Conversion Rate Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conversion Rate Analysis\n",
    "\n",
    "1.\tProportion of users who converted.\n",
    "2.\tMonthly Conversion Rate VS Monthly Average Conversion Rate\n",
    "3.\tMonthly Conversions Volume VS Monthly Average Conversions Volume\n",
    "4.\tMonthly Leads Volume\n",
    "5.\tCorrelation Analysis between \n",
    "    - Conversion Rate and MQL Volume\n",
    "    - Conversion Rate and Conversions Volume\n",
    "7.\tConversions Volume Across Channel\n",
    "8.  Conversion Rate Across Channel\n",
    "9.\tLeads Volume Across Channels\n",
    "10.\tMonthly Conversions Across Channel\n",
    "11.\tWhich channel is the most effective in generating leads and converting users?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set plotting style\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Gathering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load funnel datasets\n",
    "mql=pd.read_csv('olist_marketing_qualified_leads_dataset.csv')\n",
    "closed_deals=pd.read_csv('olist_closed_deals_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Database Structure\n",
    "<img src=\"olist_database_structure.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join funnel datasets\n",
    "\n",
    "The two dataframes that are going to be joint are 'mql' containing data regarding marketing qulified leads that reached out to Olist sales reps and 'closed_deals' containg data about the marketing qualified leads that converted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mql_id</th>\n",
       "      <th>first_contact_date</th>\n",
       "      <th>landing_page_id</th>\n",
       "      <th>origin</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>sdr_id</th>\n",
       "      <th>sr_id</th>\n",
       "      <th>won_date</th>\n",
       "      <th>business_segment</th>\n",
       "      <th>lead_type</th>\n",
       "      <th>lead_behaviour_profile</th>\n",
       "      <th>has_company</th>\n",
       "      <th>has_gtin</th>\n",
       "      <th>average_stock</th>\n",
       "      <th>business_type</th>\n",
       "      <th>declared_product_catalog_size</th>\n",
       "      <th>declared_monthly_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dac32acd4db4c29c230538b72f8dd87d</td>\n",
       "      <td>2018-02-01</td>\n",
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       "      <td>social</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2017-10-20</td>\n",
       "      <td>007f9098284a86ee80ddeb25d53e0af8</td>\n",
       "      <td>paid_search</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2018-03-22</td>\n",
       "      <td>a7982125ff7aa3b2054c6e44f9d28522</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6be030b81c75970747525b843c1ef4f8</td>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>d45d558f0daeecf3cccdffe3c59684aa</td>\n",
       "      <td>email</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5420aad7fec3549a85876ba1c529bd84</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>b48ec5f3b04e9068441002a19df93c6c</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>2c43fb513632d29b3b58df74816f1b06</td>\n",
       "      <td>a8387c01a09e99ce014107505b92388c</td>\n",
       "      <td>4ef15afb4b2723d8f3d81e51ec7afefe</td>\n",
       "      <td>2018-02-26 19:58:54</td>\n",
       "      <td>pet</td>\n",
       "      <td>online_medium</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                             mql_id first_contact_date  \\\n",
       "0  dac32acd4db4c29c230538b72f8dd87d         2018-02-01   \n",
       "1  8c18d1de7f67e60dbd64e3c07d7e9d5d         2017-10-20   \n",
       "2  b4bc852d233dfefc5131f593b538befa         2018-03-22   \n",
       "3  6be030b81c75970747525b843c1ef4f8         2018-01-22   \n",
       "4  5420aad7fec3549a85876ba1c529bd84         2018-02-21   \n",
       "\n",
       "                    landing_page_id          origin  \\\n",
       "0  88740e65d5d6b056e0cda098e1ea6313          social   \n",
       "1  007f9098284a86ee80ddeb25d53e0af8     paid_search   \n",
       "2  a7982125ff7aa3b2054c6e44f9d28522  organic_search   \n",
       "3  d45d558f0daeecf3cccdffe3c59684aa           email   \n",
       "4  b48ec5f3b04e9068441002a19df93c6c  organic_search   \n",
       "\n",
       "                          seller_id                            sdr_id  \\\n",
       "0                               NaN                               NaN   \n",
       "1                               NaN                               NaN   \n",
       "2                               NaN                               NaN   \n",
       "3                               NaN                               NaN   \n",
       "4  2c43fb513632d29b3b58df74816f1b06  a8387c01a09e99ce014107505b92388c   \n",
       "\n",
       "                              sr_id             won_date business_segment  \\\n",
       "0                               NaN                  NaN              NaN   \n",
       "1                               NaN                  NaN              NaN   \n",
       "2                               NaN                  NaN              NaN   \n",
       "3                               NaN                  NaN              NaN   \n",
       "4  4ef15afb4b2723d8f3d81e51ec7afefe  2018-02-26 19:58:54              pet   \n",
       "\n",
       "       lead_type lead_behaviour_profile has_company has_gtin average_stock  \\\n",
       "0            NaN                    NaN         NaN      NaN           NaN   \n",
       "1            NaN                    NaN         NaN      NaN           NaN   \n",
       "2            NaN                    NaN         NaN      NaN           NaN   \n",
       "3            NaN                    NaN         NaN      NaN           NaN   \n",
       "4  online_medium                    cat         NaN      NaN           NaN   \n",
       "\n",
       "  business_type  declared_product_catalog_size  declared_monthly_revenue  \n",
       "0           NaN                            NaN                       NaN  \n",
       "1           NaN                            NaN                       NaN  \n",
       "2           NaN                            NaN                       NaN  \n",
       "3           NaN                            NaN                       NaN  \n",
       "4      reseller                            NaN                       0.0  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#joining funnel datasets\n",
    "funnel = mql.merge(closed_deals, on='mql_id', how='left')\n",
    "#check results\n",
    "funnel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create variable 'converted'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
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       "      <th>mql_id</th>\n",
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       "      <td>2017-10-20</td>\n",
       "      <td>007f9098284a86ee80ddeb25d53e0af8</td>\n",
       "      <td>paid_search</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>a7982125ff7aa3b2054c6e44f9d28522</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6be030b81c75970747525b843c1ef4f8</td>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>d45d558f0daeecf3cccdffe3c59684aa</td>\n",
       "      <td>email</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>No</td>\n",
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       "      <th>4</th>\n",
       "      <td>5420aad7fec3549a85876ba1c529bd84</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>b48ec5f3b04e9068441002a19df93c6c</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>2c43fb513632d29b3b58df74816f1b06</td>\n",
       "      <td>a8387c01a09e99ce014107505b92388c</td>\n",
       "      <td>4ef15afb4b2723d8f3d81e51ec7afefe</td>\n",
       "      <td>2018-02-26 19:58:54</td>\n",
       "      <td>pet</td>\n",
       "      <td>online_medium</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reseller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
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      "text/plain": [
       "                             mql_id first_contact_date  \\\n",
       "0  dac32acd4db4c29c230538b72f8dd87d         2018-02-01   \n",
       "1  8c18d1de7f67e60dbd64e3c07d7e9d5d         2017-10-20   \n",
       "2  b4bc852d233dfefc5131f593b538befa         2018-03-22   \n",
       "3  6be030b81c75970747525b843c1ef4f8         2018-01-22   \n",
       "4  5420aad7fec3549a85876ba1c529bd84         2018-02-21   \n",
       "\n",
       "                    landing_page_id          origin  \\\n",
       "0  88740e65d5d6b056e0cda098e1ea6313          social   \n",
       "1  007f9098284a86ee80ddeb25d53e0af8     paid_search   \n",
       "2  a7982125ff7aa3b2054c6e44f9d28522  organic_search   \n",
       "3  d45d558f0daeecf3cccdffe3c59684aa           email   \n",
       "4  b48ec5f3b04e9068441002a19df93c6c  organic_search   \n",
       "\n",
       "                          seller_id                            sdr_id  \\\n",
       "0                               NaN                               NaN   \n",
       "1                               NaN                               NaN   \n",
       "2                               NaN                               NaN   \n",
       "3                               NaN                               NaN   \n",
       "4  2c43fb513632d29b3b58df74816f1b06  a8387c01a09e99ce014107505b92388c   \n",
       "\n",
       "                              sr_id             won_date business_segment  \\\n",
       "0                               NaN                  NaN              NaN   \n",
       "1                               NaN                  NaN              NaN   \n",
       "2                               NaN                  NaN              NaN   \n",
       "3                               NaN                  NaN              NaN   \n",
       "4  4ef15afb4b2723d8f3d81e51ec7afefe  2018-02-26 19:58:54              pet   \n",
       "\n",
       "       lead_type lead_behaviour_profile has_company has_gtin average_stock  \\\n",
       "0            NaN                    NaN         NaN      NaN           NaN   \n",
       "1            NaN                    NaN         NaN      NaN           NaN   \n",
       "2            NaN                    NaN         NaN      NaN           NaN   \n",
       "3            NaN                    NaN         NaN      NaN           NaN   \n",
       "4  online_medium                    cat         NaN      NaN           NaN   \n",
       "\n",
       "  business_type  declared_product_catalog_size  declared_monthly_revenue  \\\n",
       "0           NaN                            NaN                       NaN   \n",
       "1           NaN                            NaN                       NaN   \n",
       "2           NaN                            NaN                       NaN   \n",
       "3           NaN                            NaN                       NaN   \n",
       "4      reseller                            NaN                       0.0   \n",
       "\n",
       "  converted  \n",
       "0        No  \n",
       "1        No  \n",
       "2        No  \n",
       "3        No  \n",
       "4       Yes  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating variable 'converted' to establish which leads converted\n",
    "funnel['converted']=funnel['seller_id'].apply(lambda x: 'No' if pd.isnull(x) else 'Yes')\n",
    "#check results\n",
    "funnel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mql_id</th>\n",
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       "      <td>2018-02-01</td>\n",
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       "      <td>social</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8c18d1de7f67e60dbd64e3c07d7e9d5d</td>\n",
       "      <td>2017-10-20</td>\n",
       "      <td>007f9098284a86ee80ddeb25d53e0af8</td>\n",
       "      <td>paid_search</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b4bc852d233dfefc5131f593b538befa</td>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>a7982125ff7aa3b2054c6e44f9d28522</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6be030b81c75970747525b843c1ef4f8</td>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>d45d558f0daeecf3cccdffe3c59684aa</td>\n",
       "      <td>email</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5420aad7fec3549a85876ba1c529bd84</td>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>b48ec5f3b04e9068441002a19df93c6c</td>\n",
       "      <td>organic_search</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mql_id first_contact_date  \\\n",
       "0  dac32acd4db4c29c230538b72f8dd87d         2018-02-01   \n",
       "1  8c18d1de7f67e60dbd64e3c07d7e9d5d         2017-10-20   \n",
       "2  b4bc852d233dfefc5131f593b538befa         2018-03-22   \n",
       "3  6be030b81c75970747525b843c1ef4f8         2018-01-22   \n",
       "4  5420aad7fec3549a85876ba1c529bd84         2018-02-21   \n",
       "\n",
       "                    landing_page_id          origin converted  \n",
       "0  88740e65d5d6b056e0cda098e1ea6313          social        No  \n",
       "1  007f9098284a86ee80ddeb25d53e0af8     paid_search        No  \n",
       "2  a7982125ff7aa3b2054c6e44f9d28522  organic_search        No  \n",
       "3  d45d558f0daeecf3cccdffe3c59684aa           email        No  \n",
       "4  b48ec5f3b04e9068441002a19df93c6c  organic_search       Yes  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting columns\n",
    "funnel=funnel[['mql_id','first_contact_date','landing_page_id','origin','converted']]\n",
    "#check results\n",
    "funnel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Assessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8000 entries, 0 to 7999\n",
      "Data columns (total 5 columns):\n",
      "mql_id                8000 non-null object\n",
      "first_contact_date    8000 non-null object\n",
      "landing_page_id       8000 non-null object\n",
      "origin                7940 non-null object\n",
      "converted             8000 non-null object\n",
      "dtypes: object(5)\n",
      "memory usage: 375.0+ KB\n"
     ]
    }
   ],
   "source": [
    "funnel.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check duplicates\n",
    "funnel.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicate observations in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning Issues:\n",
    "\n",
    " 1. **first_contact_date** is an object and not datetime\n",
    " \n",
    " 2. **origin** has 60 missing values: change NaN to 'unknown'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Convert 'first_contact_date' to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8000 entries, 0 to 7999\n",
      "Data columns (total 5 columns):\n",
      "mql_id                8000 non-null object\n",
      "first_contact_date    8000 non-null datetime64[ns]\n",
      "landing_page_id       8000 non-null object\n",
      "origin                7940 non-null object\n",
      "converted             8000 non-null object\n",
      "dtypes: datetime64[ns](1), object(4)\n",
      "memory usage: 375.0+ KB\n"
     ]
    }
   ],
   "source": [
    "funnel['first_contact_date']=pd.to_datetime(funnel['first_contact_date'])\n",
    "#check results\n",
    "funnel.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.Change NaN with 'unknown' in 'origin'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8000 entries, 0 to 7999\n",
      "Data columns (total 5 columns):\n",
      "mql_id                8000 non-null object\n",
      "first_contact_date    8000 non-null datetime64[ns]\n",
      "landing_page_id       8000 non-null object\n",
      "origin                8000 non-null object\n",
      "converted             8000 non-null object\n",
      "dtypes: datetime64[ns](1), object(4)\n",
      "memory usage: 375.0+ KB\n"
     ]
    }
   ],
   "source": [
    "funnel['origin'].fillna('unknown', inplace=True)\n",
    "#check results\n",
    "funnel.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort values by 'first_contact_date'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>mql_id</th>\n",
       "      <th>first_contact_date</th>\n",
       "      <th>landing_page_id</th>\n",
       "      <th>origin</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>0b99dab71519032b917dc641cdd7ac5b</td>\n",
       "      <td>2017-06-14</td>\n",
       "      <td>d41d8cd98f00b204e9800998ecf8427e</td>\n",
       "      <td>unknown</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5232</th>\n",
       "      <td>6dd0ef38856fd5fb21aed5223aea9e66</td>\n",
       "      <td>2017-06-14</td>\n",
       "      <td>8f08d2ef8b9aec2ec5118fbcf7f96f11</td>\n",
       "      <td>display</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5358</th>\n",
       "      <td>70bd89abdbdba90e471cd3d0bbdbac49</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>8f08d2ef8b9aec2ec5118fbcf7f96f11</td>\n",
       "      <td>unknown</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2676</th>\n",
       "      <td>98c9832865e4ab224a1649e8e6e9a2a0</td>\n",
       "      <td>2017-06-20</td>\n",
       "      <td>b6cd08ac77c501fd6857f7916d2c24f3</td>\n",
       "      <td>email</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2976</th>\n",
       "      <td>e1e32379b464961f67fba6ddf6d4b3b7</td>\n",
       "      <td>2017-07-02</td>\n",
       "      <td>0b37d43dbde6452cdd94c17bf61a88e4</td>\n",
       "      <td>direct_traffic</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                mql_id first_contact_date  \\\n",
       "1973  0b99dab71519032b917dc641cdd7ac5b         2017-06-14   \n",
       "5232  6dd0ef38856fd5fb21aed5223aea9e66         2017-06-14   \n",
       "5358  70bd89abdbdba90e471cd3d0bbdbac49         2017-06-16   \n",
       "2676  98c9832865e4ab224a1649e8e6e9a2a0         2017-06-20   \n",
       "2976  e1e32379b464961f67fba6ddf6d4b3b7         2017-07-02   \n",
       "\n",
       "                       landing_page_id          origin converted  \n",
       "1973  d41d8cd98f00b204e9800998ecf8427e         unknown        No  \n",
       "5232  8f08d2ef8b9aec2ec5118fbcf7f96f11         display        No  \n",
       "5358  8f08d2ef8b9aec2ec5118fbcf7f96f11         unknown        No  \n",
       "2676  b6cd08ac77c501fd6857f7916d2c24f3           email        No  \n",
       "2976  0b37d43dbde6452cdd94c17bf61a88e4  direct_traffic        No  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "funnel.sort_values('first_contact_date', ascending=True, inplace=True)\n",
    "#check results\n",
    "funnel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions:\n",
    "\n",
    "- **stat_summary**: returns a DataFrame with summary statistics and statistical moments of a Series.\n",
    "- **plot_distribution**: plots a distribution of a Series (hist, kde, quartiles, mean)\n",
    "- **plot_distribution_subplot**: as above but includes additional argument 'axis' to allow using the function in subplots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define stat_summary\n",
    "def stat_summary(data):\n",
    "\n",
    "    values=[data.count(),\n",
    "            data.min(),\n",
    "            data.max(),\n",
    "            data.mean(),\n",
    "            data.median(),\n",
    "            data.quantile(0.25),\n",
    "            data.quantile(0.75),\n",
    "            data.var(),\n",
    "            data.kurtosis(),\n",
    "            data.skew()]\n",
    "    index=['count','min','max','mean','median','1st_quartile','3rd_quartile','variance','kurtosis','skewness']    \n",
    "    stat_summary=pd.DataFrame(data=values, index=index, columns=['Value'])\n",
    "    \n",
    "    return stat_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define _plot_distribution\n",
    "def plot_distribution(data, title, axlabel):\n",
    "    \n",
    "    sns.distplot(data, axlabel=axlabel)\n",
    "    plt.axvline(data.mean(), c='green', ymax=0.95)\n",
    "    plt.axvline(data.median(), c='orange', ymax=0.95)\n",
    "    plt.axvline(data.describe()[4], c='yellow', ymax=0.95)\n",
    "    plt.axvline(data.describe()[6], c='red', ymax=0.95)\n",
    "    plt.title(title)\n",
    "    plt.legend(labels=['mean', 'median', '1st quartile', '3rd quartile'])\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define plot_distribution_subplot\n",
    "def plot_distribution_subplot(data, title, axlabel, ax):\n",
    "    plt.figure(figsize=(12,6))\n",
    "    sns.distplot(data, axlabel=axlabel,ax=ax)\n",
    "    ax.axvline(data.mean(), c='green', ymax=0.95)\n",
    "    ax.axvline(data.median(), c='orange', ymax=0.95)\n",
    "    ax.axvline(data.describe()[4], c='yellow', ymax=0.95)\n",
    "    ax.axvline(data.describe()[6], c='red', ymax=0.95)\n",
    "    ax.set_title(title)\n",
    "    ax.legend(labels=['mean', 'median', '1st quartile', '3rd quartile'])\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis - Analysis of conversions over time to identify what channel could drive conversions most"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Proportion of users who converted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     0.89475\n",
       "Yes    0.10525\n",
