FinalProjectGroup045-Fa21.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Analysis of Female Homicide Victims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [ X ] YES - make available\n",
    "* [  ] NO - keep private"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we try to find out the most significant factor, among the GEI(gender equality index), the Gini index and the HDI(Human development index), that affects crime, specifically the number of female homicide victims in a country. In our investigation, we used data of the three indices for 100 countries, comparing them against the international homicide female victim dataset. We found out that the Gini index, which represents economic inequality, accounts the most for the number of female homicide victims. A higher Gini index corrolates with a higher number of female homicide victims. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Ruijia Zhang A15550228\n",
    "- Zhuotong Li A16554690\n",
    "- Haoyu Zhu A15432720\n",
    "- Joe Cai A15575572\n",
    "- Nimish Prabhakaran A15367986"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*What is the most significant factor, among the Gender Equality Index (GEI), the Gini index, and the Human Development Index (HDI), for the number of female homicide victims in a country?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are trying to investigate what impacts the crime rate in different countries, specifically the rate of female murder victims versus male murder victims. Upon initial research, we came across the Global Gender Gap report, which we used as the major guide of our project. The Global Gender Gap Index benchmarks the evolution of gender-based gaps among four key dimensions (Economic Participation and Opportunity, Educational Attainment, Health and Survival, and Political Empowerment) and tracks progress towards closing these gaps over time” (Global Gender Gap Report). A higher Global Gender Gap Index (closer to 1)indicates that there are less gap, that male and females are more equal(Global Gender Gap Report, 2021). \n",
    "\n",
    "Based on our common sense and previous knowledge, we hypothesized that gender equality correlates with female and male murder victim counts. In places where females and males are more equal, the number of female victims will decrease. However, upon further research, we find out it is not so clear. For example, the factor of class may also play an important role.  In a study, researchers looked at 238 U.S. cities and their rape rates in Unifirm Crime Reports, comparing them to the relative and absolute women’s status in economic, educational, employment, and occupational spheres. Surprisingly, they found out that “greater gender equality was related to higher rape rates, but higher absolute status of women (which often accompanies higher gender equality) was associated with lower rape rates”(Martin & Vieraitis, 2006). Moreover, in a study done in 2021 by Chu, Hebenton and Toh, they looked at the relationship female offending and societal gender equality using the Global Gender Gap Index, and their findings shows that “ countries with a narrower gender gap in the Global Gender Gap indexes of political empowerment were likely to have a higher female prosecution rate, and that political empowerment was also significantly associated with female conviction rates as well as rates of property offending” (Chu, Hebenton & Toh, 2021).As a result, we found our asked question is more complicated than we thought. Many factors impact the rate of female and male victim rates. We think in order to get a holistic perspective on our topic, we will look at more factors and see which of them contribute the most to the dependent variable.\n",
    "\n",
    "Besides looking at the Gender equality index, we also included the Gini index and HDI(Human Development index). The Gini index is a measure of statistical dispersion intended to represent the income inequality or the wealth inequality within a nation or a social group. The Gini coefficient measures the inequality among values of a frequency distribution (for example, levels of income). A Gini coefficient of zero expresses perfect equality, where all values are the same (e.g. where everyone has the same income). A Gini coefficient of one (or 100%) expresses maximal inequality among values(United Nations). We want to use the Gini index as another indicator of inequality that can possibly explain female vs male victims. \n",
    "\n",
    "According to the definition given by the United Nations, “The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and having a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions”. We want to use the HDI as a general indicator of the condition and quality of life in countries, since crime rates in general can be caused by societal conditions. \n",
    "\n",
    "\n",
    "\n",
    "References:\n",
    "- 1)Kimberly Martin, L. M. V. (n.d.). Gender equality and women's absolute status: A test of the feminist models of rape - Kimberly Martin, Lynne M. Vieraitis, Sarah Britto, 2006. SAGE Journals. Retrieved December 9, 2021, from https://journals.sagepub.com/doi/abs/10.1177/1077801206286311. \n",
    "- 2)Doris C. Chu, B. H. (n.d.). Gender equality and female offending: Evidence from international data sources - Doris C. Chu, Bill Hebenton, Albert Toh, 2021. SAGE Journals. Retrieved December 9, 2021, from https://journals.sagepub.com/doi/10.1177/0306624X20986527. \n",
    "- 3)Global gender gap report 2021. World Economic Forum. (n.d.). Retrieved December 9, 2021, from https://www.weforum.org/reports/global-gender-gap-report-2021. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hypothesize that, among the Gender Equality Index (GEI), the Gini index, and the Human Development Index (HDI), the Gender Equality Index is the most significant factor that influences the number of female homicide victims in a country."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset Name: WEF Global Gender Gap Index\n",
    "- Link to the dataset: https://en.wikipedia.org/wiki/Global_Gender_Gap_Report\n",
    "- Number of observations: 156\n",
    "    \n",
    "The dataset holds the gender equality index determined by the World Economic Forum (WEF) for global countries from 2006 to 2021. The gender equality index measures the degree of gender equality in workplaces, economic status, healthcare access, political empowerment, etc. We plan to use this as one of the variable that possibly influence the number of female homicide victims. \n",
    "\n",
    "\n",
    "### Dataset Name: International Homicides, Female (per 100,000 females)\n",
    "- Link to the dataset: https://data.worldbank.org/indicator/VC.IHR.PSRC.FE.P5?end=2021&most_recent_year_desc=false&start=1990\n",
    "- Number of observations: 270\n",
    "\n",
    "The dataset holds the number of homicides for female victims per 100,000 females in all countries.\n",
    "\n",
    "### Dataset Name: Gini index from UNdata\n",
    "- Link to the dataset: http://data.un.org/Data.aspx?d=WDI&f=Indicator_Code%3ASI.POV.GINI\n",
    "- Number of observations: 1999\n",
    "\n",
    "The dataset that holds the Gini index for every country from years ranging from 1967-2019. Unlike the previous datasets \"Year\" is the variable and years between 1967 and 2019 are the observation. We plan to use this as our outcome variable to see if any of the indices correlates with it.\n",
    "\n",
    "\n",
    "### Dataset Name: Human Development Index Dataset\n",
    "- Link to the dataset: http://hdr.undp.org/en/indicators/137506#\n",
    "- Number of observations: 189\n",
    "\n",
    "The dataset holds the Human Development Index (HDI) for all countries between the years 1990-2019. The dataset needs to be cleaned to remove all the repeated countries and keep the most recent data.\n",
    "\n",
    "\n",
    "#### We will combine the datasets to make sure the country names match each other, and also we will use it for geospatial analysis. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import patsy\n",
    "import re\n",
    "import statsmodels.api as sm\n",
    "import geopandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gender Equality Index Dataset  \n",
    "\n",
    "From https://en.wikipedia.org/wiki/Global_Gender_Gap_Report and the World Economic Forum (WEF)\n",
    "\n",
    "The raw dataset is relatively clean aside from the columns. To get the dataset usable, the current column headers should be removed since they contain metadata and the first row should be the headers. Then the column names should be formatted to be easily referenced. There are unused columns that will be removed like the Region and older years from 2006-2009. The observations are normalized based off of an index ranging from 0-1 so the observations are clean. We sorted the dataset based off of the most recent year to observe the degree of gender equality in each country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>GEI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>0.657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>0.699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>0.701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>0.492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>0.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0.732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country    GEI\n",
       "0    Afghanistan  0.444\n",
       "1        Albania  0.770\n",
       "2        Algeria  0.633\n",
       "3         Angola  0.657\n",
       "4      Argentina  0.752\n",
       "..           ...    ...\n",
       "151    Venezuela  0.699\n",
       "152      Vietnam  0.701\n",
       "153        Yemen  0.492\n",
       "154       Zambia  0.726\n",
       "155     Zimbabwe  0.732\n",
       "\n",
       "[156 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import raw dataset from the csv\n",
    "df_gei = pd.read_csv(\"./gender-equality-index.csv\")\n",
    "\n",
    "# Drop the first row containing metadata\n",
    "metadata = df_gei.iloc[0]\n",
    "df_gei = df_gei.rename(columns=metadata).drop([0])\n",
    "\n",
    "# Remove region and old data from 2006-2010 \n",
    "df_gei = df_gei.drop(df_gei.columns[list(range(1,7))], axis=1)\n",
    "\n",
    "# Remove recent data from 2020-2021\n",
    "df_gei = df_gei.drop(df_gei.columns[[7,8]], axis=1)\n",
    "\n",
    "df_tmp = pd.DataFrame(columns=['Country', 'GEI'])\n",
    "df_tmp['Country'] = df_gei['Country']\n",
    "for i, r in df_gei.iterrows():\n",
    "    for col, val in enumerate(reversed(r)):\n",
    "        if(val==val and col != len(r)-1):\n",
    "            df_tmp.loc[i,'GEI'] = val\n",
    "            break\n",
    "            \n",
    "df_gei = df_tmp.dropna().reset_index(drop=True)\n",
    "df_gei['GEI'] = pd.to_numeric(df_gei['GEI'])\n",
    "df_gei"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## International Homicide by Female Dataset\n",
    "\n",
    "From https://data.worldbank.org/indicator/VC.IHR.PSRC.FE.P5\n",
    "\n",
    "The data set contains the number of female homicide victims per 100,000 females. The dataset is relatively clean aside from the NaN observations and columns that contain metadata. The first few columns and rows contain metadata that should be removed. The third row has the header we want so we will replace the headers with that row. Also the years are in decimal form, so it will be relabeled. There are columns like 2019 and 2020 that contain all NaNs and rows with all NaNs. They will be removed since we get no information from them. We will then get the most recent female victim rate for each country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Female Victims</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.846291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1.060551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.371234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>11.194949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.213828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>3.195021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Venezuela, RB</td>\n",
       "      <td>10.706602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Virgin Islands (U.S.)</td>\n",
       "      <td>3.626999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>0.12519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Country Female Victims\n",
       "0              Afghanistan       0.846291\n",
       "1                  Albania       1.060551\n",
       "2                  Algeria       0.371234\n",
       "3                  Andorra            0.0\n",
       "4      Antigua and Barbuda      11.194949\n",
       "..                     ...            ...\n",
       "129          United States       2.213828\n",
       "130                Uruguay       3.195021\n",
       "131          Venezuela, RB      10.706602\n",
       "132  Virgin Islands (U.S.)       3.626999\n",
       "133     West Bank and Gaza        0.12519\n",
       "\n",
       "[134 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_intl_f = pd.read_csv('international-homicide-female.csv')\n",
    "\n",
    "# Remove unused columns\n",
    "cols_to_drop = df_intl_f.iloc[:, 1:50]\n",
    "df_intl_f.drop(cols_to_drop, inplace=True, axis=1)\n",
    "\n",
    "# Remove last two columns (2019 and 2020) since they are all NaN\n",
    "cols_to_drop = df_intl_f.iloc[:, -2:]\n",
    "df_intl_f.drop(cols_to_drop, inplace=True, axis=1)\n",
    "\n",
    "# Remove rows containing metadata and set column headers\n",
    "df_intl_f = df_intl_f.rename(columns=df_intl_f.iloc[3]).drop(df_intl_f.index[0:4])\n",
    "\n",
    "# Rename column names for the years\n",
    "for i in range(1, 14):\n",
    "    df_intl_f.columns.values[i] = str(i+2005)\n",
    "\n",
    "df_intl_f.rename({'Country Name': 'Country'}, axis=1, inplace=True)\n",
    "\n",
    "# Drop rows that contain NaNs for all years\n",
    "subset_f = df_intl_f.columns.tolist()[1:]\n",
    "df_intl_f = df_intl_f.dropna(how='all', subset=subset_f, axis=0)\n",
    "\n",
    "# Create a new dataframe to store the most recent numbers\n",
    "df_intl = pd.DataFrame(columns=['Country', 'Female Victims'])\n",
    "df_intl['Country'] = df_intl_f['Country']\n",
    "\n",
    "# Go through the original dataset and take the most recent number for every country\n",
    "for i, r in df_intl_f.iterrows():\n",
    "    for val in reversed(r):\n",
    "        # if the value is not NaN\n",
    "        if(val==val):\n",
    "            df_intl.loc[i,'Female Victims'] = val\n",
    "            break\n",
    "            \n",
    "# Remove \"World\" Row\n",
    "df_intl = df_intl[df_intl['Country'] != \"World\"]\n",
    "\n",
    "# Sort the rate by Country        \n",
    "df_intl = df_intl.sort_values(by=['Country']).reset_index(drop=True)\n",
    "df_intl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## International Homicide by Male Dataset\n",
    "\n",
    "https://data.worldbank.org/indicator/VC.IHR.PSRC.MA.P5\n",
    "\n",
    "This dataset has the same format as the Homocide by Female dataset. The data set contains the number of male homicide victims per 100,000 males. The dataset is cleaned in the same process as the International Homicide by Female Dataset. We get the most recent observation for each country. This dataset will be used to compare the countries male and female homicides and determine if there is a relationship between a country's total homicide rate and female homicide rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Male Victims</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>12.15626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>3.473225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2.337533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>27.006457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>United States</td>\n",
       "      <td>7.757731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>21.558143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Venezuela, RB</td>\n",
       "      <td>61.869646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Virgin Islands (U.S.)</td>\n",
       "      <td>99.241793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>0.648662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Country Male Victims\n",
       "0              Afghanistan     12.15626\n",
       "1                  Albania     3.473225\n",
       "2                  Algeria     2.337533\n",
       "3                  Andorra          0.0\n",
       "4      Antigua and Barbuda    27.006457\n",
       "..                     ...          ...\n",
       "127          United States     7.757731\n",
       "128                Uruguay    21.558143\n",
       "129          Venezuela, RB    61.869646\n",
       "130  Virgin Islands (U.S.)    99.241793\n",
       "131     West Bank and Gaza     0.648662\n",
       "\n",
       "[132 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_intl_m = pd.read_csv('international-homicide-male.csv')\n",
    "cols_to_drop = df_intl_m.iloc[:, 1:50]\n",
    "df_intl_m.drop(cols_to_drop, inplace=True, axis=1)\n",
    "\n",
    "# Remove last two columns (2019 and 2020) since they are all NaN\n",
    "cols_to_drop = df_intl_m.iloc[:, -2:]\n",
    "df_intl_m.drop(cols_to_drop, inplace=True, axis=1)\n",
    "\n",
    "# Remove rows containing metadata and set column headers\n",
    "df_intl_m = df_intl_m.rename(columns=df_intl_m.iloc[3]).drop(df_intl_m.index[0:4])\n",
    "\n",
    "# Rename column names for the years\n",
    "for i in range(1, 14):\n",
    "    df_intl_m.columns.values[i] = str(i+2005)\n",
    "    \n",
    "# Drop rows that contain NaNs for all years\n",
    "subset_m = df_intl_m.columns.tolist()[1:]\n",
    "df_intl_m = df_intl_m.dropna(how='all', subset=subset_m, axis=0)   \n",
    "\n",
    "# Rename Country column\n",
    "df_intl_m = df_intl_m.rename(columns = {'Country Name': 'Country'})\n",
    "\n",
    "df_intl2 = pd.DataFrame(columns=['Country', 'Male Victims'])\n",
    "df_intl2['Country'] = df_intl_m['Country']\n",
    "\n",
    "# Go through the original dataset and take the most recent number for every country\n",
    "for i, r in df_intl_m.iterrows():\n",
    "    for val in reversed(r):\n",
    "        # if the value is not NaN\n",
    "        if(val==val):\n",
    "            df_intl2.loc[i,'Male Victims'] = val\n",
    "            break\n",
    "            \n",
    "# Remove \"World\" Row\n",
    "df_intl2 = df_intl2[df_intl2['Country'] != \"World\"]\n",
    "\n",
    "df_intl2 = df_intl2.sort_values(by=['Country']).reset_index(drop=True)\n",
    "df_intl2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UN Data on Gini index\n",
    "From http://data.un.org/Data.aspx?d=WDI&f=Indicator_Code%3ASI.POV.GINI\n",
    "\n",
    "This dataset contains all the available gini index for 167 countries throughout the years. It is already quite clean. We only need to remove the footnotes. However, for some countries we have more data throughout the years than others. In order to get as many data point as possible, we decide to drop the duplicated countries while keeping the most recent data for each country. The Gini Index is a summary measure of income inequality. The Gini coefficient ranges from 0, indicating perfect equality (where everyone receives an equal share), to 1, perfect inequality (where only one recipient or group of recipients receives all the income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Country</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>51.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>29.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>35.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>33.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>57.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>50.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Country  Gini\n",
       "0               Albania  33.2\n",
       "1               Algeria  27.6\n",
       "2                Angola  51.3\n",
       "3             Argentina  42.9\n",
       "4               Armenia  29.9\n",
       "..                  ...   ...\n",
       "162             Vietnam  35.7\n",
       "163  West Bank and Gaza  33.7\n",
       "164               Yemen  36.7\n",
       "165              Zambia  57.1\n",
       "166            Zimbabwe  50.3\n",
       "\n",
       "[167 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import raw csv data\n",
    "df_gini = pd.read_csv('combined countries gini index.csv')\n",
    "\n",
    "#drop the last few rows of text descriptions, whose value are NaN\n",
    "df_gini = df_gini.dropna()\n",
    "\n",
    "#For each country, we want to get its gini index from the most recent years available\n",
    "countries = df_gini['Country or Area'].unique()\n",
    "df_gini = df_gini.sort_values(by = ['Year'])\n",
    "df_gini['Year'] = pd.to_numeric(df_gini['Year'])\n",
    "# df_gini = df_gini[df_gini['Year'] > 2010]\n",
    "df_gini = df_gini.sort_values(by = ['Year']).reset_index()\n",
    "\n",
    "# Drop unnecessary column \"value foodnotes\"\n",
    "df_gini = df_gini.drop(['Value Footnotes'], axis = 1)\n",
    "#Change column name\n",
    "df_gini.columns = ['index', 'Country','Year', 'Value']\n",
    "\n",
    "#Drop duplicated countries, keep the one with the most recent data\n",
    "df_gini = df_gini.drop_duplicates(subset=['Country'], keep='last')\n",
    "\n",
    "# Extract Index and Country\n",
    "df_gini = df_gini.rename(columns={'Value': 'Gini'})\n",
    "df_gini = df_gini[['Country', 'Gini']].sort_values(by=['Country']).reset_index(drop=True)\n",
    "df_gini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Human Development Index Dataset\n",
    "From http://hdr.undp.org/en/indicators/137506#\n",
    "\n",
    "The dataset is relatively clean. The data contains the value of the metadata and NaN, so the previous columns（index 0-3） of titles，rows and value are deleted. Then, set the first row to the title, so the country and HDI are set as column names for easy reference. The value range is naturalized between indices of 0-1, so the observations are clean. In addition, some columns contain the values and rows of NaN will be deleted altogether because no information is obtained from them for clear viewing.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>HDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>0.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Venezuela (Bolivarian Republic of)</td>\n",
       "      <td>0.733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>0.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>0.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0.569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>189 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Country    HDI\n",
       "0                           Afghanistan  0.509\n",
       "1                               Albania  0.792\n",
       "2                               Algeria  0.746\n",
       "3                               Andorra  0.867\n",
       "4                                Angola  0.582\n",
       "..                                  ...    ...\n",
       "184  Venezuela (Bolivarian Republic of)  0.733\n",
       "185                            Viet Nam    0.7\n",
       "186                               Yemen  0.468\n",
       "187                              Zambia  0.582\n",
       "188                            Zimbabwe  0.569\n",
       "\n",
       "[189 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import raw csv data\n",
    "dfHDI = df = pd.read_csv(\"Human_Development_Index_HDI.csv\")\n",
    "#drop rows that are not needed\n",
    "dfHDI = dfHDI.drop([0,1,2,3])\n",
    "#get the columns needed\n",
    "dfHDI = dfHDI[['Unnamed: 1','Unnamed: 58']]\n",
    "#drop rows that are not needed\n",
    "dfHDI.drop(df.tail(17).index, inplace = True)\n",
    "\n",
    "#rename the columns\n",
    "dfHDI.rename(columns={'Unnamed: 1': 'Country','Unnamed: 58':'HDI'}, inplace=True)\n",
    "dfHDI = dfHDI.drop([4])\n",
    "dfHDI = dfHDI.reset_index()\n",
    "dfHDI = dfHDI.drop(columns='index')\n",
    "dfHDI['Country'] = dfHDI['Country'].str.strip()\n",
    "dfHDI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Country</th>\n",
       "      <th>Female Victims</th>\n",
       "      <th>GEI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.846291</td>\n",
       "      <td>0.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1.060551</td>\n",
       "      <td>0.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.371234</td>\n",
       "      <td>0.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.720233</td>\n",
       "      <td>0.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>0.703711</td>\n",
       "      <td>0.673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0.358061</td>\n",
       "      <td>0.716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0.72563</td>\n",
       "      <td>0.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.213828</td>\n",
       "      <td>0.763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>3.195021</td>\n",
       "      <td>0.702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Venezuela, RB</td>\n",
       "      <td>10.706602</td>\n",
       "      <td>0.699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country Female Victims    GEI\n",
       "0             Afghanistan       0.846291  0.444\n",
       "1                 Albania       1.060551  0.770\n",
       "2                 Algeria       0.371234  0.633\n",
       "5               Argentina       1.720233  0.752\n",
       "6                 Armenia       0.703711  0.673\n",
       "..                    ...            ...    ...\n",
       "127  United Arab Emirates       0.358061  0.716\n",
       "128        United Kingdom        0.72563  0.775\n",
       "129         United States       2.213828  0.763\n",
       "130               Uruguay       3.195021  0.702\n",
       "131         Venezuela, RB      10.706602  0.699\n",
       "\n",
       "[110 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the dataset using a left join on the Country\n",
    "df_g = df_intl\n",
    "df_g = df_g.join(df_gei.set_index('Country'), on='Country', how='left')\n",
    "\n",
    "# Since the country names do not exactly match, we will need to iterate and find a match\n",
    "# Get all rows that arent matched\n",
    "no_gei = df_g[df_g['GEI'].isna()]\n",
    "\n",
    "# Iterate through the original dataset and find a country that matches with the first four characters\n",
    "# if it matches then set the GEI to the corresponding country, if no match then it will remain NaN\n",
    "for i1, r1 in no_gei.iterrows():\n",
    "    for i2, r2 in df_gei.iterrows():\n",
    "        c1 = r1['Country'].lower()\n",
    "        c2 = r2['Country'].lower()\n",
    "        if c1[:4] == c2[:4]:\n",
    "            df_g.at[i1,'GEI'] = df_gei.at[i2,'GEI']\n",
    "            \n",
    "# Drop the countries with GEIs that have not been found \n",
    "df_g = df_g.sort_values(by=['Country']).reset_index(drop=True)\n",
    "df_g = df_g.dropna(subset=['GEI'])\n",
    "df_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Female Victims</th>\n",
       "      <th>GEI</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1.060551</td>\n",
       "      <td>0.770</td>\n",
       "      <td>33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.371234</td>\n",
       "      <td>0.633</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.720233</td>\n",
       "      <td>0.752</td>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>0.703711</td>\n",
       "      <td>0.673</td>\n",
       "      <td>29.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>0.656064</td>\n",
       "      <td>0.731</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0.358061</td>\n",
       "      <td>0.716</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0.72563</td>\n",
       "      <td>0.775</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.213828</td>\n",
       "      <td>0.763</td>\n",
       "      <td>41.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>3.195021</td>\n",
       "      <td>0.702</td>\n",
       "      <td>39.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Venezuela, RB</td>\n",
       "      <td>10.706602</td>\n",
       "      <td>0.699</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country Female Victims    GEI  Gini\n",
       "0                Albania       1.060551  0.770  33.2\n",
       "1                Algeria       0.371234  0.633  27.6\n",
       "2              Argentina       1.720233  0.752  42.9\n",
       "3                Armenia       0.703711  0.673  29.9\n",
       "4              Australia       0.656064  0.731  34.4\n",
       "..                   ...            ...    ...   ...\n",
       "95  United Arab Emirates       0.358061  0.716  26.0\n",
       "96        United Kingdom        0.72563  0.775  35.1\n",
       "97         United States       2.213828  0.763  41.4\n",
       "98               Uruguay       3.195021  0.702  39.7\n",
       "99         Venezuela, RB      10.706602  0.699  44.8\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add Gini dataset into the combined dataset above with matching country names\n",
    "df_g = df_g[['Country','Female Victims','GEI']]\n",
    "df_g = df_g.join(df_gini.set_index('Country'), on='Country', how='left')\n",
    "no_gini = df_g[df_g['Gini'].isna()]\n",
    "\n",
    "for i1, r1 in no_gini.iterrows():\n",
    "    for i2, r2 in df_gini.iterrows():\n",
    "        c1 = r1['Country'].lower()\n",
    "        c2 = r2['Country'].lower()\n",
    "        if c1[:4] == c2[:4]:\n",
    "            df_g.at[i1,'Gini'] = df_gini.at[i2,'Gini']\n",
    "\n",
    "df_g = df_g.dropna(subset=['Gini']).reset_index(drop=True)\n",
    "df_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Country</th>\n",
       "      <th>Female Victims</th>\n",
       "      <th>GEI</th>\n",
       "      <th>Gini</th>\n",
       "      <th>HDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1.060551</td>\n",
       "      <td>0.770</td>\n",
       "      <td>33.2</td>\n",
       "      <td>0.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.371234</td>\n",
       "      <td>0.633</td>\n",
       "      <td>27.6</td>\n",
       "      <td>0.746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.720233</td>\n",
       "      <td>0.752</td>\n",
       "      <td>42.9</td>\n",
       "      <td>0.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>0.703711</td>\n",
       "      <td>0.673</td>\n",
       "      <td>29.9</td>\n",
       "      <td>0.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>0.656064</td>\n",
       "      <td>0.731</td>\n",
       "      <td>34.4</td>\n",
       "      <td>0.943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0.358061</td>\n",
       "      <td>0.716</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0.725630</td>\n",
       "      <td>0.775</td>\n",
       "      <td>35.1</td>\n",
       "      <td>0.928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.213828</td>\n",
       "      <td>0.763</td>\n",
       "      <td>41.4</td>\n",
       "      <td>0.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>3.195021</td>\n",
       "      <td>0.702</td>\n",
       "      <td>39.7</td>\n",
       "      <td>0.816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Venezuela, RB</td>\n",
       "      <td>10.706602</td>\n",
       "      <td>0.699</td>\n",
       "      <td>44.8</td>\n",
       "      <td>0.733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country  Female Victims    GEI  Gini    HDI\n",
       "0                Albania        1.060551  0.770  33.2  0.792\n",
       "1                Algeria        0.371234  0.633  27.6  0.746\n",
       "2              Argentina        1.720233  0.752  42.9  0.842\n",
       "3                Armenia        0.703711  0.673  29.9  0.771\n",
       "4              Australia        0.656064  0.731  34.4  0.943\n",
       "..                   ...             ...    ...   ...    ...\n",
       "95  United Arab Emirates        0.358061  0.716  26.0  0.889\n",
       "96        United Kingdom        0.725630  0.775  35.1  0.928\n",
       "97         United States        2.213828  0.763  41.4  0.925\n",
       "98               Uruguay        3.195021  0.702  39.7  0.816\n",
       "99         Venezuela, RB       10.706602  0.699  44.8  0.733\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add HDI dataset into the combined dataset above with matching country names\n",
    "df_g = df_g[['Country','Female Victims','GEI','Gini']]\n",
    "df_g = df_g.join(dfHDI.set_index('Country'), on='Country', how='left')\n",
    "\n",
    "no_hdi = df_g[df_g['HDI'].isna()]\n",
    "\n",
    "for i1, r1 in no_hdi.iterrows():\n",
    "    for i2, r2 in dfHDI.iterrows():\n",
    "        c1 = r1['Country'].lower().strip()\n",
    "        c2 = r2['Country'].lower().strip()\n",
    "        if c1[:4] == c2[:4]:\n",
    "            df_g.at[i1,'HDI'] = dfHDI.at[i2,'HDI']\n",
    "\n",
    "df_g = df_g.dropna(subset=['HDI']).reset_index(drop=True)\n",
    "\n",
    "cols = ['Female Victims','GEI','Gini', 'HDI']\n",
    "df_g[cols] = df_g[cols].apply(pd.to_numeric)\n",
    "\n",
    "df_g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The combined dataset will be used for EDA and data analysis. The variables include the countries and the observations are data for each country. For each country there is female victim rate and the three indices: GEI, Gini and HDI. If a observation is NaN then the row will be removed from the dataset. We decided to have drop all NaN rows so we can have consistent graphs when comparing two variables. The downside of this rule is that the dataset will have less countries than the raw datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the dataset for geopandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will transform the current datasets into a dataset that can be used for geopandas. We will map the countries to a specific polygon on the world map and then include the female victims and three indices. The variables will be the country, polygon, female victim, GEI, HDI, and Gini. The observations will be the values such that we are able to generate a geoplot for visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This function standardizes the names of the countries in order to be used with geopandas\n",
    "\n",
    "def standardize_country(country):\n",
    "    countryDict = {\n",
    "        \"Bosnia and Herz.\":\"Bosnia and Herzegovina\",\n",
    "        \"Czechia\":\"Czech Republic\",\n",
    "        \"Dominican Rep.\":\"Dominican Republic\",\n",
    "        \"United States of America\":\"United States\",\n",
    "        \"South Korea\":\"Korea\",\n",
    "        \"Kyrgyzstan\": \"Kyrgyz Republic\",\n",
    "        \"Macedonia\": \"North Macedonia\",\n",
    "        \"Russia\": \"Russian Federation\",\n",
    "        \"Slovakia\": \"Slovak Republic\",\n",
    "    }\n",
    "    if country in countryDict:\n",
    "        return countryDict[country]\n",
    "    return country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each dataset (GEI,HDI, GINI, etc) we make their corrosponding geopandas usable dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Female homocides dataset\n",
    "\n",
    "#get the geometries for each country\n",
    "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n",
    "df_geo = world[['name', 'geometry']].rename(columns={'name': 'Country'})\n",
    "# rename counries to merge\n",
    "df_geo[\"Country\"] = df_geo[\"Country\"].apply(standardize_country)\n",
    "\n",
    "# merge the female victim dataframe with geometries\n",
    "df_geo = df_geo.join(df_intl.set_index('Country'), on='Country', how='right')\n",
    "\n",
    "df_geo = df_geo.dropna(subset=['geometry']).reset_index(drop=True)\n",
    "df_geo = geopandas.GeoDataFrame(df_geo.copy())\n",
    "# turn the numbers from string to float\n",
    "df_geo['Female Victims'] = df_geo['Female Victims'].astype(float)\n",
    "\n",
    "#Male homocides Dataset\n",
    "\n",
    "#get the geometries for each country\n",
    "df_geo_m = world[['name', 'geometry']].rename(columns={'name': 'Country'})\n",
    "# rename counries to merge\n",
    "\n",
    "df_geo_m[\"Country\"] = df_geo_m[\"Country\"].apply(standardize_country)\n",
    "# merge the male victim dataframe with geometries\n",
    "df_geo_m = df_geo_m.join(df_intl2.set_index('Country'), on='Country', how='right')\n",
    "\n",
    "df_geo_m = df_geo_m.dropna(subset=['geometry']).reset_index(drop=True)\n",
    "df_geo_m = geopandas.GeoDataFrame(df_geo_m.copy())\n",
    "# turn the numbers from string to float\n",
    "df_geo_m['Male Victims'] = df_geo_m['Male Victims'].astype(float)\n",
    "\n",
    "\n",
    "#GEI Dataset\n",
    "\n",
    "#get the geometries for each country\n",
    "df_gei_geo = world[['name', 'geometry']].rename(columns={'name': 'Country'})\n",
    "# rename counries to merge\n",
    "df_gei_geo[\"Country\"] = df_gei_geo[\"Country\"].apply(standardize_country)\n",
    "# merge the GEI victim dataframe with geometries\n",
    "df_gei_geo = df_gei_geo.join(df_gei.set_index('Country'), on='Country', how='right')\n",
    "\n",
    "df_gei_geo = df_gei_geo.dropna(subset=['geometry']).reset_index(drop=True)\n",
    "df_gei_geo = geopandas.GeoDataFrame(df_gei_geo.copy())\n",
    "# turn the numbers from string to float\n",
    "df_gei_geo['GEI'] = df_gei_geo['GEI'].astype(float)\n",
    "\n",
    "\n",
    "#GINI Dataset\n",
    "\n",
    "#get the geometries for each country\n",
    "df_gini_geo = world[['name', 'geometry']].rename(columns={'name': 'Country'})\n",
    "# rename counries to merge\n",
    "df_gini_geo[\"Country\"] = df_gini_geo[\"Country\"].apply(standardize_country)\n",
    "# merge the GEI victim dataframe with geometries\n",
    "df_gini_geo = df_gini_geo.join(df_gini.set_index('Country'), on='Country', how='right')\n",
    "\n",
    "df_gini_geo = df_gini_geo.dropna(subset=['geometry']).reset_index(drop=True)\n",
    "df_gini_geo = geopandas.GeoDataFrame(df_gini_geo.copy())\n",
    "# turn the numbers from string to float\n",
    "df_gini_geo['Gini'] = df_gini_geo['Gini'].astype(float)\n",
    "\n",
    "\n",
    "#HDI Dataset\n",
    "\n",
    "#get the geometries for each country\n",
    "df_hdi_geo = world[['name', 'geometry']].rename(columns={'name': 'Country'})\n",
    "# rename counries to merge\n",
    "df_hdi_geo[\"Country\"] = df_hdi_geo[\"Country\"].apply(standardize_country)\n",
    "# merge the HDI victim dataframe with geometries\n",
    "df_hdi_geo = df_hdi_geo.join(dfHDI.set_index('Country'), on='Country', how='right')\n",
    "\n",
    "df_hdi_geo = df_hdi_geo.dropna(subset=['geometry']).reset_index(drop=True)\n",
    "df_hdi_geo = geopandas.GeoDataFrame(df_hdi_geo.copy())\n",
    "# turn the numbers from string to float\n",
    "df_hdi_geo['HDI'] = df_hdi_geo['HDI'].astype(float)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we want to carry our exploratory analysis on our datasets, aiming to have a better understanding of their distributions, and the possible relationships between them. For each dataset, we will first use plots/graphs to see their distributions. Then, we will run a linear regression model for each index and the International Homicide by Female Dataset.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Female Victims</th>\n",
       "      <th>GEI</th>\n",
       "      <th>Gini</th>\n",
       "      <th>HDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1.060551</td>\n",
       "      <td>77.0</td>\n",
       "      <td>33.2</td>\n",
       "      <td>79.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.371234</td>\n",
       "      <td>63.3</td>\n",
       "      <td>27.6</td>\n",
       "      <td>74.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.720233</td>\n",
       "      <td>75.2</td>\n",
       "      <td>42.9</td>\n",
       "      <td>84.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>0.703711</td>\n",
       "      <td>67.3</td>\n",
       "      <td>29.9</td>\n",
       "      <td>77.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>0.656064</td>\n",
       "      <td>73.1</td>\n",
       "      <td>34.4</td>\n",
       "      <td>94.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0.358061</td>\n",
       "      <td>71.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>88.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0.725630</td>\n",
       "      <td>77.5</td>\n",
