ireland_census_data_analysis.ipynb

{
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   "source": [
    "<h1 align = 'center'><u> Ireland Census Data Analysis</u> </h1>\n",
    "<h3 align = 'center'> Vinutha Shivakumar</h3>\n",
    "\n",
    "## Introduction\n",
    "\n",
    "\n",
    "In this project I will be analyzing the 2016 survey data collected by the Central Statistics Office of Ireland at local and county level. This census takes place every five years. The goal of this census is to collect information about the entire population residing in the country when the survey is performed. The census form is delivered to every household, hotels, guesthouses, hospitals and ships to collect the data. The form usually asks questions about their age, sex, marital ststus, education, occupation, religion etc. \n",
    "\n",
    "The results of this survey is then used by the government to provide public services to the population. It is used to plan the infrastructure of the city/ county/ local area, provide better health care, transport, housing and education services.\n",
    "\n",
    "The census data is divided into fifteen themes, each one giving different insight into an aspect of the society. Some of the themes involved are : Types of families, Education, Housing, Industries where majority people work etc. Each of these themes further have numeric counts describing how many people fall under each subcategory within the theme.\n",
    "\n",
    "The themes that I will be analyzing from this census data are:\n",
    "\n",
    "1. Education - This theme focuses on the education level and results of this education. The data gives insights about the population above the age 15  and have completed their education further classified by their sex,  population above 15 and who are still pursuing their education further classified by sex, what is the field of study pursued and finally the highest level of education completed.\n",
    "\n",
    "2. Commuting - In this theme there is data about the mode of transportation used by people going to work / college/ school. Time at which they leavel home for work/ school/ college and what is the journey time. \n",
    "\n",
    "Inisghts that I will be checking in these themes:\n",
    "\n",
    "1. Which field of study is popular in Ireland\n",
    "2. Is there a trend in number of students at different level of education in different counties\n",
    "3. What is the most preferred way of commuting to school/work/college\n",
    "4. Determine how long a student spends commuting to school/college based on the student popluation in a particular area and how they generally travel to their school/college\n",
    "\n",
    "\n",
    "## Data Cleansing / Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6973d410",
   "metadata": {},
   "source": [
    "Loading necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "79ecc039",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix,  roc_curve, roc_auc_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdae0bb7",
   "metadata": {},
   "source": [
    "Reading data into panda dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bef52e33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using thousands parameter to deal with the , in the numeric value\n",
    "# uploading county level data\n",
    "df_county = pd.read_csv(r'.\\Census_by_County.csv', \n",
    "                       thousands = \",\")\n",
    "# uploading electoral area level data\n",
    "df_electoral_area = pd.read_csv(r'.\\Census_by_electoral_area.csv',\n",
    "                               thousands = \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa1897bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GUID</th>\n",
       "      <th>GEOGID</th>\n",
       "      <th>GEOGDESC</th>\n",
       "      <th>T1_1AGE0M</th>\n",
       "      <th>T1_1AGE1M</th>\n",
       "      <th>T1_1AGE2M</th>\n",
       "      <th>T1_1AGE3M</th>\n",
       "      <th>T1_1AGE4M</th>\n",
       "      <th>T1_1AGE5M</th>\n",
       "      <th>T1_1AGE6M</th>\n",
       "      <th>...</th>\n",
       "      <th>T15_1_TC</th>\n",
       "      <th>T15_2_Y</th>\n",
       "      <th>T15_2_N</th>\n",
       "      <th>T15_2_NS</th>\n",
       "      <th>T15_2_T</th>\n",
       "      <th>T15_3_B</th>\n",
       "      <th>T15_3_OTH</th>\n",
       "      <th>T15_3_N</th>\n",
       "      <th>T15_3_NS</th>\n",
       "      <th>T15_3_T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EB9428B8-B443-407E-977B-9393FD73E1D0</td>\n",
       "      <td>CTY31_CC</td>\n",
       "      <td>Cork City</td>\n",
       "      <td>720</td>\n",
       "      <td>614</td>\n",
       "      <td>613</td>\n",
       "      <td>645</td>\n",
       "      <td>602</td>\n",
       "      <td>643</td>\n",
       "      <td>647</td>\n",
       "      <td>...</td>\n",
       "      <td>49370</td>\n",
       "      <td>30466</td>\n",
       "      <td>16572</td>\n",
       "      <td>2332</td>\n",
       "      <td>49370</td>\n",
       "      <td>34240</td>\n",
       "      <td>2709</td>\n",
       "      <td>10278</td>\n",
       "      <td>2143</td>\n",
       "      <td>49370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84B85B58-010E-45DB-BC55-E137BBE627E4</td>\n",
       "      <td>CTY31_CE</td>\n",
       "      <td>Clare</td>\n",
       "      <td>762</td>\n",
       "      <td>759</td>\n",
       "      <td>780</td>\n",
       "      <td>842</td>\n",
       "      <td>868</td>\n",
       "      <td>843</td>\n",
       "      <td>929</td>\n",
       "      <td>...</td>\n",
       "      <td>43348</td>\n",
       "      <td>29214</td>\n",
       "      <td>12886</td>\n",
       "      <td>1248</td>\n",
       "      <td>43348</td>\n",
       "      <td>27941</td>\n",
       "      <td>4701</td>\n",
       "      <td>9524</td>\n",
       "      <td>1182</td>\n",
       "      <td>43348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8A998E65-D3BE-4620-98DE-EBF44DE0285B</td>\n",
       "      <td>CTY31_CK</td>\n",
       "      <td>Cork County</td>\n",
       "      <td>2957</td>\n",
       "      <td>3154</td>\n",
       "      <td>3174</td>\n",
       "      <td>3324</td>\n",
       "      <td>3365</td>\n",
       "      <td>3579</td>\n",
       "      <td>3628</td>\n",
       "      <td>...</td>\n",
       "      <td>146052</td>\n",
       "      <td>103617</td>\n",
       "      <td>38632</td>\n",
       "      <td>3803</td>\n",
       "      <td>146052</td>\n",
       "      <td>103761</td>\n",
       "      <td>12597</td>\n",
       "      <td>26278</td>\n",
       "      <td>3416</td>\n",
       "      <td>146052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 802 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   GUID    GEOGID     GEOGDESC  T1_1AGE0M  \\\n",
       "0  EB9428B8-B443-407E-977B-9393FD73E1D0  CTY31_CC    Cork City        720   \n",
       "1  84B85B58-010E-45DB-BC55-E137BBE627E4  CTY31_CE        Clare        762   \n",
       "2  8A998E65-D3BE-4620-98DE-EBF44DE0285B  CTY31_CK  Cork County       2957   \n",
       "\n",
       "   T1_1AGE1M  T1_1AGE2M  T1_1AGE3M  T1_1AGE4M  T1_1AGE5M  T1_1AGE6M  ...  \\\n",
       "0        614        613        645        602        643        647  ...   \n",
       "1        759        780        842        868        843        929  ...   \n",
       "2       3154       3174       3324       3365       3579       3628  ...   \n",
       "\n",
       "   T15_1_TC  T15_2_Y  T15_2_N  T15_2_NS  T15_2_T  T15_3_B  T15_3_OTH  T15_3_N  \\\n",
       "0     49370    30466    16572      2332    49370    34240       2709    10278   \n",
       "1     43348    29214    12886      1248    43348    27941       4701     9524   \n",
       "2    146052   103617    38632      3803   146052   103761      12597    26278   \n",
       "\n",
       "   T15_3_NS  T15_3_T  \n",
       "0      2143    49370  \n",
       "1      1182    43348  \n",
       "2      3416   146052  \n",
       "\n",
       "[3 rows x 802 columns]"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_county.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6acfc98",
   "metadata": {},
   "source": [
    "Checking for duplicate entries in the county and electoral area level data. We need unique entries in the GEODESC field. So that is the field i will be checking for any duplicacy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60c97fe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_county['GEOGDESC'].duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "409ec6ed",
   "metadata": {},
   "source": [
    "There is no duplicate data at county level data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e8b8118",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_electoral_area['GEOGDESC'].duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94372b5b",
   "metadata": {},
   "source": [
    "There is duplicate data in electoral area data. Next step is to display the data that has this duplicacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be74b4bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GUID</th>\n",
       "      <th>GEOGID</th>\n",
       "      <th>GEOGDESC</th>\n",
       "      <th>T1_1AGE0M</th>\n",
       "      <th>T1_1AGE1M</th>\n",
       "      <th>T1_1AGE2M</th>\n",
       "      <th>T1_1AGE3M</th>\n",
       "      <th>T1_1AGE4M</th>\n",
       "      <th>T1_1AGE5M</th>\n",
       "      <th>T1_1AGE6M</th>\n",
       "      <th>...</th>\n",
       "      <th>T15_1_TC</th>\n",
       "      <th>T15_2_Y</th>\n",
       "      <th>T15_2_N</th>\n",
       "      <th>T15_2_NS</th>\n",
       "      <th>T15_2_T</th>\n",
       "      <th>T15_3_B</th>\n",
       "      <th>T15_3_OTH</th>\n",
       "      <th>T15_3_N</th>\n",
       "      <th>T15_3_NS</th>\n",
       "      <th>T15_3_T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2AE196293F6A13A3E055000000000001</td>\n",
       "      <td>LEA2014_006</td>\n",
       "      <td>Athlone</td>\n",
       "      <td>146</td>\n",
       "      <td>141</td>\n",
       "      <td>123</td>\n",
       "      <td>159</td>\n",
       "      <td>163</td>\n",
       "      <td>157</td>\n",
       "      <td>160</td>\n",
       "      <td>...</td>\n",
       "      <td>6901</td>\n",
       "      <td>4669</td>\n",
       "      <td>2070</td>\n",
       "      <td>162</td>\n",
       "      <td>6901</td>\n",
       "      <td>4196</td>\n",
       "      <td>965</td>\n",
       "      <td>1576</td>\n",
       "      <td>164</td>\n",
       "      <td>6901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2AE196293F0813A3E055000000000001</td>\n",
       "      <td>LEA2014_007</td>\n",
       "      <td>Athlone</td>\n",
       "      <td>216</td>\n",
       "      <td>207</td>\n",
       "      <td>212</td>\n",
       "      <td>208</td>\n",
       "      <td>213</td>\n",
       "      <td>235</td>\n",
       "      <td>245</td>\n",
       "      <td>...</td>\n",
       "      <td>11358</td>\n",
       "      <td>7373</td>\n",
       "      <td>3526</td>\n",
       "      <td>459</td>\n",
       "      <td>11358</td>\n",
       "      <td>7281</td>\n",
       "      <td>1222</td>\n",
       "      <td>2438</td>\n",
       "      <td>417</td>\n",
       "      <td>11358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 802 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               GUID       GEOGID GEOGDESC  T1_1AGE0M  \\\n",
       "5  2AE196293F6A13A3E055000000000001  LEA2014_006  Athlone        146   \n",
       "6  2AE196293F0813A3E055000000000001  LEA2014_007  Athlone        216   \n",
       "\n",
       "   T1_1AGE1M  T1_1AGE2M  T1_1AGE3M  T1_1AGE4M  T1_1AGE5M  T1_1AGE6M  ...  \\\n",
       "5        141        123        159        163        157        160  ...   \n",
       "6        207        212        208        213        235        245  ...   \n",
       "\n",
       "   T15_1_TC  T15_2_Y  T15_2_N  T15_2_NS  T15_2_T  T15_3_B  T15_3_OTH  T15_3_N  \\\n",
       "5      6901     4669     2070       162     6901     4196        965     1576   \n",
       "6     11358     7373     3526       459    11358     7281       1222     2438   \n",
       "\n",
       "   T15_3_NS  T15_3_T  \n",
       "5       164     6901  \n",
       "6       417    11358  \n",
       "\n",
       "[2 rows x 802 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dup = df_electoral_area[df_electoral_area['GEOGDESC'].duplicated(keep=False)]\n",
    "df_dup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e0628a",
   "metadata": {},
   "source": [
    "There seems to be be two entries for the area Athlone. Further checks on data source indicate there are indeed two  Athlones' but in different counties. The first Athlone belongs to Roscommon and the second Athlone to Westmeath. So this is a case of incorrect labelling of data. So I will just rename the electoral areas by appending the county name each Athlone is present in. This will remove the duplicacy issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "455e1482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = list(df_dup.index)\n",
    "df_electoral_area.loc[index[0], 'GEOGDESC'] = 'Athlone - Roscommon'\n",
    "df_electoral_area.loc[index[1], 'GEOGDESC'] = 'Athlone - Westmeath'\n",
    "# check for duplicacy again\n",
    "df_electoral_area['GEOGDESC'].duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb1ad60",
   "metadata": {},
   "source": [
    "There are no more duplicaates so i will proceed now to splitting data into different dataframes based on themes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8166b97",
   "metadata": {},
   "source": [
    "As I will be analysing only 2 themes, the next step is to extract the columns corresponding to these themes.\n",
    "\n",
    "1. Eductaion which has column names starting with T10. Further I'm interested in the table containing data about the field of study, counts of population aged 15 and above who are still studying and another table containing information about the highest level of education completed, hence I need columns beginning with T10_2_, T10_3_ and T10_4_.\n",
    "\n",
    "2. Commuting which has column names starting with T11. In this theme I'm interested in what mode of transport is being used and what is the journey time, hence I need columns beginning with T11_1 and T11_3.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "753522b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# county level data\n",
    "df_education_1 = df_county.filter(regex='GEOGDESC|T10_3')\n",
    "df_education_2 = df_county.filter(regex='GEOGDESC|T10_2')\n",
    "df_education_3 = df_county.filter(regex='GEOGDESC|T10_4')\n",
    "df_commute_1 = df_county.filter(regex='GEOGDESC|T11_1')\n",
    "df_commute_2 = df_county.filter(regex='GEOGDESC|T11_3')\n",
    "\n",
    "# electoral area level data\n",
    "df_e_education_1 = df_electoral_area.filter(regex='GEOGDESC|T10_3')\n",
    "df_e_education_2 = df_electoral_area.filter(regex='GEOGDESC|T10_2')\n",
    "df_e_education_3 = df_electoral_area.filter(regex='GEOGDESC|T10_4')\n",
    "df_e_commute_1 = df_electoral_area.filter(regex='GEOGDESC|T11_1')\n",
    "df_e_commute_2 = df_electoral_area.filter(regex='GEOGDESC|T11_3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a0bc4f",
   "metadata": {},
   "source": [
    "Now I will be renaming the columns to increase readability, checking for any duplicacy and missing data in each of the dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc29b38f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# Education dataframe data cleansing\n",
    "# column names to be renamed\n",
    "colnames_edu_1 = ['County','Males_Teaching', 'Males_Art', 'Males_Humanities', 'Males_Social', 'Males_Science', 'Males_Engg', \n",
    "               'Males_Agri', 'Males_Health','Males_Service', 'Males_Other', 'Males_Unstated','Males_Total','Females_Teaching',\n",
    "               'Females_Art', 'Females_Humanities', 'Females_Social', 'Females_Science', 'Females_Engg',  'Females_Agri', \n",
    "               'Females_Health', 'Females_Service', 'Females_Other', 'Females_Unstated', 'Females_Total','Total_Teaching', \n",
    "               'Total_Art', 'Total_Humanities', 'Total_Social', 'Total_Science', 'Total_Engg',  'Total_Agri','Total_Health',\n",
    "               'Total_Service', 'Total_Other', 'Total_Unstated', 'Total']\n",
    "colnames_edu_2 = ['County', 'Studying']\n",
    "colnames_edu_3 = ['County','No_Education', 'Primary', 'Lower_Secondary', 'Upper_Secondary', 'Technical', 'Advanced_Certificate',\n",
    "               'Higher_Certificate', 'Bachelors_Degree', 'Honor_Bachelor_Degree', 'Postgraduate/Diploma', 'Doctorate',\n",
    "               'Not Stated']\n",
    "\n",
    "# columns to be deleted\n",
    "col_edu_del_1 = ['T10_2_SASM', 'T10_2_OTHM', 'T10_2_SASF', 'T10_2_OTHF', 'T10_2_OTHT']\n",
    "col_edu_del_2 = ['T10_4_NFM', 'T10_4_PM', 'T10_4_LSM', 'T10_4_USM', 'T10_4_TVM', 'T10_4_ACCAM','T10_4_HCM',\n",
    "               'T10_4_ODNDM', 'T10_4_HDPQM', 'T10_4_PDM', 'T10_4_DM', 'T10_4_NSM', 'T10_4_TM', 'T10_4_NFF',\n",
    "               'T10_4_PF', 'T10_4_LSF', 'T10_4_USF', 'T10_4_TVF', 'T10_4_ACCAF', 'T10_4_HCF', 'T10_4_ODNDF', \n",
    "               'T10_4_HDPQF', 'T10_4_PDF', 'T10_4_DF', 'T10_4_NSF', 'T10_4_TF', 'T10_4_TT']\n",
    "\n",
    "# Dropping columns not required for analysis from the second table of education\n",
    "df_education_2 = df_education_2.drop(col_edu_del_1, axis=1)\n",
    "df_education_3 = df_education_3.drop(col_edu_del_2, axis=1)\n",
    "# df_education_1 contains data about the field of study further sub divided by sex\n",
    "df_education_1.columns = colnames_edu_1\n",
    "# df_education_2 contains data about number of people aged 15 and above that are still studying\n",
    "df_education_2.columns = colnames_edu_2\n",
    "# df_education_3 contains data about the highest level of education completed\n",
