Task 1 - Web scraping and Sentiment Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a639140",
   "metadata": {},
   "source": [
    "# Web scraping and analysis\n",
    "This Jupyter notebook includes some code to get you started with web scraping. We will use a package called BeautifulSoup to collect the data from the web. Once you've collected your data and saved it into a local .csv file you should start with your analysis.\n",
    "\n",
    "Scraping data from Skytrax\n",
    "If you visit [https://www.airlinequality.com] you can see that there is a lot of data there. For this task, we are only interested in reviews related to British Airways and the Airline itself.\n",
    "\n",
    "If you navigate to this link: [https://www.airlinequality.com/airline-reviews/british-airways] you will see this data. Now, we can use Python and BeautifulSoup to collect all the links to the reviews and then to collect the text data on each of the individual review links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4bece0cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Necessary Libraries \n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import pandas as pd\n",
    "import re\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de07b41a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scraping page 1\n",
      "   ---> 100 total reviews\n",
      "Scraping page 2\n",
      "   ---> 200 total reviews\n",
      "Scraping page 3\n",
      "   ---> 300 total reviews\n",
      "Scraping page 4\n",
      "   ---> 400 total reviews\n",
      "Scraping page 5\n",
      "   ---> 500 total reviews\n",
      "Scraping page 6\n",
      "   ---> 600 total reviews\n",
      "Scraping page 7\n",
      "   ---> 700 total reviews\n",
      "Scraping page 8\n",
      "   ---> 800 total reviews\n",
      "Scraping page 9\n",
      "   ---> 900 total reviews\n",
      "Scraping page 10\n",
      "   ---> 1000 total reviews\n"
     ]
    }
   ],
   "source": [
    "base_url = \"https://www.airlinequality.com/airline-reviews/british-airways\"\n",
    "pages = 10\n",
    "page_size = 100\n",
    "\n",
    "reviews = []\n",
    "\n",
    "for i in range(1, pages + 1):\n",
    "\n",
    "    print(f\"Scraping page {i}\")\n",
    "\n",
    "    # Create URL to collect links from paginated data\n",
    "    url = f\"{base_url}/page/{i}/?sortby=post_date%3ADesc&pagesize={page_size}\"\n",
    "\n",
    "    # Collect HTML data from this page\n",
    "    response = requests.get(url)\n",
    "\n",
    "    # Parse content\n",
    "    content = response.content\n",
    "    parsed_content = BeautifulSoup(content, 'html.parser')\n",
    "    for para in parsed_content.find_all(\"div\", {\"class\": \"text_content\"}):\n",
    "        reviews.append(para.get_text())\n",
    "    \n",
    "    print(f\"   ---> {len(reviews)} total reviews\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c9aaf989",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviews</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>✅ Trip Verified | Boarding at Mumbai was chaot...</td>\n",
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       "      <td>Not Verified | Mexico City Airport is a zoo, b...</td>\n",
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       "      <td>✅ Trip Verified |  Very poor service, very fru...</td>\n",
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       "      <th>4</th>\n",
       "      <td>Not Verified |  BA changed our prepaid seats a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                             reviews\n",
       "0  ✅ Trip Verified | Boarding at Mumbai was chaot...\n",
       "1  Not Verified | Mexico City Airport is a zoo, b...\n",
       "2  ✅ Trip Verified |  Very poor service, very fru...\n",
       "3  Not Verified |  Generally poor. Sent to gate o...\n",
       "4  Not Verified |  BA changed our prepaid seats a..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert raw data into pandas dataframe\n",
    "data = pd.DataFrame()\n",
    "data[\"reviews\"] = reviews\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bd306606",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert dataset into csv\n",
    "data.to_csv(\"BA_reviews.csv\")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "71c390e2",
   "metadata": {},
   "source": [
    "# The next thing that we should do is clean this data to remove any unnecessary text from each of the rows. For example, \"✅ Trip Verified\" can be removed from each row if it exists, as it's not relevant to what we want to investigate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c40844c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>reviews</th>\n",
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       "  </thead>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   Unnamed: 0                                            reviews\n",
       "0           0  ✅ Trip Verified | Boarding at Mumbai was chaot...\n",
       "1           1  Not Verified | Mexico City Airport is a zoo, b...\n",
       "2           2  ✅ Trip Verified |  Very poor service, very fru...\n",
       "3           3  Not Verified |  Generally poor. Sent to gate o...\n",
       "4           4  Not Verified |  BA changed our prepaid seats a..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the dataset\n",
    "data = pd.read_csv(\"BA_reviews.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c44ef8f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check shape of dataframe\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d705c15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  1000 non-null   int64 \n",
      " 1   reviews     1000 non-null   object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 15.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check information about dataframe\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2e591cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Unnecessary Column\n",
    "data.drop(columns='Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7311113c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'✅ Trip Verified | Boarding at Mumbai was chaotic and badly organised. Once on board the crew announcements were professional, relaxed and informative in typical BA style. The seat was quite comfortable. It was a refurbished aircraft and the padding and materials are quite good. I was lucky that the middle seat was empty so I didn’t suffer from the new narrow seat which I think would have made it a lot less comfortable. The IFE is excellent, responsive and good quality. There is an enormous selection of TV and movies and a good number of games. WIFI is also very good not quite Netflix quality but still reasonable and I don’t think £17.99 for full flight is bad value. You can pay less if you just want short term simple messaging. Cabin crew were very good. Friendly and efficient. Catering was reasonable although on a near 10hr flight, the small (but tasty) pastry for the second meal service wasn’t really enough. Landing and processing at Heathrow was astonishingly quick - it felt like it was 3am, even though it was 6:30pm! No queues or waiting at all. Even the baggage was super quick at coming through. Overall a very solid experience in economy. Significantly better than it used to be.'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first review\n",
    "data['reviews'].loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d48537af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the trip verified string\n",
    "data.reviews = data.reviews.str.split('|', expand=True)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "079e0494",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviews</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boarding at Mumbai was chaotic and badly orga...</td>\n",
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       "      <td>Mexico City Airport is a zoo, but taking the ...</td>\n",
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       "      <td>Very poor service, very frustrating. Firstly...</td>\n",
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       "      <td>Generally poor. Sent to gate on time, sat ma...</td>\n",
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       "      <td>BA changed our prepaid seats at the last min...</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Our original flight was cancelled as my wife...</td>\n",
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       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Hong Kong to London. This was my first Busin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Washington to Bucharest return via London. I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>London to Warsaw. BA European service is bec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>London to Istanbul. Due to poor weather cond...</td>\n",
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      "text/plain": [
       "                                               reviews\n",
       "0     Boarding at Mumbai was chaotic and badly orga...\n",
       "1     Mexico City Airport is a zoo, but taking the ...\n",
       "2      Very poor service, very frustrating. Firstly...\n",
       "3      Generally poor. Sent to gate on time, sat ma...\n",
       "4      BA changed our prepaid seats at the last min...\n",
       "..                                                 ...\n",
       "995    Our original flight was cancelled as my wife...\n",
       "996    Hong Kong to London. This was my first Busin...\n",
       "997    Washington to Bucharest return via London. I...\n",
       "998    London to Warsaw. BA European service is bec...\n",
       "999    London to Istanbul. Due to poor weather cond...\n",
       "\n",
       "[1000 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f501b3e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>reviews</th>\n",
       "      <th>clean_reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boarding at Mumbai was chaotic and badly orga...</td>\n",
       "      <td>Boarding at Mumbai was chaotic and badly orga...</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Mexico City Airport is a zoo but taking the l...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Very poor service very frustrating Firstly my...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Generally poor. Sent to gate on time, sat ma...</td>\n",
       "      <td>Generally poor Sent to gate on time sat maybe...</td>\n",
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       "                                             reviews  \\\n",
       "0   Boarding at Mumbai was chaotic and badly orga...   \n",
       "1   Mexico City Airport is a zoo, but taking the ...   \n",
       "2    Very poor service, very frustrating. Firstly...   \n",
       "3    Generally poor. Sent to gate on time, sat ma...   \n",
       "4    BA changed our prepaid seats at the last min...   \n",
       "\n",
       "                                       clean_reviews  \n",
       "0   Boarding at Mumbai was chaotic and badly orga...  \n",
       "1   Mexico City Airport is a zoo but taking the l...  \n",
       "2   Very poor service very frustrating Firstly my...  \n",
       "3   Generally poor Sent to gate on time sat maybe...  \n",
       "4   BA changed our prepaid seats at the last minu...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to clean text\n",
    "def clean(text):\n",
    "    # Remove all special characters & numbers except alphabets\n",
    "    text = re.sub('[^A-Za-z]+', ' ', str(text))\n",
    "    return text\n",
    "\n",
    "data['clean_reviews'] = data['reviews'].apply(clean)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "09c2ad74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting wordcloud\n",
      "  Downloading wordcloud-1.8.2.2-cp39-cp39-win_amd64.whl (153 kB)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from wordcloud) (3.5.1)\n",
      "Requirement already satisfied: numpy>=1.6.1 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from wordcloud) (1.21.5)\n",
      "Requirement already satisfied: pillow in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from wordcloud) (9.0.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (4.25.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (2.8.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (1.3.2)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (21.3)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (3.0.4)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->wordcloud) (1.16.0)\n",
      "Installing collected packages: wordcloud\n",
      "Successfully installed wordcloud-1.8.2.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "343fd8b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\alboc\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\alboc\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\alboc\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     C:\\Users\\alboc\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\alboc\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('omw-1.4')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk import pos_tag\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.corpus import wordnet\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from wordcloud import WordCloud, STOPWORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ac09786",
   "metadata": {},
   "outputs": [],
   "source": [
    "# POS Tagger Dictionary\n",
    "pos_dict = {'A':wordnet.ADJ, 'V':wordnet.VERB, 'N':wordnet.NOUN, 'R':wordnet.ADV}\n",
    "\n",
    "def token_stop_pos(text):\n",
    "    tags = pos_tag(word_tokenize(text))\n",
    "    \n",
    "    new_list = []\n",
    "    for word, tag in tags:\n",
    "        if word.lower() not in set(stopwords.words('english')):\n",
    "            new_list.append(tuple([word, pos_dict.get(tag[0])]))\n",
    "    return new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6ad71326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviews</th>\n",
       "      <th>clean_reviews</th>\n",
       "      <th>POS Tagged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boarding at Mumbai was chaotic and badly orga...</td>\n",
       "      <td>Boarding at Mumbai was chaotic and badly orga...</td>\n",
       "      <td>[(Boarding, v), (Mumbai, n), (chaotic, None), ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mexico City Airport is a zoo, but taking the ...</td>\n",
       "      <td>Mexico City Airport is a zoo but taking the l...</td>\n",
       "      <td>[(Mexico, n), (City, n), (Airport, n), (zoo, n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Very poor service, very frustrating. Firstly...</td>\n",
       "      <td>Very poor service very frustrating Firstly my...</td>\n",
       "      <td>[(poor, None), (service, n), (frustrating, Non...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Generally poor. Sent to gate on time, sat ma...</td>\n",
       "      <td>Generally poor Sent to gate on time sat maybe...</td>\n",
       "      <td>[(Generally, r), (poor, None), (Sent, n), (gat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BA changed our prepaid seats at the last min...</td>\n",
       "      <td>BA changed our prepaid seats at the last minu...</td>\n",
       "      <td>[(BA, n), (changed, v), (prepaid, n), (seats, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             reviews  \\\n",
       "0   Boarding at Mumbai was chaotic and badly orga...   \n",
       "1   Mexico City Airport is a zoo, but taking the ...   \n",
       "2    Very poor service, very frustrating. Firstly...   \n",
       "3    Generally poor. Sent to gate on time, sat ma...   \n",
       "4    BA changed our prepaid seats at the last min...   \n",
       "\n",
       "                                       clean_reviews  \\\n",
       "0   Boarding at Mumbai was chaotic and badly orga...   \n",
       "1   Mexico City Airport is a zoo but taking the l...   \n",
       "2   Very poor service very frustrating Firstly my...   \n",
       "3   Generally poor Sent to gate on time sat maybe...   \n",
       "4   BA changed our prepaid seats at the last minu...   \n",
       "\n",
       "                                          POS Tagged  \n",
       "0  [(Boarding, v), (Mumbai, n), (chaotic, None), ...  \n",
       "1  [(Mexico, n), (City, n), (Airport, n), (zoo, n...  \n",
       "2  [(poor, None), (service, n), (frustrating, Non...  \n",
       "3  [(Generally, r), (poor, None), (Sent, n), (gat...  \n",
       "4  [(BA, n), (changed, v), (prepaid, n), (seats, ...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['POS Tagged'] = data['clean_reviews'].apply(token_stop_pos)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "daae325f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtaining the stem words – Lemmatization\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "def lemmatize(pos_data):\n",
    "    lemma_rev = \" \"\n",
    "    for word, pos in pos_data:\n",
    "     if not pos:\n",
    "        lemma = word\n",
    "        lemma_rev = lemma_rev + \" \" + lemma\n",
    "     else:\n",
    "        lemma = lemmatizer.lemmatize(word, pos=pos)\n",
    "        lemma_rev = lemma_rev + \" \" + lemma\n",
    "    return lemma_rev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cb659445",
   "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>reviews</th>\n",
       "      <th>clean_reviews</th>\n",
       "      <th>POS Tagged</th>\n",
       "      <th>lemma_rev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boarding at Mumbai was chaotic and badly orga...</td>\n",
       "      <td>Boarding at Mumbai was chaotic and badly orga...</td>\n",
       "      <td>[(Boarding, v), (Mumbai, n), (chaotic, None), ...</td>\n",
       "      <td>Boarding Mumbai chaotic badly organise board...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mexico City Airport is a zoo, but taking the ...</td>\n",
       "      <td>Mexico City Airport is a zoo but taking the l...</td>\n",
       "      <td>[(Mexico, n), (City, n), (Airport, n), (zoo, n...</td>\n",
       "      <td>Mexico City Airport zoo take late departure ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Very poor service, very frustrating. Firstly...</td>\n",
       "      <td>Very poor service very frustrating Firstly my...</td>\n",
       "      <td>[(poor, None), (service, n), (frustrating, Non...</td>\n",
       "      <td>poor service frustrating Firstly flight LHR ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Generally poor. Sent to gate on time, sat ma...</td>\n",
       "      <td>Generally poor Sent to gate on time sat maybe...</td>\n",
       "      <td>[(Generally, r), (poor, None), (Sent, n), (gat...</td>\n",
       "      <td>Generally poor Sent gate time sat maybe minu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BA changed our prepaid seats at the last min...</td>\n",
       "      <td>BA changed our prepaid seats at the last minu...</td>\n",
       "      <td>[(BA, n), (changed, v), (prepaid, n), (seats, ...</td>\n",
       "      <td>BA change prepaid seat last minute charge us...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             reviews  \\\n",
       "0   Boarding at Mumbai was chaotic and badly orga...   \n",
       "1   Mexico City Airport is a zoo, but taking the ...   \n",
       "2    Very poor service, very frustrating. Firstly...   \n",
       "3    Generally poor. Sent to gate on time, sat ma...   \n",
       "4    BA changed our prepaid seats at the last min...   \n",
       "\n",
       "                                       clean_reviews  \\\n",
       "0   Boarding at Mumbai was chaotic and badly orga...   \n",
       "1   Mexico City Airport is a zoo but taking the l...   \n",
       "2   Very poor service very frustrating Firstly my...   \n",
       "3   Generally poor Sent to gate on time sat maybe...   \n",
       "4   BA changed our prepaid seats at the last minu...   \n",
       "\n",
       "                                          POS Tagged  \\\n",
       "0  [(Boarding, v), (Mumbai, n), (chaotic, None), ...   \n",
       "1  [(Mexico, n), (City, n), (Airport, n), (zoo, n...   \n",
       "2  [(poor, None), (service, n), (frustrating, Non...   \n",
       "3  [(Generally, r), (poor, None), (Sent, n), (gat...   \n",
       "4  [(BA, n), (changed, v), (prepaid, n), (seats, ...   \n",
       "\n",
       "                                           lemma_rev  \n",
       "0    Boarding Mumbai chaotic badly organise board...  \n",
       "1    Mexico City Airport zoo take late departure ...  \n",
       "2    poor service frustrating Firstly flight LHR ...  \n",
       "3    Generally poor Sent gate time sat maybe minu...  \n",
       "4    BA change prepaid seat last minute charge us...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['lemma_rev'] = data['POS Tagged'].apply(lemmatize)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "82430479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'  Boarding Mumbai chaotic badly organise board crew announcement professional relaxed informative typical BA style seat quite comfortable refurbish aircraft padding material quite good lucky middle seat empty suffer new narrow seat think would make lot less comfortable IFE excellent responsive good quality enormous selection TV movie good number game WIFI also good quite Netflix quality still reasonable think full flight bad value pay less want short term simple message Cabin crew good Friendly efficient Catering reasonable although near hr flight small tasty pastry second meal service really enough Landing processing Heathrow astonishingly quick felt like even though pm queue wait Even baggage super quick come Overall solid experience economy Significantly well use'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['lemma_rev'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ff6f2144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Boarding at Mumbai was chaotic and badly organised Once on board the crew announcements were professional relaxed and informative in typical BA style The seat was quite comfortable It was a refurbished aircraft and the padding and materials are quite good I was lucky that the middle seat was empty so I didn t suffer from the new narrow seat which I think would have made it a lot less comfortable The IFE is excellent responsive and good quality There is an enormous selection of TV and movies and a good number of games WIFI is also very good not quite Netflix quality but still reasonable and I don t think for full flight is bad value You can pay less if you just want short term simple messaging Cabin crew were very good Friendly and efficient Catering was reasonable although on a near hr flight the small but tasty pastry for the second meal service wasn t really enough Landing and processing at Heathrow was astonishingly quick it felt like it was am even though it was pm No queues or waiting at all Even the baggage was super quick at coming through Overall a very solid experience in economy Significantly better than it used to be '"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['clean_reviews'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d81b719d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting vaderSentiment\n",
      "  Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl (125 kB)\n",
      "Requirement already satisfied: requests in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from vaderSentiment) (2.27.1)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (1.26.9)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (3.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (2021.10.8)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\alboc\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (2.0.4)\n",
      "Installing collected packages: vaderSentiment\n",
      "Successfully installed vaderSentiment-3.3.2\n"
     ]
    }
   ],
   "source": [
    "!pip install vaderSentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8706a2e7",
   "metadata": {},
   "source": [
    "# Sentiment Analysis using VADER\n",
    "VADER stands for Valence Aware Dictionary and Sentiment Reasoner.\n",
    "\n",
    "Vader sentiment not only tells if the statement is positive or negative along with the intensity of emotion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "94b1ae39",