       "      <td>35.1</td>\n",
       "      <td>92.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>United States</td>\n",
       "      <td>2.213828</td>\n",
       "      <td>76.3</td>\n",
       "      <td>41.4</td>\n",
       "      <td>92.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>3.195021</td>\n",
       "      <td>70.2</td>\n",
       "      <td>39.7</td>\n",
       "      <td>81.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Venezuela, RB</td>\n",
       "      <td>10.706602</td>\n",
       "      <td>69.9</td>\n",
       "      <td>44.8</td>\n",
       "      <td>73.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country  Female Victims   GEI  Gini   HDI\n",
       "0                Albania        1.060551  77.0  33.2  79.2\n",
       "1                Algeria        0.371234  63.3  27.6  74.6\n",
       "2              Argentina        1.720233  75.2  42.9  84.2\n",
       "3                Armenia        0.703711  67.3  29.9  77.1\n",
       "4              Australia        0.656064  73.1  34.4  94.3\n",
       "..                   ...             ...   ...   ...   ...\n",
       "95  United Arab Emirates        0.358061  71.6  26.0  88.9\n",
       "96        United Kingdom        0.725630  77.5  35.1  92.8\n",
       "97         United States        2.213828  76.3  41.4  92.5\n",
       "98               Uruguay        3.195021  70.2  39.7  81.6\n",
       "99         Venezuela, RB       10.706602  69.9  44.8  73.3\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_g\n",
    "\n",
    "# Transform the GEI and HDI so that all indexes are from 0 to 100\n",
    "df['GEI'] = df['GEI']*100\n",
    "df['HDI'] = df['HDI']*100\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distribution of Female Homicide Victims per 100,000 females'}, xlabel='Female Homicide Victims per 100,000 females', ylabel='Count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_size = (15,8)\n",
    "fig, ax = plt.subplots(figsize=fig_size, ncols=1)\n",
    "\n",
    "plt.title(\"Distribution of Female Homicide Victims per 100,000 females\")\n",
    "plt.xlabel(\"Female Homicide Victims per 100,000 females\")\n",
    "vic = df_intl['Female Victims'].tolist()\n",
    "\n",
    "sns.histplot(ax=ax, data=vic, bins=30, kde=True, color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution for female homicide victims is right skewed. Most counties lie between 0-3 female victims. The 50th percentile is around 1, which is shown by the peak in the plot. There are only a small number of outliers between 10 and 14.\n",
    "\n",
    "Most of the countries have lower than 2 female victims per 100,000 females. 2 is a small number, but the rate is depedent on population size. We have the rate of female victims per 100,000 because we remove population size as a variable and compare the victims in each country.\n",
    "\n",
    "To explore and visualize the countries that have the highest and lowest female murder rates, we can make a barplot of the rates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Female Victims per 100,000 females by Country')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1728 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots(figsize=(16,24))\n",
    "sns.barplot(\n",
    "    ax=ax2,\n",
    "    data=df_intl.groupby(\"Country\")[\"Female Victims\"].agg(np.mean).sort_values(ascending=False).reset_index(),\n",
    "    x=\"Female Victims\",\n",
    "    y=\"Country\",\n",
    "    palette=\"plasma\"\n",
    ")\n",
    "plt.title(\"Female Victims per 100,000 females by Country\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This visualization can better inform the number of female victims for each country. From the plot, many countries with the highe female victim rates come from South America. We can also see that there are some countries with 0 victims per 100,000. This can be due to the countries being very small like an island country. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gender Equality Index EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GEI', ylabel='Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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54YYaatIkVp9/nqHs7NNOxwEAj6LUAQDgEosX79W+fSfVu3cjRUWFOB3Hqxlj1LdvYwUFGU2btl0lJQzDBOC7KHUAALhAZuYJLVuWpVat4tW8eTWn47hCVFSIevZsqL178/Tll/ucjgMAHkOpAwDAy+XnF2vq1HTFxISqR48GTsdxleuuq66UlDh98UWmDh8+5XQcAPAISh0AAF5u7tzdOnYsXwMGNFFoaJDTcVzFGKNevRopJCRQM2fuZDZMAD6JUgcAgBdLS8vRunWHdNttdZSYyPIFVyMiIlh33VVfmZkntGbNQafjAECFo9QBAOClTp8u1MyZO1SjRrg6darndBxXa9UqXo0axejzzzOUm5vvdBwAqFCUOgAAvNScObt16lSR+vdvoqAgXrKvhTFGvXs3UkmJ1ezZuxiGCcCn8AoBAIAX2rIlWxs3HtEdd9RV7doRTsfxCVWrhunOO+spLS1HmzdnOx0HACoMpQ4AAC9z8mShZs3aqVq1InTHHXWdjuNTbrmlturUidCcObt1+nSh03EAoEJQ6gAA8DJz5uzSmTPF6t+/sQIDeamuSAEBRn36NNapU4WaN2+P03EAoELwSgEAgBfZuvWoNm3KVseOCapZk2GXnlCrVoRuv72uvvnmsHbuPOZ0HAC4ZpQ6AAC8RhXNnr1TtWqF6/bb6zgdxqd16FBXcXFhmjVrpwoLi52OAwDXpFylzhhze3m2AQCAa9FbJ08Wqm9fhl16WnBwoPr0aaScnHwtWpTpdBwAuCblfcV4tZzbAADAVZg/f7ekm3XbbXVUu3ak03H8QoMGMWrTpoZWrNivffvynI4DAFct6FJ3GmNulXSbpHhjzE/PuytaUqAngwEA4C/y8gr08MPzJB1Sp07tnI7jV7p1q6+0tBzNnLlTDz/cUgEBxulIAHDFLnemLkRSpErLX9R5l1xJ93g2GgAA/uFXv1qqjIxcSZNYZLyShYUFqUePhjpw4KRWrtzvdBwAuCqXPFNnrV0sabEx5j1rLfP+AgBQwZYvz9Jrr63Tj398g159dbfTcfxSSkqckpJitWhRppo3r6aYmFCnIwHAFSnv24Ghxpg3jTHzjDELz148mgwAAB935kyRHnporurXj9bzz9/hdBy/ZYxRz54NJUmffrpL1lqHEwHAlbnkmbrzTJL0hqS3JDHvLwAAFWDs2C+1bdtRzZt3jyIjQ5yO49diY8PUqVM9zZ+/R1u3HlVKSjWnIwFAuZW31BVZa1/3aBIAAPzI2rUH9de/rtaDD16nbt0aOB0Hktq1q6UNGw5rzpzdatQoVqGhzAkHwB3KO/xypjHmh8aY2saYuLMXjyYDAMBHFRYW68EHP1ONGuF6+eVOTsdBmcDAAPXu3UgnThRo0aIMp+MAQLmV90zd/WVfnz5vm5XUqGLjAADg+/7yl9Vav/6wpk3rr9jYMKfj4DwJCVG66aaaWrXqgK6/Pt7pOABQLuUqddbahp4OAgCAP9i8+Yh+//uvNGRIM/Xr18TpOLiAzp0TtWXLUc2atVPlH9QEAM4pV6kzxoy60HZr7QcVGwcAAN9VXFyihx6aq6ioEL36amen4+Aizq5dN2lSmqTbnI4DAJdV3uGXN513PUxSF0lrJVHqAAAop1dfXacVK/bro496qkaNCKfj4BLOrl2Xnn63MjNzVa9etNORAOCiyjWmwFr7xHmXhyXdIIm5lwEAKKedO4/pueeWqlevRhoxIsXpOLiM/65dZ/TEEyzNC8C7Xe1A8VOSkioyCAAAvspaq4cfnqegoAC98UY3GWOcjoRyKJ3EZp6mT9+uadPSnY4DABdV3s/UzVTpbJeSFCgpRdJET4UCAMCXvP32Ri1cmKF//7ubEhKinI6DK7JU11//A/34xwvUpUt9RUUxUAmA9ynvZ+peOu96kaQ91tq9HsgDAIBPyco6oZ/97AvdeWc9Pfzw9U7HwRUr0b//3U233faJfvObZfq//2OCGwDep7yfqVssaaukKElVJRV4MhQAAL7AWqvHH/9chYUl+s9/ujPs0qVuuaWOHn+8tV59dZ3WrDngdBwA+B/lKnXGmCGSVkkaLGmIpJXGmHs8GQwAALcbP36rZs7coT/9qb0aN451Og6uwfPP36EaNcL16KPzVVRU4nQcAPiO8k6U8pykm6y191trR0m6WdJvPBcLAAB3O3z4lJ58cqHatautJ59s43QcXKOYmFD94x+dtXbtQb322jqn4wDAd5S31AVYaw+ddzv7Cr4XAAC/8+STC3X8eL7efru7AgN5yfQF99zTVD17NtRvfrNMmZm5TscBgHPK+yrzmTFmrjHmAWPMA5JmS/rUc7EAAHCvqVPTNX78Vv3mN7eqRYvqTsdBBTHG6J//7KriYqsnn2TtOgDe45KlzhjTxBhzu7X2aUn/lnS9pFaSvpL0ZiXkAwDAVY4cOaXHHpuvNm1q6tlnb3Y6DipYgwYxGjv2Nk2bxtp1ALzH5c7U/Z+kE5JkrU211v7UWvsTlZ6l+z/PRgMAwH2efHKhcnLO6N1371ZwcKDTceABTz11o66/Pl5PPLFQJ04wITgA512u1DWw1m74/kZr7RpJDTySCAAAl5o6NV3jxpUOu7z++nin48BDgoMD9e9/d1NW1gn99rfLnY4DAJctdWGXuK9KRQYBAMDNGHbpX86uXfePf6zV11+zdh0AZ12u1K02xjz8/Y3GmIckfe2ZSAAAuM8TT5QOu3zvPYZd+ouza9c98ghr1wFw1uVK3VOSfmCM+cIY83LZZbGk0ZL+n8fTAQDgAqmpaRo/fqt++9tb1bIlwy79RUxMqF59tXTtupdeWu10HAB+7JKlzlp70Fp7m6SxknaXXcZaa2+11jLWAADg944cOaXHH/9cbdrU1DPPMOzS39xzTzMNGpSk3/3uS23Zku10HAB+qlzr1FlrF1lrXy27sDALAABlGHaJf/6zqyIigvXgg5+puJhhmAAqX3kXHwcAAN/DsEtIUs2aEfrHPzprxYr9euWVtU7HAeCHKHUAAFwFhl3ifCNGpKhPn8Z67rllSk/PcToOAD9DqQMA4ApZa/X4458z7BLnGGP0+utdFRoaqNGj56qkxDodCYAfodQBgB9KSEiUMcbjl4SERKf/qR7x/vubNHlymv7wh9sZdolz6taN0t//3klLluzV669/43QcAH4kyOkAAIDKl5WVqTFjlnl8P2PHtvf4Pirbzp3H9MQTC9SxY4J+/vObnI4DL/PAA9dpwoRteuaZJerZs6EaNox1OhIAP8CZOgAAyqmoqET33vupAgMD9MEHPRUYyMsovssYozffvEvGSA8/PE/WMgwTgOfxagQAQDk9//wKffXVPr3+elclJkY7HQdeKjExWn/9a0ctWJCht97a6HQcAH6AUgcAQDmsWLFPv//9Vxo5MkXDh6c4HQde7pFHWunOO+vpZz/7QpmZuU7HAeDjKHUAAFxGXl6B7r33U9WtG6nXXuvidBy4QECA0VtvdVdxcYkeemguwzABeBSlDgCAy3jqqUXaufOYPvywp2Jjw5yOA5do1ChWf/1rR82fv0f/+tc3TscB4MModQAAXMLUqel6++2NeuaZm9WhQz2n48BlHn+8tbp3b6Cnn16sbduOOh0HgI+i1AEAcBH79uVp9Oi5atOmpsaOvd3pOHAhY4zeeeduVakSpPvu+1SFhcVORwLggyh1AABcQEmJ1Q9+8JlOny7Sxx/3VEhIoNOR4FJ16kTq9de7avXqA3r++ZVOxwHggyh1AABcwN/+tkbz5u3Wyy93UnJyNafjwOWGDEnWiBEp+sMfvtLq1fudjgPAx1DqAAD4nhUr9umXv1yqgQOT9NhjrZyOAx/x2mtdVKdOpEaMmK0TJwqcjgPAh1DqAAA4z9GjpzV06EwlJETq7be7yxjjdCT4iKpVw/TRRz21c+dxPfnkAqfjAPAhlDoAAMpYW/o5uv37T2rChD4sX4AK16FDPT33XDu9994mjR+/1ek4AHyEx0qdMeYdY8whY8y3522LM8bMN8akl32t6qn9AwBwpV55Za1mzNihF1/soJtvru10HPio3/72Nt16ax09+ug87d593Ok4AHyAJ8/UvSfp7u9te1bSAmttkqQFZbcBAHDcV1/t0y9+sVh9+zbWU0/d6HQc+LCgoAB9/HFPSdLIkbNVVFTicCIAbuexUmetXSLp+6ts9pP0ftn19yX199T+AQAor0OHTmrw4BlKSIjSe+/14HN08LiGDWP1xhvd9OWX+zRmzHKn4wBwucr+TF1Na+1+SSr7WqOS9w8AwHcUFZVo2LBZys4+o9TUfqpalc/RoXIMH56ihx5qqT//eaXmzt3ldBwALua1E6UYYx4xxqwxxqw5fPiw03EAAD7q179epkWLMvXGG13VuvWF32tMSEiUMcbjF/iff/yjs1q0qK6ePT+SMTEePb4SEhKd/ucC8JCgSt7fQWNMbWvtfmNMbUmHLvZAa+2bkt6UpLZt29rKCggA8B/TpqXrxRdX6dFHW+n++6+76OOysjI1Zswyj+cZO7a9x/cB7xIeHqxJk/ooJeUN1a//F40a1UIBAZ4p+BxfgO+q7DN1MyTdX3b9fknTK3n/AABIkjZvPqJRo+bopptq6ZVX7nQ6DvxYcnI1Sanas+eEvvgi0+k4AFzIk0sajJP0laRmxpi9xpiHJL0gqZsxJl1St7LbAABUqpycM+rXb5rCw4OUmtpPoaGVPXAF+L61uuGGGlq6NEvp6TlOhwHgMh57FbPWDr/IXV08tU8AAC7n7MQoe/bk6osvhiohIcrpSIAkqUePBtq/P0+pqel65JHrmbQHQLl57UQpAAB4wrPPLtG8ebv1+uvddNttdZ2OA5wTHByoIUOaSZImTNimwsJihxMBcAtKHQDAb3zwwSa9/PIaPfHEDXrooZZOxwH+R9WqYRo4MEkHD57S7Nm7ZC1zxQG4PEodAMAvLF26Vw8/PE933llPL7/cyek4wEUlJVVVx44JWr/+sNasOeh0HAAuQKkDAPi87dtzNGDAdDVsGKMpU/opODjQ6UjAJXXsmKAmTWL12We7tWdPrtNxAHg5Sh0AwKfl5JxRr16pkqRZswYw+QRcwRijQYOSVLVqqCZO3KZjx844HQmAF6PUAQB8VkFBsQYNmq7du3M1bVo/NWlS1elIQLmFhQVp+PBklZRYjRu3TQUFTJwC4MIodQAAn2St1cMPz9WiRZl6553uat8+welIwBWrVq2K7rmnqQ4fPqXU1HQmTgFwQZQ6AIBPeu65Zfrgg836/e9v18iRzZ2OA1y1xo1jddddDbRtW44WLcp0Og4AL+SxxccBAHDKP/+5Tn/+80o9+mgr/frXtzgdB7hm7drV0qFDp7R0aZZq1AjXdddVdzoSAC/CmToAgE+ZOjVdTzyxQH37NtZrr3WRMcbpSMA1M8aoV6+GSkyM0vTp27VvX57TkQB4EUodAMBnLF26V8OHz1K7drU1blxvBQXxMgffERgYoCFDmikiIkTjx2/ViRMFTkcC4CV4tQMA+IR16w6qd+9UNWgQo5kzByg8PNjpSECFi4gI1vDhzXTmTLEmTNimwkJmxARAqQMA+IC0tKPq3n2yYmJCNX/+PapePdzpSIDH1KwZoYEDk5SVlafp03cwIyYASh0AwN0yM3PVrdskSdL8+YNVr160w4kAz0tOjlPXronatClbCxZklPO7AmSM8eglISHRo/9uABfG7JcAANc6dOik7rprso4dy9eiRUPVrFmc05GASnPbbXWUk5Ov5cv3KTY2VG3b1rrMd5RozJhlHs00dmx7j/58ABdGqQMAuNKRI6fUtesk7dmTq88+G6Q2bWo6HQmoVMYY9ezZULm5+fr0012Kjg5V06ZVnY4FwAEMvwQAuM7Ro6fVrdtkpacf04wZA9ShQz2nIwGOCAgwuueepqpVK0KTJ6ex1AHgpyh1AABXOXbsjO66a7I2b87WtGn91LVrfacjAY4KCQnUiBHJCg8P1rhxW3Xs2BmnIwGoZJQ6AIBr5Obm6+67p2jDhsNKTe2r7t0bOh0J8AqRkSEaOTJZhYUl+vjjrTp9usjpSAAqEaUOAOAKeXkF6tFjir7++qAmTeqrXr0aOx0J8Crx8eEaNqyZjh49owkTtqmoqMTpSAAqCaUOAOD1Tp4sUK9eqVq5cr/Gj++tfv2aOB0J8EoNGsSoX7/G2rMnVzNmsIYd4C+Y/RIA4NVOnSpU377TtGxZlj75pJcGDWrqdCTAq11/fbyOH8/XwoWZio4O4XOngB+g1AEAvNapU4Xq12+aFi3K0Acf9NTQoclORwJcoX37ujp+vEDLl+9TVFSI2rWr7XQkAB5EqQMAeKUTJwrUu3eqli3L0rvv3q17723udCTANc6uYZeXV6DPPtutqKgQpyMB8CA+UwcA8DqlyxZM0vLlWfr44166//7rnI4EuE5AgNGgQUlKSIhUamq6JGaLBXwVpQ4A4FWys0+rS5dJ52a5HDaMIZfA1QoODtTw4cmKjQ2V9IAOHTrldCQAHkCpAwB4jYMHT6pTpwnavDlb06f314ABSU5HAlwvPDxY996bIqlIH3+8Rbm5+U5HAlDBKHUAAK+QlXVCHTtO0M6dxzR79kD16NHI6UiAz4iNDZP0ts6cKdbHH2/RmTMsTg74EkodAMBxu3cfV4cO47VvX57mzr1HnTsnOh0J8EH7NHRoMx05ckbjx7M4OeBLKHUAAEelp+eoQ4fxOnr0jD7/fLDat09wOhLgsxo1ilH//qWLk0+btp3FyQEfwZIGAADHbN58RF27TlJhYYkWLRqq1q1rOB0J8HktW8brxIlCzZ+/R5GRwerevYGMMU7HAnANKHUAAA8KuMQfi7UlPSKpRNK/dcMNP76qPdStW09792ZcZT7AP916a23l5uZr5coDio4O1W231XE6EoBrQKkDAHhQicaMWfY/W7Oy8vTRR5sVEhKoUaOaq1q17le9h7Fj219LQMAvGWPUvXsD5eWVnrGLigpWy5bxTscCcJUodQCASpWRkauPP96q8PAg3X9/87JZ+QBUNmOM+vdvory8Qk2btkMRESFq1CjG6VgArgITpQAAKs2uXcf10UdbFBUVrB/8oAWFDnBYUFCAhg1rpurVwzRhwjYdOHDS6UgArgKlDgBQKbZvz9Enn2xRbGyoHnighaKjQ52OBEBSWFiQRo5MUVhYoD7+eIuOHTvjdCQAV4hSBwDwuK1bj2rcuG2qXj1cDzzQQpGRIU5HAnCe6OhQjRyZoqKiEn300RadOlXodCQAV4BSBwDwqG+/PaKJE7epdu0I3X9/c4WHBzsdCcAF1KgRrmHDknXsWL7Gj9+mwsJipyMBKCdKHQDAg25Uamq6EhOjdN99zRUWxvxcgDerXz9aAwcmKTPzhFJT01VSwuLkgBtQ6gAAHrFmzUFJw9SwYYxGjkxRaGig05EAlEPz5tV0990NtHVrjubM2SVrKXaAt+MtUwBAhVuxYr/mzt0tabOGD/+BgoJ4DxFwk3btauvEiQItX75P0dEhuuOOBKcjAbgEXmUBABVq+fIszZ27WykpcZI+oNABLtWlS6Kuv766Fi7M1DffHHI6DoBL4JUWAFBhli7dq88/z1CLFtU0aFCSJCZaANzKGKO+fRurUaMYzZixQzt2HHM6EoCLoNQBACrE4sWZWrgwUy1bVtfAgUkKDOQlBnC7wMAADRnSTDVqhGvSpDQdOnTK6UgALoBXXADANbHWauHCDH3xxV61ahWv/v2bKCDAOB0LQAUJDQ3U8OHJCg4O0CefbFVeXoHTkQB8D6UOAHDVrLVasCBDS5dm6YYbaqhfv8YUOsAHxcSEavjwZJ06VcgadoAXotQBAK6KtVbz5+/R8uX7dOONNdWnTyMZQ6EDfFWdOpEaODBJWVl5mjZtB0sdAF6EUgcAuGLWWs2du1tffbVfN91US716NaTQAX4gOTlO3brV1+bN2Vq0KNPpOADKsE4dAOCKWGs1Z85urV59QO3a1Vb37vUpdIAfufXW2srOPq2lS7MUFxem1q1rOB0J8HuUOgBAuZ09Q7d69QHdcktt3XUXhQ7wN8YY9ezZUDk5+Zo5c6diY0PVoEGM07EAv8bwSwBAuZz9DN3KlQfUrl0tCh3gx0qXOmiquLgwTZiwTdnZp52OBPg1Sh0A4LLOznL51Vf71bZtTXXv3oBCB/i5sLAgjRiRrIAAo08+2arTp4ucjgT4LUodAOCyvvgiU8uX71ObNjXUsyeTogAoVbVqmIYObaZjx/I1ZUqa+NMScAb/5wEALmnx4kwtWZKl1q3j1bs3yxYA+K7ExGj16tVQO3Ycl9TT6TiAX6LUAQAuaunSLH3xxV61ahWvvn0bU+gAXFCbNjV10021JHXUBx9scjoO4HcodQCAC1q+PEsLF2aoZcvqFDoAl9W9e31J2/XII/O0cuV+p+MAfoVSBwD4H6tW7dfnn2eoRYtq6t+/iQICKHQALi0wMEDSh6pTJ1IDBkzTvn15TkcC/AalDgDwHevWHdKcObuVnFxVAwZQ6ABciVOaPr2/cnMLNGDANJ05w4yYQGWg1AEAztm0KVszZ+5Qo0YxGjSoadk77wBQfi1bxuvDD3tq1aoDeuSRebLWOh0J8Hm8WgMAJEnp6TlKTU1XvXpRGjq0mYKCeIkAcHUGDEjS2LG36cMPN+tvf1vjdBzA5/GKDQDQ7t3HNXHiNtWsGa7hw5MVEhLodCQALvfrX9+qQYOS9ItfLNHcubucjgP4NEodAPi5vXtPaNy4rapaNUz33puisLAgpyMB8AEBAUbvv99D111XXcOHz9bOncecjgT4LEodAPixAwdO6uOPtygiIlj33ddc4eHBTkcC4EMiIkKUmtpP1loNGjRDp04VOh0J8EmUOgDwU0eOnNZHH21RSEigRo1qrqioEKcjAfBBjRvH6uOPe2n9+kN67LH5TJwCeAClDgD8Uqw+/HCzJOm++5orNjbM4TwAfFnPno30u9+VTpzyr39943QcwOdQ6gDAzxw+fErSw8rPL9a996aoevUqTkcC4Ad+/etb1bt3Iz311CItX57ldBzAp1DqAMCPnDhRoJ49p0iqquHDk1WrVoTTkQD4iYAAow8/7Kn69aM1ePAM7d+f53QkwGcwxRkA+In8/CL17z9N69YdkvSh6tfv4HSkChIgY4zTIeCzOL6uTHl+X7UkPaE6dX4h6d+SSq54L3Xr1tPevRlXkQ/wTZQ6APADxcUlGjFithYuzND77/fQ/ff/3OlIFahEY8Ys8+gexo5t79GfD2/m+eNL8qVjrHy/r40bjyg1NUQ33/y+evRoeMV78Z3fF1AxGH4JAD7OWqvHHpuv1NR0/f3vd2rUqBZORwLg51q2rK527Wpr1aoD2rDhsNNxANej1AGAj/vVr5bqrbc26rnnbtFTT93odBwAkCR165ao+vWjNHPmTh04cNLpOICrUeoAwIe99NJqvfDCKj36aCv94Q+3Ox0HAM4JDAzQPfc0VVhYoCZO3KYzZ4qcjgS4FqUOAHzUu+9u1NNPL9bgwU31z392YbIHAF4nMjJEgwc31fHjBZo2bTsLkwNXiVIHAD5o+vTtGj16nrp1q68PP+ypwECe7gF4p8TEaHXrVl/btuVo2bJ9TscBXIlXeQDwMYsXZ2ro0Jlq27amUlP7KTSUiY4BeLd27WqpRYtqWrQoQzt3Hnc6DuA6lDoA8CHr1h1Unz5T1ahRjD79dJAiI0OcjgQAl2WMUd++jVWtWhVNmZKm3Nx8pyMBrkKpAwAfkZ6eo+7dJ6tq1VDNmzdY1apVcToSAJRbSEighg5tpqKiEk2alKbi4itflBzwV5Q6APABe/eeULduk2StNG/eYCUkRDkdCQCuWPXqVdSvXxPt3ZunuXN3Ox0HcA1KHQC4XHb2aXXvPllHj57R3Ln3qFmzOKcjAcBVa968mm65pbZWrz7IwuRAOVHqAMDF8vIK1LPnFO3YcUwzZvRXmzY1nY4EANesa9dEJSaWLkx+8CALkwOXQ6kDAJfKzy/SgAHT9fXXBzVhQh916pTodCQAqBDfXZg8jYXJgctwpNQZY3YbYzYaY74xxqxxIgMAuFlxcYnuvfdTff75Hr39dnf169fE6UgAUKGiokJ0zz1NlZNzRtOn72BhcuASnDxTd6e1trW1tq2DGQDAday1evzxzzV5cpr+9rdOuv/+65yOBAAeUb9+6cLkW7ce1ZdfsjA5cDEMvwQAl3nuuWX6z3826Fe/aqef/IT3xQD4tltuqa3mzeO0YEGGdu1iYXLgQpwqdVbSPGPM18aYRxzKAACu8/LLq/XnP6/Uo4+20h//2N7pOADgcaULkzcpW5g8nYXJgQtwqtTdbq1tI6mHpB8ZYzp8/wHGmEeMMWuMMWsOH2Y6WwB4992N+vnPF2vIkGb65z+7yBjjdCQAqBShoYEaMqSpCgqKNWlSmqRApyMBXsWRUmet3Vf29ZCkqZJuvsBj3rTWtrXWto2Pj6/siADgVaZNS9fo0fN0110N9OGHPRUYyOh5AP4lPj5c/fo11t69eZJ6OR0H8CqV/leBMSbCGBN19rqkuyR9W9k5AMAtFi3K0NChs3TzzbWUmtpXISG8Qw3AP7VoUV3t2tWWdIfGjdvidBzAazjxVm9NScuMMeslrZI021r7mQM5AMDrrVlzQH37TlVSUqxmzx6oiIgQpyMBgKO6dUuUtEujR8/Vpk1HnI4DeIVKL3XW2p3W2lZllxbW2j9VdgYAcINNm47o7runqHr1Kpo3b7Di4qo4HQkAHFc6/PwjRUWFaODA6UycAoglDQDAK6WlHVWXLhMVGhqozz8fojp1Ip2OBABeJFcTJvTRjh3H9OCDn7EwOfwepQ4AvMzu3cfVpcsklZRYLVgwWI0bxzodCQC8TseO9fTCCx00ZUq6/va3NU7HARxFqQMAL5KVdUKdO0/UyZOFmj9/sJKTqzkdCQC81s9+1laDBiXpmWeWaMmSTKfjAI6h1AGAlzh48KS6dJmkI0dOa+7cQWrVqobTkQDAqxlj9M47d6tx41gNGTJT+/blOR0JcASlDgC8QHb2aXXrNkmZmbn69NOBuumm2k5HAgBXiI4OVWpqP504UaAhQ2aqsLDY6UhApaPUAYDDjh/PV/fuk5WWlqMZMwaoffsEpyMBgKu0aFFdb73VXcuXZ+mZZ5Y4HQeodJQ6AHBQXl6Bevacog0bDis1tZ+6dKnvdCQAcKXhw1P0xBM36O9//1oTJ251Og5QqSh1AOCQEycK1LNnqlas2K9x43qrZ89GTkcCAFd76aVOuvXWOnrwwbnasiXb6ThApaHUAYADcnPz1aPHFH35ZZbGjeulQYOaOh0JAFwvJCRQkyb1UUREsAYOnK4TJwqcjgRUCkodAFSys5+hW7lyvyZM6KMhQ5KdjgQAPqNu3SiNH99baWk5eughFiaHf6DUAUAlOnbsjLp1m6Svvz6oSZP6cIYOADzgzjsT9fzz7TVpUppefHGV03EAjwtyOgAA+IujR0/rrrsma8OGw5oypZ/69GnsdCQA8Fm/+MXNWr/+sH71q6VKSammfv2aOB0J8BjO1AFAJcjOPq2uXSdp48YjmjqVQgcAnmaM0dtvd1fbtrU0cuRsrV9/yOlIgMdQ6gDAww4fPqXOnSdq8+ZsTZ/eX716UegAoDJUqRKs6dP7KzY2VH37TtXBgyedjgR4BKUOADxo//48de48UWlpOZo5c4Duvruh05EAwK/Urh2p6dP76/Dh0xo4cLry84ucjgRUOEodAHjIjh3H1L79OO3adVyzZg1Qt24NnI4EAH7pxhtr6f33e+jLL/fpkUfmMSMmfA6lDgA84JtvDun22z/R8eMFWrhwiLp0qe90JADwa4MHN9PvfnebPvhgs156abXTcYAKRakDgAq2eHGmOnYcr5CQQC1bNkw331zb6UgAAEm//e2tGjKkmZ55ZolmztzhdBygwlDqAKACTZuWru7dJ6tu3UgtXz5cycnVnI4EAChjjNG7796tG2+sqREjZmnjxsNORwIqBKUOACrI229v1KBBM9S6dQ0tXTpc9epFOx0JAPA94eHBmjatv6KiQtS7d6r2789zOhJwzSh1AHCNrLV68cWVGj16rrp1q6/PPx+satWqOB0LAHARdetGadasgcrOPqNevVJ14kSB05GAa0KpA4BrUFxcop/+9As9++xSDR+erBkzBigyMsTpWACAy2jTpqYmTeqjDRsOa8iQGSosLHY6EnDVKHUAcJVOnizQoEEz9H//97WefLKNPvqol0JCAp2OBQAopx49GumNN7rps8926/HHP2epA7hWkNMBAMCN9u/PU58+U7Vu3SG98kpnPflkG6cjAQCuwujR1ysjI1d/+MMKJSRE6ne/u93pSMAVo9QBwBXasOGwevdO1dGjZzRtWn/16dPY6UgAgGswduztysw8obFjv1J8fLh+9KMbnI4EXBFKHQBcgdTUNI0aNUcxMaFasmSY2rSp6XQkAMA1MsboP//prqNHz+iJJxYoLi5Mw4enOB0LKDc+UwcA5VBSYvX733+pQYNmqEWLalq9+l4KHQD4kKCgAI0f31t33JGgUaPm6LPPdjkdCSg3Sh0AXMbJkwUaOnSmxoz5UqNGNdfixcNUp06k07EAABWsSpVgzZgxQNddV10DB07X8uVZTkcCyoVSBwCXkJZ2VO3afazU1HS9/HInvfdeD4WFMXIdAHxVTEyoPvtskBISotSjxxStWrXf6UjAZVHqAOAiUlPT1LbtRzp48JTmzr1HP/1pWxljnI4FAPCwmjUjtHDhEFWvXkXdu0/WunUHnY4EXBKlDgC+p6ioRM88s1iDBs1QSkqc1q69T1271nc6FgCgEiUkRGnhwiGKjg5Rt26TtXHjYacjARdFqQPgExISEmWMqYBLVQUHP6G//GW1pC+1atUDSkyMOXd/YGBIBe3n4peEhESnf50A4OUCKuW5uEGDGC1cOFShoYHq2nWSNm8+4vQ/HLggPhgCwCdkZWVqzJhl1/Qztm49qunTt6ukxKp378Zq2fJWST/7zmPGjm1/zfu5nLFj23v05wOA+5VU2nNx48axWrhwiDp1mqCOHSfo888Hq1WrGh7dN3ClOFMHwO8VFZVozpxdmjBhm6pWDdOjj16vli2rOx0LAOAlmjWL05IlwxQWFqQ775yoNWsOOB0J+A5KHQC/duDASb355gatWnVA7drV0oMPXqe4uCpOxwIAeJmkpKpasmSoYmJC1KXLRH311T6nIwHnUOoA+KWSEqvly7P01lsbdfp0kUaOTNbddzdUUBBPiwCAC2vYMFZLlgxTzZoR6tZtkubP3+10JEASpQ6AH8rJOaMPPtiszz/PUFJSVT3+eCs1aVLV6VgAABeoVy9aixcPVePGserVK1Xjx291OhLARCkA/Ie1VqtWHdCCBRkKCDDq27exWreOZ+05AMAVqV07UosXD1W/ftM0fPgsHTx4Uv/v/93odCz4MUodAL9w9OhpzZixQ3v2nFCTJrHq3buRYmJCnY4FAHCp2NgwzZ17j0aOnK2nnlqk/ftP6vnn71BAAG8UovJR6gD4tOLiEn355X4tWZKpwMAA9evXWK1acXYOAHDtwsKCNHFiH/3oRwv04ourtGPHMb3/fg+Fhwc7HQ1+hlIHwGdlZp7QrFk7dOjQaaWkxKlHj4aKigpxOhYAwIcEBgbo9de7KikpVk8/vVi7dx/X9OkDVKdOpNPR4EcodQB8zsmThVq4MENr1x5SdHSIhg1rpmbN4pyOBQDwUcYY/exnNykpqapGjJitm2/+SDNmDFCbNjWdjgY/weyXAHxGcXGJVqzYr1dfXadvvjmsW26prR/+sDWFDgBQKfr2baLly4crIMDo9tvH6YMPNjkdCX6CM3UAfERTvfHGBh05clqNG8eoe/cGio8PdzoUAMDPtGpVQ6tX36thw2bp/vvnaMWKffr73+9UaCh/dsNzOFMHwNW2b89R375TJT2skhKr4cOTNXJkCoUOAOCYmjUjNH/+YD399E16/fX16tBhvDIycp2OBR9GqQPgSseOndGzzy5RixbvadGiDEmz9fjjrdS0aVVmtgQAOC4oKEB/+UtHTZnSV1u2HFXr1h9o8uRtTseCj6LUAXCVkycL9MILK9Ww4X/04ourNHx4stLSHpL0hYKCeEoDAHiXgQObau3a+9SkSawGD56p0aPnKi+vwOlY8DH8BQTAFQoKivXaa2vVuPFb+uUvl6p9+7pat26U3nuvh2rXZtpoAID3atKkqpYvH65f/rKd3nlno9q0+VArV+53OhZ8CKUOgFcrLi7R++9/q2bN3tYTTyxUcnKcli8frpkzB6p16xpOxwMAoFyCgwP1/PN3aOHCITp9uki33faJnn76C50+Xeh0NPgApuEB4HEJCYnKysq8wu8yklpKuktSTUmZkj7T4sVpuv32ik7obQL4XCAA+KhOnRK1adMD+sUvluill9Zo+vQdevvt7rrjjgSP7fPqXoevTN269bR3b4ZH94GLo9QB8LisrEyNGbOsXI8tLi7Rhg1HtHx5lrKzz6h69Srq3LmekpNvkTFDLvp9Y8e2r6i4XqCk3L+vq+Vbvy8AcJfo6FC98UY3DRnSTKNHz1WHDuP14IPX6YUXOnhk9uYreR2+WryuOItSB8ArFBQUa+3aQ/rqq33KzS1QrVrhuueepkpJiVNAAGetAAC+p3PnRG3ceL/Gjv1Kf//710pNTdcf/9hejz3WSoGBfEoK5UepA+Co06eLtHr1Aa1cuV+nThWpfv0o9enTSI0bxzIEEQDg8yIiQvSXv3TUD35wnZ54YoF+/OMFeuutjfrrXzuqa9f6TseDS1DqADgiJ+eMVq06oLVrD6mgoFhJSbFq376uEhOjnY4GAEClS0mppvnzB2vy5DQ9/fRides2Sd27N9CLL3ZQq1ZMDIZLo9QBqDTWWu3enauVK/dr27YcBQQYNW8ep9tvr6tatSKcjgcAgKOMMRo8uJn69Gmsf/3rG/3xjyt0ww0faMSIFP3mN7eqWbM4pyPCS1HqAFSCYH399UGtWrVfhw6dVnh4kO64o67atq2p6OhQp8MBAOBVwsKC9NOfti2bPGWV/vGPtRo3bquGDUvWr399i1JSqjkdEV6GT2AC8JgdO47pmWcWS3pOs2btVECAUb9+jfWTn9yozp0TKXQAAFxCbGyYXnihg3bvflg//3lbTZ++XS1avKvBg2doxYp9TseDF+FMHYAKdeZMkaZOTddbb23UwoUZZTNX7tQDDwxTYmIUk58AAHCFatSI0IsvdtTTT9+kv/3ta73++jeaPDlNt91WRz/7WVv169eE2TL9HP/1AVSITZuO6Cc/WaS6dd/QiBGztXPnMf3xj+2VkfGIpA9Uv340hQ4AgGtQvXq4nn/+DmVmPqpXXums/ftPatCgGWrU6D/6059WaP/+PKcjwiGcqQNw1Y4dO6MpU9L19tsb9dVX+xQcHKABA5I0enRLdelSn/XlAADwgMjIED35ZBv96EetNX36dr3++nr9+tfL9Lvffal+/RrrwQdb6q67GigoiPM3/oJSB+CKFBQUa86cXfroo82aOXOH8vOLlZwcp5de6qhRo1ooPj7c6YgAAPiFwMAADRzYVAMHNlV6eo7efHO93n13k6ZMSVetWhEaOTJFo0a1cDomKgGlDsBlWWv15Zf79NFHmzVx4jYdPXpG8fFV9OijrXTvvSlq27YWQysBAHBQUlJV/fWvnfSnP92hTz/dqfff36RXXlmrl19eI+lpLViQoZSUONWuHcFrtg+i1AG4IGut1qw5oMmT0zRpUpp27TquKlWCNGBAku69N0XdujGsAwAAbxMSEqj+/ZPUv3+Sjhw5pUmT0vTDH76u5cuztGxZlmJiQpWSEqeUlDjVq8cEZr6CUgfgnJISq6++2qcpU9I0ZUqaMjJOKCgoQF26JGrs2NvUv3+SoqJCnI4JAADKoXr1cD3+eGv98Idv6uc/X6Rt23K0ZUu2Vq8+oBUr9isyMlhNm1ZVkyaxatgwRmFhVAO34r8c4OcKC4u1bFmWpkxJU2pquvbvP6mQkEB1795Av//97erbt4mqVg1zOiYAALgG4eHBuuGGGrrhhhrKzy9SWtoxbdmSrW+/zdbatYdkjFS3bqQaN45V48axqls3kgnPXIRSB/ihw4dPac6cXZo9e6fmzt2t48fzVaVKkHr0aKh77mmqXr0asTA4AAA+KjQ0SC1bVlfLltVVXFyirKw87dhxTDt2HNfixXu1ePFehYYGqlGjGNWvH6369aNVo0Y4Jc+LUeoAP2Ct1fr1hzVr1g7Nnr1TK1ful7VS7doR50rcXXfVV0QEQysBAPAngYEBSkyMVmJitO68Uzp9ulA7d+Zqx45j2rnzmLZsOSpJCg0NVL16UUpMjFJiYrTq1o3ks/VehFJ3DRISEpWVlenRfdStW09792Z4dB+Vhd/Xlbn231espCaSksq+Rpdtz5C0RdIW7d+/T+++G6S33y68pqwAAPieAI9PIhIQEKySEu96Da5SJVgtWlRTixbVJEnHj+drz55cZWScUEZGrhYuPCZJCgw0qlMnUnXrll6kOFlrPf474+/JC6PUXYOsrEyNGbPMo/sYO7a9R39+ZeL3dWWu9Pd1+nShdu3K1a5dx7Vz53EdPXpGkhQREayGDaPVqFGskpJiFRl563e+b+zY9vx3AQDgf5RUyuujp/dxdj9XKyYmVNdfH6/rr4+XJJ06VajMzBPasydXmZkntGbNAa1YYSX9UvHx/9LNN9fSzTfX0k031VLbtrVUs2ZEBf0rSvH35IVR6gCXOn48XxkZpe+cZWae0MGDpyRJwcEBatAgWm3b1lSjRjGqUSOc6YoBAECFCA8PVrNmcWrWLE6SVFxcokOHTuvNN8eoX78xWrXqgD77bJesLX18rVoRat06Xq1b11CrVjXUunW8kpKqKjCQoZsViVIHuMDZJ8zMzBPKzCwtcrm5BZKkkJAAJSREqVOnBDVsGKO6dSN5ogQAAJUiMDBAtWtHSFqht9++W5KUl1egr78+qHXrDumbb0ovCxasUWFhiSSpSpUgXX99vFq1Ki17rVvXUMuW1RUZyWf7rxalDvAyRUUl2rw5W9JNmj17p/bvP6kDB06quLj0La+oqBAlJkaVfVg5WjVrMhsVAADwHpGRIerYsZ46dqx3bltBQbG2bMkuK3mHtX79IU2alKY339wgSTJGatAgRs2bV1Pz5qWf6WvevJpSUqpR9sqBUgc4KDv7tL799oi+/faINm06onXrDmn9+sM6fbpI0hBt2HBEdepE6Oaba6lOnUglJEQpJiaE4ZQAAMBVQkIC1apV6RDM++8v3WatVWbmCX3zTenfP5s3Z2vz5mzNn79HBQXF5763fv3ocyVPaqusrBOqXr2KQkOpMmfxmwA8zFqrAwdOKj09R2lpOdq0KftckTtw4OS5x539IPJjj7VS27a1NHJkRz37bCoFDgAA+CRjzLnlFPr2bXJue1FRiXbuPKZNm7LPFb1Nm45owYIMSUP11lvfSiqdDK5atTBVq1ZFcXFh37nub8stUOqACpCfX6S9e/OUmZmrHTuOa/v2HKWn52j79mPavv2YTp7873TFVaoEqUWLarr77ga67rrq5y516kR+p8CNHHmYQgcAAPxOUFCAmjaNU9OmcRowIOnc9uLiEgUF1dTQoe8oO/u0srPPKDv7tNLScr7zt5ZU+mZ5ack7v/RVUWxsqE9+bIVSB1xCSYnV0aOndfDgqbLLSe3bl1c2YUnpei3nzzx5VnBwgBo1ilWTJrG6885ENWkSq6SkqmrSJFYNGsT45JMJAACAJ5VOBHdEyclx/3Nffn5RWckrLXpHj5Z+3bDhiPLz/zuUMyDAqGrVUMXFhalq1bOX0tuxsaEKDg6sxH9RxaHUwedZa3XmTJHy8gqVl1egEycKdfx4vo4ePaOcnDM6evTM/1w/ePCkDh48pcOHT52boOR8kZHBqlcvSvXqRat16xrnJi2pVy9KDRvGKDEx2u9O+wMAADglNDRIdepEqk6dyO9st9bq1Kmi75zZO/v33p49uSooKPnO46OiQiT1qsTkFYNShwpnrVVxsVVxcYmKikrKrltJEcrLK1BJiVVJSenjSq9bWavvfE/pxV7keunPPP92UVFJ2f+Uj+qmmz5UXl6hTpwoOFfkLlTMzlf6rs1/36mpVy+qbMHMcNWsGVH2tfR6rVoRio0NZWgkAACAlzPGKCIiWBERwUpMjP7OfWcLX07O2Tf385WTc0br1x93KO3Vo9Rdo/OLydnyUnq95Lzr/y05Z2+f/Z7vFxxrdV7RsZJu0yuvfH2uxFyoLJVe//7tiz3u0tsr4vtLSi5WoH6nl1/+ukJ+7wEBRkFBAQoKOvs1QCEhAZICFB8frkaNQhQZGazIyBBFRZ29HqyoqBBFRAQrNjas7LR7qOLiqigqKoQhkQAAAH7k/MKXkBB1bvv69cscTHV1KHVX6a67Jkn6s37/+xUe3tMAPfXUogveY0zp2OKgoAAFBhoFBprzrl98+8UeFxISdJXff+mfe3b7U089qZ49f6qAAKOAACNjdN71s4/7b0k7+7PP3j57PTAw4KIFbOzYH+nTT//lyf8gAAAAgFeh1F2lgQOTNH/+u+rQ4b5zJeNsgQkI+G+ZKb3939Jz9nFny8zFCk5AQOntl1/uo+zsw/9Tqi5VbLzVU099qZtu+ovTMQAAAACf4kipM8bcLekVSYGS3rLWvuBEjmvx2GOt9fjjc3Tnnc95eE8nFRdXxcP7AAAAAOBWlT49nzEmUNI/JfWQ1FzScGNM88rOAQAAAAC+wIk512+WtN1au9NaWyBpvKR+DuQAAAAAANdzotTVlZR53u29ZdsAAAAAAFfIlE6bX4k7NGawpO7W2tFlt++TdLO19onvPe4RSY+U3WwmaVulBoW3qy7piNMhgMvgOIUbcJzCDThO4e0q4xitb62Nv9AdTkyUsldSvfNuJ0ja9/0HWWvflPRmZYWCuxhj1lhr2zqdA7gUjlO4Accp3IDjFN7O6WPUieGXqyUlGWMaGmNCJA2TNMOBHAAAAADgepV+ps5aW2SM+bGkuSpd0uAda+2mys4BAAAAAL7AkXXqrLWfSvrUiX3DZzA0F27AcQo34DiFG3Ccwts5eoxW+kQpAAAAAICK48Rn6gAAAAAAFYRSB69mjLnbGLPNGLPdGPPsBe7vZIw5boz5puzyWydywr9d7jgte0ynsmN0kzFmcWVnhH8rx3Pp0+c9j35rjCk2xsQ5kRX+qxzHaYwxZqYxZn3Zc+kPnMgJ/1aO47SqMWaqMWaDMWaVMea6SsnF8Et4K2NMoKQ0Sd1UuhTGaknDrbWbz3tMJ0k/t9b2diIjUM7jNFbSl5LuttZmGGNqWGsPOZEX/qc8x+j3Ht9H0k+stZ0rLyX8XTmfS38lKcZa+4wxJl6laxjXstYWOJEZ/qecx+lfJeVZa8caY5Il/dNa28XT2ThTB292s6Tt1tqdZU/Y4yX1czgT8H3lOU5HSEq11mZIEoUOlexKn0uHSxpXKcmA/yrPcWolRRljjKRISUclFVVuTPi58hynzSUtkCRr7VZJDYwxNT0djFIHb1ZXUuZ5t/eWbfu+W8uGYswxxrSonGjAOeU5TptKqmqM+cIY87UxZlSlpQPK/1wqY0y4pLslTamEXMD5ynOcviYpRdI+SRsl/T9rbUnlxAMkle84XS9poCQZY26WVF9SgqeDObKkAVBO5gLbvj9eeK2k+tbaPGNMT0nTJCV5OhhwnvIcp0GSbpTURVIVSV8ZY1ZYa9M8HQ5Q+Y7Rs/pIWm6tPerBPMCFlOc47S7pG0mdJTWWNN8Ys9Ram+vhbMBZ5TlOX5D0ijHmG5W++bBOlXBGmTN18GZ7JdU773aCSt+dO8dam2utzSu7/qmkYGNM9cqLCFz+OC17zGfW2pPW2iOSlkhqVUn5gPIco2cNE0Mv4YzyHKc/UOlQdmut3S5pl6TkSsoHSOX/2/QH1trWkkZJilfpsepRlDp4s9WSkowxDY0xISr9Y2PG+Q8wxtQqG1t/9hR3gKTsSk8Kf3bZ41TSdEl3GGOCyoa3tZO0pZJzwn+V5xiVMSZGUkeVHq9AZSvPcZqh0hEPKvuMUjNJOys1Jfxdef42jS27T5JGS1pSGWeTGX4Jr2WtLTLG/FjSXEmBkt6x1m4yxjxWdv8bku6R9LgxpkjSaUnDLFO6ohKV5zi11m4xxnwmaYOkEklvWWu/dS41/Ek5n0slaYCkedbakw5FhR8r53H6B0nvGWM2qnQY3DNlox+ASlHO4zRF0gfGmGJJmyU9VBnZWNIAAAAAAFyM4ZcAAAAA4GKUOgAAAABwMUodAAAAALgYpQ4AAAAAXIxSBwAAAAAuRqkDAKCMMaamMeYTY8xOY8zXxpivjDEDjDGdjDHHjTHfnHfpWvY9eU7nBgD4N9apAwBAkjHGSJom6X1r7YiybfUl9ZWUI2mptba3cwkBALgwztQBAFCqs6SC8xbjlrV2j7X2VQczAQBwWZypAwCgVAtJay9x/x3GmG/Ouz3IWrvDs5EAALg8Sh0AABdgjPmnpPaSCiQ9LYZfAgC8FMMvAQAotUlSm7M3rLU/ktRFUrxjiQAAKAdKHQAApRZKCjPGPH7etnCnwgAAUF7GWut0BgAAvIIxprakv0tqJ+mwpJOS3pB0UNJ0SbvOe/gfrbWTjTF51trISg8LAEAZSh0AAAAAuBjDLwEAAADAxSh1AAAAAOBilDoAAAAAcDFKHQAAAAC4GKUOAAAAAFyMUgcAAAAALkapAwAAAAAXo9QBAAAAgIv9f865gEKnB1ETAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=fig_size, ncols=1)\n",
    "sns.histplot(ax=ax, data=df_gei['GEI'], bins=30, kde=True, color='darkblue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>GEI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.704173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.067637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.444000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.669000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.710500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.742250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.892000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              GEI\n",
       "count  156.000000\n",
       "mean     0.704173\n",
       "std      0.067637\n",
       "min      0.444000\n",
       "25%      0.669000\n",
       "50%      0.710500\n",
       "75%      0.742250\n",
       "max      0.892000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_gei['GEI'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot of the gender equality index is in a normal distribution with the average score being 0.7. The index ranges from 0 to 1, but the range of the countries' scores is 0.444 to 0.892. We can see that there are  no outliers in the distribution and there is no skew."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GEI', ylabel='Female Victims'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=fig_size)\n",
    "sns.regplot(ax=ax,x='GEI',y='Female Victims',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scatterplot, we can determine that there is little to no correlation between the gender equality index. Given countries with high female homicide rates, we can find that their GEI is around the mean. Also, countries with low GEI do not show high rate. From the linear model, we can conclude that there is no relationship between GEI and female homicide since the slope is near horizontal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            fem_victims   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                 -0.010\n",
      "Method:                 Least Squares   F-statistic:                  0.007925\n",
      "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.929\n",
      "Time:                        23:02:36   Log-Likelihood:                -233.15\n",
      "No. Observations:                 100   AIC:                             470.3\n",
      "Df Residuals:                      98   BIC:                             475.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.4354      3.245      0.751      0.455      -4.004       8.875\n",
      "gei           -0.0040      0.045     -0.089      0.929      -0.093       0.085\n",
      "==============================================================================\n",
      "Omnibus:                       66.339   Durbin-Watson:                   2.014\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              253.941\n",
      "Skew:                           2.371   Prob(JB):                     7.20e-56\n",
      "Kurtosis:                       9.201   Cond. No.                         937.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "df_gei = df[['Female Victims','GEI']]\n",
    "df_gei.columns = ['fem_victims','gei']\n",
    "o, p = patsy.dmatrices('fem_victims ~ gei', df_gei)\n",
    "mod_log = sm.OLS(o,p)\n",
    "res = mod_log.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph and linear regression model, it is very clear that there is no significant relationship between the amount of female homicide victims and the gender equality index. Our hypothesis with this variable was that there would have been some slight correlation. However the p-value is 0.9 and the R-squared value of 0 shows that there is no statistical relationship. There are limitations to this analysis such as not having all countries in our combined dataset and we have no control over the way gender equality index is measured for a country. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gini index data EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Gini index')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph a scatterplot of the gini index data\n",
    "fig, ax = plt.subplots(figsize=fig_size)\n",
    "sns.histplot(\n",
    "    ax=ax,\n",
    "    data=df_g['Gini'],\n",
    "    bins = 30,\n",
    "    kde=True,\n",
    "    color='g'\n",
    ")\n",
    "plt.title(\"Distribution of Gini index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100.000000\n",
       "mean      36.637000\n",
       "std        7.828821\n",
       "min       24.600000\n",
       "25%       30.600000\n",
       "50%       35.550000\n",
       "75%       41.675000\n",
       "max       63.000000\n",
       "Name: Gini, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g['Gini'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the result above, we can see it roughly follows a right-skewed normal distribution. The range of the data is from 24.6 to 63, with a median of 35.55. The right most two values can potentially be regarded as outliers. However, we won't drop them because having a very high Gini index means there's greater inequality, we want to see if there's something special about those datapoint in relation to our other indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gini', ylabel='Female Victims'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the relationship between Gini index and female victims\n",
    "fig, ax = plt.subplots(figsize=fig_size)\n",
    "sns.regplot(ax=ax,x='Gini',y='Female Victims',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            fem_victims   R-squared:                       0.286\n",
      "Model:                            OLS   Adj. R-squared:                  0.278\n",
      "Method:                 Least Squares   F-statistic:                     39.18\n",
      "Date:                Fri, 10 Dec 2021   Prob (F-statistic):           1.02e-08\n",
      "Time:                        23:02:36   Log-Likelihood:                -216.33\n",
      "No. Observations:                 100   AIC:                             436.7\n",
      "Df Residuals:                      98   BIC:                             441.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -4.1131      1.023     -4.022      0.000      -6.142      -2.084\n",
      "gini           0.1709      0.027      6.259      0.000       0.117       0.225\n",
      "==============================================================================\n",
      "Omnibus:                       71.972   Durbin-Watson:                   1.800\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              411.936\n",
      "Skew:                           2.371   Prob(JB):                     3.54e-90\n",
      "Kurtosis:                      11.739   Cond. No.                         180.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fem_victims</th>\n",
       "      <th>gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.060551</td>\n",
       "      <td>33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.371234</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.720233</td>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.703711</td>\n",
       "      <td>29.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.656064</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.358061</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.725630</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2.213828</td>\n",
       "      <td>41.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3.195021</td>\n",
       "      <td>39.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>10.706602</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    fem_victims  gini\n",
       "0      1.060551  33.2\n",
       "1      0.371234  27.6\n",
       "2      1.720233  42.9\n",
       "3      0.703711  29.9\n",
       "4      0.656064  34.4\n",
       "..          ...   ...\n",
       "95     0.358061  26.0\n",
       "96     0.725630  35.1\n",
       "97     2.213828  41.4\n",
       "98     3.195021  39.7\n",
       "99    10.706602  44.8\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gini_F = df[['Female Victims','Gini']]\n",
    "df_gini_F.columns = ['fem_victims','gini']\n",
    "outcome, predictors = patsy.dmatrices('fem_victims ~ gini', df_gini_F)\n",
    "mod_log = sm.OLS(outcome,predictors)\n",
    "res = mod_log.fit()\n",
    "print(res.summary())\n",
    "df_gini_F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph and linear regression model, it appears to have a significant relationship between the amount of female homicide victims and the Gini index. An increase of 1 to the gini index (more wealth inequality) means an increase of female homicide victims by 0.17 per 100,000 females with a standard error of 0.027. The p-value is very small meaning that there is significant relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Human Development Index EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='HDI', ylabel='Count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histgram of Human Development Index\n",
    "fig, ax = plt.subplots(figsize=fig_size)\n",
    "sns.histplot(data=df['HDI'],bins = 30, kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100.000000\n",
       "mean      79.561000\n",
       "std       11.095098\n",
       "min       43.100000\n",
       "25%       73.175000\n",
       "50%       80.200000\n",
       "75%       89.100000\n",
       "max       95.600000\n",
       "Name: HDI, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HDI'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of Human Development Index is a left skewed distribution range from 43.1 to 95.6. The distribution has two peaks, one peak is between 70 and 80, and another peak is between 90 and 100. The left most value (43) is an outlier but we will not drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='HDI', ylabel='Female Victims'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plot showing relationship between Human Development Index and Female Victims\n",
    "df[\"HDI\"] = pd.to_numeric(df[\"HDI\"])\n",
    "fig, ax = plt.subplots(figsize=fig_size)\n",
    "sns.regplot(ax =ax, x='HDI',y='Female Victims',data=df_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            fem_victims   R-squared:                       0.151\n",
      "Model:                            OLS   Adj. R-squared:                  0.143\n",
      "Method:                 Least Squares   F-statistic:                     17.47\n",
      "Date:                Fri, 10 Dec 2021   Prob (F-statistic):           6.34e-05\n",
      "Time:                        23:02:37   Log-Likelihood:                -224.95\n",
      "No. Observations:                 100   AIC:                             453.9\n",
      "Df Residuals:                      98   BIC:                             459.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      9.1298      1.686      5.414      0.000       5.783      12.477\n",
      "hdi           -0.0878      0.021     -4.180      0.000      -0.129      -0.046\n",
      "==============================================================================\n",
      "Omnibus:                       59.476   Durbin-Watson:                   1.866\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              216.643\n",
      "Skew:                           2.084   Prob(JB):                     9.05e-48\n",
      "Kurtosis:                       8.884   Cond. No.                         585.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "df_hdi = df[['Female Victims','HDI']]\n",
    "df_hdi.columns = ['fem_victims','hdi']\n",
    "o, p = patsy.dmatrices('fem_victims ~ hdi', df_hdi)\n",
    "mod_log = sm.OLS(o,p)\n",
    "res = mod_log.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data points are mostly distributed at the bottom right, indicating that most countries have high Human Developmet Index and low Female Victims. There is one outlier at the top (where the value of female victim is near 14). According to the graph, it seems like a higher HDI is related to lower Females Victims, but the effect size is not very big. According to the table, it is clear that there is a significant relationship beween HDI and Female Victims beacause the p-value is very small."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our EDA, we visualized the ditribution across the index in each country and determined that the data we relatively in normal distribution. We explored the the correlation between the rate of female victims and each index. Each linear regression plot is unique as the model's effect size is clearly different. For gender equality index, we found that there is no correlation between GEI and female victims because the slope is close to 0. For Gini we find that there is positive correlation and for HDI we find a negative correlation. Putting the indices together in a multilinear regression model, we find that the Gini index has the highest correlation with female homicide victims. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multilinear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            fem_victims   R-squared:                       0.333\n",
      "Model:                            OLS   Adj. R-squared:                  0.312\n",
      "Method:                 Least Squares   F-statistic:                     15.94\n",
      "Date:                Fri, 10 Dec 2021   Prob (F-statistic):           1.73e-08\n",
      "Time:                        23:02:37   Log-Likelihood:                -212.93\n",
      "No. Observations:                 100   AIC:                             433.9\n",
      "Df Residuals:                      96   BIC:                             444.3\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -3.1073      3.118     -0.996      0.322      -9.297       3.083\n",
      "gei            0.0693      0.042      1.657      0.101      -0.014       0.152\n",
      "gini           0.1376      0.030      4.572      0.000       0.078       0.197\n",
      "hdi           -0.0603      0.024     -2.541      0.013      -0.107      -0.013\n",
      "==============================================================================\n",
      "Omnibus:                       66.589   Durbin-Watson:                   1.669\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              340.945\n",
      "Skew:                           2.194   Prob(JB):                     9.22e-75\n",
      "Kurtosis:                      10.910   Cond. No.                     1.71e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.71e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "p-value for gini:  1.4427406889430573e-05\n"
     ]
    }
   ],
   "source": [
    "df_cp = df.copy()\n",
    "df_cp.columns = ['country','fem_victims', 'gei', 'gini','hdi']\n",
    "o, p = patsy.dmatrices('fem_victims ~ gei+gini+hdi', df_cp)\n",
    "mod_log = sm.OLS(o,p)\n",
    "res = mod_log.fit()\n",
    "print(res.summary())\n",
    "\n",
    "#Print p-value gini index\n",
    "print(\"\\np-value for gini: \", res.pvalues[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After running a multiple regression model with the three indices together, we can see that the coefficient and the P value for each variable. With a P value of almost 0, we can see the Gini index affect the number of female homicide victims the most. An increase of 1 to the gini index means an increase of female homicide victims by 0.1376 per 100,000 females with a standard error of 0.030.\n",
    "\n",
