FinalProjectGroup077-Fa21.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project (Relationship between state economic development and vaccine coverage)"
   ]
  },
  {
   "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 hope to investigate if each state’s economy effectively contributes to the spread of COVID-19. We are going to search, collect, and examine the set of data which contain information about the number of people vaccinated for each state, which we will use to determine the proportion of people vaccinated per state and the rate of increase in the vaccinated population per state, and the GDP and personal income since each state's economy varies. Data will be collected mainly through CDC and public government sites for accurate information. Using population as a confounding variable, we explored the relationships between different measures of the state's economy and the vaccination coverage rates and the increase rate of vaccinated population. Through linear regression, we found correlation between vaccination rates and individual income, and a positive correlation between vaccination population growth and individual income. It suggests that there may be a positive relationship between vaccine coverage and a state's economy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Jianfan Huo\n",
    "- Yunhao Du\n",
    "- Yixin Jiang\n",
    "- Yixiao An\n",
    "- Celine Zhao"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there a positive relationship between state economic development and vaccine coverage？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The COVID-19 epidemic is a huge challenge that the whole world is facing together. On January 10-12, 2020, the WTO published documents about how the novel coronavirus(COVID-19) can has high transmission and infection rate, and also guiding people how to manage with this disease[1]. From the beginning of 2020 until now, COVID-19 is still continue affecting people’s live all over the world. The United States also implemented city lockdown and social distancing restriction during the pandemic to try to reduce the spread and impact of COVID-19. Although COVID-19 virus have not be eliminated today, the COVID-19 vaccine was produced and approved to use. \"Since Dec. 11, 2020, the Pfizer-BioNTech COVID-19 Vaccine has been available under EUA in individuals 16 years of age and older \" (U. S. Food and Drug Administration, 2021)[2]. However, the rate of COVID-19 vaccination coverage has not reaching 100% in U.S.. CDC indicates that the national rate of fully vaccination coverage is approximately 58.5% by November 9, 2021, and only 67.5% people have completed at least one dose[3].\n",
    "\n",
    "Data from the New York Times shows that each state has a different completion rate for COVID-19 vaccine, with vaccination coverage rates ranging from 87% to 36%[4]. Based on the statement from FDA, it is known that people can have COVID-19 vaccine for free in U.S.[5]. At March 5, 2021, Pew Research Center reports that in U.S., 14% of low-income adults reported that they had received at least one dose of the vaccine, compared with 20% of middle-income adults and 27% of high-income adults [6]. The rate of vaccination coverage meant to be thought-provoking. Does each state of the economy has an impact on COVID-19 vaccination rate? If there is an impact, what is the relationship between state economy and vaccination rates?\n",
    "\n",
    "References (include links):\n",
    "- 1) https://www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline#!\n",
    "- 2) https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-total-admin-rate-total  \n",
    "- 3) https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine \n",
    "- 4) https://www.nytimes.com/interactive/2020/us/covid-19-vaccine-doses.html \n",
    "- 5) https://www.fda.gov/consumers/consumer-updates/learn-more-about-covid-19-vaccines-fda#:~:text=FDA%2Dauthorized%20COVID%2D,or%20after%20your%20appointment. \n",
    "- 6) https://www.pewresearch.org/science/2021/03/05/growing-share-of-americans-say-they-plan-to-get-a-covid-19-vaccine-or-already-have/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vaccine coverage will have positive effect on economy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.\n",
    "•\tDataset Name: COVID-19_Vaccinations_in_the_United_States_Jurisdiction.csv\n",
    "\n",
    "•\tLink to the dataset: https://data.cdc.gov/Vaccinations/COVID-19-Vaccinations-in-the-United-States-Jurisdi/unsk-b7fc\n",
    "\n",
    "•\tNumber of observations: 21208\n",
    "\n",
    "•\tThis dataset contains information about the number of people vaccinated with various vaccines in each state. We will use this dataset to analyze vaccination rates in different states.\n",
    "\n",
    "2.\n",
    "\n",
    "•\tDataset Name: GDP and Personal Income\n",
    "\n",
    "•\tLink to the dataset: https://apps.bea.gov/itable/iTable.cfm?ReqID=70&step=1\n",
    "\n",
    "•\tNumber of observations: 59\n",
    "\n",
    "•\tThis dataset contains the sum of each state's per capita income in the second quarter of 2021. We will use this dataset to analyze the state per capita income and vaccination.\n",
    "\n",
    "3.\n",
    "\n",
    "•\tDataset Name: GDP and Personal Income\n",
    "\n",
    "•\tLink to the dataset: https://apps.bea.gov/itable/iTable.cfm?ReqID=70&step=1\n",
    "\n",
    "•\tNumber of observations: 50\n",
    "\n",
    "•\tThis dataset contains full names and abbreviations of all 50 states. We're going to use this data to abbreviate all of our state names.\n",
    "\n",
    "4.\n",
    "\n",
    "•\tDataset Name: List of All 50 US State Abbreviations\n",
    "\n",
    "•\tLink to the dataset: https://abbreviations.yourdictionary.com/articles/state-abbrev.html \n",
    "\n",
    "•\tNumber of observations: 51\n",
    "\n",
    "•\tThis dataset contains the state abbreviations. And we will use this dataset to rename all the states name as abbreviations.\n",
    "\n",
    "5.\n",
    "\n",
    "•\tDataset Name: US States - Ranked by Population 2021\n",
    "\n",
    "•\tLink to the dataset: https://worldpopulationreview.com/states\n",
    "\n",
    "•\tNumber of observations: 51\n",
    "\n",
    "•\tThis dataset contains the population of each state in America. We will use this dataset to calculate the vaccination rates per state with the vaccinated population of the state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## YOUR CODE HERE\n",
    "## YOUR CODE HERE\n",
    "# import dataframes\n",
    "import pandas as pd\n",
    "\n",
    "# import number manipulation\n",
    "import numpy as np \n",
    "\n",
    "# import data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#import Statmodels & patsy\n",
    "import patsy\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vaccine = pd.read_csv('Data/COVID-19_Vaccinations_in_the_United_States_Jurisdiction.csv')\n",
    "df_population = pd.read_csv('Data/Population.csv')\n",
    "df_income = pd.read_csv('Data/net_income.csv')\n",
    "df_GDP = pd.read_csv('Data/states_GDP.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning\n",
    "As we searched, collected, and examined the data, we found that not all of the data set are what we needed for our research topics. We need to pick out the data we need and sort and rename them. From the dataset of COVID-19_Vaccinations_in_the_United_States_Jurisdiction, we extracted the number of vaccinations by state. From the datasets of population, net_income, and states_GDP, we extracted the population of each state, their per capita income, and their GDP.\n",
    "\n",
    "### Steps taken to clean the data：\n",
    "#### df_vaccine:\n",
    "   1. Extract the number of vaccinations by state stored as the dataset named df_vaccine.\n",
    "   2. Only keep the data with 'Date' from \"4/01/2021\" to \"6/30/2021\" and delete all the other in df_vaccine.\n",
    "   3. Change the unit of data to thousands and rename all columns in df_vaccine\n",
    "    \n",
    "#### df_population, df_income, df_gdp:\n",
    "   4. Extract the population of each state stored as the dataset named df_population, rename the state, and remove excess data.\n",
    "   5. Extract the income of each state stored as the dataset named df_income, rename the state, and remove excess data.\n",
    "   6. Extract the GDP of each state stored as the dataset named df_gdp, rename the state, and remove excess data.\n",
    "    \n",
    "#### df_econ:\n",
    "   7. Combine the df_population, df_income, and df_GDP together into the dataset df_econ\n",
    "    \n",
    "#### df:\n",
    "   8. Calculate the total number of vaccinated people between 04/01/2021 and 06/30/2021 for each states. \n",
    "      Calculate the vaccination coverage rates for each states. \n",
    "      Calculate the rate of increase in the vaccinated population. \n",
    "      Add all these result into df_econ and rename df_econ to df. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Extract the number of vaccinations by state stored as the dataset named df_vaccine.\n",
    "Our goal was to analyze whether state economies contributed to the spread of vaccines. The data we need here is the number of vaccinations in 2021 for each region. So, the data we need here is Date, Location, Series_Complete_Yes, Series_Complete_Janssen, Series_Complete_Moderna, Series_Complete_Pfizer, and\tSeries_Complete_Unk_Manuf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## YOUR CODE HERE\n",
    "## FEEL FREE TO ADD MULTIPLE CELLS PER SECTION\n",
    "df_vaccine = df_vaccine[['Date', \n",
    "                         'Location', \n",
    "                         'Series_Complete_Yes', \n",
    "                         'Series_Complete_Janssen', \n",
    "                         'Series_Complete_Moderna',\n",
    "                         'Series_Complete_Pfizer',\n",
    "                         'Series_Complete_Unk_Manuf']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "         Date Location  Series_Complete_Yes  Series_Complete_Janssen  \\\n",
       "0  11/05/2021       SC              2587217                   185786   \n",
       "1  11/05/2021       OR              2661407                   228418   \n",
       "2  11/05/2021       IN              3365638                   259526   \n",
       "3  11/05/2021       NY             13038415                  1127875   \n",
       "4  11/05/2021       WI              3409332                   276848   \n",
       "\n",
       "   Series_Complete_Moderna  Series_Complete_Pfizer  Series_Complete_Unk_Manuf  \n",
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vaccine.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Only keep the data with 'Date' from \"4/01/2021\" to \"6/30/2021\" and delete all the other in df_vaccine.\n",
    "Current official state personal income data are only available through the first two quarters of 2021, so we need to filter our data again. Because universal vaccination began in March, it didn't widely spread until April. So we decided to use data from the second quarter(April-June) to analyze the impact of each state's economy on vaccination penetration. In this step, we only keep the data with 'Date' from \"4/01/2021\" to \"6/30/2021\" and delete all the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                         object\n",
       "Location                     object\n",
       "Series_Complete_Yes           int64\n",
       "Series_Complete_Janssen       int64\n",
       "Series_Complete_Moderna       int64\n",
       "Series_Complete_Pfizer        int64\n",
       "Series_Complete_Unk_Manuf     int64\n",
       "dtype: object"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Check the type of each data in the dataset \n",
    "df_vaccine.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep the data with 'Date' from \"4/01/2021\" to \"6/30/2021\" and delete all the other.\n",
    "start = \"04/01/2021\"\n",
    "end = \"06/30/2021\"\n",
    "\n",
    "df_vaccine = df_vaccine[(df_vaccine['Date'][:] == start ) | (df_vaccine['Date'][:] == end)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "             Date Location  Series_Complete_Yes  Series_Complete_Janssen  \\\n",
       "8305   06/30/2021       ME               819500                   103312   \n",
       "8306   06/30/2021       UT              1196468                   119014   \n",
       "8307   06/30/2021       PA              6345433                   537395   \n",
       "8308   06/30/2021       GU                89574                     4314   \n",
       "8309   06/30/2021       NY             10508196                   894628   \n",
       "...           ...      ...                  ...                      ...   \n",
       "14215  04/01/2021       NH               247295                    16199   \n",
       "14216  04/01/2021       KY               798150                    45857   \n",
       "14217  04/01/2021       NY              3362056                   214142   \n",
       "14218  04/01/2021       SC               792963                    49667   \n",
       "14219  04/01/2021       NC              1768023                   122957   \n",
       "\n",
       "       Series_Complete_Moderna  Series_Complete_Pfizer  \\\n",
       "8305                    327721                  388364   \n",
       "8306                    456194                  621258   \n",
       "8307                   2503367                 3304347   \n",
       "8308                     32664                   52596   \n",
       "8309                   4043410                 5569076   \n",
       "...                        ...                     ...   \n",
       "14215                   129723                  101367   \n",
       "14216                   358920                  393257   \n",
       "14217                  1434622                 1713049   \n",
       "14218                   267357                  475736   \n",
       "14219                   691778                  953262   \n",
       "\n",
       "       Series_Complete_Unk_Manuf  \n",
       "8305                         103  \n",
       "8306                           2  \n",
       "8307                         324  \n",
       "8308                           0  \n",
       "8309                        1082  \n",
       "...                          ...  \n",
       "14215                          6  \n",
       "14216                        116  \n",
       "14217                        243  \n",
       "14218                        203  \n",
       "14219                         26  \n",
       "\n",
       "[130 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vaccine.reset_index(drop=True)\n",
    "df_vaccine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Change the unit of data to thousands and rename all columns in df_vaccine\n",
    "Change the unit of data in df_vaccine to thousands and round the value up. <br>\n",
    "Renaming all columns with unclear name, sorting all data by date and location, and only keeping the data for 51 states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这个不用在conclusion里面说"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming all unclear columns\n",
    "df_vaccine = df_vaccine.rename(columns = {'Series_Complete_Yes':'Num of Vaccinated People in thousand',\n",
    "                        'Series_Complete_Janssen':'Num of Vaccinated People with Janssen in thousand',\n",
    "                         'Series_Complete_Moderna':'Num of Vaccinated People with Moderna in thousand',\n",
    "                         'Series_Complete_Pfizer':'Num of Vaccinated People with Pfizer in thousand',\n",
    "                         'Series_Complete_Unk_Manuf':'Num of Vaccinated People with Unknown Manuf'\n",
    "                        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sorting all data by date and location\n",
    "df_vaccine = df_vaccine.sort_values([\"Date\", \"Location\"], ascending = (True, True))\n",
    "df_vaccine = df_vaccine.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only keeping the data for 51 states\n",
    "states = ['AL','AK','AZ','AR','CA','CO','CT','DE','DC','FL','GA','HI','ID',\n",
    "          'IL','IN','IA','KS','KY','LA','ME','MD','MA','MI','MN','MS','MO',\n",
    "          'MT','NE','NV','NH','NJ','NM','NY','NC','ND','OH','OK','OR','PA',\n",
    "          'RI','SC','SD','TN','TX','UT','VT','VA','WA','WV','WI','WY']\n",
    "df_vaccine = df_vaccine[(df_vaccine['Location'][:].isin(states))]\n",
    "df_vaccine = df_vaccine.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Num of Vaccinated People in thousand</th>\n",
       "      <th>Num of Vaccinated People with Janssen in thousand</th>\n",
       "      <th>Num of Vaccinated People with Moderna in thousand</th>\n",
       "      <th>Num of Vaccinated People with Pfizer in thousand</th>\n",
       "      <th>Num of Vaccinated People with Unknown Manuf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04/01/2021</td>\n",
       "      <td>AK</td>\n",
       "      <td>165.32</td>\n",
       "      <td>5.69</td>\n",
       "      <td>71.94</td>\n",
       "      <td>87.68</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04/01/2021</td>\n",
       "      <td>AL</td>\n",
       "      <td>673.77</td>\n",
       "      <td>42.96</td>\n",
       "      <td>314.53</td>\n",
       "      <td>316.25</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04/01/2021</td>\n",
       "      <td>AR</td>\n",
       "      <td>432.74</td>\n",
       "      <td>27.50</td>\n",
       "      <td>204.41</td>\n",
       "      <td>200.77</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/01/2021</td>\n",
       "      <td>AZ</td>\n",
       "      <td>1267.48</td>\n",
       "      <td>48.30</td>\n",
       "      <td>543.86</td>\n",
       "      <td>674.79</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04/01/2021</td>\n",
       "      <td>CA</td>\n",
       "      <td>6485.63</td>\n",
       "      <td>368.03</td>\n",
       "      <td>2823.85</td>\n",
       "      <td>3293.46</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Location  Num of Vaccinated People in thousand  \\\n",
       "0  04/01/2021       AK                                165.32   \n",
       "1  04/01/2021       AL                                673.77   \n",
       "2  04/01/2021       AR                                432.74   \n",
       "3  04/01/2021       AZ                               1267.48   \n",
       "4  04/01/2021       CA                               6485.63   \n",
       "\n",
       "   Num of Vaccinated People with Janssen in thousand  \\\n",
       "0                                               5.69   \n",
       "1                                              42.96   \n",
       "2                                              27.50   \n",
       "3                                              48.30   \n",
       "4                                             368.03   \n",
       "\n",
       "   Num of Vaccinated People with Moderna in thousand  \\\n",
       "0                                              71.94   \n",
       "1                                             314.53   \n",
       "2                                             204.41   \n",
       "3                                             543.86   \n",
       "4                                            2823.85   \n",
       "\n",
       "   Num of Vaccinated People with Pfizer in thousand  \\\n",
       "0                                             87.68   \n",
       "1                                            316.25   \n",
       "2                                            200.77   \n",
       "3                                            674.79   \n",
       "4                                           3293.46   \n",
       "\n",
       "   Num of Vaccinated People with Unknown Manuf  \n",
       "0                                           10  \n",
       "1                                           35  \n",
       "2                                           57  \n",
       "3                                          526  \n",
       "4                                          280  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the unit of data and round the value up\n",
    "df_vaccine['Num of Vaccinated People in thousand'] = (df_vaccine['Num of Vaccinated People in thousand'] / 1000).round(2)\n",
    "df_vaccine['Num of Vaccinated People with Janssen in thousand'] = (df_vaccine['Num of Vaccinated People with Janssen in thousand']/1000).round(2)\n",
    "df_vaccine['Num of Vaccinated People with Moderna in thousand'] = (df_vaccine['Num of Vaccinated People with Moderna in thousand']/1000).round(2)\n",
    "df_vaccine['Num of Vaccinated People with Pfizer in thousand'] = (df_vaccine['Num of Vaccinated People with Pfizer in thousand']/1000).round(2)\n",
    "df_vaccine.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Extract the population of each state stored as the dataset named df_population, rename the state, and remove excess data.\n",
    "We will get population data and remove excess data."
   ]
  },
  {
   "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>rank</th>\n",
       "      <th>State</th>\n",
       "      <th>Pop</th>\n",
       "      <th>Growth</th>\n",
       "      <th>Pop2018</th>\n",
       "      <th>Pop2010</th>\n",
       "      <th>growthSince2010</th>\n",
       "      <th>Percent</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>California</td>\n",
       "      <td>39613493</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>39461588</td>\n",
       "      <td>37319502</td>\n",
       "      <td>0.0615</td>\n",
       "      <td>0.1184</td>\n",
       "      <td>254.2929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Texas</td>\n",
       "      <td>29730311</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>28628666</td>\n",
       "      <td>25241971</td>\n",
       "      <td>0.1778</td>\n",
       "      <td>0.0889</td>\n",
       "      <td>113.8081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Florida</td>\n",
       "      <td>21944577</td>\n",
       "      <td>0.0330</td>\n",
       "      <td>21244317</td>\n",
       "      <td>18845537</td>\n",
       "      <td>0.1644</td>\n",
       "      <td>0.0656</td>\n",
       "      <td>409.2229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>New York</td>\n",
       "      <td>19299981</td>\n",
       "      <td>-0.0118</td>\n",
       "      <td>19530351</td>\n",
       "      <td>19399878</td>\n",
       "      <td>-0.0051</td>\n",
       "      <td>0.0577</td>\n",
       "      <td>409.5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>12804123</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>12800922</td>\n",
       "      <td>12711160</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0383</td>\n",
       "      <td>286.1704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank         State       Pop  Growth   Pop2018   Pop2010  growthSince2010  \\\n",
       "0     1    California  39613493  0.0038  39461588  37319502           0.0615   \n",
       "1     2         Texas  29730311  0.0385  28628666  25241971           0.1778   \n",
       "2     3       Florida  21944577  0.0330  21244317  18845537           0.1644   \n",
       "3     4      New York  19299981 -0.0118  19530351  19399878          -0.0051   \n",
       "4     5  Pennsylvania  12804123  0.0003  12800922  12711160           0.0073   \n",
       "\n",
       "   Percent   density  \n",
       "0   0.1184  254.2929  \n",
       "1   0.0889  113.8081  \n",
       "2   0.0656  409.2229  \n",
       "3   0.0577  409.5400  \n",
       "4   0.0383  286.1704  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_population.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_population = df_population.drop(30)\n",
    "df_population = df_population.iloc[: , 1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_population = df_population.sort_values(['State'], ascending = True)\n",
    "df_population = df_population.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_population['State'] = ['AL','AK','AZ','AR','CA','CO','CT','DE','DC','FL','GA','HI','ID',\n",
    "              'IL','IN','IA','KS','KY','LA','ME','MD','MA','MI','MN','MS','MO','MT',\n",
    "              'NE','NV','NH','NJ','NM','NY','NC','ND','OH','OK','OR','PA','RI','SC',\n",
    "              'SD','TN','TX','UT','VT','VA','WA','WV','WI','WY']\n",
    "df_population = df_population.sort_values(['State'], ascending = True)"
   ]
  },
  {
   "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",
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       "        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>State</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>724357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>4934193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>3033946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>7520103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>39613493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Population\n",
       "0    AK      724357\n",
       "1    AL     4934193\n",
       "2    AR     3033946\n",
       "3    AZ     7520103\n",
       "4    CA    39613493"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_population = df_population.rename(columns = {'Pop':'Population'})\n",
    "df_population = df_population.reset_index(drop = True)\n",
    "df_population.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Extract the income of each state stored as the dataset named df_income, rename the state, and remove excess data.\n",
    "In this step, we need to rearrange personal income and GDP data for each state. We deleted the geographic information (GeoFips) for each state. Since our data only analyzed personal income and GDP data for the second quarter of 2021, we also removed personal income and GDP data for the first quarter of 2021."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>GeoName</th>\n",
       "      <th>2021:Q1</th>\n",
       "      <th>2021:Q2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>66191</td>\n",
       "      <td>62215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>52633</td>\n",
       "      <td>48133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Alaska *</td>\n",
       "      <td>69683</td>\n",
       "      <td>65501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4000</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>54620</td>\n",
       "      <td>50373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5000</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>53643</td>\n",
       "      <td>49079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GeoFips        GeoName  2021:Q1  2021:Q2\n",
       "0        0  United States    66191    62215\n",
       "1     1000        Alabama    52633    48133\n",
       "2     2000       Alaska *    69683    65501\n",
       "3     4000        Arizona    54620    50373\n",
       "4     5000       Arkansas    53643    49079"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_income = df_income[['GeoName','2021:Q2']]\n",
    "df_income.columns = ['State', 'Personal_Income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_income = df_income.iloc[:52 , :]\n",
    "df_income = df_income.iloc[1: , :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_income['State'] = ['AL','AK','AZ','AR','CA','CO','CT','DE','DC','FL','GA','HI','ID',\n",
    "              'IL','IN','IA','KS','KY','LA','ME','MD','MA','MI','MN','MS','MO','MT',\n",
    "              'NE','NV','NH','NJ','NM','NY','NC','ND','OH','OK','OR','PA','RI','SC',\n",
    "              'SD','TN','TX','UT','VT','VA','WA','WV','WI','WY']\n",
    "df_income = df_income.sort_values(['State'], ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>State</th>\n",
       "      <th>Personal_Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>65501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>48133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>49079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>50373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>74304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Personal_Income\n",
       "0    AK            65501\n",
       "1    AL            48133\n",
       "2    AR            49079\n",
       "3    AZ            50373\n",
       "4    CA            74304"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income = df_income.reset_index(drop = True)\n",
    "df_income.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Extract the GDP of each state stored as the dataset named df_gdp, rename the state, and remove excess data.\n",
    "Abbreviate the full name of each state(include the DC) and drop the dataset which is not the state.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>GeoFips</th>\n",
       "      <th>GeoName</th>\n",
       "      <th>2021:Q1</th>\n",
       "      <th>2021:Q2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>United States *</td>\n",
       "      <td>22038226.0</td>\n",
       "      <td>22740959.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>237303.0</td>\n",
       "      <td>243554.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>52373.8</td>\n",
       "      <td>54020.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4000</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>388598.8</td>\n",
       "      <td>400155.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5000</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>139172.7</td>\n",
       "      <td>143438.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GeoFips          GeoName     2021:Q1     2021:Q2\n",
       "0        0  United States *  22038226.0  22740959.0\n",
       "1     1000          Alabama    237303.0    243554.6\n",
       "2     2000           Alaska     52373.8     54020.3\n",
       "3     4000          Arizona    388598.8    400155.7\n",
       "4     5000         Arkansas    139172.7    143438.4"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_GDP.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_GDP = df_GDP[['GeoName','2021:Q2']]\n",
    "df_GDP.columns = ['State', 'GDP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_GDP = df_GDP.iloc[:52 , :]\n",
    "df_GDP = df_GDP.iloc[1: , :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_GDP['State'] = ['AL','AK','AZ','AR','CA','CO','CT','DE','DC','FL','GA','HI','ID',\n",
    "              'IL','IN','IA','KS','KY','LA','ME','MD','MA','MI','MN','MS','MO','MT',\n",
    "              'NE','NV','NH','NJ','NM','NY','NC','ND','OH','OK','OR','PA','RI','SC',\n",
    "              'SD','TN','TX','UT','VT','VA','WA','WV','WI','WY']\n",
    "df_GDP = df_GDP.sort_values(['State'], ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        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>State</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>54020.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>243554.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>143438.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>400155.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>3290169.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State        GDP\n",
       "0    AK    54020.3\n",
       "1    AL   243554.6\n",
       "2    AR   143438.4\n",
       "3    AZ   400155.7\n",
       "4    CA  3290169.6"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_GDP = df_GDP.reset_index(drop = True)\n",
    "df_GDP.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Extract the GDP of each state stored as the dataset named df_gdp, rename the state, and remove excess data.\n",
    "Combine the df_population, df_income, and df_GDP together into the dataset df_econ. <br>\n",
    "Change the unit of data in df_econ and round the value up. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>State</th>\n",
       "      <th>Personal_Income</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>65501</td>\n",
       "      <td>54020.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>48133</td>\n",
       "      <td>243554.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>49079</td>\n",
       "      <td>143438.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>50373</td>\n",
       "      <td>400155.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>74304</td>\n",
       "      <td>3290169.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Personal_Income        GDP\n",
       "0    AK            65501    54020.3\n",
       "1    AL            48133   243554.6\n",
       "2    AR            49079   143438.4\n",
       "3    AZ            50373   400155.7\n",
       "4    CA            74304  3290169.6"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the df_GDP and df_income together\n",
    "df_income['GDP'] = df_GDP['GDP']\n",
    "df_econ = df_income\n",
    "df_econ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the column Population of df_population into df_econ\n",
    "df_econ[\"Population\"] = df_population['Population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the unit of data and round the value up\n",
    "df_econ['Personal_Income'] = (df_econ['Personal_Income']/1000).round(2)\n",
    "df_econ['GDP'] = (df_econ['GDP']/1000).round(2)\n",
    "df_econ['Population'] = (df_econ['Population']/1000).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>State</th>\n",
       "      <th>Personal_Income in thousand</th>\n",
       "      <th>GDP in thousand</th>\n",
       "      <th>Population in thousand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>65.50</td>\n",
       "      <td>54.02</td>\n",
       "      <td>724.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>48.13</td>\n",
       "      <td>243.55</td>\n",
       "      <td>4934.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>49.08</td>\n",
       "      <td>143.44</td>\n",
       "      <td>3033.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>50.37</td>\n",
       "      <td>400.16</td>\n",
       "      <td>7520.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>74.30</td>\n",
       "      <td>3290.17</td>\n",
       "      <td>39613.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Personal_Income in thousand  GDP in thousand  Population in thousand\n",
       "0    AK                        65.50            54.02                  724.36\n",
       "1    AL                        48.13           243.55                 4934.19\n",
       "2    AR                        49.08           143.44                 3033.95\n",
       "3    AZ                        50.37           400.16                 7520.10\n",
