzWY3G/1UUk6yN+9AaOHvo8VrqUFuMgbaabBLDet5c79Vv4pvFDLLfk68kvsktU0vVsiO8//g0u7nqBHZub+NDN3+Q/P/lxErkRoiJCxmlAG+hEEtOHbmcikx5vrEQiFehOCrihoaqx7SaSIifWSxoaQr4+xzhh+EDTiOsBRspCPDMhbvh32hkUjUzuic0XcSwEI+485unjvcDB9IRUiaRJ4OWYLMcQFqWxGIYFCZ+OaWiYPovCvnVUtF/Ekfx6kkVuUln7upcRYtLEpxMe2ykbTZ2YP2FUz+vOEMiO1zuijdu4jxTS1MYc46zbTzIQGstb7C6wv1fWSZkWddsenUfaN9SAZ/L4e9GiGGkng0WmxCSQb6K7Lfxpi2WOY5yQPsJp+z4jZsgYYfTpFMSguHP6XIv+YOHY67ZyQTI4/fN9BTqHasavoUsnDDS4PSb+4gyulDXmlC+IpQikYGjKsH3lWeGxpeEjAYGl2Y/chDjgUce8OGpS4Y6Mbd+Zt5iqXtsx3ls//XoecH6OvfUCzbK1DRMl37LQdIi685GGH1/eZAfSwqCwsI+FlbsnbXd77bBDdzo7ydHRkPS31GI6v4mOiS9tkXaLsUWTAlKiuSQeOR6RHHcFaAvYjS138CALF26jNP8Ili7oTdQwnFeK5fYQjdohfJpl4HLCbTRhYhg58iN2p0W/UYQUGnGvQVbXqJu/k2Jz+m+raxItXs8Paq6hz1NJaoaln3X3+OekJij2ZzF1wYligRYaz2OfnZKyLmvYDTaYPFG/NBfHgGk9xhrQXWwvELPxWCePNi7g2SWCnKGh903ouHHEfnDtJjymReNxnUcq8tlZ4MbKBekJnE9DewQAX9oi8QrLq58q3rahFACByAK+6P3stO05bVyWsDufPDk5ODznslN0tfrnkdA8HPHXkoj58cftC+avKt7BysPfAmBFzO4l3hLeDkAk6KJoJEuLrCXrTID7kvFLShJJBg0LMuByUmMB7LQaWec+NGmy0zNTJq7M70hQ1ZNCyx4kKVwME+BZazmfd93Nbd6vMU8cR+yW1A23AOO91t9xfY+FVjdZWQ0CenoaKStvp2zj9zHc9h846J58ozwhxx2tYRmgaMnPqDVGeOJAFfdlN3L98GOsHt7PflnP/ecUc/bxYgq6dvFufXx53ROU8A+Vf82jfZ+cVPZ+q449cgHXdT+I7spxQi/jM4mPYQ5pPCVWcaHYNen4DllBE8f5D9dtgD2ZrrbhYWrTZXS6Be2Fz7LFU82QPtlxK8/lyM+4yEWXYWWKkZkStpT1cmv3T/BKyT9l/4J/dv0XAF9OLWUqKScJ7t7gIi6uewCc3O9deUEMovYEMg9o0sBAIxKpoDDUMzaJL+Fy0d9Xw4gzhJgSPiDLPeWXktHcky7iKc2uq2ySjzH9b3vEXc9mq5Ny6eGRrvMAkC6NyIQbPkCXLOVhcwOX6rY9nqEfYgXtY/vvyF3E9cZjAJRY/fTr5RT4xy+uETmPKtroFlHmm+XomgtjQkPO7w9jrXgJXQiO7z6DwugAtx/+MsPmRsrrquk4dpySsg5SqQCEF4C1lYyMclP/N0i0rqSsaBdH6jMUSx9fzX0VT3yYtGGhm2mOV9s9Dl2VD+DLtGHkbCdvz4pzsLb+kXRtKb55fj69+2OO02KHiUihcf9APWLwEq7IbsNXdA/rWz9hn7AU/NYL99cKGo/9ltEmbXM+MAwEWzGCtrOyIOgsoDB2wxBYQpDSBG3uHAszLv5yxEVZ3i8RQUlbxuTf8tKE6r9DyBTIXJz723QO7qsnmncmRd4CoulreCokGZkniBTYDsZV249MWgo+F1ww9nokMwBMX255OuNWlEzm43KWxh4IV1Fc1gFAVkpKYxl+1f45RMpEc2WptAZfc3dJQrd7v8oDx+gJreRIywSne8HwxEsNd7huYYF2gqjuRwoNvxzv+XZNyNzgEVkQgmOW3ThceshZKdI7flK+cBP5WfuX0vyS+Zd30tzlZpfXw+bEeCaMPk8xxMdjKwsmOMYA7ux4vUNBH/nD4+eUyXnGFunRpywENeqICMchzxkWWOBxRgqnOrahxjiPeUOs25MlFbTGyshLZ1jphCCs6W/lyz+zQwCEBnKyb82lDz9FJ8UIaU9MK5wwcPCLc6/gul0PUdIVpq8Q4nk6waSJlRnXrCgXY0chMyPAX54m0e+hvyBEcWSY8uYsMqOTnpKCLq8yzUhljvxuY1K2CM8ELdNlJr4eg1DcIlgUHgtXu6vmcm5I/AqA/bUCpsw/rRq0y9hXJ8aWpN/vcdOQyZGTGsOG7aW7ApPFWbHiMfpCfTS0THb83N4Ey5Y/jqtpBA5NrqvruRVkm5oBi4bzjpN12dfZnJzcmNOmrNzV67ZjWNyxIBXLWhFVkM14OLB/C9gDfYxEKiB0FCyNrDQwAI/bWXI5A3ghbNidNk2Nz5JvDJMfi8y4MJ2uW/gSjeQ0g9ur3w+xX007ZmoOY8+Ea0dp8gQ4HVtTs1JMnNQ4sMSk9IB9X6mz7FEka2oMDdBdPD5v577zRjB1we6Fjaxuaxn7n4zGZ+9aOB9LCD76mMZAcBsgsXJ5BNzfZN7xLtJeH55UkvaHHoXLNs/w7U8ds+YYCyEuBb6NnT39x1LKW2arrj+FbDZDa34amW3EwwDbytZSYrkJpmKsiB7if8rOR88z2SSfpbliHg/H11Nf3MFe10o+krqTfb1nsV7s5b+qr8SKSE5UuVmU3c+G1D4GA7V0uFYSEMdJaHn4MyOMuH0EvMO0FVcSjuTTNL+FnYl16HoHhXqMEbePSJ5gbXwvT1TVcX5nO0/n1vC4PBu3zJA+spInQwcJBga5LLWVXwxcwAZ/M8IsxZsZQLcSJAkynB/i6qJ7MYXJ4eEKyhnCyEIuHaBAQposvVQQ0IfxiRRpq4RobCkBA0SsiXBpOy5/mLTpof9YIVt8rRzKb+dW850sqmzDa0p+mbiEAmIYIUnIPQhCsrl0N7/rXc0a2YqbHLuLalmmNfN04ToWh48hUxZ/DKylMB7hpyVXMpJXwO3m+6nV+qiIn6ArV0Kuyc9vurfw59bjxHQf99Vu5szjL7J/ZDH3ik1UiSG8ZoZt5XVsTB/iyeQSqrUBzJROu7cE6c+xqngvoWQFO+RCci43y9Iu5qUsDLOYc612MulK2kIreSFTS1u0kVzKTZ0Wp7uihGd611IjBnlBX8m3K69m2J1PU08XrYkaiqqgquco8wq78JFme24Rhm8Qy5umZzhAZ7qSPlHIwZ2XsGz4IEe9DeQb/ZR4InQNzaOvuJuBhTmOHVlFK0E6++oIFo3wktlAu6uUDQUH6fUVc+3BZ/DrHSyLtNMf8LE/neCwrOKEVY5LzyPQV0qRNohV7SU2YFCntXK0cgEX9D5NWlQRjCUpJI/S/Chp4WJ11UF+fXQztb4IUa+P0vAgEZeL571LWTBynLjmxpKCO3MXEZIxbq/7K7xpN1uLV3Nd7Jds9W6iwneY9sJivqJ/lGv6O0kRJyTjBJMR9ifvxF/mp8psZESLUmEVY1ka21svZk28lZ36UtakH6ZXl7iFl7xYJV0H16PpkpWRGgZ3LSAR9uK1suRyWXQkOd3CJ7P4ZZKoR7KnScO0nsafWUjUG6E31IGek3RU9pJwN5OV4ELH8lVg6hnSGTdmzsCyBJo22anZKRuo1YKkNXPCjWg00lqAGHWEJEJqWNKZTy4shLBv11NvF17gumG4cthA6nFMt/355RZcj8YzXoukkAx5Q2iGj+WZEVymTkb30VskSPosnIX7mLCSM2agAawcVnA5T8f3EjIzZIKLMGNpNCNDpSuPQU0QyrhpGDEZcKWpEuV0pw6RC9UQ2H85PVqGQBL6D24hWHWE1FA5g70L8OcN0nvofPDvY032IJbpIaq7SQovLzGfXIHOd3If5GP6fZR7TlA4mCFtucGfxXSb5Kdz9BV6+fvoLUR1L4NWCfEGnZuOX09ZIExt4DBthcVUhKOc8JdQkxgio7v4fehMrhrZivfEORyofYKk9NJ/fAmrA22kAkPcHP8bNqcO06hF2Z+dT3kmQiqY4FimGp45h2DNAYY7Mnis7WxoLeJSz68wXCYthXksS2U4K+ahXPjY65PUmXfTnw2i51axj93cE3oHRfIQnnAFZkeSQ555LEvvI+OG7qJ8erQKfJ5BRK+fLeIw90UvoyEQJd80+etnvsW90cvx5kVpXlrK+r7D9JxVg1fbxgV+D7l+Lw9nr2BB6C6qtWFyCw22la8kFA3zv4vOpKXYRTD+R0qKi7nZewlnVB5BH8yh6yn2FS6krm8Qt5njYMU86tYOkH0ux4BLo9wr8ZPBigtMNFJLdF4qk6xq9rG7ZhGlw2HMjQZHqupIP5dAuIp4flkxlw61siO0Cmt4B+t6YFMRhLMCs1Gjdb6OPJyj3KXT46qndM0w+eYQsb4Av2y8hLP797J+8CC9QUlBo4FpmWj5Eo8rS6cooXddAc0tkmQwR6loICPW0rNoH901XRQlo1irDNpdJqEM7Fm1hkzQwpU26SrzYVycpbNf50PJHAm3m0PU8eDqpYzU7iLozVG9UyPkz0PGz8VT8HustEVDtpi7CpbSUtLEJ2I/41h1AcayDL/JqyboLqLOaifb2sC2w034A4+gWW7Swk846cZNBoFga3ETvo4cJe4Eh82zCFeu5Dc9nfzZvN0k/BqlnhzLcjpl7ir250coHxogber8MVbFiQAMDMzn2ie2sa8hwJPecl7S16HvC1FZ8SzRSAX1Ay6uaN1GdeFeKo/XsGa4mYsPwu41Z3JWw3Y63xmkL+LlvoyHLYNh0pnDrM4U4M/FyeTc7NmxgEJ3N20bTUYyHgq6DYQrRYhGspH5fLz/F+zX6ghYFpVZk9acQWzxKvKju3hhRSULo3E+nmpgj+yhNuPmjJRFWNO4OprCt97iocIigqEkwe0ufnzeBhYdbONDRphfv6uQvHCK0po6ynqbyXk1RspCHCutITjsYvsSiSELWZiM8NzFDQzV5rG3vpht8yVJnwc3K3j07LMojCepjncTlX5+ddlmLFcHPq2KB869gMXtB/BnhuzOPstPRSZJIlTOsQWLmN++FZF5beFhJxMhZwj4fsOFCqFjR/lfBBwHtgPXSin3z3T8+vXr5Ysvvvimn4dC8XZDSuksd34qz8FEiOkTUGa1TstCvFISf4XidTAX/kcKxVxHSgkSxCsts+tgmhYSC03TwJJouo40TYR+cu8VowghdkgpZ1qDZtZijDcAh6WUbVLKDPDfwLtmqS6FQuEwF27mJ9spBpRTrHhTmQv/I4ViriOEeE1OMYCuaxi6gSY0NMcZPlVO8asxW3eTamBiHqHjzjaFQqFQKBQKhWJOMluO8UxNiEkxG0KIG4QQLwohXuzv75/hcIVCoVAoFAqF4uQxW47xcWBiAtUaxubu20gpb5dSrpdSri8tLZ2l01AoFAqFQqFQKF4bs+UYbwcahRANQgg3cA3wwCzVpVAoFAqFQqFQvGFmJV2blDInhPg74FHsdG0/lVI2v8rHFAqFQqFQKBSKU8as5TGWUj4MPDxb5SsUCoVCoVAoFG8mKseRQqFQKBQKhUKBcowVCoVCoVAoFApAOcYKhUKhUCgUCgWgHGOFQqFQKBQKhQJQjrFCoVAoFAqFQgEox1ihUCgUCoVCoQCUY6xQKBQKhUKhUADKMVYoFAqFQqFQKADlGCsUCoVCoVAoFIByjBUKhUKhUCgUCkA5xgqFQqFQKBQKBQBCSnmqzwEhRD9w9BRVXwIMnKK6T3eUtrOD0nV2UfrODkrX2UXpOzsoXWeXU6VvnZSydKYdc8IxPpUIIV6UUq4/1edxOqK0nR2UrrOL0nd2ULrOLkrf2UHpOrvMRX1VKIVCoVAoFAqFQoFyjBUKhUKhUCgUCkA5xgC3n+oTOI1R2s4OStfZRek7OyhdZxel7+ygdJ1d5py+b/sYY4VCoVAoFAqFAlSPsUKhUCgUCoVCAbwFHWMhRK0Q4kkhxAEhRLMQ4pPO9iIhxGNCiFbnOeRsL3aOjwkhvjehnKAQYteEx4AQ4lsvU+c6IcReIcRhIcR3hBDC2f7NCZ9vEUJEZl+B2ePN0tbZd62j2R4hxO+EECUvU+fLaXuuEOIlIUROCHHVbH/32WSO6Xpa2Sy86fpe7WjbLIT4+ivUqez25Oqq7FaIi4QQOxx9dgghLphQ1oy6zVCnstuTq6uy21fW96tCiE4hROxV6jy5diulfEs9gEpgrfM6CLQAS4GvA190tn8R+JrzOgBsBv4v8L1XKHcHcO7L7HsBOBsQwCPAZTMc83Hgp6dan7mgLWAAfUCJ8/7rwE2vR1ugHlgJ/By46lRrc7roerrZ7JusbzFwDCh13t8BXKjs9tTrquxWAqwBqpzXy4Gu16ObstuTr6uy21fV9yynvNir1HlS7fYt12MspeyWUr7kvI4CB4Bq4F3YF1yc53c7x8SllM8AqZcrUwjRCJQBW2fYVwnkSymfk/Yv8fPRsqdwLXDXn/at5gZvorbCeQScll0+cGJqfa+krZSyQ0q5B7DezO94KphLuk7hLW+z8KbqOx9okVL2O+//ALxvan3Kbk++rlN4u9rtTinl6P+9GfAKITyvVTdltydf1ykou52gr7Nvm5Sy+5XqOxV2+5ZzjCcihKjHbo08D5SPCuw8l72Ooq4F7nZEn0o1cHzC++POtonnUQc0AE+8jjrnNG9EWyllFvhrYC+247YU+MkMh76qtqcbc0XX09Fm4Q1fEw4Di4UQ9UIIA/viWzvDccpuT5Guym7HeB+wU0qZ5rXbo7LbU6SrstsxJur7WjnpdvuWdYyFEHnAvcCnpJQjb7C4a3j5ltxMMUVTHehrgN9IKc03eB5zgjeqrRDChe3ArQGqgD3AjTMdOsO20zZNyhzT9bSyWXjj+kopw9j63o09etQB5GaqaqaPv9763irMMV3f9nYrhFgGfA342OimGQ6byR6V3b7y8bOpq7Lb6fq+5qpm2DardvuWdIwdB+Fe4JdSyv9xNvc6Xe6jXe99r7GsVYAhpdzhvNcnBMvfjN06qZnwkRqmD1+/kmP9luJN0nY1gJTyiNMLfw+w8U/U9rRgDup62tgsvHnXBCnlg1LKM6WUZwOHgFZlt3NK17e13QohaoD7gA9LKY84m2fUTdntnNJV2e10fV+u7FNut285x9iJrfwJcEBK+Y0Jux4ArndeXw/89jUWOSnuR0ppSilXO49/doYEokKIs5y6PzyxbCFEExACnvuTv9Qc4U3UtgtYKoQodd5f5JT5urQ9XZhrup5ONgtv7jVBCFHmPIeAvwF+rOx2buj6drdbIUQh8P+AG6WUfxw9+OV0U3Y7N3RVdjuzvi/HnLBbOQdmOb6eB/asZ4k9jLzLeVyOPfP5caDVeS6a8JkOYAiIYbc+lk7Y1wYsfpU61wP7gCPA98BeGMXZdxNwy6nWZa5piz0z/YBT1oNA8evRFjjDKS8ODALNp1qf00HX081mZ0Hfu4D9zuOaV6hT2e1J1FXZrQT4R8eudk14lL2abspuT52uym5fVd+vO3ZnOc83zQW7VSvfKRQKhUKhUCgUvAVDKRQKhUKhUCgUitlAOcYKhUKhUCgUCgXKMVYoFAqFQqFQKADlGCsUCoVCoVAoFIByjBUKhUKhUCgUCkA5xgqFQqFQKBQKBaAcY4VCoVAoFAqFAlCOsUKhUCgUCoVCAcD/B7MG7osS9E+FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stations = srs_stat['fromstationid'].unique()\n",
    "print(\"Number of Stations: \", len(stations))\n",
    "\n",
    "fig = pyplot.figure(figsize=(12,8))\n",
    "for i in range(0, len(stations)):\n",
    "    pyplot.plot(srs_stat[srs_stat['fromstationid'] == stations[i]]['starttime_date'], \n",
    "            srs_stat[srs_stat['fromstationid'] == stations[i]]['tripid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trend seems similar to the general ridership anyway. Forecasting on the overall dataset seems sufficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}