   "metadata": {},
   "outputs": [],
   "source": [
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "Analyzer = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2294e1f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate vader sentiment\n",
    "def sentiment_scores(review):\n",
    "    ss = Analyzer.polarity_scores(review)\n",
    "    return ss['compound']\n",
    "\n",
    "data['Sentiment'] = data['lemma_rev'].apply(sentiment_scores)\n",
    "\n",
    "# Function to analyse the sentiment\n",
    "def sentiment_analysis(compound):\n",
    "    if compound >= 0.5:\n",
    "        return 'Positive'\n",
    "    elif compound < 0:\n",
    "        return 'Negative'\n",
    "    else:\n",
    "        return 'Neutral'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "36f86b88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviews</th>\n",
       "      <th>clean_reviews</th>\n",
       "      <th>POS Tagged</th>\n",
       "      <th>lemma_rev</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boarding at Mumbai was chaotic and badly orga...</td>\n",
       "      <td>Boarding at Mumbai was chaotic and badly orga...</td>\n",
       "      <td>[(Boarding, v), (Mumbai, n), (chaotic, None), ...</td>\n",
       "      <td>Boarding Mumbai chaotic badly organise board...</td>\n",
       "      <td>0.9871</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mexico City Airport is a zoo, but taking the ...</td>\n",
       "      <td>Mexico City Airport is a zoo but taking the l...</td>\n",
       "      <td>[(Mexico, n), (City, n), (Airport, n), (zoo, n...</td>\n",
       "      <td>Mexico City Airport zoo take late departure ...</td>\n",
       "      <td>0.9917</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Very poor service, very frustrating. Firstly...</td>\n",
       "      <td>Very poor service very frustrating Firstly my...</td>\n",
       "      <td>[(poor, None), (service, n), (frustrating, Non...</td>\n",
       "      <td>poor service frustrating Firstly flight LHR ...</td>\n",
       "      <td>-0.9403</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Generally poor. Sent to gate on time, sat ma...</td>\n",
       "      <td>Generally poor Sent to gate on time sat maybe...</td>\n",
       "      <td>[(Generally, r), (poor, None), (Sent, n), (gat...</td>\n",
       "      <td>Generally poor Sent gate time sat maybe minu...</td>\n",
       "      <td>-0.4588</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BA changed our prepaid seats at the last min...</td>\n",
       "      <td>BA changed our prepaid seats at the last minu...</td>\n",
       "      <td>[(BA, n), (changed, v), (prepaid, n), (seats, ...</td>\n",
       "      <td>BA change prepaid seat last minute charge us...</td>\n",
       "      <td>0.6908</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Flew from London Heathrow to Marrakech. BA c...</td>\n",
       "      <td>Flew from London Heathrow to Marrakech BA cha...</td>\n",
       "      <td>[(Flew, None), (London, n), (Heathrow, n), (Ma...</td>\n",
       "      <td>Flew London Heathrow Marrakech BA change pre...</td>\n",
       "      <td>-0.6162</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I was meant to fly in January to Algeria. I p...</td>\n",
       "      <td>I was meant to fly in January to Algeria I pa...</td>\n",
       "      <td>[(meant, v), (fly, v), (January, n), (Algeria,...</td>\n",
       "      <td>mean fly January Algeria pay ticket day mean...</td>\n",
       "      <td>-0.5994</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>We have flown repeatedly with British Airway...</td>\n",
       "      <td>We have flown repeatedly with British Airways...</td>\n",
       "      <td>[(flown, v), (repeatedly, r), (British, None),...</td>\n",
       "      <td>fly repeatedly British Airways One World all...</td>\n",
       "      <td>-0.9442</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I was horrified by the extremely small seats...</td>\n",
       "      <td>I was horrified by the extremely small seats ...</td>\n",
       "      <td>[(horrified, v), (extremely, r), (small, None)...</td>\n",
       "      <td>horrify extremely small seat poor training c...</td>\n",
       "      <td>-0.5507</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\\r\\nThe worst cabin experience ever: cramped...</td>\n",
       "      <td>The worst cabin experience ever cramped seats...</td>\n",
       "      <td>[(worst, None), (cabin, n), (experience, n), (...</td>\n",
       "      <td>worst cabin experience ever cramp seat low c...</td>\n",
       "      <td>-0.9136</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             reviews  \\\n",
       "0   Boarding at Mumbai was chaotic and badly orga...   \n",
       "1   Mexico City Airport is a zoo, but taking the ...   \n",
       "2    Very poor service, very frustrating. Firstly...   \n",
       "3    Generally poor. Sent to gate on time, sat ma...   \n",
       "4    BA changed our prepaid seats at the last min...   \n",
       "5    Flew from London Heathrow to Marrakech. BA c...   \n",
       "6   I was meant to fly in January to Algeria. I p...   \n",
       "7    We have flown repeatedly with British Airway...   \n",
       "8    I was horrified by the extremely small seats...   \n",
       "9    \\r\\nThe worst cabin experience ever: cramped...   \n",
       "\n",
       "                                       clean_reviews  \\\n",
       "0   Boarding at Mumbai was chaotic and badly orga...   \n",
       "1   Mexico City Airport is a zoo but taking the l...   \n",
       "2   Very poor service very frustrating Firstly my...   \n",
       "3   Generally poor Sent to gate on time sat maybe...   \n",
       "4   BA changed our prepaid seats at the last minu...   \n",
       "5   Flew from London Heathrow to Marrakech BA cha...   \n",
       "6   I was meant to fly in January to Algeria I pa...   \n",
       "7   We have flown repeatedly with British Airways...   \n",
       "8   I was horrified by the extremely small seats ...   \n",
       "9   The worst cabin experience ever cramped seats...   \n",
       "\n",
       "                                          POS Tagged  \\\n",
       "0  [(Boarding, v), (Mumbai, n), (chaotic, None), ...   \n",
       "1  [(Mexico, n), (City, n), (Airport, n), (zoo, n...   \n",
       "2  [(poor, None), (service, n), (frustrating, Non...   \n",
       "3  [(Generally, r), (poor, None), (Sent, n), (gat...   \n",
       "4  [(BA, n), (changed, v), (prepaid, n), (seats, ...   \n",
       "5  [(Flew, None), (London, n), (Heathrow, n), (Ma...   \n",
       "6  [(meant, v), (fly, v), (January, n), (Algeria,...   \n",
       "7  [(flown, v), (repeatedly, r), (British, None),...   \n",
       "8  [(horrified, v), (extremely, r), (small, None)...   \n",
       "9  [(worst, None), (cabin, n), (experience, n), (...   \n",
       "\n",
       "                                           lemma_rev  Sentiment    Review  \n",
       "0    Boarding Mumbai chaotic badly organise board...     0.9871  Positive  \n",
       "1    Mexico City Airport zoo take late departure ...     0.9917  Positive  \n",
       "2    poor service frustrating Firstly flight LHR ...    -0.9403  Negative  \n",
       "3    Generally poor Sent gate time sat maybe minu...    -0.4588  Negative  \n",
       "4    BA change prepaid seat last minute charge us...     0.6908  Positive  \n",
       "5    Flew London Heathrow Marrakech BA change pre...    -0.6162  Negative  \n",
       "6    mean fly January Algeria pay ticket day mean...    -0.5994  Negative  \n",
       "7    fly repeatedly British Airways One World all...    -0.9442  Negative  \n",
       "8    horrify extremely small seat poor training c...    -0.5507  Negative  \n",
       "9    worst cabin experience ever cramp seat low c...    -0.9136  Negative  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Review'] = data['Sentiment'].apply(sentiment_analysis)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "45a02fda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Positive    527\n",
       "Negative    368\n",
       "Neutral     105\n",
       "Name: Review, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check value_count on Review column\n",
    "review_count = data['Review'].value_counts()\n",
    "review_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1f494a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Review Countplot\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Review Count');\n",
    "sns.countplot(x=data['Review'], data=data, palette='rainbow');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9af44661",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n/hZCIZEe5FjXowxHjs17v4pQqatcj5Qmx848TDR5LkO2YGTJ5qPTbjcWP0PP0M4JoZVI9zM0dpgFtVvxuksmhCbASPT4lO37R/fTWL0Zn6dy2v2rqou+4T30j+wrms9kLMG6nrye5HTvc+gF60d6VtzWlK+iqnQZXQMvT2yrKBoCMEwdw9TH568TT/UyFauElCkN6/VHSkxpXNWsc0WFuz5UTX27h7GBPD/8XA+dh63j9oc1hMKM1kwAPWdybEec8jon+56LXfPZjWdxuARb7i2n83Bq4nhfW8iiF6iLrImUBv5QPYtXvQejkOPY/u+RTg5d5jna2MwOoSmUrjtXdzO6v5dC8sLxmSWr6nGGPRNu8+ywJabGtp+h4R1rUZwqpeua0Pzui8Z6Xq+4/Rour4ppSCL95wwamkOw+d4KghVOOvYnphWaiiJYekOY9W+uwOFUeOknQ0WC3F+i8eG/X8iyrSUYBSvTPZc2cHlUqls8NCzxsfzmEs4cSNCbmHqPrW72sP6uclRNoGgCh0tBz5nse2rsgiKvbqGXm99TTaDUwdIbw9z0rmoKukkqVkABqls81C3ysnRrmC/+9lG6Dk8NRWta5ueXP91G3SIfetbatlAwqVrgoWGxj7VvKOPrf3mSQ89Hp2zrC2n88mfaWXlbqZXhHzt33FULPNQv9rHilhK6DqdIxZJTtl/7hjJ+4a9a8Zc4yKUMUvECRkHiCzsorXGxaFOIcKWTr+x/DQjNWLKH7sEdnPVFOB0+KsILWdz0FrzuMjr7X5j1g2oyDs2L31NBOjtCPD17a0gkcWaK4MrpSUBMU/dT4HGVEPBV4XaG0VQXDs2DqjpRFJXpKBhZIvEzF5yD0+HH6ykHKWmuu7k4+UIINNWJVJ04NM+E0ByNnSKdHaW94U7CgUYGRg8QTXZPhB5c67i8KovWBZASnvnuENseGJnT271pwI//vZdHvzpAOmFcN5aBujYP7/+TRh74j97XqNCcA1LicodpXXovpmlwdN93yaTsEAibawPFrVHzxqWU32BVXMiNJOn58R7khdyTiqBsSzNCVZCmydiOTmTBep4lT4+Q6Y/hX1CGpzZEoL2CyO7Xnvvc4VK44f5K3D6VvhNpTuyKX9L9b76vkmU3lRAZyPHdz3Rwem+cgi7RHIKyOjft64MESh0MzpBF/vS3+nnloWE8AY3aNi8f+v/aEbNsyC2EYPnNJSDhpZ8M8cw3+4kM5lA1haU3hnnnHy6gosHNG3+ljv/94xMY+rnfSqjCwS/+dSt1C30ceGaMR77Uw1Cn9TyvaHRzz280sPymEt77p638268eYri7+CVk/VvKWXVbKbGRPN/7TAcndscp5E1Ubfy41wUpqXLSd2KqwPWFNO75eCP+Egev/HSYR/+7h/iI5fX0+DXqF/tYemOY3Y+NXnKjzVURmplclIHRA0xWBn3De1jZ/h6aa28iEu8gluy54D6mi5BRFA1NdRFP9c9JqJ61HhZzdm7nRtJUNy11t1JdtgIpDTK56IToExdoG2+aBQrmhWPNVMWBprpASsqCrZyvmvJ6Ct3IFZWTyOQi7D3+bRqqNlFZuoTK0iWks2P0De+hb3gvhWs8c9/tVfEGNUxD0nsyMy+hWMhLEmPXbumM6WhZ6cfjn/6l5PWGomi0Lr0Xj6+CdHJw2oQ3G5vLhbexhIqb24uWCQGqx4mnNkR4VT3BRVUITSXdHeHkfz5L8uSFX4TclQFCS2oAKKTyRXGYhVSOyO4ufE2lKC6Nsk3N1ufXyUvy+ZQ3uFl3V9nE/BVVEKp0smRLmEUbQ/SdTPOtvzlFfOTSXddCwIIVfoSAfU+NsfeJ0SK7TGxY5/TexAWTRPMZk3wmT3QwTyFnxTWqyuxzFBwuhV2PjvLdz5wmnzk3yMs/GaK8zsU9v9nIwvUhfCHt3LELuPEdVTQu83PmQJKv/dlJEqPnzktiTOcbf3WKT351OVVNbm64v5Kffr7r3LEJWLDcj1Dg4HMRdj02UnR88RGdjn0zH3egzEFlo5ts0uDxr/YWufaTkQLD3dkp5/JScc0kA1nZ1acpDTYT9NVeVGgqijYleUVKE1MaqKoDweyv3elqgU1HXeU6Gqo20jeyhzN9z5PTU0izgMPhY9Pyj15kkIvNwZp7MjXA/pPfn1Eonx+/mslFON71czoHXqI81EZNxSraG+4k5K/n0OkHrukHt8MlUBTLeFvQr96d1uVR8IU1VE1QyJuk4kbRzWM6PH4VT0BF1QTSlOQyJumEUfT2Oh1CgaWbg5dy+tc1iuIgGe9nsGcnje130rrkXo7v/z6FwmvTnWhz7SCEoPKmdipvap9xHWmYxI8NMrztJENPHyM7ePGEltCKOpylXqvhwalhMv2TqpFIy31ee88KVKdmuc+9Lgqp6zP7fNmNJSy7sWTK8kyywAs/GuTpb/Rf8tqUUjIRd9mw2Eew3DFt1vjlrESi50y2/WBgynNCSjixy7IyunwqgTLHhNB0e1XWvrEcgB0PDxeJzLNEB3Ic3xGnotHDkhvCPPzFnnNloeS5ULK6hT7CFU4ig1MNWDMdt54z0XMmbp9Ky6oAvSfSmIXi59XlalZ1zQhNsKx6AOZ4RoeUJhKJoqgIRFGLSo+rFEUUW4UKRpZcPo7XXYbD4SOXv3TmeoFCabAZw8zTNfAymVx04jOnwzcx9/miF1Lk8gncrjBCCPQ5PWgluXyM3uFdDI4dYmnzfVSULMLnKSeRmhpCIK0DuuK4fSqb3lxKeb2Lkkon5fVOPAEVRRW89TfquPXd52JcC3mTn/5XH11Hi63NZbVO3vV7Dbh9xd/9kVfi/PxrAxcW9ALu/WgtLSt8/OBzPQyeybLhTaXc/t5Kalo8ON0K2ZTBYGeW7/1TNyd2T41xKa91ctv7qlixNURptROnW8EoSJLRAoNdWQ48H2Pbj4dJxc6FYniDKm2r/TQs9tK0xMfyG63yPze/o4LFG4pFZzZl8I2/7SQZvb6stPPFMPL0nH6WdHIQRXXS0Hobje130nHsUaT5+jgHNleH2RoYNK8TT10YT12YfCSNmZ85blxoCuWbmyey0sd2dU1JHIofHyQ3lMBbX4KnPoy/tYLo/gsbVq5VRvusDkBnEYp1n6+od3PD26toWubn0S/3sP/psUvqjt318xE23VdB69ogn/jiMp7/3gD7nxkjOpS/IqXuMkmj6Lgnk01ZmeOKYlk+z1Ja46KszoU0welWWXZTeJqtBS6PAkjCVU6cHqWo/uiex0a54f4qmlf4+cQXl/L89wfZ9/QYkYHcRY87NpTn4PMRNt1bwTv+YAEtqwK8+OMhOg4kLmpYebVcM0LT6y6nsnQJhpknnuoDQDey6HoKn6cSj7uUdHYUsEoh1VasRkwRmjmGIsdorruJBTVbOdX79KR4RYGmujDNAqaczwPMSp4RQikSlYrioK5iLZo6tR3XXCgYOYbGDtFcewuNVZvp6N+GMWG9FDg0D0Io5PVz4sflDGIYuSIrp2Hq5M+GAkxzH5WmQcHI4naFcDn8pF9Fhv9cCZZpvO0TdRNuYyEEqipAQH27h5qWc+cwnzXxhab+PDWHQm2rh1CFA80h8PhVNIdCJmlc1IotgNZVPtbcXsL2R8ZYe0cJ9/16LUZBkk2ZGAUrQ7J+kZf8NGUeKhtdfPxz7SxY5iWTNIgN64wNmHj8Kv6wRkV9iPqFXnY9HikSmi0r/Xzk/2vB6bZuOm6vVUqqssFNaXVx+8VUrIDmfD0V07cSh6Q06el4DrenlJrGTWTTY/R1vsR161O0uS6I7u1hdOeZomUCgeLScJX7CbRX4msqxdtUSvUdixl+4RSnvvAcudHp6w07S32EltcihKCQyRPZNbVlqx7PEt3fi6cujOJQKd/SfN0KzYPPRfjGX50qukw1pyBc5eLOX6rllvdV88H/r52v/NEJ9j89NvOO5sjxHTG++5kO7v5YPY1LfLz/L1t580frObQtyis/HeL0vgSF/OW7d2QSBfIzFKAvSq+YtDxQ5sDptrxgb/udxinbTUYIgaoJnG6Fyb+0k3vifOdvT3HPxxupW+TjvX/Wwpt+rZ7D2yK88uAwp/YkZiyMX9AlP/zsGYSA1XeUseX+Sta9uZyeoyl2PjLC7sdGGRvIXZZb7lURmkFfDe2NbwCsi9qpeQkHmnA5A3QPbieZsUoQGUaegdGDtNTdyoq2dzEaPQEIwsFGBGLaMkg9g9sJ+euor1xHONBAPNWPaepWso27lGOdjxBNzK5f82QkkuHIMcpCrSxqeguDY4cQCEqCzbgc/hkz2udC9+AOAt4aGmu2UBJcQCI9gJQSl9OPz1NJ/8g+zvQ9P762YEHNVsKBBhKpgfHxBX5vFWWhVsZipyeE+WRMWWA0epKSwAKWtdzPaOwkYIUinOnbNiUb/lIyNpDnn3/tOGdzpkqqnHz0H1pxeRW+98/dHN95zi1lmkwJhAYY6sry6V84jMuj4A1qfODPm1ixNTznudz8zgrq2j08/Z0hXvjJCCN9eYSwanrWtXnoPz117NveU8mCZV66jqb56l+esVwPBlYgdo2T1tVW7OVYf/Hv8tTeJJ/9lWMTtct+5TPNVC9w89S3B3nhJ8V1V43CtRNz6vKU4PGWompugiVNCKHgC9ZQXr0Co5BFz6dJxs9VOHB7y3B7SlA1N6HSFkAQCDdSnktQKOTIZxOkkzOXqzINnY5jj+DyhGhqv5Nseoyx4aNX4EhtXq/EDvfT9e2d03+oCFS3g/JNzbT+2lZclQGqbluE6tQ4/HePYmSmuj7DK+pwlngBSyw0vX8DxjSlkHwN59zNJWsb0fwuCsnr031+PoW8ZKQ7ywP/2knjMh9ta4Pc/os1HHo+glGYi4qZ+YXbNOCFHw1y7JUom99ayYY3WyWGbnpXFRvvrmD/M2P89N+6GDxzefIUDF0iL9KC9Hw0h0BRrTqjh7ZFpi8/NIl0vDDVNW/Cyw8Oc3xnnM33VbDhLRVUt3i48R1VbHhLBQefi/CTz3fSf3L6444O5vnan57kxR8PcfO7q1m0KUTLqgDNKwPc9os1PP6/vbzww6FL3sXpigpNKU2ryLnmoaZ81cRy09RJZ8c43fccA6MHJsUnSroGXsKUBlWly6gpX0XByDEaO0XP0A4WN72FQqH44swXUhw89UNqy9dQXrKIslALQqjoepp4qo9s7ly8jGX9S00bx3jus3M/hoGR/aiKg9ryVTTV3IBh5IgkOjne9Sj1lRtwOQNT9lMwsuQLaSQX/+L0QppDpx+gpnwVlaVLrA4+KOQLKaKJLkajJyefTUajJ3C7QoQCDZSpbSAl2Xyczv4X6RnaWVRHczI9QztRFI3K0iXUVa7DNHWS6aHLXrC9kJd0HzvnbkjHDUxDIiUMd+VmlYEtJeTSJrm0SSJSsLLN58HijUEe+PceHvpSf9HNLxkp0HFgqrVCKNCwyHqA7H4ywqm9xW71nhMZek5Mf3FnksZECIDHr07UCh0bzF/TWedVdWuobbph4u+CniYYbiQYtt7G08lhDmz/7/GKDYKaxk1U1a2dWF/PJykpb6ek3IqBi0fOcGTPtycqPFhiNVUUj6znk5w89ACLV72XxrbbSaeGyaanvjDZ2Fx2TImRzjP49DGMfIFlf/pmVLeDss3NlG1qZuiZ80rdKcLqba5angvV7aBia9tFh/E2lFju833Xp1VzJrJJg86DSdrWBqlodOMNaiTGZpczIIQVw39BJIz05HjoP7p5+hv9LNwY4ob7K1myJcyGt5RTtcDDv3/s8DXTVlLPmVZMpAqPfKmHEzvnGdonYawvx8Nf6OGZbw3Qvj7IDfdXsvTGEtbeVUZVs4fPf+wwY33Tv7joOZPDL0Q5+nKMyiY36+4qZ9O9FVQ1e3jPn7Tg9mn8/L97rt/yRunsKK8c/OI0STxyRpe2Yep09r9Az+AOhFCQSAwjD0j2nfzetNGreiFD58CLdA/tmIjjlNLENAtFgm9o7AhjsVMUpuk+MjBygOHI0SKrqSkLdA++Qt/wnnG3vYlh6EhMTvY8OW3/8CMdDyKEMusi9AUjS/fgK/QO70IR2sTcDVPnfJv2SOwEo/HT4/U0rbgOKY3xdWfGMPN09D1P18DLiPGaDvMPKbj+EEIw2pfl2R8Mz/oNW5qQiFjnZ/GGICVVQ0QGr91Eq0tBz+nn6et8ecbPLZf3WaEv6TzxBN2nnrnA+sV1W88cf5yuk09R0Iutx5nUCPtf+TJCUTAK18ZDwub1zdiuTlJdYwQXVll1NTc0MfTcCZhk1XKVnXObSykv7oIcf1woLo2yjQss9/lrLFJEdYzHKJ53XFJah6/MkOmtOgTBcue0n01HKlZgz+OjHHwuwuo7SvmFv2qjYYmPNW8o4+lvXhuNH6KDebJpA3/YQXWLd/5CcxLpeIF9T41xaFuEFbeU8kv/r426di/r31TOY1+Zrp72OUxDMnA6w8++0M0LPxrkXX/UzIa3lHPzu6t48ceDl7RSwBV2nct5u2ans84ZFxFvpqljMvPJktKYtu85WKLSLEwvvKaby0zZ3TNZFS+GaRYwubjwk9LAMOZj1ZPzntv1jpRWOaW5uqhfeGCENbeFWbwxwB99dQnbfjzCzsfHGO7JXTTb/HrENPU5VS0wjfycWkZa60//mWHkwO7yZHONYOYKZAfiltAUAk91yBKUkxRUaHktrjIfUkrSnWOc+M9nMbIzXz/hlXU0f3ALiqZSuqGJM9/cjpF+7dyTfWGNllWWly8ymCeTsO630rQSZoQCoYrpxWRprZuKBitmfy53Vj1nsvuxUba8rZLlN5dQ2fTqcicuJZHBPD3H0izZEmL1HaW88tOhaXMB5kMhL9n31Cgn3lrJ6jtLqVrgmf3G0hLBT3+jn1W3lRKscOLxa5dUaM6yRKmNzWuLsx0R5sLhl2N87a/O0H86S3Wzm3f+Xj1/+s2lfOwfW1l1S3gi2cfGxua1x1mXuPXfojiEcNxtzriFbmx3F2O7Ookd7Jvxn8Gnjlk91AFvfQn+lvIreTiXDc0pqFrg5h2/v4D6RT5MQ7LzkZGJEnZ63pwoebTilhJ84WJ7l8OtcPsv1kwkg55v81RUQXmDa8akSV9Io6TaBdLKtL5WOFsSqZCXLN4c4pb3VU97DEKBkmon3kBxsrOiQnmDa8aQAm9Ao7TGBUB0aKoRzl+iESx3TB/6KqBqgQfNKawuS5lL+5Z/zWSd29hcSeZTAsM04KWHRjnySpyNbyllyz3lNCzysvHNpay5vYRDL8X43me76Z0hVtPm9YMQ8I73eLj1djff/06aZ5+ae6JHVbXCbXe6Wb3WQVm5gpQwNGSyf0+en/woQ84uNXrFUD1OPDWhib9zo6miZBBXqY/wijqEEJgFg9FXOi5qisuNpogfGaD8xlbLfb65mdjBvst1CJeFlbeW8rv/c85qKITA5VUor3cTKHNgGrDj4RFe+em5lrKGLtn1yDArbi6heWWAX/mHhbz0wBDJaIFQuYN1bypn4cYQI705yutdU8Z0+1Q+8o+LKORNju+I03siRTpuoChQXu9m4z0V1LZ7iQ7l2TdNpntlo5v6JT6rFrJfo6zOheZUUFTB5rdWUr/IRyZRIJM0GO3N0nEgeclCGvY+McqLGwbZ+q5q7v/dBSzeHObwtgixER2HS6GszkXTMj91C318/S9OcuTF6MS2To/Kh/6/hYCVdd97LE0qXkAIKK9zs+HuchqW+IiP6Ox5YupxL7+phHs+3sjpfQlO74kz2pdDz5u4fRptawJsub8SRRHse2psomPQpcIWmjY2cyQ6rPPY1wZ57ocjtK70sfX+CtbeUcLqW8OUVDr5548eIzr02o7ftLkwgYDgAx/ysWiJA4cTnn16bmVD2to1/vHfwixaoiEE5PPjCRIOWLhI48EHsrzmAvquYco2NOGtDwMgTUn8yEBRfGZoeS3OUh8A2cEEiRND0+2mCKkbjO3stNpbCihd10Tnt3dgpK4dK9zFKKl2WdbDcax8C8hnDPpOpHnxgSGe/97ARKHxs+x5Yoym5f3c8t5qlt9cYvUsNySqJsilDB77Si+ZZIF3/0nLlDFNU1LQJe0bQizcGEJKq1qHEIw30ICB0xl++Nkz9J+e+tK/9q5y7v+9pmnjQ7e8tRLeeu5Y9j8d4b8+cWTO3q+Z0HOSH3z2DImIzs3vqWbFLSWsuKV04nMhrOOLDOTIpYrPmTQlhbzJ4s1h2tcHzx03VkyrlDB4JsOP/6WT3mNTE1pzGZNAqYPN91Ww+b4KTENOVE0RipVk+9JPh3jw810zhjTNF1to2tjMk2zS4NCLcY5uT7D69jAf+XQLDYu8rLolzLPfn75N3eXqvGBzbZHJSo4e0amqVtmzKz8nTahp8JFf97F4qcbQoMmX/jPJ8SOW5aK0XCGbkWTS9g/psiNAcWqUbmii9aM3IxwqUkpyI0lGXjp9bj1FULapGaEpSCmJHeidcIlfjMi+HgqpHI6AG19TKf4FZcQOXRvJKzNhGlY9xunqHINVrzEVLTDcnbGqgkzzU9VzJj/+5zPsf2aM5TeVUNHgRiiWUNr39Bgd+5L4whpDnVkig8VF2LNJg//5/WO0rQvSuNRHSbUbl9dqnBEbynN6X4Kjr8RmdJvv+vnItL3ApyM+qmNO6mu/76kxRrqzZFMGuRmKnA91ZvjCbx1FIqftipRNGjz4+S62/2yYZTeW0LDUhy+ooedMRvtzdB1KcXpvgtHeYpdFLm3ylT8+Qfta67hLa124vCqmIYkO5TlzIMmRl6JEp+kWBLD/mTE+95FDtK4OUN3iwV/iQHUIskmDgY4Mx16JcebA5ak/agtNG5tXiVGQHHguRvfxNIvWByipnjlbUpqSs7lbDuf8YzodLj8VTesmmhZI02CkZy/5TOwiW9pcCfQ8/M2fxgmVJBkZmpt5IBBUWL/JiZTwf19J8c2vXrslsK53Au2V1N238rylAsWp4izzEVpSTWBhFYrLanls5At0fnsHmb7oxNrOsJfwqnG3uT47t/lZsn0xkqdHKFlVP9H7PHa4/5o2VksJp/ZcvBXnxSjokmOvxDj2yvT3rMSozoFnI9N+FhnMs+PhEXY8PDLt5xdiuCvLcNf84k5Ge3OM9l44DCaTMDj4/PTzPotpQv/JzIz1LmciNpRn56Mj7Hx07sdt6JKOfQk69r36726uvLaF5kzd5S/LUAKktC1WVxBxXjy+EJfPYuhwCWpaPAx2ZslnzCnjlNU6Ka91YRow1jez60vPmRO15NpW+3G4lYm6mnOajztA49K7UFSrS5VR0EmMddpC8xoilZKkUnP3QQUCgmBQIZ+HY0deHyXHrhalG5ooXd809YPxG8vZUnxSSvREls7v7KTv4YNFQjC4tBpXhZVdnY+mLaE4S8xx93l4pSVUSzeMu8+nKQZvY3O9MmuhGWpYimkUSPQdv/jKV5TpGw8GaxdRtfI2Tj/5VQz98kbNVy0r5YZPrCLalWDb5/aip6//h4OiOimvX4WqWfE3EklirJNU5OoVFXb7FDbcVUqgxGEFcgdUFiyz4qJaV/l5/6eayCQMMimDZLTA7icil6xnuDeg8ev/1Iqel5zcnaD3VJZ0vICqCaqa3Gx8UylltU66j6U5sC06435M0yr4vuyGEMu3hviVv23m4AsxjILE7VNxOAXPfH/4sveetbl2cbmtmCkpIT8PN5bTBV6v1d61UJCkUxLd1i1TmKjnPE0WrpTSKtiuFygkc0T39dDzk32WiJzcEUYIym9oRShW7czYoX7ykblZoCO7uzDetwHN68TbWIq/pfyad5/b2MyFWQvN8kVbKORSJPpPXDOBZg5viJLmVQwfeQF5XvSq6nTjDldane0vMwu21lK/oZLqFWXs/dYxImeuvGn6UqM5PTQsfSMubxiwbrxdhx695ELTNC2j82x+UYESB+/5w0b855XDME1JZaOLO3+xamJZOm5w5lBqitCUpvXznetPuKBLkpECbWsCNC3xWvswseK4lHE30I4E3/67rosWcn/pwVGaV/jYck85W+4tY9PdZSAtA/xYf54XHxy1heYVoqpa4dd/y088JvmPzyXITfKKve2dHtZvchKPmXzx35PEoud+NHe+yc2td7h44udZnnnC2qixSeWDv+rDeV75kZe25fjZT2Z+2fX5BVtvcVFTo1JZpbCgRcPtFggBv/IxH/e9/VxNvHxO8r9fTtHdOdVS6vEI3vgWN3ff56a1XcPjUUgkTI4e1vnx9zNsezbHDKWBXzdIw6TruzsZfPIirU0lmPkCubE0mZ4I+WgaWZh6TQpFMPz8CWIHekFC4uTQtOtdiOSpYY7+0+OobgdIyM8yvtPG5nrhunad+yoaCdYtYvjoi/PfiYDmm+sQCnQ82zfn/qUAAwdGiPUkGTsdJz1q1xyZLZGhPJ/9yFEURTDYefHzFhnK848fPoqqXrxVpmlKBjqK9yklfOcfunjwC30TXX5mSypW4N9+8wSNS7w0LvZSUuXE6VEwdElkMM+pfSk6DibJpi7+kMkkDf7vr8/w4k9HWbQ+QKjcQUGXxIbznDmUJjPPtpo2c0dKuOlWF2634IffTdN5xjr3bg/c/y4PGzc7yeXg0Yey7N9rvUAIAW++x82b7nbz4nPnlGkgqHDTrS5CYQVVBbfbsipmUvKCQrOuXuWvPxPC5bJ2rggrIQhg8w0uzEk/qXTK5IEfZKYIzXCJ4FN/FeTN93iQJkSjJomESTCkcOddbm6+zc1Xv5zkv/4tSf76SWq+9JiSsR2dl2x30jAZfanjVe3DzBsMPX2teQptbC4dl1ZoCgV/ZROBmnaEqpEa7iTeewxpWA91d6iSYP1iop0HCNUvwRkoI58cI3JmP4XMOSug4nARql+Ct6weRdUmvOOpkW7GTu3CFaogVL+EkgWrcPpC1G+8D0wTPZtk8MDT56ybEpzeEIHWdTh8YXLxYaKdByhkz2WcOX0ONv3aMlLDGTq39WPMQ2h2vTzAdz/wOIZuULjEhU5fyxTyck69vue6/nQMduaAudc0BKsF5aEX4xx68dW3DtNzkiMvxzny8qvfl838iYyZ9PUYrF7npKZWnRCa4bBCS6tGLCbx+wWLl2oTQtPrEzQ1qaSSko5T515Yjh7Rec9bR/H5BOEShb/8dJDlKy/eRq+ny+A3fzUy4Xypq1f5878NoqqCf/q7BEcPnbOQG4bk9MnilyRVhd/47QB33+eh64zBv/5Tgl3b8+TzkmBI4e3v8vDBj/j54K/66Thl8NMf2xYzGxubK8el8ysLQdXSm2ja+h6cvhCqw0Xd+rupW383QrH0rNNfQvXK22na+h58lU0IRaFiyY0suPl9KA4rFlAoCvUb7qVq+S3omTimoVPaug7V6SETseJWVIfb6nsuTUyjgJ6OkU/HLLE6ySeqOJw0bL4fT2ktiqpSteJWGm98F2I8gQIgWOsj3Bh4VYcuTcjF868xkXlthEfY2FxOdB2OHimgadDSfu69u22hRiCg8PgjWTIZycrVTs6G9IVCCrX1KmNjJr095655owBjoybdXQaHD+ok4rO7htJpyc7teba/bP1zYL+OYVhhJUcP6RPLt7+cZ9cOnWSyeL/LVji4/10e0inJX34qxqMPZRkeMolFJd2dBv/5r0keeSiD2w0f+JAXn//iHoErhepUcHg11FdRgcHG5kohHI5z7gabWXPJzpg7WEHlspvo2fkQkdP7AEmgdiHNN7+P6JkDJAetumOq00O85yiDh54DaVrr3PJ+POEqUsNdODxBgnUL6dn+INHOAyAErkAZUhoTQjM90k16pBuHN4CnpIbBg89OWE0no6gOxk7vYeT4yyAl4aYVNG55O65AGdnoAAA1q8rR3Ff+hyMUgbfcjaIppEcyGHnLP+bwaHhKXRRyBpmx7AWT5hVN4Am70NyaVd8toZNLzK1m3wVmeCl2YmNzzXP4gI6UsGjxuRfQZSucKCo8/WSWDZudLFyk4fUJUklJQ6OK36+we2eOTObqv5C94U1u/H7BS9vy7Nk51S+u6/DEz7O87Z0eWto1GptUjhy6NoI1b/691TTfXMvp5/p45u932++3NtcszsZ6St/3doxEgtGvfx8zkbzaU5o3Yvy9biZ9IQQEyp24fSq5tEF8JP+qCvhcMoXlLW9A0ZwIRSPUsAQA1eUBRcFTWjMhNM1CnljPkYkjzCfHkKaB6rDaWElpIqVE0ZwgBEJREaqGzM79xmjks8R7j05YOXOJURBQt7YWf1WQ8oVh6tdXIhSoWl7G3f9805Qske3/fZiB/cU1q1SHwqaPLae8PVy0PDWSZdu/7CGXmD4ZZPk7WmncXM0L/7qPtjvqWf3+Ragula6X+nnus3twB53c/IdrqVpWRj6Z5+APT7Hra0cxzwsuF6qgfkMlK9/dTuXiEtwhF2bBJDmUoeulfvZ+5wSJvtkVpLWxeb1z6mSBdFrS3KridFmWyVVrHEQjJqdOFDh1osDaDU5KSxVSSYP2RRqqBseO6Fc9m9vltiyaALmcZOstrmkT3RqbNKS0stErq64doekJuwjW+PCEp7YatLG5lnC1LsDZVA+GiVZaQv46Fppt68Pc8M5qXvzBACe2R4s+c3oU7vxwI5vfXk2gzEEqqrPn0WEe/UInqXlWcblkQlN1uhGqg4rFW4oywLPRQYzcpLg6KTHyk2KEzt4Ux/1SeibB2Mmd1Kx5I4GaVlSnB4cnQP+ex+acKiylgaHnJi9AUQUbPrKMYG0KAWhu1erPGnRSs7JsyhDu0DQxVgJ8FR7K2kIoDgWHR8PpdRDrTaI6VWCap4+A0pYQzTfXkRrO0HxLHZlojlC9n/Y3NJIazlLSHKR0QZDUUJpQY4D1H1pK//4Renaca2emqIKV721n00eX43BrpEazRM7E0VwqgWovq39hEfUbqnjsL15m9MSrqalomxZsXh90dxnEoia1dSrBoIJhWK7zgT6DgT6D40d1tt7iYuFijd4eg7aFlmg7cujq1wxyOgWVVQpCCG67081td7ovuL6UEp/P9lbY2MyV3MkO8me6KUSiFEZGr/Z05o+ANXdVsOHeKhKjOid2RCfpMLj5/XW86dcbUVRBPmMSLHdy6y/Vo7kUfvDpk/Nqx3nJhGY+GcHQM3Q+/11yyeKG7tKcg81VStIjPQRqWon3ncDIpcmM9aFnppYMskShYC5uXtOQPPfZ3eQTPSAE6z+0lIV3NTKwb4Tn/2UPpl481/g0lkEjb/LU3+5Ac6k4fQ7a72pk62+vntX4iiZof0Mjj//ly/TtHWHlu9q44ROrWP6OVkZORPnRR58mE81xx59voPX2eho319Czc2jih9B0Yw2bProCpOSFz+/j+COd5BI6QhVULC7hpt9dQ+VS6/9/9vvbXhM1PScQAjHpu5bMo07R9TD2pLEkXLGmAyDGx2Z87Ct4fq/i2KmkScdpg3XrnZSVK7jcgooKlZe25cjl4MA+HU2DJcscbHsuR9tCB5m05NSJq39tCQEOp3XWDh/SOXZEv+g74uS4Uhsbm9mR7+5l8F/+y6pMY1y/15DLo1K/xE8hb3LspUjR/aKiwcOtv2g1D3j5xwO8+P1+6hb7ue93mll/dyUvfK+fniNzt+TOSWgqqobD7beK2QJgWSelaZIa6iSfjFK5/BYGDzyNkc+gaE7coUqSQ2cw9dln+oablpNPxUgNnsE0CyBNhKJOqZVZyCRwBkpxhyrIJ62WT0XW0umQEOtOkhqOIQRkY9a88imd0RMxDH12D3Ujb2LkTXIJndTw7LM4hRAMH4/Qs3MII29y+ple1v7yEtwhJ8cf7STaZQnq7lcGabm1nlC9D0UVmAWJw6Ox7peX4PRp7PzKYfZ+83hROaa+3cNs+5c93Pdvt1C3rpK6dZWceb5v1nM7b6bz3O7Sojk8+EsbCZQtwBuswukOoqgOTFMnn4mTivUTHz5FKtqLUZhbNrmiOihvWI2qWVagdKyf2PDJos994VpCFe34wrU4PUEUxYFpFtCzCdLxAeLDp0mMdWEUXl1ZK0V14C+pJ1y5EG+4Fqc7AEKhkE+TiQ8RGz5FfOQUhfy4d0DKSdfhqxhXc+EP1xIob8EXqsHpCaGqTkzTQM+NH+NIB8nRTgr63LKVhVAorVuB0x0EIJeOMNZ3iLN3NiFUvMEqQpVt+EsacHrDqKoTaRro+RSZ+CDx0TPERzoo5C9fKIiuW27wTVucNDVrBIMCpwv27bYslqdOFkjEJcuWO6xEoDqVvl6D0ZGrX+vUNJnoe/7iczn+6e8S10qZYxub1xxSv/ovl68Wp0ehpNpFNmkU93wXsO7uSkJVLvqOJXngs6dJjul07ItTt8jP1vfU0LY+dLmFpiTUsBR/VfPEErOg0/HsN0mPdFPIpeh68YfUrXsz7W/8tYmJZ2PDpEd7J4SmnGKdGX9gnr07CoVsfJiaVW8gUNM28UBNDp6mZ/uDFLLnDjLSuZ9Q4zJa7/gQhp4lFx+h45lvTCpvJDn/9f5SPJxfLbHu5ITlNJvIk0/puAJORk5EJ9bJRHNIKXH6HFZ7SySlLUEqFpeQT+qceLx72pqfQ0cjxAdSlDYHadjwaoTm1UXVXJQ3rKa65QY8gQqEop3r5DGJ0trlmAtvJRXtoe/E80QGjkx5IZlxDIeb+sV34vaVAjDad5D4aAfSNPCF62hY8gZCFW1W7PE0Y5fULKW2/WaS0V56jj5JdPDYvKxw/pJ6Gpa8kWBFC4o6daxQRRtVzZtIxwfpPf40Y70HMM0CpllA5eLlc6ZDKBqlNUupaduKL1yHojqmP8bqJdS23UQ6McTAqRcZ6d6LacyuEKNQNGrathIsWwBAMtpLbOgkRiGL21dG/eI7KK1dhurwTDt2uGoR1a03kkkO03f8WUZ69s76u50LUsKxwwUEsKBZpaZWJZ2WHDtqPVSGBk36eg2aWzWaWzX8AcHBfQUSiat/L8nlJH29BouWaLS2a6gqV70ou8Or4a/04PRqIASFbIFMJEcmmr9onWJFFfirvVbIkoRsLE9yKDMlTn3KdprAW+rGHXahOhQM3SQbzZEezWIaFx5TiLOeA+tv1akQqPHh8jswDZNMJE96JDPzfgR4S1x4y9yoDoVcUifRn54y5xlvDcKKVfVVeFCdCoVMgcRghnzyMoRmKApqOIjq94EQmOkMhUgUChe5rs5enxPPaYES8KOGApYRKJejEI0hszO/7AunAzUcQnG7kaaBEYlhps97eZ3pJKkKaiCA4vdaORtGATOZxogn4EIe08l9iRUFrSSM4vMCEiOVwYjGLmydFBP/mt08p0NRUIMBFL8PoapIw8DMZDDjiVkJV6FpqCVhFK/bCj1MpjCi8Qsf94WmowqcHpVUVCebPHfsHr/K6jeUg4SXHxgkOd4qWZrQsTfGje+qoXKBd15jzlpo9mz/6UTCzlkkklxseOLvzFgvp576Kk5fCYrDiVnIo6dimAXrwZQc6uTEz79UJBbzqRinHv+fCXd7acsawk0rOf30/5FPRQFweAI0bHk7JS2rGT687dy2iTFOPf4/OP1hy/qTS008iOJ9xzn52H9j5M/98LPxEU4+/mVy8asXX2Flh+cnfqfSkEhDYuSNohuLWbDa5SjaubIfFYtK0Fwq2ZxBw6YqqpaVTtm/UASaSwUgUO2zWqPNozbo1cTtK2PBqvsIVy1CCGVaEXIWIQSq5iRQ1kx7uJ7hzp10Hf75OcvfHPD4K1BUB8GqRbSsvh+nJ3TRsYXqIFDaxMIN76fz0CMMdrw8h5uQoKxuBc2r3orDHZhxLGscDV+4lrZ176YvWMVw127runLO/cJ3uPw0LnszFY1rZhTw5x+jL1RLy5q3U1K