       "4    CA                        74.30          3290.17                39613.49"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_econ = df_econ.rename(columns = {'Personal_Income':'Personal_Income in thousand',\n",
    "                        'GDP':'GDP in thousand',\n",
    "                         'Population':'Population in thousand'\n",
    "                        })\n",
    "df_econ.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Calculate and get the final dataset df.\n",
    "Calculate the total number of vaccinated people between 04/01/2021 and 06/30/2021 for each states.<br>\n",
    "Calculate the vaccination coverage rates for each states. <br>\n",
    "Calculate the rate of increase in the vaccinated population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function sum_vaccinated to get the total number of vacciated people for an input state\n",
    "def sum_vaccinated(state):\n",
    "    sum_start = df_vaccine[(df_vaccine['Date'][:] == \"04/01/2021\") & (df_vaccine['Location'][:] == state)]['Num of Vaccinated People in thousand']\n",
    "    sum_end = df_vaccine[(df_vaccine['Date'][:] == \"06/30/2021\") & (df_vaccine['Location'][:] == state)]['Num of Vaccinated People in thousand']\n",
    "    return (sum_end.values[0] - sum_start.values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the total number of vaccinated people for each states\n",
    "vaccinated_quarter = df_vaccine['Location'].apply(sum_vaccinated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the data into df as a new column\n",
    "df = df_econ\n",
    "df['Total Vaccinated in thousand'] = vaccinated_quarter[:52]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the vaccination rate for each state and add the data into df as a new column\n",
    "df['Vaccination rate'] = (df['Total Vaccinated in thousand']/df['Population in thousand']).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the number of vaccinated people in 04/01/2021 and the total number of vaccination people in this quarter\n",
    "sum_start = df_vaccine[df_vaccine['Date'] == \"04/01/2021\"]['Num of Vaccinated People in thousand']\n",
    "change = df['Total Vaccinated in thousand'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the increase rate in vaccinnated population for each states\n",
    "rate_increase = (change/sum_start).round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the data into df as a new column\n",
    "df['Increase rate in vaccinated population'] = rate_increase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finally, we get a clear and clean data frame df. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Personal_Income in thousand</th>\n",
       "      <th>GDP in thousand</th>\n",
       "      <th>Population in thousand</th>\n",
       "      <th>Total Vaccinated in thousand</th>\n",
       "      <th>Vaccination rate</th>\n",
       "      <th>Increase rate in vaccinated population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>65.50</td>\n",
       "      <td>54.02</td>\n",
       "      <td>724.36</td>\n",
       "      <td>148.39</td>\n",
       "      <td>0.2049</td>\n",
       "      <td>0.8976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>48.13</td>\n",
       "      <td>243.55</td>\n",
       "      <td>4934.19</td>\n",
       "      <td>920.85</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>1.3667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>49.08</td>\n",
       "      <td>143.44</td>\n",
       "      <td>3033.95</td>\n",
       "      <td>596.79</td>\n",
       "      <td>0.1967</td>\n",
       "      <td>1.3791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>50.37</td>\n",
       "      <td>400.16</td>\n",
       "      <td>7520.10</td>\n",
       "      <td>1661.62</td>\n",
       "      <td>0.2210</td>\n",
       "      <td>1.3110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>74.30</td>\n",
       "      <td>3290.17</td>\n",
       "      <td>39613.49</td>\n",
       "      <td>13193.70</td>\n",
       "      <td>0.3331</td>\n",
       "      <td>2.0343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CO</td>\n",
       "      <td>66.68</td>\n",
       "      <td>416.94</td>\n",
       "      <td>5893.63</td>\n",
       "      <td>1955.97</td>\n",
       "      <td>0.3319</td>\n",
       "      <td>1.9021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CT</td>\n",
       "      <td>81.85</td>\n",
       "      <td>294.65</td>\n",
       "      <td>3552.82</td>\n",
       "      <td>1418.90</td>\n",
       "      <td>0.3994</td>\n",
       "      <td>1.9083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DC</td>\n",
       "      <td>90.04</td>\n",
       "      <td>151.39</td>\n",
       "      <td>714.15</td>\n",
       "      <td>267.98</td>\n",
       "      <td>0.3752</td>\n",
       "      <td>2.6651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DE</td>\n",
       "      <td>57.50</td>\n",
       "      <td>79.28</td>\n",
       "      <td>990.33</td>\n",
       "      <td>323.54</td>\n",
       "      <td>0.3267</td>\n",
       "      <td>1.9777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FL</td>\n",
       "      <td>58.44</td>\n",
       "      <td>1198.91</td>\n",
       "      <td>21944.58</td>\n",
       "      <td>6351.89</td>\n",
       "      <td>0.2895</td>\n",
       "      <td>1.8197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GA</td>\n",
       "      <td>54.44</td>\n",
       "      <td>673.07</td>\n",
       "      <td>10830.01</td>\n",
       "      <td>2557.00</td>\n",
       "      <td>0.2361</td>\n",
       "      <td>1.9261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HI</td>\n",
       "      <td>60.81</td>\n",
       "      <td>89.66</td>\n",
       "      <td>1406.43</td>\n",
       "      <td>456.19</td>\n",
       "      <td>0.3244</td>\n",
       "      <td>1.6512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IA</td>\n",
       "      <td>56.46</td>\n",
       "      <td>220.93</td>\n",
       "      <td>3167.97</td>\n",
       "      <td>891.80</td>\n",
       "      <td>0.2815</td>\n",
       "      <td>1.4348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ID</td>\n",
       "      <td>50.24</td>\n",
       "      <td>92.30</td>\n",
       "      <td>1860.12</td>\n",
       "      <td>343.85</td>\n",
       "      <td>0.1849</td>\n",
       "      <td>1.1456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IL</td>\n",
       "      <td>67.24</td>\n",
       "      <td>938.22</td>\n",
       "      <td>12569.32</td>\n",
       "      <td>3709.38</td>\n",
       "      <td>0.2951</td>\n",
       "      <td>1.7329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IN</td>\n",
       "      <td>54.62</td>\n",
       "      <td>415.34</td>\n",
       "      <td>6805.66</td>\n",
       "      <td>1539.52</td>\n",
       "      <td>0.2262</td>\n",
       "      <td>1.3419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KS</td>\n",
       "      <td>58.38</td>\n",
       "      <td>193.14</td>\n",
       "      <td>2917.22</td>\n",
       "      <td>721.00</td>\n",
       "      <td>0.2472</td>\n",
       "      <td>1.4431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KY</td>\n",
       "      <td>49.26</td>\n",
       "      <td>234.31</td>\n",
       "      <td>4480.71</td>\n",
       "      <td>1144.26</td>\n",
       "      <td>0.2554</td>\n",
       "      <td>1.4336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LA</td>\n",
       "      <td>53.28</td>\n",
       "      <td>253.31</td>\n",
       "      <td>4627.00</td>\n",
       "      <td>827.59</td>\n",
       "      <td>0.1789</td>\n",
       "      <td>1.0607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MA</td>\n",
       "      <td>82.00</td>\n",
       "      <td>625.11</td>\n",
       "      <td>6912.24</td>\n",
       "      <td>2902.54</td>\n",
       "      <td>0.4199</td>\n",
       "      <td>2.1638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MD</td>\n",
       "      <td>70.00</td>\n",
       "      <td>443.73</td>\n",
       "      <td>6065.44</td>\n",
       "      <td>2309.64</td>\n",
       "      <td>0.3808</td>\n",
       "      <td>2.1466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ME</td>\n",
       "      <td>55.63</td>\n",
       "      <td>74.60</td>\n",
       "      <td>1354.52</td>\n",
       "      <td>547.82</td>\n",
       "      <td>0.4044</td>\n",
       "      <td>2.0164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MI</td>\n",
       "      <td>54.86</td>\n",
       "      <td>559.48</td>\n",
       "      <td>9992.43</td>\n",
       "      <td>2965.13</td>\n",
       "      <td>0.2967</td>\n",
       "      <td>1.7081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MN</td>\n",
       "      <td>64.67</td>\n",
       "      <td>407.40</td>\n",
       "      <td>5706.40</td>\n",
       "      <td>1832.51</td>\n",
       "      <td>0.3211</td>\n",
       "      <td>1.6842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MO</td>\n",
       "      <td>54.12</td>\n",
       "      <td>359.03</td>\n",
       "      <td>6169.04</td>\n",
       "      <td>1443.39</td>\n",
       "      <td>0.2340</td>\n",
       "      <td>1.5127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MS</td>\n",
       "      <td>44.13</td>\n",
       "      <td>123.78</td>\n",
       "      <td>2966.41</td>\n",
       "      <td>433.64</td>\n",
       "      <td>0.1462</td>\n",
       "      <td>0.9591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MT</td>\n",
       "      <td>55.53</td>\n",
       "      <td>57.92</td>\n",
       "      <td>1085.00</td>\n",
       "      <td>247.68</td>\n",
       "      <td>0.2283</td>\n",
       "      <td>1.1772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NC</td>\n",
       "      <td>52.57</td>\n",
       "      <td>646.21</td>\n",
       "      <td>10701.02</td>\n",
       "      <td>2609.92</td>\n",
       "      <td>0.2439</td>\n",
       "      <td>1.4762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ND</td>\n",
       "      <td>65.91</td>\n",
       "      <td>64.98</td>\n",
       "      <td>770.03</td>\n",
       "      <td>138.68</td>\n",
       "      <td>0.1801</td>\n",
       "      <td>0.8830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NE</td>\n",
       "      <td>61.29</td>\n",
       "      <td>150.51</td>\n",
       "      <td>1952.00</td>\n",
       "      <td>549.46</td>\n",
       "      <td>0.2815</td>\n",
       "      <td>1.4726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NH</td>\n",
       "      <td>68.13</td>\n",
       "      <td>93.89</td>\n",
       "      <td>1372.20</td>\n",
       "      <td>508.49</td>\n",
       "      <td>0.3706</td>\n",
       "      <td>2.0562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NJ</td>\n",
       "      <td>77.19</td>\n",
       "      <td>671.48</td>\n",
       "      <td>8874.52</td>\n",
       "      <td>3167.34</td>\n",
       "      <td>0.3569</td>\n",
       "      <td>1.8306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NM</td>\n",
       "      <td>48.16</td>\n",
       "      <td>108.24</td>\n",
       "      <td>2105.00</td>\n",
       "      <td>639.34</td>\n",
       "      <td>0.3037</td>\n",
       "      <td>1.2709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NV</td>\n",
       "      <td>55.58</td>\n",
       "      <td>187.39</td>\n",
       "      <td>3185.79</td>\n",
       "      <td>789.95</td>\n",
       "      <td>0.2480</td>\n",
       "      <td>1.5740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NY</td>\n",
       "      <td>78.40</td>\n",
       "      <td>1868.22</td>\n",
       "      <td>19299.98</td>\n",
       "      <td>7146.14</td>\n",
       "      <td>0.3703</td>\n",
       "      <td>2.1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>OH</td>\n",
       "      <td>55.77</td>\n",
       "      <td>732.12</td>\n",
       "      <td>11714.62</td>\n",
       "      <td>3215.60</td>\n",
       "      <td>0.2745</td>\n",
       "      <td>1.5983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>OK</td>\n",
       "      <td>51.56</td>\n",
       "      <td>205.56</td>\n",
       "      <td>3990.44</td>\n",
       "      <td>788.10</td>\n",
       "      <td>0.1975</td>\n",
       "      <td>1.0783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>OR</td>\n",
       "      <td>58.54</td>\n",
       "      <td>264.11</td>\n",
       "      <td>4289.44</td>\n",
       "      <td>1503.00</td>\n",
       "      <td>0.3504</td>\n",
       "      <td>2.0851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>PA</td>\n",
       "      <td>63.28</td>\n",
       "      <td>832.70</td>\n",
       "      <td>12804.12</td>\n",
       "      <td>4147.70</td>\n",
       "      <td>0.3239</td>\n",
       "      <td>1.8873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>RI</td>\n",
       "      <td>61.62</td>\n",
       "      <td>64.17</td>\n",
       "      <td>1061.51</td>\n",
       "      <td>396.52</td>\n",
       "      <td>0.3735</td>\n",
       "      <td>1.7446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>SC</td>\n",
       "      <td>49.74</td>\n",
       "      <td>266.08</td>\n",
       "      <td>5277.83</td>\n",
       "      <td>1198.94</td>\n",
       "      <td>0.2272</td>\n",
       "      <td>1.5120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>SD</td>\n",
       "      <td>62.45</td>\n",
       "      <td>60.81</td>\n",
       "      <td>896.58</td>\n",
       "      <td>199.13</td>\n",
       "      <td>0.2221</td>\n",
       "      <td>0.9843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>TN</td>\n",
       "      <td>53.34</td>\n",
       "      <td>411.69</td>\n",
       "      <td>6944.26</td>\n",
       "      <td>1442.99</td>\n",
       "      <td>0.2078</td>\n",
       "      <td>1.4833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TX</td>\n",
       "      <td>57.79</td>\n",
       "      <td>1950.36</td>\n",
       "      <td>29730.31</td>\n",
       "      <td>7778.51</td>\n",
       "      <td>0.2616</td>\n",
       "      <td>1.8727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>UT</td>\n",
       "      <td>53.54</td>\n",
       "      <td>212.85</td>\n",
       "      <td>3310.77</td>\n",
       "      <td>816.63</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>2.1499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>VA</td>\n",
       "      <td>63.97</td>\n",
       "      <td>586.25</td>\n",
       "      <td>8603.98</td>\n",
       "      <td>2931.97</td>\n",
       "      <td>0.3408</td>\n",
       "      <td>1.9676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>VT</td>\n",
       "      <td>60.40</td>\n",
       "      <td>36.09</td>\n",
       "      <td>623.25</td>\n",
       "      <td>289.31</td>\n",
       "      <td>0.4642</td>\n",
       "      <td>2.4121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>WA</td>\n",
       "      <td>70.44</td>\n",
       "      <td>661.52</td>\n",
       "      <td>7796.94</td>\n",
       "      <td>2747.26</td>\n",
       "      <td>0.3524</td>\n",
       "      <td>1.9578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>WI</td>\n",
       "      <td>57.36</td>\n",
       "      <td>363.59</td>\n",
       "      <td>5852.49</td>\n",
       "      <td>1773.63</td>\n",
       "      <td>0.3031</td>\n",
       "      <td>1.6039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>WV</td>\n",
       "      <td>46.34</td>\n",
       "      <td>85.80</td>\n",
       "      <td>1767.86</td>\n",
       "      <td>319.15</td>\n",
       "      <td>0.1805</td>\n",
       "      <td>0.9187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>WY</td>\n",
       "      <td>63.59</td>\n",
       "      <td>41.20</td>\n",
       "      <td>581.08</td>\n",
       "      <td>89.99</td>\n",
       "      <td>0.1549</td>\n",
       "      <td>0.8258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State  Personal_Income in thousand  GDP in thousand  \\\n",
       "0     AK                        65.50            54.02   \n",
       "1     AL                        48.13           243.55   \n",
       "2     AR                        49.08           143.44   \n",
       "3     AZ                        50.37           400.16   \n",
       "4     CA                        74.30          3290.17   \n",
       "5     CO                        66.68           416.94   \n",
       "6     CT                        81.85           294.65   \n",
       "7     DC                        90.04           151.39   \n",
       "8     DE                        57.50            79.28   \n",
       "9     FL                        58.44          1198.91   \n",
       "10    GA                        54.44           673.07   \n",
       "11    HI                        60.81            89.66   \n",
       "12    IA                        56.46           220.93   \n",
       "13    ID                        50.24            92.30   \n",
       "14    IL                        67.24           938.22   \n",
       "15    IN                        54.62           415.34   \n",
       "16    KS                        58.38           193.14   \n",
       "17    KY                        49.26           234.31   \n",
       "18    LA                        53.28           253.31   \n",
       "19    MA                        82.00           625.11   \n",
       "20    MD                        70.00           443.73   \n",
       "21    ME                        55.63            74.60   \n",
       "22    MI                        54.86           559.48   \n",
       "23    MN                        64.67           407.40   \n",
       "24    MO                        54.12           359.03   \n",
       "25    MS                        44.13           123.78   \n",
       "26    MT                        55.53            57.92   \n",
       "27    NC                        52.57           646.21   \n",
       "28    ND                        65.91            64.98   \n",
       "29    NE                        61.29           150.51   \n",
       "30    NH                        68.13            93.89   \n",
       "31    NJ                        77.19           671.48   \n",
       "32    NM                        48.16           108.24   \n",
       "33    NV                        55.58           187.39   \n",
       "34    NY                        78.40          1868.22   \n",
       "35    OH                        55.77           732.12   \n",
       "36    OK                        51.56           205.56   \n",
       "37    OR                        58.54           264.11   \n",
       "38    PA                        63.28           832.70   \n",
       "39    RI                        61.62            64.17   \n",
       "40    SC                        49.74           266.08   \n",
       "41    SD                        62.45            60.81   \n",
       "42    TN                        53.34           411.69   \n",
       "43    TX                        57.79          1950.36   \n",
       "44    UT                        53.54           212.85   \n",
       "45    VA                        63.97           586.25   \n",
       "46    VT                        60.40            36.09   \n",
       "47    WA                        70.44           661.52   \n",
       "48    WI                        57.36           363.59   \n",
       "49    WV                        46.34            85.80   \n",
       "50    WY                        63.59            41.20   \n",
       "\n",
       "    Population in thousand  Total Vaccinated in thousand  Vaccination rate  \\\n",
       "0                   724.36                        148.39            0.2049   \n",
       "1                  4934.19                        920.85            0.1866   \n",
       "2                  3033.95                        596.79            0.1967   \n",
       "3                  7520.10                       1661.62            0.2210   \n",
       "4                 39613.49                      13193.70            0.3331   \n",
       "5                  5893.63                       1955.97            0.3319   \n",
       "6                  3552.82                       1418.90            0.3994   \n",
       "7                   714.15                        267.98            0.3752   \n",
       "8                   990.33                        323.54            0.3267   \n",
       "9                 21944.58                       6351.89            0.2895   \n",
       "10                10830.01                       2557.00            0.2361   \n",
       "11                 1406.43                        456.19            0.3244   \n",
       "12                 3167.97                        891.80            0.2815   \n",
       "13                 1860.12                        343.85            0.1849   \n",
       "14                12569.32                       3709.38            0.2951   \n",
       "15                 6805.66                       1539.52            0.2262   \n",
       "16                 2917.22                        721.00            0.2472   \n",
       "17                 4480.71                       1144.26            0.2554   \n",
       "18                 4627.00                        827.59            0.1789   \n",
       "19                 6912.24                       2902.54            0.4199   \n",
       "20                 6065.44                       2309.64            0.3808   \n",
       "21                 1354.52                        547.82            0.4044   \n",
       "22                 9992.43                       2965.13            0.2967   \n",
       "23                 5706.40                       1832.51            0.3211   \n",
       "24                 6169.04                       1443.39            0.2340   \n",
       "25                 2966.41                        433.64            0.1462   \n",
       "26                 1085.00                        247.68            0.2283   \n",
       "27                10701.02                       2609.92            0.2439   \n",
       "28                  770.03                        138.68            0.1801   \n",
       "29                 1952.00                        549.46            0.2815   \n",
       "30                 1372.20                        508.49            0.3706   \n",
       "31                 8874.52                       3167.34            0.3569   \n",
       "32                 2105.00                        639.34            0.3037   \n",
       "33                 3185.79                        789.95            0.2480   \n",
       "34                19299.98                       7146.14            0.3703   \n",
       "35                11714.62                       3215.60            0.2745   \n",
       "36                 3990.44                        788.10            0.1975   \n",
       "37                 4289.44                       1503.00            0.3504   \n",
       "38                12804.12                       4147.70            0.3239   \n",
       "39                 1061.51                        396.52            0.3735   \n",
       "40                 5277.83                       1198.94            0.2272   \n",
       "41                  896.58                        199.13            0.2221   \n",
       "42                 6944.26                       1442.99            0.2078   \n",
       "43                29730.31                       7778.51            0.2616   \n",
       "44                 3310.77                        816.63            0.2467   \n",
       "45                 8603.98                       2931.97            0.3408   \n",
       "46                  623.25                        289.31            0.4642   \n",
       "47                 7796.94                       2747.26            0.3524   \n",
       "48                 5852.49                       1773.63            0.3031   \n",
       "49                 1767.86                        319.15            0.1805   \n",
       "50                  581.08                         89.99            0.1549   \n",
       "\n",
       "    Increase rate in vaccinated population  \n",
       "0                                   0.8976  \n",
       "1                                   1.3667  \n",
       "2                                   1.3791  \n",
       "3                                   1.3110  \n",
       "4                                   2.0343  \n",
       "5                                   1.9021  \n",
       "6                                   1.9083  \n",
       "7                                   2.6651  \n",
       "8                                   1.9777  \n",
       "9                                   1.8197  \n",
       "10                                  1.9261  \n",
       "11                                  1.6512  \n",
       "12                                  1.4348  \n",
       "13                                  1.1456  \n",
       "14                                  1.7329  \n",
       "15                                  1.3419  \n",
       "16                                  1.4431  \n",
       "17                                  1.4336  \n",
       "18                                  1.0607  \n",
       "19                                  2.1638  \n",
       "20                                  2.1466  \n",
       "21                                  2.0164  \n",
       "22                                  1.7081  \n",
       "23                                  1.6842  \n",
       "24                                  1.5127  \n",
       "25                                  0.9591  \n",
       "26                                  1.1772  \n",
       "27                                  1.4762  \n",
       "28                                  0.8830  \n",
       "29                                  1.4726  \n",
       "30                                  2.0562  \n",
       "31                                  1.8306  \n",
       "32                                  1.2709  \n",
       "33                                  1.5740  \n",
       "34                                  2.1255  \n",
       "35                                  1.5983  \n",
       "36                                  1.0783  \n",
       "37                                  2.0851  \n",
       "38                                  1.8873  \n",
       "39                                  1.7446  \n",
       "40                                  1.5120  \n",
       "41                                  0.9843  \n",
       "42                                  1.4833  \n",
       "43                                  1.8727  \n",
       "44                                  2.1499  \n",
       "45                                  1.9676  \n",
       "46                                  2.4121  \n",
       "47                                  1.9578  \n",
       "48                                  1.6039  \n",
       "49                                  0.9187  \n",
       "50                                  0.8258  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will analyze the data that we have cleaned before. We will discuss the distributions and the relationship between these cleaning data and focus on the relationship between two vaccination factors ('Vaccination rate' and 'Increase rate in vaccinated population') and two economic factors ('Personal Income' and 'GDP').\n",
    "\n",
    "At first, we can use the matrix function to find any basic relationship between the above cleaning data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='Personal_Income in thousand', ylabel='Personal_Income in thousand'>,\n",
       "        <AxesSubplot:xlabel='GDP in thousand', ylabel='Personal_Income in thousand'>,\n",
       "        <AxesSubplot:xlabel='Population in thousand', ylabel='Personal_Income in thousand'>,\n",
       "        <AxesSubplot:xlabel='Total Vaccinated in thousand', ylabel='Personal_Income in thousand'>,\n",
       "        <AxesSubplot:xlabel='Vaccination rate', ylabel='Personal_Income in thousand'>,\n",
       "        <AxesSubplot:xlabel='Increase rate in vaccinated population', ylabel='Personal_Income in thousand'>],\n",
       "       [<AxesSubplot:xlabel='Personal_Income in thousand', ylabel='GDP in thousand'>,\n",
       "        <AxesSubplot:xlabel='GDP in thousand', ylabel='GDP in thousand'>,\n",
       "        <AxesSubplot:xlabel='Population in thousand', ylabel='GDP in thousand'>,\n",
       "        <AxesSubplot:xlabel='Total Vaccinated in thousand', ylabel='GDP in thousand'>,\n",
       "        <AxesSubplot:xlabel='Vaccination rate', ylabel='GDP in thousand'>,\n",
       "        <AxesSubplot:xlabel='Increase rate in vaccinated population', ylabel='GDP in thousand'>],\n",
       "       [<AxesSubplot:xlabel='Personal_Income in thousand', ylabel='Population in thousand'>,\n",
       "        <AxesSubplot:xlabel='GDP in thousand', ylabel='Population in thousand'>,\n",
       "        <AxesSubplot:xlabel='Population in thousand', ylabel='Population in thousand'>,\n",
       "        <AxesSubplot:xlabel='Total Vaccinated in thousand', ylabel='Population in thousand'>,\n",
       "        <AxesSubplot:xlabel='Vaccination rate', ylabel='Population in thousand'>,\n",
       "        <AxesSubplot:xlabel='Increase rate in vaccinated population', ylabel='Population in thousand'>],\n",
       "       [<AxesSubplot:xlabel='Personal_Income in thousand', ylabel='Total Vaccinated in thousand'>,\n",
       "        <AxesSubplot:xlabel='GDP in thousand', ylabel='Total Vaccinated in thousand'>,\n",
       "        <AxesSubplot:xlabel='Population in thousand', ylabel='Total Vaccinated in thousand'>,\n",
       "        <AxesSubplot:xlabel='Total Vaccinated in thousand', ylabel='Total Vaccinated in thousand'>,\n",
       "        <AxesSubplot:xlabel='Vaccination rate', ylabel='Total Vaccinated in thousand'>,\n",
       "        <AxesSubplot:xlabel='Increase rate in vaccinated population', ylabel='Total Vaccinated in thousand'>],\n",
       "       [<AxesSubplot:xlabel='Personal_Income in thousand', ylabel='Vaccination rate'>,\n",
       "        <AxesSubplot:xlabel='GDP in thousand', ylabel='Vaccination rate'>,\n",
       "        <AxesSubplot:xlabel='Population in thousand', ylabel='Vaccination rate'>,\n",
       "        <AxesSubplot:xlabel='Total Vaccinated in thousand', ylabel='Vaccination rate'>,\n",
       "        <AxesSubplot:xlabel='Vaccination rate', ylabel='Vaccination rate'>,\n",
       "        <AxesSubplot:xlabel='Increase rate in vaccinated population', ylabel='Vaccination rate'>],\n",
       "       [<AxesSubplot:xlabel='Personal_Income in thousand', ylabel='Increase rate in vaccinated population'>,\n",
       "        <AxesSubplot:xlabel='GDP in thousand', ylabel='Increase rate in vaccinated population'>,\n",
       "        <AxesSubplot:xlabel='Population in thousand', ylabel='Increase rate in vaccinated population'>,\n",
       "        <AxesSubplot:xlabel='Total Vaccinated in thousand', ylabel='Increase rate in vaccinated population'>,\n",
       "        <AxesSubplot:xlabel='Vaccination rate', ylabel='Increase rate in vaccinated population'>,\n",
