FinalProject_Harrison-Q-Ma.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, I explored the relationship between the crime rate in the surrounding area of a park or recreational area in San Diego and the Yelp review (1-5 stars) it received. I used the Priority E and Priority 1 police responses as a proxy to approximate the crime rate in that area. My analysis found no statistically significant correlationg between the dependent and independent variables and conclude that San Diego parks located in areas with lower crime rate do not necessarily receive better reviews on Yelp. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Name & GitHub\n",
    "\n",
    "- Name: Qixuan \"Harrison\" Ma\n",
    "- GitHub Username: Harrison-Q-Ma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the crime rate in the surrounding area affect the yelp review for a certain park or recreational area in the San Diego area?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lot of previous work link the availability of parks in the city to the overall crime rate of the city. Studies have found a correlation between the amount of green space in the city to the rate of violent crimes in the city: the more green space, the lower the violent crime rate. CDC also found a correlation between people's behavior in park usage and the types of crimes being committed in the city. However, these studies did not explore the question in more detail. Therefore, in this project, I want to do some more in depth about the effect on parks on the crime rate of its immediate surrounding areas. \n",
    "\n",
    "References:\n",
    "- 1) Effects of Crime Type and Location on Park Use Behavior (CDC) https://www.cdc.gov/pcd/issues/2020/19_0434.htm\n",
    "- 2) The Impact of Green Space on Violent Crime in Urban Environments: An Evidence Synthesis https://www.mdpi.com/journal/ijerph\n",
    "- 3) Safe cities: California, Texas are home to many of towns with lower violent crime rates https://www.usatoday.com/story/money/2019/10/27/crime-rates-lower-in-these-us-safest-cities/40406533/\n",
    "- 4) Can Parks Help Cities Fight Crime? https://theconversation.com/can-parks-help-cities-fight-crime-118322#:~:text=One%202015%20study%20of%20multiple,up%20to%2011%20times%20worse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My hypothesis for this is that parks and recreational areas located in the parts of the city with the least crime rates would receive better reviews on Yelp. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset 1: \n",
    "- Dataset Name: Yelp Data for San Diego Parks (yelp_SD_parks.csv)\n",
    "- Link to the dataset: https://www.yelp.com/\n",
    "- Number of observations: 834\n",
    "- Description: \n",
    "    Dataset 1 contains a list of locations in the San Diego area that is commonly recognized as parks and recreational areas. I will be conduct all further analysis based on the review scores on this list.\n",
    "    \n",
    "Dataset 2: \n",
    "- Dataset Name: Crime Rate in San Diego (pd_calls_for_service_2019.csv)\n",
    "- Link to the dataset: https://data.sandiego.gov/datasets/police-calls-for-service/\n",
    "- Number of observations: 590968\n",
    "- Description: This dataset contains the specific location for each of the calls that the police department received for the year of 2019. I will not be using any data from 2020 due to the year not being over yet and the calls are probably screwed towards focusing more on residential areas due to stay-at-home orders. \n",
    "\n",
    "\n",
    "I will be using Dataset 2 (locations) to calculate a overall \"score\" of each park that comprises of its proximity to the crimes in the area. Then I will compare that score to the reviews these parks received on Yelp and see if they correlate at all. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing packages that I will be using\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import geopandas as gpd\n",
    "from geopandas import GeoDataFrame\n",
    "from geopy.geocoders import Nominatim\n",
    "import requests\n",
    "import math\n",
    "from shapely.geometry import Point\n",
    "import descartes\n",
    "import matplotlib.pyplot as plt\n",
    "import patsy\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 16})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the data files into pandas dataframes\n",
    "df_sd_parks_review = pd.read_csv(\"yelp_SD_parks.csv\") # Yelp review for SD parks\n",
    "df_sd_pd_resp = pd.read_csv(\"pd_calls_for_service_2019_datasd.csv\") # Police calls in the year of 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we deal with the reviews on Yelp. In the follow 2 cell blocks, I will drop the columns and rows that contain information that are not really needed for my analysis or contain outlying information that will skew the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I will first drop the columns of phone, id, opening status, and url as they are not related to my analysis\n",
    "df_sd_parks_review = df_sd_parks_review.drop(columns = ['phone', 'id', 'url', 'is_closed'])\n",
    "\n",
    "# I will then drop all observations with review counts that are less than 20, to make sure we don't have outliers due to \n",
    "# limited sample size\n",
    "indexNum = df_sd_parks_review[df_sd_parks_review['review_count'] <= 20].index\n",
    "df_sd_parks_review.drop(indexNum, inplace = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows with outliers\n",
    "indexNum = df_sd_parks_review[df_sd_parks_review['longitude'] <= -117.5].index\n",
    "df_sd_parks_review.drop(indexNum, inplace = True)\n",
    "\n",
    "indexNum = df_sd_parks_review[df_sd_parks_review['longitude'] >= -110.0].index\n",
    "df_sd_parks_review.drop(indexNum, inplace = True)\n",
    "\n",
    "indexNum = df_sd_parks_review[df_sd_parks_review['latitude'] >= 34].index\n",
    "df_sd_parks_review.drop(indexNum, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following cell, I will do the following: \n",
    "1. Add new column called address to add later\n",
    "2. Drop columns that I don't need in the police report database\n",
    "3. Because we are using police report as a proxy for crime rate, I want to focus only on the more significant instances, in other words, the higher priority calls. In the police report, Priority 0 (\"E\") calls refer to those that \"involve an imminent threat to life\" and Priority 1 calls refer to those that \"involve serious crimes in progress and those in which there is a threat to life\". I will drop all rows with lower priorities so we don't count a loud party in our calculation of crime rate. \n",
    "4. There are addresses listed in the police report that only contain partial information about the location, which makes it hard to determine the geolocation of it. This will also cause errors in the conversion to geocode, so I will drop these as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add new column called address to add later\n",
    "df_sd_pd_resp = df_sd_pd_resp.assign(address = df_sd_pd_resp['incident_num'])\n",
    "\n",
    "# drop columns that we don't need\n",
    "df_sd_pd_resp = df_sd_pd_resp.drop(columns = ['incident_num', 'date_time', 'day_of_week', 'address_dir_intersecting', 'address_road_intersecting', 'address_sfx_intersecting', 'call_type', 'disposition', 'beat'])\n",
    "\n",
    "# drop lower priority police calls\n",
    "indexNum = df_sd_pd_resp[df_sd_pd_resp['priority'] > 1].index\n",
    "df_sd_pd_resp.drop(indexNum, inplace = True)\n",
    "\n",
    "# drop rows with not enough information to determine the location\n",
    "indexNum = df_sd_pd_resp[df_sd_pd_resp['address_number_primary'] == 0].index\n",
    "df_sd_pd_resp.drop(indexNum, inplace = True)\n",
    "\n",
    "indexNum = df_sd_pd_resp[df_sd_pd_resp['address_sfx_primary'] != df_sd_pd_resp['address_sfx_primary']].index\n",
    "df_sd_pd_resp.drop(indexNum, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this cell, I performed some string manipulations to concatenate different columns in the police report and come up with a single string that has the information required to accurately describe the location of an address. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:38: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n"
     ]
    }
   ],
   "source": [
    "# Perform string manipulation to get a single address from different columns in the police report\n",
    "\n",
    "for ind in df_sd_pd_resp.index: #REMEMBER TO CHANGE LATER\n",
    "    \n",
    "    output=''\n",
    "    \n",
    "    # add the street number if it is not 0\n",
    "    if df_sd_pd_resp['address_number_primary'][ind] == 0:\n",
    "        pass\n",
    "    else:\n",
    "        output += str(df_sd_pd_resp['address_number_primary'][ind])\n",
    "        output += ' '\n",
    "    \n",
    "    # add the street prefix (south, north, etc) if they are not NaN\n",
    "    if df_sd_pd_resp['address_dir_primary'][ind] != df_sd_pd_resp['address_dir_primary'][ind]: \n",
    "        pass\n",
    "    else: \n",
    "        output += str(df_sd_pd_resp['address_dir_primary'][ind])\n",
    "        output += ' '\n",
    "    \n",
    "    # add the street name (e.g. Gilman) if they are not null\n",
    "    if df_sd_pd_resp['address_road_primary'][ind] != df_sd_pd_resp['address_road_primary'][ind]: \n",
    "        pass\n",
    "    else: \n",
    "        output += str(df_sd_pd_resp['address_road_primary'][ind])\n",
    "        output += ' '\n",
    "    \n",
    "    # add the street subfix (e.g. Ave, St, Pl) if they are not null\n",
    "    if df_sd_pd_resp['address_sfx_primary'][ind] != df_sd_pd_resp['address_sfx_primary'][ind]:\n",
    "        pass\n",
    "    else: \n",
    "        output += str(df_sd_pd_resp['address_sfx_primary'][ind])\n",
    "    \n",
    "    # add San Diego, US to avoid getting geolocations that are very far off\n",
    "    output += \", San Diego, US\"\n",
    "        \n",
    "    # setting the address column of the current row to be output\n",
    "    df_sd_pd_resp.ix[ind, 'address'] = output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a new column in the police response to store the geocode that we will generate later\n",
    "df_sd_pd_resp = df_sd_pd_resp.assign(geocode = df_sd_pd_resp['address'])\n",
    "\n",
    "# due to the limitation of computing power, I do not have the resources to perform analysis on all observations (as I have \n",
    "# mentioned in a Piazza post)\n",
    "df_sdpd_rand = df_sd_pd_resp.sample(n=8000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following cell, I will first create two new columns to store latitude and longitude data (default being -1.0). Then I will use the Nominatim service to generate geocode data (latitude and longitude) from a string of address and store them back to the new columns I created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  \n",
      "/opt/conda/lib/python3.7/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",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "/opt/conda/lib/python3.7/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",
      "\n",
      "See the caveats in the documentation: http://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",
      "/opt/conda/lib/python3.7/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",
      "\n",
      "See the caveats in the documentation: http://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"
     ]
    }
   ],
   "source": [
    "df_sdpd_rand = df_sdpd_rand.assign(latitude = -1.0)\n",
    "df_sdpd_rand = df_sdpd_rand.assign(longitude = -1.0)\n",
    "\n",
    "for ind in df_sdpd_rand.index:   \n",
    "    geolocator = Nominatim(user_agent=\"COGS 108 Proj\")\n",
    "    location = geolocator.geocode(df_sdpd_rand.ix[ind, 'address'])\n",
    "    \n",
    "    if (hasattr(location, 'longitude') and hasattr(location, 'longitude')):    \n",
    "        df_sdpd_rand['geocode'][ind] = location\n",
    "        df_sdpd_rand['latitude'][ind] = location.latitude\n",
    "        df_sdpd_rand['longitude'][ind] = location.longitude   \n",
    "    else:    \n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following cell, I drop the rows that are unable to generate a geocode that make sense or at all. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address_number_primary</th>\n",
       "      <th>address_dir_primary</th>\n",
       "      <th>address_road_primary</th>\n",
       "      <th>address_sfx_primary</th>\n",
       "      <th>priority</th>\n",
       "      <th>address</th>\n",
       "      <th>geocode</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50860</th>\n",
       "      <td>3900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BATEMAN</td>\n",
       "      <td>AVE</td>\n",
       "      <td>0</td>\n",
       "      <td>3900 BATEMAN AVE, San Diego, US</td>\n",
       "      <td>(Bateman Avenue, Otay Mesa West, San Diego, Sa...</td>\n",
       "      <td>32.577412</td>\n",
       "      <td>-117.048828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214253</th>\n",
       "      <td>9400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AERO</td>\n",
       "      <td>DR</td>\n",
       "      <td>1</td>\n",
       "      <td>9400 AERO DR, San Diego, US</td>\n",
       "      <td>(Aero Drive, San Diego, San Diego County, Cali...</td>\n",
       "      <td>32.809105</td>\n",
       "      <td>-117.117656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16074</th>\n",
       "      <td>10000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BORDELON</td>\n",
       "      <td>ST</td>\n",
       "      <td>1</td>\n",
       "      <td>10000 BORDELON ST, San Diego, US</td>\n",
       "      <td>(Bordelon Street, San Diego, San Diego County,...</td>\n",
       "      <td>32.813721</td>\n",
       "      <td>-117.108231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298963</th>\n",
       "      <td>600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COCHRAN</td>\n",
       "      <td>AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>600 COCHRAN AVE, San Diego, US</td>\n",
       "      <td>(Cochran Avenue, San Diego, San Diego County, ...</td>\n",
       "      <td>32.585285</td>\n",
       "      <td>-117.058899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139084</th>\n",
       "      <td>3300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WICOPEE</td>\n",
       "      <td>PL</td>\n",
       "      <td>0</td>\n",
       "      <td>3300 WICOPEE PL, San Diego, US</td>\n",
       "      <td>(Wicopee Place, San Diego, San Diego County, C...</td>\n",
       "      <td>32.816338</td>\n",
       "      <td>-117.211693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        address_number_primary address_dir_primary address_road_primary  \\\n",
       "50860                     3900                 NaN              BATEMAN   \n",
       "214253                    9400                 NaN                 AERO   \n",
       "16074                    10000                 NaN             BORDELON   \n",
       "298963                     600                 NaN              COCHRAN   \n",
       "139084                    3300                 NaN              WICOPEE   \n",
       "\n",
       "       address_sfx_primary  priority                           address  \\\n",
       "50860                  AVE         0   3900 BATEMAN AVE, San Diego, US   \n",
       "214253                  DR         1       9400 AERO DR, San Diego, US   \n",
       "16074                   ST         1  10000 BORDELON ST, San Diego, US   \n",
       "298963                 AVE         1    600 COCHRAN AVE, San Diego, US   \n",
       "139084                  PL         0    3300 WICOPEE PL, San Diego, US   \n",
       "\n",
       "                                                  geocode   latitude  \\\n",
       "50860   (Bateman Avenue, Otay Mesa West, San Diego, Sa...  32.577412   \n",
       "214253  (Aero Drive, San Diego, San Diego County, Cali...  32.809105   \n",
       "16074   (Bordelon Street, San Diego, San Diego County,...  32.813721   \n",
       "298963  (Cochran Avenue, San Diego, San Diego County, ...  32.585285   \n",
       "139084  (Wicopee Place, San Diego, San Diego County, C...  32.816338   \n",
       "\n",
       "         longitude  \n",
       "50860  -117.048828  \n",
       "214253 -117.117656  \n",
       "16074  -117.108231  \n",
       "298963 -117.058899  \n",
       "139084 -117.211693  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lastly, we drop the rows whose latitude or longitude is -1 (cant find address)\n",
    "indexNum = df_sdpd_rand[df_sdpd_rand['latitude'] == -1].index\n",
    "df_sdpd_rand.drop(indexNum, inplace = True)\n",
    "indexNum = df_sdpd_rand[df_sdpd_rand['longitude'] == -1].index\n",
    "df_sdpd_rand.drop(indexNum, inplace = True)\n",
    "indexNum = df_sdpd_rand[df_sdpd_rand['longitude'] >= -117].index\n",
    "df_sdpd_rand.drop(indexNum, inplace = True)\n",
    "\n",