    "df_education_3.columns = colnames_edu_3\n",
    "# check if there are any blank values\n",
    "print(df_education_1.isnull().values.any())\n",
    "print(df_education_2.isnull().values.any())\n",
    "print(df_education_3.isnull().values.any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ecc92aaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# Commute dataframe data cleansing\n",
    "# column names to be renamed\n",
    "colnames_comm_1 = ['County', 'School_OnFoot', 'School_Cycle', 'School_Bus', 'School_Train', 'School_Motorcycle', \n",
    "                   'School_CarDrive', 'School_CarPassenger', 'School_Van', 'School_Home', 'School_Other', \n",
    "                   'School_NotStated']\n",
    "colnames_comm_2 = ['County', 'Under15', '15-30', '30-45', '45-1', '1-1.5', '>1.5', 'NotStated']\n",
    "# columns to be deleted\n",
    "col_comm_del_1 = ['T11_1_FW', 'T11_1_BIW', 'T11_1_BUW', 'T11_1_TDLW', 'T11_1_MW', 'T11_1_CDW', 'T11_1_CPW', 'T11_1_VW',\n",
    "                  'T11_1_OTHW', 'T11_1_WMFHW', 'T11_1_NSW', 'T11_1_TW', 'T11_1_TW', 'T11_1_TS', 'T11_1_FT', 'T11_1_BIT', \n",
    "                  'T11_1_BUT', 'T11_1_TDLT', 'T11_1_MT', 'T11_1_CDT', 'T11_1_CPT', 'T11_1_VT', 'T11_1_OTHT', 'T11_1_WMFHT',\n",
    "                  'T11_1_NST', 'T11_1_TT']\n",
    "col_comm_del_2 = ['T11_3_T']\n",
    "# Dropping columns not required for analysis\n",
    "df_commute_1 = df_commute_1.drop(col_comm_del_1, axis=1)\n",
    "df_commute_2 = df_commute_2.drop(col_comm_del_2, axis=1)\n",
    "# df_commute_1 has data about mode of transportation used for getting to work/school/college\n",
    "df_commute_1.columns = colnames_comm_1\n",
    "# df_commute_2 has data about how long one spends commuting to school/work\n",
    "df_commute_2.columns = colnames_comm_2\n",
    "# check if there are any blank values\n",
    "print(df_commute_1.isnull().values.any())\n",
    "print(df_commute_2.isnull().values.any())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "611f525e",
   "metadata": {},
   "source": [
    "There are no null / blank values in any of the dataframes\n",
    "\n",
    "Performing the same data cleansing steps for electoral area data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1f65e8c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Any duplicates present?  False\n",
      "Nulls in df_e_education_1?  False\n",
      "Nulls in df_e_education_2?  False\n",
      "Nulls in df_e_commute_1?  False\n",
      "Nulls in df_e_commute_2?  False\n"
     ]
    }
   ],
   "source": [
    "# Education dataframe data cleansing at electoral area level\n",
    "# Dropping columns not required for analysis from the second table of education\n",
    "# Dropping columns not required for analysis from the second table of education\n",
    "df_e_education_2 = df_e_education_2.drop(col_edu_del_1, axis=1)\n",
    "df_e_education_3 = df_e_education_3.drop(col_edu_del_2, axis=1)\n",
    "# df_education_1 contains data about the field of study further sub divided by sex\n",
    "df_e_education_1.columns = colnames_edu_1\n",
    "# df_education_2 contains data about number of people aged 15 and above that are still studying\n",
    "df_e_education_2.columns = colnames_edu_2\n",
    "# df_education_3 contains data about the highest level of education completed\n",
    "df_e_education_3.columns = colnames_edu_3\n",
    "# check for duplicates\n",
    "print('Any duplicates present? ',df_e_education_1['County'].duplicated().values.any())\n",
    "# check if there are any blank values\n",
    "print('Nulls in df_e_education_1? ',df_e_education_1.isnull().values.any())\n",
    "print('Nulls in df_e_education_2? ',df_e_education_2.isnull().values.any())\n",
    "\n",
    "# Commute dataframe data cleansing at electoral area level\n",
    "# Dropping columns not required for analysis\n",
    "df_e_commute_1 = df_e_commute_1.drop(col_comm_del_1, axis=1)\n",
    "df_e_commute_2 = df_e_commute_2.drop(col_comm_del_2, axis=1)\n",
    "# df_e_commute_1 has data about mode of transportation used for getting to work/school/college\n",
    "df_e_commute_1.columns = colnames_comm_1\n",
    "# df_e_commute_2 has data about how long one spends commuting to school/work\n",
    "df_e_commute_2.columns = colnames_comm_2\n",
    "# check if there are any blank values\n",
    "print('Nulls in df_e_commute_1? ', df_e_commute_1.isnull().values.any())\n",
    "print('Nulls in df_e_commute_2? ',df_e_commute_2.isnull().values.any())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4a408b5",
   "metadata": {},
   "source": [
    "There are no null entries in any of the dataframe so no action needs to be taken.\n",
    "\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf5cc46b",
   "metadata": {},
   "source": [
    "### Education theme analysis\n",
    "\n",
    "-  Analysing male vs female eductaion level across different counties\n",
    "\n",
    "We have total columns for both male and female category aged 15 and above in the df_education_1 table. I want to check the overall distribution of male and female population pursuing studies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "79706513",
   "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>Males_Total</th>\n",
       "      <th>Females_Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48831.741935</td>\n",
       "      <td>51073.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>35897.656053</td>\n",
       "      <td>38213.430181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10784.000000</td>\n",
       "      <td>10800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24845.000000</td>\n",
       "      <td>25540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40673.000000</td>\n",
       "      <td>42923.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58775.500000</td>\n",
       "      <td>60326.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>185165.000000</td>\n",
       "      <td>195589.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Males_Total  Females_Total\n",
       "count      31.000000      31.000000\n",
       "mean    48831.741935   51073.161290\n",
       "std     35897.656053   38213.430181\n",
       "min     10784.000000   10800.000000\n",
       "25%     24845.000000   25540.000000\n",
       "50%     40673.000000   42923.000000\n",
       "75%     58775.500000   60326.000000\n",
       "max    185165.000000  195589.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_education_1[['Males_Total', 'Females_Total']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d647b7",
   "metadata": {},
   "source": [
    "Overall there is slightly more number of women pursuing studies compared to men. There is just a small difference of over 2000 people. So I would conclude that there is similar distribution between number of men and women pursuing their studies.\n",
    "\n",
    "-  Student population distribution across different counties\n",
    "\n",
    "Next I wanted to see which County has the most number of people pursuing their studies. This of course is a function of population in the given county too. But in this case I'm just curious about which county has more number of people studying because this is an indicative to government / other types of analysts about measures needed to provide the desired level of infrastructure to accomodate the growing number of students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "747e70ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Number of people educated in each County')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "bplot1 = sns.barplot(x='Total', y='County', data=df_education_1)\n",
    "plt.xlabel(\"Total\", size=16)\n",
    "plt.ylabel(\"County\", size=16)\n",
    "plt.title(\"Total Number of people educated in each County\", size=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf93c5fd",
   "metadata": {},
   "source": [
    "From the barplot its evident that there a lot of students pursuing their education at different levels in County Dublin and County Cork. Similarly Leitrim and Longford counties has the least number of student population\n",
    "\n",
    "- Field of study that is popular\n",
    "\n",
    "Further analysing which field of study is most popular in the counties Dublin and Cork with a big student population and counties with much lower counts like Leitrim and Longford."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7fe42661",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     Total_Social\n",
       "5     Total_Social\n",
       "14    Total_Social\n",
       "17    Total_Social\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fetch rows containing information about the counties of interest\n",
    "df_education_field = df_education_1.loc[df_education_1['County'].isin(['Leitrim', 'Longford', \n",
    "                                                                       'Dublin City', 'Cork County'])]\n",
    "# using columns that are indicative of different fields of study\n",
    "df_education_field[['Total_Teaching', 'Total_Art', 'Total_Humanities', 'Total_Social', \n",
    "                    'Total_Science', 'Total_Engg',  'Total_Agri','Total_Health',\n",
    "               'Total_Service']].idxmax(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6539b992",
   "metadata": {},
   "source": [
    "It looks like the field of Social Sciences, Business and law is popular across these four counties.\n",
    "\n",
    "Now I want to check if this is the same choice of field when i look into the male and female data specifically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4a6916b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2       Males_Engg\n",
       "5     Males_Social\n",
       "14      Males_Engg\n",
       "17      Males_Engg\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# poular field of education for males\n",
    "df_education_field[['Males_Teaching', 'Males_Art', 'Males_Humanities', 'Males_Social', 'Males_Science', 'Males_Engg', \n",
    "               'Males_Agri', 'Males_Health','Males_Service']].idxmax(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abb55d76",
   "metadata": {},
   "source": [
    "The field of Engineering, Manufacturing and construction is more popular among the male population in Cork, Leitrim and Longford. In Dublin alone there is a focus on the Social Sciences field amongst men."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a9c06182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     Females_Social\n",
       "5     Females_Social\n",
       "14    Females_Social\n",
       "17    Females_Social\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# popular field of education for females\n",
    "df_education_field[['Females_Teaching', 'Females_Art', 'Females_Humanities', 'Females_Social', 'Females_Science', \n",
    "                    'Females_Engg',  'Females_Agri', 'Females_Health', 'Females_Service']].idxmax(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e992ba",
   "metadata": {},
   "source": [
    "The female population is more interested in the Social Science fields across all the four counties.\n",
    "\n",
    "-  Trend in different level of education\n",
    "\n",
    "Observing if there is a trend in the number of people who are uneducated, in their primary level of education and other higher levels of education in the four counties Dublin, Cork, Leitrim and Longford."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9e6342f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vs7mwLSGOnSD4kuZGbzHiWe2IEbdFP8fcQXuTbqQQBEEAneZc9ij8Zzr8Ox+++RscLXDvj02GASe3fY1Jc3xr09oX296/pp39naQj41AtFgtXX301ADfccANLly5t8/iYmBguvPBCAPLy8igsLOy2naDn3c6ZM4eXXnqJiooKli9fznnnneeTa7eE1NgJgi9Z/bz7DXb0pTDgpK5f6/g6u+wWxwIKghAJ1B6FLe/Dxrcc4wmPc2yi4mDEeTDmChh2NliidAq0pSjaqItgvI/rdisPtLPfd6nf3bt3Y7VaycjIICoqyquRor6+9eyGauchOzo62nWM1WqlufnE9HFubi6rV69m/PjxnbL5pptu4qKLLiIuLo4rr7ySqCj/uV8SsRMEX1FXAYv/pteWaDjrj927Xj8PR65I6uwEIeJorNGO3CtXw0PD4aP/gr1LcTl1ygpDz4bL/gN37YIr58OoC3VNr8WqJU0ueUw7eaDrdi95DK5c4Hsdu+R+7ez3Teq3tLSUW2+9ldtvvx2lFDk5Oaxbtw673c7+/ftZsWKF61i73e7qfn3llVeYNm1at+9/11138Ze//IUdO3a47vHwww+fcFxSUhLV1dWu13379qVv37787//+L/Pmzeu2HW0hETtB8BXf/sNDjPgW6DW4e9dLzoaEDKgpEckTQYgUmhuh4GvY9Jau122qPfGYASfDmNmQexkk9G79WtYo3fm6ej4UrdQTbSZc3/XykLaYOEc3S7RGN1K/dXV1TJgwgaamJqKiopgzZw533HEHAKeeeiqDBg1i7NixjBkzhkmTJrnOS0hIYPPmzeTl5ZGcnMzrr7/eZRucjBs3jn/+859ce+211NbWopTiggtOnOc9b948br31VuLj41m+fDnx8fFcf/31lJaWMnr06G7b0RaqI3nqcCc/P99wdsUIQpco36trXWyNEJcCv1zbOd261njlGtjxiV7/eo9vrikIQnBht+uGq41vwZb3oK78xGMyx8DYK7RDlzKgc9d/5xbY4HBq7twJiRkdPnXr1q2MGjWq/QPttrZTv/6IEoYYt99+OxMnTuTmm2/u1Hkt/R8opVYbhtFifY5E7ATBF3z1J+3UAczopBhxW/TLczt2B9fC0DN9c11BEMzFMODwBq13uekdqGqhRi1lIIy9Ujt0GR1wrlrDM3twdHenHLsO40z9bnhNN0pUFumsw6Q5up4vwp26vLw8EhIS+Mc//uH3e4ljJwjdpWi1TpuAFiOe/GPfXTv7uAYKcewEIbQpK9CRuY1vQtnOE/cnZMCYy7VD1y/PN2nT4x27AVO7f82WcKZ+fS18HAasXh24chpx7AShOxgGfH63+/VZ93VejLgtnFp2IHV2ghCqVB2Cze9oZ+7g2hP3x/aEURfD2NmQM933M1xTB7nXR/f49tpC0CGOnSB0h20fwb7let3/JBh9iW+vH58KaUOhbJd27AzDP4XPgiD4lrpy2PKBduYKPTpZnVhjYcS5DnmSWRAd5z9bjo/YCWGNOHaC0FV8KUbcFv3ytGNXUwoV+yB1oO/vIQhC92ms1TWxG9+GnZ9rcXFPlAUGn67TrCMvgLjkwNjVo5cWLW6oFMcuAhDHThC6yqrn3G+SuZdB/8n+uU+/fHdH24HV4tgJQiBpb7C9rQkKFunI3LaF0FRz4jWyp2hnLvdS/zQutIdS0CsHDq2HcknFhjvi2AlCV6ir0GN8AKwxcGY3xYjb4vgJFGP8NzxaEAQPnIPtPSU8nMPt178CvUfoaRBO/UpPMka75UlScwJlcev0Gqwdu7pyPcUihKSTEhMTOXbsmN+u/+abb3LPPfeQlZXFokWLunSN008/nYceeoj8fPMnBIljJwhd4duH3FpTU26BXoPaPr47ZI3RzqOtEQ6s8d99BEHwpq3B9nuX6S9PkgdoZ27sFZCZ63/7OoNnnV25fzQxm+3NfFjwIe/sfIfDtYfJ6pHF5cMu5+IhF2MNYrmTZ599lscff5wzzjijQ8c3Nzf7dSRYdwleywQhWCkvhB/+o9dxKTD9zm5drtlm5501B3h91X4OVdTRJyWeq/P7MzsvG6tF6S7brLE6WndonY4i+LprThCEE2lvsD1Aj95ueZLsycHb3OTVQLHHOxPgA5rtzdz1zV18ue9L17bDNYdZV7qOJUVLeHDGg0RZfPe+tW7dOm699VZqa2sZMmQIzz33HKmpqZx++umcdNJJLFq0iIqKCp599llOO+00amtrmTdvHtu2bWPUqFEUFhby2GOP8fHHH7N06VL27NnDxRdfzJ///Gduu+02Vq1aRVRUFA8//DBnnHEG8+fPZ+HChdTX11NTU8PChQu56aab2LJlC6NGjaKurs5nP1t3kU8HQegsX97nIUb8P7pztYs02+zc/spaPt182LXtYGU9q/eW8/W2Ev593USirBb9JnxgtR4vVLpVO3qCIPiX9gbb9+gNv9oeGg9afpY8+bDgQy+nzpMv933JhwUfctmwy3x2vxtvvJFHH32UGTNmcM8993Dffffxz3/+E9ARtRUrVvDxxx9z33338eWXX/L444+TmprKhg0b2LRpExMmTADgnnvu4euvv3alUZ0Cwhs3bmTbtm3MmjXLNRd2+fLlbNiwgV69evHwww/To0cPNmzYwIYNG7xGmZmNxWwDBCGk2L9S61GBfqPsphjxO2sOeDl1nny6+TDvrHV8sBxfZycIgv9pb7B92tDQcOrA75In7+x8p8397+5612f3qqyspKKighkzZgAwd+5clixZ4tp/+eW6DjkvL4/CwkIAli5dyjXXXAPAmDFjGDduXIvXXrp0KXPm6Lm2I0eOZODAgS7H7uyzz6ZXL53CXrJkCTfcoIWYx40b1+r1zCDgjp1SqlAptVEptU4ptcqxrZdS6gul1E7H91SP43+rlNqllNqulDrHY3ue4zq7lFL/UkrHv5VSsUqp1x3bf1BK5QT6ZxTCFMOAz3/vfn32fRAV061Lvr5qf5v731jp2O/p2BXJXGNBCAgT2xlc343B9gEnKQui4vXaD47d4dqWH1CdHKo55PN7tkZsrBaJt1qtNDc3A2AYRlunuGjruISEBK/XKkjT7mZF7M4wDGOCxwDb3wBfGYYxDPjK8Rql1GjgGiAXOBd4XCnlrMB8ArgFGOb4Otex/Wag3DCMocAjwN8D8PMIkcDWD2D/93rdf6pWiu8mhyrarss46Nzfa4hb80oaKAQhMEy4TuvNtcSoi/QM1FBBKXfUzg+OXVaPrDb390no47N7JScnk5qayrfffgvAiy++6Iretca0adN44403ANiyZQsbN25s8bjp06fz8ssvA7Bjxw727dvHiBEj2jxu06ZNbNiwocs/j68JllTsJcACx3oBcKnH9tcMw2gwDGMPsAuYopTqA/Q0DGO5od3rF447x3mtt4AzVbC61ULo0NwIX3hImpzjGzHirOS21eb7pjiesC0W6Ouo4SjdCg3+a/0XBMGBxQpTbnW/jknUD3WXPAZXLgi9wfbO7v2aEp+/h1w+rG0ZpsuGdr2+rra2luzsbNfXww8/zIIFC7jrrrsYN24c69at45577mnzGj/72c8oLS1l3Lhx/P3vf2fcuHEkJ58oEP2zn/0Mm83G2LFjufrqq5k/f74rAujJbbfdxrFjxxg3bhwPPPAAU6ZM6fLP52vMKA4wgM+VUgbwH8MwngIyDcM4BGAYxiGllFPBsR/wvce5RY5tTY718dud5+x3XKtZKVUJpAFH/PTzCJHAqmfdwp65l0O2b7SKhqQnsmZfRav7r5rc3/2iXx7sXgSGXetR5ZzqExsEQWiD3R66ZlfOh2Fnm2ZKt/GUZSrf49MmrIuHXMySoiUtNlCcNeAsLh7S9QyH3W5vcfv3339/wrbFixe71r1793bV2MXFxfHSSy8RFxdHQUEBZ555JgMHDjzhnLi4OObPn3/CdefNm8e8efNcr+Pj43nttdc6/bMEAjMcu1MNwzjocN6+UEpta+PYlkIiRhvb2zrH+8JK3YJO5TJgwIC2LRYim7py+MaR0bfGwFm+ESOurG3iq20lre4/NzeL2ZOy3Ru8GihWiWMnCIGg4Cv93RoLA0P8b+74BgofOnZWi5UHZzzIhwUf8u6udzlUc4g+CX24bOhlQaFjV1tbyxlnnEFTUxOGYfDEE08QE9O9GulgJeCOnWEYBx3fS5RS7wJTgGKlVB9HtK4P4Py0KwI8QhZkAwcd27Nb2O55TpFSKgpIBk6QBXdECp8CyM/P71hVpRCZLPEQIz7ppz5TkX/4i+0crdGyKacMSaO0uoGdJTo9cvrwdB67fpLWsXMinbGCEFiOleroOMDAkyGmh7n2dBc/d8ZGWaK4bNhlPpU18RVJSUmsWhUZjWcBrbFTSiUopZKca2AWsAn4AJjrOGwu8L5j/QFwjaPTdRC6SWKFI21brZSa6qifu/G4c5zXugL42uhoO4wgHM/RPbDiKb2OT4XTfuWTy245WMWL3+8FIC0hhiduyOPDX0wjLlr/SRZXN3g7dQBJmZDseM6RBgpB8D+eadghZ5pnh6/ws5adEBwEunkiE1iqlFoPrAAWGobxKfA34Gyl1E7gbMdrDMPYDLwBbAE+BX5uGIbNca3bgGfQDRUFwCeO7c8CaUqpXcAdODpsBaFLfOUpRvybbokROzEMgz9+sAm743Hjf84bSXJ8NHHRVqYOTgNg66EqSqrqTzy5n6OBonI/VBd32xZBENpg11fu9dAwcOySs8ESrdd+iNgJwUFAU7GGYewGxrewvQxo8a/GMIz7gftb2L4KGNPC9nrgym4bKwj7V8Bmh6hmr8GQ/yOfXPbdtQdYWahTuxMHpHCFRx3d9GHpLN5eCsCSnUe4Ii/b++R++XroOOh07MjzfWKTIAjHYbdDwdd6ndQHMkaba48vsFh1KUnZTonYhTHBInciCMGFYcBnd7tfn9V9MWKAqvom/vKx7hdSCv58yRgsHinX6cPTXeslO0pPvIDU2QlCYCjepGVBQKdhw0U1y1lnV3UAmlrICgghjzh2gtASW96HohV6PeBkLUbqA/75xU6OHGsA4PqTBjCmn7eO0pD0BPo5tOuW7jqC3X5ceWif8aAcf7bi2AmC/yjwTMPONM8OX+OSPDGgYq+ppnSUxMTEDh/75JNP8sILLwAwf/58Dh482Oqx99xzD19+2fJ821AmRIbcCUIAaW6ELz0kTWb5Rox42+EqFiwvBCC1RzR3zjpRzVwpxfThvXl1xX6O1jSy6WAl47JT3AfEJkL6KCjZrBso7HYtXiwIgm9x1dcpGHyGqab4lOM7Y9NPfB/qKkZzM5Xvv0/FW2/TdPgw0VlZpFwxm+RLL0VZAyN3cuutbkHp+fPnM2bMGPr27XvCcTabjT/96U8BsSnQyCeCIBzPyqehvFCvx1wB2XltHt4RDMPgnvc3Y3NE4P7n3JGk9Gg5tTt9WDvpWKc9DZVwtKDbtgmCcBwNx2CfQ/y23yTo0ctce3yJnyRPjOZmDvz3HRy6+/fUrV1L86FD1K1dy6G7f8+B//pvDMfMVl9RUFDAueeeS15eHqeddhrbtukSl3vvvZeHHnqIt956i1WrVnH99dczYcIE6urqyMnJ4U9/+hPTpk3jzTffZN68ebz11lsA5OTk8Lvf/Y6TTz6Z/Px81qxZwznnnMOQIUN48sknfWq7vxHHThA8qT0K3zyg19YYOLPtMTUd5YP1B1mxR8spju+fwlX5/Vs99pShvV1SJ0t2tDAwRersBMG/FC4Fe5Neh4PMiSdekie+c+wq33+f6i++aHFf9RdfUPn+Bz67F8Att9zCo48+yurVq3nooYf42c9+5rX/iiuuID8/n5dffpl169YRH69LXOLi4li6dCnXXHPNCdfs378/y5cv57TTTnM5fd9//32748qCDUnFCoIn3/4D6iv0+qRbIXVgty9ZXd/E/Qu3Ajqj+6eLc70aJo4nOT6aCf1TWL23nDX7yqmubyIpLtp9wPGO3fgT36AEQegGuzzqrsJB5sSTlAG6Ttew+7QztuKtt9ve//bbpMxue55sRzl27BjLli3jyivdAhgNDQ0dOvfqq69udd/FF+uxZ2PHjuXYsWMkJSWRlJREXFwcFRUVpKSkdMvuQCGOnSA4ObobfviPXsf38pkY8b++2klJtX7TuWZyf8b3T2n3nOnD0lm9t5xmu8GygjLOyc1y70wfBdE9oKkWiiJDSV0QAoqzcSI2WUsMhRNRMVrovGKvTyN2TYcPt73/0CGf3ctut5OSksK6des6fW5CQkKr+2JjYwGwWCyutfN1s49Tyf5EUrGC4OTL+9zpl9N/A/Ep3b7kzuJqnv+uEICUHtHcdc7IDp03fXhv1/qEOjtrFPSZoNeHN0Jzx55UBUHoAEf3uB2ewdP131u44ayzq9gHtiafXDI6K6vt/X36+OQ+AD179