       "        <AxesSubplot:xlabel='Increase rate in vaccinated population', ylabel='Increase rate in vaccinated population'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DtfPip/9DkStpv1ieXNRw+kfglsuW/QOwMwfbFhGRImCt5YcHhjg1FuCOtU3csbYp3yGJZJzauZQDtfPiZ63l8X2DnJkIcvf6Znb1N+Y7JJGCsOfsJC8cH6evyccjO7qUeFoCR7ZWbIzZbIz5GFBnjPnogp/PA95sbVdERIpPJJ7k5GgAa+HI0Ey+wxHJCrVzKQdq58UvGE1wejyItXBY/4ciFx0emsVaODMeIhiN5zucopC1hBOwCfggUA88vODnFuBLWdyuiIgUGa/byfbuOnweJ7f0NuQ7HJGsUDuXcqB2XvyqPE62dtbq/1DkMjf31OPzONnSUUt1RS4GixW/bNZwehx43Bhzp7X25WxtR0RESsO7t7Tx7i1t+Q5DJKvUzqUcqJ0XN2MM772hPd9hiBScbV11bOuqy3cYRSWbPZwAULJJRERERERERKS8ZD3hJCIiIiIiIiIi5UUJJxERERERERERyaisV7oyxlQAHwP6F27PWvsfsr1tERERERERERHJvVyUVn8c8AN7gEgOticiIiIiIiIiInmUi4RTt7X2fTnYjoiIiIiIiIiIFIBc1HB6yRhzYw62IyIiIiIiIiIiBSAXPZzuBj5vjDlNakidAay1dnsOti0iIiIiIiIiIjmWi4TT+3OwDRERERERERERKRBZSzgZY2qttTPAbLa2ISIiIiIiIiIihSebPZz+FvggqdnpLKmhdPMssPZqXzTGvA/4WvrlJuA3gA3AI8BZ4PPW2lgWYhaRVTo7EWTIH2Z7dx0+Ty46UYrkjrWWAxf8AGzvrsMYc51viBS/YyOz+Odi7Oiux+PKRflPESkmhwdnmIvF2dFdj8upY4QUtzPjQYZnwuzorqfS48x3OEUva3eD1toPpv9cs4Lv/hj4MYAx5lVSSavHrLV3G2N+B/gw8A+Zi1ZEMmE2HOP7bw6StJaRmTCP3NSV75BEMurQ4Aw/f3sUAKfDsK2rLs8RiWTXkH+OJw8MARCIxHlgU2ueIxKRQnJ2IshPDg0DEIknuWtdc54jElk5fyjG4/tS9zLjgQgf3N6Z75CKXkGnoI0xa4ERYDvwbHrx08Ad+YpJpNy8emqC//6L0xxM9+q4FocxONIdPlyOgj68iCwqGk/y+L4B/vqVs4zNRq543+l4p0eTQ72bpIQkk5afHBrmf7x8hvOToYvLncZgLh7X1eZF5FILz4XOFZwX/aEYf/faOf5pzwXCsUQmQxNZNuMgY+e8105P8t9/cZoDF6ZXH1gRK/TxLh8FvgfUAzPpZX6gIV8BiZSTRNLy0skJAF4+Nc6N3dfuzVFV4eITu3oYngmzpaMmFyGKZNS5ySCnxoIA7Ds/zXu2tl3y/paO2osX15va1caldIwFIhweTF1qvX5mkp5GHwCttV4+enM3/rkYWztr8xmiiBSgnkYfj9zUSSiaYGvH8o8RBwf8DPnDABwfCVz3WlMkm2q9bj6+s5vR2ciq7mWSSctLJ8exFl46OcH27vrMBVlkCj3h9DCppNMdwPzYnFpgerEPG2MeAx4D6O3tzUF4IqXN6TCsaa7i9HiQtc3VS/pOe52X9jpvliMTyY62Wi/VFS5C0QRrmn2LfkaJJilF9T43jVUepkJR1rZcerzvbVp8XxARAa44ZixHX5OPN89N4XI66KzX9aPkX2d9JZ31latahyN9D3VqLMja5qoMRVaccpJwMsbcDWyw1v6lMaYFqLbWnr7Od9qBqLV2whjzOvAvgD8AHgReWew71tpvAt8E2LVrl83k30GkXD1yUydzsYQKgEtZqPG6+cLufuJJi9etQpFSPipcTj5zRx+xRFJtX0RypqfRx2P3rcVhDG4VHJcS8qEduoeCHCScjDH/DthFara5vwTcwF8Du6/z1UeAxwGstaPGmOeNMS8C54A/ylrAInIJY0zZHyilvLicDly635Yy5HQYnA41fhHJrQqddKUE6R4qJRf/Ah8Bbgb2AlhrB40x1x2PYK39s8tefwP4RlYiXIb+rz2Z7xAuylQsZ77+gYysR0REREREREQEcjNLXdRaawELYIwp70GMIiIiIiIiIiIlLhc9nP7eGPNnQL0x5kvAF4E/z8F2RcrWK6cmOD4a4PY1jWxsU4FjKU3jgQhPHx6hxuvmvTe04VLtBylh/lCMnxwapsLt4H3b2jUERUSKirWWnx0ZZWQ2zP2bWulaZVFmkXwanJ7jmaOjtNZ4eXBLKyY9g7FcKetX59ba/wT8I/BPpOo4/a/W2j/O9nZFylU4luDlkxOMz0b4xYnxfIcjkjV7z04x5A9zbGSWs5OhfIcjklX7LkwzMD3HqbEgJ0YD+Q5HRGRZRmcjHBzwMzoT4bXTE/kOR2RVXjs9yehMhLcG/AzPhPMdTkHLyeNga+1TwP8O/EdgjzGmMRfbFSlHFS7HxadG/U0awSqlq6+pCmOgqsJJa01FvsMRyareRh9Oh6HC7aCjTj0DRKS41FW6qfe5gdT5W6SY9TX5gFS7bvB58hxNYcvFLHVfBv4DMAckAUOqntPabG9bpBwZY/j4zm6C0Tg1Xne+w1mWIf8cF6bm2NJRS3WFZnWQa9vUXkNPYyUuhwOPq7iH0wUjcQ4PzdBZX6lhBrKoNc1VfOmetTgcxTGj08mxANOhGNu76zTVuUiWnRoLMBWKcWNXXcGeD71uJ5+9o49wPKlrPMmI2XCMt4dn6Wnw0V7nzem2b+5tYENbDV6XQyUdriMXe/u/Bm6w1mpsj0iOOBym6JJN4ViCf9pzgVjCcnYixMd3duc7JCkCpTLd7E8ODXN2IoTLYfj1e9ZS6Sn8hILkXrG0i5GZMD/YNwikbgju39Sa54hEStfobJgf7B/EWpiZi/HA5sLd31xOB9W6OZcM+eeDwwxMz+FxOfjSPWtznmxV4nRpcvG/chJQcQ2RInR6PMiZ8WDOtqeCe5JvU8EohwdniMQTOd3ufNM35p3fRbJpNhzj8OAMwUg84+s2LGzTatAixSaWSHJkaIaJQCTfoYhc3SpPL9ZaTowGOK86oFmVi7Tc7wIvGWNeBS4etay1v5WDbYvICr09PMM/HxwG4OEdHaxvze5sd163k4/d0s3AdIjN7bVZ3ZbIYqLxJH/3+nnCsQRrR6t45KaunG37vTe0cyQ9pM7rLo5eLFLc/nHPBaZDMVpqKvjMHX0ZXXdrrZdHbupiOhRlW1ddRtctIpdqrfHy4Zu6mMrg/vazIyMcGZrF43Lwhd39JdObWErLL93YwdHhGXoafCvq3bTv/DTPHh0D4OM7u+lp9GU6RCE3Cac/A34OHCRVw0lEisBcNLHg99zsuu113pyPwRaZl7SWWCLV1sOx3PZw8nlc7OzTfBqSO+FYdtv6muYqQIWBRXKhv7mK/gzub3Pp40IskSSWsBlbr0gmVVes7tpp/jwI5LxneznJRcIpbq39VznYjkjZiyWSHLjgp67SzfrW6lWta3t3PdF4EofDcEOnehxJ4Tg6PEsoGmd7dz1OR+aG63jdTh7e0cnZiSA7uusztl6R5UokLQcH/Hjdjqz1+Hzkpk6OjcyypUPHdyl+x0dmmY3E2d5VpwK+GfDuLW28eW6arnovdZXFVRO0GETiCd4a8NNUVUF/sxLz+bKrvwGLxet2sq5ldfdNcnW5SDg9Y4x5DPghlw6pm8zBtkXKyksnJ9h7dgqAT93Wu6reQk6H4fa1TZkKTSQjzk4E+dHBIQAi8SR3ZLiNrmmuSvfMEMmfveemePF4aq4Vj9PB2ixcCHfWV9KpGRGlBJyfDPHEgdR5YS6aYPf65jxHVPxqvW7u29iS7zBK1rNHxzg8OIMx8Kt39tNY5cl3SGXJ7XRw1zodL7ItFwmnR9N//u6CZRZYm4NtyxL0f+3JjKznzNc/kJH1yMrEE0l+cXycw0N+1rfWqPCwlKTJQJS956ZwOw07++rzHY5IVoSicQ5cmCaRtNy/STd9Itey8HpnOZc+E4EIj+8bxO00fPjmrqKb3VeKlwESySRHRwJ86+UzfPQW1Q+S0pX1hJO1dk22tyEiMDIbocLloKveR1e9l7Za1UKS0uMPx+hvqiKWSNLgq8h3OCJZUeN101brxeUwF2upiMjiuht8PLyjk2Akvqyi2UeHZ/HPxQA4ORbkpp76LEUocqn7N7USjScJRBIkLRwanFHCSUpW1hNOxhg38BvAvelFzwJ/Zq2NZXvbIuWkpbqCjvpKPC4H921qzXc4Ilmxoa2Gw0MzeF1O+pt1cSalaW1zFetbq4knLetVV0LkulZSt3J9azUHBvy4HIb+Jp1PJHc8LgcPbm1jMhRlZi7GpvbszgQtkk+5GFL3p4Ab+C/p159NL/v1HGxbpGx4XA4evb0Xay0mA+Pp9p6b4vDgDDf11GtaaykYXfWV/MZ96zLSxi938IKf/Rem2dZVpyfdklf1Pg+/fs/ajB3PFxqdDfOzI6M0+Ny8Z2t7RgvvixST1lovX753bVbOJ8Xi7ESQF0+M093gU82mHPO6nfzqnf1ZOc6XIv9cjJ8cGqbC5eB929qpcDnzHZIsUS4STrdaa3cseP1zY8z+HGxXpCxl4qRlreWFY+MkreWF4+NKOElBydaF2fPHx4jGkzx/bEwJJykI2Wjrb5yZYtgfZtgfZktHLX1NKpIv5avcb/RfPjnB6EyE0ZkIO7rrqPepeHWulXsbXKoDF6YZmJoD4PhIQPcmRSQX84YmjDHr5l8YY9YCKkggUsCMMfQ2pWYvul4387logslgNBdhiWRVX7qt9y1jaIW1lrHZCJG4TmtSHPqafBgD1RUumquXXwdtPKD2LlIq5hPOzdUeqiuu3Q9h/nwXjSdzEZrIJXoafDgdBo/LQccqZuFeqnBM9zeZkoseTl8FnjHGnCJVlL8P+EIOtisiq/DIji4C0Tg117gACUTi/PUrZ5mLJrh/Uws39zbkMEKRzPrAjR3Mbrx2m7/cc8fGePPcNPU+N5+5ow+3MxfPcURW7obOOvqbqnA7HXhcy2uvzx0bY+/ZKbV3kRJx57omtnXVUul24rrO/vzs0TH2nZ+mIb3/X+/zIpnU31zFl+5ZizGp4YjZFEzf34SiCe7d2MzOvsasbq/U5WKWup8ZYzYAm0glnN621kayvV2RcnNsZJZ956fZ2lGbkW6mDoeh9jpTBE8Fo8xFU0+6h/xhbl71VkWu7YXjY4zMRLh3QzOtGZ6J0Zjrt/nLDfnDAEyHYszFEroBl4zIZjsHqFpGUnWhoenUcIbpUIxQNEFdpdq7ZE48keRnb48SjiV41+ZWapZ5PJaVWeq/8/z5bip9vqvR+W5Jsn08LyeVntzUbZqeS53j4J12LyuX9SOFMeY3gUpr7QFr7X7AZ4z5F9nerki5+fnbowxMzfHzt0ex1uZkm90NldzcW09fk4/b1yw9+z8eiPDzt0c4NRbIYnRSakZmwrxxZorzkyFeOjmR73AAuGdDMz2NPnavb152supyM+EYz7w9ypGhmQxFJ8WoENv5vHs2tlxs73WVy2/v1lr2nJ3kheNjGpYnVzgxFuDw4AynxoLsPTed73DKylsDfp45OkogEr/qZ+bPd3dvaFYycIkK+Xhe7i5Mhfj52yMMph+kLNRZ5+WWvgb6mnzcsbYpD9GVllwMqfuStfZP5l9Ya6eMMV/inVnrRCQDOuq8nBoL0l7nzVkBQmMM929qXfb3fnJomNGZCG8NzPDYvWuz3jVWSkOt1011hYtAJE5nfWW+wwGgu8HHx3dmZjrtZ94e5dRYEM5De62XhioVby1HhdjO53XVV/Lxnd0r/v7JsSDPHxsHwGC4e0NzpkKTEtBcXYHH5SCWSOakRoukjM1GeOrwCAChSIIPbO9Y9HM9jT56GjNzvisXhXw8L3c/3D9EOJbgxGiAx+5dd8l7xhjN2phBuUg4OYwxxqa7XBhjnICuokUy7IPbO5kMRmnwFf5Tp+oKF6NE8LoduDQltyxRpcfJZ+/sIxiJ07SCYseFrsqTOiV7XMuvrSOlo5Tbuc/jxBiwFqoq9KBBLtVcXcHn7+onnrDUFcG1TKmocDtwOw2xhMWn/TKjSvl4XuyqKpyEYwl8nlykQ8pbLv6FfwL8vTHm/wdY4CvAj3OwXZGy4nQYvG4Hb56fpq/RV9DjxN+/rYPT46neWCo6KcvhdTuxFt44M0lHfSVdJfTE8IHNrfQ1+WiqrlhxjR0pDfO9PvecnaS1xlsyvQo66yv5lVt7mIsmWNtSne9wpAAV4rFvOhTl+GiANc1VK5rZsdDVet188rZepoJR1mm/zDiv27ninvxTwSgnxgKsba5SwirDPr6zm/OTc/SWyPm1kOXiqP47wJeB3yBVNPynwH/NwXZFys6TB4YY8od5zeXgy/euLdhkjsflYFN7Tb7DkCL108PDnBoL4nIYvnj3moK8QVkJp8OwoU37haQ88/Yobw/P4jCGz+/uX1HNpELUUVc6SWIpD4/vG2QyGOXNc1PpWbJKr2d2c3VFSSbTit339w0wHYqx79w0X7p3bb7DKSk+j0v3IjmSi1nqksCfpn9ERERERESKTikmm0REsinrCSdjzG7g3wN96e0ZwFprlaYVybD3b2vn52+Psrm9tmB7N4ms1kNb23njzCQWi1M1wKREPbC5ldZaL601qV4Hbw/PsKa5igqXaqyI5NIjN3VyIj2kTlbPWsup8SA+j1M9Hq/jIzd3cWI0oCHI15BIWk6OBWis8qiXXoHKxTiEvwB+G9gDaA5ckSzaf8HPmYkQ56fm6Kj3Uu/zYK0lEIlTXeHSkzkpCV63g6Mjs8yG4wz7I/zyrT1q51JyvG4nO/saSCQtf/HiKYKRBL2NPh66oU3tXCSH6n0edvU35jsMZsMxqjwuHEX+oGXP2SleOD6OMfDJW3tp14yEV1UobW8l5q/Lst1mnzs2yv7zftxOw+fu6qfGWxrDz0tJLhJOfmvtP+dgOyJlLxSNA6lsfySeBOAH+wc5NRZkU3sNv3Tj4lPdihSTpIVwLPX8Yr7Nz7fzze01vF/tXEpIImkJx1LH85dPTXBuMsSWjhret03tXKRcPHdsjL1np+io8/LLu3qKOukUiqbO39bCXEx9EUrVTw6NcGRohv5mHx+5uTtr2wlGUm0olrBE0/c+UlhykXB6xhjzh8B3gcj8Qmvt3hxsW6Ss3LOhBa/bicthLt6Qn50IAXB6PJjP0EQyxukwfGhHFyfGZmmpqWBwOsSZ8XQ7n1A7l9LicTl4eEcn+89PM+wPA5bT6fYuIuXhTPoabsgfJhzP/FTu4ViCgek5uuorVzyj2lLdtqYRY1IzEmqYYuk6m74eOzsRIpm0K0qSJpOWs5MhGnxu6n2eRT9z/6YWqr0u2mq8msmvQOUi4XR7+s9dC5ZZ4F052LZIWamqcHFjVx1/8+o5Xj8zxe71zdyzoZm3BmfY0V2X7/BEMqa3yUcsmeQH+wYBWN9azfRcjJu66/MbmEgWNPjcnJ8MYbHMhOO8Z2t7vkMSkRzavb6Jl09Nsq65KuPJJoDvvTnAsD9Mc7WHz97Zn/H1L+R1O7lnQ0tWtyH5t3t9M2+en2ZrR82Ke+S9cGKcvWen8LgcfO6ufqoXmZW4xuvmgU2tqw1XsigXs9Q9sJLvGWN+Ffgc4AQ+DTwKPAKcBT5vrY1lLEiRImet5YXj44zMhFnXUk0iaQGYmYvx4NY2bu5tyHOEIplx4MI0bw/NcnNvPYFI/OLy7oZKHt7RmcfIRDIvGInz9JERZsNxIvEkHXWVbOmoZVuXHiCIlJP1rTWsb83eFO4zc6nbqplw/DqflIX2nJ3i5GiAW9c0qrfWZbZ11a36XOVPt8toPMlcNLFowqnchWMJfnZklIS1PLilNSsJ6dXKWkTGmH91rfettf/5Gt/tAu6z1r47/boFeMBae7cx5neADwP/kMFwRYra6GyEPWenAHA7HdyzoRn/XIw71zXlOTKRzEkkLT9/exRrUxchX9jdfzHpdKNuwKUE7b8wzamx1LCE3iYfjT4Pd+i4LiIZ9ks3dnB4aIbN7dlLapWacCzB88fGAAgdHWVN85o8R1R67tvYQqXbSWtNBS01Gi63mCNDMxwbmQWgraaC29cW3jVCNlNgqzlivRdwGmN+BhwGfgw8m37vaVK9nZRwEkmrq3RTXeEiEInTWV9ZtDNaiFyL02HoqPMyOB2ms74Sl9OhbvlS0jrqKnEYg8OkLrzbajWbk4hkXk+jj55GX77DKCoep4OWmgrGZiN01lfmO5ySVFfp5j1b2/IdRkFrr/PichiSNnXNUIiylnCy1v5vS/mcMeZ3rbX/x2WL2wCPtfbdxphvAPXATPo9P7Do+CBjzGPAYwC9vb0rCVukKHndTj57Zx+haILGqsWL6omUgo/d0s30XIzGqxSPFCkla5qr+MLd/TiNoUpDCURECobDYfiVW3uYmYvp2lvypqOuki/cvQZrLTVed77DWZQj3wEAn1hkmR94Lv37z4F+oDb9uhaYXmxF1tpvWmt3WWt3tbToqbeUF6/bqROelDyX00FzdUVRTwktshy1XreSTSIiBcjtdNBUXYExuiaR/KmucBVssgkKI+G02B76ErA9/ftNwHngvvTrB4FXsh+WiIiIiIiIiIisRCE8MrNXLLB2nzFmzhjzLDBOqmZThzHmReAc8Ec5jVBERERERERERJasEBJOi/ZBtNb+68sWfSP9IyIrNBWMUulx4nU7OT8Z4uWTE/Q0+op+NrtgJM6psSC9jT7qfIXbpVRyyx+K4XE5qPQ4Ly6LxpP87MgI4XiCB7e0FXQX5EI1OD3HVCjK5vZanBramDcLj+fX8uqpCc5OhLh9bSN9TeU5bbe1lmMjAbxuR9n+G0juJZKWqVCUBp8nK8fKM+NBookkG1qr8zakay6a4KkjIwC8Z0vbJedbKU8TgQjVXhcVrsJoCydGZzHGsK6lOmvbODo8y77zU2ztqOPGbs2afLlCSDhptjmRHNh3fppn3h6l0uPkM3f08eKJcYb9YQam59jaWUtdZfHeeD++b5CRmTDVFS5+/Z41GksvHB6c4aeHh/G4HDx6Wy/16SLjx0dneXs4NX3svvPTmuVumSaDUf7hjQskrWU8EOW+jfr3y4e956Z47ugYvvTx/Go1ngKROC+dnADghePjZZts2Xtu+uL05R/f2a3ZuCQnnjgwePFh2Md2dmd03WcngnzvzQEA7t/Uws29i86nlHWHh/ycHA0A0Fnn1SzJZe6lE+O8enqS2ko3n7mjN+9Jp0ODfn56KJUQ/eD2Dja01WRlO88cHWUummDYH2FbV63uQy6TtYSTMeaPWWS43Dxr7W+l//yP2YpBRN4xOD0HpJ5GTQWjdNVXMuwP01jloarIn0hF44nUn4kk1oKO8zI4PYe1EIklGQ9ELyac2mq9eFwO4gmraYxXIJ5IkrSpU3s0nsxzNOVr/ngeiiaYCkWvmnDyuhw0VXuYCETpaijf9r6wrUbUbiVHLkyl9tP5/TWTFrbpfB6L2+sqL/beaq/z5i0OKQwD6bY+MxcjEI5TUZ3f+4tcHfu76is5MRqgs96rZNMistnD6Y0srltElun2NY3MRRM0Vnnobqikp9HHtq46arwuXM5CmD9g5T64o5MjQzOsa6ku2NnLnjk6yuHBGW7pbSj6IYzFYFd/AzPhGDVeN2ua3+nV0VxdwRd29xNPWmo1nG7ZWmu9vP/GdiYDUW7py88T9YVePjnB3nNTbO2o5YHNrfkOJ2duX9NEOJakqdpD1zUSpy6ng0/d1stsOF7Ws5ju6m/AYaDS42R96+qGVUTiCb7/5gBToRjv39Zetr3G5PrevaWVA+f93NBVu+j7F49fnbU8sGl5x6/1rdW8a3MrkXiSW3rrMxDtynTVV/LFu9cAqZmypLztXt/ML06M01VfSVN1Rb7DYXt3PYmkxeEwbO1I7YeBSJzv7b1AJJ7kQzs6aa1dfaL0Azd2MBV65+GmXCprRwZr7V9la90icnWHB2d49fQE61urLxku1FRdcUWX7sVuQIb8c0RiSfqbi+ciurm6oqCHRiWTlv3np7E2NYxLCafVicQTPHlgiFA0wfu2tdO8yEVNvc/DR29ZfAiDz3P1U9/obJjZcJy1zVV6SnUVm9sXv3nKh/0XponGk+y/MM19G1sKNuG8Gi8eH+f46Cy39jeyrStVG6KlpoKPL3GIjtvpuORYf2EqhLWU1bAyt9PB7Wszc9wd9ocZnA4DcGhwRgknuarN7bXXPF7uO58+fp2f5v6NLcs65xhj2NFTf9X3Y4kkZ8aDtNZ4l1zbcnB6jqePjNDg8/D+be1LfhipRJPM66yv5BO7elb8/bHZCP65GOtaMnMN5nSYK4Z5nhkPMh6IAvD28OxVE07xRJJ/fmuYqVCU92xto6Pu6g93HA5TEAm2QpX1bg3GmBZjzH8yxvzIGPPz+Z9sb1ekXL16eoLpUIw3zkwRjiWW9d0LUyG+8/p5vvfmAAcuTGcnwDLkcBi2ddbhdBi2q5jgqp0eD3J2IsTYbISDA/6MrXciEOHvXjvPD/YN8sqpyYytV7Lnxq7UfrWts64kk02ReILXz0wyHYrx6unVt8mTYwH+4Y0L/OOeCxwbmc1AhOWnrdZLa20FHpeDLR2Fk3yV4rO9O3X8urGrLuMPOH56aIQnDgzx7dfPEYkv7VrwzXPTTASinBgNXEyqiuTKZDDKt187xw/3D/JyuvZgNvQ1+aj3uan0ONl4jZpOA9NznBgNMBGI8ua56azFUw5ykZL+G+A7wAeArwCfA8ZysF2RsrS+tZo3zkzR0+ijwrW8nHIwkiBdnoVAJJ6F6MrXg1vbeHBrW77DKAkddZVUVTiJxJKsyWDvglA0QSKZ2gGCav9FYff6Znavb853GFnjcTroa/JxdiK06qFgcGm71jF+ZbxuJ5++vS/fYUgJyObxa35fj8SSxBOWpXRCWtdaxYnRADVeFy016q0huTUXe+caLJvnpxqvmy/sXnPdz7XWeKmrdKd6vbeoJ+tq5CLh1GSt/QtjzL+01j4HPGeMeS4H2xUpS/dsaOHW/kYqXI5lPzHb2FaNf66ZSDzBrj7NNCKFqa7SzRd3ryFpwbPMpOq19DT6uH9TC/65GLev0bBHyT9jDB+5uYtIPInXvfriqzd01qUfLFi2d6m3pUipenBrG2+em6K30XfVSQUut7m9ljXNVbgdjpLsMSqFrau+kndtbmUyFOX2Nfm/B6n0OPncXf3Ek8m8z7ZX7HKRcIql/xwyxnwAGAQyOzeoiFziajcmyaTlhwcGuTA1x/2bWrih89IbDmMMtxXAQV7kepZSW2IqGL04bfRHb+laUjHHfE0tLXI1xpirHtODkTjf3XuBcCzJh27qpO06xU+dDqMaciJFaN/5aX5xYpy1zVW8b1v7dR8oNlZ5ePeW5feq1o21rNTITJgf7BvE63bw0Vu6l5zoXOhadcnywekwOB3aJ1YrF1NT/X+MMXXA/wv418B/BX47B9sVkcv452KcGgsSjSc5eCFztW9ECtHx0QD+uRj+uRgnRgP5Dkck485MpIqfBiJxjgzN5DscEcmS/ekC428PzzK3zPqcIrlwZGiGQCTOeCDKmYlgvsORApL1hJO19glrrd9a+5a19gFr7U5r7Q+yvV0RuVJdpZv+Zh8uh7k421G2DfnnODE6i50vDiWSI+taqqjxuqjxuljbsvr6N5mUTFqOjcwyOqvCrLJyfU1VNKSLn25qv3rx02wJphNdqnkmklnxRJKjw7OMByIAbEtPkLChrZrKDAyvFcm0Te01VHqcNPjcK5q9MxpPcmRohulQNAvRST5lfUidMaYF+BLQv3B71tovZnvbC/V/7clcbq4sZerf+MzXP5CR9ZSj8UCE7785QNJa3r2ljXULbrLPjAc5MjTDzt5GPnJzbqbDHpkJ853Xz2NtqjimhutJpvhDMV46Nc65iRD9zVW8Z0vbFTUnGnweNrfXEk0k8HkK6wL9hRPj7D07hdNh+NU7+5Y03E/Kz+hMmPNTITa111Jd4eL1M5McGZrhlt4GuuorefX0JLf0NbC9uz4v8X137wXGA1Eaqzx87q7+vMQgkkvWWg4PzWAwbOmouebQtmg8yS9OjON0GHavb8a5jLpIzxwd460BP26n4fO717Czr4GdfRryPS8cS/Djt4aJxBO894Z2nUOX4PhIqndctmZ17air5Cv3rVv2994a8HNhKsRUMMrwTASv28kX7+4vuOGd06EoPzk0TIXLyfu2tWekrmK5yEUNp8eBF4CnAfUBFcmSZNLyN6+c5aWTE/g8Tnwe1yUJpycPDhGNJzk7GVrRCWElwrF3Zr0LRfUEXDLn8f0DvHRinEAkQTAS58auOjrrKy/5zNGRWV4/k5pK3ut2cte6wpnNbC6aOh0mkpZoPJnnaKQQReNJ/mHPBaLxJCfHgnzslm5ePD4OwEsnx2mpqeDMeIgjQzP0NPhoqMr9Ddf80B4N8ZFycWhwhqcOjwBgDGzpqL3qZ/edn2bf+Wkg9QDkxu6l9yyfv2aKJy3xhM4Rlzs5FuD0eGrY1oELfu7d2JLniArbmfEgTxwYAiAST3Jrf2E8APbPxS7uT4PTc3TWVxKNJy/OVldIDg74GZxO9Uo/MRrI2UiRUpCLhJPPWvs7OdiOSNlzOx1UVbhIWsva5ku7s9b73IzORKivdOcsnr6mKh7Y3EogHGdXv57MSeZYC/U+D4HIHHWVbhoXudmurXRjTPqzlYX19PPejc34PE4aqzy0XqfQswg2Vby0r8nH2YkQa5qrcTkMEMLrdubtSevDOzp5e3iWTW25H84nkg8LqwNcr1JAvS91vWVMqqTBcrxrcyt1lVN01FWq984iOuoq8bqdxBJJ+ppy02u/mC1sqoVU4aLC5aDS42QummD3+mbqfG56G334PLlIUSxPb6OPN89N43Y6rnjAKdeWi//NJ4wxv2St/VEOtiVSthwOw8d3dXNLbwO9Tb4rDoYfu6WbIX+Yzvql39wmkhaTXvflTowGePrICG21FTy8vfOqs4bdVGAzTkhpeOSmTrZ01NJeV0FnXeWi7a+rvpJP395HLJFc1cVBLJHEvYRZ8RaaCER4fN8gLqfhkZu6rrjZ8HlceiIr1+RxOfjYLd2cnwqxOV2f6SM3dxGMJqiucJFMWta2VNFQ5aHyOkNG44kkToe57sxW1zMXTfC9NwcIReN8YHsHHXWVdNTpwlvKx+b2asBiTGpI3bVsbKuh5jYXToehtWZ5DxZqvG7u39S65M8HI3G+9+YA4ViCD+3oLPkHGY1VHn7t7jUkrdXQpiVY01zFL93YwVwswY0r7JlzrXuClfK6nXz69l4mAlF6G31LWncskeTxfYOMByK894Z21jQvv17USvQ1VfHYvWtxGIPHlYt510pHLhJO/xL4t8aYKBBLL7PW2qv3QRWRFWmt8V71osbrdi7roHx+MsTj+waocDn5ldt6qPVeesN8cGCauWiCM+MhxgNR2uve2e7JsQAep4OeRj11kuyo93mWVBOspaZiVdv56aFhDg3OcENnLQ/d0L7k7x0bSc2QB3BqLMD27npOjAZoqHIv+8ZDyld7nfeSY6sxhur0VNMOh1lSYdZTYwGePDCEr8LFJ2/tWfJU1eFYgtPjQTrrKy8mTM9NhhiZSQ0pODI0o2STlJUTo7P86OAwNV4Xn7y192ICN1V7Jsz61uorHk7kah85MxFkbDZVYPzI8GzJJ5wA3fQv02omlhj2h/mnvRdwOgy/vKtn0V7lK1XjdVPjXXoPwJGZMOcnQwAcuDCdsYTT6GyYqWCMDa3VV018Kbm5MrmYpa7GWuuw1nrTv9co2SRSuPxzMcKxBCfGAsQSlkAkzoXJuSs+t6WjFqfD0Fnvpan6nRPPgQvT/GDfIP+45wLnJkK5DF0kI6y1TAajxBNJjo3MAnB0eHZZ61jXWoXP46TG62JNcxUvHB/jRweH+M5r5y8mokSyLZm0vHlummgiycxcjCH/0mdF/NHBIX781jDfef3cxRoy3Q2VNPjceFwONrRqGJ2Ul+MjARJJy3QodjHxOh2K8pcvnebHbw3z00MjeYutt9FHXaWbCreDDa2FNSurFL9T4wGi8SRz0QTnJld2bR9PJJkMRlc9a3VrjZe2Wi8uh2HrNWqoLcd0KMrfvXaeHx0c4oUT4xlZp7wjJwMkjTEfAu5Nv3zWWvtELrYrIsvz1oCfpw6PUOlx8t6t7ZybCFHpdrK25cqnB5vba9nUduUMLfPFkEGFZKU4/ezIKAcH/LTUVLCzr4G3BmbYvoxir5C6IPryguL88/tFXEXCJYf++a1hDg/NMDYb4d6NLfQ0Lr23RSjdZiOxJPP1W6sqXHx+9xqstasenidSbLb31DPkD1Pvc9PVUIl/LsZfvXSG105Psba5iu6G/PX4q/G6+eLd2jclO7a013JyNIDL6WD9ChKa1lr+/o0LjMyEl91j/HIel4NHb+/NaFtfWKh8TpMcZVzWE07GmK8DtwJ/k170L40xd1trv5btbYvI8gxMp3oyzUUTYLjuNNeLHehv6WsgYS0VLgcb2/SUTYrP/H4wNhvh4zu7uTMDs9vdu7EFX4WLluqKVQ/zE1mqgekQ1RUuarwuPnpz17Jqb7x/WzsHBvysaaq6YuiKbmilHHXVV/LFu9dcfD0RCJG0sKG1mgafm4e2rvwmOlO0b0o2NFR5+Oyd/Sv+fjSRvNgr8MLUlaMmViKTbb211stDN7QxEYhqkqMsyEUPp18CbrLWJgGMMX8FvAko4SSSYxemQvxw/xBVFU4+vrObSrfzkgP2rf2NzIbj1FW66WmoZMg/R33l9QvSLuR2Ogpq+nkpb2+em+LF4+P0NVfx8PaOS9r71Z6O3bOhmdfPTLKupRqv28lMOEYkllxVoqiqwsV9KhIuGTQbjvGPey4QiSf5yM1dtC1Ss+Xu9S28cHyUnX2Nyy702lRdwQPLKFoskk+r6e2QSFpGZsI0VXuocC39eqevqYobOmuZCcdTs8r53qlDE0sk+e7eC4wHojy0tY0NmslRsqDQe7Qt3Lfu3djCidFZdvZdv/5mPtzQubJi6tdyYjTATw4N01Tl4aO3dJdt3bFczTlYD0ymf8/8/6aILMnR4VnCsQT+uSj/99PHqfO5+cjNXReLWjZWefj4zm4Anj06ypvnpqmucPHZO/tUKE+K0lsDfuJJy8nRAIFI/GJhytfPTPKLE+Osbam+IhG1tqWatS2p3nkTgQjffu0csYTlPVvb2LbC2V1EMu3EaIAXjo8zF41TX+nmk7f1XvGZYyOzhKJJjo7Msqu/MC/yRVbrqcMjHBr0c3Nvw4oS+08eHOLkaIDmmgo+c3vvkm/gnQ5z1aFBY7MRBqdTPToOD80o4SQZd34yxA/2D+LzOPnlXUufECKXfnRwiBOjAZqrPXzmjj529pVX76HDQzNE40mG/GFGZ8N0N5TnZEq5SLP9H8Cbxpj/nu7dtAf4jznYrohcZktHLVUVThzG4HQYIrEkx0cCi352fraTQCR+SV0mkWKyo6cej8vBxraai7N7ARwa8GMtnBwNEI5dvabSVChGLJEa1z+/T4gUgiqPk2TS4nI4iCUXL8I6Opu64Z0IREle5TMixcxay6HB1PH80KB/ResYTQ/1mQxEL9ZxWa2Wmgq6GyqpcDuy0nNC5NjILNF4kulQLGPD1DJt/rppIhglXobnoG2dtXjdTroaKhfthVwusp4KtdZ+2xjzLKk6Tgb4HWvtcLa3KyJX6qyv5LF71xGMxPnemwNE4kk2dyz+1O2+jS28fGqCrvpKGpY4/WkoGmdgao6eRp96RElB2N5dz/bu+iuW39zbwEsnJ1jXUnXNIaNrm6u4ubeeYCSxqnH9Y7MRZsMx1jRXFXT3dykea1qqeXhHJ+OBCHevX3wY83u2trP//DSbO2qWPaQOwB+KMRYI099UhctZnkMBpLAZY7ilt4FDgzPc3Fu/onW8Z2sbb56bZmNbTUbaeTyR5OxEkIe2tl8yzE4kk27orOP0eJCqChd9TYXZc2ZTew0nRgPs6m/AXYbnkLUt1fzG/apnm4ui4buBfdbaHxhjPgP8G2PM/22tPZvtbYvI4qoqXHzmjr5rfqa11ssjN3Uta71///p5pkIxOuq8iw7vECkUO3rq2dFTf93PORyG+1dZx2Z+WF4iabljbRN3rmta1fpEIFUv72PpIdBXs6a5ijXNV84yuhThWIK/ee0skViSLR01vG9bx4rWI5Jt925s4d5V1Mjra6qir2ll+8linj4ywpGhWbxuJ1/Y3a8HcJIV7XVefv2etfkO46rePDfFa6cnMSZVskPKVy5SjX8KhIwxO4CvAmeB/5GD7YpIDllrCaaH3gUimlJUZN5cLHFxmEZQ+4YUiVgiSTSeGm4aiGhYtchSzYZTx/lIPEEscfUh2yKlLJg+b1ira59yl4vqYnFrrTXGPAL8f621f2GM+VwOtisiOWSM4UM7Ojk6PMsNXbX5DkekYHQ3+Lh/UwvTczFuX6PCzVIcarxu3r+tgwtTobIr9CqyGg9uaWPvuSm6G3wXJ6oQKTe3rmkgYS0+j5N1LRpWVs5ykXCaNcb8LvAZ4F5jjBPQ0VckD0ZmwhwfCbCxvZrWmswXr+tp9NHTWJjjyEWCkTj7zk/TVutlfWtuL35u7tUNu2TWbDjGgQt+Ouq8F2dVzLRN7TVsatfsWlIaIvEEe89OU1fpZmtn9h6MNVR5ePeWtqytX2ShyWCUw4MzrGmpoqu+Mt/hXFThcq5o1kgpPbkYUvcrQAT4tXSx8C7gD3OwXRG5zPfeHOD1M5P8YN9gvkMRybmfvz3Ka6cneeLAINOhaL7DEVmVnx1Jtecf7h9iNhzLdzgiBe+lkxO8cmqCnxwaZmC6MGf1ElmuJw4M8vqZSb7/5kDGZlkUyaSs9nBK92b6a2vtg/PLrLXnUA0nkbxwOx3MkaDCVX4zRYh40u3eaQzOFczYJVJI5mf8cTnVnkWWwpPeZ4wBt1P7jJSG+XbtdhrUqqUQZTXhZK1NGGNCxpg6a60/m9sSkev7xK5uzo6HWNOSudlYRIrFuza30lVfSWtNhepqSNF7z9Y2+pp8tNZW4PPkokKCSHG7c20TDT4PtZWurJQVEMmHD93UycnRID2NlTj08EEKUC6uUMLAQWPMU0BwfqG19rdysG0RWaDW6+bG7rolfTaRtCStvfgUXaTYuZ0OtnUtrf1fTSSewO1w6KJO8s7jWn57DscSmqJdypbDYVZUuymWSOJQz1gpUD6Pa8nX9ssVT8+y6NK9gKxCLhJOT6Z/lswY0w+8ChwBotbah4wxXwUeAc4Cn7fWqmCByFUcH5llYHqOm3sbqKtcfk+OqWCU77xxnngiySM3dakQuBQ0ay17zk4RTSS5tb8xa0nSgxf8/OztEZqqPPzyrT1UuHTjLtnnn4vx5rkpuuor2dC28gLeP35riCNDs2zpqOV929ozGKFI4ZiLJnj9zCQNPk9GbsLPT4Z4fN8AbqeDX7m1h3qfJwNRimRfMmnZc26KWCLJbf2Ny04aDfvD/NPeCxgDn9jZQ0tNRZYilVKX9YSTtfavjDGVQK+19ugyvvqUtfYzAMaYFuABa+3dxpjfAT4M/EPmoxUpfjPhGE8eHMLa1MwVH72le9nrGJieYy6aAODMRFAJJylobw/P8sLxcQBcDge3rWnMynZOjM1iLYwHokwFY7TXKeEk2ff04RHOTYbYd36aX6vzrng46PGRQPrPWSWcpGT94sQ4BwdSVTyaqj10rnLWrtPjQWIJSyyR4MLUnBJOUjSOjszyYvrayO10cGv/8q6Nzk4EicZTPZzOTYaUcJIVy3r/OGPMw8A+4Mfp1zcZY36whK8+YIx5wRjz28BtwLPp5U8Dd2QhVJGS4HY4LvbwqLzG0ImpYJQnDgzy8smJK95b11JNd0MlLTUVbOvMTjddkUzxeZyL/r5U5yZST7CPDM1c83M7extp8LnZ3F5Dqy68JEfm27Tb6VhS771XT03ww/2DTAYvnYnxznVN1Fa6uXNdU1biFCkElen9xWFMRiZI2dZVR0tNBd0NlaxvrebgBT+P7xtgULPcSYFbeA9wrfuBq9ncUUt7nZfOei+b2lfeu3be/vPT/GD/IMP+8KrXJcUlF0Pq/j0LEkbW2n3GmDXX+c4QsBGIAI8DtcBI+j0/0LDYl4wxjwGPAfT29q4ybJHiVOlx8qnbehmdDbOupfqqn/vFyXGOjwQ4ToD+Zh8ddZWXrOMTu3pyEa7IqvU1VfHxnd3EEknWXqPNX81TR0aYmYtxZjzEhtbqq3Y7723y8fnd1zt9iWTWg1vbWNNSRWuN97r1l0ZmwryUfohggQ/t6Lz43q7+RnYt8wm3SLG5c20TLTUV1FW6aape/YOBxioPn7mjD0gN1/vZ2yNYCzPhOJ9NLxcpRP3Nq7s2qqt086nbMnM/PRuO8fO3RwEIRuIZW68Uh1wknOLWWr8xlxTas9f6grU2QirZhDHmCWAG6Eq/XQtMX+V73wS+CbBr165rbkOklDVWeWisSnX7ngnHiMWTFy+8gpE4P9g/yPHRAG6Hoc7n1oxdUvQWG/YZiSeYCsZoram4pMj3VDDKEweHcDsMD+/opLnaw8xcjMYqt4rCSsFxOx1sbn+n0PHlx/SFjg7PcnBgmkafh9vXKrkk5cfhMGy8Sq2zq50TlsrtNNR63fjnYrRUL39o3Vw0wQ/3DxKOJ/jAjR0ZSYiJXEu2SmIkkpax2QhN1Z6LPW/PT4Z46vAITdUePnBjxyUP77xuJzVeF7PhOM1q92UnFwmnt4wxjwJOY8wG4LeAl671BWNMjbV2Nv1yN/DHwKPAHwAPAq9kMV4pAf1fW1ad+qw78/UP5GW7o7NhvvPaeeJJy/tvbGdzey3HRwMM+8PUVLhY31rFA5vbqK7QlNpSWhJJy9+9dp7JYPSKIslHhmYYn40AcGI0wAdu7GB4JkxLTQWXPRwRKShjsxH+7rVzxJOW921rZ0vHpTNuHbgwzaa2WhLJJHeta85TlCKF51rnhKVyOR08ensvE8EoHbXeZX//5FiAgfRQvEODM9y7sWXZ6xApBD/cP8jp8SDtdd6LvZX2nZ/GPxfDPxdjyB++JNnldjr49O19TIZWtu9IccvFHIf/M3ADqR5Lf0tqSNz/cp3v3GOM2WOMeQkYtNa+CjxvjHkRuAn4ftaiFSkhk8Eo8WSqs9/oTOoGu7fRR6XHicflYFd/o5JNUpJiiSRToVQNm9HZS+sFrGmpwuNyUOlx0tPow+V00N3g06xzUvAuOaank6YLbWyrweNysFND50Quca1zwnJ43U666itX1EOqp8FHVYUTt9OwprlqxTGI5Nv8PjQ2GyGZPidtbKvBYQyNVZ5FC4xXela+70hxy9qdpjHGC3wFWA8cBO601saX8l1r7Y+AH1227BvANzIdp0gpW99SzfbuOsKxJDv7UqXPGqs8PHbPWixo+JCULK/byQObWjk1HmBX36U33x11lXzlvnUY0IWPFJX1rdXs6KkjFE1cPKYv9NAN7dy/qRVPBooli5SSa50TcqXO5+bX715L0tplT1EvUkjevaWNAxem2dxee/E6alN7DWtbqnA5jHqLyyWy2bXhr4AY8ALwfmAL1+/ZJCIZkExanjs2xlQoyv2bWi/Wc5o36J/jrQE/G9tqVlRIUKTQJJOWZ4+N4p+Lcf/GVhqqPOzoqWdHT/2in3c6DPFEkheOjWOtZff65iXNACaST06HYVN7LS+dGOetAT93rG3izHiQt4dn2NpRR2+TT8kmKXv+uRivnJqgtaaCm3vfScxe65yQKw6HwUH2b8bDsQRPHxnBYHj3ltbrTjggEk8k+fnbo4SiCR7Y3Epd5eL1XQ8PznBuMsjd61uu6Mmk6yhZTDZbxVZr7WestX8GfBy4N4vbEpEFzk+F2Hd+mrMTIV47PXnF+z9+a5gjQ7M8eWDoYldYkWJ2djLE/vN+zoyHeO3MlW1+MW8NzrD37BRvnpvmwAV/liMUyYwXj49xYWqOl09O4A/FePLgEEeGZvnRW0P5Dk2kIDx/bIzDgzM8e3SM0ZnynIL94ICf4yMBjo3McmhwJt/hSBE4NR7k0OAMp8eD7D07tehngpE4Pz2cuof42ZGRRT8jcrlsJpxi878sdSidiGRGQ5Xn4tOszvori/PNP7WorXRrSJGUhMYqDxXu1Cmts65ySd9Z+PTuak/yRApNR7p911W6qfQ4L7bderVhEQDqfal9Yb5WXzlqr/XidBicDkNbrWYFk+trrq7A43JgDHQscu8AqR5MvvQ+pesmWapsDqnbYYyZT6kboDL92gDWWlt79a+KyGrUet18/q5+wrEEDVVXTt37yE1dDEzP0VGnmSKkNNRVuvnCXWuu2uYXs6a5ikdv78VaaNe+IEXi3o0tbO2spcbrwuNy8PGd3Qz5w4s+XBApR3evb6a30Ud9pYcab3neFPc0+vj87n4MlO2/gSxPY5WHL+zuJxpPUu9b/DrK43Lw6O19jM1G6F0wC53ItWQt4WStLc9HCiIFotLjvOqTPY/LoRlSpORcq81fTZum55Ui1Fz9To8Fr9up47nIAsYY+pq0T9Qq0STL5PO4uEqu6aLqCpdmuJZlUWUvERERERERERHJKCWcREREREREREQko9QfTqTETAaj/HD/IE6H4ZGbOjV2X0reWwN+Xjo5Tn9TFe/Z2oYxKoQvpcFay08Pj3B2Isju9c3c0FmX75BECo61lqcOj3BmIshd65rZ1qX9RIpHLJHkh/sHmQxGee8N7fSoNpKUGPVwEikxbw/PMBmMMjYb4eRYMN/hiGTdm+emCEYSHBqcIRDRpKhSOmYjcQ4PzhCMJNh7bjrf4YgUpEAkzqH0fvLmucWncxcpVEPTYc5OhJgNxzlwwZ/vcEQyTj2cpOD0f+3JfIeQcZn4O535+geW9Ll1LdXsOz+Ny2Ho01MSKQNbOmp58cQ4vY0+qjw6rUnpqPa46G30cX4qxNaOmnyHI1KQqjwu+pp8nJsMsaVDk2BLcWmtraC52sNUKMam9up8hyOScboyFykxbbVevnLvOoxBQ4ukLOzqb+Tm3gacDrV3KS0Oh+FjO7tJJK3at8hVOByGj96i/USKk9ft5LN39pNMWhxqv1KClHASKUE6YUm50U2GlDK1b5Hr034ixUzX7lKqVMNJREREREREREQySgknERERERERERHJKCWcREREREREREQko5RwEilRiaTlpZPjvHh8nHgime9wRHIqmbS8emqC54+NEY2r/Utxi8QTPHdsjNdOT2KtzXc4Ihnx1oCfnx0ZYSYcy3coIgXDWstrpyd57tgYkXgi3+GIrJqKhouUqMODM7x6ahKASo+DnX2NeY5IJHeOjc7y0skJANxOB3eua8pzRCIr9/rpKfaenQKgscrN+taaPEcksjrjgQhPHR4BIBRN8PCOzjxHJFIYjo8G+MWJcQDcDsNd65vzHJHI6qiHk0iJqqpwXvy9usKdx0hEcq+6woUx7/wuUsyqvak2bAz4PGrPUvwqXA48rtRtyHz7FhGoWnj9on1DSoBasUiJWttSzS/f2kMyaelp9OU7HJGc6m7w8clbe4nGk/Q2qf1Lcbupp54Gnxuv20lbrTff4YisWo3Xzadu62UyGGVtc1W+wxEpGF31lXzy1l4i8QR9Tdo3pPgp4SRSwrrqK/MdgkjetNfpxlxKh248pNQ0VnlorPLkOwyRgqPrFyklGlInIiIiIiIiIiIZZUp1tpPm5mbb39+f7zBEsurMmTOonUupUzuXcqB2LuVA7VzKgdq5lIM9e/ZYa+11OzCV7JC6/v5+3njjjXyHIZJVu3btUjuXkqd2LuVA7VzKgdq5lAO1cykHxpi9S/mchtSJiIiIiIiIiEhGlWwPJyldBy5M89zRMXqbfDy8vROHw+Q7JBERkSX50cEhTowGuGtdE7v6G/MdjkhWqJ0XN2stTxwY4vR4kLs3NHNLb0O+QxIpCG+em+KF4+P0N1fx8PYOjNF96PWoh5MUnYMDfuJJy6mxILPheL7DERERWZJwLMHR4VkSScvBAX++wxHJCrXz4heMJjgxGiCRtLyl/0ORi94anCGRtJwcDRCI6D50KZRwkqKzo7sej8vB+tZqarzqpCciIsXB63aypaMWj8vB9u76fIcjkhVq58WvyuNkU3uN/g9FLrOjuw6Py8HGthqqK3QfuhT6V5Kis62rjm1ddfkOQ0REZNnet6093yGIZJ3aeXEzxvBLN3bkOwyRgrO9u15J2GVSDycREREREREREckoJZxERERERERERCSjNKROpIT0f+3JjKznzNc/kJH1iIiIiIiISHlSDycREREREREREckoJZxERERERERERCSjlHASEREREREREZGMUsJJREREREREREQySgknERERERERERHJKCWcREREREREREQko5RwEhERERERERGRjMppwskYs80Y85Ix5gVjzF+alK8aY140xvyNMcad/tyn0597whhTm172LmPMy8aYZ4wx3bmMW0REREREREREli7XPZyOWmvvstbek369C3jAWns3cAD4cDrp9BXgXuBbwJfTn/094CHga8Dv5jZsERERERERERFZqpwmnKy1sQUvI8BG4Nn066eBO9LLDlpr4/PLjDE+YM5aO2utfRXYmruoRURERERERERkOXJew8kY8yFjzFtAK+ACZtJv+YEGoH6RZQ0LlgE4r7Lux4wxbxhj3hgbG8tC9CIiIiIiIiIicj05TzhZa39grd0GDABxoDb9Vi0wnf65fNnUgmUAyaus+5vW2l3W2l0tLS0Zj11ERERERERERK4v10XDKxa8nCHVU+m+9OsHgVeAY8A2Y4xzfpm1NgRUGmOqjTG3AYdzGLaIiIiIiIiIiCyDK8fbe58x5l+lfz9OqhB4hzHmReAc8EfW2pgx5s+BF0j1bHo0/fnfB54CwsDnchu2iIiIiIiIiIgsVU4TTtbax4HHL1v8jfTPws99i9QMdQuXPU2qiLiIiIiIiIiIiBSwnNdwEhERERERERGR0qaEk4iIiIiIiIiIZJQSTiIiIiIiIiIiklFKOImIiIiIiIiISEYp4SQiIiIiIiIiIhmlhJOIiIiIiIiIiGSUEk4iIiIiIiIiIpJRSjiJiIiIiIiIiEhGKeEkIiIiIiIiIiIZpYSTiIiIiIiIiIhklBJOIiIiIiIiIiKSUUo4iYiIiIiIiIhIRinhJCIiIiIiIiIiGaWEk4iIiIiIiIiIZJQSTiIiIiIiIiIiklFKOImIiIiIiIiISEYp4SQiIiIiIiIiIhmlhJOIiIiIiIiIiGRUThNOxpjbjTEvGWNeMMb8X+llfmPMs+mfxvSyT6c/94Qxpja97F3GmJeNMc8YY7pzGbeIiIiIiIiIiCxdrns4nQXeZa29B2g1xtwIHLTW3p/+mTTGuIGvAPcC3wK+nP7u7wEPAV8DfjfHcYuIiIiIiIiIyBLlNOFkrR221obTL+NAAtiS7vH0dWOMATaSSkLFgaeBO4wxPmDOWjtrrX0V2JrLuEVEREREREREZOnyUsPJGLMdaLbWHgY2kOrN1AA8DNQDM+mP+tPLGxYsA3BeZb2PGWPeMMa8MTY2lqXoRURERERERETkWnKecErXafp/gF8DsNZOWmst8H1gGzAN1KY/Xpt+PbVgGUBysXVba79prd1lrd3V0tKSjfBFREREREREROQ6cl003AX8NfBVa+2wMabKGDPfW2k3cBI4BmxLL38QeMVaGwIqjTHVxpjbgMO5jFtERERERERERJbOlePtfQK4FfhGqlwTvwv8iTEmCJwC/p21NmGM+XPgBVI9mx5Nf/f3gaeAMPC5HMctIiIiIiIiIiJLlNOEk7X228C3L1t8yyKf+xapGeoWLnuaVBFxEREREREREREpYMtKOBljDgL2au9ba7evOiIRERERERERESlqy+3h9MH0n7+Z/nO+F9KngVBGIhIRERERERERkaK2rISTtfYsgDFmt7V294K3vmaM+QXwHzIZnIiIiIiIiIiIFJ+VzlJXZYy5e/6FMeYuoCozIYmIiIiIiIiISDFbadHwXwP+mzGmLv16GvhiRiISEREREREREZGitqKEk7V2D7DDGFMLGGutP7NhiYiIiIiIiIhIsVpRwskYUwF8DOgHXMYYAKy1quEkIiIiIiIiIlLmVjqk7nHAD+wBIpkLR0REREREREREit1KE07d1tr3ZTQSEREREREREREpCSudpe4lY8yNGY1ERERERERERERKwkp7ON0NfN4Yc5rUkDoDWGvt9oxFJiIiIiIiIiIiRWmlCaf3ZzQKEREREREREREpGStKOFlrzwIYY1oBb0YjEhERERERERGRoraiGk7GmA8ZY44Dp4HngDPAP2cwLhERERERERERKVIrLRr+vwN3AMestWuAdwO/yFhUIiIiIiIiIiJStFaacIpZaycAhzHGYa19Brgpc2GJiIiIiIiIiEixWmnR8GljTDXwPPA3xphRIJ65sEREREREREREpFittIfTI0AI+G3gx8BJ4OFMBSUiIiIiIiIiIsVrpQknAKy1ceBlUkXDZzIRkIiIiIiIiIiIFLeVJpyeB7zGmC7gZ8AXgP9+vS8ZY243xrxkjHnBGPN/pZd91RjzojHmb4wx7vSyT6c/94Qxpja97F3GmJeNMc8YY7pXGLeIiIiIiIiIiGTZShNOxlobAj4K/LG19iPA1iV87yzwLmvtPUCrMeYe4AFr7d3AAeDD6aTTV4B7gW8BX05/9/eAh4CvAb+7wrhFRERERERERCTLVpxwMsbcCXwaeDK97LoFyK21w9bacPplHNgOPJt+/TRwB7AROJgervc0cIcxxgfMWWtnrbWvsrTkloiIiIiIiIiI5MFKE07/C6leRt+z1h4yxqwFnlnql40x24FmYJp3aj/5gQagfpFlDVxaI8p5lfU+Zox5wxjzxtjY2FLDERERERERERGRDLpur6TFWGufA55b8PoU8FtL+a4xphH4f4BfBnYCXem3akkloKbTvy9cNrVgGUDyKnF9E/gmwK5du+xS4ilGk8EoP9g3gNPp4MM3dVLjdec7JBERkZx77fQke89NsbWjlns3tuQ7HCkD0XiSx/cNMB2K8b5t7fQ0+vIdkhQpHb9Eci8YifP9fQNEYkke3tFJS01FvkMqeSvq4ZQu3P3zy3+W8D0X8NfAV621w8DrwH3ptx8EXgGOAduMMc75Zel6UZXGmGpjzG3A4ZXEXSreHp5hKhRjfDbCidFAvsMRERHJi73nppiLJth7bopksmSfM0kBGfLPcWFqjkAkzsEBf77DkSK25+w7xy9rdfwSyYXT40FGZyL452IcGZq5/hdk1VbUwwn41wt+9wIfI1WT6Xo+AdwKfMMYA6lhec8bY14EzgF/ZK2NGWP+HHiBVM+mR9Pf/X3gKSAMfG6FcZeE9S3V7D/vx+Uw9DdV5TscERGRvNjaUcvec1Nsbq/F4TD5DkfKQFutl+ZqD9OhGBvbavIdjhSxrZ21vHluii0dtaTvi0Qky3oafdR4XUQTSda3Vuc7nLKw0iF1ey5b9AtjzHOLfvjS730b+PZli18GvnHZ575Faoa6hcueJlVEvOy11nr5yn1rAXSCEhGRsnXvxhbuXt+sZJPkjNft5LN39pNMWrU7WZX7NrZwj45fIjlVV+nm1+9Zi7VW99E5stIhdY0LfpqNMe8F2jMcm1yDMUY7yQoNTs/xzNFRhv3h639YRKTEvTXg5/ljY8xFE/kOZUV0syaZNBWM8uzRUU6NXbtkgdqdZEI5tSNrLXvOTvHSiXFiiUXL8YrkTDbvo89Phnjm6Chjs5GsbaOYrHRI3R7AAobUULrTwK9lKiiRbHp83yDhWIITIwG+dO/afIcjIpI3w/4wTx0eAWAuluC9N+jZkZS3nx4eZnA6zIELfr50z1oqPYtOjCwiy3RiNMDzx9KziBu4a11zfgMSyYJE0vL4vgFiCcu5iRCfu6s/3yHl3UqH1K3JdCAiuVLpdhCOJfDqIlJEylyFy4HTYUgkLZVuHRNFvOn9wO1M7RsikhneBecYnW+kVBmgwuUkloirnaetKOFkjHEDvwHcm170LPBn1tpYhuISyZqP7+rh3ESIviZNZSwi5a2hysMnb+1hKhRjg4pnivC+be2cGgvSUefF41pR5QkRWURPo49P7OomHFOxZildDofhV27r4cLkHGtbNLkXrHxI3Z8CbuC/pF9/Nr3s1zMRlEg2VVe42NpZm+8wREQKQmutl9Zab77DECkIFS4nWzp0jSCSDd0Netgrpa/W62ZrpzvfYRSMlSacbrXW7ljw+ufGmP2ZCEhERERERERERIrbSvsKJ4wx6+ZfGGPWAsU5vY2IiIiIiIiIiGTUSns4fRV4xhhzilRtrD7gCxmLSkREREREREREitZKZ6n7mTFmA7CJVMLpbWttJKORiYiIiIiIiIhIUVppDyeAnUB/eh07jDFYa/9HRqISEREREREREZGitaKEkzHmW8A6YB/v1G6ygBJOIiIiIiIiIiJlbqU9nHYBW621NpPBiIiIiIiIiIhI8VvpLHVvAe2ZDERERERERERERErDsno4GWN+SGroXA1w2BjzGnCxWLi19kOZDU9ERERERERERIrNcofU/aesRCEiIiIiIiIiIiVjWQkna+1zAMaYb1hrf2fhe8aYbwDPZTA2EREREREREREpQiut4fSeRZa9fzWBiIiIiIiIiIhIaVhuDaffAP4FsNYYc2DBWzXALzIZmIiIiIiIiIiIFKfl9nD6W+Bh4AfpP+d/dlprP3O9LxtjOo0xe40xYWOMK73Mb4x5Nv3TmF72aWPMS8aYJ4wxtell7zLGvGyMecYY073MuEVEREREREREJEeWW8PJD/iBT61we5PAu4HvLVh20Fp7//wLY4wb+ApwL/Ax4MvAHwK/BzwEbAV+F/jNFcYgIiIiIiIiIiJZtNIaTitirQ1ba6cuW7zFGPOCMebrxhgDbCSVhIoDTwN3GGN8wJy1dtZa+yqppJOIiIiIiIiIiBSgnCacrmIDqd5MDaSG59UDM+n3/OnlDQuWATgXW5Ex5jFjzBvGmDfGxsayFrCIiIiIiIiIiFxd3hNO1tpJa60Fvg9sA6aB2vTbtenXUwuWASSvsq5vWmt3WWt3tbS0ZCtkERERERERERG5hmXVcJpnjPko8A2gFTDpH2utrb3mF69cTxUQttYmgN3AQeAYsM0Y4wQeBF6x1oaMMZXGmGpSw+kOryRuEVma/q89mZH1nPn6BzKyHhERERERESkuK0o4AX8APGytPbKcL6ULgv8zsAP4CfBvgT81xgSBU8C/s9YmjDF/DrxAqmfTo+mv/z7wFBAGPrfCuEVEREREREREJMtWmnAaWW6yCcBaGyPVa2mhWxb53LeAb1227GlSRcRFRERERERERKSArTTh9IYx5juk6i5F5hdaa7+biaBERERERERERKR4rTThVAuEgIcWLLOAEk4iIiIiIiIiImVuRQkna+0XMh2IiIiIiIiIiIiUhmUlnIwx/8Za+wfGmD8m1aPpEtba38pYZCIiIiIiIiIiUpSW28NpvlD4G5kORERERERERERESsOyEk7W2h+m//yr7IQjIiIiIiIiIiLFzpHvAEREREREREREpLQo4SQiIiIiIiIiIhm1ooSTMaY504GIiIiIiIiIiEhpWFbCyRjzsDFmDDhojLlgjLkrS3HJEllrCccS+Q5DRETKRCKp844Un2g8STyRzHcYIhkTTySJxtWmRa4llkgS07E/r5Y7S93vA/dYa982xtwO/AFwX+bDkqV64sAQJ0YD3NBZy0M3tOc7HBERKWHhWIK/ffUcM+EYD21tZ2tnbb5DErmusxNBfrBvEI/Lwa/c2kO9z5PvkERWZSoY5TtvnCcWT/Lhm7voafTlOySRgjPkn+O7ewcwBj6xs4eWmop8h1SWljukLm6tfRvAWvsqUJP5kGSprLWcHAsAcHIsmOdoRESk1I3NRvDPxbAWTo0H8h2OyJKcHg8ST1pC0QSD0+F8hyOyagPTc8xFE8STljMTugcQWczZiRDReJJILMn5qVC+wylby+3h1GqM+VdXe22t/c+ZCUuWwhjDXeuaOTTo5+behnyHIyIiJa6zvpINbdVMBqPcovOOFInt3fUMTM/hdTlZ21KV73BEVm19azVvD88SiSfY1lmX73BECtLWzlrOjAdxGMOmNvWTyZflJpz+nEt7NV3+WnLstjWN3LamMd9hiIhIGXA6DB/c3pnvMESWpbHKw6dv78t3GCIZ43U7+fjO7nyHIVLQar1uPnlbb77DKHvLSjhZa/+3bAUiIiIiIiIiIiKlYbk1nDDGPGCM+SdjzKH0zz8aY+7PfGgiIiIiIiIiIlKMlpVwMsZ8APhvwBPAo8CngR8B/80Y80uZD09ERERERERERIrNcms4fRX4sLV2/4Jl+4wxbwB/TCr5JCIiIiIiIiIiZWy5Q+raL0s2AWCtPQC0ZSYkEREREREREREpZstNOAVX+B4AxphOY8xeY0zYGONKL/uqMeZFY8zfGGPc6WWfNsa8ZIx5whhTm172LmPMy8aYZ4wxmpZBRERERERERKRALXdI3TpjzA8WWW6AtUv4/iTwbuB7AMaYFuABa+3dxpjfAT5sjPk+8BXgXuBjwJeBPwR+D3gI2Ar8LvCby4xdRERERERERERyYLkJp0eu8d5/ut6XrbVhIGyMmV90G/Bs+venSRUiPwwctNbGjTFPA980xviAOWvtLPCqMebry4xbRERERERERERyZFkJJ2vtcxnefj0wk/7dDzRcZVnDgmUAzsVWZox5DHgMoLe3N8OhioiIiIiIiIjIUiyrhpMx5hFjzG8ueP2qMeZU+ufjK9j+NFCb/r02/XqxZVMLlgEkF1uZtfab1tpd1tpdLS0tKwhHViocS3B+MkQ8seh/jYiIXEMyabkwFSIUjec7FJGMmAnHGPLP5TsMkbKg/U3KUSJ97RSOJfIdilzDcofU/RvgkwteVwC3AlXAXwL/uMz1vQ78C+APgAeBV4BjwDZjjHN+mbU2ZIypNMZUk6rhdHiZ25EsstbyndfPMxmMsralikdu6sp3SCIiReVnb4/y1oCfGq+LX72zH49ruXN6iBQOfyjGX796lmg8yb0bm9nZ15jvkERKln8uxl+/ktrf7t7QzK392t+kPPzk0DBHh2epq3Tzubv6cTrM9b8kObfchJPHWnt+wesXrbUTwIQxpup6X07PQvfPwA7gJ8C/BZ43xrwInAP+yFobM8b8OfACqZ5Nj6a//vvAU0AY+Nwy45Ysiict06EYABOBaJ6jEREpPhOBCACz4TiReEIJJylqM+EY0Xiqx/O4rgtEsmp2wf42fy4RKQfz7X0mHCOWSOJ0LFp1R/JsuQmnhoUvrLX/04KX1x3DZq2Nkeq1tNCrwDcu+9y3gG9dtuxpUoXFpcC4nQ7eu62NYyMBbu6pz3c4IiJF54HNrbx2epLeRh81Xne+wxFZle6GSm5f08j0XIw71zXlOxyRktZVX8ntaxuZDsW4c11zvsMRyZl3b2ljz9kp1jRX4XUr2VSolptwetUY8yVr7Z8vXGiM+TLwWubCkmKzub2Wze211/+giIhcoa3Wy8M7OvMdhkhGGGO4a71ufEVywRjDXUo0SRnqrK+ks74y32HIdSw34fTbwPeNMY8Ce9PLdpKq5fThDMYlIiIiIiIiIiJFalkJJ2vtKHCXMeZdwA3pxU9aa3+e8cgkp6y1vHJqkkAkzu71Tfg8y81FiojIakwFo7x6eoL2ukpu0vBkKUHJpOWVUxOEogl2r2+m0qMhECKFYM/ZKcZmI9y5tok6n4Z1S+k7PR7k7aEZbuiso7fJl+9wStqKsgrpBJOSTCXk9HiQV05NAOByGB7Y3JrniEREystzx8Y4PR7kyNAsPQ2VNFVX5DskkYw6ORbg1dOTAHhcDu7deN3ynyKSZaMzYZ4/NgZALJHU8G4pCz86OEQ0nuTsZIiv3Lcu3+GUNE2DIwDUVrovTiWpJxsiIrlXnz72et1O9fyQklRb6cZhUtcaDT5PnqMREQBfhevizKjaL6VczF9zNei+N+s0bkoAaK6u4DN39BGKxuluULdCEZFcu29jC2ubq6mvcmtYs5Sktlovn7mjl3A8SZcKvYoUhOoKF5+5ow9/KEZPo/ZLKQ8fu6WbYX+YjnpvvkMpebqilYsaqzw0VunJhohIPhhjVEdASp6GiooUnrpKN3WV6ukh5cPrdtLfXJXvMMqChtQVqLHZCGfGg1hr8x2KiIhkmLWWU2MBJoPRfIcismLhWIITo7PMRRP5DkWkpCSTlpNjAaZDOkdIeYgnkpwYDTATjuU7FMkw9XAqQBOBCN9+7RyJpGX3+mZuW9OY75BERCSDfnFigtfPTOJ2Gj57Z7+eLEtR+qe9FxididBcU8Fn7+jLdzgiJePZY6PsP+/H43Lwhd39GmYtJe+nh0c4OjyLz+PkC7vXXKwrJsVP/5MFKBRNkEimejbNKssrIlJy5o/tsYRV7xApWrPhePpPXauIZNL8vhWNJwnHknmORiT75s8jc7EEsYTafClRurwAzIZjJC0Xn3D3NPq4d2MzM3Nxbl+r3k2rNRdN4HIa3E7lV0UkO8KxBA5jrvlEbiIQoarChdft5J6NLXhcDpqqK2ivU8FKKTzWWsYCERp8nquePx/c3Mrx0QDbuupyHJ1I4YjEE8zMxWmu9mDSszCu1v0bW6nyTNJe5y26+qqBSByf24nDkZl/CykPD25pY++5aXobfVRVZDZFEYknsDZVt+lqwrEEwUhcdQazQAmnPBucnuMf91zAWnjkps6Lxct29pV2oikYifPG2Smaqz3c0Jm9C9UjQzP85NAw1RUuPnVbb8YPYCJSXhJJyxtnJgHY1d+I02E4NRbgiQNDeFwOPnlrD/WLTCv9xplJXjg+js/j5LN39lFd4eLdW9pyHb7Ikv308AiHB2dorqngxs5aZiNxbu1vvHjBfnxklh8dHKbS42D3huY8RyuSH7FEkr999RzToRg399Zz/6bWjKy3zufmwa3Fc47Ye26KuWiCcCzBgQt+Ouq8/PKuHiWdZMmaqit4Txba/OhMmH/YcwFrLR+5pXvRGVKDkTh//cpZQtEE925sprrCzZB/jp19DdR4VfJgtXT3nQOTwSjPvD1KQ5Wb+ze2XnLwHZ2NXBw+NzwTLptq+c8fG+Pt4VkAWmoqaK3JzhP+VOH1VNfksdmIEk4isipvDfh56eQEkHpStqOnnrOTIRLJ1NC44ZnwFQmnZNLy1OERTo8H6W+qwj8XUz0OKXhD03NcmAqx5+wkx0dmafB5iMaTFxOlZyZCJK0lGEkwOhOmVhflUoZC0QTTodRQoCF/GIALUyFeOjlBb6OPO9Y25TO8nDg1FuC5o2MATIei1Ps8DPnDhOMJnevK2Ew4xs+PjFLpcfLuza248jTS5ML0HNF4aoje+cnQogmn6bkYoXR5g+MjAYZnwlgL/rkYj9zUldN4S5GOAjnw2ukJzk2GODcJ61qq6Wt6J6m0paOGYf8c8aRlR3d9/oLMsUpP6gmpy2GocF69e+Nq7exrYCoUo97npqdxddONvz08w5nxILf0NtBaqyEwIuUglkjyixPjGGPYva4Jn+ed49X8ceym7npG/GEqPU7WNldfsY7zU6kb89R3HLTr+CFF4JbeBn5xcoKm6gqG/HM0+DwMTs/x47eGuX1NIzf31jMeiFBd4brkukaknNRVutm9vplzkyHuXJdKLr14fJwhf5iBqTm2tNdS5yvtZGylx4kxYC3c3FvPbDjO2pbqvCebpoJRXj09SWe9l+1ldI9VKPaeneL0eBCAviYfm9tr8xLH5vYaTo8FSVrLDZ2Lx9BZ52VnXwPjgQg7+xr44f5BYgmbkTZsreXlUxPMRRPcta754rVjOVHCKQc66ys5MjRLpcd5xTjsCpeT923ryFNk+XPPhhY66yup97mzeiJurfXy6O29q15POJbgx28NYy2MB6J8RrPxiJSFAxf8vHluGkjdWNzUU89Hb0k9pZu/yW6o8vDJ265+nGmo8tBYVYHP4+LdW1ozVuNDJJu2dNZy17ompkMx1rVU0VHn5YXj44wHooRjCT58cxefuka7FykXt61pvGRG6c76Sob8Yep9bnwVpX9z2VFXyS/v6iEUTbCupapgznHPHhvlzHiII0MzdDf4iq4WVrHrrK9k3/lp3E4HLXmsi+TzuPjYzu5rfsYYw70bWy6+/uRtvUwEoqxrWf3DlJNjAV49lSrF4HI6uG/BdsqFEk45sL27nt5GH16385rFygrFXDTBa2cmafC5s/ZEwOkwbGyrycq6s8HlMFRXuJgNx6kv8SdVIvKO+ckcjHnn94W9OaZDUd48N01PYyXrWxc/ptV63Xxhdz/hWGLR+k4ihcjtdPDp2/uYDcdoqq4gFI3z/PFxzowHaalVUVWRq7l3Yws3dNZS43WX1IQ1U8Eo+85P09PoY33rpb15OxcZppRv9ZUeIESF24HXXTr/D8ViY1sNbbVe3E6T995uy9VcXcGwP8yLJ8a5tb9xVSVZar1uHMaQtJb6yvK8hyyu//0itpSbjMlglBqvK+8npxdPjPPWgB9I7XCFeBLJNZfTwadu62V0NkJPg/49RMrF+tZqPnVbL8ZA2yJD4X56eISBqTkOXPDz6/dUXnJRMhuO4TDm4sx0xfDAQWSh+ZkUIfWUuLWmgslglAsTIQKRONWqiyiyqHzMdBWKxoknbdbqqT11eISB6fnz3ZqCr4t638YW1jRX0VjtKbqER6moy1KCJRxLZPUh3shMmKcOj1zc1mpGI7XWevn0Hb2EYwm6G1ZX3qVYae8rEM8eHeXNc9M0VXt49LbevBVWAy7WKHE6jG6QFqiqcLGmwE+uIpJ57XVXr7lUlb6I9bgcOBdMCHF2Isj33xzEYeATu3quuQ6RYlHhcnJ6PIgxcHosyI3d2ZtlVkSWbjwQ4TuvnyeWSPLB7R1X7XG7GvPDAz0uBy5nYQybuxaHw5TNZEzlJBRNzSgXjCS4d2MLO/saMr4Nr8uJy2GIJzNTx6k5j0MKC0He756NMf3Aq8ARIGqtfcgY81XgEeAs8HlrbcwY82ngN4FJ4FFr7Uy+Ys6Ggek5ACYCUeZiCWrymHC6c20TbbUV1Fa6Nd5ZROQaHrqhjfWt1bTVVlySoB/yh0laS9KmZiBVwklKwbauWvZfmKaqwsV4IJLvcEQkbXQmcnEmrsHpcFYSTu+9oZ0NrUHaaiuocOmBtOTHVChGMJKaUW5gei4rCac6n5tP3tbLdCjKupYrJ4OR5cl7wintKWvtZwCMMS3AA9bau40xvwN82BjzfeArwL3Ax4AvA3+Yr2Cz4d4NLbx8coK+Jh81eZ5a2OEwWTlRiYiUGrfTwab2K4+X27vrGJkJ43QYNi/yvkgx2thWwz0bWghF49zSm/mLfBFZmQ1t1ZydqCEcT3BTb31WtnG1851ILnXWebmpt57x2Qh3rG28/hdWqKWmgpaa8u6ZlCmFknB6wBjzAvBd4BjwbHr508CjwGHgoLU2box5GvhmXqLMop5GHz2N5TmuU0Sk1Pg8Lh65qSvfYYhklMvp4H3b2vMdhohcxu108P4by2/Wayk/xhge2NSa7zBkGQoh4TQEbAQiwONALTCSfs8PNAD1wMxly65gjHkMeAygt1dT9YqIiIiIiIiI5EPe54i01kastUFrbRx4AjhBKulE+s/p9M/lyxZb1zettbustbtaWlqyGbasgrWWZNLmOwwRkUXpGCXlLKG2LyI5lkxarNWxR0qPzqkF0MPJGFNjrZ1Nv9wN/DGpYXR/ADwIvEJqmN02Y4xzwTIpQv5QjL9/4zzRRJKP3NxFZ31lvkMSEbkoGInzndfPE4rG+eD2Ts1wI2UjlkjyD29cYGw2woNbW7mhUzPQiUj2Dfnn+O7eATxOB5/Y1Z21qe5Fcu3Hbw1xZGiWm3vrub+MhwHmvYcTcI8xZo8x5iVg0Fr7KvC8MeZF4Cbg+9baGPDnwAvA54A/y1u0q2St5dRYgIkFs7uEonFeOjnOqbFAHiNbnLWWYCR+1acObw34+dmREWbCsSWt7/xUiEAkTjSe5GQB/n1FpHxZa3n26Ch7z07in4txfDTA6GyYJw8MsufsZL7DE1m2cCzB4UE/zx8b48jQO5P7zkUTnB4P8PThkYuz5E4Go4zMpGZXPDo8e7VVipScSDzB8ZFZgpH4dT/36qmJkts/QtH4Fb0wpkNRfnZk5JLjRracHA0SjScJROKcmwxlfXuyfImkZc/ZKfafn15WT7T5fStwnX1rqcKxxMXZGDMhEk/w/LExXjs9mfEedsmk5e30seLtEjtmLFfeezhZa38E/OiyZd8AvnHZsm8B38phaEu25+wU5ydD3L62kY66a/fYeenkBK+dnsTlMPzqnf3U+dz8/O1Rjo8EMAY+f1d/QWX2//mtYY4Oz7KhrZoPbu+85L2JQISnDqfKbQWjCT60o3OxVVxiTXMV7XVeovEkWzpqr/t5EZFc+cG+Qf7m1bOEYgkwhm1dtfz1y2dTx2yn4Yt3r2E2HKet1ssda5vyHa7IdT2+b4CXT04wFYrR21hJf3MVXfWVvD08y/7z02zpqOXUeIDH7l1Hc3UF61qrGZ0Jc1NPfb5DF8mZH+wb5MLUHLWVbr64ux9jzMX3QtE4zx4do8LlwFrLwYFUAqa20nXda/5i8OqpCV46OUFLTQWfvLUHlzPVF+Hnb49ydiLEwQE/nXWV1PmyN4P2lo4aTo4FcDsdmoK+gIRjCZ49OobDQL3PzS9OTADgcTmWfA/3w/1DnJ8MUeN18cXda3A4zPW/dBXnJkI8vm8Ap9PwyVt7aaxa/f3ynjNT7Dk7BaT+jhvbMjcLo8Nh2NXXyOEhf9nP6pr3hFOxmwnHeP7YGJDaMT9527WLlc+GUxneeNISisWpw43LkTq4O41Z1Y6YDafHg6k/x4JXvFfhduJxOYjGk9RULK0pVVW4+NR1/o1ERPLh3FQIj8uBw2F47w3tdNRVkrQWCzgdhtdOT1LhcnJqLMja5ipaa735DlnkmmbDcRwOQzSe5MRoAI/Tyf7zftY0V2Ftaghpd0NqhlynwyzpwZFIqZnvfRGKxElacC64FN97dvpij6aWmtQNrjFcvHYvdvPX+WOzEWbDcRrSN/FV6et6j8uBx5Xdv2tTdQWfu6s/q9uQ5Ts44L/Yw62n4Z3kqtu59PYQSI+AmYsmSFqLg5Xf556dDBJPWuJJy+D0XEYSTtXeVDs35p02n0l3b2jm7g3NGV9vsVHCaZW8Lic1Xhez4TittRXX/fzdG5rxuAyNVRU0+Dy8dHKcrvpKuuoraampoNZ77ScIvzgxzsDUHLs3NNOVg/pHu9c3c+DCNDd2XVnLoTqdPJoMRlmrOiciUuTuXt/M2fTFt8VyfjLEr9+zlu6GYWor3fg8Tt48N43P47x4kSJSyO5c10QgEueONY1MBKMEwnGcDsOQf47339jOju561SmTsvf+bR0cHPCzvrUa52UPfltrKzAGHMbwrs1tTAaj1FW6aalJXfMfGvQzMxfnlr56KlzOfIS/qHMTIV45NUFvk++aPXJvX9vEi8fH6G70XUw2ATy4pY11LVU0V1dQ6Smcv5fkTkt1qu0bDLetaeTG7iROh2F969J7ob3/xg4OXEjtW8MzYc6Mh7ihs/aStjYvmbQ8c3SUyWCUBza30lx96X319q56Bqfn8Lgcy4rhWrZ311NX6abC5aS9Tg8Rs0VXzKvkcTn4zB19TIditC0h4VRd4eJdm9sAePrwCAcH/AA8ensvbdd5Wj4ZjPLa6VQdkV+cGOeXd/WsMvrru6mn/ppd6xurPBnJMIuI5FuDz0NXg4+3Bvz85K0Rzo6HeOy+tZf0XN3SUUuN14XPo9OnFL7XT09iLYwHo/za7jU8d2wMh8OPwxjWtlSzWUPbRWiv8171ZnNjWw1NVR5cTgd1le5LJru5MBXip4dSpSUi8URBFQV+4cQYozMRBqbnuKGzlpqrPNBe01zFmkWSzqnEQuaGF0nx6W+u4nN39mMMKy730lbr5T1bvcQSSf7suZPEEpYzE0E+c0ffFZ+9MDXHgQup++LXT0/y/hs7Lnm/zufmV27N/CiZviY9dMm20ugPmmdedyorunDM91K4011UHcbgWsJQuuoKF/XpMdTdmt1NRCSjmqo9VHqcOByGWq8Ll9OB47LjelutV8kmKRrz1xluR2pYzLauOjxOB06HoV1DQkWWpKm6grrKKxM2HqeD+VOEZxnDjHJhfqhsU7WHSrd6KMnKNFR5MlJb2GHMxfpgVxui2VjtwZfuTdfVoPvcUqKr5jy6e30zzdUeGnwemqqv3zvK43Lw6dv7CEbii3ZFlOwKROJUuBzLGrssIoVhNhzD63Zec/+t8br5/F39fOTmLkZnInQ1VGp/l6L2gRs7ODoyy+a2WowxdNZX8sW712BJPcQSkZVrrfXysVu6mQ3H2dxeWL2B7tvYwo1dddSkH56UingiyVwscdUeW1KYnA7Dr+zq4cLU3FWHw1VXuPjcXf1EYsmsFqm/nLWW2UicmgrXsjuPyNLoaiOPnA7DDZ1X1ka6llTxPiWbcu2tAT9PHxmhusLFo7f3qoeDSBHZc3aK54+NUe9z86nbevFe42mv1+3E63Zed4izSKGbiyb4xz0XmA3HqXQ72e6rB7JTGFWkXPU0+vIdwlWVWsmLWCLJt187x0Qgyu71zdy2pjHfIckyNFR5rtthYv4aLJeePDjE8ZEA61qrNXFGlpROyjvHIvEEzx0b47XTk1hr8xZHLJHkjTOTF2fQkOw4OxHC2tSMPxOBaL7DEZFlODeZKgQ+HYrhn4txaNDPnrNTJJKpY/fITJinD49wZvzK2ThFis1bA35+/vYI5yaDzIbjzMzFePrICLPp2YJEZOVOjM7y9OERxgORfIdSEKy17D8/zf7z01m/H5qZi128Bj8zofN1sTk2ktp3JoOFdR91diKU+nMV14CTwSgvn5xgdDacqbBKih5zXUcknuDYcIC22opLpsB+48wUe89OAdBY5V5yYb03zkzyxtkpNrfXZKS44CunJnjjTCqOqgrnxTHbklm7+huYCcdorPLkZHbAUtH/tSdXvY4zX/9ABiKRcnbbmibmoknaaiuYnYvx00MjjMyE+e7eC2xqr2HYH8btdHBkaIZ/8cD6K2YpEilE8USSH+wf5MLUHBvbqrlrfTORWJKnDqeKGAfDCTa0VvP4/kF6HT5+/NYwn8jBZCMiufbC8TEODc5wc089t69tIpZIcnR4lqZqDx11mbtmC8cSPHlgmKS1jM5GePT2zBcwLjaHBv//7P13mFvneef/v2/U6b2Sw97EqkZ1SpZkWZYtF8WOnVgucuJEduLsejf79RV7f1+vUy5/10422exmd7ORN8kmSmKnuXfLlizJqpREkSIlkmKdwukzGMygA8/vD4DUkJwCDgeDwczndV24ADzAObiHPDg45z7Pcz9j/PS1fiBbp2dnx6WN3LgUjVVBrl5dR/dolJtmmHlvuegNxfjO/h6qgj7uu3rlgvUMGgjHOROKsrm1Ou/PnIin+N6BMziXTc68/7rF81t066Ym9neFppyRPV/f3NfNaCTJy12jfPy29RqadwElnGbx40N9HO0bx+81fnXPOtIZxzf39XBycAKPx6gIeC9peNWLp0eIJtK8dHqUWzc153ViMxCOUx7wTllvwTtpg9ZJUuG01pTxget1YCFSakYjCaoCvnMnBicHJ4gm0+w9OYzf6+Fwb5iaMh9tteVsaKlCu1EpFb1jMU4NRXj+5DD7To/SNxano76cl06P0FSdPTG7cX0jp4YjJFIZfF5t3LL0OOd44dQIzsELp0e4YX0jPzs8wIHuEF6P8cBNa+etHozXY5QHPEzE01QGL/3kPpJI8Y2Xeoin0rxj1wqaq2ev33pWJpNNctVXZqdwXyw8C3wesphmAiy2gz0hwrEU4ViKU0MRtixAHbFYMs0/7e0kkcpwYnCCd1+18txrp4cifGNfN41VAd6/e9V5NTB9XqPM7yWaSFNVtrjSD7s66tjVUXdZ6zg7+ZfXTMmmKSyu//FFKJnOAJDOQDrjODYwwUA4TmXQx5qGCm7e2HTRVKrOOY72jxP0eS6aanFbey0/PHiGjIP9XaNcvbp+xs/f1znKo6/1E/B5+NANay760bxhfSPVZX6qy3zzehVHRKTUnR6K8Nc/P0HXSJR3XbWCe3a0MR5P0VodZG1jJWfGovi8xhVt1WxbWcvtW1p0oCAlo8Lv5eXOEV49M0Z7bRndoxHCsSRXtNWQSGe4bVMzHo/xvms76B6NckVbTbFDFpl3Zsa29hoOnRk7Vxc1lckeu2ecO/cYskmbw31hasr9c+qt7vd6+OXrV9MXis06lbpzjscODzAwHuf2zc201JRxfGCCvrHskJtXz4zRXN2c92f/+NU+DvWM0VAZ4EM3rlk0