    "df_sdpd_rand.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the follow cell, I will plot the location of the Priority E and 1 crimes and parks in the San Diego area. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(170.43749892811908, 0.5, 'latitude')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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JsRPU886Eu+UvW+8j33PekVgP+Mokx1I919hsGF9NPGBmx8r/J95mxoxu+J18QPTt4BJ8u4K84jB4NtgmnQqyI865D51zv3fOlcr3Vh+nFNNYsiSWKx+fP3KopH8kCdgPUPKrL73GzIrlP7Nb5X9sdORZSQcFs8J05lX5wcTHW9vpTocrIXANrthslHRksikU9fkPiw7/hoJxLNPlr5JVt3N7RX48zdeS19JGVz+/Sb9PgvOemGIdQLsI2oHMiF3GPzFuUKTMrFC+961HnJ9n+dvyqRKPWdyS5OaXuE/2n2usx6q9Kc9iMtp2+angNkn6lplNiqt/iHxKT484556Sn0HnXEn/3Unx2Lm2CtDN7FtKfSGmB+V75GbFB9Pm59T+t+Dub1Ksq13OuQ/lX/89JP3RzI5KLGN+LvafyaeVhMH/kx+QeJWZtRnQG7T3K3H3T0/8wRF8LmJpCjuURWZ2rZm1SU8K2hibSjU+bW2zpJFmdmRc2aHyKVxt1jvoLWZ2tvxMT5JPmfu0k4fEVtetNrPcxINmNs6Cededn250haSD5acFjfcv8jndiX4j/7m+PqHeU+TTSl6X/97oyBnyP+pqnXOXJLvp80GzM9uvppUufX6V5Psk+A79hZJcWQS6ivQYIAOcc2+b2Qr5wPE5M3tMflDSP8nntp6XhudYaWbT5HtgHw9SZbbI/+d1s5k9Kb+M+YfyU459Uz7PNNng1V5ru3Nul5ldKj8DzhNmFj9P+2dKwyAt51yynvNkVsr/R3ulmR0t30t4tKQp8oHHuSk8V0NwPvdIej4YQPmR/EDFY+TPs8dBe/BctwVpI9dLesn8IkIvyr9u4+Tf+33l5/nOOufczuAz+j+Snjazh+VnTRki3+N5qqQ18q+35Oc0Hxuc1yb5K0NnyL+O97QzULE3/UTSPDN7VtI6+fd5f/kpEQ+Wb3N84LlQfi7zp8zst/J56WfIB+wvKvPpEkfEDaAcJj8D0UmSCuV7w//Fxa2G2h7n3H+b2U3yPxo3mFmtpLfkU2COlO+JvlD+/CU/l/xkSb82v7jaK/JpaCfLDyYtls/zjvk3+e+nS4OrU0/Kd0hcIP9DbaZzLr58MrEBqHd3UGal/HfNt8wst7PZlLrx+b0jaMcDwfvdEJzrOPkBtad1cg5Ax1wI5p3kxq2v3JTi/MdB2RGS5ssP5twh6a/yA9TGKWGu76D8JnUwP3l7x+V7oj6TFJUPro+Un0/6BflBlY3y8xz/WtLBKZ5n2treQbtPl8/T3i7/Y2KRfI9qh69DQh0zlGTu9a6Ulb+kvSJowzb5oKIkrvyMJO//4iT1nyb/o+ajuNfsSsXNWx9XzslPwdjdz+Ex8tPpvSY/sHGH/LzU/yXp1ISyjythHvbOjivJXN2p1Kckc6QH+/PkA9jX5YPFDyWtDz6T8fN7XyC//sDrwef2H/IpCrMlDUnxtWl3nvZUz7+Dur8qH2A+LT9jVJP8VZbn5Qc3fiHJY86X/1tslB9UuVj+qldXX/d2jyUpG/ucxt8a5ceKPCK/UNPYdh7bURu+Lr8w1Vb575wt8gO5fyJpv4Syh8n/XX0c3B6W/5GyMqh/n4TyI+R7pDcEdf9DfhD8cSmc7xfkv0f+oU7msZf/XnOSZqf6uqb6+Q3KTpb/XtsWtOd+SV/U5+t4FKTyWePGLdkttvAAAKATZnaEfK9hm4GrADoWDDR/Q37BMtJFgC4ipx0AUhfLWe/uAkfAgGBmydY3uEJ+mssHe7k5QL/Qq0G7mZWY2aNm9q6Z7TSzt8xsWfyAKjP7spk9ZGZbzGxHUPa/zezEbjxfqZk5M3srvWcCYCAxs/wgp/c2+UvcBB1Ax14xs4fNbL6ZLTC/uNFN8vOxz8tu04C+qbd72kfK5/79UNJZ8oNajpYf4BFbZOIL8jltP5HPK70s2PdEstHb7TGzL0i6RZ8vzAAA3TVOfrGfBknTnHPJVgoF8Lk75cfYXCy/7sNB8tMuHu/8gHkAXZT1nHYzO1x+xoZ/dc7d3E6ZEfIDX6qccynNimBmVfKX4d6RdIZLbVEIAAAAIHTCMOXjP4JtUwdlPpUfsd1RmRZmdrKkiyQdq8/nZU3Zfvvt5woKCrr6MAAAAKDbnn/++a3OuaQrCmclaA8WHRks3xP+f+RTWO5LKDMoKDNafs5XSborhbpzJFVJ+nfn3Ia4tWFSVlBQoLVrO1vVGQAAAEgfM6tv71i2Zo95Rr7nPCrfG366c+69hDLL5OdqrZdfzOVs59zLKdR9pfwiEjd1pUFmNsvM1prZ2vfff78rDwUAAAAyKltB+3f1+QpqH0t6JLYEcpwrJB0vH7D/RdIfzKyoo0qDJcQrJP3QOdel5a6dc1XOuSLnXNGoUUmvSgAAAABZkZWg3Tn3inPuGefcUvnVw/bS5ykwsTJvOOeec849IL8K23uSbuyk6gWSHpWfjeYLwQwyQyVZcH942k8GAAAAyLCsD0R1zn1kZhv0+aIlycp8ZmYvSZrQSXVHyefJf5jk2IfyS7tf3t22AgAAANmQ9aDdzA6QdISkmg7KRCQVSXqtk+q+I2mPhH1XSfqypGmSWGQJAAAAfU6vBu1mtkLSOkkvyeeyF8ovWNIs6eagzJ2SPpC0Vn5u9nz5xZhGy+fCx9fXLGmJc65ckpxzTyd5zhmSdjrnHs/EOQEAAACZ1ts97U9LOl9+tdOhkt6U9Likm5xzm4Iyz0i6RNIsSXtK2hLsK3fOrU+oLzZ1JAAAQKg1NDRo69at+uyzz7LdFPSyoUOHar/99lNubm6368j6iqhhVFRU5JinHQAApMuOHTu0efNmjR07VsOHD1d31pFB3+Sc0/bt2/XWW28pLy9Pe+yRmMn9OTN73jmXdLbEbE35CAAAMGC8//77GjVqlCKRCAH7AGNmikQi2m+//dSTtYAI2gEAADJsx44d2muvvbLdDGTRiBEjtGNHl5YRaoWgHQAAIMOam5s1ZEjWJ+1DFg0ZMkTNzc3dfjxBOwAAQC8gLWZg6+n7T9AOAAAAhBxBOwAAABByBO0AAADoksWLF8vMWm4jRozQcccdp4ULF/Yobzuememaa65JS139ASMiAAAA0C3Lly/X2LFj9fHHH2v58uW67LLL9N577+n666/PdtP6HYJ2AAAAdMuECRN02GGHSZLOOussbdiwQfPnz+9R0L5z504NGzYsXU3sN0iPAQAAQFpMmjRJ27Zt0zPPPKNvf/vbLSvAHn744br66qu1ffv2VuVPO+00nXLKKVq5cqW+9KUvadiwYbr99tuT1t3Y2KipU6dq9OjRevHFFyVJzz33nM4880ztu+++ikQiOuSQQzRnzpyMn2c20NMOAADQx9Ssr1HFqgptbtisvNw8VU6uVNn4smw3Sxs3btTgwYO1YcMGTZgwQTNmzNCIESP017/+Vddff73eeOMN3Xfffa0eE41GNXfuXF177bU65JBDNHLkyDb1fvDBB/rmN7+prVu3avXq1Ro3bpw++eQTlZSU6Pjjj9fixYs1YsQIbdq0SatXr+6t0+1VBO0AAAB9SM36Gs1aOUuNTY2SpPqGes1aOUuSej1w37Vrl5qbm7Vt2zYtW7ZMDzzwgKZOnaqyss/b4ZzTySefrL333lvf+973dNttt2nfffdtOb5161Y9/PDDmjBhQtLn2Lx5s0pKSrTXXnvpqaee0qhRoyRJr776qj788EP98pe/1LHHHttSfsaMGZk52SwjPQYAAKAPqVhV0RKwxzQ2NapiVUWvt+WII45QTk6ORo4cqTlz5qisrEyLFi3Sxx9/rCuvvFKHHnqohg0bppycHH33u9+Vc05/+9vfWtVRUFDQbsD+8ssv66STTtLBBx+sxx57rCVgl6QvfvGL+sIXvqDZs2frnnvu0ZtvvpnRc802gnYAAIA+ZHPD5i7tz6QVK1boueee06uvvqpPP/1Uv/nNbzRy5EhdfPHFuuOOOzR37lw98sgjeu6553TbbbdJknbs2NGqjtGjR7db/5NPPqktW7aovLxce+21V6tjubm5euyxxzRmzBjNmTNHeXl5OuaYY3T//fen/0RDgPQYAACAPiQvN0/1DfVJ9/e2Y445pmX2mJgdO3bowQcf1Lx58/SjH/2oZf/69euT1mFm7dY/e/ZsNTQ06KKLLtKQIUN03nnntTo+YcIE3X///WpubtbatWt100036fzzz9eLL76oY445pgdnFj70tAMAAPQhlZMrFcmJtNoXyYmocnJlllrU2s6dO7Vr1y7l5OS02r948eIu12VmWrhwoX7wgx/oO9/5jpYvX5603JAhQ3TCCSfohhtu0O7du/XKK690p+mhRk87AABAHxIbbBrG2WMkn7Zywgkn6Oabb9bo0aO13377adGiRdqyZUu365w/f74GDx6sCy+8ULt379YFF1ygP/zhD6qqqtI555yjcePG6dNPP9WCBQs0YsQInXjiiWk8o3AgaAcAAOhjysaXhSZIT2bp0qX6/ve/rx/84AcaPny4zj//fN1666365je/2e06b775Zg0ZMkRlZWXavXu3Jk6cqOHDh+uGG27QO++8oxEjRmjSpEl65JFHNHbs2DSeTTiYcy7bbQidoqIit3bt2mw3AwAA9BOvvPKKjjzyyGw3A1nW2efAzJ53zhUlO0ZOOwAAABByBO0AAABAyBG0AwAAACFH0A4AAACEHEE7AAAAEHIE7QAAAEDIEbQDqYhGpepqvwUAAOhlLK4EdCYalSZOlJyTzKR166TCwmy3CgAADCD0tAOdqavzAXtjo9/W1WW7RQAAYIAhaAc6U1zse9gjEb8tLs52iwAAwABD0A50prDQp8QsWEBqDAAAkhYvXiwza7mNGDFCxx13nBYuXKjm5uaUH79p06a0tym+znnz5unRRx9N23NIUkFBQatzj7/Nnz8/rc8Vj5x2IBWFhQTrAAAkWL58ucaOHauPP/5Yy5cv12WXXab33ntP119/fYeP+8Y3vqE1a9Zo9OjRaWtLsjqvu+46VVRU6PTTT0/b80hSSUmJ5s2b12Z/QUFBWp8nHkE7AAAAumXChAk67LDDJElnnXWWNmzYoPnz57cbtDc1NWnIkCEaNWqURo0alZY2ZKLOzuy333464YQTeuW5YkiPAQAAQFpMmjRJ27Zt03vvvadNmzbJzHT77bfriiuu0JgxYzRs2DB99NFHSVNZmpqadM0116igoEBDhw5VQUGBrrnmGjU1NbWU6UqdZiZJqqysbElfmTdvnn71q19p2LBhev/991u13TmnQw45RKWlpRl/nbqDnnYAAACkxcaNGzV48GDttddeamxslOSD5kmTJqmqqkq7du3SHnvskfSx06dP17Jly3T11VfrlFNO0Zo1a3TjjTfqjTfe0L333tuqbCp1rlmzRieeeKJmzJih2bNnS5LGjh2rSCSia6+9VnfffbeuuOKKlvIPP/ywNm7cqEWLFnV6ns65pLn7Q4ZkLrQmaAcAAOiDolE/C3FxcfaGXe3atUvNzc3atm2bli1bpgceeEBTp05VJBJpKXPAAQdoxYoVLT3fyfzlL3/R0qVL9fOf/7wlV/yss87S4MGDde211+qqq67Sscce26U6Y+krBx10UJtUlgsuuEBVVVX66U9/2lLHnXfeqcMPP1ynnXZap+d97733tvkhIUnPPfecioqKOn18dxC0AwAA9DFhWffviCOOaPn3oEGDVFZW1mYGlXPOOafD4FqSnnzySUnSRRdd1Gr/RRddpGuvvVZPPPFEq6A9lTo7MmfOHC1ZskSrVq3SGWecoXfeeUcrV67UL3/5y5Qe//Wvfz1p3v5RRx3V7TZ1hqAdAACgj4lf9y8S8fezEbSvWLFCY8eO1YgRI5Sfn580TSWVGWI++OCDpGUPPPDAVse7UmdHjj/+eBUVFemOO+7QGWecobvuuktDhgzR9OnTU3r8yJEjM9aj3h6CdgAAgD4mLOv+HXPMMS2zx7QnlR7xkSNHSpLeffddHXrooS373333XUnSvvvu2+U6O/P9739fs2fP1pYtW3TXXXdp2rRpLe0II2aPAQAA6GP627p/p556qiTpvvvua7W/pqZGkvTVr361W/UOHTpU27dvT3qstLRUI0aM0IUXXqjNmzfr0ksv7XrfKUkAACAASURBVNZz9BZ62gEAAPqg/rTu39FHH63S0lLNmzdPzc3NOumkk7RmzRrdcMMNKi0tbZXP3hVHHXWU/vjHP2rKlCnaZ599NGbMGI0ZM0aSNHz4cM2YMUO33HKLxo8fr5NOOinlerdu3aqnn366zf4DDzwwYwss0dMOAACArFuyZImuvPJKLVq0SGeffbaqq6t15ZVXasmSJd2uc+HChdpzzz01derUliki402bNk2SWqaETFVtba1OPPHENrdf/epX3W5rZ8w5l7HK+6qioiK3du3abDcDAAD0E6+88oqOPPLIbDcDCSoqKnTrrbfq7bff1t57753x5+vsc2Bmzzvnko5wJT0GAAAAA8oLL7yg1157TbfeeqtmzZrVKwF7TxG0AwAAYEA599xz9fe//10lJSW67rrrst2clBC0AwAAYEDZtGlTtpvQZQxEBQAAAEKOoB0AAAAIOYJ2AAAAIOQI2gEAAICQI2gHAAAAQo6gHQAAAAg5gnYAAAAg5AjaAQAA0CWLFy+WmbXcRowYoeOOO04LFy5Uc3Nzyo9P53zpyeqcN2+eHn300bQ9hyQtWbJE5513nvLz82VmmjFjRlrrbw9BOwAAALpl+fLlWrNmje6//34df/zxuuyyy3T99dd3+rhvfOMbWrNmjUaPHp22tiSr87rrrkt70H7PPffo9ddf15lnnqm99947rXV3hBVRAQAA0C0TJkzQYYcdJkk666yztGHDBs2fP7/dwL2pqUlDhgzRqFGjNGrUqLS0IRN1dqS2tlaDBvl+74ceeijjzxdDTzsAAADSYtKkSdq2bZvee+89bdq0SWam22+/XVdccYXGjBmjYcOG6aOPPkqaytLU1KRrrrlGBQUFGjp0qAoKCnTNNdeoqamppUxX6jQzSVJlZWVLGs+8efP0q1/9SsOGDdP777/fqu3OOR1yyCEqLS3t8BxjAXtvo6cdAAAAabFx40YNHjxYe+21lxobGyX5oHnSpEmqqqrSrl27tMceeyR97PTp07Vs2TJdffXVOuWUU7RmzRrdeOONeuONN3Tvvfe2KptKnWvWrNGJJ56oGTNmaPbs2ZKksWPHKhKJ6Nprr9Xdd9+tK664oqX8ww8/rI0bN2rRokXpejnSiqAdAACgL4pGpbo6qbhYKizMShN27dql5uZmbdu2TcuWLdMDDzygqVOnKhKJtJQ54IADtGLFipae72T+8pe/aOnSpfr5z3+uefPmSfLpNoMHD9a1116rq666Sscee2yX6jzhhBMkSQcddFDLv2MuuOACVVVV6ac//WlLHXfeeacOP/xwnXbaaV19GXoF6TEAAAB9TTQqTZwozZ3rt9FoVppxxBFHKCcnRyNHjtScOXNUVlbWpqf6nHPO6TC4lqQnn3xSknTRRRe12h+7/8QTT3S5zo7MmTNHr7/+ulatWiVJeuedd7Ry5cqWHvkwoqcdAACgr6mrk5yTGhulSMTfz0Jv+4oVKzR27FiNGDFC+fn5SdNUUpkh5oMPPkha9sADD2x1vCt1duT4449XUVGR7rjjDp1xxhm66667NGTIEE2fPr1H9WYSQTsAAEBfU1wsmfmA3czfz4JjjjmmZfaY9qTSIz5y5EhJ0rvvvqtDDz20Zf+7774rSdp33327XGdnvv/972v27NnasmWL7rrrLk2bNq2lHWFEegwAAEBfU1gorVsnLVjgt1nKaU+XU089VZJ03333tdpfU1MjSfrqV7/arXqHDh2q7du3Jz1WWlqqESNG6MILL9TmzZt16aWXdus5egs97QAAAH1RYWGfD9Zjjj76aJWWlmrevHlqbm7WSSedpDVr1uiGG25QaWlpq0GoXXHUUUfpj3/8o6ZMmaJ99tlHY8aM0ZgxYyRJw4cP14wZM3TLLbdo/PjxOumkk1Kq8+WXX9bLL78sSdq+fbvq6+v1u9/9TpL/8ZGpueLpaQcAAEDWLVmyRFdeeaUWLVqks88+W9XV1bryyiu1ZMmSbte5cOFC7bnnnpo6dWrLFJHxpk2bJkldGoC6bNkyTZs2TdOmTdMHH3ygxx9/vOX+X//61263tTPmnMtY5X1VUVGRW7t2bbabAQAA+olXXnlFRx55ZLabgQQVFRW69dZb9fbbb2vvvffO+PN19jkws+edc0XJjpEeAwAAgAHlhRde0GuvvaZbb71Vs2bN6pWAvacI2gEAADCgnHvuufr73/+ukpISXXfdddluTkoI2gEAADCgbNq0KdtN6DIGogIAAAAhR9AOAADQC5j8Y2Dr6ftP0A4AAJBhOTk57S7yg4Fh+/btysnJ6fbjCdoBAAAybP/999eWLVvU2NhIj/sA45xTY2OjtmzZov3337/b9TAQFQAAIMNiUwq+/fbbampqynJr0NtycnJ0wAEH9GhqSYJ2AACAXrD33nv3ifnAEU6kxwAAAAAhR9AOAAAAhBxBOwAAABByBO0AAABAyBG0AwAAACFH0A4AAACEHEE7AAAAEHIE7QAAAEDIEbQDAAAAIUfQDgAAAIQcQTsAAAAQcgTtAAAAQMgRtAMAAAAhR9AOAAAAhBxBOwAAABByBO0AAABAyBG0AwAAACFH0A4AAACEHEE7AAAAEHIE7QAAAEDIEbQDAAAAIUfQDgAAAIQcQTsAAAAQcgTtAAAAQMgRtAMAAAAhR9AOAAAAhBxBOwAAABByBO0AAABAyBG0AwAAACFH0A4AAACEHEE7AAAAEHIE7QAAAEDIEbQDAAAAIUfQDgAAAIQcQTsAAAAQcgTtAAAAQMgRtAMAAAAhR9AOAAAAhBxBOwAAABByBO0AAABAyBG0AwAAACFH0A4AAACEHEE7AAAAEHIE7QAAAEDIEbQDAAAAIUfQDgAAAIQcQTsAAAAQcgTtAAAAQMgRtAMAAAAhR9AOAAAAhBxBOwAAABByBO0AAABAyBG0AwAAACFH0A4AAACEHEE7AAAAEHIE7QAAAEDIEbQDAAAAIUfQDgAAAIQcQTsAAAAQcgTtAAAAQMgRtAMAAAAhR9AOAAAAhBxBOwAAABByBO0AAABAyBG0I/Oqq6WTT/ZbAAAAdNmQbDcA/Vx1tXTJJf7fq1f7bXl59toDAADQB9HTjsxatKjj+wAAAOgUQTsya+bMju8DAACgU6THILNiqTCLFvmAndQYAACALiNoR+aVlxOsAwAA9ADpMUBPRKN+sG00mu2WAACAfoyedqC7olFp4kTJOclMWrdOKizscZV1dVJxcY+rAgAA/QhBO9BddXU+YG9slCIRf78HkXYGfgMAAIB+gvQYoLuKi310HYn4bXFxj6qL/w3gnL8PAAAg0dMOdF9hoe8OT1M+S5p/AwAAgH6EoB3oicLCtOWwpPk3AAAA6EcI2oEQSeNvAAAA0I+Q0w4AAACEHEE7AAAAEHIE7QAAAEDIEbQDmVJdLZ18st8CAAD0AANRgUyorpYuucT/e/Vqvy0vz157AABAn0ZPO5AJixZ1fB8AAKALCNqBTJg5s+P7AAAAXUDQjpTUrK9RwfwCDbpukArmF6hmfU22mxRu5eXSXXdJJ53kt6TGAACAHjDnXLbbEDpFRUVu7dq12W5GaNSsr9GslbPU2NTYsi+SE1HV1CqVjS/LYssAAAD6DzN73jlXlOwYPe3oVMWqilYBuyQ1NjWqYlVFllrUO7i6AAAAwoLZY9CpzQ2bu7S/P0i8ulDfUK9ZK2dJElcXAABAr6OnHZ3Ky83r0v7+YKBeXQAAAOFE0I5OVU6uVCQn0mpfJCeiysmVWWpR5g3EqwsAACC8CNrRqbLxZaqaWqX83HyZTPm5+f1+EOpAvLoAAADCi5x2pKRsfFm/DtITVU6uTDpjTn++ugAAAMKLnnYgiYF4dQEAAIQX87QnwTztSMWb1bXavmiphs8s1cHlJdluDgAA6ONCM0+7mZWY2aNm9q6Z7TSzt8xsmZkdFVfmy2b2kJltMbMdQdn/NrMTU6i/0MxuNbOXzOwTM3vHzH5vZsdl9sww0LxZXauxl0zRF1cv0dhLpujN6tpsNwkAAPRjvZ0eM1LS85J+KOksST+TdLSkp80sPyjzBUkbJP1EUomky4J9T5jZ8Z3Uf5akr0laImmqpDmSRkl6xsy+nN5T6QeiUam62m/RJdsXLZUkWcJ9AACATOjVgajOuaWSWkU3ZvaspFclfVvSzc65VZJWJZR5SNJWSd+V9GwHT3GfpNtcXM6PmT0qaZOkH0n6Xs/Poh+IRqXly6XKSsnM39atkwoLs92yPmP4zFJp9RK5+PsAAAAZEobZY/4RbJs6KPOppJ2dlJFzbmuSfQ1mFpV0ULdbGCa1tdLSpVJpqVTSjTzqaFSaOFFqapI++8zvi0SkujqC9i44uLxEb+ohctoBAECvyMrsMWY22MyGmtkXJd0p6V35XvL4MoPMLMfM8iQtDHbf1Y3nGinpGEmv9LDZvaZmfY0K5hdo0HWDVDC/QDXra/yB2lppyhRpyRK/re04j3pjbVR1M6q1sTYu/aWuTnLu84B96FDf015cnKGz6b8OLi9R4VOLCdgBAEDGZaun/RlJsRzzDZJOd869l1BmmaTzgn+/J+ls59zL3XiuX8unHs/vqJCZzZI0S5Ly8rKwgE51tbRokf6rKEfTRz4pFyRe1DfUa9bKWZKksqWPtH7M0qVJe9tr1tfojoUL9D9Vf9UoOWmJaeND6zSupNAH52a+d905qaJCmjaNXnYAAIAQy9Y87d+VdIKkCyV9LOkRMytIKHOFpOPlA/e/SPqDmSWdAqc9Zvaz4Dl+6Jzb0FFZ51yVc67IOVc0atSorjxNz1VXS5dcIrd6tS5a8ISmP996Gs7GpkZVrKrwKTHxEu/LB+yzVs5S4TNDJTntpUZJTk/evtgXKCz0+esLFkh//rMP2gnYAQAAQi0rQbtz7hXn3DPBwNTJkvaSdFVCmTecc8855x6Q9HX53vYbU30OM7tU0i8kXeOcW5S+1mfAIt+82Ewk5S+0LbK5YbPvVX/oIWn6dL9N0stesapCjU2NqjvmTUmmTxSRZLrrwLixvYWFUnk5wToAAEAfkfUVUZ1zH8mnyBzWQZnPJL3UUZl4ZvZdSbfLz0YT/nXnZ86UpJaZSKq/1LZIXm6QslNSIi1e3O4g1M0NmyVJf/tivSaW7acfHTdRE8v20xffeVY6+WTfq98PtTsOAAAAoB/I+uwxZnaApCMktRtlmVlEUpGk11Ko71xJd0u6yzn3r+lqZ0aVl0uS1t44R7eP/0yLE2aUN5kqJ6f22yMvN0/1DfWSfOD+ty/Wa8bzUvVKSVotrV7d6jn7g1hKUGNTo6SEcQDjy7LZNAAAgLTo7RVRV5jZtWb2z2b2NTObLekJSc2Sbg7K3GlmN5nZeWZ2qpl9T9LjkkYrIT3GzJrNrDru/lfl54F/SdJiMzsh7pak/zpEyssV/f0iLTsh0mq3yXRp0aUpB5+VkysVyWldx6wXrXWhReHOFuqqWEpQvJZxAAAAAP1Ab/e0Py3pfPnVTodKelM+IL/JObcpKPOMpEvkZ3LZU9KWYF+5c259Qn2Dg1vM6ZKGSfqSpKcSytZLKkjPaWRGLDCvWFWhzQ2blZebp8rJlV3qLU5Wh5t5pmxe3GyZQTpOfxFLCUp1PwAAQF9jcYuHIlBUVOTWrl2b7WakVzClpGbO7FepMZJUML+gJSUoXn5uvjZdvqn3GwQAANANZva8cy7pbIlZH4iKXlJeLj31VL8L2KXkKUGRnEjK4wAAAADCjqAdfV7Z+DJVTa1Sfm6+TKb83HxVTa1iECoAAOg3SI9Jol+mxwAAACDUSI8BAAAA+jCCdgAAACDkCNoBAACAkCNoBwAAAEKOoB0AAAAIOYJ2AAAAIOQI2gEAAICQI2gHAAAAQo6gHQAAAAg5gnYAAAAg5AjaAQAAgJAjaAf6smhUqq72WwAA0G8RtCPtatbXqGB+gQZdN0gF8wtUs74m203qn6JRaeJEae5cvyVwBwCg3yJoR1rVrK/RrJWzVN9QLyen+oZ6zVo5i8A9E+rqJOekxka/ravLdosAAECGELQjrSpWVaixqbHVvsamRlWsqshSi/qx4mLJTIpE/La4ONstAgAAGTIk2w1A/7K5YXOX9qMHCguldet8D3txsb8PAAD6JYJ2pFVebp7qG+qT7kcGFBYSrAMAMACQHoP21dZKM2b4bYoqJ1cqkhNptS+SE1Hl5Mo0Nw4AAGDgIGhHcrW10pQp0pIlfpti4F42vkxVU6uUn5svkyk/N19VU6tUNr4sww0GAADov0iPQXJLl7a9X1KS0kPLxpcRpAMAAKQRPe1Ivj5PaWnrQon3AQAA0GsI2geg+CD9zepaPXvUDD04p7b1+jwlJdJDD0nTp/ttir3sAAAASD/SYwaY2CKazkmTd9XqwZ1TVCapbNcS/bMeUl1dyeeTkZSUEKwDAACEAD3tA0z8IprTmn3eugXHzt+1lPV5AAAAQoigfYApLpYu+qxaT+lk/cPtI0lywbFT7yxlym8AAIAQIj1mgBmxvFp3NF8iSTpx92p9MP1y7asPpdJSHUwqDAAAQCgRtA8wdvciv5XvYW/632elDU9ltU1AWtXW+ilKS0sZkwEA6DdIjxlg3MUz/TbhPtAvdHNRMAAAwo6e9gFmdEW53pHvcXcXz9ToivJsNwlInx4sCgYAQJjR0z4Aja4o14EbniJgR//DomAAgH6KoB1A/8GiYACAfor0GAD9C4uCAQD6IXraAQAAgJAjaAcAAABCjqAd6M+iUam62m8BAECfRdCOdhHvZUfN+hoVzC/QoOsGqWB+gWrW13SvomhUmjhRmjvXb3kjAQDosxiIiqRi8Z5zkpm0bp1UWJjtVvV/NetrNGvlLDU2NUqS6hvqNWvlLElS2fiytg+IRqW6Oqm4uO0bVFfn38DGRikS8fd5EwEA6JPoaUdS8fGec/4+Mq9iVUVLwB7T2NSoilUVbQt31pNeXOx/cUUifltcnMGWAwCATCJoR1LEe9mxuWFz6vs7+2VVWOgvkSxYwKUSAAD6ONJjkFQs3msv8wKZkZebp/qG+qT720jll1VhIW8eAAD9AD3taFdhoVReTszXmyonVyqSE2m1L5ITUeXkyraF6UkHAGDAoKcdCJHYYNOKVRXa3LBZebl5qpxcmXwQqkRPOgAAA4Q557LdhtApKipya9euzXYzAAAAMICY2fPOuaJkx0iPAQAAAEKOoB0AAAAIOYJ2AAAAIOQI2gEAAICQI2hHl9Ssr1HB/AINum6QCuYXqGZ9TbabBAAA0O8x5SNSVrO+RrNWzlJjU6Mkqb6hXrNWzpKk9qckBAAAQI/R046UVayqaAnYYxqbGlWxqiJLLQIAABgYCNqRss0Nm7u0HwAAAOlB0I6U5eXmdWk/AAAA0oOgHSmrnFypSE6k1b5ITkSVkyuz1CIAAICBgaAdKSsbX6aqqVXKz82XyZSfm6+qqVUMQgUAAMgwc85luw2hU1RU5NauXZvtZgAAAGAAMbPnnXNFyY7R045+ifnkO8br0zleIwBAmDBPO/od5pPvGK9P53iNAABhQ3pMEqTHdFM0KtXVScXFUmFh1ppRML9A9Q31bfbn5+Zr0+Wber9BIcPr0zleIwBANnSUHkNPO9IjGpUmTpSck8ykdeuyFrgzn3zHeH06x2sEAAgbctqRHnV1PmBvbPTburqsNYX55DvG69M5XiMAQNgQtCM9iot9D3sk4rfFxV17fDQqVVf7bQ9lYz75vjRokfn2O8drBAAIG9JjkB6FhT4lpjs57WlOrYkNFKxYVaHNDZuVl5unysmVGRtA2NcGLfb269MX8RoBAMKGgahJMBC1l1VXS3Pn+tSaSERasEAqL892q1LGoEUAAJAOzNOOcOtpak2WMWgRAABkGkE7si+WWrNgQVZnnekuBi0CAIBMI2hHOBQW+pSYPhawSwxaBAAAmUfQDvRQ2fgyVU2tUn5uvkym/Nx8VU2tYtBiH5PGCYwAAEg7BqImwUDUvqVmfQ2zfKBHQrQ2GABgAGMgKvqt2HSL9Q31cnIt0y2GeZ70sOhLc8tnWojWBgMAICmCdvRpFasqWuZHj2lsalTFqoostahv4MdOa318AiMAwABA0I4+jekWu4cfO6318QmMAAADACuiok/Ly81LurAR0y12jB87bRUWEqwDAMKLnnb0aUy32D3MLQ8AQN9C0I4+jekWu4cfOwAA9C1M+ZgEUz5iIGCqTAAAwqWjKR8J2pMgaEdv2n7woRr21hvaOfYQDX/z9Ww3BwAAZAnztAMhtf3gQ7XHW2/IJO3x1hvafvCh2W4SAAAIIYJ2IIuGvfWGJMkS7mcaCysBANC3ELQDWbRz7CGSJJdwP5NYWAkAgL6HoB1Zs7E2qroZ1dpYG812U7Jm+Juva8fYQ+Qk7eilnHYWVkJXcFUGAMKBxZWQFRtroxo1ZaL2l5NbYtr40DqNKxmYK9vEAvXhvfR8LKw0QNTWSkuXSqWlUklJt6qIXZWJ/ciLXZWRxExDANDL6GlHVry1tE4mpz3VKJPTW0vrst2kAYOFlRJEo1J1td/2F7W10pQp0pIlfltbm7RYZ1e7uCoDAOFB0I6sGFtaLCfTp4rIyTS2tDjbTRowWFgpTjQqTZwozZ3rt/0lcF+6tOP7+vxq18QlczVqysSkgTtXZQAgPAjakRXjSgr1/kPrtG76Ar0/gFNjsoFVZOPU1UnOSY2NflvXT674lJZ2fF+pXe3iqgwAhAeLKyXB4krAABHraXdOMpPWrZMK+8kPyE5y2mM97SYnJ0v64zkxp13yV2UG7I88AMgwVkTtIoJ2YACJRn0Pe3Fx/wnYU7SxNqq3ltZpbGlxu1e7atbXqGJVhTY3bFZebp4qJ1cSsANAhhC0dxFBOwAAAHpbR0E7Oe0AAABAyBG0AwAAACFH0A4gI1hJEwCA9GFFVABpx0qaAACkFz3tANKOlTQBAEgvgnYAacdKmgAApBdBO4C0YyVNAADSi6AdQNpVTq5UJCfSal8kJ6LKyZVZahEAAH0bQTuAtCsbX6aqqVXKz82XyZSfm6+qqVUMQgUAoJtYETUJVkQFAABAb2NFVADIEuarB4De0d+/b5mnHQAyhPnqAaB3DITvW3raASBDmK8eAHrHQPi+JWgHgAxhvnoA6B0D4fuWoB0AMoT56gGgdwyE71uCdgDIEOarB4DeMRC+bwnakTX9fZQ3wHz1ANA7BsL3LfO0J8E87ZmXOMpb8r+I+8QfWDQq1dVJxcVSYWG2WwMAAPoJ5mlH6PTZUd7RqDRxojR3rt9Go9luEQAAGAAI2pEVfXaUd12d5JzU2Oi3dXXZbhEAABgACNqRFX12lHdxsWQmRSJ+W1yc7RYhwBgJAEB/RtCOrOizo7wLC6V166QFC/yWnPZQiI2RqG+ol5NrWQmPwB0A0F8wEDUJBqL2jpr1NapYVaHNDZuVl5unysmV4R+EilAqmF+g+ob6Nvvzc/O16fJNvd8gAAC6oaOBqEN6uzFATNn4MoJ0pEWfHSMBAECKSI8B0Of12TESAACkiKAdQJ/XZ8dIAACQIoJ2AH3eQFgJDwAwsDEQNQkGogIAAKC3sSIqAAAA0IcRtAMAAAAhR9AOAAAAhBxBOwB000tn/lgf5Oyvl878cbabAgDo5wjaAaAbXjrzxxr/p/nap/l9jf/TfAJ3AEBGEbQDQDeMfbxGkmRx9zfWRlU3o1oba6PZaxgAoF8iaAeAbnjrND8HfGzS3PpjztaoKRM1cclcjZoykcAdAJBWBO0A0A3HPnKL1p9xuT4cMkrrz7hcnxxXLJPTnmqUyemtpXXZbiIAoB8Zku0GAEBfdewjt0i6RSMlbayNyi0xfaqInExjS4uz3TwAQD9CTztCo2Z9jQrmF2jQdYNUML9ANetrst0kIGXjSgr1/kPrtG76Ar3/0DqNKynMdpMAAP0IQTtCoWZ9jWatnKX6hno5OdU31GvWylkE7gil9n5gjispVPHich+wR6NSdbXfAgDQQ+ac67zUAFNUVOTWrl2b7WYMKAXzC1TfUN9mf35uvjZdvqn3GwS0I/YDs7GpsWVfJCeiqqlVKhvvB6cqGpUmTpSck8ykdeukQnreAQAdM7PnnXNFyY7R045Q2NywuUv7gWypWFXRKmCXpMamRlWsqvB3olHpF7+Qdu+WGht94F7HoFQAQM8wEBWhkJebl7SnPS83LwutAdrX4Q/MWA/7rl3Sjh1STo702WfJK4pGfTBfXEwvPACgU/S0IxQqJ1cqkhNptS+SE1Hl5MostQhIrr0fknm5eT4Id84H7IMHS01NUnOzdMklUm3t54Vra6Xx46Uf/tAH+Uny3hmYDQCIR9COUCgbX6aqqVXKz82XyZSfm986RxgIiQ5/YI4d63vZc3L8Nt7SpX4bjUr//M++B37HDl8uIX2GgdkAgEQE7QiNsvFl2nT5Ju3++e6Wwaf0NCJs2v2BOWySdN55fuBpc3PbB5aW+m1dnTQo7qt3926fIhOn07x5AMCAQ047Qilxho5YT6Mket/Ru2prfS95aalUUiLJfwbbfA6rqz9PjRk6tHUu+403tjxWxcU+aB8+3AfsDz7YJqedgdkAgET0tCOU6GlEKNTWSlOmSEuW+G18Xnqi4mLfyx6JaPfgHD0x/S41nDNdeughqSLuc1tY6KeA/PWvpZde+jyYj9Nh3jwAYEAiaEcoJZtJRqKnERlSWyvNmNE2KI/locdcf337iyUFwfi7Vy/QRK3T2cvLddAjixUd1zYoV2GhVF7e7qwxDMwGACQiaEfo1KyvkcmSHhs5fGQvtwb9XmJvemwV0+rqNrnmev75dmd7kSQVFuqPB5brb1bYoynaGZgNAEhETjtCp2JVhZySr9T78c6PVbO+huAF6ZPYmz57ts9JN/O3u+6SFi3yAfvOnX4qx7q6dnvJ47JkZNY27k+quto/x8yZvgde7eTNAwAGLHraETodpcA0NtpBQgAAIABJREFU7W4irx3plRhVm/lpGGNd5ZJ0993SkCEpReKxlPUFC/y203WTqqv9PO6rV/ttdXXPzgcA0C/R047QaW911JhYUF+zvkYVqypU31CvwTZYu9wu5efmq3JyJT2USE00Kv3oR35e9aYm38M+eLA/Fh+gxyLxxBVMo1Fp+XL/72nTWvYXFnZhkdNFi9reD3rbAQCIIWhH6FROrmw13WOivNy8NlNC7nJ+IRumhkSXxFYwbWqS9thDuuAC6eqr/bFYMB6TGIlHo9KECdL27f5+ZaX05z93IVoPzJzpe9nj7wMAkID0GIRObBDevsP3bXMsNoNGsikhY5gaEimLT0AfPNgH7LGg+6abpF/8ov2Bp3V1rVc9TbKyaYvYwNZk9ZSX+7z5k07yW3rZAQBJELQjlMrGl2nrFVt1z7fuSTqDRmdTPzI1JFLSXgJ6LCBvbGw/GC8u/jyVRvL/TpbrHo36wH/u3PZ/AJSXS089RcAOAGgX6TEItfZm0Ogs751FaJCyZAnoY8f6lU0lvx079vNj0ejnue1//nPSnPZWYik4jY2+R7+DmWcAAGgPQTv6pI7y3lmEBt0SH4y/9ZY0fLjPVx8+3N+PlZk40QfhZr53vqKTVKxuzQEJAEBrBO3ok2K978weg7SIRrV7wkTt2uU0eLBp0Ir7pUGD2gbaib3my5dLBx7YekaZRO3NPAMAQBcQtKPPYvEZpMu7y+s0YrvTnmrUp4po29q3dGCyQDu+13z3bum66/z87YMGtZ2UPb7nvktzQAIA0BYDUQEMbLW12n7/HzRIu/WJInIy1SkItMvLWwfbsV7zq6/2A1SbmnwKze7drQer1tZK48dLP/xh+4NPAQDogv+fvXuPj6uuE///+sxkJhdaY6E3sDSppaMoVSh1FTCilBqK2yJby9qN2kv6LRcRyk8XvxCWApKyov6IhQWpTWhXY5DaBcpKG0u57EgFtw2XoNgppUmJXFtqpM195vP94zNn5sw1M8lMMknez8cjj8k5c+bMZyZ0eJ/3vD/vjwTtQogxp765ntKaUsq/odAXXUTpC49QQBcPOFfz+cImPr0kSVbc4zElMfbOMYFAuITG5yOw6BJ0T4+ZxJqsFaQQQgiRIgnahRBjirUwV2t7K19/xexTwfvKP3uUh1709F/JYrV7LCgwq6g++mgoI//2Fi9dPQ4UoIGAPyCTT4UQQgyaBO1CiOHX2AjLl5vbLLMvzNVwhtmng/d5bl4aDtiTLYjk8bCt4Rauv6SIj63uofTVy6lvrgfASxkBHHRQSA/5NH57m9SzCyGEGDSlte7/qDFm7ty5es+ePcM9DCHGhsZGuOii8PaOHVBenrWnc9zqQBP+3Ju/H5a+Ag+eAY2/DO6P19rRFnhve+zHbH+gil0f6QGg7BDsmVnA9Ss28pn8Ci4708c5fi9/cJallrkXQgghAKXUXq313Hj3SfcYIcTwamiI3c5i0D69eDruA62UHQLvdNg5y/yUFJeED0q2IJLPx7yvXc8FWqM0KA1OoE918Y8F11OxroKHXvTg9Xq4Ljif1Wokc8E0HzPastP6sb65nqpdVRxqP8T04unS+lQIIUYZCdqFEMNr6VLYvDlyO4vWz7yaC77/r6GamDmXw19PjlqQK9mCSF4vWmvG9UK3A9wBUxPvBs59/k0gssOjzwePn34d3wxsZjx/J1CYj8MRm70fDKtO3yr7aW1vZfVjqwEkcBdCiFFCatqFEMOrvNyUxCxbFr80Jllt+QAsencCBU4343rN9lffO5ENCzdEBrdWa8f162OD67IylIJjLsIzWIMK8wo52OjDu7yWg41mvEeWX8e1gRpO5Cgu/Dg6O0wWP4MdZex1+paO3g6qdvWzWqsQQogRQzLtQojhV14evySmn9ryASkrI8/hgqI8xinFnbf9If454y2IFKxzWfaN8RQf/oA3xsPDvwanBr+CpvGnsuaiOUxGozcrDu5o4owXzQTVUDcZwBGdvR+kQ+2H0tovhBBi5JFMuxiVrD7cjlsdlNaUhjp7iGE0kIy5vbY8U9npZFn0ZKwLiGuuYdMvPgjVw591BXz7K+Z2wmuTUZiVVRWatgYvr5xpMvjW1NeDH70wo6UxYOr009kvhBBi5JFMuxh1pL43Bw00Y56stnww4mXR+2O7gFBuRdkhzf6JhH4Apny2B/2S4nhwZdVpS8so2ANv/uFkxvEBDzhWcfH2uyDD3WSq51VH/DcPUOSKqtMXQggxokmmXYw6Ut+bgwaaMR9oVjwbbBcQbqebPTMLIu4uchVxxdXX8N6OJpqWree9HU3MaPNy8k2rOIW3+BDHWL7oKB4yU5tvVzG7gg0LN1BSXIJCUVJcElunL4QQYkSTPu1xSJ/2kS26D7dFoQisDQzDiERWatOHg9W7sayM+u7/7b/F4nnnwe7d4W3rW4OR+vqFEEJklfRpF2PK9OLptLa3xt0vhomVMfdmp0f5kLGV1VTg6T+TvXJlZNCudfibhpH6HqRJ+scLIURmSHmMGHWq51VT5CqK2Cf1vTnA44HKysgG5hls5TggtbUmG15bm53zV1bCxo1w5pngcmW+Nj/HWfNLWttb0ejQ/BKZGC6EEOmT8pg4pDxm5JPsXo7LhXKZ2lpYtSq8vXGjCbKzxVZaM9Svtb65nmu3X8uRziMAnFR4Ej9d8NOs/5sorSmN+61XSXEJLWtasvrcQggxEiUrj5FMuxiVKmZX0LKmhcDaAC1rWiRgzzXZaOWYrrq65NuZYM/kR3/TMETqm+tZ8ciKUMAOcKTzCCsfXZn1jLf0jxdicKR9sbCToF0IEVdWq1ey1coxHStXAqZ/ugb+9tKBzJ7fyuTv3m1us1WC04+qXVX0Bnpj9vf4e7LeUUn6xwsxcFJeJqJJ0C5GjeiMxFW/vUoyFANkW0eIOXOyELjnQivHykr+dsKU0Gbx8Xc4PGFm5q5UhiKTn4JkWe1sZ7xlfokQAyfti0W0IQ3alVLlSqknlVJvK6W6lVJtSqmHlFKfsB1ztlJqh1Lqr0qpruCxjyulzknxORxKqRuUUi3Bx7+klFqcvVclckG8jMR9e+6TDMUADUn1yjCVi9h96Pg7AKjg9kl/ez1zVyrBTH7C7SGSLKud7Yy39I8XYuCkvExEG+pM+4nAXuBq4MvADcAngeeUUiXBYz4MvAZ8FygHvhPc94xS6h9SeI4fALcA9wALgOeALUqpizP3MkSuiZeRiCYZitTlQvXKUHjvpI8DhLr6azBXKj09g79SsTrHnHtu9ie5JlE9rxqXwxWz3+10D0nGW+aXCDEwUl4mog179xil1MeAvwDf01r/JMEx44HDwAat9XeSnGsy8Abw71rrtbb9u4BJWutPpTIm6R4z8iRaUCmaLLCUumFsdjKk3pl4OpOO/IWO/A8zrvtv4TvWrIG77hq+gWXQcHWPEUIMnPUNsj0hVeQqkm+rRrlc7x5jtTSInSkVdhzo7ucYMJl5N/DLqP2/BGYrpWYMaIQi56WaeZAMRepyoHplSEw5/CoOrRk37aTIOx57bHgGlAUVsys4fP1h9FqNXqs5fP3hUfU/femwIUYjKS8T0YYlaFdKOZVSbqXULOB+4G3gwahjHEopl1JqOqbUBWBjP6f+JCa4fy1q/5+Ct59AjErxJrxFkwlwIqkVK0KdZDTwQ8/hlIK/XFgjaiyTDhtiNJPyMmE3XJn25zHBtQ/4FHCB1vrdqGMeAnqAVmAxcLHW+s/9nPdE4G86tubnfdv9cSmlViul9iil9rz33nspvgyRK+JlJK6ce6VkKETK6heVsvbCPF6bADd8Cf7vZ9v7Df6y3mVH9Es6bAghxophqWlXSp0OfAj4KPA9YArwea11i+2YjwInAacC3wbOBi7UWicsNldK/Rz4R631yVH7Z2EuEL6ltf5Ff+OTmnaRzoqqsvrqyHaw0Udbg5cb8zfw+1P+GHN/stU7a2tNwN7RYSbtrl8/bPNNx6xE81lk/ooQYiRKVtOeN9SDAdBavxr89Xml1HagBfi/wBW2Y14HXgf+Vyn138ArwO3ARUlO/T4wQSmlorLtE2z3C