mTQoEG8+eabgK5hXr9+/QnHJSUlUV1d7bP7hgri2AkCaDHiLe/pda8hkHdTty/pbJhodjRM3DlrBL0SOqaFNy47hZQeOv26ZGcpJ0zFc06gsDfB4U3dtlUQBAeeMifhVl/nxCl5Ytj0FBsfkHLF7Lb3z257f1vU1taSnZ3t+nr44Yd5+eWXefbZZxk/fjy5ubm8//77J5w3b948br31VlfzRKQQho8igtBJDAM++5379dl/8okY8UcbDrF8dxkAY/r15NopAzp8rtWiOHVobxZuOMT+o3UUltUyqLdHCsGrzm6VTzp3BUEAdn3tXodbfZ2T4ztjPV93keRLL+XY4m9abKBIOvtski+9pMvXttvtLW7/9NNPT9h27733utazZ89mtodDWVhY6HXs/PnzW9w3b9485s2b1+p5wY44doKw5T0oWqnXA06BkRd0+5I1Dc2uhgmAP10yxtXp2lFmDEtn4QZdl7JkR6m3Y5ftUfcjnbGC4BtsTbBniV73Hq4bDcIRL8fONw0Uymql3yMPU/n+B1S8/TZNhw4R3acPKbNnk3zpJQHTsRPEsRMineYG+PJe9+tz/tcnYsSPfr2Lw1V6XM9V+dlMGpDa6Wucdlyd3dxTctw7k/tDQjrUlIpjJwi+Yv8KaHTUZIVrGhb8pmWnoqJImX25z7pfha4hNXZCZLPCQ4x47JXeKc4uUlB6jGeX6jfLnnFR/M+5HWuYOJ4+yfEMz9SjdJbvLqOx2SMdoZTb1rJdUFfeLZsFQeC4MWJh7Nil5rjXPpQ8EYIDceyEyKX2KCxxihHH+kSM2DAM7v1gM002R8PEOSNIS4xt56zWcU6hqG20sWrvUe+dXnV2a7p8D0EQHDjHiFljYeCp5triT6LjIckxZqsDEbsTmreEgNGVf3tx7ITIZcmDUF+p11Nv80k9zaebDvPtTj0tYnSfnlx/UvcEjqcP9xwvdtwUCnHsBMF31ByBQw7JjIEnQ0wPc+3xN850bPkesNtaPSwuLo6ysjJx7kzAMAzKysqIi4vr1HlSYydEJmUFOg0LDjHiO7p9ydrGZv780RbX6z9fmtvphonjmTKoF7FRFhqa7Xyzo5TfnOeR1u070b2WOjtB6B4FiwCH8xLO9XVOeg2CvUvB1ghVByGl5TGH2dnZFBUVUVrawtxqwe/ExcWRnZ3dqXPEsRMik688xYh/C3HJ3b7kY4t2cbBSN0zMnpRN3sDuDw6Pi7YydXAa3+woZeuhKkqq68lIcjy99eilNfeOFmjJE8PwSeOHIEQk4TxGrCV6ecyMLd/TqmMXHR3NoEGDWtwnBCeSihUij33fwxaHmGXaUMjvvhjxniM1PL1EFyEnxUV5R9a6iWc69tvj07FO2ZOaUp8JjQpCxGG3Q4FDvy6pD2SMNteeQOCnzljBfMSxEyILw4DP7na/PvtPYI3u5iV1w0SjTXet3nH2cNKTut4wcTwzPGVPdh6XDvGqs5N0rCB0ieJNUFOi10NmRkbkWxy7sEUcOyGy2PyuTluC7nobcX63L/n5lmK+ccxzHZmVxJyp3WuYOJ4h6Yn0Tdbp1293HsFu9yhiFsdOELqP1xixmebZEUhSPdKr4tiFFeLYCZHD8WLEs7ovRlzXaONPH7obJu67OJcoq2//rJRSrnTs0ZpGNh+scu/MHAMWR8SxSBw7QegSTpkTVOQ4dnE9oYcjG3C00FRTBN8ijp0QOax4Cir26vXYq6DfpG5f8onFuzhQoYdLXzqhLycNTuv2NVvCS/bEMx0bHQdZY/X60DqwNfvl/oIQtjQc03W3oN8TenS/6SlkcKZjj+7WZSpCWCCOnRAZ1B7VunXgECP+Q7cvubeshieX6BRGYmwUvzt/VLev2RqnDumNUznFmfZ14UzHNtVC6Ta/2SAIYUnhUneHfCTInHjidOyaauBYibm2CD5DHDshMvjmAbcY8ck/84kY8X0fbnGN+fqvs4aR0bNzIpKdIblHNBP6pwCwZm851fVN7p1SZycIXSdSxoi1hDRQhCXi2AnhT1kBrHSIEfdIg2n/3e1LfrmlmK+36Sfc4ZmJzD0lp9vXbA9nOrbZbrC8oMy9w8uxW+V3OwQhrHDW18UmQ798c20JNMdr2QlhgTh2Qvjz5b1gd9Se+UCMuL7Jxn0fbXa9vu/iMUT7uGGiJVqts0sbqj+UQEaLCUJnOLpHC3wDDJ4O1gjT7JeIXVgijp0Q3uxdDls/0Ou0YZA3r9uX/M83u9l/VDdMXDS+LycP8U/DxPGMz04hOV53wHrNjbVYoJ9jvFjJFmisCYg9ghDyeMmcRFgaFsSxC1PEsRPCF8OAz30rRrz/aC2PL94FQEKMlbv92DBxPFaLYtpQLU+w72gthUc8HDhnOtaww8F1AbNJEEKaXV+715FWXwcQn+qO9h+VVGy4II6dEL5setvdTJBzGow4r9uX/NNHW2hwNEz88sxhZCX7r2GiJaa3NoXCszZIGigEoX1sTbBniV6nDfNJQ1XIoZS7zk4idmGDOHZCeNJUD1/e534968/dFiNetL2EL7YUAzAkPYGbTg38YGyvOjtP2RNPTT5x7AShffavgMZqvY7EaJ0TZzq2vkLLQgkhjzh2Qniy4imo3KfX466GvhO7dbmGZhv3feBumPjTJWOIiQr8n0+f5HiGZSQCsLygzCW3QlIW9MzWa2mgEIT2ifT6OidedXaSjg0HxLETwgNbM6x5EZ6dBQ+NdI8Oi4qDmd0XI356yW4Ky2oBuGBsH04d2rudM/yHM2pX02hj9d5y9w5n1K5yn4iNCkJ7OGVOrLGQc6q5tpiJSJ6EHeLYCaGPrRnemgcf3A77f4Bjh8Cw6X3J2ZDUp1uXLyqv5d+LdMNEfLSVuy8IXMNES3imY72mUGRLnZ0gdIiaI3BovV4PPBliEsy1x0ykMzbsEMdOCH3WvwpbP2x5X9ku2PBaty7/vx9tpb5JpzxvnzmUvinx3bpedzlpUC9iHWlg7zo7mUAhCB2iYBHgmI0ayWlYEMcuDBHHTghtaspg+WNtH7PmxS5ffsmOUj7dfBiAQb0T+PFpgW+YOJ64aCsnDdbaeVsOVVFa3aB39JkAyvEnXSQTKAShVSJ5jNjxJGZCdA+9FscuLIgwmW0hZKmr0APuS7Y6vm+Bkm1Q04FassqiLt2yodnGvR4NE/denEtslLVL1/I104f1dkXrvt1ZyuWTsiE2EdJH6n+bg2vAbtfixYIguDEMKHDo1yX1gYzR5tpjNkpB6iAo2SzNE2GCOHZCcNFwDEq3Q+lW7cQ5v6oPdv2aydldOu3ZpXvY7RABPic3kxketW1mM2N4Ov+7cCugo4qXT3L8jP3ytGNXX6mfvnsPNdFKQQhCijfBMS1bxJCZ3ZZBCgt6ORy7mhJoqIbYJLMtErqBOHaCOTTVwZEd3s5b6Vao2Nex8y1RekZq+kiwNcL2j1s/dtKcTpt3sKKOR7/SDRNx0Rb+cGFwPdUPzUikT3Ichyrr+XbnEex2A4tFacdurSP1fGC1OHaCcDy7vnSvh8w0z45g4njJkz7jzLNF6Dbi2An+pbkRynZ6OG+ONGp5oR5/1R7KotMEGaP0V/pInTpJGwpRMfoYuw3enNtyA8Woi2D8tZ02+/6FW6lr0p21Pz99KNmpPTp9DX+ilGL6sHReX7WfsppGthyqYky/5OMaKFbB+KvNM1IQghGnzAlKHDsnxzdQiGMX0ohjJ3hja9ZdpmtfhMoDkNwPJs6BCdeBpY36MluzfkMo2eJdA3e0AOzNHbt3ykBv5y1jJPQeDtHtdKFarHDFfN39uuZFXVOXnK0jdeOvbdvuFvhu1xEWbjwEwMC0Hvxk+uB2zjCH6cO1Ywda9mRMv2T97xYVD8110hkrCMfTcAz2fa/XfSdCj17m2hMsiJZdWCGOneDGqQfnGfmqKtLacDs/086TUjra5um8lW7TaVVbY8fu07OfhwPniMT1HqGL/7uKNQom3qC/ukFjs50/ejZMXJRLXHRwNEwcz7ShvbEosBu6zu7nZwzV/w59J8C+5XB4IzQ3QFSs2aYKQnBQuBTsTXod6d2wnojkSVghjp3gpi09uK0fwr8maGHP5rqOXS8x0zv6lj5Kf49L9pnJvmb+sj3sKjkGwFmjMjljZIbJFrVOco9oxvdPYe2+ClbvLedYQzOJsVE6HbtvuXa0D2+C7Lz2LyYIkYCMEWuZnv3AGqPfM6QzNuQRx05ws7YdvbfK/S1vj+/l4bw5HblRIZfmKK6q5/++3AlAbJSFP14UXA0TLTF9WDpr91XQbDdYXlDG2aMz3aPFQKdjxbETBI2zvi42GbInm2tLMGGxQmqOzrxIxC7kEcdOcFN5oJ0DFPSf4kijOqJvGaMhIT0sJAPuX7iVmkbdMHHb6UPo3yu4GiZaYvrwdP7vK+2MLtlR6nDsZLSYIJxAeaGu+QUYPF2XLQhuUgdpx67qgFYtaK+2WQha5DdbcJPcT9fUtUb2FLj588DZE0CWF5TxwXqtlde/Vzy3zhhiskUdY3x2Mj3joqiqb2bJTsd4sZQB0KM31B4Rx04QnOySNGybeNbZle/VD+5CSCKy9IKbie3oveXdGBg7AkyTzc4fP9jkev3HC4O3YeJ4oqwWpg3rDcDeslr2ltXo6KlT9qRsJ9SVm2ihIAQJzmkTII0TLSENFGGDOHaCmwnXwdCzW97XRT24UGDBskJ2FOuGiZkjMzhrdKbJFnWO6cPcEzGcY8bI9kjHHlwbYIsEIciwNcHub/Q6bZiOagveiORJ2CCOneDGYoUpt7hfx/aE/lPhksfgygWd1oMLBUqq6vmno2EixmrhniCbMNERpnuMOvtmxxG9OL6BQhAimaKV0Fit1xKtaxmJ2IUNUmMneFO61b2e/SwMn2WeLQHgr59s41iDFlD+6YzB5PROMNmiztM3JZ6hGYnsKjnG8oIjNDbbienr6ditMc84QQgGvMaIiWPXIsn9QVnBsIljF+JIxE7wpniLe50ZetGrzrBiz1HeXas7gfulxPOz00N3rqozHVvTaGP13nItNeN8Ai9aBYZhonWCYDLOxglrDOScaq4twUpUDKT012tx7EIacewEb0ocUxfikrVoZZjSbLNzz/vuhok/XDia+JjQTTVPH97btXZ1xzplT2pK9Jg1QYhEao7AofV6PeBkiAm9qHzASHXU2VXs13WJQkgijp3gxtYMpTv0OiM3LLTpWuPF7/ey7bCuuZk+PJ1zckOrYeJ4pg5OIzZK/zm7Gij6eQgTS52dEKkULAIcEWupr2sbZ5TfsEHFPnNtEbqMOHaCm6MFYGvQ6zBOw5ZWN/Dw59qBjbYq7r1oNCrEndi4aCtTBulJH5sPVlFa3XCcY7fKJMsEwWQ8x4gNPcs8O0IBrwYK6YztErZmWPMiPDsLHs7V39e8CHZbwEyQ5gnBTbE7NUlG+Dp2f/tkG9WOhomfnDaYwemJJlvkG2YMT+fbnbordumuUi4bMxYs0XrouTRQCJGIYbj165L6hPX7mk+QztjuYWuGt+Z5z1yvKoL9P8DOz+CK+QGZeCIRO8GNV+NErnl2+JHVe4/y9hpdb9Y3OY7bZ4Zuw8TxeMqeLNlxBKLjIGuM3nBwrX7TEYRIongTHCvW6yEzw7q8xCeIll33WP+qt1PnydYPYcNrATFDHDvBTYmHY5cxyjw7/ITNbnDP+5tdr39/4Wh6xIRP0HpYRiJZPeMA+HZnKXa74U7HNtXCke0mWicIJuA1RmymeXaECqk57rVE7DrP2hfb3r+mnf0+Qhw7wU2xw+lJHqC7YsOMV37Yy+aDVQBMG9qb88ZkmWyRb1FKubpjjxxrZMuhKu86uyKpsxMiDFd9nRLHriNEx7vVEMSx6zyVB9rZHxh1AnHsBE1DNVTs1eswbJwoO9bAg5/piFW0VXHvxbkh3zDREl7p2J2lbskTkM5YIbJorIF93+t134la21FoH2edXXlhQAv+w4LkdiTCkrMDYoY4doKmxGPiRBgWGD/w6Xaq6nWN2Y+mDWJoRng0TBzPtKG9sTj81SU7SiFtqB4NB9JAIUQWhUvB1qjXInPScZzpWFsjVB001ZSQY+KctvdPame/jxDHTtAUu2vPwq1xYu2+cl5ftR+ArJ5x/HLmMJMt8h8pPWIYl50CwOq95dQ02XW0ArT4dGONecYJQiCRMWJdQzpju86E61pP+Y+6CMZfGxAzxLETNCXh2RF7fMPE3ReMIiE2fBomWsKZjm2yGSwvKHPX2Rl2twK/IIQ7zsaJ2J6Qnd/2sYIbcey6jsUKEzyicnEp0H8qXPIYXLlA7w+EGQG5y3EopaxKqbVKqY8cr3sppb5QSu10fE/1OPa3SqldSqntSqlzPLbnKaU2Ovb9SzkKppRSsUqp1x3bf1BK5QT8BwxFnFInlmidvgsTXlu5j40HKgE4eXAaF47rY7JF/mfG8ePFsqXOTogwygu14DrAoOlgjTbVnJBCHLvuUeKR/brudbj5M5h4Q8CcOjAvYvf/AI+iLn4DfGUYxjDgK8drlFKjgWuAXOBc4HGllPNf5wngFmCY4+tcx/abgXLDMIYCjwB/9++PEgYYhvuXMX1E2LwJltc0uhomoiyK+y4Jz4aJ4xmfnUJSnI5KLtlRKqPFhMjDU+ZE6us6h2jZdQ9PoX+Tsl8Bd+yUUtnABcAzHpsvARY41guASz22v2YYRoNhGHuAXcAUpVQfoKdhGMsNwzCAF447x3mtt4AzVSR8mneH6kNQV67XYdQ48cBn26mo1YOs552Sw/DMJJMtCgxRVgvThuqoXWFZLfsae7olDIrEsRMiAOe0CZD6us4SmwQJju56GSvWeQ5v1N9TB+l/SxMwI2L3T+DXgN1jW6ZhGIcAHN8zHNv7Afs9jitybOvnWB+/3escwzCagUogzac/QbjhNXEiPBy7DUUVvLZSD7FOT4rl/50Vvg0TLeEpe/LNzlLoN0m/qNwHx0pMskoQAoCtCXZ/o9dpwyB1oLn2hCLOdOzR3TqjI3SM2qNQ5dCyyxprmhkBdeyUUhcCJYZhdDRs0FKkzWhje1vnHG/LLUqpVUqpVaWlpR00J0zxrAnIHGOeHT7Cbjf4w/ubXe9Hd58/iqS48EgvdxQvx2778Xp2InsihDFFK6GxWq8lDds1Uh3p2KZa90g2oX2c0Tow1bELdHvgqcDFSqnzgTigp1LqJaBYKdXHMIxDjjSrM6RQBPT3OD8bOOjYnt3Cds9zipRSUUAycPR4QwzDeAp4CiA/Pz+yH0k8I3Yhmoptttl5Z80BXl+1n10l1VTWac26KTm9uGRCX5OtCzz9UuIZkp5AQWkNywuO0HTqJFyu7YHVMOLctk4XhNDFa4yYOHZdwquBYg8khdeUHr8RJI5dQCN2hmH81jCMbMMwctBNEV8bhnED8AEw13HYXOB9x/oD4BpHp+sgdJPECke6tlopNdVRP3fjcec4r3WF4x6R7bi1hzNiF5cMPUPPCWq22bn9lbX8+u0NrN5b7nLqQE+ZsNkj87/fGbWrabSx1jYQVzD7gIwWE8IY5xgxawzknGquLaGKdMZ2jUh07Nrgb8DZSqmdwNmO1xiGsRl4A9gCfAr83DAM54yT29ANGLuAAuATx/ZngTSl1C7gDhwdtkIr2Jqg1DEcPiMXQrDP5J01B/h08+EW931XUMY7a9uZ3xemeKZjF++pg4xR+sWB1VI3I4QnNWVwcJ1eDzgZYhJMNSdkEceuazg7YuNS3A1rJmCaUqthGIuBxY51GdBizNwwjPuB+1vYvgo4oSDMMIx64EofmhrelBW4x+6EaOOEc6pEa7yxcj9X5fdv85hwZOqgNGKiLDQ221mys5RfD5ikhajrK/WbddoQs00UBN+yexGukmqpr+s6InnSeZoboHSbXmeNNTVIEiwRO8EsvBonQnPixKGKujb3H2xnf7gSH2PlpEF68PmmA1Uc6z3evbNI0rFCGCJjxHxDfKouzQGJ2HWU0m1gd5QBZY0z1RRx7CIdr8aJ0HTs+qTEt7m/bzv7w5npw9zp2JWNHk/hIlQshBuG4davS8wK2QfVoEApdzq2TCRPOsRhD2HiLHPVJcSxi3Q8Z8Q6a7BCjKvbSbNeNTny0rBOPOvsFhanQpTDyRXHTgg3ije5pTmGzAzJeuGgwunYNVS6BeyF1gmSxgkQx05wFnsmD4C4nuba0kUun9SPnnEtl4uem5vF7EnZLe6LBIZnJpLVMw6AxbsqMPo40rGHN0Bzo4mWCYKPkTFiviXVI8IvEyjax+nYWaKh9whTTRHHLpJpqIYKPZ0hVBsnADYeqKSqXtc29IyLom9yHPkDU3nginE8dv0krJbIfXJXSnHaMD1e7MixBspSHCkCWyMUb2zjTEEIMZwyJygdsRO6h3TGdhzDcL+fpo+EqBhTzTGtK1YIAkq2utchXI+yYFmha/3PayYwc2SmecYEIdOHp/Pmaj2Bb23zEM527jiwBvrlmWaXIPiMxhrY971e950IPXqZa084II5dx6ncr9UGwPQ0LEjELrIp9uiIDdGJE6XVDSzceAiAAb16MGN4RjtnRB7ThvZ2lRt9WOYhQC11dkK4ULjULdskaVjfII5dxwmi+joQxy6y8WycCNGI3asr9tFk0x1bN548MKLTrq2RmhDDuOwUAD45EI0Rn6Z3iOSJEC7IGDHfk5gB0Q6BZ9Gya5sg6ogFcewiG6fUiSUa0oaaa0sXaLLZefmHvQDER1u5Mi9yu1/bY4ajzq7JhrvOrmwn1FWYZ5Qg+ApnfV1sT8jON9eWcEEpt1CxROza5vAG9zpTHDvBLAzD3RGbPgKs0W0fH4R8tvkwxVUNAFw6sR/JPULvZwgUnrInGwwPJ/7gWhOsEQQfUr4Xynbp9aDpIfleFrQ4HbuaUqivMteWYMb5WdozOyjqO8Wxi1SqD0F9hV6HaBrWs2li7ikDzTMkBJjQP4UkhyTMpxUe8i9SZyeEOgUic+I3POvsJB3bMvWVUF6o10FQXwfi2EUuXhMnQq9xYvPBSlYWatHMkwb1YmRWaGrwBYooq4VTh+h07OcV0kAhhBFSX+c/RMuufTybEMWxE0wlxGfEvrBsr2s975Qc8wwJIZzp2AqSqOrhqEcsWiXjgoTQxdYEu7/R67ShkCqRe58inbHtE2SNEyCOXeQSwhG78ppG3lt3AIA+yXGcPVp06zrC9OG9XeutluF6UVMCVQdMskgQuknRSmis1muJ1vkecezax7NxQiJ2gqk4w8dxydCzb9vHBhlvrNpPQ7MdgBumDiTKKr/GHSE7tQeD07V8waJqjw5ikT0RQhWvMWJnmWdHuNKzL1gdUxQkFdsyTg27mCRIyTHVFCfyiRiJ2JrgyHa9zhwTUsOybXaDF7/XadgYq4VrJovESWeYPkynY1c05rg3Sp2dEKo4GyesMZBzqrm2hCMWK6Tm6LU0T5yIrdk9wSkzFyzB4VIFhxVCYCkrcKu0h1ga9uttJRSV1wFw4fg+pCXGmmxRaDFjhHbsNhs52JRVbzywxkSLBKGL1JTBwXV6PeBkiEkw1ZywxZmOrToATXXm2hJslO0Em5bcCpY0LIhjF5l4NU6ElmPnKXEiTROdZ+qgNGKiLDQQw26Lo+Pt4Fqw28w1TBA6y+5FgKPxR2RO/IeX5EmhaWYEJUE2SsyJOHaRiFfjROh0xO4qqWbpriOA1mVzjskSOk58jJUpOVpA83tnOrapBkq3mWeUIHQFkTkJDCJ50jpejl1wdMSCOHaRiafuTsYo8+zoJC8sF4kTX+Dsjl1vH+LeKHV2QihhGFDwtV4nZoWkZFPIIJ2xreN07JQlqMqaxLGLRJyp2JQBEBcawr7V9U28vboIgN6JsZw/to/JFoUuTj27tXaP0WLi2AmhRPFmOHZYr4fMDKkGsJCjl2fEThw7F4bhdux6D4foeHPt8UAcu0ijoRoq9ul1CKVh315dRE2jrgO7bkp/YqLkV7erjMhMIrNnLLuNPhzD8WYkjp0QSsgYscCRMgCcjVbi2Lk5Vgy1ujSIzOBJw4I4dpGHszUbQqZxwm43XGnYKIvi+qmiLt8dlFKcNiwdAwvrbY40S/EWaKw11zBB6Ci7vnQsFAw+w1RTwh5rNKQ4ZKVE8sRNkDZOgDh2kYdXfV1oOHZLdx1h95EaAM4Zk0VmzziTLQp9nOnY9Yajzs6wwaH1JlokCB2ksQb2fa/XfSdAQpqp5kQEzjq7in3Q3GiuLcFCEE6ccCKOXaRRHHozYkXixPecNrQ3SsE6aaAQQo3CpW4dTpk2ERicjp1hh8r95toSLHjNiBXHTjCTEofUiSVaD80OcvaV1fL19hIARvXpSf7AVJMtCg9SE2IY1y+ZdV4NFDJaTAgBROYk8Ehn7Ik4U7GJmZCYYa4txyGOXSRhGO6IXfpIXTsR5Lz4fSGGQ4N03ikDUdL95jOmD0+nhFQOGVrXTiJ2QkjgbJyI7QnZ+ebaEimIlp03jTVQtkuvgyxaB+LYRRbVh6C+Qq9DoHGitrGZ11fqsH9Kj2gumdDPZIvCC2ednSsdW7EPjpWaaJEgtEP5XvcH6qDpIfFwGhZIxM6bkq24pp4EWUcsiGMXWXhNnAh+x+79dQepqm8G4Or8/sRFW022KLyY0D+FpNgob6HigzI3VghiRObEHFJzAEe2RBy7oG6cAHHsIotij2LPIG+cMAzD1TShFNwgEic+J9pq4ZShae7OWIAiqbMTghiprzOH6Djo6ciYiGN3nNTJOPPsaAVx7CKJktCJ2K3Yc5Rth6sBOHNkJv179TDZovBk+vB0NtoHYTccT+NSZycEK7Ym2LNEr9OGQqo87AUU5wSKir1gt5lri9k4O2Kj4iFtSNvHmoA4dpGEMxUblwI9+5pqSnssWF7oWovEif+YPiydY/Rgp+F4Gj+wGle3iiAEE0WroKFKryVaF3icjp2tEaoOmGuLmdht7ibEzNFgCb4SIXHsIgVbExzZrteZuUE9W/FgRR2fbS4GYEh6AqcOFQFSf9G/Vw8G905w19nVV0iqRQhOpL7OXKSBQnN0DzRpwfxgbJwAcewih7ICt6hnkKdhX/lhHza7jhrNPSVHJE78zPTh6d51dpKOFYIR5xgxawzkTDPXlkjES/Ikgh274uAdJeZEHLtIwatxIngdu/omG6+u2AdAYmwUl0/KNtmi8GfG8HSZQCEENzVlcHCdXg+YCjEJppoTkXhF7CJYyy7IGydAHLvIwatxIng7Yj/eeIiyGh1ZvCIvm8TYKJMtCn9OGtyLPZaB1BsOTTBx7IRgY/ciXLphMkbMHHpJxA7wHiUWpEEScewiBS8Nu1Hm2dEOnnNh55wsXW+BoEdMFBMHpbPJ0G/cxqENMuhbCC5E5sR8YpMgwTE6SyJ2OoIZm2SuLa0gjl2kUOLo4kkZAHE9zbWlFdbuK2d9USUApw3rzZD0RJMtihymD0t3NVAoW4N36l4QzMQwoOBrvU7MCnoNzrDGGbUr3xOZ3fM1ZVB9UK+DtL4OxLGLDOqr9LgoCOo07AvL97rWInESWKZLnZ0QrBRvhmOH9XrIzKDu6A97nHV2TbVwrNhcW8zAs3EiUxw7wUxKtrrXQVoTUFrdwEcb9JPQgF49OH1EhskWRRYjs5LY38OdojfEsROCBZE5CR4iXfLkcPB3xII4dpGBMw0LQSt18tqKfTTZdGh/ztSBWC3yVB5IlFIMGTaGo4ZOfzfuXWmyRYLgwFVfp2DwGaaaEvGIY+dei2MnmIpn40QQCio22ey89INOw8ZFW7gqv7/JFkUm00e46+xiK3ZBfaXJFgkRT2MN7Fuu130nQIKIlZuKl5ZdBDZQODti41ODenqTOHaRgFPqxBoTlHPtPt9cTHFVAwCXTexHco9oky2KTE4bls46Y6h7w8G15hkjCACF37mF1aUb1nwiWfKkqd49vSlrbFDXeopjF+4YhnuuXe8RYA0+p8lT4mSuNE2YRq+EGCp7uQU3G/euMNEaQUDq64KNHr30rHGIPMeudBvYm/U6SIWJnYhjF+5UH9LzPyEoGye2HKxiReFRAE4a1IuRWcEpxRIppA8/2bWu3PWDiZYIAu4xYjFJkD3ZXFsEjbPO7miESZ54TW8KvpImT8SxC3eKg7tx4oXlha61ROvMZ3LuMPbadUdyXPHayHrjFoKL8r1QtkuvB88IymxDROJMxzZUQl25ubYEkhBpnABx7MIfT8cuyJ4yKmobeW/dAQD6JMcxa3SmyRYJEweksEkNAyCpuQyqDppskRCxSBo2OInUzlinY2eNgd7DzbWlHcSxC3c8Z8QGWSr2jVX7qW+yA3D9SQOIssqvo9lEWy0c6z3e9frI9u9MtEaIaGSMWHASiY6dYbg7YtNHQFSMufa0g3yShjtOqZO4FEjqY6opntjshmvSRIzVwjVTBphskeAkeehU17p46zITLREiFlsT7Fmi12lDIVXmRgcNkejYVezTqWcI+sYJEMcuvLE1uduzM3ODqj37620lFJXXAXDhuD70Tow12SLByZi8aTQZVgCiDq0x2RohIilaBQ1Vei3RuuAiErXsQqi+DsSxC2/Kdrk1oIKscUKaJoKX7Iw09lhz9LpuO01NTeYaJEQeUl8XvCRmQHSCXkdKxC6EOmJBHLvwxqtxItc8O45jV8kxvt15BIAJ/VMY3z/FXIOEE6jspZ9KE1Q92zetMtkaIeJw1tdZYyBnmrm2CN4o5SF5EiGOnVfEThw7wUy8GieCx7F70StaJ7UzwUji4JNc6wOblppoiRBx1JS5p54MmAoxCebaI5yIU/Kk9gjUV5lrSyA4vEF/Tx6gx4kFOeLYhTOeM2IzRplnhwfV9U28tboIgN6JMZw/NngaOgQ3OeOnu9bGgdUmWiJEHLsXAQ79RKmvC048R4uVh3mdXV2Fbp6AkIjWgTh24U2JIxWbMgBik8y1xcE7aw5Q02gD4NopA4iNsppskdAS8X1GUat6ANC/dgtHaxpNtkiIGHZJfV3QE0mdsZ4lTSHQOAHi2IUv9VXup4yM4EjD2u0GCxxp2CiL4vqTJA0btFislCfrhpsRaj/Lt+032SAhIjAMKPharxMzQ6JQPSKJJMfOs74uRH4fxbELV0q2utdBUl+3dNcRdpfWAHDOmCyykuNMtkhoi9gcXWcXpezs2bTcZGuEiKB4Mxw7rNdDzgwqiSbBAy/JkzB37IpDS+oEOuHYKaWmK6USW9mXqJSa3tI+wSRKPDtig0PqxEvi5OQc0+wQOkbacLdQcfO+FRgyN1bwNyJzEhr07AdWh/bo0UJTTfE7zohdbE9ICY0sU2cidouA1jyEEY79QrDg1ThhfsRuX1ktX20rAWBUn55Mzgn+zqJIR2Xnu9ZDGrezvbjaRGuEiMBVX6dg8BmmmiK0gcUCqTl6Hc4RO1uTO/uVmat/7hCgM1a2FROPBWzdtEXwJU6pE2sMpA0x1xbgpR/24gz4zD15IEpSLMFPz77UxWUAMF4VsGRHqckGCWFNYw3sc6T8+06AhDRTzRHawVlnV30QmurMtcVfHNnpFvkPkTQsQFRbO5VSOYBHlST5LaRj44EfAft8a5rQZQzDrZTdewRYo001p67RxusrdfF9cnw0l0zoZ6o9Qsex9M+HnR8zwFLK2q27YLr5DwlCmFL4nftDVGROgh8vyZPCoJHU8ikhNkrMSZuOHTAX+CNaVMgAHsU7cmc4XjcDP/eHgUIXqDoI9Y6BxUHQOPHeugNU1umxVFdP7k98jEichAqxAybDzo8BsO1fRW3jmfSIae9tQxC6gNTXhRbHd8aGo2NXHHodsdB+KnY+cAZwJtqBu93x2vk1EzgFyDIM4+n2bqaUilNKrVBKrVdKbVZK3efY3ksp9YVSaqfje6rHOb9VSu1SSm1XSp3jsT1PKbXRse9fypHbU0rFKqVed2z/wRF1jCy8Jk6Y2zhhGAYLlhUCusFtztTQKD4VHPTLcy1z2cUPu4+aaIwQ1jjr62KSIHuyubYI7dMrAjpjnRE7ZQ0px7XNR2/DMPYCewGUUmcAawzD6E4FdQMw0zCMY0qpaGCpUuoT4HLgK8Mw/qaU+g3wG+B/lFKjgWuAXKAv8KVSarhhGDbgCeAW4HvgY+Bc4BPgZqDcMIyhSqlrgL8DV3fD5tDDU1DR5MaJFXuOsu2w/pU5c2Qm/Xv1MNUeoZP0nYiBQmEwXhWweEcpZ4zMMNsqIdyo2AdlO/V68AzTy0eEDhDuWnaG4Xbseg+H6Hhz7ekEHW6eMAzjm246dRiaY46X0Y4vA7gEWODYvgC41LG+BHjNMIwGwzD2ALuAKUqpPkBPwzCWG1qD4YXjznFe6y3gTGc0L2IIoojdC8v3utYyFzYEieup6zSB8ZYCluwoMdkgISzxnDYxZKZ5drRAs83OGyv3M/uJZZzy16+Y/cQy3li5H5s9wuV/kvvrSBbA0TAcK1Z9GGrL9DpERok56YyOXYxS6o9KqW1KqVqllO24r+YOXseqlFoHlABfGIbxA5BpGMYhAMd3Z0igH+ApeV/k2NbPsT5+u9c5hmE0A5VAZLVXOSN2cSmQZN4s1kOVdXy6WYuNDk5PYNrQ3qbZInQdla3TsanqGLay3RSV15pskRB27PrSvQ6i+rpmm53bX1nLr9/ewOq95RysrGf13nJ+/fYGfv7yGpptdrNNNA9rtB5XCeEZsQvRxglov3nCkwfRDRKfAO+g06qdxpFGnaCUSgHeVUq15Qq3FGkz2tje1jneF1bqFnQqlwEDBrRlcmhha4LS7XqdOcZU5fZXftjneqqde3KOSJyEKv0mwbqXAafsyRGuOymM/mYEc7E1wZ4lep021K2PFgS8s+aA6+H0eD7dfJh31h7gqvz+AbYqiOg1GMr3QOV+aG6EqBizLfIdhze412Hs2F0B/NEwjPt9cWPDMCqUUovRtXHFSqk+hmEccqRZnfmeIsDzryYbOOjYnt3Cds9zipRSUUAycELFt2EYTwFPAeTn54dPTL1sF9h1B6qZadiGZhuvrtAKOImxUczOy27nDCFo6ecWKp5g0Xp24tgJPqNoFTRU6XWQyZy8vqrtGclvrNwvjl3BV2DYdZ1k76FmW+Q7nJJhAJmh5dh1RqA4EejWwEilVLojUodSKh44C9gGfICWVsHx/X3H+gPgGken6yBgGLDCka6tVkpNddTP3XjcOc5rXQF8bUTSLCSvxgnzHLuFGw5x5JjWpLoiL5vEWJHICFkyc13jgyZYdvFdwZHITkEJviWIZU4OVbQtvHuwnf1hj5eWXZjV2TlTsYlZkJhuri2dpDOO3YdAd+fB9gEWKaU2ACvRNXYfAX8DzlZK7QTOdrzGMIzNwBvAFuBT4OeOVC7AbcAz6IaKAnSKGOBZIE0ptQu4A91hGzl4NU6Y1xG7wKNpYs7J0jQR0lijoc94AHLVXurr61m3v8Jcm4Twwdk4YY2BnGnm2nIcGT3j2tyfnhQbIEuClHDtjG2sgbICvQ6xNCx0LhX7KPCCUsqOlhdpKb3Z5v+sYRgbgIktbC9Da+W1dM79wAnpX8MwVgEn1OcZhlEPXNmWHWGNV8TOHN2ddfsrWO/44D9tWG+GpB8/rEQIOfrlQdEKYlUTI9Q+luwoJT+nl9lWCaFOTRkcXKvXA6ZCTIK59njQ2GyntqHtnsCiijr2H62NXBmncHXsirfgKs0PsY5Y6FzEbjk6FXov8AOws4UvwWyKHRG7lIEQm2SKCS84BIlBN00IYUC2d53dNzuPmGiMEDbsXoTrAzSI6uvsdoNfvbmeHSXH2jyu7Fgjs59YxvbD3VICC11SBuLqVwwnxy6EGyegcxG7H9FCd6kQRNRXQaVjZK9Jadgjxxr4aMMhAPr3ihcx23Ch3yTXcoKlgJeLKiivaSQ1IYy64ITAU/C1ex0k9XWGYXDfh5v5cL3ux0uOj+bmaYNYsqOUgxV19E2J58Lxffhg3SHW7CunpLqBK59cxvM3TSZvYIRFsaPjoGc/qCoKLy07L6mTcebZ0UU67NgZhjHfj3YIvqBkq3ttUuPEqz/so9FRWD9n6kCsFpE4CQtSB0F8KtSVM14VYBiwdNcRLhrf12zLhFDFMNyOXWJm0Mzi/PfXu1w1wnHRFp6bN5m8gan88sxhXsddnT+A215ezeLtpVTVN3P9Mz/wxPV5kfcw22uQduzKC8FuA0sYzAJ3dsRGxXunm0OEzqRihWCnxKO+zgSpkyabnZd/0BHDuGhLZMsAhBtKuebGDlEHSaKWJTtKTTZKCGlKtkC1ju4zZKapmptOXv5hL//4YgcAURbFEzfkkTcwtcVj42OsPH1jPpdO0A839U12fvLCKt5beyBg9gYFTsfH3gSVRW0fGwrYbe5a9czckHRUOxyxU0o9184hhmEYN3fTHqE7mDwj9vPNxRyuqgfgson9SOkhabqwol8+7PoSizIYa9nNkp2pGIYhwtNC1/AaI2Z+GvbjjYf4/Xtu7bIHrxzHGSPajr5FWy08fNUEUnrEMH9ZIc12g/96fR3ltY3cdOqgNs8NGzwlT47uhtQQV0E4uhuaHNN1QrC+DjpXYzeTE2vsegFJQIXjSzATZ+OENQbShgT89guWF7rWN0rTRPjhiNgBTFAFLKsaw47iY4zIMqdJRwhxXGPEFAw5w1RTlu06wn+9tg6n4ukfLhzNZRM7JqpusSj+eNFo0hJiXNG++z7cQnlNI/999vDwf/DxTFWW7wHM/b/sNl71dcFRHtBZOpyKNQwjxzCMQcd9JQOnA4eB2f4yUugAhuFOxaaP0NpjAWTroSpW7NEKOFMG9WJUn54Bvb8QADwaKMZbtMaTpGOFLtFYA/scevd9J0CCeXOkNxZV8pMXVrlqg392+hBunta5aJtSil+cOYw/XzrGlVH+19e7+P17m1xjFcOWcJM8CfHGCfBBjZ1hGEuAR9A6d4JZVB2E+kq9NiENu0AkTsKfhN6uOZ4ux26nOHZCFyj8Dmx6Mo2Zadg9R2qY9/wKahq17v3V+f2565wRXb7enKkDefTaiURbtXf38g/7+OWra2lotrVzZgiT6pmKDYPOWJdjp0yd3tQdfNU8sZsWhIeFAOI1cSKwv4wVtY28t04XDGf1jGNWbmZA7y8EEEc6NkuVk0UZP+w5Sl1jGH9oCf4hCMaIFVfVM+fZHyir0Q7mrNGZ3H/ZmG6nTi8c15fn5k2mR4wuul+48RA3z19FTTtixyFLbCIkOGoRw8Gxc3bE9hqsf7YQpNuOnVIqCpgHhEE7TAhjYuPEG6v2U9+k0xg3TB1AtFWarcMWjzq78ZYCGpvtfL+nzESDhJDE2TgRkwTZkwN++8raJm58dgVF5XrW60mDevGvaycS5aP3rtOGpfPKT6aS2kOXxCzddYTrnv6eow4nMuxwpmOP7oZQHs1+rNTdqR2ijRPQCcdOKfV1C19LgYPAdcBDfrNSaJ9ic6RObHaDF7/Xmk8xVgvXTBkQsHsLJuDZQCF1dkJXqNgHZY5BRYNnBLweuK7Rxs0LVrK9WE+LGNWnJ0/PzScu2reyFhP6p/DmrSfTJ1nPm11fVMmVTy7jYEWdT+8TFDgdu+Y6qD5sri3doTj0GyegcxE7C3p2iOdXNfAOcKZhGE/73jyhwzhTsfGpkNQnYLddtK2E/Uf1G9WF4/rQOzHCh2KHO33Gg9IfgJOsulBaHDuhU3jJnMwM6K2bbHZuf2UNq/aWAzCgVw8W/GgyPeP841wOzUjirdtOYXC6noFbUFrDFU8sY1c7o8pCjnBpoDjslrsJ1cYJ6FxX7OmGYZxx3Nd5hmHcahjGYj/aKLSHrQlKt+t1Rm5AhT69JE5OyQnYfQWTiI53jasbb9mNBTsFpTUcCMcohOBbbM2w5kX46j73tvoqLQgbAAzD4Ddvb+SrbSUA9E6M5cWbp5CRFOfX+/ZLieetW09hfHYyAAcr67nyyWWs31/h1/sGFE8tu/IQrrPz6oiNgFSsEMSU7dKq3xDQNGxB6TG+dQyDH98/hQn9UwJ2b8FEHOnYOKOOIUrP05SondAmtmZ4ax58cDvUlbu3f3UvvDlX7/czf/tkG2+v0aXgSbFRLPjRZAamJfj9vgC9EmJ4+SdTOXVoGgDltU1c