F5m3rajB6zEcTvuYBba5tZrXesN4zRiLJhmNJOZlRrmZOMe5cgSJVOa8177/yhl+/vogHstuF1eveuMcN+jz8oHrV9M/FmNd08zfnVL07qtX8nr/OOsu2C/0h2M8dniApqoAdyzjY0wlnGbxlm1t7O8cpb2unOoyP2sbK6gK+khmMtyyqWnKwrL7Okd57PAAAO+5ZuV5P0p7NjVxqCfERCJbA2rnytqLZo8YnkhwamiCTa3V536YEqkMI5HERQmnp48NMTAe49ZN+f9oiYgsdaFokq+92MXPXx8k4PPw+JEBMs5xfGCCWDLN9o4arvPVs3VFDZVBP1evqsOzSA7gRWYzNB7nP33zFQ73hfF7jNoyPztX1lEZ9PJyZ4jrVzac255basrOKwkgstTcvb2Nu7a2ntvm37S5hZpyPy3VwfNmgX7m+BDPnhjGDO6/YTUt1Zf+vagp81OTxwxpvWMxfvJqH8cGxjncO8bn3rGdVQ0VVJf5iKcybJhmpq7p9OfOB4YnEiRSGcoDi6eX00L0rJGLrWqo4Ddv38A/Pt/Jk68P8lLnCL+2Z31Bj2XKA17uu2olnSORi4ZP1pT5SaYzRBNpDvWMnZdwAqgt91NbfvF3pzcUo3csxhVt+Q/RW2xqyvxcM0UnkmePD9M9EqV7JMqWtpplW5ZFCadZVAV93Lyx6dzzuooAv3brOoBps5STM74XZn8hu4N4rTfMitryi5JNmYzjn/d2EkmkefVMmHt3thNLpqkt97Om8fz6TGdCUZ4/OQyA1zOkyvoiIjnPnxhmYDzGeDyJP+WlutwHuXqm5QEvD9y0hrqKwLK92iSl7enjQ5wYnGA8lsbnNa7f0MCdV7RQGfRx++YWJU9l2Zm8zZcHvNy8oemi98Rzx+TOQTJd2ALXdeUBXu0N0xuKEo4lOdoXZlNrNR/bM/M5xHRu39LC3lPDrGuqWlTJJikuMyORG42TTDsWYhqr1Y0VrG68uGbwO69awSs9o4xFU/SGYgxPJGadKTGSSPEvL3SSTDs6hyO8c4mdy3bUl/N6/zhVQR/18zS0txQp4XSJMhlHNJmecVrha9dkM5xBv5eNU1zBuGdHGzesb5wyywuQynVVTGcy1Fb4zxsfO1ltuZ+KgJdIIk1bHlcvB8fjHB+YYHNrVcG7XMr86hqJcGoowvYVNfq/k2Utkcrw0ukRqsv8bFvxRvf9s/u3TS1V1FcGaKstwzA6GirZ2lbNW7a2squjjpdOj9JWG6S+Mv/aGSLFMhZL8kpXiI767AH+4Hicw71hAl4PK+rKcc6xc2Utv3rL+nPHJUo2iUztxvUNOBytNWUF72lQHvBy4/oGnjk2RH1lgHRuBre5XuSorwywsq4ir+P9UhBPZevZ1lX4NRTvMt27s51DZ8ZY31y1YEMtnXNMJNJUBrzntumqoI+37VjB8yeHqS7zkUynee7EMOuaKqetV5Zx2Vv2cfFmfS+Uq1fXs76pirKAZ1HVXrtUl3seuiwSTpmMI5VxBHye2d88g3TG8U97O+kNxbhhXcN5PZ8m83k93DDD7AlmNm3G1+Mx3ntNBycGJ9jaPnMX1YqAj4/ctJbxeGrWwoPOOf71hS4iiTSHekJ89JZ1M75fFo9EKsM3XuommXacGopoVpQim4+Z90Cz783VsyfemJmzuszHqobsVbavvdjFRDzNwZ4Qv3LLOra117C2qYJIIs3qxgquX9eI12Ps2TT1fltkMfrRwT46hyPsPTXCR25azcNPn2TvqRECXg+/fus6kmlY01gx7QUskcVsvo7P8/2sb73cQ89ojGvXFP7zAP7dmzfzxOoBGioDbGm9vGFn3ztwhu6RKM/7PPz6resX5N+skJ56fYh9naNA9gK66tCeL51xZJw7r/D2dBqrggteWuVHh7I1xdY3V57XMWLPpiY2t1VRU+bnn/Z2MjSe4MXTI9PO3FYV9HHfVSvpCUUva5a4xWy+Ji0olvk4D13yCad4Ks0/Pd/J0ESCu7a2smMOG3PfWIyKXPfV3lB2DPXxwYlpE06Xq6227KJC5NMpD3jz7lp79nuuISSlxWPg9XhIptP4NcuQLHM+T/bgy4zzZt0yjHgqTTi7i6ZvLMZjhwdJZxyrGsoXTYFVkUtxdp8fjiX5qydP8szxYVLpNGmfl7F4inu2txc5QpG5mY/j80v7vAw9o9kfiBODE9y2ufAn6H6fhzu3thb8c6YzPJEAmHVYUzGc/f020yzbFwpFkvzj3tMkUhnefdXKcxfWFuSzo0niqfSs9c1ODE6cu89k3Hk9a88ue7Zltv/d6YboyeIwH+ehSz7hNDKRZHA8u8M92h+e9gctlc7g9Vw8leHek8M8cTRbdPbDN63h2jX1nBqa4MZcD6ZMxnGkP0xNmZ8Vi7QQWDrj6BmN8o5dK+gZjbKppbDF/VLpDAe6Q1SX+dhY4M9aDnxeD+/fnZ1laKohmiLLyQ3rGqgp91Ed9NNcFeTR1/qpLvOxqbWCfXtH6Kiv4KXTI/i9Hlqqg4xEErTmmcAXWWzeur2Ngz0hnjw6yOB4nJpyH6ORDAGfhyO949yxJV3S3fRl+cr3+PxSJNMZvGYc7gtTW37+cXl5wMv16xo4PjA+4yiExertO9s53DtGR31FXr2bTg5O8I193QC85+qOWU/ok+kMvaEYrTVlBe09dXoowsB4jGtX11NfEaC23D+n4u1LjXOOdMbh83roGo0wEU8D2Q4Ol5NwOjk4QSqTyet8bCAc56vPnSaVcbxl28xJ4Fs2NPFS5wjb2mumHcb9ritXcrQ/zNqmyjl3dnDO0ROKUVvup2qGcjbFEEtme9U3VQVnnbWylM3Heeji+p8rgJbqIJtbq+kPx6asHg/wWu8YP3ylj/pKP+/fveq8CvmD43Eg250sFEnmroi8cVVkPma8uNBEPMVrvWFW1ZdfNLPMWCzJE0cGqSn3sWdjU15f4B8e7OVwb5i6Cj8fuWltwa8kPHtimOdOZIuZv2+3l456Za0vV2PV+TOtiCxXHo+xta2Gx48O8BePv86Lp0aZSKTwezw4st2zB8Jxbt3UzI0bGomnMuoFIiUr4PXwl0+e4JWuEOFYkqoyP+V+L5taq0lnHLFkRgknKUn5HJ/nK5NxfO2lbjqHI1QHfYTjKTxmfPDG1TRNOna6ZWMTtxRodEKhVQV9XLumIe/3D47HOVsSZ3AiPmvC6RsvdXOsf5xQLMk7d63gpg2N8z4iYjSS4OsvdZNxjoFwnHt2XN5v89G+MPFUZsakRymIJdN89bnThKIp3razjQ3NVXTUjxFLZdixYu71rY4PjPPNfT0A3LU1c9GschcajSTO1REeyJ3/TmdnR+2s66ut8LN7bf7b7FSeOjbEcyeGKfN7eeDmNVQEFi514Zzj6WNDDEcS3LqxmeoyH4fOjBH0edjUWs3PjgxwqGcMjxkfuWkN9YuwJ+F8udzz0CWfcPJ4jHt3zbxDO9I3TsY5hsYTDITj52WSb1rfRCrjqK8I0FF/cQ+mQsx48X+eOM6JwQnWNFbyyTs2nnel4bnjwxzpCwPQUV/BuqbZM6pDuZ1GKJrkaH+YJ48O0lZbxh2bW/jZ0QH8Xg+3b2medZxwKp3haP84L54aYSyW4s4rWmadClXD90RkPkUTaf7fbxzgiaMDjMfTZI8xjZbqAH6vh6bqIDesb6Q84OX9u1cVO1yROekfi/G/f3aMQz1jHO4Lk8k4Yqk0Pq8HnwdGIknet3uVajdJycrn+DxfwxMJvn/gDIlUhqbqAKsbKsk4RzJ98UzRFzoxOIHPYzP2Ijk2MM7+rlG2ttewqaWavSeHMTN2r6lftImOnR21DE8kMDN2rJi999jQRILOkQjDE0mePTHMqoaKeR/KZbnBVRPxFI++NkA4luKdV64470J/vk4MTvCd/WeA7PDMS0nGLQbHB8b50aE+Al4P65oqGIkkAONo3zibW6t5X+745fX+MI8fHWBbe+2s51wXSkza/mPJFC+eHiGWTLN7TcOUvdg2NFdx9eo6Iok0111momi+nO34EUumGY+npkw4nRyc4JFX+2irLeNtO9rnrWNF10iUZ3MdKHweo7k6yONHBgG47+rz//10ujuzJZ9wysfVq+oYDMdprArQfsHQi9oKP+/YNf0UjTdtaCTo91BT5p+XGS8Gx+Mc6A4RjqVIZxwXTnDZlCsO7vcadXkcaB4bGGddcxV1FQnWN1dyqGeMcCxFODaOkU22AbTXlrFjZS37u0Y5PRzh+rUNF/Wu+slr/ew9OczBnjGuWlXHy12jU+78bljXQGXQR1XQV/BZQERkefmTHx3mkUN9jMVSeD1Q5vNwRXs165urWNdUxa/fug5fHkU2RRazv/75Sb6zv4fxaAJn2e15dX05GYxrV9ezs6P2snuFiCw2mYzjydcHiSbT3LapOe8apSORBBVBL4l0hq3tNexYUUtdRWDWQtSvdIf48aE+AO67euW0F3EfOdRHJJGmczhKJJ7iqWNDAJT7vbP28iiWoM/L3dvb8n7/W7e3EUumGRpPEPB5ClLouLbCz3uuWcn3DpyhzO+hayTK6eEIm+dQUH3yjGaZIk5uNjyRoGskwqaW6mm3170nh+kbi3PzhsZzvWAOdIcYGo9zoCvEFe3VVAX9NFT6uXLV+dvTjw/1E0um6RqOXnLCaUtrNZFEmlTaUV3m4/uv9J577eYNF/f083iM27e0XNJnFNqtm5rxeozWmrJpRxG9eHrk3Lnt7jXxvOsgz6am3E/A58kmsquC521nGee4fUszTVVBmquCRZ9B/NjAOJmMY9NlTk5QKCWVcDKz/wrsBl50zn1qvta7qqGCX90zt1nbyvzeKb+0c+X3eriirYbeUDSbzLqgq/xVq+pory2jPOClpmzmH4OjfeFz2f+3bGtl+4panIPTwxGaqoKsa6riaP84HjMaqwKEY0l+8mo/AOOxFL98/flV6KOJNGV+77mpl7e1T93N0+f1cNWqurn8+SIX0axwpWM+/q9m+n/qH4vx82ODJDMZPJad2eaqVfX8v+/YlldvT5FS0B+O0ROKknGQxqgO+GioDHDDugZu3NhIU2Xwkk88RErB0f5xXjiVnYW0IuDNe+atlfXl7F7TwHg8xdt2tOfdMyeaTL/xOJGe9n0tNUFODkZoqQ5SMamOTJl/6VzcWNdUyb+7azO9oRiVQS/Vs5xjzNWqhgreur2Nb7/cQ3nAO+f6txuaq7hnRzZJtqujbn6DzFMqneEfn+8klkxzuDd8rlfSZP3hGE8czfaKSWUy52Z029pew4GuUO4CvZ9r1tRx5xUXF5hvqQ5yejhCS82lD2cys3MXJrpGIufa59KjrFgaKgMzdvwA2Nxafe7cdj4L5NeW+/nITWuYiKdpqy0jncnOGhj0edjQnK1ldO2a4l/4Of98P1PwCRjmomQSTmZ2DVDpnLvVzP7czK5zzj1f7LjmW225n4/cvIbhicS0xb1ba/LL3E7uSnm2W/GOlbVc0VZ9rkB6W20ZXo9RW+4nkcpQXeYjHEtN+YW9c2sLdbkeX5tbqzRcTkQWVLnfS0NlkIxzNFcF+Tdv3sjW9tqCHRiLFENlwMeW1mr8XiOezF7oaanJDhVYjAeSIvOlvsKP12OkM+6SThwrAj4euHktzrlLOja9elUdyXQmd7F3+iTuO3etYGA8TlNVMHfC6cVjLMlCwfPVO2Qma5sq+c07NuKxyyu9sXWaC98LJeMgncmeX01XVqUy4KPM7yWWTNNY+UbSaHNrNf+/e7eyvyvEWCw57RC2d1/1xrZ3OTrqK3jf7g5iyQwbmpfWdnvhue18qi7znzvG9HpsUXaomOp8f7EpmYQTcBPwSO7xI8CNwJJLOEF2Osn5KD6+rb2GRCpDxjmunJT9nzzcZPIPesDn4f4bVjM0nphyKFxNmX/RdbUUkeWjutzP775rG690j7FnYyNNmtlGlqDKoI8P37SGUDRJW00Z+zpH8Xs9bL+M4rEipaClpowP37iGRDqT98XVyS71ZNPn9eQ1SsHn9Zw3PE89ai9foScwWggBn4f7rl7JqaHItPvnyqCPD924mlA0edG5lZlx5SwJjAu3vcuxlCdxWs6lFKY7319MSinhVAccyz0OAdsvfIOZPQg8CLB69eoLX152zIyrL7HGQ0XAR0VDKW0WIrKcbGypzmt6X5FSNvmq6uXO8iNSSpbyTE+y9HTUV8yayJm8PxeZb3M5319opZQOHAXOpo9rcs/P45x7yDm32zm3u7k5v3HfIiIiIiIiIiIyv8y5wpb2N7PfBt7jnNtjZp8G3g2cAj7qnEua2QeBTwLDwP3OuTEzuxP4AhADPuyc6zKz9wH/HTgOdAF/7Jx7brrPbWpqcmvXri3o3yZSbCdPnkTbuSx12s5lOdB2LsuBtnNZDrSdy3LwwgsvOOfcrB2YCppwMrMg8BCwAfgF4G+cc283s98hmzj6BvBT4A7gvcBq59wfmdmjwLuAbcBHnHOfNLOvk01K7QCanXPrZ/rs3bt3u7179xboLxNZHHbv3o22c1nqtJ3LcqDtXJYDbeeyHGg7l+XAzF5wzu2e7X2FHlL3a8Df5B5fDzyWe3y26Pdm4IBzLnW2zcwqgKhzLuyce5Zs0gmgwTn3MefcDcDpAsctIiIiIiIiIiJzVLCEk5n5gTc5536aa6oDxnKPQ0D9NG31k9oAvFPEOmXcZvagme01s70DAwOX+yfIItU5HOEfnz/N08eGih2KiMyjF04N84/Pn+bYwHixQxERkcug/Xnpe+5E9v/w5OBEsUMRWTRODU3wj8+f5tnjOg/NVyF7OH0Y+IdJz0e5uOj3VG0jk9oAMhfcX/j4HBUNXx6eODpIz2iMZ44PEYomix2OiMyDWDLN40ey3+0njuiCgYhIqdL+vPRNxFP8/PXc/+FR/R+KnHX2PPSpY0OMx1PFDqckFDLhtAX4DTP7AbAd2A28KffaXcAzwBFgh5l5z7Y55yJAuZlVmdn1wKHcMsNm1mFmK8j2hpJlqqO+HIDGqgCVAe8s7xaRUhDwemitKQOYdYphERFZvLQ/L31lfi9N1UFA/4cik63MnYc2VQUo9+s8NB++Qq3YOfc7Zx+b2ZPOud8zs98xsyfJ1mD609wsdV8GniDbs+n+3CJfAH5Mdpa6B3Jtnwe+ChjZWe1kmbptczM7V9ZSVebD5y10GTIRWQgej/H+3R2EYynqKvzFDkdEROZI+/PS5/UYv3zdKsZjKeorA8UOR2TRuGNLC1d11FFV5sPrsWKHUxIKlnCazDm3J3f/JeBLF7z2MPDwBW2PkC0iPrltP7CnsJFKqdCPn8jS4/N69N0WEVkCtD8vfX79H4pMSd+LS6PuISIiIiIiIiIiMq8WpIeTiIjIfFn7me/Oy3pOfvHeeVmPiIiIiIhcTD2cRERERERERERkXinhJCIiIiIiIiIi80oJJxERERERERERmVdKOImIiIiIiIiIyLxSwklEREREREREROaVEk4iIiIiIiIiIjKvlHASEREREREREZF5pYSTiIiIiIiIiIjMKyWcRERERERERERkXinhJCIiIiIiIiIi80oJJxERERERERERmVdKOImIiIiIiIiIyLwqWMLJzHaY2VNm9oSZ/bVlhczssdytIfe+D+be9x0zq8m13WlmT5vZo2bWMWl9T5rZz81sV6HiFhERERERERGRy1PIHk6HnXM3O+duzT3fDRxwzt2euw2bmR/4BHAb8DDw8dx7PwfcDXwG+Gyu7Q+ADwDvzz0WEREREREREZFFqGAJJ+dcctLTONAJbM31ePqimRmwmWwSKgU8AtxoZhVA1DkXds49C2zLraPBOdfpnOsGagsVt4iIiIiIiIiIXJ6C1nAys3eZ2StACzAEbCLbm6keeCdQB4zl3h7KtddPagPwThHrlHGb2YNmttfM9g4MDMzXnyEiIiIiIiIiIpegoAkn59y3nHM7gG7gHc65YeecA74B7ABGgZrc22tyz0cmtQFkLri/8PHkz3vIObfbObe7ubl5vv4MERERERERERG5BL5CrdjMgs65eO7pGJAwM69zLg3cAhwAjgA7zMwL3AU845yLmFm5mVWRHU53KLeO4VwB8QzZ3lAiIiIiIiIiIrIIFSzhBNxjZr+de3wUOAM8b2YTwHHg8865tJl9GXiCbM+m+3Pv/wLwYyAGPJBr+zzwVcCATxYwbhERERERERERuQwFSzg5574JfPOC5mumeN/DZGeom9z2CNki4pPb9gN75jlMERERERERERGZZwWt4SQiIiIiIiIiIsuPEk4iIiIiIiIiIjKvlHASEREREREREZF5pYSTiIiIiIiIiIjMKyWcRERERERERERkXinhJCIiIiIiIiIi88o33Qtm9m3ATfe6c+5dBYlIRERERERERERK2rQJJ+C/5O7fA7QBf5d7/gHgZAFjEhERERERERGREjZtwsk59zMAM/sD59xtk176tpk9XvDIRERERERERESkJOVTw6nZzNaffWJm64DmwoUkIiIiIiIiIiKlbKYhdWf9e+AxMzuee74W+HjBIhIRERERERERkZI2a8LJOfcDM9sEXJFres05Fy9sWCIiIiIiIiIiUqry6eEEcC3Znk0+4Eozwzn3twWLSkREREREREREStasCSczexjYAOwD0rlmByjhJCIiIiIiIiIiF8mnh9NuYJtzzl3Kis1sB/AQ2STV68CvAv8P8G7gFPBR51zSzD4IfBIYBu53zo2Z2Z3AF4AY8GHnXFduff8bMOA3nHP7LyUeERERERERERFZGPnMUvcK0DaHdR92zt3snLs193w3cIdzbg+wH7jPzPzAJ4DbgId5oxj554C7gc8An821/QHwAeD9ucciIiIiIiIiIrII5ZNwagIOmdkPzexbZ2+zLeScS056Ggc2A4/lnj8C3JhrO+CcS51tM7MKIOqcCzvnngW25ZZpcM51Oue6gdp8/rilLJHKkExnih2GiOTJOUc0kZ79jSIisqilM45YUvtzuXzRRJpLHEQiIpcpmc4QT2kfvlDyGVL3u3NduZm9C/j/gCO5zxrLvRQC6oG6KdrqJ7UBeHP3k5NjUybKzOxB4EGA1atXzzXsRa97NMrXX+zC4zHev3sVTVXBYockIrP41ss9HB+YYPuKGu7ePpdOoyIiUmyRRIqvPNdJOJbknh1tXNFWU+yQpET96GAvB3vG2NBSxbuuXFHscESWhZGJBP+4t5NkKsO7r1rJ6saKYoe05M3aw8k597Opbvms3Dn3LefcDqAbSAFnf5VrgNHc7cK2kUltAJkL7i98PPnzHnLO7XbO7W5ubs4nxJJ0amiCZNoRT2boGokWOxwRmYVzjhODEwAcz92LiEjpGQjHGYsmcQ6OD2h/LnN3LLf9HB8YVy8nkQXSPRolmkiTyjhODmkfvhBmTTiZ2Y1m9ryZjZtZwszSZjaWx3KTu92Mke2p9Kbc87uAZ8j2fNphZt6zbc65CFBuZlVmdj1wKLfMsJl1mNkKsr2hlq3tK2ppry2jo76cza1VxQ5HRGZhZty8oYn6Cj83rW8sdjgiIjJHK+vK2dRaRVN1kGtW1xc7HClhN29opL7Czy0bmzCzYocjsixsbKliTWMFbbVl7Fy57Kv0LIh8htT9D+CXgX8mW/j7I8CmPJa7x8x+O/f4KNlC4O1m9iRwGvjT3Cx1XwaeINuz6f7c+78A/JjsLHUP5No+D3yV7Cx1n8zj85es2nI/v3z90h0yKLIUXb+ugevXNRQ7DBERuQw+r4d37NLwJ7l8V66q48pVdcUOQ2RZKfN7ec81HcUOY1nJJ+GEc+51M/M659LAX5vZU3ks803gmxc0fyl3m/y+h8nOUDe57RGyRcQnt+0H9uQTr4iIiIiIiIiIFE8+CaeImQWAfWb2h8AZoLKwYYmIiIiIiIiISKmatYYT8OHc+34LmABWAe8tZFAiIiIiIiIiIlK6Zu3h5Jw7lXsYM7P/Dqxyzr1e2LBERERERERERKRU5TNL3WNmVmNmDcDLZGs4/UnhQxMRERERERERkVKUz5C6WufcGPAe4K+dc9cCdxU2LBERERERERERKVX5JJx8ZtYOvB/4ToHjERERERERERGREpdPwun3gR8Crzvnnjez9cDRwoYlIiIiIiIiIiKlKp+i4f8M/POk58fRLHUiIiIiIiIiIjKNWRNOZvbXgLuw3Tn3qwWJSEREREREREREStqsCSfOr9tUBvwC0FOYcEREREREREREpNTlM6TuXyc/N7OvAI8ULCIRERERERERESlp+RQNv9AmYPV8ByIiIiIiIiIiIktDPjWcwmRrOFnuvhf4nQLHJSIiIiIiIiIiJSqfIXXVCxGIiIiIiIiIiIgsDXkNqTOzd5nZf8nd3pHnMjeY2VNm9oSZ/ddcW8jMHsvdGnJtH8y97ztmVpNru9PMnjazR82sI9e2w8yeNLOfm9muuf25IiIiIiIiIiJSaLMmnMzsi8CngEO526fM7D/nse5TwJ3OuVuBFjPbCRxwzt2euw2bmR/4BHAb8DDw8dyynwPuBj4DfDbX9gfAB4D35x6LiIiIiIiIiMgilE8Pp7cDb3HO/ZVz7q+Ae4B7Z1vIOdfrnIvlnqaANLA11+Ppi2ZmwGaySagU2ZnvbjSzCiDqnAs7554FtuXW0eCc63TOdQO1l/RXioiIiIiIiIjIgsl3lrq6SY8vKdmTG/7W5Jw7RHaGu9uAeuCdufWO5d4ayrXXT2oD8E4R65Rxm9mDZrbXzPYODAxcSpgiIiIiIiIiIjJP8kk4/WfgJTP7v2b2N8ALwP+Xz8pzdZr+B/AxAOfcsHPOAd8AdgCjQE3u7TW55yOT2gAyF9xf+Pgc59xDzrndzrndzc3N+YQoIiIiIiIiIiLzLJ9Z6r5iZo8B1wEG/I5zrne25czMB/wd8GnnXK+ZVQIx51wauAU4ABwBdpiZF7gLeMY5FzGzcjOrIjuc7lBulcO5AuIZsr2hRERERERERERkEZo14ZTjAQZz799sZpudc4/Pssz7yCapvpQt18Rngf9pZhPAceDzzrm0mX0ZeIJsz6b7c8t+AfgxEAMeyLV9Hvgq2aTXJ/OMW0REREREREREFtisCScz+xLwS8BB3hjK5oAZE07Oua8AX7mg+Zop3vcw2RnqJrc9QraI+OS2/cCe2eIVEREREREREZHiyqeH033AFudcvMCxiIiIiIiIiIjIEpBP0fDjgL/QgYiIiIiIiIiIyNIwbQ8nM/szskPnIsA+M/sJcK6Xk3Pu3xY+PBERERERERERKTUzDanbm7t/AfjWBa+5woQjIiIiIiIiIiKlbtqEk3PubwDM7FPOuf82+TUz+1ShAxMRERERERERkdKUTw2nB6Zo++g8xyEiIiIiIiIiIkvETDWcPgDcD6wzs8lD6qqBoUIHJiIiIiIiIiIipWmmGk5PAWeAJuCPJ7WHgf2FDEryd2xgnMO9YXasqGV1Y0WxwxEpis7hCAe6Q2xurWJjS3WxwxERkUUklkzz5NFByvxebt7QiMdjxQ5J5LKkM46njg0ST2bYs6mJMr+32CGJLDqJVIafvz6IGezZ2ITPm8/gLplvM9VwOgWcAm5auHDkUjjn+P6BMyTTjq6RCA/etqHYIYkUxQ8P9hKOpXi9f5zfuqNKJxMiInLO3pMjHOgOAdBcHWRLmy5MSGk70hdm78kRACoCXm7e2FTkiEQWn/1do+zrHAWgviLAlavqihrPcqU0XwkzM2orAgDU5e5FlqP6c98Dv5JNIiJynroKPwAeM2rL/UWORuTy1VX48ZjlHuscQGQqZ78bZm/8DsjCm2lInZSA913bQd9YjPba8mKHIlI077pqBT2jUVpryoodioiILDI7VtbSWBUg4PXQWBUsdjgil629tpwP3riaZDqjcwCRaWxsqeL+G1ZjBi3VOkcoFiWcSlyZ38uaxspihyFSVH6vR98DERGZlk7KZalpUvJUZFa6GF18syaczOwW4HeBNbn3G+Ccc+sLG5qIiIiIiIiIiJSifHo4/SXw74EXgHRhwxERERERERERkVKXT9HwkHPu+865fufc0NnbbAuZ2Q1m9pSZPWFm/zXX9mkze9LM/t7M/Lm2D+be9x0zq8m13WlmT5vZo2bWkWvbkVv252a26zL+ZhERERERERERKaB8Ek6PmtkfmdlNZnbN2Vsey50C7nTO3Qq0mNmtwB3OuT3AfuC+XNLpE8BtwMPAx3PLfg64G/gM8Nlc2x8AHwDen3ssIiIiIiIiIiKLUD5D6m7I3e+e1OaAO2dayDnXO+lpCtgFPJZ7/ghwP3AIOOCcS5nZI8BDZlYBRJ1zYeBZM/tibpkG51wngJnV5hG3iIiIiIiIiIgUwawJJ+fcHZfzAbnhb03AKG/UgAoB9UAdMHZBW/2kNgBv7n5yb6wpe2aZ2YPAgwCrV6++nLBFRERERERERGSOpk04mdmHnHN/Z2a/PdXrzrk/mW3lZtYA/A+yw+CuBVbmXqohm4AazT2e3DYyqQ0gc8H9hY8nx/QQ8BDA7t273WzxiYiIiIiIiIjI/Juph1Nl7r56Lis2Mx/wd8CnnXO9ZvY88JvAHwJ3Ac8AR4AdZuY92+aci5hZuZlVAdvIDrsDGM4VEM+Q7Q0lMiePHu7nla4QV62u49ZNzcUOR5ag4YkEX3uxCzPjPVevpL4yUOyQREQkD/s6R3ny6ABrGit5x652zKzYIYksCePxFP/6QhexZJp3X7WSttqyYockUhA/OtjL4d4w169r4Ib1jcUOp+imTTg55/4id/97c1z3+4DrgC/lfqw/CzxuZk8Cp4E/dc4lzezLwBNkezbdn1v2C8CPgRjwQK7t88BXAQM+OceYRNjfGSLjHPu7Qko4SUG83j9OOJYC4PjgONdWNhQ5IhERyceBrlGSacfr/eOMx1NUl/mLHZLIknBycILhiQQAh/vCSjjJkpRMZzjYk60OdKA7pIQT+RUNnxPn3FeAr1zQ/DTwpQve9zDZGeomtz1CtrD45Lb9wJ75j1SWm12ranmlK8SuDtWel8LY2FLF/q5RzIz1TVXFDkemsfYz352X9Zz84r3zsh4RKb6dHXU8eXSAtU2VVAULdpgssuysbaqkoTJALJlmS+ucBtCILHp+r4ftK2o43Btm50qda0IBE04ii9UdW1q4Y0tLscOQJayhMsCv3bq+2GGIiMglumpVHVetqit2GCJLTlXQxwM3ry12GCIFd/f2Nu7e3lbsMBaNKWd7ExERERERERERmatZeziZWRB4L7B28vudc79fuLBERERERERERKRU5TOk7ptkZ4V7AYgXNhwRERERERERESl1+SScOpxz9xQ8EhERERERERERWRLyqeH0lJntLHgkIiIiIiIiIiKyJOTTw2kP8FEzO0F2SJ0Bzjm3q6CRiYiIiIiIiIhIScon4fS2gkchIiIiIiIiIiJLxqxD6pxzp4A64J25W12uTURERERERERE5CKzJpzM7FPA3wMtudvfmdm/KXRgIiIiIiIiIiJSmvIZUvcx4Abn3ASAmX0JeBr4s0IGJiIiIiIiIiIipSmfWeoMSE96ns61iYiIiIiIiIiIXCSfHk5/DTxrZl/PPb8P+MuCRSQiIiIiIiIiIiVt1oSTc+5PzOwxYA/Znk2/4px7qdCBiYiIiIiIiIhIaZp2SJ2Z1eTuG4CTwN8BDwOncm0zMrMVZvaimcXMzJdrC5nZY7lbQ67tg2b2lJl9Z9Jn3mlmT5vZo2bWkWvbYWZPmtnPzWzXZf7dIiIiIiIiIiJSIDP1cPoH4B3AC4Cb1G655+tnWfcw8Gbg65PaDjjnbj+3IjM/8AngNuC9wMeBPwI+B9wNbAM+C3wS+APgA0AG+F/Au2f5fBERERERERERKYJpE07OuXfk7tfNZcXOuRgQMzuvvvhWM3sC+DnZRNJmskmolJk9AjxkZhVA1DkXJls76ou5ZRucc50AZlY7l5hERERERERERKTwZp2lzsx+kk9bnjaR7c1UD7wTqAPGcq+Fcu31k9oAvFPEms/seiIiIiIiIiIiUgQz1XAqy9VZajKzejNryN3WAivm8mHOuWHnnAO+AewARoGa3Ms1uecjk9ogO4Ru8v2FjyfH/KCZ7TWzvQMDA3MJUURERERERERELtNMNZw+Dvw7ssmlF8jWboJs76P/eakfZGaVQMw5lwZuAQ4AR4AdZuYF7gKecc5FzKzczKrI1nA6lFvFcK6AeIZsb6iLOOceAh4C2L17t5vqPSIiIiIiIiIiUlgz1XD6b8B/M7N/45z7s0tdca4g+PeBK4EfAv8R+HMzmwCOA593zqXN7MvAE2R7Nt2fW/wLwI+BGPBAru3zwFfJJr4+eanxiIiIiIiIiIjIwpiphxMAzrk/M7MdZHsblU1q/9tZlkuS7bU02TVTvO9h4OEL2h4BHrmgbT+wZ7Z4RURERERERESkuGZNOJnZ54HbySacvge8DXgSmDHhJCIiIiIiIiIiy1M+s739IvBmoNc59ytkh8gFCxqViIiIiIiIiIiUrHwSTlHnXAZImVkN0A+sL2xYIiIiIiIiIiJSqvJJOO01szrgy2Rnq3sReK6QQUlxhCJJekOxYochy0A64+gcjhBLposdioiILKDRSIL+MR1riCwm4ViS7tFoscMQWTCJVIbO4QjJdKbYoSx5+RQN/83cw/9tZj8AanIFvGUJGRqP8w/PniaVcdx5RQtXrqq7pOUTqQzDEwmaq4N4PVaYIGXJ+NHBXl7rDVNb7ueBm9ee22ZiyTShaJKW6iBm2o5ERErVRDxFJJGmufqNKgz94Rhffa6TdMZx9/ZWtq+oLWKEIkvHVN+3S1n27545TSyZ5oZ1Ddy8sakAEYrMv0zGMTAep67CT9DnvaRlv/ZiF2dCMVbWl/P+3asKFKHADAknM7toRrnJrznnXixMSFIMoWiSVMYBMDQRv6RlnXP8495OBsNxNrVW8Y5dKwoRoiwhg+PZbWwsliSZzuD1eEmlM3zludOMRpLsWFnLW7a1FjlKERGZi1A0yd89c4pEKsPtW5q5enU9AKORJOncscbgeKKYIYosGWOx7Pctnsxw2+Zmrl1Tf0nLj8dT53qcD07oeyml40eH+nj1zBgNlQE+dOOaS+r0MJTb1of0W1RwM/Vw+uMZXnPAnfMcixTRuqZKrlvbQDiW5Pp1jZe0bCrjGMolEPrGLi1ZJcvTm7e28sKpEdY1VVLmz16RiKcyjEaSAPRpuIWISMkKRZIkUtlhCpOPCzY2V3HNmnqiiTTXrb20k2IRmVookiSezH7f5jJctbWmjFs2NjEQjnPzhks7BxAppv5wdnsfiSTOXcDO11u3t3HozBg7VtQUKjzJmTbh5Jy7YyEDkeIyM/ZsmlsXWr/Xw11bWznSF+aa1TqAlNmtqCtnRV35eW2VQR+3b2nm5NAE161tKFJkIiJyuVY1lHPNmnpGIwluXP/G/tzjMd60ubmIkYksPR315Vy7pp6RSIIb188tYXT9Oh13Sem5Y0sLe08Ns76p6twF7HxtbKliY0tVgSKTyWat4WRmH5mq3Tn3t/Mfjpx1amiCsWiKbStqSqIm0o6VtexYqVoMcrGTgxOMx1NsbZ99W756df25oRciIrK4haJJjg+Ms76pitoK/7l2MyWWRC5HPJXmtTNhmquDF12gu5CZcZu+b1LiYsk0r54ZY2VdOS01ZXkts6qhglUNFQWOTC7XrAkn4LpJj8uAN5OdqU4JpwLpG4vx9Ze6cS57MDfXnkcixdYzGuXrL3UD2RoDN2/QtiwislR87cUuRiNJXjo9yq/uWVfscESWjEdf6+fVM2G8HuNXbllLdZl/9oVEStgPXunlxOAEAZ+Hj+1Zd8k9lmTxymeWun8z+bmZ1QIPFywiIZ1xuGxNTVIZTdUopetscdgLH4uISOk7u1/X/l1kfp2dyMc50KmALAdnt/nMpPNgWRry6eF0oQiwab4DkTesqCvn3l3thKJJruyoK3Y4InO2qqGCt+1sYzyW4spVdcUOR0RE5tF9V6/kaN+46mCIzLM3X9FKU1WQtpqy84ariixVb93eyivdY3TUl1MeUO+mpSSfGk7fJjsrHYAX2Ar8UyGDWk6cc7zcFSKdcVy9qg5PrsbN5tbqIkcmMjfpjOOl0yMEfV52dtRyRZtmfxARKTWhSJKDPSHWNFWycpoaMk1VQZqqggscmcjicnooQtdIhJ0dtfM29K084J1zAXCRUlRd5uemIs6SeLQvzOB4gqtX12k43zzLp4fTf5n0OAWccs51FSieJen0UITvv3KGugo/9129kqDvjY341TNhHn2tHwAzNMublLTHDvfzo4N9xFNpOuorKPN72KTkqYhIyfn2/h6ePDrIRCLFf7h7C1epl6rIRSbiKb6xr5t0xnEmFOO913ZwsCfEz44MsKq+gnt3tp+7mCyynPSGYnz75R7KA17ec81KKgJzGVi1MAbCcb6z/wyQrTn71u1tRY5oafHM9gbn3M+Aw0At0EA26TQrM1thZi+aWczMfLm2T5vZk2b292bmz7V90MyeMrPvmFlNru1OM3vazB41s45c247csj83s11z+3OL42BPiEgiTc9ojDOjsfNe83vf+BEKeGf97xBZtJxz7OscJZ3J0BvKbuc+bdMiIiUpmc4wEkmQyThe6Q4VOxyRRcnrsXMz8Pp92WOe/V0h4skMr/ePMxZLFjM8kaJ5tXeM8XiKgXCcU0ORYoczI5/H8Fj2e6zz8fmXz5C6XwP+E/BTwIA/M7Pfd8791SyLDpOd0e7rufU0A3c45/aY2e8A95nZN4BPALcB7wU+DvwR8DngbmAb8Fngk8AfAB8AMsD/At59SX9pEV3RXsPxwQlqy/201Z4/zeOm1mreeaWRzjg2t6oGgpQuM2PnyloMuHJ1HTeua2RdU2WxwxIRkTl437UdjEWTRJNp1ZMUmUaZ38v7dnfQG4qdK4exY0UtA+E4qxrKqdHscrJMbW6t5rUzYcr9HlY1VBQ7nBnVVwb4xd0djEwkuKJNIzPmWz592z4NXO2cGwIws0bgKWDGhJNzLgbEzM714LkeeCz3+BHgfuAQcMA5lzKzR4CHzKwCiDrnwsCzZvbF3DINzrnOXAy1ef59i8K6pko+ecfGaV/f2FLF8YFx/v7Z06xtrGTPJk0dL4tf92iUnx0eoKU6yJu3tmBmvHlrK2/e2lrs0ERE5DJVlfn51F2bz2tLpjP88GAvkXiat2xrpb4yUKToRBaPluoyWqrfuKC8s6OWnR0Le6oST6X54cE+Ysk0b93WpkLjUnQr68r5jds3zPt6MxnHI6/2MTie4M4rWi7qzDFXK+vKp61XKJcnnz5jXUB40vMw0DmHz6oDxnKPQ0D9NG31k9ogW6j8wliXXF+3p48PMRCO8/zJYXW/lZLw3Ikh+sZiHOgO0TcWL3Y4IiJSYMcHJjjaN073aJSXOkeKHY6I5LzeP86x/nG6R6Ls6xotdjgiBXNmLMbBnjH6xmI8d3K42OFIHvJJ3HST7Wn0u2b2eeAZ4HUz+20z++1L+KxR4Ox0VTW551O1jUxqg+wQusn3Fz4+x8weNLO9ZrZ3YGDgEkIrvrWN2aFHLTVBKhdxUTWRs9bkttnacj91upImIrLktdWUUR7w4jFj9SIfIiGynLTXlhP0e/B69N2Upa2xMkBNefa8Y4229ZKQT2bjWO521jdz95c6wPF54DeBPwTuIpu4OgLsMDPv2TbnXMTMys2simwNp0O55YdzBcQzZHtDXcQ59xDwEMDu3bvdJcZXVLdsbGJXRy0VAd+54oMii9k1q+vZ3FpNmc+j4uAiIstAbYWfX7llLemMW9QzDoksNw2VAT62Zx2ZDJQHNKW7LF1lfi8fuWkN8VSGqqB+h0rBrP9LzrnfAzCz6uxTN57PinOz0H0fuBL4IfAfgcfN7EngNPCnzrmkmX0ZeIJsz6b7c4t/AfgxEAMeyLV9Hvgq2cLln8zrrysx1SosKCVGO3oRkeUl6NPJrMhipO+mLBd+rwe/LnaXjHxmqdsBPAw05J4PAh9xzh2caTnnXJJsr6XJngW+dMH7Hs6tf3LbI2QLi09u2w/smS1ekcl6RqP8+FAfdRV+7t3Zrp44C6BvLMYPD/ZSU+bn7TvbCfj0by4istT0j8X4wcFeqst83Ltzhfb1IsKrZ8Z46tgQ65sruWNLS7HDEWE8nuI7L/eQcXDvrnZqy9XBY6Hlc3TwEPDbzrk1zrk1wH8AvlzYsKSYQtEk3z9whqePDRU7lMv2cucowxMJjg9M0DUSLXY4S9Izx4f43oEzhKLZYvf7OkcZGk9wYnCCzpFIkaMTEZH50h+O8Z39PbzcOcrLXSGGxhOcHIxwenii2KGJSIH1jWW//we6pqxsAsBzJ4YZiybZd3qUiXhqAaMTmdqRvjBnQjH6xmK8emZs9gVynj17fhPRZF6XK5+EU6Vz7tGzT5xzjwGVBYtIiu7pY4O81hvmmeNDdA6XdsJgQ0sVHjNqy/201szPtJnyhq6RCE8fG+Jwb5inXh8EYENzFV6PUV3mo03/5iIiS8ajr/VztG+cn77WT0tN8I19fa2mkhZZ6n6a+/4/8mof49Mkkza1VgHQUV9OuV9D/KT4VtVXUOb3EvB5zk3SNZue0ShP5c5vnsyd38jc5VOA5biZfY43hr19CDhRuJCk2BqrgkCYgM9zbhaAUrW5tZq1jZX4PIZHxdjnXXWZn4DPQyKVyW03sLGlik+8aYP+zWXZWPuZ7172Ok5+8d55iESksBorg/SMxqgK+tjaVsPWthq8HtNkJyLLQENlgN5QjOoyH8FphtDevKGJa9fUE/B6MNN+QYqvuTrIr9+6DiDv0irVZb5z5zcNlYFChrcs5JNw+lXg94Cv5Z4/DvxKwSKSortubQMr68qpDPoKOs51Ip4iuAAznKmuROHUlvv58E1rCMdSrKzLXuFOZxyJdIaATwXFRUSWgmQ6QzKd4c4rWtjSVk1jVUC/rSLLzFu2trKtvYbGqsBFBZsTqQzpjKM84FXxcll0LvVcs9zv5b3XrCTtOHd+I3M37RmhmZUBnwA2AgeA/5ArBC7LwIoCf7n2d43yk1f7qSn388EbVlOmbrclq6bMT01uhsVkOsNXnjvN0HiCWzY2cf26hiJHJyIil2M8nuIrz55mIpHirdvb2NpeU+yQRKQIPB5jVUPFRe2hSJKvPH+aeDLDvbva2dhSVYToROZHIpXhH549xUgkyW2bm5Rwmgczpfv+BthNNtn0NuCPFiSiEuOc40cHe/k/TxznSF+42OGUjBOD2QKjY9EkQxOJIkcjF4okUvzT8508/PRJhi/h/2csmmRoPPv+k4MqIisislg8d2KY//PEcZ47MXxJyw2E44zHUzgHp4a0X5flZWg8zsNPn+Sfnu8kmkgXO5xFqXcsRjSRJuNcydd+lTeEokn+/tlTfOW504Rjy6fPyWg0wUiuUPiJQW3P82GmhNM259yHnHN/AfwicNsCxVRSxqIpDvaMEY6l2HtypNjhlIzr1zXQUhNk24oa2lVYetE5PjBB92iUwfEEB3umn43kQg2VAa5aVUdzdZAb1zcWMEIREbkUzx4fIhxL8ezxS5uBdlV9OVvaqmmrLeOaNfUFik5kcTrYM8bgeILu0SjHBsaLHc6itK6pkg0tVayoK+PKVXXFDkfmyWtnxugfi9MbinGkb/ls+81VQXZ11NJcHeQGjdSYFzMVWTmXynTOpVT4bWpVZT5W1JXRMxpjS9vsXUgzGcfxwQlqy/00VwcXIMLFqb22nA/esKbYYVySeCpNKJKkuTq45AshdtSXU+bz0D8ez7tYXiyZZiyW5PYtzUv+30dEpBTEkmlOD0dYWVfO5rZqDvWMsbmt+pLW4fN6ePvO9nPr6w/HaKnWhSJZOs5+T1bUlVMVPP/UaH1zJfu7Rgn4PKyqv3g4mWRrpb7ryhXFDmNK/eEYNWV+le6Yg7VNlew9NYLHjNVTDKXMx9nvVnttGdVlpTERlZnx5q2t87rO8XhqWRcgnynhdKWZjeUeG1Cee26Ac85pED/g9Rjv372KZNrlVUDzqWNDPH9yGK/H+MhNa6irCNAfjvHU60O01pRx0wb1ClmM0hnHV5/rZHgiwfYVNdy9va3YIRVUXUWA9rpyXu4a5S+fPMHvvXM71TMUkE+kMvz9s6cZiya5Zk09b9rcvIDRiojIVL7xUjcnhyboD8d555Ur+I03baAsMLcTr1gyzcNPn2I8nuKGdQ3cvLFpnqMVKY5vvdxD90iU6jIfH9uz7ryLZh31FXziTRvwmDEWS/LNfd3UVQS4bVOTLq4tcj9/fZDnTgxTFfTx4ZvWKOl0iVpryvjEmzYA2eP87x04g9dj3LGlJe9JI779cg9due/Wr96yblnOXj00Hucrz50mmXbcs2N51kGcdmtxznmdczW5W7Vzzjfp8fL7l5qBmeX9xRuPp4BsAiOWzADZHeKJwQmeOT7EQDhesDhl7hKpzLlaRn1jsSJHszAOnRljJJKkeyTK4Vnqk0UTacai2U6RfaHl8e8jIrLYTSTSdI9EOROK8dKpEXpC0TmvKxxLnTuG6V0mv4OyPERy23W2DtHFr/u8Hjwe45njwxwfmODFUyN0Ds/9uyQLozd3PDoef2PfJZfG6zG8HuPlrlEO94Y51DPGq2fGZl8wJ5Kre3a2xtdyNDSRIJnO/u3L9bdT85YvsFs3NRH0e2isDNBWm+2S3lpdxsnBCFVBH9Vl+i9ZjMoDXt60pZkTAxPLZua1t2xroTcUo77Cz5rGyhnfW1vh55aNTXSNRNRLT0Rkkbh3Zzs46ByJEPR7L6s7/9nafGdCUfaod5MsIW/f2c6B7hAbW6rwztADo7UmyKtnIOj3UFtRGsODlrNbNzXx5OuDtNeW01S1fMuYzIeW6iBm4DG7pJIwb9vRxoHuEBuaq/B58+ucsdRsaK5i58paIsk0u5dpHURlNxZYZdDHHVtaAOgcjvDDg700VAb4pd0dNFQFl0V3zyN9YRKpDNvaa0qqa+U1q+u5ZvXS3lF0j0b5wSu91JT5eNdVK/jdd1UzFk1weniCoN9DzQzjr69f17BsknEiIqWgezRCKpNh99p6bt/SclF9mgt1DkcYGI+zfUUNQd/FxyO6oCBLUUtNGW/OYwKbq1fX01FfQUXAS2XQRzyV5lv7ehiLpXjbjjZWaPr0OclkHIfOjOH3ethyiTXmZtJSU8Z7rumYt/UtZ+ubq3jgprV4PEbtDCU2LjT5u/Xoa/0c6Qtzw/pGrlomxeWHxuOcHJrgunUNl/TvttQo4VRE+7tChGMpwrEUu9c05JVsOjk4wZlQjF0dtVTOcuC4EJ4/OUxvKMbNGxppzOPqwbGBcb67/wwA8VSGa5dppnexOtAV4vW+MKmMY9uKGrpHovzwYC/tteUcOhPmwzeWVqF3EZHlbO/JESKJNEf7xnn7jnacc7zcFcI5R0XAy2u9YXaurGV9cxWhSJKvvdhNxjn6x+Lcs2Np1yoUmYvJvTu6RqJ0jWSH1h3oDi2qhFM4luRAd4hV9RWsmkPB5/5wjGeOD7Oyrrzgx+r7ukb52eEBIDuEa2PL7JMwycKrv4wesvFUmn2dowC8cGpk2oTTkb4wIxMJrlpdN+VFj/kWSaR4/MgAQb+X2zY1z9jD8VI55/jXF7uYiKc52DPGR25aO2/rLjXFz1gsY5tbqzg2ME5dhZ+WmtmTNeFYkm/u68keDIZjvPuqlQWP8YVTw7zcGWJXRy27157fe6U/HOPJo4NAtibVfVfPHs/5w3eX51jexazM5+Hk0AQBn4d/fK6Tk0MTjEYS+DxGax7bqIiILB5XtNfw4qkRrmirxuMxXukO8e2Xuzk2MEEqleHq1fWcCcX4xJuqcOf9Juv3WWQ2K2rLqavwMx5LsWmRJUl+eLCPzuEIL3hG+LVb11M+zWQBr/eP88TRAVbVV/DmrS3nCqH/7PAAXSNRjvWPs76p8rKSDbPRucHSF/R52dBSxbH+cba2T92LrTcUO9cpYSKR4s4rzp8pbmQiwfdeOUPA6+Edu1ZMu01fihdPjfLqmWyd2raasnkv6H22JtwyLV91zoImnMxsLfAs8CqQcM7dbWafBt4NnAI+6pxLmtkHgU8Cw8D9zrkxM7sT+AIQAz7snOtayNgLYVNrNRuaq/IeVuYxw2PZjdfnWZhxsE8fGyKZdjx9bOiihFNV0Ed5wEs0kc57bPTGliru2dFGIpVh58raQoQsl2FtUyXXrW3ADI72jxNPZQj6vezZ1MxtmzTznIhIKXnT5mb2bGw6d9XW5zXOhGJEE2mcc4zFkqxpytboq6sIcN/VKxgIx9mh32eRWZUHvHz05rU4x6IrEeHLxePxGDNNprf35DCjkSSjkRDXrqk/l1hqqg7SNRKlMuidlxP7mVy9qg6fx/B7PWxsmb8hdbK4vOvKFaQzbtpeRN7ctuoceKc4z32lJ0T/WHZyrWMD4/PyO9VUHTj32ZdT43AqZsZ7r+ngxOAEm1sXV0J6oRWjh9OPnXMfAjCzZuAO59weM/sd4D4z+wbwCeA24L3Ax4E/Aj4H3A1sAz5LNiFVNGdCUb65r4dyv5f3Xtsxa12E6VzKD1Rl0Mf7r1tFbyjGFdNkh+fbxpZqXj0zNmX31oqAjw/fuIZQNEl77exj389ajtNBLkav94/zw4O9NFUF+IWrOwj4PKxtquS+q1cST2XY2B/mQFeIre01vPPKFcUOV0REZpFMZ/j6S90MhOPcva2VTa3V5x3cX9FWw31XreSJo4Osbqjgjiuaz5sUYk1j5ayTRIgsV845vnegl+MD49yyqYlrVtdjNnNCp1ju2dHG4d4w7XVlM5bs2NhSxZlQjLbasvMmLrp9czObW6upr/AXvL6sx2NcuUxq+ixXmYzj2/t7OD0U4U1bmtnVUXfRe5qrg7z3mg5C0SRXTFHLa21jJS93juLzelg5T8NXr2iroaEyQMDroa5i/nvxNVcHL6nI+lJVjITTHWb2BPA14AjwWK79EeB+4BBwwDmXMrNHgIfMrAKIOufCwLNm9sWFCjaWzI67bK8tO29s9mu9YaKJNNFEmtNDEbatWJgkSmtNGa15FDacL/fsaOP2Lc3T/thUBn2LopaUXJpQNMl39vcQiadIpDL0h2N01GfH+K9vziYXt7bX8JZtbcuikL2ISKkamUhwfHCcDc1VRJNpunM1ZQ72jLGp9eKD9ru3t/GmLc34PZ5F1ytDZD4kUhkO9oRoqgrOqX7RdCKJNEf6ssNvDnSFFvVEMmV+b15JnN1rG9ixspagz3NuOB1ke2fM10m9LC29oRjdo1G2tldTEcjvHDAcT3F8YALI1jubKuEEsKqhglXTrGNVQwUP3rYBjzGvM961VC/cefVytdDzE54BNgN3AHcBu4Gx3GshoB6om6KtflIbwIKdAf/oUB+PHxngX1/oYiKeOte+ta2GioCXxqoAaxov7cdsIp7CldBgTiUclp5v7cteAX/1TJgVddMnMfV/LyKyeKXSGb76/GkePzLI11/qpqkqSEd9OQGfhx0rp78QFvR5lWySJcM5d96x9WOH+3ns8ABfe7GbkYnEvH1ORcDLlrZq/F5jV8fSGXZa5veel2yS0hFLpkmmMwv6ef/yQiePHxnghwd7816upszHxpaq7HdnZd2cPz/g88xrskkWxoJ2TXHOxYE4gJl9h2wS6Wyl6RpgNHeruaBtZFIbwJTfLDN7EHgQYPXq1fMVc/Z+Utvh3jDJdIYHb1t/yTvoRw71caA7xNqmCn7hak3VKcVxYnCCg91jVJf7uHF9I37tvEVESkosmeYfnj3NU8eG6Kgrp7bcz0Q8xfrmKt6yrbUgwwNEFqNvvdzD8YEJtrbXcM+OtnPH7O6CUviXy8x4+872y1rH0b4w8VSGbe01SvrKZTk2MM53Xj5Dmd/DL1+/mtpy/4J87tk+E5kLzsaT6QwHe8ZoqAiw+oLOGGam8hzL2EIXDa/ODYsDuAX4M7LD6P6QbI+nZ8gOs9thZt6zbc65iJmVm1kV2RpOh6Zav3PuIeAhgN27d8/Lb8xbtrXySnd2SF1l0MfRvjDfO5CtoJ/KuGmndZzO8cFxAE4ORmYsnJavWDLNM8eHqC7zF3zaUlkajvaFCUWTTCRSBP0enjo2qJodIiIlZmgica7WRbnfy31Xr+RrL3YTiibZ1znKx/asA7L7/FNDEa5eXUdjnhN8iJQK5xwnBrNDdc7ev2lzM42VAZqqgvNeCPhynBic4Du5WbjiqTTXrmmYZYnFL5XO8NyJYQCuX9eg3icL6NTQBBnniCTS9I3FFiThVOb38p5rO+geibL9gnIyTxwd4OXOEGbwoRvX5D2h1FJwJhTlYPcYm1urL0q2ycLXcLrVzP6AbC+nJ51zz5rZ42b2JHAa+NPcLHVfBp4g27Pp/tyyXwB+THaWugcWKuCKgI/r173xg5CZlMbKzDAs7kwoit/ruejLdvOGJl48PcIVbTWXnWwCePr4EPtOjwLQWBlgbZMSBzK9WDJN50iU5qogQZ+HdMZxaijCeDw158L3IiKy8FqrgjRVBYilfNy7s52GysC545KzvbMjiRTfO9BLxjkGx+P88vXz0/tbZLEwM/ZsbOLQmbFzF4HL/N6LZlY+q3M4QlXQd242toU0+bwhUzqVNWb0Ss8Yz+YSTuUBL1cv4rpWS82VHXX0jcWz9beAaCJd8BkFAVbWlU9Z32vyaXEJVY6ZF9/df4ZwLMXhvjC/efsGDVG9wEIPqfse8L0L2r4EfOmCtoeBhy9oe4RsYfGi2tJWTTKdIZ1x7JxmOsZDPWP88GAvHjPet7vjvGLjO1bWzut0w2eTBB4zKoKqtyMz+5cXuhgIxwj4Pbx9ZzuxZJrygA+/VztGEZFS8tiRAQbHE5QHvOd6cfzC1St5vX+cDbmZZX0eD0G/h2giTVWZLirI0rR7bcO0CabJ9p4c5omjg/g8xgdvXLPgvZ82NFfx1u1txFPpaYsml5qqSece1drHLKjGqiAfuH41X3+pi+/sP0N9hZ8Hbl5btGTHnk1N1Jb7qa8MLLuZ2aqCPsKxFJUB1UObivYMczBbwmg0ki1QmHGOUDR5XsJpvu1eU09TVZDKgFdV9gsok3EcHxynptxf0v/OoWgSMCoDXj62Zx0nhyYIeD2cHIywvrlStZxERErESO5YI5pIE09mKPN7aawKnjdsLhRNcuO6Bsr9XtbnklAiy9VIJAlkS2KMx1JFGW63ULNaL5SNLdW8b7cX55jXGQGnMzKRYGA8zvqmSg3fyxmZyG7XY7EU6YzDV6SLyEHf9D0LF7NIIkXncJRVDeV5z7p3ofuuXknncKSg5/ylTAmnArhmTT3RZJqgz8uWKaYkTqYz9IxGaa0pu+xZwMyMdRpGV3BPHRvi+ZPDeD3Gh29cU5Su2PPhbTvaeOrYELtW1uLzeuior+Cvfn6CeDLDlrbqyy6GKSIiC+POK1p45NU+WqrLqK24uHZHKJLkq8+dJpVxXLumni3tS+tEV+RS3bShEeccteV+VjUU/sQwFE0yFk3SUV++pHs9dNQvTM2aaCLNPzx3mkQqw7YVNbx1e9uCfO5i99YdbbzcOcqmliol4ebgay9mZ+5urArwkZvWzmkdZX4vm6Y453fO0TUSpabcv2BF3RcjJZwukXOOZNoR8E3/hS7ze3nz1tZpX//2yz2cGorQUBngIzetuehH6JXuEEf7w1y1qp6W6iBPHRuittx/Xi0pWVgTiRQA6YwjlkoXOZpLk8k4Ms5xfHCC7x84w+G+MM8eH+a37tzAyvoKEqnsNBMT8VSRIxURkdmc3af3jEb56Wv9RBNpTg9P8EvXrT7vIlYslSaVeaOWk8hyVxnwcvuWlhmP4efLeDzF//35CY70hdncWs1v3L7xsj63byzGS6dHWNtUyRVtyzN5nEhnSKazx6zap71huppKhRZPZTtXnBVLpnnq2CBlPi83rm/MexbGvSeH6RqJcuP6RtpqF34Uydnzn4n4/J/fPX18iGePDxPwefjwTWuoKVueSadlkXB64ugA/WNxbt3UREvN3DfkTMbxLy920T0S5eYNjdywvnFO6znbpTcUTZJxMLnnYyqd4ZFX+3AOhsYTrGms5JXuEABtNWUXVb4/0hdmMBzn6tX1C1Iobrm6dVMTQZ+Hxsog7bWl0V0ylkzznf09PHl0kDWNFYzFUhzqCXFyKMLaxkoeOdTHJ+/cxL072+kaiXL16rpihywiIlMIx5L89LV+Mg76QzEmEimO9o9zfGCCSCJFVZmPDc3V7NnUdG6Z1poy7traytBEnOtKcJiDLH3zdXyeD+ccX3uxm9PDEW5Y38DNG5pmX+gyTMRTHOkLc2xggkgizatnxrjyEme2nuzHh/oYCMc53DvO2sbKyx4hUYpqy/28bUc7PaNRrlnCM3Mn0xl++lo/sWSaO69ooXoRJil++lofL3eG2Nxazb27sqMj9p4c4fkTI/SOxYgk0ty1bfrOF2eNRhI8cXQQgEQqw/uvW1XQuKfyzitX8FrvGFsKkMg9O9wxkcowEU8p4bRU9Y3F2HtyBMgOi7rv6pVzXtd4IkX3SBSAI/3jc0443b2tlQPdITa3Vl00U53XYzRVBRkIx2mtKaMu103e57GLCn4Ojsf5bm561bFYknt2aDhUoVQEfNy+paXYYVySV8+M8eKpUc6EYvg8HkajCZJph8eMhorAua6fm1qrp+wGKiIii8OLp0c5PjDB4HgcyF6wGokkyLgMVUEfq+rKzx0vTLazY/4mKRGZT/N5fJ6PaDLN6eEIAEd6wwVPOLXWlBHwZmcDjibTl11Eua7Cz0A4TmXQi28eZrkuVVvaqtnStrSPWY/2jXOoZwyA+opRbtvcXOSILnakbxyAo/1hMpk2PB6jrsLPicEJRiMJnjkxxHXrGmYdRlYe8FJdli243VJTnELjK+rKC1Z7ac+mpty5faBkOiwUwpJPONWU+akK+hiPp2i/zG56NWV+dnXUcnIownVr555ZX9VQMW1hPTPj/btXMTyRoLk6iNdjtNWUURn0XVTc0O/x4PUY6czMQ/xkeWqtKaOpKsDgeLZWwa2NTezvCnH9WuOeHW1LZoYUEZGlrr22DDNorg5SW+bn+OB47pimjndf1U59ZbAoQypE5mo+j8/zURHwcdXqOo4PTHDdApWouHpNPc01ZdSV+y/7hPae7W1sXxGlpTqoOj1LXHN1EL/XSGXcgnw35uL6dQ28eGqEbStqzg2d27Gylj2bmjg1NEFtuT+vxGjQ5+VDN64hFE3SsgRntqst93PPDtUaM+dcsWMoiN27d7u9e/cC2aFFE/HUeTO3LBW9oRiD43GuaKvWD9AytHv3bs5u51MJx7LDNmvL/aQzjtd6x6ivCGgWBSkpF27naz/z3SJGs7id/OK9xQ5B5mi2/XkoksTnNSqDPlLpDIf7wjRULu+rplJ6Jm/nS/n4HLLHYCcHI6xpqli2Q2mWq9n257OZiKdIpd2UE0IsZolUhiN9YVpqgiU9q7fkx8xecM7tnu19S76HE2SLeM91rPOpoQl+8Eov9RUB7rt65aLrSdRWW5Z3gbVYMk3/WJy22rJF93fI/Iqn0nzjpW5GI0nevLWFUCRJW20Z21doeIWISCmqrfATS6b56av9HOwJsbapkq3LtHiwLA2Xc3xeCqrL/PM2rDWdcXSPRGmqDuQ1dfvIRIJv7OvGyE7ZXldRmrMrL1eVwYU/RY8m0nztpS4i8TTvuLJ9ThczAj4PO1Zmt/lkOsOZ0RgtNcEl/T2X2S2LhNNsEqkMB3tCNFYGLyrK/Ur3GJFEmkgiSm8odtHrfWMxfnSwl5pyP2/f2Y5/Efcy+ucXuhgMx+moL+d9uxe+KJssjEwmw39/5CjPnBhi+4pa/vLJE7RUl7GyrrwoxfhERGTunjk+xI8P9nLN6nqGIwkeOzyAxyCVcYSiSeordSIp0jcWo2skSn2FnydfH6SuIsDbd7Qtmd7/P3illyN9YWrK/Txw05pZ/66j/eOM5iYpOjYwzrVrNHHAYnZqaIKRSJLtK2rm7Vwykkjx3f1nSKYd9+5sn7W31OnhCP1j2TqBr54Zu+zes9/c10PncISmqgAfvmntZa1LSpsSTmRnydjfFcIMPnzjmvO69l7RXs2JwXHqKwO01l7c5fel0yMMjicYHE/QNRJlXVPlQoaeN+ccoxMJAEYiiSJHI4X082NDPHdimKHxBMf6x9nanr0CPqz/dxGRkhJLpvmrJ08QiiY5PRxhU2s1zdVBekajrGmsmLUgq8hyEEum+ZcXukikMoxEEtRXBBgaT9A9GmVN4+I8Lr9UZ4/hwrEkqYzDN0uHkfXNlezrHMEw1jVVLUCEMlcD4Thff6kb57LnaHfM0yRFR/vG6cpNdnWwJ8TNG2culN9RX059hZ9IMs3meZhMaDS3zY5GkmQy7lytJ1l+lHACJpexurCi1YbmKj55x0bMpv6SrG+u4nDvOJVB77wVO+sPx3jp9Cjrmirn5Qv/6pkxnj0+REtNkKqgn50rCzusKhxL8viRQarKfNy6sUk7mAXWG4pyeiRCMpXhXVe28+6rO3jtTJjtKzT0QkSk1DRWBhiNJBiNJjnQNYrH6+E3bt/ATQWeZUuklJytSdtcHSSTgaoy32XPDLeYvGVrKy+cGmFdU2Vew5OaqoI8eNuGvNf/0ukRXu4cZcfKWnavLVxvqGePD9EXjnPLhsbLqt2VyTiePTFMLJXm5g2NBGfLwC1ibtLZ53zWVu6oL6fM7yWdybAmjw4RlUEfH71l3bx9/lu3t7G/K8SWtqo5nwt2Dkd49HA/LdVB7t7WVtRzyngqzVPHhgj6PNy4rpGRSIKfHxuipTrIjXOcuX65UMIJuHVzE3UVfhqrgjRNsfObLtkEsLm1mjWNFfhyM8bNh3/a20nncJTW6jJ+846Ki35YLjVL/OzxIUYiSUYiSX791hX0jkUZnkhcNOvdfHnuxDBH+sJAdme3oVlXVhZK5/AE33ixm0QqQ5nPS184wYbmKv0fiIiUoHAsxZ1XtLC5tYqfHu7neP8E5QEvjx4eUMJJJKfM7+U913TQNRJl24oa/F6b1+PyyZxzHO4LYxhb2i7/onC+2mrLuHdXe8HW/9SxIRKpDE8eHSxYwqk/HOOpY0NAdgjku65cQWvN3ApLH+0f55nj2XUFvZ5Ze+8sZi3VZbzryhWMRBLsXFk3b+ttrAry67euI+OytZWODYwTTaTZ1l6zIImbmWZlz9feU9kRG0PjCa5aVT9l3eJMxnF8cIKGykBBzm3Pnne/cGqEfadHAWioDHC4N8zxgQmO9Y+zrqlyztvycqCEE9kpGS9n5zqfWfXRSIJDPWMMjSfIZNxFU0qeGJzgu/t7qAr6eP91q/IqHLihpYq9J0foqC/nuRNDvNwVIuDzcP/1q3mlJ4Tf6+H6tQ1573ycc6QybtoxxmevKPm9Rr2KFC4Y5xx/+IPD7O8OEUmk8VUZm1qWRldyEZHlpn8sxhd/8Cq9ozH6w3HMIJbK0FAVpHUJ9dwQmQ9tNWW0VAcLUrOpZzTKoZ4xtrRVE4om+fGhPiDbM+WKJVK4f0NzFT9/fYCRiSQPP32S9+1eNe+FnquCPsoDXo4PjJMadkzEU3zg+tVzOlGvKvNhlh2lUlOiQ4tT6Qxej2FmrJ/mwnD3aJRXc9veXJI3Z78Pp4Ym+Na+HiA7BLWQvdjm04bmKk4NRaivCFBfOfX/88+ODLCvcxS/13jg5rVUz9OMkLFkmn/a20kokuRtO9vPzTRplp0QoLk6yPGB7EWg6jKlVGaif51FJuNgY3MVTVVJrllTf9EP55G+MMm0YySS5EwoNmvPleMD47TXlvGJN22gzO/h2/vPANlC6XtPDvNKzxgANWV+tq2oIZZME4omaakOXtSz69TQBI++1p/NIlcEuOOKFq5cVXfRZ+7qqKOttowyv1fTwM6T508Oc7RvnOvXNbCxZer/886hCD9/fZDxeBq/17hxTQP3X7+GQz1j7OscZfuKmin/v0Rk6Vj7me8WO4TznPzivZe9jvn6m+YjFpifeGaL5ZWuUT79ry/TPRIllXaUB7xsW1HL23a2c+cVLedq84ksNeFYkmTaXVJPhfF4iq8+d5poIs07rlyRdz3VUDTJD145g8/j4e072ykPTJ1g+d6BM4RjKQ73hdmz8Y2hM6n0/A1/KrZ7drQRTaQ5MTjO4HiC/rH4RRMlXa6KgI8P37iGHx/q42BPiFe6Q/zwYC8fvGHNJfdGW1lXzv3Xryaeylx2L5rLMTKRrRW2saVq2gRdNJEmHE/SUv1GYu1Qzxg/PHiGM6EY21fU8Lad7ee9ftZ39/cwEU9zpD/Mb96+cc5xpjNvbKupTOlst7s66tjcWk3A65m2Y0Q4ngIgmXbEUxnmq99h31iMofFsHaojfWHevrOd2nI/QZ+HlprsZEzrm6qoLvPl1QGkkI4PjJNxjo0thel1mc956EyUcFpkGioD3Hf1SgbHE+yaYirVnStr6RyOUFPup6N+5tkDXu8P8+2Xswmmt2xrZcfKWm7f0kxV0Htup3Y24VQV9JFMZ/iHZ08Tiia5clUtd17Ret76njsxTPdolMO9YXZ11HK4LzxtAmOqnabMTSyZ5smjgwA8eXRgyi96KJrkP337INFkGo/HKPN7uWFjIxVBHz87MkAsmWYgHGdXR+2MQ0RFRKS4ook0f/qTo/SNxYknHWbgMWNjcyUfunGNfl9lyRoIx/nqc6dJO8c9O9ry7j10ZjRKOJY96Tw7vCUfB3tC9IzGgOwJ5XTHtJVBH+FYisqANzfkyTBjydXG3L22nqGJOPUVAdrrCrOfqQz6uGdHG8cGwtRXBBgMx+kcjrB2DpMutRR5CFMqneEf93YSTaR5rTfML17bcdF7ook0f/v0SSKJNDeub+SmDdmE5dH+MCORJKeGIlQFfbx4apR7drRdtHxl0MdEPE1V8PJO2dc3V/HW7W1Ek2muKrGLz7P1tJt8bjtVaZy5WlFXzuqGitxQx+w5+YXJzamG+C20yef7d2/PsH3F/NZqzuc8dDYllXAys/8K7AZedM59qtjxFMr65irWN0/92oq6cn7t1vV5rSeeypx7nEhnH9eU+c9LJNWU+fF5jRV15YRjSULR7BSqfblpMc+Pq5LO4Qjrmipprg5y7Zr6fP8kuQxBn4e22jJ6Q7Fpr+KkM45EMkN9RYBoMs0vX7eK9+9eDcCqhnKO9o2zqqFcySYRkUUuHEvSVlNGbZkfj2Uv4LTWlPGL165SskmWtKGJ+LneF31jca64+Px7Sqsbs7VixmNJdk5xsXba5RoqeOHkCF6vsXKGi7i/cPVKOocjrKwvx+OxJdtbfFVDRd7nGJejzO/lrdvbeeTVPsr9XppKdIhwxmWTTpAtKD2VcCxJJJF9rT8cO9d+9ap6ekajhGNJqsv80yZJ33N1B10jkRm3z3xtW2IJ0rMuPLedL36vh/dOkURcbCaf709+PF/yOQ+dTckknMzsGqDSOXermf25mV3nnHu+2HEtZlvbaoglM2Sc48qOuinfM7m7bHWZn9s2N3FqKDJltf1r1zSwtb2GoM9bkEKMMjUz4/27VzEeS1FTPvVXtqEywMduXcdzJ4a5Y0szN04qJnvvznZCG5Ma3igiUgJaasp4+652rlpVx3Xr6jneH6G63LdkTxZEztrYXMXOlbVEk+lLuqgZ9Hmn7F0ym476Cn79tvV4zAj4pq/9VOb3smkeZo2WN+xYWcuaxgoCPk/JzjAX8Hl491UrOTk0wY5pepW01JRxw/oG+sfi3Dzp2Hx1YwW/cftGUukMqYybthdPeUDbnswsn/P9y5HPeehsSibhBNwEPJJ7/AhwI6CE0ww8HrvkXkjXrmng2jXTF5Ir9hjV5crrMWorZk4YvXlrK2/eenGG38yoU/F2EZGSMfnEZE2jZhmV5cHn9XDXtvnvqTCT+S6MLfmbr+LOxZTPTGw3zzCjqM/roUTzbbJIzOV8/1Llcx46k/mfyqFw6oCx3OMQcNG/rJk9aGZ7zWzvwMDAQsYmIiIiIiIiIiI5pZRwGgXO9ievyT0/j3PuIefcbufc7ubmaYogiYiIiIiIiIhIQZlzpTE1Yq6G08edcx83s/8F/F/n3HPTvb+pqcmtXbt2weITKYaTJ0+i7VyWOm3nshxoO5flQNu5LAfazmU5eOGFF5xzbtYOTCVTkMc596KZxczsCeDlmZJNAGvXrmXv3r0LFJ1IcezevVvbuSx52s5lOdB2LsuBtnNZDrSdy3JgZi/m876SSTgBOOc+VewYpPhGIwle6hxlVX0FG1tUTFVkqTg5OMGJwQl2dtTSVFWa0ySLzEbbuYiUguMD45wajnBlRx0NlZp8RgRgZCLBvq5RVjdUsKFZ56H5KKUaTiIA/OhQH/tOj/Ld/WeYiKeKHY6IzINEKsO3X+5hX+coP3ilt9jhiBSEtnMRKQWxZJpvv3yGfadH+dFB7atEzvrRoV72nR7lOy+fIZpIFzuckqCEk5ScikB2/lC/z/B6rMjRiMh88BgE/dmfpLPfcZGlRtu5iJQCj9m5fVW59lUi55QHsgPEAj6PzkPzVFJD6kQA3rq9jY0t47RWl1Hm14+gyFLg83r4petWcyYUZV1TZbHDESkIbeciUgoCPg8fuG41Z8airG/SsCGRs+7Z3sbx1nHaasoI+NR3Jx9KOEnJ8Xs9XNFWU+wwRGSe1Zb7qS33FzsMkYLSdi4ipaC2wk9thfZVIpMFfDoPvVRKy4mIiIiIiIiIyLxSDycRERGRRWbtZ747L+s5+cV752U9IiIiIpdKPZxERERERERERGReKeEkIiIiIiIiIiLzSgknERERERERERGZV0o4iYiIiIiIiIjIvFLCSURERERERERE5pUSTiIiIiIiIiIiMq+UcBIRERERERERkXlVsISTma0wsxfNLGZmPjNbZ2ZPmNnjZvYPZubNve+DZvaUmX3HzGpybXea2dNm9qiZdeTadpjZk2b2czPbVai4RURERERERETk8hSyh9Mw8GbgmdzzUeCdzrnbgBPA283MD3wCuA14GPh47r2fA+4GPgN8Ntf2B8AHgPfnHouIiIiIiIiIyCJUsISTcy7mnBuZ9HzEOTeae5oC0sBm4IBzLgU8AtxoZhVA1DkXds49C2zLLdPgnOt0znUDtYWKW0RERERERERELs+C13AysxXAXcCPgDpgLPdSCKjP3cYmLeLN3U+OVbWnREREREREREQWKd90L5jZnwFuutedc//2Uj/MzILA3wC/7pxLmdkoUJN7uYbssLuRSW0AmQvuL3w8ef0PAg8CrF69+lLDExERERERERGReTBTT6G9wAtAGXANcDR3u4rscLi5eAj4X865Q7nnR4AduQLidwHPOOciQLmZVZnZ9cDZ9w6bWUeuh1RoqpU75x5yzu12zu1ubm6eY4giIiIiIiIiInI5pu3h5Jz7GwAz+yhwh3MumXv+v8kOh5tRriD494ErgR+a2e8D7wHWmNmngP/mnPu6mX0ZeIJsz6b7c4t/AfgxEAMeyLV9HvgqYMAnL+3PFBERERERERGRhTJtwmmSFUA12VnnAKpybTPKJajuuqC5eor3PUx2hrrJbY+QLSI+uW0/sCePeEVEREREREREpIjySTh9EXjJzB7NPX8T8LsFi0hERERERERERErarAkn59xfm9n3gRtyTZ9xzvUWNiwRERERERERESlVMxUNn8wLDJCts7TZzG4rXEgiIiIiIiIiIlLKZu3hZGZfAn4JOAhkcs0OeLyAcYmIiIiIiIiISInKp4bTfcAW51y8wLGIiIiIiIiIiMgSkM+QuuOAv9CBiIiIiIiIiIjI0pBPD6cIsM/MfgKc6+XknPu3BYtKRERERERERERKVj4Jp2/lbiIiIiIiIiIiIrOaNeHknPubhQhERERERERERESWhnxmqdsE/GdgG1B2tt05t76AcYmIiIiIiIiISInKp2j4XwN/DqSAO4C/BR4uZFAiIiIiIiIiIlK68kk4lTvnfgKYc+6Uc+53gTsLG5aIiIiIiIiIiJSqfIqGx8zMAxw1s98CuoGWwoYlIiIiIiIiIiKlKp8eTv8OqAD+LXAt8GHggQLGJCIiIiIiIiIiJSyfWeqezz0cN7OPAVXOubHChiUiIiIiIiIiIqVq1h5OZvYPZlZjZpXAIeCwmX268KGJiIiIiIiIiEgpymdI3bZcj6b7gO8Bq8kOqxMREREREREREblIPgknv5n5ySacvumcSwKuoFGJiIiIiIiIiEjJyifh9BfASaASeNzM1gCq4SQiIiIiIiIiIlOaNeHknPvvzrmVzrm3u6xTwB2zLWdmK8zsRTOLmZkv1/ZpM3vSzP4+12sKM/ugmT1lZt8xs5pc251m9rSZPWpmHbm2Hbllf25muy7rrxYRERERERERkYKZdZY6M/tP07z0+7MsOgy8Gfh6bj3NwB3OuT1m9jvAfWb2DeATwG3Ae4GPA38EfA64G9gGfBb4JPAHwAeADPC/gHfPFruIiIiIiIiIiCy8fIbUTUy6pYG3AWtnW8g5F3POjUxquh54LPf4EeBGYDNwwDmXOttmZhVA1DkXds49SzbpBNDgnOt0znUDtXnELSIiIiIiIiIiRTBrDyfn3B9Pfm5m/wX41hw+q443aj+FgPpp2uo5v0aUN3c/OTk2ZaLMzB4EHgRYvXr1HEIUEREREREREZHLlU8PpwtVAOvnsNwoUJN7XJN7PlXbyKQ2yA6hm3x/4eNznHMPOed2O+d2Nzc3zyFEERERERERERG5XPnUcDoAuNxTL9DM7PWbpvI88JvAHwJ3Ac8AR4AdZuY92+aci5hZuZlVkR1Odyi3/HCugHiGbG8oERERERERERFZhGZNOAHvmPQ4BfTlai7NKDcL3feBK4EfAv8ReNzMngROA3/qnEua2ZeBJ8j2bLo/t/gXgB8DMeCBXNvnga8CRraIuIiIiIiIiIiILEL51HA6ZWZXArfmmh4H9uexXJJsr6XJngW+dMH7HgYevqDtEbJFxCe37Qf2zPa5IiIiIiIiIiJSXLPWcDKzTwF/D7Tkbn9vZv+m0IGJiIiIiIiIiEhpymdI3ceAG5xzEwBm9iXgaeDPChmYiIiIiIiIiIiUpnxmqTMgPel5OtcmIiIiIiIiIiJykXx6OP018KyZfT33/D7gLwsWkYiIiIiIiIiIlLR8iob/iZn9DLiFbM+mX3HOvVTwyEREREREREREpCTl08MJYB9w5uz7zWy1c+50oYISEREREREREZHSNWvCKTcj3eeBPt6o3+SAXYUNTURERERERERESlE+PZw+BWxxzg0VOhgRERERERERESl9+cxS1wmECh2IiIiIiIiIiIgsDdP2cDKz3849PA48ZmbfBeJnX3fO/UmBYxMRERERERERkRI005C66tz96dwtkLtBtoaTiIiIiIiIiIjIRaZNODnnfg/AzN7nnPvnya+Z2fsKHZiIiIiIiIiIiJSmfGo4fTbPNhERERERERERkRlrOL0NeDuw0sz++6SXaoBUoQMTEREREREREZHSNFMNpx5gL/Au4IVJ7WHg3xcyKBERERERERERKV0z1XB6GXjZzP7BOZdcwJhERERERERERKSEzVrDSckmERERERERERG5FPkUDRcREREREREREcnbgiaczKzCzL5rZo+Z2TfNLGhmnzazJ83s783Mn3vfB83sKTP7jpnV5NruNLOnzexRM+tYyLhFRERERERERCR/syaczGyzmX3ZzH5kZj89e5vj590DPOucux14Dvhl4A7n3B5gP3BfLun0CeA24GHg47llPwfcDXwG+OwcP19ERERERERERApsplnqzvpn4H8DXwbSl/l5x4Brc4/rcp//WO75I8D9wCHggHMuZWaPAA+ZWQUQdc6FgWfN7IuXGYeIiIiIiIiIiBRIPgmnlHPuz+fp844CN5jZQaAf+D9Ade61EFBPNhE1dkFb/aQ2AO9UKzezB4EHAVavXj1PIYuIiIiIiIiIyKXIp4bTt83sN82s3cwazt7m+HkPAD90zm0Hvks24VWTe60GGM3dLmwbmdQGkJlq5c65h5xzu51zu5ubm+cYYmnoH4sxOB4vdhgikqdYMk3ncIRkesrdl4jMQTKdoXM4Qix5uR2wRUQW1tn9Vzyl/ZfIQhqeSNA3Fit2GMtGPj2cHsjdf3pSmwPWz+HzDBjOPR4E1gLXA38I3AU8AxwBdpiZ92ybcy5iZuVmVgVsIzvsbtl6vT/Mt18+gxm895oOVjVUFDskEZnFP7/QxWA4zqqGCn7xWs17IDIfvrWvh9PDEZqqg3z4xjXFDkdEJG/f3NdD53CE5uogH9L+S2RBnAlF+afnu8g4xz072tjaXjP7QnJZZk04OefWzePn/QPwj2b2YSAJ/BLw62b2JHAa+FPnXNLMvgw8QbZn0/25Zb8A/BiI8UYSbFkaGk8A4ByMRpKsmmt/MxFZEM45Riey39uR3L2IXL7h3PdpdCJBJuPweKzIEYmI5Gd4IjtSYWQigXMOM+2/RAptNJIk4xzwxjGEFNa0CSczu9M591Mze89UrzvnvnapH+acGwXeekHzl3K3ye97mOwMdZPbHiFbWHzZu2p1HeFYCq/X2NpePfsCIlJUZsbbdrbxWm+YXSvrih2OyJLx1u1t7O8e5Yq2aiWbRKSk3LO9Pbf/qlGySWSBbG6tZiAcJ57KcO2a+mKHsyzM1MPpTcBPgXdO8ZoDLjnhJPMj6PNy17bWYochIpdgY0s1G1uUIBaZT6sbK1jdqGHlIlJ6tP8SWXhej3Hb5qVd63mxmTbh5Jz7fO7+VxYuHBERERERERERKXX5zFInIiIiIiIiIiKSNyWcRERERERERERkXinhJCIiIiIiIiIi82qmWeqmnJ3urLnMUieyGDx5dJAD3SGuWlXHTRsaix2OLEGjkQTfeKkbM+O+q1ZSW+EvdkgiU0qmM3xzXw/DE3Heur2NNY2VxQ5JRESWoIl4im/s6yaWzPDOXe201JQVOySRgvjpa30c7h3n+nX1XLumodjhFN1MPZzembt9DPhL4IO52/8BPlT40EQK44VTI8SSaV48PVLsUGSJOtI3zkgkyfBEgtcHwsUOR2RavaEYncMRJuJpDnSHih2OiIgsUSeHJugfizMWTfJqr46NZGlKpjO83Bkilkzz0unRYoezKEybcHLO/UpuhjoHbHPOvdc5915g+4JFJ1IAO1bWYAbbV9QUOxRZotY3V1IR8FIZ9LJWPUZkEWupCdJcHcTnMa5o0z5RREQKY1VDBTXlfoJ+D5taqoodjkhB+L0ermirxgy26VwTmGFI3SRrnXNnJj3vAzYXKB6Rgnvz1lbuvKIFMyt2KLJENVUFefC29drGZNEL+rx86MY1OOe0vYqISMHUlPn52J51+r2RJe9tO9u5Z0ebtvOcfBJOj5nZD4GvkO3t9MvAowWNSqTAtAOQQtM2JqVE26uIiCwE/d7IcqDt/A2zJpycc79lZr8A3JZresg59/XChiUiIiIiIiIiIqUqnx5OAC8CYefcI2ZWYWbVzjlVexMRERERERERkYvMNEsdAGb268C/AH+Ra1oJfKOAMYmIiIiIiIiISAmbNeEEfBK4BRgDcM4dBVoKGZSIiIiIiIiIiJSufBJOcedc4uwTM/ORLR4uIiIiIiIiIiJykXwSTj8zs/8IlJvZW4B/Br5d2LAkX4PjcZ47McxoJDH7m0WWqFAkyXMnhhkIx4sdisiSd2JwghdOjZBIZYodiojIsvVa7xgvd46SyagfgMhUnHMc6ApxsCeEc/qeFEs+RcM/A3wMOAB8HPiec+7LBY1K8uKc42svdjERT3OoJ8RHb1lX7JBEiuJbL3czOJ7gxdMjfPy29ZqKVKRABsJxvrmvG+cgFE1w5xWtxQ5JRGTZOTYwzvcP9AKQTGfYvbahyBGJLD4HukP85NV+ADxmbG2vKXJEy1M+Cad/45z7b8C5JJOZfSrXJkU0+aRaJ9iyrGn7FxERkWVi8lGPDoFEpmZMPlcuYiDLXD4JpweAC5NLH52iLS9m9pHcOr3AB4H7gXcDp4CPOueSZvZBssXKh4H7nXNjZnYn8AUgBnzYOdc1l89fat57TQcnBifY1FJd7FBEiuZdV67gaF+YtU2VSr6KFFBzdZD7rlrJcCTBjhW1xQ5HRGRZWt9cxb272oknM2xfoV4bIlPZsbIGM/B6jCva9D0plmkTTmb2AbLJoHVm9q1JL1UDQ3P5MDNbCbzJOffm3PNm4A7n3B4z+x3gPjP7BvAJ4DbgvWSH8f0R8DngbmAb8FmyCallr7EqSGNVsNhhiBRVbblf3clFFsjapkrWUlnsMERElrXNrbrYLDITM2PHSl0cK7aZejg9BZwBmoA/ntQeBvbP8fPeCnjN7CfAIeAHwGO51x4hm+A6BBxwzqXM7BHgITOrAKLOuTDwrJl9cY6fLyIiIiIiIiIiBTZtwsk5d4rsMLeb5vHzWoGAc+7NZvYloA4Yy70WAuqnaauf1AbZ4XgiIiIiIiIiIrIIeWZ7g5ndaGbPm9m4mSXMLG1mY7MtN40Q8LPc458Ca4GzAyprgNHc7cK2kUltAFPOxWxmD5rZXjPbOzAwMMcQRURERERERETkcsyacAL+B/AB4ChQDvwa8Gdz/LyngF25x1cBncCbcs/vAp4BjgA7zMx7ts05FwHKzazKzK4nO+zuIs65h5xzu51zu5ubm+cYooiIiIiIiIiIXI58ZqnDOfe6mXmdc2ngr83sqbl8mHNun5lFzewxYJBszaZ2M3sSOA38aW6Wui8DT5Dt2XR/bvEvAD8mO0vdA3P5fBERERERERERKbx8Ek4RMwsA+8zsD8kWEp/z9DTOuf/ngqYv5W6T3/Mw8PAFbY+QLSwuIiIiIiIiIiKLWD5D6j6ce99vARPAKuC9hQxKRERERERERERK16w9nHKz1UF2KNvvFTYcEREREZkvaz/z3XlZz8kv3jsv6xEREZHlY9aEk5ndAvwusGby+51z6wsXloiIiIiIiIiIlKp8ajj9JfDvgReAdGHDERERERERERGRUpdPwinknPt+wSMREREREREREZElIZ+E06Nm9kfA14D42Ubn3IsFi0pEREREREREREpWPgmnG3L3uye1OeDO+Q9HRERERERERERKXT6z1N2xEIGIiIiIiIiIiMjSMG3Cycw+5Jz7OzP77aled879SeHCEhERERERERGRUjVTD6fK3H31QgQiIiIiIiIiIiJLw7QJJ+fcX+Tuf2/hwhERERERERERkVLnme0NZvY3ZlY36Xm9mf1VQaMSEREREREREZGSNWvCCdjlnBs9+8Q5NwJcXbCIRERERERERESkpOWTcPKYWf3ZJ2bWQB6z24mIiIiIiIiIyPKUT+Loj4GnzOxfAAe8H/hCQaMSEREREREREZGSNWvCyTn3t2a2F7gTMOA9zrlDBY9MRERERERERERK0qwJJzO7ETjonPsfuefVZnaDc+7ZgkcnIiIiIiIiIiIlJ58aTn8OjE96PpFrExERERERERERuUg+CSdzzrmzT5xzGVQ0fEk6PjDO/q5R0hk3+5tFLsNEPMULp0boD8eKHYrIohFPpXnp9Ajdo9FihyIiIstI53CEl06PkEhlih2KyIIYmUjwwqkRQpFksUNZ8vJJOB03s39rZv7c7VPA8cv5UDP7bTN7Mvf402b2pJn9vZn5c20fNLOnzOw7ZlaTa7vTzJ42s0fNrONyPl8u1jMa5Zv7evjJq/08e3yo2OHIEve9A2d4/MgA/7y3Swc3Ijk/fbWfxw4P8K8vdDEW0wGQiIgU3shEgq+92M1jhwf42ZGBYocjsiD+9cUuHj8ywNde6ip2KEtePgmnTwA3A91AF3AD8OBcP9DMgsCVucfNwB3OuT3AfuC+XNLpE8BtwMPAx3OLfg64G/gM8Nm5fr5MLfNGJzbUwUkKzWkbE7nI2X2vc+CUhxURkQXgAEf2B0ijHGS5OHsuok2+8PKZpa4f+OV5/MxfA/4G+H3geuCxXPsjwP3AIeCAcy5lZo8AD5lZBRB1zoWBZ83si/MYjwAd9RXcu6udcCzFlR21xQ5Hlri37WzjUM8YqxsrCPjyyXuLLH1v3tpCc3WQ1pogtRX+YocjIiLLQENlgHdftZLB8Tg7V+ocQJaHX7hmJcf6x9nUWl3sUJa8fGapKwM+BmwHys62O+d+9VI/LNd76U3Ouf9pZr8P1AFjuZdDQP00bfWT2gC806z/QXK9r1avXn2p4S17m/WFkwVSXebnhvWNxQ5DZFEp83u5fl3D/5+9/46SLKsPfN/vDu8j0ntX3vuuqva0gW5ooDESgm5ASILWSHo8pJl7r6S73iyJWW/uRWLm6nE1oxmhmTsaSUALJGEbaGigm/bV3eW6vK/0PjO8PbHfHxFZnVWVVekiMkz+PmvlyjAnTvyqcseJfX5n798udRhCCCFWmZ56Nz317lKHIcSKqffYqffYSx3GqrCQoQV/DzQDjwAvAO1AeInv9yng67PuTwO+/G1f/v5cj03NegxgzskGWuuvaq33aa33NTQ0LDFEIYQQQgghhBBCCLEcC0k4rdNa/1sgqrX+n8BjwPYlvt9G4HeUUj8mN2JqH3B//rmHgdeAc8A2pZR55jGtdQxwKqU8Sqn95KbdiTJydjjMD44P0j8VK3UoooKdGAjyg+ODDAdl9TqxuoyGEjxzfIi3+4OlDkUIIUSFee3SBD96e4hgXBacEKvHUDDOD44PcmowNP/GomTmnVIHzBy5ppVS24BhoHspb6a1/sOZ20qpl7TWX1RK/WF+xbpe4P+ntU4rpf4GeJHcyKYn8i/598BPgQTw60t5f1EcaSPLj08Mk9WasXCS37i7p9QhiQoUS2V47vQIWkMwnubJA12lDkmIFfPzM6MMBROcGwnT0+DGY1/I17MQQojVrn8qxqsXcytMKwWPbmspcURCrIznTo0wHklxYTTCmgY3DuucVXdEiS2kR/tVpVQNuVXivgd48reXJb8yHVrrPwP+7Ibn/p7cVL7Zjz1HrrC4KDMWkyLgsjIZTVHrtpU6HFGhbGYTHruFcCJDnbQjscrUum0MBRN47BZsZimkL4QQYmG8Dis2i4lUJkutW2rSiNWj1m1nPJLC57Bilb5T2bplwkkpdQr4GvC01nqKXP2mNSsVmKgcSil+7Y4OxsJJWvyO+V8gxBwsZhNPHOhkIpKiNeAsdThCrKiHNzexucVHnccmKzcKIYRYML/TyicPdhFJZmiT/pNYRR7d1syOdj/1Hjtmkyp1OOIWbter/QS50Uw/UUq9rpT6faWUjNEUc3JYzXTUurBIdlksg8tmoaPWJV8aYtUxmRQdtS5cNplKJ4QQYnH8Tqskm8SqY873nZw2mUpXzm6ZHdBaH9Na/7HWei3wBaALeF0p9XOl1OdWLEIhhBBCCCGEEEIIUVEWNBxFa/2a1voPgE8DNcB/KmpUgnjKYDqWKnUYQixbPGUQjMmqKULcaCqaIpE2Sh2GEEKIMjQZTZHMyHeEWB201kxEkqQy2VKHIgps3rH7Sqk7yE2v+yhwBfgq8K3ihrW6BeNpvv56L4m0wbu3NLGtzV/qkCpWIm1wuHeKGpeNzS2+UoezKiQzBoevTuNzWmgLOPn6oV5SmSzv2dLMllb5G4jqkzayHL46hctmYXv7wo7Xh3uneOHsGG67mScPdOGWVemEEELkvXpxgtcuTeB1WPjkwa6KWX0rGEvz9kCQrjoXHbWuUocjKsjz58Y42jtNrdvGkwc6C1amRWvNkb5ptNbs6qiRsh0lcLui4f8H8GvAFPA0cLfWun+lAlvNJmdd9R6YjkvCaRleuTjOsb4gAAGXlRa/zG8vtlcvTnCkdxqA/T01JNO5KxWD03FJOImq9MblSV6/PAmA225mTYNn3tcMTMUBiCYNpuNpSTgJIYS4ZnA69x0RTmQIJzIVk3D60YkhhoIJjvRO8bn71lRM3KL0Ztr8ZDRFPG3gLVDC6dRQiBfOjgFgUordnTUF2a9YuNv1cJPAe7XW51YqGJHTVetie5ufUCLN/u7aUodT0WaWyFQKLCYpaL4SZlbYUgp66j3EUlnCiTT7uuUAL6rT7FXlFrrC3ME1dSQzWeo8NlpldU9RAbr/6Jll7+PKlx4rQCRCVL+719WTPT9Gi99Jg9de6nAWbOY70GI2YVIykkQs3H3rG3j10gRdtS68DmvB9mszL76PJgrrlgknrfUXVzKQ1e7cSJiXzo/TXe/iwU1NPLylqWSxZIwsp4ZCBJw2OusqezjsXWvrqXPb8busBfnCTmWycrDK652I8fMzI5hMih1tfra2+kApDvbUEXDa8DostAactMqqKaLCGVkNMOcw7ETawKQU+3tq6Khx016TO2YGY2meeXsIswnev6P1phFMDV47v7K3vfjBCyGEqDjNfge/uq9jwdsn0gY/OD5EIm3w3m3N1HkKn6SKpwzODIdoCzhp9M19oeR921s4PxKhNeAoi/5y2shiMSmUJL/KXkftwqZhGlnNj04MMR5O8tDmpnlfs77Jywd3KbTWrGv0LjievskYk9EUW1p91wYwLFU2q8lqvWpXc5cx/GXi0OVJgvE0x/qC7OuuxVfAzO5ivXRhnCO90ygFTx7oqqgrKzcym1RBpnFprfnnwwP0TcY4uKaOO9fWFSC6yvZW7yQD03GO9we5Mh7lu0cH8Tmt3Lehgb1dMppJVIfRUIJ/OtyPQvEre9tvOh4+e3KYS2NRrGbF3q53RqSeGgoxEkoAuQsKMoRbCCFEsVwci9A3GQPgxGCI+zc0FPw9nj05zOXxKDaLid+6p2fO6XIOq3nBtQyL7VjfNL84O0qD187H9nUsO2kgysNwKMH5kQiQq4e5kCTV2gWUOphtMpriXw4PkNWaiWiSBzctfSBIKJHmm2/0EU8ZfHBXK1117iXvq1LJJ69MrG/MfRBaAw48ttLmAbM6dzVf63duF4Mu4r4LLZYyrn2RnxsJlzia8rCuIXeVwGUzY1KK8WgSgPMj4Yr62wpxO1cmYiTTWRJpg97JKJA7ds208ZnRTzceL7vrXdgsJhxWsxROFUIIUVTtARcumxmLSbGmvjgntDPfd9msZq5uXrn1/c6NhNEaRkNJpmTl76pR57ZR67ahFKxrXFwiaaGMrCars/nby9vX4HSccCJDJqu5OBYpQHSV53ZFw/fc7oVa68OFD2f1OrCmjl2dAWxmU8mHfd69rh6vw0qNy0rTLYbMLte5kTA/PjFMvSc3raQcht3ejttuYVdHgItjEe6QuloAbG/3s6HZw9XxGFOxFGORJIPTcSaiSf7vn13gwU2NZXOVS4il2tjs5fxoGIViQ5OXiUiSfz7cj9bw0b3tvGdrMycGgrQFnLhmXSxo8Tt56r41KFi1Q6iFEEKsDL/LymfvXUNW66KN5Hlk2zvfd07bO6ObIskM33qzj1jK4IM7W8vmIsverhpCiQytfgf17sqdrSGu57Ca+dTBLtLZLHZL4YvSZ4wsPz8zQjCeYUuLj3vX1y9rf911btprnMTTxqpdCOx2Q2n+422e08CDBY6lqk1Ekjht5utOSG5UjA/NUtgt5qInVU4PhTCympFQgrFIkrYKqPPzwKZGHtjUWOowyordYmZD8zvzoUdCcf7ml5dx2cycHgpJwklUPL/TypMHuq7df+PKJNFkbhXRy+NR7uiu5eCauafYzu70x1IZ4imjKHU1hBBClA+tNWORJD6HdUVXaTObFGaKd9HaY7fM+X3XPxVjOpYG4PxouGwSTmsaPAtaNVYUz1Q0hdViwlPglXhNJoXdVJzP1mQsxeB0Ar/Tismklv0ZdljNi6rHVo1uVzT8gZUMpJod7ZvmF2dGsVtNfPJg13X1mbTWnBgIYWjNjjY/pjmK0lajHe0BhoMJ6j12miq4RpS43i/OjNE/FSOSzLCxZeGF+YSoFOsbPZwaDF27vRChRJq/ffkKvZMxHtzUwPu2txYzRCGEECuobzLGwHScbW1+PHYLvzw/zuGrU/idVj55sKvsR/EvV1etmyafg1gqw5YWudAocs4Mh/jxiWEsJsXH93dSX+YX3C6ORZiIpNja6mNNg5uhJ/HnsQAA8WFJREFUYIKdcuG8IBaUblRKbQO2ANfmV2mt/65YQVWb4WCucGwynWUqmrou4XRmOMxzp0eu3d/VEVjp8Eqip97Nb9+/ttRhiALSWjMaTuJ1WAknM5wbjrCxKbyoFSGEKHcBl41fv6t7Ua+ZiqY4NxJmLJzkh28Pc6CnTkY6CSFEFYilMnz7yABGVjM4Hecje9oZDsYBCMbTxFNG1SecnDYzTxzoLHUYoswMBxNoDWlDMx5JlnXCaTyS5PvHBtEapmMpHt/VVuqQqsq8CSel1J8A7yKXcPoh8F7gJUASTgt0oKeWRNrA77LSecMw09nLbK+SwU2iSimleGhzIz8+MYw937kyyTK0QtBR42Jtg4e0kaU94JLPhRBCVAmFwqTA4J0+/b3rG3j14gQdtS78rtKtOi1EKc3U0HJazawr86mNJqVQKDT6unNzURgLGeH0K8BO4IjW+jeUUk3AfytuWNWlxm3jQ7vnzpRuaPLCDsgYms2rZArSdCxF2tA3LS8uylMyYzAeSdHktc9b/Hhrq58tLT5ODYWwW0wyd15UpGA8TTJt0FigRRNMJsX/68F1nBoMUeu2UeO2FWS/QgghSstpy9VnGZyOs7nFB0BrwMlH97aXOLLKMhZOYjUrAi75fqwWXoeVD+6sjBICtW4bH9nTxmQ0xZZWX8H2m8pkGYskF3QOVc0WknCKa62zSqmMUsoHjAJrihzXqrKhaXUkmiA3vPKbb/ZhZDWP7WhZVf/2SvXNN/sZDyfpqXffMnE6m1KKra0y51lUpsloiq+/fpW0oXlocyM72gMF2a/VbGLnKpkyLYQQq0mTz1G0VZ1Xg/MjYX5wfAiTUnzsjnZa/OW/kJCoPh21roIXvP/WW32MhpJ01bn4yJ7Vm4ReSKrtTaVUAPgb4C3gMHComEGJ6jURTWJkNQDj4WSJoxHzMbKayUgKyM1vFqLaTeVHYELuiqsQQgghimcs37/Mas1Evs8pRKXTs9rzaj+HmneEk9b6d/M3/6tS6seAT2t9fClvppQ6APwFuanOb2qt/0Ap9b8CjwNXgc9ordNKqSeB3wMmgSe01iGl1IPAvwcSwKe01v1LiUGU1sYmL6OhJMlMlt2dNaUOR8zDbFI8sq2Js8PhVVPQXqxuPXVu9nXXEE5k2N9TW+pwhBBCiKq2p7OGUDyDzaLY1CwzH0R1UErx6LZmTg+F2Fmg0fKVaiFFw3+mtX4IQGt95cbHFukq8KDWOqGU+ppS6l7gAa31PUqpPwQ+pJT6DvCvgPuAjwK/DXwZ+LfAe8gVL/9jcgkpUWEsZhMPbGosdRhiETY1+9jUXLj5zEKUM5NJce/6hlKHIYQQQqwKDquZR7c1lzoMIQpuQ5NXysdwmyl1SimHUqoWqFdK1SilavM/3cCSKoBprYe11on83QywA3g+f/854CCwAXhba52ZeUwp5SJXSyqstX6dXNJJlIHTQyF+cXaUcCJd6lBEgRlZzasXJ3jt0sS1aZBCVLuLYxF+cWaUyagM6xdCCLE6BeNpfnF2lHMj4VKHIkTRpDJZXr4wzltXJ9FaznWK5XYjnH4b+H1yyaXDsx4PAf95OW+qlNoB1APT5KbXAQSBGiCQf4/Zj9XMegzAfIv9PgU8BdDZ2bmcEMUCTESS/PjEMACRRIYPVMhKBGJhjvdP89qlCQBcNnPBiicLUa7iKYMfHBsiqzUjoQQf3y/fI0IIIVafn58Z4cp4jGN90zT5HPid1lKHJETBvXl1kkOXJwHwOaysl9FIRXHLhJPW+ivAV5RSn9da/2Wh3jA/auo/AR8D9gIzy175yCWgpvO3Zz82NesxgOwtYv4q8FWAffv2VWWaMpkxCCcy1HvsK/J+L50f51j/NNvb/Ny34fppJjaLCatZkTY0LtucOUBRgaLJDBlDc3IgxKErk7T4HLhsC1nQUojK9vrlCQ5dmcDvsLKmwV3qcIQQQogl0VozEU3hc1ixWW69RtTJwSDPnx2jvcbJB3a0YjIpAJzWXL/Pas719YVYCfGUQSJtUOO23fTcaCjBd48OYrea+PDuNryO5SdB3fnzG6XAKeeyRbOQs8i/Vkr9v8nVVILcFLi/1loveg6VUsoC/APwv2qth5VSbwC/C/w58DDwGnAO2KaUMs88prWOKaWcSikPuel0pxb73tUgmTH4h9d6CcXT3NFdyz3r64v+nkf7pkgbmqN90zclnLwOKx/f38lkNMXaBk/RYxHFNxpO8M03+sgYmslYio1NHhxWM+sa5e8rqt/hq1NkDE3/dJz2msIujSuEEEKslOfPjXG0d5oal5VPHuzCYp476fR2f5BUJsulsSjBePraif7DmxvpqXfT4LXLRUexIsKJNF97vZd4yuCBTY03LVZ0diRMJJkhkoSrEzG2tfmX/Z47OwJ4HRbsVjNtAeey9yfmduuU9zv+itxIpL+adfu/LPH9fhW4A/gzpdTzwFrgl0qpl4BdwHfyiay/AV4Efh346/xr/z3wU+DPgC8t8f2L4kjvFN89OsBwMDH/xssQTRqE4rk832AwXtT3mrG9PYDZpNjePveHut5jZ0OTF7NJrn5Ug7FwkrShMbQmFE8zEUlxR7es1CVWh45aF0pBT72bsfDNS9hORJJ879ggr+enmgohhKhusVSGH709xM/PjJAx5pxgUZaGpnPnJFOxNPG0ccvttrX5sZgU3fWu66bNWcwmNjZ7qZ1jpIlYHRJpg2dPDvPTUyOkMsVv+1PRNPFUrq0OTd98nruhyYvLZibgstJZV7iLgmsaPJJsKrJbpqyVUpZ84e47tNY7Zz31c6XUsaW8mdb6G8A3bnj4VXJJpNnb/T3w9zc89hy5IuJlJZRI8/zZMSA3DHCpNT9iqQx2i/m2iZtat42Da+oYmI5z97q6Jb3PYt2/oYH7N8iKTavFhiYvfZNxLo5FaPE7MSmwyFBqsUp8dE87dR4bwXiaAz25RKvWmljKwGUz89KFcS6NRbk4GqGn3k2jz1HiiIUQQhTTkd5pzgznCmc3+5z01LuxmtUtRwyVi3vX1/PapQm66923nXq0rc1fkJEiovqcGAhyajBXQrneY2Nziw+TUredorkc7TVOdnUGmIqmOLDm5vPcJp+D375/bVHeWxTX7cZIHgL2AIZSaq3W+iKAUmoN7xT6XvUcFjNeh4VwIkOjb2l1lQ73TvHC2TFq3TY+sb/zth/kO9de/wGMpwxeuzSB12Fhn4xEEYswGU3x1tUpOmqdbGrOlUizmk08uq2Z4WCCv37hImeGQyQzWXa2Bwi45CqXqHzZrObQlUmSmSwH19Rit7wzZ99kUjy4qem67X/49jDnRsKsb/LQ4LFzaSyK02bG45ApBkIIUe1maqaaTYrJWJKf/HIYj93CJ/Z34raX7/dAR62LjtriTw3PGFleu5QrunxwTW3ZJ+LEwtV57CgFCkXG0PzNLy9hMik+tq+DBm/hawmbTIoHNjYWfL8rYXA6zomBIBubvXTVSQ3QG93uSDkzrOF/AX6hlLqUv98N/EYxg6okNouJTx7sYiqWonkBV7uHgwl+fmaUWreNnR1+fn5mlHPDYXxOK5PRFNOx1KKumr92eYKjfdNA7kuxu14aeTEkMwZHe6ep89hY11gdKxg8d3qEgak4r14c5551DexfU0ssleG5U6P4nVY6a12cGw0zGk4yOB2XhJOoWPGUwbH+aRq9dtKG5tWLuSlxVrPirrU318I7NxLm9UsTrG3wcGE0zLmRMKcGQ3zpo9tZ0+DB67BITQshhKgSpwZDvHV1kk0tvpvKCGxs9lLnsWE1mXjl4jhaQziRYSycxG4x8eOTw4TiGd69pakoJ+Hl7sRgiDeu5BJObruZ3Z01RX2/qxNRBqcT7Ozwy/dwkfXUu/n0nd0o4MxwmExWQ1YzMB1fcFs/PRTizSuTbGz2YWQ1F0bD3NFTe+1CdzkYjyRzFxYbvUv+DP/w7SHCiQznRyP87rvWopTMDpntdp/UBqXUv87f/mvADEQBB7Ab+EWRY6sYDquZFv/C5n6+eXWSkVCCkVCC0VCCiWgKk8oNzd3S4lt0Q/fmr66YlCrrKy2V7sVz47w9EATgyQPWqphK47VbSBtZzo9GcNrMTMdTuGyWa+3TyGZxWMwkM3OvFiFEpfjF2VHODodRCt6zpQmlQGvw2ueeZvDKhXGmYmnGI5N01Dg40jdNk9fO0b5pHtrcNOdrhBBCVKaXL4wTSWZ46fw4uzsCN43SmRnltLerhslYioDTRnuNk97JGOdHIkButsIjW5tXPPZS88w69/AWeeRvOJHmO0cGyWrNaDjB47va5n+RWJaZGl5b23z0TcYwmxQbmxZ+4f3lC+OEExlGzo0CCrNJ8fKFibJKOH3nyADhRIYTA0Geum9pU/ZmZju5bWZJNs3hdkcGM+DhnZFO5O8DVMcQjxLornNzYTSCx25hS6uPly6M01bj5IkDnUvK1O/rrqXeY8dtt6zKKysrxZyvY6QUVVMg/d1bmuisdWFSYDaZsJhNdNe7ODMcwm2zsKHZi9lkos5ju9bZEqISzXxmTUrRVuPiE/s7SWWyt5xu0NPgYerqFK0BBw9vbmY8miaZztJVwCKVQgghykN3vZsTA0E6al23nRLW6HPw5IGu6+677WZiKYPuVTqNZl2jh4/d0QFQ9MLLJqUwKchqsJhk6t5K8jms1/7Oi9Fd5+btgSBddW40MDAVZ02Zzcax5PuIy2lTj+9qo38qRqsUH5/T7TIcQ1rrf7dikVSYZMbg1YsTOK1m9vfULjibua3Nz5oGNzZz7gR/S6vv2u2lkml0xXfvunrq3XYCLit1VZJ8iacNhkMJ9nXVEnDb2NTsxWE101XrxmJWWM0m9nbV4LTevpi9EOXugY2NNPscNHjt+J3W61bigdxU57cHgqxr9NBT7+b+DQ3s7arBZTVjMil+8+4ejKzGaTPf4h2EEEJUqoc3N3JwTS3uBVz4vTAa5vJ4jF0dARq8dn7j7h4yxur+flipFb7cdgu/uq+DkVCCTS0y9qESPLyliQP5z9bZ4TBOq5mdHYFSh3WdD+9p5/J4lJ5lJI0dVnPVlFwphoXUcBKzRJMZvn9skDMjYexmEw6rmVq3jfWLGF44eySTzD+uDBazie3t1bGKRzSZ4XvHBjnaN03AacVhNfPEgU4c1lxnaXanySPTNEUVsFlM13VwpqIpfvD2EFaT4gM7W/nRiSGmY2nODIX43QfWYTap69p+sVZkEUIIUXpKqduu5DYjkTZ45vjwtSldTx7owmo2YV1grimeMvj+sUESGYPHtrdUzQXMldTsd9Dsr/yyFtWobzLGT0+NUOex8dj2lmuDKbwOK9FkhmdPDaN17nOwlNFSxeJ3WtlVZkmwanO7XvRDKxZFBTk/GmEomCCeMhgLJ1EKWa1IVJTzoxGGgwmSaYOxSBKrWV1LNgmxGpweCjEeTjIUTFyb4gzgsluQwXxCCCHmYjYpnLZ3TqIX6+JYhIHpOBORFCfzy80LUS2O9k0TjKe5NBZlKJi47jnLrHMNOW9efW75F9daT65kIJWis9aFy2ams9bF/p4aOmvdNFVBAWmxenTWunDazKxr9LCvu4YNTb6bphgJUc16Gtwc6ZvGYlJ01LrY1OKlbzJGi98pxR6FEELMyWo28fH9nYyGEkta+ryjxoXbbiaVybKmQcphiOqyocnLpbEoAZf1prrCdouZT+zvZCycWLX1zlYzSTHO49RgiOfPjdIWcPKBHa3Uum187t41aFameHQibfDmlSn8TmvVTOkSpTUZTYLWxNMG8VT22goUQlQbI6t56+oUkFtdaOaY3eJ38q/uX0swnuK7RwfQGj68u01W+hRCiCpwYTTCT0+N0OC186Fdrcuqk3ojn8OKbwmjmwD8LiufvWcNWa0XHdNkNMXx/mm669xSu1WUjNaaH749zJWJKPetb7ju3LSn3s2ergAumxn7HKUI5qqhKVYH6V3P43j/NMl0lktjUabjaWrdNkwLSDRNRVNYzDfPCb88HuWX50bRwEObmuZcJSmZMZiIpGjyOXjl4jjH+oIA1LittNfIKkliadJGltODIf7z8xcYmIphMZu5PBblju4aPEvsPAlRzt4eCPLM8UFGQgnGI628b3sro+EEPoeVsXCSb73Zx0g4SbPPwfnRCPt7aksdshBCiEU6Mxzi9FCI7W0B1jV6ODEQJJE26JuMMRpOXrdy1EQkicNqXpELDKcGQ1waj7CnM0BrINd/N5kUpiWUyX325DDDwQTH+4M8dd8aKYUgCCfSPH92DJfNzOYWHw1eO9YCJlfnfM9khnMjYQCO9U9fl3B68+okb17JXeTzO60LLqIdTxlMx1M0+xxVN8o8lsrwxpUp6tw2trWt3oEjknCax7Y2P6PhJO01zgVnZc+PhHnm7SHMSvFrd3TQOGvK3c9Oj/Di+XGSaYNQPMPv5QvUztBa8803+xkPJ1nb6KHekxt9YlIKu+XmL5epaIqpWIruOveCEmFi9fqXw/38j5evMByMYzGZcNstdNQ4OdoX5J719aUOT4iCuzQa4fmzo2SyGpvFhNNq5mhfEI/dgskEiUyWoek4XbUu1sr0BiGEqDhaa35ycgQjqxkJJVnX6GFLq4++yRiNPvt1U3uO9U3z8zOj2K0mnjzQVdTRFmPhJP/w2hVODIZo9Tv5tx/YsqzV3Jz5BJPNYpKVgwUAb16Z4sJohPMjYeo8Nja1+Hhif2dRkzYem4U1DW6uTsRuSqA4rWayWhOMpUkZ2QXtL5kx+NrrVwknMuzqDPDAxsZihF0yvzw3xumhXIKu0Wu/LiewmkjCaR7b2vzUe+y47QtfGn4klERryGjNWCR5XeNq8NqxmBVmkxmXzXxTgVojq5mMpAAYDSV4//YW6j12fI6b58OGEmm+9vpV0oZmb1cN921oWN4/VlSVUCJNLGlcW83jratTTESSGFlNs9/O3evq8DqsNPpklRRRHWa3+beuTvLm1XeutLlsFsbCSQAiyQxtAScha4aHNjfxyYNd0oEXQogKpJSi0WtnKJigKd+f2dDkZcMcq0ePhHKFjJPpLFPRVFETTjaLibMjYSYiuTIGE5HkshJO793ezMXRKK0Bx4JHsUxEct95shpedZrpv8fSBu1WM2PhXB/fYl56fyYYS5PMGLdMjJhMisd3tc353K6OACcGgkSSGZ4/O0ZPnee6la/nEk8ZhBMZgGt9tGrizK9GbzapVb3isSSc5vHGlUleOj+OzWLikwe76J2IcWUiyh3dtbdclnN3Z4DpeAqb2cTGG77w3r+jlZ3tAUKJND317puy0BaziXdvaeL0UJC9XbWYTGrOL03I1XdKGxrg2od1PsmMMedIKVFdJiJJnn6jj2TawO+y0uRzsK7RQ99klHha8xt3d/OujY0YWS01nERFyxhZzCZFMJ7mf75yhayG+zc2EEpkyOpcjbJtbX5+511rsZhMvHxxnGafkwM9tYyGk9S4rZJsEmKV6v6jZwqynytfeqwg+xFL89G97UxEUtdmBdyKz2llOpZiR3uAzjlKWhSS32llf3ctJwdD1LptbG7xLWt/douZtY1ubAtMNl0ej/LdowMAfGR3O5111VeSI5vVK1ZTtxxtbfXT5HPw7mAjZ4cjbGz2Lqte2Vg4ydOHeslkNe/e0nTbKWBXJ6Ic7w+yqdnL+vx5qlIKj8OCz2Elmc6SzBiYTNz2vDPgsnHfhnr6p+IcXFO35NhXipHVKFjwrKJ719XT4ncQcFkJuFbv+ZYknOYxc3UglckyEkzwszMjaJ1L8DxxoHPO17jtFt6/o3XO58wmNW+xv3AiTe9kHKWm6Kpz3XJoZKPXwYObGhkLJzmwZv7aIz84Psj5kQjb2/w8vKVp3u2XKpXJcrRvGo/dwpbW5X3BisXJZjX/fLif1y9PMBZO0uh18NKFcaxmE/u6avjsvWupcdvY01lT6lCFWLbj/dP8/atXafQ5qHVZeeXiBI1eO1tbfdzRXct3jgywrtHL+kbPtfoZH97dfu31t7poIIQQonJYzaZ5j+eJtMFrlyYIuGzEUpkVKUPx5MEuTg2G2NTsW3ZtnVcujvP6pUk6al18dE/bvNOmJqO52RYA49Fk2SScLo5FGA0l2dURmHf0y+0EY2m++WYfKSPL47taV22N23qPnXqPnW1tgWXvazqWIpPNNZqxyO1HG/3k5AiRZIYr41HWNniufZ4e2NjIG/YpWgMODvdOcawvyLpGDx/YOfd5McDerlr2di07/KLrn4rx3aOD2MwmPravA5vFxNG+aRq8dtY1euZ8ze0GjqwmqyLhdGE0nDu4dQZw2Rb3T75zTT1GNlewu7vehcduIZzIUDfPVZTlODOcm+t5dSJGLGXctrDhzo7AvPs7NxLmtYvjHOsP0l7j4sxwmIc2NxZtju/rlyeuFY3zOixzFkYXxRFJZbgwGuHo1SkSRpZkJks8ZWBxKCLJ3PQhIarFP7/Vz1tXp9BodncEaA04yWrNwbV1uKxm6j12grE0NW6ZTiCEEOVmOf3zxbKaTfgcVoLx9IpNMWuvcRUsEXJ+JAJA32SMeNqY9/9rW5ufyWgaBWxrnb9Y8fH+aY71TbO1zV+0i5JT0RTfPzaI1jAeSfLY9pYlJ/76pmJEkrnZHZfGolWXcDreP00inWVPZ6CgqyzeztoGD3u6aoglM+zvvv1Ahlq3jUgyQ80Ni2kFXDbenR/U8OL5cSCXZMxm9ZL+1gPTcX5xZpQmn4OHb3PumjayWEyqqPWrLo1FSWWypDJZ+qZi9E3GODMcRin49J3dMmPkNqo+4TQZTfGD40NoDdPxNO/b3jLndkZWzzkk0++y8tiOd17z5IEuxmfNw9Zac3EsgteRm7ZUCPu6a3j14gQ99e45k02JtIHFpBZ8AHrlwjhTsTSQuyIQzxh87fXea9nZQrOY3tnncuYRi8UbmIpxdiTMWCSFx24imsywvsmDy2bhg7tufXVBiErznSP9nBwMEk6k8TttBJw2nHYzD25qwuewcn4kjMNqIuuwUC/1K4QQoqwstH++GDPTXS6NR/A5rNfVoTGbFE8c6GTshlXrKsX+nlpeuTjBmgb3gpJzdov52on/Qrx4fpxUJsvzZ0bZ2R4oyjQ1k0lhUoqJaJLnTo0wEkrwa3d03LSi90KsbfBwMhAkmclWzWyKmXPRC6MRfnZ6FCB3EW0ZU836p2JkDD3v7BrI/X3uX2A94A/uamU4mLhtHdgDPbUc7p1mc4t3yYnFQ/kZG2PhJNvb/HOOZDwxEOS50yPUeez8WoHPbY2sJm1kcVjNbGn1cWUiit1iYk2Dm6FgriacSSnMVba6XqFVfcLJnD+4GVrfcjjr2eEwz54cpsZl5WN3dNx2rqnTZr5uxM5rlyZ57dIEJqV48mBnQU5sNjX7WNvgmXPJ0/MjYX749jBuu5mP7+/Ec5vRTxkjy2uXJgknMxhZzf6eWiwmE72TMcbCSaZiqYIlyWY70FOLz2nBa7fS4q+8L/VyFIynGQrG6al337J9Huub5vvHBklmMiTSWdKGprXGREeNi82tPjY1V8cXsljdLo6GefbkCD8+OQzoa6t+HOmfpsFj55GtzUCuYKvFZMLnNOGwrt5CjUIIUY4W0j9fqGxW852jA/ROxq6NYjKbFE8e6LxuNJPDai7IqPtUJrcC10oWAd7c4lt2HajbWVPv5qUL40xEU/ztK1d4Yn/nsqa8zcXvtPKr+9r54fFhvI4kVyeinBgIcufaxa+U7LSZ+bU75i5tspLOj4S5OBZld2dgyedUibTBN9/sYzqW5r3bmq87/1vOZ+PqRJR/OZyr4/XuLU1sbPaS1bogtXytZtO8n6XdnTXsXuZouZ56D1fGY/kaSHMnJs+NhHOj5sJJJqOpgpVLiKUyfONQH+FEmke2NrO5xcen7+y+9vy7NjbQ7HNQ57Hhv0VsK2HmfN/QmjvX1BXluLSQ89DbqfqEk99p5WP7OhiPJNnYPPccyrMjYYysZjySYjSUXNSXUSyVG86Z1Zp4ylh2vIm0wdOHepmOp3loUxPb268fBntpPEpWa8KJDCOhBJ6GueeMApweCvPGlUkA9q+p5Z619fROxpiOp2n2Oah32zgxEMRmMRV0fqnJpNi6gOG7YmGMrOYf3+glmjToqnPxkT3tN20zFk7y8zOjnBoKcrwviNkEfqeF9Y1erGYT50cijIWT/MbdPSX4FwhRGNms5m9evMzbA9NcGY/hdVjY31NHi9/B0b5pgvE0k9HcKp9ddW4+vLuNZCbLhqZbHyeFEEKsvIX0zxcqmspwdSIGwIXRCA1eO0ZWk8gsbGn2+QTjaS6MRuipd5PKZPnnw/0A/Ore9qpZ5vzRbc3EUgZXJ6KE4mnGwsWp+9Tid/KBnS385+cvMBxK8sqFCTY0eStyJb1kxuCHbw+T1ZqxcIJPzUpGLMZoKMlEfoXycyMRHtvRwkf2tJFIZ2ny2Xnr6hRr6t3ULHLKVjT5znnpaDjBSxfGSWeyfHBXK1118494Kge7OgJsaPJgM5tuObNnT2cNU7E0TT77TSu6L8dYOEkonpshdHk8elPC12o23XSeXgqzz/fdNjP75pkOuVgLOQ+dT0UlnJRSfwHsAw5rrb+w0Nc1+x23zXbuag8wGkpQ77HTssis6N3r6nPzwp3Wglw1mYqlrk1/uzQeuakh7+4MMBZO4nNa511hw+uwoBRoDc0+B6Z8wfLfuieXdDjSO8XzZ8cAMO1U1wqeaa3nnAM7OB3npfPjJNIGjT47+3vqZL7qCjCymkQ612mK3SKp6bSZsZgV5/P1v5RSbG3z83sPrOdHJ4YIJzLEUsYt/7ZCVAKlIJPV9E/GSGay+JWi3mPj/dtbiKYMGr027lr7ztDzhQwhF0KsnEKtCieKrxB/q/lW77tV/3yxfRW3zYzFpLg6GeMju9tIZzU1Luu18he3Ek1meO70CGaT4t1bmm555f67RweYiKQ4fHWKnR2BayOc+qZiVZNwUkpx17o6YqlcXZ62muLNUGj0Obh3XQNvDwQBCpYYXGkWkwm33Uw4kcHnvP0IlxvbdDCe5pWL41ydiFHjslHnsZHKZNmRP++bSQj97cuXmYqlOdI7xWfvXbOo+DY1e4kkM2SMLB6HhWN9uf/vy+PRikk4AfNOIZ19bltIbQEn65s8TMXSZb3Y0uzz/fna4VIs5Dx0PhWTcFJK7QHcWut7lVL/RSl1h9b6jULsu7POtegP8QyH1cx9C5zvuhBNXgebW7yMhZPs7bq5cTd6HXzy4MJK+XfXu/nE/k6MrJ5zvnp+IQIgdyCcPST5vg0NN324Xr4wzoWxCMf6ptnZ7ieWMpaU5RSLY7OY+MDOVq6MR2+ZSffYLdS4bCQNTTYL9T4b//t7N9NW4+T9O1o5NRRkQ5NXkk2iomWymlgyQ9rQOCwmPHYL79rYyPaOANsXsICCEEKI8hVOpPnmm/0k0saiVh67MhEjk9W0BZwkjSwPbGxc0OuO9U9zaSwKQEeN65YL8WTzHeas1mxq9nBlPAqKqitV0OJ3LnmUzmLdva4eq8W0oMRguTKbFJ/Y38lIKHHLQQdpI8s/vdXPWDjJu7c0XRsl8+L5Md64PMmZ4TDb2/zc0VN7rSTAbDPnalmtb3puPiaTYn9PbrRLIm1wcSxCPJVlR3tg0ftajSxm0y1XnS8n853vL9dCzkPnUzEJJ+BO4Ln87eeAg0BBEk7lxGRSPLpt+YUTZ9xuPvHujlxRQItJsb7JSzCevjYk+fRQ6KaEU3uNi8vjUTx2CzaL6ZZzaUXh9dS76ZlntMZEJElHjYuRcILdHf5rQ2/nG+EnRKWYiKRw2swEXDaUgt++f21BE/5CCCFKp28yfm0Ky4XRyIITTl6HFbNJYWQ1gUVc4W/1O3MFf03ctp/0wV1tnBsJs7bBg89p42N3dCz4PcTcnDbzggtUlzO33cKa25Q3mYikGM4Xlz4zHLqWcAo4bThtZhzW3AyFWyXdPrT7nba3HA6rmQ/vlkEC1aoYNZlnW8h56O1UUsIpAFzM3w4CW0sXSnUwmRS7Zl3N8TksbGr2cnUydt3jM+5cW8emfMG5SDJDR5UtQVrpDq6pw2RS1DitPLajddFzvYUodw1eO/eub6DeY+fONXW8a9PCrmILIYQof931Lpp8DuJpgy2LKJDd4LXz5IFO4mljwUmq3Pu5+c17ujGb1G2n7dS6bctaKUysXg1eOz31bkZCietGFt29ro7OWhd2qwmH1Yz/FolSaXuiGlRSwmkamPn28eXvX0cp9RTwFEBnZ+lXLqg0SineO8+ytDNJjEos7lftDqyp44B8KYkqZjYpPrS7jQ/tbit1KEIIIQrMZbPwxIGl9d+X2i/1OmS0viiemX7LjZRSRSnKLkQ5qqS1ol8FHsrffhh47cYNtNZf1Vrv01rva2io/GGaQgghhBBCCCGEEJWoYhJOWuvDQEIp9SKQ1VofKnVMQgghhBBCCCGEEOJmSi+h6n0lqK+v193d3aUOQ4iiunLlCtLORbWTdi5WA2nnYjWQdi5WA2nnYjV46623tNZ63gFMlVTDaVG6u7t58803Sx2GEEW1b98+aeei6kk7F6uBtHOxGkg7F6uBtHOxGiilDi9ku4qZUieW5sp4lItjkVKHIVbAaCjB6aEQGSNb6lCEKJpgPM3JwSCxVKbUoQixIi6MRrg6ES11GEIIIYQoQ+V+vl+1I5xErpP6/WODALxnaxNbW/0ljkgUSyiR5h/f6COT1Qy2+3loc1OpQxKi4LTWfOvNPsKJDM1+B5/YL6uRiup2YiDIT0+NAPCh3W301LtLHJEQQgghysXs8/1HtjazpdVX4ohuJiOcqlgibcy6LaNeqlk6kyWTzdVjk7+1qFZaQzKTa9+zj29CVKvrv8elzQshhBDiHdf1EzLl2U+QEU5VbEuLj2TGIGNodnUESh2OKKI6j533bW9hNJxgT2dNqcMRoihMJsXju1q5OBZlS0v5XcERotB2dQTIZDVWs4lNzd5ShyOEEEKIMjJzvm9kYWd7oNThzEkSTlXMZFLs7aotdRhihWxs9rJRTkhElWuvcdFe4yp1GEKsCIvZxME1daUOQwghhBBlqBLO92VKnRBCCCGEEEIIIYQoKBnhJIQQQgghhKhY3X/0TEH2c+VLjxVkP0IIIXJKNsJJKfUXSqkXlVJfmeM5pZQ6ppT6bP7+Z5RSZ5VSzyul/nzloxVCCCGEEEIIIYQQC1WShJNSag/g1lrfC9iUUnfcsMkHgdEbHvuy1vpdWuv/bUWCFEIIIYQQQgghhBBLUqoRTncCz+VvPwccvOH5TwD/eMNjv6+U+qVS6qFiByeEEEIIIYQQQgghlq5UCacAEMrfDgLX1nFXSj0CvABkZm3/HWAH8FHgPyilzHPtVCn1lFLqTaXUm2NjY0UIWwghhBBCCCGEEELMp1QJp2nAl7/ty9+f8Vngf8zeWGs9rbXOaq3HgHNA01w71Vp/VWu9T2u9r6GhoeBBCyGEEEIIIYQQQoj5lSrh9CowMzXuYeC1Wc+tJzei6d+Qm0a3SSnlA1BKOfPPy/AlIYQQQgghhBBCiDJlKcWbaq0PK6USSqkXgWNa60NKqb/UWn9ea70LcivTARat9Rml1J8opR4llyD7ktY6XYq4hRBCCCGEEEIIIcT8SpJwAtBaf+GG+5+/4f7fzrr9ReCLKxOZEEIIIYQQQgghhFiOUk2pE0IIIYQQQgghhBBVShJOQgghhBBCCCGEEKKgJOEkhBBCCCGEEEIIIQpKEk5CCCGEEEIIIYQQoqAk4SSEEEIIIYQQQgghCkoSTkIIIYQQQgghhBCioCThJIQQQgghhBBCCCEKShJOQgghhBBCCCGEEKKgJOEkhBBCCCGEEEIIIQpKEk5CLFAwnubcSJhUJlvqUMrSdCzF+ZEwGUP+f0R16ZuM0TcZK3UYQpTcZDR3nDeyutShCCGEEBVhOJjg8ni01GGUjKXUAQhRCdJGlqcP9RJLGaxpcPP4rrZSh1RWEmmDrx/qJZnOsrnFy6PbWkodkhAFcW4kzDPHhwD44K5W1jZ4ShyREKURTWb4xqFeUpksO9r9PLS5qdQhCSGEEGVtOJjg6Td60Rru39jAns6aUoe04iThJMQCGFlNIp0buRNNGiWOpvykjOy1kV8R+f8RVSSSzFy7HZ11W4jVJpWZfZyXz4IQxdb9R88UZD9XvvRYQfYjhFi8aCqDzg8KXq39SEk4CbEADquZx3a0cHUiyq6OQKnDKTs+h5X3bmthYDq2KjP3onrtaPOTSBkopdja6i91OEKUTI3bxiNbmxkOxdnXXVvqcIQQQoiyt6bezb3r64mlDO5Ypd+dknASYoHWNXpY1yjTaW5lY7OXjc3eUochREFZzCbuWldf6jCEKAtbWn1safWVOgwhhBCiIiilVv1FGikaLoQQQgghhBBCCCEKShJOQgghhBBCCCGEEKKgJOEkhBBCCCGEEEIIIQpKEk5CCCGEEEIIIYQQoqCkaHgZers/yMsXx1lT7+Y9W5tLHY6oIFprnj05zJWJGPesq2dbm6yqJarX5fEoPz01TIPXzgd2tGIxyzUUUd2ePzvKmeEw+7pqVn0RUiGEEGKljIWTfP/YIHariQ/tasNtlzTKQpWsd66U+gul1ItKqa/M8ZxSSh1TSn02f9+rlPq+UuplpdSnVz7alXW4d4p4yuDkYIhoMlPqcEQFCScznB4KE08ZHO2bLnU4QhTV8f5pokmDK+MxRsPJUocjRFEZWc2R3mniKYPDvVOlDkcIIYRYNU4PhQjG04yGklwej5Y6nIpSkoSTUmoP4NZa3wvYlFJ33LDJB4HRWfc/B3wDuA/4rFLKtjKRlsbWVh9KwZoGNy6budThiLzRcKLsE4Aem4XuehdKweaWhS1dnUgbDAcTaK2LHJ0QhbWp2YfZpGjyOaj32G+77WgoQTxlrFBkQhSe2aTY3OJFKdjSsrDRq9FkhtFwosiRCSGEEJUpY2QZDiZIG9nbbreu0YPdasLrsNBZ51qh6KpDqcaC3Qk8l7/9HHAQeGPW858A/vGG7X9Pa20opY4BG4G3VyLQUtjXXcuezhpMJlXqUETeW1cn+eW5cRxWM5882InXYS11SHMymRQf3t1ONqsX1H5SmSxfe72XUDzNro4AD2xqXIEohSiMjc1e1jd65m3rr1wc5/VLk7jtZj51sBunJPJFhXp0Wwvv2dK8oON7JJnh71+9SiJtcM/6eu6QKXhCCCHEdb57dJDeyRhtNU4+tq/jltu1Bpz8zv1rUUrOzxerVFPqAkAofzsI1Mw8oZR6BHgByCxk+9mUUk8ppd5USr05NjZW4JBXliSbystIKDddJ5E2CMbTJY5mfgttP/G0QSj/7xkJyVVwUXkW0tZH85/faNIgnCz/z68Qt7PQ43swniaRzo3qk+O7EEIIcbPh/Pfj6AK+JyXZtDSlGuE0DczM9/Hl78/4LPAp4ONzbJ+YY/trtNZfBb4KsG/fPpkfJArmzjV1pI0sdW47bQFnqcMpGL/Tyn0b6umbjHNgjVz9FtXp7nX1KAWNXgeNXkepwxFiRbT6HezvqWU8kuSutfWlDkcIIYQoO+/Z0sSJwSBbW2WhpWIpVcLpVeC3gW8CDwN/O+u59cB3gDZy9cNfym//kFLqm8Au4OwKxioENW4bj+9qK3UYRbG3q5a9XaWOQojiafDaq/bzK8StKKW4e50kmoQQQohbWd/kZX2Tt9RhVLWSJJy01oeVUgml1IvAMa31IaXUX2qtP6+13gWglPoMYNFan1FKDQJfBz4PfFVrLcsRCSGEEEIIIYQQQpSpUo1wQmv9hRvuf/6G+38763YIeP/KRCaEEEIIIYQQQgghlqNURcOFEEIIIYQQQgghRJWShJMQQgghhBBCCCGEKChJOAkhhBBCCCGEEEKIgipZDSexeiXSBlqD02YudShiAbJZTSSVwWu3oJQqdThCFF0qkyVtZHHb5StSrC5pI0sibeB1WEsdihAl0f1Hzyx7H1e+9FgBIhFCVDo5h8qR3rRYURORJE+/0YeR1Ty+q5WuOnepQxLz+PaRAXonY2xp9fHI1uZShyNEUQXjaZ4+1Es8bfC+7S1skKVyxSqRSBt841Av07E0921oYG9XTalDEkIIISrWd48NcGU8xuYWL49uayl1OCUjU+rEihoKJkhlshhZTd9kvNThiHkYWU3fVAyAqxPREkcjRPGNhRPEUrlRmFcnYqUOR4gVE4ynmY6lAeidlOO9EEIIsVTZrL7Wj7yyyvuTknASK2pdo4c1DW7aa5xsb/eXOpyKobUmnjKW/Pp4ykBrvejXmU2Ke9fX0+C1c8+6hiW/vxCFlEgbZIxsUfbdXedmQ5OX1oCDPZ2BoryHEIsRTxlks4s/fi9Wo9fOjnY/TT4H+3vqiv5+QgghRLUymRT3rm+gwWvn3vX1pQ5nWZZ7HrrsKXUqNyHxSWCN1vrfKaU6gWat9aHl7lsUx3QshdNmxm5Z+RpKDquZx3e1rfj7VjKtNf98eIC+yRh7u2q4b8PtEz/BWBqHzXTt7/uTk8OcHAyxrtHDB3a2Lvr993bVsrerdkmxC1FoF0YjPHN8CIfVxHu3NdNR6yrovHiL2cRjO1bvsGdRXl67NMGrFydo8Np5ZGsz9R5b0epAKKV4aHNTUfYthBBCFEspz21vZ29XTcVPT1/seehcCjHC6a+AO4FP5O+Hgf9cgP2KInjjyiT/4+Ur/P2rV0mkl56pFCsnmcnSN5kbinlxLHLbbY/0TvH/vHyZ//nKFWKpDAAX8q+5NBZd0ignIcrJ5fEoWa05dGWSv3nxEs+eHC51SEIUzcWxCFprfn56lP/+0iWeOz1a6pCEEEKIsiHntsW1mPPQWylEwumA1vr3gASA1noKsBVgv6II+vP1eMKJDKF4uijvkUgbvHh+jCO9U0XZ/3IFY2mO9k0TShTn319oDquZO7prqXFZObjm9tMc+qdydbEGpuI8c3yI8UiSu9bWU+Oycte6ulW9QsJKOzcS5vxIuNRhVJ2dHX5q3TasZhNuu4UXzo1xtG+61GFVtP6pGG/3B4s2TVEs3YGeWrwOC16HBYvJxC/PjfHmlUm5eFBEibTBsb5pxsLJUocihBBiHrPPbYNFOredSySZ4fmzo5waDN303GQ0xdG+aSLJzIrFUyyLOQ+9lUKsUpdWSpkBDaCUagCk11qm7lxTT9oYo8nnoMFrL8p7vHZpgiO90wDUum1ltxLdPx3uJxRPc6zPxq/f1V3qcBbknvX13LOA+b8H1tQSTWbonYjSNxnjRyeG+dTBLnZ1BIofpLjm5GCQn5wcAeC92zWbmn0ljqh6NHod/Ppd3exo9/O9o4OYleIXZ0apddnorHOVOryKMxFJ8k9v9aM1jEeTPLCxsdQhiVnWNXpZ1+jlrauT/PjEMAp48fw4fqeV9bKCYlH86MQQV8Zj2CwmPntvT9lN0RBCCPGO2ee2jUU6t53LC2fHOJe/sNzos1Pvyb13Nqv51pt9xFIGp4dCfGJ/54rFVCwLPQ+9lUIknP5v4NtAo1Lq3wO/AvzbAuxXFEGz38HH9nUU9T3c9lyzUgqc1vLrqM1cxU9X4dX8Rq+DX93XQSiRJpo0cNvK7/9/NTBmFfjNGDISoRh2d9aQymR55eIEJqVw2GQNjKUwspqZwTKGtNWytberFrPJxC/OjOa+W+XYXjTp/OcgO+uzIYQQojytxLntXFz572GrWWGzvNMH1UAmfx6QWYEFPyrBshNOWuuvKaXeAh4CFPAhrfXpZUcmKta+rhpqXDY8dguNPkepw7nJh/e0cWEkwobm6rw6bDYpPr6/k8HpOD315TW6bLXY1urHyGqUUmxtldFNxbK/p5Y6jx2vw0Kjt/yONZWg0efg/TtamIimZCRkmdvVEcDnsOCwmmkNOEsdTtV6dFszJwaCdNS4cJThRTMhhBCld9+GBtpqnNS6bfgc1muPm02Kj+xp4/JYlM0tcg4AhVml7u+11p8CzszxmFiFlFKsa/SUOoxbavQ6qv7k1Oew4mu2zr+hKAqTSbG7s7JXpagE5X6sqRTrm7ysL3UQYkHWNEh7Lzafw8pdayt7CWshhBDFZTYpNtxianuL30mLXy4MzSjEHISts+/k6zntLcB+hRBCCCGEEEIIIUQFWnLCSSn1x0qpMLBDKRVSSoXz90eB7xYsQiGEEEIIIYQQQghRUZaccNJa/59aay/wZa21T2vtzf/Uaa3/uIAxCrEsoUSaRNoodRirivyfi9UmLG1erEJyrBdCCFHuYqkM0WSm1GGsWoUoGv7HSqkaYD3gmPX4L5e7byGW68RAkJ+eGsFlM/PkwS489kIszChu59RgiJ+cGsZhNfPEgc7rCukJUY0ujIb5wfEhrGYTn9jfSa3bVuqQhCi600Mhnj05jN1i5on9nfhdcqwXQghRXoaDCb71Zh8a+PDuNjpqXaUOadVZdg0npdRngV8CzwJfzP/+0+XuV4hC6J+KARBLGUxEkiWOZnXon4qhNcRTBmNh+T8X1a9vKo7WkMpkGQ0nSh2OECtiIN/uE2mDMfl+FUIIUYaGgnEyWY2R1QxOx0sdzqpUiKLhXwDuAK5qrR8AdgNj871IKfUXSqkXlVJfueHxP1ZK/VIp9YZS6sP5xz6jlDqrlHpeKfXnBYhZlNhkNMX3jg3yysXxor7Pvu5a2mqcbGvz01EjGe2VYDGbGAsnaAs46a5zlzocIQoqmTH4yclhfnJymGQmN5VoT0cNnbUuNjV7WSuriIkqo7XmxfNj/OD4IMF4+trje7tq6Kh1sbnFR0+9HOuFEEKUn80tPtY2euipd7O93b+g1xy6PMl3jw7IhfMCKcT8ooTWOqGUQill11qfUUptvN0LlFJ7ALfW+l6l1H9RSt2htX4j//R/0Fr/n0opD/Ac8O3841/WWv+3AsRbVeIpg9cuTeBxWLiju7bU4SzYyxfGuTga4eIo9NS7i7Z0ZL3Hzsf2dRRl35VuIpLkratTdNa52NTsK8g+p6IpjvVN0+B1oFRuyVAhykk2q3n98iTJjMGda+uwW8yLev2JgRAnB0MA1Hns7O2qwe+y8tG97cUIV4hlOzsc5upElD1dNdR77It+fe9kjDevTAFgNZt4ZGszADVuG78i7V4IIUQZc1jNfHBn64K3H48keflCbkCEkdV8ZM/833MD03FODgTZ2OylSy6236QQI5z6lVIB4DvAT5VS3wUG53nNneSSSeR/H5x5Qms9c/nMCZyY9Zrfz498eqgAMVeN1y5NcLRvmpfOj3NlPFrqcBaswZvr9DqsZrxS46ckfnZ6lJODIX58YphIgQrpOW3ma3WyGn2OebYWYuWdH43w2qUJjvRO89bVqUW/vsFjx6QUSkG9R2o1ifIWS2X40YkhTg6GeO7UyJL2EXDasFly3cWZ724hhBCiGnnsFtz23MXIRu/CzmV+9Hbue/YHx4fQWhczvIpUiKLhH87f/FOl1C8AP/DjeV4WAC7mbweBrbOfVEr9FfBh4N/kH/oO8HdAHfATpdQ+rfVNy6IopZ4CngLo7Oxc7D+lIvmcuT+hSSncFVQQ++CaOnrq3fkPdeXEXU18TgsD07mkn9VcmJFIDquZTx7sYjqeolkSTqIMeRwWlAKtWVJB+846F5++swvIjfAQopxZTCacVjOxlLHkizt+l5VP39lFPGXIhQQhFqD7j54pdQjXKVQ8V7702LL3UU6xCDGXmXOZUDxDs39h33leh4VwIoPHbkEpmd1xo2Wd6SulTMBxrfU2AK31Cwt86TQwM4fHl79/jdb6d5VSfwy8Anxdaz3z/JhS6hzQxByjqLTWXwW+CrBv375VkV7c21VLvceOy2apuCuPTdJxLamHNzexvslLg9e+6GlFt+O0mXHaijNFUojlags4eWJ/J8lMdskrlUiiSVQKm8XEEwc6GQkl6a5beh1Dr8Mqo5GFEEKsCi6bBZdt4WmSx3e10T8Vpy0g5z9zWdaUOq11FjimlFrscKJXgZmpcQ8Dr808oZSayZrEgVD+MV/+txNYzwKKkq8mXXXuiks2idKzmE2sbfAsaZSHEJWs0eeQZXHFquF1WFnX6MFiLkQVBSGEEELM5rCaWdfowWkr3AX8alKI3kcLcFIp9TOl1Pdmfm73Aq31YSChlHoRyGqtDyml/jL/9FeUUs8DzwNfzj/2B0qpV/OPfWlWnSdRBEZWc2IgSP9UrNShiArQPxXjxECQjJEtdShCrLhE2uB4/zSj4USpQxGi6KS9CyGEWA0GpuNyflMghSie88WlvEhr/YUb7n8+//tfzbHtF5f6PmLxXr4wzltXp1AKPnmwa0mr2ojVYSKS5J/fGiCrNRPRFPdvaCh1SEKsqGdPDnNpLIrNYuK37unBYZWrW6J6/fTUCBdGI9gsJn7z7h65miuEEKLqTEZT/NOb/WS1Ziyc5IFNjaUOqaIVomj4Qus2iQqRzmdytc6NdhLiVjJZTTa/GkM6I1cAxOqTMXLt35j1WRCiWs30D6S9CyGEqFazv+PSMsJp2WR5MHGTu9fV47ZbqHHZpLC3uK0mn4PHdrQwEUmxuzNQ6nCEWHHv2drE2/1B2mqciyowKUQleveWd9q7rDArhBCiGjV47bx/Rwvjcn5TENJbEDdxWM0cXFNX6jBuSWvNUDBBjcsmw/nLwIYmb27dSCGWKZE2mIimaPY5MJsqY1lZr8PKXevqSx2GqCCV2M5nSHsXC1XJ7VwIIdY3eVk/x/lNNqsZDiWoddukjMICScJJLMvpoRDDoQT7umpWbMnkn50e5e2BIF6HhU/f2Y3NIivvFIOR1Ry6PIlScEd3rXQYRVFls5qnD/UyFUuzsdnL+7a3lCSOi2MReidi7OwIUOu2lSQGUb3KpZ1DbsGH8yMRNrV4afHLUs6icMqpnQshxK2kMlneuDKJw2piT2cNSs1/rvPc6RFODobwOa38+p1dsgLsAiw74aSUuhv4U6Arvz8FaK31muXuW5S3iUiSH58YBiCSyPCBna0r8r7jkSQA4USGZMaQhFORHO+f5rVLEwC4bGZ2tAdKG5Coaikjy3Q8twDpWDhZkhjiKYMfHBsiqzUjoQQf399ZkjhE9SqHdj7je8cGSaazXBiN8Ln7pMsmCqec2rkQQtzKm1cnOXR5EgCfw8r6Ju+8rxnLn4eG4mmSmawknBagECOc/jvwB8BbgFGA/YkKYbOYsJoVaUPjWsGpbe/a2MjrlyforHWt2Kiq1Wh2PRqpTSOKzWE18/DmJi6ORdjTWVOSGMwmhd1qIp4yZLquKIpyaOczXFYzyXQWl13auiiscmrnQghxK+78+Y1SLLjf98DGRt64Mkl3nVtqGS5QIf6XglrrHxVgP2IBpmMpfn5mFJ/DyoObGjGVcJqT12HlE/s7mYimWNvgWbH3bfY7eHxX24q9X6W5PB7ljSuTrGv0LKujt7HZi9NqRinoqHUVMEIh5ratzc+2Nv+822WMLD87M0o8ZfDg5kZ8BUo82ywmPnFHJ0OhOGvqV+6YJlaXhbbzGUd6pzg/GmFfVw1rCvhd+6v7OuibitFV6y7YPoWYsdh2LoQQK21nRwCvw4LDaqY1cP3U8mxW88K5MSajKd61sYE6jx2A1oBTzkMXqRAJp18opb4M/Atwbdys1vpwAfYtbnDo8iRXJ2IArGlwL6jzGYylOTkUpKvOTVvg1nUagvE0ibSxqJXp6jz2ax9AUR5eODvK+dEIhy5Nsq7Bg8+59JPxzrriJJqyWc1gME69xy4F98SiXRyLcmowhNaayWiSPV217Gz3L2ju/Xz8Lit+V2lGTs4siOB3WuWqmQAgmTF44dwYWsPlsSjv2tjAzo5AQY6bbruFTc2+AkS5OMFYmqRh0OiVVXCFEELcWjxlcKx/mgavveCDG1KZLKPhBB21LqxzTIvrn4pztG8ayJ1/v1dq0S1ZIXq0B/K/9816TAMPFmDf4gatAScnB0PYraYFJ3p+dGKIoWCCw1eneOq+tXPWPJqIJPnGoV7ShubBTY3s7AgUOHKxUhxWMxdGI7hsZt66OsUDmxpLHdJNnj05zJnhMAGXlU/f2S0FycWiNHjt2Cwm+qdiTEZTBOMZzEqxvb2yr6a/dGGcN69M4bKZ+fSd3TKtT2Azm2jw2rk8FmUomOCVixOEExke3lKZS4OOhhM8fagPI6t5z9YmtrZW9mdWCCFE8Tx/dpQzw2GUgl+/s5uaAi7m8i+H+xkKJmivcfKr+zpuer7GbcVpMxNPGTeNfhKLs+yEk9b6gUIEIhZmW5uf9hondot5wScjMyfzJpPiVgMApuNp0oYG3inKDXB+JMyJwSBbW/1sWEAhNVF6D2xs4NJ4FLvFNGfGfqHODIc4PRRiR3ug4FcVZtpYMJ4mbWQxm+TEWixcrdvGb9zdzYn+EC9fHAfAYl5+0vLCaJi3B4JsafGzsXnlj3czn4tYyiCaykjCSaCU4mP7Org8HuWHx4fQLL+tT8dSvHBujBqXjXvX1xdkZODC3zuNkZ3pa6RW7H2FEEJUnmvnsEqRyBg8c3wIu8XEuzY2LLtY90yf61bfRV6Hlc/c1U08ZRQ00bUaFWKVOj/wJ8B9+YdeAP6d1jq43H2LuQVci2v0j+1o4dxIhPYa5y0TED11bvZ11xBOZNjfU3vt8Z+cGiGVyTIwFZeEU4Vo8jv51MEugvE0m1uWNl1Ca81PTo5gZDWjoSRr7y9swunBzU28dXWKNfVumVInlsRls7B/TS0Bd276WyGOTz85NUIynTvelSLhdO/6BiymCVr8DuplqrLIs5pNbGjy4tpnZiqaZnPL8trma5cmuDQWBaL01LtXtEbf2gYPuzsDJNIG+7qkmLQQQohbe2BTI835PtGlsSjnRsIAtAQcyx4h++i2Zk4Ohm5ba85hNct5SgEUYkrd/wOcAD6Wv/8p4H8AHynAvkUBuGwWds0zRc5kUty7vuGmx5t8DvomYzQuoq6TKL2OWhc3Dw5dOKUUjV47Q8EEjb7Cn/i2BZy3rScmxEIVMhHe5HXQOxkrWW2Zeo+dD+xsLcl7i/LXXuOivQA5mkafg9NDYexW04rXKzObFO/aWH7TvIUQQpQfq9nEjvYAANFkBqVyo50KcVFuXaOXdY0ymGIlFCLhtFZr/dFZ97+olDpagP2KMvChXa2MR1LUe2Qo4Wrz0b3tTMjfXqwij+ePd3XS5kUV29NZQ3uNE7fNIsXphRBCVIT1TV4+7bFjNin8y1gQSay8QvQ04kqpe7TWLwEope4G4gXYrygDFrOJZr+MblqNrPK3F6uMHO/EaiErxAkhhKg0tVJLqSIVIuH0O8D/zNdyUsAk8JkC7FcIIYQQQgghhBBCVKBCrFJ3FNiplPLl74eWu08hhBBCCCGEEEIIUbmWnHBSSn1Sa/0PSql/fcPjAGit/69lxiaEEEIIIYQQQgghKtByRji587/nKu+ul7FfIYQQQgghhBBCCFHBlpxw0lr/df7mc1rrl2c/ly8cXjX6JmP4HNYVXz5YiOWYjqUIJzJ01LpKHYoQK2I6liKSzNBeI21eVK9sVtM3FaPOY8cjq8wJIYQokMloinjaoC3gLHUooooUoqfyl8CeBTx2HaXUXwD7gMNa6y/MevyPgfcCTuD/0Fp/WynlBb4O1AJ/rbX+uwLEvSCvX5rglYsTWM2KTx3sLmnSKZvVfP/4IP1TcR7c1MjmFl/JYhHlbTqW4h9eu0ra0Nyzvp47umsX9LpIMsO3D/eTzGT54M5WGn2ykpGoDJPRFF977SqZrObe9fXsW2Cbny2ZMfj24QGm42net62FzjpJXIny87Mzo5wYCOKymfnM3d3YLeYFv3Y6luLbRwbQGj60u01W/BFCCAHAaDjB04f6MLKaBzc1srMjsKLvn8wYfOfIAFOxNO/d1kxXnXv+F4mKYFrqC5VSdyql/g3QoJT617N+/hS4be9HKbUHcGut7wVsSqk7Zj39H7TW9wEPAH+Yf+xzwDeA+4DPKqVWrIc0FUsDkDY04WR6pd72FrGkuDQWJZXJ8nZ/sKSxiPIWTmRIG7mZrVPR1IJfd2U8yngkNzLqzHC4WOEJUXCRRIZMNt/mY0s7Vg9OJxgKJoinDE4NyTFWlKepWO6YHksZJDPZRb324liE6ViaYDzNhdFIMcITQghRgULxNMa1ftTCzx0KZTiYYHA61wc7OShrkFWT5YxwsgGe/D5m13EKAb8yz2vvBJ7L334OOAi8AaC1njlTcAInZm3/e1prQyl1DNgIvL2M2BfsrnV1KAU1LlvJp2kEXDY6a10MTsfZ0iqjm8StddS6uGttHdPxNHeurVvw67rqXARcVpKZLBua5irPJkR56qh1cufaOkKLbPOztfgdNHjtBONpNjXLMVaUpwc3NfLG5Unaa1z4HIsbdb2m3sOR3mm0hrUNcvVYCCFEztoGDwd6aommDA70LK0ftRzNfgeNPjvTsbTM4qkyy6nh9ALwglLqb7XWVxf58gBwMX87CGyd/aRS6q+ADwP/Ztb2oVnb18y1U6XUU8BTAJ2dnYsMaW4+h5VHtjYXZF+3E01mODkYoq3Gect5s2aT4qN724sei6gOB9Ys/svC67DyG3f3LGjbRNrgxECQBq9dhr2KklNKcXAJbX42h9XMJw92FSSebFZzYjCISSm2tvqureAqxHLVe+y8d3vLkl5b47bx2XvXLOv9z42EiSYzbG/zYzEveaC8EEKIMqKU4q519SV7f7vFzJMHFtYHuzweZSKSZHu7f1HTykVpFKKGU0wp9WVySaNrBV+01g/e5jXTwEzq0pe/f43W+nfztZxeIVe7aWb7xFzbz3rdV4GvAuzbt6+iVsp79uQwVydiWEyKz923Boe1sj48l8ejXBiNsKPdT5PU/VkVnj87yumhMCal+PW7ugi4lj/T9fxImCsTMXZ3Bqj32AsQpRClcXwgyC/OjAJgMat5R0xNRlO8dXWKzloXG5tldKEoT70TMZ45PgRAPG1w19pbn5wc7p0iGEtzYE0tLpsUNxdCCLF8E5Ek3z2aq0V4ZSKG32mlp97NukZPqUMTt1CIS1NfA84APcAXgSvkp8fdxqvAQ/nbDwOvzTyhlJo5y4zzzqimV4GHlFJmYBdwtgBxl5WZi9+VeBE8Y2T5wbFBTgwE+dHbQ6UOR6wYNevW8htuPGXww7eHOTEQ5KenRpa9PyFKafYnwrSAA/tzp0Zyx9ATQ0STmeIFJsQyzG7Kt2vX/VMxXjg7xtG+aV6+MLECkQkhhFgNZo8YP9o7xYmBIM8cHyKRNkoYlbidQlxyqtNa/3el1BdmTbN74XYv0FofVkollFIvAse01oeUUn+ptf488BWl1CZyNaK+nH/JfyM30unzwFe11skCxF1WHtnazOmhEG0BV8WNbjIphdNmJpzI4F1kPQlRuR7Y1ECjz06Dx16Q1RstZoXDaiKWMmSpb1HxdrT7MZsUFrNaUD00jyPX5h1WMxZzBV55EKtCR62LD+xsJZbKsLXVf8vtXDYLZpPCyGq8DjmeCyGEKIxat42P7G5nPJrk8liE3sk4LpsZi0n6TuWqEL2AmSLfQ0qpx4BBYN5CQ1rrL9xw//P53/9qjm1DwPuXH2r5ctks7O1a/DLe/VMxzo9G2NLiK9lUNpNJ8fH9nQwH43TUyjLiq4XdYmZP55zl1JbEajbxiQOdjIYSi64JNRpKcHIoxLoGj7RBURaUUmxru/UJ+Y3es6WJDU0eGryOsq9HcGIgyGQ0xb7uGpkqtQotZNpCrdvGEwc6CScydNeV/picymR588okdquZPZ0BqakmhBAVrLPORWedi22tfnonozT5HHPWFMwYWd68OoVJKfZ11WCSpFRJFKKn+P9VSvnJFfj+S3I1lv6gAPsV89Ba892jg6QyWS6ORpZdCHQ5PHYL6xql7ohYHp/DuuhVlwCeeXuI6ViaU4Mhfuf+tfKFIiqOxWyqiGPocDBxbcprPG2syKIaojLVe+xlU4vvzauTvH55EgC/U/orQghRDWyW2/edjg8EefViblq3y2Ze1IVAUTjLTjhprX+QvxkEHlju/sTCKaVwWs2kMlmctvK+Ii5EMTmtZqZJY7eYKrIOmhCVwm4xXZsq5ZLvHVEhnLNKFVRa2QIhhBBLI8f+8rDshJNSqgH4HNA9e39a699c7r7F/D52Rwd9kzG6ymDIuhCl8viuNq5MRGmvccpUCSGKqMZt4+N3dDAdT7OuQVaEEZVhd2cNPqcVh9VMW8BZ6nCEEEKsgM0tPpxWMyal6JRz5ZIpxJS67wIvAs8BUh6+CEZDCZw285wFuT12C5tbbr/c9oxUJst4JEmj1z7nPFdRndJGlrFwkgavHavZRDxlMB1P0exzVE1yxmkzL/hzIKrTrY5vk9EUilyiRBRGo89BY4lqBq52c7XzYCxNysjS4C2P6Wvlaq0kSIUQoqjGwklsZhN+l5WMkWU0nKTeY8dmKd15Z3f94urCisIrRMLJpbX+wwLsR8zhSO8Uz58dw2Yx8eSBTgKupZ80/dNb/YyEEnTVufjInnnruosq8c9v9TMUTORXF2rha69fJZzIsKsjwAObGksdnhAF8c+H+xkOJuisdfHRvbnj29WJKN8+MoBC8eHdbXJ1S1S8G7/HR0MJnn6jDyOreXRbsyTehRBClMTpoRA/PjGM2aT4+B0dvHxxnCvjMRp9dp480FXq8EQJFSLd+AOl1PsKsB8xh7FwEshd1QzG0/NsfWtaa8Yjyev2KVaH2X/3RCpLOJG5dl+IaqC1Zjzfnsci77Tr8UgSrSGrNeNRae+iss31PT4ZS2FkNfDOsV4IIYRYaTPfQUZWMxlLMR5OATARSZHNf0+J1akQI5y+APzvSqkkkAYUoLXWcpmtAA6sqSNlZPE7rXQuY7l3pRSPbG3mzHCI7VKhf1V5ZGszp4ZCbGvz43dZuW9DA/1TMe5cU1fq0IQoCKUUj2xr5vTQ9ce3bW1+JiIplFJsa5Xjnqhsc32Pr2/0MtSRIJE22NtVU+IIhRDVrPuPnil1CKKM7e2qIZLI4LCaWd/oxW4xc7x/mo3NXlk9epUrxCp1srZsEVydiBKKZ9jS6uP9O1oLss+NzV42Nsufa7VZ3+RlfdM7f/e9XTUremJyZTxKJJlhc4sPs3zhiCLZ0ORlQ9P1xze7xcx7tjaXKKLbSxtZTg+FqHHZ6FjGxQSxutz4PW42qZJOjQ7G0lwaj7Cm3oPfdXOdSSGEEKuDy2bhvdtbrt3vqXfTs4j6SfGUwZnhEG0Bp9SJrDJLTjgppTZprc8opfbM9bzW+vDSw1rdRkIJvn1kAK0hGE9zz/r6UockxJIMTsf59pEBAEKJNHetlbYsBMCL58c41hfEpBSfurOLWilqLirQvxzpZzqW5nDvNL91T0+pwxFCCFGhnj05zOXxKDaLid+6pweH1VzqkESBLGeE078GngL+4xzPaeDBZex7VTOyGp2f6prJZksbjBDLYMyas23I/G0hrjHyh3aNls+GqFgzbVfqcwghhFiOzKzvEy1fKVVlyQknrfVT+d8PFC4cAdAacPLYjhaC8TQ72wOlDkeIJeuodfHe7c1EEhl2dgRKHY4QZePe9fX4nVZq3TZZzl5UrA/tbuP8SIR1jZ5ShyKEEKKCPbK1iRMDIdprnDhtMrqpmiy7hpNS6veAr2mtp/P3a4BPaK3/arn7Xs1urEUiRKXa1CzrBwhxI4fVzP6e2lKHIcSy1Hvs1HskYSqEEGJ5vA4rd66VBY2qkakA+/jcTLIJQGs9BXyuAPsVQgghhBBCCCGEEBWoEAknk1Lq2tJTSikzINVPhRBCCCGEEEIIIVapQiScngW+qZR6SCn1IPAN4McF2K+4Ba01p4dCnB8JlzoUIcQCTUVTHOmdIpRIlzoUIapGKJHmSO8UU9FUqUMRq0jvRIzj/dNkDFnYRQghKs2F0QgnB4NoqU6+IpZdwwn4Q3Kr1f0OoICfAP+tAPsVt/D2QJCfnR4F4P07YL3UexKirGmt+ae3+okkM5wYCPKpO7tLHZIQVeG7RwcZDydx2yf53L1rmDXgWoiiGAsn+Zcj/WgNU7E0929oKHVIQgghFujKeJTvHxsEIJHOsrerpsQRVb9CJJycwN9orf8rXJtSZwdiBdi3mENm9lLzkpkVoiLMfG4NWT5ciIIx8iNMZKCJWClZ/c6S3UZWGp4QQlSS686jpU++IgqRcPoZ8DAQyd93khvldFcB9l2WMkYWDVjNhZiRuHi72gMowGIysVFGN4klKHUbXm2UUnx0TxsXx6JsbJbPbDlIG9nccVQ+AxXtg7vaODscZm2DW0Y35SXSBg6rLCldLE0+B+/f0cJULM3ODn+pwxFCiLKRzBhYTSZMpvL9Pl7X6OHdW5pIZrLs6giUOpxVoRAJJ4fWeibZhNY6opRyFWC/ZWkikuSbb/aT1ZoP7W6jLeBc8RhMJsXuThn+J5Zmdhv+8O42WkvQhlejRp+DRp+j1GEIYGA6zrcP92M2mfjYvnbqZFn3ilXrtskyyrP8+MQwp4dCbGr28t7tLaUOp2pJKQMhhLje6aEQz54cxu+08on9nWV94WNbm1wsWEmFuLQbVUrtmbmjlNoLxOd7kVLqL5RSLyqlvnLD43+ilHo1//NQ/rHPKKXOKqWeV0r9eQFiXrK+qTiJtEEqk+XqeLSUoQixJLPb8JUJacNi9bk6HiVtaBJpg/6peb+uhKgYF0bD+d+RebYUQgghCufiWAStYTqWZiycLHU4oowUYoTT7wPfUkoN5u+3AL92uxfkE1RurfW9Sqn/opS6Q2v9Rv7pv9Naf1EpFQC+R27KHsCXtdYlL0a+ocnDuZEwGUOztVWyo6LyXNeGW6QNi9Vna6ufKxMxLGbF+iZPqcMRomDuXFvHsb4gO9rl2C6EEGLl7O6sYSKSotZtk9kT4jrLTjhprd9QSm0CNpJbpe6M1nq+db/vBJ7L334OOAi8kd/f5fzjSWB2Ja/fV0p9Gvii1vpnlIjLZuFj+zqK/j6pTBabRWqLiMJKZbIr1oaFKAdGVqO1vq5Wk99l5YkDnSWMSojCSGWyWM3qWv2qvV217O2qLXFUQgghVpu2gJNfv6t70a/TWpM2tJz3VrFCjHCCXLJpC+AAdiul0Fr/3W22DwAX87eDwNY5tvlT4K/zt78D/B1QB/xEKbVPa23c+AKl1FPAUwCdneV7MpFIG1ydiNEacOB1WG96/rtHB7g0FmV3Z4B3bWy87b6O9U0zHkmyv6d2zn0JMeM7Rwb42elhmv1OfuueNTT7pZ6QqF7xlMFzp0d45eI4NrOJX9vfwRYZ0SeqRCSZ4elDvRzrm2Zdk4fffdc6WQRCCCFESU3HUrxxZYrWgGNBM4EyRpZvvdXPcDDBfRsa2NtVXTWKs1nNxbEIAZeNBu/qrRe67N6JUupPgL/M/zwA/DnwwXleNg348rd9+fuz9/lhoE5r/XUArfW01jqrtR4DzgFNc+1Ua/1VrfU+rfW+hoaGpf2DVsD3jg7yw7eH+Mc3+tD6+uUYM0aWS2O5ujrnR25fg2E0nODnZ0Y53h/kl+fGixavqA4/PzPKxbEoPzoxzN+8eInhYKLUIQlRNK9dnuCVC+O8cXmSo33T/M9XrpLKyBLmojq8dH6M58+Mcbh3iufPjPHqxYlShySEEGKV+8XZUU4MBPnJyRGmY6l5tw8lMtfOR86PhIsd3op76cI4Pzg+xNOHegnG5psAVr0KcTnsV4CHgGGt9W8AO4H5Univ5l8D8DDw2swTSqkdwO/lf2Ye8+V/O4H1wFgB4l4xo6EEX3+9lx++PYSR1URTGSA30il7fb4Ji9nEHd21eB0W9vfcfli8w2rGas4No/c6CjVYTVSrXR1+TErhd1qxmBSxfDsspLeuTvH3r17hxECw4PsWYjF8Dgs1bhtWswm33UKNy4px4wF3hRhZzQ/fHuLrr/cyGpZEr1g+r8NKjceK1WyiwWvHUqQlqJ8/O8rfv3aVK7JIihBCiHl47bnZNnariVjK4B/f6OW7RwduecGvxmVlS6sPn9PKvu7qGt0EXDvXymQ1ycxNk7NWjUJkKeJa66xSKpNPDI0Ca273Aq31YaVUQin1InBMa31IKfWXWuvPA18mN4LpWaVUUGv9OPAHSqlHySXIvrSAGlFl5XDvFCOhBCOhBFtafLxvewsnB4Osa/BinqOTeM/6eu5ZXz/vfn0OK08e6GIqlqKn3l2M0EUV+Y27e7h7XT0XRiM0+52saShssWStNS+eH0NrePH8uCw5Kkpqb1ct9R47H97dRt9UjM5aN05baZbo7ZuMcXY4d+Xu8NUpHt0my9WL5blrbR3NPjtXJmLYLWb2dRe+btN0LMWR3mkAXrs0Qbf0M4QQQtzGA5sa6WlwU++2c7hvisHp3EW2i2MRNrf4btpeKcUjW5tXOswVc+/6BhxWM/UeO42+1VvKpBAJpzfzK8r9DfAWEAEOzfcirfUXbrj/+fzvR+bY9ovAFwsQa0l01ro5MxzGbbPQ4LXjtltoKlCjq3HbqHHbCrIvUd2UUmxs9rGx+eYDfqH2313n5vJ4lJ56V1HeQ4jF6KrLnSCvb/KWNI4Grx2P3UI0laGzVk7axfIppVjb6GVtY/Hatsduod5rZzyclGSTEEKIeZlNirX5C9pdtS6O9wWxWUy0rNK6sW67Zd56zKvBkhNOSqn/BHxda/27+Yf+q1Lqx4BPa328INFViS2tPrrrXVjNJinqKara47taiSQzeOwyxVOIGW67hc/c3U3ayK0SKUQlsJhNPLG/k3jakGO6EEKIRVnT4OGp+9ZgNilZgW6VW85f/zzwH5VSV5RSf6aU2qW1viLJprm5bJY5k03ZrOa1S7nithlDCtqKyqaUwm4x8/KFCQ5dnrypKL4Qq1Uyk+X1y5NS30xUjLFwkufPjjIakrpjQgghFs9pM1dMsunt/iC/ODtKJFn4Grer3ZIvWWmtvwJ8RSnVBXwc+B9KKQfwDeBprfW5AsVYtdJGlu8dHeR4/zQBlw271cTersLXYRBioTJGlhODIfxO65Lrgh3uneKNK5MA+JwWNhVpCp8QxRJJZjg7HKKjxlWwOfe/PDd2rY5To89Oo3d1Di8XKysYT3NhNEx3nZs6z+KWZP7xyWHGw0lODob4V/evrZiTBiGEEOVnLJykdzLKhiYvXoe11OFcZySU4LnTIwAkUgbv3S61Ngtp2b0HrfVVrfWfaa13A08AHwZOLzuyVeCl8+Mc75/mzHCYaDIjUy1Eyb16aYJfnBnlO0cGri1TuljuWe3YLW1aVKAfHh/il+fG+dZb/aQLNPLUnZ+SZDHlRgEKsRK+d3SAX54b55/e6l/0iFOPPddOnVbznAucCCGEEAuRMbJ8660+fnlunGeOD5U6nJs4LOZrq726ZQp5wS37f1QpZQUeJTfK6SHgBSq4wPdK0mgCLhtbW308tr1lzur9Qqyk2avGa5Y2HW57ux+vw4LNYqI14CxQZEKsnNltv1CzQu9dV09bwEHAZcPvLK8re6J66Rt+L8b7trdwdSJGs98hCSchhBDLMtOfKsdiG36XlU8c6GQ6lmJNfWFX8RbLKxr+buATwGPkVqV7GnhKax0tUGxV7551DfidVmpctuuWqB8LJ5mMpljX6JFOnliUcCJN/1Scnno3DuviR1HctbYOj92C32mhxb/0ZJGsaCSKLZ4yuDIRpb3GWfCh2e/b3sLpoTCdta6CTSMymRTririimKhOwViawWCcNQ3uJY2M++DOVs6NROipd6PU4voTdouZDSVe4VEIIURhDEzHSaSNa6vIrSSL2cSv7G3n6kSMTS3l+b1S77FTv8ip52JhljPC6X8Hvg78L1rryQLFUzFCiTQ/Pz2Kw2rm4c2NWJaw+pzN8k7NpuFgAp/TQtrQPH2ol0xWs6szwAOylKJYIK01//hGH+FEhtaAg1+7o/O224+Fk7xwbox6j437NzSglMJqNrG3q+baNuORJFazSUZkiLLznaO5aZ9+p5XfvKdnWfu6MBrm8NVpNjZ72dkRwOuwsr8nd2xOG1nGI0nqPXZZZVSsqIyR5ek3eomlDLrrXXx4d/u8r9Fa88vz44yGEty/oYFGn4P9PbWMhZOEE+myq5shhBCi+PqnYnzrzX4A3rWxgd2dNfO84maT0RRmpfC7lvY9cmU8ytXJGC1+Bz75LlpVllM0/IFCBlJpjvROc3k8N5iru961rMLIL18Y59DlSdx2M+/b3kImP68pnjIKEqtYHbSGRDrXZmILaDuvXZqgbzJG32SMDU3em6a/nR4K8eMTw1hMil/b3yFFjkVZmWnj8bSB1nrRozdme/7sGOFEhsFgnK2tvusuIHznyAD9U3Haapx8bF/HsuMWYqEMrUlmcjXEFnJMBxgJJTl8dQrI1eR7fFcbx/un+dnpUWwWE0/s76TGbStazEIIIcrPzPkBLO388uJYhO8fG0Sh+JV97bQtsmRGKJHmlYsTALx4fpwnDtz+orioLlIVa4la/Q6OKLCaTTQsc/jdaDhXnDmaNLBbzDyytZmxSJJ9XYvPPi/Wck/URPkwmRSP72rj/GiYbW3+ebdvDTi5MBrBbTcTmONqxWg4CUAmq5mMpiThJMrKY9ubOT0cZn2jZ9nHsNaAk7PDYZp8jptGq858Dsbyv4VYKXaLmQ/saOHKZIyd7YEFvcbvtOKxW4gkM9dOCEZDubabymSZiqUk4SSEEBWkEOdqaxs83LehgWTaYF/34ldEHwsn0TpX43Iiklx0wsllNVPrtjEZTdEakPOJ1UYSTku0vsnLb/gcWM2KaNLgxECQ9U2eJdVYuGddAyY1TrPPQYPXToN3ZeaPjoQSfPvIABaT4lf3dix5iKQoH4PTcd7uDxFLGbx/R+ttt93bVcOaejdOm3nOek8NHhteh4WeejfrpfaMKCPH+6f5xZkx2muctKxffmH6R7c2s7+nlsAcU0ffs6WJk4Mhtrb6SBtZzg6HafDaafJJh0kU16nBEM+dHqHZ58DnWFh3zWkz86k7u4injGuJpf1raklkDMwmRTiRkal1QghRIZ4/O8rRvmm2t/l5aHPTkvejlLquZMZCXRgNo5RiV0eA6VgKs8m0pEWuLGYTn9jfSTiRpk7qJK06knBaBr/TSiJt8M03+0hlslwej/KBnbc/yZ9Lg9fO47vaihDh7V0YjVwbVnl1MsoOV2DFYxCFdXIwRFZrzo9ESKSNeQuH3+pK95XxKM+eHAGgzmOX4vWirJzKt/PeyRiheHrZIzZMJnXLQpHrm7yszxdO/umpEU4MBDGbFL9+V7fUNhNFdXoohJHVDEzHc6NMF5jkdFivv4jgc1h5/45W/vbly5wZCnOk18pn7l5e3TMhhBDFd3IwhNZwYiC0rITTUpwaDPHsyWEAHtvRwqPbWpa1P5vFJMmmVUoqoC6TkdVkjFzNpVS+1kKl2NDkxee0Uu+x0SOrilWF3Z0B7FYT29r8S1qlbkbaeKctV1q7FtVvZ0cAh9XMukbPiiZ9Zj4XWa0xsuW4sK+oJjs7/DhtZtY0uAvSSU/l22/KkGO6EEJUgpl+/Z6uwIq/d0rOBUSByAinZXLbLTy+q5X+qTg7Ouavm1NOGrx2fmuZqzuJ8rK7s2ZJK0/caF2jhwc3NZLMZNnTGVh+YEIU0OYW35KGdC/XuzY2EHBaafTZqZU6OKLI1jV6WVfA6cwf2tXG2ZEwG5tkirQQQlSCu9bWc9fa+pK89442P0Y2i1KKLSXoc4nqIQmnAuiud9MtI4REFVFKsbMjUOowhCgrLpuFu9aVpuMnxHI1+hwLnpYnhBBidTOZFHu7Fl9gXIgbyZQ6IYQQQgghhBBCCFFQknASQgghhBBCCCGEEAUlCacSGA4miCQzpQ5DiNvSWjM4HSeWkrYqVrdgPM1oKFHqMIRYtIyRZWA6LgVfhRBCrLiZlVaTGaPUoYgSkhpOK+yNK5O8dH4cu9XEpw524XXIstqiPL1wbowjvdO47WY+fWf3sla9E6JSTUSSfP31XjJZzcObm9jeXlmLQ4jV7fvHB7kyHqPRZ+fJA12lDkcIIcQq8szbQ1wcjVDnsfGpg10opUodkigBSTitsLFwEoBkOksokamohNNEJMmhy5O0BJzskoLSZenNK5OMR5LcuaYev2t5bWumrUaTBvGUIQknURYSaYMXz4/jtJq5a20dJlNxOy9TsTSZrAZgPJIs6nsJMSNjZHnpwjhGVnPP+nrslqUdf2eO4xORFNmsLvrnRYjF6v6jZ0odgqhAhWo3V770WEH2sxRaa167NEkwnuae9fV47NV3Wj6e/w6aiub6UlazfAetRiVr2UqpvwD2AYe11l+Y9fifAI/m7/5/tNY/U0p5ga8DtcBfa63/bsUDLpC71taR1Zpat422gLPU4SzKC+fGuDoR48xwmK5aFzWyLHhZGQ4mePH8OACZrOb9O1qXtb/7Nzbw2qVJ2gIO+VuLsvHGlUlODAQBaPDa2dhc3CXe19S72dNVQzSZ4Y4eWa1FrIzTQ2GO9E4D4LFbOLCmbkn7ec+WZo71T7Op2SfJJiGEKCO9kzFeuzQBgNmkePeWphJHVHgPb27iSN8U6xu9WM1SyWe1KknCSSm1B3Brre9VSv0XpdQdWus38k//ndb6i0qpAPA94GfA54BvAP8I/EIp9bTWOlWK2Jcr4LItOxFQKjUuG1cnYjhtZpw2Ge1Sbtx2MzaLiVQmS61r+QmiRq+DD+6szLYqqldNvm2blCKwzFF8C2EyKe7f0FD09xFitoDLilKgNctK+HfXu+mudxcwMiGEEIXgdVixmBSZrKbWXTkzXhajs85FZ52r1GGIEivVCKc7gefyt58DDgJvAGitL+cfTwJ61va/p7U2lFLHgI3A2ysXrgC4f0MD6xo9BFxWmV5VhrwOK5882EUonqa9prJGzwmxUNva/NR5bNjMJuo89lKHI0RRdNS6ePJAF1mtafI5Sh2OEEKIAqt12/jUnV1EU0bFzXoRYjFKlXAKABfzt4PA1jm2+VPgr2dtH5q1fc1cO1VKPQU8BdDZ2VmQQG+USBtMx9I0+ey3LHyWSBu8cnEcm3llaoysFJNJ0VErWepy5nda8Tuvv0oSSqRJZ7LUeexcHo9yZijEtja//C1FxWrxL6xjlswYTEXTNHrtmEyKqWiK1y9P0uJ3sFPq0Iky1+CdO6E6c0yH3BTT9hoX29qkmL0QQlSagMtGYJnd8cHpOMf6plnf5GFdY3HLDMwwspqxcJI6j02myol5lSrhNA348rd9+fvXKKU+DNRprb9+w/aJubafobX+KvBVgH379um5tlmOjJHlG4d6mY6l2d7m5+FbzLU9fHWKY325GiN1HhubW3xzbidEsY2GE/zjoT4yWc17tzfzs9OjpDJZeidj/Pb9a0sdnhBFY2Q1Tx/qYzKaYnOLj0e3NfPCuTEuj0c5PRSivcYpI6RExRkLJ3n6UG7VRLNJYWQ1p4fCdNS4lr1QhBBCiMrz4xPDBONpzo9G+N13ubGsQALoB8cHuTQWpdnv4BP7izPIQ1SPUqUkXwUeyt9+GHht5gml1A7g9/I/122vlDIDu4CzKxPm9RKZLNOxNAAj4cQtt5vp9CnFTaNNhFhJk9HUtRW2RkNJfPn2uBK1b4QopbSRZSqWK/U3mj9ezxyb7VaT1KETFWn2Md3Qud9Omxm7Va4wCyHEajTTp/c6LJhXaFbNSCjXrxoLJ8lmCz7GQ1SZkoxw0lofVkollFIvAse01oeUUn+ptf488GWgCXhWKRXUWj8O/Ddyq9R9Hviq1roka1N77Bbu39jA1Yko+3tuvWLM1lY/Na7cEMNbDYkXYiWsa/Cwo91PIp1lb1cN+3tqGQomaA1ITRBR3RxWMw9sbOTSeIR9XbnV5e5f38CaejcBlw2XrfqWHxbVb12jh50dfmIpg/vWNzAZTVHnsUldRSGEWKU+sLOV/qk4zT7HLcu9FNpDm5s4LiugigUqWY9ba/2FG+5/Pv/7kTm2DQHvX6HQbmtPZw17OucsIXWdVin+JsqAxWzioc3XT/3skRWLxCqxsyNwXa0mk0nRVSftX1Qus0nx4KZ3juk+GUUthBCrmtVsWvG+/doGD2sbPCv6nqJyyRhsIYQQQgghhBBCCFFQknCax1AwzreP9HPo8mSpQxGiYC6ORfiXw/2cHAyWOhQhSiaZMfjxiSF+9PYQibRR6nCEWJIzwyH+5XA/50fCpQ5FCCFElXvr6hT/crifgel4qUMRFUKKWMzjxXPjDEzHuTIeY0OTh4DLdt3zY+EkPz8zgt9p5d1bmlesWJsQt/PLc2P0T8W5Z109nXU3r7f6s9MjRJMGfZNxNsv8a1HhEmmDZ08OkzY079nahM+xsGlGJwZCnB7KnaQ3eO3s664tZphCLEnvRIyXLozTXuPkvg0NNz3/3KkR0oZmOJRgfdPKLIkthBCivM3uGz2ytQnvAvtGtxNOpPnlubH8/rM8cUBWqBPzkxFO82jy54orex2WOYvMvnV1isHpBKeHwvROxlY6PCFuMhVN8dbVKUZCCV69ND7nNk2+XLtu9Nkl2SQq3rmRMJfGovRNxni7f+Gj9hq9dkxKYVKKRq8U0hfl6dVL44yEErx1dYqpaOqm5xvzx/Nmn7RhIYQQOUvtG92O02q+tipek08WxhILIyOcgIyR5fRQmBq3lfaa60eD3Le+ns3NXnxOKzbLzfm5zloXZ4ZDuGxm6j22m56vRom0QSievtbJFeUlkc4QS6VJpDUH18y9muL7d7QyHklS614dbVZUl3AiTdrQ19pvjcvGRDSJ22aho+bmEX230lHr4jN3dQPgd0nxZVFepqIpLGaFz2HlcGiaDU0ePI6bu20f2d3GRDRFnRzPhRCiYoyFk3jsFpw2M32TMYLxNJuavVjMhRkP0uJ3YrOYMLL6pvPbpbKYTTxxoJNgLC0rsYsFk4QT8MvzYxzrC2JSik/d2XXdSbhS6raJlS2tPjrrXNjMpjkTUjcaCyc5NRRiXaOHtgpcyS6RNviH164STmS4o7uWe9bXlzokMcuxvmm+caiXqxNRNrX4aPDOfQJiUjA4HefyeJS9XTVYC/TlJkSxjUeSPH2ol7SheXRbM5tbfLxxZRK/04rVbKLJv7AO0FQ0xdsDQbrr3HNOOxWilM6NhPnh20PorCaV1dS4rATybXy2M8MhxsMp9nbVFOwkRQghRHG9fmmCVy5O4LKZed/2Zv758ABa5/o479rYuKB9pDJZ3rw6icduYUd74KbnG7x2fuueHrJazzlLZ6nsFjONPnPB9ieqnyScgIyhAdBojKxe9Os99oX/Nz5zfJCpWJoTA0F+5/61FTedKZYyCCcyAAyHEiWORtxoOJQgqzVZDdmsJm3M3Z4vjEZ4/mxuDrYCDtxiJJQQ5WYikrrWrkdCCTa3+MhkNRaTCbNS6AUewn90YpiRUIJjfdM8df8a7BbpPInyMRJKoDWks5poMkONy8aN3ZPRcIIfvT0MQCSZ4dFtzSWIVAghxGKNhJNA7rwqGM9c67ss5jz00OVJ3riSW9TK57DSXe++aRuHVfo2ovQk4QTct6EBn9NKvcdW9OGBdqsZSGMzm1CVlWsCoNZt4661dQwG49y1VkY3lZsDPbXEkhk2NHm5o7uGTc1zF5CdfXJtly8jUUHWNXrY0e4nljLY21UDwKPbmjkxEKSjxrXgzpXDmhsNYrWYMFXiwVhUtT2dNQTjaRwWM2sb3AyFEmxr81+3jc1swmxSGFmN3Sqjm4QQolLctbaObFbT4LWzrc2PzWJiKppiZ0dgwfuYOe4rxYJm2QhRKpJwIpf9vVWtm0J7fFcrl8aidNS6UBV6kiOjYcpXwGXjw3va592us87FR/e0kzIM1jZ4ViAyIQrDbFI8tLnpusd8DuuiE+Dv297CxbEIrX6nTCkVZcdtt/D+Ha3X7vfMcZwOuGz82h0dTEZTbJDV6YQQomLUe+x8aHfbtftLOYbv66rB77TisplprcAyLWL1kITTCnPZLDddpRSiFKRujVjNHFYzW1vlWCwqW5PPcW3VUSGEEKuHUkouNoiKIAknIYQQQgghhBCrSvcfPbPsfVz50mMFiESI6iXzCIQQQgghhBBCCCFEQUnCSQghhBBCCCGEEEIUlCSchBBCCCGEEEIIIURBScJJCCGEEEIIIYQQQhSUJJyEEEIIIYQQQgghREFJwkkIIYQQQgghhBBCFJQknIQQQgghhBBCCCFEQUnCSVzn6kSUly+ME0qkSx2KWKTxSJKXzo8zEkqUOhQhiubCaJhXLowTTxmlDkWIopF2LoQQ1UGO52K1K1nCSSn1F0qpF5VSX7nh8d9USl1WSv3DrMc+o5Q6q5R6Xin15ysf7eoQS2X47tFBDl2e5CcnR0odjlik7x0d5I0rk/zL4QG01qUOR4iCm4ym+MHxIV6/PMkvzo6WOhwhikLauRBCVAc5ngtRooSTUmoP4NZa3wvYlFJ3zHr6e8C753jZl7XW79Ja/28rEuQqZFIKs0kBYDWrEkcjFstqyX2crWaFUvL3E9XHbFKY1MwxSgboiuok7VwIIaqDHM+FAEuJ3vdO4Ln87eeAg8AbAFrrcaWUZ47X/L5S6tPAF7XWP1uZMFcXh9XMx/Z1MBxMsL5prj+BKGcf3t3GpbEIXbXuUociRFH4nVY+tq+D8UiSjc3eUocjRFFIOxdCiOogx3MhSpdwCgAX87eDwNZ5tv8O8HdAHfATpdQ+rbVMhC2CBq+dBq+91GGIJfDYLexoD5Q6DCGKqtnvoNnvKHUYQhSVtHMhhKgOcjwXq12pEk7TgC9/25e/f0ta65nnx5RS54AmYPDG7ZRSTwFPAXR2ds68lp+fGWU0nOT+DQ20BpwFCF+I8hdPGTx7chgjq3lkWzMee6k+7kKsvGTG4NmTIyTTBu/Z2ozfaS11SEIsWTCW5tlTwzitZh7Z2ozNIlMzhBCiGqUyWZ49OUwibfCeLc34XdJ/EZWtVD2WV4GH8rcfBl673cZKKV/+txNYD4zNtZ3W+qta631a630NDQ0AjISSHO8PMhxM8PrliULFL0TZOz0c4vJ4lN7JGCcGgqUOR4gVdX4kwsXRCP1TcY73T5c6HCGW5UjfFANTcS6MRrg4Fil1OEIIIYrkwmiEC/n+y1Hpv4gqUJCEk1LKrJRqVUp1zvzcbnut9WEgoZR6EchqrQ8ppf4yv6/3A/8APKSU+uf8S/5AKfUq8DzwJa11eqGxBVxWfPkr251S20asIm0BJ1azwmJStNfIyD6xurT4HdgsJkxK0V7jKnU4QixLR60Lk1LYrSaafTI1QwghqlWz34Hdmuu/dEj/XVSBZc+xUUp9HvgTYATI5h/WwI7bvU5r/YUb7n8+//sHwA9ueO6LwBeXEp/DaubTd3aRSBt4HTIkUaweTT4Hn713DVqD02YudThCrKg6j53fuqeHrNa4bDKdVFS2tQ0ePntvD2aTwmGV47kQQlSrWreN37xb+i+iehSiFX8B2Ki1Ltv5alazSZaiFKuSnJiI1Uzav6gmbqnDJ4QQq4L0X0Q1KUQWpo/cSnNCCCGEEEIIIYQQQqC01svbgVL/HdgIPAMkZx7XWv9fywtteerr63V3d3cpQxCi6K5cuYK0c1HtpJ2L1UDauVgNpJ2L1UDauVgN3nrrLa21nncAUyHGZ/fmf2z5n7LQ3d3Nm2++WeowhCiqffv2STsXVU/auVgNpJ2L1UDauVgNpJ2L1UApdXgh2y074ZQv6I1Sypu7q2W9XiEKLG1kmY6lqffYUEot6rVGVjMZTVHjsv7/27vvMEmq6+D/39NhpifnmY2zs4G8LMvukrNAESUkIQmhgBK25FcSsq2fZVt6hWy/r5VsWZYs20ivlUEZECABAgFCsCxxMyxsDrM7s5Njz3Q4vz+qerd3dnLXTHX3nM/z9NPd1VXVp7pvV1fduvdcQpbLzOSBroFhCkIBS6Zp8p6q0t4/THkkTEHI9t/GmPzR2T9MJBy0gW2MyXNejFK3EvgRUO0+bwPer6rbMl23MQaSSeVnzxzgaO8QZy0o5zVnzZvS8vdubmb30X4WVRVx/brFMxSlMbNjW3M3D25roSAU4MYLGqkszpqGtcZ47tGXj7JxfxdVxWHee+ESu2hgjMkLmw928fCLrUTCQW68sJFyG0ncmLzlxZHLbcBfquoSVV0C/BXwHQ/Wa4wBhhNJjvY66dGauwanvPzh7uix+0xzthnjtyNueR6OJ2nrG/Y5GmNm1uEup7x3DsQYiCV8jsYYY7zR7O7borEEnf32X25MPvOiP0KJqj6SeqKqj4pIiQfrNcbgDI16xWl17GrtY11T9ZSXv+q0ejYd7OLM+eVT7o5nTLZZt6SanmiMssIwS2vtr8bkt8tOqeWp3e0sqSmxFgDGmLxx/tJqBobjVBaHWVxV7Hc4xpgZ5EWF024R+TxOtzqA9wJ7PFivMca1prGKNY1V01r2tHllnDavzOOIjPFHRXGY685d5HcYxsyKxdXFLK62kzFjTH6pLingbWvsv9yYucCLLnUfAuqAXwN3uo8/6MF6jTHGGGOMMcYYY0wO8mKUuk7gkx7EYowxxhhjjDHGGGPywLQrnETk31T1FhG5BzgpE7GqvjmjyIwxxhhjjDHGGJNVmj57nyfr2fulaz1Zj8lembRwSuVs+poXgRhjjDHGGGOMMcaY/DDtCidVfc59uFpVv5H+moh8Cngsk8CMMcYYY4wxxhhjTG7yImn4B0aZdpMH6zXGGGOMMcYYY4wxOSiTHE43AO8BlorIb9JeKgPaMw3MGGOMMcYYY4wxxuSmTHI4PQkcBmqBf0mb3gtsziQoY4wxxhhjjDHGGJO7MsnhtA/YB1zkXTjGGGOMMcYYY4wxJtdlnMNJRC4UkWdEpE9EhkUkISI9XgRnjDHGGGOMMcYYY3KPF0nDvwXcALwCFAEfAb450UIicoGIPCkij4vI10e8dquIbBKRR0XkL91pZSJyj4g8ISLv9yBuY4wxxhhjjDHGGDMDvKhwQlV3AkFVTajq94CrJrHYPuBVqnoZUC8iZ494/a9U9UpV/Vf3+UeBO4DLgY+ISIEXsRtjjDHGGGOMMcYYb3lR4TTgVv5sFJGviMingZKJFlLVI6oadZ/GgcSIWb4sIg+JyGr3+UXAQ6qaADYBp3kQuzHGGGOMMcYYY4zxmBcVTu8DgsD/AvqBxcDbJ7uwiKwCalV1e9rkf1fVtcDHON49rxJI5YbqBqpGWdfNIvKsiDx79OjRqW6HMcYYY4wxxhhjjPHAtEepS3FHqwMYBL44lWVFpBonB9Q7R6yzw71/RURSk7uAciDq3neNEsttwG0A69at06nEYowxxhhjjDHGGGO8Me0KJxHZAoxZqaOqqyZYPgT8GPiMqh4Z8Vq5qvaISG1ajOuBq0Xk58BqYMd0YzfGGGOMMcYYY4wxMyeTFk5vzPC9rwfOw8nVBPC3wHtU9RPAV0VkJU6Xv8+6838XuB34BHCbqg5l+P7GGGOMMcYYY4wxZgZMu8IprSvddJe/A2fUuXTr3df+bJT5e8i8kssYY4wxxhhjjDHGzLCMcziJSC/Hu9YVAGGgX1XLM123McYYY4wxxhhjjMk9GY9Sp6plqlru3iI4I9R9K/PQjBd2HOlle3MPqpZDPd8d7Bxg44EuhuNJv0MxZsa09w3x/P5OeqIxv0MxZsapKtuau3m5pdfvUIwxxoxwtNc5JukfivsdijFZK+MWTiOp6l0i8tmJ5zQzbceRXn675TAAiaRy9qIKnyMyM6V7IMavnjtEUpWWniivPWue3yEZ4zlV5ZfPHWRgOMH25h7ee+ESv0MyZkZtOtjNIy+1AhA4R1hRX+pzRMYYYwBiiSS/eO4AQ7EkO1v6eOd5i/0OyZis5EWXurelPQ0A6xhn9Doze5JprZoS1sIpryVVUfdnZ63ZTL5ShaRbvJNWzs0ckEgeL+dW5o0xJruoHZNkrOmz93mynr1futaT9RjvedHC6U1pj+PAXuAtHqzXZOj0eWUkkko8qaxaaK2b8llVSQFvWb2Qo71DrLKWbCZPBQLC29csZHdbP6fPK/M7HGNm3LmLKwkIhIMBTm2wMm+MMdkiHAzwtjUL2dc+wBnzLXWxMWPJuMJJVT/oRSDGeyLCSqtomjOW1pawtLbE7zCMmVH15RHqyyN+h2HMrAgEhHMbq/wOwxhjzCjmVxQxv6LI7zCMyWoZJw0XkWUico+IHBWRVhG5W0SWeRGcMcYYY4wxxhhjjMk9GVc4AbcDPwfmAwuAXwB3eLBeY4wxxhhjjDHGGJODvKhwElX9karG3duPsaThxhhjjDHGGGOMMXOWF0nDHxGRzwI/xaloehdwn4hUA6hqhwfvYYwxxhhjjDHGGGNyhBcVTu9y7/9sxPQP4VRAWT4nY4wxxhhjjDHGmDnEi1HqlnoRiDHGGGOMMcYYY4zJDxlXOIlIGPgYcLk76VHgv1U1lum6jTHGGGOMMcYYY0zu8aJL3X8CYeDb7vP3udM+4sG6jTHGGGOMMcYYY0yO8aLC6TxVPSft+R9EZJMH6zXGGGOMMcYYY4wxOSjgwToSIrI89URElgGJiRYSkQtE5EkReVxEvj7itS+IyHr3drU77SYR2SEij4rIVzyI2xhjjDHGGGOMMcbMAC9aOH0GeEREdgMCLAE+OInl9gGvUtWoiPxERM5W1S3uaz9U1S+KSCXwG+Bhd/pXVfW7HsRsjDHGGGOMMcYYY2aIF6PUPSwipwCn4VQ4vaSqQ5NY7kja0zhpraJUdY/7cAjQtPluEZH3A19U1YcxxhhjjDHGGGOMMVnHi1HqIsDHgUtxKoceF5H/UtXoJJdfBdSq6vZRXr4V+G/38V3AD4Ea4EERWaeqJ3TdE5GbgZsBGhsbp74xxhhjjDHGGGOMMSZjXuRw+iFwFvBN4FvAmcCPJrOgiFS7y3x4lNeuA2pU9XYAVe1S1aSqHgVeBhpGLqOqt6nqOlVdV1dXN93tMcYYY4wxxhhjjDEZ8CKH02kjRql7ZDKj1IlICPgx8JkR3etSrZ7+Arg2bVq5qvaISBFwCnDUg9iNMcYYY4wxxhhjjMe8aOH0gohcmHoiIhcAT0xiueuB84AvuyPPXSQi33Rf+ypOC6YHRORud9qnRWQ98CjwJVWNeRC7McYYY4wxxhhjjPGYFy2cLgDeLyL73eeNwIsisgVQVV012kKqegdwx4jJ693XXjvK/F8EvuhBvMYYY4wxxhhjjMkDTZ+9z+8Qjtn7pWsnnmkO8aLC6XUerMMYY4wxxhhjjDHG5ImMK5xUdZ8XgRiT7eKJJH1DcSqLC/wOJSvFEkkGhhJUFIf9DsUYTw0MxwEoLvDiGo0xuWs4nmRw2Pbzxpjc0D0Qo7gwSDjoRRYZY8x02NGzMZOQTCo/feYAR3uHWL24kqtOr/c7pKwSSyS5fcN+OvqHuWBpNRevqPU7JGM8cbBzgDufPwTAO9YtYn5Fkc8RGeOPoXiCnzy1n+7BGJesqOX8pdV+h2SMMWPasLudJ3e1U1EU5sYLGykMBf0OyZg5yap7fbD1UDc/XL+XDbvb/Q5l1qkqmw508czeDuKJpN/hTFo0nuBo7xAABzoHfI5m+tr7hrh9w37u3niI4fjkPv/D3YP86ZU22vuGxpynfyhOR/8wkNufj8k/O1t7+eH6vfzx5ckPbLr1UDdP7W5nOJ7kcHeUeFKJJ5XmrugMRmrM1ERjCe584SA/fXo/XQPDnqxzZ2svT+5sO9aqL133YIzuQWe8lgMdtp83ZjzJpHL/1iP86Kl9HOoa9DucrJZIKs/s7WDjgS5U1bP1Huh0PvfuwRg9gyfv04wxs8NaOPlg/a52+obiPNnXztolVYTmUDPPna19/OGlVgBUyZkrpMUFIS5eXsPutn4uyJGYR7PpYBctPc5J8972fk5tKBt3/mRS+fXzTuXUztZebrpk6ajzVRYXcF5TNQc7B7h4ubVuMtlj/e4O2vuGae8bZs2SKkoLx//bO9AxwO+3twBO96F1TVUc6hxEBM5aUD4bIRszKTtb+9jb5lT8bDnUzWWn1GW0vs7+Ye7dfBhV6ByIce2q+Se8XldayJolVRzuGuSi5TUZvZcx+e5IT5QXD/cA8OzeDhauXuhzRNnr+f2d/OmVNgCKwkFOmzf+selkXbS8hngiyfzKIurKCj1ZpzFm6qZd4SQivcCY1dCqakfmY1haW8KWQ90sqSmeU5VNwAl9qAtC/mz74e5BXmnp4/R5ZdSXRya93AXLarhgWW4fZC+pKWHroR4KQwEaJrHtIlAQDDAcT1IwQVPkS0+xiqaURFJ5fn8nAqxprCIQEL9DmrOW15bQ1jvE/IoIxeGJm9OHgwFEnArxcDBAcUGIt56b3ycK25t76BwYZu2SKiKT+IxMdlhQWUQkHCSeSLKkuiTj9QWDQkCEhOqo/88iwhWnZlaplQu6B2JsOthFY3UxTbWZf65mbqouKaCyOEz3YIylVo4At5fDwW6isQRrl1QdOycomKFzg4WVRbz7/EbP1meMmZ5pVzipahmAiPwDcAT4ESDAjYA3VdN56pozG7hoeQ3FBXPvwL6ptoS3nruQ4XiSUxtKfYnh7o3NDA4neLmll49ctsyXGPyyvK6Umy9fRjAgk0qgKCJcv24R+zsGWF7nz/eVi7Yc6j52ta4wFOTsRRU+RzR3XbyilnMWV1IUDk6q4m9eRYS3r1lEbzTO6R5dZc1mLT1RHth2BHC6xr7mrHk+R2Qmq7qkgA9fupSkqicVheWRMO9ct5i2viHPWhjkoge2HeFQ1yAbD3Rx8+XLrBLWTEskHOT9FzUxHE9SNAeP90ez62gfj6T1cki1lFy1qIKiAiext1XOGZN/vOhS91pVvSDt+X+KyAbgKx6sO2+VTNCtI5/5/WdSGAowOJyg0KcWVn6b6sFzZXGBjcw3RZHw8bJVGJ6b5SybTHV/u7i6eIYiyT7hYICACElVCu3EOud43VJ4XkWEeRWTb/mbj1L77FBQEGucajIQDIhVNqVJT9qdfmwkIhOmeDDG5C4vaj0SInIj8FOcLnY3AAkP1mvMjHjH2kXsax+wpvJmxpw+r/zYibzfFazGjKe6pIB3nbeYzoFhO+A3Bnjdynm80tLHgsoiG9XKGA8tri7mHWsXEY0lWFFvreaNmSu8qHB6D/AN96bAE+40Mwu6BoZ5ZEcrpYVhXnV6PUHLFTOhskiYlQuti9OBjgHW725nSXVxzuemykbWBTE7xRJJHn6xhaF4kledXk9ZJOx3SL6zVi357Zm9Hexp6+fCpTU01syd1nvTVRgK2jGCMZMUjSV46EVnoI1rzmiYsBX9XGpBbIxxZFzhpKp7gbdkHoqZjmf3dh4bpWZpbYldMTCT9qedbRzpjnKoc5DT55dTUWQn3ib/vdzSy4uHewGoLunKeGQvY7JZ31D8WD65P8aP8t6aJT5HZIzJJ9uau3mlpQ+AeeUR1jXl7kjOxpiZkXHnfxE5VUQeFpGt7vNVIvK5zEMzkzG/0rkqXRgOUFeaPUN+xhNJkskxBzE0s2A4nhz39QWVRQBUFYfnZAJ7kx9iU9zX1JdFCLu5WeZXFM1gZMZ4b6r/rZFQgOoSJwffwkor78YYbzWURwgGhGBAJjX6saoSS4x/fGqMyS9edKn7DvAZ4L8BVHWziNwO/JMH6zYTOGtBBQvdPAPZkphw99E+7tt8mKKCIO8+v5HSOZwg3Q+qyl0bD7G3bYDzl1ZzyYraUee74tQ6Vi4opywSntSIdcZkm13uvqakMMS7z1s8qeTgdWWFfPCSpcSTaq36TE450DHA3RsPURAK8K51jVQUT1x+Q8EAN5zfSE80Rk2JDf5gjPHWoqpiPnhJE8CEXdSH40l+9uwB2vuGuOaMBuu6aswc4cVZZrGqPj1iWtyD9ZpJqiwuyJrKJoBdR/uJJ5XeaJwj3YMz8h6q1npqLEPx5LFuljuO9I47b01poeejHBmTian8tne29pFIKj2DMQ53Rye9XElhyCqbjK+m8x+2u62fWELpH0pwsGtg0ssVhALUlhYiNuSamWV2rJb7JvMdlkXCk8qH2NE/TFvvEKrwSuv4x6fGmPzhRdOTNhFZjpMwHBF5B3DYg/WaHLVqUQXNXYOUFoZmJDngxgNdPLbjKI01RbzlnIUELFH6CSLhIOc2VrKztY/zl1pfepMbVJX7thxmV2s/Fy2vmVTZPWdRJYe7BikvCtNoiUhNDphOOU85a0E5+9v7KQwHWVZr+RpN9sqknJvsoKr8ZlMze9sGuPSUWtYuqcp4nfVlhZzaUEZLT5RzF2e+PmNMbvCiwukvgNuA00XkELAHuHGihUTkAuDrQAJ4VlU/nfbaAuDHQAT436r6kIiUAbcD1cB/q+oPPYg9K8USSfa1D9BQXpiTIyg1lEf4wMVNM7b+bc3dJFXZ2zZAbzQ+qW4Fc0lPNMaiqmIuXVFLyLrKmRwxFE8eSzy6rbl7Uico9WWFXH5qHVXFBdZSz+SE6ZTzlNrSQt6+dhHNXVECVtxNFsuknJvs0D+cYPfRfsD5Dr2ocAoEhGtXzc94PSktPVGG40kb+c6YLOfFIYuq6jVAHXC6ql46yfXuA16lqpcB9SJydtprnwU+B7zGvQf4KHAHcDnwERHJ22QE9289wj2bmvnp0weIW2K9k5yzqJLCcIBTG8ooL7L8UOmG40nu2LCfezY18+D2Fr/DMWbSIuEgZy0opzAcYPXiykkt86edbdy9sZmfbNhHTzQ2swEa44HplPMUVeVnzxzgnk3N/GZj88wEaIwHMinnJjuUFAQ5Y35Z1n6Hh7oGuePp/fzyuYNsPdTtdzjGmHF4cbb+K2CNqvanTfslsHa8hVT1SNrTOE5Lp5RVwKdUVUWk123ddBHwF6qaEJFNwGnAFg/izzqpE6eB4QTxpBLKnvRMWWHlwgpLNDiGeDLJYMz5KfUM2gm4yS2vOWser5nC/L1RJ11gLKFEhxOU52CLUDP3TLWcpyQV+twynyr7xmSr6ZZzkx1EhNet9K41ktf6onFS6aXseNeY7DbtCicROR04C6gQkbelvVSO0xVusutZBdSq6va0yUE9nqWuG6gCKoGeEdPy0mvOnMcL+ztZWltCJGy1TWbyigtCvH7lfPa197PGg+bPxmSzy06tpTAUoK6skPpJDMdsTC4LBoQ3nrOAV1p6OXuRXXQxxsxdp9SXcuGyGobiCdY2Zf/xbtNn7/NkPXu/dK0n6zFmNmXSwuk04I04FUFvSpvei9P9bUIiUg18C3jniJfSWzuVA13urRyIpk0bub6bgZsBGhsbJxNCVqorK+Q1Z80b9bXeaIw/vdJGeVGYi5fX2Kgz5iSlkRBD8SStPUPUlhb6HY4xnksmlT/tbGNgOMHlp9ZSXGBda03+evFwDztb+zi3sZKltSUsrS3xOyRjTB7qHozxxM42qooLuGh5jd/hjCsQkKyP0RjjmPZRuqreDdwtIhep6vqpLi8iIZzE4J8Z0b0OYLOIXARsBspVtUdE1gNXi8jPgdXAjlFiug0ngTnr1q3Ly7FYN+zu4CV3qPuFlUU0zcCBZ2f/MI+9fJSqkgIuP6XWKrVyzMMvttDeN8yuo30sq5vZVnKqyuOvtNHRP8zlp9ZRXZK3qdVMFtl5tI/n9nUCUFQQ5IpT63yL5VDXIBt2t9NYXcy6JkuMa7w1HE/y4LYWkqq09w1x0yVLfY2nuWuQp3a3s7i6mPOsvBuTV9bvamOHe46xuLqIRVUnJuNO/d8tqSlm7RL7/RtjJseLy8IviMhf4HSvO9anQVU/NMFy1wPnAV92KzT+FniPqn4C+ArwQ6AI+II7/3dxRqn7BHCbqg55EHvOqSl1TujDQaGiaGbylTy1u509bf3saetnWW3JjIz+cLh7kEdeOkptaQHXnNFAIGCVWl6pLimgvW+YiqIwBTM8St3BzsFjJ/4FoQBvOHs+j+xopblrkMtPqbORQ8yMqCwOEwwIiaRSM8uVnAPDce7f6lwjee1Z8/jjy0c50h1lX/sAp84rszxSxlOhgFBRFKJzIEaNTy1WE0nlwW1H6B6M0RuN0TeUcMp7Q9mMHYcYY2afs4/ppSAUGHWU7Md2HKWlJ3rs95+aZzie5IFtRxgcTvDqMxuosouPxpg0XlQ4/Qh4CXgt8A/AjcCLEy2kqnfgjDqXbr372kHgVSPm78HpwjenndtYxbyKCMUFoRk70JtXEeGlI71EwkEqi2fmPZ7e00FLT5SWnihnLaxgYWXRjLzPXPT6lfM5Z9EgdWWFM16RV1kcJhIOEo0laCiP0N43xMb9XQCs39VuFU5mRtSXRXj/RUsYiidpmOXcTdube9jXPgA4XZ3mlUc40h2loihMkeXcMx4LBIR3n9/I0d4h5lf4k6dsf8fAsZbViWSSYCBARVGY4gIr78bkk/OaqllYWURpJDTqxZN5FYW09ESPHful7GnrZ2drHwAbD3Rx1en1sxazMSb7eVHhtEJVrxeRt6jqD0TkduABD9ZrxjC/YmYrZ85trGJxdTHFBcEZy43SVFPC7qP9lBeFZ72FQr4LBmTWKnrKImE+cPESBoYT1JYWEkskqSl1WlgtqbHKJjNzKov92W8srCoiHJRjj+eVRzhrQTkVxWHCM9yi0MxNkXDQ18r7urJCSgqDDAwneP3Z86krK6Q8YuXdmHy0YJwLwFedVs/KBRUn/d81lBcSCQcZjiftQqMx5iRe1CakxqLsEpGVwBGgyYP1zgkDw3F+u+UIyaTyurPnZU13jJlONn3O4kqW15dSGArYQWuOKy4IHauYDAcDvOHsedy76TCHugaJxhI20qLJK/MrivjIZcsAjpXtyYyQd6hrkD+81EpdaQGvOXOedSM2We+lIz1s2N3BKfWl3HTxUmKJJCWFlqDfmJEe2dHKgY4BLllRy/K6Ur/DmTEiMur/XWVxAR+6tIlEUm0QD2PMSbw4079NRKqAzwO/Abbj5GAyk7DjSC8HOgY41DXItkM9foczq0oLQzlV2dTZP0w0lph4xhwzHE/S3uddSrQXD/fSORBjX/vAsSbWxvitw8PfbyQcnHJF6rN7O2jrHeLFw70c6Yl6EocxE4nGEnT2D09r2fW72unoH2bDng6SqlbZZMwougdibNzfRXvfMBt2d/gdjm8KQzPXKyLdUDzh6TGrMWbmZbxnUNXvug8fA5Zlur65ZmFVEQWhAKpKYw52Qdre3ENLT5R1TVWjJhjMFy/s7+TRHUcpLghy44VLKM2TA+9YIsntG/bRORBj7ZIqLk8b7SueSPL03g4E4fyl1QQn2SJjSXUJG/d3EQoGxm2abcxs2bC7nSd3tVMWCfHeC5eMW1m0s7WP/R39rF5c5emoi8tqS9nT1k9FUdhGczSzon8ozo+f2sfAcIIrTqtjTWPVCa8f7Bzg5ZZezphfPmpX/WV1pTy/r5OFVUUUhnLn4pAxs6mkMEh9eSGtPUMsq/N+5Gi/dA/EeH5/JwsqizhtXpnf4QBOZdOPn9pPz2CMC5ZWc/GKWr9DMsZMQsZnzSJSCLwdpxvdsfWp6j9kuu65oL4swkcvW4aiFIb87XrUNxRnKJaY9Eg47X1DPLDtyLFl33TOgpkMz1fNXU6LhIFh52pxvlQ4DQwn6BxwesU2dw2e8NrmQ93HrtaVFAZZtahyUutsrCnmo5cvIxiQMVuwqSqtvUNUFod9L/cm/zV3O2W7NxqnZzA2ZoXT4HCC+zYfJqlKa88Q7z6/0bMYzl5UwYr6UgpCgXErb1t7o5RHwtYV1WSsc2CYgWGnVV9z1+BJFU6/2dTMUCzJrtZ+Pnr5ydcLrzi1jnVLqigKB3FHE/ZEbzRGLKFW8WryQigY4IbzGonGEzndnWxwOEHvUIz6MqfL3B92tLC3bYBNB7uYVxHJihEp+4cS9Aw6x6wHRxyzGmOylxd7xruBbuA5wNo4TkNBFlw57Owf5van9zMcT/LqMxtYubBiwmUKQgHCQSGWUEoK8/vk6IJl1URjCapLC1hUlT+tdiqKwly0vIb9HQNcvLzmhNfSK9WmehA10cnyg9tb2N7cQ01pATdesGTSraeMmY6Ll9cSTxxlXkVk3HxLwYAQCQcYGE5QPAOVykUTjOr15M42NuzpoLQwxPsuGr8lljETWVhZxOrGStr7hrlwWc1Jr5cUhBiKDVM8zv+3193o2vqG+OnT+4kllNefPY/T55V7un5j/BAISE5XNg0Mx/nReqc15MXLa7hgWc0JuTkLsiT9RXVJARcsreZQ1yCXWOsmY3KGF3vHRar6Og/WYyapJxrj/q1HCAWE16+cP+FJzGR0DAwzHE8C0NITnVSFU1kkzA3nN9LRP8yyPE6SCE4S9bevXeR3GDPiwmU1o56MnNpQRmRNEBE8H3Wkxc1h0943TCyRJBjI/xPrZ/d28OLhHs5trJrU78t4p6E8wvXrFk84X0EowLvPb+RId5SltbPfNSKV26lvKE5vNG4VTpOw9VA3L+zv5Iz55axrqvY7nKwiIlx12tjDk79j7SIOdg7SOIujSjn7fAXgSHc0JyucEknlgW1H6BwY5pozGmiYxKABxoxmy8FuNh7o5MwF5axd4t/+q2cwfqw1ZOp/6OrT61laW0JdaaEn5xlesW50xuQeLyqcnhSRs1V1iwfrMpOw7VAPhzqdpqQ7WnpZvbgy43UurSlh9eJKeqKxKR2015QWTroLnsk9M5VX7KrT6nlmbwfL6krnxEm1qvKnnW2owp92tlmFUxarKAr71nXg0hW1iLQxr7yIujLbr07Gn3a2MTic4E8721jTWGWj/01BSWFo1nOzrKgv5eyFFQzGEqxdUjXxAlnoYOcAO470AvDcvk7ecPZ8nyMyueqJXc7+6/FXnP2Xl11Xp2JeRYTzl1ZztHfoWMuhUDDAqQ3ZkbvJGJPbvKhwuhS4SUT24HSpE0BVdZUH6zajWFxdxLN7hUBAWOhRUuZAQLjq9LGvhBrjpcXVxZ63mspmIkJTTQl72vppqsmfpKLGW/XlEa47Nz9bUs6UpbUlbG/uoammxCqbckAwIFxzZoPfYWSkrqyQskiIvqG47c9NRppqSnjxcA9La0t8q2xKsS5qxpiZ4kWF0+s9WIeZgkVVTlJmESzhsjE54i2rF9A/nKAki5qmG5PrXnNmA5esqLXflZk1xQUhPnBxE7FEMqfz9hj/vfasBi49xfZfxpj8Nu1/ShEpV9UeoNfDeMwkzYVuSF5o7hrkwW1HqCop4A1nzx9z1DTjnSPdUR7YdoTyohDXnr0gK5LiZwMRyZvRDc2JjvYOcf/Ww0TCQd50zgLbP88i+135p6Unyv1bj1AWCXHtqvlz6gJYOBiw4wmTsXzcf21v7uHJXW0sryu1nhPGGCCzFk63A2/EGZ1OcbrSpShw8hi7xnNP7+lgW3M35zZWeZLLKd9sOtBF50CMzoEYhzoHafIhEfBcs+lgFx39w3T0D3Ogc4Dls5BQfvPBLp7b5yQOHi0BujEzaeuhbtr6hgHY09bPGfNzLxHyZCWSyu+2Hqaj30mYvMCjbt0m92w+2H18X98xwIr67M330tE/zO+2HiYSCnLtqvlWKWxyXjSW4L7NhxmKJ3n9ynlUlRT4HRIAz+ztoDcaZ+OBLs5fWu35SJfGmNwz7cszqvpG936pqi5z71M3q2yaBarKk7va6BqIsX5Xu9/hZKXl9aUERKgoCttIMrNkeV0pwYBQXhRm3ix95ut3tR/7HSSSOivvaUzKsroSQgHnSvXCqvyugGnuGuSVlj7a+4Z5fn+n3+EYHy2vKyEYEMoiIeZVZHe533ywi9aeIfZ3DLD7aL/f4RiTsZ2tfezvGKClJ8rW5m6/wznmlAbnIuPi6mKKraugMQYPcjiJyHXAH1S1231eCVypqndlum4zPhFheV0pO1v7WF5nLXdGc2pDGUtrSwiKWELZWbKivpSPXbl8Vj/zFfWlbD7YzTL3BMiY2bSkpoQ/n+Uy75e6skIqisL0RGMsq5351osmey2rm/19/XQtrS1hy8FuCkIBzwZbMcZPCyuLKCoIEosnsyp5/cXLa1m3pJpwUHxPhG6MyQ5etHP8gqremXqiql0i8gXgLg/WbSbwxlXzicaSFNlVhDFZnoXZN9uf+dVnNHDx8loiYfuujT/myn4mEg4eS5hs3ZJMrpT7JTUl3HzFMoIihHIkZmPGU1VSwEcuXUpSybpcmdkWjzHGX15UOI22V7EOu7NERKyyyRiw34ExsyQYEIIB+72Z3DKXkpqbucEqT40xucCLPdWzIvKvIrJcRJaJyNdxEomPS0QWiMjzIhIVkdCI1/5NRB51b53utJtEZIc77SsexG2MMcYYY4wxxhhjZoAXLZE+AXwe+Jn7/EHgc5NYrgO4Grhz5AuqeguAiJwL/FXaS19V1e9mEqwxxhhjjDHGGGOMmVkZVzipaj/w2WksFwWiEySUuw74ddrzW0Tk/cAXVfXhqb6ngUNdgzy24ygN5YW86vR6S+iXZ1p6ovzhpVZqSgq45oyGrE/kasx0Pbu3g5eO9LJ2SRVnzC/3OxxjZtRTu9vZ2drHBUurOaWhzO9wjDFm2jr6h3loewvFhUFee9a8nMkFZ4yZnox/4SLye3dkutTzKhF5INP1ul4H3O8+vgtYBbwd+JqInNQZX0RuFpFnReTZo0ePehRCftmwu52WniibD3bT2jvk2Xq3HurmrhcOsa/dhhv209N7OjjSHWVbcw/N3YN+hzNlGw90cdcLhzjUlXuxm9kTTyR5/JU2jvYO8adX2vwOxxOHuwe564VDPL+/0+9QTJaJxhKs39XulPeduVHeVZXHXznKPZua6R6I+R2OMXlNVfnTK238ZlMzXQPDfoczoRf2d3Koa5BXWvrY02bnDcbkOy+qlGtVtSv1RFU7gfpMVyoipwCHVHXAXW+XqiZV9SjwMtAwchlVvU1V16nqurq6ukxDyEtL3KFTK4vDVBSFPVlnLJHkoRdb2NPWz0MvtnqyTjM9S2qKASiLhKgpKfQ5mqkZGI7zyEut7Gnr55GXrByZsYWCARZVOUObp8p8rnt0x1H2tPXz2I6j9EbtBN0cVxgKsKAyApBVw5+P50DHIM/u7WRnax/rd7f7HY4xee1g5yDP7O1gV2sf63dl/++tsbqYgDvoUUNZxO9wjDEzzIscTkkRaVTV/QAisgRQD9Z7HWn5nUSkXFV7RKQIOAXIqyZMQ/EEr7T0Mb8iQk3pzFUUrF1SxakNpRSFg56NbhEKCDWlhbT1DtFQnluVHPlm1aJKltWVUhgKzHgT5XgiyY6WXupKC6kvz/yAoSAYoLI4TNdAjPoyK0dmfG9fs4i+4Thlhdk7KOrO1j6CAWFp7cSVBPVlhRzpjlJeFCYSttG0zHEiwjvWLqZ/OE55xJsLRZk40DHAwHCCUxtKx+yWX1EcpiAUYDietOMCY2ZYRXGYwnCA1p4hovEEqprVKTNOaSjjI5VFhIJio0caMwd4caT+98CfROQx9/nlwM0TLSQiYeB3wDnAAyLyd8B7VfUT7ixvBN6StsinReR1OK2yvqSqeXUJ+IFtLexq7aMwHOBDlyyd0ROOMo8PWEWEd61bTHv/EPV2pcJ3pbN0Av7ojqNsOdRNKCB84JKmjE+EQsEAN5zfSOfAMPM8qMAy+S0QkKw4+R7L1kPd/H57CwBvOmcBK+pLx53/VafXc9aCCiqLw5bPwpwkmCXlvblrkF8+dxCAnmgt5zVVjzpfRVGYD1zcxMBw3I4LjJlh5ZEwV59ezy+ePcjetgFeONDFmsYqv8MaV0kWXywyxnjLi6Th94vIGuBCQIBPq+qESQbcCqNrRkzekPb65SPm/yLwxUzjzVZDsQQA8YSSVC8aiHkjmVS6B2NUFIXHTUBdEAowv6JoFiMzMy313VcWh0e9UjYUTwKQUCWe8KbMRsJBK0fGN5Pd301G6vfhPE5MOL+IMK/CTszN7OiJxigMBabcuuCEch1LjjOnc/Fjti6AGDPXBQNyrBJnot/mSF0Dw5QUhuxihzFmRnh1JJAAWoEIcKaIoKp/9Gjdc8JrzprH5oNdLK4qprggew7Q7tnczO6j/SytLeGt5y70Oxwzi+584RD7OwY4taGMa1fNP+n1K0+ro6IoTH15IdUlBT5EaIy37t1ymF2tfZ7s71YvriSpSkCEM20UPZNFUq3vSgqDvOeCJVOqFFpaW8LVZ9TTP5Rg7ZLsbkFhzFyyor6Mq05PEI1N7bf55M42NuzpoLqkgPdc0GiVTsYYz2VcsyEiHwE+BSwCNuK0dFoPvCrTdc8lFUVhLjsl+xKdH+x0Rgs70DHgcyRmNqnq8e++c/TvvqQwxKWn1M5mWMbMqNR+zov9XTAgY3Y3MsZPqfLdP5Sgo294yq2QVi2qnIGojDGZWr24csrLpI7xOvqH6R+KU1lsFxCNMd7yohr7U8B5wD5VvQo4lzxL6J0PhuNTa16bcsWpdcyriHDV6RkPPGhmUTKpxBLT+87B6d5z5WnOd3/FqdlXEWpMSiKpJJLedOlMlfkrT7P9nck+me7XU85bWs3CqiJWLqw4NtqjMWZuunh5LfMrIpy/tDqrelgYY/KHF3uWqKpGRQQRKVTVl0TkNA/WazySai7bWF3M29YsnNLIFSsXVrByYcUMRme8NjAc56dPH6BvKM4bzp7Hivqyaa3nnMWVnDONq2XGzJbW3uixBMbvWLso4+TEZy2o4KwFtr8z2ScaS/DTp/fTPRjndSvncdq86e3XAWpLC3nnusUeRmeMyVWLq4t59/mN/H57C//xyE5On1fG688+OY2CMcZMlxctnA6KSCVwF/B7EbkbaPZgvcYjL7f0ArC/Y4DoFBMJmtxzpDtK92CMRFLZ2drvdzjGzJh97QMMxZIMxZLsa7duvyZ/tfYM0TkQI6nKK629fodjjMkzqXOFl1v6fI7EGJNvvBil7jr34a0i8ghQAdyf6XqNd9Y1VfPU7naW15dSVDC1EWlM7llUVUxTbTHdAzHOWWytNUz+Om1eGa+4B8eZtPgwJtstqIywrK6Ejv7haeVpMcaY8Vy4rJoX9ndZrwZjjOe8SBr+DeBnqvqkqj7mQUzGY9Ytbm4pCAW47txFfodhzIwrj4R5zwWNfodhzIwLBQO8ZbWNFGuMmRlrl1SzdokNdGGM8Z4XXeqeBz4nIjtF5Ksiss6Ddc558USSaCxx0vTtzT08tL2FroFhH6Iy+S6ZVJ7a3c5jLx9lKH5y+ZuOscqyMdno+f2dPPJSKwPD8Rl/r2gs4VnCc2MmI5FUntjZxp9eaSPuQQJyVWVgOI6qlWNjvDY4PP3/iPa+IX6/veVYVzljjPGLF13qfgD8QESqgbcDXxaRRlU9JePo5qi+oTg/fXo//UMJXn/2PE5tcLqKdA/EeHD7EVShJxrjbWusFYvx1iutfazf1Q5AOChcvLw2o/X1RmPc8fR+BoeTXLtqPivqS70I05gZcaBjgMd2OIOsJpLKNWc2zNh7bT3UzUMvthxrpRUJW3dnM/O2Hurm6T0dABQXBlnTWJXR+u7feoSXjvRyakMZ166yRMPGeOX5/Z08tuMoNaUFvPu8RgpCU2sj8NCLLTR3RdnW3M3CyiJKCm0EOmOMP7zc+6wATgeagO0ernfOae2J0ht1rq7vPtp/rMKpIBSgIBRgKJak1P44zAwoKQwiAqpQVhjOeH0tPVH6h5zWTXva+q3CyWS1ksIQwYCQSCqlkZndx+5u60cVugdjtPUNsaiqeEbfzxjghHJd5sFxxK6jTg613Uct0bAxXtp91Bn0pb1vmK7B4SmPwlpaGAaiRMJBwkEvOrSYbND02fv8DsFMglff094vXevJevzmRQ6nLwNvA3YBPwP+UVW7Ml3vXNbWN8SRnij1ZYWsaaw8Nr2oIMiN5y/haN8QS2tL/AvQ5KTNB7vYcqibcxZVjpnTa1FVMe8+r5HheJLGmsxPgJfUlLCsroS+obglujWzbiie4IFtLQzHk7z6zAYqisavRK0uKeCG8xvpG4rT5EH5H8+6JVV0DwxTU1rI/IqiGX0vk58Odg7wx5fbmF8Z4cpT6xCRCZdZXlfKO89bjKp6Usl58YpathzstjyRxoyibyjOA1uPEAoKrz1r3pRasp7fVM3AcJx55RHqSgun/N6vPauB0+aVUlcWmXLrKGOM8ZIXl3D3ABepapsH65rz4okkT+xsZ155hNLCEPXlJ17RqCgOU1GcecsTM/c8tuMo8aTy2MtHxz05mFcxtato4wlbolvjo1da+tjV6rS82Hywi8tOqZtwmbqyQurKpn5wP1ULKot430VNM/4+Jn89tbuDlp4oLT1RVi6omHS5XVjpXQXnmsaqjLvlGZOvthzsZn/HAAAvHemd0oW3xppi3p/Bf0QoGGBFvY3eaozxX8ZV3qr6X1bZ5J1QMMDCKudg0IsWJsakpMpTY7WVKzM3zKtwruwGA8Ji67Jm8swSd59eVRyesPWeMWb2LaoqIhgQCkIBFnh4Mc8YY3KJJQLKQu9Ys4jeoTjlM5xDxMwtb1q1wMqVmVNqSwv58KVLSapSXGDl3uSX85qqOW1eGUWWo8WYrLS4upiPXrYMEWxgCGPMnGVH4FkoEBC7Wmk8Z+XKzEV2kG/yWXnE9unGZLOiAvsPMsbMbZ5cEhORoIgsEJHG1G0SyywQkedFJCoioRGv3Soim0TkURH5S3damYjcIyJPiMj7vYg7G8QSSZJJ9TsMM0epnlz2rEyauWjkb2Eonhj192FMLkgvu0PxhI+RGJN/0n9fyaQSSyR9jMYYY7KbF6PUfQL4AtACpPa4CqyaYNEO4GrgzjFe/ytVfSjt+UeBO3BGwntERH6qqsPTDjxDzV2D3L2xmUg4wPXrFlM6jeGFdxzp5f6tRygvCnHD+Y12Jd7Mqp2tTvmrKS3k7WsWURAKsLO1l99uOUJJYYgbzl9s3ZDMnLB+VztP7+ngtHllvG7lPJ7c2caGPR00VhfztjULJzX6lzHZIJZI8qvnDnK0d4jXrpzHoc5BNh7oYkV9KW86Z4Hf4RmT01SVezcfZvfRfi49pYbT55Xz02cO0D8U5w1nz2dFfanfIRpjTNbxooXTp4DTVPUsVT3bvU1U2YSqRlW1c5xZviwiD4nIavf5RcBDqpoANgGnZRx5Bna09BKNJegaiLG/fWBa69jZ2kdSla6BGK09Qx5HaMz4tjX3EEsoR7qjtPZGAdjZ2k8iqfQMxjjSHfU5QmNmx9ZD3SRVefFwD/FEkpdbegHY3zFANGZXrk3uaOsb4nB3lHhS2d7cc6ws72zts5arxmRoYDhx7Nh966EejvRE6RmMkUgqu472+R2eMcZkJS8qnA4A3R6sJ92/q+pa4GPAN91plUCP+7gb8HUc3jPnl1MWCVFXVkhT7eRHP1JV9rX309k/zOrGSqqKwyyrK2FBpY1eYWbX2QsrKCoIsri6mIZyp/ytXlxJdUkBS2tLWFhZxJ62fnqiMZ8jNWZmrW6spCAUYNWiCkLBAOctraa8KMy5jZUUFQRp6xviQMf0LiwYM5vqSgtprC6mqCDI2YsqOLexklgiyerGCgIBa6lnzGiG40l2H+1jcHj87qfFBUHOmF9GYTjA6sWVLK4qpqm2mOqSAs5ZVDk7wRpjTI7xor/MbuBREbkPONZMR1X/dborVNUO9/6VtK4MXUA5EHXvu0YuJyI3AzcDNDZOmEYqIw3lET5y2bJJzds/FGf9rnaqSsIMxZNs2N1BOCi878Imbrpk6YzGmS4aS3C4O8qCygiFIeu+N1eM9b0vqyvlz684sfn3vIoIH7i4CYCHtrew5VA3kXCQD17SNKkun3vb+nnpSC8rF5azyIahN7Mok/3beU3VnNdUfez5WQsqOGtBBQCtvVHu2HCApCpXnlbHuY3Tu9bx3L4OOvtjXLS8hpJpdME2BiYu56FggLevXXTs+XP7OgkHA+xvH5zW+21r7uZg5yDnNVVTXVIw7biNyWb3bGpmf8cAlcVhbrq4acxu1CLC61bOP2HadecuOmm+9r4hnt3XyeKqYs5cUO5JjPFEkgOdg9SXFdp/iDEmp3ixx9rv3grcW8ZEpFxVe0SkluMxrgeuFpGfA6uBHSOXU9XbgNsA1q1blzVtx5/c1c7WQ04jsLqyQgBiCWUgFqeC2Rth5ufPHqC9b5iFlUW887zFs/a+xl+/dPN5zK+I8O7zJ18R2z3otGyKxhIMxZITVjg5uQ2aiSWUAx0DfPTyyVXIGuOFXzx3kLbeIRZURnjXed5dcOiNxkm6CWJTv4mpOtAxwB9fbgOcBIevPrPBq/DMHDPV//Eet8z2RmMkkzqlVk490Ri/396CqrOe69fZcYPJT93HfidxkgrBDBsDPvxSK4c6B3nxcA+Lqos8GU3y/m1HeKWlj7JIiJsubiIU9GTcJ2OMmXEZVzip6hens5yIhIHfAecAD4jI3wHvVdVPAF8VkZU4Xf4+6y7yXeB24BPAbaqaM0mPyiPOxxwKCFecWseLh3uoKS1kfkXRrMWgqscOPKd70mRyU/c0v/erTq/nmb0dLKwsoqJ44oMlEaG8KEx73zDlRXb1zcyemdy/Last4aLlNfQPxblgac201lFaGCIUEOJJPfZ/YMxUTaecv27lPLYe6ubUhrIpd6krCAaIhIMMDicoL5q9i2PGzLbXrZzH5oNdrKgvI+hB19OKojCHOgeJhIMUeFQx1DXg/Ob7hxLEk4p1VDDG5IppH/mKyL+p6i0icg/ORdsTqOqbx1teVWPANSMmb3Bf+7NR5u8B3jjdeP10/tJq5lVEKIuEqS4pYHG1d12Ndrb20to7xJrGqnFboIgI165awI4jPce6ipi54dqz5/PSkR5W1Jeyflc71SUFnDavbMLlqksKeO1Z86b0XtevXUxz9yCLqmavMtUYEeENZ88/Yf82HE/ywv5OSiOhjPZ5IsKFy6ZX0ZRSVVLAjRcuoTcao9HD/b+ZW1Ll/OWW3mP785rSAk5tGHt/vqiqeNrdmyPhIO+5oJG23iGW1JRMN2xjst6CyiIWVHp33HLNGQ2cUl+KIDy3r5NT6kupL88sV+trzmzg+f2dLK0ttVGtjTE5JZNLrT9y77/mRSC5IpFUntrdTiyR5OLltRSEJr5yISLTPljrGhjm0R1HqSgKc8WpdSdcoWzvG+LezYfd5u5xXrdy/MqBpbUlLK21g8a5pqm2hKbaEh5+sYXNB7s52NFPUUGINUuqeOOqBZMqw5NVVBBkeZ0NC2xm38j921O729mwp509bf1cuKyGd65bfOwgfTie5MldbYSDAS5cVuPJFe2JVJcUWA4ck7FldaUsqyvlwW1H2NbcQ1KVxupiCsNBXnV6PZ39w2xr7uHM+eU01mReuVkeCXvSHciYuSQYEJbVlfLdx3fTG42z9VA3f3bFcpJJ5ak97QzFkly0vGZKFUfRWJK+oQT9w/EZjNwYY7w37QonVX3OvX/Mu3Cy34uHe3h6TwcAxQUhzl9aPcESmdmwp4M9bf2Ac0LVlHZCFQwIARESqoQz7XBu8l4oGCAaS/BySx/hUIAjPVEWVRXPeBk2xg/hYIDWniHa+4bZ29bPtuYe1i5xEn5vPNDFC/u7AKfrw8qF1urT5Jawe6Ggc2CYoViSooIglUVhtjX3EI0l2N3Wx8evXOFzlMbMbWG3O10q39LLrb1s2O2cQxSGAly8onbS63r05Vba+4Y50DHAGfPKKSqwVk7GmNxgySSmqDwSRgRUmZU8NfMrImxv7qEwHKBqxNXxyuICrl+3iPa+YU6fRBcpM7dduqKWikiIaCzBS0d6KS0MUWF5OUyeumBpNdFYgsdfaaOyOExDeeGx11L7bhHsN2By0mUraqkrLUSBx3a0Ek8q8ysiHOwcJBpLWLk2Jgtct2Yhe9v6j/VyOPEcYmq/0fkVRbT3DVNTWuBpy3RjjJlpVuE0RY01xdxwfiPxpLLQw/7eY1m1qJJFVcVEwgGKC07+uuZXFM1q8nGTu4IBYXVjFWcsKGdXaz+VxWFPcxYYk00CAeGq0+tZs6QKEU7oFnT6vHLKImHCAck4r4YxfggFA8da5i2tLSGeSFJZ7OSIbO4atH27MVmgPBJm1aLKY88XVBbxngsaGY4np5xb7Zoz6jlncQWVRQWz0g3cGGO84lmFk4iUqGq/V+vLZg2zfIJieT+MlwpDQc5cUO53GMbMirFaeszGBQNjZkNp4fFDuUg4yDLLo2dM1qovm945hIhMe1ljjPFTxm0yReRiEdkOvOg+P0dEvp1xZFnqQMcAT+5soyfq7dDbxsyUrYe6eWZvB/FE0u9QjJlVBzud/XX3gO2vTf6wfbox02P/CcYYM/u8aOH0deC1wG8AVHWTiFzuwXqzQiyRZNfRPurLIhQXBLnrhUPEk8qhrkGuX7fY7/CMGdeuo338fnsLAPGEctFyZ3j3weEEe9v7WVRVRJmNQGTy0HA8yV0vHCKWUPZ3DPDu8xtHnW9/+wCK2rDvJifsTtunp0bL7R+Ks79jgCU1xaN2vTdmrkkklV1H+6gqLqCuzMnfNxRPTOo/wRhjjLc8OTJR1QMiJ/QnTnix3mzw0PYWXjrSS0EowI3nNxIICCT12MgTxmSzcOB4OS0IHf+N3r3xEIe7o5QXhfnwpUv9CM2YGSUCwUCAWCJxbISgkXa29nLPpsMAXLtqPqc22OALJrulH3sUuI9/+dxBOvqHqSsr5L0XLvErNGOyxh9fOcrG/V2EAsL7L26ioihMQGTC/wRjjDHe86LC6YCIXAyoiBQAn8TtXpcPBoadurNYIkkgKLxz3WIOdw96fmKSSKolATSea6wp5q3nLiQaS5wwkmG/W66jsQTJpDoVqaNIJJWAOLkDjMkl4WCAd65bxMHOsffXqf37yMcjxRNJO0ExWWFxdTHXnbuQwbR9eqrsDo5Thm1fbuaSgSHntxBPKsNxp+vpZP4TJsN+S8YYMzVeVDj9OfANYCFwEHgQ+LgH680K15zZwPP7O1lUWeSMchThWPNcr2w91M1DL7ZQXxbh+nWLrPWU8dTS2pO7Cr1p1Xy2Nfewor50zMqmAx0D3L3xEJFwkHeet/iEUb6MyQU1pYXUlI69vz5rQQWDwwkUONsd8Wukh7a3sOVQNysXVvDqMxtmKFJjJq9pxD79zasX8PKRXk6fP/pJ9J62fu7d1ExJYYh3nbeYkkLrdmfy25Wn1VEaCVFXWnjCMftE/wkT2dnax2+3HKY8EuJd5zVSVBD0IlxjjMlrXhx1nKaqN6ZPEJFLgCc8WLfvKorCXHVa/Yy+x44jvahCS0+Uzv5hG6bbzLj68siE5Wzn0T5iCSWWiHOwY5AzF1iFk8kvwYBwwbKaced56UiPc3+4xyqcTFZaWFk07qiLr7T0Ek8q3YMxDndHWVFvo9iZ/FZSGOKKU+s8X+8rLb0kkkrnQIyWnuhJlb/GGGNO5kVTmm9OclreiieSdA9Of8SL1Y2VlEVCnNJQSm0GV16M8dKKulJKCoMsqIyM2krKmLlgXVM1JYVBzltaDUBPNEbMRgczOeTsRRVUFIVZWFlERSRMMql+h2RMThkYjhONJVi1uJLyojCN1cUsGKeS1xhjzHHTbuEkIhcBFwN1IvKXaS+VA3OmjWk8keSOp/fT1jfMBUuruXhF7ZTXsbyulOV1dsXRZI+2viF+s6mZRFJ5zZk11mzczFkXLqvhQrcV1HP7Ovnjy0epKArzngsaiYTtd2Gy3/yKIj506VJ+s6mZH2/Yx/L6Ut58zgK/wzImJ+xp6+c3G5sJBYV3n7fYBloxxpgpyqSFUwFQilNpVZZ26wHekXlouaF/KEFb3zAA+zoGfI7GGG8c6Y4yHE+SSCoHOwf9DseYrLC/ox+A7sEYXQPTb9VqjB/2t/efcG+MmdjBzgGS6iQfP9wd9TscY4zJOdNu4aSqjwGPicj3VXWfhzHllIriMOc1VbO/Y4CLl4+fC8SYXHFKQym72/oZjidZtXj0ZMrGzDUXLK0hGkvSUF5IQ7l1fza55fJT69hyqJtVCyv9DsWYnLFqUSUtPUMUhgKc0mC9EYwxZqq8SBo+ICJfBc4CjmUhVtVXebBu3yWTyiutfVQWh2kYI8nypadMvRudMTPtSHeUnmiMFXVjj0Q3lsJQ0LpcmJyTSZmfjAWVRdxwfqPn6zVmKmKJJDtb+2goj1BdUjDp5VYtqmTVosqZC8yYPFRRFOYdaxf5Hca07G3rRwSW1FgeTmOMf7xIGv4T4CVgKfBFYC/wzEQLicgCEXleRKIiEhrx2hdEZL17u9qddpOI7BCRR0XkKx7EPSlP7mrnt1sO89OnD9DRPzxbb2tMRtr6hvjZMwe4b/Nhntrd7nc4xsy4o71pZX6PlXmTv36/vYX7tx7hjqf3E40l/A7HGJOFdhzp5c4XDvHr5w/xSkuv3+EYY+YwLyqcalT1/wExVX1MVT8EXDiJ5TqAq4GnRnnth6p6EfB64Atp07+qqleq6v+XcdSTNOgezCVVGYrbgZ3JDUPxJEl1RiIatBMSMwcMxRPHyrydhJt8NjjslO94Qm3ERGPMqNKP/ew40BjjJy+61KUypx4WkWuBZmDCtqeqGgWiIid3e1DVPe7DISB9/N5bROT9wBdV9eGMop6kS1fUEgkHqCouYH6FDYFqcsPCyiJefWYDXQMx1jVV+R2OMTNuUVUx15zRQPeglXmT3645s4GNB7pYWFlEWSTsdzjGmCx09sIKhmIJAgFh5QLLxWmM8Y8XFU7/JCIVwF8B3wTKgU97sF6AW4H/dh/fBfwQqAEeFJF1qnpClb2I3AzcDNDY6E2ejaKCIJedUufJuoyZTSsX2gGGmVvOXmRl3uS/iqIwV5xqxyXGmLEFA8IFy2wwI2OM/zKqcBKRIHCKqt4LdANXeRKVs+7rcLrr3Q6gql3uS0dF5GWgAac11TGqehtwG8C6devSW0YZY4wxxhhjjDHGmFmSUQ4nt4XRmz2K5RgRWQX8hXtLTSt374uAU4CjXr+vMcYYY4wxxhhjjMmcF0nDnxSRb4nIZSKyJnWbaCERCYvIQ8A5wAMicoGIfNN9+as4LZgeEJG73WmfFpH1wKPAl1Q1dtJKjTHGGGOMMcYYY4zvvMjhdLF7/w9p0xR41XgLuRVG14yYvMF97bWjzP9F4IvTD9MYY4wxxhhjjDHGzIaMK5xU1bO8TcaxvbmHo31DrFtSRUmhF3WCZq440DHAztY+zlpQTn15xO9wjJkxLT1Rtjf3cEpDKYuqiv0Ox5gZsaetn71t/axaVEFNaaHf4Rgz5yWSyjN7OxBgXVM1wcDJo20bY4w5zmozsszR3iEe2HYEgP6hOG84e77PEZlckUwqd288RCyh7G3v54OXLPU7JGNmzL2bD9MzGGP74R4+fuVyROyg3+SXoXiCezY1k0gqzd2D3HjBEr9DMmbO23ywi/W72gFnJOtViyr9DcgYY7KcFzmcjIcKQgFC7tWSooKgz9GYXCICkbBTZoqt7Jg8lyrjReGgVTaZvBQUoTDkHKbZPt2Y7FBcEEp7bL9LY4yZiLVwytDAcJwNuzsoLwqzdklVxuurKArz7vMb6RwYZnldqQcRmrlCRHjXeYs52DnIUDzBQ9tbWLukiqqSAr9DM8Zzb129kL3t/SyuLiaZVDbs6SCWSHLhshoKQnYtxeS+UDDAu89rZMeRHtr7h3nxcA9nzC/3Oyxjslr/UJwNe9qpLC5gTWPmx+UjnTavjEg4gCA01lh3bmOMmUjGFU4iUgz8FdCoqh8VkVOA01T13oyjywFP7mxny6FuAOrLCllcnfmfT11ZIXVllqvBTF1ZJMyCSvjeE3tQha7BGO9Yu8jvsIzxXFFB8NjJ9/bmHp7a7XRxKAwFuGBZjZ+hGeOZiuIw+zsHOdAxwI6WXhZWFVEeCfsdljFZ64mdbWxr7gGc4/KZyPG3pKbE83UaY0y+8uIy8PeAIeAi9/lB4J88WG9OKIs4dXbBgFjTWpMVCkMBCkNOWUyVT2PyWWlhiFSvulIr8ybPlLqDhxSGghQErfWeMeMpPeG43P4PjDHGb17siZer6rtE5AYAVR2UOZRQ4/yl1dSXRygtDNkIMiYrRMJB3nN+I0f7hlhaa1fhTP5rrCnmXectJp5QT1qZGpNNrjmjnhX1pdSVFh7L02eMGd1Fy2qYVx6hvChMtaUUMMYY33lR4TQsIkWAAojIcpwWT3OCiNhJvck6FcVhKoqt24WZO+ZXFPkdgjEzIhQMsKLecjoaMxkiwjLLgWqMMVnDiwqnLwD3A4tF5CfAJcBNHqzXGGOMMcYYY4wxxuSgjCucVPX3IvI8cCEgwKdUtS3jyHLcoa5BwkGhvizidyjGHBONJWjuGmRhVdGxPE/GzBXJpLK/Y4CqkgIqiqwFoMltCbc815QWWCJxYyZpf/sApZGQdbczxphZknH2SRG5BIiq6n1AJfB3IrIk0/Xmsq2Huvn5Mwe4fcN+DnUN+h2OMcf84rmD3L2xmTufP+R3KMbMusdePsqdLxziJxv2MTAc9zscYzLyh5daueuFQ9y+YT/RWMLvcIzJes/s7eBXzx/kJ0/to6N/2O9wjDFmTvBiuJP/BAZE5BzgM8A+4IcerDdn9QzGAFCF3mjshNcOdg7wuy2H2dna50doZo5Llc2ugRhP7GzjDy+12ImKmTO63fI/FEuyv32A+7ceZseRXp+jMmZ6ugdjHO4eZOuhbo72zZnUmcacZE9bP7/bcpgDHQPjztc94PwHxJNKX9QuOhhjzGzwIodTXFVVRN4C/Luq/j8R+YAH681Za5ZUMRRPUhgKcGp92QmvPbCthZ7BGK+09vEXV60gGJgzA/qZLHDt2fN56UgvkXCAp/d0ABAJBbl4Ra3PkRkz8648rY6igiDzKyI8s6+Ttt4hdhzpY0lNsY3+ZXLOygXlPLqjlbJIiC0Hu1lcZSM0mrlHVblvczOxhHKgc4CbL18+5rwXLa8BnIFVGmvs92KMMbPBiwqnXhH5W+C9wOUiEgTmdDKBSDjIVafXj/paRVGYnsEY5ZEQVtdkZltTbQlNtSU0dw2y8UAXqthodmbOqCwu4LVnzQNgX/sAbb1DlBQGCQe9aOxrzOxaUFXEGfPLGY4nLSeZmbNEhIqiMG19w1QWjZ+XqaQwxDVnNsxSZMYYY8CbCqd3Ae8BPqyqR0SkEfiqB+vNS28+ZwGHugaZVx5BxGqcjD8WVBZx4wVLiCWSLKi04eTN3PP6lfM42DlIXVmhtTQ1Oak8Eua9FyyhezDG4mrbj5u56/p1izncHWVBpQ3UY4wx2Sbjy7qqekRV/1VVH3ef71fVCXM4icgCEXleRKIiEhrltT+IyJMico07rUxE7hGRJ0Tk/ZnG7ZeCUICltSUUFVj3DeOvurJCq2wyc1YoGKCptoSSQi+uuxjjj1TXILuAZeaySDjI0toSG33XGGOykBej1F0oIs+ISJ+IDItIQkS6J7FoB3A18NQor30W+BzwGvce4KPAHcDlwEdExMYzNcYYY4wxxhhjjMlCXiSu+BZwA/AKUAR8BPiPiRZS1aiqdo7x8ipgvar24eSIKgMuAh5S1QSwCTjNg9iNMcYYY4wxxhhjjMc8yZSqqjuBoKomVPV7wJUZrjKoquo+7gaqgEqgZ8Q0k4FkUtl0oIvtzT0Tz2xyQk80xtN7OmjpifodijEzZnA4wTN7OzjYOf4Q2MbksmgswbN7O9jfbuXcGD91DQzz9J4O2vqG/A7FGGNyjhfJKwbc7m0bReQrwGGgJMN1JtIelwNd7q0ciKZNO4GI3AzcDNDY2JhhCPnvhQNd/PHlowAUhIQV9WU+R2Qy9dvNhzncHeWZvQFuvnyZjb5l8tKD24+w+2g/wYDw4UuXWh4mk5f+8FIrO470EhDhpkuabCQ6Y3xy1wuH6ByI8cL+Tm6+fJnlTDPGmCnw4ij9fTgtpf4X8GlgMfD2DNe5WUQuAjYD5araIyLrgatF5OfAamDHyIVU9TbgNoB169alWkgRTySJxpOU2knJCU78v7Q/z3yQ+k5FnG80GnPqbiNhS6Rp8kfqYF8YuR870VA8QTKJDdJgclKqaKfK+OBwgkAAS4xs8kauHJ8f+88RrLLJGDNrmj57n98hHLP3S9dOe9mM9/Cquk9EioD5qvrFyS4nImHgd8A5wAMi8nfAe1X1E8BXgB/i5IT6grvId4HbgU8At6nqpNq1RmMJbt+wn+7BGFeeVse5jdYTL2X1okoKggHCwQAr6kv9Dsd44NpVC9hxpIfF1cV09A/zi+cOkkwqb1u7iIU2Ip3JE685s4FtlUXMr4hQXDD631h73xA/e/YA8YTy5nMW0FSbacNbY2bXVafX01ARob6skPa+Ie7ZdJiCUIB3n7eYqhIbN8Xktlw6Pn/r6oXsPNpLU439jxhjzFRlXOEkIm8CvgYUAEtFZDXwD6r65vGWU9UYcM2IyRvc1w4Crxoxfw/wxqnG1zUQo3swBsDe9v6s/kObbYGAsHJhhd9hGA+VFoZYu6QagOf3dzIcTwJwoGPAKpxM3oiEg6xdMv6+/HB3lKGYW/47B6zCyeScSDjIGveY5dEdrSRVicYSHOmJWoWTyXm5dHxeURw+dmxljDFmarxow3orcD7wKICqbhSRJg/W64mG8kLOXlhBS2+U85fW+B2OMbPmjHnl7GvvJ5HEKhbNnHNKQym7jvYxFE+yamGl3+EYk5HViytp7RkiUhBkeZ21SDa5z47PjTFmbvCiwimuqt3Z2qdZRLjmzIZx51FV9rT1U1EUpqa0cJYiM2bmHOgYIBQUrjt3kd+hGDPjYokk+9r7qS+PUB5xEisXhoK8ZfVCnyMzJnPdgzHa+oa4bs1CGwjC5I3JHJ9PRU80RmtPlCU1JfY7McaYLOJFhdNWEXkPEBSRU4BPAk96sN5Z86edbTy7t5NQQHj/RU1UFNtIMCZ3vXi4h/u3HgHgbWsWssRyDpg8d//WI+xs7aO4IMgHL1lKQchONkx+GI4n+enT+xkYTnBqQxnXrprvd0jGZJ1YIskdG5zfySkNpbxx1QK/QzLGGOPy4qj8E8BZwBBOUu9u4BYP1jtr+qJxAOJJZdAd1cucLJ5I8kpLL539w36HYsbQ1jfES0d6UHUGaex1y7Yx+WRPWz+HuwePPe8bcsp5NJYkkdSxFjMmpxzsHGD30b5jxyW90ZjPERmTneIJJerm7Jvqcc++9n4Odg7MRFjGGGPIsIWTiASB36jqNcDfexPS7Lvs1DoKwwFqSgqZVxHxO5ys9YeXWtnW3ENBKMAHL2kac3Qo44/uwRh3bNjPUDxJUUGQcxsrOWN+ud9hGeOpjQe6eOSlVkTg+nWLWVhZxKvPbOCF/V001RRTVGBDxpvct7etnztfOATA6fPKCAUDrGms9DcoY7JUUUGQN5w9j73tA5w7hd/JtuZuHtzWAsB15y60wSWMMWYGZNTCSVUTwICI5HRG4tLCEK86vYFzFlf6HUpWGxhOkFRl88Euvv3ILrY39/gdkkkzFEvQ1jfE8/s7OdQ5wJrGKoKB7MytZsx0dfQNselAF8/s7WBfex8AtaWFvPrMBk5pKPM5OmO8sbe9n+f2dbLxQBelhSFefWaD5Zg0ZgyDwwme2tPBztY+Bocn31NhIG3e/mFrEW6MMTPBiyYqUWCLiPwe6E9NVNVPerBuk0WuOr0eATr6hwkGhK3N3Zy5wFrQZIv68ggLK4voGoxRWVzA4e4oS+1qnckztWWFVJWECQUCRIeTfodjzMxQmFdeSFKhvMjyShoznoOdA7T1DgGw40gvi6uLJ7Xc6sWVDMeThALCGfPseNYYY2aCFxVO97k3k+cqisK88ZwFJFGau6KcvTCnG7blpdetnE9CobI4zHzrHmry0Ir6UtYsqWZwOM4ZVuFt8tQZC8p5ubWXSDjI8vpSv8MxJqstqiqmrqyQ/qE4p8+ffEvXcDDAJStqZzAyY4wxXlQ4/RKIut3rUnmdrN13ngoGhOvOXeR3GGYMjTXFfOzK5X6HYcyMKS4I8b4Ll/gdhjEzqqE8ws2X277cmMkoKgjyXvtfMMaYrOTFKHUPA0Vpz4uAhzxYrzHGGGOMMcYYY4zJQV5UOEVUtS/1xH08uc7TJuu83NLLQ9tb6Ogf9jsUM0XNXYP8fnsL+9tteF+Tv7Ye6ubhF1vosSHiTR6zcm7mIlXl6T0dPPbyUYbik0/+bYwxJnt50aWuX0TWqOrzACKyFhj0YL1mlvUPxfntlsOoOonB33neYr9DMlNw3+bD9A3Febmll49fuRwRG6HO5Je2viF+v90ZwnpgOMGbzlngc0TGeM/KuZmrdrb28cTONgBCAbH8SsYYkwe8qHC6BfiFiDS7z+cD7/JgvWaWhYJCJBxkcDhBacSLomFmU2kkRN9QnJKCoFU2mbxUGApQEAowHE/aPsrkLSvnZq4qKQwhAqpQWmhl3xhj8kHGe3NVfUZETgdOAwR4SVWtDXgOKgwFueH8Ro72RmmqKfE7HDNF1527kAMdAyysKpp4ZmNyUFkkzA3nN9LRP8yyWttHmfxk5dzMVQsqi7jh/EaisQRL7DjUGGPygleXD84Dmtz1nSsiqOoPPVq3mUUVRWEqisJ+h2GmIRIOckrD5IcDNiYXVZcUUF1S4HcYxswoK+dmrmooj/gdgjHGGA9lXOEkIj8ClgMbgVSGPwWswskYY4wxxhhjjDFmDhJVzWwFIi8CZ2qmK/KYiBwF9s3S29UCbbP0XlORrXGBxTYdo8W1Bnh+jNfyRb5uW75uF3i/bblczi3m2ZOLcafHnCrnI6f7yeLIrhgg9+NIL+d+ypbPcabY9vlrJsp5tm/zRCx+f81E/EtUtW6imbzoUrcVmAcc9mBdnpnMxntFRJ5V1XWz9X6Tla1xgcU2HePFla0xeyFfty1ftwtmbtty8TOzmGdPLsY9VszZsi0WR3bFYHF4J9fjn4htX/7J9W22+P3lZ/xeVDjVAttF5GlgKDVRVd/swbqNMcYYY4wxxhhjTI7xosLpVg/WYYwxxhhjjDHGGGPyRMYVTqr6mBeB5Ljb/A5gDNkaF1hs0zFeXNkasxfyddvydbtg5rYtFz8zi3n25GLcY8WcLdticRyXDTGAxeGVXI9/IrZ9+SfXt9ni95dv8U87abiI9OKMRnfSS4CqankmgRljjDHGGGOMMcaY3JTxKHXGGGOMMcYYY4wxxqQL+B2AMcYYY4wxxhhjjMkvVuFkjDHGGGOMMcYYYzzlxSh1xphZJiJrgQuBKqALeEpVn/U1KGM8ZuXczBVW1o0xZvJE5FRVfdnvOGaaiJTg/i+oap/f8cw19vl7w3I4TZGIBIG3MuLAELhLVeMW18ksNm/jAr4KFAIPAd1AOXANkFDVT85+tN4RkQXA3wFn4rTATALbgS+p6kE/Y8tEvm4XzNy2icjXybFyLiIrgX/CiTWAM7BGF/AFVd3sY2jjysW48yzmPqAXn8t6tuynLI6T4vC9rGdDDJkQkauAW3G+w/9W1Z+60+9U1ev8jM0LIvJW4G+ALcAfgM/h7Eu+qqp3+ReZN0TkQyMnAZ8C/k1V/8eHkGaciLwK+DzQ497KgTLg/6rqQ37GNlkicouq/puInAN8E2e/EQI+q6qP+xvd+Ozz95iq2m0KN+BHwGeANcBy4Fz3+Y8tLottNuIC/jjGMqNOz6Ub8DBw3ohp5wMP+x2bbdfsblsulnPgcWD+iGkLgMf9ji3f4s6zmLvHmH9Wy3q27KcsjkmXm1kr69kQQ4bxP4FzwhgBvoQzPHgYeMTv2Dzavg1AMVAHHMI5MS4A/uR3bB5t30bgAeBDwPuBDwCbgff7HdsMbvOfgOIR00qAJ/yObQrb8Af3/kFghfu4Nhe2wT5/b2/WpW7qmlT1fSOmvSAiftfUZmtcYLFNx3hxPSMi/4VzNTxV63418PwsxzgTioBtI6Ztc6fnsnzdLpi5bXs2R8u5TPA8W+Vi3PkSc2+WlPVs2U9ZHCfLhrKeDTFMl6hqj/v4s26LoN8C1f6F5Kmoqg4AAyJyl6r2AohIzOe4PKGqq0XkzcANwKPA94F3qeoP/Yxrhg0BZ+NUJqacDUT9CWdaqt2WQtWquhNAVdtEJBe6V9nn7yGrcJq634jIvTg7vNSB4RXAPX4GBdydpXFBdseWrd/nmHGp6ldE5FzgIuBUnGbtt6nqCz7F6qW/B+4VkQGcLiapK5Kf9zWqzOXrdsEMbZuq/mUOlvM/B74lItUc74LTDnzc16gmlotx51PMr8c5HvO7rGfLfsriOFE2lPVsiCET94vIElXdB6Cqd4nIbuArPsflld+JSFBVE6r6FwAiUgC85HNcnlHV3+AcG78GpxfAPJ9Dmmnvxakc/RLOby6B26rL16im5k7gMuAeEalU1S4RKQO2+hzXZNjn7yHL4TQNIlKL06x6LbAT2Kmqz/gc03wg5sZVCTQB+4Gfqv95kt4MPAecgxNbF/CMqh71MSzg2B/yu4ElON9lGOez+3dV7fIvMhCRy4GVQAXOZ3aP5ni+n8kSkSLcsqKqgz6H45l83S7I720zZi7Jlt+yxWGMMcbkPqtwmiIRuV9VXycit+Ak9bwXuAQ4pKqf9TGuP6jqq0Tk34F+4BFgNbBOVd/pV1xubM3APqAFp7b1N6ra6WdMKSJyJ/A0TmLutcB9OFft3qOqr/Uxri/hNNvfBFyF04QzATyZz02IRaQU+DNOTpb+36km4rkoX7cL8nvbpiotyfAZQJAcSQ6fi3FbzN7Llt+yxXFSHL6Xm2yIYSaIyDdU9VN+xzFT5sD2/btm6SAiMyUfvtNc3oZcjj3Fj22wCqcpSqvYeQy4SlWT7vQ/qeqlPsb1kKpek7pPm/6Iql7lV1zpMYjIUuBtwJtw+sberarfzobY3MdbVPXskdN9iuthVb067fnvVfXVI7/ffCMiv8FJjD5ytKb3q+qb/IwtE/m6XZDf2zZVIvIwzugfz6RNOx/45/Tfc7bJxbgtZu9ly2/Z4jgpDt/LTTbEkCkRWcuIykNVfdbXoDxk2zc3iMj5qvq033FkIle2QUTOwhkt9qW0aReo6oZxFssqY2zDhar61GzGEZjNN8sTZ4rID3FGDitMmx7xKZ6UH4jId4EDIvJjEfmoiHwTyJqdsaruUdV/UdUrcfrA+trVz9UvIp8TkX8EjojIX4nIB3EqxPzUKiJ/IyKvd1s7bXenB/0MahbUAL9U1Q43F0En8CtyP7Fnvm4X5Pe2TVU2JRmeilyM22L2Xrb8li2OE2VDucmGGKZNRL4OfBg4DKzHGcntgyLyDV8D84htX/4RkcAotyDwf/yObbJyeRtE5F+Av8XJ43SPiNS5L/2zj2FNyTjb8H9nOxZLGj51F7j3n8etMHGbXfua/FdVf+RegXot0IDz3X5XVTf5GZfrSyMnqGoLzrC0frseeB2wC+cH+AGcysN3+RkUTrK663BGRFjP8STmN/oW0ez4D+BREdmMkyy9AjgL8LUlnAfydbsgv7dtqrIlyfBU5WLcFrP3suW3bHGcKBvKTXoMqc8im8ruRNaq6uUjpt0pIn/0JRrv2fblnz6cLrwCpLojCbDKt4imLpe3YZ2qXgEgIquAX4jIZ3yOaaqyZhusS50xJquISAhnpKZUsvRX/E5874V83S7I722bDsnRJMO5GLfF7K2033Ilzm/5ZT9+y9myT8mWz8ONxfdy48ZQBXRmW9kdj4j8K1CM0z0yNfLv1cCQqt7iY2iesO3LPyLyHPAqVe0eMf33qvpqn8KaklzeBhF5Aid1zrD7vAqni/U6VW3wNbhJyqZtsAonY0zWcJvavpWTk7TelcsVGPm6XZDf2zZVkiVJhqcqF+O2mL0n7rDJ7uM34oySugunW9msHSxmyz4liz4P38uNiJyuqi+JM7LvzRz/LL6tqv2zEUOmRORc4CKOVx6uV9UX/IzJS7Z9+UWc0cfbU5UFadNDuXJslcvb4Oao26uqrWnTgsD1qvpT/yKbvGzaBqtwMsZkDRH5EbCFk5O0nqOq7/Uztkzk63ZBfm/bVGVLkuGpysW4LWbvyfFBUf4Z56TubpxReBep6gdnMY6s2Kdk0efhe7lJ+yz+C9gL3IXzWbxRVa+bjRiMMcbkJsvhZIzJJk2q+r4R014Qkcd9icY7+bpdkN/bNlWpJMNJ93mniPwKuMW/kCYlF+O2mGfOxam8D8D94ozKO5uybZ/i9+eRTeXmdFX9c/fxSyIy8nsyxhhjTmAVTlMgIgmcq24h4EXgA6o64G9UDhG5CadP5v8a4/VbgT5V/dpsxjVZIvJb4D2p5uuTmP+tOLkUtrvPHwX+WrN0iFQR+T5wr6r+0u9YstzdInIv8CjH++lfAfzGz6A88Js83S4Ye9vuGW+hPJUtSYanKhfjtpi9t8ZNwntmqjuZiASA0lmOI1v2KWvcSq4zfP48sqHcLHA/i+q0z6IAKJvFGIwxxuQg61I3BSLSp6ql7uOfAM+p6r9OYrkZ76ua6xVOUzWyAscqnPKHiNQC53O8n/4zOFe8n/ExrIyJyKU4ow524XSLeAZYpqob/IzLCyKyDiexbhBIAAFV/bG/UfkjW5IdT1Uuxm0xe09EVgIJVX3RfV4MrFLVp2Y5jtT/wFpgJ7Bztv8DROQtwO/TLyy6n8cpsz0CcDaWGxEJA1Xp+UHmIvf4859V9YG0abcAp6rqxz1Y/3eBf01dYJ3islcCw6r6pPv8z4EBVf1hpnFNIYa/U9VZH4bdL+nnivlmZHma5DLrcLr/fnKm4pqKqTZwGLHsW0lr7DCF5XwpEyLShHPuuXKCeS5W1dvd5zPyfQW8XNkc8ziwQkRKROR/ROQZEXnBPUBBRG4SkV+IyD3AgyIyX0T+KCIbRWSriFzmzneDiGxxp305tXIR6ROR/yMim0TkKRFpcKe/SUQ2uO/1UGr6VIjIoyLyZRF5WkReToslKCJfc+PZLCKfcKdf7b7fFndbC93pe0Xk/4rIehF5VkTWiMgDIrLL/VNLvd9n3M9ns4h8cYyY9opIrYg0iciLIvIdEdkmIg+KMypK+rwXA28Gvup+nsvdl64fZZsiIvI9N/YXROSqtO/nW2nrvFdErnQ/g++738cWEfm0+/pH3W3YJCK/cg86cef9dxF5UkR2i8g73OkiIt8Ske0ich9QP9XvaS4S5+pxB3A/8FP3vh3I6YMVEfkXnKSvFwDvAZ5R1aPAP/samAdE5P8BHwMuA96Ck9j2dBG5zdfAfCBOMsa3AB8EPuze3uqeLGatXIzbYvaeu5/6G+BvROQeEalzK1tmdf8rIverahtOBcsFOJUsnxSRL81mHMB/Ag+LyF0i8gERqVLVAR8qm3wvN+4xzbUi8jr3fxpVjeEkcZ7r7gDePWLau93pGVPVj0ynssl1JXBx2rr+y+vKJrd8jufvvHy/ucDP/4QJ3vtK0srTZKjqs9lS2QSgqm+YTmWT663Amd5FkxWacM5LgBn8vlTVbpO84bQQAqdL3d04J1n/F3ivO70SeBkoAW4CDgLV7mt/Bfy9+ziI0wx5AbAfqHPX+Qfgre48CrzJffwV4HPu4yqOt0z7CPAv7uObgG+NE/utOC2AwGmmnlruDcBD7uOPAb8CQu7zaiACHMC5UgPwQ+AW9/Fe4GPu468Dm93tqgNa3emvAW4DBKeC817g8lHi2wvU4hT8OLDanf7z1Oc7Yv7vA+9Iez7WNv0V8D338enu5x0Z+Xm5cV2JczX192nTK937mrRp/wR8Ii2OX7jbdibOVViAtwG/d7/rBThXJN8xcjvsdtL3OuD+DtJvj+CMcuF7fBls12Npj1e55fU84A9+x+bxtm1Je/yI37H58Fn8CPj/gDXAcuBc4DPAj/2OLd/itphnJL6s2E+l3g94DKe1ZGr6n2Y5jkfc+6XuscSjwAPAx+daucFJWv5POMeSjwOnpX9Xc/mGk2PrKFDoPm/COdYUnErLZ4FtwBfTljkPeBLYBDyNc+wcBL6Gk7pjM8ePMx/F6cEA0Af8H3e5p4AGd/qbgA3ACzjJ5RvcOI4Ah4CNOBeFbuX4ucBqdx2bgTtxWqul3u/LblwvA5eNss1X4hyb3Q5sd6fdBTznbuvN7rQv4bR63gj8xJ32XnfdG4H/BoJ+f4cel4fUueKV7mf5S+Al4CccP38b7fu/Ced84h6cY98S4H9wWsO/ALwlrXw9Djzv3i52p88H/uh+rltT3xvOedh6d95fAKWjxPwozvnsYzj7usmWpzqc88Zn3NslY5SVe93Ht7rb9CiwG/jkKPN/DPhK2vObgG+OVcbc6a9zt28T8LA7rRT4Hsd/T293p+/l+Pnmi8B33PU9CBS583zU3Z5N7vYV41S0dQB73O1f7t7ud2N6HCfHHTj/GevddfwjbpkYsZ1NOOXiB258vwSK3deudj/7Le7nVZgWe+q3+TSwwp3+fU48J+5Le4+tE5Sbp3B6XWwEPj3i+6p2P/PN7nyrJvs9nrS9fv8wc+nG8Z3mRuCbQAHOH8nWtOn7gTNwfiDfS1v2cpxm4bdyvDLlLcAP0+b5ME6zWYAhju+Y3gV81318tvuj2ALsAO5P+0FOpcLpEvdxA8crSX4FvHrEcucAf0x7fjXw67SCv9B9/CHgO2nz7cepgPuaO1/q89kJfHiU+PZyfAfwStr0v8GtbBsx//c5ucJptG26E3hV2nyP4xxIn/B5cbzCqQpnqN9v4uzAAu7rV7jLbsHZ2fxXWhw3pq2n173/N+BDadN/jVU4TeY39hxQMcr0389mHDOwXU8ABWnPq4D7gBa/Y/Ni29Ievynt8aN+x+bDZ/H4VKZnyy0X47aYZyS+rNhP4ZzU/BDnol1R2vRnZzmOR0aZ1kDaic5cKTfp+3Oci2gP4hzDzvkKJ/czuY/jFQKfBb7qPk5ddA66x6mrcM4ddgPnua+V41x0Pumib+qz53iFkzK1i9G34h77j3yOcxJ5hfv4H4B/S3u/ky7gjtjeK4F+YGnatFS8RTjnRTXu8760ec7AqVAJu8+/jdN9x/fv0MOykF7h1A0swrkovR64dJzv/yZObKgwVoOGYiDiTj8Fd7/I6A0banEqoUrc6X8D/O9RYn4U+Hba88mWp9uBS93HjcCLY5SV9AqnJ4FCN7b2VFlIm78O9xzOff67tPc4qYy58x9IlcW0eb6cKtOpbXLv9zJBAwfGb2SQfu75ME4Xa3Ba46YulvwmVa6Bv2DsCifl+Lnr/wB/zcQNPVLf8fvTPteRcY1W4TRWuTn2/YzyfX0T+IL7+FXAxsl+jyNvWdGMO4cMqurq9AkiIji1pjtGTL8AZ2cMgKr+UUQuB64FfiQiX8VJ/jiWmLrfKk5FV+q7+iZOpdRvxOlLe+s0t2VolHULTuFPJ5NcTzLtcep5yF3+n1X1v6cRWyq+orFmHGO5kds0mjgndimNAKhqp4icA7wWZyfxTpzKtO/jtD7bJE6+rCvHiDf9/UZ+lmZibwQGR5n++tkOxGOfxjlgaIVj5ezNwPV+BuWRm0UkqKoJVb0HQJxkshPmt8tDd0tuJofPxaT22ZJYeipGxlyBczEqW2LOlv3UBe7953H+qxGRUvf5bDqpC5+qtuC02p5N2bBfCYhIuar2qGqziLwR53NYO4sxZLNUt7q73fsPudPfKSI34xyTzsdpCa/AYXVzkqlqD4CIXINzMTPuTu8Y5X2GcS6QgnOB7tXu40XAz0RkPk6Fxp7xghWRCpwW/KkRF3+A0/ol5ddp79E0xmqeVtX09/mkiFznPl6Mc1LbPmKZq3HKzDPO6RNFuPubPPW0qh4EEJGNOJ9lN6N//+BcXE19768B3iwif+0+j+BU6jQD3xKR1TjnO6e6rz8D/I84udXuUtWNInIFTpl7wl1/AU7F12h+lvZ4suXpGpxBJlLPy0WkTFV7x/lM7lPVIWBIRFpxKvEPpl5U1aNuipILgVeA03AuhsDoZawOp2HEHnf51Od3DWldXVW1c5RY9qjqRvdxellfKSL/hPN/WIrTsvUE7n/SxcAv0ra/0L2/BHi7+/hHOJVfozmgqqlt+zHwSZzeMXtU9WV3+g9wzkf/zX1+R9r918dY72jCjF5uxnMp7nao6h9EpMbdd8AE3+NIVuGUuQeAT4jIJ1RVReRcVX1h5EwisgQ4pKrfEZESnKbRXwa+IU5yzE7gBpwKpfFU4DRnBPiAZ1vheBD4cxF5VFXjIlKN09yvSURWqOpO4H04TS4n6wHgH0XkJ6raJyILcSrTMv2D6WVyo6P8EbgR+IOInIqzs96Bc8D2cTcXwUKc5KSpRKXDqvorEdmFU9GE+16H3R35jRz/DsZ73z8TkR/i5G+6CudKgBmHqh4eY3rWJNWdDlV9epRpCZw8VTlNVbeNMm2Y7K6smBGq+jVxBghIJb0/iHOw0ORfVBNT1a+KyHqcFrS9HI97ma+BjcON+RGOJ6vvA36mWZys3o35ezjlowLnKmZEVb/ib2SObNlPqeq+Uab14Vzpns04TjrJ8EOW7FfeT9pFOncff5OI/M8sxpDN7gL+VUTW4LTKe15EluK0WDjPrbz9Pk6lwWgXdxlnerqZvhidMtoF3JGOXVR33/Ma4CJVHRAnkXpklGUE+IGq/m2G8eWKkRfQUxfix/qe+9Mej9Wg4VagBacHSgCIwpgNGzpxKrFumESs6e892fIUwPnOR7tQPJbRPpORfoZzwf8l4E73/PpKRi9jmfyexmrg8H3GbmSQEgC6RjZESTOZRgcj51Embuihozw+1ojCbQhTMMpyn2aUcjOB0WJJvedkvsdjLGl45v4Rp9Zws4hsdZ+P5kpgo4i8gFNb+A335PpvcfpBbwKeV9W7J3i/W3FqUx8H2jIP/wTfxekKt1lENuFk8Y/iJKr8hYhswWm59F+TXaGqPohT0bLeXf6XeDOM7k+Bz4iTCHz5OPN9Gwi67/0z4Ca3RvYJnBr7LTjd/p5351+IM/zwRpwdTupP8fM4/Zl/j7MDnMidODXzW3D68E+lks4Yk4MkR5PeSw4mtZccTFbv/m//GqfLzcdxkuneIiJ/9DUwk9WyZL+yH+gRkUD6jdlvdZaV3ArRR3G6xaRaIJTjnMR3izPAT6ql9kvAAhE5D0BEytxEzamLviF3evUUQhjrYvSoF2dVtRvoFHeAHaZ+MXm09+90KwJOx9kfp8Tci7XgdEF6h4jUg7ON7gX5uWSs73+kVIMGcec7151egdNCKonzvQXd15fg5M/9DvD/cBo2PAVcIiIr3HmK3YvvE5lseXoQODY6utt6xgu/xknQfQPHW16NVcbWA1e4Fbzpv5uRsVVN4f1HNjJIObb9bsu0PSJyvbt+EaeHDDjnmKnWVenLj9QoIqmBF24A/kRaQw93+sjf5rvS7lOt1fZyvLXpW3DqJUYatdwwfgOOVKONVKVyW6pF3lRZC6cp0FGGNHRrdf9slOnf53jrGFT1BzhXpEbOdzujtHxJfy9V/SVORQ1uhdRJlVIj32+U129Ne3xl2uM23KtkbiuSv3Rv6cs+jJOkcuQ6m8Z6/xGvfQP4xlixjZi/DViZNv1rY8z/BCeOFHBl2mvp2xTF6Rc9cnll7J3AmlHm/0+ciqOR028a8bw0bf3/a+T8xpi81odzgJdOcPJ2ZLN1qnoFgIiswrnA8BmfY5rIirSYt6hqaoTQR/wNa1x34pSF76vqowAi8jtVzfUuw2ZmZcN+JRVDequBXNi3zaY7cE6U3w3gto54ASch8W7cbkGqOiwi7wK+Kc4ozIM4LTe+i9Nic7OIxHCSGX/rpHcZ3a04++1DON/TUnf6PcAvxRlB+xMjlvkA8F/ijLq8G+fi8nTdj1NZthmnF0F6eb0NZ5ueV9UbReRzOKN3B4AYTnehk1o15qtxvv+R/hGnG9Vmt9JpL07aiW8Dv3IrOh7heMukK3EuxMdwfq/vd7un3QTcIe4I48DncPJBjedWJleePgn8h/u9h3AqKP785NVNjdsicDtwZlrL21HLmLuNNwO/dstUK05X039yY9uK0wLnixzvKjqRVCODfTgNB1IVMj8FviMinwTegXMe+Z9umQ67r28CPgXcLiKfwsnLNpYXgQ+IyH/jNFL4T1WNikiqoUcIp6tkekOPQhHZgNNoKNVy7Ts4Xa+fxqnUTW+tljJWudkMxN2GJt/HSVaecivwPfczHyCDnlWphGDGGOM79yrg13GuXHTi5Cv4iqre6dau341zYFSM0zT0K6p6r7vsrTgjSxzF+eP7O1X9zYj1vxnnD2xSw2uLSCVOS79vu8+vxEmY+MYMNnPGiEgTTrK/lRPNa7wnIs/hDFLQPWL671X11WMs5jsReQK4yu0mk7oS+GOciqgGX4Mbg4g8oaqXuI/fpMfzhz2aflEl24iT3+wjOLmbbscZ6dUqnMyYsmG/kg0xGGNMvpjO8bqI7MU5LvO6h9OMsy51eUZE/l5ENo64/b3fcRkzEfcKzl04yf+WqepanCuFi9Jme1xVz1XV03CurHxLRK5Oe/3rbn/q63GSJ56wj1PV30y2sslVidP1xZjJyNWk96lk0cCx5JpvxrlKl61uFpEggOZQsnpVHXYrsN+LM8LOJp9DMtkvG/Yr2RCDMcaYHGQtnIwxWcGtOPrfqW4yo7x+JSNaF4nIh3CGCL7ObeHUl+qGKSJHgFWalqDebVq8TlX/lzgJPHuAdcA84P9zu6+mv+dPcfpD78DJ33UfThPTVNfP53CGUVU3/q/htK56BqflwlD6FQkRWQd8TVWvFGf0kFRXU8Vp8aA4rbiqcJrnfk5V73avhPwOp3/3xTh969+iqoMishYnb8SA+/rrrYWTMcYYY4wxxm/WwskYky3O4njy9sl6Hjh95EQRuQAnwf3RCZafjzPs5xsZZQhsnOS+u1R1taqmctqcC9yCk0NsGU5CxghO3+d3qerZOJVOH5vgvf8a+Au3RdZlOFePo8B1qroGZ2TDf0kljMQZ/vU/VPUsoIvjQ65+D/ikql6EMcYYY4wxxmQJq3AyxmQlEfkPEdkkIs+MN9uI558WZ4TBr+FU/kzUhPMuVU2q6nZgsrlqnlbVg+5IDxtxEtSfBuxR1VQixh/gtFgazxM4wyh/Eqh0k/YL8H/dBH0P4YyamIprj6pudB8/hzOKRYW7bGoEix9NchuMMcYYY4wxZkZZhZMxJltsI22EQFX9C+BqoG6cZc7FGeUh5etua6TLVPXxSbznUNrjkZVXk1kmgdOaabxl4xzf10ZSE91cUh8BioCn3GFeb8TZ3rVuy6eWtGXGel/rF+0DEUm4OfK2isgv3JF+vFz/o24XzPHmuSX9fUXkt26i+0zf+89F5P1TmL9JRN6T9vwmEZns6EqzTkSuFJF7/Y5jNohITVo+xyMicijtecGIeW+ZTDkerWyKyK0i8s8jpq0WkRfxwFTL5IhlTyifU1ju+yLyjlGm/4OIjDaq1FjrWS0ib0h7fquI/PVU45kt2f77NcYYk1uswskYky3+AEREJL0r2pgnP+IM3/554D9mMKZejg+HOp6XcFocrXCfvw9ItTraC6x1H6e6wSEiy1V1i6p+GXgWp2tgBdCqqjERuQpYMt6bqmoX0C0il7qTbpxErMYbg27l5kqc0RQzHgp4Gm4h7Teiqm9wy0RGVPW/VPWHU1ikCZjyCb2Zeara7pbT1ThDK6cq5VenRiVMcwvj7HMncAfwrhHT3o0zEl/GplEm0zXhYflU1f+tqg9NYZHVwBsmmskYY4zJR1bhZIzJCm73t7cCV4jIHhF5Gqdr2t+kzXaZiLwgIjtwKpo+qaoPz2BM7cATbiuWr44zXxT4IPALEdmCkz/qv9yXvwh8Q0Qex2mZlHKLu95NOPmbfgf8BFgnIs/iVB69NIkwPwj8h4isZ/RRhMzMexxYISLVInKXiGwWkafcStFUi4YficgfROQVEfmoO/2EljYi8i03sf0JROQ/ReRZEdkmIl90p30SWAA8IiKPuNP2ikit+/gv3fK1VURucac1iciLIvIdd10PikjRKO93rAWG25rlyyLytIi8LCKXjbL9X8L5bW4UkU+70xaIyP3u9n4lbd03iMgWN64vp03vS3v8DnGS+iMi16d+JyLyx7TteFxEnndvF6d9no+KyC9F5CUR+YmIkwNNRF7nTvsT8Laxv8r8JyJXu/vRLSLyPyJSOEZ5OqncjUVVdwBd4uTPS3kn8FMR+aiIPON+h78StxWViDSIyJ3u9E1p3+P73d/QJhH5kTttwjI5VrlgRPkUkaCIfNWNabOI/Jm7vLi/we0ich9QP8bnd6zlk/ub+6L7flvEaamaPm8B8A/Au9z3T1XKnelux273s0/NP9bvdmvaPH8tziAZiMgn3Xg3izPIBSJyvog86X7HT4rIae70m0Tk12P8Lj/ofpaPAZeM910bY4wxU6KqdrOb3exmN7vZbQo3nBERwenaeDdOkvhvAl9wp78K2Og+vhXYhNN9shY4gHNyfyVwb9o6vwXc5D5+FGd0Q4Bq9z7oTl/lPt8L1KYtv9dd/1pgC1AClOJ0Vz0Xp6VHHFjtzv9znFEWR27brTgjQqbi+Bf38RuAh0aZf+R23ATsxmmxFwH2AYvdbd6P0200hNOq8a3pn6f7+B3A993HW4CF7uNK974YiLiPTwGeTYujG1iEc0FtPc6gABH3Mz8Fpxvqz9PjnSs393v9nPtZnOpO+yFwyxjlaaxyd6xsjlj/Z3BaUAFcCDzjPq5Jm+efgE+4j3+W9t5Bt7ychTMqaO2IGCYskxOUi/TyeTPOCKAAhTgtTJfiVET+3o1lAc7gDO8YZTu/n5rufmap7fk48N1R5r8J+NaI7+FJ971rgXacUUnH+91uTVv+r4Fb3cfNQOGI30c5EHIfXwP8aoLf5XyO/y4LcPILfmvkdtjNbnazm93sNp2btXAyxhhjpq5InAT1z+KcrP0/nMqNHwGo6h+AGnESuwPcraqDqtoGPAKcP4X3eqeIPA+8gHNCfuYE818K3Kmq/araB/waZyREGCX5/CTe/9dTnB/gYVXtVqf133ac7qHnAY+q6lF1kuT/hMkl1/++OK3Cgu60MPAdcVoT/oITP4/RkvqfjrPdr6iqAj+e5DbkoyCTH+BgquXup8A7RCSA053uDnf6Srfl0RaclptnudNfBfwngKomVLXbnfZL93eCqnaM8V6jlcnxykW61wDvd3+/G4AanAqqy4E73FiacSpEJ2M6v4/7VHXI3c5WnMEhxvvdjmUz8BMReS9OZTI4FUq/cFtFfZ3jnzeM/ru8gOO/y2GcikBjjDHGEyG/AzDGGGNy0KA6eXGOSXXfGkFH3KdPT08oD2lJ5dPWuRSnRcN5qtopTlezk+Ybudg4r41MPn9Sl7pxlkklq5+MqSbXT/980pPr/7nbTetaYKOIrAY+gZNQ/xyczy86wfuOXP9c1j+ZmaZT7lT1gIjsBa7AyVd3kfvS93Fasm0Sp8voleO9NZP7rkYrk59m7HIx8j0+oaoPnDDRSew9nXIy07+P8fYT1+JUlL0Z+LyInAX8I/CIql4nIk04LcLGe1+w34cxxpgZYi2cjDHGGG/8ETdxu4hcCbSpao/72ltEJCIiNTgn3M/gdGk5082hU4EzKuNI5TiVBN0i0gC8Pu21sZLa/xF4q4gUi0gJcB1OnqmZMtnk+htwcrTVikgQuIHjyfVbROQMt3XMdakFxEmuv0FV/zfQhtMFqAI47LZieh/HWz6N5SVgqYgsd5/fMNkNy0MRxh7gIP17HK/cjecOnFY1u1T1oDutDDgsImFOHNjgYZyuqLh5lcrdae90fyeISPUUtm2scjGyfD4AfMyNBxE51f2d/BF4txvLfOCqKbz3eCb7+xjrd9sC1Isz4mAh8EY37gCwWFUfAf4/oBKnK14FcMhd502TeN8NwJXu+sPA9ZPdMGOMMWYi1sLJGGOM8catwPdEZDMwAHwg7bWngfuARuAf3S47iMjPcbrFvILTdekEbquQF3DyuezG6WKWchvwOxE5rKpXpS3zvNsi5Wl30ndV9QW3tcNM2AzExUmA/32gc7SZVPWwiPwtTpdCAX6rqne7L38WuBcnv9BWnBNngK+KSCr30sM4ubC+DfxKRK531zVuqx1VjYrIzcB9ItIG/AlYOc1tzXXpAxyEcCo+UwMcnFCexil34/kF8A2cVmgpn8ep1NiHk6MoVfnyKeA2EfkwTmubj6nqehH5P8BjIpLA+U3cNMn3HqtcjCyf38Dp+va82yrxKM6AFXfidOnbArzM8Yq4TD0CfNbtwvfPY8001u8WQET+Aecz3MPxwSSCwI/dymrByZ/V5SYD/4GI/CWT6Bbo/i5vxcl5dhh4nokrcY0xxphJESedgTHGGGNmgnsy16eqX/M7FmOMMcYYY2aLdakzxhhjjDHGGGOMMZ6yFk7GGGOMMcYYY4wxxlPWwskYY4wxxhhjjDHGeMoqnIwxxhhjjDHGGGOMp6zCyRhjjDHGGGOMMcZ4yiqcjDHGGGOMMcYYY4ynrMLJGGOMMcYYY4wxxnjKKpyMMcYYY4wxxhhjjKf+f65uJagJnHUWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## YOUR CODE HERE\n",
    "## FEEL FREE TO ADD MULTIPLE CELLS PER SECTION\n",
    "pd.plotting.scatter_matrix(df,figsize=(20,20),)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scatter diagram above, we can see that there are some trends between our data. It also looks like there are some outliers in the scatter plot of \"GDP in thousand\", \"Population in thousand\", \"personal_income in thousand\", and 'Total Vaccinated in thousand'. Hence, in order to further confirm the relationship between our variable, we can make outliers analysis for further data analysis and rearrange our data to check whether the data has outliers and decide whether we need to delete outliers if any. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to get a sense of what information we have in the df dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Personal_Income in thousand</th>\n",
       "      <th>GDP in thousand</th>\n",
       "      <th>Population in thousand</th>\n",
       "      <th>Total Vaccinated in thousand</th>\n",
       "      <th>Vaccination rate</th>\n",
       "      <th>Increase rate in vaccinated population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.088627</td>\n",
       "      <td>443.130588</td>\n",
       "      <td>6496.932157</td>\n",
       "      <td>1900.006275</td>\n",
       "      <td>0.281612</td>\n",
       "      <td>1.619025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.951744</td>\n",
       "      <td>575.011856</td>\n",
       "      <td>7428.830471</td>\n",
       "      <td>2364.218883</td>\n",
       "      <td>0.076545</td>\n",
       "      <td>0.426720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>44.130000</td>\n",
       "      <td>36.090000</td>\n",
       "      <td>581.080000</td>\n",
       "      <td>89.990000</td>\n",
       "      <td>0.146200</td>\n",
       "      <td>0.825800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>53.440000</td>\n",
       "      <td>101.065000</td>\n",
       "      <td>1813.990000</td>\n",
       "      <td>482.340000</td>\n",
       "      <td>0.224150</td>\n",
       "      <td>1.354300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>57.790000</td>\n",
       "      <td>253.310000</td>\n",
       "      <td>4480.710000</td>\n",
       "      <td>1144.260000</td>\n",
       "      <td>0.281500</td>\n",
       "      <td>1.603900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>65.085000</td>\n",
       "      <td>572.865000</td>\n",
       "      <td>7658.520000</td>\n",
       "      <td>2583.460000</td>\n",
       "      <td>0.336950</td>\n",
       "      <td>1.941950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.040000</td>\n",
       "      <td>3290.170000</td>\n",
       "      <td>39613.490000</td>\n",
       "      <td>13193.700000</td>\n",
       "      <td>0.464200</td>\n",
       "      <td>2.665100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Personal_Income in thousand  GDP in thousand  Population in thousand  \\\n",
       "count                    51.000000        51.000000               51.000000   \n",
       "mean                     60.088627       443.130588             6496.932157   \n",
       "std                       9.951744       575.011856             7428.830471   \n",
       "min                      44.130000        36.090000              581.080000   \n",
       "25%                      53.440000       101.065000             1813.990000   \n",
       "50%                      57.790000       253.310000             4480.710000   \n",
       "75%                      65.085000       572.865000             7658.520000   \n",
       "max                      90.040000      3290.170000            39613.490000   \n",
       "\n",
       "       Total Vaccinated in thousand  Vaccination rate  \\\n",
       "count                     51.000000         51.000000   \n",
       "mean                    1900.006275          0.281612   \n",
       "std                     2364.218883          0.076545   \n",
       "min                       89.990000          0.146200   \n",
       "25%                      482.340000          0.224150   \n",
       "50%                     1144.260000          0.281500   \n",
       "75%                     2583.460000          0.336950   \n",
       "max                    13193.700000          0.464200   \n",
       "\n",
       "       Increase rate in vaccinated population  \n",
       "count                               51.000000  \n",
       "mean                                 1.619025  \n",
       "std                                  0.426720  \n",
       "min                                  0.825800  \n",
       "25%                                  1.354300  \n",
       "50%                                  1.603900  \n",
       "75%                                  1.941950  \n",
       "max                                  2.665100  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 找了前四列的outlier，每个数据都不能删除，51个states不能少"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Personal_Income in thousand Outlier Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lower Outlier = 53.44 - 1.5 * (65.085 - 53.44) = 35.97 <br>\n",
    "Higher Outlier = 65.085 + 1.5 * (65.085 - 53.44) = 88.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Personal_income in thousand Outlier Analysis')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iSkS0RUTb0KFDWx2mmdlqq4qunl2AhyJiUUS8DFwMeFBzM7MWqSLxPwJsJ2mQ0iOVdgZmVRCHmVmRqujjnwZcBNwC3JljmNLqOMzMSlXJoxcj4hjgmCrKNjMrnf9z18ysMA0lfknbNzLNzMz6vkZb/D9pcJqZmfVxXfbxS3oX6VbLoZK+UDNrHdJQC2Zmtorp7svdNYG183JDaqY/A+zdrKDMzKx5ukz8EXEdcJ2kMyLi4RbFZGZmTdTo7ZxrSZoCjK5dJyJ2akZQZmbWPI0m/guBk4BTgFeaF46ZmTVbo4l/SUT8vKmRmJlZSzR6O+fvJH1W0kaS1m//aWpkZmbWFI22+Cfm35NrpgWwae+G0/uGjxzF/Llzqg7DzKzPaCjxR8QmzQ6kWebPncOEk2+oOozlnP8Zj0RtZtVoKPFL+mS96RHxy94Nx8zMmq3Rrp5ta14PII2hfwvgxG9mtopptKvnsNr3kl4LnNWUiMzMrKl6Oizz88DmvRmImZm1RqN9/L8j3cUDaXC2twAXNCsoMzNrnkb7+H9Q83oJ8HBEzG1CPGZm1mQNdfXkwdruIY3QuR7wUjODMjOz5mn0CVwfA/4G7AN8DJgmycMym5mtghrt6jka2DYiFgJIGgr8CbioWYGZmVlzNHpXzxrtST97YgXWNTOzPqTRFv8fJV0BnJvfTwAub05IZmbWTN09c3czYMOImCzpI8C7AQE3Auf0tFBJ65LG9h9Luk30oIi4safbMzOzxnXX4j8B+CpARFwMXAwgqS3P26OH5f4I+GNE7C1pTWBQD7djZmYrqLvEPzoi7ug4MSKmSxrdkwIlrQPsAByYt/USvj3UzKxluvuCdkAX8wb2sMxNgUXA6ZJulXSKpMEdF5I0SdJ0SdMXLVrUw6LMzKyj7hL/zZIO6ThR0sHAjB6W2R/YBvh5RGwNPAcc2XGhiJgSEW0R0TZ06NAeFmVmZh1119VzOHCJpE+wNNG3AWsCH+5hmXOBuRExLb+/iDqJ38zMmqPLxB8RjwHjJb2XdAcOwO8j4pqeFhgRj0qaI2lMRNxLGtv/7p5uz8zMVkyj4/H/GfhzL5Z7GHBOvqPnQeBTvbhtMzPrQqP/wNWrIuI2UpeRmZm1mIddMDMrjBO/mVlhnPjNzArjxG9mVhgnfjOzwjjxm5kVxonfzKwwTvxmZoVx4jczK4wTv5lZYZz4zcwK48RvZlYYJ34zs8I48ZuZFcaJ38ysME78ZmaFceI3MyuME7+ZWWGc+M3MCuPEb2ZWGCd+M7PCOPGbmRXGid/MrDCVJX5J/STdKumyqmIwMytRlS3+zwOzKizfzKxIlSR+SSOADwKnVFG+mVnJqmrxnwB8GXi1swUkTZI0XdL0RYsWtSwwsxWyRn8k9bmf4SNHVV0z1of1b3WBknYHFkbEDEk7drZcREwBpgC0tbVFa6IzW0GvLmHCyTdUHcVyzv/M+KpDsD6sihb/9sCHJM0GzgN2knR2BXGYmRWp5Yk/Io6KiBERMRrYF7gmIvZvdRxmZqXyffxmZoVpeR9/rYi4Fri2yhjMzErjFr+ZWWGc+M3MCuPEb2ZWGCd+M7PCOPGbmRXGid/MrDBO/GZmhXHiNzMrjBO/mVlhnPjNzApT6ZANZtYk+TkBfc2wESOZN+eRqsMonhO/2erIzwmwLrirx8ysME78ZmaFceI3MyuME7+ZWWGc+M3MCuPEb2ZWGCd+M7PCOPGbmRXGid/MrDBO/GZmhXHiNzMrTMsTv6SRkv4saZakuyR9vtUxmJmVrIpB2pYAX4yIWyQNAWZIuioi7q4gFjOz4rS8xR8RCyLilvz6WWAWMLzVcZiZlarSYZkljQa2BqbVmTcJmAQwatSo1gZmZs3h5wT0CZUlfklrA78GDo+IZzrOj4gpwBSAtra2aHF4ZtYMfk5An1DJXT2SXkNK+udExMVVxGBmVqoq7uoRcCowKyKOb3X5Zmalq6LFvz1wALCTpNvyz24VxGFmVqSW9/FHxPVA3/t2x8ysEP7PXTOzwjjxm5kVxonfzKwwTvxmZoVx4jczK4wTv5lZYZz4zcwK48RvZlYYJ34zs8I48ZuZFabS8fjNzPqEPvqcAGjOswKc+M3M+uhzAqA5zwpwV4+ZWWGc+M3MCuPEb2ZWGCd+M7PCOPGbmRXGid/MrDBO/GZmhXHiNzMrjBO/mVlhnPjNzArjxG9mVphKEr+kXSXdK+l+SUdWEYOZWalanvgl9QN+CnwA2ALYT9IWrY7DzKxUVbT43wHcHxEPRsRLwHnAnhXEYWZWJEVEawuU9gZ2jYhP5/cHAO+MiP/qsNwkYFJ+Owa4t6WBtsYGwONVB9EHuB6Wcl0krodkZeth44gY2nFiFePx13vawXKfPhExBZjS/HCqI2l6RLRVHUfVXA9LuS4S10PSrHqooqtnLjCy5v0IYH4FcZiZFamKxH8zsLmkTSStCewLXFpBHGZmRWp5V09ELJH0X8AVQD/gtIi4q9Vx9BGrdVfWCnA9LOW6SFwPSVPqoeVf7pqZWbX8n7tmZoVx4jczK4wTfwtJmi3pTkm3SZqep60v6SpJf8+/16s6zmaTtK6kiyTdI2mWpHeVVg+SxuTzoP3nGUmHl1YPAJKOkHSXpJmSzpU0oMR6AJD0+VwPd0k6PE/r9bpw4m+990bEuJp7c48Ero6IzYGr8/vV3Y+AP0bEm4GtgFkUVg8RcW8+D8YBbweeBy6hsHqQNBz4HNAWEWNJN3zsS2H1ACBpLHAIaXSDrYDdJW1OE+rCib96ewJn5tdnAntVF0rzSVoH2AE4FSAiXoqIpyisHjrYGXggIh6mzHroDwyU1B8YRPq/nhLr4S3ATRHxfEQsAa4DPkwT6sKJv7UCuFLSjDwkBcCGEbEAIP9+fWXRtcamwCLgdEm3SjpF0mDKq4da+wLn5tdF1UNEzAN+ADwCLACejogrKawespnADpJeJ2kQsBvpn117vS6c+Ftr+4jYhjQy6X9K2qHqgCrQH9gG+HlEbA08RwGX8Z3J/8T4IeDCqmOpQu6v3hPYBBgGDJa0f7VRVSMiZgHfA64C/gjcDixpRllO/C0UEfPz74Wk/tx3AI9J2ggg/15YXYQtMReYGxHT8vuLSB8EpdVDuw8At0TEY/l9afWwC/BQRCyKiJeBi4HxlFcPAETEqRGxTUTsADwJ/J0m1IUTf4tIGixpSPtr4P2kS7tLgYl5sYnAb6uJsDUi4lFgjqQxedLOwN0UVg819mNpNw+UVw+PANtJGiRJpPNhFuXVAwCSXp9/jwI+Qjo3er0u/J+7LSJpU1IrH1J3x68i4jhJrwMuAEaR/gj2iYgnKwqzJSSNA04B1gQeBD5FaoSUVg+DgDnAphHxdJ5W4vnwDWACqVvjVuDTwNoUVg8Akv4CvA54GfhCRFzdjHPCid/MrDDu6jEzK4wTv5lZYZz4zcwK48RvZlYYJ34zs8I48ZuZFcaJ37ok6ZU8bPBMSRfme8/7BEkHSjqxi/nHSvpSK2NaEZIul7TuCiy/l6Qtat5fK6mtq3WqJOkMSXtXHYctz4nfuvNCHj54LPAScGgjK+WRFq0LEbFbHpm0UXsBW3S3kFl3nPhtRfwF2CwPP3GapJvzCJt7wr9a4BdK+h1pFNKNJE2tuWL4t7zcfvmBNDMlfa9945IWSzpO0u2SbpK0YZ6+h6Rpuaw/tU9fEbl1/D1Jf5N0X00s/ST9IMdzh6TD8vSdc3l35n1dK0+fLenbkm6UNF3SNpKukPSApENrypuc6+eO/J+p9WKaLWkDSaOVHkjzi/wAjislDeyw7HjSYG7fz/X5xjxrnzr7NEDS6Tn2WyW9t+b4nFizzcsk7Zjr4Ix8PO6UdESef0jeh9sl/br9ai8v+2NJN0h6sL1Vr+RESXdL+j1ljKi5SnLit4bkFvwHgDuBo4FrImJb4L2kZDQ4L/ouYGJE7AR8HLgiP2xkK+A2ScNIIxDuBIwDtpW0V153MGk88q2AqaSHUgBcD2yXR/M8D/hyD3ejf0S8AzgcOCZPm0QaGXLriHgbcI6kAcAZwISIeCtpiI3/qNnOnIh4F+mD8Axgb2A74Ju5rt4PbE4ahG8c8HZ1PxLr5sBPI2JL4Cngo7UzI+IG0pgtk/MV2ANd7NN/5nXeShoL6My8T50ZBwyPiLF5ndPz9IsjYtt8PGYBB9essxHwbmB34Lt52oeBMcBbScdufDf7bBVx4rfuDJR0GzCdNE7IqaQB5o7M068FBpDGEQG4qmYckZuBT0k6FnhrRDwLbAtcm0djXAKcQ3owC6SupMvy6xnA6Px6BHCFpDuBycCWPdyXi+tsexfgpBwLOfYxpBEj78vLnFkTI6QEDOlDcFpEPBsRi4B/5j779+efW4FbgDeTEntXHoqI2+rE15N9ejdwVt6fe4CHgTd1sY0HgU0l/UTSrsAzefpYSX/J9f4Jlq3330TEqxFxN9B+BbYDcG5EvJJHor2mwX2wFnM/rHXnhdxi/xdJAj4aEfd2mP5O0vj6AETE1NzS/SBwlqTvszSp1PNyLB086hWWnp8/AY6PiEsl7Qgc28N9ebHOtkV6QE4tNbidV2tet7/vn9f/TkSc3IPY2uMb2NmCnazXcZ/qWcKyjb0BABHxD0lbAf9Oulr4GHAQ6Wpmr4i4XdKBwI6dxFtbngf/WgW4xW89cQVwWP4AQNLW9RaStDGwMCJ+QbpS2AaYBrwn9233I3VFXNdNea8F5uXXE7tasAeuBA7NXVlIWh+4BxgtabO8zAENxFjrCuAgSWvnbQ5XHm53JT0LDGlguamkFjqS3kS6GrsXmA2Mk7SGpJGkrigkbQCsERG/Br5OOk7kshZIek379hood9/8ncFGpG5A64Pc4ree+BZwAnBHTv6zSX29He0ITJb0MrAY+GRELJB0FPBnUkvx8ojobnzxY4ELJc0DbiL1yfeWU0jdIHfkOH8RESdK+lQusz+py+qkRjcYEVdKegtwY/5sXAzsz8o/QOM84BeSPkf6XqEzPwNOyl00S4ADI+JFSX8FHiJ1Uc0kdUMBDCc9CrO9IXhU/v110gf1w3md7j50LiF9d3MncB8r9mFpLeRhmc3MCuOuHjOzwrirx1Z5ko4G9ukw+cKIOK6KeMz6Onf1mJkVxl09ZmaFceI3MyuME7+ZWWGc+M3MCvP/e1pnlmOgzgkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='Personal_Income in thousand',data=df).set_title(\"Personal_income in thousand Outlier Analysis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>State</th>\n",
       "      <th>Personal_Income in thousand</th>\n",
       "      <th>GDP in thousand</th>\n",
       "      <th>Population in thousand</th>\n",
       "      <th>Total Vaccinated in thousand</th>\n",
       "      <th>Vaccination rate</th>\n",
       "      <th>Increase rate in vaccinated population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DC</td>\n",
       "      <td>90.04</td>\n",
       "      <td>151.39</td>\n",
       "      <td>714.15</td>\n",
       "      <td>267.98</td>\n",
       "      <td>0.3752</td>\n",
       "      <td>2.6651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Personal_Income in thousand  GDP in thousand  Population in thousand  \\\n",
       "7    DC                        90.04           151.39                  714.15   \n",
       "\n",
       "   Total Vaccinated in thousand  Vaccination rate  \\\n",
       "7                        267.98            0.3752   \n",
       "\n",
       "   Increase rate in vaccinated population  \n",
       "7                                  2.6651  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Personal_Income in thousand'] > 88.55) | (df['Personal_Income in thousand'] < 35.97)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find that there are an outlier in the diagram of \"Personal_income in thousand Outlier Analysis\" which is DC. However, we cannot eliminate this state's data because it is part of the 51 states in the United States. If we remove it, we will miss data for DC in the penetration analysis. Hence, we don't need to change our data here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GDP in thousand Outlier Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lower Outlier = 101.065 - 1.5 * (572.865 - 101.065) = -606.635<br>\n",
    "Higher Outlier = 572.865 + 1.5 * (572.865 - 101.065) = 1280.565"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'GDP in thousand Outlier Analysis')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='GDP in thousand',data=df).set_title(\"GDP in thousand Outlier Analysis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found there are some states that had signifcantly more GDP than other states. We wanted to find which state there were."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>State</th>\n",
       "      <th>Personal_Income in thousand</th>\n",
       "      <th>GDP in thousand</th>\n",
       "      <th>Population in thousand</th>\n",
       "      <th>Total Vaccinated in thousand</th>\n",
       "      <th>Vaccination rate</th>\n",
       "      <th>Increase rate in vaccinated population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>74.30</td>\n",
       "      <td>3290.17</td>\n",
       "      <td>39613.49</td>\n",
       "      <td>13193.70</td>\n",
       "      <td>0.3331</td>\n",
       "      <td>2.0343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NY</td>\n",