5FU9OQf6+t2ICYYT+dYkXsONvqYdNEcJqPZjmJORQn7Z7VGHJOsvdpY6bKTy6YXT6e1vTXufiGEGE2GfSKq1vpvmBr005Ic0wO8nOyYoD8B+cDMqP1WLXt/5TVCpPV1u3w1P7K1NXhRaE6gA9CUvXJqzDHJgr9cWCNqrJMFnIQQY8WwB+1KqSnAx4GEa4grpYqAucmOCdqBqYOPTnF+A3hFa31wEEMVY0Q6C1rI4hcj27SlZWgUxykCFH88K3I+S0Twl2DG6VjpspOrpMOGEGKsGNLyGKXUw0ATJmv+d8ADXAf0AT8JHnM/poxlD6Y3ewlmMaaTgW9Gna8P2Ky1rgTQWr+rlLoLuEEp9UHwuf4ZuAC4JNuvT+SOwdSZp/N1u3w1P7LNKPdwcEcTbQ1eSsum8fO233BD9wc8k/9W5H831oxTrU0djKTVc0rF7AoJ0oUQo95Q17Q/B1yGWe3UjVkI6WngDtsk1OeBVcBq4ATgr8F9lVrr5qjzOYM/dlXAMeBaYCqwD7hMaz16mi6LpAZbZ149rzrughbxvm5P51iRm2aUe5gxA5gzh1O15imtoeoHcPGScGDu9ZqA3Zpx6vVK0C6EEGJIDfuKqLlIuseMbKU1pXGz38m6gEST7jFjjL0NDIDbDS5XOKMumXYhhBBDIFn3GAna45CgfWSTFnAibVZQ3tsLPT1mX3QPR5/PZNjLyiRgF0IIkRXJgvZhn4gqRKYlqieXOnORkNUG5uabobAwfg9HmXEqhBBiGEnQLkYdaQEnBsTjgaoqePFF6eEohBAi50jQLkadVFvA1TfXU1pTiuNWB6U1pUmXqxdjiGTUhRBC5CCpaY9DatpHv+gOM2Cy8dLfeWyTsnUhhBDDSWrahYgiK5mKaNZc1GuuMbdRaygJIYQQw0qCdjEmyUqmIpq9FbvWZlsIIYTIFRK0izFJOsyIaGVlpmFMvMYxQgghxHCToF2MSdJhRkSzuj5K4xghhBC5SIJ2MSYl7DCT/xmzOqYUNA8pn2+Y3vaoJ+63ccz8+Wal1Pnzh26MQgghBAPoHqOU+gjwXeALwInAIq31K0qpNcAftNbPZ36YQ0u6x4we9c31VO2q4lD7IaYXT6d6XnXi7jCyVP2wGLa3Pd0nnj8fnngivH3hhbBzZ/bHOQqk9e9QCCHGsIx1j1FKfRJoBr4JvAmUAO7g3SXAtYMYpxAZZbV1bG1vRaNpbW9l9WOr4/Zjr2+u5/qbz+FYz3Ho6KAv0JtbMxGHLRWdfdETQF/aMkSvNYWZp4dOn0+vcnHo9PkEnnqaiBTH008Do/pPkxHp/DsUQgiRWLrlMT8BXgVmAP8EKNt9u4HPZWhcQgxaqm0draDikUnvA3DMBV3+HrZNPjpkY01qlPcitE8AnaV9LK4eotcab+apLQI/dPp8Tv3LE+TRx6l/eYLD/nEA6OAPX/wiBxt91MyuZf3VvtH4p8kIaa8qhBCZkZfm8Z8HlmqtjymlnFH3vQNMzcywhBi8ZG0d7V/XO5QDv/azfyLMuRzKDoF3OvQcuIdFfG+IRx2HPSNcVGS2R1HZjjUB1OuFr7ztxbFuiF6r/YmtVjFWuYzWnNLZBZjMhAYm8AE7uJB5PM3hj36GU774RU5ddCY/6lFoFOcVNOH1ekbTnyYjpL2qEEJkRrpBeyDJfROBzkGMRYiMml48ndb21pj9JxaeGLEaql/7Q/ftn2h+AFSuBBXTpoHfD4WFo7YXoccTjM19ZXDHEPZdDD0xJsNuXRwR+zXkPmbxtaKdzNI+mt6aA7ftxdnTwwnAcYo4L+ClrEwi9miJ/h1Ke1UhhEhPuuUxfwRWJLjvMuDZwQ1HiMxJ1NYRiPm6Pp6cCCp8Pli82ASwfj9s3TqqsuwxhqLvYqIidKtcxu1GE/5wDAB9wAk7HmX9ethR5cWhNPT0oICAy43bpbn1qrfxIPUx0aS9qhBCZEa6QfsPgIVKqd9hJqNq4EKl1GbgUkA+hUXOSNTW8UjnkX4fmzNBhVUa09UFeXnQ1jbcI8q+BH0X65vrKa0pxXGrg9Ka0oFNZEw2P8C6YLj5ZvpchRyniA4KudV1O4/cvo8Z5R4qK2HqkmBwX1AALheOf1mKKw8mbVg3KuccDFbC9qrSPUYIIdIykJaPXwFqgJm23S3At7XW2zM3tOEjLR9Hr/rmer75X99EE/vfvVM5CehAbrWky0A/RJ8vXLY9UpP01mRh+zckRa6i9II/nw/WrYOHHoLOTlOCs369uUCIcrDRx50LvXgp4/U8Dy++GPXeNTbCJZeAw2G+AXE6+z2nEEII0Z9kLR/TrWlHa/1b4LdKqdOAycARrfW+QY5RiCFRtasqbsCuUGy+dHNuBOp20ZMlBxCwpxTz53hkn6wDSUp/M+uN8PvNtxb9zA/oneFhc57HxOPxDmhrM4F6RwcUFBDwB/C7i3BqhWMUzjkQQggx/NIO2i1a69eA1zI4FiGyLlHHCo3OvYDdYp8smaboxjMvbfHhmRoVnNsD2kAAtm2D8vIMvoAE7BcK1mATXDQMugOJvcyooAAuuwxuvDHh++r1mpi+pydBExtbu8iAVnxVbeUjuo0/UMZDeMi9yx4hhBAjXb9Bu1LqW+mcUGv9nwMfjhDZlaiTRUlxyTCMJvvi9kBXJu1+cGsTe/bAl/eso7i310SoYMo+Xn45uxl3+1cAVomeUgm/Dhh0B5LonuxJAvZ4h8ckz23fgGx9u4xd6zyjtSOnEEKIHJFKpn1T1LZVW6Di7AOQoF3krOp51XFro3Ni0mkW2KtrFr2yBce9JjgPFBaxeeEWvtd7Bw4CaHrC/6ADAdiyBaqyuPiN/SsAd3BR5YRp7fDf7WhVB3mYbi4TqtP4u6VZZpTS4cFvQD7tA3XH0HWpFEIIMTb1OxFVKWVPQU4DfgX8FngQs6DSFGApsACz8NIfsjPUoSMTUUcxn4/nGn7EDd2P80z+W7k16TSbfD4480wzWRLodRVSrav4Xt86xtFBX14+ebrPlMiAqfmOmX2ZoXF4veb3q64yEzlV8HJBKejrg098Aq6+OmYyZ49SuGzbvYA7zYn02ZLjUwKEEEKMEIOaiKq1Dn0nrZT6KfCg1vr7tkP2Af+jlPohcD2m9aMQuSdYkvE5rXlKKWj6y9iJsKwibQC3m79dVcUj9y/he313cJwiCl0KLr8S7r3XZLyVSq3OI51oNXoyaH6+ybY/8gjMmGE6u2zebC4WVq0yj7EF7taHlbVC6YAn5GTBIKYdCCGEEClJt0/7PGBngvt2Bu8XIufUN9dz7Y1zONZzHDo6ON5znOcafjTcwxo69iJtl4tJVy7hoRc9bL+9iQ9uX4/jxSa48kpwuVKv87D3PD/zTKiuju1Rbl/IyD4ZFKC723RgaWszEe/+/ZGPrauL2OwL3uqobSGEEGIsSDdo7wbipuyBzwA9gxuOEJlX31zPsoeXsf3k4wAcc5nAr/L9TQNboCcD4+lvkaBEi3b2x3rcwcaoE3g8ZjXVJUtCq6p6PLCkysPUqspwqjid1UjtdemdnXDbbZGLC0UvZDRtWnhRIohtu7hyZeT5o7bdWtOL+dvlUmmMEEIIMRTS/Yb5IeAWpZQf2EK4pv0yYC1Qm9nhCTF4126/Fr/2s38izLkcyg6BdzrsP7Ev9T7f6UpQNhK9SFBreyurH1sNEBrHQNdTsh430+/jn7vmECjUOBzBEwAsXmxO+pvfJD5pf3Ue9tdlZe/dblNS09NjVm21ymqi+022tZkLhoaGcKBuf3+sUpi6OhOwx1mgyArU3f2/HSNefXM9VbuqONR+aOzMvRBCiGGU65+76Qbt3wXGA3cA/27brzETVL+boXEJkTFHOo+Eft8/0fxYUu7znY4kUXcqiwRFx7qpthC0Hje3y4tC4+i0nQAGdtLo13XmmeEVQF980by2LVtMaYzVstEekNv7Jk6b1v+FQ2Wl+bG+Migro777f0Mfoud3n8wd+RfzuaX/yjPrvEzYWsfRxSs5f1Nl5DhH+KzQVC7uhBBCZM5I+NxNK2jXWncC31RK/QD4LHAy8BbwvNY6zS/yhRh+Kff5TkeSqDuVRYL67RFuZwtQy8o8KAV7CsrQXYpAYREO+wlSPmkCW7aEus+EtquqzM+SJbGBcnTfxFSvRmwXPb3az51XKFqLu5h1GB67/00UG+n9wWa+4O81x2/ezTNgAveBfk2RYwa9AqwQQoi0jITP3QE1YAgG6BKkixHhpMKTIrLtdtnoz76r18fnuzvoywP6Otk1+SiLgvf1u0iQz4fH66V5axlPtnmSJ4ujAlRPUxNNTR68Xg/vTWtiRltUEJ1Gn/K4Dh9OvJ2orCZ6fyoXDvfdZyar+v30uBVzD2henmPKmgBO6AV/cBqq1UlmwtY62FQ58K8pcsygV4AVQgiRlpHwuZtW0K6U6jctqbXOnVcnBPDTBT9l5aMr6fFHzpO+cu6VGb963vbYj7ng2jvxK3BquLJcU/CAWaRo0cLvJV/cyRaEz1CKyv6yxHECVE+lJ/gQT/DHZrB9CSdOTL7dn1RWLGpshJqa0KbDr/EGP3Ws22MuyPMr8gM61Enm6OLgpNW0vqYIyoFymughDHoFWCGEEGkZCZ+76WbaW4hc/TQe58CGIkR2WIH5UEwu+X39HVygoagPOvLgvsehz9GD+u310Lwo+Vhqa9PLEg8kQB2MJUtM7bpV075kSfrnsF84xAuWGxoiDn/6E4Xsn2hKcuwTiV//5FRuefO22Jr2VFc+tZ7bXmc/TOU08Sp6xtrKvUIIMdxGwuduukH7SmKD9pOArwAfBX6QiUEJkWkVsyuyEqRHx52PTnqfmwlmgwPgVzCuF465dCgITzgWKwgvKDArg06bFv9JLKkGqJni8ZjJp5l4vgS15z3/+cvQqqcKcF/1HYrevyf0Ibp/Ivz15CI2LLyT82dXmJKYeOPsrwOO9dx9feYCpLMzI+U0A+k8EK+ip7Jy6C40hRBCDG2Cb6DSnYi6KcFd/79S6heYwF2I0aGfsol4cWf3zBLmXN5K2SF4Yzw8/GvocgYXROgvE271Ur/kEhNILl5sthNlgoejrCNTS3/GiVR7PnEGLu0PHdKDYt5z77Dtc1dT2fXrzH2I2p+7oAACgYx8WzHQzgNHJ2+js28euDSdfYqjk3cBi7J2oSmEECK+XP/czeRK4L8EHgBuyuA5hRgeKXQhiZchrZ5XzeqO1dRN7GBWcJ6mAtzOFDuLt7WZgN06aUNDZIC5bh0sXQp79kS2WRxpXVLitIPMC3aDsSaXutCweTPzNkPLmjVmxdZMvMbo59661bzvg7z4GUjngfrmetY2r0av/ggcKkNP97K2+a+cXLohp//HIYQQYuhlMmifDBRk8HxCDB9bRN7rLuJv921h0hlTIwK7eCXlHk8Fzx56loMP/oyqpzVKQ74fyM9LrfQiukSmrMz0NC8oMB1VHnwQNm8Glwt6gy0PR2KXFHtpT7CuXAXvijtp5u674ec/z8zFSZbKigbSeSAU6E/cb36Ajl5yqsWYEEKI3JBu95gvxNntBs4AbgC8mRiUEMOurIyAVnRShOrRFNVUEyhU4RVGPZ64sV99cz1tv6nl8V+GQ89OJ+RpP64kpRfhShcPHnuJzLXXhlcRfeihcJ90K2B3u4dmEmom2Mt5IKZ/u5Vh10CfcuK2lcrg95uLqGQXJ+mUC2WqzMdmIJ0HRkKLMSGEELkh3Uz708QmwqwE2TPAlYMdkBDDyhb4ba1q4snbvEzoeZtbWIuj048fJ05b4OjBhwcvUAZ4qNpVxdoXTGtJKwjdezL82zdP4qkEQWJ0Jc5rN7Qx1V4i09ZmSmIefBDy86G7GwoLzYOthY1yPctuf5E6+BFilfZs3Rr6ykIphWpqwu3xmPaPP/sZbN9uLmCSXZyku6hSFuYDDKTzwEhoMSaEECI3pBu0X0Bs0N4FtGqt387MkIQYBj6fWeHTVic+d2sTK1yVbO65FBd+NODAzzu33MOUyvirb+YfaOX9YDxt/UO57Xx4Jv+thE8dUxtPGUui6r1ZvNj8HgjAxo3mgcPYVzxt9hfpdpvfe3uhsJD3G3bQ7plP8adKOfFGW916ebn58flMLf9LLyXOtKezqFL03y1DNe0D6TwwElqMCSGEyA3pdo95OkvjEGL4WEFcby/0BBdgKipiRpuXpiYPJ3/8CdDhzPnkthdNT3WIDBS3bKFpg0JrTacTts+C+8+GnbOgJEnmNLo2/tNLPLCkKaZ8hK4ucxBAZZxWh7nM/iIDAfNaAN3ZyYTNNUwAeAHeKLuIU6MDZ6/X1PEDrFplbqNffzo96+0BvssFF19sbvPyBl0zn27ngVxrMXb00uUUbd9Kx4LFTHh407CMQQghRHyOdA5WSvmVUv+Q4L6zlVL+ePcJkdOsIM4K2G114h4P/PVziwETsFu1YNTVxQaKQL7TxbheCDjgcY8J2PvLnFq18evX22JGj4f6fyig9PEv87EXV3G8r5O+wgzUr/t85oLD58vMcamyv8grrjABMqCDH0HW+9pZ1xD72Lq65NvR5+8v8Lb+bvn55kItEDAlR3195r+FaJl+L6JUzK6gZU0LgbUBWta0DGvA/uFHNuPuPsaHH9nM0UuXD8s4hBBCxJdueYxKcp+T/ldLFSL32INvrWPqxD++exPvnPoSk9teDAfuF18c24UEyLvjDvoKQfl7+f10TUlxSUqZ0+h5kRE9vyfCWas18/4KC1bcwqKBZoJTrftOtz48VdY5vvMdEyADuJzQGwh9cBSuXBr7uJUrYffuyO1E509lnNbfbcWKyPP6/bEXRNl6L3JQ0fatQPgbJbO9aRhHJIQQwi6loF0p5SAcsDuC23aFwALgcAbHJsTQSKEF4JRbrjaDeqJ2AAAgAElEQVR9wnt7TZZ4/34T0EUHik1N5Hm95JWVsS+DPb/3T4T9E3vYfuAeFvG9gZ001brvdOrDBzKG4LcSuN04br6ZN6bOpbOugcKVSzm1sjz2MVYpTF2dCdgzURrk8cDNN8NFF4X3/exnka/TqqUPBDK2Ymou61iwGPcjm0MXUB0LFpM/rCMSQghhp7ROnhxXSq0Fbk7xfPdqrb8z6FENs7lz5+o9e/YM9zBELrEyrn6/qccuLASHI2uZV8etDnScL64UisDawMBOOtyZ9kycu7HRtL9cutRMUh2sROcb4r93rpCadiGEGF5Kqb1a67nx7ksl0/60dR5M8F4LtEUd0w38GfjvAY5RiNxmZePXrQv3S89i5jUrrQBTXVQoS4sPpXPu+ub62MmZb04MZ8Y3b4YdO2DGjPTGGd3q0epQE80++begAC67DG68MfwcWWgZmQtMoL5JMuxCCJGD+s20Rxxssu4btdZ/zd6Qhp9k2kVCQ1TjHFHTHlTkKmLDwtG/vH2i1/7y82czc5ttouhXvwo7d6bXmz3Vv12yY7PUMlIIIYRIlmlPq3uM1vrW0R6wC5FUOl1KgD3VjXhPW86e6sa0nqZidgUbFm6gpLgEhaKkuCRxwL58OYwfb25HgWu3XxsRsAN09HZwy9R9kQeWloZr763VUhNpbDQTT3t6zPF+f/Ljk/2d7TX/gQAsWgTXXGMC+Sx1mBFCCCFSqWmvA36gtT4Y/D0ZrbUeYQ2kY0mmXWTCnupGzr4pPNGx+cI1fGrnXZl9kuXLwz3MAZYtg02bMvscQ6i+uZ5v/Nc34t7nOQz78tZAS4tpGzljRmqZ88bGyAmnlh07BlYXb8+0+/3mua0e+uvXj7we+kIIIXLGYGvavwT8NPh7vBVR7aTloxBBnQ+YnuNWC73ZT9TwVvUZnFyVwaBu69bY7REctFftqoq7f9ZhaNqgIG+DCZJ/+MPUa+8b4vR+Lyw0JS0DYX9ea7XaVBZ1EkIIIQah3/IYrfUMrfVLwd9Lg9uJfj6a/SELMTIUrjA9x+2LMqkH+vuyKoHqajjtNHNrt3hx8u0R5lD7obj7yw6ZhatiSmE8HpPZTtQFp7Y2NpDOzzedYBIF2KkspmQ9b3l5WuVSQgghxECltbiSUuoLQJPW+lic+04AztZa/0+mBifESDa3qpyXn17D7CdqQl9B6RUJFgZKproabrrJ/G7dVgUz0lZWfetWE7CP4Cw7JO6a84rnw+Tt6oWivNQy2tGTRTdujFgEK2FmfiATjVNd1EkIIYQYhHRXRH0KOAf4Y5z7Ph683znYQQkxWnxq5128VX0G6oE69IqVAyuNuf/+yO0NG2DqVN5gGi3eNqYtvZEZORisx23bOLsiaW/0B9vnc3n7L3m5uCu0u8hVxDXfugf+z2dSb7MYvUAUpHZBk82FpYQQQohBSLflYwD4nNY6JmhXSp0LPK21dmdwfMNCJqKKnOHzwRlnmJVYLS4XAUceqruTTgoI4OS9HU3MKM+d4LK+uZ47H1jF3ANdeIOt5ef91c03P/0tzr1lY/hAazKoLcPdq/18ec1JPJP/VmSwn44kGXNvrY/9dV5mrSyjrNKT8uOEEEKIbBvURFSlVClgr1Wfq5QaF3VYIbASiF+QKoQYGK8XXK5w0H7WWbBvH46ODjRQRBcdFNJ72zqYcWPOBJgbG67n2XtMtlwF8wJa9ZD/yMbIAxsaTNBuy3C7iop4auZtg+vCkmCSqrfWx1mr5nAWGnYrvDRFBu7ZXFhKCCGEGIRUymOWAWsx8+k0cDfheXUQnmfXB3w70wMUYkwrKzMZX6s7yR13wOLFBPILUd2ddJNHIZ2c9sdfwZzf5ExmeOaf3gRgXC90O82HREEfdKuoA2fNMrfRr3MwXVjsq5VGBf7767ychWYcHRyjiP113thsu9SoCyGEyEGpBO2bgKcxgfmTmMD8z1HHdAM+rfX7mRycEEMhYe11LoiX+W1qwuH1csT7CidurgHA0dcLDpW9Gmx7IJzC+Q988hTY/ibHXOFM+zGXCdwj7N9vbjOV4e6nvGXWyjLYrThGEaDMdrrnH+gYB/NYIYQQY16/QbvWuhVoBVBKfQnYG697jBAjUX1zPasfWx1agbO1vZXVj60GyK3A3RNVwgGcdPnlkcf5/dnpEz6AOu9VS+/kvK7ImvZ/2ZdH1VMa+vzhA5cuDf+eiQx3PxNJyyo9eGlKXNOezGDq3YeiVl4uCoQQYlTrt0+7ndb6GQnYxWhStasqFLBbOno7Ei7ykzO8XnBGNWq6//7sBGv2QNjeI91i9TVvbAz1N6+YXcH1Kzay60slvDZR0TOzhIvOW47LnW8e43TCmjWxK5Km0iM9mRTKbMoqPax8tjK9gB3M6w4EzPvQ1wfr1qU+zv7ew8FqbITZs+Hqq83FwUDfPyGEyJL65npKa0px3OqgtKaU+ub64R7SiJNuy0eUUuXAFcDHgIKou7XWemYmBibEUEi0mE+i/RkXLztq7Zs2zazaGS9zWlZmJqiCybDff//gJm4mkywQtjLIgQB0dkJBgQnIm5qomF0R+W2Fzwc/agif58orI55m22M/Zt7XrkdrjVKKXb+5k0ULv5feWBOV2WQiCz1tmnmNAN3d8OCD8JsU5xFksmY/ms8Hl1wCPT1mu6AgcZmUZOOFEMNgRHyrPQKku7jSxcBjwBOYvuw7gCLgPEwJTYbTR0JkV6LFfKYXT8/+k8crmQCzz++Hri4oLDSrd0YHhkPZ5STZc1kZZCuY7eoygemWLTB1auTxwfP41m3h5ZdgihfKgnfVN9fz+wequEBrxvXCMZdm+wNVfFB6cvze7skubOxlNj6fGUt1tXmPB1Oa0tZmAuKuYA/57m7zt0llHkE2/15erxmHJRCIf1HQ2GiCe4cj/n9TQgiRJcm+1ZagPXXpZtr/DfgP4DqgF7hJa92klPIAjcD2DI9PiKyqnlcdcfUPZjGf6nnVAz9pqtnMePXXYPZZgWFnZ+JFfoayy0mi57IyyIWF4Uy71gmDZK8Xztp8B6egYdUdoZaLVbuq8BT1kBeAjuCn0oGiHsZXX0PF7JbwSrCbN5vVTa+9Nm52P2KMjY2waJE5ri84A3YwCyaVlZnnyc83ATuY5582bXDv4WCVlZkgvLDQvNZHH43/rc2iReFsfGGhLBwlhBgyw/6t9iiRbtD+ceBmIIDp4pYHoLX2KaVuwQT1D2VygEJkk3WFn7HuMelMOIwqmdg2+Sh3/eEuHuvtwOGCwl5QhYWZL6fIJHsGedo02LPH/DQ2xkwGrW+u59nbfx+35WL+gVYe/rXpNOPUcO9n4Le/Ar96H359U+jpNKBXrQKlcFgLw3V1xZaEBEtGdE8PKvi4Hic4tB/XQN9L67WuW2ey/j095nnb2gbzDg6O9Q3ET39qtqNLgqz/Fv3+yMclysYLIUQWDOu32qNIukF7AOjTWmul1HvAdMBaHfVNQOrZxYgTU3s9GP10L4lgC3i3TT7Kxv+q4lsv9PCdi0z3xvdOdHNjaQWfW/qvuZ0RtTLIPh8sXhxZ2hO84LDqGT8y+0xoCbdcPPJl8xFU+XoxRX3toQD7u8+bU7tsCzZbvyoArQlYvwN0dfFkr4951rbXSx8B8oKP63HArefDbz+tuL77f6lggO+nx2NKdB58MFy6NFzBb2MjXHSR+X3zZrO6bLzSpY4OM1a/31xkRGfjhRAiy7LyrfYYlFb3GGAfUBr8fQ+wRil1slJqEvBdoCVzQxNiBEp3wqHHA5WVbPnvH/Hoph6WvwR1j8Eb4+Gx0h6+Xrxz5ARXVpBoZb4vuyz0TUPVrio+8lYHZWo3//QlD9dOu4w5Cz/NfxSvBWDhhHNiTmcF8NaPfb+ly2m2O/PgN7+rCXcjKCujW/fRkWcWd1q4FO74Arxc3DW4zkDWhYlSJgjeunX4/j4NDcm37f8tOhywbRvccw80N8d27RFCiCyqmF3BhoUbKCkuQaEoKS5hw8INUs+epnQz7fXA6cHf12ImpFrfDfuBf8nQuIQYmaKy59c8/mUONRzixMITAXi/8/24JTjznn8XCAeqS1+BnbNGSL2fvW7afsFy442hgDb/QCt777ce8AJzLn+B/RNBtZsQ/PTTP49mR8RpraWWA8qB0oGIIB7glfGFfLSrk2PB1MOuj/Sw3ZrU5PEwZ7Xm84fAOx32Twyfd1Dv6ZYt0NtrSmOKikwpUKIOP9m2dKnJsNu37YZysrIQQvQjo99qj1FpBe1a6/+w/b5XKTUbWAAUAk9oraNXShVi7PF4qO/+X1Y/tjb0VeCRziOhu+O1unrnlGJ4qT0UkO4zMX7u1/tF1/Bv3RobxNbWsqPBSZ7fT0HArIxadsgE0qHXt2QJqroa/H6U0wl33w11dbB3L47ubjTQjZs8egHN3g+fwGfXHGfWYXMuKzBXtoC8e2YJdRMHWEOZqBVndXV4Mqffn5muNANVXm5KYuxddaIN5WRlIYQQWZVueUwErXWb1vrnWuv1ErALERavvZVd9AJOX3N9CgiXfnzs/RFQ7+fzmUmZfn940aC2NtMv3haws2oVpUf85AegK/iJ450Obqc7/Po8HnjxRbj3XnNbWQkPPAB5eVBUhC4opPmrN3Nox19was1la03qfP9EqJsTzqTbA/LqedUUuYoihpzSe2pdiFxzTeRCRV6vCc4B3G5YsMBsZ2vBpFSUl8OmTVLuIoQQY0C/QbtSKqCU8qf40zcUgxYi16VSgmE/ZuZVJoC3Mu1PfnZybtf7WYHtQw+Fa9jj1fDX1QHhi5E3imHO5SbI1lpHHnvwoAl8Dx4021Z5x/r1OF56kbkPVzGj3FwMpBKQ22soAZzKGbpYSroSX6LVS+014i4XXHFF9hZMEkIIIaKkUh5zG5HzwIQQ/UjU3ir6mJDycpSt1OEXuZ45tS+qVFhoJp3aathDVq6E3btDHyDrPg/f/x/42l/gNx/vpWp8FRV/7IKf/ARefdUcZHVCKS9PWN6RaqtOazutlfjitOK8pqaUQ+2HOP+ak7kj/2I+97mvmW8V4pUDCZFEfXN95lrMCiHGFBWT7RLMnTtX79mzZ7iHIUaw6CWboxW5inI7k04/a0TF60d/8CD87GdQWgpXXhlRIvPsLavYeBZ8vhVWvhw+TS/gjvfky5aZso8ox846l8KXnqfz059l3Au7U3odpTWlcS+gSopLaFnTEv9BwRe/bfJRljavNZ1vgrXzha4C9vxM41LO/mvZo1dyFWNavM+FkfBZIIQYOkqpvVrruXHvk6A9lgTtIhPsGbX+usdkRKorsaZ4qpg1oog6v/35Dh4M9wwHs2royy+HxmEFzn9bBx/qCXfJsSiiWJl2m2NnncsJL/4htH38zHMY9+tN/b5mx60OdJwvCxWKwNpA0vehtKYU94FWmu4P71tXBlW/V5zQo002fv16U4Mfzd5HPcFrEmPLgC4ghRBjSrKgPd2Wj0KIFA1pe6t0VmKN99iowDd6jagD9zXiuXeR6fftdIbPbz3HunWR5+zpMe0Rq0ytvrWwxm8+3sHKl8PtHNNJGRS+ZFZcsh5X+OJz4dccCJgA+YorYgLjwazEd6j9ECuCUw/G9ZrONxCsx++vlj1eH3UJ2sc0WcpdCDEYg+oeI4TIEYkmT/YnQacUe1n3LO2j/N5LTCDe1WW6xUSfP7pHuNamHWLwfNak0B+sKKHXOiR42wswaVLk46MDXqDz05+NfNypM8KvuasLHnnEBO6NjRGPG3AXGUxg7w3G9lbA/ptPwMLvnWIy7MkujqLfk+htMeYkulDM+dauQoicIEG7ECNJdTWcdpq5tUtnJdbrroPJk82tPdjv7oavfx0aG+2NW9hR5cXhtH1U9PWZhZTsrJ7hp59usvFgxmEL7itmV9By8e9wb9wY7LYerGnXGn7xi8jzxQlwx72wm+NnnkNAOTh+5jkUPLHdPIfTGXlgVMA/mJX4qudV89eTi5hzOVy7AP7pMpj3VzfXnXNdZGvLeKz3ZNkyKY0RwOAuIIUQAq21/ET9nH322VqInHP77VqbENv83H575P379mm9caO5TWTNmshzLFumdWFh5D7QeseOyPOecILW+fnmPpdL97ry9DdWT9bqFqVL7irRv3z5l+Y4+7kKCyPHYp2nqMjcd/vtkffv2GHGY3/u/uzbF/ua0nl8Cn758i91yV0l2nM1+phb6d5Ct3kdyd5nIRKw/nuK+LcjhBBBwB6dID6ViahxyERUkZNOOw0OHAhvz5wJr72W3jkmT4b33gtvT5oE114LN99s6sIt0d1brIWUGhqgp8esUOqET11peq4vbHGz8YUSJr/aaspo3G5zzqrwAlLU1poynI5g5wy3m4DTxdaqJj69xJPe3NnrroP6eqiogLvuSq9Ly0An7NrHb01ABdOLfuXK+JNRhRBCiDQkm4gq5TFCDIbPZ4I5a9XMbFqxIvl2P+qb69lweicaQj9MnAhTp5og2669PVxCAya4vfFGIDyJNKCg7BDM3w+Pbuph0kv7wwG7ywVLloTP5/PB22+bXLj1XD09dHZqnrzNG7HwaKK39K3qWt4+7Tw+OHc+1NSYi4+aGjPGVFcGTbTaaSqiS5DefhtWrYLdu81tbW3q5xJCCCHSJN1jhBiowXRsGQgra/3AAyZgt7ZTyDKH+kNf0MGEd2CRD9wa9Kuvolatgo0b4ZVXoKXFPOCRR8xtTQ0cPWoCYo8Htm2j+ysXEVAmaPdOhxuCZeuhto1TpsDZZ8PnP28y4VdeGX6fAK66Cu6/n16/gh7NhJ63menw4fWa9y7eW/pWdS1Tb1plHn8gfOFgXly9ybb3Y/t1jXxk4218oruPvL5uE3x7van/zaxCfytLH33RVFcn2XYhhBDZk6huZiz/SE27SMnGjaY+G8ztxo1DP4YdO1Kq5y65q0RzC3p+BTpA+Ceitt0yaVJsjbutfvuLN5yiVy5Cz7qamHPGPA60vvDC2Pdp3z797prbdQf5ugu3Pkahfn3HvoRv6Vszzw2dP+a51qzp9216fM2OiNfd58xAXfrGjZGvczj+/kIIIUYVktS0S3mMEAOVTseWbInXCzwOqw/00lfMtpWlDs1osXdrqYjqquJ0RnSBWbX0Th78rOmAsbIJWibAPec60SpmiSTjuedMZxq3O/w+eTxMmggFdJNPD0V0MmPPloRvqV6xMmK8x8650NTjr1kTm2WPU18TqG+IfN3+Xti6dXDfjFRWmm8ozj3X3EqWXQghRBZJeYwQAxVdLpFiAPhWdS3qgTr0ipWcXDXIQG/pUti8Obw9YYIJVqPGYi0w1HAGLH8pHPw2Tz6BE086B90Gp1oH33WXKYnZvNkE7AUFERckFbMrGN/yFvNuvR4V0Dg17P23FTheqIfOztgxHjtmbv1+uPfeiLGpqNtEb+nJVZW8BagH6nhv4Ur+eEZl/Lc8XskScOqnJ8ATtkWd8lzQ1tbv29uvykoJ1oUQQgwJ6R4Th3SPEdkSUZsNvH37xsEH7o2N8LOfmV7gDkcoWK3v/l+qdlVxqP0QJxaeyN+7/05voJf5+03G/UDRFH7wh3dCp2nbuINTK2018cm6rNTWwne+Ew7S3W7Ytg2sfzf798Pjj5uJrq++Gn6cvSuNzwdnnmmCeacTXnwx8YVPcCwHp5Uxe7En8TSC6A4vN94Id9wBWtPX0wd9fnSeE1e+K/tzEIQQQog0JeseI5l2IYaQeqDO3BLM+j5QB/0F7T4ffPvbJsj81rdiy0HKy03W+He/CwWrzzX8iNWuX9HRa9orHuk8gtvp5qTCk3hi1vv45k6nftMngcdDY+msawB70O7xJA5qy8oiW0Q6HGYM9haPYC4oLroovG0vw/F4TKDe3zcVtuz5NL9ipmri5S5P/Hmk0fU1+/eHVnHNKyqCW2423XLSbfcohBBCDDMJ2oUYQnrFSrhpd6g8Ra9YSX1zfSgjPr14OtXzqsOrdfp88MlPmlVIwXRzgdjAPSpYvaH7cTroiDikx9/DOPc4Dl9/GIA3xjfCqsdDYylcGbsKaUIeDzz6KCxaZAJ2pzN+Tb+1Kmii7jbJLgwstlVbnYVFnOf38lqRJ/40Ant9DZhWjBa/37ShlGBdCCHECCTlMXFIeYzIJntN+5OLCkwrxt5wgF3kKmLDwg0mcK+tjQw8wUzAfPfd2BP7fPD978PevfzklDf48xTTknH/xPAhCkVgbThD/kZtI511DRSuXBpZGpOqdBcqCi6K9Kcvn81X5r4a/0Il3nPY6tQPbm3iyTZP/0+5fHlkvf9XvwoPP5zOqxNCCCGGVLLyGAna45CgXQyV0ppSWttbY/aXFJfQsqYlNtMOkR1T7EHzli1w002AKXfpAbQTFn0dds6KOu9wuO46qKkJZfZ/8ln41wXm9yJXES8+dzaznnoBFi+OXI0VBraKaXRpzo4d/S++JIQQQgwjWRFViBxltWJMuN/jgT/9CS68EE480QTsR4/C+PFw6aWRq3vef3/EOfKBfD9sexBmHTZZ9tb2VkprSqlvru9/cJle7bXePKfVKeabzeG71v+6g9Me85pOM5s3myx50DsTTyfwsY/xzvd/zAdfWEBAKT6YOrP/57NKc5Ytk4BdCCHEiCdBuxCZkmqQaztuevH0uIdE7Pd4YOdOOHIk3Irx2DGzamlXl5l8qrVZhTQoNNEVs3LpFw6BDua4W9tbWf3Y6uSBu1WSYl0QpBu4x3svgv3frUz7L2aH7/raX6Iev3UrYAL2yUf+ggImH/kL4955HQWMe+f11AP3TZskYBdCCDHiSdAuxCAcvXQ53QXjaZ9/aWpBblQwvH7m1RS5iiIOKXIVUT2vOvJx8+eDywW/+EXkfr8/3Cnlhz+E22+HmTNh2TJUfj4UFqIdiv+Jujbo6O2galdUpxc72+RPtI5YXKlf1mu8+mqYPduUqYAp6VmzhsMnqIjSGIDffDzqHIsXAzDpiInm7cs2Wb+f8M7rqY9JCCGEGOEkaBcigfrmekprSnHc6ohbUnL00uV8+JHNuLuP8aEnHiHQ2QkdHejjx+n52MfinzQqGF707gQ2LNxASXEJCkVJcUl4Eqpl/nx44glT125vswhmcuX69eGe41VV8NprJrv88stw993MWa0jJqNaEpXmAOmv9mrPrHu95mKiqwt6euCSS8IXMXfdxe/+8AvWLoq8ULnmn4t4bWEZjBsX0cv9vZNMNG+feWP9fnzKR5OPSQghhBhFpOWjEHHUN9ez+rHVXLathMrmj1I7283qjtUAoYC6aLsp4QiVogQCoYDSBfQoJ27tjzxxnGC4wuNJ3DmlttYE7NHGjYs/YdMu2E6xu+YHEGeya6LSnNBjU13tNXoV0q1bY3u42xqqW681us3lrBtj34Mph1/l8ISZnPS314PlPgqN5viUjzL+7QOJxySEEEKMMpJpFyKOql1VXLathLrnX+W8jgPUPf8ql20riSgp6VhgSjhCPdeDt1b5Rh5RWXEIB8P27HgiwXaPOnjuULb5wgvhgw+SB+w21fOqk5bgHGz04V1ey8HGqJIejwcqK2PGGHN8dClNW5tZHTVYnoPDEZOpr5hdQcuaFgJrA7SsaUl80QJM/PGNqKIiHIAqKsSxcaME7EIIIcYcybQLEceh9kNUNpvyCyuTXtncw+YF4ZKSCQ9v4uilJuPesWAxJzzyC1yEs+19OHDHO3kqCwoB1NWFJpNa5zw85eNM3LkzrddiBcTXN2zkzT/N5JRPHuDOf1pFxewK3qht5JRVlzAFB4HNDg7uaGJGeeKxHWz0MemiOUzBj2NzgD23b2PukjilNB6PKc9Jt01jPOmW6gghhBCjkATtQsQxvXg6tbPdnPd8OGCune2OKSmZ8PAmYBP5AGyiRznJI2AC9ujSmHStXAm7d0cE7m8eP5E45en9+kx+Be3rKyjS0L4LPrMc8Pk4+YpFOOlBAR0U0tbgTRq0tzV4mYKfIrrQwBk3XcIbUx/l1BtuMAfYVxxN9eKkP+mU6gghhBCjlATtQsRx8ayLuW/BfcDpVDb3UDvbzaYFr3LlrCuTPs4K1ONm2PsTvYBQZSX7ar187A+bQxcORxevHMiZIypYioqCJeZ4cTgdqD5zQeAkwLSlybPY05aW4dgciLiQOOWKS8DtNFnwJUsGNL5+ZeoCQAghhBihJGgXIo7H9z8OwKYFr7JpQez+jIuezBmsd//47k08s7yMCVvrOLp4JedvqhzQ6eNXmJThyHMSoICAP8C7P3uUGTMwtfQJMtozyj3suX0bZ9x0CQEcOPCjnM6oqwEJroUQQohMk6BdiDjitUNcvhdWvdAK42vNBM1MipsKN8Hv+ZsqIUmwXt9cH9OJJXpiZ/wKE7PT4fXiKCvjVIh74RBtblU5B+e+TFuDl9KyaZx67WJwSr25EEIIkU0StAsRx/Ti6bTa2iQu3wt1jwU3Vq0yt5kI3K2SmGnTBjTZ0mpN2dHbAYRXOwXiBu5WDH6w0Udbg5dpS8uYYb2O2tr4Fw7BMR6cVsaTbR4T9Jd7wrXvZVJvLoQQQmSb0lr3f9QYM3fuXL1nz57hHoYYRtHBsHcjnNdmW5nz3HPh2WcH9yTx+pu3tYUD9hQC4dKa0oiLC0tJcQkta1riPsbqAGM6niveszrGxCvRAZgzh0BA09GpOK+giQNOT7/dKoUQQgiRPqXUXq313Hj3SZ92IeKomF0RsVLpo+eeGHnAyoFNCI0Qr7+5lfWeMweuucbc+nwJT5FoVdNkq522NXjJo48T6CCPPtoavOaO6B7yAOvWQSCAo7MDhWZulxetzdCFEEIIMXSkPEaIFLz94ah/KtOmDf6kifqPJ6lvjxZdxmPfn8hpsyCfbjTm9rRZtjutGhor6+73Q1cXgfxCdLdiT0GZlK4LIYQQw0Ay7ULEYZXHtLa3otFc8Py7ofs0sOXfFlPfXB/zuHevq6Z98mm8e111/0+SaP5pgJUAACAASURBVHXUNBYT6m+103hO3m/S5CpqO4J14dDVBQ4HDgL0fvVrXHOPlMYIIYQQw0GCdiHiqNpVFapnB2g4w9xaM0B+7jnO6sdWRwTu715XzaSam/jQeweYVHNT6oF7ZWVkFJwomI8juoynpLiEDQs3xExCjbB0aShgV8HtGNaFg9MJgQB0dzPhkc1UvnKdBOxCCCHEMJCJqHHIRFThuNWBJvLfxvz9sPQVE8DvDJaU2Cd8tk8+jQ+9dyC06NDfJ82k+N3XUn/S+fPh6afhi1+EnTsz8CqSaGyEhgYTsJeXxz/G54Ozz4Zjx8L7Jk2Cd9+Nf7wQQgghBkUmogqRpng14TtnwcpLwwE7RE747K5YAYSz8dZ2SubPhyeegL4+czt//kCGnbrycti0KXHADibDb7W3tFQkyeALIYQQImskaBcijni14vHYg/vJd1Xx3prb+fukmby35nYm31WV+hM+9VToVw307nqC0prSuHXzNDbC8uXmNtvuugvWrDEZ9jVrzLYQQgghhpyUx8Qh5TECIlcaPbHwRP7e/Xd6A72h+4tcRf3Xj6dq4kT0kSOh0pq3C+GU74Pb6abukrrQc+xf9AVOeyw8cfTJ+65n3hU/HPzzCyGEEGLYSXmMEANQMbuCljUtBNYGOHz9YR746gMxEz6XfOobBJSiR6n+T4i5ECitKcVxqyOUSa9vrufe049FHNfwKXPb4+/h4ZorYfnyiIDderZjd/+Y5279P0l7uQ+neK9XCCGEEOmTTHsckmkXqehRCpdt2w/kbdxousFceils3w4LFsDDDwMmgN33/y3nm3v7qD0Tfni+yaT7A3523evkC+/0hM71P1PcfPHKHubvh8Z6Qhl4iPy90wnaqTjBVdRvp5mhFr2qLGT42wkhhBBilJFMuxBZYC23ZGW9nWAmbp51FjzyCHR3m9tLLwWg7fvf5tYn+jjtKNzxFHz/GZNJ92s/n3unN+Jc1vbSV2KfN/oy+4QeTV+gN+eWKY1umwnQ0dtB1a40av2FEEIIAUjQLsSA9QVv7RlwAF56KfLA7dsB+Kfn2iOOq3wxfMhzU1wR57K2rf7wlv0TIp/LFYBjLujy97Bt8tEBvIrssXfWSWW/EEIIIRKToF2IBPqrx3bv20cvsZlvPv3pyO0FCwBo+Ewh2I6vPTN8SOOMcfgJB+PTjplLgqc/5uLJ+66HZct48r7r+d4l7ohzrP5HuHYBzFkN1xy4Z6AvNSvitc1Mtl8IIYQQieX1f4gQY090PXZreyurH1sNmAmq+HwwZw7uoiKzcugNN8Djj8PKlXDLLZEnC86PeIcpHM77Kx/y97LNY2raAWbtL+HfnnsHJ+Fg/KPHAzx/t4v9Tz3AvNkVcAXMA94+71OU934jZpEnAJVjGezqedVxa9qr56WwUqwQQgghIkimXYgo9c31LHt4WfJ6bK8XtIaODnM7dSo8+yz1/1BAoK0tFHxrINDWxooLpnDP71qY2NeLW8PX9sHyveaYr+09