+/T3LHW8f4Y8x4sUhypOxy6+V0AzX76mU46dUmqsUuotpVSpUqpZKVWilHpDKRW6rm044NU4ETjH7gUviZMQVxsXOk52vms5wbILgPs+2MzsJ5bxxsr94a/bJXSe9a/C1g9b3rf1Q9jwml9v/59vCvjPEu1wxERZeHpuPrl9k/16z+NJjI3iuXmTOX9sFgC1jTZumr+ChRsOBdQOvxAOqdimejiiBabJGhsUI+66SmeaJyYDP6BlpT8CHgQWoidSfK+UymvjdMGfeDVOBKYjtrq+ibdW66ff3okxXDAudJ9uhE5y3AQKgPpmO6v3lvPrtzfw85fX0OwQexUEANa+2Pb+Ne3s7wZvrtrPXz/ZBoBFwaPXTmTq4DS/3a8tYqOsPHrtJK47STeZNdkMbn91DS85GtBClvhU/QWh69iVbgXDURYQwmlY6FzE7q/AJiDHMIybDMP4rWEYNwGDHNv/6g8DhQ7gqWGXMSogt3xnzQGXqOe1UwYQGxV6g5KFLtJ7OE3WHoBbqNiTTzcf5p1IG4QutE1lO78Pfhoe/+WWYn7zjrtu6q+Xj+Wc3Cy/3KujWC2K+y8dwy9mDgW0Osjv39vEo1/txAhlqRCnUPHRwtCUPPFqnIgcx24q8FfDMKo9Nzpe/x042ZeGCZ3AOSM2ZSDE+n9up2EYrqYJq0W5nj6FCMFiZbtFfyiNVPuI5URtrjdW7g+0VUIwk9yvnf0dm8vaGVYWHuXnr6xxlQb8+twRXD05ON6rlFL8atYI7rnQXTrzjy928KePtmAP1VIGZzq2oRJqj5prS1cIk8YJ6Jxj195vW4j+NoY49VVQuU+vA5SGXbrrCLtLawA4NzeLPsnxAbmvEDystQ8BIErZyVWFJ+wPS60uoesMO7ft/ZPm+PR2Ww9V8aP5K2lo1iUBN08bxG0zhvj0Hr7gR9MG8cjV44my6Hqu578r5FdvrqcpFEsZQr3OzunYWWOg93BzbekmnXHsfgB+p5TyCgkppRKA/wG+96VhQgcp2epeB6hxwnMu7I3SNBGRHEp0P0RMdDRQeNI3RZx9wYPSLa3vG3URjL/WZ7faf7SWuc+toLped9peNrEfd58/qtujwvzFZROzefrGfOKi9cfxu2sPcMsLq0JvVF8oO3aG4R4llj4y4KLZvqYzjt3vgFxgr1LqBaXU35VSC4C9wFjgbn8YKLRDsUddQACkTvYfrXXpQI3MSmLKoF5+v6cQfIzKO921bqnObrL8XghOirfAxrf0ukdvyJ4CPbOh/1S45DG4cgFYfFOje+RYA3Oe/YGS6gYATh+RzgNXjMNiCU6nzskZIzN46eaT6BkXBcCi7aXc8OwPVNY2mWxZJwhlLbuKvdBQpdchLEzspDMCxSvQdXZfA+cAdwDnOl6fZBjGSr9YKLSNZ+NEpv9HoLz4/V5XXey8U3KC9ilY8C/nn5pHhVV3Fo5XJzp2L32/l62HqgJtlhCMLP4Lrkqdc+6HH38Bd2yGmz+DiTf4zKmrrm9i3vMrKCyrBWDSgBQev35SyMyuzs/pxRu3nkxGkp7es3pvOVf9ZznFVfUmW9ZBQjliF0b1ddCOY6eUsiilLlJKjQEwDGODYRhXGIaRaRhGtGEYmcCfgZwA2Cq0hLNxwhoLvfxbQ1LXaON1R1F8cnw0l0xopyBaCFusVgs9h04FYKClhNE9G8gbkMKkASkAVNc3c+NzK9hbVmOilYLpHFzr1q/rPQLGXumX29Q32bjlhdVsOqAfJoZlJPLcvMn0iInyy/38xcisnrx92ykMTNNd59uLq5n9xDL2HAmBv6OEdIhJ1OuQc+w8O2JDd0ask/YeZW4AXgXa+q2qBl5VSvmuSELoGIYBJQ4Nu/ThYPXvm9j76w5QWadTA1dP7k98jEicRDKWbLee3cdXJPL2z07l9Z+ezJkjMwAorW5gzrMrKAmViIPge77+X/f6jN/5LDrnic1u8N+vr2P57jJAj/B64eYppPSI8fm9AkH/Xj1469ZTGN2nJwBF5XVc+eQyNh2oNNmydlDKQ/Ik1Bw7j4hdADJf/qYjjt3zhmG0mjA3DKMQeBaY60O7hI5QdRDqHX/sGf7tiNUSJ1pEUym44SRpmoh4PISKObAagGirhceun8SUHF1jt+9oLTc+tyK0aoUE37B3Oez6Uq+zxsKoi31+C8Mw+MP7m/hk02FAj+564eYpId+pn54Uy2s/ncpJjlrVI8caufap7/ne4bwGLc46u9oy92dTKOB07FIGQHyKqab4gvYcu0nA5x24zpdAfrtHCb7Fa+KEfxsnVhaWu2qmzhyZwQBHqkCIYPpOBBw1lg7HDiAu2srTc/MZ5Yg4bDtczY8WrAy9Lj+h6xiGd7Ru5h/A4vtat0e+3MkrP2i5px4xVp6fN5kh6Yk+v48Z9IyLZsGPpnD26EwAqht0ecPnmw+bbFkbeNXZhUgDRV2Fh2RY6NfXQfuOXRJQ3s4xOI7xvzKu4E1J4EaJeUuc5Pj1XkKIEJ0AifpDh4Kv4Jmz9Wgou43k+Ghe+NEUchwPAKv3lnPby6tDU59L6Dy7F8HepXqdPQWGzfL5LRYsK+RfX+0EINqq+M+cPMb3T/H5fcwkLtrKE9dP4so8LeDc2Gzn1pdW88aqIBUAD8UGiuLwmTjhpD3H7gjQkZzbAMexQiAp9hwl5j/H7nBlPZ86nhIHpycwbWhvv91LCBFszfDWPDjmiB4YdihaAR/cDm/OBVsz6UmxvHjzSa4uv8XbS7nzzfWhq6wvdIwTonW/9/lA9Q/WH+TeD/WDrVLwyNUTOG1Yuk/vESxEWS08cMU4fjpdO012A3791gb+882J3eim4yl5EiqOXZh1xEL7jt1SOlY7N89xrBBInFIn8amQ5L/5hy//sNc1lmfuyTlBrwklBID1r7q7HY9n64ew4TVAF4K/6KHP9f66g9z34ebQnokptM32T9yp+UHTYfAMn15+yY5SfvXGOpfs0p8uzuXCcX19eo9gQynFb88fxW/PG+na9tdPtvHXj7cG19+SZ8QuVLTswqwjFtp37P4JnKmUekQpdUKLkVIqWin1f8BM4BE/2Ce0hq0JSrfrdUauz5+InTQ023h1ha4/SIixcvkkkTgRgLUvtr1/jXv/iKwknr9pCvHRuiNywfK9/J8jhSaEGXY7LLrf/XrmH3x6+XX7K7j1pdU02bQz8//OHMacCCoN+emMIVpw2fF2/58lu/n1WxtoDpYSh6S+WnoLQqfG7vAG/T22p563Hga06dgZhrEc+BXwS6BIKfWSUup+x9dLQBHwc+BXhmHISLFAcmQn2B2dhn5snPh44yGOHNND3q/IyyYpLrRHrQg+ovJAO/uLvF7mDUzlyTl5RFv1J9I/v9zpVbcphAlb3nXXLA07B/pP8dmld5Uc46bnV1DraMK5YeoA/uusYT67fqhwVX5/nrghj5go/fH95uoibnt5DfVNQdCcZLG407GhkIq1NUHpNr3OGuu3AEmgabdNyTCMfwJnAKuAy4DfOr4uc2w7wzCM//OjjUJLeE2c8F993fxle13rSHoyFtohuZ3IbXL2CZtmDE/nH1dNcL13/vGDzby/rh0HUQgdbM2w6C/u1zN/77NLH6qs48Znf6DcIZtzwdg+3HfxmIidfHNObhYLbppCYqwucfhiSzFzn1tBVX0QyAo5teyqD0Fjrbm2tMeRHWDTgYtw0K9z0qH+c8MwlhiGcT668zXL8dXTMIwLDMP41p8GCq3gKXXip8aJdfsrWL+/AoDThvVmaEZ4yAgIPmDinLb3T2p5/8Xj+/KnS9xvoL96Yz2LHLOHhRBnw+tQtkuvR18KfXwzc7O8ppE5z67gYKUWuj51aBoPXz0ea4TX+p48JI3XbplK70RdJfXDnqNc85/vKXXMyTUNrzq7QtPM6BBh2DgBnZgVC2AYht0wjBLHVxDEfSMYz4hdxsjWj+sGL3ikyuZKtE7wZMJ1MOqilvdFxbW+D5gzdSB3nD0cgGa7wW0vr2ZV4VF/WCkEiuZG+OZveq0sesqED6htbOZHC1ayq+QYAGP7JfOfOfnERsnUG4Ax/ZJ589ZTyE7VgsxbDlVx5ZPL2H/UxEhZKHXGejl2ERaxE4IQp9RJykCI9Z2EYLPNzhsr93Pxv5fyzlqdJkvtEc304eEpJSB0EYsVrpgPlzwG/adCz2zo4ZDBaa6HJQ+1efovZg7lplNzAKhvsvOj+StdAthCCLJmAVQ4RF7HXQ3pI7p9ySabndteWsPafRUADO6dwPybJrvSj4JmUO8E3r7tFEZk6s+BwrJaZj+xjG2HTfp7CiUtO6djp6yQPspcW3yIOHahSH2lh1K279KwzTY7t7+yll+/vYENRe5xMOW1Tfzy1bXB03klBAfWKJh4A9z8GdyxGX72PcQl633fPw4lW1s9VSnFHy4YzWUTda1eVb1W1d9bFgLDzgVvmurcjrwlCmb8T7cvabcb3PXmer7ZUQpAZs9YFvxoCmmJsd2+djiS2TOO1386lbyBqQCUVDdw1ZPLWb3XhEi4Z8QumCVPDMPt2KWPgOg4c+3xIeLYhSKeH5g+dOzeWXPAJUR8PJ9uPuyK4AlCiySmw5l/1Gt7Myz8FbShsWWxKB64YhwzR2YAUFrdwJxnV1BSVR8IawVfsfIZt1D1xDneH+xdwDAM/rxwC++tOwhAz7goXvjRSfTvJWMM2yKlRwwv3XwSp4/Q2ZWq+mauf+YH7vtgM7OfWMYpf/2K2U8s442V+126pH4heYB28CG4I3ZVB6HO4fiGUeMEiGMXmng1TvhO6uT1dsbUvLEySMfYCMFD3jzoO0mv936nC+rbINpq4bHrJjE5R0ca9h2t5cbnVlBZGwTdfUL7NFTDUoeEqTUWpt/V7Us+vriA578rBCAu2sJz8yYzIksmVnaE+BgrT9+Yz6UTtGBzfZOd55cVsnpvOQcr61m9t5xfv72Bn7+8xn8ZGGsUpAzQ62B27MJwlJgTcexCET9JnRyqqGtz/8F29gsCFitc+DDg6Fj8/PdQ1/a46fgYK8/MncyoPj0B2Ha4mpsXrKSuUfqzgp7vn4TaMr2e/OP2ZXDa4bUV+3jwMy28brUoHr9+Evk5vbprZUQRbbXw8FUTOHVIWqvH+D0D46yzqyzSjTXBiFOYGMSxE4IAZ+OENRZ6DfHZZfukxLe5v287+wUBgL4T9Yc8QE2p99zQVkiOj2bBjyYzME2n21btLee2l1fTJHWdwUtdOSx7VK+jE2Daf3frcp9uOszv3nV3KT4wexwzR2Z265qRisWiqGtHsNivGRinlp1hdzfVBBthKnUC4tiFHobhTsWmD9dhbx9xdX7/NvdfNbnt/YLgYubvIcHRSb3yWTiwpt1TMpLieOnmk8hI0gXyi7eXcueb67H7sx5I6Drf/QsaHE1WU2/VNZZdZHlBGb98bS3O/+rfXzCK2XknilwLHedwZdu1qn7NwIRCZ6xzRmxSH0joba4tPkYcu1Cj6oD7zdTHBZ+z87LJ6tlyZ9C5uVnMniRvtEIHiU+BWc6ZoQYsvAPs7adW+/fqwQs3T6FnnH5geX/dQe77cHNwDToX4Fgp/PCkXscmwym/6PKlNh2o5CcvrKKxWUdnb50xhB+fNrids4T2MDUDE+yOXUO1264wi9aBOHahR7GnMLFvZ8Q22exUO0bSxERZ6NMzlvyBqTxwxTgeu35SxCu9C51k3FUwcJpeH1wLq5/v0Gkjs3ry/E1TiI/WIrQLlu/l/77a6S8rha6w9GFocojgnvILiE/t0mUKj9Qw7/kVHGtoBuCq/Gz+59zua+AJJmdggl2kuHgL4HhYDLOOWBDHLvQo8eiIzfStY/fdriPUOArW552Sw/LfncVbt53CVfn9xakTOo9ScMFDbumDr/6kIz0dIG9gKk/cMIkox+/dP7/cyQKPSSiCiVQe0Ol1gB5pOg3bBUqq6rnxuRUcOaaL688alclfLhsbsfNffc3svGzOzc1qcZ/fMzApA3E1UAWjll1x+NbXgTh2oYdXxM63M2I/2eTWsDt3TMtvCILQKTJGwck/1+v6Svjing6fevqIDP5x1Xicn/N//GAz768TLUXTWfIg2BzzSKf9d4cn3zin2sx+YhlT//IlMx5czD7H6KspOb3493UTibLKR5KvsFoU/75uIg9cMY6x/Xq6to/ISvJ/BiY6DpIdjmMwRuy8Gid8M9M4mJC/olDDKXUSnwpJvnO+mmx2vthSDGiV9wnZKT67thDhTP+1HjkGsP4VKPyuw6deMqEff7rY/QDzqzfWs2h7ia8tFDrK0T2w9kW9Turj7n5uB8+pNqv3lnO4qsHVtZkUG8WTN0wiLlrmv/qaKKuFq/L78+EvTiO3r3bu9pTWUNvY7P+bO9Ox5XvBFoD7dQZn40R0j24Lagcj4tiFErYmKNUaT2SOAR+mLL7fXUZlna6vOzc3C4ukXgVfEZsI5/3N/Xrhr/TvcgeZc3IOd5w9HIBmu8FtL61mVaEJo5IE+ObveqoIwPQ7IbpjBfhtTbWpbmjmy23irPubWaN1IKDRZneNavMrTskTexNUFfn/fh3FbnMrS2Tmau3NMEMcu1DiyE79RwI+b5z41CsN28en1xYERl4Iw2bpdelW+P6JTp3+i5lDmXdKDqDV9H80fyVbD5k05DxSKd3uniSSMgAm3tjhU2WqjfnMynVrAn6+udj/N/TqjA2iOruyAmh2SL2EYX0diGMXWnhNnPCdY2ezG3zm+ENPS4hhyiBRehd8jFJw3gMQ5ZDTWfw3rUrf4dMV91w42jUqqaq+mRufW8G+slp/WCu0xKL7teAswIzfQFRMh0+VqTbmMzIrif69dIR10bYSl7yM3whWyRPPxokw7IgFcexCC68Zsb5rnFi9t5wjx3Qx9NmjM6UDVvAPvQbBab/S66Ya+PS3nTrdYlE8eOV4Zo7MAKC0uoEbnv2Bkuq2hVgFH3BoPWx5X6/ThsG4qzt1ekJs20LqMtXG/yilXOnY6oZmvt9d5t8bBqtjF+aNEyCOXWjhGbHLGOmzy36y6ZBrLd2wgl855ZfuN/ytH8DOLzp1erTVwmPXTWJyjtZN23e0lhufXeGqDxX8xNf3u9dn/LZTE2/2HKmhqLztiJxMtQkMs0Z7pGO3tFzz6DNSc9zr8kL/3qszuBw75XPJsGBBHLtQwhmxSxnYYYmB9jAMg88c9XVJcVGcMiS8RqsIQUZ0HJz/kPv1x3dBU+cibvExVp6ZO5mRWfpvYNvham6ev5K6xvYnWwhdYP8K2PmZXmeOhdGXdfjUyrombl6wss25pTLVJnDkDUylV4JOoX+xpdi/4/piEyHR4UgGVcTO0RGbNgRiEsy1xU+IYxcq1FdCpaPA2Id1ARuKKjnomCl49qhMYqLkV0LwM0PPhNGX6nX5Hvjun52+RHJ8NC/cPIWBaT0AWLW3nJ+9vJomm5/rhiKRr//sXs+8Gywde4/QEidr2F1aA8DknFT+cvkY8gem0jc5TqbamECU1cKZjlKG4qoG1hdV+PeGzuj80T1gD4K/zWMlcMwRqQzTxgkQxy50KNnqXvswfOwpSnyOpGGFQHHuXyEmUa+/fVh3qnWSjKQ4XvzRSWQkxQKwaHspd7653r9RiEhj9zewZ4le98uD4ed2+NT/XbiVb3ceASA7NZ4nb8jjuikDeeu2U1j22zNlqo1JzPKYRvH5Fj93xzodu+Y6t0NlJofDe+KEE3HsQgWvxgnfOHaGYfCpo76uR4yVGcPTfXJdQWiXnn3hdEfzhK1Bp2SNzjtkA9J68MLNU+gZp2u+3l93kPs+3IzRhWsJx2EYx0Xr/tBh7cxXftjHfMcIuIQYK8/OnUxaYqwfjBQ6y2nDervmMH/eiragz0j1nBkbBJInxZvc60xx7ASz8ZI68U1H7PbiagodchFnjMgQ5XchsJz0U3d3d8FXupmiC4zM6snzN00mLlq/nS1Yvpd/fbXLV1ZGLjs/h6KVej1wGgw+vUOnLS8o45739QeoUvCvaycyIss3NcFC94mLtjJ9uK6lLiitYVfJMf/dzHOqQzDU2UnETggqnBE7ayz0GuKTS36yUWbDCiZijYYL/uF+/clvoKG6S5fKG9iLJ27II8qR1nvkyx28sLzQB0ZGKHa7d7TuzI5F6/aW1XDby6tpdqTDf3PuSM4cldnOWUKgccqeAK5Rkn4h2CRPnI5djzSfjuQMNsSxCwUMA4odEbv0EZ2SGmgL57SJmCgLZzgKagUhoAw8GSZcr9fVB/XIqi5yxogM/nHVeJf/8ccPNvP+ugM+MDIC2fq++0Nw6NkwYGq7p1TVN3HzglVU1GrpmdmTsrll+uB2zhLM4MxRGa7aRr/KngRTxK6pTk9vAh2t8+FIzmBDHLtQoOoANFTqtY/SsLtLj7G9WEdHpg/rTWI7AqKC4DfO/hPEpej18sfdDzFd4JIJ/fjTxfpvxDDgV2+sZ9F2mUPaKew2WPQX9+uZd7d7is1u8MtX17rSevkDdQesCuMPz1AmpUcMJzkmDK3dV0FJlZ9EvuNT9RfoDngzKdkKhkN2J4zTsCCOXWjg+UHno8YJz4HcMhtWMJWE3nDWH/XasMHCX3WpkcLJnJNz+O+zhgPQbDe47aXVrN571BeWRgYb3oAjO/R61EXQd2K7p/zl460s3q4Hy/dLiefJOXnERknNbjDjKVb8xdYApGOP7unW33W38ayvC+PGCRDHLjQo8eiI9ZHUiTMNG2VRnDVK0rCCyUyaq+U0APYtg/WvdetyvzxzKPNOyQGgvsnOTc+vZNvhqm4aGQE0N8LivzpeKDij/Wjd6yv38exSHY3pEWPlmbn59JYO2KDnbE/Zk80BcOwaqqDWz2PM2sKzI1YidoLp+HhGbFF5LRuKdGr35CFppPTo+DBvQfALFqtupFCOt6TPfw915V2+nFKKey4czaUT+gJQVd/MnGdXsM/RBS60wrqXoGKvXo+9EjJGtXn4D7vL+P177g7Yf149gVF9evrbSsEH9EuJZ0w//X+1rOAI1fV+GssXLA0UzoidNRZ6DzPPjgAQUMdOKdVfKbVIKbVVKbVZKfX/HNt7KaW+UErtdHxP9Tjnt0qpXUqp7Uqpczy25ymlNjr2/Us5ijmUUrFKqdcd239QSuUE8mf0C85UbHwvn3TyfLpJumGFIKTvRJj8Y72uPQJf/bnt49vBYlE8eOV4Zjoag0qrG7jh2R8oqfZTPVGo01QP3zyo18oKp/+mzcP3H63ltpfX0GTT6bW7zhnhJX4rBD/O7tgmm+FKpfucYNCys9vdo8QyRuqO/DAm0BG7ZuBXhmGMAqYCP1dKjQZ+A3xlGMYw4CvHaxz7rgFygXOBx5VSzsKNJ4BbgGGOL6ck+s1AuWEYQ4FHgK632QUDtiZ3vUtmrk86eT5z1Ncp5d32Lgimc8bdkOAoDVj1HBxY3a3LRVstPHbdJPIH6mfFfUdrufHZFVTW+Sk6Ecqsek53JgNMvEHP0myF6no9A/ZoTSMAl0/sx20zfCPDJASOWbnuOju/TaEIhohdxV5odEgphXkaFgLs2BmGccgwjDWOdTWwFegHXAIscBy2ALjUsb4EeM0wjAbDMPYAu4ApSqk+QE/DMJYbWmL+hePOcV7rLeBMFcqtWUd2gt3xIeSDxomS6npW7dUprsk5vUhPkloYIYiIT4Fz7ne8MOCjO3SXZncuGWPl2XmTGekQyd12uJofL1hJXWP3rhtWNByDbx2agtYYmH5Xq4fa7Ab/77V17CjWHbATB6Twl8vHSgdsCDIiM4kBvfS85UXbSmho9sPfRDA4dl7CxOPMsSGAmFZj50iRTgR+ADINwzgE2vkDnNX8/YD9HqcVObb1c6yP3+51jmEYzUAlkOaXHyIQeE2c6L5j99nmYldj0rmSNhGCkbFXQs5pen1onY4kdZPk+Ghe+NEU14fYysJyLnv8Oy5//DtO+etXzH5iGW+s3I8tUufM/vCkTn8D5P8IUvq3eugDn27j621aQqZvchz/mZMnU2tCFKWUqzv2WEMz3+/2Q/d4Qm/3XOhgcOwyx5hjQwAxxbFTSiUCbwP/ZRhGW61qLT0CGm1sb+uc4224RSm1Sim1qrTUT7UFvsCzk8cHjRPO2bAg9XVCkKIUnP8QWBzail/9GY51X4suo2ccL918EumOjs1th6tZs6+Cg5X1rN5bzq/f3sDPX15Ds83e7XuFFHUVsOxfeh3dA6bd0eqhb67az3+W6A/n+GgrT8/NJyMpLgBGCv5illd3rB/EipVyCxWbpWXn1RErjp3PUUpFo526lw3DeMexudiRXsXx3fkuXgR4PjpmAwcd27Nb2O51jlIqCkgGTngMMQzjKcMw8g3DyE9PT/fFj+YfvDTs2u5Qa4/ymkbXE9n4/in0TYnv1vUEwW9kjISTb9frhkr44h6fXHZAWg9umDqg1f2fbj7MO2sjbFrF8n9DvUMAfcotkNTyCLBVhUe5+133B+QjV48nt29yICwU/EjewFR6JWhlhC+2FGP3R9TamY6tLdMPEoHGGbFLGQhx4f87G+iuWAU8C2w1DONhj10fAHMd67nA+x7br3F0ug5CN0mscKRrq5VSUx3XvPG4c5zXugL42lGHF5o4U7GpORCb2K1LfbG12JVqOk+idUKwM+PX0NPx/Lb+VShc6pPLLtl5pM39b6zc3+b+sKLmCHz/hF7H9oRT/1+Lh+0/WstPX1xNoyOaeees4SJsHiZYPbRMS6obWF9U4fubeNbZBTpqV3sUKh1/0xHQOAGBj9idCswBZiql1jm+zgf+BpytlNoJnO14jWEYm4E3gC3Ap8DPDcM5E4TbgGfQDRUFwCeO7c8CaUqpXcAdODpsQ5L6SvcvpE/SsB4yJ1JfJwQ7MQlwnkdT+8Jf6S7xbnKooq7N/Qfb2R9WLH0EGnUTBCffDj16nXDIsYZmfvLCKsocHbCXTOjLz88YGkgrBT/jqY7gl+5YMyVPPHVgI8SxC+iAUMMwltJyDRzAma2ccz9wfwvbVwEnJMsNw6gHruyGmcFDyVb3upuNE9X1TSx1RCpGZiWR0zuhW9cThIAw8gIYdg7s/AxKt8H3j7caVeoofVLiOVjZupadUmC3G1gsYd7lWXUIVj6j1/G9YOptJxxitxv812vr2HZYS0WM75/C32ePkw7YMGPasN7ER1upa7Lx+ebD/M+5I317AzM7Y706YiPDsZPJE8GMV+NE9xy7r7eVuNIo50kKRQgVlNJRuyhHgf7iv0FlUdvntMPV+a13fAIcqKjnRwtWUu6IUIUtSx6EZoeDO+2/IO7EiREPfr6dLx1zRLN6xvG0dMCGJXHRVmYM17XmBaU17Co55tsbeDl2gY7YeXyORkBHLIhjF9x4Nk508xfSMw173lhJwwohRK9BcNqdet1UC592r7pidl52q6UIziDd4u2lXPCvb1m9t+tjzYKa8kJY84JeJ2bC5J+ccMg7a4p4YnEBAHHRFp6Zm09GT+mADVc8xYq/8HU6NqmP++Es4BG7Dfp7bDKktN44FU6IYxfMOBsnrLHeTzydpK7R5hoXM7h3AsMyuteEIQgB59RfQi/HZIOtH8KOz7t8KatF8e/rJvLAFePIH5hK3+Q48gem8sAV43jv56cyME1r3R2srOfq/yznmW93E8r9Vy3yzQNu4fPT7oSYHl67V+8t5zdvu1NYD181gTH9wr+bMJKZOTIDq+PJ5jNfy55YLLoBEALbPNHcCCXb9DprrE8mN4UC4tgFK4bhjtiljwBr18shv9lRQl2T7jk5d0yW1McIoUdULJz/oPv1J3dBU9ebHKKsFq7K789bt53Cst+eyVu3ncJV+f0Zl53Ch7+Y5uoab7Yb/O/Crdzy4moqa8NkDFnpDt1lDJDcH/Lmeu0+UFHHT19c5Srd+O+zhnP+WCnfCHdSesRw0iDdPLNufwXFVT6eqewMTlQfgsYa3167NY7scD/ARIB+nRNx7IKVqgNavwv0jNhu4JWGlfo6IVQZeibkXqbX5YWw9J9+uU3PuGgev34S9140mmirfgj6YksxF/77Wzb4Qwoi0Cz+KxgOEeYZv9ZOs4OahmZ+vGAVR47p+sILx/Xhl2dKB2yk4JxCAX5Ix3pJnhT69tqtEYGNEyCOXfDi2aLdjcaJhmYbX23Ves/9UuIZ0+/EAmlBCBnO+Yt7PNHSR6CswC+3UUox79RBvHnrKfRzCHnvP1rHFU8s54XlhaGbmj28ETY7dOF7DYHx17l22e0Gd7yxjq2H9DCgcdnJPHjFeInwRxBn5/pR9qSXp+RJgOrsxLETggpPx64bEbtlu8qobmgGJA0rhAE9+8IZv9NrWwN8fBf40cma0D+Fhb+c5hJwbbTZuef9zdz+6lqq60MwNbvoL+71Gb/zKvF4+IsdfLZZf5hnJMXy1Jx84mOkAzaS8Hz4X15whCpf/o6boWVX7HDsLFGQ7mMJlyBGHLtgpcSzI7brjt0nHrNhZdqEEBZM+am7S7zgK9jyftvHd5OUHjE8fWM+vzt/pKu4fOGGQ1z87+/YcrCtUddBRtEq2P6xXmeMhtzLXbveX3eAfy/aBUBslIWnb8wnK1k6YCMRp1hxk81wNd35hEBr2RmGO2LXe4RXyUG4I45dsOJsnIjvpeUIukCzze6qk8hIimXSgFRfWScI5mGNggv+4X796W+hodqvt1RKccv0Ibx+y1SyHJIfe47UcNnj3/Hqin2hkZr9+s/u9Rl3605FYO2+cu56a4Nr10NXjmd8/5QAGycEC56yJ5/7sjs2ub+OnEFgHLuqA1DnkCuKoDQsiGMXnNiadDcP6GhdF9OnK/YcpdzRyXdOblb4K+kLkcOAqTDxBr2uPqiFiwNAfk4vFv5yGtMdYq4NzXZ++85G7nhjPTWOkoegZM+3sHuxXvedqCd6AIcq67jlxdU0Nutmil+eOYyLxvc1yUghGBiRmeSS/Fm8vZSGZls7Z3QQaxSkDNTrQKRiD3sIE0dQRyyIYxecHNnpbtHuRuPEJ56zYSUNK4QbZ90HcSl6/f0T3nWpfiQtMZb58yZz56zhLkHjd9ce4JLHvmNnsX8jh13CMODr/3W/nvl7UIraRj0DtrS6AdClGv915jCTjBSCBaWUqzv2WEMzywvKfHdxZwNFVRE0N/juui0RoY0TII5dcOKDxgm73XCJTKb2iHbpEwlC2JDQG866V68NGyz8lV8bKTyxWBS3zxzGSz8+ifQkXbuzq+QYF//7O95Z072RZz5n15ew/3u9HnAKDDkTu93gzjfXs+mArhHM7duTf1w1XqL6AgCz/NUd66yzM+xQsc93122Jw+7yAjLFsRPMpqT7jt2afeWUOJ7Ezx6dSZRV/quFMGTSXOiXp9f7lruFdwPEKUN6s/CX0zh5cBoAdU027nhjPf/z1gbqm3yUwuoOhuFdW+eI1v3zq518vFE/+KUnxfLM3Hx6xHRdBF0ILyYNSCUtIQbQenZ2u48emALZQOGcEZvUFxLS/HuvIEM+7YMRzxmxXWzRFlFiISKwWOCCh0E53so+/wPUHg2oCRlJcbz045P45cyhrnLY11ft59LHvmN3qY+HqXeWrR/CofV6PWQm5JzKh+sP8q+vdgIQE2XhqTl59EmON9FIIdiwWhRnjdLp2NLqBtb5Spg7NUBadg3V7utHWBoWxLELTpxSJ6k5ENv5ua6GYbjq65JiozhlaGQ9rQgRRt8J7iH2tUe8I1QBwmpR3DFrBAtumkIvR6Rj2+FqLnp0KR+uPxhwewCw22DR/e7XM3/PhqIK7nxzvWvTg1eMY6J0ywst4N0d66N0rFfEzo8NFJ7lTBHWOAHi2AUf9ZVQuV+vM7qWht10oIoDFXqO5sxRGcRGicioEObMvBsStIgwq56HotWmmDF9eDof//I0JudoZ6mm0cYvXl3LH97b5Lvuwo6y6W0odQxAH3khhxNz+ckLq2hwdMD+/IwhXDKhX2BtEkKGU4f2podDoPrzLT6SPUkdCDjC2v6M2EVw4wSIYxd8FHdfmFhEiYWIIy5ZjxsDwICF/60jViaQlRzHKz+Zyk9nuKMTL36/lyueWM6+strAGGFr8pgyoaif9htueXEVxVW67vac3Ex+dfaIwNgihCRx0VZmOGR9dpfWsKvEB2UFUbFazw4C6NiN8999ghRx7IINr8aJzkudGIbhqq+Lj7YyY3iGrywThOBm7BWQc5peH1oPq54zzZRoq4XfnjeKZ+fmkxwfDcDGA5Vc8Oi3rm51v7LuZSjXqS5jzGzuXNLEhqJKAEb16cnDV02QDlihXbzSsb6K2vXK0d8r9oLNT9qPTscuOsG7ri9CEMcu2PCM2HUhFbuz5Bi7j9QAcPqIdJn1KEQOSumJFE51+6/+DMdKTDXpzFGZLPzlNNckh+r6Zn764mr+/NEWlyiwz2mqh28e0Gtl5YXYa/log47i907UHbAJsdIBK7TPzBGZrjF6Pq+zszdrPTtfY2t216ln5romrEQSkfcTBzvOX0hrrHehaQf5ZKOIEgsRTPoIOOUXet1QqbtkTSY7tQdv/vRkfnSqO3Lw7NI9XP3UclctrE9ZPV+PUwL2D7yUP36n068xVgv/mZNHvxTpgBU6RnKPaKYO1hqo6/ZXUFxV3/2L+lvy5GgBNDvsjMD6OhDHLrgwDHfELn2EHsHSSZz1dTFWCzNHShpWiECm3+Wu49nwGhQuNdcetKzIPReN5onrJ5HkiJat3VfBBf/6lkXbfBhVbKyBbx8CwG6JZm7B6a5df5s9lryB0gErdI5Zo90Bgi98IVbsb8fOq74u8jpiQRy74KKySEcZADI7/wtZeKSGbYf1SKNpw3qTFBftS+sEITSISYDz/u5+vfBX0Nxonj0enDe2Dx/9chq5fXsCUFHbxE3zV/L3T7fRbPNBanbFU1BTCsDbnMXuJi11dOuMIVw+Kbv71xcijrNHe9bZ+cCx89Ky84PkSYQ3ToA4dsFFiWdHbOcbJz7dLGlYQQBgxPkw/Fy9Lt0G3z9urj0eDExL4O3bTuGGqQNc255YXMB1z/zQvVRXfSUs/ScADcTyQO2FAJw1KpNfnyMdsELX6JsSz9h+yQAsLzhCVX1T9y7YK0COnbJ0a9Z6KCOOXTDhKarYhV9Ipyix1aI4e1RmO0cLQhijlI7aRcXp19/8HSr2m2uTB3HRVv730rH83zUTSHA0OK3Yc5Tz/+9bvt1Z2rWLLn8c6isAeL75bEpJZWRWEv+8Rjpghe4xyxG1a7IZLN7exd9PJzEJkOgIPPgzFdtrCMT08P31QwBx7IKJkq5r2B2sqGP9/goApg7uRapD/V4QIpbUHJh+p1431cKnvzHVnJa4ZEI/PvjFNEZmJQFQVtPIjc+t4JEvdmDrzHzO2qOw/DEAqo14/tN8IWkJMTwzN59E6YAVusmsXHcGyCdyPc46u/I9YPdhd3h1MdQ4alYjtHECxLELLpyNE/G9ILFzEbfPvNKwMhtWEAA45ZeQNlSvt30EOz4z154WGJKeyLs/O5Wr8nUNnGHA/321kxuf+4HS6oaOXWTpI9Co62ufs53HMWsy/5mTR3ZqZEYsBN8yPDORgWn6d2nxtpLuT1FxpmOb6+GYD3UdiyN74oQTceyCheZGOLJdrzNzcU0T7yDONKxScM5oScMKAqCV7s9/0P3647ugyQ8SI90kPsbKA1eM56ErxxMXrd+Wv9tVxvn/+pbvd5e1fXL1Yew/PAVAhZHAM83n85fLxpKf08vfZgsRglLKlY6tabSxrKCd38n28Kqz82E69vAm91ocO8F0ynZqwUbodBq2tLqBlYVHAcgbkEpGzzhfWycIocuQmZB7uV5X7NXRrSDlirxs3v/5NIakJwD6b/u6p7/nsUW7sLeSmq396u9YbLrp4j/NF3Ht9DFcmd8/YDYLkYFnOrbbYsX+kjyJ8BmxTsSxCxa8Jk50rnHi8y2HMRzv+dINKwgtcM5fIEbXsbH0ESgrMNeeNhiRlcQHt0/jkgl9AbAb8OBn2/nRgpUcrfGWbak/Ukj0uhcAKDWSKRxyPf9z7siA2yyEP5MGpJLmqN3+Yktxqw8aHcLfjl1CeqfLmcIJceyCBa8ZsZ2L2Dlnw4I4doLQIj37wBm/02tbI3x8J66noSAkITaKf149gb9cNpaYKP02vXh7Kef/61se+HQbs59Yxsl/+ZJPHruDaHSk/834q3jgupNdI6AEwZdYLYqzHGoLR441sNbRrNcl/KFl11SnM1+gdWA7Wc4UTohjFyx4RuzSO/7EXVnbxHJHvcO47GQplhaE1phyi1v4u+Br2PKeqea0h1KK604awLs/O4UcR+F6aWUNpd8+y28P/T/ebbiZS+1fAXDI6MX5c38jouSCXzlnjKdYcTeaHuJTdJMg+C5iV7IFDEeHbQSnYUEcu+DBqWGXmgOxiR0+7YutxTQ7QuLn5Eq0ThBaxRoFFzzsfv3pb6Gh2jx7Okhu32Q++MU0xvdN5N/R/+LB6KfIt+wgS1W4ghJHjJ6s3B/8P4sQ2pwypDc9HLqLn28uxuhO1NuZjj26xzfRc5k44UIcu2CgrgKqivS6k6PEPnXMhgU4T9KwgtA2A06CiXP0uvoQPJoPD+fCs7NgzYtg76aMg5/oGRfN2U1fc5515QmfgYYBYy2FlCxdYI5xQsQQF23l9BHpAOw5UkNB6bGuX8zZGdtYDTVHum+cV0dsZM6IdSLKlcFAyVb3uhONE8camlmyU/9BjMhMYnB6xyN9ghCxzPwDbHgNbE1uDa2qItj/A+z8DK6Yr6N7TgxDH2trdHx5rO3NLWxvOvF4e9OJ59paObeVY2+qXgWcWDrkfD2t+hPgXn//6wkRzqzRWXy8Uf/dfLa5mKEZSV27kGcDRfkeSEzvnmHOiJ01FtKGde9aIY44dsGAV+NExx27RdtKaGzWNQXSNCEIHWTn59ppaomtH8IDOXrOpNOxcsoQmUxCO/v7qm5qiwlCBzhjRAZRFkWz3eDzLcX8/IyhXbvQ8Z2x/ad03Si7HYodEbuMUd4PZhGIpGKDAS+pk453xEo3rCB0gbUvtr2/oRrqK/UYsmBw6qwxEJ2AXVnbPi45OzD2CBFNco9opg5OA2D9/goOV9Z37UK+lDypKIRGR1o4whsnQCJ2wYGzccIa6/3L3gb1TTYWbdcz8XLSerhmTQqC0A6VB9reb4mGfnlgjdZOlTXaY+14bfHc19FjYnQkoTPHWKLcudbVL8CHv8AwvNOxztdpp93sv38zQfBgVm4mS3fpMqAvthYzZ+rAzl8k1YfTJ6Rxwgtx7MzGMNw1dhkjOxxCXrKjlNpGXeh97pg+qAjW7BGETpHcz92s1BL98uDm4Jspa5l4Pfadn2PZ9qHXdqXAGHkRlgnXmWSZEGmcNSqTe97XAYnPNx/ummOX0FuLhjdWd1/Lzsuxi+zGCZBUrPlUFkFDpV53MQ0r3bCC0AmcXbGtMamd/WZhsWK5cj5c8hj0nwo9s/X3Sx5DXbUALO2kagXBR/RNiWdcdjIAywvKqKxrpWa1LZRyd8Z2O2Ln0RHbSYH/cEQidmZT4lFf18HGicZmO19s1bP6+ibHuf7ABEHoABOu092vWz88cd+oi2D8tYG3qaNYo2DiDfpLEExk1uhMNhRV0mw3WLy9hEsm9Ov8RXoNhsMboO6olv2KT+maMc6IXWoOxJn7edhss/POmgO8vmo/hyrq6JMSz9X5/Zmdlx2wqTDi2JlNsUdHbAelTpYVHKG6Xhd1nzMmS9KwgtAZLFYtabLhNa1dV1mkGw8mzdFOnUS+BKFdZuVm8dDnOwD4fEtxFx07jzq78j0QP7Hz16g92mUdWF/TbLNz+ytr+XSzO6N2sLKe1XvL+XpbCf++biJRVv8nSsWxM5vizs+I/WyzZxq2j68tEoTwRyJfgtAthmUkkpPWg8KyWhZvK6Gh2UZsVCcfio7vjO3bBceu2FOY2NzGiXfWHPBy6jz5dPNh3ll7gKvy+/vdDqmxMxtnKrZHGiRmtn0sYLMbfL5Zp2F7J8aSNzDVn9YJgiAIwgkopZjlGGNZ02hjWUEXdBR9IXni1ThhrtTJ66v2t7n/jZVt7/cV4tiZSXMjHNGhbDJGnygp3wIr9hylrKYR0C3ngcrZC4IgCIIns0a7gxHOgEOn8HLsutgZG0QdsYcq6trcf7Cd/b5CHDszKdvpFkDtYBpWZsMKgiAIwcDEAan0TowB4IstxdjtRjtnHEdiFkTF6XWXHTtHKjYuGZL9n+Zsiz4p8W3u79vOfl8hjp2ZeE2caL9xwm43+MzxVJQc71b/FgRBEIRAY7Uozhqlo3ZHjjWwdn955y5gsbiFiruSim1uhNJtep01rkNZL38ya1Tb5VRXTQ6M4ymOnZmUdK5xYl1RBYer9PiWs0dnEh2A7hpBEARBaI1ZuT5Kxx47DI01nTu3dBvYHRp6JnfEGobB93tarzM8NzeL2ZMCM/ZPPAMzcXXEKkgf2e7hXrNhcyUNKwiCIJjLKUN60yNGd8N+tvkwhtHJdKyn5Eln07FeHbHmNk58sukwi7aXApCRFMukASn0TY4jf2AqD1wxjseunyQ6dhGBMxWbmgOxiW0eahgGnzjq6xJirEwb1tvPxgmCIAhC28RFWzl9RDofbzxMYVktu0qOMSyzE7PLj9ey60wDRJB0xFbVN3HvB+4M3L+vm8SUQb1Ms0cidmZRV+Ehqth+GnbLoSr2H9UdNTNHZRIXLSKqgiAIgvnMGu3OIH2+pZPp2O5InjgdO0sUpI/o3Lk+5IFPt1FS3QDANZP7m+rUgTh25lGy1b3uQOOEzIYVBEEQgpEzRmQQ5Ugzft6KQG+rdNWxMwy3Y5c+EqJiO3dfH7F6bzkv/7APgN6JMfzmvPbLqvyNOHZm4dU40b5j94nDsYuNsjBjeLq/rBIEQRCETpHcw63SsL6okkOVndBr65mtI27QOceusgjqK/TapDRsk83O797ZiLOs8A8XjialR4wptngijp1ZeI0Sa7umYFdJNbtKjgEwY3g6CbFSGikIgiAED+d4dMd+2Zl0rDUKUgbq9dHCjp/n2ThhUkfs09/uZntxNQCnDevNxeP7mmLH8YhjZxbOxomoOO9QdAt8stEjDTtW0rCCIAhCcHGW5xSKrtbZVe6H5oaOnWNy48Teshr+78udgM6k3X/pWJTJOnpOxLEzA8Nw19iljwBL240QzqHC0VbFzJHtz5MVBEEQhEDSJzme8dnJACwvKKOyrqnjJ7uCGwaU7+3YOYc3uNcBduwMw+D3722iodkOwP87axgD0noE1Ia2EMfODCqLoKFSrzPa7ojdV1bL5oNVgNYLSo6P9rd1giAIgtBpZjn0VZvtBou3l3T8RC8tuw7W2TlHifXsBz0C24X6wfqDfLvzCAAjs5L4yWltZ90CjTh2ZlDiMUqsncaJTzfLbFhBEAQh+Jk1uotTKDzLkco7IFJcX+U+LsDRuoraRv70of4MVwruv2xs0E2BCi5rIgWvos+2I3bObliL0mPEBEEQBCEYGZqRyKDeCQAs3l5CfZOtYyd2VvLEs/kwwI7dXz/eRllNIwDXnzSAvIGpAb1/RxDHzgyKPSJ2baRiD1fWs3ZfBQAnDUojLdEcnR5BEARBaA+llCtqV9NoY3lB67NTvUgZAMrhjnTIsTOnI/aH3WW8vmo/oMeG/fpc8zXrWkIcOzNwpmJ7pEFiRquHfeYh9HiupGEFQRCEIGdWrmd3bAfFiqNitZ4ddGxerAmNEw3NNn73rrsT996Lc+kZF5w17+LYBZrmRjiyQ68zRuskfSs4Z8MCnJMrjp0gCIIQ3Ezon0pvR3bpiy3F2OxGx050NlBU7AVbc9vHOqVOYhIhdVDbx/qIJxfvpqC0BoAzR2YEdc17QB07pdRzSqkSpdQmj229lFJfKKV2Or6neuz7rVJql1Jqu1LqHI/teUqpjY59/1IO8RilVKxS6nXH9h+UUjmB/Pk6RNlOsDt+aduorys71sCKPUcBmDQghazkuEBYJwiCIAhdxmpRnD1aZ6KOHGtk3f7yjp3orLOzN2s9u9awNbvlwjJzweJ/N6ag9BiPLdoFQHy0lfsuyQ0azbqWCHTEbj5w7nHbfgN8ZRjGMOArx2uUUqOBa4BcxzmPK6Wcgm9PALcAwxxfzmveDJQbhjEUeAT4u99+kq7iNXGidcfuiy3FOB90zhvTx89GCYIgCIJvmDXaHc3qcHdsRxsoynZBc71eByANaxgGd7+7kUab1qz71azhZKcGj2ZdSwTUsTMMYwlw9LjNlwALHOsFwKUe218zDKPBMIw9wC5gilKqD9DTMIzlhmEYwAvHneO81lvAmSrY3GpPx66NxglnNyxIfZ0gCIIQOpw8JI2EGB2H+WzzYQyjA+lYTy27tiRPPCdOBKBx4q3VRXy/W7stuX17Mu+UHL/fs7sEQ41dpmEYhwAc353dBP0Az3hskWNbP8f6+O1e5xiG0QxUAml+s7wruDTsFGS03FFTWdfEsgItfpjbtyf9ewX304EgCIIgOImLtnL6CP1RXlhW65p13iZeEbs2HLtiz1Fi47poYccoO9bA/R/rtK9FwV8vH0tUkGnWtUQwW9hSpM1oY3tb55x4caVuUUqtUkqtKi0t7aKJXcApdZKaAzEJLR7y1dZimmza7GAu0BQEQRCElvDuju1AOjY1x71uKxXrjNgpC2SM6ppxHeT+j7dSUatHo809JYdx2Sl+vZ+vCAbHrtiRXsXx3TmHpAjo73FcNnDQsT27he1e5yilooBkTkz9AmAYxlOGYeQbhpGfnp7uox+lHeoqoMoRbGyjvu5TrzSs1NcJgiAIocXpIzKIsuhYi6d0V6vEJECS4/OuI45d2lCI8V8267tdR3hnzQEA+iTH8atZI/x2L18TDI7dB8Bcx3ou8L7H9mscna6D0E0SKxzp2mql1FRH/dyNx53jvNYVwNdGh5L7AcJrlFjLjl1NQzPf7NARxGEZiQzNSAyEZYIgCILgM5Ljozl5iK6E2lBUycGKuvZPckqXlBeC3X7i/upiqHFk2PzYOFHfZONuD826P10yhsTYKL/dz9cEWu7kVWA5MEIpVaSUuhn4G3C2UmoncLbjNYZhbAbeALYAnwI/NwzDOZ/kNuAZdENFAfCJY/uzQJpSahdwB44O26DBq3Gi5Rmxi7eX0tCsf6GlaUIQBEEIVTxnx365tQPpWGedXXM9VB86cb9n44QfHbt/f72LwrJaAM7JzQy5cZ4BdUENw7i2lV1ntnL8/cD9LWxfBZzQDmMYRj1wZXds9CsdiNh5ihKLYycIgiCEKmeNzuQP7+uAxuebi7nx5Jy2T/DsjD26G5L7ee/3nDiR6R/HbkdxNf9ZUgBAYmwU910cuJFlviIYUrGRg7NxIirOuwPIQX2TjUXbdInhgF49GN2nZyCtEwRBEASf0Sc5nvHZyQB8v7uMSkcjQqu0p2XnOSPWDxE7u93gd+9sdDUv3nXOiJAcDiCOXaAwDHfELn0EWKwnHLJ05xFqGnW2+bwxWUGtbC0IgiAI7THLMQ6z2W6waHtJ2we3p2XnTMUmZECS79Ojr63cz6q9elLG+P4p3DB1oM/vEQjEsQsUlfuhoUqvWxFV9BQlPkfSsIIgCEKI41ln9/mWdrpjU49LxXrSWKunTgBk+T49WlJdz98+0Zp1Vovir5eNxWoJzeCKOHaBotijvq6Fxokmm91VXJrVM44JIaKXIwiCIAitMTQjkcG9tWbr4u2l1DfZWj84PgV6OGYKHO/YlWwFw9Ep64c07J8/2kpVvZ7j/uNpgxjdN3RLocSxCxQlnjNiT3Tsvt9dRmWdrj84d0wWlhB9UhAEQRAEJ0opznaIFdc22lxTlVrFGbU7ukeXMDnxbJzw8cSJxdtL+HC9lsPNTo3n/501zKfXDzTi2AUKr4jdiR2xMhtWEARBCEdmjXZ/pn2+uR3ZE2cDReMxqPFwAv00I7a2sZnfv+duyvjzpWPoERM6mnUtIY5doHA2TvRIg8QMr102u8HnDmXutIQYJuf0CrR1giAIguAXJvZPoXdiLKD17Gz2NuYGtNYZ6+yIjYrTUyd8xP99tZOici2e/P/bO88wKaqsAb9ncoIhBwUEQaICIgYEsyCioJhzDmvYNbvmHDbquvu5u+666uqaAyooqBgQBLOoBCWIkoc8MEyeOd+Pc3uoScSeru7hvs/TT3dXVVedqq5w7onH9m3PYT3abOEX8Y9X7GJBeSmsmmOf2/SGGtmuX/68hlUFpYD110vUgE2Px+PxeGqSlCRVRX5XFZTyzcK19S9cl2JXWQnLnWLXpjckR8eiNmvpeh6fbNm3TTJSuGNk3Y0DEg2v2MWCVXOg0oIy6zIhT5jpe8N6PB6Pp/EyrE8wO3Yz7tiaRYrBSp+UbbTPUcqIrahUbh7zfZX18Kaje9KmSeLVrKsLr9jFgmodJ6qPCFSVd1x8XdOMFAbt3jKWknk8Ho/H0+Ac2LUl2WlWv/Wdmcupt4170GIXqWVXrZVYdBInnv3sF75dtA6Agbs15/R9O0VlvfGAV+xiQbUesdUTJ75dnM/S/GIAjuzVlrQU/5d4PB6Pp3GRnpLMoT0tfu2X1YXMXVFQ94JZLSHdlRqJWOyi3CN2eX4xf5jwIwCpycIDJ+zVqCpReC0iFlRZ7ATa9Kw2y/eG9Xg8Hs/OQLVixTPrKVYssskdG1Hsgq3E6umzvi3c9eZMCkosPOrSg7vSvW2THV5nPOEVu1gQKXXSvDOkZVdNDrphs9KSObh76xCE83g8Ho+n4TmsZxtSk80yttk4u0gtu6K19opY7Jp3gfQdU8Lem5VXFdfeuWUWVx4evQzbeMErdg1N0VpYv9g+1xhp/LB8Az+vLgTshM9Ird0/1uPxeDyexkDTjFQOcHHk3y3OZ+m6oroXDMbZLfkK1i+xzzuYOFFQUs4db2yy/t0/eq9G+dz1il1Ds2L2ps81FLtqRYn7eDesx+PxeBo3wwLPukgbzVoEFbtZb276vIOJEw+9O4dlLqb9hL13ZXC3Vju0vnjFK3YNTbXEieoZsRNcfF1aShKH9Uz8oogej8fj8WyOob2CcXZbodj9MG7T5x1InPh+cT5PTbUs22ZZqdx6TK/tXle84xW7hqZaqZNNFrv5KwuYk2dZQQfv0Zqc9MRuYeLxeDwez5Zol5tBv47NANcjvbCs9kLBWnaFqzd93s5WYuUVldw85jsiDS9uGdGLlq4TRmPEK3YNTSRxIiWj2ihkQsANe7TPhvV4PB7PTkIkO7a8UvngxzqsdjntICWz+rSMZpDbYbu299TUn5mxZD0AB+zegpP32b71JApesWtIVDdZ7Fr3hKRNQZoRxS4lSTgyYJr2eDwej6cxc1SfLbhjk5KqW+3A3LCy7bXmlqwr4qH3rKVnWnIS94/eC9mO9SQSXrFrSPIXQYmNEoJu2EVrCvl+ST4Ag7q2JDcrNQzpPB6Px+OJOV1b57B7Kyv9NWnOSorLKmovFIyzg+2Kr1NV7nh9BoWltv7LD+tK19Y527yeRMMrdg1JXiC+LpA48c7MoBvW94b1eDwez86DiDDUWe0KSyv4ZN6q6gtUlENZYfVpxeuhsg4FcDNMmLGc939YAUDX1tlcdmjX7ZY5kfCKXUNQUQ5fPwNvX7dp2vqlVSdlxA0rUr0xssfj8Xg8OwPDem+KLa/mjq0oh1fOg/kfVP/B9P/By+fa/K1gfXEZd765qSrFA6P3Ij2l8dWsqwuv2EWbyEn55pWQv3jT9E8fhZfPZcW6Ar5auBaAfTu3oFUjzszxeDwej6cu9u7YrOr5N3F2HhWRlNVvn4fZY+v+0eyx8N0LW7X+P074kRUbSgA4ZWAH9neFkXcGvGIXbbZwUs6b+Djqzl+fDevxeDyenZGkJGGoy45dvbGUr53Bg2+e2fwPv97CfODrhWv532e/ANAyO41bRjTemnV14RW7aLOFk7LlnJeqPg/3ip3H4/F4dlKqZ8e62PP8JZv/UdATVgdlFZXc8tr3VQaU24/tTbOstB0RM+Hwil202cJJ2aTETt7+HZvRPjdzs8t6PB6Px9NYGdS1ZVVx/ndn5aGqkLvr5n+0hVp2j09ewA/LNwBw0B6tOK7/LlGRNZHwil202cJJuUTNz++tdZ4I5ZXljJk7hrPfPpuhrwzl7LfPZszcMVRsYwaYx+PxJBLpKckc2qM1AL+sLrRuTHufvfkfDah//sLVhTzy/hy37iTuO37PRl+zri58H6tos/fZsOizeme/VHEo4OPrPEZ5ZTk3TLqBiQsnVk1bvnE501dO5+PFH/PHQ/5ISpK/TD0eT+NkWJ92jPvO+qa/O3M5PQ47A+a+U3eseq+R0O/0Otejqtz2xgyKyyoB+M0Re7Bby+wGkzue8Ra7aNP/DDv56uCdyv14teJgerVvutOecJ7qjJ0/tppSF2TiwomMnV9PIo7H4/E0Ag7t0ZrUZLOqvTsrzzo0nfQUHPcodDwAmnaw9+MehZP/W62DU5A3v13Kx3NWAtCjbRMuOXj3OpfbGfCmgGgTOSm/e8Gyd/IXQ24Hvml1LJdN60wlSd5at5NRqZWsLlrN8o3LWV643N7da8qSKZv97Zh5Yxi9x+gYSerxeDyxpWlGKoO6tuLjOSv5fkk+S9cVsUuzTNj7LHttBesKS7l33KaGAA+csCepyTuv3cordg1Bckqtk/KxZ76iEkuc8PF1jQdVJb8kv5rCtmzjsqrPeYV55BXmUV65dUU1a/LDmh/4dNmn7Nt2X5LrGal6PB5PIjOsd9sqa9t7s/I498DO2/T730/4gVUFpQCcuX8n9tmtRbRFTCi8YhcDCkvL+WiOtTXZvXU2e7Rp/L3qYk15ZTlj54/ltbmvsbxwOe2y2nHCHicwquuoHVKICkoL6rS0LS9cTt7GPJZvXE5xRfF2rz9JkqjUynrnF5UXcfG7F9MyoyVHdT6Ko7scTb/W/XbKgGCPx9M4Gdq7Lbe9PgOAd2ct3ybF7vMFa3j+80UAtG6Szo3DezaEiAmFV+xiwKQfV1YFdB69Zzv/UI4y25uAUFxevEWlraCsYLvlSk9Op112O9pltaNtdlv77L5HPk/8ZSJ3TL1ji+taXbya5354jud+eI5dsndheJfhHN3laHo07+HPJ4/Hk9C0bZpB/47NmL5oHZ/+tIb8wjJys1K3+LuS8gpuGfN91fe7RvYhN3PLv2vseMUuBkyIFF4Ejt6zfYiSNE62lIBw/6f306FJh1pK3LqSddu9zRRJoU1WG9plB5S2gMLWLrsdzdObb1HpGtV1FB8v/rhO+Y/oeASn9TyNd355h/d+eY/8knwAlm5cyhMznuCJGU/QJbcLR3c+muFdhtMlt8t274/H4/GEybA+bZm+aB0VlcoHP+Yxeu/N16sDeGzST8xbYYPvw3q0ZsRePswJQDRSnnknZuDAgfrll182yLpLyisYeO9ENpSU06F5JpNvPMxbWHaAkooSVhWtslfhKlYWreTx7x8nrzBvyz/eSgShdWbrzSptLTNaRi3mray0mMmP30vFm++StbaIwuaZJI8axsEX30lKqlVML6soY9qyaYxfMJ4PFn5AYXlhrfX0atGL4V2GM7zzcHbJ2fmKcno8nsRl3ooCjnxoEmCerX+ctc9ml/9pZQHDH5lMaXklmanJvHvNwXRskRULUeMCEflKVQfWNc9b7BqYT+atYkOJBc4P7+PdsHWhqqwvXc+qIlPUIkpbte/u84bSDTu8vebpzaspaTUVt9ZZrUlNio05X8vLybvuRtq/917VtBb5BfDX11g+ewO7PvwQkpJCanIqB3c4mIM7HExxeTEfL/6YCT9PYNKiSZRWWtDw7DWzmb1mNg9/9TD9W/dneJfhHNX5KFpltorJvng8Hs/20q1NDru3zuanlRuZNGclxWUVZKTWPXhWVW4dM4PScgtxunZo951KqdsSXrFrYMZ/H3DDJoCZOJpJCGWVZawuWs3qotVVCtrKopVVSlvwFVFOGoJOTTpxx6A7zAKX1ZaMlIwG29a2kv/GG2wIKHVBNrz3HvlvvEmzE0+oNj0jJYNhnYcxrPMwCkoL+HDRh4xfMJ5pS6dRrjaImL5yOtNXTucPX/yBfdvty4guIzii0xHkpuc2+D55PB7P9jCsdzv+OWk+haUVfDJvFUf0alvncq9+vYRpP60GoHf7ppw/uHMMpYx/vCuW6Ltiyysqee3rJbzwxUK+WbQOVWiakcJXtw0lNSV+a+vUlYQQ4chOR/LHQ/5IsiRTWF7IysLqlrSgkrayaCWri1aztngtyo6fX6lJqbTKbFX1ap3Z2j5ntaJVRitmrprJv7/9J4d8rxz2bSWtNsCqJvBhvyQ+2ku4e8i9MakFpxUVVG7YQMWGDVSsX2+fq943ULlhfbX3ig3rKZ4xEy2uP6s2c8AAOj/37FZtf23xWiYunMj4BeP5cvmXtY59SlIKQ3YZwvAuwzms42FkpfoRrsfjiR++XriWE/4+FYBTB3bk9yf1rbXMmo2lHPHnj1hbWEaSwJjLB9OvY7MYSxo+m3PFesWO6Cp25RWVXPncN9USJiIM79OO/ztjb1LitHDimLljNpuh2SKjBUXlRRSVF0Vle03SmlQpai0zW9I6s/Wmz1mtaZXRitZZrWma1nSzLuzy0hLePecoukyvHWe3oH9bhj39Dilp6VuURysqqCwooGLDBirXb1K+Kqu9R+atrzWtcuPGHToedZKaSssLLyBnyBAy+/VDUrfORbyicAXv/PwOExZM4LtV39Wan5GcwSEdD+HozkczpMMQ0pO3fHw8Ho+nIamsVPZ/8H1WbiihZXYan996JMlJ1e/91730La9+vRiA8w7szF2j+oQhauh4xW4LRFOxe+mLRdz4au0HaYQ/nNSXUwZ2jMq2os3p405nxuoZO7SOZEmmZUZLs6bVUNqqLG5ZrWmZ0TJqLtF1r77KsltvQ4HgLSDyPffEE8novgcV+es3KWaB94iCVlmw/aVNtoek7Gy0tBQtK9vq5bMOOICcIYPJHjyYtE6dtup3izYs4p2f32H8gvHMWTun1vyc1BwO73Q4I7qMYL/2+8UsvtDj8XhqcsuY73nus4UAvHTpIPbrsqnY8NR5qzjjcevF3q5pBhOvO4Sc9J0zoswrdlsgmordif+Yyle/rK1/W7s155XLDozKtqLFkoIlPDPrGZ6b/dxmXafJkky/1v3MmlaXazSzFc0zmpMk0bdIqiqV+fmU5a2gPG85ZXl5lC/Po3xFHuvfeZfK9eujvs0tkZSVRVLTpiQ3aVL1npzblKQmTUlu2iTw3oTkpk2r3pObNCEpJwdJSalSSuslORkqKuqcldqpE9mDDyRnyBCy9t+f5JwtF76ev24+4xeMZ/yC8SzcsLDW/ObpzRnWeRjDOw9nQNsBDfJfejweT3189OMKznvyCwAuGtKF247tDUBxWQVHPzKZBavMM/LY2ftwVJ/4j1tvKLxitwWiqdgd+OD7LM2vP2Zql9wMpt58RFS2taPMWj2Lp2Y8xbu/vEuF1q08BNm7zd48ffTTUZdDy8spX7WK8ry8agpbWd4Kypcvp2xFHuV5KzYbi7Y9SFaWKWMRJSyioDVtSlLTJiTXUtACilqTJkjKjo8UtaKCJVdfU2cCRZOhQ2n/wP0UffMNBVOmsPGTqZTOn1/3ilJSyOzfj5whQ8gePJiM3r2R5PqTXVSVWWtmMf6n8Uz4eUKd5WLaZLVheGcrhNynZR+f0e3xeBqckvIK9rl3IgUl5XRqkcWkGw5FRHjo3R/56wfzAGtB9q9z6tRpdhq8YrcFdiaLnaryydJPeGrGU3y2/LNq87JSsigu3RjVJITKoiJT2KqUtdqKW/mqVVBZf1utHSWtSxfa3X7bJsWsaVOSc3K2Ol6todHycvLfeJN1r75K2bJlpLZvT7MTTyT3+ONqKWdlS5dS8MknbPxkKhunTaMyP7/OdSY3a0b2gYPIHjyE7CGDSW1bd3YZQKVW8s2Kbxi/YDzv/fIea4rX1FqmY5OODO88nBFdRtCteTeg4dq4eTyenZsrn/uacd8tA2DC1QeRLMKIv06mrELJTktm4nWH0D43M2Qpw8UrdltgZ4ixK6soY/zP43lq5lPMXTu32rxOTTpxbp9zGdHxKD6+4LitSkJQVSrWraM8L6+apa1shVPc8vIoW7GiXsVja5G0NFLatiW1bVtS2rYlpZ373KYtqe1sWsHkySy/485619H+/vtrlQxpDGhFBcUzZpg1b8onFH33Xb1u2/Q9upF94GCyhwwha9+BJGXUHd9YXlnO58s+Z/zP43n/l/fZUFa7bmC3Zt0Y3nk4X+d9zdRlU2vNj2RQ19XGzePxeLbE698s5uoXvwWgSXoKChS4erB3juzN+YN9lx2v2G2BaCp2FZXKFc9+XW9W7KNnDqiV5dOQFJQW8MqcV3hm9jOsKFxRbV7f1n05v8/5HNbxMJKTkreYhJDRty9JaWmmxK1YgZaU7JBsSbm5pLZps0lha2OKWkRhS2nbluRmzbboAtySO3PXvzy8WbdkY6Fi/Xo2fvqpWfOmTKFsyZI6l5O0NLIGDiTbuW3Tu+9R5zEurShlypIpjF8wno8WfURxxda7wu858J6YlJjxeDyNi/KKSi595ive/2FFrXm5mal8fssRpNdTuHhnwit2W6BB6th9s4SXvljE0nVF7NIsk1P27ciJAzrETKnL25jHs7Of5eU5L9dqZH9ox0M5v8/57N1m72oP9AUnn0Lx99/XXNW2k5RESqtWpLRrR2rbNqS0qdvSlpQZPVP6trgzdwZUlbJffqFgyidsnDKFjZ9/jhbWbkMGkNK6NdmDzZqXfeAgUlq0qLVMYVkhHy36iPE/j2fKkimUV5Zvdvudm3bmkcMeoVPTTt5y5/F4tpp49XrFG16x2wIN2Ss21sxZO4f/zvwvb//0dlUXArAiv6O6juKcPuewe+7uVdMrS0spmDSJ9W+OrbcDQhBJT69tXQsqbm3bktKqVVQSCzzRQ0tLKfxmOhs/MUWveNasuhcUIaN3b6foDSarf38kLa3aIvkl+Yx4bQTrS7eciZyenE7XZl3p3rw7PZr3oHvz7nRv3p1mGc2isFcej6exEe9x6vGCV+y2QKIrdqrK58s/58mZT/LJkk+qzWua1pRTe5zKGb3OqOoZqpWVFH31Ffljx7F+woStLhWS0bcvnV98wWdHNgLK16wxl+0nn1DwyRQqVq6qc7mkrCyy9t+f7CGDyRk8mNTddkNEOPvts/ku75t6E210C5bpNlltqhS9Hi3sfbemu3nrnsezk5NIlSXCZHOKnb+LJjDlleW898t7PDnjSWavmV1t3i7Zu3B277M5YY8TqlpHlcydS/7YceSPG0v50mW11pfcujUVK1fWu73mp57qlbpGQkqLFuSOPJbckceiqpTMmcPGKZ+w8ZMpFH75FVpqvXsrCwsp+PBDCj78kDwgtUMHsgcP5pwue7D81S8ZOG/TOluth55LKhkwX5h7zSgyM3L4cc2PzFk7h8Ly6m7gFYUrWFG4gslLJldNS0tK22Tdc8pej+Y9vHXPEyo++zu2tG+WuXnFrtnOnQ27NXiLHYlnsSssK2TMvDE8M+sZlhRUD5Dv1aIX5+95PkN3G0pKUgpleStY/9Zb5I8dS8ns2bXWldy6FbkjjqHpqJGk9+jB0muu3emTEHZ2KouKKPzySzZOMWte6bx6audBvYk2be+7lxYnnWTr00qWFCxhzto5zFkzhzlr5/Dj2h9ZtGHRVsnTJrMN3Vt0r+bO7Zzb2Vv3EoxEVJDKK8u58YPrKH97Yi3LdMoxQ/nDYX/y52GUSfQYu1id594VuwUSRbFbVbSK52Y/x4s/vlgrvmnwroM5v8/57NduPyo3bmTDu++xftxYNk77FGr8x0lZWTQZOpSmo0aSvf/+1eLhfBKCpyZly5axcepUCqZMoXDqNCq2ooSNZGaStd++lvXcujUpkffI55YtKaLMlL0ar41lW+65G7TuBS18zTOaR2OXPVGmvLKcGybdwMSFE2vNi3V5nPLKckorSimpKKGkooSyijL7XFlSNb20opTSilKmLpxMpz+9zP4/1n5OftZDyH7wDk7ufVpM5N5ZiLfKEttCLM9zr9htgXhX7BbkL+C/M//L2PljKa0srZqeIimM2H0E5/Y5lz2yO1Mw5RPWjxvLhvc/qF2KJDmZnCFDaDpyJE0OP4ykrKwY74WnMaAVFRTPnMnCCy7csd66IiS3aBFQ9tx76zbkN0licfpG5qasZpYuZfb6uSzasGiz7e4itM5sXad1r2b/20S0HiUyY+aO4a4pt9cbk3lp/8sYvOvgaopVSUUJpZWl1ZSwiMJVbZmKUkor65gWXM4pbWVlJaSUVpBeBhllkF4G6aWQXq42rdRNc/O7L65kn/n1W6an9RQWdM0mvUkzMnNbkp3biqbN29CsxS60bNmBVq060a7FbmSnZYdy3G2g/gbrXnmVsuXLSW3XjmYnnUju8cfH9UA9HipLbA+vznmVu6bdVe/8aJaB8ordFohXxe6bFd/w5Iwn+WjRR9Ueatmp2Zzc/WTO6HkGuXOXs37sWNa/PZ6KdetqrSOjX19yR46i6dHDSWnZMnbCexo1P59+BkXffFP/AikpUL75kihbS3Lz5iS1aklJsyzymyazIrOUX9IKmJu8kmUZJazNgXU5UJZS9w0/NSm1mnWvW7NuPD/jfyS987F3r+0AqsqGsg2sK17HuhJ75Zfk1/l5Rt63/OqVjfVavh4+PonKJAFVUiqopXjZ503KV1q5m1Zq06uWrVpeN30PKGpp0Tklt4lKgeI0oTQjmYrMNDQrg6TsbFJympDWJJeMps3JadaazKYtSMrJITk7m6ScHJKys93LPifnZCNZWVsd56zl5SzZXGjNww/56gVRYPnG5UxbOo1pS6fx7i/vohXl9Q5g+rcbELW2nF6x2wLxpNhVVFbw0aKPeHLmk3y78ttq89pktuGs3mdxXOq+VLzzAfljx1G2qHacUupuncgdOYrcY48hrXPn2Aju2amIFLOuj/b330/uqJGUr15N+cqVlK9YUeN9JWUr7XPFqtW1wgW2h+LsVNblwIrMctbm4F5S4x0qkuGa1yvrVTJmXzWC/ToMIis1i8yUzKpXVkoWmambPqcnp8c8maistJjJj99L+ZvvkL22mI3NM0gZdRQHX3wnKalpW15BfeutKKtSwjanoEU+55fkk1+Sbz2m1ZSorBLILIWsYsgqUbJKqHr1XljJwHn1W742poOoKWDJ/pFUPyIkZWVtUvxyckjKziI5J4ekrOzAtGxK5s1l/dhx9R7zxtqRp6EpLCvki+VfMG3ZNKYuncqC/AVV85IqdbP3lufP2IV3Tq3tpt0evGK3BeJBsSsuL+bN+W/y9Kyn+WX9L9XmdWvWjQt3OZH9Z5ZR8Nb4OosIJ7doQdMRI8gdNZKMvfby2aueBiWa3T60vJzy1WvqVgCDn6PUU7gkBdLL61cypvQWZnUSSlOgNAXK3Htp6qZppSlQliokZWSSnJFJRkZ2vUpg1ffI55oKo/seWSYrJYvU5Np9jMtKi3nvnOH1tvwb+vQEUlLT2Vi2sZZCVuu7s7LlF6+jqGAdbCw0JawOpWyz34tNmUtpuFbP244IkplJUtUrA8nM2vQ9K9PNt2mSmVH1udq8rEx+uPU6MhfUjvWKUNypLd1uvpPKgo2UFuSzYW0eBetWUbh+NaXr11FasN5CFgqLSCoqJa24jIyS+Dhmqd260uW550hu2jRcQeKcisoKZq2eVaXIfbvy23oLtB/5nXDJW2X13lvGndaZG+4aHxW5vGK3BcJU7NYVr+OFH1/g+R+er9V8fXDzfbhg7Z60m/IjG6dOq9UHVDIyaHLEEeSOGkn2gQfGTVN7z85BrBNttKKCijWmAJbVUgBXVVcAo+QG3loqBEpToSzZ3oMKYE2FsPZyUjU9skxFajKkp5OcnkFSpimPXb5fycgJa+t/aAxOY1YHJb2oopYSllmPkpZZEh8WMk1OIqv/3rWVr4zailj17xlOOTNFLCkzE8nIiNrAds3LL5N3+x1blf29NVRUVrC6eDV5BctZkb+UVasXsnbNUvLXLqdg3UoK81dTvH4tqcXlZJZCZonau3tllEBWqZIRmeb+xx1REtO6dCGz715k9O1LZt9+ZPToXqso+c7GkoIlTFtqitxnyz6rtxh7dmo2+7XbjwNbDmS/wnasv/lu0pevqXNZgMJeu7HPmAlRkdErdlsgDMVu0YZFPD3zaV6f93q1HpwpKpy7cW+G/ZhO0uQv0aKi6j9MSiJ70CByR40k54gjSc4JJyjX44lXtLKSirVr67UArv3gfVIq/H1ve5HMTHP9NWlCUpMckrMDn3Pce5MmJAU+L3/wQUp/nFPvOjMG7E2X556L4V5sHVpRwaKrr2bje7XdZzlDj6TDX/4S9UGMqrKuZB15hXmsKFzB8o3LySvMI2+jfc8rzGP5xuXVakOmlJsCmFFqil5GKVwyvoKOq7d9+5KWRkbv3mT260vGXn3J7NeX1A4dGrUXqKC0gM+Xf26xcsum1fKaRUiSJA5I78mhxZ3pszab5ovWUfrDHEoXLNgqb0JKu3bs8dGHUZHZFyiOMZvLuJu9ZjZPzniSiQsnUqnuRFCl98p0zl60G92+XIau+dwmB9aZ0acPuaNG0nTECFJat479Tnk8CYIkJZHSsqUlC/XsWWt+3uijSJm9sN7fF3dqQ7eb7kJLiqksLnHvxWhxCZUl9q4lmz5XTSsuprLEvRcXU1FSjJaUoMUlUDNLPSwyM0hq0oSUJk2cEhZUyJqQ3CSnmkKWlOOmNWligf05OdvlGWh5zjksu/W2ei1fzU/ceqtXLJHkZDo+/HBMLdMiQvOM5jTPaE7PFrXP3wgFpQWm+BUuJ29jXpUiGFECx635gcverqj3mH/eXUipgG5LlaYB+4GWllI0fTpF06dXTUtu0YLMvfYio59Z9TL32pPk3Nwo73nsKK8sZ8aqGUxbZkkP3638zuJFA0il0m4tDFzfgn3Xt6bzSsj6eQWVK78DrM7ettYFSN1ll+jswBbwFjuia7HbXB2bZunNWFeyrup7m7XKsB8zOPKHNLKW1e6Nl7rrrjQdeSy5I0eS3rVrVOTzeHZ2ou1e2xpUFS0tdUpfQFkscUpivQpkZPkSFr76P3I21O9iXtc8jR5X3FhdEWviFLacHJJyckLLgoxmTKZn6zhn3Fkc/u+v6w3kf+j4JGv9p0rbdabgdVuq7LFU6ZwHaRW11xkkrXNns+oliAt30fpFVXFyny/7nA1lG6rmpZUpnVZA5xXKHitT6L0mk9ZLC0kqKdviepNbtCCjVy/Se/agcsMG1r30cr3LRjNhxVvsYsjY+WP54Of3OKzOdOe1NCmGQbOVI39Io/PCYmCjexnJubk0OXo4uaNGkbn33o3a/O3xhEHzE06g4OOPa7nXBHOvNR8dnTpT1dYtgqSnW9zcdho6ZjZdT85fX6tXIS06ZyQtzjpzx4VtACQ5mV0ffsgXP48ho3ucyF3HT+fgGcrh31bSaj2sagof9Eti0p7C1QOvpUtuF+asncPcdXOZ03kO09b/QqVWklxhyl23pUq3Zabs7VIjdKz0558p/fln8t940yakpZHZq9cmq17ILtz1pev5fNnnTF06lWlLp7G4YDEAuRuV3fNs/zrnKZ1XQPs1SlKV/lvqXjUQIW233Ujv1ZOMnr3I6NWT9J49SWndumoftaKCirXr6h3A5B5/XMPsbE1RvcUuuha7c8aexeGP1z1KWpsNTYpqB7pKWho5hx9ucXNDhsT1qMfjaQwkYoeV8rJS3j17WL1ZscOeeXeHSp54GhcVlRVcP+n6ersg/OmQP9Uqxl1cXsxP+T+Zsrd2btX76uLVZBeZktdtKezhrHtNi2qtujrNmpLVrz9ZzqqX2XevBnPhllWW8f3K702RWzaNmSu+p82aSqe8bVLkmm+5sQ2Snk56jx5k9OxZpcBldO9OUvaWY9pjdW/xyRNbIJqK3e03H8gZY2pnrtVChKz99yd35EiaDBtKcpMmUdm+x+NpvJSVFjP5P/dZHbs1RWxskWl17C66wyt1nlpE4r3HzBvDso3LaJ/dntHdRm9zh5XVRavNqud6Pc9dN5f5a+eRu7qkSsnbWhduRYe2ZPXrT7O9B5LZrx8ZPXrUMmZsTb1GVeWX9b8wdelUvvhlCnnffU7bpUWmyOUpnVZaUeotEXGlmgJn72m77Rb3xZt3OsVORIYDjwDJwOOq+rvNLR9Nxe7d4QPp+HP9Q4LStCQ6XHUtTY85htR27aKyTY/H4/F4YklFZQULNyysZt2bv/JHUhYsqabs1XTh1lpPahIlu+9KRt+9aLPvELL77MlHt1xEl29X1Fp2fr/WpF5/GQu++Yj1M76jxaJ8dlth20jakiqzFa7URGKnUuxEJBmYAwwFFgNfAKer6qz6fhNNxe67IQeQuqr+JullrXLpO+XTqGzL4/F4PJ54YmPZRuaunVtl4Vu4dDaVM39k14WFVcreFl241F9AfGuQDOdK7bHtrtREYWdLntgPmKeqPwGIyAvAcUC9il00adKxC8Wrptc/v9PusRDD4/F4PJ6Yk52aTf82/enfpn/VNB2t5BXmMWftHL5f8yPL5n5L+YzZ5M7Lo+uSijpduDWVuPqUuvJm2WT06k3unv1MgevVy1ypcRorGwsao2K3KxBsoLoY2D9WG29+0kks+2Z6wtVr8ng8Ho+nIRAR2mW3o112Ow7ucDD0BU60HsU/5f/EnBWzyPv+c0q/m8Gg1+dtNjauJAXSLzqLDgOGkNmrt6/rWgeNUbGrS7Gv5W8WkUuASwA6deoUtY3nHn88BR9NqpXuLMQ23dnj8Xg8nngmNTmVHi160KNFD+g5Gk6Gd7/YfJz6io45DLv61hhKmXgkhS1AA7AY6Bj43gFYWnMhVf2Xqg5U1YGto6jxR+o1tb//fjIHDCClfXsyBwyg/f33+yKcHo/H4/FshpRRRwG1rTFaY76nfhpj8kQKljxxBLAES544Q1Vn1vebMHrFejwej8fjqY6v17h1bC55otFZ7FS1HLgSeAeYDby0OaXO4/F4PB5PfJCSmsbQpyew7KoTWdQlhzW5ySzqksOyq070St1W0ugsdtuDt9h5PB6Px+NJFHYqi53H4/F4PB7PzopX7Dwej8fj8XgaCV6x83g8Ho/H42kkeMXO4/F4PB6Pp5HgFTuPx+PxeDyeRoJX7Dwej8fj8XgaCV6x83g8Ho/H42kkeMXO4/F4PB6Pp5HgFTuPx+PxeDyeRoJX7Dwej8fj8XgaCV6x83g8Ho/H42kkeMXO4/F4PB6Pp5HgFTuPx+PxeDyeRoJX7Dwej8fj8XgaCaKqYcsQOiKyEvilgVbfCljVQOtuaBJV9kSVGxJX9kSVGxJX9kSVGxJX9kSVGxJX9kSVGxpW9t1UtXVdM7xi18CIyJeqOjBsObaHRJU9UeWGxJU9UeWGxJU9UeWGxJU9UeWGxJU9UeWG8GT3rliPx+PxeDyeRoJX7Dwej8fj8XgaCV6xa3j+FbYAO0Ciyp6ockPiyp6ockPiyp6ockPiyp6ockPiyp6ockNIsvsYO4/H4/F4PJ5GgrfYeTwej8fj8TQSvGLn8Xhiioi0ClsGj8fjiUdEpI2IpO3IOrxi5/F4YoIYbYGvROS4sOXxeDyeeMHdH9sDLwPHi0j69q7LK3aeHUZEBohIf/fZn1OeOlEjD7gJ+IOIjAhbJo/H07gQEQlbhu1FVZcBfwHOAY7a3vX4h3ACEccn7PHAQwCqWhmuKLFHRFq793j9f+pERHqIyD4x2pZE3lX1eeAO4P9E5JhYbD9eqeuc8YMjz7aSKPeewH1gh1yNm1u/uoxQEUlNpGtJN2WylgLZwP9E5FQRSd3WdaVEVTJPg6KqKiIHA0OAccBPqloQazmCF4+T6w4ReVxEhqjqlJrzGyvuptEKmCoip6rqV2HLtLWISCZwEfA95hptsP+sxrpbiUi+qr4oIhXA30QEVX2rIbYdz0SOi4gcDRwApAL/p6pLQxatGgE59wYKgTRV/T5subaGgOxDgFzsNvp22HJFk8A+Hgq0x/bxhXClqk1AzmOBI0WkEPgzsCZa956AUnc5dk2tFpEpqvpqNNbf0IjI6cBVwAnA6cCvgXJgm+RPGG3WAyIyGPgb0BO4HzgxYi2KJe7iPFBEThORg9zkZcChkfmxlikMVLVSVVdgtYqOEpHkRBkhqmoRMBW4XkS6NOR/FrjZXo+dv2NEZD9VfQW4FnhYREY31PbjFXcdDQfuAd4FDgL+GG/WFyfnMcATwEjgaXcvinuc7EcBj2FWkHEickrIYkWVgLL0JyANu6avD1msWgTO9/uBfwOj3Xv/aG5HRC4BTgR+B/QAhkZz/Q3MLsDHqrpUVf+MnbePiciZIpKxtStJiIeQB0SkN3AfcLGqngM8DxwIjBCRNjGW5WDgFmAf4EYReQD4HDhbRPrFUpawEJHO7l2ASUBfVa1Q1cp4ezBDNRfIbu4mkaaqY7BA3f5uXnIDbv9XwNHYKLQ18KiIHKuqr2Nu2TtFJLuhth8v1HFuHAichVl+BbjJPQC3O3A6mohIkojsAtwMjADygSJgTrwPYlwwei5wOWYBWQVMByaHKVe0cW7N07D/pwhz5T0bT/+PO4/SMIXrAqAjdi6tB/4kIntv7/2njv3MBs7ADA1JwJUikiIiHbdX/oYgeC8I7PuPQFN3zaGqzwCfYUrwVh8f74pNANwJ0B7ogj0EPlfV50SkEjgGSBWR/6pqWQxk6Y0pdZeo6kLn0rsX2BfY1b1/KyJJjTXeTkT2BV4QkTexm9P92H9wm6reF48WS6csjMBGrxnAxSJyN3ZetQPGqGpFtLZXh2s3G7gQuAaz7k4C/ioi6ar6goiMU9WN0dp+POJG3O2An0WkO3YcsoEHgKbAuaq6SESOB9qJyL/CuIZcTI+qajn2MFkDfAEcApzn5FwpIsNFZIaqLo61jFuDO//yRWQ6Fgc8EjhdVZeJyJnAj6r6ZYgiRgsBKoAbsPvvuW4fjxWR9ar6cWiCbboPiKqWishVQEssJvtIVS0QkYXAZcD1mKK3LetvBbQBZonIyZjluwXwJfC1qg53y10CZIrIo+68Dp2AJ+NSoK2IlALPAecCl4rIN0AOsBa4ZZvuj6rqX3H4YlNXkBZAC/f5MOAl4PrAcmcCe8VIpo7AP4GJNWV1r8uwEUdu2MevAY/BIHfx7YdZLP/m/pMngTci/1u8vZysE4Ah7vulwHXAR8BG4NQobksCnw8FWgbOn3cC8z5zxy077OMTw//gJuBO4CdMmesHrACuc8sMcdfQoSHJmIJZVQa692fd9Lexh27rgJzTgD3CPq415I/cNzthCoS4410WkH1v4Ftg/7Dl3cF97IFZeiOWsHXAsMD/8wOwbxzIeyhwN3C4+94SmIiFHvR098/9tnPdvTDv1ZPuummCKUNvAP9wy1wIzAR6hn0s6pD/IuBjoBumnJ+OGUjuwNzU7wF9tnm9Ye+Yf232Tz8es2x85S6M/THl7gVMg4+FDMGHdBo2Yh/v3tPc9KTAMi8AA8I+dg10LPYEHgbOqzH9cExRWgOcGbacdci9K6bAPVjHvO5Yev0dDbDdX2Our93c9yxgLHAFcIo7V3YL+/jE+L/4G+YuuyEw7RBgvns4fQUcE7KMBwNzMeXzmMB58or7zy52itFxYR/PeuQfAXwN/AF4100bB7zljv/X8Sr7NuzjscCnmLX3FUwRv8b9b7/HkqKOjQM5BzqZ7nHn+PmYseJKbLAwFxi+g9u4DSgALnPfk4Dd3f/9GvDB9ihHMTg2ye587I4pn+8A6TWWabI96/a9YuMUEdkDeBZToEqwTJmVwD+AAdjD8WpVXdCAMkSymA4CmgGrVHWaiJyLmfynAq+oaqlbvjc2ejpOVX9uKLliTeA4XIhdgF8Cd6nqmhrLHQacDFyhcXRhOXfF1Zgb/1RV/cy595NUtcLNnwCcoKoLd2A7uaqa7z7vDzyCnQt5Lg5GgOOweJE+wFmqOmtH9i3RECsvMxqz1r0DTFHVfBHZDbMqZarq/Dpc2bGQLVgq4kks9vI6YJqqFolIFvBbIA9zY74fhpybQ0R6YffNUzCF+Qagt1rs69FusdWq+nm8yb61uHPlVSwM5xJsYDlaVdeJyIHYdVasqg2a7b4Z+SL3yw6Y5bStqo4RkWGYxfrfwIeYYtNCtzHDuuY+uXjn/bF785PAq5FnkpufrXEQ5iEizYFOqvqtiOwHLMGe7wMxZfQkVS0TkbuAear6v+3+/8LWWv2rSjNvj428Isp2f+B9nJsK6IDFuZzpvreIkVwjgW+w0eBE4HI3/SzgP8DZgWVzcW63xvAK/BftAtNGAf/DgrGb1lj+QszCmhoncvfG3DW57vv1wJs490yNc20ezlW1ndvcHYu9jFhxewH/cp+zcFbdyLGM1fkb9itwjAe7a6m/+34F9hA62E2/kRDd+AE5d8UUg2RMYZgEnOzmdSGOwyyAzkBXrLjrKCyha/fA8U8OW8Yo7eN+WCLdsQTc4ZhrMydsGZ0sQzHFZbL7H5oEpn+OxWhv93nqPp/lrpuIi/cod76OwpJ9nnHncejhMe6a6gvchRk/3nfTz8Ys4wPd9xOx522PHdpe2DvsX9X+/N5YzEGW+/4E5o5t7r5fiTM3x0ie3TCrQgd3s/wKc8Ne7+afRxyauKO07xFFZAQWA/GAO/6CWeWewjLRct1ygsVHhBrHEXl4OblnuwfA90AbN/1qbMCwf+A3LbCR5I5stxNm1e2PZXq2B+YABweWORdzmyTtyLYS7cWmwdHdmCv6V2765Vg5g58wi0s8yPkx8ChmCUpxD8uPnOwbcDGa8fbCFOQ3geHAYmBB4BoejLlid+gcD3HfIkr3vu786eL+pyWB6/pwbOC9axzIuyfmYhyExY79FTMCRJS7YcABO7iN32BK4+WYongHNoAc5rY1Edg77GNRQ+Ys4GksVjUYJ38bMMa9JgN77vC2wt7Znf0FpAQ+J2MxXEuweLYTsBppf8ZGJz8Tw6BqLMi1B1bocTqm6J0F/ALcHPaxa6B9Tg98PhhTinpg7oMvMAUvGUtaeZ6ANS9kuZsHPu8DfIdZL07CMnfzgF3c/OtxI8QobDc4gs7BRqRPYpafo7DEgFswBfO7aNy0EumFWTLfxhTdM4AZ7ub+Gze/KdC55rEMQc4hWOzZru7B+BWWdJCCuYouAQ4L+3jWI3tvpzwc7b4fByzHBl7nEcfxgNuwjwdjsXOjA/v4TGSau7ZC3Ud3X2yCDRwnY0qdAHu459rzbGfMWI37TD93jqZiCt4X2GDkTsxrlET8WC6lxvd9sbCqv2KlyyLT98Ket9vtNQm+4qbOzc6IiKRgZSf6idV/+52qXoNZxT7DRmf/xILyD8ROhI8aUJ5IrbO+IrI7NuL9ESvR8JKq/oIpCS9jF2+jwsWaXScig9ykXTAFbjcsrvEBzJx+H/AicI2qLg9D1iCu5tkLInKPm/QzZj3shNVFy8Vi6GaISDtV/ZNGocxDjZgsUeuC8hhmqbsLe9gMx5IFKjGX3owd3W68E7iOsrFB2vXYf3EDNlj7HCtncIeqrlcXjxo5liGxC5ah19e9HsIsXfcCS1X1X6r6IcRl+6pRmOLTX0QyVfUNLCxiKGZBvlFV34hDubeFnpj7vr37/hFWgLcV9n/9Nqx9DGxTVXUDlki2AfM2ZajqXOw5thqzNm7r+pMC95mTgXTMQjcEU2b3xQYiZ2KVGZI0hI5MNalxfzxZrMh3mqo+gsVp7y/WMuw4zOK6RlVXRmXjYWu0O/sL0+ArMStYr8D0J9yfn+m+p8VInsOwJI0nsbIefZ2MC4BbMaVhcNjHrYH2vQ82srwL52LGbiLP4Fys2KjzJXYwBqIBZO+NxdtcFZh2K86yCpzqzqdDorS94Aj6aneM7sHc9s3ctv9BnLlDYvA/RNxmQzErQq77fiJwm/t8DPB3YlSmaAtyDsS55d25/h+gu/v+P+C/xFlJk8A+9AHOcJ+vxrwbh7ApzjP02Koo7GNPYJT7fBFW0qN/2HLVcR4d4Z5Zd7lnSDusVMe1bIoTz9rBbR2IxaFHQpNOB152n08EHse5puPphVnoPsVCUX5kU8zq6ZiR5CcCz/5ovLzFLkTcSOQLLI6tpXsBoKoXYLV3ZrmMwqgVj61DjoiFoRmWej0KOxm/wKxTC7GTcBVmNfykoWQJCze6moll+u4BXCYie6pqCWZtOUpE+mJdE+5Ss2SGTmC0nIZZgq4RkavdtGVAV7H2QlcA56jqpGiM6jVyV7csvOOx0gJJmMWuKabUrMa6kWQmuLVkqxCRZFVVETkEU9xeVpcljA2W7hErCv034EUNqd9qIGvxWCxWNFKovhRzxV4v1he2A/AXNYtLXBC4Vx2IJXRdIyInqOpfsEHnKcDBIpIaOUcTjcA+HoSdKw+LyAhVfRz4P+AZl10dOu48OhSTayoW8vFHzIJ6HhYKcpl71hVuzzbEulYcAEzBivOvdcdoLDBQRN7GvCl/UmvxGDc4T9wxWC2/3YClwIMicpaqPo/Frg9S1dlR3XDY2uzO+GLTKKdJYNphWFBlJIYiks0Vk2B8rN3T09jIIpJl1AobcU3EWmaFfuxicAw+xSxP07CbRUcspuMzLG7kxLDlrEPuwzD35/GY2+8zrIZcOqbQ/Qc36o/ydk/GbubHu+8tMUvdOFwGJY0oS3ozx6ErmyzrSViszyXuewrVg/h/AxwRkpzZgc/tMYtKJBsvknTTFlPSPyQOEjrq2Y/DsOK7p2IK9H/YVC3gHsxyl9BZ11iG6zfu/R+Yp+BYN+96rP7bdsWrRUG21u7YZ7jv51PdU7CPe2Y0x0JYtrkQNHVYW7G6hGsJZGZjg8jjiZN6mG6fIxbvfpjFtQ024Jjspl+HDaIuaCg5fEuxGBMYLR8HnCsiCvxVVT8UkdOAV12s1Lkicoaqft2AsiSr1TGLBHQ+CvwKOFFEflDVpSLyDPawSmsoOeIBsXZPpwD3qeo4EXkDC76+EYszOhC7ka6Lw/pXHYHHVfV1Z92dhLnQSlT10chCOyq31G4TNw0rKzAaeF1VV4vI37Hsrz9g7Zvion1PQ+HiG08FPhCRz9TqpW3E4mdeUNX1brkDgeWq+teQ5MwE/iMiV6jqaqygazFQ8/8pVdVjRKS5OstIvJzrzkojWOeIf6rqiyIyEYvjPFdEClT1DhHpojVqTCYKgeM9GPhQVScDk0XkWuA+EalU1T+JyKtq8WxhMByzQKWJyIvY8+FMrG4lWLLKUqD99jy/asSmDccsx1+p6o0iUgJ8JyL7qOoqd329vqM7FEW6AaPEer22Bk5T1UIRaYrFZYN1CPkDltncIHhXbIxxSt0wLMX5Cjf5nyJysqq+jV00LYErG0qpE5HuItLVKXXtsZPsB1Udiyk3zYBbRKSjWjDnI9o4eirWi6oWY0H+R4lIiqp+hfUdHIm5fLJVdZ1bNtQHXR1uzWTgTHdDrMSSFr7GEkF2jyy/g0pdakSpE5FDRGSgWo/Qo4C9RORBt421mCvmVzuBUrcnVqLg/7A4mQViCRPjMIXpcBFp7twxf8auq7DkHI4FlrcQkVFOKVgK9BaRJu5ecADwBxFp6f7H0M912HS+q1GJhRicISKdnJL6Jna8jxGRQaq6INFc/wF5I+/fYP9VLwBVfQhz558oIp1D3sfnMUv9fphV9z/ATBEZLyJNMCvdXmxD0/ogAaXuWjYNqq8Xkd+p6u2YZ2m+WMHfuEBE9hFrKvAdVu7leOAN3eR+TgZGiMgjmMXun6o6r6Hk8YpdOPTAXDL7YkGmTwAPiMjZqjoJy3Ca2IDbHwjsKtaAfRkW43esiBylVqH7IizW5ja3TFkDyhIKgTiWbmJVwMESRkoxCwxYC5wvgacilpd4wA0ODhWRX4vIXqr6JDb6+1Ass3dvrFvJMar6044+nEWkB+buQkTOwlxDfxeLF0vCakcdLiJ/c/Ktcw/cRouzjF6BlS9pij10J2FxQD9hme3HYD0rHwP+4AYLsZazJaZ8pmKZinsAfxPrDPIoZmn5vfMSPA28GU//XcDDcZCIXCsiR2LH/FUsvq4j5lauwLwKe0N8KKRbS2AfDwEuF8ueXIn1cB7q9r2P+94MSxSJ+T4GFOxyzCPwBXbdn4oNGpZgCXd/A+7U7YwhFSMLc0MPU4s3f8jNOt0pd/8kEJMeJiIyAnuG91eLyf4vJm8/ETkBQFUfw9rxfYl1mFjcoDIl0PmfsAQu3L7AClVdLpao8DRwrarOE5HxbvFztQEDQAOyZGMj9iGq+r2IXIY9iB5R1fec66aHqk5vKFnCxl2Qf8UeeDOxeMJjgSOxh0U7rDn726EJGSDw3+2P3UhmYO60aVhW6v3YoKEtcI+qvh6l7XbDjk0LLF7sFDftSuzY/R+gWLbwKQ15/sYDbrBTIiIjsfiiA7C4udki8gQWY7S/qhY7pbhYVX8JuphiKGtrzF3+ExZDejLmOr8JC25fgsWsdQA+VdXJYci5OcSSPO7Hsh5PwVxvk7BabqMwa93pmCKwJxaDVhlP+7AlRGQophDdhu3nr7H/7EjMMtYG+7+6Ym7am2uERcRKzkOx+Nl8VX3NKXWHAe+p6qsikorFwK3alvPIDUg3uOtqf7W2h59jlq0n3DJXY9UKLm6AXdsuxNpIPox5KD4NTE/Daj/2ZZMLtgPwTEz+N42DgMOd4YXdVOdiN/2IQv0/rHjrAdgIdJuDTLdTlmOxTM/rMOWul5t+CRY0fVTYxysGx6AH1iA6UsbkLUxBaYbFiB1MHHbVwG7y7wL93PdTgb+wKVhfcAkLkfNsB7aVFPjcDaubtTCw/j3dtv+MPXgafUcJt58Xu89DMMXoRWBAYJl/AYsIsUgqFt/TzX3+DxZmcHVg/pmYuy8ur/XAPTIbU3jaY7W+pgOtAsvt4q7ZI7AEot5hy74d+9oEs+r2x7wp09lUTDzTHYOWmBI7nRiXyWFT8s8ALGnlAfeceM1NPwXzdpyLWfC3+b6DuS6fw5TyzzEL82jMMhcp93ImZg3L3NF7WxSPzWVYPVMC5+GDbn/2wJ6p47ByZjErkRX6gdkZXlj1+U+xtObghXIMVm5gNuY2a0gZgm1p3sPVM8MsMSvZpOBcjusl2lhfWObSX7Ag30gNr2TMbfYCLsMxHl/YCL6UTfXpkjArzL+xWMCUaNz0guvA4rM6YH0qn3I31xZuXn+s+n1UKqbH+wuLHdoDU/COct+vxpTbYYHlniCGXWJqyJiMWem6Y+7JizHl6M/u/InUeTvX3XvaECd9VLFM6hycUuqm3Y/F0U3FZT+6e+dg97mN27+EUOrcPu5SYx9/AzyLKTWd3bQzcYo3pug8RgyVOkzhjNSgOwQbsBwbmP8e8A/3+ZLtOf417jPvYAOQvu57Z8wiPssdm7nEWeca97+9iA3y3sLq7L3pni2/dsu0I8at3kI/MDvDCxs9P+VusqmBG2tbzMLSYD0M3Um1u7v55WC1f/7n5kWUvasxl15C3Bi38zjUbO3SDRsN3s6mYsTJWPunfmHLW1NubMTezH0ehgXpnhtZBrPcRbXIpVv3Ze7GupvbTlfgT9gIPWK5i0nx7JD/h2DrvxaYMvugu4ZbYZXwf4dra1XfeRfD8yUVi5O9E1fQFnPzPYbLaHTT2od9bAOy98as0a9gFo7fu+v0aOATrA4jWDD9HAJ9a3GlN+L95fbxfawTzGQsNrYp1m5uOjDULdfPXXdHBH4bs3MJUz7vxg3YsAoBBVj8d2SZDjWfJdtzrrrPPYGzsUHjuwQKDWPPsAHEQR/cevbjKcwq9zc2lQ/qh1k1c8OQycfYNSAishd28n+EjTbvVdXX3LwhmEv0DlUtbaDt98Rir9ZhN8rPgEIs0+hGVX0lsOz1wLeq+l5DyBImgdi0o7G4nAwsuLUcSxRZDYxV1e9CFLNexErjXI49AF7BHgw5WGzHv1T13w203T6Y5ekUtXZykel7YJZesAQC1UZ8I3HxMgdgrf3aYQ+9dZiCXYS5OjdioQ2ZwAOquioEOTMwRW2BiHTFzvMbsXCL/2DJQL8FemHxvR/Ey//mYhGfxUpmjMcGog9irr+JWKzSUKzWZx9MwRgntUvwxC1uH5/GBpSvYnGpT2L7ejx2LXXHPArtsDjZN0MRFhCRXbHB7v6q+rKLp7sfuABLEjoY+7+OBFZt77kkItdgYR03qepKEfkPptAfjhXwTVYrzhxXiCsX5j430UD5GZdkdhrWZaIo5sKFre02xhdm2cjAasKNwdw1+2IKxG1YzbgZNGDTZmxk+A2bAqOvwIp5tsdM/OOoowApcRK70ADHYwg2Ih6NPezmY/Fqu2NZnndiylJc7T92g5uOPcwOwSxD92IWvBFY0seuRCG+zZ23wVF0V+D5wLwU97kl5iaJu/Y9DfQfZGE36bFYqY0D3PQDsdIut2MWzZYE3GshyDkAc8HejQ3kmmAuv79j1sSubrnbiSOXljt/vwcud98joSqdMQXodve9gzvmkZjguLpWt7CP7bFBwCnue2pg3mvAu+5zVyzOLpR9xLxKwULWp2GuxRPc93Ox+NH/YVnVx+3g9o7Fsmvb1Jj+OGbV/I44KY6PDdoi98BIceaanqBWWJLLV2FeY75AcQOg9g8Xi8gErA/szdjDeAgWD9UOq9T9fgNmoLXAXIofAojI+9hFVIIpm2XAr92I99UasjdG+gLvqOoYABFZjPXpG4BZNddoHDSOroOmwFq1dmeIyCpslPyJqr4tIp9rFKxDYkVdF7jPrTGL1EKgm4jcoqoPAOUiciE2ur5BG3mdOqiy9haKSDHmXpmEqzWmqlNFpBw4C7NiPKghlgpR1a9F5ALs4XuvmgVhg4g8gFnqfi0ij6rqvWHJWA/pmCehRKyQ6wYAVf3ZeRImi8jbauViqspEJNK9SlWXicjXmKL0kqqWiUimqhap6gkiMlVE+muNKgSx3Eexmnn3ALlihYAnY7HIRcBp7lnxX7EC3A9jMWRv7KDVtAMwSVVXiEgkPrhMVS8Ske7AOo2DLHsRycESIxa7kjvJYnX1au73EVgbtXNVdUas5YzgFbso4B6EyWplTPpjKdoHuBvTBMycfQ9Wy+qe4G8b6sJV1SkiMkJEflLV3TH3SwpQ7h5U451cv2x2RQlKwP3aXVXnYNbSAa4WU5KqPudS91up6tQwZQ0SkHsgFtMyH1giIucAL6jqTBGZilkaiZJSNwL4i3PdX4HFNP2EucFOACaISBfMpXcM1gqn0St1UFUz8CDgQsxt3w04WUTaqOobWJB0a2CeWpHr0BCRQViW7j+xOpUjsTZGi0XkIUy5i6vape58/0lELsKUheZYkP56VzpjMRZCkheimDtExGWnqoNFZKKITFTVI1W1yLnPKzBltiREGSOu8MexpIhBmCHiP9h5nwaMFCve/oJYIeJ/i8gyVf1sK7dRlxFjMbC3WFHs1W65U4EiDdENXRNVLXCDjiex5+hJdSmzat1Q3lFXzD4s4uoiT0TECpU+iDX2betGXBtF5GMAVV2IjXzaAzeKSGv3mwZHVScAV4pIAdbD8zhVXe8usA3YyLFRdpRwD+ThWIu2rtjNqjumYHd3MY6HEGeDm4DcL2LWumI2xRg94pSwszGFYocRkaOwZIjTsTiyk7EMt2ZY1uEiLNZlNrAK68kZl7GIDYGLMzwReEJV38Ws3SuAg0Tk95gFb4aq/hiimJFag7/GrukbMHfZcUB/ERmFZfBeH7acNXHnu6gVs70Gy8C+WKylWRmWdd2OBH5WqXX1SHafjwQQa4WGGwzsi7nNYx+LZbLsgsXt/kNV/66qc1X1aazGZxEW+/YypmCPEpF2akXRb8AGzFuzjWCbsLNE5CoRuQJztbYFLhKRU0TkbOAuLFQpdJwhIMLbwM/YvbCFWC3aWsuGrdThhPCv7fe5B+ORXsEekFnu+5uYuwzMdfUMrqRICHIeDiwOfE8NQ44Y7/OemMXpoMC0Ntio9D9YMkuDlpjZTrl3xQKTg3JnYEVJ/4DV2js6StsahllCXsEsgKMx1/SvsMy0VLdcl7CPSwj/QySx7DqsPdt9uKbr2IPoNCw287iQ5UzCFJ+p2IOnY2De9Vgc1E+4GKl4fQWO917YIOxCTMmbHvYxjuI+Jgc+T8Ri6/bGuhGEso9YTNhoLGbuCmpkF7t5bwW+d9zB7V2AKXOnYTHg12Nxk7e6+/LzxEn8Z43ne3d3nWVgrtZncKVf3H/YNGx5gy+fFRsFxDoBXIeVEZgKnK2qG8QayWdjD83fqOq4EGUcjmVk9VDXB7IxEnBlDsbKI1zqRsspalXNk9VG0Luo6tIGjHHcLlwsx6NYgs0GrCxFkYi0VssYS9UotHgTkSMwxeRu7IbVHHMpjgamq+owt9zFWED3XRqyqzEWBM6fDsBKd86cjlm8ngOmqOv/GIktCuMcqrlNF/dzNxYTNU5dJp5YL+hUVV0Yb+d6TQLHfi+cOxm4QlXfinfZN4dUz54Mfp6MDdiOVYuXjek+Olf3hcBazBp/ApYc9Ee11liR8+d14Hi19pM7sr0cLMv+P6r6jrN4/Rf4WlXvdstkahhZpJtBRC7HjtOP2H3yeMyKPxSrrDAUq1G7Q8cnmiSseTtMRCQ98LkPNtK4E+sqAfCQO0EjZSqGqqXmh9aYWs0tex4WAN7oCBzbyHsecKCIHKEW31Li3JhXuPnLIPwA7IjcItJErKxGIZZheZGTu8jFTt0rIjnRUOoc64HzVPVZrLBmBXbj+hhL/OnsbmhXYm1wdialbgT2MHtQRN5S1eex7OPRWG/MTAB1MTZhKXUicpSI/EVEfovFYt6F3W+GBWRcphYOEvq5DtXO97Sa89w+Rdyy52OlIhJWqRORA8FcsZFpWt0texCwj7qWhbHeR3cvKcNqr72PtQRsB1wv1qsVLBt/FXZf2iZEZA8ROUBEDheRFmrJaT8BXd29bB1W4Hd/p/QRD0qdWPxg5PNBWGjKidjzcz52j3wRU0q/wjwocaPUAd5it604Re5PWKzRGhHpjRXSPF0twDIb6zLxC9bmaWmI4tZJot4o6yPwoDsCiyv6GrsAd8fi6D7BqpY/Atyq8df79Rgsvmg+plw9j90wXsIU1NOwptqvN4AMEatTTyzOrgjrubkGiz+8T11GbmOlhhWlHxZAfgZWKugaLLu81ClQvbBYtZjXqQsiVpPxXqx0yeVY9uDZInISVs7n91jLp7i5zgPn+5HYtfm8Bmp/BZZLmNp09SGW/PQk5r2ZXsf8FA0kIAXPwVgjIi8DX6nq78R61o7EwlhmYxbg21R17Dau8xjs/PwFKyPVC7N698DuM49i97hhWBH0Y+Nh8CgWj30p8LKqfiEie2JleC4PnL8vAK9ooA5svOEtdtuAWObQY1jMwRo3+lyGucz2dqOQSCp4O6z+VdwRTzf7HSVwsQ3Fgn3fx8zmI7CR1RvYQ/pCTDl6O0zLaRAn92DMhfZrLKPxQjf6OxAL1K3Aeny+3hByB6xOP2CuxlTshvsPbPDS2JW6PYDrnEIHptj+FauEfxEWh1kqIgNV9ffYORRG8eG2zkUZoR/WoxPMhX4bgHvY3A8sibfr3J3vw7G6erPqUuocEatelrvnJhRiGeQPAw+r6nTn8qxJJJEgRywZIRSlznE1lgzQGeuW8DaWSPU6cIuqjt2We4/7j2/HeqiOVtWhWMvDN7G4utewNnfPYYXOr40Hpc6Ri5UoGy1W4WINcJSIHBu4npZiIVbxi8ZBoF8ivIAuwDys1APYA/ARrGTIVVhNtBuwh8F7NPJ+q2G/qN5yJhm4CXvY7YdZ7HZ181Lc/EhhyVCLmgKdMItYpKXTYVjl9mMwS29nN71zSPL1xuouNvriw5gVYQb2YDvUTeuL3bjn4YLdsbIP44B2IcnZ053Tt7CpZdHvsGz7SbiAdqxO5cVhH9c65I94hlKwpIGj3fej3HU7PLBs5Jg3w+KVo94mLwb72wVLSPqGTW33kgLzI/uY65YZEMJ/sae7F+3uvt+NeZ0iz7ahwH7B32zl+ltgilEksSAjMO8ezBKYiiWydQ3rmqpD7maBz30wa+OfMAPNEKyf+k1YksfXQPewZd7s/oQtQCK8sBHkJVirm36YpXMMrgGyW+ZEzAXyLFHKWvSvev+Pnlhm1fFsqlJ/OZZd9gWu/yXmUjibKHRliKLcs7E2OS3ctJPZVKsrx007HHMHtgxJzp0ha7oVlnF5bh3zTgKWu/PrHKy0zKiQ5NwD6xl6fo3pHbGG8Q+474dg/VOPiLWMW7kfo9x5fQXWYvEVrMfmI1hAfQbVlbr3gIPDlnsr9y2iLO2OZfWmY4kfD2DZk1XKHdWVuvcJ9LuNobwj3f3mPmzA0h0rK/MFcFh9+7cN6z8G6yQS2e/0wLxJxFCR3Up5j3TXziNY6Zn2mEJ3F1bKrJ07Pr/FBlRxP9gIXYBEeWHZMNdiJvbvgD/VmB8pcxIXlqHG+sIKxH6Ns5wGpvfGTP03ue/7uQfisLBldvLsigXgX1DHvDuxkeyuTpmYiRvx+leD/R89gDcC35OC1yzmyn8Kc0kf5abF/Jp295zba0yL3GMOwKxaYzD3edyV73FyDnAKxEHuIXlm5OGOWazH49pYBRSeg8KSdyv3SWq8j8QGbe+410jMCHA/psS2DPy2GWadD0Op6+TOmZZYqZHJQGs3byiu80QUtnM0FjPc3H2PlE56gzgpZxKQtT9WL3Q9liw2F/O8/ckpc39mB8u8xPoVV8VZ4xUXx7VWRJ7ELECLsT52kfmHAbeLVcxeBY0rji3OOA/rq/gEgFhZiv0wxWg8sKeIfIKZ+29SKyobD3QHPg3IfThW3T0Js85txGK7UrDg/PGNLcklztgApIl1kFihrmwJVHVwmK2q5wV/ENJ/UYn1qERcqRvdFHS/AlOWWmFWkYUhyLdZRKQV5uYrV9XJbvKzbt4R2ED5NrXYZDCF6A5V/STmwm4b2VhnGBGRdlh252mq+q2IXInV4HsUGxhci7XOihTz7Yu15JvS0EIGkzRcNm4+5tk4HMvyPletjNIhWOZrO0y5zt+R7br715XAly4+da1Y95x22HkbN6jFQQ7ArInrsYSOw7AqF7mY4pfkkqfKEuGe7LNit5JAkH5TLPBzF2z0UYbdnH6nDZC16KmOWGXyXljm6PnYjeJILND3c8z10QnYqHFUp06sM8BEzJR/BPbAbo5lwQ7EXGnlWOxdvAQSN1rEamiNAV5U1X/WmHcCZm35HVAc5vkjVkdwlKqOdN9TMB2zQkSuAz5Q1W/Ckm9LuMSBE9nUUvFxN31XrKftd+pKQcXDdbo1uFIgszBL6jOuEsKr2P594JZ5DAsBuVhCqs3mjv2ZmIWuPWaUuBTzbAzA4sAXu0HmXZiit5eqfhRFGY7GCqv/3W3/Eg2xh+rmEJF9sXv0Var6lFOE+2GK3huqOjtUAbeFsE2GifRikyLcHCuD8DyWFTsiON+/GvQ/OAjrJTkTGIvF7jTHskqfClu+Lcg+EnPTPImNAptgFrungV3Clm9ne2GZx2uwThuRUIpBmEstLlz4TqZPsT7BNWX/FugTtnw15IrcIwdhSsVwd54fg2VDnhtYNjP4m0R6Ya761WxKOLgLS8Lp7r4fATxEyPG9To58LBlov8C0/7jX6Vg83Oi6/scoyXAsUBpv52o9su6LFWy+PGxZduTlLXZbQXA0GbDcNcdGnF+r6sfhSrhz4Y59M1VdIJs6SRyGuUPOA9ZrnJ7YIpKmqqWB74OxNmEnqer88CTbuQjU7zsQC5pehJWg6IW58ENvQB44t7MxS8JKLKlgA5Yhe62G2M2mJhG3nys99G8sKeJkrJDrh5jV6BrgWXUhCYmGc9eLO3eOwqohnIHFON6NuWgXY9av63Qb679FW1b3rHoVi8c8TlW/FCsG3BxLOFuLdZpp0JAVEclS17El3hGRfbBEkosS9jyN0+dfaAQuhpoP4KqimYFlqto7hVlgcmehPneNcyX8Bau5FDcPuprUGCDkYi7kuzBF4q0wZWusiEhbrNftp3XMiyh3bbCA9pbAGlX9MV5cgzXOmZuw2pgpwEeq+m48yCkiHVV1kfucg8WKjlPV18QKuP8aq1v3NxEZDSxS1S9DFHm7CNz3d8FCPfJF5FDMtXkSprweiSVyfaqqn4Tx/wTkbKWu5qKIHIv9L1eoxb91A5YmirIVa0Rkb6BQVX8MW5btwSt2AQIXxFGYe2YZMF9V/+zmB5W7yOg0HUBdbz1P7HDxjkdi9QMfVNU34+RBFzmP6lT2XZzUHljc0X/jWRlNZFyM0cVYHapHtY6A/Hg4X4KISCdMuSwITKt2HgXOr7iQXUSeA/qq6p7u+x8w19uDqrpRrJf2o1gv7Y3xIPP2IiLHYZauFZiy+qCIDMPCcq5Q1RdCFdAhIqOwZ9hKrEvCWBc7+iesD+/VwAl1DXg8iY/vPMGm/oXuZjkIO/mfxoKrLxORB9z8iFKX7JS6ZlicQptQBN8JEJEjROT4uuap6nosw+usOFTqhmOZ0rWuMVUtVwvEvSii1EXOQU/0cNb0V7BYtPOci6XmMhFrWHKMxasi8t87Bej/sBZMVdSl1MVWws2jqmcA88Wy0cE6FzTDFGqAdVgsY3K8yb4tiHX+uBXr+FEAHCEizZwb8wzgCRHZJcxzycl5GFZC6UIso/puEblEVV8DLsAsvmd5pa7xstMrdiLSGrjIuRDAKme/rapjVPU9LDZhhIgc7JaPxL3kYsHA/464ITzRxd1Ib8Jaa9XHokhsWjw8NJxStz/Ws/Z9rafnpTuP8oODiljK2dgJKMp7AXtjpQt+IyJD6lg2ck03EZHRsVay3TlzEJZc8y9VXV7Xck5OFZF0EWkeT+eMqh4HrBWRD9SyKr8FzhKR8VjrqMdUdYdKaMQBrbHB/iFY8tMlqrpORHqr6jtYrbOlcRCSswfWf3UfYDcsWesiEfkNFhP+gLrsXU/jZKdX7IDBwP7YiD4bKMFq2ADgYhQ+xMqaEFDqxmL1libFXuTGj4jsjhWLXKquiXbNB27gQZcrIqfUsZqYIkYa5hoegZUZiLheg8tFFInmwJ0ikhF7aRs37rzojrmd7sWKsc4CzhZr0A5U+y+aAW8BC2OpMAXO6UOw1oSt3PSkGssF5XyGGla9WBOwMvYRkYNFJENVjwU2iMg7qvpvrLTGA8A5qvpqolmlA/sYkfsnrP7b/Vg27E8iMhL4o4i0wBIRQrO+i8geYnUZ/4W1yjsbq633NyysaBDQNAzZPLFlp1XsAubysVhh2x7YDWgi8LWIfOFuWkdiFbkrAr+7BbhVY1Bgcmeixg1xI9azs6NYLaTIwzpysw0+6MYAS2Itb4SA3JGEm99gWZb/AXO9Rs63GhbfV4GP1detiyoBpagZsFhVZzoLxVis7uFNYtnIBM6hl7BCuV/FSMbIOdPRuVfvw5TP60Skq1YvmBw8118B/h62l8Bdi8dhGa9nAWNEZB9nuSsRka9UtVBVJ6vqzMhvwpR5a3GW2+ZuH4cBN4rIxar6M/Ax1hLtCDfvAeCfqromYp2P9X6KSJJYrPcDTtYOQBGQBlwhlgiQDvxZVRfHUjZPOOyUip2I9AB+J5aWn6Kqr2JxIb1cLMKvsHpjN2AN0X+rqp9DVbzLg7qpironCkRih0TkEBE5CWs5dDdW5uEwp2BHHihJgQfdy8CdGmKleifTUcBDInINpjycArQSkX+4ZSpqyP2Kk9u7RKJEQFmKWLO+Ada4/wRVnYWVMViBq6wvm4rL3qcxLFvkzpkRWBeGB0TkUVV9zMnyooj0iCgIgYHA68DdGsUCstuLUx4uwSyNk4C2WJkPVHUUsEKslExCISJNgN9j4TdHYkkfRcDNIvI7Vb0XmAIcjBVevkktMSHmVrqAJyBdLXnveqxw/mXY/3EX0AUrPfN3TcBMZM/2sVNmxYq1T/kQ6wn3Eta8+Y+YZS4NWKKqT7llm6rqenfhSn0xU54dxylHf8YClMdgNbCmYY3YOwJvuliWyAN5IqZ0h1pH0J1P/8ACk/+Fjeivwm6uzwK/qOoF7hxKwx6EN4Ytd2NELGnlYmA6ZjVdh13X6di1/iAWG/WVW34wsEFVv4uxnHti58ZxwBVYPNSxqlooIvcCJ2DFUosAwZSNN+LFSyBWIuYmLCliBBaM/5NYm7APE/k+KdbtYzBWL/AzVf2fWAb+dOBlVf2tWy5DVYsjg9IYytcBy5wuFJFemMv7/1R1noh0xDohLccGK8tFpL2qLou1nJ7w2CkVOwCxIOpxWHzdiVjBxtHYqLMbNtp5Ahtc75wHKUY4N2Um5ta5FdgVa0NznFrLm5bYKPRVdW1d3A0tKeLmCYOIso+NlL/BYmz+iVVxXySWkNME2DU4WpZA3S9P9HAWon9jGYr3A3lYE++WWHkHBSZofBQf3hdTHqZjSlskZqu/Wu/Kbqo6L7B8tm7qpxoaYvGgqqolIvIIFo98vqp+JZaNGSm2nTjtlxwSKCvjwj+uwlr+/c4pRrnAHEzBviQMRcndc36HxfodglnorsTaEf5DVeeLxZZ+jtWtu08D9Vg9Owc7rWIHVRfvH4ADVXWDs7zshbkYrlHV90MVcCdBXFVyZ6kowXrzXaxWKPYc4Dusp2RcWgFE5GTsIZANHKPWo/ZkrE3YI4HlfBHrBkREzsDOn0WYC+1kVf05YLGI1J4M44EciZOLvHfELM7pQH+17Mojsdpjl6rqave7UK0sItIKO4+/EytyewGmIF+DNUi/DlMqfsKC9W/QBKzLGAgFOQoYqqrXu5CQUVhSzYequsIpd3uH7Q4XkcexCg6nARG3eCo2SEjDCrb/XlW/CEtGT3ikbHmRxotaBW4BpovIvmoZrpNcvEvcFABtrDhL3W7A606pzsdcZV2cpW5vLM7x0nhR6gIPgAGYe3g6Vo5lKTAZWOfm3YFZi6rwSl10CfwX7THr3GLMFV4GHK6qK8WyFgeJyD3qklRi7DZrCZSoaoFT3IaJ1Xsbiz2EhwHHi8hSbJB5Z0Spi7WsNRFLQrkBSBORD7Hz+R4su3Ia5ir+LdYPtinm4p6ciPdNdx4NwxSiq920V8SSEoYD6WLZvnnAR2FZ65ych2B9aocAL2L9Xh/DBgXjMC/BVV6p23nZqS12EVxcztNAD1VdG7Y8jZ2aN0URuR9ooqq/EZH/Ap2AHzA3+d2q+kZIotaJWAuzf2MPtx5Ys+/27nUgplg8rKpvJOJDLpFwitu1wEVYPNptWEb1P7HSIf/CMthjbkUSkUwstCAJ+AArfP4acCrWn3YqFod5LfALVj9zXDydMy6e63rMOrRSVa9z02/FYruGaoK2XQrilNj7sFZgb4pIJCEBETkTqzF4jaouC1nO/lgLszMxw8xp2ADzBBfvtz9QrKrfhielJ2y8YucQkWOwdjcfhS3LzoCLcXwUCxwvxgLcJ6nqVPdfbMD+j6/i7EHXA7NaPKyqn4r1vhwGvKSqH4rIrkCFC1qOG7kbI2L16B7DWjl96qYNxiwZJ2DtlP4dloLtvAFHY7FQvbD2ca86GW8EJgBPaKAdYbycMwHrUJabdC8We3xfxBIkIndjpX06YMpEQlukReT/sLjdywPTBgDfA81UdWVowlWX52JVvcx5PLKxwUIFcKIGWtF5dl52ynIndaGqb6nqR+5m7GkAahzbJVh80QlYk/A9gSOg6r/4WF3mYrw86MSKD5+AVZ0/BEBVxwCzsfZhaaq6RF3ngHiQu5HTHJjsFOwsALWyN3/AzqXTQ1TqktV4GytTkoR1Ysh1Mj6INY6/XKynLU7+uDhnAvFmbwIZmNt4EeY23sctcycwUFU3JppSF7kXibUA281NfhIoFpFT3bx9MMtvn7CUujqeRwXAcSJyjKpWqLVV/BCz3vWKuYCeuMQrdjWIlxtrY8Q9LPYVkWtVdQHWOPt7zC3VE+tpeFOoQtYgcGNtopZd9justMmuYgVawepabcCClz0NRB0POcHKhKCqhW6ZwVjZkAJV3eDmxfyaVkuQGCQif1XVaVjvzsXANWIllD4FbscU07JYy7clxLJ2/w+4R6347grMVZmDKaj7ukUXhCXjjuDuRSOxWMe/OWvdAmAmcIqITMQUvfvVdb6JNQGr6WEicp9Y3cOfgfOczBe4MKJDgV/5mDpPhJ06ecITChuBc52V4hcsVf8UzHpxCaYkxQ2BB8BtIrIOq1H3KBaoHMmc2xVzzYZejqIx4/6LI7HM9e9U9V0ROUtEPsayNHfBanj9KiwZAw/jwVhG5akiUq6q17pz/jTgFhF5QFWnhiXnVtAVCy/4WKwQrrjwgnuxUlARpTkukpq2FZeAcC9Wg+8YzKWvWG2+/2EDzQ1qJWhCcY+78+hoLDbzIWwgcCSmcJ+LZeJXYsWH58ZaPk/84hU7T4MSeNANwEb7yzErSyQgOwm4G1PqbteQylHUkLkF0FZVZ4vIHtgI+XosE+05IFlV7xORCqA78Iyqjg1L3sZO4Bzqi9XmmgzsJSL7q+o5Ysk3l2HJK79WawsYaxmTnWtMRWQQVnz4QqzTxbki8k9V/VVAuWsLrI+1nNvAWuAQsQ4YPwI4pboU+E2iKXQi0hbIAgqcW3UdlnDTD7u+u2Mu82eAM4PJB2Hdi1y87gnYAKELFnqQjCl0v1PVk8S62VSGfc/0xBdesfM0KO5BdwzmxvkGs6p8paq3ikgnrOvHSUArdUV7Q1bq0rGA8GwRmeQ+rwU+VyvKegTwuYh8gxVUvgDYX0QWq+p7YcndmHHn0OHYA+1CVZ0mIgdjLrNbgbtUtUxEmkTcr7HEKQ3DRORF565vCjynqu+7APdpwDgR+YOq3iiuj2qs5ayPgOK8D9Zfd56qvhM4xh9jsV1/AK5LQKWuB5Zg8Bmwp4icpKrfOkvko1iCzTwR+Q+WzNURK0QcGiJyKKbEPeje78My7rtjISzlznqaDz6EyFMdH2PnaVDESj78GrhaVS/ARscDROR6VV2oqlcCQzROOjGoZSi+h1km9sDqozXDZM5RqzH2FJCjqquwEf4PgC8v0LCUYyUnhrnv04AXgM5Yr9VkzM0fBm0wy1xTEdkFs8SdKyLdnRVvCZYBe4CI3BCnSt3RmMIwCPhCLOP4VaAQiyu9B7hXVT8MT9ptR0R6YyVv/uzuP1OAFmKdPMqxzhIHisiFWKzaKaoatlLXG7P2rlbVnzDPRqmqrsEUua8wZXSdV+g8deEtdp6oI9U7LFRiSlIkJme5iPwdl1XqmB9jEWvhlLYCsMxKEVEs/uYbYD+saOkXIjIbOB9z46DW0eBviZYVGO8EFI5crHzMx85q95aIzFPVZ0XkU2xwuiqM4y8irbGByj/VOtc8CizDFKF7gTdF5AKsE0BvTHHaJdZybg53jPsAD2ClWbphsWb/A65U1T+LyL/dsusTyeXnlP0XgHWq+oRLvjkFaAcMFJHzMPfrqcBZwEOq+nVI4kaSg9ph95z/qrWWS1LVz0QkyXkQdsGKD/8Qlpye+MfXsfNEDRHpgjWnzhfXvslNvx1ztw5T1TwROR4bkZ6C1b8K9SQUK5UxAfiPqv43MP1ArOr8L1iCxEnA28A4VZ2SSA+5RCKg1B2PKU7JWJPziHvwNeBGVX0iRDERkYOAc7Bs1wex3q/HY2VBHsfOl5Owsj5XAn0wBeIUrBtFWLFbXbHYsgp1xb/F+ou2Bh5R1YEi8ltMOR2uqh+EIWc0EJFuwCTMatcdGwRcJSKXY8rsnmpdbjJVtSiMazo4iHH3zkuxWNLB6npMO6XvGGCJqn4TS/k8iYd3xXqiSVfgZxFp5pIg0gBU9V6sHtanLibqj1gmV1E8KEbONfYw8BtxNazc9KlYjaizMffrE1itqAJnlQxd9saIe8gNw7o2XIq5Nh8VkbNV9WNMMfo/sRpkYd7DPsXqnLXALLqTgZcw9/AFwLOqOhwbHLTBFKWbVTW0wYxT4N7AlNDfisivAJz7sRfWPB5s36ZgrtiExA0u5wEHAxdjStxVAKr6d+AVTNkDK5Ie81i1gFK3H/A/ETlYVR/DzqeJ4srKqDHOK3WercErdp6ooZaNeDrwlYg0V9VSl4yAqt6O1fKaBpyrquNDFLUWaoWGbwdulU0FSpNcTNEPwB6q+hesGPGN+Jp1DU0r7OF2AKYo/QW4S0R+5SxIu6rq0lgH8otIF+ceRq3+3LdYUPtQTBH9AsuI7QP8WkQysAK/XYDjVHVmLOUN4mK3XsSUy+uAv9tk6e8W+RFoJSKPYCU2blLX0SMRcYPLZFWdj3UjaSciNwOIyAFYTN1at2woirZT6o4CrsOyuv8jIgeq6j+wTPxPZVPNQI9nq/CuWE/UcYHY/wfs6wJ+I26rk7G+nTHPXNxaxDJ4fwf8XlX/5x4ATwJnB9wirVzihCfKiEhPYDTW6aApduxvU9WZIvIqkAucpa67R6xdZ2IlP14BmruH8uvAT1j83GnAGswivR8WljDL/S4YdxoKYm38PlbVJPf9O6wDTHvga8yqdRimBH2qqhPCkjWaRI69c0FPAj7G2qD9Tq0zSBgyRSx1HYFxwPmq+rWI3IZ1TbndhXtcjmUpvxuGnJ7ExCdPeKKOqo4XkSuBL4HdXXD2q8Cl8azUgbUzE5ENmFtkEOayul5Vv5RNtcq8Utdw7AZ0dZa4dSKSD5whIuOxThO3RpQ6iL2lRVUnishpwHwRmYspQHcCiEhTbPByC3C3e3CLc6OFnlzjFIVjROQnTBl9RVXvcSETM7DM9T8DEyF++tZuLfXJ65S6ZFWdL1ZGZDJweRhKnViVAFXVYrEyOauBWVivV9TqY+4OPCkixzuXccL9F55w8RY7T4PhLHevYSn6v1LV1xPlBuVG0mlAiroCrZ6GQ6z8xEbnuh8HfKmqN4vVDTwD2B/4raq+FaqgDifXO0BqRIFzsw4Hlqrq7PCk2zwB2dMirmyxch/NnGKXsIi1+eviwiZqzktx7tkMp1jF2tqbhA0UD8dCOo7GvANXYS7911R1pVjXkt9hhpeDNQ5bznniG6/YeRoUsRIVzVT1tURR6jwNj1Oc+6nqOJe5eCUwSVXHOJfZTdjDbYFaZf0Oqro4TJlrIta78xFgUKJZcZ3sf1XVbu74j8M6SiSsy0+su81vsQzqyfUskxqmoiQizbDuNftjscbjnCJ3MzAXKw11GJawdRvmkv05HGk9iYp3xXoaFBfo7l0JnprsDdzjDF2rsaKr97kA/9VAEdDNBb4Tb0odgKq+LdZWbqaI9FTVtWHLtLU42StFpBBYgLlhE1mpy8X6BXeLKHU14xrd9zIRaQ5cEEvrZOD+V4DV7SwAjhWRz9XqZl6NtVrcAysF1Q4YQAJnJXvCw1vsPB5PTAkEjl8DHItV0X/BWeqOAvpivYN/xB52cVEWpz5cws1GVf0obFm2FeeWbaqWFZ5Q1BwsitWdfAh4Ty0LP5LZXhlIoMgFxgD3q+r7sZRTrC7j3lj9vFzMKt1Krd9xG6C3qn4kVqvxCWC0qn4fCxk9jQuv2Hk8npgjIsOxLMwKLOboClV9MTD/WuDrRFKWEtkqnWiyB5SlQ7Fiy2VYKZee2KBgrqre55aNKHfNgJexxJYpMZb3GKyO4S2qOsHFZHYBLsPauLUALnEJLjmYwvdzLGX0NB68YufxeGKKiLTDsqRvVmsVdirmRvsrlqlZGlg2oRQOT+xwSt1TmAXsXKyY+HhssHALNjC4yy2bgRVcvlatyHUs5UzC6jCOw9qFHQCMwuodfo8VsF6sqpPioSyOJ/Hxip3H42lQRKQH5oKaEomVE5EnsXpwE5yL7HrgPqwW3Fj/cPPUh7N2CVZEeY6q/lNEWmCuzWRVvc65ZYs00KlBRLqo6oJYyVjDTXwv0ANoC3yA62CjqhfV9xuPZ3vxyRMej6fBcA/hS4DfAC+LyAqsA8lq7EE3CwvefxcrzLrQK3WezeGUHxWRn4F+ItJGVVeIyAPAeyLSXq0dILBJYYq1Uucsip2ASuAe4BCsFM4sEekFPB7M9vZKnSdaeMXO4/E0GO4B9w5W3uF2LLj9Gqzh/ECgmYi0xuKMfq2qX4cmrCduCShLfYFmWN238cBewKEi8hGWkFCBKVJVxLhWXUTOSLUgQQAACelJREFUYVgpnH9jbRaHqeo5bpnjMPfxTfGY7e1JfHyvWI/H06C4MhqrgDNU9TjgF8x60R7Ica9L66s95vE4ZWkoMAG4HCuPswJ4CUu+eR54GvijqubFWj4RaSfWMaK9m3QS8JCqPqSq+2I9eJ9183oAV6nq2EBha48navgYO4/H02AEMhL3A0ZicXXPYsHkC7BK/G+o6rfhSemJd8R6CF8LPKGqn4rIH7Gewvup6ho3v1xV54XQUaInplTmA4uANzDlba2q/tstk+uWGY09d324gafB8K5Yj8fTYKhrWQX8jGUDXo8Vw30cQESmqaovwuqpE5dRmo4pdQOAtwFU9QYRqQTmicheqvpD5DcxVup6YwOVa7HOEccBh7rPl4rIV8B0rAxLO6ysyepYyefZOfGKncfjaXBccPutWEmTN6HKmueVOk8tAla3VFUtEpHfAncBA0XkJ1X9TlV/KyLJmHVsSUiitsBa433o5P4AOEZVr3Fu1t8Di4H+wJ2aYK3nPImJd8V6PJ6YICKpwD+xBvSvBKx5Hk8VgQSEo7ACvnnAZ8D/MBf+KuD1YKJNmKVCXLHtv6vq7iIyGut7fJyqFjiLXiWQpqrf+ZImnljgLXYejycmqPXpfAxI8UqdpyaBsiQqIoOwOnV3YH1V/wW0BK7DBgejRWSuqm6AcEuFuE4SV4pIAfADcLCqFrr9mVVjWa/UeRocb7HzeDweT6i4kjcXAv9Q1XwRORY4SFV/G5j/AVY6ZCOQo3HWR1VEDgeeVtUO7nuKqpaHLJZnJ8SXO/F4PB5P2PQEdgeuFZGmQBFwWGSmqq4E3geaqeqCeFPqAFT1A+AiEVkhIs29UucJC6/YeTwejydsPgUeA5oCv1LV94EvReRLEekjIkcCRwKlm1tJ2KjqBOA8oF/Ionh2Yrwr1uPxeDwxR0S6AGtUNd99TwGmAeuB91X1ARG5G9gN6IgV/H0rNIG3EZ8o4QkLr9h5PB6PJ+Y4K9wrQHOXMPE68BPWReIMYDnwsKqWikhTVV3vlSWPZ8t4xc7j8Xg8oRApFYIV9P1UVe90048ATsZKm9wFVPpMao9n6/CKncfj8XhCwylx72DFiDXQP/VwYKmqzg5POo8n8fCKncfj8XhCRURGAI8Ag3x3Bo9nx/AFij0ej8cTKqr6tohUADNFpKeqrg1bJo8nUfEWO4/H4/HEBSJyDLBRVT8KWxaPJ1Hxip3H4/F44gqf/erxbD9esfN4PB6Px+NpJPjOEx6Px+PxeDyNBK/YeTwej8fj8TQSvGLn8Xg8Ho/H00jwip3H42lQROQ8EdF6Xuu24vdPicjPDS/pZmXoLyJ3iUiLOuapiNwVY3kOdds9Mpbb3RLuGPnAbY8nRHwdO4/HEytOBhbXmFYehiDbQX/gTuB/wJoa8wZRe788Ho8nFLxi5/F4YsV0VZ0XthDRRlU/DVsGj8fjieBdsR6PJ24QkSNE5GsRKRaR+SJyaR3LRNyQh9aYHnH5dq4x/WK3ziIRWSsik0TkwMD8u938fBFZJSIfiMgBwfUCT7qvcwNu5M5ufi1XrIgMF5Fpbpv5IvK6iPSoscxHIjJFRI502y8UkRkicvw2H7h6EJETRORTt+51IvKyiHQKzH9bRL6q43ftRaRcRK4OTOsiIs+KyEoRKRGR6SIyOlqyejye6OAVO4/HEyuSRSSlxqvqHiQivYC3gSLgNOAW4GrgiO3doIj8CfgX8DVwCnAW8DHQKbDYrsDDwPHAecAK4GMR6evmvwXc5z6fjLleBwHL6tnmcPebAuBU4DJgT2CKiOxaY/GuWI/Uh4AT3DpfEZFu27O/NeT4FfAqMAs4CbjUyTFJRJq4xZ4GBohI7xo/P8O9P+/W1RH4DOgHXAOMwo7pqyIyakdl9Xg80cO7Yj0eT6z4oY5pbwHHus+3ARuAYaq6EUBEpgLzgaXbujGnHF0DPKyq19bYZhWqelHgN8nABGAmcCFwlaquFJH5bpGtcSffB/wEHK2q5W6904A5wHVAUJZWwMGqOtct9zWm3J0CPLANu1sNEckBfg88qaoXBKZ/5uS4EPgL8AawHjgbuDmwirOBd1U1z32/CxDgEFVd7aa94xS+e4A3t1dWj8cTXbzFzuPxxIrRwL41XlcH5g8C3o4odQCqugj4ZDu3dyR2j/vX5hZyrtAPRWQ1lsxRBnQHemzud/WsKxsYALwYUeoAVHUBth+H1PjJ3IhS55ZbgVkMO7FjDAKaAs8GLaRYkscPwMFue0WYVe9MERG3D3thlrmnA+sbjllT82us7x2gn4g03UF5PR5PlPAWO4/HEytmbMHa1R7Iq2N6HtBlO7bX0r3Xm7EqIgMwheUdzIq1DKgAHgcytmObzTHLVl1u2uXAbjWm1cywBSjZzm0HaePeJ9Yzf23g89PA+cChwIeYtW4DZs0Lru8c96qLlpjlz+PxhIxX7DweT7ywDGhbx/Sa04rde1qN6S1rfF/l3ncFfqxnmydiVroTVLUsMlFEmgPrtiBvXawFFGhXx7x2wOo6pjcEke2ch7mVa7Ih8HkSsBA4S0QmAacDrzhrXnB9kzH3bl1ss6vc4/E0DF6x83g88cI0YISIZAdi7DoCg6muOPzi3vcE3g1MH1FjfROBSuASLLatLrIwC11VUV0RORxzhS4ILFfi3jM3twOqutFlmZ4sInepaoVb527AgcDfNvf7KDIVU966qep/N7egqqqIPAtcAYwBOlDdDQsWdzgImFlD4fN4PHGGV+w8Hk+s6C8ireqY/qWLR7sPyzp9V0T+iFnk7qaGe1ZVlznL0s0isgqLSTsLyzANLjdfRB4GrnVZoG9iStx+wA+q+iKmsFwNPCUiT2KxdbcDS2rIOMu9XyEi/8Xi8L5T1dI69ud2LEFjnIj8Hchx+5EP/HlzB2g7OEhEmtWYVq6qr4vIDcCjItIaGO+2vysW5/eRqj4X+M3TWPLEP4FFmBUvyB3A51i28P8BP2Nu5z2B3YMJGh6PJ1y8YufxeGLFy/VMbw2sUtXZIjIC+CPwIqZc/R6zFB1a4zdnAf8A/oq5Zp/AFMN/BxdS1etFZB5wOXAusBH4DmfpU9V3ROQ3WKbqicAMLI7sthrr+dbVqrsEuBhLyuiCKTjUWHaCiByDdap4CSgFPgJuVNVouyzvqGPaRiBHVR8TkUXADVj5klTsmH4MTK8h8w8i8iUwEHhQVbXG/IUiMhDLjn0A+89WY8drsxZBj8cTW6TG9evxeDwej8fjSVB8uROPx+PxeDyeRoJX7Dwej8fj8XgaCV6x83g8Ho/H42kkeMXO4/F4PB6Pp5HgFTuPx+PxeDyeRoJX7Dwej8fj8XgaCV6x83g8Ho/H42kkeMXO4/F4PB6Pp5HgFTuPx+PxeDyeRsL/A+igP7Pn2X4qAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fetch rows containng data of the counties of interest\n",
    "df_ed_3 =  df_education_3.loc[df_education_3['County'].isin(['Leitrim', 'Longford', \n",
    "                                                            'Dublin City', 'Cork County'])]\n",
    "# using data melt to change structure of dataframe to build a plot\n",
    "df_ed_3_melt = df_ed_3.melt('County', var_name='ed_level', value_name='count')\n",
    "plt.figure(figsize=(10,8))\n",
    "plt2 = sns.pointplot(x='ed_level', y='count', hue='County', data=df_ed_3_melt )\n",
    "plt2.set_xticklabels(plt2.get_xticklabels(),rotation = 45)\n",
    "plt.xlabel(\"Education Level\", size=16)\n",
    "plt.ylabel(\"Count\", size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c9c998",
   "metadata": {},
   "source": [
    "1. The number of people that are uneducated remain low across all counties.\n",
    "2. Leitrim and Longford show similar trends in education levels. Both counties have a student population in Primary - Upper secondary level of education. Beyond this there is a decline in number of students pursuing higher levels of education. This could be due to lower education options available in these counties and students moving elsewhere to pursue higher studies.\n",
    "3. Dublin City has a big number of student population in Upper Secondary level and Postgraduate studies. Dublin does appear to be a popular destination for higher studies for students.\n",
    "4. Cork County also shows a similar trend but with a much lower count compared to Dublin. Other than upper secondary students there is a higher number of students pursuing Honours Bachelor degree.\n",
    "5. As we are looking into an age group of 15 years and above it explains why there is more number of students in the Primary - Upper Secondary levels of education. They represent the bulk of population in their initial levels of education.\n",
    "6. Number of people pursuing their doctorates is low across all these counties. This level of education is clearly not popular in these counties or there could be a lack of infrastructure to support the doctorate students needs and they are moving elsewhere to complete their doctorate degrees.\n",
    "\n",
    "### Commute theme analysis\n",
    "\n",
    "- Preferred mode of travelling\n",
    "\n",
    "In the commute theme I have mainly picked columns assosciated with students and how they commute to school/college. I will continue analysis on the four counties as before(Dublin, Cork, Leitrim and Longford)\n",
    "\n",
    "The first question I would like to address is what is the preferred mode of transportaion when it comes to students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "380e5b99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(26.445772569444443, 0.5, 'Count')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 502.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fetch rows containing data of the counties of interest\n",
    "df_comm_1 = df_commute_1[df_commute_1['County'].isin(['Leitrim', 'Longford', \n",
    "                                                    'Dublin City', 'Cork County'])]\n",
    "# using data melt to transform data for plotting\n",
    "df_comm_melt = df_comm_1.melt('County', var_name='Transport_method', value_name='count')\n",
    "plt.figure(figsize=(10,8))\n",
    "plt2 = sns.catplot(x='County', y='count', hue='Transport_method', kind=\"swarm\", data=df_comm_melt,\n",
    "                  palette='dark')\n",
    "plt.xlabel(\"County\", size=16)\n",
    "plt.ylabel(\"Count\", size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b844cf6",
   "metadata": {},
   "source": [
    "1. In Cork , Leitrim and Longford County majority of student population travel by car as a passsenger to either college / school.\n",
    "2. In Dublin walking is the choice of transport to get to school / college followed by travelling in a car as a passenger.\n",
    "3. The mode of transport travelling by car as a passenger is quite popular as we have a bulk of student population at their schooling level and they usually have their parents dropping them of to school / college. It also acts a means to get to school/ college faster\n",
    "4. Walking is the next most popular mode of transport as its free. Its more popluar when the school/college is within walking distance from the place the student lives.\n",
    "5. Other modes of transportation like cycle, bus, train, motorcycle and cars are used but not popluar because of the cost factor. Cycling is popluar in Dublin but not so much in other counties taking into account the weather conditions and the maintenance cost that goes into the upkeep of a cycle and to some extent cycle theft which is quite common in Ireland.\n",
    "\n",
    "- Time spent travelling\n",
    "\n",
    "This table contains the amount of time spent by the general popluation to get to work, school or college. So this analysis is combination of both working and student professional and a much larger age group( 5 and above). We could still see how much time is spent travelling. It could be indicative of how efficeint the transport system is in the county, status of roads for people driving, cycling and footpaths for popluation walking to work/school."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0e7ba3a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2402d7650a0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fetching rows containing data of counties of interest\n",
    "df_comm_2 = df_commute_2[df_commute_2['County'].isin(['Leitrim', 'Longford', \n",
    "                                                    'Dublin City', 'Cork County'])]\n",
    "df_comm_2_melt = df_comm_2.melt('County', var_name='Commute_Time', value_name='count')\n",
    "sns.catplot(x='County', col='Commute_Time', col_wrap=3, data=df_comm_2_melt,\n",
    "           kind='bar', y='count', palette='Set2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32e4bda5",
   "metadata": {},
   "source": [
    "1. Looking at these distributions we can see that in general people spend anytime under 15 to under 30 minutes travelling to work or to school.\n",
    "2. The time spent travelling is a function of how many people use their own mode of transportation to get to work/school i.e., cars, motorcycles etc vs usage of public transport. If there are more number of people using their own mode of transport this could lead to more road traffic and thereby increase the time spent travelling.\n",
    "3. The distance also plays into a huge factor to time spent travelling. The trend above suggest that people usually go to school/college near them or live in a space close to work. \n",
    "4. The proportion of popluation that spends time travelling greater than 1 hour is quite low across all these counties.\n",
    "5. These counties have an efficient transport system as a bulk of popluation spends lass than 45 minutes commuting.\n",
    "\n",
    "So far the analysis of commute time has been on the bulk of poluation. I would like to further analyse the commute time of the student popluation aged 15 and above. For this purpose i will be combining dataframes of the education and commute theme. Also\n",
    "for the purpose of building a good prediction model i will now start using the electoral area level data as it has more datapoints available for training and testing purpose. Moving to the electoral area is good for my analysis as from the above analysis we know people generally live close to school / place of work. So having information about the transport system at the electoral area level gives us a better picture about its student popluation and how they are travelling to school/work and how this is going to affect the commute time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5514dfb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Electoral_area         object\n",
       "Studying                int64\n",
       "School_OnFoot           int64\n",
       "School_Cycle            int64\n",
       "School_Bus              int64\n",
       "School_Train            int64\n",
       "School_Motorcycle       int64\n",
       "School_CarDrive         int64\n",
       "School_CarPassenger     int64\n",
       "School_Van              int64\n",
       "School_Home             int64\n",
       "School_Other            int64\n",
       "School_NotStated        int64\n",
       "Under15                 int64\n",
       "15-30                   int64\n",
       "30-45                   int64\n",
       "45-1                    int64\n",
       "1-1.5                   int64\n",
       ">1.5                    int64\n",
       "NotStated               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using merge function on county to combine different dataframes\n",
    "# df_e_education_2 just has a count of number of people aged 15 and above that are still studying\n",
    "# df_e_commute_1 has the  count distribution of different modes of transport\n",
    "# df_e_commute_2 has the commute time info\n",
    "df_edu_transport = pd.merge(df_e_education_2, df_e_commute_1, on='County')\n",
    "df_final = pd.merge(df_edu_transport, df_e_commute_2, on='County')\n",
    "# renaming the column from county to electoral area to avoid any confusion\n",
    "df_final = df_final.rename(columns={'County': 'Electoral_area'})\n",
    "# checking the datatypes of these columns\n",
    "df_final.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "850236c2",
   "metadata": {},
   "source": [
    "We are dealing with a majority of numerical data only the county column is categorical."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e711954e",
   "metadata": {},
   "source": [
    "## Statistical Analysis\n",
    "\n",
    "First step is to transform the commute time counts to represent the student population aged 15 and above. As this is the population proportion we are interested in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "96e979a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transforming the commute-time count to denote the student proportion\n",
    "df_final['Under15'] = df_final['Under15']/df_final['Studying']\n",
    "df_final['15-30'] = df_final['15-30']/df_final['Studying']\n",
    "df_final['30-45'] = df_final['30-45']/df_final['Studying']\n",
    "df_final['45-1'] = df_final['45-1']/df_final['Studying']\n",
    "df_final['1-1.5'] = df_final['1-1.5']/df_final['Studying']\n",
    "df_final['>1.5'] = df_final['>1.5']/df_final['Studying']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9482f4",
   "metadata": {},