       "      <td>78.40</td>\n",
       "      <td>1868.22</td>\n",
       "      <td>19299.98</td>\n",
       "      <td>7146.14</td>\n",
       "      <td>0.3703</td>\n",
       "      <td>2.1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TX</td>\n",
       "      <td>57.79</td>\n",
       "      <td>1950.36</td>\n",
       "      <td>29730.31</td>\n",
       "      <td>7778.51</td>\n",
       "      <td>0.2616</td>\n",
       "      <td>1.8727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State  Personal_Income in thousand  GDP in thousand  \\\n",
       "4     CA                        74.30          3290.17   \n",
       "34    NY                        78.40          1868.22   \n",
       "43    TX                        57.79          1950.36   \n",
       "\n",
       "    Population in thousand  Total Vaccinated in thousand  Vaccination rate  \\\n",
       "4                 39613.49                      13193.70            0.3331   \n",
       "34                19299.98                       7146.14            0.3703   \n",
       "43                29730.31                       7778.51            0.2616   \n",
       "\n",
       "    Increase rate in vaccinated population  \n",
       "4                                   2.0343  \n",
       "34                                  2.1255  \n",
       "43                                  1.8727  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['GDP in thousand'] > 1280.565]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found the outlier in the diagram of \"GDP in thousand Outlier Analysis\" are CA, NY, TX (California, New York, Texas). However, we cannot eliminate these three states' data because these three states are parts of the 51 states in the United States. If we remove the GDP data for those three states, we will miss data for those three states in the penetration analysis. Hence, we don't need to change our data here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population in thousand Outlier Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lower Outlier = 1813.99 - 1.5 * (7658.52 - 1813.995) = -6952.80<br>\n",
    "Higher Outlier = 7658.52 + 1.5 * (7658.52 - 1813.99) = 16425.32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Population in thousand Outlier Analysis')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='Population in thousand',data=df).set_title(\"Population in thousand Outlier Analysis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found there are some states that had signifcantly more population than other states. We wanted to find which state there were."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>State</th>\n",
       "      <th>Personal_Income in thousand</th>\n",
       "      <th>GDP in thousand</th>\n",
       "      <th>Population in thousand</th>\n",
       "      <th>Total Vaccinated in thousand</th>\n",
       "      <th>Vaccination rate</th>\n",
       "      <th>Increase rate in vaccinated population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>74.30</td>\n",
       "      <td>3290.17</td>\n",
       "      <td>39613.49</td>\n",
       "      <td>13193.70</td>\n",
       "      <td>0.3331</td>\n",
       "      <td>2.0343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FL</td>\n",
       "      <td>58.44</td>\n",
       "      <td>1198.91</td>\n",
       "      <td>21944.58</td>\n",
       "      <td>6351.89</td>\n",
       "      <td>0.2895</td>\n",
       "      <td>1.8197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NY</td>\n",
       "      <td>78.40</td>\n",
       "      <td>1868.22</td>\n",
       "      <td>19299.98</td>\n",
       "      <td>7146.14</td>\n",
       "      <td>0.3703</td>\n",
       "      <td>2.1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TX</td>\n",
       "      <td>57.79</td>\n",
       "      <td>1950.36</td>\n",
       "      <td>29730.31</td>\n",
       "      <td>7778.51</td>\n",
       "      <td>0.2616</td>\n",
       "      <td>1.8727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State  Personal_Income in thousand  GDP in thousand  \\\n",
       "4     CA                        74.30          3290.17   \n",
       "9     FL                        58.44          1198.91   \n",
       "34    NY                        78.40          1868.22   \n",
       "43    TX                        57.79          1950.36   \n",
       "\n",
       "    Population in thousand  Total Vaccinated in thousand  Vaccination rate  \\\n",
       "4                 39613.49                      13193.70            0.3331   \n",
       "9                 21944.58                       6351.89            0.2895   \n",
       "34                19299.98                       7146.14            0.3703   \n",
       "43                29730.31                       7778.51            0.2616   \n",
       "\n",
       "    Increase rate in vaccinated population  \n",
       "4                                   2.0343  \n",
       "9                                   1.8197  \n",
       "34                                  2.1255  \n",
       "43                                  1.8727  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Population in thousand'] > 16425.32]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we found the outlier in the diagram of \"Population in thousand Outlier Analysis\" are CA, FL, NY, and TX (California, Florida, New York, Texas). However, we cannot eliminate these four states' data because these four states are parts of the 51 states in the United States. If we remove the population data for those four states, we will miss data for those four states in the later analysis. So, we don't need to change our data here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Vaccinated in thousand Outlier Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lower Outlier = 482.34 - 1.5 * (2583.46 - 482.34) = -2669.34<br>\n",
    "Higher Outlier = 2583.46 + 1.5 * (2583.46 - 482.34) = 5735.14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Vaccinated in thousand')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='Total Vaccinated in thousand',data=df).set_title(\"Total Vaccinated in thousand\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found there is one state that had signifcantly more total vaccinated than other states. We wanted to find which state there was."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>State</th>\n",
       "      <th>Personal_Income in thousand</th>\n",
       "      <th>GDP in thousand</th>\n",
       "      <th>Population in thousand</th>\n",
       "      <th>Total Vaccinated in thousand</th>\n",
       "      <th>Vaccination rate</th>\n",
       "      <th>Increase rate in vaccinated population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>74.30</td>\n",
       "      <td>3290.17</td>\n",
       "      <td>39613.49</td>\n",
       "      <td>13193.70</td>\n",
       "      <td>0.3331</td>\n",
       "      <td>2.0343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FL</td>\n",
       "      <td>58.44</td>\n",
       "      <td>1198.91</td>\n",
       "      <td>21944.58</td>\n",
       "      <td>6351.89</td>\n",
       "      <td>0.2895</td>\n",
       "      <td>1.8197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NY</td>\n",
       "      <td>78.40</td>\n",
       "      <td>1868.22</td>\n",
       "      <td>19299.98</td>\n",
       "      <td>7146.14</td>\n",
       "      <td>0.3703</td>\n",
       "      <td>2.1255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TX</td>\n",
       "      <td>57.79</td>\n",
       "      <td>1950.36</td>\n",
       "      <td>29730.31</td>\n",
       "      <td>7778.51</td>\n",
       "      <td>0.2616</td>\n",
       "      <td>1.8727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State  Personal_Income in thousand  GDP in thousand  \\\n",
       "4     CA                        74.30          3290.17   \n",
       "9     FL                        58.44          1198.91   \n",
       "34    NY                        78.40          1868.22   \n",
       "43    TX                        57.79          1950.36   \n",
       "\n",
       "    Population in thousand  Total Vaccinated in thousand  Vaccination rate  \\\n",
       "4                 39613.49                      13193.70            0.3331   \n",
       "9                 21944.58                       6351.89            0.2895   \n",
       "34                19299.98                       7146.14            0.3703   \n",
       "43                29730.31                       7778.51            0.2616   \n",
       "\n",
       "    Increase rate in vaccinated population  \n",
       "4                                   2.0343  \n",
       "9                                   1.8197  \n",
       "34                                  2.1255  \n",
       "43                                  1.8727  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Total Vaccinated in thousand'] > 5735.14]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found the outlier in the diagram of \"Total Vaccinated in thousand Outlier Analysis\" are CA, FL, NY, and TX (California, Florida, New York, Texas). However, we cannot eliminate these four states' data because they are parts of the 51 states in the United States. If we remove the population data for these variables, we will miss data for these four states in the later analysis. So, we don't need to change our data here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vaccination coverage rate and population\n",
    "We considered population to be probably the confounding variable that may affect both a state's economy and Vaccination coverage rate. We wanted to determine if there was relationship between these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Vaccination coverage rate and Population')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#draw the scatter plot\n",
    "sns.scatterplot(x='Vaccination rate',y='Population in thousand',data=df).set_title(\"Vaccination coverage rate and Population\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the Vaccination coverage rate doesn't have a positive relationship with the population. In our diagram, all the points are hovering near the X-axis. Near the top of the graph, there is also an extreme outlier. From the above graph, we could initiatory get the conclusion that Total Vaccinated and population don't have a linear relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between Vaccination coverage rate and GDP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we mentioned in purpose, we believed that state economie(GDP) is our confounding variable which may influence vaccination coverage by state. So we need to analyze whether there is a connection between these two data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Vaccination coverage rate and GDP')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using the catterplot for analyzing \n",
    "sns.scatterplot(x='Vaccination rate',y='GDP in thousand',data=df).set_title(\"Vaccination coverage rate and GDP\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the Vaccination coverage rate doesn't have a positive relationship with the GDP. In our diagram, all the points are hovering near the X-axis. Near the top of the graph, there is also an extreme outlier. From the above graph, we could initiatory get the conclusion that Total Vaccinated and GDP don't have a linear relationship. We will choose another angle to analyze whether Vaccination coverage rate has any relationship with the economy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between Vaccination coverage rate and Personal_Income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Personal income is one of the most important indicators of a state's economic performance. As we did in the previous step, we use scatter plot to analyze the relationship between the Vaccination coverage rate and Personal_Income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Vaccination coverage rate and Personal_Income')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using the catterplot for analyzing \n",
    "sns.scatterplot(x='Vaccination rate',y='Personal_Income in thousand',data=df,color=\"black\").set_title(\"Vaccination coverage rate and Personal_Income\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above graph, we find that there seems to be a positive linear relationship between the Vaccination coverage rate and Personal_income. In order to see their direct relationship more closely, we use the lmplot to draw the line of their relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd4c19ecc10>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 806.4x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship between the Vaccination coverage rate and Personal_Income\n",
    "sns.lmplot(\n",
    "    x='Vaccination rate',\n",
    "    y='Personal_Income in thousand',\n",
    "    data = df,\n",
    "    fit_reg=True,\n",
    "    scatter_kws={'color':'red'},\n",
    "    line_kws={'color':'blue'},\n",
    "    aspect = 1.6,\n",
    "    height= 7\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the new graph, we can see intuitively that here is a positive linear relationship between the Vaccination coverage rate and Personal_income. This also suggests that individual income has a positive impact on vaccination coverage rate in each state. Vaccination coverage rate increases when a state has high personal income."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OLS Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear regression analysis\n",
    "dependent, predictors = patsy.dmatrices('Q(\"Personal_Income in thousand\") ~ Q(\"Vaccination rate\") + Q(\"Population in thousand\")', df)\n",
    "mode = sm.OLS(dependent, predictors)\n",
    "result = mode.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Q(\"Personal_Income in thousand\")</td> <th>  R-squared:         </th> <td>   0.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                           <td>OLS</td>               <th>  Adj. R-squared:    </th> <td>   0.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                     <td>Least Squares</td>          <th>  F-statistic:       </th> <td>   18.34</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                     <td>Fri, 10 Dec 2021</td>         <th>  Prob (F-statistic):</th> <td>1.21e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                         <td>16:52:53</td>             <th>  Log-Likelihood:    </th> <td> -174.57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>              <td>    51</td>              <th>  AIC:               </th> <td>   355.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>                  <td>    48</td>              <th>  BIC:               </th> <td>   360.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                      <td>     2</td>              <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>              <td>nonrobust</td>            <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "               <td></td>                  <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                   <td>   35.8800</td> <td>    4.144</td> <td>    8.658</td> <td> 0.000</td> <td>   27.547</td> <td>   44.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Vaccination rate\")</th>       <td>   82.5142</td> <td>   14.242</td> <td>    5.794</td> <td> 0.000</td> <td>   53.878</td> <td>  111.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Population in thousand\")</th> <td>    0.0001</td> <td>    0.000</td> <td>    1.019</td> <td> 0.313</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.821</td> <th>  Durbin-Watson:     </th> <td>   1.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.020</td> <th>  Jarque-Bera (JB):  </th> <td>   6.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.799</td> <th>  Prob(JB):          </th> <td>  0.0324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.821</td> <th>  Cond. No.          </th> <td>1.35e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.35e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                   OLS Regression Results                                   \n",
       "============================================================================================\n",
       "Dep. Variable:     Q(\"Personal_Income in thousand\")   R-squared:                       0.433\n",
       "Model:                                          OLS   Adj. R-squared:                  0.410\n",
       "Method:                               Least Squares   F-statistic:                     18.34\n",
       "Date:                              Fri, 10 Dec 2021   Prob (F-statistic):           1.21e-06\n",
       "Time:                                      16:52:53   Log-Likelihood:                -174.57\n",
       "No. Observations:                                51   AIC:                             355.1\n",
       "Df Residuals:                                    48   BIC:                             360.9\n",
       "Df Model:                                         2                                         \n",
       "Covariance Type:                          nonrobust                                         \n",
       "===============================================================================================\n",
       "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------\n",
       "Intercept                      35.8800      4.144      8.658      0.000      27.547      44.213\n",
       "Q(\"Vaccination rate\")          82.5142     14.242      5.794      0.000      53.878     111.151\n",
       "Q(\"Population in thousand\")     0.0001      0.000      1.019      0.313      -0.000       0.000\n",
       "==============================================================================\n",
       "Omnibus:                        7.821   Durbin-Watson:                   1.874\n",
       "Prob(Omnibus):                  0.020   Jarque-Bera (JB):                6.862\n",
       "Skew:                           0.799   Prob(JB):                       0.0324\n",
       "Kurtosis:                       3.821   Cond. No.                     1.35e+05\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, 1.35e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table we can see that the P value is 0.000 which is lower than all significance levels,  it indicates there is a positive linear relationship between the personal income and vaccination rate. We can also find out the P-value for Population is 0.313. It is much larger than 0.05, making us fail to reject the null hypothesis. Therefore, we can state that there is no relation between vaccination rate and population.\n",
    "\n",
    "After we analyzed the relationship between Vaccination coverage rate and economy, we get that there is a postive linear relationship between the Vaccination coverage rate and Personal_income. We also want to analyze the relationship between Increase rate in vaccinated population and economy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Increase rate in vaccinated population and population\n",
    "We considered population to be also probably the confounding variable that may affect both a state's economy and Increase rate in vaccinated population. We wanted to determine if there was relationship between these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Increase rate in vaccinated population and Population')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#draw the scatter plot\n",
    "sns.scatterplot(x='Increase rate in vaccinated population',y='Population in thousand',data=df).set_title(\"Increase rate in vaccinated population and Population\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like Increase rate in vaccinated population doesn't have a positive relationship with the population. In our diagram, all the points are hovering near the X-axis. Near the top of the graph, there is also an extreme outlier. From the above graph, we could initiatory get the conclusion that Increase rate in vaccinated population and population don't have a linear relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between Increase rate in vaccinated population and GDP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will try to analyze the relationship between Increase rate in vaccinated population and GDP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Increase rate in vaccinated population and GDP')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using the catterplot for analyzing \n",
    "sns.scatterplot(x='Increase rate in vaccinated population',y='GDP in thousand',data=df,color=\"green\").set_title(\"Increase rate in vaccinated population and GDP\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the increase rate in vaccinated population doesn't have a relationship with the GDP. In our diagram, all the points are hovering near the X-axis. Near the top of the graph, there is also an extreme outlier. From the above graph, we could initiatory get the conclusion that the increase rate in vaccinated population and GDP don't have a linear relationship. We will choose another angle to analyze whether Increase rate in vaccinated population has any relationship with the economy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between Increase rate in vaccinated population and Personal_Income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we did in the previous step, we use scatter plot to analyze the relationship between the Increase rate in vaccinated population and Personal_income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Increase rate in vaccinated population and Personal_Income')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using the catterplot for analyzing \n",
    "sns.scatterplot(x='Increase rate in vaccinated population',y='Personal_Income in thousand',data=df,color=\"red\").set_title(\"Increase rate in vaccinated population and Personal_Income\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above graph, we find that there is a positive linear relationship between the Increase rate in vaccinated population and Personal_Income. In order to see their direct relationship more closely, we use the lmplot to draw the line of their relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd4bf7c8fa0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 806.4x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship between the Increase rate in vaccinated population and Personal_Income\n",
    "sns.lmplot(\n",
    "    x='Increase rate in vaccinated population',\n",
    "    y='Personal_Income in thousand',\n",
    "    data = df,\n",
    "    fit_reg=True,\n",
    "    scatter_kws={'color':'red'},\n",
    "    line_kws={'color':'blue'},\n",
    "    aspect = 1.6,\n",
    "    height= 7\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the new graph, we can see intuitively that here is a positive linear relationship between the Increase rate in vaccinated population and Personal_income. This also suggests that individual income has a positive impact on Increase rate in vaccinated population in each state. Increase rate in vaccinated population increases when a state has high personal income."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OLS Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear regression analysis\n",
    "dependent, predictors = patsy.dmatrices('Q(\"Personal_Income in thousand\") ~ Q(\"Increase rate in vaccinated population\") + Q(\"Population in thousand\")', df)\n",
    "mode = sm.OLS(dependent, predictors)\n",
    "result = mode.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Q(\"Personal_Income in thousand\")</td> <th>  R-squared:         </th> <td>   0.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                           <td>OLS</td>               <th>  Adj. R-squared:    </th> <td>   0.293</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                     <td>Least Squares</td>          <th>  F-statistic:       </th> <td>   11.36</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                     <td>Fri, 10 Dec 2021</td>         <th>  Prob (F-statistic):</th> <td>9.15e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                         <td>16:52:54</td>             <th>  Log-Likelihood:    </th> <td> -179.17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>              <td>    51</td>              <th>  AIC:               </th> <td>   364.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>                  <td>    48</td>              <th>  BIC:               </th> <td>   370.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                      <td>     2</td>              <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>              <td>nonrobust</td>            <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                       <td></td>                          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                   <td>   38.7768</td> <td>    4.650</td> <td>    8.339</td> <td> 0.000</td> <td>   29.427</td> <td>   48.127</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Increase rate in vaccinated population\")</th> <td>   13.0174</td> <td>    2.903</td> <td>    4.485</td> <td> 0.000</td> <td>    7.181</td> <td>   18.853</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"Population in thousand\")</th>                 <td> 3.638e-05</td> <td>    0.000</td> <td>    0.218</td> <td> 0.828</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 5.058</td> <th>  Durbin-Watson:     </th> <td>   1.866</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.080</td> <th>  Jarque-Bera (JB):  </th> <td>   4.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.720</td> <th>  Prob(JB):          </th> <td>  0.0852</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.504</td> <th>  Cond. No.          </th> <td>4.54e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.54e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                   OLS Regression Results                                   \n",
       "============================================================================================\n",
       "Dep. Variable:     Q(\"Personal_Income in thousand\")   R-squared:                       0.321\n",
       "Model:                                          OLS   Adj. R-squared:                  0.293\n",
       "Method:                               Least Squares   F-statistic:                     11.36\n",
       "Date:                              Fri, 10 Dec 2021   Prob (F-statistic):           9.15e-05\n",
       "Time:                                      16:52:54   Log-Likelihood:                -179.17\n",
       "No. Observations:                                51   AIC:                             364.3\n",
       "Df Residuals:                                    48   BIC:                             370.1\n",
       "Df Model:                                         2                                         \n",
       "Covariance Type:                          nonrobust                                         \n",
       "===============================================================================================================\n",
       "                                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                      38.7768      4.650      8.339      0.000      29.427      48.127\n",
       "Q(\"Increase rate in vaccinated population\")    13.0174      2.903      4.485      0.000       7.181      18.853\n",
       "Q(\"Population in thousand\")                  3.638e-05      0.000      0.218      0.828      -0.000       0.000\n",
       "==============================================================================\n",
       "Omnibus:                        5.058   Durbin-Watson:                   1.866\n",
       "Prob(Omnibus):                  0.080   Jarque-Bera (JB):                4.926\n",
       "Skew:                           0.720   Prob(JB):                       0.0852\n",
       "Kurtosis:                       2.504   Cond. No.                     4.54e+04\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, 4.54e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table we can see that the P value for Increase rate in vaccinated population is 0.000 which is lower than all significance levels,  it indicates there is a postive linear relationship between the personal income and vaccination rate. We can also find out the P-value for Populaion is 0.828. It is much larger than 0.05, making us fail to reject the null hypothesis. Therefore, we can state that there is no relation between Increase rate in vaccinated population and population.\n",
    "\n",
    "After we anlayzed the relationship between Increase rate in vaccinated population and econmy, we get that there is a postive linear relationship between Increase rate in vaccinated population and Personal_income."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our research question is to check if each state is contributing to the spread of COVID-19. In terms of data, we will be collecting from the government’s site, so we can guarantee the accuracy of it. Our goal is to focus on a specific season for data analysis, which will include information about the number of people vaccinated each date for that whole season, the GPD, and personal income. There might be bias here since it is based on a seasonal term, especially with GDP and personal income. For example, in sales services there are busy and non-busy seasons, meaning it affects the GDP accuracy when we only look at one. Moreover, people might prefer getting vaccinated at a specific season than others, hence will create a difference. Another bias is that we don’t have many research data to choose. Since the vaccine only became available in March this year, we had to select data from march onward for our research analysis. And because the government did not release GDP per person for the third quarter of this year when we did our survey, we have a narrower range of data to choose from. Finally, we only chose the data of the second quarter of 2021 as our research data. However, we think our research is still very necessary. Data from early vaccine research can help governments choose the number and region which vaccines need to be spread.\n",
    "To avoid any biases we will bring our data in a broader view because there isn’t any other season for comparisons. Furthermore, due to sample sizes and variation, there may be bias in a specific state. If we are collecting 1000 samples from both California and Michigan, then the sample will better represent Michigan than California because California has a bigger population. Therefore, we hope to be consistent with all samples and be as balanced as possible. There is also the problem of sample size mentioned above. The data we have so far is still sparse. To better improve our research and improve the accuracy of our research results, we can carry out more in-depth investigation and research. We can run the same survey again next March when the problem will be gone.\n",
    "\n",
    "In order to prevent the invasion of personal privacy, we selected all data from official government websites. The data is publicly available, which has been publicized by the Bureau of Economic Analysis(BEA) and Centers for Disease Control and Prevention(CDC). In the process of data cleaning and data analysis, we did not select any data of each body. This is also a good way to prevent information leakage."
   ]
  },
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   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After deciding the final version of the hypothesis, our first step was to collect data and do data cleaning for our dataset  in order to perform data analysis logically and tightly. We collected four main dataset, they are vaccination rate, personal income, GDP and  increase rate of the vaccinated population, and one ancillary dataset to indicate the 51 states correctly. In order to find out the dataset we need, we extracted the number of vaccinations by state. And then, filtering the personal income data to get the data from 04/01/2021 to 06/30/2021 in the consideration of the limitation on official state personal income data integrity. After we extract the columns of value we need and renaming them, we calculated the vaccination rate of each state. Moreover, we combined all these data to form a new dataset named df.\n",
    "\n",
    "In the step of the data analysis, we found that personal income and vaccination rates showed a positive linear relationship. We used the matrix function to draw a scatter plot and used lmplot to add an upward line to the scatter plot to show the upward trend. However, there is no linear relationship between GDP and vaccination rates. We inferring this phenomenon is probably due to the fact that GDP contains too much non-personal related data, which is the market value of all final products produced by economic activity in a region at a given period. The reason for the correlation between personal income and vaccination rates is that both vaccination and income are related to individuals, and no unrelated data such as market value are included. We also found that population is a confounding variable because it is not related to the vaccination rate or the increase rate of the vaccinated population. The reason we still add population in our OLS regression model is because population contains many other factors that might affect personal income such as demographics and the age distribution. \n",
    "\n",
    "To sum up, even though our project was able to conclude that personal income and vaccination rates have a positive relationship, we still need more information to make a more complete statement. Since we only collected two months of data and covid-19 is the new disease we are facing, vaccination policy is also changing. Variables like government policy, individual’s age, and much more information are needed to be included to make a complete statement.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jianfan Huo: Topic Choosing, Data cleaning, Data Analysis, Data description, Edit cleaning and analysis <br>\n",
    "Yunhao Du: Topic Choosing, Data obtain, Data cleaning, Data Analysis, Data description, Video powerpoint <br>\n",
    "Yixin Jiang: Topic Choosing, Data obtain, Data cleaning, Data Analysis, Data description, Video content <br>\n",
    "Yixiao An: Background, Data description, Conclusion & Discussion, Video <br>\n",
    "Celine Zhao: Overview, Ethics&Privacy, Team Expectations"
   ]
  }
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