1iDMHHiKXjsx5XJcnjOb04AlU0Lb+PXgCleP38QsftzdYTevad+AJVNB95AmkeemV1LGjOnld0tKqUd+gMTxk0t1pjZNJS44d0bnjjW5WrXHg9QoOjmeqX230PLzyUp5b73CxcrWDBS0aJ49fHaXpLXez/G0ttN9ZT6DGh8OrIcCqoBHNMXIixrOf3UO8d6p12tRNytvDbPzIUurWVuAOOkFKsgmdgf2jvPKlQwwdnfqbc4edLHxDA6231VPaEsQdcqJqCkbBJBvLM3R4jJ1fO0r//tFpQwoaNlVxw28s58wLA2z/8iHq1law8SNLqVhSgsvnwDQk2ViOYz/v4vl/2TdlH/5KD+t+eTEtN9fiLXOjOBTySZ3EYBo5KZbNKJg8+f92MHqquG6uv8rD2l9YRMuttZbQdCgUsgax3hRHHuzg4I9Pk09dgu9TCFwtTQTvvAVncyOqzzshNPM9fcSfeI7s0RPTihfhcFD6i+9EC4cY+d9vI3Wd4BtuwbtuFVooCIqCzOXRR0YZ+cq3KAwOF+9AVfGuWkbg9ptwVFWcE5rROGamWGhmDh4l9rPHJ+6hit+Hd+1KvKuX46iuQvF5zom1VJp8Zw+xx54m39E1Zd7h+96Ee1Eb0Z8+ipFIEnrLnbjbW1C8ltA00xlyHV3EHn6cfFfv1HOmKJTc/xZc7S1Fi81UmtH/+x5G7CI1kIXA2dxI6M5bcC5oQPGdE5oyk6UQiZA5cIT409uQmWk8YqqKZ/ligrffhKO2GsVzVmimyZ0+Q/yxZ8h3dl94DtMgJdO+NLWsCVHd6iUxmufAU8UJ0JmkgWlabZLnw6yFZi4+cvGVAGkUyMWHp/3M1LNkxootbNIsTJQtQghC9YtJj3QT7z12buzEGHo6PkXoAhh6lkxkYOryfIbMeW50aehkxq5+D9np3PPSlBd98/ZXeUFYCUpbf2f1RcdRXSpCna/QvDpPUbe/nPYN78Nf0jAhgKSUSGmiZ+PouRSmoaNoThwuH06X5WI+KzirWjajuXyc3vOjOYtNh8tPSdVimlbcjdMTGh/bRM+l0HNJjEIORXHgcHlxuIMTIlgIgeb00LjsTWQSQ8SHT81qvFBlGy2r70dz+YqO1TR08pmYNX8BmsNrhQxoThTVQd3CW1FUB1LO3bqnuXy0rn0nJTVLEOM1LuS45V/PJtBzKes4VQea0xpXKOr4caqU1q3A4Q5wYse35yw2Vc1FsKKV2rateAKV1thAIZdCzyUo6FkURbWO1xOcEMFnxW5N201kkiMMd+6c83FfjOEhk6FBk7aFGhWVKrt35Emnz10D+/fq3Pd2D+s2OJESjh6+dlxojz+S5Rd+yUtDk8rv/EGAv/mzGEODU+8x4RKB16vQ13vprcIlTQHe8FcbqV5ZhgDSkRypzgTStLwyvgoP0pAUstOP7S1z8+bPbCZY5yPalWTkRBRPyEnJgiAtt9YSbvLz4O++MBFadJbytjBbf3sVmlslNZJl5ESMfFLHU+KiZEGAllvrKF9UwkOf3Mbw0eiUcd1BJ5VLS8lE8yzYWsMdf74Bh0cjNZIhOZjB6dPwlXusSiLn3RI9YSdv/OuNNGysItaX4uCPT5Mey1LSGKD55lrcISdGzhKNqd4shXzxdxJu9HPX32yiekUZ2XiekeNR8qkCvgoPJQsCbP3tVZS1hXjmH/a86lh779qVlL77rSgBP0YsQa6jC0yJVlaCe1EbzqZ6oj95lOTz07woC4GjugpHdSWO2ir8WzbiXb0MI55AHxqxLHYBP4rbPdVCCfg3raPkXfeCopA9coJ8Vw/C4cC9pB1nQx0IgRGNWRbOVPE921lXQ8n9dyMcDox4HL23HzOTQw340Koq8KxahqO+huEvfg29p/jZrpWX4mxuxLdxDc6mRrSKUgpDI+R7+1F8XhxVlXhWLsVRVcHQf32VwtBU3SINE6FqCKcDxeNG8fusskazqKXpamum/EPvRw0HMaIx8p3dSF1H8XlRwyGcjfWgKMSf3jZ1Y0UhePtWQm+5E6E5KIyOku/pQ2gajspyvGtW4FrQyOjXv2e9IMyBQs4kMZonVOnCX+ogkyigOgSb7q9GcyoceSHCaG+x8HW4FIQi5pUIBNdaC0opyUQGLKvmgpXoqSiKw02ofjEufwm9k8Tndc08NZzqsIRBNpbn6EMdGBdxJ0XOJJAXEa8zc+VjNDWHh5bVbysSmaZpEB8+Rf+pbSQjPRRyaSQmQig4XH4CpY3UtG0lULoAoSgIoVBWt4JCPk3Hvp/OyfqlOT00r7kfzWH1lk7HB+g/uY3Y0Eny2ThSGggEmstHsGwBdQtvw1dSPzFXzeGhbuFtJMe6MI0Lu70c7iALVtxTLDJNg8jAEfpOPE8q1ocx7glQNSdufzmVTeupaFyHqrmoabtpzt+QUDSalr35PJFpkoz00H9yG/GRDvRcAilN6zidXnzhWqpbthCuXowyLvwCZQtoWX0/x3d8G2MOcZtCUa1jHrfC5tIRBk69QGTgCLl01IrHRqA53PjC9dQtvIVQRRtivGWOJbJvITJwhELu0sZsxqImXWcKbL7BhaLCgf16kQv62BGdbFay6QYX8bhJZ0fx78rvF2zZ6iIQFAQCCsGQoHGB9fa/Zp2DX/8tP8mESTIpiUZMXn4xPxFb+Wrp6zX4579P8FefCXH7G1w0NZfy5GM5zpy25lheobBwscbylU6efSrLP/ztpY3jdAUc3PbHa6lZVUY2mmfnV49w4ske0iNZywoSdFLSFMDpd5Aemz6WuWFjJcnBDI/+yct0vjKAkTNRHQott9Ry+6fWUdocZNnbmnnh3/YXbTdwaJTd3zjO8PEIgwfHSI9lkaZEdai03FLLbX+yjmCNl2VvbeHZY7tnPO6KhSFu+cO19O8dYefXjjJ2Jo6RM3AFnJQ2B0kMTH1pbX9DA/UbqkgMpHnwd55n9HQcpFWjsPW2eu76f5vIp3Qe+ZOXGD0VK8rp0zwqN39yDdUryuh6ZZDn/2UvYx1xpGlVQmm7o55bPrmaxXcvYPh4lL3fmpuYmIyjroaSd9yL4veRemUXsZ89QWEsAlKiBvwEbr2R4J03E77vTRSGRmYULkJVCd9zF2rAz+i3fkj28HGMZAqhqqilYbRQEDNZfF0qAT/BN96CcLqI/ewxYo89PeGmV556nopf+QVcC1tJbd9D9MGfT3Fj5850EX/yOfLdveQ7ezASCTAlQtPwrFpG6XvehlZWin/LRiI/+OkUkSyEwLdpHUY0zshXvk32yHGkrlvbr1hC6fvfjlZVgf+GDUQfeLj4gE2T6E8eIfrQz1GcTjzLl1D2wffO7qQLQfD2m1DDQTJ7DzL2/Z9aFlApLWEeCuBqrEeaclprpmfpIkJvudNqmfr9n5DeuRczkwUh0CrKKHn73XhWLKXkHfcy9PkvWyEEsySTLNB1MMGmt/m488MNPPvNXto3hll+Sxm5lMGLP+yfkn9aVutGUSAZmV84x7UlNIHhoy8AULH4BlTNiWkWyMaGOfP8d0iP9Fzl2V1dcuOu9XxSZ8dXjpCNzq+V4uy48hbNqubNhCrai4TXwMltdB99AkM/v2+5QT4TY7T3ALHhUzQuexNVCzZNiM2KpvXEh08z0rN31uMLoeBwepFSEh08xundPySXiRaPO275G+09QDLSw8JNH8A/LjaFEATLmvAGq0lGLuTSEFQt2IA3VD3JkmkyeGY7nQd+NiWpydCzpCI9nIn2kRzrpnn129Cmse5fjNLaZZQ3ri2yZI72HKBj30/Qc8U3KolEzyWJDh4nPnKG2vabqV98+0QsZ7hqIdUtW+g99jSz/a0IIXC4fEgpSUV7Obnre6Rj53sYJIV8mtjQcVLRHlrXvYvSmmUT59fjryRY3sJY74E5H/+FMAzLSnnTrS503co0n8yZDoNoxCqB1HHanGIxrK1X+bt/CeH1Fst/KSXLVjpYtvJcMfjREZP3v2OUrjOXzrL42KNZdF3yW58M0LZQo+3jU2/tyYRkbPTSJzAtuLGGunWVGHmT5z+3jyMPdRQ9qNKjWdKjF0mWk/DSFw5y6plzLsxCzuDEEz0s2FrLknuaqFtTgeJQiiqDFDIGL3/h4JTdFXIGJ57soXFzNcve1kz1slKEqiBneDn3V3npfmWQJz+9k2zsXAxyJpKjNzK9h65+QxWKKujaPljkEpem1ZY40pmgvD1MzapyRs4rNdewvpKmzVWkRrM88w+7iZw5d/3p6QJHf9ZJ1dISVr2nnWVvbeHIg2dmrM98QYQgcNNm1HAQvbuPyI9+VlRo3IgniD3yJFpVBd41KwjecTPZkx3TBvoKVcFRW8XI/36bzL5D547XMCgMDFEYGJqyjaOyAq2sFDOTJrVrX1EsqJlIktq9H1d7C56lC4k9/ATyPKEpc3liDz02Zb9S10nv3o9nyUJ8W9bjWtAAioAZDCvRnz1GZt/B4u33HsCzfDG+zetxtTWDpk6NVZUSCgZmITPF2nohhKailVuhbZlDRzEi0XMfGgbGWJT0WHT6bR0OArffhHC5iD/xbLGVWUoKg8NEH3gEV3MTjtoqPCuWkHxh+6znJk148Yf9rLyjnBveWcPaN1Xg9KqoquClHw1wZl9xSIBQYMEqq3vi0Jn5tYC+5oSmkc8ysP9JBg8+M7FMSvMKllm5domciSMluMMuAtXeyyw0r6xF0+Utoapl84T1SkpJZOAIXUcem4jxnYlCPk3nwYdx+UoJVy603Oiqg9qFtxAdOj4nF7qUkkxiiNN7fzxFZJ5PLh2h5+gTLNz0i6jjbU0VzUmgbMEFhabD5aOice25EkZSkor00n34sQtmzktpMty9B2+omtr2Wy4YW3k+qsNDbftNKOPtYKWUJCPddOyfKjLPxzTy9B5/Bqc3ZIn58UYK1a1bGO3ZTzY1u7Cas+i5FKf3/GgakVlMIZ+m+/DjBMuacbh81kIhCFe0XXKhCfDtr6fY8UqeQkFy8DyhGY2YfPITUQIBhciYSTZbfD/q7Tb4xK9FUNWLfyd6Xk7r2j5LX4/Bb300ghBW7OhsMA148rEce3bpbNziZO16J1XVCtKE0VGTE8cK7NuT5+SJwqW9lQpou6MeRRP07xvjxBPd86rGFR9I0/nC1N+DNCVDR8ZYck8TrqADh1slN8vKINKQDB+LgGzGGXCMV++Yfl2zYHLgh6eKROaFEIqw4kiBzDRWWrMgycbzCAU8Ja7ztrUsnopDoW/vCNGuqVm80pR0vjTIqve0E27wE6z1MXwsOqu5TUbxeXEvaQcgtXvftN1spK6TemUX3lXLcLU04agoQ+8fnHZ/uY4usodn71lUPG5QVWROR+am3tvMZMqqb+3zgjrHNqSmSb6nD9/4OEJRkMbU30ZhLErm0DRzNiX5rl58m9ej+DwoTidmYW6VNWZCGiZGLI6jrgbvmpVkj56gMBadlY7RKspwLWhA5vKk9xycdht9aAR9aAR36wLcC9vmJDQBOvbG+f6nT3DXx5ooq3OTSxoceGaUB/+1A0MvHq+k2kXD0gDpWIHOA7O3nBYd07y2ugJcjuzSKWNwLl5ScajWG9E1zNCRMVLDGfyVHlpvq2fkeOTKlVi8zJTWLsPlDU/8behZeo89c1GRWbz+0wTLFqBq1o3dG6ohXLlwTlZNkAycepFcauziqwKJkQ5yqVG8wWoABGI8/tCqFDAdgbImXL7SSUkwkoGOl9BzsygbIU0GO7ZT0bjOKoE0S4LlzfhCtUUW1L4Tz6FnZ3fjkGaBvuPPUVqzdKJckdMdorxhFT1Hn5z1PKSUjHTvJhmZnXciEx8gFe0lXLUQsM6qe7wKwaVOCurvM+nvm17omybs3TWz6EulJC8+P7vf6sVIpyUvbZvfvsZGTR59KMujD726cluzxelzUNJk/Q7794/MO5Yw0Z8iE53+mPXMuKtVVRAXEPJCEVbfdIeCogkrMXK8vbC4SLnlQtaYk5CTppwoaxduCFix8JOsaQ6vRqDKa603VCxeNLdGWZuVaOgrd7PxI0um1R/BGi9I0Fwq3tK5ezAA1FAQNRQCwyDfPXMFEn1gGDOdsWIXa6qmF5rSEmZzKfFjJFNWXKLXjRoMWNnSk9Aqy0FRLNfvxWpTKgLhdCEcmmWQUITVexwu+AUbY5EZ20WaOes3JxQVlDkK3QthmiSefxlXSxPupQup/O2Pkt65l/Se/ej9Q8gLlIZw1FYjnE6kruNbtxLP+IvC+agBv/X/oQCo6pxqe0oTdjw4xOFtEQKlDvSsSXQoN0VkAhgFyc8+f4Z0osDAqdeIRfOKIiE5lEaaktLmAP5KD7HuOdaIEhRZliZ+75fBAJsYSHPskU7WfmAxK97ZxuipKKef7i1KLhIKOLwOwg1+Rk9bcUbXOkLRKKleApMsfMlIN6nY3EozJce6SUV7CZQ1j7taFUprlzHau3+aslrTo2cTRAaOzHpMo5AnkxieEJoIgdMdQAhlxmSdYEUrQpzL3tOzSWJDJ6dddzpy6QjJSDcl1UtmbdUsqVmCUM5d7tnkyJzGnLxNecOaiXFLapbSf3LbrGuYmoU8oz37me0FIqVJKtY3ITQRAs3hRVE1jMuQfW4zNxweDaffetjH++b3EAIr7nw2peem+7X7qzy03lpHw8YqgjU+XAEHmltFdSiW0JzFJWIUJPk5iuTjj3fTdmc9jZurWHLPAk491YOeKeAOu1j7i4sI1fmI96Xp3lHsUladykTLzdrV5dSuKr/gOFJKNPf8sn1Vv288ZKAwbaLOWcxMZkJAqqHpX2Dl+HpzQe8fJH+mG1d7C6E330nkgYcpjI4hFAVXSxP+GzaCKUnv3o/MT/8ip5aE8a5ahntRG1ppGOH1oDgcoGkoTse020zGSGeY+X5z+TylmQNHGP369wm95U4cNVUE33Q7gVtvIHeqk+RLO8gcOjatlVcNBS0R7XISuP2mC44hpQRVHbfmzv1Zn4ropC4SdxkbyvPyj6cmXM+F17fQBLq3D5KN5fBXennj/9vMsUc6ySXyODwa7pCTE493TxGfzTfXUtIUxOnXcPoclC8MA1YttBt+axWZsRy5lI6e0unZOcTI8eilmayEPV8/SnlbiKYbarjzLzbRe88Qg4fGyKd1XH4HoXo/FYtKQAh+9GtPkb4OhKbD5ccbrCoSTbHhUxdNqDkf08gTH+kgUGbVehVC4C9tRHV4Zl3wOx0fJJeZfetOKc0prnlFdcz4siGEij9UV3SsmeQw+bmMaRZIRXrHxfnFUTQXgUkJVgDJSPc8iqBLokMnKG9YDYzHTAYqcXrCZBLTu9rOJ5eJkY7Pbt2z6Ocl/iiqNhFnanN1EQrjdX65aK3LC3GxihszsWBrDbf8/hrCDX7yKZ1od5LBIxFy8Tx6pkBpS5CmLdUX39E84gm6XhpgzzePs/p97dz+qXVs/JWl6JkCrqADf7mHxGCaZz+7e0p3OYFAGbfMdjzfT/eOwYvqnZGT82wnPFH/kgsf40TNaWF9qRdcb/bIXI7Ij39G2QfejWflUlytCzDiCYSioJaEQREkX95J4vlXpt3es2IJJe+8F62iHJnJog+PoHf2YKYzyHweR031RGjAjBjG1SmiYpqk9xwge+I0nhVL8G1ci6upAfeyRbgXt5E9cZrIDx5E7ysWcWI8hEBmssSfeBYzf2HvhhGNz0tkXkle90Jz9ESMl79wkM0fW0718jKql5dZ5YCEQBomAwdGi4SmUGDle9pp2FjF5Fdl05Bobo1Fb15wbudS8sK/7TsnNKW0ssxk0SrWsvNLEJ1dft6FnYnkeOKvt7PhI8tYeFcjTTfU0HRjTdF2hZwx3nno2v7xncXtK0Edz/QGQMqLxu/NRCrWb53Us5ngTi9uXwnJOQjNucUjyKlhHhewMqoON47zXN6ZxNCsLa4T2ySnT1CYDofTh9MdmrREkorOr5B/JjGEWdBRHZZFRlEdeAKVsxaa2eTwnF8gprrIr+0Ql9cTRt6cKFl0fizi5SZQ7eXWP1xLqN5H9/YhXvj8fsZOxzF0w6pPLGHlu1pp2jwLoTkPDN1k77dPULOyjKqlpaRGMkhTEutJsu/ASU491Uu0e2poimmY5FPWNTB6Ksaebxy/LPMDLEFmWlYvxT3z96O4nAhVw6ovOX/L9HTkO3tIPL2Nknfdh5FMWXMqFMgeP0Xm4BGyx04h9an3BLW0hJJ3vxWtrJTs0RNEf/Io+sCg5XY2rSYvgVtuuLjQvMqYyRSpl3aS3rkPR30N/i0b8K1bhXtxO2W/8E6G/ut/i7L1zXTG0gp6geT23Rijc69ZfK3xuhea0pQc/MFJencN07CxklCdHwRkYnkiHXGGjkTOWx+e/btdOHwXN9mD5Zq3NoTdXz/KkYfOkBo5537IJ3V+9vvbUDSF6CRB27NriO9/8An0jD6l7mZ6NMtz/7ib/d87Qe2aCkqaAmhOlXxaJ9aTZOhIhMiZ+Iw16641HK6AZQUcR0rzook4M5FLRZDICSmiqg4c4zGFsyE/z3Fni6o5J8onnSWXnvuY+WycCQvERdCcnqIatFIyr4LrAPlMfLwjkfXQEkIpiq296PbZ+JxFtc21Sy6RJzWcIdzop3JJyZRYxctJ3boKgrU+cgmdbf+6b8q9GsAVdF629xKhCjb96lJqVpXz/D/v5cAPT2HOos6gnjGI9iQpawtRuSiMoolZbTcfjFgcM5lELQlb8ZDHpg+XUUtLEB43GAb6wOxfYmeDc0ED4XvuIt/Tx8iXv2F145kF7oUtaKUlmOkMkR//DH2aGFOry8/1gdR18h1djJ3pJnv4GGW//F6cjXW4WprI7D88sZ4+MASGieJ146issIXmawUpYex0jLHTs7sAonON4xwnOWgVAS4a25SMnZ7aYSCf1K2MyRmQpiTSESfScZHuBPPmyvkaNGdxC0LT1Ods9TqLUciOx0aOxzQJZU6lgObaL32uCEUrEtXAnGpRntsmN265vfi6qsNd5GqW0pj3cZpGfrze5Tm0OXQnsmqD2hUkXiuYBUnX9kHq1lVQu6acioXhaQXf5cBb4kIIyKesTjzno7lValdXXLbxPWEXzTfXUsgU6HxpYNZi0SyYdL04QOstdVQtK6ViUQmDh2aXfDhXjESS3OlOvOusOMfUyzunJvMIgXflUoRDQ+/tn7Zw+avBs2wxStBP7uWdsxaZMJ7sIgQym8OITN1OOB24WhZcwpleIaQke+I0hUgUR3XlFLGs9w+iDw3jqK3Gt3412eMnYZps+leDL6wRqnQx2JGeNgGotNbF6jdWUN7gYawvy/4nRxg6M/+MfDvQyeaqMzlJBUCa5rx70ktpIie1UbNK8cz+fUrOs3/sbLHmU3zZmfOpsCBnf44U5byECCnnf5yyOFRACDFRMml2m9vWzNcaxx/tIt6bwhN2cdsfr6NyaQmK49xvXKgCh0ejtDmIw3PpbBvJoQxSWgXhy1qKvRaaS2XFO1tpWF95ycY7HyNvoKcLOP1ObvqdVax4RyuL39LE4rc0sehNjTTfVEO4wT8RjzmZE0/1MHwsiivo5NY/XEPF4nBRu2HrnKmULwxTtXRqq+FZY5oknn0RmcniXtiG/+YbEJMTaFQVz8ql+DavB9Mk+cL2CyYNzWsK465g34bVhN58J75N6/BtXItv4xq8a1fiamlCTOPWL0RiVukjrwdHdfH3KJwOAjffgGthy5TtLjlCWBnp6uTvR7GWzfCir3g9uBe1WSLy/Gx2RcG1oBEtHELmdQrnWSzNVJrE8y+DYeBdv4rg7TeheDznQrKEAE1FCfhxL2m3LNFzZP3dVfzuN9Zw928umHIMdYt9/MaXVvL2P2zl5vfX8rbfb+ET/7OKxTeWzHmcs9gWTZsZuHJxcOdnZwuhTNSYnCtns83P7ftViKrLxPkCcS71MCf2MafxjPO87GJeY1qbTnN+59EK0+a1Q7QnyQv/cYDb/3gt1StKedu/38zA/lFiPSmkKfGUuChtCeIKOPjJbz1fVJz81dC3d4RYd5JwU4DbP7Wewz/tIDGYwVvqomFjFXVrK+jeMUjjZYrRzCV19n3vJDf97ipabq2j5da6os/NgtUn/fjPu3n5iweLCq5nxnI89897eONfWy0o7//3W+jbO0K834rV85S4KF0QJNTg5+CPTjN4eP4Wz9zpTmI/f5rQ3XcSvu9NeJa0kzvdiTQMnHW1eJYtQrhdpHftI/nypW/vmtl3yGoD2VhP6N43Fn8oJTKvo/f1E3ngEXInTp+b96kOCiOjaBXllL7/HaRe3okRiVkCa3E77rZmskdP4lmy8JLPGay2nc6mBhS3C+F24RgvwK54PJS++60YiRQyl8PM5sgeOU52UliCEgxQ9sH3IXM58l096MMjmOkswqHhrKnCvWQhwu0is//QtP3KUy/txFlfi3/LesL3vQnvutXku3ows1kUlwu1NIyzphoEDP7LFyhM1yt9BlRNsHBzGLdfJTaUL3qYON0K9/52M9WtXlIRnY59ccobPFS3eHnr77XQfWgfqejcq33YQtNmBq6ce9PIZ5FSTogfRdVQ1Pn9NBXVeZ7FUGIUrkxNwdkgTWM8xtE5sUzR5p5EoYz3H58NBT1bFLcqFAVFc15wm5nHnfrdGPrlDTewucaRcOKxLnLxPBs/spTKJSUs2Fo7kbwsTSsBZvRUbErcuKGb6JnCBRMXpWElHBk5oyiRMjmU4em/321lnTcFuOETK2E82TIXz7Pzf49w9JFO3vqvN82YLC1Nq/96IWfMyYuiOhVWvLONVe9uIxvLM3Y6bpVHGk9EVB0KvjI3ZW0hVr23nXxa56X/Olh0W+3ZNcxDv/8Cmz+2jPp1lbTcWld8zgomiYEUIyejs57XtJgm8Sefw0ylCb7xVkukLVk0MZCZSpN4/GXijz2NzE5/LcuCjszrc85udjTUEr73TWhlpVYLyVhs4sVfCMUScNWVOJubKPuFdzL4b1/CGO+YY0RijH33AUreeR+OynLCb33zePYsmOk0sUefIrVzL5Uf//C0NSRlwcDM55Hnd/s579zIvD5tMpJn1TK8q5cX73N8PVd7sSVVFgpFQlNmcxRGRnE21uEtLxu3Rp7r7mNmsiRf3EHswZ8jc1OzymU+T+QHP6UwOIz/5i0466pxNk56kTFNzGyO3PFTmDN8ZzPh8qlULvCSSxmc3lMcktC2McyiLSVkkwZf/9QxDj8/RqDcwUf/fTn1SwK0rA1x4KnROY0HttC0mZErZ9HM5xJIs4AYj10UQrEys+Nzr93l9ISKrKGmUZhSHudqYhg6hp7DMSmucS6F18+iOSe5Ui42Zj6DoWdRxrvrCKHg9Mw+QWoyDre/OBRBmuSz8yy9YvOaQZrQ+eIAfXtHKG8LUb4wjCfsQkpJNpYncibO6Kk4mfO6mT33L3t56QsH0TOFGd9tTzzZQ8/uYcyCSS5R/FDuenmQH37sGepWlxNqCCAUKxZ+4MAo0e4E0oQff/w5hGJV4zifzhf7+eZ7f440JdkZCsZPx5K7F7D1t1YyejLGg5/cRrQzURyjKayi7Wt/YSGbP7qcllvq2P31Y8VtJCUMHYnw8B++RElzkKolJXjL3EhTkonmGOuIEzmTmHLO5oVhkHzhFTIHj+BqabISg4TAGIuS6+iiMDI6Y+kimc8z8uWvIzQNYw5tGJVggPIPvButupLoDx8i+fKuqXUjVRVHdSWVv/4htIoy3G0tpLbvnvg4e/g4g5/7Iu62ZrQKS7AZkZg15+ERkJKhz38ZEFPEYuTHPyP28OMTRdmnI733ELlTZ5CGMaXFZOQHDxJ78OezOtbzww2MaIyh//wKjsoKHDWVqAE/wuFA6gUK0Sj5rl4KI2MXLLIuc3niTzxLascenI31OKoqEE4HZi6PMRYh39uPMRa9YPH36dCcCv4SjUyiwFj/ue9D0QQb76vC4VLY9/gQR14YwzQkscE8e34+TOPyAE0rArbQtLk+yaUjFPQszrNJMkLBG6wiNnRizvvyBqqK6sAZenbeGdaXA0PPUsgnwXcu3sXtv3DB5ulweUuZ7cuAnkuRz8bPtXEEfMGaC2wxM25feVEyk2kaZBKXNnnA5vpFTxfo3z9K//7ZPYwyYzkyXFhI6enCBTsOpUeynHhi5k5TZzv4TLvvjIHeO7cXUdWpsPCuRhSHwpGfnWHs1DQJmRL0VIHePSMYeQN30CoiP12/8kLOYPhohOGjl/8+ZcTipPfMvX3r+R19ZoO7dQGOmmr0wSGS23dPW5zcynIfQh8eQS0NowanvnSb8QTp3fvnPDczkcS8SJSGzOUoTDevWW5/wX1nsuQ7u6d1jc8FIxYnc+AwmUvUdVcIUDSFXDRfVPu2rNbNwk1hCnmTVx4YKEoSGunOYhqSUMX8SpjZyUA2Vx09EyeTKO6eEShbUNQ9ZzYIRSVQ1lS0LJMYml1rxyuEaeSnCDNvoHJOmdsAvtDshaJRyJKK9hYt85c2TLTqnAuB8gVFMZr5TIxs+vJkzNrYXIsIVeAKOEDKi5aQK2sOojpVsrH8dVNu7lKheNygCCvL/QJx8qrPi1Yats5n7HJVUbE5i1mQ5FIGLq+KOikBbeWd5QTLnPQdT9Gxr/h7MMdDUhRtfp5OW2jazMCVi9E0zQKRgaMTGclCCIJlzVY/8Dng9pcTKGsq6ucdHTp+yXtiv1riI6eL4sFc3lJ84boLbFGM6vDgn3ScsyHSf7ioLJEnUDWnMcHq4FRStWjS+ZXERzoo5C5tgWcbm2sZM28y1hFHKIJFb24i1OC3uiONtx8WilW/c+FdDaz/kNW968yLA+SS8yvZdr2iDw4jdR1HTSXe1SsQDm1K5rSjttrq/FNeZrnxT525qnN+PZDLGIz2ZPCXOGhaEQAB4WoXm++vRgI7fzZENln8UuQLOVBVQT49v5cl23Vuc00w1nuAmratON1BhBA43AFqWm/gzP4HZ1USRwiFmtYb0Zzn3MN6Lslo78HLOe15ERs+jZ5LTsRmCkWlqnkziZGOKTUqpyNc2YY3MLeyLbHhU6Rjg/jCtVZJItVBbfvNJCM9mMZsYtMEFU3ritz8pqEz3L0buy7mVDwVXupubMTQDXqeOYOeen2JjNcypiHZ++0T1K2poH59Be/68m0MH4+SGs0iFIG31EW4IUCw1gcCzrzQz55vHn/dXSa5zh5SO/bi37Ke0ve/ncAtN6APjSALOorLhVZWilZZjuL1YMQSRB74GcbYtRPm9FolnzE58PQobRvCvPvP2zn07CiNywPUtHkZOJ1m18PF3kUE1LR7QUBkYH7xwrbQtJmBK9vmL5saY+jMDuoX387Z8juVTRtIRnosMXOhjFChUNG0norGtUXWtuGuPWSTI1fmAOZALjVKdPAYFY3rxssxCUqqF1PeuJahMzu40BPJ5S2hbvEdc6oNClDIpxk49QIta96OUDWEEISrFlHbfhO9x5+Z2kbzPMLVi6htv4WzvwspJdHBYyRGO+c0j9cLwcYQ6z+5hXwyz/C+QVtovsYYPDzGQ3/wAmt/cRG1q8qpX1+J6rQchEbeJBfP079vhGM/7+L4Y93k4rNPNHrNUCgQ+dFD6IND+DasQassx9lQa9WVNE3MXA4jniC9cy/JF7aT75lfW1ybubP9p4Msu6WMRZvC3PwLlmcrEy/wyH90WiWPJuF0K7SuDWEa0HN0fmFottC0mTUuXynB8vkVyNVzySlxmMVI+k9uI1TROh6fKVA0J82r34onUMFgxyvkM/Gimo1CUXG6Q1S3bKa6ZQuKapXskVKSivbSf+I5rkUzgpQm/SdfoKRqMQ63H7B6hi9YcQ+a08Nw5y70fGqSuLYskMHyBTQsvQtfqBaQ45VUZv9CMNKzl1BVO+X1q8etmhr1i+/A6Q3Tf+J5sqmxojADIRQ0l4+KhjXULboNzelDCIGUklw6SveRx6+5sIQL4S71UMgWKKRt0WfzKpEwdDjCz//sFbylLjylbjSXFVNeyBlkYznSo7miZIvXIzKTJfHEcySffxk1HEJxu63C54aBmbGEpsxeO+XnXi8kx3S++gdH2HRfFQ3L/KRjBXY9PMSp3VMriJTVuUHAie0Rug7OLzvKFpo2MzBVoFU2raeiYc289jbau5+TO797wXUK+RSn9z7Awo3vxxOoRAiB5vBQt+g2KhrXkYz0kE2NYBbyKJoTt78cf0kDTndgIkHFEkEROvY+MN4P/NokFe2l98QzNC57E0JYNTE1p4emZW+msmkjybFOchnrone4/PjCtXiD1eMZ35Khzl0Ey5vxzCFj3TR0Og88jMsdIlDePOFCr1qwibKa5SSjPWQSQxh6FqE6cHtL8Jc04PKGx2uTWiKzoKfpPPAg6Vj/5Tk5lwF3qYfbPvcmzvz8FEe+OXMGq43NXJCmJDWSJTVii6ULIXN5CoN2dYpricRInie+0o0QF3YYDp3J8O8f3o9pSLIpO0bT5jJiuXhVUOaWCT6x/Sy3S8f6OLHj27SufQe+cJ1V1FcouLxhnJ7QtPM6i5SSTHyQU3t+SGLsWnfpSgZOvYjTFaC69UYYL8AuFBVvsBJPoLhH80RIgGky0rOXzgMP0b7hvXMSmgD5TJQTO79Dy5r7CVcuQijKeEysn5LqxYSrFk07LljnN5+Jcmb/g9dk7OuFCLeWEGwOo13CFog2NjY2s0UogLywqLsaXGw+RkGSjr86z5V917WZgSsbozmZVLSXoy99jdr2W6hoXIvm9E7EMk6HZWXLMNqzj97jz5BLXR/ldkxDp+vwz8llotQuvHUiEQqmusTPWhIHO16h99jTGHqWdGyQkuolcx43l45wfPu3qWm9garmzVaR+xnGPTu2WcgRHTxO99EnritL5lkq1tQU9ZK2mRuaCh/5ZT+lJSpPPJNh+67XYcyhjc0cEAqU1XtoXV9CxQIvLr/GaFeG577ehWnIiXUcbhWkJJ957YZZ2ELzdYyqCaQppy1xZho68ZHTRVncr4a5ipN8JsaZAw8x1LmDstrlhCracPlKUB0ehKIizQKFfIZcOkJ8+BRjfYdIxwdmlaF+FmkUiI+cLqprmc/MtsuN1X9IIkknBokMHJv4JBXrnfY1USCQ54UkmIZO/8kXiAwco7x+FeGqRbh8JWgONyAwCnnymSjx0Q5GunaTjPZZbViA2PBJvKFqQEycj9li6Bl6jj7FSPdeSmuXEa5ahNtXhubyIhQNaRpWsftMlORoJ6N9B0lGeuYUkymlSXKsq6hFZXYexd1z6UjR+c1nohdNXkKA6lTxlHvx1QSovaEBAH99kJrN9UXfj2lKRvYPYpzfOUaAt8JH6dIKypZW4KvyWYWOYzmiJ8cY2NFLoic+7zDgYFMIX7UfaUoix0fJxaZmdLpLPdRsrqd8RSWusBs9pTN2dIS+F7tJ9SeuWAiypsFHPxSgtVkjnjBtoWljcwFcXpWbf7mRG97TQKDMiVCsl/hTOyI8/80uGL/VeEMO3vvpZWgOhe/++SGi88zqvtYRF+rvKoS4xoy8NpeKNbeGePvHazi+J8l3/qkXPXdtf9VC0dCcHlTVCUIgpYlZ0Cno6YuLjstAo1hISJRywHx5Vus7cbNU2UCXPM6YHJxxPUV1oDk8ViymEJhGAUPPYBQu7w1ICBXN6UHRnFa8q5Tj7TIzmMb1lzxTsrCMjX+yFV+VH0fAiaIpE4lM51PIFHjkl35EsmdSoLuA9rcvYekHVuGp8CIUgTQkUsoJy2hmOM2+L+6k4+ETUwRf1boabvvcm8gn8zz2qw+S7CmOF65cW8PmP7sZb6WPjkdPsudfXyY/ub2igNotDaz++AZCzVYXKdMwUVQFBKQGkhz8nz10PHwCaV7+a9ftgpeeqKG1WeNTfxPl37/0Klqm2Ni8htGcCvf+fjs3vLceIQSZuE5BlwQrnJzeGeULH9k10XVH1QS/+I8rWH5HBd/788Ps+MmV9RZpDoEnqOFwKVYt2IsQG8pTyM9szJFSTrsT26L5OkQIWH9nmLZVPirrXTz45UGiw9e2mJBmAT2b4FqZpUO4cIvZW3slJnlyGPLCFkHT0Mm/CmFXJqpRURmSvRdfefL8pGF1UHqNvFDraZ2BV3oRmoKiKTTe0Yy3wsfIgSGG9xcLfVM3ikUegIR8PIfqVBl4pZeBnX0ke+KYBZNQc5i2+5fgrwuw+tc3MHpomPiZ6KznVrm2hs1/fjPeCh+nfnKUvf+5Y0r5o8rV1Wz605twBV30buui+5kzZCMZ3GE3Dbc3U7ulgbW/s5l8PEfPc9d6PLLN9YCqOvH7qsnlE2Szc69n6XKF0DQ3qdQQ12K1jytF68YSNr69lnza4OmvdLLv54N4Qg5+8+vrp6xrFCRn9kVZ+cZKmlaH2PnT/isWw9m6LsTtH6ynaXkAt1/jYgVMJPCfv7qf03vmnmRrC82rRPUCF2tvC7PtJ6PEx65siRgp4dDLCZZtCnB4e4J04vopUXO9opPnsLn9so9TKerIyDQwN6H5WiPZE2ffF3YCoHk0ypZW4K3wMbC9lwP/vXtW++h5vovRwyOkBhJI49zdv+/FbkYODnHLZ9+Iu9RD9cba2QlNAdXra9n0qZtwl3k5/v1D7P/iLgrZ4uvP4Xey8mPrcZd4OP6Dw+z9j+1Fbv3uZ86w8Y+30vTGVpZ9aDVDewfIx18jbwivExwOLxXly4gnekgmZ7ZiOTQv5eVLSSb7SCQvb51Jn7eS1Ss/RG//Dk6eenhO2yqKxsK2ewkFG9i9779Jp69uhrmqujBNfU6hVJcCocCqN1bidKs89oXTPPnfHUgTqttmNkoMdaRBQkWT1+rVeAUcdA1L/Xzon5YQrrLaEBfyEmlKHC4FKaGgmyiKQHVY6jOfMek+lCAVnZ9WsIXmVWLLW0q558NVHHwxfsWFJsDLD4+x97kYetZEz79+3z5nwoGTClGHX4QpSJ0R2Uec4iQjiSRICRVKHQoqo3KAiByaiMNUUGkSC3GLs33MBYOye4rrXMNBhaglIEowKDBi9hNjtGgdgSBEGaWiCqdwoZMnYg4xhlWbtFRUUSoqKRd15EQaj7TGjMoR+uXr2+I1XwuBkS2Q7J3+7X308DDRU2NUrKrGXxuceWxTYurWk6NqfS2b/uxm3CUejnzrAIe+uhcjO/Xar1hZRdnicrJjGY599+CU2NFCpsCpnx6j/pYFhJpLKFlYxuDO+YsQMfGv+Z0rMc9tJ1tQ5vsdvdqxr/S4Z9E0Dy3Nb2BgYDcnLyA0vb5K2tvewslTj152oWkxvyRQKU3iiW5MU0fXr25LWlV1snzJe+jsfp5orOOKjq0ogup2P4W8yaGnR5iNzs3EdCTgDjimjeO/1AgFbnx3LeEqF8kxnee/00fH3jjSlPziZxYz2pvloX/rwOVRaVoZYPP9NaSiOt/56+MMdszvu7WF5lXA4RQsXu+/qnMwTUjHr3xs4/WAEzfLlU2oQiMih3ELDyuUzZw2D9Mvz0ys58FHq7KCJFEcuFiubOKUeZBeeRqw3OVROYILDw5cLFAWk5JxxjgnNDWcLFU24BYexuQQLjysUDdz0jzIwLhAFAgWKEuoF23E5ShZ0ngJgCIYMy3LgYpKXuZASPIyS1JaAinH7BOEbGaPNCXZUevcqs6ZS3eZBRMjb1C9qY7Nf3ozzoCTg1/Zw5Fv7sfUp3kKCahcU43iVEl0xzByBg6/c8pq2UgWU7c+Cy0Iz1loOhywcpmTN9zmZuUyJ2WlCgUDhoYNDh/TeWl7jpd35ChM8w5smhAKCu55k5c7bnFTW6OSyUiOHNN58JEMO3bnKExza9E0WNCoceMmFxvWumhsUHE6BNGYyeFjOo89mWHXvjz6eZEjigJveYMHl1vw8yczKALuucvLHbe6qatRyebGx340w/Zd088ZwO0W3LTFxV13eGhv1VBVwbETOnsP5DEmzTedNnnkiQy584zELhfcuMnNm9/gYXG7A4cDevsNntmW5ZHHMoyMzd56pusZCnoGt7sExtMKp8Pp8KEIdV6u7CuJlCadXc9e7WkA4PGUEQjUo8yxe9olQYDTrVLIm+TSszMgaa7xjlJXqLi/26exYGWAQl7yo78/xfYHB0GC06OQSRTIpQqceCWKlHDgmVEOPTPGr/zrUt72yRb+95NHyM2j37ktNK8CoQoHjYs8l2RfDpcgXO7A6VYo6JJkrEAqdnkFpOYQhCscuDwKet4kOlwgn33tlGaoFc24hIfdxrPjQk3QINpoVpYyZgxOiDcFhZPmfhJEEQhalRU0KG0MGj0UyCORRBgGaQnBOpqnjFUl6vGLIHuM58iQQiBoFstYoCxmxOinQB4/YRpEO6fMA/TJM0hMxPj/zj6ghmUfCgo1NBGVo3TLE1fwjL2GUQSeUg+BhiD+2gDOkBvNo6G5NcJtpVY5qAsYgWTBJNxaysY/2oqnwsvhr++fWWRiZaYGGq1yU2VLK3jTV98247wcPidCCJxB15wOqaxU4VOfDPGet/sIBaef/M49ed76viHiiWIBJIFgQPDfny/jzls9CAG6LnG5BHfe6uYD7/XxmX+K8cX/TU6pZvGB9/j5sz8IUV6moCig62AY1rZ33+Xhox8M8G9fjPNP/x4vEouaBn/1J2GamzQ+8NERfum9Pu66o3jsO25x84H3+vn7z8X4zy8nMM4b2+8TfPrPw7zvnT5UVTAwZGAYkrUrnfzaB/0TiWJSwvGTBZ57MUcud24npSUKf/tnYd5xnxevV5DPgynhxs3wjvu87Hp/nt/70zH2HZhdfLVhZNH1FG53GCGUoo5nk3E6/Uhpks8XJ19pmhuH5kUoKqZZIJ9PYpozj60oTpxOH4qiIaVJoZBB1zNcKJbS4fDh0DzW+HoKw5ganqGqTlyu0Pi9yBKcmezYtC5rh8OHprnJZMas360zgKo4MM0CuXxixnMghIrLGUBRHRPjnEUvpMnnk+PH6MDh8FJWughNc+F2h/F5KyfWzeWTFApTLXKK4sDp9KMIFcPIk8snppwXRdFwu0vI5WIYRn7GcyMlJMfyVDb78Jc4GZ6FBbB+aRAhYKwnO22y4qXG4VIIljtJjOU58sLYxKFKCYW8ieZSUTRhJSxJ6Ngf55UHBrnzww20bwxz8JnRCw8wDdes0PSHVbwBlZG+PFchqfiSU9/uZuEaP42LPbQs9xEqcyAl/Oqnm8ilii/K43uSfP9f+6a4ZW55exlb7yvjsW8OseupKKtuCnHPh6toWOTB7VPRcyZjgzoPfnmA535c/GNw+xR+6VMNVNYXP5T6z2T5+t/1zFjDS9UEH/7LRkIVDv7v010ESx3c/+s1tK7y4Q2o5DImPScy/Owrg+x+Ovoa+K4EpaKSqByZZA2UjMh+WliKX4TISWt5ljQp4uNrSMbkIPWiFRcuCly8/ItAUCIqMSgQECUEsLKLFSFw48WFmwJ5QqKMAjrDsheJOTHe5XaxvN7x1wZY8oGV1G5pwFPmQUrLkom04pkuZMk8i8PvZOMf3Yiv2vJgVIyXKcoMz/AAEuAct2CahsTQZ76gUoPWA1afQ0tNn1fw2b8t4e33eikY8OSzWR5+LMPpzgKaAu1tDm7c7OLFV3IkElN/X4qAj/xSALdb8KWvJnjk8QyJpKSpUeMTvxZg/Ronf/y7IV58Jce+g8XzOnlaJ5U2efaFLE8/l+XEaR1dh+Ymjd/4SIANa5387m8Eef7FHC9unypqnE747N+WEAoq/Pf/JXn4sTTxhKSxQeXjvxpk0zonf/jbIba9lGPP/nPXnxDwgff6+KX3+xkZNfmjvxjjuRez6LqkrcXB3/5ZmK1bXOzck+fv/jlGV69BLH7ufuhxCz7zF2He9y4fnV0Fvvi/SXbtzWEUYPEiBx/7cICN65z8x2fLePcvD9M3cPGboCXIIoSCjWiaG11PAWJCCJ4VXU5ngEIhi17ITPzd1HAzJSWtuJwBhLCEZjozQmfXs4yOHTtvJEFFxTIa62/E4y5FUR1IaaLnU0Sipzl5+tEpAlIRCk0NN1NTvQ6H0wcSMpkROjqfnrL/ULCRJYvfiao4EIqKUcixY9d/kMtPDTmpq91ITfU6Dh/5PnW1GykJN6OqbkwzTyR2htOnHyOTLQ5PCvhraV5wBwF/LYriQFFUq3EIkoKRo6fnRc50PYPD4WPJorfj81XhdPoRQqW99e4iwXu64zF6+iZXCRGUlS2iqX4rXm8FiqJRKGQZi5zkTNczRVbks/GrJ08/ikPzUluzDofTb52b7ChnOp9hZPQIpiHp2BOlbVMpa++uputAbCLDfDpK6z2sf2sNpiE58crYrFztrxahgOpQyCSKM8ilKckmDTx+FYdLOXfvkXB6dwz5oXpa14VeW0Lzzb9cxdb7SvnL9x0jOnSt5BrPn1vfUc4tby8DBKomrLpaEmqb3VPE2UwZ4JWNLpbfEODU/hSegMovfaoBRRGkYgUy4z+QmiYXufTUX6sQ4A9rVDa40JwCb0DF7VXxBlXUCzwvhQJtq3w0LPTQfayCrW8txe1VGe3PM9qfp7zWyeL1fpoWe/m/z3TzzA9GXsVZujZQhTYlO1xiYmKiTrpkJBJzktgzsLZRmH33JA0NJx7qldai5VE5PCEqNTRMCpi8dqzG1zq+Gj83fvp2SheXkxlOc+wHhxk9MERqKIWezGMWTNZ/cgu1WxouuB9n0EU+kefA/+ym5e52KlZVs+YTm9j+d9um77kumeiPPbirjx1//wLyfPPceZyfTHQh3nq3l7fe7cUw4R8+F+Pfv5QglT73G370ySxf+ErCaks3zfZCCEpLFD71N1G+/LXEhMt51948h4/mefA7VVRVKtx1h2eK0Hx5R443vG2IkVGjyFW9a2+efQfzPPTdSmprVO641T2t0BRCUF2p8hefifKF/0lMuOd37YWDh/WJ7d98p7tIaLpdgrff60NV4OvfSfLjh9ITL/G79ub528/GeOCbFTTUa5zoKHC6o/h8vvF2N++4z8vgoMGHPz7Kzj3n9r1jT57tu3I88M1KVi5z8Evv9fH3/xqfVexmJjtGWelCHJoHXU9RWtLGovZ7GR07wfGTDwIClzNIoZChULBaXCpCJRCoIx7vJp7ooVDI4vNWUFu7kUXt97Fn/1fIZM4JgWCgnkXt95FJj9Bx5kn0QgaHw0sw0IAQCuY0dXHLyxZjGDr9g7vJZCL4vBXU1W6adv/R2Bl27fkiToeP1pa78PtqmCl9WaDgcgZYvPBt5PIJOjqfxjB0ykrbqaxYgUBw6Mj3JkS2w+Fj8cL70TQ3J049TCo9RMBfQ2vzXZjS4OChb5Een4tRyNHZ9SxCUamuXE111Ro6Op8knug5d74zxSK2onwZixe+lUw2wpmuZ9D1FH5fNTXV6/D5Kjl4+NvkcuOCWVgvAU0NNyMx6R/cSyYzNn5uNrKw/V7S6WHSmRH2PjLIxvvr2PC2WnJpg5e/34PTo549CWhOBbdfoWF5kDd8rIWaNj99x5IcfubKJFCZBUkubeAJaDhcKpmEdb5NExKjeapawvhCDrLJcxdpLm1gGhAsnxrGMxuuTaEpIFTuwB/WmEVpp+uCh/5nkKe+b4mwlmVe/n/2zjq8jvPM2/c7cJjEaMnMmMRhZm7aprRlbnfbLW23y7vt7rbfdksL5S0zNw04zImTmBklWcw6jAPv98fIsmWxLMtOovu6kuuydGbmPXOOZn7zwO/54OfnYxiSb37mGB1Nw+fk5tLWuBeqdVeGuOKuErY+GuPhn/XQ1ZzHNiWRMp36FT72bxnpcZdN2fz3xxvR3QKvX+WWd1Vwx/sqJ71+ocDt76tg/4tJfvbFNjqbciChfJ6bt36mlg1Xh7n7o1Uc3JKkq/nl3AErycoUXgLDCrNdeFBQyckTkSgNHR0dYzB66cWPjTX074mPJMmSQZEqO+1nh4Tlyb8HJ3Kq48aFG3MSBk+nppfmmCICFty6hOJlTkPOs3//GH17e4YpL6GKYZ3oY2GkCmz+7JP07e1h4EAfl/zzVdRdu4B0V5I9390+IoUupSTTk0ZKiSvoxkgXMLMz0yzo9QjecrcfXYOnnsvz9e8OF5lDa57gK7b/kMEvf5ceJhYBGppMtu3Kc9uNXhYv0lBVhr3GMKG7Z/RoX0OTyfZdBaqrvNTVjn1bOnjE4Oe/SY+oAW1qNtm6o8CdVV4WL9LRNIbS7x6PYF6timXBnv3GiGtrc4vJQMymqkKlrkYdJjQ1Df7sDX48HsGfNmXZvmvk3/aRBpNHnsjx7rf5ufkGL//73SSp9MTfjVw2iqq60HU/ZPsoKV6CpnkpKlqErvsxjAxud3AwNescN5ePsWvPjwb/ffwYAtPKs2jBTYSCtcOEYDBYg6a6aW59hr7+A0M/b+elMVP2qupm/8HfEosfG9q/LS0W1F9LUWTBsP3btkkuFyWfj0+qCUgIFcPMsO/ArwajuNDXfwCPO0IksgCPOzwU1QwFa/H7y2k89hg9vXsASKe78fnKqau9DEXRhqKxtjSJJ1oACIfqAUkq1UUsNnozkK55mV9/NYaZZd+BXw29p272kEp3s3zpa6mtvoSGpoeGb+fys2ffz4nFGofOjWUbLJx/PUWRhWSyfXQ3prn/q0d43d8v5+p313Pha6tJ9hdQVUHVkgDv++YGQqUuIlUeNJdCorfAn750mGTf7AxByGctBtpz1K0JUrHQR2LwuLYp6WzIsO6GUpZfVsRzv+kc+oqV1XvRXApGfnrBjnNyJpuigMenvKJul7Feg46GHB0NOfo6Ck4azpL0tOaHfn78v4Gu8a/081f62P1cgh98toXGPRkyCYtcxqarOc+LD0ZJjtHFbhqSbMpJryejU7t5CSFIJyx++oVWWg5mMfISoyBpb8jx48+3Eu02KKlyccmtRVPa77lIp2wmKCJUiHm4cOMlwHxlOQk5MJQqB/Dgo1LU48KDjyA1YiEx2UeB3Dh7H0633YJPBKgRC4eahvyEKRLlHC/+i8peTAwWKWsIEMaFBy8BwpQMvQYcYZqXOUKieGhfGvpMnZaXN8fVxSQuKkIRlKwoQyiC/kGBeGp4T3Vr+ConbuizTZtc1Hko69jcyq5vbsU2LJa9cTWL7ljGiCdpCX17e5C2JDw/gr8qOMk3ODGlpQqrVujYNtz3YGZSYuhUpJRs3ZEfllo+jmVBX5+NEIKAz6nDnPx+obffHqzdGz0oJqVk+84CsfjIY9s29PZZCCHw+8SwY0spMU1nnz7PyB27XAKXLrCskSK7tFhhzSoXUkJDk0FVhUpN1fD/qipV+gccwVY3TyMQmNwbz+XjSGnjdodRVRfhUB29ffvRNS9+XzlCKLhcgcEU7kmZk0FxpapudN2PyxWgUEgjhIKuDa/9z2b7kNKitvoiwqF6FOX49UCOWROZSLYNiwSCJJFoBcDtGtthYbL09OwZEpnO+ymQTHWgqR5U7URpl8vl/H3l88OnteXzcRACTZt+n0MgUIXfV0Z//6Fhwhkk/QOHhqLNpx4jmWwnMShoj78+kRw8N+7Q8R+x44EufvrpPTTviuP2q1Qu9iMUgS+ss+iCIkrrHVeQxq0xfvJXuznywuyNTS5kbRp3xHF5FFZcPvx+ffiFKGZBcsuH67nkdZXULPOz7oZSrn/PPBQF2g+lpnXMmY9ozoA6VDWBx6/OVaCNQTZl8fDPesjP4mxUKSVNe9N0HhsZrexpybP/pSSX3VHMyotD3P+Dbgq5l++nNyC7abT3MV9ZzkJWAZCWCQ7ZO4bS4yDpkW0ERYQaZSEaGhnSNFh7ppTijtHPYXsX85Xl1LMMABubHtk6aJXkdI7vt7awSFnNevWKoW17ZTsJe2Do70QiaZVHWKqs5wL1GiwsuuxmjsmDM3BWXr7Ypo2ZNZFSOrWSYzf5DjE0bUcZvdmn+pJaQvOmeNOV0Hj/YXzlfla+Yx1rP3g+6a4UHc+3DntZ10vtZLpS+KuCLH/LarZ+efOoNkgAiqYMpdonoqpCxecRGAYcOjK9KKnE6bQeK+Ni2ydmOI92K9A0qJ+nsWKZzuKFGqXFKn6/wOsRXLxx4qam9k5z1JG54DTnACMEbjoj2bYzT/08H7fd5OXeBzNDTU6qCjdd56G4SKG51aSxefh5KStViYQVhIDP/V2Ef/pMZNRj64N+g7rmNB5Nhnw+gW2beDxhPJ4iPJ4ijrU8id9fQVFkIelMD5rqIZuLDW0jhEpx0WIqytfh95WiaV4URUNV9eMvGHaMaKyJpuYnmVdzCevWvINkqoOe3n309u2nMEodJUA2Fx2RUrePi1Jx+rGpdPbU8io5tH9x0v5zuShSSgKBSnp69jhNkEIhEKjCtswRAnQqOE1Y6qh+n5ZlkMvFnPpZ1Y1pnnDuGO3cOJPp5LBzI204+Gw/x3bGqFsbpn5dhNI6L26fipGz6WvNcGxHnGM7Y+TTs9/YsPvxPi56TSVur4qiiqHZ6817kux/ZoD1N5byls8uxTQkqur4afY2Z9n31NTrM2GGhabuFtz5gUr8wdPbraJB/fKZ6cp+JRLrNek6Nvmo2UzR1ZzHHKWw2bah+UCGy+4opqLOjcujUMi9fLuCJJIO2USv1YELNzY2eTLDBGSrfRSJxMTAgw+BIE/2JCE6+aP1yDYGrG5ceBAITAwK5IY1+8TpZ6f9LG68qKhYWOTJjmgI6pddbLOeQMeNRE4puvpKRZo2/