    "On the other hand, with a P value of 0.013, the Human Development index also plays a considerable part. An increase of 1 to the HDI means an decrease of female homicide victims by 0.0603 per 100,000 females with a standard error of 0.024. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing limitations and constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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estbDeOiVtKtWKRUEtgLOia9prV9f5HP+Qmv9lUU+VgghhBBCCCEeevMGckqp3wf+HtAInANOAG8BH1jkcz6tlPo18AOt9b9c5DGEEEIIIYQQ4qFVSh+5vwccBTq11k8DB4HhRT5fP7ANeBp4Rim1b5HHEUKINfHevTF+cWWQeCa/1kMRm9iNwRg/u9TPQGR9VlsUQmwO0XSOn18e4HRnaK2HIhahlNTKtNY6rZRCKeXQWl9TSm1fzJNprTNABkAp9QKwB7gw8X2l1DeBbwI0Nzcv5ikWpPUPXlzW4937w08s6/GEEOtL91iSN26OAKC15sO7a9d4RGIzyuQLk30Eh2IZfueR1rUekhBik/r1jZHJdjUNgTJq/c55HiHWk1JW5HqUUgHgR8AvlFJ/BfQt5smUUlN33D4K3J76fa31n2itj2itj1RVVS3mKYQQYsV4HFasRrGQbynNVIVYDKth4HEW51n9ZeuvibUQYvMIuIrnGLvVwOWQVjUbzbwrclrrz45/+D8qpV4B/MDPFvl8jyul/heKq3JvaK3fWeRxhBBi1QXddr5yooV4Jk9T+frsSyU2PouheP5YE4PRDI1B6fslhFg5JzsqaAyW4S+z4XPKxNFGs5CqlU1AbPy/PcCZhT6Z1vol4KWFPk4IIdaLoNtO0D3zalw2b/LGreIW4se2VGG3lpL08L5oOkehoGc9vnh4uOxW2ipLukRvKolMnnSuQMVD1PPsan+U28NxDjUHqQ9I4C5Wl1Jq1mbepqkZjmcIuuwLvp5NFUvnePPWCL4yG4+0V6CUmv9BoiSlVK38X4DfBe4A5viXNYuvWimEEJvSxd4I57sjQDH18lBz6c2IB6NpvvteN/FMnhqfg/1NszdivZ/WmvfuhYilc5zsqKTMvvD0mEy+QCSVo8rjkIvsQyhXMAkls1S6HRjGyv79bw3FuTUU50BTYNp+nHAyy7ff6SKbN3lmZw17GzduQ/u7Iwmu9EXZVe+jrXLmm2Qovu9+fnkArWEskZX9kGJd+esrg1ztj1LhsfOV4y2LPje8dXuUd+6OMRhJk82ZPLWjet7HXBuI0jma5FBzkCrvyk3sJLN50jmT8g06gVrKdN/ngQ6tdXalByOEEBtZudvORAxUPsMeujNdIYaiaU60V0zuscsVTFK5AsOxDHlT0zmaZDiWIZLK01ReRrV3/o3n90aTvHmrWITFMBRPb5//IjlVrmDy7be7iKRyHGwO8NQCHy82Nq013z3VzVA0w/ZaLx/fWzfvY5LZPG/eGsVlt/BIe0XJN3jZvMlLF/spmJrBaJqvnWyd/N5YIks2X5wvHoim2cv7gVzB1MTTefyujZH69dLFfrJ5k86xBH/7qS2z/pzVMPCX2Qgncxv2RnIziqVzOKyWJa1CbQaD0WLV3LFElmzBxGksbg9dhcfOneEE2XyBd+6O8ujWSmyW2X+3yWyen10qTnCEElm+eGz2AojZvMmbt0ZQCh7dMvdx7xdJ5vj2u51kcht38qiUQO4SEACGVnYoQgixsbVVuvny8RaAB2YQh2JpXrteTLvM5E2ePdBAJl/gz97pIpzMcaK9nN31PvKmidbgsBm47aWl1nkcViyGomDqRe1xSGaLq3GAlLt/COVNzXAsA0B/iX//d++Ocam3uPpc43Owpdo7zyOKrIbC7bASTeXwlU1/fbdWuDnQHCCaynGstXzy66ap+Yv3uhmMpjnQFODpEmbz11q5285AJD3jhM5Uxf2QzQzHMpJWuU5c7Inwy6uDeBxWvnS8Gbfj4UtxnvD09mpOdY7RUeXBaVt8IZTDLeU8vb2KgUiaap8TyzxZH1bDwGW3kMgU8M1T8OlCT5hz3WGgmAlzoClQ8rjGklkyueLkUX8ktWkDuX8OnFVKXWK8dQCA1vrTKzYqIYTYoGZLAXHbrThsBpkpKRzRVJ5wshhA9YXTfO5wIx/aVUNPKIXfZSv5BqLK6+BLx5tJZgo0Vyy8CIu/zMZjWyvpGk1yoqNiwY8XG5vNYvCBHdXcGIxzqDlQ0mMmXsMWQ817ozWVYSi+eLSJgWiapqDrge/NtJqcyZuTKwNdY8mSn2stPXeogYFIuqRS7k6bRYonrSMTr7F4Js9YIvtQB3LNFa5FXVNm8pUTLfSGU1R7nfOu4NutxuQER/M8742JPeVKQXCBK/Yt5S4ONAWIpHIcayuf/wHrUCmvzv8M/BFwkff3yAkhhFgAt8PKV060EEnmJisRVnrsHGwO0B9JTwZQSqlF3dRVehzgWfz4jraWc7R1cReyZDaP1TAe+jSkjWxfY4B9jQGgmGoZy+Tx2K2z3nDtawxQ6XHgtFkWnBLodljpqCr9xVpmt3Cyo4Lbw4kNc7PlsFpmLSAh1rejbUHimRxBl50GWSVdNlaLsaD3hNdpw1tChklHlYcvH28GRUlbEaYyDDXjCn8mX8A0WdR+89VWSiA3orX+1ys+EiGE2OR8zunlnZVSG34/2vWBGD+91E+ZzcKXjjeXdOEV69vPLw9wtT9Ga6WLzx5snPXnVjMV8Hh7BcfbZbVYrLxqr5MvHJ19T5ZYf6p9y9fEPJTI8p33usnmTT61v472BUw6rYVSpk9PK6X+uVLqEaXUoYn/VnxkQggh1r3O0QRaF/fZDcUy8z9ArHv3RoupZZ2jSbTWazwaIYRYPQPRNOlcAVPrDZHKXcqK3MHx/5+Y8jVpPyDEBnOpN8JrN4ZpDJbxqX31K15iXBSNJbLYLGrTrlQdagkylsjiddpokX0+S6a15sWL/dwbSfDY1qoFbdxfLo9tqeRsd5hddb5la0WRzhWIpnLLOnMuhJhdwdT81ble+iNpPrizmh21vrUe0rKaOKdUeZe3ZU5HlYeOag/pbGFNzr8LNW8gp7V+ejUGIoRYWZd6I2TzJneGE0RSOWk6vQom0g4tSvGFY00Lzt/fCCo9jjlLQ4uFSWQL3ByMA3CxJ7wmNxJ7GvzsaVi+6m3pXIE/fbuTWDrPkdYgj2+tWrZjCyFmNpbI0jm+un6xJ7KpArls3uTb73QRTeU40BxYcMududitBp/eX79sx1tp86ZWKqVqlFL/t1Lqp+Of71JK/d7KD00IsZz2NPixWRRtlW78C6gyJxZvMJpG62J599G4tOIU83PbLWyt8WA1FHvHi49sdMlsgVg6D0h7CyFWS7nbTnO5C5tFLevEzHqQGl+NAxiKPtznlFJSK/8T8P8A/3T88xvAXwD/9wqNSQixApZ7lv1hMRRLMxzLsK3Gu6BGo2/dHmUwmqbe76TC42Br9YMbprXW9IRSBN12PA9xiWvxPqUUn9y39NngoVgaQ6liNdNlkCuY3BiMUeV1LHhludxt52RHBX2RFCc7Kuf9+eFYhrfujFLvd3JkkZVUhXjYWQzF5w7PXqxoOeQLJv2RNFVeB5FUjtF4lm01HobjGd69O0ZrhZv9K5BV4C+z8fjWSrpDSU485EWQSrlzqNRaf1cp9U8AtNZ5pVRhhcclhBAzyhVMTneGsFsNDjYFljU3/n6xdI6/eLebvKnpDaX48O7akh7XH0nx9p1RALZUe3hmV82MP/f6zRHOdIYos1v42iOtG6LUsVj/bg3FeeFCHwCfO9S44HYWQ7E0V/tjbK32TFamfOXaEJf7olgNxe8+2rrgPZ8LqTj5+o1husaS3B6K01bppmKZglEhNoObgzEGomkONgfXfALwxYv93BlOUGazFEv2axiMFlvqDEbT3BlO0FHtWZFxHmktl4keSqtamVBKVVAscIJS6gQQWdFRCSHELE53hnjr9iivXR/m5lB8RZ+rYGoK41X7MvnS22h6nTactmJQNtEgvDec4runuvnNrZHJnxtLFKs8prIFktn8cg1bPOTGElm0Bq2LHy/UT873c6YzxI/O9U5WrZx4/Re0pmAuvZLlhZ4w332vm5uDsQe+N/GecTssD3UzZrHyLvVG+O573Vztj671UEoSTmZ58WI/p+6F+NW1obUezuSWgbFkllxh4lxRmHwP+8psOKS/6Ioq5Qz5D4AfAx1KqTeBKuC3VnRUQggxi4kACcBpXdkVrIDLzif31TEQyXCwOVDy4zwOK199pIV4Ok+tv5iG9uatEXpDKXpDKTqqPdT4nDyxtQq7ZYy6gFNWHR4i2by5os3T9zf5iaZyWAzFrvqFFzhw2gyiKbBbjMkV7w/sqCboslPrdxBwLa1QkmlqfnVtCK0hnMqytcY77fuPb61kS7WHgMs27f0uxHJ75doQeVMzHM+ws279FwOxWgyshiJX0DjXQYD0oV01nO8Js7W6+B4eiRevlU6rhd31Psrd9gVtSViMlT6frnezBnJKqWatdZfW+oxS6klgO6CA61rr3KqNUAghptjf6MfjsGC3WGiuWPly91uqvWyp9s7/g/fxOKzT0kkaAmX0jCXpHEvyX9/q5LGtlZxor+AT++oWdNx37ozy3r0xdtT6Zk3ZnM/ZrhA9oRQn2ismZ07vd60/yjv3Rqn2OPnw7los0q5iWbxybYhz3WG21XgX/LcvlcNqWfRrA+AzBxq4O5KY9v5yO6w8tnX+/W3dY0nOdofZWu2Z9cbYMBR1fid94TQNgQffw0qpyZTOcDKL02ZZUkD3yyuDXBuIcrS1vOQUz7duj3JrKMbRtvJNVe1vrWmteev2KOFUjse2VuJb47Ys9YEyusaSNAZXr7n9UngcVj5/tImRWHEv2koqmJpwMkvQZZ+1XVFTuWta6vZ23r9WTryHAbpGk7x4sR9/mY3nDjUs6v2sdXECqC+S5smtVTRXuHjpYj/XB2Lsb/LzgR2LP+dtZHOtyP0ImGj8/Rda68+t/HCEEGJuSqlFBVZLVTA1b98ZJZs3ObmlAscCVwMf3VJJS7mL77zXjcVQ3BiMLWqT9vmeMLmC5mJvhKd3VJcUYEWSOV661I/FUDy+pZJXrw8DxbLwv32k6YGf//XNYf78nS4S2QJ7x4vkLHSflZjZtYFiKuHNoRha167oHs/FcjusMxZGOtsVYiiW4URbBX7XzDfgL18dJJTMcXc4QUeVZ9aZ8s8daiy2QRlf3cvkC7x0sZ9EpsBH99RS6XFwrjvMK9eGcNktfOVEy6LSLPMFk4u9xd0gF3oiJQVymXxhco/rm7dGJZBbRj2hFO/cHQOKxTg+UuK+45XymYMNk8HKRlHtda5KK5sfnOmhJ5RiS7WHTy2xHP+V/gjpXIF0rkB/JE1bpbvkxw5F05zpChNw2bjQU3wvv3N3lKbyMm6Mp2ZfH4ivy0Du7kiC164P0RB08czO6hU538+1Fjn12dqX/ZmFEGIDuT4Q4927Y5zrDnOmM7yoYzSWuzjUEsDrtHKkZXGbtPc2BLAaxXLSpa6SXe6PMBBJ0xtK0R1K4nYUg9DZKhr2hlL4XTbSuQIOq0GFp3iTo7XmZ5cG+Lev3OJcd3hR43/YHWsrx+u0cqy1fF0GcbMZiqV59fowV/qivH5zeNafKx9/TQVcNqxzvD6tFoMKj2Nypv/eSJJ7I0mGYxku9ISB4usQiu0LFrPXb+J5dtf7sBqK9io3//k39/iPb9xlNJ6Z9TF2izG5QtO+gBtOMT+f0zYZ3Fd61j54shhq2utwI7s5GOOPX73Nj872Lnkfq9aavnCxrH/P+PtwKXbW+XDYDKp9Dur8CwtCf3F1kKv9Ud68NYLXWZzMaa9yo5R6/3zatj6Lnrx3d4xQMsel3siiz2HzmWt6S8/ysRBCPHR8ZVaUKhaQWEgfvky+gKHU5D6BD+yo4QM7oHM0wfdP97Cl2rOg8syPdFTwSMfCVvJaKtyc6QxhMQzaqzzsafAzlsjSEJg5neixrZUYSvGRXbU8trUS6/jYU7nCZFGA891r06x6o3j37hg9oSSPdFRQ53//93y4JcjhluAajmxxXHYrdqtBNm/O+fr/+J7ayXLkC7k5rgs4cTsspHMmbZXFlLHj7eWkcgUq3PYlpb59aFcNj22t5NpAbHJG/8ZgnEdmmchQSvG5Q40kc4U1rwq42fhdNr76SAuJTH7a+0Is3YWe4qrX3ZEEo/EM1b7Fr9oppfjAjmqu9kdnvD4ls3nKbJaSJ6NaKtz87ae2TPva2a4Qd4YTHGsrnzPjI1BmZyiawV9m46snWihojctefF+e7KgsqaXJWumo9tAbTlHldeBbof69c52h9iulohRX5srGP2b8c621llwDIcRDozHo4kvHm8kX9LTc/7l0jib4V7+8yWgiw1eOt0xrX/Dq9WHGElm6Q0m213pL2jPQPZbk1lCc3fW+WS/SM+0pagiU8c0nOjAUjMSzvHC+n2qfY9ZArjHo4vNHH7ywltksbKn2cGc4wZ4GuQRMuDeS4J27o7RWuDneXkE4meXN8eqk+YLm80cfTF+dkM2b/OhsL6OJLB/dU7uglKPV5HFY+crxFsKpLM1z3HRZLcai0nB9Thu/91g7edOcTFuu9Dj4rSl9sCKpHDaLmryJK9WPzvVybyRJR5Ubr9NKwdRsmaGv41SGoeYM4qLpHGc6Q9T5y9heu/qp3huZz2lb871xm9Gueh+94RS1fifl7qWvdu5t9LO38cEU619cGeRSb4T2KjfPHmiY9r3BaJrXrg9T5XPw1LaqWQO9dK4wmeKfyOb5nUdaZx3HR3bXsKveR7XXgcNmIZUt8PqNYYIu+4zjm+m50rnCkos0QTEz4cfn+rBZDJ471DBvG5bDLUF21/uwW4wVW/Wd9SyltZZSUQvU+gcvLuvx7v3hJ5b1eEKIpZltX4Jpal661E/3WIont1VNVgq8O5KgL1xMS3nr9ui0QK7G52QskaXCbcduMegeS3KlP8rOWt+MRVxMU/Pj831k8yb3RhN8/dG2B37mQk+Yl68OUWa38PzRJs50h4mmcjy1rXpyT9Pbd0bpDafoDafYWeejZgGztkopPrW/Hq31hkoLXCl3huOc7QpzeziOzWLQF06zu8GPy27FV2YjmspRM08a0WA0Te/4a+RSb2TdBnJQXE2ZbW/cYoSTWX50theAZw80kDc1Z7pCtFa4HwiObg3FeOFCPzaLwReONpXc6DxfMLk3kgRgMJrhG08sz06RV64NcWc4wTkVpsa39Eqe4uEwEs/w43N9WC2KzxxsWNaAdmedjx213hU/N98eLrb9uTuSwDT1tABl2vWl1jdZtfl+V/qiDMcy+Mqs816DrBZj2nnxjVsjXBrf91rhsVPutvOra0MYCp7eUT1t/3o8k+dP3+4klS3wgR3VS25OfmMgTixdbBV0byRZUiC50pV3H956nUIIsUyi6Rw3B+OkcwXOj+/vAdjXGGB7rZeg284T26qmPebDu2r48vFmvnC0GcNQvHixnyt9UX4y3sj5fkox2Y/HNUvj8ImgMZUtcKkvwrmuMHeGi6tFEyZS1LxO64JSRKePRYI4gF9dG6JrLElvOIWpNZUeO2U2C3arwZePN/PlE808ed/f/X41Pie1fid2q7GoVgEb2a2hOKFkjlAyx63hOL+4MsiVvig/vdRPKluY9rO94TRaF1cwh2Oz72+7n9Vi8EhHBUGXbcEpyXMpG785s1mMFS+vLjaPGwMxIqkco/Esd4YTy3781Tg3nxx/P53sqHxglWnq9SUwy6TPUCzNazeGKXfbaSp38eEFVtiduP4ZSuG0WbjYG+H6QIyr/TGu9E3vBzgWz06eSyYmzJZia40Hl91CwGWjpXJ9FACT5G8hhFikdK7AD870kMjkqfTYGUvk2DWl5Hq5287//OyeGR9rGGpaeqTPaSOVLcyaR6+U4gtHm+gJpWZdtTnaWk4iUyDgsrGrzs+57gjZvDltVvRIazlbqj2U2S0lVd58c3z282BzcN1uKF8rtX4nsXScY23lfHxPHR6ndcYCNK9cH+LGQLGU/aHm6fvj7FaD5481T/vaSDzDzy8P4LZb+dje2gVXSN0o2irdnO0Ko9G0VboZimYYjKZx2S1YLdN/jwebA4QSWZw2Y960yPudaK9YVIXYuXxgRzUtFW4qPXZpWi5KtqXaw4XeCFZD0boK7XNWwr7GAPsaAzN+73BLOR1V719fRuIZXHbLtHToicmubN6kKehacPD5SHsF1V4H/jIb5W47sbQDQymU4oF2Oo3BMvY3+RlL5DjaGuSnF/sZiWd4ekc1jcGF//5rfE6+9WTHgh93vjvM23dG2VrjWfbqmnL2EUKIRTp1b4yfnO/D1PDsgXr+7ge3LHpG9LlDDfSGU7PuWwPwOm3srJt9Fa3C4+BzU/YU/e7JVtK5wgPNxktNA9Na8969MbSGU51jEsjd52N76jjamnmg6W02b/Knb3cSS+fZ1+TnQncxDehMZ+iBQG4mF3rCDEUzQIZ7I8lNuwerwuOYlur4kd017KjzUu11PLDK5XPa+MzBhvsPsWasFmPT/l3Eyqn2OfnWE+2bOqth4vpyujPE6zeGcdosfPWRlsk9p16njS8fbyaUzC0qmDUMxdaa9997LRVufvdkK6gHC5EZhpoMnHrDqcn2L6c7Q4sK5BbrdGeIZLbA+e4IJzsqlzXdUvIBhBBikWzjG5gNpbBbjXkvzlpr/vryAP/utdtcHK+eN8Fps9BR5VnwCd40NZFkDq0fLC7sdlgfCOImDMXSRNO5OY+tlJps6jxbc+eHmcVQ1PicDwQdyWx+ch/FaCzL1vHGvaX+Dlsr3FgMhdthmXWPSakKpqYvnCKTL8z6M7+4Msi/e+32ZNn/taC1JpEp0FrhnreAgBAb2WYO4qYaiBTbF6RzBcLJ6aX3u8dS/PzyAL+4MjjjY+OZPIPR9KzHzuQLxKZcv/wu27xbBSrcdgIuG0pBR9XKNlO/38S5v73KPblFYrnIipwQYlOJpnMkM4Ul3wAPxdKYJnMe53BLkK+eaCWZzU8rZDKTeyMJfnF1gIs9xYIWZ7tD1AWcvHV7lDq/kyOts692pbLFfm4T+xG01vRF0pS77Pz0Uj+do0m21Xj5xL66kv5tF3si/PLqIKlcns8caKClws2pzhCVHju766dv3v7I7lo+tLNm3fZZimfyXOyJ0BgsW9Wm5RN9yGYKlAMuO49uqZxsP1DtdVLrC+GwWkoqFNNe5eFbT7ZjUWqy9QMU/+5nusKkcwWOtpbP2mw7ms7x04v9GEqh0dwYiOMrs/F3P7j1gZ9NZQuThQPOdIampUwttMT4TCLJHNF0jsZg2ZzH+dW1IS70RKj1O/nCkUYGohkCLlvJFSqHomnevjtGY7CspFVPIcTKeqSjglzBpNxtfyDT5ExXiFS2wOW+KPua/GRzmqby4jkils7xX9/uJJMzeXRL5WQmSL5gkjc1eVPzZ+90ksjkOdAU5Hh7eUnniVzBpMrjoLncxe4F7Ec+1x3mfHeYPQ0+Ds/T/7U/ksLrtOFxWLnQEyaczHG0tZz6gJMvH2+m2ufkznCcoViGA02BZVmZk0BOCLFpRJI5/vSdTrJ5kye2VS26X1f3WJLvn+lBa/jEvjq21cycQmW1GHx0Ty2mqfmr8710j6V4ens1exv95AsmV/tjBN02GoMu3r07RiyVJ5M3SedMttd4+eNXb9E9lqK10k1bpXvGoOA3t0d4584YtX4nnz/ShMVQkze9HoeFWLqAUtA1liz53zccTzMaz3BzKM5AJEOuYGIbrwxW5XU8UJ1zahD3m9sj3BlOcKK9YsF7lVbCzy8N0DWWxGIofv/xtgWXpl+MztEEPxyvtviZAw20zrBn8Vhb+eQNyOnOMX59s9iOwGEzZn09TTXTvrjbwwlev/F+M+5HtxT7Jw3F0vzq6hABl40P7arlcm+UvnCakXiG6wMxktni6y6ZzfP7j7dPq/jotBm0V7m5O5Jg15Qg/s/e6eRyX5RDzQE+d3j29glziabffz8eby+fs9/TxOt3IJLm5WtDXOqN4nZY+J1HWku62Xn1xjC9oRS3h+J0VHrmrK4Zz+T5y1PdpHImzx6oL7mdiBBQnPz4y9PdRNN5Pr2/flUnkDaScrd9xnToSDLHSDzNvZEkj22p5Aene8nkTQ40B3h6ezXRdJ5MzgSYLGwUS+f4zrvdJLMFDjT7SWQK3BsttuO5ORTjZEclDqsxLeXyfu/eHePmULHiZlulmxqfkxcv9mOamo/vq5u1guibt0bI5k3evDU6ZyD31u1R3r4zisNm8MzOGl6+OgTA5d4I6byJxVB8fG8tL1zoR2sIJbLsrPPxxq0RmstdDxREK5UEcuuYtDMQYmEiqRzZfPECMBIvvbLd/cLJ3HiFvAKvXh9CAVtrvGTzJi9c6KNnLMmeRj+PbanCbjWIpnOTJc4v9UXY2+jn17dGONcVxlCKr5xopr3KTW84xeNbq/jtw430hlOMxrPjFcwy0womnO4M0RtOcaK9nKt9Ue4Ox7k9HOfJbZXUB1yT/7Z4psCx9nK6RpMlN+fO5AvYDAOH1aDO72QwmmYskcViKJqDZTgsFvrCKd6+M0pj0DVtX1wym+edO2OY4ymibZUdMxb3mOu5h6IZ6vzOaStNSzFRFMMynuK6GkYTWSYyWUcTmRkDuakshjHl4+IY45k8V/ujNJe7Zi2/3RNKcqYrjN9pJZUrMJbIcqYrRLnLzpPb37/on74Xoj+Spn88lak3lCKeydE9lsRfZqU/kiLosnNvJMEL5/v5nUdaMAzFcCyD22Hh2QMNXOwJc3Ugxo2BYvvYn13uJ1cozn5/+kDDgiozTqw6JjL5yfdjODl3Gu/jWyt5926IrTUeukaL76VEpkAiky8pkKvyOOgNpfA4rDjtc4+1eyxJaHw81wdjEsiJBekNJxmJF1MFr/ZHHwjkfnFlkHsjCR7dUrkpK9FqrbkxGMdhNeY9983kVOcYY4kcFgMagmX0jZ+3JtIv6/1OjreVM5rIcnK80uxgNE08U0xXT+dMdtX76I+kqPY6uDeSYCSepcxm4WN7NDtmSWGfmMCyWRQBl53rgzF6Q8VKllf6orMWROqocnO5N0reNHnl+hCPdlRyYzDG9YEY+5sCkxOaw7E0naMJsgWTY63lGEphak0im+fWUAKHzSCZzaMoTr45bQbne4r3CMOxDPsbA9isirypF9SWQgI5IcSySueKzTodNguPbalc0I3+UjWVl1Hnd/LTS/04bcYDPW5KtbPOSyiZ5Y1bw0RSOV682M/XvU5CySy3huKc7QpxbTDGrcE4TrsFn9NKXcDJaDzLvvG+MvlC8U7f1JqC1hxpLWd3vR+bobg2GCOXN9le66Xcbefj++omb1Yv9oT53qluqrwORmIZ3rw9zOnOCPV+J2e7wtQHXDy9vZq3747RFCzjYHMQtpT+b3vl2jBX+6O4nVYOt5bzk/N95E2NRSlq/WX817fvEUvncNqsdI4m2V7jnVzdcFqLe7ZevTaE027hJ+f7FlSA4runehiJZWitdPHZg43zP6AEH9ldy7WBGPV+56LTVJLZPC9fHcJiKD64s3reKpF76v1c6AmjNdNSdAYiad64NUK938nJLe+vPu1v9OOwGtgsanJvxksX+ukNp7BbDb7xePsDaZIFU/Mf37jL5fF+S09uq+LeaIKt1R7ypmZnrZc/e6eTt26P0lLhwmIYOG0WLvREsFkMav1O3HYrv745TLXXQdBtJ5TMcWckztt3R3FYDV6/MYLTZuGLx5p4+doQvaEUg9E0TeUuXHYryWyeRCbPf3zjLp/cXz8tPap7LEl3KMmeBv/kTUckleN7p7rJFkw+vqeOsWSW3fU+TM28pf+3VHvZUl2cTW+tcPPtdzqxGKrk88dT26uKrT5c9nn/fi0VLiq9DtLZAjtrN9+NtlhZjcHi5EssnXsgUItn8pOpyqc7x1Y0kCuYmnPdIewWS0n9zOZzczDGr2+O0FTu4pmd1bOmQp/rDk82837uUDE1fyFcdgu3huJYDEV/JM3TO6oZiKRoDLp44UIf22q8086fANcGYtwaitEQdHGwOUC118mh5iBv3xklmc1zbzTJ2a4QoWSWbzzePuMq6f6mAHV+5/g120bB1DhsBloXzwkz6R5LsqvOR4XHzhs3i5OzLruFV64NMZbI0htOTaasN5a7GI5lCKdyvHChj99/rJ1UrsC1gShdY0nsFgOv087+pgA/vTTAC+f7iKRyFEzNx/fVk87l+dN3eskVTD65r77kjBcJ5B4issInVsPpzhCXx3u5VHsdiy6SMRLP4HPaZt0HNJXWmkzexGmzcLU/Sjpn8ur1YT64s5ptNcXnT+cKvHV7FJfdwrG28jn361gtBk9sqyKVK3ClL4rVUFgtxcIWxfYAigq3g4t9EfIFTSiR5cntVdNuyB/fWonXaaXCbZ9MVSyzWzjdGeJX1wY50xWircLNt57soKXCzWg8w3udY7x7d5TeUIp0rkC110EuD16HhYIuXnSgmLI2EstQZittz9VwLEN3qBiUTbAog5MdFTisBme6QgTK7AxE01gMRSiZo85fvDz86TuduO0WPne4Ea/TxuePNNEbTqG15mxXiKe3V5fUJNo0i78ngNF4dp6fLp3TZil5NXI2F3si3BpPuakPlM16vFAiy4sX+xmNZ8jki+mot4cTk/sK37g1UgxwxpJsq/VOzgBPLRqTzhVw2iwMRdMMxdKzVilVaNK5AlprTK3pi6RoqXDhddpor3Izmsjy88sDjCWyZAsm//ijO2gIlPG90z0MxzJYlOJqf5SReDF952N7a7k1FCdf0Lx9exSXw4rWmvPdYUbiaawWA5fdgsdppcrj4GhLkFvDcZLZAslsgat90cmxpnMFfnS2l7yp6Qml+PyRYupl12hyssjLX5zqxm4pBq9fPdFCwXywGM9sCqZG6+JkyK9vjvCp/fXzPkYpVfLKmstu5asnWkoejxBTOW0WvnS8ecbvuWwWmstddI0lZ10ZWi5nu0KTKdtOWzGtsGs0ydWBKLvqfDMGM6PxDB6ndcbJjvfuhYikckR6IxxtDc5a3Xhilf3+jydMZH3MNom6q97PkZYgmmLK5MXeCHvqfbx9Z5RYOs+d4QQdVZ7JSZxUtsDNwThbqr0EXLbJ62mV18Gn9tdjmpofn+9jLJ4lUGbj5lBsxn+71hpfmW1ywq/K6+Abj7ejNQ/cZ4zGM7x+Y5irAzHKbBYOtQRIZAuEEzmsStE5mmQskSVXMPnPv7lHKldgW7WX3nCKSCrHe3c1R1vL+cS+elK5Am2VHuxWg6DLRirrpNbn5I1UjkzexG4xsBrw2s0RYukcDmsxK0YCOSHEmih3F0/+hlIESyxzf7/XbgxzpjNE0GXjyyda5kzr0lrz/TO9dI8li5uKg2VcG4jhcVgpd7+/F+idu2Oc6w4DUOl14HPaCCezdFR5Zr3gfHBHNU1BF1Vex2Tq47eeaOeDO6oZihV7Xr10sR9fmY1sXpM3TezjxYCdNsuMqRpaa+4MJ+gaTTISyzIYvcLH9xZXLwYjaW4OJNjb6Ke9ys2z++tJZgqMxDNUeh3Ujl/ASr3gQnGD+PdOd5PJmdwajPPswXqqvHaqPE4CLjsf2FHN7no/AZeNX98c4Wp/lM8caGBLjYfz3WEu9UbJ5k26xpLsrvdjMRQf21PLf/nNPSyGwXfe6+L3HmubN1XSMBQf3VPL9YHY5KplqaLpHD+7OFB87r21y74PrtbvxFAKQ0GNb+Yqn8Dk6thQrNicusbnxBy/j8nkizOvV/qiHGgKTJbanuqPX73FO3fG2FbjwWIxyBc022q8D9xEaK354dk+XA4LFR47FR4HlR47zx5oYG+DH6vFoD+SIpbOM5bI0hQsozHowm41+MLRJkLjM8U/Pt+HMV484EM7a9hW4+XFC/2YWjOWyFLtceC0GXgcVoJuOx/dXYehitVYX7zYj6mhO5TkYFNwWql9pcb3TZoa25R+b21Vbqp6HGTzJhYDxhLFVOdvv9NFJm9ysqOC4yX0c/M4rThsBpmcSYVncecQIdaCYSg+d7iRfMFctvTx2UxdrZ74+CcX+sjmTe6OJPib9/U7e/PWCO/eHcNXZuMrJ5ofCOa21XgYjKap8zvnrBx7uCWIUmrGPWkXeyK8fG2QoMvOF481zRgw+stsfO1kK6OJLC9fHSSXyo+vBJYRS+cJuGzT/m1OW7HVx+2h+IzXjolrSypXTMXeVTfz9eXFi/3cHIyzu943WZxstnuLH5zp5dZwnIFIenxirzgxlsubnLoXYne9n3Ayi9WiGBufoHz77iitlW6u9kVx2iyTqaD7GgPU+csos1vwOKz4y2wMx9Kc7w5xbzRJjddJXySNxTC42h+jwm3nkY7SW/2seiCnlPqXwBHgjNb676328wshVtbOOt9kX62JoG6h+sLFvPVQMkcqV5gzkEvnTLrHCyXcGorx9ZNtHGkJUud3Tivq4HMWT3eGUmgTvvNuF3lTc6glyJOzbDK2WowHUmOUKvaw2VrjRWvNoeYgt4ZjtFa4SwowDjYHOdkRpTecIpHJU2a30DWWxGoo3A4ruxt8fGBHNfsaA9isBh/fV0cyVywdf3c0yZ7GAFurPQxE5r/gAmiY3M9V0BqH1TJtw7ZSarIy54d21fChXe83K93XGODuSAKnzTItfWZ3vZ99TQGGohmyeZOC1iVdTLbVeEsq9HG/y73F3xfA9YFYMZ10GbVUuPn6Y60omPP32Vrp4lx3iJYKN3vr/XjLrOxpKL4+BiMZ3HYre+p9bKv1zpjm+eatEXIFzanOEI9vraI+UDZjyezMeODstttoDKrJ15XdakzeHFqU4mRHJdm8yaGWwGQwaLMYVPuchJI5nFYLZXaTx7dW4XHa2F3vZyCS5kJPhIDD4PnjTbxy3UnnaJJDzcFpFVoNpbBZDA41l/OtJ9qnTXY4rBY+f6SJvnBq2t/T47DylfGVrokUM4fVmEzDmtjDN5+J40RTuVXt9STEclnpIA6YrHpotxq0j6ds+8psjMQyM+6xmjiHRlM5EpnCA0HWkdZy9jb6sVvmbqVjtRiz9hS9PRxHaxhLZBlLZKnzz7xKXu1zUu1zcnckwfWBGC0VLj6+t46BSPqBpt5KKT6+d+6KzE6bheePzbxKCsXJsdtDCQBuDcf58JxHK6rxOnBYDU60V2A1FOjiWEYSGb7xRDt3hhM0B128fG2QVK7AoeYALpsFq6Hw2C0MRjOTGRhT/01KKTqqPexvCrK/KcjWag+DsQyRZHGFrzFYxtmuCCfaZy8ONdWqBnJKqUOAW2v9uFLqj5VSR7XW763mGIQQK2+24g2lenxrJW/dHqW53DXvpt8yu4XDLUFuD8c53l5BIpOn2uukPjD9BvBgc5BKjwPneDpifjzVKzk+a7YYSilaK90zbvgOJ4uFTJrLXdMuihZD8dVHWtnXFOD2UIy+cJr6QNn4Hqgk7ZVuglMC4PpAGVtrPIzGsxwar8JZ6gUXijf2zx1q4N5IcsH7NWp8Tr75RMeM3/vYnjou9IRpq3TPuydpqZorXJzuHEMpRUNwZQpTlLK5vDHo4ltPdmCoB/du1fgd1PmdjCWz7J9Swn+q4+0VvHd3jMe2VvLBHTWkcoUZ0zidNgtHWoPcHIzzoV3v71XZNSVVq9rn5ON76xhNZGaszprOFTjeXk42b05bGX56e3WxQqrbgdth45P7Zk5b/NT+Om4MxmmpcM24Yl3ldTxwwzWVx2GdfN5iT6jMZOGCqQajxdXNqUGkaWrG4lnKZTVOiFlNTdmeMFFIa6aU7ce3VvLGzREagmWzTrJOPZcPxzJkC+as6d8TTFPTNZak3GPncEuQcDJLjc9JjXf++4CP7anl8a2VeBxWlFIrVgFUKcUjHRVc6o1wsDkw788/d6iBuyMJtlR7CLjsRJI5TnRUMBLPFtsIeJ2TKZ5ffaR18nGPdFTyg7M99IeL57XZUsobg8XANZbOsb8pQDiZ43JfhKDLTjiVo20BRWTUTE1kV4pS6r8BhrXW31VKfQ6o11r/m5l+9siRI/rUqVPTvrbce7zE+rLce+5kTyAcOXKE+99Hm1kokeVP3+4kb+pp/Wdmcqk3wkg8w9HW8mkVI5dDNJ3jv75VLLt+tLWcx7aWNrMmZpfOFSYbr6+m5XwPaa1JZgu47EvrzVaKgql5794YWhdbIaxm0aFS3RmO81fn+gD49IH6yUIwv7o2yPnuCE6bhd892UqZfWUnCsTKetiuQ5tBfyTFd9/rwdSaD+2qYU/D7Onwr1wb4lx3WN6v48LJLGe7wjSVl00WcCrVbNcIpdRprfWRmR6zYitySikr8KdADfCe1vofAZ8EmpRSzwL/BQje95hvAt8EaG6efYlUCCFmEs/kJ1faJkoZz2auC9NSpbKFyU3gkdTcZddFaZajcepaU0ot+6TBbCyGmrWc9nox9b0x08fpXIF0rvDQ3xgKsdqiqTzm+ELPfNewcKp4rU3nCqTk/UrAZefpHdWLeuxirhEreUX5LHBea/3PlVL/Rin1JNAE/M9AG/AM0D31AVrrPwH+BIorcis4NrEOPWwrrrJiuPyayl08trWScDI3b7nzlVTjc/Lk9iqGY5l1fzMtxFrZ0+AnOl7lcu+UiZWntlXzjm2M+oBzWpqxEGJ1bK32cLytnHS+MGPq9lRT36+L3RcvFm8lA7l24ML4x+eAPcAvgQ9SDNb+b8ZX34QQYrkcbS292tNKOrTMBTmE2GxsFmPGQkNBt52P7qldgxEJIaBYCfL+Xm6zkffr2lqxPXJKqc8AJ7XW/0gp9afAVWAU2AmcpFj0ZNd9j5lMrXS73Yd37NixoOec+Kes8NYDITaMe/fu0drautbDEGLDWuh7yBzv6yeXISGK5DokxOxMrTHmCVxOnz6ttdYzbhBfyUDOAvwrYBdwDzhDMXj7F+PVK7+itf4Hsz1+pmIncxmMpvnL0z2Ypua5w43zVtkR4mEgm8yFWJqFvIdO3Rvj1zdHKHfbef5Y86oXZhFiPZLrkBAz+83tEd65M0al18HzR5tmbVsxV7GTFbvKaK0LWuv/Vmv9QaAA/AR4cvzbzwBvL+fz9YRSZPMmeVPTM95TSgghhFgtd0eKfYrGElkpciOEEGJOnaPFeGUkliGRKSzqGCsWyCmlGpRSryqlfgX8RmvdBbyulHoDOAD8aDmfb2edl5YKF03lLnavYDU6IYQQYiYn2iuo9jk40BSgUnqgCSGEmMOJ9gqqvA4OtwTxu+bvZTqTFSt2orXuBZ6672t/BPzRSjyfy27luUONK3FoIYQQYl5N5S6+fLxlrYchhBBiA2irdC+o+fdMVqehjRBCiAdICwohhBBCLJbsxBZCCCGEEEKIDUYCOSGEEEIIIYTYYCSQE0IIIYQQQogNZtPvkcsXTJK5Aj7n4qrBCCHEhOXe0yYeHqlsAaXAabOs9VCEEEKskVzBJJ0r4F2muGRTB3K5gsl33u1iJJ7lRHsFj3RUrPWQhBBCPGS6RpP86FwvFkPx+SNNVHkdaz0kIYQQqyydK/Bn73QRSeV4cnsVh5qDSz7mpk6tTGTyjMSzAHSOJtZ4NEIIIR5GPaEkBVOTzZv0R1JrPRwhhBBrIJLKEUnlgOWLSzb1ilzAZedwS5DuUFJW44QQQqyJPY1++iJpbBbFthrvWg9HCCHEGqj2OtjX6GcwmuF42/LEJRs6kLvcF6F7LMmR1nIqPTOnqjyxrWqVRyWEEEK8z+e08VuHG+kcTfDq9SF21vloqVhaE1ghhBAbi1KKD+6smfX7d0cSXB+IsrveT1O5q6RjbtjUyngmzy+uDHK1P8bLVwfXejhCCCHEnF66OMDV/hgvXuxf66EIIYRYR7TWvHihj6v9MX56qfRrxIYN5GwWhcterP7lL7Ov8WiEEEKIufnLilXKAnLNEkIIMYVSalHXiA2bWumwWnj+WDMj8SzNJS4/CiGEEGvluUMN9EfS1Pmdaz0UIYQQ68xvH2miP5KmPlD6NWLDBnIAXqdt2fowCCGEECvJabPQVil744QQQjxoMdeIDZtaKYQQQgghhBAPKwnkhBBCCCGEEGKD2XSBXDpXIJMvrPUwhBBCiCWTa5oQQmw+WmvimTxa6yUdZ0PvkbtfXzjFD870oJTitw83Uu2TDeVCCCE2pp5Qkh+e6cUwFJ8/0kSVd+Z+qUIIITaWn18e5Gp/lPYqN88eaFj0cTbVilz3WJJcQZPNm/SEU2s9HCGEEGLRusaS5M3iNa1XrmlCCLFp3B1JAHBvJLmkVblNtSK3q95H11gSQyl21HrXejhCCCHEou1p8NMTSmE1FNtr5JomhBCbxWNbKjnXHWJXvQ+l1KKPs6kCOa/Txm8faVrrYQghhBBL5nPa+Lxc04QQYtPZ2+hnb6N/ycfZVKmVQgghhBBCCPEwWLFATinlUkq9qJR6VSn1V0oph1LqHyql3lBKfVsp9dB28jZNzbt3x3jz1gi5grnWwxFCCCEmDUTSvHx1kO6x5FoPRQghVpXWmlP3xvj1zeENUTF4JVfkPgq8o7V+CngX+CLwtNb6MeAC8JnleJKhWJqzXSFS2fX/y55wbSDGm7dGePfuGGe7wms9HCGEEBtQ91iSCz3hZZ8QfOFCHxd6Ivz4fN+SS2MLIcRGcns4zq9vjnDqXohT90Kr8pzxTJ6zXSHGEtkFP3Yl98jdBg6PfxwYf65Xxz//JfAl4HtLeYJMvsD3TvWQzZvcHUlwsqOSX1wdJOiy8dHdtYwlsiilllSyOVcw6QunqPE5cdosSxnuJLfj/eO47MtzTCGEEA+PsUSWH5zpxdSakXiGbTVevA4bftf7yS7JbJ4XL/RTMDUf21uHv6y0RBi3w0osncdltyxpE/56NBzLoLWW9kRi09Fa0xNKEXDZ8Dof2qS3JXPZrSgFWq/8PbppFv9mL18bZCSWoXMsySPtFXx8bx1Bt72kY6xkIHcTOK6UugwMAf8BmCi7FQGC9z9AKfVN4JsAzc3N8z6B1sVfAkDB1JztCjESyzASy+BzjnKmqxhJf/ZgAy0V7kX9I35yvo/O0SQVHju/80jroo5xv5YKN58/2kS+YC56XEIIIR5eBVOjKV7/rvXHON8dwWZRfOVECwFX8QbgxmCcnlCxbcHlvggnOypLOvZnDzbQOZqkMVi2MoNfI12jSX5wtget4VP769hSLZVAxebx+s0RznSGcNosfO1kCy77pqpnuGrqA2V84WgT6ZxJW+XK3qP/8uogl/uiXBuIEiizMZbIMhhNc6E3wpPbqko6xkqmVn4N+LnWejfwIsWg0Tf+PR8Qvv8BWus/0Vof0Vofqaqa/x/gtFn47KEGTrRX8NE9tbRVuTGUwuu0YjGKgZ7WzLpUORRNE0nm5nyOiceGkzkK5vKlmDQEyiSIE0KIh0A6V6AnlCS/jCmQVV4Hn9xXz6NbKmmucAGQK2hi6fzkzzQGy3DYDGwWtaDrjdNmYXutF7djc90IhlNZJjJFQ/Nc+4XYaELj96vpXIHkBtputB7V+cuWNYgrmJqeUJJ0bvrfJTx+HmqtcHNySyUHm4PYLAat4+f0UqzkWVoBY+MfjwCtwDHgXwDPAG8vx5M0Bl10jSX5L291sqfBz7eebMdmMSiYmmxegyr24rnfpd4Iv7gyiNVQfPFY86zplx/ZXcuFngjbaz1YDMVAJE0klWNrtQfD2FwpJ0IIIZaX1prvnupmNJ6lo9rDp/fXL9uxt1R7AIilc1gNg4DLRlP5+zcAlR4H33i8HVNrHNYHU4S01rx0cYB7owme2Fq1LKWw17NddT7GEllMrdk3z781kszRG07RXuVetm0VQqykJ7ZV4bAa1PqdVHoWv6VoPRuJZxiOZdha7cFqWfvC+8lsnh+c6SWVLfCp/fXU+mdO2f7ppX5uDsYJumz8ziOtk/HDUzuqOHUvRFPQxd5GP7mCOev5ejYrGcj9GfAXSqmvAjngC8A3lFJvAF3Av1quJzrbFSabNznXFeZAk58XL/TjtFn40K4a7NaZ/9Aj8QwAeVMTTmZnDeSayl00lbuIpHL859/c5d27Idoq3Zxor+CxraWlqQghhHg4FUxNKFGcdR2OZVbkObxOGx/dUwvA6c4xbgzGOdoaZEu1F9scNzuxTJ4bgzEAzveE110gNxRL8+q1Ycrddj6wo3rJk6dWi8FT26vn/bmCqfmLU10kMgUag2XSn1ZsCOVuOx/bW7dsxzvfHeZKf5QDTQF21vnmf8AKS2TyfOfdLnIFTVe9j4/srl2zsZzrDnO1P4q/zDZ5Xr/aH501kBsZ/5lwKkfONHEYxUCt2uvk41P+ZnOdr2ezYoGc1joMfOS+L//R+H/Lal+jn7NdYcrddv7jG3eJpwt4nFZaK13srp/5wnS0tZxUtoDLYaWjyjPvc5zvDtM1lmQknsFfZiOVW9ll63zBxFBKVv1m8M6dUc52h9lT75dgWgixrlktBh/eXcONwRiHmh/YGr6sMvkCr98YAeD1GyPz7gHz2K20V7npHE2yp8FPNm9Om/zUutgqJ5HN80h7JWWrXJzr3btj9IZT9IZTbK/1TlttXEmm1mRyxTTYdF5aBJUiVzD5yfk+RuNZPry7RraObGCj8Qw/Pt/Hr2+OsK3GQziZW/NA7uZgjEu9USKpHC679YEUxaUwTU1B65KDKNPUvHp9CK1hOJYm6LKRyplsq539fPvMrhpOd4bYUu1Z0GpbKTZFAvzjW6s40V7BH796m1Ayx53hOIdbyvE4rLx9Z5SusSQHmgJsq3n/l+x2WBc0c9EQLMPntLO91svxtnIe27JyAUT3WJK/OteL3WrwhaPNJVcae1ic7gqRyZmc7gzx6JaKTVdVTQixueys8y3LjVC+YPLytSFS2QIf2FmN777KdHaLQZ3fSX8k/UDQk8zmyebNyUIoAIahePZAAwA/ONPDC+f7eGxr5eSq1d2RBL+5PVr8WaVKWs1aTk1BFzcH43gcVspLrOC2HGwWg2cPNHBnJM7eGbZmiAcNRNJ0jhb7Dp7viUggt4FdH4gRTuawGopQMjfj9qTVlMkXeOniAKbWlNksHGoJcqg5sCzHHoqm+d7pHgqm5uN76ybT1ediGIqGQBk9oRRbq70zxhKmqXn1xhDhZI6ntlfjtlv58K7aFZkM2xSBHIBFKdwOK4PRNA6bhWQ2z3fe7ebtO6O0V7kJJbLTArmF6qjy8HuPt2E11Irny98ejpMraHKF4gZ5f5lcSKbaWefjXFeYnXVeCeKEEA+NW8NxrvRFATjTGXogsFJK8dtHmoilc9MmAG8MRPne6R7cDisf3VPLjtrpQWU2X+CFC/0kMnlCyezkcT1OK4ZSmFrjW4MJxf1NAdqq3DisxrLPYs+nucI1WURGzK/a56DS6yCUyLJjjpUJsf61V3k41xPmSEuQj+yppXmVVsJnYzUM3A4LsXSejmpPydUc59MXTvEnr9/hWn+U7bVebg3FSwrkAJ471PjAeXaq7lCS890RAP7yVDd9kRQeh41vPN4+rUXMctg0gZxhKJ4/1sQPTvdQZrMQz+SJJLOEU1nO9+TQWvP2nVFOtFfMeZx8wSSWzhNw2R4IElw2C1f6ozhtlpL/2Iuxp8FP91gSh81SUtrnw+bp7dU8ubVK0k6FEOtGKJHF67SWvAFfa004mVvQY6o8DuxWg1zBpD4wc2sAi6Gmrbq9dXuE/+NXt4ikcuxt9NMfTj8QyCml8JfZyBVMAlNuMqq9Tr58oplUtlByWmN8fN9dc7lrWQou3L/qKNYnh9XCV0+0YJp63mtz52iCSCrHrjrfuihYIaar9Tv5W092ACxqsjySzFFmt8xao2I+WmveuDXCWCLL41urKHfbef5YM4PRBzMNlmIwmsbjsOJ32VAKDjQFSn7s/efZ+wXddpw2C+lcge5QihuDMZSCp3dUcbilnGQ2j9YsS2XgTRPIATitFrIFTXcoSVulm2qPk4IJ8WwOh9XCW7dH2VrtoWKWi4tpar57qofBaJq9DX6e2VUz7ftnukL8+mZx/8FnDzbQukL9JSo9Dr66TD3rNisJ4oQQ68Wr14c42xWm0uvgS8easZRwfnrl+hDnuyNU+xw8f7S5pHNahcfB1x9tJZfXJc/qvnJtiFzBJJM38ZfZONz64D49m8Xgd0+2FvfxtUz//kKDsRfO99EfSeO0WfjmE+0l/S7E5jHf63gomuaHZ3vRulh6/YllWl0Ry2ux2U6nO8d4/cYIXqeVr5xoWVQGW284xal7xT7QVsPgE/vqcDustC/zwsbOOh/9kTTba708s7NmWdMefU4bX3+0lVS2wOs3hxmKprHbDMrddvrCKb5/ugcNPHeogcbg0oLTDR/IRVI5OkcTtFa6sVsMIqkcO2p9BF02nj/ezFu3R7kxECOeyeN1WueMfrMFk8FoGoCeUHLa9852hXj9xjCJbAGPw0pBL19POSGEEBtX93jT7ZFYhlSueI2Yz7muMCPxLFprsgUTp1HaTYTLboUFbBfb3eBnNJFlZ72V/+5D22ZNUdzfFGD/AmakZzNxbTS1RmtNsRPR/ExT8/rNYVLZAk9sq9p0PexEkTne3xeQ+6hNqGf8XBhL5wknc9T6Fx4cBVzvr2bV+FaujYLTZplWMXIljh9N58gVTA63BjnUHKSt0sPpzhB5UxNJ5fjNrVF+63DZkhYnNvyZ8vune4ikcpS77XztZCuPb63k1lCco23lOKwWntpezVPbqxmKpvGV2WadHUjnCgzHMpzcUsHd4QRH28onvxdJ5Xj1+jAADqvBR3bXSsqjEEI85PIFk/5ImmOtQc52hWmtdJcUxHWPJQmncgxG0+yq963ovutP7qvnRHsF5W77okpbL/j59tZzdSBKa4V7QWlzt4fjnO0KA1Bmt6x6YRWxOmr9Tj61v45wMse+xsBaD0css+NtFWRyJlU+x6KDMI/DytdOtpDIFGZtDXa/kXgGBbNm3K2V164PTwa3E3HD7nofl/siXB+M4bJbePvuKCc7Fl9AccMHcrmCOe3/R1rLOdJa/sDPVftm7u0AxZ4xf/ZO1/hqnpcvHmue9v0ymwWv00osnedIa4Bd9WvfT2OpLvZEeP3mMM3lLj65r06KhgghxAK9cKGfuyMJKjx2vnqipeTzaLZgEnTZCbrsKz4paDEUNXNc/5ab32Wbdy/6TIJuO1ZDkTf1sjUz7gun+Mn5PlwOK5871FBczRRrbr62GGLjqvU7+fzR0vouprIFfnC2h1g6zyf31U1LMXTZrSW/X+8Mx/nx+T4AnjvYuK6KFFV5HfSEUngc1snUTafNwtPbqxmNZwHIFZa2Mr3hz2qfPdjAzaH4kipS5gom0XSOsUSGU505nt5RPW2G1G41+PLxFkLJLHWzNPvbaM73FJuo3xqKE03npcWBEEIs0GiieCEOJXKYGiwlzod1VHl4ZmcN6XyBg8uQzrgZVHoc/M7JVjL5AtXeB6+z+YLJ+Z4wDqul5HLo1waiJLMFktkC3WPFXnRCiPWhJ5RkKDrRTDu26L1iY4nsZLruWDL7QCB3czDGWCLLgebAqle/fXJbFdtqvARc0zMCm8pdfGR3LbF0joNL7C+64QO5ap9zztW2UjhtFg63BPmzd7qo8zv59c0RPnRfoZMyu4Uy+8xVwlZCJJnj7buj1PicC6qkU6q9DX5evzFMc4ULr+xFEGJTaP2DF5f1ePf+8BPLerzN5kM7azjXE2Z7jXfBRT32Nj68bWVuDcW4NRRnf1OAOv/719XihOLMk4qnOkO8Nd7TzmW3lFT4YHutj+sDcdwOC03lq3f9FkLMrzHootLrIJbOzdiy4mp/lM7RJIdbgnOmWO5t9BNO5lCqmLY41WA0zQsX+gFIZPN8YEfNTIdYMUqpWSsML1d230NxB39tIMqF7gg763yzXjx31fnYUevD1BrrOqiy9drNYW4PFXsG1QecM85QLsVybWwXQoiHlfQaW7hs3uTFC8XmvoPRDF872QoU06NO3QvRUe3hcMuDM9RTr8tWo7S9dw2BMv7WUx3LMm4hxPIqsxdbVswkmc3z88sDaA2hZJbnp2x5Mk3NK9eHCCVzPL29igqP44Eq8xMMpVCqWGDHUuJ5Y6PZFIFcJJVjOJahtcI1bXO11pp7o0leON+PxVD0R9LsafDNuI+hwuPgc4cbCCVy7Kxb+/SLiVRHu9WQvP41MpbIEk5maa1wS7sDIQRQLFRisxiTFfcaZpltFTOzGgqP00o0Nb2Z7ms3hgknc/SGU+yeoQDMoeYgLrsVp81YUPAcz+QZiKRoLncvuq+VEGJmo/EM0XSe1grXstZasFkMXHYLiUzhga0/3aEkF3qKzbbfuzfGR/fMXnmyyuvgc4caCSdX/t4+nzd58/YIjeWuVS2IuOEjhEy+wJ+/20UqW2BnnXfaH/TtO2O8fWeUuyMJGoJlbK32zPlCawy6CLjypPMmnlmqbV3qjXB9IMaB5sCK/qGe2FpJS7mLoMteUhU0sbwiqRx/9k4nuYLmSGuQx7eu31436VyBK/1RanxOuakUYgVd6o3wiyuDhJNZDKXwldn41P46Kd5wn0Qmz7WBGE3Bsge2PhiG4vljTQxEpjf3bQiUEU7mqPY5sM9w/TUMteBUpIKp+c67XdwejqNQ/P7jbcvaiyqUyOJ2WCVAFOuSaWqu9Edx2owVOUeFk1n+7J0u8qbmeHv5nJUXw8ksr90Yxldm48mtVfNOjtssBl863sJwLENTcPp9TbnbjlKQy5uzpi1O1VTuounBGojL7t//+g6v3xjGZlH80W/toyGwOtkaGz5CyOZN0rkCANF0HiiuxCmliKVzAHRUufnwrhp21s+9J6F3vEkfzNykr2BqXr46hKk1Y4nsigZySqkVazgu5pfJFSYrCcXGX1fr1a+uDXF9IIbFUPzuo634nFK4RoiVEM8UzwXpvIllfFIwuoLnh4lr2f0fr3cvXeynJ5TCbjX4xuPtDwQ6LvuDzX0/tKuGwy1B/GW2ZcuAyJsm8XSeO8MJyuwWfnVtaNkCubduj/L2nVH8ZTa+fKJ51YsoCDGf010h3rg5AsBzhwxaKpb3njKZLZA3S7tPeufuGHeGEwC0VbhLur/1OKwzLmTcHUmQK5jYrMaC389LPY/O9fixxPtVKCPJHA2BRT/NgmzoQO7mYIyfXRqgYGp21vmo9jj4Z391iWg6x4n2CrbXeHkvm2dPg589JfQrGYikKIy/KAci6QcCOYuhqPY5GIikqd0k1SvFzKp9Tj6wo5qReIZjbaswlbME5niKl57SaFUIsfwONgfI5E0sCoqtrhX7SqyguFAvXOjj1lCcR9orSOUKnOsOs7fBzwd3Ls9m/XzB5NpAjIDLNmO1uK7RJGe6Qmyt8bB7nknQqcd87cYwF3oieJ1WbBaFprSTklJq2XtAOawWPrm/npF4FqfNWNbrdm+42BsqksoRS+dxeCSQE+uLab7/3jNX4N6gPlDGk9urCCezHG+bu+VIrc/Jlb4oDptB0G1f8HOZpubXt0aIpXOkcwWshoHWMBbP4nFYMU3NazeHSWYKPLm9asYA8BdXBrncF+Fgc5Anty0syyqUyPKXp3soaM1zhxpmrFvx+4+18efvdtFc7mJXCefMWDrHneEErRVu/K7FT8Bv6EDu6kCMvKkxlGJfo5//85XbvH1nlGzBJOCy0zWaxGW3Tjbjm8+uOj+94TTArBeu3zrcSCiRXbY+N6vtYk+Es90hdtX52NPg50xnCL/LVvKF+mGyUYrBfHBHDdVeJ7U+p7SREGIFOayWBd8ALEY6V+DmYByAy31RUrkCWhc/Xq5A7te3RjjXFcZQiq+caH4giPrF1UGiqRydo0m213hLau59fTDGhZ4IHoeFCredj+2tW7OVqjvDcXrDKfY3Bfhnn9617Nftkx0VvGFq6gLOdX0/0BNK8sr1Yaq9Dj60s0b2ez9EjrSWY7caOG0W2irdxNI5znWHqfOXsaV66SvTBVMzEEkzlsgSS+dxz7ENaH9TgMZgGWV2y6LqPtwbTXCmMwRAa4WLxmAZ5W47jeNpl7eH45zrCgPgchT7tE2lteZyX2T8PBpZ8Hn83mhiMiPj9lBixkCuLlDGP/jw9pKP+aOzvYzEs3idVn7/8fYFjWeqOX+bSqkYzDidpgCttV7Tzth7G/z0h1NUeBw4rAaZXAGroYikCgxE0jy6pZLhWIaqEk+yZXYLn95fP+fP2CzGktsdlCKazvHmzRECLjsn2suXLaXmzdsjpLIF3rg1QjSV4/z4htGAyy77qzaoMrtl3a8aCiFK57QVe6XdHIpxsDkwuSK3bxlzdfLjqeOm1pOFW0bjGX54thelFG67hWgqR7nbVnJrhQq3A4uhcNgsnOioYCyRxWE1ln2lbT7xTJ6fnO/H1JrhWIbnDjUu+3W7PlA2Z+PjO8NxLvdF2V3vW9Z9eQt16l6IkViGkViG/Y0BySZ6iFgMNa1H2ctXh7g7kkCpEH/jsbYlb8PoDaW4PhAD4HRniE/sm73oCEAqV+A3t0fZWuNhR+3Cwoegy47NosgVNB3VHvbdl2UXdNuxGoq8qWe851dKcag5yKW+CIcW0beto9rDxd4IBVOzrWbm93MqW+D7Z3roj6Q43FKsrWCbYwJsYvtO3tRLSvmcM5DTWq/rHdxtlW6+cLSJi70R/tOb96jxOxlJZDnUHKDGX8aR1iA+p23O/hMTrg/ECCfXpmHgTH5za5SzXSHujCS4ORTjS8eaS5oRnU9bpZsrfVHaKt2TVcGUYsbN5UIIIdbGh3bVTOtnOlchgcV4fGslXqeVCrd9cnb55lB8cq9LW6ULl93C/qZAyTcYtX4nX3uklbxp8pvbo9waimO3GvzeY20PVKGcSSiR4T/9phOnzeDzR5oWHQBalMJqUWTzes2u5z+9NEA2b9IdSvK3n9qyJmMAaK10c3ckQcBlI7CE9C2x8TnG96paDVVym61bQzHeuTvGlioPx9unp09Weu14nVbimTytlfMX9igWispN7pULJXIcbA6UdG4Iuu187WQrqVxhxtWwSo+Drz3aSiZnznrP/8S2Kp5YZEaFz2njmZ013BtJzDqx1RNK0htOca4rRNdokmxe89E9tbMe89kD9VwfiLFlnkKM85lvRe4DWutfjX/cprW+O+V7z2mtf7DoZ14mPzzby2g8w/meMAebghxuDmCzWvCVWWmtKAYrxXza2aPd/kiKly4WGwYmswWe3lE948/NJ5MvYDOMRacuaK25PZzA7bBQ4bHTH0mTyBboDaW4N5pclqXwD++q4bEtlbjsFrSGco+95GB3qbTWZPJmSW9aIYTYaExTkzPNdTEZmCuYKJh1AtBps3DivhuzrdUeLvVGUEpxcyhOMlOgO5Tibz7ZUfKq3MRej0zeBIorf4USN+j82bvdvH1nFEMpmoIuPjklQyaSzNEXSdFe5Z7195vOFXBYDcrsFr5wtInBaJqti6zWt9TrVYW7eA2vWMR+oOV0oCnAthoPDqtlwU3rH1amqbk9HMdfZluVDKxSZPMmFkMt6W/4zK4amitcVHudJac3vnFzhFAyx1A0w/6m6UGXy27ld0+2EkvnS5okKHfbCSdzWA346cUBACKp7JztA6byOm1451hF9DltMM+fa7G/x3zB5Pune8jkTe6MJPjKDP3vGoMuyl12DENR6XGQyRfmPGaFx8HJLUu/957vL/m/AYfGP/7+lI8B/gdgTQO5128M88bNYbJ5k9ZyF4dbguxt9FPhcRBJ5XjpYj/9kRTprEldoIzPH2mc8aJmMd5vGGi1FP+4d0cSjMQz7G3wl3Qiv9of5eeXB/CX2Xj+WPOiTv6nOosVhpSCLx5t5vNHm3jzVjG9stq3PIGWUmoyj1kpFry8vVj5gsl3T/UwGE3zxLZKDrdIKqAQYvNIZYutcGLpPB/ZU4Pbbp3sXbrUXqCRZA4UJe+B7Y+k+MGZXoZiaYJldp7aXsWBEtKJKjyOyb0a33m3i2SmuF1hMbeOH9pVw4WeMI1B15x7Z6YKumzYLApTM61XXL5g8p33ukhmC7RWuvjswcYHHnu6c4zXb4xQ43Py+SONVHoci967prXmh2d76RxNcrgluKhZ/OcONTIYTVOzDgIB6UW7ML+5Pcp798awGIqvnmhZVHGO5XR7OM6LF/pxjU9QzBXMzMVmMRZcD6Glwk0oGabO75xc0ZvqjVsjnO0Kz/q+nOoTe+voj6RJZfP8/35xk2yhWDhqW7WX5vv6QK+EW0NxXrpY/D1+8VjznK298gWTsUSWCk8xXXw0keVUZ4hsweSpWc4HZXYL33iinad3VDEQyXCgObBC/5Lp5nt3q1k+nunzVXdzKI6p4cZgnHg2T33QxY66YmBypitE52iSS70Rav1ODENxczCOx2md1rsGoNrr5HOHGomkcuys8zEaz/BX53qLHeUTWT68+/2l0WzepHM0Qa3fOe3NdGsojtYQThabk9//HKVIZovRu9bFmcXHt1ZxsDmIzaLWxQzvUsTSeQajxUIyNwfjEsgJITasfMHknbtjaA0n2suxWgyGYxkiqWLLm8t9UXpDxSrI/ZEUzx5omHxsOlegeyxJQ7CspBvs7rEkPzjTCxTb4pRybbk3kiSbN/nNrVFsFsWV/ij//quHZ81KiaVzDETStFS83zT7U/vruTUUp6nctagsE3+ZbcH9N5871MjWGi9VXgetU0qlF8ZXx+D96+T9bg0Vi8MMRtNE03nKl3DznS2YdI4mgeJ9xmICObvVWNR9gFh7yWwxvbhgatLzrKqshjvDCQqmJpbOMxBJLzqQW4ynd1RzqCWIx2Gd8fwx8b6bOOfM1VPRaim+J16+Okilx867d8eIp3Oc743wkd21fP7I7HtOZ5PM5ukNpWgqd827gHJnOD7l95ias7feD8720htKTQaofeEU22u9RFM5ttV4uTeS4MZgjD0N/gd62W2p9q5qb9H5riJ6lo9n+nzVHW8r59VrQwRcNoZjWS70hHHZLfzuo200Bso43x2mpdJFvqAxteZnl/sBxUd21z7QWLSp3MXES0gphaJYNtm474X74sU+7o0k8TqtfO2RVq4NxHDYDA61BBlLZCl320tqUDiTE+3lZHIFrg5EuTl+Ad0szcADLht7Gvz0hJIclcIcQogN7FJflHfvjgHFCmmHmoPUB5x0VHsIJ7Psb/TTN14t+f5ryI/O9tIfSRNw2fj6o23zPtdgND3ZYmSoxEnCXXVe3rxVzO5w2a0PjGGqfMHkO+92E8/kaa9yTwadbod1zsq90XSOV64N4bJb+cCO6gWnKo3EM3SOJthS7Z1caXTaLBxtffD64LBa+NT+eu6NJmZt93CktZzXbwzTGHQRXOJeMIe1OI4bgzEpJLXJjSWy3B2J01HlIeAqBv+Pb63CabNQ7rZT51/7InD7m/wMRFJ4nNZl7wVXirkyAU60V/Du3TG213rnDOKmagiWMRTLkM6b9EfS+Jw2BiPpRY3tu+91MxzLEErmeGxrJR/YUT1rQHegKcBgtBgIN5fP/XucGM9AJEM4mSWRzVPrd9JW6eZgU4A/faeTXEHTNZZcUsXJ5TBflNCulPoxxdW3iY8Z/3zOK5BS6qPAH4x/uh34W8BW4FmgE/hdrXVusQMH2NPg5599ahd/caqbm4PFwGdipmJrjZe/4XdyvT/GG7dGGIqlMVQxb3WihOhMTFMTSWZ5Zkc1WdN8YBk6Pr4RPJktcKozxNt3RgH4zMEGvnaydcZjllqNxmG1YLMamCZc6o3QUuFiW826rjdTMqXUtI37QgixUXmd7186feMfWy3GtKrHv3WkmFq5q276pOHE9SeZLUxeG0KJLNF0juZy1wPXij0NfoZjGQB215eWCt85lsRiKI61BvGV2fn43rpZr0F5U0+ucs3X1Heq0/dCk0ULSr1WTfx7tdb85ekeUtkCV/pjfHWG/Sb3a6t00zZHE+GOKg8dc1SHXGhVuMe2VvLY1uUtMCPWnx+c6SGWznOhJzI5sVJmtyy6KMZKqPY6+eojrWs9jGkm3k97GvzsWWAvzR21Po61lVMfcDIYzXCivZxj8/Shm20M8UyewViGwWia6wMxav3OWatSVvtK/z0+s6uGK31R9jX6+eHZXsLJHF6nleePNdMbTk327PWtg5ZP8wVyz075+H8b/7++7/MZaa1/BvwMQCn1DnAa+KbW+jGl1D8GPgN8r9SBFkxNPJ3HVzZ9eXdLjZd/+oldJDJ5+iNpmsdnK3tCScYSWSZSbqs8DhqCZdQHyjg4R97qj8/38b3T3VgNg//1M3semGH4yJ5azndHaK9yT3Zxh/ebMkeSOdwOy2Su788vD3C1P8qh5tLy7Ov8Ts6rYi7zSm6SNseXl71Oq/SVuc/9f0MhhJiqo8rDF442oWHWti11/rIZZ/M/vreOy31Rttd4UUoRSeb49vjs7vH28geqUzptFj62t7RiABMmbjIqPE4+trd2zr3QTpuFT+yr495IYtoK3K2hOLmCyY5a74wBUK3fieopXqvm24uWL5j84Gwv/eE0H9hRzZ6GKePRMyf3dIeSVLjti9rfFU3ncFotk9fvn17s5/pgjKOt5Ty6Zf0FZ/FMHquhpBDYGph4+c3yMnzoaa2JpvJ4nNbJVffXbwxzpivEzjofJzsqsFsN0jkTl90yrdz+XPdSzx5o4Hx3cW/dYtIQC2ZxH+tYIkulx46pNaFkdtn2pO6s87FzfBLu1zdHgOJr5EpfsR5G3jQ52VHJkdaFtzJYbvOdIQNAo9b63wIopd4FqigGc/+4lCdQSrUDg8A+4NXxL/8S+BILCOS+f6aH3lCKXfU+PrL7wXKebod1sqpjKJHl+6d7MbVmX6OfZ3bWoFRxNnO+Gbnbw3HyBU2+UODGUIzGGfbTfWhX8YXSVqGxWQycNoOOKg+vXh/ibFeYKq+D5481oygWQdEarvRHSwrkdtT6qPU5sVmMkjeIL8YLF/u5PRSflkoj4M1bI7x7d4wKj33ZWj4IITafxabQ1wfKpj02kc1P9hOKppaUpDJpX6MfQykMA7aXsFK2pdozrSry7eE4PznfBxSrTx6YIcVyZ52POn9p16pwKkfveKrp1f4oexv9fO5QI3dHEjOO7/958y4/uzRA0GXj//vb+xe0J+hsV4hXrw/jdVr58vEWbBbFtfFeV1f6ousukLs5GOPFi/04rBaeP9Y0md4nVsdzhxq4PZxg6zJUBd+MXrk+xPnuYq2JLx5tQqninlut4bXrQ1zpizKayOJ1WKn2OfjS8WYcVguv3xjmdGeISq+DLx1rfiD1usrr4JklZGnF0jm6x5J4nTYKpqba4wD1ftbccvrswQZuDsXpqHJzY7C4J9BqGARd9nVRv2K+SOEfAV+c8rkdOAK4gf+H0gKx54AfUgwKo+NfiwAPhLFKqW8C3wRobm6e/Hq+YE5eBLrHkvM+YUFrNHpy2fWDO0tb9k1m83zmQD1DsQyBMtu8BTkMQ027wE2MbTiWIZ0r4HZYOdQc5HJfdEENCKeeyG8OxvjrK4NUeRx89lDDnM0FF2JirF2j8/8+HyZd47+X0XiWRKaA3yWBnBBi5dQHynhiWxVjiSwn2pdnP5ZSir2NC0t3mmpqu4CCac76c6UGHeUuO1uqPfSFU5OV3Kq8Dqq8DtK5AulcYdpq1NX+4q1CKJmjf4HFHSbO4bF0nlAyS32gjAPNAa71xzjUEpj8uWzepGBqyuxreyPWHUpyYyDGWDJLrc/BZw/NXflPLK8Kj2NR/QpzBZN8YXGvn3SugFKsiyBgPhP3iAOR9GQ7jkPNQU53hiZ/b4PRNBa/k3DSIJrKU+W1TL4PR2IZktk8Y4ksL1zoJ+Cy8blDjUteffaX2dhe66VrLEmdz8mdkWKad36O89ViBd32yb2yh5xW0vkCdovBjtr1sfVpvkDOrrXunvL5G1rrUWBUKVXqjstPUQzmTgATSz8+IHz/D2qt/wT4E4AjR45MXkmsFoMntlVxfSDG4ZaZA6J4Jk8snaPOX0alx8FH99Tyvfe6uTEQ4507ow80MrzfxZ4IL18bxOu08T9+avei3pyPbqnk7TtjtFW6J2col9KAEIrVz7J5k95wiuFYZtGzwPd7clsVF3oi7F1gbvNm92hHJW/eHqG53DXZD0kIIVbSbNe1pRpLZNFaL/hGdWu1h2d21pAzTfY3BpY8DsNQfGrK/sEJA5E03z/Tg9aazx1unExF/fyRJr79ThcdVe4F7xM/3lZBOlegyuugzl/Mnnl6ezVPb3+/P2w4meU773WTzZt8cl8d7XPsrVtpu+p85Aom5W47o1O2a4j1K5rO8Z13u0hlTT62t3ZBr9HusSQ/OtuLYSg+f6RpVXr4LsVjW6t49+4YW6o9k8HXsbZyjrWVMxLP8KurQ9T6nWitaQi6Jv89j22p5K07o7RUFGtXvHlrhGzeZCiaoT+SnnO/aymUUnx8POXcNDXne8IYSj2wJ3mpJrZQTVTBdVgt084l68F8gdy0q4vW+u9M+XTe6EQpVQtktdajSqn3gL8N/AvgGeDthQz0cEtwziDuP//mLreHErRXufm9x9qp9jrxjM/i3RtNTAvk3rg5wivXh4in8xxrK+fZA/XcHU2gdTG1ZTSRodG+8LLB7VWeZb8g7Gnw0xdJUeVxLOsbfjEbVB8GzRUumiua5/9BIYSYhWlqXrjYT08oydPbqyf3WpSqP5LihfP9uBwWnjvYOOvE4ql7Y4zEszzSUfFAZbmu0SQ/ONsDwGcONNC6gBunpa7olaonVCxZXvw4NRnIHWkt58gM1StLUet38oWjc5/Di72sigVeOseSaxrI1frL+NzhJm4Pxzm4gMwdsXaGohkSmfHXz2hyYYFcKEne1GBq+sKpdR/I3Z92PVWlx8Hnj87cMmDifPOzywP0hFIcag5ybzSJv8w2OcGyXAxDlfTeyRVM/upcH8OxDB/ZXTPv+75zNMEPzxZbv3z2YENJFUO7RpNc6ouwo9a7aueV+QK5d5RS39Ba/19Tv6iU+hbwbgnHfxb4KwCt9ZBS6nWl1BtAF/CvFjHeGRV74GToDacYiWewWQy+drKVA80BekIpTty3Gne2K0RPKEk0laPW72QoluFoa5BYOkeF20H9Oig3O6H4Jtqy1sMQQghRonAqx+3x/krnu8MLDuSu9keJZ/LEM3m6xpJsnyGFpz+SmtyEXzA1n9g3vSDKSCIzWcBhNJGhtdLNjcEY0VSO/U2BZUvTX4qddT46R5NoWPaZ9Ll0VHlor3KTzhU4sAwrjkv10T0P7vsX61dLhYutNR5i6fycxfNmUmzDlMJmURu2KnlhfAXMYZ27wfjF3gipbIHebIpH2iv4m092rOIoHzQUy0xuK7rYG5k30BqJZyfPoSPxbEmB3E8v9ZPMFrgzHOe/eXrLgirlLtZ8gdx/B/xIKfUl4Mz41w4DDopVJ+ektf73933+R8AfLXyYc6vzl/FIRwUD0RSj8QyvXB/i+mCMv/uBrTMuge5p9DMUy+CyW6nzO6nyOrBZDL58fPYSyPdGEjhtFmqXeSZhqV6+OsitoTgn2ivm7PkjhBBidfjLbLRUuOgJpRaV+bCtxsuLF/qJpvM80vH+ROS57jDv3BllW42Xw61B7FaDbN4k6H4wDXxPvZ/ReDEtaE+Dn95wihcv9APF1geLSfkfjWcIJbO0V3qWpdqx22Hlc4dXf0+Y3WpIkS+xaDaLwSf3PZgqDMUqj7+4MsjdkQSPbql84P3vc9oW1fh6JXWOJvjry4NUeu18al/9vEXeznSFeGN8Eslhtcy6YlecqElQ7nZQ7Vv8ymM2b9I5mqDa55yzp918qjzFdOuReKakiaM9Db7J1MpplXbnEHTZSWZTBFz2VQniYJ5ATms9BJxUSn0A2D3+5Re11r9a8ZEt0Mf31rGr3se/f/U2d0YSZHIFzveEZ0wnuT9ffj7nusO8cm0IpYq5+8u1T22p0rkCF3oiAJzuDEkgJ4QQ64DFUDy3hKIVlR4HjcFiev+VvuhkVsnpzhDJbIFz3WFObqngK8dbiKZzNAYfvCbZrca03p2WKTcVC23eDcV9QX/+bhe5guZAc2Dd7RMRYj1IZAtc7isW6znbFdoQW1jOdYcnMwAGounJc89sjBLPJVuqPfydD2xd8vh+dnmA20NxXHYLf+OxtkVnE9itBl88VvrWGYfVsuD+x5852EB/JLVsbRBKUVJ9+/HAbd0Fb/drrXDzW4cb+b9+fYdKj2PGdJTFSIw3cNW6WNlyJvFMnhfO95E3NZ/cV7cqJYQdVoP2Kjd3hhMLTt0RQgixPk2c2892hbBbFYPRNDU+JzvrvLxzp1h4wGG14LBaSi7KVOt38tmDDcTSeXaV2Fh8qpuDcd69O4bHaWXLGu4pE2I9c9kstFS46BpLbpj7sh21xRTnoNte0p69g00BnDYDh9UyWbSkYGpeutjPcCzDMztraK6YOxi81BthOJbhSGtw3qq0E/fg6Vyx0ux6brdotxolpWAup5VrVLZGLvZG2F7rw6Kg1ufkJ+f7cDssPLmtelGzkABHWoOTZYo7ZrmA3RyM0R9JA3C1PzYtHWalKKV49kADBVMv+t8mhBBifVFK8cl99XSNJsjmizdIX3+0jZMdlRxvq1j0+X4hBU/ud3ckQUOwjESmwO4S04y01rx5a5TRRIbHt1ZNVn4TYrMyxlfjTVMvS/rxathe62Vrdenp0oahHtgbNxBNc2t8X/DZ7tCcgdxwLMMvrgwCxTTvCo+dgUiakx0VVM+wkvXhXTWc6w7TUuFectuCzWjTBXIuh5VQMofLYeVsV2jyhdUYdC16Y6nDapl3P0FzuYsyu4WCqZdcVnWhJIgTQojNxVDgctiIpnLTGm6v1fl+W42HnlCSSo+DpvLSqjr3R9K8d28MKI57tn1FQmw2GyWIm7DU8VZ67FR47IwlsrPumZvgsBnYLIpcQVMwTd66PQoUV/Vm2jNb4XHwwZ2Lbx6+2W26QO7T++vpGktSHygbb2QYwWZRVKzwTGCFx8E3Hm9Haz3vRlEhhBBiLkopvnC0ib5wiuYSA6eVtK8xwM46H1ZDlbyJ31dmo8xuIZUtrOqeESHE6nJYLXz1RAt5U8+7h83ntPH8sWZCySz1/jIGo13EM/l1V0xwo9h0gZzTZplcedtV76PG58BuNebNwV0OxZnSjTULI4QQYn3yOKzrqkT5QosMeBxWfueRFuKZPNVeuUkTYjNTSmGzlHYPXOFxUOEp7sf7yokWYuncjGmVYn4bNpC7NhDl9lCCQy2BySaiM5l4oYi1MZbIMhBJs6Xag90qK5VCCLEacgVzskT4o1sq1+z867Jbcdk35q3GraE4DqtRciqpEGvpznAci6FWvdjGUpXZLZTZN8/et6FYmlP3QjSXu1alaumGPLtm8gV+fmkQU2tGExl+55HWtR6SmEE6V+A773WRyZncHvbwqf2yP0IIIVbDpd4I57rDQDHF8XBLcG0HtMGc7w7zq2tDADx3qGHD3RyLh8uVvig/vzwAwKf218+7T02snFeuDdEXTnNjMEZLhWvFMwI35BKJ1TDwOsdjUA3/4dd3+Iv3ukjnCms7MDGNqTW5vAaQv40QQqyioMuOUqAUBKe0KLg5GOPfv3abH5/vwzT1Go5wfZt6zUrnzDUciRDzS42/XrvGkvzp252TBUTE6ptoP+a2W3FYV36lcUOuyFkMxfPHmhmOZbjSH+Fqf4xYOk9PKMmW6vWzn+Bh57Jb+dT+OrpDKQ40BtZ6OEII8dBorXTzpePF5rdT96ed6w6TzBa4PRRnNJEtqW/Uw+hQSxBTFyvsbauR1Q2xvh1oCpAvmAxE0/icVs50hValDZZ40Id21rCz1keFx74qKe0bMpCDYk5tc4ULjebWUByPw0p9YPa9cmJttFd5aJfmsUIIsepmKjCys85HXzhNrd8xbaVOTGezGHIjLDYMi6E43l5BLJ3nUl+EXRukGflmZBhq3oboy2nDBHIj8Qydo0m21Xim5Zu2VLj5209t4e5ogn/98k3cditfPNZMlddBrmBypS9KwGWT/HYhhBCbylAszZu3RqjxOjm5pbKkx+xp8LOrzrfkvlFnukLcG0lwtLV80cVAtNZcH4yhNeyo9U62NUjnClztj1Ltc9IgE7RCzCiRyfPK9SHsFoOnd1Rjsxg8s6uGD+yoXtT7uy+c4u07ozSVuzjaWs6toRjpnLks54uVkM4VuNIfpdbnfKgXcjZEIGeamr883UMqW+DaQJQvH2+Z/J7WGsNQ/PhcL2e7wiigMVjGZw818satEc51hVEKvny8RVJIhBBCrCmtdcl92Obzm1uj3BtJcm8kyZZqT8nlu5d6U5bI5Hnt+jAAyWyBr5xomecRM7vaH5ss0GBqze76YoW3X10b4vpADIuh+PqjravSPkiIjeZ8d5ibg3EA6gNlkxUSF/v+fv3GMP2RNJ2jSZw2C7+8MggUA6YjreUlH2c5z3FzefnqEDcGY1gNxdcfa8Pj2BAhzbLbMP9qU+vx/7//tVtDMX56cYAKj4P6gAubJYzdalA3Hpnr8cdo/f7HQgghxFp45foQ57vD7G8M8PSO6iUfr8bn5O5IArfDsqrBjsNqEHTZCCVz1C6h95M55bo89RJtTr12L/roQmxu1T4nSoFFqWVZqKjxO+mPpPGX2XBM2du1kJpI1wdi/PXlAaq8Dj53uHHBvScXYvI8wcN9j78hAjnDUPzWoUbujCTYWft+3u+V/hh5UzMYTfPcoQYeaS/HZjUm+8o9tqUKn9NGwGWXRoNCCCHW1KWeCFoXWwMsRyD3SEcFHdVufE4bTtvq9WGyWgyeP95MJJlb0g3k7nrfeLCm2V3//rX9gztqqPY6qfU58clqnBAz2lLt4WuPtGK1qGWZyHlqWxW763z4yornk/xuTTpfYP8CitVd7Y+SNzX9kTQj8cycfZ6X6pmdNdT4iueJh3nVfkMEclCcebg/GNvb4Kc/nBpfkSt7IPK3W40FLQcv1vWBGDeHYuxvDEjjUCGEEDM61BLkfE94QTdG85mpoMlSdY4muNgbYUetd9ZK0A6rhWrf0oJHpRR7Gx9smFtmt3CsbeWv3UJsRKc7QwxE0pxoL6fCs3xbhpRS0+6zd9UvvGDKngY/A9E01V4HVcs4tpnIeaJowwRyM2mrdPOtJzvWdAz5gsnPLg1gas1gNMPvPda2puMRQojl0voHLy7r8e794SeW9XgbzaNbKnm0xKIka+nnlwdIZArcHU7wdz7gWZX9LkKI+Y3GM7x+o7g/NVso8NmDjWs8oum2VHukGfkq25ANwdcTi6EIjJdwrnDb13g0QgghxNKUu4sz6QG3XYI4IdYRl91Kmb24Ej7xPhUPtw29IrceKKX4wtEmhmMZ6vyyD08IIcTG9uyBegYiaap9cqMoxHpSZrfwlRMthBJZGoMPb8l98b4NHchFkjl+fKEPBXz6QP2abYp22iyyN04IIcSmYLMYy3ZNK5ialy72MxhN88GdNbRVSk9XIZbC47AuqtT+raEYr14fpj5Qxkd3167L3nBi4TZ0IHdtIMpILAPAzcEYh1tk06MQG91y7st62PdkCbHWhmMZbg0Ve12d7QpJICfEGjnTGSaWznN9IMbxtuUtlCLWzorukVNK/Y5S6mWl1KtKqQal1D9USr2hlPq2UmrJy2dtVW6cNgtldgstFXJxEEIIIdaTCo+dap8DQym21cxcAVMIsfK213pRCur8TvxlD2+5/s1mxVbklFINwJNa6w+Of14FPK21fkwp9Y+BzwDfW8pzVHudfOuJdmDxneyFEEIIsTJsFoMvH2+hYGoscp0WYs3sbwqwp8Ev78NNZiVTKz8CWJRSLwNXgJ8Br45/75fAl1hiIAcSwAkhZrfc5fOFEIsjN49CrD15H24+K5laWQPYx1fkkkAAiI5/LwIE73+AUuqbSqlTSqlTw8PDKzg0IYQQQgghhNi4VjKQiwCvjX/8K6AVmGgT7wPC9z9Aa/0nWusjWusjVVVVKzg0IYQQQgghhNi4VjKQ+w2wb/zjA0A38OT4588Ab6/gcwshhBBCCCHEprVigZzW+hyQUkq9ChwFvgO8rpR6g2Jg96Plfs7ReIZ4Jr/chxVCCCHEKkplCwyPtxcSQmx+BVMzGE2TK5hrPZQNZUX7yGmt/1/3femPxv9bdpf7Ivz15UHsVoPnjzVT7ravxNMIIYQQYgUlMnn+69udpLIFHt1SybE26RErxGb34sV+bg/FqfE5+dLx5rUezoaxon3kVtNQtDhzl82bjCVkFk8IIYTYiKLpHKlsAYCBaHqNRyOEWA1D4+/14ViGgqnXeDQbx4quyK2mw61BYpk8HoeFtkrPWg9HCCGEEItQ5y/jaGs5I/EMj3ZUrPVwhBCr4Okd1ZzrCrO91ittEhZg0wRyPqeNT++vX+thCCGEEGKJHttaudZDEEKsoo4qDx1VshCzUJsmtVIIIYQQQgghHhYSyAkhhBBCCCHEBiOBnBBCCCGEEEJsMBLICSGEEEIIIcQGI4GcEEIIIYQQQmwwEsgJIYQQQgghxAazadoPCCGEWN9a/+DFtR7CnO794SfWeghCCCFEyWRFTgghhBBCCCE2GAnkhBBCCCGEEGKDkUBuFeQKJr+5NcLpzjG01ms9HCGEEOvYneE4r1wfIpTIrvVQhBAb2I3BGK9eHyKWzq31UMQKkT1yq+DUvRDv3B0DwOu0sa3Gu8YjEg+z9b5PSYiHWTpX4IUL/RRMzVA0zReONq/1kIQQG1A4meWli/1oDZFUjmcPNKz1kMQKkBW5VeCyWyY/LrNZ5vhJIYQQDzNDKWyW4qXZKdcLIcQi2SzG5LlE7j03L1mRWwX7mwJ4nFYcVoPGoGuthyOEEGKdslsNnj/WRH8kTUeVZ62HI4TYoNwOK1842sRIPMMWOZdsWmq97tmqrKzUra2taz0MITa0e/fuIe8jIRZP3kNCLI28h4RYmtOnT2ut9YxZlOt2Ra61tZVTp06t9TDEOhTP5Lk3kqC5woXPaVvr4axrR44ckfeRWJDbw3EAWQ0aJ+8hIZZG3kMbz83BGFaLQVule62HIgCl1JnZvrduAzkhZvPDs72MxDL4ymz83mNtaz0cITaN6wMxXrrYD8DH9tayo9a3xiMSQgixmi72RPjl1UEAnj1QT7tM6q1rUuxEbDiZXAGAbN6Udg5CLKNs3pz8OJMz5/hJIYQQm1EmX5jysVwH1jtZkRMbzqcP1HN9IMbWai9KqbUejhCbxu56H9lC8SK+t8G/xqMRQgix2g42B9GA1VDsqJV2WeudBHJiw6n2Oqn2Otd6GEJsOoahONxSvtbDEEIIsUYshuJoq1wHNgoJ5IQQQohNpPUPXlzW4937w08s6/GEEEIsD9kjJ4QQQgghhBAbzIoGckqp31FKvayUelUp1aCU+odKqTeUUt9WSkndeCGEEEIIIYRYhBUL5JRSDcCTWusPaq2fArLA01rrx4ALwGdW6rmFEEIIIYQQYjNbyRW5jwCW8RW5fwMcA14d/94vgRMr+NxCCCGEEEIIsWmtZCBXA9i11h8EkkAAiI5/LwIE73+AUuqbSqlTSqlTw8PDKzg0IYQQQgghhPj/s/ffwbHdV4Ln+b03vc+ESXj3vOfzjl6kJEqU96JKvkptqqdnpqN7qmJ3Jya6d3eiqtdExU70xo6md2emy6pUMlUypZKlSEriI59/fN7Bu0yk95n33v0jAfDhwQOZABI4nwgG8YA0v0wg773n9zu/c2pXNQO5OPDrya9/CXQD3sl/e4HY43cwDOObhmEcNwzjeGNjYxWHJoQQQgghhBC1q5qB3G+BQ5NfHwYGgGcn//0i8GYVn1sIIYQQQgghNq2q9ZEzDOOyoihZRVFeBcLAK0CLoihvAP3An1XruYUQQgghhBBiM6tqQ3DDMP7tY9/608n/xDoZiGS4Phxnd7OXngbXeg9HCDGPQknnt/fDqIrC2e31mE3S9lMIIcTGcb43QiRd4Mz2ejx26Sq2HqoayImN5yfvjJLKl7g3nuIPn9+BoijrPSQhxByuDsa41B8DwOew8ESHf13HI4QQQkwZimV5/W4YAN2Alw40r/OItiaZ4t1ifM7yjInXYZEgTogNzD/5WVWUciAnhBBCbBRuqxmLqXwdKeeo9SMrclvMRw+3MhzL0eKzr/dQhBAL2BH08PmTFhQFmrzyeRVCCLFx+JwWvnCqi2SuREedY72Hs2VJILfF2Mwm2RsnRI1olgkXIYQQG1TAZSXgsq73MLY0Sa0UQgghhBBCiBojgZwQQgghhBBC1BgJ5IQQQgghhBCixkggJ4QQQgghhBA1RgI5IYQQQgghhKgxEsgJIYQQQgghRI2RQE4IIYQQQgghaowEchtcrqhx7sEE90Opij5uUdMZimUplPSKPq4QW5muG1weiHF5IEYyV2Qknl3vIQkhhBAroukGQ7EsuaK2qscplHTeehjhzliyQiMTU6Qh+Ab36u0QN0cSKAp86Uw3dRVqvPi9S0MMRbO0+u189kRnRR5TiK3u+nCCX90ap6DppPMlAk4rp7fVc2Z7/XoPTQghhFiWf7o+yu3RJAGnhS+d6UZVlRU9zm/uh7ncHwPAc9JMi89RwVFubbIit8GZJz80Cgor/PzMKZzKT/6/ULkHFWKLM01+SAsljZJmAO9+1oQQQohaMnX+imWLFLSVZ3BNX8sqYFIqeDErZEVuo3t2dyNBr416tw2/szKrcQDv39/MO0Nx9rd6K/aYQmx1+1q9mE3lk1QkXSCcyvPk9oZ1HpUQQgixfO/ZE+RCX5TtjW7sFtOKH+fs9gYCTiteu4Wg117BEQoJ5DY4i0nlULu/4o+7vdHN9kZ3xR9XiK1uV5NnvYcghBBCrFp7wEl7wLnqxzGpCgfafBUYkXicpFYKIYQQQgghRI2RFbkNajSe42c3xwg4Lby0vxmzSWJuIbaCgUiGV2+PE/Taee/ephVvLhdCCCE2ihvDCd7ujbCzyc1Z2XJQMRIdbFAX+6OEk3nujqUYikkJcyG2ivN9EcKpAjeGE4wnpVCKEEKI2vfb+2Ei6QLnHkSk9VUFVS2QUxSlW1GUMUVRXlUU5aeT3/t3iqK8oSjKXyqKYqnWc28GPQ0uFAU8djONHtt6D0cIsUZ6Gsp7V+tcVgIuOUwKIYSofduD5XNbR50Ti0kyTSql2qmVPzMM4/cAFEVpBJ43DOMpRVH+CPgY8O0qP3/N2tvipafBhVlVJK1SiC3kcIef3U0erGZ1up2BEEIIUcue3x3kVE8dDosJRVoQVEy1A7nnFUV5HfgucAd4dfL7PwdeYYMFcplCiZ9eHwPgffubcFrXdwvhakq9CiFql8M692f/Yn+UWyNJjnb52dMsrUOEEELUjkpcV48nc/zq1jgBp5UXZR95VffIjQC7gOeBF4HjQGLyZ3Eg8PgdFEX5hqIo5xVFOR8Khao4tLndGE7wMJzmYTjNjeHE4ncQQog1oukGr90JMZbI8fqd8HoPRwghhFhz53ujDMdyXB9OMBiVGhJVC+QMw8gbhpE2DKME/BC4B0xNIXuB2Bz3+aZhGMcNwzje2NhYraHNq9XvKKcyqgqtfseaP78QQszHpCq0TR6XOupW39dHCCGEqDWddU4UBVw2E/Vu63oPZ91VLXdQURSPYRjJyX8+CfxPlNMp/yPlFbo3q/XcK9Xqd/D7T28D5k9tEkKI9fLJo+0kcyW8Dukcs566//hHFX283j95uaKPJ4QQm9WBNh9d9U5sZhNWs9SQqOY78LSiKBcURfktMGwYxjngNUVR3gAOA9+v4nOvmMNqWjCIuzee4nyvlE5dqrceRvj2+QEGIhkGIhneehghUyit97DEFjcYzfDt8wOcezCxruPIFTXeehihfyKzpNurqoLPaVmTjeKFks753gj3Q6mqP5cQQoi5lTSdC31R7owl571NtqDxgyvD/PjaCPmStoajW1v9E+XrSLOqShA3qWrTuoZh/Bj48WPf+1PgT6v1nNU2nsjxgyvDACRyRd6zp2mdR7SxJXJFfnOvvJfn5zfHSOZKaLrBSDzLRw+3rfPoxFb2+t0wo/Ecg9Ese1q8+BzrU+b/FzfHuTOWRFUUvvJk97qNYy6v3w1xdTCOosArpzoJeuzrPSQhhNhy3nwQ4e3eCACOY6Y5U+uvDsa4N16edGv1Ozjc4V/LIa6JRK7I9y4NoRsGY4kcH36idb2HtCFIOLscCkxNhEvp1MU5LSYCzvKFaYvPztQ7Ju+dWG9Te2D9TgvOdUyjfvd48u7XG4X6yIAUNtjghBBii3i0KON8FRqbfXZUpVzjIbhJew8rvHueVDfaCXMdyUaLZQh67Hz8SBvRTJH9rVL6ezFmk8orp7pI5IrUu6w80ZFjJJ5jX4u8d2J9Pburkf2tXrx2C5Z17NP4nj1Bmn12mrx2vPaNsxoH8NTOBgIuKwGnhcZNemGwUVR6z91GJ3sMhVi6U9vqcdvNuG3m6YJXj+uqd/HVp7pRFQW3bXNe2nvsFj55rJ2xhFxHPmpz/rYnFUo6yVyRenflLkK66l101Vfs4TY9q1mlYfL9b/E5aPFJNVCxMTRU8LgAKzve2C0mjnbO6sSyIVhM6qZMzxFCiNXKFjQKJR2fs/oTcCZV4VC7f9HbbbTJwGpo8zvmDWa3qk0byBVKOn/xZh/xbJET3XU8tbNhxY+VzpdwWqUTvRBibkVN5y/P9RHLFDnWFeCZXWvfPmWt6LpBtqjh2qSzvkIIsZB4pshfvtVHvqjzvv1N7G/1rfeQpmULGlazimmLN8neSjbtmTidLxHPFgEYjq28YeDPb4xxbShOd4OTjx9pr9TwhBCbSCavEcus/niz0em6wbcvDDAcy3G8O8DTOzdvwCqEEHMJp/Pki+XK5SOx3IYJ5C72R/n17RB1LiufP9kpVR23iAV/y5OtAlAUJakoSuKR/5KKoiTWZogrE3BZObWtjvaAgyeXsRqXLWj86OoI/zhZwvVBuFwFqDecQdMN3hmK83cXBqerA4nNK1MoMZ7MrfcwRA3wOS2c2V5PW8AxY/U/mSvyD1eG+en1UUpaZVqW3BtP8e3zA7x6a5xccW3LTOdKGsOx8mfiQSi9ps8thBAbQXe9i4OTvcyOd2+c1PiHk8fkSLpALFOo2vPcG0/ydxcGeWcoTjpfIpTML3qfaLrA9y8N8atb4+i6UbWxbUULrsgZhvHU5P89azOcyjq7ffnplNeG4tO9Opp9ds5ub+Bif5Q9zeWNlT+/OYZhlP8odwTdFR3vWnpnKM7rd8M0eW28fLAFm0UaoD8qnS/x52/2kS1oPLmjgZM9des9JLHBnd5Wz+ltMzfQXuqPcX9y0qejzsneZWzQ1nSDVL6E126ekdb9q1vjvDMUZzSR414oxZfOdM8583p5IMZv74fZ0ejmffubV/iqZnJazZzoruNBODXrtQohxFZgUhVe3Lfx2k+d7KkjUyjR7HPMW6AqkSvitJgwr6LI1y9ujpMpaDwIpXDZTBRKBs/sauRY1/xB7Vu9ER6Gy4FmT4OL7gYXuaLG9y4NkcqVePlQy3Q1abE8S06tVBTFBDQ9eh/DMPqrMaj11OS1oSoKigJBr502v4MDbb5Hfm5nNJ6j2VfbPZUuD8QYiGR49fY4g9EMX3tq26atdLQcJU2ndyKDbuhkC+XVjrGErMqJ5YukC5R0HTCwmNRlFVcxDIPvXBhkKJblQJuP9z5y0dDit/PmwwncNjPJXIlsUZszkLvUHyVf1Lk+nOCZXY3YKzRZ89TOhlXtORZCCFF5HXVOvnime96fv/lggt/dn1h16mWL38H98RRum4nRRB6bWWV8keukFp+dG8MJbBaVgMsKwEAkw2i8fL/rwwkJ5FZoSVfuiqL8V8D/AIwBU/lBBnCoSuOqGMMwGI7nCDgtOK2Lv9yuehdfebIbRZm7AtCnjrUTzRRocNV2Oe79rV7OPZjA77BQ0gxCybwEcpRXXG+OJHFYTRzu8BPPFjm7XVYexPLEs0X+6lwfRc1gT7OXp3c1zvn5MgyDkXgOn8Myo3hIUTMYmtxr1zcxM4Xxgwda6Kp3cns0SXe9a94m4vtbffz2fpjtjW5ssldCCCG2tP5IBihPMq6movvLB1uYSOe5M5rkzoVBCiWdz5/sXPA+h9r9tAec2C3q9LV4W8BBnctKKl9id9PaJP6NJXLYLaZ5z5u1aKlX7v81sNswjIlqDqYaXr0d4vJADLfNzJfOdmEzLz4rvdAv2GJSCXpqezUO4EhngP/+Qw5+dXscn8NCZ51zvYe0ISRyJQByRY3j3QE8W6Ccr6i8fFGjqJX3ARgw7yTJ63fDXOiL4rSa+PLZ7ulVM6tZ5amdDdwZS3K8a2Zar6oqHGzzc7DNv+AYTvbUcaI7INV2hRBCcGZbPa/fDdMWcKyqLZdJVQh67FwZiNNV7wJYUj/WusmVuClOq5kvn+3GMIw1OU9dGYjxy1vjWEwKr5zqmjWeWrXUQG4AiFdzINUSSpU3YabyJXIFfUmB3FYR9Nr57ImFZ1G2mhf3NnGhL0pHnUOCOLFiQa+d9+wJEk7lF9xfGZ48PmUKGul8aUb644nuOk50r25vpgRxQgghoJx6+cqpyl3znd1ej0kFn8NKxyoWA9bqPDV1vi1qBrFMYcsFcg+AVxVF+REwXZ7GMIz/Z1VGVUHP7WrkzYcR2gOONWncKGpbncs6Yz+SECv1xBKaaT+zq5Hf3Z+gxWdf1QypEEIIsZZcNjPv2VM710sne+rIl3Q8djM9Da71Hk7FLDWQ65/8zzr5X80Ieu185InWJd02W9BIF0rLKkoghBAr1eC28eElHp+WaiKVx2E1LWlPsBBCCLGZZQolsgWNereNDx5sWe/hVNySzvSGYfz7ag9kvaXyJf5istz8M7saONa19uXmR+JZfnR1BLfNzMeOtFWsypwQYmPSdIMfXRthJJblhb1BdgRXt+H78kCMX90ax2ZR+b3TXXMWbBJCCDG//okM/3R9lIDLykcPty5p/5fYmJK5In/xZj+5osZzuxs50rlx+v5VyoKBnKIof2YYxn+jKMoPKO/Zn8EwjI9UbWRrLJYpTJebH43Pbm5YKOm83RvBblE52lmdAgLXhxIkcyWSuRL9kQy71qiKjxBifUyk89N95i4PxFcdyI3Gy5Uu80WdWLq44kAunilycSBKu9/BTjkObXndf/yj9R6CEGvm6lCMVL5EKl9iJJajs35rFoMbjGa4O5ZiX6uXJm9tFvmLZYrkilPX9puzldRiK3J/Pvn///sj35sK6NZ0F/1YIseDUJq9LR78ztnZnfFMkV/cGsNlM/PCniBmk0q+pHF1ME7AaVn0AqnN7+BYV4BIusDpbbNX4873RXjrYQQotyWY6+ImmStyfThBR52TthX0w9jV5OH2WBKn1bSi+wuxESRzRX5xcxybWeWFvU0r7lWzUnfHksSyRZ5o96/5cz/q1dvj9E1k+NjhVnxzHLMA6pxWWv12RuN59jSvPmA61VNPrqjjd1roqFv5MeRnN8cYiGS4MhDj6z67FP4RQmwZe5q9PAyl8busBL1bc6uNYRj8/eVhCiWd+6EUv//0tnlvmy1oXB2M0eyzT1exnLoebg84aA8sLRC+O5bkUn+M3c2eJe0xzxRKXB2M0/LI8z6uPeDgaFeAaLrA6W2bs5XUYoFcu6Iopw3D+E8AiqK8BTRSDub+qNqDm6LrBt+5OEi+qHMvlOKLp7tm3eZCf4S+iXKPjG0NLnY2efjNvTBXBsrFNl85ZVlwRmEwmqXOZeXJHQ2Y1NkxqtWkki1qOK0mHNa5Ux7/8Z1RhqJZzvdG+P2nty07NbKz3sm/fG779GpfMlfkjbth3HYzT+1okAp0oiZcHojxMFzufdZZ72R/q2/NnnsknuWHV0eAcrr087uDa/bcj7ozluT/8+v7GEZ5FvCPPrBnztuZTSqfPdGJrperaP1/X3+A32nlgwdb5j3OLCTgsvKxI21z/iyVL6EqLGnvnHPyuS0mVdKKhBBbyo6gmz98fgfqHNeCW4WiKDitJsKpPKOJHNeH4/Oey39xa4y7YylUReErT3bjc1j4p+vlyUCzqvD7T2+bdT4bjmUJp/LsbfFOn2NevR0ilS8xHM9yoM0357X4o35+c5z74+Xn/dpT3XNOOCqKwrO7Glf4LlRGLFPAaTVXbWJ5sTP6fwd87pF/W4HjgAv4X4FvV2VUc1AngxjTPMFMq9/B1cE4VrM6Xaxk6j6j8Rx//VY/p3rqOTNHc+fxRI7vXBzEMCCaKfD0zpm/9KKmc304Qa6osa/VO+/swtTYFEVhpTHXo8HaWw8j3BpNAuUVw22N7pU9qBBrqMXnQFVimE3KmvdcNE1+9gxj/mPFWrCoCgoKBgYW0+LjUFWF//n1B1zuj2Ezq7T47Jzd0bCk5/rd/QmuD8c52hXg6Dz5/30Tab5/aRiTCp8+3rFomsx79zWxrdFF0GOXvbpCiC1nKwdxUz59vIP/96/u4bCY+NmNMbY3uuc8H7x77QtTb9vU/J+qzr4ejmUKfPv8ILphMJ7I8+JkpfC2gIPbo0lafPYFg7iipvPjayO8cTeMx24m4LRu2IWO394Pc+5BBL/TwhdOdVUlmFsskLMahjHwyL/fmGwKPqEoyprV7lRVhad3NPBWb4Qnd8y9NLqn2UuLz4HVpE5H/k/taKDOZeWHV4cxDDj3cILT2+pm/cI1w6Co6YzEcjR6bDy9c+ZjZ/IakXSBgNNKbnIf3Vw+eLCFW6MJ2gKOivSrmypHbjEpc6aTCrER7Qi6+epT3ZhVZc0rJwa9dg61+RiJ5zjW5V/T534YTjMcy3Ko3UdPo5v/9r076Z/I8MFDS6uSZTOrKIBusORWBIZhcO7hRPn49iAybyA3HMuhGwa6Vp7YWiyQs5hU9jR7lzQGIYQQS6PpBpf6o5hUhcMd/g0bgAA8CKWwmU2UNB2fw4J1nuyM9+wN0uJ3EPTYplfFXto/eT3sd8wK/nQDjMldWiVdn/7+S/ubOdVTh8+xcCr/UDTLg1CaoNeG22rmY0fbcNs2ZpXmwWh533osUySVL1Fnrvy1/GKvfMZVgWEY/+qRfy5prVJRlH8DfMIwjKcURfl3wEeBPuArhmEUl/IYmm7w6p0QhZLOr++E+NKZuWPIx3/5ZpPKoXY/44k814bi7Ax65vzQtPgc1LtsjMXzjMSzjCdzM1YSfE4Lp3rqGIhmOLv93VnydL7Eq7dD2Mwqz+1uxGE1VbQiTkfAwSeOtuF3Whf9wxZiI1mvaonDsSxXBsvp1OceRtasx00qX+IfLg+jGwZjiRyfONrOyZ56Tva8O/E0lsjx2/th2gPOORt9f/F0N/tbfHTUO9i9xP1yiqKwM+jhzliSrjoH0XSBwBxNTg+2+xhL5DCpypIfWwghNrvbo0muD8c51O5bdbGppbg8EOX1u2EA7BYTe1s25oTZvfEUv7g5jmEY7Gp28959zfOuUtrMJg4/tqdtoevhOpeVDx1qJZzKz7ifqiozJjELJZ14tkiDe+aKW5PXjs9hIZkr8cK+Jlp8G7emxNnt9fzmXpg2v7NqDcgXC+TOKYryB4Zh/C+PflNRlH8GvLXYgyuKYgOemPy6EXh+MqD7I+BjLDE1U6G8KlUoMe+MwEJe3NfE07saFlwl62lwEc8WURUFizr7OeZKc7rUH+POWDn1sdXvYF9r5T6Qbz2M8Jt7YVw2E1883V2xxxViM7OY1OnUSqtp7VICTYqC2aRQKBnz7in79Z0QQ9EsveEMOxrdswKuRo+N9x9oXvZzv3yoheMJP393YYjbY728b3/TrL0MUy1NhBBCvOun10cp6QajidyaBHKPnpfWsxjXYqautRVFoS3grHiK/Y6gmx3B+bcLlTSdv36rn0i6wOEOP8/veXe/u8Nq4itnu9GM+c+3G0V7wMlnT3RW9TkWC+T+W+D7iqK8Alyc/N4xwEY5EFvM7wP/O/AfgJPAq5Pf/znwCksM5FRV4TPHOxiIZNkenLkap+vG9G0Wsliq49M7Gwh6bdS5rHPOaM8l6LWhKOWLuAb33PcZjGa4O55if4uX4DLKt45MlhFP5zUSueKKCh8IUcs03Vh0s/PjGj02PnWsnUS2tKYrTw6ric8c72AskWNn07snp0JJ53xvBLvVRJPHxlA0i8duxmmr7Oc5mdMolMopKqPx3JoWmBFCiFrV5LUzFMvStEb7uQ+2+7BbVEyqsi51D5Z6Xu2sd/KJo23kijq7mtZ+nLmSTiRdAGB48nr4Uaqq8GA8zWA0w5GOAD7n1s1aWzCQMwxjHDirKMp7gP2T3/6RYRi/XOyBFUWxAM8ahvGfFEX5D4AfSEz+OM5jaZuT9/kG8A2Azs6ZEazfaZ21T2w8meM7F4ZQFPjUsfbpIidTSprO1aE4Tqtp0f0eZpO67IufXU0eGtw2zCZlzlSyR8u3Pgil+fpTPUt+7DPb6ylpBk1ee8327xBipS70RXn9bog2v4NPHm1f1sbz9oBzjqPL8hiGwfXhBJpucLDNt6Tnb/TYaPTMPAad74twbrJtyYcOtbCnxYvPYanIHtpH9TS4ONTuI5krcbxrdtqmEEKI2T5+tI2JVGHeyfhqWK/enFcGYvzq9jgtPjufPNqOeZHVrPlK+lda/0SG0USOg22+6UULt83M0zsb6J3IcKpn9jktlS/xo6sj6IZBOFXgU8fa12SsG9GSdgdOBm6LBm+P+SLwV4/8OwZM5fZ4J//9+PN8E/gmwPHjx2c1IH/cw1B6utHfa3dCWEwqRzr901Ul3+qNcO5B+SLKYTFV5Y9yoZxXRVGwW0wUSjqOZS5LBz12PrmF/zDF8sSzRZxW04ZPM1iqW6MJDKO8UTiZK635bNudsRQ/uzEGgG4YK977+ujn3mE1LTopYxgGv3swQTxT5MmdDXNOEGUKJWBmGwGTqvDC3rXZDyiEEJuFxaTS7Nu8k+WpfAmzWr4WvT2axDDKxa/i2eKSi2pVUzxb5HuXhtANg1Ayz8uPFAc73l3H8Tn2kwOY1Xe3Myz3+rraLvRFGY3nOL2tbk3e42qWedkNHFYU5Z9TXs07Tjm98j8CLwJvrvYJ9jR7uTOeQtcN7o2nALg5kuBfvWcHTqt5uv0AMOPrlcqXNM49iGC3mDjRHVhStaHPHG9nIJKlu2FpDRGFWK5zDyb47f0J/E4Lr5zqrPhqz3o41hXg9TthOuqceB1rX43q0QW41Rw7Dnf48dgt2C3qkpqiDkaz05NPqqrw/v3NFDWd3nCaoNdOOl/iOxcGAfjEsXba/Cvb5H1rNMFQNMuxroBUxBVCrKlL/VFi2SKneurWvLLxVnI/lOKHV0YwmxQ+f7KTo11+4tkibQEHgQ1y3FeU8n8YM8+7i7FbTHz+ZCdjidz0XruxRI5sQaO7Yc2K6s8SSRd47U4IKMcMnzha/QWZqn2CDMOYbhiuKMobhmH8e0VR/khRlDeAfuDPVvscPqeFL57uwjAM/svv+nj19jg2s8p3LgzyxTPdnOiuw2Ut70fpqFt9IHWhN8qFvigAfqeFXUtYHvfYLexr3bq5u6L6Bh4pb5vIlmj01H4gt6fZu67l73c2efjgwXJp5H2rqCqmKMqCG7of57VbsJpVCiWd+snV/n+6PsrdsRQOq4kn2n2UJvcFj8SyKwrk4tkiP3lnFMMof70WJxohhIBy3YBXb5cvdEuawXv3SSZBtQxGs+iGQaFkMBrPsa/VuyYFXZbDa7fwyWPtjCdyyy4YWOeyTmfFjcZz/M3b/RgGPLu7cd5WPNXmtJpwWE1kC1rVqlQ+bk2mQgzDeGry/38K/GmlH19RFD57ooPReA6zSSGVL6dbmlSFg+2V2/Tvtpsnnw9cG7Rnhdh6yvspdVr8jll7tMTKrUeZfp/Twu+d7iKdL9E6GaSl8+VUynxRZ1eTh/FkHsNgxVVyrSYVq1klX9TlOCaEWFNOqxmTqqDpxobt/bVZHO7wE0rmcVhMy5pQXGttfseKs0umpAsljMkNWalcqQKjWhm7xcQXT3cRzRRW/ZqWatN8igwDTm2rI5ErcqSjOpH4oXY/XrsFu8VUtZzqXFHjt/fDOCzmOZuXC/G4Nr+Dz52sbnnbWlLSdO6FUtS7ZhcfWW9XBmKMJXKc6qmfd9+fz2GZ0TfyvfuaudgXpaveSb3bxkcPr66NgMNq4pWTnYSSeXrWMQVFiErp/uMfVfTxev/k5Yo+nnhXncvK5092ksgV2SbHn1W7H0phNalzZp35HJYtUQSkqOkMRDJ47WZ6Gt2cnKM4ylpy2cxrOkm6aQK5v788xEg8h8dupj1QvSi42rm35x5GuDJQbmjc6LFt6FkUITaiV2+HuDYUx2JS+PLZbjzr1Jz8caFknl/eGgfKpZU/8kTrku5X57LyYoXTj+aqAiyEEGthrgq/YvmuDsb4xc3yOeUTR9vWrMrkRnN1MMal/hgA9S5rxXvebXSbJpDLTlavzJd0DGNy82SFJHNFfnlrHIfFxHv2BOct2Xp9OI6mGxxoXVq58rlMzcSrioLHvml+PUKsmaljQUk3KGqLFr9d1EAkw1gix64mD7+9P0FB03lhT3DZM24Oq2l6/9ujK25CCCHEcmUL2rtfF7UFbrk2esNpwqk8B9t9FS26NhDJcO5hhJ4GF8e6ZmfcPXo+9W7Bc+umiRRePtTCtcE4LT57RYM4gIv9MR6E0kC5SeJcRRhujyb56fWpcuXl3OSVONzhn55RkBkrIZbv+T1BvA4LTV7bqjcbx7NFvnuxXBr5fF90+sRZ77Ly5I6GZT2W22bm906Vc+e76jdmFVtdN0jmSngdZknrFkKIDexoVwDNMLCZVXavU2+6KZF0ge9fHsIwyl+/b39zxR771TshxhM57o0l2dvimVXpdEfQw+dOmlFQNnUrifnUfCBXKOn0R9I0ee1MpApcHYwzHMvxwt4gqXyJsUSONr9zusngSkwFh2ZVmdV0fMqj1zyrvfypRIVNIbYqt83Ms7saK/JYj5ZG9jssFEs6mmEs2g9uSlHTMavKdFDkc1rm3RuXK2oMRjOzjlclTad3IkPQa5uzr1wlfffSEAORDHtbvLx0oHInYiGEEJVlMamc3b68CcVqUlAwMCo+CdjksfHqrXGyRY3vXxzildNds24T9FQ/gEvlS4zGs3TWubCaN07P3poP5H54dZi+iQxOq4l0voSiKNwPpRiKZXntbogmj529rV6+OMcvfql2NXkIemxYTOqc6VQX+6PcGU2yr9VDm9/J/kWqyeVLGj+7MUZJM3hxX5NUbhJiGZK5Ij+/OYbVZOK9+5qqekD12i184mgbY4k8+1u9lHQDTTOW1KD81miCf3pnDJ/DzOdOdi6at/+di4OMJ/I0uK188Uz39Pf/6foYd8aSOK0mvvpkT9Ver6YbDEYzAPRH0ku+XyRd4Je3xvHYzby4twnTCtPKhRBCLO61OyFG4zme3tVAi29tKiMups5l5eNH2gin8xxorVy1eICndzbw6zsh7o6l+KcbY+xq9sxoFD6WyPF3FwZRFYVPHWtfdTZbUdP5+Y0xskWNF/Y24XNY0HWDv3mrn2SuRFe9c0O17an5CCI5WWY0X9I5s72eC31RbowkiKYLFDWdiFogmSuu+nnmKwxQ0nReuxPCMMrR+vv3t8x5u0fdGU1xd6zcwPzqQIyzc6RoJXJFzKoizTKFeMzlgRi94XLA0VXv5EBbZU8aj2sPOJfUzPtxd8dS6IZBNFMklMwvutI+VTI5nivyvUuDDEWzPL8nSCpfPn5lixpFTa9KIBdJF7CZVZ7o8DMcyy6rB8/53ggDkfLvY0fQzfZGKdAkhBDVEErmp/sZ//beBJ/cQFUpO+uddFZh24DDauZkdx0j8Rxtfsf0df+U3nCaQkkHoD+SWXYglytq5Iv69ATtvfEUt0aTQLl5/XO7g2iGQWZya8Xjzz8lW9D4u4uDpHIlPnSoZcY5P54pYrOoVSnEUvNRwksHmrk6GKfVb2dfi5dbo0lG4jlimQKH2/0c7gzw9M4GNN3gzQcTlHSDM9vqK3YxZDaptPjsDMdyS77Ya/LasJgUNJ3pXlGPujee5IdXR7CYVD57omPedE4htqI2v4OLSgyzSVlyiuN6ONLpJ5zKU+ey0vJI3v6dsSS94TSHO/0z0kFePtTCzZEkTV7bdCWy60MJXtzbxMX+GF31zqqUNH719jgX+qLcG0+xo9HNiZ469j7SBD1f0lAVBcs8RZ7aA05ujCSwmU1yrBJCiCryOsx4HRYS2WJVK7RX23gyx6X+GNsaXOxcwv6+jx5po73OQSJb4vS2+hk/29Pi5V4ohaoo7Gpa3kRiPFvkr871kytqvHdfEwfafDR57VjNKiXNmH6PLSaVDx1q4X4ozaHH+lMXNR1NNxiKZQgn8wDcGk1OB3LvDMX52Y0x7BYTr5zqrHixs5oP5Jq8djQ9xk+vj/EglKaolaPy9oCT3zvTxaF2P1B+I996GAHAYTFVtM/Ep451kMgW8S8h3Qog6LXztad60A3mTKsciuUwjPL+v3KqlVwcCTFlW6Obrz3VjVlVV7X3tdraA06++mTPjO/lihr/eG0U3TAIpfJ84VTXjNu3B5zousGDUJqhWHZ6tfFAm7dqKTRDsWw5eyFdoFivMxTLTv+sbyLNP1wexmJW+dyJjjkzE/a1emkLOLCZqzPbKIQQosxmLjeczhRKNd1C5mc3xhhP5Lk1kuSf1TmXdO441jX3dbvPYZlxLl2OSLpAbrLi59Q5t85l5WtP9lDS9Rnti7Y1utn2WMZJPFPkb97uJ1/Sed/+JoJeG8lcib0t7wanw5Pn1FxRYyKVnw7kCiWdkXiWJq99VefOmg/kAK4NxdB1+N39MB6bmTqnlef3NLLvkVllr92CopQbh3sdlX3ZJlUhsMzqeAulTB7p9BNJ53FYTOxc5uyCEFvBSnvDRdIFrGZ13falmlQFp9VEKl+at3CJqip87Ei56fdoPMdfvNmPbhjTs4WV9szORn53f4JGjx2PzcyZ7e/OdvZOZCjpBqWCxlAsO++Fg7RTEEKItWE1q1jN82/3iaQL1LttG3q/ssduYTyRx2k1YV7HcXbVlbdnJLJFTj6y7648Sbx4cDUcz06nXI7Ec3MGlCe66yarQVtm9Pr7/uUhhqJZGjy2VdXxqPlA7p2hOOFUgXAyz6EOH8lsiTqXlSfafTP6vXXWO/nciU5Kur6i/S5ryWu38PEjGyfvWYjN4MZwgn+6PorVrPL5k52rak0QSuYZS+TY3eyZN+VwLhaTyudOdjCezNO5hOq08WwR3Sj3wotlVr/Xdy4ddc559+8davMxFM1it6iy900IITa4710aYjCarWpBDsMwuDeewmY2rXhP3AcONNMfydDktc/bm3ktqKrCe/c1rfj+2xpddDc4yRZ0npjMAHxcwGWdcy9jLFMAIJ4pYBgrr/ZZ84HcaDxHs9dOs9fO2W0N/MW5PkyKwt2xNNeHk4wlcry4r4meBhdNXpv0RhJiixpL5IByOsPE5N61lcgUSnzr7X6KWrnK40sHFi9w9CiP3bLkFcWdQTcnuuvIFTWOdy+9AMlSlDSdsUSO/kiGO2MpOuudPL87OOM2AZeVV051VvR5hRBCVMfUeW508v/VcGUwzq9ulfdxf/Jo+4qCOYtp4clBwzDQdKOqQV46X+LH10bQDYMPHGxZUXsfm9m0pIWXvok0Lpt5xlapl/a38M5wnN3NnlXFJjUbyOWKGpF0geNdAR6G02SLGpliiWxBw2ZReRhOcW04zkgsx9sPI3Q3utgZ9PCZ4+3rGv0LIdbHse4AyXwJl9XEtkY3qXyJVK607AaiJd2gpJdXyfIlHV03GE3kqHNZl5znbhgG7wwleBBO0ey1c6QzMGcBJlVVeGpn5fsE9YbT/G+/ecj9cBoF6GlwEUkXONYVWNLJ7PpwnHeG4hxs87NvkXYrQggh1saL+5q4PpTgYHv1qjnnJ/eUQbkY1nIVNZ0rAzHcdjONbht3xlLsCLqnq03mihrfenuAWKbI+w80sae5OueY22NJBqPl/Ws3hhOziqhAeTsGsKyJX8MwGIm/e03wdm+EN+6GMakKr5zqnA7mHq/yOdX2IF/SeWFvcMkTvjUZyE31c4hmirT47UQzBa4MxPj1nXHqXFbMarnf20gsRzRTIF0ooRvgspqJZoqr7jExl3imyOv3QtS7bDP2mAghNgav3cJHnmgFyq1C/vx3feSKGme313NqjgP4Qo/zoUOtjMZzPNHh4+c3x7g+nMDrsPClM13zplqOJ3OcexChLeDAZTXzvUuDXB6IYTWrvLi3id9/eltFXudS3BtPkS5oFEo6XruZfEmn2WfHtcR2J7+6NU5RMwinxiWQE0KIDWJPs7dqgc+UY13l7BCbxYTNrPL3l4fY1eSZUe14IeceRHi7t1x8UDMMTIrC1cEY/+zZ7UB568JUAHV3LFW119MecGA1qxiGMedWh76JNN+7NEQ0XaCz3smzuxrZEVy8wuYvbo5zbSiOx27mS2e6SWTL2yI03SCdL81bwPDu2KNtD2I8s6txSa+jJgO5gqYTm3xjoukChZJOSTdwWU3ok2/UcCzH6W313BiJo6KgGzouq4n6VeyLWchv7oe5O5biLuUUpbY52goIITaGZK44XakqlMov+/47gm52BMtpIeFU+YSTyBbJl/R5A7lf3w4xGM1ybzzFs7saUFCIZ4vYLSauDyeYSOWpX6MKtQfbffRNpPE7rZzdXsfxrjpcNjPq5Kbzu2NJXrsbpiPg4L37mmalfbQFHPSGM3KcE0KILcZsUqcnP/9/bzwkni3SG86wI+he0p7xR4uwWFQF3Zj5vRafnW2NLiZSBQ53+Cs+/ilBj50/mJxAnSsjJpwqYBjlic9sUSNb0PhX7ykHcr+9F+bGSIJjXQGOPNZ3NTx5TZHMlciXNM5sr0dRypPAjxY7mTUerw2rWaWo6XO2JptPTQZydouJF/c2cT+U4mhnAEWBV2+HcNvMFDWN3nAGVQGTqvLCniCJXAlVUfnQE63TFyqV1uC2cZskVrOKx16Tb6sQW0aLz8HpbfWEU3me3L661MXn9zTy1sMIXfWuBathNnhsDEazuGwm9rX68NgteJ1mRmJZuupdVekRN58mr52vL7ACeL4vSiJb5Hq2yInuuhlVeQciGeqdNg4fDtBVheavQgghakO920o8WyTgsiy5+uTJnjo8djMum5k6l5UHoRQ9De8GOGaTykcPt1VryDMs1FP6QJuXiVSeWLaA+5H9bbpucG6yndlbDyOzArnndgc593CCzjrndHrke/bMXVAlkStydSBOe8BBd4OLrz7ZTUk3lrVfr2YjjgNtPg60+SiUdOLZIl841cmdsSTvDMWxmFVimQJ2swmDcunPfa2+VVWpW8zJnjo66hy4beYVl0YXQqxevqTxzlCCoMc2bzVGoCIp0JpuYFIVXj7Ysuje2+d2NbKryUPAacFuMbGzycO/DroZiefwOSwbqgfbriY3o/EcLT473kdaC+RLGt+/NERpsvmpy96E32Fd8GQohBBi48sWNG6MxGn1O5bct/Tlgy2MJnI0uJdeTNCkKjNa6TweCG0UNrOJ9+1v5vk9QcYSOYKe8n56VVXY2eTm7liKXY81M9d0A1VlSdcEAD+7PkZ/JMP5XvjYkTbaAg6c1uWdT2s2kIPyG/bXb/UTSRcIemz8+NoIJd3gxb1NvLi3iZ/dGAPKDbirGcRNqVbDXiHE0v3q1jg3R5KoisJXznbjc1ZvYuWHV4d5EErT5nfwmRMdC95WUZRZqYiKoiwrhWKtHOuq42CbH4tJmXFyVhUFs0mlpGvcHU8xmsjT4LbyhVNdVct2EEIIUX0/vTHKg1Aas6rw+09vm+yltjCzSd3wLb1WyzLHa/zQoVYKJX3WJOb0NUHAwWeOL3xNAO+uCD4Ip/n2hQHa/E4+f7JjWVUsazqQm6pcCeVNidpkJbmxRBa3zcyHDrUQSubLjWwdFoLe5VWnE0LUnsm2a1UTSRdI5Up01jsZib9b6nk1fWDWW6ZQ4nxvlAa3bbp4yVyrbBaTymdPdDAUzXLu4QTJXIlwqkBB07GrG2dFUYjV6P7jH1XssXr/5OWKPZaoDal8ifFEjs46Z01VSX/03GlQ5RPpJjDXOXI4NnlNEM9xdSBGPFfenjBfxs379pfbo/3i5hi6US6KVm67sAECOUVRDgDfBDTgHvA14N8CHwX6gK8YhrGqDrcum5lndjXwMJzheFeA7oYw/ZE0mYLO354f4Mz2eq4OximUdB6E0nz9qZ7VviwhxAb3/J4gjR4bQY+94qtx0XSBv3yzj5JucHZ7PS/sCXJ5IMa+Vm9Vg7h0vkQ6X6raZNRrd0LcHClXy2rwWKdTSOZS57JS57LidZh5uzfK9kbXhkoLFUKI9VLUdP76XD+pfIldTR5ePrS8PqPr6X37m7g+nKDFZ8e5xArGW0ksU0A3Fm5F8MLeIFcGYjR6bPxistdeoaTzwt6598jZzCYOtPlwWE1c7Iuyu9mz7OC/mr+p24ZhnAVQFOV/BY4DzxuG8ZSiKH8EfAz49mqfpKQZjCVyPAyneeVUF9/89T1euzNMwGllZ9CD3WKiUNJxyIWGEFuC3WLieHddxR4vninitJmwmFRS+dJ0D7l4tsipbfXsbFq8HPHjErki37s4RFHT+diRtnnLEUO5wuafv9lHvqjz9M6Gir62KVOBmElVsJmWdqzsqnctWIFrLrFMgb6JDNuD7gULwwghRC0qaQbpQgkonyNqidNq5kQVzi9zKZR0skUNn2Nj1pT49Z0Q7wzFOdLh5+yOBgajGb5zYQgDg4880cq2eZqZ72rysKvJQziV58pAHN0w5o0/krkiD0JpuutdbG90L9ggfSFVO5M+ttqWB3YBr07+++fAK6wykEvlS3zn4iCqolAo6TS4rfz42iihZJ6SbnC4w0+Dx8pAJEt3w8z8Vl03eBBOE3Ba1qTkd1HTURVlRonVSngQSvEglOZQh2/BWXQhoFyswmpSazYFcK2dezDBb+9P4HdaeOVUJx11Tpq8NsYSeY50+oFyCkWuqNHdsPSg5kEoPZ0Wfmc0ScOO+Y9B8WyR/okM2YJGV72zKoHc0zsbafE5CLgsVdtTaBgG3z4/SCpf4p3hOF841VWV5xFCiPXisJp46UAzveEMR7v86z0cDMOgoOnYzPNP0KXyJd5+GKHebeVQu7/qY8oVNf7izT6SuRLP7Gqc7ktXbQORDOd7IzT57JzuqV9wX/fl/hi6YXBpIMbZHQ1MpArok7mnoWR+3kBuSoPbxmdPdJDIFdkxz22/f3mYcDKPx25eVR/Zqk6JKoryEeB/BO5MPldi8kdxYNZvTlGUbwDfAOjs7Fz08d+4G6KkGQzHMxztCqAZ5QaFXoeFzoCTjjoHZpPKvtbZFyav3Q1xqT+GxaTwpbPdyyr1uVwDkQx/f3loen+J31mZwiuFks4Pr46g6QYjiRxfPC0XRmJ+53sjvH43TLPPzqePtddU7v56GYhmAYhliiSyJfKlPGOJco+YywNx9rUafPv8AIZRTqlY6kmwp97FBYeFkqazo2nhE4JJUciXdPIlHb1K2xZMqsLu5uWvLC6HYUBR1wEolvSqPpcQQqyXtWjKvRRFTedbbw8QTuV5bndw3p5sr98JTTeibvbaq15PIpopkMyVVy0Ho5k1CeQMw+Bvzw9wvjeKWVUoacaCDbcPtnt5ZyjBwcnqmntbvIwn82i6zhNL7G3X7LPT7Jv/vZw6DxY1Y1V77KsayBmG8Q/APyiK8j8BJWDqL9sLxOa4/Tcp76vj+PHji16yuGxmOuqc9DS4eP++Zjx2M58/WW5D8J49wQUvVNP5cjPgomaQL+pQxb/bh+E0Rc2gqGkMRrMVC+RMqoLDYiKVL+G2SeqoWNi98RRQXkFK5kozeoOJuZ3ZXk9J02nxO2j02IikC5hUBU03cNvMpPOl6Q3iqckT01L4nJYl79l1WE1sa3BR0g2aa7hgk6oqfOJIO/dDKfZUOWgUQoitLpYpEkqWJx7vjiXnDeSmepiaVQXbGmxDavbaOdzhZzyZ41TP6tsALYWiKFhNCrphYDWXr5sX8p49TTN6v1nNKu/dN/c+t5X6yOFWbo8m2RF0rypLqprFTmyGYeQn/5kATMCzwH8EXgTeXO1zPLm9gVa/A7/j3XSgjx1ZWhPBZ3Y14LSaaPTYaPS8m9b0IJTil7fGafLa+eDBloqkQh5o89EfyWAzq+wIriwHdi4mVeFzJzsYjeeWvVdFbD3Hu+t4/W6I9oATfxVL8m8mbX4Hnzv5bnZAncvK5092kswVpxuYPrmjgVxR41j33LOKv7kX5vpwnKOdgRWlRfqdVj5/qpN4tsi2ZaRvbkSLzVAKIYSojAa3lb0tXkbi2QXPPU/tKF9LB5yWNdmzpigKz+8JVv15Hvf1p7exrdGN2aTy5I6GFT3GRCrPD6+OYDWrfOSJ1ukgeCUa3LYFt1UsVTVX5F5SFOXfTH59F/jvgRZFUd4A+oE/W+0TqGq5L9NIPIfbbsZmNtE/keHhRJqDbQs3APfYLXP+IV3qj5HMlUjmUoRTeZoqMANe57Lye1VKe/TYLdKAXCzJjqC7ohMJW9Xjkz8ne+Y/QRqGwdu9EQwD3u6Nrnh/W4PbtmBBlMVkCiXGEnnaAw4KJZ2L/VGCHnvV0ymFEEKsD0VReOlA86K3U1VlS1wbOK1mXj7UuqrHuDmSnN7f/iCU5mC7b5F7zKbrBhf6o8QzRXY2uVe9EFPNYid/D/z9Y9/+08n/KubvLgwSSuZp8dn5xNF2/v7yECXdYDCaWdFm+t3NHgajWRo9tjkDwUSuyK2RJF31zooEeUKIzUtRFPY0e7g5kmRPSzloGolnGYhk2dviWZNJGE03+Ou3Bkhki2xrdGFWVe6MJVGU8oztWhR7EkIIISotlMzzMJxmV5O7YtuWFrKt0cWVwRhWk0pn3coaoV8fTvDT66NcG4rTVefkMyc6V7VPsGbrPxuGwf/2217+y+/6CHpsPLOrEVUp57GWChr2yQo9JU0nXVh6idMDbT72tnjnTan88dURRuI53u5V+cYz27BIwQgh1t0/XR/lfijF2e0N8+4DWC8vHWjhxb1NmE0q+ZLGdy4MUtQMeifSfOZ4R9Wfv6Tr0/v34tkibX4HUC6iYpmjoelGNJHK8/eXhzGbFD56uG3W8TyeLeKymqSAjxBCrKN74yl+fnOMoMfGR55oreox2TAMvntxkExB4+ZIgi+f7a7ac01p9Tv4F89uR1FYeXEStVwp1DDAbFKJZwvTP/v1ndD0VozT25a2f7BmA7lEtsS5hxG89nLBgePdAcyTVSGHYlm2N7rLjRnf6mciVeDUtjrObn83JzacyvOTd0ZxWk28fKhlRmnWhfbFTf3eFAWkgLsQ6y9b0LgxXC6Ie7k/uuECOWDGyax88DdQ16gFhM1s4gMHm7k/nuJIZ4AGt5VWv4N6t3VJ1Xrfehjh+nCcwx1+jnSuTZnox90ZS033ZHoQSs0Yxxt3w7zdG6HBY+PzJzokmBNCiHXyzlCcbEGjbyJDKJWnxeeo6vM9ek2+VhZqWzCfG8MJzj2coKvOxcOJNBZV4fS2Og60+acLvhiGwaX+KIYBF/ujmz+Q89jNHG73kcgUeaLDz5GO8ond77ROL6/GMgUmUuVIt38iw9nt5fsWSjq/uDnGaDyHSVXoDWdm7RVJ5ookcyVa/TP/CF8+1Mrt0QQddU65YBBiA7BbykWEHoTS7G9bfr76asUyBQqavqQ+jjaziU8da2cwmmFn0MNgNEOD2zbdkLtappqUTtnbsrTS2IZh8Lv7E+iGwe8eTKxbILc96OLqYAyTqkwXmZnSH8kAEE7mJ7Mv5LgshBDrYW+Ll4FIhkZPeV/3SDyL22aecxtBrqgRngz2VlJYUFEUPnm0nd6JNDsaN/Z+7zcfTBDPFhmMhrFbVAIuG90NzhmVMBVFYX+rjxvDCQ60Lv1apmYDOVVV+OfP7eAbz2xDVec+cfudVo53BxiIZGesxn3/0hDXhxOMxnMc7w7MqqKWypf48zf7yBd1Tm+r58z2d6Nit83Msa7KN+QVQqyMoih8+InWVfVhWamxRI5vvT2Aphu8dKB5SQFSk9dOk9fOj66OcGcsScBp4Utnulc0y1dtiqKwPeji7lhq3qamayHosfPPnt0+58/Obq/nt/cn6Kp3rknFNSGEEHPb3exhV1O5nP5bDyP85l4Yq1nli2e6ZmSAlPdu9xPLFNnT7OEDB1tW9Hz1bltN7PPeEXRzoS/K3mYPAZeV8WR+ztYL793XxIt7g8u6lqnZQA7KF1GXB2Jsb3TT5LXxgysj6IbBhw+1TrcjeHrn7IZ/oVSegNNKvdvK157smbWylswVy73lgIl0ftb9hRAbz1oHcQCRdAFtskv31Or/UoVTeXJFjQv9KQ51+DjauTEniF4+2EJ+r171VcOV6m5w0b3KtgyxTIEfXB3BpCh85HAr7lWUlBbiUd1//KOKPl7vn7xc0ccTotKmzsXhVPn6uVDSSeZKMwK5oqZPp8tP3W4j03SDNx9MUNINzmyrx7rM/eXP7GrkZE8dNrM641rl9miS1++G6Khz8r59TSiKsuxrmZo+W/3knVEi6QK3R5Oc6qljLJED4NZoglML5Ja+dKCZ68MJ9rV4ZwVxbz6Y4N54is46BzaLiSe3r6zXhBBi89vV5GEskSNX1Dna5V/WfV/YG+R/fu0BHpuZX98O01PvXpMm7cOxLK/eDhH02HhhCTN/iqJs2CCuUm6OJAlPNs69M5bk6DqlkAohRC179Pxydls9hlFuwdX22DYlu8XEi3ubeBBOr6pi41q5MZzgrYcRAJxWEydW0EporvPohb4oyVyJG8MJTvXUrajyZk0Hcl6HmUi6gNNqoqfRxaWBGLphzNpD8bjtjW62P5YmdKk/yu3RJLdGk/gcFvxOC189Vv2KckKI2mVSFZ7bvbLGpu0BJ8/vbuTKQBybRcVmqezeLl03+PXdEIlsked2BaezFN56GGEskWMskeNAm08adFMuKX2xP4pJVehaYUlpIYTY6h49v+xv8/LyoflTJg+0+ThQgX3tuaLGL2+Noyrw/J7gjOKFleJ1vBsuLaVI2FLtbnYzlsjR5nesuB1RTQdyLx9sZSCaodlr59zDCXTd4Hh3HcHJ/m4DkQw/eWcUv9PCRw+3zbsUmi1ovHo7hGEYxDIFfA4L3ats0CeEEHMJJfP84MowVrPKR55opbveRb3LhtO69MPxzZEEv74ToiPg5IMHm+dcVeuLZLjcHwPAYZngffvLjWE76508DKenJ6xEed/iP392Oworq0gmhBACuh45vwTWoK8bwLWhOLdHk0D5WF6Nolxd9S4+f7ITzTBo8zv49Z0QN4YTHO8OTK/OPQyn+dmNURrcS2+9cKyrjifa/asqnljTgZzVrLK90c1Pr4/yV+f66axzcnUwxsme8pt6bShOKl8ilS8xHMvOu4/Calapd1uZSBV46UAzT+5oWJNGvUKIrefWaGJ6b0DfRIaD7cufkfzZjTEu9kW54Ujw5I76OdMx6pxWbBaVfFGfsep2tDPAriYPdrMqlXcfsZKqaUKstUrvuas02cO3tR3pDLBzjc8vQY8NVVFQFGj0VK/wydR5VNcNLvZFAbjYF50O5F69Pc5v70+UUy976ugILC27Y7XvU00HcgDxTJHrwwmcNhNDsSwvHWye/tnuZg8PQil8DsuC6UMmVeFzJzqJZQo0emzrUjRBCLE17Ai6uTYUx2pS6axfWRpfvqiRK2pYzSr6ZLGVx/mcFr5ytptsQZtV1UuKeQghhKiGtT6/dNW7+MrZblBYk8rFqqqwt8XLrdEE+1rfrVSdL+rkihqqoqBrc5+Xq6Hmz+Zuu5mmyVTKM9vrZzTQ297o5g+f37GkwMxqVqdTMoUQolpafA7+xbPbVzVh9OzuIGaTQtBrx7vAictpNS8rZVMIIYSoNb413ibw0oFm3r+/acZ5/KmdDeSKGj6nhaY13Hte82f48mpaB/mSjsM6e4OjrK4JITaa1R6XTvbUcbDNh82syp4uIYQQYo09fh4/0OZjR9CNxaSuaaq+Yhhrt/y3HA0NDUZ3d/d6D0OImtbb24t8joRYOfkMCbE68hkSYnUuXLhgGIYx52a6Dbsi193dzfnz5xe8zbXBOH2RNCcnK1UWNZ2rg3H8Tsus9gJCbEXHjx9f9HMkxHJNpPLcD6XZEXRTtwa97/IljdfvhDGZFJ7e0bCmRVoq+Rl6EEpxY6Tcw3TbGp2jrg/H0XSDA60+Wb0V60LOQ0KsjqIoF+f72YYN5BaTzBX5+c0xxhM5fnMvzL9+YSe3RpPT5bZfOdU5vXdOCCFE5Xzv0hDDsSzRTJHff7qH/a2r7wW0kCsDca4NxQGod1k51O6v6vNVy0+uj5Iv6vRNZPjD53es+vHefDDBRKowb+XSO2NJfnp9DABNN6pSllsIIcT6qdlAzmY2YTWrPAinafTY+Nn1MSbSBW6OJOiud1LQNH56fRRFUXh2V+O8PeSEEGIjK5R0NN2Ycw/werg2GOed4Tij8RxWk8rPboyxvdGN3bL4+HTdIF0o4baZl7VPsM5V3siuKFSlN1GuqPHrOyFMisKzuxuxrHDFL5TM87sHE7T67ByfLEn9qDqnlZF4jvplrGLmihqKwqwmtyPxLL+7PwFAplDCYTXhd1h5ckf99Hv76DuszvF+ZwolLCZ1xa9XCCHE+qrZQM5qVvni6U7ujiUJJfPcGk2QKWhc6otydzzFriYPA9EsAA1uq8xECiFqTjRd4G/eHqCo6Xz4iVZ65umFuZhf3hrj7liK09vqOdjm4+FEmjqnlcAy0yKzBY1f3Boj6LGTzBbZEfTgc1iWHAh8//IQfRMZ9rV6ef/+5sXvMGlH0MMXTltQFYUGd+X7BF0djHNjOAGU+xA90eFf0eO8didEfyTD/fEUPQ2uWW0fPnG0nbFEDq/dwl+82cdILMunjrXTM0+a5UAkw/cvDaGqCp8+3k7Q826Widtmnu4TOBLPoU22oeisc063tdjZ5OEDBw1KmsH+R8pkA7wzFOfnN8dw28x84VTXhpkoEEIIsXQ1G8gBeB1WOuqcBFxWTKpC30SGvKaj5ku8djdET4Obe+Mp8iUNh9XEnmbv4g8qhBAbxEg8R66oAdAfyawokMsVNa4MlNMSL/RFiWYKXOqPYTWrfOlMFx770ss2W80qPocFw4BPHm3nUIefBrdtSRW6dN2gP5IBoDecXvbreDSIedSN4QSv3Q3RVefkpQPNK6oIWu+2oiigoKxqz1+jx0Z/JIPLZsL1SC+lcCrPD64MYzGpfOxIG/0TGd64GyKaKTKRLvB/+diBOff9DUQzlHQDdIPhWG7Ge+CxW/ji6S6SuRIj8Ryv3QlN/34eNd95r3cijWFAMlcinMrTUbeynoZCCLHVdf/xjyr2WL1/8vKybl/TgRzAwTYf14cTPNHh4+UDLfzxd69iUlV2N3l46UALf3GuD5OicLk/NuOEVijpXOiL4rKZana/hRBic9sedLFt3EWuqPFE+8r2odnMKtsaXTwIpdnb4iWcygPlY2AkXcCkKuSLOm67ec6VtZF4lrtjKfa0eAh67Hz+ZCeRdIFmr31ZxTNUVeHpnQ3cGElyZIUrXnO5NBAlW9C4NZrk7I6GFTWE3d7o5ounu1AVZdmrlI96emcDO5vc+ByWGammb9wNcWUgRtBjK6/WNbpQFAVVVXBZzZR0A/McC2IH2nwMRbOYTQp7mj2zfu6xW/DYLbT6HbQHHDitpjkD82xBo6TrM352oruORLZEnctKm9+BphskskX8Tou07RFCiBpR84Hc+/Y388LepukZ4f/upT18//Iw6YJGLFtkKJolnS9xomfmfoW3eyO89TAClE+Gi8103w+lGIxmOdzhX5PO8UIIYTOb+OjhtlU9hqIovHywhbceRrCay8GU02rCMODvLw/TF07jd1noqnfxysnOGStDhmHw/UvD5Ioad8dTfP2pHuwWE61+x4rGcqyrjmNds/eOrca+Fi+hZIjOOice28pPaY+nQa6Eoii0+Ga+N5pucHMkQSiZJ1vU6K534bVb+Pcf2c+lgdiC+wu9dgufPt6xpOeer7hXOJXnW28PUNIMPvREy3RF5yavnVdOdQLl3/Pfvj3ASDzH3hYvLx1YetprJV0eiJHJlzjeXSf72oUQYglqPpADZqT1qKrCtsmg7O5YkjqXFU3XSeVLM+7z6ElisRNGKl/ih1dG0A2DcDLPJ4+1V3D0QghRXVcGY5ybnLhyWMy8sLeJtx5G0HSDaKZIUdfpn8hQ77bywQMt7xbLUBSsZpVcUduwF9ZHOgMc7vBv2FUkhXKQeKwrQFvAgc9ZnggMeu3L2icIUNJ0fn5znGyxxHv2NC1pUnEskaNQ0gEYjmXnbM1T0g1G4jkABqOZZY2pUh6G0/zq1jgAugFP7WxYl3EIIUQtqclAzjAMfnxtlPuhFGe313O8u47zkytsrX47dS4LXoeFZ3Y28Ktb41jNJvoe25NxvCuA127BaTXRtsjssllVMJsUCiVjSZXZhBAbW6Gk892Lg4RTeV460MyO4Oy0taVI50s8DKfprHfiXcZes/nousEvbo0znsxxrCuw4L5eXddR1aUFV49WPLRbyvc50OZlJJ7F57RwcziBxaRyZzTFkY7cjBW3Tx9vp39iZfvz1spaBnGGYSzr+VRV4bPHOxmIZlbd3/ReKMXNkXJRlov9UfY2e4mkC+xu9szapxhJF/juxUGKmkGLz47VrM5bxMViUnl2dyN3RpPsavYQzxZXnXnSP5Hht/fDdNY7Obt98aDMZlZRFDCMd/9GhRBCLKwmA7l8SefOWBKAq4MxHoTS/PVb/RQ0HatJZUfQTXeDi8FoDpTyZv/WwMxgTVEUds+x52AudouJz53oYCyRZ0dQGo0LUevGk7npFYjrw4kVB3LfvTREOJnH67Dw9ad6Vj2ugWiGywNRrg7G+e29MJ872cnTOxtn3e7vzg/wnYtD9DQ4+Q8fOYB5kdWyA20+HFYTFlWdrmjotJqn0zYv9EV57U4It82M3znzAt5rt3Cgrbp94mrFaDzH9y4NUdLKK1w2i8onjrYvWknT57Tgc67+PQx6ygFZUdPx2Mx86+0BdMNgLJnj+d3BGbd9EEqRzJUzUbY1BjjZs3BK69HOAPUuK9+/NMwbd8N88lj7opOcC3njXpixRPlztr/Vt2hg2Op38Klj7WQLmpxnhRBiiWoykLNbTOxt8XI/lGJH0MOvbo+RKZRI50s0+8qbtgslnYv9UXY3eSYLBfhX9Zz1bltF9lAIIdZfk9dOe8BBOFVYVZAylbJWKOnLXqmZS73bhllVKZR0vA7LdLD5uN/cn0A3DO6H0gzFs3TVL75attBq0LGuANsbXdgtphlZB/mSxu/uT2C3mDjVU7dh0xfXyr3xFLmixnAsi0lVaPLaeRBKV6UlwlzqXFa+9mQPRV0nV9R4/W4YgHxRn3XbHUE314biGAbsXCAwuj2apD+S4Winn7FEHt0wwCinZK4mkOuoczCWyFHvtuJaYmuD9oBUzhRCiOWoyUAOmN6MbRgG37s0RMBlJei188XTnQzHcjS4bRxo8/HTG6O01znlBCGEmGYxqUsuIrGQDz/Rwu3RJDuC7mUFOeFUHrOq4H+subXbZuZfPLedQ+0+YpkiT+6YOyXthT1Bvn1hkO2NLtp8K7/YftSjYzGM8p6p26NJLg/EgHIj7qVmMWxWu5s93B5L4raZSRVK2EzqgkFSNTisJhyY8NotfOBgMxOpAkcf6ZOaL2mEU+Wqol99cuFV4nS+xD++M4JhQCSd56OH2wgl86gKs/rOLdfTOxs50OrDbTfP2VpBCCHE6tVsIDdFURROdAf46Y0CGPAgnCFb0EjkypUqv/HM9mU9Xr6kcWM4QaPHJsGfEGJBQY993v5mUyLpAg/DaXYEy2Xp74wl+dHVEUyqwmeOd9Dsm3l/u8XEC3ubFnzMDz3RyoeeaF31+Ofzq9vjXBmIk8oXcVhMmE0qbnvNny5WrdFj4+tP9dA/keG7lwYpaDrxbHFVLQugvJ8skimwv9W75ObqMLtHnK4bfOvtASZSBXY2ufnQoYX/RiwmFYfFRKag4baVWya8fKhlRa9hLqt9X4QQQiysps/MyVyR39wLkytpeO0W2gMO+sJpNMPAZTUzkSrMW5J5Pr+6FeLmSAKTqvDls93SakAIsWKGYfB3FwZI5zWuD8f50pluwslyHzdNNxiJZxmKZQl6bOiGwfXhBHtbvLMKi/SG00QyBQ60+takemQ4WQDAbbPwvv1NNLhtyz6WbmbhdB7DKH89kc7TPfn76g2niWbK6bpLDcjCqTzfvTSIYUA0XeD5PcFZt4llCtwbT7Gt0b1gw/KSbhBJl393ocm/s4VYzSqfP9XJeCJPd/3MicuhWJYrAzF2Bt3sbNraK7FCCLFR1XQg98OrI/zknRGKmkF3vZMdQTfRdJGL/VEKJZ1mn518SeONu2FUReGpnQ2LnlyNybOzYQDGGrwIIcSmpk8eRxK5Ij+6OkKD28reFg9Ws8pgNMO98TQmVUHXDQzKZdj/8Pkd0/cPp/J8//IQhlG+oH/PnoVX6yrh2d2NvPlggo46J/tbpdDJ4w60+phIFTAMY3qP5Xgy9+7vKVucVXxkPsYj5xlNn/uk892LQ8SzRS4PxPj9p7fN+1hWs8p79zVxayRBUTP41e1xntqx8HnPa7fMWXH1p9dHiWWK3BtP0dPgkvRIIYTYgGo6kDMMcFhMZAsFMoUSsUwBq7lctVJVFEyKwtXBOFcH40A5zePwPOWXpzy/J0i920aT1zbd70cIIeZzazTBb+5N0F3vnJUSqSgKnzzazoNQilujSe6MJbkzBl99shun1cyP3xkByscyv9NCNFOcVTXy0Qt9Y40ml5q89lU3It/MpgKmeS3j99TosfHhJ1qJpgscbJ87aC5pOiV9dkGTuexv9ZHOa/zy1hg/uzHG2w8j/PPnti+7PUbAaSWWKeK1m2e1NhBCCLEx1HQg96EnWphI57k1miCV14hnS3TUOXh6ZwNBjx2f00LAaWWqBkGdc/F8fbvFtGiZ5tVK50v84MowRU3n5UOtC6bKCCE2trd7oySyRa4OxjnRUzfrgrnRY6PRYyNdKBFJF3BaTaTyJf7yXD+FksbuZg8H2/wEvTZG47lZKYyNHhsfPdxGJJ3nYJt/DV/Z0ozGc/zjOyN47RY+9ETLjJ51W0nQY+cjT7QSzRQ5uMxKqNsb3TC7ywRQfn8TuRITqQIfXmTP25Q6l5WJVIFUvkS2oHFrJLms89qFvgijiRzd9U4+cLBly1crFUKIjaqmAzmv3YLVpNJd7+LGcAIw2NfiY98j1bZ2BN28cqoTVVHWrET0Yu6HUtNlxW+OJOatTCeE2Ph2Bd2Ek3naAg7c1vkPqc/vDrIz6CHgsnJvPDXZuqBcubKjrrw/ab42Aj0Nrg3bkPvKYIxYpkgsU2Qgkt3SPcC2rbLh91wGopnpVgepfGlJ99kRdPPVJ7v58bVRnDbTrP1vi3nrYZRcUaMvksG2BnsyhRBCrExNB3IAT+5o4EJflGeebeRIZ2DOQgCLVZVbax0BJ06riZJuzHtx1htOY7eYZlW0E0JsLKe21XOkM4DFpCy4cqEoynTAtrvJw/3xFCVd3zB70OKZIuPJ3LL3Q+0Murk9Wi7J3yLHq4rb2+LlYTiNMvn1Uu1r9bG72YthGEv6fabyJUZiWTrrnexp9nB5IMauJo+sxgkhxAZW84HcgTbfnA19i5rOL2+NUyjpvGdPEJdt47zUgMvKHzy9DQPm3HtwZSDGL2+Noyjw6eMdq2rKKoSovuVWknRYTXzyWHuVRrM8g9EMb9wNc20oToPbxp5mDx84uPQS9Nsa3fzL57ZjUhcOZMXKuG1mPrPCnofl84uCphv86tY46UKJ5/cEZ6X/6rrB37zVTzJXoqPOyaeOtS+pOJgQQoj1tXGimwq7PZqcTLcs7xfYaOmL6gKbx9OT6TOGAZklptIIIcRKvHYnzEA0w73xFB67meQKjjlS0XBjux9KcW2oXPTLa4/OanGgGwbZggZAKlcEkCBOCCFqQE0HcppucHc8ScBpnVUgIOixYVYVMgWNvok0TV4bO4Lv9sIZT+T49Z0QjR4bz+5q3FAzyce6A5R0A7vFtKX3mwix0RU1nV/cHCNX1HlhbxDPMisDJnNFLvbHaPba2d08f6+usUSOaKbAzqCn4hUEm302xhI5jnYFONLh50R3dYs9icXFs0V+eGWYbFHjcyc6cC/z7+px9S4rVrM63ZbncWaTyoefaOXeeIpD81TO3Oji2SKX+qO0BxwzzvVCCLGZ1XQg9/rdEJf6Y5hUhS+d6cL/SFXKoNfOV57s5vuXhhhL5Pnh1RG+/pR9+kLrdw8mGIxmGYxm2d3socXnIFvQyBRK1K9zURSb2cQzu+YpYSaE2DDujqW4OZIE4FJ/bNmf21/eGudBKI2iQJPXNuMYNiWaLvA3bw2gGwbjXflFn2MilcdhNeFcoPDKo57fHeRAmw+fw7JlK05uNG8/nOAfJ3uk5oo6/+o9Oxa/0wLq3Ta+fLabYkknME+V5O4G13Rj840qlilgNqm459gq8YubY/RNZLg8EONrT9mX3W5BCCFqUU0Hcrliua+OphuTFeBm8tgtNLhthFMFLCZ1RqpIm9/Bg1Aat82M32EllS/xF2/2kS1oPLOrkWNdgTV7HUKI2hT02rCaVYqaTqt/+YU+HJZy4GRWlXlT2Qqajj7ZQC5b1BZ8vMsDMX51axybReX3Tnct6WJWUZQNVxBqq2v2OdD08h43q7kyK7Bumxk2RuHmFbkzluTH10YwqwqfPdFJo2fmi5n6LFlMKhZV0kKFWE/df/yjij5e75+8XNHH20xqOpB7ZlcDLpuJepeNoHfuC5EX9zXR0+gi6LFjt7w723y8u47tjW4cVhN2i4nBaGZ6j8BIPAtIICeEWFiD28ZXn+ymqBn4HMtfAXjPniCd9U4a3LZ5CzI1ee28b38TkXSB410Lpz2OxrMA5Is60XRBViVq1IE2H/+nl/cyGMss+jvfKkbiOQwDippBOJWfFcg9eq53WGVlWQixNdR0IOe0mnl658w0o5Kmz9h4bzGp7Gmeu2TzoykmbX4HR7sCRNJ5zmyrr86AhRCbzlJTGOdiXuD49KjdTZ4lFRQ51VNPrqjjc1rorFte7zCxsexv87F/mY3FNwJdN+atyLwax7oCxLNF7GaVnXPsHV/oXC+EEJtVTQdyj/v5jTGuDcXZ1+plX4uXiXSB/a3eJVXfCqXyuG1mjncF1rVVga4bXB9O4LSZ2F6F5rJCiLVR1HSuDyeoc1rpnGzIPBjNEErm2d/qW3LLgrd7I7xxN0x7wMEnj7YvWPE24LLysSNt0//uDadJ5Irsa/FKZckqSOaK3BlL0V3vXPe91ZV2ZyxJUdPZ1+KdsxjYXOeqWKbA354foKgZfOxIW0Vb57htZj7yRGvFHk8IITaDTRXI3Rottxu43B/j1kgS3TC4M5rg/QdaFkx7KpR0vn1+kEJJ50EoxacX6NkzEs9yqT/G9kb3glXmHpUrajPSOhfyVm+E392fAOBTx9qnGwgLIZYmnimSLpRoXef+i6/fDXFlII5uGPze6S7sZhPfuTDEeDLHT94Z5WtP9cyqtjuX26PlYiqD0SypQmnJ6ZKj8RzfvzyEYUAiW+KpnRurBctm8A9XhhlP5HnbauIbT29bMMieMhDJcG0ozu5mz4adrLs3nuJHV0eAcirj4Q7/rNvMda4aiGRJ58tbFB6EUjMCOcMwKGj6kgrqGIbBYDRLwGWds7CJEEKIsk11hDzZU8+VgRidrU5ujiQYima5H0rRH8lyvDvAgTYfDXPMmhoYGJPFBBLZIuPJ3Lyb/392Y4yJVIG7Yym6G5yLnpR+cGV4uqTzC3ubFn0NU0UNHv9aCLG4WKbAX7zZR1EzeGZXA8fWcX+RrpdLot8eTaIoCp880kZR07k3nqLBbePnN8f4wqmuRR/nWFeAN+6G6ap3YlEV+ibStPod05kG4VSeh+E0O4PuGVUvdcNg6hCiybGkKnRj6v9Lf39/8s4oqXyJ++Mp/tV7dmyo1jdTjEdej6bP/doefc1TX24Purg+bKcwuZI3pajp/O35AcYTeZ7d3cjRztl70JO5IrdGk3TWlc/fl/pjOKwmvnK2e8kToWtpKtj0Oy3LbjsihFgeKZ4yv6oFcoqiOIFvAy4gDnwG+NfAR4E+4CuGYRQr+Zwne+o42VO+cNsZdPPjayNkixrXhmKk8yXujCX5xjPbZ93PZjbxiaPtvPVwgttjKf7qXD8feaKVbXPMluZLOiPxLDuDnkUrY+m6wf1QCoC746klBXInu+uwmU24bCa66jd2KWghNppEtkRRK19VhlOFdR3L07saeBBOoRsGCpAqlPjYkVYSuSKeyWq5S7G3xcveFi+GYfBfftdHJF2gq97JJ462A/Ddi4Ok8xrXh+J85cme6fu1+h186FALiVyRQ+3+ZY+/UNK5Ohgj4LJu2JWj9fbhQy3cGk2yrcG1pNU4AL/TQipfwu+0bMggDmBnk4f37tMp6QaH5tmn9+i5aip12Gk187mTnbNuG88WGU/kAbg3lpozkPvR1RFG4jmsZpXgZCGTckugpWe0VNJYIkdvOM2eFu+cGT2v3glxeTLY/PKZbimwIoRYF9VckXsJOGcYxn9QFOX/CHwOeN4wjKcURfkj4GOUA72KGY3neBhOs6/Fy7ZGNwfafPzgyjAOiwnvIhXlWv0OOutdPAxnMAyIZmbGmLmixkQqTyJTxGEx0V5nX/TEraoKp7fVc2M4wdEltjMwm1RpfSDECnXUOTi9rZ54tsCZ7ZUvWpQvaRgGS7qwtJlNfPJoOz9+ZxSHpbyPyG4x8Ucv7SGcytMeWFra9K3RBG/cDdMRcBLLlIPTSHp2kDpXULCzaXmNkccTOe6H0uxp9nCxP8rVwTiKAq+c6pQWBXPwO62cXmZxrI8ebmM4lp2zMfdGoukG53sjXB+Ks6fFw6F2PwrldhhOq3lZ56p6l5V9rV6GY+XsmMU8vauBC70xWvx26ubpe1dNJU3n7y6Ut1vcD6V55dTs4DQ6+RnMFjSyRU0COSHEuqhmIHcfODb5tX/yuV6d/PfPgVeoYCBX0nS+c3Fyn1s4xRdOdfHOUJz2gJOSpvP0zgZ6Fml2erDNRzxbDuAOtb87C/kwnOYHV4bJlzQ03cDvtGK3LO2tO72tftkneiHEyiiKUpUADsqNtr91fgBNM/jo4bbpVYiF1LttfPH0zPRJl828rIJK53ujJHMlbowkeH5PI8OxHAcfWSX55NF2HobT7Jijkt9y6LrBdy4OkStq3B1P0vFIoKmwMVeOapHVrG74xttQLrIzGM1ycyTBaKKORLZE70SaWKbI83uCc+6bm4+iKLx/f/OCt3n5UAu3J1Mrg147Lx9a3z2uU/Mi883XPrc7yLkHE7T4HesSbAohBFQ3kLsLnFIU5TowDvxnYGp6OE6FG7UpioI6eeQ1Tf5/d7OXd4bi7G/z4XdaFi2HbDGpPL87OOv7fRNpNN3ArKoc7fRR77axv7W6ZY4fhFL8/OYYQY+dDz/RWvFSzkKI5RmO5cgXdQD6I5klBXKVsLvZQyiZpy3g4FCbn8MdM48F9ZM96P7+8hDJXImXD7XQ4lv8IvhhOI3Lapruwako7160qorCUzsbCLisBJyWWT27RO3IFTUGIhla/Y5lTSDsavIwFMsScFpQFYVMoURsMlOlN5xeViD3uF/eGuPOWIpTPXUcmUyz9NgtHO/eGD3zzCaVTx/roD+SmbeoWZ3LygcOtqzxyIQQYqZqBnJfBv7JMIz/m6Io/3byuaaiHy8Qe/wOiqJ8A/gGQGfn7FSGhZhUhU8fb6c/kmHXZDrRe/c18dzuRl69HeIHV0awmlW++mT3svs+PdHuZzSew2E18dTOhiVV3VqtK4Mx0nmNh/k048ncki7MhBDVs7PJzb1QkmLJmLEiVm0nuus43OFfsI3KQCTDcCwHwDtDiUWPFxf6Irx2J4yqKHz+ZAdBrx1FUfj08Q56J8qrexaTuqqLdbEx/ODKMIPRLF6Hha892b3kfXnP7GrkzPZ6BqNZ0vkSu5s8WEzjjCfznOhZecCVL2lcGYgDcLE/Nh3IbTSNHptMYAghNrxqBnIKEJn8Ogx0AyeB/wi8CLz5+B0Mw/gm8E2A48ePL6vM2nAsyw+vDuOymdn7SFNQi0kllS/PIhY1nUJJx7nMLIiAyzrnBu5q2tPspX8iS6PHRr1LTiaidowncvzDlWFsZpWPH23fNOXD7RYTHz/Svi7PvVgvzLaAA7/TQjpfYlfT4imWyVwJKFceTBe06e/XuaySJrbJpPLl33UmX0I3wLSM5A6LSZ2xJeF9i6RHLoXNbGJH0M39UIq9LXOvdhU1nb+/PEwomed9+5uk2I4QQsyjmldYfwV8S1GULwJF4LPAHyiK8gbQD/xZpZ5oPJHjZzdGSeVKpPMaA9Hyqlw8W+SXt8bAUNjf6qU94JxRnnsj29viZXeTZ8mV0FYinMrjtVuW3JhYiKW4NZokmSuRpJyCdWANV68qJV/S+PmNcUq6znv3NS17FX8p+icylHR9zuq4y+W0mvnqkz3oujF9zDAMg4l0AZ/DMisQnNq367GbF907LGpXOl9iR6ObWKbIwXbfmqfoG4bBq3dCTKQKPLurcXqF68NPtM74W33ceDLPQCQDwDtD8VUFcvFMEYtZqcpnWAgh1lvVjmyGYcSA9z/27T+d/K9i4pkif/P2ANF0gWS+xIFWLzazSlHTudAboTdcPhm8fKgFq0nl3nhqzqIAmUKJXFGnzmUtn3xuhwil8jy3q3F6D8laq2YQ9+rtcS71x6hzWfnCqU7Mi8z4C7FUu5o83BhJYDWpdK3RPrJKuzWS5M5YuRH3a3dCPLc7WLES6JF0gbcfTvDrOyHGEnnev7+Jz5zoRNMNJtJ56pzWFX8eHz1m/OzGGNeHEzR6bLxysnPGz+wWE8/NsR9YVM5v7oUZjGZ4ckfDkiuUPq6o6UQzBRpcthWdD753aYjxRI6Ay7pggZVousBQLMuOoLuipf6H4zku98cAePPBBB9+onX6Z3O9HsMwCKcK+OwWWnx2wqn8jH50y3VrNMFP3hnFYlL5/MlOWW0WQmw6NT9FldfKlSS9DgunttWh6fDdi0PYzCqhZLlR7uEOP+l8iR/dDgHlvXOPrhIkckX+4s0+8kWdF/YGaXDbuDwQA+DNhxE+8sjJZ7OY2k8TSRfIFjU8EsiJCmn22fnnz87u11hLmrx2zJPNt2OZAoPRLL93umvVF7mFks633h5gIJLhfG+UvKYz9rschzv9XB9O0BvO0FHn5FPHVp/CORzLAhBK5iloOnZVyqOvlWi6wFsPyzsLfnMvzGdPrCw1/+8uDDIaz7Gzyc2HDi3/PHR9OM7DUJrWgAPDMObcH1fUdL51foBsQeP2aJJPVuBvb0rAacFlM5HOa7QFFt/n/ctb41wdjFPnsvJ7p7tWvYI4EsthGOXPXTiVl0BOiHlUuuG2WDs1H8gFPXbev7+ZcCrP8e4Af3WuH4DbY+UyxrubPTy3pxGr6d2LmHxJm/EY0XRhuhrdcCzHjqAbt81MKl+i/bGTTyxToG8iw/bJ21TLeDLHL2+O47GbaQ84afTYaPVXruDJ0zsb+N2DCbrqnHjsC/fYE2KrafbZ+epTPXz/0hChZJ5krkQqX1pSIHd3ciVvrh5uumFQ1HQa3FaavOUVh0aPjQeh9PTkyu3RBH/9Vj9NXhvP7w7OuvhO5UvcH0/RVb9wqvizu4O83RuZ7l8n1o7LZibgtBDNFFe8GqfpBmOJ8t/EaDy3oscIOK1kfBp1C/yd6IZBsVQ+/+Un/18pTquZL53pJlvQCCwhiBqJvzvBmCtqy6qyOZejXQHi2SK6YUxuvSit+jGX6upgjGtDcQ61+TnYvnB6+UQqz2A0y84mt6SACiGWZVMcMfY90grg+T1BLvXHONLh514ohcNqYleTB4fFRLZYXr073DGzSlZHwMnhDj/xbJFTPXXlk8/ZLnIFHZ/z3SDHMAz+9vwA6bzGO8NxvnBqZn+oSrrQG2UknuM391LUuazUu218+Uz3jPGsRkedk4662kx7E2ItuG1m3ruviTfuhmnx22lwL1506OZIOZUL4AMHDfY0z0wLs1tMfPRwK30TGT5/soMfXxulqOnsb/XR0+Dm2lCcsUSW0XiO0XiO/a0+mh5L7f6Hy8OMJXK4bCb+4Olt81Yh7Glwyf63dWI1q7xyqot0vrSkAGYuJlXhhT1N3B5LcqTTv6LHeGpnA5f6Yxxo8837d2Izm/jo4TZ6J9JVqcZqt5iWPJHw7K5Gzj2M0NPgrEjA5XNY+MgTrfznNx7QN5Hh9liSz69R4bLX7oQoaga/To8vGMiVNJ2/PT9IrqhxZyzJp493rMn4hBCbw6YI5B61vdE9vTH6uT0z94Ac6yoHcJf6o1wbinOwzceRzgCqqvD8Y7e1mU2z2gwYBhS1cjHNkja7qKauGwzHszS4baueAe+oc3J7LInFrOK0mtENg5Je2dlSIcTCmrz2BVPNckWNn7wzSq6o8dKBZorau5/ReKbISDw7qxVAV72LrvpygPX1p7fN+NnuZg8X+qK8dieEz2HB55g9cTP1HEXNwDDebVwsNg7DMKb3yD29s3HFDcAPtvsWXc1ZyNntDZzd3rDo7TrrnWvWF3EhUxOMhmEwFMvid1gqEtBNnbcf/XxWW2e9i/vjKTrrF/7d6wZo+rufaSGEWI6aD+SGYlk8djPeZaQH/uZemKJWPtE+3sMmV9QIJfO0+h2z8vNVVeETR9t4EEqzZ44moT+9McbNkQReh4Uvn+laVQGRA20+uhtcaJrBjZEEjZ7yqpwQYrZsQSOcmvtzW033QykehtMAXBmM8/SOBkq6Qa6gcb4vym/vT3B6Wz1nttcv+TGPdQXY1VROh5yr7cCHn2jl5kiCbY2uFRXAmLpI9jksklZdIZpuMBwrT+I5rCZimeL0PutzDyemA7lCSWcskaPJa5dqwQt47W6Yi31RnFYTXz7bvaqJ0UfP23tXUThluT50sIVkvoTXvvBlltWs8rEjbfRPZGZkFwkhxFLUdCD3u/sTvPlgAqtZ5UtnupZ8UdLd4OLuWGrWLKmmG/z1W/3EMsV5N5e3+BzzNtu9NhRjIlUADAqavupKkFN78JZzESjEVjP1uY1ni+xq8vDyoZaKPv5AJMN4spzm+PgFZavPgd1ioqjpdNU5UVWFo50BRuJZzk0Wu5hI55f9nAsdy+pcVp7csfgqy3x+c2+Ct3sj2C0mvnSma832DG1mP7sxys2RJB67mS+f7cZtN9PgsRFO5ulpeLdK8vcuDTIcy9His695b9LVyhU1rg/HCXrsVU/LDyfLn5lMQSNT0Fad4bLQebtaVFWZc0V9Lu0B54r3UgohtraaOYPfHi2XAz/c4Z8+iUTSBaA8y5nKl5YcyL18sIX0bg2XdebJoaTrxLPFGY+9nPGlckWimQInuutkw7IQa6So6SRyU5/b5QdNC4lni3z34hC6YTCWyPPBgzODxIDLytef6kE3jBkXmy0+BwGnlYFohgOLzLJnCxpWs7pmK4lTgWWuqJEurF3xh81sYvJ8kcqXKGo6TquZV052zirYEU4VZty+mvIlDQWlYit/v7o1zq3RJKqi8JUnu5ccpKzEgTYv14fj7Ai6pdKkEEIsoCbO4CVN5yfvjKIbBuPJPF9/qgeAp3Y0oCjQ4LYta7ZNUZQ5K07azCbet6+ZB+EURx9LuVzM/fEUt0ZT2MwqHUsos7xeLvRFudQfZV+rd0l7J7aasUSOcw8jtPsdDMayhJN5XtzbtCH2j4i52S0r/9xWwlwXyqPxHNeGYmQKGm8+jNDdMHdD44v9UX59O0SD28pnT3TOeKx4psgPrg6jKgoffqJlxkTVnbEkr90J0VHn5H37muYtZDGXZ3Y2YlZVmrw2gp716ZG52bywp4nzfRG6613Tk3gmVZkVJH/gQDPXhxPsr3IK3XAsy3cvDqIoCp8+3l5zv+frwwk8dgtjiTyRdGFZwVw0XeA398M0um2c2lbPr26Pc388xelt9TPaDm0VfRNpfn5znKDHxgcPtqx5U3ghRHXVRCBnUhUCLgsTqQL1jxzQfU7LrBnytx5GGIlneXJHw5KqzD1uT7OHfKm8T67FZ1/yBZJmGOwIutENgxb/xj1pvvUwQq6o8dbDCGe21S/rAnArePX2OMOxHJf7o1jNKjaziUsDUQnkNrh9rd6q7C/xOSx84mjbdGrllPO9EYZiWc5sr5/zIjmZKzISz1HSdM73RjjQ6pvzIvJBqLy/LpwqEM8WafS8e8y6OZogNJlidmcsNV2sCcoTMslciRvDCU711C3YhuBxAZe14umnW12zz76kPm/bGt1sa5w7qK+kvonMZOEMg6FolqDHjmEYvH43TCJX5JldjcvaVw7litDByeC/mqtxUE4f7pvI4LSacFqXl1b5xr0w98ZT3B1L0eyzc7k/RipX4ltvD9Dmd6y4imitutgfJZEtksgWGU/m1jzFVAhRXTURyCmKwmeOd0wHV/MJJfP85l4YKPfG+fiR5Tc2vToU59XJxuFmkzLj4m0hx7sDxLJF6l1WWv0b96J/d7ObKwNxdjV5JIibQ73LxnAsR9Brw2pWSWY1dgZnF7YRW8fjrTqi6QKv3y0fZwolfc5y4S1+Bye667gzlsQw4Gc3xnBaTbMu4k9215EplGjxOWhwz7zA7GlwcbE/ioJC12MTCbuaPIzGc7T67VKwRMyyv81LfySNqijsnizM1TuR4UJfFChnn7x3X9OyHtNuMXGsq67iY53Ls7sa2RF043dal70/rt5t5d54ebx1Liud9Q6+c2GIZq+dH14d5otnuqsz6A1qZ9BD30SGereNepcUTBNis1kwkFMU5ehCPzcM42JlhzM/u8W06AZrt82M02oiU9BWnEpifiTtYK6KcfNp8Tn44unq9ZWrlPfsaeKpHY1SMW0eL+wNsrfVS53Tit2iUtKNZf0diM3PYTXhtplJ5UsEvXMfZ9w2M3/wzDZ+czfEjZFyg3CzOvvvqLPeyZfmubBs8tr5Z89sB5iVDnWsK8Chdh9mVZEJGTGL127hsydmFlMJOC1YzSqFkk7Qs7Ev6BVFWXHxj7PbG+hpcOGxW3DbzHz8cDtj8Tz5ko5pjs/gZnegzcfuZo8cK4TYpBZbkft/LPAzA3hPBceyIE03iGeLBJyWeQ9GDquJL57pIpEt0bzAyt1C9rd6sZhUVAV2Ns1ciXkQSmGzmGjzr01qwmA0w4+vjeCxW/j4kbZVV+6aYjWr/OZemMsDMQ61+3h6Z2NFHnczUBRlxu/XYpITX63JFTXyJb1q6V92i4nfO91FLFugeZ5Arji5rzeUzHGg1cv2oHvR9NxUvsT3Lg6SL+l85HArQY99wf0s1ZxgKGo6D0Jpgh7blktF26z8TitfOtNFtqDNOwGxHtL5EgAum5lcUeP7l4ZI5Ip88GALrT4H90MpfE7LsiZnH00fVFWFz53spD+SYUew+mmtyzUSz/LDKyM4bSY+caQdxzJTSZdCJiOF2LwWDOQMw3h+rQaymL+7MMBwLMfeFg8vHZh/f4fTal5VxUjlkVSUR10eiPGrW+MoCnzqWPualAq+MZwgnddI5zUGoxl2VDDF71J/lKJmcKk/VnOB3Fgix82RBLuaPLSuUVAtakMyV+Qvz/WTLWi8d19T1YobOKwmHNb5//bGk3kGIhkAMkVtSfuiHobS01UNb48m16VAha4bnO+L8uaDCYqajsNq4mtP9lRsEkmsL499Y/UOHIpl+e6FQQzgE0fbyBV1RuI5oFzw5GE4zfneKCZV4Yunu1Y8qVDnsm7Y6pc3RxKk8iVS+RL9kcyc1x+14vpwnEi6wLGugFTuFmKNLDpNoyhKvaIo/5WiKP9p8r9/pSjK2iTKTypp7x7cB6PZeW83nshxYzhBSdMrPoZMoTxraBjlcuFrYU+zF6tZpd5tpa3C++4OtPlQFYWDNVjF6wdXhrnUH+P7l4cwDGO9hyM2kIlUgXS+RCiZ5/pwfN3G0ei20eKzYzEp7FtiE+KuBidehwWH1bRu+zLfGY7zm3thrg3FGU/mKZR0ilU4norKKGk6N4YTjCdy6z2UFRmNZynpBppuMBrP0eYv7xW1mlX2NHvITJ5rNd0gX9qcf4e7mjzYLCp1LivtG7ji9WLGEzl+en2M871RXrsTXu/hCLFlLLZHbi/wS+CfgEuAApwA/g+KorzHMIxb1R8imE0qz+5q5M5Yct7y4olckW+9PUBJNxiO+XhxmRu5F3O8qw7DALtFXbP0jM56J3/4/I4FbxNNFxiKZdne6F5WSsZzu4M8u6uxJnPm7RYTyVwJu9lUk+MX1dNZ50Q3yoWPesNpErnisqvzLWYoliWeKbK72TNv6qPVrPK5k50YhrHkv1Gv3TLdWmW9TK28bW9w0Vbn4HRP/YZawdlKlnJsf/V2iGtDccyqwpef7K7433q17WvxMRTLYRgG+1t9k9sjuqd/3uixYbeYqHdZV7xdYqNrDzj5l88tfJ6vBVO9MDXdqEp6qBCV1P3HP1rvIVTMYmvf/2fgvzYM428f/aaiKJ8E/q/AJ6s1sMcd6QxwZIEeUSXNQJtcnanGzJ3VrPLkjo3Vd62k6Xzr/ADZgsbNQGLO6nkLqdUg6ONH2uibyNBRV7uzl6I6VFVhb8vUxv7ycaGSQsk83z4/gGFAJF3gqZ0LHxNq7TO2q8mD9Ug5UaO7wbXOo9m6lnpsz5UmV6wMo+J/62vBYTXxkSfmb9vgtJp5dldtpf5vVX6nlc+d6CCaKW7IvYhCbFaLBXIHDcP41OPfNAzjO4qi/I9VGtOK1LmsfPBgC2OJ3Lo0BV4PusF0GmlhC6U/uWzmqvQME5vD87uD+BwWmrz2iu+LKWo6U9m8BW1tUqzXmgRw689gacf2av6tC7FcQa99QxXSEWIrWCyQS6/wZ2tmIpVHURTqXFZ2NXnY1VS7G4WXy2pW+ejhNnon0hxYYr+7x43GczhtpppLyRGbm64bjCRy1Dmty07TcdnMVSvg0+p38P79zcSyhapPGOWKGuFUnhafY8HqlWLzsZiWdmyv5t96JaXzJeLZIi0++5qtUmcKJaKZIq1r+JxCCLHWFgvkgoqi/Js5vq8A6372eBhO8/eXhwD4xJH2Rct7b0aPNytejov9UX59O4TVrPKFU534nTKjKzaGX94a59pQHI/dzJfOdG+ovodrsRpsGAZ/e36AiVSB7UH3gulnYnNazbF9I8kUSvz5m31kCxone+rWZItCoaTzV+f6SeZKPNHh4z17KrtnXgghNorFro7+F8Azx39u4D9Xd2iLm0jlMYxyJcmJdH69hzMvXTe4Ohjj9mhyvYcyQzhZfs8KJZ14trjOoxErZRgGA5EMydzCv8NsQeNCX4Sh2PyVXzeKcKr8t5nMlciXNmcK40JKukE0Xf59Tn1Op4wnc4wna7NKoVia0XiO872R6R5rtUbTDfonMmQK5bL6U5Wepz7X1ZYtaiRz5fcunCysyXMKIcR6WKyP3L9fq4GsxMF2H9FMEQXYv8LUwrVwsT/K63fL5XgtJmVJPaXWwunt9RQ1A7/TQucmmPndql69E+JyfwyH1cSXz3TPm4r40xujPAilMasKX3+6Z0P3+Xl+T5BzDyN01jm3ZNVEi0nlvfuauDs+s1Lvo1kIHz3cRo/sZ9t0ckWNv7swQFEz6J3I8Klj7es9pGX7p+uj3B5N4rGb+crZbp7c0cBYIsfZ7fVr8vw+h4XndjcyEM1yumdNuyUJIcSaWqz9wP9roZ8bhvGvKzuc5bGZTTy3u5GrgzF6J9JV2x+XzpdwWjdfqXuv3cLLh+Zvri5qQyxTnnHOFjSyRW1Je8o2evu9Jq+94umEJU3nymAcl83EnuaNXyxnX6t3VhpnNFOY/t1FMwV6WPtATtcNskUNl23jTgSI+WULGteG4jR5bXTVV+fvJ5IuH5NS+RJFzeDkOgRT81W6nlrht5mlRL4QovYtdia+8MjX/x74H6o4lhU59yDC270RAJzHTbQHKruy9LMbY7wzFKe7wcnHj6xsZvRoZwCrWcVmNm2Y1bi1cnkgRt9EmlM99Zu2D9B6e2ZnIzZzhBbfwpXr3revmevDcVr8ji15Ef5Wb4RzD8rHinROYzCWobPOuWBbk43mYJuPeKaccrnSAkeroesG374wwHAsx7GuAM9IafiKs1tMfPJYO4PR7JKbyS/HL26NcXcshaoofPWp6vSee3FvExf6ovQ0uDZUT7HxZI5vnx/EMAw+frSdNv/8LWyyBY1f3xnHalZ5ZmcjZtPG2acrhBBTFkut/N+nvlYU5b959N8bxbvF3AzSeQ1dN1ArWOHtYTgFQG84g6YbK6oep6oKh9r9FRtTrUjmivzq1jgAmYLG5092rvOINqd6t40PHlx8ZdVhNXG8e2ukGSVzRewWE5ZHLr7UR1bU3+qdIFfUeRBKsz3orpmqrRaTyvN7guv2/LmSxnCsvD/vYTgtgVyVtPgctPiq0ydz6nOgKOWqZSul6QapfAmfY/Znp9ln35DZHkPRLIXJPrMDkcyCgdyl/ig3R8r72pu89g29fUMIsXUtZ1p+QyZjndpWj8du4WJ/lB9fG6GjzlnRPQVntzdwsT/KnmavlABfJrvFhNdhIZEt0ui2rfdwxBZxoS/Ca3fC+BwWXjnVid1SXhE42V2Hy2rGZTPxMJzm6mC5KqZdUqyWzGk1c7KnjvuhFGe2rc1+J1FZL+wN0uKz0+S1r3j/qaYb/M3b/Ywn8jW1Mru72cODUBrNMNi/SPXZBk/5nGVSFepdcv4SQmxMNZ9fZVIVDrb7+N2DcjGRwejKV87mcqDNx4G2hWfirg7GuDOW4kinn+1bLHVyIRZTua1BJF2gRdIqRYWk8iV+eWscm1nlPXuCM1bdAPomMgDEs0Xi2eJ0IKdOHisAehpc7G3xUueybqjWBrXgyR0Na1JCXlSHzWxaNJ347liSK4Nx9rZ45lyJyhY1xhPlCpR9kUxVxlkNTquZTy5xondXk4e6M1YsqorPWRsr9kKIrWexYidJ3l2JcyqKkpj6EWAYhrFhKgY8vbNxXVbOSprOL2+NYxjlohMSyM1kt5hoXSB9RYjlutwf4/54OeW5PeCYdaF5als9+ZJOk9dG0DP3TLqiKPJ3KcQ8fnlrnExBY2hyn97jhb7cNjOneup4OJHe1CuzDZJJIoTY4BbbI1edMpBVsLfFy94qbAxfjElVaPTYGE/kq7anYTPQdYN/uDLMYDTDc7uDi65yCjGfZp8NRQGTUv7sPa7N76iZ/Zi3RhP8/MYYLT4HHzvSJunbYkNo9tl5EEpPftbm/ps8u6OBs5t4ZfYn74xydyzJ6e31nNgie4uFELWn5lMr15uiKHzmeAfRTIEGyaOfVzxb5GE4DcA7Q3EJ5MSK7Qh6+MpZG2aTirvGq29eG4xT1Az6Ixkm0nmCHklBFuvvQ4damUjnqXPOXwV3M8uXNG6OlBOQrg7GJZATQmxYtX0VtEFYTKpcgC3C57CwrdHFYDQ7vU9JiJXyb5ILzIPtPsaTeVp8dimoIDYMk6ps6XOazWxib4uXe+NJnpDzldiAuv/4R+s9BLFBSCAn1oSqKnz0cNt6D0OIDWVPs7cmmpMLsdW8dKAZaF7vYQghxIKkXJsQQgghhBBC1JiqBnKKonxJUZRfKIryqqIobYqi/DtFUd5QFOUvFUWRer5CCCGEEEIIsQJVC+QURWkDnjUM4wXDMJ4DCsDzhmE8BVwFPlat514JwzAYjGZI5IrrPRQhRA1K50sMRDLourH4jYUQAISSecaTufUehhBC1KRq7pF7P2BSFOUXwA3gJ8Crkz/7OfAK8O0qPv+y/O7BBOceRLCaVb54pguvXRYMhRBLky9p/OW5PtJ5jQNtPt67r2m9hyTEhtcbTvP9y0MAfPRwGz0NrnUekRBC1JZqplY2AVbDMF4AMoAfmGooHgcCj99BUZRvKIpyXlGU86FQqIpDmy2aLq/EFUo66XxpTZ9bCFHb8iWddF4DIJourPNohKgNkUwBwwDDgIh8boQQYtmquSIXB349+fUvgePAVN6iF4g9fgfDML4JfBPg+PHja5qf9NSOBlQF6t02aewthFgWr93CC3uDDESynOyRnlNCLMXBNh/xTBEDg4PSW1QIIZatmoHcb4E/mPz6MDAAfBb4j8CLwJvVeuJIukAsU6C73oWqKku6j89p4QMHW6o1JCHEJhFO5UnmSnTXO1GUd48vh9r9HGr3r9/AxKZXKOn0R9I0+xy4bbXfPchiUnl+T3C9hyGEEDWramcCwzAuK4qSVRTlVSBMeU9ci6IobwD9wJ9V43kTuSJ/da6PomZwrCvAM7saq/E0QogtKJIu8Ffn+tF0gzPb6zm9rX69hyS2kB9eHaZvIoPHbuZrT/YseaJSCCHE5lTVKT3DMP7tY9/608n/qiZX0Chq5azMlOx1E0JUUKZQQpusSpnMyfFFrK2pc1qmoKEZBioSyAkhxFZW+7kZjwl67bywN0gomZe9KkKIimoPOHl2dyOxTEFW48Sae//+Zq4OxtnW6MJiqmobWCGEEDVg0wVywPQ+lbtjSS70RTnSGcDnkHYCQojVO9o5q+AuAPfGkwxG+ODdswAAEaVJREFUsxzpCOBzyvFGVF6T185799nXexg1zzAMLg3EyBY0TnTXYTVLUCyEqE2bMpCD8l65H10bmS5r/Imj7es9JCHEJpXKl/jh1fLxJpwq8KljcrwRYqO6H0rz69vvtjh6ckfDOo5GCCFWbtNOQ1lUdTr1xGk1rfNohBCbmVlV5HgjRI1wWE1MFZx1yOdVCFHDNu2KnMNq4vMnOxlP5tje6F7v4QghNjG7RY43QtSKNr+DTx/vIFvQ2N7oWu/hCCHEim3aQA6gzmWlzmVd72Esy3gyh81skj19QtSYlR5vMoUS8WyRZq99Rl86IcS7ckWNiXSBFq+9Im0X2vyOCoxKCCHW16YO5GrNtcE4P785hllVeOVUJ/Vu23oPSQhRRbmixp//ro9MQeN4d4Cnd0rfSyEep+kGf/1WP7FMkT3NHj5wsGW9hySEEBvCpt0jV4vCqTwAJd0gmimu82iEENWWypfIFDQAQsn8Oo9GiI2pqOnEs+Vz4tR5UgghhKzIrYquG1wbimOzqOxp9q768U701JEtarhsZrY1SN6+EJtJtqBxfThOs89Oe8AJQIPbxlM7GxiJ5zgjfenEFhJK5umdSLMr6Fm0XYfdYuLFvU3cD6U41jV3+w8hhNiKJJBbhYv9UV6/GwbAalLZtsoiB26bmQ9KyogQm9LPbo5xfzyFSVX42lM9uG3lw++J7rp1HpkQa8swDL5zcZBsQePWSIIvnule9D4H2nwcaPNVf3BCCFFDJLVSCCHWgJQxEUIIIUQlyYrcKhztDGA1q9gtplWvxgkhNrf37mui1W+n2eeYXo0TYitSFIVPHWunN5xmZ5NnvYcjhBA1S64mVkFVFQ61+9d7GEKIGmC3mDjWJWmUQkB5f2iDVGYWQohVkdRKIYQQQgghhKgxEsgJIYQQQgghRI2RQE4IIYQQQgghaowEckIIIYQQQghRY6TYiRBCCCGEEJO6//hHFX283j95uaKPJ8QUWZETQgghhBBCiBojgZwQQgghhBBC1BgJ5IQQQgghhBCixkggJ4QQQgghhBA1RoqdCCGEEEIIUSWVLp4ixBRZkRNCCCGEEEKIGiOBnBBCCCGEEELUGAnkhBBCCCGEEKLGyB65ZUrnS5hUBbvFtN5DEUKIFSlpOumChs9hWe+hiE3GMAwS2RJuuxmTqqz3cMQGJXvGhKgMCeSW4UEoxQ+ujGA2KXzuRAf1btt6D0kIIZalqOn81bl+IukCp7fVc2Z7/XoPSWwiv7g5zrWhOK1+O5853oGiSDAnhBDVIqmVyzAQzaIbBoWSzmgit97DEUKIZUvmSkTSBQAGIpl1Ho3YbPom/6aGYzkKmr7OoxFCiM1NVuSW4XC7n/FEDrvFxM6gZ72HI4QQy1bnsnKsK8BgNCurcaLint7ZwNu9EXY1ebCZZQuCEEJUkwRyy+BzWvj08Y71HoYQQqzKM7sa13sIYpPa1eRhV5NMdAohxFpQDMNY7zHMSVGUEND32LcbgPA6DGc+Mp6FyXgWV+0xHQUuVvHxK2Ej/l4qSV5f7WoAOln/z9Bme4/l9WxslX4963ke2qi/GxnX8mz1cXUZhjHnDOyGDeTmoijKecMwjq/3OKbIeBYm41ncRhzTWtvs74G8vtq1UV7bRhlHpcjr2dg20+vZqK9FxrU8Mq75SbETIYQQQgghhKgxEsgJIYQQQgghRI2ptUDum+s9gMfIeBYm41ncRhzTWtvs74G8vtq1UV7bRhlHpcjr2dg20+vZqK9FxrU8Mq551NQeOSGEEEIIIYQQtbciJ4QQQgghhBBbngRyQgghhBBC/P/bu/dgu6rCjuPfnwkIIoXy0IGpAkqLNRCgoRREEkLSQi1VKFScFKrDUIaWlmKJIzo4dUofoGAkTFvKoCOWV6dTQ1DKKxASHlJAeUSQFCgRMLQ8JCCPEgi//rH3aU4uN8977lnrnPl9Zs7cc1b2uee31s45+66z114rYsCkIxcRERERETFgJpYOsD6StgJ+EVhh++XSeWqUNlo7SROBPWjbB1hq+42ioQBJAt4LPGt7Vek8/SJpAnAkcACr98mdwFW23yyXrDdSv8FVS91qydErqU/dhqk+tdYluZJrXHPVOtmJpEOBLwEvtbdfALYG/tb2gkKZTrP9dUl7AxcApukMn2H71gJ5qmqjCtvneOBE4D5Wt8/ewDdtf7tAnrNtn9Hut3OB/wR2B/7O9r/1O08Jkv4ZeAC4CXiRZp/MBPa2fVzJbL2Q+g2uWupWS45eSX3qNkz1qbUuyZVc48p2lTfgNuBdI8q2Am4vmOnm9ucNwO7t/R1KZaqtjSpsn1tpv6zoKpsA3Fa4fW4Bdmjvbwl8v0SeUvtkY8oH7Zb6De6tlrrVkiP1SX1KZxuWuiRXco3nreahla8DewH/0VW2F/C/ZeIAsF17NmU7248C2H5OUqnTmrW1UW3t8wJwrKQFrD4jN7MtL2FnSScA29t+DsD2awXbp4T5kr5H05nt7JNpwHdLhuqhYa/f1UNcv1rqVkuOXhlZn22AqQxPfQZ9/wzTZ1at+6bWNq41V637scpcNQ+t3Ak4A5hMMynLKppTml+1/dNCmf6y6+H5tldI2rrNdHKBPFW1UYXt827gj4ADgW1pOnDfB75h++cF8ny66+E82y+17fNZ23/V7zwltP9n3wD2p9knuwJPAFd6wK7HGI2kjwM/oBnCuy3NGPq7bT9bMFbPSNoc+BSwC/AosBnNPpxre0W5ZL0haSqwJ01nYwXwXdtPFcixH/ArNCMIVgHvsH1pv3P0iqQdaN7znXbdz/ZZRUONQVd9ptC8Dx61fXfZVJtm2D6Ta9w3tbZxrcermo8ztRwj1shUa0cuIoaPpJttHyppLvAKsBDYh+YPu08WDdcDkpYDPwH+B5gHXG271BngnpM0D7iL5kLvKcA1wPPALNuHlcw2VpLOphnqfD8wnWZkwyrgDvfxmlpJ32jvrgR2BJbTfPv7Htsn9StHr0i6leZ6aQC1Pz8MPGh7aplUm07SdbYPl3QazQiP7wEHAT+1fUbRcJtgmD6Ta903tbZxrcerWo8ztRwj3qbkuM5NudH0yIvnGJHp/NIZam6jCtsnecrVdUH3z67yhaWz9ah+C9ufuwGn0wzBuB74k9LZelm/9v6SYdp/wE0jHt/Y/lzQ5xyLhqmNgb8AvgUc0lV2belcY6hP51rnRTRnSjvlRa697kF9huYzudZ9U2sb13q8qvU4U8sxYuSt5mvkkDSFEdN82j61cKZJwCrbD3cVX1EqT4ekPWlO9z5Wqo3a0/QLbL/aXW77z0vkWZvkKeoSSRcDT0q6lOaAOxm4p2ys3rL9OHAecJ6k9wKfKBypV16RdCbwTuC/JZ0O/Izmet1B94ykz9MMT58GPNSWT+hzju7j8he77mvkhoPA9tfaoVInSjoZuLx0pjH6sKRvAx+keR+81pZvUS7SmAzTZ3Kt+6bqNq7weFXrcaaWY8Qaqh1aKWkOzU5cwJrTfL5Z6g9fSefRrP31JrA9cILtZzunzQvk6R5GMIPm9PNBwFO2v1AgT1Wn6Wtb80OSgI/RnIq/wfZbbfknbM/vd55SJO0MHEbzXnqRZljC/WVT9Yakw2xfXzrHeJG0JXA48BjwCPBpmg7G5bZfLJltrNrPi6OADwBLaa59eEvSzraX9zHHJOBhd60v2XaEDrd9db9yjAc163oeD+zhARyGCCBpl66Hy22/0V6PfbDta0vlGoth+Uyued/U2Ma1Hq9qPc7Ucox4W66KO3KLPcr4+bWV9ynTItvT2vuTgbnA54BzCnXkOuOuFwHTuzoGt9n+aIE8C21Pl7Qb8HvA79J8gzLf9j8UyFPVmh/tN3HLaL4ImAGcaHtpqS8CIiIiImJw1Ty08h5JF9KcketM8zkD+GHBTBMlbW57pe0HJB0FXApMKpSnymEEFZ2m39X28SPK7m0vvi/hlzodSEkXAd+S9PeFskRERETEAKv2jByApH1ZPXX8CpqFk+8tmGd/YJntZ7rKJgC/b/vKAnmqGkZQ22l6SbOBQ3j7mh+LbX+1QJ7FwBG2X2ofbw5cBBxle5t+54mIiIiIwVV1Ry5irLrWlNmWdo0UmjN1fV9XRtKuwAqPWAdF0lTbi/udJ/qjPSs9h+ZazRdoppX/Snt/PvB41+azbS+Q9LLtd/c9bERl1vH+eRL4w/VN7iXpDtsfGfegERUbeUyR9Bma5Q/+VNKXadbcfRbYClgCnGn7oXbbW2iOTVVMzhJrqnloZcSYSHoHzUxH1434p8uB3+x/Ip6A/8/V7UuUyRPjrJ3g5irgEtuz2rJdgI/T/FF6q+0jyiWMqNe63j+257EBs/6lExexQebYPhdA0rHAzZL2cuHFwWP90pGLYfYyzSyV3UQz7W8JnTxizQVyS+WJ8XcosNL2hZ0C2z8BLpB0SKlQEQNife+f2baPaM8ovJ9mNrn3A1+3PRfefiYiItbN9r9I+h1gFnB+6TyxbunIxTD7Mc31Z2tMVyvpxuSJPpnEuidoOljSfV2Pj7b92PhGihgY63v/dPsQMB3YGlgq6R9tvzFuySIGy5YjjjXbAetazuSHNO+pqFw6cjHMjmD1TJ7dfrvfQVq15Yk+a2cp/SjNdT6fI0MrIzbYKO+fbtfYfh14XdIzNOt1PdXniBG1es32Pp0HnWvk1rG9xjtQ9MbIa3Uihobtp22vHKW874uB15gn+uJB4Nc6D2yfQrOMyo7FEkUMjo15/7zedX8V+aI6Yiz2pRlFFJVLRy4iYvzcDGwh6Y+7yt5VKkzEgMn7J6LPJB0N/BZwRekssX7pyEVEjBM367scCUyT9Liku4BLgM+3mxws6b6u2zGlskbUZgPePxHRG59tj0GPAMcBh2bGysGQdeQGhKRVNGt7dBxpe9k4vdYymvVFntuAbb8MvNP2F7rK9qH5JmcGMNf2Wv84lXQacJHtV9vH/w7MGrnWWkRERERErJaO3IDo5xTKG9mR2wO41vYHusrOBl6xfVYvXysiIiIiIhoZWjnAJE2RtEjSDyRdL2mntvwWSXMkLZb0Y0m/Luk7kh6R9Nddz7+qfe6Dkk5ay2scJ+mu9pT7P0ma0P3vtpcCKyT9RlfxJ4ErJe0q6Uft75kg6VxJSyQ9IOnPJJ0K7AwslLSw3W6ZpB3a5z4s6WJJP5J0maSZkm5v67F/u/20rmFp90rauodNHBERERFRpXTkBseWXR2WeZI2Ay4AjrE9Bfgm8Ddd26+0PRW4EJgPnALsCXxG0vbtNie0z90POLWrHABJvwocCxzUTlu7CviDUbJdAXyqfc4BwPO2HxmxzUnAbsC+ticDl7ULti4HptuePsrv3Z1mMcrJNOuZzKKZeno28MV2m9nAKW2+gxl9ev+IiIiIiKGS6XkHx8g1QPak6ZjdKAlgAvB01/adhR6XAA/afrp93n8B7wOep+m8HdVu9z7gl9vyjhnAFODu9jW2BJ4ZJduVwB2STqfp0I0209FM4MLOVPu2f7YBdX7c9pI294PATbYtaQmwa7vN7cDXJF0GfMd21g2KiIiIiKGXjtzgEk0H7cC1/HtnTZ23WHN9nbeAiZIOoelcHWj7VUm3AFuM8hqXdE9kMhrbT7bXuk0DjgZGyyRgYy/IHJm7u04T29c+W9I1wMeAOyXNtP3wRr5ORERERMRAydDKwbUU2FHSgQCSNpM0aSOevw3wQtuJ+xBwwCjb3AQcI+k97WtsJ2mXtfy+K4A5wGNrOSt2A3CypImd39WW/xzY5OvaJH3Q9hLb5wD30AzBjIiIiIgYaunIDSjbK4FjgHMk3Q/cB3xkI37FdTRn5h4AzgLuHOU1HgLOBG5ot7sR2Gktv+9fgUk0wyxHczHwBPBAm3dWW34RcG1nspNNcFo7Gcr9NNfHXbuJvyciIiIiYmBk+YGIiIiIiIgBkzNyERERERERAyYduYiIiIiIiAGTjlxERERERMSASUcuIiIiIiJiwKQjFxERERERMWDSkYuIiIiIiBgw6chFREREREQMmHTkIiIiIiIiBsz/ARWlcWGGQCrkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get a rough idea of the relationship between our variables using scatter matrix\n",
    "fig = pd.plotting.scatter_matrix(df, figsize=(15, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The OLS model states that the condition number indicates strong multicolinearity between the independent variables. We created a scatter matrix to understand which variables are highly correlated with one another. In the matrix we see that GEI has a slight positive correlation to HDI and HDI has a slight negative correlation to Gini. This is one limitation of our analysis where there is some mulitcolinearity between the variables. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data on a map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing to observe is that for different variables there are different numbers of countries who's data has been plotted.\n",
    "The main reasoning in looking at the geomap data is look at the regions. This is something you can't get from tables. Looking at this data will help us see if the factors related to \"FILL\" is regionalized or isolated cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rate of Female Victims of Homocide for 100,000 by Country')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lHLAVuOyw9z+ryTidIdWv7PDX80je53Uk3cmAg+e/lEPJJLqtL//cV6KJIylrHbCk3N1xMMeC7q+5I+nuuKO86OfAqSJyMknLYF+Bxa1AnqRrZF96+y46cJx2khtAB+rRI5PjMPT2fh4I/K4k6dI8kr9PxphBsq53xpj+fBZ4cnkQcSXJH/Q9kCQ2IGlROmA3ME/Kg+bLd3y/RjKOZ0Z5n7n9jNP5PvBWEVksSUryjwI/HKCLzEDuAFrKA6VzIhKIyMkicuYwykqTdEXbA0QichlwSR/bDue4NwNNJGNrfqCqxT62+wbwCknSMrvya3pg8PxuehncPxwislxELiwHiHmSoDc+fLty69uPgI+ISHU5oL6GpPvQSLme5JxXlevzUeD2cje/kSzr18AsEXmLJAP6q0Xk8b2U8RPgaSJyXvnz/iG6/036CsnrsRBARKaLyGAzqv0WWCZJ+upQRJ4PrKDvwLmH8n5ZkgQogYhku7Sg3ACcLCLPKW/zPuC+Lt03vwNcU/5czSFJ7X5dl7L/JiIfONKyNBmfdQ/w/nL9nkWSGa9bUobDnCEizy6fy1tIxtjdVi4vT/K+XA/coapbeiug3BXzfcCXROSZIlIhSbKEy0TkE+XNvg+8p/y+1Ze3P/B5vhc4qfz5yZK0uA7FYH9Hf06S7OLNJK+jMcYMiQVKxoyycv/87wDvVdUHSMae3Eryx/4Uku4hB/yF5G7wLhHZW172DpLBzLeJSAvJQPnlfRzumyTdZW4CHiO5OH/jEdY/Jhnbsapc5l7g6ySJB4ZaVivJIP0fkQzOfxHwy5E6brk70ndI7mT3eWGkqndQTiRBMqbj7xy6+/05krEwjZKM0ToSGeDj5brvAmaQdB/szRtJ7rRvJOkydz29pKYeLlW9kWQc0E9JWnOW0vd4r2GXVX6Pn0zy3u0iGYP3pF7KWAe8nuQ8d5J8HrrOBfU5ks/GHyUZ73cb0FvA1Vv99pG0iLyNpJvYfwBPU9W9/e7Y3XtIAtt3krRKdJaXHfidfg5JgoHGcr26vpZfJUlEcj9Ji/Fv6J7QZD7l3/sRKOsFwOryvh8HntvHuLwDfkEyHq6RZNzOsw8bo/Vtku+lfsfzqOpnSIL595Dc+NhK0lL+8/Im/0WS6vy+ct3vLi87EOB9iOS77BG6dxEdjA8A3y53y3xeP3XsJPmMLiZJwW6MGWPC5O56J713mTbGGGPMSJMkE+aPVfXs8a5Lb0RkAUnii1mq2jLe9TlSIvI+YJmqXjXgxsaYEVcvS/Sp8pERL/c7+qI1qrp6xAs+jE3KZowxxowRTeb8mqhBkiNpJfrBURIkTSXJcDmUbHvGmBE2mbuvWaBkjDHGHOPKSSR2k2Snu3Scq3PEROTVJONDv6uqNw2wuTFmFImOXfKFkWaBkjHGGHOMK6dnHygF96Shql8jSYRjjDHDZoGSMcYYY4wxZsQdSOYwWU3muhtjjDHGGGPMqJhQLUr19fW6aNGi8a6GMcYYY4wxE86aNWv2qur08a7HUEzeEUoTLFBatGgRd91113hXwxhjjDHGmAlHRDaPdx2GajJ3X5vMdTfGGGOMMcaYUTGhWpSMMcYYY4wxRwtBJnHnO2tRMsYYY4wxxpjDWIuSMcYYY4wxZsRZenBjjDHGGGOMOcpYi5IxxhhjjDFmVEzeEUoWKBljjDHGGGNGyWTuvjaZ626MMcYYY4wxo8JalIwxxhhjjDEjzpI5GGOMMcYYY8xRxlqUjDlKaNyMFtei0XY0bgDfgvo28G2gJcADMaoxECPBLHAV4FvBt6K+tbxtESQDkj74v3T5+cBDum1TibhqcNWIq0VS8yGYjYjdizHGGGOOZZbMwRgzalQVtAON96CFf+Hzt6PFR0CySFCLZFYjwXSixo9DvGfw5Q6lDkOvNrhpBNXPIZzy/4aztzHGGGOOApP5lumIBEoi8lbg30iup+4HXgFUAD8EFgGbgOepauNIHM+Yo1HccSO+469otB18E2ix3CK0H7TQ6z4K0PHnsaxmFykktRCCesRNgWAaEkxH0stwqWUQzrUWJWOMMcZMWkccKInIXOBNwApV7RSRHwEvAFYAN6rqx0XkncA7gXcc6fGMmezUtyUtQkRJNzcUyZyGy6yEeB9e2/H5O4HiONe0H5JFMqfjsmfhcmcj6ZMQsQZqY8zYU1XIt6Kt+9BiB27GUiSdHe9qGWNIut3JaPS9G1ZXl6EbqSubEMiJSImkJWkH8C7ggvL6bwN/wwIlcwzzhXuJW3+IRjvQwprurURSRVD1LFzFBQR1byKoexu+/TdocS2++ABEW7uUJEAIEgJBuZzSqNdfUicg6eVIejkufQqSWYW4zKgf15gDVAtAAZGag8u83wGUEJmOSMW41a0vGpXQ9v1o697k0dkKPkaqpuGmzSXacAe6bytkq3H1CwlPugBJ2UV+f7SzlXjLvcSb7iHecj/avAviLt+BLsTNWU6wcBXhCU/A1S8Yv8oaYya1Iw6UVHW7iHwK2AJ0An9U1T+KyExV3VneZqeIzDjSYxkzWUX7P0bc8q2+N9A24tbvErd+99CyYAYSzMalT8ZNfT8uvZy47QaQFEiIECRJFcLZaNyM77wZ33kz+L0jWHOHq7yMoPbfcekTRrBcc6xTVUC7dc9UzaPaSdKaWkkc/5FS6Qa834PqPqAVAJFpOLcU1f14v6FLqVU4Nx2R2Th3PEGwHOdWEgRLx/DMupMwhdTOhNqZ+IbHKPzxi/j923F1swkWrcLNPRFZdBpSNRWpqEMCa5nti6rit64leugm4s33oPu3976hj/Db1uG3raP0z//DLVxJ6vQrCI57POKCsa20MebYHqMkIlOAZwCLgSbgxyJy1RD2vxq4GmDBArvrY45OQc3LUd+K7/gr+H2D2yluQOMGtHgvvuNPBHVvBBTf8Re08C8gGuFKTsdlz0aC6RBMQdxUJLsal1o0sscxx6QoupMo+jOq2/B+K95vw7m5pFLPA5LWomLxqxwIhiBNEKwmCE4jDGchsgDv7ySKbkK1Fe8fRrXtsKPEqDaVW560HCjNHMOz7J+bsZjcVZ8e72pMWiJCsOAUggWnoIUO8j/9IH7r/QPu5zffS2HzvUj1dHL/9lXrlmfMGDvWs95dDDymqnsARORnwDnAbhGZXW5Nmg009Lazql4LXAuwevXqMepxaMzQRPv/G422gatN0l+Hc5DUAgjnJz9Lut/9JZxNqv6jqHqIG8plfgjfcRODGoskGbT0CJJeSTj1PfjCHcT7PzL8EwqmE9S8mqDyUpAcSLac8nsyf52ZiSwITqRQ+BTe33twmfePUCj09TkuEse3EMe3lJ+ncO4kguAswvBxBMF5JH/CkqAIMpY85BgimQqyL/gofs8m/PYHiLc/iDbuRNv2o+37wcc9d0pnkXSWeNs6Srf/BG1ugHQOV78AN/dEgjknIlPn2vegMeagkQiUtgBnSdI5vBO4CLgLaAdeBny8/P8vRuBYxhwxX1yPb/spvnAfEszEVTwJl3sSEtT2uU8w5a341p8QtXwd4iag1GWMkUNSiwmm/AdBxZMAiNv/QLT37SCZpGUmcwoudwGu8nIknIWq4rJno9FOtLiuz+NKegWu6krQIr79F/iOvxJr+5G/CPEefNsPCCov7fe8jRkpIlVUVHyXKPoNxeJ38f6BQe/r3PGkUs/EuSU4t4Qkh5BHdQeqbQTB8tGruJmwxAUEM5cSzFxK6vQrDi5X9dDRjG/bnwTPmQokU4GGGYo3f4/SrT8A9Qe399sfgHt/nzzJ1ZB93ocJZh0/1qdjzFFJOMa73qnq7SLyE+Bukr5A/yJpIaoCfiQiryIJpq480mMZcyRUPZq/Dd/5T9S3J13MXGWSjlv677cukiaoeRFBzYvKZSnEu/HFtWhhHRrvwbf9DHE1uOwZuIpLCKe2Erf/GrQDNEpapOIGFAeulqDmJQQ1L0kmgPXtyQSx2gqkEJdLWnrcFLT4IBptIqh7M0JA3P5rfPuvGXLXO8nichcg6ROQ9Im47FnJcYwZIyJpUqlnEYbPJI7vJop+SRw/gPfr6atlVWQqudyXcW4ekPzulUrf69YSFYbPJAhOJwhW4twyaxE4xok4qJxCUDnl4DL1MfFdv0Db9pI681mQrUJy1Ugm+Z9sFZKtRrJVSLZqHGtvjJlIJBlQOzGsXr1a77rrrvGuhpkkVBUtPYTv+Bv4tiTY0RiN94CrJDXt/d239wW0cA++cA8abUbCBUhqSXnen0WjW9d4P77jj6jPg+ZBUgRVz0SCaQPu6ztvo7T7peVnDgnng+TQ0kODOLJDUscjmZUE1S/CZVYc0XkYM9Li+CHy+fd365KXqKKi4ts4dwLS5UaGqieO1+D9ZlS3knTJO4EgOAER6zZlBs837cLv3pB039uzGb9/G666HrfwVMIVT8JV1493FY3pQUTWqOrq8a7HYM2Wpfpy+eiIl/txfcGYvA6WXsdMOr74IL7tBuKOP0O0rZctHEHd63ssFZdBco/H5R7fY51G21HfiYSzEVd5xHVUjQ7OKxS3/YKo8dOIZEAyIDlc9oykpchVH6pfMBMJZyX7x01EjZ9IWruiHSSN1wp4NNo8+IoEM5HMKqCE7/iTBUpmwgmCE6iouJYouoUougnvNyCSIQyfQRCc1GN7EUcYngmcOfaVPUqoj/F7NqHNu9F8G+TbIFNJePJFx0TWvXj3oxT/+g385nt6rmvZgxY78dseRLJVBMvOITz+LLSjGY2K5QlhBESSlquKGhsbZ8xR7Oj/RjRHBVWP7/wbcct1aP62LmuCpBtZ5nQkcyoSTEVSi4GAqPk6tLgOCWYg4VwI5ySJF8L53bqcxa0/Itr3XkDB1ZKe8wsknDNgnXz+HrT0CGiEL96PFv6FRjuSxAiAy55JWP9xXOXTSaVXoIW7Ud8MvhX1rUSNn0GLa7uUKAR1byasex24Glz27GQclBaTlMm+GY33J8tcVRLQSSXiqkBSaLwPjXZDvBuNdwMBLnMakj0NlzkNgtkj8VYYM+JEakmlLiOVumy8qzLpaamA37sZbdsPxQ600I4WOpKf8+34vZvxuzdA6dA8blI1FTdjKW7WcQQzxy+V+pFIukOXAEHCVLIs30q8/UH8tnVoRzOIA3HEj61Bm3f3XlCxIxmzVBZtuI1iRR26v7ebckC2imD2ctzs5cncTYtWIUFqhM9udGkcJa+dC8Elr5G1zJqR5GzCWWNGm+AyK5G6N6DR85Iua8H0JPBxhyaZVFV820+J9v9XMjaoV2lc7gm4ystwFRcSVD8PXC3RvveD309x11W4zGq0lMzP4iovJay9ukcpvnAncfNXQSqS7nS+qVyJzmR9xx/R6E249DIkfTykj0d9AbQVCeqTroOFOyntOpBNX4lbrsPn1+Cyq3AVlybnKVmQ7MG7lqoRRDuSVjBtB98J2lFO5z0XJIekjsOlJ+cFjzFmcLTYiW/YiN+1Ab/7UeLdG9C9W7olKjhAKqcgU+fhps0nOO5xuLo5uClzkLpZSHpij1XUOEIbdyRBX7EzCfqKnURrb8Q3PAZRMXmgyWSzc09A823ons0c8dVUvi1pdetnffzYGuLH1gAg1fVkr/o0rmb6kR13lKl6in/4IvGme9CWhp6fmSCZ/8tNmYNMmYObOhc3ZW6SFbC63gIpMyST+dNigZKZFEQEgmkDjunxHX8ibvsJuGqgKkl5TQokfehBjM/fju+8sVx4OXEC5T8U0Tb8gS59kkWC3luXwtpXE9a+Otml+Vrixk91W+9yFyCp41FV4tbv4Ft/jJYeBTySPY/UtPfhso9Dsmej+VvLJ9CE5m8mzt9M3PSFrq8ASCW4Coj3M5hEDpJeQWr655M05saYSU9LBeKNdxGXJ1P1ux/tPSiaNp9gyWrc9MW4afNwU+dNmgQFvqUBbdkDUZF49wb85vuIt62DUn6QBUT4rWsH3m6kuQA3eznBktXIJBjb5Hc/SrzlPrR5V+8bxCV0/zbiw1rSwjOeTvqiq1GF4l++ht+2DjKVSK4maZmsmoZUJw8354RJ17pmzOEsUDKTjqr2eTcrqLyEoPKSXtdFjf+D7/wbWnwYJIWkT0bSy5JkB6kliKsDV4FIDlzlwfmFBtP/PKy9mqD6RWi0K+kS56qTbnEiqJZwqSVQeWmS+c63A4LiESBV/99JJr5oOxqXW4qiHRA3wsEJNTX5OT7w3IGrAqkqd70L0Hhfsk8wHUktxGVOScZEGWOODsUOCr//PORbuy8Xh5t7IuHxZxMc93jc1LnjU78joPlWiv/8PtHdv+p9DqQJRupmJYHRnHK3uxlLkNTk+b4NZh1P7tVfw+95jPihf+BbdietjFPnQLaG5G+OJoG4avI8SBEsPuPg39/MRVejURG/eyN+z2NooQ0tJH+j3NwTLUgyQDk9+CRuUrJAyUxYvvM2oqbPJmNzfEfSlc53JP2nwwVIuABcDUgKJEwmfXW1uNx5SPqUHgFOUPMqgppXgIRJ97QBUoIPlbgqJH1cz+WSQnJPwOWegPo2fP62JBjq/Cdx/nYghGB60rUwnIe4Q7PGqxbBt5J81ZRnI5BUuf49v3n6CyKNMZOPRiXiR+8gWncj8aN3ge/SmiyO1FlXklr9TKRiks+HJkGSVGKiB0niCE96EsHSx5W7pM1DUhk0LqHFzgnfjbErESGYsYRgxpLhlxGmCeaeQDD3hBGsmTEThwVKZsJyubNwhScRN326+woFLT2SJFLoRdz0OQjmEk55C67y6QcDBwlqhnR8jfcTt/4gGX+E4rJn4XLn9tyutIW49Qf4jj8lCSNSS0CLSYCnnaTqP4MEtUn2u73vBkq9H1ByBLWvI6h91cGMeVp6DM3fgca70agBfCvBlLfh0sehPo+WNqKlR8uPDUhmNUHNVWj+DqLmL5PMyVRRTvpQg+TOxmXPRdzkufNpzLFKVYk33EbxxmvRtn3d1rkZS0hf/tYJn3wh3v0opVt/iLbsQVv3onEJcUGSOCAIwPsk2UShnTEbnX0k1BOtvZFo7YGu2w6pmY627Uvek4tfQ7zhdrR1H9reiKpH0jkkXZFMfJvOIVPmkDrlyX0eIt61gdJtP0IqapNHrgZckLTspLKES85AuswRNWKnFhWTc1v3F7RtP1rsIJixlGDRaYSnXDz5g3Ezbibz7VsLlMyEpKrEjf9N3PLNwe8UzkOy5+DC+aCd+PbfEu3/byScQVj/KSScV570dTeSWZl0WQM0bkajrUg6SUUc7f8QWliLhLMhmAHxDrT4CK7i4u519AXAg4Tl7nKbIdp8aLyRVBNO/zQSJH9c4rYb6BkkpZDUIiSzkrDuDQez7Wm0m9KeN6OFu7tt7SouR1KL8J23U9rzevAt3Yvr+CO+7acEU96By11A3PLNZF6pYAbiqqHzb8SSThJU1L1x8K+tMWZMxbsfJbrrF0QP/r2cza0slSV93lWEZzx9VFJ5+z2bibfeB2Hm0JiTqqmQq+7WSu9b9yGVdUnQ059CB/H6f3RbNAnCocFTfzCDnt/5MPnvXjOo3fzWdZDOImEGwjSk0uWfM0l3uMNes66KCG7+SaTOej7hkjNG5DSApGvdktW4OcsBSTIjapz83MtYOGMGy7reGTOCkiDpY8Qt1w28sasjqHkVkj4F3/YDfNvPiA9LdKClDkq7XwFxl3SwksVVPBlcDb7thiRrXLiY1Kxv4rKPI2r9P7R4aAJMV/N6XGZl93KLa5O5jgrrkjFNhHRLsqCtRA1XE6eWEE7/PGHdmyntuh1Iupa4qisJp77rYMB2cDffQanhNWhxXY/T9R2/pbRzK1p8iL5aprS0nmjP60gvuBtX/XKId6Klh/GtP0BLm5KxWcEMfPERJLXU5gAxZgLKf/+d5VaWQ9yCU8k89W2jllEt3vEQ+e/9v14visMzn0XmwlcffF78/eeRmnoyTzl0w0XzrWipAOV001JZh6tfgNTMSDKrmYOi+/94BHsrfutaCnu3ELzmWyPS3S/esZ7SLdcTb/pX8v4BZKqQXHXyyFYnwXL20HPJVcOB57maZJtspf1NMUcVC5TMhOM7byJu++WA27nqFxLWXYNGmyjtvhp8Y+8bagfEh6UK1zy+/VfdF0WPUdr9GsJpH04yzOmhixTf8hV85cW4zKEJMF32DNKzf4hGe/Cdf8F3/Bnf+U8Oz0inpY2Iq4XU8QR1b0PjHUhqEUHVc3qf3Na39hokHSyveH+f6w4RSjufg5Y290iTrtEmfPsNyZNgNkHl5biq51o6cWMmgOKtPyT61296BEkAbuq80QuSdj1C4Zef6L3lIFtNePxZAGhHM9EDf8Pv34ZzAfGW+yjd9wf81nU9gyFxuPknE57wBEr3/A6KfU3ZYIYjtfIpkMr2WK7FTrR1X5LKe5BjVt3UOfh9Ww8FSQDl5AzatHMItRKC5eeSecobki6D5pgnKDKJ25EtUOrC791C4TefTuZhqF+Am3Uc4amXTKjMLaox7F8D+Z1QsQAqFiCZ/lNmTzZBxRNx829Fo81o4e4kAOn484G1SYa69AmEta9Hglq0FBPWvREtbULjXWjcmMyB1Ffg1A8trae063m9rImJmz6Lm/m1HmsknE5Q/XyC6ufjS4/hW/4vmfDVVeOyq3GZ1YAnavh3fOffDpXY+FmC6isJ6t6QdIs7WN5MXMXlyfgj3wrx9iGfB9qJFh8ceLt4J3HLN/CdN+Mqn4pGO9BoBy61iKDuzUMe12XMRKTaDsSITPzPc7DoNIhL+P3b8Xs2oXs3H1wXPXQTrm424cpLkrv3IyDe/hClW75PvPHOPrdJn/NC3LyTiTffS/6nHzg4Wa0sPRPtbCF+8KbekzCox2+5D7/lvhGpq+mudNuP8Xs2kb7w6oOZDjUqkf/Zh/Gb70FqZxIsPh3E4fdtTSbNlQCZlsynJbka4odvSZJQVNRBOBJjV5V4/T/o2Hwv4XGPI1iyOsmUN0nS0/dGVfG7NxCv/wepxz03aUkzxwxRnThR3urVq/Wuu+4at+P7lr1Ea/+MpLNI9XSCeStGZcDkYGl+NzT+C5rvTwa+xp2w+89Q2NN9w3lXQtVS6NgCLgOZ6bDwRSOe1W28qCq+8yYkqEdSx/WaiEA1Im78RNIS5fd3Xyk50Ig+kygMgat8Oi53AS73hINjjwYjav4aceMne10nqeNIzfhKt/mODsy9FDd+5uAEtmPO1RNUXpa85unjkm56wdTxqYsxA4jjBygUPofqDiBVfnTg/W4gGcvn3HEEwekEwRkEwemIzJvQWSLVxxT/+L9oR2PSpa2UT4IUEcJTn0K46rKBxwj1I979KPnrBjdWMf3Ut5E6+SLiho0Uf/tZ/O4Nwz6uGXkyZQ7BgpVo217iR/sOeseFONy8FQRzTkDjCIqdaCl/2P8FJFNZTmBRg5u9nPDki5Fw/G9UazFP8e/fQsI0qXNeiGQqBt5pFInIGlVdPa6VGIK5bom+NvzoiJf73tILx+R1sEBpAtG2DbD1J9CxCdoeg/yO4RcmQRIwpeshaoGoDXLzoHIRVCyE3GwIKiCshLAKKuaDyyYtVaUmqFyCpEb+7qv6VrS0EXC4zCmoeqKGq8HVEtT+Gy594qFt42ZwlQczwA1YtsZJhjrfjkZbUd+YzCuEB8mg8T40fwc+f2eX+YmGK0AyK3HZs3G5s5HMqiQ9+cG6KBo9hoSLEHFEjZ8nbv5i38W5aaTn/AIJZ3Qpw6OFu5NzifdBtDv5OdqClrYAxcFVVarB5SAegTEC4QLCmlfgql9w1ATiZvJQ9ahuJo7XobofyCCSIYpuIYr66q5bg5PpeN3MoW6xtaTTLyMMLyIIlo9N5ScgVaVww4eJH7ltwG2DZeeSfdZ/osVOOr97TbeWLmNGQ3jyRWSe+rYRKUtLBbSzFamsnVC9hIZjMgZKrxuFQOk9YxQoWde7CUIf/Sps6OdCesgFxpDflTwOKO6H5sF3gdDcfKg+HnJz8TMXQmZGMs9POA9xvQ8eVfVoaT3Ee8FNRVwNvng/vuNv+Pwt3S7WJfuEJGgqdy3z7b/CVV5BUPMqfOeNxM3fIjX7RxDU4wv34luvTyaADerLaa7P6pYIQSRAyRC3fpm4+RscSJowOmK0tAkfN+I7/05Y/wkkfRyqEZq/jajpK2jhDnBTcLlzcZXPITXrR8TtN+Bbf0z3cUwBQdWzkxTf2p7MESUBIg7JrgZ6fg8U97wjSU4haUQqwFWA1CCpeaD58pxSdcm8UplTwVUm46fiBnzHX/Cdfx3eaUdbiPZ/EFdYk5zzIINYY4ZLtZNS6edE0W+J4weBwd3kCDgd17oDijsQmlBXia9bSSxrgGai4t8J9p9G8ZFvE+98mPD4swlPPH9Sj6vQYifFv18HxQ6kaipSzlgnVfW4afN6dH8SEdz0JYMKlNyU2QAU//J1C5LMmPCte4k334ubvRxKnfh929C2faiPk7F04nCVU5Dq+iQzo4/RQgdaaMdVT+vWI6jwh88Tryv/3ctUJQFTRV2S8lwV4hIaFZMyp83DTVuQDMGoX2hp0Y9x1qLUhcYRODcmGVtUFbSUdK3b8SvY+dvk+QRVOm4GGmwtPwuR7Jm4iotw2bORYAb4ZqLmr+M7fg++aeQOLBnQQh8rQyS9LBmzdOARLkJS89HSFnz+1iS9d7QNLdyPz/+zPCfSANwUJH0CEi48OHZIo83lOYs2c6ALn4QLSc3+YbfuaBrtIW7/Nb7jD+DbwFUlGeq0g6DujYR1b8SXNhLteWufY4jSc28EyRC3fh9X9XRcanGPbQrbLoRoWy+vVzVB9ZWEU9/Z5+mpeuLGjw8uq2CvAiRzCmHdNbjcWcMsw5j+eb+fUun/KJWuRzUZbyg6h4BZUBIIhcitB1p77OtkOcHeDUiX7raq4JsuRVoVdil+xy4oHJZcwIW4+ScnF1kVdVCRXEy5KbNxc044om5uY0FViR+5leJfv9HLAHxB6hcQzFtBcNxZBAtXIWEKjYpoRwvRPb+ldNuP+kwDnX7ya0mdfgWltTcS3ft7tHEH2j70caDGjIkwTXjaU0k//kqksg7ftp/89f+BNg69p45UTknGWh1/NsHSM8f9e2CytSjNc0v09amPjHi57y6+yLrejbV4x3oKN/wXwfFnJb8QC04Z0SZaLbXA3W+EpnuAyTUnQem46WjQy4X5RCXVgAct4SouJKh6Lurq8K3fBVeVtESVJ2HFVZeXVQMhcfsfQFtAO9BoFxLU4yovxVVclpTtm5OucJrHZU7u9fDq29HCPWi8F1InAjEufRwiKVQjitsv6T3QAYKalxO3/ricdS8gnPpugpqXHFzvC+so7XwefY25crnzSc38ep8vjaqHaAe+9Aha2oAWH8B33txzTqZeKzeLsPa1uMpLkODoSiJiJgbvN1MsXkep9DOEDIEuRimhtBPEdWjxHiBAghOROMQXH0V8HoJqyM6DOA+d63tOcNixiOhXR9ACmq0mWLKacOnjCJb0PzhdfUz82JokZXL1dKRqyqBvwGlcwu98mHjrWvyW+9GoQOYpb8TVL8A37UIL7UkwE8f4hkeJH1uDb9hE6owrCFdeSnTfHyj+4/96zZrXTTpHsORMwmVnEyw5E8lUJOnBr39H93mbDnBBcuF53lUQhHR++83ovq09tzNmIkllCOadTLxjPRSOtMs95F7/PVzV+I7VtUApMVCgJCLfBJ4GNKjqyeVlU4EfAouATcDz9MCduL7KsUDpkHjHevLffeuhBekKwmVnE558MW7BKUfc0qTqoX1j0orU8BfY2/eEchPNpAuUDpBqgpoXE1Rf1W38Dxxo1cujpYeTDHWpJfjig8TN38S3/6JnUakTcFXPAN8OEuCyZ3Ybm5T8LpWAEC2upbTzuYd2djWk5/yOuPV6NN6Nb/vJoE8hnP5FgspLkmNE2ynufH7f442CmclYp36SLvj8v4ia/gfiBlzuiYRT34VqCc3fQdT8VTQ/cDccAEmvwGXPSZI9pOYjqeORoG7Q52VMV1F0N6XSN4miG3G6CKdTEB91m8+sN+JmIcyF9v7/dqiC7Hsc/uFa/NbHjqyyLsDNO4nwuMcTrngSUlnXY5N4x0Pk/+/tSTY4FyA108lc9laCBad0r1cxj9+5nnjbOuKt9+O3PwTRoVZ0t3AVrmYGvqUBv/meAeoVgo/636Y3QUiw8DT8/q1o064+N5OaGeRe9WUQR+FnHybedHef2xpzNLJAaejmuSX6hvTIB0rvKgwYKJ1P0lf7O10CpU8A+1X14yLyTmCKqr6jv+PYAIP+FDuI1t5ItPZGpHYW4SkXE558Ea525rCKE3FQdVzymH8l2rYRHvgwNE6GBBYTJ6AeEm0lbv4Kccu3kCDpY6/aAb6jnE3uwDimAFf9fCSYXZ4LqZeiSg8RNz508HkMIBkktRx8Cxo39Jiz6CDfQrT//V3SnA9e1PjfSFCPRtuI9n0QtGdXo0OV2k2p4U2kZ3+v702av3QwGPLl10QkheTOJZU9k9KOZ6ClRweslxYfIC4+cGiBVBDUvYWg5iWW6MEMmmqRQuHjlErXA+B0MS6/C3TjoL511O9CJc9At7FEgPo7kOqZSNtStPEIkpv4GL/lPopb7qP4z++TueLthEvP7L7J1nUHU2a7OSeQOvNZkD00b1rxn9cTP3IbvmFjn93dAPzmewbf/2A4QRJAHPWbHhzAzVhC5unvQNI54u0PojYnkjFmAlPVm0Rk0WGLnwFcUP7528DfAAuURoI276L0j+8R3f8nMk9/J6U1v0QyFUmayHQFrmY6wfLzkDA9cGFlUrUEPfPr0PDXpIWp4W8Q9XMRbIZPC2i0qZ8N4iRZxHDKLQ4uQcZwgiQAoq2Udr1g4O2kAldxMUH1C/vdLJz2YTTei7jaHinJfetP0dIwu9NoB3HjR5OkHFXPQCQHwQyCiicOrzxz1PN+L/n8m4njNUAyBsnl9/d9w6Hvkga1lcYh/m9L0Ka+gySpnIJ2NPcbvHRTaCPefG+PQCk89cnE29YSb7gdCklyBVc3m7hhI7pnE6V/9H0zY6IJz3g66QteCUEKv+cx8j94F0SDzLppjDnmTaBZGGaq6k4AVd0pIjMG2sECpSHSjiaif/2G+IGeWcPkr98gdeazCU+7HEn3kRWusBea7k2yzzXdn6TuXvU/yMyL0cIeuPft0LhmtE/DTGKu8ulI+iQ0f2uSBTCchaRPwVVchLiB53eQcDYSzu62TLVEtPc/8O2/GbgCUo3LPR6CGWjxQbTwr+5lFe8n3n8/AEHta8ACpW7U7yMu3Y36PaB5gszluGBWt/Xq9yLBcYgE+HgzceGPxKU1STdPyZGquAYXDK9le6KI4/vp7HwDqrsBEJ1CUIhBm4ZemHairgYZYJydBBHurFbiv+Sg2HN+Mpkyh9TZzydcdi7avBu/d/Ohx57N5W5p5XauTBVu5hJSpz2VYNk5xI/dTfTwLRCkCOYsI1hyZjKeCJLg4rvXDP28JoDwtKeSWnU5pTW/ILrrF2jb/oF3MsaYLpyMSq+kehHp2iXrWlW9dqQPYoHSYVLnvohg8Rl93zErFYjW9t4yoO2NFO/4KaTShKc97eBEhtryYJLZbs9N0NFLWtXbXpjMAeSj8sSoE49O1q53RxnJnkNQ88pkjFTtK0asXN/+28EFSYCrOJ/U9P8BknF3vu1nRI2fOCzbYYqg7vUEta8esToeDXy8m0LzVag/1GpX6vgMQfoSgvRZRIU/40v/BGKQKsRNR+PDx9OkSVVMzovuA0qln5LPf5ADc4GJziIoAP7wTG39k9QqXGoVLjgOqZ2LdvyDuP13B6cc6HWf2rWET3gc0Y09AyVt3EHxt/9D6aZvk73yw4QrLui+vpTHN+5I0gpXTkm6Z9//Zzq/8Vp0/6ExnNEaQNzgW6UmsOhfvyH61+C+G4wxZgztHcZYrd0iMrvcmjQbGLAPtgVKXYkg6QqCuSeSe9nn0c5mpKoezbcSb7yrz64S6Se/lmDBSjgwq3Rw2MtauTiZ6LXhL70ft9Q8sudhjkqu8qmE9Z+gtOtlaGENQe3rwVUQVD0HCaYMuL8vbcS3/pTkjngMGqG+FXxrMjnvIPn231DSAkHdW3Hp4wmqn4uruJC47aflYMsR1n8Mlz52J/Lsiwtmkp3yq2RcTbyVUscX8dG9xMXfEhd/231jbUPj3rI0Fck3XU668l2E2ef2sn7iUu0kn/8QUXTDwWVOF+HyzaBDaanIkKp+B0HuRQdvSAGQPYdgylspbn9Kn1kl1Qfog1VA38fTtv3EDRtxMw6l5vdNu5DqeoIZSw4uK979a0o3fbuPQiZ/kGSMMUdKZEJ1vfsl8DLg4+X/e2buOowFSl24WccTzF6W/Fy/oMua2bhZx+PmnIDf8RDx5nvwW9ce2m/GksO2706CLMx/HjrrUlj/GWh9ENofA1+CVB24FOR3QsVCmHIGhBWw7WcQ22BZk3C5JxFO+wjETWghaWmOm5MJiuPGTyPZ1bjck3C5c5HUsu4XjyTpyku7Xw1Rz/FHEi5G/b4h1cd3/Bnf8WckfSKu8qkE1S8irH011L4aVe1xfNOFthAX/0FcvBEfPTKsIpIWqMnVpdH7x+jsfDPeP3xooUJQ9OiQgiSQYC5hxYu7LdNoD3HLdcRtP+5/Lrc4N7i/2vlD40WjjXdR+PH7k1TD808mWLASt+AU3IzFyZgmm0/ImKNPrgZXOwMt5gnmnEBw4hNt8tlJRES+T5K4oV5EtgHvJwmQfiQirwK2AFcOWI6lBx+euGEjuncLhGmC4x4/5AnIVD0gh7rnde6CTD3iQrTpfrjvHdA5ceaoKB43DYKhT9RmhkfSp4KrRVwFruq5BxMixB03EjW8tuf2qeVoaT0A4bSPJKm6MycjcuheiEY78J3/AMl1n0sqnA2uEt95S9IFr+PPoEObb0JSx5Oa/TPEZY7grI8tPt5NqeOLxIWfMGBWSaknSJ2BS51BkDoHFx43JnUcCd7vp1j8KqXS9znQ1Q5VAk4EwBU70XjToMuT4DjCqtcTZi9PiiptJWr5Br71J4fKH4D6AP+nx6FNe/reKFdDsOBUgnknERz3eIp/+RrxI7cOup7GmMkvPOlC0k9+XZK4a4KYbOnB5wdL9K25/xrxct/W/uIxeR2sRWmYghlLoEsXDN1/J+y5OemXnqmH+vMhNwdxvb/Eh8/JJLlZaNsGtLAfCg1JOebY46aSmvn1Xiey9YUH0PxduMor8O2/AgJc7gkEta9DMivx7b/C528jbvsZ+DaC2lcSVD3r4P4SziGofl6fhw4qLiCouAD1BaJ97+l1LimkEsmcghbuL0+Im9DSI8TNXyKcMrnHzowlF8wkXfVutOKVaLwL9TtRvwsf70R9Ay6YiQtX4lJnIG7hpGul834/pdJ3KRa/DXRvHQ/1DMgnSWsGe6tOwhMIK68myFyKSID6AnHT/xC3fJtDaf4HWVZ+Ptra1P9GnS3E6/9BvP4fcONX7TvZmGNQtC4ZMpF52v8b55pMbjI6yRzGhAVKI2XKGVB7CkgAu2+Ex74J1cej869EXO8pw3X7z2H3nyDIJY/tv2SwaW7H3uT9kE8OaSSci+TO7TVIAnCZFbjMClQjfPZMXMXFyfxKqmjpYSS1hCBzCi61uNf9B0tchnDqO4nTy/BtP+s+p5K2o4V1BHVvQlILky54+duTLn06tItVAyI5JFgMwZG9ZxOF9zuJoj8RRX8mju+i1+8zFcivG3SZLv1EwspX4FJnHWqB922Udr0ELQ6+nG4qNhFcMQ22nEl8/1YoFQbex8YcGXNMitb9BYIU6Uvf2OMmtzn6WaA0QkQcBNnkyexLk0cfVDXJgrfuQ6ClMarhEZpcN7MnFVdxMeHUDyLh9EFtLxISVCfzKqkq0Z434jv+iGTPIqx7A5QDJVVFi/chqeWIy6LxXuK2XwMeSS3BpZZCOBuREI22o6VtSHY1IgESTDs05ijakXTLy/8Tn18D8S7ixo8hqRMIp72XVP1H0Xg/yODnEDNHlyi6nULhU3h//yC29gy2i1yq6l2ElS/vWULbL4cfJJVJZh86YyvEFuAbM5lI5ZRkvFAQUrr9JyN/gCCFm3cSbsYStKMRbdlDvHM9FDogWzXyxzsGTLIOEd2MSKAkInXA14GTSZoeXgmsB34ILAI2Ac9TVRvxCvDQx2DL98e7FmbcOcJpH8FVPXvY3apEhHDaB2Hqu5FwTrd1mr+F0u5Xk5r9/aRrXuF+4saPHl4CuNpDg9+DGQTVLySovgoJkkGrSZe95xJUJxnW1Heg0Wa0tAlfuA/JnI4EU4dVfzP5lUo3kM+/m0G3OouA1Aw4X5K42QQVL+p1nat6BjR9AYaYhOQAjTKw8UnE925KpmUwxkwgArlq6OxtXjQhc8XbCRauonjzd0fmcGEaN20BwcKVBItOw807CUnZeFuTGKkWpc8Bv1fV54pIGqgA3g3cqKofF5F3Au8E3jFCx5u0NM5D+5axO2B2FuR3jd3xzKCF9R/rNoZouCSY1utylzuX9IK7EZe0dLrMqb1spd0zhMUNxE2fI27+OmH9Jwgqn9zzeK4CSZ8I6ROPuO5m8lPtYKhdc322Fpf3oH1PEBvknptMsNsLcZVJt9MhBkqqIHvPwt+laMuGIe1rjBkdMnUeqTOuQOpm4+pmITUzwTny33sbfmc5S6Y4guXnklr9TIK5J+JbGijd8dPhH9SFhCsuIHXms5DpiybdGNDJRBi1CWfHxBEHSiJSA5wPvBxAVYtAUUSeQZKWD+DbwN+wQClJFb76K+itz4OWB0fvQHWroO40iFphx6/B5w+tqz0Vak8GX4Bdf4Ko/9nszcgL6t46IkHSQA4ESZAEVBIuRKNeJj0+nLYT7Xk90Z40uAokmEV67i9HsaZmsnKu76kR+uLZidP+L0yi9q/j0mcSpB/fY51qEY13D+mY2rEAvW858ebDJ/A1xowPIfW4Z5M676peW3Ayl19D/of/SXjyhYSnPQ1XMx1tb6R48/co3fMbiAbXhbebXA2plZcSnv40XHX9CJyDGdDEmkdpyEaiRWkJsAf4loisBNYAbwZmqupOgPIMuDNG4FiTluZ3w9YfQnE/TDkTTnwPbPvx6CRwmHExnPQ+uPlyiHpJ87zkamRGkm5aT3w37L8TGtdA47+g+f4kgAoqAT04l1PYuhDChfhUOz5cD24YX1Am4aYS1Lx8fA5d/eJeut/1pwi+iA5i/JHP34mE85J04+aY4dz8Ie8TsAxYO8BWnRQbX06QewGpqrcirubgGpE06fn/wHfcSLT33YcyMEoumdsrcyqSPgUJpoPLgVQQP7aTqHAEd6CNMSNGps4lc9lbCeat6HMbV7+A3Ou+jYhD2xsp/P7zRGv/DHHP7rJd5zNzs44nOO7xBMc9HqIipVt/QLxxDemLX0N46iVIaONpzeCNRKAUAqcDb1TV20XkcyTd7AZFRK4GrgZYsGDodyYnC8nOROc8E/bdAtkZSN2pUHcquuhl0LYhmWxWY1j7fmh7eMDyelVzIix9DUy/IGlFOhAkpafBzCdD7QqoPA6qDmXYEpeG+nOTB6C+BEgyn5N66NgKLetwzWuh5QFc82aQSuI5pxHn7oADzam+hqC4HA2b8OHwJtE8Vkh6GeJy43PscObwdowb0Gh3n/vHLdcT7f9geT6l7yOu+ghqaSYTkblAChhcYppAlyP5wXY/9vjSPSCpXo6bJqi8DEkvh7gZCeehbhpCKxrvBBQJFiKuAu89EjbCEOe7M8YMleDmnwSqaHsj2tLQI7CR6npyL/kMkh3E3wlVSmt+TvHm70Hx0DQDqfNfhuRq0Lb9BItW4eacQLzhdtzs5bjq7t3Rg+d+EN+611qQxo0e213vgG3ANlW9vfz8JySB0m4RmV1uTZoNNPS2s6peC1wLyYSzI1CfCUsqF0Bl92BQqo6DqkOTR+qBzHlBDlwGSk2DKzysgRPehUw5LSmn7hRY/vYkeKpbhbieFxq91rHLdiIOKhcmj9nlyR0Le+GxbxFs/SGu5lRK9XsI2xfidt0Peh8EOeJ5q4kzd1mmvD6IZFHVMe8THbf+gGjfB4a9v+/8J0H1s3uW23Ej0f4PAUma8tLuV5Oa+fVkQltz1BNJEQTnEMd/72N9PZnM2wBHKf9NpHMDMMgECq6edN2XEOn7xoJLLSHWNZRa3osv3Qt+/2FbZKB5Ef7XAvEkyTJqzESQzuFmL0cqpyCVdcmjYgpSkST6odiBFjqg0IEWOyBME664AFd76IZa/hcfJ37opvIzQaqmItX1lP71W1JnPa/fv4Nxw0aKv/40fk/37rLBkjN73Tdcdk6fZVmQZIbriAMlVd0lIltFZLmqrgcuAh4oP14GfLz8fy+zV5oeFr08aQmaeTF0bE4yMm28FmZcWO4GJ/Dwp3vOWRO3Q/N9UA6UpHIxVI783CySqYcT3o4ufgXy2DdJb/kdFO/uUo9Ogs3346pPxNeHxOnBpAs+tvjOv1HcshIJZhHUvpqg+spRP6ZqEZ//F0fSzTPa906ips8QVF9FUPvvEG0hbvtpecLPQ+Vq4W5Ku19JasbXkaCm7wLNuFFV4s4bys88SJYg+9RhB+/p9FXk8w+i2vV+WIpU6iVkMq9DpBw0F/cQ8ckBy5NwGemajyHhcqSX1qQe2xPgi2t6TQ6hPkJ/nz7YjdgYM7DwpAtJXfBKXNWRZTRNn/siOOeFkK1CKmqQYODfZ1VPdOfPKd50XY/WqNTjnkPqiS+35AuTiDC5J5wV1SOvvIisIkkPngY2Aq8AHPAjYAGwBbhSVQ+/1dfN6tWr9a677jri+hzN9J/PhrYuXdskgPonwJJXQWY6dO6AqmVIunZs6qOaBHQdW5MJGTVKLkhaHoLN3yGev4q44vaBCzpWBTNIz70RcWOTitR33k5pz5vAH2Gm/nABRP13n5L0yaRmfcdaliagUsuniNq/ljxx08lM+y4uPLIbK6oxcXwb3q/HueNx7kScqz9smzyFvU9Bff+ZOMOKfyNV/fahHd+3UGz8BH73A2huD5LtErT99SL81k1DKs+YY5HULyRzyesI5p8ypsfVzlb8/u34xu1Ea/+M33xvj23cwlXkXjCUMbZHJxFZo6qrx7seg7UwXKzvqPngiJf7+saXjcnrMCLpwVX1HqC3yl40EuWb5CKEXX9IxjMB1KyAec+FGU+Cjm2w/lPQVP5imfccdMX7x+SOi4hA5aLk0bW+s58GTfcQbL0XFq0kzvT80jMk6bhbvklY91oAVCNERm8eaJd7PKnpn6O0+xXAEUy0OUCQBKDFtZQaXkdqxtfGLBA0A/Ol9YeCJMCFSyg1fxjVNlx4POm6jwyrXJGAMDwXOLfbcvVtRB3XIcFswtxzCKveQqml/2GsLnXagMeLNt5F6e7fEMw6ntS5L0RcDdHPG9E9BaAWt2QVMi9CfQAtrcM6J2OOGekc6fOuIjz9CiQYvb9BvVFV8j94F75hY7/b+c330PHFFxOsuIDMha8eo9qZkSBuvGswfGP722CGTFWTrHSPfC7pWncg+cKJ70ays5Jtio0QF2DO02H+lUjdqvGtNEkApSs/BQ98kGDLneji5Um2PNND3Py/+M6/o6VN4BuR1DIkewYucwYu98QR777mcmfhcufiO28aeOMjpPnbiJu/TDjlLaN+LDM4cf533Z774qEWX8mO3L2tqPPnRJ3Xo9FW0P1kpv0GgCD7TOLCjfjCn3rfUSpx6f5vEkab7yP/4w+CeuINdxA3PIarmoLu2VTeQvEbNyb9GwA370Ro3jMyJ2bMUSY44XzSF766RxKEsaDtjRR+85kBg6Su22vz0KYGMONMjvF5lMzo0T03wcOfLXe1c3DSB2DWU5Cwe1cmqT4ezvnxeFSxX5KbjZ7+Zdj1O8IHPoyfeTZRxSZwO8e7ahOLFtDCoXFeWlqPltbjW68HyeAqLiOc8v+QcGQy7KuW0LjfXrAjymXPHLNjmYEFuecQdfwY/N7Dlj+LsPLfRuw4pdaPdBszVGr9FOm6LyISkq75CPl994I/0D3OgWRBO0hVvglxdf2WHT96B2QrCKaVfyfathO3bCNYcBza1orf3/1CyjdsQrJVkO9lugRjjlFSN5v0Ja8nXHz6uNWheOuPiB9bM6R9pHLKKNXGmJ4sUJpANGqDrT9OutcVGqDp/mSi2PrzYP7zkBlPGu8qDpmIwOzL0U3fxm2/mzSgdadSmnnfeFdtctACvv3nFDv/gqu4BJdZicuei6TmDa84nyfa/yG0ONAcNiOntPuVuNz5BLVXW9A0AbhwPi51ChptQOOt5aUBaJ6o9XOE1W9GZGTTaAe5l5Cq/g/i/K/whZvx8SMEmUuIO78HQFj1FlKV/45qgcH8WQpqKiDc07OVqDydUjBrOpqbS7x3L8HcKQT1O5DUI2hxOfH26ahX/N490Gld8swxKAhJnfW8JHPcOM4ppD7ukhFvYMGKJ5G5/K1j3jXQHLnJnMzBPm3jTFWhdT3s/lMyIW1uLsy8BBa/KslapzHijoK36ZSPwrafwLafIp37QLH04UPhW/BtP8G3/QQQJHsuLvt4cDlEMuCqETcNyZ6ZpHU/jGqM77yJaP9HBjW+aGQpvvPv+M6/46qeTVD1AiS91OZaGkPeN0O0GdxUXDiPVM3b8cV7KTW/q7xFfKhLnqRJVb/hiI8ZZJ5InP8VkCJV9TpE0qAF4uLfEKkjzD0PlzoBpJIg85Tk0DLwWDb1MeSmQ+VstH030lsmx/Y9SPseMqfNxKV+f2h59l8ES8vlHAfFdU/EN2w74nM9KriQYNESXDZGSwFa8MQNu6wV7ijjFq4ic8nrcFOHd7NtJGnz7oOTxA5G6uSLLEgyY84+ceNEVWHbT2Hnr5NscXWnwuO+jVQt7b7hKA7sH0tStRROeAe64EUUd73QgqQjomj+H8T5f3Rf7OpIz++eYdCXHiNu+lIyHsk3jV0V++DbfoZv+1nyJJhBUPVMgro3JRfRZsSpeqLSDRQ7P4frbAMtEFReRarqzUjYTFD5MjR6DF+4meTuBURtX0TCxQTZiwcVuPQlrHgd2vkwSEjc/huCqhcTVryAIHsJ6htx4VJcanmvdfb+AeLSP/F+Ky5YTCp9FZCm8KtPEG+4HUr5ZOOgknDuPNj/YPdCqmcQLm9A3N09yu92rNIxOq9SppJgxiwkGyLpIhI043Qron8rr08epdwFROuHOQG6mXDcvJNIrXwK2roXrZ017kGH1M2CbDXkB2jZDULc/FPQQvvYVMyMqCQ9+HjXYviOjqvwSUhE0JkXw7znHNXzAfjOf0D5Ystlz0Qq5uNqzjt0sWxGjARTe3yWJFyEpBZCe9P4VKo/cQNx87VIuHBM5pI61sTR3RTyH8PH94NWIOECNN6Nlh4GyRCkTydIJ2MT4s7fU2x6G8lEsEqp6RoiN4vMjD8gkh3W8SWch/omiHcRFdcSNX+W1NT/Qn0rvuN3qN9HUHEFQeWV4HLE0d+ISn8jjm5Cdd+hgkpQLFyLaBpWVsHyGqRUHqcpSlzdjNuxCFmzCcQhs5fgZjYhbnv/9RNIn7KV4oOL0T39bzsZSe10pKISSaeQtIPQI0ERkU4cjyDaZZqJPqZXCzKb4MTjcalG1NfgWxzxlg2HNkjnCObNx2VjSg8+ltz0MxOW37aOwrZ1AIRnPJ30ha9GXED04N+Jdz6CVNSQWnV5Mp6P5IZu6aZvkzr/ZaNynSLiCBacQvzwLX1uEyw7l8xTr0HSfU86bSY6tWQOZngkXTfeVRh1Ucu30M6bcVUvODg+JVX/cXzVcynteQvEDf0XYAZNSxvRaAcSzjm4TEQIaq4mbv4m6MS8Gxc1fhpfuIeg+nm4zMrxrs5RQVUpdH4I7x9KFkgHUeohCJUg9+yDY5B8aQNICkn3fN3V7yJq/x6pqoETPKgW8fGDKOBbf4X4NnBZiLvMl6StlPa9udt+UfNDlNq+ja/KotpPFzhtTtq76pIxSYf/ydXa2WTcXgRFZOvhe/fJhVvJrGgnaj6D6MFdUDg6JqV1c5aQnvKP3u/iDuF6xflNuGDTwUBKa3LorFVIOoWrLRDoPQiPJtuechylXdPwDYN//c1hwgySq4YghKiEtu0beJ9hitb8kuj+P+OmzcPvPNRqGN37BzLPfDfBzKVo007inesJtq0dtXmVgvn9B0rhSU+yIMmMKwuUzKhKTf8s0d534zInd1vusqtxuQvxbT8Yp5odneKW/yOc2n2iTnEZXMWF+PZfjVOtBuD349t+jO+8mcz80U9ZfiwQEdLZN5LveP3hKxDpBJL5jQp7nwn03f0savtfgvRqJLWi1+6R3u+mmP8YUelmDmRSCAszoTSEzJbxfqB+wM36o+xEp6/A7V03tP1UiBoed0RTik1Efucm/LTVBDqyE7gLnWTqb02eHBZwOb+B9PRNFMPH43c8NqLHPZpJ1TTSF7wCt+BUpGpat5YbbW8i3r2BeMPtRGtvPNTldKQUO7oFSQDatJP8d68h8+z3UfjJ+0E9Ud3s0QuUFva8SeNmLsXNOwlXv4BgHDPymZEzmZM5TOIpoMxkIK6asP6T+MJ9+EL3SWfDujcSTHk3kj51nGp39Ilbvokv9LxYlPTYzrI+LH7wg3rNwOKo5/icMPUMwlS5m6NUEOSu6L8Qbaew7/nkd60i6vxlj9XF/MeJSr/nYLo5qtHSrh7b9UeIcH7BkPY5XKCnIm1Dn1tFRMEp0cMPQ6EdsmnIpI6oLuPOhWROCUc8SBoMkYj01H8h02aP+bEnK6muxy1chauu79l1urKOcMlqMpe8ntzLPp+0NI2FuITf9fDBrpTx9gcH2GH4pH4hUjO927LMFf9B5uLXJN0AU8Pr+mvMSLFAyYw6cVnCaR9Ggu7zAEk4nbD25QR1r+9jTzN0MaWGN+A7bz24RLWEb/2/cazTIEg1Qe3rkiQn5ojF0b1EpRsAEJlOKv0icpXfIZP7aDI+UpVS83uJ838YVHkudSpB+vHdlkXRbUSlQ5PXiiwk8MuQofTtOrBv/sgyz6m04/J7IbUIcqdBdhVkToSgnwk0g2mQOZPUnD1kn5Qld+52cqc/TO6sFtyMuiOqz7jJVJI5OYfzm8atCkIHmTlbkKqJM9dNMO94gvnHj12gMQTa0gDxwElF3LR5pB7/vDGoEZCuwM1bcfDpaCZ9EBGCpY87+NzNPxmZABn5zAiSZEzoSD/GysT71jBHJRGBsPe7jC51XHmyyRHuVnCsirdT2v0yXNVzCaqeS9z6PTTaPN616snVIOlTkezjEZfDt/8elzsfyZw03jWb9IJwJZU1t6BaBIIe8yKJCGHVq3DpM4g7f4Uv3kafI/oBJQaX3OjQ4g58562U+DmBPw1RRxTci4umIO13Dqu+WmpAKhagOrzU9V63QepkaH0QOGyMTHoWhHUQZEEcOJf8lfUKzUmrW7e/uaXdSO0SaMtDx9h8J8nU2aTmgYvvJekHqGiwjLgwh3hPK7p/kF0ZC+0U1oYEC55IWLMJ58fn9150D+kllRTWVyCVtVAqoK1jN8l1V8HCZYTciogSLqxEU/NQavHFNH7fXrRl78CFjBKZtoDcVZ9EsoObKiF1zguItz+A33zPqNYr89RrCBesJDrpScTr/orftw3Ntw66nkMVrrqMaO2NpM+7inD1M47qBFfHIgFL5mDMcKnvRH0rwdT34VuvR4sPctQNGBgnh+Zdmlgkczrh1Pci6ROBmGjP24g7fk9Q90acBUkjqr+06xrvotT6GfDlSVulArQDpBoXHgeuLlnsphJWvY6o7X+JOn5CGM1Gmh4hHTUfLCuoXICvqxhyW5KEiyA1A2UvEgmSWoXqHlSHmIVOCkRTcoS9ZRku7kkeQ5CefivMqiS/dgnaOHqT0kpNPeHCHIG/A/HaLWIT/zAu9TCpOeDnzSMuLSVuaEPSGYL6GCf7Sb4rhbg4n3jbHqR2Ci4bTojkc85vIrN8GaIPge/Eu9Pw+ZpyOvL9aDyF0vodEBWHWHCAmz4fbW9B2wYIvsQRpB5FouSTKb4dKawHIADiOSsptQzj5EZCRR3ZKz84pOBDgpDMJa+n8+v/PnoZBsURHH82AOmznk/nur9CVCC670+kHvfsUTlkMGMJFa+9DsnVjEr5xhwJC5TMOIsoNbwGfCOSWYWrei4iOeLiI1D453hXzgxAUkuRzEpEqg+2hasWIN6DRnvQeA9oEQmnI8F0XO58XPWLD7ZwRE1fw3ckLUlB9YvG81SOKaXW/yXu/AVh7lmo30OQexoutSoJlNz0Hnd01e8narsWyBO5HUj9LIK26bi2JFW0tG+FypmDPr6EC9F0LbG/Hyi3InmgAOJm4VJngi/iZTuqA9/xDztWEDSNcFbHuJ3sqbvxLMa3pHEVntKmAn77EbZAZKsIF88m2riD9KJOnL9vwHnlnN+GC7aROtAor3RLpuDCRwkXCqCHuqRMiGCpnChAINA1BAem5fLJMlmxiqihAr9r08BlzV5MUKu44oOIvwvNgs5eCC6N6jRKj22FUqH7TkGIhtMh6n38miusI3XCKnxHBt+0D6mqRXIpJPCIRKhP49sK+J1DS07hZiwA7/F7++5SGq64AFc7+N+Zg2VPnUuwZDXxo3cMed9ByVUf+v3v8j1QvP3HhCufgmQqR+WwFiQd3SZzMgcLlMy4EldNes7P0eI64pbr8W0/BMBVX4XUXIVv/hJaXDvOtTS9cRUXE9Z/CnEVB5fFHTei7b9D0icS1P570rWuj24UvrSRuOkLyc/Fhyhuu5D03N9BMBu0gDgbxDtawqpXEFb9W48WJ9VKfPFOXPpM0Hbiwl+JO39XnpD20J1/9buIKhxStRihBilmIBUl0zANgvpmvN/We0dzvwstHEgIEeDcPDToRLUNpNBj83Tz6YSb72NUIoNSA44GXA5QyCzKoMsWglbgO6uJd+SJtw6xtWpZDYH/O+Hx1RB3jtjk25PxQiTw9xDUQ1R/NlrIQhgTN3Sg+3YkG4gjmH8cQXYXrngndOkJKQJSOtC18BHcvAy4KtRVoa4e9VkcW3H5e3sc92AZGhHk7yJwwNTywpjunRoy4JcfT6khgzb2k6jEhbjZCwkrG3HFpEtnPO0kkCqkZg6aWUz8yK0QhKTOfDbB8WcN8dU6JHX28yFM4xs2oo07GVLO9wG4+oUHf9augWdHM6Vbf0j6gleO2LGMmQwsUDLjToKpSO4JSPZcNH9rMq9O6/eQ/J246hdB8Fp801e6XDxNXs5PxRWmEFXeNt5VGR6pIqi+Elf5NCS1BN95E8QNqG9B0ifgchfhMqfhO28hbvsZ4v5GOOXNPYpR30G0990cvPguz6dV3PFMJJyLFh/GVV5Kavqnx/Dkjh0ivc9LIiJErZ9F/V403knX4Kgnj/pNySVaCJTqk7nSfAl8Kxo92s+uTbjSbMjU4XV9+eC1oJ3djinqCItT0fxacNVJgoZoH5AC3wqZhQR7mxiz5hMtIIWklSQIQBadQzyUaYNSWZwmr4vQOmJB0mQXciuUW5vCpacSn/ISZOdvkNJ2xN/S/8ewTLQAcQGJ9wEjOzbLFR4hPaUCPyPpGqyaQksOCUDCJly0C0q7ENnVra5BYR0EGdzqtyPVC+CcF4xIfYK5JxLMPTGpS6GD0p03ULrtR4NKCjFg2UtWH/zZ73iwvOxM3IzF+N2P4tv246qm9rW7Mb0SN/lu5BxggZKZMEQckjuXVPpkfMfv8J3/IG78FOG095Ca82O4+WnQOdknM2yBcC/hjLPRMEbDdnywHVzTeFdsYMEMUjO/DhoRt/4omZep2yS2jnD6Fwgqn0xQ9TSCqqf1WoxGuynteika9dKdxTehxaZku3gfcdsvCKqeMfLnYgAotX4RX/gnPt6OBLNw4WKUCI2Hc6G5Fx8l3dJcuBr6C5QA4p1IaSaBOxUN0tB6N7gckl0OkoYoD9FuNL4n2d43QmcjkjoJFDTeQtC4GGnf2P9xgkrQCHzP1qgjVXpwaBPUBvMWIDpAfY9VlQuh+njcye/Gpafg//IX2DtxJskW30FQOCxZScShVtQ+gl5Z9eYkSBqtemUqSJ/3YsKTLqTz229K0tz3JlsF+bYBywuXnHHwZ78/GSuYOv+lBDOXjkh9jZlsLFAyE44EtQTVL8AVFsC2HdC+DuYugzlXwKP/O97VO3JRG25H1zluBK05CTJVUOyATAU+qyCKa/UgENcW8eFaGK/uNeGCZPLg/Z9E8zf3uklq5nW43MDdSTTagvqBB8hr/haiwr24iosQVzXkKpuBabwTX0o+i+p3E5f67qY0+EIVLQ+Y74+ES9H8PQhdrjF9K3TcCwqSPhWNGwCHhKtwkYeoFZrXIYBKCuRBdMrpyUQXpTwSdSY3U3Jz8VX1+GyJoHE3km+D6hVJa1frSHTlFQiqkboc7G0a9F7a1oGvnoHzDSNQh6OLPP5aJHdozI570pfRDT9Ft/wB9j8weskLRlO2Hln6bNTHiAvQYis03AWzzkLC3lt1h8tNmU146iVEdybTAhCmCU++mOD4s3Ezkm5/xT9+qd8y0hddjZu++OBzqZyCm7vCgiRzRMY6nfdIs0DJTFzTzk7S+W79Iey7FZZdM941GiWKtGzotiQ4bItwH5CZg582n6hyA7ixS2krmdWEdW8i2vVB1N8LmgPX2cuWgxyggiOsey2qBwYDOMRVoXEDvv33qG8knPpeoj3XgLbjO/5EUPWskTshc1C67iNozf/DF+8iav8uvnj7kRUYzEeCWUCM0gEUEGbhogKKB8IkyInbQHK934RXCONTYO99qKsC34nQc/JcUOL6uWh0R7d9oQLkUSBp0fLTZ5LarkjbGnCVkJ4BxeEHKj59JoVbmqBYAgaZtvvAvnu2UWipRSrnE8yqIOV6v+lw1HOpJGg9IMhBqvvNEHEhsuz5sOz5aLENffCb6EPfG+OKHqH8XvwvLoNiC1TNg/xeKLVDqgpZ/S7cgieP6OHS57+c9NnPT56E6YOTtfrGHRRv+k4/ewrpp7yB1KrLui92AX7fVvyeTbjpi0a0ruZYopYe3JjRICLonCtg/Xro2ALrPjDeVRpfhb24HXtJS4BOPYNoSgMajG5XREmvwOWegN/2RVKbNoKbBuopLq6GoPvFZtzx52RupGAGEs7qtk61BPEeJJyDy54B2TM4nEY7wTcT1L4G9c1ABFKBy10wimdoxE0hyD4Zl7mIqO0rRG2fG15BwQJwMT4+rHuSK6IyA+87ESf4TBUSV0HLnb0HSqmlaMvapKXJ991VyNevQqO7DjsZgMO6w/ndRPOWEm6NkLgdcvOgtD/pjjcU054MU86BphypVXso3XnD8Fo5Cu1ooZ1oPwSnLsb5chfU7EyYezkU9kPTWggy0LoB/BDTZ08C8vhroWoxtG2E1o0w5VQk7DubmqSr4NQ3oi2bYMc/xq6iI6HQmPzfWu7O6lKQqkTv+ijqUsi8C0bsUBKmIEz1rMKvPgn5vlvx009+Tc8gCdDWvZBvJf/D/yT7ks8MK0ufMZOdBUpmYqs5NDs4paZxq8aEojGyby2p9lnEM84kzt45aoPCtfgAceFR0psrkgu28kWbMLtHniXf9gtcZjVx81eT+XFcDa7yKUi4kGjf+3HpEwhqXtrnsSScTTj13cnPwVTS829NBs8HU0bn5Ew3Io5U9etAAqLWzwx9/3Am6g9rYZFKJJyFV0/sdgHlSWsCYEoVTubi/ExU86hrQXU/4uogPItw30MQNR4sSkk+5hpWo3Un9AzI+qHxo2jlUqSUBlcBFUsgnIIsfTfkt6Gb/gc6N5WDpwCIIbsAmX81TDkbghokrMQ3PEbxL+8deP6eQYoLx+NOeioy7XSYsrLHxMD+rmtg919G5FgTRtVSmLIqyYY59fTkMQgiAlFvLdmTQLYeOeU1yNzzIV2b3ARUHZPuhL5lL35n311hpX4h4arLe19XNY3UBa9EghC/b6sFSmbYJmNWzgMsUDITW27+eNdg4srvItiyC1dzEqWZj4Eb2sDywQo7V8GBAfUHaC+RmbYT7XsPUnEpEs5G4ya081biwv/i23+Od7VIZjUus6Lnvr2QwDIrjYdU1b/j83/Fl/41pP20cCekTkLSM4AAlQZAibi7j0C+hEoJ75rx8f3gkyQQMXcRV0IxV0Gm/UxcoYSvqCFy60A7cMGJ0DH0OWQkKkGpBIVHILcQWf5xJDMTKo9Hpj0JH+XxD/+Kwh++R7BoOqkTH4eb1X2CTe1oQgcxIH5A6Rxu+mKCU67ELT3zyMubRGTBc/ucMqA/WmyBPUP7TE4IU1fgzvkYUjm722IRATm8k/XIK93R96Tjbs4JZJ/9PsT1Xo/UykshW3UosDPmGGSBkpnYAptLZyDSsoF0eyXxvJOS1qWRKjeeR9A+H7fr/h7rwl0B0bwVkK1DgvokS1nciO/8K9r+M+L2nwHleSXDhQQ1ryaofZUFP5OE6vCCAY024VMO6H9/504gW/l1nKunre1luOBCVHcQ+3sQNw2R6SCeQvVDUF0CdYSlaWi8Hx/fias4E+1ch+jgbw5EU3IExVpwiwnmfxRJT+9yvkp0yw8p3fM7KJaIH95B/MgvCdZuhzBD+rwXE93/Z0p3/wqiI+sKJ/ULyb3w40hFbY91qgqbrkfbNiG52dDU83cPgMrFSQvU1FUwZSW0b0U3Xgf7exvLNQFkZ0LtCqT+8TD/mcMrI1WNnPwadPcd0LYNOveAxgPvNxYk6F6XOechCy9DZpyBZMf2O8/v30782N1I7QwodhCt+WWv2wXLzyPz1LchqUyv6wGK4XdJ6bMQmT2s4NaYA6xFyZjR0rl9vGswOcTtuP2txHOGsI8GhB1n4vbuLHep00P9myDJMOZ7v/CSQhOpzHuQWZejpS1EzV9P5lTCAylcxYW43BNw2XOQ1LwjOjUz9jQe2iSqB0hqDrBh4O3cHJyrByCX+0+CYBn5/MfwpY0QCaoPJdsd3P44VFsO7u/ju3C5M9HCZtBOxLccfoge1K1H619EUPGsbkESJHf30+e/jGDRaeS//05kyhxSp19BsPRxSO0MxAW4Bacij96B7hv+uECpnk72+R/pPUjq2A4uRB/4ZPK8rzJWfQSZ+9TuC6sWIzPPRxvvRR+9Dnb/rZ8SxlB2BnLu9Ui2/oiLEhFkxcthxcsBUPVQaER3/BNd9w3oGFpijREzYzXu/M9Bxy5oWg/Z6cj0leNSFd+6j86vvXrA7VJnPY/U+S9FxPW5jWoHhdKXKUbXU5H5OoE7YSSrao4hggVKxoyejsk+b9LYkbYtoBmQgeeLkXgB4a4c0jaUriwOpp4Jsy6B2U9DwgoAirtfDtE2IE1Q90aC6hdby9EkploAbRrevsVHCFJnEutdhKmnEYQXEkd/JCr9HTg0viSO/4X3O3BuDkGwDIBM5i1E0W8gzKGlHNJle40fwQWno373wWU+vhPCEKcroNhbWvMQCReAVKPRetA8QdWLcanj+6y/m38KuVd9BZk2r8dFZLj0TIIlq9G9mynd9Qui+/4w9NenbR9+x0O4Zef0XLnrL+jDXx64EOn7z7ZMWYms/h+0+QH0jjdAcWTGUg1bvgEa74XZF4140SIOstOQJU9HF14GO/+Jf+SH0LBmxI/Vbz0qZiFBCqrnJ49x5KqnIVPnofu39bFBQPqSN5Ba+ZQBy4r9OqCAagPt+eeQCp5KKnwWIrXE8X2kUyMzea4xE50FSmZia3t4vGsweWiMixfgw0e6L/eVBMUTQR0goBDsegxKu3stpofcXFj2Vph2FpLqeSc8qLgMjXcQ1L0Rl1py5OdhRoxqTFT8LT6+B3GLcO7AYGxJWnWChaAllCJJ6u6QYsunhn08AVw8jVTVlwnCCxARUunLUW2jveUcoNxtTRvpaL2MVOblpDNvSVoLJEdFxXfo7HwzpGYgni4T36aT7p29HbO4H/wsJJwGLptM2KGK5h9EiuVJb10drvpSJOx/PhgRQer7nhxURJDpi8hc9mbCE8+nePO30PYmtHmQ6frVU/jFx3Gv+CLusOPIkpckmzz46YEKGfAwUrsCzv4WuvajkJmStOyEVUka7jiPPvZdKA3cCjcS9OH/hVkX9EhUMZIkSMG8C3DVC/C/fyFj2ZqmpTY0LiJB75/PMef76I4YZsg8892EgxwTF/v7ujwrUYp/Tin+OQBOVligZAZPsPTgxowG9SXY/ovxrsbkkZ2FDzb2XC4Rwdb1w0szHFbDGV9GKhf3vcnUtw+9XDPq4mg9+fZrUP/okPYL3OkQzEPjbX0mUwyr34pLnYZLnZTMdRVtQePNiJtOkH3iwe1UPfhGJJhGEJ5HHHXN4FagVPgqzi0llX46AM4toaLil3j/MMXiN/BShY/WEep8tLQBl1pdvhAUfHw3EKG5mbjmOyHq3vWqa90lmEo47cP9djUaqmDRaWTnLyZu+Spx683QHqH5KbBnOv7RRrRlX+87+qjvi9mFz01SZjf8Awp9dH8c5LgcqVqInPXV3lcueA56/4dh918HLOfAGP4DQ1Q62y+m1ACp6UKqchOqGdAAxCNEiHQgtCA0JvtUzE1SZI9A97uBSO0S5JTXovePwcTkmam4sz6AzBp4ku2x4pt2ooV2Uk94CdrRnIxPylaTWnUZ4elPw1X3/x6oKp3F1xHFtwPtPdYH7hwCdxqBO2mUzsCYiccCJTNx+UIyTsYMzGUpzXUg5YsoBfxMwsIiZP8+8MPswli3st8gyUxcLlhCMqHv0MT+bkgB6ak4WQDkESqh1AwaIeFcUlWv6bJHFUEwE+h+p1o1orj/FbjUKaRq/oNs7qPkO95MHHef1LbQ+WFEcoSpZPJNESEIlpPLfYI4fpio+Gfitq8hMgtKaWi7BYdCzZl4fRhpuWfAcwqqnoZI34PWh0uCOsIp7yCofROlhpdDcQ1MA7ccZM9ZoIIWs+gmh9+2+eB+hT98MRmrlM4eVl4OOfX9qCq65q3lsUaOZOxf2QiklJbMVJh+LjpAoFSKT6ft3gC8Es6cSlgHnffvhFKRaAt04oBS+XFAFsgSzF5Fbkkr6eVvG5ExSoPlVrwcnXNuEuFpBJ170Y4G6GxIJjDPTYewAtq2oQ1rYM8ACTDStTB1BZKdAi6dTBY763Ew/fSJ04pUVvrH/1Hxqq8glXUApM+7ClKZpMVtEGJ/M1F8IwCBW0ngTqUY/RToIBU8j2z6w5bUwQyD2hglAEna1e8Ctqvq00RkKvBDYBGwCXieqjb2XYIx3UlYhc54Euz87XhXZcLTqcvQ4E5c6QSCpgqk6VHwTcA9R1Zw4xq06V4Ickj1shGoqRkrIilCXY5GWeLUXkRmoNqI6iAHvWsjvutXdrnnlLjBtsqU8KW1SOrE8n51ZCu/QanwFYqF/+XQxX8r+Y43EqaeQxCcjAtXEwTJOKIgWEaQW4ZPXUq0/6PQ+neoOA9lP5pfi9NOpOJMaO+ZLlyDeiS9BCgQF+/FaYT0Mb5H1aOl9Wj+LjTeXQ5GYiT7OFzuiX3ud4C4HK7yqcTFZMyfCDDjtuRngNkVBI88Eb+pGW1qwO94kPwvPkLmOe/BuZ4BnIig1ccjx/0bVC+Fzt1JN7p9d4zc3DtVi6Hu5GRy2x7rlsDSVxKta0Fbk1b90sbWbuHQQCTM4esuRCrHPpmL1HUfh9b3pf2r0eaN6Mafo+07AU3GOIWVyPIXInOfCJVzJ01wkL78rd1SfUu2aogl5A7+lEldQxicTSb1Fkrxn0gFT580r4OZeEawMX/MjWSL0puBB4Ga8vN3Ajeq6sdF5J3l5+8YweOZY0F67O5ETmay9z7STdMgeuxIS0pS+WZmQM2JUDE/Sagx/YkD72omHBeB73yAsBOgAa04lSgYbnYwIUidT6bivwa3teRI1XwUSZ3QZVlIOvsGgtRFFPOfJI7WcmAS2qj0U6LSTwlTz0ayb8K5WYfOI1xCEK4k5u8gBbT04MGLX5UCVJ4ELoNPCa5YhFITWjEL8k1QTMbsRQ2vJ6j9NyS9AqTi4NwwvuNPxI2fRnv73Wn5FgQzCaquxFVejqSW9rhYVFU0fzu+4xYkPAUklUy8G+8EQgjn4sI0uuJmwqXT8A+uIH50O8H8h9Gtb0YXfAGRnnf83fLXH3pStQhW/w9699uTFo4RINPOgEUvRO/5zy4HTSMnvxvmXUG8YwuFuz5BMHshiBDv2DSocoOZ86h56f8jmD574I0nAKldgpx2zcHnmt+XTArrJl+Hm77mQxqswJ1ELv0lVBuA5G+vSDXp8Nn972jMUWxEvglEZB7wVOAjwIFvnGcAF5R//jbwNyxQMkPVVz9901N0BBNhSpB0K5l6JlSfADMvRmpOHLm6mXHhsmfjO3538Hkc7hjyOHdx88lWfhYXLEakYtD7lYo/ItLf4+KTCVLXdFsXBCeSq/xmsl3heoqFL6Oa/K5HpZ8RlX5OEF5IOvtagiAZD+GL6wHQ/GNI5TmoKxLLI6APgsSIW0iQOhPJLkT9DqTYAMW7kNRxBHVvRFwtqjGlLavLL05Nef6vAZKaxLuJm79I3PxFCGbhsqvBt6LRDpAcaCdaOiyBCgFUnIr39wONpNpno1EKfCPh4t8SLBJEFG0F3/AFgpnX9HbkbiSsRB43wmNv5lyGzLoIfAm9/8NIzXJ0zmri4q+gvsCUd30JcQ5VpXjvrbT+4Iu9jq+SyhpqX/2fuLp6JFc5qVseJDttvKswjkIKpc/i9RECdwZh8P3xrpA5Clh68MRngf8Aqrssm6nlPh6qulNEZozQscyxpGPLeNfg2JCbC+f9elJf4JiefOHQ+At1Naj2HKDdH5F6spVfIAiHPoeKagtxdAtxdAup9MtwrvcL0FTmRYTpF5DveAtx9EcAXLCSIDyDYv4zBMHjSWevRtJL0XYgM5UouJcD6cbDzLOIin8EQlLZ1+KCucm5u1uJdSm4Cnz770GLaOlRDo7b8sPoCR7vwrf/ejAbkgz0UpBW4oo5BPs7QPNA94sG3XsdcdyKm3kNElT3XtwoEXHJpN5BFjn9E6jGdLZcgfrHCMKzYO8SRKdRXH8PnX/9xcEgyU2bRe68S0ktPRnNdyCpNOGcRWNadzPyvD6C1yToj/0aYv8oges/U6QxR7sjDpRE5GlAg6quEZELhrH/1cDVAAsW9J2W1RyjVn0a/vGMJLGDGT01J1qQdJRR34rP35L8DFB5Askw0sFLV7xjWEESgMiBXtgOkf67i4k4cpWfp5j/CsXCZwlTl5BKv5xU+mV4n1y4BVPeAoQU5RegnV12TpOr+w1oO+IOzbgs2TOg7Sf4ll8Nq/5HrGMNQXoFGlTheYggMx/y5ZYnSZXTyUVI1TkEc94/PnUsU20DjSjmr4XIU/zeWQTLKmi/+dMQdb8THC4+gdqr34cEo5fu24wPJwu7PY/9GguUzJETa1E6F3i6iFxOku6mRkS+B+wWkdnl1qTZQENvO6vqtcC1AKtXr568r6QZFZKbi1YugdYHx7sqRxeXTtKFz7osyQx10gfGu0ZmhEWNH4M4+doVwIsfcre7IDxj2McXmYu4+YSpKwZMhnBAKvNKotLvKeb/G+fmE6YuJgiWl8sTgimvhObfdDuPqPAjwvQVBKnV3Qvz7WjcR4ruMZKa/gU0/xh++7sp1TWRcicjmRW4mW/H7/kKuu8bSM2Tx7WOAIWOjxAVb0ieOEi/dBMAwVlVSOcyiNKQykOqnVTmBAuSjlJeG0i+LZJfMJGZ/W5vzGBN5nmUjjgPhaq+S1Xnqeoi4AXAX1T1KuCXwMvKm70MsAlxzPAc/4Z+Z6M3wzCrPDP73pvhlI8kk1Gao8yR/2GKS3cOe98wdS4VVb8jk33TkPZTbSWT+yRBeH6PdSKVZKu/DtK1G58Sl25BNeq+bTAFlx7fTI2+cBeu+nyCJdeTrno/MudDBHM/goR1EO1B6p6Jm/Kcca0jQLbyY6Szb+xlTQnN3Y1W34Zm70GDR4j5PVHpH6gOY142M6EVSl/gwPdGOryaVGBJfIwZzavPjwM/EpFXAVuAK0fxWOYoJtPPR0/9GNz/HuuCNxIkBJeF+c+HYmPysznqhFPeR+yqiVuvA0B0aPfFUtmrSWWecUR16Dsdt1IqXodIFZBCfQOqu3DuNMLUk0ilr+izTBfMI13xVort7zm4rJS/Fh+vJ1v9pS7HiPGF+46o/kdMk+8rSS8gSHfvWu7mfRJKu8ajVr1yQfegMpDV0LaWuGoKdEkTH0e3E7fdTjr3TtLZl49xLc1oUFVK8c+I4mQqjsCdTib1tnGulTmaHOtd7w5S1b+RZLdDVfcBF41k+ebYJbMuRVsfho1fG++qTH4awf47YPnbYMpqG5t0lBKXQ1LJxa9Khlj2DK6RSWpJpZ9BKvOiUaxdHh/fR1S6EUhaJkSmUlH9LkT6DpIOCFLnk/z5ioAU6Yp34cJV3bYRCUjN+g5Rw+vwnTeN9An0TaYARdB2fMdfcFXP7/V3TMRBek7P/ceAxvuJO/+Ey56DuBqQaor5rwEO5xYhfiq0rUO0g4CTiFkDgHPHk0yoFSfzTZlJS1UREbzuIV9838GJZgGEWvu7YEyZ9Wcyk8fCq2DLDyBqHe+aTH7F/ZCbi6TGNsuWGWsOV/E0FMFlTyGO7iIu/bmf7SvIVX2DIDy5zy1US6g2Ae2otiMyFdVOSqUfliezTRGGT8G5eQTBil7LEMmRrfgfVFuISn8ljv5OHN3GwVltBzorN4Mw82yiwo9w4emksr0HdSJpwqkfoLjjsoOtO4N3IBCDA8EBbhoS1KOlJFW5pE/GVTwZCaZBMA3dcC/+jhsQMmjVLFge4Tv/QrBo4twzLO67Bt/5V+iaATG1HElVEnSmQRoQv+HQOhWcOwGJstB2HxJMx6VXEYTLDl5sm8klim+hGH2XMHgyheLHUZq6rZcuc5gZc6QEtRYlY8aCpKeix78JHvzIeFdl8otaYfN34eQPj3dNzCgKqp5NUHVoskjVF9HR8oxy+ufHk869DZEpJOmsA0SqEDelWxlRdDdR9Fvi+G5UG0g6C/T/Ry+KfkM2++k+A6UDRGqS1qv0M1BtTVpZBild8UGC1DnEpdtRbSt34+vlGKl5pOf+CbSERluIGj+NFtcT1L2RoPIyNG4kavwkWiiPx5IKwmnvw1VcghbuI2r8BJI6jrDuLRDOTSaq9R2AIC4HgMYl/O1fRB/4OVKeClfa2mHNBnz1tbj5T0CC9KDPbbRotAPf0Ut689J6pHRgo+6rXLGToOrfcZUnIVPnDek9MhOPaol88cN4fbRbK1JX6fDFY1wrYyYuC5TM5DL/Smi6F3YOZi4T06/2zeNdAzPGRNLkqr5IqfQbUumX4lxdr9vF8Xq8f5go+gtR9LtetxmI6v4h1m1orZsiQpi+lDB9afl4JURSvW8bJnfIJTWfVPZsID64raQWkpr13WTiWVd3MPgBkNw5BPErcLnzklajA8vdYRPvugD/UB9pyFt3Et/8SYLV/4ZUjU0WscNbeuLOG/Gdfybu+P2Qy0pN+S9c2iafnsxUm1DtQGQ2xeh7eH10gO2t14YZWdaiZMwYEQnQkz8McR4a+utCZAZUd+p418CMg0LxWqLoF5SiX5BKPRvVVpybgXOnACmKxS8Rxzcf8XGGOrntkR2rSLHjw4hbhPpNiNQTpJ5AkDq9x7ZJi8jhrSIRSBZ8MyrZbkFGUDWIhBb5FlDfd/02/IFYhPCJ7x7kGR0Z3/kHopZrCSqeCtpJ1PqN7l3thiDu/MOgAiVVhQ0/gFlnI9WLhnUsMzJUlWL0daL4Rrzf2KVrXQ3QfxfUMLiIwPX8vTFm2ATEWaBkzJgRF6JLX2OB0nBVLoGZF8Fxrx/vmpgxlnSj+yUAqtspFr/Qbb3IDFSbR+BIQthLeu/RIpImU5l0I41Ld4BUIG764AuI9xPteh2k5hHUvgLJnjak4+uWf/YbKFE1i2DVS4ZU5pGIO36PltYSNa89onIkmEdY1b3eqh62/xVy06F6MQRpaN+JbrgeNv4MHpkFT/omkptxRMc2w6PaSr7435TiHx22phJo5VDfypD/3959x8lV1/sff33Ombo1m2x6DyQhIRBK6IJ0EBRsKFZsqFzL1atesVx7u+rP3kAvdlFURFRQKYL0XkIKkAKk9022TTvn+/tjNsludjdbZmZnZ/f9fDzmwc4p3/PhsMzO53y/38/Xs1lgCYw4njeXqP9yfO9ozTsT6USJklQkq5uPO/UmaN8AG/4EOx6ATHkXlxz2LJpfM2nyS8odiZSBc7tJpz/JweYXObcVzzuMMFxZ0LUSia/1OT+pVPzo8QM+xyKTiE67fsDnuSCDW/VPgsd+ftDjvLnnYfXTB9z+YLiwlTB1R4GtxMDieIlTuww5BOC5v+AeOcjcxrbNuMf+Fzv5/xUYgwxUJvc7UpmvA7vxbA4R/6WY1ROGzxKLXE5b5vU4twVjPMn4d4n46jmSoaGhdyJlYFXToWo6jDsRl94Oq74H66+nGAttjiheDA77KEw4C4uP6/t4GVGcayGb/R3p9NVAf3qLCl982PePK7iN4cxl2gif/gvh0uugbVvfx29biXPhkBRCcLn1YElwbYNvxB9HfNLN3ediAUw6GcwHFxzk/GTv+6TonGsjm7uBVPYrJKJXEvFPJxvcRjr7efLD7ZqJRs4mHn0vudwtJGJfxPOGZr6cSDmZ2QeBd5D/YrgUeKtzLjWQNlS+RkYEizdih38GjvxKuUMZXiwK8z6ETX+NkqRRKJv9Ky0tp5NOf43+JUnAAaWCByOd/irOtRTcTjmFwVoyLZ/Dhfv/PZzLkGv9Gdn1HyJ84Pv9SpIA3PoHCB/9GS7M9X1wocyHgu59hEjtO3pvPjkeZl5w8CY2/RvXsr6AGKS/nMvQkrqQVPYzQIpU9jME4VJywc343snUJu+lOvFXIv5pxCKvpSrxEyVJMuTMXNFffV/TpgLvB5Y45xaRX+fh0oHGrh4lGVkmvYTsM3cTNLcSpHyCVo/Y+DTx2INYUMAT1ko07kRY+Kl8z5uMOrncQ6RSHyVf+rv/wnAVnjePMHymgGv/jfb23SSTP8KsMv/M5NqvJcxtJNv6f0SSLyXMLifX8k1cuAFqxuNj+0qB90f42M8In/073hGvxVv4ypLNAzGv9uC9PX3wa99GpPbg86nsiP/EBVlY10sVvVkXQ6590DFI/5nFSMQ+RSrzBZxbB4DvLaQq/nMcuzGL4dvcMkcpo1mZ11GKAEkzywJVwMbBNCAyYpgZqfA8dvzh1503YtGjqVtSQ82cPURsBZZrKluMQ2LKxXD4ZzBP/4uPVvmiDf3/wux5xwLthGFTQUnSXkFwN+3t76Kq6v8KbmuoOdcOZPGjx5Nr+V+Cth/ktwMWmYyLTSH7CvB3JLBVTXgbt/ev4ZbNhPd9G4vXYoeeW5LYzZ+IxY/evy7UQM+P9OPBSrQGq5uD62UInk07GxujL+dDJeqfgYtsIJX9HL53Mp6X/29oNJY5MpHycc5tMLOvAy8A7cA/nXP/HGg7+hYlI07NKSfR9NebCXbk13GZ+MH3Eba1kX52NRuuvRPcAmqOrKF2foZochtu31Ph/D+3/3si7WvyvU/Vi2poPOYxLNvPL0LDQc2hcPinlSSNYs41kc32b60xs4k4lyMMHyl6HEFwb8dCsgNbI6nccqk/EAZrMbf/4aNLHkPgv4BjE7AJEhBMBaaCnz6KyJMe3qoN/Wo/fOE+vBIlSgCR2ssJrIYw9a8+j7XIXLz4Yvyat4BFscisvi+w4Xbcsh/0utut+i2MmYdFNFdpqARuDVBFPHp5uUMR6cpKVsyh0cwe7vT+aufc1fsum19N/WJgNvkx5b83szc65341kIvom5SMOF4sxrjXvpqtP8j//5Lbvp26M0+n5sTjCdraaL3vAVoeb6HlcYD6HlrYP0Sv9akW2p87jMmvyBHN3T8U4Reu8RTM63nhTRkdwnAb+QdofanCbDrOPdz3oYPiCILHhrRUeDG4YANh9n48Dt2/sX0pVjsX57o/NAnijxMcB5HDjyPyzy1Y+8HXqnG7X8AFWcwvzf+nfvJ0zJ9Apo9Eya/7D6L1Hxhw+27PmoMfsP4W3K7lsPiDMPnFKjc9BBLRK4lH3onnTSp3KCJDZbtzbslB9p8NrHXObQMws+uBk4EBJUoq5iAjUtVxxxKfewgA2Q2b9m2vXjLwcqhhS8CGX0IuNoSlVM2HSS+BOe+C2W+HKS+DmW/Mr31U18fij5mdQxOjDFueNwc4+NN8z1sCZAnDUiVJeUEwuCFg5RRJvApowAWr9m0zsnhBD1XgOslVPUT64izhIVMOfoEdz+I2PVaESHvmwjayTV/s8zi/6uUDb3v1H2DFT/o+sHUD7t4P425/M25b8XsrpSuzmJIkGbY8c0V/9cMLwIlmVmX5pzVnASsGGrt6lGREMjPGveFSNn72SzTfdQ81p5xEfM4sqo5eTOKweaRWDnQOhrHjrgYmnmDsLT8exueTTU8gGt+El1518NMPlJwGY44EPNj5AERqILt7f5LjApj7fqxqWrdT3ZzLYeXX83HMeD3sehiWfWZfXGy7CxdmMC82wH9HGSmcW0/3j/c4njeVfC9qUPIEaa9s9iZisQ8OSWnsYvEic4nETiNovwEAF19EGAsIXd/JjbNNpI/fSnT2ifi3r8bCrn/Qw7Nm46rX4bkWnHMl6W0JU3dAsLWHPR2fX/5U/OQ5mD+ARXkBt+VB3GMDrCxqEaidNbBzRGREKUensnPuATP7A/AokAMeA64++FndKVGSESs+ayZTv/Bptv/8V2z66jeY+IH3kDxsPo3veCvrP/yxAbfXvqaNbcmzaTh2F563i/W/H0/YmgMamXTJdBKRvucDEG2AxV/Dxp3QbZfLNMGz34K29VC/CDK7oIdEycyHBR/dv6F6Bs7lYHnHIpDZJthyK0zuo4SvjEhBsJr29rcBzQCYjcXzDiEIVhCGfQyZKgHnNhCGa/H9Q4b82oUIoxNx/hJwRs6WwoCW3gjIjr+H4FXziTw3FjIhlglhTDVB7ZMQ7IQxVrIhaX7VBXjJl+Bya3DZlVhkBrgQ8yeCJcCrH/C1XZAeeJIE2BHvwxKlXZrAbX0IWtbDmPm4VddiC9+F1XT/7BSR0cU592ng04W0oURJRrTY1ClM+vAH2PKt77H1Oz9k8ic/SmzKZOovOI/dN/1jwO21LmuhdVkUGM/+imLG5j+mmPHmBXjpXnp1zYeJ58C8D2DJqT0fEhsDh39mwDEBMO0SWP8n2PNU/v0z38SNPx2LHHyokIwczjkymavIZL5H/uEZmE3Duc1lH/5mFi/r9Qcjx92ELGMAFcC7CSNPkzm0+/ZIeCy57N348TMG33gfzAyLHgLRwhNUF+ZwD30GWl4YYBA+ND2Nq5uDxccUHEe3uFo34R77X9h8d9cd4xZDzauLfj0RGTijZMUchkTljIUQGSQvFiMybhxhWxtbvv19nHM0XPJKxrziIiw22MnUB3x7CmHb3VNwfg+JSd0COPVv2OKv9ZokFcrMINe8f0NqM7QMcDigVKQgWEUm8ztSqQ+QyXyLvUlSXvSA9+WQJL/uX+VwLodnpVt/LOc9QtquJQwHvKTHkHGZZtzu1bi2LbgH/wfW3zKIRgLcE/8P97cLCZ/8Ni7d1P9T0024Pc8RBltIN3+STOtXyaX/jgu345zDbX0Yd9ubuidJgFt+FcHtbyW9/c0EmaEZYioiI5N6lGREc2HI9p/9ipa77gEgt2Ure/55G/XnnU3DxS9lzIXn88IHPkLY0lrwtdpXtbGj+kU0LryFffOF/CQcexUWayi4/T5F67q+3/qvjnlQMlLlcnfR3v4eINPjfs+bShCsHdqguskBIflF0SuDWYSqxLdJpb9AJvfLEl0loD19JYn45/C9WSW6xuC49q24f78Hmov0uxOm4Zlf4lZfh5txAXbIq6HukIMuYeAe+gxUNZKefX+Xohq4WuLPHoVtPMh8sfQuwkMmE2YfINv039iYb+HF9FkoUhYG5qlHSWRYMs9j3OsuYczLLsQSCQB2XvcHWh7MP2W0SISxr301eMX5X6HliWbC+Lz9G5LThyZJApj/4a7vN1yPC8vdmyCl4lwrqdSn2J8kxfD9FxOJvATPm08s9gHi8feRSHwHs4FN2i8ms8n5eXUVKB77bzxvUcnaD8IHaE//J0FY7mR2PxdkipskdRakYe2fcLe+AXf9iYQ3nk34xDdxLux+bGIcbH0Qb1cCwghe+0wsPRHLxXHRnhdSdpEouSMXkXnROIKx2zv+fTYQZCpkaQcRGXbUoyQjnpdM0vCqi6k7/xxa7rqHXdffwLYfXE1kTD2JeXOpPfUUYjOms/V7PyK3rfCFZZuWzaRh/kYsaM4/SR0i1nAMburLYcMN+Q2Znezr2ZIRJQzXkEp9Aef2l76PRl9OIvG5bsf6PkQiJ5BOf51s9vdDGSYAZpU7T84sRlX8m7S0XwBkS3KNMFxJa/urqa16uF8FFpzL4NiDZ40licf8GJz4Zdytb8hX3yylTBM8+2vcC3/H1UyF+kOx+nkQqconatEGoo8uI2rV4HZ0OvHJHpvLnhjHufv0sScyrDjNURKpBH51FfXnn8OUz3wSzGi5Z/9TxvjMGUW7zp6Hmll33RHs2Xwe2fQ0wj09lektkXn/BdWz8j9H6rTw7AjkXAttbZcRBPfu2+b7JxGLfbDXc8zqSSQ+TzL5M8yK97veH5HIqUN6vWLzvBnEom8p6TWM/P+n6fS3yR1kYWvncqQz38DRUtp46g+FmtLN0eomvQN2PAlrrsc99hXcQ58CL46d+TPstB/CxBP7bMJV19OxrmQ3XqSHihoiMmTMXNFfQ0WJkow6sSmTiYxvpPnue8msW79v+4T3XUHVIBak7UmYcez81x623jYBqynNk9+eWKwBjvspROth6kVDdl0ZOkHweLcvhLHYm/C8vod4RiInUl39Z2KxdzBUc4YymR8TBI8OybVKJR79EL53csnad+wik/1Zx716sOdjXIpU6oNkM7/Ao/QLi9rxX4BYfcmv06ttD8Pq67AJx2FzXgX1c/PxxOrp6atLbnEj3QuXxIiN+RZ+4syhiFhERiAlSjIq+WPqIQjY+fvr922Lz5jOxPe+m9ozTy/adWpOOQkr0vyn/rJ4I5xxF3bYR/s+WCpOEDzQ5b3nzcH3+99rY5YkHv8wVVW/w/MWFDu8HmWzt+Jc5Q69MDMikdKV8gZIZ3+AeTPxvGO77XOuiba2y8jlbsGLHEU27F7prdis4TCYclrJr9OrSSfBzAvzsUw5De+ca/Euug3votuws37R5VDneYS2tFsTfuJs/ORLhiRcEeldJfcoaY6SjEqTr/wwe267g903/4Omv9xE/UvOxSL5/x3GveG1eIn4oNZZOlD80DkFtzEYpVrIUsornf4hmcxP972PRM4hkfgyZgMfYun7i6iquo5M5hoyme/TW+W8Yshmf0Eu93d8/0ji8Y/jeRNKdq1SiUVeTRg8STa4mdKUXN9DyB6C8AG8YBxh+HzHf9c46fTXCcP8Gm2hbSQVfBrfm4Nvxft8cc7Byp/idj+zf2Pzc0Vrf8Ba1kPLOmjomsy7ts24Ff+X/9mPQBAQLlwA9DBk0Z+Ac1lwWcyr3LlyIlI+SpRkVDLPo/6cM0nMn8uWb36X3I4djLvsjflFGn2fsa95FbEZ09l21f9BAU/C/draIkYto1k2+xcymW/vex+NvoV4/KMFJcVmUeLxdxGJnEM6/ckSDpHL4dxGcrmNhOEWqqp+jVllDWgwqyKZ+DrR4JW0pd5GqSoG5HJ3ks1c3eM+zz+GnJdPCFqyFxKxk/DtSGL+K/EKnXu26S7csh8U1kYxtbzQrZiECzK4298CyXwVRzd1JtkZq4GlPf7nCNr+SND6a/zqNxCr+1jpYxaRbrTgrEgFi8+YTuPbLqN92QpcpusT9ZoTj2fcG19XUPteIl7Q+f3hXIDbvRS36Ubcqm/jnv85bt21uF0P91x2VypSOv3dLu+j0UuK1nPo+3NIJn9FPP4poHRP3j1vAfH4+ysuSeos4p9M1H9ZydoP3Uo8bx49PccM/c2d3qXJuTtIh9+hJfsGQreroOvalNOwE78C0eHzcMdtvKvrBi+KzX8LdvI3AQ8Xi4BrBtfLOniuGcgSpm4lSN9X6nBFpCdW2UPvKvevlUiRJBctZOoXPo0X757U1J11OrWnD7JqVySCP2ZMYcH1wW29Be45H57+ArgQWlfDs1+Dp78Ij7wF7ntZPmHaeT+ufX2f7cnw5FwTzm3Z996sAc8r7rBOM49Y7PVUV/8V3y/u3BSziSQSX6aq6o9EIicVte1yiMXeXrrGDQKexlkA3lQ8/0g8/zg8/9he53k5NtCWexuBe76wS087Gzv1ewW1UVQbbuvy1sywuZdiyUYYfzRuXP+accF6ci3DqLdMRCpGwUPvzGw68AtgEvnl1692zn3bzMYCvwNmAc8Br3GuwEdeo5Db/A+YeHbFLthYCcwM6yFJ2mvMxS+j+c67Bz4EL5cju3UbscmlqVDldtwDL/wS5n8Cxp2CeVFc46kw+SJIbYLtd8HOe/MJE4CXxB32cZj8cs1hqiDOtdLe/hFg/5pc8fhHSvbf0POmkExeRS73F9LpL+FcUwGtVRGLvZNY7DLMksUKcRgofU8xFuJYT+D2P+Dw3OEE9JwMBW4NVoTeQLfx3wW3UTRtW3DO9fi77haeSRB8awAjID3CYCOePwXnMpjFihmpiPSqstdRKsYcpRzwIefco2ZWCzxiZrcAbwFuc859xcyuBK4EVIZrgGzSeUDHRNvVP4A578I8TS0bSpGGMdSddQZ7br19wOfu+uMNTHzvuwd8nstmST/3Auk1a3HZ/EKXXjJJ3Vmn7zvGxp0C407pcp7FxsGEs/NvZrwJl9oETY/Brodgw+9h+f/AjntxCz4Ffo0Spgrg3HY8bxKe90bMEsAYotFXlvSaZkY0ehG+fwrp9BfJ5W4acBuRyIXE41fieeNLEGF5eTaZfHn1Ei/IeoD8f/8e95D0P4Fnhd9rW3g5rm0jvHBz1x2xesjsLrj9Ps28EJt6FtTOAD/Rc5LkMgTRJyG3s9/Nhpn7SW89Fy92ArhW4o2/LWbUIjJCFfyN2+WXht/U8XOzma0ApgIXA6d3HPZz4A6UKA2amcGh78Flm3Et62HnQzDzjRU91r+ShO3tgzqv7fEnCdPpHof19ab57nvZee3vCVu7jruPH3pIl0SpPywxGSZNxo07BVpWQfNy2HJz/lU1G3f0VRAfr4VphzHPm0ki8fkyXXscyeQ3yOVeSir12S7D/3pj1kA8/hmi0fOGIMLyMEsQi76DTPaqIb1uaNt73J7wryTmv6Yo1zAvAsd9Fmpm4LbcB2OPyK9jtO0h3KNfLso1Dnr9xmOwPsuS+wTtfxxE61kwI9YwtP/dREa7Su5RKuq3bDObBRwNPABM7Eii9iZTlVcPdhiyaC1WtwCmXATtGyt6bZJKETQ307b0qcGdnMux6/o/D+iUlrvu6ZYkedVVjL/8rYOLAbBoPXbcL+G0OyAxOb+xbS3ccy7cfQ5u2x2DbltGvkjkTKqqrgN669HYe9zZVFX9dUQnSXvFo+8l4l/AUE31NZtIyKqu25hE0v9fYt6bi3wtD1t4Od4Z1+At/iBWOwPqDinqNXrUsBCmn9OP+HwiNe8aWNvOw2cx0fibMK+MC+mKjELmuaK/hkrRxnCZWQ3wR+ADzrk9/R3SY2bvBN4JMGNGgeVNR5NoHdx/KVTNwh3zXfUIlJBfW0ty4QJa739wUOfv+cetxGfOpObkE/o81oUh2S3bum2vOeUkohOL8KyhdQ3MeW/+9yfXDGEG4hOh/kgAgl0/xAU7sNih8MIuLNWKTT0VGhdh0erCry8Vy/MmUl19E9nstWQyP6frukv1JBIfJxK5aNQM5zSLUZX4JtncGbSnP0p+im4JrsNYzJ+d70064NZGvYuI+ZeU5LrdlHLId90cbP6bYfr5/R5aHqn5AFg9QesvcOGmXo8zGjGm4GXaIP0YQepLeLNfXKTARWSkK8onn+VXxfsj8Gvn3PUdm7eY2WTn3CYzmwxs7elc59zVwNUAS5YsUfdIP5l5uNg4qDkEwiwoUSqpxre8EZyj9YGHBnV+019vomrJ0Xix3icQO+fYc9u/CJqauu0LU+nuJwyC1S+G+sW97vfGXE52/Uthzy/wth8Jm9bhnv09jF+MN/912PQzihKHVCbPm0I8/iGC4AmcS+P784hEzsb3T8RsCAocDEPRyEXkgofJ5n5XtDb3JkfOdhOyEqznIY+OPUW7Zt9BlShRmv9WbNEVAx5GbmZEa95GmH0sPxezF352CrQ/vn9DZi3ZNWcQmXUz5h28h1RECmdW2UPvilH1zoD/A1Y4577RadeNwGXAVzr+ObDxR9K3467B/NH55WSoeYkEE664nPQF57P1+z8it7V7r8/BZDduYvPXvkn1cUuoOnox0fGNAAStrWTWrSf19LO0Pf4kmbXPdT3RDC+ZoHrJ0QX/O6RbsmRaA2omxLs99Q/b7wWrwiLT8BveS7D987Brx/4Dtj1BuO0JaJiPd9aPsGgVrn0n5FKQHIdF9Hs4mlRV/aLcIQwr0cgrB5UoGWOJRE7FsxmYTcPzpuPZdNLBNWRcz4vOAng2l6T/RXybX0jYA1M3K58suVzx2jz0ddii/yioFzJa93HS6XvBtXTbZ8yB9qUHbHXg1Q6rJMmlt4NfBX5y1PTIilSKYjwiOgV4E7DUzB7v2PZx8gnSdWb2duAFYIjGB4weSpKGXnzmdCZf+WGa77yLtieWkt20GZfuX29P+tnVpJ9dTdONf6X6uCW0PfEkwc7eK+ZbIk585kySRxxOcsFhBcd+65dWcNuXV5Coi3LyFYfw0q/s71lyuS241ONYJF/K3Kt5KUTuxU7+OARpbNLx+aE3TWuwaL4EcXDPV3F7XsDi9fjnfwfz1aspo1PEP4po5GKyuf4/D4xH30cs+vYey6bH7e1ksj+j6/DGTvu99xDxlgwy2sExP4FrWAA7D0w8Bmnem7Aj3l9wYuD5k4nUvIdc8/922+fnaumpMqFFp+Kcw7U/iMVmETZdh0s/jT/+v7BYcdcn64vLNMGd54CfgNr5uCVXY55Kl8vIMqp7lJxzd9Nt5PQ+ZxXavshwExnbQMMrLqLhFRcRtLax/Zqf0/bIY/0+P2xppflfd/a4LzdxMbtWpzGD6sY63NMPUnf+2Vi08CTkwi8dyfFvnU22PWDCYbVd1ifxa18Bta/ocrw77sF8gtTZpLH7foyc/ZWCYxIZKRKxT+FchlxwM57NxrkmzOqACKF7AcjuOzbin0cs+p5ekwTPxhPzLiET/rrL9qrIVUTsdPKj3ctg3BGFJ0qRauzoj2AzX1qcmAA/fhK55u7bg8hmfK8awq7FcVzmBYJ1r8e1P9xley71BJEZ12HRyUWLrU/b7sj30uVaoPkZCHOgRElk2NCCPCIF8KurmPi+K2h/+hn2/PM22h57AsLBTer26uvZ/Vyato35UuTxCXX5a1QVr4jC+Lm1/T62W5IkIr0yqyER+wgt7XcRj32EiH/mvkTIuTTObcXRTjb7B2Kxd/fZkxL330km/C3GeKLeGUAtETu1fEkSYGMX9X99157UzsJO/T5WNbFYIXXo4Z5YLc5tIaxZgLdnJV16ljLP9Pzvkduc72WKXlzk+A4i1Wn+Wa4Ztt8Dk/qu/CdSObTgrMiol5w/j+T8eQS795Bes5aWBx7KF34YQPn2cPduxjfmCLPTaNucwosaXk010WlTShi5iBSL500lGf8svjevSyJkFsdsOgB+/OP9a8umUxO9GY/pw6dQxtjDB39utBab+/oSJEkQpu/u8t5Pvpxo3ZWkt72CMFyBV7MEWvpXtdRl1hU9vl6vFbRDtnNBDg8aCp+PKjLcKFESEQD8+jqqjl5M1dGLyb7yItqeWErbo4+TfnY1iQXzqT/vHKLTppBevZY9/7yV1NPP5s8bP4Hm1CRwEKTzPUrZ1pDq17+Ftk2ttK/bQGJiLWE2YPu9a4g1VBG0Z5lwxjyqZ40r57+yiHQSjRRxSJkdWrS2iqJqCtTOgubn+n/OtLOxyafBtDMxvzQFFCwyAy92HC7cQ7Tuv/HjLwLAr3o1uZbv4rxsr/MDDhTu+AGWXIxXfWpJYu1ixwOwsWNeW7QBlvwIizeW/roi0m9KlERKJDphAvXnnEX9OWfhwhDz9pe/jRx7NFXHHMWuP/yJ3X/7O82pSWx7ZHeX85uWbyP87VKanvgHLuj5acyaq+5m1mUnMOutJ+EnVExBRErHzODYT+LuuBz6MwgvWosd95mSJUh7+Ykz8RNndttukdlAFZbqYQJTr7IEWz6LN+fWosXXq9gYyHZ87o9ZjNUtLP01Rcqgkos5Ds2S4iKjXOckad82M8Ze8koaXvVyMnu6V2aqO3w6ux7d0GuSNOOCgOnnZRk/7XrCbQ8UPWYRkQNZ41Fw6KX9OzhWV/Ik6WC8yByMSZBZNaDzzK8rUUQHqD8CxhwNsUYI2ofmmiIyIOpREimzMS+7gMmxpbR+45/kmlMA+FMb+PM2OLOxhtz2A9cHcRz+rjYmzfnj/k1Vrxq6gEVkVLO5r8OturbvA1s34oJU2ZIli8zD2U6oOf6gc5S8+kshPh+LTsZlN2OJAuZiDSQ+83HHfB8e/wDsegS35icQpqHhWBh7gtZUkhFh1C84KyKFm3TeEYw/bT6pzbtpfnYLb/rcQzz02EZ+VxdlzoQ6PjolJLdzDyd+Zj1V1SuwXKdKSfGJMPZF5QteREYVq56CS06E9i19HOmgdSPUDe3aRHuZ+fiJF5Nru4FIbBZknut2jDfuP/AbPzjkse0TqYaxJ0D1nHyJ8LU/BfOg9jDcib9RsiQjghIlESmYn4xRPXs81bPHc8u5i9i5s53fXbuMTRubmXdqjGDVrVTH74DcASfGJ+mPqYgMrYbD+pEoAZmBzA8qvmjtlQSpWyAyFrJbiMz5F+HOqwh3/RQAr/6SssbHnuWw5mpY8DFs2qtwE86CtudhxZehfT1UTS9vfCKjnBIlkWFq7NgkV7xnCQBuzzKouQ0yPRyY3VX0a7swgFQrVjVEY/VlVMtuu4JI47cx00KblcImnYLb2PPC2fvEG/IJVRmZ37FIth8BfwwWGYcll+D5DbiWW3Cp5Vh0Wvniq1+Em/NO6KjLZ2OOgDFH4KpnQds6XNPjUL8Iq55dthhFCuZVbo+SijmIVACrOxyO+yPEe1gxfsK5Rb2Wy6ZI//VbtP7onbhctqhti/TE4ksI2/9V7jBkIGZdlC8VfjAzLsD88q4B5VwIOHK2BkscAYBXey7+uCvwZ/wRqzqurPEBMOdymPwSXLj/SZjVL4JxJ8Cmm+DxD5UxOJHRTYmSSIWw+Hg4+ieQOGAoRrLwoRku047Lpsitfpi2q64Az6fq8u9hEZUcl9JyLkOYugeCHeUORQbAvAh27Cc52NcIi9UOXUC9xWAefvISfKbj1b38gH2G+Q1licuFuY4kbm8cSVjzf7gnPoLbenv+oPaNUD0LWtfgUlvLEqdI4RxmxX8NFQ29E6kgVjULN/eTuBVfhcQYzALItg66vbBpC+lbf0y4ZS1ud36+gT/zSOJnvQNL1hQl5mDLGghy+FPmFaU9GTmcy5DddgUudTcku6+DI8ObNR6Fm3wKbLqr3KEclMuuwEs1Y5NeXO5Q9ttwAzQcAzWdCl0EbbDrUahfhNv5cH7bjgfg1JvB00MrqVAVXvVOPUoilcai0PI8bH8Ct+0p3NNX4do3Dqqp1J+/RvDM/fuSJOJVJF79yaIlSS7IkvrTV8g9pWFV0l3Y+jdcKv8lO9f8M5xLlzkiGSib9bKed9TPg/r5QxtML7xcAq/qpGEzB85ld8Pmm8Gv6rpjykVwyvWw+ipY/lmoOQSmvRIyO7FYeXq+REY79SiJVJr6BTD1IvAisO56yLXi7n8HHH8VVt11GJ5zIbgA6+FppAsDws1dF2L0JszB4lXdjh20MCDxyo/jNUwqXpsyYoRtN2GRmfgNH89/iQ3bocxzWmSAJp0CkSrIte3fFqnCXvQtSIzBZddh0TJXbmtfhzW+urwxdHBBGp7+Biz+f1hsTJd9VjsXt/yLkGvOV8GLNcDMN+IyO3FhDvP0lU0qk3qURGTIWLQOO/Jz2KJPYSf9Amrng2vHPf02XGY7zrn8a/f9uJXvwj14LOGyy3Abf7pvTLxr2036L9+EoGutcX/64bigeAUcLJrAnzALi5ZnwUkZvsL0I4SpO3FhC+ZPw0ucgvljyh2WDJD5cZh8ateNVZMgUoVLPURm06sI00+WJzjApVdDZg1hyx2EbQ+XLQ4Al9kFT/0PzHh9tyRpn6kXw/gXw+QL929rWgr3XYLLNA1FmCLSiRIlkQpkZvlXw2Js1qWQ2gHZnbjHL8CteDvuqdfilr8l35t09C1Qswj3/P/inrmS7LI7afvlleSW3dGt3ey9vyO3fHjPN5DK51xI0Hxt/k24g+yW1xC0/LG8Qcmg2fzLIJpfSsAWvgs753dYtIaw/W4Im8ht+wguLM+wyrD9AQDcnj8SbPwPXJgqSxzOhbDlFlj4Cayu9yGJVr8IjvgStG/MP/Ba+gl48r+hZVV+zSWRCmOgYg4iUkZTL4L4GMxL41qWwpbfQtgGky/DZnw4P+xuxn/hdvwTdt9J+l8rIQh7bS5z97VEjyj+xPqguYXtv/kDVUceTu1Jw6Akr5RN0PQ1wrYb929wreR2fx+v+uWY+eULTAbFxsyD036AW/ETmPeGfQtgh+nHAHC5tYStf8KvvXToYyOCN+bNhE2/gGAXruU2rO7Cvk8sOgfTLjno4uDOOXjhN7D2mnzxBi8Krc/tP6DlWWg8ufShisg+SpREKpx5EWxCPrGxxpfg6k/EbbwGmu6FGR3HmI+rmkewNQfBhoO3F6/GOXfQP+iDsenr3yW99nla7rmf3LbtNFz0kqK2L5XBhS0Erdd33xGsx+VewKJaWLMSWcNh2Mlf77rNa2Dvc99gz8/KkyjVvxLDB/MJd/0U1/4olCFR6tcDgO13wdNfh6rpMOutsOp7Xfe3rStNcCIlVslzlJQoiYww1nAaVM0DL9a1iMOhXyN778f6PD/cvIr0X7+JP/UwXNserKqeyKIzsNjg5xk554jPnkl67fMA7PzjjYx5ydlYVCVvR4Mw/Sgu2IGXPDufJIW7uh3jJc7AU5I0olhk6v43rjxflMzyX3O88VcSNt9MuPtarPpkrPrMoj8MKlhqK5zwS6x+Ub53af0fIL0VYuNgwpnkBzGJVJqhHSpXbEqUREYgi3evMudFqoid+XZSv/l4n+fnlt5Obunt+94Hzz1B/BUf3ffFwrkQs/5Pcdzwua+SXr12/wbnWHvFfzHxPZdTffSR/W5HKotzIUHLrwh2fRXIQGQ65Hp6Kh7FH/O+oQ5PSsyrPo+g+RcAWPKkssZi5uFP+S6u7QGCzZ8EjMi0H2OJw8saV2c2fX9lPjPD7e2FqpoJc94J6S1likxk9FKiJDKKeI2DK9ObW3k3wVVrIJMCz8cS1VS943t9nwjkdu4i/dwL3ba7TJbN3/4R4y55GdFJE7BIlNQzT2N+hMT8ecTnz8GLJAcVrwwPYfstBLu+sH9Dj0kSeNUvw4stGqKoZKh4ieOINH4Di87AYkeUOxy85FGQPAqv+kXkNrwLl904rBKlbhZ9Dlb9ALbfDbExWFLLLEgFMhjAc9VhR4mSyCgSbnxm0Oe6nfsXtY0edW6/z4uMbWDsa17Onlv/RW7bzgMCCmm5525yW7o+KU098wj1Y/+GnziVaM1/4EUPG3TcUj7Bnqv6dZxZdYkjGTjXvhq37QZs2vt7XIdM+seveWm5Q+guvpDoIXeXO4o+WfUsWPxVXLYZ87XEgkg5VHCOJyID5U3tvSztQPhzTxjQ8fUvOoaGsU8TaRzbaasjedgsclu3dju++vR1GG2EqX+Q3v5acu1/KzBiGWphdi0u81S/jnXhjhJHMzCu7Wnc8jfBph9DdnjFJoUbdnOT+mDR2nKHIFIQlQcXkYrgmrcXp6FM97VIXHsLLpvGasfmx9dn2gnWP0125X3klt8DQZaamT5NHSH4tbVk1qzucZJ32N65hyFFtum/YFcN/uTTKu5LzmhlfiMWOxKX6Xux0bDtn4Sph/AS5S8bH2xZDuvehgVNALjll+Li07Dxr4bGi7v9/rnWZRCdgMXGlyFaEZHhbe86SpVKiZLIKOI1zsRqx+GaC3tKnn3oRrxpC7p+afQjtP/ms4R7tuPPWEhuxb0QBl2vH7Tu+zlMpYiOqSdoaurWfsuttcTmw97mzc2l7ZpvUPWmcURmai5LJTCvlui4/0d21+dwqb4WMc6R2/MjovElQ5oI51beBX4EcATrlkG6ldyyW4kevZhIzZ35gzKbIbMZ1/ww7PwHTLkcYpMgNgH37Adg160QacBNfB3etPcPWewiIlJ6SpRERpHMPb8rOEkCCJu3gQuh09ogFktgVbW4DU+TW9bzF+NMMB7Ilwh32RxBECHS2Ehue9eeLr8mSufvy7m7GiHcSfapfytRqiAWnUl03FfJbHgxkDnosS51F7ldnyfS8IkhW3Q2/bevQpDrtj1YFxJZ0MMJTf/CNf0r/7OXhLA9/7NfhU26rHSBiohUKgMquEdJc5RERgHXupv0rT8me+91RWnPGzutx+3+rCOxht4rMx04yi7Y2YQ3Zlz+3DH1RCY04jeMIX74/i/VFiwifDpfBCK38n7cAb1UMsxZEsj269Cw5VeQO/iCyMXicpkekySAcOt6wmBqj/v2H9S+70eb8h9YpL6Y4YmIyDCgHiWRUSD3zH1kH/xz0doLXlhKbsVdRA8/vcv22JKXgBnp238Jue49CPHoNlo7b4j4ZHftJjZ7JvE6R7TpYVwCfH8iub8uIXJCNdk7mtj7Rdu1NhGsfZLIIUcX7d9FSswiRCffBC4AAsyrx+XWgT8OXBpcBhdsIWy7lbD9DlzueSw6o/Rx+VH8eacQPHNPj7uDpoV44/qZtJn+lIqI9EZzlA7CzM4Hvg34wE+cc18p9TVFZL8wzJF96u8FteFNXUjYsjs/aciMYOdmgg3PdEmUXCZF5r4/kb7z2t7baV5D7eITaH12K1WHTiBmG/Bbn4QQaMofYwbhI/ly4dkbmru10fbrT5O89H+Iziv/xH/pm1kMix7SdVtkSrfj/KrzcS5H/pehtJxzZP7xnV6TJACvevdAWiw8KBGREWloq9QVW0kTJcsPNP8+cA6wHnjIzG50zi0v5XVFJP9lkC23w6ofkxi/lmDSsWRW7sA1t/Z98oFttezAkmMI1j+7b1vm0dvxJs0lMnsxmYf+Rub+P/fYi9SZEZJovY/EFKBt9YDjAMCL4FWPGdy5MqzZEPXMBCvvJPdk7w8PvOlz8OK3DaDFyv0SICIivSv1X6XjgVXOuTUAZvZb4GJAiZJIyYW4Jz4JQRsAfngPiUVzyO2eDg5yG5twu7v32PQoXk+w+bmu2zJtpG74ZnFD7ofkxe/Hnzp3yK8rI0duxb9731lVT2zWowys+J4SJRGR3qhHqXdTgXWd3q8HuqxUaWbvBN4JMGPGEIxLFxkt1v1pX5K0l7WvIRpbA0B0bg1B9bEEW1vJrdhy0KZckIVc/ybkl1rmidsJtm/Aq67HGz+dyOzF5Q5JKszB1hOLTJ+AeY8PtMWC4hERkeGp1IlST8/kuvxFcc5dDVwNsGTJEv21ESkC5xxu3fUHPyjXgp/Yhj/tUbyq08k8cpCJ682bIRLrc2jdUAjWPE6w5nEA/KnzsYvGknn4JiJzFhOZdSQWrypvgDKshTteINzybK/7nRvEOk5OlRhFRHqjHqXerQemd3o/DdhY4muKyHO/gd3L+j4uFoEURMbdh518Eul7e0mWMu14DdMJtw1N6eb+CndtovWn/w2pVrIP/Q0iMSLzTyB65JlEDjkK84ZmPR6pIObhzzku33O05S84PMwF4FL5Knz2MHAyuEfA0v1q0m36OYx7GeYnSxu7iIgMqVKvo/QQMNfMZptZDLgUuLHE1xSRqj7WgAFonAfpx/M/uzR+9d14k3pfC8al2nvdVy6ubQ+kOhWnyGXILbuL9ms/S9tPP4rL9u+Lrowe3thpJF79Ofxj344F2/GCrVi4A3OtGDksTMHGJ2DXJPCO69+oOjNID6+HCCIiw4EZmFf8V/+ubWPM7A9mttLMVpjZSQONv6Q9Ss65nJm9F/gH+fLg1zjn+vGYW0QK4kWhagbEx8Gux7rvn7AAWAqu84KbOeLH7CZsmopVZ8BCwMg+A8GGZlzzDnoeTTs8BRueIdjwLIQ5XKoFf9aReFV15Q5LhgG36RZc29aDH5TaDuu3Q93hUBdAuLLn46oXYwt/jnmJ4gcqIjISlG/o3beBvzvnXt3RYTPgsfklr8XqnLsJuKnU1xGR/Wz8KXDa9biHrui00Ycpi8C2Q7qH5Amw7LP41R3zNzo+1+ILptLeMh/aK++JeduvPgXh3mTQ8KYcSmTmIojGIcji1Y4jesy5WCRW1jhlaLgghVv1Y1j7c6g+rH8n7XkO9gCNx0PieQg7FT7xa7DDr8X6+3hTRESGhJnVAacBbwFwzmWAAU+01nLiIiOQcyFu3U9hx0P7N05bBKkHBtxW5vk5WLymiNENobBzj5kj3PgsmY1dJ/Kn77+BxFmXEVn4ImxgNaGlwpifwO16HMIsAx55vn0ZTD4KrFOiNP7VSpJERPpQomIOjWb2cKf3V3cUiNtrDrAN+KmZLQYeAf7TOTegxST1CS8ywjjncKveB7vv3r8x2QipBwfRmk901haIj9yPCte0lfY/fo3Ujd/BBbm+T5DKFq2F5AzYuWrg525ZBnZSR2+rh019d7GjExGR/tnunFvS6XX1AfsjwDHAD51zRwOtwJUDvYh6lERGmpZHoOlWiE6A6TNgWzM0ToHU+n434SJTCfbMJbuqnWBDE7CMkf5cJfvEbYTNO6i65EqVGB/JkofApicgHEShjzALG56EMUfBYS/FImOKHZ2IyAjjylUefD2w3jm3dyjNH1CiJCIkZoGXhOzW/Ks+CalN/TzZyLW/mNRdWyDofM7ITpL2CtY8TvO33050wSl4Yybgzz6SyLR+zmWRimDxCbjsnsIayaaxSW8oTkAiIiOZlWcdJefcZjNbZ2bznXNPA2cBywfajhIlkRHGoo24+tNg1z/yG8L+l/V20Zmkbu5vUjVCpVrJPvbP/M93x6n9yK9V7GEk8Qr/s2czX6r5bCIiw9/7gF93VLxbA7x1oA0oURIZYZwLGGwZ72DPLGCUJ0qdZdME61YQmb243JFIscQbitBGY+FtiIiMEmUaeodz7nFgSSFtjI7xNCKjiJmPd+i3sTnfBIv3+zwXPYTs8wOunDni5dY8Xu4QpJgmnQbTLyyoCffUN3Hr/1mkgEREZLhSoiQyQtm4C7A5/9vv47PrpxOs3VHCiCpTsOGZcocgRWReBDvmU5CcNPhGss35BWlFRKRvniv+a6hCH7IricjQazgXknP7d2xQtpWzhzV/wqxyhyBFZl4Em/OawhoZd3RxghERGcGM/NC7Yr+GihIlkRHMzMfm/hCs72IE0ZnrIaqPhAP5Mw8vdwhSCrNeCVPOKncUIiIyjOlbkcgIZ/Hp0PjKvo/LriZ55rghiKiCxKvwZy4qdxRSAharxTvhq9j5N2FHfQyqpw7ofLf6NyWKTERkBDGwEryGihIlkVHAJr0D8Ps8zotvInHa5P3vx1bjTxnTV+sQSxQU37BkHsmXfxCvqq7ckUgJWXIiNvvV2Dl/xs7/Oxxyaf9OXP8PXPvW0gYnIiJlpURJZBSwxHQYe0Hfx2VXE6m+g6oL60me30jVyavxGnqunOdNORQbOxF8w598SLFDLi/zSL7iv4jOP6HckcgQMTMsOR5b9AGo68fvswtwy76LC7Mlj01EpHIVf36S5iiJSNHZlP+gP71KAF74IL7dA8FuyIRd942bhjdhOuHmZ3FNm8EFJYi2jMwj+aoPE110WrkjkTIwLwoTTu77wEg1NuGk/PEiI1SYXVHuEETKSgvOiowSlpyDG/8q2HbdgM6LLVhPbIGR3XAI2TUpwuatkEsfcNTIqZgXP+vNRBe+qNxhSBlZw0Kc11EAJey0ttjYI6F2DtZ4LEw5HYtUlSdAkYMIMo8QZO4gknwNnj998O1knyS9680kxz+I9aMgkEhvyrXgbDEoURIZRWzKe3Hb/wzuwETnIOdknwMgOtWReaznP5Zh8w6sZiyuZWcxwiwba5hE7PiXlTsMKbepZ2NTzwGXg6aVkN4FNdOhejrm6c+mDG9+7FiCzD2kdl5EtPr9+PFzMEti3jhcuAvnWvqVQAXpfwHthJn78ePqYZcCKFESkUpgsYm4ye+Ajd8f+LnZ54gfdzrphzZ22+eaNkOiFm/KXFzTVlzb7mKEO7Q8n+RL34tFNJRqtDPrGJVuURh7RHmDEekH5xxB6vcEmXuACEH2AXBtZFu+QrblK4CPFz2KMPso4IiPuQY/dkof7f0JgGzbT/Fip2JDWWpMZJhQoiQyytjkd+N23gypNQM+16XC3nemmgk3N+evMXYSZhHCHRsGG+bQileTOO8dRGYfWe5IREQGLNf+c7ItXz7IEQFh9hEAzJuCF1ncR4vtOPKFSsLsvYTZew+aWIn0ysC8yu1RUjEHkVHGvBg242ODOje3rb1fx7mmzYS71mPjJuPPWIg3zKvixU9/HbGjtPioiFQeF+4kSP+9X8eafwjxht9iXs1BjwtzT0O4vdP75woJUaRiqUdJZDTq449krzIDq3Dndm0k2JUfqudNnU+44ZnBXbfEItMWlDsEEZFeOZch1/YTguyjRKve2qV3J7Pnfwizj/WrHS96LJ4/sR9Hdv166IK1OOeANGYjcN08KRlDxRxEpMJY7TG4+tNg97/7f1JkAmFz/3qUerym37/S5OXgcpm+DxIRKYNc6mYyLV/Y18OTztxFtObTRKtejwtbCDJ39Ksd8yYQq/lI/461ZNcY2v9AmHsKrIZ4/feULMkADO26R8WmoXcio5Qd+j1ovKTfxwfp+ZAtYM0kG8YfN9n+VwEUERkqudRNZPZ8oMswOIBsy2dp234KqV2XAP0rQONcmjDXv1598xoP2NJOmH2MMHMX2dbv9qsNkZFgGH9zEZFSMi+ON/sL2LxrID6rz+NdurAeITeM11py4QhbNFdEKl6YfYrMng8d5IDtuGAN0M+efrebdNMbSe/5nz4TpiD9z4O0ox54GSArwWuIKFHqxO1Zi8u2ljsMkSFl9adgi/6CTX0/eMlej/Pr11PIwrLh9nX40xfgTV+AP20+VjN20G0Vm984tdwhiIgA4FyOIH0H2bafAwepNDq41glS15HaeRGZlq/hXK7Ho4JM78Oyg+wDRY5p+HOpnYTP/BaXbip3KDLENEepsxotJiijk3kxmPIeGH8pbtNPYNtvIEwdeFRhF2nfQ7Bh+b633tjpw2OB2mgCa5hU7ihERAgyD5NL3YBZDUH6xhJeyZFr+wlhdjmR5Gvw42djlh/Cl6+id2fvZ+aeJsg8iB87voTxDR9u66OEK3+BzTgPooMshDTKVfIcJWUFnShJktHOouOwGR/FTX4b7vnPwa79wy/SK8YD23s/eaCq6iAaL/v8IG/8jP0LjIqIlFFmzwdx4Q68yGFDcr0wey+Z7L348ZcQrfkY5o0l2/pj4ODD6zItXyI59oYhibFcXOsmaFkPsTr8075V7nAql9ZREpGRxqLjsWn/DdVHAeDqjsLG9m/CcH+F65fhTzm0qG0ORmTm4eUOQUQE5zK4cCsQEOaWDem1g/TNpHacTvuO08m1X9Pn8S73NM5lhyCy8gkf+SrhHe/BrfxluUORMiooUTKzr5nZSjN70sz+ZGZjOu37mJmtMrOnzey8giMVkSFlienYgt/iZr6PIPI4/vxni3+RsOfx8UPHiJ/++jLHICKjnXMh2ZZvljmKsFt1vd5FwI3sOd3eiZ/DO+cX2PH/U+5QKp+54r+GSKE9SrcAi5xzRwLPAB8DMLOFwKXA4cD5wA/MbPguoiIiPTIzrHpe/kMp3IA/p8gFGMrcG2/1jVg0Xt4gRGTUC1J/7ldPzvCRIdP8BVy4o9yBlIzFarGxh2G+/kaMZgVNynHOda4feT/w6o6fLwZ+65xLA2vNbBVwPHBfIdcTkTKIzt7/48lrCdbU9/9c3yO+ZDJedRYvuhvcHiBC0DqD1INbCTaUoJdqAPwJM8t6fREZ3ZzLEKRvJdPy+XKHMmBB+i+0p28hUvUmotXv1SK00isVc8h7G/C7jp+nkk+c9lrfsa0bM3sn8E6AGTNmFDEcESkG88ftf9M+G+h/pbrECZOJxO7MV7jtVLMhEl1L1Wkvpu22reCKXf62fyxZS+Il7y7LtUVkdHIuxAVryLZ8A+daCXNPgmsrd1gFSJFr+zFB+lZitV/Ejx1b7oBEiqrPRMnMbgV6qp37CefcnzuO+QSQA36997Qeju8xnXTOXQ1cDbBkyZLKTTlFRiqr2v9zZGALDfrVq6CX+b4ej4FNLiCwQhjJV3wIb8yEMl1fREaLMLeyo9y2T67917hwY7lDKjoXrCXT/HESY29CMy2kM8ON7B4l59zZB9tvZpcBLwXOcs7tvRPrgemdDpsGjLxPBpHRwOLkn3048B/Fm3484bqmvs+LRbDcQSYGB3uAKcWJcYC8ibOIHHpMWa4tIqODc2kyez5Z4vWQhg8XPEe25SvEaj9R7lBkODGGtPhCsRVa9e584KPARc516Tu+EbjUzOJmNhuYCzxYyLVEpEzC3ezvEHZEj47t22VVtfhTZkA8uf/4aBzMI7ZgAriDrJFkEQiCkoTcF3/irLJcV0RGPhfuJNv2U1I7XjJqkqS9cu2/xYXN5Q5DpGgKnaP0PSAO3GJmAPc7597tnFtmZtcBy8kPyXuPc64834hEpCAu+0KX91a3HohBPEni+Bb86KM4B7ndZ5LbAImFt+fPS4yDloM1nKP6fHDhWMzbRZiaSPsdm+h55G7xxM99O7HjLijpNWT4CYOQ5/68hqd+uJRIwicxoYr6Q+o59uPHlTs0GUGCzCNk235AmLm73KGUSYb0ng/jx04ikrgI84pcKVUqUiWv6V5o1bteV4t0zn0R+GIh7YtI+blc10SJcC3xV5yC50fxd/8bADOIjrkdv3YC1pHnWPpeqD8Wdj8B9PycxDLP7kuLfO8Z/OknEKzrf7GIfvEj+FPmYslarHYs8RMvLm77Muxtf2Ib93zw3zQ93dRl+3rPWHj5IpLjkz2fKDJQbs8oTpLywswdhJk7ACNadVm5wxEpSDGr3onISJR9vtsmS96Due5zfDx/a9cNbY9A3SJoWQ1hqs9LeXWxXlKqgYscfiqxJS/BnzoPi8T6PkFGpOdveo673ncHQar7b5YLHc//bS2HvWVhGSKTkSgMNB17rzDzIC75ZsxKO0pAhr8RXcxBREY3l13bfWNkBrZtVf8aaH8KkjMgMgky26D9uV4PjU1vIjp5PNlNPtllmwccqzdhJtGFpxA55Fj8qXMHfL6MLJndae750F09Jkl7rfnTaiVKUjS5th+XO4RhI8jcSmbPh4nVfQkzLdo6qilREpGR6sBEycsdiW1big1kLlHmhfzLWX44XsvTEHSfwGSZpRgQGX9ab1XFe+ZFiL/4UmKnvArzVJp2pHLO9fvp9I6l23nyO0+Q3XPwkvY7ntyOCx3m6am3FMa5EKwW2FTuUIaNIP1X0rs2EB/zY8yrLXc4IgNWwdOrRKTUnHO4zJou26x57cCSpC4nO2h7GPws1B8F0cYeDwt2DKBNP0LVmz5H/NTXKEka4R769P2kdnYfwpnelWb/6hSQac5w88v/ygs3Pddnm7Wz6pQkSVGYeUSrryh3GMNOmHuM9O534lx7uUORcrD80Ltiv4aKEiUR6V2wHVxr123FWEzQpaDtMUhO677Pi0M8gSVr+tVU8qL3E5m5qPCYZNhr29TGw5/vutLE0u8/wW8X/YqbXvYXVv5sOc9e+zR3vvv2gw6322vCcROZenoPv4Mig+T5c/CiJ5Y7jGEnzD5KkPp7ucMQGTANvRORXrlgS9cNloAwV5zGY7Nhz5PdtydmEBv3D+yoU0nfd7D64mBjJhI94vTixCMVoWnlLiDf27niJ8t44huPAbD9sW1sf2xbv9uJ1kY57QdnUD2luiRxyujjXJZM67dwYZErd44QQeZuIslXlDsMGXIO8zRHSURGouDAL55R8Lzeqn0PgA+BA8Kumy0C0SrIglnY45ldDu9nr5OMHM3P72Hlz1ew+Z6NPP+35wbdTqw+TrQ2CsCKa5ZRO7OOaWdNL1KUMhqZRTGvgTDzr3KHMkwNaOapjCQq5iAiI5HLbe/y3rMFkH2IgheFtRjEx0F0DOR2Q7YJYuPB93Ctj5PbfSbpx7b31Qrh9g0DmuAvlS+zO8MDH7+34HZa17fw91f+jYbDGlhz/WpO//FZRYhORrtYzSdoT98JbiATLUVkuFKiJCK9cqmH9v3sRZbgbXqYgpMkANeeX2NpLwOyuwjbp5NafibhpvX9asYbP11J0ggUBiGeX/optLuW72TX8vwwqcajxpf8ejLymVeDF11EmLmz3KEMO0H6FsLcSrzIYeUORYaQUdnrKKmYg4j0yGXXE7b8Jf/Gn4Rtfqqk1wtSR9B+74R+J0kA0blLShiRlEMYhNzyur+z7EdLu2zPtmVp29JWkmvWzKylalJVSdqW0SdW8xGwunKHMQyFhNke5qWKDGPqURKRHoWt/2DvZCQvnI65rSW7Vq7tBFL3tkDQ3O9zoseeT+yUV5csJikPz/eIJCM8/vVH2Xz/Zia/aDJBOmDlNctp2zy4RGnKi6dywpdOZvujW7n3I3d3qYhXd0g9p191pkqES9F4kbn48XMJUn8odyjDj9fzkhAyghlU8sAPJUoi0jNv/xNRay9dFadc23Gk7m2GoP/V9CKHnUjywv8oWUxSXsd+4jhuPOtPrL/lBdbf8kJBbU0/fyZn/PgszDMSYxPE6uO0p/IJ14QTJnL2L88jWh0tRtgi+0Sr361E6UBWhR87udxRiAyIht6JSM+8/WWTnZ8sySWC4AiCbY3g+q5wt08sSeL8d5UkHhkexsxrYN6bizOPYezCsZhn7Hp6F/d+5G7aO4bvRWuinH7VWUqSpCTMSvOZWcn82CmYJcodhpSD54r/GiLqURKRHrm2O/I/WA3WurEk17BdNURaHyKycA7pDRA27QZ38A/AxFlvxqsbV5J4ZPg4+iPH8vxf1pLakSqonSe+8Rgt61vYcPs6Utv3t7Xk0yeQHK8vs1Ia5jVikYW43PJyhzJsmK/y+6OTUzEHERl5XC6fHHkswLJNxW+fBG772vyb3WuI16whOT9OZPrUXs+JHn0O0WPPL3osMvzEx8Q56WsvKkpbq697tkuStPDyw5n3+vlFaVukN9Hq95Q7hGHFvInlDkFkwNSjJCI9suhsXOrBgQ2LGwh3GIQHVLhr3UiUjUQWH01q2UbIdSxQaB7x015L7LRLVQ58FJlx3kwmnTKZzfdsKlqbR334GI78wFFFa0+kN37sLMybhjvwc64YrBo/ehJe/EV4kfmY1YBV48Kd5NquIkjfwfBa4NXDj2utslGrgnuUlCiJSI8sOhMAFwn6OHJwXK6m92vvfIzkYYfQvmIPBAFVb/wskdmLSxKHDF/OOSKJ4vyZMt848csnM+8NWsNFhoaZYf7UIiRKUfzEy/EiC8G14EWPwIsei1ms+6H+VPz67wEQBhvItf2UXPvvgcKGsBbKj1+Ip6F3UoGUKIlIj1xmVf6f4RocRVlm9oAL9LF/z1oikxZA7UwlSaNUZneGzfcV3ptUO6uWk792KpNOnlyEqET6zwWDT5IssoBI4mL8+Pl4/sB/dz1/KrHaT2LeJLKtX+vhAvXg9tD3h3FhIsnXEa3+SEmvIcOYVfaCs0qURKQb50LC9rvyb8I9uInHYVseKe41dvSxJk5yPOGuKFXnXV7U60rliI+JM/XM6Tz/17UDPtdP+DQePZ5DLpnLnIvn4BepZ0qkP5wLCbMP4tyuQbbgEa/9El50YcGxRKrejnOt5Np+sH+jVREf8xPSu6+AcHvB1+iNFzmaaM2nNWR6tBvCKnXFpr8cItKNSy+FYNv+DaX4Ixer7+OAkKrLvoiX7Os4GcnGHz2+x0QpWhNl9sVzGHdkI/GGOOYbu1bsomZaDWHgmH3xHCJJ/YmTMnF7SDdddtBDvOjx+LETMG8yWJIwtwKIYt5YvMicoiRJkB8CGK1+P2H2YcLsg3jR44jVfgoXbCxpkgQQrfmAkiSpaPorIiLdhHt+1emdYS07inwFD/+ES2H7+bhMC+HKG6Bl/xArm3QU/nHvw5QkjXqHv/sI6ubUs/R7T7Bj6XYOf/cRzDhvJg2HNXTrJZpx/qzyBClyAPPGkBh7E9nW7xKkb+6yz4segx+/kEjytZh1XsfrgtLFY4YfP4swt5J43Tcxfzyp5i/hRU8kzN5fkmt6sTPwYyeWpO1COZcil76OSPxNSuRKzNDQOxEZQVxuG2HLTfveW+RorOXxIl8lhFXvgomvxzv0I3iHvxay7RDmoHUr1qjSzbLf9HNnMPXMabhcqCF0UjG8yCHE679FkLmUMLcSXAbzJ+LHLyrLl/No1VuIJF6NeTUEmfvxIjOJVv837TtOBdda9OtFEhcXvc1iMUsQ5laSzv0X0fjr8aPHlTskGab0F0dEugibf0/nsrIWlvBjYstvYPLlWHwyxGvz25INpbueVCwv4kFES/9J5fFjJw6bnhXz8tVG98YU5tbix88jzDyEC9cV7Tpe7Az8+PBe8y5W/UlSey4j1XwZVWPuxryx5Q5pxKrkTjv91RGRfZwLCZr/uH+D1WDtu0t70e034FxpSpCLiEjvvMhs4nVfJjH2BixSvJ78aPL1w35Im1kVidof4fnzyGX+Uu5wRi5z+WIOxX4NESVKIrKPSz0Muf1PFb30dGzPs6W95vrvQNhe0muIiEjvzKshmnxLkVrz8GLHF6mt0jJvHPGa75Fp/y659F/IZf5V7pCkiMzMN7PHzOyvg21DQ+9EBIAwvZzcto922Wa5Pkp4F8OUd2F+74vPiohI6Xmxk4AonYdeD66hcZglihHSkPD8aURi55Nu/TR+9DgisTPKHdKIU8ZiDv8JrADqBtuAepREpIOD3IauW+JjSnvJ2iXYtPeW9hoiItInz59MvP77YLW9HtPz8DwfP34B0ZoridZ8ikTD70sXZIl4/kKgjSD7b1LN7yCb+j3Z9A20NV1IkL2v3OHJIJjZNOBC4CeFtFOUHiUz+zDwNWC8c257x7aPAW8HAuD9zrl/FONaIlIaFulh5fd4tPu2YqhejDW+DBovwkwd2yIiw4EffzHJxnsJs48Q5lYSZh8nyNwNrp1ozSfx42eR2f0+wtwTAFhkIfH67+D508sceWFcuHnvTwTZuwmyd+/bZ/6h5QlqJClPj9K3gP8Ges/8+6HgbyhmNh04B3ih07aFwKXA4cAU4FYzm+c0Y1tk2DJ/LBY/Epd+Mv8+shDbsqKwNmd8rOMHD2ITIDoR4pOx2MRCwxURkRIwi+HHTsKPnQSAcxlcsBkvMgOAxNjrCLKPEmbuJVL1jooaZteTbPrP5NI3dNtuNhE/+iI8b/zQBzWSGFhpii80mtnDnd5f7Zy7GsDMXgpsdc49YmanF3KRYjzK/Sb5jO3PnbZdDPzWOZcG1prZKuB4QP2XIsOUC9twwa597y0dwwotshCfgo09p8DIRESkXMxiWEeStJcfPQY/ekyZIioe5wJy6T/j3LYu2/3oGSRqf1imqKSftjvnlvSy7xTgIjO7AEgAdWb2K+fcGwd6kYLmKJnZRcAG59wTB+yaCnQuyL++Y1tPbbzTzB42s4e3bdvW0yEiMgTCPb/pUvHO9qwpvNHqIwpvQ0REpATMfJJ115Csvw2z/T1Hnr+gjFGNQOaK/zoI59zHnHPTnHOzyI9wu30wSRL0o0fJzG4FJvWw6xPAx4Fzezqth209/lt1dJNdDbBkyZKylcUQGe1ceun+N5EZWG79wBqwOLh0p/cRyO2EeE8fHyIiUmnC3HNAGvPn7VsnyYUtwP7FbCtBLvMvzBuPH1kEgHmTMX8uLrcNzz+SaPKKMkcow0WfiZJz7uyetpvZEcBs4ImO/1mmAY+a2fHke5A6z+ybBmwsOFoRKZkws7LTO3/A59vif0CwB5ruhOg4qD0WS8wsXoAiIlJWQeY2si1fxbzpeLETCHPLcbkVYDVUjX+47waGgSD7EOmWK/AiS/AjSzCrxYssIMzdh1kj8ZqvY1aiQkajkitneXCcc3cAdwz2/EHPUXLOLQUm7H1vZs8BS5xz283sRuA3ZvYN8sUc5gIPDvZaIlJaLmyD7HP7NwSbcTisx87hXoRtWNU8qJpX9PhERKT8Isk3kGu/Dhc8R5DqNMPCteCc29fLNFyF4TYybV/O/5x7mDD3MH7sfKLJt5Oo/RVeZAFmVWWOUoaTkqyj5JxbBlwHLAf+DrxHFe9Ehi+XXgZE8Md9GryxeN6igbex+mO4jT8mXPu54gcoIiJlZ5Ygknxzt+1+7Cx6mWExrGTa/pcwWL7vvdlEYlX56qx+9FglSaXiueK/hir0YjXknJu1dw2ljvdfdM4d4pyb75y7uVjXEZHic+nHsMSReHWvBzPABtabBNC6FJfZio1/RUliFBGR8vPjp0PnhMLG4SdfjllJnr0XhXMBmbavEmT+un+j1ZGovRrP03IVpWRWmtdQ0UqPIoIlT8WPL4bsagh24CJjcBbBXK5/DXhJbP6PsLoTShuoiIiUledPJVb7WbKt38e8ScTrvw/DeE5PkHuSTOtnCYNlXbYnan+CF5lfpqikUihREhEsOg3zagl2/woAl1uNqz8Ca1rWx5kdqhYoSRIRGSUiiYuIJC4qdxi9ci5HkL2bXPpPBNl/dNnnR1+MHz0LI16m6EahMhZzKJQSJREht/FNQIBz2X3bXBXQ1M8GVMBBRESGARfuINXyX4S5B7rtiyauIFb1n2WISiqVEiWRUc6FaVzmGaDrMDvLxg5+okVh/CUQbcDGXVi6AEVERPohzD1NquVduHBzD3uTRBKvG/KYhCEtvlBsSpRERjmXWcaBSRIAwUEKVVoUW/QHrErju0VEpPycayXV8v5ekiSIJt+O503ocZ+UVjnXUSqUEiWRUcxlnyO39UM97rOW9b2fWH+ykiQRERlGHF7kcFw4lTBYCq45v9lqiCauIJp4U3nDk4qkRElkFAvb78Nih+XnJgVb9++IzMIyL/R+YtO/Cdd9C5v2Xsz0MSIiIuVlVkOi5hsAuHA3QfbfODJEoqdj3rgyRzeKmVMxBxGpTH7d67D4YnJbPwidRtp56fo+znSw8Ue47Tfi6k7A5nxp2K/ILiIio4N59UTiLyt3GDICKFESGcXC1OPkNr4B2F/tDn8Stu1J6M+Cs5mNEKaVJImIiEiPTMUcRKQSBU0/pkuSBICP9SdJ8qph4uuxyW8pQWQiIiIyImjonYhUIpd+ousGbyxe+xhg08FPtDh25J+x+LRShSYiIiJSVkqUREYx8xtwnYo4+Ls8LLOi7xMnX6YkSURERA7OAK/cQQyeEiWR0cxrOOB99ODHRxuxia+HSZeVLiYRERGRYUCJksgo5tWcT5C6f9971zAN1z4Wa1refZ5SZCy26I9YbOIQRykiIiKVqpIXnK3gzjARKZRX+xrwxux7H+YeIowu6/ngMAWRhp73iYiIiIwwSpRERjGXXQNhU/ftYxd3Pzhsg123lT4oERERGTn2LjpbzNcQ0dA7kVHMi83Dq3s94Z7f0XnF2dB/inDSOLAkRhz8SXh2OK71SbzaYzT8TkRERPrBQQWvo6QeJZFRzh/3SfwJ38WrfR37PxJyEGwDHF796yAyliD9S1x0C3hVZYxWREREZGgoURIZ5cwiWHQyLvMUXt0bsMSx+/Z5NRfgUg/jWv8MrhWX3YB77tNljFZEREQqhuWLORT7NVSUKIkIXvxwIpOuxuXW4dJPY8kzsdpLIGwlbL2p68E7biJc9w1cdld5ghUREREZApqjJCIAmD+O6KSrCdsfImj6Hq759/T6zGbj1bjNv8Q1XoTVHANV86FqHmZ69iIiIiKdVPAcJSVKItKFlzwOL/lzwvQKwubrcdnVuPRyCHfiwjUEk+cCDi9+Drbt37imu6DhLGzmx8sduoiIiAw3FbyOkhIlEemRF1+AF/8EAM5lcG134DLP4FwKr+osLH4UNvE/yxyliIiISGkoURKRPpnFsOpzofrccociIiIiFcIs/6pUmlAgIiIiIiJyAPUoiYiIiIhIaaiYg4iIiIiISGeuoos5FDz0zszeZ2ZPm9kyM/tqp+0fM7NVHfvOK/Q6IiIiIiIiQ6WgHiUzOwO4GDjSOZc2swkd2xcClwKHA1OAW81snnMuKDRgERERERGpAEZFD70rtEfpCuArzrk0gHNua8f2i4HfOufSzrm1wCrg+AKvJSIiIiIiMiQKTZTmAaea2QNmdqeZHdexfSqwrtNx6zu2dWNm7zSzh83s4W3bthUYjoiIiIiIDBvmiv8aIn0OvTOzW4FJPez6RMf5DcCJwHHAdWY2h3xH24F6/Ldyzl0NXA2wZMmSyu2bExERERGREaPPRMk5d3Zv+8zsCuB655wDHjSzEGgk34M0vdOh04CNBcYqIiIiIiIVxEbxHKUbgDMBzGweEAO2AzcCl5pZ3MxmA3OBBwu8loiIiIiIVBIrwWuIFLqO0jXANWb2FJABLuvoXVpmZtcBy4Ec8B5VvBMRERERkUpRUKLknMsAb+xl3xeBLw6kvUceeWS7mT1fSEzSq0byvX1SWrrPQ0P3eWjoPg8d3euhofs8NHSfS2dmuQMYkAovD15oj1JROefGlzuGkcrMHnbOLSl3HCOd7vPQ0H0eGrrPQ0f3emjoPg8N3WcZKYZVoiQiIiIiIiPF0JbzLjYlSiIiIiIiUhoVPPSu0Kp3UjmuLncAo4Tu89DQfR4aus9DR/d6aOg+Dw3dZxkRLF+kTkREREREpHiWzK1yD35jXtHb9S964pGhmAenHiUREREREZEDKFEaYczsEjNbZmahmS3ptH2WmbWb2eMdrx912nesmS01s1Vm9h0zG8KlvCpTb/e5Y9/HOu7l02Z2Xqftus8FMrPPmNmGTr/HF3Ta1+N9l8Exs/M77uUqM7uy3PGMJGb2XMdnweNm9nDHtrFmdouZPdvxz4Zyx1lpzOwaM9vasbbj3m293ld9ZgxeL/dan8/SM68EryEMXUaWp4BXAv/uYd9q59xRHa93d9r+Q+CdwNyO1/mlD7Pi9XifzWwhcClwOPn7+AMz8zt26z4Xxzc7/R7fBH3edxmgjnv3feAlwELgdR33WIrnjI7f4b0PWq4EbnPOzQVu63gvA/Mzun+u9nhf9ZlRsJ/R898wfT5Ld+aK/xoiSpRGGOfcCufc0/093swmA3XOuftcfsLaL4CXlyq+keIg9/li4LfOubRzbi2wCjhe97nkerzvZY6pkh0PrHLOrelYWPy35O+xlM7FwM87fv45+nwYMOfcv4GdB2zu7b7qM6MAvdzr3uheS8VSojS6zDazx8zsTjM7tWPbVGB9p2PWd2yTwZkKrOv0fu/91H0unvea2ZMdQz/2DqPp7b7L4Oh+lpYD/mlmj5jZOzu2TXTObQLo+OeEskU3svR2X/U7Xhr6fJauDMxzRX8NFa2jVIHM7FZgUg+7PuGc+3Mvp20CZjjndpjZscANZnY40NM8GZVCZND3ubf7qfvcTwe77+SHL36e/L37PPD/gLeh+1tsup+ldYpzbqOZTQBuMbOV5Q5oFNLvePHp81lGHCVKFcg5d/YgzkkD6Y6fHzGz1cA88k92pnU6dBqwsRhxVrrB3Gfy93N6p/d776fucz/1976b2Y+Bv3a87e2+y+DofpaQc25jxz+3mtmfyA9D2mJmk51zmzqG6m4ta5AjR2/3Vb/jReac27L3Z30+y35DO6eo2DT0bpQws/F7J0+a2RzyxQTWdAxFaDazEzuqsL0Z6K23RPp2I3CpmcXNbDb5+/yg7nNxdHzR2esV5ItqQC/3fajjG0EeAuaa2Wwzi5GfiH1jmWMaEcys2sxq9/4MnEv+9/hG4LKOwy5Dnw/F0tt91WdGkenzWUYi9SiNMGb2CuC7wHjgb2b2uHPuPOA04HNmlgMC4N3Oub0TMa8gX8EmCdzc8ZKD6O0+O+eWmdl1wHIgB7zHORd0nKb7XLivmtlR5IdtPAe8C6CP+y4D5JzLmdl7gX8APnCNc25ZmcMaKSYCf+pYHSAC/MY593czewi4zszeDrwAXFLGGCuSmV0LnA40mtl64NPAV+jhvuozozC93OvT9fksPargbhnLF+ASEREREREpniXzku6hHxxS9Ha9c5Y90ml5hZKp4BxPRERERESkNJQoiYiIiIhI8RngueK/+rqs2XQz+5eZrTCzZWb2n4MJX3OURERERERkJMkBH3LOPdpRQOcRM7vFObd8II0oURIRERERkdLoaSWtEuuoNrx3selmM1tBfqFjJUoiIiIiIjIM9GOo3CA0mtnDnd5f7Zy7uqcDzWwWcDTwwEAvokRJREREREQqyfb+VL0zsxrgj8AHnHN7BnoRJUoiIiIiIlJ8Bq5MpePMLEo+Sfq1c+76wbShqnciIiIiIjJiWH5V7/8DVjjnvjHYdpQoiYiIiIhIaZgr/qtvpwBvAs40s8c7XhcMNHQNvRMRERERkRLo37pHRb+qc3dThHp76lESERERERE5gHqURERERESk+IyyrKNULOpREhEREREROYB6lEREREREpDQquFumgkMXEREREREpDfUoiYiIiIhIaZSh6l2xKFESEREREZHiUzEHERERERGRkUU9SiIiIiIiUhKugofeqUdJRERERETkAOpREhERERGR0qjgOUpKlEREREREpPiMih6/VsGhi4iIiIiIlIZ6lEREREREpDQquFumgkMXEREREREpDfUoiYiIiIhI0TnAWeWWB1eiJCIiIiIixadiDiIiIiIiIiOLepRERERERKQ0KrhbpoJDFxERERERKQ31KImIiIiISEk4K3cEg6ceJRERERERkQOoR0lERERERIqvwqveKVESEREREZGScBWcKFVw6CIiIiIiIqWhHiURERERESkNq9xqDupREhEREREROYB6lEREREREpPissucoKVESEREREZHSqOBEqYJDFxERERERKQ31KImIiIiISEm4yq3loB4lERERERGRA6lHSUREREREis+o6G4ZJUoiIiIiIlJ0DnBe5Y69q+AcT0REREREpDTUoyQiIiIiIqVRwd0yFRy6iIiIiIhIaahHSUREREREis9UHlxERERERGREUY+SiIiIiIiURgVXvVOiJCIiIiIiJeEqePxaBYcuIiIiIiJSGupREhERERGR4lMxBxERERERkZFFPUoiIiIiIlIaFdwto0RJRERERESKz1TMQUREREREZERRj5KIiIiIiBSdA5znyh3GoKlHSURERERE5ADqURIRERERkdKwyu1RUqIkIiIiIiLFZxp6JyIiIiIiMqKoR0lERERERErAgReUO4hBU4+SiIiIiIjIAcy5yh03KCIiIiIiw5OZ/R1oLEHT251z55eg3S6UKImIiIiIiBxAQ+9EREREREQOoERJRERERETkAEqUREREREREDqBESURERERE5ABKlERERERERA7w/wGgmNkZcsxPXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 8))\n",
    "df_geo.plot(column=\"Female Victims\",  ax=ax, legend=True, cmap=\"plasma_r\")\n",
    "plt.title(\"Rate of Female Victims of Homocide for 100,000 by Country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rate of Male Victims of Homocide for 100,000 by Country')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots(1, 1, figsize=(16, 8))\n",
    "df_geo_m.plot(column=\"Male Victims\",  ax=ax2, legend=True, cmap=\"plasma_r\")\n",
