Analysis/final_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.) Import Initial Libraries & Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import numpy as np\n",
    "import pandas as pd   \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime as dt\n",
    "import matplotlib.dates as mdates\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from pathlib import Path\n",
    "from datetime import date, timedelta\n",
    "from pandas.plotting import register_matplotlib_converters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>...</th>\n",
       "      <th>deposit_type</th>\n",
       "      <th>agent</th>\n",
       "      <th>company</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>reservation_status</th>\n",
       "      <th>reservation_status_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>304.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0  Resort Hotel            0        342               2015               July   \n",
       "1  Resort Hotel            0        737               2015               July   \n",
       "2  Resort Hotel            0          7               2015               July   \n",
       "3  Resort Hotel            0         13               2015               July   \n",
       "4  Resort Hotel            0         14               2015               July   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                        27                          1   \n",
       "1                        27                          1   \n",
       "2                        27                          1   \n",
       "3                        27                          1   \n",
       "4                        27                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  deposit_type  \\\n",
       "0                        0                     0       2  ...    No Deposit   \n",
       "1                        0                     0       2  ...    No Deposit   \n",
       "2                        0                     1       1  ...    No Deposit   \n",
       "3                        0                     1       1  ...    No Deposit   \n",
       "4                        0                     2       2  ...    No Deposit   \n",
       "\n",
       "   agent company days_in_waiting_list customer_type   adr  \\\n",
       "0    NaN     NaN                    0     Transient   0.0   \n",
       "1    NaN     NaN                    0     Transient   0.0   \n",
       "2    NaN     NaN                    0     Transient  75.0   \n",
       "3  304.0     NaN                    0     Transient  75.0   \n",
       "4  240.0     NaN                    0     Transient  98.0   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  reservation_status  \\\n",
       "0                            0                          0           Check-Out   \n",
       "1                            0                          0           Check-Out   \n",
       "2                            0                          0           Check-Out   \n",
       "3                            0                          0           Check-Out   \n",
       "4                            0                          1           Check-Out   \n",
       "\n",
       "  reservation_status_date  \n",
       "0              2015-07-01  \n",
       "1              2015-07-01  \n",
       "2              2015-07-02  \n",
       "3              2015-07-02  \n",
       "4              2015-07-03  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data \n",
    "hotel_path = Path('../Data/hotel_bookings.csv')\n",
    "hotel = pd.read_csv(hotel_path, infer_datetime_format=True, parse_dates=True)\n",
    "hotel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Defenition:\n",
    "\n",
    "- hotel: Hotel (H1 = Resort Hotel or H2 = City Hotel)\n",
    "- is_canceled: Value indicating if the booking was canceled (1) or not (0)\n",
    "- lead_time: Number of days that elapsed between the entering date of the booking into the PMS and the arrival date\n",
    "- arrival_date_year: Year of arrival date\n",
    "- arrival_date_month: Month of arrival date\n",
    "- arrival_date_week_number: Week number of year for arrival date\n",
    "- stays_in_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel\n",
    "- stays_in_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel\n",
    "- adults: Number of adults\n",
    "- children: Number of children\n",
    "- babies: Number of babies\n",
    "- meal: Type of meal booked. Categories are presented in standard hospitality meal packages: Undefined/SC – no meal\n",
    "- Country: Country of origin. Categories are represented in the ISO 3155–3:2013 format\n",
    "- market_segment: Market segment designation. In categories, the term “TA” means “Travel Agents” and “TO” means “Tour Operators”\n",
    "- distribution_channel: Booking distribution channel. The term “TA” means “Travel Agents” and “TO” means “Tour Operators”\n",
    "- is_repeated_guest: Value indicating if the booking name was from a repeated guest (1) or not (0)\n",
    "- previous_cancellations: Number of previous bookings that were cancelled by the customer prior to the current booking\n",
    "- previous_bookings_not_canceled: Number of previous bookings not cancelled by the customer prior to the current booking\n",
    "- reserved_room_type: Code of room type reserved. Code is presented instead of designation for anonymity reasons.\n",
    "- assigned_room_type: Code for the type of room assigned to the booking. Sometimes the assigned room type differs from the reserved room type.\n",
    "- booking_changes: Number of changes/amendments made to the booking from the moment the booking was entered on the PMS\n",
    "- deposit_type: Indication on if the customer made a deposit to guarantee the booking\n",
    "- agent: ID of the travel agency that made the booking\n",
    "- company: ID of the company/entity that made the booking or responsible for paying the booking. ID is presented instead of designation for\n",
    "- days_in_waiting_list: Number of days the booking was in the waiting list before it was confirmed to the customer\n",
    "- customer_type: Type of booking\n",
    "- adr: Average Daily Rate as defined by dividing the sum of all lodging transactions by the total number of staying nights\n",
    "- required_car_parking_spaces: Number of car parking spaces required by the customer\n",
    "- total_of_special_requests: Number of special requests made by the customer (e.g. twin bed or high floor)\n",
    "- reservation_status: Reservation last status\n",
    "- reservation_status_date: Date at which the last status was set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.) Data Cleaning & Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119390, 32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the shape of the data import\n",
    "hotel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotel                                  0\n",
       "is_canceled                            0\n",
       "lead_time                              0\n",
       "arrival_date_year                      0\n",
       "arrival_date_month                     0\n",
       "arrival_date_week_number               0\n",
       "arrival_date_day_of_month              0\n",
       "stays_in_weekend_nights                0\n",
       "stays_in_week_nights                   0\n",
       "adults                                 0\n",
       "children                               4\n",
       "babies                                 0\n",
       "meal                                   0\n",
       "country                              488\n",
       "market_segment                         0\n",
       "distribution_channel                   0\n",
       "is_repeated_guest                      0\n",
       "previous_cancellations                 0\n",
       "previous_bookings_not_canceled         0\n",
       "reserved_room_type                     0\n",
       "assigned_room_type                     0\n",
       "booking_changes                        0\n",
       "deposit_type                           0\n",
       "agent                              16340\n",
       "company                           112593\n",
       "days_in_waiting_list                   0\n",
       "customer_type                          0\n",
       "adr                                    0\n",
       "required_car_parking_spaces            0\n",
       "total_of_special_requests              0\n",
       "reservation_status                     0\n",
       "reservation_status_date                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null values\n",
    "hotel.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will address the null values outlined above\n",
    "\n",
    "# For the following, features, we will fill null data with a unique value\n",
    "hotel[\"country\"] = hotel[\"country\"].fillna('Unknown')\n",
    "hotel[\"agent\"] = hotel[\"agent\"].fillna(0)\n",
    "hotel[\"children\"] = hotel[\"children\"].fillna(0)\n",
    "\n",
    "# For the \"company\" feature, given it is essentially irrelevant (almost entirely null), we will remove the column\n",
    "hotel.drop(columns=['company'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "          ...  \n",
       "119385    False\n",
       "119386    False\n",
       "119387    False\n",
       "119388    False\n",
       "119389    False\n",
       "Length: 119390, dtype: bool"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Next, run a check for duplicates\n",
    "hotel.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotel                             0\n",
       "is_canceled                       0\n",
       "lead_time                         0\n",
       "arrival_date_year                 0\n",
       "arrival_date_month                0\n",
       "arrival_date_week_number          0\n",
       "arrival_date_day_of_month         0\n",
       "stays_in_weekend_nights           0\n",
       "stays_in_week_nights              0\n",
       "adults                            0\n",
       "children                          0\n",
       "babies                            0\n",
       "meal                              0\n",
       "country                           0\n",
       "market_segment                    0\n",
       "distribution_channel              0\n",
       "is_repeated_guest                 0\n",
       "previous_cancellations            0\n",
       "previous_bookings_not_canceled    0\n",
       "reserved_room_type                0\n",
       "assigned_room_type                0\n",
       "booking_changes                   0\n",
       "deposit_type                      0\n",
       "agent                             0\n",
       "days_in_waiting_list              0\n",
       "customer_type                     0\n",
       "adr                               0\n",
       "required_car_parking_spaces       0\n",
       "total_of_special_requests         0\n",
       "reservation_status                0\n",
       "reservation_status_date           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify that the nulls from above have been addressed\n",
    "hotel.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.) Preliminary Analysis of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>children</th>\n",
       "      <th>babies</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>agent</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "      <td>119390.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.370416</td>\n",
       "      <td>104.011416</td>\n",
       "      <td>2016.156554</td>\n",
       "      <td>27.165173</td>\n",
       "      <td>15.798241</td>\n",
       "      <td>0.927599</td>\n",
       "      <td>2.500302</td>\n",
       "      <td>1.856403</td>\n",
       "      <td>0.103886</td>\n",
       "      <td>0.007949</td>\n",
       "      <td>0.031912</td>\n",
       "      <td>0.087118</td>\n",
       "      <td>0.137097</td>\n",
       "      <td>0.221124</td>\n",
       "      <td>74.828319</td>\n",
       "      <td>2.321149</td>\n",
       "      <td>101.831122</td>\n",
       "      <td>0.062518</td>\n",
       "      <td>0.571363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.482918</td>\n",
       "      <td>106.863097</td>\n",
       "      <td>0.707476</td>\n",
       "      <td>13.605138</td>\n",
       "      <td>8.780829</td>\n",
       "      <td>0.998613</td>\n",
       "      <td>1.908286</td>\n",
       "      <td>0.579261</td>\n",
       "      <td>0.398555</td>\n",
       "      <td>0.097436</td>\n",
       "      <td>0.175767</td>\n",
       "      <td>0.844336</td>\n",
       "      <td>1.497437</td>\n",
       "      <td>0.652306</td>\n",
       "      <td>107.141953</td>\n",
       "      <td>17.594721</td>\n",
       "      <td>50.535790</td>\n",
       "      <td>0.245291</td>\n",
       "      <td>0.792798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.290000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.575000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>535.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         is_canceled      lead_time  arrival_date_year  \\\n",
       "count  119390.000000  119390.000000      119390.000000   \n",
       "mean        0.370416     104.011416        2016.156554   \n",
       "std         0.482918     106.863097           0.707476   \n",
       "min         0.000000       0.000000        2015.000000   \n",
       "25%         0.000000      18.000000        2016.000000   \n",
       "50%         0.000000      69.000000        2016.000000   \n",
       "75%         1.000000     160.000000        2017.000000   \n",
       "max         1.000000     737.000000        2017.000000   \n",
       "\n",
       "       arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "count             119390.000000              119390.000000   \n",
       "mean                  27.165173                  15.798241   \n",
       "std                   13.605138                   8.780829   \n",
       "min                    1.000000                   1.000000   \n",
       "25%                   16.000000                   8.000000   \n",
       "50%                   28.000000                  16.000000   \n",
       "75%                   38.000000                  23.000000   \n",
       "max                   53.000000                  31.000000   \n",
       "\n",
       "       stays_in_weekend_nights  stays_in_week_nights         adults  \\\n",
       "count            119390.000000         119390.000000  119390.000000   \n",
       "mean                  0.927599              2.500302       1.856403   \n",
       "std                   0.998613              1.908286       0.579261   \n",
       "min                   0.000000              0.000000       0.000000   \n",
       "25%                   0.000000              1.000000       2.000000   \n",
       "50%                   1.000000              2.000000       2.000000   \n",
       "75%                   2.000000              3.000000       2.000000   \n",
       "max                  19.000000             50.000000      55.000000   \n",
       "\n",
       "            children         babies  is_repeated_guest  \\\n",
       "count  119390.000000  119390.000000      119390.000000   \n",
       "mean        0.103886       0.007949           0.031912   \n",
       "std         0.398555       0.097436           0.175767   \n",
       "min         0.000000       0.000000           0.000000   \n",
       "25%         0.000000       0.000000           0.000000   \n",
       "50%         0.000000       0.000000           0.000000   \n",
       "75%         0.000000       0.000000           0.000000   \n",
       "max        10.000000      10.000000           1.000000   \n",
       "\n",
       "       previous_cancellations  previous_bookings_not_canceled  \\\n",
       "count           119390.000000                   119390.000000   \n",
       "mean                 0.087118                        0.137097   \n",
       "std                  0.844336                        1.497437   \n",
       "min                  0.000000                        0.000000   \n",
       "25%                  0.000000                        0.000000   \n",
       "50%                  0.000000                        0.000000   \n",
       "75%                  0.000000                        0.000000   \n",
       "max                 26.000000                       72.000000   \n",
       "\n",
       "       booking_changes          agent  days_in_waiting_list            adr  \\\n",
       "count    119390.000000  119390.000000         119390.000000  119390.000000   \n",
       "mean          0.221124      74.828319              2.321149     101.831122   \n",
       "std           0.652306     107.141953             17.594721      50.535790   \n",
       "min           0.000000       0.000000              0.000000      -6.380000   \n",
       "25%           0.000000       7.000000              0.000000      69.290000   \n",
       "50%           0.000000       9.000000              0.000000      94.575000   \n",
       "75%           0.000000     152.000000              0.000000     126.000000   \n",
       "max          21.000000     535.000000            391.000000    5400.000000   \n",
       "\n",
       "       required_car_parking_spaces  total_of_special_requests  \n",
       "count                119390.000000              119390.000000  \n",
       "mean                      0.062518                   0.571363  \n",
       "std                       0.245291                   0.792798  \n",
       "min                       0.000000                   0.000000  \n",
       "25%                       0.000000                   0.000000  \n",
       "50%                       0.000000                   0.000000  \n",
       "75%                       0.000000                   1.000000  \n",
       "max                       8.000000                   5.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate summary statistics \n",
    "hotel.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Or5fmTFN19xzWdbRTu+KJGD0MIQR+XSMpb6Lsp+89QOchAwCIGD2cU7sscTq7uCCsc2NUltX+EOFq8KkZueXs6oJ3cCDFdi5Tx3cm0HXsUIQQ+HeLoLy4hOKcvEaxn9lzgPBhlpEwXccO5fgOS+wIQUVpGVJKKkvL0Hu4o9Ge30vU/KPJuHYIwLWDpQwEDBraqINK5+WNV3gkQtt4pINPt544uTu2c62hE7viibSWGf9uEZQXlVBso8yk7j1A2FBLmekydhgndsaf8z5Sk/bj1bEDHh0c0yEBkLIjkZ5jhyCEILB7OGVFJRSZ8hql848IwSvAdhwJ/11Pl2FRuHnbf6RPQyd3xhMx2lLeO3SzXK/Yyve0vQcJteZ75JjhnNxZex44+MtaQocMwKWJeNOS9uMZYN98z01Jwb3OuSl4yBDSYxu06bGxdKrTplefm85m78qV9Lz1VofNz9PW2EtMJtITEug8Zky9dYQQVJZazv+VxcW4WK/NzpfD25PoM34wQgiCe1jKfqGNst9lUG+EEAghCOoWSkFW4zTnw8FtSfS/bBBCCEJ6hFFaVEKBjXi7DepVE29wt1DyrfHqXfU156mK0jI1nZPSIqrj6hIhhCi0/hsohNgghIgXQuwRQjTZHS+EmCSEiBVCJAgh1ljfGyyE2CKEiLP+2936/r1CiH8KIX4VQhwWQrxzlu24CyEWCiF2Wrc1xcb+baYRQrgKIb4WQiQKIb4B2j6M5hLj5+pMZnFFzeuskgp8XZvu+JgYZmRXWgEAx/NL6ePnjqdOi14rGNTRiw7NrGsPpTk5uBqNNa9djUZKc+p/cSrJycXVt24aAyU55/blKvvgIfTe3nh07NjmWEtMObj51o+1YRwlpsaxlubkNLtu/7vuIGnVt/z85N9JXPkNvW+9qc2xNnkMNvK74TGUFxSic3dDY/0C5uZrrPmS3NT6hRmZ6D092TlvIatfmsnO+YuoLLXvKBpHxd6c6LtvJ3Hlt/zniWdIWPEtfW+90a7H4GKjLNtM06AzoSgzk7zjxzF0qb1tIeWPP1j7j2nEfvkV5UVFbYrzbEpzcnAx1n5pcjUYKWtwHKU5JlzqHofBSGmOiZKMDHSeXiQu+JJNM6aRuPArKh084spmG2JqnO9uNsrH2dZNXr2G31+Ywa55CykvdEy+F5ty67UfbkYDxabcemnKCgpxdnOtLftGQ71OqJM7YvnP36ex/u2PGPrIfY32UZiRhenYCfzaeCtsQ0XZubjXid3d6ENRdv28LysoQl+n3rr71h5fryvHkXsqlZUPPs8Pz8xi6H23IjTWS1QBv8z+kH89/zoHft9g17jrxZdrqlfe9QYjZbnt02l8ropNubj7GWpe183TamUFhejc6uR7g3KVeSiFfz83i9/f+Iick2ca7ePo5p2EjxjkkPirFZry8PDzqXnt4etDYYPjaHb97FyStyfQd+L5GYFSnJNTr7y7+RoosZnvDeuqJU2xKYcTO+PoekX9TpS6jm3ZSdiIwXaNuyQnp357beP8WJqTU2/UlEuD67Vtc+awccYMjq9bV/NeWmwsLgYDXp0b3955ocS+d8UKet5yS6OOtV533sm+r7/mj6efZt/XX9Pj5psddgy2FGTn4VWn7Hv6+lCQ3XSnVFVlFXvW7SQixvEjC23Jz8rDq0Ntm+Pl51PTKWVLVWUVCWt30WVAj5r39m9J5JOH3mDFK18y5W+3OzTei5JGnL+/i4y6VfDScwfwm5TydSGEFnCzlUgI0QH4EhgtpTwqhKg+GxywvlcphLgceAOo/hYXBUQDZcBBIcQnQGkT23kZWCulvF8I4QPsEEL80SCMptI8DBRLKfsJIfoBsdgghHgIeAjAqdu1OAUNPNc8ujRJ22/36+DBhDBfnv3zMAAnC8r47lAGb4yMpKTSTEpeCVWyiZUdGluDBtNWDOf4U8yprdvtMtqqKaJRHOcea/W6KX+so/9fbid48EBObdvB7i8XMfofz9k50qbDa5jf0kaimkNoYn1priLn2HGi770D3y6RxC1Zyf6f/kvfW25oY8D1ArO57/pJWh57c478sY6ou26j0+CBnNy2g53zFzH25TZ8NjbL8tnT1C1nlaWl7Pj4U/reeQfOrpa++/DLxtPjOstvAPt/+Cd7Vn5NzINTWx/n2ZxLs2CzKgjM5iryjx+j91/uwicykn0rlpPyf/+h242O67A9pzbE5kcjml038opx9LrBMtfR3u/+ReKKbxj48P1tjdaGcyg3ttQ5xpDBMYQMjiF9/yESv/2Ry6bV3nJSUVrKxg8+Z8A9t9rltur6mi/PYBmB0jiR5Z/T8XvxDQvhqpl/Jz8tk19mf0jHnl3Qubly7WvP4270oSQvn19mfYR3cEcCe3Wzc/w2D4Fz+wDaURvOmwC+4Z256fM3cXZx4VRsEmvnfM6NH9fOI1lVWcnJ3QkMuMOObbwtNq9Bzv04/vzqe0bcfd35G6VnM9yzx1tdJ3Yu/oaYO25Ao7Edb1VlJad2JxB9u53z/SznnbMZMW0aLgYDZfn5bHvnHTwCA/EJD+fIf/7DkOccdD1TrQ2xp8fHo/fywic8nKz9++stO752Lb3vuIPAQYM4s307iV99xdAXWj9VQIudy/VCHb99/i0hvSMJ6XP+5uE6m+Y+h/9+9h2hfSIIrRNvz+H96Dm8H8eSklm77BfueeOxJtdXlLpUx9WlZyewUAjhDPwopYxvIt1QYIOU8iiAlNJkfd8bWCKE6Irl1Fx3KM4aKWUegBBiHxAKGJrYzgRgshDiWetrF6DhTzFNpRkNfGzdXqIQItHWAUgp5wPzAVzHzXJwz8uFJaukgg5utR+Nn6sz2aUVjdKFebnwt5gQpm9OoaC8qub91cdMrD5m+aju6R1IVonj5reC6tEkpprXJSYTLg1ux3I1GijJrpsmp+Z2uuaYq6o4s3M342a/cta058LVaKA4u0GsDeKwFauLjw/mysom1z2+cTP9774DgOAhg9j95SK7xFvt8Oo1HF1nGY1giAhvlN8Nb3/Te3pSXlSMuaoKjVZLcXZtrK42Pi9Xgw8IgavRgK91BFCnIQM58NPPF0fszTi+YQvR1s+m05BB7PxycZuOx9VorBdDqSkHVx/DWdNU1wlzZSU7Pv6UkOHDCBpU2yHv4l17O3Do2DFse+/DNsV5Ni5GA6Wm7JrXJTkm9AZDgzRGSuseR44JvY8BgWX0lY91ktuOAweR/N//c2i8NtsQG+1MsY22yFxZ2eS6dfM9fPwYNs/5yG4xH/xtLclrNwJgjAyr134Um3Jwa1T2PagoLqkt+zbSAAT07MbW9ExK8wtw8fLEXFnJxve/IGzkUDoPHmCX2Pf9so4DazYB0CEyjKI6sReZcnEz1o/LxcuDsjr1tii7NvZD67bQ/7pJCCHwDvTH09+P3NNp+HcNx926HVdvL0IHR5F5+JhDOq70BmO98l6WY0J/nm8bOhf7f13HoTWWMuMXGUZRVu2oE0ue1p82QO/pQXlxnXw35dR8Nro6HZidYvqy9auVNWUGLHNo+YZ3xtXHy+7HkfDzn+xZvRmAgK6hFGbl1iwrzM7Fw+jdxJqNZRw5wS/vLgSgtKCQY7F70Wi0RA6138MrDv62jsNrLOcp38jweuW9ODsHV5v5Xr+uVqfJTjnGxo+/BKAsv5DTcXsQWg2dB1nmszoTtwejA/LdtWF7beP6xsVgoCQ7u34aa7tf/a/ey4uOAwaQm5KCs7s7xZmZbJg+vSb9hhkzGPnKK3a97a4tsafu3El6XBwZiYmYKyqoKCkhbu5coh95hFObNtH7zjsBCBw8mMSFC+0Wc1N2/98G4n/batln187k1yn7Bdm5eDZR9jeu/IXi/EJufMKBP1jZsOM/G9ltjTe4a2fyM2vbnPysXDx9bZfT9St+pSivkFuftP1DT1jfSH58P4uivELcvc/vbdYXNHX/ZJPUrYKXGCnlBiwdP6eBZUKIu5tIKrD9m9FsYJ2Usg9wLZbOpGp17/OowtLx2dR2BHCjlDLK+tdZSrm/BWn+pzqiWupQTjFBHnoC3HQ4CcGYTga2ncmvl6aDqzPTh4UzZ+dxThfWv0XHW+9Uk2ZEsDd/nsx1aLyGiHAK0zIoysjEXFnJqW07CIypP+FoYEw0JzdtQUqJ6Ugyzm6ujTq3bMncsw+PoMB6t/nYO9agAY1jPb7REmv24WScXV1xNfg0u66rwYes/QctMe/dj0fHALvEW63rhMuY8OarTHjzVYIHRnOsXnxujb7ACyHw79WDU9stzzw4tnELwQMtsQYNiLK5vquPN26+RvLPpAKQvmcfXsFtn+D5fMTeHBeDD5nWzyZj7348A9r22fhEhFOYll6nHGynY4Py3jEmihObNlvL+xGc3Fxx8fGxPBVxwUI8ggLpcmX9ycDrzjOSuiu23qTjjuAdHkFRejrFmZbjSN2+jYDo+scREBXN6c2W48g5cgQnVzdcfHzQ+/jg4mukMNVSVrL27cUjyH6TgdtiiKyf7ye3bidwQFS9NEEDohrUXUv5aG7duvNMnd5p33zvPnE8V739Cle9/QohA6NJ2bAVKSVZh5PRubnaLPsBvbpzYrtl7qWUDVvoNNASZ0Faes2oJtPR45grK9F7eiClZNu8JXgFB9Lz6gl2i73XleO44d3p3PDudEIHR3F4/TbL/HOHUtC5uTbqQBFCENS7O0e3WgZRH16/jdBBlo4Fdz8jp5MOAFCcm0/emXS8AjpQUVpGefX8c6VlnE7Yh6GzY8qRZ1gEJRlplGRmYK6sJH3nNvz626eTz556ThrHlDkzmDJnBp0HR5FsLTO1+e5TL70Qgo69u3Nsm6XMHFm/lc7WMlOcm1dTZjKPHAWzGb1n7RfIlM07CLfz7WrV+l81hjs//Ad3fvgPIof0Z//67Zb5uA4eRe/uinsLOq7umz+L+7+czf1fzqbLsGjGPXyrXTutALpPHMc177zCNe+8QsigKFI2WMp75iHL9YqtfA/o1Z3j1nxP/nMLIdZ8v+HTt2r+Og+NYcjUO2s6rQCObt5B2HD757t3eHi9Nv309u2N2/ToaE7Va9Mt56bKsrKaSc0ry8rI2rMHz06d8AoJYcKnn3LZe+9x2Xvv4WI0MnrWLLvPFdWW2HvecguXf/ghl733HtGPPopfz55EP/IIAC4+PmQfsLQ92fv24d7G8/+5GHDNaKZ+8gJTP3mBbsP6sWftDstcrQeOondzsdlpG//bFo7G7mfKc/fU3kZ9ngy+dhSPfvo8j376PD2G9SVhzU6klJw8cAy9u6vNjrbdv27lSOwBbnrh7nojC7PPZNa0OWeOnKSqsgo3L8fOf6lcOoTNodvKRUcIUSil9BBChAKnrbf6/Q0Ik1L+zUb6Dlhuwau5xU9KaRJC/AtYLqX8QQgxE7hXShkmhLgXGCilfMK6/v8B7wJ7m9jOG4AX8KSUUgohoqWUcXWfKthMmr8DvaSUDwgh+gDxwNDmniroiBFXS6bdwKioUPy83cjIKWL24vUs+TnervsY+8TksydqwqCOnjzULxitEKw+ZuLrg+lcFW65t//no9n8NSaEEcHeZFjnwqqSkr+uPQTAnDFd8NI5UWmWfJl4mvjMwhbvv3/n4halT4tPIHH5KjCbCR0ziu5TruXoGsscCeGXjbM8pn7JcjISk9DqdMQ8NBVDRDgAOz+dS+b+A5QXFqL38qLnjdcRNnY0ALvnLcDYJZLwy8adcyxnu607NT6BxGWrkGYzYWNG0eO6a0n5wxJrxOWWWOMXLyfdGuvAh2tjtbUuQNbBQyQsXYk0m9E4OxN9310YwsMA+OWvz1JRUoq5shKdmxsjX3ymyS/HWnH2oi6lJHbxctIS9uCk1zHo4fsxWuPb8PYHDHroXlwNBgrTM9j2yTzKi4rwCe3MkMcfROvs3Oz6OcdOsOvLRZgrq3D378Dgh+9H5+HOqZ27iVuykrL8Apzd3PAJDWHMS42fjNOesW/9ZC6Z+w9SVlCIi7cXvW+cQsS40WQeOET80lWYzVVonZ2Jue8ujBFh9eIqr2rZhWJafAJJKyyfd+joUXSfMpmja9YCllv+pJQkLllGelISTjo90Q9aylD2wUNsfO0NvEI61fzq1uvmm+gY1Z9dc+eRf/wkCHDz8yPq/nvP6ctBS2OvKyMhgX0rl4NZ0mnUaLpMnszxtZbjCB1vOY69y5aSlZSERq+j39QH8Am3zJ+Uf/w4iYu+QlZW4dahA/0eeLDFE7S76SpblD41LpGE6vo3diQ9r7uWZGvdjaxTd9MS9qDV6xhYp3zYWhdgx+dfknv8BAKBWwc/YqbefU4T+Os15hbFLqVk56KVpMZbYhv2yH34RoYBsO6tDxny0L24GX0oSM9k88fzKCsswhjWmeFPPIDW2Zm9//6Foxu3otFq0eqcib7zZvx7dCXjwGF+n/k2Pp2DEdanFfW/7XqCo/s1GYurU1WTy5qKfcuCVZyK34uTXsfox+6hQxdL7L++/gmjHr0Ld6MP+emZrPtgAWWFRfiGhTD2r/ejdXamyJTLhk8XU5ybBxL6XT+RrqOHkp+eyR/vWJ7SZ66qInLUYKJvvKrZWJJzWv/LfXZSPIe/XoaUZgJHjCHs6us4vd4yy0Hw2Mspy8tl92vTqCwtQQgNWr2ewbPewcnVjb3zPyX30H4qCgvQeXoRNvkmgkaNbdH+Q71bNn+alJLtX63idMIetDodIx+7Fz9rmfn9zY8Z8fDdNWXmzw+/tJSZ8BBGPzkVrbMz+39dy8HVfyKsZWbw3bfg390ySrKyrIzvHn2RGz99HZ2bzVkn6vHWNR713ZLjWD//W47H7sNJr+OKp/5CQBfLAwV+nPUZlz9xJx5GH+L/bx27//UHRTn5uHl7EjagN5c/cWe9ba3+aCnhg/rQdXjMOe/fVKZrcbw7Fq7kTMJenHQ6hj96b01dXfPmRwx7+J6afN/40XzKC4swhHVmpDXf69r8+UI6xfSvmcS9sqyMHx57ges/eeOc8j2ntGWxpycksG/FCqTZTMjo0XS10abvWbaMzMREtHo9/R94AJ/wcIoyMtj18ceW46+qInjYMLpObnwNu+aZZxg1c6bNJw+2VWtjrytr/35SfvmFwX+3PAnPdOgQe5cvx2w2o3V2ps/ddzdax5a+vvaZHF1Kyeq535Gyez/Oeh1X/+1OArtablD55pW5XPXU7Xj6evPW5L/h7W9A52oZT9B9eD9G3t66Jwy39NzUMN6fP/+BI9Z4pzx9O8HdLPEunzGPyX+9DS9fb1695u/4+BvQuekBy+2BY++YxKbv/iBhzS40Thqcdc5cMXUKob1bNufi7ZFXXtJDklyvfu+8dc6U/PeZiyovVcfVJaJOx9U9wHNABVAI3F19G5+Nda7EMoeVBsiQUl4hhBgGLAEygbXAXc11XEkp1zexHVfgQ2A4lpFVx6ydVWOp7bhqKo0rsAjohaXTqgvw1PnuuDof2tJx1d5a2nF1IbkI5yOscS4dV4r9taXzp71dzLG3tOPqQtKWLwftraUdVxeStnRctbeWdlxdSNrScdXeWtpxdSFpaceVYh/26rhqDxfzuQlUx5U9XWwdV2qOq0uElNLD+u8SLB1P57LOL8AvDd7bCtSdPGK69f3FwOI66a45y3ZKsEyy3nCf64H1Z0lTAtx2LsegKIqiKIqiKIqiKBc9NcdVky7en2EVRVEURVEURVEURVGUS5oacfU/QAixHdA3ePsuKWVSe8SjKIqiKIqiKIqiKEodF/N8Jg6mOq7+B0gph7R3DIqiKIqiKIqiKIqiKC2lOq4URVEURVEURVEURVHak1AzOTVF5YyiKIqiKIqiKIqiKIpyQVIjrhRFURRFURRFURRFUdqTeqpgk9SIK0VRFEVRFEVRFEVRFOWCpDquFEVRFEVRFEVRFEVRlAuSulVQuSSMfWJye4fQKus//am9Q2i13m9NaO8QWi2vxLm9Q2g1f8/S9g6h1fJLL958v5jLjItzVXuH0GoGl/L2DqHVzPLiHe6v15jbO4RWc7qIY7+Yn4J+uti1vUNoNa2Q7R1Cq2UW6ts7hFbr5Z/f3iG0Wm75xXtNcDGfm/4nqMnZm6RyRlEURVEURVEURVEURbkgqRFXiqIoiqIoiqIoiqIo7eliHnrrYGrElaIoiqIoiqIoiqIoinJBUiOuFEVRFEVRFEVRFEVR2pNQI66aokZcKYqiKIqiKIqiKIqiKBckNeJKURRFURRFURRFURSlPamnCjZJ5YyiKIqiKIqiKIqiKIpyQVIjrhRFURRFURRFURRFUdqTmuOqSWrElaIoiqIoiqIoiqIoinJBUiOulP9pAwI8eaR/MBoh+PVoNt8dyqi3fFyIgZu7+wNQUmnm07iTHM0rBWBKFz8mhfkiBPx61MSPRzLPe/xNmfv8tVw5tBuZuUUMvH9ue4fTSEZiInuWr0CazXQeM4au115Tb7mUkr3LV5CekIBWryPqwQfxCQurXW42s2HGK7gYDAx55u/nNXbTngSSv12KNJvpOHIcnSdNrre8OO00BxfPo/DkMcKm3ELIBMuxmSvKSXh3FubKSmRVFX4xQwibfJPd40tLSCJ+6UqkWRI+bhQ9Jl9db7mUkoSlK0mNT8JJp2PgI1MxhIc2u27iim9JjY1H4+SEe0AHBj48FZ27G6YjKez+akn1hul14xSCBw2w27FkJSVyYOUypDTTadRYwq++ttGxHFy5jMykBLQ6PX2mPoRXaBgAx1f/wqkNf4IAz+AQek99EK2zDoATf6zmxJrfEVotHfr1p9stt9stZlty98Zz/PslSLMZ/xHjCZowpd7ykrTTpCyfS9HJo4RceyuBl1uOsyT9DEe++qgmXWl2Bp2uvpnA8Vc5NN66THsSOPLNMqTZTODIsXS+skF5Tz3DgSXzKDxxjPDrbiFkgqXMlJqyObDwCyry80AIAkePp9Nlkxwer5SS3UtWcTouCSe9jmGP3o/RWr7rKszIZNNH8ykvKsIQ1pnhTzyA1smJk7viSPz2R4TQILQaBtx9G/49ugKw/7+rSV63CQCfzp0Y9sh9aHXOdo09dskqzsQnodXpGNpM7Fs+nkdZURHGsFCGPm6J/dimbez76RcAnF30DJx6F4bQEAC2zV3ImbhEXLw8uWrObLvF3NRxbPzqW47H7sVZr+OyJ+6mQ2TnRukSf15Pwv+tJT8tk/sXz8HVywOAg3/uIO7H1TXHMeah2/EL7+SweLOSEjm4ytLOBI8aS/hVNtqZVcvIsrYzve+v386c3vgnAB6dQuh9f512Zs1qTlrbGb9+/el2s/3bGSkl2xd9w8lYS3kf9fi9+EU0LjMF6Vms+3A+5YXF+IZ3ZvST96N1diLp37+RvHE7AGazmbxTqdzx1fvoPd0t71WZ+enF13E3+nDFS0+2OD5HnI/KCwvZ9vFcijOzcOvgx9CnHkXn4U5ZQSHbPvocU/JRwkaPIPq+v9TsZ/3stynNzUWrs3w2o158BhdvrxYfT92445asItVaVwc3U1e3fjzP2s6EMsRaV/NPp7Jj3kJyjp6g763X0+MaS9tYVV7B2llvU1VRgawyEzJkAH1uvq7VcZ7NxXZuqi7vp6zlfWQz5X39h/Mpa1DeAVL3HmTHom8wV1Wh9/TgqlnPUVlewS8z5lBlvT4LGzqA6FsnN9quPY9js7WNdNLrGN9EG5n083oSrW3kvXXayEMN2sjRDm4jpZRsWfgtJ2L34KTTMfbJe+gQ0Tje/PQs1nywgNKCIvwiOjP+qfvQOjtRVljE+s+Wkp+WhVbnxNjH78bYORiAsqJi/vx8GTknzoAQjHn8bjp2j3DYsVwUNGrEVVNUx5XyP0sDPB7ViX9sSiaruIKPxndje2oeJwrKatKkFZXx/J9HKKyoYmCAJ0/FhPD0usOEerkwKcyXv607RIVZ8trISHak5XGmsLz9DqiOZb8mMPdfO1nw0nXtHUoj0mwmaelShj7/PK5GIxtfmUnHmGg8g4Nr0mQkJlKYnsb4Oe+Qm5xM0uIljJr5Ss3ylN9W4xkUREVJyXmP/ciqRfT920voDb7EvTkN334xuAfVXjA4uXnQ5bZ7yIrfVW9d4eRMv6enoXVxwVxVScI7r2Ls0x+viK52jS9u0XJGvfQMbr5G1kybRVBMFF6davM2LT6JgrR0Jr3/JqYjKcQuXMpls6c3u65/3170ue1GNFotiau+48BP/6Xf7TfjFRLMZa/NQKPVUpKTyx8vvUJgTBQardYux7J/+RIGPPMCLkYj22bNoENUDB51yklWUgJF6emMfPNd8lKS2bd0EUOnv0ppjonjf6xmxGtvo9XpSPj8E9K2byN45GhM+/eRERfL8FlvoHF2piw/r82xnu04jn27kB5PvozOx5e97/wDn74DcAusU2bcPQi9+V5yEnbWW9c1IIi+/3i7Zjtx/3gUY/9BDo23YeyHVy6m39MvoTcYiX1jOr79G5R3d3e63HY32XG7660rNBoib74Tz9BwKktLiH1tGoaefeqt6whn4pPIT81g8odvkH0khR0LljPp9ZcbpYtb+QM9rr6CsOGD2b5gGclrN9Jtwjg69ulJpwFRCCHIOX6STR/N49r3X6PYlMPBX9dyzXuzcNLp2PjhXI5t2UHk2BF2iz3VWjev+cAS+66vljHhtWmN0sWv/J7uV11B6PAh7FywlJR1G+l6xTjc/f24fMbz6DzcOROfxM4vl9SsHzFmBN0mXsa2zxfYLd6mHI/dS15qBn/57FXSDx1l/fxV3Pz2C43SBfaIJGxgX36c/n69970CfLlu9tO4eLhzPHYP6+ausLm+PUizmQMrlhDzzAu4GIxsn21tZ4LqtzPF6emMeMPSzuxftogh0yztzIk1qxk+29LOJH7xCenbtxE0cjSmA/vIjItl2KuWdqbcQe3Mqbg95KWmc9Mnr5F5+ChbvlzB5Df/0SjdzhU/0Oeay4kYMZjN85dzaO0mek4cS98pE+k7ZSIAJ3YlsPf//qjptALY9/MafIIDW3WuddT56MBPP+Pfpyc9Jl/NgZ/+y4H//Ey/229G6+xM75uuI+/UafJPnm4Uz+DHH8IYEd7i47Cluq5eZa2ru79axhU26mqita52Hj6EXQuWcnTdRrpcMQ6dhzvR99zB6V1x9dJrnJ0YO+1ZnF1cMFdWsmbmW3SM6otf10i7xF3XxXhuOhW3h/zUdG60lvetX67gWhvlfdeKH+htLe9b5i/n8NpN9Jg4lrKiYrZ+uZIJLz+FRwdfSvLyAdA6OzHplb/j7GrJ9/9Of4fg6D74d3NMB8qJ2L3kpmZwh7WN3DB/FTfaaOM69ogkdGBffmqijdRb28g/566wub69nIzdQ15qBrd9OouMw0fZNH8l17/1YqN025f9k77XXEaXkYPYMG8FB9ZspvekMcT+8Cu+4SFMfOFRck6lsWnBKq6d+TQAWxZ+S0h0byY89zBVFZVUll8Y36OUC5O6VfB/iBCi0E7buVcI8Wkzy68TQvSq83qWEOJye+zbnroZ3ThTVEZaUTmVUvLnqRyGBnnXS7PfVExhRRUAB0zF+Llafl0P8dRzwFRMWZXELCEps5DhQT7n+xCatDnxBKb889upc65yklNw9w/A3d8fjZMTQUOHkBYbWy9NWmwsISNGIITA0KULFcXFlObmAlBiMpGRkEDnsWPOe+wFR4/g6h+Aa4cANE5OdBg4jOyE+l/YdV7eeIZFIhp03ggh0Lq4ACCrqpBVVXa/j910JAWPAH88Aix5GzJsCGd2x9dLc2Z3HKGjhiOEwLdrJBXFxZTk5Da7bsd+fWo6o3y7RFCSnQOAk15f8765ogKw3/HkpSTj5h+Am7WcdBwylIz4+nmdGRdL0PCRCCHwiexCZXExZdZyIqvMmMvLMVdVUVVejt7HAMDJdWsIv+oaNM6Wuqz3ql/n7a3w2BFcOnTExc9SZowDhpOTWL9T09nTG4/QxmWmrryDSeg7BKD37eDQeOvKP5psLe+Wz8B/0FCb5d3LRnnX+xjwDLV8SXRyccUtMIiy3ByHx3xqVzwRo4chhMCvayTl1vJdl5SS9L0H6DzEMjowYvRwTu2KB8DZxQVhrZeVZfUvoKW1LJmrqqgsK8fN4GPf2HfHE2atm2eLPWTIQADCRw/nlPXLb4duXdB5WDod/LpEUGyqzW//nt1rljna0R0JdB87FCEEHbtHUF5UTJGpccdNh4gQvPx9G70f2CMSF2usAd3CKcp2XLmpaWesZbzj4KFkNuiEzYyPJbCF7cypdWsIq9PO6BzUzpzYGU+XMZby7t8tgvKiEoptlJnUPQcIG2op713HDOPEzvhG20rZtJOIkYNrXhdl53AyNolul41sVWyOOh9Z1rF0GIeOGsGZXZbrBycXPX49uqF1tt8oyKacblBXK5qpq52sdTVs9PCajioXby98I8Mb/cgjhMDZep1grqrCXFVV0x7Z28V4bmpNee8yZhjHreU9ZdMOQodE49HB0u64WkfdCSFwdm2Y7447jmMN2siyFraRHXtEore2kR0d3EYCHNuZSLcxlngDukVQVlRCUU79eKWUnNlzkIhhMQB0GzuMYzsSAMg9lUpw3x4AGDp1pDAjm+LcfMqLS0jdd5gel1nqs9bZCb27m0OP5aIgNOfv7yKjRlwpjnAd8H/APgAp5Yx2jaYJfq7OZBZX1LzOKqmgu7HpBnNimJFdaQUAHM8v5Z7egXjqtJRXmRnU0YvDOcUOj/lSUJqTg6uvsea1i9FIbnJy/TSmHFyMtSdrV6PR8p6PD3tXrKDnrbdQWVp63mKuVpabg95QG5feYKTg6JFzXl+azcS+/jIlmWkEjZmAV3gXu8ZXkpNbL29djQZMR1IapMnBzVg3jZGSnJxzWhfg2PpNdBpW++Um+0gyu+ctoigrm8GPPWCX0VYApbk5uNSJ08VgJC+lQTnJaZDGaKQ0x4R3eARhk65iw3N/Q+Osw7dPH/z69AWgOD2NnMMHOfzP79A6O9Pt1jvwDnfcsPTyXBO6OmVG52Ok6Ni5l5lqpl1b8R0w3J6hnVV5rgl9nXqo9zGSfzS5mTVsK83KpPDEcbzC7T9qoKFiUy5udcqxm9FAsSkX1zqdTGUFhTi7udaUVUua2gv/kztiif/6n5Tm5TP2hb/WpOl5zUR+fPwFtDpnAvv1JrB/b7vGXmLKwf0ssZcXFKJzd6uJ3dXXSImp8ZeW5PUbCYzqa9f4zlWRKRcPP0PNa3dfA0WmXNyNLe+82f/HFjpH2zef6yrLzUFfpw3RGxqX8bKG7YzBSGmuCe+wCMImXsXG563tTO8++FrbmaL0NHIPHeTIP79D4+xMt1sc084Um3Jx962f18Wm3HqdqmUFhejcasuMm/XzqKuyrIxT8XsYNrX2dsbti75h0F9upKKV51pHnY/K8vJr6oSrwYeyvIJzimfXvIUIjYbgQQPoef21beoQKjHl1GtnXI0GSs5SV918jfXamaaYzWZ+/8csCtMy6DJhHL5dHHN+uhjPTa0t78XW8p5/Jh1zVRW/vPIuFSWl9Lr6MrqMGQZYbov9zwuvkZ+WSY9JY+nQ1XHXBQ3bSI82tpEhDmwjwRKve7023Yfi7FzcDbXxlhYU1SvvHr4+Ne2MMawTR7fFEdizCxmHj1KQaaIoOweh0eDi5cH6T5eQffw0HSI6M/z+W3B20Tv0eJSL18XX1abYhRDiOSHETiFEohDi1Trv/yiE2C2E2CuEeKjO+/cJIQ4JIf4Emrw3QggxHJgMzBFCxAshIoUQi4UQN1mXHxNCvCGE2CqE2CWEiBFC/CaESBZCPHK2+Brs6yHrNnad/P0HO+QKIG2/3a+DBxPCfFm45wwAJwvK+O5QBm+MjGT2iEhS8kqokk2srDRgK5/O4QJSQHpcPDpPL3zC7TPcv+VaGXt1So2GAdPfZOhbn1JwLJmi0yftFxqArTLY8OLcVhLEOa27/8f/ILQaOo8YWvOeb5dIJsx5jctem86Bf/9MVXlFw620zrkci42DEUJQUVRERtxuRr39PmPe/5iqsjLObN0MgNlcRWVREUOmzaTbLbeT8MUnyPNdd1v4hclcWUlO0m58Y4aePbE92SzuLYu9qrSUvXM/JPLWu3ByPR+/pNos4GdX57hCBsdw7fuvMfrZJ0j89kcAygqLOLU7nimfvMUNX7xLZVkZRzdutU/I1WyUw3Oovo0Spe89QMq6TUTdbv859M5J25rJGqeSDrJ/zRaG3319m0Nqks263zDTbXwuWNuZ+N2MfPt9Rr9naWdSre2MrKqioriIwS/PpNvNt5M41zHtjO1tigZpzpYCTuxKJKBHl5rbBE/sTsTF2xO/yMbzB7UgOBs7tt/5qCWGPP4QE96ezdgZL5J18DAnNm5p9baAc8pU283n2Y9Bo9Ew8a2ZXPvZu5iSj5J78lTrYmyNC/3cdE71tenVzVVVZKcc5/KXnmTCtL8S//1/yTuTDoBGq2HKuzO4Zd7bZB05Ss6Jxreb2ou9ivdpaxs5zJFtJJxbI9LM+Sv6+omUFRXz/TOvsefn9fiFh6DRapFVZrJSTtJr4hhuevdlnPQ64v/1m/3jv9gIcf7+LjJqxNX/ICHEBKArMBhL0/OTEGK0lHIDcL+U0iSEcAV2CiF+AHTAq8AAIA9YB8TZ2raUcosQ4ifg/6SU31v31zDZSSnlMCHEB8BiLB1hLsBeYO5Z4qu7r/nAfIArf4hv8RVhVkkFHdxqh5T7uTqTXdr4S3eYlwt/iwlh+uYUCsqrat5ffczE6mMmAO7pHUhWibov+1y4GIyUZJtqXpeaTLg0uOXGxWig1JRd87rEZMLFYCB1507S4+L4IzERc0UFFSUlxM6dS8wjj3A+6H2MlOXUxlWWY0LnY2hmDduc3Nzx6dYT094E3IND7Bafq9FQL29LTDn1fgGuTlNsqpvGkv/myspm1z22YTOpsYmMfvlZmxffXsFBOLnoyTt1yi7ziLgYjJTWibM0x4Tex6f5NCYTeh8D2fv24ObXAZ2X5TaAgJhB5B45TNCwEbgYjPgPGIQQAu+ISITQUFFQUJPW3nQ+RsrrlJnyXBPO3i0rM7l743ELCcPZy8fO0TVPZzBSVqceluU2/gyaY66sZO/cD/EfMoIOMY6b/+Tgb2tJXrsRAGNkGMV1ynGxKafRLX16Tw8qikswV1Wh0WptpgEI6NmNremZlOYXkL7vIB4d/HDx8gQsnVuZh5IJHzWsTbEfWr2W5LWWU5tvRBhF2Saqb7gptlF/9Z4elBcV18Rekm2qlybn+El2zF/MmBf/ht7To02xtUTSL+vZ+7ul0yagSyiFWbUjS4qyc3Bv4W2VWcdOse7z5Vw7/QlcHHgceoORsjptSJmNdkZvtNUWGTDt24OrXwd0npa2w3+ApZ0JHDYCF6MR/5gG7UxhQU3attj36zoO/WEp735dwurdJlSUnYNbg1EbLl4elBfXlpni7BzcjPWPMWXzTiJG1NbRjANHOLErgVNxe6gqr6C8pIQ/P/6KMU9NPec4HXU+0nt7WUZkGXwoyclF7+15TrEAOLu60nn4EEzJRwkd3bL56Q6vXkuKta4aI+q3M7aOrWFdLW5QV89G5+5Gh57dSUvYg0+I/ecGvFjOTftbWN71zZR3d18DLl4eOLvocXbR07FnV0zHT+IdFFC7vrsbHXt351T8Xgydg7GXPb+sZ5+1jfRv0EYWZts+BzUn+9gp1n++nKsd1Ebu+WU9B/6wPIykQ5dQiuq16bmN2hAXr/rlvTC7diSczs2VcU/cA1g621c++jKe/r5UlpXj7utDQDfLNWPEsBjVcaU0S424+t80wfoXB8QCPbB0FAE8JYRIALYBIdb3hwDrpZSZUspy4Js27v8n679JwHYpZYGUMhMoFUL4nCU+uzmUU0yQh54ANx1OQjCmk4FtZ/Lrpeng6sz0YeHM2Xmc04Vl9ZZ5651q0owI9ubPk7n2DvGS5BMRTlF6OsWZmZgrKzmzbTsdo6PrpekYHc3JzZuRUpJz5AjObq64+PjQ85ZbuOKjD7n8/feIeexR/Hr2PG+dVgCeYZGUZKRRkpWBubKSzF1b8e1/bk/RKy/Ip7K4CICq8nJyDuzBrWOQXeMzRIZTmJZOUYYlb09u3U7ggKh6aYIGRHF84xaklGQfTsbZ1Q1Xg0+z66YlJHHwPz8z4tkncdLXDuEuysjEXGXpzC3KzKLgTCrufn52ORav8AiK09MozrTkddr2bfhHxdRL0yEqhjNbNiGlJDf5CE5ubuh9fHAx+pKbkkxVWZnlOPfvxSPQktf+0QMw7d9niTktFXNlJc6eZ//i01oeoZGUZqRRai0zpt1bMPRt2ZMXs3dvxm+g/SYBP1deYRH1ynvGzm3nXN6llBxa+iVugcGEXOHYJ011nzieq95+havefoWQgdGkbNiKlJKsw8no3FwbfVkUQhDQqzsntlvmMkrZsIVOA6MAKEhLrxnFYjp6HHNlJXpPD9x9jWQdSaHSWqbS9uzHOziwzbF3mzCeK9+ayZVvzSR4YDTHrHUz63Ayzm5utmPv3Z2T2y1z0RzdsIVO1npalJXNpg8+Z+jjD+AV2LHNsbVE3yvHctv7L3Pb+y8TPrg/B9dvs+TTwRR0bq4tugWmINPEL+/M5/K/3otPnS+UjlDdzpRUtzM7ttGhYTvTP4ZUW+2Mry95ddoZ0/69uAdZ2pkO0QMwHWjQznjYp53pNWkc1707g+venUHooCiO/Gkp7xmHLHnd8AuwEILA3t05ts1S3g//uZXOg6JqlpcXFZO271C99wbeeQO3zXuHWz5/k7FPP0hQnx4t6rQCx52PgmKiOb7R0gFwfONmggZE0xxzVRVl+ZbbCc2VlaTGJeAV0vIOia4TxjPxrZlMbEFd9e/dnVPWunpswxaCGhx/Q6X5BZQXWaadqCwvJ33PfryC2t7O2HKxnJt6ThrHlHdnMOXdGXRuRXk/Uqe8dx4URfr+I9Z5CsvIPHIUn+BASvMKKKvO97JyUhP34xNs3za0z5VjueX9l7nFRhupb0Ub+es787nMgW1knyvHctN707jpvWmEDY7i0J+WeNMPpaBzc6l3myBY8j2oT3dStlrmnDu0fithg/sBlicHVlVUAnDgj00E9upq/ey88fAzkns6DYDTSQfw6eSY8q5cGsR5v0VCaTdCiEIppYcQ4j3gkJRyXoPlY4HXgAlSymIhxHpgJuADXC+lvMea7imgm5TyiSb2s5j6I65qXgshjgEDpZRZQoh7rf9/wpruGDAQeMlWfM1pzYgrgEEdPXmoXzBaIVh9zMTXB9O5Ktxyz//PR7P5a0wII4K9ybDOhVUlJX9dewiAOWO64KVzotIs+TLxNPGZLZ/7fv2nP509USssmXYDo6JC8fN2IyOniNmL17Pk53i77uPxtya0et30hAT2Ll+BlGZCRo+m2+TJHFu7FoCw8eORUrJn6TIykhLR6vREPfAAPg1G8WTt30/yz78w5Jm/t3j/eSWtn7zVlBRH8rfLkGYzHUeMpfNV13Hmzz8ACBpzOeV5ucS+MY2q0hIQAq3ehYEz36E0O4uDi78AsxkpJR0GDCX0mhtavH9/z+bnG0mNSyRh2Sqk2UzY2JH0vO5akv9YB0Dk5eOQUhK/eDlpCXvQ6nUMfPj+mhFSttYF+OXpFzFXVKCz/qrn2yWSmKl3c3zjFg7+9DPCSYsQgp7XTyZ4UIztwID80pble2ZiPAdXrUCazQSPHE3EtVM4uW4NACHjLkNKyYHlS8jaY3kkee/7H6yZR+bIjz+QtmM7QqvBq3MYve+disbZ2TIKaOGX5J88jkbrRLdbb8e359nnh2hLmcndE8fxHyyPHO8wbBzBk64nfePvAASMuoLyvFz2vPMPqkpLEEKg0bvQb9q7OLm6UVVeRvy0x+n/6setvtXOxbnq7ImakJ0UT/I31eV9DKFXNy7vu1+vLu8atHo9g159h6JTJ4mfM8syotA6Qi/8+lvx7RvVov138mrZQyaklOxctJLUeEv5HvbIffhGhgGw7q0PGfLQvbgZfShIz2Tzx/MoKyzCGNaZ4U88gNbZmb3//oWjG7ei0WrR6pyJvvNm/HtYfjdJ/O7fHN+6E6HRYAjrzNCH72l2ImizbNkwfCkluxetINVaN4c8fH9N7Ovf/pDBD96Dm9FAYXommz+ZR3lhEYawEIY9/iBaZ2e2z1/MyR27cfeznMM0Gg0T37BMMbn543lk7D9IWUEhLt5e9L1pCpHjRjUZi1Hf+hHEUko2fPk1J+L24aTXcdkTd+PfxXLL2X9e+5Txj/0Fd6MPCf9dS9y/fqc4Nx9Xb09CY3oz/vG7WPvZMlK2xdVMoKzRarhlzkvnvP9DOS0bfZCZGM+hry3tTNDI0URcM4WT663tzFhrO7NiCdnWdqbX/Q/iHWZpZ5J//IG0ndsRGks706tuO7PoSwpOHEfj5ES3W27HeA7tTEgryvvWr1ZxOt7ymPpRj9+Ln7XMrH7jY0Y+cjduRh/y0zNZ/8GXlBUW4RsewpinptaU3cPrtnAqfg/jnn7I5j5S9x5kz0+rueKlJ5uNJb+i8U0cjjgflRUUsu3jLyjJysbVz5dhf30UnYflM//5qeeoKCnFXFmJzt2NUS/+HTc/P9bPesvyUBSzGf8+veh/120ITe1v91rRsktIKSWx1rrqpNcx+OH7MVrzfcPbHzLowXtwtdbVrda66hMWwlBrXS3JzeP3l2dTUWJp851cXLhyzmyKMrPY/sVXSLNESjOdhw6i942Tm43lmKn1D11o73NTL//8syeqQ0rJNmt51zZT3gsalPfRdcp70r9/4/C6LQiNoNtlI+l99eWYjp9i46eLkNbrs/BhA4m6+ZpmY3HRtv68KqVk45dfc9LaRo6r00b+97VPGWttIxP/u5b4Om1k55jejHv8LtZZ20jPOm3kTS1oI1tzbtq04GtOxe3FSa9j7OP30MEa78+vfcKYx+7C3ehDflomf3ywgLLCYvzCQxj/1/vQOjuTdjCFdR8vQqPR4BMSyNjH7qqZXD7r6En+/GIZ5ooqvAL8GPvE3TXLmvL3PuMuvnvcWsD1zoXnrXOmZMX9F1Veqo6r/yF1Oq4mALOBy6SUhUKIYKACGAY8IKW8VgjRA4gHJgEHsYzAigHygbVAQjMdV58AsVLKRdbXi2lZx1WMrfiklBlNHVtrO67am6M6rs6HtnRctbe2dEK0t7N1XF3IWtpxdSG5mMtMWzqu2ltLO64uJC39cnAhaUvHVXtracfVhaSlHVcXElsdVxeLlnZcXUja0nHV3lracXUhaUvHVXu7mM9NoDqu7Oli67i6eM8ySqtJKVcLIXoCW61z1RQCfwF+BR4RQiRS21mFlDJVCDET2AqkYrl9r7lHh30NfGkdmdXi2WGbia/JjitFURRFURRFURRFuWhp1ExOTVEdV/9DpJQedf7/EfCRjWRXNrHuImDROe5nM9Crzlv31lkWVuf/i7FMzm5rWVPxKYqiKIqiKIqiKIryP0J1XCmKoiiKoiiKoiiKorQnG0/uVixUx5XSakKIl4GbG7z9nZTy9faIR1EURVEURVEURVGUS4vquFJazdpBpTqpFEVRFEVRFEVRFKUt1IirJqnZvxRFURRFURRFURRFUZQLkhpxpSiKoiiKoiiKoiiK0p6EGlfUFJUziqIoiqIoiqIoiqIoygVJjbhSFEVRFEVRFEVRFEVpTxo1x1VTVMeVckno37m4vUNold5vTWjvEFrtsxdXt3cIrbbt+0ntHUKrfX+svSNoPXddZXuH0GpuzlXtHcL/pLwy5/YOodUu5vKeVuLS3iG0WjdDYXuH0Gqni1zbO4T/ScVVF+8NKD075Ld3CK0Wn+rT3iG0WrcOBe0dQqtlFurbOwRFaRXVcaUoiqIoiqIoiqIoitKe1FMFm3Tx/sSgKIqiKIqiKIqiKIqiXNLUiCtFURRFURRFURRFUZT2pJ4q2CSVM4qiKIqiKIqiKIqiKMoFSXVcKYqiKIqiKIqiKIqiKBckdaugoiiKoiiKoiiKoihKe1KTszdJjbhSFEVRFEVRFEVRFEVRLkhqxJWiKIqiKIqiKIqiKEp70lw4I66EEJOAjwAtsEBK+VaD5c8Bd1pfOgE9gQ5SSpMQ4hhQAFQBlVLKgW2NR3VcKYqiKIqiKIqiKIqiKAghtMBnwBXAKWCnEOInKeW+6jRSyjnAHGv6a4GnpZSmOpsZJ6XMsldMquNKURRFURRFURRFURSlPYkLZianwcARKWUKgBDia2AKsK+J9LcDqxwZkOq4Uv7npCckkbhsJdJsJnTsaLpPvrreciklictWkh6fiFavY8BDU/EJDwNg9/yvSItPQO/lxeVvvVazzo5PPqcwNQ2AiuJinN3cGP/GLIceR0ZiInuWr0CazXQeM4au117T6Dj2Ll9BekICWr2OqAcfxCcsrHa52cyGGa/gYjAw5Jm/OzTWlpj7/LVcObQbmblFDLx/bnuH04iUkkUfLCNuawJ6Fz2PTXuIiO5hjdJ99to89sUdwM3DDYDHX36IsG6hNcuP7Evh5Ydm8vSsJxg6fnCbY4pbupK0+CS0Oh2DH5mKITy0UbrCjEy2fTKX8sIiDOGhDH7sQbROTs2un5qQRPzSlUizJHzcKHpa60vCim85ExuPxskJj4AODHp4Kjp3N9KS9pK06nvMVZVotE70u/MWAnr3bNVxtbauFmdns3vuAkrz8hBCEDZuDF0mTQDg9Pad7P/njxScSWXsq9MxRIS3Krazxp6YSFKd2LvZqJ9Jy1aQnmCJPeahB/AJC6OqvJyNr7+JuaISaa4iaNAget54vTX2HRz4lyX2MTNnOCx2Rx+HI2UkJrJ3RW272OUaG+3iihVkJCSg1VnaRW9ru7jmmWdwcnFBaDQIjYZRr74KwMF//YsT69ej9/ICoPtNNxHQv7/dY5dSErdkFanV9fDR+zE2UY+3fjyP8qIiDGGhDHn8AbROTuSfTmXHvIXkHD1B31uvp8c1kwCoKq9g7ay3qaqoQFaZCRkygD43X2f3+KulJSSRYC0z4U3U24SlK0lLSESr0zHw4akYrOfYXfO/Ii3Oco694u3XbGzdsaSUbPzqW47H7sVZr+OyJ+6mQ2TnRukSf15Pwv+tJT8tk/sXz8HVywOAg3/uIO7H1QA4u+gZ89Dt+IV3cli8ra2fxdnZxM77sk4bOZbIiZY2Mu/4CeIXL6GqtAxXP18GPvYIzq6uF0zsF0IbmZmYyL6Vy5FmMyGjxxB5zbWNYt+3YjmZiQlodXr6PVDbzqx75u9oXV0QQoPQahg5s/Za8djvqzm+5g+ERot///70uPU2u8cupWTHom84FZeEk17HyMfuxTeicTtTkJHFnx/Op6ywGN/wzox68n60Tk6k7j3I2nc+w8PfD4DQITFE3WT57MqKitkydyk5J08jhGDEo/fg3y3S7sfQUP6+eE79cxHSbMZ32GV0vOK6estNOzeSvubfAGh1LnS69QHcgsMcHlddjmrfq5nNZn7/xyxcjQZGP/9Xhx1H9p4EDn+9DGk2EzhqLGFXTq63vCj1DPsXz6fgxDEir7uZzhNr2//9i+eTlRiPztOLIa++1XDTSjsQQjwEPFTnrflSyvnW/wcDJ+ssOwUMaWI7bsAk4Ik6b0tgtRBCAvPqbLfVVMeV8j9Fms0kLFnGiBefxdVoZN2MWQQOiMIrOLgmTXpCIkVp6Vzx3lvkJKcQv3gZY1+dDkDo6JFEXnEZu+YtqLfdwU8+VvP/pBVf4+xm/4u8hseRtHQpQ59/HlejkY2vzKRjTDSedY4jIzGRwvQ0xs95h9zkZJIWL2HUzFdqlqf8thrPoCAqSkocGmtLLfs1gbn/2smCl65r71BsituaQNqpdD7+9l0O701mwZxFvLHgVZtp73r8dpudUuYqMys+/5qoIX3tElNafBKFaelc+f6bmI6ksHvhUi6fPb1RusRV39Htygl0Hj6EXV8t5ei6jXS5YlyT65vNZmIXLWfMS8/g6mvkj2mzCIqJwrtTMAF9e9H3thvRaLUkrPqO/T/9l/6334ze04ORzz2Fq8FA3slTbHjrfa797P0WH1Nb6qpGo6XvHbfiEx5GRUkJ66a/in/f3ngFB+PZKZghf32C+IVL2pTn5xT7C8/hajSyfsardIyJbhR7YXo6l7/7NjnJySQsWsqYV2egcXZm5Esv4OTigrmyko2z3yCgf1+MXbrg1akTg//6JPELFzss9vNxHI6Md8/SpQypbhdnziQgunG7WJSWxrh3rO3ikiWMfKW2XRz24ovoPD0bbTti4kQir7rKYbEDpMYnUZCWzlUfvEH2kRR2f7WMK16b1ihd4srv6X7VFZZ6vKC2Hus83Im+5w5O74qrl17j7MTYac/ibP0s1sx8i45RffHrav8vlNJsJn7xMka+9CxuRiNrp88iMCYKr061n0FaQiKFaelMfO8tTEdSiFu0jPGzrOfYUdZz7NwFTe3CoY7H7iUvNYO/fPYq6YeOsn7+Km5++4VG6QJ7RBI2sC8/Tq/ftnkF+HLd7Kdx8XDneOwe1s1dYXN9e2hT/dRq6XPHbfiEWdrI9TNm0qGPpY2M+2oRfW6/Fb+ePTj+5wYO//dnet1044UTezu3kdJsZu+ypQx+7nlcjEY2v/oK/tEx9dqZzMREitPTGfP2HHKTk9mzdDEjZsysWT70hZcatTPZ+/eRHhfLyNmvo3V2piw/3yHxn47bQ35aOjd8/BqZh4+ydcEKrnnjH43S7V7+A72uvpyIEYPZMn85h9duoseEsQAE9OzK5S8+2WidHYu+ITiqN+OeeYSqykoqy8odcgx1SbOZk999RZfHp+Hs48vBd1/Cu89AXANrO4x1vv50fWomTm4e5O2L4+TX8+n+zBsOj60uR7Xv1Q7/8jtewY69ppdmMwdXLiH66RfRG4zsen0GHfoPwD2otuw7u7vT7ba7yIzf3Wj9jsNH02ncFexbOM9hMV4SzuNTBa2dSU11KNkKRDaR9lpgc4PbBEdIKc8IIfyB34UQB6SUG9oQrnqq4IVECLFACNGrleseE0L4nWPamUKIZ8+S5rrWxnIhMyWn4B7gj7u/PxonJzoNHUzq7vongdTdcYSMHI4QAmOXSCqKiinNyQXAr0d3nD08mty+lJLT23fQaZjNDmm7yUlOwd0/oOY4goYOIS02tl6atNhYQkaMQAiBoUsXKoqLKc3NBaDEZCIjIYHOY8c4NM7W2Jx4AlP+hdWZVteujbGMnjQSIQTd+nShqLCYnKzcFm3jl+9XM2TcILwMXnaJ6fTuOMJGWcqsb9dIKoqLKcmpH5OUkoy9B+g0xDI3Ytio4ZzeFdvs+qYjKXgE+OMR4I/WyYnOw4ZwZnc8AB379UGj1QLg2yWCkuwcAAxhobgaDAB4dQqmqqKCqoqKFh9TW+qqi8GnZpSks6srnkGBlJos+eEVHIRnUGCL42mJnOQUPAIC6sQ+hLQGsafFxtF55Ahr7LX1UwiBk4sLAOaqKsxVVVRfO3gGB+EZ6NjYz8dxOEpuSgrudeINHjKE9AbtYnpsLJ2aaBfb2+nd8TX10K+Zepxetx6PHl7zRcbF2wvfyPCaellNCIFzg89COOjCuLreetSpt2ds1NvQJtqrDj27o2vmHOtoR3ck0H3sUIQQdOweQXlRMUWmvEbpOkSE4OXv2+j9wB6RuHi4AxDQLZwia7voCG2pny4+PjUjsC1tZBClJkushamp+PbobjnOPr1J3dn4C2h7xt7ebWRuSjJuAf64WWMPHDKU9LgG7UxcLMF12pnKc2hnjq9dS+TV16B1dgaoGeFpbyd2xRM5ehhCCPy7RVBeVEKxjXYmde8BwoYOAKDL2GGc2Bnf7HbLi0tI33+IruNHAqB1ckLv7uaIQ6in+PgR9B06ovcLQOPkhCFmOHlJO+ul8YjojpObpV1xD+tKRW62w+NqyFHtO0BxtokzcYlEjBvl0GPIP5qMW4cAXDtYyr7/oKGNOqh0Xt54hUcibMRp6NYDJ/f2a9+VFjsFhNR53Qk400Ta22hwm6CU8oz13wzgX1huPWwT1XHVTqwTntV7LaV8oO6EZ+3sOuCC6bgSQthldGBpTg6uRmPNa1ejkdKc+heWJTm5uPrWTWOgJOfcLj6zDx5C7+2NR8eO9gi3SaU5OfVidLFxHKWmHFyMtRfWrkZjzYXp3hUr6HnrLee1V/9SYcrMwS+gNu99OxgxZZpspl01/zuevesfLP5oORXlFdb1Tez4cxcTrrvMbjGV2CjXDctseUEhOne3moseN19jzUVTU+uX5OTi1rAumBrXhaPrNxEY1Xj02Kkdu/EJ7VxzId4S9qqrRZlZ5B0/gSEyosUxtFbD/HSxEZfNNNa8lWYza1+ezi+PP4V/n94Yuzj+VgtbLrbjKMnJwcVYv11sGK+l7fStl6Zuudo2Zw4bZ8zg+Lp19dY7tmYNf778MgkLFlBeVOSY+E05Nupbbr00tupxsY062ZDZbOa3F2fy74efpmPfXvh2cUx9aHwMjT+DElPjetuwbreXIlMuHn6GmtfuvgaKGnwG52r/H1voHN3bTpE11tb6Wa0oM5O848cxWOunV6dOpMVaviyf2bGTEpPt81t7xt6ebWRpToNrK4ORskbtjKl+W2QwUppjzUcBO959h02vzODE+tp2pigtDdOhQ2yeNZNtb75ObkqKQ+IvNuXi3qCMFzco42UFhejcatsZd2P9NJmHUvj3c7P4/Y2PyDlp+R5bkJGFi5cnmz5fzE/Pz2bz3KVUlJY55BjqKs81ofOp/Tx0Pr5U5DVdZrO3rsWrZ7TD42rIke173NKv6X/HzQgHP42uLDcHfZ1yrTcYKcu9MNruS4kQ4rz9ncVOoKsQIlwIocPSOfWTjXi9gTHAv+u85y6E8Kz+PzAB2NPWvFEdVw4ihPhRCLFbCLHXev8oQohCIcQsIcR2YJiN1+uFEAOFEI8KId6ps617hRCfNLXdc4znZSHEQSHEH0D3Ou8/KITYKYRIEEL8IIRwE0IMByYDc4QQ8UKISOvfr9Z9bxRC9GhiP55CiKNCCGfray/raDDnprYhhLhWCLFdCBEnhPhDCBFgfX+mEGK+EGI1sNTGvh4SQuwSQuyK/9e/Gy62zeYAxwYVV9pIdI4dPKe2bnf4aCsLWwdyDjEKSI+z3F/uE+74+XEuRdJG+bDV+N/xyK18uOod3vzqVQrzi/j38v8DYPGHy7nzsdvQaO3Y/J5DuZY2EtWE3dT651AX9v34HzRaDZ1HDK33ft6p0ySu+o6BD9zTZNjNskNdrSwtZcdHn9L3L7c7/Pbds8d19jTV5UhoNIx/fTYTP3qfnJQU8k+eckCQ5+BiO45zrJtNGTFtGqNnzWLws89ybM0asg8cACBs/HjGz5nD6Nmz0fv4sH+Vg+YePYf8ttnyn8MxajQaJr41k2s/exdT8lFyz2OZahxf68+xDtfKU2tDp5IOsn/NFobf7cB53dpYP8HaRn78KX3vvKNmHqvoB+8n5Y81rJv+CpUlpQinxiMn2uxia1vqxdW6NNWxD3t5OiNfnc2gZ57l+Jo/MB20tDPSXEVFURHDp79Cj1tvI+7zT21eb7RZG65xAXzDO3PT528yZc4Mek4az9o5n1s2W1VF9tET9JgwhsnvTMdJryPpx1/tFXUzzv14Cg7tIXvbOoKm3OngmGxwUPt+JjYBvZcnxoiwVod2zmzVScfvVWknUspKLHNW/QbsB76VUu4VQjwihHikTtLrgdVSyrq/6gUAm4QQCcAO4L9SyjY3CGqOK8e5X0ppEkK4Ynl85A+AO7BHSjkDanog676uXvd7YCvwvPX1rcDrTW1XStnsmFchxAAsvaTRWD7zWKB6bOc/pZRfWtO9BkyVUn4ihPgJ+D8p5ffWZWuAR6SUh4UQQ4DPgfEN9yWlLBBCrAeuBn607vcHKWWFEGJ+E9vYBAyVUkohxAPW437GuskBwEgpZaN7x+rel/vizi3ndHa3/GJX+0tMicmEi8GnXhpXo4GS7LppcnD1qZ/GFnNVFWd27mbc7FfOmratXAzGejGW2jgOF6OBUlNt0bAcq4HUnTtJj4vjj8REzBUVVJSUEDt3LjGPPIJi268//M6an9YDENkjgqz02rzPzjRhqPPrZTWDnw8Azjpnxl09mv+s/BmA5ANH+WjGZwDk5xUQtyUBjVbD4DEDWxTT4dVrOLrOcqu4ISK8Ubl2bVAe9J6elBcVY66qQqPVUpxtwsVarl1t1AtXgw/mykqKG9aFOts9tmEzqbGJjHn52XoXV8XZJja//ylDHn0AjwD/Fh1XtbbWVXNlJds/+pROw4cRPKhledtWrkZjvdhLTTm4+hjOmqbh8enc3fHr0YP0xCS8Qhw3wXNTLrbjsIwqbdAuNmi7XQwGSrKz66ex3tpa/a/ey4uOAwaQm5KCb48e6L29a9J3HjOGnR98YLeYD69eS8paSz02RoQ1W98A9J4ejepxwzTN0bm70aFnd9IS9uDjgM/C1WhocAyNPwNb9bZhmvMp6Zf17P19MwABXUIpzKodQVCUnYN7C/IXIOvYKdZ9vpxrpz+Bi6fjbotpa/00V1ay4+NPCRk+jKA6baRnUBAjXngOgMLUNNISEi642Ku1RxvZ6Noqx4TeYGiQpkFblGNC79O4nQmIsbQzxu49cDEY6ThgIEIIfCIiEUJDeUGBXW4Z3P/rOg6t2QiAX2QYRQ3KuJvBu156vacH5cW17UyRKQc3ow8Aujo/AnWK6cvWr1ZSml+Am68BN18DHbpaRnOGDR1A0o+/tDn2s9H5+FJe59a/8txsnL0aX5OVnD7OiVXziHz0JZzcG89j6Ajno33POniEM7EJ/Cc+yXpNX8q2T79k6BMP2vtwLCOs6pTrshwTOp/Gea20zYXyOw6AlPJn4OcG781t8HoxsLjBeymA3Z9io0ZcOc5T1l7GbVjuD+0KVAE/1EnT8DUAUspMIEUIMVQI4YtlhNTmZrZ7NqOAf0kpi6WU+dQf5tfHOvopCbgTaDSuXQjhAQwHvhNCxAPzgOYmEVgA3Gf9/33AorNsoxPwmzWG5xrE8JOtTqvWMkSEU5iWQVFGJubKSk5t20FgTP0hw4Ex0ZzctAUpJaYjyTi7uTa6WLIlc88+PIIC690C4Sg+EeEUpadTnGk5jjPbttMxuv5xdIyO5uTmzUgpyTlyxHIcPj70vOUWrvjoQy5//z1iHnsUv549VafVWUy68QrmLHmdOUteZ/DoAWz4dRNSSg7tOYKbu1tNJ1Vd1fNeSSnZuWE3IRGWi+rPfviAz/5p+Rs6bhAPPHtvizutALpOuIwJb77KhDdfJXhgNMc2Wsps9uFknF3dGl3sCCHw79WDU9t3AXBs4xaCB1rKTNCAKJvrGyPDKUxLpzAjk6rKSk5s3U7QgCjA8rTBA//5mRHPPomTXl+zn/KiYjbO+ZB+t92IX/dzaZ5sa0tdlVISu2ARnkFBdL1qYqtjaC2fCEu+1ca+nY4NYu8YE8WJTZutsR/ByVo/y/Lza25FqyovJ3PvPofPydWUi+04vMPrt4unt28noEG7GBAdzak67aKTqyXeyrIyKq2T2laWlZG1Zw+enSx1tu7cNGm7d9e8bw9dJ4xn4lszmfjWzHr1OOtwMs5uTdTj3t1r6/GGLTV1siml+QWUFxVbjq28nPQ9+/Fy0Gdhq94GDWhcb4/Xa29cW9T5Zm99rxzLbe+/zG3vv0z44P4cXL8NKSVpB1PQubnibvQ++0asCjJN/PLOfC7/6734BAU4MOq21U8pJXELFuIRFEiXK+s/nawszzIpuDSbOfjvnwgfP+6Cir2920jv8Ih67Uzq9m2N25moaE7Xa2fcbLcze/fgGWxpTwJiBpC93zJbSGFaKuaqSpsPimiNnpPGMWXODKbMmUHnwVEkb9hqmffykKWMu9loZzr27s6xbZbfuI+s30rngVEAFOfm1YwEyzxyFMxm9J4euPl44+5rIO+M5QnbZ5L2490pyC7xN8etcyRlmamUZWdgrqwkJ3YL3n3rX1OVm7JI+epdQu96Ahd/x8dU7Xy07/1uv5HJn73LtZ+8w7CnHsa/dw+HdFoBeIZFUJyRRkmmJa8zdm7Dr3+MQ/alKLYIhwxD/R8nhBgLvAZMkFIWW0cgzcQygsmjTrrCBq/XA89KKXcJIaZi6cA5AHSXUj7T1HallOuFEMeAgVLKLBvx/A0wSClfsb5+HzgjpXxXCHEUuE5KmSCEuBcYK6W8Vwix2Brv90IIL+CglPKcrwysnWtPAXOklIOb24b1ON6XUv5kPcaZUsqxQoiZQKGU8t2z7e9cR1wBpMUnkLh8FZjNhI4ZRfcp13J0jWWegfDLxlke1b1kORmJlkfWxjw0teaRyjs/nUvm/gOUFxai9/Ki543XETZ2NAC75y3A2CWS8MvO/SKv0tz6vuP0hAT2Ll+BlGZCRo+m2+TJHFu7FrDc2iKlZM/SZWQkJaLV6Yl64AF8GjwaOmv/fpJ//oUhz/y9xfv/7MXVrY69OUum3cCoqFD8vN3IyCli9uL1LPk53q772Pb9pLMnaoKUkq/eW0LCtiR0Ljoee/lBIntafmF885k5PPziAxg7GHj1iTfIzy0AKQntGspDz9+Hi5tLvW199to8BgyPtvnkwaZ8f6zxROdSSmIXLyctYQ9Oeh2DHr4fo/Wz3vD2Bwx66F5cDQYK0zPY9onlMcs+oZ0Z8viDaJ2dm10/NS6RuGWrrI+2H0mv6yyP/f756RepqqhAbx1RYOwSycCpd7PvX/9h/0//xbNj7Ze20S8+g4u3FxXmlv2M1Nq6mnXwEBtnv4lXSKeakWC9brmRjlH9ObNzNwlLV1BeUICzmxveoSGMeKHZZ1VY87jlsSetsD7qffQouk+ZzNE1a62xW+pn4pJlpCcl4aTTE/2gJfa8EyeJnf8l0mxGmiXBQwbT4/opAJzZtZvEpcvrxN6Z4c+fPfa2cMRxtERlC8tMekIC+1assD6mfjRdJ0/muLVdDK1uF5ctIzMxEa1eT/8HHsAnPJyijAx2ffwxYLnlJXjYMLpOtjzmO27ePPJPnADAzc+Pvvfdd04jhNx1lS2KXUpJ7KIVpFrr4eCH78cYGQbAhrc/ZNCD9+BqNFCYnsnWT+ZRXliET1gIQ631uCQ3j99fnk1FSUnNBNZXzplNUWYW27/4CmmWSGmm89BB9L5xcrOxtOXclBqfQKK1zQgbM4oe111Lyh+WehtxuaXexi9eTrq13g58uPYcu/3TuWTtP0BZQSEuXl70vOk6wq3n2HPV0bW01bFLKdnw5deciNuHk17HZU/cjX8XyyPr//Pap4x/7C+4G31I+O9a4v71O8W5+bh6exIa05vxj9/F2s+WkbItDo8Oljl3NFoNt8x56Zz3f7qoZbc0t7Z+Zh88xMbX3rCMUqpuI2++iY5R/Un+bTUpf6wBIGjgAHrdcrNDJvO/kNrI8qqWlfeMhAT2rVwOZkmnUaPpYqOd2btsKVlJSWj0OvpNfQCf8AiKMzLY/clHAMgqM0FDh9HF2s6YKytJ/GoB+SeOo3Fyosett+PX6+xTzfq7tay8SynZ/tUqTifsQavTMfKxe/GztjO/v/kxIx6+GzejDwXpmfz54ZeUFRZhDA9h9JNT0To7s//XtRxc/SdCq0Wrc2bw3bfg390yx1j2sZNsmbsUc2UlHv5+jHzsXvTWhxXYkpDm06LYm5K3N5bT/1yCNJvxHTqOjhNvIGuT5RrVb+QETqycS27CdnRG6zOsNFp6PPdWm/bZrUNBi9I7qn2vOw1Cxr4DHPi/3xj9/F+bjSWzUN/s8uZkJcVz+OvlSGkmaMQYwq6ewun1lvYieOxllOXlsuu16VSWliCEBq1ez5BZb+Pk6sae+Z+Se2g/FYWF6Dy9CJ98I0GjxrY4hi9GD7qAxiTZn8eT/zpvnTOFn1x/UeWl6rhyACHEFOABKeW11nmc4oFJtKzjyoDldr7jwAtSyh1NbfccOq5isAzhG0LtrYLzrB1XWVgmYc/BMhTwtLXj6hMgVkq5yLqNLcAHUsrvhOUKpp+Ussnx40KIZ7Dc7jdbSvlFc9sQQsRZj2u3EGIREO7IjqsLSVu+HLQ3R3VcnQ9t6bhqb7Y6ri4WLe24upC0tONKsY+WdlxdSFracXUhuZjPTW3puGpvLe24UuyjpR1XF5KWdlxdSOzVcdUeWtpxdSFpS8fVhUB1XNnPxdZxdfG21Be2XwEnIUQiMBvLbX0tIqXMAfYBoVLKHW3ZrpQyFvgGS0fXD8DGOounA9uB37GM7qr2NfCcdcL0SCy3EU61jqTaC5ztp/MVgIH6j8ZsahszsdxCuBFo1PGmKIqiKIqiKIqiKJcyjTh/fxcbNTm7A0gpy4ArbSzyaJCu4euxDV5fc47bRUoZdpaYXqd2gve6738BfGHj/c1YRmLV1ZJhKiOB76WUuXW2edTWNqSU/6bOIzTrvD+zBftTFEVRFEVRFEVRFOUSozquFLuz3mZ4JXBVe8eiKIqiKIqiKIqiKMrFS3VcXUKsTyBcY2PRZVLKbBvvt3V/LwM3N3j7Oynlk/bel6IoiqIoiqIoiqJcqhzxMIxLheq4uoRYO6eizuP+bN5+qCiKoiiKoiiKoiiKYg+q40pRFEVRFEVRFEVRFKUdqQFXTVNPFVQURVEURVEURVEURVEuSGrElaIoiqIoiqIoiqIoSjtSc1w1TY24UhRFURRFURRFURRFUS5IasSVcknQXKSd03klzu0dQqtt+35Se4fQakNv+rW9Q2i1h1+f2N4htJqLc1V7h9BqWo1s7xBarcp8kTaQXNxlptJ88f42qNdevPl+qtC1vUNoNbeLuLxXyIu3nXF1unjzPa/84r2ODPAsbe8QWq3qIi7vXi6V7R2C0gw14qppF+9VlaIoiqIoiqIoiqIoinJJUyOuFEVRFEVRFEVRFEVR2pEacNU0NeJKURRFURRFURRFURRFuSCpEVeKoiiKoiiKoiiKoijtSM1x1TQ14kpRFEVRFEVRFEVRFEW5IKkRV4qiKIqiKIqiKIqiKO1IqGFFTVJZoyiKoiiKoiiKoiiKolyQ1IgrRVEURVEURVEURVGUdqTmuGqaGnGlKIqiKIqiKIqiKIqiXJDUiCvlf0JaQhIJy1YizWbCx46m++Sr6y2XUpKwdCVpCYlodToGPjwVQ3hYs+vmHjtB3MIlVFVUILRaou+7C2NkBGUFhWz/6DNMKUcJHT2C6HvvcsgxmfYkkPztUqTZTMeR4+g8aXK95cVppzm4eB6FJ48RNuUWQiZcA4C5opyEd2dhrqxEVlXhFzOEsMk3OSTGpkgpWfTBMuK2JqB30fPYtIeI6B7WKN1nr81jX9wB3DzcAHj85YcI6xZas/zIvhRefmgmT896gqHjB5+v8Js09/lruXJoNzJzixh4/9z2DgeAzKRE9q9cjjSb6TR6DJFXX1tvuZSS/SuXk5mYgFanp+/UB/EOCwOgoriIpEULKTx1CgT0vf8BDF26Evf5pxSlpQFQWVyMk5sbI2e9ZvfY0xOSSLTWvdAm6m3ispWkxyei1esY8NBUfKz1dvf8r0iLT0Dv5cXlb9XGlnv8BPELl2C21tv+91rqrb21pc3ZNf8r0uIssV/xdv18PfLbHyT/vgah0RAY1Z++d9xi99jTExNJqpPv3a69plHsSctWkJ5gyfeYhx7AJyyM4uxsYud9SWleHkIIwsaNJXLihJr1klf/ztHf1yC0GgL696fP7be2Oda0hCTil65EmiXh40bRo4l8To1PwkmnY+AjUzGEhza77t7vf+Toug3ovTwB6HPLjQRG96MoM4vfnn0Zz6COAPh2iSRm6t1tPoamjssR5ed8kFKye8kqzsQl4aTXMfTR+zGGhzZKV5iRyeaP5lNWVIQxrDPDnngArZMTp3bFkfjtjyA0aLQaYu6+Df8eXR0Wb3piIknLV4LZTOcxTZT35SvIsJb36Act5b2qvJxNb7yJuaISaa4iaNAgetxwfc16Kat/J+WPNWis5b33bW0v7w1JKYldsooz8Ulodc3n9ZaP51nzOpShj1vy+timbez76RcAnF30DJx6F4bQEAC2zV3ImbhEXLw8uWrO7DbHeinV1bbme/7pVLbNW0jO0RP0u/V6el4zqWadAz+vJnntRoQA75BODH3kfrQ6Z7vF3trzanF2NrvnLqjTvo+hyyRL+77vu3+SGhuHEAK9lxcxD0/F1WCwW8zVqq9nsF7PRDRxPZOVmICmzvVMYWoqCV98VpOuODODrtffQNgES74f/2M1x9f8gUajpUP//nS/5bY2x+qI8n5q2072/fBv8s+kMn72NIwR4QCYKyvZvWAJOUePIYSg/9134N+rR5uPodrFdE2gXNpUx5VyyZNmM/GLlzHypWdxMxpZO30WgTFReHUKrkmTlpBIYVo6E997C9ORFOIWLWP8rOnNrpu06lt63jCFjlH9SI1PIGnVt4yZ9iJaZ2d63Xw9+SdPk3fqlMOO6ciqRfT920voDb7EvTkN334xuAd1qknj5OZBl9vuISt+V711hZMz/Z6ehtbFBXNVJQnvvIqxT3+8Ihz3xaChuK0JpJ1K5+Nv3+Xw3mQWzFnEGwtetZn2rsdvt9kpZa4ys+Lzr4ka0tfR4Z6zZb8mMPdfO1nw0nXtHQpgKSd7ly1l8LPP42I0smXWK/hHxeAZXFv2MxMTKUpPZ/Rbc8hNSWbvssUMnz4TgP0rltOhT19iHn8Sc2UlVeVlAEQ/9kTN+vu/XomTq5tDYk9YsowRLz6Lq9HIuhmzCBwQhVed2NMTEilKS+eK994iJzmF+MXLGPvqdABCR48k8orL2DVvQb3t7l31LT1umELH/v1Ii09g76pvGTXtRbvH3to2ByB0lDX2ufVjz9i7nzO747j8zVlonZ0pzcu3a9zVsScsWcaIF57D1Whk/YxX6RgT3SjfC9PTufzdt8lJTiZh0VLGvDoDjVZLnztuwycsjIqSEtbPmEmHPr3xCg4mc99+0mLjGPfGbLTOzpTZIXZpNhO3aDmjXnoGN18ja6bNIqhhPscnUZCWzqT338R0JIXYhUu5bPb0s67b9coJdK/zZbKaR4A/V7xpu62yF0eVn/PlTHwSBakZXPvhG2QfSWHnguVMfP3lRuniV/5A96uvIGz4YHYsWEbK2o10nTCOgD49uXJAFEIIco6fZPNH87jmfcd0wEmzmcSlyxj+vKW8//lK4/KeYW0jL5tjLe+LlzJm5gw0zs6MePEFnFxcMFdWsvG1N/Dv1xdjly5k7ttPamwc4163lvd8+9dVgFRr+b7mA0te7/pqGRNem9YoXfzK7+l+1RWEDh/CzgVLSVm3ka5XjMPd34/LZzyPzsOdM/FJ7PxySc36EWNG0G3iZWz7vO3l6FKrq23Nd52HOwPuuYNTu+LqpS825XDo1zVc9e5snHQ6Nn34Bce3bidizEi7xN2W86pGo6XvHbfiE25p39dNfxX/vpb2vevVV9Lr5hsASP7tdw786yei77/HLjHXjX3fsqUMsl7PbLVez3jUiT0rMZHi9HRGvTWHvJRk9i1bzLDpM/EIDGSE9cc1aTaz7um/EhAzEIDs/fvIiItl5KzX0diprjqqvHuFBDPs6cfZ/dXSevtLWfsnABPenk1pXj6b3v6Ay16bjtC0/caqi+ma4FKh7hRs2iV1q6AQYoEQolcr1z0mhPA7x7QzhRDPniXNda2Nxd6EEGOFEP/XzjGECSH2tMe+TckpuAf44+Hvj8bJiU5DB3Nmd/2LhdTdcYSOGo4QAt+ukVQUF1OSk9v8ugIqSkoAqCwuwdXHBwAnFz1+3buhcbbfL2QNFRw9gqt/AK4dAtA4OdFh4DCyE3bXS6Pz8sYzLBKh1dZ7XwiB1sUFAFlVhayqOu+t5K6NsYyeNBIhBN36dKGosJicrNwWbeOX71czZNwgvAxejgmyFTYnnsCUX9LeYdTITUnG3d8fN2v5DRw8lIy42HppMuJiCR4+AiEEhsguVBYXU5qbS0VJCaZDB+k0egwAGicnnN3c660rpSRtxw6Chgy1e+zVdc+9Tt1LtVFvQ0Za6q2xSyQVRcWU5uQC4NejO84eHo03LKDSWm8riktwMfg4LPbWtDkAHXp2R2cj9pQ16+g++Sq01rbFxdv+ZT8nOQWPgIA6+T6EtAaxp8XG0XnkCGu+d6HCWmZcfHzwsY7Wc3Z1xTMoiFJTDgBH16yl6zVX18Sut0PspiMpeAT44xFgiTVk2BDO7I6vl+ZMU237OazbXhxVfs6X07viCR89DCEEfl0jKa8TWzUpJel7D9B5yAAAwkcP5+SueACcXVxq5vioLCt3aKw5ySm4+9eW9+ChQ0iLbZDXsXGEjGhc3oUQOFnPpeYG59JjaxuUdy/HnKdO7Y4nzFoOzpbXIUMsX9TDRw+v6TDp0K0LOg9Lu+7XJYJia30F8O/ZvWZZW11qdbWt+e7i7YVvZDiaBtdnYLkuqyovx2z919WO56i2nFddDD41I5ot7XsgpSbLMTu7udasX1lW5pA5enJTknGrcz3TcfBQ0htcz6THxRJkvZ7xiaytq3Vl79uLm78/rn6Wr3wn160l/Kpraq7Z7VFXHVXevYKD8AwKbLS/gtNn8O9j+crp4u2Fs7sbOSnH2nwccHFdEyiXvot2xJUQQiulrGrw+oH2jKmB64D/A/a1cxyXBCGEk5SysjXrlphycPM11rx2NRoxJSc3SJOLa700Bkpzcppdt/9dd7Dp7fdIWvkNUkrGvtL4F2VHKcvNQW/wrXmtNxgpOHrknNeXZjOxr79MSWYaQWMm4BXexRFhNsmUmYNfQG2++nYwYso0YfDzaZR21fzv+H7Rj/QZ2Is7H70VZ50zpkwTO/7cxSuf/IMv9qecx8gvLqU5ObgYa8uJi9FIboOyX5prwsVY+1m4GIyU5ZgQWi06Ty+SvvqS/JMn8A4Np+edf8FJr69Jm3PoIDpvL9w7dnRI7K7G+nUvp2G9zWlcb0tycprtjOr7lzvY8s577LHW2zEOqLdtaXOa+5JSmJpG9oFD7P32n2idnel7xy12v82xpEG+uxgN5CSnnDVNiSkHF5/a2IsyM8k7fhxDl0hL7GlpZB88xP7vfkDj7EyfO27FENG22G19/qYjjWN1a1COSnJyzrpu8uo1nNi4BUNEGP3uvLXmC3xRZiZ/vDQTJ1cXet9yAx16dGvTMdg8LgeVn/Ol2JRbL343o4FiU2692MoKCnF2c6354u5mLUPVTu6IJeHrf1Kal8+YF/7qsFhLc3Ia5WPD8l5qatgW1ZZ3aTazfsYrFKVnEH75ZRgja8u76dAh9n//A1pnZ3rf3vbybkuJKQf3s+R1eUEhOne3mrx29TXWy+tqyes3EhjlmBHMl1pdtWe+1+VmNNDjmon89MTzaHXOdOzXm8B+fewWt73Oq0WZWeQdP4Ghzvln77c/cHLTZpzc3Bj1j+ftFnO1spwcXBtcz+Q1iL0s11T/3GS9nql7bkrdvo3AOj+2FaWlkXPoEIf/+T0aZ2d63HI73hfwuckW784hnNkVR8iwwZRkm8g9eoxikwkjbW9zLqZrgkuFRg25atIFO+JKCPGjEGK3EGKvEOIh63uFQohZQojtwDAbr9cLIQYKIR4VQrxTZ1v3CiE+aWq75xjPy0KIg0KIP4Dudd5/UAixUwiRIIT4QQjhJoQYDkwG5ggh4oUQkda/X6373iiEsHnzsRBCK4RIERY+QgizEGK0ddlGIUQXIYS7EGKhdb9xQogpddadY30/UQjxsI3tD7KuY7N1sI4mW2jNyxQhxFPW9+uNmBJCPCuEmGn9/3ohxAdCiA1CiP3WffxTCHFYCFF3fL+TEGKJNbbvhRBu1vUHCCH+tObNb0KIwDrbfUMI8Sdg1yvXxr8GSVuJml035Y919P/L7Vz1yfv0/8vt7P5ykT1DPAsb8XLuDZ3QaBgw/U2GvvUpBceSKTp90n6hnQMpG8dv6xe6Ox65lQ9XvcObX71KYX4R/15uGTi4+MPl3PnYbWi0F2wTdsE6l6KPEMiqKvKPH6PzuMsY+epraPV6Uv77n3rJzmzfRtCQYY4J1FZcDcu4jXJ0ttGDR9eso++dtzPp4/fpe+ftxJ6netuWNqdmDbOZ8qJixr06jb533ML2T76wWZfaxGaenj1N3eOrLC1lx8ef0vfOO3B2tfwSL6vMVBQVMXrmdPrcfis7P/m87bGfy+dv83BEs+tGXjGOKz98m8vfnImLjzeJK74BwMXHm6s+fpfL35xJ/7/cxo5P51FRfH5GWNqj/Jw/tsrHOaxWJ1HI4Biuef81Rj/7hGW+KweRthuaRqkaqv48hEbDuNdmM/HD98lNSSHfOj2ArDJTXlTE6Fem0/u2W9n1qR3Ku83Qzp7XtpvS+onS9x4gZd0mom530HyXl1pdtVO+N1ReWMSpXfFc+/HbXPf5e1SWlXF049bWx9mQHc6rlaWl7PjoU/r+5fZ6I61633Ijkz5+n5DhQ0n5fY194j2bc7yeqWaurCQjPo6Og2qnn5DmKiqKixg67RW633Ib8V98esGem5oSNnYUrr4G1kybRfyyVfh27YJG03g0X6tcTNcEyiXvQh5xdb+U0iSEcAV2CiF+ANyBPVLKGQBCiIavq9f9HtgKVHf53wq83tR2pZTZzQUihBgA3AZEY8mzWKD6vqx/Sim/tKZ7DZgqpfxECPET8H9Syu+ty9YAj0gpDwshhgCfA+Mb7ktKWSWEOAT0AsKt+xll7ZzrJKU8IoR4A1grpbxfCOED7LB2qN0J5EkpBwkh9MBmIcTqOscxHPgEmCKlPNHMIfcAxgGewEEhxBfN5Y9VuZRytBDir8C/gQGACUgWQnxgTdPdmj+bhRALgceEEB/ViSlTCFH9Wd1vXcdHSjnG1g6tHY8PAUx66Xmib5hiMzBXo4HibFPN6xJT/V9fqtOU1Etj+aXAXFnZ5LrHN26m/913ABA8ZNB57bjS+xgpy6kttmU5JnQ+LZ8I08nNHZ9uPTHtTcA9OMSeITby6w+/s+an9QBE9oggK702X7MzTRj8GsdfPQLLWefMuKtH85+VPwOQfOAoH82wTLSZn1dA3JYENFoNg8cMdOgxXGxcDAZKTbXlpNRkQt+gnLgYjJSaaj+L0hxLGiEsy3ysIwg6DhpEyn9r7zg2V1WRvnsXw1+Z5ZjYjQZKTA3qXoPRJLbqrWuDut3QiY2b6XdXbb2NW2D/etuWNuds2w0eNMAyHD8yAiEE5QUFdr0NydVorJfvpaYcXBuUGVtpqj8bc2UlOz7+lJDhwwgaNLDOOgYCrbEbIiNA0/bYbX7+NspIsY1yZK6sbHJdF2/vmvfDx49h85yPANA6O9fc1mCICMM9wJ+CtLSaCXLtxVHlx5EO/baWI2s3AuAbGVYv/mIbn4ve04OK4hLMVVVotFqbaQD8e3ajMD2T0vwCXKwTcNuTq8HYOB8bTCrt0qC8l5gaj+p0dnfHt0cPMhKT8OrUCVejgaCB9i3v1Q6tXkvy2g0A+EaEUZRtooN1WVN5XV5UXJPXJdmmemlyjp9kx/zFjHnxb+g9HXOL6aVQV+2d77ak7dmHh79fTVkPGTSArENHCB9lnx+I2npeNVdWsv2jT+k0fBjBg2xfb4UMH8qWdz+k543X21zeWnqDgZKzXM/oDZa6Wv1u9fVMtczEBLxCw9DXKTcuBiMBAwZabi+MiAShoaKgAN0FeG5qikarJequ22ter33ldTw6+rc6/vpxXjzXBJcKWz/kKxYX8nCFp4QQCcA2IAToClQBP9RJ0/A1AFLKTCBFCDFUCOGLpcNkczPbPZtRwL+klMVSynzgpzrL+lhHQiVh6Tjq3XBlIYQHMBz4TggRD8wDGt+kXGsjMNr69yYwEhgE7LQunwC8aN3WesAF6Gx9/27r+9sB3zrH1xOYD1x7lk4rgP9KKcuklFlABhBwlvRQmydJwF4pZaqUsgxIwZLPACellNWfw3LrcXUH+gC/W+OeBtTOMA7fNLVDKeV8KeVAKeXApjqtAAwR4RSmZVCUkYm5spJT23YQNCC6XprAmGiOb9yClJLsw8k4u7riavBpdl1Xgw9Z+w8CkLl3Px4dzyWb7MMzLJKSjDRKsjIwV1aSuWsrvv0HnNO65QX5VBYXAVBVXk7OgT24dQxyZLgATLrxCuYseZ05S15n8OgBbPh1E1JKDu05gpu7m83bBKvnvZJSsnPDbkIiLEXjsx8+4LN/Wv6GjhvEA8/eqzqtbPAOj6AoI53iTEv5Td2xDf/o+mXfPzqa01s2I6UkJ/kITq5uuPj4oPf2wcVopDA1FbDMC+ERVFtOsvftxT0wsN7wcHuyVfcCYxrX25ObLPXWdCQZZzfXs85Z5XIe6m1b2pzmBA2IIWPffgAKUtMwV1ai87Tvl3mfiHAK09LrxL6djg3yvWNMFCc2bbbm+xGc3Fwtt01JSdyChXgEBdLlyvqTJQcOiCHLGnthahqysqrNsRsi68d6cut2AgdE1UsTNCCqQT67Wdr2ZtatO1fN6Z2xNRPqluXnI81myzGkZ1CYlo6HfwfszVHlx5G6TRzPVW+/wlVvv0KngdEc3bAVKSVZhy31smFsQgj8e3XnxHbLb4BHN2yh08AoAArS0mt+eTcdPY65stJhHSo+EeEUpadTZG0jT2/bTscGbWTH6ChObq4t787W8l6Wn09FUe25NHPvPjwCLZd2HQfEkFmnvJvtUN6rdZswnivfmsmVb80keGA0x6zlwJLXbjbzOqB3d05utzyo5eiGLXSylvWirGw2ffA5Qx9/AK9A+9/yXe1SqKv2zPemuPn5knU4hcqyMsscknv24x1sv+uztpxXpZTELliEZ1AQXa+aWG+dQutThgFSY+PxDGzuK07reIdHUFzneiatieuZM9brmdzkIzhbr2dqYmtwmyCAf8wATPsts7oUpaUiKytxvkDPTU2pLCujstTy8Jz0pL1otNp6E8G3xcV0TaBc+i7IEVdCiLHA5cAwKWWxEGI9ls6Z0rrzWtl4Xdc3wC3AASydTrKZ7Z6LpsYvLgauk1ImCCHuBcbaSKMBcqWUUee4r43AI0AQMAN4zrrdDdblArhRSnmw7krC0kX7pJTytwbvjwVSsRxrNHDmLPsvq/P/KizlpJL6HZ0N8616HXOD9c3UlrOGeSitx7JXStnUz0lFZ4n1rDRaLVH33smmt99Dms2EjRmFV6dgUv5YB0DE5ePoGNWPtPhEfvv7CzWPFm9uXYCYB+4lYanl8bAaZ2diHri3Zp+//PVZKkpKLZ0Fu+IY+eIzdjuJAAitli633cuej95Cms10HDEW96BOnPnzDwCCxlxOeV4usW9Mo6q0BITg9JpfGTjzHcrzcjm4+Aswm5FS0mHAUHz7xdgttnMRPbw/sVvjeermZ9G56Hjs5Qdrlr35zBwefvEBjB0MfDzzc/JzC0BKQruG8tDz953XOFtqybQbGBUVip+3G0e+/RuzF69nyc/x7RaPRqul1513s/O9d5BmSadRo/EM7sSJdWsB6DxuPB369SczMYE/X3gOrU5Hv6m1UwX2+stdJMz/AllZhWuHDvSbWvs5pTryNkFr7P3vuZPN77wHZjOh1rp3dI2l3oZfNo6AqH6kJSTy+zOWehvz0NSa9Xd+OpfM/QcoLyzklyf/Ts8bryNs7Giip95L0rKVmM1mtM7ORE291yGxt7bNAdj+6Vyy9h+grKCQn5/4Oz1vuo7wsaMJGzuKXfO/4vcXpqFx0jLwkQfs/sucRqul391/Ycucdy2Pvh5dne+WMhN+2XgC+vcnPT6R3599HiednugHLbGbDh3m5OYteIV0Yu3Llifc9br5JjpG9Sd0zGhiv/yKNS++jMbJiZiH2h67JZ//wsa33rfk89iReHcKJtmaz5F18vnXp19Eq9cx8OH7m10XIGnVd+QeP4FA4NbBj5ipdwOQeeAQ+777EaHVIDQaYu6/2yGToDuq/JwvQdF9OROfxH/++g+0eh1DH6ltt9e99SFDHroXN6MP0XfcxKaP55H4zb8whHUmcpzlyWknt8dydONWhFaLVufMiL8+7LBfoKvL+9Z33kVKM52ry/taa3kfby3vCYn88dzzaHV6oh+w5HVpbh5x879ESjPSLAkeMpiO0VEAhI4eTdyCr1j7kv3Kuy1B0f1IjU/i//72Elq9jiEP31+zbP3bHzL4wXtwMxqIuv1mNn8yj8Rvf8QQFkLEuFEA7PnnfygrLGTXwuWW/NBomPjGDAA2fzyPjP0HKSso5MfHn6XvTVP+n72zDo/qaPvwPdlk4yG7CZaEEME1aHD3Ure3LlS/9tX6WypAW6y0pfK2UMEK1N2LFdcoDglOXCBuO98fZ5PsJhsh2U1IO/d17ZXsOTPn/Gb2mefMmfPMHMLN+S6VP1tbbWy9F2Rf4Ndn51BSUIAQgiM/r+OKhXPw7xRGcOQAfvnvbJycnLR2Md5+bbcx19WMo8c4s9Xs3/+r2UiPm66nXURfDnz6BTlJyQgh8PD3I+Ie+75RsFx7j9vuZG8d/Zn0uFg2m/szvS36M2VFRWQc2E/Pu6z7kUEjRxH/4QdsnfkMTjpnet/3wGV7bTq3Zx8xK9ZQdDGHbQsW49uxAyOfeYyiizlsmbcIIZxwN/gy6GH7LfnckvoEfxZUNdSMuBznk5rXbLpPSnml0NaCigGmoE2987JIl1vl+ybgcSnlXiGEAW2a3SngKSnl7pqOK6XcJIQ4CQw0RxlV1dMfbYAqksqpgkuklK8KIdLRpvVlAT8B56SU5WtqRUkpl5mPsR14XUr5uXmAqY+UMraG8rsCR4BEKeU481S96cB08wDZK4AP2iCVFEL0k1JGm6fOTQNulFKWCCG6AOfQorUeB2YAvwH/lFJuquHcLwK5UspXzd/3m899Dm3wqyuQC/wB/CKlfLFKvY8x/z/d8jcB0oETwDAp5Q4hxPtog4pvoS1gf4d5uwvQRUp5wPK4trRa8t+92y8/Q64HabmudSe6THm0d01jxpc/Q274pbklNJgHX55cd6LLFDeXlmszTi24I1FmarninXWm5pbQYKRsufXuqmu5bTWv+LJ8JlsvPFqwjyxpwfbuIlpkFxKAwrLLefJM7RSU2GkdpmbAy7VB74u6LChtwTYDMH/w0JbrbOpB4LM/NZlDOvfytBZVl5er5f6CtpB3HDAHbVrfJSGlzEIbEOkopdzdmONKKaPQIrhi0KYmbrHY/RzatLzf0QZiyvkEeEJoC6GHo00jnGGepngAqHFum3mK3RkLfVvQ1puKN3+fA7gAceaBpTnm7R+Yyxxl3r4Ei6g6KWUKcCXwjnmdrXojpSwBZpvL+kOVstaXQ8Bd5vo3Au9KKYuBG4D55rqJQZtWqVAoFAqFQqFQKBQKxV8CIUSTfVoal2XElUJxqaiIq6ZHRVw1DyriqnlQEVfNg4q4ah5UxFXzoCKumgcVcdU8qIir5kFFXF3eBM38uckc0tmXpraoumy5V3eFQqFQKBQKhUKhUCgUij8BLTAQqslQA1cWmN9AuN7GrvFSygwb2xt7vmeBG6ts/lxK+bK9z2Xj3PcA/6yyeZuU8hFHn1uhUCgUCoVCoVAoFAqFoj6ogSsLzINTEU14vpcBhw9S1XDuZcCy5ji3QqFQKBQKhUKhUCgUikpES16bwsG07EmuCoVCoVAoFAqFQqFQKBSKPy0q4kqhUCgUCoVCoVAoFAqFohlRa1zVjIq4UigUCoVCoVAoFAqFQqFQXJaoiCuFQqFQKBQKhUKhUCgUimZEqJCrGlEDV4o/BTohm1tCg2jjXdjcEhrMFyebW0HDefDlyc0tocEsefbX5pbQYJ5aNK65JTQY0UJ9DECpaLnB1WWy5XbgWup1CaDU1HJtxlNf2twSGkxLrnfZgttqSXML+Ivi5mxqbgkNpiXbu7Ou5da74q9Ny71CKhQKhUKhUCgUCoVCoVAo/tSoiCuFQqFQKBQKhUKhUCgUimZEzRSsGRVxpVAoFAqFQqFQKBQKhUKhuCxREVcKhUKhUCgUCoVCoVAoFM2IWpy9ZlTElUKhUCgUCoVCoVAoFAqF4rJERVwpFAqFQqFQKBQKhUKhUDQjKuKqZlTElUKhUCgUCoVCoVAoFAqF4rJERVwpFAqFQqFQKBQKhUKhUDQjTirgqkZUxJVCoVAoFAqFQqFQKBQKheKyREVcKRQKhUKhUCgUCoVCoVA0I0KFXNWIGrhS/CWQUhK9cg3JMfHo9HoGPzQDQ2jHaulyU9PY+dZ7FOfmYQjtyOD/ux+ds3Ot+Yvz8tn7/jIunDkHQjDogXvw79KJMzv3cODLb7l4PokJc2ZiDAu9ZN3JsfHErFyDNElCx46k21VXVCtX7Mo1JMXE46zXM9BCV01541Z/RlJUDE7Ozni2bc3AB2eg9/Qg83gi+z5cUX5gelx/NYGDBlySXkfWc1KV8nQ3lyd29WecN5fHq21rBpnLkxx/gPi1X2AqK8VJ50yf226ibc/ul1SectLi4zi05mOkyUTQqNGEX3FltXIfWvMxaXGx6PSu9J5xP61CQgAoyc8jftlH5J49CwJ633sfhk6dif7f2+QlJwNQmp+Ps4cHI2a/1CB99uC9J69k6pAupGXnMfDe95pNhyVSSqJWrOW82R6GPHwvxhrsafubSyjKy8MY0pEhj9yHztmZi+eS2LnkI7JOnKbPzdfSffqUijzFefnsXrqc7LPnEAgiH7wb/y6d7Kp934q1nI+Ox9m1du3bFi81aw9m6KOa9hNbd3Lou58BcHZ1Y9B9t2Po2AGAwz/+RsLGrQD4Bgcx5KF70OldGqXVUe1295KPSIqOxdXHhykL5lgd79iv6zj+23qEk472/frQ99abGlyGcpJj44ldtQZpMhE6ZhRda/CZybFx6PR6Bj44A0NoCAB7l35IslnrxPmVbfHgl99wYuMfuHp7A9Dz5utpH9G3QfouJx+ZYeHzpZT0vP5qgurp81vqNbWmsjiirV48n8zWxUus8ve58Wq6TZvYKK1NbT/xn33F+X0xCCeBq48Pgx+6F3eDocFlKMcRbfXsrj0c/PIbcs4nMW72cxjsZCOO6IsV5+ay8833yE9Lx6O1P0P+8TB6L09SbPRd2jSw72KLlNh44sz13rGGeo9btYaUmDh0rnoGPDAD39AQ8jMy2PfeBxReuIAQgpCxo+k0ZRIABz//iqSoaITQbKT/gzPsYiO1liMujniLcnS5cnq1csSvWk1KrFaO/g/ch29ICGXFxWx5eS6mklKkqYyAQYPofv21dtfXGPuuKW/2ydNEf7SCspIShE5Hv3vuwBgexultOzj6w88Vx75w5izjX3oR35DgBmu3t73v/+wrkvbFgNmPDDL7kaKcXHYu/h+ZCScIGTWcfvfc3iDNir8Gaqqg4i9Bckw8uckpTH1tLgPvu4t9H620mS5u7ed0mTqJaa/Pw8XTkxMbt9SZP3rlGtr17c3URa8wad4sfAIDAGjVIZBh/36E1t26NEizNJmIXvYxI578N5MXvsSZ7bu4ePZctXLlJKcw5bW59L/vLqLMumrL26Z3DyYumMPE+bPxat+Ow9/9CIBPh0DGv/Q8E+fOYsRT/yHqw5WYysouSbOj6tlkMhG17GNGmstzevsuLpjL07Z3DyYvmMNkc3kOmcvj6u3FiCf+weT5cxj88Ax2/+/9SypLOdJk4sCqlQz89+OMfHkeSbt2knPO+ndIi4sjLyWFUfMW0vPueziwannFvkOrP6Z1r96MmjufEbNfxitAs49+//coI2a/xIjZL9F24EDaDhjYIH32YtUvsVz91Opm1VCVJLN9T3/9FQbffyd7P1xlM13Mmi/oOm0iV74+F72nB4lme9J7eTLgrlvpNn1ytTz7Vqylfd9eTF/0MlPmv1jRbu3F+Zh4cpJSufINTfueDz6uQfuXdL1iIle98Qp6L08SN2javVr7M+H5J5m2YBa9rpvO7qVaW8jPzOLILxuY/MpMrnh1NtJk4tT23Y3S6kj/GDpqOKOe+k+1Y6UeOMS5vdFMmjebKQtfousVU6qluVSkyUTM8lUMf/LfTFrwMmd22PCZsXHkJqcwedE8+s+4m+hllTbVceQIhj9ZXStA56mTmDB3NhPmzm7woBVcXj6yVYdAJrz0PJPmzmLUU/9h3yX4/JZ4Ta0JR7VVn4B2TJv/AtPmv8CUuc/hrNfTYVD/RmltDvvpNn0qk+fPZtLcWbTv14cDX33fqDKA49qqT1AgQ//1KP52tBFH9cUOf/cTbXp1Z8rr82jTqzuHv/8JAL23F8Of+AeT5s9hUCP6LjWVJXbFKoY9+W8mLHiZszt3cbFKfyYlNo685BQmLppHvxl3E7Ncq3cnJx29b72ZiQteYfSLM0lct6Eib+crpjJ+7hzGvTKbdv36cvjr7+ymubZyDH3iP4yf/wpnd9guR25KChNenU/EvXcTu0z7TZxcXBjxzFOMe2UOY1+aTWpcPJnHj9tdX0Ptu7a88Ws/o/t1VzNh7mx63HAN8Ws/AyB4+NCK69Ogh+/Hw9+/wYNWjrL3rtOnMnH+bCaa/cghsx/RubjQ84Zr6HNb4x9c/VkQouk+LY0/1cCVEOIDIUSPBuY9KYTwr2faF4UQj9eR5pqGarFxrFx7HKce51kohDgghFjYFOezOK9VXQkhNgkh7HoXf25fNCEjhyGEwK9zOCX5+RRkZVulkVKSeuAwQZHaqUNGDuPc3qha85fkF5B++CihY0YCoHN2Ru/pAYBPYAA+Ae0brDnzeCJebdvg1bYNTs7OdBgayfl9MVZpzu+LpqMNXbXlbdenF046HQB+ncIoyMgCwNnVtWK7qaQEuHSP5qh6tiyPztmZ4HqUxxDSseKJn09QIGUlJZSVlFxymbITE/Bs0waPNlpdth88hNToKKs0qdFRBA4bjhACQ3gnSvPzKczOpqSggMyjRwgaNRoAJ2dnXDw8q9VH8u7dBEQOuWRt9mRb3GkyLxY0q4aqnN0XU2EP/p3DKa7BnlIOHKaD2Z5CRw3j7N5oANxa+eAXHlphH+WU5BeQdvgoYWOrt1t7cW5vDKGjhtZLe3DkgArtZ/bGANC6ayf0Xpqt+HcOIz8zqzJfWRllxcWYysooLSrG3eDbOK0OarcArbt3rSiHJcfXbaT7VdPQuWiRYm6tfBpVBoDMhEQ827bBy9xWg4YM5vy+aKs0STX4zEqtXo3WURuXk4+09Plll+jzW+I1tcayOLCtlpMSfwivtq3xbO3XOK3NYD8uHu4Vxy4rKm5Az6A6jmqrPoEBeNvZRhzVF9PyDAeg48jhnDf/RlX7LqYG9l1slsVc754W9Z5ko947jNDKYuwUTklePoVZ2bgZfPE1RwS5uLvjHdCewsxs7buFjZQWFSEcfEeclZCIV9u2FuWIJLlKOZKjogkeMdxcjk6UmPtlQgic3dwAMJWVmQfr7au3MfZda14BJQVaP600vwB3X99q5z6zYxcdhkU2XLuD7L2qHynH2c0V/25dKvoCCkVttNipgkIInZSyrMr3+5pTUxWuAX4ADjazjkvhQaC1lLKoic97DQ6uq4KsLNyNxorv7kajts3ihq84Jxe9p0dFR97Dz1jRSaopv3DS4ertzZ4lH5F96gyG0I70u/NWnN1c7aA5G3c/y3MayDyeWK1cHjZ01ScvwMlNWwkaOrjie8bxBPYtWUZeegaD/+++ajf8dWt2TD0XZGXjUY/ynNi0lWCL8pRzdvc+fDsGN+jCWJiVhZux8mbDzWgkOyHBOk12Jm4Wut0MRoqyMhE6HXpvH+I/fJ+LZ07TqmMo3W+7HWfXSvvIOnoEfSsfPNu1u2Rtf3YKMrPwtPjdPYwG8jOza7Undz8jBTZuHC3JTU3D1cebXe99RNapMxjDQhhw5y12abfl5Gda26wt7UU5ubh4uFe2BaPBpvaEjVsJiOhVkabb9Ml8+8hT6PQutOvTk/Z9ezZKq6PabW0DarnJKaQdOUb8Z1+hc3Gh7203Ywxv3LSegsysKn7CSGaVtlqQWd03FtahFSDht/Wc2rIdQ1gIfW77G3rP6oNx9dJ4mfnIjOMJ7FmyjPxL9Pkt8ZpaE45qq5ac2rGbjo24mSynuewn/tMvObllOy4eHoyZ+UTjy+HAtmpvHNUXK7pwsaIs7gZfii7kVDv3uUb0XWxRaOP3z6pa7zY0F2Rl4WZR73lp6Vw4dRpDeFjFtgOffcmZrdtw9vBg5H+ftIvemqhqx25GA1kJ1X+TqmkKMrNw8/VFmkxsfO4F8lJSCZswHmOncPvqa4R915a37x23snX+IuLXfIqUkjEvPFvt3Gd37mbof/7RcO0OvPfY/+mXnDL7kdF28CN/Vhw98NuSuWwjroQQ3wgh9pkjgB4wb8sVQswWQuwChtr4vkkIMVAI8bAQYoHFse4WQrxV03HrqedZIcQRIcQ6oKvF9vuFEHuEELFCiC+FEB5CiGHAVcBCIUSMECLc/PnFfO4tQohutZwrVAixw3zcORbbvYQQ64UQUUKIeCHE1ebtc4QQ/7RI97IQwqbXEhoLhRD7zce42bz9O8AT2FW+zUbe5UKId4UQG4UQiUKI0UKIj4QQh4QQyy3S3WI+9n4hxHyL7blmbbFCiJ1CiLa26sqc/EYhxG4hxFEhxMga9DwghNgrhNgb9dW3NVWnhrR5hCpJqieq8B015JemMrJOniJ8whgmzX0RZ1fXimkYjUbaOGlVZ2YrCaJeeQ998z1C50Tw8MpIH79O4Uxa+BLjX3qOw9/+RFnxJT7lc1A916c8B7/5Hqcq5QG4cPYccWs/Z+B9d9Uo+1Kpdk2xpVsIZFkZF0+dJHjseEbMegmdqyuJP1pPszi/aycBkUPtpu1PhY3fvR5NoM74Z1OZiawTp+g0cSxT572Is6ueg9/91HCdNqlbu02qJEo5cJiEjVuIuPUGAIpz8zi3L4ar3prHte++SllRESe27LC3VOzSbmvBVGaiOC+P8bNn0ufWm9jx5rtIW+28kVTvANbDr1YhbMJYpry+gAmvzMLN15e41Z80XNBl5iP9OoUzZeFLTLhUn98Sr6k14pi2Wk5ZaSnn9sUSPOTS1oy0STPZT++br+fKtxfRcfgQjv+2ob5qLwl7tFWH4OC+WE1cOHuO+LWf09+OfZd6+eo6NJcWFrJ78dv0vv0Wqyianjddz5Q3X6PDsCEk/r7ePnprwqbGutOU25hwcmLcy3OYvPg1shITuXjmrANE2j63hUBbiWrNm7huI31vv4Vpb71G39tvYd/7y6zSZR5PQKfX06pDUMOFOtDee918PVe8vYhgB/oRxZ+byzni6l4pZaYQwh3YI4T4Em1gZb+U8nkAIUTV7+V5vwB2AOVD/jcDL9d0XCllRm1ChBADgL8B/dDqLArYZ979lZTyfXO6l4AZUsq3zANBP0gpvzDvWw88JKU8JoSIBP4HjKvhlIuBd6WUK4UQj1hsLwSulVJeFNq0xp3m83wIfAUsFkI4mbVWDzvRuA6IAPoC/uY62CylvEoIkSuljKitLgCDWfdVwPfAcOA+83EigFRgPjAAyAJ+E0JcI6X8Bu332ymlfNY8sHi/lPIlG3UF4CylHCyEmAa8AEyoKkRKuRRYCvDcvm3VvOWx39ZzYuNmTXRYKAWZmRX7CjIzqz21c/X2pjgvH1NZGU46HfkZmbiZw3DdjQbb+YXA3WjAz/y0JihyIIftdAPsbjRQkGF5zupPGt2NBvKr6HIz+GIqLa0178nN20iKimPUs4/bHNn3CQzA2c2VC2fP1rkAblPUs6m0lPx6lGd0lfLkZ2Sy7bW3iXz4Przatqm1HDXhZjBQmFnpIgozM3H1NVRJY6TQQndhlpZGCG2fb7hmH+0GDSLxxx8q0pnKykjZt5dhL8xukLY/I0d/20DCBs2e/MJCyMvIpLV5X76NNuDq7WVlTwUZ1W2uKh5+BjyMBvw7aU+LO0QO5NC3jW+3R3/dwHHzujd+4SFWNluT9pL8gsq2UCVN1qkz7FqygjFP/xNXb21aTPL+Q3i29sfNR1soPGhwf9KPJhA68tIGP5vEP9aCh9FA0KAB2lSDTmEgBEU5Obj5NHzKoLvRUMVPVGq0TFPVN1ZNUxW3Vq0q/g8dO5rtr75xSbouZx9Zjk9gALo6fH5Lv6Za0hRttZykmHgMIcG4+7aiIVwO9lNO8LBItixcTK8brmlQWcpxVFt1BI7qi7m28tEiVAy+FGRl49rKuyJdfkYmO157m0GN6LvYws3G7+9moyzVNJvr3VRayq7FbxM0bCiBg2yv6NFh2BC2v/qGQxY8r9RotCpHYWYW7lX6ZbbSVC2r3tMT/27dSImLx6cxgz3V9DXcvqu3xcq8p7Zso++dtwIQGDmo2sDVmR276TCscctOOPLeo5wOwyLZtnAxPRvpR/6sqICrmrlsI66AfwghYoGdQAegM1AGfGmRpup3AKSUaUCiEGKIEMIPLUJqWy3HrYuRwNdSynwp5UXActXBXuYIqnjgNqDanA0hhBcwDPhcCBEDLAFqm4Q/HFhr/t9yNWIBvCKEiAPWAYFAWynlSSBDCNEPmARE1zIYNwJYK6Usk1KmAH8Ag2rRUpXvpfZYPB5IkVLGSylNwAEgxHysTVLKNCllKbAaGGXOW4w2JRC0gb+QWs7zVT3T1UjnSeOZNHcWk+bOInBgP05u2Y6UkoxjCbi4e1RzpkII2vToxtldewE4uWU7gQP7ARAwIMJmfnffVnj4Gbl4PgmAlP0H7bbIsyE8lNzkFPJS0zCVlnJmxy7aD4iwShMwIIJTNnTVljc5Np4j3//E8Mf/bjVlLS81rWJh3ry0dHLOJ+HpX/eyb01Rz0ZzeXJT0ygrLeX0jl0EmMuTFBvPYRvlKc7LZ8vCN+jzt+vx71qfZm6bVqFh5KWmkJ+m1WXS7p206dfPKk2bfv04t30bUkqyEo7j7O6Bm68vrq18cTMayU3S7CPj4IGKxdnLv3u2b28Vzv5Xp8ukcUyd9yJT571oZU/pxxJw8bBtT217duWM2Z5ObN5OUJV2UpXKdqu91TFl/yF8ghrfbrtMHlexGHPQwH6c2LzDQrt7DW2hK6d37avUPlDTnpeewZbX/sfQR2bgE1A5jdTDz0jG8URKi4q0dXf2H8In8NLXdGmKdlsbAQP7kXrgEAA5ScmYSksr3trXUAxhoeQmp1b4vbM7dxMwwLqttu/fr4rPrP67VMVyDaHze/fhExR4SbouVx+Ze4k+v6VfUy1pirZazsltu+k4vKZniXXT3PaTk5RScezzUTE2y3ipOKqtOgJH9cUC+vfj1Bbt1uTUlm0V5S/Oy2fbwjfo1ci+i82y2Kj39v2r1/uZrVpZMo9r7cHN4Ku95feDZXgHBNB5mvULT3LNb0gGSIqKwbu9/deis8Q3zLpez+7cRbsq5WjXP4LTW7eZy3EcZw933Hx9Kbp4keK8PADKiotJO3DQ7uuiNca+a8vrbvAl/dARANIOHMKrXduK40mTiXO79lgt/9Eg7Q6yd0s/khQVg7cd/Ijir4dwRGh+YxFCjAFeAiZJKfOFEJuAF9Gicrws0uVW+b4JeFxKuVcIMQNtEOkw0FVK+VhNx5VSbhJCnAQGSinTbej5F2CQUr5g/v4acF5K+aoQ4gRwjZQyVghxNzBGSnm3eercD1LKL4QQPsARKWW9PKMQIgNtQKrUnPe8lNLLfPypwO1SyhKz5jFSypPm6X3DgHbACimlzUeUQog3gDgp5Ufm76uAz6WU31WtTxt5LcsUYv6/l+U+oBS4Tkp5p3n7DKCnlPI/lscXQtwATK9aV+Z9m6j8Hf2BvVLKkNrqzFbElSVSSqKWf0xy7H6cXfUMevDeiqfKm+e/zqAH7sbdYCA3JZWdby2hOC8P347BRD5yPzoXl1rzZ508zd73l2EqLcOzTWsGP3gvei9Pzu7ZR/SKNRRdzMHFwwPfjh0Y/cxjVrpMtYkGkqLjiF21FmkyETJmBN2vuZKEdRsBCJ8wFiklMWZdOlc9Ay102coL8PO/n8ZUUoLe/FTYr1M4/Wfcyakt2zny3U8IZx1CCLpfexWBtbwBydaotyPrOSk6jmhzeULHjKCHuTw//ftpykpKKp5yGzuFM3DGnRz8+nsOffcj3hYX9lFPP4ZbKx9yii4t2DQ1NpZDaz9GmiRBI0fR6cqrOL1RC3MOHjsOKSUHP15JWrz2ivE+M+6jVagWzXPx9Cnil32ILC3DvXVr+sy4Hxfz+jhxHyzFN7wTwWNrCr6szpJnf70k7fVlxczrGBnREf9WHqRm5TFn+SZW/BRj13M8taj+5QTza+qXrSbJbN+RD96LX3gIAJvmv8Hg++/Cw2ggNyWNbW8t0V79HtKBoWZ7Ksi+wK/PzqGkoKBiQdYrFs7BxcOdrJOn2b10OWWlZXi19WeIud3WhBCXdq2UUrJ32RqSYjTtQx66p0L7xnlvEPnA3XgYfclNSWPrm+Xagxn26H3oXFzYtWQ5p3dH4emvra/mpHNiyivPARD3+bec3rEH4eSEISSYyAfvqnUNlFJT7c+oHNlud7z1HmmHjlCUk4tbKx96Xn81YWNHUVZaWrGOkZOzjr633UxbG698L5OX9ugxKSaWuHK/N3ok3a65kkSzzwyz8JkpcfGVryA3a9319nukHzqsafXxofsN1xA6ZhR7/reU7FOnQQg8W/vT79676nUDrbNhM5eTjzy5ZTuHv/sJJ2cdCEHPOnx+U5WjMddUAGenuq6s1cviqLZaWlTEN488yVVvzkXvUfcLIJqzrdZkP9tef4ecpGSEEHj4+zFgxp14GA3VtF0ObfXcnn3ErlhNUY5mI606dmDk07W+Swmo2787oi9WlJPLzjffpSA9A3d/P4b+82H0Xl4c+vp7Dn/3o9WgxEhz38UWZaZLq/fkmFjiPl4LJhMdR4+k69VXcmK9VpbQ8VpZYld8TKq53vs/oNV7+pGjbJkzF58OQRVRmz1uup52EX3ZtfhtKxuJuOcu3G3YSFXkJdpM1XLEr16DNJnoOGokXa++ihPrN5jLofXL4lasIiU+Hme9K/3u18px4fQZopa+jzSZkCZJYORgul179SWfX+dUh800wr5t5QVIP3KU2JVamZ1cXOh3zx0YzAvmpx08zP5PPmfs7Ofq1N4c9r6jih/pP+POChv56R9PUFJQiKm0FL2nByOf/k+tD4heHjD8Tx2T1H3B+iYbnDn05PgWVZeX68DV1cB9UsorhbYWVAwwhUsbuDKgReucAp6SUu6u6bj1GLjqDywHIqmcKrjEPHCVDvRAmxb3E3DOPBjzFhAlpVxmPsZ24HUp5edC8/h9pJSxNZT/O+AzKeXHQoiHgYXmgat/Ap2klH8XQowFNgCh5oErPVoUlAvQWVosXF/l2NehLcI+DTACe4FIKWWynQautqFFs5VPFfwVeEtK+W0tA1dV62oTdh64uly5tO715cXlHK5ZF5c6cHU54aiBq6bgUgeuLicudeDqcqKum+HLmUu9Gb6csDVwpXA8lzpwdTmh2mrz0JL9+6UOXF1ONGbgqrmpa+DqcqYl2zv8+Qeueizc0GQ/0MEnxtVal0KIKWhLGOmAD6SU86rsHwN8C5wwb/pKSjm7PnkbwuV6hfwFcDZPiZuDNhBySUgps9DeUtdRSrm7MceVUkYBn6INdH0JbLHY/RywC/gdLbqrnE+AJ4QQ0UJbcPw2YIZ5muIBoLbh/X8Cjwgh9gCWiyKsBgYKIfaaj1dxPillMbARbcDL5qCVma+BOCAWbeDrSSllci3pLwkpZRLwjFlLLNqA1Ld1ZKtaVwqFQqFQKBQKhUKhUCiaGCGEDngHbbZXD+AWIUQPG0m3SCkjzJ/Zl5j30jRdjhFXikvHvCh7FHCjlPJYc+tpalTEVdNzuY561wcVcdU8qIir5kFFcTQPKuKqeVARV81DS26rLdm/q4ir5kFFXDUff/aIq56vbmyyH+jA42NrrEshxFC0JZUmm78/AyClnGuRZgzaLKnpl5q3IbTcK6SiAvMI5nFg/V9x0EqhUCgUCoVCoVAoFApF/RBCPCCE2GvxecBidyBwxuL7WfO2qgwVQsQKIX4WQpS/pK6+eS+Jlht24ADMbyBcb2PXeFnzW/oac75ngRurbP5cSvnypRxHSnkQCKty7N5Yv5EQoEhKGdlUuhQKhUKhUCgUCoVCoVDUjWjCeDIp5VJgaU1SbGWp8j0KbVmmXCHENOAboHM9814yauDKAvPgVEQTnu9lwCGDQVLKeBpYFkfqUigUCoVCoVAoFAqFQnHZchboYPE9CDhvmUBKedHi/5+EEP8zv1itzrwNQQ1cKRQKhUKhUCgUCoVCoVA0I8LpslnCaw/QWQgRCpwD/gbcaplACNEOSJFSSiHEYLRlqDKA7LryNgQ1cKVQKBQKhUKhUCgUCoVCoUBKWSqEeBT4FdABH0kpDwghHjLvfw+4AXhYCFEKFAB/k9qb/2zmbawmNXClUCgUCoVCoVAoFAqFQtGMNOUaV3UhpfwJ+KnKtvcs/n8beLu+eRuLequgQqFQKBQKhUKhUCgUCoXiskRFXCkUzcjFQpfmltBgPPWlzS2hwbi5lDW3hAbz1KJxzS2hwcx/bENzS2gw4dNHNreEBjMm0rO5JTQYNxdTc0toMM5OLVe7v1txc0toMBdLWm7X1lXXcq9N6fmuzS2hwWQV6JtbQoPp4pfT3BIaTGZhy613d+eW21azWvC9x18Bp8sp5OoyQ0VcKRQKhUKhUCgUCoVCoVAoLkta7mMphUKhUCgUCoVCoVAoFIo/AUJFXNWIirhSKBQKhUKhUCgUCoVCoVBclqiIK4VCoVAoFAqFQqFQKBSKZkQFXNWMirhSKBQKhUKhUCgUCoVCoVBclqiBK4VCoVAoFAqFQqFQKBQKxWWJmiqoUCgUCoVCoVAoFAqFQtGMCCc1V7AmVMSVQqFQKBQKhUKhUCgUCoXiskRFXCkUCoVCoVAoFAqFQqFQNCNCrc5eIyriSqFQKBQKhUKhUCgUCoVCcVmiIq4UfwmklESvXENyTDw6vZ7BD83AENqxWrrc1DR2vvUexbl5GEI7Mvj/7kfn7Fxr/t1LPiIpOhZXHx+mLJhTcaysk6fZ99FKTCUlCCcn+t9zB36dwhpVjvT4OA6vWYWUJoJGjiH0iiurlfPImlWkxcei07vSa8YD+HQMAeDUbz9zdvMfIMA7sAM9Z9yPzkUPwOl1v3F6/e8InY7WffrS5aZbGqXTFimx8cStWoM0meg4ZhRdr7qimva4VWtIiYlD56pnwAMz8A0NIT8jg33vfUDhhQsIIQgZO5pOUyYBcG7XHg599Q0555MYM+s5DGGhdtfdGO0A+5Z+SHKMZh8T5r1UkSf71GliPlqh2YdOR9+778AY3jj7sIWUkqgVazlvtt0hD9+LsQbb3/7mEory8jCGdGTII/ehc3bm4rkkdi75iKwTp+lz87V0nz6lIk9xXj67ly4n++w5BILIB+/Gv0snu5ehLt578kqmDulCWnYeA+99r8nPfykMDzHy9NjO6AR8uT+JD3eftto/KMiXN6/pzbkLBQCsO5bOeztPNqnG9Pg4jqzV/EzgyDGETrPhZ9auIt3sZ3reW+lnTv/+K2c3bwQgcNQYOk7U7OXoZ2tJi43GydkZ99Zt6Hnv/bh4eNpde2pcHPs/Xo00mQgePZrOV06vpv3Ax6tJiY1F56on4v778Q0JqdxvMrH5+RdwMxiIfOw/FdtP/PY7J9atQ+icaNs3gh5/u9nu2lu6n9nx0aecid6Ps17P6Efvxj8suFq6iynpbHj9fYpy8/EP68CYv9+LzsWZ4rwCNr75IbnpWZjKyuhz1US6jhtO9rlk1r/+fkX+nJR0Btx8Jb2nT7Cr9ugVa0kqv77X4iN3vLmE4rw8DCEdibTwkbvNPrL3zdfSzewjy4pL2DB7PmUlJcgyEx0iB9Drxmvsprtc+57ln3A+Oh6dq55hD9+DX1h17TmpaWxZ/D7FuXkYQ4MZ/ugMdM6VtwDpx0/wy8y5jPzXg3QcMoCy4hJ+fXEBppJSTKYyOkYOoO9NV9tVe1p8HIfWfIw0mQgaNZpwG/2ZQ2s+Ji1O8zO9Z9xPK3NbLcnPI37ZR+SePQsCet97H4ZOnTn61RekRkeDEOh9fOgz437cDAa76q5K9oEYTn6+AilNtBk2jsDJ1vVUkHyOhFXvkXfmBB2uvJmAiZXlLM3PI3H1EvLPnwUg/I6H8A7r4lC95TZzzmwzw+uwmSKzzYww20zygSNsXPgOXm38AAge3J++N1SWyWQy8eMzL+Fh9GX8U/+wq/bUuDgOrK70752m2/Dvq1eTGhuLTq/591ZV/PuWFzT/Pvg/mn+/ePo0ccuXU1ZUhLu/P/0eeggXd3e76i7X5qi+2JGffydhw2akhPBxo+g2baJdtZe3VcxtNayGtpoeF4uTRVvNTUoi9t13KtLlp6XS+drrCJk0hWNffUFKdDTC3FZ7N0FbbSmogKua+dNEXAkh/iWE8GiC88wWQtivx9RwHSFCiP0OOO5yIcQNDch3lRDi6TrSjBFC/FDDPof+fskx8eQmpzD1tbkMvO8u9n200ma6uLWf02XqJKa9Pg8XT09ObNxSZ/7QUcMZ9dR/bB6r53VXMWnuLHrdcC1xaz9vVBmkycShj1fQ/99PMPyl+STt2kHuuXNWadLjY8lLSWHE3Ffpcde9HFy5DIDCrExOrfuNIc/PZviceUiTieRdOwHIPHSQ1Ogohs1+heEvzaPjlGmN0lmT9tgVqxj25L+ZsOBlzu7cxcUq2lNi48hLTmHionn0m3E3MctXAeDkpKP3rTczccErjH5xJonrNlTk9Q4KJPKfj+Lf1XEdvcZoB+g4agTDn6huHwfWfka3665m3Cuz6X79NRxY+5lD9CfFxJOTnML0119h8P13svfDVTbTxaz5gq7TJnLl63PRe3qQaLZ9vZcnA+66lW7TJ1fLs2/FWtr37cX0RS8zZf6L+AQGOKQMdbHql1iufmp1s5z7UnASMHN8Fx7+Kparlu9mWte2hBmru72os9ncsGovN6za2+SDVtJk4vDqFfT79xMMmzOf5F07yD1f3c/kp6Qw/JVX6X7nvRxapfmZ3LNnOLt5I5EzZzHkxZdJj40hLyUZAL8evRg6ey5DZ72CZ9t2nPzxe4doj1+5ksjHH2PsvLmc37mTnCptNTUujtyUZMYtXEDfe+4hfvkKq/2Jv/6Gd4C1HacfPERyVBSjX36JsXPnEj5tqkO0t2Q/cyZ6PxeSUrnprTmMeOh2ti613R53f/wVvadP4Oa356D39OTIhm2azl824hvUnusXPcf0WY+xa+UXlJWU4hvYjutffY7rX32Oa+c/i7OrnpDIfnbVXu4jp73+CgPvv5N9NfjIOLOPvMLsI09Y+Mh+d91K1yo+0snFmTEzH2fK/FlMnvcCSbH7ST+WYFft52P2k5OcytWLX2bI/Xew60Pb9R69+ku6T5vANYtfRu/pwfENWyv2mUwmotZ8Sfu+Pa20T3z+MaYvfIHp85/nXOwB0o7aT7s0mTiwaiUD//04I1+eR9Ku6m01LS6OvJQURs1bSM+77+HAquUV+w6t/pjWvXozau58Rsx+GS9zmw2degUj5rzMiNkv0aZvBMe/+8Zummsqx4lPP6Lbo0/T97lFZOzdRn7SWas0zp5ehNx4N+3HT6+W/+TnK/DtEUHEC6/R59kFuLcLdKhegHMx+7mYnMo1i19maC02E2W2mWsXv4xrFZtp070TVy54gSsXvGA1aAVw+Kd1tApsb3fd0mRi/8qVDH7sMcbMncu5Gvx7XnIyYxcsoM899xC/wtq/n/jttwpbKSf2o4/oftNNjH75ZdoNGEDiTz/ZXTs4ri+WfeYsCRs2M+mlmUyd/yLno2PJSUqxm25pMnHQ3FZHmNtqtXuPuDjyU1IYOW8hve6+h4PmturVvj3DZ7/E8NkvMezF2ej0rrTtPxCobKvDZ79E674RJDi4rSr+HPxpBq6AfwEOH7iSUj4vpVzn6PO0NKSU30kp5zXiEP/Cgb/fuX3RhIwchhACv87hlOTnU5CVbZVGSknqgcMERWpONWTkMM7tjaozf+vuXdF72Y4cKCkoNP/Nx93g26gyXEhMwKNNWzzatMHJ2Zl2kUNIjdlnlSYtOoqAYSMQQuAb3onS/HyKsjWdssyEqbgYU1kZZcXFuPpqTzbObFxP6LTpOLm4AODq06pROm2RmZCIZ9s2eJq1Bw0ZTNK+aKs0Sfui6TBCq2Njp3BK8vIpzMrGzeBbEVXg4u6Od0B7CjO1MvkEBuAdYP8Okr20A/h364qLl1f1AwsoLdCiakryC3BrpH3UxNl9MRW26985nOIabD/lwGE6mG0/dNQwzu7VyujWyge/8FCcdDqrPCX5BaQdPkrY2JEA6Jyd0Xs63AXbZFvcaTIvFjTLuS+F3u18OJ1dwNkLhZSaJD8fSWFcJ//mlmVFhZ9pbfYzg4eQFl3Fz8RE0d6Gn8lLOk+r8E7oXF1x0ukwdO1GWtReAPx69a6woVbhnSjMyrS79qyERDzbtK1oqwFDIkmOirJKkxwVRYfhwxFCYOjUiZL8fArNPrIgM5PU2FiCx4y2ynNyw3o6TZ+OrsJH+thde0v3M6f2xNJ5zBCEELTtEkZxfgH5WRes0kgpOb//MKFD+wPQZcwQTu6O0WQKQUlhEVJKSgqLcPXyxEln3UU9H38Yn7at8W7tZ1ft56r4yJr6BymW/YNRwzhXh48UQuDi5gaAqawMU1mZ3dcuObMnhrBRWr237qLZRL4N7ckHjtBxyAAAwkcP48yeSts68vMGOkYOwK2Vd43aZWmZXcMAshMT8GzTpqI/037wEFKjrdtqanQUgcPMbdXsZwqzsykpKCDz6BGCRmnt1MnZuSJ60zJSpqyoyOGhC7knj+PWuh1u/m1xcnbGb8AwsmL3WqVx8W6FV0g4oop9lBbkk3P8EK2Hja0oh7MDolCrcmZPDOEWNlNcT5s5vSfaxtGsycvI5Gx0PJ3HjbC77uzERDzbVvr3wMhIUqr495SoKIJq8e8psbEEj7b273lJSRi7dgWgdc+eJO21/v3shaP6YhfPJeHXORxn83W3TfeunNljXS+NITsxAQ+Lttpu8BBSqrTVlOgoAsxt1Tfcut7LyTh4AI82bXD31/o8zk3cVlsSQogm+7Q0WuTAlRDCUwjxoxAiVgixXwjxAhAAbBRCbDSneVcIsVcIcUAIMcu8bbwQ4muL40wUQnwlhNCZI432CyHihRD/ruXcFRFJQoiTQohZQogoc75uteSLF0L4Co0MIcSd5u2rhBATzBoWCiH2CCHihBAPWuR9wmL7LBvHDhNCRAshBgkhwoUQvwgh9gkhtpRrMut+UwixXQiRaFEGIYR4WwhxUAjxI9Cmjrq3WWYhxN1CiLfN/4cLIXaaNc8WQuRaHMJLCPGFEOKwEGK1+fz/sPz9LuX3qC8FWVm4G40V392NRgqysqzSFOfkovf0qLgoePgZKy4q9clflX533kLcms/4/tHHiF39Gb1vvr5RZSjMzsLNQoObwUhRFQ2FWVXSGI0UZmXiZjASMmUam5/4F3/8++84e7jj36s3APkpyWQdO8LOOS+wZ95LXDiR2CidNrXbqL/CKtoLsrJx97NMY6hWx3lp6Vw4dRqDA6a61IS9tFel9+23sn/tZ/zyj/+wf+2n9Lz5kgMd60VBZhaeFto8jAbyzQN/5VS1fXc/IwWZtevPTU3D1cebXe99xM9Pv8iupcspLSyyu/4/E228XEnOKaz4npJTRBsv12rp+ga04ss7BvHudX0I92vawcCi7CxcLezd1WCkKNvaFoqq+hmDkcLsTDwDg8g+eoTi3BzKiopIj4ulMLP6ANW5rX/g37uv3bUXZmVZtUM3G221MDMLN2PlwIe70Uih2dYPrF5N95tvqtaBzktOIfPoEba8OIttL79CduLl6yOr0lR+Ji8jGy8LbZ5GX/IyqthNTh6uFn7G06/SF/WYOpbss0msvv9JvnxsNkPvuRnhZN1FTdi2h/ARg+yuvSAzC4+q9VqHj/TwM5Jfh48ELZrp16df5NsH/0273j0avVxAVfKzqvh3v+rai3Jy0Xu4V2q3uAbkZ2Zxek80nSda38yXa//hyVl8fv9jtO/Tndad7add66tUtkObbTU700afJ5OCtFT03j7Ef/g+W1+YSfxHH1JaVHntOfrl52z8z784v3M7na+5zm6abVGcnYneUFkOvcFI8YX6DcoXpafi7OVDwqp3iXvlaRI+XkJZUWHdGRtJfpa1vXv4Ve8T2LIZS7tKO5rI90/MYt3cxWSfqYy+2bPiUwbcdgNC2P/2ssBG/7aq/9OuAbbt6sDq1XS/qbp/9w4KIiVaGxxK2rOHAhvXLLvod1BfrFWHQNIOHaUoJ5fSoiLOx8SRn2G/MhRlZeFepa1Wvfcoys60un6Vt1VLknbtpH3kEKttR7/8nE3/+RdJTdBWFX8OWuTAFTAFOC+l7Cul7AW8AZwHxkopx5rTPCulHAj0AUYLIfoAG4DuQojW5jT3AMuACCBQStlLStnbvK2+pEsp+wPvAo/Xkm4bMBzoCSQCI83bhwA7gRnABSnlIGAQcL8QIlQIMQnoDAw26xwghBhVflAhRFfgS+AeKeUeYCnwdynlALOe/1loaA+MAKYD5dFR1wJdgd7A/cAwO5R5MbDYXJbzVfb1Q4uu6gGEAcOllG9i/ftFUI/fQwjxgHlwcm/UV9/WrljaPEKVJNUTVVzf6pG/KsfXbSTijr9x5duLiLjjb+xZeilmZQNpQ0S10XJbZRCU5OWRGr2PkfNfY/Rrb1JWVMT5HdoUDZOpjNK8PCJnvkiXm24h9t23kLbO1SjttjZW0V5H+UoLC9m9+G16334LLh72X3+gRuyg3RYn1m+k9223MOXN1+h92y1Evd9I+6gJG9qqSrNdxNr1m8pMZJ04RaeJY5k670WcXfUc/M4xIfZ/FmxVadW6P5iaw8T3d3D9qj2siT7Lm1f3bhJtlYJsWUPd9i4QeAUEEjL1CqIWzSfq9YV4dQhGVImaSfzhW4STjnZD6nOpuVTqod0WAlKiY9B7++AbWn2dPFlWRklePiNeeJ4ef7uZvW+/c1n6SFs0mZ+xVYAq2mzWmTnJ2ZgD+IV04Lb3F3Ddwpls+3AtxfmVUZRlJaWc2htL6NAB9hRdLqxGXRVJbGSrz9NqJycnJs97kSvfeZXMhBNknzlbZ55LwrawOrOVa9+z/FP633odTk7VbwecnJyYvuAFrn93AenHT5J1+ly1NPakHt0ZEAJZVsbFUycJHjueEbNeQufqSqLF1OMu19/I2NfeIGDIME6vb44JEvWLYpCmMvLOnKDtyIn0+e88dHpXzv9WR1/WHthsqvXxk1oaY2gw178zjysXvkC3KePY+Kp2m3F2XyxuPj4218uyCzb7MvWr65SYGFx9bPv3vjNmcHLdOrY8/zylBQXVIprshoP6Yq0CA+h+1VQ2vrKITfNexxDcwXFlqNBU5XsdfshUWkpqTDTtBg22StLl+hsZ89obtB8yjFPN0lYvT4STaLJPS6OlLs4eD7wqhJgP/CCl3GLDed0khHgArYztgR5SyjghxCrgdiHEMmAocCfgDYQJId4CfgR+uwQtX5n/7gNqGy7eAowCTqEN+DwghAgEMqWUueYBqj6icn2pVmgDVpPMn/IYXS/z9tNAa+Bb4Hop5QEhhBfawNPnFvVh+Tj/GymlCTgohGhr3jYKWCulLAPOCyE22KHMQ4FrzP+vAV612LdbSnkWQAgRA4QAW7EmkXr8HlLKpWgDdTy3b1s1t3nst/Wc2LgZAENYqNVTlILMzGpT91y9vSnOy8dUVoaTTkd+RiZuvloa7Qls7fmrcmrzdvrdeSsAQZGD2PP+8lrT14WbwWgVvVCYlYmrr2/taTIzcfU1kHFwPx7+rdGbp7i07T+I7OPHCBg6HDeDkTYDBiGEoFVYOEI4UZKTU5HWHrjZqL+qU1bcjQYKMizTZOFuLp+ptJRdi98maNhQAgcNtJuu+tBY7TVxess2+tyh2Udg5CCiP7DfDeXR3zaQsEGzfb+wEPIyMikfrc/PzLJh+15Wtl+QUbd9e/gZ8DAa8DdHEHSIHMihb9XAVW2k5BTRztut4ntbb1fScq2j1PKKyyr+33Iik5njBb7uLmQXlDSJRleDkSILey+y4WdcjbZ8kTb1OHDkGAJHjgHg2Jef4WaofAp7ftsW0mNjGPD40w4JUXczGK3aYaGNtupmNFCYmVHxXWvPBpL27CElOpp1cXGYSkooKSgg6r336P/QQ7gZjbQbOMA8ZSkc4SQozsmx65TBluhnDvy8kcPrtct36/AQci205WVm42m01ubm40WRhZ/Jy8jCw1zGoxu30/eaKdp1qH0bvNv4k30umTadtRvNM9H78Q8NxsPXPnV+7LcNJJp9pDEsxCpCoaAePjK/Hj7SEr2nB627dyU5dj++HYIapf3Irxs5tt7s38NDybPQnp+RhbvBerq/q7cXxfkFldozK9NkJJ5ky5va4vdFF3M5F70foXMieFDlOmJ6Tw/a9ujC+dj9GILtswaTm8G6HZb3VazT2PYzQmj7fMPDAWg3aBCJP1ZfPjVgyFD2vrGIztc6LpJD72ukOKuyHMVZmehb1W+Bab2vH3pfI96hnQEw9o/k/K/fOUTn4So2k98Im9FbPDgM6tebXR+upvBiDqlHEji7L4YvY+IpKy6hpKCQLW99wMi/32eXMrhXve5kVvbRy3EzGCjIsLYrS/+eauHfo997j34PPYRXQABDnnwSgNzkZFJiY+2iF5qmLwYQPnYk4eZlG2I/+RIPo/0WOXc1GCioo626GowUZGZSvtWyTwCQFheLT8cQXFvZXookYMhQ9jm4rSr+HLTIiCsp5VFgANoA1lwhxPOW+4UQoWiRQOOllH3QBj/K7xaWAbcDtwCfSylLpZRZQF9gE/AI8MElyCm/6yij9oHAzWhRViPN50kDbkAb0AJt/PrvUsoI8ydUSvmbeftci+2dpJQfmvNcAM6gRXKB9ntmW6SNkFJ2t6G1/HzlXOqj4/qWuba8NeZv5O9RQedJ45k0dxaT5s4icGA/Tm7ZjpSSjGMJuLh7VLsYCCFo06MbZ3dp89tPbtlO4ECt8xYwIKLO/FVxM/iSdugIAKkHDuHdtm2t6evCJzSM/JRk8tNSMZWWkrxrJ20i+lulaR3Rn/PbtyKlJDvhOM4eHrj6+uJm9CM7MYGyIm0NkYxDB/Bqry1Q2abfADIPHQQgLzkJU2kpLt7e1c7fGAxhoeQmp5KXmoaptJSzO3fTvr/1Arvt+/fjzFatjjOPJ+Di4Y6bwVd7E8sHy/AOCKDztOoLhDuaxmivDTeDL+lm+0g7cAivdo2zD0u6TBrH1HkvMnXei1a2n34sARcP27bftmdXzpht/8Tm7QQNiKj1HO6+rfDwM3LxvLb4dsr+Q/gENc/i7C2F/ck5BPu6E+jjhrOTYGrXtmxMSLdK4+ehr/i/VztvnIRoskErqPQzBeV+ZvdOWlf1M337k2TDzwAUX9TWNSrISCc1ai/tIocC2psKT/78AxH/+Dc61+rTI+2Bb1goeSkp5KdpbfX8zl2062fdVtv168eZbduQUpJ1/LjWVn196X7TTUxc/AYTXltE//97GP/u3en/0ENangH9ST94CIDcpGRMpWXoLyMfWRuO9DM9p46tWDg9ZHAExzbt1NZoOZqI3sMdjyo3w0IIAnp25cQObX2Uo5t2EjJImzLq5W/kfPxhAPKzL3LhfAo+bVtX5E3Yat9pgp0njWPyvBeZfAk+sk3PrpX9g83bCajDRxZezKE4Lx+A0uJizUfaYU3GrpPHMn3BC0xf8AIdBkWQuFmr97Sjmk142PLvPbpyaqe2Vl3CH9vpMFDTft3b8yo+wUP6EznjNoIH9aumPXn/IVoFtGu09nJahYaRl1rZVpN276RNlbbapl8/zm03t9WE4zi7e+Dm64trK1/cjEZyk5IAbe2c8gW385KTK/KnxETh2d6x1ySvjuEUpiZTmK75y4x92zH0qV9UoL6VL64GPwpStIkJFw7vx729YxZn7zZ5bMVi6sGDIkioh820q8FmCrIvVERPph8/gTRJXL296H/rddzw7kKuf3seo/75AO16dbXboBVAq1Br/35u1y7aVrGZtv36cdbCvzu7V/r3CW+8wfhFi+j3sObf+5n9e9HFi4C2CPmxb7+l47hxdtPcFH0xgMILWhny0jM4syeKjsMi7VaGVqFh5Fu01eQa2up5c1vNTjiOi7mtlmNrmqBlW01tgrbakhCi6T4tjRYZcSWECECLVPrYvH7S3UAOWuRUOuAD5AEXzJFFU9EGQZBSnhdCnAdmAhPNx/MHiqWUXwohEoDl9tYspTxjPo9eSpkohNiKNrj2qDnJr8DDQogNUsoSIUQX4Jx5+xwhxGpzZFYgUH4XU4wW2fSrECJXSrlGCHFCCHGjlPJzoT3S7iOlrO3xwWbgQSHESrT1rcaiRUk1hp3A9cCnwN/qmafi93PE79E+og9JMXH89O+ncXbVM+jBeyv2bZ7/OoMeuBt3g4E+t9zAzreWsP/zr/HtGEzomJF15t/x1nukHTpCUU4u3z/6GD2vv5qwsaMYeN9dxKxci8lUhs7FhQH33dWoMjjpdHS7/U6iXluINJkIHDEKr8AgzmxcD0CHsePx79OX9LgYtj79ODq9np733g+Ab3gn2g4cxI5ZzyF0TvgEhxA0WptVGzhyNAc+ep9tzz2Nk86ZXvc9YPdoCCedjr533ca2BYvAZKLj6JH4BAVyYv1GAELHj6VtRB+SY+P4/bGn0On19H9gBgAZR49xZut2fDoEseG/2hh1j5uup11EX87v2UfsytUU5+Sw49U3aNWxA8Ofqm3GbtNqB9jz9nukHTpMcW4uP//9P3S//hpCxoyi34y7iV+1BpPJhM7FhYgZd9tVdzkB/fqQFBPPD/96Bp2rnkgL2900/w0G338XHkYDEbfcyLa3lhD32TcYQjpULLpekH2BX5+dQ0lBAUIIjvy8jisWzsHFw50Bd9/KjreXUlZahldbf4ZYHLspWTHzOkZGdMS/lQfHP/sXc5ZvYsVPMc2ipTbKpOSVDUdZcn1fdE6Cr/cnkZCRz019tA7bZ3HnmdSlNTf3DaTMJCksLeOJHw80qUYnnY6ut91J1Ouanwko9zObzH5mjNnPxMew7RnNz/Qw+xmA2P+9SUluLkKno9ttd+HiqS02fHjNCkwlpexbNB+AVmGd6HHnPXbX3uvOO9i5YCFSmugwahTeQUGc3KAFEoeMG0ebvn1JjY1jwxNPoNO7EnFf3TdWwaNGEfPBB2x65r8IZ2f6PXD/ZeUjofn9TIf+vTgTFc+nj87E2VXP6P+rvN798vJbjHz4DjyNvgy+4zo2vP4Bez/5Fr+QDnQdrz1363fDFfzx9nK++M8skDD49mtx89EWmy8tKuZc3CFGPni7Q7S3N/vIH//1DM6uegZb9Q/eYND9d+FuNND3lhvZ8dYS4j/7Bt8qPvJ3Cx959Od1TF04h8KsbHa9+yHSJJHSRPCQQQT0t+/aboH9enMuOp5v/vkszno9wx6+u2Lf+rmLGfrgXXgYfel/2/VsWbyU2E+/wRASTKc6Fs8uyLrAtv99hDSZkCZJyNCBBA2wn3YnnY4et93JnkULkCZJ0MhReAcGcXqj1laDx46jdZ++pMXF8sdTT6DT6+kzo7Kt9rj9DmKXvossLcO9dWv6zNB80JEvPiMvOQkhnHDz86PXXXfbOr3dEDodITffw+G3X0GaTLQZOhaPgA6kbP4dgLajJlJ8IZv98/9LWWEBCEHyxp/p89yrOLt7EHLTPRxf9jaytBRX/zaE3/mQQ/VCpc18XQ+b2bx4KTGffoMxJLhiwfVTO/dx5PdNODnp0OldGPVP+/tDWzjpdPS84w52LdSuTeX+/ZTZv3cs9+9xcWx84gl0rq70rYd/P7dzJ6fWadPU2g0cSIeRI+vI0TAc2Rfb+vr/KMrNxUmnY+A9t9X4wqiGUN5W99bRVtPjYtlsbqu9LdpqWVERGQf20/Mu6+v9UXNbRTjh7udHTwe3VcWfA2H3dRqaACHEZGAhYEIbxHkYbXraI0CSlHKsEGI5EIk27awI+E5Kudyc/2/Av6SUQ8zf+6JFYpVHoD0jpfy5hnMvR5ue+IUQ4iQwUEqZLoQYCLwqpRxTi+5VgE5KeasQYhjaFLnWUsoMoa1k+BJwJVo0VBpwjZTyghDin0C5F8hFixgrM+voJYTwBX43549Dm4rYHnABPpFSzrbUbdaSK6X0Mg9uvQWMA46az/FxeTobZbBZZiHE3ebtjwohOgMfm8vxI/CAlDJQCDEGeFxKOd18rLeBvVLK5UKIv2P+/dDWwKrX71GOramCLYHsQpfmltBgPPWlzS3hL4mbztTcEhrM/MfqMxP58iR8umM6s03BmEjHv6nKUbi5tFx7d3Zqudr93YqbW0KDuVjSIp/JAqBvwTaTnu+YSMqmIKtAX3eiy5QufjnNLaHBZBa23Hr3bsF94KwWfO8BsHhYZAuMFao/Qz/Y0WT3tDvuG9qi6rJFDlw1FvOASbTFlDuFHRFCeAAFUkppHiS8RUp5tSPPqQaumh41cNU8qIGr5kENXDUPauCqeVADV82DGrhqHtTAVfOgBq6aBzVwdXmjBq5qpuVe3RuIEGIf2jTCx5pby5+YAcDb5miubKB55g8pFAqFQqFQKBQKhULRAnBqiYtPNRF/uYErKWW9Vk0UQrxD5aLn5SyWUtb6Sh4hxD3AP6ts3ialfKT+KpsfIcTXQNX3xj4lpfy1rrxSyi1oi6srFAqFQqFQKBQKhUKhUDSYv9zAVX1p6ECTeWDLfu+bbiaklNc2twaFQqFQKBQKhUKhUCgUf23UwJVCoVAoFAqFQqFQKBQKRTOiZgrWjFPdSRQKhUKhUCgUCoVCoVAoFIqmR0VcKRQKhUKhUCgUCoVCoVA0I8JJhVzVhIq4UigUCoVCoVAoFAqFQqFQXJaoiCuFQqFQKBQKhUKhUCgUimZEqEWuakQNXCn+FBSXtczgwQsFLs0tocF4uJQ1t4QGo3OSzS2hwQjRcrWHTx/Z3BIaTMIPW5pbQoMZEzmluSUoWhhOLdjPtGRa8u1KS+2HQcvWXlima24JDabM1HItviX3xVpurSv+6qiBK4VCoVAoFAqFQqFQKBSKZkQFXNVMy33EoFAoFAqFQqFQKBQKhUKh+FOjIq4UCoVCoVAoFAqFQqFQKJoRtcZVzaiIK4VCoVAoFAqFQqFQKBQKxWWJirhSKBQKhUKhUCgUCoVCoWhGhJOKuKoJFXGlUCgUCoVCoVAoFAqFQqG4LFERVwqFQqFQKBQKhUKhUCgUzYha4qpmVMSVQqFQKBQKhUKhUCgUCoXiskRFXCkUCoVCoVAoFAqFQqFQNCPqrYI1owauFH85UuLiiF+1Bmky0XHMKLpcOd1qv5SS+FWrSYmNQ+eqp/8D9+EbEkJ+RgZRS96n8MIFhBCEjB1D+ORJABz66mtObfoDvbc3AD1uvIF2EX0dWo7sAzGc+mIF0mSizfBxBEy62mp/QfI5Ej9+j7wzJ+hw5c20n3Cltj3lPMc/XFyRrjAjlaArbqT9uGkO1dvQei8rLmbLy3MxlZQiTWUEDBpE9+uvBeDcrt0c/vobcs4nMfrF5zGEhTpEe3JsPLFm7aFjRtH1qiuqaY9duYbk2Dh0ej0DH5yBITQEgL1LPyQ5OhZXHx8mzn/JKt/xX9eR8Pt6hJMT7SP60vvWm+yuXUrJvhVrOR8dj7OrniEP34sxtGO1dLmpaWxbvJSivDyMIcEMffQ+dM7OnNi6k0Pf/QyAs6sbg+67HUPHDgAc/vE3EjZuBcA3OIghD92DTu9i9zJUZXiIkafHdkYn4Mv9SXy4+7TV/kFBvrx5TW/OXSgAYN2xdN7bedLhuhrCe09eydQhXUjLzmPgve81txwA0uPjOLJ2FVKaCBw5htBpV1rtl1JyZO0q0uNj0eld6XnvA/h0DAHg9O+/cnbzRgACR42h48QpABz/+gvSYqJACPTePvS89wHcDAa7a0+Ni2P/x6uRJhPBo0fT2YafOfDxalJiY9G56om4/358Q0Iq95tMbH7+BdwMBiIf+49V3oSffuLgJ58y6Z23cTX7ekeREhtPnIW/tOVz4latISVG85cDHpiBb6h2ndr33gcW16nRdJoyyaFay/Vs/+gzTkftx1mvZ8zf76J1WHC1dBdT0ln/+gcU5uThHxbMuH/cg87FmaLcPDa9s5KLyeno9M6MeeROjMGBZJ9LZt1rH1jlH/i3K+kzfbxdtUevWEtSTDw6vZ7BtfjIHW8uoTgvD0NIRyIf0XzkxXNJ7F7yEVknTtP75mvpNn2KVT6TycTv/52Nu9HAqCf/aTfd5dr3LP+Ec9Hx6Fz1DH/4HvzCqmvPSU1jy+L3KcrNwxgazIhHZ6BzrrwFSD9+gp9nzmXUvx6k45ABABz6aR3H1m9BIuk8bhQ9rphgV+0Z+2M5unYV0mQiYOQYQqZdVa1sR9euIiM+Bp3ele73PoBPR+0af3rdL5zfvAmQBIwcS/DEyjo/s/43zm74DaHT4dc7gs433mJX3bVx4WAMZ79cBiYTfkPH027SNVb7M/dsIXndtwDoXN3ocNN9eASFNJk+MNv7yjUkl9v7QzMw1GDvO996j+LcPAyhHRn8f/ejc3auMX9+Ria73v2AwmzN94SNG02XqRPtqj0tPo5Daz5GmkwEjRpN+BXVr02H1nxMWpx2beo9435amf17SX4e8cs+IvfsWRDQ+977MHTqzLFvvuLMH5X99y7X30ibvvbvvzuyL/bto0/h7O6GcHLCSefElFees6t2R9T7xdOn2L9iOaaSEoTOiZ533IVvWLhddSv+fKiBK8VfCmkyEbtiFcOfegJ3o5FNz8+iXf9++AQGVqRJiY0jNyWFCa/OJyshgdhlKxk963mcdDp63fo3fENCKCkoYNPzL9K6V8+KvOGTJ9P5iqlNVo6Tn31Et78/i97XjwML/otv7wF4tA+qSOPs6UXHG+8mK3aPVV73tgH0/u/8iuNE//dhjH0HOVxvg+vdxYURzzyFs5sbptJStsx5hbZ9e2Ps1AmfoCAG//PvxHy03KHaY5avYsQzj+NhNLLhudm07x+BT1Cl9uTYOHKTU5i8aB6ZxxOJXraKcbO1jkPHkSMInzieve99YHXc1AOHOL8vmglzZ6NzcaHwwkWH6D8fE09OUipXvvEKGccT2fPBx0x++dlq6WLWfEnXKyYSMmwwuz9YReKGLXSeNBav1v5MeP5J9F6enI+OZ/fSlUx++VnyM7M48ssGrlg0G2e9nq1vvMep7bsJGzPcIeUox0nAzPFduP+LGJJzivj0toFsPJ5OYma+Vbqos9k88k28Q7XYg1W/xPLe13v44JlrmlsKoNn74dUr6P/YU7gZjOya8zytI/rjFVBp7+nxseSnpDD8lVe5kJjAoVXLiJw5i9yzZzi7eSORM2chnJ2Jfn0h/n0i8GzbjpApV9Dp2hsAOL3uVxK//4Yed95jd+3xK1cy5MkncTca2fLCi7Tr3w9vCz+TGhdHbkoy4xYuIDshgfjlKxj54gsV+xN//Q3vgABKCgqsjl2QkUHa/gO4+/nZVXNN5YhdsYrhTz+Ou9HIxudn035ARDV/mZecwsRF88hKSCRm+SrGzHoOJycdvW+9Gd9Q7Tq18blZtOnd0yqvIzgTtZ8LSan87e3ZpB47wdala7h23tPV0u1a9RW9p4+n04hBbF6ymsPrt9FzymiivvwFv9AOTH7qYbLOJrP1g7Vc+eK/8Q1sxw2LZgJgKjPx8QNPEzo4wq7ak2LiyUlOYdrrmo/c9+EqJr40s1q6uDVf0HXaRIKHRbL3g5Wc2LiFThPHovfypN9dt3Jub7TN4x/7+Xd8AqvblD04F7Ofi8mpXLP4ZdKPJbLrw9VMe/m/1dJFrf6S7tMmEDp8MDvfX8XxDVvpOmkMoA2sRa35koC+PSvSZ50+x7H1W5j2yn9xcnZm3SuLCerfG5/2be2iW5pMHFm9gn7/eRpXg5E9Lz2Pf8QAKz+TER9LQWoyQ19ZxMXEBI58vJxBz84i99wZzm/exKBnNT8T88YC/PtE4NG2HZmHD5IWs4/IF+fi5OJC8cULdtFb3zKd+fxDOj8yExdfP44sfIZWvQfibtEv0/u1ocs/X8TZw4sLB6I5/clSuj3+SpNpBEiOiSc3OYWpr80l83gi+z5ayYQ51Qc64tZ+TpepkzR7/7DS3mvKL5yciLjtZgyhHSkpKOD3Z2fTtncPWgXZx/dIk4kDq1Yy+PEncTMa2T77BdpE9Lfy72lxceSlpDBq3kKyExM4sGo5w557EYBDqz+mda/e9H/k75hKSykrLqrIFzJpMmFTHfvw1lF9sXLGP/c4bj72f6DiqHo//NmndL76Glr36UtqbCxHPvuUyKer+y6FwhK1xpUNhBD/EkJ4NMF5Zgsh7PsIq2E6QoQQ++1wnJ+EEL51pNkkhBhoY3uEEMKxVw0gKyERr7Zt8WzTBidnZ4KGRJK8z7qzmRwVTfCI4QghMHbqREl+PoXZ2bj5+lY8mXdxd8c7IIDCzCxHS7ZJ7snjuLVuh5t/W5ycnTEOGEZW3F6rNC7erfDqGI7Q6Wo8zoUj8bi2bourX2uH6m1MvQshcHZzA8BUVoaprAzQwmi9AwPwbt/eodozExLxbNsGrwrtgzlfRXvSvmg6jhyGEAK/zuGU5OdTkJUNQOvuXdF7eVU7buL6jXS9aho6Fy1Cya2Vj0P0n9sbQ+iooQgh8O8cTrGFtnKklKQcOExwpPakPXTUMM7sjdH0d+2E3ssTAP/OYeRb2LwsK6OsuBhTWRmlRcW4G3wdUgZLerfz4XR2AWcvFFJqkvx8JIVxnfwdfl5HsS3uNJkX7X9D21AuJCbg0aYtHq01e283eAhp0fus0qTFRNF+2AiEEPiGd6I0P5+i7Gzyks7TKrwTOldXnHQ6DF27kRal+SVnd/eK/GVFRQ5ZfDQrIRHPNpV+JmBIJMlRUVZpkqOi6DBc8zMGCz8DUJCZSWpsLMFjRlc79oE1a+j+t5ubZNXUcp/jaeFzkmz4nA4jhpn9ZTglefkUZmXjZvDF1xztqV2n2lOYme1wzSf3xNFl9BCEELTtEkZRXgF5WdaDBlJKzu8/QtjQ/gB0GTOUk7tjAcg+m0Rg724AGILakZuaQX629WD+ufjD+LT1x7uNfQcPz+2LIcTsv/2r+G9L7SkHDhMUqXVfQkYNqxiocmvlg194KE42rrX5GZmcj44jbOxIu2ou58yeGMJHafXeuks4xXn55NvQnnzgSEUkVfjoYZzeU2lPh3/eQHDkANxaVd70XjiXhH/nMJzNbbldjy6c3m17YK4hXDyRgHubtrib/UzbwUNIj6nqZ/bRbqjmZ1qFd6I0P4+i7CzNz4SFV/qZLpV+5tymdYRMvRIn83VV79PKbprrIu/UcVz92+Fq7pcZBgzjQrz1g0OvsK44e2j9Ac/QzpRkZzSZvnLO7YuusPeq/ZVypJSkWtr7yGGc2xtVa353g29F5JaLuzs+ge2rHbcxZCcm4NmmDR5mv9h+8BBSo639e2p0FIHDzP7dfG0qzM6mpKCAzKNHCBql+XYnZ2dcPDztpq0+OLIv5kgcVe8CKDUP5pcW5OPq69sk5WkJCCGa7NPSUANXtvkX4PCBKynl81LKdY4+T1MhpZwmpcxuYPYIwOEDVwVZWbgbjRXf3YwGCrKy6k5T5QKRl5bGhVOnMHSqDGtNXLeODf+dSdT7H1Kcl+egEmgUZ2eiN1R23vW+RkqyMy/5OJl7d+A3YJg9pdmksfUuTSY2PPscPz/yD9r06omxU9OFExdkZuHhV6nL3Wisrj0zG3erNAYKs2rvVOQmJZNx+Cgbnp/DH3PmkZmQaF/hZvIzs630exgN5Fe5kS3KycXFw73ixsvDhs0DJGzcSkBEr4o03aZP5ttHnuLrhx7DxcOd9hZP7B1FGy9XknMKK76n5BTRxsu1Wrq+Aa348o5BvHtdH8L9HO7O/zQUZWfhatEOXQ1GirKtbaEoKws3y7ZqMFKYnYlnYBDZR49QnJtDWVER6XGxFGZW+qXjX33O5sf/SdLO7YRfc73dtRdmZVm1QzejsVo7LMzMws1Y6TvdjcaKBxAHVq+m+803VRucSo6Kws1goFVw9alvjqCwii90t1GOgqzqPqeqX8pLS+fCqdMYwsMcKxjIy8zG079y6qenny/5GdlWaQpz8tB7elT4GS8/X/LMvsgYEsSJndrASOqxE+SkZZKXYV2ehG176TTC/tHB1X28gYIqPrI4J9dKu4efsV43jtErP6HvrTcinBxzg5CfZa3dw8+2f9dX8+9amvzMLM7siabLROvBWt8OgaQcPkphTi6lRUWcjY4nL+PS+xg1UZiVhZuhip+pYr9F2dZttdwXeQUEkXXsCCXlfiY+lsIsbQAoPyWZ7GNH2PPyC+xb8BIXTyTYTXNdlFTpl7n4+tXaL8vYsQGfHv2aQpoVVftatvo0tuy9fJClPvnz0tLJPnkaPzv6nsIsa3uw6d+zM6tdm4qyMilIS0Xv7UP8h++z9YWZxH/0IaVFlRFXp9evY+tzzxL34fuUOKj/7qi+GABCsPGV1/n5mdkcX/eHXXU7qt6733obhz/7hI3/+ReHP/2ErjfYf6kMxZ+Pv/zAlRDCUwjxoxAiVgixXwjxAhAAbBRCbDSneVcIsVcIcUAIMcu8bbwQ4muL40wUQnwlhNAJIZabjxUvhPh3LedeLoS4wfz/SSHELCFElDlft1ryxQshfIVGhhDiTvP2VUKICWYNC4UQe4QQcUKIBy3yPmGxfZaNY4cJIaKFEDZ7h0KIu83l/EUIcUwIscBi30khhL/5/+eEEIeFEL8LIdYKIR63OMyNQojdQoijQoiRQgg9MBu4WQgRI4S4WQgx2vx/jFlPtfhXIcQD5t9lb8zX39RUXdZIaaNQdaexHJUuLSxk95tv0/u2W3ExRxGEjh/HpEULGfvSbNx8W7F/zSf102NPLnHk3FRaSlb8Pvz6D3GQIAsaWe/CyYlxL89h8uLXyEpM5OKZsw4QWX+qP6WwVb7afw9pMlGcl8/YWTPpfetN7HrrXaStemo0tuq1HtmqJEo5cJiEjVuIuFWb7lWcm8e5fTFc9dY8rn33VcqKijixZYc9BF+KLKB6CQ+m5jDx/R1cv2oPa6LP8ubVvR2u60+DTRsUdaYRCLwCAgmZegVRi+YT9fpCvDoEI3SV3YxO193IqFcX037IMM6s/93OwsFmO6zmaGwnSYmOQe/tg2+o9Tp5pUVFHPvue7ped519JNYHm26g7t+AqtepxW/T+/ZbcPFwr57W3jTYx2t/+107maK8fL547CX2/7QJ/9AOVhFMZSWlnNoTS9iwAXYUXbOuatVtI1tdT6vPR8Xi6uONMSykwdLqxKYZ1MfmtTR7ln9K/1uvw8nJ+nbAN6g9va6awrqXXmfdK4sxdgyyGVHWcBpmLyDwDAgkZMp0ol+bR8wbC/DuEIxw0rTJMhMleXkM/O+LdLrhFuKXvO2g66ot6t8PyDm6n/QdGwm8+jYHa7JBPfyLrK3fUEf+ksJCtr/+DhF3ON731KcrhhDIsjIunjpJ8NjxjJj1EjpXVxJ//B6A4LHjGb3gVYbPmoObry+HPlnjILWO6YsBTJz1NFPnPc/Yp//F0d82knroaGPFXoqkBtX76Y0b6H7LbYx97Q2633Ir8cs+sHGQvyZOouk+LQ21xhVMAc5LKa8AEEK0Au4Bxkop081pnpVSZgohdMB6IUQfYAPwjhCitZQyzZxnGVrkUKCUspf5eL6XoCVdStlfCPF/wOPAfTWk2wYMB04BicBIYCUwBHgYmAFckFIOEkK4AtuEEL8Bnc2fwWhXme+EEKOA02atXYFPgHuklDG16IwA+gFFwBEhxFtSyjPlO4U2FfB6cxpnIAqwjAF3llIOFtrUwBeklBOEEM8DA6WUj5qP8T3wiJRymxDCCyikClLKpcBSgKd276hXz8TdaKTAIgKgMDMLd19DnWnczFOgTKWl7H7zbToMG0rAoMoZj26tKsPRO44Zzc5Fb9RHToPR+xopzqoMMS/OzsSl1aUtdJx9IAaPDiG4+PjaWV11Glvv5eg9PfHv1o2UuHh8OgTRFLgbDeRbPGkuyMzErUpIs7vRQIFVmqxqaWwdN3DQAG2qT3gYQgiKc3Jw9Wn8lMGjv27g+IYtAPiFh1jpz8/Mqjalz9Xbi5L8AkxlZTjpdNXSZJ06w64lKxjz9D9x9damOSTvP4Rna/+KNRWCBvcn/WgCoSOHNlp/baTkFNHO263ie1tvV9Jyi6zS5BWXVfy/5UQmM8cLfN1dyC4ocai2PwOuBiNFFu2wKCuzWgi/q9FoFUlVmJWJq7k9B44cQ+DIMQAc+/Izq6iKctpFDiN68at2j7pyMxit2mFhZmY1H+JmNFCYWek7CzIzcTMYSNqzh5ToaNbFxWEqKaGkoICo996j0xVXkJ+Wxh8zn6s45ubnnmfkiy/U2cYbXA6jwcoXFtgohy2f427WYyotZdfitwkaNpTAQdVm5tuN/T9v4vA67eUMrTt1JC+98il8XkY2HkZrzW4+XhTn5Vf4mdyMbDzM5dJ7uDP20bsAbbrMmoeftZoSeCZ6P/5hwXj42mdK9bHfNpC4YTMAxrCQKj7eto+01J6fkVnn1Oj0I8c5HxXL9zHxZpsqZOfb7zPk0fsbpf3wrxs5tl7T7hceau3fM7JwN1hPj3P19qK4mn/X0mQknmTzm+8DUHQxl3PR+xE6J4IH9aPzuJF0HqdNcYxa+xUeRvu9TMHNYKQwq6qfsT6+q8Fo1VYtfVHAyDEEmP3M8a8+rfAzrgYDbfoP1KYXhoUjhKAkNwe9t2Om4lvi4utn1S8ryc6w2S/LP3eKU2uX0OnhZ3D2dOxLHso59tt6TmzUbMYQFlrNv1S3d+9q9l7u79xt+Cd3iz7y9tffIXj4EIIG23eQ2c1g7bsLM6vbjJvB9rVJCG2fb7gWrd9u0CASf/xBK6tF/z1o9Bj2vfGa3TQ3RV8MqPC1bq18CBrUj4zjJ2jTvYtdyuCoej+3bSvdb73dvH0w8cs+tItexZ+bv3zEFRAPTBBCzBdCjJRS2lrJ8SYhRBQQDfQEekjtEc4q4Hbz4NRQ4Ge0gaQwIcRbQogpwKWsuPyV+e8+IKSWdFuAUebPu0BvIUQgkCmlzAUmAXcKIWKAXYAf2oDVJPMnGm0wqZt5O0Br4Fvg9joGrQDWSykvSCkLgYNA1ddijAC+lVIWSClzgO8bUM5twGtCiH8AvlLK0jo01QvfsFByk1PIS03DVFrK2Z27aNffOlS7Xf8ITm/dhpSSzOPHcfZwx83XV3uTygcf4RXQnk5Trd8aVL5GCkDS3iirhbsdgVfHcApTkylMT8VUWkrmvu0Yel9aJyFj3zb8Bzp2Ie1yGlPvRRcvVky9LCsuJu3AQbwDHLuulSWGsFByk1MttO8mYIC19vb9+3Fqy3aklGQcS8DF3b3Om5qAAf1JPXgIgJykZEylpRVvtWksXSaPY9r8F5g2/wWCBvbjxOYdSClJP5aAi0d1bUII2vToyuld2vjyic3bCRoYAUBeegZbXvsfQx+ZgU9Au4o8Hn5GMo4nUlpUpK3LsP8QPoGO/132J+cQ7OtOoI8bzk6CqV3bsjEh3SqNn4e+4v9e7bxxEkINWtUTn9Aw8lOSKUjTfEvy7p20juhvlaZ13/4kbd+KlJLshOM4e3hU3FCWL4ZckJFOatRe2kVqA5l5KckV+dNio/BsH2B37b5hoeSlpJCfprXV8zt30a5fFT/Trx9ntml+Juv4cVzMfqb7TTcxcfEbTHhtEf3/72H8u3en/0MP4dOhA5PfeZsJry1iwmuLcDMaGTVntsMGrcC2z2nfv7rPObN1u9lfau3azaBdp6I+WIZ3QACdp012mEaAXlPHcMOimdywaCYhgyM4+sdOzRccTUTv4YZnlQEUIQQBvbqSuENbH+Xoph2EDO4DQFFePmUl2mX+8LqttO/RGb1FtMbxrXsJt+M0wc6TxjF53otMnvcigQP7cdLsvzUf6WHbR/bsytld2lpKJzdvJ2BARK3n6HPL9Vz1zqtc+dYChv7jQdr07NboQSuAbpPHcuWCF7hywQsED4ogYbNW72lHNTvwsKG9XY+unNqp+feEP7bTwezfr3t7HtebPx2H9Cdyxm0ED9JsrcD8wpDc9AxO744mdPjgRmsvxzvE2s+k7N6Jf98qfiaiP8k7ND9zIeE4zu4eFTfM5X6mMCOdtKi9tB2sLXnQut9AMg8fBCA/OQlTaSkuXk0zOOQZHE5RWhJF5n5Z1r7ttOptPXBcnJnOiQ9eJeSOR3FrY38fWBOdJ41n0txZTJo7y8retf5KDfbeo1ulvW/ZTuBAzS4CBkTYzC+lZM/SZfgEtqfrFfb3Pa1Cw8hLrfTvSbt30qaKf2/Trx/ntpv9u9lm3Hx9cW3li5vRSG5SEgAZBw/gFaDVv2X/PWXfPrwD7fdQtCn6YqWFRZQUFFb8nxx3kFYd7HcP4qh6d/X1JfPIYW37oYN4tm2HQkM4iSb7tDT+8hFXUsqjQogBaOsrzTVHJlUghAhFi34aJKXMEkIsB8of9y9DG5QpBD43D65kCSH6ApOBR4CbgHvrKac8bKCM2n+bzeZjBwPPAtcCN6ANaIEWTfV3KeWvVcoyGZgrpVxSZXsIcAE4gxbJdaCeOmvSWldLqLOcUsp5Qogf0X6XnUKICVLKw3Uct06cdDr63Hk72xe+qr1mfNRIfIICObF+A6BN+Wvbty8pMXH8/viTOOtd6Xf/DAAyjx7jzLbt+HQIYsOz2tP3HjfeQLuIvuz/5FMunjoDAjz8/Ym49+7GSq0VodMRctM9HHnnFaTJROuhY/EI6EDKFm36TduREym+kM3+Bf+lrLAAIQRJG3+mz8xXcXb3oKy4iIuH4wm9pfGd6PrQmHovzL5A1NL3kSYT0iQJjBxMu34RAJzfu4+4lR9TnJPDzkWv06pjMMOefLwmGQ3WHnH3bWydvwhpMhEyWtOeuG4jAGETxtIuog/JMXH8+p+n0On1DHxwRkX+XW+/R/qhwxTl5PLTo/+h+w3XEDpmFCFjRrJ36Yf8/tRMnJx1DHzoPocslBjQrzfnY+L5/p//ReeqZ8hDlW9y2zjvDSIfuBsPoy/9br2BrW8uIe7TrzGEBBM+dgQA+7/8nqLcPPZ8tNpcH9qrlv07h9EhcgC/PDMH4eSEISSYTuNH2V1/Vcqk5JUNR1lyfV90ToKv9yeRkJHPTX20ztBnceeZ1KU1N/cNpMwkKSwt44kf63JpzceKmdcxMqIj/q08OP7Zv5izfBMrfoppNj1OOh1db7uTqNcXaq+pHzEKr8AgzmxaD0CHMePx79OX9PgYtj3zODq9nh73VvqR2P+9SUluLkKno9ttd+HiqS3EevyLT8lLTkI4OeHm50f3O+z7RsFy7b3uvIOdCxYipYkOo0bhHRTEyQ2anwkZN442ffuSGhvHhieeQKd3JeK+mgKbmw8nnY6+d93GtgWLwGSi4+hyf6n5nNDxY2kb0Yfk2Dh+f0zzOf0f0HxOxtFjnNlqvk7993kAetx0Pe0i7P96d0uC+/fidNR+PnnkOZxd9Yx55K6KfT+99Baj/+8OPI2+RN5+Lete/4A9a7/DP7QD3cZrD0+yziaz8c1lODk54duhPWP+746K/CVFxZyNPcTIBx0zrap9vz4kxcTz47+ewdlVz+AHK7tsm+e/waD778LdaKDvLTey460lxH/2Db4hHSoWXC/IvsDvz86hpEC71h79eR1TF85pkimagf16cy46nq//+SzOej3DHr67Yt/6uYsZ+uBdeBh96X/b9WxevJSYT7/BGBJM53Ej6jz2H6+9S1FOHk46HZH33oqrl/0Ws3bS6eh6611Ev7EATCbaDx+NV2AQZ81+JmjMePx6R5AeH8uO/z6Gk15Pj3seqMgf9+5iSnJzcdI509XCzwSMGM2hZUvZ+fzTODnr6HHvg022ALHQ6ehw470c/9/LSGnCb8hY3Nt3IG2rdlvResQkkn75gtK8XM58pk2LEk46uj05r0n0ldM+og9JMXH89O+ncXbVM8jK3l9n0AN3424w0OeWG9j51hL2f/41vh2DCR0zstb86UeOcWrrDlp1COK3Z7Q3tfa+6Xra9+tjF91OOh09bruTPYsWIE2SoJGj8A4M4vRGzb8Hjx1H6z59SYuL5Y+nnkCn19NnRqV/73H7HcQufRdZWoZ769b0maFdt4589gkXT59GCIG7vz8977L/tQkc1xcrvHCRzYveAbQlKDoOH2y9/lUjcVS997r7Xg6tWY00leHk4kKvux1T74o/F6Lp5n5fngghAtAilQqFENcAdwPhwFVSyhPmQaiVaNPeWgNxwFNSyuXm/N8D/YGJUsqD5jWeiqWUF4UQEcByKWVEDedeDvwgpfxCCHESbapcunmq3atSyjG16D4KXJRSDhRCPAU8CjwqpfxWCPEA2oDPjVLKEiFEF+Ac2qDUHGC8lDLXHKVVgrYQ/Q9AJPAr8D8ppc1J3kKIu7Ge0veDWeum8jIAocASYBjawNQ+4H0p5atCiE3A41LKvea62iulDBFCXG+u87vMxw2XUiaY///GXI/f1FQf9Z0qeLmRdNGt7kSXKe19qs3ebDHonFqkuQDgqiurO9Flyucb7BI42Swk/LCl7kSXKTPmTKk70WWKm4upuSU0GGenlqu9jXtR3YkuU7KLXZpbQoNxbcE2cy6nCdZScxBZBfq6E12mdPLLbW4JDSanqOXGTxjci5tbQoNpyfYO8MawyJYXKnQJTPkipsluUn65IaJF1WXL9Rj2ozewUAhhQhvEeRjztD8hRJKUcqwQIhotCikRbQqbJauB1lLKg+bvgcAyIUT5NMxnHKR7F1C+UuYWYC6w1fz9A7QpeFFCe9SUBlwjpfxNCNEd2GF+ApUL3I4W+YSUMk8IMR34XQiRJ6X8tiHCpJR7hBDfAbFo63DtRYvoqo2NwNPm6Y1zgRFCiLFmbQfRpmEqFAqFQqFQKBQKhUKh+Avxl4+4aixCiLeBaCmlWlXOAiGElzmqywNtauMDUsooR51PRVw1PSriqnlQEVfNg4q4ah5UxFXzoCKumgcVcdU8tOQIFBVx1TyoiKvm488ecTX1y6aLuPr5ehVx9ZdBCLEPyAMea24tlyFLhRA90NYDW+HIQSuFQqFQKBQKhUKhUCgUf07UwFUjkFLW6zVuQoh30NaXsmSxlHJZHfnuAf5ZZfM2KeUj9VfZMMwLuc+vsvmElPLa+uSXUt5qf1UKhUKhUCgUCoVCoVD8+WiqF0q0RNTAVRPQ0IEm88BWrYNbjsL8RsJf60yoUCgUCoVCoVAoFAqFQuEg1MCVQqFQKBQKhUKhUCgUCkUzIpxUxFVNONWdRKFQKBQKhUKhUCgUCoVCoWh6VMSVQqFQKBQKhUKhUCgUCkUzopa4qhkVcaVQKBQKhUKhUCgUCoVCobgsURFXij8FxWUtcwzWzaWsuSX8JSkztdzHGaWiZdo6wJhIz+aW0GDGRE5pbgkN5sPnfmluCQ3mH/MnNreEvyRuupZ7bZJS39wSGoyHc8ut90DvguaW0GC8XUubW0KDEUI2t4QGczrbo7klNBgPfcttq/kluuaWoFA0iJZ7B6RQKBQKhUKhUCgUCoVC8SdACNFkn3pomSKEOCKEOC6EeNrG/tuEEHHmz3YhRF+LfSeFEPFCiBghxF571I2KuFIoFAqFQqFQKBQKhUKhUCCE0AHvABOBs8AeIcR3UsqDFslOAKOllFlCiKnAUiDSYv9YKWW6vTSpgSuFQqFQKBQKhUKhUCgUimakPpFQTcRg4LiUMhFACPEJcDVQMXAlpdxukX4nEORIQWqqoEKhUCgUCoVCoVAoFArFXwQhxANCiL0WnwcsdgcCZyy+nzVvq4kZwM8W3yXwmxBiX5XjNhgVcaVQKBQKhUKhUCgUCoVC0Yw4NWHAlZRyKdr0PlvYUmLzbRBCiLFoA1cjLDYPl1KeF0K0AX4XQhyWUm5ujF4VcaVQKBQKhUKhUCgUCoVCoQAtwqqDxfcg4HzVREKIPsAHwNVSyozy7VLK8+a/qcDXaFMPG4UauFIoFAqFQqFQKBQKhUKhaEaEkE32qYM9QGchRKgQQg/8DfjOWqsIBr4C7pBSHrXY7imE8C7/H5gE7G9s3aipggqFQqFQKBQKhUKhUCgUCqSUpUKIR4FfAR3wkZTygBDiIfP+94DnAT/gf+ZF5UullAOBtsDX5m3OwBop5S+N1aQGrhQKhUKhUCgUCoVCoVAompHL56WCIKX8Cfipyrb3LP6/D7jPRr5EoK+99aipggqFQqFQKBQKhUKhUCgUissSFXGl+EuTFhfHwTUfI00mOowaTfj0K632Syk5uPpj0uJi0eld6XPf/bQKCQGgJC+P+GUfkXP2LAjoM+M+DJ06N5n2zP2xHP90FdJkov2IMQRPvcpqf37SeQ6vWELu6ZOEXnMTHSZdAUBhZgaHP3qXkosXQAjajxpH0PgpTaYbICUujvhVa5AmEx3HjKLLldOt9kspiV+1mpTYOHSuevo/cB++ISGUFRez5eW5mEpKkaYyAgYNovv11162evMzMoha8j6FFy4ghCBk7BjCJ0+qyJfw2++c+H09QudE27596XXLzXbRK6UkeuUakmPi0en1DH5oBobQjtXS5aamsfOt9yjOzcMQ2pHB/3c/OmfnWvPvXvIRSdGxuPr4MGXBHKvjHft1Hcd/W49w0tG+Xx/63npTo8qRHh/HkbWrkNJE4MgxhE6r3j6PrF1FerzWPnve+wA+HUMAOP37r5zdvBGAwFFj6DhRs/Gjn60lLTYaJ2dn3Fu3oee99+Pi4dkonU2l/fjXX5AWEwVCoPf2oee9D+BmMNhde31578krmTqkC2nZeQy89726MzQxLc7PxMYTZ6G361VXVNMbt2oNKTGa3gEPzMA3NASAfUs/JDlGa5cT5r1UkSf71GliPlqBqaQEodPR9+47MIaHObQcUkr++OBzTu47gLOrnkn/uIM24cHV0sX+uIno7zdyITmdB1bOx93HC4Cz8Uf5fu4SfNr4AdBpaASRN0+zqz5H+cfivHz2vr+MC2fOgRAMeuAe/Lt0Yseb75KTlFyRRu/pwaS5s+xWpvJybfvoM05H7cdZr2fs3++idVj1et//00biftzAxeQ07lr2akW9Z51NZtM7K0hLPMPgW68i4upJ1fLaW++e5Z9wLjoenaue4Q/fg19Y9d8hJzWNLYvfpyg3D2NoMCMenYHO2ZnkA0fYuPAdvMx2Ejy4P31vuJK89Ey2vvMRhdkXwEnQZfwouk+bYFftjugTHPrqa05t+gO9tzcAPW68gXYRjQ9YkFISvWItSeX2+vC9GGuw9x1vLqE4Lw9DSEciH7mv0t5ryH/kp99I3LAFBPh2CGLwQ/ei07sQ/9nXnNsbg3ASuPp4E/nQvbgb7Xut6ufvxb09AnASsO5MFl8nplntHxXgyzVh/gAUlplYuv88J3MKcXESvDQkDBcngZMQ7Ei+wKfHUu2qzRbJsfHEmm0mtAb/HrtyDcmxcej0egY+OAOD2b/vXfohyeZ+18T5lf794JffcGLjH7iababnzdfT3g42Uxst+d6jpeBU99pTf1nUwJXCIQgh7gYGSikfrSVNCPCDlLKXECICCDCHJDYJ0mTiwKqVDH7iSdyMRrbNeoE2/frjHRhYkSYtLo78lBRGz19IdkIC+1cuZ/jzLwJwcM3HtO7dm/6P/h1TaSllRUVNJR1pMnFszXL6/PsZXA1Gol55Dr++/fEMCKpI4+zpSae/3UlG9D6rvMLJifAbb8O7YyilhQVEvTQTQ/deVnkdrT12xSqGP/UE7kYjm56fRbv+/fCxqPeU2DhyU1KY8Op8shISiF22ktGznsfJxYURzzyFs5sbptJStsx5hbZ9e2Ps1Ony1KvT0evWv+EbEkJJQQGbnn+R1r164hMYSNrBQyRHRTP2lTnoXFwounDRbpqTY+LJTU5h6mtzyTyeyL6PVjJhznPV0sWt/ZwuUycRPCySvR+u5MTGLXSaOLbW/KGjhtN50nh2vfuB1bFSDxzi3N5oJs2bjc7FhcJGlkeaTBxevYL+jz2Fm8HIrjnP0zqiP14BlfWeHh9LfkoKw195lQuJCRxatYzImbPIPXuGs5s3EjlzFsLZmejXF+LfJwLPtu3w69GLTtffhJNOx7HPP+Hkj9/T+ca/NUprU2kPmXIFna69AYDT634l8ftv6HHnPXbVfims+iWW977ewwfPXNNsGmqixfqZpx/H3Whk4/OzaT8goprevOQUJi6aR1ZCIjHLVzFmltYuO44aQfjE8exdYt0uD6z9jG7XXU27vn1IjonlwNrPGDnzaYeVA+DkvgNkJ6Vx17svknz0JBve+4S/LXyyWrr23cMJHdibL2a+UW1fQI9OXD3zYYfoc6R/jF65hnZ9ezPsX49QVlpKWVExAEP/UVmWmI8/wcXDw+7lOh21nwtJqdzy9mxSj51gy9I1XDev+m/drls4wQN7893zr1ltd/P2YPiMmzmxK8bu2mxxLmY/F5NTuWbxy6QfS2TXh6uZ9vJ/q6WLWv0l3adNIHT4YHa+v4rjG7bSddIYANp078T4p/5hlV7onBh4x434hXWkpKCQH56ZQ/s+PfANCrCLbkf1CQDCJ0+m8xVT7aKznKSYeHKSU5j2+itkHE9k34ermPjSzGrp4tZ8QddpEzV7/6DS3mvKn5+ZxbFf1jPl1Tk46/Vsf+NdTu/YRejoEXSbPoXeN2mD/Ud/WceBr75n4H132q1MTsD9PQOYtfsEGYWlLBgezp7Ui5zNreyHp+QX89zORPJKTfRr7cVDvQN5ensCJSbJC7tOUFhmQifg5aHhRKflcDS7wG76qiJNJmKWr2LEM4/jYTSy4bnZtO8fgU9Qpc0kx8aRm5zC5EXzyDyeSPSyVYybbfbvI83+/b0Pqh2789RJdLGzzdRWjpZ676H4c6CmCiouFyIA+z1SrQfZiQl4tG2DR5s2ODk70z5yCCnRUVZpUqKjCBw+HCEEhk6dKM3PpzA7m5KCAjKPHCFo1GgAnJydcfG0f9RGTVw8kYB7m7a4t9a0txk0hIxY64uE3qcVPiHhCJ3OarurrwHvjqEAOLu549E+gKLsrCbTnpWQiFfbtnia6z1oSCTJ+6Kt0iRHRRM8Qqt3Y6dOlJjrXQiBs5sbAKayMkxlZYBjJ4M3Rq+bry++5gg9F3d3vAMCKMzU6vrE+g10nn4FOhcXAFxb+dhN87l90YSMHIYQAr/O4ZTk51OQlW2VRkpJ6oHDBEUOBCBk5DDO7Y2qM3/r7l3Re1W39ePrNtL9qmkV5XFrZHkuJCbg0aYtHmYbbzd4CGlVOkJpMVG0HzYCIQS+4Vr7LMrOJi/pPK3CO6FzdcVJp8PQtRtpUXsB8OvVGydzm2gV3onCrMxG6WxK7c7u7hX5y4qKmn0dhG1xp8m86LjOfmNoaX4mMyERz7ZtLPQOJqmK3qR90XQYMcysN5ySvHwKze3Sv1tXXLy8qh9YQGmB9huV5BfgZvB1aDkAEnfH0X1MJEII2ncNpSivgLzMC9XStQnrgE9bP4frqYqj/GNJfgHph48SOmYkADpnZ/SeHtWOe2bnHoKHRtq9XCf3xNFl9BCEELTtEqbVe1b1evcPC8anjX+17e6tfGjTKQQnZ121fY7gzJ4Ywkdpelt3Cac4L598G79D8oEjdBwyAIDw0cM4vSfaxtEq8TD4VkRuubi70SqwPfmZ2bXmuRQc1SdwFOf2xVTYq38t9p5iae+jhnFub3Sd+U1lZZQVF2MqK6O0uBh3s39x8ai8VpUWFtl90Z5Ovh4k5ReTUlBCqZRsTbrA4LbWfY4j2fnklZoAOJqVj5+bS8W+wjJtu04InIVAOjjApdy/e1n49/M2/HvHWvtdNvx7E9OS7z1aEqIJPy0NFXGlaBBCiG+ADoAbsFhKuVQIcQ/wDJAEHAWKzGmXo0VWfWH+niul9LI4lh6YDbgLIUYAc4FkYLE5iQRGSSlz7FmGwqws3IyVnWZ3g5HsxIQqaTJxMxorvrsZjBRmZeLkpEPv7UPcB++Tc+Y0PiGh9LjtdpxdXe0psUaKszNxtdDu6mvk4omEWnLYpjA9jdzTp/AJDbenvFopyMrC3bJOjQayEhLrTFOQmYWbry/SZGLjcy+Ql5JK2ITxGDs5Vntj9ZaTl5bGhVOnMJj15iYnk3HkKIc+/xInFxd63XozhjD7TOGpqsfdaNS2Wdy0Fufkovf0qBjE8fAzVnSS6pO/KrnJKaQdOUb8Z1+hc3Gh7203YwwPbXAZirKzcLXQ4GqobuNFWVnV22d2Jp6BQRz/+guKc3PQuehJj4vFJ6S6lnNb/6DdoCEN1tgc2o9/9Tnnt2/F2d2dgU9Wj05QaLQ0P1Noo81lJVjbTEFWNu5+lmkMFGRl1ToY1fv2W9m+YBH713yKlJLRLzxrd+1Vyc28gJd/pSYvP19yM7PxNLaq9zGSj5xg5MlVDQABAABJREFU9b9ewdPYipF3X4tfsH2iZcBx/lE46XD19mbPko/IPnUGQ2hH+t15K85ulf2C9MNHcWvlg3f7tnYrTzl5mdl4+VdOx/Ly8yUvIxtPQ/3rvSnJz8rCw8KePfwM5Gdm42HxOxTl5KL3cK/8HYwGCiwGodKOJvL9E7NwN/oy8PYb8O1QGcECkJuaTuaJM/h3avi1qCqO6hMAJK5bx5lt2/ANDaXXrX9Db4cHogWZ1vXsbq7Duuw93zygVlN+Y3gI3aZP5odHn0Snd6Ftn56069OrIl3cp19xcvN2XDzcGftc9YjLxuDn5kxGYUnF94yCEjr71hzFOKGDkei0ylsIJ2DhiE6089Dzy6lMjl1w7AOY6nVoJLOqf8+s7t8L6+h3AST8tp5TW7ZjCAuhz232sZmaaMn3Hoo/ByriStFQ7pVSDgAGAv8QQgQCs4DhwESgR30PJKUsRnud5qdSyggp5afA48AjUsoIYCRQ7aoihHhACLFXCLE3/ptvLr0E9XnCYiONEAKTqYyLp07Scdx4Rsx+CWdXVxJ/+P7SNTQUW9ov8YlWWWEhB957g/Cb78DZ3f7TFmrE1qOtqtJtpDG/UhXh5MS4l+cwefFrZCUmcvHMWQeIrF3LpegFKC0sZPebb9P7tltxMUfNyDITJXl5jHrxOXrdcjN73vof0l6P/WweRlRJYktz/fNXxVRmojgvj/GzZ9Ln1pvY8ea7jSuPzbyizjQCgVdAICFTryBq0XyiXl+IV4dghM76cpf4w7cIJx3thgxruMaacKD2TtfdyKhXF9N+yDDOrP/dzsL/RLQ4P2NrY902U5ffP7F+I71vu4Upb75G79tuIer9ZQ2WWG/qY/+10Dq8A/csnc1tb/yXvtNG8/3cpfbTBg7zj9JURtbJU4RPGMOkuS/i7OrKoe9+tEp1evsugofZP9pK01WL5suRGvpXdWJOYwwN5vp35nHlwhfoNmUcG1/9n1WyksJCNr32LoPuuhm9RQRQo3FQnyB0/DgmLVrI2Jdm4+bbiv1rPmkyvba7lKLW/MW5eZzbG8MVb87nqv8toqyoiJNbdlQk6XPzdVz1zqt0HD6E47+ub7j+emO7v9HL6Mn4DgZWHk6u2GYCHtt6nPs3HKaTrzvBXk3z0NmS6rZ+6f49bMJYpry+gAmvzMLN15e41XaymZpoyfceLQgnIZvs09JQEVeKhvIPIUT5arUdgDuATVLKNAAhxKdAl0YcfxvwmhBiNfCVlLLaXYOUcimwFODfO3ZdcutzMxoozMyo+F6QlYlrlYWO3YxGCjMrpxIVZmXi6mtAoEVI+IZrTwvaDRxEwo8/XKqEBqM3GCmy0F6UnYmrxZO8ujCVlnLgvTdoEzmc1v0HOUBhzbgbjRRY1mlmFu6+hjrTVI0q0Ht64t+tGylx8fh0cNwc+cbqNZWWsvvNt+kwbCgBgwZa5DHQftAAbRpqeBg4CYpzcnD1adgUu2O/refExs0AGMJCrfQUZGZWe2rn6u1NcV4+prIynHQ68jMyK54Ga09Ua89fFQ+jgSBzefw6hYEQFOXk4NbA8rgajBRZaCjKqm7jrjW0T4DAkWMIHDkGgGNffoabofJJ5vltW0iPjWHA40/X70bpMtJeTrvIYUQvfpXwa663u/4/Ay3Nz7jZaHNVtbgbDRRkWKbJwr0Ov396yzb63HErAIGRg4j+wDEDV7E//cH+37YB0LZzR3LTsyv25WZk43UJ0VauFoMMoQN7sXHJpxRczK1YRLwhNIl/FAJ3owE/cwRNUORADn9XuWynqayMs3uimPjy8w0uR1X2/7yJQ+u2AtC6U0dy0yun3uRmZONh9K0hZ/Nw+NeNHFuv/Q5+4aHkW9hzfkYW7lWiw1y9vSjOL6j8HTIr01gORgX1682uD1dTeDEHNx9vTKWlbFr0LmEjIukY2d+uZXBUn8CtVWXZO44Zzc5FbzRY47HfNpC4QatnY1iIVT0XZFaP4nH19qpm7+Vp3P2MNvOn7D+IZxt/3Hy0hcEDBw0g/ehxQkYOtTp2x+GRbF6wmF43XtPg8lQlo7DUauqfn7sLmUWl1dJ19Hbj/3oHMmfvSXJLyqrtzy81cSAjj36tvTmd67h1at2Nhip1mGkVfVeepqp/r5qmKpY2Ezp2NNtffcMecmukJd97KP4cqIgrxSUjhBgDTACGSin7AtHAYWqOYSrFbGtCu0vU13UOKeU84D7AHdgphOjWaOFVaBUaRl5KCvlpaZhKS0natZO2/fpZpWkb0Y9z27YhpSTr+HGc3T1w8/XF1dcXNz8juUlJAKQfPIBXgP2mMtSFT0gYBanJFKSnYiotJXXPTvz6DqhXXiklR1e+j0f7QDpMbNJlxQDwDQslNzmFvFSt3s/u3EW7/tb13q5/BKe3avWeefw4zh7uuPn6UnTxIsV5eQCUFReTduAg3gHtL1u9UkqiP/gIr4D2dJpq/faU9gP6k37wEAC5ScnI0rKKtwk1hM6TxjNp7iwmzZ1F4MB+nNyyHSklGccScHH3qNZRFULQpkc3zu7S1k86uWU7gQO1cgUMiKgzf1UCBvYj9YBWnpykZEylpRVvumkIPqFh5KckU5Cm2Xjy7p20jrC+AWndtz9J27cipSQ74TjOHh4Vnajii9raLgUZ6aRG7aVdpNaZTo+P4+TPPxDxj3+jc9DUXkdpz0upfGKcFhuFZ/um8zktjZbmZwxhoeQmp1ro3U37Knrb9+/Hma3bzXoTcPFwr3PNKjeDL+mHjgCQduAQXu3sP0UNoO+00dz2xn+57Y3/Eh7Zl0ObdiGlJOnICVw93S9pmmBe1oWKaM3koyeRUuLm3bjpL03hH919W+HhZ+Tiea1fkLL/ID6BlW00Zf9BfALaWU0Zaiy9po7hxkUzuXHRTEIHR3D0j53aekVHE9F7uF120wS7TR7LlQte4MoFLxA8KIKEzZretKOaPXvY+B3a9ejKqZ3aOjoJf2ynw8AIAAqyK+0k/fgJpEni6u2FlJLt763AN7A9Pabb/82IjuoTFGZnV/yftDfKauHuS6XzpHFMnvcik+e9aGXv6ccScPGowd57dq20983bCRgQAUBg/74283v4+5Fx7P/Zu+/wqIqFj+Pf2U02lSS7aaSSRq+B0HsXFHvvgqhXvffqay8IAiqIimK5io2O9erVa0Mp0msqHRI6aaQQ0svO+8duwm6yCSTZJcQ7n+fxMezOyf7OZGbO2dk5Z9OoLCsz3RNu976a9n4uPbPmd5/alYSXncfPw2eLCfJwIcDNGSchGBLkzY5M6y+E8XN15une4byTdJL0ovKax710WtydTG9/dRpBDz9PThY59suVbI3vwX3qju/HrMYVtwued1neq+z0zl3NajMXozW/91D+GoTdLk1R/mcIIa4B7pdSTjJPKCUC9wFzgN5AAbAGSJJSPiqEeBFoI6V8RghxLfCdlFLU+lbBG4CrpZT3mF8jWkqZav75e2CRlPL7+jI1ZcUVQFZSEntXLAOjJHToMGKuvppja9YA0G7UKKSU7Fm6hDMpKWhcdPSYcj8+kab7EBUcO0by558iK6tw9/enx/1TG32D9qLypt8ENSclkVTzV9K2HTycdldey+k//wAgePgYys/ms+uVF6kqLQGhQeviQt+XX6fo5AkS583EIySsZolv5HW34Nu9V6NeX+9WceFC9chITCJlufmrpIcNpeM1V3NktaneI0eb6j158VIyU1Jw0rkQO3UK+qhIzh4/QfzCj5FGI9IoCenfj07XXdPkHI7Om3PgIBtmv2paqWGu6+qvuDZWVhL/8aecPXYcjZMT3W67Bf+uF77CVqc1XrCMlJL4RcvISNqNk4uOvg9OxhBlusfH+rnz6fvAvbjp9RRmZrH1XdPXX/u0C6f/I1PROjs3uP2Wdz8ke98Bys4V4urtRdcbriFq5DCqKitr7u2icdLS845bCOza2SpXXolznawNyU5O5OAXy5FGI8FDhhF11TWcWGe65CBsxGiklOxfvpic3aav6e4yeSreEab+uWPOLCoKCxFaLR1uuQPfLl0B2PjcExgrKmtuZO0dFeOQb+ZzRPak99+hKCMdodHg6utL57vus7kaq7ZPp/1q9/0DWPzi9Qzt1Q4/b3ey8oqYtWgdi39OtOtr/GPu2CZv29LjjGjkMvyMxCSSl60Eo5F2w4fS8ZpJHFm91px3pOnr0hcvIyvZ1GZ6P2DKC7DjvQ/J3ref8sJCXLy86HzDtUSMGMaZAwdJWboCo9FouvfcvXfVfMV6Q8I9ixu9v9WklKxb+BXH4vfi5KJj7D/uJDDGdLPs72e+z5hH78DT4EPif9ey67s/KMorwN27DRF9ujLm0TtI+mkdyb9uQKPV4qRzZujkGwjudPH3/0svbviyMEeOj3lHj7Pz488xVlbhEeBPvwcn13yZxfYPP8UQE0XMmJH1ZjO4lNf73IVIKdn4yRecSNiDk4uOEY/cQ4C53n+a/S4jHr4LD4MPKT+tIfH7VRTnF+Dm3Ybw3t0Y8fBdFOed5dunX6O8pBQhBM6uLtzyzvSLvsyuuLJx5zNSSrZ/toJTSXtw0ukY9Ld78YuOAGD1a+8w8MF7cDf4cC4zm/XvLKS8sAhDRDhD/j4FrbMz+39dw4Hf16HRaNHqnIm7+2YCOsaQuf8Qv01/HZ/wkJoVtbG3XU9obPd6sxSUN+7Y5Ihzgp0ffkTBsRMgwN3Pj16T773gihsAF6e6K4ksSSmJ/3w56eb22u/ByRjM9bx+7tv0nXoPbgY9hZnZbHn3I8oLi/CJCGOAZXuvZ/vdX3/P8a07EBoN+ohw+j5wL1pnZzbNf5+C0xkIocHD35c+U+7C3aCvk213RtMnVnv7t2FylyA0wOqTeXybms24cNPxcNXxXB7uHsKAtt5kl5j6VJWUPL0plXZtXPl7j1A0AjRCsCn9LF8fzmr063cKaNxtd9MTk0heuhJpNBIxfCidrp1E2h+m8T1qjGl8T1y0jEzz+B734Pnxfdt7H3Jm337TeZeXF51vvJbIEcPY8cFC8o8dByHw8PcjdvI9F5zsAjhTdMH1A/Vq6fceAAuHx13OF0E3262/JV2yyZkvxvdsVXWpJq6URhNCuADfAyHAAcAfmAFEcv7m7ImA1jxxFQj8B9Oqq9XA36WUnrUmrgzAb4AzppuzDwFGAlXAXuBeKWW9H4k0deKqpTVn4qqlNWfiSmm6i5m4ulw1duJKsQ9HTVxdCs2ZuGppjZ24upw0Z+KqpV1o4upy1pyJq5bW2Imry0ljJ64uJxeauLqcNWfiqqU1duLqctKciavLgZq4sp/WNnGl7nGlNJp5AmmCjafWAXVuoCGlzAQsv7rrOfPjR4Fu5p9zAcsLnr+0T1pFURRFURRFURRFubxd1l+q0cLUPa4URVEURVEURVEURVGUy5JacaUoiqIoiqIoiqIoitKCNK34NgOOplZcKYqiKIqiKIqiKIqiKJclteJKURRFURRFURRFURSlBalbXNVPrbhSFEVRFEVRFEVRFEVRLktqxZWiKIqiKIqiKIqiKEoLUve4qp9acaUoiqIoiqIoiqIoiqJcltSKK0VRFEVRFEVRFEVRlBYk1E2u6qUmrpS/BHddZUtHaBK9a3lLR2iys2XOLR2hyVydq1o6QpNVydZ7RHN1NrZ0hP9J/5g7tqUjNNmCZ35v6QhN1prr/UypS0tHaDInTesdZ86Wt97j6rly9ZaiJXi04suKCgoqWjpCk5XotS0doclcnFrvGKn8b1NHGUVRFEVRFEVRFEVRlBYkWvFktKOpe1wpiqIoiqIoiqIoiqIolyW14kpRFEVRFEVRFEVRFKUFqVVF9VN1oyiKoiiKoiiKoiiKolyW1MSVoiiKoiiKoiiKoiiKcllSlwoqiqIoiqIoiqIoiqK0IHVz9vqpFVeKoiiKoiiKoiiKoijKZUmtuFIURVEURVEURVEURWlBGtHSCS5fasWVoiiKoiiKoiiKoiiKcllSK66U/zkZSSkkLlmBNEoiRw6l09VXWj0vpSRpyQrSE1Nw0umIe2gK+sh2DW6755vvObJ2PS5ebQDodvMNBMX2sHt2KSW7Fq/kVEIKTi46Bv5tMgZzNkuFWdlsfGch5UVF6CPCGfTo/WidnDixM4Hkr75HCA1Cq6HP3bcS0Kk9APt+WkXq2o0A+ISHMvCh+9DqnO2WPSs5mT3LlyONRsKHDyfmqqvq7Nue5cvJSkpCq9PRa+pUvCMiAFj9xBM4uboiNBqERsPQl18G4MB333F83TpcvLwA6HjjjQT27NnsrJeyjRRln+G3J1+gTXBbAHxjouk95e5m74PlviQtXYE0GokcMYyO9exLRlIyWp2OuAenoI+MAGDnwk/JSEjCxcuLsXNn12yz99vvObL2T1zamPal6y03ENSr+fVeW1ZyMruXnW8z7SfZaDPLlpOZlITWxdRmfMxtBkAajax/aTquej39n/i/msePrPqdI3/8gdBqCOzZiy633tJqsgOk/vwze7/4knHvv1fzN3CUzORkUsztp92IYXSwsR8pS5eTmZSM1kVH7wfuxycigqrycja88hrGikqksYrgvn3pfMN1Ds3aGB8+PYkJAzqQnV9E3OQPWzoO0PS6Ls7JIf6jjyk9exYhBBEjRxA9fpzVtod++oU9X3zJhA/edUibcdSxqaq8gt9fnktVRaWpL/XvQ4+brnFI9tPm7AMayL7pnYWUFRVhiAhnoDn7kY1b2ffDLwA4ubjS9/470bcLA+A/jz6Dk5vp2KXRarji1Wl2zx6/eCWnE1PQ6hrOvnnBR+bs7RjwiCn70Y1b2WvO7uzqQtyUu2qyb/3wM04nJOPq1YaJ82bZNTc075wATGPkhummMbLf/5nGyILjx0letIiqsjLc/PyIfeghnN3cLqvs9Z3PnN6+nYPffUdhejpDpk/HJzLS7rmrs7XWvmqpb1svHu4dikbAL2k5fLEv0+r5QSHe3Ns9GKOUVEnJv+JPsvtMEf7uzjzTPwK9qzMSyU+pZ/juYLbDclbLTk5m74plSKORsGHDib5qktXzUkr2Ll9GdnISWp0LPe4/32bWPvF/aN1ca+p8yIyZNdsd/X0Vx1b/gdBoCejZk0633Gr37GdSktm/YilSGgkdOoLIK+tmP7BiKdkppuzdpjyAVztT9mOrfuHk+j9BQJuQMLpOmYrWWceBr1aSnZiAxskJd/8Auk6ZirO7h92zt0bqHlf1UxNXSoOEEIuA/0opv6n1eDCwQEp5oxBiBPCklPIqG9sfBeKklGccn/bCpNFIwufLGPrcE7j7Glj94kyCe/fCKzSkpkxGYgrnMjK54q3XyD2cRvxnSxg9a9oFt20/YRwdr7rCoflPJ6ZQkJ7F1W+/Ss7hNLZ/sowrXnmhTrmEFd/S6cqxRAzqx7ZPlpK6ZgMdxo2kbbfOhPbphRCCvGMn2PjOR0x6azbFuXkc+HUNV705Eyedjg1vf8jRzduJHjHYLrml0cjuJUvo//TTuBkMbJgxg8DYWNqEnK/3rORkijIyGPn66+SnppKyeDFDpk+veX7gs8+is/GGK2r8eKInTrRLzuqsl7qNeAYGMPa1l+22D5b7krhoKUOeexJ3g4E102YSVHtfkpIpzMhk/JtzyD2cRsLnSxk10/Tmqt3QIUSPHc3ODz+p87vbTxhHhysn2D2zZfaUJUsYUN1mps+gbe+6baYwM4NR88xtZtFihs4432bSfltFm+BgKkpKah47s3cfGfHxDH9lNlpnZ8oKClpNdoCSnByyd+/BzdfX7rlt7UfS4qUMfuYp3AwG1r30Mm17x+JlsR+ZSckUZmYy5o255KWmkvT5Eoa//BIaZ2eGPPcMTq6uGCsr2TDrVQJ7dscQE+Pw3Bdj6a9JfPjdDj557tqWjgI0s661Wrrdfis+ERFUlJSw7qUZ+HfrWrNtcU4O2Xsc22YcdWzSODsxetqTOJvb0arpcwnu1Q2/9tF2zX4uPYtJ5uw7PlnGeBvZE1d8S0dz9u2fLCVtzQbajxuJp78fY156Gp2nB6cTUti+cInV9qOnPYmrl2MmmNPNx6Kr5puy7/x0KeNmv2gj+zd0nDiWdoP6s+OTJaSt3UD7sSPxCLDInpjCjo8X12wfNXwwHcaPZusHdcf/5rLHOcGRVavwDA6m0mKMTPrsM7rceiu+nTpxfP160n7+mY433HDZZbd1PtMmNJS4f/yD5EWL7Jq3ttbcV6tpBPw9Loxn1h4iu6SC98d2ZPOpsxwvKK0pE595js2n9gEQ6e3GtMGRTP55L1VGyYeJJzmcV4Kbk4Z/jevEroxzVtvamzQa2bN0Cf2eehpXg4FNL08nILa3VZvJTk6mODOT4XPnkZ+ayu4lixj80oya5wc881ydNpOzby+ZCfEMmfWKQ89n9i1bTJ8nnsHVYGDrzJfw79UbT4vsZ1KSKMrMZMhrb3