ft7qbyohupL5zHvqvn07OhE2hKhKmhezUlXD15spSXpP9BLzeV1lK2toPrSeXRv7UDaEs2rU3NZHavftwHbkihT7BK1DZt9P96Fr8LPgluWcMGnLuHZgSwDB0/cfNNdKfb/ZDfnfeJiFtyyBM2j0fCnQyRa4kjLRg+4CdaGKF1TjupS2fmNLWN2sZ+MzytQVUcMJlPTfEiVkJvmQ+SSRRqf+XiY667yUFysUMhL8gUwTYllQSAwvkCTErLT+DobBnz9u0kuudDNrTd6+el3y3jsySzZnGT9Ghevuc1HNif5n28n6eoeft3yuMWQiOzstkYtNTiZZFJijNP8cTIFI41p5XG7gkTC9RhmlniilXiihUhkPr19+1BVF/mThOa8mktYMP9aUqluurp3ks70YppZgsFali6+fcQxpLRoaX2a3r79VJStpqx0FYsX3kxtzcU0HXuUnt59nPrUNVrd5kwyXnf8ycQTrfQPHKK6ciOq4iKT6cUfqKS8bDVdPTtIn4YpvBDOSFp7jO4bKe1BR4nh30k5xXOTS1kcfn6Aw8/PXsRyMrTuS/Ff79xJf3tuSGQCGHmb33+xAVUTLLkw4jQFmZL2Qyn++KVGBjqmVxY3s0LTpXDtG8ooqZpeweippBNzE2tGI5+1zkrEMBUf+/NIRk2kDV6/iu6apfEGZxiDPMYYMxlPFnBZJk4nKKgoqNhjnBcTY8L6SxtrUscqkKfwSpklOUM0PnCEedcuIFQf5pJ/uZrcQBbbtNG8GmbO5NEP3Tfkiwlw7MGjzLt6PkVLS7j0X64m2ZrAylt4S714ir10vthO374e1n7g/CmvxcqZ7Pz6FrylPiovrOGCT1/Ks3/3GJnuwXTiYOTTFXSx4u3rqLt+ITVX1GMVLJCOOFZdKoqu0P5sy/gHOwnDcKJ+iuLULU6X6TiwzKtR+f7XS1m/RqelzeLbX47z4tY8HV0WyaSkYEj+47NFvOl1E4x1nab9y5btBT78iQH+90vFXH25mysucSOBdMrm0BGDb34vyT33Z0bs3jAlliWxpeDv/zXGE89MoHQlZCYQo8exbYN8Po7LFcLlCpJKdWIYGaLRo1SUrcHvr0AIdaiDW9f9VFdtJJ9PsvfAL4dF9DyeyLjHymb7ONbyJG0dL1BctIRFC25k8aJbSSY7RnR6nyvYtkHTsccIrKqkqGghwWA1hUKKow2b6OndM2bqfzLk8ymktPG4R2YljpcsGGYW+5RxwC/fPN1wLFPS0zy6x/JAe47vf2o/VYv9hEpdZJMmnUfSZE5Dj52RZqBsyqJpXwZrEsXyo6EoULfcN6Uan1cTTupo9r/y41kv2LazIiFAUV9J1bXTp1RUo6AgsSkRVSioxOT0Ug9znB6ptgTP/u2jLHvTasrWVeCOeLBNm0IiT/TIAHZh+E0r3ZniuX94nOVvWUP5hkq8ZT5s0ybdmeLwb/dz9E+HcAXd+CsD9J0yOx0g25eh8YEjWDkTIz2yyD83kGXLF59n+VtWo7pVStdU0NLdOPR727A58LM9dG/vZOHtSyldVY6nxItQBIVkjnRnkv59vTQ/2jipaCZAT59FPi8JBRXm12k8u3n2Hkbufo2Pdat1+vpt3v3nfby0bfg5GSV4NKMoCpy/3kU4pPDN7yX59R8yTpdtyqaj0yKTHf16OhC1SSYlPq+gqkIllZq5666UFrlcHK+nCJc7yLHmJwBJMtWBZRsUFy0GIJ93mjtV1YWme0mlOoe8I8ERRpHwSI/ewd9y8r3CNHP09O7B7ytnfv3VuN2hc1ZogqC25mIsq8CuvT8efM8Tn38n6ihQlbHr01PpTvL5OEVFi2lpe26YxVPAX4HPW0o01jBkK/Vqw8jZtOwd2VQ8Xc6I0Gw7muWrH22YloM8OO3/H/3KQpaeN8HT7Ryzisc/tm2Px3/chNnGKMxe7ei5jJ8QZUrVULf6fnsLKWJne1mvWuJNMbZ88Tl0v46iK0h7cHJPzhy1zjHZmmDrl55H8+uouoq0JVbeHOoCN5IFtvzHc6MeK9Ec56UvPDvuelLtzv7HQtqS/n29DBzoQ/NqKC7VSfeZNlbeHDGaciK6ui2OtVhsWKtw/dUefvX79IQd5jOBELBmpQtFEew/aLBr70jh7XYLFtSfOROUJQs1PvKBIO0dFv/vqwmiscldo7p6LI40GFRWuLnmCg8/+nmK/Aw1B0tpk8vHKCleipTWUAOOUciQTLZTUrwE2zYoGI6oNM0chXwCv6+CcKiOVLoLVXVRXrqastKVjCbCKivWIYRKItE6ZJHk8UQoLl5CoZA6Yd9zGgihoKoup75S4HhqCvW0Io7H9+v3VaCqOgF/JXk9AdIR6IaZGbPL/XgNaEXFOlKZbiyr4HhkGllMyzkH+XyC9s6tzK+7ioULbqC940UsM4/HU8SihTcCkrb2F6b1HurXhSmb76Nxa5SB9rnSJThDQjOTtMgkLSxzek9/pmmRSb38U6+vNEqrXSgKI4rxhYDKeg8AA10GxitoStDp0CwP0mI5Bsdz5urnBtKWFJKTVwrSlhjJwiRMpc4c0pYYaQPSp7eKVFryx/syrFsd5ubrvdx1m4/f35sZYVMEDLMHOm0k5AfrFt1ugaYJ8vkTfw9CwM3XeVi94sw5JBRFVPx+hUBAsnKZzt4DhRMWSdLpmC8U5FBD0XEKBfjFb9NcvNERmq+5zcfv7smMmDzkvDdnX1MRornsAJrmJpnqGPJ5lNgMRI9SXraaVKprqKbRNLM0tz7NkkW3smbVWykYKVRFw7QKNB57lIULbjhl7wKvt5S62sucaUCDUbvjDS4NTY+QPY3RlprmZeniO/B4IqiKjtdb7MwZX/VnWFYB2zZoaX12FBP5ySGlTd/AQRbOv4G1q9924hoqbfL5BK3tm+no3DJiClEs1kRv315KS1YSCc/Htk2EUGhoepiu7h3H905r2/Ooik519UYqy9dh2Qaa6qZgpDl85N6T7J0mj1AEF762moteX8MLv2njd/92cFZM2M80br9KqNRFrCs/LYujGRWaUkoKeZvEgDltkQlOina60dCXA8cbaoQi0PSXR5pZCMH8FT6CxRrxvuF3II9fYcVGx4ri2P7MrHbDn+vMCcw5zhWkhJ/8KsVtN3m58HwXX/l8ERvPc7HpkRztnSaqKqipUlm3xkV5qcrn/iM2YfPLpI6LY9b+ltf7WbtK5+1v8nPP/VnyBSeNf8sNXj7+50EyWYnHc2b+Xg4dNdi2I8/FG9385sdlJJL20AOzaUp6+2yefzHPD3+eouEUw/Y/3p/hpuu83Hmrly/9WxHrVrvY9GiW7h4Lly6oqlJZt8rFFZe6+fYPkmx6ZPJRrGi8icamR0hneoY1yfQPHKah8WHyhfiw5pzuHsfnMRKqR9Xc5PMJYrEmCoZjb5RMnTzzXtLS+gwD0SP4feVomgcpJfl8nESilVw+NmwtuXycpubHSCY7OJVcLkrjsUeHWftIaRFPtJDOjCwdOU7+pNGZ0VgDtjTJndTcdJyBgcOYRmYowipQqK25mOrKC+jofIlUuntIUOq6j/LS1SycfwPJVOcpdkOO/dPBw38kHNqB3++MoCwUkkRP8dS07QJNzY5HZyg0D1V1zmc80TyKpVKCpmOPk0p3jnJuYjQee4xEshVFgcrFjrNF0874K0JkAqy+qoS7/34x3/mLvTTtnHoUfEaFZj5j8z+faCSTPD2RKCWk4xZmYeZu0+FyF0s3hjn0Upxkf+GsfgES/Qb5rI0vpLL0vABN+0YWoZ8xxKBjizjp34pwJoFMsIaKOjc3/lk593y7k8JgRELVBVe/vpR5S70UcjYvbIqOaT8yxxxznF16+2z+/FP9fOnfirn8Yjcffm+Q970z6EwnE6AqjuXP9l0FlMkPuJqQezdluePmHDdc4+EL/1zExz4cIpuVFBcpBAIKmx7J8vDjWb72heKZO+ggigIL52sMRG1MC1y6M90InApGoQjqamHjeS5uus7D297fx+GGE+IumZJ86h8GKBhF3HGLj49+MMiH3xt0rnMnnbNCAf7vx1PzGcxm+2lufXrEzwuFJC1tz4yyhSSd7iadHinuOjq3jPiZZeWJx5uJx5snXEuhkKSldbRjOt6VLaes07IKtA8b6Tg+sfixMaOE0Vgj0diJGmWvt5i6uivp7d3HkYYHRkQtU6ku1q15BwF/xQiheXxtA9EjDESPjLsmKW1S6S5S6a5xXzf25wH5QmLo3Ki6wO13ymx6j01sYP9ywR/RCBbrqNr0AmMzKjRtGxr3zszJfeCH3TxzTz+J/pnJ34RKdN7x+WXEuvPsfqKfbQ/20XYwNeaM7zNJtMfgwEtJNt4Y4XV/UUVplYv2xhyqKvBHVHpbCzx//8wVaCsqXHp7MeW1brx+FW9AYdFap/61rMbNu/6xjmTMJJd2Sh62PxGno3H4U7mUku6WHLe9t8KZPvRiAqMgWbI+wIU3RlBUwXP3DnBglKlEc8wxx7nDoSMmb3t/L7fc4OXm670sW6wTDCoYpqS312L/IYP7HsySSZ/cRALf+0mKkmKFbTtGzw1LCQ8/kWMganPwiDEsvTwQtfnwJ/p5558FuOFaD1UVKghH0N7/UJbf/DGNrguqK+M0HjOHPfRaFvzgZylKSxRe2j72sR99MkciYXPoqDGsHOCu23x8+d+LSKUln/y7AbZsL5DLOUEMgWP7tGqFzt98IsyKZTrv+LMA//hvsWFr6O6x+Yu/GuC3f8zwmtu8rFrhIhJWKBiS7h6L/QcMHn8mx5MTdaXPMSl03e9YOw2a2p+K2xUcNOI/t9w1pIRMzEAIMW1Rdi7iCZyeVDw3R1DiiLHoDM44N/I26bhBSa2Ha99Rw2V3V9GyL8nWB3rZ+/QA0a78rEU5jYLkV19rJ1Kms2itn9veUzFUGySAp/7QP6rQlLZjS2BbckpN55ouuPVdFdQt9w0zUbZMiS+gcuntxcN+Fu0xRghNgKd+30+oWOOaN5Rx3jVhZ70KGHnJ8/cO8IsvtWMW5lLF5zIuVxC3N0I23TvUHDB7xw7g9hadlWNPFX+gCl0f7uVr2ybJRPuUGgSCQcGqlTqdXRbNzedOOVA8Ifnl7zL89p4Muj44SUeCZUkKxsg6bNOC//3O+A+RUsJ9D2a578HRO3V7+22+/L8J/vc7CVRVIHHS1oUh7Sj5j6+NTMtZluODOdGxH3g4ywMPDz92KCT45EdCFEUUPvPP/fz6D6MHQvbsN5hfp/F3nwqzeoUzwvLURqlsVvLAI1keejyLy+WcMzl4zoxRztkc0yeXi2IUUlSUryOZ6iCT6UMi0VQ34fB86uuuJJPtI56YOFI7m9iWpHF7jIUXFFGzIkjT9tjZXdAMaF0hwBs4vfTGOSs0Z5ruY1m+/LZdrLysiA03llK3KsiSjWGWXBAm1lNg/3NRtj3QS+OuxLBh8meK9qM5vviBIyzfGGT+Sh8en0I2bdHXUeDw9tHTL4/8rIcXH4xSyNmjFvCPhZGXg3POJ+cX1d81RjW7gJ//ZxvP3zfAyouChEt10gmLQ9tSHNmZwsjPicxzGUXRWbbqboqKF9HR9iJHD907i8fWWLry9RSXLKGzfQtHDt4za8eeKkIoLFp6C0XFS4b9PJ+Ps/2lb1CYQqfuiuU6v/p5Cd/7forP/fu5F+03TUfszRZSQi4Ps2XPVlKkMq9GJZ2RHDg8fuBCGZwCZVnjL8+yHNE5x5kjX0hwpGETixbcyKoVb8a2DSQSRagIoZBIttN47JHTmg50RpCw5Y8drL2hnI13VbP3sV5iXWfvofqqt9ZQVneaw28ELL4gfFq7OGtCUyiOwbuqCYy8PerEmZlE2tDbkuOplk6e/0M3VQt9rL22mHXXllK5yMelr6tg421ldBxOs/3hPnY91k9vS9apXTpDpOIWWx+NsfXR2KReH+83iU+jlEBKZ6rP6aIIgWVCw54MDXvOfv1JUCmiWK2k02iam4AzCRRFxe0OIoR6Yi7vLCEU55hCqLhm+dhTRUqblqan6OvZh677CIbrKCldDogpBwgEMxJUmGOa5POSTFZSWa6wfInO3v3GqLXoy5do3HWbFyRs23lSR/ocZ42+/gPEEy0E/JW4XEGEEJhmjmx2gGxuYNIThmab/tYsv/7n/dz9zyt487+v5JFvNtFxKEkubY77AHMm+jQ23FTGko2RGdmXPI0Fzq7QFFBZ7+aSW4pZeXGQULGGogr+8I1Onr/vRKo4XKIRKXeRHDAY6J75L5ORs2nZn6Jlf4pHf9hO3aoA591YxsrLi6hdHqB+TZAb3lPLkS1xttzXw+EtcTJxc/YaduaYFMVKJUv08xmwuinYc0JzIkwzR9PRh4kULaSrc8fEG8wglpmnqeFhiooW0d21c1aPPR1i0QZi0QYAyivWUVK67CyvaI7p0N1r8egTOd75Z34+/08RFi/U2LK9QCplo+uCslKVCza4uOs2H/NqVfYdMPj5b1Jz1/pzBMNIE401nO1lTInKxX7q14bpa86w6poy5q+PEO3MkUuaWKP49R7n6R+3sPuRnhlfj1mwad6TnPZYaCGgfL6Poir3tNcwa0JTKHDRTUX82V/XUlbtGnrMlzb4Q8Pz/4vW+vnQ/5vPwa0p/ueTjWc0JZtLWRx+Mc6RLXECRTqLzwux6spill9SxIYbSll7TQk9zVl2PNLHS/f20HMsO3cROkdQxZnz3Xul0t93kP6+g2fl2AN9hxjom56n3tllBix+Bkc/lpcphEIK2Zykq8sa1zA9FBKUlSqoqiAWt+nrs8etA9R1KCtTCPgd4+x0yqa3zz6pBvIEmgbl5U7Hd6Eg6e62R6SDS0sVfF5Be4dFVZWKpkJbu4VlQUWFc5yOjtGn6rhcUFGh4vMKsllJV7c16jrONJYF//7lOD6f4PabvPztp8IU8mBaEkWA7hJoqtNZft9DWf7ti3GOtcyFM+eYPssvL+WOT58ou1FUQfkC34Tb7XxwbJuo0yEVM/jJ3x4k1j3NrKaAOz62gGvfVTvtNcya0Fx1UZD3/EsdgYhGb0eBloNZyue5qF08sn6gaV+GQk6yfGOQ4goX3S1nvrNMSkf557M2ZkFiGrbTlagIyud7ufVDdVz+hiqe/U0nj/2w/bTmfs4xM2hzQnOOlwFSOoLmM58O8vq7vBQVKeTykueeL/BvX0jQcoqwcbngjXf7ePc7/dTWOgIvGrPZ9FCO//6fFL19w9WmEHDpJS4+9IEA69boBENOij+RlGzbXuDjn4oRj58Qg8uWanziY0EuuchFICAoGJKDh0z+5+spnnwqPyRm3/5WH9dd4+G3v8/wsY8G8XoF3/hmikOHDT73z2FKSxUefzLPJz8dI5k8sf8Lztf5xMeCrFuj4/UJclnJrt0GX/mvJNu2j566PpN0dVv8+Sf7+dZqF5de6GbhfI1AQME0JX39NkcaDLbtKnDoiHFWxPAcryxa9yV46kcjLZcmov3AmanhNnI2qagxLaP146Rjp5dZnhWhqemC299bSSCi8cwf+/n1f3UQ7S7whr+sHlVopuImHY1ZVlwYpHax54wKTUUTlNd7Oe/GUjbcWErlAi+aWyGftTmyJc5L9/WQS1lcdGc5yy6OcMsH66iY7+Nn/3yY7Gn6hb5aUdFwCx8FmcPkxJVdQ0cRk+1uE7iE58ws8Azh9oQJhevx+UoRiko+FyMeayab6RvVwuPk7RShkcvFkNJC1TyEw/UEglUoqo5RSJNKdpBMtI+oW1IUDbc7PGKQtGlkMYz0pNat6V5C4Tr8/opBy5EEiXjLMCNpkORzw82lp3tsIRQ8niJsaZLPxVEUjUCwhlB4HpruxTJzpJKdJOKtw2YUj1i35sHnL8cfqMTlDiIQGEaaVLKLVLJj3G1nEiHgztu9NDeb/M83UsTjNpdd6uYNr/cSCoZ5/4ejQ0JNUeC97/bzqU8EeeKJPN/5vxSFAlx0oYu3vtlHZYXKxz4ZI3OSkfp117r5769EALh/U459+w0UBZYt1cnn5bD53PV1Kt/+ehGlpQo//XmGAwcNSktV3vxGL1//rwgf+2SMhx/ND61l+XKNq69y8x//meDd7/TzoQ/6aWqy+Pb/pVmzWuPu1/n45a9dPPa4s836dTrf+noR8bjkv7+eorPTor5O451v9/Gt/y3i7e8Z4MCB2X9Izxdgy/YCW8awR5pjjpmiYUuUhi3Tn7g0k5gFm0zcwJjiuNpTOd0G6VkRmqXVLhas9jHQVeDXX2unv3N8dWwakoFuA1UTFFe6Zn5BAnwhjaUbw1x4RzlLL4zgD2tICbGePHufGuDFP/XQsj81NE5x12N9rLqimDf+3SI23FjCoRfLeeZXI6cEvNKwDMn3P9uC16/QeWxmbsxLXOdRrS0ianWzO/80Fs6NZ6nrfIrVqknv5+UiNBVFp7r2QmrrLneacE4SXqaRpatzB81Nj2OOMrtXKBorVr2JQLCaHVu/BQiWLL+DULgOMSTKJaaZY/f275FMtA/bPhCqYc26d6Cqw+tr2lqfo/HIpgnXHilaxKKlt+APVA4eSp5Y/5B+kdi2ye4dPyARP/Ek7w9WsWb9u9BOOXZ722YaDt8/5jHdngjnXfQX5HNx9u3+GfMXXk9p+UoU5fjlSjhTSaLHOHLwHjKZ3lP2IKiedzHVNRvx+spQTnEet22LZKKNo4fuJTXKFJQzgW1JPvFXMRqbnAv2Aw/mEALe9AYfV17u5v5NTo3x/HqVP/9ggMefyPOxT8aG0tn33u/Y9rz1LT4uu9TFI4NiMBwWfPqTQYQi+PBHojz9zImIpKI4H9VxhwpFgfe/18+CBRof+2SUe+7NDUUXn3gyx89/UsJffSrIS1sLxGKDIyNdgl/8KsNDD+fRNMEXvxDmhRcz/OBHaVYs17j9Vi8rlmk89ngelws+9tEAioAP/UWUI0dPCMpDRwy++80i3vsuP3/9t/E5K6A55pgFfveFowhFnHazdTZpYuTGL90Zj1kRmsEiDX9I5cCWLNFJNPdIGwo5GyHA5ZmcJc9kUHVBxXwv591UyoYby6hY4HW63nM2jbuSbLm/h92P9xPtzI9I75gFya7H+gmW6Lz1X5aw+spinv1N5ytmxNRYSAmHtk1t2sX4CHwihCo0/EoYBXVIaLqEF58SnNLe7HP8AxBCoX7BNcybfyVSSgb6DxOPNWPbJv5gJSWlK6ituxS3O8ShA7/HGsVfUigqiqoTKVpAZfUFuNxB+nr2k80OoCgq/kAlqqKTzfSP2DafjdLa/AwuVwBN91FcuhSXK4AQE/9d+XxlLFv1ejyeCL09e+lq30qhkMLrK6G27nJC4TpMI0N31y4ymd4Rx8/n4rQ1P4PuCqDrPopLljiRxUkcW1E0PN5ilq96A4FAFdGBBhKD5y0YqqGkbCWR4kUsXnYHe3f/FNs6OVIl8XgieLwlpFPdJOIt5HJRkHJo23BkPkuW38meHT/CNEf3fZxJtm03aGk9ERUoFOBP92Z5y5t8XHyRiwcedETfpZe4KSlReGlLgdISZUjLC+DgIQOXCzaef0JoLl+ms3SJxqYHczzzbH7YjeDUm0IwKLjmKjdNx0yefGr4Na7pmMWjj+V421t9rF6p8+zzzvk0TUnToDg+XlN64KDTGBmL2ximJBhyPs/qKpWNF7jYvdsgn5fU1p4Q+N3dNum0ZP06nYBfkEjOFbrPMceZpvPozLjD7Hmyn/Y3b6e3ZXrXytlrBhIC25zcSElFBV9QRUpnrOVMEC5z8aZ/WMTSiyL4Q87bTg4Y7H82yov39tC4IzGp+epO95ZNqExHMFtucK8kJEeNHcTtPuJ2HwbDU1mmNGgy9pCXE/2BCGq0xYSVsjO31BkgXLSQmrrLAGg6+jAdbS8MpbeFUIgULWT56jdSWr6KZKKV1ubRx5wJoVC/8Doy6V52b/8+mXTPULpdKBqa5hnVBD2fT9By7Mmh163b8B5cxYFJrb2sci0eTxGpZAeHD/xxKOKaSnaQSfWw7vz3oaguerv3ED9ljjBA4eRjC5W1G96Nyz35BwlVdREMVtNwZBOd7S8NpeWFUKmZdwmLltxCuGg+wWA18dixYdu2tzxPX89+0qlOLMvg+F+qs+3FLFxyK4FgDcFwLdH+8cfUzQRdPRbmKRnjjk6LQkFSXaWiqo6n5ZLFGqoK//C3Qf7mr51zdTz+raqOeCyKnBDq82pV3G7B3v3GhN66kbBCSYnKth2FETPMpYSjDSZul2DePA0G/y4tm6FmH8t2LE6SKed7Z9uAdEYwAlRWqgSDClde4eaJR5y/y+PTdwC8XkEiIdFdc1fOOeZ4OZFNmLSfRl/KrAjNVMwknTAprXHhD6qk4uNfEUNFGgtW+TAKko6mmbGtCZXqrLuuFNuStB5IsXVTLzsf6ae3NTulqKRtORdbI2/PXSqnScLuJ2GPjL4BWJh0mk3k5MT1g0Gl+JwWmkIoVNdsRFVdxKINw0QmOH6N0YGjdLS9wPyFN1BVs5Guju2j1i+KwXT10UP3kk4Nn8srbROjMImos7SZ3KOeQzBUA0Ai3jIirZ/N9pNJ9xIuWkA4Uj+q0Bx26CkeG5z3HI01DROZzr4serp3U1t3GW5PBH+gYoTQzOfjo5o5S2nR3bWLmrrL8HiK8HqLmY1qqtFSTrZ0/lNPyuzrujOR5oc/znDoyOjZn4aGE9dPTXPS45NpYjmeSneuYSN/b1nO77WT7gpyUEyezFjXS01zjvHCiwV+98fRHxRTKUkqdW5nIeaY43QIl7sprp26SXp/a4ZE7xmoIRZOn8zZnNo3K0Kzr7NAy6Esyy8IcsVdJTz0054xjdBdHoVb31NBZb2HtqNZWg7NTOjXyNtsf7iXrff3cnhLnOw01blp2hzdlqBhe3zuofwMIKWNJSfX4TbZ150tNN1HKFwHQH/vwTENhgf6DjGv/io83hL8gQpi0cZRX5eIt5BOnRkLjJGIoRrQsUYuHheOQpm58pZh+5eSgb5DpzQdOVhmjkIhhcdbhKZPbB1yMrZVcLb1FKGoZ6AGfBSKIwqqyrCoY1FEwe0S9A+cqH3q7LJRFNi7z+C3v584TTUwYGOaTmRzIhIJR+SVlCi43WLERKCqKhXTZERX+2SJxWxyWUkiafPLX83ZwM3x6mTtTRXc9vHFU97uT188zPO/apvx9ZTVeXnNpxay5d5u9j7ZjzXFek23T6V+TRDLkBzbk5jy9jBLQtPIS+7/fjcLVvl44ydqqFns5aWHooSKdRAQCGvMW+pl3hIPl91RwrorQ1iW5IEfdJOKzUxnd/exLD/49MHTnvTT3Zjlfz+4d05kngHSdgwLA4vJfUgm57bQdLmCaLoPaVtkMn1jvi6fS2AaWdyeMD5/+ahCU0pJNtM/pTnbp4ckneykuGQpwdA8VM0zrH7U7Yng85UipXXGxK+UNtnMwBi/k0OTKsar+VRVN253CJc7hKZ5UFQNVfOg6z6EmPqkn+mydq1OJKLQ3z9Y7iDgisvdCAE7dxlDQvOFF/Lk8wFuv9XD/ZtyI7wthRgejdx/wKS3z+Laa9x89/sqbW1jfz8SSZuXthS4/joPy5dpbNt+4u8nHBJcebmb3l6Lvfum93d17JhFQ6PJ+ee5WLZU4+ChkQ8Ip65/jjleaUhbYltjf8mFcNxuNJfiTDsq2HQ3pIl3n5mhI2uuLmHd9aWU13s58mKMjDG1IJvHr/LWf1+GP6zztXfspO3A1Hs2Zq1Gc/ezcX711Xbe9IkarntTKVfeVYIQzkl/zYcquf19Fbi9zkjKQk6y6Ufdw6YFnS7SnkFtOHehPCM0GLsAgT1JoZmxE0Tt7nM2sqlpboQQSGwsc+yOfcs2hqJ2+jjROdua3ffZ3bWD8qr1hEK1LFn+Gro6tmIU0ng8RdTWX47LHSIWbSQ2MHoE9nSR0saa5pg5lztEdc2FlJavxO2JoCgaQijOEEkx+4Mha2tV/vpTQb7/wzSptOT8DTrvfoefY80mjz1x4ruxa4/BPfdmed1dPv7tsyF+8csMA1Ebl0uwYL7G2rU63/tBmr7BqGNHp8XPfp7hEx8L8l9fifCtb6dobLIQAqqqFBYu0Pjt77KkMxLDgO/9MM0VV7j53D+H+fz/S3Cs2SIYFLz7nX7WrtH5768n6eiY3sNMKi355ndSfOU/I3zlPyP899dTHDliIISgrEzhoo0utmwr8NzzcxZDrxSEgPJKFZcLOtqsCeuEXw3seKBrXHsjzaXgL9KZtyrEebdX4Q/rPPLtRg48M3o52emgaoIFG0IIAQeei5JNTj2Tmxww6G3OUnKph3krA+e20LRMeOTnvbQdznLzOytYvjGA169i2+ByK9i6xMhLju5K8+CPe9j6aOyMzz8fDyGguMaDZdjEuucujLOBzdRSdj1WKz1WG+eq8j/ZG3O8qJtADNVgjuenOdvvMp3q5vCBP7B46R1UVK6nvGLt0Cps26Svdx+Nhzed4a7tqb9rj7eYlWveQjBUi2FkiPYfIZFoI5+LYRo5ELB46e34AxVnYL0jMS3Jl7+a5PprPfzhNyWYJoTDCp1dFv/wT4lhwq5QgM/9e5JMRvK61/p4/Wu9FAqg6aAIOHjI5Ac/PFHDa9vwze+k0XTB2/7Mx/e+U0yhIEGArgn27Tf44z0nPp9t2w0+/Zk4f/+3QX78w2JSKYnHI7Atyf99P803v5U+Leuh+x/I4fXE+fhfBvn214soGBIB6LoglbbZd+DcfCh8NePxCmrmqVTXavh8AsOEeNSmrcVkoG/86VW19Sr/8/1SQhGFf/pUlGefmBsFnI4apKMTf88PPtPPtvu6ePuX1nDnp5fS05im6+jkvI0ni6oLyuq8WKakcXt8WtkE25Z0Hk2z/NIiKiYx4Wg0ZnXWubRh/0spDu9MU1Lponqhh3CphqIIMkmLjqYcPS15cjPUaX466B6F9391BT3NWb7/V2dnZN8ck+HcFJkARiGNbVuoqgvdNfYfqJPSdaYc5fOJ2VrepHBM2A2ymT66OrZh2xammSWd6iKd6h6z7vTsIaiddynBUC2FfIL9e35BIt4yTMCrqnvW1t3SavKF/0jy+z9k+fkvMlx8sZt5tSrRqM0LLxVGTXUPDNj802cT/ORnGdau0Z1JQllJ4zGT/QfMofT7cTIZyZe+kuTXv82wfp2L8jIF24L2Tovdu41hVkJSwqaHcuzYWWDjRhdVlSqplM2OnQZHjprDOuOffiZPIiGJxZ3jNTSYfOGLSQ4fcV6UTEq+/LUU+08Sj6YJv/pNliefznPeehc1NU7taGeXxb59Bq3jpPZfLaihIEoggNHVPXqX2Czh8QpuuNXLa9/sZ9FSHZ9foGnOkgoFSSoh2buzwPe+kWTvztGDLcUlKuWVKh6voLJ6ssM2HIRgyG3h1cpAW5Znf9bCWz6/mvNur+KB/zo6o7c0RRF4AhrSltMfQSkh0ed8/v6i6U3jm1WheRyzIOluyc/KaMnporkUQqX60AmeY46pUjBS5LL9jo1OqJbe7j2jvs7rL0XX/di2MYvNPhOjKBoLl9yC11fK3l0/nhUboNNFVXVCkXqEEPT3HiAea+bUK7eq6ui6f1bW09ll893vnYhSPPjQ5CI+pun4VR44OLm7sGVBU5NFU9Pkostd3Tb33jf+WrZsNdiy9YSIbGm1+PZ3T7yXdFryvR+MjMBICV1dNg88OBfdGoEQhG+8Ds/SxXT91zex0zMbwZosxSUKn/yHMDfe7kPXIZuRNB4xiEdtXG5BRaVKabnKRZe7+en3xh6NeOSgwU/+L0UwqPDUo1PLbFx5nYe73uTn8/8Qo7f71fsA0nkkhVmwqV8XRlE47T6S0REIZfrlQsrgtqo6vX2cFaH5csDlUVC1M9NNO8erA8vM09dzgECwhtKylbS3biafiw17jaJoVFadjxAKiXgLmUzP2VnsKGiaB3+gAiktpz70ZdHJcaJb3rILjBYeKCpZOiU/zznmmCmEruNetAChTi36N5N4fYJP/3OEm+7wYlvw2KYsP/pOiqajBrmcRFUFwZBg1VoX8xdp7B5nbGcmLfnmV6aXhbnuZi9rz3Ph8cxuvfQ5h3SaGz0BDWbYnduyJIm+AsXVHioX+Ti2a+qflRBQVu/YNWWmUeMJsyg0FcW5R032PjXV15+MmIHvrduvokxTvc9xerjw4FUCaMLFRH3BEknM6sU6RzvQuzq2UlaxBn+ggiXL7qTp6EODHegSXfdTPe9iSstXYVkF2pqfnfGGHyEUhHAmC6mqa2iMo6Z5cLlD2JbTiCSlNaI+1DRzZFI9FJUsYcXqN5NOdQ2lnKWUGEaGZKKV/r5Do/p4nnpsVdEnfezpYtsm2UwvgWAVRcVL8HpLyGadpkJF1SkuWcqCRTcgUIa61kc5awhFQVF0VEVDd/mH3o/LHcK2LWzbwLaPr/tcF9+vUI7bah2/N0/lhiEAoZzYdiop7ONdrNM4rlZWgl5SjJWcyWlrU+PWu3xcf6sjHP7w6zRf+3ycdOrE+k1Dks9Jnno0x9OPnZlny1BYsHLt7FiLnevMWxPG5VHJxI1xrknTwyzYtB9MsXBDiA03lbF9Uw+F7NSutcFSF4vODwPQNc1JQ7MiNIWA132kGt0lePzXfROmzOuWebnj/ZX0tef5/Tc6MfKTP/nVS3xc+ebJz8seC39Ex+M/e0+dr0YECrXaEur0FXiEH4WJI8oSyYu5B0jaM+dQMJPk83GOHPwjy1a+npKyFYQi9eSyA9i2iccTwe0JY1kGzU2P0d93YEaP7fWVsnzVG9F1rzPGUqhDoqm8ch1FJUuRtoktLQq5OAf2/YbCyTWiQqG/7wDhSD1uTxi3JzzKUS4klezk4L7fDDOS93iLWbH6TY6N0CnHLqtYQ6R48Ylj5xMc3PubUQ3Wp4qUFh3tL1FUvBh/oJJ157+PZLIdadt4fSX4/GVEBxqIRZuoqNowyh4Ei5feRlHJEqdTXVEH58QLXC4/a9a/E9s2h8RmW8tzdHVsPe11zzF59IpyfOvX4p5fhxoOgRDYmSxmTy+5pmPkDh3Bio8RuVEU3HXz8J23Dve8WhSvBzubJd/cSnr7Tgqt7aMrKyFQI2E8SxbhWbQQvaIMxePBNgzM/ii5I0fJ7N6LPYqAVLxe1OIIenk5vlUrEB4PimURvOISZH54tDDf3EK+8dgMnKXRiRQpvOkdfjQNDu03+NZXEsNE5qmMdip8foHfPzwAIIFEzJ5wcICmORHVVetc1NapZLOS4lKFXHb4tT6XkyQTJw4eKVKcFH9WkhpvfKmASGSSrz1DaC4F3Tv+vUsAmktl8YVF3PDBBSCgedf0mnXGQ9qw+7E+Ln5dJcsvKeLyN1Xz5E/bsc1JPhi5FK59Vy2l87xk4qbjHz4NZkVoevwqF99cRNUCD0d2picUmsmYycoLg7i8YZ7+Yz+dTZOv5Syp8XDVn1UPdfHOMT5CVfFVL6QQ68VIxs7qWsrUWpa6zkdBRSIpyBwSG7fwYmNjyIIjWNABQU6m6bFaydlnp85pssRjx9i768fMq79ySAAJITDNHNGBBtpbnmeg/9DoUT0phwTcqWn3iZHYtoFxkntDbox9nGqKrrsCLF3+GopKlhCLHWOg7xCmmUVKiUCgqM4s8vLKdQSC1dQvuJYDe3857D04xz7xBDzZY9u2SSLeAginS3yM9+acF0k+N/ziFxto5ND+3zFv/pX4/BWUlK5ASot8PkF762Zajz2NP1iJ11c8avOVlPao4zzHWsccs4dv/VqKX3sHaiTiTLoqFEAoCJcOSxYRuORC4o89Rey+TSNUktA0QtdeSejaq1B8PjBNpGki9HLcixYSuOgCYpseIfns5hERTr2ynPL3vxutuMiJpFoW0jAQmoa7vg7/+rUELjiP3p/8AmvgJGsbTaXkLXfjXb4MoWtDY6AUv5/IrTeNeH/xhx47o0JzzQYX9Qt1bBt+/4s0/dMw57/7rX7e/5HQMIewQkHyj58Y4Pmnx75X33G3j2tu9DKvXqOqRsXtEbjcgq//uHTEtKkH/5Th8/8QQ0rndH/0r0PceLuPzc/k+Lu/HBizgai4ROHrPyqlulbj61+K8+ufzP694bzbKrnmvfPHfY0Q4Alo+MI6qi6ItufY8UDXGbmcHN0WZ//TA6y7oZTbPjqfYLHOkz9pJz7eFCLhjO2+7t3zuOIt1QgBux/vo7vpHI5oFpXrRMp0ElGT5oMTLzQVM2k7mmXVRUFqF3unJDSP09OSpbd5+rYrbp/KgrWv/DouT2k19Xe8i65n7mVgzwtnbR0ChRptMQoqCbufBmMXKTuGisYFnhuI2b0cKmxBQSWoFLNAX40iNDrMBgzO3aay42TSvRza/3tcrsBgZE9gmTny+cS4JuxSWhw+8IdpHTOb6WfXtu9OY0tBbd1llJavordnH4f2/QbLGv2ilEp2sGL1mwmGatB1H4XBFHouO8Cubf83rXUX8okJt7Vtk8MHfj/GbyV9vfsZGDiK2x1CVXVs28YopIbGe8YGGtgx0DDqtg1HHpjWuuc4s6jhMEV33ooaCZPevoPUcy9gJpIIRaCGQrgXzsezeBHZvftGDcUFLruYyM03YufzxDY9THbfAexcHjUQIHDxBQQ2nk/RHbdgxRNkdg1v3DMHYph9fRQ6OsnuP0ihrR07l0fxePCfv57g5ZfgXrSA8NVXMPCHe08c37JJvbCF7H7HueS4oLWTKWIPP4bMD792Fdo7z8zJAxCw7nwXLhfEYzabxxGF43Fwn8GD92YIhhVKS1XWb3ThdgtUbezgjhBQXaMSDAliUQspJUuW604T2xGnNvRkOtutoVNo2/DEwzluf72fiy/3sGCxzpGDo5cYnbfRzeJlOom4zUvPnZ37gi+iU7Fw/GZDKSXSdiYWth9Icv9Xj9DdeGZEsZGz+eOXGymu9jBvVYDr31fH+pvK2PNEP007Egx05Milnbm0nqBGcbWHhRvCrL66mLJ5XhDQsjfJpm80TzoSeiqzIjR9QRWPT6G/MUe8b+JiUrMgGeg2UDRBUfn02uk3/76LR74//XFOFQt8fOKHa6e9/csFX2U9ist9tpeBLlwElDAmBgcLW4jbvQBouLCwkEhy0nlIyVhJknaU8zzXsty1kR25JzB5ObgDSAqFJIXC2F2c5wKKohKJLAAg2n9kTJEJjoWTlDZCUZ2at3MI2yqQHWci0xwvL/SyUrSSYqx4gui9m4ZFDo2uHnKHj5LQn0KOEu7SSooJX3cVANF77if14pYhMWj29FJoa0PoLvznryd03VVkDx4eJgJlPk/vD36GXShwqit5oaMTNRjAf8F5eJYuRrhdyNzgtlIOiUwH4QjNXI701h2z2nWuCFiwWEcIQUebxUD/9NqbX3ouz0vP5VFVqK3T+Mk95RM29EgJ3/9Gkh9923kQvfhKN1/6ZgnJhM1nPxOlrXn4WqxTJuvs3Jqn4ZDB8tU619/i5eghY8SzhKrBtTd70TTYujlPW+vZ8U06vLmf335ugjIoCYWcxUBblo7DKXLTbLKZLL3NWX746f287jOLWXF5EWV1Xq59Zy32WyVGwR7sdJcoqkB3KSiDDw22KTm4OcrvvnCU/rbpu0jMitBUVIGiCixTYpkTh+qldJS+EKC7p3fzSvQZpzVEPhM3saap3qeMEAhVG5oZLaVEWtbkfA5O3daWSMtkRC5ijG398xYz21NSRkNFQxNu8jJDyj55qoLExkJFcxo4Bk3dMzJBh9nIQn0NRWoFvVbr2Vn4KxRnjrkY1wZICJXi0mUoikY+F59CunmOOaaOlDZIiXC70MKh4Snq468Zw13cu2YVajhMob2T9M5dIyKesmCQ3rod/4a1uKqq0CvLKTQPv6bY2TEyZKZJdv8h/OetRw0GEJqGPAezLEI4qWWA6IA9rKRmOliWkzKfbLrXMBg6pjH47Col5HOMiGieSiopefDeDMtWhbnuFi+/+GGKWHT4Pa6ySuWCS9yYhpN6N89Sf2jHwRQdB89es9dYdDdl+f4n97PmuhIuurOSutVB3D4Vl0cdihFIGyxTkhkwaNmX5KU/dbPniX7y6dPzXJqlWec2RkHi9at4/Crp+PiLVjUIRDSkZModUrYtyWdsUgOn9y0r5Kxph4knjRD4KuuJrDgfb3ktmi8ACKx8hkKsn0znMRINeynER46mUj0+/DWLCMxfhqe0Gs0XcOr+chnyfZ3EDu8i1XJ4hFgVmgt3pAR3cQWeshr8tQsBCC1ZhytSNuy1diFH3/ansI3ZiRYKBLa0B0WOg0RiSgNduFFQsE6aHpQcFKQhpXhOaM4gtm0RizYSjsynet5F5PNxYtHGoY54RdXxeospr1xPRdV6pLQcM/dxIp9zzHG6GF09GJ1d6DXVlL7tzSSeeZ7Mrj1Ysfj4rdGqgmfBfBACKxZDKyoavct80OpEuF1oRUUjhOZ42JkM2NKpF1XOrcj+yeguJ6iQz8kz5Nd45nji4Sxvf3+QugUaF1zs5tFNw4X/RZd7KC1VaDxqsmPL3LVoNPIZi6339rDr4T7C5S7K5/uIVLhx+RSkDbm0Rbw7T8+xDIl+AzM/M24gsyI0430GyahJcaWL+St87Hth/NRhqERnwSofpiHpbp3ak2HjjiRffddueo5Nr2j1OGbBJpM0MQtnanKDoHjNJVRcdiuqy4M0DaxCDqEoaP5yPKXVhBavQXG56Xnh4VM2FVRecTtFKzaCEEjbwi7kQIKnJIS3vJbw0nV0b36Yvu1PDYtuBuuXUnPDm1B0l9MwpTjF6YF5SwjULh52GCOdoH/38yceP88gNhamLOASbjR0CoPzzm1s8jJLRClDF24seSLFcFyQ6mLOJmNmkbS3biYQrKa4ZCnLV74ew8g4EUsh0FQ3mu5FCAXDyNDW8hzdndvP9qLneIVjp9P0/+4eSu6+C72qkuLX3kH4mivJHjxMetsO8k3No0Y0haKiFRchhMC7agXeFcvGOIIA1RGJitczyq8FalEE97wa9MpK1HAQxeNFuHS0SHho23MZYzDLp2nnXKXLhLS3Wjz/VI477/Zxy10+nngkizV4O9BdcP0tXoQCTz6cHRHtnGM4Rt6mrzVHX+vsZKFmRWjG+gyO7kpx0c1FvO4vquhqztPfObp4cXsVbn9vBeXz3PS05ifVPHQy2aRJ857Tr4ErZG2+/Zf7sYwz84V1hUsov+gGVN1N/85niO57CTObBiHQfUG8lXUEaheTODrKNBkpSR07hCtYTKJpP5mOJsx0Agm4iyuovPRmvJX1lJ5/FcnGfeSjJ0zAM92ttD30C+ei6XJTecUdaP4QA3ueJ3ns0PDDWCZ2fna+iIYskJVpQkoxfiVMwXaOK7FJ2VHK1Foq1HqazQMwmNYNK6UIFEx5bnpovpwxCikO7P0lJaUrKCldhtdXhqZ5QEIuFycfO0Yy0UZ/70HS6R7muq/nmA3yRxvp/sZ38V94AYGN56GXlxG89CL8528g19BI/KFHyTc1D49wCuF0pQNWIoHZPzLlfipWanjqU/H5CF9/Df4LNqCGQ0jLQubySNNE2jZCU2fGwPkMIqWTMgeIFKvouhgSni8HbAvu/0OGm+7wcf5FLuoXaDQOjkOdv1Bj9XoXyYTk0Qen3wQ8E3hDGoFiF8m+PLnUxGFj3a3gL9LJJs3TTlGfq8yK0LQteOinPay6JMSKC4N8+luLeeTnPRzZlSYVM7Et8AYUahZ5uep1JWy4OgwSHv91H4n+s1PQKyX0HDtzX1hXUSmaP4QRH6B3y+OYmRPi2EzFyfa0MbD3hTGNhOMNe0g07nPqMU/CTMXpKORY8NoPonn9eCvnDROaZipOMuXYwageH+UX3Qh+yPV2kGzcdwbe6eSwsYhaXUSUMsrUWqL2iVGMfVY79fpKFrrW4Fa8JKwBAkqEefpSJJK4PdfwcSawzDw9XTvp7d6NEAonanklUtozZrI+xxxTwUokSTz6BKnnX8SzeCH+jefhXbYU74pluGtr6P3xz8kdOnlcqkQaznUys2c/0T/cO7Ex9skNP5pG0V23EbhoIzKXJ/HkM2T3HcAciGLnckjDxLtyOWXveMvMv9kZREpoaTKRUg52gCtkXmbCZs+OAof2FVh7novrbvbSeDQJEq68zksgKHj+qTyNh89u4OGC11Rz68cW8bvPHWTrnyZ2EahfF+YdX1nLsz9r5eFvNs7CCmefWZsMdGhril9/rZ03f7KG+hVe3ve5erJpm1zGQtqguwX+kIaiOsWoz97TzyM/7zn3J95NE2maYNuoHi/u4nLMTIoRUaHxplXY9lBjzKnkB7opJPrxlNWg+V4+Fk3dVgvFahUZOdzbMGEP0GU2UaMtoV5bOexb2291MGCdO/PBX4nMicrZRREapb75eNQAA7k2UoW5B6nRsDMZMrv3ktm3H/f8+ZS84S706irC119DvvHYUBpdWjZmNIqrrhatuAhp2yM6x8fDVVWBf/1akJLYpodJPPXsmKbu5zJSwr7dBUwTwhGF9Re4eOjesxv9myrZjGTTPRlWr3dx3S1efvXjNKYpufoGD7YND92bmdA0/kyjagLdM/nJgrmUiSegUbsyiFAm18f7cmPWqjRsGx77ZS//88km9jyfJJu28fgVisp0iit0AhEN25K0Hs7yky+08YN/bSGXfgWe8UFyfR1k+zpQ3F5qb34r5ZfchLu44jQKZ46PRRu0JSjkEUI4tjMvE5L2ANtyj9JmHhn2c4nNkcIOms39ZGUKgzw5maLdPMr+wgvn7PjJOeaYDpX+pawrv40VpdextvwWdMV7tpd0bmPZ5BsaSTz5DNg2ekW5Y44+9HuLfNMxkBJXbQ1aSfGUdq+VliB0F7JQIHvw8Kgi01VZcWIk5jgMPbCddK2eTXZtK9DTZaHp8No3+/H5z444Ph5Rns7Rn3osR0+XxfxFOms3uFi6QmfhEp3OdosXnn35OV+YBRuzYBModr1iB83MWkQTnBT6zqfi7NucoLzOTfUCD5EyHUUVpBMmXU15Oo/lSCdmPpyvaoJAsY6Rs8kkJk7HCwW8AUf85s5AesHKZeh47LdUXfUafJX1lF90AyXrLiXT3kT04DZSLYcnrI/UfAF8VfPxlNfiChejeXwIzYWiu3AXl8/4mmeDsUSjQZ7DhW0cYx+q0LAwKcg8c7WBc7zSCLnLUYTmNK9oEdyaH6Pw8oo8nQmExwOWOZQGH/5L4Uz7EcJJZ5+SDcrs2kvoqitQiyJEbrqOgd/+ETs7yvVV01B0fZiVkTQMjo+oGa1JSCsvw7/xvEmJBDuVBilR/H7UcBA7Nbs2OL09Fvf/PsP7PhrkgovdvPMDQb7/zST5CeyFZppcViKl0wU/VbHb3WHx9GM53vh2P1fd4CE6YOPzC+75TY6+npdfcErVFMcCchzD+5c7syo0j2MUJO1Hc7Qfnb2nj8qFPj72/TXseKSPX/7r0QnD04GIzl98exUDHXm+/+mDp+XJORbZ7laa//R9QovXUrRqI97yGoKLVhOYv4JsTys9LzxCquXQyCdooRBeup7yi67HFSlFIDBzaex8Dtsyx0+5v8wpkJvTlnO8osmYcZw6WMhbaQrWnMgECF56Ed7lS8kePkKhuRUrmULaNorbjWfpYkLXXAlCkN13YMQMcbN/gNhDj1L8utfgP38DaihEeusOjO4epJSowQCuqgrcixZidPcS/eOJ6T5GRxdWMolaFCF843XE7n8IK5lE6DruunmErr8Gxe1yvI8nwOjqxkokUcMhim6/lfgjjzv7UlWEx42dTGH2D5yR8wdOWvZXP05x0eVu1p7n4l0fDlJTp/Kbn6ZpbjTJZiWaBoGgQnWtSs08jQfvHe5JKQRoOrhcgnBE4Xj5djCkEAg6DUaGMf5tqKvTIhG3KSpWuPlOH81NCXJZ53wrqtPAP4YlKrYNm+7JcOfdPi642E0uJ8llJY/cn3nZldopmmDFVaXoboVEb37i2uGXKWdFaJ4NFBX8ER23b3Kp5ELexhvQqFvlwhfSSPSdmfSslcsQ3fsCsYPb8FXUEVl+HqHFq/FVzWferW+j7cGfk2zaP2yb4Pzl1Fx3N4ruItG4j4Hdz5OP9mDls87sXlVj/ms/gL96/hlZ8xxzzHHm6EjuRxUaHi1EZ+ogBWv25zWfiwhVwbN0MZ7lS0FKZMFRM8Klg6oiTZPMjl3EH3ty1PR26sUtAERuvgHPsiV4li0Z0Z2OZWH2Dq+JNaMx4o88TtGdt+FdtQLP4oXY2RxC11C8XgrtnfT+6BcUv+EuVP/4owfNgSiJx54kcttNeFctx7t8CXah4AhNTSN6z/0knnj6tM/VePT32fzLX0f5x88Xse4CF7e91sf1t3jp6bLIpCWaLgiFFYpKFI41mjy6KYs5aLR+/S1e3vLuAF6vM6fc5xP4AwIh4BN/H+Z9HwmSz0sKecn2l/J8/UuJUeeSd3dYPLYpy91v9fPW9wbYeKmbrg4TXXfE65OP5Pjht8Z2jzmwp8DeXQXOu9CNlLB7e4EDe89OCVW4ws3Fb6g97hRI/bowQsCaG8opqRu77EVRBRUL/Sy5uAQp4fDmgVdkfSa8ioTmVJGDKfPSefq0pxNN6XimQbq9gXRHEwN7NlN93d14K+ZRuuFKUi2Hh7rLhaJStOpCFJebTOcx2h/5FVZuuAWUUFUU7eX90XqEj6BSQkCJoAs3AoElTbIyRcLuJ23HsXl5dUzOMcdkMO08TbEtZ3sZ5xzJzS9hJVO4F8xHKy1B8TlernYui9HVQ3b/AbIHjyDH6gaxbFKbXyJ/tBHf+jW4Fy5ADYcRgJVOY3T3kDvaSO7w0eECVEqSz7+I0TdA4IL16JWVCE3FSiTJHWkgtWU7VjxO7L4HUYNB7Nw43s9SknjmeYyeXvznrUevLEeomvMeevvJH2ue0XM2Fk1HTT714X7ueqOfm273Uj1Po3qehqI4fVLZjKSjzWTzUznMkwaX+AKCSJEyVF6ay0mONQxXkm63wO0WRIrUMYswLQu++dUEtuWMjVy6QmflGh3DgHTKRp9g8nQ+D5vuybJhoxsh4JEHskMR0dnGF9bZ+JoqwhXuoQYgIQSrri5j1dVl424rpTNZ6dDz/ezc1DUbyz0rzKoaUVWoXuilfqWX4nIXmmv8mgSjYPPoz3vJno2mIOE8caiaQCizWDshbcfaaPdmaq6vdVLjqnZCaKoarnAxQggync0jRCaA5g+hByITH0rKE1N4zpGmIZfwUq+toEpbgEt4EcevVIKhlLmFSdzuo6mwlwG7i7lc+hxzvDLQXWLoZm0actgYYDuVJvXCFlIvbh1sphn8hcQRhpNJO0qJ0dNL/OHHneadye7DtskdPETu0OETx5YMyw9ndo3ieTwalkV2/0GyBw4N39dk38MMEe23+cE3k/zyRymqalQiRQqq5qS+kwmbni6LdEoOS4Hf97sMD/xhct7WUo7f3B/tt/niZ2P88NtJKqpUdJcgn5PEBix6uiYOIrQ1mxiGJBmXPP3Y2Ssv6TqS4n/etoWqpQEWX1jM6uvKKKv30decIdk/dgu8tCGTMDj6UpTt93WSjr5ym1pnTWh6Ayp3f7SKK+4qJRhRJyXe0gmT5/40cFaE5rwVAUqq3WSTFsYMjWE6GdUbQFoGdmGUp18h0ENFIJzay5ML26W0h7bR/SHnYnnS74WmU7rhSlTv+CkcAGkWsPPOH6i3rNq56J3FGhGX8LLGfTnFSiUAJgaGzA8asktUoaHhQhduipVKQp5iDha20Gm+Mr3H5pjj1YSqwZs+VsnqiwP4QxoP/7yPe3/QO/KFMyXIplPLPpNicJaF5VhkM3LI+HwibHtmWwBsG7o6LLo6ppadUhS4/jYvHo/g4fuydLWfveyWlBDvzhPvznPwmX6iHVnu+ttlPPmjZrbeM46PpgTTsF+x6fKTmRWhKQTc+u5ybn5HBYoCqbjFQFcB05DjxqJyGWuoNmSqqLpg421leIPOWyyqdCMUpynomrfXIO3R9yuEIFLuYsONpXhDGke3D5yRJ42SdZcRXLCCdFsDma5mzHQSOeirGaxfRtGqC0HaxA/vQp5UiS1Ng1TbUXw1CwkuWEnp+iuc1Lpt4woVU7RqI/66pZipOFogPO4apGWRajmKt7Ke8NL1FJIx0q1HQdoI3YWi6iSbD42Yl36mqNdXUKxUYmLQahyi22ohaycHU+TONCCX8BBRyqjTVxBWSlmsrydm9ZCVs9u9+WpFU9yE3RWE3ZX49Aia4nbm0dt5MkacVKGPVKGPnJka0+d17P1WUeSpxqdHUISGaedJFfoYyLaRLPQhJ1kq4dVC+PVix8w/34lpO1EFRWhE3FUUe+cNHcOwc6QLA8TznSTyPVgnTZkSKEQ8VahCRwKpQh95a/Lfs7C7El1xupQzZpyMMXIizfE1KWL0jIIpC8RynZxO1F4g8GghIp4qgq4yPFoQRWjY0qJgZciYMZL5XtJG/6Qbj1ShEXJXUuSpwe8qRhU6ljRIFwaI5tqI57ux5dSGbVgm/OE7PTz1xygf/c86/OFzI8syx7nHkuU6193sJZeV3PvbzFRsUc843Q1pzILEKkiM3KtARU6CWRGawWKNy+8sQSjw9B/7uefbXUR7DWxz/IunBArT/KA0l8KVb66mblUAVTtRN1G/OkD96sCk9hHvLfDI99umLXbHQ9oWnrJqvBXznH9bliPwVM2x6Cjk6N/1PAO7n+PUm8zArufxVc0nMG8JlVfegV1wutVU3Y2Vz9L93AMomk7llXdOuI6B3c/hq67HX7OIiktuRl5oAhKhaBTi/aR//hXswpn/K9ZxU6bOG/TM3DbCS/M4OZmmy0oTtXtY776GkFJMiVpNm3n4jK/x1YxApTKwlAWRCwi4ShCMJQIkeStDLNfBof4nyZqJMV53fL8KZf6FLIpcTNBdhhjF2te0C/Rlmzg68DxpY+KO3Ar/UpaVXIVEsrXjNwzkWgnoJSwtuZISbx2KGHnZs6XJrp776UkfHfbz+ZELKPctBqAlsYMDfU8wGdHnVv2sq7gdrxZGItnT88CoQvP461yqb9T9pIx+Nrf9BFtO72/QrxezIHIBZb5FYx4DnMlcOSPB7p5NxPPjTTMRFHlqWFx0CRFP9ZjnMppr4+jAZmL5jimtNx13Mkj57NwNeo4ThMICTXPKDOYv0vjLvw5TXKKw6Z4su7aNUxN7FuhryZKOFrCtsx+tPleYFaFZXOGiuFynty3PL77UTqz3zNci5NMW3/n4fupWBll6YZjlF0eoWeYnm7SIdY/9xZTSGTjfdijFs7/umpG56aPRv/NZcn2d+KrqcUdKUd2OB5yVz5Lr6yTVfIhsdytylGiimUnS+sBPCS1eQ6BuCZovgG0UyPV2kGjYQ7anHc0XwEgnyPWOf6E3UnFa7v8J4cVr8NcuQvMFsS0DM5Ug03kMe7SWwTOAJnTcwktOZug2WyZ8fV5m6LaODc5GD83CCl/NCOrDG1hSfNmQsJDYGFYOW5oIoaIr7sHfCTxagJC7AnuCnJBAYX7kAhYVXYQqXKfs10JTXGiKG01xUelfRtBVxp6eTcTzkyuaFwjcmp+Aq5R15bcRcJUOHkMipYUQymANsBiKxp2MxKYrdYgy30IUoVLqXYBLfYGCNXGNWrF3Hh4t5NRSG3H6s62jvk5Ki4wRw5YWqqKjCh1l0C/mdM2bS30LWFl6HV4tjBBiyDpFYmNLC0WoQ+9fQUUIhdy4DwaCqsByVpReM2Qi70Szc1i2iaroQ9+DEu98ApVl7Ot9mN7MzJa2+IIKF98UYck6H0ZBsv2pBHueT2KZEAir3PauMnY9m+Sy2yIc3J7m2P4sN7y5hL5Og4d/0Yfbq3DrO8rY/VySS2+NsH9LmtbDzmt62go88st+Cnmn+3r5+X7WXBqguEInk7TZ9WySXc8mh9WOToTuFrz2gxW8+HAMX1DlohvDeLwKezan2PxgbChhFC7VuPSWCPOXe0knLV56JM6hHWmkDZFSjTd8pJJHf91P0/4TUedIqcbdf1HB478doHHfK9cG6xN/F+HiK5zGn3CRgtstOLDH4JtfTYxpg3S2SPTl+fEndxPtfPmZx58pZkVoujwCza3Q1Zwn3j9734pYV4FYVz+7H+9n3soAf/XTdex/doBffPYo9hipcynBMuQZqcs8GbuQI9m4b9rzxa1cmujeF4jufWHU35vpJPFDOya3r2yKgT2bGdizeVprmQmcIgqJKQ0sJidujxu2TyRo5jg9AnoxC4suRBEaEpuO5AHak3vImskhwaYpboKuUoq9dRR759GbbpgwzVwVXMHioksG07gmXalDtCX3kjUTSGmjKS6KvfOYH74Anx7BrxezquwGtnX+YdIp7JC7gnmhdfhdJcTzXXSmDhLPdWLaeYRQ8etFlHjrUBUXWTM+Yvv+bAtZI4HfVYRXD1PsqaUrPX70XKBQ4V+KwBF3fZnGMS2KclaKbV2/RxUaitBQFY1y3xIWF196ohFuGkQ81awuuwm36gcxGBXONNGTbiBV6MOSJopQ8WphIh6nnCCabSM/jpVSibeOFaXXoiseJDa9mSbaErtIFQawpYkqdMKeSuaHzyfkrsSt+llZeh3bu5IkC6PUWk4Df0jlQ/8+j5JKnZ1PJ/AFVT7wL7Xc/+M+HvhRLy6vwhV3FFE9300qbvHOv6mmYW+WzmN57nhPGd0teZoOZLnyziKq6t1kUhbv/vtqGvZk6WzOc+f7yulsLrD9yQTegMId7yljoNugozFPzUIPf/mfdXz7n1rZvGnkd2UsNE2w4cog1QvcBCMqzYdyWCaU17qGSjSLyjU+/pV6bBv2vpCipFLnY1+q4yf/2cnzD8TIpCwq6lxce3cx3//X9qHt1l0eZP2VIX7/rZ4ZOb/nKn29FqbpdLS3NVtseyHPT76XpL3lHMqZD2KbkpY942dyXm3MitAsZG2MvO10cHN2eoTjPXkSfU5daCZpvioKcF9OmLJA1k7jEm504SIvJ3469wmnBCJlj0xJzjFzRLw1QxGsgWwrB/oeG1bL6JAkVeijM3UIl+qd0HjYq4VZVHTxkHhtiL7AsfjWYSnivAVpY4B4vov1FXfi1UIEXeXMC63laPT5Sa29NrgWVVFpTezk6MDzGPbwKEOy0ENX+jCKUEdNTxesLL2ZRnz6eQgEFYFldKePjlt/6tPDFHlqEEJg2gW6UuMLU9POY3IiyxJwlXE6V0lNcbGk+HJHZAJ5M82BvsfpSTeMqHNNFnrpyRwdjCiPfUxNcbO4+NLBmlNJW2IPh/qfxpLDu2ozqRixXAfrK+4k5K7Ao4VYENnInp4Hp1SzOxYXXBti0Sov//qeRjqP5REK9HYY3PL2Up5/wLkOKApsfjDGnhdSrLzIT8OeDL/7Zjc1C93ULvbQdCCLosJz90c5uC3N6osCHN6Z5p7v9lC3xEPtIjfbn4Rk1OIrH2umkHcaNtxehXDpfNZfHpqS0ARQVUF5jYsvffQYA90GQjhGH8fvQ1e/rhhfUOXf39dIrNdEUeFtn67mzveWseOpBNm0zeZNMV7zvnLCJRqxPhPNJbj45gi7nk0SO0M+z+cK3/2fBD/+ThJFEZimJJuVs9U6MMcMMCuzzns7CvS2Faha4KGk2jUbhxxBKmbS356bq5s4RzEx6DQb0IWbKm3hhNGcgIhQqS0gZcfot8erKRsbV3klwfM24l+91vHeGgNP3XyC523Et3T5WZlPfLZxnTRrO2vERxGZJyMpWBkMe/wHhergCnxaBCEE0Vw7zfHtY9YhJvLdtCV2Df27KrB83HrD4wgh0BQXfZlmDvc/O0JknrzmsRtXJF3pQ4MlAoJiTy1effxSjTLfQlyqDyklyXwviXz3hGudSUq884eEri0tDvU/RXf68LjNVJYsjPu5lvkWEHZXIYQgbURpiG4eITKPkzUTNMW3ILERQlDqW4BPj5zu20IIWHGBH8OQrLjAzzWvK+bqu4qJlGoUlWmUVjn3FsOQRHtNLEOSSdp0t+aRNuQyNq5BT2SjIIn1mU7gIWXR3VrAtiGXPfEacMTg4jU+LrwhPJTydvumd9vc/XySgW7nHEvpND8BaC7BivP9FHI2G64Mcc3rirnqNcV4/QoV89wEi5140M5nkiiqYPXFzgN29Xw3i1Z7ee7+6CtedBXykExI4jHbsVw6h9+vqjsZ3DlOMCsRzXTc4pFf9PC2z8zjdX9Rzc+/2EYyOju1f8exTck9Xz2Gbcu5aOZZRseDMopg67XaCFtlLNDX4BVBega7zq3BG6SCgi7cFKkV1GhLECgcKmylIKdXC+NbupySW+7AjEXJHjmMPUbrYvC8jYQvvIRsUwPZhqNDnqavFnKmU6cshKDIW4NPLxq1sWWyqEKj3Ld4sG7QqYMcX7xCX7aZRfISVEXHo4UIuEoYyE5cK2lLi5b4jjFF0WRI5ntIFHoo8tTgUr2U+hbQEh+9LEURGhX+JUPvrTt9eML3NpMIBOX+RUNNVfF8Fz3p0RvrprLXCv+SoVKA3nTjuCl2gGi2nYKVxaMF0BUPEU/VpBq5JsLjV/GHnDrHk4Pmu55NkhtsIJKSEwGFkwTdoHHF0Gusk4IOQzWXJ+1z4Sov7/77GhDQ0Zgnk7bwh1X6u6f+eUoJuTFs+hThREuLynUuunG4U8iu55JYg82oA90Ge19IcvHNEV54KM7514To6zBo3PvKrc18OXLR62tYc3052+/rZOufOuf0BrPoo/nsnwaoWejhujeXMX+Fj5cejtJyKEM2ZY8ZZbQtSePezIx1fTftPjONPXNMBcEa92UElMiwnzoVmjYCgYZOrbaEGm0xNiaWNJGAIhQ09KGbaF5mKdPmkSxEMTi3Og9fSURz7eTMJF49hF8v4bzKu2iOb6M73TDYGDO1v0+PFsKrOzdUS5qTau4pmBkMO4eqOM0yfr2YgTEabE4mb6VIFE6vfs0arB+NuKsBQaV/KW2JPaNGQYOuMoLucmfNVpaeGW6EmQhF6ITdlUPNP72ZBqwp2gydiq54CLrKhsTzZDrJLbtA3kzh0Zzom1MOcHpICV3NeSrmufjaJ5rJn+JIYltQXOmMlDndO4aiwq3vLMOW8MUPNZFJWWiak/6eaUxT0tNWIBW3+Nonm4c3GskTvpVSwnMPxPjwv89j3hIPF1wX5oWHYuQyc0rmXEFRBUsvKWbpJcW07I7PicxBZsdHU4H3fraO1ZeEUDXBglU+5q/0YlvOk+RY5VyZlMXfvXY/0Wk8Qc5x7uISHjzKxIbyAoGCC02MfnH3CB/zxDI6zAYMe05onimyZpyG6AssK7kKTXERcJWwovR65kc20ptuoCt9mGS+d9KRO7cWQFfcgNM4UxNcNWQhNBbaYEc2g1vp6tgzhE8mb6ax7OlHM4/Tm2liYeQi3JqfkLucoKt0VIFc4V/i+G5KyUCulawxtVq+08Wleoe8OyU2yULfBFtMjK56cGvHLeEEZb5FBF3l426jCHVYeYNrEp+XEE6HtjegoqoCl1vB61cwDYlRcG4SLzwY47Lbi7jjveU8e28UI28TLtEornSx7fGZP9eaJvCHVDSXYNVFAZas87F/y8zOnrctePpPUf7883Xc8KYStj+ZwLIkJRUuvAGFnc8kh+6RR3dliPeb3PiWEkLFGlsfn2s6OZcQAoqrvUgbmnbEzvZyzhlmzbC9Yp4bX0DFLDixq5N/N1bZ22xOfpxjtpAcNrahGTMXGcja50ikWignvrRSTmOEhgBVOdEwZ9uTnxyiKCf+kKay3SRpT+4hb6VYVHQJIXc5AgW/XoQvfD7zQuuI57vpSO6jO310wvpMp+bTWauqaNSHz5vSWoQQKJMsL7ekMSOuBFkjTn+2harAclThosK/ZITQ1BUPZf6FIJwJXp2pgzPSADMVTthMOX6WxiQN2Cfa5/EsghCC2tDqKW1/4vMavxV00Rofb/54Jf6QStV8NyWVOks3+Ij2mvzfv7QR7zdpPpTjO//Yyuv/vIJrXleEUASFnM3WxxNseyLuTFspOOVRUjpjLI87jJimdLybT34NI19jWU4N4IM/7eO9/1TDP3x/IZmkRevRHE/+PkpxxQSDuE/BOYY9LFV/KvteSPHj/+jg9neXccd7yxHCSbU//rt+dj6bHDpt2bTN5gdjvPYDFex8JkF36+k/RM0xwwhnvPMreaTkVJkVoWlb8LW/bESfYLb5iO1siM9gN11prYfkQIH8KKkGRRPUrwqwcEMIaUuadiVp2Zeakl/aHJNjwJqcD+LLBcXtwbdiJf7lq3CVloGqYsbjZI8eJrl7B1ZiZKRF8XoJnX8RVjpFctcO9NJSQudtxFO3ANXrxcplyXe0k9y5jXxby5jCUQ2FCa7dgHfRYrRQBGlZ5DvaMKMDnDx3y+jvI71vz7QFqETSm2kkmmunzLeQ2uBqwp5KVOFCVXSKPDUUeaqpC6+nIfrCYIfzWDVpJxqvpJRYsjDlZU3XwHy6HPfUrAwsRREqZb6FNMW2DGswinhqnAYnBGkjRjTbNqtrBBxv0KFnnZmx/lKEOmyfljQmdBU4lcmk71uP5Pje59o5tQ/QtiSpuDl4fNizOcXhnRlCxSqqJshlbBIDJrYFsT6D//hwE7E+A7Mg+Z9PN5NKON+Vn3yxY7BByOL/faiJWK+BaUi+9olmUnHnNT/6QvtQ9PTo7gz/+u4GQkUapuk0D2m6wDPFZqB8xuarn2ges0YTnHvd5k0xdj6dIFikoSiOqExERzqkPPzzfrY8miAVN+fuT+cYUkJ/W5bqZUECxWen8flcZNZqNPs6zu6Tlz+i8YH/WkE6YfLTfzhMf/uJVKuiCm54dy03vX8e3qAKErIpiyd+0s6mb7cMRmHnmGMkWqSIsjtfh2/JMhAK0jCQ0sZVWo5v8VKC511A7z2/I9fcNGw71een+IZbsFJJbKNA6c13oEWKhrbXy8rx1M0nsGYdfX/6Pam9u0Yc21VRSfndb8FdVYOdyWDEoghFIbB2PYrbSZ8ipTNqdM9O0vv3nnak07TzdKYO0J0+QshVRkVgGeX+Rfi0MCAIuspZU34LRwaepTm+g9EiWCeLDsPOsrP7Pgxrag1dY/lSnkmiuXYyRpSAqxS/q4iIp4rezPHPVQyJUCklPZmGcbrczxy2tIZEoBAK6iiTe6aKJS3nayNAYrGn50EyRmxK+zDtHBNVTuazNp3HJlcCk8/a9LaPFG62Bb3tJ+41fZ0nAhWx3hPfu7FeE+0ZLogzSZtM8sRrzYIkl7bRXYLX/3kFFXXuMddomZLffr2LruYCfR2TC5hk0zbZ9Pj3ylxm8udpjtnFtiR7H+th5VVlLLushIPP9s3VaTKLQvNsU73YT/USP72tObKp4dGQZReFueWD83D5VHqOZclnbaoW+7jhvbW0HUyz45HTr3OaYzIIgkqEIqUSnxJEE64JU6QSyZHCDnJnYda54vFQ9pq78S1djjnQT/SZJ8geawTLRi8tI3LZlXgXLqb89W+m62c/oNA9MpKrhcKU3/UGrFSK3j/8mmxLM9g27qpqiq67EVd5JcXX30z2WCNW6kSJgFA1im+4FXd1LdnGo/Td+3uMgQFQBO6qaspf+yb0snIS214ivvkZrGRiGqn8sbGlSSzfSSzfybHYFir8S6iPnI9Pi6AKnUVFlwzOKB/ZiFOwMoONXyoChawRH9Us/VzDsLN0pxvw684Izgr/Mvoyx5BIPGqAYs+8Qe/MPN0TeGeeuTXmhpqUFKHh0nycbp+cYWUdQ3Y0QKFgpUf9XF9NWJbk0I40HeMIPmlBOnEO+/DMcUbY82gviy/qZMOtlRx9aYD9T/a96m0VXzVCs2a5H1UTNO1MkE2eeGrV3QrXvL0Gt19l/7NRfvz3h8lnLG54Ty23fLCOi19bwe4n+odSFGrIh14eQZoW+dZesEa5eQuBe14pwqVjZwsUOvpnvGbulUittpjFrg3ojB0lOBWJ5JjYR+40Tq/q81N65+tgtHGbAjz1C0bdLrB6Hb7FS7GzGXp+/yuyTQ1DvzP6e8l3tFL5lnfiqV9A0ZXX0f27X4wQe0JVkZZF929/4aTIT9reyuWoevu70UtKcdfUkjl0YOj3WqQI7/wFSMsi+tRjFHpOeDXmmo8Rf+l5Sm99De6KKoyBAWThzEVA8laalsROBnJtbKi4E59ehK54KPcvGlWQ5MwkhpXFrQVQhEbAVfKyEJoA3ekj1IXWo6tuSrx1uFQ/eStFxFONRwsgpSSe6yI1A00408GwsuStNG4tgEAQdleNmN8+5X3aObJmAl31IAaj1tFc+wyt+OWJbcGOp86R2vA5hhACauqc0oO2ZvO0n629PsH8RTq2hOajBrlJ3GgKWYtN/3UURRW88bMr2fNIDwef6yfePb6Pd6wrR2rglVnXOatCU9UF1Qs91Czy4AuoKBOUupiGZPMDUfLZ04/EFFW6kUD74fSwUHbtcj9LNobJpS02fauFeI+Ttnj+d11c/oYqqpf48IU1koOjM4MXLqPqw7dj9Cc49jffxxwYebEJblxK9V/ehdBVun/wsCM05xgXHQ/1+ip0XMTsHqJWN3mZm0QzhSRnn14aVXG7CW24YErbCF0nsHYDKArpQwfIHmsa8RormST+wrN46ubjW7ocvbgEo2/kKL70gX3DROZx8h1tmLEYemkZenHJsN+pfj+Kx4udyWBGR3paGn29SMtCLylBcbuwzqDQPE6q0Ed3+igLIhsBZ/rPaOTMJIlCL6WqH0WolPsXD6agz/2HsVShj3i+ixJvHW4tQLG3lq7UYcr9izheXNiVPnzalkLTxZIW0Vw7QVc5QgjKfYtojm2lMEGD1niYdp5Yrp3goEVRuX8R7cm9s+oPOscck2HZKhdf+l4Zqir4mw/1smvr6V33Fi3T+cYvKsjnJX/+lm6O7J/4O7/h1gqufe98fBEXvojOxW+s4eI31Aw2qI19jfvj/zvEc7+Y/bru2WDWhKY/pPKmT9RwyW3F+ELqYJPs2M1BUjpF27ufTcyI0NTdCtKGTOLEDUAI2HBDKZ7BaGbzvhPp1+SAQbK/QHG1B5dXBQa/YAJQxJhrd8+voOLdN6L4XEQf3ErssR1z0cxJoAkNl/CQkSl255+e1AjKmcLKZoi/8BzSHO0iIvCvWIWntm7YTxWvD1dlFUhJ7lgjYxXi5NvbsDIZVL8fd1XNCKEppXTS7aMgTQM7n3e6dl3uU35nIm0LNBXhGtkFq7g9CEXBMgzkaFH3M4SqnLik2GMIEWde+n5KvHUoQqXSv5Su1EH6syPF9rmGLU2604cp8c5DEQplvkUMZNuIDE7iyZlJ+jIjHzpmD0lX6jA1wdVowkXAVUxd+DwaoptPqwO+I3WQ6uBKNMVNkaeGysAy2pN7Z3Ddc8xx+hSXKUSKVVQFikpnZjqPoggUMaJHbUwCxS5K6x1rLyM3+dKJGaxsOueYHaEp4OZ3lHPdW8pQBGSSFtEeAwRUL/SQHDBJDJh4AwqhEh3dJWjck+GJ3/SRjM1MZCCfthCCQdHoECjWWXddCdKGLff3YpxsACzBMiSqJiaMvB5HjfipfP8t6JVFpLYdofcXTyKNuRqdyWDIPGk7hlv4hqxUZgs7lyP2zBPYudGbN7RwZITQ1AIBFN2FtCzMeGzMfVuZDHYui+r3j4hKOi+whtVejmTwIeWUBxsjOoDR24ursorguvMY6OtFGo6wU7w+AuucaGuu5Rh2fvpNKQKV+vAGMqYzw9qwRo8yCxSKPLVU+JcMrTuaG9vYuyfdQF/mGGW+hWiKm1VlN3Fk4Bl60o2DkbKTLNAQKELFq4Up9tWRMxL0ZBrG3PeZpjfT5KSn1QBhTyVl/oV4ND9SSvqzzWTN00upjryhTc2tI5brpDt9hOrASkCwIHIBqqLRHN9O3kyf8vkJFKHgVv2E3BUMZFtHbWKK5zrpSO5nXmgditBYVnIVqtDpTB0Y9LCVp+xTxa06EV+BoC25Z0rvYY45psPubXl+8q04UsK2zWenYWrHpm4at8WmvF2sa/abB2eLWRGa/qDKJbcWIwQ8f/8Af/xmJwM9BqXVLj77i+U8/cd+/vitTjRdsGCVnzd+vBqXR+HQ9hRGfmaigV1Nzri6heuDPPtrgWVJzr+5jLI6L72tWfY/Nzz9qGgCT1DFNMaeXHQywqVR/tZr8a2oI9/aS/f3HsJKzo0GmywmBgcLW1nu2shK18W0m0dJ2TEszGE2PaNRkNkJXzMhU9xcaPqgLaBEjlbbeXy3ljX0qCr0kXYXUtowxujL8bCzGaJPP07Zna8nfMkVuCqryXe0IRQFT/0CPPPqMAb6iT79+LT2fxwhBGX+hRR5asibKeL5blKFfnJmAtM2EELgVv2EPVWUeOuGzMJj+a5xI3uWLHCo/0k8WpCgqwyfHmZ12c2kwn3E853krTRSSjTFhUcL4teL8ephdMXD4YGn4SwKTSdq2UxNcBVeLURtcDUCFVtadKUOM9kvk0v1E3ZXoCkuNMWFKtxoimvIpxTArfpZUnw5pp3DtAtD/xlWlli+Y1SbJ4nF0YHn8OvFhN2VqIrO/PAFVPqXEst1kDaiWNJEFRpuzY9fL8avFyOEwovtPx9VaEpsjkY349MjlHjn41K9LC+9mnnhdcRyHeTNJLa0URUdtxrA7yrCpxfhUjy0JffOCc05ZoVUQvLtL5/deu9Uf4FU/5y/6cnMitAsqtApqtDp6yjwsy+2MdDlRF58AWtovGR60Mds51Nx+jsLfOa7i3nb39Ty1Y80zMiIrYbtCRJ9BdZfX0omYZJLWVz2hkoANv+hm0Tv8C9GuMyFP6yTiZsUchMcXxEU3bKR8NVrseJpur6ziULnOHN9FYG7vpzAeUvw1JcjXDrmQJL0nibSu5uw0yMv9EJT8a2qR5oWmYOtKB4XgfMW4189HzXix87kyTV2ktp6ZNxjC5eGb0Ud/nUL0SsiYEtyjV3kW3uG3R+tdI7sobZZTfun7Ti9VhsL9NUUq1VIbEdoyuEm/ycjkWzPPUZKxmZtnQC2UXDOjaog9LENnIWmgqoObjPKE/ZpnN7Unp0ITaPs9tfiXbjYaQ6ybaxUiuT2rcSee2rUTvepIZFSIlDw6mG8enjo8zgxOnrw/4MjCuP5bvb3PjKhvU/aiLKr+z6Wl15NibceRaiEPZWE3BXDXne8TOX4cWfCG/L0kHSlD1IdXI5AJeyuAiBtDBAbJ4p7KqXeelaV3eh4XwInRy6Pv2eX6mV++Pxhx5Y49k4vtP98aA79qWTNBLt77md5yTWU+uYPfX4eLTTsdSeXADnR6rEpWBn29DzI0pIrqfQvRREaQVcpAX14pH745wVylj1P55hjjnOLWRGaXr+K26vSsDvtpMwHsSyJUbDxBlSEcqLMre1olu2Px7nydSXMX+nj4NbTt67pb8vx+E/auf0j9VzzthrAuRAefinOc78ZeTOuXx3EG9JoPZAimxg/fR88fwllb7gCLJuenz5GZt+xMV+reFyUvP4yim6+ADXgBVs6/2kKRTeeR+ZgK13fe4h80/A1KQEPNZ96PdKyaPvibyh701X418wHBNKWCE0hfPVaim+/iK7vbCK17ciIY6tBLxXvvYnQJStBiCFBG7xoOUJXT9wgbElmXzMtn/vprKX+BQpLXRuo1ZYCAhsLC+e8O+saI314lkSHlUxi5/OogSBauGjM16k+P6rHGb9n9M9sU5jq9RJYsw67UKDvvj+Qb291TLoLeexMZkYeEmxp0Rh7kYKVJuKpQle8KEJDEQpi8DORWJi2QdaI05U+THty7+AM9IlJGwPs7LqPisASqgMrCbrLHFurQVN3iY1pm5h2nmS+h95MI12pkd/tkzHtAjkzAQjyk1zHVInlOolm2/Hpxz97SWfq4JS8My1pkLfSQ+dxKhTs7ISm6Rkjxq7u+yj3L6Y6uJKQqxxNOX5uBRKJbZuY0iBrxOhJN5A3x2+sy1tp9vU+THfqMDWhNYTdlcOmEUlsLNvCtAukCv30ZZroSp8dq6eXE8Gwgs8viEdtcllJcanCugs81NRpGIbk6MECB3YXyKTH/swjxQpuj2Cg18IwwO0RLFvlYslKHZ9fIRG3aThU4OgBg1x29P14fYIlK1wsXqETCCikkjaH9xc4enD0bRQVSstULEsy0GejarBomYtV61z4gwr9vRZ7tuVpbxm/A9zrE1TP01i0TKe0XEUogmTcoumIQcMhY9z3HQwr+P3D/4ZsCdF+C2MSgUUhoKJaZd0FbsorNbIZm4N7CxzebyDly6FF8dxn1pqBhBh53zMLknzGJlKmo6oCc3AMmLSh9UgW3aVQs8gzI0JTSnj8J+0kegucf2s5bp9C444ET/2ik+QplgJCwKLzQiT7C+x6vH8o6joa7rpyKt5zE4rXTf8fnyf21O6xv5mqQukbr6TkzouxDZPYw9tJ7TiKnTdwVZdQdN0GfCvrqfn4a2n7j1+P2q2uBX1Uf+Q1aGEf0Ye2kd7VhF0w8NSXU3TrhejlESredQO5xk7M6EnnTQhKXnMp4SvXYPTG6f7hI2QPOxYl3qU1VLzzBvSKCOkdR4k9tpNCxwDSnL1IhFt4KNfqkUjazEN0mc0UJtV1Djl5ZsTEeNjZLPn2NnzLV+JduJjE1hdGreb21NaheL3YmTSFrslHuyaDb/kqfIuXkdyxzTF0P0PR54FsC9FsG6qi49GCuFTfoBhUkMhBYZciZyam1YlsyQIdyX10pQ7iUYN49BC66kYoCrY0yVsZcmYS08450Uwhx51m2J7cS2fKsYKSjpw6jXc/OqadZ1vXH4aJxKlOK+pJN9Cfa6b+zlUUr6lk/zc3k+ubvIPCZM61JQ167aOUv7kCo1RB00Iowk3LvQfo2daCaefImSnyVnrIf3MibGnRk2mgN9OEW/Pj1cJD1ke2NClYOcfCys7O+gSnmUAVLoo9NcTz3RTs2bm2vPPDIV73tiDf+I8oHa0mH/27IuoW6KiDd+hCXrJzS56vfi5K05GRn7sQ8Nf/WszFV3n51Ht76Gwz+cu/L+LSq714vAIhnMtTJi35h4/0svmpkQ9EK9a6+PBfRVi30Y3LLYbu2fmcZOdLeb7+H1GOHBh+7JIylf/7QyXppM2nP9DLW94T4ubX+vH5nQYa24aBfotffC/Jr36QGCH8hIDrbvPxhncGWbLChcd7oidCSud9H9hd4L//Pcr+3aOrxnf+eYi73x4c9rNsRvJX7+th387xlaaqwR1vCPDuj4Qpq3SaiGwJ+azkqUcy3P+7NNKek5qny6wIzWzaIpexiJTpuDzK0AjI46PDahd78AZUktHhHeFCAbdPHWu3U8bMS164p4cX/9SDUBwvtNGQEu7732Ye/HYrqXHmlapBLxXvvtFp/tlymL7fPQvm2Dc134o6im6+AGlLen78KNGHtjnfaiC9o4HUS4eo+dTr8S6rpfSNV9L5v/cgT92fqqCXhuj81n3En9ozbPt8Sy+1n34DrqpifKvqSTy778RmIR+hS1cA0Pf750huPuHJmNycQA16qfrQbahhP6mdjaOm788kx/+UMzLJ0cJOTM5t6xRpmSS2b8G7ZCm+pcvwLV5K5vDBYa/RwmHCl1wOQpA5cghjYGYjmorHC0LgKi/HU1uHEYueEJuDtaN2/tRGjenhRBbzpM6gTZItLTJmjIwZI7igmPV/cy2qZ+QlyjYs9v7XMwzsGb0sQGJjTTLSrQVclJ1fS/cLzdj5qYmiyQqzsXDWaRBcVkT5FfPY/4Pnz4hlkLQkmd4kuARiMYQXl5J4sove06xzldjkzOSY6fuXKyXeeawrvYUdvffRlz02K8d0uQT+gOC2uwOUV6rEoja/+XGSRMyitl7n8uu8XHSFh3/8zxI+/f4e+ntP+X4LJ4LpDwhWrHHxno+GWbHWze5tedqOmSgK1C3UKSpRaBxFqK5c5+Lf/7eMyhqVIwcKbNucJx61KKvUuOQqD5dc7aGqtpTPfKiXY0eH36e9PkGkWONvP1/M8tUutjyX5ehBA10XnH+Jh1XrXXzoryLkc5Lf/jg57HlYSqhboLNyrYvOdov9u5zop2XB4mU6l1ztZcNFbv72CyX85Tu6ifaP/Lve8WIet1sQDCuUV2lsuNBx6FAnIR2uvcXHx/+xCI9HcGhfgZeezZHPSRYv17niOh8LlrhQlHGeasdBUQVuv4ruVlDUibMW2YRJPvPyezCbDLMiNKPdBrFep/mnvNZN62GnSaaQs2k5nOXq15dw0c1FPP6rXmwb3F6FlRcFR9gRzRRSOlMbxuO4b+aYKILiOy7Gv8Yx87ZzBaQxzloVQfiqNSheF9kDrcSf3D0kEo9j9Mbpv2cztZ96PYHzF+OqKSXfPNzwWghB+mArief2j9g+c7CVfEc/noWVuGpLh/1ODXjQioPYeWNEWh4gf6x7KLKqhXwUZlloFmSWDqOBKm0BfiVM3D73pzFlDh0gtXsnwfXnU3bXG4g//wzZxqOOf2VZGZFLr8RdM89pynnq8Rn3r8geOYTR34u7to7q937IaTw6rjMtEzORIHv0ELHNz446b/1cJtefpun3e3BFPJSsqabyigX0bm2ld0srhXiOdEdiRo5TvKqSlR+6lIHdneTzZ7t5b+op9Mlg5U2O/HQbCEH9HStZ8/ErzshxXikUuWsQYnadL8C5tq9c52Lzkzn+7a/76e91Rn+qKlxytZd//nIJK9a6uOONAX70jcSYCYy3fiBEImbz1x/oYe+OAoWCRAAutyBSpNDbPfzm5w8IPvI3RVTVqmz6Q5r/+rcosagN0hGSdQs1/u2/y1i6SufdHwnzr5/u51QnOLdbsHqDm//3dwM8el+aQQMMQhGFv/psMTfe6ePP3hfi2ceydLYNv0/+6Vcp9u/Oc2BXgVTSHupd1HS4840BPvnPxSxcprPhQg+PbxoZYX7u8SzPPZ5FUWHJChf/97uKEa8ZjUixwrv+IozXJ3j+ySz/+ul+BgYFvK7Dtbf6+JvPl+ByC/JTnAhSWufl6nfPZ9HGIgLF+qBYHZ97v3yEF37zyhyEMCtCM5O0OLAl9f/ZO+8AOc767n+eadt3b693naRT78WWezfY2GAwGJsaQhJISEIC6f3NS0h5SYOEhBR6Mca94I57la3e+0nX6+5t39mZed4/5nSn1e2dTtJJluT7gMG3U56Z2dmZ7/OrXH9nJSuuDI8KTYC3nolx1Qcr+NjvNzBnqZ+ew3laVwRYdU2EbMrm4I6z7xadClpZkMjVyzB7htCrygitW0ho/Z4iK+KxKB4d/8ImANLbD+FkS5v0s3s7sOJptIoQvvkN44SmlJLMtkNIc7yolQUbO5FBCIEa8B630N1WCFFyqic0N0ZT2s4JY7/OBBLJYWsXilBZ5rmSmN1LWg5TkCZSOpNmlQ/YHRQ4+1l+0iow8Pgj4DgEV6yi4ub3uxnojjOaIGT2dtP/yAOYvd3TOrYwDLzNs8Bx3P7oljVmzRQCoet4amvx1DfgaWym5+7vu3Gb5wmFRJ6Op/YAYMZz1FzeQv/6dg7cs3lax6lY1YDQlDOl8U6SM/y7kxJ5nhTrU1DdWFIhkNI+qQL4AgVFqKNiUUoHR564egWAKnSi3vpTPm44/tinPjZALiv54X8NM9A3JgZtG15/IcsvHs/woY8HueHWAPd+P0k6NX6fQgiCQYWv/MEgG98Y8z5IXBd4b/d4C8uaS70sX+thoNfmf/4lTnxo7B6REg4fsLj3Bwn+5O8quOQqHw3NGocPFH8fQgg2vpnj2Z+PiUyARNzhu/8+zLorvdQ2qFx6tZcHflwcCjfQZxed71GsAjzzaIY7PhNizjydltaJky7B9VBahanXH1m1zktLq04mLfnevydGRSZAoQC/eDzD1e/xc/0t/inu0SVcZfCJf1hG83I38c62JNKWqLqCEGAVHPcdrbv1uO2CQ8+BNMO9F27/+rMiNKWEVx8dJFyuceg44bhrfZI3n4px+fvLue6jVSPrSxwbXnpwkM79525tqcQrO+i/+wWqP3Et4auWUfnRq8jsbscaGG9tUUN+1JCb/GP2jO/kchQ7nceKp9Arwxj1leNXcCSF/omsOXIsOea4uovWcBqzawjvnFqCq1vdjPKjLx1FEFjVivDo5Pd1YSfeGUEyX19NtdaMioZfd2NuTiR6JZI3c49TcCbJ8p+AzIF9yEcfdJNnShZrd0lt3YTZ242VGHaLpB+Dk0nT/8gDpHZuI7h4GUZ1Dagq1nCc7P59pHZsdfuMH4edTjHw+MMAmIOlrbfSsoi//AJqMEjuyOHRz4WqUvHeW4hcdCmZ/Xvpe+g+CoMDY9+nECgeL95ZLVTe/AF8s+finzuf1LbNJ3eBzhnkMf87MYqhUr60luiSWoyIFztvke4YJrazl3RHHDlSpkwPe6lYWU+ktZL6a+dihD0s/LVLsHPuy1PaDgd+urkoXlIxVEIt5USX1uKvDbkJc10JBjZ2kO4othZXrm6gcnUjB+7ZjCfqo3pdM96qIGYix8DbHQzv7R8X9yUdiVAUoktrqVrTiOrTSewfoO/NIxSSYy8goSq03LYEI+pj34824uSPcWNqCrM/vAzNZ7D/xxtxzsMavqrQqQssoMbfil8vQxEaOStJ0uwvirW1pU1bYkNR0plHDVLpm0WFt5mgUY6u+ACJaWdJmv10pXcRy3WOE32a4iGgRwnqlUQ8NYT0SgSCptByqnwtRevm7DRtw2+XFI6q0Knxt1IbmE9QL0cRGgUny3C+l47UduL5Hk50Fw8N2OPiIMEVm2+8mOW2u4LUNqjUNWrs3136mXVgb4FtG6YmWhQF1l3pQ9dh93aT/p7S98zubSa2BZEyhVlz9HFCU0rJpjdzJZNvutot9u8usPYyD8vWeHjwJ6kph5Pncw593TZz5hn4/NM3GxQClq4y0DRo22+xf/f4A7cKsOGNHNfefHJCc9kN1TQtDVPIOmx8vIfdrwyQTVjc8qVWQpUeHvib3QgBta1B1t5Whyeg8tDf7+Hg2xPrgvOds5YMtPutFHs3pkd7hh/FzEm+/zdH6GvPs/raCP6QSnzAYv1TMX7x0/5x658q3qBK44IA3QcypIet0zYeWPEU/T95nkL/MP0/ewnfwiY8jVVU3n4FPd95clyspjA0UBXXqpCbxC1v2aMueNVfoue3lDjmycdxOekcQ4+9Sd3n30fF+9ehRQJkdrQhJQSWzCJy1TKcbJ7Bh1/HybwzM6u0TDBkn1w5HonEkqdmzTS7OzG7T+yqyB7cT/bgxP2ipVUgs3un24v8qMCXctLkHCeXI/Hma5MP7DglxaFRW09o1VrsbIaBnz/kiszjsFNJCkMDBJcux79gMUZNLZznpQwne81ofp2lX7yS+mvnYibzWGkTLWBghL2kO4d5/UuPYMZdT0p4bgWzP7QUxdDQQ16EphKeXY4z8pt1LAfVU/xobP3EauZ+dAXSkeRjWYQi8FYFKCTybPna8/S9MdbVKDKvitkfXoZTsGm+ZREIsPM23soArR9bxa7/foPDj+woegZJRzL7w8uovXIOVtpE9WjMuWM5Q9t62PS3vyDX51qBhCqou2oOgeYyDv5sS7HQVAUN17ZiRH0c/Nnm805oasJgccV11AbmY0uLpNmP5ZijtUoVoSKlgy0tslaC9uTW0W0VobG08kYqvc1I3PhZy8kDCkGjgrBRTU1gHruHXqQzVex1qg8sZH70CgQKQojRGqauyCz+DSfNQQ4Pb0RSfG0N1c/i8mup9s8FoODkcaSNT4sQ1Cup9reyP/4a7cmtk1o3B/sdchN0wus4bGEVIBBUqKhSSwpNKSVd7dakWdrHounQ0qqPuO09/NuPa0q+Gz0+gaa5cYdl5eM9YrYNvV2l77d8To64yz00zdJKJgaD634vq1CorFYJBBUMj8DnF0QrVDdnYxq9DooKjbPc8+48YmFOUK+764g1YS5HKYQKcy+KIhR4+cdHePLfDoxqmER/Hn9EZ9+bQ5gZm+3P9bPlqV4+9U/LuO0P5vOd395MvGd63r3C40EI4cboT0HVC1UDRUFOJU3/FDhrQlNKJhSNqbjNvd/o4rFv96LpgkLemZbamcfSuibCr/7TQtp3p/nPL+w4/dhPR4629jM7Bxl84FVqf/UmItcsJ7VxH6m3i0uwSMt2YyqFcGsrToSiIEbrLk5vfOrwy9tQvAbVn7yOshtXUXbDSpBufGm+vY+Bh14n+cbuE+7nTHGosO2USr2cdrH26eIE4nK60KPlKLqBGYthpyepyKAoo0XiNbA2BAABAABJREFUZeHcTq6aCpNd2YoV9TRcP49DD23j4D1bsPMWiq4SaIxglPkwE2OekaEtXaz/414Uj8ZFX3kvgcYyNvz1M+RjmdGB7ONCU/rePEJuIM3g5i7M4RxCQOWaRpb//tXMvXMlA5u6ikSfFjCYc8cK9v7gbTp/sQ+nYFO2oJpVf3o9rXetouflQ+SHxqxxmt+gYnk9G//6aRIHh1B0hZYPLqX146uZ98nVbP/6y6MW2QuV2sB8agPzMe0s2waeIpbvQkoHTTGYHVnL7PAaMtYwW/qfIGslikpJOdKiP3MI2ynQlznIsNlDwc4hhCBsVDM/egVBvYI5kbX0Zw8VWUL7s4fIFOJuQo0aZGG52/XoQPxNhs3iia/lFHCOE5kKKvPLLqfGP49UYZAD8TdHiuk7eFQ/TeEVNAaXMK/sMtKFGIO5iVutZjPOhBXb8jmJacpRATYRuayc8mNIUQThMldYl0UVAsHxTSVGx8+7dWxLdcpzHCYUyADplLvM61dQNXCO0TPhiMLNtwe47mY/zXN0vD6BpgtUN/pgyp35TgZFuLGpR49toi69mfTUr6W7X0G03oeVd9jydG+R5inkHFRNoBsK5kjST//hDC987zAf+9slrHpfLc9/5/BEu546QlB756dQQyG6f/idE8fnC0H0uhsJLFhM789+jNl3urWXx3PWhOYJkZBNnbkZePPiIJ6ASmLAJJee/gSj+IvbCK6dT3DtPKo+di3Z/V3Y8THXm53M4mTzqGE/WmV4wv0oPgM15ENKSaF/ehM4hBDo1REQMPjAq6S3tYHjYCWzFLqHcHLvfDeDc0Y0nsPYmTTSttEiEfSqGvLt4x9OQtMILl2Bt7EZaRXIHWk7+wd6FtECBkIV5AbS5GPZ0UngsWLuKNKR2Hm3EYB03IL0dr4w6jovRXxnL/GdvUWf9bxyiFnvX0ywOYrqUYuEJkD/W+0cemDbaOWI/rfb6XmtjeabF+KrCRYfm5QcfmQHg5vHSmAdvGcL1euaqb1iNvt+uIFc/9RLH52PVPvnIlDoyxwoEmMFJ8eRxGbqAgvxqiEM1UfC7B23fXty64jFsFg19GfdDlWrqm/Fq4UJ6hUMHSM0s1aCrOWGt/i1CI60UYXGsNnLQPbEL/6ot4G6wAIKTpbtA88UiVPTybBn6EV8aohKXwst4dXEcp3jxOpRFIUJTfdCuAIJJs8rPOln6Mjqzz+Z4e5vT5xkdJSujvG/E4FbJWYiRksWOcVz8Wilwl/9YyXrrvKSy0q2bcyzY7NJd4dFKuFgWZJf+Z0Ii5aV8O6dBpLiKLNpM5YK0D0KlumQTxd/x9mkheZR8IY00vGxiX/79gS5lMXci6K88L3D01IaWg0G0UJhxFRUupQIRcWoqcWorbvAheYZJhh1A4m79qVPyhQ+VWTOpP+nL+CbV493di0VH7iEvh8/z9GpkpMzyR7oRq+J4pvfiNDUknUqjfpytPIQ0rTIHZjeBBLfwibK33cx2T0d9P/0xcmz5N8BQiKKIbwnXvEYJDDs9I8Wd383kO/qINd+GN+cVmo/+gmSWzZi9vYgCwWEYaCVRfHNnotv9hyEppHY+Ba5EmL0QmJoazfJtiEW/NJFhOdU0vnMXmK7e7FS0zd50sMegs1RAvVh9KAH1afjrQygaAqKOv6B3r+ho7g8mYRcfxqhKqje4sQGx5bEdhcn/hXSeeJ7+mmeU4G/PnJBC02B228dIGONn2Bbjolpp/GqQXxa6Yn6ZPVSE2YfeTuLVw2iK9MrWo52SerPHmK4hAC2pUVv5gCVvhbCnho8WmBU2B5PKKJMWE4nEHLdyVZhzEJ4uti2JDbkvoekhF0jsZgni6JCKFxa1AgFIlF3WTLhjL5/hYDb7gxyydVeksMOX/2jQV59PlsU52l4BHf+cqjEXk8P6UBi2L2G4YiCopbu1BsMK5MK6HH7lZBLWuizVbzBYnmV6M/jDWqU1XoZbB9LiLbyDlbeIVThurvfCWOLk8uAEKjB4BnZ/1kTmtVNBrmMQ3LIOuGMyRtQiFbr5LPOaLvK0yWfde+i6Yr5LEXuYA+Dj75J9cevJXrjGtKbDrhWQwDbIfHKDkIXLSCwdBa+RU1kji4bQXh0ou9ZgzA0sns7yR2cXqGpV0YQhobw6GjlQayhZHFSgpTjSiadTeYYy6lSG09qG4lkfe4Jks6FG0h9PE4uR//D97mJPnNbiV5zw5jb/mgwk5QU4jGSG9Yz/Mark/ZjvxDI9qV4+y+fYu6dK6m9Yjb1180l1RbjyBO76Xh6z+kJTkXQcF0rrZ9Yja8qQG4wg5U2cQo2Rplvwt/MqCv+WEYefuPizaTESh0XnyWhkMiBEOiTuDQvDCT2SKy1Rx2ffKEIDU3xAnIk9nIqHA3Ecf/XkRZCCJRpLF2kCoOwUQ2A7RQo95Z+fhmqO4HWFQ+G4idLaaFZUaVSFlVKZofPXaCjqhCPuQky04FVgH07TdZd6WX+YoNgSGE4dvIiVlFg1hy9ZPyl3y9oanEnVof2FUaXazqsvsSLogjefi3HK89lx5VN0nWIVkxfLe2j2DYcOVhASklTi47Pr1Awx593U4s2pXqcR5GOpK8tQ8vqMhoWhWjfPvY99+5PoWoKS66p5OCG2KjlMlztwRvSGep6BxOfhTIS1ndmJOFZEZqqCp/5i2ZqW7x8/2+OsOWlyWvg1c7y8OVvthLvK/APn9s32gf9dDiyPYVjS2pafEXtLqcVKYk/tYHg6lb8i5up+vi15P72p9hJd/aS2rif5Ju7CV+xhLrfuJX+Hz9PZucRpGWhRYNEb76Y8BVLcDJ5Bu9/ZdqTcrL7Oin0D+Ob10DL330WJ5Ub60dsWRQGEqQ3HSDxyg7s1NmvKXhs28nSCLd0yEiwvoVJwhmkcIrJQOczhYF+eu7+AUZNLZ7GJrRwBKHryIKFnUpg9vVi9vZgpy6sYtqTke4YZuu/vMT+uzdRc1kLjTfOZ8lvXkblqgY2//1zWOlTu0+iC6tZ9qWryHQneOvPniRxaAg7674RL/rqTZQtqC653UnFVApKxm4rugrI8Y0bSqCoCkKf/pfy2UAi6c8cIuppoNo/l670blLmABKJIjTqggvwaSFydorh/HiroYvAp4WIehoIG1V4tTC64kEVGqpiTGgJPR00RcdQ/QghqA8upj64+ARbCDRl4jI90QqVtZd5+fn9xdZrr09wzXv9CAUO7i2UFKKngpTw0rNZbv9kiIZZGje+P8D9P0xOaAyaKJFHCMG6q3z84FsJEvHie3XhMoPZ83QKBdjwem6sChtuAhBAOiVLehqXr/XQ0HxmZMrGN/N8Ii+pbVBZdbGHF58ufud5fYJLr/adVBKSdODghhgXfbCOhVdW8tZDXdgjnQXbdyRI9OdZ9+EGEv0me14bxB/WueHXZ2P4FHr2pd6RLkTC8OBtbnETjc9QCbyzIjRDFTpN831EKvQpxWH2tZvk0jbNC33Uz/ayb/Ppu4z2rI/Tti3Fwkui1LT46Dl4ZoSUncrSf/cLNP3JnfgWNBK9aa3bMciRyHyB3u8+jeLRCa6ZR/3vfhAnlcMpWKgBL4rPg53O0v+TF0iW6FV+WigC1e9x635WhtEiAYgExpYL8M6pI7R2PsHVrXR94+GzLjb3mhs4wNYJl6uoGIqPqFJDvTYHC5N95iZy8sJ1KU6GtArkO9vJd7a/04dy7jBSdujQfVvpeGoPS3/nSuqvbaVsYTUDGzrGrX7UCDxZlFbFqgY0v07bg9sZ3DIWQ6n5dYzwyYV6TIRQFAL1YZIHx7pHKZpCoCGCU3DIjmSdIyWO4yAUgaIXW+b0oAdPmQ/nLLaOnU46U7uoGClPtKb6NuL57tGs8zJPHba0OBB/s6RrXREqzaGVzAqvxKMGkdLBdDJYjokjLWzpWq+mv17qmIU0YfaVPLYipCRvT/xcVVX47Bcj9PXYbHkrR6EA/qDgw58Mse5KLwUTHrs3NWGW9Kmwc3OeZx5N84E7g3zuSxEUBV54MsPQoFsw3u8XlFepLF7uQTcEj92bGhcjKqWkdYHO578c4XvfTDDYb4OA2fN0vvCHUXx+wc4tJhveGLPaWTYcPlhg5cUeVqz10DRb48hB19CgabBsjYff+bMohkdMXOZuJFlI14VbkL5cQSAQQhKJqoQjCqYpKZgS57j40B2b82zfaLLmUg+f+3IZ/b02e3aYOI4bBnD7J4KsWnfyYRb718cYbM+i6QLDq5IdCVGLdefY+Fg31/xyC7d8eR7v/c05CFWgexSyCYuNP+855VxST30jRm3d6EVR/QGEbhBcugI7M/H7UdENfK3z8bfOx8nnyXWOf0ZOB2dFaEbKNUJlGrE+k562E1vp8lmHrkM5Gub6qG2ZHqGZGbb46Vf28+mvzudjfzWP+//hIF3701jmyX2z+Y5B4k9vxE5lcfKl3fqZnYfp++Ev8M6pRfHoKD7PaEtHayhJ578+SOSKpYSvWIxRV4FiaBQGE2T3dBB/dpPbg/y4O07mLYZf2ILQNQq98ZLjSkeS2rifwkCC7O5jxIcQlN2wippP3YAVT9Hz30+QO9SDPFr+RIDi9+Bf2ETFBy4luGYeoXULiP9i80ldm9MlL7PACcStHWPQ7qLfbmeF5xoWGGvZlHse6x0o2D7DuYMR8WKb9qilEaCQNkl3xBGKQJRoAScd6ZZB8ht4or6SiUNH1wOBODYOUxFUXdREaE5F0ZinilAFDdfPo39Dx+j+wvOrKF9WS2L/AJke1zLtFBxyfWkqltVRtqiG3lfbRrZXqLt6Dp4KP9ne89OKbToZdgw+y8qqWwgZVUS9DYBbKqg3c4DO1A4Gc+2Uil9sDC5lfvRyJJLO1HY6UzvJWokRoWljqF7W1d6JXymb1mOWOKP93LvTe2hLbDit/W3baBIKK/zDf1Vx+ECB4ZhDdZ1Ky1wdocBTD6dLdsc5HQoF+NY/xomUKVx5g58v/WWUT30+THzIbd7h9StEyxVCEYXHH0jz2H3jK12Yeclrz+d4/0eDXH6dn/a2AqoqmD1Pp7xSITbo8F//FC9yyzs2/Py+NFfd6Kd5jsa/fq+GbRvzmHlJ4yyNeYsN2vYV+Pl9ad5/R2DcmAAfvCvIB+4K4vUIdI/A7xcYHpBS8Cd/V0Em5WCaEjMveeW5LN/++vDoqzWTlvzXP8X56jcraV2o86/fr+bg3gL5nKSuUaO6VuXxB9Jc814/uj71GcpwT47v/JZbqsjMjk36pAPPfbsNf5nBivdUo/tUpCMZ6szx9H8e5OCGUw//8i9cTPTq68e5vitufv/UduA4JDe9fUYSgeAsCU2PT0H3KKQTNunEiWfbtiVJxiwUFYKR6XEF6R4FVROsf6yPmz7XxBe/vYzD25MMdOQo5Cd2S3Xvz/DqfWMXP7vrCNldE5enAMCRxJ58e+LFmTyxpzcQf2ELikd3O0hYtpv1PYHp3Mnm6f3O05OPazsMPbZ+3MdaRYjKj1yJMDR6/ucJ0lsPldw8s/0wnqYqIlctw9taD2dZaJ4Mw84AffYRGrX5RNUa+u0Zq96FhOJRCTaWoYc8ROZVIQSEZ5dTuboBM5kn3TFcJPBaPrSU6oubie3sdUWZhFBLlLpr5jK8t5/EvvG1RqXlMLChg9rLW1j25avofvEg0nbQ/AaHH905WndzcFMnVsZk7l0rUQyVQiJHZH4V1ZfMItOVwBP1nfb5FpJ5QnMrWP2XNzK4uRPNZ9B44zwUQ+PAPVuKzrXr+f3UX9fK0i9eSdmCasxEjsi8SqKLa0brbR6LrzqIp9yPFjCItFa67XDnVZJd04iVNjGHs2S633lxKhDUBxYT0Cs4NLyBI8nNONJxe8I7hQmTfVRhUB9cjCJUutN72DX0wqj4G8PtFjTd2E6BvJXCq4YI6uWnvb/YoM3X/mKQX/7tCKsu9rJwqYLjQH+vzdOPpPnhfyXIZUu8I6RbGH3frsKE9SwnY2jA4St/MMgtH85zy0cC1DdrVFbrICCflcRjDpvWZ3nqofSEGe+P3ZfizVey3PHpECvWutbPbEayeX2e7/77MG++PD4GceuGPF/9o0E++9sRWubq3Ph+P7YN8SGbF57M8L9fj1NVozFvkT6udSa4/d0NXeBIt/xTPieJDY0/QE0XeH0j6eXHXL6tG/L8xW8PjGa2r1jrwSpIujts/uP/xXnq4TQ+v6CuQZtyG0opoe9Q6clAZtjigb/ZxSs/OUJFo49CzqF7X4rhvvxp1fYefv1l8u1H8LbMxjd7Lt7GZlAUrESCybKfpXSwk0lS27eQ2LC+dEbUNHB2OgON/L+iCBSFCQo7HINw61EBiCn0CJ0KNbN9/N4PV6BqYiTuVbDkyvITdp7Z9vxQkdCcTqRpjavXdyYwaqJokQDWcJp8e/8kBzRWG/R8qDKUdGIIIKJUzAjNC4xgc5R1f/c+VJ+OUARWzqLu6jnUXjkbx7TZ+JVnGNg4Vmw/vquP8uX11F41B92vIyWY8SzdLx7kwE83kY+VtpS3P7kbo8xHw/WtzP/0GpyCQ6Y7Mdr+EiC+t59t//wScz66gvm/tBZpO6QOx9j5H69iRLzM//TaIgeEU7CxMoWx3xLHLzNxjonftE2L2I4edv7n67R+YjVzPrIC1aOS7hhm9/+up/fV4olh/9vtbPuXl2i5bQktH1yCtCWJg4Ns+5eXqL50FlVrm8aORxHM/8xF1F09B3Cfp3bOoummBTS+Zz4AAxs7efsvnjyp7+dM4FEDNIWWYTl52pNbyNtT82Rpij6asR7LdZUQmW7ZIl098YTAfR+MJGxx4qQhWxYYyncR8dQR9TbiUYPk7Ulq254Aj1dwcG+BP//tAeoaNMrKVRzHbR052GdP6FqVEv7l/8ZgghjKqZBKSu75XpJHfpaitl4jGHHPP5txGOq3iQ85E4pMobiJVw/8KMWzj2Woa9TweAXppEPnEau0OB457pefzbLh9Rx1jRqBoIJlSQb7bfp7bRwbujtsfvmDPSXzKn72vST3/mBqkyR5TOO8Yz/btD7Plz/bT0OzRijsutp7Oiy33zvw118eBDGpXjspLFPStTtF1+5Tv0+