   "source": [
    "1. Using Label Encorder to convert categorical to numeric datatypes. Here i use this on the Electoral area column just to make model building easier.\n",
    "2. Normalizing data of all dataframes at county and electoral area level for further analysis to remove any kind of bias that might get created and help build a more accurate model. I will be using Min-Max scaler to perform this transformation. The data we get will be in the range [0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8ef1a23a",
   "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>Electoral_area</th>\n",
       "      <th>Electoral_area_cat</th>\n",
       "      <th>Studying</th>\n",
       "      <th>School_OnFoot</th>\n",
       "      <th>School_Cycle</th>\n",
       "      <th>School_Bus</th>\n",
       "      <th>School_Train</th>\n",
       "      <th>School_Motorcycle</th>\n",
       "      <th>School_CarDrive</th>\n",
       "      <th>School_CarPassenger</th>\n",
       "      <th>School_Van</th>\n",
       "      <th>School_Home</th>\n",
       "      <th>School_Other</th>\n",
       "      <th>Under15</th>\n",
       "      <th>15-30</th>\n",
       "      <th>30-45</th>\n",
       "      <th>45-1</th>\n",
       "      <th>1-1.5</th>\n",
       "      <th>&gt;1.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adare - Rathkeale</td>\n",
       "      <td>0</td>\n",
       "      <td>0.246414</td>\n",
       "      <td>0.070554</td>\n",
       "      <td>0.010911</td>\n",
       "      <td>0.279771</td>\n",
       "      <td>0.005517</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.383779</td>\n",
       "      <td>0.302188</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.139535</td>\n",
       "      <td>0.445233</td>\n",
       "      <td>0.613247</td>\n",
       "      <td>0.744569</td>\n",
       "      <td>0.350421</td>\n",
       "      <td>0.147825</td>\n",
       "      <td>0.214192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ardee</td>\n",
       "      <td>1</td>\n",
       "      <td>0.242616</td>\n",
       "      <td>0.143868</td>\n",
       "      <td>0.025674</td>\n",
       "      <td>0.492220</td>\n",
       "      <td>0.008966</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.173077</td>\n",
       "      <td>0.267412</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>0.570218</td>\n",
       "      <td>0.609780</td>\n",
       "      <td>0.524829</td>\n",
       "      <td>0.360379</td>\n",
       "      <td>0.492510</td>\n",
       "      <td>0.671571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arklow</td>\n",
       "      <td>2</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.195618</td>\n",
       "      <td>0.010270</td>\n",
       "      <td>0.248968</td>\n",
       "      <td>0.064828</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0.128763</td>\n",
       "      <td>0.226617</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.162791</td>\n",
       "      <td>0.683067</td>\n",
       "      <td>0.437363</td>\n",
       "      <td>0.413197</td>\n",
       "      <td>0.464787</td>\n",
       "      <td>0.742163</td>\n",
       "      <td>0.940262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ashbourne</td>\n",
       "      <td>3</td>\n",
       "      <td>0.201406</td>\n",
       "      <td>0.242884</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.361385</td>\n",
       "      <td>0.036552</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.164716</td>\n",
       "      <td>0.218878</td>\n",
       "      <td>0.309524</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.139535</td>\n",
       "      <td>0.567398</td>\n",
       "      <td>0.648606</td>\n",
       "      <td>0.995025</td>\n",
       "      <td>0.815849</td>\n",
       "      <td>0.778624</td>\n",
       "      <td>0.570608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Athenry - Oranmore</td>\n",
       "      <td>4</td>\n",
       "      <td>0.313783</td>\n",
       "      <td>0.123512</td>\n",
       "      <td>0.034660</td>\n",
       "      <td>0.449984</td>\n",
       "      <td>0.057241</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0.313545</td>\n",
       "      <td>0.426961</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.209302</td>\n",
       "      <td>0.551742</td>\n",
       "      <td>0.796874</td>\n",
       "      <td>0.980904</td>\n",
       "      <td>0.488394</td>\n",
       "      <td>0.245009</td>\n",
       "      <td>0.235678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Electoral_area  Electoral_area_cat  Studying  School_OnFoot  \\\n",
       "0   Adare - Rathkeale                   0  0.246414       0.070554   \n",
       "1               Ardee                   1  0.242616       0.143868   \n",
       "2              Arklow                   2  0.176371       0.195618   \n",
       "3           Ashbourne                   3  0.201406       0.242884   \n",
       "4  Athenry - Oranmore                   4  0.313783       0.123512   \n",
       "\n",
       "   School_Cycle  School_Bus  School_Train  School_Motorcycle  School_CarDrive  \\\n",
       "0      0.010911    0.279771      0.005517           0.111111         0.383779   \n",
       "1      0.025674    0.492220      0.008966           0.111111         0.173077   \n",
       "2      0.010270    0.248968      0.064828           0.185185         0.128763   \n",
       "3      0.052632    0.361385      0.036552           0.222222         0.164716   \n",
       "4      0.034660    0.449984      0.057241           0.185185         0.313545   \n",
       "\n",
       "   School_CarPassenger  School_Van  School_Home  School_Other   Under15  \\\n",
       "0             0.302188    0.214286     0.000000      0.139535  0.445233   \n",
       "1             0.267412    0.523810     0.000000      0.279070  0.570218   \n",
       "2             0.226617    0.190476     0.058824      0.162791  0.683067   \n",
       "3             0.218878    0.309524     0.058824      0.139535  0.567398   \n",
       "4             0.426961    0.928571     0.235294      0.209302  0.551742   \n",
       "\n",
       "      15-30     30-45      45-1     1-1.5      >1.5  \n",
       "0  0.613247  0.744569  0.350421  0.147825  0.214192  \n",
       "1  0.609780  0.524829  0.360379  0.492510  0.671571  \n",
       "2  0.437363  0.413197  0.464787  0.742163  0.940262  \n",
       "3  0.648606  0.995025  0.815849  0.778624  0.570608  \n",
       "4  0.796874  0.980904  0.488394  0.245009  0.235678  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_std = pd.DataFrame()\n",
    "df_final_std['Electoral_area'] = df_final['Electoral_area']\n",
    "# define label encoder\n",
    "labelencoder = LabelEncoder()\n",
    "df_final_std['Electoral_area_cat'] = labelencoder.fit_transform(df_final['Electoral_area'])\n",
    "# getting all columns that need to be normalized\n",
    "df_final_col = list(df_final.columns)\n",
    "# removing electoral area as its categorical\n",
    "df_final_col.remove(\"Electoral_area\")\n",
    "# define min max scaler\n",
    "scaler = MinMaxScaler()\n",
    "df_final_std[df_final_col] = scaler.fit_transform(df_final[df_final_col])\n",
    "# dropping columns that arent useful\n",
    "df_final_std = df_final_std.drop(['School_NotStated', 'NotStated'], axis=1)\n",
    "df_final_std.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d109ae8e",
   "metadata": {},
   "source": [
    "We have few more steps of data preparation before we can building a model for predicting time spent commuting. We need to create a classifier column in out datarame in order to train a successful binary classifier model.\n",
    "\n",
    "1. Determine which commute time count is highest and that will be the classification category for the particular row\n",
    "2. Perform numeric encoding to the new category column which will be used to train our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d79632db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Under15', '15-30'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determining which is commute time has the largest proportion\n",
    "# Using the original data for classification purpose as normalization for this step isnt required\n",
    "df_final_std['classification'] = df_final[['Under15', '15-30', '30-45', '45-1', '1-1.5', '>1.5']].idxmax(axis=1)\n",
    "df_final_std['classification'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7980fb12",
   "metadata": {},
   "source": [
    "So we get two main categories to build a binary classification model. I will be using Logistic Regression to build a model that will predict whether the commute spent by students aged 15 and above is under 15 minutes or in the 15-30 minutes category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "864f0efa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting the category value to numeric value\n",
    "df_final_std['classification'] = df_final_std['classification'].map({'Under15':1, '15-30':0})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea8fde10",
   "metadata": {},
   "source": [
    "Building a correlation matrix to visualize the relationship between different coluumns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eb320b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'classification')]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df_final_std.corr()\n",
    "plt.figure(figsize=(10,8))\n",
    "corr_plot = sns.heatmap(corr[['classification']], \n",
    "    vmin=-1, vmax=1, center=0,\n",
    "    cmap=sns.diverging_palette(20, 220, n=200),\n",
    "    square=True\n",
    ")\n",
    "corr_plot.set_xticklabels(\n",
    "    corr_plot.get_xticklabels(),\n",
    "    rotation=45,\n",
    "    horizontalalignment='right'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48adc8b7",
   "metadata": {},
   "source": [
    "There is some good co-relation between the classifier and the other columns of interest.\n",
    "\n",
    "1. There is a negative relationship between the classifier and number of students, counts representing how many use train, cycle, car etc. This can easily be understood if thereare more number of students travelling to school/college by any means of transport there is bound to be more traffic and thereby increase the time spent commuting.\n",
    "2. The columns actually representing the counts of each commute time will be disregarded for model building purpose as that is what im trying to predict.\n",
    "3. Finally i can use the transport methods and number of students to determine how long a student commutes\n",
    "4. I will also be removing School_Home as a student being homeschooled doesnt commute and it doesnt in anyway contribute to commute time prediction\n",
    "\n",
    "- Splitting data into test and train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c7c0a93b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cols = ['Studying', 'School_OnFoot', 'School_Cycle', 'School_Bus', 'School_Train', 'School_Motorcycle', \n",
    "          'School_CarDrive', 'School_CarPassenger', 'School_Van', 'School_Other']\n",
    "X = df_final_std[X_cols]\n",
    "Y = df_final_std['classification']\n",
    "# Using a 80-20 test split between train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c72c348e",
   "metadata": {},
   "source": [
    "- Feature sampling to get the best model\n",
    "\n",
    "I will be using Recursive Feature Elimination to construct models repeatedly to see which group of features gives me the best performing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3576ac98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True  True  True  True]\n",
      "[1 1 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "log_reg = LogisticRegression()\n",
    "rfe = RFE(log_reg, 20)\n",
    "rfe = rfe.fit(X_train, y_train.values.ravel())\n",
    "# the below statement gives a binary output which helps us to decide if we keep or\n",
    "# reject a feature\n",
    "print(rfe.support_)\n",
    "# each of the feature are given a rank \n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d7efc52",
   "metadata": {},
   "source": [
    "The above result states that we can use all the columns specified in X_cols to get the best model for predicting the commute time. Next step is to fit the model with these features and analyse the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f8a7103d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of this model : 0.89\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "# fit the model on training data\n",
    "logreg.fit(X_train, y_train)\n",
    "# predict using the test data\n",
    "y_pred = logreg.predict(X_test)\n",
    "print(\"Accuracy of this model : {:.2f}\".format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153980a7",
   "metadata": {},
   "source": [
    "This model gives an accuracy of 89%  accuracy in classifying the commute time. Let us further analyse the confusion matrix to check the sensitivity and specificity of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6f984b5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "conf_df = pd.DataFrame(data=conf_matrix, columns=['predict:0','predict:1'], index=['actual:0','actual:1'])\n",
    "sns.heatmap(conf_df, annot=True,fmt='d',cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7fe957",
   "metadata": {},
   "source": [
    "The model has predicted some false positives but no false negatives. Next lets determine the sensitivity and the specificity of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5d083468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity :  1.0\n",
      "Specificity :  0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "true_positive = conf_matrix[1,1]\n",
    "true_negative = conf_matrix[0,0]\n",
    "false_positive = conf_matrix[0,1]\n",
    "false_negative = conf_matrix[1,0]\n",
    "sensitivity = float(true_positive)/(true_positive + false_negative)\n",
    "specificity = float(true_negative)/(true_negative + false_positive)\n",
    "print('Sensitivity : ', sensitivity)\n",
    "print('Specificity : ', specificity)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dfd7ae2",
   "metadata": {},
   "source": [
    "The sensitivity is very good meaning the model will not incorrectly classify a positive as negative.  The specificity score is 66.6% meaning the model only classify the aforementioned perecentage of positive values correctly.\n",
    "\n",
    "- ROC Curve\n",
    "\n",
    "The Receiver operating characteristic curve is used to show the trade off between the specificity and the sensitivity of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "abc9c8e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51c2b0c4",
   "metadata": {},
   "source": [
    "As the curve is far from the dotted red line we can conclude the model performs well.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Ireland has a substantial population consisting of students at different levels of education. Social Science, Business , Law, Engineering are the most popular fields of study in the country. There is very little gender gap in the population pursuing their education which is great both for the popluation and the country as a whole. Analyzing the number of students at different levels of education showed that majority are in their initial years of schooling. There is a trend that students might move to a bigger county that offers more options for education and has more opportunities. There is also a very small percentage of population pursuing their doctorates in Ireland.\n",
    "\n",
    "Commuting is a big part of life for anyone. Be it getting to work / school, the mode of transport plays a very important role in determining our commute time. We have seen that travelling in a car as passenger and waking is the most popular means of transport in four of the counties in Ireland. This could be extended to other counties too as we have seen that there is a good amount of students travelling to school/work and they usually have their parents or other dependants dropping them and picking from school/college. Being a student also means being on budget so walking is usually preferred as one doenst have to pay to get to destonation. Other than walking cycling is also quite popular. Public transports are used by students but at a much lower proportion taking cost into consideration. Overall Ireland does have an efficient transport system as majprity og the population spends less than one hour to commute to school/college/work\n",
    "\n",
    "Next I combined these two themes to determine how long students usually commute. Logistic Regression model built on features like number of students in the electoral area along with the number of students using different types of transport together are used to classify whether its under 15 minutes or in the 15-30 minutes range. The model predicts the commute time with 89% accuracy. The 2016 dataset points to these two ranges as a classifier for all the electoral areas which is great because students arent soending too much time getting to their school/college. It only speaks more to the efficiency of the transport system of Ireland and also the strategic placements of schools and colleges in the area so that very little time is wasted travelling. There is a lot of scope for improvement to this model by changing the thresholds and even a different sampling method for testing and training the data and test this with other algorithms like Random Forest to see if it gives better accuracy. Further in depth analysis could be done to see if there are any more features to make this model more accurate. This model is built as a binary classifier but it can be improved to work as a multi class classification model to work with future datasets where we might get more classes for prediction."
   ]
  }
 ],
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