    "plt.title(\"Rate of Male Victims of Homocide for 100,000 by Country\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From both the Male victims homocide rate and Female victim rate in the maps above one thing jumps out from a side by side comparison. Both maps are very similar when it comes to the higher murder rate values. South Africa, Brazil and parts of Central America all show one of the highest rates of Male and Female Victim's of homocides. This could mean that gender has little impact and that countries with high murder rates in general will trump the charts regardless of how equal they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'GEI by Country')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gFEGgCJwCxApO9ZCVUkqdRsYdUBljqkTkv0kt7IoCDxpjHhSRYmNMTc81NSJSNN6+lDpZJR/+Dt7WB4a8xj+8Ef/wxr4HnTCSWYhz5huxF1xM8qmfpMrlHAmq7ABWZhEkY/jVr+LXbJvYgUeycc68EeeM1yPhjIltW522jHEhWQtWGuLkHT0e290TNCUxTj5Et2HqfwyJA/0bsXPA7waT6H/OKYTwfCSyEMl5DZK2YvLejFJKqcEZMFOwhupEm4gpf7mkdg+eTaoqxh9E5J2juP+DwAcBKioqxjscpaalwGWfwCqcg7f3Ofz6PamDAiSiQ6+VcmOYlkMkH/seftUmJL8S016HX7UR0zrOtVNOKFW5zwkhThDJKsEqPxNJz0ciWal1UfmzkEB4fP0oBZj2J/AbboHEYUjUAC7YWUj2NT0XxDAt9wDe0ZucIsg4GwrejkSWQbIGOp7CeJ3gdYLfCV4HYMCK9PmS4Cwk53UQWToF71YppVSK6JS/EboC2GeMaQAQkT8D5wF1IlLak50qBeoHutkY81PgpwBr1qyZmkr16rRjjIHuVghnpEqCDyP55I/xDr6cyhiFM5HMIiSnDCu7BMkuS30FI0fbj7VjYp2IE4RwFuKEUpmeM2/s7T/xx8/gV20e0XilYA7kV6bKkRfOgWAE75U/j+m9AxDJIXDezdhLr9a1T+rEyLwAab0H0/nc0WNeO6b5jsHvceuh9W5ovTtVWdLKgIxzsPLeBFkXITL8312llFJqsk1EQHUQOEdE0khN+bscWAd0Ae8BvtHz37smoC+lxs1v3Ie75QFMRx3ihLBmrcaefQ4Szhz0HueCD2Ad3oi74U6ItuNHWzE7H4d4Z+oCO4hz9jtx1rwZsWz8hj0k/vzF3nVSUjAHe+75OOe8s7f6nXPue/C2PIBfv6tnH6hOSEZTe0AFwuCEsWetxlp6LaZxD+6Lv8Xrbp2YH0K0FXfLfdiz10JGwcS0qdQQRGyk4r8wBe/GNN6Kab0ntfZpJDIvxCp4L4TnYJwS8BogugPjNmC8ztS0PtEHA0opNS3plL/hGWNeEJE/Ai8DLvAKqYxTBnCHiLyfVNB103j7Umo8jBvH23xPavPbWAfEOzHBdLAc7LnnD3mvWDZ2xSrsilVH2zMG016Lqd+F6WrGxDowdTuQ0iXY5WcSvOk7eLuehGQUiWSDE8Q/sB5EkPR8rBlnYM/su7bD+B6IhcjRXz7e/pcwsQ4CF30YScvFr9mKu+lu6Goe1fuXzELspddiFc5FCudiZRWP6n6lJoKkLUsFVqWfx7TdD9GtmK5XIL5ngIsDkLYKa9Z3EDsrdcz4mOY/YWq/23uZafglknk+knMthBf1+fujlFJKTbYJqfJnjPkK8JXjDsdJZauUmhQm1oG37wX8fS+kKtxZFvg+pqsZySggdP3X+1wvTgh70eVIzgxMa1Vqml5eOZJVMqZpbyKCZJdCdumA5+2yJdhlSzDGx3S34jcfxPge4iVT/aflQFpu3zYHGIek5eC+8JtjqgIKpOf2u25A4SzsyrOwShZhL7gYSRvhfUpNMgkUIAXvxES3YaJb+18QKMZaeB9iH18MRZD8tyGZF2Dih1LT/iKLIThT9z5TSqlpSItSKDXNGC+Jt/MJvG0PpSri+V7/i9JycM58w4D3SyQbe/bZfdv0Pbx9L2ASUay8CqzCOWMbmxtPBXNOKBX8RHIQ28Hb8TjJh74FXk81MieMVbYUq60GCR39sCiZhamiEJaNibYT/8uXU3tR9ZvmZ0aenfJdTFsNXncLJtpK4Nz3jum9KTVZJLIYa/6dEN+D6XgSYrtBgkj2FQMEU6kHGTh54ORp9T6llJruDOAPe9VJTwMqdVIwxsfb+iDJZ38JXU2pg5aN5M7EKl2SClCKFyFp2RDOxq/ZSuKhb+HX70pVrcsqThWSyCzCyp+FFMzpnRbkbXuI5CPfTVXbsxxCN30bq3Qxxpghpw55e57FO7QBfBe/biemYXefAE/yZhF64zdxFl2GPe8CTMPu1LTAeBckuvBrt+PueAy8o+tI7BXXE7z04xDOJHjlZ8BNYLxkqtpfrAOibRg3gQTTIJiWKoQRTAMrAN0tmK5GTGcjprMJLBurdDFW6dJUJk6f3qtpSkQgPA8Jz5vqoSillFKjpgGVOimIWDhLr8Gee14qCxTJgUhWvyDBuAmST/9fnwp4pqH/2gzJLsWefxHWnPOwZq4geMO/kXz8B5jmg8Tv/CKSXda7ma69+CqCV3y6Xxsm3pkKogLhVDGJ47JlpvkgJtmNkJuq9lc0H5oOgBPEWnAxzhmvw5x3M7Gfv6N3Op+38S6iu5/GKl2CPf9CJK3n3vR8JLcCCYQxXU2YthpMey1+Ww0kYxg3Br6HBCIQCGPPPhtrzjmpbJlSSiml1FTRKX9KTS8SzkLCWYOe9w+9Am4ca9ZZgAE7ALaD2MHU9yKYlsP4DXtw190O624foBEf09WIpOenNrZdds2AfTlLrsJZchUA7pb7cV/4LaajHgIRJJSBveBirJwZGM8l+eh38bY93LvnlGQWErj2y9hly7AXXoq3/ZGjDXc14e9+Cn/3U2P+OQEQTCN0439hlSwcXztKKaWUUmpQGlCpac/4HiRjkIxiEtFUlbysEsTu/8fXnn12vzVSRyQe+1/8/evAd1NTBfNnIwWVWPmVqexPOBMCoTFNjXOWXoOz9JoBpwmK7RA49z3Ys8/Gb69NbeYLWPmzAQhc/mmcM9+Aaa/H76jDtNdh2ush1oaJtmPiHYCASKoCoOWkpvuF0num/aWBZWO6WzHRNiQ9D8mZgVW8ECmYPer3opRSSik1UYxu7KvU1PEObSD58LcxbTX9T4qFZBVDKAMsJxVcWQ4SzkztKzXnvFQVvWMEL/3EpI7XJGNIIDzgOckowJ53AVK3A2//Okx7Lcmn/w8sOzXu9DwkqwS7bDmy8g1DVh0cbm2XUkoppdS0YESn/Ck1lawZy7Hnno/78h/7nzR+n0DLHHPK2/UkbtbvcM59D1bFaiScOWA2azimu5XkMz/HRFtTswfnnY+ztP/0P79mG8nnf41/YB2kZWPllqcKScQ6wA4QuunbSDiT5PO/xn3+1kH7k+xSAhd/FKsnmDJuAm/30/iHN6XWTXU1gVgEr/4ckjcLv/lAqhhG08FUSfbmA1gzlhM4//29RTmwndS6qp6MllV+Jva887HyZo3656GUUkoppfrTgEpNS8aNk/jb1/D3vzSyG5wwUr4CO382OEFMy2HcZ3+JeeA/IZxF8PVfR2wbv7UG09WIXX4mVuFcAPz6XfgNe1Ob+4bSSdz9VfyDLyOZhUjBbCScjYl1YM9a03eMnY2YjgZwgkh2SWofrO5W/CNlzoPpBC77BPQEc3797n7DltxyrMK5WCWLsc94baoABeA37CH+5y9AtK3P9VbFKiQtH3fzPSQf/d4xe1OleK1VeLufwTnvZgLXfAHv5T9hOhpSGwsH0vBqtuHXbMUqXkjgnHeN7GerlFJKKTVGug+VUlPAeEkS93x9RMGUFM0ncO57wA6QfOJHuPte6H9RrIPEHZ/qc8iFVLAUysSv2gRA8rH/JfSm/8ZZ8xYS+57HtBzCtBwCwF54GZJR0HeciS7cLQ/g7X0Wulv695voInn/N0jaAUI3fYfARR8ifuiVVEVAO0jggr/DXnl9/0qF3a3E7/qnfsEUgH/wZWK/uhmirYP/UOIduM/8nPCH/pjKlrUcxq/fhffqfamiGYVzMbF23B2PYZef2W9qpFJKKaWUGjkNqNS041dtBjfeU448Nuh1zkUfwjnzRrwdj5F88L97K+j1ZwY+2riv7xk3TvyvX+kN0HDjvae8HY/iLbqsT8ELK28WwSs+jTGfxNRux9vzLO7WB/sHV5aD5JSCE8Ze+QbobkayS1PZpoEKYFjOwAHaEUMFU0cko8R++Po+e1z1vu/6XXj1u/A2/IWkWFgVq3BWvQl71urh21VKKaWUGo3ToCiFGDPwh82psGbNGrNu3bqpHsa05nc2421/Cr+9ASu3FCtvJlbF8lNy01bje5jGvXh7nsF98XcAqep8BXOQwrk4y69DwpmYrhb8lkO9mRjTfAC/+eCAGZ7xkOKFhN76v0MWhDCJbrydj2M6m1JrlmYsRwrmYBr3kbj3XzGtVX2utypWEbjsk1g5M/ocTz7/a0xrFX797t79sCaTZBRilS3FdNTjt9dh5c4kcOnHsfIrJ71vpSaK6ZkCe+T3oTEuJBshWQPGg7RliDVw4RillDoZiMh6Y8ya4a+cHlZXZJlnPrt2wtuNfOqRafVz0IBqmjLxLryqbfiHt+DV7EQsB5OM4R/e0m/djDVjCVZ+OX5rDeIEkfQ8Ame9AckqQELpGDcB8W5Iy56W1eGM8YcNCP32OiQtZ9CNao3vkXzyx/h7nwffS7XphCAQAieUOtbRMLLszhCkYE5q09zZZ2OVLBqyGt+xki/+DvfZWwY+Gc4keN0/Y5evPPp+jMHbcCfJJ3/S7//3CWPZWGXLkLwKrPxZSN4srMK5qfLySk0jxviY1rsxNd+BZDXggBUAPwEcs+G2BCCyDElfjaSvhvQzESdvqoatlFKjdlIGVJ8ZeDub8Yh8+uFp9XPQgGqaML6Ht+MZkhvuxTRXYTqbGWyq2qg4QXATqe8DYay8GUjuDKysQghGUnsYhdKwckvBCWHa6zHRDqyCCqyiOUgwMv4x9DDR9lQ1upZDIBb24isg3kX8tg9DKANnzVuxF17SG1z59buRvIreQg0j6sP3MA27U8UnOhswnY1g/NTGvhj85oOpKYWJ7vG/oVAG1swV2OUrU2usIkc3HDaJKP6Bl7BKlyAZBbgb/0ryiR8NPi0xLY/wO36Y2kz42PcTbcd01GO6W1JFMNpqMG01+K1VqQAx3jnEVMcUya9EIjn4hzeM9x2DHcBecjWBC/4utQ+WUieIMQaSVdC9BRN9FdwmkDBYIUzHsxDb1v8mCUN4FtjpEK9JZaoA7Bxkxj8iGecggeIT+0aUUmocTraAalX55ARUaX8/vQIqXUM1TSTu/x7u5ocmvuEjwRRAMoZftwfq9hz7zHYIguSUINnF2CWzkLQsJLsEK7ccKZwzYFbJdDbiHdqA6W5BItlIKAO/bifevhcwDX2r3LnP/wrT2ZTKwHQ0kLz/P3DX3Y6z+k14e57D3/0Uwev/FQJh/NrteBvvTlXUS89Llf+etRopmt83SyQWfvOhVPASax/Tj2ykJLMILAu/fjfW3PMQsjBdLbjbH8bbcGcq4CEV0DhLryX0d7fh73qK5Au/he7mPmO255yDt/OJ1NS72Wt7M3ESyeoTqB0Rv/1TqWqGkRmpn3MkK/VzmXFGKki2g6ly8eFMSM9DLBu/vQ66mvD2Pof78p8GXF81LC+Jt/lv+A27Cb3hGxpUqUln4gcwDb/AtN4LXuvI78tag++tA9meOhAEK7IWaX8JvFZM46+R9LMmZ9BKKaVOK5qhOsFMrDOVffI9jJfEr9qKu+VR/Nr+JbWnE6sgHzpqjx5Iy8Wecw5W+ZmprEqsA3f9Hfg1W0/swOwAkjMDySvHyq1AcmcgWaUQCGMa90I4E3w/VYhh64OYzoah2wtnYRXOQQrmYOWUQTAdRDAtVZiWQ/gthzHdzan1WcE0wu/5RaokeQ/jufhVm/B2PZnKggXTU+ugGnbhnPU2Aue+B5PoJnHXP6UyZQMI3fxrTFcT7vo/4Cy5CmvOuf2masZ+/o5Uxb4BWJVrCd3wb4O+ReN7uM/+Enfd74f+WQxCcsqwZq7EWXYtVsmiMbWh1HBM1wb8hp9D24P0ZuutRYiXj5+WwARiWIlucFtBguB3gdeemtaXsQLPfwn6/LWxcbK+CE4hkn4mEiw78W9KKaXG6aTMUP39ORPebtpnHppWPwfNUJ1AJtZB9NefwTQfnuqhjN7xgXd3C96r9+G9et/UjOcIL4lp2o9p2s9gK40ktxznrLcRfMv38HY8msrgBNN6pjtmpLIswXQklI5XtwNv/R/xa7biH3w5tYdTei72/IsIXPbJ3vVDxviQjPebEim2g12xCiu/EnfHo9DVQmD1m6BoPlZ2ac9FFn7T4IUmko99r7dkfGLPMzhnv4vAue8++pZ3P9Ob/Rrs/Q5FLBvn/PdhLb0WWg/jN+zG2/s8pnaAKVPHyyjAnnsBVuUapGDO8NcrNQrG+ND+aCqQ6loPUor454BlQFykKwFt67ABQkX4+TPwwo1gNaZiLrsMTAzM8cEUEJiJFLwLkZGte1RKKaVGSgOqE0jCmURu/gF+3W68Xc+R3HB/ag2MmjzBdOx5F6QKSESycFbeAMkYJhkDNwrxbvzabUg4G5l7HnbxQvy8CvxX/tTbhEl04b54G+7Gv2LPOReTjCJiYc1YnsrQ5VWkAruuJoh1gGVjWqtwn/xJn6GE3v1zEs/ckspuDTEd8fj9t6zC2b3fe4c3kbj/Gwy6vi49H2fZtUP+SNydT5B87PsQbcOqWEXoxm8QWPt2TGcjyRdvw9t09+A3dzbirr8D1t8BTjj1M8ivQLLLsEoWYhUvHLJvpQZijItp+gOm4ReQ2A+yCvHOQWLt0PHywDfF67Gq67GsIKbwXNzM58CvHryT5AGSBy/Hzn4XVuZNiN1/Kq1SSqmJJrqxr5p44gSwZyzGnrGYwHlvI/H4Lbiv3DPVwxqB6TM1dFQSXbgv/Q73pd8Ne6k1YzmSUYi38/GBL4h34m07us7N2/Vk6hsn1GfPqkGHcv83MPW7RjLqvvc98E0C0Q5MW00qmBmq6l9XE8mHv03oLd8d9BJvx2OpaoeBMHJMWXTJKCBw6Sfwa7b1W+82IDeGf+Al/ANHA0B76TUELvwAEtYPq2pkjNuMf+DT0Pl86oC1Eqt6Gwyacz6On0Ca98JIik+6Vfid92JlvBbQP6NKKXVCaEClJpMEI4Su+hiBNdfj7noed/PDmKaDUz2s09Zga5qGNYJgChhTMAVAMkryke8Me5kUzMFedBnOwsuGvC547ZdT39iBPmuzjJsg+eSPRxZMDcLbcj/evuex55ybymDlleOc8boxt6dObaZ7K/7+j/aUOgesJVg1uxlxMNXb0MjK7ICDU/SfiKOV/ZRSSk0cDahOIBPvxq/ZgVe9Hb96O171Doh1En77f+Isvghn7lriD/8Y/8CGqR6qmsacs9+FNWMZ/sFXUlUPMwuxihdiFcwe/mYYsAy9iXcR/9PnRhT0SWYh9pKrkLT81PqrV+/te0F3a+/aOuvST4xoTKcTz99G0jyEMfUY4gSt67HlfEQEY3w8sw7f1OBYqxEKcM2TJP37cP11CEGQNNKdn2JJ6VS/lXHxW+7GHPrH1JonAJmNVVcLZizVJ6Pgl4N1aJgLXZI17yNQ9mskMGv0/SillBodA8bXDJWaAN7hrSSe/g3+oVcH3DMo9tvPTcGoRukknfF3qrFXXI/MWIaJthG44P0T1q5/YN2IM2iSPxtn7TsRO/Xrw196NYlHv4dp2HP0orRcgpd9Cnve+RM2xlOBb+qIel/AMzuB1JYGSf9PWLIAR84l6T+EoSdb4wHYHLsxrQEwQXxTc9IGVMZPYGr+G9P4y6MHrcVYdXXgdY2iJYGZN0LOCkifSyCtCD/+PH7nPZjuJxj0l5Zbg9vwVQJlvxjHu1BKKaWO0oDqBLBnLiF4wTtIPP3bkzj7dOo/XZjunLPfgX32u0n89kOYpv14u55EAmHsZddhlS7uux/XALyDL6f2nzImNUXK9zDxLoh3pv47Qv7+F4n/5oM457wLe+55WKVLCL3tB/gHX06tzxIhcNGHdB3VACwpJt35C4YmPLOduPc/eOYVfLOThNk5wB0DTWVL0OW+ibD9BUL2hyZ7yBPKJGpS66W6Xzl60FqZmuY3msxUIBdWfgvJ67uPlB28AStyNsmqt4I7SIEKKwOn4CujH7xSSqmx0TVUaqLYM5cSfvPXcV/+G37dHvymg+C5kJaN2A5e1TbsmcuwZ52B6W4juf6vkIhO9bDVdGAHcFa9Eefsd2E66jBN+wHwdz8NgLftYYjkYFeehTVrDfbc85BAuE8TJhEl+ej3MK1V/Zq3Zq4AN46p3T7iIZmWQyTv+3eSgQj23PNwzn4nduVZ2JW6UepwDE24/ot4Zj2ptULC6FLAGYTtTxK03j38pdOIaX8c/+Dn+27OawSrIzH6aX52pF8w5UfX4bX+uCc7NQQ/ih99BjtYObo+lVJKjYlW+VMTSiybwJrrBzxnjI+Ilfo+2o63/xX8moGeWKtTXjANe/5FqT2vAhHsRZdj5ZRhjMHf90L/6wNh7Fmr8bY/grftIcxZb0WySrHnnIOk5wE9BVDe9X+Y+t0QCB/dhyuY1pvZ8tvr8XY9gbfzcUzdCP/sJaOpfg++TPhdP0MimpUajiWFBO3r8M1qYt638Mw2jkz/G1gajpyJbZ2FI2dhy0pEIkNcP72Y7o34Nd+BzmePORpAWJ2KJeOD78k2oOKrYN5HUm0bg+l+Aq/1J5jYSDeF9/Aav4oVORcJ6l5qSimlxk8DqgniHdiIu+s5sById2LPXYuVOwPJnznsVKwUwd3+NKazCdPZjN9aO+ljVtOPZBYRevuPBgxMTMMecII4574X97lfIhmF2IsuxznzRiQ9F3/VG/GrNuO3HMZUbUIy8rFnn320bTuAlC4etG8rqwhr9U0EVt9E8qn/S5VoP14gglV+JqarsW/Q1d1C8pmfE7zi78f1/k8nlpSQ5nwTY/4LQxPG1OBTg29qMKYWkVJsWYEtSxAJTPVwR8UYD7pexm+4Bdof6Xde/DVI/SD7Sw1EHCi5GmbfjGSm9jozbh1u/Zcw0adGP0CnAnSDX6WUmnxGQItSqJGyZ63AnrUCAL/xYCrD5ISw82cOek/iiV/ibn0stR9QMDLNM1JalWIySXYpUjQPu2zZoFkeq2geVtE8jDFY5WdilSxELBvjxnG3PwpeEsktJ3jmjeMej3P++7BmrsDb+iDewZch3pE6kYzi730Oe/l1OJf/PX7VJrxDG/FrtyO5g/9ZV4MTEYQCkAJslk/1cMbM+AnofA7T9iCm/RFwmwe50EbqXxn43PGcDCh/M1S8DQmXHG0iWU2y6o3gNY5tsO5Bkgevwkq/Grvwa4idO7Z2lFJKKTSgmhRWQQVWQcWg542bIPnUr0m++Kejx07EwNT0Y9k4Z70VZ+07EHtkWQgRwS5bAqSmPCXu+n/4dTuwl1yFU35T73XGTeBtfwS78iwkowC/+SDu+j+A8ZHccqy8CiSrGEnLwa/fjWmrwZ5/IZKej1g29uy12LPXYnwPU78b7+A6/OotmJYqvFfvxdv1BIHz30/o9V/DGIP+KT49GWMwTb/F1Hwb/M4R3OGBhEa2d9TSryAl1/Q77EefHXswdbQVkCBYOeNsRyml1FDMafDxYEICKhHJAX4GLCP1qep9wA7gdqAS2A+82RjTMhH9ncyMMcT//HW8feuneihqqgXTCN7w79hlS8fchIgQeuN/DXjOr9uBu/4PWDOWp8oedDbibbl/yPaST/wIe94FOGe9DatoXqoPy0ZKFmKVLOy9znhJTFsNprUK43s901pP/ZS+6s803IKp+c+R3yBAuBiig1ThOyJzYWq91ACszBvwWn4E7hg3QndKcQr+H1b6lWO7XymllDqGNUHtfBe43xizCFgBbAO+CDxijJkPPNLzWnlJrMLK1FqrySYW9uzVYGsictpxwgSv/7dxBVPDsWcsJ/yeW7B6puJZxQsZNugxPt6uJ4nf9hGSL/wmtR6rs7FfWXWxA1h5Fdhzzh3hGkF1yrIzRn2Ln90CwWGm2RVc0Fuo53giDshYAvgAVs5HCJTfr8GUUkqdAIZUlb+J/ppuxv1JW0SygIuA9wIYYxJAQkSuBy7puexXwOPAF8bb38lOnCDBS9+Pd2jzpK6ZcpZfiWTk4x3cBL7f55y99FLsmcvBS+BufRy/euTlstXECFz+SewZy05onxJKR4rmjXgDX/e5X+E+96vUi7QcIh/8wySOTp2sJFgxhsmeSUgOVdkQ2HcLJlwC5W9BjguejEkgVi6GkVYIFCRyIU7B/0O0XLpSSp1YWpRiROYADcAvRGQFsB74FFBsjKkBMMbUiEjRBPR10vI7Gkk++Sv85irsOWsIv+lruFseJfHc7RBtB7FSmSR3mA8ZwxGLwLlvxVl5LdEf3wy+2+8SZ/55OAvPByCw+vX4HY34h7fgHd6KX7UV4yaRcDoYg9+wD5JxCOZDpBhxbExXHXTUjG+cpzHJKsFeeNmU9O2seiPJ+78x+huNwRjT74Pt0dMGb9PfsIoX9JkaqE4DoVmjvkVkCZhNw1xlYNu/Qe19mBXfQkIFx9wfJDDzD5jEbpI1HwD3MFi5SKAcCS1FQmcgoeWIUwiSBhIa9M+uUkopNV4TEVA5wCrgE8aYF0Tku4xiep+IfBD4IEBFxeCFHE52VmYBwas/iV+zA8kuRtKyCZz1BpwzrsJ0NCE5peDGiP3xa/hVW0ffgVg4Sy8jcO6bsfJmktz8UG8wZVWcgbP4IqzieVgFFf02fbUyC7AWX4yz+OJ+zRrfwzQdwqvZiV+3G2/vOkxrI1b5CkzzDsSNpS7MqYRQDmKSmPotox//aURyyqZkmpwxBtPRMLabo22Y1qoBK/kZY0g+/gO8jXdBWi6ht3wXK7t0nKNVJ41AMVjp4HcNcZGABMAkENYita+OvH0nC4J5A7canEeg/K+AIFYGfkcrbn0NfmsTxtThFNlIWgLcJHbxTA2qlFLqRDNTt7GviFxDalmSDfzMGPON485/DnhHz0sHWAwUGmOaRWQ/0AF4gGuMWTNUXxMRUB0GDhtjjuw4+kdSAVWdiJT2ZKdKgfqBbjbG/BT4KcCaNWtO6Tog4gSwy/tO85JQOhJKT71wAr1BkOSVg4Bpq+ubtbKd1BQ+03caH4EQ1sylWHmpD7z27NWE3/JvWMVzx7XZqlg2UliZWvfFVRjfw93yKMlnfwd2HuRmgwt+7W6gChCsipWYxm2IFx9zv6c0L4nfWo1kFCBO8IR0aXyP5JM/xtvwlzG34W1/BDnzDUi4758n75U/p4IpgO4WEn/6PME3fhMru2SAVtSpRsRGsi7DtN498AVpK7Fm/BMYH7P/e8jhl0bYsA05Z8IZ/zHoWioAsTKJb32Zjjt/gd888AOD4NI1ZL/rkxA4MX/flFJKTS0RsYEfAFeSilVeEpG/GmN6sxbGmG8C3+y5/nXA3xtjjt3z41JjzIhKyo47oDLG1IrIIRFZaIzZAVwObO35eg/wjZ7/3jXevk4Hwas/Ab6HVTIf09GA2AGSL/0Fe/65qT2Amg6TePhH/W+0HCTj6FNcKyMPMgZ+qjseYtkEll+Js+RS3C2P4m57Ar/u2IyawT+4BcKZWMWL8es2IfiDtnc68qs2E//lewBw1r6DwHnvPSH9SmYROGE4klUcJfeF3+C+8BvshZcSuOxT+Ic24G69H3/vC32uM+21JP74DwTf+N9YOWUTMXQ1wYybwH3ulp4XPpJVir3ihjFncKTwfRi3GTqfpbd8fqAYKfkHJPf6owFR7htgJAFV7mpY9UPESRtR/3ZBCVYogglFMPFo35OWRdbbP4ZoMKWUUlNjajJUa4Hdxpi9ACLye+B6UvHJQN4G/G6snYmZgOLwIrKSVNn0ILAXuJlUBcE7gArgIHDTcVFfP2vWrDHr1q0b93hOZd0/eT+m9ej6JcnIx1l+BfbyqzFNBzBt9djly7CKZp+wMRnPxXS1pvaV8T1MMoZfu5vEff+DVb4EU7dxbAW5TgfBNMLv+3W/rM9k8dvrSdz5RUzLofE1ZNngD7OPUEYBoTf/D1ZW8fj6UhPKGJ/kPV/D3/sMAFIwl+CN35yQP4MmUQvx3RCeB05xnwAt9W+ND8/eBJ3DFEaZ+1Fk3keG769n/zMRC2MM3ffegmnYgdftEt+TypgDZN/8WULLhpytoZRSJwURWT/c9LPp5MyyXPPYBy6Z8HZz/+UvQ/4cRORNwDXGmL/ref0u4GxjzMcHuDaNVBZr3pFYRUT2AS2knhL+pGdG3aAmpJ62MWYDMNCbunwi2ldguttIvnRnbzBlLziPwNo3YpUuwNu3ntitn4JYBzghAufcRKCw8oStFxDbQbIK+hyzi+bg7XkRb+ez2BUrMXUbTshYTjqJbpLP/pLApZ+AaCumqxnJLZ+0qYBWVhHB132N+O8/DonusTc0XDAF0NlI4s4vErrpO0haztj7UhPKVG/pDaYA8JLEf/N3kOjGKltK8IZR7Cl1HAmWQLDvVE+/pYrkU7dglS4icNZNmIWfhfUfGrqhnBXD9uW+8ge8zXchRQsJXPlFxA7gND2G6azBAULLizDhMrBDOCX5Y35PSimlpqUCETk2C/PT44KegT4ED5ZFeh3wzHGJn/ONMdU9RfUeEpHtxpgnBxuMblA0zZl4N+7mh0i+8EdMZxOSNxNn4QUEzn4TEkpNh7GK5uDMPSv136WXIek5UzvoHqHrPksy9zaSL92JPWMZpn4UC9FPI96mv+Ftf+RogGMHsIoXYpUtw55/AVK0YEKDYyuvHGvmSvy9z05Ym4MxLYdxN9xJ4LybJ70vNTLegb5T7kzL0c1x7YUT9wws8cyvcNf9CdzUWsrApamMkxSch5n1bjhw68A3hosh98wh2/Z2Por7zI8BMO01JJr3QTgL035M9dHOeqQztXTXWxfGvuafx/mOlFJKjcnklE1vHCZTdxgoP+b1TGCwHeXfynHT/Ywx1T3/rReRO0lNIdSA6mRjjCH54h9JPvt7SEQhECL8ru9glfb/cG1lFhB67WenaKSDk2CY4CXvw158MbFf/wNW8VKwPEzDTl1X1Yfpmy3ykvjVr+JXv4q77vdI4TyCl30Cq3TJxPQW68Q07Z+QtkZCcmZgfE83AJ4mnNVvwd/1BKatqu/x8z+AvfiqCevHXf/n3mAKIPHwdwld/9XUn4M5H4XGp6Frb+qknQaZ8yHRAvM/jdiRIdv2G3Zilc5C0nuuM6QqCM5fhEl4eLVVED/6d8rf/zxe9Y4Bf38qpZQ6Jb0EzBeR2aSqpr0VePvxF4lINnAx8M5jjqUDljGmo+f7q4B/GaozDaimEb/xIMnn78BvPIBJRJFIFpJdjDNnDc6yK7AKTs6y8nbxXKyZS/APbEwdSC9A3AGLPqoBmIbdxG//NFK8AKt0MfasNViz1owpQPHb60k+8J+YtsEe0ky85IPfxH3+1zir34S9/LUaWE2x1AbP85GcMvwj2Sqx8LY/hF+1mcC1/4gEhg5oRsQOQDJVACVwxScJrLiOxDO34u16GtNaTfiqK5AjAdXSryClrxl503lpmMOvQlv/cwJY2Q4m+wy8Ngvjh3Grq0n+4jNIJIvAWa8D28EuW4BdvhSx9Z9BpZSaNAYmoFzD6Ls1xhWRjwMPkCqbfosxZouIfLjn/I97Ln0D8KAx5ti9P4qBO3sewDnAbcaY+4fqb0KKUkyU07EohYl34+56Dm/nM3gHX8WZfw72oguxZ604YSW1TwQT78Ld8QzJ534PThDa92ihinGQjEKsGcshEAInhATTkbQc7DNeN2DAYjoacLc/gvvib3s/5E4FKV6Es+xaJK8CK69iXCX91cj5LXvxq3cgGQVYFWsg3om39T7cp/uvsbUWX0Xwys+Pu8/E07/EfeE2sAKEbv4/7JwyvP3rSTz2IySYRuiNX4VYFThpSMbcUbVt2g/gb/4RtO2BjgMDXxTMJt4xG+/Q4A8P0t7zTeyZi0fV96kq2dBA+8OPkDx0CDs/Dyc/n4xzzyVQrEVllJpOTraiFCtLc82j77tswtvN//c/T6ufgz6amyLGGJIv/BH/8KtIZiH2/PMIvfbzSDA8/M0nIQmlEzjjKpxFFxL96Zs1mBon09mAt+PRvgczCnBW3tDnkN98iMR9/4Zp2HPiBjcEU7edZN323tfW7LMJXv1FJJwxhaM6dZlEJ2bzz/B3/hG3LS9VeKJ8FYHXfAV7/sUQysS0HsbbdDckU1Pk/G0PkkwvwF50BVbe2LPigRVXYFX9DiyBXXdgFr8Fa9Yqwu/5CSbamgqkcpb3u89rbSW65VViW7fiNtQTKCkl67Wvw4pEsCIRxHGQrFnY56f2Z/Sf/wrmwH19G8mYTfemGPhDZ2LN8fv5nSbiBw4Q3byZZF0dbm0dybo6/K7+GzObZJK8m24atj3jumBZiDX4fmFKKXUq0wyVmlTuhr+AWBCM4Cy+EoDk+j/gPn/rlGZKTkWSVULo5lv7rRFxtz9K8v7/mKJRDc85//0EznrrVA/jlGKMwey5C7PxJxBvwVhp+KFlmEQCKVxI8LK/73O9X7+TxB8+Dd4xm4gH0wi959dIJHtsY/BdvDvfBN11vcdk+c2Q7MIcehLi7Uj5RVhL34nnZtH53LPENm0kcWCQjFMPKy0NiURSc0h8A7aQviBMZsFGEAffLiNxsB2/achdOlJCaYSv+ySBxReM6T1OV8b36d6wAa+5Ga+tHa+tDa+97ej3bQPMlRyAnZNDsKKC+L59BIqKCC9ZTM711yMiGGNIHDhA5zPPkKyto/BDH8TO0AcjSk22kzJDdfPEF/3O/48/Taufg2ao1KTyG/bgbbkfZ9WboGdmTWD1TTgLLyX+p8+Pfz8k1cu012KaDyD5lX2O2wsuJvnkj6C7dUrGNRz3+Vvx9jyDs/INOIsmflrA6UhE8A8+AvGW1Gu/Gzv6Yur7imt6r/N2P5l64GEH+gZTAIlu3JduI3DR8PtB+c1VeIdexQBW/T2Q6EACaX2CKQCz+Rd9X++7H3f/o9RvWIXX1j6i9+Z3d0N335L/XVuzsHIMIglg/4jaASDeTeyv3ya54SHCr/kYVnbRyO+dxmLbttHwwx+Ne+GC19pKtLUVgHhHB4lDh7Azs/A6Ouh++WWSVUcLm1R9+R/JffNNZF5wagWnJ4rb0UWiugGvoxvjeUjAIVw5g0CeTotW6mSgAZWaVIEr/h5Jy0HScvscl4wC7NlrcTWgmlDui78jeO2X+hwTy8aeewHe5r9N0aiG4SUxtdtJPvEjDagmkLXkPfi1L/U7LuE8AEy8k+S9X2fwbTnA23hnar1b8SIkdybihPqer99H7K5vYJoO9x4LzglhxWuGaPU4Pox3DrDX1o5XuRynbdMo7xTC138WSc9BMk+dvaqcoiIiK1YQ3bBhQts1iQTNt9024Dm/q4umX/wSk0ySdemlE9rvqaxr216qf/on4gdqBjzv5GaRedZS8l97EeGZup5NnYwEMzll06cVnfCsJpWIhXPezZhkFG9X3/L9zgUfIHjjf2Iv1A/RE8Xb8Sjevhf6HZfMk+DJe7yT6TQF+WRmjMHf9ce+B8VCVnwUmXlR6mUoA3vFDcM05OM++h0Sv/sQ3s7H+/URf/BHfYIpSc9FYgN/MByMSILMpeN7thdZNBu7c8cY7jT41TuwCufg1tUR37ObZF3d8LdNY25zM7X/+V8THkyNVPNvb6N7ivo+GQWL8/Gj8UHPuy3ttDz4HPv+6fu4Hf3XuSmlpgfNUKlJJ2IRWPuO/sctG7tiFSS6+hdYUGOWuPffCJz/vt6KfybRjfvKn6Z6WEOSgjk457wLY4zuEzQRmrZCogMyKyCtCKm4HCm/+Gh2yhiSd30R/+D6ETVnzT4Xe27fqVzulsfwD2/pfW3PmIedFkO6j797eEFnHzD2qU3Jpg5C6UncnJX4fiqLJpaHZbqQroNYbv8Pon76TKJt+XQ8tBVz7xfx21NTDiUUoujzXyJYfmK3qTCui9vYiPE88H2ckhKsQGBUbXidnTT+8ld4LS2TNMoRMIaGn/yUks99ltCcOVM3jt7hGNrvvRs8j7Szz8UpKp5Wv2OSDS34scSw13ntXdT+6m5mflzXm6qTjAFjps/fucmiAZWactbMFZCWM23X+Jx0klGSj/8Ad/sjOEuuxt1yH0RHtgj9hHFCWEXzsUoWISWL8as3k3zsfwllFUPR/Kke3UlPCpZiX/6Dwc+LELjua/iHXsbb+Bf8wxtgiIp3JtoKwTT8WDum/lVM9Sbcg/tw5i4CXNzqOqx0g9U12il3KbapITR7MfF9VcNfPAC3sZlOWYx3YKCqfulIKA8rIx2xbQL5mWAMXrdLsqr/lGMTj5PYtxfjeYQqZ49pPKNhfJ+uF1+i9a6/4NY39B6XUIjI8uWkrTqTtOXLsdLSBr7/mIcQdkYGJf/w9yQOHaLp1l8T37t30sc/4JgSCeq+97+UfvlLJA5XIY5NZNmy3iqAJ+rBiTGGtr/8kY777wWg/Z6/IuEwTlExwco5pJ11NuEFCyd9HIPpeHkbB/795+CPrNpk66Mvkr54NrmXnz3JI1NqgmlApdTkMtF2vP0vELjgg3iHXsHUbsO0HB7+RjUsU7udZO324S88wexlryFw4QeRUDom3kX8T5/D1O8icM2XsDSYOiGMMXhb7sV9+v/AT0IgjGSWYLqbe/YImwXhbMAgkRzs5a/Fe+zf8Hc9jJQuw8SacDqO/j21stORcBmMckaSl7GK7toMOl9txfj1BOcvwMRjJA8eHOUb8rEysvAaGgY+HY/jxVPTqo4NWgbT8ptbwbIo/sd/ntRMVXTLFprv+APJw/1/55l4nO516+hetw5sm8jSpaSfdRZeVyedTz6VmproeSBCZMUKMi+5mGRVNW5TI+IESDY2Ttq4R8Lv6KD2G/+J194OxuAUFhJeMJ9ETS3JqioCZaUUfezjOLk5o2rXbagnunkjTmER4SXDbMzs+8S2vNrnkInFSB48QPLgAdyaasKf/eIY3t34xQ7Vcuhbt444mDqi9ta/kXXeSuxIaPiLlVInjAZUamoFwrgb7sLU7YBINlZeBVbpUgim4W28a8in5mrqWXPOwZq5AgmmQ08pZfGSmO5mTFfqCy+JpOchaXlYlWdhl6/svT/5zM8x9buwF12OZORjPHfoD0hqQrjP/wLTsIfAlZ/FdDVjzTkPyS4D6Jc5MMZg6rbg734UMJiazeBEoHA5pr7nw6qfxHdTW9GPhMmYQ9PLBcRruoCj60fiO3ch6WkEZs/Ba2/HTkvDbajHxIbaYsEQmr8Ar2n4QGlUfJ+6f/0awdlzCM6eg9tQT/q555O2enxVepN1dTT//nZybriB5t/fTrJ66L2yAPA8ops2Ed00QAbQGKIbNkzZmqmhHFue3W1ooPOYgDexbz913/42mRdfRMZFF2EFB9/I3o9F6Xr6KTqfeRK3+pgspgjiOARmVZL71ndi5+ZihVN7laX6b0WcwadNxnftoPZr/0R4+QqCc+bi1tWSrKnGa23F7+7Cyc0jNG8B6RdejBUe2R6RfjxO9/PPgO2QtvacAd+X8X1aH3tpyLVTg/E6umh/fhO5l5416nuVmiqnw5Q/3YdKTTljDHQ14W68C3fdHamnzRWrcS74AN4rf8Lb/ogGVtONHcBZ82acc96NyNHaNt6eZ3E3/Q0CYezKs7AXXoIEIgM24VVtJvGHf+hpL/WhI3TTt5GCSkzLYaRgzrRa63A6MMbgb7sba94VmLbD+Hsfw9/7OLQP8qHfciC9EDc8h0SjQ0bkoZF1ZIepe2UJbsvwHyglPZ1gWQl+ewdufV2qIqAxSCSCiUYJLZhPYtfOkb/JcZJQCDsrm+CcOURWrhpVgGVcl5p//4/UXltH/mxPo3+Dp0po7lwCJSW4ra1kXXYpaStXprKojQ10Pvk4nU89jolGR9aYZeEUlxAoLSO2fSumewyL+o5vMiODvHe/j8iKM/udM8bgd3fhNTUSe/VVOh55AL+zEwA7Nw8rKwvJLCDWESLr7OX4SZemu58kUTP2BwCB4nxyLlxFuLKM8JwZhEoKxtyWOvmcdPtQleSZh9511YS3W/Tft0+rn4MGVGpaMZ2NJJ74If6upyCYhr34Cuxlr8F95c/49UNv+HlSsEOpucT1G6d6JGMi2WU4a9+OPe8CTGcD3s4nUpmoeAdWwVycVTeCE8I0HcCv2oxJdA+4aa/fVkvizi9gWgf4oB5Mg0Q3VtlSQm/+n8l/U6c5YwwkOiGYgYiQvPfzmKp14HujbktKFiDBIBgXmoZZT2UHiXpn0/xEagPeYHkpfjSO23h0Q147O5vCj3+c0OzZGNdNTRWbMQPjJpFAkK716+i4+y7cutpRj3UiZN94E1nXXDvi6/1YjMNf/BJ+R8ckjurkFqicg2vnY9e+gomPPoMzaUQIzCwHwM7MxC4oxESjxLZvw+8YfA81KyOD9oYMvM7Jey9ZZy+n5ObrCRblTVofavo4GQOqB98x8QFV8benV0Clc2vUtCIZBQSv/Uf8M3fg7X0ev2ojVsthQld9juitn8avOXFPoieNWFglS8AJgADJTkzzPoT+WbgjzzumQ6JGiuYTvO6f8Q9vJPGXL+PXbO1z3j+4AatkIfasNUjBbKyCgRf0m85G4r//BERbB+4okXqibHyP5HO/JHDueyfwXahjuY/9B/7+p1MBlRNBcmZiktExBVMApnZnanPfiqXDX+x7hPO6yV/r40kBwdizqcOzZ2ACeYRe/084OTm907fEcQjMnEnT3Y+BCBkrF5PYvXvoYMpxCJYW4UXjeI1NY3pPQ4mcccaorm9/5BENpgYRmDUHY4Vp3VSNcRvJP2s27v5ptAbUGJKHUmv7kqO4LRmaidc5uaX421/YTMcr25n//S8SLMjtd95t6yRR20jawspJHYdSAzJoUQqlpoJYNnbZEvANyRf+inH/BgRwFl1E4lQIqIyPX7Or77FQFmTkYrpakYw8JJKBIJi2+tT+QdkF+G17kdjgT0InkzVzJYHz30fiz1/AtA08/Sv05u9g5c8ati3v8Cbw3WGvM7XbcRv24JzxOiT91Nl0dTqR7JnghFMBlRvFNO4a/qZhGWjZBlYQjJf6GkhmOd6+bTiAw4HUwwXATlZhEtW0Pr6O7IvX4kdjtD76PF0bd5Bsauldd1L/m78iAZvcNWdg4nHEEjAefmszVnYuniu4rV2E8roJRLfgl88j6RXTtWn/uN+hU1SEnZOLSY7mozVknHce3Rs2kpii6nvTWceBKG7L0UIaTS/VkT5vAeFcC7+tHr+leYi7pycrM4uumnbCZdnE6zsIlWaTVhyhu7qLWPXEVl41iSRNdz9JwfWXAKm1Vs33P0v7C5txWzsoeuvVGlApNYl0yp+a1qK/+yL+wU0QySJ42QdI3POtqR7S1LFsrJK5QBzTuPuEZa3sZddhV64m8dKfMe3VqTVR7f3LWwev+2fs+RcO257fXodpr01lQXwPLBsJpmE6m3DX34ExPsGrPkv8tx8BL0ng6i/gLL5iMt6aomfKX3sV3qt/wn/1z+NqSwrnIJEQkmyDZAckuqBgGX7cByS1x1Iyid/agGTlYer7V/MzBg5vu4BkQ+uQfVlpYUJZgtc69AdTKxKmYHkbTqIK34rQ2b6UZNXoNh8+VuY115L9hjeNeX2f6anq1vn00zT96tYxj+NkZufmYVy3d6qcnZND+2HBJAfPjGavKIXqrYOen/Z61v4dYVcupXldderp/SRLWzKHyv/3QazQ4IU/1MnjZJvyt6I43zz49qsnvN2S//ndtPo5aIZKTWvO4otJHNwE0XYS935nqocztXwPvzqVoZPsmZAWguY9k9qlFM4DL05i3V/xD+4AwNCBlV8Mx01j8XY/laq6lVWCVTSvzzkT68R01GMVzsHKKoas4n59+R312Asuxj7jdZiOBghngciIgjQ1diIC2TNxzv8UftmZuA9/dYzrp+ZjRffB8UUAWvcg6bOJN2fixmySzT5W2gzC/m7EHFObQWywM0iGF+C2DZOJFUiryCWxf/h1lX40RtPOQgrmdmJ7baQX1NHelpEqHGDZSCg4ZMEDiUSw0tNJP+c8wkuXESgtG1exlCN7MWVedBHtjz5G8tAhECG8dAkZ556L195OYt9+JBCg+5VX8CegqMJ0E41mkKhpxUorIFSSTbShA5McqpIjtG2sIf+sBbj7T9JZCsZgZWZhpWVAMATxNjIXl9CxdfLX/5V9+CYNppSaZBpQqWnNKjhmCplW+utl2uow3WGskhWYll1IcnI+dJmG3bhtdZiu46boWf1/dXi7nsKafS7epr9BvAOcEM7atyOZRST+9lWsksVIwexBP4xamUVYZ94IgOSUEf6736W+nw4LyE4T1uyLcK74Ku5DXx18qt4gxLYhYya09nzgtYOYQBbxxEKsTuh4pRY/drTNGDYSmkvakhmQ6MZrrMckk9hlIfJXpNFR6xA/dDRoD+QFscM2EszCSQ8Q3zPyaXNeazu+k48fnouXjGCldxNevoK8d7+XZNVhOh64j2RNNcnaWpz8Aty6WoJz55H7lrcRnMTNffPf8Xb87m5Cc+diZ2T0O9/wU5euF16YtP6nQmDGTDpfbQXA704Q3TvyancmenKuP7OLy4h2RojubQKiPV8w6o3bxiA4o4jwzP4PsJQ6kU6HsukaUKlpzcotneohTF/JGP6hrUhGHmTlDjgNbyJI7lxMx5a+x+xQ/5kqvkvy/n9HihdgzT4HEtGe6oy7MLXbMd2tqTLqhXNH1q8GUlPCmn0RUrIMUzO6SpR+1Xa8tHmYwPl4Meh8uRkR8GP1A15v5xUQqJgNXjNuXQ0kYtgVi0nsTmVCM4D0lfPxJJv0shiR7tQ+WF7OGVTfF6d30dVI2SE6tndguusILVpM7tvegdg2wYpZ5H/gw6n3EI9jhUJ4ra24DfWTGkwBhOcPvZG13z38B24JBlNruabR9P2huJIBjH4tqJUWxKufmmqOY2VlZGIVlNG8qRkTjzLqP7MToORdrx3zvcYY/T2sJoYGVEpNrem0xm+6Mp3NmE6wKlZC3YbR3++EwAoA0jP/KvWLTzJKwBP8g1v63eO1tuLMOR/JLkHSchA7iF+zBW/305i6nXh1PVmKYDpW+Uqcq7+AvfBSxBrp1q9qKploy9hujLfRtsHBxFJP4Af72xuYM5+CT38ZsW2avv0FAssuIrlzI4k9O7BLK1Ol8wHinQT8GhLb64ksLYWOauzWTcy4djn1L9okm4aeJnas5q1BAjn5ODMyKPj4xwbccNUKhYDUmh47J2cUb3xiGN+n6dZbSdbW4eTnDZiFs7KyCM+fT2j+PMLz5hEsL8dtaaH9gQfpfPrpURfKmGxWRgZ2XgHGiZBoTdC9uYqxBBZ+dwK/chGBDBA3jol3Y7o68bs6J37Qo2QXFOG3t2ISCSQQwJoxn66qbuL7WmHfwA8UJoUlZK5ZSuaZi0g0tGCFQ2StXTbm5nb/x58puX4tmUvLJ3CQSp2aNKBS05ppPbmeSE4l0940qo8pJpiJZFViqnYA/fdIMe2DT6mysotxzrgBu3Il3r4XcV/8bW8ZdckqwV5yVap8evECDaJORt1jLDFupfcGU0OxMzJTUwSB3A//P6y0DLoeu5uu++8g0Q7Jmr5ro6ysHPCOTv+zWjdTuHYFXXV5dB/oHlFg5bV2kHPNlWRddO6AwdRUi27dCr5P51NPAxAfpOBi/jveQfqa1X2OBQoLyX/nO8i5/vW0P/IIHY8+ht81+dPJhmNlZdN+GDh0bIW+sT+p7r/eKEBa5TwiuR7u4X1jbnc8nMqFNL1UB5JOqLQMvztJ8qUjRU9O7FN5OyOdis+9F7Gt4S8eRrK9m4ZHNtP0xFbmfflG8i9aotkqNTYGjH/q/9nRgEpNa37r2KtxnW5May1kpiOJEXyQyl+IaWnEVI18nxfJKsSefx7OoguxZizu/cc1+fgPUqXUMwoIXvJRrLnnIzL+f9DV1DCxtlR1vjGwEvvJXHM2Ha80kPnaGwkvX0Vsw0tEX1mHW3W0ol9s22biO7YSWrgktUgfSLvwWuIvP420NuLnZuG1HJ0W5rd3kbTnEODoehu7dSNZaUH8iktw26oxbt91fk5BHsHCTOwgRKvacRtbyDjrTOzM/muVpgOTSND0m9+O4MLB15LamZnk3nADWZdeSu23v0Py8OEJHOHo+e1tZC9fQtuGyRtH9/4muvdDsKiCzFnhE1+04kgxDQPxCS6FPlpeeyexgzVEZs8Yd1ud26vBN/gJl51fvYP0+aUUXrUCJzNC9EADsz545QSMWKlThwZUalo7JTbyPYEkkt/vw7AJZyHZc/qssfCrd4I3/F5QAFbZYkKv+TSSN3PAJ5TO2rdhuppxVlyPhNLH9wbUhDKxVvxNv8fUb4XsciS9oOeMIBnFkD0z9efAS6QKjVg23iu3jbyDjBJIdqWKkABiO0SWLiZy3WdwilIL4QOlM8i48rXUfuFjR7NXySRN3/sGoeVnkvd3n0AcB3Eccj7y/+j48y3I3u34heXEdh4CwM7OQgID/HPlJUlLryJQVo2J5Kf2c5MAGB9p3YZ0A90QSLcIXPoGnMKJ3c/MravCa6jBKa3Ays7r3YR4LNJWrsSPxWj8v58Ned2RsutDsbOzKfn852i7+26szEyc3FwkHMEKhzCJBC133kny8OSsuTyeaTmMBOwhS6JPhER9B+0Jl7SIA+7IfreNl4RCmEAG4nRi3OlRNMlOj4zrfi+WwG3rpnXd7j7Hu3bV0LUr9YAzUlGgAZUaFS1KodQUMokY7pZHp3oYJw0pnIVpHWB/Ki+JX7UTvNGvrZDMAsJv+goSyRr0GmfpNaNuV00+07gD955PQrIniKndMLItb/LmIsVLMK2HId4OYvXNioiNc9XXkeKlSCQn1VesHdNehYQyUyX9j4wh3gndjUhuJZHV59D9zGN9uopvfoXOR+8j86rXAWClZZD9zk9i4jG6nrwXOxKme+cBQhkJul7ZT9rStYSXLIdgBO/5n4Ebw8kJ4x4CiTVB7Jipisf8PXAqFpDxurdP+JQlu6CY5ME9tP/h/0jufBUrM5vAvCWEV51PcOGKUQdY6WvX4nd20r1pM7Ft22Cg4GmE60rt9HTy3vrWAc9Fli2j+fY76Hh0BL9fw+mptZXR1FolU7qc6IEGwuUFOAEXIxaktiFPjc144CXwW+oh3o1dUIY0Nk16QAWQbI1iZsxHGncM/LObQE5ZOZ21QvylgTc6P5Gc3Cz8RJK0hZUE8rPH3I6fdNn0kf8jun/gdV+lbzqXzKXlpM/VqoFKHU8DKjV9WRbo+puRScuGREPP3pGCZBRCJBckhKndC17/NVIjIXkzhwym1DSWvwAyy6D9MLij+P/fs7eZAGSXgtsN4TwIZoAxSCQXq/L8PrdIOAsJ9/1zYtw4yT++H2vWuTgXfJrst7wLbJvuJx/uc13HX/8InkfG1a/v3aNJQmEyrryRtItiRDa9SPThvxDIz8EKBLGXvh7Cmfhd3fj7niK65cCwK1UClZOz/kNsh8hZFxFecyFtt3yLxJb1xF95jvgrz2EXlmB8Hys9k8jaS4icN/zm1GJZZF1xBVlXXEH9j35M9/r1OEVF+B0dR/ejmoBAQRyHQOnwFVTtOctpfSm1ns3Jm0moKJuubYfA8+na2n9T5j73ZueQPnsOnYda8bvG9vtnLNq31IKVA76PODaB3HQC2WGciIMx4HYn8ZMegawQwXTwavZhYgOvwZNgCLuwGBPIwPiCMYLxfLoPtJLcOPXFMLAs0hbMovhdr8VOCxEszu9dmzgWLS/s6g2m8s5fRO4586n+4/NEDzRQ+ZGrKXvzeRM1cnW6OQ3qi01YQCUiNrAOqDLGvFZE8oDbgUpgP/BmY8wYS0ep05E4QZz55+BuenCqhzLtWdlFmGgNFJ8BLQ34zY3A+P+6+Qc3kdxwP1ZBBfbMJeMfqDphRASCpfgmipWfCXYEkh3QMorNoDt71jDGjlkbUnzGCAdggxtNTcMjFXzkvOXdhBYupvW2WzBHiiYYQ8ff/kxsy0YCM2cRWXU2oQWLAbBCYdLOuojwkjOJP/Jj2P0QXfdk0/bcfvyO1DTD0PwzCLS90q/7ZHg2NbVLaD8QI1yVYNVFSexwYMChJlq7aXp2Hy2vHCLe0InxfYxnyF1VzozrzyBUMPS6KxEhOH8ZiS3re495DakCCn5TPR2H9pI4uBcq15J19gpEBK87Bp6HnTnwNNnIsmUUvO9mrFAIPx6n+fe30/nkkyOa8jcS4UULiaxcSXTDhn7n0lavJuuaa2i87yUgFVC5ze24zSMrd26lhQmVFZJoT5Coa++fNZ9svgEE4/okGjpINPTfv+pICCWhTLIWz8UOABjoaEAi6cS7Q3TurIeqDmD67H8VKMojWd9MziVrKH3/G8Y9xe9YwbzM3u/L33cZ6XOKKbpuNV27akifr1uYqLESnfI3Sp8CtgFHHlN+EXjEGPMNEfliz+svTGB/6nQQmp4LyKcbvyZVEsy0bhtfQ2k52MVzkYw8rNIFSE4pVnouUjhr+HvV9JNehNn7Il5HIwB2+eKxtSMWZJZizb4Ea9XNI7vFdrCv+g8kre+6pcjKswjNW0THA38leWA/if27wfNI7ttDct8evKZGjOcRWrikN2NlpWfiZGfjAomajt5gysrMxCdELPtsfNcmVhclrTSEZcWItoaINhpiNZ3Eajp57m2/YP7HLiJclo0dDhDICmMFHQ7dsZ79t76I153o9x6aX9jP3p8+Q+FF8yi+ahF5Z1USzEl9gPVdH7EFrztB9d2bqf5rFcm2CwhkBiie10q6vxMrPRvScjAIiS3rib5aT9eG7WRffBbV3/s1VnoalV/7xIAfijMvvKD3eysUIv9d78SKRAgUFY3o5z+cYFkZmZdcQrK6CuN6eC0tWOnpFH/6U4RmzyZ2qI5kYws5F68GS2h76hWMO8S0PcvCyUwjUJJPbO8hojtSVfeme2E4E3dp23DMtD0BzNQWlxhM8Tuvo+D1l9C1fR92emRCgymAjIWlLP3uzXTuqCbZ3AlzihERMhaUTWg/Sp2KZCL2+RGRmcCvgH8D/qEnQ7UDuMQYUyMipcDjxpiFQ7WzZs0as27dunGPR506Yn/6F7zdz0/1ME59oQysvBlYhbOQzAKcBedjFU3upqZq8rmb7sJ9/H97X9u5od4CEiMlhYuxr/se4oRGdV/HPXfS/cLTBCoqyX3fx3qDo+PFt2+h46G/kdjed78zu7CYzNe8gchZ5yIixP/6L/i7n8ZYIdq5jGh1F9F99RxZLBVZNIesc1YQLC0k2dBC04ZG9v9hB4UXz2f+Jy4mkBWmY08DL3/k9lG9jz4EMuYU4HbGidV3YAUdEMGP9V+fWLwqHbfqEOJYZC0qIL73IKl5L30jjKzzV1H2kbdhhaa2lHvrXX/FKSnBDRXT/MwurHCAyvdf3Hu+7flNHPzmreAdzZCJbWM8j0BRHm5La59zauQic8vJvfIcmu57mviB/pVtM9csofCmK0mbP7kPtpLt3Wy4+QckmzuJVBax8paPaqn0aUhE1htj1kz1OEbqjKICc+8bx77B9GDKf/yrafVzmKgM1f8AnwcyjzlWbIypAegJqibmsZo6vbj9nxqriSfhdMLv+rb+43mK8Q++fPRFei4kR7m/VO5s7Cv/Y9TBFICkpeE1NeA1NeB3dmBnDbxYPrRoKaFFS2m/5890PXwfJhEnvGotaeddQnzLBpL7d5P95ndjV67B3/scUfsMmp/cf6QXCt5wJW3PvEyytpHMs5YTLEplxILzGkhfNo+m5/ex8XN34ic8Eq3do34ffRjo3NPY+9KPD15Nzg9kImlpmO5uYo1xrGAAk+gfeHVu2E797fdS+MarJzzjMBo5178eL5pg3Tt/hFiCk51GWr5FIDNExyvbaXt6Q++1kfnlmKRL7FANViSI3x3VYGoMnJxMSt77erIvXAW+T3T3wT4BlQQDzPjYW8i5cNUJGU+iqSOVmQKi++vp3HpYN/VVaoTGHVCJyGuBemPMehG5ZAz3fxD4IEBFRcV4h6NOMaEbvkz3D98NiXF+EFKDEMBgzz9Xg6lTjN9ajV+1seeVwS4ogYa6Ie85nn32x5G0vDH1L6FwTyM2Vkbm0BcDWdfdiFNQROutP8XOySNYOZfw4mX4namMmnPGa/D8CI3/80Cf+2IHa5j3vX8EpM+GpsG8dA7d8TLtW6ZmL7uGF2pJXzibjAqB1sOEKiuI7z+ISSRJREqwvW7sRDsZKxdR8u4bpmSMR3QfaCTR3EXVHc8jtpBR6IMTp+HOx/Hbjq7FNAbSFlYQ250qSCGAiSdOh/XmEy5cWUbl1z6C07OGrvFvT9Hy8As4uVnkXn42kfkVpM2vwMkZ/u/ORInMyKOnshEA3fvrNaBS46cb+47Y+cDrReQ1QBjIEpHfAHUiUnrMlL8B63AaY34K/BRSU/4mYDzqFCKhNKzCSvyqrVM9lFNLJAui7ThnXIlkFhA4721TPSI1wdznfwHxI5XIZEyLWSQytmAKIDCjnNCylaStPW/Q6X7Hi6w9n+7nn6Lr0fsJLVxKeNmKPsFYcNlFhCq30L1lV++xzpe30L1zP+mL5/Zpy8kIkTm/cMoCKoB92yKYHQ4rliSINVo4M2exb185O+/uZMX1DoX+i+RcsnbKxndE3f2bqP7TS73ryGLHLCkKFVVgRwK4XQncjhiOq+tax8oKByl+x2tw8rJJXzq3N5gyngcCRW+9hvzrLpyyTGXnjpqjZfkti7RKnVikJshpUJRiZP/KDcEY8yVjzExjTCXwVuBRY8w7gb8C7+m57D3AXePtS52eQld/TItTTDBn3tkAuJsfJrD2TYiWpz/lSOZxH4b8UZauDkQw3QPvRzMSwVlzyP/IPxBZfc6I7zHxGMmqgxR+6V8JL1vR77w4NuWfez+h8pJjbjI0/+1x3I6+G1pbARu388SV6x5IyQKblt1dPHnvPNavW8ATdxSz88FOMOC6DtkXn03GyjEWCplAsz90GZUfvLTPMXEs7JIc4vUddB9oJtHYiZ9w6aqNY1UuQTJ0O4XRSl8+n7zXXEj2uStwso7+mya2TcHrL6HozVdN6bTPg784ui/ZvM9fr9kppUZh3AHVEL4BXCkiu4Are14rNWpW4WzCb/lXJCN/+IvV8CwbjCFwyfsInP1GJBie6hGpSRA4/wM4F3zomCOjWwdlrXofVvm5EzuoHsb3afn1/9H2h9/Q9uff0fKLH9H4nX8j9upGsl77JgIzB5/+badHKLyp72bSsX2HOfRfP+vbh+djDVIm/UTxe4ri+a6hdU8XsZaja0K3/C3G47emMZrCUIefrGXHHft4/DMv8sw/v0zdK6NcEzcEJ7Pv74FnC2bymns6scty+xyPHmym8cUapGAmanQ6XtpC1Q9vp/2lvgVY3NapLcvuJ5Ls+8H9tL+SqsyYs3YeRVevnNIxqVOLMTLhX9PNhG7sa4x5HHi85/sm4PKJbF+dvuzSBThrrif5+C1TPZSTn+/hHd5CcNGF2JVnTvVo1GQK9jztDoShY9/I7smuwD7jrcjsS4e/dhjGGDp31hLISSNcfExRCs/FKSii+8Vn8OpT+zVZmdlEVp89orV86WcswoqE8KNxnNwsit7xun6ZHrEtln7lNVhBh6o/bxj3exkJcSyCs4rA9/HbunC8ZiwnjD9I7YqmrW088P6nmXlhMQvfPIdAev9/kn3Pp3l7G9t/t5dXvt93W4SXv7eVGRcUM+OCYmZdWUbJ6oJhx2iM4eX1tfz+tle59LJKcnLCnLmikAM/fxK7MItodiZ/q4XbHqzFILRkZJJFCwi0zq8gaSAo4GKjE8JGr/WRF2l/diOz/vEDBEsKqLnlTtqf3UjGmYtIW1hJ7hXnEMib3OyfMQY/4WKHAnTuqGLXf9xJ9EBD7/mRTtFVSh01oQGVUpMpsOq1uK/cg2kb3cJ61Z/pakYCYcTWXwGnMskoxDn3fRjALluAqX4Zf+Nvjl5gOakv46f2mkovxHnNt5H0/h+VjTEkW6PE6jtItERxuxJ43QmCuWl48SSH/rSRaE07VsCm5PIF2EFouPdlYjWthEtzKLtxDVnLy8laNhMJBMm89noyrnk97uGDxLZsJLZhXWr9xggCKjs9QsEbr6b+t3djZ6aTdd6ZAwZiIsL8j19M41O7cTvj+AkX46UyQmIL4tgDVuqTgE2kNIvug6mCDE5GCOMbgmX5GCeA39yKCEhuLo2NIaLths4mD784hy13NPf0HWHpOWHCK9PpXjf476xdfz7Arj8fYMOPtrP6U0vJrEgnY0Y6XbVRmre3sveeQxx8ZPB1YFVP11H1dB0vfXMz5//LKlZ+dNGQQen5Z9/Cq5tTH55/8qPURsTZGQ7nzs3g/o0d2NKNd8zT341tsGR+Bffs8zi8zlCSkcbcSAYZ6wwVsy9lXvA5bC82YF9qYH40zr5/+j5Y0rMJMcSrG7AiIdy2jkkPqBoe2kTVbU+Re/Z8qv/4PBy3WXTanOJJ7V+dbqZnRmmiTcg+VBNF96FSw3F3PU/8z/8y1cM4JdiLLyb8et1r+3RijMG77zOY6nVYi2/AWvMBJDR4FTE/6XH4rs3UPridlo3VmOQQG7sep2h1Pp3bqnpfF16+lMVfu3HIsY220mTsUA3dW3eTfdFZ2JHhp67G6trZ8+OnaN9Wx9wPXUDRpQtwu+Ls+v4T1N63lUB2hGBeGgv+/jJyzphBvKmLjZ/7M7FkiGf/4HL8HlJHWAELZ142+zb03xA2LTfArLQkXmf/kumTIX9JDrOuKOP8f+lfavuZpw9y7ZW3jaq9965dSMVhh44DA1daPfN1EebJ42MZqupR+v4byL/uohPSl9sV45V3/29vefSBnPnrTxKZqVPsp6uTbh+qgkLz1+uvn/B2Z9/y82n1c9DH0+qk4sw/B/+i95B88ldTPZSTnmmpHv4idUoREaILv0Rz88vkla4lc4Bgyk+41D6yk47djTQ8tYfO3Y0DtDQCTt9/XroPNOJ2xnAyBg58xlK2P1xeSri8FIBkSxuB3IH3uuq9vjiLpV+5ru8w00Ms/sJVLP7CVf2uD+WnE169jCe+sJ3BgikAP+lzeNvA62C6W5K0z8yioNgluqd/wDXRmra2ggWrv3wG4XDq/8H3v/sif7hjK6+8XDu6xgzMbgvRemDwNT7bnnKxL72I/Eg96d17sP0TEzieCtIWzab4ndeRvmTOpLRvjCHZ3En0UBNeNEH63GJq/vz8kMEUgNupGUelRksDKnXSCZ77Fky0HfelO6d6KCc154z+HyDVqc0Yw8tff4Hapw5hRw5SekkFyY4EkaJ08s4oIphps/+Wp4geHv8H/+PDj67ddWMq3T4SftKl+d4nsTPS6HhxE052JtkXribr3JGtEfQ6O0kcrgLPI1BWhpOb03vu1bua8QbfvxeAjLIIVftiDBZ0VW1uJ+PCfGDyAyqA9jRhfuX/ct3r5tPdleSev+0imRzDxrsCHflDz2KJtSZZdydYTg6rrrmAypL9SP0I1+udxiJzy6n82kewAhP/McxPuOz577tofmYHXvfIK12KY1P2lvPIXDRjwsekTl8GTospfxpQqZNSYNVrcTfeD4noVA/l5OKEsOetxVlwHs7ii6d6NOoEO3TvHmqfOgSAF3U5fN/e3nP7/7wDJyNAVvb4S43bEYd4XUufY6U3rMZJH12lwZGyAg7F73gdADmXno0dCSOj+KAqtk3nM8/i5ObiFPddPzZjRYT6zQFiLYNnXtIrM2Df4D+3kkWZyO7GE7YB7vpoB21tcW77zavjamftnGLMs33L0QfSbBZemEEiKXQ0+ngJn4x8h7xIO+zew+HqADNXzECaqgZpVQEY16Vr004yVi2e0E3Vu/bWse9799K+cX/vsWBBJqHiHDp3VGPc1LRdJyed7DNnY4UcrGCAtMpCCi5bRiA7fcLGotTpRAMqdVKyckpJ+/QfoLud5OYH8Xa9gF+9bfgbT2OSWUjoDf+IXbpgqoeipkDr9iZe/trTQ17jdiYJLsgj2jm+6aA5syNE9x+tGhYszGTeZ64dV5sjdez+PiNlRSIUvv/mfsebXzpA4tkXyamYT+0AAVV6SZjInExefb6l37ljFZWFiNUMk+aaIKHcIH/ZsH9cbZTnZ7C4OJfV2XnEdrT3OVe5NpPwgW2Egd7SCcdUb3e7k7R1FpGDBlRDiR2o4cC//YyS991AwWvHv37KGMP+H95P3d9eRmwh76IlZC6eAcbQum4vsz54Bdu/cjuJujbSF5ax6F/eSqho6CmySk0YXzNUSk1bIhak5xA8582YVa/H3fwgiSd+CUmd/91HJIvwjf+MPXPJVI9ETYHWHU3s+L8NHLhrF8YfQY4kNP59ybzj1mAkGjpY944fseif30DmotJxt38iGGPY+T+P0RBcQO36vlP1sivTsYrT2PpsE/6B4feCqj0UIydgwVim3Y2SneYwtyiTrdVjn14YjbusPhgh1tLe79yB9Z2cMTsIicQAd/aMIWDB1O6pfNJoeeBZ8q+7cMxZKmMM3fvqqb3rJervfZlZH7yC0jedy/4f3s+BnzxExqIZxKpSlSfnf+lGWp7fSfl7L8UOTe0ebUqdajSgUqcECYYJrH49VukCYr/+h6kezrQhmQUEr/64BlOnqc3feZGt318/qns6DkexGaoEw/C8QAbBIiFRf/QDefRgE5s/81tW//KDhAontyz0RIhu2UpReTXPPnh0ClRGcYhlF8XJyKnnV98fvDri8ep3dZJ9fiGBwy24rYMHIhNB0my27R17MFWYGebjMxfQumngQhSJDpd42WxC+3cM2kbjhkYy15ZC8+Dl3lVKvKqeloeeJ++qsW2ibVyPHV+5ndjhVGC//4cP4GSl4bZFKbxyBfO/fCNedxw7LTXdNntF5UQNXakROx3WUOnubeqUYpctwl56JRQtgry5mIwyKF6C5JVP9dBOOGf164n83U9x5q6d6qGoKXD4gb2jDqYAorVdRCrHtw9Nx54WXEnDyeyb7TIJD+skeTKe3P4KkdkzWXttF3MuymTVjUEuPOsFCrpeIq11H7YzuhVRu55pZkeNYK0smbTiHADx6ig5acEx3//+ZQsHDaaO2PJwB90VizCDbABbfmEWJDVFNVK1v/wr8eqG4S8cgBVwWPjVN5N15mwgVVgi9+z5zP/yjcz7QqpU9ZFgSqkpYVIB1UR/jYSIXCMiO0Rkt4h8cYDznxORDT1fr4qIJyJ5I7n3eBpQqVOOPXctXtUuvLr9+C11+M01uHXV+KECpGwZUjgntYnpICSrEMktQ3LLIDz69RjTQeDi9xK64sNIcPzTt9TJ6cDdu0Z8bSAzSOl5BeTPMmRE2ontH//m2Z37WvFCR9dopM0pZM1tHyGQFRl325PN62jH7+7CKZpJTucmlmQ8SlnsGaxAkHjBUhpZyUXnCJe/Pp0zL8lCZGTBlZfw2f5EE+FVJZM2drfL5bJFo28/5FhkhgOEY8N/UAmmO4TtOHgDT2G0/W7oaB71GE5XfizOgX/9KW7b0OXMB5M+t4ScNXMByLtgEYGcVFZVbHvCxqjUyUZEbOAHwLXAEuBtItJnuo4x5pvGmJXGmJXAl4AnjDHNI7n3eDrlT51ynIXnYJXMxa/dA0DkTV9GIhl4h7YRf/zXmO52CKXjFJeD3T+w8hprMZ2pD5SSkY1dUIlp3H8i38K4WDOXEjj7pqkehppC3dWdVD04stLV6eWZ2G01dG9JfQCeyORJ18FWskrTyD9vHvM//1qswOR/wDv0h5c5+Lt14Pk42RHyzppFwflzyT5jBpZj4cWSHPj1C7SsP4SfcMk/fy4lVy8mVJCBHQ5gPJ+ORx7Abaija+/RQhRd+avY+UAU8IHW1MED7QSAi9ZmEMvJZt3D7Xgj2Ps44U5uvb8LuyLMvWgx//Xk0IV6Apbw1tXzKbcjxJ7twMQMbS8OP12w7Ix0rH3bBp0X2lKfSV5GHnRqUDVSidomunfsJ2vtsjHdb5IeobJcZr7zxGwQrNTIjTyjNMHWAruNMXsBROT3wPXA1kGufxvwuzHeqwGVOvWIZRO+6gN035rK0HrVOwmd8wbswll4VTtJbnwI4t24hwZfA3CE6WzD7WzFqVyGqdoCJ6zw8djZM5dMaBledfLxXR/jDf9nNWtuDulFDt39aw9MECFclkuwMOuEBFMALS8fJHqop+pedRsd22o5cOsLOFlhcleV0/LyIdz2o0Uz2jZXc+DWFzCeT2RGDn4syYxVcYINu0mYhb3XpTW+TMXF53LwidZ+fUarO6G6k4svzGXrAYfaIUqoA1Tv6KRkEotUdO3vomyATZuP96U1K+h4po0oo9uMNzsjAUPMUGvb0UxnOIeStbMINm3V6X8jkHXeCiLzxj41fea7L2bG2y+YlH2tlJqmCkRk3TGvf2qM+ekxr2cAh455fRg4e6CGRCQNuAb4+GjvPUL/5qlTklOxFGfx+bjbnsGv3997PLDyilRANSqCu38bgcql+FXj29dlxCybwAXvxMotA9/F72hCIllIOANv57O4Wx5NXRdKh3g3xwZ6frOWKz7dpZdnEswOkWgb/INs6Tl5dG87SHfj5I7FLsyn/J3nTW4nPYwxdO0Z+A257TEaHh94GqTXnSoU0bU3dW+1H6ByeRGJ7cNX8DtW554WZkccKq4s4cWH2hgohRMMC7G2BGnnFlJcHKD9YCdezKN1z9DrlkYjXBTmF621w15nDoy+QMbKazOx9+0c9jov5lL1ZBOB7ApmXRAhKG1gCdFdBwedKng6Cxbm4uSOvViLiIxq7zWlTqRJylA1GmPWDHF+oE4He9L4OuAZY8yRtPpo7gU0oFKnsPDl76Nr/yaSmx7DmbuGwNILccqXEFh+KcnNj426PbehCiuYBoluAKRsCRgLxMdUb2PE2atAGLtyJVbZIhDB378RQmmYtjr82p4PfL6Hs+A8rPz+TyydBedhlS8DBGfppfh1e4nd/uXecvHevvWYeDcSShv1e1SnhoYXqvFiffc9Sp+ZSXppBDtgcBua6d528ISMpfH5QyfsqbmIYEdGX5AhVJiB7/okW1J/t7trkyTOng+ksthd+Ws48KJLvKV12La8qAvbD3PFdcU8/0yCztZj5wAarrlW6N5ey9p/P4+iNaVYPdOO/3LDIxx+YvggaCTshVksaYEDjV20difAgCUwrzgH1/dZVV7AEieLrhdGl5qcd3EOzr7R7fdXtDSEt/dVoj1BVKA4Hwk6JA6Nf53eQIyZ1Jofk6bxrsfBsih512uneihKTbypmfJ3GDj2Q9RMYLBNFt/K0el+o70X0IBKncKsnCIyPv4zYg/+jOid38S4cYIrriB89QdJvvoEmNE9JTVd7fiRDOwZy8F4uAd3pv71BuzyxVA76NTaXlJYSfjGf8bKOWbReM96J+N7uK/cg99ai106H7zBNwINrLim93t7xiLCb/gnYnf8U+pAMo67+UECa24Y1ftTJz9jDFUP7uP5zzyCF099kM9ZnEck3aNrZxXxiUuCjFiyNUrnviayFhSdkP4KLphL+9bRleuON3RSet0yau5JZaDt9CB1HQto9fKwA0LNA0Nv3DuQ9k11rJqbhlWWh2fA9yEjW5BoO9gWpj2G7RydBnnBv6zi95fcOyGzijufrGfNkiJmRMtJnxmmuyaG2IJpNLgxH6oNHYyutHo4J0BO14FRjsSQbtWSPCYjlaxLZf2CJfm4HV34XePbN9BYFm7xMtxkgGAgTvNBoaC0DWk8MQ8MJlIgXzfaVWoCvQTMF5HZQBWpoOntx18kItnAxcA7R3vvsTSgUqc0CaURfu0nMG6C2D3fR9JzCMxbQ/i1nyB29/cY7acXE+3E3d8/cPIO7SBQsQi/ZvvA48jIwznztQTWXI8EB65yJpZNYPXrRzWeI+zZq7AqzsA/uAmAxNO/xVlyKZKm/0CfLry4ywufe4xD9+zuPZY5JxtTfZBupvapfSjvxGVLreDY1mr5SY9gXhqJ5m7yz65k/7pG2na1jmsssfpuqO/ufX0kLCs5p5yqJw5TdtW83nOFK/I44wML2fTT4dd2DkcE/NpWytfmcfD5ZvxxFsEIZTosPzOOqeke/uJjWULcKSFU5pCs7pt9S9Q2YWdnEFlYidveSbJm9HNPTWEldfvSiG5v7XM8tyIb+5i3fLJkrDLOXDTVQ1Bqwhkz6ufXE9SvcUXk48ADgA3cYozZIiIf7jn/455L3wA8aIzpGu7eofrTgEqd+nwP4l3ge8Tu/i7Op39FcMUVSDBC/Jk/9FYDHC+3qQ47Ix/T2Xfdhb3wAkLXfQYJTO5eIObYtVPxLvzGg9gVyye1TzX1qh7aR+0zh2l4qYa2Ua75ORHscIBgfvrwF06xuoe2sejzV7Hnx09SdsMKTFELr/5wA/4kFI6ofb6e2ufrmfuO5eQtye89fv7XzqT2xQbqN4y/Ol6iJUGipZa5FxSz6/HRt5dbmUZueZBkzFCS0YKpGcMUPR8OPN4OBCg5aylZ6U19AiuvrZNoWyeIEJ49AysSwrgeJukSjzn4rhAMdNPRmo0TsQjnCERbcNtiuFmVHH6mDeP2LzV+6Ik2AlnzqJhXS7A0n9iu0WbWTqzw3JmUvvd6QqWFUz0UpU4pxph7gXuPO/bj417/EvjlSO4digZU6pRmjE/sb9/D3Z0qBGO6Wond/2Mi136UwOLzcRadS+f3P4Bpqx9/X13teJKLZTng90zXi2SdkGAKwCqeg3dMMOdufVwDqlPcnt9tZd0/PTHo+ay5OXS21kxtEQAL8A3Y0zxNYKDm3lc5784PEcgMU3AuBHNCrPvac5PW5ZMffphzv3kRxWeXAuBEHN782LU0b2/jpf/ezK4/jT8QMHEXJ2LjRkdQz71H0eIMKiKHoSGaamPcU0WF2pc6qCVAwbLF5M1MQrQdv6sbr70TjCG2r28xnVjOUnw7nZZdtSSaj671MhjSFi6keYj1ZuIIM84K4R3oJJ5IEJ49o1/704UEHGZ+8u2EyydvbzKlptoUlU0/oXRjX3VKE7EIv+5TRG78PFZhBQDJ9fcRe/w3GOMjYhF53aeRyPAlhkfCdLYghbN7X1sZ+SckmAIIXvYBsI8+I3G3PobxXIwxxDatJ7r+OeI7tmCSo6/spaafaF0XG79x9MN+MDvE4g+fybK/X8us6+dzwU+uYdYbFpNIKyNUcWLWLw0kmJuGDLDf23TUtrmaju1HMzGL3reU8qtnTVp/Hfvbeerjj3L4kaPrfUSE/MU5XP2zC7jkW2tJLzs6XXLu68q55pcX8sb7riJ34cim87ZubmL2OTkjHpMVEGblN0F3dMT3jJzQ+GoXO+9PsPOJMLvX5RHNWoqd2T+DGcyAlu1tBGeUEJqRS/rimaQtKCV9finGDDyFMb0sxPyrg8yaewi7JRWM+rEEVvjE/A4ei6I3X6XBlDrlGSMT/jXdaIZKnfLEsgksuRBn8QV4e18heu8PSDx9O3bZfAILzsapXE7GR39C161fxG8Y/0Jmv6sLK78C03QQE+8a/oYJYuXNJHD2TSSf7SlUk4yBCCKCScSxc/Kx8gvoeuJBMq7QSlIns9qnDrH+q0+R7DwaHM99+xLO+Ow5fa5rWF9H54EOOg9CyTnzSO4/gImPbs+h8UqflXdC+xuvfT9/lryzUkGUiHDuf11E7XO3k2yfnAcR0bpunvzIw7xtx829fR757/L3L2DZ++bTsjOVoclbmE28NUayPcHSd8/j6X9cP2i7xkDemmI6Wjx2PTbyqaB+0rB5cwZLK9qQ5MizWmNVu64DyCeQUUT27AgZhYLlQOtuj/wlEWpfqMe4PjD8+q3ivIMk96S2CvDaj04FnK67B4bKi8l//SVTPQyl1ATQgEqdNkQEZ+4qMv7uu3R8510kNz5CYEFqnzaJZPZW7Bsvv6UOH7BySpGMfPy2eqzsE5MhCJzzZvza3Xh7X0Iy8hErtUA/sia1D5Dx3N7v1ckp2tDNMx97ALfraGA04+o5tO5q5+mPP4zlWIhtYTlCvLmngpqB2ucaiBQXkDfXIrrnxE1/CuZEMMacNJtNt6w/SO2D2yi5ajEAodwwq798Ns9/8alJ69MOOxjPsP6z91F65TxmXnd0Q2ERIa8nG+V3d9N8223kvOltzL9xFgcerqbjcBft+zr6FZ7InJvFrifGthYr1pbEKirEVE1MGfeRSHZ6NG7u5EhpitxluZRYj1B4ST4N0ZXUPDP0e8meHcFP9H9YIKEgXseJe7A1YiKUfegm3YhXnQamZ0ZpounfZHXakUgGVnYh7o7ncPe8jDN3FQDp7/kvEi/dTfyp309ISRq/tR7JyD9hwRSABEKEXv8FYn/6Ks7CC/qcS+zbRWDWXOyckytjoPpqebWhTzAFUPGauTz98UeHvTda101VHRSdNQ9TX4XXMRnTuvqqvncrJVcvoujCuZPe10TZ+a1HKDh/Dk56aqrY/Lcvou6FGvbduXuYO8cm0Rpn/defp+aRPWQtKBjwGq+1hYbv/w/uoYOEbn4HViTCDXdeDoAb82jc3Ezjqy0072inZWcbfiREZU6CjNIIoUwHsQXLFiJ5ITLLwmSVRtj1QA0v/XjgojxbNoZYOj8L0zK6vaomSsurLWRfeRF58SfJzTqIOaeCeLsBgWhjnFhd34xVcWUHiUN9f2/bmWk42ekkDk/OnlfjUfTWa0hfMmeqh6GU+v/t3Xd8HFe58PHfmdle1HuzZFvuvab3OIUUSgJJCITeErhwgUu4lAuXem9e6qWE0ENLQkkIkArpPbZjx73LllzU+/ad8/6xsorVVtKqWHq+fPSJdubMmaNhvTvPnHOekyISUIkZySxbjNV8nPBz93YHVMrtw3nejeD0EH78Zyk5j33xeSmpZySU04P7pv/tt73ltz8l7U034lqycsLbJFJDa031w31vgAvPLyNreXJBu6fQS2sDVG/qxObOpmixnci+Q+OeVvrEo7vJWFKIze/CsE39+VSRpk52fe0Rlnztmu6etYKzi8YtoALY+7tdFK4pJXtd/8W8oyeOU//dO4g3N3MifjaRBw4y58bF3fttLpOCtbkUrB1Zlrh5VxURj2o2//xgv32BhgjRdfnYJimgKjw7i8zQv0CBq3MfRY590BVrRkpns/2RnjlkNq9JpKa6Xx3O4jyCe6smqMXJ869bQu5bLp7sZggxYaSHSohpyn31v2FfeiGhR+4k8Jc7cF/5YZTLB4Bz/bUoTxqhf/wAYmObN2EUzh2+0ARJv+HdhDa/LAHVacqKW7z0iX/1rDOlYPFH17DottWJ9Y6SEG4OEW41AEW0I8aBp8Lkr5qHO3Icq2X8Vv099tBOjj+2G295Fsu+ciVp8/PH7VypUvv4bvwLCih/Z2JY8Ow3VxJqCLLjzteJtIRTfj4rYnH0hQYKHt+EO1ZFrL4OZdpQdjttjz2M1daKcrlp2qGo+vrLuAv8FF1YNqZzKqW48L+WsPU3VWitWXGJE7OjZ8FfXTc5wRRA3aY2Mi5ciLtzV5/tIe8Cjh4oBFpwptuweU0KltiIHxxgyLZpYM/PRkeixCYpMOxN2W3k3XA5OddcgDKm/oMFIVJCAxJQCTF92cqX4bn5awTu/W8Cf/4fPDd+qXvOkWPphZjZJXT+9nMQGf2wKOWaOuvvOCsXYqZlTHYzxCjt+dnWPov2rvz82cx71zIg+RGq8VAcZ7qLcGvPkMHazc3YfT5K1+YT3bt/3HqrdMyiY38DW//zH6z67pvxlmb0K2PFLXRcY45ycd5Ui3f2BE6GzWDJR1ZQdkUFf9/wZ+Lh8UnYcOyFevxVfxlwXzh9AU0HEksyPP2eh/EU+chalsv8dy0hb33RqM6XVuTmUzXXcOjB/VR97wmsCUhEkYx4KIYi1mebpWzsfS6drAWJ90fGbDfu9h3EDw38pg3sSPS8eRZWjDCg0kBq/yF45pdT9JG3SkY/IaYpCajEjGb4MnFf8RFCT94NlgVGz42cWVSJ5y23E7j3vxOLA4+CcnqGLzRGVkcL0T2biNcfxWqoSSTYsNkx82fhWHYOhqcnJbwtv3Dc2yPGx95fbO3zuvjSCqKdUf5x6X0EjiU/6d6d4+wTUAFEO2IcfLKVnCWVpNnqiTW0pKLJfWgNGcuK6axp56mrfoGnNJ2cM8uZ96EzcWS5qf5XNRu/uolwa4SlH17C/LfPw+61p7wdI9G+t454OIbp7PmqTKtIZ/4ti9h517ZxOefxrXFmr1yOI1aHbqmHeE9QsedpD9DzWRQ41kHgWAe1zx9lwwNvIm12xqjO6clysvhdiyk5I4/Nn36IjkPNY/wrUqOlvYwCY1/ihVIYOkbluR04g9tocCzC4VPQzrAJhcI1tXgWVBDYfWjIcspm4F9cgtXaCO4MQidaiDWPLaGFmeaj4J1XkXHBGumVEjOSRob8iSngdMqOdboyiyrxvv0rA+6zzVmF87ybCD/1m1FUbEP5MsfYuqGFnn2A4D9/j2PJWTjPuprI5icIv/xw9/7go3fjvuydYMUw88qwz10+ru0R46Ojuo1IW09viafYj7fYT+v+lhEFUwCujMGDlIbtLTQ7nZSdNY/4gX2JBXlTwDs3l3hE07SlJ2tcoLqVI9VbadpYjT3dTUernebdieFmL3/xFbZ+dyuLP7iYRe9eiCPNkZJ2jFTDcwfY+P7fseybb8Rd1DNnp/LtC8ctoAJ4/ldeoAJlVpBTaZJdDi5fDLzpQP9sd9H2CE+8/e+c//MryFyUPerzpi/IZdUdV/DMdb8fdR2p1Lw/TMG8xO+OOfPI+fjtBDe9TPMvd5C9NB2Xp5NkFgGItwf69bwZToOMZblE26JYgQDEYsTb2ons39NVogF3UQHtI4wt/euWEG1soeAdV+EsKcCW7jtt1mETQozemAMqpVQpcDdQAFjAXVrr7ymlsoB7gXKgCnir1npqPPY6jcT3PI8570yUYaKtePeQNDFxHKuvJPzcvSOfTxWPEa/Zg6104bi0K7pvC/G6atI+/gPMzERSArOgHOdZV2G11BPdu5nI5icI/v2n3cc4z7oK94Z3oGyT++RfJC/UGOTZ9z+EFekZ17f+josA8JX4Rlyf3TX0zV08bHHoyWYyKmeTldFG7Hj9iM9xkrMwDUeGj5btg2dZ6ziYCBB8S8v7bA81hdn0jc1s++F2Fr5nAUvevxhXtmvYc2atL2e+J7UBWPue2j4BVYpHgw1Kx6F+d5z63YmTZi0e/PNfGQp/edqYz1n33OEx15EKht3EikPadTehO9qxl1WgDAPP2jPpfPYJCjtbaNuS/LqB8Y4AzvJiQoeOYs9Ox3RE8YReIphxBp01A9+axJqa8S0spXPf8cRaWIaRGMkwAO+SOWRctJ6M81cDyINQIXqRHqrkxIBPaq03K6X8wCal1OPAu4B/aa2/qZS6Hbgd+EwKzjej2BYkUl9rrYm+eC+21ddCLIzhk9TXE0W5fTjOfAuRZ/8w4mPDT/4a2zu/OeLjrOYTxA6+RvzYPnRXIGd403FteH93GXvlCuyVK/q21TQxswsxswuxz1mG+7J3YNUfJbpvM8FH7ib8wt+JHdqB56r3YRaUo5zuEbdNTKxYR4T0+dlkLsnDsBmYbjvth9vpOJJ4km7z2vulUR+KYSb3xdayr41WU1F27nx01X6IjWDYq2mQvrSE5i3HCNQk14OmoiFQut/k5UhbhK3ffZ0dP9nJ/HfMY+mHl+AtHHxuYtqCAtIWjO88FV+xH5vHRiwQG75wCtn9Az8IsfnsrP+f87F5xv6gpPJ9a1EKdn3n+Z7zpjlx5ni7g9/xVPmBdZS9ZTHuwjSUMfB71VG5iLZnDkI8+eUtIscTK1zZMvw4sr3oYBsoA2d4C0FvEVZn/8QuOhQmdmQ/GasXkv2263AU5RLcd4S6+x6jc9s+sq8+H8/CCtxzS3HkjO9oBCHE1DbmgEprfRw43vV7u1JqF1AMXAtc0FXs18BTSEA1akopHGffhLbi6EiQyDN3Yz/3ZpSSoQQTwaqvGtVx8SM70cH2xLymJIWf+QPhZ/oPubFVrhvx+ZVhYuaXYWQVEG+qJX54N/Hjh2j/6efA5sB7w6cw88u6e7jE1GM4bTRta6LtYOvwhZOg48kHRjquOfxUE/7SUvJKw0Srjw97jLssCysGTZtGtnhw++4TVJxfzqGnBv47Y8EYO+7aya5f7abybXNZdtsy0mYl/+8qlQy7werPn8HL//nchJ43eHTgTIzrvn4eBeeUpOw8c9+7Fm9ZJvUvHCZzeQFFl8/n2KN72fK5x1J2jsH4KjLxFKcPWcaz7gyCd784qvq9s9Pxx59HuSzQYMRD+OenEQ3momMWwX096deVw0HOzdeTdv5Z3fOfvIvnYDjsmH4vBe+8CmXKqBEhhqRTsrTnlJfSu3GlVDmwEngZyO8Ktk4GXXLHlgLKMDHScnCc906sI9vQkdBkN2naswKtxA5uGeXRmuCD30WPIKlF7ODmfttUeh7uaz4xyjaAsjvwXvNBfO/+L4z0rsVcYhE6f/t12r5zK+HXnhx13WJ8eQq8XPCrK1BJ9iwNJx4aea9Ke3Unh7eCmTnEkDJDkbGyjM4j7QSqRxf8RWuOo4Z5zGdFLPb8Zi9/OuvPPHXbMzTvaRnVucaq8u0LWPHpNdgmKHGGvyKdwLG+AVX6vEw2/PXNlL0h9YsmF146l2X/dTGlb1yM6bLhKx//HpiSqxdSfOX8YcvZ8wsp/shbR1S36XOSuaYYb0YHir53d/aWLXjCL+OJvYq9sGstL8Og4KPvJ/3Cc/olk8i+9gLybrgcPZJeWyFmLIXWqf+ZalKWlEIp5QP+DHxca92W7PhhpdQHgA8AlJWNbU2NmURri/Df7wC7C/ct30M5p0567unG8KTjWHsVkef/OKrjY/teIfLyX3Ge+eZhy+pYlHhDTb/t9vlnoNwjny/Tr/5gB963345yedHhAMSiGGlZqLT+E9lP/OiXxJpa8K5ehm/NCuy5o5/sLsbGletBx1OTICLaOro1lGKBGId3uylYXYi94RA6lBiKqjV4Z+dgxaFx48h6pU7lLstG709umQId1xz40wEO/OkA5W+YxZr/XE36nKF7NlJJKcXSj66k4Kwi/vWOh4l2JD/sMulzGIrMhVnYvSZt+/vP8ym5tJzsZSNbzHfUBhl+N2YKijZUMue9a8lYlPxz18wL12DL8HH85w8QrqkbtJy7LBtXvgur8RixQ7sIZGbgy3BDJNhviYB45hKoTgRJ2dddg3fFkj77o01tHLvzPjq27UeHI8Q7A+Rdd2nyf6sQYtpKSUCllLKTCKZ+p7U+uYBGrVKqUGt9XClVCAz4iae1vgu4C2DNmjWpuWOYAZQyMEqXYlasAvvwE7XF2DjPfzvK7iL84l8gPPI0upGX/4p92UUY3oxBy+hIiNA/fwGhjjG0dGhmbvLDgiK19YQPVBHcvY+Wx54i7dwzyLjsQkzv+KeCF32ZLhNnlotw09h7pEMNQRKDE0Z+g2xFLY691MTsC8sAjdcfp/NQE+0D3OyPhqUcwMjXfav6x2Hy1uSx9MMTF1CdlLs6n3nvXMSOH20dvvAwtAad7iS91IfHa9J+oJm23QMnBTEcJrHgxM3hGpe1wRSs/PpllFw9usQ9/pULaF04e+iAKjNO9GDP4sDx5hZa29NIL4qC1XP9tLIR7vQQrU8kuvCtX9Wvrro/PEz7xp0AOEvzybnmglG1W4iZZir2KKVaKrL8KeDnwC6t9bd77XoQuAX4Ztd//zrWc4m+XNfIlLSJogwT5zlvxbH6CgJ//BrxIztGdLzuaKLzZx/HNmcV9qUXYpu1FCvQhlV/BKv+MLHD24gd2to/mHK4MTILsS08e8x/Q6y2GqutGTOnEDNz6KfaseZWItVHwTQhHscKhmj95zO0Pfk8s+74IoZLgviJpJTCV5aWkoDKisRxZroIN4++R+Xgk4khfXNWxgg3jG2dnt6CraMfaJ+qHrzRmHP9PHbe9To6NrI2RCoyqXW6aQ1aNDZFqDsaIHg8xgXFTmZv6t9TfVL+WUWc9b1LcOVMXFIZ35xsTLedeDB1PXHLvnjxqIOpkwrffQ0dW/YQre8f1KctKyZas6vfdkdBHsrqm3lS6Rje6Cu4lpXRdsRF80P/JO9dN/YpY89NDHu0ZaZR8K5rMRypGe5pHXsN5c4EXz7YXJIhUIjTUCp6qM4G3gFsU0pt6dr2nyQCqfuUUu8FjgDXp+BcQkwq5fbjufFLxPZvIrbvVeInDmDVVSV1rG5vJLrlcaKvP4lZVEn86J7+MzVNGxi2xHZ3GmGrHLNgPd6SBWNue+zQLgIP/xatNa51F+N5wy2DfnHbMtOxZWVScNt7wbJwlpeCUsSbWyWYmiSODGfK6vLkjC2gGi8th0c3HBFAp2jNrNFIn5PB4g8tZ/sPtiRV3gK2l+Wz5flGEivT9vXSS02UlzowOvsu1WDz2HAX+Fj0kZUTGkwBGDaDzOUFNLxUPXzhYSibwfIvX0LptYvGXJfpcZH3tg0c/cG9/fc5NQP14RlOOzrmJJ6+CEMHiIS8xJo78BRozJZ9pJXPo/mJZ/EsWYhvzYru4/LeuoGMC9ZgS/NiuFLz71G31xL728cTL9KKsb/1V2BOzrprQowHWdg3SVrr5xh87MjFY61fiKlG2V3YF56NfeHZaCtO+Nl7iLz4l+TXqbJixAd4aooyaOg4k+PPJdL3pi8rwtm8haILS1KSzdF1xgacay5Ex6IYruGH7RV9+lYc+X17smxZGWNuhxid4InU9QQ501OUSCHF35GBhvCoK7VGkEJ7PKz41Bq0pdnxo62E5uQAmrjdxELhbg9iNgYgZqFiFo2Li9jy+ODD1EKdMY6V5lGyu28v1UV/uGbi5kwNIHNZ4ZgDKl9FJiu+uoHM5YUpahV4F1YMuL11ZyNpBZnEW/r2XkUbW4laRcQPn1zHKpFSvb3dgb+iCLNtL76l66n7+W9xzS7v87nnyEvtkiXWkV7ZCjvrIdwOHpmvKqYXCaiEEENShonr/LfjWHU5kc2PEt3yGLq9cXSVudOI9rrHOtl5ZKRwzpKy2ZNe1PfUYEpMnvaqVlpTNE8JwO5MRYJXjY6mLsuZ4TD7rUE1otZM4pA/SCSQWPSBZVT97QBbQiavv9r/c8C0maw7K5+NTzcMW9+LG1u4LsdG/so8Si+vwO53kLl4cm+0M5bmj/n4s351PaYztbce8c7+8+6cZQWEj5wgFMzA4Qygwz29n/GWlgHr0eEI2lkCHcdwdm4ikraI2p/8iqLPfKxfpr+UCfZqSzyCrt2BqjhvfM4lhBg3ElAJkQKGPxvX+TfhPO9GdGsd0R3PEHnpfnSw/3Ae5fYPuJ1AC4X5LxKbv56WPSEMu8I5qxRnWerWlxGnp5bdDSy+ysQ049gC9VieTA6+6qL1yOiG7RnG2Htz5m1Ip3Nb1ZjrgcQQMNfcMqhuGXUdkx1QAbiyXBS8fyUHPrlxwP3xmObFZwZOMtGbP8vJv/3wTNZfUYzLN3WGf2UuHf2CyRlL8pl/25kpD6YA2l/b0/27ctjIv+kKcq4+n/2f/DahqmO4Vs0mfniAUQEDiEftmCTmVPkyjtNWF+XY/3yfvPe/A3tOagNaHWhEh1p6Njh8qOLVKT2HEJNOSw+VEGKElFKojHycZ1+PY/21xGt2E9vzErGaXdjKl+FYdw2GP5t40zEiL/+V6KaHANBZlRx8NjFMKNSYeJIaagjhv+xsAltfJ153FDMzC+Ixgttfx/SnocNh3GvW4VnRPxuVmF6c7Qcwq7cBdM0Jqac8TxFfuZTtfx15UJWK+Ubhw8Mv8psMZ66PjlgGx59sGdXxylT4y3yYzqmxwGpDUNPZOrb5aedfX8751w88jG0yOXO8ZK8pHlF6/MoPrqPw0krSF4xfj3famkXEWtuxOoPk33gFjoJE4JNx3kpOVB1Dj+DtHth5CGP5SmyNr2GE6knPCdN8KEj1F75B6Vf/E3t26ob86fq9WDsfTLzw5WN/810ohyyBIsTpSAIqIcaJsjmwlS/DVr6s3z4zqwj3FR/GLFlA6MHvUneogMCxQJ8ykcY21MGXaH568CergVdfJrBiFZk3vQMzPSPVf4KYAiInGmh84In+O7TGrHmdORet5MATIbLXFAFgRWIoh43242HikXjX00ES/+067sTrA/SQnsKR7iDSmpgXmD3PQ065gdIWOhIl0tBOpGn0CSR6M3JzaRxlMAWw+P2LWP+ldSlpSyq0NY7+usxfk8MV753HRTfNTmGLUmvZly/h6bf8DiuJBaKduV7mf+QMlDlOw+W6uOeUUDynf0++ozAXR64f1V47wFGDC1a34+8aaa0ibbjKFxLYdZi6u+6m+LMfT0GLu/hyQSeGzaq8BSh3RurqFmLKmJoL8aaaBFRCTCLH0gtRdhehO57pty9/jQ+resvAB5qKzHMST2Edns2o1uWQLuPup5tYeydHvvlzrNDgN+m+jq3MueIMdj3eSqRtbD0jpsuk7Mw03B1VWB3tqEXZxCwXoRPH6diWuvlS3RTUbg8MX24Qdp+dlZ9ckbr2DKP95S3EA0FMnxd7bhb27ExMf98ehbWXl/DrL72WdJ2zl2Xypo8tZt3lxWQVTP013nyzMllw65ns/Nazw5ZVinEPpobiKitAGYp4W+uIjjP9XogDDhdEQuhYYohscNde4p2dmN7U9CKprDkYlZeiW47ACIM+IU4rlgRUQohxZl9wJuWfKuPAt/9O5/4TYGkylufjte8hXlJC9MQJiJ18Gqwx01xkneHGFngusakdlGviFzQV48/m95L/zms48rW7Bi8Ut/A4W4m0je3j3JXlpHylRtVsRZPItacb64m78rEi4xBMkbjZTityEqhPMkPmKbzFXuy+FGUsHITWmsP/eQe27EzCNceJHu87B8q7agn5t7wZR0FiSNu81dnkFHtoODp4oOhwmfgyHay9rITbvn8GLs/p9VVccu3CpAKqUF0n0fYwdn/qUv6PhKMoFytmoErmoQfKrAooux2jaC6x9gjRtiD2dDdmmomKZVN/MB97ugPiXe9PrQnt2Y931fKUtE8phXnBZ4n+8V3QUk3sX/+NjkcxCpZhLLhShv8JcRo5vT7FhZim/AuLWfHTDwLQebCW+Ev3EDsShfb9mD6LqFFCvLmJgstiqM7tEOh1g+svReWl5gteTD2+lQvIu/EK4p0hPPNmYUVjxJpaaX3hNUIHEmm1HdEGIJ/RpBx357hwptso9B2Emr4LBysFaYUmDU0G+pS05KbPga8si0hrCGUYOHM9tO44jhUePPjSSqG6JrQ4sj04Mtwoj+bEiFudUHRu4bgvghqtbyR0sBoODpwuvHPzdqqPnqDkU+/HWVaEUoozrynjbz/e3a+sP9PBsvMKePdXVzNrYca4tns8ObM8eMsz6awaPvNk8Fgb9vmTkzFUKUX6OSto+OtTZC4rIXa0BiPNn8jm15X1zyiYQ9vWnvT00YZ2AgcAfECAcG2AtOVlQKIHKbh7X8oCKgBCrZiL34RWBsrhxXruu8SPbyW+9R7sN/9JFvkV04IM+RNCTDjv7HyY/W9oyyJ6aCdWWzOGM05854Oojhf7H+DwTXwjxYRRShEPhom3d3L8Z38m1t6JLc1HrLmtu0zsRC1n3pTLi78fWWKGwjWZpLdtTbwIDVwmXHWMtPkVtO7s2zPjn51N85aeUKjzcDO+2Vk4Mt0Ej7XhLkojdKIVK65x5fqItIQI1LTiKkrHXeCnbU89oUgAVAfzLs1l7+MjX2crb3XeiI8ZqdanXxm2TLS2gepv3knF/96O6fOQW9zTs+BJs/Oh/7eODbfMxZzE4W+plr4wN6mAKj5OvZvJyn/7FTQ9/hLK4Ua53ZR95