iQK6Qll26/bsI70xEzYrZlfgm1sSs8gT5F4Gu2J2BRsWbojpuCOTUIUQQoj0SdAuhE19cz0rHlmBX/vj3h+qxy4rMxl2K9NeVhbKzh8cbw6xAveD48P16wpbTfsL5rZg4rsRz2E97vC0aXHHEG/hp1zNYNvbZrasaZGAXQghhBggCdqFsLl2+7URCyhFm148HWprYcUKUxKzfn2o1aLVLeW078Lr403w/fp4OO274fp1ja2m/SwTbJ95/bX0ut30BsN5BQSAKdFlNkGSwRZCCCHGHqlpF8LmSOeRhPcVuYr4Vft8uG6V2bF7N2zcGOqNbu+Kctp3Ix+76fx3gcnc8HwH5HXx7+f38dQFJWyYV82i2RXQvAi2bKHv9tvoDvSitWbhgZtZ1VwQNxivmF0Rsd/nM9cSZWU51apdCCGEEBkiQbsQKdqwcAPnXnFv5M66OrOYEiYL39remvDxm85/l03nQ0lxCS1rWiLv9HioX1TKnUcczD2g8U6H/flv8kf75NcEgnNi0dpU6uTYGktCCCGEyAApjxHC5qTCkxLur5hdYbrD2Nm249WaR/vKSzOo3/RJ3qhtjLmvalcVLxd3UTcH9k80++ItRmRl1X0+sx09JzbH1lgSQgghRAZIpl0I4KrfXsWGvRviTkB1O938dMFPzUYwq05dnQnYrW3C2fCqXVUcaj/E9OLpXDzrYh7f/ziH2g/x9T+fQf3DzcBBWPU4b7CDUyvLQ49PZTGi6Kx681YfX3nby926jP1FHmtObPhgr1dqZoQQQohRQIJ2MeZd9duruG/PfXHvKykuoXpedWR5SmVlRLBuF11rbuc7bznQjMJMRu2sawBb0J6ovMbeytGeVf9UgY9pl8zB5dQ0BTR7F1Qx8YolzPB4IqL7vp5e2sZ/kg+WXc3su+KPWwghhBC5TcpjxJi3Ye+GuPudypnRNoWFK5cC4e4x1rYlXnnNp9oLeLB9fqgWxt5p8ryAF6fDRPCqu5OzH11LySVnhjPsweje2ddLydEXOaNmFc3X1WbktQghhBBiaEnQLsa8RD3ZE+0fqFMry2nbuIP95y6jbWOwNMZWoB7dyvGL3aew52eaz935K5M19/nweMxE0/Xr4V+3leFwKAJ5LgAc2o/q7uS9+7aEonu/rY0kQH59XUZfkxBCCCGGhgTtYsxzKifz90PdwzB/f+T+TDu1shzPs5vCAfucOXDNNaGgPLQY0dK/8NSb83HhiJlh6vGY6pwZ5SaCP3TGVyKeo6UleFBTE29e+C0gnN0/xfm2uUgQQgy5+uZ6SmtKcdzqoLSmlPrm+uEekhBiBJGgXYx5d6mLaKyH5S9BY304cF999ursPrHXSyBgSlgCAVvbl8ZGmD0bGhqgsxMKC4mcYWrj8eC8+gogHJhP/ce5oftO/Y8babtwGYeLTF38uLdfh1WrJHAXYohZKya3trei0bS2t7L6sdUSuAshUiZBuxhVrEyWulWRd1texG2izNZ3Dpj+ilYJScUr8J/vn8+9616C2lreva6a9smn8e511Rkd68FpZXR0Ko5TREen4uC0MpN9v+QS6OkxP243XHZZTPN1+5hOpQ2dX2gmuOYXcipt5qBgJv/U3VuY1NUWen2A6X4jhBgy1orJdvFaugohRCLSPUaMGlYmy/ofo1WTbt1amS2IWqxo6VLU5s2ACdyXfWkN1NQAoHfvZpJ1XM1NvAtMvisz/5N9ss3D+oIm5nZ52VNQxjVtHirbasERdS19440xAfukmptCYzp27oWMcwBFRTjsGXl7qxmXCwKB8Dmj+80LIbIqlZauQgiRjGTaxahQ31zPsoeXxWSyHmqAztvgj/fBrMMJMlvl5bBjByxbZm6feSbm/OGJnA9kbMxlZXDA6eHBokoOOD0m1i4rM0F7YSHk58O2bTE91q0xWGM6Yfcu88uNN0Zm5O2tZtxuuP12OPdc2LgxYctKIUR22Fu3prJfCCGiSdAuRjwrw+7Xfp6/20XvLfD83S4eaoCv7YP8AMx9B165x9SrX/hUa3g5UUt5OWzaBDNmwJ//HHGX1VcdoLtixeAHHOwY48EX6gQTirWt9jB33w0vv2zGFeXti80YrDEpgistTZ0aGeDbW800NUFVFTz7rATsIi6ZJJld8Vq6FrmKqJ6X2bI7IcTopbTW/R81xsydO1fv2bNnuIchUlRaU0preyvP3+3iM0d6Q/v9gJNwRjoA9DhBOxSFDjc8+mhsUFxba7q5dHSYQFjrUHD89wu/SvHOhwc32OglTc85x5SxLFgAD6d27tpaaLm8mkr//ZzMOzjcebhcKqbuXYhURZeWgQkoNyzckLF1ClIdh31F4ZiFzUa40f76hBCDp5Taq7WeG/c+CdpjSdA+sjhudaDR9N4SDtJ18Mc++bIP6HNCodV+3e2G5ubIQDc6qL7hBnj8cVMDnokMdZyLgpCvfjWlwN0+xFnax44qL1OXlEnALgbMuvCNVlJcQsualiEZQ65cOAghxHCSoD1NErSPLBPvnMiRziO8VJPH7L/1hfY3fziPfVP6uGSfybD/vgS+1GLKZQAoKIB77okNxq0VRcuyEAjbI+6OyPp78vOhqyvl02RriGLssS584ykpLhmSzHAuXDgIIcRwSxa0S/cYMWo8OKeP2U+GM+2eD/o4Og5cgMsP5a+bkhny8syP05mw93nWImGrztzrhQcfhCeeCN+3YEFap5FgXWTK9OLpcQNmhQrtT9h9KUOku4oQQiQnE1HFiPd+5/sAnHY4cn++H74QbFmugj9OMGUpN900NDXgwUmnERNfrSVNd+40JTH5+f+PvXOPj6K6+/979prdABHlJgQCYla8gIC0VWysNxrUglGLT2lsg8ADYhHD81Qthipqom1tC1KrQBOFX6GxIK3ghaRiL+YRq2JE8JYgVyOggIK57+38/jg7szO7s7ko1oDn/XrNa7NzOXPmzKKf+c7nfL8dtsYoFF8GdpMkNbSk6PuXmVdcZVdRKBSKtlGiXXHco/9PfUCDdb2WsJ/hcXc6kzOtdJBOZdjQrTBz5sjPxIw1tbXwve/JLDF2gt1O8JvXV1bab1coOkn+8HyWTVhGVkYWGhpZGVkp7TJfVuRbZVdRKBSKtlH2GMVxT8llJcx4egZv9Wniuzvj682Sw/hb01LbYtph1bZVlN/9c+5+zU35qFN5/ry27QIH1lRxcmsET7iFqDcNR1VV/EEhccJrYtQ/1fbY+mg4gtbagvD6cLgc1u2dMLt3avcpU2DtWrjuOpkeU3FCkT883/I7TuUx/7Ii3/q5VXYVhUKhsEeJdsVxj/4/9dKG2/nJK/twC+srJD3iLoCWYeeS9tSfP1eU/dniX/L0X3cBMKUOcunP8+fto+iFoiRhUVsL/3tPJuvDLTLC39rCB2QyUN/BXK3U75ffzX1Ktb2qimhU4GiV7Tpam4k6/PKBAFI/CNio8/aeGyxMmQKxqrHG5/Ei3BOvvbISysth8mTbPPgKif4wnJjN5cuMfCc+OCgUCoUijrLHKE4I8ofn84+h95JmEuxmsa5H2sW777JrV+p22rK/jNvUbGl38hvyVb6dXaCqCvpH62jGhwY042N3VV18B3O1Uk1Ljvzr29PSiIbCbHgrU7pgcnKIRDWaSAOgCR+RaOz4NWsgFJJCPxSS30GK1OHD4eab5WdlJdTW8tH9ZQyN1NLUBJeGKuHGKXKbHWvXtv39WFFby7/v+W8uuXPAsSnyk2hRKiuD8ePlg8f48amvV2FrmVHpFxUKheKrQ6V8tEGlfDwOqa2Fs86CiEzCLoAwDlxEjV00oBE/1QWLyVmenHO9vTzRP/yvEaxavc3YljtBRtrtUtLV1sL3R9SyqXU0upv+YEU1Q3ITLDB6BBiSfSqVlUQnXk1L0IEAHnQXUfD0JAAenFhFHZlkUsdt63MYMgQYORKam+Pt+3zSK3/11dDaGl/v8YDTSRSNpmaNn7of4tHQdGOMKC1NToNpjrQDFBQc+0h7bS2hkSMIhlsRwOiZsL3X58jVrY9rZqaMqK9eLcfF75dju2XLl3sdCoVCoVB8TtpK+agi7YoTg6oq6VU38dGZlyK8PiMy3ooHgUbmZHs/e9ELRRbBDtZsGVfNv4MJ1wxheebphmBPZRcIBODJrQH+WFjNP/MWWwW7PpEU4uLYZsLqkSXlEAzipxk/zcwL3Uvm1aMZMgRuW5/DbZNjgj1X2mbQEqbeapoUrY6Ef+bRKEQiOJqb8PsECwY9JnfXt8+cmTy5dflyKXC7dbMK3VSTZcvK4MIL49fZEaqqCEaCpMeK2ubEXmB0KmOJHlm/5RYZSS8vl4Ld55M+oLfftu4/eXKHmlTzfRUKhULxVaM87YoTg5wccLvl35EI2tKlZObkSAHn9COigm3jizjlpknWaLeJVFkxJq/fAw+fTv6NN8I998Umyu0nKyOrzYlygQAEFgaAdiaYVlURjoZwNQdpcMO9d13AJb1/yPinZGRbfxfmJUjU4YI1axjywAMMEQKejLWh22l8PilS09Lk98mTif55NTicaNGIFObhsEwz6ffj0DT6zZsK0zfF++h0Gh76w1Pm4lu7iubr8jll+XJrVDrhWhb8diLFB1bzo80RHns6ts+mTUxbN52/X5pirMxvG3JyEELQELuNVab5jh3OWKLPBdDfOASDciyuvx6ys+H++6V1yOmUwr4dT3unfP8KhUKhUHyJKNGuODEIBNi1tpq68ioyJ+fEhXlMFDtychjTjtqyKzBzx7/g/n8A7ID588k/VEj+wt2d65tZmNpMMF3f51MujQRlFSjgqd6fkPcn6UfXC0UR+3To3xInqWZmwgUXyG0/+IH8zMlh1y7o0wIeTP/YfT4oKpJpL012nOiMmUSFhkNz4sjJ4fCUuZy8YpE8ZMUiDgOnLF8Yvy7TtbR6XXzwbDmR0TD7NSx9/8lr8Lhdph0bRTzhp/057e19VA2S1hjzvQEIahouIAx47Kx9dg8vTifceafc/sAD8XkEs2a1e+vamy+sUCgUCsV/CmWPUZwQ1NbC8OsCjF8zjYbxVxPVNA71OzNeyKgDSssuT/R/v5mw00MPdc4nkTgRMjMzaQLqnB0PM3oG3HpFzMdNNn/oMxqIC3YN0DweKbYnTbK2AdIKsnGjXH7yE0OM15VXARpuWQuWiNNDFAdrmERtTnxcdmXm0BL1EBEOWlph1y7wrV1lnBug54pF0tuuY5pMG4qGjcj4m32tQ6B/T7K5mBWxEFBVxfTJv+KJb/ktgl23IAU1DXesP26kgLeMs27FWbtWRtZLS+Hhh+Phcb0a7eLFHQ6ZtzdfuCvTqZoCCoVCoejyKNGuOO5ZtW0VF9x1O43BBl5uOpNzeA8NOOWj9/isR78Oi2y7bBlHfzjJWqRJt450lERhWleXJBz3Ht3L9l7wQXeYt7E74x79Fct3rGaq9ggHTh2JcLulanS7pWBPFJ+J/QmFpA2ktpbMyTkINBrx04SPHTfcxWiqmXJ/IG6fr60ldO/9gLTgCDTqyqtovk5GxM0PDqxYAVOmSI1cJd9usHgxo6/PYvveqXAom1/kQItDRsNbHPALk9C12FxsFLHdPXh745nkf2Oa8aZAvx/GmwPzg9E558BVV8ETT8Ctt1on9nYgKX2i0H2tdVVndX6XQJ9UvefoHgSCPUflmw4l3BUKheL4RWWPsUFljzl+MDK+7B8AS6sJh7rjIG7NEIAjPf2LKa65c2WE3emUXvDOtNUBU/TgRYMJbN5DpUlP5VLBRs+3+cMj6UzLaUNs1tbCHXfAU09Z17tcUuRv2cKuXRi2ob/XBZgzJ273WH5nLZMeiBdrasZHFIcxcfbwlLn0XLFIRvpjTYf93ThJqzcuae1aGD/BVI525miy2U7OXpJsLhN2e/nR2y5KA43UjMli8dDZTPy4Z2ohfc01xrUl/pcqRMwiU1aGcVFmvF74/e/lm5YO3If2sgcdT6QqjGSX6UihUCgUXQeVPUZxwmJkfOm1HWaO5m239GLoAs8BMg1kLPL8uVi4EN57D5Ys6bz474Alo+SyEn70jsx8owvj33IrZ2m7pR0jlcWnshJGjICnnyaJcFh6utesYUhugJzl0+SnKbg9NFJL4Mn7iYYjsliTN426sddbMt2csnwhjoICy9uGfYPOt7w8KC8Hl8MNoW5yh705bO8Fj422CvZx22Hd8la+/1ojFasgsHkPedW3c3O/15OvTc8+88wzxirzg5gh2CEesU/IHkQ4bE2nae70mjVJKWHayx50PJFq4m6HJ/QqFAqFosuhRLviuMYsQrLZzhM5HxHGFJX1eKClRebq1v0gtbVQUiKXjgr5ROHcmTyA+rG7dklPeGWlXGJ/5w/PZ+jVN4Kp32dTw1bGENhVaX+eykppA2ltNXLTd/Qyqqvh1zNkDvnsLavRWluIetNwuJwEHr/Tml1HLz40cqSxauB7G7ksUmm4WmZnV/J4eCbjXTGBPcjePjT5LfmpPwD8/F8gECzZvMRq2ygrg+nTYdMmKbxNODQNR2mpdRJqICDD/dnZ1hPecEP8fpmfVoSQ9z4hxWYqQevdsee4y/moT9zt6HqFQqFQdH2UPcYGZY85ftBtANmHoHopuKLgjcSEoc8Ho0bBG2/Ei+vceacUbHpKQJ9PFtvpTPT88+QBrKyUk0VjmP/VaaWlRGffgtZirbiK0ykXl8t6ntpaGWE3F0xyu+E734EBA6SfWwh5bIprq5pSxugVc0iniSZ8fDQylyHfHxP3zNv02czRvAL2fwTZ1atxtjYb11NXWsHAabm29ow7/gUP/MPazmMjYPq1CbaNCy+Ugl2nRw/47LO46E4cb/1+RKPyvjoccjy2bpX7VVbK1wF61P3AAfnmRfcILV4M06bZ9jn7ELyxTMOlOQlFw4yeCTt7O5lx3gweueoR27HpCpxIVh+FQqH4OqHsMYoTFj3ji16IJy0WdI64XVK83XWX/DRnWjFHpiORzk0sBdusJ/EOlcDpp8tPgNpa/n3Pf1Nx2zWW1I0QF+ctv3kYEYoYvnHdAoLDIZfE81RVWQsmOZ3SIvP88zKP+tat8MgjbT+M5OgTVNNwEmbg2xukkDVFnikvTzkEGQ0fMuzlFThbrQ8aPZ6Rx9hl4hn2Sfy69f2nboUpr8so967KWqqmlPHxN6+0nuy3v4WaGvjDH+wfkMy52X0++NGP4oK9rEw+eKxYIaP3mZnJ2XdiYt6uz5d96MGBhrdVRvxz9kJERHh086Pc/OzNKcfnq8ZuQq8S7AqFQnGcI4RQS8Jy3nnnCcXxw8qtK8XF8/qLejeiwaOJoNctRHGxEDU1coeaGiFKS+VnTY0QPp8QUubJv/X9OkpNjRDp6UL4/fJTP764ON4uCFFYKII+r2hwIxqdiCjWRcQ+372gQETSfMb6EIhP8wqEqKgQIj1dRHx+EfSmi50VNdbz+3xCeL1yv05SWirEVe4K0YxXtOI2+iP8frlRCNmu+XqKi4UoiPWrWzdjvfmaJngrjOFYuXWlyFqYJbQFmshamCUW33uV7fVXZSK+PeObop500YBf1JMuPiosFmLs2HhfPs/9qKkREbT4tYEQBQViZ0WNeDWvWHxcWJx07xP7vG79g6LejbFkz0awQC7Oe5ydHneFQqFQKNoC2CxS6FNlj7FB2WOOUzqQ0s/Yb40sXmSxg3zRc51+OuzYEd+nd28ajx4iPSirfC49D05uhhdP7sMj/ziKizBhXByo2MqQikcRixYZh2rFxVBUxK7KWh6cWMVLjhx2OAPxQHPi+du59lXbVsUque5lUMYgZg9dzLlX/47LxEYcyMi+5vFIW4k5mh2zlnyQM5m/kUtODrzWugrX1BlcvzluvXif01jHRC7UXiX446l8Z/m0+MnnzoVVqyA/n9t2uZm1rpwh1BmbZ13j5oLIT/n++odIp4lG/FQXLCbH3IYdJSXw+ONw443yPiZc/5FrppDx1ApLgaqjeQW4n1oNaAg0y6TbVARu0Wwz4QCIu9V/PxUKhUJx7GjLHvOVR7W74qIi7YrPhU2kPVWUNjt/sHixoDQePe/Xz3psv35CCBlo9vuTg+AWUkWaY6zculL4S/zGuVmA+O3IU6yRf6fL+nYiRfNeX1CkzR0hWIAoHYE44kE8NuwkMd211BpF1ztaWGi5rk/yCkQ96aIZtwjhEPdfNFis3LpS7KyosUTajXHp6FgXFyd1+rOeAy0R/SiaCDs9xroWPOLVvGL79k0473Faxk5F2hUKhULxZUEbkXblaVd8vfk8mWTMx5qzihQVQXExDB0qPxcuZMJP+8crnZqitMExwkjDCEC/fta2Y9/1pCcTPJUsbZ3Cd6lM7kdbHnvsUxn+8B2ZV93w1Xu7Q1ER9RddQVTTqO831Lb5YCRIyw4ZAJh+LZx0J0z9wREKes0x2tMAfvMbmR3n8cct5+25cS1+nyCNEA5fGvN+PJ/84fkMGQLNhfN4J+9O++j33LnQp4/8hKR2Ld9jE1PTGz4C4lF24XbjjASNdR6CnLeh/fs+47wZnVqvUCgUCsWXgav9XRSKEwzdVjFhAixdGs8kU1IiJ29C+zabVBlkiorkEtvnAe+VzBy6ku0ZLcahfrefkstKrO394hfWTC2/+AXU1hKoqmL7POg3fzoA2vQVkFkBubnxfW0qi5qxS2X4x2Fp/O/WVkPQbjm3gDN6DaXn4Z0AdPtoJ/X9htL9xQ1MfGsNH4Tgr2mT2Brpn5TS8d0HvQQaZSYbY5Ltu+/Cu+8mj9t11+F48knw+3HofY2NZW8h6K1p8MtJ1mPmzgXdOqR/3ngjzJ8f3+fGG+N/x54yHKEgUbeH+pMG0pp/I33+8AC4XRAKE4kIXNEQmlOT+7dhkdKzxCx7fRkREcGpdf3sMQqFQqE4AUkVgv86L8oecwJRURGfPClEsq3C6Yz/7fHI7W1YTQza862YPCVBn1dcPK+/Mblx5daVll13VtSIFwtKxb7i0nhfzZ4Ul8va54KC5P6YJ9smkLUwy9besWTMAFHv7y0WOgqF3y9EJGGCaESfqKtPkHWniW/P+Ka098xGTJ2IeN/nTppga5n4CUIMGyZE797SKmPX1/bGsndva3u9e8fv5dChttaYVBNTRWmpMcG33Xt8jJn1zCzDauO8xylmPTPrP3JehUKhUBw/0IY9RkXaFScu5jzjK1ZARUWyrSIajf+tV9TUvSB+f+oobE4O0WAI0CAYwpEQ3TZ7Stx+P/8Yeq8ssJTArspaeo8fTZ9YosdPCmZy8ubNsHlzvB9er7XI0OTJyf3R+6hbY0z5ycvO/y8mNj2clLO722O/5M+v5lM0By5sqjSsMnq0vLX7KfhaPgN5lbhCLfzOk0XB0a28tFS+OUgPhYztwvRpYdEi65uBQMA6pu28KSA/Px5h17+D9a1G4lhUVye/LTGf1277l8jNz97Mo5sfNb7raSMBpHnfdwAAIABJREFUFbFXKBQKRYdQol1x4lJebhGST/78OoZecSWjHzZleLn1VugVM5tPitkyHnggtYCMsX9NFf3CUrASDrF/TRWnFtkL0ajQWHsgh3Nrk/VhXXkVfRCk04QA/CukOI1604hENZw+Pw6HBr//vRSZkydbBbBOol3noYdkXnLgshWw/tHbmdbyZyN7TMllJeQPz6fWK3e/QSsHER+rFocX3+ZNcM45ltOM7HcuS8MZaFoZ6UFBBIzsM2Aj2E85xb6/ZlKJbJ2FC+VnLAON8b29Nu3EuDnLjs1D1JfFsteXAbJYkzkTzbLXl/3nRXtHsywpFAqFomuRKgT/dV6UPebEYOOjt1ssG+PyEf4Sv3h99iSrrSLRrtGG1URn/9CxFivJ/qFjk3eqqRH7i0vFub6alE4MPWtKCx6LraQFj7jbVSxmeUrbz6QihLwWjyduMRk1ytI/W0tNvJtiVUFFssWlokIubre06Og57WO57oMuh2hxWI95rS+i1Wxl+SLWkw7ch0631wlbzN7SClEztkDsLTXlwf+cfdItRfVuRL1LE/VOlxg3ob+YOvELjlFn6eQYKBQKheI/C8oeo/g6Urx3JRc6wR2FkAN294QB+5t44uALjH7u5Xh+c7sJpW1EIGtroWb4VL63Y1M8wnzj1OQdAwGe7Rdgu5babTMkN8CuimoOL1nDqGfvwxGSEzrDOPlTeBIf+gOcVwfT9BPb5WbPzJSTaIMyMwqRCNFt2yxWlY97ZtMnxfUEdlUSoJyW087Eu/NdwybDkiXw17/CW2/F89qbiIoozpi7yIjQuyF3Xn9pB/oikdxU96UzxOxBxtsJcxqctqxPwAdllWROj1mrNq3gAyoYmDOkc30y3S+n5iRnbwSERrewoBEX658+RNilwfMjpc2nEzUDEvPu629O2qUTY6BQKBSKroUS7YoTlrGv7MMbkYLSEYGbXoMZ1aCJT2Dt2XDxxdL+0QkRE9eS07jBBfNPfQzXzKmcWmRvtWjPrg1SuA/JLWLD3DEEf7eE9yODWcos9ngCuPVjEkXs2rVw3XXyeyQi1wF4PHDFFYh1Txv5XAVw5NXt9qLd5PtPS9w2eHD87wcekOcqKTHsLt4ItDjAGY0/HJSNgn95939x64mNuGw+ZzTeUCOt7nR8wYbUx1ZWws9+Fs8EpM9n6MjNiNH8WDkQfxiR33M6/ltJuF/zf3s9fxpUDsJJAx5chInioFtYQLgZ7r1XjnEHHk5WbVvFjKdnGHMU9hzdw4ynZfrJdoV7J8ZAoVAoFF0LladdccKSkZZh+Z51FDQB6WHkxM6NG+UEx0ikwyLGrCVXeqZx77TfcEH6fTjucTB40WBWbVsld4zlcA9QS3U1LF7cjh6rrSV36XVcHvkbM/kDaV646y7TMQm52MPjxxNtbJTfHQ45odbvlxVNb7oJzeWKTW2VfHblZCOt/Htjp9Dk7s4H46bISLQZp1MuaWkwa1byRTc3wzPPIJqbaXI4CTs05l0CL2XC1Amw/DwYlDEo9QAm5rZPRYK4bLnpFtJCjWhAWqiRZk83++P0hxBdsOuUl8e98+3eDPBNlZN9hfl7ZwRvwv1a4LyMy8fPYszkQdzq/BUTnX8mipOo2yP3DwZtc+zbYZd3vynURNELNpNyE+nEGCgUCoWia6Ei7YoTlmE3zafp+dtwRiECLD3pu3wv+jdjcqpBTg4MGCBtFO2IGLNui4gQK4/OpIU9QDzi2X33fiZOXmBEWQPV1QSmBQy9ausaqarCockJqSGPn7/9vIp+KSa2iqYmYnluEEC0uRlnRQXU1RmNO97ayq47HuXNdbspc93ECyW5UAK/b57CGawAIHPjCj4eebk1Ar90afx8sU7uL3uGvk1NgIYDAeEwzaTxpDaJYm0u28/+L375ne1Aihz0Op2xvCRMTvWccQYQj3x7Q432xyU+hOjoGXfasT7pDJyWywdU0PxYOb6pkxk4LTaZtqNZZ2wE/iOBaXAV1P5cNnEws5ohm9fItxea1uHIt13e/bbWJ9HBMVAoFApF10KTnneFmTFjxojNmzd/1d1QHAPWP/1r/rWklKf/Vo6mabwROh8frVbR7vXK6LKmwbx58NxzMHVqSouHblW+a8cl7PP+M2n7be+ezK+ebonbKBYvpjZnWtt6NUHQ7lpbzd/rAlZtGDtxdPp0o/KoHk132Pw7LiuDOXNkNzyxgO7Hwe70oCFu+3B1w//Mk1bvt4kPxk0hc+MK43sEDeHx0RrUGE01e3yn0W3inXx65q/b91abOxQbl47aaJo93UgzCfWWVBaZkhJr0SWQ1WntUkN+2XQ0S0sns7kMXjSYPUf3JK3Pyshid+Hu5AMSvf1f8PwKhUKh+PLQNO11IcQY242pZqh+nReVPebEQq/dM5VS0UCsiI/LJcTIkTKrip51JfapZ0L5JK+gzXa1BZpRZCh7drxoUWA2IuJ2iwiaiLjdQtTU2NcPSpG1ZmdFTZsJPloTsryEIbkgkbAmCvH55PIYBZZj912QZz0goT8tDq8lC01Q84iPC4vbzIiTki+YuaTJnS4iyOw0ERAt3nTrDhUV8aw1IO9vRYV9Y12ZdjLUrNy6UvhL/JZCWf4Sf1LRLiFE8phUJGTCKS4WYc0hIiAiaMfneCkUCsUJBG1kj/nKBXJXXJRoP7HQteKINJleMeKLicaKChHRnPEUh5pmEahREO8VWKttvlZcIV4cWiBeK64QF8/rL1P4xRZduN9/0WCLMP6osFjU1Ajh9QWF5mkQuOvFt2d8UwR9Xvmg4HRaRHd7BUKFEIZwtVQf1XfURV9pqTiSVyCeK6wwMjWWlgrx/sg8EQER1pxx8ZwoqGNVQ8MJDwghHEKkp4udFTW2unLl1pUia2FWyuqvXzSNY4s33dIfXcQLIeQDmHk8CgqOfdrIL5sOPti0O846dmNiOk9EcyRXwT1exkqhUChOQNoS7crTrjjhidujA9JHXCetALsv+h5ZImJYRcJC4Iz9ra8buuLn7Jo8iSG5ATaXVHLe/FgawPkruOfSa9H4C+khaHDLojkfnupn6jZrlVDvqsd5bepgtJt+hdgxhuzuLzD1lT1EWmU6SgEwfTp1ZDJwWm6H5jt6hIALL4RNm+IrH3tM7jx6tJxo29pKBnAFK2B8BeTmxia1ZsCbmi7R4pMfhSkzSnk5BIPGeICcF+BCSrwhdVVMmxZg1bZVfHeRTD14su9k6oP1BCMy9aTu8d+/uztZW4eRQxX9JtkUNWrLnlFWJq9r6lR46y3crY2WsdUANxDUNDwVFTJTjI4+FuILpI38Eqgqq2X7Y1VkT80hZ1pCfzqYkjF/eH7chqSPn9emeldOjnVMdG9/7DwOETXur5EiVKWBVCgUii6JEu2KrwXxuXcBQIrNaz/aZWzXkKmUXuICLuRlQxCGcVFXXsUQdpH5i58Y+wrA/34Uj8tLoyYzf+w8uz/LJvwK7eBuWDTfEEPRC79N0QtFtGTsIXvQVmoejvfL/IDwr5nlfDMnt90CoQZTp1pF+9SpcdHX2mrd9957YcgQud0s4sLh+FOB+Ulh8mT4f//PuF4Al6aBzweaxgdk8ver7+TXJ/+FPYOlv/pw8+GkLjbtH8AfFgzi9dBoNATREg3HFpN4tqvkqld+raszqrrq16n3JVFoukD6tSsq5ANHTo5sJxKBlpYuk5O8qqyWUdNHMwoBmzSqqLYKd5sntjZzsuvjF43KZd26uG+9tlZW/PV65TgsWRLfFjtP1OFEi0bi9QY0h0oDqVAoFF2VVCH4r/Oi7DEnPlkLs8TqvpkWq0UriID7DVHkKBYhNNGMW9STLvYVl1osM/qyI68wpf3ik7y4d1yA+M350j4TMq1LbG8NeclWmPbsHaWlQowda7XGpKcL4fVabRFer1w/cqR1/ciRqc+Vl2fdNy9PiNJSsbe0QtSTLurxi3rSRXZ+lq23/8ERGeKgI11s5QzRiE8IEEF3Wty2ovdf9wK53dbzDRtm/Z5i3PR7Z7mO9HRp4gch0tKObfXPL2C5KRtbKupj8yrq8YuysTbeJ1P77frXS0vj1wnSbqX3K7FKbuKPq6JCiIIC8dHIy0Wz0y8ODbtAWWMUCoXiK4au4mkHcoG/AweAVqAOWA2cZdrnMmAlsANojn0+CvTp4DlOAR4CdsaO3wU8DPTuaD+VaD/x0RZoggWI1X0zRTMu8abvZDFv2GiRPWGsmL7gZfFiaY14sUBOCjX7ghPFoq3hXIgkL3FQk753sw89mvD3AXpbNZOdzzyFWDR7nC+e11+8vGB6XNDrAt7vT/Y4p+q/fn6PRwhNs4jBFwviE3rr8Yt5w85L8vY/OCIjaaxacMvr9fmSvfRpackCfexYW9FuvhcWT7uO+UHA57M+JHSQo87uIgLiqLN78pi05TkvLhZi6FD5acOLpTWWB54XS9vuV9bCLItg15eshVnx/pgf0NLS4r+RmJiX46TJ+RkxoS5KS7/QpGCFQqFQfDl0JdE+GXgQ+D7wHeBHwNvAZ0BWbJ81wAbgxtg+04EPYyK8Wzvta8BLwEFgFnAxcDNwGHiZWIrL9hYl2k98zGIoOz/LENMREOdMOcM6sc+UgSNRaDefdqb9CRKzdoBocJOU+cW8/H1kobWNRPHp8diKrDajsYkpZIqL5WKOzreFHvXNyxOiWzchCgrEzgqr8FwzxC9CsWusd8uI+wFPPOuMWWC3OGNj4dHkg4UQYl9xqTjSe6iIOF3WMdMfUlyupLE0Frc7WXR+wSw1R53dLffFItzN98TjsYrz4mJr39oQ7mVjS9sV7ELEHy4TF22BFt+pokL2xeuViz5usX4mPWjqiy72E69DoVAoFF8ZXUa023YAzgAE8L+x70kRceCi2D5T22krENtvRsL6m2Lrz+hIn5RoP/ExC91DWNMaHsIbj2TqVFQI4U0WovtPuyD1SUpLZWaY2P6hNI+4eF5/I/NLK4hNFxSKg47eYtMFhcnHm8Wn1xuPRidYHToUjS0ulqI9UcimsHqYI/dPjPFbrrth2EjxacZAsem0S0TRBX0tgrDJgTjjFi0p0m4Wj40uKe5HzE0Tqxfcbc0G43JLkW5+oCgstBfsupi3s358AQtL4tuQiDmSb4pgGw9T+jmGDrX2b+jQzp3Yps/t3lsdXbjrViAj+09yRiSjf7HfZhREyOOTb5WOQzqcSUehUCiOA9oS7Y7PaYU/luiz10IAQoiDNvu8Fvsc0E5bsRIyfJaw/kjssytcr6ILkD88n2UTlgHQE+ukzZ60GtUla6aUcKD76dSUb4ZZswCMgkYAoTvvbvtEXq/89Hho+vaVlK8ezOHiUhxC4BGCCzYtpFfkY3YuHcPgRYNx3ONg8KLBrNq2ylpyft06WQDKJqWMd8ceplZD9iHrqY0KmYEA9Osnj2tqkpKtqio+iXHOHPlZWwvAqm2rmPH0DPYc3YNAMH5rk9GmAPzvbSHj6Aecv/Mf/HR7BIhPEPVGYfWfBLdde5TfjMjgM6fLOE4DWh3w57Nh9EzYmtFC2h9WWY4/3P9sePtta4aZhQuhsDA+lmZSpdgJBGQbnZl4WlYGF15IC16jzwANzu7WdouK4tWqNC2efefGG43fhoh97zAp7kXJZSX43X7LrrZVZ+vqwOWSk26FkN+rqzl4wdWWa7Fwww1E3R55X4IaD06s0k973JD4W9UzFq3atuqr7ppCoVAcc74SEatpmlPTNI+madnAUqTH/Yk2DvlO7PPddpp+G3gR+LmmaWM0Teumado3gbuADUKI9o5XfI3IH55PVkYWdW6rSKtzexmUMYiaKSUEVsynb8MOAivms+GP5RxxuQlrDlodaezKK4yXt4+xv6SMA6dfyP6SMksmkGhU0H3jU/TdsYl+86fL7THaFB66+MzNhbVrYdIk+WnKvlK9TOOhDVC91CrcB2UMin+xyyNpTi+oC3mg6IUimkJxof7kMCzjA3GR7W6J/61n4Bl+GN78Hdx27VHWnh0mijVF4/ZT4u1s72V90nBecrFxbbW1UkfX1iKF+7p1FlF827U9+PftPzw2qRzLymSmmk2b8NNKM14EUO/sTo9wQgxg0iRwuyEtTWbfycwEYNeYSQRxI9AI4mbXmEkdP7/NvdhcUsmga57nof13kZWRhYZGVkYWyyYs4xve/PjYgP39DQToN+17RJxuNJCfhYVQUCCz7OTkQCRKC14EGi85coznj+OFxN8qQFOoiaIXvoIKuAqFQvFlkyoE/2UuwGbi/+/dDpzZxr7dgfeAdwBXB9pOB/5ial8AzwC+do6bEevX5kGDBh2rtxyKLo5uk9nj9lqqbbaCeN/TN2WmF6OiaHHclrGvuDR5W8zy8PHAkZa29g8daxynWyDMGVge/Bbi43RNWkOESO3TLi0VIZ/MEKL7yVNWyLSrwGrTZsAmE0zpCMQRj7TGJBaOEqWlFluIbimZMsE6VkEN0eyUvvYGl2z/4nn9RcSbJiIOp4h404zJqfuLS5Oqrq7culJMmOIRj52LGJffTiXQznLmmVZry9ixbe8fs0sZpWaLi8WrecXGBN0G/OLFgg7MGdBJuBdbC62/pa2Fpal2lbdNt0AVFsrPdu6xPudCb3+mu/S4nI/aIc+/QqFQHEfQ1TztwJnAt5ATU19HZpEZbLOfKya464ERHWz7T8A+YCbSCz8TGcl/FnB0pA3laf96oXtiEyeJ2k0aFQmfH/szjeOrevZJKcxtBX0MbYEmsmfHs6+0agn+48JCsb+4VAQ9NmVSY6Is5HaJoIa4cQKd8/XaCPkGj2bJBJM9GzHvUsQvr8ywHSMhRFJ6xsNDThWvDvZYxuOI2zp2d49zxSfLxlJJvpZXLCJpPhH0xNJJUmNcbof93Tb3tk2/s82k4XYn6ZonpIIQHo+IeH2iEZ9oiE3Q7ZBHXM/mUlFhuRevDcyzjFVIcxr3SD/1OCrECq1ArM8rtWbg0SctjxwZv7eJ/v6EjEg1YwuOO8EuxOf7TSgUCkVXpsuJdksH4CSk53xJwnoHsApoAS7rYFtXxSLrlyWsHxdbf3VH2lGi/euJ3QTEVJF2Q0yB+O3IUwQLEFMu6WPZJ4RDfDTycqP9fcWlYv/QsRbBLoQUHlMnSpEsEs4rQAR79hbn+mTGlgb8IuJLCIkmRLpFRYVc/3kmYyZE7uddKqPi0RRjYpmkaRbuPp9tBN587BuzJ1kEez3pooW40G/AL27yxCPA7UVVEwX6rGdmtZ3jXCcxDaZZ7JpJFNjp6fE86LGHqY8Li+OpQs3Y3YvEhwXTfQu5vZbxCrk8xoNETY0Q33NXWCfwuhPy8puXCy6It603kDi59zjNHtNuHnuFQqE4zujSol32j83AxoR1y4AwkNeJdn4WE+c9EtZnxNb/rCPtKNH+9aQVq6hsK9KeuNRrTvHgiAwx5ZI+4rDmsVpITMLdjpVbV4oRc9NSRtrfvLxQ+P1CZFMjbvKUitXFCYIwUXTqeck/T9pDPXLv84gGjybuvDSeplEQLw5ljrQf6T1U2mTMWVXMwn3sWCF69UoWk7EMJyItTUQcLtGExxjfsNMjImk+8VpesSGA24qq2om3VCLfNvOKuV9er62dxPI7qKgQOytqxKt5xSLitWblOXJ5ngi6vOLI5XmWMU1q0+6+CZGUrjHscFnz2peWinfPtEbiIw5XPNKuafbi3ZxCM7GQlfmh4ThDZY9RKBQnEl1atAN9gQZgqWndb4Ao8KNOtjUlJs4vT1j/3dj6DrWnRPvXk2G3OCzCLPuSa8RzzvNFC07RkCBWU0XhHxyRIVpwWLYFNVe75165daW4eF5/MW2i9Hm/lT9eiN69hSgsbFd/b3z0dksfNj56u9W+YZcSUQhr5NiMOSocS3Got92Az+L7TxyTJCGok5jDvLBQniMmNPU2GkkTDfjE4YLCeIpKl0uIk08Wb+WPTxlV1QW97r0vHZEs1pP8zubr1MeisDA+bmnx6q1HM4dZ7unhvsMMF8pwr/Tg64LdPCZHLs9LfS9sIu3676DeLXPZh9I8cY+66YcQ8Xgt5/k0r0Dup1+H3b24/HL79YkPDf9hlOhWKBSKOF1GtAN/BX4OXA1cEvObvxezxwRi+9wRE9hlwPkJy9CE9sJAmel7D2Qhpn3I4kqXxD4PAHtppziTvijR/vXEMhn0kmsEROPLxGlij08TERB7XeniwTMG2kbgD3i84rm+J1m2vXvGqC/ct1ROFz3CPC4fY4Kmv8Qv1q1/sG2lnygYCwtTV1ytkRHlu9zFFo/5kd5Dk60y5kXTrA8EidVC9Yqrsf0jHq+oGSuLN4niYqv1JLa8lT/eVuBpCzRROsL6EPF+uiN1pD0mgCM+vwh6pf/8teIK8drAPKNQlPG2ID1dNDq7Wf35WkaSo6aiQoigy5rzP+jytv3Ww1Sh9OUF08WIuWmW3+GIuWlxEZsg/g8XFIp3z5RR/YgveYJy0uTa9kT7VxBpV/YWhUKhsNKVRPsdsYmnR4AmoAaZ8nGwaZ9/JmR+MS/LE9qzWzcwJvh3xfzwu4A/AAM62k8l2r+eWOwXmVUxwS7kZ2ZVkvh7cESGqMeZFGlnAeK5vieJFhziub4nfa5JcbOemSWc9zgFCxDOe5xi1jOz5AZdvccE9sXz+otPcYoIiE9xJgvTVJ72xGisw2ERqeYKqPppE3WnOaqsi1RzMakOiUFzFhazBSTRaqMvPXrYvh3IWpgljniS336sHpJCEJaWSqGL9M4vchTaWqF0gfzxqMst254dVmjbvfdH2kTazffN7l7EBlefAGzO2pNUJMt0E3ZW1IhZnlIjY03EZ/NGxfw2JfFBTc82k5f3lVlj1ERShUKhsNKWaNfkdoWZMWPGiM2bN3/V3VD8h9HzpTeFmsje8N/kvPJNqshhOwGYMBXOW2573IN/yeBH77Xwx2Fp3Hbt0aTtGhrRu6Md7sfNz97Mo5sfTVp/96mTWfA/6yEaheZmSEsj2NKC27TPUdz0nD0Yrdf7bZ+zshLGj7ff5nRCJGJ8/TSvgL98bzmZmbJmT04OBKiFs89GhMNGnnYBfFpQyMl8CitWxNsrKIDly2VS8aoqI4e4QeL6sjJZZKjJmn87iYoKmb8eee/CP/4xP94ir1nPC9/qdDHs1wPYe3Qv32k9lSee89N3+z747ncJbXieYKtAoPE848jjKUs+eYP0dFi7lsiVV6FFIwiHk73PvcPZ1wSMWkbmS33ow2vw/3MDTRdfQcbzf237GsByvQ1uuPUKeGx0fLPl92Maq7KqAItn1/JSy2g0BB6vhntrOznrKyuhvBwmTzbG7qvEcY8DYVP6qbP/ZhQKheJEQdO014UQY+y2uf7TnVEouir5w/MBWPLwYja88idk8iKN0d/6IdtTCHaQRYRuA0iorKpjKXLUAZa9vsx2/YfPPgHCJwU7QEuL8Q9YF5s9CJG95M9M+O5kKfBSCbjcXCl6lyyBDRtkQZ6WFvD5ELH29TadT61lzt+Wo2mmOkZlVeB0ooXDFpG782gv0jaU4TP1i8mT4xU/hcDaEPLT3E+9UFDCwwM9esBnpkJH5eWG8Mwfns+q/wcfjL2RgQ0hQwa2XvI9dhf+VZ7/rLPi7T31FE2X5zHvxe/xkiOHzPAu8sJPGccZ19SzJ/z731BVhTPNKx8k0rwMqatiy5YAjz4KixbFuzR5MmTkSqGeYT/yycSut9GjgRBUJfxcLL8f01jlALc6A1yYVs2F0SpuW5fDkPaKTOXmdgmxrjMoYxB7ju6xXa9QKBQKK19JRVSFoquSPzyf+1tn4CBKN5rw0sLMgy997vbcDndyyfl2iIiI7fp/DYoJXl9MErvdFpEJ0ICX6nAO9z+/R4rkturS5+bCX/8K69bBf/0XlJbyWm4Rodh/FvQ2X9Aup6kJhkZq+ej+WBnOnBxwuYi6ZZw/hJMmfAzb8Gt8rfXGKZq83eV5UlRftSUQkKL+lluMVQL45YWaxSvH5MmWw/KH5zOoPshnl+cRdnn57PK8eKS7qsr6AABk/HsjhdumMefhAAvfzuX14gp29RxJFE0K9sxM+OQT2R+biqOBgCzUWlERLzL6ufRw7Hq33TmNC2ensb1XfJPf7U/5+9GHac7DAQq3TWNI7hesCvsVUHJZCX6337KurWtWKBSKrzNKtCsUCQzOycRHMwJwEeF/dr7DuO32+7odbjxOD9mHYN6Lcsk+FN+uaZr9gW3g1Jy26ye9A5x8slSGxcUQCmHeUwC/cMh53N5QC9HGJqJnnMGnQ0elPlltLVx3HaxZA7feSnV1rN+xzRE0xonnmeCp5KWW0Vy4eo58GACoruadnzzKVEpZyQ1cw1/xtx6xHJ/WWi/PceCAFOsm0dsmJkW8Y2IOV0/x8LNvHSU3H5afC1dP8bCq/6Gkw94YNQXHCxt565wfWK0pb72VfI7zzydQVca0nFoCARjDZk77dAtO/XHlppus/amuhsWLrW8JkLdj+fIOCvbaWmmHSXyYCgQ4/+4/cPuNpZziO8VY7XP5aItAAKZNa9sR05XJH57PsgnLyMrIQkMjKyOLZROWGW+9FAqFQhFHedptUJ72rzllZURnzMQRlZFZATz5jXT+66omTvadDMAnzZ8wKGMQJZeV0H33fi6/7jZ8IXl4k0tj1EXXsv2srdBrO1kZWewu3N3h09t52u/4FzzwD5PXeuRI2LLF6J8GNDgd3N/rau48vB5/OGLxZR85bSQ9d7xhe62Gf9zn4xPXKfSsrzM2a0DQ7Wf3NyZx+htrcDQ3SeG9eDFkZrL5v5dw1gcbiOIENIKam57iiHF8k+8k0h2mfCxFRTBpUkqVuWrbKopeKGLv0b3G+Ba9UGRroUgc1zdGTWHklriX/rG+53Dfz+opuayE/Gvuhh07rA14POByxe06V15p3WfoUHj/fdt+Aqk9+m3tn8oiZLp+fV6Fjt/tV0JWoVAovia05WlXkXaFIpEMiqHhAAAgAElEQVScHBzu+HQPDZh031qid0c5dPshDt1+iOjdUXYX7iZ/eD4TP+6JX/Ogxfb1hh088PcQ2Y+uhkPZ7D26t1Onf+SqR5g1ZpYRcXdqTv7nvR7WyZF7pIjVH7mbHUDEx5OHf85oXrP0HaDHzjdTXqthuWlu5qSYYNePiwIul0bgrsk4HCZ7CMD48Zz3wVP4aKUbTYDg0I9/ajQtgD99I0wk2CIfCjQN+vVrU7DPeHoGe47uQSDYc3SP8d2OxHE9/c21lr5//6NdRhvVV4y2WmucTnA4oKmJcGMj0TPOoPnAYesJbrzRfswgLsDnzGnfhqTTAYtQ0QtFFsEO0BRqouiFovbb/w+xatsqBi8ajOMeB4MXDWbVtlVfdZcUCoXia4ES7QpFIoGA9Hm73VLcpaXBkCGp948JX10QOolwLet5IzKW7M2XfK5JdY9c9Qjhu8KIuwXhu8L0+cnt1h0+/ZSjl+fxscfLY2d0Y/bwbzPaVcX28Ci2a9ls9vUH4qL+s9POTX2t1dVw/fXg8Rj/QdCP2z3kLPb8tZoNFVB/wTiYMQPWroXHHgPiAjmMk7Q0jabub9LskutbnfDj/2vAEYrI9oSwt8XELCOl5bfbCtZUdqHEcf2sz+mWvtek+402bg3uIYQHgUYEJ/vvWQpOJ2GIvSOAtMYjNKefBEOH0nTaMEIL7uPouGvsx60zHn0dG198Iqke8Dr74PdlkerBSgl3hUKh+PJRol2hsKOuTor2SIRQ1MGBNe2IMoeDSOyfkx5xdxHh4s/2H5tJdUVF0hITQwA9Nj5Fn2ArU2saiKYdZruWzYPcwoHQEGr7nEQotl8IOPLIn1M2XUuArR/2JBoMGlYbAVT19XJ2novZE7YzftF4um18CrFoEVx9NZjsYxrgKrwF11Nrqf33s/jC8vi0CEQ0uT2sATNnJkfZ9Yj1TTex8YF9PLghuX8REenQZMUB3eqN/gD09Bw0tgVe8RDChQNBK17e3w5UVxv/AdSP8TYe4eiQ4fh2vocr3EqPjU/ZC3cbAZ7Krh7vRGpfvE6qB7yukk3leHgToFAoFCcqSrQrFHbk5BAVGo34aQ1qjC/JSS3GqqpA03Ah80rrEfcoDr51yxXHzos8e3bSKl1sTqs9zIOnncn/8jB9OET+nncAJw4ghJ+6cvuHjtpaWHl2CcM3LjLaCgJNbpg+qZWWHWP4begWy7kIhyEYlJ7wsWNl2pRZs+Caa5i0ucnYN6SBU8ixcAlg6dJkRVtVBa2tEA7jAP73FenfN6NPTmx3smLMzqJH2svizzjUfiuIQN5PgUbmZOlDb+x7muWYxr6n4f/nBsv16t8tJAjwWgKMHg3rbq7k1bOm8EFZpe14280c/aCsktoL5TFdPZtKV38ToFAoFCcySrQrFHYEAqwtquannsWMppqtkQGccdt0ew9vLOoacUgfvAZEHC4aCucz7aZZx7ZfXi8AwinPpYvNv5x/Ej/addA4P8hIv0Wk2lBVBQXhxy3HfeqH0TNhey94cPdaAuyyHqRHmN1uePzxeErHSMR4yyAAt4CnsyHojLWtack2ksxM+RBgOv+0LdZdrsy+kvzh+ewu3G2ZS5BEURFvzJ7EzpNh3iXwy+/I1X63n5tmz+FgRTXVBYs5WFHNkNwAb4yaAh9/TKMjHQE09D2N7gd20HTxFZax1b8nYRLgVVVwaaiSdcHx5EdWkDl9vCxk1A4flFWSOX082ZvkMRe92svygHLDvj5sfeU88vf1aret/wRd/U2AQqFQnMgo0a5QpGDMGBAiAq4mhBAw6EV7D28gwLb/fghHVIpPfaJj71mTjm2HYrnR8ftxpHk5WFjMR0PHcqC4lN8+V4Nr8qz4+YEPLy+wiNRUTb7JcMtxQSH/szDuX6O4eWsjEBfiGsAzz/Dv23/IJXMycJQPY/Ciwazv8yk4ncZbBl2AaxqEHNDqddn7uOvq0LOn20XIAZ7b/lyHh2j071bz73+u5ImJyVH5IbkBcpZPMwT7yC0r6CYaSI828ubIArofkJljMp7/q32u9zbIyYHrI+XGWAGy+FM7ND9mPab5sfL4A8r5G/jjso8Zur5KVq/twEPAl01XfxOgUCgUJzRCCLUkLOedd55QfM2pqRHC5xMtTk00OJwiOz9LsABjyVqYZdn9xaEFIhpLbBgF8drAvC+vX6Wl8tNm3aGCQtHYrbc4VFDY4Sb3Dhxr6XsURKvpb30RIERBgVi5daXwl/gt4+Ev8YvXZ08SondvyzHj8hHZsxE3XeMR69Y/mHTuxPO0Itv70OUWEeQnCzgWI2fhM62b5Zo/07pZd7Ab53bYW1phHauKik4dEwWxt9R0TEGBnijTGPtjycqtK0XWwiyhLdBE1sIssXLryi/1OIVCoVC0D7BZpNCnKtKuUNixZg00N+ONCPzRCN/fb007mOjh9d0oq3MaVQ9m3kR7dDp1nk1e8PVP/5rG4cNomDWdxuHDeOm6AfjrP+aU5Qs7dJkflFXKtwjEI+RyEq3E8Lk7vFBYCMuXU/RCEQP2NzG1Ol5I6vp/NzHq4TVwUFp0/i8TcvPh+Wxps1lybpA5Ox5OOn/ieVzAh8VuTg2H0IBTwyE+LHZ36Fo6w/vnXgfE75f+Hfh86RyBgdNy0Soq0GLlUWuH5LY9MTV2TF1pBdvHFlBXWsHAaaYKTQkVX5O+f05WbVtFr1/14oa/3PC5ssB0yKqkUCgUimOOq/1dFApFIoke3jFFuWymgubHy/HdOJkxRW2Xx0wsoqOLJsBeBNkU5lnV+hr/93gRlwpBtxC0OgXVv5tH/eBTOySkdD+1GV3Eagnft927zrgm7449vL4UNCEnmk78AUxLqNukIQW7GbvJimHAbTpPGOgXDhltCNP3Y8moN5bzxiiZ2/39c69j1BvL4xvN6Rz9fvk99pBkLv5kV2grPzcfcnM7UkfJYOC0XJhm83vJzZWTfMvLpWDvUMnVtrEr3qSjZ4FRIlyhUCi6JirSrlDYMWkS+HxE3C6a3fDkWfFNqTy8Y4pyOWlCDj0eupdtc8vabL7TqfNs8oIXvVDECwOCaLEMLZ4I3PaPMKXlt9u3kUCin/pgj9OS9nnr1Mt5vbjC8hBy9cGT0QSkh8EbgfVPwDMJAr1sFPyzFIIL5CdgiFwzHiEsqSm9C+CAS0bWdSF/wOXueB7wdvMuxhn1xnK6R+utgh1S5lNPzFF+uPkwh5sP20aq16yBUChFGvf2+mjenpsLy5cfE8EO9r87MyoLjEKhUHRdlGhXKOwIBKi5voiD3iz+Of5GgkPbSTcIbJtbxjmLppN9cBPnLJrepnDvdOo8c+XSSAQyM9l7dC/be0HJRbKQkQYIDYa+va9Dl+ibarX07PNI0a5bZACGf3dA0luDb+fPwx2J22miGqS5XOyYmAMjRzLnunSmvAEX1clXeRfVQdM9MOcFe7HoEQKHEKzZuhINjQHzQ+x3uRHAfpebAfNDHcsD/jltLUmkyKfenuDVH7pqa+G++2RWTEioKVVWBmefDT/5SVIfXx14DS2al9AZZ33xa0hBe6JcZYFRKBSKrosS7QqFDdvmlhFYMZ++DTu44unHeXrPz9v18HpXWauE6t/tSCWOHJrDPqocCMC8eTKneSgEV17JA6/0AORbgLADGmLW7x1n9+/QNSb6qcOFPwXieeYB26qdEz/uiTu2gwBcUSh6UeP0ja/D9u387dRGLqyT2/WxSBPw8781Q0nqLCP5w/MRsTMPmB/CuUB+QgcjwOa3EaGQzNv+eTKuVFbC/ffLv9eskX2urbX24VA2VE+Vnyb2Ht3LmjXyNukYNaUqK2H6dJniMpabnjVroKyMrf3G8Y26p/ASxEUEmpqIBsMcuPLG1DnfPwdtiXKVBUahUCi6Nkq0KxQ29Cj9DRAXnc5H7mH907+mOaMXUU2jvt/QpGNa86cCccGrf7fDLnUeyOqfthMCKyth/nyIRo3l9g1HKXpiDNvJZvRMuPUKuHB2GtMn/8o4rL3JrgOn5RJ4aTkDp+UypiiX3XmFRNFktN3jse/8Y48Z0XgN8GQOQnNo0NREQ7CRi/ZqvH9SfHdz5J7HH085JiALKdmRKDbrx44j7HRTP3ZcfKX+NsLjkWHuTZs6nyqxslIes2KFFNjz58tl5EgqF55B4wIPbxb3p/bhCHesz4Sl1Rbhbu5nNrXMo4SJb5XIdu+913qucFg+EMyZwzkfbQTi4xQFtFArfXdsInP6+GMm3FP97k7xnZLyDZJCoVAougZKtCsUNhx2a5bvTc4IV068jbTPDqMB3T7amSTchy+cxluFpWzvPZa3CksZvnBayvbzh+ezbMIynJozaZuttz1Fzu8F71UTWLoaPh3MKb6Tue+S+wzhlejB7kiGkCG/nIUz3R8vnmSKtH8wbgpN7u4c/kRYjjl4sAFHc5CYG4R/DRJMvAHDq26J3MeqlpoxP1g0BBvwOK0PC4kR4Pqx4+j28kac0TDdXt4YF+66rWXMGGNfAez62ZKOu0xSjHO4uZnLj76HjyDDw/s4nZ08wL3cEXoQ9soxGnE0jSeOjuOHY2oZ7q1lCyMpYT4Xb5wP48cTefUVy3jsGzNMPmQ0NRGOyXWRcF79V+h+4N5jYpXRf3fm6rIrr13JodsPKcGuUCgUXRxNT/emiDNmzBixefPmr7obiq+IVdtWsaLoDiqf/tBY99sRGfzP1qOWrCoCcHzBfz+OexyGJcSMhkb07mh8hR4BNp1fA1pw84D7buY7HsDttKYqGbxoMHuO7klqOysji92Fu1N3KpZacn2fT5mz42H2Ht3LH1cN4Yfbdxq7fDLsAk45WeOTbXvpWV9nrN/m68m5d3wKwLDDDq59O8p3P/ByXn063Wb/DxRZH0bsspm4HW56eHtYsrJ8w5tvZLs87Uw3zmjYyC4TcbhwRUwZZiorEaaxasbLWN9WVm8JpMzgYj42cZz1T3O1V/3zI3px6uyefDPtIFWLGnCFI+BycST/J5z0x9/hiEYsxyfe6ZDbgcedBprGK2kjGH64mre9vTk3WIdHmLLquDy4ve6209AoFAqF4rhH07TXhRBj7LaplI8KRQJFLxSx57wPyaU/k9/wUz6qid1Z+7h1K5jj4o19T6M71jSARuq/DkYtB2UMshXWSd5jc/q/o0dh3TqCwkUYDxrgdFhTFH5QtYvf/BJ2981gac5RAHL2QtUgeJ92/OGBAKtaX2PG03cbYvp778sHGF14+t7fBqF6MjSHZf1ZzZ8azdScIih5oe2HGrvJnaFoiG6ebhy6XSaBT0yfuHPExfTestEQtM3fupjupuN3MYQBuHAb8WsHF0SqqKrqgGg3jfP+7ByW3nMAAVwWepYcXraIeIC+HOJ1LuG09/24wyvkOIRDnLTiIRwIy77mSrH692eGRun/gx/y8egzmLztbppCrUx5vY7Hno4fF8KJJxwk7HThMqWf7Gp8kX8HCoVCoWgfZY9RKBLQJxw+f94+pk5/n+fP28f2XnDWbPjYI7OaNPQ9je4HdthaUAZeeAdhzcmegWPbPVd7ZeF3jLqGkKYR1hy8U7gE7rwTnn8ezevF5daoKlxLwdOTcDjiKQr3H4DM6eO59sAe/ufNo7z5e3hjCTy0AaqXwndaT223X4li+snB0qSuC8nDF8tiRAf6nmtZX53e2zgm1aTHw1Pm0tS9D4enzDXGOvsQlmJN5kmf5vmlNwTLiNY30XzaMCIOFw0XXE73Tc9b2q8rryKCw3iQcBDlZWeOZU7trspaqqaUsasywXJSWWnkRf8/cvhI60d5aBLlnmmEYl5//f2HLsBH//5JtJf+z9KMZvv2JNkutOQ8+EHG88zZ8bAx3hfttbavodGAn3BEs50Y3BX4PFYshUKhUHQOFWlXKBJIFf1+v5fGxs2Pkz88n/XbVlFkYz/5529OJadeRqUH1b3MnoFjyfpgEwD/uuEGMp96hj+c4+SJH3S3RCLtIpQ7Rl3DaVueirUsOPO9p9h30W76CwEtLTj8fq44pw6GDJGZZQAOHeLk+wqBuOjzCAgL8Iah0Q0PeK9sdwwSs7VML/gIVvTl+7uP8NllP2Dg88sBGHDgDbb3OoMhh2upTu/Nt26TFVHdDrdtJpLDU+Zy8opFAPhWLOLRMQP4zfkfUr00vs/omRAcOsiw6VyamYOmBZjpLuPR0HTYEbu20lK65+TINIqmKrGDczJJWxE0hPHzeb9n9S/jUfZdlbX0Hj+aPgjECo1dFdUMyQ1YrTErVnCd28uVIScCjR9qa3H5/KAJtKam2B2JCXEh6P7JbmNdImaxnsh3t2WwMds61uXnwJQ34/vPYAkOYMqSHHK6aJS9rboDKtquUCgUxwYl2hWKBEouK0nyWWto3DTmJvKH57dZVXJs/YHY/lJ0Dah7BZCC/aJVMur4wCuQfSTIjKZ4BVQ7YTPgzQ1GW8TaO+WjdyDdHS/8k5kpvSMtLTJ/O+Ax7Q/ydZoWgUaPhsfp4fzJt7U7BnYPLtMLPuK+jCx2Fy63rH/1H3dxzrqpBCMH4+OlWSfy6vjWrjKuSQA/eKeRLQM9QJBuIZm28rIPPVzx3dmGJ2aIprFtbTXh6Q9BXfzYrT+fyVmfOHALDZxO2LIFAgEGUkfU68PR2kyzy8G66HQ2Pncfs2sW0/PjifR9popLieCnhSbSqCuvkqI9YRKqIxIinVZC7jT+eEU5jpvWQl0dkZvn4Ao2WfztmsfDkdzr0V76P3oc3IHd1e/z+ugRCtHN5Mf/0XstPBx7I6GP9/PZkJsPk9+CDf0vxd9nGtdPhpxjU1/pS6HTdQcUCoVC0WmUPUahSMAuw8Yfr/0jj1z1CNB2kZ39Tml10QXzh5nfAiDzqWeAuACfWtPAhe+0UQEV+PDcK4y29PY0gkQbGwk2NUF1NZ/9egmisdEQ7OZzmP/WPH7SH/kD7i1bO+SJ/uf6XjTdB6tNOjZVHu+iF4oIRoKWdcFI0Lg2PTuMdssZlJ0SMK4JoOayHKqHyjFrcINTwOXvBtlx/220hltlZpXGRrLGn4F3327Lsdt7RHC1hiAYRDQ38+k5w4hqGodunkE01EyTE6JEefA52Dl3D9dPnMHNt4RZ+mwmPlqkN58WBudkygYnT7ZeWDQKHg/uUAsZlav5/+yde3hU1bn/P3vPLTMJhPtFAwQwARUQItaCndOjYhMvKF7QctKeIKSoFTX+ejsSVLSJ2toWxDtNEFpy0oqKisVJBa2NgheM3FpJkHvkGiCRkMvc1u+PNbNn75lJAGuPguvzPPPMzN5rr73WnsDz3e/+rvfl+uvB66X5ismWcYQ1HXSdbr+aRXpW30TBPmECms9H6YtTeXZEquXY/z0njdJLSxNsUm9kwcwbPVzzq2lfZkHUfxsdWaFUsSaFQqH48lCiXaFIQv7IfHYU7ZAFlc5+lvzfvmHk++4oevjoS+kMCB0zvh9x92XQ7tVUbKzg9yPkElbzYsQpmzqPRA79eBnbRk8iCITQCACOyPEOwD9sGF1Wvmz0m4zo9uYrJsP06Se2iPHaa8l86yNSQnBDLSytpNNKsJ350g2v81YnPPsRd+7x8VtupymlOx9OnMjF317FB2mN5NwCC3IgJQjXbYai90BrD+JHLv7VgAHho+zWu1DTPY3nuo1jF1ZB2C0g0ICe/jC2MLhD4A5Ct2DkePazpTWDDK2eoN0to90uNwOop64OyutzOVxQJKP2QAidoD8oRXlrqzTWV1fTfdkiGicV4Hel8fmESei/XxDL6jItLjd/SQm88Qbk5vLUlU8x5rZbLLtH3TLdeNISf6N4KuVNP97aDIVCoVD866iUj0lQKR8VBnEpAPH5yPzklqSe930PuejjbzesD61pvVm2eq5hpSmrTGNabbPRPjcf6sYeJ/2iibCmWdIORjF/1wDS0uB//ocja7fgef1FWi6/nu7LFp34nFNSrCU9XS5pv+mAzHmZOLfupOZZ0MPgCMOMq+CtS2ShpJ1NO5n6p8uZvvkI5RSyyHETPa57kC7ffp6LX21l+jooHw3/cfgAU9cnT41o9oW3kAoINAQuWi0ZfeJTMhrXxHR8jruWGnLQNZmOZvuLNYy8PhshIEvUUUMO4dZWbMRSboY1GzZPyomlXCwvh4ULpYCfHperf+pUWbgpSkEBLFrUeX+nCCp7jEKhUPzrdJbyUUXaFYrOiC+2U1nZYVRxZ+73AJNtYuR5lFX+3LDSFE5pJjcfFp0nBfu755xcJDIYeU8mRo33ggI4ehSKi+m+bBGutqMnJ9gBLr+88+9xlF5ayiW1GehBDU8I7AIWLofylJvY1bSLqW/3YeHm17mI91hIIVODf+Bw71e4+NVWFr51gIuOHGDhWwc4JNKNecRbfKJzrKcnIEijBUecYE+4FqbP5mu2/PHt6OtqYP58qKnhzfpsIzvNFi2bF4trCGoOoz1AAHtSwV5XJzW6pe7R9Olw3300PvQkrfZUAq5UuPtuuS/eghP//V/geNVv/91Ynk4V7VCCXaFQKL5shBDqFfc6//zzhUIhhBDC5xNCGiPkq6xMCCHEkg1LxKC5g0T2TMTPbuohXnn1USGEEA0FRaLV012EbDYhPB5x1IHImolgTuJryYYlJz2cdhAhEGHTS0TeQ9DpsdExa3M0MWjuoM7PP2mSEC6XfD8OtbVCnOvaJPzYLONZM9AmqjMQte40y/bqrmeKQXMHiTU9z7Bs3+twira4uZlfn2mpImvieHGUVHEUj3EdzPMPgfjMhVh4HuK356WLFpzGfuO9oMAY++Eho0UQTXzIaOHxCJGaKufzybgCy7k/GVeQdN6pqcJyXG2tECuKfEnHL4qK5IE+nxyDz3fca3uiLNmwRHhKPZa/L0+p5wv9jSkUCoXiqwNYKzrQpyrSrlB0xuDB0h4S5Y47oK5ORhWv+Cu1z6Xy6+VtXD1lDtTV0XPRXFLmP4ruckFLC5qm4U1iWx+UPqjDSKQ5YnrxrDN574EfGaFcpxDoQqDV1hKtAWpUzQTsD9r58V9+nLTPE82jXV1ex8IDV1H95AZYtuy4l6i6GrbbzmUGz1rGc+GuEBfVQ1Zrs2X7H76lUXppKUemXGfZ3jfgNzLffK45uWfUEF754QVoQ4eilZTw1vpnOfydWnImnsddGTfyjmuY5fiqLmdhmwNn3gPTrgWf+0JS8Fui7xoY0e0jQ8fQbds6dATns45P3GOMYPrw1YuoHVfAMT2N2nEFDF+9KOm8hYAbW8r567GL+Oj2cm4cXUfWPHn9NdNL/giRa52by5e9urSzlIsKhUKhOD1Qol2h6IyoMosSCslt5n0tLcYiRUDmDNdksSOnzcnaoSmWLhMW6JWWwllnQWmpRVyf1SBY/ps9jCwtIzB6lNWDkZ2Ns6zM0u99F/ag4KVxrKxckSDcT1TUVZfXMaYwhxtX38mYwhyqyyPnTOoDsU73ec90rnH5eH5kXz6TSVIMwbqlq513u/dh2sV9sBdNJH9kPpc//ji+mTN5r0d/Wm3W9jj85DV3I2/JBrZ2GQnFMt93w88bqHv1Xcp3P8d/tG1mc8YEAtip6pJFT+dOAnPg/ccd2DQbw9K30EKK0WcYZLXTiFjuum295ZxnHlpPnwsHENY0GrsPYPjqRXy84CNWa97YdYib9w/85SykkHGs5vsrC1nTOoLBbDPaWPKzn3ce26vqeHtqOS1jLpLX80tCpVxUKBSK0x+1EDUJaiGqwqCuDkaPltlDANxuIx84dXVGLnE0DWpqqCOb6mq4JKOOwfXV4PVS0f5hwgK9yaN+gB0ZHXcSE3b3XAy/+q78PK1GVjFNC0BAA8ddRTB3rjG0I0PPpNu2PYbnOwS0k4KNMJOu7oPvld1GW/0BHZG0SqdG+P7YgsuFF5Vz4+o7SaOFZjw8P34+057zJswzmbe7Wk6XR36qUb7cep5pE6FvM0xfB388386w3y2y5Lxf86ifkY1Bo/3BLn3offSA8X3b6EmE/ryM6mo40udVnth6p+V6XnzNvfTfvt1oX9Mrhf/6fhubn7COI4B8WnHk2ql0fXmxJWoR76U/4u6Ls7XZ2PNxWQ3e6dZ57x54ERm7V5t+Aw173JUOIQtihDUdv7DjIpYeUysrS1ys+gXITFLoC+QTnRNd6KxQKBSKr57OFqIq0Z4EJdoVFurqYOlS+XnyZKtgjajVV/sc4bY1f2HPb5ajabKI0YZ1joREI9ur6jgzbxiOuFNERd+n3SH7Lnj+8bOYeGgnOgGjrQZGpLjF5sQdDhBPtB+/pvHOUz/jUkc2eL1krvjeCYm6aKTdLFTHLLyd1NUr5fk9HrmAsxOh+eFgF2N3+I2xNNht/NYb4uG3Ym1+fXk6v1jRSOa8TGY+18hPNjQZ+w47ID2gIWuRyj7acdErtY1AOIg/1MZlY65lSl0L/Zz/5KIjjaT5Y1aUaHR7+EzY/IR1O8Cx0eNIXbcm4ZrFf5eZajzWG5h3rfM2V3gFCKOhR8Yd1jSaegym26Ftxs1AiEixq+gVHj8e3n23w2t5oiQr+OVxeBLTRprvrr6m1VUVCoXim4zKHqNQ/CtkZ+OfPYfw7Nn4hw2Hi2LWhor2D+l16BdcU/Mz9vxjCADCn0p70M+jle9ZutleVccZeecYZYjNUd2oaCwfLQX7DYc+xRUv2AEqK2l1phmCPd43HY0WO4XgO7f/muAdP4acHOYPnXlCebS907P5uKyG58fP5+OyGs6pfprU1SuNvkW7Xwq+TghM/W/LnHoFQ/zkHZtlHte9J0X6zqad/HBzm2VfjwDYIkdH+9iQcTlCgL/VzmWBN6l6fyVTj6wmb38jXfyJmXQ0YNMT1ow70Tbu9e9bztdRNp5Gd19AoxkPoJE1LXHePTliOdY24VI0h4Ow3UGrSMFxaJ/R1jwG4yYhPq/7F+SE8rxHnwzdead8T2J1UigUCsXXFyXaFYpO2F1ehV/TcBCMFDUSiNWrobCQ1VSf8J0AACAASURBVA/8iBnLZ3Co9ZBsPDDiaXdIS8WK9nssfdmLbsdJyPhH19EzrisPSR9yMlHPlCm4AseM/cL0Soa91Q9CcPWB7idcvMc7PZtp707HOz0b94sVlrGIUBCTCyWBio0V/Ff6GxywpVmO6xEIWeZRd2YKDel92ftAOiF/imWfFvceBLqtWoamAY5mpogXkraLP96OtB6ZF+xqQOt5F1raJ3s/2KUPOQ+lkDNxND/P/G+envMk3n1LjbUHBlOmWMfx/e+D3Y4WDOChFSctlnMfnTCJvxeU0Tp6/JdmjYly3JSLHa3BUCgUCsUpgf34TRSKbya7y6vIKIwVVoov3BMuK6OlMNb+siNb+LPoQnoQ6rrY+NV7PTl8xjm4+nYj9ZH76b/3I0v/Ark40mbqc/o6+EvPgdxw6FNDRP6z70jOzcuRWU9yc2l3pJISOGbsDwAaOg7CCCCgaYQ1gR6GFht4NA28XvKzs086d3br9fm4F88z5h1A590rH4K/zGJwrtVeEbVobLlX0DvcaswRYtGBJpvOW1mCSZvagOQFm+JFtB3o/Yup1NQsYtx9D1K5Yy9T309s58eBi5hlKIwU/JanFePG8Vn2lWSb7DFbCkoYuGohrvpttGcM4aUVD0qrSdMBOH8nW85/l3vfKUOslDduzJ4NwO5+Yzn8RCWHJpRwftoW0m+dAmvXIlpbjd/Tgfx9dE8a9u9NIP2qq/iu1wvZX55YP2FMC6SJ/E0oFAqF4hSio1yQ3+SXytOuEEKI2vEFlvze8a+pE2M5sX9xccf5xaOv10akWL6HdJtox2HZ9uiwDMHV08Tz3c4Vx3AaednbseZgP2pPESEQhzRdZOUPMnKXN+MW5cO6WPODR3LLx7NkwxLxn/ecIX57Xjfx6plniefn3J+03eFJBZEx6CIM4hhu4XdFEpObGDR3kDgYN5/4XOpNWorYf16WsS0+h/pRXGLheRj52qPbg2hif1GJkY/8sgsniIX8UKzgYtFImlh/8cUJ+dGnjhonjtmwnEukpYm9aUOt+eHThlrm0RYZdysyx/60qxHbu1r78ffuZznXVQ6f2OarFcLlSpo/f5svSVL3r4LaWvn38FWdX6FQKBSdQid52r9ygfx1fCnRrhBCiF1lvqQCNPoqH54msmYiLsu3Fjoyi3zz90YtRXzi6CGLIIHw2+yizVT8Jxh5PcpMgeOoaI87X7xwX7JhidAf0AVzEFn5g8S0874jLpt4hqjO6KCYUFSw+XxizZxCMXGqU7TYNMs5OhLu23y14o+2AnEMtxAgQm5Pws1A/HVIds3+MHSAmDqx4xubsm7jBHMQZcPSku73d+1uuZHpcdPPxKOvvmKMYUWRT7zQ/UZx/X9cL7Q5mvjTWI9VtBcUiJ09R1v63FxQYhzfjpZwzY86EG269fc8MGC0ZZ6LtQLx94IyIZzOhL+BdhC3OcvkNQMp3Du4kVIoFArFN5vORLvKHpMElT3mm03FxgqKVxWzs2knuTUZPPV6I4MDzcZ+wx4DtDrg3QHwvW0dZyGJx4+DgD2IIyywhW2EAQchS/t7uJ+HeCAhI4oe9++1YmMFP3jpBwBkNUDNs2APgsvUTPP5ZJGonByZZ76tjVaHhhYW2EMyRSGR/v3AzlqRNMHI9qo6Mq7JwaYLCIXYdc7l2GbeyoDpMu/54tEa/73eOu9DpOC3h+gXDLDP7uD1cwLcsBnW9YGBDR7c7Q4a8TBYHOTN1LO44mebjblsekJaY+Kzv5gRwIpxJVy1upMiQlOnwosvwvXXs7kOhq1ZbOza3XM0Axs+Nr6HNS3xmkeuy5osF98NZ8DNN7O731iLdepWexklM/fR++lfQnu7YSfyA7dRRn/bPh4I3Ydmt6G7nDJtJqhMLgqFQqGw0Fn2mK88qv11fKlI+zcXczn497ulihCJFg9zNLlVR3zYy5kQXRYgQnaHCKGJFmyW/dsZIH4z5JzEMvemNnUMMSLt0W3xkfYot712m2COtHEcs1nHueyHF8hGZbFIb7TPZjuiXU+MZh/BLTweIa5y+MS60QViz7hJ4lhab9FQUCTEpEkiZLNb2u8q8wkhkkfaAx1E1MMgykZZn1KEQTRhEyEQR9FFsz35NU/2WywfV5L02sRzVE+z2nH0NCGEEPuLSkRj76EJTzfMr6BNt9hKdpXJ6/PqpDIRckesLy6XEOPGCWGziZDdIZpxi2JbiaWf/UUlsp+vg11GoVAoFF8r6CTSrrLHKBQmilcVc+beFj79lYMLGo8lpFOMYiw0DMMRrXdcLzq43ehz7kev3cxvx46wHP8C13Bhy1ajH3P/RurHUfDHwX0tx829PD3pmJ+68imWXLeEj4a6cYSwpBactFcesz3DS0urRgsyU4tfly2umhLrP3pMV1q5qKWKVwN5jFq3mH5rXsbdfJAei+chXn4ZLRS0tG9dWAlA39Q+xpiiY9Dj+jZ/vmEzTNlk3dYl8sQhlTCeYOLYzAuBzdvPef+5pNcmnvoLrzf6iX4/cHcpvefNpuvBrcbCUfNvEf2NbKGwzNcfqQ47wDuY8z5exMSrQNciWVna2+H99yEUQhdh3M4wd7metYw1vHyFyuSiUCgUipNGiXaFwoRr605qnoXBrdY86GAVcdHPIQ2CN11r2a8Tltk5+vWD7GzGRewjUW7Xn+Bb+9st28yiduPFF7OxT2+mbd9v2XeDY5QhGONzbOePzGfd71pwjBuXkAoR4M36bC5KqaGE2bSRgqbZ0TTY0R0+j/w3EB1/ELhNe8bSR7zoNl8D97QpAMzZN8zS1ix8zTc8xs3LcKgckbg/mcBPdrz5s9/dhWZbFzaPn0pnDF+9iNpxBRzT06gdV8DwRbNIK3vMcq5o8aP4eQggPPs+wueOsOY693ohYCp0FQ7L91AI3d9Oz5bdlrGKPn1UJheFQqFQnDRKtCsUJq452APAkks9Kraqeloj3xpgE3D56NHUl/nYOXoSwulKEGKXvmcV3ylhcIrI99GjYcIEY78GjBo4kJmbzzS2Rc839OqC4xfHuf9+46MA6j+TqSu9Xthqy+aIsx9hdByhAE67k0kHe9BjjqCNWBTb7vbQ/9uZlrnG+8mj12VfSZnhae8yJNfYZxbA5rlFOWSH1b36cMFHY6nv14uP+lr7jr4HkZHvYyldOFxQhFZWhjZe5jj/y7gStutDqU0dzbBj60gNNzNszWIOdx1AwJFC02XyZqqh+1DCmsZRT2+oq2P46kWkhY4yfNEsyMkhpe2I5bzJxiyM72G0YEBGyEMhGSHPzoZzz038LZL0B+D5dKM85sUXZYXdF19UnvYviYqNFWTOy0R/QCdzXiYVGyu+6iEpFArFl0dHvplv8kt52r+5vPLqo+KoU2YMMWcpKcsYIlZkpCR4q0Ng9SPX1op2t/TCt3frKbf5fIZfPeFVUpK43+cTH5ZYM9dsLiiRGUc8J5CBxOcTR8dNsBy/I2OcaMUp/po2wvCZB6Njr60Vwu2Ond/tFqK2VuyeUCCCcumrTC85fJwQum4Zp5na8QWd+s7jr1tLXHrIVj35MeEk54qyp6RMBCKpKJMde0x3W75bfi/T9QzZ7eJYWm/pN3c4EvrraF3DnpJI+sSCgsT1CZom+9Ztln3rJxTFPO0pKTLjTAfzU5w45vUo0Zen1COWbFjyVQ9NoVAoThiUp12hODGunvhT3nzhUX53YQZzhucwPH8QrjlQWLiNS+qlpcUc/T7cd7glSuq/cDyOVumFdzQewt+1O9TXW09it4PTCW63jLTm5oLPBwUF8j03l7HFuXxU4uOdoQV8VOJj2KLiE7dU5OayR4tF6gEG1q/BhZ8JzZuwRbbpgH/YMHjoISkzQY6rWGZi6fv28+iRgk1tuNk2/EpIkZ54PJ6EeUVtMvHR+WSVXUOAm4BlvzMsM/JEt1mi85WVCdPcW1pOv9mF2CJHJbPYpESKPFn6i/rHf/MbGTEHdJcLz0fv0GduMWzaZCmkBdA2enzSOfWYfRvh80YT/tOfLecWAKmp4POh//AHBLChAe3YeWzlCDY9XW1k8sHvh2uuSf7kRHHCFK8qpiXQYtnWEmiheFUnmYUUCoXiFEKJdoUijpHOq/l/7xxh1ubN1FQ0kLUlk0Hpg6gfNRKwisNepT+1HGtvPATERJ39aCM88YSxXwD+br1g7Fh4/PGY4M/NhUWL5HuEscW5eD9dxNjiyLbsbJkqcP58+d6JpSJeQJvHZP7sAEKLF0vxmJICDoe8kaiuxmbXImkLnfzaUUzPWyfHbhqEgH37LEJzwPRc6st8bBlfwMYJRdR7BhpjMNtMNnaz43fotEZqlcYv+IxuM1uTmDIlYY7acwstcwmhc6RLhqXPNt2d2J/XCwMGwObNsc7S0mKfs7OhrMzi5/c8ch/1ZT52jJlEWHeY7DIgAiH09jajrTHmwkKorCT0/FIAmvEQxEU1Xv64wxvzvgPoulqM+i+yq2nXSW1XKBSKUw0l2hWKOOorq9EQpNGChqDcPpsdRTvocsmNCZFgFi60HBt2OI3PhkhuaLAIUEfDPsTq1VLUVVWd3OCys2H69ON6oM0CelfGOMAq4M1C2gbgdrN/TB4vZN0jtaPXi65r+F0ugjbBzu/MZ+29Ofxx+lge/U8HxwKtHHvwXgKjRyUI9+x3FzHqjbkMmDUjYdFqXj6cVxRkzC1hZl7Sg3uG5/BuPxef47Jcs1bsbHGn8U5fl8wzn2tdzAsgbp5mmdfBkgX0+Hw3n0+YRNDu4vMJk/CEWjjUbQgCaHb3Qq+tleI4/unH/v3WdQLTp7O7zEfd+AJ2l8nzD5iey+CaZexZsJx2nLSQQgA7msNG2JWScI154QVYvBi9tQUHIWo4jxxq2EI2l9yaDa++Ci6XfOKi62ox6r/IwPSBJ7VdoVAoTjVUcaUkqOJK32y2V9XROy8HDYFA46CvhlTfUnrPm53Q9lD3IbQ++pRcjFlXBw89hFgcK94Tv5Ax/jOTJsGyZf++yUR45cyzuGzPLkJ6iLRwOKFYURDw40ZEZPa2CT/izA/+l7/3auXVc4+ycHms7e++Dbd8BGkBOOaAjcWFfPv+31vO979Tq5j4hxtIE83GufakQYb1wYTBo5UZ/KQ2JqRfyOjJjYWHGJQ+iB1FOzqc197ScrTnFiJunkb/4ukndjEuughWr07c7vHIpxjTp1NXJzW8EPLhwuFjOnYEQcBZVsZuMthRXU/GFC+DBwPV1YRvvRUtGEywA0Xn/zlp9LA1seBZnenRodbVqQJLXxIVGyuYsXyGxSLjcXhYMHEB+SPzv8KRKRQKxYnTWXElFWlXKOIYnJvNQV8NNQXzOeirYXBuNq4KmQdci2vb88g2Mgrz2FtaLlVeZWWiHzuCxe4RJTMz9rmqSlbvPNno+wnQ7HuA3qUOSr9r9X+bo+0CjTRacNDOyJXz6PH5ASZtO8rtqx2WtpmNkT4dsp972ldYzlVy9ZNMWZxHmmi2nOvxCzoe38+m1PNCRk/j+w31h5i4w0nppaUdH1RVRf8t1fR78r6OBXuyFJnTpiW2M68TqKqCm6dySaCKlhZoOKbjiNzOOIBQYSED7roe7ywvg3Ozjacf+lVXWX7zz/t2t8z/hYy+OO8cQ8q3TBlNTvDJieL45I/MZ8HEBQxKH4SGxqD0QUqwKxSK0woVaU+CirQr4okW4DFjjpzvHzqefnvXGQsbcTql5aGtzWj/+YRJfP5ZM2ds+ZvMyWK3w4YNUrBVVUFeXqzzDiwh/woVGysoXlXMlFd3cv9bRAwpkfn1HE7qod2AwE0rekSkCmCPy80Z7a1G29x8md/duwuqB8KWXjAofRC7mnbRw92DR+/9LlP9L5kizKk89r0w949vJR4t8jwDYOEymLo+FpnedrWXoa/83XpAebm0JH3rWzBvXmx7kuvVNOBsutRL37rudsO6dTFxHO1n2jQp1KPR7u3bIS/PENrXOH287M+z2Hw0sETlLVx7Lbz+Olx+OZnf/Zh7n9vJDZtlTvrC62ST4z09UCgUCsU3l84i7Uq0J0GJ9m8uUWG7q2kXA9MHMn/oTK4+0B28Xg48vRRXxXO0nDWSfmtethy3r6SM/g/fFUvcWFwsF5tecw0Eg1aBHrVEZGRIb7XXKzO4mGw1FBTIhan/JrZX1XFm3jDsSGvMZ75a6uthy8Jqxno2MXJlTBDfc2EPas46zJRNshjSG1md933ZS5Op2rDU+D5tWB6Lp1ThsDnwh/zGdo/DQ8F5BazYsoJdTbvI39ObPyw4EItWxwvx0lKYnWhRAhKu15GhY+i2bR1gEtslJUZmnA6ZOtX4HQSwZXwBmasXR5bMSsKALTX1uIuB9Qd044bEjIZG+P5wkiMUCoVC8U2nM9Fu/78ejELxdSXeE5v9ZoDv/eQXBOx2HA4HfWpqYG4x6Ugvte2x39CW0gPt/vukp32y1+pPLi8Hm02Wtne5YoV4okLPbJp+7DGraP83L0qsr6ymDx6ctBDAQ31lNd5ZXrwAGXmE//YEBIMENHjpgsNs6SXFelYDTKuJRdiT8cZ1S5nWnseC2jcIAI9v8/FuA+zoI+jp7snh1sMMTB9I6aWlidaFgaXw3HNw881WwV5XB/fe2/GEzNllqqpINwn2k2LKFON30IDs+6bQdM0KHO0HDfHfbk/DU/PRcS0tA9MHsrNpZ9LtCoVCoVCcLEq0KxQRilcV88J8J5fsb+O9bt25sLEBF2E0v5+wZkOPim6QHup4H7VZkEPHedWrquDBByEQkDm63W7eq3+PF2/swUMvHCakg+2O23H8Oxcner2IxRrNeAANd1ZG7CYiFEK32yEYxJbi5Cfv9cG5L4W/Z33O4+8cMLrIuSW5cM/aMoirGv9JWAvhEdCMtNJs6RUgzZlGw88bko+pqioWSZ89Gxoa4Lbb5DVYujSWSz6es8+OCfyIzchsXYq+76Mf/SPf9w8YQ6/69TRknId/ziO0LqzEPW2KvPny+WRe+KwsqKykJeciuq552eir6YIJeB56SAr8TixMpZeWJl0Y2alPX6FQKBSKDlALURWKCE8/8jl5+xtxEuY/Gg+h4zeiq+FAkA9f28f2qpMogJMsr3p5ufSur14tBTsgWlt5aMcfONx2mHYbpATBH2znvcpH/y3zBKgjm3HOGu5iPuOcNbRuqZeiuKVFevHDYfB40P1hZqytZ2r9pyx86wD2oMwao4c1vEnSX2dtGcTHFQe4bv8unCJiSxEyMg8kjTwbxBdQmjcPRo06ftGhfFO0PtJHfIRdA/rNlik29w8YQ5/6degI+tSvI6Mwj6zVi8kozGN3eRUMHgzdu8sbh8WL6b/mZZqGjKHZ2Z194ybRf83