A2LZW9Sz5nwLSXKc3L5dgfqxg8ey5anY6kD94lY9tWQoYMw7dLN9rfcDMarZaDX3/BkZ9+pMNN9p90U/5a1KWCSpNIKU9LKW9syrbCpEXaXu7hNDwDA/AMDEDj5ETYwP6c3pVoVeb0rgTaDR2EEALf9tFUFBdTkpd/Uds62smdiUQNG4gQAr/20ZSbs1mSUpK5Zz/h/fsAEDVsECd3mnI6u7oihOni6cqycuvtqqqoKi/HWFVFZVk57nofu+XOT0vDIzAQjwBT3YX0709mfLxVmcz4eEIHD0YIgT4mhoriYkrz823/Qgdq7W3EUm5qGh6BAXia6z10QD9O70qwKpNez74A+HfuiM7TswWSQ15qGh4B59tM8ID+ZNRqMxnx8YTV02ZKcnPJSkoifMRwq22OrllNzFVXoXU2rSasXq3XGrID7Fmxgs633gLC8TdByEtNw9Oi34YO6E9GrfaTEZ9A+BDTfhgs9kMIgZOrKwDGqiqMVVXA5XPjhk3Jx8ktKLlwwUukOXXt6uNTs1rP2c2NNsHBlObm1Wy3e/lKut5ys0Or31HHJiEEzg5uR6d2JhLZyOyRwwZxwpzdv2MMOk/TSgG/9lEUW9S9o53clUiEefy+UPaw/nE12U/uNLUt/w4W2WOsswd07ljznL0195ygJDeXzKQkwodbj5FF6ekYOnY07VvXrqTv3HnZZa9Pm+BgPIOC7J63ttbcV6t1NHhw+lwZ6UXlVBol647nMTjE26pMaaWx5mdXJw3SvIglt7SSw3mmsb+k0sjxglL83Ox3dYEt+WmpuAcG4G5uM0H9B5CZUKvNJMQTYtFmKi+izRxbs4boKx17PnM2LRX3gMCa7G37DyArcZdVmeyEeIIHDUEIgU+0KXuZObusMmI0v7eoKi/HxUcPgF+37mi0WgC8o2Iozcu1e/bWSiMu3X+tjVpxpVgRQtwNPAlIIBmoAoYJIf4PaAs8LaX8RggRgWklVrda2/sCKwF/YDvmo5a5/C/AWmAgcK0Q4mbgZsAF+E5KOd2i3EZgEHAKuEZKaZd3GCV5+bj5Gmr+7WbQk3s4rVaZPNwNlmUMlOTlXXDb1FWrOb5hM/qoCHrccYtDTviKc/Nxt8jgbtBTnJuPm8UkU9m5Qpzd3WoOCKYy509GT2yPJ/GLf1N6toARz/yzpkznq8bz/SPPoNU5E9SjK0E9u9otd0leHq4WdepqMJCXmmpVpjQvz2pFgKvBQGleHq4+pn3bOm8eAggfOZJ2I0fWlDu6ejUnN23CJzKSzrfdhs6jefXeEm2kKDubP56bgZObK11vvh7/Th2atQ81OXPzrNqLm8FAbq16L8mtm7k0L8+qTdmSumo1x2r25dZm13ttpvZg3Wbya7eZ3DxcDefbjJvBYHrMx4c9y5fT+ZabqSy1/hS1KCOT3IMH2P/NN2icnel62634REW1iuwZ8fG46vV4h4fbNW99SvLycLPqt3ryUuv2hdplSsz7IY1G1k6bTlFmFlFjRmOIsf8n738Vza3rakXZ2Zw9dgy9ua7T4xNMbaadY9uMo45NAEajkV+fm8W5jCw6jBuJX3v79temZi+xMUGVunYjwb0sTouEYO2r80FA+9HDiRlTdzK6OUpy8/C4QPbyc4XoPNxrsrv5GmxnX7eBoF7d7ZqvPs09J9izfDmdb647RrYJDSUzIYG2vXuTvmMHJbn2fzPsyPOZS6E199Vqfm7OZBWfnzTLLqmgk8G9TrnBId5M6RmCj4sTL6xPrfN8oIeOGL07+3OKHJKzWmlereO93kB+Wu02k2vdrvQG02M+PiBg+xuvA4LwkSMJH2FqM0UZGeQePMiBb79B6+xMp1tus//5TH5enVxn62Sv2ydK83Lxjowi4oqJrH/qMTTOOny7dcOvW90x5tTGP2nbb4Bdcyt/TWrFlVJDCNEVeAEYJaXsCVQfjYKAIcBVwJwL/JrpwEYpZSzwA2B5ttwRWGJ+riPQHugH9AL6CCGGmcu1B96XUnYF8gH7rfOu/sjFUu2VC7aKIBrcNnrsSCa8PZcxr83A1ceb5OVf2iGsLTbDXZjFPob1682kt2Yz7MlHSf7qewDKCos4uSuRa96dw/X/eoPKsjKObNhin8hgs+5EI1aMDH7xRYbNnEm/J5/k6OrV5OzfD0DEqFGMmjePYbNm4eLjw76VKx2S1ZFtxNXHm4kL3mDMazPoeeetbH/vIyqKHbcSpG69X8T+1hI1ZiRXzH+dMa++jKuPD8nLv7BfwIZyXUxjF5CZkIiujZfNe4TIqioqiooZMv0lutx6Czvfex9p6+/WLPbPXllWxqEffqTj9dfbJ+LFsNmeL1ymuo0JjYZRr8xi/DtvkZeWRsGJkw4I+RfRzLoGqCwtZfuC9+h+x+04u7lRWVbGwf/8eInuLeaYYxOARqNh4tzpXPfBPHJSj5B/4lTz41qxVa8XsVmtQpl79pO6dgO9bj+/GH3sy88yYc5LjHz2MQ6uWkvWvoPNDWvNZpuoVcTWdjayp63dSK/bmrSQvvGacU6QmZiIi5ft8b3nlCkc/eMPNrz0EpUlJTUTL3bloPOZS6c199U6URq06dRZJv+8l+kb07ivu/VqNlcnDdMHR/FBwkmKLVZnOcTFnGLYPBUz7ejAF6Yx5OVZ9H3iSY6t/oPcA6Y2I41VVBQVMWjadDrdcisJH7xn//OZizkntjmGCiqKishK2MXQuW8x/K0FVJWVcXrLJqtyaT/+B41GS9CAQXYM3boJ5CX7r7VRK64US6OAb6rvRyWlzDUPmt9LKY3AXiFE4AV+xzDgevP2PwkhLD/WOyal3Gr+eZz5v+prITwxTVgdB45IKRPNj+8CImy9kBDiAeABgAnPP0Xs9Re+CaSbQU9JzvlP4Epy664scTPoKc61LJOLq94HY2Vlvdu6ep9fohw5ajib5r1zwSwX68Bva0hdswEAQ3QExRYZinPz6lzS59LGk4riEoxVVWi0WptlAAI7d2BLZjalBefI3HsAT3+/mvtwhPXrTfbBVCKHDrTLPphWk5zPXZqba7VKAMBVr6ckJ8e6jF5f8xyYlkG37dOH/LQ0fDt1wsWi3sOHD2fH/Pl2yHpp24jW2blmmbc+KgKPwADOZWRgiGr+jVndDHqr9lJio95t7W/tMrVZ7cvI4Wx+4+1mZ63zGnqDVa5Scx1blTHoKc0932ZKzG0mfccOMhMS+CM5GWNFBRUlJcR/+CG9H3oIV4OBtnF9TMvxo6MRGkH5uXN2XWLviOwxV15JcXY2f744reZ3rp/2EkNnTL/g36up3AwGqxULpbl5uJmX+TdUpva+6jw88OvUiczkFLzCQh2StbVrbl0bKyvZvuA9wgYNJLiv6ZKwoqwsirKzWfPCtJry66ZNZ/iMl+zSZi7Fscny3lA6D3cCunTkdOJufMJC6mzXGAd/W8Nhc3ZfG9lrj/m2sluWyTt2gm0fLWbEs//Epc35y6vdDaYyrt5ehPaNJefwEQI6N29F7cFVa0hds96UPSqCopxc/C+QvbyouCZ7SU5unezbFy5i+LOPWWV3pOacE1SPkVkWY2TChx8S+9BDeAYHM+DppwEozMggMynpsspe/RzUPZ9xpNbcV23JLq4gwF1X829/N2dySirqLZ+SXUiQpwteOi0F5VVoBcwYHMXqY7lsPJlv93y11Tne5+XiotfXKlOrXeXl1lxWZ9lmAnub2oyhYydc9Qba9okzXaIXFY0QGoecz9TN5dNwmVxT9py9u3H380dnzhPYuy/5hw8RPNB0/9xTmzaQnZxI3JPPNmryV/nfpVZcKZYEtj8XKKtV5kLqm8K1XIsrgNeklL3M/8VIKT+18XpV1DPBKqVcKKWMk1LGXcykFYA+OpLCjEyKsrIxVlZyYss2gvr0sioT3KcXxzZsRkpJzqFUnN3ccdP7NLit5f0BTu2It7r5dXN1HD+KiXOnM3HudMLiYklbvwUpJWcOpaJzd6tzkiqEILBLR45vM12DnrZ+M6FxppznMjJrPo3JPXIMY2UlLm088fA1cOZwGpVlZUgpydi9D+8Q+91rwTsykqLMTIqzTXV3ats2AmNjrcoExsZyctMmpJTkHT6Mk5sbrj4+VJaV1dx8tbKsjDO7d9Mm1PTm1/L6/4xdu2oeb45L3UbKCgqQRtOnfYWZWRRmZOIZ4I896KMiKczIqslzcut2gvtY13tQ79ha+1K3TdVmuS+nd+6ya3uv5hNl3WZOb91G21ptpm1sLCcs2oyzu6nNdL75Zsa+8zZj3nqT3g//Db/Onen90EOmbfr05sxe0w1bC9MzMFZW2bzp/+WW3SssjPHvv8eYt95kzFtv4mowMGzWTIdNWlXvh2V7Prl1G21719qP3r04vtG0H7mHD+Nk3o+yggLKi0xDflV5Odl79tIm2PH3b2mtmlPXUkoSPvkMz+AgYiac//IH77AwJn7wLuPnv8n4+W/iatAzYtbLdmszl+LYVFpwjvKiYgAqy8vJSNmHl/kbWJujg0X20LhYjlhkd64ne4BF9iMW2YvO5LDhrQ8Y+MgUq2yVpWVUlJTW/JyRvBdvO7yJ7zBuFBPmzGDCnBmExMVy1Dx+m7K72673rh05sW3n+ex9zmffOP8DBjxyP15Bza/Xi9Wcc4LON9/MmLffZvSbbxL7N9MYGWse36tvTi2NRg795z+0GzXqssre0PmMI7XmvmrLgdwiQtq40NZDh5NGMCJcz+ZTZ63KBHu61Pwco3fDWSMoKK8C4Ml+7ThWUMq3B7Ickq8278goqzaTvm1r3TbTK5ZTVm3G3Xab2bObNiGmNhPYuw85+/YCUJiRjrGq0u7nM16RURRnZlCcnYWxspKMbVsJ6NXbqox/r96c3rwRKSX5qYdxcnfHxccHV4Mv+WmpVJnfW+Ts24NnUDBg+qbCoz//l9i/P47WxcXWS//PEuLS/dfaqBVXiqXVwHdCiPlSyhwhhOGCW9S1HrgDmC2EmADo6yn3GzBLCLFcSlkohAgB6v+4xE40Wi297r2TDXPeQhqNRIwYgndoCKl/rAUgesxI2vbqQUZiMr8+/ixaFx1xD05ucFuAlJVfk3/sOAKBu78fvafc7ZD8wbHdOZWYwg//fB6ti46BD91X89zaOW/T/4F7cTf40Ov2G9m04COSvvwOQ0Q40SOHAHB8WzxHNmxBo9Wi1Tkz5J8Pmm/QGUV4/z788twshEaDPiKcmNHD6ovRaBqtlq533cW2efPMXwU8jDahoRxbswaAdqNGEdCzJ1nJyax96im0Li70vP9+AMrOnmXnggWA6TKvkIEDCejRA4B9X35JwfHjALj7+dH9vvtsvHrjs17KNpK9/yB7v/4eoTV9NXbvyXfb7Ybopjx3sHHum6Y8w4fiFRpCmnlfoiz25bf/ewatTkfcg1Nqtt/23oec2befsnOF/Pzo/9H5xmuJHDGM3Su/Iv/YcRACD38/YiffY5e8tbN3u/sutr4+DynPt5mj5jYTUd1mkpJZ89RTaHUu9DK3mYaEDxtG4iefsO655xFOTsQ+MNXun/Q5KvulptFq6XH3nWye9wbSaKTdMFP7ObLatB+Ro0cR2LMnmYnJ/P7k0zjpXIidamo/pflniV/4MdJoRBolIf370Ta2VwvujbXFL17P0F7t8PN25/BXjzFr0ToW/5zYYnmaU9e5Bw9xYtNmvMJCa1ZXdbnpRtr26nnJ8jvq2FSSl8+Wf31W047aDexLaB/77ldwbHdOJ6bwozn7gHqyx95+IxsXfETyl9+ht8i++9sfKSssYsdnywHQaDVc8eo0Ss8WsP7N9wHTREq7wf2s739ll+w9SE9M4b+PPYfWRUd/87EIYN3ct+k39R7cDXp63XYTm979iOSvvkcfEUbUyKGm7P/+kbLCQnZ+tsyUXaNh/KsvAbBpwUdk7TtA2blCvn/kSbrfeA3R5u2aqznnBA05tXUrx/74A4C2cXGEDbVPXntlb+h8Jn3nTvYsW0b5uXNsf+stvMPD6f/UU3bP35r7ajWjhHd3nWDO8Bg0GsGvaTkcKyjlqmg/AP6beoahoT6MjTRQaZSUVxmZvfkIAN38PBgb6UtafgkfjjetdPss+TTb0+3/jXzVNFotXe+823SfKqMkdOgw2oRYtxn/nj3JSk7iz6efQuOio8cUU5spP3uWXe+aVujLKiPBAwbib24zYcOGkfzpJ6x/4Tk0Tk70uP8Bh5zPdLrzbuLfMrX3kCHD8AwJ5cTa1aYMI0fj16MnZ5IT2fjsk2h1OrpOngqAT3QMgXF92fLyNIRWg1d4BKHDTffn2rd8McaKSna9ORcA7+gYutzd/HN45a9N2P/eHkprJoS4B3gK00qn6sv4/iul/Mb8fKGU0tPy5uxCiBHAk1LKqyxuzu4H/InpssE+mC4FtLqZuxDin0D1mUghcKf5dWvKCSGeBDyllDMayv3Crk2tsiG7aBx8Xb0DnS1z7LewOJKrc1VLR2gyKVvhRyRm5VVqkW9L0IpWOTwCsOCZ31s6QpP9Y+7Ylo7QZB7OlS0doclab2tv3eP7uXL1WXhL8HZx+Ge+DrP+gGO+tfJS6N6u9dZ7pbH1jjMA7w7u17p34AIe3bj9kh3G3hvSuupSHWUUK1LKxcDiBp73NP//KNDN/PM6YJ355xxM966q9rj5/2eqy1v8rncAWzeD6mZR5o3G7YGiKIqiKIqiKIqiKH8VauJKURRFURRFURRFURSlBbXGe09dKuq6DUVRFEVRFEVRFEVRFOWypCauFEVRFEVRFEVRFEVRlMuSulRQURRFURRFURRFURSlBWla8RfqOJpacaUoiqIoiqIoiqIoiqJcltSKK0VRFEVRFEVRFEVRlBak7s1eP7XiSlEURVEURVEURVEURbksqRVXiqIoiqIoiqIoiqIoLUioJVf1UhNXyl+Ci8bY0hGazChb5wjVRldJa631SmPrXWyqbcU3bXRqxf1UaRn/mDu2pSM02YJnfm/pCE321JujWjpCk8lWekxt7cqrWu9x1d25qqUjNFllK27vfaLKWjpCk8nWeypGWaV6+6+0TqrlKkoLaq2TVkCrnbRSFEVRFEVRFEW53KhvFaxf6/14RFEURVEURVEURVEURflLUyuuFEVRFEVRFEVRFEVRWpC6x1X91IorRVEURVEURVEURVEU5bKkVlwpiqIoiqIoiqIoiqK0IA3qHlf1USuuFEVRFEVRFEVRFEVRlMuSWnGlKIqiKIqiKIqiKIrSgtQ9ruqnVlwpiqIoiqIoiqIoiqIolyW14kpRFEVRFEVRFEVRFKUFCaHucVUfteJKURRFURRFURRFURRFuSypFVfK/xwpJbsWr+RUQgpOLjoG/m0yhsh2dcoVZmWz8Z2FlBcVoY8IZ9Cj96N1cuLEzgSSv/oeITQIrYY+d99KQKf2FJ3JZcsHn1KSfxah0RAzahidJo6xe/b4xSs5nZiCVqdjQAPZNy/4iLKiIgwR7RjwiCn70Y1b2fvDLwA4u7oQN+Uu9O3CANj64WecTkjG1asNE+fNsmvu6uwJi1eSbs7er4HsWxZ8ZK73dvQ3Zy84lc72jz4j78hxut9yHZ2uugKAqvIK1sycS1VFBbLKSFj/PnS76Vq756+WkZRC0tIVSKORyBHD6Hj1lXX2M2nJCjKSktHqdMQ9OAV9ZAQAOxd+SkZCEi5eXoydO9tumaSUJCxZQUZ13T40BX09dbv13Q8pLyxCH9mOfg9PRevk1OD26UkpJC5ZgTRKIkcOpbN5f5OWf8Xp+EQ0Tk54BvrT98Ep6DzcyTmcxq5PF9fk6nrDNYT27dOk/cpMSiHZXNft6qnr5KUryExMRuuio88DU/Ax1/WuhZ+SkWiq6zFzztd1/rHjJH62GGNFBUKrpee9d2GIjmpSPkfvR3FODrs+/ITSs2cRQhAxcjgxV4xzaNbm5IWWr/fM5GRSLLJ3mHRVnewpS5eTmWTK3vuB+/GJMNV1/EcfW9T1CKLHW9f1oZ9+Yc8XXzLhg3dxadPG7tkv1odPT2LCgA5k5xcRN/nDFsnQ3PG8oe0P/LyKtDUbQIBPWCj9HpqMVudM3rET7Pp0CZWlZXj4+zHgkak4u7s1LbsDxsuC0+lsffdDq+273XgtHSaMY/c333Nk7XpcvEztpvvNNxAU2+OyyQ5QXlTMzo8/5+yJUyAEfR+4D78OMQAc+u0PDq9ajdBoCYrtQc/bb2509trOpCSzf8VSpDQSOnQEkVdOqrOvB1YsJTslCa3OhW5THsCrXQQAx1b9wsn1f4KANiFhdJ0yFa2zjsP//oasxHiEEOi8vOg6+QFc9fpmZ21IU8ecqvJyNrzyGsaKSqSxiuC+fel8w3UOzVqdxxHnYgDbzeeRLl5tmOCA80hHjO9njx0ncdFiqkrLcPPzJe7hh3B2a/y4clHZl60Ao5Hw4fVkX7acLHP22Knn28nGV63bSafrTe1kx3sfUJiRDkBFcTHO7u6MnG3/es/ZncTBlUuRRiPBQ0cQMfHqOtkPrlxKTkoiWp0LnSc/gFe7SACO//Erp9evAyTBQ0cSPtbUXs4dP8b+ZZ+Zzgk0WjrecS/eUdF2z678tagVV8pFE0JECCF2N6L8OiFEnI3HrxZCPGvfdBfvdGIKBelZXP32q/SfejfbP1lms1zCim/pdOVYrn77VXSeHqSu2QBA226dmTh3BhPnTmfAg/eybaHpTbpGq6H3XTcz6a3ZjJ/1PAdXreXsydN2zZ6emMK5jEyumv8q/abezc5Pl9osl7jiGzpOHMuk+a+h83Anba0pu0eAH2NeepqJr79M1+snsePjxTXbRA0fzIhnH7drXlvZJ85/lbipd7OrnuzJ5uxXmrMfMWfXeXoQe8/tdLxqvFV5jbMTI158kivmvsz4OdNJT9rNmUOpDtkHaTSSuGgpg59+nHGvv8KJLdsoOHnKqkxGUjKFGZmMf3MOvafcS8Ln5/ez3dAhDH76/+yeKyMxhcKMTCa89Rpx99/Drs+W2CyXvPJrOkwYx8T5c3D28Kip2/q2NxqNxH++jKFPP874ebM5vnkbZ837G9i9C+Nfn8X4uTPxDGrLvh9+AsA7LIQxs19i3GsvM+yZ/2PXp0swVlU1ep+k0UjS4qUMevpxxrz+Cie3bqPglHVdZyYlU5SRydg35xA75V4SF1nU9bAhDH6qbl3vWfkVna6/hlGvzqTzDdeyZ+VXjc52qfZDo9HS/fZbGPv6qwyf8SJpf6yps+3llBdatt6rsw986v8YPfdVTm6xnb0wM5Mxb8yl1+R7Sfrc1NY1Wi3dbr+VMXNfY9j0aaT9sdpq2+KcHLL37MHN19fuuRtr6a9JXPPM8hbN0NzxvL7ti3PzOPTrasa+Oo0J82YhjUaOb9kGwI6Fi+hx641c8fpMQuJi2f/fX5uU3VHjpVdwEONee5lxr73MmFem46TTERLXu+b3tZ8wrub5pkxaOTI7QMKSFbTt2Z0Jb77KuDkv4xUSDEDWnn2c2pnAuDkzuWLebDpeeYXN12wMaTSyb9liej/+FINnzyV92xYKa/XVMylJFGVmMuS1N+hyz2T2LvkcgNK8XI79sYoBL81k8Kw5SKORjG1bAYiYcCWDZr7KwJdfwa9HL9J+/L7ZWS+0H00ec5ydGfLcM4x6dRYjZ88kKzmF3MOHHZoXHHcuBhAxfDDDHHQe6ajxPeHTz+l6802Mem02wXF9OPTTzw7JnrxkKQOf/D9GzXmVUzaOq1nJyRRlZjJ63lx63ncvSYvOt5PBzz7DyFdmMWLWTDIt2knfRx9m5OxZjJw9i+C4OILj6rzlskv2A8sX0+uxpxkw63Uyt2+l8LR19pyUJEqyMhj46pt0unsKB5YtAqDw1AlOr19H3xdept/0VzmTnEBxZgYAh79ZSeSk6+k//VWirrmBw9+stHv21kojLt1/FyKEuEIIcUAIcdjWe3dhssD8fLIQovfFbtukurHHL1GUxpBS/iClnNNSr39yZyJRwwYihMCvfTTlxcWU5OVblZFSkrlnP+H9TStFooYN4uTORACcXV0R5q98qCwrr9nGTe9T86mVs5sr3iFBFOfm2Tf7rkQihg66qOxh/U0HsMhhgzi5MwEA/w4x6Dw9APCLibLKF9C5Y81zjnCqVvaKBrKHmrNHDBvEKXN2V28vfKMj0Wi1VtsIIXB2dQXAWFWFsaqq5u9jb7mpaXgEBuAZEIDGyYnQAf04vSvBqkz6rgTamffTt9Z++nfuiM7T0+65Tu1KqKnb2q9ZTUpJlmXdDh3EqZ3xDW6fezgNz8AAPAMD0Do5ET6wP6d3JQLQtke3mr+Fb0wUJTmmtuTk4lLzeFVFBdC0v0V1XXtY1HW6jboOG2LKbYiJpqKomFLzfvt16oizrboWUFlSAkBFcQmuep8m5bsU++Gq96lZyeTs5kab4CBKc/Mv27zQsvWel5qGZ2CgRfb+ZNTKnhGfQPiQwebsMVQUF1Oan4+rjw8+ERFAdV0HU2oxPu5evpKut9zc1OZsV5uSj5NbUNKiGZo7nje0vbGqiqrycoxVVVSWl+Nmbivn0jPw79wBgLY9unJy+64mZnfMeGkpa/deUz/y92tSxkudvaK4hDP7DxI5YigAWicndB7uABz+Yy2dr56I1tkZMB2Lm+tsWiruAYG4m/tq2/4DyEq0/ntmJ8QTPGgIQgh8omOoLC6mLN+0r7LKiNHcRqrKy3HxMa2qcrJYKVNVXtbsnBfSnDFHCIFTrXOXSzHAOOpcDEznkS4OOo901PhemJ6Ob6eOAPh360r6jqaNKxfK7hFwPnvIgP5kxNc6rsYnEDb4wu1EVlXV+do5KSWntu8gZEB/u2cvOJKKW0Agbv6m7IH9BnCmdl9N3EXbgaa+6h0dQ2VxEWX5eRSln8Y7Khqt+bxQ36ET2fE7TRsJQZX5nKCypLimDyuXDyGEFngfmAB0AW4TQnSpVWwC0N783wPAvxqxbaOpSwWVxnISQiwGYoGDwN3Ak8AkwA3YDDwopay+s9ydQogFgBcwWUq5XQhxLxAnpXxUCOEPfAiEm8s/JqXcJIQYDrxjfkwCw6SU5+yxA8W5+bj7Gmr+7W7QU5ybX3NyDFB2rhBnd7eaA7OpzPk3MSe2x5P4xb8pPVvAiGf+Wec1CrPOkHv0OH4x9r0UpiQ3D48LZC8/V4jOw70mu5uvgRIbE2ip6zYQ1Ku7XfM1pCQ3z6re3Qx6Si6Q3d3XcFGTf0ajkd+fn0lhRhYx40bia+d6r1Z3HwzkpqbWKpOPW639LM3Ls9pPu+fKy8PNYJ2rpNZr2qrb6pPV+rYvycuv8zfLPZxW5/WPrNtI+MB+Nf/OOZzKjo8+p/hMDv0evt/mCe6FlNrIlFe7rvPq1nVJXl6DkyLd77ydza+/ye4VXyKlZPj0FxqdrTHstR9F2Wc4e+w4egdf1tia6712O3Y16MlLTbtgmZLcPFx9zmcvys7m7LFj6GNMly2kxyfgqtfj3S4cxaS543l92xuiI+h01Xj+++jTaHXOBPboStse3QDwDg3h9K5EQuJiObF1B8U5uU3L7qDx0nL741u2Ez7Q+k3k4VWrObZhM/qoCHrdcUuTPihyVHah0eLSpg07PvqM/GMn0Ee2I/bu23FydaEwI5PsA4dI+erfaJ2d6XnHLRiiIxud3VJpfh6ulv1Qb+BsmvU4U5pXq4zBQGleLt6RUURcMZH1Tz2GxlmHb7du+HU7fy5z6NuvOb15I07ubvR96vlm5byQ5o450mhk7bTpFGVmETVmNIYYx18q5chzMUdy1PjuFRpKRnwCQX16c3r7DkpymzauNKQ0L6/OMbN29tLc2n3Tup2se8nUTiLHjMYQbd1Ocg4cxMXLC8+2bR2S3VV/PpeL3kBBrb5alp+Hq8HXqkxZfh6ewaGkfvc1FYXn0DjrOJOShFeEaezocMudJLz9Ooe+XgFS0ue56XbP3lpdRjdn7wccllKmAQghvgCuAfZalLkGWGJ+379VCOEjhAgCIi5i20ZTK66UxuoILJRS9gAKgIeB96SUfaWU3TBNXlleuO0hpRxkLveZjd/3DjBfStkXuAH4xPz4k8AjUspewFCgzsfLQogHhBA7hRA7d377QyN2wcaAcDEfcll8whHWrzeT3prNsCcfJfmr762KVZSWsmH+B/S555Ym3X+jQbJu9tqLi2wOd7UKZe7ZT9rajfS67Ub7ZbsQG9lr17ut7Bezekqj0TB+zgwmvf8GualHyD9xsmkZm6BuPlv76eBPUW3/0WsVaaDt1Le9zb+Z9e/d+/2PaLQawgcPqHnMNyaaK+bNZszsaez/z89UlVc0GN+mi9ini8lX25HVa+l+x21cseAtut9xG/Eff974bI1hh/2oLC1l+zvv0f3O2+w/ptTWmuv9IsYY22Norbpe8B7d77gdZzc3KsvKOPifHy/JvWdaleaO5/VsX15YxKmdiVy5YC5Xf/AmVWVlHN2wBYB+D97HoVVrWPX8TCpKStE4NfGzV0eNl2ZVlZWc3pVI2IDzl+3EjB3JxLfnMu61Gbj5eJO4/MtGx76Y1zYVaXx2aawi7+gxoseMYNxrM3Bycam5/NtYZaS8qIjRM1+kx+03s2XBv5C2/n6NcVFjiO2+WlFURFbCLobOfYvhby2gqqyM01s21ZRpf8NNDH/zHYIGDOL4mt+bl/NCmjnmCI2GUa/MYvw7b5GXlkbBpTh3ceC5mEM5YHwHiJ06mbQ/VrN22nQqS0oRTo3/oO1CbPVJW6Vqs2wnI2fPYvzbb5GflkbBSet2cmrrVkIH2n+1VX25LqbeQeARHELEFVeR8NYcEt9+nTZh4QiNqX5PrltNh1vuYMi8BbS/5Q72LfrY7smVC7N8L23+7wGLp0OAExb/Pml+jIsoczHbNppacaU01gkpZfUZwjLgH8ARIcTTgDtgAPYAP5rLrASQUq4XQngJIXxq/b4xQBeLA4uXEKINsAl4SwixHPi3lLLO0VxKuRBYCDAzYUODR4UDv62puUeVITrC6pPa4tw83GutFHBp40lFcQnGqio0Wq3NMgCBnTuwJTOb0oJzuHq1wVhZyYa3/kXEkAGE92vaDalrO7hqDalr1gPgGxVBUU4u/hbZa6/kcWnjSXlRcU32kpxcqzJ5x06wfeEihj/7GC5t7H/ZmqVDq9aQZs5uiLKu95KLyF5cK/uF6Dzc8e/ckYyk3fiEhdpjF6y4GfS19iHX6pO86jIltfazdhl7OLRqNUfWmupWHxVp9SlhSW7denNp06ZO3VbnMn2yV3d7Y2Vlg3+zo+s3kR6fzPAXnrR5UusVEozW1YWzJ09iiGrcJ/SuNjLVXtFjq67dLlDXxzdsosddtwMQ0r8vCZ84duKqufthrKxk2zvvETpoICF97X//Cnvnrc+lqHc3g8Eqe2luHm61Lj+wVaZ6/4yVlWxf8B5hgwYSbK7roqwsirKzWfPCtJry66ZNZ/iMlxzSry9n9hzP3XwNNrfP3L0XjwA/XM03MQ/p24czBw8TMXQgXiFBjHj+CcB02WB6Ykojsjt+vKyWkZiCPrIdrt7eNY9Z/hw1ajgb5r3Dxbok2YXAzaDH17wKJbR/HPt/MN3rx92gJ7RvH9PlhTFRIARl587h6tX0SwZd9QZKLfthXi4utfpTnTK5ubj46MnZuxt3P3905tcP7N2X/MOHCB442Gr7oP6DiH/nDWKuvaHJOS+kuWNONZ2HB36dOpGZnIKXA85dLvW5mCM4YnwHaBMczOBnngKgMD2DjKQk+2fXG+qeF9b60gDXWtlLbLQTZw8PfDt1Iis5Ba9QUzsxVlWRvnMXw2fOsHtuMPfDvPO5yvJy61zW56I3UJqbU6uMKXvw0BEEDx0BwOF/f1mzeit9ywY63HYXAAFx/dm3+BMUk0u5qsjyvbQNtmara7/frq/MxWzbaGrFldJYtRudBD4AbpRSdgc+BlwvUN6SBhgopexl/i9ESnnOfA+s+zGt4NoqhOjUnNAdx49i4tzpTJw7nbC4WNLWb0FKyZlDqejc3eockIUQBHbpyPFtpuu409ZvJjSuFwDnMjJrPm3MPXIMY2UlLm08kVKy9aPFeIUE0flK+33zV4dxo5gwZwYT5swgJC6Woxs212R3dne3nb1rR05sM11HfmT9ZkL7mLIXnclh4/wPGPDI/XgF2X9JcW3tx41i/JwZjG9E9oCuHTlpzn50/WaCzdnrU1pwjvKiYgAqy8vJ3L0Pr+AgR+wO+qhICjOyKMrKxlhZycmt2wnuE2tVJqh3LMfM+5lzKBVnt7rtyx7ajxtdc6Nfy7o1vWY9ddul0/m63bCZkDhT9uA+vWxub4iOpDAjk8KsbKoqKzm+ZVvN3yM9KYX9P/7M4Cf/jpOLS83rFGZl19yMvSj7DOdOp+Ph1/j7vNiq66Dedev6xEZT7tzDqTi7u13w3kmueh/O7DsAQPaefXi2DWx0tsZozn5IKYn/5HPaBAfTfmLdG+Febnkbcinq3SfK1F7PZ99G21rZ2/buxfGNm8zZD+Pk7ma6FENKEj75DM/gIGImnL/5tHdYGBM/eJfx899k/Pw3cTXoGTHr5f+5SSuw73ge0runze3d/XzJOZRGZVmZ6V5Nu/fV3CS89GwBYLpZ8J7v/kv06OGNyO748bLa8c3brC6dBqzuIXRyRzzeoRf/wfOlyO7m4427r4GC06ZvKMvcvbem3oPjYsnasw8wTRiaznma962aXpFRFGdmUJydhbGykoxtWwno1duqjH+v3pzevBEpJfmph3Fyd8fFxwdXgy/5aalUmdtIzr49eAaZshaZb/wMkJ0Yj0fb4GblvJDmjDllBQWUFxUBUFVeTvaevbRx0LnLpTgXczRHjO8AZRbjyoH//EDkqJEOyV6UmUlRtin7qa3baBtbK3tsL05sOp/d2aKdVNRqJ55B59tJ9p49eAYFWV1maE9tIkx9tcTcVzO3b8WvZ92+mrHF1FfPph7Gyc29ZnKrvOAsAKU5Z8iO30lgv0EAuHjryT9gGlfy9u/BPcDx70mURjsJhFn8OxSo/a1j9ZW5mG0bTTR7ua/yP0MIEQEcAQZJKbcIIT4G9gNPY7qWVQtsBb6RUs4QQqwD9kspHxJCDAH+JaXsXuseVyuABCnlPPNr9JJSJgohoqWUqebHvgcWSSm/ry/bhVZcWZJSsuPzFaQn7kbromPgQ/fhGx0BwNo5b9P/gXtxN/hwLjObTQs+oqywCENEOIMevR+tszN7/vMLRzZsQaPVotU5E3vHTQR0ak/W/kP8PmMuPuEhCGGaE+5563WENPDtQUbZuKXXUkp2fb6c9CRT9v4PTq7Jvm7u2/Sbeg/uBj2Fmdlsevcj01dhR4Qx8JGpaJ2d2bZwESe278LDz3QtukajYfyrLwGwacFHZO07QNm5Qly9veh+4zVEjxxaf/ZGJTdljzdnd3LR0e/ByRjM2dfPfZu+U+/BzZx9izm7T0QYA8zZS/LP8vsLs6goKam5WeWEebMoyj7Dtn99ijRKpDQSPqAvXW+4usEslcamz9mnJyaRvHQl0mgkYvhQOl07ibQ/1gIQNWYkUkoSFy0jM9n0VdNxD05Bb15ttO29Dzmzb7+pjr286HzjtUSOGNao19fauPZdSkn8omVkmOu274OTa1Y4rZ87n74P3IubXk9hZhZb3zV9vbVPu3D6m+u2oe3TE5JJMO9v5IghdLnW9JXlPz/+LFUVFTWr9gwx0cRNuZujGzaz/4ef0ThpQQi6Xnc1IX1NJzkVxsa194zEJJKXrQSjkXbDh9LxmkkcWW2q68jRprpOWryMLHNd937gfF3veO9Dsvftp7ywEBcvLzrfcC0RI4Zx5sBBUpauwGg0mu7Xcu9d6M03QHeUpu7HmQMH2TDrNbzCQmtWtHW5+Qba9up5WeYF+9e7bOQYmZGYRMpy89elDxtKx2uu5sjqNebso5BSkrx4KZkpKTjpXIidasqec+AgG2a/alrtUF3XN91Yp65/e/wJRsyccVFv3Bc845jLlBa/eD1De7XDz9udrLwiZi1ax+KfE+36Gk+9OarB55s7nje0/e6vv+f41h0IjQZ9RDh9H7gXrbMzB3/5nUOrTO0wtF9vetx6g82VnhdqM44cLyvLyvjv359k4ttz0bm717zmtg8+Jv/YcUDg4e9Hnyl3N+kDDUdmzzt6nJ0ff46xsgqPAH/6PTgZnacHVZWVNfe+0jhp6XnHLQR27VwnW36pc6P2JTs5kQMrlyONRkKGDCNq0jWcWLsagLCRo5FSsn/ZYs7sNo0zXSdPxTvSdI+/w99/S8b2bQitBq/wCLreOwWNszOJ779DUUY6Qmhw9fWly933Wd2fpz7uzo3/5ttqTR1zzh4/QfzCj5FGI9IoCenfj07XXdPo13dxalx2R52LObu7saXWeWS3G68hqoHzyLLKxl2W54jxPfW3VaT9YWp3wXF96HLzTRd1WWRj3z5nJiWRsmyF6Tx12FA6Xn01R9aYs48yZ1+ylKyUFLQ6F2LvP99OEhZ+jJTn20nHa8+3k/iFH6OPiSZyVMNjtqXC8sZdcHUmOZGDXy4Do5GgwcOJvOoaTq4z1VnoCFNfPbBiMbm7k9HodHS57wG8Ikx9defcmVQUFqLROtH+ltsxdDbdszD/0AEOrlyKNBrRODvT8Y57a+5/dSEfDO17GXxNiuO8FL/xkk3OzOw9pN66FEI4Ybqf9WjgFLADuF1KuceizJXAo8BEoD+wQErZ72K2bQo1caVcNPPE1c/AemAQcAi4C3geuBU4iul61mMWE1dbgOHUf3N2P0zfOtAZ06Wr680TXe8CI4EqTDdyu1dKWe9XxDRm4upy0tiJq8tJYyeuLifNmbhqabYmrlqLxk5cKUpjJ64uJ46auLoULjRxdTlrzW2mNWvsxNXlpDkTVy2tsRNXl5PGTlxdTlrz2+fGTlxdbtTElf00NHEFIISYCLyNaXHKZ1LKV4QQDwFIKT8Uplne94ArgGLgPinlzvq2bW7e1t1ylUtKSnkU01da1vai+b/a5UfU83sWAYvMP58BbrFR5u9NDqooiqIoiqIoiqIorcjl9NG6lPJnTItWLB/70OJnCTxysds21+VUN4qiKIqiKIqiKIqiKIpSQ624UhRFURRFURRFURRFaUGiFd8SxNHUiitFURRFURRFURRFURTlsqRWXCmKoiiKoiiKoiiKorSgv/Sd55tJrbhSFEVRFEVRFEVRFEVRLktqxZWiKIqiKIqiKIqiKEoL0qh7XNVLrbhSFEVRFEVRFEVRFEVRLktqxZWiKIqiKIqiKIqiKEoLUve4qp+auFL+Etycqlo6QpO4aIwtHaHJMkpcWzpCk7loW2d7Aag0tt6Fsn6u5S0docla89Jt11bc3s+UurR0hCZ76s1RLR2hyeY9saalIzTZ/bOvaOkITbZ9T2VLR2iy0b1bb3YPp9abvaIVnxOEexa3dIQmO5jv2dIRmszHraKlIyhKk7Te0U5RFEVRFEVRFEVRFEX5S1MrrhRFURRFURRFURRFUVpQa17h72hqxZWiKIqiKIqiKIqiKIpyWVIrrhRFURRFURRFURRFUVqQUHdnr5dacaUoiqIoiqIoiqIoiqJcltSKK0VRFEVRFEVRFEVRlBakFlzVT624UhRFURRFURRFURRFUS5LasWVoiiKoiiKoiiKoihKC1LfKlg/teJKURRFURRFURRFURRFuSypFVeKoiiKoiiKoiiKoigtSN3jqn5q4kr5nyOlZMtnX3IyYTdanY7hj96LX1R4nXLnMs+wZv7HlBUW4xsVxoi/T0br7ER5UQlrF3xK0Zk8jFVV9Lh6LB1GDQbgi789j7ObC0KjQaPRcO3rLzh0PzZ8+hXH4vfg7KJj9KN34x9ddz+Sf15H0n/XUJCRzeRF83Dz8gTgwJ/bSfh+FQDOri4Mf+A2/CJDHZbXUkZSCklLVyCNRiJHDKPj1VdaPS+lJGnJCjKSktHqdMQ9OAV9ZAQAOxd+SkZCEi5eXoydO/uS5K2dbdfilZxOSMHJRceAv03GENmuTrnCrGw2vbOQsqIiDBHhDHz0frROTpzcmUDyV9+D0KDRauh9960EdGpv94wJS1aQkZiCVqej30NT0NeTceu7H1JeWIQ+sh39Hp6K1smpwe3Li4rZ+fHnnD1xCoSg7wP34dchhhNbd7Dn2/9QcDqdMbNexBAVaZf92PLZl5xI2I1TA321wKKv+tnoq4UWfbXjqMHkn8pg9fyPa7Y/l3mGPrdMovtVY5qd2TL75s++4ni8KfuIv9+Dfz3ZV8//hNJzRfhFhTPqH/ehdXairLCIde8voSDjDFqdEyMeuRtDeAj5pzL4461PrLaPu3USPa4abbfstffjz0++5uiuPTi56Bj3j7sIsDHOJP20joQf13I24wwPLJlbM86cTDnIj699hFeALwAxA3vR/5aJDslanXfX4pWcMvfPgQ30z43vLKS8qAh9RDiDzP3zhLl/CqFBaDX0MffPqvIKfn95LlUVlUijkfD+fehx0zXNzpqweCXp1f2sgaxbFnxkztqO/o/cf76f1rP9gZ9XkbZmAwjwCQul30OT0eqcyTt2gl2fLqGytAwPfz8GPDIVZ3e3Zu1HY3z49CQmDOhAdn4RcZM/vGSv25AzKcnsX7EUKY2EDh1B5JWTrJ6XUnJgxVKyU5LQ6lzoNuUBvNpFAHBs1S+cXP8nCGgTEkbXKVPROus48NVKshMT0Dg54e4fQNcpU3F293DofgwI9eH/BkWiEfDD/iyWJJ2yWa6zvyefXtOdF1cfZM2RHAI8dMwY2R6DmzNSwvf7M/lyd7pDs4JjzgOSV3xJenwiGicnPAMD6PPAFHQe7nbPLqVkx6IvOJWQgtZFx+C/3YdvVN2+ey4rmw3vfExZYRGGyHCGPDoFrZMTGXsOsHbe+3iax8Xwfr3peeMkis7ksvH9zyjNPwsaQYfRw+g80X7Hpers8YtXcto8bjR0DrN5wUfmc5h2DDCPO0c3bmXvD78ApnPHuCl3oW8XRlFOLls/+ITS/AIQgpjRw+g4Yaxds9fej/WfVh+bnBn797ttH5t+Xkfij2s5m5HN1MWv1xybUrclsXXljwjzudiwyTcS3CXGYXmzU5LZt2IZGI2EDhtOlI1xZt+KZZxJTkKjc6H7lKl4R0RQmJ5O0r/erylXnJ1F++uuJ2LcFSR+8B5FGRkAVBQX4+zuzuCZ9j8vdkRf3fP1vzm9KwEhBC5eXsQ9NAU3vd7u2ZW/FnWpoPI/52TCbgrSs7jp3VkMfehONi1cbrPc9mX/pttVY7j5vVm4eHhwYM0mAPb+uhZ9aBDXvzmNK19+gm1LvqGqorJmuytnPMH1b0xz6KQVwLH4PZxNz+LO919mxEO3s27hSpvlgjpFc82Mf9LG32D1uFegL9fOepxb579I3E0TWPuh7XqwN2k0krhoKYOffpxxr7/CiS3bKDhpfYKdkZRMYUYm49+cQ+8p95Lw+dKa59oNHcLgp//vkmS15XRiCufSs5j09qv0m3o3Oz5ZZrNc4opv6XjlWK5++1V0nh6mN5FAYLfOTJg7g4lzp9P/wXvZvnCx3TNmJKZQmJHJhLdeI+7+e9j12RKb5ZJXfk2HCeOYOH8Ozh4eHFm74YLbJyxZQdue3Znw5quMm/MyXiHBAHiHhTDo8Ufw79TBbvtxImE3Z9OzuPndWQx56E42NtBXu181hlvem4XOoq/u+XUtPqFB3PDmNK6y6Ks+IW254Y1p3PDGNK6b+wJOLjoi+sfaLTfAiXhT9lvfm8mwv93BxoUrbJbbtvTfdL9qNLe9PwsXT3f2rzZlj//2V3wjw7hp/jRG/v0+Nn32FQA+IW258c0XufHNF7n+9edxctER2a+XXbNbOrprD/np2dzzrxmMfvh21nz4hc1yQZ2juf7lf9QZZwCCu8Rwx9vPc8fbzzt00gpM/bMgPYur336V/lPvZns9/TNhxbd0suifqeb+2bZbZyaa++eAB+9lm7l/apydGD3tSa58fQYT57zE6cTdnDmU2qys6YkpnMvIZOL8V4mbeje7Pl1qs1zyim/oOHEsV85/DZ2He00/rW/74tw8Dv26mrGvTmPCvFlIo5HjW7YBsGPhInrceiNXvD6TkLhY9v/312btQ2Mt/TWJa565NMeaiyGNRvYtW0zvx59i8Oy5pG/bQuEp6+PRmZQkijIzGfLaG3S5ZzJ7l3wOQGleLsf+WMWAl2YyeNYcpNFIxratAPh26cagWa8xaOaruLdty5GffnTofmgEPDUkisd+2cutXycyLsaPSJ+6E5IaAY/2a8e2k/k1j1UZJe9sOcqtXycy5T/J3Nilrc1t7clR5wGB3boydu5sxs6ZhWfbQA788F+H5D+VuJuCjCyufecVBk69i22f2m7T8cu/pfPEMVz3ziu4eLhzeM3GmucCOscw6fXpTHp9Oj1vNE1iCK2GuLtu4pr5s5g4+3n2r1pL/snTds1ePW5cNd90DrOznnEn0TzuTDKPO2nmcccjwI8xLz3NxNdfpuv1k9jxsXmM1GiIvfMWrnxzNuNmPc+hVWs5a+fslo7F7yH/dBZ3fzCDUX+7g7Uf1XNs6hTNdTaOTWE9OnL7/Be4ff7zjHn0TlZ/4LhxSRqN7F26hLjHn2TIK3NI37a17jiTnExxZiZD58yj2733sXfpIgA8g4IYPHM2g2fOZtCMmWh1LgT2jgOg18OP1jzXNi6OwD5xDsnuiL7a4coJjJ0zizGvzSQotif7/v2D3bO3VhohL9l/rY2auLIDIcTmls5wIUKIEUKIQU3Y7qgQws8RmS7y9Z+39+88tiOJ9iMGIIQgoEMU5cUlFOedtSojpeT07v1EDuwNQPsRAzi2PbE6FBWlZUgpqSwtw8XTA4320nelI9uT6Gjej7YdoygvKqYo92ydcv5RYTWrHSwFdYrG1dP0CXBgh0iKcvIcnhkgNzUNj8AAPAMC0Dg5ETqgH6d3JViVSd+VQLuhgxBC4Ns+moriYkry8gHw79wRnafnJclqy6mdiUQOG4gQAr/20ZRbZKsmpSRzz37C+/cBIHLYIE7sTATA2dUVIUwLgSvLyh2TcVcCEfXUn2XGrD37Ce1vOtGJGDqIUzvjG9y+oriEM/sPEjliKABaJ6eaT7O9QoLxCg6y635Y9tXAi+yrHUYM4Ki5rwqLvlpRT189nbIfr0B/2vjX7SPNcXRHMh2Gn89eVlRCkc3sB4iqyT6Qo9uTAMg/mU5I904A6EPbUpiVQ3F+gdX2p1L24xXoRxsb/dte0rYn03lEf4QQBHWMNO2HjXEmICoMr0DH5bhYJ3cmEtXI/hk1bBAnL9A/hRA4u7oCYKyqwlhVRXMX9J/alVjTz/wa6KeZlv102CBO7Uy44PbGqiqqyssxVlVRWV6Om94HgHPpGfh3Nk0ut+3RlZPbdzVrHxprU/JxcgtKLulrNuRsWiruAYG4m49HbfsPICvRuk6yE+IJHjQEIQQ+0TFUFhdTlp8PgKwyYjTXc1V5OS4+phUDft26o9FqAfCOiqE0L9eh+9HF35OTZ0s4fa6MSqPk99QzDIuoO4l8c9cg1h7JIbekouaxnJIKDuQUAVBcYeRofgn+HjqH5nXUeUBgj2419W6IiaYk1zHnNSd2JBI9zDS++3eIpryomGIbfTdjzwHaDTCNM9HDB3F8R4KN33aeu96nZuWWs5sr3iFBFOfmN7hNY52sNW40NEaGmcedyGGDOGked/w7xKAznzv6xURRbK5jN71PzcotZzc3vEKCap5zhLTtyXQaaXlssn0OHFDPObDO7fxYX1HqmHOxavlpqbgHBJwfZ/oNIDMh3qpMZkI8wYMG14wzFcXFlJrHmWo5e/fgHhCAm5/12zIpJRnbtxPUf4Ddszuqr1qu9K0sKwOhLpBTLkxdKmgHUspGTwhZEkJopZRV9spTjxFAIXDZT7LV8jzwqj1/YVFOPh6+50/oPAw+FOXk4a73rnms7FwRLh7uNSdAHr76mpOHLhNG8vuc91kx9WkqSssY9fhUhMb8ZljAL7PeBiHoPHYoncYOs2d06/3IzcfT7/yyWg9fPUW5+XgYvBvYyrZ9f2wmPLarPePVqyQ3D3eL+nczGMhNTa1VJh83qzJ6SvPyat58taTi3Hyr/O4GU9uwzFZ2rhBnd7ea9uNu0FudQJ/YHk/SF/+m9GwBw5/5p90zluTl4WawruOSWvVXfq4QnUUbd/c11Jxo1Le90GhxadOGHR99Rv6xE+gj2xF79+04ubrYfR/A1Fc9m9lXV815n+Xmvjrasq+apW7aQfSQvvbPnpuPh1X/9KE4Jx8Pi+yl54qs/gaevj4UmbMbIkI5sjWBoM4xZB06wrnsXNO++3hZZN9JjAOyWyrMPYunn0/Nvz19fShs5DiTceAIyx97FQ+DN0PvvQ7f8GAHJDVpav8srtU/E839c4RF/zQajfz63CzOZWTRYdxI/NpHNStr3bFQbxr7LtBPq7PWt70hOoJOV43nv48+jVbnTGCPrrTt0Q0A79AQTu9KJCQulhNbd1Cc49gJlctdaX4erhZjnavewNk06+NRaV6tMgYDpXm5eEdGEXHFRNY/9RgaZx2+3brh1617ndc4tfFP2vaz/xtKSwEeLmQWnX/znVVUTtcA6zeL/u46hkcYeOSnPbzgb/uSqCBPFzr4ebAnq9CheS/FecDRPzcQNqCfXfLWVpxnnd/dfNxxrzXO6OqcB+TXPJ99MI0fn3oZN4MPcXfeiE9YiNVrFGadIffICfximn/ZvaWS3Dyrc2BbY2TtccfN12BzEjB13QaCetVt84XZZ8g7ehy/mOaNkQ0pzMmnje/5Y6ynr77Rx6bUrYlsXvYfis+e4+oXHnZETADK8vJwM5yfPHM1GDhbq72X5edanXe56g2U5eXi6uNT81j6tq02J6fyDh5A5+2FR9u2ds/uyL66+6tvOb5hE87u7gx74Wm75m7N1BRe/dSKKzsQQhSa/x8khFgvhEgUQuwWQgxtaBshxEwhxDZgoBDiTiHEdvO2HwkhtBbl3hRCxAshVgsh/M2PRwshfhVC7BJCbBBCdDI/PkkIsU0IkSCE+EMIESiEiAAeAh43//6hQgh/IcS3Qogd5v8Gm7f3FUKsMm//ERfoP0KIaUKI/UKI34UQK4UQT5ofXyeEiDP/7CeEOGr+WSuEmGd+zWQhxIP11Z0QYg7gZn6szhpeIcQDQoidQoidW79pzDL8uksjRa2ZfiltLJ80FzmVuAffiDBu//h1rpv3Ips/XUl5sekT5Emzn+a6eS9yxQt/Z++vf5K+92AjcjWSrRWeTRjtTqYcYN/qzQy6+7pmR2qq2vVvc+cum09jbLWfi9jMolBYv95c9dZshj35qOl+V/Zmc/VvrTbe0H7Us700VpF39BjRY0Yw7rUZOLm4sO+Hn5oZtiEXbgcN9dWT5r56x8evc/28F9lk0VcBqioqObYziciBfewZujpYvbkaKlO9e7HXjaesqJhvnpjN7p/X4RcZVvMmAszZdyQRNcgB2S+QsTEDjX90GPctnMkdbz9Pz4nD+fG1hfbLZtNF1LsttfrnJBv9U6PRMHHudK77YB45qUfIP2H7HkIXH/XCWW3WfnXWerYvLyzi1M5Erlwwl6s/eJOqsjKObtgCQL8H7+PQqjWsen4mFSWlaJz+xz+/tFmHFz4eCSGoKCoiK2EXQ+e+xfC3FlBVVsbpLZusyqX9+B80Gi1BA5r1+WaT1N61xwdF8P72YxjruTrEzUnDnLEdmb/5CEUVjv4stS57ngfs//5HNFotYYMHNj+YLTajXUQ2cxlDZDg3vD+HSfOm0+mKUax94wOrYhWlpax761/0vecWdPa+B10Dx52aIra2q1Uoc89+0tZupNdtN1o9XlFaysb5H9D77lsdfP+8C5/LX0j0gF7c9d50rnr2QbaudOzlvHVcRHO3rHNjZSVZiQm07Vt3MtY0oeWgtm4zln36arebb2Diu28RNmgAqatW2yec8pf2P37GYne3A79JKV8xTzw1dEdID2C3lPIlIURn4BlgsJSyQgjxAXAHsMRcLl5K+YQQ4iVgOvAosBB4SEp5SAjRH/gAGAVsBAZIKaUQ4n7gafO2HwKFUso3AIQQK4D5UsqNQohw4Degs/n3b5RSzhRCXAk8UN8OmCembgBiMbWleOBC1x1MAc5KKfsKIVyATUKIVcD1tetOSrlBCPGolLKXrV8kpVxorgfmpaxr8ELdvb+sZf9q070F/KMjKLL4lLkoNx93g49VeVcvT8qKijFWVaHRas2rPExlDq7dTM9rr0AIgXdQAG0C/Mg/lUFA+0g8zL/HzduLdv16kX3oKEFd7HfPn5Rf1rHnd9OJcWBMOwrPnP8ErCgnD49Grkg6c/Qkaz9YxqRpj+La5tJcfudm0Ft9yl+Sa/2JUnWZEqsyeXXKXEoHf1vDYfM9cHyjI6zyF+fW/VTJpY0nFcUlNe3HVhmAgM4dKMzMprTgHK5ebZqV8dCq1RxZux4AfVQkJbnWdVw3YxvKLdp4cc75v4Np5YaN7YXAzaDHNyYagND+cez/4edm5a5tT62+Wlirr3rYqa+C6R5afpHhVquYmmP3L+vY/4c5e0w7iqz6p+1xxvJvUJhz/hN7nbsbIx+9BzBNzq342wtWlwSeSNiNX5T9sltK+vlPdq8yjzPt21F4Jr/mucKcfDwb8Ym2i8Ubl8i4bqz96EtKCgprbpBrDwd+W1NzjyqDjf7pfhH9s3YZgMDOHdhio3/qPNwJ6NKR04m766ySuJBDq9aQtsbUTw1REbXGQttjSe1+Wl3Gzddgc/vM3XvxCPCryRzStw9nDh4mYuhAvEKCGPH8E4DpssH0xJRG5f+rcdUbKLUY60rzcnGpdaypUyY3FxcfPTl7d+Pu54/Oy9QHA3v3Jf/wIYIHmr6s5dSmDWQnJxL35LONfkPdWFlFZQRaXN4X4KHjTLH15U+d/TyZNdp0PuLj6sygcD2VRsn6Y7lohWDO2I78ejibdUcdvwrPkecBx9ZvJD0hiaHPP2XXet//21oOrTb1Xd/oSOtxJicPN731uOjSxpPyOucBpjKWk1Ghsd3Z9unymnHGWFnJujf/RdSQ/rTr39su2Q+uWkOqedzxjTKdA/tXZ7+Icackx/ocIu/YCbYvXMTwZx/DxeLc0VhZycb5HxAxuD9h/ez/oUrSz39anQOfs7jFRWFOntWK5sYI6dqesxln7H5squai11OSm1Pz7+oxxLqMgZLcXKofNY1F58tkJyfh1S4CF2/rfTRWVZG5ayeDps+0e264NOfsYYMGsPmNt+lyY8t9gH45Ea3w3lOXipq4sq8dwGdCCGfgeyllYgNlq4BvzT+PBvoAO8wHWTcgy/ycEfjS/PMy4N9CCE9gEPC1xUG5+lqdUOBLIUQQoAOO1PP6Y4AuFtt7CSHaAMMwTSIhpfxJCNHQBepDgP9IKUsAhBAX83HFOKCHEKL6IxpvoD2Nq7tG6zJhJF0mjATg+K4U9v6ylqjBfck+dASdu5vVpUdg+jQhuGtHjmyJJ3pIXw6t20q7vj0B8PAzcCplP227tKc4v4CzpzPxCvSvuZeOzs2VitIyTiXtJfamK+tkaY7uE0bQfcIIAI7uTCHll3W0HxJH5kHTfjRmifS57Fx+eX0hY/55Lz7BgXbN2RB9VCSFGVkUZWXjZtBzcut2+j3yoFWZoN6xpK5aTejA/uQeTsPZza1FLxPsMH4UHcaPAuBUfDIHf1tDu0H9yDmchrN73WxCCAK6dOT4tl1EDOrHkfWbCY3rBcC5jEw8AwMQQpB75BjGykqrE7+maj9uNO3Hmb5Z7nRCEodXrSaspv7c68nYiZPbdhI+qD9HN2wmJM50g/LgPr3q3d7d10DB6XS8goPI3L235ubs9tJ1wki6WvTVPb+sJXpwX7Iusq8eXLeVCHNf9fQzcDplP0G1+mq11I32vUyw24QRdDP3z2O7Utjzyzqih8SZs7vWOakWQhDcrSNpW+KJGdKXg+u2ENGvBwBlRcU46XRonZ3Y/8dGgrq0t3qzc3jjTodc4gjQc+Jwek4cDsCRnbtJ+vlPOgztQ8bBo7h4NG6cKco7i7uPF0IIMg4eRUqJaxv7frtax/Gj6GjRPw9Y9E9dPf0z0KJ/pl1E/ywtOIdGq0Xn4U5leTkZKfvocvUVjc7aftwo2o8zZT0dn8ShVWsIrxlL6umnXTue76frNxPcx5Q1pHdPm9u7+/mScyiNyrIytDodWbv3oY+KAKD0bAGu3l5Io5E93/2X6NHDG70PfyVekVEUZ2ZQnJ2Fq95Axrat9HjQ+pIh/169Ob76d9r2H8DZtFSc3N1x8fHB1eBLfloqVWVlaHQ6cvbtwTvCNCl+JiWZoz//l77PvIDWxTGXUlval11ImLcbQW1cyC4qZ2y0H9PWWK/4vu6L8/fUmTY8hk3H81h/zPRm88Xh0RzNL2FliuO/TRAcdx6QkZTCgR9/Yfi0Z3Cyc713Gj+STuNNx6aT8cns/20tEYP6ceaQ6Tyg9uS3EIK2XTpybOsuIgf3I/XPzYSZx5mS/LO4epvGxTOHjyCNEpc2nqZvo/1wMT4hQXS5apzdsncYN4oO46rHSNO40+4C405g146c2LaTdoP6m85hzONO0ZkcNs7/gAGP3I9X0PlL06SUbFu4CK/gIDpdOd5u2S1ZH5tSSP75TzoMiTMdmxp5DpyfnoV3W3+EEGSlHqeqstLux6Zq3pFRFGdlUpydjateT8b2rfR48G9WZQJiYzm++g+CzOOMs5v7RV0mmLN3Dx5BQVaXM9uTo/rquYwM2pgvbUyPT6RNkH3vkar8NQmbl1kojSKEKJRSepp/DgauBP4BzJNS2vw6r1rb/B0IllI+Z6NcFeAipawUQkRhmuwaDhyQUtbp5UKIdcBbUsofhBAjgBlSyhFCiBlYr7g6A4RVTzpZbJ8IXCelPGL+dy7QQUp5xsZrPQ74SCmnm//9FnBaSvmGEOIP4Hkp5XYhRCimVVwRQohvgYVSyt9s/L46dWdZTw250IorS1JKNn+ykpOJpq93H/bwPfjHRADw6yvvMvRvd+Fh8KEgM5u18z+hrLAI34gwRvxzMlpnZ4py81n/3iKK88+ChB7Xjaf9sAEUZGbzx+umr/c2VlURPbQfsTc0/A1aLhrjxca2uR/rP/6C4wl7cXLRMfrRuwmIMd0Y88fZ7zHq4TvxMPiQ9NMaEr77neL8Aty829Cud1dGPXIXa95fStrWBDzNN6XWaDXcPK9OE6xXRolrk7OnJyaRvHQl0mgkYvhQOl07ibQ/1gIQNWYkUkoSFy0jMznl/FfrRpneEGx770PO7NtP2blCXL286HzjtUSOaNy9xFy0Tb8MQkrJzs9XkJ64G62LjgEP3YdvdAQAa+e8Tf8H7sXd4ENhZjYbF3xEeWER+ohwBj16P1pnZ/b+5xeObNiC0GrR6pyJveMmAjq1v+jXrzRe+ApvKSXxi5aRkbQbJxcdfR+cjMFcf+vnzqfvA/fiptdTmJnF1nc/oryoCJ924fR/ZCpaZ+cGt887epydH3+OsbIKjwB/+j04GZ2nByd37CJh8QrKCs7h7O6OT7swhj/3hFUub11FnawX2o/Nn6zkhLmvDm+gr66x6KsjLfrqnxZ9tae5r4LpxtsrHnyWW99/BZ3HhS9naOy3r0gp2fjJF5xMMGUf8cg9+Jv758+z32X4w+bsGdn8Mf8TygqL8YsMY9Q/70Pr7EzGgTTWLvgcjUaDT1gQIx6+CxfzDXEryspZ/sBz3PbBbFwuIrtrM9v7uoVfcSzeNM6M/cedBJr34/uZ7zPm0TvwNPiQ+N+17PruD4ryCnD3bkNEn66MefQOkn5aR/KvG9BotTjpnBk6+QaCO138fU/OlDbuzaeUkh0W/XNgPf3zXGY2mxZ8ZPqaeov+ucfcPzW1+mfesRNs+ddnSKMRaZS0G9iX7jdMajBLpWx4tYeUkvjPl5Nu7mf9HpyMwZx1/dy36Tv1HtwMegozs9nyrmks8YkIY4BlP61n+91ff8/xrTsQGg36iHD6PnAvWmdnDv7yO4dWmcba0H696XHrDTZXpcx7Yk2j6v1iLX7xeob2aoeftztZeUXMWrSOxT8n2vU17p/duAnF7OREDqxcjjQaCRkyjKhJ13BiremylbCRo5FSsn/ZYs7sNh2Puk6einekqQ0f/v5bMrZvQ2g1eIVH0PXeKWicndnw7BMYKyprbkrsHR1Dl7vvu2CW7XsqL1imPoPCfHh8YCQajeDHA5ksSjjFdZ1NH0p9ty/Tqmz1xNWaIzn0DGzDwmu6cyinqOYqsn/tOMbmE/mNev3RjVwc5IjzgF//7xmMFRU19W6Iiab3lHsumMXDqXH1LqVk+2crOJW0ByedjkF/uxc/c99b/do7DHzwnppxZv07Cyk3jzND/j4FrbMz+39dw4Hf16HRmMaZuLtvJqBjDJn7D/Hb9NfxCQ+p6Zext11PaGzd+0hVq7iIc4La2XeZxw2ti47+D06uGSPXzX2bflPvwd087mx6t/ocJoyB5nFn28JFnNi+Cw8/87mjRsP4V18ie/8h/nh5Dt5hoQiNKXvPW64nOLZHvVn8Xcsalb32fqxb+CXHEvbi7KJjzN/vqjk2/WfW+4x+xOLY9P3vFJuPTe36dGXMI3ey89+r2L9uW82xacg91xHcxfa932w5mN+4Dxyzk5LYt3IZ0igJHTqM6ElXc3ytaZwNHzkKKSX7li0hO8XU3rtPub9mnKkqK2PdE48x7PU3cXa3vpgn+ZOF+ETHED5y1EVn8dA17pzAEX11y9vvUZieAULg7udL78mm493FeDVu0OVy7xCHeHvPmks2OfNY11Gtqi7VxJUdVE+uCCHaAafMk0yPARFSysca2sb8cxfgP5guFcwSQhiANlLKY8K0XvA2KeUXQogXgUAp5d+F6ZsM50spvxamo1sPKWWSECIBuF9KuUsI8TkQaZ64egLwsphkWgEkSCnnmf/dS0qZKIRYAGRJKWcLISYAPwP+9Uxc9QU+wrT6ywnTZYIfmyeuPgF2SSn/Za6Lx8wTVw8AE4GbzJdFdgBOAX626s684itAStngO97GTFxdTpozcdXSmjNx1dKaM3HV0i5m4upy1diJq8tJa/za4GrNmbhqaY2duLqcXGji6nLmqImrS6GxE1eXk+ZMXLW0xk5cXU4aO3F1OWnsxNXlpDkTVy2tsRNXl5PGTlxdbv7qE1cLLuHE1T9a2cRV6x3tLk8jgETz5NENwDsXs5GUci/wIrBKCJEM/A5Ur6YqAroKIXZhuodV9UXMdwBThBBJwB7gGvPjMzBdQrgBsJxs+hG4znyj86GYVjXFmW+QvhfTzdsBXgaGCSHiMV3Wd7yB3DuAH4Ak4N/ATqD6u2jfAP5mnmCz/N7WT4C9QLwQYjemiS8n6q+7hUCyrZuzK4qiKIqiKIqiKIry16ZWXF3mLvZSuZYihPCUUhYKIdyB9cADUsr4C21nb2rF1aWnVly1DLXiqmWoFVctQ624ahlqxVXLUCuuWoZacdUy1IqrlqFWXF3e3t27+pKdcP69y+hWVZfq5uxKcy00X+roCixuiUkrRVEURVEURVEURVH+mtTElYMJIbZx/hv/qt0lpbyo76C+HFZbCSF8gdU2nhotpbz9UudRFEVRFEVRFEVRlL+S1ruG0vHUxJWDSSn7t3SG5pJS5gC9WjqHoiiKoiiKoiiKoij/W9TElaIoiqIoiqIoiqIoSgsSrfieqo6mVqMpiqIoiqIoiqIoiqIolyW14kpRFEVRFEVRFEVRFKUFqVVF9VN1oyiKoiiKoiiKoiiKolyW1Ior5S8hNa/Fv3yxSZw0xpaO0GQd9IUtHaHJTha6tXSEJvPQVbZ0hCYrqFCHnJYgpa6lIzRZax4jpRQtHaHJ7p99RUtHaLJPXvy1pSM02S3TrmrpCE3moi1q6QhNVmFsvZ/jV7TiceZcZes9J9C7VrR0hCZzbsXH1f8F6h5X9Wu9I7WiKIqiKIqiKIqiKIryl9Z6p7oVRVEURVEURVEURVH+AlrvGkrHUyuuFEVRFEVRFEVRFEVRlMuSWnGlKIqiKIqiKIqiKIrSgjTqHlf1UiuuFEVRFEVRFEVRFEVRlMuSmrhSFEVRFEVRFEVRFEVRLkvqUkFFURRFURRFURRFUZQWJNTd2eulVlwpiqIoiqIoiqIoiqIolyW14kpRFEVRFEVRFEVRFKUFaVA3Z6+PmrhS/qfl7E7i0BdLwWgkaOgI2k242ur5ovTT7F/0EeeOHyXq2psJH39lzXP7Fi0kJzkBXRsv+r0895LkPZOSzIGVS5HSSMjQEUROnGT1vJSSAyuXciYlCa3Oha6TH8CrXQQAx1b9wqkNfwLgGRpG18lT0TrrADi+ehUnVv+O0Grx69GTDjfd5tD9kFKy4dOvOBa/B2cXHaMfvRv/6PA65ZJ/XkfSf9dQkJHN5EXzcPPyBODAn9tJ+H4VAM6uLgx/4Db8IkMdljczOZmUZSvAaCR8+DA6TLqqzv6kLFtOVlIyWhcdsVPvxycigqrycja++hrGikqksYrgvn3pdP11NdulrfqdtD9Wo9FqCOzZk6633mL37FJKdi1eyemEFJxcdAz422QMke3qlCvMymbTOwspKyrCEBHOwEfvR+vkxJGNW9n3wy8AOLm40vf+O9G3C6PgdAYb3/nIavseN11Dp4lj7Zo9YfFK0hNT0Op09Gsg+5YFH1FeVIQ+oh39HzFlLziVzvaPPiPvyHG633Idna66AoCq8grWzJxLVUUFsspIWP8+dLvpWrvldmT2akajkd+fn4mbQc+wp//Z/KxLVpBRnfWhKejrybr13Q8pLyxCH9mOfg9PRevk1OD25UXF7Pz4c86eOAVC0PeB+/DrEMOWBf/iXHpGTRmdhzvjXnu5WftRvS+OaO8A/3n0GZzcXBEaDRqthitendbsrI6o94LT6Wx990Or7bvdeC0dJoxj9zffc2Ttely82gDQ/eYbCIrt0az9ANOxaf8K07EpdOgIIq+0cWxasZRs87Gp2xTrY9PJ9X+CgDYhYXSdYjo2HfhqJdmJCWicnHD3D6DrlKk4u3s0O2tTffj0JCYM6EB2fhFxkz+88AaXWN6eRI5+tQQpjQQOHknI+Gusni/JOMXhJR9RdOII4VffQvDY88ex+Bf+jsbVDaHRIDQaejz3qsPztvZjU/zilZw2972Gsm9e8JE5ezsGWIzvW83je49brqOzxfi+/+dVpK7ZgBDgHRbKgIcmo9U5NzlrRlIKiUtWII2SyJFD6XT1lVbPSylJWrKC9MQUnHQ64izGofq23VNrHOlmHkeMlZXs+mQxeUeOIoSg5923E9ClU5Oz1yalZNvnX3Iy3tRmhjxyL35Rdev9XOYZ1r29kLLCYnwjwxn298lonU1vedP3HGD7519irKrCpY0nE2c+BUBZUTGb/rWEfPOxasjf7iGgY7Rds7eWNmMr+45FX3AqIQWti47Bf7sPX1v1npXNhnc+pqywCENkOEMenYLW6fxUw5nDR/jlxdcY9tiDtBvQB4C9P/3OoTUbEAh8wkMY/Lf77Jpd+WtRE1fK/yxpNHJwxSJ6Pf4cLnoDO1+Zhl/P3ngEn58AcfbwoP2td3MmcVed7YMGDSV05Fj2fXZpTmCl0cj+5Yvp/cQzuOoNbJv1Ev69euMZHFJT5kxKEsWZmQx+9Q3OpqWyb+nn9H/xZUrzcjm+ehWDZs1Fq9OR/K93ydy2leAhw8jdv5fshHgGvvwqGmdnygvOOnxfjsXv4Wx6Fne+/zKZB4+wbuFKbpr7TJ1yQZ2iiYjrzvfT3rJ63CvQl2tnPY6rpwfH4nez9sPlNre3B2k0krxkKYOefgo3g4E/p79M296xeIWcr/es5GSKMjMZPW8ueampJC1awvAZL6Fxdmbws8/g5OqKsbKSDbNfJaBHdwwxMWTv3Ud6fAIjX5mF1tmZsoICh+Q/nZjCufQsJr39KjmH09jxyTLGv/JCnXKJK76l45VjiRjUj+2fLCVtzQbajxuJp78fY156Gp2nB6cTUti+cAnjX3kBr+C2TJw7HTBNonz/tycJ69vbrtnTE1M4l5HJxPmm7Ls+XcrY2S/WKZe84hs6ThxL+KD+7PxkCUfWbiBm7Eh0nh7E3nM7p3YmWJXXODsx4sUncTb/XVbPmEPbXt3xa2+/k1RHZa926Jff8QoJpqKkpNlZMxJTKMzIZMJbr5F7OI1dny1hzKy6kzLJK7+mw4Rxpqyfns/a0PYJS1bQtmd3Bj32CFWVlVSVlQMw8B9/q/m9icu+wNndvdn7AY5r79VGT3sSV/ObteZyVL17BQfVTAIajUb++8j/ERJ3vm+2nzCuzkRoc0ijkX3LFtPniWdwNRjYOtN8bAqxPjYVZWYy5DXTsWnvks8ZMM10bDr2xyoGzzYdm5I+eJeMbVsJGTIM3y7daH/DzWi0Wg5+/QVHfvqRDjfdarfcjbX01yQ+/G4Hnzx3bYtlqI80Gjnyxed0+cfz6PS+pMx5AX2PPrgHnT+fcXL3JPLme8hN2mnzd3R9/EWcPb0uVeS/xLHpKvP4vvPTpYyzMb4nmsf3doP6s+OTJaSt3UB78/je557bOVlrfC/OzePgr6uZ+MYsnHQ6Nr79L45t2UbU8CFNyimNRhI+X8bQ557A3dfA6hdnEty7F16h5/tmhnlfrjCPI/GfLWH0rGkX3Lb9hHF0rDWOpK0xfTg6bu4sSs8WsHHufEbPnobQ2OfONCcTdlOQnskN784m+9ARtny8nEmvPV+n3M7l39L1qjFEDe7H5oXLOLRmI53Gj6CsqJgtH69g3Av/wNPfl5Kz58+7tn3+JaGxXRn15ENUVVRSWV5ul8zVWkubseVU4m4KMrK49p1XOHMojW2fLmfiK3XrPX75t3SeOIbIwf3Y+vFSDq/ZSMdxIwBTX4xf8S3BPbtaZd//y2qufmsmTjodf87/kCObtxMzYrDdsrdG6h5X9VP3uGomIcTPQgifls7hCEKIdUKIOPPPR4UQfhco/3ytf292ZL7mKjiSipt/IG7+AWicnAjsO6DOBJXOyxuvyGiEVltne58OnXHy8LxUcTmblop7QCDu5rxt+w0gO8E6b3ZiPEGDhiCEwCc6hsriYsry8wGQVUaM5eUYq6qoKi/HxUcPwMm1q4mYeBUaZ9MnHDovb4fvy5HtSXQcMQAhBG07RlFeVExRbt0JM/+oMLwCfOs8HtQpGldP0yfvgR0iKcrJc1jWvNQ0PAIC8Qgw1XvIgP5kxFufOKTHJxA2eDBCCAwxMVQUF1Oan48QAidXVwCMVVXIqqqaI9LRNWtof9WVaM317uLlmDcMp3YmEjlsIEII/NpHU15cTElevlUZKSWZe/YT3t/0CVjksEGc2JkIgH/HGHTmuvZrH0Vxbt26zkzZh2egPx7+df9Wzcq+K5GIoYNqslc0kD20fxwAEcMG1Uz2uHp74RsdiaZW/xVC4GzxdzFWVSHsfKbgqOwAxTm5nE5IJmrkUDtlTajJ6ttA1izLrEMHcWpnfIPbVxSXcGb/QSJHmHJqnZzQebjX+b0ntu4gfGB/++zLJWjv9uKoereUtXsvHoEBePg3ePhulppjk3mMbNt/AFm1jqXZCfEEN/LY5Nete037946KoTQv12H7cDE2JR8nt6D5E8WOUHj0MK7+bXH1D0Tj5IRf3EDyak1QOXt54xlh+3ymJbTmY9PJWuN7Q9nDzH03ctigmkmHhsZ3ae4H1f3BTe/T5Jy5h9PwDAzAM9DUN8MG9uf0rkSrMqd3JdDOxjhyMdvWdu7UaQK6danZR2cPd/LSjjY5f23HdyQSM9zUZgI6RFFeVEKxjXpP372fCPOKnpjhAzm2w5Q7beN22vWPxdPcHty8Tedd5cUlZO49SPtRpskerbMTLh72+TClWmtpM7ac2JFI9DDTObt/h2jKi4pt1nvGngM1K6mihw/i+I7z58r7f1lDeP8+uHpbf/BjNBqpKq/AWFVFZXk57nbOrvy1qBVXFoQQWillVWO2kVJOdFSeVuh5oGZ9uZRyUAtmuaCy/FxcDedPZlz0BgqOpLZgooaV5efhYjDU/NtW3rK8PFwtyrjqDZTm5+IdEUXE+IlsePoxNM46fLt2w7dbdwCKMjPIP3iAw//+Go2zMx1uvh3vyCiH7ktRbj6efvqaf3v46inKzcfD0PhJs31/bCY8tuuFCzZRaV4ebr7n69TNoCcvNc26TG4ebgbrMiW5ebj6+CCNRta9NJ2izCwix4zGEG1a1VOYkUHuwYPs++ZbtM7OdL3tFvRR9q/34tx83C3yuxv0FOfmW53YlJ0rxNndreaEyN2cv7bUtRsJ7tWtzuPHtmyn3SD7TDxYKsnNs8puqlfr7OXnCtF5uJ/P7mu4qMmG6kvtCjOyiBk3Et8Y+9a9I7MnLPmCnrffRGVpqX2y5tVuvwbTYxfIWn3SXd/2QqPFpU0bdnz0GfnHTqCPbEfs3bfj5OpSU/bM/oO4envRJijQLvvi0PYuBGtfnQ8C2o8eTsyY4c3K6qh6t9z++JbtdSYFD69azbENm9FHRdDrjltq3vw3VWl+3ePO2TTrY1Np7WOTwUBpXi7ekVFEXDGR9U+Zj03duuFnPjZZOrXxT9r2G9CsnH9l5fl5uOjPn8/o9L6cO3L44n+BEOxb8BogCBw6msCho+0fspbWfmzyuED22n3XzddgM7sld4OeTleN54dHn0arc6Ztj64E9ai7XxedMy+/zvlL7uG0WmXycLcxjlxo29RVqzluHkd6mMcR7/AwTu9MIGxgP0pycsk/cpTi3FwM2Of4Wpybj4ev9bljcW6+1WRH2blCdO6WY6apDEDB6UyMVVX8Mv0NKkpK6XLlaGKGD+Rc5hlcvdqw8f1F5B47iW9UO/rfdwvOFseq5motbcaW4jzr8xn3euu9dl/NN22fm8eJHQmMfekJtqQescre9apxfPvwM2h1zgT36GK1Iut/lVpwVb//mRVXQogIIcR+IcRiIUSyEOIbIYS7eSXRS0KIjcBNQohxQogtQoh4IcTXQghPIcQEIcRXFr9rhBDiR/PPNSuRhBD/J4TYbf7vMYvX3W2x7ZNCiBnmn/8hhNhrzvNFA9k9hRCfCyFSzGVvMD/+LyHETiHEHiHEyxbljwohXjbvQ4oQotMFfk+dfb5AXX4vhNhlft0HzI/NAdyEEIlCiOXmxwrN/xdCiHnmekkRQtxiUY/rzH+L/UKI5cK87EEIMceibt6oJ8cD5v3fufeHfzcU2Tab9767jIcLaSuwuGAZgaCiqIisxF0MmfsWw95cQFVZGelbNpk2qaqioriIfi/MoMNNt5H84btIm69lRxexKxfjZMoB9q3ezKC7r7tw4SaSF3WTRBv1bl7BIzQaRs6exfi33yI/LY2CkydNW1QZKS8qYtj0aXS99RZ2vveBg+rdVraL2KxWocw9+0ldu4Fet99o9XhVZSWndiURbv6Uza5s1UftJm9js4tZPaXRaBg/ZwaT3n+D3NQj5J842bSM9XFQ9tPxSbh4tcEQFdHkaHVcxFhoqx/URK1ne2msIu/oMaLHjGDcazNwcnFh3w8/WZU6vnkb4XZ9Y+m49j725WeZMOclRj77GAdXrSVr30F7R8Ue9V6tqrKS07sSCRsQV/NYzNiRTHx7LuNem4GbjzeJy79sdOw6bLb12pVue4ysKCoiK2EXQ+e+xfC3TMem0+ZjU7W0H/+DRqMlaMBl/VlYy7J17G/EMbXbkzPo8fxrdH70GTL+XEXBoX12DFefv9axqXZ2292z4R0sLyzi5M5EJi2Yy7UfvEllWRlHNmyxa86LCSoQDW4bPXYkE96ey5jXZuDq402yeRyJGDEUN189q1+cSeLSlfi2j0GjseMKv4s6D65/c2NVFTlpxxjz3N8Z9+I/SfzmJ86ezkQaq8g5cpxO44dzzbxpOLnoSPn+V/vlhtbTZmyx2RQuorOay+xY9CW9b78eTa1LRssKizixM5Hr33uNmz6cR2VZOWkbttojsfIX9b+24qojMEVKuUkI8RnwsPnxUinlEPME1L+BMVLKIiHEM8D/YVpF9JEQwkNKWQTcAlid7Qkh+gD3Af0xjaLbhBB/Ag1NlT8LREopyy5wueE04KyUsrv5tao/bnhBSpkrhNACq4UQPaSUyebnzkgpewshHgaeBO639XvM+/yijX2e2UCeyebXdQN2CCG+lVI+K4R4VErZy0b564FeQE/Az7zNevNzsUBX4DSwCRgshNgLXAd0klLK+upGSrkQWAjw0PqdjX7H76I3UJqbU/PvsrxcXHxsvtRlwUVvoCz3/KUStvK6GAyUWpQpzcvFxUdP7t7duPn5o2tjWhYd0Kcv+YcPETRwMK4GAwG9+yKEwDsqGiE0VBSeqylrLym/rGPP76Y3JIEx7Sg8c75rFOXk4dHI5cFnjp5k7QfLmDTtUVzbOO6STTe9gZKc83VakpuHq15vVcbVYKAkt3YZH6syzh4e+HbqRFZyCl6hobgZ9ATH9UEIgT46CjSC8nPn7HLJ4MHf1nB4zQYAfKMjKLbIX5ybV2cZuUsbTyqKSzBWVaHRauuUyTt2gm0fLWbEs//EpVZdpyemoI8Ix83HPpeYHlq1hrQ1puHBEGWdvaSe7OVFxeez5+Q2apm8zsMd/84dyUjajU9Y827wfymynzlwmNPxSfyYmIKxooKKklK2vvcxAx6d2sisqzmy1pRVHxVZq/3WzeHSpk2drK7m8ce0mszG9kLgZtDjG2NaZRjaP479P/xcU85YVcXJHfGMfeWlRmWv7VK1d3eDqYyrtxehfWPJOXyEgM4dGpX1ktS7WUZiCvrIdrh6n++blj9HjRrOhnnvNCq/La56W8cdn4bL5JqOTTl7d+Pu54/OPO4F9jYdm4IHmu5xcmrTBrKTE4l78lm7X877V6LTGyjLO38+U56Xg85b38AWtbb3Ma2mcPbyxtCrL4VHU/Fq39nuOVvzsengqjWkmsd336gIinJy8b9Adsu+W3IR43vG7r14BvjV3EcvrG8fzhw8TOTQgU3K7GbQ1zl/qZ3BzaCnuNY44qr3wVhZWe+2luNI5KjhbDKPIxqtll53nf9ynzXTX8GzbUCTslfb9+taDv5hajN+MRFWt4YoysnDvdZKfRcvT8qLLcfMvJqx28NXj6uXJ86uLji7utC2c3tyj50gsFN7PHz1+Lc3rQyLGNiHlO9+aVZuaJ1tptr+39ZyaLU5e3SkdV/NycNNX6ve23hSXqevmsrkpB1l/YKPASgrKORUwm6EVoOxssoqe3i/WLIOpBI19H97da1GqG8VrM//zIorsxNSyuqP8pYB1Xeuq56EGgB0ATYJIRKBe4B2UspK4FdgkhDCCbgS+E+t3z0E+E5KWSSlLMQ0AXahG5AkA8uFEHcClQ2UGwO8X/0PKWX1qH2zECIeSMA0+dPFYpvqJUi7gIgGfo/Nfb5A7n8IIZKArUAY0P4C5YcAK6WUVVLKTOBPoK/5ue1SypNSSiOQaM5aAJQCnwghrgeKL/D7m6RNRBQlWRmUZGdhrKwkc8dW/Ho64JM5O/GKjKI483zejO1b8e9lfcNR/569Sd+8ESkl+amHcXJ3x8XHB1dfX86mpVJVVoaUktx9e/AIDjZtE9uH3P17ASjKSMdYWYmzp31uPmyp+4QR3PrWC9z61gtE9uvJgXVbTdfEH0hD5+7WqMsEz2Xn8svrCxnzz3vxCbbPJUb18YmKpCgzk6LsbIyVlZzauo22sbFWZdrG9uLEpk2muj18GGd3N1x9fCgrKKCiqAiAqvJysvfsxTMoyLRNn95k7zV9ql2YnoGxsgpdG/vUe4fxo5g4dzoT504nNC6WI+u3IKXkzKFUnN3d6pwICSEI6NKR49tM96U5sn4zoXG9ACg6k8OGtz5g4CNT8ApuW+e1jm7aTrvB/eySG6D9uFGMnzOD8XNmEBIXy9ENmy2yu9vO3rUjJ7eZ7udydP1mgvv0avA1SgvOUV5kGlYqy8vJ3L0Pr+CgVpG9x203cPX7bzDp3dcZ+I8HCejaqdGTVqasoxn32suMe+1lq6w5h1Jxdqsna5dO57Nu2ExInKkfBPfpZXN7Nx9v3H0NFJxOByBz9168QoJrfmfm7r14Bbe1ugShKS5Fe68sLaOipLTm54zkvXiHhdBYl6Leqx3fvI3wgdZ90/KeKid3xOMd2vh9qK362FRcfWzatpWA2semXr05bevYZPAl3+LYlLNvD55BpjZyJiWZoz//l9i/P47WxX6X7PwVebaLpjQrg9Izpr/BmZ1b0Pe4uPOZqrJSqkpLan7O35eMW3DzJvHr05qPTR3GjWLCnBlMaMT4Hti1IyfMfffI+s2EXmB8d/fz5cyhNCrN/SFj9z68LcbMxtJHR1KYkUlRlun85cSWbQTVyhDcpxfHbIwjDW1rOY6c2hFfc8P2yrIyKkvLAMhM2YNGq7W6EXxTdL5iJNe88RLXvPES4X17cfhPU5vJOmg6d6x9TyQhBEFdO3J0q6nNHP5zC+F9TbnD+/Yic99h0/2UysrIPnwEn5Ag3PXeePjqOXvK9E236Sn78Alter1Xa41tplqn8SOZ9Pp0Jr0+nfC+vUhdbzpnzz5o6qu26r1tl44cM9d76p+bCTP31evfm8MN5v/aDehN/yl3EN43Fg8/A9kW2dN378c7pG5fVpRq/2srrmpPYVb/u8j8fwH8LqW8jbq+BB4BcoEdUspztZ6v76PASqwnCF0tfr4SGAZcDUwTQnQ1T5LVJmpnF0JEYlpJ1VdKmSeEWFTrd5eZ/1/F+b9znd9Dw/tcN4gQIzBNgA2UUhYLIdbVel2bmzXwXJnFz1WAk5SyUgjRDxgN3Ao8Coy6mHyNodFq6XD7vSS9PRcpjQQNHo5HSCin1v0BQMiIMZSdzWfX7BepLC1BCA0n//iFfjNfx8nNnT0L3yP/4D4qCs+x+alHibj6RoKHjrB3TKu8He+4m/j585BGI8FDhuEZEsqJdasBCBsxGr8ePTmTksim555Eq9PRZbLpDa13VAyBffqydabp2128wiMIHTbStJ9DhrPn84/ZPO1ZNE5OdJvygMM/2W7XpxvH4nez7OGXcHLRMfrRu2ue+3H2e4x6+E48DD4k/bSGhO9+pzi/gC8en0273l0Z9chd7PjqJ8rOFfLnwi/MdaPh5nnPOSSrRqulx913suX1N5DSSPiwoXiFhnBkzRoAIkeNIrBnTzKTkvnjqafR6lyIvX8KAKX5Z0lY+DFSGpFGSUj/frSN7WWqg2HDSPjkU9Y89wIaJyd6P3C/Q+o9OLY7pxNT+PGfz6N10THgoftqnls75236P3Av7gYfYm+/kY0LPiL5y+/QR4QTPdI0r7/72x8pKyxix2fLzfWh4YpXTd98VllWRkbKXvpNvcvuuQGCYnuQnpjCT489h5OLjn4PTq55bv3ct+k79R7cDHp63nYTW979iJSvvscnIqzmpuUl+Wf5/YVZVJSUIITg4C9/MGHeLErz8tn2r0+RRmn6mw7oS3Dvnq0iu7O7m11zAgT16kF6YjI/P/4sTi46+lplnU/fB+7FTa+nx203svXdj9j99Xf4tAuvuel6Q9vH3nMH295fiLGyCo8Af6t6OLFlO2F2vv+Mo9p76dkC1r9p+txHGo20G9zP5j11GsOR9V5ZVkbm7j30uf9uq9dMXvk1+ceOAwIPfz/6TLF+vik0Wi2d7ryb+LdMx6aQ6mPTWvOxaaT52JScyMZnTcemruZjk090DIFxfdny8jSE1nxsGm46Nu1bvhhjRSW73pwLgHd0DF3uvs92iEtg8YvXM7RXO/y83Tn81WPMWrSOxT8ntlgeS0KrJfLWe9n37mtIo5GAQSNwDw4jY/3vALQdNpbys/mkzHnBNEklBOlrfqHnS/OoLDzHgY9M39wrjVX49R2Mvmsvh2duzcemYPP4/t/HnkProqO/Rd9bN/dt+k29B3eDnl633cSmdz8i+avv0dca33+zGN8P/PIHV86bhV9MFOH9+/Dr8zPRaDSm/R09rMk5NVotve69kw1z3kIajUSMGIJ3aAipf6wFIHrMSNr26kFGYjK/Pv4sWhcdceZ9qW9bgBTzOCIQuPv70ds8jpQVnGPDnDcRQoOb3oe+f7u/ydltCe3dnZMJu/n27y+g1ekY+si9Nc+tenUBQx66G3eDD3F33sC6+R8Tv/I/+EaG0WGUaQWnT2gQIb268v0TMxEaQYfRQ9CHm/ap/+Tb+HPBpxgrK2kT6MeQh++1kaDpWkubsSUktjunElL47p8v4KTTMehv99Y8t/q1dxj44D24G3zo/f/snXd4VMX6xz+zm2yy6buBBAKk0nsPhN4FAbvYG2K5lp/32q8iCBYQG1jBRlevevWq14I06TWVDgmdNFKA9LLz+2NPkk2y6RtCvPN5njxJzpk55ztz3jMz5z3vzLn9BjYtXEL01z9gDg4sXey+Klp2CCUovB8/P/cKOp0Oc0ggHcc6VntzRMUWV41o9LVsrhCEEMHAcSBCSrldCPEJcAh4DOgvpTwvhGiJNUJptJTymBDCDWgrpTyiTceLB3YD30gp/6Ud9wTQHwgElmKNYBLATuBOYB+QiHWaYhbWaKPfsE7FC5RSnhBCOANngE5Sykw72ucBrlLKJ7T/Tdr5lmOdatcSa/TWs1LKpSWatDL1B96UUo6s4jhO1ZR5I/CUlHKPTTmHAPdLKadoa2dFA1dJKTcKITIAPylloXb8LCmlhxY19SAwCTADe7BOqeysHX+ylv59bd+3gJuUMkUIYQaOSSmrfSVfn6mCVwJOOktTS6g3HU1ZTS2h3pzJcvzD/+XC3VBdcOaVTbFU3XFTIJtxvTfnNrLI0nyD2jPznJtaQr359EUHr01zGZk2c3JTS6g3IabsmhNdoTTnNrKwGWv3dG6+45m8oivj65z1wbkZ96sAL/Qe3nyNvhasPPbbZXumvaP9Vc2qLpvvqKp+HATuFkLEYnWgfGS7U0qZCtwDfKml2YHVuYL2tcGfgYnabyrkjcTquNqF1Wn1qZQySnPizNG2/YzVWQagB1YKIeKwTvV7x57TSuMVwKQtbh4DjJJSxmj59gOfY10fqibsHafKMlfBb4CTlnaulr6EJUBsyeLsNnyP1bEWA6wHnpFSJlVzDk/gZ+0cfwJ/r0XZFAqFQqFQKBQKhUKhaJbohLxsP82N/7WIq5+llI79RqjiikBFXF1+VMRV06AirhR1pTlHEzTnNlJFXDUNKuKqaVARV02DirhqGlTEVdPxV4+4Wh3/62V7pr0tbGKzqsv/tTWuFAqFQqFQKBQKhUKhUCiuKJqVJ+ky03xfB9YRKeWJKz3aSghxrxAiusLPBzXnVCgUCoVCoVAoFAqFQqFoXIQQZiHEH0KIo9pvk5007YQQG4QQB4UQ+4UQ/2ezb7YQ4qyNz2NSTedUEVdXEFLKL4AvmlqHQqFQKBQKhUKhUCgUCoUdngPWSSnnCSGe0/5/tkKaIuBJKWWkEMIT2CuE+ENKeUDb/46U8s3anvB/JuJKoVAoFAqFQqFQKBQKheJKRAh52X4ayDXAMu3vZcC1FRNIKRO1D9ghpbyE9UN5bep7QuW4UigUCoVCoVAoFAqFQqH4H0EI8YAQYo/NzwN1yO4vpUwEq4MK8KvhXMFAH2CnzeZHhRCxQojP7U01rIiaKqhQKBQKhUKhUCgUCoVC0YRczqgiKeUSYElV+4UQa4FWdna9UJfzCCE8gO+AJ6SUF7XNHwFzAan9fgu4r7rjKMeVQqFQKBQKhUKhUCgUCoUCACnl2Kr2CSGShRCtpZSJQojWQEoV6ZyxOq1WSSn/bXPsZJs0nwA/16RHOa4UfwmCvLObWkK90DXjb56ezTY2tYR64+Zc3NQS6k2RpfnO8HbRN996b8a3Km5OzbfeLxQ4N7WE/0l27S9qagn1ZtrMyU0tod58PbfGcfsVy98XVPl8c8Xj5tR87d3SjMcEzblfvVTQfB+h9Q1f20jRiAjRbO6MH4G7gXna7/9UTCCshfkMOCilfLvCvtYlUw2B64B9NZ2w+bZ2CoVCoVAoFAqFQqFQKBSKy8k8YJwQ4igwTvsfIUSAEOIXLc0Q4E5gtBAiWvuZpO17QwgRJ4SIBUYBf6/phM3XXaxQKBQKhUKhUCgUCoVC8RegucRbSSnTgDF2tp8DJml/b6GKIkkp76zrOVXElUKhUCgUCoVCoVAoFAqF4opERVwpFAqFQqFQKBQKhUKhUDQhzWiNq8uOirhSKBQKhUKhUCgUCoVCoVBckaiIK4VCoVAoFAqFQqFQKBSKJkTFW1WNirhSKBQKhUKhUCgUCoVCoVBckaiIK4VCoVAoFAqFQqFQKBSKJkSomKsqURFXCoVCoVAoFAqFQqFQKBSKKxIVcaVQKBQKhUKhUCgUCoVC0YSojwpWjXJcKf7nkFKy64uvORMVh5OLgaF/uwff0KBK6S6lnOfPd5eQn5WDb0ggwx67D72TE4n7D7P+jQ/w8GsBQFB4X3rfOJkL55LY+M6S0vxZKefpffNUul091qHad37xNacjrdqHPXIPLexpTz7PhneXUKBpH/7YfeidnYj7z+/Eb94JgMVi4cKZRG777G1cPN2t24ot/Pjcq7ibfRj3/GMO0w2QHBtL3IrVSIuFoJHD6ThlcqWyxa1YRXJMLHoXA30fuB+f4GBy0tKIXPwJeRcuIIQgeNRIwiaMB+DCyVNEL11GcV4+xha+9P/bQzgbjQ7VXaItctmXnIuOQ28wMOjh+zCHVK73rJRUti1aTH52NubgIAY9cj96JydObNnBgR9/BcDZ1YX+0+/EFNQOgB0ff865qFhcvTyZtGCuw/RGLV9NkqZ34EPTMVWhd8d7H1OQlY0pJIiBf5uB3smp2vyJMXFEL1+NtEhCRg2jy9SrAYj71785tzcaoRO4eHkx8KH7MJpMDS7H7qVfcS4qDr2LgYiH763iXk1l88JPKMjKxhwSyJBHp6N3Kuvezh87zm8vvs6wJx4kaFA/igsK+X32G1gKi7BYigkK70evm69pkNaqtJ/VtA+pQXu+pn2oHe2/vvg6wzXtAAd/WcvRdZuRSDqMHk5XB7Yx9sqx9fN/cSpyH04GA6Meu5uWoYGV0u37ZQOx/13PxaRU7v7iTYxeHgBknEli4wfLSE04zcDbptL7mvGNprVEb3O5VxvzPi3IzmHPJ19w4fRZEIIBD9xLi47tATj6+1qOrVmH0Olp3acnvW67ucFlsWVQWx/+ERGCTsCPh1JYHnPWbrouLT347JoevLjuCOuPp+HnbmD2qA6Yjc5ICT8cSubrfYkO1VYTGfujOfGv5UhpwX/IKNpMKN8u5Cad5djyxWSfPk7g1GkEjCvrxyJfeAydqxGh0yF0Ono+/9pl1V4dHz8zhYmDOpKamU3/+z5uajkAJMXEEaONCUJGDqeT1peUIKUkZvlqkmJi0RsM9H9wOqaQYAD2LPmMpKgYXLy8GDf/ldI8sau/JjEyGp2TEx7+fvR7YDoGdzeHa2+svgms9+72xcvIPH0OgIiH76Flx7AGaY1a9iWJJe1ENW3i9kWLKcjOxhQcRLjWJlaX/8ivfxC/fhNICB09nE6TxpU75qGffyNm1Tdcu/hdXLw8610G27I0xhi4MD+fTe9/Tm7mRYQQdBo7nG5Xj2mwXltSYmPZv2oV0mIhcMQI2k+uPAbev2oVKTEx6A0Ges+YgXdwcNl+i4XNs2bhajIx8B//AODCyZPELVuGpbAQodPR/a67MIXV31aqIjk2lriVq8FiIXBEFeP3latI0cbvfWZYx+/FBQVsee11LIVFSEsxAQMG0Pn660q1xyxdRnFhIUKnp9fdd2EKC3W4dsVfCzVVUPE/x9mofVxMSub6Ra8w+IE72f7pKrvp9q78jq5Xj+WGRa9gcHfj6Potpfv8u3TgmgUvcc2Cl+h9o7UB9w5oVbptyvwX0RsMBA3s41DtZ6L2cSExmRvfe4UhD97Jtk/sa9+96ju6Tx7Lje+9gsHDjSOa9h7XTODaN1/i2jdfov9t19Gqa8dSpxXAgV/W4dOmtUM1g7XDjVm2gsFP/4Mx81/jzPadXDxb/mEmOSaWrORkxr45n9733UPMF8sB0On1dL/tFsbOf53hs2aSsHZdad6oz76g2803Mfr1Vwjo34+j//3F4doBEqPjuJSUzOR3XmPgjLvY89kKu+miV39Lp0njmPLO6xjc3UjYsBkAd78WjH3pGSa98TLdrp/C7k+WleYJHTGEkc/93aF6k6LjyEpKZuLbr9P//rvZ+/lyu+liv/yGjhPHM+mdeTi7u3Nc01tVfovFQuQXKxn2zN+ZsOAVTm3byYUz1mvRefJEJsyfw/jXX6Z1n57s//dPDS7Hueh9XEpK4ZqFrzJoxp3s/My+vUet+o4uk8Zy7cJXMbi7cczmXrVYLESu/o7WvbqVbtM5OzHupSeZvGAWk+e/xNmY/aQeiW+wXlvORu/jYlIK1y58lcHVaI/UtF+38FVcqtAeYKM949RZjq7bzKTX/smUN2ZxJjKWi4nJDtVuy6nIfVxITOHW9+cw4uHb2bxktd10rTqHMXnW/+HR0lxuu6unG0OmT6PX1MZzrtnSnO7VxrpPAaKWr6ZVrx5MfOs1xs97Ga82AQCk7D/I2T1RjJ83h6sWvEKnq69yWHkAdAKeHhrKE78e4JZvohnfvgUhPpVfJugEPDowiJ1nMku3FVskC7ef4JZvopn+n1hu7NrKbt7GQlosHP/qC7o8+iy9X3qT87u3kZN4plwaJzcPQm6+m4Cxk+0eo9vfX6TXC/OuKKcVwIrfYrjmWfttUFMgLRail65gyDN/Z/wbr3J6+04unik/JkiKiSUrKZkJb82j7/R7iPqi7F4OGjaUIc/8o9Jx/bt3Y9z8Vxg3by4erfw5/OPPjaK/sfomgN1LvyKgV3eueWcukxfMwruBY7KSNnHSO6/Rf8Zd7K2iTYzV2sSrtTaxpJ2pKn/m6TPEr9/EuFdeZML82SRGxXDJpi/KSUsnOe4Abi3Mds9XHxprDKzT6xh4103c8O4cprz2PAd/30CG5jh0BNJiYd/y5Qx88klGvv46Z3fs4FKFMXBKbCzZSUmMeuMNet57L3HLlpXbf3zNGjwCAsptO/j113S85hqGz51Lx+uv5+C//uUwzbbaY5evYPBT/2D0vNc4u6Py+D0lNpbs5GTGLJhPr3vvIWapNn53dmbIc88y6tW5jJw7h+TYONKPHQNg/9f/otO11zLqlbl0ueE69n/9tcO1K/56NJnjSgjxixDCp5HPMVsI8ZQDjjNSCFGp9xNCTBVCPNfQ4zsaIUSwEOK2ptZhDyHEUiHEjXVIHyyE2OdIDaf2RBM2fDBCCPw6hlKQnUtORma5NFJKEvcfIlh7A9Z+5GBO7Y6u9TkS4w7i1aolHi19HagcTu2Opv2IWmjfV6a9wwj72hO27CZ06MDS/7PTMjgdGUfHMUMdqhkgIz4BD39/3P380Dk50XZQOEl7o8qlSYqMInDoEIQQmNu3pzAnh7zMTFx9fPDR3jo5G414BgSQl54BQFZiIr6dOwHQsns3Enfvdbh2gDN7owkeFoEQghYdwijIySHXTr0n7z9Eu/D+AIQMj+DMHmsZW3Zsj8HD6iBs0T6UHE0/gF+XTqX7HMXZvVGlen07hFFYhd6U/Ydoq+kNHhbB2T2R1eZPP5aAh78fHv5+6J2cCBwczrm90QA4u5U9YBbnFzhkacnTu6MJHT4IIQQtO4ZRmJ1j196T9h8ufVsdNiKC07vLbOvwr+sJCu+Hq3fZ214hBM6urgBYiouRRcUOj80+vTuaMBvtBbXUfspG+6Ff1xNYQfuFs4m06BCKk4sLOr2eVl07cmpX+XvJkZzYHUvHEdZy+HcMJT87l+yMC5XStQgNxEuLQrXF6O2FX/tgdE76RtNoS3O6VxvrPi3MyeX8oSOEjBwGgN7JqTTq5NjaDXSZOgm9szMArt5eDisPQNeWHpy5kMu5S/kUWSR/xJ9neHDlB9ebu7Vmw/E00nMLS7el5RZyOC0bgJxCCycyc2npbnCovurIOnEM15atcG3pj87JiRb9B5MRs6dcGmcvbzyCwxD6y2PPjmJr7CnSL+Y2tYxS0uMTcPf3w6N0TDCQcxXGBIl7owiq4v5o2aUTBg+PSsf179kdnXZtzO3DyLW5fx1JY/VNBTm5JB88QvvR1nGY7b1bX85WaBOrameSbduZ4RGc1drEqvJfOpuIb4ew0r6oZZdOnNkdWXrMqOVf0fO2m8CBC0031hjYzeRTGrnlbHTFp01rctIzK+WpL5kJCbjbjIHbhIeTHBlZLk1yZCRth1jHwCabMTBAbno6yTExBI4YUS6PEIKivDwAinJycPXxcZjmEjLiE3D3s9E+KJykyAr3amQU7YZUHr8LIXCyHWsV2461BEW51japMCcXV5+GRej/ldAhLttPc8MhjishRJ17cCnlJCllpiPO31RIKX+UUs5rah12CAauSMfVlUBOeibuLcoaSHdfU6UOKv9SFgY3t9IBkLu5fJrUIwn85+k5/PHaQrtvZY5v3U3IkAGNo923btrdfE1kV0hTlJ/Pmeh9BIf3Ld2284uvGXDHDQid4xuy3IwMjOayhxdXs4ncjIya01QYdGanpnLh5ElM7a2h0F5t25Z2oOd27SY3Pd3h2gFy0zNw9y3T5mauXO8Fl7IwuJfVu9HXbHfQHL9xM61792gUnaV6K9Sl0WyuVN8V9br5mksHs1Xlz83IxM3Xdnv5axT39Xf89OiTnNy6g243XdvgcuRkVKh3XxO5du3dWFYOm2uTk57Bqd1RdBhXfrAH1rfdPz/zMt/MeJLWPbvQsoNjQ9RzMjLK1ZVblfdqee25NtpP746iYwXtPu3akHzoCHmXsqz3cVQc2WmNY/cA2emZeNi0lx6+PmSnZVadoYlpTvdqY92nWSmpuHh6snvx56x5fja7l3xBUV4+AFlJyaQePsramXPZMGce6fHHHVomP3cXkrMLSv9PyS6o5Hxq6WZgRLCZfx9MqvI4rT1c6NjCnf0pWQ7VVx0FmRm4mMpeNhlMvuRn1sHxIQQHF71O7Gv/JHnzukZQ+NchNz2jQl9S2fZz0zMxVuhv8jJqfz1O/LmZVr0a5/5trL4pKyUVVy9Ptn30BT8/O4ftHy+jULt360vluq6s1V47U+K0ryq/d7s2pB48Qr7WFyVGx5Kj9UVn90RjNPuUTrN2FI05Bi7hUsp50o6fomWHEIfpzs3IwLXc+LayvedlZGD09S2XpsTe969aRZebb670gq3r7bdz4KuvWPv3v3Pgq6/ofNNNDtNcXlf192FeesW+qGxsKC0WNrw4k98efZyW3bth1qYy9rj9NvZ/9TW/P/EP9n/1FV1vrnVMg+J/mBodV1rEyyEhxDIhRKwQ4lshhJsQ4oQQ4iUhxBbgJiHEeCHEdiFEpBDiGyGEhxBiohDiXzbHGimE+En7+4QQooX29z+EEPu0nydszrvPJu9TQojZ2t+PCyEOaHq+qqEIvYQQ64UQR4UQM7T8QgixQDtfnBBiWnXbK9THACFElBAiVAhxjxDifW37UiHEIiHENiFEQklUkRBCJ4T4UAixXwjxsxZpVrJvnk053qzmGlR17Kr0zgOGCSGihRB25zUIIfRCiDe1fLFCiMe07S8JIXZrx1wihLWVFEJsFELMF0LsEkIcEUIMq+E4/YQQfwoh9gohfhdCVIp1riqNtj1GCLEdeKSaenlACLFHCLFn17d1mJYkpb2D1Tq7b0ggN374OtcseIkuV41m/YIPy+0vLiri9N4Yggf1r72mWiLtaa/gMbdbvAr/n9oTi3/n9qXTBE/tjcXV25MWYZXXCnAItRFlJ42wuS5FeXnsWvQ+PW6/rXQdqz4z7iNh7To2zJxFUW4eorGiOuxqq5DEXr4KiZL3HyJhwxZ639rIHbR9MRWSVFOmqvLXcO/0mHYDU95/i6Ahgzi2Zn1t1VaNXXOv+V4tsZvdS7+m723Xo9NV7up0Oh2T35jFDR+9wfljJ8g4ZX8dnnpjt6pq0c7UoN2nbWu6T72Kta+8w9rXFmIOals6QG8UamH7VxTN6V5tpPtUWorJOHGSsLEjGf/6bJxcXDj4438B6zqGBdnZjJnzIj1vu5ntiz6qol9xHBUP//eIYD7YdRJLFac1OumYN64T72w7TnZhcaNqK0cDbb37U7Pp+c/X6fLosyT9uYaLRw86UNxfn8rtY/3Haod++AmdXk+7IYMbLswejdQ3yWIL6cdP0XHcSCbPfwknVxf2/+fXBmqtefxlvzii2vxebQLoMnUiG197i03z3sEnsB06vZ6i/HwO/PAz3R3w8qoijTUGLqEwN4/1b35M+L3TMLg5cJpyDePb6kiOjsbFywufkMqOtJPr19PtttsY+847dLvtNmI/+6zBUitirw+yl6oiJeUTOh2jXpnLhHffJjMhgYtnrNOvj69fT/fbb2XCu2/T/bbbiPr0c0fKbtYIcfl+mhu1XZy9EzBdSrlVCPE58Ddte56UcqjmgPo3MFZKmS2EeBb4B/AasFgI4S6lzAamAeUmsQoh+gH3AuFY25adQog/gepeqzwHhEgp80XN0w17AoMAdyBKCPFfYDDQG+gFtAB2CyE2ARFVbC/RGgG8B1wjpTwlhBhe4VytgaFAZ+BH4FvgeqwRUD0AP+Ag8LkQwgxcB3SWUspalKOqY9vT+xzwlJTS/iIMVh4AQoA+UsoiTQ/A+1LKOVp5VwCTgRKvkJOUcqAQYhIwCxhr7zhCCGebekrVHGqvAvfZ1GV1ab4AHpNS/imEWFBVAaSUS4AlAK/H/Flty3rwtw0cWWedr98iLJjs82XmlZ2WgZvJu1x6F08PCnJysBQXo9PryU7PwM3sA1CuM2vbtwfbP1tN3sVLuGoLT56N2odvSCBGH8dMwTjw2waOrNW0tw8mO62CdnN57a5e5bXnpJVpLyFh625CbSLCUg4d49SeGM5E7aO4oJCC3Fz+XPQZIx6f7pAyGM3mctFQeekZGCuEBdtL42qy6rYUFbFr0fu0ixhMwIAyh6BnQABDnn0agKzEJJJiYhyiF+DImvXWRUcB39BgstPSaanty0nPwKhpK8HF04OC7LJ6z01LL5cm4+Rpdi1ZyojnnsDFs/IUh4ZydM06jm+w6jWFhpSry9z0dDt6PcvpzUlLLw0zt74tq5zfUlRU+kbVur1yPQAERoSzecFCut94bZ3Lcfj3DRxdp9V7WEi5aKKctAyMdu/V3LJypJelSUs4weZFnwCQfzGLs1H7EHodgQPK1p4zuLvh37Uj52L2YQpsU2e9thyqoD2ngdo3VaG9w+hhdBhtnQYW+eW/cTM7NsR+368bObjWuiZIy/ZBZNm0l1lpmZXak6amOd2rl+M+RQiMZhO+WmRq2/D+HPrRuv6fm9lE2wH9rNOv2oeCEORfuoSrl2P6q5TsfPxtIqz83A2czykol6ZLCw/mjukIgI+rMxGBJooskk0n09ELwbxxnfjtWCobTzReJKE9DCYz+Rlppf8XZKRh8K79vWXwsQ6jnL28MfceQNaJeLw6dHG4zr8CRrOpQl+SXmmak9FsIrdCf1ObqVAnN20hMSqGYf98utbOgdpwOfqmlh1CcfM1lUYAB4b3Zf9/fquz1qNr1pOgtYnm0OAa++2KbWKOTZto9DVXmT901DBCR1n7otivvsNoNpGVnEp26nl+f3Z2afo1/5zD2FdexOhTvo5qw+UYA4N1nLn+rY8JGxZuNxKrIRjNZvLKjW8r27uryURuWlr5NCYTibt3kxwVRUpsLJbCQgpzc4n6+GP6PPQQZ7ZsodvttwPQeuBAYj93vPPHaDJXvg8rfHjHtcL4Pddm/F6Cs7s7vp07kxIbh1fbtpzespUed1i1BwwcQPRnynGlqJnaOq5OSym3an+vBB7X/i5xQg0CugJbtU7CAGzXHBm/AVOEEN8CVwPPVDj2UOB7zbGFEOLfwDCszpmqiAVWCSF+AH6oQft/pJS5QK4QYgMwUDvnl1LKYiBZc5QNqGb7RaALVifJeCllVSv2/SCltAAHhBD+NuX7RtuepGlAO2Ye8KnmTKtpBcmqjl2V3poYC3wspSwCkFKWtDijhBDPAG6AGdhPmePq39rvvVidcXaPI4ToDnQH/tDsQQ9U/DRQJ3tphBDegI+U8k8t3QpgYi3KUy1drhpFl6tGAXA6MpZDv20gZMgAUo8ex+BmxK1CAyuEoFW3TpzYsZfQIQM5tnE7gf17A5CTeQGjtxdCCFKPHQeLpdzDTcLWXYQMGYij6HrVKLqWaN8by4HfNhBag/bWNtqP/rmdwAG9S/cXZOeQdOBIOadU/9uvp//t1wOQuP8w+35c4zCnFYBPaAhZSclkp6RiNJs4s2Mn/f/2ULk0rfr2JuGPdbQZFE5GfDxObkZcfXysX7T59HM8AlrTfmL5hYTzL1zExdsLabFw+D8/EjJ6lMM0dxw/mo7jRwNwNjKGo2vWExQxkLRjCTi7uVUa+Akh8O/WidM79xAUEc7xTdto2683ANnn09jyzocMeuR+vFq3cphGWzqMH0OH8dav4JyLiuHYmnW0GxxO+rEEnI329fp17cyZnXsIjAjnxOZttOlvdegE9OttN7+LlydZSclkadfx1PadDHr0QQAuJSbj2draNJ2LjMYroH7l7DRhFJ0mWK/jmchYDv++geCIgZw/moBzFfbu37UTJ3fsJWTIQOL/3EY77V69/v2ymdxbP/yctn17ETigD3kXL6HT6zG4u1FUUEDSvoN0m9rwRao7TxhFZxvth2qhvVUdtQPkXriI0duLrPNpnNoVxcS5jl1qsfvEkXSfOBKAk3vj2PfrRtoP7U/K0eMY3FxxN9X94aMxaU736uW4T8E61efiuUS8AlqTvO9A6eLsAf37kLL/IH5dO3MpMQlLUREung3/2lcJB1OzaOdtpLWnC6nZBYwLa8HM9UfKpbnuq7K1XWaOaM/WUxlsOmkdgrw4IowTmbl8GXd5vyYI4BEURl5KEnnnUzD4mDm/Zzsd7nu0VnmL8/NASvSuRorz88g8GEvbSdc3suLmiyk0hKykFJsxwS4GPvJguTSt+/Yhfs062pbat9HuixJbkmLiOPzTr4yY+SxOLi4O1Xw5+iawTn+7cC4J74BWJO07hHfbui/O3mH8aDpobeI5rU0MrKFN9OvWqayd2bSNAK1NbNO3V5X58y5cxNXbi+zzaZzZHcnYl/+JwcOdaxe/W3rsnx57hvGvzqz3VwUvxxhYSsnmj5bj3aY13aeU/zKiI/AOCSE7OZmc1FRcTSbO7txJ34fKj4H9+/ThxNq1BAwaRGZ8PE5G6xi4y803W6cJAucPHiTh11/po+V19fEh7dAhWnTpQtqBA7j7+1c6d0PxCbVqz05NxWgycXbHTvo9XGH83qc3x9eWjd+dtfF7/sWL6PR6nN3dKS4oIHX/ATpcPamS9vMHDuLeyvHamyuiGa49dbmoreOqYjRLyf/Z2m8B/CGlvNVO3q+xTvdKB3ZLKS9V2F/V1Smi/FRGV5u/rwaGA1OBmUKIbiWOk1pqr+qc1VlKoqahD1CV48p2Irqo8Lu8CKtTbyAwBrgFeBQYXc35a33sWiKoUDdCCFfgQ6C/lPK0sE7NtK33Eg3FlNlOpeNo2/ZLKauL0babRos8a9R5C2379OBs5D7+/fgL6A0Ghv7tntJ9f7y+iCEP3oWb2Yf+t9/An+9+QtRX/8Ec0o4Oo4cAcHLHXg6v+ROh16M3ODPiiQdK3+oV5eeTGHuQiAfuaBztfXtwOmof3z72Ak4G66eAS1jz2iKGPqRpv+MGNr7zCXu//A++Ie3oqGkHOLkrmja9uuLs6thBXXXo9Hp63nUH2xa8ibRYCBo+DK+2bTi+zjqdLGTMaPx79SI5OpY/nnoGJ4MLfWZYBxXpR45yeus2vNq1Zf0LMwHoetONtOrdizM7dpCw1rqWSED/fgQOH9Yo+gP69CQxOo6fn3gevYuB8AdLgwfZOP9dBs64Gzezid633sTW9xYT+68fMAW3K30Tue/fP5GflcWez1da60OnY8JrLwGwddFiUg4eJv9SFj888hQ9bryGsFENK0fr3j1JjI7ll78/h5OLgQE2ejfNf4cBD9yD0WSi5603suO9xez75nt8ggJLF3OuKr9Or6fvPXewad7b2ifMh+Ld1hqlFPvVt1xKTEIIgVsLX/pNv6tBZQBo06cHZ6Pi+OH/rPYe8fA9pfvWvb6QwQ/ejZvZh76338DmhUuI+foHTMGBpQvbVkVuxgW2fvg50mJBWiTBg/vTtl+vBuu1p/37WmjftHAJ0V//gDk4kA41aAf48+2PyL+UjU6vJ/y+23Bx8OL+tgT27c6pyH18+chMnFwMjHzk7tJ9/33lPUb+7U7czT7E/Xc90T+sISfzIt/8Yy6Bfbsz8m93kpNxge+eeZ2C3DyEEMT9vJ5pC2c5dhqGDc3pXm2s+xSgz923s/ODJViKinH3a8lAbV/IyGHsXvw5vz0zE52TnoEP3+/QqJRiCW9uTWDRxK7odIKfDidzPCOX67pYH0q+P1j1FzB7+XsyqaMfR9OyWXG99X78aPdJtp3OdJi+6hB6PSG33MPB915HWiz4RYzELaAdSZv+AKDV8HEUXMgkbt4LFOflghAkrv+VXi8toCjrEocXvw2AtBTTYsAQTN16XxbdtWHZi9czrHcQLbzdOPavJ5i7dCPLfoluMj06vZ7e99zOlvlvIS0WgkdYxwQJa63vdkPHjqJV754kRcfy+z+eRW8w0P/BMkfDzvc/5vzBQ+RfyuKXR/9BlxuvJWTkcKKXrcRSWMjm162rb5jbh9F3+t12NTSExuqbAAbceytb3vsUS1ERHn4tyx27PrTW2sT/PvE8Ti6G0rYAYNP8dxkw426MZhO9br2J7e8tJu5fP+Bj0yZWl3/rOx9SkJWF0Ovpd+/tDv/QTEUaawycfOgY8Zt2YApsww9PzQGg323X0a6vY9ZI0+n1dLvzTnYuWIC0WGg3fDiebdtycr11DBw0ejR+vXqREhvLhqefRu/iQq/776/xuD3vu4/9K1disVjQOzvT4957HaK3ovaed93B9jfeREoLgSXjd017yGht/B4Ty9qnn0FvcKHP/dZ7NS/zAlFLPkFK61irTfhAWvXpDUDv++4lbtUqZLEFnbMzvRtBu+Kvh6hpbQMhRDBwHIiQUm4XQnwCHAIew+rgOC+EaIk1Cme0lPKYEMINaCulPCKsC7fHA7uxRh79SzvuCaA/EAgsxRq1JYCdwJ3APqzOok5AFvAn8BswBwiUUp7QppudATrZW+hdc7xci81UQe3vQcCDwCSsUUV7sE5VjKhie2fgKWA6sAb4PynlRiHEPVodPCqEWAr8LKX8Vjt3lpTSQwhxE3A3VidbS6xTBR/QyuImpUzRpukdk1La/WZsNce+vgq9bYC3pZSVVyQuO+ZDWKOlbrGZKmgBDmONptIDO4BvpZSzhRAbsU4/3KNNDd0jpQyu4jhZwAHgTs1mnIGOUsr9JWXBGlFXVZpY4G9Syi1CiPnA1VLK7lWVBWqeKnil0gjroF820vMu39eeHI3R6TKum+JgimXzNRoXffOt9+Zb6+DWjO39QoFzU0uoN835Xl2zt/lq79LeteZEVyhfz60p+P7K5e8Lxja1hHrj5lTVu+8rnwJLk30gvsG4N+O+KSXn8r38dTR60SwfmUp5I3xw8+2gasF/T/9x2S7Q1e3GNau6rG1rdxC4W3MomIGPbHdKKVOBe4AvtTQ7sDp70Kax/Yx1ulelHllKGYnVcbULq9PqUylllJSyEKuTaqeW75CWRQ+sFELEYXVEvVPD1wl3Af/VNM3Vpvl9j3W6YQywHnhGSplUzfYSrcnAFOADIUR4tTVWxndYnWv7gMVaeS4AnsDPWn39CdhdRL0GqtIbCxRpC5xXddxPgVNArBAiBrhNq8dPgDisUzB310KDveMUADcC87Vt0VidgqXUkOZerHW8Hbhyvt+sUCgUCoVCoVAoFAqF4rJS24irn2uKeFFUjRDCQ0qZJYTwxepIG2LrEFM0HBVxdflREVdNQ3OO4lARV02DirhqGprzvaoirpoGFXHVNKiIq6ZBRVw1DSri6srml9NrL9sFmtRubLOqy9qucaVoGD9razcZsEZ9KaeVQqFQKBQKhUKhUCgUCkUN1Oi4klKewPr1tysWIcS9wP9V2LxVSvlIU+ipiJRyZG3SCSFeAG6qsPkbKeWr9T23EGICML/C5uNSyuvqe0yFQqFQKBQKhUKhUCgUjsOB30v5y/GXiLiSUn4BfNHUOhqK5qCqt5OqimP+DvzuyGMqFAqFQqFQKBQKhUKhUFwO/hKOK4VCoVAoFAqFQqFQKBSK5opo1quqNi7Nd0U/hUKhUCgUCoVCoVAoFArFXxoVcaVQKBQKhUKhUCgUCoVC0YSoqKKqUXWjUCgUCoVCoVAoFAqFQqG4IlERV4q/BN6GwqaWUC/O5hibWsL/JIWy+c4fl81Y+/kcl6aWUG8Kipvve542nrlNLaHeXCpovsOU5mwzY/oWNbWEeuOiz25qCfXm7wvGNrWEevPO02ubWkK9eWzeuKaWUG9cnYubWkK9ych3bmoJ9abY0nzHYipsRdFcab4jQoVCoVAoFAqFQqFQKBSKvwBCNGOnaCOjfK4KhUKhUCgUCoVCoVAoFIorEhVxpVAoFAqFQqFQKBQKhULRhKh4q6pREVcKhUKhUCgUCoVCoVAoFIorEhVxpVAoFAqFQqFQKBQKhULRhKg1rqpGRVwpFAqFQqFQKBQKhUKhUCiuSFTElUKhUCgUCoVCoVAoFApFE6LirapGRVwpFAqFQqFQKBQKhUKhUCiuSFTElUKhUCgUCoVCoVAoFApFE6LWuKoa5bhS/E8jpeTPT7/hxN79OLkYGP/4nfiFBVZKF/PfjUT9tIELSed5YPl8jF4e5fYnHT3Jv55dwMSn7qNDRN8GaUqKiSN6+WqkRRIyahidp15dSXPM8tUkRsfhZDDQ/6HpmEKCqs1bkJXFjkUfk5N6HreWLRj0+MMYPNzJv5TFjoUfkh5/nODhQ+hz7x2l59k4dz55mZnoDQYAhj33JK7eXvUuV3JsLHErViMtFoJGDqfjlMmVyhW3YhXJMbHoXQz0feB+fIKDKS4oYPOrr2MpLEJaigkYMIAuN1wHwNmduzj0/Q9cOpfIiNkvYQoNqbc+WxrjGuz/9geOb9iEi5cnAN1vvoHWfXqSnXqe3596Ac+AVgD4tg+j7/S7HFKOEj0xWr2HjBxOpyrKkhQTi95goP+D0zGFBAOwZ8lnJEXF4OLlxbj5r5TmObNzNwe+s9b76DkzHVbvFUmNi+Xg6pVIi4W2w0cQdvWUStoPrl5JamwMeoMLPabPwDvYqr0wJ5u4Lz4n68wZENDjvvsxte/AkX9/S0pUFAiBwcuLntNn4GoyOVx72r4Yjny5AmmxEDBsJMGTplbSfuTLFaTFRaM3uNDlvgfwCrLW46m1v3Fu00ZAEjBsFIHjrirNd3rdGs6sX4PQ6/Ht0ZsON93qcO1SSnYv/YqzUXHoXQwMefhefEODKqW7lJLK5oWfkJ+VjTkkkKGPTkfv5ETS/sNsWPABHn6+AAQO7EuvG6eQfT6dLR98Tl7mBdAJOo4ZTpdJYx2qPSU2lv2rViEtFgJHjKD95MrtzP5Vq0iJiUFvMNB7RpnNAEiLhc2zZuFqMjHwH/8A4OKpU8QuXUpxfj7GFi3o89BDOBuNDtUNcD4ulkOrVyClhbbDRhJix94Pr15BapzV3rtPfwCvIKv2k2t+5cymP0GAZ5t2dJs+A72zgWP//paU6EiEZu/d7nugUey9MdqZ2NVfkxgZjc7JCQ9/P/o9MB2Du5vDtUsp2bvsS85FxeHkYmDQw/dhDqls71kpqWxduIT87GzMwYEMfvR+9E5OHN+yg4M//gqAk4srA+6/A1NQOy6eS2LLwsXl8ve86Ro6TxrnMO3Nud5ry8fPTGHioI6kZmbT/76Pm0yHLcmxscStXA0WC4EjqhjPrFxFijae6TOjbDyz5bXy45nO119Xmi9hzR8krF2HTq/Dv1cvut0yzeHapZRELfuSxOg49AYDA6ux9+2LFlOQnY0pOIjwR6z2fvFsIrsWf07G8VP0mHYdnSdb+6ectHR2fvgpuZkXEUIQNmY4HSc6ztYrkhwTR6zNmNKe7ceuWE1ytPUa9HtgOj4hweSkpbH340/Ju3ABIQTBo0bQ/qrxjaazhMYYzwCcWLuGU+vWInR6WvbqReebb3G49ob0q+uefBInV1eETofQ6Rj28ssAHP7uO5IiIxE6HQZPT3rPaJyxmOKvhXJcKf6nObF3P5mJqdz90WySjpxg/cdfccuCZyqla90ljJD+Pfj2xXcr7bMUW9i6/AcCe3dpsB5psRD1xUqGPf8kbr5m1r04h4C+vfFq26Y0TVJ0HJeSkrnq7ddJP5ZA5OfLGTN3ZrV5D/34C37du9B56tUc+vG/HPrpF3reehN6Z2e63XgtF86c5eLps5X0DHzkAcwOcEpIi4WYZSsY8uzTGM1mNr70Mq369sGrTVm5kmNiyUpOZuyb88mIjyfmi+WMePkldM7ODH3+WZxcXbEUFbF57mv49+qBuX17vNq2ZeD/PUb050sbrNFWa2NcA4AOE8fTafJVlc7p4e/HuNdfdlgZbMsSvXQFQ59/CjezmfUz59C6YlliYslKSmbCW/NIP5ZA1BcrGD1nJgBBw4YSNm4Mez7+tNxxvdq2YfATjxL5+TKHa7bVvn/FcgY+9QyuZjPb5szCr3dfPG1sJjU2luzkZIbPW0BmQjz7VywlYuZsAA6uWknL7j3o+8hjWIqKKC7IByBk4tV0vP5GAE78sYZjP/5A97vvdbj2w6uW0ecfz+FiMrP7lZdo0bsfHgFl2tPiYshNSWLwa29xMSGewyuXMuCFl8k6e5pzmzYy4IWXEU5ORL/7Bi169sbNvxXphw6QGr2X8Nmvo3N2puDiBYfqLuFs9D4uJqVw7cJXOX80gZ2frWLSq/+slC5y1Xd0mTSWkCED2fHJCo6t30Kn8SMB8OvSnjHPPl4uvdDr6H/nTfiGBlGYm8fPz8+ldc+u+LQNcIhuabGwb/lywp95BqPZzObZs/Hv06eczaTExpKdlMSoN94gMz6euGXLGDprVun+42vW4BEQQFFubum2mM8/p+stt+DbuTOnNm0i4Zdf6HTDDQ7RbKv94Mpl9HvyWVzNZnbMeYmWvfviYaP9fFwM2cnJDH39TS4kxHNg+RcMmvkyeRnpnFy7hiGvzEdvMBDz4Xsk7dxBm6HDCZ54Ne01ez/5x+8k/PQDXe9yvL03Rjvj370b3afdiE6vJ+7Lf3H4x5/pcevNDtUOcC46jkuJKUx59zXSjiWw+9OVTHj1hUrpold/R6erxxEcMZBdn64gYf1mOowfhUfLFox96RkMHu6ci4pj15LlTHj1BbwCWjFpvtW2LBYLPzz8FO0GNOzFli3Nvd5ry4rfYvj4+918+vy1TabBFmmxELt8BRHPWMczf86qPJ5J0fqmMQu08czS5YyYbR3PDHnOZjzzymv49bSOZ1IPHCQxMopRr85F7+xM/sWLjaI/URu/THrHau97P1vBuFderJQudvW3dJo0jsCIcPZ8upzjGzbTftwoDB7u9Ln7Ns7uiSqXXuh09LpjGuaQIApzc1nzz7n49+iGt4Pad1tKx5TPPYXRbGbDS3No3a93pTFldlIy496aR0Z8AtFLVzDy5ZnodHp63DYNn5BgCnNz2TDzZfx6dCuXtzH0NsZ4Ju3gAVKiIhky59VGsxlH9KuDn3sOg6dnueOGTppU2o8eX7OGI//5Dz3vucfh+psjKt6qatQaV42AECJYCLHPAcc5IYRoYWf7toYeu5bnz7oc52lKEnbF0mVkOEIIWncKIT87l+z0yg+DfqHt8PL3tXuMmP9upP3g3rh5e9rdXxfSjyXg4e+Hh78fOicn2g0O59ze6HJpzu2NImhYBEIIfDuEUZiTQ25GZrV5rXmGABA0bAjn9kQC4OTqQovOHdE7OzdYe3VkxCfg4e+Pu59VW9tB4STtLT/oSYqMInDoEIQQmNu3pzAnh7zMTIQQOLm6AmApLsZSXExJs+7ZJgDP1q0dqrWxrkFTkB6fgLu/Hx6l9T6QcxXqPbGKsgC07NIJg4dHpeN6tQnAM8Cx9V6RzIR43P38cNO0tx44iJSoyHJpUqIiaRNhtRlTWHuKNJspzM0l/chh2g4fAYDOyQlnN3eAcpEyxfn50Agh2RePx2P088fY0qrdf+AgzkfvLZcmNXovrQYPRQiBd1h7inKyyc/MIDvxHN6hYehdXNDp9Zg6diY1cg8AZzeuJXjiFHTa/Wrw8na4doDTu6MJGz4IIQQtO4ZRkJ1DjmYTJUgpSdp/mKBB/QAIGxHBqd1Rdo5WhpvJpzRyy9noineb1uSkZ1abpy5kJiTgbtPOtAkPJzmyvM0kR0bSdohmMzbtDEBuejrJMTEEjhhRLk92YiLmTp0AaNmtG4l79jhMcwkXEuJx8/MvtfdW4YNIqWgzUZEERFhtxkez93xNuyy2YCkowFJcTHFBAS4+1jfXTrb2rj3sOJrGamf8e3ZHp9cDYG4fRm56RqPoP7snmpDhgxFC0KJDGAU22kqQUpK8/xCB4VZ7Dxkewek90Vb9ndpj8LC2Ly06hJJjR2dy3EE8/Fvi3tL+OKI+NPd6ry1bY0+RfjG35oSXiYz4BNz9bNqZQeEkRVao98go2g2peTwji4tL+6AT69fTYfLVpeMxF6/6R7hXx9m90QRrNtGigk2UUGLvbcP7AxA8PKLUUeXq7YVvWEipjZRgNPmURm45G414tWndaLZTYvvuNrafaMf22w2N0K5BGIXZOeRlZOJq8sFHizp0NhrxDGhNngP7IXs01njm1Ib1hE6a3Kg209B+tSoqjsWUs0ZRG1TEVTNEShnR1Br+KmSlX8CjhU/p/x6+PmSlZ+Jurt0DYVZaJvE7Y7h+zv+RfPRkg/XkZmRi9DWX/m80m0g/llAhTQZuZts0ZnIzMqrNm3/hIkaTj3W7yYf8C5dqpWfP4s8ROh1tBvSjy3VT6j3vOjcjA6ONZleziYz4yuWqmCY3PQNXHx+kxcKGmbPITk4hdOwYzO3D6qWjdlob5xoAxK9Zx6nN2zCFBtPz9mmlDzvZqamsfX42TkZXut18PS07d3RMWdIzcPMtrzM9Pr5Cmsqa8zIySu2lqcjLyMDVXPaQ52o2k1lBe15mOq62NmMyk5+RjtDrMXh6EffZJ1w8fQrvoBC63H4HTi4uABz57hvObt2Kk5uRgc883zjaTWW6XExmLiaU156fWb58LiYz+ZkZeAS0Jf77byjMuoTO2cD5uBi8gq1RjznJSWQePUz899+gc3amw0234hXi+HshJ6O83bj5mshJz8TNxibyL2VhcDOWPry4mU3k2gz+U48k8NPTL2M0+9D/jhvxaVf+bXZWynnSj5+mRXvHTTPNzcgobw9mMxkVbSYjA6NvebvKy7C2M/tXraLLzTdTlJdXLo9n27YkR0XRqm9fEnfvJjc93WGaS3VlZlSy5QsJlbVXLF9eRjreIaEEXzWJTU8/gc7ZgG/37rTo3qM03dHvvuHcti04uRkZ8HTlyLmGcjnamRN/bqbdoIEO0VuRnPTM8vZuttq7sYK9O1ey98oP5fEbthDQu3ul7Se37yIoItyhupt7vTdXrG1I+TqtOJ7JSy8/njFWGM9sfMk6ngkZOwZzmLUNz0pKIv3IEQ5++501Gv7WaZhCQx2uv7LdWNtuW5souJSFwd2tzN59zXYdslWRnXqezBOn8G3veP2gXYMKY7CKbb298VhuRgauNuXMTj3PhZOnMIU1jk5bvY0xnslOSiLjyBGO/PtbdM7OdL75VnwcbDMN7VcBdixYgAACR40iaNSo0nSHvv2WM1u34mw0Mui55xyquzkjlBuvSlTEVePhJIRYJoSIFUJ8K4RwE0KMEUJECSHihBCfCyFcAKraXoIQwiiE+E0IMUP7P0v7PVIIsVE7/iEhxCqheRaEEJO0bVuEEIuEED9XJVQI4SGE+EI7f6wQ4gabfa8KIWKEEDuEEP7atilCiJ2a5rU222dr+jcKIRKEEI/bHGempucPIcSXQointO1hWtn2CiE2CyE6a9tvEkLs0869yTGXxA5S2quRWmf/87NvGXLXtej0DrqV7Omp6CyylwRRu7x1IPyRBxg/fy4jX3qO84ePcmpzAwL97GqrOU2Jo0zodIx+dS4TFr5NRkICF0+fqb+WmmikaxA2bhQT353P2Ndn4+rjTeyqrwFw9fFm0qI3Gfv6bHrdcQu73l9MYU7jvV2u7Hx0rN00JrWRjhDI4mIunjxB4KgxDH35FfQuLiT896fSJB1vuIlRb79LwKAITq1b2whK62fvIHAPaEPwVZOJense0e++gWe7QITO+vAgiy0UZmfT/5+zaX/jrcQtfh9p9zgNxK5J1MImtDTmkEBu+GAeUxbMovNVo9nw5oflkhXm5bHx7Y8YcPc0DG4OXCuqmjakJpKjo3Hx8sInpLIjrdf06ZxYu5bNL71EUW5upUgDh1Cr9tt++Qqzs0mJ2suw+W8z4u1FFOfnc2771tI0HW64iRFvLaT1oAhOrf/DwcLt48h25tAPP6HT62k3ZHDDhdnFXr3WIluFRMn7DxG/YTO9b7ux3PbioiLO7o0hUItObEyaV703T6TdjqdyqorYjmdGvTKXCe++TWZCAhfPWMczsthCQXY2w2fNpNst09jz/oeN1L7X3D/Z71prZzeFeXlsfedD+tx1C86ObN9tsVstFQtRve0X5eWxa+H79Ljj1sbTWQ2OGM9ISzGFOdkMfnEWnW++heiPGmFM0IB+FWDIiy8yfM4cBj71FCfWrSPt0KHSfZ1vvJGx77xDm8GDObG2McZiir8aynHVeHQClkgpewIXgX8AS4FpUsoeWKPdHhZCuNrbbnMcD+AnYLWU8hM75+kDPAF0BUKBIdoxFwMTpZRDgZY1aJ0JXJBS9tD0rte2uwM7pJS9gE3ADG37FmCQlLIP8BVguyhUZ2ACMBCYJYRwFkL0B27QtF4P9LdJvwR4TErZD3gKKHnCeQmYoJ27/KrGGkKIB4QQe4QQe7b86781FLGMmF/+ZNUTr7HqiddwN3uTdT6zdF9WWiYetYy2Akg5dopf3/ycz2fM5Nj2KDYs/pr4HTG1zl8Ro9lEblrZ2/zc9MpvRo1mEznptmnScTX5VJvXxdurNBQ8NyMTl1pMazSarVNNnI1GAiPCSY8/Xs9SaRFJNprz0jMw+phqTONaoewGd3dadO5McmxcvbXUrLVxroGrt3fp4pQho0eU1qfe2RkXT+t0DVNoMO7+flxKSnJYWXLSKuj0qVyWiporpmkKXE0m8tLTSv/PS08vnf5UlsZMnq3NZFjTuJrNuJrM+GhvslsNGMDFk5UjIgMGDSZp7+5G0G6NhCkhP6OydheTuVz5rGl8rLqGjWTgS6/S79mZOLm74+bvr+Ux4de3v3V6YWiY1WmRVbvoyZo49PsGfnrmZX565mWMJp9ydpOTloHRVL5ddPH0oCAnV5u6CznpZWkMbkactekwbfv0wFJcTN5Fq05LUREb3/qI0KHhBIU7br0fsLYh5ezBjr27mkzkppW3K1eTifQjR0iOimLdk08S9dFHnD94kKiPrQtBewQEMOiZZxg2Zw4Bgwfj5ufnUN1WXfZsuaL2yuVz8TGRdmAfbi1aYvDysk5N7TuAzGNHK52jdXgEyY1g743ZzpzctIXEqBgG/O0Bh35p6cjv6/nl2Zf55Vk79m6nzXfx9KCwkr2Xpck4eZqdi5cx/KlHS9vzEhKj4zAFB2L0cezU3uZY738FjCZz5TqtsKi0a4XxTK6d8Yyzuzu+nTuToo1njGYTAf37aVPFQkEnKLjkmPb96Jr1/P7cbH5/bnYle7c3xnHx9KAgO6fM3tPSaxWlZykqYts7HxI0JJy2AxvPUWuNyK88BrPF7nhMs31LURE7F75P24jBtBnQn8amscYzriYz/v2sYwKf0DAQOofZTAkN6VdL9oF1GmOrfv3ITCgfnQgQMHgwSY0wBb+5ohOX76e5oRxXjcdpKWXJK8+VwBjguJTyiLZtGTAcq4PL3vYS/gN8IaVcXsV5dkkpz0gpLUA0EIzVeZQgpSzxNHxZg9axwAcl/0gpS+KBC4CSSK292rEB2gK/CyHigKeBbjbH+q+UMl9KeR5IAfyBocB/pJS5UspLWB1xCCE8gAjgGyFENFZnW8nCOVuBpVqUmd3X21LKJVLK/lLK/kNvvtpeErv0mjSC29/9J7e/+0/CwntxcONOpJQkHj6Oi7ux1tMEAe5dMof7PpnLfZ/Mpf3gPox6cBphg3rVOn9FTGEhZCUlk52SiqWoiNPbd9K6X+9yaQL69ebk5m1IKUk7Go+z0Q2jyafavAF9+3Bys9UcT27eSkC/PtXqsBQXk2/zoJkYFYNXu/ovXOkTWl7bmR07adW3vIZWfXtzastWpJSkHzuGk5sRVx8f8i9epCA7G4DiggJS9x9o1PWVGusa2K4hcXZ3ZOkCuvkXLyItFgCyklPISkrGw68mX3MtyxIaQlZSik2976p07Vv37VOhLMYmnyYI4B0SSnZKMjmpVu2Ju3bg16e8dr8+fTi7zWozGfHHcDK64erjg4u3D65mM1mJiQCkHdiPR4B1gdhsG6dgcnQk7q0dv3CsZ3AoOclJ5KamYCkqInnXDlr0Ku+kadm7L0nbtyCl5IKmvWQgW7Loel7aeVIj9+A/0Do7vGWf/qQfOgBATlIilqIinD0avrYeQOcJo5jyxiymvDGLwAG9id+0AyklqUficXYzlpsmCNY3rq26duLkDus6TPF/bqNd/94A5GZeKH3re/7YcaRF4uLpgZSSbR8vw6dNa7pOdvxXnLxDQshOLrOZszt34l/BZvz79OHMVs1mjh3DyWhtZ7rcfDNj332XMW+9RZ+HH6ZFly70eeghgNIFb6XFwtH//Ieg0aMdrt0rxGozOZrNJO3cgV/vyjZzbpvVZjLjj+Hk5oaLjw+uZl8yE+Ipzs+33scH9+Oh2XV2cpm9p0ZH4t7K8fbeWO1MUkwch3/6lYgnHy+d5usoOk4YzaT5s5g0fxZt+/fh+KbtSCk5f9Rq7xW1CSHw69qJUzut9n580zbaavaefT6NzW9/yOBHpuOlfR3WlhNbdxE0xPHT7Zpjvf8V8Am1tjPZJe3Mjp20qtDOtOrTm9Nby8YzzjbjmcIK4xkPbZ3OVv36knrgIABZiUlYioorLWhdXzqMH82EebOZMG82bfr34YRmE1Z7d7Nv7906cWan1ZlwYtM2AiqMgyoipWTXkqV4BrSm09UTHKK7KuzZfuu+lW3/9JZt2jWw3teuJh+klER++gWeAQF0mNS4OktorPGMf99+pB20jgmykxKRRUUOs5ky7fXvV4vy80s/dFKUn8/5ffvwbNsWsE6NLSE5Kgp3B69Xq/hrIholDPV/HCFEMPCnlDJI+3801qgmvZRyuLZtDPAIMAdYVHG7lPJ6IcQJ4BfAC7hTahdLCJElpfQQQowEnpJSTta2vw/sAWKAd6WUI7TtU4EHStLZ0RsJ3CylPFZhe5aU0kP7+0ZgspTyHiHERuBtKeWPmobZUsqRQojZQJaU8k0tzz5gMnAd4COlnKVtfxs4hzXa6rCU0m5rJYQIB64G7gV6SynT7KUD+PDg2noZspSSjUv+xcnIAzi5GBj3+B34t7cuLvnDnA8Y++jteJh9iP55A3u/X0t2xkXcvD0J7teNsY/eXu5YaxYuJ2RAdzpE1D6K4GxO5fDkxKhYYlZ8ibRYCB45lC7XTiF+7QYAwsaOQkpJ9NKVJMXsQ+9ioP+D95V++c9eXrCuz7Fj0Ufknk/D2MKXwf/3cOmirL88/jSFuXlYioowuLsx7Ll/4NaiBRvnzEMWFyMtFvy6d6XXnbcgdGW+7qLiuvm9k6JjiFulfbp4+DA6XTOV4+uswX0hY0ZbP128bAXJcXE4GVzoM2M6ptAQLpw6TeSST5AWC9IiaRM+kM7XXQPAuT17iV2+koJLl3B2c8M7KJCIZ56qUYuT3lLt/sa4Brs+/ITMk6cQCNxatqDv9Lswmnw4s2sPB775AaG3RmN1veHaageIUtbtFUlidAyxJXpGDKPztVNI0MoSalOW5Ni4ss+la2XZ+f7HnD94iPxLWbh6edHlxmsJGTmcs7v3ErNsFfml9d6OYc/VXO85BXWbYpUSE8PBL1ciLZK2w4bTfspUTm2w2kzgKKvNHFi5nNQ4q/ae0+/HO8S6vsPFUyeJ++IzZFExxpYt6Tl9Bs7u7kS+v4jspESE0OHq60v3u+8ptx5VVRTU0d7Px0Zz5OuVYLHQesgIQiZfw5mN6wBoO3IMUkoOr15G+r5YdAYDXe99AK9gq/Y98+dQmJWFTu9Eh2m3Ye5iXTPHUlTEwS+WcOn0KXROetrfdBvmLt2q1FBCG8+6TT2VUrLr89WcjdmPk8FAxMP30CIsGIB1ry9k8IN342b24VJyKpsWLqEgKxtzcCBDH5uO3tmZQ7+t5/AfG9Hp9OgNzvS/62b8OrUn+dBRfp/1Bj6BbUqjOPrcej1t+/SoUkt6nqFO2pNjYjigfba73fDhdJg6lZPrrTYTNNpqM/tWrCA1Nha9iwu97r+/0vTA8wcPkvDrrwz8xz8ASFizhpPaNIZW/fvT+aabahWFUlebSY2N5vCXVu1thg4ndMo1nN5gtZl2o6w2c2jlMs7vs9p7t/tmlNr7sR++I2nXToReh1dgMN3umY7O2ZnoDxaWs/eud91bK3v3dCmqk/bGaGd++8ezWAoLS/sqc/sw+k6/u0YtLvriOmmXUrLni9UkRlvb80EP3YuvZu8b5r1L+AP34Gb2ISs5lS2LFlOQlY0pOJCIR+9H7+zMzsVLObUrEvcW1jVedHodV71m/XJfUX4+PzzyDFMXvY7Bza1GLfnFdWsjr6R6f+fpxpnqs+zF6xnWO4gW3m6kZGQzd+lGlv0S7dBzPDZvXJ3SJ8fEELdyNVJaCBw+jE5Tp3Jca2dCtHYmdvkKUuLi0Btc6HN/2XgmasknSFk2nul0rXU8YykqIurTz7hw8hQ6Jye63TqNll271qjF1bnu9h75xSoSY/bh5GJg4IP3YdbsfdP8dxkw426MZhNZyalsf89q7z7B7Rj0yAz0zs7kZl7gjxfmUpibW7rY/MQFc8k8dYb1L8/Du11bhBbK0WPa9QT06Vmllrq2kbYkRccQu/JLsFgIGjGMTtdM4fg6q+2HjLHafsyylaRott/3Aes1OH/4CJvnvo5Xu7al7XjXm2+gVe+6vXTOK2z68YylqIi4zz7l4umT6PROdJ52K761sBm9rm6PTPXtV7NTUtizaBEAsriYNoMH02GqdRLNnvfeIzsxEYTA2KIFPe6+u9y6ZdXx1qBBzTBWqPZsTvrzsjlnhrUa0azqUjmuGgHNcXUciJBSbhdCfAKcAB4ERkspjwkhlgJRWKOMjlTcLqVcqDmu+mN1ehmklA9rx6/JcfW1dsxhUsoTQohVgHc1jqt5gKuU8gntf5OUMqMax1UUcL+Ucq8Q4gsgpAbHVUutnBFYp0LuBT6RUr4prF9IfEdK+Y22PldPKWWMECJMShmvHScKuFdKGV1VndfXcdXU2HNcNRfq6ri6kqjJcXUlU1fH1ZVEXR1XVxINGWA3NXV1XF1J1NVxdSXRnG2mro6rK4m6Oq6uJOrquLqSaCzH1eWgro6rK4m6Oq6uJJpzG1lXx9WVRF0dV1caf3XH1ZakTZftAg1tNbxZ1WXzbTGufA4CdwshYgEz8A7WyKFvtCl2FuBjKWWeve0VjvUE4CqEeKM2J5ZS5gJ/A34TQmwBkoEL1WR5BTCVLIYOjKomLcBsTe9m4Hwt9OwGfsQaCfZvrM61Ej23A9O18+4HrtG2L9AWi9+HdX2t+i8cpVAoFAqFQqFQKBQKhaJZoiKu/qIIITyklFlaFNMHwFEp5TtXgB43rI6oB6SUkY46voq4uvyoiKumQUVcNQ3N+c2wirhqGpqzzaiIq6ZBRVw1DSriqmlozm2kirhqOv7qEVdbky9fxNUQfxVxpbgymKEteL4f8MY6Va8pWaLpiQS+c6TTSqFQKBQKhUKhUCgUCsVfE6emFqBoHLToqnIRVkKIe4H/q5B0q5Tykcug57bGPodCoVAoFAqFQqFQKBTNEUGzCoK6rCjH1f8QUsovgC+aWodCoVAoFAqFQqFQKBQKRW1QjiuFQqFQKBQKhUKhUCgUiiZEqICrKlFrXCkUCoVCoVAoFAqFQqFQKK5IVMSVQqFQKBQKhUKhUCgUCkUTota4qhoVcaVQKBQKhUKhUCgUCoVCobgiURFXir8E6fmGppZQb/RCNrWEelEEFBQ3T9+3k96CczOt9wIpEM1UO0BGbvO8V90NRc1WexvPXC4WODe1jP9J3JyLm1pCvXF3KmpqCfWiSAqkbL5vrN2aab0DPDZvXFNLqDfvPfdHU0uoF0+/NRqDztLUMupFcx1DlpBTqG9qCfXC06UISzNtI3XNePxbW9QaV1XTvFsMhaKZ01ydVtC8BxzN1WkFKKdVE9GctSunVdOgnFZNg3JaNQ3KadU0NFenVXOnuTqtgGbrtILmrV3RcFTElUKhUCgUCoVCoVAoFApFE6LWuKqa5hsyoVAoFAqFQqFQKBQKhUKh+EujIq4UCoVCoVAoFAqFQqFQKJoQFVVUNapuFAqFQqFQKBQKhUKhUCgUNSKEMAsh/hBCHNV+m6pId0IIESeEiBZC7KlrfluU40qhUCgUCoVCoVAoFAqFQlEbngPWSSk7AOu0/6tilJSyt5Syfz3zA8pxpVAoFAqFQqFQKBQKhULRpAghLttPA7kGWKb9vQy4trHzK8eVQqFQKBQKhUKhUCgUCsX/CEKIB4QQe2x+HqhDdn8pZSKA9tuvinQSWCOE2Fvh+LXNX4panF2hUCgUCoVCoVAoFAqFoklpcCRUrZFSLgGWVKlEiLVAKzu7XqjDaYZIKc8JIfyAP4QQh6SUm+ooFVCOK4VCoVAoFAqFQqFQKBQKhYaUcmxV+4QQyUKI1lLKRCFEayClimOc036nCCG+BwYCm4Ba5bdFOa4U/3NIKdm99CvORcWhdzEQ8fC9+IYGVUp3KSWVzQs/oSArG3NIIEMenY7eqeyWOX/sOL+9+DrDnniQoEH9uHAuic3vLi7dn5Vynl43XUOXq6u85+ulPWrZlyRGx6E3GBj48H2YQyprz0pJZfuixRRkZ2MKDiL8kfvROzlx8WwiuxZ/TsbxU/SYdh2dJ18FQHFBIevnzKe4sBBZbKFdeD+633Stw3QDpMbGcmD1SqTFQrvhIwibPKVS2Q6sWklqbAx6gws975+Bd3AwABue/Ad6oytC6BB6HUNnzynNd+KPNZxctxah0+PXqxedp93iUN0l2iKXfck5rd4HVVPv2xYtJj87G3NwEINs6n2HVu89p11HF63eAQ79sob49ZsRArzbtWXQQ/ehNzjXW2tSTBzRy1cjLZKQUcPoPPXqSmWJWb6axOg4nAwG+j80HZNWlqryFmRlsWPRx+SknsetZQsGPf4wBg93kuP2E/flt1iKi9Dpneh5+834detSb+3Vkbk/mhPfLENKC34Ro2kz4Zpy+3OTzhK/4mOyTx+n3ZRpBIwrs6+inGwSVi0m59wZAMLufAjP0I6NorMmLhyI5sx3X4DFgu/gMbQaf225/em7N5O09j8A6F1caXfz/bi1Db6sGpNjY4lbsRppsRA0cjgdp0wut19KSdyKVSTHxKJ3MdD3gfvxCQ4mJy2NyMWfkHfhAkIIgkeNJGzCeAAO/vt7Tm78E4OnJwBdb7qRVr17OVx7Smws+1etQlosBI4YQfvJlbXvX7WKlJgY9AYDvWeUtTPrnnwSJ1dXhE6H0OkY9vLLAJzbtYsj339PVmIiQ2fNwickxOG6K1Lfa1BcUMDmV1/HUliEtBQTMGAAXW64rtH1lvSrZ7V+dUgN/Wq+1q8O1frVpP2H2bDgAzz8fAEIHNiXXjdOIft8Ols++Jy8zAugE3QcM5wukxzXp5Zoby7tuz3tjTGeASjIzmH74mVknj4HQMTD99CyY5jDtCfHxhK3cjVYLASOqMLGV64iRbPxPjPKbHzLa+VtvPP1ZTaesOYPEtauQ6fX4d+rF91umeYwzXXl42emMHFQR1Izs+l/38dNpsOWxrSZbR8t5UxkLK5enkx962WHa0+OiSPWpl3sZGd8E7tiNcnRVpvp98B0fEKCAdi75DOSomNw8fJi7LxXSvPseu9DshKTACjMycHZzY3Rr82hMUnfF8Oxr1cgLRZaDx1J4MSp5fbnJJ7j0LLFZJ06Qci1N9NuvLWceelpHPr8IwovXgAhaD18NG3HXGXvFA6lIeP3wuxs4r74nEtnzoCAntPvx9S+A4m7dnH0h+/JSjxHxEuz8AkJbfRyNBcuX7xVg/kRuBuYp/3+T8UEQgh3QCelvKT9PR6YU9v8FVGOK8UVixDCB7hNSvmhI497Lnofl5JSuGbhq5w/msDOz1Yx6dV/VkoXteo7ukwaS8iQgez4ZAXH1m+h0/iRAFgsFiJXf0frXt1K03sHtGLyG7NK93/30NO0G9jHkdJJjI7jUlIyk955jbRjCez9bAXjXnmxUrrY1d/SadI4AiPC2fPpco5v2Ez7caMweLjT5+7bOLsnqlx6nbMTI198CmdXVyxFRaybPY9WvXvQooNjBqnSYmH/iuUMfPoZXM1mtr48C78+ffFs06Y0TWpsLDnJyYyYv4DM+Hj2LV/KkJdml+4f9OzzpQ+9JaQdPEByVCRD576K3tmZ/IsXHaK3IiX1Plmr9z2frWC8nXqP1uo9KCKc3Z8uJ2HDZjpo9d7v7ts4U6Hec9IzOPLbOia9ORcng4Et737Eye07CR0xtF46pcVC1BcrGfb8k7j5mln34hwC+vbGq21ZPSdpZbnq7ddJP5ZA5OfLGTN3ZrV5D/34C37du9B56tUc+vG/HPrpF3reehMGTw+GPP04RpOJC6fPsHne20z+4O16aa+pXMe//pwuj7+AwceXffP/ialnP9xaty1N4+TuQfBN95Aes7tS/hPfLMOna286zvgHlqIiLAX5DtdYG6TFwulvPqPDIy/i7OPL4QXP492jP0abchh8/ej4f7NxcvPgwv4oTn21hM5PvXZZNcYsW8GQZ5/GaDaz8aWXadW3D14292pyTCxZycmMfXM+GfHxxHyxnBEvv4ROr6f7bbfgExxMYW4uG1+aTcvu3Urzhk2YQIerJzaq9n3LlxP+zDMYzWY2z56Nf58+5dqZlNhYspOSGPXGG2TGxxO3bBlDZ80q3T/4uecqtTOebdvS//HHiV26tNG0VyxHva+BszNDn38WJ60t3zz3Nfx79cDcvn2jaj4bvY+LSSlcW0O/GllNv+rXpT1jnn28XHqh19H/zpvwDQ2iMDePn5+fS+ueXfFpG+Aw7c2lfbdHY41nAHYv/YqAXt0Z8Y+HKS4qoji/wGG6pcVC7PIVRDxjtfE/Z1W28ZTYWLKTkxmzQLPxpcsZMdtq40Oes7HxV17Dr6fVxlMPHCQxMopRr85t1DFBbVnxWwwff7+bT5+/tkl12NKYNhM2IoJOE0ax9YPPHa67tF187imMZjMbXppD6369K7WL2UnJjHtrHhnxCUQvXcHIl2cCEDR8KGHjxrBn8afljjvwsb+V/h236iuc3YwO116xHEdXL6Xn35/HxWQm8rWZ+Pbqi3uA7XjGnfa33EVa1N5yeYVOR9hNt+MZFEJRXi6Rr7yIqUv3cnkbQ29Dxu8HVq+kZY8e9H30MSxFRRTnW8dfnm3b0Pexx9m39ItG065odOYB/xJCTAdOATcBCCECgE+llJMAf+B7bSF4J2C1lPK36vJXh1qcXXEl4wP8raZEdeX07mhChw9CCEHLjmEUZueQk5FZLo2UkqT9h0vfIoWNiOD07rJB6eFf1xMU3g9X7/IPOCUkxR3E078lHi19Har97N5ogodFIISgRYcwCnNyyLWjPXn/IdqGW784Gjw8otRR5erthW9YCDq9vlweIQTOrq4AWIqLsRQXO+JrE6VkJsTj5u+Hm58fOicnWocPIjkqslya5KhI2gwZghACU/v2FOXkkJeZaf+AGifXryfs6snona1vsF28vBym2ZYzFeq9oJp6b6fVe8jwiNIHmarqHUAWF1NcUIBF+200+dRbZ/qxBDz8/fDwt9Zzu8HhnNsbXS7Nub1RBGll8bWxoeryWvMMASBo2BDO7bFeO1NwEEaTCQCvtm2wFBZSXFhYb/1VkXXiGK4tW+Hawh+dkxO+/SLIiNlTLo2zpzcewWGICnVclJvDpWMHaRkxCgCdkxNObu4O11gbsk8ew6VFK1y0cpj6RXAhrryjzSO0E05uHgC4h3SgMDPtsmrMiE/Aw98fd+1ebTsonKS95R/IkyKjCBxqvVfN7dtTqN2rrj4++GhvWZ2NRjwDAshLz7hs2jMTEnC30d4mPJzkyArtTGQkbW3amcJatDOeAQF4tG7diMrL05BrIITAqUJbfjne357eHU2YTb9aUMt+9dTuKDtHK8PN5FMaDeJsdMW7TWty0jOrzVNXmkv7bo/GGs8U5OSSfPAI7UdbnWx6JycM7m4O050Rn4C7n829OiicpMjytpAYGUW7ITXbuCwuBm28cmL9ejpMvrrRxwS1ZWvsKdIv5japhoo05hjYv2tHXDwap39Nj0/A3d/Ppl0cSGKFdjFxbxTthkZoNmMtW55WthadO+Hs4VHl8aWUnN25i7aDwxtFfwkXj8dj9PPH2NJaDr8Bg0iLKe+gMnh542VnPOPiY8IzyBrx6+RqxK11APmZjdvHNmT8XpibS/rhw7QdPgKwjr+c3a324RHQ5rL2q82J5vJVQSllmpRyjJSyg/Y7Xdt+TnNaIaVMkFL20n66SSlfrSl/daiIK0WDEEL8ALQDXIGFUsolmuf0WeAccBTIl1I+KoRoCXwMBGrZn5BSbhVCzNa2hWq/35VSLsLqiQ0TQkQDf0gpn3aE5pyMDNx9zaX/u/mayE3PxM1mQJl/KQuDm7F0IOpmNpUOlnPSMzi1O4pxLz3J+fjjds9xYttugocMdITccuSmZ+Bmo91otmq3HQwXXMrC4O5Wpt3XTE4tHh4tFgt//HMOWUkptB8/Ct/2jgvbzcvIwNVc5sQzmsxkJsRXSJOOq7msbK4ms3Wbjw8I2PXmG4AgcNQoAkdanRDZSUmkHznC4e++Re/sTOdpt+IT6vhw49z0Cjaj2UN19W70NZNbQ727mU10njyBHx99Br3BmVY9u9G6Z/f668zIxFjBPtKPJVRIk4Gb2TaNmdyMjGrz5l+4WFpWo8mH/AuXKp377K69+AQFlj4wOJKCzHQMpjL7MZjMZJ04Vqu8+edTcPLwIn7FR+ScOYV7YAjBN92N3sXV4TprorBCOZx9fMk5cbTK9Gnb1+PV1bFRmzWRm5GB0fY+NJvIiK9sQxXT5KZnWO9VjezUVC6cPImpfVnUZsLatZzeuhWfkBC633YLBnfHPuDkZmSUb0PMZjLiK7YzGRh9fculycso075jwQIEEDhqFEGjRjlUX21p6DWQFgsbZs4iOzmF0LFjMLd33PSuqsjJKN83ufla28ia+tVcGydU6pEEfnr6ZYxmH/rfcSM+7cre6IN1+n368dO0aO/YqZrNpX23R2ONZ7JSUnH18mTbR1+QcfIMviFB9L/nFpxdXRyi23oflu9vKtp4Xnp5GzdWsPGNL1ltPGTsGMxhVhvP0sYEB7/9Dr2zM91unYapEcYEzZnLMQZuDPIyKtpD5fbd3jgmNyMD11o4jNMOH8HF2xuPVvbWoXYcBZnpuNiMh118zFw8Hl9NDvvknU8l69RJvEIat31vyPhdp9Nj8PQi9tNPuHT6FF7BIXS9/Q6cXBzTjij+91ARV4qGcp+Ush/QH3hcCNEGmAkMAsYBnW3SLgTekVIOAG4AbON1OwMTsC7YNksI4Qw8B8RLKXvbc1rZfsJz93c/1l6xtLOtFl7nEs/07qVf0/e269Hp7N8+xUVFnNkbQ9Cg/rXXVFukHfEVpNsvXs3l0+l0TJg3mykfvEl6/HEyT5+pn0Z72BNVizQluge/MJOhL89lwJNPcXLdWtIPH7JmsRRTmJ1NxMxZdJ52C1Efvo+0V0cNxc4xK1ap3bPWUO8FWdmc2RPNlEXzufbDtyjKz+f45u0O1VkboQJRu7xVcOHMWeK+/Ia+999dq/SOoXbapKWY7NPH8R82jp7/nIfe4MK5NTVOo28kal/Hl47s4/z2DbS55vZG1lSBWrQx9u+HskRFeXnsWvQ+PW6/DWejddpFyJjRjH9rAaNemYOrjzf7Vn/lSNW10lUTQ158keFz5jDwqac4sW4daYcOOVJd7WngNRA6HaNfncuEhW+TkZDARUe25VVRTftdLVoac0ggN3wwjykLZtH5qtFseLP8CgGFeXlsfPsjBtw9DYOjp/I0l/bdHo00npHFFtKPn6LjuJFMnv8STq4u7P/Pr45QbD1+PQcFtjY+6pW5THj3bTITErh45kyp7oLsbIbPmkm3W6ax5/0PG2dM0Jxp5DFwo2H/JqyQpv7jmDPbdzZ6tBVQ7/q3pTgvj/0fv0vYtDtxMjouEtIuDRi/WyzFXDx5gqDRYxg65xWcXFxI+Pknh0v86yEu40/zQkVcKRrK40KIklUx2wF3An+WhPsJIb4BSlZBHgt0tRnMegkhSuKM/yulzAfyhRApWOfEVovtJzxfid5UbdN6+PcNHF1n/fKmb1gI2Wll0Yg5aRkYTd7l0rt4elCQk4uluBidXk9OelmatIQTbF70CQD5F