zs0/fGPtD3yKACh+o5Bjhz4BnCo4bejEg8Tf+UuzJU3Yy5+I0bxanRrNbF/fQXdsBeVOz+15xNCjAsJqISYopRh4JizBAAdaMA49Cvov9wKWGObNzMQKxQkdvwojoq5Ka9bjJzpdmI47OTfci3KNNDRGOETzbS9dgAVT7wpora+N+uONPuwc6rSM4Ko4fNTYFUfIntOJlF7Om27E2sohWr7B0AdB5u6fw8e76k41GtR4uDRNoJHe4LB9CX5ZHhrsPvyiHaM7L184sUTzH5jxbg9xY8cq6Pl8eeSKhtrbKbudw9Q+MGbuPpD88kv95Fd4KZwjp+80un30KPo8nkce3jvkGU8pelkLBr/oHcohsOOjsWwLHv33CfvmjW4KisJbH6NzsNNRE4MNcdKo+M970vTn9r/L5W/EPO8T3cv3q48WShPFvY334VuO0bspTsxStZilEg6dXF60pI2XQgxVShPDubldxF/9EPQ2HcugCo5N6XnijXU0XL3j4k1NpL/pW8RbWohuHMv6ReendLziOTlXreh37YT979E48ZjPRtcEZae76ddFeNwxHDW7aQzaykHnxj4ZnHuxemorlTsw1EKovXN+FdkYuuwiGfk07Jt+MVpk9G6vRb3WXkULPdQ/fzIkgfsvnsP6/97HTZX6r/Kog3NVH3+/2ENsGjsQAyfh5zrrgDAl+Hkohumbqa+VCi4aA5Zq4tp2jR4CvXiK+ZPalIKAG1ZmG4X7Xvryb0o0dvjrEikpPesWIHt2U10bP9t/wMVeOeXoGKdRA4e6t5s+lIfHJtzL0Zrje6sR3kTwyNPBlbW3keJPfUN7G//owz/E2IKmz7jD4SY5pTpwLz4u5C3Eoyeifgqfezr1cQa6ohU7afjn3+n/uu3EzMy8N/wYTQQ3LmXhnvup/7u++jcsn3M5xJj0/raQXZ84udU/eihvjtCnaimE6Q1bsJ1fCsqHsXbsAVvkbtfHabTwHZiZ9LntKW7cZVk0r7lAPH2Tmg41pWDfezsaU486hg25+gq3Pq911PTkFM0/PGhpIMpAM+COdizM8elLVORUooVX7kUY4j1v4baN1GUYZBx4Vr883PxnXVmv/3pZ63EUdS/F82/dBaxI/uJHutZb821oBLfWWtT0i4dDaDjPannlVLEd/6N6H23EN/1dwCshv3ocNdDhrbk1/4SYqrROvU/U430UAlxGlHONNSS9xJ//HMofy7Kk4ZuroJRxlSR/Xto+d1dxFubIdo1rKV4Me2v7cMxZyEnvv9TrGBick3LY0/R8vjTFH3qI3iXL0ZrjdUZwPC4UUbi2Uy8M0C8tR0rHEbHYtgyM9B1e0AZOBauT8EVEOFjTbS/XpVcYdPEneWg81jfwKBwdTqqsW8dWasL0ZEIxC0wDaxQBE+JH93RRnOVJny0ie4H5MEQ3rJcOo+MrEfpVDavg7JFzajmE5iOkfXo2Nw21n9lHRVXl4+pDQMJ7NpP+8tbRnRM5+u7OfjJr5N19UVkXHBGyts0FXnLMijaMI+avw22Vt7U6FEJ7NyHy9WKa968fvuUaVB869uo+vKd6EgUV3kejnQX9BrmZ7hdZF5zBRlXXIwyUxMkWtv+hCpd33eOVCwEzjR0aw3xfY+j0oog1Ir9LT8De/8HI0KcLmTInxBiylHagmgQ3XQE3QQc3YUqPgsjf/GI62r946+JN9T1bHD76dhRjeF24SjI7Q6mumnN8e/+BGW3o6NRdDSGZ/lisq69HGW3U/ez3xE+nMiGlnbh2eTccA2dj/4K+/ILQAKqlMi9cjXH/vg8oeqeIXdag7Kb6JiF6tV1pGJRiioC2FxZhNtiZJUo4paJy2vhdGYTPtbYXTZa30y0/pQAKRLGbnTgSM8j0muEnwZCDaNfP6r7b1mehmpJZMPL4TWOpC1Mei2t8394HuVXzhpzG06lLYua//fT/u/94Y6LRInUHCdyrG74wtNI6RsXDRhQ5V84m9yzyiahRf25C1y4Z8/ufvBzKs+C2Sz87TcIHzlK3U/vJryvZ26YZ9ki8j/4Lsw0f8rao1uPYjXux7b4TX22mytuQmtN7A83gDcX+5t/grHwanSwGUMW/RViSpMhf0KcZlThCsz1H8E84zYwHaDjRB/6NLGa/vNhrFCA2ImB0z3rWIzY8Zo+21R6AWiNb/0qfGtX4pxV2v+4aAwrEERHE8NVAlt3UPPf36L6C9/sDqYA2p55kaPf/CHBtPNxrLtmLH+y6EUpxZIffpDMsxYAYM9JwzWrkHgIXKX56FN6BbyeMO66XWSE9mHt34s6uIvwtj0EWzSeheU4Z8/CPb8Ce2Zav3OF69rBZuKInkDZeup1zZlFPDD2ZChus46Iv4LX9pzNlk2LiQWTywhXcW3FuARTAKGDR7A6Rh8sNj34Tzq370lhi6a27DXFOLL6LkjsyPKw8huXkTYng/ZtByepZQnasogfPYijtP9nWW/KNHFVlJH3gXd2b3MvXUjBv30otcFUoJH4a7/BdtEXUM6+9Sp3BtYrP4FYCNt5n0Q5vJhzL0GZDnSwJWVtEGJiKbRO/c9UIwGVEKcZZdoxl70Nc+n12G/8I6r0TDROWn/yLWI1B9HRCDoSJvjcQ7T+3+dovfPLNH75fbTf9yN0JPHUPXr8KA3f+lK/gcjadGK4XLS/sJG2514mfKRmgBYkKW4RPnSEtief49j/+9EY/mJxKpvXReXn34pvcRmG10vgYKJXJHCoHvesvmv+BA8d7RMMnRRp7KRp83Fad5ygecsxLHPgxVcNhwMrECJ9fjYA9sJcGnYkkRpwGFnLcjAaq7B11pBe4iDUFMaKWgOW9ZX4yF+XR87yHM76nzM5//upTcTSW7xtsDTaSdKao3f8lJYnXiQ+wl6u05EyDQovmdP92rCbZC4rAKB9ywH2fvZntLywY7KaR6SqinhLC+1PPUXHCy8MWz68P5GAIuPKSyj691sxHKlbOFo3VxF/8UeYa9+PMgeu11zzHowVb8coWpk4JhrEqt1B9J63oztmVu+nEKcTGfInxGlMuTMwKjdgVW9Ex720fP+zGJm56HAQHejAddZleK99N+FNz9Bx34+IN9VjFC8i8NwT6MAAN45Ht+Oes4zOHYeovfPXKWtnuKqaln89g3fpIux5OSmrdyYznHZyr1jD/m/+tc920+/FXeInWNMGKAybiTvfR+Do0EFQLDDwLF8zI51oXSN2X9fcEV8m8eBol+LtqtNjJz2zCerBsKJUeJ7Ef/k57H1kgJ4hBas/u4q5b5nTf984iI+hd+okKxTmxE9+T+0v/4h/7XLSzl2Dd9mClM2/mWrmfWg9Da/U0FnVzLIvXUzptYsAaN20FyyLw//3F3xLK7D5PcPUlHqhAwcS/925i/D+A7iXLRsyU5//nDOw5eXgXTbyIdRD0fEYuvkI5oWfRRmD33opXx7mvA1YJ7ajMsuJPXI7+kRi9IFu2IfyTW4aeiFGYyr2KKWaBFRCnObMORdB+lwy4gaxmgN0PvR7dKADz6XX47n0OgCcq88j+OQDxKr3E91TBbHYoPUZ7anPJqVjMVoe+ifhFx7Cs3Ilviuum7Y3lxPJv7j/MCaHIwLhI3jWLKRx4wkcpYU0vzj8k209SNo+K5YYyHDyC9FKwTqtJeucmHU92fmUgqz465jO+cTDPSdwpDm44EfnUXrJ0MO1UkFbFoe/8B1C+6tSV2ckStvzG2l7fiNmup+0s1fjnleBs7QQZ0lhys4z2Vx5Ps765XVU3fs6xW9Y0L09WJXIkBdr7aT+7y9SeOPFE942W3Y22e+6hcZf342ORAi89hr+cwfv4TRczpQHUwDKtKFmnzdkGR2PEn/uu1j7HgPTnkiMEY/07G8+BOWyfIU4vcyUdahkyJ8Q04CZU4YtvwTX6vNJ/+AXcSw/i8juzd1peZVS2OctQ2XNGjKYAsDmQDkdKW+j2x9AN1bT+c8Haf3DXSmvfybylOVQ/uFLu19nrS6G6kRqe5stSs66AiLtkcEO78NmDTzUzbAlhuHpFOWpdWS5sTXu7rfdjHcw/8Zyzvjqes799tlcfu8Gbtp+w4QEUycV/du78J+xclzqjre20/zQUzQ9+E9sWRnjco7J5Mr1suC2MzF6LTBtz07v/r3+kVcmo1l4Vq7Ef+65ZL/jHQCE9gy9GPFksvb/C+vw8xizzsL2hm+hMvom9dBtxwY5Uggx2aSHSohpxpZXjP9tt4Jp9lkI0nP1LUR+9j3g8JDH6+bj+MrL0Q4fyoqhDRvBqkbi7aOfW2L4vGh3BrQknliHNj5H9OKrsReWjLpOkVB8w9l45xVS85tnMNv2d/czRQ7sB8BwLxq2Dme2m3B17cA7VdcNclfF2hp5YJW3Lhe706J2czv5y5youv5BXvp561nwoaGf4I8nZRg48rIp+ugtHDxcQ/R4fcrP4SgpoOSzH8H0zIwU2M78nnW5bL7J+ZtPfgb6zjuXtiefpPPFF7EXFuA762xsmRkT0oZYR5C2V3eSef7KQTMNAihXGva3/BzlzU4s9BtLzMFTmeUY8y5Dh8c4v0+ISaIt6aESQpyGlM3WJ5gCMAwD34bksu3puiqo2Y4+thtqtuMptKMcjsSaUy4nZvrIsl5586JwvO9Cso3/8xk6n/h7yno+ZrKMVbNZ/O1byLy0f2r6aMfwY/T8ZYPPKQke7wSlsNlj2NI9uPJ8pC3MI31xPvZ015D1GnaDsgv8+Ntfxt34KrPK9+Ks29yvnJnuJ/vNG4Zt50RQNpOK//0srtmpTfltz8+h9HO3YUsb/FpPNzkb1oBpgGmQffGqSW2LUoq8j94GJcvoePEVaj7zGTpe3Tgh5z72y79z5Nv3cOL3jw1Zzph1FsqbSP6ilOrTQ2UseAPm/MvHtZ1CiNGTHiohZhDD3z81djJ0/RE8PhOlu27OM/LpSHJNVzPdjw7UDrjEZ/uDfyBWX4uZngE2O5H9u1A2G/Y5C/AsW4jKqugXGIqBKaXIfdvVuMpLqP3lH4m3tAGQYe4h8+IlHP7X4P+HtR9ux2M30dFTgi9D4SjKJe+KS/AuqqAyO6PP7kBNKy994M8EqgeuO29NNo66l7vXd1V64Cx+/jXLcEyhZCWGw07u26+l+iv/l5L6bJnplH7+VuxZ6cMXnkZcpXks/O6tOAtzMD0DZ5GcSPbsbCzDR/uxFtKKSzl29+P4tp+g8F1vGJfPGa01dX98gqbHEsMdGx9/FUdBNpnnLMdwDT+s2nbR57G2/wXr4FNgc6EyZtb7R0wfkzWHSil1OfA9wAR+prX+5gBlLgC+C9iBBq31+V3bq4B2IA7EtNZrhjqXBFRCzCCx2uOjPrY7mAIsVz5wKKnj4q3txHIX4PQGsY4f6pfVILJnG/GmU4ZXhVuI7f1fSC/Ftu4DGMNM5hY9/OtX4l44l2Pf+inBPYk1gGyxZnoPSFCmwlWUgTPLQ7Q1SGdVE1nriwjuS6wjphw2Ct96LrlXrsWRM/hNnKckneVf2cCL7/rjgPu9VnLvkQGj7UmmY3Fcc8oIHaoBa+BAMBmm30vp52+dUgHjRPLMKZ6wczXc/ziBXQew52Riz83Ct2YJrtK+yT+sSITwiXbquxJVBqqexlmSR/al61LentaXdnD8N490v441tVH9vfs4etdfqfjcLfiXVw55vHJ4MVe9A2PlzfJgSZzGJmfdKKWUCfwQuBSoAV5VSj2otd7Zq0wG8CPgcq31EaXUqWk0L9RaN5AECaiEmEEc5alJPR1tbBtR+XhnmNb9zaTPy8LqFTyZ+cXEa/tnFXT66iAEtFYTe/wLGCtvxlz7PrmpSJItzU/J5z9K0/2P0vzo04n5cPYwuefOoeQtK8lcXYph68myGGkJEmtpo/Wl3Zg+N5lnL8KemdzQtKxVxRRcPJcT/9rfvc2R5aboolmonQ8lVUesZWTvp/HWsWk7R7/9c3Qshr0wd8TzqXxrlpJ70zUYLieGx43pHnpopEgNz5JK6v/8GDqcmKPX+LcnKf/ax3EW9twj5d9wKR2vH+hz3InfPZbSgEpbFsowiNQ2Dbh/1qduwrd4dtL1yeeeEKOyDtivtT4IoJS6B7gW6D3/4CbgL1rrIwBa61Ev9iYBlRAzSLwxNRPtTa+b6Knb0v3YMjOJ1jdgdQawF+ZjpvmxOoKEjxwFFMH2dJwk2qDSMgcMpgAsndH39Wu/JdhaiO+iKyTdepIMu52ct15F1tWX0HnwBGW352NPHzgxgCPDjSPDjac8f8TnUUqx4uuXU/PgTkynjdxzZuHI9KCU5ui3q+nYuG3YOjo2bqPtpddIG6cMeyPR8dKLHP3hfeiubJjRuiYMjxvnrGKC+w5BrFcPq6FwzS0n3t5B9Hg9mVdegGt2GWlnrx4y+YAYH57Kcko/8z6qv3EXOhoj3t7Jka/8mFlfvBVHQaKH0DuvDAwFvZKrxFo70FqnJHCJNLRw+I7f41s6m+ZntuJbWp5If64UyubAv24JvmVzUTb5HBMzxOSlTS8Gqnu9rgFOnWg8D7ArpZ4C/MD3tNZ3d+3TwGNKKQ38RGs9ZHpiCaiEmEHMnDyMjEysluYx1WO3Bwidsi3e2o6Znkm8PYg9L5dITR1w8mFP4sPUnu6Crs5z3daMmV9EvLZ/KuDAwXbsRYn1iQDi6Us5/ouHKMooxbd2+ZjaPtMYbhf+xeXjeg6bx075Df3/fym89R3U//5BWh5/btg6Gh94HP+apSjbxH0ttf7tAegKfML79qKDQSJVB0lbvIjW144kCsXjWIEgwV37sedlY8tMx4pEibd3oAyD0N7EsMbMK84n/5a3TFjbxcB8S+cz6wsfoea7vybW1Eq0volDn/sOpZ9+L54FszGcdvLfdgm1f3i8+xhbmjclwVQ8EOLQV39F8MBROnceAkNhRFqwOhOLRee8eQN51wy+BpYQYkRylFK9M8vcdUrQM9A/6lOzYNmA1cDFgBt4USn1ktZ6L3C21vpY1zDAx5VSu7XWzwzWGHmEJsQM0van34w5mALQDs/AOwwFGqK1Aw85tnv6fpbFG+sw8/vPsbBlZ3QHUyiDuldcgKL9pf4Z4sTUZXrc5L3jTSiHfdiy4UPVVH/zTqzoqX2f40NrTdtDf6Ptwftpe/B+wrt2EKlKzDnTbQP35EbrGgnuOUj4UDWxhmaidY0AuCrLybv5jRPSbjE8z8I5VHztExiuRCKMeFsHVV/6P1qeTiSHKLjxUpylPcMAo01txDuDozqXtiwCB4/S9MRGDn75FwQP9PS6+xaWdQdTAJ5FqRlyLcTpRJPooUr1D4kEEmt6/Zzag1QD9F7IsAQ49QluDfCI1rqza67UM8ByAK31sa7/1gH3kxhCOCgJqISYAWJ1J2j8wTcJvjJ8T0EyVDQ44E2yMhXOubPAHPhpr+aUYS6xGIY7MQzNzC/FLJ2HWTwHR0YUnIlkCDH/CsLHEzc7na9tn7AbbpEaOhpDx3uGyRld84kG6okKbNtDZBzWfxrQEAtcxxvrcZYkn0Qic8O5E9qzJoZnz8kk7x29lomIWxz78R/o2LobpRSlt74FR35W9+6jP//7qM5z6Gu/Zu+/fZcj37k30SvVxZbuJXxgf5+yx37wO6LNSaZHFUKM1atApVKqQinlAG4AHjylzF+Bc5VSNqWUh8SQwF1KKa9Syg+glPICG4DtQ51MvgGEmAHCu7cT2bMjZfUZDgP33BICO/tmcYser8VRVDToccHjAXonT1beNOIxhW3OUmIdmraNNQAECtxEm2eRviKd5udaustbwRCt/3qO9IvPwbAP3+shJp/p87Dg999LrDemNcowiLd3YnhcibH1sRjRphY6Xn2djo3biLdN0OKlSuFasozQ61sG3O3KdROumZimiPGRteEcHAW5HP3+3cRbOyBuUfP/foF36TyU3UbFZ2+m4eGXaXz0ZZoef4Xcq8/GXTH45xeAFYnS9spO0tYtItrQSvuWvQOWc5XlEtnXdzRArKWNWHMb9syxpT8PVR+HuIWrfOIyKAoxFpMxh0prHVNK3QY8SiJt+i+01juUUh/q2n+n1nqXUuoR4HXAIpFafbtSajZwf9dQYBvwe631IwOfKWHcA6pkcsALIcZPvLOd0KZBh/0mxZy1mJbN1WAolGGgG+pJW5fVp4w9PxfD7+9O1T2QcE0TjmWLsRkdxLWf9v31WAf790iETyR6pJpf7j88sf7Xf8KWmY5//eQuFCpGRinVPSnO9Ht7tttMnEX5OK+9lOxrL52Qtuh4jPrv3EF4/8A3wwDhplNnCQ5VoSxOPVX5ls0n78arOH7nPQBYoTDtryYSpXS8tovS29+Pd2E5tX96ktDh2mEDquN3/oHQiXYOf+sP6K4EJYbHhXduAVpDtKkD0+saNAHQ8Z/cS+l/vA97rzXdIrUN2HOz+iUy0XGLhr88Rvo5q3EU5nZvb370OTq27Cb/7VeTduaKkV4SISbcZK1DpbV+CHjolG13nvL6DuCOU7YdpGvoX7LGNaBKJge8EGJ8aK0J/uvPBJ9/GB1oxzV/HrGWILHakWcF1U3V+JeU0L6tpmv4lqLtlT24F1VihUKgDMIHjtCdcWII7a+fnGMwuqEvhteDs6xkVMcKAdDx1BNDBlO2igV0vibdU9NF+nlrqf3NX7FOmSdlBUMc/d7dzPnW7WRdPOSand3aN+0k3hnEt7ASUKAt4q0thPf2vJ9ijQMfq5wOnGWFtL/yOpmXn4tSiqZHnuXEL/6MPTsD19xZ2LPTMTxudCRKYPdB3PPK+wRTkMg8Ga1tIHioWgIqIaaI8e6hSiYHvBBiPEQjBJ76K0TDAMSr92L4M3EtnAsa4u0Bokf7Z9gbSCiSRXhbNX2T5iiCO/cPdsi4KfzYe3AUnrr2nhDJC+0cfPirmZNH2/aRLYCtpYdqSjPsNtLPWkXz48/32xdraqXqi9+n8ANvxbNg6LWhdDyOFQyhFIT37RtxO7IuP5f8m3vmdXVs3U3n9r3kvf1qTL8Xw25D2UwidU1EGppJP3cNmZec2a8ed+UstGXhXTh3xG0QYuJNzsK+E228A6phc8ArpT4AfACgrKxsnJsjxMwRfPbv3cHUSbq9mXh7zzA655LVWIEokQN7GCprsCPTQ/jowItUTrS6n9+Do6wYW7oP1+xZpF909mQ3SZxGtGURqx+8l9ZIy0JHR9g7JfHUlJd/yxuJNjTR8dqufvvC1ccJHjgybEClTBN7XhbR2kG6oIbhLCvs87rlXy9R9OGbML0Drw83GO+y+dhzs/CtXDjgfh23iNQ24CjMlUWBhZgg4x1QDZsDvivN4V0Aa9aska8lIVLACnYS3pzEvKlgO5H9R3BULiB6YPfg5Wp3YXhzsTpHMK9knETrGojWJYYWhqtqsGVl0PTg43iWzMe3eimOsmK5iRCDih6tIVZ7IrHQqssN2kIZJthtKJsdbCNPdmKFJv/fhRia4XRQ+un3cehz3yF0qH/AbPq8SS3um3nxmdT9fhQZAU0DW5qv+6UVCtO+eQfup18h+8rzidQ1Eth1kMjRWgDybrpq0Ko88yuIHKvts03H47S/8jqhw8fo2LyT0KEazHQf3sWV5L/zjX3mbAkxoTRoa/p/J493QJVMDnghRIqFNz5JvGH4YUuWdgAQ2bcHx5z5RA/tGbig1jhyfISmQEDVW7jmOEfvuBMsi+CufTT+8e84SgpJO3c9aeetw5YxtmxaYvpRkRp8a0rBVLQ8d/LrKAZ09eaeaMG/ZA7B4+3EGtuSqrPx/sdIO3MVtoy0cWmzSA1lt5H39qs58tUf99t37Ae/pfnRZ6n4+r8PWUfWGy4gsOcQHZuSz5pqy0qn9FPvxV05q3tbx9bd6HCE2l/+hfCR47Q+twkdiVLwnreQedk5w9aZceEZfV4r0yRyooHW5zZ3rwMYb+2g7YXXcM+dRfbVFybdXiFS6eQ6VNPdeK9DlUwOeCFEihmZw88xUsVLiOzvmQMVObAXs2DwNLzR5sCg+yaLDkfAsvpsi9Qcp+EPD3DkC3cQa22fpJaJqco48ivS0h/G731s0DKRQwcwY034l8/CcDsHLXeSe15Fn7W2xNTlW74A7/IFA+4L7j9C+NjQSXsMh53Sz7wfNYJlGzwLZvcJpgBCB3t6yVr+9SK2jDTmfu9zZHUlqxiNnDddSuUPvkD+O6/tsz1c3ffhmo7Faf7nCxz8jzvY//Gvc+zHfyB4sBohxOiNaw/VYDngx/OcQggw0rNwLFoDpo3Itpf67NOGiVG4iPCe/pOqrc4Ator56GAgEagoheF2Ez1RQ7yhk4FH8U5NsYZmAjv2EG9pI94ZwLN0Ae65FSibOfzBYlrSoWZo3I5OW0tHXRFwePDCsRiR/Xtw+r2Yc2bRsePwgHOl0i88k4IP3NAv5bWYugrfex0HPvU/6Mgpi4RrTfX//BTv0nnkXn8FtnTfgMcrpbBnpxM5MXxWUwDD1TcojwdDtDz9Sk99NpNobQOhw0f7ZfQbjaw3XIBrThmdW3YTOnKc0JGegUHxjgBVX/we4eoT3dsiR2tpeeIlvMsX4FuxAMesUrwLKjDsY/usbNtdi6ckA5tv+IcSYnqbCXl7xn0dqoFywAshxpe9dC62G/+Nlv+7vddGF2TNIXb0KHqAYArAam3Bam3ps81WUISRkQM1o0tzPplO/N8vu39v+svDKJcT9/w5GA47OhbHlplO1puvwJ6dOYmtFBNFa0XctprYjj0ojyOpY6zOTqxDe/DOyiUWcxGu6VlfyEz3U/DBG2XO3mnGUZhL0Udu4uj37u53pxc5VkfkWB2x1g5KP/nuQevIveENBPcdRtlMmh97HisYwlla2K83COjXOxWtayLW2AJA5qVn4yjOo/ZX93Psx/fgriwfcL5TPBAifPQEpseNszif+j8/RtuLW9CxGK6yQnxrlpBx3tpE/Y0t1P7q/p65Ykpx4pd/Iee6yzj2f7/pE0z11rl1N51bd6Pzyqnb56D4mqWUvHEZnpL+7UmGYTc58IsX8VXkUPSGxShD/p2I6WvcAyohxMSzgkHa/34v8bqjPRuz5hA5cGDEdSmbAXYvo103airRoTCBrX1XbWh79hUy33AxWW+6HMMx8oQE4vRhuDOwol5AEYtnAZ1JHxurr8cxZx7hXvkMst+4QYKp01T62auIt3Vw4hd/HnC/MofucUw/exXpZycWF08/dzW2ND+2zDSCh2oIVx3lxK/vx+oMYs/Pxru4ss+xrllFFH/8Fo5+/zeJdOldPVhWIIgVCvc7FwDaov6eh7Cl+yn+2DvwrViIZ34FptdNrK2Dtpe24q6chbMwD0duFqX/8T72ffhLeJZUEtxbRdNDT9P8+AvoaHTg+nv/7VoTaezk0C9f4tAvXyJ9cSF5F8wl7/xKvLOzk37P++bkMP9jF3Di8d3sv/M55n7oHAmqZihrBsyhkoBKiGlGx+M0fOermD4nGoVCozIKCI8mmPJ40cpBLDp9h8npaJSmBx4hsGsvxZ/8IKZ/4GE+YnpQTg8Rz3LaNo584d7Igb34l1XSubcWHddkXNR/jSBx+si8/FyC+6pofXZTv33uOaUDHDEw16yeuafuihLcFSXY87KxZfix52SiBnhQk372KurvfZh4Z4C8i99ArKWd+nv+wbEf/4Hy/7oNZe97e2Z6Pcz6wkcGbZ9v+YI+66HZsjMo/PCNpJ+9ilhrOyd+8Rc6Nm1P8i/q22vXuuM4rTuOs++Hz5K9vpyl/30lzpzkPicjzQGibSHcxelU/fZVKt65Lsk2iGlDS1IKIcRpKLx3F7Gj1YT3HSQWz0DlzELbs0ZUh610DlZWJR31Tlq21tPx+vSfsBzac5Aj//UtwtWSiHS8nXjwVY784olJOXcgtpDmza2jHtQfObAPV45Jyaff129ujDi9KKUo+ug7KLrtZky/t88+M90/prq9i+fiLM7HcDoG7NHRWqOtePc8Lu+iOaSdvQpbZjoND/5rVOfsfR6lFJkXnYHhdODIy6bs9vcz57v/Sel/fpD089aCOcRDsiH+aTS+XMVL7/wNHYeSW4sr0hRg59cfw1+Zi7d8ZN9DQpxOpIdKiGnGMbsSw+fH6mhHB4OEq4LJH2yYWNlzaNk6fMr16Sh6vI7D//E1XHPLsedm41m+iPTzzxj+QJE0Hbeo+d2zLL/rQ5NyfltOAcSt4QsOVUdGBt7li1LUIjGZlFJknL8W3/L5HP7qjwkfTjxQMezje3sUrW0kWtuIVVkOgGfhHNzzKth365dxFOak9Fxaa9AaZ3E+zuJ8/CsXkXfz1TQ/+jxNDz2FFTxlmOEwnQmh2nYO/fIllv73G/psj4djmE4bgaMtdB5uwnTZee0Tf8FXmUva/HzUop5n+PUvHCT3rKEXUhbTg0bNiB4qCaiEmGYMpxN7+RzC27eM+FizoJiO7TMzmOottL+K0P4q2l9+Dd/a5Zge92Q3adpQpsHc/7gWKxKl+ZX9xINhFCpxE6cUymaSdea8cTu/FYmMuQ7vGetT0BIxldgy0pj1xVup/dX96FgM5XTSuesAptuFs7QANVSPzig4CnKo+Pq/Y8/pSYijTIOKb/x7ytcza3/ldaxgmIwLeobb2TPTybvhSuzZGRy/696+ByTRe3vsHzsI1LRQdNViCi5dgM3nZMunH6Bjfz3R9hBWOIbhsrPwUxcnElKcMietaeMRQifaKH3zilT8iUJMOgmohJhmtGVhLy0nvGcHJDEBuc+xNj/QPD4NOx1ZFsGd+/CtWTbZLZlW4p1hNr3124PuX/Hr2/CUjT199EAcJSVjr0SPrYdLTE22NB/FH3sHVjTGoc9+m/DhRFIfe142mRvOJvOSMzG9nu7ykdoG7HnJJ2k41anZ/yAR6KRax+adpJ2zasB9GRedQf2fHiHW1DvpUHLDYVu2HqVl61H2//g5zvvbB1n9/evoPNJE/dP7MZx2Cq9YhN0/8LDY2e86gxffcTem20HRFdLbO91JD5UQ4rSjDIO0q96Ma9lKmu78LlZrcgGSrWQ27VVN49y6009g224JqFIs69yFZKyvpOXlgdP31/5tIxW3XtFnm45bBA7XY4Wi+BeNPihyL5hP5lveTPOf/zLqOpru/SNoTfplG0Zdh5i6Isfr8K1YQKyllXhrB9G6Rup++yDtr27DUZCD4XahTJOmfzyFmebDu6SS9PPW4F+9ZLKbPqCct2zAkZc94D5lGngXzx0wMUcyss+sYMl/XY7hSNxOesuy8L5j+MQT9jQXq39wPe27akd1XiGmGgmohJimHGUVZL7nIzR+52tJlQ8HTOItHePcqtNP+KgMgUw1pRT5V64aPKB6cCP5b1iNpzyve9uOT/6atq1VOAszceamkb56NqXvvGBU50+/8nI6X3mFSPXIM/0BoDVmWmqHZYmpw1VWhOvma/AtX0Dbi68R2HOIcPUJwjW1BPdW9RkSF2/roO2F12jftINZX7wVZ3E+pndqDREeLJgCCO4/3H+B4iGG/Nm8DmKdPcNmW18/SqQxgCt35Ek8vKWZeEtlDcCZQHqohBCnNceceTgXLiG8a/h0uTa3rME0EPfc8sluwrSUsXYuhsOGFYn122dFYuz9yp/Iv2o1HbuOgoK2rVUAhI83Ez7ejI5bow6oAPwXXUDjr3876uPthYWjPlacHrxL5+FdmpjPZ4UjqK4FwQPb99H20hYCO/d3ByM6HKHqc98BwPC6STtjBTlvugRHfk+CCR2PE+8IYOuVQfBkwghlJOYYxVrbMT3ufmnTx4MVCnPkm3cRb+3AOauYWFML8fYB1mZTUHTlYmbduBr/gnza99Zx/NFdVP36FWKdEV56129Z/PnLKL5qavbQiUmmQVsSUAkhTmNKKTLfexu1X/wkOjD0Iqaq4zhmmpt42wiyAs4A7gVzJ7sJ05LhsuMuy6Fz/4kB9wcO1nLo+w8NenywugGt9Yjmr7Td/1vCu14n3tSAMgx8S2cTOFSL1REYcft1ZJAFWMW0ZDgdACi7Dd/KhfhWLgTACoaJtbUTD4RQNpPoiQZan91IpLaBzu37sOdmoQyDlidfouXJVwhVHcWWmUb5lz9KrKWd6v/5KQD+dcuIdwaIdwQo/cz7u8+rLYvO554ksm8PVkc7tuJS0t98Q3cANhZWNEbJv78bZbPhnlMGhqLthdc49vsngJ55gobDxpL/uqI7sUTa/HzS5ucTaejk2D92oKNxdn79MTJXluApzhhzu4Q4HUlAJcQ0Z7g9eM+/lI6HHxiynNXajH/uAlo2H52Yhp0GbDlZuBfMmexmTEvt244MGkwlw5GbPqJgKtZYT+CpR7pfayBetROnoTAri4lrH+Fj9ViB5IKrlr/9g4JPjl82QnF6MNxOHO6exAuu0kL8a5f2K6dsNrKvvQhXeQm2rMR710jzMftbtxNrbUMpA3teVnegpLWm/aUttDzxIo7wIdwr15B2zVuw5eanrO02vxfbor4PjNLPXoXyZUD5IWKdYdp2niBr3SysmIV5Sqa++Z+4kLbdtXQcaMAKx9j2+b9T8qblFF/T/+8XM5sM+RNCTAu+Cy6l818PoYdJGW01HSdjVVFXUKVwzcoFSxOqbhiwvLLbsGWmgYZo/fRKaGG4XRT9+/sxXK7Jbsq05K0sxPS6iHeGRnV8qLqBzkO1eCuSu8E0s3IwfGlYHW19d2hN/EQNGAYxzxLMLAe6Zsew9QV37CRy9BiO4qLRNF/MMOnnrunzuuWZV6n95V/IuPAM8m66CmXrm5a95Z8vcPyu+0g7exV5t38TI8Vp24eStnw2acuHXyPKkelh7U9uYMt/PEDz5hpaXj9GoKaFSEuA8nesG3X2QyFOR2PvMxZCTHmGz4/nnAuHLWe1tRKr2kP6Aj/pSzKxdVahnP2fuyi7De/SCjxpbThjh3EUpD7V72Qy3C6K//M2XBVlk92Uact0O8i/avWoj7ciMZqf35P8AfE4VmCIpCuWhS8nTOPG49iKS5OqsvH3f8AKtCffBiG6ZJy3lopvfBKNJtrYPxNruPoEuTe+gaJb3z6hwdRIOTI9rPnR21j8+cuY9fY1pC8ppPrPW9n8sT/RcXDgB3FiZjm5sG+qf6Ya6aESYobwXXoVgeeeSmruR7yuJ7Odjlkou4nhdhPvCOCeU4IZOAJHX+8uo5Jct+R0YHjdlHz2o7jm9F8jRqRW6bsuoP6xrUSbR5ddUjlMYu1BAlV1mC6FZ1YByuEYsGy8sQ6sYdaPUiag0PbhM5bZC/Nw26uI7X4Ox6orhi0vxKkcBTkUvPONA+4reM9bJrYxA9DxOOFtr+JaccaQ5Qy7Scmblne/joeitLx+DFfeyDP/ielpKgZAqSYBlRAzhJmWjvfiy+l4+K8jOs4eqsaeDfaSXCIHj0Nj6/AHncby3n2DBFMTxHQ5cGT7Rh1QHb7zcQ7/+DEA8tZlkFURxczKxb32HBwVlX3Kxpvqh60v0JzIdBnptHAX56PjmmhtfSKNtGngmV+GzRnHjJ/A6NiK6mTIFNNCTIbIwT0EnnsM9/oLcM4f/Xym6MHdtN79fzgXrRr0QcVATJed7HXyGSpmFgmohJhBfBdfSeDZJ7A6kh+mdHLeVayhDkwbxPunuaapCmdpIeHq03vNJldlBf6zRj8MTYyMtizCdWMI0HsFM8rQRHYlek1DW17BvfYclM2GDgVJu/5dGGkZGJnZWM2NQ7QnUV9acQh3+xYArEo32H0YoXoIH4WTHbwnH7iOMaDS0QjKnvzNqhDDccyeT/TQXlru/AaudefhWroW5fJg79puBTpwLl41bKbA0OuvgNaEtryIe935E9R6MR1Z0kMlhJhODLcb/zXX0fr7X474WKutFfuceUQP7O2/M9iOzQhgX7qYWGeUSH0z1kDrmUxhhtdNwQdvlonUE+jo758llqI0/fFeI1l1ZzuBpx7ufh3a+ireS64m93N3EN65lZZffG/AOuyexA2m09kBXc8cjHgQ4oO30WoZfabC0I6tBJ5+jMwPf0red2JMdCxK+4O/J7JnG8o0iZ1ILFodeuUZQq88A4DyeLuXz8h436dwLl41eH1aE962CYDAs4/hWn0OagrP5RJisklAJcQM4znzfIKvvkRk364RH6uHSCmtrDgcfR0bYPq96OLZhI7UYgWm/rpW9rwc8j94M47igsluyozR8PQOjvz8iTHUoJl9uZOTXUWG1YI1SGxjtbfS/sDvsOUX4ly4fMAyRlYedS/VA4pwyIc72VYER5+UIlp9iPCu1wlt3Yh7xdpR1yNExyN/Jvjso0OWORlM2WfNxTF/2bBldTjx2R2rOUR420ZcK9anprFiZtEyh0oIMQ0pw8B/5bU0fm/kAZUVTK7XSYU6UaFtuDO8qDnlaG0S2Ht02LTtkyX7LVfiWVQ5fEGREvFgmIPf/tvYKjEMONbzHh4m3QRoTfOddwy623LnA4lsay374riWzEc1JpFF0BpgCOxwh4RDxI7V0PmvxMLFwVeek4BKjFqs9ijh7ZuSKmsrnkXmbV9A2Ya+/Ysdr0aHeh6G9VtuQIgkaWZGQCVp04WYgQxfGoxiiJGOjezmUYU64egO1LHXcc+euuv1SM/UxGp6bveYh/oZqXocqBS2gkJc/hDlV3vBVLQfDLDvQRs6O4kgOzLydbR0OEzjt7/cfcMaOyGLaYuhxRpqafn196n/0m10PvUQuitjpbYsWn9/J/Ha5N5DziVrUDb7sOUMr6/P6+ihPVihIPHW/inehRDSQyXEjGQvKsF73iV0Pv140sfYCoqI1Y4+6YRpm5q9UwBWeOq2bTpqeXX/mOtQ5hieB5omztkVGLYwqr0KFdkFTWAC885zE3Iu58gjHTRU5ZDr3zdkVfHjQ+8/lY5EaPvzb/rW0dggySnEgLTWdD7yZzqf+BvEogB0/PW3xBvr8F32ZjCMxMLUSTBz8vFeem1yJz5lvlRoy0tE9u/E8PrJ+ODtmOmZI/o7JoK2NC07aslcKg/Ipho97BCC05/0UAkxQ6VdfzNp190M9uGfVgIYXv+YMppZyjnqY8fbVB2KOB3puEXra4fGXI8yxzCERCnMlo0YjdtQkVPmQEWCmLZET2yofvj3hW324BP7TxVvbqThW18itPnlvjusOMFNLyVdj5gZtNZ0PvoXOh/7S3cwdVLwuceo/8KHqP/8B9Gn7BtMvKWJ4MtPo5P4HFfOU2YRWhZWWwux49UEnvpH0n/DhFLw8oce6M7WKcREkh6qU2itJduSmBGUUvgu3IBr6Upa/3g34e1bx/V8hk7uS39SyL/5CdO65RCRhtEncjjJGE1AZdpwVMzGam1E5+aDFYXmgyjddyhrqN0BhIZ/qmracJz91mFPG29qILRtM51PPUq8oa7f/rS33oItT56qi76i+3fS+eifhy6kdfIPumJR2v/4c0KvPI3n/Ctxrlg/6P1OaNNzg58yNDUTDSmlWP3/riTcEsSV5Zns5ohuakbMoZKAqpdg1QkceRmYHtdkN0WICWPLySXrQ/9O6PXNtN1/D/H62gHLxRqHXxh1KLr+EP5lc9Eo0BCu6yBa2zCmOlPFUTp153dNN537UrNWWaQtilEwG+vEwaTKK5cbe3ExsUOJRBbhpsR2M7cce7YbmnahuhJM+DKasKe5KFu6Dwa9d1S4rrgVI7Nw0HOGXt9Ex78eInpo35A3vWZmNo7Z85L6O8T0ZwU6CG16ntDWV8al/ujh/bTe/X0cr64g/eaPYHh8/cpE9mwb9PjIvh1T9uFzzhml7LzjGRZ+4hwMe2rSvGvLomPHIZqf3EzhO6/AntH/egkhAVUv7nJ5QihmJqUU7uWrcS1ZQfDVF2h/+K/9nqSb6RlYLU2jP0k0hK7Z3nNO15zR15VChs+LLTN9spsxY8QDERyZBnnrFTqmOPrPKD2r5I6AhsMv2Zm1Kh+raeCHACcZ/jQMl41YVf+sffH6WuL1YJ+9BFvrFgBUw24qzsiGzoEymynMOatwrH8TtooVQ5438OLTRA8OsG5b79ocTszs3CHLiJkj+OqzhDa/gGPuQqIHRp6JdSQiu7bQ9J0v4Fp1Fp5zL0skKyKRACOyb8egx8Ub6whtfBb32vPGtX2joZRi0afPS2mw17nzMK0v7yTnqrMlmBoNLQv7CiFmGGWaeM44F/faM2m7/x46n3wsscO0EWttSem5nBmKeCCNeMvkpuN1lhROySet01XhORYlGQ9D1zC7whVLiUb9KKUBjUKD0igsUBAJ+dj9Gx+x9v49PNG2KI1NORQu8WL3d6BUnOAxD9Gq6u4yhteHYVdYTcP0sMZPqb+zse9r04591RU41lyFkTV8j2b06BHC218btpxjwVLshSXDlhMzQ/uff4UOB4keGXvilmTEG2rpfOx+Int34L38LdgKSuj42x+GHUbY+ehfcK05d0p+dqaqTR07DhHYfxR7dhol77s6JXXORDMlbboEVEKIfpRpw3/5tehIhMALT2Mvn4vWEBtLD9Up9In9uEuX0DHJAZVvzdALXIrUsmWkoXvNWbKFtg35ReQwYPX7MrHMPCztwrKcxGNODJuFaYthhR2YjgiG0UagNp9QTSPu2XPQ4SDK7UW3txBvGGTF396MIb7wHW7cb/0CtlkjeK8keVNnZk69bGlicuhIuHsx3ZOL8E6UaNVeWu78RtLl4y2NYMXBnL63kfV/f4GW514n703nknmOfE+IoY3pX4JS6g7gaiACHADerbVu6dr3WeC9QBz4mNZ66CW8hRBTiuHzk3HTezDzimn54z0Yfv9oBmYNKdErMYlMg4wrLpzcNsw0uWuh/I1Q9UDSh6hoM2a0mZMzIhp2n0n4SEfXqwCJZ6AmkOhVinXGoekoWMnn6jUHSUKpfFm4r/scZvH8pOsCEsP4bDYYZu0219LVI6pXTE86EqblV9+b7GYkT2us1mbMrOk7XLXiM2/H+sRbMRzJZcIVg5sJPVRjTZv+OLBEa70M2At8FkAptQi4AVgMXA78SCmVmtmBQogJZctPzC202tuxlY1t3pNGdY8k0Rqi7fGxNm9M7LnZKENWj5hISinUys9A2hjeS/3iJEXveVjhw0cxy0YWAGH1X6DXLF+G9yN3jTiYAjCcLlxLVg5dxp+GY+6CEdctpp/AM48S2bVlspuRPMui9bc/JFI19BzB050EUyJZY7qT0Fo/pnvGbrwEnBwIfi1wj9Y6rLU+BOwH1o3lXEKIyWHLy+/+PdY8wuF5Tg+ULMHKqiSscwm1uwl1eqFoMSoti9Dh1GR8Gy33vNmTev6ZShl21KzRz0lIZp2ZwJ4jmCXJ//+rjb7r7tiWXoj7+i+i7KPP+uqoXDjkfteqM1CmPGucqbTWxOqO0/HQfXQ8dO9kN2fEoof20vy9L9F81/8Sb24c/gAxY2mtUv4z1aRy8Ot7gJOfCMUkAqyTarq29aOU+gDwAYCysrIUNkcIkQqGtyerkS0nh1h78unTVd5sAjtPSWutNcE9h3AtqAQ99gVeR8tekEfuLddP2vlnvPJrYOedEO/fMzSsZEaKxuIE9p/AW1lJrHrfsMXVKeukOS98F8oxtiU0IvuGztLmXnv2mOoXp594eyvR/Tvp+Me9WOEQumNy55CmQmTXFhr/9z/wX3sz7jNkCLU4lZIsfwBKqX8CA+UT/5zW+q9dZT4HxIDfnTxsgPIDfgVqre8C7gJYs2aNLG8txBRjOHsml1jBkS3oGD4++FPL8IEqlGmgYxM/7E/ZbBR94n2YHvfwhcW4UI40dPk1cOC+ER+b7DqmxC06d1fjWbyIeNXOQYsZWTlQv73PN1f82F6M+WeOuG0nhfftIrTl1UH3mzl52MsqRl2/OD3oeJzQlheJ7NwChkHotZcgPvS8utORDgXpePwBnCvWY7hkUV0x8ww75E9rfYnWeskAPyeDqVuAq4C3a939NVcDlPaqpgQ4lurGCyEmgM3WnbEsWlONWTBgZ3N/Lg/x1vZBd+toFD2CpAGp5JxVjLMsyb9DjBu18APgGMUaYCN830Qb21HOQXqblMKR4+qXlC/82F1oa3TBfqyxnva//XHIMq7la6ZkymmROvGWRpq++0XafvsjQptfILTxuWkZTJ1kNdXT9oefTHYzxBSj9fj8JEMpdblSao9Sar9S6vZBylyglNqilNqhlHp6JMf2NqY5VEqpy4HPANdorQO9dj0I3KCUciqlKoBKYHyW/BZCjCurva3Pp5dy+7t/N3PzcM6fi724JBF4AbaiImz5+Rh55UPf+NpsPfVO8H2lZ8nIkwyI1FPODNSaL0GSOYti7lWE9HnE2yMjOk/0RD2hJg1ZszGycvukNHfNL0c19h+ap9vqsY4PP1RwIKGtG4keGvpY14q1o6pbTG1aa2K1R2n9w500fPXjxGomb1jzZAhv20i8tRmddDeyEOOjKxneD4ErgEXAjV1J83qXyQB+RCKOWQxcn+yxpxrrHKofAE7g8a4nbS9prT+ktd6hlLoP2EliKOCtWuvJTeclhBiVWF1d39cNjWgNtqwsnK46VHPixtE+t5JY1Iu9fUuinGsp2EwYbEhfLIbN48CWk0WsqRl7USHB/TX9F1hNJaUo/uxteJdKZrWpQhWei85bB7UvDls2WJtO20sNozqP4XPhrYhi69yLzrVhmWkoTzaxQ68Pekx0+9OYxaN4r8SH/rozMjKxzxpbxkwxNUV2baHj7/cQO149fOHpSGuaf/Q17BXz8F58NbbcwslukZgCtDUpvfHrgP1a64MASql7SCTN6z3++ybgL1rrIwBa67oRHNvHmAIqrfXcIfZ9DfjaWOoXQky+WH1tn9fx5ibsJeXYMl2oxp6nr6ppHzaj5yPF1rKNzCWVtO6NYwUGnnulIxGixxKLrkYOVuEoKCRytG7AsqPlKC3CPX82ps+Lme6XYGoqcg80TTd1/CvzcZvboDUxkEIRxox3QuQ4KqMM3TLwXD/lGGRxqmE45swbcr9zwVIZ7jddKWPmBlNd4nXHiNcdw5ZbiO3i0WfzFNPHOGXly1FKbez1+q6uvAwnFQO9/zHWAOtPqWMeYFdKPQX4ge9pre9O8tg+pu8S10KIlDi1hwoSc6mcGf2fPCqr7/wAs30fGXOKaa1OI94yfDYrwzO2rGrd7bDbyHrzFWRcfA6m3zf8AWJyGeP3VZR5Vj72zpcHWLsqwZ6XTWSwgMqfM6pz2isqsRWVEjs28I21c8HSUdUrpr7o4f2T3YQpI7xjE55zN4z6wYQQw2jQWq8ZYn8yCfJswGrgYsANvKiUeinJY/tVJIQQg4rWnui7wTBwzpmFah58qFSf4oGjZOT7iM+uIBL0Eth1eNCyOtCBe3YB2N0E94x87oH/zNX4z1yNe2Elpk8yTZ0ulCs7qUzoI2XP8WIPbhq6ULiWxPdk/+9P5RhdFkilFLbc/AEDKuVw4ly8fFT1iqlNa03g2UcmuxlTRvTQXpp++FUy3vPvmOmZk90cMUk0TFba9GQS5NWQCMw6gU6l1DPA8iSP7WNMSSmEENNf7JSAyj/HjrNzU791e4aiYh3YWrfhibxE5gofjqK8gc9VV0+k+ijKNrIPXzPdT9EnP0jhx96Db+1yCaZON1lLkio2kveFf1UBmQs7wBomq1qgf++UOWc17hu+jH3ZxUmf71T28oHnSLlWn4HhknT905W9uHyymzClxI4coOm7XyTemNqh3EIk4VWgUilVoZRyADeQSJrX21+Bc5VSNqWUh8Swvl1JHtuH9FAJIQalLatfQEV4bAtRmu378LvAWl5OR20a0RP9FwqOdyS/2Kvh81L65U/iyM8dU7vEJMpaQqKHaOh+Kpe/k0C+n2htr3T8CtyVOYSrW7GCiSDfzHDjtl6BtiTSq3vzob4RlIF9xQbs667FzCkd/rjhqj1/AxgmgaceJd7ciHPRMtzrzsW5SHqnpiulFN7L3kxk347JbsqUYrU00vSjr5H9ia9g+NImuzlioo0gzXlKT6t1TCl1G/AoYAK/6Eqa96Gu/XdqrXcppR4BXicxMPxnWuvtAAMdO9T5JKASQgwq3tKMjp7SE2Ukl+J6KEqB2VGFzb+O6KkjCj1uPPkhrFAW0dqmYSsq/uQHJJg6zSm7D529HBq3DF1QW2Qs1PTOk5J9fhZm80b8y9KJu+eCVpjRaugcPpgyKi8GbwFUPYDrqn8bU4/UqZTdge+iK/Cev4F4Yz22vPFNvCGmBivQiXP5OsJbZaWY3qymekKbX8Bz3uWT3RQxg2itHwIeOmXbnae8vgO4I5ljhyJD/oQQg4q3tvR5bXi9EEu+92gosfQlBPf1n2PiyM/C0fY6/lnDP9JyFOXjXjBoslFxGlGF5yVXzup6/9kNci7IxGzenHgdasVs3oTZshE6awev4CRPFrbzPo7tzPei/DnEqraOsuXDtNc0JZiaIXQ8RvD5x4ke2jvZTZmSIgf3THYTxCTRWqX8Z6qRHiohxKCs1tY+r3UkAjYnREa2sGq/ek037Qf612F4PbjTW6AdtE5Nxj9xmshJcihcew3Z5y/DtOqg+bXRn09bEG4HhxfH7BJUlkyaF2OjTBv2WZVE9myb7KZMTcbUuwkWE0FNVlKKCSUBlRBiUPG2XgGVzYZ3fhaqKYmn/8NQ8TBpFXFilBEPK+LBKDafAyeHINBKwHkW7a8eGbaeWFMLWmtZ02c6yFoCaXOg7cDQ5awoZsswmfuSEWwh8udbUZ5MdONBbMvfOPY6xYzn3fAmQhufJd7Uf27oTKc7Oya7CUKMGwmohBCDCm7t6QHwLCzFaEzBjSygsDDbD2DSdfNsAkGI+ebTUpNJvHX4YArAOatYgqlpQikTVn0O/exH0NoO8Q6UGueZzMFmdLA5cf7sgbPyCTESyjSxlc6WgGoAkb3biezbgaNy8WQ3RUwgzeQkpZhoModKCDGgyJHDhLYnhq7YCgswGzaP7/nSVtP4WpB4a/vwhbv4z1k3ji0SE01lL4OzvkMsUITl7LsovbYXQKB1kCOT4PQPuoCwylsA7ozR1y1EL2nXvRsjLWOymzElSaAppisJqIQQAwrt2tn9uzPHMa69BWHfeppfqYV4EmmuAZQi791vJf2is8etTWJyGHlrMYpXYlXvINpZQdy+nri5jtixCLr1+KjqjKato+b5MhprV4Mzve/5ytZjf8M3pKdTpIzhS8O9/oLJbsaUZGTmTHYTxCSYCUkpJKASQgzITOtZL8SINo/becL+dbRsPDqiYzI2nEfGhvPlJniasq19N9hc0NGAVbMT69gusJJfSLq3qH89xx9px4pYRFuiEA937zPmXYrtDV9HSe+USDH3WZdMdhOmHOX145izcLKbISaaBkurlP9MNRJQCSEG1itY0aZnXE4Ry1hBJODC8LqTPsaWlUHO264Zl/aIqUF5szFX35ySugwzDArSV2WRv7q5J+2/Nwfb+f+OUvI1KFJP2R19PkMFOBcuR5ljX8dQiKlIvkmEEAMKbt4IgJmejmodJvPaKAXqFOG9+7B5TFxzyzD93mGPyXv3WzHcklJ9ujNXXI/KKB17PS1bKL0sRLr3VVTbocRGZWC/6DMou7yPxPgwvD5JvnAKw5c+fCExLWkr9T9TjQRUQogBWV1rTbnK0lDW2NadGoi2+YjUJFKwx1taiR4+hCKMe9FsGOTBbu473oJvTZLrFYnTmjId2C75HBhjf6Kt2g5B/OR7WGE7/xMYpWvGXK8QQ/Fecf3EnUwplNMNdvvEnXOEbPlFk90EIcaNpE0XQgzIXlBIeNcOxutjIu6tAN3QZ5sOhYns24e7ophIUyfxlkTGP8PjJv8DN+Ffv2pc2iKmJiNvPkbZeqyqF1JUoR3bpZ/DnHN+auoTYgiO8kpshaXEjlenvG5b6WycC5fjmL8MW1EZyuFEGQZWsJPAUw8T3vka8eYGdGfyWVPHld2Bc8nqyW6FmASJtOnTf/irBFRCiAGZmVkARNvjjMeod41z0H2R6qOYWZlorxsdjlL29c/gyM8dh1aIqUzHwuhwim4InWnYL/8yRvGK1NQnRBKUZ/hhzMMxMrLwXngVtpJydCiIrXQ2pn/g4XOG24vviuvwXXEdOhYluPE5Av/8K/HGujG3Yyw8516G4UsbvqCYhqZmEolUk4BKCDGgSNVBAKLHTuAscKFOTuZPEa2HHnFsBYOYvjQ8ZyyQYGoG0y01Y67DKFuP7dyPodJlyJGYWPHG0a+75Fi8Cs+5l+GYswBlG/lQPmWz4znjQpRh0HbvT8HqO/HEzC1IBFrW+E5I8V/3bsl6KKY9mUMlhOhHx2OEdu5I/B6JEPcvSfk5AjWdQ+437HYcpcXkvv3NKT+3OD0omxOjZOXojs2Zi7H0TdivuxPbG74hwZSYUDoeJ/DMI1jto1uMWjndpL3pnTjnLx1VMNWbe935pN304T7bjMwcMj/8OQyvf0x1D8e5ZDWesy+VJS5mMg16HH6mGumhEkL0E9q1Cx0K9mwYh+9CV+UcOuqbBt1vZqRT9G/vRdnkY2omMyrOwTrw7CnrUClUbiXmgstRufPAlYZSBlbjIZQ/H6w4KrcSlYKEFkKMho6E6HjoPojHBi3jPvsSHPOWYGbkoJxOojVVKNOG4UvDyMjCzM5LWXvcq88m9NqLRHZsxrX2PPxvvJnQlpdHHfAlxbThe8Pbxq9+IaYQuVMRQvTT8eQ/e17Y7ZjBqhSfQZG+Ipe0iz+FFQjR9viTRI4eR8diWIEA/vPPIfPqyyWYEphzL0RlzyG+5V507S7M1TdjlJ+Jsvdfu8xML56EFgrRn+H2kvvfP6bzqYfo/OcDEO16IKAU7jMvwrXyLBxz+y5ya8sf3/eve935WC1N+N/yLpTdQfDFJ3CfeTHBF/81LudzrTwDW0HJuNQ9VtoKoQ//L2rWZ1DG4PN5xdhpkDlUQoiZJ1p7gtD217tfu+eWolo3pvgsmvCjP8csX4Ln+s/gXf7RxFat0aGwrDMl+jAyyzAu/PRkN0OIEVEOJ74Nb8K99lxidcchFsNIz8ReUj4p7XEtW4tz6RqUUkT278K9/gLca88ltPl5dDi1c2QB3OsvSHmdqaIMF9qeid5xA+S9DZV/w2Q3SZzmJKASQvTR8fSTfV4rY/yeLMWrtvcZDK2UQkkwJYSYRszMHMzMnMluBkD3XCbH3IU45i4k3lSP9/LriOzaQmTv9pSdx33GhTjmLkpZfeNBFd+KDu5HV30J0s9BuaZmb9p0MBXnPKWaJKUQQnTT8RiBV17sfm1mZ2OGj4zrOUMP34WORYcvKIQQIqXMrFy8F1xJxgdvxzEvdcmHXOun/lpvShmoOf8LmZei6+5Fz4S7/kmitUr5z1QjAZUQoltw2+tY7T3r/rhzw6hA7bieM7rnFdDjm7ZXCCHE4JRhpC61uWnDXjo7NXWNM2U4URVfgtrfw9EfomvvmewmidOUBFRCCADCB/bR+NMf99mmrPi4n9d95QdRdpkULIQQk8kxdyHK6xtzPba8QpR5+swoUfZsyL8BffRH6Ia/onXP956Od0rPVQpYOvU/U83p844XQowr5XBCrG+KX+3MQXUcHbdz2pddgH3FxeNWvxBCiOQYXj9Zt36B1nvuInbkwAAFTJzL1hLe8lLfzf4M3OduwF42B2WzYy87PXqnelPOYjQWdLyG3nIJOvMSMOxw4reoyu9B5oWT3UQxxaUkoFJKfQq4A8jVWjd0bfss8F4gDnxMa/1oKs4lhBgfttxcME2I9zydi8ftKe/GVm4/tgXrcSy7EHPWIlnwUQghpghbYSnZn/gK8fZWYseOED28j/COzcQb68i45d+wFZTQ4XIT3PwCRMI4V5xB+o0fTDyQO43pUFXPi8hxqP1N4ndlB/+aSWnTdDFVF+JNtTEHVEqpUuBS4EivbYuAG4DFQBHwT6XUPN27H1UIMaUYLjfOufMI79kFgGvebGzNG8e0qK/nrbejnB4wDJQ/C8OfhXL0Xz9ICCHE1GH60zHnL8U5fym+DW9Gx2Pdw/jS3vZ+vFdcT7RqX3ca9tOV1hpd/f+g7o99dygbpJ2JSluPsvknp3HTiKxDlZzvAP8B/LXXtmuBe7TWYeCQUmo/sA54cYDjhRBTQLy1lWhNT0Y/m72dMX9PGia22cvHWIkQQojJdOqcKDMtA3PZ2klqTSpZoONgeMAK9mzOuAhj3vcnr1nitDOm0TxKqWuAo1rrrafsKgaqe72u6do2UB0fUEptVEptrK+vH0tzhBBj0PH0E1idnYkXhoFqPTTmOs2iOWOuQwghhBgPSpkYs25HrXwS3HN7tmecO4mtmn5ODvtL5c9UM2wPlVLqn0DBALs+B/wnsGGgwwbYNuCfr7W+C7gLYM2aNVPwEgkxM0Sqe3qn7Hm5KKthRMcbWUVYHU0QCQGgvBnoUCf4s1PaTiGEEJMjcnA38eZGnAuWY3h96FiMaM1BlDKwz5o7fAVTgNYxOP4LcM9DZV7QldVPgdE1DyzzUsi9bjKbKE5DwwZUWusBFyZQSi0FKoCtXeNnS4DNSql1JHqkSnsVLwGOjbm1QohxE63p6VTW8fiI+69977sDTJPY4R0Y3gyM3FJJhy6EENNIrO447ff+FEwTW3E5sRM1EAmjnG7yvvnzyW5eclqeQVd/G1yz0XX3gbMIlXUpBKvAtwI1+yun9bywqWgq9iil2qjnUGmttwF5J18rpaqANVrrBqXUg8DvlVLfJpGUohJ4ZYxtFUKMk3hnB/Gmxu7XseYWdC4jmkNltdZhFszGNncVSskSd0IIMd24151P6JVniB7a0ye1ug4H0ZaFMqb2Z78O7Ecf/ELiRehg4ifvrai09bD8IbDnSTCVYpqZkZRiXN75WusdwH3ATuAR4FbJ8CfE1BU5dAjldJL3qc9iZmXjWVA64oQUnb/9EoG/fJvA7/57fBophBBiUinDwL3+/H7b3edsmPrBlI6hq74MsZ6Hh3gWoco+A4By5EswJUYtZQv7aq3LT3n9NeBrqapfCDF+IocO4Jw7D8fsOViBTrSVMeI6dDyGrXwp9kVnpb6BQgghpgTnkjUY6X/Cam0CwF5eiXPRislt1DB0rAV94DPQ/mrPRtds1PyfoEzv5DVshpgBI/5SF1AJIU5fvgsvxlp/JpHqw+hQiEhtC7a0dFS4NanjlduP7/3fwsjMH+eWCiGEmEyG10f6O24j8MzDmBnZ+K69eWTjwyeQ1nFoeAB95FsQa+qzT839X5Qjd5JaJqYbCaiEEMSbm7Hl5xN8bVPidWMjuiAv6YDKyC+XYEoIIWYIx5wFOOYsmOxmDErHWqDuPnTDgxDc37PD9EHejajMi8EuwdSE0GDNgC4qCaiEEDT+7E7ijY0op6N7W6jexJPkJ4RZVDlOLRNCCCGSpzt3ofd9FMI1/fapko+hCt45Ca0S050EVELMcFYwSKz2BGiNjka6t7vyDGga/DjlzcB5wQ0YnnTMWYsmoKVCCCHE4HTLs+i9t4EO999py4bcN098o2Y4jUIPuDzt9CIBlRAzXHjv7pEvEuH04PvI/2F40sanUUIIIcQI6Ggz+sCnBw6mAFX6byjTN8GtEiBD/oQQ01xo904af35X/x1KodoOD3qcrXgeyu0fx5YJIYQQI2B6UQXvSsyfan0BgnsT211zULNuR2WcO6nNE9ObBFRCzGA6GsV/0SUEt79OtPpI93ZHSREqtm3Q42IHtxC49+u4r/mo9FIJIYSYdMpwQPGHUIDWFgT3gRUBzwKUYZ/s5s1oM6CDanwW9hVCnB7cS5djLy4hVl/fs1EpnN72YY+N7XmF9jveSccvbkePdMigEEIIMU6UMlCe+SjfUgmmxISQgEqIGSy49TUaf3YnOhTs3mZmZqLaqpKsQWPml8vq8kIIIYToR5OYQ5Xqn2QopS5XSu1RSu1XSt0+wP4LlFKtSqktXT9f7LWvSim1rWv7xuHOJUP+hJjB2h9/tN82w+VM6lgjtxTnuddjX3xOqpslhBBCiGliMsawKKVM4IfApUAN8KpS6kGt9c5Tij6rtb5qkGou1Fo3JHM+CaiEmMFiDfV9XtuLi3B5m6Fj6OOU24/vPd9EuSRjkhBCCCGmnHXAfq31QQCl1D3AtcCpAVVKyJA/IWYww9c3IHLZDqE6jg57nPP8GySYEkIIIcSwJmnIXzFQ3et1Tde2U52plNqqlHpYKbW413YNPKaU2qSU+sBwJ5MeKiFmMMN3Supzhx+inYOWN4sqcZ57HbZ5a8e5ZUIIIYQQg8o5ZW7TXVrr3uvADDS5+9RQbDMwS2vdoZS6EngAqOzad7bW+phSKg94XCm1W2v9zGCNkYBKiBnMf/GlhHft6H4djhVgyyrGbNyMUn0/d1RaNt5bvopyuCa6mUIIIYQ4TY3THKoGrfWaIfbXAKW9XpcAx3oX0Fq39fr9IaXUj5RSOVrrBq31sa7tdUqp+0kMIRw0oJIhf0LMYM55CzA83u7XkUNHCGw/xEAff7qjFbQ1ga0TQgghxOlMA9Y4/CThVaBSKVWhlHIANwAP9i6glCpQXWmKlVLrSMRFjUopr1LK37XdC2wAtg91MgmohJjB4i3NWMFA/+3Zq9E2L1r36jG3YkQ2PjKBrRNCCCGEGDmtdQy4DXgU2AXcp7XeoZT6kFLqQ13FrgO2K6W2At8HbtCJhTXzgee6tr8C/ENrPeQNkAz5E2IGs+cXkPG2t9P28N+xWlu6tyd6qZyAE+V04lq8GOfcCizTwGprxEjLnqwmCyGEEOI0Mhlp0yExjA946JRtd/b6/QfADwY47iCwfCTnkoBKiBnOd94FGGlpxGpP0PHPx7A6e3Km22eVk7bhCtoefZjg5s24Fs7DuWD1JLZWCCGEEGJqkSF/QsxwyjRxzZ1HeMd2nPPm4163HiMtDQD30uV0PP0k0SNVAOhYlMD930XLXCohhBBCJGGS5lBNKAmohBCY6enkfvxT2PLzCb22GeXx4l53BpEjVYT37u5TNn5kJ4F7v0Hs6L5Jaq0QQgghTgca0Dr1P1ONDPkTQgCg7HYy3nQ9/gsvoe2xR+h8/hl0KDRg2dieV4jteQWzdAFm6ULM/HLsS85FGeYEt1oIIYQQYnJJQCWE6MPMyCTzrTeSfvW1BDZtJHbiGOFDB4ns30f4wGEijiKUzUbGW96Cqt+K1VCDmVUowZQQQggh+pmKQ/RSTQIqIcSADLcH3znndb+O1p4geuwoOhrBtWgpps8HXDBp7RNCCCGEmAokoBJCJMWeX4A9v2CymyGEEEKI08gUnPKUcpKUQgghhBBCCCFGSXqohBBCCCGEECmnkTlUQgghhBBCCDFqMyGgGvOQP6XUR5VSe5RSO5RS/9tr+2eVUvu79l021vMIIYQQQgghxFQzph4qpdSFwLXAMq11WCmV17V9EXADsBgoAv6plJqntY6PtcFCCCGEEEKI04MkpRjeh4Fvaq3DAFrruq7t1wL3aK3DWutDwH5g3RjPJYQQQgghhBBTylgDqnnAuUqpl5VSTyul1nZtLwaqe5Wr6drWj1LqA0qpjUqpjfX19WNsjhBCCCGEEGIqOJmUItU/U82wQ/6UUv8EBlp85nNdx2cCZwBrgfuUUrMBNUD5AXv8tNZ3AXcBrFmzZib0CgohhBBCCDEDaPQMGPQ3bECltb5ksH1KqQ8Df9Faa+AVpZQF5JDokSrtVbQEODbGtgohhBBCCCHElDLWIX8PABcBKKXmAQ6gAXgQuEEp5VRKVQCVwCtjPJcQQgghhBDiNCJD/ob3C+AXSqntQAS4pau3aodS6j5gJxADbpUMf0IIIYQQQojpZkwBldY6Atw8yL6vAV8bSX2bNm1qUEodHkubxKBySPQeivEl13liyHWeGHKdJ45c64kh13liyHUeP7MmuwEjNf1nUI29hyqltNa5k92G6UoptVFrvWay2zHdyXWeGHKdJ4Zc54kj13piyHWeGHKdxUkns/xNd2OdQyWEEEIIIYQQM9aU6qESQgghhBBCTB9ajcOgvyk2jlB6qGaOuya7ATOEXOeJIdd5Ysh1njhyrSeGXOeJIddZzCgqkZRPCCGEEEIIIVInR83Wb1AjylGXlLv1TZum0jw96aESQgghhBBCiFGSgGqaUUpdr5TaoZSylFJrem0vV0oFlVJbun7u7LVvtVJqm1Jqv1Lq+0opNTmtP30Mdp279n2261ruUUpd1mu7XOcxUkp9SSl1tNf7+Mpe+wa87mJ0lFKXd13L/Uqp2ye7PdOJUqqq67Ngi1JqY9e2LKXU40qpfV3/zZzsdp5ulFK/UErVda2NeXLboNdVPjNGb5BrLZ/Pop+TWf6m+8K+ElBNP9uBNwPPDLDvgNZ6RdfPh3pt/zHwAaCy6+fy8W/maW/A66yUWgTcACwmcR1/pJQyu3bLdU6N7/R6Hz8Ew153MUJd1+6HwBXAIuDGrmssUufCrvfwyQcytwP/0lpXAv/qei1G5lf0/1wd8LrKZ8aY/YqBv8Pk81n0o8fhf1ONBFTTjNZ6l9Z6T7LllVKFQJrW+kWdmFB3N/DG8WrfdDHEdb4WuEdrHdZaHwL2A+vkOo+7Aa/7JLfpdLYO2K+1Pti1gPs9JK6xGD/XAr/u+v3XyOfDiGmtnwGaTtk82HWVz4wxGORaD0autZj2JKCaWSqUUq8ppZ5WSp3bta0YqOlVpqZrmxidYqC61+uT11Ouc+rcppR6vWvIycnhO4NddzE6cj3HlwYeU0ptUkp9oGtbvtb6OEDXf/MmrXXTy2DXVd7j40M+n0U/M2HIn6xDdRpSSv0TKBhg1+e01n8d5LDjQJnWulEptRp4QCm1GBhoHs/U60udBKO8zoNdT7nOSRrqupMYNvkVEtfuK8C3gPcg1zfV5HqOr7O11seUUnnA40qp3ZPdoBlI3uOpJ5/PYsaSgOo0pLW+ZBTHhIFw1++blFIHgHkknhSV9CpaAhxLRTtPd6O5ziSuZ2mv1yevp1znJCV73ZVSPwX+3vVysOsuRkeu5zjSWh/r+m+dUup+EsOfapVShVrr411DhOsmtZHTx2DXVd7jKaa1rj35u3w+i5M0TMk5T6kmQ/5mCKVU7slJoEqp2SSSIhzsGgLRrpQ6oyvr3DuBwXpfxPAeBG5QSjmVUhUkrvMrcp1To+uG6KQ3kUgOAoNc94lu3zTyKlCplKpQSjlITCh/cJLbNC0opbxKKf/J34ENJN7HDwK3dBW7Bfl8SJXBrqt8ZqSYfD6LwciQP3HaUUq9Cfg/IBf4h1Jqi9b6MuA84L+VUjEgDnxIa31yQumHSWTscQMPd/2IIQx2nbXWO5RS9wE7gRhwq9Y63nWYXOex+1+l1AoSD72qgA8CDHPdxQhprWNKqduARwET+IXWesckN2u6yAfu71o1wQb8Xmv9iFLqVeA+pdR7gSPA9ZPYxtOSUuoPwAVAjlKqBvgv4JsMcF3lM2NsBrnWF8jns5ipVCLhmBBCCCGEEEKkTpaarS8xvpLyev9o3byp17ITk06G/AkhhBBCCCHEKMmQPyGEEEIIIUTKacCaAUkpJKASQgghhBBCjIupmEQi1WTInxBCCCGEEEKMkvRQCSGEEEIIIcaBlnWohBBCCCGEEEIMTgIqIYQQQgghRMolklJMzsK+SqnLlVJ7lFL7lVK3D7D/AqVUq1JqS9fPF5M99lQy5E8IIYQQQggxbSilTOCHwKVADfCqUupBrfXOU4o+q7W+apTHdpMeKiGEEEIIIcS4sNAp/0nCOmC/1vqg1joC3ANcm2STR3ysBFRCCCGEEEKIcaFV6n+AHKXUxl4/HzjltMVAda/XNV3bTnWmUmqrUuphpdTiER7bTYb8CSGEEEIIIU4nDVrrNUPsVwNsO7VrazMwS2vdoZS6EngAqEzy2D6kh0oIIYQQQgiRcomkFJMy5K8GKO31ugQ41qdtWrdprTu6fn8IsCulcpI59lQSUAkhhBBCCCGmk1eBSqVUhVLKAdwAPNi7gFKqQCmlun5fRyIuakzm2FPJkD8hhBBCCCHEuJiMhX211jGl1G3Ao4AJ/EJrvUMp9aGu/XcC1wEfVkrFgCBwg9ZaAwMeO9T5VOI4IYQQQgghhEiddFWhzzD/K+X1PhZ/96Zh5lBNKBnyJ4QQQgghhBCjJEP+hBBCCCGEECmnk08icVqTHiohhBBCCCGEGCXpoRJCCCGEEEKMi+nfPyUBlRBCCCGEEGKcWGr6h1Qy5E8IIYQQQgghRkl6qIQQQgghhBApp0GSUgghhBBCCCGEGJz0UAkhhBBCCCHGxfTvn5IeKiGEEEIIIYQYNemhEkIIIYQQQoyLmTCHSgIqIYQQQgghRMpJUgohhBBCCCGEEEOSHiohhBBCCCHEuLAmuwETQHqohBBCCCGEEGKUpIdKCCGEEEIIMQ40egbMoZKASgghhBBCCJFykpRCCCGEEEIIIcSQpIdKCCGEEEIIkXoKLCU9VEIIIYQQQgghBiE9VEIIIYQQQoiUS8yhmv4koBJCCCGEEEKMC0lKIYQQQgghhBBiUNJDJYQQQgghhBgXM2EdKumhEkIIIYQQQohRkh4qIYQQQgghRMpptMyhEkIIIYQQQggxOOmhEkIIIYQQQoyLmdBDJQGVEEIIIYQQYlzMhIBKhvwJIYQQQgghxChJD5UQQgghhBAi5TTSQyWEEEIIIYQQYgjSQyWEEEIIIYQYF5aa7BaMPwmohBBCCCGEECknQ/6EEEIIIYQQQgxJeqiEEEIIIYQQ40BLD5UQQgghhBBCiMFJD5UQQgghhBAi5TQQnwE9VBJQCSGEEEIIIcaFDPkTQgghhBBCiNOMUupypdQepdR+pdTtQ5Rbq5SKK6Wu67WtSim1TSm1RSm1cbhzSQ+VEEIIIYQQYlxMRg+VUsoEfghcCtQAryqlHtRa7xyg3P8Ajw5QzYVa64Zkzic9VEIIIYQQQojpZB2wX2t9UGsdAe4Brh2g3EeBPwN1YzmZ9FAJIYQQQgghUk6jiStrMk5dDFT3el0DrO9dQClVDLwJuAhYe8rxGnhMKaWBn2it7xrqZBJQCSGEEEIIIU4nOafMbbrrlKBHDXDMqWMPvwt8RmsdV6pf8bO11seUUnnA40qp3VrrZwZrjARUQgghhBBCiJQbx7TpDVrrNUPsrwFKe70uAY6dUmYNcE9XMJUDXKmUimmtH9BaHwPQWtcppe4nMYRQAiohhBBCCCHExJqkdaheBSqVUhXAUeAG4KbeBbTWFSd/V0r9Cvi71voBpZQXMLTW7V2/bwD+e6iTSUAlhBBCCCGEmDa01jGl1G0ksveZwC+01juUUh/q2n/nEIfnA/d39VzZgN9rrR8Z6nxK6+m/2JYQQgghhBBiYplGifY6b0t5ve2hz24aZsjfhJK06UIIIYQQQggxStJDJYQQQgghhEg5pdQjJBI+pFqD1vrycah3VCSgEkIIIYQQQohRkiF/QgghhBBCCDFKElAJIYQQQgghxChJQCWEEEIIIYQQoyQBlRBCCCGEEEKMkgRUQgghhBBCCDFK/x8yQsITf7St/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3, ax3 = plt.subplots(1, 1, figsize=(16, 8))\n",
    "df_gei_geo.plot(column=\"GEI\",  ax=ax3, legend=True, cmap=\"plasma\")\n",
    "plt.title(\"GEI by Country\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this map we can see that North Africa, Middle East and South Asia as a region have low scores for the gender equality index. Unfortunately for a large number of these countries we lack data regarding homocide victims. But for the countries in this region that we do have data for they don't end up having very high rates of female murder rates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'GINI by Country')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4, ax4 = plt.subplots(1, 1, figsize=(16, 8))\n",
    "df_gini_geo.plot(column=\"Gini\",  ax=ax4, legend=True, cmap=\"plasma_r\")\n",
    "plt.title(\"GINI by Country\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this map we can see that Sothern Afcrica, South America (Brazil especially) and parts of Central America Have incredibly high scores on the GINI index (which means more inequality). This regions map very closely to the rate of female murders.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'HDI by Country')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig5, ax5 = plt.subplots(1, 1, figsize=(16, 8))\n",
    "df_hdi_geo.plot(column=\"HDI\",  ax=ax5, legend=True, cmap=\"plasma\")\n",
    "plt.title(\"HDI by Country\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this map Central Africa and South Asia have incredibly low scores on HDI (less development) while Southern Africa and parts of Central America also have low values. We dont have data for most of the Central African countries, but for the countries that remain in both maps (HDI and female Murder rates) there is little corelation just by looking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got all our data from open resources, which are publicly available and no license is needed. The data has no personal identifiable information because we are analyzing different indexes and the number of victims (personal information such as names and heights are not in the data). Thus, there should not be any privacy concerns.\n",
    "\n",
    "Our data might be biased in some ways. For example, some countries do not report all criminal activities to females and the numbers of feamle victims might not be what it truely is. Many factors can cause this, such as different legal standards, different cultures. Also, we did not find all the data we need for some of the countries. For example, one country might have the Gini Index and the Gender Equalilty Index but does not have the number of females victims (we did not find the data). We decided to drop of countries that do not have missing data and do the analysis on the 100 countries that have all the data we need. This means our conclusion might only applies to those 100 countries. However, we believe that the the conlusion should be representative if most of the countries we anaylized show that there is a significant relationship.\n",
    "\n",
    "To make sure the integrity of the data, we did not change anything in the original data or try to skew the data, we honestly present our data using different graphs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, our topics is to analyze the female homicide victims and the relationship between human development, gender inequality, economic inequality. We cleaned up the datasets, consolidated and deleted invalid data. We then use histograms, subplots, OLS regression, and geoplots for visual analysis. With our analysis, our hypothesis is not supported because the GEI has little to no correlation with the rate of female victims.\n",
    "\n",
    "The answer to our question on the most significant factor on female homicide victims is the Gini index. To help answer the question we explored data on the three indices, Gini, GEI, and HDI and used the linear regression model to determine which index is the best predictor to the rate of female victims in each country. \n",
    "\n",
    "Using the multilinear model, about increase of 1 (+1) in the Gini index would mean a 0.1376 increase in female homicide victims per 100,000 females. The p-value is less than 0.05 specifically 1.443e-05. The R-squared value is 0.333 meaning that most of the data fit within the linear regression. The GEI index has a high p-value meaning that it is not statistically correlated. The HDI index has a low p-value however it does not have a bigger impact than the Gini Index.\n",
    "\n",
    "When answering our question we found many limitations and factor such that it constrains our ablility to provide a direct answer. When gathering data we noticed that a lot of data on the Internet is not made public, resulting in incomplete data in some countries. We took the most recent year and compare data across a range of years due to many missing values for countries 2020. When we looked at the data of the rate of female homicide victims we noticed that the highest rates are based around a certain region. It opened our views to understand that there are many more factors that contribute to homicide against women like politics and overall homocide rates. Also the OLS model suggest that there is multicolinearity within the independent variables. This occurs because the human development index could be affected by the wealth inequality in the country. We decided to explore gender inequality and homicides against women due to the problem of gender inequality globally. We hope to provide perspective to which factors affect homicides against women and provide insight to what can be explored next. The next steps could be to explore gender-based violence for each country like domestic violence and sexual harassment and compare that with the same factors we have explored."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ruijia Zhang: Looking for datasets. Research question and background/prior work, cleaning up Gini index data. Conducted EDA and visualization on Gini index data. Talked about overview, background and data in presentation video, editing the final video\n",
    "\n",
    "Zhuotong Li: Helped to find the data of the Human development Index. Also wrote a draft code and got a graph. She did the description about the Human Development Index Dataset. She also did the summary of what we learned in exploring data and helped Joe do a half conclusion and discussion part. In the video, she talked about this project conclusion.\n",
    " \n",
    "Haoyu Zhu: Found the data of Human Development Index, cleaned the HDI data, and wrote the code for Human Development Index EDA. He also did the ethics & privacy part in the project and talked about the limitations and improvements in the video.\n",
    "\n",
    "Joe Cai: Gathered data for the Gender Equality Index and female homicide victims. Cleaned data, described the data using EDA, and carried out analysis on the relationship between the female victims and the three indexes. In the video, talked about EDA and our analysis results.\n",
    "\n",
    "Nimish Prabhakaran: Found data on Female and Male Homicide rates and cleaned it. Implemented geopandas for all variables by creating and cleaning a new geopandas specific dataset. Made the maps with the geopandas specific dataframes. Talked about general visualizations just from looking at the heatmaps of all the variables in the video. \n"
   ]
  }
 ],
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