LMhqfl9fpouEvsjCyYmMFmfMy0R/QyZyXScXGig7bKhQKheKbhRLtCkWES/c3AabCSMAxUvDjIBC2cc7LD9E7L+fkhXt0sWJpKcyYkdAkBEz62G8I246ysnyZgs7rhe2ObP7kmc52RzYZU0xPBXRd5hCfMQMtFARi18QVEeLukGC3OCOx300DsBEyFm4GNY3S/4hF5G2aLeGYKKu+3TexqFJ7u/T7T54s85lHK8kWFclxOhzw8MMxYR9XhEmLe+fBB+lVn2idiX4OPvkM4uyzEfPmWdzo3XZuoIvDT/++cYNOUqnVTP7IfHYU7SB8f5gdRTsMwZ4sqU3UnrWzaScCwc6mncxYPkMJd4VCoVAASrQrFAbbhp0HxARjdbee/L8xF/LShTMIo5MaKbZUX3mSlSujSb8ffBARTlyAaANuXg8X7ZSWk7sul+9vu/YabY4r6MrLZf7x8vITGlL8Q4DBuZENs2bBDTfAM8/Apk1AXJVPYgI37x/dEvqtHrGbEDZDePtt8MI5sf0hEUo6noqNFVx9+Aly8+FQSmy7gNgC3nXr4Kmn5PuIEbKCayAgnxBEq4nm5sKECQnjNrK+fPCBte+4z312r4VIHntLm1BInsecohOSVmo1SKLMt1fV8dq15dwwqo4777TWcypeVWyx0gC0BFooXnWcxbMKhUKh+EagPO0KRYThm2vYPDyHIbXrWdU3ndv+J43SS3/Et/dcgJa3kHacaAgZle6E6vI6dv5mKd17wDn3TWaw72lobTXK1scL4Ki4vP0DWHR+LCo9yLRgsTNBl/9Bm6yuCrGiQfGpCO++GyoqpJVk7lwg0YIPwC9/iWhvt2yKjjdEpHpqhEFYrS6/eBumr9vJ06PSOew/B9K380JkEauZzHmZCYtQo/N7Iwt+dhksXG66TrffTtIBJ1svAPDTn8LKlbJJ3PQIBrF5PBy1dSHt6H55DlcKO8/Jw377rfT/0ZXxR0gcDnme226TtpjKys497fHVmWpq2L4deuflcAlhPiDM1bzCu55cY33yrqYkfiM63q5QKBSKbxZKtCsUJoZvrgHgcmBHdKOrjrALwkFw2GEw26E8SRXLujrqHlrK+Yt/yXeQwrc97wEEAUvUN/rZUhkVaPDEPscvWOxU0C1daN24cKFVtN99dyynefQ9ItwtVFcTDgaMx28C+EcvGHYIHAJEZODReTQ6YqP/xdvw8Fvy80+ONHHPxR/xq+8mHbLxlCBK8apii9d90fnyffrHsHAMLIy/AYHYo4Jk1URzTYtJu3e35nOPiO8uv7gDHnoIraUFbDqDb78KqCfgSYVjnycUn8LjkRltli6VVh1Tesmt3ceQ2bieHd3OY+iRj41raawR8Higupr6auhLGA+tCOBVrubbYiNerxy7yjajUCgUis5Q9hiF4nhUV6PbNOwhP7omZO71eG9DXR2MHs2Qxffjpt2IoNsJGt0cL/3gt/bBzR9hLFgEDA+7riX/pzowfWBihc/I96gHfv8Cqz+big480l4v/khm8Wj7347sQ7td+uxtwjqPaz+VNyaD0gcx8x9uy77p6zqfa0ughbtev8uw/MSz6HzwFsKr3+mZ5Ghiue47yrCTm0vdrEWUj5jL3pIyGD8eJk2CgQPhnnuk8DZH6vftg8JC7Mc+B6xPQwBoapLif/ZsGDnSWIC6tfsYhjTKRa1DGtextfsY6upg6T4vYWF9EpAxxYuNsHGzptt1fMXS1lNeDjOHzsfj8FhOq7LNKBQKhSKKEu0KxfHwemXU1OmU3mZdlxFUs5d66VJobcWO9GxHhW8g8jDLLISj73pZmXEKDejRKm0hO7rIfORmD3syL7gh6KZPh7KIMC0rg+nTLR74P46MnVeANduKmexsrr7gMjYxjEa68lt+zKIuV5FzCyw4J51G3WFp3j0UYEXDTHYU7SDjzmLL04SFo6WYX3LdErQktytZDXDNu4c4c29Lwr7jErWexN84mfbvKy3nxtHSN5718HT2XjENXn4Ztm6Vwru62mrqX7HC8uRDAw4PH5f09MLvhyuvhLo6MhvXG+0BMhvXk5MDJSWwpnUUoTByjUB2NoNzszlQ9gohu5OwKwWny0brWK8xlTlTruaBkZUnlW1GoVAoFN8cVEXUJKiKqN9ckgZwI1F0Q7BrWuwVTeVosqAI4JPu41natQ/vjvqA3IN7+FY9tDjhT+dCb08Pfv3gGnlcVRX88Idw8GBsEOPHkzn5s6QRaJtmIyzCHRcnipA5L9Ny/KOvww83witj05jx9tEOJ9929jBcprWy0y7uw2dpdqqW77E0jQr093uewfg79jIwfSAvbf0WOa/XSBuJqfJo/FiyGqDm2VhfHeV7l+cxVQ+N/jj79smMMi0tcjHqTTfJBbTRarM5OQQCgna/Rg41fObJZmv/i+i3dXWs4/Hj4d13Y9/LyxGRdQHRua3SJnBJyhp0EUK0tSXMvWnIaA4dhiGNsccKIeAtJvBd3sZBwGhPUVHMkmT6IyuvzubOO2MumvnzE5cjKBQKheKbg6qIqlCcAEnWDkrhXl0tN/j9UlnNmgX9+lmVfa+Y6tQAf/6ZVLCUmmdBF/J19fdlVVGNI/w6elxurkxZGF1ICjBtGrvqf5R0jGERjonYToj3wP/scvnSOEZi0knJe5WPMjYcixoL4LHqA6QFY/OKf1rw7Kigkc3G2/8gC5YlRobjiwx5d8m+UgPSdiMLLyUfk+HnNv840UBDSgq0tcHzz8MLL8Q87kLg8LcQxsl/2ZfyG60YcfM0mG0S7fGWIq+XY+MnkLp6pTG3i0Q1a3Pv5VsHVnD0k110OVJveWaQvm0d+wtKOPh8A71a5T4bMIGVCesVWLxYZrzxeuUNx4svwoQJXDn2Kh4XXrZ4shPW0/5LHM8+pFAoFIpTDmWPUSgimNcOmp0vCZVNJ0+W4VCzGDLlEQ+4HBQ4luPdBc4AeILgCsGrf5JR5oSFhUnsLR0tPjzRRYkne3zFxgpuaVpC0Ga18nQJWkV81KsPsMndg0XfjVVI7Sg9YXyRoU3Z3UCTgh1ihZeScUXWFfLDTTfBsWPyx9E0Gcm/6SZ5zVtbYz9Y1MoEOPFTrJWy8cU6WcE27hobRG4I0tatRtgcBNHRACftXPDybMTq1XQ5Um9cA7MgP2vxvfTRDqPHXZv4NJk0NcGPfgTDh0sB39wML79Mv/tmUEMOi2bVxW4S40iW071TjmcfUigUCsUpiRLtCkWEeG1uRD2TVTaNJ5pH/L77mHuph5xP2yh6FxyYxG4ILv3MyRVZVyQWSZo+Xdo1ImKy9NLSf2lR4skeX7yqmA3pbYy6DZ4bBTX9IBrPN4tRsxBtTD+W0E+yLDd1ddD2QT5/vWIHf7zuj2zq7mfMjFg++k97xWLSU9/uQ/VjfZj6dh8AVmxZAddeK69tlPZ2eZM0a5a0K5l/sOxsKeidTjTA4dAYXB+5+4q7xgbV1YSjNwShADaHnTCx/xyjo2sdPZ5mRzfLddAxVZGNu14aQNeucPbZMt2n+SlBlHAYXRNM7lfdoWDPyYH5M+uYN7I8sbBXMkXf4d2nQqFQKE5llD1GoYjQWRbB5EnNI0StCBkZ8PDD3NF2jBTTcKfUoAAAIABJREFUutFo1NUO9M27nkfXLzasIub0h2ZbSfRz8apidjXtOq6HPZ6TPX5X0y6ytgzCu2kAj4zczZasnax/HEYeSpJJJcKWPu1c9tEZLHntCD1FKx916caND6YnXBqz5Sj9zjJaXC1s6WW2xMgzTH27DwvfkpH7i96Cvi09aOi7E3x7LX2i67HfItkPNnmytBzZ7Yk53M0Dixx3+BcP0Z3YzVVTah+6NsrIujli7nnkPsjN5f2zp3L+5j9gj2TaCdmc2FIc0KWL9NtHsdngd7+DJ57o4AoSWxvRgS+muhqGhup4ty0HDYHzGg021Fj8+wl+rg7vPhUKhUJxKqNEu0JhojNtnpSqKsITryaEjk2E0DVwh2IC0Gw1CWnw9o63aXF1UCQpTlDnj8z/lzKHnMzxF+25gNcr/gEchPUaOfmDeOy8VsrePGBEmoNYiyvhT7MsUL3gaCNvPtIbimJN4tOV7/nHUMj5W9IxRNNERq9byfuHaXdqEAxaG15pKoCU7Afr9O4Lq9gNBOgWiC0YFUDXxnr8fTNw7a+P/XYOp/GfZZ95s2jLewGdEDZCtE6cQrexWXKdg3ltgt0eE8zm7bouI+82G/zgB7FFtEnwemF3aCkOArjwE0TmfCc7G542Fe2y2zn49FJeHVGM15tNdmfzVygUCsUpicoekwSVPUZxQtTVER45Cs3fnuhhNiGQVoug28mtl/kpz0lsY8mS8hXw6IRCbltVSRotNOPhrvNyyNvWzA1HExOuC6DVDr5eA7lu3y6LdUYAuun/lMRI+8Xscf0toc+e7p5c94abZ1fWG9vaNUgRSLWfnQ2ffAKXXw7Llv1rky0vx0jZQuLvdqjvcHrt+4S2fgNw7q+32IMoKYF+/di7D/rMuR1NB93fLhfF2mzw2GPw2muQmSkrp9bXS+FcXS2LXl1xhXwKYI6Ob9/ecYXVujpCo0ajt7cC0IKbA751ssBXXp7RTABtpPDtlPVstWV36OJSKBQKxdcblT1Gofh3UF1NWOjYsVY7xenk07wL+XhvDa/3O8bjVRoumwO77mDrub2APQldfdVVL18dupHbVmk04wE0qkfs5nfrpVUl/oZEAxxBePaCINctty7M1IBpV2vMWd6TDA6RpvVl21134Fn4BLYz+rN64GVcffgD7ljZwvR1UD4azj6iM6Wumd1nncl76Tpp/mZeu6ArP33/M1mGVQj485+/PBUatY9osm/zE5HPu2XQK+9C/JqGk9gNl8Hs2eB00h/AYYNWWd1Ua2sj7PbItsuWJbeuRL30kyfHouDbTeJ78WJZydUs3KurCQsNG9COk4W2WxhdWc3gz/5kmZLMXBNibFs1n3qyjWC8QqFQKE4flGhXKL4oXi+6Xac94MBGkBB2HG4Huq5x1qNlnJWdzWSw+KcL2z/kA1P6Q/h6VL1894wPyckfiHfTAKpHSE/7CxlDmFa/zWL1MfvzgYQFmwJ4arkNF4cA6C/2w7zZUtR/vo9LbvuYRs2GPXIX8PBb0V7aGbJhA0MiffX63q9xfHiHzI3/ZZOdLVMuXnWVYb3RAK2oiG7z5iEWLyZaRso8bwO/HxwOCIUsKTBbWwUHMrwMhkRfUFRFl5bCggVS0Hu9MsJuprLSKtq9Xmw2jWN40AkxI/Qkjj/ZIGy9LtGbjrUpXmVjVygUitMUlT1GofiiZGejP/4YTgLYEDgJoN8yw5JhpmJjBZkrvode/yMyV3wPwJL+8KuuelmxsYLMeZkIBFuydrLw2nfIZCcL/9SFP/ccS7xhx/xEoWr5Hj7q6TC2R/c5I1VhE+ugSuwicb8W//7cwlhufE2D6mpaBwwlrGm0Dhhq7TCaQaWqCsrL2VtaTt1FU9ldXtXxxH2+WCYXTYOCAjhyJOlYkiIE/PjHhG0OmZcfB4/ai3mzPhLeTrYYtLRURup37ZLVWaN5281MmWLNCJOdjb7sRcITvodLC8q/sfY2QsGwNWONw8GBsuXc+YSyxigUCsVpixBCveJe559/vlAojseSDUvE8xekimgyvzAIUVBg2e8p9QjmYLw8pR6xZMOSr27QJpKN77J8OY/o6zN6Gp9F3CsMYmHGWWLFYIQfxCEn4hejhogg1j7M1yf+c0fb9pSUCZGaKoTHI0Rqqmjtm2HZ35IxRE6iqMgyppDDYWm3q8yXOHGfzzoXl0uI2lpje7Jxxc9dlJQI4fNZ2k50+eT5CgpkX7W1QpSVyXchhBg61NqHpgkxYYIQ6elCDB8ujykpEULXhXA45Px9PvlusyVcp6Bul+2cTtnOPL/oGJIQPyyFQqFQfH0A1ooO9KmKtCsUX4CKjRXMWD6D32fLXOXRKPOqb/c12hSvKrbYYKDjAkRfBcnGN2WTfI9GmftHbC4Qi6abo+qVY1q4ogCcc6DnLBi7pa9RaAggBGiTJkG/fgS6WnOcB92p7B4+wbItFPncc3ahJTe+c38sBSOAq36bjFzPm2cZv27KBANwZmEeLSldrRM3WVIEEG5vxz9sGKxdC/37o40eTcC0PyHirmkyU0x9PVpKChoQdKSw6Ps+BhTmSW96Xp70q5uLcN18s7UfIWDlSkRTE2LzZj4vLJKR+HAYAgHw+2kr/LHMIW+yCRlPAsIhCAQIYGeTr57ycuTThTzTGKqsTxtU3SWFQqE4dVGiXaH4AkQF7xtZMG0ivJsh36e3/dlok6zQUGfb/69JNo7KEfI9fnGpWYQ34+Qj9xnkTjyDHV0dlJedhX+ORmgOXNe6xtKfDaQVpKQEZ9MRGicV4Hel0TipAEdLMwM/eYMPJ5VQ7xpKINJeQxal8g8bZoje9owhxrgA+f255xLGLzTN0k4D3O1HDeG+c8B4Qov/YLnxiJ5PzJ4Ne/fCunU4U1OtWWPMRFM5er0yY4zHg8Npo0fTjtg4gK2X386jl5lEc79+UvCbx2s6R1r9ZsuCXxEI4KrfZlkIbB53WLdzDA/tfo3r5nmZORPevqXSmg3nmWcs51N1lxQKheLURYl2heILEBW8WQ3w5Aq48DN49i8wbO1Oo01HGWG+6kwxUZKN440smY/djFkENjhcnD/Tz9OXyQw46yv2cHP9p9gRCSI3+l0A+x5eSF0ddF+2CFfbUbovW2S0+9ayYga0fWosbo32YaySr6rCfakXf8++MrVhxhDcu7cmRq4B7a67aBk9znJ+gJT2owQ1jYH1a2QVU6yiPYFjidVeGT1aet83bZJR9IcegsceY9+s+WwY9yMC77xv6XeI2MpPV+ZJ4V5VJXO1m1Psxt1gxP9nHNLtHY5v9/AJLHtwEz91zieHGraQTVsbbCbL0ic+nyWcruouKRQKxamLEu0KxRcgKnhvWSuLKTkEOMKwvBJDJJVeWorH4bEc93XIFBMl2fgA/jAsDUguansF2vnkCfj9q7Bi+R5cBAxxbBbJ8TaaObumHdeOEX+zEAQpdiN2D9eh/eg+nxTsAMXFMm96//5SUPt8MHcuqY/cbxlDdA420+fjkppq/d6lC3z8MSxaFEvTuHgxorCQjbP/xMiV87A37EUADXS3nOe/VhYkRLwBSEmhfkIBh+hOADstuBG6Hc4+G8rKaB91QcIh0Ws8sO5Nxo6FxbbpbCGbLOr4DXfzy9Bs6wG6bgmnR+tORVxHasGqQqFQnEIo0a5QfAGigjezMbZNA+xohkjKH5n/tcoUE0/8+KIUTmlm4bA0mm1S5poj7Tbkfxo6MStLMuuGABptUNO1B9P0J3g2ML1TO8bm8VMJah5CyASQQcBZW5uQEvHg//yG6rOmsrY0YjspLoY9e6SgjqZKzM1F8/kIRGLqzZFjzZF/gL19R1vmpxUVwdCh8kaguVkK9ei8jh7F36ufvImIG9MEVhr9A3Sn0dLvGewn9MqrluuDzQaPP86A73v5pOw9HpvwGi57CN2uw9atsGkTqe2xfsw3Q9HjB9dXU1wMl9uq2MBI7mS+ZRxh3ZY0nJ6dbbXaKxQKheLUQFVETYKqiKpIiinfOtnZVGyswPfE/+MPCw4YTTSXCzZsOCUVUea8THY27bRs+9vTTv5jv9/4Hp+vvTPuGTaaX112DG3Bx7jtqUaNofhLs3n8VIatWWx83ztkHGe8vkg2jEbak5xPAK3DR5P6yccJ544uFG4JtJDVALVPWPcHAKcQMREerUZ6991QUQH5+fDBB4jVqxPm2jipgO4vL7aMqaPrYQhoIIwNGyG5zW6XUXC7nXAwREP6YHo0bDHSYZrnmLRPux1ee43tDKZ/3ihcJFblPVJQRI9Zt52Sf4sKhULxTaWziqhKtCdBiXZFAskqXEbFUFWVtD9kZsJtp65IqthYwQ9f+iEiTiquKEvhsvo2w15iJiECTEzAhtF5KaM7V+5rpXbEZFL/vIhsrDc+rZrTYrERwDE9jbTQ0ViHEWG98a+fMWLvygRx6u/ZF1fDPsu44m9Ashpg0xPSJx8G7LW1cof5N73hBpl1JcqECYiV1ii6AELoYLNjD8VuZpLN30wQjSBO3LQnZKSJbxtv3zHfJBn7bDZISYF77iH0YCk2f6uln5ZufUl9/+/Sd79+PcycGavIqlAoFIqvLUq0nyRKtCsSKC+XefKiFS7nzz8tRdCP//Jjnln7TIJwjxe90bSO8QK1s0h8c98hpB7YAXY7utNB27F2XAlOdjhs68WeOx5h5Fzr9V1bWsX5s/MSzgXS2lJ321zjfiBzmIYdabNxzYnNoeZZSHNEVmHec48UtdHfVNelLSaK3U5rv4Gk1G+znDOATggHYXTctKJpGkKIhDm3DB+NffunhDypPHL0Dn4efBgnfhyRmxSAMBpaZBEvSa5lPJZ1Bk4nnHMO/POfCL/fck0EoDscEAgYx+wrKaN/8en3N6tQKBSnE52JduVpVyhOhNMs7Ua0Eqr+gE7mvEwqNlYA8NSVT/HH6/5o8eEDeHeB3yH/w2hzJPYnOngHU0rD/dvQRBg94CfU2o4zItjjs870CDUwYl4hG+8ut5xj7OTBhJ0plnMY4nfePH46qor5M+vIHKbhIJbKsX0OHHtA538rHWiaFst3CNbf9PrrrZMKBnFHBLtZVNsJ8xZeXrTdyN8LyuCuuyxzbiSdwwVFpM57BFd7M54j+3kgOJuPCh6jqeh+NJcr5m/XY+sGovPRgLCmyxSR48ZJj31BAYcKighhau/3w7p1smps3HXXQOZ6N429ZfbDbK9SidkVCoXiVEVF2pOgIu2KpMR52k9VzH7vKB6Hp8NFsr1+3Yseuw9R82xsmzMgBXGUALDGfi7jg5/wsT6UseEtCWK8My+8JWoe+byz+2gyD5v86qanHcmi+wGcBLDJ6HcH5wprGja3O2ZxgthvCnD77fDWW5ZiRpA4NoBWUkhx29DX1cD27TQ9U8nqzCkMvS1X/nlMnWq120yaBFddxeHqTaQtfhInAVpIYf+kW2lfu4mh9X/DHrmREQ4Xuo68ubDZYN06qh+qZvziGdgIJ1zPZO/mcQP4caC7nDg2qLQxCoVC8XVF2WNOEiXaFaczyRacAvR09yTNmcaupl0MTB9I6aWl5I/Mp2JjBTe/fDOZBwJ4d0H1QNjRx0HzfQHDguJ5wEZIhLBpNmacP4N5Vz1NkoC8heMJ+PoJBQx4Y5Gx3a/ZcBCW1g9Ngx494FCsYmsQENhwEEoQ2dH3kM2J/dmnEm+8qqrgmmukRSYQgGCibSdZn9hsHP7BHSzzzk28lzMtogWkB13XCQZC2ALtxmZfkY/L5+ayvaqOhmeWkpkJvWmwVnstKmL3iDwyCvOOe+0AaZ0B2nv2pXFvG91oxEWAsNuD/rjV2nWa3IsqFArFaYES7SeJEu2K042KjRUUrypmV9OuBL96Z/R09+Sxyx8DMI43C/rO8GsxX3mzpxfv9E7hip31Caki6wpKGMYW8HoJ3nobWjCIsNux/2OToSL9mo6jg3EnE7AdidrPJ0wi/Y1l1o11dYTPHYEWDFieDEQ5XoYYgGucPt505LLxxToG15si908/DTt2yEXKCxZASwthh5NgIIyTIG24aCx5kn5xXvPGa6eS/vLi2A2CroPLRbitHU2EE+YaHWdL/6H848KbOSu9gR6L5xnbQw4Xut2GrlsXUXe2vlqhUCgU//d0JtrtyTYqFIrTh2R2mBPlUOshZiyfwYKJC9hRtCOxQSdhWqcQ7C6vIqMwj+4tDUzcCfWOFDICbUabo6n9GLao2Phu93pjQnf7dsjO5vW7q8iLyOVkC12TWWWC2NAddnaeezld2/bT9dMa2kfk4Fj3EUFNI6w5OPrft9PTOwL/7AdwRAR7QkGp8eNp/mQ3aUd2G/uCpmh+9JgC/zOMEWsZcHUp2DWrZ17T4MUX4fe/B48HPRTCQRABuGinX8MmeR1NQnrnK58xwTSWcDiMHgqhizDhyFn1JNdgzN4V1Fdls8A/lXxiHnn7lZfDVVcl/E7V1XKo0bW41dVKtCsUCsXXFbUQVaE4zSleVfyFBHuUlkALxauKE3dEw7R33klH5U5bF8pCRFER/M/0HkBMZHa9IRfOOgtKI1Vit2+XtpCXX5bWkqoqwhWxPpItQk3Guu4X0xbQ6bPur7g2b+DIzHtJXbcGd8NubIBDBOixeB6isBDHvt2WYy393ncfXd5bScjuJIxGUHcQwEkbLss8rsDHrMCD2PytUgGHQvIVXfRaXy/D2JMnQ58+aA5HzPP/1FOW61ddDf/URhhDEEBYixRKstnA6SQYee5gviYfMpotZNPaCs/bplgvyK23Jq2odJqtr1YoFIrTGiXaFYrTnF1NuzrcF80Q09Pd8+T7MIdpOyh36p4mxWNUWI5s2o8AjtrguVHIhZpbt8Ls2VK4x1UbpbKStCu8lj7iI+tRkR3CRjtOWnAz6shK3LSSSgs6YXrNu9fSXovrI75fADIyZMGl6mrsTjs2BA67RlvBLXxY8CT7SsrQJk3i2PgJ2B3gwh9LxWizyZdZDVdXy/nu3i1TMeo2eU6/X16/pUuhvJxLMuood9xGGykEsRHAweG7HoD2dgiF0P3tNBbdT8juJBQZ9yaG83ttJlnU4XbDm45c6st8UFAAPl+sWmwc2dnyXmL+fGWNUSgUiq87ytOeBOVpV5xOdLTwdFD6IMPycjwLjbmtwQkaov/rplFctrqV3P3b6R+IZWUJYs1Aw9Ch8OST1sWbZTKlYqA9AMEQi8c9S25qNRkrFxPPX8aVcEDrxw9WF1r6jf4Pp8d9jxLLbW7NmU5JiUy7CLJoViSFIm63TLUIcv5+v9xnt8vouq7DxRfDmWdKW8z118OiRXDRRbB6tTEGS5YXV4r0m0fsNNtfrGFN5XZyPniGPt/KpEfTDvn0IUpBgbwRWLiQ2qwrOHPxw0aP8wtquGFWthLgCoVCcQqiPO0KxTeY0ktLk6Z4LL201PgeXVR61+t3caj1kOX4+LYG0TBtZ6lH6uq485wLueXMJRTMlYI9KpLN//kIQGzdSkt+IWk+n4y4T5kibSVC4Aj6weOhcDowfRFHrgXPa0s56OhH39adBLHzn2se5qCvBntE85v97/HR9DAaukmgazYbWigko+R2O+GWVrTZsxEOJ3rALwV5FE2LPVWICnaIZZwJhSBSSRWQ0fXu3WHaNEO0g0xR6cJPO072nJPH4Nq/GubywWuXMrjyQdn/JxjZYAyamqCwUP4Mq1fThgs37TTjod+WarKVYlcoFIrTDmWPUShOR+rqZF7zujryR+azYOICS8GkZDnZ80fm0/DzBpZct+S4bQ2ys5N6pY0x5OTw7V//L2ufEezzSOFrWezpcBAyfU89VM/nkwtkZDo3N7npuqqK7ungeu0ltt84C39EsGoI6iurjRqr8RF1gbTQfFTiw1byywRPfNjlJhwIIVpa0CJH6wG//BQV5E5nbBxeb9LUkPEIoP3pMrZneOWTg/HjOVhUQgAHx/AQwIH99ltlvykpss+1ay1Fk/D7oahI5npPSYHlyy3zsxOiGQ+gkTVNGdMVCoXitEQIoV5xr/PPP18oFKcstbVCpKYK4fHI99raDpsu2bBEDJo7SGhzNDFo7iCxZMOSL28cZWVyDCDfy8pEa98MEQIRjm5LTY19j7yHQWzzmcZcWyv7qq0VoqhI9hd57S8qEa04xTFSxFFS5XElJSIISfutZYi4DJ/4e1mtEG63EHa7ECBCDocIYLMcYz5OpKTI9iUlsetZW2sc39Erenw79tj4Imzz1Yq/F5TFtvl8Qrhc8jw2W2J/kyZZrmnY9Fo/oUiUjy8T708qEXuHjhd7Ssq+vN9RoVAoFP9nAGtFB/pURdoVitONE1ggCjEf+86mnQgEO5t2MmP5DCo2VpzU6So2VpA5LxP9AZ3MeZmx45NEyVP27UYXAq22FubPZ+89j1msKxoQBuorTWOORvOrq60Fh4A+T8zB6bLhsguOlL3I4LVLYfZsbMjHiPHR9Cy2UUUe7/1pu/Sl5+eDy4UeCGAjZKmkGo1kN04qgCeekO2Li2NPFaqrY7YVTZM+80mTwPX/27v3+DjLMv/jn2smSZtAqRwKRdI2HBwQLWKprqBZWYukiCgIVbpV29JuAVmxuPwWIayCEtAVl4Igtfa4a4zSH3IUGqX+cCPgSqmFosAo9EBdKqW0pTRpTnP//rhnModMkpl0knmSfN+vV15PnmeeeZ577qTpNfdc93WPgtNPh3e/m46SctopoYwOjE7+9IWbaVrmq8QcWxOheuU8jq2JX2/bNj95taWFWLgkrQ2AT7mJ92ksFPa3jT80unkX53wCPnD/9Rz18pOMv34+r9Uty/2HKCIigaegXSQPPQaoQZJjHb9spSB7LO/Yg14D/95Kk8QD8b2P+OA8EXzGgBYOonJmljYvX97tUMyMUGsLFjYmsA1WrOheBebQQ9n17jPS7vOPNPg2XHcdkD4pNDVdB+DQsWRPAUrt54oKf6377oMHHoCnnoIXXqCko4VOStjHaCrYzz/suIf3z5/CurrGrvQlGhthzpz4TY1YeQXNbSU8yeldbXPAi5MvJIrv091f+HLXY+Cr9NiK5WntTuyLiMjwoKBdJEeFGpkecDnW8eupFGRvJSIz9Rn495bzTveSkL8/YyE71qxPjj6neHPj1rTR5z1nnY+1t/uAu3U/G5+HF3cfmXY9BzB7Nofd9vW048dcPTPZvgcewMrKiI0aTWdZOXvPn50MfMFPiM2mp35OKVtpQMdZ03l83OdooZwKWggR431f/5Svb3/KKb5azqpVfmLp7bfzxGfv4MOj1/MRnuQ/Q7NpLTuY/wzN5rRnV/py7kQ4bOVtbFu6hj+fMZttS9cwoRLGHOLSX/vcS7K3W0REhiQF7SI5KsTI9EDr+iSg4SSq9n6L+tanezx34tiJeR3P5kAD/wnzatKCzw89cVtXwP7SnDq2jzmBVy64ijfHTuDQvdu6nvfmmEqeO+aTtFAeT6kxTlx0KSfsfApIprh0gl9dFbA1a7DZs7HMuuU1NbBxI6G77qRk4wbecd9KX9u8jxrnQPY3JRlB/pirL+OQW64jRoi3qSBEDAuHfPpS5kTWpiZOeBcsbp3LpaXLuKJ8Jd/7+l6+NHplt2ynCfNqiDyxkgmVwPTpHPQH/9p3jX83229aytG18/r+AYiIyJChOu1ZqE67ZBO6MYTrnnyBYcS+EStCi9Jlq7VeUVrRY/WXfM/PJpca8Pm0v3ZtLVv3bOWbD59C7bpnu52TCMZjQPOEk6h4NUqIWLfHU1cK7cprX7rUB9iDobERFi+GsWPhXe+CqVOJNqzjuWdh8vvgxPobfP46+EWTEhYuhEWLutq+/aal7J0xr/dy+HPm+JH6hNnx6js5SO3ziWMnUjetLuefvYiIFJ7qtIsUwMSxE7MGqIeVH0bVoqqiBz59fRLQU3CWOP7R1qO5hU/woVEfyPmeudSAz0XmG4jPPfe/QPcgPHWhpINffbHr+akj6yGyBOwAl17acz35Qtu2DR56yNdsj4uMHk0kFoMN8VKOHR3+jURlJTu+divbt0PVzxsZk/J6xtx8LR3jK1m/voamJvhYZZRjm5pgU6W/R3W1H9lPDdp7SufJkNnnW/Zs4Qs//wJPbH2CH5z7g8L0g4iIFIxG2rPQSLtkk21kujRUipnR1pmsqZ3vaHWh9PRJQKJNvY6o57i6aTaFGK3NHLG/5ufHcctzr3Ttx8IlWGcnDkd7STmjOlq6LZgE0EEJJfFK7ZmVYygp8aPfAz3a3tiYvqprL9oOP4rQzh2E6f2Tmm1L1zCh+lj/M4rFoKXF12sPh/2qq+ef7xd5Ki2FZ5/N6WfX06ckhvFfn/kvjbiLiBRBbyPtymkXyVG2RYoOGXVIWsAOxctz7ykXPWzhvnPxcywTmc2sybPYvHAzsW/E2Lxwc7+Cvcwc+O985hWuPeU4/lJ2JJvOX0j4h4sJvfQiYecYvfj7QHLkvZ0QL4w+legZs2mnNGswD/iAtodKOgWVMhE1TUn6B5sOKN35t66APbV6TeYnBc9d28DzdzclA3aA/fv9z6qhAUIhP6ofCuX8s+tp3oHDBWqehoiIeAraRfKQGaC+2fJm1vPyqcBSKHXT6qgorUg7VlFaQafrzHp+WhtzLBM5ULK94fjOZ17h498p57j7bkuf7DlvHttvWsoGO5X/tNlMHfUCJc/+gRNXXMfoslhXOo0BnHUW3HST/9qwYWBSYxIlGxsb/X629JRRo+DMM9MOuXhInvkmowToiB9NHLt9x0y+uqgSlwjYIbk668yZ/frZ9TbhuBi/vyIi0jsF7SIHoBAVWAol2ycBif1s0tqYY5nIgdLTG46ecuOPrp3HQS/+gY4freT/PhfxzY1ECD3oyzcyejQcdBDcdZdfECl1UaRCSqTCrFrlt42NvtrM0qXJiabgR8Df+14/2g9QXk7rcScC6Z8IJHLZSzM+JziGbUxgG22h0f7AqFE+WF+/3t+vHz+7uml1WPckIqA8JN4VAAAgAElEQVQ4v78iItI7TUQVOQCFmohZKLMmz8qanpJTGyORQQ/WEzInxeaSG5+1ufHyjTQ1Dc6k08xUmIYGOPZYePjhtEmofPaz8MMf4trbAXCdMcoffYC9c66g/H8epzXyXg56cUPWCbQOuI5buIK7CJeFIRQfUb/uuuTr68fPbtbkWTyx9QkWr1ucNheimL+/IiLSM01EzUITUSUfQ6Fs3lBoY38U/XVlTjpduhS+8hVoa/MTQxPOP5/Yo2sIte6P5+GX8dc1G9MXkWpshFtvhcceS7uFPz9MSdgI3XgDjB9f0DckRe9DERHp0ttEVAXtWShoFwm+QtSZL4jGRj/CPnOmL8N45ZV+Qi/4FJmyMrjvPjo++SnCHW0Y0Ew5z8z+PtUr45Vsli2D5ct9Tfef/tTXbi8pgTFjYPduP+E0oa8Fn0REZMhS9RiRAdK1AumNIaoWVVG/sb7YTRoxArNCbU2NX8yopiZ9Qm95Odx4o58AW1PDa4sfpI1RNFNOjBCVM+MTRpctg/nz4cknfW58YrGljg54++30gB16rk4jIiLDmnLaRfop2+I0Cx5aAKD0gkHQU4WTolY+SUzozZJTP2FeDZsqn2NbQxOVM6uTqTHLl6dfwwyc84Ug29u7j6zkuHiSiIgMLxppF+mnwIz0jlBBqtyTJhJJL1HZl0suSd8PhYiRLFvpiJeAPP98pcaIiIxgCtpF+inbapKgGteDJd8ykYMiGvXpLtFot/rtmxqjjJs+hSmrrmTc9Clsaoz658yb5yewnnSSz4Hv7OyqHpPYtlk53HefAnYRkRFM6TEi/VC/sR7D0krlJRR9pHeE6E+ZyAEVjcKUKT4HvbMzmZu+ahWsWcO2hm0cieMgmmmljDcWr+bYmvinMtXVcMUVXWUiEyPsid+urR+awUmD/XpERCRQFLSL9EPt2tqsAbthqnE9iHqqS18UTU0+YG9uTltYyQG/vaKB7XOv63qbV0Ybpz1aB9EZPo2mqckvwJSiE6PNyn3A/uTKQX0pIiISPEqPEemHnlJgHC44QaQMrtTKMSV+PCTxtq755b/yo+s38fJZlxKzMAaEwuaD9cRzQyFfcaasDBYupOSlF6mI7VPALiIigEbaRfKSWIgm2yg7wKSxkwa5RRIYmZVjNm1iw+dv5dQ3HuNs/Ff7Y6WE6MQB1tYGlZW8uqyRluUNjLn2do4ez+Cs5CoiIkOORtpFcpQo8djTBNSiT4KUotu0ycfsmzYBNTW0jzsGIGViaXKSqevoIHbeeVTOn867nlzF+Ovn8yqVOQXsWh9ARGTkUdAukqNsJR4TJo2dNPgrcUqgZKsOc/S/+Jrqic9lHOZH2eNftHcAyaC+ZXnfCyelvnl0uK71ARS4i4gMbwraRXLUUx67YWxeuFkB+wi3raEJi1eHMRzbGpqYUH0srrSs65wYYdopTVaGKU3PfS+/pO+Fk7Q+gIjIyKScdpEcTRw7MWtqjEo8CkDlzGrcKmMfFTiMypnV0NREyJLnlJXCM+d+g6oqGHcE2IwZvNq0iZblDZRfMpMJ8/quwx7IlWBFRGTAKWgXyVHdtDoWPLQgbZRTeeyScGxNhE1r1rOtoYnKmdUcWxOBKGnlH0MlYT7wnRlpeesTIhHIIVhP0JtHEZGRSUG7SI4Ct5iPBM6xNREfrCdEIrBhA6xe7fdnzDjgyjB68ygiMjKZc9lL141kU6dOdevWrSt2M0REskqUHtWbRxGR4cXMnnHOTc36mIL27hS0i4iIiMhg6y1oV/UYEREREZGAU9AuIlIojY0wZ47fioiIFJCCdhEZ8TYcfwH7bTQbjr+g/xdpbITp02HVKr9V4C4iIgWkoF1ERrQNx1/A+165n1G08r5X7u9/4N7Q0Pu+iIjIAVDQLiIj2kmvPAqAZeznbebM3vdFREQOgIJ2ERnRXjzuHABcxn7eampgzRqYPdtva3JfMElERKQvKvmYhUo+iowsG46/gJNeeZQXjzuHU1++r9jNERGREaq3ko9aEVVERrxEoH5q6sFoFJqaoLr6gFcxFREROVAK2kVEMkWjMGUKOAdmsH69AncRESkq5bSLiGRqavIBe3Oz3zY1FbtFIiIywiloFxHJVF3tR9grKvy2urrYLRIRkRFO6TEiIpkiEZ8So5x2EREJCAXtIiLZRCIK1kVEJDCUHiMiEo3CsmV+KyIiEkAK2kVkZEtUirnySr/NCNzrN9ZTtaiK0I0hqhZVUb+xvkgNFRGRkUxBu4iMbL1UiqnfWM+ChxawZc8WHI4te7aw4KEFCtxFRGTQKWgXkSHvgEbDe6kUU7u2lub25rTTm9ubqV1b2/06SrEREZEBpImoIjKkJUbDE8F1YjQcYNbkWd2f0NgIDQ0wcybU1PRaKWbrnq1Z79ntuBZjEhGRAaaRdhEZ0vIaDW9shOnTYdUqv21s9McjEZg3r1ugPXHsxKz37HZcizGJiMgAU9AuIkNazqPh4EfYe9vPUDetjorSirRjFaUV1E2rSz9RizEFkiYRi8hwoqBdRIa0nEfDwafE9LafYdbkWSw5bwmTxk7CMCaNncSS85Z0T7tJpNjccYdSYwJCk4hFZLgx51yx2xA4U6dOdevWrSt2M0SGjfqN9dSurWXrnq1MHDuRuml12fPN+3nt1Jx28KPhWYNr6J7TLsNS1aIqtuzZ0u34pLGT2Lxw8+A3SEQkB2b2jHNuarbHNBFVRAZU3hNF85S4Rs5vCmpqFKyPAHmlTYmIDAEaac9CI+0ihaMRTykG/d6JyFDU20i7ctpFZEBpxFOKIedJxCIiQ4SCdhEZUHlNFBUpkJwnEYuIDBHKaReRAVU3rS7rRFGNeMpAmzV5loJ0ERk2NNIuIgNKI54iIiIHThNRs9BEVBEREREZbJqIKiIjllbFFBGR4UA57SIybA10jXgREZHBopF2ERm2atfWpk2ABWhub6Z2bW2RWiQiItI/CtpFZNhSjfj+UUqRiEjwKGgXkWFLNeLzl0gp2rJnCw7XlVKkwF1EpLgUtIvIsKVVMfOnlCIRkWBS0C4iw5ZqxOdPKUUiIsGk6jEiMqxpVcz8TBw7kS17tmQ9LiIixaORdhGR4SQahWXL/LYflFIkIhJMGmkXERkuolGYMgWcAzNYvx4ikbwukfhUonZtLVv3bGXi2InUTavTpxUiIkWmoF1EZLhoavIBe3MzVFT4/TyDdlBKkYhIECk9RkRkuKiu9iPsFRV+W11d7BaJiEiBaKRdRGS4iER8SkxTkw/Y+zHKLiIiwaSgXURkOIlEFKyLiAxDSo8REREREQk4Be0iIiIiIgGnoF1ERIa9+o31VC2qInRjiKpFVdRvrC92k0RE8qKcdhERGdbqN9az4KEFNLc3A7BlzxYWPLQAQKUtRWTI0Ei7iIgMa7Vra7sC9oTm9mZq19YWqUUiIvlT0C4iIsPa1j1b8zouIhJECtpFRBKiUVi2zG9l2Jg4dmJex0VEgmhQg3YzqzGzX5vZdjNrNbNtZnaPmZ2ccs40M/uxmb1sZi3x7d1mdmQe9znGzJan3GeTmd0yMK9KRIaFaBSmTIErr/RbBe7DRt20OipKK9KOVZRWUDetrkgtEhHJ32CPtB8GPAP8M3A2cC3wHuB3ZjYpfs5lwOHATcB04BbgU/FzDu7rBmZWBfweiABXxu9zA9BRuJchIsNOUxM4B83NftvUlPNT28JlxMxoC5cNYAOlv2ZNnsWS85YwaewkDGPS2EksOW+JJqGKyJBizrniNsDsROBF4Grn3PfMbJxzbkfGOX8P/AaY55xb3sf11uDfHHzYOdfenzZNnTrVrVu3rj9PFZGhKjHS7hyYwfr1Oa0s2hYuozSW/FPTHiqlrLNtIFsqIiLDlJk945ybmu2xIJR83BnftgNkBuxxT8e3x/R2ITM7HqgBvtjfgF1ERqhIxAfqTU1QXZ1TwA5QEg/YDXAp+yIiIoVUlKDdzMJAGJgEfBvYDvy0l6d8NL59oY9Lfzi+bTGzXwF/DzQDDwFXOed29vhMEZFIJOdgPaEjVEpprB2Xsq8kGRERKbRiVY/5H6AViAKnAB9zzr2e7UQzGwMswgfs9/dx3XfGt8vj1z4HuAY4F2g0sx5fr5ktMLN1ZrZux45sg/0iIt2VdbbRHirFodQYEREZOMUK2r8AfAj4R+At4FfxCaRpzKwEaMCnxVzsnOtrMmni9TzunLvCOfdr59wS4EvAafjUmaycc0ucc1Odc1PHjRuX7+sRkRGsrLONkHMK2EVEZMAUJWh3zr3gnPsf51wDMA04GPha6jnxUfFVwFnA+c6553K4dCL95VcZx38Z376//60WERERESmOok9Edc7tNrO/ACdkPLQY+BxwkXNubY6X+2Pisj08HutHE0VEREREiqroK6Ka2VHAScDLKce+B8wH5jrn+spjT/U7/KTW6RnHE/tPIyIiIiIyxAz2iqj3mdm/mdmnzewfzOxSfP31DuB78XOuAb4KrAD+bGYfSvk6PuN6HWa2LLEfz3n/GnCumS02s7PN7EvAD4DHgV8PxusUkWFszhwYM8ZvRUREBslgp8f8Dvgs8C9AGfAqPpi+xTm3OX7OOfHtJfGvVKuAOSn7idKRXZxzq8wshq8aMxd4E/gxcK0r9kpSIjK0zZkDq1b57xPblSsLeotoNO9S8SIiMgIUfUXUINKKqCKS1Zgx8Pbbyf2DD4a9ew/8uo2N0NDAq9UzefdXanJflDX+PGbOhJoei2OJiMgQ0duKqEXPaRcRGTIuvLD3/f5obITp02HVKirnT+