       "Name: converted, dtype: float64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "funnel['converted'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **10.5% of the users converted** over the period under examination."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Monthly Conversion Rate VS Monthly Average Conversion Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define funnel_ts in order to resample data (datetime index is needed)\n",
    "funnel_ts=funnel.set_index('first_contact_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.494163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.989626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>4.772472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1st_quartile</th>\n",
       "      <td>2.309602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3rd_quartile</th>\n",
       "      <td>13.375111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>32.317573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>-1.847776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skewness</th>\n",
       "      <td>0.299031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Value\n",
       "count         12.000000\n",
       "min            0.000000\n",
       "max           14.494163\n",
       "mean           6.989626\n",
       "median         4.772472\n",
       "1st_quartile   2.309602\n",
       "3rd_quartile  13.375111\n",
       "variance      32.317573\n",
       "kurtosis      -1.847776\n",
       "skewness       0.299031"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get montlhy conversion rate\n",
    "monthly_conv_rate=(funnel_ts.resample('M')['converted'].value_counts(normalize=True).unstack().fillna(0))*100\n",
    "stat_summary(monthly_conv_rate.Yes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution(monthly_conv_rate.Yes, 'Distribution of conversions rate', 'Conversion Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **monthly average conversion rate** is 6.98%, the **median** is 4.77%. The median is smaller than the mean, that suggests that the distribution is skewed to the left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1092.2486569455102"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get overall percent change\n",
    "((monthly_conv_rate.loc['2018-05-31','Yes']-monthly_conv_rate.loc['2017-07-31','Yes'])/monthly_conv_rate.loc['2017-07-31','Yes'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2a7dc930eb8>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot conversions rate\n",
    "monthly_conv_rate.Yes.plot(figsize=(12,6), title=('Monthly Conversion Rata VS Average Conversion Rate')).set_ylabel('Conversion Rate')\n",
    "plt.axhline((monthly_conv_rate.Yes.mean()), color='orange', xmin=0.02, xmax=0.98)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The conversion rate consistently grew until February 2018, the overall percent change of the conversion rate is 1092.24% during the period under observation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Monthly Conversions Volume VS Monthly Average Conversions Volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     12.000000\n",
       "mean      70.166667\n",
       "std       77.034624\n",
       "min        0.000000\n",
       "25%        8.500000\n",
       "50%       16.000000\n",
       "75%      149.750000\n",
       "max      183.000000\n",
       "Name: Yes, dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get montlhy conversion rate\n",
    "monthly_conv_vol=funnel_ts.resample('M')['converted'].value_counts().unstack().fillna(0)\n",
    "#check results\n",
    "monthly_conv_vol.Yes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution(monthly_conv_vol.Yes, 'Distribution of conversions volume', 'Conversions Volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2a7dc9f4048>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot conversions volums\n",
    "monthly_conv_vol.Yes.plot(figsize=(12,6), title=('Monthly Conversions Volume VS Average Conversions Volume')).set_ylabel('Conversion Volume')\n",
    "plt.axhline(y=monthly_conv_vol.Yes.mean(), color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Monthly Leads Volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1352.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>666.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>430.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1st_quartile</th>\n",
       "      <td>293.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3rd_quartile</th>\n",
       "      <td>1149.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>240352.606061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>-1.795064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skewness</th>\n",
       "      <td>0.277575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Value\n",
       "count             12.000000\n",
       "min                4.000000\n",
       "max             1352.000000\n",
       "mean             666.666667\n",
       "median           430.500000\n",
       "1st_quartile     293.750000\n",
       "3rd_quartile    1149.250000\n",
       "variance      240352.606061\n",
       "kurtosis          -1.795064\n",
       "skewness           0.277575"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get monthly conversions volume\n",
    "monthly_leads_vol=funnel_ts.resample('M')['mql_id'].count().rename('leads_vol').to_frame()\n",
    "#check results\n",
    "stat_summary(monthly_leads_vol.leads_vol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot distribution\n",
    "plot_distribution(monthly_leads_vol.leads_vol,'Distribution of monthly leads volume', 'Leads Volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2a7dde4af60>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot leads volume\n",
    "monthly_leads_vol.plot(figsize=(12,6), title=('Monthly Leads Volume VS Average Leads Volume')).set_ylabel('Leads Volume')\n",
    "plt.axhline(y=monthly_leads_vol.leads_vol.mean(), color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution and Evolution of Conversion Rate, Converions Volume and Leads Volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Leads Volume')"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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31OsxreHDscQtuOD2LVs2sWvXTqxWK0VFhYwaNZavv97B8eMZTJ78f9hsdlateh+1Wk1CQk8mTZpKYWEh8+bNRFEUgoKCXccaOfIe3n9/LTk5p1i69GWcTgWzuYInn3yK+PgejBkzgvj4HuTmZmMy+bNgwRI0GlmhqakYOnQo2dnZrtthYWEA7Nmzh5UrV/L+++/X2d/Ly4vHHnuMUaNGceLECSZMmMBnn32GVnvhU83AQB+0WvmZEEK0PJk5Zezcd5q24SZG3dEZrabuGJPQUJObkommJDTUxCvTglm6OoWdKTnMf+8n/vrwdXRrH3zpBzcDUswSQgghWpDKykpefnkZW7d+zqpVH/DGG++yd28yq1a9T05ONm+9lYSXlxfz5z/L7t3f88MP3zN48FDuvXcE27Z9wfr1a+sc7/jxTKZMmUaHDh354ovP2LJlE/HxPTh9OodXX11O9+6xjBw5isOHD9G9e7ybnrWoD1u2bGH58uW88cYbBAUF1dkWExNDdHQ0KpWKmJgYAgICKCgoICIi4oLHKympbOjIQgjhcRRFYdmaFBQFRt/WgZJiS53toaEmCgpa9sTe4sr8bmgcrYN9WP3lMZ5ZvovRAzsyuG9ks1lQ4ELFXSlmCSGEEG5giVtw0VFUDSU2thMARqOJdu1iUKlUmEwmqqqqKC0t4amnngBqi145OTkcP57J0KF3AxAf3+OcYlZISBjvvvsWBoOByspKfH19AfD3DyA8vBUAYWHh1NS0rKHvzc0nn3zCqlWrSEpKIiAg4Jzta9eu5ejRo8yZM4f8/HzMZjOhoaFuSCqEEJ7tpyMFHD1VSq/YELrFBF36AUJcgkql4o7r2hIdbmT5hlQ+3JbO8dxyfndnZwz65jsCWubMEkIIIVqQC31Kp1KpCAsL55VXXuOf/3yDkSMfoFu37kRHR3Pw4H4ADh8+dM7jXn31BR577PfMnDmXDh06oijKRdsRTY/D4WDhwoVYLBamTp1KYmIi//jHPwB4+umnOX36NCNHjqSiooKxY8cybdo0Fi1adNFLDIUQoiWqsTlY/WU6Wo2K0bd3dHcc0cx0igpk9qPX06G1H98fymdh0k/kN+NR0HKWIYQQQgg0Gi0PPPAgU6ZMxOFwEBHRmttvH8Ljj09i9uwZbN36Ba1btznncXfccRd//eufCQoKIjQ0jLKyUjekFw0hMjKS1atXA/Djjz+ed58lS5a4vn/ppZcaJZcQQjRVn/14kqJyK3fdGEV4oI+744hmKNBkYPqDvflwWzpf7clh3rs/MfGervToGOLuaPVOpfz8EWoT4mnXEMt1zbVaej+oVJCSWUxJWVWjtnv3LYMA2PLNtkZt19V+0dn2g2vbNxm9qDBXN2ibk5Nr21zWZxtarZr+8RF40m+ypvRaCOrTHYDi5NR6P3ZT6oeGIn3gvj6QyXM9X0t/bQghWpbi8mr+9sb3eBu0LJp4I96G848rkXMHUV92Hcjlvc+PYLM7ubdfO+69JQZ1Exw5f6FzOrnMUAghhBBCCCGEaEBrtmdQY3dy/60dLljIEqI+9YuP4G8P9SHE34uNu07wj7X7sVTb3B2r3kgxSwghhBBCCCGEaCBHT5Xyw6F82rUycXN8K3fHES1IdCsTsx65ju4xQezPKGLeu7s5dcbs7lj1QopZQgghhBBCCCFEA3AqCh9uTQdg3JC4JnmZl2jajN46nhzVg9/eHE1BaTUL3/uJ7w7muTvWNZNilhBCCCGEEEII0QB27c8lK7+CG7uF07GNv7vjiBZKrVZx34AOTL0vHo1GxZubDvHBf49idzjdHe2qSTFLCCGEEEIIIYSoZ1VWO+t2ZKDXqRl5awd3xxGCXnGhPPu762gT4svW5Gxe+HAvpWaru2NdFSlmCSGEEEI0U5s2beLll1+mqqqKDRs2uDuOEEK0KJu+PUF5pY3f3NSOID8vd8cRAoBWQT4883AfruscRnp2GXNX7CY9u9Tdsa6YFLOEEEKIFubgwVSmTJl40X3y8vL45pudDZ4lI+MYKSl7AJg9ewY2m42FC+fw/fffNnjbzd2LL77Ijh07+OKLL3A4HKxbt47nnnvO3bGEEKJFyC+u5L+7TxHi78XQ69q6O44QdXjptfxhWDceuL0jFZU2lnywl60/nUJRFHdHu2xSzBJCCCFakPff/zfPPz+fmpqai+63Z89uDhzY1+B5tm/fxokTmQDMnbsYnU7X4G22FN988w0vvPACBoMBo9HIihUr2Lmz4QuUQggh4KNt6TicCqMHdkSv07g7jhDnUKlUDL0+iqfG9MTHS8sHW9N569NDWG0Od0e7LFp3BxBCCCFaIl/fmRgM9XvZl9U6HItlwUX3adMmkoULX2D+/Fmu+z7+eA3/+c+nqNVqEhJ68oc/TGHlyneprq4mPj6BW2651bXv2rUf8emnGwkKCkalUvHggw+Tl5dLVtYJJk2aitVq5cEHR7J27Sb27k1m5cp3sNkcVFdXM3PmXHQ6HdOnT8PPz5/evfvyn/98ilarIy6uM7NmzeD999e62rLb7bzwwiKys0/hdDqZMGESvXv3rdc+a87U6trPLFVnV86qqalx3SeEEKLhHMgsYl9GEZ2jAujTKdTdcYS4qM7Rgcx+5Dpe25DKdwfzyS6wMPm+eMICvN0d7aLqtZjldDqZM2cOR44cQa/Xs2DBAqKjo13bV69ezUcffYRWq2XSpEkMHDiQyspK5syZQ3Z2NjabjWeffZaEhIT6jCWEEEKIs267bRC5uafr3LdlyyaefPIvdO8ez/r1a1EUhYceeoSsrBN1CllFRYWsXbuK995bBcDjjydetK3jxzN54YUXUKt9eO+9d/jqq63cccddFBcX8fbbK9HpdCiKQnBwMF27dj/n8Zs2bcDfP4AZM2ZRVlbK5MkTWblydT30Qstw55138uSTT1JWVsa7777Lxo0b+c1vfuPuWEII0azZHU4+2paOSgVjBsW6PlAQwpMF+XkxfVxvPtx6lO0pp5m3YjcT7+1GQodgd0e7oHotZm3dupWamhpWrVpFSkoKzz33HMuXLwegoKCApKQk1q1bh9VqZdy4cfTr14+3336b2NhYlixZQlpaGmlpaVLMEkII0exZLAsuOYqqsfztb7P48MOV/OtfS+nWLf6C++Xl5RET0wG9Xg9A9+7n+3v9y1wLoaGhLFy4ELVaR0HBGeLjewAQEdH6si4nzMg4xv79ezl0KBUAh8NOWVkp/v4BV/DsWq6JEyfy9ddf07p1a3Jzc5k6dSoDBw50dywhhGjWvtqTQ25RJbf1akNUuMndcYS4bDqtmofv7ExMaz+SPj/Kq2v2MeyWGH7brx1qDyzK1msxKzk5mf79+wPQs2dPUlNTXdv2799Pr1690Ov16PV6oqKiSEtL45tvvuGuu+7isccew9fXl9mzZ9dnJCGEEEJcwsaNG3jqqRkYDAb+9KcpHDiwD5VKhaI46+zXtm1bTp48QXV1NTqdjvT0IwwePBS9Xk9RUSEAR46kufZ//vkFbNu2jaoqhQULfvn7rlL9cqmbWq3G6Tz/ZKPR0e0ICwvj4YfHY7VW8+9/v4PJ5FefT71Zmz9/Ps8++6zr3Axg+vTpPP/8825MJYQQzVd5ZQ0bvjmOj0HL8P4x7o4jxFXpn9CatmFGln2cyoZvjnM8t5wJ93TFx8uz5jWt12KW2WzGaDS6bms0Gux2O1qtFrPZjMn0S2Xa19cXs9lMSUkJ5eXlvP3222zYsIHnn3+eJUuWXLSdwEAftFrPmkQvNFSq7iD9QGYxJmPjLrv7c5W8sdt1tV9ybvsNnUX1q+es1agICfG8n7sm81pQ1/ZlQ+VtMv3QgDyxD6xWX3Q6jStbz57dmTTpUQIDA2nVKpxbb72JjIwM3n//Xfr27eW6NC001MQTT0zlySf/QEBAAIriJCDAh5tu6sPmzRt44omJdOvWDT8/E6GhJoYPH87o0aPx8/MjJCQEs7mUoKC6bV9/fW+WLFlCjx5d0WjUhIaa8PLS4e/vzdChv2PmzJlMmzYJs9nMuHHjCA/3d1OvNR3PPPMMp06dIjU1lfT0dNf9DoeD8vJyNyYTQojmbcPXx6my2hk7KBY/H7274whx1dq18mPWI315Y+NB9mUUMe/fPzFlRDyRYcZLP7iRqJR6XHtx8eLF9OjRg7vvvhuAAQMGuFbN2bZtG19//TVz5swBYPLkyfzhD39g/vz5zJo1i+7du1NWVsbYsWPZsmXLRdspKKior8j1IjTU5HGZ3KGl94NKBSmZxZSUVTVqu3ffMgiALd9sa9R2Xe0XnW0/uLZ9k9GLCnN1g7Y5Obm2zWV9tqHVqukfH4EnrSLblF4LQX1q5ykqTk69xJ5Xrin1Q0Np7n0we/YMhg27/6KTsrurDzyxiNhYsrOzycnJYeHChcycOdN1v0ajoUOHDgQEeMZlms35tSGEaHlO5lcw993dtAryYe7469Fqrm7BjeZ+7iCaFqdTYf3XmWz+Lgu9Ts0jd3Xmxq6tGjXDhc7p6nVkVu/evfnqq6+4++67SUlJIS4uzrUtISGBV155BavVSk1NDRkZGcTFxdGnTx927NhB9+7d2b17Nx07dqzPSEK0CIpS+we0qNyKpcpGZbUdS7XtnGVVtRo1PgYtvl5afLx0BBj1BPt7EWA0oFZ73nXQQgghrlxkZCSRkZFs3LiR0tJSqqqqUBQFh8PB4cOHuemmm9wdUQghmhVFUfhoWzqKAmMHxV51IUsIT6NWq7j/1g60a+XH25sP8cbGQxw/XcGogR3c/nNer8WsIUOGsGvXLsaMGYOiKCxatIgVK1YQFRXFoEGDSExMZNy4cSiKwrRp0zAYDPz+979n5syZPPDAA2i1WpnHQYjL4HAq5BZZOJln5ra+dpyKwva9dVcn89Jr8DHUfYnb7E6KyqspLKt7PI1aRZCfF21CfIgMMxJoMsjKK0KIS5o7d7G7I4iLWLp0Ke+++y52u52AgADOnDlD9+7dWbNmzWU9ft++fbz44oskJSWRlZXFX//6V1QqFbGxscyePRu1+peT2Orqav7yl79QVFSEr68vzz//PEFBQQ311IQQwqMkHykg7WQpPTuG0L29567+JsTV6tMplNYhfVm2PpX//nSKrPwKJg3rhr/R4LZM9VrMUqvVzJs3r859HTp0cH0/evRoRo8eXWd7QEAA//znP+szhhDNkqIonCmpIuN0OSfzK6ixOc/eDxq1mp4dgwkJ8Mbko8PHoEVzgUq5oihU1ziwVNkorrBSWFZNUVk1haVVFJRWkXKsCB8vLW3DjHRo40+wn+cXtjwxnidmupirzetJl3cKIepav349O3bsYOHChUyaNInMzEw++OCDy3rsm2++ycaNG/H29gZqp5J48sknueGGG5g1axbbtm1jyJAhrv0//PBD4uLimDp1Kps3b+a1116rc4mjEEI0VzU2B6u+PIZGreKB2+UqI9F8RQT78kxiH1ZsOcxPRwqY++5u/jgino5t3DOfab0Ws4QQ9c/pVMjKr+DQiRKKymrnovI2aOkS7U+7ViZ8vWtfxgkdQy7reCqVCm+DFm+DlpAAb+La1t5fY3OQU2gh+4yZnEILR06WcuRkKYEmAx3b+BPT2g8vvWctvAC1l05+fzCfGofz0js3EpNvMRUWq7tjXJY7amovRf36QO4VP1anUXNj13ApaAnhocLCwjAajcTGxpKWlsYdd9zBSy+9dFmPjYqKYunSpTz99NMAHDx4kOuvvx6onRN1165ddYpZycnJPP74467tr732Wj0/GyGE8Eyf/3iSovJq7rwhivAgH3fHEaJBeRu0TBrenc9/PMWa7cd4/v09TB4RT8/Yy3svWp+kmCWEh3IqChk5ZRzIKMZcZQMgKtxI56hAwoO86320lF6nISbCj5gIP5xOhdNFFo5ll3HqjJndaWfYc7SAjpH+dG0XiMnDVmexOZzY7Z5TzLI7FI/Kc3G1laimk1cIcbmMRiMbNmygW7durFy5krCwMKqrL2+BjqFDh5Kdne26rSiK6++Or68vFRV1Jyf+9arV59t+Pp64OrUQQlyJwtIqtvxwkgCjgUfv7Y6Pl65ejtuSFzERTUPib7vRo3MYr65KocrhdMvPrBSzhPBApwumxN7+AAAgAElEQVQtJB8poKTCikatolNUAF2iA/HzbZwiklqtIjLUSGSokeoaOxk55aRllbhGa0WFG4lvH0ywv1ej5BFCCHHlFi5cyObNmxk+fDhfffUVs2bNYtq0aVd1rF/Pj2WxWPDz86uz3Wg0YrFYLrj9fEpKKq8qixBCeIo3Nh3EWuNg3KBYLBXVWCqufUVvWc1QNBUR/l48N/FGoGFXKL5QoUyWWRDCg5grbXyZnM3Wn7IpqbDSoY0fIwa054au4Y1WyPpfXnot3WKCGDGgPf17RBDkZ+BkvpnN32WxfW8OTqdcYyZEU+FwOFi0aC6TJo1n8uQJ5ORkX3R/q9XKyJH3NHiujIxjpKTsAWD27BnYbDYWLpzD999/2+BtN2fh4eGMHz8egL/+9a9s3LgRp/PqRmF27dqVH374AYCdO3fSt2/fOtt79+7Njh07XNv79OlzDcmFEMLzHcsu4/uD+US3MtEvIcLdcYRocaSYJYQHUBSFIydL2LjrONkFFsKDvPnNTdH0i4/Ax8szBlCq1SpiIvz4zU3RDO4bSYi/FyfzzVRa7VTXODBX2twdUQhxCbt2fQ3A8uXv8Nhjv2fp0r+7OVGt7du3ceJEJlC7QqJOVz+XabRUW7dupV+/fvzmN78hKysLqF2ZcPTo0SxatOiqjjl9+nSWLl3KAw88gM1mY+jQoQCMHz+empoaxo4dS3p6OmPHjmXVqlVMmTKl3p6PEEJ4Gqei8MHWowCMGxyLuqmt/CNEM+AZ75KFaMHMlTa+Tc0jr7gSvVZNv/hw2rf289gVBFUqFa1DfIkI9iGnwILarsLucLLhm+N0iwnipnj5ZEqIy+E7ZyaGTRvq9ZjWe4ZjmbPggtsHDLiNm2++BYD8/DwCA2uXD58yZSIBAYFUVFSwcOESFiyYRUVFBW3aRJ73OGvXfsSnn24kKCgYlUrFgw8+TF5eLllZJ5g0aSpWq5UHHxzJ2rWb+PHHH3n55VcBqK6uZubMueh0OqZPn4afnz+9e/flP//5FK1WR1xcZ2bNmsH77691tWW323nhhUVkZ5/C6XQyYcIkevfue95cotYLL7zA3LlzOX36NMuXL6ddu3a8/vrrPPTQQ7zzzjuXfZzIyEhWr14NQExMDCtXrjxnn18f7x//+Me1hxdCiCbg2wN5nMir4Iau4cRGBrg7jhAtkhSzhHCjE3kVfHcgD5vDSWSoLzd2a+UxI7EuRaVSERlmxKdIi92h4KXTcCCjiMzT5fSKDSEmwuSxBTkhWjKtVsuCBbPZuXM7CxY877p/yJA7ufXWgaxbt4qYmA78/veTOXgwlT17fqrz+KKiQtauXcV7760C4PHHEy/aXnp6OrNmzSckJJT33nuHr77ayh133EVxcRFvv70SnU6HoigEBwfTtWv3cx6/adMG/P0DmDFjFmVlpUyePJGVK1fXQ080X3q9nsGDBwNwyy23kJ2dzaZNm4iMPH9xUgghxOWrstpZuyMDvU7NqNs6uDuOEC1W03jXLEQz43AqJB85Q1pWKVqNin7xrTx6NNalaDUqhvWPITWziEMnSvhmfy5HTpZyfZcwmSReiAuwzFlw0VFUDWnmzLkUFRUyceIjrFy5BoCoqGgAjh/P5IYbbgKgW7fuaLV1TxXy8vKIiemAXl87j1/37gnnaeGXufTCw8N55ZUX8Pb2oaDgDPHxPQCIiGh9WZcTZmQcY//+vRw6lAqAw2GnrKwUf3/5JPxCNJpfVgj08vLi9ddfx9fX142JhBCi+fj02xOUW2oY3j+GID85zxXCXaSYJUQjM1fZ2JlymsKyavyNem7t2ZoAo8Hdsa6ZTqumV1woPTuFsWNPtmuS+I6R/vSKDcHbIL9uhHC3zz7bTEHBGRITH8XLywu1Wu1ape7nr1FR7UhNPUD//rdx9Ggadru9zjHatm3LyZMnqK6uRqfTkZ5+hMGDh6LX6ykqKgTgyJE01/4zZ85k1aoN+Pj4smDBbNf9KtUv03aq1eoLLiYRHd2OsLAwHn54PFZrNf/+9zuYTJdeKa8l+/UHIyaTSQpZQghRT/KLK/li9ymC/QzceX2Uu+MI0aLJu0shGlFBaRVf7cmhusZBTISJG7u1QqdtXusw+PkauK1XG3KLLOw+fIZj2WVk5VXQKy6EuLYBMkGmEG506623s2jRXCZPnoDdbueJJ/6EwVC3mH7ffaNYvHgukyY9RnR0u3NGT/n5+fPII48zZcpE/Pz8sNlqi1033HAzGzasY9Kkx+jUqYurgDJs2DAmTnwEk8lEYGAwhYUF5+Tq1KkLr732Ku3axZyzbdiw+3j++QVMmTIRi8XMiBGjXIU3cX6nT59mxowZ53z/s8WLF7sjlhBCNHmrvjyGw6kw+vZY9DrNpR8ghGgwKkVRzv9RqAcrKKhwd4Q6QkNNHpfJHVp6P6hUkJJZTElZ1Xm3n8irYNf+XJxOheu6hNEpKqBeLiu8+5ZBAGz5Zts1H+uq2i86235wbfsmoxcV5moAnE6Fo6dK2ZteiM3uJNjPixu6hRNyjZceTk6ubXNZn214GbQ4HE5s9qtbbr4h/LoPPN3diWf//5Ku/OdHq1XTPz6CC/0Vaem/E6Bl9MHs2TMYNuz+C07K7q4+CA01NXqbnmL9+vUX3T5ixIhGSnJxzf21IYRoXlKPF/H3VfuIaxvA9HG9Gmx6kJZw7iDElbjQOZ2MzBKigSmKQmpmMXvTC9Fp1NzWpw1tQlvGJR9qtYrO0YFEtzKRfKSAzNPlbPkui05RAfSKDZFPtIQQogF4SrFKCCGaC7vDyUfbjqECxg2ObbLz3ArRnEgxS4gG5FQUfjiYT3p2Gb5eWm7vE0mgqenPj3WlvA1abkmIoGMbf344lM+Rk6Vk5VXQt3OYrHooRBM3d65csiaEEKJ52743h9OFFm7t2Zqo8JY78lcITyKTTgjRQBxOhW/25ZKeXUaQn4G7b4pukYWsX2sV7MNv+7WjV2wINruTb/bn8t/d2ZSZre6OJoQQQgghxDkqKmvY8PVxvA1aRgxo7+44QoizpJglRAOwO5xs35PDibwKwgK9ueO6trKa31katYr4DsEMuyWGyFBf8oor2bTrBHuPFmB3eM68V0IIIYQQQmz45jiVVjvD+rXDz0fv7jhCiLPk3bUQ9azG7uDL5BzOlFTRJsSXW3u1RquRuvH/MvrouL1PJCfzK9h9+AwHMos5nlvB9V3CiAwzujueEEI0eR9//DHPP/885eXlQO0cjiqVisOHD7s5mRBCNA3ZZ8xs35tDRLAPt/eJdHccIcSvSDFLiHpktTnYujubwrJq2rUy0S8hAo1a5oO6mKhwExHBvuzPKOLQiWK+3JND2zAj13UJw+itc3c8IYRosl577TWSkpKIi4tzdxQhhGhyFEXhg61HURQYMyhWPpwWwsNIMUuIelJltfPJjgwKy6pp39qPm+NboZaJzS+LTqumT6dQOrT244dD+Zw6Yya3yEJCh2C6tgtCLQVBIYS4YmFhYVLIEkKIq7TnaAFpJ0tJ6BBMfPtgd8cRQvwPKWYJUQ+qrHb+vjqF3KJKYiJMUsi6SgEmA3dc35bM0+UkHylgz9FCMnLK6ds5lNYhvrLqoRBCXIFu3brxxBNP0K9fPwyGXxYgGT58uBtTCSGE57PZHaz68hgatYoxg2LdHUcIcR5SzBLiGlXX2Hl59T4ycsrpFBXIdV1CpZB1DVQqFR3a+BMZamRvegHpp8rYllw7V0HfzmEtfkVIIYS4XGazGV9fX1JSUurcL8UsIYS4uM9/PEVhWTVDr29LqyAfd8cRQpyHFLOEuAY2u4Ol6w5wLKeMG7qGc323VpRVVLs7VrNg0Gu4sVsrOkUF8lPaGXKLKvl01wk6tPHHqYBceSiEEBe3ePFibDYbx48fx+FwEBsbi1Yrp35CCHExJRVWNn+XhclHxz03x7g7jhDiAuSMRoirZHc4+dcnBzmcVUKv2BAe/20XUrNK3R2r2Qk0GRjcN5LThRaSjxRwLKeMSqMdnUZFldWOl0F+jQkhxPmkpqbyxBNPEBAQgNPppLCwkGXLltGjRw93RxNCCI+1dnsGVpuDsYNj8fGS80whPJW8OoW4Ck5FYcWWw+xNL6RLdCB/GNZNVjhpQCqVijahRiJCfDl+upwvT4DN4WT9zkw6RwfSLSYIg07j7phCCOFRFixYwMsvv+wqXqWkpDB//nzWrl17Vcf7+OOPWb9+PQBWq5XDhw+za9cu/Pz8XO3t2bMHX19foHY1RZPJVA/PRAghGkdGThnfHcwjKtzILfER7o4jhLgIKWYJcYUUReGD/x7lu4P5dGjjx9T749FppZDSGNRn59PyydVidzjR2zWkZhZz8Hgx7Vv70S0miACjzKklhBAAlZWVdUZh9ezZE6vVetXHu++++7jvvvsAmDt3Lvfff7+rkAVw8OBB3nrrLYKCgq4+tBBCuIlTUfhgazoA4wbHyWraQng4KWYJcYU2fXuCL/fkEBlq5MlRPfDSy8uosalUoNOqGTGgPdkFZlLSa1c9zMgpp1WwD53aBtA2zNgsT0KcTgVLtQ1zlQ1LlZ0qq52qGjvVVgdWmwO7w4ndoWB3OIHaAqBKBWq1CoNOg0GvwUuvwcegxc9Xj9OpyIIFQjRT/v7+bN26lcGDBwOwdetWAgICrvm4Bw4c4NixY8yePdt1n9PpJCsri1mzZlFYWMjIkSMZOXLkNbclhBCN5bvUPI7nlnN9lzDi2l7770ohRMOSd+FCXIEdKTls+Po4If5e/OmBHvh66dwdqUXTqFV0igqkQ2s/Mk+Xc/hECXlFleQVVeJt0NKxjR8xrf2a5Ggtp1OhvLKGkgorpRVWSsw1lJmtmKtsKMqFH6dSgVajRqupLVApSu0njU6ngt1x7gMfstoB2PjNccICvQkN8CYs0Bujtw6VFLmEaNLmzZvH008/zTPPPANA27ZtWbJkyTUf9/XXX2fy5Ml17qusrOShhx7i0UcfxeFw8PDDD9O9e3c6d+58weMEBvqglZHNQggPUFlt4+Odmeh1Gn5/fw9CA927gmFoqFyiLcSlSDFLiMu052gB731+BKO3jj890LNJFkiaK5VKRVS4iahwE6VmK0dPlpJxupwDmcUcyCwm0GSgXSsTUeFG/Hz1HlWkURSFKqudkooaSsxnC1cVVsrMNTj/p2pl0GkI8ffC5KPH6K3D11uHj0GDl0GLt16LQadGrVZd8PnZHU6sNQ6qaxxUWu2UWWrQadQ4FQVzlY1Scw1HT5UBYPLR0TbMSNtwI6EB3jJ6S4gmKCYmhjVr1lBZWYnT6cRoNF7zMcvLy8nMzOTGG2+sc7+3tzcPP/ww3t7eANx4442kpaVdtJhVUlJ5zXmEEKI+rN2eQUmFlWG3xKCyOygoqHBbltBQk1vbF8LTXKi4K8UsIS5DenYpr288iE6r5slRPWgV5N5Pa8SFBRgNXN81nF5xoWQXmDmeW8HpAjN7063sTS/E10tLm1BfWof4EhrgjXcjroZYXeOgwlJbtPplxJWVGpuzzn4atYpAk4EAk772q9FAoMlwzVm1GjVabzW+3jqCgbaAQV87KmLMoFiKK6o5U1LFmZIqThdaOHSihEMnSvDSa4iJ8CO2rb8UcYVoAp599lnmz59PYmLieYvb77333lUfe/fu3dx8883n3H/ixAmmTZvG+vXrcTqd7NmzhxEjRlx1O0II0VjOlFTyxe6TBPkZuPOGKHfHEUJcpnp9F+d0OpkzZw5HjhxBr9ezYMECoqOjXdtXr17NRx99hFarZdKkSQwcONC1bffu3Tz11FPs2LGjPiMJcc3yiytZuu4ADofC/41KoH1rv0s/SLidTqsmJsKPmAg/rDYHp/LN5BRayC20cPRUmWsEkq+XlhB/L4L8vfDz0ePnq8PorUenvfLVKRVFwWpzUFVcSV6hmfLKGsotNVRU2iivrDmnaAW1I6BaBfnUKVoZfXSNPhJKrVYR4u9NiL83XduBw+Ekt7iSU/lmTuabOZxVwuGsEkIDvIiNDCCmtQktsoKnEJ7ogQceAGDq1Kn1fuzjx48TGRnpur1ixQqioqIYNGgQ99xzD6NHj0an0zFs2DBiY2PrvX0hhKhvq748ht2hMHpgR1kdW4gmpF6LWVu3bqWmpoZVq1aRkpLCc889x/LlywEoKCggKSmJdevWYbVaGTduHP369UOv15Obm8s777yD3W6vzzhCXDNzlY1X1uzDXGXjkbs6E98+2N2RxFUw6DR0jPSnY6Q/TqdCUVk1uUUWCsuqKSyrJivfTFa++ZzHGHRqDHoNBp0GjUZdO5H62SKTw6mcnWzdic3upMrqoLrGft75rNQqMPnoCQvQ4eerJ8BoIMBkIMCoR6vxzIKQRqMmMtRIZKiR67sqZJ8xk55dxulCCwWleexNL6R7+yD6xoXKIghCeJju3bsDtasXZmZm0rlzZzZt2sShQ4eYMGHCNR378ccfr3P70UcfdX0/YcKEaz6+EEI0poMnitmbXkhspD/XdQ5zdxwhxBWo13cgycnJ9O/fH6g9gUpNTXVt279/P7169UKv16PX64mKiiItLY1OnToxe/Zs5s+f71ruWQhPYLM7+ee6/eSXVHH3jdEM6NHa3ZFEPVCrVYQGehMaWDuvi6IoWKrsFFdUU1Fpo6KyhvJKG1XVdqw2BxWXmHAdQKtR4aWvHeHlbdASYPLCS6fGdHakl6+XrkmvrKhRq4huZSK6lQlzlY20rBKOnipl9+EzHDxezJC+bbnjuraNesmmEOLS/vKXvxAZGUlNTQ1Lly5l2LBhzJgxg9dff93d0YQQwu0cTicfbU1HBYwbHOdRc6oKIS6tXt95mM3mOpOLajQa7HY7Wq0Ws9mMyfTLxF2+vr6YzWbmzZvH+PHjCQ8Pv+x2PHH1G1lxolZz6QdFUXjp/T0czS6jX4/W/P7+HpdXjMgsxmT0aviAv/LzSKHGbtfVfsm57Td0FtWvnrNBr8bhOP9KfZfLzwQRYef/2VUUhRqbE4fTiVOpva0oSu38U1o1Wo26SU+OfqU/PyajFxGhJm5KaM2hzCIOHi/mk2+OsyPlNGOHduKOG6LrjDZrLr8TroX0gfSBu2RnZ/Pqq6/ywgsvMHLkSCZOnMj999/v7lhCCOERtu89TU6hhQE9IohuJX+nhGhq6rWYZTQasVgsrttOpxOtVnvebRaLBZ1Ox08//cTJkydZtmwZZWVlTJs2jZdffvmi7Xja6jey4kSt5tQPG77OZMfebDq09iNxcCxFReZLPubnekaFubqB09X184p3jd2uq31n3fZNRq8Gz6L86jnbDFocZy/1aywqwGF34LA7sJ5ne2P0QX25lp+fLu0CeXhoHJ/9cIrPfjjJ8nX7+firY4y5vSM9OoY0q98JV0v6wH19IAU0cDgcFBcXs3XrVpYuXUpBQQFW6/l+awkhRMtirrKx4etMvA0a7hvQwd1xhBBXoV4na+nduzc7d+4EICUlhbi4ONe2hIQEkpOTsVqtVFRUkJGRQUJCAp9//jlJSUkkJSXh7+9/yUKWEA1t14FcNu46QYi/F1PvT0AvE0EKcUFeei3DbonhuT/cxMBebSgoqeLVtftZ9vEBCkur3B1PiBbt8ccfZ/To0dx6663ExcXx0EMP8cc//tHdsYQQwu02fJ2JpdrOvf1i8PPVuzuOEOIq1OvIrCFDhrBr1y7GjBmDoigsWrSozio3iYmJjBs3DkVRmDZtGgaDLPEuPEtaVgnv/icNH4OWaaN7yB83IS6Tv6+exKGduL13G977/AjJRwv445JtDOsXw6C+kWjUnjnRvRDNWXp6Olu3bnXd3rJlCxqNfEAjhGjZss+Y+WpvDuFBPgzqE3npBwghPFK9FrPUajXz5s2rc1+HDr8M2xw9ejSjR4++4ON37dpVn3GEuCK5RRb++fEBAKbcF09EsK+bEwnR9LQJNTL9wd7sOpDL2u2ZfPTlMXanneGx33alVZCPu+MJ0aJ89dVXPPnkk655BqWQJYRo6RRF4cNt6SgKjB3U0WNXlRZCXJosPSUEUF5Zwytr9lFptfPYb7rQOTrQ3ZGEaLLUKhX9E1oz6IZ2LF21lx8O5TPnnR8ZeVsHbu8T2aQnzBeiKQkICODOO++kW7dudUbDL1682I2phBDCffamF3I4q4T49sEkdAhxdxwhxDWQYpZo8Wx2B0vX7aegtJp7bm5Hv/gId0cSolnwNxr4/b3d6BUbQtLnR/hgazp70wt5/LddCTTJZeZCNLQRI0a4O4IQQngMm93Bqi/T0ahVjBnU0d1xhBDXSIpZokVzKgpvbz5MRk45N3YNZ3j/GHdHEqLZub5LOJ3aBvDuf9LYl1HEnBU/MuG3XenePtjd0YRo1kaMGEF2djbHjh3jlltuITc3l7Zt27o7lhBCuMUXu09RUFrNHde1lelEhGgG5CJh0aKt35nJj4fPEBvpz6N3d3bNKyKEqF/+RgNPjExg7OBYqqx2/r56H2u3Z2B3ON0dTYhma8uWLUyaNImFCxdSVlbGmDFj+OSTT9wdSwghGl1JhZVPv83C6K3j3n7t3B1HCFEPpJglWqyv951m83dZhAV6M+W+eHRamRhXiIakUqkY0rctf0vsQ1iAN1u+z2LJh3spNVvdHU2IZunNN9/kww8/xNfXl+DgYNavX88bb7zh7lhCCNHoPt6RgdXm4L5b2+PjpXN3HCFEPZBilmiRDp0o5r3Pj+DrpWXaqB6YfPTujiREi9GulR+zH72O6zqHcSy7jLnv7uZYdpm7YwnR7KjVaoxGo+t2WFgYarWc+gkhWpaM02XsSs0jKszIgITW7o4jhKgnckYjWpycQgvL1qeiUsHU+xMID/JxdyQhWhxvg5Y/DOvGA7d3pMJi4/kP9vDlnmwURXF3NCGajdjYWFauXIndbufw4cM8++yzdO7c2d2xhBCi0TgVhQ+3pgMwdnAsarVMKSJEcyHFLNGilFlqeHXNPqqsdh69uwtxbQPcHUmIFkulUjH0+ij+PKYn3gYtK784yr8/S5N5tISoJ7NmzSI/Px+DwcDf/vY3jEYjs2fPdncsIYRocJZqG7sO5PLqmv1kni7nus5hdIoKdHcsIUQ9ktUMRYthtTn4x9r9FJZVM/yWGG7q1srdkYQQQJfoQGY/ch3//PgAO/flUlBazR9HdMdX5rQQ4pqsWbOGRx55hD//+c/ujiKEEA2uzGxlT3ohe46cIe1kKQ5n7WjvqHAjD9ze0c3phBD1TYpZokVwOhXe2nSI47nl3Ny9FffIKiZCeJRgfy/++mBv3th0kL3phSx8L5n/G5VAeKBcBizE1crLy2PUqFG0b9+ee++9lyFDhuDt7e3uWEIIUW8Ky6rYc6SA5KMFHMsu4+fJCtq1MtGnUyi940KJCPZ1a0YhRMOQYpZoEVZ/dYzkowV0jgrgkbs6o1LJ9fJCeBqDXsPk++JZuz2Dz344ycL3kplyX7xcDizEVZo+fTrTp0/np59+YsuWLSxbtowePXqwZMmSqz7m8OHDMZlMAERGRrJ48WLXttWrV/PRRx+h1WqZNGkSAwcOvObnIIQQ/yu3yELy2QJWVl4FACogNtKf3p3C6B0XQoi/FO6FaO6kmCWavW3J2Xyx+xQRwT5Mvi8erUamihPCU6lVKkYP7EirIB+SPj/CCx/u5ZG7OtMvPsLd0YRokhRFwWazYbPZUKlU6HRXf/mu1WoFICkp6ZxtBQUFJCUlsW7dOqxWK+PGjaNfv37o9bJasBDi2iiKwqkzZn46UsCeowWcLrQAoFGr6BYTRJ9OofSKDcXfV37fCNGSSDFLNGt70wv4YOtR/Hz1TBvVQ+bgEaKJGNCjNSH+Xry2PpW3Nx8mv6SS4f3bo5ZRlUJctgULFvDf//6XLl26cO+99zJz5kwMBsNVHy8tLY2qqirGjx+P3W7nT3/6Ez179gRg//799OrVC71ej16vJyoqirS0NBISEurr6QghWhCnopB5upzkI2dIPlJAYVk1ADqtml6xIfTpFEqPjiFybi9ECybFLNFsHc8t5/VPDqLTqvm/kQmEBMhwYyGakq7tgnjm4T68umY/n36bRX5xFY//tgs6rcbd0YRoEqKjo1m/fj1BQUH1cjwvLy8ee+wxRo0axYkTJ5gwYQKfffYZWq0Ws9nsuvwQwNfXF7PZfNHjBQb6oJXXsxDiLIfDSWpGEd8eOM33qbkUl9eOBvU2aBjQsw03J7Smd+cwvA3N/y1saKjp0jsJ0cI1/98EokUqLK3i1bX7sTmcTL0vgZgIP3dHEkJchYhgX555uA///PgAu9POUGa2MuX+BIze8kmsEJdy77338umnn1JaWoqiKK77p0yZclXHi4mJITo6GpVKRUxMDAEBARQUFBAREYHRaMRisbj2tVgsdYpb51NSUnlVOYQQzYfN7uTgiWL2HClgb3oBlmo7AL5eWm6Jj6BPp1C6tgt0fZBlLq/i4mXypi801ERBQYW7YwjhMS5U3JVilmh2LNU2Xl6zj3JLDQ8OiaNnbIi7IwkhroHJR89TY3rx1qeH2J12hsUrk5k2uodM7irEJTz55JOYTCZiY2PrZeGTtWvXcvToUebMmUN+fj5ms5nQ0FAAEhISeOWVV7BardTU1JCRkUFcXNw1tymEaH6qa+wcyCwm+cgZ9mcUUV3jAMDfqOf23m3oExdKXFQAGrXMcyuEuDApZolmxWZ3suzjA+QWVXLHdW0Z1CfS3ZGEEPVAp1Xz+2HdCDQZ+GL3KRYmJTNtVA+iwmUYvhAXUlhYyIoVK+rteCNHjmTGjBmMHTsWlUrFokWLSEpKIioqikGDBpGYmMi4ceNQFIVp06Zd0/xcQojmxVJtIyW9kD1HC0g9XozN7vx/9u47vql6/+P4K6PpSvegrEJbWgqUVZAhe4pCL7OAhTpQUS7qBRVBVIYyRdB7ReCi/hyobMEqeK+CCIq2F4plt2wopXSvdJ2k7jEAACAASURBVKc5vz8KkcrGNun4PB+PPtLknOS8z0l6kn7yHQB4utjRu11DQpt74d/AWcbGFELcMSlmiVrDpCh88t1x4i9k0yHIi9F9m1k7khCiEqlVKsb2C8TdyZb1P55i0RcHmDy8Na38Kmc8ICFqmxYtWhAfH09wcHClPJ5Op2Pp0qUVbgsNDTX/Pnr0aEaPHl0p2xJC1Hw5hmJ+P5lO7Ik04s9nUWYq7+7cwNORDkFedGjuRWNvfaW0HBVC1D1SzBK1gqIobPjxFL8dTSGggTNPhbWUb3aEqKUGdvLFzdmOD745yrsbD/L4Q8HcH1Lf2rGEqHZOnjzJ8OHD8fDwwNbWFkVRUKlU7Ny509rRhBC1VHpOIQdOpHMgIZWTF3O4OlpfEx8nOjb3IjTIi/oejlbNKISoHaSYJWqF72Iu8P2+ROp7OPD8qDbobGR2JCFqs/uCvXF2sOG9zYf58NvjZOUV81CXJvLtrhDXWL58ubUjCCHqgOSMfA6cSCM2IY1zl8sHLlcBzRq50KG5N6FBnjLOpRCi0kkxS9R4ew5eYtNPp3F3tuXFMe1wctBZO5IQwgKa+7rxyvhQ3tl4kM27z5CZW8y4AUGo1VLQEgKgQYMGrF27lujoaIxGI126dGH8+PHWjiWEqOEURSEx1UBsQhoHTqSRlF4+k6lGraKVnzsdgrxoH+iJi17GzRNCVB0pZokaLTYhjU//E4/e3oYXx7TD3dnO2pGEEBbU0EvPq5EdeWfDQXb9nkS2oZiJf2uFrbTOFIK33nqL8+fPM3LkSBRF4auvviIxMZFXX33V2tGEEDVAcUkZKVkFXM4sICWz/PJyZiGXMwsoLDYC5RO0tA/0JDTIi7bNPNHb21g5tRCirpBilqix4s9n8e+oo+i0GqaEt5X+90LUUW5OtswYF8r7Ww7z+8l03l77O8+PaiOtNEWdt3fvXrZu3Yr6yvT2vXv3JiwszMqphBDVicmkkJ5bxOWMawtW5T9ZecXXra9Rq/B2s6dNgAehQV609nfHTif/UgohLE/OPKJGOn85j39tPoSiKDw7og3+DZytHUkIYUUOdlqmjm7L/20/TvTRFBasiWXqmHZ4u8oYHaLuKisrw2g0otPpzNc1Gmm1KERdoygKeYWl1xWsUrIKSc0qwFimXHcfNydbWjRxw8fdAR93B+q5O+Dj4YCHsy2aKwVyIYSwJilmiRonJbOAZRviKC4p4+mhrWjl527tSEKIakCrUfPkkJa4O9mxPfo8Cz7bz5TRbWnqI8VuUTeFhYXxyCOPMHjwYAC2bdvGkCFDrJxKCFFVikvLSLlSpLqcWVBevMoqvyy40i3wWva2Whp7O+Hjbv9HwcrdgXpuDtjqpPAthKjepJglapSsvGKWro8jr6CUyIFBdGpRz9qRhBDViFqlYlTvANycbPnyhxMs/uJ3Jg0LoU2Ah7WjCWFxzzzzDC1btuS3335DURSeeeYZevfube1YQoi/wGRSyMgtqtAd8Gprq8zcm3cLbO7rWqFg5ePugJODjcwCLISosaSYJWqMnPwS3l73O+k5RQzr7kef0EbWjiSEqKb6dWiEq96W1d8c5V+bDvHooOb0aNvA2rGEsJicnBzKysro2bMnPXv2JCYmhsDAQGvHEkLcAUVRMBSW/qlgVd7a6k66BZYXrMpbW3m42Em3QCFErSTFLFEj5BWUF7KSMwoYeF9jwro1tXYkIUQ116G5F9Mc2/PPTQf5+Lt4MvOK+Vu3pvIttKj1jh07xsSJE1mwYAE9e/YE4Ndff+Wll17igw8+IDg42MoJhRBXGctMnE/J40RiNklp+eZWVvlFN+oWqKGxt75C6yofdwe83exlEHYhRJ1TqWc9k8nEnDlzSEhIQKfTMW/ePJo0aWJevmHDBtatW4dWq2XSpEn06dOHS5cuMXPmTMrKylAUhTfeeAN/f//KjCVqOENhKUvXxZGUlk+/Do0Y07eZ/DMqhLgjzRq5MDOyA+9sOMjXv5wlK6+IyAeay7fUolZbvHgxS5cupXPnzubbpk6dSseOHVm0aBGffPKJ9cIJUccZy0ycu5xHwoUsEi5kc/JiDsWlZeblV7sFBjZyxcfDocIA7M7SLVAIIcwqtZi1Y8cOSkpKWL9+PXFxcSxatIiVK1cCkJaWxpo1a9i8eTPFxcVERETQrVs3/vnPfzJ+/Hj69+/Pzz//zLJly1i+fHllxhI1WEFRKcvWx3Eh1UDvdg2I6B8ob+JCiLtS38ORVyM78O7GQ+w5mEy2oYRJQ0NkcFtRa+Xm5lYoZF3Vo0cP3n77bSskEqLuMpaZOJucS/yFbE5cyOJkUg4lpSbz8voeDjT3dSPY15Um9ZzwdJVugUIIcScqtZgVGxtLjx49AGjXrh1HjhwxLzt06BDt27dHp9Oh0+nw9fUlPj6e6dOn4+TkBJRPGW1ra1uZkUQNZigsZen6OM5fzqN76/qMf6C5FLKEEPfERW/LyxHtWbn1CIdOZ7D4ywNMCW+Ls6PO2tGEqHRGoxGTyYT6T/8Qm0wmSktLrZRKiLqh1Hi1eFXe8up0Ug4lxj+KVw09HQnydSXY142gxq64yPuQEELck0otZhkMBvR6vfm6RqPBaDSi1WoxGAzmohWAo6MjBoMBd3d3AM6cOcPixYt5//33b7sdNzcHtNrq9Y26l5fT7VeqAyrrOOQYinnns/2cv5zHgE6+TA5vh0ZdAwpZZzJx0ttZdJPqKwU+S2/XvP2s67df1VlU1+yzrU5NWZlyw8FQrclaz8fd+iuvH61Ghafnrf/mq9u58c1J3Vi+MY6d+xJZ9OUB5j7VlQZe+tvf8S+obsfAGuQYWNZ9993H8uXLef755yvcvmLFCkJCQqyUSojaqdRYxumkXBISs0m4kMXpS7mUXlO8auTlSHNfN5o3diXI1xVnByleCSFEZajUYpZeryc/P9983WQyodVqb7gsPz/fXNyKjo5m7ty5vPXWW3c0XlZWVkFlxv7LvLycSEvLs3YMq6us45CTX8Lba38nKT2fPu0bMqZPAJkZhkpIWLWuNhrLMxRZdLsmRbHKds3bN1XcvpPersqzKNfsc6mtlrIyU4UPjtZmiWNQWf7K60erVZOenodykzpidT03RvRthoONhm9+PceL/9zDP8LbENDApUq2VV2PgSVZ6xjU5QLaCy+8wMSJE9m6dSvBwcHY2tpy7Ngx3N3dzcM/CCHuTUlpGaeTckhIzCb+QjZnLuViLCv/DKICGnnrae7rSvPGbgQ1dsFJildCCFElKrWYFRoayq5du3jooYeIi4sjKCjIvKxNmza8++67FBcXU1JSwunTpwkKCiI6Opr58+fz4Ycf0rBhw8qMI2qgzNwi3l4Xx+XMAvp3bMTD/WSMLCFE5VKpVAzv6Y+bsy1r/pvAki9/56mwVnRo7mXtaEJUCr1ezxdffEF0dDTHjx9HrVYzbtw4OnbsaO1oQtQ4xSVlnLqUYx6w/cylXMqufJGnAhrX0xN8peVVYGNX9PY21g0shBB1RKUWswYMGMDevXsZO3YsiqKwYMECPv74Y3x9fenXrx+RkZFERESgKApTp07F1taWBQsWUFpayowZMwDw8/PjjTfeqMxYooZIzshn6fo4MnOLebCzL6N6B0ghSwhRZXq3a4ir3pZVXx/h/S2H+Vu3pvytu5+566UQNZlKpaJr16507dq1Uh6vtLSUmTNnkpSURElJCZMmTaJfv37m5R9//DGbNm0yDx8xd+5cmZ1a1EhFJUZOJeWQcCGbhAvZnE2+pnilgib1nCq0vHKwk+KVEEJYQ6UWs9Rq9XWFqICAAPPvo0ePZvTo0RWWR0VFVWYEUUOdTc7lnQ0HMRSWEt47gAe7NLF2JCFEHdCumSczx3fgvc2Hidp7jsRUA08OaYm9baW+PQpR40VFReHq6sqSJUvIyspi+PDhFYpZR48eZfHixTIml6hxCovLi1fxF7I4cSGbc5fzzMUrtUpFE5/y4lWwryvNGrriYCfvD0IIUR3I2VhY3dFzmSzffJgSYxmPPRhMz7YNrB1JCFGH+NZzYtZjHVm59Qi/n0xn/ppYnhvZmnpuDtaOJkS1MWjQIB544AHzdY2m4kQ8R48eZfXq1aSlpdG7d2+efvppS0cU4o4UFBk5eTHbPGD7+csG8/iRapUKv/pO5tkGmzV0kS83hBCimpKzs7CqXw4l8+l/4lGp4O/DQujQ3NvakYQQdZCTg44XxrRjw4+n2BF7kTc/2c8zQ1sR4u9h7WhCVAuOjo5A+czVzz//PFOmTKmwfPDgwURERKDX63n22WfZtWsXffr0ueVjVsfZqcXNZeQU8tvhZE4mZqNRq9Bo1GivXmru9LoajUaFVnPl8s/XNWo06vJLtfrGt2uu/H6nDIWlHDuTweHT6Rw5nc6ZpByuNLxCo1bRvIkbIQEehAR40qKpuxSvRLVQlycxEeJOydlaWIWiKGz5+Szf/noORzstz45oTXNfN2vHEkLUYVqNmogBQTSup2fNfxN4Z+NBRvUOYFAnXxm/TwggOTmZyZMnExERQVhYmPl2RVF49NFHzbNU9+rVi2PHjt22mFXdZqcW18vMLSI2IY39CamcupjDTSavtTgVoNGo0KjVVwprqvLLK8Wxq7+bFIXk9Hxzbo1aRUBDF5r7utHc15VmDVyw1f1RUDXkFlL9588WtZ3MhCxERTcr7koxS1hcqdHEx9uPE30sBS9XO6aEt6W+h6O1YwkhBAA92jSggacjy786zMZdp7mQYuDRQc2x08lbpqi70tPTmTBhArNmzbpuUHmDwcCQIUPYvn07Dg4OxMTEMHLkSCslFX9VZm4R++NT2ZeQyumkXKC8eBTY2JWOzb1o5eeOWq2irEyhzKRQZjL98XuZiTKTgtGkXLnNdOX2P//+x/2Mf36Max7nZr/f6vGLS0zmx1eU8tzBvq40b+yKf0MXbG2kNaAQQtQG8slcWFRWXjErthzm9KVcmjV04dmRrXF20Fk7lhBCVBDQwIXZj93H+18dJuZYChdS8pg0LIRGXnprRxPCKlatWkVubi4rVqxgxYoVAISHh1NYWMiYMWOYOnUqjzzyCDqdjq5du9KrVy8rJxZ3Iz27kP1XWmCduXSlgKWCYF9XOgZ7Exrkhave1sophRBCiD9IMUtYzKmkHN7/6jA5+SV0aVWPxx8MxkbGyhBCVFOuelumjwtl467T/LA/kXmf7mfcwCB6tJFJKkTd89prr/Haa6/ddPmwYcMYNmyYBROJvyotu5D9Cansj0/lbHJ5lyaVClo0cTMXsFwc5QtHIYQQ1ZMUs4RF7I5L4vPvT6AoMLZvMwbc11jGoBFCVHtajZqH+wfS3NeV/9t2nI+3xxN/PovxA5vLIMFCiBonNauAffGp7E9I4/zl8gKWWqWiVVM3OlwpYEmLeSGEEDWBfBIXVaqoxMgX359g75HL6O1teGZoK1o2dbd2LCGEuCuhQV74eutZ+fVRfjuawsmLOTwV1pLARq7WjiaEELeUknmlgBWfyoXU8uHNNWoVIX7udAz2pn2gJ05SwBJCCFHDSDFLVJnEVAOrvj5CckYBfvWdmDQ0BE9Xe2vHEkKIe+Lpas8r40OJ2nuWbb+dZ9EXBxjctSl/69YUrUZt7XhCCGGWnJFfPoh7fBoX0/4oYLX296BjsBftA73Q29tYOaUQQghx76SYJSqdoij8FHeJtTtOYiwzMfC+xozqHSD/7AkhajytRs2IngGE+Hnw4bfH+PbXcxw6nc6Eh1rgW+/G0wYLIYQlJKWXF7D2J6SSlJYPgFajom2ABx2DvWkX6ImjnRSwhBBC1A5SzBKVKiuvmE//E8+h0xk42mn5+7AQ2gV6WjuWEEJUqqDGrsx5vBPrdp7kl8PJvPnpfgZ3bcKQ+6WVlhDCMhRFMRew9sWnkpxRAJQX3ds18+S+YG/aNvPEwU4+7gshhKh95N1NVApFUfjpwEVWbT5IfpGRVk3dePyhFrg721k7mhBCVAkHOy0TBrfgvhbefPJdPFF7z3HgRBqPDAqmWUMXa8cTQtRCiqKQmGpgf0Ia++NTuZz5RwGrfeAfBSyZoEIIIURtJ+904i/LzC3iyx0nOXAiDZ2NmsiBQfRu31BmKxRC1Amt/T2Y92RnNuw6xe64SyxYE0vPtg0Y1TsAL2uHE0LUeIqicCHFwP6E8kHcU7IKAdBp1XRo7kXH5t60CfCQApYQQog6Rd71xD0zmRR2xl7kq5/PUFxSRit/DyIHBOLt5mDtaEIIYVH2tloeHRRM11Y+rPk+gT0HL3HgRBoTwlrRxs8NtRT3hRB3QVEUzqfksS8+ldj4NFKzrxSwbNR0DPbmvmBvWvu7Y6eTj/JCCCHqJnkHFPfkdFIOn39/gvMpeTjaaXn4wWCG9w0iI8Ng7WhCCGE1QY1dmf3YfezYf5GvfznLvzbE4VtPz5i+gbRo4mbteEKIakxRFM4m55lbYKXnFAFga6OhUwtvOjb3pnWAB7Y2GisnFUIIIaxPilnirqTnFLJ59xlijqUA0LWVD2P6NsPZUYdaLS0PhBBCq1EzqLMvnVp4sy3mArtiL7Jk7e+0a+ZJeJ8A6ns4WjuiEKKayCso4UKqgSNnMtgfn0ZGbnkBy06noUvLenQM9ibEzx2dFLCEEEKICqSYJe5IflEp30Vf4Pt9iRjLTDT1cWJsv0CCGrtaO5oQQlRL7s52vBDRge4hPqzfeZK4U+kcPJ3O/a18COvuh7ervbUjCiEsxGRSSMkqIDHVUOEnK6/YvI69rYaurf4oYNlopYAlhBBC3IwUs8QtFRQZ+WF/It/vS6Sw2Iibky2jegXQuVU9GQNGCCHugF99Z6aPC+X3k+ls+fkMe49cJvpYCt1a12dI1yZ4SlFLiFqlsNhIUlo+ial5XLhStLqYZqCk1FRhPTcnW9oEeNDYW0+zhi60bOqOjVZtpdRCCCFEzSLFLHFDhsJSfjxwke//l0hBsRG9vQ3hfQLoG9pIxmoQQoi7pFKpCA3yol2gJ/uOp/L1L2fZc/ASvxxKplMLbwZ19sW3npO1Ywoh7oKiKGTmFpOYauBCap65tVXqldkGr9KoVdT3cKSxt57G3np865VfOjnorJRcCCGEqPmkmCUqSM0q4Pt9ifxyKJkSowlHOy2jegfQN7ShzJgjhBB/kVqlonPLenQM9uJ/x1P5Lvo80cdSiD6WQoifO/07NiLE30NavgpRzZQaTVxKzzcXrS5eKVzlFxkrrOdopyXY15XG3k7molV9D0dpcSWEEEJUMqlOCEwmhSNnM9kdl0TcyXQUwMPZlgH3+dKjTX3sbeVlIoQQlUmjVtO1lQ9dWtbj8JlM/hNzniNnMzlyNhNPFzv6hDakR5sG6O1trB1ViDont6CExJSr41qVdxW8nFFAmUkxr6MCvN3sadHEjcb1nMpbXHnrcXOyRSXFaCGEEKLKSZWiDsvMLWLvkcvsibtknj2nqY8TD3TypWOwFxq1fIsohBBVSaVS0SbAgzYBHpy/nMePBy4ScyyFjbtOs2XPGdo18+T+1vUJ8XNHq5FzshCV6eqg7BdS/hiQ/UJqHjmGkgrr2dpoaOrjZC5aNfbW08jLUVqsCyGEEFYk78J1jKGwlP0JqcQcTeFEYjYK5R/SerZtQK92DfCr72ztiEIIUSc18XHi8YdaMLpvM/YeSmbPoWT2J6SxPyENZwcb7mtRj/uCvWnW0AW1Wlp+CMszmUzMmTOHhIQEdDod8+bNo0mTJublGzZsYN26dWi1WiZNmkSfPn2smLaiwmLjn2YSzCMpLZ8S4/WDsrcN8KBxPX15V0FvPV5u9tL1VwghhKhmpJhVB2TlFRN3Mo0DJ9OJP59lbiYf1NiVLq3q0blFPelKKIQQ1YSjnQ0DO/ky4L7GnE/JY+/hy8QcS2Fn7EV2xl7ExVFHaHMv2gd60ryxKzZamZRDWMaOHTsoKSlh/fr1xMXFsWjRIlauXAlAWloaa9asYfPmzRQXFxMREUG3bt3Q6awzyPnJi9kcP5d1ZTbBPNKyiyos16hVNPB0xPdKS6urra6ka68QQghRM0gFoxYqNZo4lZTDsXPl46+cv5xnXtaknhOdWnjTqUU9PFzsrJhSCCHErahUKpr6ONPUx5kxfZsRfz6L/QmpHDiRzq4DSew6kITORk3LJu609ncnuIkbPu4OMl6PqDKxsbH06NEDgHbt2nHkyBHzskOHDtG+fXt0Oh06nQ5fX1/i4+Np06aNxXMWlRhZ+PkB83W9vU352Fbm2QSdqO/hIF13hRBCiBpMilm1QHFJGWcu5XDyYg4nL2ZzMimHktLyZvMatYoWTdzKp4Rv5ikFLCGEqIG0GjUh/h6E+HsQ+YCJExeyOXQmg0OnM4g7lU7cqXQAXPU6gn3dCGrsSkBDFxp6OkqXRFFpDAYDer3efF2j0WA0GtFqtRgMBpycnMzLHB0dMRgMt3w8NzcHtFXUsnDOU11QFPBr4Iy7s50UeYUQNYqXl9PtVxKijpNiVg2TV1BS3mQ+5Y8ZdpLTCzApf8yw08DTkZZN3WjV1J3mvq4yQKkQQtQiGrWaFk3dadHUnTF9A0nLLuTI2UwSLmQRfz6L6GMpRB9LAcBWp8HPx4mmPs409HKkkZee+h4O6Gyka6K4e3q9nvz8fPN1k8mEVqu94bL8/PwKxa0bycoqqJqggK+HQ3nGEiPp6bcuqgkhRHXi5eVEWlre7VcUoo64WXFXqhzVUJnJRGZuManZhaRlF5KWVUhSej6JqQay8oorrGtro8GvgROBDV0JbORCQCMXnB2sMz6FEEIIy/NytadP+4b0ad8QRVG4lFHAqYvZnL6Uy9lLuSRcyCb+QrZ5fZUKvN0caOTpSEMvRxp66fF0scPTxQ69vY20YBE3FRoayq5du3jooYeIi4sjKCjIvKxNmza8++67FBcXU1JSwunTpyssF0IIIYSoTFLMsrDikjJyCkrIzS//yblymW0oLi9cZReSkVNcoaXVVW5OtrQJ8DCP9yAz7AghhLiWSqWioacjDT0d6dWuIQAFRUYuphlISs8vv0zLJynNQOyJAmJPpFW4v62NBo8rhS0PFzs8ne1wdbLFycEGZwcdTg46nBxsZKyhOmrAgAHs3buXsWPHoigKCxYs4OOPP8bX15d+/foRGRlJREQEiqIwdepUbG1trR1ZCCGEELVUpRaz7mXK5szMTF566SWKiorw9vZm4cKF2NvbV2asu5KaXUhpaRmKAiZFqXCpKArGMhOlZSaMRoXSMhOlxjLs7DPIzCqgxGiisNh4zU8ZBddczysspbik7Jbbd3HU4d/AGS9XO7xc7c0/9T0ccJIWV0IIIe6Sg52WoMauBDV2Nd+mKArZhhKS0g1cSi8gPaeQjJwiMnKKSM8p4lJ6/i0eEexttTg52OBkb4O9rRY7nQY73ZVLWy32Og12Og06Gw3ubjkU5Bej1ajRatVo1Sq0WjUuDjo8Xa33fi/unlqt5o033qhwW0BAgPn30aNHM3r0aEvHEkIIIUQdVKnFrHuZsnnFihUMGTKEESNGsHr1atavX89jjz1WmbHu2N7DyXy07XilP66dToO9rZZ6rvY4O+pwdtThcuXy6u8ujjo8Xeyx1ck4JjWZ5so/a5ZV3jLP8tu9uvmK29dqVBbI8sc2bTRq1MD1bRmtxzLHoLLc++vHRlrn1FgqlQo3J1vcnGwJ8fO4bnlBkZGM3CLScwrJyS8hL7+EvIJScgvKL/OuXGbkFFFmuve/vjef6ERDL/3tVxRCCCGEEOIalVrMupcpm2NjY3n66acB6NmzJ8uWLbttMauqZncY1teJYX1lfIe/oq7PvNHH0xr7fxGA4X2ssOlrt2/BLQ7vc9GCW6vlLlft81fXzwlQc49Bk9uvIsRfUlP/NoQQoqrJ+VGI26vUr9VvNmXz1WU3mrL52tsdHR3Jy5OZG4QQQgghhBBCCCHEjVVqMetepmy+9vb8/HycnZ0rM5IQQgghhBBCCCGEqEUqtZgVGhrKnj17AG44ZXNsbCzFxcXk5eWZp2wODQ1l9+7dAOzZs4cOHTpUZiQhhBBCCCGEEEIIUYtUajFrwIAB6HQ6xo4dy8KFC3nllVf4+OOP2blzJ15eXuYpmx999FHzlM2TJk1i27ZtjB07lt9//53x48dXZiQhhBBCCFHLRUZGcvr0aWvHuCu3yty3b1+Ki4stnKii1atX0717d6vnuJ3Vq1fz2GOPMWHCBJ544okKY/ZWV4mJiTz33HNERkYyduxY5syZg8FguOG6ly5d4scff7RwQoiJiaFjx44kJyebb3v77bf56quvLJ7ldmJiYujatSuRkZGMHz+esWPHsn37dmvHqhUuXrxIaGgokZGR5p/ly5ffcF1rn4djYmJo3rz5dc99WFgYM2bMsFKqO1Odz7fV+bhW6gDw9zJls6enJx999FFlxhBCCCGEEEL8Bd988w0PPfQQ27ZtY8SIEdaOc0OnTp3ixx9/ZO3atahUKo4fP8706dOJioqydrSbKioq4u9//zvz5s2jbdu2AGzZsoUXX3yRf//739etHx0dzZkzZ+jbt6+lo2JjY2NunKC6Mnt1ddWlSxfeeecdoHzomsjISPz8/GjRooWVk9V8zZo1Y82aNdaOcUf8/f359ttveeihhwBISEigsLDQyqlur7qfb6vrcZV51YUQQgghRI23fPly1q5dC8Dp06eJjIwEyr89fvPNNxk/fjyRkZHVarKhm2W2tpiYGHx9fRk7dixffPEFULHVxdq1a3nvvfcAeP/99xk+fDhPPPEEERERxMTEWCynu7s7ly5dYtOmTaSkpNCiRQs2bdpEQkKCuRXJc889R15eHjExMTz++OM88cQT/O1vfzPvl6X99NNP3HfffeZCFsDw4cPJysrizJkzjB8/njFjxvDoo4+Snp7O6tWr+fbbb9m5c6fFs3bp0gUXF5frjtX//d//MXLk9jfajQAAIABJREFUSMaMGcOSJUsAGDFiBBcvls+Q/N133zFv3jyL573K0dGRMWPG8J///IelS5cyduxYxowZw3fffQfAwYMHGT16NOHh4Tz77LMUFRVZLWtNdaPjCvCvf/2LRx55hCeffJLMzEyL5woODiY5OZnc3FwAoqKiCAsLA+Dzzz/nkUceISIigqeffpqSkhK++uorxo0bx8MPP8xvv/1m8bxw8/PtrFmzzK0N09LSiImJITw8nIiICLZu3WrRjHd7XF988UV++uknoPy9beLEiVWSS4pZQgghhBCi1srPz2fw4MF8/vnneHt7m8d3FTe3ceNGwsPD8ff3R6fTcfDgwRuuFx8fz88//8ymTZt4//33SUtLs2hOd3d3Vq5cyYEDBxgzZgyDBg1i165dvP7668yePZs1a9bQs2dPPvzwQwBSUlJYuXIlGzZs4JNPPiEjI8OieaG8i6Gvr+91tzdq1Ijw8HAmTpzI+vXrGTNmDPHx8UycOJEhQ4bQr18/i2cFmDNnDp988gnnzp0Dyv+evvvuO9atW8e6des4f/48u3btYtSoUeZ/sLds2XJdbxxL8/Dw4D//+Q8XL15k3bp1fPbZZ6xatYrc3Fxef/11Fi5cyMaNG+natWuN66JsaadOnarQzTAqKuqGxxVg4MCBfPbZZ/Tp0+eGLQ0tYcCAAfzwww8oisKhQ4do3749JpOJ7OxsPvnkE7788kuMRiOHDx8GwNnZmbVr19K1a1er5L3Z+TY0NJQ1a9bw4IMPmo9lcXExX375JcOGDbN4zrs5ruHh4WzZsgWATZs2MWrUqCrJVKndDOuiH374wVz1h/KB7+fPn49Go6F79+48++yzVk5oGYqi0LNnT5o2bQpAu3btePHFF60bykJMJhNz5swhISEBnU7HvHnzaNKkibVjWcWwYcNwcnICyj+ULVy40MqJLOfgwYO8/fbbrFmzhvPnzzNjxgxUKhWBgYHMnj0btbr2f3dw7TE4evQozzzzjPmc8PDDD5ubJtdWpaWlzJw5k6SkJEpKSpg0aRLNmjWrU6+FGx0DHx+fOvdaEJaRn5+PTqfDxsYG4JbdoFq2bAlA/fr1rTomyd1ktpacnBz27NlDZmYma9aswWAw8Pnnn1dYR1EUoPwb99atW6PRaNBoNISEhFg06/nz59Hr9ebPG4cPH2bixIkUFRUxd+5coPy85OfnB0D79u3R6XQABAYGcuHCBTw8PCyauV69ehw6dOi628+dO0dxcTHt27cHMJ8nrT1GlZubGzNnzmTGjBmEhoZSXFxM27Ztza/hjh07cvLkSSIiInj44YcJDw/HYDBUmAjMGi5dukRYWBhRUVHmFo9Go5FLly6RkZFhHgpn3Lhx1oxJZGQkc+bMqTA0T3Xz526GH3zwAUePHr3uuEL56wGoMMmbpYWFhTFnzhwaN25szqNWq7GxseGFF17AwcGBy5cvYzQaAcznB2u41fm2S5cuQPmxvDpunjWz3s1x7dy5M/PnzycjI4O9e/fywgsvVEkmKWb9BfPmzeOXX36p0Bd79uzZvPfeezRu3JiJEydy9OhRWrVqZcWUlnHhwgVatWrFqlWrrB3F4nbs2EFJSQnr168nLi6ORYsWsXLlSmvHsrir/xzUlD71lemDDz4gKioKe3t7ABYuXMiUKVPo3Lkzs2bNYufOnQwYMMDKKavWn4/BsWPHePzxx5kwYYKVk1lOVFQUrq6uLFmyhKysLIYPH05wcHCdei3c6BhMnjy5zr0WhGXMmDGDcePG0alTJzIyMujWrZu5ZdDRo0crrFtdikZ3k9laoqKiGDlyJNOnTwegsLCQfv36ERgYSFpaGgEBARw7dox69eqZ/8k1mUwYjUaOHTtm0awJCQmsXbuWVatWYWtri5+fH05OTtSrV4/FixfToEEDYmNjzcf4+PHjlJWVUVJSwqlTp6zy5WO/fv1YtWoVhw4dok2bNkB5ywx3d3d69erF4cOHuf/++4mKiiInJwcnJydMJpPFc16rb9++/PDDD2zZsoW///3vHDp0CKPRiEajYd++fQwbNgy9Xk9ISAgLFy60+pg/BoOBjRs3MmrUKDp37sybb76JyWRixYoVNGrUCG9vb86dO0fTpk1ZvXo1fn5+tfq9ubL5+/vf8LhCeUG5Xr167N+/n8DAQKvka9y4MQUFBaxZs4YXXniBxMREDAYDO3bsYOPGjRQWFjJixAhzUd6aXzLe7Hzr5ubGkSNH8PHx4cCBAzRr1szqWe/muKpUKsLCwpg/fz7dunUzF78rmxSz/oLQ0FD69+/P+vXrgfITZ0lJibnpcPfu3fntt9/qRDHr6NGjpKSkEBkZiZ2dHa+88gr+/v7WjmURsbGx9OjRAyhvkVYTZtGpCvHx8RQWFjJhwgSMRiMvvPAC7dq1s3Ysi/D19eW9997j5ZdfBsr/Hjp16gRAz5492bt3b63/kPTnY3DkyBHOnj3Lzp07adKkCTNnzkSv11s5ZdUaNGgQDzzwgPm6RqOpc6+FGx2DuvhaEJbx+OOPM2/ePGxtbRk+fDiDBg1iypQp7Nu3z+IthO5UTci8ceNG3nrrLfN1e3t7Bg4ciI+PD2+88Qb169fH29sbgObNm9OrVy9Gjx6Nm5sbNjY2aLWW+/di4MCBnD59mvDwcBwcHFAUhZdffhkfHx+mT59OWVkZAPPnzyc1NRWj0chTTz1FdnY2kyZNwt3d3WJZr3J0dGTVqlUsWLCA7OxsysrKaN68OcuWLSMrK4tZs2axcuVK7OzsWLJkCZcuXWLlypW0atWKwYMHWzzvVa+++irR0dE4Ojry4IMP8vDDD2MymejQoQP9+/cHIDw8nCeffJIFCxZYPF90dDSRkZGo1WrKysp47rnnGDBgAIsWLSIiIoKCggL69++PXq9n7ty5zJw5E7VajZeXF4899pjF814rKyuLZ555huLiYrKzs5k8eTL9+/cnLCyMTp06kZCQgEqlYsWKFeYeENbUt29f/ve//113XKH8S/5PP/0UR0dHFi9ebLWMDz30EF9//TV+fn4kJiai0Wiwt7dnxIgR6HQ6vLy8SE1NtVq+q252vt20aRNbtmzhk08+wd7enrfeeosTJ05YMWm5uzmuI0aMoHfv3nz99ddVF0gRt7VhwwZl8ODBFX4OHjyoKIqiREdHK1OmTFEURVGSk5OVUaNGme+3ceNGZdmyZVbJXJVudDxiYmKU7du3K4qiKPv27VNGjBhh5ZSWM3PmTOWnn34yX+/Vq5dSWlpqxUTWER8fr6xfv14xmUzKmTNnlH79+tWp45CYmKiEh4criqIo3bp1M9/+66+/Ki+++KK1YlnUtcdg06ZNyuHDhxVFUZQVK1YoixYtsmY0i8rLy1PGjx+vREVF1dnXwrXHoC6/FoSo7dLT05XPP/9cURRFKS4uVvr3768kJSVZOdWNXfuZXYjqYvz48cqaNWuU6OhoRVEUJTY2VnnssccURVGUPn36KLGxsYqiKMoLL7ygfPvtt1bLKSxr/PjxyqlTp6wd4y+5fPmy8sgjj1TpNqRl1h0IDw8nPDz8tuvp9Xry8/PN1/Pz83F2dq7KaFZxo+NRWFiIRqMByvtKp6SkmJsY1nZ/ft5NJpNFv5WsLvz8/GjSpAkqlQo/Pz9cXV1JS0ujfv361o5mcdc2Aa6t54HbGTBggHm/BwwYwJtvvmnlRJaRnJzM5MmTiYiIICwszDzLE9Sd18Kfj0Fubm6dfC0IURdc7QozcuRIVCoV4eHhNGjQwNqxhKjW/jx2XseOHVm9ejWbNm1CpVKZx3KC6jPenxB347///S/Lly9n/vz5Vbqd2jsKrRXo9XpsbGy4cOECiqLwyy+/mAdHq+2WL1/Op59+CpR3N2vQoEGdKGRBeXfTqzMjxcXFWX3AS2vZtGkTixYtAspnCzIYDHh5eVk5lXW0bNnSPDX5nj176sx54FpPPPGEeYDbutLdOj09nQkTJjBt2jTzrC117bVwo2NQF18LQtQVarWahQsXsnnzZjZt2lRl069Xhs6dO/POO+9YO4YQzJgxg9jYWEwmExkZGSxYsIChQ4eyZMkSOnfubB7LCarPeH/CstasWVOtJwW4nQceeIBvvvnGPC5gVal7zUeq2Ny5c3nppZcoKyuje/futG3b1tqRLGLixIlMmzaN3bt3o9Fo6tQsdgMGDGDv3r2MHTsWRVGsMk5AdTBq1CheeeUVHn74YVQqFQsWLKiTLdQApk+fzuuvv86yZcvw9/evMIZQXTFnzhzefPNNbGxs8PT0rBOtca5OTb1ixQpWrFgBlI8xMm/evDrzWrjRMZgxYwYLFiyoU68FIYQQ4mb+PHZe/fr1mT9/Pv/+97+pX78+WVlZ1o4oRI2gUq4t/QohhBBCCCGEEEIIUY1JN0MhhBBCCCGEEEIIUWNIMUsIIYQQQgghhBBC1Bh1c0AbIYQQQgghhBDCAkpLS5k5cyZJSUmUlJQwadIkmjVrxowZM1CpVAQGBjJ79mzzjNjnz59n8uTJfPvttwBcunSJl19+GUVRcHFxYenSpdjb21tzl4SwOmmZJYQQQgghhBBCVJGoqChcXV358ssv+eCDD3jzzTdZuHAhU6ZM4csvv0RRFHbu3AnA1q1bmTp1aoWB4D/55BMefPBBvvjiCwIDA9m0aZO1dkWIakOKWUIIIYQQQgghRBUZNGgQ//jHP8zXNRoNR48epVOnTgD07NmTX3/9FQAXFxc+//zzCvdv0aIFubm5ABgMhjo7Y7gQ15JilhDitgwGA3PnzmXIkCEMHTqUyMhIjh49au1YZocPH+bVV1/9y4/z1Vdf0alTJ4YOHcrQoUMZMmQIAwcOZMeOHbe8X2JiIjNnzvzL2xdCCCGEELWPo6Mjer0eg8HA888/z5QpU1AUBZVKZV6el5cHQJ8+fXBwcKhwfx8fH7744gsGDx7Mnj17GDRokMX3QYjqRkq6QohbMplMPPXUU3Tu3JmtW7ei1WqJjo7mqaeeYtu2bbi5uVk7Iq1bt6Z169aV8lh9+/Zl0aJF5us7duxg1qxZ9O/f/6b3uXTpEomJiZWyfSGEEEIIUfskJyczefJkIiIiCAsLY8mSJeZl+fn5ODs73/S+b731FgsXLqRHjx789NNPTJ8+ndWrV1sithDVlhSzhBC3FBMTQ3JyMs8//7x5UMouXbqwcOFCTCYTAKtWrSIqKgqNRkO3bt2YNm0aycnJPPvsswQGBnL8+HE8PDz45z//SVRUFOfPn+f1118HYNGiRfj4+BAeHs4bb7zByZMnKSsr46mnnmLIkCF89dVXbNmyhezsbPr06UNgYCAffvghGo2GRo0asWTJEuLi4li+fDlr1qzh7NmzzJo1i+zsbBwcHHj11Vdp06YNM2bMQK/Xc/ToUVJSUpg8eTIjR4687f4nJSXh4uICQEpKCjNnziQvL4/U1FSGDx/OP/7xD+bNm8fFixeZO3cus2fPZvXq1Xz33XeUlZXRvXt3pk2bZv7mTQghhBBC1C3p6elMmDCBWbNm0bVrVwBatmxJTEwMnTt3Zs+ePXTp0uWm93d2dsbJyQkAb29vc5dDIeoyKWYJIW7p2LFjBAcHmwtZV/Xq1QuA3bt38+OPP7J582ZsbGx47rnnWLduHb169SI+Pp4FCxbQsmVLnnvuOb755huGDBnCsGHDmDlzJmq1mu+//57169ezcuVKWrVqxeLFizEYDIwdO5a2bdsC5UWk7du3o9Vq6devHxs2bMDDw4PFixdz5syZCrmmTZvGxIkTGThwIHFxcfzjH//gv//9LwCXL1/myy+/5MSJEzzyyCM3LGb9+OOPDB06FIPBQFFREd26dWPFihUAfPvttwwZMoThw4eTl5dHr169iIyM5LXXXmP58uXMnj2bPXv2cOTIETZt2oRKpWLatGlERUUxdOjQSn9uhBBCCCFE9bdq1Spyc3NZsWKF+XPlq6++yrx581i2bBn+/v488MADN73/66+/zhtvvIHJZEJRFGbNmmWp6EJUW1LMEkLcklqtxtbW9qbLo6OjGTx4sHl64JEjR7J161Z69eqFh4cHLVu2BCAwMJCcnBzc3d0JDg4mJiYGGxsb/Pz88PLy4tdff6WoqIjNmzcDUFBQwMmTJ4Hyb66uDnTZp08fHn74Yfr3788DDzxAixYtiImJAcqbaF+4cIGBAwcC0K5dO1xcXMwFr27duqFSqQgKCiI7O/uG+3O1m6HBYGDixIk0bdoUPz8/AJ544gmio6P56KOPOHnyJKWlpRQWFla4/2+//cahQ4cYMWIEAEVFRTRo0OAuj7oQQgghhKgtXnvtNV577bXrbv/zQO/X2rt3r/n3Zs2a8dlnn1VJNiFqKilmCSFuKSQkxDxl8LVd5ZYtW8b9999v7mp4LaPRCFChCKZSqVAUBYChQ4eyfft2bGxsCAsLA8rH5lqyZAmtWrUCyptju7i48M0332BnZ2d+nNdee434+Hh2797NtGnTePbZZ/Hx8QEwP/61FEWhrKysQp476fKn1+tZvHgxYWFhdO3alfbt27No0SISExMZMmQI/fv359dff71um2VlZTz66KM8/vjjAOTm5qLRaG67PSGEEEIIIYQQd0ZmMxRC3FLHjh3x8PBg+fLl5qLQzz//zFdffUWzZs3o0qUL27Zto6ioCKPRyObNm2/Z5x+gX79+7Nu3j7179zJgwACgfByutWvXApCamsrf/vY3kpOTK9zPaDQycOBA3NzcePrppxk6dCjHjx83L9fr9TRq1Ijvv/8egLi4ONLT0wkMDLynfW/cuDHjx49n/vz5KIrC3r17eeKJJ3jwwQc5e/YsKSkpmEwmNBqNuYDXpUsXvv76a/Lz8zEajUyePNnczVEIIYQQQgghxF8nLbOEELekUqlYsWIFCxcuZMiQIWi1Wtzc3Fi9ejWenp706dOH48ePM3LkSIxGI927d2f8+PFcvnz5po9pZ2dHaGgoJSUlODo6AvDss88yZ84chgwZQllZGdOmTcPX15f9+/eb76fVann++eeZMGECtra2eHh4sGjRIk6dOmVeZ8mSJcyZM4f33nsPGxsb3nvvPXQ63T3v/9NPP82mTZv45ptvePrpp3n55Zexs7PDx8eHkJAQLl68SIsWLcjLy2PatGksWbKE+Ph4Ro8eTVlZGT169GD48OH3vH0hhBBCCCGEEBWplBv1yxFCCCGEEEIIIYQQohqSboZCCCGEEEIIIYQQosaokd0M09LyrB2hWnNzcyArq8DaMQTyXFQn8lxUH/JcVB+1+bnw8nKydgRxG/J5Tgghrleb35uFuBc3+0wnLbNqIa1WZk6rLuS5qD7kuag+5LmoPuS5EEIIIaoXeW8W4s5IMUsIIYQQQgghhBBC1BhSzBJCCCGEEEIIIYQQNYYUs4QQQgghhBBCCCFEjSHFLCGEEEIIIYQQQghRY9TI2QyFqElUKmsnqP0UxdoJhBBCCCGEEOLuKYrCxbR8jpzN4MiZTBJTDYzp24xuretbO1q1JsUsIaqISgW7YhPJzi284fKQgMUAHDk9/cbLc68sd77x8pro0zPl+/Sof+Xtk41GTZeW9Wp9QctxzmsA5M+ZZ+UkQgghhBBCiL/CUFjKsXOZHDmTyZGzGWQbSszLtBo1H207Tn5hKQM7+VoxZfUmxSwhqlBZmQmj0XTDZQ28/gNAXMK0Gy8vvLLc4cbLa6Jf08r3aZxv7dknS7H9ZisgxSwhhBBCCCFqGpNJ4WxyLofPZHD0bCZnknPNX8br7W3o0qoeIX7utPLzwFBQwtL1caz78RSGolKG9/BHJd19riPFLCGEEEIIIYQQQohKlJVXzJGz5cWro2czyS8yAqBWqWjW0IUQfw9C/Nxp4uOE+ppilYujjlfGd2Dpuji+/fU8+YVGxg0MqrCOkGKWEEIIIYQQQgghxF9SajRx6mI2h8+Wdx+8mGYwL3N3tqVDc29C/Nxp2dQNBzubWz6Wl6s9r4wPZen6g+z6PYn8olKeHNISrUbm8LtKillCCCGEFcz59TW+Ob21Uh8zLGAYc+6XrqhCCCGEEJaQmlXA4TOZHDmTQfyFbIpLy4Dyca9a+bnT2s+dVv4eNPBwuOuugi56W2aMa8+7mw7xv+OpFBaX8ffhIdjaaKpiV2ocKWYJIYQQdcT27d+wd+8eiouLychIZ8KEx9m+/b+cPXuayZP/QWmpkfXrv0CtVtOmTTsmTXqO1NQU3n57ESUlxeTm5vDYY0/Rs2dvHn10LO3ahXL69CkAFi1ahl6vt/IeCiGEEEJUnaISI/EXsjlyJoMjZzNJzfpjsi8fdwdC/N1p7e9BUGPXSik6OdjZ8OKYdqzYcoTDZzJYuj6OKaPa3LZlV11wT8Usk8nEnDlzSEhIQKfTMW/ePJo0aWJevmHDBtatW4dWq2XSpEn06dOHzMxMXnrpJYqKivD29mbhwoXY29uze/du3n//fQBatmzJ7NmzZXAzIYQQtd6c++dZpRVVQUEB77zzPjt2/Je1a9fy/vsf8fvvsaxf/wVJSRf58MM12NnZ8eabr7NvXzSgYuzYcYSGduTw4YN89NG/6dmzN/n5+fTv/wBTp77M3LmvER29l/79H7D4/gghhBBCVBVFUUhKy+fw2QyOnMnk5MVsjGXlI7fb6TS0D/Sk9ZWxrzxd7askg62NhudGtubDb4/xv+OpLP7yd14Y3RYXvW2VbK+muKdi1o4dOygpKWH9+vXExcWxaNEiVq5cCUBaWhpr1qxh8+bNFBcXExERQbdu3VixYgVDhgxhxIgRrF69mvXr1zNq1CiWLFnCZ599hru7Ox988AFZWVm4u7tX6k4KIYQQolxgYHMA9HonAgICUKlUODk5UVhYSHZ2Fi+99DxQXvRKSkqiTZt2fPrpR2zb9jWgwmg0mh8rKKj8sby961FSUnLdtoQQQgghahpDYSnHzpWPe3XkbAbZhj8+4zSp50SIvzshfu4ENHSx2BhWWo2aiWGtcLSzYdfvSSz8/AAvjm2HVxUV0GqCeypmxcbG0qNHDwDatWvHkSNHzMsOHTpE+/bt0el06HQ6fH19iY+PJzY2lqeffhqAnj17smzZMgICAggKCmLx4sUkJiYSHh4uhSwhhBCiCt2s9bNKpcLbux7vvrsCrVbL9u3fEBgYxIcfriIsbBhdu3Zj27Yovvvu22vvZZnQQgghhBBVxGRSOHs5t7x4dSaDM8m5KOWNr9Db29ClVT1C/Nxp5eeBi6POajnVahXjBwbhaG/Dt7+eY+Hnsbw4ph0NvermMA/3VMwyGAwVxsXQaDQYjUa0Wi0GgwEnJyfzMkdHRwwGQ4XbHR0dycvLIysri5iYGLZu3YqDgwPjxo2jXbt2+Pn53XL7bm4OaLUy6NmteHk53X4lUfXOZOKkt7vhoqtTq950edatl9dEqtvs873QalR4et7Z671G/12oy49djd6Ha9SW/ahpnJzscHDQ4eXlhItL+Td5Xl5OpKc7Ym9vy5gxTzB16iTKyspo2LAho0cPJzNzCP/61zLWrfuM+vXrYzDk4uXlhEajxsvLCVtbWxwcdDg52cnzKoQQQogaIdtQbG55dfRsJvlF5S3P1SoVzRq6EHKl62ATHyfz/23VgUqlYkRPf/R2Wtb9eIpFXxxgyui2BDRwsXY0i7unYpZeryc/P9983WQyodVqb7gsPz8fJycn8+12dnbk5+fj7OyMq6srrVu3xsvLC4COHTty/Pjx2xazsrIK7iV2neHl5URaWp61Y9R5V895eYaiGy43XSn333S56dbLayLlNvt8L7RaNenpeeZvT26mpv9duF95PWTW4H24qqY/FzVZjx4D6NED0tLyaNGiPT179iQtLQ9Pz0YsWvQuAPff39e8vsFgpHPnXnzxRa8Kj5OWlsf69V+Tm1sClPDoo0+bb68upLAmhBBCiKuMZSZOXszhyJkMDp/J5GKawbzM3dmWDs29CfFzp2VTtxoxuPrATr442Nnw8XfHeXttHM+ObE2rpnWrl9s9FbNCQ0PZtWsXDz30EHFxcQQFBZmXtWnThnfffZfi4mJKSko4ffo0QUFBhIaGsnv3bkaMGMGePXvo0KEDISEhnDhxgszMTJydnTl48CCjR4+utJ0TQgghhBBCCCFE3ZNfVMrBU+kcOJHO0bOZFJeWAeXjT7Xyc6e1nzsh/h7U93CokZPQdW9THwc7Lau+PsI/Nx5kYlgrOgZ7WzuWxdxTMWvAgAHs3buXsWPHoigKCxYs4OOPP8bX15d+/foRGRlJREQEiqIwdepUbG1tmTRpEtOnT2fDhg24ubmxdOlSHBwcePHFF3nyyScBGDRoUIXCmBBCCCGEEEIIIcSdyMwt4veT6Rw4kUbChWxzb5h67g609nentb8HQY1dsbWpHcMWhQZ5MTW8Lf/66jArvz7Co8XB9GzbwNqxLOKeillqtZo33nijwm0BAQHm30ePHn1dCytPT08++uij6x5r8ODBDB48+F5iCCGEEEIIIYQQog67lJ7P7yfTOHAijbPJfwx54FffmdAgT0KDvKjv4WjFhFWrRVN3Xn64Pe9sOMgn38WTX1TKg52bWDtWlbunYpYQQgghhBBCCCGEpZkUhbPJufx+orwF1uXM8jG1NWoVLZu60T7Qi/aBnrg7156JtG7Hr74zM8aFsnR9HBt3nSa/0MjIXv41svvknZJilhBCCCGEEEIIIaotY5mJhAvZHDiZxu8n0sg2lACgs1HTIciL9kGetG3miWMNGLy9qjTwdOSV8aEsXRfH9ujz5BeVEjmwOWp17SxoSTFLCCGEEEIIIYQQ1UpxSRmHz2Rw4GQah05lUFBsBMDRTku31j6EBnrR0s+91ox/VRk8Xex5ZXwHlq2PY3fcJfKLjEwMa4lWo7Z2tEonxSwhhBBC3JHz58+xZMkCli9fzezZr/Da/7N35+FRlWfjx7+zJ5mZ7BsJJMFAgJCEEFBQFkH8AgvlAAAgAElEQVRELIosCiKKxQUqBVptrfzktYCI4t5aFF61VCsuEEEQFKtFBWRRSiCEhIR9TUL2bSbJJDNzfn8EY/OyBEJgZpL7c11cZM5zlvtMJmdm7vM89/PMAnS69nsHVAghhBCtq6q6jvTDxew5WEzW8VLq7U4AAn0N3JQQTkpcCF07+aFRt73kTGvxNep5alIKf1u1l105hdTY7Mwcm4hB37aSfpLMEkIIIVzAePAZDAVrW3WftrAxWOMWtuo+L+TZZxddk+MIIYQQom0rrqhprH918HQ5ZycgJDLYSO+4EFLigokOM7fp+k+tzcdLyx/uTWbp2kz2Hinh1ZV7eHx8rzY1DFOSWUIIIUQ7sWHDerZt24LNZqOkpJiHH36IDRu+5tixI8yY8Xvq6+2sXPkRarWapKRkpk+fRXFxMQsWPIOiKAQGBjXu6557RvHRR6vIzT3F4sV/welUsFiqePzxJ0lM7MXEiWNJTOzFyZMnCAwMZOHCl9Fo2tYdQSGEEEJcPkVRyC22svtgEXsOFnOi4JcZCGMjfUmJCyGlawhhgT4ujNLz6XUaZoxL5B8bsvkxq4CXPtrNH+5Nxt9kcHVorUKSWUIIIYQLWOMWXrNeVP+turqav/zlLTZu/JpPPvmEt95axp49aaxc+RG5uaf5+9+X4+XlxXPP/Zn//OdHfvrpR269dQR33TWWb7/9hjVrVjXZ37FjR5k58wliY7vwzTf/YsOG9SQm9iIvL5c33lhKWFg406c/THb2fhISEq/5+QohhBDC9ZyKwtHcSnYfLGL3wSIKy2uAhhkIEzoHkhIXQnLX4DaTaHEXWo2aR++Mx+il49u00yz6MI0/TuxNqL+3q0O7YpLMEkIIIdqRrl27AWAymYmNjUWlUmE2m6mpqaG8vIwnn/wd0JD0ys3N5dixo4wYMRKAxMRe5ySzgoNDef/9v2MwGKiursZoNALg5+dPWFg4AKGhYdTV2a7VKQohhBDCDdTbneScLGvogXWomEprwwyEBr2Gvt1DSYkLJum6YHy8JC1xNalVKibd2hWjl5Z12443JLQmJNMx1OTq0K6IvGqEEEKIduRC9SZUKhWhoWH89a9L0Gq1bNiwnq5d4zh58jhZWRl07RpHdvb+c7Z7441XmDt3ITExnVm27G3y8/Muehzhefbu3curr77K8uXLycrK4rHHHiMmJgaA++67j5EjR/Lmm2+yadMmtFotc+bMISkpybVBCyGEh3EqCpv25KLVaXHU2/EyaPHSa/DWa/EyaPDSa/HWN/yv16nd9n22xmZvmIHwYBH7jpZQY3MAYPbRMSipAylxIcTHBKDTSumBa0mlUjFm0HUYvXV8svEQL360m8cn9KJLpJ+rQ2sxSWYJIYQQAo1Gy7333s/MmdNwOBx06BDBLbcM59FHpzNv3tNs3PgNERGR52x3222/4v/9vz8SGBhISEgoFRXlLoheNKeuro5ly5Zx7Ngx5s6dy/vvv8+0adPQ6/UX3e7dd99l3bp1eHs3DEfYv38/Dz30EA8//HDjOllZWezcuZNPP/2U/Px8Zs2axerVq6/q+QghRFuz+0ARH35z8JLWValoSG41SXJpGhNg57b9V0LsKiTGKqx17D3cUMB9//FS7I6GCu7Bfl4MSoogJS6ELpF+qNXumYBrT4b37YTRS8s/vszh1RV7mDkukYTOQc1v6IYkmSWEEEK0EyNHjmr8uX//mxg1agRFRVV07dqN119fDNA4pPC/vfLKG+csW7VqPQATJz7AxIkPnNO+bt3XjT/LzIeut2DBAgIDA9m/fz8ajYaTJ08yZ84cXn311YtuFxUVxeLFi3nqqacAyMzM5NixY3z77bdER0czZ84c0tLSGDhwICqVioiICBwOB6WlpQQGBl6LUxNCiDZhU3ouAI9P7E11tY3aOge1Nge1dXZq6xzU2M7+f/Zx7dnHFRYbBXUOHE6lRcf9OTHmpdfg3dgbrGkCrGlbw/8GvYZThRZ2Hyzi8OkKfj56p1ATvbsGkxIXQqdQk9v2IGvPbkrogI9Bx5K1mbzxaQbT7urJ9d1DXR3WZZNklhBCCCFEG5eVlcWaNWvYsmUL3t7evPTSS4waNarZ7UaMGMHp06cbHyclJTF+/HgSEhJYunQpb731FmazGX9//8Z1jEYjVVVVzSazAgJ80MowEyGEIK/Iwv7jZSTEBjHs+qjL3l5RFOrtTmpsdqpr7dTY7Gd/rm+y7Jf/zy632an5eZnNTlV1PYVl1Y09qy6FSgXx1wXRP6ED/RPCCQ8yXnb84tobHmImPMzMc8t+4n8/z0Sj68WI/jGuDuuySDJLCCGEEKKNU6lU1NXVNd4hLysra9Hd8uHDh+Pr69v483PPPcewYcOwWq2N61itVsxmc7P7KiurvuzjCyFEW7Tmu8MADOjZMHFKUVFVi/elBcx6NWa9HswXH0p+IfV25zk9wJr0DDvbYyzQ14vkLsH4Gs8ex+m8otjFtRXua+Cp+3rzemo6b366lzNFFkb2j3Z1WOcICTn/Zwr1NY5DCCGEEEJcYw8++CAPPfQQRUVFPP/889x99938+te/vuz9PPLII2RkZACwY8cOevbsSUpKClu3bsXpdJKXl4fT6ZQhhkIIcYnq7Q627svH5K0jJS7E1eEAoNOq8fXRE+rvTVSYmbhO/vTqEky/+DCGJEdye78oxgy6jsG9In5JZAmPFB1u5v/dn0Kgr4FVm46Q+v1hFKVlQ1avNemZJYQQQgjRxo0ZM4aEhAR++uknHA4HS5cupXv37pe9n/nz5/Pcc8+h0+kIDg7mueeew2Qy0bdvX+69916cTidz5869CmcghBBtU9qBIiw19fyqXxQ6rfQ1EddehyAjcx7ow2sr0/nXTyex1tTz69u7u33B/hYls5xOJ/Pnz+fAgQPo9XoWLlxIdPQv3dFSU1NZsWIFWq2W6dOnM3ToUEpLS3nyySepra0lNDSURYsW4e3tzcKFC9m9ezdGY8PY2iVLllxS13QhhBBCCHFp6urqOHnyZOPnrZycHHJychgzZkyz23bs2JHU1FQAevbsyYoVK85ZZ9asWcyaNat1gxZCiHZg056Gwu83J0e4OBLRngX6ejH7/hT+krqXHzLyqbbZmTaqp1snWFuUzNq4cSN1dXWsXLmS9PR0XnzxRZYuXQpAUVERy5cvZ/Xq1dhsNiZNmsSAAQNYsmQJd955J+PGjeOdd95h5cqVTJkyhaysLP7+979Ld3QhhBBCiKtk6tSpKIpCZGRkk+WXkswSQghxdeQWWTh4uoKeMQGEBvi4OhzRzvn66Hnqvt4sXp1B2oEi3rDtZea4RLz07jmgr0VptrS0NAYNGgRAcnIymZmZjW0ZGRn07t0bvV6P2WwmKiqKnJycJtsMHjyY7du343Q6OXHiBHPnzmXixImsWrWqFU5JCCGEEBeTlZXJzJnTLrrOmTNn2Lp1y1WP5ciRw6Sn7wZg3rynqa+v5/nn5/Pjj9uv+rHbk7KyMj744AMWLVrU5J8QQgjX2ZSeB8CQ3pHNrCnEteFt0PLEhF4kdwlm//EyXl2RjqWm3tVhnVeLUmwWiwWTydT4WKPRYLfb0Wq1WCyWJsMEjUYjFoulyfKfp2yurq7mgQce4KGHHsLhcPDggw+SkJDQbA0Hmcq5eReq+C+usaOlmE1e521Sn51F6oLtZRdv90SqZs65JbQaFcHBl/Z69+i/i7Nj1j36HP5LWzmPK/Mn4NNW3ud44JWLrvHuu++ybt06vL29gQv/Ln744d8cPXqUsWPvaOUYm1qxYivBwcGEhNzMkiVvAuDlpcPPz1teJ62of//+bN++nf79+6NWu++QASGEaC9s9Q62Z57Bz6SnV5dgV4cjRCOdVsOMcQm8vyGHbZlnePGj3fzx3mQCzAZXh9ZEi5JZJpOpyRTMTqcTrVZ73rafp2f+ebmXlxdWqxVfX1+8vb158MEHGz9Q9+/fn5ycnGaTWTKV88WFhJhlSlQ38POM51WW2vO2O8/OEnHBdufF2z2R0sw5t4RWq6a4uIrmJt3w9L+LwLOvh1IPPoefefrvorUYjXUYDK07W4zNVofVevHn1t8/hGeffZHnnmso0l1UVMVnn33KV199gVqtJikpmccem8nSpf9LbW0tsbHdGTjw5sbtV61awRdfrCMwMAiVSsX99z/ImTP5nDhxnOnTZ2Gz2bj//ntYtWo9e/ak8d577wJQW1vLM888i06nY/bsJ/D19SMlpS8bNqxHq9URERHD3LlP89FHq6itraeioob8/DJeeeUFTp8+hdPpZOrU6aSk9L2k50ISYU1FRETw8MMPN95UUBQFlUpFdna2iyMTQoj2aWd2ATU2O7f2iUGrkZsMwr1o1GoeuqMHPl46/r3rFIs+TOOPE5MJc6PhsC1KZqWkpPD9998zcuRI0tPTiYuLa2xLSkrir3/9Kzabjbq6Oo4cOUJcXBwpKSls3ryZcePGsWXLFvr06cPx48d54oknWLNmDU6nk927dzN27NhWOzkhhBDCXVmtC7FaF17z4w4ZMoz8/LwmyzZsWM/jj/+JhIRE1qxZhaIoPPDAFE6cON4kkVVSUsyqVSv54IOVADz66OSLHuvYsaPMnfscwcEhfPDBP/j++43cdtuvKC0tYdmyD9HpdCiKQlBQEPHxCedsv379Wvz8/Hn66blUVJQzY8Y0PvwwtRWehfYnNTWV7777jogIKTAshBDuYNOePFQqGNxLrsvCPalVKiYO64LJW8uaH46x6MPd/GFCL6LC3OOGYYuSWcOHD2fbtm1MnDgRRVF44YUXeO+994iKimLYsGFMnjyZSZMmoSgKTzzxBAaDgenTpzN79mxSU1MJCAjgtddew8fHh1GjRjFhwgR0Oh2jR4+ma9eurX2OQgghhLiIOXPm8sknH/K//7uYnj0TL7jemTNn6Nw5Fr1eD0BCQtJ51vqlt1lISAh//esreHv7UFRUSGJiLwA6dIhAp9M1G9eRI4fJyNjD/v0NtTkdDjsVFeX4+flfxtkJaPhd+PvL8yaEEO7gxJkqjuVX0is2iCC/tlNSRLQ9KpWKUQM6Y/TW8dE3B3np4z08Pj6Jrh1d/5miRckstVrNggULmiyLjY1t/HnChAlMmDChSXtwcDDLli07Z19Tp05l6tSpLQlDCCGEEK1g3bq1PPnk0xgMBv7wh5ns27cXlUqFojibrNepUydOnjxObW0tOp2OQ4cOcOutI9Dr9ZSUFANw4EBO4/ovvbSQ1NTP8fExsnDhvMblKtUvwynUanXjsOr/Kzo6htDQUB588GFstlr++c9/YDb7tuaptxv+/v7ceeedpKSkNEkkShF4IYS49jan5wJS+F14jltSOuLjpWXZF9m8tiKd345NJCk2yKUxuecci0K0E04FisprANBq1Og0anRaNXqdjJsXQlw7sbFdmDr1Qfz9AwgJCSE+PgGj0cgHH/yDuLju3HrrCAB8ff2YMuVRZs6chq+vL/X1dgD69buJtWtXM336I3Tr1gOj0QjAiBEjmTZtCmazmYCAIIqLi845drduPViy5A1iYjqf0zZ69DheemkhM2dOw2q1MHbseCle3kJDhgxhyJAhrg5DCCHavRqbnR37CwjyNZB4nWuTAUJcjv7x4fgYtLy1JpPFqzN45M4e9I8Pd1k8KkVprmyy+5HCwRcnxZXdg0oF6UdLKauood7upLCshoKyagpKa6i01vHWUw8D8Ojz756zrUGn4X/jH0WtUvGu83Mig40Y9J4/g+eMtGEAvNXn21bbp1arZlBih7ZfAL5PQz2h0rRMF0dy5Tz9d9GWXOnvYt68pxk9+u5LLsp+LUkB+Kby8vLOu9yVNbTkOiCEaI++35PL8q8PMHZQZ0YNOPdGjnxOEu7u4Kly3liVQa3NzgO3xTE0peNVPd6FPtNJzywhrhK7w8mhU+XsPVhEXom1MdmiAnyNejRqNSoVxMcEoFKpsDuc1Nud1NU7KLfU4XA4cQBbM/NRqSAs0IeoUBNRYWZ8vORPVwghxKV74IEHzg4dVbDb7RQXF9OjRw9Wr17t6tCEEKLdUBSFTXtyUatUDJLC78JDxXXyZ/ak3ry+Mp3l3xxEr9MwILHDNY9DvhEL0crKqmx885+TbM88Q1V1PQBBvgY6BBkJC/QhJMALvVaDt6Ghp1Xf7qHn3Y+xRIfTqdC7azAnCy2cKanmTEk1/8kpJCbcTI+YAIL9vK/ZeQkhxP/17LNSb8lTfPfdd00eZ2Rk8NFHH7koGiGEaJ+O5ldyqtBCn24h+JsMrg5HiBaLCjPz9AN9+PsX+3G6aLCfJLOEaCWWmnq++vEE36adps7uxOSto3dcCJ1CjPibL//NSgVo1CoSY4NIjA3CWlvPyQILh06Vcyy/imP5VYT4e5EYG0RksBGVStX6J+UhLvXU28JT5Opz8LyB6UKI80lKSmLOnDmuDkMIIdqVTXvOFn5PlsLvwvOFBfrwPw+6rtSEJLOEuEJ2h5Ovd55kw48nqbHZCTAbmDSwMzclhJN1spyyippWOY7RS0eP6AC6R/mTX1JN9okycousfJeWS3igDyndQghuh1P7ajVqfswqoM7hvOh6ZmMpVVbbNYqq9d1W5wDgh335LotBp1HTPz5MElpCeKA333yzyeNDhw4RFCSFh4UQ4lqx1tazM7uQUH9vesQEuDocITyeJLOEuAInzlSx7MtsThdZMHnruPeWLtySEolOq7lqPWhUKhURwUYigo2UVdnYfbCI3CIrG3acIKaDmT7dQjB66ZrfURtS73Bit188mWV3KM2u494aMkiefQ5CCHdxww03cMcdd7g6DCGEaDe2Z56h3u7k5t4RqF3d1V6INkCSWUK0QL3dyfrtx9iw4yRORWFwrwgmDO1yzQuzB5gNDOvTkTMl1ew6UMjx/CpyC62kdAshrpNfux56KIQQ4hczZ850dQhCCNFu/Vz4XatRuaRQthBtkSSzhLhMheU1LPlsHycLLQT5ejFlZHd6xgS6NKbwIB/uuDGaw6cr2HWgiJ/2F3A8v5IbE8LxNepdGpsQwn04HA5eemkhp06dQK3W8OqrL+PtfeGhDjabjfvvv4dVq9Zf1biOHDlMVVUlyckpzJv3NM88s4CXX36eYcNuo3//m67qsdu67t27n/fGhqIoqFQqsrOzXRCVEEK0LwdPlZNfUk2/+DB8feSzuRCtQZJZQlyGjCMlvLs+C2utncG9OnDvLV3xNrjHn5FKpaJrJ38iQ0z8tL+AU4UW1m87Tt/uIcR18pdeWkK4GeP8ZzCsX9uq+7SNGoN1/sILtm/b9gMAS5f+g927d7Fo0SIWLHi5VWNoiU2bviUoKIjk5BSZIbGV5eTkuDoEIYRo9zal5wEwJDnCxZEI0Xa4x7dwIdycU1H4YttxPt96DI1GzUMjuzMoyT3fjHy8tAzpHcHJAgs7ss7w0/5C8oqruTEhHC+9xtXhCSFcaPDgIdx000AACgrOEBwcDMDMmdPw9w+gqqqK559/mYUL51JVVUVkZMfz7mfVqhV88cU6AgODUKlU3H//g5w5k8+JE8eZPn1Wkx5de/ak8d577wJQW1vLM888i06nY/bsJ/D19SMlpS9fffUFWq2OuLjuzJ37NB99tKrxWHa7nVdeeYHTp0/hdDqZOnU6KSmumznHU9XU1PDmm2+yY8cOHA4H/fv35/e//z0+Pj6uDk0IIdq0yuo60g4U0iHIh7hO/q4OR4g2Q5JZQjSj3u7g3fX72XWgiCBfL2aMSyAm3NfVYV2USqUiOtxMiL8XWzPOcKrQQsm24wzs1YHwQPniIoQ7sM5feNFeVFeLVqtl4cJ5bNmyicWL/9a4fPjw27n55qGsXr2Szp1j+c1vZpCVlcnu3buabF9SUsyqVSv54IOVADz66OSLHu/YsaPMnfscwcEhfPDBP/j++43cdtuvKC0tYdmyD9HpdCiKQlBQEPHxCedsv379Wvz8/Hn66blUVJQzY8Y0PvwwtRWeifZlwYIFeHt788ILLwCQmprKvHnzeOWVV1wcmRBCtG3b9uVjdygM6R0pIyWEaEWSzBLiIqpr61m8eh8HTpUT18mfGWMTMHvQOHcfLx23Xt+RrKOlpB8u5t87T5HSLYT4mAB5MxWiHXvmmWcpKSlm+vSH+ec/G5JSUVHRQEPyqV+/GwHo2TMBrbbpR4UzZ87QuXMsen3DtTAhIek8R1AafwoJCeGvf30Fb28fiooKSUzsBUCHDhHodM3PvHrkyGEyMvawf38mAA6HnYqKcvz85O725cjKymLdunWNj+fOncvIkSNdGJEQQrR9TkVh8548dFo1NyWEuzocIdoUSWYJcQFlVTb+kprO6SIrfbuFMHVUPDqt5w3TU6tUJMYGERbow+b0PNIOFFFcUctNCeHotGpXhyeEuIb+9a8vKSoqZPLkh/Dy8kKlUqFWN1wHfv4/KiqGzMx9DBo0hIMHc7Db7U320alTJ06ePE5tbS06nY5Dhw5w660j0Ov1lJQUA3DgwC91ml56aSGpqZ/j42Nk4cJ5jctVql+uP2q1GqfzlwTYf4uOjiE0NJQHH3wYm62Wf/7zH5jN7t071h0pikJlZSW+vg3PXWVlJRqN572nCSGEJ8k+UUZheQ0DEsMxejV/A0cIcelalMxyOp3Mnz+fAwcOoNfrWbhwIdHR0Y3tqamprFixAq1Wy/Tp0xk6dCilpaU8+eST1NbWEhoayqJFi/D29m7c37Rp0xg2bBj33Xdf65yZEFegoLSaV1fsoaTSxi0pkUy6NQ612rN7MoUGeHPnTdFsTs/jxJkqyi02hvaOlNkOhWhHbr75Fl544VlmzJiK3W5nzpw5GAyGJuuMGzeeRYueZfr0R4iOjjmn95Svrx9TpjzKzJnT8PX1pb6+IdnVr99NrF27munTH6Fbtx4YjUYARowYybRpUzCbzQQEBFFcXHROXN269WDJkjeIiel8Ttvo0eN46aWFzJw5DavVwtix4xsTb+LSTZkyhfHjxzN06FAAvvvuO6ZNm+biqIQQom3btCcXgCG9I10ciRBtj0pRlPPfCr2Ib775hu+++44XX3yR9PR03n77bZYuXQpAUVERDz/8MKtXr8ZmszFp0iRWr17Nyy+/THx8POPGjeOdd95Br9czZcoUAF5//XV27NjBuHHjLimZVVRUdbkhtyshIWZ5jq5AQWk1L328m3JLHeMGX8cdN0a3aEieSgXpR0spq6g5b/vIgcMA2LD12/O3l5xtDzp/e0s5nQppB4rIPlGGXqdmSHIk4UHXpo7WjLSGc3qrT+udk5dBi8PhpN7uvOh6ZpMXVZbaVjvutTZy8tnXw/LWfT1cDq1WzaDEDlz+u0ZTco1yH63xu5g372lGj77b7Yqyh4SYXR2C2zlw4AC7du3C6XRyww030K1bN5fGI9cBIURbVlZl409LthMZYmT+Q9df8vcJ+ZwkRFMX+kzXolubaWlpDBo0CIDk5GQyMzMb2zIyMujduzd6vR6z2UxUVBQ5OTlNthk8eDDbt28H4F//+hcqlYrBgwe3JBQhWtV/J7Im3tKFO2+KaXO1pdRqFdf3CGVAYjh2u5N/7zrFoVPlrg5LCCHEVTBu3Dg++eQTqqqq6NatG/fffz+TJ092eSJLCCHauq0ZeTgVKfwuxNXSomGGFosFk8nU+Fij0WC329FqtVgsFszmXzJnRqMRi8XSZLnRaKSqqoqDBw/yxRdf8Le//Y233nrrko8fEOCD1gNrF11Lckf68uUVWXhlRTrlljoeuSuBMTfHXvlOj5ZiNnmdt0l99k3tgu1lF2+/UsndvAgNNPLVjuPsyCqgpt7JjYkdGuO6GlTNnHNLGPRqHA4Fu6P57kJX67m8Fpp7vVwLWo2K4ODWubbINcp9XOnvYsmSN1spEnE1PP3006xdu5Y333yTG2+8kbvvvpsbb7zR1WEJIUSb5nQqbNmbh0GvoX98mKvDEaJNalEyy2QyYbVaGx87nc7G2Y7+b5vVasVsNjcu9/Lywmq14uvry9q1aykoKODXv/41ubm56HQ6IiMjm+2lVVZW3ZKw2w3pmnr5isprWPRhWmOPrAHxoVf8HP6cE7rQ0Dbn2bFaF2x3Xry9NZi9tdzeL4rvdueSfrCI4vIaBiV1uGqF4ZVmzrkl6tvJMMPmXi/Xglarpri4SoYZtiFt+XchCdMG119/Pddffz11dXVs3LiR999/n/nz53PXXXcxbtw4OnTo4OoQhRCizdl3tISSShtDkiPwNsica0JcDS36xpqSksKWLVsASE9PJy4urrEtKSmJtLQ0bDYbVVVVHDlyhLi4OFJSUti8eTMAW7ZsoU+fPjz11FN8+umnLF++nLFjxzJlyhQZbiiuuQprHa+tbOiRNWFoF267IcrVIV1TvkY9v+ofRXiQD6cLLfzrp5NYa+tdHZYQQohWpNfrGTlyJG+//TYff/wxBQUFDB8+3NVhCSFEm/Rz4febk6XwuxBXS4vSxMOHD2fbtm1MnDgRRVF44YUXeO+994iKimLYsGFMnjyZSZMmoSgKTzzxBAaDgenTpzN79mxSU1MJCAjgtddea+1zEeKyVdfa+cvKdArLarjzpmhu79e+Elk/M+g03NqnIzuzCzh4qoINO04wNKUjwX6eOyxPCCFEU8ePH+eLL75gw4YNhIeH89JLL7k6JCGEaHNKKmrJOFpC5w6+RIdLL2EhrpYWJbPUajULFixosiw29pf6QhMmTGDChAlN2oODg1m2bNkF9zlr1qyWhCJEi9XVO/jb6gxOFloYkhzB2EHXuTokl1KrVfSLD8PXqGdXThFf/3SSgUkd5E1YCCE8WGFhIRs2bGDdunVYLBbGjBnDsmXLZHihEEJcJVv25qEoMKR3hKtDEaJNkwG8ol1yOhXeXpfFwVPl9O0WwgO3dZNZRmgo0B4fE4ivj54te/PYnJ5Hn24hxMcEyPMjhBAe6ICa6NIAACAASURBVPbbb+e2225j9uzZ9OvXz9XhCCFEm2Z3ONmSkYe3QcsNPaTwuxBX09Wp8iyEm1vx3SH2HCqmR3QAU0f1RK2WRM1/6xhqYkS/KLwNWtIOFPHT/sLGgvRCCCE8x5YtW3jxxRevKJG1d+9eJk+eDMCJEye47777mDRpEvPmzcPpbJh848033+See+5h4sSJZGRktErsQgjhafYeLqbCUsdNCeEYdBpXhyNEmybJLNHu/HvXKTbuOk1ksJEZYxOu2sx9ni7I14uRN0YRYDZw8FQ53+/ObXbGQCGEEO7FZDJd0fbvvvsuzzzzDDabDYBFixbx+OOP8/HHH6MoCt9++y1ZWVns3LmTTz/9lNdff51nn322NUIXQgiP83Ph9yHJMsRQiKtNvsWLdmXPoSJWbDyEn1HP78cn4eOlc3VIbs3opeP2flFEBhvJLbbKTIdCCNHOREVFsXjx4sbHWVlZ3HDDDQAMHjyY7du3k5aWxsCBA1GpVEREROBwOCgtLXVVyEII4RKFZdVkHS8jrqMfkSFXdiNBCNE8qZkl2o3jZyp5e10WOp2a392TRLCft6tD8gg6rZqhKZHszC7k4KlyNuw4yS19IgnylZkOhRCirRsxYgSnT59ufKwoSmMNRaPRSFVVFRaLBX9//8Z1fl4eGBh40X0HBPig1cowHCFE2/DlTycBGDU4lpCQK5tA6Uq3F6I9kGSWaBfKqmz8bVUG9fVOZo5LpHMHX1eH5FEaZjoMxddHx64DDTMdDu4VQcdQueskhBCe4LPPPuOll16isrIS+CUplZ2dfVn7Uat/6dRvtVrx9fXFZDJhtVqbLDebm/8iVlZWfVnHFkIId1Vvd/L1jycweeuIizBTVFTV4n2FhFzZ9kK0NRdK7sowQ9Hm1dU7ePOzfZRb6rhnaCy940JcHZJHUqlUxHcOZEjvCBQFvtudS+axUhRFCsMLIYS7W7JkCcuXLyc7O5vs7GxycnIuO5EFEB8fz08//QQ0FJfv27cvKSkpbN26FafTSV5eHk6ns9leWUII0ZbsPliEpaaegYkd0EmPUyGuCemZJdo0RVF4/185HMuv5KaEcG6/IcrVIXm8qDAzt/fT8f3uXHYfKKK8ysaNPcPQaCQ3LoQQ7io0NJS4uLgr3s/s2bP585//zOuvv851113HiBEj0Gg09O3bl3vvvRen08ncuXNbIWIhhPAcPxd+v1kKvwtxzUgyS7RpX/10kh+zCoiN8OXXt3drrPMhrkyQnxcjb4xm055cjuZVUmmtY0jvSHy85JIihBDuqGfPnvzud79jwIABGAyGxuVjxoxpdtuOHTuSmpoKQOfOnfnwww/PWWfWrFnMmjWr9QIWQggPkVds5cCpcnpEBxAW6OPqcIRoN+Sbp2izMo4Us3rTEQLMBmaOS5Quv63Mx0vLbTd0YkfmGY7lV/HljuPcnBxBaIC8iQshhLuxWCwYjUbS09ObLL+UZJYQQogL25Te0CtraO9IF0ciRPsiySzRJhWUVfP2uv1otWpm3Z2In8nQ/Ebismk1agYmdSDQ14vdB4v4eucp+nYPpXuUv/SCE0IIN7Jo0SLq6+s5duwYDoeDrl27otXKx0AhhLgSdfUOtu87g59RT3LXYFeHI0S7Ip9iRJtjq2so+F5js/PIHT2ICZeZC68mlUpFz86BBPl6sWVvHv/JLqS4vIb+PcPRaaWOlhBCuIPMzEx+97vf4e/vj9PppLi4mLfeeotevXq5OjQhhPBY/8kppNpm584+0WilfqwQ15Qks0SboigK732VTW6RlWEpHRmQ2MHVIbUb4UE+3HFTNJv35HEsv4qSiloG9YogyM/L1aF5NEVRqLc7cToBFEoqauFspzeNSoVBr8Gg06BWS084IcSFLVy4kL/85S+Nyav09HSee+45Vq1a5eLIhBDCc23ak4sKGNxLCr8Lca1JMku0KV/vPMXO7EK6dPTj3mFdXB1Ou2P00jGiXxR7Dhax/3gZX/14gpRuIfSIDpBhhxehKApV1fWUVNZSYamjqrqOqup6LDX12OocKMCttnoAvtxx4rz70OvUGL10+Bn1+Jn0+Bn1BPt5Y/TWynMvhKC6urpJL6zk5GRsNpsLIxJCCM92sqCKI3mVJMUGEezn7epwhGh3JJkl2ozs46V8uukwfiY9vx2TIF19XUSjVtG3eygdgoxs25fPrpwi8oqt3Ngz3NWhuY26egeFZTUUlFVTVF5LaWUtdofSZB2VCkzeOsw+egw6NTqNGlTQIzqgcR2H04mtzkFtnYPaegdV1XWUVTX9curjpSUswJuwAB86hhrx8dJdk3MUQrgXPz8/Nm7cyK233grAxo0b8ff3d3FUQgjhuTan5wEwJFkKvwvhCi1KZjmdTubPn8+BAwfQ6/UsXLiQ6OjoxvbU1FRWrFiBVqtl+vTpDB06lNLSUp588klqa2sJDQ1l0aJFeHt789FHH/HZZ5+hUqmYMWMGQ4cObbWTE+1HSUUtSz/PQq1SMWNMIv5S8N3lIkOMjBoQw7Z9Z8grtrJu63HqfZ3tso6Ww6lQVF5DXpGVvBIrpZW/JJxUgJ9JT6CvF4G+BvxNBsw+OoxeuiZDBw36htk4r+8ResHjKIqCtdZOhaWOCouNwvIaCstqOJZfxbH8KtgPwX5eRIWbiQ4zYfbRX7VzFkK4lwULFvDUU0/xP//zPwB06tSJl19+2cVRCSGEZ6qx2dmedYZAXwNJsUGuDkeIdqlFyayNGzdSV1fHypUrSU9P58UXX2Tp0qUAFBUVsXz5clavXo3NZmPSpEkMGDCAJUuWcOeddzJu3DjeeecdVq5cyV133cXHH3/M2rVrsdls3HHHHQwZMkSGxIjLUm938NaafVhq6pl8WxxdOvq5OiRxlrdBy7A+kRzOrWRXTiG2egd2h0JVdV2bT6TY6h2cLrRwqtBCfnE19Q4nAGqVirAAb0IDfQgL8CbE37vVEnwqlQqTtw6Tt47IECPxNCS4Kq115JVUc6rAQkFpNcUVtew+UER4kA9xHf3oFGZGIzW3hGjTOnfuzKeffkp1dTVOpxOTyeTqkIQQwmP9lF2Arc7Br26IkrqlQrhIi5JZaWlpDBo0CGiouZCZmdnYlpGRQe/evdHr9ej1eqKiosjJySEtLY3f/OY3AAwePJjXX3+dKVOm8Pnnn6PVasnNzcXX11cSWeKyKIrC8q8PcvxMFQMTOzCkt3TzdTcqlYquHf2IDPbh37vVOJxOPv/hOPExASTGBrWpnlo1NjunCiycKKjiTGk1ytmRg2YfHbHBvkSEGAkL8Lmm56xSqfAzGfAzGegRHUBtnZ1ThRaO5lZypqSaMyXVeOkL6drRj+7RAXgbZPS5EG3Jn//8Z5577jkmT5583s9YH3zwgQuiEkIIz6UoCpv25KJWqRgkhd+FcJkWfWuxWCxN7uhpNBrsdjtarRaLxYLZbG5sMxqNWCyWJsuNRiNVVVUNAWi1fPjhhyxevJjJkydf0vEDAnzQajUtCb3dCAkxN79SG/DV9mNs3ZdPl45+PHF/H/Q6N3tdHC3FbDr/bH7qs18qLthedvF2T2M2eeHjpcXucOLj0JJ5rJSj+ZX0T+hAt+iAxufjchn0ahwO5ZyaUxeKobVVWus4mlvB0dwK8kusjctDAry5LsKP6yL9CPS98uM293q5VGYgJNBESvdwyqpqyTpaSs6JUvYdLWX/8TLiOweSHBeKr/HcnnNajYrg4Na5trSXa5QnkN9F23bvvfcCMGvWLBdHIoQQbcPxM1WcLLCQEhdCgFlKmwjhKi1KZplMJqzWX760OZ1OtFrtedusVitms7lxuZeXF1arFV9f38Z1HnjgASZMmMDUqVP58ccf6d+//0WPX1ZW3ZKw242QEDNFRVWuDuOqO5Jbwdtr9mHy1vGbUT2pKHev18XPuZkqS+15251nu+1csN158XZPpCgKGrWKuwbGkHWslMyjpXy36xRp2QUkxgYRE26+7K7a9QYtDoeTervzouuZTV6t8lwqikKFtY6TBRZOFlQ1qX8VFuBNpzATUWFmTN6/FFpvjeM293ppCa0KesUG0jPGn8O5FWQdLWXfkRIyj5YQG+lHcpdgfLx+eZvQatUUF1c19jhrqfZyjfIEbfl3IUm6BgkJCUBDT/qjR4/SvXt31q9fz/79+5k6daqLoxNCCM/z/Z5cAIYkS68sIVypRWNdUlJS2LJlCwDp6enExcU1tiUlJZGWlobNZqOqqoojR44QFxdHSkoKmzdvBmDLli306dOHo0ePMnPmTBRFQafTodfrUavbzpAjcfVUWOtYsjYTp6Lw2OieBPm1jd5L7YVWo6ZXl2DGDOpMbKQvldV1bM3I5/Otxzh0ugK74+KJqWvN6VTIL7Hyn+xC1v5wjHVbj5N+qJjyKhsRwUb69wxj/NBYRvSLIj4msEkiyxNoNWq6RwUwdvB1DEgMx9dHz+HTFazZcpQ9B4uosztcHaIQ4gr96U9/Yv369WRkZLB48WJMJhNPP/20q8MSQgiPUl1bz879BQT7eRHfOdDV4QjRrrWoZ9bw4cPZtm0bEydORFEUXnjhBd577z2ioqIYNmwYkydPZtKkSSiKwhNPPIHBYGD69OnMnj2b1NRUAgICeO211/Dx8aF79+7ce++9qFQqBg0axA033NDa5yjaGIfTydufZ1JWZeOeIbHEx8gbiacyeusYkNiBXrHBZB4r4fDpCnZkniEtp5DrInzp2snfZd23a+sc5BVbOFVoJa/Y2tjzS6tREXW291XHEKP7DW29Amq1ithIPzp38OVIbgXph4vZd7SUg6cqSIkLpntMgKtDFEK00OnTp3njjTd45ZVXuOeee5g2bRp33323q8MSQgiPsiOrgDq7kyG9I1tcIkMI0TpalMxSq9UsWLCgybLY2NjGnydMmMCECROatAcHB7Ns2bJz9jVz5kxmzpzZkjBEO7Vq0xFyTpbTJy6EX/WLcnU4ohWYfHT07xlO4nVBHDxVzuHcCnJOlpNzspxAXwOdQk10DDER6Gu4apNE1NudFJbVUFBaTUFZNcXltfw8ms7krSM2wpeOoSbCAr3RtPEepGq1iq6d/Inp4Ev2iTIyj5awI6uAw7mVRIeaiQqT4VtCeBqHw0FpaSkbN25k8eLFFBUVYbPZmt9QCCEE8Evhd41axcDEDq4OR4h2T6atEh5lZ3YBX+88RYcgHx6+o4fMftnGGL119I4LoVeXYE4XWTh0uoL8YiullTb2Hi7Bx6AlLNCbID8vgvy8CDR74XWZHbcURaG2zkFVdR2llTZKKmsprbRRbrE11oJSqSDY34uOoSY6hZjwM+nb5WtNp1WTFBtEl0hfduUUcfxMFc++/x9uSenI3Tdfh5de3kKE8BSPPvooEyZM4JZbbiEuLo4RI0bw+9//3tVhCSGExzh0uoLcYis39Dj/RDlCiGtLvokIj5FbZOG9DTkY9BpmjE3E2yAv37ZKrVYRFdbQA6jO7iCvuJrThRZyi6wcy6/iWP4vBau9DVqMXlp8vLR46bVo1CrUahUatQqnU6HO7qTe7sCpQIXFhqWm/pyZDzVqFcF+XoQF+hAW4ENogDc6bdvufXU5fLx0DE6OoFtZDXsPFfNt2mn2Hi5myq+6yzBfITzEoUOH2LhxY+PjDRs2oNG0nWHSQghxtW1K/7nwe6SLIxFCgCSzhIeorrXz5ppMbPUOfjsmgYhgo6tDEteIXqshJtxMTLgZRVGoqq6nuKKWkopaSqtqqa61U1plo7ii+Vn+dBo1Zh89Zh8dJm8dgb4GAn298DXqpe7BJYgMMXL34Ov4fOsxvvrxJK+uSGdIcgTjh3aR5LIQbu7777/n8ccfb+xlKoksIYS4dFXVdezKKSI80IduUf6uDkcIgSSzhAdwKgrLvtxPQWk1t/eLom/3UFeHJFxEpVLha9Tja9RzXYQvAF4GLXa7g6rqemrrHDidCk5FwelUUKlU6LVqdDo1gX4+1NbWtcvhgq1Jp1Vz982x9OkWwrIvs9mUnse+o6VMHRVPXCf5cCeEu/L39+f222+nZ8+eGAy/jM9etGiRC6MSQgjPsG3fGeyOhsLv8llSCPcgySzh9r768QR7DhXTPcqfu2++ztXhCDekUqnwNmgv2jtIr9Ngs8mHj9YSE+7LvCnXs27bcb7ccZyXPt7NnTfGMGpADFqNDNEUwt2MHTvW1SEIIYRHcioKm9Nz0WrU3JQQ7upwhBBnSTJLuLWsY6V8tuUoAWYDj41OaPOzyAnhSbQaNeMGX0dC50D+/sV+1m8/TtbxUqaNiic0wMfV4Qkh/svYsWM5ffo0hw8fZuDAgeTn59OpUydXhyWEEG4v50QZBWU13JQQjslb5+pwhBBnSWZAuK3iihreXpeFRq1ixthEmTVECDcV18mf+Q/dwI09wziaV8mz7+9i98EiV4clhPgvGzZsYPr06Tz//PNUVFQwceJEPv/8c1eHJYQQbm9Teh4AQ3pL4Xch3Ikks4Rbqrc7eGtNJpaaeiYNj2usjySEcE8+XlqmjurJI3f0wOFw8uZn+0j97jB2h9PVoQkhgHfffZdPPvkEo9FIUFAQa9as4Z133nF1WEII4dYqLDb2HCyiY4iRWPk+IoRbkWSWcDuKorD8m4OcOFPFwKQO3NwrwtUhCSEu0YDEDjzzYF/CAn34186TvPLJHsqqbK4OS4h2T61WYzKZGh+HhoailqH7QghxUT9k5ONwKlL4XQg3JJ9ihNvZvDePrRn5RIebmXxbnLxxCOFhOoaamPvrvlzfPZRDpyt49r2dZB8vdXVYQrRrXbt25cMPP8Rut5Odnc2f//xnunfv7uqwhBDCbTmdCpvT8zDoNNzYUwq/C+FuJJkl3Mqh0+V8/O+DmLx1zBibgE6rcXVIQogW8DZoeWx0Tybd2hVrrZ1XV6bzxfbjOBXF1aEJ0S7NnTuXgoICDAYDc+bMwWQyMW/ePFeHJYQQbivzWCkllbX0iw+76IzZQgjXkL9K4TaKK2p467N9OJ3w2OieBPt5uzokIcQVUKlU3Nq3E507+LL080w+23KUw7kVPD3lBleHJkS78+mnnzJlyhT++Mc/ujoUIYTwCJv25AIwpLeUPBHCHUnPLOEWauvsLF69j8rqeiYN70p8TKCrQxJCtJLYSD/mTbmehM6BZBwp4Y9vbCG/xOrqsIRoV86cOcP48eN59NFHWbduHTU1Na4OSQgh3FZpZS17jxQTE24mJlwKvwvhjiSZJVzOqSj8/YtsThVaGNo7kltSOro6JCFEKzP76Hl8fC9G9o8mr9jKwg92kX642NVhCdFuzJ49m++++47HHnuM9PR0xowZw1NPPeXqsIQQwi1t2ZuHosCQ3pGuDkUIcQGSzBIu9/kPx9h9sIjuUf7cd2tXV4cjhLhK1GoV9wyJ5cn7+2B3KCxelcGXO46jSB0tIa4JRVGor6+nvr4elUqFTqdzdUhCCOF2HE4nW/bm4W3Q0K9HmKvDEUJcQItqZjmdTubPn8+BAwfQ6/UsXLiQ6OjoxvbU1FRWrFiBVqtl+vTpDB06lNLSUp588klqa2sJDQ1l0aJFeHt78/777/Pll18CcPPNNzNz5szWOTPhEXZmF7B++3FC/b357dhEtBrJrwrR1t2c0hEfnYrFq/exevNRThVaeOhXPTDoZcIHIa6WhQsX8u9//5sePXpw11138cwzz2AwGFwdlhBCuJ29h0sot9RxS0qkfDYRwo21KJm1ceNG6urqWLlyJenp6bz44ossXboUgKKiIpYvX87q1aux2WxMmjSJAQMGsGTJEu68807GjRvHO++8w8qVKxk2bBjr1q3j008/RaVSMWnSJG699VaZKrqdOJZfybIvs/HSa5h1TxImb7lDLER7ERPuy9wp17NkzT52ZhdypqSaWXcnEeTn5erQhGiToqOjWbNmDYGBrVOTcsyYMZjNZgA6duzIvffey/PPP49Go2HgwIFyc1II4bEaC78nyxBDIdxZi5JZaWlpDBo0CIDk5GQyMzMb2zIyMujduzd6vR69Xk9UVBQ5OTmkpaXxm9/8BoDBgwfz+uuvc//99/P3v/8djaYh42232+UuYTtRVmVj8eoM7HYnM8YnERlsdHVIQohrzM+o50/39eajfx9kc3oeC/75H347JoFuUQGuDk2INueuu+7iiy++oLy8vMnQ3pYknWw2GwDLly9vXDZ69GgWL15Mp06dmDZtGllZWfTs2fPKAxdCiGuosLyGrGOldOnoR8dQk6vDEUJcRIuSWRaLBZPplz9ujUaD3W5Hq9VisVga79QBGI1GLBZLk+VGo5Gqqip0Oh2BgYEoisLLL79MfHw8nTt3bvb4AQE+aLXS5fNiQkLMza/kIrZ6B4s+2k25pY6HR/VkWP/mf+ce62gpZtP5e5qoVSqAC7eXXbzdE6maOeeWMOjVOBwKdkfzdZc8+bls7vVyLWg1KoKDW+fa8t/XqCcnX0987DHeWbOPV1ek85uxifzqpjZ8XXAz7vx+IVrP448/jtlspmvXro3X4pbKycmhpqaGhx9+GLvdzqxZs6irqyMqKgqAgQMHsmPHDklmCSE8zpb0PBRgSHKEq0MRQjSjRcksk8mE1frLtOpOpxOtVnveNqvVitlsblzu5eWF1WrF17dhilObzcacOXMwGo3Mmzfvko5fVlbdkrDbjZAQM0VFVa4O47ycisLbn2dx6FQ5AxLDGRAf6raxXqmfvytUWWrP2+48e2f8gu3Oi7d7IqWZc26JeoMWh8NJvd150fXMJi+Pfi6be71cC1qtmuLiKq60Xvv5rlHXdw3Gd2Iyb63JZMnqDPYfKWbS8Dipo3eVufP7xZWSJF1TxcXFvPfee62yLy8vLx555BHGjx/P8ePHmTp1auPnOmi4aXnq1Klm9yM3J4UQ7qTe7mRbZj5mHx23D4zFoHPd9Unew4RoXouSWSkpKXz//feMHDmS9PR04uLiGtuSkpL461//is1mo66ujiNHjhAXF0dKSgqbN29m3LhxbNmyhT59+qAoCr/97W/p168f06ZNa7WTEu7r0+8P85+cQuI6+vHgiO5XfHdYCNF2dIsKYO6UvixevY9N6XnkFluZMTYRX6Pe1aEJ4fF69OhBTk5Oq9Ql7dy5M9HR0ahUKjp37ozZbKa8vLyx/b9vWl6M3JwUQriTndkFVFjquO36TlSWu+761JZvNAnREhdK7rYomTV8+HC2bdvGxIkTURSFF154gffee4+oqCiGDRvG5MmTmTRpEoqi8MQTT2AwGJg+fTqzZ88mNTWVgIAAXnvtNTZu3MjOnTupq6vjhx9+AOAPf/gDvXv3bvmZCrf1712n+HrnKToE+TDz7iR0WulxIYRoKtjPmzkP9OEfG7L5T04hC/75H2aNSyI6XO5QCnElDh06xNixYwkKCsJgMKAoCiqVim+//fay97Vq1SoOHjzI/PnzKSgooKamBh8fH06ePEmnTp3YunWrFIAXQnicnwu/3yxDDIXwCC1KZqnVahYsWNBkWWxsbOPPEyZMYMKECU3ag4ODWbZsWZNlw4cPZ9++fS0JQXiYtAOFrNh4CD+jnifG95KZC4UQF2TQa3hsdE+iwkx8tvkoiz5M46GRPegXH+bq0ITwWG+++War7euee+7h6aef5r777kOlUvHCCy+gVqt58skncTgcDBw4kF69erXa8YQQ4mrLL7GSc7Kc7lH+dAiSiamE8AQtSmYJcTkOnirnnfX70es0PD6+F8H+3q4OSQjh5lQqFXfcGENkiIl31mXx9rosThVaGDf4OtRqGZ4sxOWKiIjgk08+4ccff8Rut9O/f38eeOCBFu1Lr9fz2muvnbM8NTX1SsMUQgiX2JyeB8CQ3pEujkQIcalknJe4qk4WVPHGqgycToXpYxJkqJAQ4rIkdwnmmQf7EhbgzYYfT/DGqgyqa+tdHZYQHufll19m69atjB49mnHjxvHjjz+yaNEiV4clhBAuV1fvYNu+fHx9dKTEhbg6HCHEJZKeWeKqKSir5vXUvdTa7EwdFU9SbJCrQxJCeKCIYCPP/Lovb3+exb6jJTz3QRq/uztRhgEIcRm2bdvG2rVrUasb7mMOGTKEUaNGuTgqIYRwvV0HCrHW2rnjxmiZRVkIDyJ/reKqKKuy8dqKdCqtdUwaHkf/nuGuDkkI4cGMXjoeH9+L2/tFUVBazcIPdrH3cLGrwxLCYzgcDux2e5PHGo3rpp0XQgh3sWlPHipgcC8p/C6EJ5GeWaLVVVbX8frKdIorahkzqDPD+nR0dUhCiDZArVYxYWgXokJNvPdVDm+syuCuATHcNaCz1NESohmjRo3iwQcf5I477gDgyy+/5M4773RxVEII4VqnCy0czq0g4bpAQqSurxAeRZJZolVZaup59ZN0coutDO/biVE3xbg6JCFEG9O/Zzgdgoy8tWYf67Yd52heJdPu6imzpApxEY899hjx8fHs2LEDRVF47LHHGDJkiKvDEkIIl9qUngvA0GQp/C6Ep5FhhqLVWGvreXXFHk4XWRiaEsnEYV1QqaS3hBCi9UWHm5k75XqSYoPIPFbKs+/t5Fh+pavDEsItVVRUUFpayuDBg5k9ezZDhw4lKSnJ1WEJIYRL1dbZ2Z55hgCzgaQuUttXCE8jySzRKqpr63ltRTonCyzcnBzB/cPjJJElhLiqTN46fndPEmMGdaa00saiD9PYlJ6LoiiuDk0It7F//37uuOMOMjMzG5dt376d0aNHk5OT48LIhBDCtXZmF1Jb52BQUgc0avlaLISnkb9accUsNfW8tjKd42eqGJjUgckjuqGWRJYQ4hpQq1T8f/buOzyKcn34+HdrNr2H0AIBEnoLICBNulLkCAQQiGI//AAFG4iCiDRF0PPCAQ+CohEkgICg2OgIEjFIL0oLpBDSe7LZ3Xn/SFiISQwlySbh/lwXF7NT73l2dzJ7z1Me7eLPlBGt8Ua8dwAAIABJREFUsdNp+OKHc3y6/QzGPLOtQxOiUnjvvfdYtGgR3bt3t86bMmUK8+bNY8GCBTaMTAghbGv3H9GoVNLxuxBVlSSzxD1JzcjlvbVHuBSbTteWNRn3SBNJZAkhKlyLBp68/VQH6vs6c+DENeaFRhCXlGXrsISwubS0NDp27Fhkfrdu3UhOTrZBREIIYXuXYtOIvJZOm0ZeeLgYbB2OEOIuSDJL3LXE1BwWrDlCdHwmvdvVYdwASWQJIWzHy9WeN8YG0aNNLa5cz2DWZ4fZfzxGmh2K+5rJZMJisRSZb7FYyMvLs0FEQghhe3sLOn7vIR2/C1FlSTJL3JW4pCwWrIkgLjmbgZ3rMbpPgCSyhBA2p9NqePLhJrzwaHPUavhs+1k+/uYUWTnyo13cnzp06MDSpUuLzF+2bBktWrSwQURCCGFbWTkmDp2Ow8vVQAt/D1uHI4S4S1pbByCqngvRqfxn43EysvMY1qMBAzvXt3VIQghRSMdmNWhYy4UV205z+Ox1Lsak8tzg5gTWdbN1aEJUqJdffpnnn3+eLVu20KRJE+zs7Dh9+jQeHh4sX77c1uEJIUSF+/XUNYx5Fnq0qYVaLQ/jhaiqJJkl7siRP+P539ZTmM0KTz7cWKrmCiEqLS83e6aOacu2A5fZdvAy7609wuAH6zO4S30ZtUjcN5ycnFizZg2HDh3izJkzqNVqxowZQ/v27W0dmhBCVDhFUdh7NBqNWkXXVtLxuxBVmSSzxG3bGRHF2p//RKdT8+LwlrRq6GXrkIQQ4h9p1Gr+1a0Bzep78Mm2U2w9cJnTl5N5bnAzvN3sbR2eEBVCpVLRuXNnOnfubOtQhBDCpi5EpxEVn0n7Jj64OuptHY4Q4h7c1aNpi8XCzJkzGTlyJCEhIURGRhZavn79eoYOHcqIESPYvXs3AElJSTz99NOMHj2ayZMnk52dbV0/KSmJfv36kZubew+nIsqLyWzhy5/OsebnP3F20DF1dJAksoQQVUpgXTfeefoBOjTx4Xx0KjM//Y09f0RL5/BCCCHEfWT3H/kdv/dsI7WyhKjq7iqZtWPHDoxGI2FhYbzyyissWLDAuiw+Pp7Q0FDWrVvHqlWrWLx4MUajkWXLljFo0CDWrl1Ls2bNCAsLA2D//v08/fTTJCQklM0ZiTKVlmVk0bqj7DoSTW1vR958oj3+NV1sHZYQQtwxB4OOfw9pzjMDm6JWqfjix3N8sO4oCSnZpW8shBBCiCotIzuPw2evU8PDgSb13G0djhDiHt1VMisiIoJu3boB0KZNG06ePGlddvz4cdq2bYter8fZ2Rk/Pz/Onj1baJvu3btz8ODB/ADUaj777DPc3KRT3som8lo6764+zLmrKbQL9ObNkHbSLEcIUaWpVCq6tKzJnGc70qqhJ2cik5nx6W/sjIjCYpFaWkIIIUR1dfBELCazhYfa1EIlo7ALUeXdVZ9ZGRkZODk5WV9rNBpMJhNarZaMjAycnZ2tyxwdHcnIyCg039HRkfT0dAC6dOlyx8d3d3dAq9XcTej3DW9v59JXKoGiKPx4KJJPtpzAaLIw5uEmjOgdKKN93I2LSTg7GYpdpC74I1ri8uR/Xl4VqUo557thp1djNiuYzKUnIqpyWZb2eakIWo0KL6+7v7bc6l6uUWV1/Dnju7A74iortpxkzc9/cvjcdSYMb0OD2q42ja2i2fq9EEIIIcqboijsPhqDVqOmS8uatg5HCFEG7iqZ5eTkRGZmpvW1xWJBq9UWuywzMxNnZ2frfIPBQGZmJi4ud99ULTk56663vR94ezsTH59+V9tm5Zj4/IezHD57HUeDln8PaUGbAC8SEzPKOMrq78YDn/SMnGKXWwr66ilxueWfl1dFSinnfDfy7LSYzRbyTJZ/XM/ZyVCly7K0z0tF0GrVJCSkc6/dTN3LNaqstaznzpxnHmDdrvOEn45jyod76dehLo92rY9BX/3HSKlM70VZkySdEEL8M0VRyDGayczJIzPblP9/jonM7DzrvIycvILXBcsLplWAh4sBL1cDnq4GPP827eZkV6kehJ+9kkJcUhadm9fAyV5n63CEEGXgru7Ug4KC2L17NwMGDODo0aMEBgZal7Vq1YqPPvqI3NxcjEYjFy5cIDAwkKCgIPbu3cvQoUPZt28f7dq1K7OTEGXjUmwaH39zkviUHBrVduWFR5vj6Vp1a7IIIcTtcHWy44VHm9OlhS9f/HiOH367wqHT1wju2YhOzWpIUwQhhBCVmsWikJWbn4TKyMkjy5qQujnvZrLq5nRWjgnzbTaxVwEOBi2OBh3u3nZYLJCYlsO1pOIrGWjUKjxc7AqSXPZFEl7uznZoNXfV481d2Xs0v+P3h9rWrrBjCiHK110ls/r27cuBAwcYNWoUiqIwb948PvvsM/z8/OjduzchISGMHj0aRVGYMmUKdnZ2jB8/nqlTp7J+/Xrc3d1ZtGhRWZ+LuEt5JgtbD1zi+0NXUBSFgZ3rMaSrf4X+gRFCCFtr0cCTd5/tyPeHItl+6AqfbDvN7iPRjO4bQH1fGfhCCCFE+TKZLQXJp5u1o7KsCanCNaNurT2VlWu67WNo1CocDVqc7HXUcHfA0aDF0V6Ho0F3y/Qt8+zzE1gOdtpia1rlGE0kpuaQmJZDYmoOCX+bPnslBUgpsp1KBe7OdkVqdN1MfNmhK6NuZVIzjUSci6e2lyON7rOuBISozu4qmaVWq5k9e3aheQ0bNrROjxgxghEjRhRa7uXlxapVq0rc565du+4mFHGPIq+ls/K700THZ+LpYuDpAU1oWt/D1mEJIYRN2Ok0/KtbA7q2rEnY7vNEnItn9urf6dS8BkO7NcBLBsEQQghxFxRFIT07Lz/xcyPpU5D4uZEAyr6DpJReq8bRXoeHix11DU5/S0JpCxJRt07n/2/Qa8q0xrFBr6W2txO1vZ2KXZ5nMpOYlnvLuWbfLIO0HM5Hp/JXVGqx27o66ottwnhj+na7A/jleAxmi8JDbWtLbWshqpHq3yGIKFaO0cS2g5f5MfwqFkXhoTa1CO7ZCHs7+UgIIYSXmz0THmvJmctJhO0+z6FTcfx+9jo929Zh0IP1cHbQ2zpEIYQQlYhFUUjNMBYkaQonbG5MG0vo39NOr8HLxYBrTeeiSajiaksZtOh1VWMwLJ1Wg6+HA74eDsUuN5ktJKfnFk7s3ZL4iryWzsWYtGK3dbLX4emSn9jyKkh0WaddDTjYaVGAvUdj0OvUdG7uW45nKoSoaJK5uM8oikLEuXi+2vkXyem5eLoYGPdIE5r7S20sIYT4u6b1PZg5rgO/nY5j076L/Pz7VfYdj6FPuzr061BXklpCCHGfMFssJKflFkq43JqoSkrPKXFkZUeDFl9PhxL7j3I0aO/bGkNajRpvN3u8S6j5bLEopGTkltiMMSYxk8i44gcyMeg1uDrqSUjNoVurmjgY5KevENWJfKPvI1HXMwjb9RenLiej1agY9GB9Bnauh10VebIjhBC2oFap6NTcl3aNfdhzNJrtv0by3a+R7IiIondQHfo9UBcXSWoJIao5i6KQlmkkPSsPnVaNXqtGW/C/XqupVCPX3Y08k4WktJsJKmsNodRsEtNySE43WkcW/jsXRz11fZxLHNlPWj7cPbVahYeLAQ8XAwF1ii5XFIX0rDxrkuvvzRgTU3PQa9X0aV+34oMXQpQrubLeBxJSs9my/xK/nryGArTw92BM30BqlFDdVwghRFE6rZq+7evSo3Ut9h6NYXt4JNsPRfLz71fp0rIm/R+oSw13ua4KIaoms8VCSroxPxlQpLlXDklpJdc8gvyOxfU6NTqtBr1WXZDw0qDT3Ux43UiC6XS3rlOwje7mNvnrqNFp1Oh1miLr6bVqtBr1HdVmyjWaC5Ib2UVqVSWk5ZCaYSx2OxXg5mxHg9ou1qZstyaqPF0MVabJX3WkUqlwcdTj4qinQa2ig7UoioKiUOWTrUKIoiSZVY0lp+fyfXgke/6IxmRWqOPtxPCHGtKygcd9W5VZCCHulV6noW+HuvRoU4v9x2P58bcr7Pkjmr1/RBMU6E2f9nUIrOsm11khRKWSZ7KQlJ5TuAPyW2ohJafnllzzyEFHXR8nPF0MODvqMZstGE0W8vIK/jeZMZosGPNuTudk5WE05ZCXZ6HkFNjdU5H/kEGnLSbhVZAM06rVJGfk98eUkZ1X7H40ahXuznY08XMrOqKeqwEPZzsZ4bsKU6lUyJ9jIaonSWZVQ7EJmaz54SwHTsRiMit4uhgY2r0BHZvXQC1XcyGEKBN6nYbe7erwUNtaRJyL5/vwK0T8GU/En/HU8nKkZ9vaPNjCV5qXCCEqRG6e2VqT6tZk1Y1mV6kZxmKTStaaR7VcCnWkfaP2kYeL4Z66pFAUBZNZuZnwMlnIyzMXJMEsGE1ma1LMaDJjMllurmcyFyTICtb7W8LMuo88M7lGM+lZeeSZzIVqkOm0ajxcDNTzdS7SWbiXqwE3JzuptSOEEFWQ3GFXE4qi8FdUKjsjoog4dx2LAj5u9gzoXI/OzX3RaeWJkhBClAeNWs0DTWvQoYkPf0WlsvuPaH4/e501P//Jhj3nad/Yhy4ta9LYz00eKAgh7lpWjumWRFV2kaRVelbxNY/UKhUeLnYE1nUrtk8nDxdDudY8UqlU6LQqdFo1FdUQ22JRyDNZMFksONjdv52rCyFEdSbJrCouO9dE+Jk4dkVEExWfAUD9mi7071CX9k280agliSWEEBVBpVIRWNeNwLpujOodwP5jMew/HsPBk9c4ePIani4GOjXPT3rV9XGSH1dCCCtFUcjIziucoLqlv6rE1Byyck3FbqvVqPF0saOuj5O1xlF+7SN7PF0MuDnr77v7QbVahZ1egx3Sl5UQQlRXksyqgiwWhTORyRw8GUvEn/EY8yxo1Co6NPGhV1BtugTVJSEhw9ZhCiHEfcvVUW8dMfavqFR+ORHL4bPX+a5gJERfDwfaN/GhXaA3fjUksSWqB4vFwqxZszh37hx6vZ45c+ZQr149W4d1T/KbyFmK9Ad1o2lbnulm87ib/UfdbBKXV0Kzupv9TeWvl56VR26eudgY7HQaPF0NNKztWmzNKhdHvdT6FEIIcd+RZFYVYbZY+PNKChF/xnPkz3hSCkZc8XGz58EWvnRrXQt3ZzsA+VEkhBCVxK21tcb0DeTEhUR+O3ud4+cT+PbgZb49eBk3Jz2tG3nRqqEnTfzcpY8tUWXt2LEDo9FIWFgYR48eZcGCBSxfvtwmsZyJTCYuKavYfpeKS0blmW6dvrksz1ROnZeruDm6n06Nj7v932pV3UxaOdnr5N5OCCGE+Bu5Y67E0jKNnL6cxMlLSRw7n0BmTn71ckeDlofa1OLBFjVpWNtFbnCEEKIKsNNpaN/Eh/ZNfMg1mjl+MZGjfyVw4mIie4/GsPdoDBq1Cv9aLjSr507Teu7Ur+lyTx0vC1GRIiIi6NatGwBt2rTh5MmTNokjx2jig3V/UMLAfMXSatTWEfD0WjUOdnY3R8fTFYyOp1XnJ6B0fxs179ZR9HS3rFeQqLqx3q3raNQquX8TQggh7oEksyqR9Cwj56NS+SsqlTORyUTGpVuXuTnp6RlUm3aB3gTWdZMhgoUQogqz02vo0MSHDk18sFgULsSkcvxCIqcvJ3MhOpXzUalsPXAZtUpFHR9HGtZypUEtFxrUcsHXw0F+BItKKSMjAycnJ+trjUaDyWRCqy3+dtPd3QGttnySte++8CDJ6bnYFSST7HT5SSS97sb0jX/5iScZzU4IUZl4ezvbOgQhKj1JZtlIbp6Zq3EZXL6WRuS1dC7GphGbmGVdrlGraOLnRnN/D1r4e1K3hpP0hyCEENWQWq0ioI4bAXXcGNYDsnLyOBOZwl9RKVyMSePytXSuxGWw+49oIL92rn8tF+r6OFHL05FaXo7U9HTAoJc/6cK2nJycyMzMtL62WCwlJrIAkpOzSlx2r2q5GajlZihhqYKSZyI3z0RuuUUghBB3x9vbmfj49NJXFOI+UVJyV+58y5HFopCSkUtCag7xKdlcS8oiOj6TmIRM4lOyC/XBYKfX0Ly+e8EPGlca1HLFTi9NS4QQ4n7jYNDRrrE37Rp7A2AyW7h6PYOLMWlciEnlYnQaJy8mcfJiUqHtPF0M1PJypJaXA74eDtb+djxcDNJUUVSIoKAgdu/ezYABAzh69CiBgYG2DkkIIYQQ1ZQks+6CoijkGM2kZ+eRnmkkPSuP9CwjqZlGElKzSUjNISElfzhls6Vohw1O9joC6rrhV8OJ+r7O1PfNbzYiVdyFEEL8nVajxr+mC/41Xejdrg4AGdl5RMdnEJOYRUxC/kOS2MRMTlxM5MTFxCL7cLLX3ZLcssPVUY+zg546vi5YTGacHXS4OOgx6DXShFHctb59+3LgwAFGjRqFoijMmzfP1iEJIYQQopq6q2RWaUMvr1+/nnXr1qHVahk/fjw9e/YkKSmJV199lZycHHx8fJg/fz729vbFrmsrGdl5HDgRS2ZOHjm5ZnKMZnLyzOQYTeQa819n5uSRlpmHyWz5x325OOio5+uMl6sBL9f8EWpqeDhQ28sRF0d9BZ2REEKI6sjJXkdjP3ca+7kXmp+Vk0dMYhZxSVkkpuWQmJpDUloOCWm5xCRkEnntn5staDUqHO11GPRa7PUaDHpN/rRd/v8GfX6n1lpN/r/8aZX1dX6n1gD5/1vTYipQocKiKFgsChZFwd5OS3N/D2lCX42o1Wpmz55t6zCEEEIIcR+4q2TWPw29HB8fT2hoKF9//TW5ubmMHj2aLl26sGzZMgYNGsTQoUNZsWIFYWFhDBw4sNh19XrbJHsizl0nbNf5YpepyG8K6GjQUcc7PyHlbK/D2UGPs0P+/y6OOjwLElfSpEMAaDRqtNqSOuvP/wFX4nJVKcurpLI/J51GjRpKHTpdq1FV8bK0/edBJwNPVHoOBh2NarvSqLZrkWWKopCelUdiWg7pWfm1ii0qNbHx6dbX6Vl5ZObkkZ1rIjktB6Ppnx/c3KtZT3XAr4Z0ciuEEEIIIe7MXSWz/mno5ePHj9O2bVv0ej16vR4/Pz/Onj1LREQEL7zwAgDdu3dn8eLF1K1bt9h1W7Vq9Y/HL6/RHYb3bcLwvk3KZd8VTUbAqBx6ev3T+xAFwGMlVkYsWF6mEdnWYz2jbB1C1XWten0e5BplGz5AQ1sHIUQBuQ4IIUTx5PooROnu6jF7SUMv31jm7Hzzy+fo6EhGRkah+Y6OjqSnp5e4rhBCCCGEEEIIIYQQxbmrZNY/Db3892WZmZk4OzsXmp+ZmYmLi0uJ6wohhBBCCCGEEEIIUZy7SmYFBQWxb98+gCJDL7dq1YqIiAhyc3NJT0/nwoULBAYGEhQUxN69ewHYt28f7dq1K3FdIYQQQgghhBBCCCGKc1fJrL59+6LX6xk1ahTz58/njTfe4LPPPmPnzp14e3sTEhLC6NGjefLJJ5kyZQp2dnaMHz+e7777jlGjRvHHH38wduzYEtcVQgghhBDidoWEhHDhwgVbh3FH/inmXr16kZubW8ERFbZixQq6du1q8zhKs2LFCsaNG8fTTz/NM888U6gv38rq6tWrTJo0iZCQEEaNGsWsWbNK7GolJiaGXbt2VXCEEB4eTvv27YmNjbXO++CDD9i0aVOFx1Ka8PBwOnfuTEhICGPHjmXUqFFs377d1mFVC1FRUQQFBRESEmL9t3Tp0mLXtfV1ODw8nMaNGxd57wcPHsy0adNsFNXtqczX28pcrnfVAXxxQy83bHizS9kRI0YwYsSIQsu9vLxYtWpVkX0Vt64QQgghhBDCdrZt28aAAQP47rvvGDp0qK3DKdb58+fZtWsXX331FSqVijNnzjB16lS2bt1q69BKlJOTw//93/8xZ84cWrduDcDmzZt55ZVX+N///ldk/UOHDnHx4kV69epV0aGi0+mslRZUBaNsV1adOnXiww8/BPK7rgkJCcHf35+mTZvaOLKqr1GjRoSGhto6jNvSoEEDvv32WwYMGADAuXPnyM7OtnFUpavs19vKWq4yzroQQgghhKjyli5dyldffQXAhQsXCAkJAfKfHr/77ruMHTuWkJAQ0tPTbRlmISXFbGvh4eH4+fkxatQo1qxZAxSudfHVV1+xZMkSAP773//y2GOP8cwzzzB69GjCw8MrLE4PDw9iYmLYuHEjcXFxNG3alI0bN3Lu3DlrLZJJkyaRnp5OeHg4Tz31FM888wyPPvqo9bwq2p49e+jQoYM1kQXw2GOPkZyczMWLFxk7diwjR47kySefJCEhgRUrVvDtt9+yc+fOCo+1U6dOuLq6FimrTz/9lGHDhjFy5EgWLlwIwNChQ4mKyh95+fvvv2fOnDkVHu8Njo6OjBw5kh9++IFFixYxatQoRo4cyffffw/AsWPHGDFiBMHBwUycOJGcnBybxVpVFVeuAP/v//0/nnjiCZ599lmSkpIqPK4mTZoQGxtLWloaAFu3bmXw4MEAfPnllzzxxBOMHj2aF154AaPRyKZNmxgzZgyPP/44v/76a4XHCyVfb2fOnGmtbRgfH094eDjBwcGMHj2aLVu2VGiMd1qur7zyCnv27AHy/7Y9//zz5RKXJLOEEEIIIUS1lZmZycCBA/nyyy/x8fGx9vsqSrZhwwaCg4Np0KABer2eY8eOFbve2bNn2b9/Pxs3buS///0v8fHxFRqnh4cHy5cv58iRI4wcOZKHH36Y3bt3M2PGDN5++21CQ0Pp3r07K1euBCAuLo7ly5ezfv16Vq9eTWJiYoXGC/lNDP38/IrMr1OnDsHBwTz//POEhYUxcuRIzp49y/PPP8+gQYPo3bt3hccKMGvWLFavXs3ly5eB/O/T999/z7p161i3bh2RkZHs3r2b4cOHW39gb9682eYtbzw9Pfnhhx+Iiopi3bp1fPHFF3z88cekpaUxY8YM5s+fz4YNG+jcuXOVa6Jc0c6fP1+omeHWrVuLLVeAfv368cUXX9CzZ89iaxpWhL59+/Lzzz+jKArHjx+nbdu2WCwWUlJSWL16NWvXrsVkMnHixAkAXFxc+Oqrr+jcubNN4i3pehsUFERoaCiPPPKItSxzc3NZu3Yt//rXvyo8zjsp1+DgYDZv3gzAxo0bGT58eLnEdFfNDEXl8vPPP1ufPEB+p/xz585Fo9HQtWtXJk6ciMViYdasWZw7dw69Xs+cOXOoV6+ejSOvnqSsbevYsWN88MEHhIaGEhkZybRp01CpVAQEBPD222+jVqtZunQpe/bsQavVMn36dFq1amXrsKuVvLw8pk+fTnR0NEajkfHjx9OoUSN5L2zAbDbz1ltvcenSJTQaDfPnz0dRFHkvRLWQmZmJXq9Hp9MB/GMzqGbNmgFQs2ZNm/ZJcicx20pqair79u0jKSmJ0NBQMjIy+PLLLwutoygKkP/EvWXLlmg0GjQaDS1atKjQWCMjI3FycmL+/PkAnDhxgueff56cnBzeeecdIP9vkr+/PwBt27ZFr9cDEBAQwJUrV/D09KzQmGvUqMHx48eLzL98+TK5ubm0bdsWwNqcx9Z9VLm7uzN9+nSmTZtGUFAQubm5tG7d2voZbt++PX/99RejR4/m8ccfJzg4mIyMDJsP6hUTE8PgwYPZunWrtcajyWQiJiaGxMREaxc5Y8aMsWWYhISEMGvWrEJd9lQ2f29m+Mknn3Dq1Kki5Qr5nweg0OBvFW3w4MHMmjWLunXrWuNRq9XodDpefvllHBwcuHbtGiaTCcB6fbCFf7redurUCcgvyxv95tky1jsp144dOzJ37lwSExM5cOAAL7/8crnEJDWzqrg5c+awaNEiLBaLdd7bb7/NokWL+Oqrrzh27BinTp1ix44dGI1GwsLCeOWVV1iwYIENo67epKxt55NPPuGtt96y/lCZP38+kydPZu3atSiKws6dOzl16hS//fYbGzZsYPHixdabXVF2tm7dipubG2vXruWTTz7h3XfflffCRnbv3g3AunXrePHFF5k/f768F6LamDZtGhEREVgsFhITEwkMDLTWDDp16lShdStL0uhOYraVrVu3MmzYMD799FNWrVrF+vXrOXDgAGq12hrr6dOngfwfuSdOnMBisWA0Gq3zK8q5c+eYNWuW9e++v78/zs7O1KtXj/fee4/Q0FBee+01evToAcCZM2cwm81kZ2dz/vx5mzxs7N27NwcPHiyU0NqwYQMeHh706NHDWltk69athIaGolarC93n20KvXr3w9/dn8+bN2NnZcfz4cUwmE4qicPjwYfz9/XFycqJFixbMnz/f5n3+ZGRksGHDBpydnenYsSOhoaF8/vnnPPLII9SpUwcfHx9rTbMVK1bw888/2zTeqqZBgwbFlitg/fz+/vvvBAQE2CS+unXrkpWVRWhoKI8++iiQ/5nYsWMHH330ETNmzMBisViT8mq17VIiJV1vk5KSrINZHDlyhEaNGtk81jspV5VKxeDBg5k7dy5dunSxJr/LmtTMquKCgoLo06cPYWFhQP4Hymg0Wqsvd+3alV9//ZX4+Hi6desGQJs2barESC9VVUREhJS1jfj5+bFkyRJef/11IP+HwQMPPABA9+7dOXDgAP7+/nTt2hWVSkWtWrUwm80kJSXh4eFhy9CrlYcffpj+/ftbX2s0GnkvbKRPnz489NBDQP5Tai8vL/bs2SPvhagWnnrqKebMmYOdnR2PPfYYDz/8MJMnT+bw4cMVXkPodlWFmDds2MD7779vfW1vb0+/fv3w9fVl9uzZ1KxZEx8fHwAaN25Mjx49GDFiBO7u7uh0OrTaivt50a9fPy5cuEBwcDAODg4oisLrr7+Or68vU6dOxWw2AzB37lyuX7+OyWTiueeeIyUlhfHjx9vkGufo6MjHH3/MvHnzSElJwWw207hxYxYvXkxycjIzZ85k+fLlGAwGFi5cSExMDMuXL6euat9KAAAgAElEQVR58+YMHDiwwuO94c033+TQoUM4OjryyCOP8Pjjj2OxWGjXrh19+vQBIDg4mGeffZZ58+ZVeHyHDh0iJCQEtVqN2Wxm0qRJ9O3blwULFjB69GiysrLo06cPTk5OvPPOO0yfPh21Wo23tzfjxo2r8HhvlZyczL///W9yc3NJSUlhwoQJ9OnTh8GDB/PAAw9w7tw5VCoVy5Ytw9nZ2aaxQn5y87fffitSrpD/UP/zzz/H0dGR9957z2YxDhgwgG+++QZ/f3+uXr2KRqPB3t6eoUOHotfr8fb25vr16zaL74aSrrcbN25k8+bNrF69Gnt7e95//33+/PNPG0aa707KdejQoTz00EN888035ReQIqqE9evXKwMHDiz079ixY4qiKMqhQ4eUyZMnK4qiKLGxscrw4cOt223YsEFZvHixMn36dGXPnj3W+T169FDy8vIq9iTuE1LWtnX16lUlODhYURRF6dKli3X+wYMHlVdeeUX573//q6xZs8Y6f/To0crly5crPM77QXp6ujJ27Fhl69at8l7Y2Ouvv660bdtW2b9/v7wXQogyk5CQoHz55ZeKoihKbm6u0qdPHyU6OtrGURXv1vtlISqLsWPHKqGhocqhQ4cURVGUiIgIZdy4cYqiKErPnj2ViIgIRVEU5eWXX1a+/fZbm8UpKtbYsWOV8+fP2zqMe3Lt2jXliSeeKNdjSM2sKiI4OJjg4OBS13NyciIzM9P6OjMzExcXF3JycgrNt1gsFfrk7H7y9/dAytp2bq2Ke+O7UNx3pDI85apuYmNjmTBhAqNHj2bw4MHW0Y5A3gtbeO+993j11VcZMWJEof6C5L0QQtwLd3d3Tp48ybBhw1CpVAQHB1OrVi1bhyVEpfb3vvPat2/PihUr2LhxIyqVytqXE1Se/v6EuBM//vgjS5cuZe7cueV6HOkzq5pxcnJCp9Nx5coVFEXhl19+oX379gQFBVlH7zl69KjNO2WszqSsK49mzZpZhwjft2+f9bvwyy+/YLFYiImJwWKxSFOqMpaQkMDTTz/Na6+9Zh29RN4L29iyZYt1BBx7e3tUKhUtWrSQ90IIUSbUajXz58/n66+/ZuPGjeU2/HpZ6NixIx9++KGtwxCiSN958+bNY8iQISxcuJCOHTta+3KCytPfn6hYoaGhlXpQgNL079+fbdu2lftgQlJdpBp65513ePXVVzGbzXTt2pXWrVvTsmVLDhw4wKhRo1AUxSZt2e8Xffv2lbKuJKZOncqMGTNYvHgxDRo0oH///mg0Gtq3b8/IkSOxWCzMnDnT1mFWOzeGaF62bBnLli0D8vvamDNnjrwXFaxfv3688cYbjBkzBpPJxPTp02nYsKF8L4QQQggb+XvfeTVr1mTu3Ln873//o2bNmiQnJ9s6RCGqBJVya+pXCCGEEEIIIYQQQohKTJoZCiGEEEIIIYQQQogqQ5JZQgghhBBCCCGEEKLKkD6zhBBCCCGEEEKIcpKXl8f06dOJjo7GaDQyfvx4GjVqxLRp01CpVAQEBPD2229bR+KOjIxkwoQJfPvttwDExMTw+uuvoygKrq6uLFq0CHt7e1uekhA2JzWzhBBCCCGEEEKIcrJ161bc3NxYu3Ytn3zyCe+++y7z589n8uTJrF27FkVR2LlzJ5A/EvGUKVMKdQS/evVqHnnkEdasWUNAQAAbN2601akIUWlIMksIIYQQQgghhCgnDz/8MC+99JL1tUaj4dSpUzzwwAMAdO/enYMHDwLg6urKl19+WWj7pk2bkpaWBkBGRgZarTSwEkKSWULc5zIyMnjnnXcYNGgQQ4YMISQkhFOnTtk6LKsTJ07w5ptvlsu+X331VVasWFFonqIo9O7dm7Nnz5a43bRp09i0aVO5xCSEEEIIIaoXR0dHnJycyMjI4MUXX2Ty5MkoioJKpbIuT09PB6Bnz544ODgU2t7X15c1a9YwcOBA9u3bx8MPP1zh5yBEZSPJLCHuYxaLheeeew5XV1e2bNnCN998w4QJE3juuecKVW22pZYtWzJ37txy2fewYcPYtm1boXkRERG4ubnRpEmTcjmmEEIIIYS4/8TGxvLEE08wZMgQBg8ebO0fCyAzMxMXF5cSt33//feZP38+3333HW+++SZTp06tiJCFqNSkfqIQ97Hw8HBiY2N58cUXrX9QO3XqxPz587FYLAB8/PHHbN26FY1GQ5cuXXjttdeIjY1l4sSJBAQEcObMGTw9PfnPf/7D1q1biYyMZMaMGQAsWLAAX19fgoODmT17Nn/99Rdms5nnnnuOQYMGsWnTJjZv3kxKSgo9e/YkICCAlStXotFoqFOnDgsXLuTo0aMsXbqU0NBQLl26xMyZM0lJScHBwYE333yTVq1aMW3aNJycnDh16hRxcXFMmDCBYcOG8euvv7Jw4UIAa2eZHh4e1vPv1KkTmZmZnDt3jsaNGwPwzTffMGzYMIASj3dDVFQUTzzxBLt27QJgyZIlAEyaNIkuXbrQu3dvjh8/jpeXF8OGDSM0NJRr166xYMECHnjgASIjI5k1axYpKSkYDAZmzJhBs2bNyvMtF0IIIYQQFSwhIYGnn36amTNn0rlzZwCaNWtGeHg4HTt2ZN++fXTq1KnE7V1cXHB2dgbAx8fH2uRQiPuZ1MwS4j52+vRpmjRpUujJEECPHj3w9PRk79697Nq1i6+//prNmzcTGRnJunXrADh79ixPPfUU3377LS4uLmzbto1Bgwbx888/YzabURSFn376iYEDB7J8+XKaN2/Opk2bWLNmDR9//DFXr14FIC4ujs2bN/Pyyy/z0Ucf8emnn7Jp0yZq167NxYsXC8X12muvERISwrZt23jjjTd46aWXMBqNAFy7do21a9eyfPly3n//fQCWLVvGrFmz2LRpEw8++CCnT58utD+VSsXQoUOtI8UYjUZ2797NoEGDSj1eaRISEujevTtbtmwhNzeXHTt2sHbtWiZNmsTnn38OwNSpU3nttdfYvHkz7777LlOmTLnt904IIYQQQlQNH3/8MWlpaSxbtoyQkBBCQkKYPHkyS5YsYeTIkeTl5dG/f/8St58xYwaLFy9m7NixzJ07l5kzZ1Zg9EJUTlIzS4j7mFqtxs7OrsTlhw4dYuDAgdahf4cNG8aWLVusya4btYgCAgJITU3Fw8ODJk2aEB4ejk6nw9/fH29vbw4ePEhOTg5ff/01AFlZWfz1119A/lOpG51Y9uzZk8cff5w+ffrQv39/mjZtSnh4OJBf/frKlSv069cPgDZt2uDq6mpNeHXp0gWVSkVgYCApKSkA9O7dm4kTJ9KnTx969+5Nly5dipzjY489xhNPPMHLL7/Mzp076dSpEy4uLqUe73Z0794dgNq1a9OuXTsAatWqRVpaGpmZmZw8eZI33njDun5WVhbJycm4u7vf9jGEEEIIIUTl9tZbb/HWW28Vmf/3jt5vdeDAAet0o0aN+OKLL8olNiGqKklmCXEfa9GihXU44BsdUAIsXryYBx980NrU8FYmkwmgUBJMpVKhKAoAQ4YMYfv27eh0OgYPHgzk9821cOFCmjdvDuTXWnJ1dWXbtm0YDAbrft566y3Onj3L3r17ee2115g4cSK+vr4A1v3fSlEUzGZzoXhuPY9x48bRs2dPdu/ezcKFCzl+/Djjx48vtI/atWtTt25djhw5wjfffMO4ceNu63h/P+8bZXPr6DJ6vd46rdFoCu3LYrGg1+v55ptvrPOuXbuGm5tbkeMKIYQQQgghhLhJmhkKcR9r3749np6eLF261Jqk2b9/P5s2baJRo0Z06tSJ7777jpycHEwmE19//fU/tueH/NpQhw8f5sCBA/Tt2xfI75vqq6++AuD69es8+uijxMbGFtrOZDLRr18/3N3deeGFFxgyZAhnzpyxLndycqJOnTr89NNPABw9epSEhAQCAgJKjCU4OJjMzEzGjRvHuHHjijQzvGHo0KFs3LiRyMhIOnbseNvHc3FxISUlhaSkJIxGI/v37//HsrmVs7Mz9evXtyazDhw4wJgxY257eyGEEEIIIYS4X0nNLCHuYyqVimXLljF//nwGDRqEVqvF3d2dFStW4OXlRc+ePTlz5gzDhg3DZDLRtWtXxo4dy7Vr10rcp8FgICgoCKPRiKOjIwATJ05k1qxZDBo0CLPZzGuvvYafnx+///67dTutVsuLL77I008/jZ2dHZ6enixYsIDz589b11m4cCGzZs1iyZIl6HQ6lixZUqj209+9/PLLTJs2Da1Wi4ODA3PmzCl2vf79+zNnzhyefPLJQjW7Sjues7Mzzz77LMOHD8fX15eWLVuWXui3uLH/lStXotPp+PDDDwsdXwghhBBCCCFEUSqluLY0QgghhBBCCCGEEEJUQtLMUAghhBBCCCGEEEJUGVWymWF8fPpdb+vu7kByclYZRlP9SBndHimn0kkZ3R4pp9JJGZVOyqgwb29nW4cgSnEv93NCCFFdyd9zIQor6Z7uvquZpdVqSl/pPidldHuknEonZXR7pJxKJ2VUOikjIYQQouqTv+dC3J77LpklhBBCCCGEEEIIIaouSWYJIYQQQgghhBBCiCpDkllCCCGEEEIIIYQQosqQZJYQQgghhBBCCCFEJaAoiq1DqBKq5GiGQlQWKpWtI6j8KqKM5HovhBBCCCGEqKoUReHPqynsiIji2PkEHAw6PF3s8HAx4OliwMO5YNrVgIeLAWcHHer7/MeoJLOEuAsqFeyOuEpKWratQylVi4bvAXDywtSy2V9awf5cSt+fs2MS6Zm5ZXLc4nx+8T3UKhUrHv1QElp/4zjrLQAyZ82xcSRCCCGEEEKI4uQazfx66ho7j0QRHZ8JQA0PBxRF4er1TC7Fphe7nVajLkhw2eUnu24kugqSXh4udhj01TvdU73PTohyZDZbMJkstg6jVLW8fwDg6LnXymZ/2QX7cyh9fyazUq5ldDD+B0AFfFhux6iq7LZtASSZJYQQQgghRGUTl5zF7iPR7D8eS3auCY1aRcdmNegdVIeGtV1QqVQoikJ6Vh6JaTkkpeWQmJZL0t+mz15JKfEYjgbtzZpdtyS9bky7OunRqKtuz1OSzBJCCCGEEEIIIYQoRxZF4eTFJHZGRHHiYiIAro56+nXwp0ebWrg52RVaX6VS4eKox8VRj39Nl2L3mWeykJyeQ1JabtGkV3ou15OzuXo9o9ht1SoV7s563ItLeDnb4elqwMFOi6qSNmeUZJYQQgghhBAFtm/fRmTkZcaPn3RX27/99hsMGTKMoKD2ZRxZvkcf7c/WrT+Wy76FEEKUvaycPH45HsuuI9FcT8nvpqZRHVd6B9WhXWNvtJq7rx2l06rxcXfAx92h2OWKopCVayIxNT/hlZSeU5D0upn8uhidxvmo1GK3t9NrrIkuD2dD4X68XA24O9mh09qmdpcks4QQQggbmHXwLbZd2FJm+1OrVQz0H8KsB6Vpqage1u86z+Gz18t0nx2a+DCiV6My3acQQghRnKjrGew8EsWvp65hzLOg06rp2qomvYPqUM/XuUJiUKlUOBp0OBp0+NUo/phmi4XUDCOJaTcTXUl/S3jFJGQWv39gdN9AererU45nUTxJZgkhhBD3ie3bt3HgwD5yc3NJTEwgOPhx9u/fy6VLF5gw4SXy8kyEha1BrVbTqlUbxo+fxPXrcXzwwQKMxlzS0lIZN+45und/iCefHEWbNkFcuHAegAULFuPk5GTjMxSi7GzcuI6ff/4RlUpF7979CA4excWL51my5EMsFoWMjHQmT36Vli1b8/XX6/n22y14enqRnJwMwJUrkcyb9w5arRaNRsNbb72Dt7dPkeOYTCbGjBnO6tVfYW9vz9q1X6DRaOjevScLFryLyWRCpVLx0kuvEhAQWNHFIIQQ4g6YLRb++DOBnRFRnLua35+Vl6uBnkG16daqFk72OhtHWJRGrS7oS8tAQAnrZOeaSErPJbkg4XWjKWNqRi6eroYKjfcGSWYJIYQQNjDrwTllWovK29uZ+PjiR7y5VVZWFh9++F927PiRsLC1rFixmj/+iCAsbA3R0VGsXBmKwWDg3XdncPjwIUDFqFFjCApqz4kTx1i16n907/4QmZmZ9OnTnylTXuedd97i0KED9OnTv8zOR4gRvRrZrBZVTEw0x48fZdmylahUKiZP/j86duzEpUsXmThxCg0bNuKnn35g+/Zt+Ps3ZMOGdXzxxTrUajXPPDMWgMOHw2ncuAmTJr3MsWN/kJ6eVmwyS6vV0qNHL/bs2ckjjwxix46f+PDDpSxcOI/hw0fSrdtD/PXXORYseJdVq0IruiiEEELchrRMI3uPxbDnj2iS0/NHc29e351e7erQuqEXanXl7Hfqdtnbaaltp6W2l6OtQ7GSZJYQQghxHwkIaAyAk5Mz9ev7o1KpcHZ2Jjs7m5SUZF599UUgP+kVHR1Nq1Zt+PzzVXz33TeACpPJZN1XYGD+vnx8amA0Giv8XETZO3bsGB988AGhoaGcOXOGd999F41Gg16v57333sPLy4s5c+Zw5MgRHB3zb2iXLVtGXl4er776Kjk5Ofj4+DB//nzs7e1tfDZ37+zZM5jNJl56aTwA6enpREVF4eXlw+rVK7GzsyMrKwtHR0ciIy/j798AvV4PQNOmzQEYNGgIa9Z8ziuvTMLR0YkXXphQ4vEGD/4XH3ywgHr16lO3rh+urm5cvnyZ1q2DgPzv7fXrceV81kIIIe7UxZg0dkZEcfhsHCazgkGvoXe7OvQKqk1Nz8qT+KmOSk1mWSwWZs2axblz59Dr9cyZM4d69epZl69fv55169ah1WoZP348PXv2JCkpqdgbmuLWjYmJ4fXXX0dRFFxdXVm0aFGVvvkRQgghKrOSRqRRqVT4+NTgo4+WodVq2b59GwEBgaxc+TGDB/+Lzp278N13W/n++29v3apighYV4pNPPmHr1q3W+7C5c+cyY8YMmjZtyrp16/jkk0944403OHXqFCtXrsTDw8O67Zw5cxg0aBBDhw5lxYoVhIWFMW7cOBudyb1r1CiA3NxcFi36f6hUKsLC1tCgQSOmT3+FmTPnUL++P6tW/Y/Y2Bhq1arN5csXyc3NQavV8eef5+jX7xF++WUvrVu35emnn+fnn39gzZrPmT797WKPV7euH6Cwdm0ojz02HID69etz/PgfdO3ag7/+OoeHh2cFloAQQoiS5JnM/HbmOruORHEpNr9WfE1PB3oF1eHBFr7Y20mdoYpQainv2LEDo9FIWFgYR48eZcGCBSxfvhyA+Ph4QkND+frrr8nNzWX06NF06dKFZcuWFbmhGThwYLHrrl69mkceeYQxY8bw4YcfsnHjRkJCQsr9xIUQQghxk0ajZeTIMUyc+Dxms5maNWvRq1dfevbszX/+8wGhoZ/h41ODlJQUW4cqyomfnx9Llizh9ddfB2Dx4sX4+OQ3izObzdjZ2WGxWIiMjGTmzJkkJCQwfPhwhg8fTkREBC+88AIA3bt3Z/HixVU6meXnVw9XV1f+7/+ewWjMo2nT5nh7e9Ov3yNMm/YKHh4eeHv7kJqagru7O88++2/+/e+ncXNztyYDmzRpxuzZM9BoNKjVaiZNevkfjzlw4BBWrlxuHQVxwoTJvPfeHL766ktMJhNvvDGj3M9bCCFEyZLSctj9RzT7jsWQnpWHSgVtA7zo1a4Ozeq5l/jAUJSPUpNZERERdOvWDYA2bdpw8uRJ67Ljx4/Ttm1b9Ho9er0ePz8/zp49W+wNTd26dYtdt2nTply7dg2AjIwMfH19y+M8hRBCiPvegAGDrdOdOj1Ip04PAvlNmBYvXgJA//4DCm3Tt+/D9O37cJF9bdy4zTo9fvyk8ghXVLD+/fsTFRVlfX0jkXXkyBG+/PJL1qxZQ1ZWFmPHjuWpp57CbDbzxBNP0KJFCzIyMnB2zh8lydHRkfT00vtvc3d3QKvVlM/J3IMnnxxtnX7ppcJNAydNGs+kSeOLbDNq1DBGjRpWZP6mTRtv+7hjxgQzZkyw9bW3d2PWrCnaR9avvx687X0KIaomb++KGelOlE5RFE5eSGTbLxcJP3UNi0XB2UHHsJ6NeORBf2p4ONg6xPtWqcmsjIyMQqMTaTQaTCYTWq220I0L5N+8ZGRkFHtDU9K6vr6+LFq0iG+//Raj0cjEiRNLDfpeb37k4lA6KaPbcDEJZyfbjNxwJ9QFTwjKKlZ18p3trzzLSKVSoVKBl1fV/7yW+XeuoJPJ6vRdrk7nUl6kjERZ2759O8uXL2fFihV4eHhYE1g3ah916tSJs2fP4uTkRGZmJgaDgczMTFxcXErdd3JyVnmHX6nk5eUxZUrRfrP8/Orx+utv2iAiIURldLsDuojylWM08eupOHZFRBGdkAmAXw0nererQ8emNdDrNGA2y3tVAUq6vy01mXXj5uQGi8WCVqstdllmZibOzs7F3tCUtO7MmTOZP38+3bp1Y8+ePUydOpUVK1b8Y0z3cvMjF4fSSRmV7kYN0vSMHNsGchssigKUXawWy+3vz9nJUK5lpCgKiqIiISGdgtOsksrjO+dR8D4lVZPvslyXSidlVJgk9u7dN998Q1hYGKGhobi5uQFw+fJlpkyZwubNm7FYLBw5coTHHnuMoKAg9u7dy9ChQ9m3bx/t2rWzcfSVj06nY+nSf77HFUIIYVtxSVnsOhLNLydiyc41oVGr6NisBr2D6tCwtos0JaxESk1mBQUFsXv3bgYMGMDRo0cJDAy0LmvVqhUfffQRubm5GI1GLly4QGBgYLE3NCWt6+LiYq2x5ePjQ1paWvmdrRBCCCGEKJXZbGbu3LnUrFmTSZPym5F26NCBF198kcGDBzNixAh0Oh1DhgwhICCA8ePHM3XqVNavX4+7uzuLFi2y8RkIIYQQt8eiKJy8mMiOiChOXkwCwNVRT78O/vRoUws3JzsbRyiKU2oyq2/fvhw4cIBRo0ahKArz5s3js88+w8/Pj969exMSEsLo0aNRFIUpU6ZgZ2dX7A2Ng4NDsevOmDGD2bNnY7FYUBSFmTNnVsR5CyGEEEKIv6lTpw7r168H4Lfffit2neeee47nnnuu0DwvLy9WrVpV7vEJIYQQZSUzJ49fjsey+0g011OyAWhUx5U+7eoQFOiNVqO2cYTin5SazFKr1cyePbvQvIYNG1qnR4wYwYgRIwotL+mGprh1GzVqxBdffHFHQQshhBBCCCGEEELcqavXM9h1JIpfT13DmGdBp1XTrVVNegXVoZ6vdFNQVZSazBJCCCGEEEIIIYSoqkxmC0f/SmBHRBR/Xk0BwMvVQK+gOnRtVRMne52NIxR3SpJZQgghhLgtkZGXWbhwHkuXruDtt9/grbdmo9PJzZ8QQgghKqfo+AzCz8Rx4MQ1ktNzAWju70HvoDq0auiJWi0duldVkswSQgghbMDxz7ewi9tSdjvUqHD0GkJm4Jyy2+c/eOed+RVyHCGEEEKIO3E9JZvfTsfx25k4ouIzATDoNfRuV4deQbWp6elo4whFWZBklhBCCHGf2L59GwcO7CM3N5fExASCgx9n//69XLp0gQkTXiIvz0RY2BrUajWtWrVh/PhJJCQkMHv2WyiKgoeHp3Vfw4cPZs2ajURHX2XJkg+xWBQyMtKZPPlVWrZszahRj9GyZWuuXInEw8ODOXPeR6PR2PDshRBCCFFdpWTkcvjMdcLPxHExJg0ArUZF2wAvOjarQeuGXtjp5T6kOpFklhBCCGEDmYFzyrQWlbe3M5nx6aWul5WVxYcf/pcdO34kLGwtK1as5o8/IggLW0N0dBQrV4ZiMBh4990ZHD58iPDwQ/Tp059HH32MnTt/YvPmjYX2d+nSRSZOnELDho346acf2L59Gy1btiYmJpr//Gc5NWr4Mn7805w5c5oWLVqW2fkKIYQQ1c2FmFTMajWScrk9Gdl5RJy7TvjpOM5dSUEBVCpoXt+dB5rVoF2gNw4G6Q6hupJklhBCCHEfCQhoDICTkzP16/ujUqlwdnYmOzublJRkXn31RSA/6RUdHc2lSxfp338AAC1bti6SzPLy8mH16pXY2dmRlZWFo2N+1X1XVzdq1PAFwMenBkZjbkWdohBCCFHlnLmcxMJ1RwHwdjPQvL4Hzep70LS+O46SkLHKMZo4+lcC4afjOHkpCbNFAaBRbVc6NqtB+yY+uDrqbRylqAiSzBJCCCHuIypV8R2dqlQqfHxq8NFHy9BqtWzfvo2AgECuXLnMqVPHCQgI5MyZ00W2+89/FjJz5hzq1/dn1ar/ERsb84/HEUIIIURR3/92BYCgxj6cuZzInqMx7Dkag0oF9X2daVbfg+b1PWhY2xWdVm3jaCtWnsnCiYuJhJ+O49j5BIwmCwB+Pk50bFaDDk198HK1t3GUoqJJMksIIYQQaDRaRo4cw8SJz2M2m6lZsxa9evXl2WfH8/bbb7Bjx0/UqlW7yHb9+j3CtGmv4OHhgbe3D6mpKTaIXgghhKi6ouMzOHkxicA6rrzzfGeuxaVyKTad05eTOH0piQsxaVyKTee7XyPR69QE1nWjeUFyq7a3Y7V8gGS2WDgTmcxvp68T8Wc82bkmAGq429OxWQ06NqshHbnf51SKoii2DuJOxd9GnyAl8fZ2vqft7wdSRqVTqeDoxSSSU7NtHUqpBnTtDcD2X3aWzf4SC/bnWfr+nJ0MpGfklMlxizMhojeg4vSzZ6h6V7KbyuM759GuBQBJESfLdL+2Itel0kkZFebt7WzrEEQp5PMqhBD5Ptt+hv3HY5k0tCX9ujQocn3MzjVx7mpKfnLrcjIxCZnWZS6OeprVd7c2S3R3tqvo8MuMRVG4EJ1K+Ok4fj97nbSsPADcne3o2DQ/geVXw6laJu9EyUq6p5OaWUIIIYQQQgghhA2kZhr59VQcPu72tG7kVew69nZa2jTyok3B8uT03ILEVn5y69CpOA6digOgpmHY1WsAACAASURBVKdDfmLL34PGdd2wt6vcP/kVReFKXAbhZ+I4fCaOxLT8PjadHXT0DKpNx6Y1aFTHFfX/Z+/Ow6Mqz4ePf2dNJjOTZLLvCVvYA4R9VWRTRFTcsfZ1RXmFVltbqq8FpCii1bY/qbb6oy64AEK1aLVYBEFQWQJhyUYgkI3s+0yWycyc94/AILIkQJLJcn+uiwtmzjPPuc8ZZjn3PM/9SAJL/ETH/p8thBBCCCGEEEJ0Udv25+Fwupg2Ihq1umUJG4vZi/GDwxk/OBxFUcgvsZF6spyUkxVk5FawJSmPLUl5aNQqekb4upNbPcLNaNQdo95WYXktu1OL2J1aRGF5LQAGLw3jB4UxekAo/eMsHSZW0TFJMksIIYQQQgghhGhn9kYnW/fnY/TWMmFw+BX1oVKpiAoxERViYvqoGBodLo7nV5GaXU7KiQqO5VeRmVfFpztPYPDS0C/G0lRMvkcAoRZDu07ZK6+uZ3daUwIrp8gKgE6rZkS/EEb3DyWhVwA6rabd4hGdmySzhBBCCCGEEEKIdvZdSiHWukZuHBuLl751kjg6rZp+sRb6xVqYMwls9Y2kZ1eQcrKC1BPlHMgs5UBmKQCBvl70P11Ivn+cBV8ffavE8GPVtXb2pRezO7WIzLwqADRqFQm9AhndP5ShfYI6/FRI0THJ/xohhBBCCCGEEKIduRSF/+7NRaNWcV1iVJvtx+itY3jfEIb3DQGgpLLOPSUx7WQ5Ow8VsPNQAQAxoaamUVtxAfSJ8kOvu7IEW229g/1HS9iTVkTqyQpcioIK6Bfjz6j+oQzvG4y5DRJnonuRZJYQXVRNrZ2SyjrsjS5cisIX32fT0OgEcK/856XXYDLoMBm0mAx6gv29sZi9ZIUQIYQQQggh2tDh42UUlNUyblBYu65AGOxv4JqhkVwzNBKXSyG7qMZdSD4zr5KcIiv/2Z2DVqMmPtrPndyKDjVdsgi7vdHJweNl7E4t4tDxMhxOFwA9ws2M7h/KyP6hnXqlRdHxSDJLiC7C5VIoKLORX2Ijv9RGzemlbO+/qSmBVV5dj5degwoVnP4cqqhpoKyq/px+9Do1oRYfwgN9iA0zy7BfIbqglJQjvPHG/7Bq1ZsXbVNYWMixY0eZMGFSm8Zy/PgxamqqGTo0kSVLnubZZ5fx0kvPM2XKdMaMGdem+xZCCCE85au9uQBMHxntsRjUahU9wn3pEe7LjWPjaGh0kplbSerJClJOJ7hST1awgeOYDDr6x1oY2COAAXEWgvwMOJwuUk6UszutiAOZpTTYm647IoOMjBoQyqj+IYRafDx2fKJrk6tUITq52noHmXmVZOZWUdvgAECraSoEGR7gg0GvRaVWMXda/HkrpCiKQl2DE1tdI1U2O8UVdRSW15JbbCW32Mre9GIiAo30jPAlOtSEViMrigjRWozGZ/Hy+rQVe1RhNN6Mzbb8kq0++OBdNm/+Am9vwyXb7d+/l+zsk22ezPrmm68JDAxk6NBEnntuRZvuSwghhOgIcopqSMuuoH+shZhQs6fDcfPSaRjUM5BBPQMBqLLZSTud1Eo5Wc7e9GL2phcDEGIxYKtrxFbfdP0R5OfN1OFRjO4fSlSIyWPHILoPSWYJ0UlV2+wcPFbKycIaFAV0GjV9Y/yJDTUTbDGgOZ240mia/r7QUr8qlQofby0+3lqCLQZ6R/kBYK1rJK/YStapavJLm0Z66XVq+sVYuMECMgtRiM4rMjKK559/mT/8YbH7vn/+82O+/PJz1Go1CQlDeeyxBbz//jvU19czeHACEyZc4267YcNaPv98EwEBgahUKu699+cUFhaQnX2S+fMX0tDQwL333s6GDZ9x4EASb7/9FgD19fU8++xz6HQ6Fi16El9fPxITR/Dll5+j1eqIj+/H4sVP88EHG9z7cjgcvPzyC+Tl5eJyuXjkkfkkJo5ov5PVDR08eJA//vGPrFmzhuzsbH73u9+hUqno06cPS5YsQa1Ws2rVKr755hu0Wi3PPPMMCQkJF20rhBDifJv3NI3KmjEqxsORXJqfUc+YgWGMGRiGoigUlteScqIpuZWeU4GXTsPUEVGMHhBKz3BfKVUi2pUks4ToZOrtDg4dL+NoTiUuBfxNevrG+NMzwg+dtnUuHEwGnXsVlCprA8dPVXM0t5JDx8uoHeRAq1Vhq2vEaNC1yv6E6I5stuXNjqK6HMHBZmy2mmbbXXvtFAoKTp1z3xdffMYTT/yGQYMG88knG1AUhZ/97P7TI7POJrLKykrZsGEd7723DoCHH77vkvs6cSKLxYv/QFBQMO+99w+2bdvC9Ok3UF5exurV76PT6VAUhcDAQAYMGHTe4z/77FP8/Px5+unFVFVV8vjj83j//fUtOR3iCrz11lts2rQJg6Fp1N6KFSt44oknGD16NIsXL+brr78mIiKCPXv28PHHH1NQUMDChQvZuHHjBdtOmzbNw0ckhBAdT0VNA3vSiggP9GFQzwBPh9NiKpWK8EAj4YFGpo6Idhd1lwSW8BRJZgnRSbgUhfSTFRw8Xkajw4XJoCOxbzCxoaY2/RDxM3mRGB/M4J6BHMurAhU0Olx8+u0JBsRZGNgzAL22dZYSFkJ4xjPPLOajj97nb397jYEDB1+0XWFhIT169EKvb1qBaNCghAu0Utz/Cg4O5s9/fhmDwYeSkmIGDx4CQHh4BDpd88nw48ePcejQAVJTjwDgdDqoqqrEz8//Mo5OtFRMTAyvvfYav/3tbwFISUlh1KhRAEyaNIldu3bRo0cPJkyYgEqlIiIiAqfTSXl5+QXbSjJLCCHO93VSHk6XwoxRMZcsqN7RdebYRdcgySwhOoFqm51dhwsoqaxHr1Mzsl8I8TH+7qmE7UGnVdM/zoKxTEuj04Vep+FwVjmZeVUM6R1Inyj/C05lFEJ0fJs2fcpTTz2Nl5cXv/rVAg4fPohKpUJRXOe0i46OJifnJPX19eh0OjIzM5g6dQZ6vZ6yslIAMjLS3e1XrlzO+vX/wsfHyPLlS9z3q1RnR5Gq1WpcrrMJsB+LjY0jJCSEn//8QRoa6nn33X9gNvu25qF3SXa7ndWrV3PixAkWL17MO++8w7x589xJyIuZMWMGeXl57tuKorh/LDEajdTU1GC1WvH3P5tMPHP/hdo2x2LxQSs/hgghupG6BgfbD57Cz6Tnpmt6o9dd+D0wOLjj1NESoqOSZJYQHZiiKGTkVJKUUYLTpRAbZmb0gBC89Z596eo0am6d1IPUkxUcySpjd2oxmXlVjBkYRpCft0djE0Jcvl69evPIIz/H399CcHAwAwYMwmg08t57/yA+vh9Tp84AwNfXj/vvf5gFC+bh6+tLY2NT0dfRo8fx6acbmT//Ifr27Y/RaARgxoyZzJt3P2azGYslkNLSkvP23bdvf15//S/ExfU4b9vNN89h5crlLFgwD5vNyq233iF1mFpg2bJlBAQEkJqaikajIScnh2eeeYY//vGPl9XPj8+1zWbD19cXk8mEzWY7536z2XzBts2pqKi9rHiEEKKz+zopD1tdIzdP6EFV5YXfA4ODzZSUNP+DgBDdxcWSu5LMEqKDsjc62XmogLySpuLr4waH0SO844xI0GrUJPQKpE+UH/szSjh+qpovv8+mb6w/w/oEt1r9LiFE6wsPj+DNN99x377pplu46aZbzmkTH9+Pjz7653mPnTbteqZNux6AJUueBsBsNrNq1ZvntV248FcsXHj+/n+873HjJjBu3AQANmz4DID/9/+Wurf//vfLWnRM4qyUlBQ++eQTduzYgcFgYOXKldx0002X3c+AAQPYvXs3o0ePZseOHYwZM4aYmBhefvllHnroIQoLC3G5XAQEBFywrRBCiLNcLoWv9uag1aiZPCzS0+EI0elJMkuIDqiypoFtB/KpqW0kPNCHCQnhGLw65svV4KVlfEI4vSL9+D6lkPTsSnKKrIwdGEY/k4zSEkKI9qZSqbDb7e5pfxUVFVdUW3HRokX8/ve/59VXX6Vnz57MmDEDjUbDiBEjuOuuu3C5XCxevPiibYUQQpx1ILOUksp6Jg2JwNd46WnfQojmdcyrYyG6sezCGnYdLsDhVBjYI4Bh8UGdosBiWKAPs8fHcTirnMNZZXydlEdhRR0JPQNklJYQXdRzz63wdAjiAn7+85/zwAMPUFJSwvPPP8+WLVt4/PHHW/TYqKgo1q9vWjGyR48evP/+++e1WbhwIQt/MuTuYm2FEEI02bw3B4DpI6M9HIkQXYMks4ToIBRF4UhWOQcyS9FqVEwaEk5cB5pW2BIajZqhfYKIDjWx61ABKVllZBdUMz4hjFCLT5vttxPk+prVVsfQ3udGuXAdbyFEO7rlllsYNGgQu3fvxul08sYbb9CvXz9PhyWEEN3W8VNVHMurIqFXIBFBRk+HI0SX0Gwyy+VysXTpUjIyMtDr9SxfvpzY2Fj39vXr17N27Vq0Wi3z589n8uTJlJeX89RTT1FfX09ISAgrVqzAYDBcsG1tbS1Lly4lLy+PxsZGfv/735OQcKGlvoXouhRFYW96MenZlfh4a5kyPAqL2cvTYV2xQF9vbhwXS1p2JQcySvhqdy6DegUypFdgq694qFLBDylF2J2u5ht3UGZjOTW2hlbtc7rdCcC3hwtatd9L0WnUjBkQKgktITzMbreTk5PjLsSfnp5Oeno6t9xySzOPFEII0Ra+2pMLyKgsIVpTs8msLVu2YLfbWbduHcnJybz44ou88cYbAJSUlLBmzRo2btxIQ0MDc+fOZfz48bz++uvMmjWLOXPm8Oabb7Ju3TpuvPHGC7ZdvXo1ffr04aWXXnJ/2ZJkluhOnC4Xuw4VcrKwBn+TnikjojB66zwd1lXTqNWMHRxBiL83Ow8Vcvh4GQWlNiYOCcfs07p1AhqdLhyOzpvMcjiVNoi/KaPUmc+LEOLKPPLIIyiKQmTkuQWGJZklhBDtr7Syjn0ZxUSHmOgfa/F0OEJ0Gc0ms5KSkpg4cSIAQ4cO5ciRI+5thw4dYtiwYej1evR6PTExMaSnp5OUlMSjjz4KwKRJk3j11VeJjo6+YNudO3dyww038NBDD2E0GlmyZEkbHaoQHY/D6WLb/nwKymoJ9jdw3fBIvHQaT4fVqkIsPswaF8vu1CJOFNTw+a5sRg0IoWeE7xUVJBZCCHFpFRUVbNq0ydNhCCGEALYk5aEoTaOy5LuvEK2n2WSW1WrFZDK5b2s0GhwOB1qtFqvVitlsdm8zGo1YrdZz7jcajdTU1Fy0bUVFBdXV1axevZpPP/2UlStX8tJLL10yJovFB632yi/4g4PNzTfq5uQctUBWOearWK2v0eHi37tOUFBWS1y4L9NHx7ZJofQzxeOvJtZz+qu4vP7OtJs5vicZ2RVsP5DHrsOFFFfWc01i1FUl71QqFSqVCpPRC4ezc89ta63n54zWft5bQqtRERTUdu8d8r7UvObOkdPp5Nlnn+XEiRNoNBpWrFhBTEzMRds3NDRwww03sHXr1tYO9RwZGRlUV1czcuRInnzySVauXMnixYuZOXMmkyZNatN9d0Vjxozhu+++Y8yYMajVsgCHEEJ4Sm29gx0HT+Fn0jN6QKinwxGiS2k2mWUymbDZbO7bLpcLrVZ7wW02mw2z2ey+39vbG5vNhq+v70Xb+vv7c9111wEwefJk3nzzzWaDrqiobfkR/kRwsJmSkporfnx3IOeoeWd+VKmx1l/R4x1OF1uT8iksryUm1MT4wWHU19u5st4uzXW6gNGVxnpef66W92c2eZ/TLiLQwKxxsXx7sIDM3EpOlViZOCSckCssDq+cPjarrYHGTjyd7qfnqTW09vPeElqtmtLSmjapmdUV35eMS5/F67NPW60/jVpF7Y03Y1u6/KJtduz4hvr6Rl577S3279/Hc8/9gRdffPWi7RsaGnA6XW1+7j/55DMCAwOJi+vHM88so6qqgfr6Rqqq6q543905+RkREcGDDz7oHgGgKAoqlYq0tDQPRyaEEN3LjoOnqLc7uXFsLFqN/LggRGtqNpmVmJjItm3bmDlzJsnJycTHx7u3JSQk8Oc//5mGhgbsdjvHjx8nPj6exMREtm/fzpw5c9ixYwfDhw+/aNvhw4ezfft2Bg0axN69e+ndu3ebHrAQnuZwuti6vymRFR1iYuKQCDStXBS9IzP76Ll+dAyHjpdx+HgZm3fnMrhXIAltUBxeCHGuSZOuZdy4CQAUFRVisQQCsGDBPPz9LdTU1PD88y+xfPliampqiIyMumA/Gzas5fPPNxEQEIhKpeLee39OYWEB2dknmT9/IQ0NDdx77+1s2PAZBw4k8fbbbwFQX1/Ps88+h06nY9GiJ/H19SMxcQRffvk5Wq2O+Ph+LF78NB98sMG9L4fDwcsvv0BeXi4ul4tHHplPYuKINj5Tndv69evZunUrERERng5FCCG6LafLxZakXPQ6NdcMjWz+AUKIy9JsMmvatGns2rWLu+++G0VReOGFF3j77beJiYlhypQp3HfffcydOxdFUXjyySfx8vJi/vz5LFq0iPXr12OxWHjllVfw8fG5YNtHH32UZ599lrvuugutVsvKlSvb47iF8Ainq6lGVmFZUyJr0tDulcg6Q61WMbRPEOGBPuw8VMCh42UUlNmYkND6xeGF6KhsS5dfchTV5QoONmNrwSgmrVbL8uVL2LHjG5YvP/uZO23a9VxzzWQ2blxHjx69ePTRx0lJOcL+/fvOeXxZWSkbNqzjvffWAfDww/ddcn8nTmSxePEfCAoK5r33/sG2bVuYPv0GysvLWL36fXQ6HYqiEBgYyIABg857/GeffYqfnz9PP72YqqpKHn98Hu+/v74lp6TbCg4Oxt/f39NhCCFEt7YvvYTy6gauS4zEZOj8izsJ0dE0m8xSq9UsW7bsnPt69erl/vedd97JnXfeec72oKAgVq9efV5fF2rr7+/PqlWrLitoITojl6Kw81AhBWW1RAUbu20i68dCA3y4aXwcP6QUcbKwhs+/y2b0gFB6Rvh6OjQhurRnn32OsrJS5s27n/ff/xiAmJhYoCn5NHr0WAAGDhzkLi1wRmFhIT169EKvb0o8Dxp0oRWIz841DQ4O5s9/fhmDwYeSkmIGDx4CQHh4BDpd81/ujx8/xqFDB0hNbVqAxul0UFVViZ+fJGsuxt/fn1mzZpGYmHjOOV6xYoUHoxJCiO5DURQ278lBBUwbGe3pcITokppNZgkhrp6iKOxJLSK7sIZQi0ESWT+i12mYOCScyGAju1OL2HmogFOlNkYNCEF/FQs9CCHO95///JuSkmLuu+8BvL29UavV7gLhZ/6OiYnjyJHDTJx4LUePpuNwOM7pIzo6mpyck9TX16PT6cjMzGDq1Bno9XrKykoByMhId7dfuXI569f/Cx8fI8uXn12xWKU6WztErVa76/H9VGxsHCEhIfz85w/S0FDPu+/+A7NZEt6Xcu2113Lttdd6OgwhhOi2MvOqOFlYw7A+QYReYW1YIcSlSTJLiHZw8FgZR3OrsJi9mJwYKQUgf0KlUtEr0o9gfwPfHiog61Q1xRV1TEwIJ9hi8HR4QnQZ11xzHS+88ByPP/4IDoeDX/ziV3h5eZ3TZs6cO1ix4jnmz3+I2Ni480ZP+fr6cf/9D7NgwTx8fX1pbGxKdo0ePY5PP93I/PkP0bdvf4xGIwAzZsxk3rz7MZvNWCyBlJaWnBdX3779ef31vxAX1+O8bTffPIeVK5ezYME8bDYrt956h6zQ14zRo0d7OgQhhOjWNu/JAWDGqIuvGCyEuDoqRWmLdafa1tWsqtQVV8RqbXKOmqdSQXJWORVVdc22Tc+uYE9aMWYfHdePjsHg1b455JkTpgDwxc6vW6e/stP9BTbf35Ws0udyKRw8VsrhrHJUKhjSK5BBvQJRq84fyfZ40hRUKhV/H7VVVjP8iZn3nX6e1rTO894SWq2aiYPDZTVDD/HUOVqy5Gluvvm2DleUvTuvZnjdddehUqlQFAWHw0FpaSn9+/dn48aNng7tHPKaFkJ0RUXltTzz5g/EhZt59ucj3CvLtpR85xHiXBf7Ticjs4RoQ7nFVvamFeOt1zB1RFS7J7I6I7VaxbD4YMKDjOw8VEDysTJOldUyISFcimcKIUQLbN269Zzbhw4d4oMPPvBQNEII0b38d18uCk2jsi43kSWEaDkZpy9EGymrqufbg6fQaFRcNzxKVum7TGGni8PHhpoorqjjs10nOVFQ7emwhBA/8dxzKzrcqCxxroSEBFJSUjwdhhBCdHnWukZ2Hi4g0NeL4X2DPR2OEF2aDBMRog1Y6xrZuj8Ph1Ph2mERBPl5ezqkTslLp2HS0AiO5VezN62Ibw8WkFtkZWT/EBnlJoQQF/HTVaIzMzMJDAz0UDRCCNF9bE/Ox97oYurEaDRS31GINiVXg0K0MrvDydakPOoanIzsF0JMaPet29IaVCoVfaL8CLUY2HmogJOFNZwqszE8Xn7tEkKIlhg1ahQ33nijp8MQQoguzeF0sSUpD2+9hokJEZ4OR4guT5JZQrQiRVHYebCASqudfjH+9I+zeDqkLsPXqOf6MTEczankwNFSvk8pos7XiZde4+nQhBCiQ1mwYIGnQxBCiG5nd2oRVVY700dG4+Mtl9lCtDV5lQnRipIzS8krsREe6MOIfiGeDqfLUatU9Iu1EB1qYm9aMU6bi9p6FzsPFZDQK7DbTT10Ol3UNjioa3BS1+DA7nDidCq4XApTG12oVHA0txKdRo1Oq8Zk0GHy0aHVyLB3Ibqifv36XbDYsKIoqFQq0tLSPBCVEEJ0fYqisHlPLmqViqkjojwdjhDdQve68hOiDWUX1nA4qxyTQcekIRGo1bJ6SVsxeuu4dlgkH+3RYm90kpFTyfH8Kgb3DKRfrAWdtmslaxxOFxXVDZRV11Nls1Nls1NttVPb4LjoY+wOJwA/pBSdt83gpcHP6EWwvzfB/gaC/Q0ywk2ILiA9Pb3V+/znP//JJ598AkBDQwNpaWm88sorvPTSS4SHhwOwcOFCRowYwdKlS8nIyECv17N8+XJiY2NbPR4hhOiI0rIryCuxMqp/CEF+Bk+HI0S3IMksIVpBRU09uw4XoNWomJwYKYmBdqLVqNBqdIyNDWV/RikHMktJPVlB/zgL/WL80es63/OgKArWukaKyusorqyjtLKOKpsdRTm3nY+3lrAAH3y8tRi8tBi8NHjpNGjUKjQaNQa9FgWYkBBGo0PB3ujEWtdITW0jNbV2CstrKSyvdfcX4OtFTKiZmFAT/iav9j1oIUSrqqurY9WqVXz//fc4nU7GjBnDL3/5S3x8fC67rzlz5jBnzhwAnnvuOW677TZSUlL4zW9+w4wZM9ztvvrqK+x2O+vWrSM5OZkXX3yRN954o9WOSQghOrLNe3IBmD4yxsORCNF9SDJLiKtUb3ewbf8p98qFFrMkAtqTSgUD4gKIDTWTll1B2skKkjNLST1RTt8Yf+Kj/TEadJ4O86IURaGsqo6s/EqKy+soqqij7kcjrjRqFUF+BoL8vAnw9cLf5IWvUd/s6DONpmlkYM8Ivwtutzc6Kamsp6SyKWlWXF5LeXUDyZml+Bn19IzwpU+0H956+ZgQorNZtmwZBoOBF154AYD169ezZMkSXn755Svu8/Dhwxw7dowlS5bw8MMPk5aWxrvvvktCQgJPPfUUSUlJTJw4EYChQ4dy5MiRVjkWIYTo6PJLbRzOKqNPlB89I3w9HY4Q3YZcpQhxFVwuhR3JBVjrGknoFSgrF3qQXqdhSO8g+sdZyMiuJPVkBYezyjmSVU5kiIm+0X6EBxlRX6CeTHtyuRTKquspqmhKIBVX1mFvdLm3e+s1xIaaCLH4EBJgwGLyapMpq3qdhshgI5HBRqApuZVXYiOnqIb8EhsHMks5eKyMuHAzfWP8CfaXIfNCdBYpKSls2rTJfXvx4sXMnDnzqvr8+9//zuOPPw7A+PHjmTp1KlFRUSxZsoS1a9ditVoxmUzu9hqNBofDgVZ78a+aFosPWm3nG0ErhBA/tnbbcQDumBpPcHDrXAu0Vj9CdGWSzBLiKiRllFBYXkt0iIkhvQM9HY4A9FoNg3sF0j/OwomCGo7mVJJXbCWv2Iq3XkNMqImYUDOhAT5o2qGuWaPDRWlVXdO0wYo6SirrcLrOzhk0GXT0iPAjwKQnNMAHs4/uggWc25pep6FnhC89I3yxNzo5fqqajJxKsk5Vk3WqmrBAH4b1DiLYIkktITo6RVGorq7G17dphEB1dTUazZUnjaqrq8nKymLMmDEA3Hbbbe6+p0yZwubNmzGbzdhsNvdjXC7XJRNZABUVtZfcLoQQHV21zc7WfbmE+BvoGWKipKTmqvsMDja3Sj9CdBUXS+5KMkuIK5SSVUZadgV+Jj0TEsI9koAQF6fVqOkT5UefKD9Kq+rJzK0kt9jK0dwqjuZWodOqCbEYCPE3EGIxEOjnfVWr/CmKQr3dSZXN7i7WXl5dT5XVzo/LXfmb9IRYfAi1GAgJMGD01mE2eVNjrb/6g24lep2G/rFNdccKympJOVFOQVktX5blEBlkZGifIAL9vD0dphDiIu6//37uuOMOJk+eDMDWrVuZN2/eFfe3d+9exo0bBzS9182ePZu1a9cSFhbG999/z8CBAwkKCmLbtm3MnDmT5ORk4uPjW+VYhBCiI9u6Pw+H08W0kdGy+JMQ7UySWUJcgeP5VWxLykOvVTN5WGSXWz2vqwny8ybIL4zRikJxRR05RTXkFdvIL2n6c4bRW4vZqMfXR4ePlxadVoNOq3Y/vy5FweVScDoV6u0O6uxO6hoc2OodVNvsNDpc5+xXq1ERbGmqdxUa4ENIJ1s1UKVSERFkJCLISFFFLclHS8kvtZFfaqN3lB+J8UFSU0uIDui2225j0KBB7Nu3D5fLxWuvvUbfvn2vuL8TJ04QFdW01LxKpWL5sRPOoAAAIABJREFU8uUsWLAAb29vevXqxZ133olGo2HXrl3cfffdKIrirtclhBBdlb3RybYD+Ri9tUwYHO7pcIToduQqRIjLVG2z89dPjuBSFCYPjcTXqPd0SKKF1CoVYQE+hAX4MKo/1NY7mgqgV9RRXl1PTW0jhWW1FJZdfr++Rh2+Rj2+Rj3+Ji8Cfb0wG/Uer9HVWkItPkwfFU1heS1704o5lldFTlENiX2C6R3t12WOU4jObM6cOdxxxx3MmjWLvn37XlUC68cefvjhc25PmDCBCRMmnNdu2bJlrbI/IYToDL5PKaSmtpGZY2I71Y+VQnQVkswS4jI4XS7+9q8jVNQ0MC4hnIggo6dDElfBx1tLbJiZ2LCz87AdThc1tY3UNThodLjcfwDUahVqtQqNWoW3XoPBS4vBS4OXTtMtppmqVCrCA43MGhdHek4FBzPL+CG1iGP5VUxICCdAph4K4VFPP/00n376KatWrWLs2LHcdtttjB071tNhCSFEl+NSFL7am4tGrWLK8ChPhyNEtyTJLCEuwyc7TpCeU8mwPkGM6BdCZXXHqXMkWodWo8Zi9sJi9vJ0KB2WWq1iQFwAcWG+7Esv5mRhDZ/tOsnIASGMHxSGiq6f2BOiIxo5ciQjR47EbrezZcsW3nnnHZYuXcrs2bOZM2cO4eEyDUYIIVrDkawyCspqGTswTL4zCuEhUuhHiBY6cLSEL37IJsRi4OFZA7rFSBwhLsXHW8ukoRFMGhKORqPihyNFvLo2mYqaBk+HJkS3ptfrmTlzJn//+9/58MMPKSoqYtq0aZ4OSwghuozNe3IBmDEq2sORCNF9ycgsIVqgqKKW//13GnqtmsdvHYyPt7x0hDgjLtyXEIsPP6QWknKygiX/2MOjswcysEeAp0MTots6efIkn3/+OV988QVhYWGsXLnS0yEJIUSXkFNUQ1p2Bf1jLcSEmpt/gBCiTcgVuRDNaGh08td/HqGuwcFDN/YnOsTk6ZCE6HB8vLVMGxlNY6OLj7Zk8uq6ZGZP6MFN4+JkqWoh2klxcTFffPEFmzZtwmq1csstt7B69WqZXiiEEK3oq70yKkuIjkCSWUJcgqIovL85g7wSK9cOi2S8LLsrxEWpVE1FUOPCfHnj08P8a+cJjuVX8ejsgZgMOk+HJ0SXd/311zN9+nQWLVrE6NGjPR2OEEJ0ORU1DexOLSI80IdBPQM9HY4Q3Zoks4S4hO0HT7HrSCFxYWbumdLH0+EI0Sn0jPBlyQOj+N/PUzl0vIzl7+7jF7cnyOqfQrSxHTt2YDLJ6GEhhGgrW/fn4XQpTB8ZjVrq5wrhUVIAXoiLOFFQzYf/PYrRW8v/vXUQOq28XIRoKZNBxy9uT2DWuDiKK+t4fs0+DmeVeTosIbo0SWQJIUTbabA7+eZAPiaDjrEDwzwdjhDdXrNX5y6Xi8WLF3PXXXdx3333kZ2dfc729evXM2fOHO688062bdsGQHl5OQ8++CBz587liSeeoK6u7qJtz9i7dy/XXHNNax2XEFfFWtfI658cwelUeHT2QIL8DJ4OSYhOR61SMWdST+bNHkCjQ+HPHx/kq725KIri6dCEEEIIIS7LzsMF2OodXJcYiV6n8XQ4QnR7zSaztmzZgt1uZ926dfz617/mxRdfdG8rKSlhzZo1rF27ltWrV/Pqq69it9t5/fXXmTVrFh9++CEDBgxg3bp1F20LUFBQwD/+8Q8cDkfbHakQLeRyKbz5WQpl1fXcPKGHzIcX4iqNGRDG7+5NxNdHz9qvM/loSyYulyS0hGhLZ75jZWdn88033+ByuTwckRBCdF4ul8J/9+ai1aiZnBjl6XCEELQgmZWUlMTEiRMBGDp0KEeOHHFvO3ToEMOGDUOv12M2m4mJiSE9Pf2cx0yaNInvvvvuom0bGhpYsmQJS5cubZsjFOIyffbdSY5klTO4ZyCzxsd5OhwhuoSeEb78/v+MIDLIyJakPP72ryM0OpyeDkuILmnVqlX87ne/49SpU9x777288847vPDCC54OSwghOq3kY6UUV9YxdmAofka9p8MRQtCCAvBWq/WcGgwajQaHw4FWq8VqtWI2m93bjEYjVqv1nPuNRiM1NTUXbbts2TIefPBBQkNDWxy0xeKDVnvlQzuDg83NN+rmuus5SkovYtOuE4RYDDz9wCjMPpf4sMoqx2zybr/grtCZ4pStFau64vL6a8tzpFKpUKlUmIxeOJyde6RPa5+n1n7eW0KrUREUdPH3juBgM3984hqef3s3+zJKqGs8wrMPjMJ0qdfZTx4vLk3OkQDYunUrH374Ie+99x6zZ8/mt7/9LXPmzPF0WEII0Wlt3pMDwPSR0R6ORAhxRrPJLJPJhM1mc992uVxotdoLbrPZbJjNZvf93t7e2Gw2fH19L9hWp9Oxb98+cnJy+Otf/0pVVRVPPvkkf/rTny4ZU0VF7WUf6BnBwWZKSmqu+PHdQXc9R6VVdby8Zh8atYrHbh5Iva2BelvDBdueWbykxlrfjhFeGdfp+kStFeuZ6WEt6c9s8m7Tc3Sm9pLV1kCjo/NOoWmL89Taz3tLaLVqSktraK4k1sJbB/HW52nsSy/mqb/s4Nd3D8Xf5HXJx3TX96XLIefoXN05sedyufD29mbbtm088cQTuFwud/1SIYQQlyfrVDWZeVUM6hlAZLAstCFER9HsNMPExER27NgBQHJyMvHx8e5tCQkJJCUl0dDQQE1NDcePHyc+Pp7ExES2b98ONC0TPXz48Au2TUhIYPPmzaxZs4Y1a9bg5+fXbCJLiLbQ6HDy+idHsNU7uHdaPHFhvp4OSYguS6fV8NjNA5kyPIr8UhsvfrCfsqqOnxgWorMYO3Yss2bNorGxkZEjR/Kzn/2MyZMnezosIYTolL7a2zQqa8aoGA9HIoT4sWZHZk2bNo1du3Zx9913oygKL7zwAm+//TYxMTFMmTKF++67j7lz56IoCk8++SReXl7Mnz+fRYsWsX79eiwWC6+88go+Pj4XbCtER/DBf49ysrCG8YPDmDQkwtPhCNHlqVUq5k7tg7dew7+/z+bFD5J46p5hhFp8PB2aEJ3eokWLuO+++wgLC0OtVvP73/+e/v37ezosIYTodEqr6tiXXkJUsIkBsRZPhyOE+JFmk1lqtZply5adc1+vXr3c/77zzju58847z9keFBTE6tWrz+vrQm1/bNeuXc0GLERr256cz46DBcSGmrlvel9UZ+YQCiHalEql4rZreuGl0/DPHVm8+P5+nrp7qAzhF+IKPf3005fcvmLFinaKRAghuoYt+/JwKQozRkXLNYIQHUyz0wyF6MqyTlXzwX+PYvTW8vitg9DrrnxhASHElZk1Lo57pvShymZn5YcHyC6Uuk9CXIlRo0YxatQobDYbxcXFjBkzhgkTJlBdXe2uMSiEEKJl6hoc7Dh4Cj+jnlH9W75YmRCifTQ7MkuIrqraZuevnxzG6VR49PaBBPkbPB2SEN3WtJHReOk1vPtlOi99dIAn7xxC70g/T4clRKdy6623AvDhhx+ybt061Oqm3yxvuOGGS46MF0IIcb4dB09Rb3cyc0wsOq2MARGio5FkluiWnC4Xf/vXESpqGrjtmp4M6hHo6ZCE6PYmDYlAr1Pzv5+l8craZH5x22D6xwV4OiwhOp2amhoqKysJCGh6/ZSWllJbe+UrQd9yyy2YzU2rQ0ZFRXHXXXfx/PPPo9FomDBhAgsWLMDlcrF06VIyMjLQ6/UsX76c2NjYVjkeIYRob06Xiy37ctFr1Vw7LNLT4QghLkCSWaJb2rg9i/ScSob1CWLmGPmyLURHMWZAGF5aDW/86wh/+vgQC28bzOCekmwW4nI89thjzJ49m8TERBRFITk5mWefffaK+mpoaABgzZo17vtuvvlmXnvtNaKjo5k3bx4pKSnk5+djt9tZt24dycnJvPjii7zxxhutcjxCCNHekjJKKKtuYHJiJCaDztPhCCEuQJJZotvZm17Mf3bnEBrgw8OzBkgxRyE6mGHxwfzi9gRe23iY1zYeYsGcwUwJNns6LCE6jVtuuYVx48Zx4MABVCoVS5cuJTDwypLC6enp1NXV8eCDD+JwOFi4cCF2u52YmKYl6idMmMD3339PSUkJEydOBGDo0KEcOXKk2b4tFh+0WqlVKYToWBRF4ev9+1Gp4O7p/Qj2wMI0wfK9R4hmSTJLdCv5pTb+8e80vHQaFswZjMFLXgJCdESDegTyy9sT+J8Nh1j1z8OYfQ30CDZ6OiwhOoXy8nK++OILbDYbiqKQnp5OXl4eL7300mX35e3tzUMPPcQdd9zByZMneeSRR/D19XVvNxqN5ObmYrVaMZnOXvBpNBocDgda7cU/ZysqrnzqoxBCtJWjuZVk5jbN4NChUFLSvgvTBAeb232fQnRkF0vuSiU70W3U1jtY9c/DNDQ6efDG/kQGyYWxEB3ZgLgAfnnHENQqFSve2cOBzBJPhyREp/DEE0+QlpbGpk2bqKurY/Pmze5i8JerR48ezJ49G5VKRY8ePTCbzVRWVrq322w2fH19MZlM2Gw29/0ul+uSiSwhhOiovtqbC8D0kdEejkQIcSmSzBLdgsul8NZnKRSV13L96BhG9gvxdEhCiBboH2vhyTuHoNGoef2TI+w/KgktIZpTXFzMypUrue6665g+fTrvv/8+qampV9TXhg0bePHFFwEoKiqirq4OHx8fcnJyUBSFnTt3MmLECBITE9mxYwcAycnJxMfHt9rxCCFEeymqqOXA0RLiwszER/t7OhwhxCXIT2aiW9iw/TgHj5cxMM7Cbdf09HQ4QojL0DfGwtKHx7D0rR9449MjPHbzQIb3lYS0EBfj5+cHNI2qSk9PZ8iQIVfc1+23387TTz/NPffcg0ql4oUXXkCtVvPUU0/hdDqZMGECQ4YMYfDgwezatYu7774bRVF44YUXWutwhBCi3WzZm4cCTB8VLXV1hejgJJklurxdhwvcBd8fu2UQmiucaiGE8JxBvYJ48s4h/Onjg7zxaQqP3QwjZISlEBc0ZswYfvGLX7Bo0SIefPBBUlJS8Pb2vqK+9Ho9r7zyynn3r1+//pzbarWaZcuWXdE+hBAdQ6PDRW19I9Z6B7X1jdjqHNjqG7HVO7DVNWKrb6S23oG1vhF/kxezx8UR5G/wdNitxlrXyLeHTxHg68UI+dFMiA5PklmiSzuWV8W7/0nHx0vLL29PwOgtS+sK0VnFR/vz6zuH8ur6ZP72rxTmKQqj+od6OiwhOpwnn3ySnJwcIiMjefXVV9m7dy+PP/64p8MSQrQDl6JQ3+BoSkBdICF1JhlV+6ME1Zm29kbXZe1rd2oR14+KYebYWLx0nX9l0u3J+dgbXUydEI1WIz9+C9HRSTJLdFmlVXW89s9DuFww/9ZBhAX4eDokIcRV6h3lx6/uGsqr65J5c1MqigKjB0hCS4ifOnjwIBs3buSxxx4jMzOT0FB5nQjRmTQ6XKeTUWeTTWcSUO6RU+6ElMPdtrbBgaK0fD8GLy1Gby3hAUaMBi0+3jpM3lqMBh0+3lqM3rrTf5ruM3pr8fHWcuBoKeu/OcZn351k15EC7pzcm5H9Qjrt1DyH08XXSXl46TVMGhLh6XCEEC0gySzRJdXbHfzPhsPU1DZy77R4BsYFeDokIUQr6R3px6/vPp3Q+iwFRVEYMzDM02EJ0WH88Y9/pLCwkJSUFB555BE2btxIeno6v/vd7zwdmhDiRxxOFycLa8jIqSAzr4ry6np3gsruaPkoKa1GhdFbh5/Ji/AgIybvM0knHUaD9pxklI+3tmm7QYfBS3PF5TfGDgpjWHwQ//4+m817cvjbv1LYuj+fuVP7EBNqvqI+PWlPWhGVVjvTRkTj4y2XyEJ0BvJKFV2OS1F467NU8kqsTB4WyZThUZ4OSQjRynpF+PHru4bxyrpk3vq8aYTW2EGS0BICYOfOnXzyySfceuutmEwm3n77bWbPni3JLCE8rNHh4kRBNRm5lRzNqSAzv+qcqX3uUVJBxqbk0+mkk/vfpxNUptMJKp/TCSq9Vu2REVHeei23XdOLiQnhrNt6jAOZpTz3zl6uGRLBrZN6YvbRt3tMV0JRFL7ak4tKBdNGyHWDEJ2FJLNEl/PJjiwOZJbSP9bCPVP7eDocIUQb6Rnhy1N3D+WVtcn87+epuBSF8YPDPR2WEB6nPj3S4szFrd1ud98nhGg/jQ4nWaeqycipJCO3kmP5VTT+aMRVRJCRvtH+9I3xp2+0P34mLw9Ge+VCLD4svC2BlBPlfLjlKN8kn2JPWjE3T+zB5GGRHb7+VHp2BTnFVkb0C+lSBe2F6OokmSW6lO9TCvn399mEWAzMv2VQh//wFEJcnR7hvvzmnmH8ce0B/vHvNBQFJiRIQkt0b9dffz1PPPEEVVVVvPPOO2zatIkbb7zR02GJTq60qo49acXklVgJC/AhKthEVLCRIH8D6k5aJ6m12RudHM+vIiO3koycSo6fqsbhPJu8igo20jfaQt8Yf+Jj/PHtJCOXWmpgjwCee3AU2/bn8+nOE3y0JZPtyae4Z2qfDl3yY/PeXABmjIr2cCRCiMshySzRZRzNreTtL9IxnF650GSQlQuF6A5iw8w8dXdTQuvtL9JQFIWJUrxVdGPz5s3j22+/JSIigoKCAhYuXMjkyZM9HZbohKptdvamF7M7rYhjeVUXbOOl0xARZCQq2OhOcEWGmLpcouZCGuxOjuVXkZFbQUZOJScKqnE4m6qvq4DoEBPxMf70i7EQH+3fLb6bajVqpo2MZvTAUD7ZkcWO5FO8sjaZYX2CuGtKH0I62MinU6U2Dh0vo3ekH70i/DwdjhDiMkgyS3QJp0ptvLbxEIqi8H9vGUR4oNHTIQkh2lFsmPn0CK1k3v4yHZeicM3QSE+HJYTHTJw4kYkTJ7pvL126lKVLl3ouINFp1DU42H+0hN2pRaSerMClKKiA/rEWRg8IJT7an+KKWvJKbOSVWMkrtpFTVMOJgupz+vE16t0JrsjTf0cEGfHSaTxzYK2grsHRlLzKqSQjt4KTBTU4XaeTVyqICTXTN7opedUn2g+jd9dPXl2Mr4+e/3N9P64dGsmHW45yILOUw1nlzBgVzY1jY/HWd4zL0P/uk1FZQnRWHeNdRIirUGlt4E/rD2Krd/DQjf0Z2KPjDmMWQrSdmNCmhNbLHx3g3f9koChw7TBJaAkBsGnTJklmiYtqdDg5dLyMH1KLOHS8zF3XqUe4mdEDwhjZLwSL+Ww9p7AAHxJ6BblvO5wuisrPJrjyT/+derKC1JMV7nYqIMRiOCfBFRViIsTfgFrd8aYq1tY7yMyrdE8bzC6swaU0Ja/UKhWxYWb6xvjTL8af3pH+sgreBcSGmfndvYnsSStm/bZj/Pv7bHYdLuCOyb0ZMyDUI4Xrz6iutfPdkUKC/b0Z1ifYY3EIIa6MvOOKTq2uwcGfPz5IWXU9t07sIcWfhejmokNM/HZuU0Lrvc0ZKIrC5ERZmUgI5fQFuBBnOF0u0rIr2J1axP6jJdQ1OAEID/RhzIBQRg0IJdTi06K+tBo1kcEmIoNNjCbUfX9dg4P8Uht5xdamUVwlNvJLrCQdLSHpaIm7nV6rJvycqYpN0xV9jfp2TXbU1jdyNLeK9JwKMnIrySmq4cxLR6NW0SPC7K551TvSD4OXXEq1hEqlYvSAUIb2DuKLH7L5cncOb32Wyrb9+cyd1oe4MF+PxPXN/nwaHS6mjYjukMlUIcSlyTuw6LQcThevf3qEnCIrk4ZEMGtcnKdDEkJ0AFHBJn57eoTWmq+O4lJgynBJaInuzZOjH0THoSgKx/Or2Z1axN70IqprGwEI9PXi2qGRjB4QSnSIqdX+vxi8tPSO9KN35NlaRIqiUGm1n05uNU1TzD89miu7sOacx5sMurMJrpCm0VyRQcZWm6JmrWvk6OlRVxk5FeQWWzmT9tWoVfSO9Du90qCF3pF+eOk77xTJjsBLr+HWST2ZmBDOum3HSMoo4Q/v7GNCQji3XdMLX2P71VlrdDjZuj8PHy+tLBwjRCclySzRKblcCm9+lkrKiXKG9Arkvhnx8kVdCOEWGWziN3MTefmjA3zw36O4FIVpI6Qehuja7rvvvgt+FiqKQkNDgwciEh1FXrGVH1KL2JNWRGlVPdCUKJqcGMno/qH0jvJrtxUJVSoVFrMXFrMXg3sGuu93ulwUldedM4Irr8RKek4l6TmVZx8PBPl7n56qaHInu0IDDGjUl17FurrWztGcM9MGK8grsbm3aTVq4qP9Tyev/OkZ6dep63t1ZEH+Bh6/dTBpJ8v58OtMvj1UwL6MYmaP78GU4VHtshr59ylNydwbxsR0mPpdQojLI69c0ekoisJ7mzPYl15MfLQ/828Z1OyXFyFE9xMZZGTR3GG89OEBPtqSiaLA9JGS0BJd18KFCz0dguhAiivr2JNaxO7UIvJLm5I2XnoN4waFMXpAKP1jLe2SNGgpjVpNRJCRiCAjo/qfvb/e3jRVMb/k3OmKBzJLOZBZ6m6n1aiJCPRpSnCFNCW4gv0N5BTVuGtenSo9m7zSadX0j7XQ93QCq2eELzqtJK/aU/+4AJY+MJJvDpzi02+zWLf1GNuTT3HP1D7nJDpbm6IofLU3F41axRQpRSBEpyXJLNHpbNh+nB0HTxETauIXtyWgl1/NhBAXER5o5Ldzh/HSRwdY+3UmiqIwY1SMp8MSok2MGjXK0yEID6uyNrAnvZjdqUVknWpaXVCrUTM8PpjRA0JJ6BXY6b43eeu19Irwo1fEuVMVq232sysqnk5wnSq1kVNshZTz+9Hr1AyMsxAf05TA6hHui07bcZJ53ZVGrWbK8ChGDwjlkx1ZfJOcz5/WH2Ro7yDumtK7xXXbLseRE+WcKrUxdmAoAb7erd6/EKJ9SDJLdCr//v4kX/6QQ2iAD7+6c6isGiOEaFZ4oJFFcxN56cP9rNt6DJeicMPoWE+HJYQQraK2vpGkjBJ2pxWRll2BooBKBQPjLIweEEZifHCX+76kUqnwM3nhZ/I6ZxVrl0uhuLLOPYKrpLKOiCAjfWMsxIWZO9RINHEuk0HHfTP6cu2wSD7acpTkY6Uczipj+shoZo2La9Vi+5v35AAwfaT8uCVEZ9a1PtlEl/af3Tls3J5FgK8Xv75rSLsWiRRCdG5hAT4sujeRlz48wMfbjuNyKdw4Ns7TYQkhxBVpaHRy8Fgpu1OLOJxVhsPZVLa8V6QvYwaEMaJfCH7d8HuSWq0iLMCHsAAfRvQL8XQ44gpEh5j4zT3DSMooYd3WTL7cncN3Rwq5/dpejB0UdtW13XKLraSerKBfjD+xYeZWiloI4QnNJrNcLhdLly4lIyMDvV7P8uXLiY09+4v2+vXrWbt2LVqtlvnz5zN58mTKy8t56qmnqK+vJyQkhBUrVmAwGC7Y9tSpUzzzzDM4nU4URWHZsmX07NmzTQ9adD5f7c1l/bZjWMxe/PaeYQT5GTwdkhCikwm1+DTV0ProABu3Z2Grc3DH5F6yeIQQolNwOF2knqxgd2oh+zNLabA7AYgKNjJ6QCij+ocS7C/fj0Tnp1KpGNEvhIRegfxndw5f/JDN6n+nse1APnOnxtMzwveK+/7qzKgsKTkgRKfXbDJry5Yt2O121q1bR3JyMi+++CJvvPEGACUlJaxZs4aNGzfS0NDA3LlzGT9+PK+//jqzZs1izpw5vPnmm6xbt44bb7zxgm3/8pe/8LOf/YypU6fy7bff8uqrr7Jq1ao2P3DReXydlMfarzPxM+n57T3DCGmDufNCiO4hxOLD0/cO55V1yfxnTw41tXbun9lPFpEQ4iIaGxt55plnyM/Px263M3/+fMLCwnjssceIi4sD4J577mHmzJmsWrWKb775Bq1WyzPPPENCQoJng+8CXIrCsbwqfkgtYl96Mda6RgCC/LyZerrOUFSwycNRCtE29DoNsyf0YPzgcNZvO8be9GKWv7eP8YPDuP2aXviZvC6rv0prAz+kFhEW4ENCr7YrMC+EaB/NJrOSkpKYOHEiAEOHDuXIkSPubYcOHWLYsGHo9Xr0ej0xMTGkp6eTlJTEo48+CsCkSZN49dVXiY6OvmDbRYsWYTY3DfF0Op14eV3em5Lo2v67N5ePvs7E19iUyAoNkESWEOLqBPp58/TPEvnzxwfZdaQQW72Dx24e2OmKIgvRHjZt2oS/vz8vv/wyFRUV3HrrrTz++OM88MADPPjgg+52KSkp7Nmzh48//piCggIWLlzIxo0bPRh556UoCjlFVnanFbEnrYjy6gYAfI16dwKrZ4SvjCoV3UagnzfzbxnEdTkVfLglk12HC0nKKOGm8XFMGxHd4lpoXyfl4XQpTB8ZfdXTFYUQntdsMstqtWIynf3FR6PR4HA40Gq1WK1WdyIKwGg0YrVaz7nfaDRSU1Nz0bYBAU1FG7Oysli5ciV//etfmw3aYvFBexVL5wYHy/zo5nSEc7RuSwYffZ1JgK8Xyx8bT3So52M6R1Y5ZlPHXwHlzId1a8Wqrri8/tryHKlUKlQqFSajl7teSGfV2ueptZ/3ltBqVAQFtd3rtDXfl4KBFxdMZMU7e0nOLOEvGw/z/x4Yddm/8nY0HeG9W3Qt119/PTNmzHDf1mg0HDlyhBMnTvD1118TGxvLM888Q1JSEhMmTEClUhEREYHT6aS8vNz9PU80r6i8lt2pRexOK6KgrBYAg5eWCYPDGT0wlH4x/jKKVHRrfWMsLLl/JNsPnuKTHVl8vO04O5JPcfeUPgzpHXTJxzbYnXxzIB+TQce4QWHtFLEQoi01m8wymUzYbDb3bZfLhVarveA2m82G2Wx23+/t7Y3NZsPX1/eibQF++OEHnnvuOV566aUW1cuqqKht+RH+RHCwmZKSmit+fHfg6XOkKAobt2fxxQ/ZBPp68dQ9w/BW06GetzM/5tRY6z1lvdldAAAgAElEQVQbSAu4lKYkT2vF6nK1vD+zybtNz5Fy+tistgYaHa42209ba4vz1NrPe0totf+/vfuOq7LuHz/+OuewN8hwoshQ0VBwoGmWg1QUy8GQJFdZfh233WqOHJSDyrT7l/O2LI1yQamomTmjSHGLO0FFcbAEEWSf8/uDu1MoOEo4jPfz8eAB5zqf61zv8+G6rnOd9/UZStLS7qGpgLxiRZ2XxrzSki9/OE/suWQm/edn3gloXW27Muv63F3VSGLv2TA1NQVKbm5OmDCBiRMnUlBQgL+/P61atWLFihUsW7YMc3NzrKysSq137969Ryaz/unNyZqguFjN7sPX2BWbSPz1TAAM9JR0bl2fFz0b0ra5vbQaFeIBAS9b4NulKet+usiOmCv8v8g42ja3541XWtHQvuxz/46YK+TkFRHo40aD+lZllqlK5DNMiMd7bDLLy8uL/fv34+vry8mTJ3Fzc9M+5+HhwX/+8x/y8/MpKCggISEBNzc3vLy8+Pnnnxk4cCDR0dG0bdu23LKHDh1i/vz5fPHFFzRo0KBC36yo+tQaDet3X2Lv8SQcrI2ZHORJHcuq3/pJCFE96espedPPnToWRvxwKJF5Xx/jX4M9cG5gqevQhKgybt26xdixYwkODsbPz4+srCwsLEoGYPbx8WHu3Ln06NGj3JuW5fknNyerO41Gw6mEdCL2x3Mr/T5KhYLnmtbB290eT1c7jA1LLtHvZtbeOhLicQZ0bkIHN1vW7bnEsQspnPx9Pz3bNcTveSdMjP78mqvWaPh+/yX0VAo6Nrev8jd+5OaUEKWVl9x9bDLLx8eHmJgYgoKC0Gg0LFiwgK+++gpHR0d69OhBSEgIwcHBaDQa3nnnHQwNDRkzZgxTp05l06ZNWFtbs2jRIkxMTMosu2DBAgoLC5k2bRoATk5OfPDBB8/23YtqobComC+2n+fIhRQa2JkyObBNte/yI4So+pQKBYNfcsbW0ojwny7y8foTjOrbgg4tHHQdmhA6l5aWxsiRI5k9ezadOnUCYNSoUcyaNQsPDw8OHjxIy5Yt8fLyYuHChYwaNYrbt2+jVquli2E5riXfY+O+eM4nZqBQwEtt6tO/ixNWcs0jxFNrYGfG5KA2HP89jY37LrHr8HUOnrnNoBed6exRD6VCwalLaaRk5NLFox6Wpga6DlkI8Yw8NpmlVCofSi45Oztr/w4ICCAgIKDU87a2tqxevfqh1yqrbFRU1FMFLGqmnLxClnx3mt+vZ+LW0JJxgzwwM9bXdVhCiFrkJc8GWJsbsjLqLCu3nuVmWg79uzjJILGiVlu5ciVZWVksX76c5cuXAzBt2jQWLFiAvr4+tra2zJ07FzMzM9q1a0dgYCBqtZrZs2frOPKqJ+NePt9HJ/Db6dtogOea1iGgmzMNZDZCIf4RhUJB22Z2eDjb8OPh6+w4eJWvdl5g/4kbBPu4sevIdQB6tW+k20CFEM/UY5NZQlS0O1l5LN50iptpObRrZsebfu7o1/IxNIQQutHaxZb3hrbls+/iiIq5ys20HEb1dcfQQM5JonaaOXMmM2fOfGj5hg0bHlo2fvx4xo8fXxlhVSt5BUX8GHuNHw9fo6BQTUM7UwK7u9LSSVquCfEs6eup8Hu+CZ1b1SXyQAKHziWzIPwYAK2cbCRxLEQNI8ksoVMJN+6y9PvT3M0poGe7hgT1cJVWEEIInWpob8bMYe1YvvkMRy+mkpJxjLEDn8POyljXoQkhqhG1WsOvp2+x+ZfL3M0uwNLUgOCeTenyXD2USrnWEaKi2FgYMbp/S7p5NWDd7ktcS7mHb8fGug5LCPGMSTJL6EzM6Vus/fECxWoNQ3q44iNNf4UQVYSFiQGTg9rw7e7f+fnkTT5Yc4Q3/dzxcH701N9CCAFw9sodNu6LJyk1GwM9Jf07N6G3tyNGBnLpLURlcW1oxazh7bh3v1DGyhKiBpJPVFHpitVqIvYn8NOR65gY6vH2qy1p5VRH12EJIUQpeiolw3o3x6meBd/89Dv/LyIOv85N6N/ZSVpVCCHKdCM1m037Ezh9OR0F0OW5egzo2hRrcxncXQhdUCoUksgSooaSZJaoVHez81m17RznEzOoV8eECYM8cLAx0XVYQghRrq6t6+PoYMbyzWeIirlKwo27vNHPXWZbFUJo3c0pYOsvl/n51E00GmjR2JrA7i44OpQ9nbgQQggh/hlJZolKcz4xg1VRZ7mbU0AbF1ve6OeOiZHsgkKIqq9JXQtmD2/PF9vPEZeQzuwvDzOqbwvpdihELVdQWMxPR66z41Ai+QXF1KtjQkA3Fzyc66CQMUCFEEKICiOZBFHh1GoNOw5eZcuvV1AqFAR2d+Hl9o3kIk8IUa2YGeszYbAHe44mEXkgnv9ExNGzXUP8X3JBX0+p6/CEEJVIrdEQezaZ76ITuJOVj7mJPgEvOfNC6/roqeR8IIQQQlQ0SWaJCpWSmcvq7ee4lHQXGwtD3n6lFS4NLHUdlhBC/C1KhYKX2zeiuaMVK7eeZc/RJM4nZjDStwVO9Sx0HZ4QohJcvJbBhn3xJN6+h55KiW/Hxvh2bCytzYUQQohKJJ+6okJoNBp+ibvF+r2XyC8opm0zO4b1bo6Zsb6uQxNCiH/M0cGcOcPbs2l/PPtP3GD+18fo09GR/p2dpJWWEDXU7Tv3idgfz4lLaQB0dHdg4ItNsbU01nFkQgghRO0jySzxzKXfzSP8p4vEJaRjbKjHm37udHR3kG6FQogaxdBARUivZrRtZsdXP1xgx8FETsanMax3c2mBKkQNkp1byNZfr3DgxA2K1RpcG1oS2N2VpvWlNaYQQgihK5LMEs9MsVrNnqNJbPnlCvmFxbg3sWakbwtsLIx0HZoQQlQY9yY2fDCqAxEHEjhw4gYLwo/RtXV9Br/kLK1RhajGCovU7D2WxLbfrpKbX4S9tTH+Lznj5WYnN+iEEEIIHZNklngmEm7eJfzHi1xLycbMWJ+hL7vxfKu6crEnhKgVjA31eL1XMzq6OxD+00WiT93k+O+p+L/kTGePeijlXChEtaHRaDhyIYXIAwmk3c3D1EiPoB6udPdqIIO7CyGEEFWEJLPEP5J+N4/vfk7g0LlkADo/V5eAbi6YmxjoODIhhKh8bo2smDO8PXuOJrH11yt8tfMCe48lEdjdhRZNbHQdnhDiMeJv3GXjvksk3MhCpSyZ8KHf802klaUQQghRxUgyS/wtuflF7IxNZNfh6xQWqWnsYE5QDxeaOVrrOjQhhNApPZWS3t6OdGhhz3c/X+bg2dss3HASD+c6+L/kTAM7M12HKIR4QGpmLpEHEjhyIQWAts3sGPySMw7WJjqOTAghhBBlkWSWeCq5+UXsPZbErsPXyMkrwsrMgEEvOtOpVV3pRiOEEH9hY2HEm37u+LRvyMa98cQlpHM6IZ32Lezp39mJ+ramug5RiFrvfl4h239LZM+x6xQVa3CqZ0FgdxfcGlnpOjQhhBBCPIIks8QTuZ9XxIGTN/gx9hrZuYWYGukxoGtTXm7XCEMDla7DE0KIKqtJXQveDfbkVEI6W365zOHzKRw5n4K3uwO+nRrTUFpqCVHpiorVHDhxg6iYq2TnFlLHwohBLzWlQwsHuTknhBBCVAOSzBKPlHY3lz1Hk4g+dZO8gmJMDPV49QUnerZthImR7D5CCPEkFAoFbVxsae1ch5OX0tj66xUOnUvm0LlkWjnZ0MvbEffG1jJphhAVTKPRcOJSGhH740nOyMXYUIX/S870bNcQfT25OSeEEEJUF5KNEA/RaDScT8zgwIkbHLuYilqjwdLMgL6dGtPNswEmRjIIqhBC/B0KhQJPNzvauNpyKj6dHw9f48yVO5y5coeGdmZ092qAt7sDxoby8SzEs3b1dhYb98Zz8XomSoWCbl4NeKWLExYyaY0QQghR7cjVstC6m1PAb6dv8euZ29xKywGgoZ0pvTo44u3uINNRCyHEM6JQKGjjaksbV1uu3Mpi1+FrHL2Qyte7LrJxfzwd3R3o2ro+TeqaS2stIf6hO1klMy8fPFsy83IbF1sGv+Qs49YJIYQQ1Zgks2q5+3lFHP89ldhztzmXmIFGAwZ6Sp5vVZcX29THpYGlfJESQogK5FTPgrdfaUVG93x+jbtJ9Kmb/Hyy5KeujQkd3R3wbukgs6qJKk+tVhMaGsrFixcxMDBg3rx5NG7cWGfx5OYX8cOhRH46UjLzsqO9GYHdXWjRxEZnMQkhhBDi2ZBkVi10Nzufk/FpnLyUxtmrGRQVqwFwrm9Bx5Z16dfVmdycfB1HKYQQtYu1uSF+nZ3o26kJZ66k89uZ25y4lMaWX6+w5dcrNHYwx9PNFk9XOxramcqNBlHl7Nmzh4KCAjZu3MjJkyf58MMPWbFihU5iiTl9i4j98WTdL8Ta3JCBXZvKzMtCCCFEDSLJrFqgqFhNwo27nLuawZkrd7hyK0v7XAM7Uzq0cMDb3QF7K2MAzEwMJJklhBA6olQq8HC2xcPZltz8Ik5cSuXQuWTOX80gMfkeW365gq2lEc8516FlExuaO1rLhByiSjh27BgvvPACAG3atOHMmTM6iSM3v4gvd5zHQF/Fqy840auDI4b6Mri7EEIIUZPI1W8NlFdQxJWbWVy6cZf4pLv8npRJQWFJ6yulQkFzRyvauJYMQPxHAksIIUTVY2yox/Ot6vF8q3rczyvi9OV0TlxK5fTldPYfv8H+4zdQKhQ41TenjZs9DWxMcGloiZmxTNQhKl92djZmZmbaxyqViqKiIvT0yr7ctLY2Qa+CZhD85F9dsbMyxtrCqEJeXwghKpKdnbmuQxCiypNkVjV3N6eA6yn3SErJ4XrKPa6nZHMz7T5qjUZbpl4dE1o2scG9iQ3NHK1kliwhhKiGTIz08HYvaUlbVKzm8s0szl29w7mrGVy+mUXCjT9b3da1McHRwYxG9mY0sjenkb0ZVmYG0jVRVCgzMzNycnK0j9VqdbmJLICMjPsVFou1sR5F+YWkphZW2DaEEKIi2NmZk5p6T9dhCFFllJfclaxGNVBUrCb9bh6pmbmk3s0jJeM+Sak5XE/JJiunoFRZA30lTetb4NLQEpcGJT8WpjLltBBC1CR6KiVujaxwa2TFqy+UtMi9k1PEkbO3uJSUyZVbWRw+f5/D51O065ib6P8vuWWGg40JdlbG2FkZY2NuKLPVimfCy8uL/fv34+vry8mTJ3Fzc9N1SEIIIYSooSSZpUMajYbc/GKy7heQlVPA3Zw/f2fcyyM1M4+0u7lkZOWjKWP9OhZGtHGxpaG9GY7/+4JiZ20sg5sKIUQtY2SgR+sG1tS3LulSpdFoSL+bx/WUbK6lZHM9JZvrKfc4dzWDc1czSq2rVCiwsTDEzsoYW0sj6lgaYWFqgKWJQclv05LfBjLmkHgMHx8fYmJiCAoKQqPRsGDBAl2HJIQQQoga6rHJrMdNs7xp0yY2bNiAnp4eY8aMoVu3bty5c4fJkyeTl5eHvb09YWFhGBsbP1VZXckvKKawWI1Go0GtKflCoFZrUGs0aDT8+ft/ywqL1BQWqSkoKqawUE3+/34XFKkpKCzmfn4R9/OLyM0vIjfvz7/v5xeRlVOonUmwLArAytwQ10ZW2Fkaae+i21oZ0cDWFBMjGRNFCCHEwxQKBbZWxthaGePpZqddnptfRFJqNikZuaRm5pL2R6vfzFzOJ2Y84hXByECFhakBJoZ6GBvqaX+X/KgwMdTDyFAPfT0lBnpK9PWU6KuU6Our0FcpMdBXolIqUCoVqJRKjA1VGBnIPbWaRKlU8sEHH+g6DCGEEELUAo+9inzUNMupqamEh4fz3XffkZ+fT3BwMJ07d2b58uX069ePgQMHsmrVKjZu3Ejfvn2fuOzw4cMr+n2X6eiFFFZsPYOmrGZQz4gCMDLUw8RQRUM7UyxM/3Ln28QAS7OS31bmhtSxMEJfT7p+VFUqlRK9avH/KWmp98xiVTz56+mpFBVcRyWx6KuUZbZerC4qpp6e8f/9CehLV7Uqz9hQD9eGVrg2tHrouYLCYtKz8kjPyiMrp4CsnMI/Ww3/rwVxVk4BGffyKSwq/0bMk9JTKflgVAfq2pj849cSQgghhBC1y2OTWY+aZjkuLg5PT08MDAwwMDDA0dGRCxcucOzYMd566y0AunbtyuLFi2nUqNETl31cMuufzu5Q3vp97Mzp84LzP3rtmkJm0Hi8brbVpY6SABjQ7Rm/3rN6uX9gQLckXYdQdd2uOv+nZ0XOS4/3T+uoQf1nFIgQT0COaSGEKJucH4V4vMfeRi9vmuU/njM3//NAMzU1JTs7u9RyU1NT7t2791RlhRBCCCGEEEIIIYQoy2OTWY+aZvnB53JycjA3Ny+1PCcnBwsLi6cqK4QQQgghhBBCCCFEWR6bzPLy8iI6OhrgoWmWPTw8OHbsGPn5+dy7d4+EhATc3Nzw8vLi559/BiA6Opq2bds+VVkhhBBCCCGEEEIIIcry2GSWj48PBgYGBAUFERYWxvTp0/nqq6/Yu3cvdnZ2hISEEBwczLBhw3jnnXcwNDRkzJgx7Nixg6CgIE6cOMHQoUOfqqwQQgghhBBPKiQkhISEBF2H8VQeFXP37t3Jz8+v5IhKW7VqFV26dNF5HI+zatUqhg8fzsiRIxk1alSp8X2rquvXrzN+/HhCQkIICgoiNDSU7OzsMsvevHmTffv2VXKEEBsbS7t27bh165Z22SeffML3339f6bE8TmxsLJ06dSIkJIShQ4cSFBTEDz/8oOuwaoSkpCS8vLwICQnR/ixdurTMsro+D8fGxtKsWbOH/vd+fn5MmzZNR1E9map8vq3K9frYAeDLmmbZ2fnPQdIDAgIICAgo9bytrS2rV69+6LWepqwQQgghhBBCN7Zt24avry87duxg4MCBug6nTPHx8ezbt4/169ejUCg4f/48U6dOJSoqStehlSsvL4//+7//Y968ebRu3RqAzZs3M2nSJP773/8+VP7QoUNcvnyZ7t27V3ao6OvraxsyKP43m3VV1bFjRz799FOgZOiakJAQnJycaNGihY4jq/5cXFwIDw/XdRhPpGnTpmzfvh1fX18ALl68SG5uro6jeryqfr6tqvUq86gLIYQQQohqb+nSpaxfvx6AhIQEQkJCgJK7x3PnzmXo0KGEhIRUqcmGyotZ12JjY3F0dCQoKIhvv/0WKN3qYv369SxZsgSAZcuWMWDAAEaNGkVwcDCxsbGVFqeNjQ03b94kMjKS5ORkWrRoQWRkJBcvXtS2Ihk/fjz37t0jNjaWESNGMGrUKPr37699X5XtwIEDtG/fXpvIAhgwYAAZGRlcvnyZoUOHEhgYyLBhw0hLS2PVqlVs376dvXv3VnqsHTt2xNLS8qG6+vLLLxk0aBCBgYEsXLgQgIEDB5KUVDKT8s6dO5k3b16lx/sHU1NTAgMD+fHHH1m0aBFBQUEEBgayc+dOAE6dOkVAQAD+/v6MGzeOvLw8ncVaXZVVrwCfffYZr7/+Om+88QZ37typ9LiaN2/OrVu3yMrKAiAqKgo/Pz8AvvnmG15//XWCg4N56623KCgo4Pvvv+e1115jyJAhHDx4sNLjhfLPt7Nnz9a2NkxNTSU2NhZ/f3+Cg4PZsmVLpcb4tPU6adIkDhw4AJR8to0ePbpC4pJklhBCCCGEqLFycnLo27cv33zzDfb29tqxYEX5IiIi8Pf3p2nTphgYGHDq1Kkyy124cIFffvmFyMhIli1bRmpqaqXGaWNjw4oVKzh+/DiBgYH07t2b/fv3M2vWLObMmUN4eDhdu3bliy++ACA5OZkVK1awadMm1qxZQ3p6eqXGCyVdDB0dHR9a3rBhQ/z9/Rk9ejQbN24kMDCQCxcuMHr0aPr160ePHj0qPVaA0NBQ1qxZw9WrV4GS42nnzp1s2LCBDRs2kJiYyP79+xk8eLD2C/bmzZsf6o1T2erUqcOPP/5IUlISGzZs4Ouvv2blypVkZWUxa9YswsLCiIiIoFOnTtWui3Jli4+PL9XNMCoqqsx6BXj55Zf5+uuv6datW5ktDSuDj48Pu3fvRqPREBcXh6enJ2q1mszMTNasWcO6desoKiri9OnTAFhYWLB+/Xo6deqkk3jLO996eXkRHh5Onz59tHWZn5/PunXrePXVVys9zqepV39/fzZv3gxAZGQkgwcPrpCYHtvNsLravXu3NhsPJYPXz58/H5VKRZcuXRg3bhxqtZrQ0FAuXryIgYEB8+bNo3HjxmWWrak0Gg1du3alSZMmALRp04ZJkyaxb98+li1bhp6eHoMGDSIgIIC8vDymTJlCeno6pqamfPTRR9jY2Oj2DVSy8vaZ2uzVV1/F3NwcKLkQCwwMfOJjrTY4deoUn3zyCeHh4SQmJjJt2jQUCgWurq7MmTMHpVLJ0qVLOXDgAHp6esyYMQMPD49yy9ZEf62js2fP8vbbb2vPSUOGDMHX17dW11FhYSEzZszgxo0bFBQUMGbMGFxcXGRfErVeTk4OBgYG6OvrAzyyG5S7uzsA9erV0+mYJE8Ts67cvXuX6Oho7ty5Q3h4ONnZ2XzzzTelymg0GqDkjvtzzz2HSqVCpVLRqlWrSo01MTERMzMzwsLCADh9+jSjR48mLy+P999/Hyg5hzo5OQHg6emJgYEBAK6urly7do06depUaswODg7ExcU9tPzq1avk5+fj6ekJoO3Oo+sxqqytrZkxYwbTpk3Dy8uL/Px8Wrdurd2H27Vrx6VLlwgODmbIkCH4+/uTnZ1datIwXbh58yZ+fn5ERUVpWzwWFRVx8+ZN0tPTtcPmvPbaa7oMk5CQEEJDQ0sN41PVPNjN8PPPP+fs2bMP1SuU7A9AqUneKpufnx+hoaE0atRIG49SqURfX59///vfmJiYcPv2bYqKigC05wddeNT5tmPHjkBJXf4xbp4uY32aevX29mb+/Pmkp6cTExPDv//97wqJqUZezc6bN49FixahVqu1y+bMmcOiRYtYv349p06d4uzZs+zZs4eCggI2btzIpEmT+PDDD8stW1Ndu3aNli1bEh4eTnh4OJMmTaKwsJCwsDC+/PJLwsPD2bhxI6mpqaxfvx43NzdtNnj58uW6Dr/SlbfP1FZ/fCH4Y/8JCwt7qmOtpvv888+ZOXOmtp7CwsKYOHEi69atQ6PRsHfvXs6ePcvhw4eJiIhg8eLF2ovvssrWRA/W0blz5xgxYoR2n/L19a31dRQVFYWVlRXr1q3j888/Z+7cubIvCQFMmzaNY8eOoVarSU9Px83NTdsy6MFrt6qSNHqamHUlKiqKQYMG8eWXX7J69Wo2bdpETEwMSqVSG+u5c+eAki+5p0+fRq1WU1BQoF1eWS5evEhoaKj2M8TJyQlzc3MaN27MRx99RHh4OFOmTOHFF18E4Pz58xQXF5Obm0t8fLxObqz16NGD3377rVRCKyIiAhsbG1588UVta5GoqCjCw8NRKpWlvtPoQvfu3XFycmLz5s0YGhoSFxdHUVERGo2GI0eO4OTkhJmZGa1atSIsLEznY/5kZ2cTERGBubk53t7ehIeHs3btWvr06UPDhg2xt7fXtjRbtWoVu3fv1mm81U3Tpk3LrFdAu/8ePXoUV1dXncTXqFEj7t+/T3h4OP379wdK9ok9e/bwn//8h1mzZqFWq7VJeV3e4CvvfHvnzh3tZBbHjx/HxcVF57E+Tb0qFAr8/PyYP38+nTt31ia/n7Ua2TLLy8uLnj17snHjRqCkkgsKCrRNert06cLBgwdJTU3lhRdeAEpaJJ05c6bcsi1bttTNm6lgZ8+eJTk5mZCQEIyMjJg+fbr2/VtaWgLQtm1bjh49yrFjx3jjjTcA6Nq1a61MZh07duyhfaY2u3DhArm5uYwcOZKioiLGjx//xMdabeDo6MiSJUt49913gZLjrUOHDkDJMRQTE4OTkxNdunRBoVBQv359iouLuXPnTpllfXx8dPZeKsqDdXTmzBmuXLnC3r17ady4MTNmzODYsWO1uo569+5Nr169tI9VKpXsS0IAI0aMYN68eRgaGjJgwAB69+7NxIkTOXLkSKW3EHpS1SHmiIgIPv74Y+1jY2NjXn75ZerWrcsHH3xAvXr1sLe3B6BZs2a8+OKLBAQEYG1tjb6+Pnp6lff14uWXXyYhIQF/f39MTEzQaDS8++671K1bl6lTp1JcXAzA/PnzSUlJoaioiDfffJPMzEzGjBmjkx4GpqamrFy5kgULFpCZmUlxcTHNmjVj8eLFZGRkMHv2bFasWIGRkRELFy7k5s2brFixgpYtW9K3b99Kj/cP7733HocOHcLU1JQ+ffowZMgQ1Go1bdu2pWfPngD4+/vzxhtvsGDBgkqP79ChQ4SEhKBUKikuLmb8+PH4+Pjw4YcfEhwczP379+nZsydmZma8//77zJgxA6VSiZ2dHcOHD6/0eP8qIyODt99+m/z8fDIzMxk7diw9e/bEz8+PDh06cPHiRRQKBcuXL9f2htCl7t27c/jw4YfqFUpu/K9du1bbi0dXfH192bp1K05OTly/fh2VSoWxsTEDBw7EwMAAOzs7UlJSdBbfH8o730ZGRrJ582bWrFmDsbExH3/8Mb///rsOIy3xNPU6cOBAXnrpJbZu3Vph8VTrZFZERARr164ttWzBggX4+vqWGnwyOztbe4BByYfI9evXH1quUqnKLVsTlFVfs2fPZvTo0fTp04ejR48yZcoUpk+fXupEaWpqSnZ2NtnZ2drlpqamVWoA1cpS1j5TVFRUqRduVYmRkRGjRo3C39+fq1ev8uabb2JhYaF9/lHHWm2ot169emkHQwW0dyrgz2MoOzsbKysrbZk/lpdVtiZ6sI48PDzw9/enVatWrFixgmXLlmFubl6r68jU1BQoOf9MmDCBiRMn8tFHH8m+JGo9Ly+vh7pgfffddw+V+6N7BsDkyZMrPK5H+TsxV7ayZgIMDfYenekAABBgSURBVA0F4O233y61PD09HQsLCyIjIykoKKBv377Uq1evMsLUGjNmDGPGjHlo+YOzr6WkpODs7Kyd8U6XHB0dWbly5UPLLSwsHrpWt7KyYteuXZUVmpa3tzfe3t7ax2ZmZuzfv1/7eMSIEQ+t4+XlxfHjxyslvr/y9vYud/Du6dOnP7TMw8ODdevWVXRYT+zChQuMGDECb29vjh8/zpIlS+jZs6d2vL9Zs2YxadIkoqOjKz2h2bBhQzZt2lRqmUKhKLNedT3j4V/32T/G94KSG3ldu3bVZWjlKu98m5CQwL///e9S3U8fPCYry9+t1+LiYtq2bVuhXWir9TdJf39//P39H1vOzMyMnJwc7eOcnBwsLCzIy8srtVytVpdbtiYoq75yc3NRqVRASR/n5OTkMuvA3Ny81PKaVC9P48G6UavVNT4h8yhOTk40btwYhUKhbdqfmZmpff5Rx1ptrLe/Ng3+o27KO97KKlsb+Pj4aN+rj48Pc+fOpUePHrW+jm7dusXYsWMJDg7Gz89PO3sUyL4khNAda2trzpw5w6BBg1AoFPj7+1O/fn1dhyVElfbg2Hnt2rVj1apVREZGolAotGM5QdUZ70+Ip7Fr1y6WLl3K/PnzK3Q7NXLMrAeZmZmhr6/PtWvX0Gg0/Prrr7Rr1w4vLy/tjDYnT57Ezc2t3LI11dKlS7V3gC5cuED9+vVxdnYmMTGRzMxMCgoKOHr0KJ6enqUG8ouOjqZt27a6DF0nytpnarPIyEjt+FfJycnk5uZiYmLyRMdabeTu7q5tNRodHa2tm19//RW1Ws3NmzdRq9XY2NiUWbY2GDVqlHYckT+6eNf2OkpLS2PkyJFMmTJFOxuM7EtCiKpAqVQSFhbGd999R2RkZIVNv/4seHt7V4lWWUI8OHbeggULeOWVV1i4cCHe3t7asZyg6oz3JypXeHh4lZ4U4HF69erFtm3b8PDwqNDt1JqmEe+//z6TJ0+muLiYLl260Lp1a5577jliYmIICgpCo9Fo+3eXVbamGj16NFOmTOHnn39GpVIRFhaGvr4+06ZNY9SoUWg0GgYNGoSDgwNDhgxh6tSpDBkyBH19fe1MkbWJj49PmftMbTV48GCmT5/OkCFDUCgULFiwAKVS+cTHWm0zdepUZs2axeLFi2natCm9evVCpVLRrl07AgMDUavVzJ49u9yytUFoaChz585FX18fW1tb5s6di5mZWa2uoz+mvF6+fLl2rML33nuPefPmyb4khBBCVDMPjp1Xr1495s+fz3//+1/q1atHRkaGrkMUolpQaP6a+hVCCCGEEEIIIYQQogqrFd0MhRBCCCGEEEIIIUTNIMksIYQQQgghhBBCCFFt1Joxs4QQQgghhBBCiMpWWFjIjBkzuHHjBgUFBYwZMwYXFxemTZuGQqHA1dWVOXPmaGcgTkxMZOzYsWzfvh2Amzdv8u6776LRaLC0tGTRokUYGxvr8i0JoXPSMksIIYQQQgghhKggUVFRWFlZsW7dOj7//HPmzp1LWFgYEydOZN26dWg0Gvbu3QvAli1beOedd0oNBL9mzRr69OnDt99+i6urK5GRkbp6K0JUGZLMEkIIIYQQQgghKkjv3r3517/+pX2sUqk4e/YsHTp0AKBr16789ttvAFhaWvLNN9+UWr9FixZkZWUBkJ2djZ6edLASQpJZQoi/JTY2lpCQkGf+ut9//z3Tpk17bLns7Gw8PT1JTk4utfzw4cMMGDDgkes2a9bsH8UohBBCCCHEkzI1NcXMzIzs7GwmTJjAxIkT0Wg0KBQK7fP37t0DoFu3bpiYmJRav27dunz77bf07duX6OhoevfuXenvQYiqRpJZQohqyczMDB8fH3bs2FFq+ZYtWxg8eLCOohJCCCGEEOJht27d4vXXX+eVV17Bz89POz4WQE5ODhYWFuWu+/HHHxMWFsaOHTt47733mDp1amWELESVJu0ThRDP3KpVq9i5cyfFxcV06dKFKVOmoFAo+PTTTzl48CB3797F3t6eTz/9FFtbW7Zs2cKKFSswMzOjQYMG2rtRH330ETExMSiVSnr27Mm4ceNKbWfgwIF8/PHHjBw5EoD8/HwOHDig/YD/7rvv+Oqrr1AoFLRs2ZJZs2ZhamqqXX/JkiUAjB8/HoDu3bvz9ddfc/jwYQ4cOEBmZiYpKSkEBQVx48YNDh06hJWVFV988QWGhoZs2bKFtWvXolaradmyJXPmzMHQ0LDC61cIIYQQQlQfaWlpjBw5ktmzZ9OpUycA3N3diY2Nxdvbm+joaDp27Fju+hYWFpibmwNgb2+v7XIoRG0mLbOEEM9UdHQ0Z86cITIyki1btpCcnExUVBSJiYlcvnyZDRs2sGvXLurVq0dUVBTJycl88sknfPvtt2zcuJGcnBwAbty4QXR0NFFRUaxfv574+Hjy8/NLbcvb25usrCwuX74MwJ49e+jUqROWlpZcvHiRlStXEh4ezrZt2zA2Nmbp0qVP/D5Onz7N8uXLWb16NWFhYXTt2pVt27YB8Msvv3Dp0iU2bdrEhg0b2Lp1K3Xq1GH16tXPqBaFEEIIIURNsXLlSrKysli+fDkhISGEhIQwceJElixZQmBgIIWFhfTq1avc9WfNmsXixYsZOnQo8+fPZ/bs2ZUYvRBVk7TMEkI8UwcPHiQuLo6BAwcCkJeXR/369XnllVeYOnUqERERXLlyhZMnT+Lo6MiJEyfw9PTE1tYWAD8/Pw4dOoSDgwOGhoYEBQXRrVs3Jk+e/FCrJ4VCwauvvsr27duZMGECW7duZfjw4QAcOXKEbt26YW1tDUBgYCDTp09/4vfh5eWFmZkZZmZmANq7aA0aNCArK4vY2FgSExMJCAgASqZcdnd3//sVJ4QQQgghaqSZM2cyc+bMh5Y/OND7X8XExGj/dnFx4euvv66Q2ISoriSZJYR4poqLixk2bBgjRowAICsrC5VKxZkzZ5g0aRLDhw+nV69eKJVK7cCXGo1Gu/4fs7Po6ekRERHB4cOHiY6OJigoiPDwcJycnEptb+DAgYwcOZLg4GCuXr2qTTqp1epS5TQaDUVFRaWWKRSKUuUKCwu1f+vr65cq++CsMcXFxfTp00d7YZKTk0NxcfGTV5QQQgghhBBCiL9FuhkKIZ6pjh07snXrVnJycigqKmLs2LHs2rWLI0eO0KFDB4YMGUKTJk04cOAAxcXFtG3blpMnT5KcnIxareaHH34A4Ny5cwwdOpT27dszdepUnJ2duXLlykPbq1+/PvXq1eOzzz6jf//+2llhOnTowL59+8jMzARg06ZNeHt7l1rX2tqa+Ph4AOLi4khNTX3i9+nt7c3u3btJT09Ho9EQGhrK2rVr/1adCSGEEEIIIYR4ctIySwjxtx09ehRPT0/tYz8/Pz744AMuXLhAQEAAxcXFvPDCCwwYMICUlBTGjRuHn58fAK1atSIpKQlbW1tmzpzJ8OHDMTY2xsXFBSgZFLNNmzb069cPY2NjvLy86Nq1a5lxDBo0iHfffZfdu3drlzVv3py33nqLkJAQCgsLadmyJe+//36p9Xx9fdm1axe+vr60bNnyqboJNm/enHHjxjFs2DDUajUtWrRg9OjRT7y+EEIIIYQQQoi/R6H5a/8eIYQQQgghhBBCCCGqMOlmKIQQQgghhBBCCCGqDUlmCSGEEEIIIYQQQohqQ5JZQgghhBBCCFFDTZ8+nR49etCsWbMnXicuLo6FCxdWYFTPZlubNm1i+/btT7VO9+7dSUpKKvf5e/fuMXbs2L8VjxCi8sgA8EIIIYQQQghRQ23evJm4uDgMDAyeeJ34+HjS09MrMKpns63jx4/ToUOHZxrP3bt3OX/+/DN9TSHEsyfJLCGEEEIIIYSogd5++200Gg3PP/88hYWFnDp1imnTppGZmUliYiJTpkzhyJEjxMTEoFQq6dmzJ6+//jqfffYZ9+/fZ8WKFYwZM6bM19ZoNHzyySfs2bMHlUpFYGAgw4YN48qVK8yePZvMzExMTEx477338PDwYNq0aZiZmXH27FmSk5MZO3YsPj4+pbYVEhLCjBkzSE5OJiUlhU6dOjF//nyAh7bl6urKvn37OHToEHZ2drzwwgtlxpmZmcmUKVO4ffs2zs7O5OfnA5CdnV3mtubNm0dKSgpjx45l2bJlbNmyhbVr16JWq2nZsiVz5szB0NCwYv5hQognJt0MhRBCCCGEEKIGWrlyJQBbtmyhTp062uVWVlbs3LmTZs2aER0dTVRUFOvXryc+Ph5DQ0MmTJhA9+7dy01kAfz4448cP36cbdu2ERERwffff09qaipTpkwhJCSEbdu2MX36dP71r39RUFAAwO3bt1m3bh0rVqzg448/xsLCotS2Dhw4QIsWLdi4cSO7du3iyJEjnD17tsxtubq60r17dyZMmFBuIgvgs88+w93dnW3btvHaa6+RlpYGUO62Zs6cib29PcuWLePSpUts2rSJDRs2sHXrVurUqcPq1aufxb9GCPEPScssIYQQQgghhKhFPDw8AHBwcMDQ0JCgoCC6devG5MmTn7jV0ZEjR+jTpw8GBgYYGBiwdetWcnJyuHbtGi+//DIAbdq0wdLSksuXLwPQuXNnFAoFbm5uZGZmPvSa/fr1Iy4ujjVr1nD58mUyMzO5f/9+mdt6UocPH2bRokUAtG/fnkaNGj1yW1ZWVtp1Y2NjSUxMJCAgAIDCwkLc3d2feNtCiIojySwhhBBCCCGEqEWMjIwA0NPTIyIigsOHDxMdHU1QUBDh4eFP9Bp6enooFArt46SkJCwtLR8qp9FoKC4uBtAmyv663l+Fh4eza9cuAgICeP755/n999/RaDRlbsvGxuaJ4lQoFGg0Gu1jlUr1yG39VXFxMX369GHmzJkA5OTkaN+LEEK3pJuhEEIIIYQQQtRC586dY+jQobRv356pU6fi7OzMlStXUKlUFBUVPXLd9u3b89NPP1FYWEhubi5vvPEGaWlpNGzYkJ9++gmAkydPkpaWhqura7mv89dtxcTEEBgYSP/+/cnPz+fChQuo1eoyt5WcnIxKpXpscqlTp07allxxcXFcu3btkdvS09PTxuPt7c3u3btJT09Ho9EQGhrK2rVrn6xyhRAVSlpmCSGEEEIIIUQt5O7uTps2bejXrx/GxsZ4eXnRtWtXrl+/ztKlS/nkk0+YPHlymev6+Phw5swZBg4ciFqt5vXXX8fJyYmFCxcSGhrKkiVL0NfXZ8mSJY+cSdHDw0O7rWHDhhEaGsqqVaswMzPD09OTpKQk/P39y9zW888/z+LFizE3N6d3795lvv6ECROYNm0affv2pWnTptpuhuVtq127dtSvX5+QkBDCw8MZN24cw4YNQ61W06JFC0aPHv3PK14I8Y8pNA+2pRRCCCGEEEIIIYQQooqSlllCCCGEEEIIIR5y9OhR5s6dW+Zzq1atwsHBoZIjKtuaNWvYvHnzQ8vt7e35/PPPdRCREKKiScssIYQQQgghhBBCCFFtyADwQgghhBBCCCGEEKLakGSWEEIIIYQQQgghhKg2JJklhBBCCCGEEEIIIaoNSWYJIYQQQgghhBBCiGpDkllCCCGEEEIIIYQQotr4/zg6/kP6TWjjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(20,8))\n",
    "\n",
    "plt.subplots_adjust(left=0.1, right=0.9, bottom=0, top=0.9)\n",
    "\n",
    "ax1=fig.add_subplot(3,2,1)\n",
    "ax2=fig.add_subplot(3,2,2)\n",
    "ax3=fig.add_subplot(3,2,3)\n",
    "ax4=fig.add_subplot(3,2,4)\n",
    "ax5=fig.add_subplot(3,2,5)\n",
    "ax6=fig.add_subplot(3,2,6)\n",
    "\n",
    "plot_distribution_subplot(monthly_conv_rate.Yes, None, 'Conversion Rate',ax1)\n",
    "monthly_conv_rate.Yes.plot(ax=ax2).set_ylabel('Conversion Rate')\n",
    "plot_distribution_subplot(monthly_conv_vol.Yes, None, 'Conversions Volume',ax3)\n",
    "monthly_conv_vol.Yes.plot(ax=ax4).set_ylabel('Conversion Volume')\n",
    "plot_distribution_subplot(monthly_leads_vol.leads_vol, None, 'Leads Volume',ax5)\n",