7M4G7UPodcROMAaGXEuah/mkECMPo4JUT+6Zj0J663azaHB5Nhoz03PqDT1wMXTg4LsnDLtael1mp5gcHejZZdOJMXsw6edY+bNu5pN5KWXTXnKzUjHRZtiVpbGTF56WdnyMtJx8bGmcdXSunh54d+3H5kJCZg7dcbVZKZVv/4IIfAJDUMIHQWXLjlkesCRNeuJ1+rdNzSY7LR0Wmr7cmpR77m1qPekfQfw8GuBq5f1Nmg3oB/njxwjZNjgemk2mk3k1mAfRrOJnHTbNOm4mnywFBVVmdfF28v6JtPkQ25GJi420wNy0tLZ/vb7DHj4fjz8/eqluyYMPmYKMsrspyAjHYO3qZoctnl9MfiY8QzpAIC5bzjnfq+Do9uBOPv4litHYWYaznbKkXP2JCe/XEz7h5/Hyd3+dOTGwmg2k2t7H6ZnYPQx1Zim5I22paiIXYvep13EYAIGlDnvXb3L2tigkSPY8da7jaK9XBuSnl4uCgysbUluWlr5NKbK7UyrftZ2xrdzZy43Db0GJRjc3WnRuTPJsXF4Oagtt+VQhX41pwH9qq0zqm2fHuz8bBV5Fy/h6uWJpaiIjW99ROjQcILC+zpEe3Ns30u4HOOZlh1CcfM10bKDNVopMLwv+//zG47CaDJX6m9c7YwJctMrpvEpl8bZ3R3fzp1JiY3Dq21bjGYTAf37WacshYWCTjhsTNCcuZxj4MbCGlVaeexii90xUIU+wB6W4mLO7d7LqLmzakzbUAwmM/k24+H8zHRcaqGxBEtREfs/fhe/8CG07DugERSWpyHjd4E1+spHi4hs1X8A8f/9udE1K/66qIgrRb0RQozE6owaLKXsBUQBh6vJotPS9tZ+2kgpS+Yc2a6WXIyDnaqdJoxi8huzmPzGLNoN6E3Cph1IKUk9Eo+zm7FciDRY3xT4d+3EyR3Weefxf26jXf/eAFz//rzSn8BBfQmffnu5Dvv41l0ERzhummCH8aOZMG82E+bNpk3/PpzYvA0pJeePxuPs5lZp8CyEwK9bJ87stK4BdGLTNgL69a72HHkXL1GQnQNAUUEByfsO4hXguLnn3iGhZCcnk5OaiqWoiMSdO/DvU36Q49+7D2e3bkVKScaxYzgZ3XD18aEoP5+iXOsaEUX5+Zzfvw/PNtaHMP++/Ug7eACArKRELMVFlRZWri8dx49m4rzZTKxDvft368Rprd6Pb9pG2xrq3a2FL+ePJlCUn29dU2LfQbzb1H/RYVNYCFlJyWSnWOv59PadtK6gIaBfb05qZUk7Go+z0VqW6vIG9O3Dyc1bATi5eSsB/azXriA7h60L3qX7LTfQolOHeuuuCY+gMPJSksg7n4KlqIi0vdsw9exXq7wGbx9cTL7kJlu/jHXh0D6MrdvUkKtxcA8MIz81kXytHBl7t+Hdo3xkZkH6eY5/+ibBdz6Kq5/jFqCuLT6h5e3gzI6dtOpb/l5t1bc3p7ZY79X0Y8dwcjNap+9ISdSnn+MR0Jr2E8t/6ch2HanEPZHlPhjgKLxDQsq1M2d37qzczvTpw5ly7YzRfjuzbx+ebRtvwdvqaMg1yL94kYLsbACKCwpI3X8ATwe25bZ0njCKKW/MYsobswgc0Jv4WvSrraroV3MzL5RGxpw/dhxpkbh4eiClZNvHy/Bp05quk8c7THtzbN9LuBzjGaOPN+6+Ji6cs35tLWnfIbzbOs6OfEKt92p2yb26YyetKtyrrfr05vTWMht3trHxwgo2XrJWTqt+fUk9cBCArMQkLEXFDhsTNGcu5xi4sTCFhpCVlGLTLu6idYV2sXXfPpzesk2zGWvZajNNMHXfATwCWpebZthYeAWHkpuSRK42DkjZvQPfXrUbz0gpObL8E9xat6HduEmNrNRKQ8bvLj4+uPqayUpMBOD8gf14BFz+cU1zQ8VbVY1QIbSK+iKEuAa4X0o5RQjRGYgGpgOvAn2AS8A6IE5b42o1ECWlXKDl7y2ljNbWuMqSUr6pbd8HTNbyR0opK3+ntwI1RVzZIqVk1+erORezHyeDgYiH78E3LBiAda8vZPCDd+Nm9uFSciqbFy6hICsbU3AgQx+bXmn9nq0ffk7bvr1KF7Asys/nu789y3XvvYbBrebw3QJL3XzHUkoiv1hFYsw+nFwMDHzwPsya9k3z32XAjLsxmk1kJaey/b3FFGRl4xPcjkGPzEDv7Exu5gX+eGEuhbm5pQucTlwwl+zU8+z86DOkRSKlhcBBA+h2w9RqtVzKr9taRikxMRxYvRIskrbDhtN+6lROrl8PQNDo0dbP1K9Yzvm4OHQuBnpOvx+fkFByUlLY+95Ca/mLLQQMGkz7qVZtlqIiYj/7lIunTqJzcqLztFtp0bVrjVq8Xeq2gLiUkr1avetdDIQ/eF+pzWyc/y4DZ9yNm1bvW7V6NwW3Y7BNvf9eod6vXjAXZzcjcd/8wMkdu9HpdJiCAxn4wD3VrhNVKKvvahKjYolZ8SXSYiF45FC6XDuF+LUbAAgbOwopJdFLV5KklaX/g/dhDg2pMi9Y17vYsegjcs+nYWzhy+D/exiDhwcHv/+JQz/+F49WZQGSw557Eldv+2+3z2TWP6Q9Y18UJ79dhrRY8Bs8ijYTryN50x8A+A8fR8GFTPbN/yfFebkgBHoXV3rOfBMnoxvZp0+QsGoJsqgIlxZ+hN31UOkC6LWloNgx73ku7I/kzHfLkNKC76BRtJ5wPalb1gDQcuh4Tq7+mMzonRjMLQAQOj2dn5nXoHMG+uTUKX1SdAxxq7RPjg8fRqdrpnJ8nfVeDRljvVdjl60gOS4OJ4MLfWZMxxQaQtrhI2x+5TVrdI8WWdv1phtp1bsXez5ezMWTp0GAW4sW9L7vnkrRUPYostRtaJUcE8OBVau0z3YPp4OddmbfihWkxsaid3Gh1/334xMSQnZKCnsWLQKsC2q3GTyYDlo7k7hnD/tXrqTg0iWc3NzwDgwk/Omal1x00tV/fFXfa3Dh1Gkil3yCtFiQFkmb8IF0vu6aOp/fy1D3NnLX56s5a9OvtqiiX92k9atmm3710G/rOfzHRnQ6PXqDM/3vuhm/Tu1JPnSU32e9gU9gm9KpSn1uvZ62fXpUqaWwHv3qldK+O+ksddbeWOOZ9BOn2L54OZaiIjz8WhLx8D3VLrx9sY5jguSYGOJWrraOOYYPo9PUqRzX7tUQ7V6NXb6ClLg49AYX+txfZuNRSz5ByjIb73St1cYtRUVEffoZF06eQufkRLdbp9GyFmOC9577o07aa8uyF69nWO8gWni7kZKRzdylG1n2S7RDz/HCOyPrlL4xbWbzwiUkHzhC3qUsjN6e9LxpKh1GD6tSS1Zh3d5RJ0XHELvyS7BYCBoxjE7XTOH4Ouv4JmSMdXwTs2wlKbFx6A0G+j5gtRmA3e9/TOrBQxRkZeHi5UWXG64leORwAPYu/hRz+zBCxtR+XcP0HEOdtNuSFhdN/NcrkBYLrYaMIOjqazn351oAAkaMpeBCJntffVEbz+jQu7gw4OU3yD5zmugFc3Bv0660jw25bhq+PXrX6fzuhuI6pa/v+B3g4smTxH7xGbKoGLeWLel5/wyc3d1J2ruHAytXlParXoGBDHzqmVrpeWdweHP0udSa6LSdl80509u3edWlclwp6o0QwgX4AWiDNdKqJTAb69TAp7Auzn4QSJdSviCEaAF8AHTBGlG1SUr5UFWOKynlCc3Z1RP4tbrF2eviuLqSqKvj6kqiro6rK4m6Oq6uJGpyXF3JNMRx1dQ4ynHVFNTVcXUlUVfH1ZVEQxxXTU1dHVdXEnV1XF1J1NVxdSVRV8fVlURjOa4uB3V1XF1J1NVxdSXREMdVU1NXx9WVxl/dcRWTtuuyDR56+Q5sVnXZfFsMRZOjrUk1seJ2IcQe7euCTsD3wBot/Xlgmp3jzK7wf3ebv29zsGyFQqFQKBQKhUKhUCgUzQTluFI0BrOFEGMBV6xOqx+aVo5CoVAoFAqFQqFQKBRXMHX8yuT/EspxpXA4UsqnmlqDQqFQKBQKhUKhUCgUiuZP810IQKFQKBQKhUKhUCgUCoVC8ZdGRVwpFAqFQqFQKBQKhUKhUDQhaqJg1aiIK4VCoVAoFAqFQqFQKBQKxRWJirhSKBQKhUKhUCgUCoVCoWhSVMxVVaiIK4VCoVAoFAqFQqFQKBQKxRWJirhSKBQKhUKhUCgUCoVCoWhChIq4qhLluFL8JcjIMzS1hHqRmuXS1BLqTZeWF5taQr25UODc1BL+J+noe6mpJdSbvGJ9U0uoN0LIppZQb9ybsfYi2XwHn4WW5huQX9iM693SjOvd1bm4qSXUmxfeGdnUEurNq3/f2NQS6k1zrnejV/O1dzen5qtd8b+NclwpFAqFQqFQKBQKhUKhUDQhovm+e2l0mu+rHYVCoVAoFAqFQqFQKBQKxV8aFXGlUCgUCoVCoVAoFAqFQtGkqJCrqlARVwqFQqFQKBQKhUKhUCgUiisSFXGlUCgUCoVCoVAoFAqFQtGEqK8KVo2KuFIoFAqFQqFQKBQKhUKhUFyRqIgrhUKhUCgUCoVCoVAoFIomRMVbVY2KuFIoFAqFQqFQKBQKhUKhUFyRqIgrhUKhUCgUCoVCoVAoFIqmRKiYq6pQEVcKhUKhUCgUCoVCoVAoFIorEhVxpfifIyU2lv2rViEtFgJHjKD95Mnl9ksp2b9qFSkxMegNBnrPmIF3cDAA6558EidXV4ROh9DpGPbyy+Xyxv/yCwe//prx77+PwdOzUcuRuT+ak98uQ1os+A0ZTcD4a8rtz006S8LKj8k+fZx2U6bReuwU6/bkcxz7bGFpury0FNpefROtR09qVL1SSnZ98TVnouJwcjEw9G/34BsaVCndpZTz/PnuEvKzcvANCWTYY/ehd3Iicf9h1r/xAR5+LQAICu9L7xut1y4/O4dtHy8n4/RZhBAMefhu/DqGOUx7ckwcsStWIy0WgkYOp9PUqyuVLXbFapKjY9G7GOj3wHR8QoLJSUtj78efknfhAkIIgkeNoP1V4wE48M2/SYyMQgiBi5cXfR+cjtFkcpjmv4J2KSW7l37F2ag49C4Ghjx8bxU2k8rmhZ+Qn5WNOSSQoY9OR+/kRNL+w2xY8AEefr4ABA7sS68bp5Tms1gs/Pf5V3Az+zDm2ccdrj1q+WqSouPQGwwMfGg6ppDK2rNSUtnx3scUZGVjCgli4N9moHdyqjJ/Tlo6Oz/6lLxM63UJHT2CjhPHNVzrsi9JLDnXw/dhrkLr9kWLKcjOxhQcRPgj95dprSL/4V/WkLB+MwjwadeWgQ/dh97gTNy/vufsnmiETuDi5Un4Q/dhNDfchqSU7F32JWe1dmZwNWXZsnCJVpZAIh61luX0nihi//UDQugQeh397roFv84dKC4o5I+X51NcWGTtO8L70fOma+woaJj2hlyHi2cT2bX4czKOn6LHtOvoPPmq0jy7Pv6cc1GxuHh5MnHBXIfqLtEeuexLzmnaB1WjfduixeRnZ2MODmKQpv3Elh0c+PFXAJxdXeg//U5MQe3ITktnx4efkpd5EYSg/ZjhdGqgvSfFxBG9fDXSIgkZNYzOdtrEmOWrSYyOw8lgoL/NvVtV3v3f/sDxDZtw8bL2+91vvoHWfXpiKSpi76fLyDh+AiEEve66Db+uneutvTHv1SO//kH8+k0gIXT0cDpNKl/Ph37+jZhV33Dt4ndLy9kQGsvec9LS2fnhp+RmXkQIQdiY4Q1uI+1p3730K85pfVNEDX1TgdY3DdH6phLOHzvOby++zrAnHiRoUD8Atn20lDORsbh6eTL1rZcrHfNy8fEzU5g4qCOpmdn0v+/jJtNhS3Ou95K+6ZzWN1XXRm5duERrIwMZrPVNZ7S+CaFDp9fRV+ubAHZ8/AVnNe1XvzmnUbRv//xrzkTtQ28wMOLRe2gRGlgp3aXk86x/5xPr+D20HSMfuw+9sxMF2blsWPQZ2eczsBQX03PqODqOHkJRQSH/felNiguLsBQXEzK4L/2mTXW4/uaG+qpg1aiIK8X/FNJiYd/y5Qx88klGvv46Z3fs4NLZs+XSpMTGkp2UxKg33qDnvfcSt2xZuf2Dn3uO4XPnVnJa5aalcfurWXsAAN5hSURBVH7/foy+vpelHCf+9TmdHnmOnjPfIm3PVnISz5RL4+TuQdBN99B6THnHnNE/gB7/nE+Pf86n+3Ovo3c2YO41oNE1n43ax8WkZK5f9AqDH7iT7Z+usptu78rv6Hr1WG5Y9AoGdzeOrt9Sus+/SweuWfAS1yx4qdRpBbDri69p07sb1787l6kLXsK7TWuH6ZYWCzHLVhDxzN8Z+8arnNmxk4sVbCY5JpbspGTGvTWPPtPvIXrpCgB0Oj09bpvGuDdeY8TsF0lYu740b4erJzLm9bmMfm0Orfr04tD3PzpM819BO8DZ6H1cTErh2oWvMnjGnez8zL7NRK76ji6TxnLdwldxcXfjmI3N+HVpz5Q3ZjHljVnlnFYAh35Z61BbsSUpOo6spGQmvv06/e+/m72fL7ebLvbLb+g4cTyT3pmHs7s7xzdsrja/0Onoffs0Jr75KmPmvMCxP9Zz4cxZu8euLYnRcVxKSmbSO6/Rf8Zd7P1shX2tq7+l06RxXP3O6xjc3Uq1VpU/Jz2Do7+tY9xrM5m4YC7SYuHU9p0AdJ58FVe98TIT5s0moG8v9v/7pwaVoYRz0XFcTExh6ruvET7jLnZ9utJuuqjV39H56nFMffc1DB7uxK+3lqVV9y5Mmj+bSfNnMejBe9i5xNr+65ydGDPzKa5+YzaT5r3Eueh9nD8a7xDNJTT0Ohg83Olz9210mjyhUp7gEUMY/tzfHarXnvbJ77zGwBl3sacK7dGa9ima9gRNu7tfC8a+9AyT3niZbtdPYfcnWr3rdPS5YxpXv/UK4+f+k6NrNnDhzLl665QWC1FfrGToM39nwoJXOL1tJxcr3D9JWlmuevt1+t5/N5HavVdT3g4TxzPu9ZcZ9/rLtO7TE4CE9X8CMH7+XIY9/xSxK79GWiz11t9Y92rm6TPEr9/EuFdeZML82SRGxXApMbn0eDlp6STHHcCthbne2h1dlqrsXeh09LpjGpPeeoWxDrAZe5yL3selpBSuWfgqg6rpm6K0vunaha9iqNA3WSwWIld/R+te3crlCRsRwZjn/8+heuvDit9iuOZZ++VqKppzvZ+LjuNSYgpT3rW2kbur6JuiV39HJ5u+KUHrm/y7d2Gi1jeFP3gPu5aUPZuEjhjCqOefaDTtZ6L2cTExhZvem8uwh+5g6xL79b5r5b/pPnksN78/Fxd3dw6v3wrAgd82YGrbmuvfmsnVLz/JzuXfUlxYhN7ZiUmz/s71b83k+jdnciZqPylHEhqtHIrmj3Jc1REhxGwhxFONePypQojnGuv4VZzzFyGEj/bzN5vtAUKIbx18rtL6E0LMEUKMrSbttUKIro48f2ZCAu7+/rj7+aFzcqJNeDjJkZHl0iRHRtJ2yBCEEJjat6cwJ4e8zMwaj71/9Wq6TJt2WeYmZ504hmvLVri28Efn5IS5XwQZsXvKpXH29MYjKAyh11d5nAuH43Bp6Y+Lb8vGlsypPdGEDR+MEAK/jqEUZOeSk5FZLo2UksT9hwjW3oC1HzmYU7ujqz1uQU4uyQeP0GH0UAD0Tk64uLs5THd6fALu/n6lNtN20EAS90aVS5O4N4p2QyMQQmBuH0Zhdg55GZm4mnzwCQkGwNloxDOgNXnpmdb/3Yyl+Yvy8xGNYDfNWTvA6d3RhA0fhBCClh3DKMjOsWszSfsPl741DRsRwandUXaOVp7stHTORMWV2o2jObs3iuBh1nr17RBGYU4OuXa0p+w/RNvw/gAED4vg7J7IavMbTT6l0R/ORiNebVpXOm7dtUaXnqtFNVqTbbUOj+Dsnqga81uKiykuKMBSXExRQQFGk49Vu60N5eU7rN08syeaUK2dadEhjIJqyhIYbrWZ0OERnNkTbdXl6lpqz0X5BaV5hBA4u7qWlslSXIyjv/3T0Ovg6u2Fb1gIOjttvl+XTrh4uDtUry1nKmivrt7badpDhkdwRtPesmN7DJq+Fu1DyUnPAMBo8imNSiix95J99SH9WAIe/n54+FvbxHaDwzm3N7pcmnN7owiyc+/VJm9FLp09h1936zDG1dsLZ3c3MhJO1Ft/Y92rl84m4tshDCcXF3R6PS27dOLM7rKxUdTyr+h520040uYby97t2UxuA2zGHqd3RxNq0zcV1rJvOm3TNx3+dT1B4f1w9S4fvebftWOj3qu1ZWvsKdIv5ja1jHI053o/uyeakDr2TSHDIzhdQ98E4NelIwb3xtN+cncMHUYOKhu/5+SSk3GhkvZz+w4RMrgvAB1GDuLkLqt2hKAwLx8pJUV5+bh4uKPT66z9qrFx+1XFXws1VfAKQ0r5I9A44QtVn3MSgBAiGPgb8KG2/RxwYyOe96UaklwL/AwccNQ5czMycDWXvTF0NZvJiC//1jwvI6Nc1JSr2UxeRgauPj4A7FiwAAEEjhpF0KhRACRFRuJqMuEVWDl0tjEoyEzHYCrTaPAxk33iWJ2Pk75nO779IhwprUpy0jNxb1E2Fcjd10ROeiZu2oMsQP6lLAxubqUDUXezNU0JqUcS+M/Tc3AzedP/zpswtQvgUsp5XL082fLhUjJOnsE3NIiB90zD2dXFIbrzMjIw2tiM0Y7N5GZkYvS1TWOy2ppN2bJTz3Ph5ClMYaGl2/b/6ztOb9mKk5sbw/75jEP0/lW0A+RkZOBmo82tSpsxltqMm9lEbgWb+enplzGafeh/x434tGsDwO5lX9Pv9hspzM1rFO25duo+NyOj1HEDUHApC4N7mb27+ZpLB7K1yZ+dep7ME6fwtbku9dKaXr6ejVod1qS1xIFQVX5zWDCdJ0/g50efQW9wxr9nN1r17F6aLvbrf3Ni0zac3YyMmukYG8pJzyxvM1obYqxgM84VbMbWGXJ6VyTRX/2bvAsXGfls2Rt4i8XCb8/P5VJSCh3Hj6JFh4bVe0Uaeh2aktz0DNxrqPeK2o2+ZrsOhfiNm2ndu0el7Vmp58k4cYoW7etf7/bau/RjCRXSZOBm596rKW/8mnWc2rwNU2gwPW+fhsHDHe/AdpzbE0W7wQPJTUsn8/gJctLTMVO/MjTWverdrg2xX39P/qUs9AZnEqNjMWkvLs7uicZo9sEU1K5emhurLLWhtI1sgM3YIyejgr37WrXX1DeVjGdy0jM4tTuKcS89yfn44w7V9lemOdd7ffum3Ap9U4zWN4149vJF5WWnZZard3ezD9lpGbiZvG20Z+Nic6+WjPEBuk4cxR/zPmD1jGcozMtn9N9nIHTW2BlLsYUfnn2Vi0mpdJ0wAr+OIZetXFcqaqpg1aiIq1oghHhBCHFYCLEW6KRtmyGE2C2EiBFCfCeEcBNCeAohjgshnLU0XkKIE0IIZyHE40KIA0KIWCHEV9Wc6x4hxPva30uFEIuEENuEEAlCiCqdSEKID4UQU7W/vxdCfK79PV0I8Yr29w9CiL1CiP1CiAds8p4QQrQA5gFhQohoIcQCIUSwEGKfja5/CyF+E0IcFUK8YZN/uhDiiBBioxDikxL9tajXpSVlEkLMs6mfN4UQEcBUYIGmp9KCRUKIB4QQe4QQe2J/+KE2pwQp7emoXV5gyIsvMnzOHAY+9RQn1q0j7dAhivPzOfbTT3S6/vpaH6dRqGPEgqWoiIy4vfj2HdRIgipgp+7rotk3JJAbP3ydaxa8RJerRrN+wYfWwxYXk3b8FJ3Hj2DqGzNxcjEQ98NvjlINdmRXeiNUQ9mK8vLYtfB9etxxa7lIk24338BVi96mXcQgEv5Y5xi95XTZ29hMtINd/bW6X7U05pBAbvhgHlMWzKLzVaPZ8KbVZs7sjcHVy8vu2hgOoxZ1L+0kKi1eDfkL8/LY9s4H9L6z/HWpF3ZtoEISe2pKxFaRvyArm7N7orl60XymfvgWxfn5nNi8vTRJz2nXM/WDNwkaMohjvzvKhmoui11s7KrdwL5MefsVhj/1qHVNEQ2dTsek+bO47sMFpMUfJ/N0w6ZoVqKh16Epsdu3VkhiL1+FRMn7D5GwYQu9by0/3CnMy2PLOx/S965bGmbvtemH7F4GUW3esHGjmPjufMa+PhtXH29iV30NQPDIYRh9Tax7cQ7RK77Et0N7dLqqo6Drp79G+TXeq15tAugydSIbX3uLTfPewSewHTq9nqL8fA788DPdb7q2/pqropHtvTAvj63vfEifhtqMPewLqzFbifbdS7+m723Xo9Opx7A60azrveY20i4V+qbJdvqmxqfmZydZzf18Nno/vsHtuO2TN7huwYts++xLCnKs0Xw6vY7r35zJrYvnkXrsBOmnHNyvKv5SqIirGhBC9ANuAfpgra9IYC/wbynlJ1qaV4DpUsr3hBAbgauBH7R830kpC7XpfyFSynwhhE8dJLQGhgKdsUZiVTV1bxMwTEvTRsuHlrfEUXaflDJdCGEEdgshvpNSptkc4zmgu5Syt1au4Arn6K3VQz5wWAjxHlAMzAT6ApeA9UBMHcqHEMIMXAd0llJKIYSPlDJTCPEj8LOU0m6ZpZRLgCUAT+7YYXdMXBGj2Uxeenrp/3np6aWRVCW4mkzkpqWVT6MtPF3y28XLi1b9+pGZkICzuzs5qalsmjmzNP2ml15i6KxZlY7tKAw+ZgoyyjQWZKbj7F23hY0z90fj1i4YZy8fB6sr4+BvGziyzjo/v0VYMNnny94cVXxbA+Di6UFBTg6W4mJ0ej3Z6Rm4ma36DDYDz7Z9e7D9s9XkXbyEm68JN18TLbXoh+BB/Yj74VeHlcHVbCLXxmZy09PLRSOB9qY4zTZNBkbt2luKiti58H3aRgymzYD+ds/RLmIQ2958ly43XOcw3c1V+6HfN3B03SYAfMNCyLHRlpOWgdGuzeSW2kxOelmacjbTpwc7P1tF3sVLpByO58zeaL6LjqO4oJDC3Dw2v/cpwx67v0Haj65Zx/ENVu2m0JBKdW+sUPcunp4UZJfZe05aWXtktHPtSvJbiorY9s4HBA4ZRNuB/eqpdT0J661azaHB5eo5Nz3DjlaPSlpL0hh9zXbzJ+87gLtfC1y1hZzbDOjH+SPHCB42uNyxg4aEs+mNhfV+OD78+/rSNarMYeXLkpOeUe5tfElZCivYTMU0AP5dOrI9OZW8i5dKywBgcHfDr2snzkXvK43gqy+OvA6XmyNr1lsX8wZ8Q4PJTkunZNJ5Ti2051bQnnHyNLuWLGXEc0/g4ulRut1SVMSWdz4keEg47epp7yXYbe/stIk5dtpNS1FRlXldvcvapZDRI9i6wPoBFJ1eT+87by3dt37Wq3i08quT5stxrwKEjhpG6KhhAMR+9R1Gs4ms5FSyU8/z+7OzS9Ov+eccxr7yIkaf8m3x5S5LdVjbyA8JGhJe7zayIocr9E3ZDeib0hJOsHnRJwDkX8zibNQ+hF5H4IA+DtH6V6I51/uR39dzTOubfO30TfbsvWLfZM/e/bp0JMtO3+RIDvy6gUPrrGuDtQwLLlfv2emZpWPzEly9PMi3uVetY3xrmiMbttHr2qsQQuDd2g9PvxZknk3Cr0NZdJWLuxutu3XkTNR+zIEN61cVf12U46pmhgHfSylzADRnCkB3zWHlA3gAv2vbPwWeweq4uheYoW2PBVYJIX7Q9tWWH6SUFuCAEMK/mnSbgSe0NaEOACYhRGtgMFDyuazHhRAlT5ftgA5AWqUjVc06KeUFACHEASAIaAH8KaVM17Z/A3SswzEBLgJ5wKdCiP9inR7YKHiHhJCdnExOaiquJhNnd+6k70MPlUvj36cPJ9auJWDQIDLj43EyGnH18aEoPx8sFpyMRory8zm/bx8drrkGr3btGP9+WZDZuiefZNjs2Y36VUGPoDDyUpLIO5+CwcdM+t5thN3zWJ2OkbZ3Ky36D2kkhVa6XDWKLldZp1Oejozl0G8bCBkygNSjxzG4GSs9LAohaNWtEyd27CV0yECObdxOYP/eAORkXsDo7YUQgtRjx8FiwcXTAyEE7r4mLpxLwjugFefiDuLdNsBhZTCFhpCVlEJ2SipGs4kzO3Yx4G8PlkvTum8fEv5YR9vB4WTEJ+DsZsTV5GP90tanX+AZEECHSeUXkM1KSsKjVSsAEiOj8Wzt+EXCm6P2zhNG0XmC1WbORMZy6PcNBEcM5PxRqza7NtO1Eyd37CVkyEDi/9xGO81mcjMv4KrZzPljx5EWiYunB31vu56+t1kjJJP2H2b/z7832GkF0GH8GDqMHwPAuagYjq1ZR7vB4aQfS8DZ6FZpACqEwK9rZ87s3ENgRDgnNm+jTX/rADqgX2+7+aWU7F7yBV5tWtPp6sqLcNde62g6jB9t1RoZw9E16wmMGEjasQSc3arQ2q1TmdZN2wjo1xuANn172c3v1sKXtKMJFOXnozcYSNl3EFNoMACXEpPxbG3t0s7ujcEroP421GnCaDpNsJblbGQsh39fT5CmxeBmtFsW/66dOLVzL8ERA0nYtI22ms1cSkrGw98PIQTpx09iKSrCxdODvIuX0On1GNzdKCooICnuIF2nXkVDceR1uNx0HD+ajuNL6t2qPagG7f7dOnF65x6CIsI5vmkbbTXt2efT2PLOhwx65H68WrcqzSOlZOeSpXgFtKZzA+y9BFNYCFlJyaVt4untOxn4aPk2sap7z8XLs8q8JevPAZzdHYlXW+uDV1F+PkhwcnUhOW4/Or2+dF9tuRz3KkDehYu4enuRfT6NM7sjGfvyPzF4uHPt4ndLj/3TY88w/tWZ9f6q4OWwdyklu5YsxTOgYW1kRTpNGEUnm77pcC36Jv//Z++u46O49j6Of3678YSQbCCQBEnw4sFd6+5GnXp7763LbSlUbwv19lao0VKo260L0uIWxSHBiUCUuOx5/phJsjEIyS6b7XPeffFqsjuz+53JmTOzZ88508i56cLXn61ebsUb79NpyCDdaNUIT97vvU6bQi+Hc9P2Xx3ryIbPTeEO56ZdTTg3uUrfMybT9wxjv+/dkMzmn5fQrdb1e+0GQxEhsl9vdq2Ko/u44exYupquwwcBENjOxoHkrXTs25Oi3HzyDmYQ3KE9xXlHsHhZ8Q0MoKK0jANJWxl0vvOOWe3vRzdcNU1DvXnmAecrpRJF5DpgEoBSaoU5xG4iYFVKbTSXPwuYgDH8bYaI9FNKVTThvUsdfm60U6lS6oCIhAKnY/S+sgGXAgVKqSMiMgk4GRitlCoye4b5NeH9G8tSiVF+WjxGQSlVISIjgKkYvdTuBKa09HUbYrFa6Xf11ayZMwdlt9N5wgTadOrEnsWLAeg6ZQrhgwaRmZTEkvvvx+rry6AbjQ+0pXl5rH/1VSNzZSVRo0cTPnCgK2Iek1itRF96Pdv++wzKbqf96MkERHYmY9nvAHQYfwpleblsnP1vKkuKERHSlvzMwEefx8s/gMqyUvK3JhNzxU3HeCfn6RQ7gANxG/n6n49g9fFh3O3XVT/3+39eZewt1xBgC2HYtIv48+V3iP/0O2wxnek5xWhc27N6A9t++xOxWrH6eDPxrpuruyqPvOEK/nr1PewVFQSFt6v12i1lsVoZdO00Vsx+Aex2uk4cT3CnKHYtWgJAzNTJdBg8kPTEJH6/90GsPj4MuXk6AFnbd7Bv+UqCO3di8b+NKd36XnoRHQcPYtNnX3IkLR0RIaBdGIOvv9Zpmf8O2QGiYgdwID6Zb/71CF4+Poy57brq5xb95xVG33ItAbYQhky7iL9emUvCZ99ii+5SPeH6ntUb2Pb7UiwWo8xM+NdNJ2xYVcTggaQlJPHT3Q/h5evD8FtuqH7ur+deYvjN1+EfGsrAKy5m9Wtvs/GLbwjp2oWYSeOPuv7hbTvYs3wVbTt34reHZwIw4NKLqu9i1qyssQNJS0jmx7sexsvXhxG1sr7M8Juuxd8WyqArLmHVa2+T/Pm3hER3ru6d0dj6YT260XnkUH779xOIxUJodBe6T50AQNKnX5J/MB0RC4Htwxg6/epm53cUGTuAAwnJ/O9f/8bq68PoW6+vfm7Jsy8z8ubrCLCFMPjKi1nx6tskfvYNtugudJ9slJm9a+LYtWwVFrOeGfevWxARinNyWfXm+yi7HWVXdB09nE5DBzklc5WW/h2Kc/P4/ZEnKS826vztP//BGXOexDvAn1Wvvk3mlm2UHingf3fcR/+Lz6tezxkizew/3PUwVl8fRjpkX/rcy4y46VoCbKEMvuISVrz2Nkmff0uoQ/aNX39PaUEB69837rRlsVg47ZnHOLxtJ7uXGeX954dmATDosguJbGZ5t1itDL7uKpY9+yLKbid60jjadooi5Q+jTux+8mQ6Dh5IekISv9z9EFZfH4aZ29LYugDJn3xB7p69CEJA+3YMmX4NAKX5R1j27AuIWPAPDWH4bS1rIHfVsQqw4qU3KCsoQKxWhl4/rXqyfFdxVXnP3bufPWaZ+dUsMwNaUGYaUnVu+rYJ56Zlr8wl8bNvCY3uQo8m3Axk2Stzydi8nZIjBXx12/0MvORcek5x3rHaVB8+eiHjB3elXdsAdn5+F0/OW8qHPyWc8ByOPHm/R8YO4GBCMt+b56ZRjZybYq+8mOWvvk3SZ98Y50zz3LTPPDdVXQOPNc9NACtenUvGZqN+/+b2+xl48bl0d2L2zkP6sy8umc/vfBQvXx8m3F5zzffL068x/rarCbSFMPzqC1ny0rts+PQ7wqI703uqcf0ee/FZ/PX6PL6653FQMPyqC/ALDiJr937+en0edrsdlCJmzFC6DHPP56rWpFUM/2+lpMExqVo1ERmC0Ug1kpqhgm9jDKvrC+QAPwEHlFLXmevcC9wLPKmUelNELEAXpdRuc/6r/UBvpVRuA+93HTBMKXWniMzDYaiciBQopRptXjeXn2L+C8MYVvilUupuETkPuFEpdY6I9AESgNOVUktFZDcwDKOBLk4p1dV8vWjz/fs75jKf+wF4HtgBrMAYQngEWAQkVy3XQMZZGI1pz1dtH/ALEKCUyjSHDe5UStnMoYhxSqkPGtvmKk0dKtjaHCpwzgTi7nBS+3x3R2i2vDJvd0f4f6mNd1Pa6lunksoWzEvjZiIeWT0C4OXB2SuU5158evLMO+UevN+tHlzePZmPxe7uCM329N1L3R2h2R55aZK7IzRbpQfXMwFele6O0CL3D5jkuTu/CbblJZ+wE0HvtgM8al968rXJCaGUigM+w2jo+QpjSB4Y8zqtAX4HttZZbQEQCnxi/m4FPhaRZCAeeKmhRisnWAZ4KaV2YjSw2Rzy/gJ4iUgS8CSwuu7K5nxXK0Rko4jMacobKqUOAM9g7Is/MIYp5h11pfraAD+Y2f4E7jYf/xS4X0TiG5qcXdM0TdM0TdM0TdP+HuQE/vMsuseVC5h3yjtPKeWcsQ+tnIgEKaUKRMQL+AZ4Xyn1zYnMoHtcnXi6x5V2vHSPK/fQPa7cQ/e4cg/d40o7XrrHlXvoHlfuoXtctW7b8jaewB5X/T1qX+o5rpzMHN52BnCmu7OcQLNE5GSMObN+4/gmn9c0TdM0TdM0TdO0/9c8qiXpBNMNV06mlGrSrd1E5HrgX3UeXqGUuuMY6w0A5td5uFQpNbLpKZ1LKXVf3cdE5BHgkjoPf6GUevrEpNI0TdM0TdM0TdM0zdPphis3MSccP+ak4w2slwwMdnogJzMbqHQjlaZpmqZpmqZpmqYdg+g+V43y5GkMNE3TNE3TNE3TNE3TtL8x3eNK0zRN0zRN0zRN0zTNnUT3uGqM7nGlaZqmaZqmaZqmaZqmtUq6x5WmaZqmaZqmaZqmaZob6f5WjdMNV5rmRn3D890dodkS0kLcHaHZOrQpcXeEZvPzsrs7QrNll/i4O0KzVdo991Jib26AuyM0W35+ubsjNNvQbqXujtBsXYKK3B2h2Y5UeO6lrefWMpBT6u3uCM1WVum5A1AeeWmSuyM029N3L3V3hGa79ZnT3B2h2XYU+Lo7QssMcHcAzV08t6bWNE3TNE3TNE3TNE3T/tY892spTdM0TdM0TdM0TdO0vwHx6L63rqV7XGmapmmapmmapmmapmmtku5xpWmapmmapmmapmma5la6x1VjdI8rTdM0TdM0TdM0TdM0rVXSPa40TdM0TdM0TdM0TdPcSHSHq0bpHleapmmapmmapmmapmlaq6R7XGmapmmapmmapmmaprmV7nLVGN3jStM0TdM0TdM0TdM0TWuVdI8r7f+dzKQkNi1YgLLb6TJxIj3OPrvW80opNi1YQGZiIlYfHwbfdBNto6NrnrfbWTZzJn6hoYy45x4A8vbsIfnDD7GXlyMWC/2vuYbQ7t2dnl0pxZoPPmN/XDJevj6Mu+M62nXrWm+5IxmHWfryXEoLigiL6cKEf9yA1ds43NM2bWPtB59hr6zEt00QZz5xPxVl5fz82BwqKypQlZVEjxpK7GXnOj1/Q/I3J7D/6w9Qdjtho6fS8ZTzaz2fvW4ZGYu+A8Dq40eny24kICr6hGSrcig5iS0LPwa7nU4TJtLtrHNqPa+UYsvCjzmclIjFx5cB040yU5CWRuKb/61eruhQJj0vuJDoU08HYM8fv7Fn0R9YLFbaDxpE70svd+l2ZCQlkTx/Icpup+ukCfQ6p37ZT56/gIzEJKy+Pgy5+UZCoqOpLCtj2dP/wV5egbJXEjl8OCdddIFLs4JrjtX8vXtJmjePytJS/Nu1I/bWW/H293d69qoyo8wy072RMnMoKRGrQ5kBKC8qJPmD9ynYvx8EBtxwI6E9erLj26/Z9+ef+LRpA0Cviy4hfNAgp2d3FNsuiBv6RmIR+GNfDt+kHqr1/ITIEM7v1g6Akko7czceZPeRErwtwlOjuuFtESwirErP47MdmS7NWtfwjsHcPqQTFoGfU7P4dEtGrefHRLXlugGR2JWiUinejNvPxsOFtA/w5sGR0YT6eaNQ/JhymG+2H2rkXZynucdnUVYWcW+/Q0leHiJC9ORJdD/tVADy9uwlYd6HVJaU4t8ujGG3u6a8183513tfsHvDJrx8vTnlH9cQ3r1LveUSf1pKwvdLyEs/xE0fzsY/OAiAlDWJrP7ke0QsWKwWJtxwMZF9e7g0ryvOqwClhUWsePMjcvcdABHG3XYt4b2dd21QlX2fmX38UbIveXkuZXWyJ3/3KynL1gBgt9vJ25/Gle+9SHlpKX+9/j7FufmICL1PnkC/s6Y6LXddGYnJJDmU/d7nnlVvO5PmLyQjwSj7Q2+eTkiMUfY3vPWuQ9mfSI/TT3VZzpbmBdgw9z3SExLxDQ7m5Gefql5n7WtvUJCWDkB5URHeAQFMeeYJp2dXSrFu3qccjE/G6uvDmNuuJ6yhMpN5iGWvvENZQSG2mC6MvXM6Vq+aj42Hd+7il0f/w/i7bqHrqKEArHxzHvvjkvALbsO5Lzzu9OxN9dYD53DGqF4cyi1k2A1vuS2Ho0NJSWw2rwk6T5hI97PrXxNsXlBzTTDwxpprgiX33oPV3w8RC2K1MG6WUS7i33i9usxUFBXhFRDA+CefwpXyNiew98t5KLud9mOmEHHq+bWeL04/wK6P36Ro/y6izr6ciJON7SzOOEjK+y9XL1ealUnUWZfQcXLtY0cD0T2uGqUbrrRWRUSuA4Yppe50xesru52NH33EyAcewN9mY9msWXSIjaVNVFT1MplJSRSmpzN59mxyU1JI/vBDxs2cWf38rt9+Iygykori4urHtnz2Gb3OO4/wQYPISExky+efM+bhh52ef3/8RvLTMrjotac4tGMXq95ZwDn/+Xe95dYv+Ip+Z59Mt7EjWDn3Y3YsXk6f0yZRWljEqncWcuoj/ySofRjFefkAWL29OH3mPXj7+2GvqODHGbOJiu1PeK9uTt8GR8puZ98X79HjjkfxDglj2/MP07b/MPwjOlUv4xMWTs9/zsIrIIi8zfHs+3Quve99xqW56mbcPP8jht/3AH42G6uemEn44CEEOZSZw0lJFGVkMP7ZOeSlprB5/jxGz5hFUEQEY594qvp1ltz9LzoMGQZA1pbNZMbHMe6Jp7F4e1Oan+/y7Uj8cD5jH7wff5uNpY89TschsQQ7bEdGYhIFGRmc/Pxz5KSkkPjBR0x8/DEs3t6Me/hBvPyM8rHsyWfoMGgAth4u/DDpomM18f336Xv55YT16cPev/4i9aef6H3RRU7Pvmn+R4wwy8xKs8w4Zj+UlERhRgYTnp1DbmoKm+bPY8yMWQBsWfAx7fsPYMgd/8BeUUFlWWn1etGnnka3M850at7GWICb+kXy+NpdZJVUMHtsd9Zl5rO/oCZPRlEZM1anUlhhJ7Z9ELcOiOKhlSmU2xUz1+yipNKOVeDp0d2JP3SE7bnFjb+hM7ML/GNYZx5csoNDxeX895TerDyQx978kupl4jKOsPLAFgBi2vozY2wMN/y0mUq74q2E/ezMKcbfy8Kbp/ZhQ/qRWus6W4uOT6uV/ldeTkh0NOXFxSx9bBbt+/cjOCqK+Pc+oP8Vl9HupD7s+fMvdvz4E30vdm55r2tP3CZyD2ZyzRuzSN++myVvf8plsx+ot1xEn+7EDBvAV4++VOvxzgN7023EQESEw7v38/Pz73H16zPrre8srjqvAqz54DM6xfZjyn23UlleQUVZmdOz56VlcLGZfeU7Czi3gezrFnxFfzP7irkfs33xck46bRIDzjuNAeedBsDe9Yls+uEPfNsEUllRzohrLqFdt66UF5fw3YNPETnwJEI7Rzo1PziU/Yfuw99mY8ljTxAxdHC9sl+YnsEpLzxLTkoqCfPmM+nxGVgsVgZceRkhMUbZXzLjccIH9Ku1bmvKC9B1wji6nzKV9W+/W+t1R/zj9uqfkxd8ineAaxqYDyZs5Eh6Jue98jSHd6Sy5r0FnPl0/TITv+ArTjrzZGLGjmD1O/PZuXg5vU+dBBiNnHELvyJiUL9a63SfOIbep01mxX/fd0n2ppr/SyJvfbOOdx8+3605qlRfE9xvXBOseHwm4bH1rwmKMjKY+NwcclNS2PjRPMY+Nqv6+VEPPlz9pVWV2NtrPipt+WQhXgEBLt+OPZ+/T687H8EnJIzNcx4mZEDta3avwCC6XHIduYnra63r3yGS/g/Prn6dhEduJXTQCJfm1f5+9FBBzSOIiFMaWXNTUwns0IHA8HAsXl5EjRxJRlxcrWUy4uLoNHYsIkJojx6UFxVRkpsLQHF2NhmJiXSZOLFuPipKjA81FUVF+IWEOCNuPXvXJdBj4mhEhPBe3SgrLKYoJ7fWMkop0jZuJdr8BqzHxNHsWZcAQOrytXQdGUtQ+zAA/NsGV+f39vcDwF5Zib2y8oTc1aJoz05823fEt10HLF5ehA4ZQ17yulrLBHXrjVeA8U18YHRPynOzXB/MQW5qCgHh4QSYZabjiFFkxNcpM/FxRI4xykxI99plpkrW5k0EhIfj387onbJvyWJizjwbi7c3AL7BwS7djpyUVIIcyn6nUSNJ3xBfa5n0uHi6jDO2w+ZQ9kUEL7/a5cPVY/BddawWpqVh690bgPb9+pG2vvbFlXOypxDoUGYiRowis06ZyYyPI8osM6Hde1BhZi8vLiZ7+zY6TTByW7y88A4IdHrGpugREkBaURkZxeVUKMXytDxGdKhdTrflFlFYYQdge04RYX7e1c+VVBqPW0XwEkGpE5e9ty2Qg0dKSSsso8KuWLo3h7FRbWstU2LmBvDzslTnyy6pYGeO0cBWXGFnb34J7fy9caWWHJ9+ISGEmN/Me/v70yYykpLsHAAK0tII62OW9/79SFu3waXbAZC6Nok+k0ciIkT0jqG0sIjC7Lx6y4V360xweFi9x338/RDzBFRe4tyGnoa46rxaVlRMxubt9JwyDjC+IPINdO4Hy+Zk7zlxNHvN7I5Sl6+j2zjjg2RAaEh1zy1vfz9CoiIoys6tt44zZKekEtgh3KHsjyCtTtlP2xBP53FjzLLfnfLCIkpycvELDanuyWSU/QhKXJTTGXkB2vXpjXdQUKOvr5TiwJq1dBo90iX5961LoNuEUYgI7XsZ2RoqM+mbtlX3pOo+cQz71tVs47afF9N15FD82tZuSOnQtxe+Qe45XzlakbSX7PwT8yVJU+SmphDQweGaYGTD15FRDtczFQ1cRzZGKUXaurVEjhzlgvQ1CnfvxLddB/zMa3bbkDHkJNW+Zvdu05agrj0Qq7XR18nfloxf+w742tq7NK+nkhP4n6fRPa60E0pEvgU6A37AK0qpuSJyPfAwkAZsB0rNZecB2UAsEAfc29L3L87Jwc9mq/7dz2YjJyWl1jIlOTn4h4XVWqYkJwe/kBA2LVjASZdeWt1IVaXvtGmsmTOHzZ9+irLbGTtjRkujNqgoO5fAsNDq3wPDQinKziUgNKT6sdIjBfgEBGAxTxoB5jIA+QczsFdW8vPM5ykvLqHvWVPpMXE0APZKO98/+BT56Yfoc/ok2vd0bW8rgLLcbHxCava1T0gYhXt2NLp81qrFBJ8U6/JcjkpzcvC31S4PeXXKTGluNv6O5SrURmlOdq0GzLQ1q4lwuKgoTE8nZ/t2dnz9JRZvb/pcegVtu7lunxfn5NTOaAslJyX1mMsUZxtlX9ntLJkxk8KMTLqdPBVbD+cPha2bxRXHaptOnciIj6fjkCGkrVtHcXa207OX5OTgV6fM5NbNnptde/vMMiNWKz5tgkl+7x3y9+2lbdcYTpp2FV6+vgDsXfQHB1euIDg6mpMuvxLvQNd9SAjz8yKrpLz696zicnqGNP7B++TONuIPHan+3QLMGdeDjgE+/LInmx15J+6DRDt/bzKLaho9DhWX08dWP/vYqLZMHxRFiK8Xj/yVUu/5DoE+9AgNYGtWoUvztvT4rFJ46BB5e/YQah6fwZ06kR4XT8TQIRxc65ryXldBVi5tHM5TQWGhFGTnEmhre5S1aktZncDKj7+jKO8I5z5y+7FXaAFXnVePZBzGL7gNy/87j+w9+wnr1pWR11+Gt5+v27MX1mncqSgtZX/CRkZPv6LeexzJPEzWrr207xnjtNyOSuqUa/8G6vrinFz8wxyXCTXOEQ7bWXjoMHl79hLa3bXXLs7K25isbdvxbduWoI4dnZbZUVFODoEO2QLCQilusMz415QZW015L8rOYe+6eE557F4Op+xySca/m7rXBP6hNnJT617P1L8mKKm6jhRY+/xsQOgyeTJdJk2utW7O9m34BAcT6KIyU6UsLxufUIdr9tAwCnfvPO7Xyd6wEtvQsc6Mpv0/oXtcaSfaDUqpocAw4J8iEgU8DowFTgH61lm+F3CyUqrFjVYADX3lL03sWpSRkIBvcDAhMfUv3vYsXky/K6/k5Jdeot+VV5L03nstjtqgBrss1Ml/lF4N9spKslL3cPLD/+DUR/9Fwpc/knfQmPfFYrVw3vOPcenbz3F45y5y9h5wXu5GNRC2kb/Hke0byVq9hMjzprk4UxPUjdjgn6VmIXtFBZkJ8XQcXtMtWtkrKS8qZNSjM+l96eUkvPk6ypVdUhp67Xrb0fjxIRYLU55+ktNeeZGc1FTy9+13QcimZTmWox2rg6ZPZ/cff7DssceoKC6uvjB3tXrRGykzqrKS/D276TJ5KuMefwqrry+pP34PQJfJU5k4+3nGPv4kfiEhbPl0octz19dwGe1vC2Rq51A+2ppe/ZgduHf5Tm5avJUeIf50CXLeB/ZjaWqP0RUH8rjhp83MXJ7K9QMiaj3n52Vh5thuvBG/nyKH3lku0cLjE6CipIS1r77OgGlXVs9jFXvTDaT+sYglM2ZSUVyCeJ2I8t78Y7dK91GDufr1mZz90C2s/uR7ZwVrmIvOq8peSdauvfQ5bSLnzZmBl68Pyd/+4uTox87elKK1d30SHfr0wLdN7Ybw8uISFj//FiOvvwwfFw1da3jf1t3/R79WqCgpYe0rrzPgqitcNsSuJktDDx5f3qPZv2qNy3pbAce8XmlM1TG8bt5nDLnyQiwW/RGyyZpyaddgkTH2+ehHZjDu8ScZfu997Fn0B9nbttZa7uDq1USOHO2EoMfK2PJrVHtFBbnJG7DFurZ3mEeTE/jPw+geV9qJ9k8RqZrVuTNwNbBUKXUIQEQ+w2isqvKFUqqyoRcSkZuBmwFOfvBBBp5//jHf3N9mo8ThG+eS7Ox6w/r8QkMpzsqqvUxoKGnr1pERH09mUhL28nLKi4uJf+stYm+9lf3Ll9NvmtGgEjFiBEnvO298/5ZflrD9j2UAtOsRTWFWTvVzhVk5BNT5Fts3OIiyoiLslZVYrFaKsnIIsBnbGBgWil9wEN5+vnj7+dLxpJ5k79lH28gONesHBtCxX2/2J2witIvr5okAo4dVmcPQv7LcLLyDQ+stV3xgD3s/eZvutz2MV2Cbes+7km9oKMXZtcuDb0honWVsFGdnU/VoSU7tZQ4lJRLcNRrftjV/K79QGx2GDjOGF3brDmKh/MgRfFw0ZNDfZqvV26IkOwf/OtvR0DJ1vyH2CQykXZ8+ZCQlE9y5E67iqmM1KDKSUQ8Y8+0UpKeTkZjo9Ox+oaGUHKPM+IXW2T6zzIgYz4WYN3foOHw4qT/+AFCr/HSaOIkNL7/o9OyOskoqag39C/P3Jru0ot5yXdv4cfuAKJ5cv5uC8vrVdVGFnU1ZhcS2b8Neh/mxXOlQUTnhAT7Vv7f39yaruLzR5ZMPFRAR5Euwj5X8skqsArPGdmPRnmyW7891ed6WHp/2igrWvvo6nceMJnL4sOpl2kRGMvZBY6LwgrR00l1Q3gESf/qTTb+vAKBDj64ccThPFWTlEBja9N5WjqL69SQv/TDF+QXVk7c7w4k4r3bo05PAsNDq3svRo4eS/M3PLc6++Tiz+x0le5XUFevoNnZ4rcfsFRUsfuEtuo8fSfTIIS3O3Rij52BNuS7Ozq533vG3hVKc5bhMDv7m+cBeUcGaV16n05jRRDmU/daa92jslZUcXLeByU86d063bb8uYceivwAI6x5DoUO2oqwc/Oscn75tgigrKq4pM9k1y2Sl7mbZq+8AUJpfwIH4jYjVQpfhJ7Y3vCfxs9W+JijOycY3tM41Qd1rHofrSD9zWd/gYDoMGUpuaiq23n0Ao8ykb1jP2FnOn8i/Lp+QMMpyHK7Zc7Lwblv/mv1o8jbHE9A5Bu/gECen0040EbEBnwHRwG7gUqVUTp1lepvLVOkGPKaUellEZgE3AVV3v/m3Uuqno72nbi7XThgRmQScDIxWSg0C4oGtHP27iEbHZyil5iqlhimlhjWl0QqgbUwMhRkZFB06hL2iggNr1tAhtvbJtkNsLPtXrEApRc7OnXj5++MXEsJJl17KyS+/zNQXXiD2tttod9JJxN56KwB+ISFkbTW+AcnavJnADh3qvXdznXT6ZM57/jHOe/4xugwfzM4/V6GUInN7Kj4B/rW6d4PxDU1Ev97sXm3MZbLzz1V0GT4YgC7DB5OxZSf2ykoqSks5tHMXIVERlOQdobSwCICK0jLSkrYQEuXaLscAAV26U3oojdKsTOwVFeTEraTtgNoXnmXZh0l973m6Xn0nfuHOnxj2WNrGdKMos6bMpK9dTXidMhMeG8vBlUaZyU3Zibd/wFGHCQKEDxlK9pbNABSmp6EqKvBu47pGuZBuMRSkZ1CYaWzH/tVr6Dik9nZ0HDKYvcuN7cjeuROvAKPsl+bnU1ZoHIqVZWUc2rSZNpERDb2N07jqWK2aBF/Z7ez47ju6TpniguzdKHQoM2mNlJkDZpnJSdmJl1lmfNuG4GezUZCWBhhzowVFGuXecb6LjA0baBPluoZDgJ15RUQE+hLu742XCOMi2rIuo/ZNBNr5efPAkC68kriftMKaoXnBPlYCvIxLDB+LMLBdEPsLT0yjFcC27EKi2vjSMdAHL4swqUsoKw/Unmcp0qEHWI9Qf7wtQn6Z0fB234iu7Mkv4attJ+ZOiC05PpVSxL/7PkGREfQ44/Ra65Tm1ZT3bd/9j5gptYeYOMugMydy5Uv/5sqX/k23kQPZumSNMe/Ktl34Bvgf1zDB3LTM6p5EmSl7qayowK+Nc4fEnojzakBoWwLDQsk7YPRCTEveQkinlp/D+p4+mfOff4zzn3+Mrs3IvsMhO0BZYRHpm7fXekwpxbI3P6JtVAT9zzmlxZmPJrRbDAXpmQ5lfy0Rdcp+xJBY9i1faZb9FLwD/PELNcp+3Lsf0CYykp5nnubSnM7IeyyHNm4mKDKi1jBDZ+h92mTOnj2Ts2fPpPPwwaT+tRqlFIe2G9kaKjMd+vZmj1lmUv5cSedhgwG48PVnq/91GTWEkdOn6UarY2gb063W9UzamtX1r2cGx3Kg1vWMcU1QUVpafYOZitJSDm/aWOvcn7VpE0EREbWGr7pKYNfulB5Kp/Swcc2eHbeS0IHH11icvX4FtqFjXJTw78GD5rh6CFiklOoJLDJ/r0UptU0pNVgpNRgYChQB3zgs8lLV88dqtALd40o7sdoCOUqpIhHpA4wC/IFJIhIG5AOXAK75ShiwWK30u/pq1syZY96SdgJtOnViz+LFAHSdMoXwQYPITEpiyf33Y/X1ZdCNNx7zdQfecAObPv4Yu92O1dubAddf75L8nYYMYH/8Rr76xyNYfYxbX1f57ZlXGXfrNQTYQhh21UUsfekd4j75jrCYzvSaYowlD+kUQdTgfnx77xOIReg1dRyhXaLI3rOfZa9/gLLbUUoRM3oYnYcOdMk2OBKrlU4X30DKG0+j7HbCRk3GP6Izh5f/BkC7caeS/suXVBYWsP8L8w48Fit97n/W5dmqWKxW+k67hvUvzEbZFZ3GT6BNVCf2LjHKTJfJU2g/cBCHkxL568H7sfr4MGB6TZmpLC0la9NG+l1bu0x0Gj+B5PfeZfmjD2OxejHgxpuPezjN8W7HwGuuYuWc541beE8YT3CnKHYtMrYjZuoUOgwaREZCEr/f9wBePr7E3jQdgJLcPOLmvmOUD7siauQIOsYOdlnWqryuOFYPrF7Nnj/+AKDjsGF0Hj/eJdn7TruGdccoM4eSEvnTLDMDHcpM36uuJnHum6iKSvzbt2fg9JsA2Pb5p+Tv3YuI4N+uXb0y5Wx2Be9uOshjI2KwAIv257CvoJRTuxgXyL/tzebSnuG08fHi5v7GB/JKpXhgRQqhvt78Y2AnLAIWEVak5bEh88hR3s352V/bsI9nJ/bAYhF+Sc1iT34JZ3c3bo7wQ8phxncK4ZQYGxV2RVmlnadWGvO19G8XyCkxYaTmFvPWaca32u8nHWRtmuvu/NmS4zN7+w72rVhJcOdOLH7EmF+x7yUX03HwIPavXk3qH4sAiBw2lC4TnF/e64oe2p/dGzbx4W0z8fb14eR/XF393HdP/pepd0wjyBZCwg9L2PDt7xTl5LPwrqfpOrQfJ99xFTtXJbB16RosVitePt6cce90l9aNrjqvAoy84Qr+fPU97BUVtOnQjnG3X9dAgpZl3xe/kS//8QheTci+oU52gD1rE4ga1LfW3FsZW3eS8tdqQrtE8e19Rk+OoVdeQOchA5yaH4yyP+jaaayY/QLY7XSdWFX2lwAQM3UyHQYPJD0xid/vfRCrjw9DbjbKftb2Hexbbpb9fz8GQN9LL6Lj4EFOz+mMvADrXn+LQ1u2UlZQwM//uIeTLjqf6EkTANi/eg2dXTlMEIiKHcCB+GS+/ZdRZsbcdl31c4v+8wqjb7mWAFsIQ6ZdxLJX5pL42beERnehh3mTgaNZ9spcMjZvp+RIAV/ddj8DLzmXnlNcX+fU9eGjFzJ+cFfatQ1g5+d38eS8pXz4U8IJz1HFYrXS76prjHmqHK4JHK9n2g8aRGZSIn8+cD8W35prgrK8PDa89goAqtJO5KjRtB9Yc31+cM1qIk7EMEGMa/Yul97Atv8+A8pOu1GT8I/oTOay3wEIH38K5fm5bJr9MJUlxYgIGUt/YsAjL2D1D6CyrJS8rcl0veLmE5JXc7nzgEnmzx8CS4EHj7L8VCBFKbWnuW8oLp1TRdMciIgv8C0QBWwD2gOzgBhqJmdPAKxKqTvNydl/UEp9eazXvnf1ao8syO0DTlwPBGdLSAtxd4Rm69DGdbe1dzU/LxfPteNCFXYPHFBvqvTg7HtzXXuLbFfKz298eF9rN7Sb59bvXYKK3B2h2Y5UeO53sp5by0BOqWvvvKk1LMi7/vBtT/H03UvdHaHZbn3mxPTwc4VDBSduvklX+PiUwZ5cVR7TnoKdJ+wzbXSbnrdgTrtjmquUmtuUdUUkVykV4vB7jlKq0bGjIvI+EKeUet38fRZwHUbHlfXAvXWHGtbluWd3zeMopUqBMxp4ainwQQPLX+fiSJqmaZqmaZqmaZrmdk4YwtdkZiNVow1VIvIH0NDcMY8cz/uIiA9wLkZHlSpvAk9iTBn0JPACcMPRXkc3XGmapmmapmmapmmapmkAKKVObuw5EckQkQilVJqIRABHmxT0DIzeVhkOr139s4i8A/xwrDx6cnZN0zRN0zRN0zRN0zR3khP4r2X+B1xr/nwt8N1Rlr0C+MTxAbOxq8oFwMZjvaFuuNI0TdM0TdM0TdM0TdOa4lngFBHZAZxi/o6IRIpI9R0CRSTAfP7rOuvPFpFkEUkCJgN3H+sN9VBBTdM0TdM0TdM0TdM0NzqRc1y1hFIqC+NOgXUfPwic6fB7ERDWwHJX133sWHSPK03TNE3TNE3TNE3TNK1V0j2uNE3TNE3TNE3TNE3T3MhTely5g+5xpWmapmmapmmapmmaprVKuseVpmmapmmapmmapmmaG+n+Vo3TDVfa38KAsDx3R2iW3DJvd0dotl7tj7g7QrNVKs89LSgPzu7vVenuCM0motwdodkCfDx3vxeHWt0dodmU5xYZtucGuTtCs4X6lbs7QrMdKfPcy/JKu+eem4rKPbee8Q/23Pr91mdOc3eEZnvr37+6O0Kz3fjU6e6OoGnN4rlnSE3TNE3TNE3TNE3TtL8D8dwvAVxNz3GlaZqmaZqmaZqmaZqmtUq6x5WmaZqmaZqmaZqmaZob6bsKNk73uNI0TdM0TdM0TdM0TdNaJd3jStM0TdM0TdM0TdM0zY10f6vG6R5XmqZpmqZpmqZpmqZpWqukG640TdM0TdM0TdM0TdO0VkkPFdQ0TdM0TdM0TdM0TXMn0YMFG6N7XGmapmmapmmapmmapmmtku5xpWmapmmapmmapmma5kaip2dvlG640jSTUorf535FyvrNePv6cPZd0+jYo3O95b6b8yHpO/dhsVqJ7NWF0++8HKuX1S15V7z3OXviNuHl68OUO6+hffcu9ZZL/mkpST8sJj/9ENfNm4N/cBAA2/9cS/y3vwHg7efLhJuvoF1MJ5fmjf/wE9ISkrH6+DDithuwxXStt1xB5iFWvfo2ZYWFhEZ3ZeQdN2L18iL/QBpr336fnF17GXDZBfQ5+/Ra69ntdn7/9xP420KZ8MC/WpQ1PTGZhI8WouyKmMnj6XPuWfW2JfGjhaQlJOPl48OwW6cTam5LY+vuX72OzV99R/7BNKY8+Si2bjFG7ooKNrz7ITm7diMiDLrmSsL79mlR9sT5C1F2OzGTJtC7kezpiUlYfXwYdst0QmOij7pu7u69xL//IZXl5YjVSuz1V2Pr3o29K1ax/Yefq187b99+pj41i5Do+uWwOZRSxH34CQfNMjPqKGVm5atvU1pYiC26K6Mcysxqs8wMvOwCTnIoM9t+/p2UxX+hFHSfMoE+Z57ilMyO2Td8+AkH45Px8j169hWvzDWzd2H0nUb2XctXs+V/xr718vVj+I1XEdrVqI++u/NBvPz9EIsFi9XC6c/McGr2lpSh9XPfIz0+Ed/gYE557qnqdTZ/9S27lvyJb5s2APS77CIiBg9yam6AQ0lJbF74Mcpup/OEiXQ/+5x62Tcv+JhDSYlYfXwZeONNtI02si+59x6s/n6IWBCrhXGznqheb/fvv7Fn0R+IxUr4oEH0uexyp2fPSEoi+eOFYLfTZeIEep1zdr3syR8vIDMxCauvD7E33UhIdDSVZWUsf+Y/2MsrUPZKIocPp8+FFwCw7vU3KEhPA6C8qAjvgAAmP/Wk07MfSk5iy8KPwW6n04SJdDur/n7fsvBjDiclYvHxZcB0Y78XpKWR+OZ/q5crOpRJzwsuJPrU00l443UK09NrZR/7xFM4myvrma0//UbK4mWIQNvOnRh16w1Yfbydlj0zKYlNCxag7Ha6TJxIj7Prl5lNCxaQmZiI1ceHwTfVlHcAZbezbOZM/EJDGXHPPQDk7dlD8ocfYi8vRywW+l9zDaHduzstc5WqMqPMMtO9kTJTdaxWlRmA8qJCkj94n4L9+0FgwA03EtqjJwC7//iNveax2n7QIPpc6vxj1VH2xkR2fjYfZbcTMW4SXc44t9bzRWkH2frh2xTs3U3M+ZfS+VSjPi3JzmLr+29Snp8HIkRMmEKnqac39BZO1dJz0/718SR9/i2Icf4Zcs3lhPcx9v3qtz7gQFwSfsFtOOv5J+q9Zku5on6Pf+N1CtKMeqaiqAivgADGP+n8eqap3nrgHM4Y1YtDuYUMu+Ett+VwdDg5ia0L56OUnU7jJxHTwLG6beF8DiUb+73/9JsJ7hoNwJ7ffmb/X3+CQJuozvSbfhNWbx92fv0lmQlxiAg+wcH0u+Fm/EJD3bB1mifRDVeaZkpZv5mcg4e4de4MDm7bzS9vfM51L95bb7l+k4Zx7n3XAEYjVuJvKxly5vgTHZe9cZvITcvkyv8+Tsb2Xfw19xMueu7Best17NOdrsMG8L8ZL9Z6PLhDGOc/eTe+QYHsidvIn28taHB9Z0lLSOZIegZnvvQMWTtT2fDefE556tF6yyUt/JLeZ55ClzEjWf/uR+xasowep0zGJyiQ2Guv5MD6+AZff8fPvxMcFUl5cXGLciq7nfgPPmb8w/cSEGZj0aNPEDlkMMGdoqqXSTe35fQX/0P2zlTi3v+IqU/OOOq6wZ2jGH33HWx476Na75e6+E8ATn3uSUry8ln+3EtMfWoGYjn+kdzKbidh3nzGPXwfATYbi2c8QUTd7IlJFKRncNoLz5K9M5X4D+Yz5YkZR103+ZPPOenC8+g4eCBpCYkkf/I5Ex99iC5jR9Nl7GgA8vbuY+WLrzmt0QpqyszZZplZ/958Tm2gzCSYZabrmJGse/cjUpcso6dZZoZeeyX765SZ3H37SVn8F6c+9SgWLy+WPvsSUbEDaRPRwWnZDyYkcyQtk3NeNrKve/djTnv6kQayf0Xvs04heswI1r47n9TFy+h56mSC2rfj5McewCcokIPxyayd+1Gt9afOuA+/4DZOy1ulJWUIoOv4cXQ/ZSrr33q33mv3PONUep11htMzO2bfNP8jRtz/AH42Gysen0l47BDaRNVkP5SURFFGBhOfm0NuSgobP5rH2MdmVT8/6sGH8WlTe79mbdlMRnwc4558Gqu3N6X5+S7JnvTRfMY8cD/+Nht/znycjkNiCXbInpmURGFGBlPnPEdOSgqJ8z5i4qzHsHh7M/ahB/Hy88NeUcGyp54hfOAAbD16MPzO26vX37jwE7wDAlySffP8jxh+n7HfVz0xk/DBQwhyyH7Y3O/jn51DXmoKm+fPY/SMWQRFRFQ3Rim7nSV3/4sOQ4YBMPj2O6vX3/rpQrz8nZ8dXFfPFGXnsP2XRZz5/JN4+fiw/OU32bNqDd0mjnNKbmW3s/Gjjxj5wAP422wsmzWLDrGxtcp7ZlIShenpTJ49m9yUFJI//JBxM2dWP7/rt98IioykwuG8ueWzz+h13nmEDxpERmIiWz7/nDEPP+yUzI7ZN83/iBFmmVlplpm6x2phRgYTnp1DbmoKm+bPY8yMWUbGBR/Tvv8AhtzxD+wVFVSWlQLGsZoZH8fYJ1x3rNbdjh0L5zHw7ofxDbUR98wMwgYNITCy5ktAr8BAelx+DVnxG2qtKxYL3S+ZRpuuMVSUFBP31KOEntS/1rqu0NJzU4f+J3HG0MGICDl79rHilbc5+0XjGO42cSy9TpvCqv++5/TcrqrfYx3qmS2fLMTLBXXk8Zj/SyJvfbOOdx8+3605qii7nS0ff8jQex/Ez2Zj9ROP0b5u/Z6cSGFGBuP+87xRv3/0AaNmPE5JTjZ7/viNsU89h9XHh8Q3XiN9zWqixk0g+oyz6HHhxQDs+f1XUr//lr7XXO+uzWxVdH+rxv0t5rgSkWEi8moLX2O3iLRzVqYTobHMIrLSHXk83Y41yfSfMgIRIapPDKWFxRRk59VbrsfwfogIIkJkr64cOVx/mRNh99pEek8ahYjQsXc3SguLKGwgb/tunQkOD6v3eMc+3fENCjR+7hVDYVaOS/Me2JBA9PgxiAjtenanvKiI4pzcWssopcjYtJVOI40PLtETxlQ3VPm1DSasewwWa/3ebUVZ2RyMT6Lb5JY3IGbvTCWoQzhBHcKxeHnRefRIDm5IqLXMwQ3xdDW3JcxhW462bnBUJG0iI+q935EDBwnv37d6G70DA8hJ3d287CmpBHYIJyjceP9Oo0ZwcEPtD1NpjWU/2rpCdYNgRVEx/iEh9d5736o1dB4zslm5G7O/TpkpO0qZ6WyWmZgJY6o/QDZWZvIPpBHWsztevr5YrFbCT+rNvnVxTs1+YH0CMRNGNyl7l5FDq7PvW58AQPvePfAxj892PbtRlO3a47NKS8oQQPuTeuMTFHRCstaVm5pCQIdwAszsESNHkRFf+++aER9H1NixiAihPXpQUVRESW7uUV93z+LFdD/rbKzeRk8Z3+Bgp2fPSUklMLwDgWb2qFEjSY+rs9/j4ulsZrf16EG5mV1E8PLzA8BeWYmqrKw3uatSigNr1xE1yrnHKJj7Pbxmv3cc0fB+jxxjZA/pXpPdUdbmTQSEh+PfrvZljVKK9LVriRg5yunZwXX1DICqrKSyrAy7+X//0BCn5c5NTSWwg0OZGTmSjLg6+z0ujk4O5d1xvxdnZ5ORmEiXiRNrrSMiVJSUAEYPFL8G6vuWZ08h0KHMRIwYRWadMpMZH0eUWWZCu9ccq+XFxWRv30anCUZui5cX3gFGXbl3yWK6nenaY9VR/q4U/MM74N/e2I7w4aPISqzdQOUT3Jbg6O5InfLhGxJKm65Gz2svP38CIiIpzXV9Pd/Sc5O3nx9i1i8VpWW11gs/qRc+gYEuye2q+r2KUoq0dWuJdFE901QrkvaSnd+yL2CdKS81hYDwDjX1+8hRZCbULuOH4uOIHDOuun6vKCqi1NzvqtKO3aEO9A0xelV5+ftXr1/V8Kxpx+K2Hldi1HqilLK39LWUUuuB9Q28h5dSqqKlr98SrsogIo2OTVNKjXH2+/1/cCQrj+B2IdW/twkL4UhWHkG2tg0uX1lRycYl6zjlpotOUMLaCrNzCWpX0602KCyUwuxcAhvJezRb/lhJ59h+zoxXT3F2DgFhturf/W2hFGfn1rqQLztSgE9gQPUHgIAwW5M+sMd/9CmDrryk+mK7RTlzcvGvkzN7Z2qdZXIIsDkuY6M4J6dJ69bVtktnDq6Pp/PoERRnZZO7azdF2dnY6Hb82evtYxvZKSl1lqmfsSQn56jrDrr6SpY/9wLJCz9DKcWkmfW/nd2/ei2j7/nncWc+1vYEOmQKsIVSdIwy4x9mo/gYZaZt5yiSPvuG0iMFWH28OZiQhM0c6uYsRdm5tfZnQ9lLjxTgHeBfU95toQ1mT1mynMjB/WseEGHJMy+BQM+pE+lx8sR66zRXS8rQsT6Up/y2iD3LVhLaLZqB0y53+geckpwc/Gw1jfT+oTZyU1PqLJONn8Ox6xdqMx4LCQGBtc/PBoQukyfTZdJkAArT08nevp1tX32J1dubPpddQUi34z8+j5W97j7NSaldd5Rk5+Bvq1uH5uAXEoKy21n62EwKMzKJOXkqtjpDu7K2bcc3OJigjh2dmhugNCcHf4f97mezkVenzJTmZtfK7hdqo7Rqv5vS1qxusHEqZ/s2fNoGE+iC7OC6eibAFkqfs0/jf3c+gNXHm44D+xExsP9R1zmu3Dk5tcuyzUZOSt3ynoN/WO2/TUmOUWY2LVjASZdeWu+82XfaNNbMmcPmTz9F2e2MneHcochVufzqlJncutlz6x+rpTnZiNWKT5tgkt97h/x9e2nbNYaTpl2Fl68vhenp5Gzfzvavv8Ti7U2fS51/rDoqy83G12E7fENs5O9KOcoaDSs5fIiCvXsIjnH+kMy6nHFu2rc2jsRPv6YkL5+JD7ZsaoamclX9XiVn+zZ8gl1Xz3iqktycevs0r95+r18XleRk0zamG9Gnn8lf99+FxduHsP79add/QPVyO776goMrl+MV4M/w+//t+o3xEHqOq8ad0B5XIhItIltE5A0gDpghIutEJElEHndY7hER2SYif4jIJyJyn/n4UhEZZv7cTkR2mz9PEpEfzJ9nichcEfkN+EhE2ovIV+b7rBORseZyYSLym4jEi8jbHKNnnohcY+ZMFJH55mPniMga8zX+EJEODWVo5PWuE5HvROQXc1tnOjz3rYhsEJFNInKzw+MFIvKEiKwBRjs87m++zk1Vyznsl6Ui8qWIbBWRBWaDISJypvnYchF5tWr/NZJ1oogkmP/iRaSN+dp/icg3IrJZRN4SEYu5/Jsist7M7/h3HS4iK819uNZ8HauIzHEoB7eYy0aYr58gIhtFpF5XGhG52Xyf9Us//elof76mUaqBjW988V/f+JzO/brTub/rLzQa0mDcZtR1B5K3sWXRSkZfc0HLQx1NE/ZvA0tUf7PXmINxifgGt8HWLbrZ0WqHaMKObXBTpFl/lOhJ4/EPC2XRo0+QMP8Twnr2wGJx3pxp9fdf0zNWrZv6xxIGXXUFZ772IoOuuoIN73xQa7nsnSlYfXxo29nJQxwa2J9N+FMcc5+3jYrkpHPPYMkzL7D02ZcI7dK5wd4SLXPs7A2qs1DGpq2kLFnG4Csvrn7slMcf4oxnH2PyQ3ex/bclZG7Z3tKwx4jU/DJUpdvJkzn9pdmc/Mzj+IWEkLTgU+cFPEqspixTtX2jH5nBuMefZPi997Fn0R9kb9tqrGKvpLywkDEzZtLnssuJf+N1VEPHeguoZoavyi4WC5OfepLTXn6R3NRU8vfvr7XcgdWr6TTa+b2tGtWEIuNYZuwVFWQmxNNx+Ih6ixkNWqPrPe40LqpnygoK2b8+gXNefY7z33iBitJSdi1b1fycdTWYu2kXARkJCfgGBxMSE1PvuT2LF9Pvyis5+aWX6HfllSS95/xhXw1pSjWDCKqykvw9u+kyeSrjHn8Kq68vqT9+b6xir6S8qJDRj86kz6WXk/Cm84/VpmQ8HpUlJWx662W6X3a1y4bD1tbyc1PnEUM4+8WnmHDfncZ8VyeCi+r3KgdXrybSlfWMp2rSdW3DdVF5YSGZ8RsY/9yLTHzxVSpLSzm4akX1Mj0vuoSJL7xCxKgx7F38u5ODa39H7uhx1Ru4HvgWuBgYgXGJ8z8RmQAUApcDsWa+OGBDg6/UuKHAOKVUsYgsBF5SSi0XkS7Ar8BJwExguVLqCRE5C7i5sRcTkX7AI8BYpdRhEalqVl4OjFJKKRG5EXgAqJoUqTrDUXKOAPoDRcA6EfnR7D12g1IqW0T8zce/UkplAYHARqXUY2YugCDgU+AjpVRDjWSxQD/gILACGCsi64G3gQlKqV0i8slRMgLcB9yhlFohIkFA1ddzI4C+wB7gF+BC4EvgETO/FVgkIgOBrcBnwGVKqXUiEgwUA9OBPKXUcBHxBVaYDX4XAr8qpZ42X6fe2VwpNReYCzBvx6/NujLZ8MNfJPxqXEhG9OxC/uHc6ueOZOXSppHeS8sW/kxRfgEX3Tm9OW/bbBt/Xsrm341KP7xHVwoO13wDVpCVQ8BxDkPI2r2fpW98zFkz7sSvjfOH9+z4bTGpi/8CwNYtmqKs7OrnirPr99DwbRNEWWER9spKLFYrRVnZx+zFcXjbTg7GJfJ9QjL28nLKi0tY/fo7jLrzpmZl9reFUnyMnP62UIqyHZfJxi80BHtFxTHXrctitTL46iuqf18882mCOoY3O3vtfZxdb5hHQ9vnF2Jkb2zdPctWMOiaKwGIGjm8XsPVvlVr6TzGOd3rt/+2mBSzzIR1i6YwK5v25nNFTSgzxU0oMwDdJ4+nuzm0NPHTrwiwtXxS0O2/Lmbn4mVG9u61y3tj2cuLimvKe51lcvbsY83bHzLpoX/h63B8BtiMZfzaBtNpeCxZO3cRflKvFueHlpWho/FrW1OXxkyeyMrnX3ZG3NrvYQulJDurJldONr51Jnv1s9kocTh2S3Kyq4cuVE0M6xscTIchQ8lNTcXWuw9+oTY6Dh1mDIPo1h0RC2VHjjh1GJJ/qK3+Pm0ge3F23WVCai3jHRhIWJ8+ZCYlE9zJaEi2V1aStn4DE5+Y5bS8jnxDQyl22O8l2TX7tGYZI3vVo477HeBQUiLBXaPxbVv7nGuvrCRjw3rGzHTuRM8nop5J37iZoPB21XPRdR4+lMPbdxIz3jkfjv3rluUGjlW/0FCKs2r/bfxCQ0lbt46M+Hgyk5LM82Yx8W+9Reytt7J/+XL6TZsGQMSIESS9/75T8tbNVXKMMuMX2vCxKmI8F2L2Kuw4fDipP/5QvU4Hh2MVFxyrjnxCbZQ6bEdpbja+xzG00l5Rwaa3XiZ85FjaDxnugoQGZ5+bqoSf1IuCjEOU5B9xyZyLjlxVv4NRz6RvWM/YWc6tZ/4OGj4OQ46+jHk8Z23eSEC79viYx1+HIcPJ3bmDyNFja60fMXIMca88T4/z3TOCpdXRHa4a5Y45rvYopVYDp5r/4jEap/oAPYHxwDdKqSKlVD7wv2a8x/8cGoxOBl4XkQTztYJFpA0wAfgYQCn1I3C0Pt9TgC+VUofN5auOzk7AryKSDNyP0UDUUIbG/K6UyjKX+xqomrHznyKSCKwGOmPsF4BK4Ks6r/Ed8EEjjVYAa5VS+80hmQlANMa+TlVK7TKXOVbD1QrgRRH5JxDiMPRxrVIqVSlVab5GVf5LRSQO42/bD6NxqzeQppRaB6CUyjdf51TgGvPvswYIM7d3HXC9iMwCBiiljhwjY7MMPXsC0197kOmvPUiv0QPZuHitMRfI1l34Bvg1OEww4deV7Irbwnn3X9usCbRbov8Zk7j0xUe49MVHiBkxiG1LVxvzf2xLxTfA/7iGCR45lM0vs+cy9V/XERLpvEmpHfU8dQqnPTuL056dRdSwWHYvW4lSisM7UvAOCKh3ISQihPfrzf41xsjf3X+tJHLo4KO+x8ArLuLc/z7POa/NZvQ/byG8X59mN1oBhHaPoSA9g8LMQ9grKti3ag0RdTJEDh3MHnNbsnak4O1vbEtT1q2rorSUihJjfH9G8iYsVmutibCPK3u3GArSM6vff//qtUQOja21TMSQ2DrZ/Y3sR1nXPzSEw1u2AXBo0xaCOtaUF2W3c2DNOjqNrt9Tojl6nTqFM56dxRnHUWY69OvNPrPM7PprJZ2Osc8BSvKMSXsLD2exb10cXZ0wP1ev06Zw5nMzOfO5mXQaFsuuv1Y5ZPdvuLz37c3eNRtqsg8bXJ1r2YtvMPqO6QRH1gxdqCgppby4pPrn9KTNtO3cvPLSkJaUoaNxnEPl4PoNzS7jR9M2phuFGRkUHTKyp61ZTYfY2tk7DI7lwIoVKKXI2bkTL/8A/EJCjOOwah630lIOb9pImyij4afDkKFkbdkMQEF6GvbKinoT/LZUSLcYCjMyKDSzH1i9ho51sneMHcw+M3v2zp14B/jjFxJCaX4+5YWFAFSWlXFo02aCImrm0zu0aRNBERG1huo5U9uYbhRl1uz39LWrCa+TPTw2loMrjey5KTvxNvd7lcaGCWZt3kRgREStYSjOcCLqmYB2YRzekUpFaalxnt64hbZRkU7bhrYxMbXK+4E1a+qX99hY9tcq70aZOenSSzn55ZeZ+sILxN52G+1OOonYW28FwC8khKytRm+UrM2bCezg/OuDtjHdKHQoM2mNlJkDZpnJSak5Vn3bhuBns1GQlmZm3ERQpLFfHY/VwvQ0VIXzj1VHwdHdKM5Mp/hwptFrcN1qwgYNbdK6Sim2f/QOARFRdD7lTJdlBOeem46kZ1T3YsvetQd7RUWtL1ZcxVX1O0CWi+tITxYc042ijHSKDhllPH3NasIHD6m1TPvBQzi4cnl1/e4VEIBvSAh+tjByU1OoNOvArC2bCIowjtXCjPTq9Q8lxBHY0Xl1o/b35Y4eV4Xm/wX4j1LqbccnReQuGu8QWkFNY5tfE94Dc/nRdRuRzN5KTe2lI40s+xrwolLqfyIyCZjVSIbG1H1NZb7OyRiZi0RkKTXbWmI2EjlaAZwhIgtVw/2hHWe8q8T4mx9XW65S6lkR+RE4E1gtIicfJX8MRg+t4UqpHBGZZ+ZvbB8K8A+l1K/1njB64J0FzBeROUdpnHOK7sP6krJ+E2/d9ATevj6cdde06uc+m/kWZ/7zCtqEteWX/35O2/BQPrrvJQB6jxnIuCtcd6esxnQZ2p89cRtZePtjePn6MPnOa6qf+/Gp15l0+1UE2kJI+nExCd/8TlFuPp/f/RRdhvRj8h1Xs/7zHyk5UsBfc40hOxarhYvnOPfOQY4iYgeSlpDMj3c9jJevDyNuuaH6ub+ee5nhN12Lvy2UQVdcwqrX3ib5828Jie5cPeF6cW4evz/yJOXFxYgI23/+gzPmPIl3gH9jb9ksFquVwdddxbJnX0TZ7URPGkfbTlGk/LEEgO4nT6bj4IGkJyTxy90PYfX1YZi5LY2tC3Bg3QYSPlxIaf4RVsx+hZCunRn/8L2U5h9h2bMvIGLBPzSE4bfd2MLs01j+3AvG+08cT3CnKFLN7N0csv96z4NYfXwYdsv0o64LMOTG60j8aCHKbsfi7c2QG6+rfs/DW7fjbwslKLx5vcSOJtIsMz/c9TBWXx9GOpSZpc+9zIibriXAFsrgKy5hxWtvk/T5t4TWKTO/OpSZbT//wVlmmVn+0huUFhRgsVoZdv206onQnZd9AAcTkvn+X//G6uvDqFtr7paz5NmXGXnzdQTYQoi98mKWv/o2SZ99Q2h0F7pPNtr/N371PaUFhax7fwFgHJ+nPzODkrx8/nrhv4DRaNh17Ija81+1UEvKEMCa19/i8JatlB4p4Kc77+Gki88nZtIENn7yObl79oIIge3bEXvDtU7L7Ji931XXGPOY2BWdxk+gTVQn9ixeDEDXKVNoP2gQmUmJ/PnA/Vh8fRg43TjeyvLy2PDaK4AxmWzkqNG0HzgQgM4TJpD03rv89cjDWLy8GHjjzU0eknU82QdecxWrZj+PUna6TDD2+y4ze8yUKXQYNIiMxCT+uP8BrD6+xN5o7PeS3Dzi576DUnaUXRE1cgQdYwdXv/aB1WuIcuEwQYvVSt9p17D+hdkoh/2+d4mRvcvkKbQfOIjDSYn89eD9WH18GDC9pp6rLC0la9NG+l1b/45SLh8miOvqmXY9utFl5FB++fcTWCwW4/ieOsFpuS1WK/2uvpo1c+ag7HY6T5hAm061y3v4oEFkJiWx5P77sfr6MujGY59fBt5wA5s+/hi73Y7V25sB1zv/Tl9VZWbdMcrMoaRE/jTLzECHMtP3qqtJnPsmqqIS//btGTjd+LKq0/gJJL/3LssefRiL1TXHqiOxWulxxXUkv/wcym6n49iJBEZ24uCffwAQOfFkyvJy2fD0o1SWFINY2P/Hzwx/fDaF+/eRsXo5gVGdWf+Ece0Vc8FlhA0Y7LK80PJz0741cexatgqxWrH6eDP2X7dU7+MVr84lY/M2So8U8M3t9zPw4nPpPsU5d912Vf0OcPAE1DNN9eGjFzJ+cFfatQ1g5+d38eS8pXz4U4Lb8lisVvpcdQ1xLxr1TNS4CQRFdWLfkkUAdJ48lXYDB3E4KYHlD92H1ceHfjcYx2NI9x50GDacVY/PQKwWgrtE02miMbfYji8/ozA9DRELfmFh+o6CDvQcV40Tl479rvtmItHAD0qp/iJyKvAkMFUpVSAiUUA5Ri+mecBIaoYKvq2Uel5E3gU2KKXeNBu47lJKRZuNPfcppc42e+gUKKWeN99zIRCvlJpj/j5YKZUgxl0IM5VST4nIGcBPQPuqXlV1cvcDvsFoTMoSEZs5FC4euFEptUFEPgBilFKT6mZoZF9cBzyDMVSwGKO30Q1AlPma54hIH4xeUqcrpZaKSIFSKsjhNXYDw4AZgI9S6jbz8QKlVJDjfjEffx1jEvvPgO3AeKXUbhFZALStWq6BrN2VUinmz9+af59c4Gdqhgr+jDFsbyfGvF6xQHsgCXgQWIgxXLBqqGAbc7tvwGgQu0QpVS4ivYADQDvggFKqwvxbRyul7mpsfzZ3qKC75ZZ5uztCs2WX+rg7QrNVKs89KSgPzu5jafG9ONxGxCOrGABKK509f9eJU1zuudm9Pbi8l1Z67k2nQ/3K3R2h2Y6Uue2eSS1Waffcc1ORB9czUcGt5w50xyu3xHOvI9/6d73v2z3GjU+d7u4ILfLa2BGeW9k0waGSAyfsgrO9X5RH7Uu3XZkopX7DaMxYZQ61+xJoo5SKw2hYScAYFrfMYbXngdtEZCVGw0ZT/BMYZk78vRm41Xz8cWCCOaTtVGDvUbJuAp4G/jSH8L1oPjUL+EJElgH1GryaYDkwH3NbzfmtfgG8RCQJo2FvdRNe5y7AT0RmN+VNzd5ntwO/iMhyIAPIO9rrmxOkJ2I0Nv1sPr4KeBbYCOzCGOKZiDFEcBPwPkaPMJRSZcBlwGvm6/yO0RPrXWAzECciGzHm3vICJgEJZuPgRcArTdk2TdM0TdM0TdM0TfM0cgL/8zQntMdVczSl95InMntcDVNK3emm9w8ye7oJ8F9gh1LqpeNYfxIOvbncTfe4OvF0jyv30D2u3EP3uHIP3ePKPXSPK/fQPa7cQ/e4cg/d48o9dI+r1u1wycETdsHZzi/So/al516ZaC11kzkh+iagLUZPJ03TNE3TNE3TNE3TtFaj1X+1o5SadaLeS0TCgEUNPDVVKZXVwONNec3TgOfqPLxLKXUBxlxRbmH2rqrVw0pErgf+VWfRFUqpOxpYfymw1FX5NE3TNE3TNE3TNE3TWn3D1YlkNk4NdvJr/gp4RH9SpdQHwAfuzqFpmqZpmqZpmqZpmga64UrTNE3TNE3TNE3TNM2tjOmntYboOa40TdM0TdM0TdM0TdO0Vkn3uNI0TdM0TdM0TdM0TXMjQfe4aozucaVpmqZpmqZpmqZpmqa1SrrHlfa34GuxuztCs9iV57aqHyrwdXeEZgv2q3B3hGbzsnpmWQfIKfF2d4Rm89wjFYrKre6O0Gy+Xp5b3ksrPPcSK8S/3N0Rms3bQ68HAKyi3B2h+Tz4q/BAn0p3R2i2AC/Pzb7Dg68jb3zqdHdHaLZ3H/3F3RFa5LUlI9wdwaU8+XrT1Tz4NKNpmqZpmqZpmqZpmqb9nXnu14Gapmmapmmapmmapml/B/qugo3SPa40TdM0TdM0TdM0TdO0Vkn3uNI0TdM0TdM0TdM0TXMjfVfBxukeV5qmaZqmaZqmaZqmaVqrpHtcaZqmaZqmaZqmaZqmuZHub9U43eNK0zRN0zRN0zRN0zRNa5V0jytN0zRN0zRN0zRN0zQ30nNcNU73uNI0TdM0TdM0TdM0TdNaJd3jSvt/TSnFz29/zY51W/D29eb8e64kskfnest9NXs+B3fsxeJlJapXF875x2VYvaxsXZXM4vk/IRbBYrFy+i0X0LVfN5fmXfn+5+yN24iXjw+T/nEt7bt1qbdcfsZhFr30LiVHCmnXrQtT/nk9Vm8vSgsKWfrfj8hPP4zVx4tJd1yDrUsUAKWFRfz5xnxy9h4EESbecQ0de7tmW7I2JrLj0/kou52I8ZOIPuPcWs8Xph1ky7y5HNm7m+7nX0KX086qfm7LvLkcTkrAp00wIx9/1iX56spISiJ5/kKU3U7XSRPodc7ZtZ5XSpE8fwEZiUlYfX0YcvONhERHU5SVRdzb71CSl4eIED15Et1PO7V6vZTffmfX74sQq4UOgwbR/4rLWpw1PTGZhI8WouyKmMnj6XPuWbWeV0qR+NFC0hKS8fLxYdit0wmN6XrUdTd+/jVpGxLAIvgGBzP81hvwDw2l9EgBq195g+yUXURPGEvs9Ve1OL+jQ8lJbFn4MdjtdJowkW5nnVNvW7Ys/JjDSYlYfHwZMP0m2kZHU5CWRuKb/61eruhQJj0vuJDoU09nx9dfkhEfj4jgExzMgOk34Rca6tTcjtmVmb17I9kPJSVidcgOUF5USPIH71Owfz8IDLjhRkJ79CR/7x42fjgPe3k5YrXQ7+prCenW3enZHWVvTGTnZ+axOm4SXeocq0VpB9n64dsU7N1NzPmX0vlUo8yUZGex9f03Kc/PAxEiJkyh09TTXZoV4HByElsXzkcpO53GTyKmgf2+beF8DiUb+73/9JsJ7hoNwJ7ffmb/X3+CQJuozvSbfhNWbx+2ff4JhxLisXh5EdA+nH7Tb8I7INDp2bM2JrL9E2NfR46fRPSZtfe1Uortn8wnKzkBq48vJ91wM8FdYwDY+8cvHPxrKaCIHD+ZLqcY+/rI3j1s/fh9o8xYrPSedh1tXVBm0hOTSTTryJhJE+jdSL2TnpiE1ceHYbdMJzQmGoD1c98jPT4R3+BgTnnuqep1Nn3xNQc3GMeqb3Aww26djr8LjlWlFOvmfcqB+GSsvj6Mve16wrp1rbfckcxDLHvlHUoLCrHFdGHcndOxetVcRh/euYufH/0PE+66ha6jhgKw+cff2bF4GYIQ0iWKsbddj9XH22nZM5KSSP54IdjtdJnYyLnp4wVkmuem2JuMc1NlWRnLn/kP9vIKlL2SyOHD6XPhBQDk7dlD4rwPqTTLzKBrryG0u/OvBTKTkti0YAHKbqfLxIn0OLt+9k0LFpCZmIjVx4fBN9XUkYvuvRcvPz/EYkEsFsY//jgA2776ivS4OMRiwadNGwbf5KL6PSmJzWb93nnCRLqfXb+e2bygpn4feKND/V5o1O9HzPp94HSjfk9bu5Yd335DQdpBxjw2k5AY11x/KaVY9f5n7I/fiNXHh4l3Xke7Bq4jj2QcZvFL71BaUERYt85M+scNWL29KCssZsmr71F4OAd7ZSUDzz2FXlPGUlFWzo+PPU9leQX2ykpiRg9h6GXnNpDAOfI2J7D3y3kou532Y6YQcer5tZ4vTj/Aro/fpGj/LqLOvpyIk42/UXHGQVLef7l6udKsTKLOuoSOk2vXWc7minPTzq+/JDMhrvp6pt8NN7ukvDfVWw+cwxmjenEot5BhN7zlthweT3SPq8bohivt/7Ud67eQfeAQ/3z3EfZv28OPr3/BTS/fU2+5AZOHcuH9xofyr2Z/RNyvqxh+1jhiBvfitlH9ERHSdx3ki//M4x9z/+2yvPviNpKXlsnlrz9B5o5dLJ+7kAuefajecmvmf82As6fSY9xw/np7AVsXraDf6ROJ++oXwmI6c9qDt5GzP53l737CObPuBmDl+5/TObYfp95/C5XlFVSUlblkG5TdzraFHxJ790P4htpY//RjtB80lMDIqOplvAMD6XX51RxK2FBv/Y5jJtBp8ilsfv9tl+RrKG/ih/MZ++D9+NtsLH3scToOiSU4qiZvRmISBRkZnPz8c+SkpJD4wUdMfPwxLFYr/a+8nJDoaMqLi1n62Cza9+9HcFQUhzZvIT0unsnPPInV25vSvHynZI3/4GPGP3wvAWE2Fj36BJFDBhPcqSZrekIyR9IzOP3F/5C9M5W49z9i6pMzjrpu77PPoP+lFwKw45ff2fL19wyZfg1Wb2/6XXw+efsPkL/vQIvz192WzfM/Yvh9D+Bns7HqiZmEDx5CkMN+P5yURFFGBuOfnUNeagqb589j9IxZBEVEMPaJp6pfZ8nd/6LDkGEAxJxxFj0vvBiA3b//Rsr/vqXftdc7Pfum+R8xwsy+0szexiH7oaQkCjMymPDsHHJTU9g0fx5jZswCYMuCj2nffwBD7vgH9ooKKstKAdj6+Wf0PO982g8cRGZiIts+/4yRD7muvlF2OzsWzmPg3Q/jG2oj7pkZhA0aQmBkp+plvAID6XH5NWTF1z5WxWKh+yXTaNM1hoqSYuKeepTQk/rXWtcVebd8/CFD730QP5uN1U88Rvu6ZSY5kcKMDMb953mjzHz0AaNmPE5JTjZ7/viNsU89h9XHh8Q3XiN9zWqixk0grG9/el50KRarle1ffMquH7+n1yWXOz37tgUfEnuPUS+ue+ox2g0eSpBDvZiVnEhxZjqjn3mB/NQUtn08j+GPPE7BgX0c/Gspwx95HPHyIuHl2bQbOJiADh3Z+eUnxJxzIe0GDOJwUgI7v/yEoQ886vTsCfPmM+7h+wiw2Vg84wki6tY7iUkUpGdw2gvPkr0zlfgP5jPliRkAdB0/ju6nTGX9W+/Wet1eZ51Bv0uMemfnL7+z5ev/MWT6tU7NDnAgYSP56Zmc/8rTHN6Rypr3FnDm0/WPq7gFX3HSmScTM3YEq9+Zz87Fy+l96iQA7HY7cQu/InJQv+rli7Jz2PrzIs598Qm8fHz486W32LVyLT0mjXVKbmW3k/TRfMY8YJyb/pxZ/9yUadYzU+eY56Z5HzFx1mNYvL0Z+9CDePn5Ya+oYNlTzxA+cAC2Hj3Y9Nnn9D7/fDoMGkhGYiKbPvuMcf9+2CmZHbNv/OgjRj7wAP42G8tmzaJDbGytOjIzKYnC9HQmz55NbkoKyR9+yLiZM6ufH/3QQ/i0aVPrdbudeSa9L7oIgF2//cb2775j4HXXOT37pvkfMeJ+o35f8fhMwmPr1+9FGRlMfG4OuSkpbPxoHmMfmwXA5oUf037AAIbcadbvpUb93qZTFEP+8U82zvvAqXnr2h+/kfy0TC557UkO7djFirkLOO/Z+n/ftR9/Tf+zT6b7uOEsf3sB2xavoO9pE9n8yxJCO0Vw2sN3Upx3hC//9Rjdx4/E6u3FmTPvxtvfD3tFJd8/OpvOsf0J7+X8Bjhlt7Pn8/fpdecj+ISEsXnOw4QMGIZ/hOO5KYgul1xHbuL6Wuv6d4ik/8Ozq18n4ZFbCR00wukZ6+Z1xbkp+oyz6GFez+z5/VdSv/+Wvtc493rmeMz/JZG3vlnHuw+f77YM2t+bxw4VFJEQEbn9GMtEi8iVTXitaBHZ2Mwcn4hIkojc3Zz1W0JEbhWRa46xzCwRue9EZWouETlfRPqe6PfdtjqZQVOHIyJ07hNNSWExR7Lz6i3Xa3hfRAQRIapXV/IPG8v4+vsiZst4eUmpyxvJd69LotfEUYgIHXp1o7SwmMKc2nmVUhzcuI1uo4cY2SeNZvfaRABy96cRNaAPAKGdOlKQmUVRbj5lRcWkbd5Bn6nGBbXV2wvfwACXbEP+rhQC2nfAv304Fi8vwoePqtdA5RPcluCY7ojVWm/90F598AoMckm2huSkpBLUoQOB4UbeTqNGkr4hvtYy6XHxdBk3FhHB1qMH5UVFlOTm4hcSQoj5Lau3vz9tIiMpyc4BYNeixfQ8+yys3sa3775tg1ucNXtnKkEdwgnqYGTtPHokBzck1Frm4IZ4uo4fg4gQ1rM75UVFFOfkHnVd7wD/6vUrS2saNL38fGnXp1f1NjhTbmoKAeHhBJj7veOIUWTEx9VaJiM+jsgxxn4P6V6z3x1lbd5EQHg4/u3aGZn9Hbel1CXfbOWmphDokD1ixCgy62TPjI8jyswe2r0HFWb28uJisrdvo9OEiQBYvLyqe/cIUFFcDEBFcRG+ISFOz+4of1cK/uG1j9WsxAaO1ej6x6pvSChtzN5AXn7+BEREUpqb49K8eakpBIR3qCkzI0eRWaduORQfR+SYcdVlpqKoiFKzzKhKO/ayMuyVlVSWleEbYnxz3a7/ACzm9rXt1oOSnGynZ6+7rzuMGMXhutkTNtBxtJG9bfceVBQVUpqbQ2HaQdp2647V1xeL1Uporz4cijM/rIlQWavMOP/b+OyUVAI7hBNUXUeO4GCdOjKtkXoHoP1JvfEJql+nO9Y7FS46VgH2rUug+wTjvNq+V3fKCosoMrNVUUqRvmlbdU+q7hPHsHddzTZu/XkxXUYOxa9t7YYUu91OZVk59spKKsrKCAgNcVrunJRUAsNrzk1Ro0aSHldnv8fF03ls/XOTiODl52dkrKxEVVY67F+prmfKi4rxc0GZyU1NJdDhvBo1ciQZcXXq97g4OpnZQ3s0XL/X5V2nfndFiclNTSGgg0P9PrLhc1OUQ/Za9fu2OvV7oFG/B0VGERQR4YLEte1Zl0jPSUZ5D+/VjbKiYooavI7cSox5Hdlz0ij2rE0wnhShvKQUpRQVJaX4BgVisVoQEbz9a8qUvbISV90frXD3TnzbdcCvXQcsXl7YhowhJ2ldrWW827QlqGuPBq8jq+RvS8avfQd8be1dkrOKq85Nta5nzC+43GlF0l6y84vdHUP7G/PkHlchwO3AG0dZJhq4EljoigAi0hEYo5Sq36f8BFBKObUfpoh4KaUqnPmax+F84Adg84l80/zDeQS3r7koC24XQv7hPNrY2ja4fGVFJYmL13PGLRdUP7ZlZRJ/zPuBwtwCpj1+k0vzFmbnEtiuJm9gWAhFWbkEhtbkLTlSiE9gQPUHraCwEAqzcwGwRXdi1+p4Ik7qQeaOXRw5lE1hVg5iseAXHMTS1z8ka88B2nfrwpgbLsXbz9fp21Cam4OvzVb9u2+ojfxdKU5/H2cpzsnB3yGvny2UnJTUYy5TnJ2Dn0PDQuGhQ+Tt2UNoD2OYTkF6OlnbtrPli6+weHvT/8rLCO3Wsm8mi3Ny8Q+ryeFvCyV7Z/2sATbHZWwU5+Qcc92Nn33FnmUr8Q4IYOKj97coZ1OU5uTgbwur/t3PZiMvpXY5Kc3Nrr3fQ22U5mTX2u9pa1YTMXJUrfW2f/UFB1eswCvAnxEPOLcnAUBJTg5+dbLn1slekpuNXwPZxWrFp00wye+9Q/6+vbTtGsNJ067Cy9eXk66cxroX5rD1s09RSjH6kRlOz+6oLDcbX4ft8A1p3rFacvgQBXv3EBzj2mGNJbk59fZpXmqd/Z5TZxmbjZKcbNrGdCP69DP56/67sHj7ENa/P+36D6j3HgeW/0nHEaPqPd7i7Dk5+IXWqRdT65b32uXKN9RGaW4OQZGdSPnmC8oLjmDx9uFwciLB0UajYa/LriL+5dns+GIhKMXQh2fibMXZOQSE1a5TsuuU9+Ls+vVLSU4O/sdoyNn4+VfsXbYC74AAJjzygFNzVynKqZ0/ICyUouzcWo1MpUcK8Anwrz6vBthCKTbPq0XZOexbF88pj93LqpRdNa9jC6Xf2afy1e0PYvXxJnJg31o9slqqJCen3j6te24qya59bvJ3ODcpu52lj82kMCOTmJOnYutuHJ8Dpl3JqjnPs/HTz0DZGT/DuT30wDgP1T0Oc+rWkTk5+IfVrkdLcmrOq6vnzEGALpMn03Xy5Orltn75JftXrMDb359RD9Xvkd5Sdet3/1AbufXqmfr1e0lONhaLUb8nvfsOR/btJTg6hr5m/X6iFGblEuhQbgJtIRRm5RDgcB1ZeqQQX4fryEDzmADoe8Zkfn/2vyy86QHKS0qZcvdNiMXoB2GvtPPtg0+Tn36IvqdNJLxXjEu2oSwvG5/Qmr+BT2gYhbt3HvfrZG9YiW2oc3pAHo0rz007vvqCgyuX4xXgz/D7XdcDWztx9EDBxnlsjyvgWaC7iCSIyBzz30YRSRaRyxyWGW8uc7fZs2qZiMSZ/8Y05Y1ExE9EPjBfO15Eqs6QvwHh5uuPb2Tdf4rIZrNX1qfmY7NEZL6ILBaRHSJyk8Py94vIOnP5xx0ev8Z8LFFE5ju8zn3mzzeZ6yWKyFci0qTuMiKyVESeEZE/gX+JyFAR+VNENojIryISYS431HztVVX72nz8OhF53eH1fhCRSebPp5rLx4nIFyISZD7+rMM+ed78O5wLzDH3ZfeG9tuJIkf5VvfH/35B1/7d6Nq/5gPYSWMG8o+5/+byGdNZPP9n14ZTqv5jdeM2sEzVJsVecBqlhUV8ee9TbPxpKe1iOmOxWlGVdg6n7qPvaRO5+PlH8PL1IeGbX52fv7F8rnkn52j2Pq9ZqKKkhLWvvs6AaVdWfyOsKu2UFxYyYdYM+l9xGeteewPV0Hu1OGudsA1ujhxz3f6XXcRZr79Al7Gj2Pnb4pblbK56+72hZWoWsldUkJkQT8fhtYcB9LroEia9+DIRo8awZ9Efzs959FiGRrKrykry9+ymy+SpjHv8Kay+vqT++D0Ae5cs5qQrpjH5xZc56YorSf7g3QZexImOsX+borKkhE1vvUz3y67Gy981vTirNaX8N7BRIkJ5YSGZ8RsY/9yLTHzxVSpLSzm4akWt5VK//w6LxUrEqCZdOhyn5tUzIARGRhF9+tnEv/gsCS/Ppk3nLojF+MC5f+kiel02jXFzXqXnZdPYMu8dpydvSP3zaFP+NvX1v/QiznztRTqPGUXKb4ucE66uBqM1oZyby6yb9xlDrrwQi6X2JXVpQSH71idw4ev/4ZK35lBRWkbqstXOSAyAavAArb9UXVXbJhYLk596ktNefpHc1FTy9+8HYNfixfSfdgWnvfwi/a+8kvh333da5ppYRz9nHsvYRx9lwhNPMOK++9i9aBFZW7dWP9fn4os5+aWXiBo9mt1/uKB+b95uR0Sw2436veuUqYx74im8fH1J/eF7p0c83nB1932D1yLmIgcSNhEW3Zkr35nNBXMeZeV7n1BWZPSysVgtXPj8DK54+1kO7dxN9l7nTiHgELDFL2GvqCA3eQO2WOd/EVGPC89NPS+6hIkvvELEqDHsXfy7k4NrWuviyQ1XDwEpSqnBwGpgMDAIOBmjASTCXGaZUmqwUuolIBM4RSk1BLgMeLWJ73UHgFJqAHAF8KGI+GE0tqSYr7/sKDljlVIDgVsdHh8InAWMBh4TkUgRORXoCYwwt2eoiEwQkX7AI8AUpdQg4F8NvM/XSqnh5vNbgOlN3DaAEKXURIz98RpwsVJqKPA+8LS5zAfAP5VSo5vygiLSDngUONnc3+uBe0TEBlwA9DP3yVNKqZXA/4D7zX2ZQuP7zfE9bhaR9SKyftGnTW8wWvv9Mt68czZv3jmbNrZg8g/VDF/JP5xLm7CGh2wtXfALhXkFnHbT+Q0+Hz2gOzlphynMK2hylqbY+PNSvrz3Kb689ykCbG0pPFyTtzArlwBbSK3l/YKDKCssMrtpQ0FWzTfHPgH+TL7zWi5+4VEm//M6ivOP0CY8jMCwEALDQuhgfjvWbfQQDqfudep2VPENtVGaXTPUpjQnGx8XDEVwFn+bjWKHvCXZOfjXydvQMn7mPrdXVLD21dfpPGY0kcOHOawTSsTwoeZQsW5gEcqOHGlh1lCKs2pyFGfX79HgbwulKNtxmWz8QkOatC5A5zEjObC2/txjzuYbGkpxdlb17yXZ2fWGOfmG1tnvObWXOZSUSHDXaHzbNtyDMnLUaDI2rGvwuZbwCw2l5BjZ/UJtlDSQ3c9mwy/URojZ+6Hj8OHk79kDwIEVy+kwdJj5+AhyU2v3rnA2n1AbpQ7bUZqbfVzDE+0VFWx662XCR46l/ZDhLkhYW8P7NOToy5h/m6zNGwlo1x6f4GBjqN6Q4eTu3FG93IEVyziUlMCAm287rg/Yx5U9p3a92FB5dyxXpQ7bFzl+EiMee5qhD87AKzCQgA4dAEhbtax634cPG+mS3q3+tlCKsurUKXX2e0P1S91ljqbzmFEcWOe8emfrr0v4/oHH+f6Bx/EPDamVvygrB//Q2nWGb5sgyoqKq8+rRdk1y2Sl7uavV9/hqzsfYs/qONa8t4C96+JJS95CUHg7/ILbYPHyosuIWDK3OW//+4fa6u/TOhMz+9U5NxU7nJuqeAcGEtanD5lJyQDsW76CiGFGPRM5YrhL6hl/W/3jsG558AsNpTirdj1atX1V//cNDqbj0KENZowcPZr09evrPd5Sfrba9XtxTja+Dez3hup3/9A69fuw4eSZ9bsrbf55CV/f9yRf3/ckAaEhFDqUm8Lshq8jSx2uI40eWcYy25esJHpkrDFkOSKcNuHtyD2QXmt938AAIvr1Yn/8Jpdsj09IGGU5NX+DspwsvNse33Vk3uZ4AjrH4B0c4uR09bny3FQlYuQYl1zPaCeenMD/PI0nN1w5Ggd8opSqVEplAH8CDV0lewPviEgy8AXQ1DmVxgHzAZRSW4E9QK8mrpsELBCRqwDHYXjfKaWKlVKHgSUYjVWnmv/igTigD0ZD1hTgS3NZlFINTbDR3+xNlgxMA46nP/pn5v97A/2B30UkAaPhqZOItMVo3PrTXG5+E15zFMb+XWG+1rVAVyAfKAHeFZELgaJG1m9sv1VTSs1VSg1TSg2bevkZTYhkGHHOeG57/QFue/0B+oweQOKidSil2Ld1N76B/g0OE9zwyyp2xm3l4gevqfWtatbBQ9XfTB3cuY/KikoCgp17t6n+Z0zi4hce5eIXHiV6xGC2/7kapRQZ21PxCfCrNUwQjG9oIvv3JnWVMefC9qWriB4xEDDuHFhZbuzOrX8sJ6JvT3wC/AkIbUtQO1v1xceB5K2EdHLNXAttortRlJlO8aFMo0fMutW0GzTEJe/lDCHdYihIz6Aw8xD2igr2r15DxyGxtZbpOGQwe5evQClF9s6deAX4G0MxlCL+3fcJioygxxm176gWMXQIhzdvAaAgLR1VUVlvotnjFdq9dtZ9q9YQMXRwrWUihw5mz7KVKKXI2pGCt38A/qEhR133SFpG9fppcQm0iezYopxN0TamG0WZGRQdMvKkr11NeGzt/R4eG8vBlcZ+z03Zibd/wDGHCRam11xgZybEERgR6ZLshQ7Z0xrJfsDMnpOyEy8zu2/bEPxsNgrS0gBjjq6gSCOjb0gI2duMngVZWzYT2MG1f4fg6G4UZ6ZTfLjmWA0bNLRJ6yql2P7ROwRERNH5lDNdmrNKcEw3ijLSKTLrlvQ1qwkfXLtuaT94CAdXLq8uM14BAfiGhOBnCyM3NYXKUmPulqwtmwgyy8bh5CR2//QDsf+4G6uLhvS0iTayV9WLGWvr14vtBw8hfZWRPc8sM1WNW2X5xhw1JVmHORS3ng4jjF5hvm1Dyd1m1DM5WzcREO78MhPaLYaC9EyHOnItkUNrl/eIIbF16h3/Yw4TPOJwrKbFJdDGifP/9DltMufMnsk5s2fSZfhgUv4yzquHtqfgHeBfby4qEaFj397sWW00nqX8uZLOwwYDcOHrz3KR+a/rqCGMnD6NLsNjCWxn49COVCrMMpW2cStto5y3/0O6xVCYkUGhWc8cWL2GjnXqmY6xg9m3oubc5G2em0rz8ykvLASgsqyMQ5s2V8+v5BcSUt2D6fDmLQR27OC0zFXaxhjZq+rIA2vW0KFO9g6xsew3s+fs3ImXv5G9orS0Zq6/0lIOb9xIm07GpNwFDmUmIz6eQBfMGdU2plut7GlrVtfPPjiWA7Wym/V7SAh+YTX1+2GH+t2V+p4xmQufn8GFz8+g64jB7FhqlPfM7anV14CORITIfr3ZZV5H7li6mq7DBwEQ2M7GgWSjfBTl5pN3MIPgDu0pzjtCaaFxSV9RWsaBpK2EOLG8Owrs2p3SQ+mUmuem7LiVhA4cduwVHWSvX4FtqCt6z9bnqnNTYUZNeT+UEEdgR9eXJU1zJ0+e48pRU5sM7wYyMHpmWTAaUJz5+g05C5iA0Ttrhtl7Cur3CVXm+/xHKVXrdmki8s8Glq9rHnC+UipRRK4DJh1HxsKqtwI21e1VJSIhR3n/Cmo3gPo5vNbvSqkr6q4gIiOAqcDlwJ0YDXN11dtvrph/q+fwvuxYt4VXpz+Ft68P591dE/fjx97m3H9dTnBYW354/QtCwkN5996XAWN44KQrT2fLikQSF63H4mXB28ebix+61iXfxlfpMqQ/e+M28ukdM/Dy9WHSHTV3WPrpqdeYePvVBNpCGHnVBfzx0rus++R/tIvpXD3pes7+dJa8+gEWi4WQzhFMuv3q6vXHTr+MRa+8j728kuAO7Zh051Hn/W82i9VKryuvJeHl2ShlJ3LsRIKiOnFgqTEMJGrSVErzcln/1AwqSooRsbDvj18Y+cRzePkHsHHu6+Ru30J5QQEr7v8HMedeROT4SS7JWpV34DVXsXLO8yi7na4TxhPcKYpdi4zhcjFTp9Bh0CAyEpL4/b4H8PLxJfYmo8Nj9vYd7FuxkuDOnVhszkfU95KL6Th4EF0nTiDunfdY9NAjWLy8GHLzjS0uOxarlcHXXcWyZ19E2e1ETxpH205RpPyxBIDuJ0+m4+CBpCck8cvdD2H19WHYLTccdV2AjZ9+yZG0dESEgHZhDJleUzZ++uf9lBeXYK+o4OCGeMY/dE+tu4m1ZFv6TruG9S/MRtkVncZPoE1UJ/YuMfZ7l8lTaD9wEIeTEvnrwfux+vgwYPqN1etXlpaStWljvTsGbv/ycwrT00As+IeF0e/a61qctbHs646R/VBSIn+a2Qc6ZO971dUkzn0TVVGJf/v2DJxujCbvf90NbFm4AGWvNOZFu861dw8Sq5UeV1xH8svPoex2Oo6dSGBkJw7+aQy/iZx4MmV5uWx4+lEqS4pBLOz/42eGPz6bwv37yFi9nMCozqx/wphHLOaCywgbMNhleS1WK32uuoa4F+eg7Haixk0gKKoT+5YYdUvnyVNpN9C4u97yh+7D6uNDvxuMfRvSvQcdhg1n1eMzEKuF4C7RdJpozAqwZcGH2Msr2PDCcwC07d7D6Xduslit9L7yWuJfng12OxFmvbjfrBc7TZpK2IDBHE5OZNW/78Xi40Pf62+uXj/pzVcoLyjAYvWi97Rrqyd8Puna6Wz/ZD7Kbsfi7U2fa46nM3bTsw++bhrLn3vBqDsmGnVkqlnvdHOod36950GsPj4Mu6Umx5rX3+Lwlq2UHingpzvv4aSLzydm0gQ2fvolBWnpUFXv3OD8OwoCRMUO4EB8Mt/86xG8fHwYc9t11c8t+s8rjL7lWgJsIQyZdhF/vTKXhM++xRbdhZ5Txh31ddv37EbXkUP54aGnsFgs2GK60OvkCU7LXXVuWjX7eZSy06Xq3LTYPDdNMc9NiUn8cf8DWH18ib3R2O8luXnEz30HpewouyJq5Ag6xg4GYPAN15O8YAGq0igzg693fj1jsVrpd/XVrJljHKudJ0ygTadO7DGzd50yhfBBg8hMSmLJ/fdj9fVl0I1GHVmal8f6V40BE6qykqjRowkfaHw5t/WLLyhMSwMR/Nu1Y8C1zi8zFquVflddw9rnZ4ND/e6Yvf2gQWQmJfLnA/dj8a1dv/ebdjUJbxv1e0D79gy80aiD0jesZ/PH8yk7coT1L71IcJcujLjP+fO6dR7Sn31xyXx+56N4+fow4faaffTL068x/jbjOnL41Rey5KV32fDpd4RFd6a3eR0Ze/FZ/PX6PL6653FQMPyqC/ALDiJr937+en0edrsdlCJmzFC6DBvo9PxgnJu6XHoD2/77DCg77UZNwj+iM5nLjKFy4eNPoTw/l02zH6aypBgRIWPpTwx45AWs/gFUlpWStzWZrlfcfIx3cg5XnZt2fPkZhelpiFjwCwtz6x0FAT589ELGD+5Ku7YB7Pz8Lp6ct5QPf0pwayaP5Oo7fXkwafGcKm4iImFAnFKqq9lz5xbgTMCGMSxtJBAFvGgOg0NEXgL2K6VeEJHrgfeVUiIi0cAPSqn+jbzXPRhD26aLSC/gd4weVxHHWM8CdFFK7RYRb2A/Rq+muzAmIx8FBGL0sBqF0dvpSWCqUqpARKKAcqA98A0wWimVJSI2pVS2iMwCCpRSz4vIYYweTjnAT8ABpdR1jss0knEpcJ9Sar2I+GBMjn61UmqVmbmXUmqTiCQBtyullovIc8BZSqn+IjIOmI3RKy0K2ITR2LQJ2IAxvHGnOedWJ+AgEKCUyjSHDe5UStlE5DXz7/lBY/tNKZXb0DYAfJLys0cW5LRiv2Mv1ErtyD5xd/ZztmA/d92DoOW8rHZ3R2i2glLP/a7Eky8jisobv6tSa+fr5bnlvdLuuaUmxL/c3RGaLdDLc+v3vFLn36H1RKlUnlve7R6cPTLIc+/ilpge4u4IzRYaUHbshVqpdx/9xd0RWqR4yWOee8A2QUFF9gn7TBvkZfOofemxnyLMBpwV5iThP2MMLUvE6Bn0gFIqXUSygAoRScTokfQG8JWIXIIxPK+w4Vev5w3gLXMYXgVwnVKqtAm9I6zAx+ZQOwFeUkrlmuutBX4EugBPKqUOAgdF5CRglblMAXCV2XD0NPCniFRiNHRdV+e9ZgBrMIYxJgPHPeZIKVUmIhcDr5qZvYCXMRqhrgfeF5EiwHHW7hXALvM9N2IMcUQpdcjs+fWJiFSNrXgUOAJ8Z84RJhi94AA+xRjG+U+Mnljv1d1vx7s9mqZpmqZpmqZpmuYJPKol6QTz2B5XnuxYvaBau2P1UHMH3ePqxNM9rtxD97hyD0++kNA9rtxD97hyD93jyj10jyv30D2u3EP3uHKfv3uPq8IT2OMqUPe40jRN0zRN0zRN0zRN05rKE+/2d6LohisHInIa8Fydh3cppS5owrr/BcbWefgVpdQHdZdVSs1qdshmOp58x6KU2o0xH5emaZqmaZqmaZqmaZrL6IYrB0qpX6k9f9PxrHuHk+M4VWvPp2mapmmapmmapmn/X+keV42zuDuApmmapmmapmmapmmapjVE97jSNE3TNE3TNE3TNE1zJ93hqlG6x5WmaZqmaZqmaZqmaZp2TCJyiYhsEhG7iAw7ynKni8g2EdkpIg85PG4Tkd9FZIf5/9BjvaduuNI0TdM0TdM0TdM0TXMjOYH/tdBG4ELgr0a3RcQK/Bc4A+gLXCEifc2nHwIWKaV6AovM349KN1xpmqZpmqZpmqZpmqZpx6SU2qKU2naMxUYAO5VSqUqpMuBT4DzzufOAD82fPwTOP9Z76jmutL+FK7qf4bIRwSJys1Jqrqte35V0dvfQ2d1DZ3cPnd09dHb30NndQ2d3D5dmH+CSV62m93vDXlsywhUvW82T93tr4Gdte8JmuRKRm4GbHR6a6+S/XRSwz+H3/cBI8+cOSqk0AKVUmoiEH+vFdI8rTTu2m4+9SKuls7uHzu4eOrt76OzuobO7h87uHjq7e+js7qGzay6nlJqrlBrm8K9Wo5WI/CEiGxv4d15jr1lHQ41wqrl5dY8rTdM0TdM0TdM0TdM0DQCl1MktfIn9QGeH3zsBB82fM0QkwuxtFQFkHuvFdI8rTdM0TdM0TdM0TdM0zVnWAT1FJEZEfIDLgf+Zz/0PuNb8+Vrgu2O9mG640rRj8+Rx2jq7e+js7qGzu4fO7h46u3vo7O6hs7uHzu4eOrvWqonIBSKyHxgN/Cgiv5qPR4rITwBKqQrgTuBXYAvwuVJqk/kSzwKniMgO4BTz96O/p1LNHmaoaZqmaZqmaZqmaZqmaS6je1xpmqZpmqZpmqZpmqZprZJuuNI0TdM0TdM0TdM0TdNaJd1wpWmapmmapmmapmmaprVKuuFK0zSthUTEKiJ3uzuHpmmapmlaS4hITFMea23Ma7E57s7x/52IWEQk2N05tL8f3XClaXWIyJCj/XN3vqYSkXEicr35c3sPuuj42N05jpdSqhI4z905/j8SkX+JSLAY3hOROBE51d25mkJEAkXEYv7cS0TOFRFvd+dqCg/P7isiV4rIv0Xksap/7s7VFJ683wFEZH5THmuNRGS2Wdd4i8giETksIle5O9f/JyISKiID3Z2jKUTkuaY81kp91cBjX57wFMfJvBYbKiLi7izNJSJjRSTQ/PkqEXlRRLq6O9exiMhCs34MBDYD20Tkfnfn0v5e9F0FNa0OEVli/ugHDAMSAQEGAmuUUuPcla2pRGQmRvbeSqleIhIJfKGUGuvmaMdk3k71HKVUmbuzHA8ReRpoC3wGFFY9rpSKc1uoJhKRscAsoCvghVHelVKqmztzNYWIJCqlBonIacAdwAzgA6VUq29kFpENwHggFFgNrAeKlFLT3BqsCTw8+y9AHrABqKx6XCn1gttCNZEn73cAEYlzPDZFxAokK6X6ujFWk4hIglJqsIhcAJwP3A0sUUoNcm+yxolIMtDohb5SqtU3AonIUuBcjHNTAnAI+FMpdY8bYx1T3bJuPpbUmve5iPQB+gGzAcdGh2DgfqVUP7cEOw4i8gLQE/iC2tdiX7st1HEQkSRgEMZnjvnAe8CFSqmJbg12DA714zRgKPAgsKE1l3fN83i5O4CmtTZKqckAIvIpcLNSKtn8vT9wnzuzHYcLgFggDkApdVBE2rg3UpPtBlaIyP+ofdHxotsSNc0Y8/9PODymgCluyHK83sP4EFbrg7yHqPpm9UyMBqtED/q2VZRSRSIyHXhNKTVbROLdHaqJPDl7J6XU6e4O0Uweud9F5GHg34C/iORXPQyUAXPdFuz4VPVsOxP4RCmV7QFVzdnm/+8w/1/Vu20aUHTi4zRLW6VUvojciFHHzzQ/3LdKInIbcDvQrU7ONsAK96Rqst4YZSYEOMfh8SPATe4I1Aw2IIva114K8IiGK6BCKaVE5DzgFaXUeyJyrbtDNYG32fv3fOB1pVS5B9SPmofRDVea1rg+VY1WAEqpjSIy2I15jkeZeeJTYAwvcXeg43DQ/GfBuNDzCFUNnh4qTyn1s7tDNNMGEfkNiAEeNhto7W7O1FQiIqMxPkRONx/zlPOyJ2dfKSIDHOt3D+KR+10p9R/gPyLyH6XUw+7O00zfi8hWoBi4XUTaAyVuznRUSqk9YPSqrdPj+iERWUHtL1paKy8RiQAuBR5xd5gmWAj8DPwHeMjh8SNKqWz3RGoapdR3wHciMloptcrdeZpDKXW9uzO00BGzof9qYLzZK9UThoO/jfHFcyLwlzm8Mc+tibS/nVZ/saNpbrRFRN4FPsb4tuYqYIt7IzXZ5yLyNhAiIjcBNwDvuDlTkyilHnd3huYQkQ7AM0CkUuoMEekLjFZKvefmaI2SmjnblogxoenXQGnV854wzBHjw/tgINXsiRIGeMqF67+Ah4FvlFKbRKQbsOQY67QWnpx9HHCdiOzCKO9VQ2M9YUjDXXjufgf4QUQClVKF5vxQQzB6Fexxd7BjUUo9ZM5RlK+UqhSRQjxnbsNAERmnlFoOICJjAE/5QusJ4FdghVJqnVnmd7g5U6OUUnkYH9ivMBsdOmB83goSkSCl1F63BmyaC0RkE0Yj7S8YQ9fuUkq12jlIReQ1jj4s9p8nME5LXAZcCdyglEoXkS6AJ0w4/71S6tWqX0RkL8ZnD01zGj3HlaY1QkT8gNuACeZDfwFvKqVa9TesVUTkFOBUjA9lvyqlfndzpCYxv8V+AGOeBb+qx5VSrXrInYj8DHwAPGLOueQFxCulBrg5WqMc5nNriGrt+xxARBYppaYe67HWSEQuUUp9cazHWiMPz97gRLee0HhSparxx905jpenzt9SxWzwicbhi1+l1EduC9REIjIUeB9jHkaF0bByg4d8OeGRROROjLkjM6jpBewRDeQeOp9b1XC6sUBfjPlGAS7BmGvJY+78bJ6jeiql/hCRAMCqlDri7lxH08icbhuUUkPdlUn7+9E9rjStEUqpEhF5C/hJKbXN3XmOl1LqdxFZg3mci4ittXdTNy3AuOA4G7gVuBZjMtbWrp1S6nOzizdKqQoRadXzRTnM59ZNKZXq+Jz5rXarZTYsBwDtRCSUmrmugoFItwU7Pg9jTCB7rMdaI4/NrpTaIyLjMD4YfGA2lge5O1dTmMME38PI20VEBgG3KKVud2+yJvPU+Vuq7n7YHWOC8Kq6XQGtvuFKKbUBGCTGLerF7BXkEUSkF/Am0EEp1V+Muwqeq5R6ys3RjuUujBvkZLk7SDN43HxuSqkPAUTkOmCyUqrc/P0t4Dc3Rjsu5iiJmzHm6uoORAFvAa3yyziHCf3bisiFDk8F4/Dls6Y5g2640rRGiMi5GN1zfYAYc36rJ5RS57o1WBOIyC0Y3euLMb7pE4wL7FbdGGEKMz/M/Esp9Sfwp4j86e5QTVBoDlOrmldsFJ4zvv9LjCE7jr7AuDNMa3ULxgeDSIxJ5auuqvOB/7opU5OIyBkYHwiiRORVh6eCgQr3pGoaT85eRRzuuorRS9IbY0h4q7/rKvAycBrwPwDzZgQTjrpG6+Kp87eAUWb6Kg8cquCJQ9kdvINxh7u3AZRSSSKyEGjtDVf78JxrgLo8bj43B5EY86NWfVEbhOd8mQXGjRRGAGsAlFI7RCTcvZGO6u8wob/mIXTDlaY1bibGyWMpgFIqQUSi3RnoONwH9FNKHXZ3kGYoN/+fJiJnYUzU3smNeZrqHowPk93NSW/bAxe7N9LRefI3ZUqpV4BXROQfSqnX3J3nOB0E1mPc4n2Dw+NHMIZktGYHMTJ7YvYqnnzXVZRS++r0fmjVPTvr8NT5WwA2Ah2BNHcHaYZ5mEPZzd+3Y/Rs9oSGqwCl1No6Zd4TGslTgaUi8iO1545s7XdI9vT53J4F4h2mQpiIMWTTU5Qqpcqqyrs57USrbSz/O0zor3kO3XClaY2rUErltfbu0Y1IwXNudV3XUyLSFrgXeA2jEaXVfyBWSsWJyESMb58E2FbVVb0V8/hvypRSr3navDNKqUQgUUQWekAZqcUh+8dKKU/48NgQT77r6j6zvCsR8QH+iefcNASzseoroKf50GHgGzdGOh7tgM0ispbaDRGtvhc2HjiU3cFhEelOTW/mi/GMxsO95j8f81+rJyJTlFKLHb/IqnMN/PWJT3V8zOHfv2L06tyCMbn8QfemOi5/isi/AX9zrtrbge/dnKkpPG5Cf83z6IYrTWvcRhG5ErCKSE+MDwgr3ZypqR7GuOX7GmpfYLf6u6oopX4wf8wDJrszy/Ew51y6HeOOZQpYJiJvtebJ/P8O35R58rwzwAgRmQV0xTgfV93drtUO6RWRZGo+QNZ73hMmHsaD77qKMe/fKxjznuzHmLvlDrcmOg6eNn9LHbPcHaAFPHko+x3AXKCPiBwAdmHc5blVq7pDsofdSGEisJjaX2RVUXhAw5WI3Ihx19tOGNcFo4BVQKu/2YzpIYy7JSdjTInwE/CuWxM1zalKqQfMCf33Y0yKvwRjGL6mOYW+q6CmNcK8k8cjONyZD3iyNTdEVDG/EV6OceKruptN9eSVrZmnTsQqIp9j9FSqOklfAYQqpS5xX6qmMeevuIn6vZZa/a2MRWQLnjvvzFaM3oQbcBju1Zon85VG7shXpbXfmU+M1rZOQB888K6rnk5EEjDnb1FKxZqPJbfmu686MueKGm7+ulYplenOPE0lIkMwejD3xxjy2B64WCmV5NZgx8HsGWlp7XdXq+J4IwWllCfeSMEjmV+uDAdWm3dG7AM8rpS6zM3RmsQs5yVKqUrzdyvgq5Rq1aMoRGSTUqqfiLwDfKWU+kVEElvznSg1z6N7XGlaI8yTxCPUzAnhSSqUUve4O0QzeepErL3rnKCXiEii29Icn++AZcAfeNZ8OeDZ887kKaV+dneI49HaG6aOxRwi+K15i26PaawSkQeUUrNF5DUamO/EE3rTmjxq/hZHInIpxnxcSzEaPF8TkfuVUl+6NVgTeOhQdgBE5J46v4PRW2yDUirBHZma6GU87EYKdfd1XZ4wPxdGo0+JiCAivkqprSLS292hjsMi4GSgwPzdH6Nn7Ri3JWoaT57QX/MQuuFK0+oQke85yoW0h8xnsUREbsYYF+84VDC78VVaDU+diDVeREYppVYDiMhIYIWbMzVVgFLqQXeHOB4Ox2kbPGzeGbP3AxjH6RyM4ReO2ePcEuw4iMgRaupJH4w7wxUqpYLdl6rJVov8X3v3HWVZVW97/DsbUSQ0QTFLvISLSFIkCoJZwSsCchEVMSKGNmFWxHhF4YkYEEVERERQJDwkqECTGxoQVOAZCGK8KCgCDQLz/bH2oU8XFU51V9fa+/T8jNGjzt6naoxJUXVqn99e6/fT5rYvqx1kEnp9rC6vmmLRdbV/C5SbWJv3Vlk1b8x+QpnK2kojhm70W1cStlu/9YsyzfHpzP85eTFwGbCvpBNsH1Qt2QQ6OEihM0MqxnGLpJWAHwFnS7qNbvW4WsZ2r2iF7X81O0BabZSG/nfRnYb+0REpXEU81OdrB5gCr2g+fqDvnIHW9s7p06lGrH09f5YGXi3p5uap1YBfVQs2OadJepHt02sHmYQu/54ePOL46X2PTQd6cdhe4A2OpJdStoB1wQ7AmyTdBNzJ/N5ire3PZfvU5uPRAJJmlsNubJvq09X+LVC2qfVvDfwbMKNWmAGN1quopxM9i4BHAZv13sxLOoBSLNyOss26rYWrzg1S6PXlmoikD9j+zOLOszBs79I8/JjKZMEVKc3Cu+JOSZv1bmBJehplFVOrNcW1t1Cufd8IPIGywvO08b4uYjLS4ypiHJIeCaxm+/raWZYUktaiNGLdGriN0oh1r7ZuUep6zx94cPXMcsC9QG/7iDuyeiZaQtIltresnWMiY/3OduR39enAUZSVEQJuB15re27NXEuCZnXkRsBxzak9gKu7tlq1a5o+hhvbvrc5fgRwle3/lHRlr1da20h6NGWQwnMov6tnAbPa3MNwUJKusL3ZxJ8ZkyVpc+B7zF8l9nhgj7a/xks6nlJIfnXTn/aRwMW2N6mbLIZJVlxFjEHSzpRVHQ8H1pS0CfDxlm9Besgo434d2RZwk+3ndKURa/+bXUkrA09mwdfW1r8ZHrl6pktGbFnr+QdlS9W7bf9u+lMNZox+Il3o3TJyC9IMyqqxVt8Jk7RK87DVrykT+Cawn+3zASRtSylktXa1WD9JNzB6j67Wrwa2vb+kXYFtKIWII2yfVDnWQJqm8p8GnmD7hZI2ALayfWTlaIP4LmV778nN8c7Acc01QmtXNdu+Fdirdo7F5KEjZWNK2L6saSjf60d3XUf60a1tew9JewLYvlsaZfRwxCJI4SpibB+jbH05F8D2VZLWqJhnEJ0fZQz8RtKJwDdtt3pZfT9JnwBeA/yW+W/MOrHtC0DSSyhbLwDOtd2V5d2HUO5MfpdykffflGbt11Pe5D+rWrKJdbZ3Cwu+xtwH3Ej7+1nMpfxOirKd4bbm8UrAzcCa1ZIN7o5e0QrA9gVN8bYr+rfFLkMZmb7KGJ/bOrZ/APygdo6F8C1KgbM3bOb/AcdTpt61mu1PSPox8wuG+9ru9XprbWFI0hdHOf0P4HLbJ4/yXJe0+ibFEFgP2IDyGrlp04/u25UzTeTeZpVVr83H2vT17oyYCtkqGDEGSZfa3qJ/Kbqkq9vcB6VH0pq2b5joXBtJWoFSfNiHspLjm8D3bP+zarAJSLoeeGpvO0OXSPofyvjoY5tTe1JW/by/XqrB9H5PR5y7xPaWbR/FLOlMYNe+3i3LU3q37EL5/m9QM9+wknQ4cEqvp5ukFwLPsf3uusnG1tfQ/1XAspTtaqZsV7vNdhen3wIg6QLb29bOMZZevlFWd/Z6o7V+S7Wky2xvPuJ65qqubOORtBTwWPpuuNu+eeyvqE/SEcD6wAnNqV2BX1JWZf/O9jsqRVtkbd6i2XVND7dnUQpXpwMvBC6wvVvNXBNphm18mJL7LEqh+TW2z62ZK4ZLVlxFjO0Xkl4BLCVpHUpjzYsqZxrUD4CR/QdOBJ5WIcukNFsDvw58vRkdfRzwf5pVWJ+w/ZuqAcf2C8rKjb9O8Hlt9CJgE9sPAEg6GriS0ki57R5QGVPfm+zVf3HX9jszq1H6ivX8G1i9WWLfyjuVkg5j/Kmrb5/GOAtrc9v79g5s/7hZMdlmIxv6H9D3uO0/5w/qK8DB/C2mrd6q3CuqdXlLNaXh86OYvxpiS8rqn9aT9DbKz/tfKFP5RPnvaPtNxP8AdrR9H4Ckr1Le0D+XMpygy06Y+FNiIe0GbAxcaXufZptv6wdY2D5b0hXAlpTf0VnNdtmIKZPCVcTY3kZZVn8PZRvSmcAnqyaaQLMv/inAiiN60MykLDluvebO6ospK67WoLxhOxZ4JuXu07rVwo3vM8CVkn5B3/LoNvdEG2El4O/N4xUr5pisvSgNcL9CeTNzCfDKZsn6W2sGG0AXe7f0tuhsQ7mzenxzvDtlK14X3Crpw8B3KD8zr6RMiGst2zvUzjBF+gtwvS2mL68TZXIkHWP7VROda6l3AacAa0u6EFiVBYv8bTYLWK+DTc2fSBl60isQLkfpMXZ/W29M9Ey0zdH2p6c70xLkbtsPSLqvmR77VzowEby50Qzze0hu0GxxnF0rUwyfFK4ixmD7LkrhqktbMNYDdqIUIfp70NwBvKFGoIXwa+Ac4HO2+1e4ndj3h7GNjgY+S7mT+kDlLJPVK7qdQ7lTth3wgbqRBtM0Xx9r5PsF05llspreLacD29KR3i22jwaQ9Bpgh17T2Gb73VkVo03GnpQVHL3G2rObc50g6cWUGxQP3oyw/fF6iQbX8QLcU/oPJD2MDqxiBrB9haTtmd/w+fqONHwG+D0dWR02wkHAVZLOZf7f1U83NyZ+UjPYAJZh9G2Or5O0Q5e3OXbA5ZJWouw8mAv8C5hTNdFg9u97vAylR/BcOtLnNbohPa4ixiDpbGB327c3xytTei09v2qwAUjayvbF4zz/Adufmc5Mg5K0fK/nzxjPtzK7pPNsb187x8KS9HhKnysBl9r+c+VI45L0XtsHjbV1rc1b1iTNtP3Pvil3C7D999HOt0nT022rXtbm9fES2+vVTTbcmgLhssAOlO0juwFzbL+uarABdXGSpqQPAB8EHgnc1TtN2eZ7hO3WF/klvQU4dsT1zJ62v1I12AAkHUkpuP1fFlzNfEi1UAOS9ARKX7rrKCuubunCChRJPwOe17fN8WH0bXNM/8Xp0QyEmmn76tpZJkvSk4GDbHfmplC0X1ZcRYzt0b2LPADbt0l6TMU8AxuvaNXYnbLKpnXGK1o12pp9rqTPULZj9F9cX1Ev0qSs2nxcCti6WeLd5imUvYmTl4/7We30XcrKyN6Uu55e75bWbwsA/of5q/SgTDT9WL04E5N0KuP35+rCtt6tbW/UDAo5UNLBdGNabE/nJmk2N0o+I+kzXShSjeENtr/cO2iuZ95A2WLddjc3/x7e/OsESa+nbHN8EnAVpffPxXRjBUpntzkOg6bVx7aUv1cXAJ0rXAG3ABvWDhHDJYWriLE9IGm13uQaSavToSa4E1DtAIugrdl7E3a27DtnOnCRKumblEa3v2T+NkfT4jfEtk9tPh5dO8tk2d6p+bhm7SwLy/ZRzVTEV1GKiGcAf6ybakKfrx1gCtzdfLyrWc3xN6BLP0ePAjbrm6R5AGWwwnaUQm7rClc9tj/QrFRahwW3abZ+BQ0wQ5LcbLNoekl2oghk+8DaGRbSLMoq5kts79D0IO3Kf0uXtzl2mqSvUBr7H9ecepOk59h+S8VYExqx+n0GsAnw82qBYiilcBUxtg8BF0g6rzneDnhjxTxTqcsFuFZm73jvli27uvRf0rrAeyiN/PtHpbe+YAgg6YnA6iyYvfVvhLu4msD2eRN/Vuud1vQ/OYj5zfBbP3GqT+cmafZ08We+z5nA95utpgb2pRSbW0/SqsB7eWhft7Z/3+fZnicJSY+wfZ2kTmyltn1k03/xGZTC1Qdt925M7D/2V8YU2B7YsK/IfDTdmELZv/r9PuA42xfWChPDKYWriDHYPqMZ3d0b7frOIRrt2tZVS4NoZXZJHx3tfEeaJl8saQPbbZ1kN54TgMMpb97vr5xlUiR9FtiDMkGwl92UZuFt17nVBJK+b/vlkq5h9L5oG1WINVmfB95MmbJ6MXA+8NWqiSani5M0ezr3M9/nfcCbKD87ovQr6krB81jK9NKdKAW3vYH/rZpoMLc0ReYfAWdLuo32r0rtN4PyfX4Y8B+S/qMLN1WGwPWUAv9NzfGT6cBWwS6ufo/uSXP2iHF0dTXERCR9sKvjjNuaXdK7+w6XoVxkX2v7tZUiDayZ1ngq8GdKfy4B7sIbeUlzbXdistdITYPzjWy3eqXJaCRdZntzSVcBW9i+R9JVtjepHG1Mkh5v+0/Ntu+HsH3TaOfbRNL3KVNiv9Oc2hNYyfbL66WaHElPY/4kzQv6JmkiaWXbt1ULN44u/swPg95rfNPXbaPmXKeGoTQTHVcEzrB970SfX1vfTZUF2gd0pA9gpzW7PDZn/iTBzSk3Ke6C9vZiHOuGEB26noz2y4qriDGM9YebFq+GGGvCWk9v0lobCz89zdavrwKPtb2hpI2Al9j+JLQ3u+2D+48lfZ7SqL0LvknpVXQN83/Wu+JUSfsBJ7FgU/zWT+YDfgcsTV/uDuncagLbf2o+tr5ANY71bG/cd3yOpE71EbE9l/nbHEf6KbDZNMaZjM79zPdI2oYyPKF3I673ZrILgyD+3Xz8k6QXU77nT6qYZ9I6uE35pZTXmi7+beq6UVfvd8CPm4/HNB/3ohTbshIrpkxWXEWMoYurISTt3TzcBtiAsrweyiS+ubbfWSXYJDR3m/YHvmZ70+bcL2x3ajpJ08R3ju11ameZiKSfdaBfyKgk3TDK6U68IZP0A2Bjypv1/qLb26uFWggdXE3wMuCzwGMob+B7b+JnVg02AEnfAg63fUlzvAWwt+39qgabIpKu7L3ut1kHf+avA95JKRg+uKXa9t+qhRqQpJ0oW2KfDBwGzAQOtN2VG0OdI+nHwO4DTHmOKdZsm77b9gPNjdz1gR/b/vcEX1qVpAttbzPRuYhFkRVXEWPr3GqI3h5zSa8Bduj9oWsasp5VMdpkLGt7jrRAK6v7aoUZ1Ihl0ksBqwJd6G8FcJ2k71K2C/YXUFo7VbBnosl8kp5r++zpyjNJp9CdVXlj6uBqgoOAnW1fWzvIoPpeX5YGXi3p5uZ4ddrfG2oyWnc3VdIqo5zuNUteHujC6s5/2P7xxJ/WPrZPax7+A+jyEJQuuYsyVbDTN1U6ajbwzObm508pTc/3oKxgarPlJG1r+wIASVsDy1XOFEMmhauIsXX5D/cTgBWYf0G9fHOuC26VtDbNGxhJuwF/qhtpIDv1Pb4P+Ivt1hfcGo+k/Iw/r++cgdYXrgbwWaCVhauJmplK+oHtXacrzxLkL10qWjV2mvhTYjGZS3k9HG0wiIHWr+6kbCn9HOU1vf965op6kQbTTBV8Aw+dHNv6/pEdNhQ3VTpKtu+S9DrgMNsHNX312u51wDclrUh5XfwHkN/RmFIpXEWMrct/uP8HuFLSOc3x9pT+Fl3wFuAIYH1JfwBuoP13mrB9k6SlgMdSXlufIAnbN1eONiHb+4z3vKQP2P7MdOWZYq2cQjmgLrwh7oxmiyDA5ZKOp/Qq6sQKw4735ZqM1v2+TrSqsyO2aD4+ve+cgS5sET+ZslXwJ3RscmxXZUJcVZK0FeW693XNuaUq5hlI07twY0kzKcW3f9TOFMMnPa4ihpSkxzH/YvVS23+umWdQkta0fUOzz3+G7Tt652pnG4+ktwEHAH9hwSk8nZ+kIukK221tmDyuZI8eSUc1D0dbPeOs4Fh8xthu96DeMAVJq7RtsIKkcX8Hu7BqqcsyuXH6SPq+7ZePNSFuGK5n2q6Z8vwe4ELbn5W0FvCOtu/2kPRY4NPAE2y/UNIGwFa2j6wcLYZIClcRY5C0DvAZSpPzZXrnu9D0GR5sDr4OC2Zv7UTEntHerPfGYdfKNAhJv6GMSG99s9vJ6krD5NF0ufjT5extJuloYJbt25vjlYGDU7hafJohCr2C4WrAbc3jlYCb27yqqW/l8mjclcEWzUS+p7DgNUHr+zBK+iRwke3Ta2cZdpIeb/tPklYf7fklaOVna0k6zPbbaucYqWnofxTwIdsbS3oYcKXtp1aOFkMkWwUjxnYUZQXN/6E0BN2HFm5jGI2k1wOzKCOjrwK2BC6mxdsCJK1PuahesW9LD5QJQsuM/lWt8nvKnv5h1OU7HDfWDrAIOvF600Eb9YpWALZvk9TJwmxX9ApTzaCQU3pFCEkvBJ5TM9tEbHe+IXjzfV+Wci3zDWA3YE7VUBOQdAfzi50flHQP8G86NAW0a2z3+onuZ/t9/c9J+izwvod+VUyztk7pe7Tt70v6AIDt+yRla29MqRSuIsb2SNs/laTmLtPHJJ1PKWa13Sxgc+AS2zs0RaEDK2eayHqUBsQrATv3nb+D0pi17X4HnCvp/7Jg35xD6kWaMq0roIwobj5Er1+R7XE/ryZJT2v6QvSf29n2qc1h3iQsHjMkrWz7NnhwG1uuh6bH5rb37R3Y/rGkT9QMNChJywLvAlaz/cZmVfZ6fVPv2mxr2xtJutr2gZIOpuXDN2yvUDvDEuy5PPTvzwtHORfRc6ekRzF/sNKWDO/N3KgkF2oRY5snaQbwa0lvBf4APKZypkHNsz1PEpIeYfs6SevVDjUe2ycDJ0vayvbFtfMshJubfw9v/rWepM/afp+k3W2fMM6njvdcLb3i5mOArYGfNcc7AOfS8jdlja9L2tv2NQCS9gTeAZwKYPusitmG2cHARZJOpFxkvxz4VN1IS4xbJX0Y+A7le/9KoCvbq4+iTBjcujm+hfLa2IXC1d3Nx7skPYHyPW/t9sx+knYBftZr9ixpJeBZtn9UM9cwkvRmYD9gLUlX9z21AnBhnVTREe+iDLRaW9KFwKqUlZ0RUyY9riLGIGlz4FrKCqBPULasfc72JTVzDULSSZStje+gbA+8DVja9otq5hqEpGUok1RG9uLodP+ZNvYlaBqwbkZp3t/JXkqSTgPe0NviIOnxwJfbvNKqp2m6eiJletC2wKuBnTKNZ/FrGsfuSFlN+FPbv6ocaYnQrG47ANiOUriaDXy8bQ3ZRyPpcttP7+/5J+nntjeunW0ikj4CHAY8G/gy5Xv/DdsfqRpsAKM1Z+9y38U2k7QisDKlv+v7+566owu/o0uCNv7sNxO13055jVmP8nf1etv/rhoshk4KVxFDTtL2wIrAGbbvrZ1nIpJOAK4DXgF8nPKm/lrbs6oGW0RtbLQt6XPAG4HlgLtoeof0Pnahh4ikX9jesO94BnB1/7k2k7Qu8CNKj7SX2r57/K+I6D5Jy9v+V+0ckyHpIkrh50Lbm0laGzjO9jMqR5sUSY8AlulKgbzZ3rjRiHPXpOnz1JM00/Y/x5oCmuJVfZJeY/tbtXOMJOlc28+qnSOGWwpXEWOQdDaw+4jJU9+z/fyqwQYkaVtgHdtHSVoVWN72DbVzTaR3N6l3sSppaeDMrkxuGksbC1c9kk62/V+1cywMSV+iTM88jlJ0+2/gN21b3dZvlFHjj6H0grgHMnI8hpekrSnNwZe3vZqkjYE32d6vcrQJSXou8GHKpOGzKE2SX2P73Jq5BiHpLcCxI65n9rT9larBBiDpm8DtzF8p9jZgZduvqRhrKEk6zfZOI6aA9rgrU7W7TNKpPHQgzj+Ay4Gv2Z43/akmJulTlJvkxwN39s7bvqJaqBg6KVxFjGG05bhtXKI7GkkHAE+nNI5dt+lpcYLttk4jeZCkObafIWk2pdfCn4E5Xb9ganPhCkDSYykN/aFsHfzfmnkmo2nU/szmcLbtk2rmmchYo8Z7MnI8hpWkSyl9T07p2263wKrJNmuaD29JeUN/ie1bK0caSJe320laDvgI86dPngV8yvadY39VRDdJOpTSH+q45tQelOvgRwIzbb+qVrbxSDpnlNPu+k3naJc0Z48Y2wOSVrN9Mzz4ZrMrld5dgE2BKwBs/1FSVyb0HNHcDf4IpdHj8sBH60aaEq2bzNcjaXfg85Sm5gIOk7S/7ROrBhtQM0GwC83YgQULU6OtjKyXLGLxs/17aYGXw06MTO9rEv5/m+OVJL20I03CZzQTknsTv5aiI0NEmgLV+8d6vo39I4dBcx22Dgv2Gp1dL9ESY1Pb2/Udnypptu3tJP2yWqoJ2N5hvOebQTRHT1eeGE4pXEWM7UPABZLOa463o/QD6oJ7bVtS7yJ1udqBBmX7G83D84DOrLKStIbtG0ec29z2Zc3hodOfamAfpoyp/ytAU0D5CaVxeKs1q60+S9luJ7rVn+vBlZGUiWVLU6attX5lZMRC+n2zXdCSHk5p6Htt5UyDOqB/Naft25vf4R/VizSwM4HvSzqccgNuX+CMupGmTF4vp5ik1wOzgCcBV1FWGV5MGWgRi9eqI26arwY8unmu9X1qxzELSOEqFkkKVxFjsH2GpM2Yvy3gnf3bAiQ9xXZb7358X9LXgJUkvQF4LfD1ypnGJeld4z1v+5DpyrKQfihpZ9t/gAeb4n8JeCpAG5tp9pnRK1o1/gbMqBVmkg4CdrbdlTe//bq8MjJiYexLKeI/EbiFsu2r9f2tGqO9JnblOvp9lBtvb6Zcz5xF6TUWMZpZlNYBl9jeQdL6wIGVMy0p3k25af5byu/qmsB+zQ3oLhd+WrvrILqjK39wI6poClWnjfH0MUArexbZ/nzTSPaflNUcH7V9duVYE+m9YV+PcsF0SnO8M2Vketu9CfiRpJ0pPxefBl5UN9LAzpB0Jgv2VDi9Yp7J+EtHi1bQ4ZWREQtpPdt79Z+QtA1wYaU8k3G5pENYsEn43LqRBmP7AeDw5t9DSPqB7V2nN1W02Dzb8yQh6RG2r5O0Xu1QSwLbp0taB1ifUuy5rq8h+xeqBVt0XWm1Ei2W5uwRC6krjU27RtJZwK6272iOV6A0ln9B3WQTk7QV8DVgHvDiDjY435ZyodT6Buc9TSPTx1G269zTO9/0vWo1Se+h9BB5LvAZysrI79o+rGqwiMVktCEVbR9c0TOiSXhv1dInh6FJeJevZ7qcva0knQTsA7yDsj3wNmBp2125GddpzXbqNehbYGL729UCTYH8nsZUyIqriIXXuqqvpDsYPVdn+v4Aq7HgPv57KX/AW2mU0cXLUkYXHykJ2y+pk2xyxmtwLuli21tNc6RBzQTuAp7Xd850oFl7R1dGRkxaU9TfmtK/pX9b+ExgqTqpJmeiJuEd17rrmUloc//ITrK9S/PwY820uBUZnp5orSbpGGBtSm+x3uAKA50uXNGNVbXRcilcRQwR28PQH+cYYE5zx8+UPkBt3tf/+doBpsEyE39KHbb3qZ1hEf0/SlH5J5KWlbRCb7VhxBB5OGVi5sOYvy0cStF2tyqJJknSusB7eOhKiDSsXoxGuTkE5ebQ5cDXWt4/spMkfRw4H7jI9nkTfX5MqacDG/QmgLbdoP1pbb91ehLFMEvhKmLhdXm6R2vZ/pSkHwPPbE7tY/vK3vOSVrZ9W510D9V/USdpdWCdXhGCjqwkGEBrL6CaN5NfBR5re0NJGwEvsf3JytEm1AxOeCOwCuUO6xMpPWieXTNXxFRrXifPk/Qt2zfVzrOQTqD8fn6D+SshhkWbGyf/DliVBXsw/gVYlzJ05lWVcg2zG4E9gS82K/nPp7QQOLlqqiXDLyjtD/5UO8iAhuGGeXREelxFjKFpGHuV7TslvZLScPvQDl90D4W29kPpL0LYXrtprnm47c4XIdr6PQeQdB6wP+XO+6bNuV/Y3rBusolJugp4BnBpX/ZrbD+1arCIxUTSqsB7gafQt5KzC6uWJM21/bTaORaGpFm2Dx3rnKTn2T6rTrrxSZpte7vRzkn6pe2n1Mo27CQ9Dng5ZaXhykOyqr/Vmq2ZmwBzWLBvZyfaTkQsTllxFTG2rwIbS9qYcqF9JGWP+fZVU0Vb7wy/haYIAWD715IeUzfSlGnr9xxgWdtzpAUi3lcrzCTdY/veXnZJD6PFq9sipsCxwPHATsC+wN5AV4ZYnCppP+AkFnxD+fd6kQa2Nw/tBfWa3rm2Fq0aq0pazfbNAJJWAx7dPJeV74uBpG8AG1BWtp1P2c57RdVQS46P1Q6wMCQtA7yOh96UeG21UDF0UriKGNt9zaj6/6KstDpS0t61Q0Vr39h3tggh6a3AseNswWzzVoxbJa1N872WtBvdWWJ/nqQPAo9smrTvB5xaOVPE4vSo5m/prL7tg13podP7+79/3zkDa1XIMhBJewKvANaUdErfUysAf6uTatLeDVwg6beUmyhrAvs1Ux7b3P+yyx5FaXVwO/B34FbbXbkh1Gkd7il2DHAd8Hzg48BewLVVE8XQSeEqYmx3SPoA8EpgO0lLAUtXzhTt1eUixOOAyyRdAXwTOLO/MajtX1RLNrG3AEcA60v6A3AD5YKpC95PuUN5DfAm4HRK/5yIYfXv5uOfJL0Y+CPwpIp5BmZ7zdoZFsJFlEL+o4GD+87fAVxdJdEk2T692Xq/PqVwdZ3tec3TX6gWbIj1pgpK+k9KIeIcSUvZ7sTvahdJusD2tqNMB+/KVPD/sL27pP+yfbSk7wJn1g4VwyU9riLG0OztfwVwme3zm+Xpz7Ld9ZG0nSbpyl4/oDaRNINShHge5ULjTOAbHZoMI0r2fShTbb4PHGn7t1WDTUDS02zPbe6+z7B9h6Sdbbe+aChpR+AS23fVzhIxHSTtRNl69GTgMGAmcKDtU8b9wpaQtCFlC1X/VphOXBNIeiyweXM4x/Zfa+aZDElb89Bpjp34vndR83v6TGA7YGXgYuB829+sGixaS9Ic28+QNJty4/bPlNeZ1q5Ije5J4SoiWkHSKuM93+sjImmVtvYUkfRIYDXb19fOsjCafm77AC8AzgG2BM62/d6qwcbRrBLb2/Y1zfF/A++0vUXdZBOT9G3K9/hvlDfz5wMXtGlqZkQUkg4AnkUpXJ0OvJDy+7pbzVyDkLQ78HngXMqNlWcC+9s+sWauQUg6hjJ19SrmT3O07bdXCzXkJH0ZmE0pVv2xdp4liaTX2T5yxLn/sf3+WpkGIen1wA+AjYCjgOWBj9o+vGqwGCopXEWMMATLdTtJ0g2U7/dojcDd9rs2kl4CfA54uO01JW0CfLwLk2AkvZ3Sv+VWyla1H9n+d7OK7Ne2164acByS1gJOpGwP3BZ4NbCT7X9UDTYJkp5AaX77HuAJtrONP4aSpHUpg08ea3tDSRsBL7H9ycrRJiTpGmBj4ErbGzcrmL5he+fK0SYk6efAc3urrJrpjj+xvXHdZBOTdC2wQVdWLy8JJF1se6vaOYaRpB8D37F9bHP8FWCZNDmPSI+riIewvW3zMWN/p1FH+4f0O4AyVfBcANtXSVqjZqBJeDTwMts39Z+0/UCzZaC1bP+uWWX1I+D3wPNs31031WAkvZKy8uGplKLhlyirriKG1dcpzc2/BmD76qYXSusLV8DdzWvifZJmAn+lxY3ZR5gxYmvg34AZtcJM0i8ofRi7MnRjSbDMxJ8SC+llwCmSHqCs6vy77f0qZxqTpFfa/o6kd432vO1DpjtTDK8UriKidSStDKzDgn1EZtdLNJD7bP+jN1WwS2x/FEDSY1jwe36z7VZOhWlWP/TfgV+FMgXpUknY3qhOskn5AvBb4HDgHNs3Vk0Tsfgta3vOiNfJrkwru1zSSpTi21zgX8CcqokGd4akM4HjmuM9KNsdu+DRwK8kzQHu6Z3swmrmIZbVb1NsRLuM11Nuxl0IfLzNLTKA5ZqPudkfi122CkZEqzT75GdRJk1dRekBdLHtHWvmmoikI4GfUibF7Qq8HVja9r5Vgw1A0s7AIcATKKsIVgeutf2UqsHGIWn18Z4fuXqsrSQ9hdIAd1tKsfZ626+qmypi8Wi2wbwVOMH2ZpJ2A15n+4WVo01Ks5p2pu1OTOYDkLQrsA1lO/5s2ydVjjQQSduPdt72edOdJQpJV9jerHaOYdLXLuPBU32PW98uI2I6pHAVEa3SrKTZnDJtbRNJ61OmTu1ROdq4JC0LfIj5UwXPAD7ZN7a7tZr+JztSep5sKmkHYE/bb6wcbSCStgXWsX1U07tleds31M41kWa70TbA9pQtg4+m/NzvXTVYxGLS9KQ7AtgauA24AdirC4VmSbsAP+v1z2tWXz3L9o9q5oqYbm2d7tx1TV/RrWxfWDvLZEk6Gphl+/bmeGXg4PTmiqmUwlVEtIqky2xvLukqYAvb90i6yvYmlaONS9Jatn9XO8fCkHS57ac3BaxNmz4uc2w/o3a2iTSTvp4OrGd73abR+Qm2t6kcbUKSrgYuaP7Ntn1L5UgR00LScpS+S3fUzjKo0f4OdeUNvKSXAZ8FHkO5sdL6YTMZlNNekja0/YvaOYZRVxvfj/Za2JXXx+iO9LiKiLa5pbmT/SPgbEm3AV0Yx/wtSU8ELmP+GOlrKmca1O2SlqfkPlbSX+lO35ldgE2BKwBs/1FSJ3otTNSHS9Jhtt82XXkiFjdJj6IMstgWsKQLKNNX/1Y32UBGa2belevog4Cd29qzcDQZlFPPRIXOFK0Wq7Oabb0/7NgkzRmSVrZ9GzzYs6srr4/REVlxFRGt1fS2WBE4w/a9tfNMRNLDKdscnwW8ibJlbZVxv6gFmtUP8ygXp3tRvufHduHNZG9lWK/nRvPfcnFHmrOPK31EYthIOptSIP9Oc2ovyna759RLNRhJ3wRuB75MWQH0NmBl26+pGGsgki7swirU0Uh6ne0jR5z7H9vvr5Vp2En6DR0rdA6LZoXhcpSbh73rstavMJT0auADwImU18eXA5+yfUzVYDFUUriKiFaRdChwvO2LameZjKbP0jObfytRGsufb/u4cb4sFpGk91Camj8X+AzwWuC7tg+rGmwKpHAVw0bSXNtPG3HucttPr5VpUE1R/CNAr8h2FuWN2Z31Ug2m+bv6OMpK5v7JfD+slWlQTUP/79g+tjn+CrBMeucsPl0udEY9kjag9EsV8FPbv6ocKYZMClcR0SqS9qaM6l4XOIlSxLq8bqqJSbofuJxSPDm9IyvERvYOWUDb7/D1SHou85vin2n77MqRpkQKVzFsJH2e8jr5/ebUbsBTbB9QL9XUaPPWXklHjXLaXSj+SHokcArwTeCFwN9tv6NqqCHX5ULnMGgam68DLNM7Z3t2vUSD6eqgnOiOFK4iopWa/fG7Av8NrGZ7ncqRxtX05doG2I6yXfABypa1j9TMNQhJHwf+DBzD/O2CK9g+qGqwAUlanXKx9JNmuuNSXWr6PJY0No1h07cN5gFK0XwpoLdiqfXbYcbT5UKzpA/Y/kztHP2aa4CeFShFlAuBjwLY/nuFWEuELhc6u07S64FZwJMoK/e3pFxL7lgz10S6PCgnuiNN0yKirf4DWB9YA2j9cmPbt0v6HfBkygXH1sDSdVMN7Pm2t+g7/qqkSykNfVtN0huANwKrAGsDTwQOB55dM9cUObR2gIiplEbbrbU7ZbVwm8ylFDfV9/HFzT8Da9WLNtxs71M7wxJsFuXm5yW2d5C0PnBg5UyD6OygnOiOFK4iolUkfRZ4GfBbynaST9i+vWqoAUj6LXA9cD6lcLJPF7YLNu6XtBfwPcobgj2B++tGGthbgGcAlwLY/rWkx9SNNBhJ6wL7A6vT9/e4d2fV9rfqJItYPCT1VnSuafsTkp4MPN72nMrRlnSqHWAk22vWzrCkkfRe2wdJOoxR2gjYfnuFWEuaebbnSULSI2xfJ2m92qEGcK9tSzI82BMwYkqlcBURbXMDsJXtW2sHmaR1bD8w1pNt3IrR5xWU1T2HUi5WL2zOdcE9tu8t74dB0sMYp29Xy5xAKXJ+ne4UCiMWxVco2wR3BD4B/IsypW/zmqGmSOuKP5PQ2tdMSUsDb6Zswwc4F/ia7X9XCzW8elMEW99XdIjd0rSe+BFwtqTbgD9WTTSY70v6GrBSsxL+tZRrm4gpkx5XEdEKktZv7iyN2iPE9hXTnWkqpf/J4iHpIMqI+ldTxtPvB/zK9odq5hrEaBPWIoZZ73Wwv3+bpJ/b3rh2tsmQNIPSePiffede09VVkm3upyfpG5Rt90c3p14F3G/79fVSLdnaPIhgmEjaHlgROKMLK/j7BuUAnDUsg3KiPbLiKiLa4l2UXkUHj/KcKXfou6zLd+Pb2P+k5/3A64BrgDcBpwPfqJpocKdK2o8yPbN/clOaDsew+rekpWhW+DSTp8Zcqdomkr4L7EtZHTkXWFHSIbY/B53f2ntC7QDj2HxEYfNnkn5eLU1AGUQTi0lzA3dbmhXwXShaNa4BHknJfU3lLDGEZtQOEBEBYPuNzV3sD9veYcS/rhetoMVbMQbQ5qLbs4Bjbe9uezfbX3d3lhLvTelxdRHljfBcskUjhtsXKYXax0j6FHAB8Om6kQa2QbPC6qWUAvlqlNU/rSfpIEkzJS0t6aeSbpX0yt7zttv8/+B+SWv3DiStRbZWx5CS9FHK6sJHAY8GjpL04bqpJtZMQ5xD6VG7G3CJpEyhjCmVrYIR0SqSLra9Ve0cU63NWzEm0uZtjpK+TRkX/TdKY/zzgQts31Y1WEQsoLkxsSXwd8rUTwE/tX3tuF/YEpJ+CWwCfBf4ku3zurLNUdJVtjeRtAul8PZO4JyOZH82cBTwO8rPzOqU4SfnVA22BGvzNUHXSboW2NT2vOb4kcAVtv+zbrLxSboe2Nr235rjRwEX2e5CY/noiGwVjIi2OUvSrsAPO7RyZhBt3ooxkdauuLL9agBJT6Dc5fsy8ARa/PdN0o62fybpZaM9b/uH050pYnGz/YCkg5sbE9fVzrMQvgbcCPwcmC1pdeCf435FeyzdfHwRcJztv/cGWrSd7Z9KWgdYj/K36Drb90zwZbF4deOHp5tuBJYB5jXHj6BM2W67W4A7+o7vAH5fKUsMqdZe2EfEEutdwHLAfZLmUS6QbHtm3Vjjk7Qu8FXgsbY3lLQR8BLbn4TWb8WYSGuLbs12l2cCTwVuBb5EWXXVZtsDPwN2HuU5AylcxbDq7I0J21+kbHXsuUnSDrXyTNKpkq4D7gb2a3qLzZvga1qhmSr4JvqmCkrKVMFpMtogAsoE4lg87gF+KelsyvXAc4ELJH0RwPbba4Ybxx+ASyWdTMn9X8AcSe8CsH1IzXAxHLJVMCJao7lA2sr2hbWzTJak8yj9ir7WNy3rF7Y3rJtsYhMV3dpM0q2Uu5GHU7a+3Fg30dSRtLftoyf+zIhukHQHzY0JSuGkEzcm4MHeMw9h++PTnWVhSFoZ+Kft+yUtB6xg+8+1c00kUwWn32iDCIAHBxHE4iNp7/Geb+s1gaQDxnve9oHTlSWGVwpXEdEqXe1xJeky25uPGPN+le1NKkebUJeLbgCSnkK5G78tsA5wve1ONE0eT/qIxJJG0lNs/7J2jtFIenff4TLATsC1tlvfgHiMbcn/AK6x/dfpzjMZo/UR60pvsa7q64m2F/A04H3AXNsbVY62xJP0A9u71s4xnjFW6UUssmwVjIi26epWklubyUe9Me+7AX+qG2lgy9qeM6LnyX21wkyGpJmU6V6rA2tQ7gw/UDPTFEofkVjSHAO0slhr++D+Y0mfB06pFGeyXgdsBfQamj8LuARYV9LHbR9TK9gA7pe0tu3fQqYKTpOlmy2aL6UMIvi3pC5djw2ztWoHGM1oq/QkZZVeTKkUriKibTrZ4wp4C3AEsL6kPwA3AHvVjTSwLhfdLuj79yXbt1TOM5XyRiGWNF0q1i5LS99EjuIB4D9t/wVA0mMp28O3AGZTCoZttT9wjqTfNcdrAPvUi7NE6PIggmHX1uuCDWz/s1mldzrNKj0ghauYMilcRUSr2F5B0iqULV/L1M4zCbb9nKZ3yAzbd0has3aoAXW26DbR1gVJh9l+23TlmWJdehMfMRXa+qYMSdcwP99SwKpAJ/pbAWv0ilaNvwLrNtMF297k/EJKIeXZzfHXgIvrxVkifLkZRgCApJuBrgwiiDqySi8WuxSuIqJVJL0emAU8CbgK2BK4iPkXrW31A2Az23f2nTuR0h+i7bpcdJvINrUDLILODSmIGGI79T2+D/iL7U5sqQbOl3Qa8yfE7kpZSbMccHu1VIP5NmW1zyea4z0pK8R2r5Zo+P1G0gnAUbavbdo2dOVnfdi19YZWVunFYpfm7BHRKs1d7c2BS5rmoOsDB9reo3K0UTX5ngIcRNnS0DMT2N/2U6oEm4TRmoBLmmu7C0W3cbW5wbmkWcBRwB3AN4BNgffbPqtqsIhKJF1ie8vaOcYiaWPgmc3hbNtX18wzKJUGhrtSCvmibK3+QRf6SKY5+/STtALw35QtmTOAbwLfS7PtxUvSUsDRtl85zuc8rwvXCM1rzlK94n6mJMdUyIqriGibebbnSULSI2xfJ2m92qHGsR7lTvxKwM595+8A3lAj0KD6im4rjpg6NZNubdPsqtfaPlTS8ynbjvahFLJaf1EasTAkbQNcZftOSa+kNGI/1PZNAC0vWs2ivKb/sDl1rKQjbB9WMdZAmgLVic2/rrlS0pa2LwGQtAVZjbpY2b4D+DrwdUnbAccB/0fSicAnbP+masAhZft+SatKerjte8f4nE5cH4yySm8WkMJVLJIUriKibW6RtBLwI+BsSbcBf6yaaBy2TwZOlrSV7a713ehs0W0S2rqsHuZnexFlS8bPNWK0Y8SQ+SqwcbNy6b3AkZStYNtXTTWY1wFb9LaDS/ospddS6wtXzY2JzwKPobzutH7oSV9PsaWBVzd9lkyZIPurmtmGXbPy58WUmylrAAcDx1JWG54OrFst3PC7EbhQ0inAg60nbB9SLdHUyLVNLLIUriKiVWzv0jz8mKRzgBWBMypGGtSVkt5CWcH04Gol26+tF2l8HS+6DerQ2gHGMVfSWcCawAea7RkPVM4UsTjdZ9uS/ouy0upISXvXDjUgUUa999xPd96MHQTsbPva2kEmYaeJPyUWk18D5wCfs31R3/kTmxVYsfj8sfk3A1ihcpap1PptydF+6XEVETEFmkam1wGvoEya2gu41vasqsEGIGkZymqCzhTdeiStS+kttjp9N2Ns71gt1IAkzQA2AX5n+3ZJjwKe2JW+ORGTJek8yo2IfYDtgP+lbB18atVgA5D0LmBv4KTm1EuBb9n+Qq1Mg5J0oe0uD6qIaSRpedv/qp0jhoekK21vWjtHdFsKVxERU6D3R1nS1bY3asYCn9mRAkqXi24/Bw4H5tK3GsL23GqhBtT0CzkK+LHtrLSKoSfpcZTXmctsny9pNeBZtr9dOdpAJG0GbEtZaTXb9pWVIw1E0qHA4yhb8O/pnbf9w7G+JpZcXb6Z1XWSVqVsox75vW/ltaSkWU2vzm1sj9l7TtKXbL91OrPF8EnhKiJiCkiaY/sZkmYD+wF/BubYXqtytAl1vOjW2emHkp5DWXmyJWVM/bdsX1c3VUT0kzTT9j8lrTLa87b/Pt2ZJkvSUaOcdgoRMZou38zquqZ9wPHAe4B9Kas8/9f2+6oGG4Okq5oJ4K2d4BzDI4WriIgpIOn1wA+AjSiraJYHPmr78KrBBtDxotvHgL9Stu/0ryRo/ZvJHkkrAnsCHwJ+T5nm9B3b/64aLGKKSLrA9raS7mDBXiddaBJ+mu2dJN3A6Nlb/zoZMRldvpnVdb2bcb3vfXPuPNutHGAh6ThgK8pk5N/2P0V5fdyoSrAYSilcRUQs4TpedLthlNOdeTPZ9LV6JfAqSkPWYylbkZ5q+1kVo0VEx0l6r+2DJB3GKM2Rbb+9QqxouS7fzOo6SZfY3lLSmcAXKdcFJ9peu3K0MTVbwM8EXjLyOds3TX+iGFaZKhgRsQiahr1j6sIIY9vfaB6eB3TqwtT2mrUzLCxJPwTWB46hTPz6U/PU8ZIur5csIkaSdDLwPeBk23fVzjOg3hTBvJ7EZBwhaWXgw8AplJtZH6kbaYnxyWYV9ruBw4CZwDvrRhqf7T8DG0t6OLBuc/r6rBqPqZYVVxERi0DSAc3D9YDNKRd5ADtTmve+vkqwAXS56CZpR9s/k/Sy0Z7vQtPh3n9D7RwRMTFJ2wN7AC8G5lD60Jxme17VYAOQtIbtG0ec29z2ZZUiRQuNcU2g5qPbfE0QdTWvj98GbqT8zDwZ2Nv27Jq5YrhkxVVExCKwfSA82FBzM9t3NMcfozTcbrMVmo+jFt2qJBrc9sDPKFlHMtD6wlVTeNsQ2IAFpwd1YsJaxJLE9nnAeZKWAnYE3gB8k7Iiou1+IOkltv8AD77J/BLw1LqxomW6fE0wFCQdDcyyfXtzvDJwcAcGKRwCPM/29QCS1gWOAzo5PCfaKSuuIiKmgKTrgI1t39McPwL4ue316yabWFN027Wv6LYCcILtF9RNtugk7W376No5RtOs1nsWpXB1OvBC4ALbu9XMFRGjk/RIypv4PYDNKCuu3lY31cQkbQ58hZJ9M+DTlO3Jv68aLFppmK8J2q7XGH+ic23T30x+vHMRiyIrriIipsYxwBxJJ1FW/OwCtLJgMorVgHv7ju8F1qgTZcrNor3/H3YDNgautL2PpMcC35jgayKiAknHA1sAZwBfBs61/UDdVIOxfZmktwNnAfOA59r+38qxor2G+Zqg7WZIWtn2bQCSVqEb79cvl3Qk5VoYYC9gbsU8MYS68IsQEdF6tj8l6cfAM5tT+9i+svd8/4VIC3W56DYRTfwp1dxt+wFJ90maCfyVjjXHj1iCHAW8wvb9tYMMStKpLDhNcFngH8CRkrD9kClgEQz3NUHbHQxcJOlEyvf+5cCn6kYayJuBtwBvp1x3zaas8oyYMtkqGBExDSRdYXuz2jnGImkz5hfdZneo6DauNn/fJX0F+CDw35QJQv8CrrK9T9VgEfEQkpYF3gWsZvuNktYB1rN9WuVoY2p6WY2p6dsV8RDjXRPE4iVpA0ofPQE/tf2rvuc6eT0m6Qe2d62dI7othauIiGnQhR4FY2lz8WciXfm+S1oDmGn76tpZIuKhmq2Cc4FX296w6Xd1se1N6iYbTLMVefPmcI7tv9bMExGT19Xrsa5ci0W7zagdICJiCdHluwRt3m43kQtrBxiLpJ/2Htu+0fbV/eciolXWtn0Q8G8A23fTkddGSS8H5gC7U7YeXSopQyAiuqcTrzmj6PI1cLREelxFRMREWnvBIWkWpffMHZTG5psC77d9FoDtt1aMNypJy1B6zTy6GXXduxCdCTyhWrCIGM+9zSorA0haG7inbqSBfQjYvLfKStKqwE+AE6umiojJau31WMTilsJVRMT06OpdsrZ7re1DJT0fWBXYh1LIOqturHG9CXgHpUg1l/KzYUrx7Uv1YkXEOA6gTBR8sqRjgW2A11RNNLgZI7YG/o3suoiI6ZNr4FhkKVxFRCyCZlTxmGz/vXn47GmIs7i0+YKjl+1FwFG2fy6pzXmxfShwqKSPAl+w/U9JHwE2Ay6umy4iRpI0A1gZeBmwJeV1Z5btW6sGG9wZks4EjmuO9wBOr5gnIhZOq69vxvG+2gGi+9KcPSJiEUi6gbJaZrSLCdtea5ojDWzQopukVfoKcK0i6SjgicCawMbAUsC5tp9WNdgAJF1teyNJ2wKfpozB/qDtLSpHi4gRJM22vV3tHAtL0suAbWlG1ds+qXKkiGh09XpM0jWMvn1RlGvgjaY5UgyxFK4iIpZQXS669TQrITYBfmf7dkmPAp7Yhel8vSk7kj4DXGP7u5m8E9FOzarIu4HjgTt759v0JnI8zVTBZ1Be8zNVMKJFRlyPrQbc1jxeCbjZ9pr10o1N0urjPW/7punKEsMvhauIiCnSNNpeB1imd8727HqJhp+kEyk9rX5s+4HaeSZD0mnAH4DnAE+jvCmeY3vjqsEi4iH63lguoCMF/pcDnwPOpbwZfiawv+00Z49oEUmHA6fYPr05fiHwHNvvrpssor4UriIipoCk1wOzgCcBV1H6oFxse8eauQbV1aKbpOdQGrJvCZwAfMv2dXVTDUbSssALKKutfi3p8cBTexMRI6I9momC+1G22xk4Hzjc9t1Vgw1A0s+B546cKpgieUS7SJo7stWBpMttP71WpkFI2hI4DPhP4OGUtg132p5ZNVgMlUwUiYiYGrOAzYGbbO8AbAr8b91Ig2mKbrOBM4EDm48fq5lpULZ/YnsvSmPzG4GzJV0kaR9JS9dNNz7bd9n+oe1fN8d/StEqorWOprwp+yLz36AdXTXR4DJVMKIbbpX0YUlrSFpd0ocov69t9yVgT+DXwCOB11NeJyOmTKYKRkRMjXm250lC0iNsXydpvdqhBtQrul1iewdJ61MKWJ3Q9LV6JfAq4ErgWMqqiL2BZ9VLFhFDZL0RK5TOaVYydUGmCkZ0w57AAcBJlJWds5tzrWf7N5KWsn0/cJSki2pniuGSwlVExNS4RdJKwI8oq35uA/5YNdHgOlt0k/RDYH3gGGBn239qnjpe0uX1kkXEkLlS0pa2LwGQtAVwYeVMA7G9/4ipgkdkqmBE+zTDHmZJWt72v2rnmYS7JD0cuErSQcCfgOUqZ4ohkx5XERFTTNL2wIrAGbbvrZ1nIpJOovSJegewI2WazdK2X1Qz1yAk7Wj7Z7VzRMRwk3QtsB5wc3NqNeBa4AE6MPZd0uOALSh5L7P958qRImIESVsD3wCWt72apI2BN9ner3K0cTXTBf9C6W/1Tso18Jdt/7ZqsBgqKVxFREwBSYcCx9vu9NLorhXdACRtCGzAgo3lv10vUUQMmy6PfW/6GH4U+BllxdX2wMdtf7NqsIhYgKRLgd0okwU3bc79wvaGdZONT9Is24dOdC5iUaRwFRExBSTtTekbsi6lN8HxtjuxVa3LRTdJB1D6WG1A6dnyQuAC27vVzBUR0RaSrge2tv235vhRwEW2O7ElPGJJIelS21tIurKvcPXztk8AlXSF7c1GnHvwvyFiKqTHVUTEFLB9NHC0pFWAXYHPSlrN9jqVow3iCuDDkjpXdKPcmdwYuNL2PpIeS1lmHxERxS3AHX3HdwC/r5QlIsb2+2a7oJueUW+nbEluJUl7Aq8A1pR0St9TM+nGNMTokBSuIiKm1n9QmoWvAfyqbpTBdLzodrftByTdJ2km8FdgrdqhIiJqk/Su5uEfgEslnUyZVPZfwJxqwSJiLPsChwJPpBSczwLeUjXR+C6iNGJ/NHBw3/k7gKurJIqhlcJVRMQUkPRZ4GXAb4HvA5+wfXvVUJPXuaIbcHkzzfHrwFzgX+QNWUQEwArNx982/3pOrpAlIsYhaSngC7b3qp1lUE1vv5uArZoV75s3T11r+756yWIYpcdVRMQUkLQvcKLtW2tnmaxRim4/7GDRDUlrADNt5y5fRMSAJB1m+221c0Qs6SSdCezcleE4PZJ2Bz4PnEsZAPFMYH/bJ9bMFcMlK64iIhaBpPVtX0dZ5bOapNX6n7d9RZ1kk3IDsFVHi24/tf1sANs3jjwXERET2qZ2gIgA4EbgwqZf1J29k7YPqZZoMB8GNrf9VwBJqwI/AVK4iimTwlVExKJ5F/BGFtzb32Ngx+mNM7guF90kLQMsCzxa0sqUO3xQGoI+oVqwiIiIiIXzx+bfDOZv9e2CGb2iVeNvlP+GiCmTrYIREYtI0gzKiqULa2eZDElH2H6jpHNGedq221x0mwW8g1Kk+gOlcGVKQ9AjbH+5XrqIiO4YbZR9RMSgJB1EmfB8XHNqD+Bq2++rlyqGTQpXERFTQNLFtreqnWOyulp065H0UUoz039K+giwGaUxfmtXi0VEtImkK21vWjtHxJJK0hdsv0PSqZSbcAuw/ZIKsQbW9Eq9FNiWciNxNrBlClcxlVK4ioiYApIOpIz+/aE79sLa1aIbgKSrbW8kaVvg05Qtmx+0vUXlaBERrdPcrFje9j/7zr3G9rfqpYpYskl6mu25krYf7Xnb5013pskYbdVm7/qsVqYYPilcRURMAUl3AMsB9wHzaLau2Z5ZNdgAOl50u9L2ppI+A1xj+7tZPRARMZ+k7wL7AvcDc4EVgUNsf65qsIjoNElvBvYD1qJMpu5ZAbjQ9iurBIuhlMJVRMQiGoLtdl0uup1G6XH1HOBpwN3AHNsbVw0WEdESkq6yvYmkvSivk+8D5mY1RES7SLqB0bcKrlUhzoQkrQisDHwGeH/fU3fY/nudVDGsMlUwImIR2X5A0ueBzm23a4puL+hq0Q14OfAC4PO2b5f0eGD/ypkiItpkaUlLAy8FvmT735Jy5zqifZ7e93gZYHdglUpZJmT7H8A/gD1rZ4nhlxVXERFToOPb7Trb4yoiIsYn6e2UVVY/B14MrAZ8x/YzqwaLiAlJusD2trVzRNSWwlVExBTo+Ha7zhbdIiJi8iQ9zPZ9tXNExHyS+hucz6CswHpz2h9EpHAVETFlJK0CrENZ3g20fxIMdLvoFhER45P00dHO2/74dGeJiLFJOqfv8D7gRkorhOvrJIpoj/S4ioiYApJeD8wCngRcBWwJXAQ8u2KsgdheYbSiW0REDIU7+x4vA+wEXFspS0SMwfYOtTNEtFVWXEVETAFJ1wCbA5c005vWBw60vUflaBMaq+hmu/VFt4iImBxJjwBOsf382lkiYj5J7xrveduHTFeWiLaZUTtARMSQmGd7HpQ3BbavA9arnGlQsyhFt5uau32bArfWjRQREYvJssBatUNExEM8HXgz8MTm377ABsAKzb+IJVa2CkZETI1bJK0E/Ag4W9JtwB+rJhrcPNvzJD1YdJPUlaJbRESMo1kR3NtisRSwKpD+VhHt82hgM9t3AEj6GHCC7ddXTRXRAilcRURMAdu7NA8/1jTXXBE4o2Kkyehy0S0iIsa3U9/j+4C/ZKJgRCutBtzbd3wvsEadKBHtkh5XERHxIEnb0xTdbN870edHRET7SdoYeGZzONv21TXzRMRDSfoQ8HLgJMoqyV2A79v+dNVgES2QwlVERERExJCSNAt4A/DD5tQuwBG2D6uXKiJGI2kzFiwyX1kzT0RbpHAVERERETGkJF0NbGX7zuZ4OeBi2xvVTRYRI0naFljH9lGSVgWWt31D7VwRtWWqYERERETE8BJwf9/x/c25iGgRSQcA7wM+0JxaGvhOvUQR7ZHm7BERERERw+so4FJJJzXHLwWOrBcnIsawC7ApcAWA7T9KWqFupIh2SOEqIiIiImJI2T5E0rnAtpSVVvukb05EK91r25IMD27rjQhSuIqIiIiIGDqSZtr+p6RVgBubf73nVrH991rZImJBkgScJulrwEqS3gC8Fvh63WQR7ZDm7BERERERQ0bSabZ3knQD0H/BL8C216oULSJGIekKSo+r51F+T8+0fXbdVBHtkMJVREREREREREWSvgx8y/ZltbNEtE0KVxERERERQ0rSycD3gJNt31U7T0SMTtKvgHWBm4A7e+dtb1QtVERLpHAVERERETGkJG0P7AG8GJgDHA+cZnte1WARsQBJq4923vZN050lom1SuIqIiIiIGHKSlgJ2BN4AvMD2zMqRIiIiBpKpghERERERQ0zSI4GdKSuvNgOOrpsoIiJicFlxFRERERExpCQdD2wBnAF8HzjX9gN1U0VERAwuhauIiIiIiCEl6QXA2bbvr50lIiJiYcyoHSAiIiIiIhab2cAHJB0BIGkdSTtVzhQRETGwFK4iIiIiIobXUcC9wNbN8S3AJ+vFiYiImJwUriIiIiIihtfatg8C/g1g+25AdSNFREQMLoWriIiIiIjhdW8zVdAAktYG7qkbKSIiYnAPqx0gIiIiIiIWmwMoEwWfLOlYYBvgNVUTRURETEKmCkZEREREDCFJM4DdgJ8CW1K2CF5i+9aqwSIiIiYhhauIiIiIiCElabbt7WrniIiIWFgpXEVEREREDClJHwHuBo4H7uydt/33aqEiIiImIYWriIiIiIghJekGmsbs/WyvVSFORETEpKVwFRERERExpJqJgvsB21IKWOcDh9u+u2qwiIiIAaVwFRERERExpCR9H/gncGxzak9gJdsvr5cqIiJicClcRUREREQMKUk/t73xROciIiLaakbtABERERERsdhcKWnL3oGkLYALK+aJiIiYlKy4ioiIiIgYUpKuBdYDbm5OrQZcCzwA2PZGtbJFREQMIoWriIiIiIghJWn18Z63fdN0ZYmIiFgYKVxFREREREREREQrpcdVRERERERERES0UgpXERERERERERHRSilcRUREREREREREK6VwFRERERERERERrfT/AeTWOG0mTI43AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identify and plot correlation via heatmap, we may remove any features we believe are highly coorelated\n",
    "correlation = hotel.corr()\n",
    "plt.figure(figsize = (20,10))\n",
    "hotel_corr = sns.heatmap(correlation, vmin=-1, vmax=1, annot=True, cmap=\"GnBu\").get_figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_year</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_week_number</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>adults</th>\n",
       "      <th>...</th>\n",
       "      <th>reserved_room_type</th>\n",
       "      <th>assigned_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>deposit_type</th>\n",
       "      <th>agent</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>304.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
       "0  Resort Hotel            0        342               2015               July   \n",
       "1  Resort Hotel            0        737               2015               July   \n",
       "2  Resort Hotel            0          7               2015               July   \n",
       "3  Resort Hotel            0         13               2015               July   \n",
       "4  Resort Hotel            0         14               2015               July   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                        27                          1   \n",
       "1                        27                          1   \n",
       "2                        27                          1   \n",
       "3                        27                          1   \n",
       "4                        27                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  \\\n",
       "0                        0                     0       2  ...   \n",
       "1                        0                     0       2  ...   \n",
       "2                        0                     1       1  ...   \n",
       "3                        0                     1       1  ...   \n",
       "4                        0                     2       2  ...   \n",
       "\n",
       "   reserved_room_type  assigned_room_type booking_changes deposit_type  agent  \\\n",
       "0                   C                   C               3   No Deposit    0.0   \n",
       "1                   C                   C               4   No Deposit    0.0   \n",
       "2                   A                   C               0   No Deposit    0.0   \n",
       "3                   A                   A               0   No Deposit  304.0   \n",
       "4                   A                   A               0   No Deposit  240.0   \n",
       "\n",
       "  days_in_waiting_list  customer_type   adr  required_car_parking_spaces  \\\n",
       "0                    0      Transient   0.0                            0   \n",
       "1                    0      Transient   0.0                            0   \n",
       "2                    0      Transient  75.0                            0   \n",
       "3                    0      Transient  75.0                            0   \n",
       "4                    0      Transient  98.0                            0   \n",
       "\n",
       "  total_of_special_requests  \n",
       "0                         0  \n",
       "1                         0  \n",
       "2                         0  \n",
       "3                         0  \n",
       "4                         1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are choosing to drop the \"reservation_status_date\" column and the \"reservation_status\" column\n",
    "hotel.drop(columns=['reservation_status_date', 'reservation_status'], inplace=True)\n",
    "\n",
    "# Verify the dropped column\n",
    "hotel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 119390 entries, 0 to 119389\n",
      "Data columns (total 29 columns):\n",
      " #   Column                          Non-Null Count   Dtype  \n",
      "---  ------                          --------------   -----  \n",
      " 0   hotel                           119390 non-null  object \n",
      " 1   is_canceled                     119390 non-null  int64  \n",
      " 2   lead_time                       119390 non-null  int64  \n",
      " 3   arrival_date_year               119390 non-null  int64  \n",
      " 4   arrival_date_month              119390 non-null  object \n",
      " 5   arrival_date_week_number        119390 non-null  int64  \n",
      " 6   arrival_date_day_of_month       119390 non-null  int64  \n",
      " 7   stays_in_weekend_nights         119390 non-null  int64  \n",
      " 8   stays_in_week_nights            119390 non-null  int64  \n",
      " 9   adults                          119390 non-null  int64  \n",
      " 10  children                        119390 non-null  float64\n",
      " 11  babies                          119390 non-null  int64  \n",
      " 12  meal                            119390 non-null  object \n",
      " 13  country                         119390 non-null  object \n",
      " 14  market_segment                  119390 non-null  object \n",
      " 15  distribution_channel            119390 non-null  object \n",
      " 16  is_repeated_guest               119390 non-null  int64  \n",
      " 17  previous_cancellations          119390 non-null  int64  \n",
      " 18  previous_bookings_not_canceled  119390 non-null  int64  \n",
      " 19  reserved_room_type              119390 non-null  object \n",
      " 20  assigned_room_type              119390 non-null  object \n",
      " 21  booking_changes                 119390 non-null  int64  \n",
      " 22  deposit_type                    119390 non-null  object \n",
      " 23  agent                           119390 non-null  float64\n",
      " 24  days_in_waiting_list            119390 non-null  int64  \n",
      " 25  customer_type                   119390 non-null  object \n",
      " 26  adr                             119390 non-null  float64\n",
      " 27  required_car_parking_spaces     119390 non-null  int64  \n",
      " 28  total_of_special_requests       119390 non-null  int64  \n",
      "dtypes: float64(3), int64(16), object(10)\n",
      "memory usage: 26.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Review data types to see if we need to make alterations for modelling\n",
    "hotel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 119390 entries, 0 to 119389\n",
      "Data columns (total 29 columns):\n",
      " #   Column                          Non-Null Count   Dtype\n",
      "---  ------                          --------------   -----\n",
      " 0   hotel                           119390 non-null  int64\n",
      " 1   is_canceled                     119390 non-null  int64\n",
      " 2   lead_time                       119390 non-null  int64\n",
      " 3   arrival_date_year               119390 non-null  int64\n",
      " 4   arrival_date_month              119390 non-null  int64\n",
      " 5   arrival_date_week_number        119390 non-null  int64\n",
      " 6   arrival_date_day_of_month       119390 non-null  int64\n",
      " 7   stays_in_weekend_nights         119390 non-null  int64\n",
      " 8   stays_in_week_nights            119390 non-null  int64\n",
      " 9   adults                          119390 non-null  int64\n",
      " 10  children                        119390 non-null  int64\n",
      " 11  babies                          119390 non-null  int64\n",
      " 12  meal                            119390 non-null  int64\n",
      " 13  country                         119390 non-null  int64\n",
      " 14  market_segment                  119390 non-null  int64\n",
      " 15  distribution_channel            119390 non-null  int64\n",
      " 16  is_repeated_guest               119390 non-null  int64\n",
      " 17  previous_cancellations          119390 non-null  int64\n",
      " 18  previous_bookings_not_canceled  119390 non-null  int64\n",
      " 19  reserved_room_type              119390 non-null  int64\n",
      " 20  assigned_room_type              119390 non-null  int64\n",
      " 21  booking_changes                 119390 non-null  int64\n",
      " 22  deposit_type                    119390 non-null  int64\n",
      " 23  agent                           119390 non-null  int64\n",
      " 24  days_in_waiting_list            119390 non-null  int64\n",
      " 25  customer_type                   119390 non-null  int64\n",
      " 26  adr                             119390 non-null  int64\n",
      " 27  required_car_parking_spaces     119390 non-null  int64\n",
      " 28  total_of_special_requests       119390 non-null  int64\n",
      "dtypes: int64(29)\n",
      "memory usage: 26.4 MB\n"
     ]
    }
   ],
   "source": [
    "# We will use categorical data assignment to address all non-numerical data types so our ML models will run properly\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "label_encoder.fit(hotel[\"hotel\"])\n",
    "hotel[\"hotel\"] = label_encoder.transform(hotel[\"hotel\"])\n",
    "\n",
    "label_encoder.fit(hotel[\"arrival_date_month\"])\n",
    "hotel[\"arrival_date_month\"] = label_encoder.transform(hotel[\"arrival_date_month\"])\n",
    "\n",
    "label_encoder.fit(hotel[\"meal\"])\n",
    "hotel[\"meal\"] = label_encoder.transform(hotel[\"meal\"])\n",
    "\n",
    "label_encoder.fit(hotel[\"country\"])\n",
    "hotel[\"country\"] = label_encoder.transform(hotel[\"country\"])\n",
    "\n",
    "label_encoder.fit(hotel[\"market_segment\"])\n",
    "hotel[\"market_segment\"] = label_encoder.transform(hotel[\"market_segment\"])\n",
    "\n",
    "label_encoder.fit(hotel[\"distribution_channel\"])\n",
    "hotel[\"distribution_channel\"] = label_encoder.transform(hotel[\"distribution_channel\"])\n",
    "\n",
    "label_encoder.fit(hotel[\"reserved_room_type\"])\n",
    "hotel[\"reserved_room_type\"] = label_encoder.transform(hotel[\"reserved_room_type\"])\n",
    "\n",
    "label_encoder.fit(hotel[\"assigned_room_type\"])\n",
    "hotel[\"assigned_room_type\"] = label_encoder.transform(hotel[\"assigned_room_type\"])\n",
    "\n",
    "label_encoder.fit(hotel[\"customer_type\"])\n",
    "hotel[\"customer_type\"] = label_encoder.transform(hotel[\"customer_type\"])\n",
    "\n",
    "label_encoder.fit(hotel[\"deposit_type\"])\n",
    "hotel[\"deposit_type\"] = label_encoder.transform(hotel[\"deposit_type\"])\n",
    "\n",
    "# Here, for consistency, we are changing the float types to interger types\n",
    "hotel[\"children\"] = hotel[\"children\"].astype(\"int64\")\n",
    "\n",
    "hotel[\"agent\"] = hotel[\"agent\"].astype(\"int64\")\n",
    "\n",
    "hotel[\"adr\"] = hotel[\"adr\"].astype(\"int64\")\n",
    "\n",
    "# Review data to ensure the above code has been properly implemented\n",
    "hotel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hotel  is_canceled  lead_time  arrival_date_year  arrival_date_month  \\\n",
       "0      1            0        342               2015                   5   \n",
       "1      1            0        737               2015                   5   \n",
       "2      1            0          7               2015                   5   \n",
       "3      1            0         13               2015                   5   \n",
       "4      1            0         14               2015                   5   \n",
       "\n",
       "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
       "0                        27                          1   \n",
       "1                        27                          1   \n",
       "2                        27                          1   \n",
       "3                        27                          1   \n",
       "4                        27                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  \\\n",
       "0                        0                     0       2  ...   \n",
       "1                        0                     0       2  ...   \n",
       "2                        0                     1       1  ...   \n",
       "3                        0                     1       1  ...   \n",
       "4                        0                     2       2  ...   \n",
       "\n",
       "   reserved_room_type  assigned_room_type  booking_changes  deposit_type  \\\n",
       "0                   2                   2                3             0   \n",
       "1                   2                   2                4             0   \n",
       "2                   0                   2                0             0   \n",
       "3                   0                   0                0             0   \n",
       "4                   0                   0                0             0   \n",
       "\n",
       "   agent  days_in_waiting_list  customer_type  adr  \\\n",
       "0      0                     0              2    0   \n",
       "1      0                     0              2    0   \n",
       "2      0                     0              2   75   \n",
       "3    304                     0              2   75   \n",
       "4    240                     0              2   98   \n",
       "\n",
       "   required_car_parking_spaces  total_of_special_requests  \n",
       "0                            0                          0  \n",
       "1                            0                          0  \n",
       "2                            0                          0  \n",
       "3                            0                          0  \n",
       "4                            0                          1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review our updated data set\n",
    "hotel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the encoded file for forthcoming usage\n",
    "file_path = Path(\"../Data/hotel_encoded.csv\")\n",
    "hotel.to_csv(file_path, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.) Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define X (indpendent) and y (dependent) values for modelling, here we set \"is_cancelled\" as our target value and the \n",
    "X = hotel.drop(columns=\"is_canceled\")\n",
    "y = hotel[\"is_canceled\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    75166\n",
       "1    44224\n",
       "Name: is_canceled, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check target counts: canceled (1) or not (0)\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='is_canceled', ylabel='count'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = hotel, x = 'is_canceled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the X and y into X_train, X_test, y_train, y_test\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "\n",
    "# Create the StandardScaler instance, then scale the X data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the Standard Scaler with the training data\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the training and testing data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.) Model 1: BalancedRandomForestClassifier (Primary Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BalancedRandomForestClassifier(random_state=1)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model & fit with the BalancedRandomForestClassifier\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier \n",
    "train_brfc = BalancedRandomForestClassifier(n_estimators=100, random_state=1)\n",
    "train_brfc.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make \"target\" predictions using the testing data\n",
    "predictions = train_brfc.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score - BRFC - Primary Model\n",
      "0.8895068346287859\n",
      "Classification Report - BRFC - Primary Model\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.89      0.91     18853\n",
      "           1       0.83      0.88      0.85     10995\n",
      "\n",
      "    accuracy                           0.89     29848\n",
      "   macro avg       0.88      0.89      0.88     29848\n",
      "weighted avg       0.89      0.89      0.89     29848\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import dependencies to evaluate the model\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Calculate the accuracy score\n",
    "brf_acc_score = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy Score - BRFC - Primary Model\")\n",
    "print(brf_acc_score)\n",
    "\n",
    "# Render & display results\n",
    "classification_report = classification_report(y_test, predictions)\n",
    "print(\"Classification Report - BRFC - Primary Model\")\n",
    "print(classification_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.12616706953229856, 'lead_time'),\n",
       " (0.11608233304888897, 'country'),\n",
       " (0.10665261267954108, 'deposit_type'),\n",
       " (0.07021625182725132, 'adr'),\n",
       " (0.056757619750620235, 'total_of_special_requests'),\n",
       " (0.05633161133686403, 'agent'),\n",
       " (0.05523392243759516, 'market_segment'),\n",
       " (0.05200711503726862, 'arrival_date_day_of_month'),\n",
       " (0.04985575344988292, 'arrival_date_week_number'),\n",
       " (0.033200533896708045, 'stays_in_week_nights'),\n",
       " (0.02869476610584411, 'arrival_date_month'),\n",
       " (0.02847900034314672, 'required_car_parking_spaces'),\n",
       " (0.027485519350371853, 'customer_type'),\n",
       " (0.025626370603860065, 'assigned_room_type'),\n",
       " (0.02558397238881633, 'arrival_date_year'),\n",
       " (0.024865865466122503, 'previous_cancellations'),\n",
       " (0.02190040698342993, 'booking_changes'),\n",
       " (0.02155004825162837, 'stays_in_weekend_nights'),\n",
       " (0.015032437150745148, 'reserved_room_type'),\n",
       " (0.012310312219976145, 'adults'),\n",
       " (0.011503315335811419, 'meal'),\n",
       " (0.01147200309947324, 'distribution_channel'),\n",
       " (0.00801477793657849, 'hotel'),\n",
       " (0.005696736549110013, 'children'),\n",
       " (0.004558221990557867, 'previous_bookings_not_canceled'),\n",
       " (0.0022147430653603013, 'is_repeated_guest'),\n",
       " (0.0016907438267050121, 'days_in_waiting_list'),\n",
       " (0.0008159363355435853, 'babies')]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review the model's important features\n",
    "importances = train_brfc.feature_importances_\n",
    "sorted(zip(train_brfc.feature_importances_, X.columns), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Impoprtant Features'}, ylabel='1'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the model's important features for review\n",
    "importances_df = pd.DataFrame(sorted(zip(train_brfc.feature_importances_, X.columns), reverse=True))\n",
    "importances_df.set_index(importances_df[1], inplace=True)\n",
    "importances_df.drop(columns=1, inplace=True)\n",
    "importances_df.rename(columns={0: 'Important Features'}, inplace=True)\n",
    "importances_sorted = importances_df.sort_values(by='Important Features')\n",
    "importances_sorted.plot(kind='barh', color='green', title='Impoprtant Features', legend=False, figsize=(15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.) Model 2: LogisticRegression (Supporting Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model & fit with the LogisticRegression model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "train_lg = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "train_lg.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make \"target\" predictions using the testing data\n",
    "predictions_lr = train_lg.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7966697936210131"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the accuracy score - this is significantly less than Model, so we will end the analysis here\n",
    "lr_acc = accuracy_score(y_test, predictions_lr)\n",
    "lr_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.79      0.92      0.59      0.85      0.73      0.56     18853\n",
      "          1       0.81      0.59      0.92      0.68      0.73      0.52     10995\n",
      "\n",
      "avg / total       0.80      0.80      0.71      0.79      0.73      0.54     29848\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Review the classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, predictions_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8.) Model 3: EasyEnsembleClassifier (Supporting Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EasyEnsembleClassifier(n_estimators=100, random_state=1)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model & fit with the EasyEnsembleClassifier model\n",
    "from imblearn.ensemble import EasyEnsembleClassifier\n",
    "train_eec = EasyEnsembleClassifier(n_estimators=100, random_state=1)\n",
    "train_eec.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make \"target\" predictions using the testing data\n",
    "predictions_eec = train_eec.predict(X_test_scaled) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.824316620485913"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review accuracy using the \"balanced accuracy score\" - this is significantly less than Model, so we will end the analysis here\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "eec_acc = balanced_accuracy_score(y_test, predictions_eec)\n",
    "eec_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.88      0.85      0.80      0.86      0.82      0.68     18853\n",
      "          1       0.76      0.80      0.85      0.78      0.82      0.68     10995\n",
      "\n",
      "avg / total       0.83      0.83      0.82      0.83      0.82      0.68     29848\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Review the classification report\n",
    "print(classification_report_imbalanced(y_test, predictions_eec))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9.) Model 4: Naive Random Oversampling + BalancedRandomForestClassifier (Enhancing Primary Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 56313, 1: 56313})"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with the RandomOversampler\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from collections import Counter\n",
    "\n",
    "oversample = RandomOverSampler(random_state=1)\n",
    "X_resampled_oversample, y_resampled_oversample = oversample.fit_resample(X_train, y_train)\n",
    "Counter(y_resampled_oversample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BalancedRandomForestClassifier(random_state=1)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the balanced random forest classifier with resampled data\n",
    "train_brfc.fit(X_resampled_oversample, y_resampled_oversample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make target predictions\n",
    "y_resampled_oversample = train_brfc.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8841154223841445"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "nro_acc = balanced_accuracy_score(y_test, y_resampled_oversample)\n",
    "nro_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.91      0.92      0.84      0.92      0.88      0.79     18853\n",
      "          1       0.87      0.84      0.92      0.85      0.88      0.77     10995\n",
      "\n",
      "avg / total       0.89      0.89      0.87      0.89      0.88      0.78     29848\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Review the classification report\n",
    "print(classification_report_imbalanced(y_test, y_resampled_oversample))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10.) Model 5: SMOTEENN + BalancedRandomForestClassifier (Enhancing Primary Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 46617, 1: 56485})"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with SMOTEENN\n",
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "smote_enn = SMOTEENN(random_state=0)\n",
    "X_resampled, y_resampled = smote_enn.fit_resample(X, y)\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BalancedRandomForestClassifier(random_state=1)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the balanced random forest classifier with resampled data\n",
    "train_brfc.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make target predictions\n",
    "y_pred = train_brfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9027391310000517"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "smoteenn_acc = balanced_accuracy_score(y_test, y_pred)\n",
    "smoteenn_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.94      0.91      0.90      0.92      0.90      0.82     18853\n",
      "          1       0.85      0.90      0.91      0.87      0.90      0.81     10995\n",
      "\n",
      "avg / total       0.91      0.90      0.90      0.91      0.90      0.82     29848\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Review the classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11.) Review Accuracy Scores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BalancedRandomForestClassifier accuracy score: 0.89\n",
      "LogisticRegression accuracy score: 0.80\n",
      "EasyEnsembleClassifier accuracy score: 0.82\n",
      "RandomOversampler accuracy score: 0.88\n",
      "SMOTEENN Oversampling accuracy score: 0.90\n"
     ]
    }
   ],
   "source": [
    "# consolidating accuracy scores for all models\n",
    "print(f\"BalancedRandomForestClassifier accuracy score: {brf_acc_score:0.2f}\")\n",
    "print(f\"LogisticRegression accuracy score: {lr_acc:0.2f}\")\n",
    "print(f\"EasyEnsembleClassifier accuracy score: {eec_acc:0.2f}\")\n",
    "print(f\"RandomOversampler accuracy score: {nro_acc:0.2f}\")\n",
    "print(f\"SMOTEENN Oversampling accuracy score: {smoteenn_acc:0.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Accuracy Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BalancedRandomForestClassifier</th>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EasyEnsembleClassifier</th>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomOversampler</th>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMOTEENN Oversampling</th>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Accuracy Score\n",
       "BalancedRandomForestClassifier            0.89\n",
       "LogisticRegression                        0.80\n",
       "EasyEnsembleClassifier                    0.82\n",
       "RandomOversampler                         0.88\n",
       "SMOTEENN Oversampling                     0.90"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Intialise data to Dicts of series.\n",
    "acc_combined = {'Accuracy Score' : pd.Series([0.89, 0.80, 0.82, 0.88, 0.90],\n",
    "                       index =['BalancedRandomForestClassifier', 'LogisticRegression', 'EasyEnsembleClassifier', 'RandomOversampler', 'SMOTEENN Oversampling'])}\n",
    "  \n",
    "# creates Dataframe.\n",
    "acc_df = pd.DataFrame(acc_combined)\n",
    "acc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot accuracy scores for better visual\n",
    "acc_df.plot(kind='bar', figsize=(10,5), title='ML Model Accuracy Scores')\n",
    "plt.ylim(0.7,1.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The End"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}