dj7Y00N4NzfsS9J68ua8RNn45btco/f9ky/xWNdj85Gu35MRERGRKKXj1GRGTISKTC3HuvD9gLkRrT0JC2+9nOBtZW1GDmU2SyiUbhhUsXUwkYvlyWXXoplJZCZydccQVcfrkfpo9GYcoUch++FxGRINJIu4iMTP0dfV650qfErFzJbjuEmBm77ZD+32fmzK5vDfjoD2dyxx29x9ZNTbCVqu4P7N8P7e2waBGcemoyQd45chq+FxGRwNJIu4gEw2DOwOxr9DmHtrxlBzOWfQCMZS/7bDQHvfRc9+tMmQIdHX4EfPFimDcv/UI1NbBmTVdu+oSaGjLO6Ka6Gj5bdjnzW+6mhA7CpSEM/D0SOjuTr8EMKirodfheREQCTUG7iBRfNErs1Cl0djrCYSO0YYBTOFJHnysq/H7ifrmkk0SjHBwP2BPpKeW00vG+KZQ8mzz/tTnXcNS+fcmPNOfPh8rK7pNGa2rymkgaicA9GyI8vPo5qmli/Ixq2LQJPvUpaGvzJ4XDyTcd69erJI2IyBCnoF1Eim776ibGtDgOopl9VLB3dRPjawcwuOxt9Dk1oC8vh5tv9iks27Ylg96mJmL4P6CJ+lshoGV/J/uuuZk3L7uOP964mnOf8mvDOXxwD8DXvlaQSi+RCERqI0AkeWDjRli92u/PmJEM0CMRBesiIkOcSj5moZKPIoNrdV2Uc66fQiK8ffSm9cwYyKAdkikwlZXpAXlipD0Wg5YWCIX896NH+9Hr9ev986dMoX3fPkqAdqCd0VSwn46Sclo7QuzkMCbyanKiaOK+RxwB3/52+qj3QKUGqei7iMiQ0lvJR420i0jRvW9GhI/Uref0ziaeCldzz4xBCDATQWy2VJj1630Flsce8wE7+EmeZWXQ1MTrz2+nfNRhuNM/zrMXf4fLL4d/7byZi2L3UNHRQhsVPM1pTOTVrpH4rsB9925/7USOe3V18k1CLAYPPJA+Ep8ZeI8fD3/7Gxx1FGzf3vPrU9UYEZFhRdVjRKQo6jfWU7WoitCNIc5+pIrP/+xFpv5gHvdsiAxIbJko4rJzzlVw5JFw1VU9V1aJRPzCSZna2tjz04cZt+h6Dn7zVcY8dj8nN93Nz5+PUP7N6xhdHiJWXgE4ni+ZyjOcmvZ0B35Samur386fD3ffnRzVb231eemJSjONjXDKKfDlL/sA/IgjfMAOfjt+fM8vePVqX0lGVWNERIYFpcdkofQYkYFVv7GeBQ8toLm9uetYRWkFS85bwqzJswp+v8Sgc93+q7iycxEQH/WePZvYz+7BtXdipWFCz25IjkYnFj3K0DrqYMpa3+5Ke3FA6KWXkqk1d9+Nu+sHdBIm3N7SdS+XsSX+ffshh1L29p7kiP7o0XDnnX5kffJkXFsbBsRGlRNqben+4rL9DY9GfcnHlvj55eWwYYNG2kVEAk4roopIoNSurU0L2AGa25upXVs7IPdLDKjP6VwKJIPmjvsfpGU/dHRCy35fgKVLTQ17Tz+LTguRGhbvrz4LSAm+Q+H0Uewf/ABrb6OkvSUtOE8E7DEr6Qr2AUrf2oVLBOylpcmqL01NxCzU9bz21hht7zg8/YUddVT3FxuN+smzHR1+v6QEamsVsIuIDHEK2kVk0G3dszWv4wckGuXiO89gZ/MoDuFtIDlC/r9HnwYYo2gDjG0NyeB71wVzOPipxwg5H1C3Vh4Ha9Yw9uJP4sIlXcF0KBxKVp9pavITV1MkAvc2oJUy/vdHD9MZLkl7LHGtNw8/gac/fi2vrW6Chx8mFnM0M5pWyphR9gD/desbyUA9W0574iOFn/zEp8aAD957S6MREZEhQUG7iAy6iWMn5nW836JROPlkDtrwVDwwTwbKu0YdReeiu3AY+6jAYVTOTJZ+rHj03rTz2fG6nyBaXU2orDT5WGqQXl3tR8pHj/aj5qNGQUUFblQ5z55/EzuWPsgEttHywTMByExsOWT7S5xy/zcZf/183P33E25vZVnJZfzd6I38urTGvzfYvt1/bLB9e/fVVhMfKSQC9gTls4uIDHmqHiMig65uWl3WnPa6aXWFvVFTU9cqoak56AB/vbyOyTURXl16Ly3LGyi/ZCbtx0ZYtszH3pXxcD1x/v7qsxgFPs3k0kvh+9/313bOT/pMpKCkLmQUb0OoupoPQFc1lzFm7D39LMp//xtcZzuGEcYRJkYJbWl573PO3EzFxZHuVRuzVYdJ1J8vLU0P3GfOLGy/iojIoNNIu4gMulmTZ7HkvCVMGjsJw5g0dtLATEKtriZm4bRgfU/4HTy/cCmTb5sH0SgTvnIhkQ2rOebLF3LRKVG+9CVoPfHdlLfu7bpMJzD2qV/5QDkahR/+sOvNAG1tUFcH0Sgbr1pG9CNz+f3D22m6ucnnyM+b17UgU2qlmjHnnknJ6DJCZeU4wrRS2vXGgpTtmN+uYV51tHtKerbKN5EI3HuvH/0vK/P57EuXFmQxJxERKS6NtItIUcyaPGtAKsWkiUT4+ak38qk/XE8Z0EaIn//drVxy2zz/eErgGwuX8cnO1WyklpN5EUiOzpcAsbZ2Qok0E7P0+5jx6hU3897HVvn9+5+klVI6VpWxac16jq2JdF+FFcA5wm0tuNLRlHTGiKfPk3b1UAiamogSSV8nqadVXbdt8yk6zc3+sf5obISGBj9Cr4BfRCQQFLSLyLB21BUzaJt/C510UEInH/hg8rE9N9zKIc0+RSfc2cb1fIt1TCURrqdWfXGdncnAOLPMonN0PvMspJw/mnb2Ucq2hiYftANce63fzpjht7fcAhUVlJix713vp3zDU+kff8ZXY91UWZ1lnaSMVJzEUHxPwXxfEos4ga8fD7BqFaxZo8BdRCQAlB4jIsNa9bwImxbezmhaKaGDyYvmw7Jl7Bs3gUO2vdh1ngHltPILzqWTUFq5RoDXv/lDHxhHIj5/vazMP1BWBrW17J39z2nn76cUcJx06HY/cj1lii/FeMst/oRE0H3HHey86J+o2PBUsixkolGxGOzfz4trNmVdA4pIJJl+A92uu+ne9SxrinTNU+1SVwcnnOC3kMyPv/JKn6+fqqEhr/4WEZGBoZF2ERn2Jm9+OP3AZZdREa9jnppHDlBCPFfdDFdaxpb3nEPJFZcxYV7KaPOMGT74Linxo9kzZjA5EmEjMKp+Obs//AlCwJQ1dRy05Ga4s80H4LGYHwFP5J/Hv0quPSFrWxLO2NyAWU3ug+eRCFEiWUbn8YH69df78xLb8eN9jv7+/X4SayJfHzSJVUQkIDTSLiLDWzQKjz6afiwc7jaSHsMHzYkvnCMUDnHsT7+TDNg//nEf1F5xhZ/wOWMG3H473H03XHABk6dXEnn9CT54Xy1TPzmeUMj88HhHR3LF09Q0m7jWWXO72tKtjCQw9rKZicHzZPDdh2zzVAFYsSL9xBUroLLSB+zgq87cdBPMnq3UGBGRANFIu4gMb01N6RNHS0p8UFxRAc0tOBxGDyMYnZ3JUfEJE/wkT4DHHoNf/9rXYV+1Knn+/fcnA91Ebnk4nD5yfc453aLuI2+rZeeuNzh01e24kjChUaP8m4Gmpq7JoBHyW9S0x9T2uXOTI+yJ/W3boLwcWlr8dvx4nwIkIiKBYS5zQpUwdepUt27dumI3Q0QKIRqFyZN9aUbwQWlqQPq978GuXdmfGw7Dn/7kR9IXLerzVg54a9zxvHXLXX50Phrt9txXl67hl9R0r7ueaGvmxNIeXlK307IcjEYh/LkLqPrTo4Q/cQ7cd58/t67Oj7DPnev7IlvN93zeIYiISEGY2TPOualZH1PQ3p2CdpFhprERPv1pP8IeCvnUlgsv9KkgiWA+m6VL/fayy3yKS6pQyI+0t7QA3XPRdyy8iSNvq/UrlsarsTjg8tKl/FfpvH7Hxlnja3oIui+4wI/+J5x/fjJwz3bhHN4wiIjIwOktaFdOu4gMfzU18NxzfhXT9et9Oohz3QP2CRN8eksinxtw8+fjOjrSg/LKSnjhBX+9NWtg4ULePnQCkKyxPm7R9f7NwvLlabeY3bG8e555HrLmqveUwJ6Zy5+5nypbJRoREQkM5bSLyMiQqNaSYObLNaYG7t/4RnLiZUMDrb9uooxkVZdYOMyuz3+Z+6tvoxqIzItfr6aG3e+dzsHzp6fVdqehAS65BJ58susWq0ouoaI0vxLqqbLnqveQwH7OOekj7eeck/8NRUQkEJQek4XSY0RGgEQ6yPbt8MgjPrieN8+Pjk+fDiRTXhJB+LOnzuYjf17ZlYWy8d4ox25LppS8flWdH2GPP6drUupVV0F9PcyaRfTy2w44CyXXnHbAp8g8+qgP2HtKjRERkUBQTnueFLSLjGBz5nRVhHFAZzzhpZ0SVi58nquXRGhuhktLl/GDzssIlYZ9RZprr/XB/wc/6Ce2xqu+pL4JALqXUWxs9CPyifNFRGTEUtCeJwXtIiNYRpD92k1L+cufoXJmNe3H+gWLPtbeyANt/hwDYqEQFq/DbuAnsFZW+mD8r3/1JSITzjoLjjkmuWhRbwG9iIiMKAra86SgXWQYy6FKytrF1/Dm8rtYGtnHS1MnUTetjlmTZ3U9nblzeNeTq7qtYJrY33fIeA56azvW/dLp57///fCHPyQPnHUW/OpXXbvr6hppWdFA+dyZTK1VMC8iMtz1FrRrIqqIjBw51COv31jPgjfvpPncZn9gzxYWPLQAgFmTZ/nTvz4TpicXVWrHKMN1BeTlb21Pv298dP2ttb9nzLYXuoL7judfSP8j3Nzc9e26ukZOuz4+Cn/9KtaxRoG7iMgIppKPIjJy9FQaMUXt2lqa25vTjjW3N1O7NmVBppqartKQ229aSozSrqoxAOH4tmtU/eqrYeVK3nor/V7h9v1d3ztg95Hv6tpvWdEAKddM7IuIyMikoF1ERo7s9RLTbN2zNetTux2vqYGVKzm6dh47lj5IrGQUsXD6h5et7z41LU991yX/AnRfiCmxX3r/PSy+oJFrj1zGS0dWpz1WPndmzi9TRESGH+W0Z6GcdpFhrI+c9qpFVWzZs6Xb8UljJ7F54eber7t6NbFv3YTriGGlYULPbuh2j41XLWNU/XJKPvxBjrt/UVpZyX2MJoyjgzBg/PT02znx9SbltIuIjBCaiJonBe0iI1f9xnoWPLQgLUWmorSCJect6ZqM2qscJrp2aWxk762LCT/2KI4wYTooo40QEANqxy3lltfnHdDrGWnqN9ZTu7aWrXu2MnHsxLRJxCIiQaegPU8K2kVGtsEO/DY1RtnW0MQHVl3KKDq7jjug+fBKDn7j1QG793BSv7GeSx64hLbO5Cq3ZeEyln96uQJ3ERkSFLTnSUG7iBRDzAyDbqUk9ylwz8kR/34EO1t2djt+ePnhvPGvbxShRSIi+ektaNdEVBGRFPUb66laVEXoxhBVi6qo31g/aPfuiNeKSc1zB6jYuY1oFJYti9eJl6yyBey9HRcRGUoUtIuIxCXy2bfs2YLDsSVeo31QAve6unjhyKTEXkv5O9h50od5+vJlTJmiwF1EZCRS0C4iI0Zfo+g51WgvhGzD5itWAKSlxzhg/0HvoKJlNx9yT3J3+3w+37YsW3l5wafB5HNcRGQoUdAuIiNCLqPoOddoPxCJVVmvvJKuYfNoFCZPBpLBOsCu2Qspf9/JQDJVZnbH8mzl5QW4/ZzbKQ2Vph0rDZVy+zm3F6lFIiKFo6BdREaEXEbRJ46dmPW5PR3vl5RVWdvbHTvuXg2nngqPPIKVlNBZcTDgg/TDVy2CI49Mm5ha9a1LiESgLVxGzIx9ZkXJvw+iWZNnseL8FUwaOwnDmDR2EivOX6HKMSIyLChoF5ERIZdR9LppdVSUVqQ9XlFaQd20usI1pLqamDP2UUFrm/HTO9/AtbRAWxt0dFAyqrQrRQaAjRth6VLsjDOwpUs5unYebeEySmPtGFABvHXDIOffB9isybPYvHAzsW/E2LxwswJ2ERk2FLSLyIiQyyj6rMmzWHLekrSR2pwXVcpVJMK9teu5uuwOprCenRyR/vhpp6Xvz50L8+bBE09Q/8HRVC2qoiTWDqRUl4lvByT/XkREAkF12rNQnXaR4eeAVzotoERau3PwLhdlvTuVUKwTwmHYsAFWr/YTU+fOhdrabu3fewMclHK9fcCYG/z3hhH7RoIwQ6gAABALSURBVGxQX4+IiBSGFlfKk4J2keEpSEvcR6M+vb26GiKk7kSynl+1qIote7Z07e+9wY+wN5MM2AEmjZ3E5oWbB7DlIiIyUBS050lBu4gETejGEI7e/14X65MDEREpDK2IKiIyxPWUkx+28MDl34uISGCUFLsBIiLSt7ppdYHJyRcRkcGnkXYRkSFgUCrbiIhIYCmnPQvltIuIiIjIYFNOu4iIiIjIEKagXUREREQk4BS0i4iIiIgEnIJ2EREREZGAU9AuIiIiIhJwCtpFRERERAJOQbuIiIiISMApaBcRERERCTgF7SIiIiIiAaegXUREREQk4BS0i4iIiIgEnIJ2EREREZGAU9AuIiIiIhJwCtpFRERERAJOQbuIiIiISMApaBcRERERCTgF7SIiIiIiAaegXUREREQk4BS0i4iIiIgEnIJ2EREREZGAU9AuIiIiIhJwCtpFRERERAJOQbuIiIiISMApaBcRERERCTgF7SIiIiIiAaegXUREREQk4BS0i4iIiIgEnIJ2EREREZGAU9AuIiIiIhJwCtpFRERERAJOQbuIiIiISMApaBcRERERCThzzhW7DYFjZjuALQW63BHAGwW61kig/sqP+is/6q/8qL/yo/7Kj/ord+qr/Azl/prknBuX7QEF7QPMzNY556YWux1DhforP+qv/Ki/8qP+yo/6Kz/qr9ypr/IzXPtL6TEiIiIiIgGnoF1EREREJOAUtA+8JcVuwBCj/sqP+is/6q/8qL/yo/7Kj/ord+qr/AzL/lJOu4iIiIhIwGmkXUREREQk4BS0i4iIiIgEnIL2HJjZV83sITN7zcycmd3Qw3mzzexeM9sSP29llnOq4o/19HVxHu0qNbON8efN7/8rLKwg9ZeZfd7MnjCzHWbWamabzWypmU0ozKs9cAHrr38ys0fM7K9mts/Mnjez/2NmZYV5tQcuYP31XjP7oZk9Y2ZtZha4fMMg9Vf8Gu8xs1+a2dtmttPMVpjZYQf+SgujkP2Vcm65md1gZn+O/x36m5k9nMu/KzO7yMz+YGb7zWy7md1pZmP6/woLK4D99Ukz+62ZvWlmu8z//f90/19hYQWpv8z/f9jTv+XFB/ZKCyNI/ZXy/C+a2dNm1hz/HfutmU3u3yvMj4L23PwTcCRwfx/nfR44HvgV8FYP57wGnJ7lay3QCvwyj3ZdjV9AIGiC1F+Hx8+dD5wN3AzUAE8G6D++IPXX14HtwFeATwI/A74F1Pf1IgZRkPrrNOATwFZgXQ5tL4bA9JeZvRN4HCgHLgKuAM4CHjazoPx/VMj+wsxKgUeBucD3gI8DXwK2AeHebmBmM4HVwLPAp4EbgJnAz/t+GYMmSP01HXgQ/zdsFvCPwN+A+8zs3Bxey2AITH8BF9D93/J344892MdzB0uQ+gszuxlYDPwCODd+37VARV/PLQjnnL76+AJC8W0J4IAbejsv/v02YGWO16/A/5KtzqNNxwH78H+YHDC/2P0U5P7KeH5NvF0XFruvgtZfwLgsx74eb9dxxe6rAPZX6j1u8n9Si99HAe6v24DdwDtSjv19vF2fKXZfDUR/AV+L98+EfrTlL8DjGccuirfrE8XuqwD210+AV4FwyrFw/H4Nxe6roPVXD9dbi39zHi7E9YZTf+Hf1MSA84vVH0EZ2Qg051yskOdl8RlgDLAqj+fcDfwUeKKf9xwwAe2vVDvj2/Z+Pr+ggtRfzrkdWQ4/Hd8e08/7F1TA+qu/9xg0Qeov4FPAL5xzu1Pu+9/4TyoCkcIwAP31JfwbmlfzaYeZHYEfOXw046E18e0F+VxvoASlv+LKgH3Ouc6U+3YCbxOQzIKA9VcaM5sI/ANQn9qHxRSw/roc2OSc62vUf8AE4pdYmA28TvKPca/MbBYwFbhmIBsVYHn1F4CZhc1slJmdAvwH8CfyS0UayvLurwwfxY8uRAvWomA70P4aaXLqLzMrB44Fns/y8B+BkwvftOKKB0ETgFfM7Edm9lY8N32tmZ3ax9MTQVNbxvF2/Ijjewvc3KI7wP4CX5v7BDOrNbMjzGycmX0dqALuHMCmF0UB+ivTFwCj/wNigVaA/voI8KyZ/av5eV8d5ud9zRjYlicpaC8yMzsG+Bj+nW1HDucfig86r3HOvTHQ7QuafPsrxd+A/fjc0HLgLOfc/gFoYqAcQH8lnn8KPr99uXPub4VuX9AcaH+NNHn216H4gGBXlsfeBAIzGbWA3hnfXoNPabwYn5M+Dng8HkRk5ZzbBewAPpTx0N/h+1H9lcE590v8pzlX4/vu9fj3n3HONQ1Uo4vogPoriy8Af3DObSxcEwPlQPvrnfg5OJcD/wc4B3gBuGewJjuPuKDdzM7qZbZ06tfjg9SkL+B/Drm+s/0u8DKwbMBalGIY9FfCNOAMYB7wDuBXZvaOArdtOPUXZnY08AD+9+2rBW5X4h7Dpr8GwxDvL4tvs1XYsSzHDlgA+ivxf2wzcJ5z7hHn3H34CWzl+Im4vbkduMjM/tnMDjOz0/CpkZ34T78Kaqj3l5l9CPgx8Ag+oJqOnzC42sz+odCNHer9lSredycCKwvdyJR7DPX+CuFTAS90zv3EOfcr4HP4TwqvG6A2pykZjJsEzJPAu3M4r3mgGxL3RWCDc+7Zvk40s78D5uAD0LFmBnBI/OHyeBC6x8VnTBTIkO2vVCnnP2Vm/w8fiF4GfLvA7RsW/WVmh+Nn4RtQ45zbOxCNY5j01yAayv21Cx+wZxshPhQ/2l5oxe6vxPyZJ5xzXfdwzr1qZi8C7+/j+d8FJgKLgO8DHcBdQAu9VMg4AEO9v74P/NE5NyvlWKOZ/Rb/CXVfz8/XUO+vVF/Ep141FLB9mYZ6f+0Eypxz61OeGzOztfh4YsCNuKA9/oN6sdjtADCzD+B/ga/K8Snvxs+EfzzLY3fEvw7FV2coiCHeX1k55zaZ2ZvACQVpWPq1h3x/mdkhQCO+XGa1c+6vA9A8YHj012Aayv3lnGs2s83Ae7I8fDLwm8K1LnlPittfr+AD7J4+Xeh1tNw51wZcambX4IP3bcBe4A38KHxBDfX+AiYDP8hy/Gl8SkNBDYP+8ieajcKPGD/SQzGCghgG/fVHIFvuu/VwzYIbcekxATMbP3LykxzPX4Of2Z36NTP+2K3x/bcL3MYgybe/sjKz9+AD0pcL0agAy7u/zKwC/3HyscDZzrm/DFDbgqggv18jSH/660HgXDMbmzhgZh8BJhGcutAF45xrx/97qjazgxLHzefOnkiyMlNf19ntnHvOOfcmPsVvFLB8AJpcVAXor+3AB7Ic/yAwYIMPxVKo3y/gPPwnYIFMCyyUAvTXfcDhZjY15bkhfJ57rn19QEbcSHt/xH9AVSTf5JxsZhfFv38k8TGLmZ1MsgJCOTAp5bzfpL6DNV/g/2LgUefc6z3c9+v4GtnHO+e2OOe24/8opZ5TFf/2Jefc4/19jYUUlP6KH/st/h/ai/iJqKcA/4IfsfrRgb/aAxek/gLuBT6Mn3x6UDzPMeHlgRyFyVWQ+iv+JucT8VNOih9L3GOzc67oCy4Fqb/w6R6fBx40s1uAscC/A7/H/zstugHor2/gX98vzOx7wOj4sd2kVDTp4ffr4/gqMc/Hn3c2vmTdl51zmwv4svstSP2FT4+51cx+gs9tB5/2cQb+b1rRBay/Er6IT/34RUFeZAEFrL+W4fPe7zWz6/GfeC3AB/xnF+xF98YFoHh+0L/wEzNcD19VKefd0Mt5Z2Zc8wL6WOAn5XpVvZxTRfAWVwpMf+FXPNuI/0j5bXypx+8CRxa7nwLaXz1d3wFzit1XAeyvql7usbLYfRW0/oofn4yfL7EPn+e+Eji82P00wP31QeD/4XN19+BXdzwhh9+vj+JH8PbG++sJ/AS6ovdTEPsrfnwW8D/x361d8e9nFrufAtxf4/C57N8vdt8Mkf46Gv+G8E38QOBT+E+lB6U/LN4IEREREREJKOW0i4iIiIgEnIJ2EREREZGAU9AuIiIiIhJwCtpFRERERAJOQbuIiIiISMApaBcRERERCTgF7SIiQ5iZ3WBmRa/da2aPm9njKfunxtt22ADcy5nZDYW+rohIkGlFVBERKYQvZeyfil9pMLEQiYiIHAAF7SIicsCcc38qdhtERIYzpceIiAwjZnaImd1pZv9rZq1m9pKZXWVmlnLOmfEUk0/Fz33DzHaY2Y/N7B0Z1xtnZg1m9paZ7TKzFfHnOTM7M+W8rvQYM5sDrIg/9Of4uc7MquJfLn5O6n3OzHLNsJndZGavmVlz/B7v6eF1v8/MHoy3scXMnjCz6gPqTBGRAFHQLiIyTJhZCPgFMBf4HnAesAb4D6Auy1NuBxzwj8A3gQvjx1L9HDgHuBa4GGgHvt9HU34B3BT/fgZwevzrtbxeENwAXAfUA+cDvwQezDzJzKYATwKHAf8Ufx07gcfM7LQ87ykiEkhKjxERGT4+AXwEmOucWxk/9kszOwj4FzP7D+fcGynn/7dz7ssp550IzDezOc45Z2Znx6/3OefcPfHzGs3sQWBiT41wzu0ws5fjuxucc39JPJYy4N8rMzsUuApY4py7OqWNncC3M07/LrAV+Jhzri3+/EbgeeDf8AG/iMiQppF2EZHh4++BGNCQcfzHQBl+tDvVLzL2NwKjgKPi+x8COoH7Ms77vwfc0r5NBg4C7sk4/tPUHTMrBz4KrAZiZlZiZiWAAY/h+0REZMjTSLuIyPBxGPCmc6414/j2lMdTZVZ1STxvdHx7NLDLOdeecd7fDqiVuTm6h3tl7h8GhPEj6v+W7UJmFnLOxQrbPBGRwaWgXURk+HgTOMzMyhJpInHj49udeV7vNeBQMyvNCNyP6ukJOdgf35ZlHD88y70T9/pjL/fejf904S7gP7PdUAG7iAwHSo8RERk+foP/uz4j4/gsoA34XZ7X+x1+FPuCjOOZ188mMWpfnnH8b/HH3ptx/NyM/eeAfcBnM45fnLrjnNsHNAHvA9Y759ZlfuXQVhGRwNNIu4jI8PEo8FtgsZmNw49QfwKYD9ySMQm1T865X5rZb4ElZnYE8BfgInyADH6EuyeJuu1XmNkqfNWZ55xzbWb2M2CemUWBl/AB+5kZ995tZrcBtWa2F1855gPAvCz3+irw3/hJssvwo/RHAFOAsHPua/m8bhGRINJIu4jIMBFPAzkXWAVcg59oei4+qK3t52U/gy8b+R38pNDRJHPH9/TSlmfxJRvPw7+ReBp4Z/zhr+BLSd4A/Cx+zS93u4h//GbgC/hSj2fHr5d5r/X4gH4ncAc+wL8dP5n1v3N6lSIiAWfOuWK3QUREhhAzuwuYAxyWZdKriIgMAKXHiIhIj+Irl47Fp9qUAdOBy4DvKmAXERk8CtpFRKQ3+4CFwPH4Gu6b8KuUfreYjRIRGWmUHiMiIiIiEnCaiCoiIiIiEnAK2kVEREREAk5Bu4iIiIhIwCloFxEREREJOAXtIiIiIiIB9/8BqtDfY4KahHcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "geometry = [Point(xy) for xy in zip(df_sdpd_rand['longitude'], df_sdpd_rand['latitude'])]\n",
    "geometry2 = [Point(xy) for xy in zip(df_sd_parks_review['longitude'], df_sd_parks_review['latitude'])]\n",
    "geometry[:3]\n",
    "geometry2[:3]\n",
    "\n",
    "crs = {'init' : 'epsg:4326'}\n",
    "\n",
    "geo_df = gpd.GeoDataFrame(df_sdpd_rand, crs=\"EPSG:4326\", geometry=geometry)\n",
    "geo_df2 = gpd.GeoDataFrame(df_sd_parks_review, crs=\"EPSG:4326\", geometry=geometry2)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (15,15))\n",
    "#sd_map.plot(ax = ax, alpha = 0.4, color=\"grey\")\n",
    "geo_df2.geometry.plot(marker='o', color = 'green', ax = ax, label = \"Parks\")\n",
    "geo_df[geo_df['priority'] == 0].plot(ax=ax, markersize = 8, color = \"blue\", marker = \"o\", label = \"Priority E\")\n",
    "geo_df[geo_df['priority'] == 1].plot(ax=ax, markersize = 8, color = \"red\", marker = \"o\", label = \"Priority 1\")\n",
    "plt.title(\"Parks and Major Crimes in San Diego Area\")\n",
    "plt.legend()\n",
    "plt.xlabel('longitude')\n",
    "plt.ylabel('latitude')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following 2 cells, I will generate crime rate scores for each of the parks using 3 methods. In the first one (score_1), I will calculate a score for each park as a sum of the product of its distance with each of the crime locations and the priority. In this way, lower score means higher crime rate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:21: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n"
     ]
    }
   ],
   "source": [
    "E_Rad = 6373.0\n",
    "df_sd_parks_review = df_sd_parks_review.assign(score_1 = 0)\n",
    "\n",
    "# Calculate score based on priority and all crimes\n",
    "for ind1 in df_sd_parks_review.index:\n",
    "    score = 0\n",
    "    lat1 = math.radians(df_sd_parks_review['latitude'][ind1])\n",
    "    lon1 = math.radians(df_sd_parks_review['longitude'][ind1])\n",
    "    \n",
    "    for ind2 in df_sdpd_rand.index:\n",
    "        lat2 = math.radians(df_sdpd_rand['latitude'][ind2])\n",
    "        lon2 = math.radians(df_sdpd_rand['longitude'][ind2])\n",
    "        \n",
    "        dlon = lon2 - lon1\n",
    "        dlat = lat2 - lat1\n",
    "        \n",
    "        a = math.sin(dlat / 2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2)**2\n",
    "        c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n",
    "        score += E_Rad * c * df_sdpd_rand['priority'][ind2]\n",
    "    \n",
    "    df_sd_parks_review.ix[ind1, 'score_1'] = score\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the second one (score_2), I will calculate a score for each park as a sum of the product of its distance with each of the crim locations and the priority that are within 0.5 kilometers radius from the park. This should eliminate the effect of farther away crimes on the scores. We default the score to be -0.01 (impossible to result from calculations) and will drop those columns later in analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:28: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n"
     ]
    }
   ],
   "source": [
    "# Calculate score based on crimes within a 1km diameter circle\n",
    "df_sd_parks_review = df_sd_parks_review.assign(score_2 = 0)\n",
    "rad = 0.5\n",
    "E_Rad = 6373.0\n",
    "\n",
    "for ind1 in df_sd_parks_review.index:\n",
    "    score = -0.01\n",
    "    lat1 = math.radians(df_sd_parks_review['latitude'][ind1])\n",
    "    lon1 = math.radians(df_sd_parks_review['longitude'][ind1])\n",
    "    \n",
    "    park_loc = Point(df_sd_parks_review['latitude'][ind1],df_sd_parks_review['longitude'][ind1])\n",
    "    for ind2 in df_sdpd_rand.index:\n",
    "        lat2 = math.radians(df_sdpd_rand['latitude'][ind2])\n",
    "        lon2 = math.radians(df_sdpd_rand['longitude'][ind2])\n",
    "        \n",
    "        dlon = lon2 - lon1\n",
    "        dlat = lat2 - lat1\n",
    "        \n",
    "        a = math.sin(dlat / 2.0)**2.0 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2.0)**2.0\n",
    "        c = 2.0 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n",
    "        distance = E_Rad * c\n",
    "            \n",
    "        if distance <= rad:\n",
    "            score += distance * df_sdpd_rand['priority'][ind2]\n",
    "        else: \n",
    "            pass\n",
    "        \n",
    "    df_sd_parks_review.ix[ind1, 'score_2'] = score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the third one (score_3), I will calculate a score for each park as a sum of the product of its distance with each of the crim locations and the priority that are within 0.5 kilometers radius from the park. This should eliminate the effect of farther away crimes on the scores. Unlike score_2, score_3 will be adjusted according to the distance squared (like GRAVITY!!!). We default the score to be -0.01 (impossible to result from calculations) and will drop those columns later in analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:28: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n"
     ]
    }
   ],
   "source": [
    "# Calculate score based on crimes within a 1km diameter circle with priority decreasing as the square of the distance\n",
    "df_sd_parks_review = df_sd_parks_review.assign(score_3 = 0)\n",
    "rad = 0.5\n",
    "E_Rad = 6373.0\n",
    "\n",
    "for ind1 in df_sd_parks_review.index:\n",
    "    score = -0.01\n",
    "    lat1 = math.radians(df_sd_parks_review['latitude'][ind1])\n",
    "    lon1 = math.radians(df_sd_parks_review['longitude'][ind1])\n",
    "    \n",
    "    park_loc = Point(df_sd_parks_review['latitude'][ind1],df_sd_parks_review['longitude'][ind1])\n",
    "    for ind2 in df_sdpd_rand.index:\n",
    "        lat2 = math.radians(df_sdpd_rand['latitude'][ind2])\n",
    "        lon2 = math.radians(df_sdpd_rand['longitude'][ind2])\n",
    "        \n",
    "        dlon = lon2 - lon1\n",
    "        dlat = lat2 - lat1\n",
    "        \n",
    "        a = math.sin(dlat / 2.0)**2.0 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2.0)**2.0\n",
    "        c = 2.0 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n",
    "        distance = E_Rad * c\n",
    "            \n",
    "        if distance <= rad:\n",
    "            score += distance * distance * df_sdpd_rand['priority'][ind2]\n",
    "        else: \n",
    "            pass\n",
    "        \n",
    "    df_sd_parks_review.ix[ind1, 'score_3'] = score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following cell, I will make a separate dataframe which excluded the points that have no crime in the 0.5 km area around it so these do not skew our data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>rating</th>\n",
       "      <th>review_count</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>geometry</th>\n",
       "      <th>score_1</th>\n",
       "      <th>score_2</th>\n",
       "      <th>score_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Balboa Park</td>\n",
       "      <td>1549 El Prado San Diego, CA 92101</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2105</td>\n",
       "      <td>-117.153150</td>\n",
       "      <td>32.734502</td>\n",
       "      <td>POINT (-117.15315 32.73450)</td>\n",
       "      <td>61016.290384</td>\n",
       "      <td>0.413502</td>\n",
       "      <td>0.169354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Civita Park</td>\n",
       "      <td>7960 Civita Blvd San Diego, CA 92108</td>\n",
       "      <td>4.5</td>\n",