    "monthly_leads_vol.plot(ax=ax6).set_ylabel('Leads Volume')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite a drop in leads volume and conversions volume happened in December 2017, the conversion rate grew steadily. Although less leads reached out to the company, the company incresed its effevtivness in converting leads.\n",
    "\n",
    "The distributions of the three variables are very similar, rightly skewed and two pikes, this suggests that they might be correlated.\n",
    "\n",
    "The most interesting relation to look into is between conversion rate and leads volume. The relation between leads volume and conversions volume, to parity of conversion rate, is quite obvious. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Correlation Analysis between - Conversion Rate and Leads Volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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>leads_vol</th>\n",
       "      <th>conv_rate</th>\n",
       "      <th>conv_vol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_contact_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-31</th>\n",
       "      <td>239</td>\n",
       "      <td>0.836820</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31</th>\n",
       "      <td>386</td>\n",
       "      <td>2.331606</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>312</td>\n",
       "      <td>2.243590</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>416</td>\n",
       "      <td>3.365385</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30</th>\n",
       "      <td>445</td>\n",
       "      <td>4.044944</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>200</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>1141</td>\n",
       "      <td>13.321648</td>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-28</th>\n",
       "      <td>1028</td>\n",
       "      <td>14.494163</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>1174</td>\n",
       "      <td>14.224872</td>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>1352</td>\n",
       "      <td>13.535503</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31</th>\n",
       "      <td>1303</td>\n",
       "      <td>9.976976</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    leads_vol  conv_rate  conv_vol\n",
       "first_contact_date                                \n",
       "2017-06-30                  4   0.000000       0.0\n",
       "2017-07-31                239   0.836820       2.0\n",
       "2017-08-31                386   2.331606       9.0\n",
       "2017-09-30                312   2.243590       7.0\n",
       "2017-10-31                416   3.365385      14.0\n",
       "2017-11-30                445   4.044944      18.0\n",
       "2017-12-31                200   5.500000      11.0\n",
       "2018-01-31               1141  13.321648     152.0\n",
       "2018-02-28               1028  14.494163     149.0\n",
       "2018-03-31               1174  14.224872     167.0\n",
       "2018-04-30               1352  13.535503     183.0\n",
       "2018-05-31               1303   9.976976     130.0"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create unique dataframe containing monthly conversion rate, conversions volume and leads volume\n",
    "leads_convs=monthly_leads_vol.merge(monthly_conv_rate['Yes'].rename('conv_rate').to_frame(), on='first_contact_date')\n",
    "leads_convs=leads_convs.merge(monthly_conv_vol['Yes'].rename('conv_vol').to_frame(), on='first_contact_date')\n",
    "leads_convs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2a7dc9d8b70>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='leads_vol', y='conv_rate', data=leads_convs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9225479152627392, 1.9253648642743192e-05)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "#Pearson correlation\n",
    "stats.pearsonr(leads_convs['leads_vol'], leads_convs['conv_rate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conversions volume and conversion rate appear to be strongly correlated:\n",
    "\n",
    " - Pearson: corr coeff 0.92, pvalue 1.9253648642743192e-05\n",
    "\n",
    "The pvalue is less than 0.05, that suggests that the correlation between the variables is statistically significant.\n",
    "\n",
    "This does not mean that the volume of leads directly affects the conversion rate. \n",
    "\n",
    "The factors that influnce the conversion are the willingness of the lead to become customer and the performance of the SDR/SR. Having a large pool of leads certaninly helps in increasing conversions but the conversion rate is mostly influenced by the performance of the sales team and the quality of the product.\n",
    "\n",
    "The overall increase of the conversion rate could have been driven by:\n",
    "\n",
    "- Improved quality of product. As the number of customers increased the company could have collected more feedback and translated it into a better product.\n",
    "- Increased pool of leads led the sales team to have more insights on leads behaviour, translating them into more effective negotiations.\n",
    "- Sales team became more structured."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Conversions Volume Across Channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "origin\n",
       "organic_search       271\n",
       "paid_search          195\n",
       "unknown              193\n",
       "social                75\n",
       "direct_traffic        56\n",
       "referral              24\n",
       "email                 15\n",
       "display                6\n",
       "other                  4\n",
       "other_publicities      3\n",
       "Name: Yes, dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_vol_channel=pd.crosstab(funnel.origin, funnel.converted).Yes.sort_values(ascending=False)\n",
    "conv_vol_channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a7e9a28e48>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conv_vol_channel.plot(kind='bar', figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Conversion Rate Across Channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "origin\n",
       "unknown              16.652286\n",
       "paid_search          12.295082\n",
       "organic_search       11.803136\n",
       "direct_traffic       11.222445\n",
       "referral              8.450704\n",
       "social                5.555556\n",
       "display               5.084746\n",
       "other_publicities     4.615385\n",
       "email                 3.042596\n",
       "other                 2.666667\n",
       "Name: Yes, dtype: float64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_rate_channel=(pd.crosstab(funnel.origin, funnel.converted, normalize='index').Yes.sort_values(ascending=False))*100\n",
    "conv_rate_channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Conversion Rate')"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conv_rate_channel.plot(kind='bar', figsize=(12,6), title='Conversion Rate Across Channel').set_ylabel('Conversion Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Leads Volume Across Channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "organic_search       2296\n",
       "paid_search          1586\n",
       "social               1350\n",
       "unknown              1159\n",
       "direct_traffic        499\n",
       "email                 493\n",
       "referral              284\n",
       "other                 150\n",
       "display               118\n",
       "other_publicities      65\n",
       "Name: origin, dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads_vol_channel=funnel['origin'].value_counts().sort_values(ascending=False)\n",
    "leads_vol_channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Leads Volume')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "leads_vol_channel.plot(kind='bar', figsize=(12,6), title='Leads Volume Across Channels').set_ylabel('Leads Volume')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.\tWhich channel is the most effective in generating leads and converting users?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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>leads_vol</th>\n",
       "      <th>conversion_rate</th>\n",
       "      <th>conv_vol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>organic_search</th>\n",
       "      <td>2296</td>\n",
       "      <td>11.803136</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paid_search</th>\n",
       "      <td>1586</td>\n",
       "      <td>12.295082</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>social</th>\n",
       "      <td>1350</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>1159</td>\n",
       "      <td>16.652286</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direct_traffic</th>\n",
       "      <td>499</td>\n",
       "      <td>11.222445</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email</th>\n",
       "      <td>493</td>\n",
       "      <td>3.042596</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>referral</th>\n",
       "      <td>284</td>\n",
       "      <td>8.450704</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>150</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>display</th>\n",
       "      <td>118</td>\n",
       "      <td>5.084746</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other_publicities</th>\n",
       "      <td>65</td>\n",
       "      <td>4.615385</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   leads_vol  conversion_rate  conv_vol\n",
       "organic_search          2296        11.803136       271\n",
       "paid_search             1586        12.295082       195\n",
       "social                  1350         5.555556        75\n",
       "unknown                 1159        16.652286       193\n",
       "direct_traffic           499        11.222445        56\n",
       "email                    493         3.042596        15\n",
       "referral                 284         8.450704        24\n",
       "other                    150         2.666667         4\n",
       "display                  118         5.084746         6\n",
       "other_publicities         65         4.615385         3"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge leads volume, conversions volume and coversion rate across channels\n",
    "channel_df=leads_vol_channel.rename('leads_vol').to_frame().merge(conv_rate_channel.rename('conversion_rate').to_frame(),right_index=True, left_index=True).merge(conv_vol_channel.rename('conv_vol').to_frame(),right_index=True, left_index=True)\n",
    "channel_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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>leads_vol</th>\n",
       "      <th>conversion_rate</th>\n",
       "      <th>conv_vol</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>organic_search</th>\n",
       "      <td>2296</td>\n",
       "      <td>11.803136</td>\n",
       "      <td>271</td>\n",
       "      <td>organic_search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paid_search</th>\n",
       "      <td>1586</td>\n",
       "      <td>12.295082</td>\n",
       "      <td>195</td>\n",
       "      <td>paid_search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>social</th>\n",
       "      <td>1350</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>75</td>\n",
       "      <td>social</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>1159</td>\n",
       "      <td>16.652286</td>\n",
       "      <td>193</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direct_traffic</th>\n",
       "      <td>499</td>\n",
       "      <td>11.222445</td>\n",
       "      <td>56</td>\n",
       "      <td>direct_traffic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email</th>\n",
       "      <td>493</td>\n",
       "      <td>3.042596</td>\n",
       "      <td>15</td>\n",
       "      <td>email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>referral</th>\n",
       "      <td>284</td>\n",
       "      <td>8.450704</td>\n",
       "      <td>24</td>\n",
       "      <td>referral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>150</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>4</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>display</th>\n",
       "      <td>118</td>\n",
       "      <td>5.084746</td>\n",
       "      <td>6</td>\n",
       "      <td>display</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other_publicities</th>\n",
       "      <td>65</td>\n",
       "      <td>4.615385</td>\n",
       "      <td>3</td>\n",
       "      <td>other_publicities</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   leads_vol  conversion_rate  conv_vol            channel\n",
       "organic_search          2296        11.803136       271     organic_search\n",
       "paid_search             1586        12.295082       195        paid_search\n",
       "social                  1350         5.555556        75             social\n",
       "unknown                 1159        16.652286       193            unknown\n",
       "direct_traffic           499        11.222445        56     direct_traffic\n",
       "email                    493         3.042596        15              email\n",
       "referral                 284         8.450704        24           referral\n",
       "other                    150         2.666667         4              other\n",
       "display                  118         5.084746         6            display\n",
       "other_publicities         65         4.615385         3  other_publicities"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create col 'channel' for legend\n",
    "channel_df['channel']=channel_df.index\n",
    "channel_df.sort_values(by='conv_vol', ascending=False)\n",
    "channel_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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>leads_vol</th>\n",
       "      <th>conversion_rate</th>\n",
       "      <th>conv_vol</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>organic_search</th>\n",
       "      <td>2296</td>\n",
       "      <td>11.803136</td>\n",
       "      <td>271</td>\n",
       "      <td>organic_search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paid_search</th>\n",
       "      <td>1586</td>\n",
       "      <td>12.295082</td>\n",
       "      <td>195</td>\n",
       "      <td>paid_search</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>social</th>\n",
       "      <td>1350</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>75</td>\n",
       "      <td>social</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direct_traffic</th>\n",
       "      <td>499</td>\n",
       "      <td>11.222445</td>\n",
       "      <td>56</td>\n",
       "      <td>direct_traffic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email</th>\n",
       "      <td>493</td>\n",
       "      <td>3.042596</td>\n",
       "      <td>15</td>\n",
       "      <td>email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>referral</th>\n",
       "      <td>284</td>\n",
       "      <td>8.450704</td>\n",
       "      <td>24</td>\n",
       "      <td>referral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>150</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>4</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>display</th>\n",
       "      <td>118</td>\n",
       "      <td>5.084746</td>\n",
       "      <td>6</td>\n",
       "      <td>display</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other_publicities</th>\n",
       "      <td>65</td>\n",
       "      <td>4.615385</td>\n",
       "      <td>3</td>\n",
       "      <td>other_publicities</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   leads_vol  conversion_rate  conv_vol            channel\n",
       "organic_search          2296        11.803136       271     organic_search\n",
       "paid_search             1586        12.295082       195        paid_search\n",
       "social                  1350         5.555556        75             social\n",
       "direct_traffic           499        11.222445        56     direct_traffic\n",
       "email                    493         3.042596        15              email\n",
       "referral                 284         8.450704        24           referral\n",
       "other                    150         2.666667         4              other\n",
       "display                  118         5.084746         6            display\n",
       "other_publicities         65         4.615385         3  other_publicities"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove unknown\n",
    "channel_df.drop('unknown', axis=0, inplace=True)\n",
    "channel_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanatory Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2a7ea2d69b0>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "plot=sns.scatterplot(x=channel_df.leads_vol, y=channel_df.conversion_rate, size=channel_df.conv_vol/channel_df.conv_vol.sum(), hue=channel_df.channel, legend=False)\n",
    "\n",
    "plt.title('Which channel is the most effective in generating leads and converting users?')\n",
    "plt.ylabel('Conversion Rate')\n",
    "plt.xlabel('Leads Volume')\n",
    "\n",
    "for line in range(0,channel_df.shape[0]):\n",
    "     plot.text(channel_df.leads_vol[line]+0.2, channel_df.conversion_rate[line]-0.2, \n",
    "               channel_df.channel[line], \n",
    "               horizontalalignment='center', verticalalignment='top', color='black')\n",
    "        \n",
    "plt.axhline(channel_df.conversion_rate.median(), color='orange', alpha=0.5)\n",
    "plt.axvline(channel_df.leads_vol.median(), color='orange', alpha=0.5)\n",
    "\n",
    "plt.axhline(channel_df.conversion_rate.quantile(0.9), color='green', alpha=0.5)\n",
    "plt.axvline(channel_df.leads_vol.quantile(0.9), color='green', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a7ea24d278>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(channel_df.corr(), cmap='Greens', annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "- The 10.5% of the users converted over the period under examination. The monthly average conversion rate is 6.98%, the median is 4.77%. The conversion rate consistently grew until February 2018, the overall percent change of the conversion rate is 1092.24% during the period under observation.\n",
    "- Despite a drop in leads volume and conversions volume happened in December 2017, the conversion rate grew steadily. Although less leads reached out to the company, the company incresed its effevtivness in converting leads.\n",
    "- The most effective channel in converting users (ignoring unkown) is paid search (12.3% conversion rate), the second one is organic search (11.8%) and the third is direct traffic (11.22%).\n",
    "- Despite being slightly less effective than paid search, organic search converted more users, 271 versus 195, and attracted more leads, 2296 versus 1586.\n",
    "- Social positions as third most effective channel to attract leads but presents quite a low conversion rate (5.55%). \n",
    "- Direct traffic is quite effective in converting users, but the ability to attract leads and the volume of conversions is not as attractive as paid and organic search.\n",
    "- The monthly conversion rate is strongly correlated with the monthly leads volume. The correlation between conversion rate and conversions volume weakens if the analysis is performed across channels instead of comparing monthly metrics. In general conversion rate could depend on the volume of leads that reached out to the company, but when the data is grouped by channel, the idiosyncracities of each channel take over influencing the relation between the conversion rate and the leads volume.\n",
    "- The factors that influnce the conversion are the willingness of the lead to become customer and the performance of the SDR/SR. Having a large pool of leads certaninly helps in increasing conversions but the conversion rate is mostly influenced by the performance of the sales team and the quality of the product. The overall increase of the conversion rate could have been driven by:\n",
    "     - Improved quality of product. As the number of customers increased the company could have collected more feedback and translated it into a better product.\n",
    "     - Increased pool of leads led the sales team to have more insights on leads behaviour, translating them into more effective negotiations.\n",
    "     - Sales team became more structured.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Limitations\n",
    "\n",
    "- There is no complete view in the overall marketing funnel, for instance additional data that could have produce a more extensive analyisis would be:\n",
    "    - Amount of impressions for paid search, social media and ads displayed on websites.\n",
    "    - Amount of emails sent to potential leads.\n",
    "- Important economic data are not included such as:\n",
    "    - Customer acquisition cost across channels.\n",
    "    - Budget for SEO."
   ]
  }
 ],
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