       "      <td>46</td>\n",
       "      <td>-117.147278</td>\n",
       "      <td>32.778315</td>\n",
       "      <td>POINT (-117.14728 32.77831)</td>\n",
       "      <td>60853.334927</td>\n",
       "      <td>5.363585</td>\n",
       "      <td>2.405990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Waterfront Park</td>\n",
       "      <td>1600 Pacific Hwy San Diego, CA 92101</td>\n",
       "      <td>4.5</td>\n",
       "      <td>242</td>\n",
       "      <td>-117.172479</td>\n",
       "      <td>32.721952</td>\n",
       "      <td>POINT (-117.17248 32.72195)</td>\n",
       "      <td>66079.701093</td>\n",
       "      <td>2.137766</td>\n",
       "      <td>0.792201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trolley Barn Park</td>\n",
       "      <td>Adams Ave And Florida St San Diego, CA 92116</td>\n",
       "      <td>4.5</td>\n",
       "      <td>102</td>\n",
       "      <td>-117.143789</td>\n",
       "      <td>32.762463</td>\n",
       "      <td>POINT (-117.14379 32.76246)</td>\n",
       "      <td>59188.213435</td>\n",
       "      <td>0.490161</td>\n",
       "      <td>0.073387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Centrum Park</td>\n",
       "      <td>8578 Lightwave Ave San Diego, CA 92123</td>\n",
       "      <td>3.5</td>\n",
       "      <td>22</td>\n",
       "      <td>-117.138397</td>\n",
       "      <td>32.828037</td>\n",
       "      <td>POINT (-117.13840 32.82804)</td>\n",
       "      <td>73893.924112</td>\n",
       "      <td>2.352769</td>\n",
       "      <td>0.600311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>The Cottage La Jolla</td>\n",
       "      <td>7702 Fay Ave La Jolla, CA 92037</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4211</td>\n",
       "      <td>-117.274957</td>\n",
       "      <td>32.843422</td>\n",
       "      <td>POINT (-117.27496 32.84342)</td>\n",
       "      <td>107575.925516</td>\n",
       "      <td>15.039988</td>\n",
       "      <td>5.908432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>Brockton Villa Restaurant</td>\n",
       "      <td>1235 Coast Blvd La Jolla, CA 92037</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1749</td>\n",
       "      <td>-117.271558</td>\n",
       "      <td>32.849777</td>\n",
       "      <td>POINT (-117.27156 32.84978)</td>\n",
       "      <td>108224.952319</td>\n",
       "      <td>1.570587</td>\n",
       "      <td>0.376360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>Capehart Dog Park</td>\n",
       "      <td>4747 Soledad Mountain Rd San Diego, CA 92109</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85</td>\n",
       "      <td>-117.225335</td>\n",
       "      <td>32.807007</td>\n",
       "      <td>POINT (-117.22533 32.80701)</td>\n",
       "      <td>80255.086142</td>\n",
       "      <td>14.190472</td>\n",
       "      <td>4.088440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>San Diego Metropolitan Transit System</td>\n",
       "      <td>1255 Imperial Ave Ste 1000 San Diego, CA 92101</td>\n",
       "      <td>2.5</td>\n",
       "      <td>268</td>\n",
       "      <td>-117.153400</td>\n",
       "      <td>32.705760</td>\n",
       "      <td>POINT (-117.15340 32.70576)</td>\n",
       "      <td>67869.559286</td>\n",
       "      <td>34.225871</td>\n",
       "      <td>14.166824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>Petco Park</td>\n",
       "      <td>100 Park Blvd San Diego, CA 92101</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1667</td>\n",
       "      <td>-117.157054</td>\n",
       "      <td>32.707246</td>\n",
       "      <td>POINT (-117.15705 32.70725)</td>\n",
       "      <td>67762.990518</td>\n",
       "      <td>36.441518</td>\n",
       "      <td>13.251613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>280 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      name  \\\n",
       "0                              Balboa Park   \n",
       "1                              Civita Park   \n",
       "2                          Waterfront Park   \n",
       "3                        Trolley Barn Park   \n",
       "5                             Centrum Park   \n",
       "..                                     ...   \n",
       "818                   The Cottage La Jolla   \n",
       "819              Brockton Villa Restaurant   \n",
       "822                      Capehart Dog Park   \n",
       "829  San Diego Metropolitan Transit System   \n",
       "830                             Petco Park   \n",
       "\n",
       "                                            address  rating  review_count  \\\n",
       "0                 1549 El Prado San Diego, CA 92101     5.0          2105   \n",
       "1              7960 Civita Blvd San Diego, CA 92108     4.5            46   \n",
       "2              1600 Pacific Hwy San Diego, CA 92101     4.5           242   \n",
       "3      Adams Ave And Florida St San Diego, CA 92116     4.5           102   \n",
       "5            8578 Lightwave Ave San Diego, CA 92123     3.5            22   \n",
       "..                                              ...     ...           ...   \n",
       "818                 7702 Fay Ave La Jolla, CA 92037     4.0          4211   \n",
       "819              1235 Coast Blvd La Jolla, CA 92037     4.0          1749   \n",
       "822    4747 Soledad Mountain Rd San Diego, CA 92109     3.0            85   \n",
       "829  1255 Imperial Ave Ste 1000 San Diego, CA 92101     2.5           268   \n",
       "830               100 Park Blvd San Diego, CA 92101     4.0          1667   \n",
       "\n",
       "      longitude   latitude                     geometry        score_1  \\\n",
       "0   -117.153150  32.734502  POINT (-117.15315 32.73450)   61016.290384   \n",
       "1   -117.147278  32.778315  POINT (-117.14728 32.77831)   60853.334927   \n",
       "2   -117.172479  32.721952  POINT (-117.17248 32.72195)   66079.701093   \n",
       "3   -117.143789  32.762463  POINT (-117.14379 32.76246)   59188.213435   \n",
       "5   -117.138397  32.828037  POINT (-117.13840 32.82804)   73893.924112   \n",
       "..          ...        ...                          ...            ...   \n",
       "818 -117.274957  32.843422  POINT (-117.27496 32.84342)  107575.925516   \n",
       "819 -117.271558  32.849777  POINT (-117.27156 32.84978)  108224.952319   \n",
       "822 -117.225335  32.807007  POINT (-117.22533 32.80701)   80255.086142   \n",
       "829 -117.153400  32.705760  POINT (-117.15340 32.70576)   67869.559286   \n",
       "830 -117.157054  32.707246  POINT (-117.15705 32.70725)   67762.990518   \n",
       "\n",
       "       score_2    score_3  \n",
       "0     0.413502   0.169354  \n",
       "1     5.363585   2.405990  \n",
       "2     2.137766   0.792201  \n",
       "3     0.490161   0.073387  \n",
       "5     2.352769   0.600311  \n",
       "..         ...        ...  \n",
       "818  15.039988   5.908432  \n",
       "819   1.570587   0.376360  \n",
       "822  14.190472   4.088440  \n",
       "829  34.225871  14.166824  \n",
       "830  36.441518  13.251613  \n",
       "\n",
       "[280 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sd_parks_review_2 = df_sd_parks_review\n",
    "indexNum = df_sd_parks_review_2[df_sd_parks_review_2['score_2'] == -0.01].index\n",
    "df_sd_parks_review_2.drop(indexNum, inplace = True)\n",
    "df_sd_parks_review_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have an longitude and latitude for each police response and each parks. Now, I will calculated a score for each park from the priority of the police call and the distance from all police response calls (priority * distance). Parks with a lower combined score indicate that there is higher crime rate in their area. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's make a scatter plot of the rating of parks (x-axis) versus scores of the parks (y-axis). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7efe2d7cd490>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## YOUR CODE HERE\n",
    "## FEEL FREE TO ADD MULTIPLE CELLS PER SECTION\n",
    "sns.relplot(x = 'rating', y = 'score_1', data = df_sd_parks_review)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we perform a OSL Regression test on the dataset to find the correlation of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 rating   R-squared:                       0.003\n",
      "Model:                            OLS   Adj. R-squared:                 -0.000\n",
      "Method:                 Least Squares   F-statistic:                    0.9568\n",
      "Date:                Wed, 16 Dec 2020   Prob (F-statistic):              0.329\n",
      "Time:                        17:28:17   Log-Likelihood:                -282.06\n",
      "No. Observations:                 280   AIC:                             568.1\n",
      "Df Residuals:                     278   BIC:                             575.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      4.0613      0.109     37.419      0.000       3.848       4.275\n",
      "score_1    -1.249e-06   1.28e-06     -0.978      0.329   -3.76e-06    1.26e-06\n",
      "==============================================================================\n",
      "Omnibus:                       41.196   Durbin-Watson:                   1.341\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               57.357\n",
      "Skew:                          -0.948   Prob(JB):                     3.51e-13\n",
      "Kurtosis:                       4.151   Cond. No.                     2.32e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.32e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome1, predictors1 = patsy.dmatrices('rating ~ score_1', df_sd_parks_review)\n",
    "\n",
    "mod1 = sm.OLS(outcome1, predictors1)\n",
    "## fit the model\n",
    "res1 = mod1.fit()\n",
    "\n",
    "## look at the results\n",
    "print(res1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that in this model, the crime rate around a park does not explain its review on Yelp very well, as it only has a 0.003 R-Squared value, meaning that only 0.1 percent of all variance in the dependent variable (Yelp review) can be explained by the independent variable (crime rate). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's try plotting and analyzing the second model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7efe2fd6e050>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x = 'rating', y = 'score_2', data = df_sd_parks_review_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above scatterplot is the rating of the parks (x-axis) versus the crime rate in that area as modeled by the third model. Again, we see no clear trend in crime rate as the rating of parks get higher and that there are quite a few outliers with an extremely high score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 rating   R-squared:                       0.029\n",
      "Model:                            OLS   Adj. R-squared:                  0.026\n",
      "Method:                 Least Squares   F-statistic:                     8.400\n",
      "Date:                Wed, 16 Dec 2020   Prob (F-statistic):            0.00405\n",
      "Time:                        17:28:17   Log-Likelihood:                -278.38\n",
      "No. Observations:                 280   AIC:                             560.8\n",
      "Df Residuals:                     278   BIC:                             568.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      4.0391      0.047     85.412      0.000       3.946       4.132\n",
      "score_2       -0.0090      0.003     -2.898      0.004      -0.015      -0.003\n",
      "==============================================================================\n",
      "Omnibus:                       38.619   Durbin-Watson:                   1.359\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               51.849\n",
      "Skew:                          -0.922   Prob(JB):                     5.51e-12\n",
      "Kurtosis:                       4.021   Cond. No.                         18.5\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome2, predictors2 = patsy.dmatrices('rating ~ score_2', df_sd_parks_review_2)\n",
    "\n",
    "mod2 = sm.OLS(outcome2, predictors2)\n",
    "## fit the model\n",
    "res2 = mod2.fit()\n",
    "\n",
    "## look at the results\n",
    "print(res2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that this model accounts for the correlation between crime rate and Yelp review much better because it has a much higher R-Squared value of 0.029, which means that 2.9 percent of all variance in the dependent variable (Yelp review) can be accounted for by the independent variable (crime rate). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's move on to the third model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7efe413d2e90>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x = 'rating', y = 'score_3', data = df_sd_parks_review_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above scatterplot is the rating of the parks (x-axis) versus the crime rate in that area as modeled by the third model. Again, we see no clear trend in crime rate as the rating of parks get higher. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 rating   R-squared:                       0.026\n",
      "Model:                            OLS   Adj. R-squared:                  0.022\n",
      "Method:                 Least Squares   F-statistic:                     7.308\n",
      "Date:                Wed, 16 Dec 2020   Prob (F-statistic):            0.00729\n",
      "Time:                        18:40:38   Log-Likelihood:                -278.91\n",
      "No. Observations:                 280   AIC:                             561.8\n",
      "Df Residuals:                     278   BIC:                             569.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      4.0290      0.046     86.908      0.000       3.938       4.120\n",
      "score_3       -0.0207      0.008     -2.703      0.007      -0.036      -0.006\n",
      "==============================================================================\n",
      "Omnibus:                       38.896   Durbin-Watson:                   1.358\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               52.238\n",
      "Skew:                          -0.929   Prob(JB):                     4.54e-12\n",
      "Kurtosis:                       4.014   Cond. No.                         7.19\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome3, predictors3 = patsy.dmatrices('rating ~ score_3', df_sd_parks_review_2)\n",
    "\n",
    "mod3 = sm.OLS(outcome3, predictors3)\n",
    "## fit the model\n",
    "res3 = mod3.fit()\n",
    "\n",
    "## look at the results\n",
    "print(res3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surprisingly, this gives us a lower R-squared value than the second one. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, all in all, the best model I have generated is the second model. We have reached a p-value (P>|t|) of less than 0.05 for both the second and the third model, we can reject the null hypothesis that there is a relationship between crime rate around a park in San Diego area and its Yelp review in favor of the alternative hypothesis that there is no statistically significant relationship between crime rate in the surrounding area of the park in the San Diego area and its Yelp review. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Privacy is not a huge concern in this study since all the data are not personally identifiable. The exact locations of the addresses of the police department calls will not be directly related to my analysis and I will only distance from where these calls are placed to the parks. The data I received are collected from San Diego municiple and county government are authorized for all public and education uses. The police report contains little to no bias because it collects all police reponses regardless of where the location is or who the caller is. The Yelp review dataset is made by taking the average of all Yelp review scores, which will average out all potential biases and represent the population without excluding a certain group. I do not identify any way my analysis could lead to problems in terms of data privacy anmd equitable impact as my analysis concluded that there is no statistically significant relationship between the variables. This does not provide incentive for the municiple or county government to distribute park resources more in favor of areas with lower crime rates in order to be better received by the general public. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I started the project with the question of whether there is a correlation between the crime rate in the park's surrounding area and how much the public like it. I decided to model public perception of the park by Yelp reviews that the park received and crime rate by the amount and priority of police reponses in its surrounding areas. I used two datasets, one containing each parks' review score and the other one containing all police reponses in the San Diego area in 2019. \n",
    "\n",
    "In my analysis, I proposed three models to approximate crime rate as a score for each of the parks and conducted a OLS Regression test for my results for each of the three models. The second model, which characterizes the crime rate as the sum of the distance between police responses times their priority in a circle around the park with a radius of 500 meters. All three of the OLS Regression tests resulted in a very low p-value or a extremely low correlation score, which led me to conclude that there is no statistically significant correlation between the crime rate in the area and the Yelp reviews received by a park in San Diego.\n",
    "\n",
    "There are certain limitations and additional considerations for this analysis. First of all, due to limited computation power, I was unable to use the entire police response dataset. The dataset has around 600,000 entries at the beginning. I removed all responses with lower priorities to make computation faster. In addition, from the reduced dataset of around 90,000 entries, I randomly sampled 8000 reponses to approximate all Priority E and 1 responses, which could lead to inaccuracy in the result. In addition, the data parsed from Yelp only have scores for parks on a 0.5 interval (e.g 4.5, 5.0, 3.5, etc). If I were able to calculate the exact score of the parks to several decimal points, that could potentially increase the accuracy of this analysis. \n",
    "\n",
    "This project shows that there is no statistical significant bias in the location of parks against areas with higher crime rates, communities which can benefit greatly from easy access to parks. The lack of correlation between crime rate in the area and public perception of the parks provide incentive for municiple and county government to be more unbiased in selecting location for the next parks. "
   ]
  }
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