Ox8lmyezbTWbfboSuU//Zz6OXV9Lzo+9gDccn3E46Nk7enL6Tm4CzIjSzKZtcxiZcoRGp1BnomtzNaRiCqkYDx4ZUfHqEmJl1OLBxuKQLbTI6953/8X+O6b701IAXrSKMFSt9g3vn1hFY2oJ0HHIHukqucy6iisl74M5w/pE8NMTLv35/6Zg6SVEBdnALqg9s6kTz6ygjSTW2aVFITewlALBzFnu//xaH7t+K6tFGa2xamWOeUY6k8xf76H29DW1ExFppEztnoRgqfevbMYfHjufIE7vpfvlg0WdHaXtoOx3P7MWMu8ukI9n9P2+CEBQSOTb//XPoIY8rrjOFkm55aUvan9hN94sHUH26m52eNnHyFvE9/Ry4ezP20e5jjmTnt15jz3fHezpGT888R2I6hduZRxMGdYGFDOQO4zgjbUGRONLGdLI4x/U9d6Rr8QTwauPLs+iKl9mRNSic2KJpSRPbKWAofsJGFf3Zgyfcpju1i4bgIvxahNaydeyJvVIyM15XvAgUTOfErm+rAO1tFu1tU3//TVcb+2xGcmj/KYSDjPxWE3GHRPzkQpkyacmBPROPOZEOkhJKzCtOGjMvObSv9PjTdV3PFrJQwOzpRo9WYGfSkzf1OEucFaE51FNgsNuksdXH2hvKeOqHfZPOulqW+JmzNICZc2jfOz0JKX2Hs3zj17afdCz4iSye5wNmxwC5Qz34FzdT9xu3MvTYm+SP9CELFkLX0CIB/EtmEb5yKVplmOyeDpJvT3My0jQjUIgq1YCgIN+ZlpkznDmk5RT1Gp8Kjmljnopokq7r+tie4qWwMgWsTPHLyDFt8oPFwsHOlhaIE+3j2HGl5WBOYH2dyr6stDkuu76QyFPg3P+N5K0U3ak9NIdXMj96OfO4jNE+99IVgenCEEcSW+jN7ButN1hwcsTynQT0KA3BJWStBPF8NwJBQI/SFFxOwIhiOplRy+dEWI7JUK6ThmCE5vBKbGmRyPeCcFtkSuRoAfijJAsDHIivZ370ChqCSwnqlfRm9pO1hhEIPGqQkFFFmaeWzvQuDg2/dUau3wznHooqCFUalDf48Jfp6B6FQs4hHTcZ6siRGir2bkwX+Z5u/AtOVAHh7HFWhGYmZfPWM3Ea5/n44G/UkYxbvP1MnPxxLatUDVoW+/nMnzcTiKhsfy1J18Hpqy3lnMEamucyTtak99tPUvu59+Frraf+tz+Akyu4UzVFQRnpw+5kTZKv76LvR89hD599S66GPqU4Kh2DWm0ONdosJA7Dzvj4uxlmmOH8QSCo9M0iZFTiSIuCk0ceY6oSQkEVulu6qLIaOSDpzRydDEsODb9NyKgkYtSwuPxaLFlAAIrQyVkJdgw8S7m3iZbI6kmPQ+JwaPgtQkYlYaOaBdErR7v4KKjE8l30Z9s4PraoPbkVR1rMiVxMmaeOqLd+VAgL3KxpS5onUQN0hvMZocCs5REu/3gTc9dGR0WmEO69UMg5pGMm+9fHeOUn7XTsOHFHppMhs2cXTjo9acb52eTsFGyX8Iuf9rPiqghzl/n5/FdbuOHONHs3pejvdBV9uEJjztIACy8KEirTSMYsHvpW9zgxOsOpkTvYQ/vf3I1/WQv+Rc3o1WUoHg1ZsLHiafJH+sjsaSff1veOdQyab6ylXK2ddB2BQBcGKq67fNDuIm73TbrNDDPMcG5T45/HksobMO0Mm/t/znC+B/sYF7kiFLxqkHlll1Ptn0tjaCn92YOj8ZgZK87Gvkeo8bcS9TSgq14sJ08i30tv5gAZK06yMEAs38lwfvKY+7QVY2Pfw9T4Wynz1KErXmxZIGelGJog613i0JHazkDuCJXeZso89XhUPxJJ3k6TNAeI57tImoPjB5zhgkIocMlHGnjf77bij7ihLY4tyWdsHEui6gJNVyir9bL2Nrfe5mP/tJe3H+metvreVjxGKn7uNDE5a52BhnoLfOuPD/HZv5rFgjVBFl7k/jMOCb1H8tz9j53sfPOdz4S8kLBTWZKv7yL5+q53+lBKYggvfmXidmMSiftfSYE8A3YX+81N76r2kzPMcKGhCo2m8HI0obM3sZGBbNu4dRwJKWeIrvRuqvyz8akhFKEVJf6Ydma033kpju1pfiLydpojyS0cSW45qXPJWQk6UtvpSG0/qe1SSYf+HovhmHM+5GGO4tgw2Gfj9Uny01jb83xm9qoy3ve78/CHNbr2pNjwaDeHNw+THHGTq5rrTp+9qozVt9ZROzfArb8/n76DGQ5vPUEd1vMUMVkMohBi2u+cYJnKmuvKuPg9UWpbPPgCKkIBM+cQ6y2w7bUErzwyRG/76aX7nzYCorUebEuS6J+p0Xg2iCq1eJXx7eeKkBILi4yTICOTk/Y3nmGGGc59dMXLJXV34dNCbO77OX2TJOA0hZazuPxaEmYfb/Xcj3WBdAXzeAS64VaXyWbOH8EmBPgDAiEgm5Wn1Cf9QkIo8JG/XMQldzSw97Uh7vmLHcR7Jg6XiNZ7+dhXlzD34iiv/KSdB/92z3lR8WUipJQl02DOmkXzKKm4zYsPDPLSQ4P4gyoev4KiCMy8Qzphj7ZreqcxvAqf+9dF9B3O8t0/3HPiDWY4bWJOD+9m3egPKtTP8VDTaOALqtiWJD1s09dp0t9pkkk50xrHM8MM5wKOtMjbafxaGRW+WQzmjhS5zcFN/gsb1cwKrQQEsVwXljz9QvnnCvm8JH8ehm9K6baPnMFFUQQNi0I4luSF77ZNKjIBYl05nv/uYWavKaNxURhFEdOSHKR4fW5JpmzpxEI1EMQ7ew6qz08hNkSu/TDyDN6AZ11oHkU6TLmA+zuBZihEqg2SQxfOw2yGcxNVE1x2c4T3f6aSplYvhldBUd2HuG25XS0Guwu88vM4D/xXH9Y5Mhk7Hs1wH5JnuCTbDBcYtrToTO0gYtTSFFqKX4sQy3di2lmEUPCofrdTkKcBXfGSNPs5nNzMeW36meHCRLjPczNnE+ueWiJzrNttGqOeRPehE1F2+VUEFi9j6BdPkd65rWiZUVdP9Yc+iqeuARQFWSiQObCX/ofvx06cGdf9OyY0z3UMr4Kqnbhg7wynRkgpxydKxOieAkN2Nxbn54RAKHDLpyv5xO/VohuCWJ/Fvm0ZsikHw6tQUatTWafTONdDtFrDmaQm5DtJIKLyu19r4u3nkzx190zCwwwnR1dqNwKVlvAqyr1NVPhmIUaK0UnkaO/y7uRu2hIbpxxrOcMMZxPpSPoPZ6meE8ATnJq88kc0dENh4HBmWsopCk3HP38RRs34xFrF46XyfbfhaWhC5vMU4jG0sjICC5dgp1L0P3zfGSkcOiM0J8DwqSgnWdx9hqnToM2jSZt32vuRSN7MPU7SOXcy7E6G+hYPH/pcFboueP6BGPf8Wy/x/gK2JVFUgcenUFWvs+zSIDvWp89Za2HTXA/LLwtxcMf01L09bRSBVlkOioI9nEBmz0O/5BlEiYRQ/F5kLo8de+dFm5u1vY3ezH5CRiV+LYKqGIDEcvJkrQTpQoy8nWHGkjnDuYpjw5anell8dSXLrq+mc2dyUle4ZghWva8W25JsfaZvWrLO1VAIrawMO5Uk39VRtMy/YBG+ljk42Sy9999N7tBBvM2zqLnj4wQXLyX+8vMUBibpHniKXHBCMxjV+cDvzMIf0Xns3w/Tc8AtplxWY3DDZxoRUzRSBqM63sD098adwUXg1sZzpE3KGSYnUxSk6VoxhJhyYX0pJYXzOCFg4Wo/4ajGYG+Bn/xrD4PdY5ZZ25YUTJvUsM2hXdNXT/ZMsGhNAMN77kzMFL+P6t/7NZRQgKHv3Udm/cllD1/QCEHZB24gcMVaMht3MPi/P4US7TLfCQpOlqFcO0PMtJSd4fxkxwv9vH5vB5d+tBHpSDY81kO8O4dtOyDd9teqLqho8rHu9gbWvr+Otx7uYu/rQyXd544tT0qAKl4fiteH2duNlRybRApNJ7zmYlAUkls3ktm9E6Qks38vmf17CS5dgaeuYUZoToXGBQEu+3Atqibo3p/m5988AkCoXOfaTzWgaufOy/DdTEGaFKSJhk5QKcOLn5yTIekMkbAHSDiDZEfE54WcWR4q01BUGB60SA+furlSCKis05m/yk9NkwchYKDLZM+mDL0dZskHlT+kuDVr4xaZpEOoTGXB6gCNcz1ommCor8C+LRk6D+VLWlKFAF/A3cfKK92yVIGIRk2TUbSedCSDvYWznJEqQFMRmgrKzG9+HIqC0DSEOhMeNMMM00llsw/LdADJDZ+bzWV3NpIcNEnHC9gFB81QCJYbBCsMfCGNfNomUu3lU/+0rKQXdctTvbxxb+eUxxeqglAU7EymyA1u1NTgaWrGyeVIbnyb0cxSx8Hs7wMhUMOR0z39klxwQrPvSJZ9bw8TiOgc2DjeJTTcb9J76MS9Zg2fStOiyduVzXDqHCpso9c+TEBECKsVhJUoPhGiVptNHXOQOJgyR9oZJuEMMuwMkHYS5GR6tFPHhUBy2MZxoKxSIxhRyWVOXlR7fIKbPlHJrZ+uJFqjo4xoB+m4AvbpewZ56H/7yaWL9339R8r5xJdrefS7A2x6Ocln/6yeWQu8qKpbrsSRkBq2efLHA9z/rT7M3JgLqL7F4M4v1tIw10N1vUEwoiKE4KaPV3DDR8uLxsmlbf74jv30HDl/Lc8zzDDDDFNhwWUVXPcrLYBrvQxEDQJRY8L1vUGNpddVTbi8a/fJ1ROXto20bRRdd60BUoIQBJeuQPF4ye7fi9nbXbyNZbmeRPXMeHEvOKE51JXnP7+wA0UV5FLjBcnmZwf42d8eQJ4gqaJmtp/f++GKM3WY73psLJLOEEmG6LEPjXT88eAVAUJKlLBSQVApI6REiao1KCgUMMk7GRLOEAlnkKQzRMZJYpLnfI3b2rc1QzphU16t86HPV/Pjf+omk5y62NR0wV1frOX9v1xFLmPz+hNx2vbkEALmLPGx6soQd3yhBn9I5Qf/rxvLlEXbev0qF98Y5qrbouiG4MWH4nQeyuHzq6y8KkTrMh+3f76aoV6rKMlH9yoYXkG8zyIxaDF/pZ9gRKOvw6SrrbgGrpl3yOcuXKv0DDPMMMNROncleemHR6Ztfwc3xE9qfXuk9aQWrUALR7DiMbRoOcFlK8FxSG7egCwUJ8+qXh9I6QrOM8AFJzQBzEnaVqZihSnV6sylbewLqDe6ELBogY7HA1u3F7DPMaOgRGLKHKbMkXAG6WQ/Ciq68OAXYUJK2Yj4jFKtNVHPXBxs8jLLxtwvyMh3PqHhVOg8kOPFh2Lc8ulKbvpYBU1zPTz+w0G2vZGaUumvlVcGufmTFWSSNv/2x+1sfDEx6qLWdMF1H4nyq3/ewI0fLWfji0k2v3zM7Hjk9m6e56W33eQffuMw+7ZlRt3kj/94gC//SzPLLgly7e1RXngoNtoS9sieHP/0O0dAgNen8Bffns2CVRqvPh7nZ//eWyz7Je9sSaYL52c8wwwznOPsXx9j//p3LjnVTiXJdxwhsGQ5lbd+kMyeXQSXrkCLlpPv7iS9Z2fxBoqKXlEBUmKnU2fkmC5IoVkK6UA+a5McnFoZHDN7fglNv0/gOJLcBMm1Hg/841fLaG7SuOz6HhLJc//cHGwKMkdK2tgUsCjgYGMID5owUFDxCB+KUM9bMWFbcPfXe1B1wXW3R1l2aZDFFwXpOJDjzWeGefXxYToP5EpODHRDcNPHKvH4FJ6+Z4C3n08UxWJaBclLD8e55rYoi9YGuOr9ZWx9NTkWtjMSDuQ48PC3+9mzuTikJN5v8dz9MZZcFKRulodwuUp/p7uxPEY8FtSxYHXHlhTMc+zLkBIUBa2qHKO5HsXnxU5lMA93YA8Nc6Iq+MJjoNfXoNVUohg6djpD4Ug31mBs8lIgAoTPi15diVZVgfB5wLKxhxMUOnuxh5MTj60IFL8P6Uhkxs3kVwI+9OYGtIoyAOx4gkJHD3Z8gkmWqqLXVaE31iEMHTuewGzrwEnNZG7PMMMFi+MQf+UFvE2zCCxaSmDRUvfjXI7Yc8/gZIqf86rPh6e+EWlZFPr7zsghvWuEZn97lm9+fjv97VPL3rVMSTZpjQT1ntsoCnzlL8rYudvk2z9IT7ruuZ4WoWHgET6CoxbMMgJKBEN4UVCROORllkG7i2FngKQzRNY5uRiWc41M0uG7X+1i4wsJbvl0FQtW+5m1wMusBV5u+nglrz0R56H/7ae3vTjGMVqt07rch1WQbH0t5WqW477gfM6hfX+eRWsDzFniQ9MF5nE9iTNJmy2vlp7J9nWY2Jbbn9cfVOF8q1cqAUUh8oEbCF51MUrQ7/5gbAd7OEHymVdIPv8GTOAy8iyYQ+T912PMbkIYIzFPtoOTzpB+czOJJ17ASYy/dsLvJXzjlfhWLUGrjLrbKsqIQrexE0lSL79F8umXkeb4a6pVVVD9pc9iDQ3T9y/fxtPaQtmHb0JvqEVoI49t2yJ/qIP+r38XedwMUwkFKPvge/CtXY7i87oJUZaN1T/I8CPPIk1rWmr2zTDDDCdG1QSaoeA4ksJZCCPKHTlCzz0/ouzyq9DLK7ESwyTWv0Z6z65x6xr1DQjdIN/ZTmFw4Iwcz7tGaOYzTsnkoIkwszb/87u7sArnvtCMlilcdbmH9s7zp9GsQKCg4RE+AkpkNC4zoITxCD8qKg4OFgUyTuIYYRkj56QpYHIhWWUKpuTt55NseTVF8wIvV9xSxiXviVDTaHDTJypYui7I1//gCPu3jdWprGky8AVUVE3wyd+r5SO/Xl1y39WNbiC6L6Dg8SmY+WLzaCpuMzxY+t6xLYnE1VfK+VjtS0Do2kvRG2sx2zopdHSDEBhzmzGa6yn78M0gJclfvDrudvIuX0jFL9+BEvRj9QyQP9CGk8mhVUbxzJ9D6IbL0etrGPz2PePEptA0/BctR41GsHr7MTt7cRIpFJ8Xz7wWtLpqIu+/AZk3ST7zyvjDVhSUgB9NCPwrFlN2160A5HcfwElnXEtpfQ1WT/84oSoMnehd78e/biWyUCC3cx+Frl4UvxdPawvRT34Ia2Boeq/zDDPMUIQnqLLwikqWXltF1Sw/mlehY0eSe/9q56i3VDUElU1+HFsy2J6ZxjrJklzbQXoOH0IoKtKxJ/Se5DuO0Pm/38TJ53HyZ6aM3rtGaJ4sUkLPwTETc32dimHAkSM2R/OImhpUfD5Be4dNdiQjN1qmUFmh0HbE4th422BAUFWpoKqCRNJhYNCZ0Ovm8UBlhYrfJ7AdGB52GIqN73NtGO54l63z0NigUlWpsmDe2FeaSEq6e0rfuX6/oLpSQdMFiYRD/8DEfbR1Haqr3OPJ5SV9fTb5EgnEHgOamzR6em2SKYnfL6ipVtFUSKbcMY66gBu0eTTpC/AIPxo6AoGFSV5mGbA7GHYGSdkxUnIYS5rYnD8i+nQomJID27Ic3J7lse8NcMMd5dz6mUoaWz186g/q+LtfbxvNTPeHFFTNzRCvneWZNMEtl3Ew8xKlRKmfXNaZlv665ySKgjGrgfhDT5N6/nXkyI2rBANE77oV/yWrCL3nKrJbd2P1jSU7qeURone8DyUUILthG7G7Hx1zUasKnjnNlH/2DryLWwnffA3xex8vcqM7iRSxex7DyeYodPa6FseRH5haXkbF5+7C09qC/5JVpF55G5kt/YBXwkHK7ryF/L42hh962nXXWzaoKkrA52aJHvcg8S5biH/NUrAshu9/ktSLbyIL7u9HjUYo//SH8C5biBDnun9jhhnOTyoafXzoTxcw/7KKotqY2WGryOvk8Wnc9dUlhCoNvv2FzXTvneYYSSmRJ6gr52SzE/ZEny5mhOYU+e3Ph7jpBi/Xv7+PoZiD3yf43rcqWLrE4Jc+N8CTz7ovil/7TJCP3+Hn5tv76O510DX44Pv9/Ppng8xu0dA1wVDM4clns/zzvyfo7Rt7SagqfOwjfj52R4C5c3SCAYFtQ/+AzQOPZPjGfyZJpd2XlWHAP/zfKFdd5qG2VsXnFfzaLwX5zMfHSjI9+FiG3/79WJGAtGy47BIPX/z1MAvmaXg8gsEhh/sezPDP30yQThcLjmVLdP7wd8OsXW0QDCjkcpLN20z+8esJ1m8wi/Y9Z7bOY/dW8af/J05Xj82f/F6ExQs1DEMwMOjw+38W4+lfuNcppEQJKVEcaZN0YqSdODmZxpRjGeQBJUKAyet6SSQ9VhsFLqzOL1LCYE+Bn32zl+Ehi1/9ywbmrfDTPN/L3pFYyqPXvpCX/NsftdPdNvk1sAqS1PD4h86F7EIVQpA/3EHqhTdGRSaAk0oz/Ogv8C5qRY2G8a1YVGRZ9K9djlZbhT00TPz+J4vjIG2H/L42Eo89R/mnP0zgkpWkXl6P1VUc35TbtqfkMdlDcVIvvIlnTjNaZbkbMzqB0BS6jj2cJHb3I8VWU9su6bJHVQlcshI0jdyu/aReemtUZALYsWGGH34WT2sLwu+b5MrNMMMMp4IvrPGR/7OI+ZeWk0/bHNmaxMzZzL+sYty62WSB7r0pmpbWM29d+fQLzXOEGaE5RbbvMvnERwM0NqgMxRxqa1SamzQyGYc1qwyefDaHqsLSRTo9fQ6DQw5CwCfvCvDXf1rGa2/m+cHdw2RzkovXGHz8owEaGzR+/XcGSabcF70Ali42yOfhv7+T5EiHTTgkuONDAb70W2EGhxz+6zvujWhZ8P2fpHjw0QwXrzH4oy9HuPu+NPc/PGaF7eu3x1kpK6IKf//XUV55PccP707h8wk+dkeAL/5GiL4Be3T/AIsX6nz3PyqwHfivb6do77RpalD59McDfPubFXzyVwfYvG3MbCsEeL2C66/1smShzpZtBR7+eQaPIViyWOdQ23iRI1BGMsrL3eSJk4widaRD3Omn4FxYQvMo0oENLyT42O/WEoyoVNTqo8tSMRur4KCogmTcOnfaP55DSCnJ72sbF8MIYA3GMA934luxCE9rC8lfvOZaB1UF79L5IAT5fYewBkpnkOZ27MNOJFHLwnjnzyHVNfVAensghixYCF0di7mcgMzb20qLyhIofi/GrAaQ7vFJc7zrodDdh9U3iNHSOOXjnWGGGabG0uurab0oysDhDPf99W7atsSpaPTx5fsvGbeudODItmHWfbiepqVhhMK0tKE817gghWYwqnP7H8zGMh0e+uc2MonxAqes2iBS4yE5aDLUdWKRsmevhVBg9iyNrdsLLJinkclKtmwzWbHUQNfcLOB5rRqvrzcxC9BYr/Kl3wrzxtt5PvfFwdFM7wceyZDLSz73yyGuvcrLI4+7AsGy4Sv/MIxlySLX9Ktv5Hns3mrec72X//6um/ThOLB5qyvy/H6BdODQYYuXXp38XAJBwfd+kuH//t0wR0O71r9t8sjPqrj1Jh/f+WGKQsG1rv7Ob4QIhxXu+FQ/W7aPCcot201+9L+V/PqvhvjCl4bGhQC87z0+vvwnMR54JDOaY6GqFMWf9NudmPL040Ekkry88AWWAJAUubi7j+RJxm0qanUWrvKz9bXUyYWtTqMhc3RX55o3VkrX3VwKy6bQN4hXSrTqCjdhxgHF43H7pAOFrt4JM8udTBZrIIZWXobeWFt6DOG2flPCQRS/F2EYCE1Fb6h1xyuRwHU8hY7uyVc4BrUsjOL1gnSwekq3kpOWjTUYmxGaM8wwzSgqLLqqEqEKnvt2G/vePHEs9FBHFikhWu9FCIGczgezEG4XMKG4/z4pclx9zenighSaHr/C6vdWYmYdfv4fR6BEDtClt9dw0+eaefmebu7/fwdPVOGE9g6L4WGH+fN0BFnWrvZwqM3i9fV5fvXTQcrKFEIhhcoKlc1bXZW47iIP9bUq3/5+imBQIRgc29+2HQUMHS692DMqNAHSGfdAfD6BzyvQNLBtSKUcyssUVMUVpKdKMim598EMx+YPtB2x6Oy2qatR0VRBoSCpqVa54jIPe/YVGIo51NeNZYJ099gMDzusXGYQCSvE4sUv4p27CzzyeKYokff48jwDdgcDdsepn8gFgqaLSWtMCgErLgvhD6ukEzbdh8dmIIlBi00vJbnxznKu+kCUFx+J0ztZ9x1BsbicJlFoWxIz7yClJFx+jj1SpCxpzRxdPOKyFh4DoapIy3b/3XAtx24poAm2te3R7dVQYNz11eqqCV6+Bu/ieahlYYShj7TEHHnoC1Ey43zc8ZcKiJ4A4TFAc7PbnfQEEzDp4GQvTA/AmUBoGsLjceNhJUirgJPPT17a6mT3bxgIVRupamDjmCayMI2dtBQFxfAgdI2jZjNZGDmPMxk6o2ooHo973zPymzEL03tu5xBCCCqbfBSyNoe3Dk9pGzPrvhwN3/RmWxp1DYTXrsPb2OxOPk8gNKVp0vWD/8FOTH9N6nPsrXD2UDUFj18t2cS+FENxhyMdFosX6Hi8guVLdfbuL/D2RpM/+pJCY4NKOKRgGIK9+92Xx/xWDU2DP/5ymN/77fBx47u/72i0uNfw3Nkan7wrwMVrDKqrVAJ+gWEIomUKw4nCaYuDdMahs6tY9dmOW89R8THavrCmSiEaUbjkYg+vPnOctUZAwC+wbRuPZ/wYBw5ZlPDYzXAcigof/a1qCqZk+xtp+jpNUsMWlilRdUFZpc6aa0Lc8Zs1qCpseDFB16ExgWDb8Nj3B1h5ZYiGOR6++A9N3P31Hg7tzJLLOKiaIDTSe3zZpUFeeyJO+/7pFxhWQdKxP8/yS4OsujLEnCU+2nZnj3Y+QzcUzNzEyWZnnMkesMcuOnqAUo79+6QPZzG6/Pg4V+/ieZT/0u2o5VGcRJL8gcNu7cxECpnLo1aUEbnl2hMf+8leMznVbS7cuNyTJbT2Ynyt8wDI7NxBautmAJRAgODS5QSWLEWvrELx+cBxi1rnu7tIbdlMdt+eU7MCCYFeUUlgyVJ8c+ehlVeg+n0gFJx8HiseI3e4jfTWzeS7u05Z1Cr+AP4FCwksWoxeXYMaDCI0DVmwsJMJ8t1dpLdvJXtgf8kwi8kILFlKYJnbPS936CCJN18fXaYGQwSWLce/cDHG0WsHOPkc1vAwZncX6Z07yB7YN22C/Zxg5JHgOEw5g9wX1hFAPjN9XVS8zS3U3PlJtLLo1Dawbbff+Rl6SL9rhebJYlluR51LL/ZQXaWwcJ7O/Q9lONJhEY9Lli8x8HgE6bTDwZFYRMMQWBb8+Gdptu8q/TA6eGjM7Ldssc53v1VBOKRw74MZ1m9I0dtvI4D/+sb4QOJTwbHBnEJBbU0TqKpg42aTn96XLvlaSqclicT4JaYp3zlRcR4hBMxe5GPtdWEsU5KI2WSSNlbBFZqhMpVIhYZ0YMurKX78Tz3jrJ9H9ub4r7/s4HP/p5HFFwX4i/+dw2BvgXzWGal9qRCp0LBt2Pp6Co5NmprG7+j5B4a47KYINU0Gf/md2XTsz2OaDoGQimPDP/xmG/GBd6BygBCjL7lSKAE/4Fo25cgLz8nlR62Iajg44bZCU1FGEmqcxFjYguL3UfaRm1ErouT3HGToRw9i9QwUPcQ982cjb77mdM6sJDKfR9o2QtdQAhOct1BcC8cMAHgamwiuXA24ZaVS27bgaZ5F5fs/iKeh0e0BfcyEQw0G0atrCC5dTnr3TgZ//ijW0OBEux+H4vURufwKwpdcjhoKueMeu3+/H62sDG/LbMLrLiG1eROxXzyDnTwJS5OiEFi8lOj1N2LU1o07B3yghkIY9Q2EVq4me+gAQ089Qb596q0TjZq60eumGAaJt9eD4+Cb20rFLR8oOa4aCKBFy/G2zMaorSPXdnD0d3chIB0Y6spROy9IZbOPvoMnqGutQOu6KAjo3Z86YWvsKaEolF15LVpZFCeTIb1rG/muToRuUH7DTWQP7Se9aweqz4+3ZQ6+2XPJ7NvDwKMPYCfPTE3qGaE5RaSEHbsK3PJeH6tXGBiGYMeuAkNDDoc7LJYs0jEMwcE2i0TC/eH09NoIAdt3Fvjejye/4QA+/tEALbM0fuv3hvjZA5nRiV5FuXLWJ33DCYd0xiGZdPjBT9LYF86z4JzBseHFR+IYXoW6WR4CEZVwuYqiCuyCJJOy2bs5wyuPxXnh4RipeOkZ74YXk3z1c4e49ZcqWX5ZkPJqHd0jsC1JJmlzeE+OjS8m6TxYHBMbG7DYvzVD56E8zgQPuFzG4eD2rBs3nJ34IXhge5Z/++N2PvIb1TTP97JwbQBpS3JZh86D+XfUmqnVVLiq/riDELqGVlMFQKGnn6N1y6RZoNDVh1Zb5cZequr42A/cEklaZRTpOJjtY3GUWl0VWk0l2A7JZ17B6h4fK6kE/K6rfpp7wdrxhOvO9/vQ66vJbt45bh2hqaiVU7R0vIsQQqCVV+Cb20rV7R9FK3fjdJFy1MUsDGMk5k2AphFYsgwtGKL37h9hxU/cdlAJBKj64EcILFnqfv8j96R0HKRpIqVEGRkDQPX5CV9yGUZNLf33/4zCQOm42yJUlbIrriJ63Y2jEwoppftPoeBORDRtNAlNaBq+1vnUVtXQ//D9ZHbuOOnrpgaCCFXD1zqbqo/ciRoa8+BNVNUi13bwjMUEvlM4tmTva4MsvrqKyz/WxOEtw6RjE5yjgNaLy1lzax2W6bDr5cFpeU6qwRCehkZkoUD/I/eT2rEVHAetLEr06usxe3tJvPmaewi6TuSSy4leeyPBFauJv/z8GbFqzgjNk2DXngJ+v2DdWg+xEVd6wYLNW03WrfVgWZIduwqjiTxvvJUnk5XccpOP+x7KjJYmOsrx7776OpVcVrJzd6FIWC5eqFNdpRCPl1Z7hYJrTPF7py8To73DZs8+i5XLDRYv0tm2Y/yPpcS7e4aTQEp45bE4rz0RJ1yuEYyoeLwKiurGbWYSNrH+wmgnHy3ip/6Xr6X7hy9RGCzuWX5kb47//PMOwuUa0SoN3aNgW5L00X3kxn9RLzwY48WHY27c2QTf4+E9Of70Y/snXefouWx8McmON1NU1hv4Au7kKJdxiA8UyCTfmZmKEALPgrkoQT9Osniyp9VVY8yqB0eS23NwzIXnOGQ27cC3YiGeeS3o9dUU2o9LyBHgX7UYJRTATqTI7z00tkg/2gXIwU6WKkGk4Fu5CFSlpIA9HZxsjvzBdvwVUbzLFpJ8/nXkcfGYekMtenXltI57oWBU14yKTDuRILV5I5k9u7FTSaSUqMEggSXLCK1ag+L3u/fXrBbKb76F/vvumVQ4CcOg8tbbCCxb7iZ9SImdSpLavInMnl1YiQRIieLz4Zs9h+CqNRg1tQgh8M6eQ9VH7qT3x98/odUpvPZioje8170PAcc0yezZRWrrFgr9fUjLQhgGnlrXIumbMxehqqiRCFW330FvLkfu4IGTum5qMIinsZHKD34YNRTGyWbIHthP9tBBrMEBpG2j+gMYtXV4W2ajV1WT2bf3pMY4X9jydB9rb6tnwWUV/NK/LOf1n3VgmQ4CUHVBeYOPcJWHBVdUsO5D9QQrDLY/1z+lxKGpoPoDqP4AhYE+0rt3jD7XpOMgbcudyIy8vGWhwPAbr+GbO4+yy68ivWPrGekONCM0T4IjHRaZjOTqKzzs3V8YLUu0aYvJp+4KkEpL7rk/M/pC3rmrwL0PZvjknQH+7q/L+P6P0wwM2ui6YFazxpqVBt//SYqeXvdG2LOvwPtv9vG+9/jo6bVxHFi0QOcv/ziCpk4sIo+0W2QyDu+53sfjT2fp6XXHyOUl/QOn9oLP5iTf/O8k//Ev5Xz9H6L8078l2LXHQgJVFQoXr/WwY5fJ8y/NJBWcLo7t9hWP90/uWva31hJZO5fee18vuVxKGB60JuzyU3KbKdweJ1NuI5+TdB48d+4JKSV6fTWRD9zA8CO/wBkRfmpFGWUfei9KwI/VO0Bue3HNy9yWXeT3H8azYA7RO29l6McPu1ncUoKu4Vsyn9BNV4MQZF7bgNU/9pKwY8PIXB4R9ONbthDzcOdoBp/weQhefhH+tcvPzAnbDpk3Nrklm+Y0EbrxSrfNZS7vWneryoncdiPC57mg66eeKorHgzAMCv199P3sbvId7UUzrEJfL7lDh8ju20PVR+5CC4UQQhBYvJTUvE2TWgODK1cTXL5yVGSaPd30P3Cv664+7rvIH24juWmDK0yXLkcoCt6W2ZRdcz2DP39kwrhGvaaW6PXvQTEMV8hmswz+/GFSmzYij2uzanZ2kNq2hcillxO94b0ohoEaDFHxvvfT871vY6em7kZVwxEqb70NLVpO7tBBBh9/lHxX5/iJ1JZNCN1Ai0anZAE+H0kNmtz/lV3c9TdLmHtRlNmryiiYDoomaFwS5os/vgjdq6J7FZDQtnmYR762F3OaYjSFpiJUFTudKv7OHRtZKLihRMdYiWTBJHtgP/458/A0Ns8IzXeaRFJysM3iyss9/PS+Mdf2nn0WQkB1pVIUi1mw3HJFpim588N+Pnybn3xeomkCTYWDhy1+/LMxK8uP70lz9RVefucLIe76SADLkgSDCj++J01Xt82sptJf16HDFv/zvRRf+LUQD/+0mkTKQdcE9z+c5s/+79Qy30rxxDNZ/uDPY/zRl8J8+5sV5Ef0g+GBXE7y5T+5MB8Up4LQ1ZEsYmv0b6EqSEciLXvULVtqO6EorltrovUUgaKrBJfPQhgaikdH8Y7V03RMa2w7IVA8GtJy3P0dvx9DQ1o20jrmRaUqKJqKY46Yxkf+hpEs0WPWFZqCUBWc/DHnefT4C9Y5l2Mi8yaZDdsJXL4W76JWCt1urUu9qQ6tshyZyzP86C+wh4p/J04mS+zuR6j4tY/hWTiXmj/4Ncz2brcFZTSC3lyH0HSym3aQePLFohe/NRAju2kHgSsvIvTeKzFmNVDo7kN4PXhaGtBqqsi8uRnv0vkowQDTTXbHPtKvbyR45cVEbr0O/8rFFPoGUDweNxTAkWTe3oZ/7bJpH/tCQFoWQ089PnG8onTI7N5F/IVfUHHLBxCKgtB1whdfSmbP7pJWajUYpOyKq9wwDMDJZhh46H7yRw5PeBz28DADjzyIVhbF09QMQGjNWlJbNpXeTlEou/xK1PCI21pK4i8+R/LttyZ0R0jTJP7yS6ihMJErrnIttA2NhNasJf7i85NcpWKErmPUN5A73EbvT3+EPTzxe0cWTAp9vVPe9/lIx44k3/7NzVz5yWaW3VBNuMpAOqCoAl9Yx7Yksa4cm57o4eUfHiHRP33Zs9KykbaN4hnJ7h25H6Vl4eSy6NEKNyHsmOQvO5UCRUELT94g5VS5oIWmZigsvjxKOj7enVEz2w2Ur2z0svzaigln94nBAm1b3ZldLif51neSvPhKjiefHSsdcuiwxd//cwJNhQMHi8eKxR3+/CtxfvjTNKtXGFRUKORzrmDdst0s6gx0uN3mE78ywNVXeGluVElnJBs2mWzdYXLRag9zZ2slJ7KWBf/4jQTPv5RjxTI3KWloyGH9hjHLklWAH96dJhxWyOWLz7VQkHz7Byk8HoF5TLKJbcPPHsjw8qt51q42mNWsIYRb3mjrdpNDh4tnyH39Nv/vXxPs2lN417nUmz7/HtSgl8P/+nPKrlhAxfXL0KvCOBmT4fX76Pnpa0XCTw14KL9+GWWXzkevCGFnTdI72un/+UbynWPWMaO2jNo7L8PXUo23uRLFozP3/3y0KLbv8D89Smp7OwC+2VXM/qMPMvTCTnruLu6h7W+tpeUPbmPgyc303f/G6OeRi1qp++SVHPr7h1ADHqpvuwj/3BqkhNzhfjq/8xxmr/viqLp1DWVXLOLwPz9K5OJWyi5fiFYWwEpkGH5zHwM/34idPgcsmo6DPTCEmUwTv/fnmG0dhG64DN+yBaBpyEKBQkcPiSdeIPPW1pK7KHT0MPCfPyL8vmvwLV2Ad2ErqAqyUMCOJUi/vpHUc6+NLyNk28QffMoNZ1m5GO+SeXiXLXAzfWPDDD/8DKmX11Nm225SUAlhIm0bq38I4TFOXALpeCyL+P1P4KQzBC5d7brKm+qR+TxmezfDDz7ltuZsrMUeTp5zk4N3GrOne0pu3dTmTYQvvRyjssp1bzfPQo+Wl4yj9LXOR6+qGrVmprdvI3e47YRj2Mkkw6+8RPWdH0eoKorXR2jVmpJWUK0sin/R4tExCv39JN9af+L4Jsdm+LVXCC5fgRYpQygKwVVrSKx/Y8qtCYUQOPk8sWeenFRkvpuIdeV49B/38vx326iZHaC8wYfuU7FyNkPdOXr3p0gOmNP+rrQzaexsBi0SQQsEsRLu9+Hk8xTiMXyz52JU1ZDvbB/dRouMCMxzOetcKBPtxg3sckXc2X+a+UIqn/zKvJJDixFX9JKryll8RXTCw9v24hDf+q2xgPonns7xxNPFSRW5nCzqqHM8luUmEu2YIPP8WAYGnaLuPkd57c08r7058QvcNOH19Savry89M7JsuPu+0jUBLQt++NPSyUpSQlePXVTrc7Jj//p/nJmstXMdrcyPt7mSmo9cQvm1S8i29WPu7MCojqD6PUWZlWrAQ/Nvv4/Q6tmkth8h8+Y+9PIg0asXE1o1m0P/8BC5NvdlJQs22UN95NoHKb9mCd6mCoae24Y1PPZdmn1j2ahC0zCqI2ih8VnFQh9ZFiyuSaV4dbyNFUTWzaPixuVY8TTpPV2oQR96NDhqvXSP3YtvdjVNX3gvRm0ZqW1HcPb3EFzSRP0nr0IvD9Hx389MaME9WziZLL1f+x/3BrZtUs+9Ruatrei1lQivByeTw+ruw8lMfl9bPf0Mfe9+1IoytIooQtfcbfsG3BqbEzyYnWSa2I8eIvnki6gVUYSm4mRzWL0DOKk0SIj99FE37dQaH+pg9Q3S87f/4ZZfOoXCuTKTY/jBp0m98CZatWvBcJIpCj39bka9EHT/9dfd7+kCyvo9XaSU5A4dnFLtUjudInfwAHpFJWKkuoGnobGE0BT4Fy5yv2tcy1Jq+9Ypv9SzB/ZjxWOj4/jmzUfx+nCyxc9z76wW1MBYlYTM3t1Tdn9b8RjZA/sJrlqDEAKjqhqjrv6kYjXznR3k2g6deMV3EdKBZL9Jst8Ezo4H0E4lKfT14pvbiqexGWvntpGDkWQP7CewaCkV73kfA08+hpWI46muJbRqLTgOhZOonnAynLbQ9AYrWXDRJ1FVo+hziUQ6FoV8ikyyj3jfXob7D+DYZ77AouNAZtjCnqQQ9lQxczMP4Rmmhqe2jNDKFg585X5yh914PrfFoFokvCpuXE744rl0/NczDD27DWk7IAThtXOZ/Ue3UXvn5bR97WFwJIXBJP2PvA2KwD+3BqO2jMFnthZZPacDoSpU33YRXd99ntiru90QACFQAx7sVPHESvHqGNURDv7N/aOCWK8M0frXd1J22QJ673+DQv/0F/09aY4TcE4yRb5Ucs6JcBzs/iHs/pO85o6D1TeI1TfBw9t2gEmeLyUE6EkhJfZQHHsoXnIZhXeg3NS5jpSYvT1MyTAiJfmuTkJHC8YK4Zb02bKpaDXF68GorRst8+Nks5jdU+/2ZKdTmH296BVuApcWjqBXVo5z7Xuamt2i8iPk2g5OeQwch9zhNrdckRCgqnibW6YsNEcF+uneszOcPo5Dauc2vM0teOobSB8VmkB613Yil12Br3U+Db/yGzj5HKrPj/B4MHt7yB05MxOF0xaaiqLhDVaiqgZSOhz7AxUICNcSqZpHbcs6hnp2cXDLgxTyZ7ZxfKLf5Ou/su3EHZemwHQWUZ3hAkcI+h95i1zbWM9rWbCRhbF7SPHoRK9cjNk7TPy1Pe6vRXUtHantR8j3xgkuakAvC1AYOrO/k+NJbT9C7OWdYzGZUo4TmUcZemHHqMgEKAymSO1op+K9K9FCvnNDaM4ww8niOFgnUa/Sig25lo2RckRaWdm4chyK14t2TLkfO5nAyZ1E21wpKfT3waIlgJu9rpVFi4WmEBiVVWObmCZWPD71MYDC4GDRuehVVVMvLSLlGUkiOZfxRzQ8gemLPswMF8inp0dvpLa5FQZyHcWTESseY+DxR6i65YNoZVE3MUhKCoP9DDz+sBureQaY1hjNg5sfIBkbOzFFNfCFqqhsWE60egEV9cvIZ2K07fj5Ga2LY1uS3kMXfv/rGc4t7EyebNvkde7UsA+jJoLiM5j/D58cZzjx1EVxChZqwHN2haaUZPZ1FycJTUJmf8/xO8DOmQhFIDSl5DYzzHCuI0/QsvR47EwGKSUCN05R8RYnYAAIw4M4poWanU6fdLjC8WWytJFC76MoCmpgLLFMFgo4udKTxAnHSKeQjjN6LmogiFBUpD0FK6XjuLVG30Vc/ZlZrLu9Ydr29/jX97P+ga5p2ZeTSZM9uL/kssyuHXR2d+Gb04oaCGINx8m2HcROnLnY2mkVmrnMEJlE8QsoFTvCUNcO5q+9i2jtYsrrltCx9zksc+IewjPMcD4ibafIelkKRVMRhoadzJLe2z0uljG9pwvHLGBnpv+hLYSYtIWpk5t60omTnekxOsOFiBzxzE1x7eNcxUJV3WScYz/TtKLfnbSsky4t5RzXG1wYxaFqQlFGM9rhaM3Ek7OOSatAkUdS10ARMNXdvMsyQL0BjVCFUXLZaDSFIsa+66Kb4ph1HUjFTOzC2QvTs+IxkhvfOmvjnZWsc9vKMdS9k2jtInRPAE33zwjNGd6VOAULJ2diDiZp/4+nRsshTR9Hn2bjFaUa9EymM09ymHfXS2WGdwvHtWo80drHrVtSQDrOcSJDjBOjJxxHOc5LcLxFVB7XUcE1S57ECIwkKx2zjXOCLg3vcl6/t4M9r42Pv7YtSe3cAO/5wlwKeZt9rw/RsSPBcF+eQt5BMwShSg+Ni0MsuLwCoQju+z+72P3KmUnEORc4a+WNbLsw8mM4WyPOMMO5hzWcIdcxhH92NZ76aFGc44mQjkQoYtIXiJO3kI5EDXlda8RofU3wz60djQedYYYZSiDGWwsnXd3jKfo5yoI5rl+1UzBdV/pIpx7F4zlpEXh8b3r7OLe4tO2iuohC1UY7A015jGOOS4603ZyW3tsXKD370vTsG1+tpaLJx21/NJ94T46f/tkOOnclcezx11EoUD8/xF1/u4T3fGEOHbuSxLtPLtxhuhCahlA1HDN/RiYXZ+mtIwhE6kAo5NIDZzwZaIYZzlVkwWbg8Y0IQ6PxV6/HN6cGxaujeDS0sI/AkiYCi0rE/TgSsz+B4tEJLW9GeDQ3o93Qil5aVixFYSBJaPksAgvqEbqG8GgEl8+i/LqlZ/FMZwDwGIJg4MT/+CZpHyuASFhh3RoPt90c4LabA1y0ykMoODWxomswZ5bGe6/1cfutAd5zjY+WJg1NPfG27zaEUIpKBJ0ILRQeK1skpZtM4RT7mmU+78ZljqCGwkXZ4VMap+yY3vRSYg/Hx61TiI1VRVAMA9V/cs0AtEikyHJqJ4bHncsMJ0DARR+qp2qWn+e+3Ub79kRJkQmuy7xzd5IXvttG3fwgaz9Qd5YPdozwxZdS/9nPowZDJ175FDizFk0hUDUvZVWtVDevxbELdB14Bdt6Z1T7DDOcCwy/sY+eu1+h5iOXMO/vPk5hMIm0HbSQDzXopfeBN0nv6hy3XfyV3ZRfs4SGz15H+bVLcQo2qs+g/ZtPjibnWMks/Y+9Tf0vX8ucv7yD3JEBFF1FrwwRf33vjNg8iygK/PmXo3z8wycWLi+/keOzX+wbV37UMOCuD4b4rV+N0Dpbx+9zxWU6I9m11+Rr/x7n58+mJ6yENLdF509+p4z3XuenvExF09yqRoNDNg89nub//XuMzu4ZMTGKoqCXV0x5db2qajRLGyiZeW1nsxRig+jl5SMlwwJoZWWYPccn1JVGaBpGTe3o3042WyQqAbcsU3cXcuVq152vqhi1teQOTb0OplFTV3Qu+Z6pl2CawUVRBXPXRCnkHTp3Tq16QffeFJYpab04yi/+59BJtfydLvTyCozq2pOeAE2VaRWasxbfROGY2EshVAxvCI8/Si49QNf+Vxjo2DydQ84wwzlBZl83Tr6Akz9xQo20bHofeJPE5jYiF7fiba5EqAqpHR2kd3eS3NJWcrvswV4OfuU+olcvwdsQxbEcMnu6KMSO8RBIGHh8E/muGJF189DLg+RiKeI/fInsgR4UTSXXUfySKgwmGX77AGbfibMOc51DJNbvx0oeN1mUkGsfZPit/edGZ6BzhFxekkxN8OYQglmNGsGAQmV5wTVdHiM0dR3+8Lei/OFvlaFqgh27THbtMxECliwwWL3cw3e+Uc1f/P0g3/peYlzY3uL5Oj/4jxqWLTIYGHJ49sUMfYM2tdUqa1Z4+fXPhFmx1OAzv9VHW/tM/UNwYy49zc1uYs0JkmmEpuGdNXssTtO2yXd0jF/RtskdOIBv7jwErhvc2zJnykJTq6hEr6oe/bsw2I8VG1/8O9d2CGnmER7Xze6fN5/E+jdOeB7gtpD0zW0dPReZz03aHnOG0ggBvrA2UlZ1ah4HRRUIBXxhfSR29yyHKwiBYnhOvN5pMK1CMxhtHveZEIJcepBYz25SsfaTzrabYYbzgZ57Xju5DRxJdn8P2XFlgiYne6iP7KG+SdeRtkNiw0ESG8YXbD7yb0+M+yy17QipbRP0dT6O2As7iL2wo+SyoWe2MvRM6XaO70YcB/7lW3H+4zslBLyAi1Z6+N6/1wAOdz+YHCcUb7rOz5d/owwE/N+vDfGt7ycYTrgrlZcpfOGzEf74d6L81R+Us3WHyStvjon/gF/wd39RwfLFBhu35vnCHw6wZUce2wZNg5VLPfzn16q47CIvf/Mn5fzKl/rJ52eezQDeplnoFZUn7Mdt1NbiaWwa/bswNIjZV/r3nN61g8gVV6EGAghFIbR6LanNG09cgkgIQqtWu/UOcbPJ09u3IwvjJ7T57m7ynZ14Z89xW2LOnounvmHinu3H4G2ZjaexcfTvXPuRkq00Z5gc6UCiP0/tvCDzL6+gZ39q0pBHIWDB5RXoHoXkQP6d0UdCFJXfOhNMq9Bs3/MsmeExc7tQNLz+KGXV82mYdw3Vsy7iyM6n6Gt/eyabbYYZZrjgyWQlmez4Z11Tg8Zf/UE5ZRGFf/xmnJ89XBy37jEEX/jlCMGA4OEn0nzjf4bJ5sb2MxR3+Of/jHPxKg83Xe/nN38lwvpNOY7mg1x+sZfrrvCRyUr+9KtDbNw6ZmW2LHh7c54/++ogP/t2Lbe+J8DFqxK8/MZMSBOAEghQdtU1DDz8QElBB27CUNnV140JQClJ79yBky7dytfs7SG9Yxuhi9a5VtPGJsKXXUn8xecmtTj65rYSvuiS0TGsWIzU1s0l15VmnsQbr+FtngWahuLzUX7jTfTe8+MJjwtADUcov+G9CN1NgnIKBRJvvD7T5ecUcGzJnlcHmX9pBdf9yizMjMWWp/rIJguMduIeyef0hXRW3lTD1b80C+nA7lcGT85trigEl644fWukqmBUVp94vdNgWoVmYuAgw/3ji4R2HXiFpoU30tB6FS1LbyGT7CUVm5oFZYYZZjg/0cvKCbYudrPfjyHb3kauu33yjYUgtGAZWjhS9LGTz5PYuRlZOH/reIaCgq/9VQVrVni479EUX/v3GMfrmVlNGquXe5AS7nkoVSQyj5LJSu59NM2N1/i58hIvddUahztccXDzDQE8HsHbW/K88XZpAfnq+hx79hdYtczgfTf4eeXN3Lt+/n/UohRatQZpWcRffB5rOD5WTkgItEiEsmtvILBkmevqlNKtS/jWmxPv2HGIvfAc3pY5o3Gd0WuvR/F6SLz6ituN6BiTtuL14V+0mPKb3ocSCLjjWBbxF37hdiOagPSO7aS2byO4YuVoX/Tqj36M2NNPYvb2FIlHoWkYDY1UvPd9eGa1jJ5LevtWMnt2ndZ1fDez4dFuFl9TRevFUW7/84Vc/ZlZdO9JEevJYeUcNI9CWa2H+gUhKpp8KKpg35sxNj1+ct4toWlUvPeW4kSx0+DYqgXTzVkpb+TYJj0HX6W6aTW6N0R57cIZoTnDO4RAL4via5qDp7oO1evFMU3MoX6yHYcxB3qn1gljhhNiVNZQdd0tI2VWXLEphKDvucemJDTLVl+Kv2VecVZ9Ik76wG6s81Ro6jr8/heifODmABu25PnjrwySTI1Xd7OaNCJhhXRGsu/gxHG/u/eZZHOScEhh7mydwx0Wug6L5rulbfbuL5S0qIIbP7prr8nq5R6WLDAwdEHefHcrTSeTwezpxjtnLuF1l+JfuJj8kTYKQ0NI20YrK8M3ew5atHw0Q1taBWLPPUthYPIWjNbgAAOPPkj1Rz+GGgyhGAZlV15DcNkKcu1HsIYGR5ICQ3gaGtFraxGq+4qWtk3izddJbnx70jGkVWDoicfQIhG8LbMRioJ/wSK8TbPIdxzB7O3FyedRfF6Mmlo8jc0oXu+oyMwfaWPoyZ9PaMmd4cSkhgrc8+c7eN/vzmPpdVVUzfJTNctfFLN5dEJTyDlseaqPx/91H+nYKV5z28ZKDJ+Wl1g9pkXqmeCs1dG0CjkKZhrDF8bwRRgX+T7DDGcYxeOlfN3VRFauQwtFxi13zDzJHZvoefKBmbIe00D2yEEO/+CbqD4/ejhC5VXvRY+UT21jx6HnifvQAmFUv5/A3EWUrb70zB7wGUYI+PjtIb74uQidXRa//ScDdHSVvs+qKlSEgGzWIZWe2J8WiztYlsTrUaiIusLHYwjCIQUhBP2DE9/HjgMDQ+7yinKVM5Rwet4x+PijRK9/D/6Fi9CjUbd/+TEIgBFhJk2T2AvPkdzwFlN5n2X37qHvnrup/MAH0auqESNZ7lq0+HcxmpQjJdLMM/zqK8Sef3ZK7mwrHqPvnp9QeduH8M9fiFBV1EAA3/yF+OYvHDeOlBJp22T27WHg4QdPukf6DOMZ6szx0z/bTuOSCEuuqaRufpBguYHuUSiYDqmhAt17k+x8foD2HcNYpzHBM/t76f7Rd5Gn2gJUU6n+0J34Wuac8jGccIgztufjUDUDzfC5boZCjhmROcPZRPF4qXnvhwgvXQOAOdBLpm0/ViaFFgjha5iFUVmDlU7OiMxpwjHz5HvcLFzF8BBde+XUhSZQiA1SiA2Obn++C83LLvLyN39aju3AH/71IJu2TfxiOG0X9hS3n7ZOURcIQtNw8nn67r2byOVXEl67DjVSPCmVALaN2dNN7IXnSO/YNqXM7qNk9+2h+7v/S9mVVxNYsgw1PN6aJKVE5vPkjhxm+JWXyOzbU+RaV8MRhBCuW78EVmyIvp/+mOCai4hcfImbtX58dyHcdphmfx/J9W+S3PjWSfdHP3qsM4zHMiVtm+K0bYqjGQJVV0aFvV2QWOb01DFyslnXonmSLUdHURSczJnt1Hh2hKZQqGxcge4JIaVNKnYCt9kMM0wnQhC9+CrCS1eDdIi9/SqDrz6LnRkLkFcMD77GFvIDk2eazjDDqTC3RePrf1tJNKLyf742xCNPpScVk/0DNlKCz6cQDEzcVyMaUdA0gWVLhuLuiyuflwwnHKSUVFVMbKZUFCgvd5cPxexTfk9dUAiB4vFQ6O8j9uzTJN9a72ZkNzSiBoNIx8GKx8gdPky+/TBONntKw1hDgww88iDxl17A29yMUdfgFnIXAjubodDXS669nUJfzzgrpuL1UXfXp1E8Xjq/85/Y6dINUJxcjsSrL5PatAFPQxPepibX5a/pOGYeKzZEvv0I+a7OUzqP5Ma3yR46WtlCYvbOPDsnwjIllnkGfmCOg53LnrrIHNmHY57ZknTTKjQNXwRvoHL0byEUDF+E8trFVM9aixAKyaE24n17p3PYcWjoNCvzEQi6nTYyvHs7Ealo1IlZSCQ98jA27763iVFeRXTNZSAUkru30P/CE+OSSRwzT/rgnnfoCGe4kCmLKPzjX1eydKHBd+9O8h/fGT7he6Gt3SIWdyiPKsybo7NlR+mY1IXzdHxeQSzucLDNjfEqWLB9t8n1V/lY0OoWeS8Vp+n1CBbN05FSsmtfAbMwY5lyC12OCPuRJJ/U5hipzRunfywpsWJDpGJDsGXz1DezXSuk0DScKcRSOpkM2X17yO6b3uebFY9hxcfX85zh7CAti8Gnfj7hRONkcE7V7T5FplVotq78CJIxc7BAgFAQQgEpScWOcGDzA1jmmTbT6swSCxAoxMUAGfnuFZpRUc0CZRUAWSfNkHz3zTpDi1agBkI4+RxDb7xwyhnLiuHB3zIPf0sreiSKdBzMgV7SB3aT7Wov6XL31DagBcPkujuws2l89c0E5y/BKK9yu3nEB0kf2EO2/SByEvWhGB78s1rxz543NvZgH+n9u8l2HZnQ3a8GQnjrGrGSw+R7uxCajn/WXIKti9DCUfelNdhH+uAesu2Hxo/r9eGtbcTXNBujvArF40GaJubQAJnD+8l2tp1TZVD08ko8FdU4pkmm/dCkYRBGRTVGeSV2Pke2o41xhSynAY8Bf/alKDdd7+fF17P8xd8PTpiccyxHOgqs35Tj5uv93HFbkMeezpA7rs6l1yu4/dYgqgpvbMjR1Tt2ro8/k+Fznw6zcJ7ButVenn91vMXqkjVeFrQa5PKSp57PvOszzs8XZKFA38P3un+cgXt2hvMExyG1fcu07Cq56S1y7W3TIlpLcdpCUzo2ZiaGohqllmJbebdge+9ehrq2UTAnruc1w3gMPGgYI1bZk38T2NLCGfmPLc8dQXC2EJruZi4D+d4u8r1dp7Qfo7KG6htvI9DSCspI1xAhYOFyohdfxfCW9Qy8+CROvjjGqXzdNYSXrqLv6YdQfQGiF1+F4vEibdvNWhWC6JrLiW18nYEXnkBa4y0URkW1O/bsecVjKwrRi64kse1t+l94Aic3Xkz4GltouP1TJPfuoPfx+6i67hbCS9egaCM/feEm5emR6Dih6a1rpPaWOzEqq93sVymRjjN63OWXXkNixyb6nnl43Hm/UxjlVdTf/mmkbXHkR98ajREdh6JSfcMHCLQuIrFjoys0pxlFgV+6K8znPx2mo9Pir/5+CNOUhEPjIyMdx20reVTsmQX4t/8d5rKLvNw8Uifzf344TCLprhAJK3z+l8LceLWfVFryn98bLiq4/vqGHE88m+FDtwb46p+V8xt/0M/23Sa27Ta9WbLQ4G/+tJyAX/DzZzK8/ta58f29KxACLRRGOg52KllyFTUYQigKVio5KiaF4UH1+0fXkY7j9iOfbChNQ/H6EKqKdBycfO6MlrGZ4fzE7O3B7D258konw2kLzWxqgC0vfIOJw8ol0rGR70QDzwuA2coSykQlG+wXsDj5B0ScfjbZLyGRJJi4/tqFiur1YZRXIYQg23X4lEoXaaEIde+/C29dE7nuDoa3rsccGkDRNPwt84gsv4jo2suRjkP/c4+NszIIoRC9+Cq0QIhsRxvD2zdgJROovgCRFRcRmD2f6NrLyXW3k9yxqXjsYNgdu6HZHXvLW5hD/e7Ys1qJrLiYstWXIR2HvmcfndCCp4ciVF13C6GFy0ju2Ub28AHsfBY9XIaveQ6pfTvHbWOlUwhVJd/TSfrQXnI9nTj5HGogSGTZWgJzFhBZtpZ8Tyext1856et6Jsi2HyLf1423vpnwouX0TyA0jfJKfA2zwLHda34GLEPVlSq/94UyvF5BKKjwjb+rmtBq2N1r8Su/08dgbOw4Xng1y999PcZf/n45f/1H5XzofQG273afAcsWG6xc4sGyJV/7ZpznXymeZORykj/720GaGjTWrvTw6I/reP3tHL19bgvKS9Z6qalS2brT5E/+ZmpW1hmmB6Eb1NzxCVS/n87vfgs7VWxFUnx+Gn7pczhWga7v/ffoJC60dAWVN7/f9RIqCoXYIO3f+kZJD43QdMJrLia4ZBl6xYgnwrIoDA4wvP41kls384401Z7hXck0uM4ljj1Tc+tMoKIRFVUIJk4GOBESSZzJ67udOwgMPYCu+TELKQpWBoFSFI5xsig+P6rXi5QSc/BUroOgfN3VeOubybYfpPOBH2KnEqNLU/t3YQ70UnPThylbuY7kzs3kusbXiNXLKkju3ETP4/cVWf8yh/bSeOev4G2YRXjJKpI7NxelHEfXXYW3YRbZjja67v8+1nFj5wd6qb35w0RWXExy5+YJLXPeukb0SDndj97jispjXzLrXyq5jZWI0/HT/8XKpMa9zNIH99D4kc/ga55LcP5S4pten9T1f7Zw8jkSOzbhrW8iuGAZQ2++WJT0dZRg6yIUn598X7frYj8DqCokkw49fe51qa2eODHHsiSKKo77DL7xP8N099h8+TfKWLrI4KJVHiSukNy1z+RfvhXn3kdSlIpeONBm8fHP9/KnX4py641+3v+eAJrmxnDGhx1+dG+Sv/3XGAfa3n2ejncSaebJ7N9D+XXvxdcyd5z709fcglFdQ+zl54qeFamd28h1d6CFIlTdcpvbyWci+46iEFi0BOlIEhvWYyUTaKEw4VVrqfrAR7ASCbKHxjdXmWGGM8FZK280w8njJ4iPADlOLbPxfEJVDGY3XEVtxTI01cv+9mfp6HuLlvorSGX76I/tPqX9Kh6P6x6WEscsdg8KTcNTXV9UEBzc0kdHH/BaKExo0XJwHIbWv1wkMgGQkuSebUQvuQajvIrgvMUlhebR+NDjXcx2Nk1q/y68DbMwyipQdGM0A1ANhgktXOF2FXnr5SKReXTs1J5tmOuuxqisIThvycQuYEVleOt6Uvt2jK+dM4k1rzBc2gruZDOk9u3E1zwXPVLm+onPAaEJkNq7g/JLr8WIVuKf1UpyV/GLXBgGwflL3XX3bCsZcjAddPXYXHd71/G3V0kcR5JKj7cqWhbc/WCKJ5/LsHSRQUOdhpTQ0W2xc7dJbHjySdjhDovf+qN+/vGbOovm6wQDCsmUw+59BdraC+M6Es1wdkjv3kH0imsJLllOasfWsd+kEASXLMcpFEjt2l60jZPLYnZnKQwO4GRzKMe40Y9Hmnl6fvZjpJkviqHOd3dSe+enCMxfOCM0ZzhrnBdC86hlr0LUEiCMJnQcbPIyS1LGGZJ9E7qFJRIvfmpEM1FRhSEMCrJAkhi9TjtJ4iW3U1AJEqFcVBMSUTzCh0BgyjxJGaNfdk647VG8+KkWjSPjenGkTZI4fbKTYTmALBFzqaLhwYdfhKgWDaho6OjUixZsii0PCRkjyfisvzKqCIjQcdfBoV92UZii+11BHb3mQcJowsDBIS+zpEau+TCDU9rXiRE0166jsmw+bV0vU1OxzE0gw/3+aiuW0R/bw6nEqB7dDyNFiY9Fj5TT/KnfRGjH/Awch/a7/5tM2z4APNV1aMEIdj6LNRxD9ZV6uAvXalZehaemflTYHktheGjC0klW0o2zEpruHsuI0PRU1aKHIzj5HIXhodJjC3dsIcSEY4PbMcS1ZE6fi9RKJd39qSpCUZGcG6qlMDxE+sBuIisuJrx0Dam924u+e291Pd6aepxshuTurWfsOKSERPLE1njF63PvTzlxnGRs2OHVTcAm66TjYQsW7DtYmLTD0AxnF7O/n9yRQ/hmz0UvKx+tF6uFI/jmziM30sXndHBGLPlC09wYayGwEnGkbaEGg6d9DjPMMFXOeaHpJ8R8ZSXlogbleBeygBrRTI88zHZnPccLEYkkQJj56kqCRJA4SCSKUKmgljq1hd3ORvplJ8fvuFVZToNoQcVt5eaMlAVShEqVqKeRVvY5m+mWh0sed5WoZ56yAj+h0e2FUIhSTYOYTYc8wEFnZ5F4VFBYolxMuahBRUXgFng1pJf5yspxYxx0tpOU44VmrdJMo5hb9JmNRdIenpLQ9BFgnrKCSlGHwnHuPgE1oomQ7GSb81pJsXyyKIpKZdkCDna+QN/QTspCs0aX5fJxqsoWIBCnNJZTKLi3hRDFghJGM66FqiI0zS0mLgTH+qOM8ipQBKrXT+PHPjdhXJPq9SGEQDE8rug6LhbUSiUmzM6Wo32UKbKuGuWVIBQUr4/GO39t0rHBzUwXqlpyHGnbFE6QODARitePUV6JUVGFFgiheLwITcdTXTfOGnxOICWJ7RsJL16Fv3kORkU1+b7u0cXB+UsQhof0vh2Yg/3v4IG6IqD+9k+BbdN53/cnjCFWDA8Nd3wGO5Om++GfnBNhCjOcBo5NcvtW/K0L8LfOZ/it1wHwzZ2HFgwx9MKzJRMDTwajppbQ8tV4GpvQgmEUw0DoOorHe27+bme4YDmnhaYXP0vVdYQpx8aiR7YzIHvIywyq0AgSoUxU0SPbKWXtUlCYoyzGwWa/s5WY7MfBISTKaFEW4SdIq7KMYXsQk2OtBJK0HCYnsgzJNoZkH3mZQULRtnOVZcTtAbIUx4CVUcUi5SIMPAzKHjrlQbIyjYpKpVJHg5hLs1iAVOCAs21UQLm1LtuJSfflVyYqqaEJkzxHnL3jLJrDsrRFsc3ZTa9oR8cgRBmzlUVTvuYGXpYo6ygTldhY9Mp2BmQXOZlFESpBwkRFFb3yyLSITBeBoqiYhfGxdEKoSGlzqp2knFwG6VgIoaP5i2fxhfgQh7/vJrJ5qmpo+vjnUQxv0TqKxwsIkG7G5kQWwaN1yKwJskhPpXfw6AvBmerYiYkvk+OcdMcjxeOlbNUlRJZfhF5e5RpLbQekM6Ldz92XVbbzMLmeDnxNswktXD4qNBWvj+C8xW7Iw45N73hfezlSLNlTVYtieLCzpY9H6AZ6WQWFeGxsYjLDeU3mwF6sxDCBJctJbHobKR2Ci5djp1Nk9p1aqNBRfHPnUfPhj7nhNTu2ktz0NnY6jer3U33bHdN0BjPMMDXOaaHZrMwnTDkWBXY5b9MnO8bEjYQBuhFyLxO9XRWhIKTCDueNovqRSRkj72RZoVyOnxARUTHOqtktD9Nnd4yzACZljJyTYYVyOR58hEUFWTkmkFQ05iiLR0XmdueNon3EnUHSIskiZQ2NYi4DdBOnf+SUJH1yrGuSxKFGNFHApEMemHLWeY40uZFjypKmhakLzSaldVRk7nY20iOPjCXjSBikmyNyH9PZQlRKm0xukKroAhLpsfJDmuqhKrqA4VTHKYtaO5/DSibweP0YVTXjxx4RgPKo5fP45SOWIyudpOOn/1MysaRofccuLV5OwWU9OnYmRcc9/3vCGmcTjn0KCFWl6rpbKVu1DmlZJHdvJX1wN4WhAexcFqdgElqwjOobPjAt4003smCS2LERX2MLoYXLib31MnY247YaLa+iEB8k3XYOxKg5DoX4EIFZrSgeD3a29P2l6Aaq10chEZvW8IcZIL1jG1bMDb2SjoMVOztFyO1kgvS+3YSWr0KvrEJaFt6mWWT27Z6wteRUEKpK2aVXonq9dP3ou2QP7htdZtTUzVSAmeGsc84KTQMv1aIRgG7ZRq/soJQSOFFG8qDsISb7xn0+LIfIkiYgwvgZH6/iYI+6y48nMbJtkAhefEXLQpQREZU42ByRe0u4ql0xWc9solRRp8wi7ryz7ruj6HioEU0goNfpoEceLinwTicLvBRSOhzufp2lcz9EWbAJjxHG7y2ndiRWc9+RZ055304uR763C6OyBl/9LBSP96Ri3KxEHKRE0XU3HnICIXAmcMd2UDQdoShndWxPbSORZWvcCd2LTzH01svjLKKnWvj+bJHat5OKy67DqKhySzjt3UFo4XJQVFL7dmKnS1ufzzaFeAyh66iBIIV46Vhz1edDqFqRANHLyvHPnoevYRZaIIRTMMn3dpPcvRVzcPwzr/KamwEYeOlpfA3NhJesQo9EsXNZ0vt3kdi5pSg8Q/F4Cc5bgr9lrrt/26IwNEDm8AEyhw8UuXaFpuNrnIW/pRWjsgahqljJBOmDe0kf2D16r1Re9R5Uf5DeZx4G20aoKtXv+SBauIy+Zx6mMORWhoisuAh/Syu9Tz10xpK1jpLdt5fsvjPbra4kUpLavoXwqosIzF+Ik8uheDwkt20+vcmEqqJFyrDTacxjQkYAvA2NKHqpmtfF1H34IoIL6jn4jaewUzM1Vmc4PU69bs4ZJkAIDz4cHPqc0iLzREgpicm+kmLJwcaigECgCf2k9uvgYEkTIcS40kNhUYGKikmeRIn4SQAbm/iIezwiKlDPEb3vI4CXAFK6Ynj6XOMnJpHuZMu+e4glD5PK9GEW0gwlDrJt/71k86dR/1M6JPftAOlgVNaMFm+fKrlet3ak4vHha5x96sdxCuR7u0ZePt6zPranug6h6di5DMk9W0u63fXyynM61stKxN0EKEUlvGglWjCMv2Uejpkfl4n+TnJUXOqRcgCErhO96ErK1l4+GlesBsOAdC2auMW7a2+5g+obPoCvYZYbYxytoOKKG2i861fdagpFCLx1jQRaFxJZvpb6D30S/+x5qKEw/pZW/HPmF5XKUTxeam+9k9r3fQRvXSOoKnq4jMiKi6h574fGJaYF5i6k4SOfIbLiYjeOVzcIti6i/kOfoOqam0fbOgpNJ7hgKaovAIAWLiO0cBnBuQvx1TePDK4QmLsQPVr5joc2nGnyne2Y/b0EFiwmsHgphUE3SWhCFAWh6aC4X5b778XvIGnbWLEh1GAQ36w5KF4vis+Pf94ColdeO6XfrH92FeGVzSj6xCW5ZphhqpwbCqcEPhFAIChgkuPUWlZKJPlJSgOdSEgpqAQIERRl+AigCw/qSB54QIQnOG4/QgjyMjuhRRRw22IKt/OPjjEu/vKd4Og1tykUhQOcLdLZfva3PzuWcT5NLp70gT3kejrx1jVRedV7MQf7MCfIAD+eQmyAdNs+QguXE73oCjJt+yjEJ8i2V1U3FnKaXJtmfIj0ob2EFq8kuvZy0of2UohNUAt0msce24/bRvZ49GgFoflLz+k4TYDE9k2El63F1zSb4LzFaKEI2Y5D5E6xQ9SZoDA8BNLtzgRuF6rKa24C6ZA9fIB8fw9aMIyUEmskoUuaeQZefgZZMMkP9rmlpRSVyIqLqbnxA0RWrKXvmUfGjeWpqCa69jJ6nrif7OEDSMdG9fpBVYrKXHnrmgjOW0Rs/csMvPKM28lKCLRAGDUUHheLnDlygO5H7ibX3Y6dyQASLRSh/vZPE162mtjbr1KIDZDr6aRszWXo4TLsVMJNtoPRIvuJ7RsRmoZRWU2us/2UYpvPJ5x8ntTObZRfcyM4DvHXXsLJjrcgamVRKq6/CTUQRPF4MCqrQVGo/8Qv45h5HNNk6PmnMXu6wbaJv/4ynoYmqm+/k8LQIEJRUXw+Em+9TmDxsnfgTGd4N3POCs2j2d4SB3sSwTYZEolzim7eKNXMURYTFuWoaNi4rRyPZq5rlLaCHrVOOtKeVMgeFZYCZXw2/TuEioYAHOQpX/PTQ6BrPnTNi5RQsLJY9um7zZxchoEXn6T+g5/AU11H4x2/TGzDa2Q7DuGYJqrPj7957risdHCtA4OvPouvrglPdR0NH/4lYm+9RLbzCI6ZRzEMtEgUX10zntoG+p5+ECuZKHEUp3LgNoOv/gJvQzNGVS0NH/40sbdeJttxeGzscBRvXRO++iZ6n37IdbdPA/neTpyCe22iay9n8PXnkWYeoWp4auupvOom1EAIOZGwVVRUfwBF01EMD1rQfUECGGUVeOubcUwTx8wjLXNUnIxu7vWh6B4Uw0AxPBiVbnyt0DS89c0UhmPutgUTO5+b0I2f6+kg13UEf/NcIisuRigKyZ2bzym3v5PNYOeyrtAUAn/TbDceVwh8TbNdoRkK4+Sy2MeEfWSPHCzekW2T2rONyiuuxyivLjmW0HRib71K+phOUOPqs8KodVMxPAihIG0TiSuKS9VWdbIZUnt3FH1WiA+R3r+LiitvdMMCYgOYQ/0gJUa0glzXEXwNs8j392IO9uGpca3oWiCEFgyT624fN86FSGrrJtedrQgSm96mZIiYbWMNx0fjtHPt46udSGvsmZ09dIDO7/4XgfkLUYMh7Eya7MH95Hu6KcSGZhLKZjirnLNC86g1UIxIsbNJhEqWqZf8f/b+O8qS68rPRL8T5nqb3pvK8g5AVcETjqAnQTZtU93TanVLM9KMNFrSaK2R5unN0ryZkeaNpKXX8uqWprvVhrZpmhYECMJ7FMq7rMpK7zOvd2HP+yNuZuWtvJnlslAG+WERRN5748SJayJ+sc/ev42PADlSTLnD5GUGkwo2NgoK96iPEqdx1bYO9rKF0nqxHrVqGySrfchvB27le66pAfraH6W1cQ+65i3LGWae6YVjjM++jePeWGSjePEcs8/9JS1Pfw69oZmWj33eswGSEhSx7DMnHQdkrcg2ZiaZ/ul3aP3kF/G3ddL22V/HtS1vWyE8eyRV80RenejfjWDMTTHzk2/T+skv4W/tpO0zX6u/73x2Q6OLxtw02ePvkjzwCMn7P0Jkxz6cUgHVH0SLxbGyaWZ//j1aPvGFutsHO3ro/OrveB5+QlTtpbybsyV/S8870sVKLTD+rT+4VGglFNo+/RXCAzu97YSCUL33VQ1F6Pjib3mV71KC67Lw8rNrtsCUtkXu5PuEegYIdPRg57MUhm6sonejcU0Dp5hHi8YRqtfWtDQxjKLphPq3kTn6Dlokhl0sIKseqwCoKv7mNq/rU6IB1R/0BLo/6EUo8frY1+zLqFCerG/JtpLK1DjFi+eI3/sg/tYOcqeOULxwdjn6Wg81HCHQ0Y2/uR0tHEHR/d6yuxCI6vKulUnhlEv4WtrhzHEC7V2YC7OUJ0eJbN+DGgiiJxoRiooxf/N6L99OWKlFFp//2bqvcfI5Fn/582sbd2GOzMLqXN380cPXNM4mm9wot63Q9JbLvcihn+B1L59fKwJBtzKAjwAFshxzXl+1/O5F/upf1MuyCBL8BFBQ14wMBqsFSBYm9m1idF2RZSQSFY2ACFKSH1SxhKC3/RFaG/cyMfcehdIsIIhFOuhqvR9XOozNvHlju5CS3In3MOamiN/zIKGefrSwl/7gmBXsXNZrRzgySHlqdSSlNDzI+Dd+v9rjezt6ogGh6ri2iZ3LUpmdpHDu5KroUGnkPK5pYMxMrnmBNlPzZN5/E6dUrLtUWBq5wPg3/4DY3oOEt2zHl2hE6DquZWLnsxgzk+QHT2HViaRamRSZI28hbRv3GqJ40nGYf/GnWIvzRHffi55oQI8lsAt5MkffJv3ua9jZFFo8geLzr8qlc8oF8mePsXaPvJWvLV7m/SkpT4zgXGURiJlav5iuOHQWK5dBTzRQvHhuw6K+G4X3OebQIlG0aBx/SzsLr/wCRdNoePij3uORGE4xv/wZqqEIzU9/juiOvTjlIubiHE65VP2Orb2S4trWVX0PXKPC9I++RXzfQeL776flY8/gfuRj5M+dJPXWS8sG40tEtu+l+aOfRQtHMBbnsHNZL+J8WfqLa5mYi7P4m9tQQyF8TS3kz53EmJtGqCq+phZ8TS24lrlmYdQmm2xyZ3HbCs2izGNi4CNAo9JG1t2oLjTro6ASFjGEEKTdubo5njo+/JdVmy+RkykcHHwEiIhE3Yp3BYWEaAIgLzNrCs0VWXJXcbm+ccoUMKgQECEaRTupOnO/GSiKRlNiG0MTv2Jm8cTy44vZ89h2hZbkLsZn3tqQ4iRjdoq553+IovsQajWq7LpIx7miQbKdy7D4+i9Jvf2y18VHCKR0kY69Zi5Z9tg7ZI+9s+64lclRKleIMtm5DKk3XiD9zivVKKHiXcQd51KEs+7xTjL7i+8TjOs0dPjRfDFcR2KWbIopE6uydoqENE3S771G5tjb3vHiCVDXMpf3l3rjV3W3NRfnmf3599Y9prV3LEm/++r1bVsPITwrVNta1Uv+tkBKrEwKX1MLgbYOFF2nMjkGikLTEz4CbZ1okSiVFTcriUOPEN93kNTbr5B660XcShnpumjhKKH+ayt4Wwu3Uib97mtkj79HsKuX+P77ie8/RKCjh8nv/uFyvqgWS9Dy8c+DgInv/CGVmUnvxsOVND72cQJLRT4AjkNldprIwE78TW0ogSCVmUmsXAanXMLf3I6vuRUrm8YprW/ntckmm9wZ3LZCs0KJRTlLu+ilUwyQYpYM9QshrrdrTD1W5nUuRS5Xji0QdIh+fPjrbp8nTVYu0CBa6RHbqsKzNtrTKNqXLZBm1ugsBGDhLZPp+NHxX3X7yOvFoMyCnKaLATpEH4vMkKJ+0YxA2UCbI4njWlTM1RE5w8xV8103ECmX+4lf1+a2dcNdO66EHlT50j+7j1DCx+t/dIHBV+ZW7Vuogif+5ja2PNjMhdfnePW/nF/VPCjZFeKh3+xn66MtRJr86AEV15EYBYvsbIWRdxd5688ukptd28JEWtYdXZQR2bEXPZakMj22di/4W4yZXiCq+whv3YW5OF+tLheYi/OEB3bWWh8pCqHuLbhGhcyRt2r8VbVo7Krsa64F16hQHDpHcfgCTR/5GI2PfZxg9xbyp44A4GtsQYvFyR55m/L4ioppIfAlG1aNZ8xNEd9/iGB3H06xgJ1Le328F+cJtHehxZMYs1ObeYSbbHKXcNsKTZCMumdJqE0ECbNPfZgpd5iUnMXEQEElKMLERSOOtLgoT195yKvAxSErF4nTSLPoYEF0kpELgMRHgHalj07Rj42NJlcXBDk4XHRPEVETNIkOdiv3M+5eoEIJFYUG0UafshMVlelq16G1KMgsFgY6fgaUvYy4Z7AwEQhUNCzMVRFXHT8+/KhoaEInSgLwBHKDaEHHhy2tqrmTgUmt4Bpzz9GgthAiyl71QabkCIvuDCaV6nseIiYakUguuic3ROC7rsN8+ixNiW0USnPVAiCBTw/TlNjOXPo0G2kQfyegqILOvQmizQGO/qj+TY0Q0NQXofdAA+mJIpfn5MU7gnztXx6kfXecctYiNV7EKNj4IxqRpgBtO2IkOoK8//2xD+agbgF6ooHkwUdBSrJH372hG4ybiZVeROg+Qj0D1WIlT9iXRi8Q3X0faiB0SWhKL69TaBpqMLS8HqL4AyQOPVrtZnVjaLEESFntNrX0nZLVHGbpVbkvPWpb4LqooTAoatUKSxDq2UJ4YHWzCGNuBkXTCXb1YaYWlpf8K1PjxPYeAIEnYm+3yPMmm2xyXdzGQhMKZDnlvs0O5QBR4vQru6tdbiQsZ0kKZhjdUB0y7l4gqbYQJcE+5SEMKkjkchRz3D2PK1y2iD11t8+wwBn3PXYo99IqumlRO5eLiFQ0XFxm5BiD7rH1LZAoMO5eoE/ZSavoplntwMGuluqonHePMi5ru5v0KTvoFttXvDvevwUq25T9K94myaQc5qxbmxheosBJ5212qgeIkaRP7KRX3cHl7/nq/vDXjxAKmhqgs/kAzYkdGFYegULAH0NTg/h9MVqSu5dfP7NwjOnF4xu2/7uVe5/pon13nLkLeX7wj48yN5RHOhKhCMINPtp2xom3BVkc++CtrG4WWiyBr7EFXActliB58FF8Ta2URofInT56q6e3JnYhD0KgxeI1HYuKFwdpeMjzPrTz1V710iV/5hjhLdtp/9zXyA+eRAhBsGfAW4ZPrWGBdQ1Ed91Dw4OPY8zPYKUXka6Lr6GJYHc/5YkRSisq3o25acpT40S27ab981/HXJzDl2z0Xjs5SmRgZ83YTjGPXSoQ6hlg8bXnlwVlZXaSxkc+inQdKpcZjW9y+yOE6qX04CKvsd3tB4NAUaquMHWKS4WirvLFXuu1m1wbt7XQBMjIBY44r9AiOmkQrYREFLVaZGPIMjnSzLm1xRs2FsPuGQRVv8o6SFwvQsosaWqLCcoUOO68TqfYQlJpQcePi828XGRWjrEgZwjIEFLIZeP1y5mXkxSdHO2il6RowScCuDgUZJY5OcGCnF5XZC7NckSeoejmaBXdhEQUBQUbi5IskJeZVVuk5ByOvLofeZ7V2wPkSHHEeZVm0UmjaCUsolWLJwdTVqrv+fW3hayHlJKZxZM1jxXK9aO91gZYHt3tCAFN/V7B2ci7i0yfzV66GXMkudnKusvldyqh3gHaPvM1z/5T9U5vxuwUc8/9ENe4fb83diGHOT+LlC7G/CWRZcx7FdlqIIi9opNR/syxqm/mIWK77sGpVCiNnCf93uskDz2KVjV4v4TELuRRA+kav8y1KA0P4mtowt/aQah/+/IcU2+9TPbo2zX5k65RYeYn36Hh4ScJdvV5leSpeeZ++WPM1AJ6PFlT7OVUyl4OKoLyxKXUIWNuGjM1j3Tstf1iN7lt6R14mpaO+8ikhjh/6vu3XavLWKKHHfu+hmNXOH30z6iUVzZUEWzb/UUSDQM121hmgdNH/wyjkv1gJ3uXcdsLTQCTChNyiEl5sRpT85YIvfrK1V9mG4sReWbdMSWSKbl2B4YyRS7IEyjOJZuQpX+85wsMX2G5vkSeIXkSIZXqvGvHuBpcXGblOHNy4rJjrz/OopxhkRu3BbEwmJIXmZbDK/a7tOeNbkHpcHHyxeV9XMUWG7r/uxEpIT/nCcm+Bxpp2Rplfii/KofzbsOcnyV3+ghaKIxrWZQnR8mfOXbbVZpfjp3PMvan/x6gRpS5lRIT3/zPnvXWirxg6Tjkjr9L/tT7nqWWlF4USUrmX3q2rtXV7M++Wx3nys0hjLlpZp/9PkJRL3WSkV7RXD2s9AKzP/9etcBOePmV1ajW6B/9m1pXAtdl5qffAaHUPG5n04z+8b8F5FXNcZObi+LXaf/y/Tjl2tqA4uAMmXcvrnq97gsRDDVQKnwwRaTXiqLqBIMNWHYJIS7veCSplBapBBKomh+fP4o/kEBV9eUGIptcP3eE0FziWkXaRrARHpeeyfuNjvHBH/sHtV8hFBrjA+SLMxhWgU0huTEc/+kk+z7TSctAlN/6Tw9x+vlpTv58kpnBHFb5dlzaunEqMxPM/Phbt3oa18Va4mq9Noye8Lvss3TrF8+tJRLXHlxeWwtIWV8g1iucqzvvNV67yQePna9g5ys0Prl71XML+sm6QvNOZ+zii4xdfMlzQWndy859X7vVU7pruKOE5iZ3J0Ko9Hc8jk8Ps5C5wGzqFLni1GZuzA0yM5jje//LET76t3fQtT/Jg7/Rx32/1s3U6QzHfzzBmV/NUM5uvsebbLJJLWN/9AoTf1q/CYJr3503qR4S17VwHJPNgMfGsSk0N7nluK7F8QvfpTE+QGvDHvZt/QoVI8Ns6jQL6XOUzcxtl+9zuyCUddINpJef+ed/+x22PtrMPc900XNfA32HGuk90Mihr/by/O+dYfidD8aj9kZRwn5wJW759mkfuckmdyPStHGu+2e2KdA2qWVTaG5yW2CYOabmjzCzeIJwoJmWhp20N+2np/VBsoUJZhZPkM6PYjt3XwHL9SKEQPdfOX/IKNqcem6acy/N0rQlwv7PdnHP5zrp2JvgC//7vfzZf/82C8O3tzl2cEsbXX//87hlg7F/8UOs+c3k/E02uT3xhKYQCj5/DE0PIqWLaeSxravp8CfQfWF0PYRQVKR0sa0yllm4qoCDECr+QBxV8yNdG6OSw3FuT1uzDwubQnOT2wrXtSmUZrFdA9e16Ww5SCLWSzLWT8XMMjL1KnPps9z1d80SpOsdo6rVF5OqTyHWWr9DVT1s02XmbI6Zc6c5/fw0v/6vDpLoCLLrY+28+p/Pb8i0bxaBgVYCPc0gJb7W+KbQ3GST2xTXdQiFW+je8iSJhgF0X8hzUyhnmJ16n8mxN3Ds1cJPVX00tuyhqW0vkWg7ui+ComhI6WCZJQr5KSZGXiWbWjs/NJbopWfgo0Tj3WhaANe1KBcXmBx9nUols+H1Bsmm7fRu+Si2XWHw1PcxjdVNR5bo6H6Ilo77KBfnOX/mR7jXHzK+49gUmpvcNqiKTizSRXvjfhri/RhmgdHpN5hPD6KqOm2N+9ne+ylMq0imcPeajAO4rqSSt4i3B4m3By/3YgegqS9Msit07YNLmDqVYep0lh0tAaJN9Q3hbyeKp8YpHL2InStRGVu/t/kmm2xy6/D5Iuy85+sEgkkqpRTl0gJ+f4xguIm+rR/HH0gwdPbHuG5t4Vgg1MTWXZ9H04NYZpFKaRHbKqPpQYKhJhqbdxGNdXH66J+Ty4ys2m8s2ceu/X8FfyCObZcp5KaQ0iEQTLJt968xP3uCjQ5QlIrz+INJYv4oDU07mJl8t+7rVNVPa9chorEusunhD5XIhE2hucltgEDQ3nwvHU33EvQnyRTGOTP8EzL5MRz30g/y4uSLhIONJKI9d73QdEyXmXM5WrfH2PXRNo78cIzUWHXZSUA46ePx/3Yb/kj9n7CqK3TtT7AwXKCctXCdFW1UBTT0hGjZGkVK7gjDdnMqxfD/p1pN7t7l0exNPlQ0+3roDd1DyprkYvEIK8VQV2AXDb5OzuRfw5J3RtpQLNlLqTjPqff/hFxmDCkdNC1IZ9+jdPc/QWvnQTKpC8zPnKjZrlSYZXLsdSqlNNnMMGYlh5QuQqgkG7eybe+X8PmjdPQ8RD47VrOMrmp++rd9En8gTjE/zeCpH1DITQISnz9Gz8BHaes8tOFWRUYlw+L8GS9a2X4Pc9NH6xaxRmIdhCNtOHZl1XF/GNgUmpvccoSi0da4j0x+jLMjP6VYWaibiyOlS7mSwf4Q5Nu4juT4TybY+dE2mrZE+Prv3c+ZF2YopU2izX62PtpCvD3I1KksHXviq7bXgyqf+od7CUQ1pk5lmR/KU8qYKKog2RVi22OtJLtCzA8VOPurG/dd/UD4EAtMxR9A8fs9X0s8eyDXMnGNymarxjscvxqmyd9FVGtgtjJM0blkJB7S4iR9bShCvWOyhaR0Gb3wPJnU0PJjllVk/OJLROPdJBu30dZ5Pwuzp5ErmotI6TB64Zd1x1ucP0ts8n26+58gGutEUfSavMt4oo9YogfXtRg+/wvy2UuBCKOSYXjwWaKxTqLxro0+WOamjtLWcYBooptwtL1m3x6CptY9KIpGOj1MsTC7sXO4A9gUmpvcclzX4vj571QF5Ppn0+GpV3CvsvPRnc7wO4v88vfO8vh/t5XmLVFaBrwIJFKSm6vw7D8/hVDgc/94P65d+765lkt6rMi2x1vY/bF2xMfbL72zEmzTYeiNeX75b86Snd7gjjmKQCgK0nEQqkKgtwUl5MeYWMROe0VHWjyEv6cZ17AwxuZxK3VsllRldVW9BHklexXF2046LkiJ0FV8bUn0pNcpyc6VMGfS9fcJCO2SmFvz66iu2MfNEsBC4G/tIH7gQYLdfWjROIrP5/U6twycYgFzYY6Fl36BOb/64qX4/DR//HNo0dU3IgB2qcj8cz/Crdz456/FEiQffgI1FCZ37F1KF29tzq9QVZo//gx6ogEpXdJvvUp5dOjKG94iHGkhhEJXcCfnCm9xZVUplhsCw+rGJWJFgw2BstzexHuu9u/V2y1te31OH6aRJ5Na3QzFcUwWZk+SbNxGONaOPxC7rDvPesjqUriLqlVvuFacBhKNAwihUi4ukE2v3rdtlUgvnCcS67yuY1qPQm6SXHaCRMMWWtr3rxKaui9EQ9MOpHSZnz6OdD98zQjuGKEZD3QQ8Tcxkz9bs5y6EQgUWqM7sF2DheLdZ0R7J3C11eQfpqpz15G8950RLrwxR899DSQ7QwghSI0XGT28SGaqjB5UGT+axijay8VDAGbZ4Yf/61Ea+yK0bIsSawmgB1VcR1JYMJg5k2X2Qv6mGLc3fPxemj7/AJP/4WdE7t1C0+cfQPFpVCYWmfi9HyEdl+6/9wyB3hak7ZB7f4jJf/tTnMKKz1YRtP+1jxI9tK32PSlWGPuXP8Ccyay5/5avPUrisd3MfvMVjMlFWn/zScK7ulDDAW+MiklldJ7Zb75C4dhwzfVWa4zS/7/+OiiCyf/4LKUz46vGFz6Nrr/7OYID7aR+9h4LP66fl3WjRPfcS8unvoAajnqX/hXdfhS/Hy0SQ43EkO7P624vVI1Q/zZ8TS11n7eyaRY0HbhBoSkEyQc/QvKhxwHwt7Yz/l//A275Frb8FArBvq0E2jqQjkPh9PEbPcqbiuWazBsjtAe2Ml4+Q8nJ1H2dQKHJ102Lv5+o3oAqdEy3xIIxznj5DLb0onx9oXvxKQHydoqe0B5y1gJDxcN0BXfR4u8jbU1zofAutjSr4woafd10BncQVhM40mbRnGCifJqKe22pNV51ef13u5ifRkoXXQ/h868nNIXXlUfREEJBCO/vpedWdr4SQiUUbkYIQbE4h+vUv4H0Iokbf1PouhZzU0dINPTT2Lyb8eGXMY1L7WLjyT4CoQbMSpbUwuCG7/9O4I4QmgKFLU0P0RTeQtFcJFOe3NDxA3qMna1PYzol0qMTGy5kN1kfRWhs6XqS8dl3MMzaqr14pItIqI3Jufdu0exuLVJCerxEery+LYhVdlgcrX8hMMsO02eyTJ/5YCu01WgQf3cTTc88gL+nmeLJUYJb2gj0NtP6G08gNAWhquSPXCS8p4f4QzsovH+R1C+OXBpEgp0p4WSLKEEfajSI3hTDyZeXI45roSXC+LubSDy2B39nA3pLgsroHNZiHi0SILCljdCuLrr/3jOM/LO/oDw4tbytUBV8HQ0IVUEJ6PV3IMDXHCfQ1Ygau45irKtATzbS/LHPooWjSLz+4OWJMaz0IkgHNRhGSySxc9k122u6lkn67VfRG5pQQ2HUcBg9msDX0la3ReV1I8Ry1FQIgRoKo+i+Wys0bwVCXHcagxAKM8ZFGv2e2DtfeLvu6xSh0hvahyJU0uYUljRJ6m1sjdyPTwkxWHgTicSnBGkL9OM3w+SseToCOwiqERxpk7Pm6QrsImvNMl25AEBXcBfbIg+SteaYNS7iU4J0BXeS9LVzLPs8pnv1n6VtV1hL0Nm2gXRtFNWHqq0uQtR9YZJNO2ho2kYg2IiuB1E0H4rQ1mwHKYSCpnu/Q8ssItf4DK7OWun6SC2co1JOEwgmSDZuY3bq/aXZ0dS6DyFUUgvn1q1Kv5u5I4SmxGU8fZRMeYqCsbDh4xt2gYuLb2E6xU2ReQtYakE5s3Acg9ofok8L09awh6m5w7ekBecm1094fx+j/9+/oHhilOjBrfT+L18menCA8tAMF//JN7EzBVp/4wlavvYokXv6ST1/9NIytJTMf+8N5n/wJoquEd7fR9//+6tXvW8hBLGHdmAt5Bj759+ncGwEadmgCMJ7euj++59Hb4rR+KkDTJyfvu3yHKO79qPFk0jAzueY/v6fUx69SE2zekVB0XWkVT+CI22LzLuv1zwW7B2g+7f/Fqzq9XwDuC7liVEiu/aDqlAZH8Up3f4FZhuGECQOPUKwu4/UGy9hzFx7IEQgMJwSU+VBOoM7mCifoeysFiWOtDiW/SWONJfbI6tC5774p2j193GxeBirGtXUhJ+h4mHKTo6o1kREa+Dt1A+xpUnS105Ma2aaC4TUOAPhg8wZI5zJvYqDt7S7aE6wP/4xOgM7GS4dWTWXNVnvtyQvncUvF42xRC9bd3+BSLQd17GolNOUSgvYVhnXMfEHEjQ076j35l26cVrHZ1NK96bluZpGnoXZU3T1PUZz+73MzxzHdW38gQSJhi1e1HP6GHdMou0Gc0cITYCF4sWbtqztSpux9IczYna7o6p+FEWjrr/PJrctQgiMiUXK56dBQun8FNZCHl97kty7g9gpb2mpdG4SaTvojVEUn7Y6b9KVuIaFW76OAjApmfvu6+Tfu1AzXvHkKNlXT9P0xYcIbe9A8eu4ldvoBlNRCXT3Lv9ZOHvCyy+8/ALuurjGNb4vN6nDVvbI21jpRRSfn9LIhTX7tt+NCF0nfvBh/C1t5E68f91CEwFTlXN0BnfQGdjOheLhuq+9vPrckRYFe5GY3ohYUTRkuCVMt4zExagufxtuCZDY0kRTfAA0+DrQlQDTlfPLIhMgbc5gOiUafB2Mlo7j1ulNXw9V9QH1I+aKqqMoKiCrbR49ND3E1l2fJxLtoJCb5OK5n5HPTeI6VrUwVNLcto9k0/bVg0qJY5vVffvXvFQoqm+taW0AkvmZ47R3P0As0UMo0kohN0mycQCfP0ouO04+t7ErsXcSGyA0BQE9StTfgqb4MO0SOWMWy1kdatcUP82RATLlScpWjpCeIBpoQREahl0gV5nBdi+dOH1qmMZwH0r1zkdKl7nCeex1oo6aEiAWaMWvRZa3W8KwiywWh5cjY7FAG1F/84rnSywUb9+E8bsNVfXT1XIInxbCr0fpbnsQ07oUCVEVjabEDhazQ9edmL7JrcNazOEannCUho1TrIAEY+zSqoRbMsBxUfwaKBtrPWKnC7UicwkJ5eFZkKAE/ShB320lNBVNQ4vGEUIgpaQyNX7bRVwvR1oWxfNnbvU0bgm+hmb0RMOGjFVyckxXztMR2MFk+VzdVZygEqXZ30NMb8anBFHRCGkJoFZHudJZPm9KXFzs6t8C5KXCn5AaR0FlR+RhXHlJaAqh4FdDWNJAEcpVF2H6AlFU1Ydtr9YAwVAjQijYVgXLuNSNLBrvJhxp86rGB5+tqVhfQtWCdVM+XNfxzNilJBDyxLaUq290AsEEN1FpUsxPk02P0NC0g6bWvZQKszS17gVgfvr4h847cyU3JDQVodKdPEhfwyF0JYCLi4JCyUozOPfyqghkQIuyu+2TDM69BEi2ND2Krga8KjjpcmL6J8zmz116vR7xxlaD+LQwAkFmeBrbTNWdTyzQyu7WTxL0JbAdA1XR8WsRQFIyM8wXh0iVRpctFZLBLrqT91VfFyVbnqrO+fY+qd8tSNfBtApEQ22oqo94uKsmdUFKyWJ2iJHp127hLDe5HqSs9iSvLoVLKZGOi3QcnBW9yqXrehpKUTb8GmClCtiZ+ku4ruldiIQiEOrGCtwbRgiEfik/dCOqwje5eQQ6e1D8/g27GZgsn6MjsJ324LYa+x+AhN7GvthTAMwZo+SseWxp0R7YSly/vOhLrvvnEgKBxCVrzS0vuy+xaE5iuEXca4iE+3wxwtG2VdXfQigkG7cBgko5jVHJrNgmjBACx7YolxZXz1EoxJN91D9JSPKZMdo6DxKKNBMMNVK6zEJICLW6/c3DdW3mpo7S0LSdxuadLMyeIhrvxjQLLM5/OG/ClrghodmVuIftzY8zXxhiNP0ell0m6Euwtekj7G3/DEcnf7CqcEcgaI/vRhU6FxfeIFeZRREK0UDrqtfmKrO8NfpnaIqP3a2foCVaW4G6ElXo7Gh5moAe5fjkj8hWptHVANtbnqIlspUzs8+zUBxm5a9tNH2Y8cxRgnqcQz2/fiNvxSbXgSttpheOMZc6g08Lc378OQqluZrXrGXDscntT10bIgk4H0x02ilW1iwMuK0RtdY1t3s086YgBEJVYWlVSrpVu6mNeS+EqlYj6F4Bj3QdcK/jeykEof61r0vXQ9HJMl0ZojOwgwWj1iqnN7QPTfHzXvrH5O1LgiyptxNfo3ZtfSRlJ49EMl05T8qauvImV0BRdbq3PEn55MKK6mtBsnFbNcLnLTOvXDo3jTxSuiiqj3C0jUr5UjBJCJXmtv00te5Zc5+phUGMShZ/IEF3/xNcOPMjHLtS3V6hoXlnVeReBUJU80fF8vZXm7qVXhykXFwgGG6irfMAmh5kfvoYxlXbON2dXLfQ9KkhepOHKFkZTs88h+l4kYOCuYBpFznU8+v0NTzAscm/vGzZUxD1N/P++F+QLk8sP7ryv1cipYPlVHBk/YT3JQJ6jGighbn8eRZLIwDYrsF4+git0R3EAq11cjwlrrSxnPKdeUG6S3Bci7n0GSy7vLlEvsnGcdMM3kWN1dCNoPgDaPEEWjiKFo2hRqLo8SRaNLb8mtj+gwQ6e1ZtKx2HzHtv4BQLq567mWjRGPEDDyLU+pcPM71I7sg71zW20DSCPVuI7NiDv60TLRIFwCkVMWancUr1j7V44SzlsdX+iUssnd/1hiaie+4l1LsFLZ5EqCquYWAuzlEcOkfx3Kn1C5lUFTUQRItE8TW14m/rINQ34KU5ALF7DhHs7qu7qVOpkHn3tTWLt1bMlsnyGdoDW2ny99Y84xMBHGlRdi69D0E1StLXdoUx12bRnMByDbqCu8jZC8uWR+AFcKSUuFxdzq2Unt9lLNHL3oO/Q2r+HJZZJBRuoql1H5oeJJ8dY2aytiaikJukWJglEutkYOfnCIVbqJRTaHqQRMMWkk07yGfGiSV76+7XqGSYGHmVLTs+Q0v7vQQCCdKL53Fdh3C0jcbmncvj16OpdS+RWCea5kfV/ARDTZ6bghZgYOczmGYexzZx7ArpxSGy6fr1IpZZZGH2FN1bnqCl/V5AMjdzrG4Dkg8T1y00I/4mgr4EY+n3l0XmEnljjrwxTzzYgU8LYdi1J4dcZZZM+cbvnFaiKhoCgXNZ+yfvb4ki7pi6pw8hkom599iMXG5yJyBUBcW3MeeT2P6DNH/i854xvVCWBaxY8f/RPffW3dY1DfKnj3/gQlONxGh45KPecnEdihfPkzv67jVHH7VojOaPfY7I7v0IbSk8J0F6AnEtASelxKlU1haaUiIdm9i+AzQ9/Rm0eBKgJt/P39ZBdPd+KgceYvan31uzoCf50OMkDj2CFo56NltVj8el8WJ771vz+KxshtyRt3GuKDSh6GSYNS7SE9xLxb30+S6aEwz4DrEz+jCL5gR+JUSrfwtOnZzEq6XoZBgqHmZb5AEOqJ8hbU7h4hBQwkT1Ji4W32fOGLniOK50cRyDsaEXUDU/fVs/Tk//E9XvtEBKh0xqiAtnfoRl1n5nLavEhTM/YtvuLxKKtNC//ZPLNweuYzIz8R4To6+x+57fwOeP1t3/9Pg7qKqfzr5HiTf0E2/YAkhcx2Z+9jjjw6+w+57fQNMD1F5rBE2te2mu5lMusWSsnmzcWvs4rCk0AeZmjtHe8yC6L0whN00ufeX37m7nus+Wfi2CQKFirbZgkFJSsfLEA+1oih+D2i9VxcptuFVN2cph2AUSoU4CWoyKnUMRGs2RAaR0yVXukDZ7HxKEUNC1q/MglNLFsm+eB9ommywj8ZZQNWXN3E015EdLhjdmd66LW7n8u+15UQpV9aJJlTLSXi1OXMsC94PvkuWUS+TPHEeLxlACQdRgyPPO9Aeu259T8flp+cyXiez0LvZOqUDh3GmM2SlwXfTGZiLbd6MnG5eLpKzUAubiPHY2TWV8ZM2xJZJQ31Zi+w+iBIK4lTJWehG7WEDx+fE1NKJGoghFJdDVS9szX2Pim/8Fp5BfNZYaDKFoGq5RBgMQAjW44rMql5BOfdHnFPN1V84c16LiFmqiXhLJePk0Db5OHGktr/SMlU8hhEqrv48GvYOKW2S8fIayk2dn9JHl66otDQy3hKw+YroV5IqPxnBLWCsKbyfKpyk5ObqCO2gN9KOgYrhlstZszRL9eowNvcDk6OsYlSzStcmkLpJo2EIg1IB0HfK5KXKZERy7vlNCLjPK8ff+C/Fkv2fArqiYRo5cZoxSYRYpXU4d/TMURcMyV18PXNdi7OKLLMyeJN6wBb8/im1XyGXGyOcmka7NicN/hBAKRmWlt7Dk4rmf1m1/WQ/rCn6clVKKcnEBLd7NwuzJqq/oh5vrFpqXfhT1TyyXTjirf1g3ww/RcsoMLbzBrtaPcbDnaxSMeXQlSCzYxmT2JIvFkQ3f592GEPDRTwV58LEAP/9hkSPv3LwquXCgiXt3/GZtLtqq74z3d744zbHBb276aG5y05G2g1My0EN+fG2Juq8J7exE2yCj9tzx9yicPVnzmBII0Pn138Xf3ArA3LM/pHjhXJ2tJU7pg78BszMpZn70bS93TQhQFJIPPkbTRz993WOGd+wlsn23N34+W/UNHb5kxyQEmXdfp+PLv4W/vRNcl/Tbr5I9/JaXX7lO9FSoGvGDDyOEIH/yKKk3XsRcmPNEuhBokRjJh58gcegRhKri7+gktu8A6TdfXjVW6tVfkn7zleW/1WCQzq//rtd9SUpmn/0hpaE1ur9IF7eyWnTMGBeZM0dXpYcV7BRvpb4HsByxtKXJUPE9hotHq4LbrdoOCd5J/3D5dcPFI4CyvOR9Lv/myolwLPtLVl6bJZJFc5yUOVl1a1ka2+VqV5oss4hlXlrdNCqZFcblV4dlFliYPbHm81c2PJeUinOUinN1n11ZgFQ7bh5YfWNxPQRDjQRDTdhWiYW5Uxsy5p3OdQvNsp3DlTZhX3LVc0IoBPUEhl3Ecq7D/+46WSyOkDfmAc+EvegsMpx6m3Rp7EPRH1sIePjxAN39GuWi5PmfliiXrl6chaOCv/F3Y2zbpdPcqnLknY03x1+iYuYYHH12WVwG/Uk6mu5hLn2WYtn7DCPBFhriWxibeXNTZH5YqfZNF3r1VCUEQtcQmop0N77PuFMoY4wtoDfFSD59D/l3L2DOZpbnEuhtoeWrj8IGVapLy1q1lCodu6Ywxa2UcYobcxHcMFy3Jp9aWiaeILmOiKaqEt29f9neKvv+25RHLrO3kRJrcZ7UGy/S/mt/BVSV2L4DZN9/+4pL9EIIUFXyJ95n9id/gWvWXpOsTIr5X/4UPdFAePtuhFCIbN9N5t3XV/mBuoYBK7xLpet438Ol58ula/6sJC7OGjl8ay2Ju9iX6T9Z81pPIK6Y12UemGvlXK43l02ujBAKbV33e0VAM8cpFeoL3g8b1y00i8YiBWOBxnAfQT1B2cosP5cMdhHxNTJXOI/lfHB33J2JfYR9Sd4Z+wYl88NX5RWJCf7HfxRn2y4d04DxUZsj71y90LcMyeS4TU+/xsXBK+cR3Qi2U2Eu7Vk+CKGwe8sXGJ1+k6mF2g4UPXaRpsQOUrm1E/1vJboP9t3nZ2LUZm7m7r+Z+aBIPLGXyP5elIAPJeBDb4hUlyl9dP4Pn8bJlnArJk7FIv/+ELk3zm7IfqXlkHr+COE9PQQH2uj7J1+ncHwEt2SgN8eJ7OvFNSzKF6YJbm3fkH1+2FF0H76mFi9C57qrReYKKhOjuKaBGgqjJxvRojGvLecVcEpFFl/71SqRuYS0THInjxDetguEQE80ogSCdZfPN9mkHoqi0dS6j9aOAzi2wdTYm6vsqT6sXLfQtF2DoYXX2dv+GfZ3POMVBdlFwv4GehsewHTKDC++fUORKE3xoSg6uuJHVwOAIKQncF0bR1o4rlkTqVSFhq4G6E0eIm/Med59uFSs/CozeIGCpvhQFZ2AHkMRKqqiE/Y1YLsGjmtXX3/nRNJ27PbR3e99pD4/PPZ04JqEpmHA//kP0zS3qoyPfnCdPRShEgm2MDm3uhNG2cjQktztea3ehhXp23b6+Ff/pYnf+6cZfvitD1HbvXWQpo1TqFzW5cfz1XQKZeRKeyPHxSlUcEpGzU8ttKuL2MM7a8Z1Ct6yo7+zETobLz2eL9cITWlY2PkyTtlY8+crbQenUK5rgZR9axDtj1+g+YsP4WtL0Nh5EJBIy6E8NMP0H71AaGcnvrbksh/nJtePUNTlwiLp2DjG2jltrml4Nkd4HXnWKki6nMrkmLdcvg7m/IwXSVYUlEAARdOvshfOJh9W4sl+urc8iXQdfP4o4WgbQqiMD79MNjN6q6d323BDpZNzhQucnP45A02PsLvtEwgUXBzylRnOz79K3qj3w7464aYpfg50fYWw3+skoAkdIVTu6fwCUnrLNkMLrzOW9nJABAp5YwFXOnQl7lkWuAIFkKTLE5yc+ikV27tDTQQ72d/5DKrQvfEVHz4tzIN9v4WULrZrcGT8exTMm7d8vJEIAY88GSAQEIyP2LR1ajz4kQCRWI5C7urFcibtkkl/sILOK/ap0JzcSb44i1O9IdDUAM2JHZh28bZdOt93n49w+OZ1m7gTSf3iCJlXTi13BQKvM9DYv/gBQlWWBSNAeWiGC3////GKYla0mZz5kxeZ+9arV7W/lfsBmP3Wq8z/4C2ktbY3YuHIRQb/9u8jXYmTv8wQ3XFZ/Nlhcm8PEhxoQ0tGQEqM6TTloWncokH54gyZl07eVh2F7lSkdJctf4Siovh8a75W+HyeByaA41xVq0spJZXpiSsWTknbRroOAg2hKCtyxjfZpD6q6iOe6EPVfEjpYhp5r0J+5JWb1u71TuQGPTokc4VBUqVRQnoCRdGxnQolK1PTymqJspXl8Ph3MB2v3+p6OK7FublfoSpru9CWzMzyf3cn72NL4yMML77NYnGkGukUKIpKS3gr/Y0P0pnYz9DC6wDkjXmOT/24thhl5ZFJSdnK1n3udiQUFjz8eADpwg+/XeQ3fjdK7xaNbTt91xTV3AhUDXRdIIR3nbdMua4XsisdxmffYs/Wz9EY20LJSAGCUKABTfFzaviHrPd9URTQfQJF8dK1bEtyPa2WhfCWwlXV+07YtsRaR0coChx6xI+4zRrL3Gpcw1ol/oDVgg6QjoudXR0JdkuG157yevZfNr2uROsgLWfNrkHeCyTWQg5roX7xgTRt7M1o5oYgTQNjfha9oQkUhVDfVsqj9e1jgl19KD4vimnnc9j5KxWHAFJi567yXL58mtkUmZtcmUx6mGPv/D6q5sN1HYxKtlpYdHsGRm4VG2IGZ7sGOWP2iq9zpLWmMfvlSFyylemreq2mBOhO3EfemGMk9c6qwp+KlaM7eR+hFYVLtlshXRq/qvHvBLbt0unp18hmXV5/scKBB/w8+lSAR54McPRdY818+bZOlc9/NYzuqz2xHj9s8OoL69syKAp8/mth2jpUvvunBdIpl0MP+Xnmq2G279YJBBWyaYdzpyz+63/KMTF66XP56KeC7Nij8/xPykxP2jz5uSmeeOo5MjNbSc3GkBIuDg/z0otHyBbqf7eEAjv36Hz2S2H23ucj2ahiVCRD5yye/0mJ11+sYBj1D7y7T+OzXwoxMmTz7F+WSDYoPPO1MB95KkBLm4bjePmqr/6yzPf+vLgslANBQUe3Rv9Wje27dO57wLvoPfWpIJ09tT+nYsHlW39UoFLePOlssslaSMchf/IIkW07QVGJH3iI8vgIpZELNRFpX0sbDY9+1LMScl3yp47irrPMfmkHslqstMkmG4vrmBTyG+sJfjdyV7iYK0JBUarLKXXuRGOBNhRFo2zeORHKa+XRp4L4A4ITR0xGhyzeeb3CI08GeOSJAH/073OUivXFTmOzypf/mwixmIJQwOvMJvjmH+WvLDRV+MwXQ9xzyM/htwz6BnT+9v8cJxAU3iqVgPYOla4ejW/9cQGqGU9CwKMfDfCFr4URArp6NZ76ZAjTTGFU3iEUUvD5BaWig/Z/F/jBN1evgKoqfOk3Ivx3fz9GIqlgmpJSQZJIKvQNaDzxiSDP/rDI7/3TLLns6nBqZ4/Gb/+tGEfeMTh51OQf/7MkBx7ys+SUoijQ06+xOOfwvT+/FPl66PEA/+v/nSQYVFBU0Kq/oIcfD/DQY4GafczPOvzgm8VNobnJJlegcO4U+TMniO6+By0Wp+Orf5XSyAWM2Wmk4+BrbCbUvw0tFkcCpeHzZA6/ecVxl9ns/LbJJreMu0JoWk6ZdGmcttgudrQ8xUJxGMe10NUA8WA7HbG9GHaB6dzpWz3Vm0I4KnjoI57IefOVCpYF775hUC7J6vK5zrHD9e/oB0+b/O6XZonGFJINCn/3/5Vg+661c6TqoQh45qthHnoswBsvl/nFX5YYH7FRNcG2nTpdvRrjI6uXUoUQfO23oxgVyb/75xle+1WFSkXS0aXyu387xkOPB/hb/1OcsyctTh2rnf8TnwjyP/6jOD6f4EffKfKDbxaZn3MIBAQPPxHgt/9WlM99JYxRkfyr/zOz5hJ4a4fK//y/J+jo1vgP/yLL+28b5HMujc0q9x7yc/KYUSNyzxw3+T/+YRohIBwR/J3/OUFTi8L3v1Hk7ddqhblpSEqFzTydTTa5EtIymfv5D3ArFWL3HEINhoju2k901/5Lr5ES16hQOHuChV/9/APviHSzUX1Bwg3dBGJNqJof17EwS1mKqQnM4ofPReVuxhdOkuzcTSk9RX7+9nRU2UiuW2iKKySmfZC9PSWSwbmXcVyLpsgAHfE9gFLtk15moTjMaOpdiubVdTi409iyTadvm0axIDn8ppfXNjZsM3zeYs+9Ph55KrCm0LRMmBxzAAdFhUzq2j83ocAnPhfiG/9Pnt//vRxG5VL04PwZC6GsnRcdCgv+47/M8p0/KSwHHWanHP7pRJp//cfNDGzX+NJvhjlzwlwWfLG4wu/8DzFCYcHPflDiX/xvmZqo4dhwgblph//9/9fAZ78c5sVflHnn9fr5fj39Grou+Ed/e5HTJ8zl1JqRIZvDbxmr6gFmpx1mp71cw3hS4a//HRdQOHvS5MVnV+cgbrLJJleHUypSGh0iuns/UgjKE6PLXXaWep2XLg5izE4tV57fLUSaeuk99GuEEu0IRcVbmZNI6TJz5hXGj/70Vk9xk41CCDp2P0XLtoep5OY59Yt/g2Pd3d2Drkto6v4IOx/8q6h6oO7ztlni3Dt/hmV8cHecplPkzOzz+BZeR1cDXgW8dLDcCpZz9woAIeDRJwMEA4KzpyyGL3iRw0pZ8tZrBnvu9fHw4wH+5D/lKRZuzvKREIKpCYtv/GG+RmQusd49x+K8w0vPlVetbM1MObzwsxID22McfChAPKmQXvQG2nefj607dEoFyXf/ZHUOpJTw+osVTh41OfSwn09/Mcy7b6ydp/rDbxU4s0JkXj7WJptscvOJ7NxH62e/jKL7WHz5OVJvvFS39ebdhuoL0nvw1wg3dGOWMqQnTmGWs6iaD18oQWZ6YzxibzcUzY8eCGMWMx9oYGoj8IUSuI6FbVyHpZ0E17FBShzLqNuW9G7juoSmUFSC0RYAipnJVW+UbZVv2ZtnOqVqVfuHg0DQWypGwHtvVWpyMd99vcJv/vUI/Vt1+rfqnDx6cxLipYQzJ8xlIXgtzM04pBbqRydOvG8iXWhsUmhuVZfH332PD90HUxPOsrC+HMOQHD/sCc2de3VCYVFXaFsWvPfm2iJ0k002ufkITSP50OOo/gDm4jyZw2/eGSJTSq8FZhWhr+2Sshbhhi5CiXZcx2L47b8gO32O5bteody1d7vNA/fTtuMxzr7w+xjF1K2ezlWj+kJsf+J3yM6cZ/zIT65jBMn06V9RWBihlJnBXaP3+93EDeVoFtLjnH3rT3DdO+CEcJcysN0TkVJCKKTwhV8PLz8XjggsE6IxwSNPBjh1zLxJ5yxJasHlelazMil3Teuj1KKDZXliOhr1UjUUBVrbVYQQLMx5z6/F1IS37JZIKkSiCsXC6gk6tiR1HQJ5k0022TgUnx8tGvNajGo6WiSKUy6t6YN6uyBtC7dSDawIQaC1k8Lp49c0hj/cgFA1jPwCxdQ4NUsrd1ik76oRgnjbdlTdf8f5lQZjzQRjrTeUW2lVCqTGru17cidzw8VAVzTSFgptfQ/iDyUYP/sCrnMpqiaEQtuWR9B9ISYGX8R1PNWgqDod2x6nlJ0mNX2GSKKTROt2NF8Yq5InPXeOUrbW+khRfcQa+4g29KJqPirFFOm5c6vulIKRZlr7HmRm+A1c16GhbTeBcANmJU969hzlfH0rHVUPEG/eSiTRhVAUyvl5MrPnMCurfdw0PUg42UUk0YXuj+A6FqXcNJm5C9jmilC7UGjrewAJzI8eJtLQTbxpC6oWoFJKkZ45i1Fa/07voccChMICIQRf/s0IX/7N+q975MkAf/oH+WvqfX4tOM71jWvba3+DHMcb1ycEut87GQkBvqoVk2lK5Dq9rpeW8TVNoK7xTXddcK9z7ptscsehrMytFwhlgy7yQmHJ8UMIUY3EXf2dp2tUsNKLXlvJeILOr/91yhOjOOViTURP2jZ2MY8xPUllegK3cmvToqRtU5maINg7AEBs/0GKF85SnhhZFYkUqlo3t1TV/QghcB2zJjp6N6MHooQSd2YL12hzP2KtC8omdbnp75YQgob23YQTnUyef6VGaCIUGjv3EQg1MHXh1WWhKRSNlu6D5MIj+AIxund9HPCqDjU9iFDUGqHpC8TYcu8XSbRswzJLuLaJLxClc/uTjJ76OQsTx1i6S/SHkrRteRgpHRKtO9D0IK5r4w/G6dz2OEPHfkhq6mTNMQSjLQzc+yXCiU4sIw9SovfHMMtZLh77Adn5S715Vc3PtkN/hXjzFhzbwDZLqJof3R+lmJ3k3Dt/jlFKL783yfbd+ANxgpEmWnoOYptlEAr+YIyOrY8x+O43KKTr+30GQ4JHnvJacw4NWowPrzaQDkUEBx/0s2WbzpbtOqdu0vL59eIPKGtaI+s6aLrAdS+JRimhVJJICcGgUr1Q1heKofAlQXonrMJtsslGoSUaiN97P2ogiOIPeC0V/QG0SJSlDgNaOELHr/8ObqWMW6ngGBVco4JTKpJ9/22c4uo+34GuXiLbd1fHDKL6vXH1RMPya/ztnXT/9n+Paxq4RgW34o1rZdNk33+rbjcf6TgsvvQceqIBvaEJPdmAnmyofc1K4eY6GHMzLL70CwqDp2/p8nLuxPvE7jmEFo6gJZJ0fP13KI8NY2XTCCFQAkG0cASnVGLmR99CERq+UALNH0bzh4g09QJegKKhZz/uipOVbRTJzl5YFdkMN3YTiDSSnx/GLGXRgzEaeu4h2tKP5gvhmGWK6UlSo8eo5OeXtwtEmwg3dGNbZXIz5+sKW9UXJN62zTu2mQvY5upUNM0fJt6+g2jLFvzhJEJRccwylfw8hYUx8gsj2JXaGg2haOj+MP5II7HWAXyhOI5tkezcg1Wp/a4ZxRSFhdUtHEMNXQSjTeQXRjGLafRAlIae/URbtqD5wzhmmVJmisXRY1Ryl3UmFAr+cJJocz+Rph4vkqyo2GaZcnaazNRZiqnJOlFkgar78YXiBKJNJHv2V9/LZhr77qsNQEuX7Mwgjll7AyQUjXj7dlSttmWqZRTIzQ6tE7kWJDp2IlStmlIhSHbuJt6+fTlPtJydJT1xksLi+JrjCEUj2tJPomMXgVgzQiiUs7MUUxM1vx3HNslOn0O6G9uM4oaEpqL5CEaacF2Hpcu9AGy7suqLcz0kmrcRirZw8egPyadGkVJWo4+XoohCUenZ82kSLdsYO/M88+PvIx0HfzhJ//7P07//8xilFPnUWM02bf0PM372eebHjyKlQyTRzcCBL9O35zMU0xMY5QzgCcf+/V/AH0oy+O6fk68ubYTjHWw98BX69/8ap17/g+XjdWyD+fH3mR15m0JmAtc2EapOx8CjdGx7gpaeg4yf/WXNcYZibSiqzuB736KQHkcIhebu++jd82k6tj7G+fe+VTdZurdfY+sOHduS/Id/keWVF1bf3ScbFP7zd1ro6dd4+PHAbSc0G5sUNF3UjYi2tKloGhQLctkL03VhcsxGSklLm0ogIOoWIIFnyg6QXnTJ5+7SJahNNqmDr6GJxsc/7rVSXAOhaQQ7e1Y97lomxfNn6grNUO8ADY99zItaroEaCBLq3bLqcTO1QO744bpCU082Et6+ezlSJKUE16nRj0KISxFZRcXf1knbF77O9A++QfH8mTXnc7MxZiZZfPFZmp7+zLKojO7at+p1lZlJEArNAw/SufdjCFVDUTWWIsG+cJL+B79Ws01xcYz8C/8J1649b7dsfYjmgQcZfus7lDLT9D/4FULJjuWxABJde6jk5muEZrx9B333f4lSZobTz/+7VYIIIBBpZMtDXwfgzAu/j71YK/hCyU76H/gy4YYuEMqyKBHK0mfnkho5wtBb364RPt33fprGvgOoegBF1RFCoOoKPQc/v2oOC8OH6wrN5i2HaN3+EUbf/T75hRH6H/gq4caumuNOdu3FKKRWCc2WgQfo3PcJ9EAUhPCuqdKtzns/bTseZ+r0r5g+83LNvAOxZrZ95LfwhRIouh8hFIQQxNt3EG/fUbMP6Tqc+sW/pnTZ+6rqfnoOPEMg2rz8mBCC/NwwZ+d/H9dZSyAqdB/4HIFII+df+a80DzxAonM3QniuOkLRSHTupmXrQ0yeeI6ZwddW3XQpmo/uez9L85b7EYqKVSmAdIk296FojyOEWL6Jq+TmOL0wgm3cRkIz1tjHPU/93VWPL0wc5fzh79zI0ABovhBDR75HevZS1Z1l1J78QrE2Gtv3kJ49x/TQa8t3aHamxOjJn7H70b9Ba/9DFNITNWKtkJlkZvit5ShqZu4cs8Nv0bPrE8RbtjM3+o53jE1biDf1M3Lq56RnLs0jO3+B2ZF36dn9CeJNW1mYOFJz/DVYZWaG36al9wHC8Y46RyqZuvAKmdlzy4/Mjb5HS+/9RBJdKKqOUydh+OEnAgRDgslxh+PvG3Vb+S4uuBx+y6CnX+eRJwP8+X+5ecvn10NLm0pHl8rwhdovthBw4MEAQngFQ3PTl54/+q5BpSxpbFbYtd/HW6+stoYIR8Ry156TR8ybcszSvZRCpm2upGyyQUjHoTRyASu9iATswrXftDvFAoVzp9YVhGvv316z446ZWqB47tQ1jwlgF3J1I2ih/m20fu4r6A1NOMUC6Tdfpjx6EadS5jKlieL342tqJX7PQXwt7SjBEA2PfpTSyIXlfuneQbiURy5gZ1JI6WJdRQtK1zAonD+Noum4toV7td2EpCRz+E2MuWniBx4i0NmNFo4iNA3pOLhGBTuf8+boOhQWR5k+89Ly5tHmfhKdu7DKOeaH3vYqkquY5ey6y+mR5j5adzyKovqYPPlLSukpkC7BeCvhhu66Yu1GUDQfPQeeIdzYQ3FxjNnB16kUPNtAPRAl2txHtGWA7Mzgquhafm4Y2/Cio4FoM01bDuLaFnMX3lx+fIlSZv2ugOGmHpq23I/mCzJ16oXlSGQg1kKksYf8/MiqbSqFFNJ1SI2fIDczSDk3j3QdAtEm2nZ8hFBDFx17niY/d7HmfXPMMvPD76EoGkIoNPYfIBhrobAwSmay1ptbShezvDqdzjHLXHzzW+jBGLo/TEPPPcTbt697jCsRikbvoS+i+cPMD71DZvIUtlnBH2mgbcdjhBu76dz3cXJzFymlJ2u2be6/n5ZtD2MbJcbe/9FyJDsQa6Hnvs8Sae6nmBpn6uQLGIWUt6q6wdzQ5bFcWGBu9L1V0ba18hyvFaOcIZ8eW/c1kXgnqu4nO7d6GaCUn8Uopog29KBofhzr0htYzEwui8wlcgtecm840Q7V71mssQ8hFMLxdrp2PF3z+nDCu4MMxVoum5XAF4wRirbgCyaqS+dhFFWrm9vhOCa5xeHLHrOwzRKBcCNCqKu2CQS8Ah8h4PCbFTLpNSJ2El77VZlnvhJm6w6d3i0aZ0/ePuvI8aRXwPTv/0W2xlR9YLvORz8VBOCNl8oU8pcuOINnLN570+CxpwP85t+IcOaESXbF8asqfPrXQmzf7SOfk/z8L6/DguIqqJRdCnlvv1t3+lCU4u1eu7DJHcCSefmNYMxOMfXtP9qgGV2icOY4hTMbV8SgRqK0fOoL+BqacMolpn/wDUpD59bdpjh4mtLFQbp+62+ihSP4WlrR40nMhUsRLOk4zD37w2uai53PMv0Xf3o9hwFSUh4bpjw+ghIIovj8CEVBShdp27iGsdwGszA/QmGFEGrb+YQnNCsFps+8fE2eik39BygsjHL+1T/BKFzyiU5PnEIo6obnfPqCcSKN3bi2wejhv1wlZNPjJ1B1P26dXNT0xEnSE15aWrxjB039B3Adk9nBN2rmfjU09t5LMTXBuZf+n5qILax93Pm5IU49/++wyvkaEVxYGKGYmmDn038TPRAl3r6j5risSp6ZMy8DnuCLNPUSjLV44uzUC1c1XyndmjH1UPzahKYQ+MIJJo79nJkzLy9rLm/uk+x6+m+iB2Mku/bUCE1F1WnouxchFBYuvsPiyBGW1voLCyOMH3uWnU/9t/hCCUqZ6Wv+HK6WGxKaRjHF1IVXN3w9fwnHKl/xh6L5w4DArOPZKV0H2yoRDLShKCorR3Ls1T9m267gug6aHmLJMNcXiIFQSLRsRzatPk6jlKoRrKrmp3Pbk7T0HkQoKmYlh1Nd9lCUpbe7Nq9Qum6duwjp/U9Qr6smvVs0tu70Ydvw2q8qdaOZS5w8ajI349DepfLwE4FloRkICnbu1QlHvKrsWFzQ2u6J2h27db74V8IU8y6FgqSQczl7ysTcYCeGfFbypd+IEIkp/PInJfI5SU+/xn/z30Vp71KZGvfaOK4MbBgVyR/8XpZtO3Ue+kiA/+vfNfKDbxaYGncIhgSPPR3kC78eRlHgL/6scNNsncyqNdKe/T4++fkQ0xOeybvrQiAk8PsE77xRwbk5P49NNrnjCXb342tu89pKjgxRGr5wVduZqXnsbBotHEHRfCiB0M2d6NUiJW65hFv+YCz2pHSZOP6LugLhphQWLV2LhFjTR9uxPgC7HgkTx39xmcisPrXGcUvXwSrVj2yXc16+YqJjF/5wckOnulFUcnPMD72zKrBn5OcpLI6R7NpLINbMyg4pQtW845EuxdQUl9czGIVFbLOIHozhjzTcnkLzRhE1PcpXI6W8YlX7cqW6Uu9QBELRkNJe5esp6rxeCNXLfVghnF3XxnVtLh79AflU/WUIZ4XQbO17kM4dT7IwfpSJwRcxy1lcx0IPROumGVSP9JoNax94LEA0JpgadzhxZP0fdibl8v7bBp/rDvHok0G++YeeyXlXr8a/+ePm5aKZldz3gH956Rlgcd7lr39ljonRjVVNb7xcJp+TPPOVEM98JYxtS3w+gRAwM+nwz/9JmrE6RU5nTlj8k3+Q4h/8kwSHHvZz/yN+TNNbwlZVyOckf/r7ef7w3+VuntCT8N0/LXDPQT/7D/r4H/9RHNPwvre6TzA+YvNbz8xSsm+fVIVNNrmd8DU2L9vbWOlF1r1jXoEQyqV8Ttdd7iD0YaOSX1y1VHozMUs5iqlJoi1b6Lv/S0yffpH05GkvSnglB5oNxCimvEKWjUKyXGdxO1aUSykpLIytSjFYes4q572cV81fzblc+bynLZQ6+V1CUb1OVJf5wW40N/8dld6BCiFWiUFV96qxb4RyYQHp2oTj7atyIzVfEH8oQaWYqq12BwLhhmpC7SWBF4w0IhSFSjHF0o+mlJtFUVT0QPSKnY6EUEi07sCxTSYGX6Scv7SUo/lCa4jh6+PkEYP/7R+kWJh3SS2sL1IdB/7kD3K8+2YFoyKXv4Rz0w7/1z9Oo66t9ZcxDElqsfaL6NjwR/8hz4++U2Ro8PqW4zVd8K//WZrXXyzz1KeCdPVomAacOGrw3I9KjAzZaxaUHn7L4O/+9jxPfjLIoYcCNLWolEsug6ctXnq+zOnj5poic+icxf/xD1O4LqSvo+3mEjOTDv/wf1jgY58NceBBPw2NCoYhWZhzOfaegWlsisxNNlmLlXY/6lJF/FXcdAe7+5ar0p1yEfsqcjDvRmyj+MFEEKu4tsH40Z/S/+BXCcbb6Lv/S7Tvfor05GlSo0cppiZv2grnSmyzhGNeR9tGoeAPJQjGWwlEm9D8Yc9eStU92yKxxhLibYBZyrK2mF96vHb+rm1SXJzAH24g2bmb1NiJSwbx1YImzRfGKKVvWjQTPgChKaWDUc6Q0LYRbexlcdLL7xFCobF9L75g/IYq1AvpMUq5WRo79jE3dnhZ3Amh0tJzEN0Xqdoq1QqhWNMWgtEWSrkZAFQtQHP3Aa+8f+GSXZHnlfkk7VseJjc/RLmwwNKHKhQVTQ9hm8VLglW6CERNL3ihaLT0HETVg9d9nJdz5B2TI+9c/ZLwxUGbi4O1J4Bc1uXnP7z+JR4p4c2Xb6xHq6p6IvbVFyq8+kIFIarv7lXqs4U5l7/40yJ/8afFa9p2ftbhZz/YmOWtxXmXb/9xgW//ceGa57/J9aMoOk3te1FUnczCEJXS+ifKaKKHQDBJeuE8tvXh6R52O2PMTCIdB0VVCW/dSXhgO8WLg2satQvdR6h/Gy2f/DxC9yGlpDh4Gqf0wbU7vp24Fb6bhYVRzv7qD2geeICm/oP4I4207XiMloEHyc1eYOrUr6r5iDfvJOgd97WNH4g207H3aeLtO9EDYa9Qy/VaQUrpomq+mzPZDeLyYNnVIF2HmbOvEG3pJ9m1l22P+chMncV1bMINnTT23ovr2sycfaUqZG8ONyQ0g5FmenZ9YtWyr2ubTF98YzkPcnHyBM3dB+jf9znC8Q4so0A43k60sQ+jlL6hSJ9tlhg9/Qu2HfwaOx/8LRYnT2JZJSKJThra95CZP8/86HuXbSVxbZPth/4Ki9OncKwK8eYBEi3bmR8/TCF9KSRfLiwwfuZ5+vZ+ll2P/C6ZuUGsSgFV9xOMNKP7w5x584+xjAJSuqRnzxFv2Urfvs+xMHEMIQSJlm0Eoy0bYvl0t3Mjdni3Q6e2jZ6DP5io5gwvmYfh/bf00jocu4K18kbnQ4Sq++nf+Wl8gRhnj3xrXaHp88fYed/X8QeTjA0+z9iFX32AM91kLSqTY5RHLhAa2IEaCtP+pd+kMHia8thFrGwG6dgomo4ajuBraiHY00+gvXu51aMxM0n6rVdujx//XYJQVC+dYZ331CrnmDr5S+YvvE2sbRtNfQeItmwh0bmbSGMPw+98l/TE9bkT3Ax8oQQDj/4m4YYuzFKGqZMvkJ8fwSxnccwK0rXpPfRrNPbee6unuuEUFka4+Na36X/gq8TbdxJv84qQHNukkl9g9txrLI4eucIoN8b1KTwpcWwDzR+mbctDq562jCJzY+8tC83swhBDR79P+5ZHaO17AKSklJvh4rEfEm8aoLFj72W5mN74rmNe1U1LZvYs597+Uzq2PU5L70EUVces5Ji+8CpTF1/HtlaX68+Nv4/rWLT0HMQXiGKZJaYuvMLk4EuXhf4ls6PvYJTStG15mGTrzmpVnY1RTJGaPl1TJTg7+g6aL0hT17307f0sjm2QT40y+O636Nj6KKoWZOVBubbpWRfV+VE7tukti2yeRD+09G7/BK2dB1Y9LpG4joVtlSgV5pideJ/FmVOb7WDXQnirDNXeNbd4Mpss4ZoGsz//AW2f+yrB3i2ooTCxew4Ru+fQZdZGy/8CvI48pYuDzD//Yy+3c5OrxmuXufbzmi+07NN4JaxKnsWR90mNHSPS3Efvfc8Qauiic+/Hyc1erHF6uZU09t1HuKEL2yhw4bU/rWP7dBefE4RCKNGOqgeYG3yDhdH3PQ1nVTCKmQ+k1/p1CU2zkufEy/9+zR6lUspav0spWRg/Qnr6NIrmA+lVfbuORSE1xvTF12vyTBzL4PQbfwi4Vx0uzi0Ok0+PeZ2DhILjWOt8yb27tanzLzM78g6qquO6drXyu46ok5LM3CDZhSE0LeDZJ0gXxzZXzc+1TcbP/JLpoddRFA0pXexq9fzwiZ/ULKlL12Ho6PcRioZ1WfcF6TqcP/wdhKJgX4PdxSZ3F0IIhKLg2AaVahOBpccVVUf3R0kGkyQaB5iMtTNy7rmrukB82DArOQaP/wWBYJKFmdsn0rIJWIvzTH33vxLdex/RPffia2xC8Qe8ooxqZE3aNq5p4hQLVCbHyJ857vlSmrdXA4rbHS+FTFaLRuon54eSHddsjSRdh/zsEBPHf8G2x3+bQKwZzR9a+xosL+UUXo/X67USTnYihKCUnvY8Ny9DUTX8kYY6W17OpQJlcYeI00C0ifbdT2GVs0wcf7Zup6ebzXWuWcu6Pb6vhGMbq4zH6z0GcpUx+1XNynWuWLCzak5W+arvuqTrYJlX48ko636Y9fzR1vvQ7ava1yYfBvKZcU6+84eX3BOEQNMChKOt9Gz/GPGGLbT3Pczi3FlyqeH1B/tQIsksXJ11ziYfPE6pSOad18gefgs1GkULR1F8fq+Di+siLROnXPJM3y1rc5XnOjGKGaTjoPsjhBu7yE6drXleD8Zo6L13TfHnubWsU6Fc3c517HXTeWyzjJQSRfOhB2NU8gvXdTxXi1udr1CqzjKXPR/v2EUo3nbFcaR0vaV2KfFFGm6KV+lGo/lDqJofVzMJxJopZWZqCu6kdG/6Mdx+dfyb3PVICa88X2Fu2vGqyj986YXXjERWT5Zy6QEss0BmsYB5ssg9D/1NNF+IROOWTaG5yR2LdGzsTBo7k77VU7krKaUnqRQWCMbb6Ln3s4wBpfQ0QkAg1kL7rifxh5PINYqxkl27aey9j+z0OYqpCaxKHum6CFUj3NBF576PIxSNwsK5dWsSjEIao5gmEG2iY8/TjFsGVjnndX9SdaTrYJYyG3bchYVRmvoOEG7ooqn/AKmJk0jXQdX8JLv20L77o7iOjVD19QeSksLCCI199xJrGaBl60OkJ04hXQehqKi6n0p+cVXlvR6Momh+VM2PqgcIRJsAr698rHUrllHEsbxVXqtSQDoblwJVyc1TTE0Qaeplx1P/rRfcqt6oSelilfPk5y8yP/QORiG1YftdyabQ3OSW8PLzZV5+3oske0s4LvUTckXVF2xj1ejl1lZ3MpXSIkYli+4P4/NHubwhAHg+s9FED4nmrQRDjQCUCvOk5wcpZCfXXW7XfWEi8U6iiW6CoUaEquFYFUqFOdILFygVZteMMPmDSYLhRsrFBYxyBlX1kWjaRqJpAN0fxbENSvlZUvPnKBfm6owgCEfbaGzdRSja6nXYyE6yOHsaxzbX9dnV/VHC0VZq868kpcI8ZmXtCktNDxKJd2EaeUr5GXz+GA2tu4gmulE1H5ZRJJceIT2/fvW6qgVING0l3tCHzx/FsU1y6RGMy1aDjHKacvHmRnQ22QQ8O6TJE8/T/8CXCSba2f7472CbpWrf8QCO5dkXte18HF8wvmp7RfWR6NpNsmuv5zFtm7iOjar5qn3ABaX0JOPHnl3X29Q2CsycfYWeA58j3r6DaHM/tlVGCBVV8zF7/k3Gj/x4w447NXachu59xNq20Xvoi1VhaaL5I2i+AJnJM2SnB+k99GtXHGtx7BiNvfcSae6n9+AX6Nz3CaRro6g+XNfh9HP/tsYqSNH8bH/8dwkm2rzldnHJlSYYb2Xb438Nz0vby7u/+Oa3VrW2vH4EvlACs5wF6aJoOj41tvwcQhCINlULufZw/tX/inETossfOqHpuna1W88H5z22ydr4g0l2H/hvmBl/l+mxt1Y9397zII0tuxg88T1MY510jSX/s6sQj/GGLfRu+zgjg8+SS29sL+Bbg6i2u5PYdoXLRaY/kGDL7s/S0LoLRdE9X1sAodA18DhzE+8zMvh83VSNxtbd9O/8NIFwU/XkKKu+uF5+l22VGB96mcmLr9YVqy0d99C389OMXXiBmdG3Gdj7BRpadq7KD1PO6YxfVgkuhEJbzwP0bv8Eui/M0sm4pfMAHf2PMnL2F+su+SQaB9hx76/X5EWD5MLJv2R69M01t4vEO9n34N8gu3iR0fO/ZOuezxOKti3fmAih0NH7MNn0COeP/0VdkRgMN7Nt/5eIJfuQroPjmNXjuZ/Lhe/E0CsMn/3ZmvPZ5O6mmBpn+vRLXnOPq/SgzE6fwzErlHP1bs7WJzV2HKuSp3nLA4QbOlF9QWzLIDN1lrkLb1FcHAcp8YWTXpRxBZmpM4wd/jHR1i34I41oPq8mwqrkMRZGyc4Msjh6dNV29ZgfehurnKOp/yDBeBuKpmGbFUrpSYqL9VtP52YuIB2HSuHaxJBtFBh64xu0bHuEeMcOfIEoiqpTzkyTnjjJwvBhhKrhC8Wv+J7alQIXXv8zWrY+TKxtG3owBtLFLOUoZaZXpch5/dWPo89dvOI8pXQxiqmavxeG3kUPRNZ8T0CSnTmPa1uUc7M1AZRk91767v8y0rEYee+HFBZGL+tk6COU6KBj79OEEu20DDzI+NGfsdHWVB86oZlbHOHoL//VVf+gN7m5KIpGKNxcFRKrCYabCEfbPI+zde4N2roewHEM5qeOXnGf/kCccLQV3Re5zlnfXkTinfgDCVzHIrPCAxZA00Ns2/9lks3bMSs5ZideJZ+dQCCIJntp7TpAe+9DqFqA8ye+t8pv1rJKaHqQXHqU7OIQxfwsrmMRCCVp636AcKydnm0fJZ8ZJ7tYu++VhMLNDOz5ArFkH3OTR8lnxnFdi2CoiVhDb938yUTTNvp3fhpV85NLDTMz8R6WWSQYaqS16yADez6Pss5SVy41zLlj30bXw/gCMdp7H0TTrt7LNhxrZ8c9XwMhGLvwAoXsJCBINA7Q2n2IeEM/vTs+wbkj364R2YrqY2DP54g3bCGXHmF08HlKhXlU1UdT+166tz6FqujMjL9LeuE8hezUVc9pk7uP/NxF8lchQlaSmjhFuZJCKBpqIIxTWT+fX48kUINhjNQs0rG9fc6PoKja8uqO69jLN+pzF1bf9IMXEZ0dfI25828iVHXZ4FxKiXTtay4gSk+cJDN1BqFoyytX0nXXNH1f2S/9cvwNbaj+AFYxh5VbvQRsVfJMnniO6dO/qhb0ermmy/uyYOL4s1c1d7OUZeL4L1BOvVC9yQek66U3XRbskK7N9OkX1x1PjyTQowkco4KRmV2xsWT6zEtXnE96/ATp8RM1j6l6gI49T6P7w1x861ssDB+uu20xNYEvFKdz/yerhWDKhudsfuiEJtKt2+d8k9uTsQsvMDX6JpXS2rkjiqLT3L6fbGptobOShZkT5LMT6455u6EIdTmq5yHQ9CDRRBfdW59CUTSmRt8km6q9YLX3PEiyeRumkePMkW/U5G8uzJwkPT/IrgO/QXPHPWQWh5gdf7dm+3x6nGNv/j6VcnrVyT89f559D/51/MEkjW171hWaDa27McsZTh/+E3KpWjNnUXVnqDleVad765OoWoBs6iJnDv85lnmp0G9h5iS7D/02kVgHa919G5Us85NHAVA1P83t+65JaGp6ENcxOf3+n5NfEflenD2FZRbp3f5xkk3bCISSNVHNSKydWMMWHNvg4umfkM+MLz83MfQywXAzrV0HQQgWpk+uOf9NNlkLLRCi97O/gx6OMf7cN8gMru2DqAbD9H3ud/EnW5h+4ycsHnvNe0K6uPb1Ve1L6SDtjREjnti7wbGEoPOJLxLu2srC0VeYfvUv19qbdzO9ITmQS2Pd+EgNex6k5YFPUJwcYvhH/xlp3/j8NH8Yf7gBxza9AqD1WLZovjnnog+f0NwERdHxBWKoqg8pHSyzVK2mr/2SqaoPXyCOoqhYZgnTqO1nqygagVAjRjmN69r4AwlUzY9jGxiVTN0cSFX14Q8kQAiMNfPkBKFI87KRvyvdur5uQijovjChSBuRWDvl4nxVeHiUS6mam4pAMIlW7c4kkSiqjrPOyVL3RZbFnVnJY9vly54Po+khysWFFe+VgmkWr8s1YT2iyV4OPfE/XZKZS76QikYhO8ngie+xMH285oSt6UFaOu8DBHOTR8ilRlaNm1m4wPzUcdp7H6Kt+xALU8dwVlh2SelQLs7XnVO5OE82NUJrV8Ny3udaKIrK+NDLdedQL3oRirQQiXd6NmTDb9SITACjnGFm7G227v3iuvu9tJOre9nlzE4eIZ++bMlKShZnTtG15TE0LUAgWCs0A6EkquqjXJynfNnNjJQu+cwErV0HiSa6b0r0YJMPC1fXLlHR/WjhGELT0cOJmz6rW4b4YKySbhYbPXfXsXEdC90X8KrN06ttncDrmJTs3g9AITV+U85Hm0LzQ4WgsWUXnVseIxRuQVE1kBLLKjF89ucszJxYfl1D8w56tn6UQKjR8yW1KyzMnGRs6MXlAohAqJH9D/wNRs4/TyzZS6JxK5rmx3VtUvODDJ/9aY0dVDTRQ/+OTxOu5rtVSilmJw/j1olmbdn1OcLRNjQ9iG1VOPbWf1wVgWxq20fP1qfw+WNoeojWrkM0d9yz/Py5o98mNb9k3yHo6HuUprZ9VS9UhTPv//mK5y+hqj46+h6ltfMgui8EeNGxieFXmZ86uiygWzruo6PvES6e/gldWx4nGG5CqXqiTo6+zvTomxtbcLRsOI6Xkyq8oh9NDxKNd1LITHiFOVUCoUYC4UakdEnNnaO+2pKk58/R1nM/4Wgb/mCyZowrsVRU490UrC5CWsIyCqQXzl/1uOFoO6rqxzILFLITdV+Tz0zgOAaqepNax0mX9Pwg9Y7Jsko4tlmtIr0sSiqrtvBCqeu1pyjV/FQpN4OZdVDxlnNd6eBuRLjoQ46VzzDz1s8JJFtJnVw7N3mTuwurkic7c47mLQ/Qc+AZApFGCgujOLaBUFR0f4RIUw8NPffijzRQzs6ycPHyLoobw6bQ/BDR2LqL7fu+SqWcYmTwOSrlVNWLsZ1i/lJoPd6whe37v+JFyo5/F8sqEU/20bXlcTQ9yPlTP/SiUEIgVI3ebR8jlx7l/Inv4TgmTW176eh5mEppkbELLwBeBfC2vV9EVf0Mnf4R5dIi0XgXXf2PrRIKrmNy5sg3UFSd3q0fo6ltD/Xu3FPz58hnxglGmtl1328wM/4OUyOXTqSmuTKqKBkdfI7xoZdoatvDll3PrNFwQNA98CQdvY8yPfYWi3NnUBSVlo4DDOx6BindS3mgQniFNrs+x9zU+wyf+zmqFqB7y5P0bfs4+cxYzbLpjZDPjHHm/W+syP8RqJqfcKyd9p4H6eh7hIaWXZw7+m1y6RHAa1+pKBq2VV7X97ZSTuM6djVCXV9o6r4wgVADwXATmi+MqvpQVZ1445armr9tG6uikusRCCUBsMxStcBpNZZZxLWtmyY0HceqRvHr4CVlAasjEcXCLLZVxheIE0v2sDh7qYJU1fwkm7YCnlC+W5wPNg7B/vBTxLQmxo0zXKwcvdUTuvORLulTb9/qWWzyQSNdJo//AlXzk+jcTdc9n0Y6dnVl0Lt2C6Hg2gaZqTNMHHu2plp+I9kUmh8SVNVHV//jWFaRM0e+UdMX+lIk07Ma6ur/CI5tcv7kDzAqGcAzDEco9G79KLOT79fkAtpWmQun/nJZSBTzMyQbt5Js2sr40EtI6dDQvINQpIXzJ77P3NSR5TFVzU/f9k+umq9jV7z/OWtXAC29RtV8ICW2VaZSXjvv0nFMHMesdmGqH0oKhBpo636AhdmTjAxe6rKTz0wQCCXp7n+C9Ny55WV0IRRS82cZvfCrZRHoOiZ77/9d4sn+DROaS24JNfM2qsvXi0PsPvRXiSX76N3xcU6988e4roWm+QFvaXq94jfHNr1xhYJa3WYJ3Reho/8RmtvvIRD0xJ9ELguttbqLXI6UziWz+atgaR6uay+bLa8a07VXpVNsJFJeX+5YKT/L/NRR2nsfYuveXyMc66CYm0bVfDS17yPRvB2jnGZq9E02Q5q1CEBXAvhFCE1cwdNwk002WRezlGXozW8Saewh2txPINqEqvuRrotlFKnk5igsjFHKTq9rR3WjbArNDwm+QJxwtJ3ZyffWLYLRfWEisQ6y6ZFlkekhySwM0jPwJPGGLTVCM5saqYlWOXYF0yzi80eWV1NjiR4c2yBbjbYtjZldvHhbRXUisQ40PUh6/lyNiHEcg/T8IN1bnyIYaVoWkK5re8urK46hUs4gXXc5H/RmY5lFFmdPE0v2EY624wvEqJQWL4lLoVxm8VPL8lLusim8h6YH2X7PV2ho2YltVZibPEJmcQijnMayyriOSffAU7T3Prjhx7TcyaP6T12W0wduEteZGC+lw8i5X4BQaOu+n97tH2fJmsmxDbKLQ4wOPk8xt1ltvsn6aKEI4Y4t+BLN4LpUFqcpzozWRNTroeg+tOBqVw27UsQ1r87aT9H9BJraCTR2oAVCnn1apYCRmsPIzOGU1690V4NhQq09+BtaUVQdu5SnNDvmVb5ffgOnqOjhGK5l4BgVIl0DBFt6MLML5EfP4loGWihKrH8Pqj9AcXqU0sxI/d+odEEIfPEmwh1b0MNRHNOgPDdOeW5iXUElFBU9miTQ1I4v1oCi6biWiZFZoDw/gV1ce2VI0f1owTCOUcYxyiAU/Mlmwu19aKEorm1jZuYpzY5hl649h18Lx1BUr3DSKuSuysoPuOQycI3OBhvJptD8kKDrIYSiYpQzrHeC0vQAiqrXXTK0zBKu6+APxGoer7+8KFmZrK77Ql4/+cvafVpW+bYSmj5/1Psh1/GUNIwciqKj6aHlx7xiqnonXHlzRdBleL6wEiEU1Krlj1HOIqWLqvnXFb0+f3S5ldrKJfaGlp00NO/EdWzOH/8ei7OnVn1WrrtxHSxWstRKVtMDKJqvbmRb1QLLBWO3G0Io+P1RbKvM+IUXKJe8qn2jkqVSXNy0V9vkikR7d9L+kWfwJ1pAUbye79KlPDvGzJvPIp21o+3Rvt10Pf21mhtMKSXTr/3oqvI0Q+19tD3yWUIt3dWe8+D5FHuWQFY+zdSrPyI/UsdYXCjEt+6j5f5P4E82IxRleVvXMsgOHWf2rV9gFTLLm/jjDQx8+e+QvXiS8uwY7R/5PIov4HlQnnqL+cO/ovvjv0G4ox+EgmOWmXrxe3Wr7aWUNN37BM0HnkQLhqHq/+vaFrmhk0y//uO6gjHY0kXLA58g3N6H6guCcun8LV0XK59h7t1fkj77Xl2RF992Dx2P/xqLJ95g7p3naD7wFI37H0X1hy5dC1yXheOvrVMVX59o3y46n/oqqs/P4ok3mH3nOaR9+1w3r8TteZbeZMNxpQ1IlCvks7mu16NWUVYvW3meawLnMmsIyZW/8K7rIBAromceQqwZr7oleBXXoq6AUVW96vNWKxKu5vhvLoJQtA0QuK6FVRXzlVIKs5LFH0wSS/asWVQTTfSgKDqV0mJNSkU00Q1CUC56HYRW3RAIhcAVqs2vl1J+BilddH+UQDBZt4o/GGpA1QI3Zf83Smv3IRpadzEx9DKTw2+wuUS+ybUQbO2h6+mvoYXjmNkFchdPYpcK+OKNxPp30/X0V1H9a988VlIzLB57DTUQRguGifTuQNF9VdG3Pr54I90f+zq+RBOV+Sny44PYpTyK5sOfaCLY2u2ZvBfru4Ykdx6k44kvIhSV4sQFitMjSMfCn2gh2r+b5K4H0MNxxn7xZziVpc5aAqH5iHZvJ9jSxeKJN9CCERI7D5LccRA9HEf1B5l953nC7b1EenbQdO/j5IZP41q1N6Gx3l3o0STl+QlSJ4dwbYtgSxfR3l0kdhxAqCrjz39zlYWQovkId2zBtQwKE0MY6Vkcs4IvkiC2ZS++eCMdj30eM5+iOLHa91coqvcexZtouf/jNO7/CEZqhvL8MVzLQo8mCDZ3Upy6lhbBgtiW3Z7I9AdZOPZqVWTenBv8m8Wm0PyQYFbyWGaJaLwbRdHWjKiYRgGjkiUcbUVR9JqIlVep7qOYn77m/ZeL8zS07CIQaqiJgAbDjVed57cWl3L/blyyLrVTDEfbSc2fW/GMIBLrwrHKVEq3UR9mIUg0bqW5bR/g+V4uRQNNI09q7iztvQ/T2nWQhekTq6LPvkCcls57AUjNnqnmr1ZZ+b7WeWuj8S5iyZ6NPiIACrlpjHKaQKiRls77KGRrC2eEUGlq37/crej2QhAMNbFUsKXpwdqOTRJu/pwvOQD4RYiAEsaUZcpuofqsQliNo6BSdvNYcu3lVF0E8CtBVHRAYkuTilvCYa2L3bIp31XOs/5rl37XChohJYImfDg4VNzCuvNdiYpGUImiCR0XF9MtY8jSuq1LbzVCUWk5+FG0cBxjcYbRZ/8EI1Ut0BOC1Om36fnUX0UNhNZM7zAWZ5h582cgBKovyNZf/3v44ld3Uxjp3o4v3kRlcZaRn/4RVn7F+U4oqD4/vngj5cXV1wF/soXWhz6FUFRm3vwZiyfeuCSKhEK4o5+eT/9VIt3badj9APPvv1SzvR5NsnDsNRaOvuK1S0x4y9/hji0M/+g/U54dw59sYeArfwdfogktHMPM1Nqv+RtbWTj6KrNv/wLX9AoJhaqS3P0gHY99gVj/HqK9O8kN1Rqcl2bHGHv2T6kszmCXC7C8vC9Inz1M72f/GnokQWLbvXWF5hLhrq2E2vuYfu1HZAbfxzWrOfCKguoP4lpXKxIFsf7ddH70a6i+AHOHf8X8e7/a0D7oHxSbQvNDgmnkWZw9RVvX/XT0PsLsxOFqWzyB5gt7y6ZGDtcxmZ04TN/2T9LSeR/zU0dxpYPfH6ez/yMY5fQ12dQskZofpLPvI7T3PEypMI/jGOh6mLau+69iiXn95227jONYXn6lFsBxrKo9ir3GiXjt8Yq5GbKpi7R130964Xy1AlsQb+insXUXi7OnL8td/WDwB+JeLuQK7adqASLxTpJN29D0IGYlx/jFl1bklkomh18j0ThAJN7Jtv1fZuz8r5Y9H0ORFnq2PU0o2ka5uMDkSG3kLZ+dACkJRppp7riXhenjuK6DomjEG/rp2/nJqtn6xl+0LbPAzPi79G3/BG3dhzCNPLMTh73+xHqQ1q5DNLXtqbbDXP15CqF6y+6Kjqr50P2R5Sh1INRAONaBYxu4jonrWGtWtl8fksziBVo676Wt50GSzdsvFVzhRc3LhXkWZk6RXhjccN+6kBJnX/gJZsyLFJwUu0KPElSimLLM+fJ7zFkjbA8+QLtvAIFKwUlzqvQqeedSNFsgaNS7aPcNkFBb8CkBVDQkEkfalN08E+Ygk8YgLpduWoNKhL2hJ1CFxvnyeyza9b37AJJaOzuCD+BIm1OlVym5tcuZLi6NWidbAvcSVRvRhIaLS9nNM1Y5xaR5fk37IwWVdt9Wuv07CalxtOrcTVkhZU0xXDlOwb2NbhhX4Es0Ee4aACmZP/rKJZEJICXl2XFSJ9+k7ZHPXnmw6lL3tdzYqAFvqdcxSisijkvjuThGmfJc/dWRxM6D6JEExYkLtSKzum1xcojcheM07HuE+Lb7WDz51rIYBHAtk+LUEN5St0l5doxwxxaM9CyVqrC1SnmsYg5fvBE9FFklNK1CjoWjr9SMKx2H9Jl3SWy7l3DnAIntB8hdPFWzBC4dm8LYOVYjKc+Nkx89S8Oeh/A3tLKelZvqDzJ/+FekTr1Ve/1x3SvmtUrX8bYRgtiWfXQ99WWE7mP27V+wcOTlO9Zzd1NofmiQjA+9iD+QoHfbx2jrfgDTyKOqOv5gguFzzzI74XlozU68RyjSQv+OT9PadRDbqhCKtACSoVN/ueydeC0UshNMjb5BR99HCEdbMSpZ/IE4pcLcqihbonEricYtqFqQeEN/tTL9E5iVHJZVYmbsbSzr0gnQMgoszJygrft+9t7/u5hmEVXVGRl8jnzGM9sORVpoatuPpgcJR9tQhEZH7yMkGgdwbIP56eOUCrO4rsXFsz9j+74vs+fQX6OYn0ERCuFYO4XsJGMXXrglOaXBcPOa5uRSuhSyEwyffXaVuXi5uMD5Ez9g2/4v09Cyi3jjAJZZ9Kp7fWEU1Vd9zfdrls0BUnPnSC9eINm0jYE9n6ez71Fsu4LuC+MPxinmphk+81MGdj9zU455evRtooluGlt307f949X9G+i+IIqqMzXyBrFED7GGvlXbJpoG2L7/qyiqvmxuv5Q20tn/KO09D0K1b3sxN82p9/5kwzqGCUXDsU2MSo5guIlAqGHlswDEkn00d9zLxMWXGTu/sd8pVahE1Qakz6WNfkxZRrouYSXBQOAAYTVOi95L1l4grMaIqY1sCdzD8eJLy2kgChoDgXuJqy1Y0qDk5DBlBYFCRE0QVRvZEXwQTegMV44t79twS9jSIqm10eHbxqI9Rf0LsqDN109MbSJlT1FxS6teEdea6PbvBCDvLOLiEFJihJUEO0IPoSl+hivHV42voLIteIge/24kkqKTxZAlNHTCaoJ231biWjPHiy+Rc66tZ/YHQaCxHdUfwjHKlKbqF3AUpy4ibcvLn9xgKgteBXKotZvmg0+xeOLNNZfJVyI0nUjXNgAKExfWXN4tzU3QAPgTTWiBMGaNILRrCmXssve9sAq5S+O5Lq7lBUlEnRa0RnoWq7B6vtK2KIyfJ9w5QKCpHdUfvGLrzpWY1baWiqZ7+ZtufaHpmhWyF45fVzGha1sgJfGBfXQ++RWEpjHzxs9InXjjjhWZcIcLzcij+4g+cR+FN0+Sf/H9Wz2d2x7TyHPu2LeIN2wh3tCP7gvjOAbF8XdIzV0yLncck4tnfsLi3JllE/bM4gUWZ0/XiBHbKjM/dYxibnV7q/TCea8ApXoBldJl9MKvyKXHSDRtQwiF2Yn3yCwO0dn3WM1yvM8fwRdIIIBibmq5Olf3hVG1AOKy/EkpXUbOPUs+M04s2YsQCoVKtqbzkKoFCAQTCKFgVrLLFku6HkLTQzW2PqXCLKcO/wmNrbuIxDqR0mV64t0aWyO4ZGNTs9yMV5gzN3Ws2hv7xsimRi7VVUHNNdV1bUwjTz4zRi49uqrQ6tIYFzn5zh/S3vsgyabt+AIxQFIqzpNZuMD06NurRCaAbZUYPPodOvs/QkPLDnR/FN0fxqzkmBp+ncmRN3Adi1CkBeNy66UqxfwMsxOHvSKjaxRTtlVi8Nh3ae99iKa2vfiDSTTN5405/h7zU8dp7thPuZRa5aRgmUXSC4MIqjlp9VZoq48tFU0tYVbyzE28j+MY1SKr1biOyfz0CTQ9WLNvoWj07/w07T0Pks+OM3b+BSql1PL4S92sGtt209J5gI6+R1mYPlHjY7tRxNUWRozjDJXfx6eEOBT5NCElRpdvJ6dKrzJvjdGgtXNf5BMktBZ04ceU3nfIwWKkchK/EmTRmqTsFqrRQ0FYibEr9CgNWjtdvh1MGoPL27m4TJvnadK7aNQ7CCnRVZFKAJ8I0KR1AZIZc7gmKrpEk9bFgjXJufJblNwcEolfhNgaPEinbzt9/n0sWpOrxGKHbxs9/t2YssLZ0lssWOM42AgEETXJruAjJLRWdgQf4P3C8+ukANwa/IlmAOxyHnsNIWQX8ziWgXYThGZx4gKZwSMkdxyk5f6Pk9xxkOzFk2QvHKM8P7mmgNQCIfRIAoDk7vuJ9u1c43VeNbxQVdRAEFZ8PaTreGLr0gMAONblN4HVk2Kd1QyrkFnzXGNk5gGJHoqi+Px1haZQVVR/CD2aQPWHUDQdoWoEGtuXXoFArBkjdi1zWZReC1JKpG0R6d5Gx5NfRvH5mX71RyyefPOaz523G3es0BQ+ndjThwju6UeNhcm/egw2qPfq3YzjmKTmz9btiLMSz7bnHOn5eksJHmYly4VTP6z73MTFl1c9Jl277r5Hzz9X8/fc1FHmlkzRrxLHMZmbOrIsIC/HM08fq/tcPZaWbuHdNV+TXhgkvTC46nHbKnHh1A+uel/rMTP2NjNjN262XCktMnzm54xpv6p2hPIqxtcSUkuYRo7hsz9n7MLa2w2d+tGa26fmztbcxFwrXtX2i0yNvIGi6iA9qym3mqc0O3GY2YnDq7YrZCcZPPbd69pnqTDLuWPfXn9edoWLp3+86vF4so/23oewjDznjn4Ho1x/eTabukgs0UMg3EQ41n5ThKaLzaw5gotLxS2QsecI++MUnSyL1iQSSd5JY7hldOHDrwQxnUs3K7NWvaIFSdHNMlw5RjLSSkAJE1QiNdst2lOU3CxhJUGz3suocWLVKA1aGwElQkWWWLBXe80KITBdg8HKOxTdSzeMhixxofweSa2VkBKn3beVXPmS0NSFn57AbgQKFytHa47BO94U5yvvcSDySRJaK0mtlQW7/jLwrUL1ewVu0rbXtOJxHWtFDuHG4tomUy//gPLsOI37H8GfbKXp3sdp2PMgpZkxFk+8Tn7kzKq5CVVH0b1VAz0cRwtG196HZXq5hpcJRSll/Uige/VCaz37JtcywZVe4c5l0VChasSqxUrB5k4Unx+hqF4e+DVYqd1I73ZfvImOJ7+MFowgXQeheXnRdzp3rNCUto1xYQJfdwvlUxfBuUMUv6YSuX8X5vQC5sjGX1w22WRtZNXk/oPabuNw7LWji7cTkUQniqJRKsyvm2Iipbuq9epG40gbY8WStCGLSCkpO3mcagTRxcGR5nIO5tVScnNY0sQnAqiXGatb0mDGHGYgcB9tvn4mjbPYK6KGAkGrrx+BYNGaoOKujipJ6YnCkrM6GmrIMovWJCF/nKTWioq+HJWMqU2ElQSmLLNgrdG61ElhuCVCSoyk3nbbCc3anOdb48nhWgaLJ14nc/4o0d4dJLbfR7hjC5HubYQ7+kmfeZfp13+8pqibe+c58qPr32BKKTEyNyF1YR3PYE8wLk1gRXGhotL60Kdouucxz2ljboLi1DBmZt7zHrUMEtvuJbn7ajyDr18YBls6sXJpckMniPXvpvWBj2PlU95S/B3MHSs0cSWp775I5tm3cfOl6zZX/qDxtTXS/DeeIfPTNzaF5iab3GUsFf34AjFUzb9GOoOgoXkHwVAjrmNRzF99b/lrwZUOjrRW/O1dWC+v2F6qwF7P1F9FQxUaAs/83y9CLF1Q60mhWXOYHv9uomoDca2lpigooERIam24OEyba5tIl938msU+eSe1PJYm9OXjjKmN1YVNhW7/rqqtWy0CBU34EELgF2HWK+y4FSwVjCi6v2oYvlrMeVZFN//y7VSKZM69T/bCcQKN7TTd+zjxbffQsOdBKgvTLJ54ffm1rmXimhWvstpxKM/feOrQ9aAFQus/JxRcu1yzRB9q66Vp/0cQisLsO8+vKibyXtN3s6YMeFH8SmqO0Z/9V6x8io7Hv0hy1/20P/YFrELOM6i/Q7lzhSYgLRsntbZT/+2If6ADJfzBdIzZZJNNPliyi0NYRpFwtJVt+77MzPi7GOWM501bLbxraN5Bc8c9KKrGzPh7dXvLbwSy+s/luFfp+6qi0ah30qL3ElYT+IQfVegoeMuJ60VAi26WlD1Fq95Pu2+gpiioSe/CJwJknQWy9vyaY9jSXPM5SxpIJKrQUIS6rBP9iicydOGnz79v3eOTUqII5TaTmVBJexZrWiiKFo55VjuXoUcSKD5/na1vDtKxKc+NM/Gr76BoOrGBfUT7d9UITadSxMguoEeThDv6WTj2yjUteW8UvngjiubDtVd/f4LNnQCY+QzOCiEZ6uhHaDpWPk3qskr4JfRI/OZNuopdymPmFpG2xfQbP0ELx4j27qTzqS8z+rM/xszenF7kN5tbIzQ1FTUSRDoubqG8HI1UQgGEX8ctG8jK2ieZDUFVUMMB0DRwXNyKgTRuclK4gODeLTe0GiJ0DSXkB1X15l2uIM3NLiObbHI7UCrMcfH0j+nb+Sma2vfR2LZnRdcmFVXzAQLbLDI18iajg8/fgmrSK8uqgBJmV/ARmvQuJF50seBkMGUZW1ooqHT4t60pNiUuU+YFWvReGvVOgkqEsptHQaVV7wMEs+bwuoU4V+t1ubLlw1LxlyGLjFROXrGZQsnJ3XaempWFKaxiDj0SJz6wz7P1WbliJwSxvt1exfkHvJInbWu50OXyCLh0HbIXjhPp3Eqkc4BIxwCFiWu3wrtR/A2tBJo7KE2P1DyuhaJEenYAUJoarhGTS/ma0rHr+lTq0SSRrq11rdRuFk65yNTL36f3079NoLmTjie+zPjz38Cpc+Nxu/OBCE2hqTR8/WNIyyb787do+PrThA/twq0YZH/+Ftnn3iG0b4DGv/IxtMY45vQiqW/9kvKp2mR0NRGl4atPoQRr7+Qq5yfIPvvWmj86EfDR9FufRFoOqe+8gN7ZQuIzDxPY2okSDiJNC2smRf614+RfOYo0a79o4ft3EXl4L9ZsivT3XkLWKTrSO5pIfvFxpO2Q/osXsRdzy/v293fg72nF19dG+L7tCCGIPLwXX1dLzRjStFj4xnO4udVWH0LXCB/aSfTJ+/B1t6AE/UjDwpycJ//qMQpvnrz5QnmTTTa5InNTR8llxkg27yCa6Mbnj6IoGo5jYlayFHJTZFPDlAvzt1X71UsIBgIHaNZ7MGSZwfLbzFvjONKuijJJUInQ6utDFWtfQtL2DAUnQ1RtoFnvZsw4TVhNEFObMGWFOWv94jxNrN3FTBU6AoErHVx56Xy8lBZgS5tJc3DdqOjtilXIkh06TtM9j9G47xEqqVlyw6eQjo2i+Yht2Uty16H1BxEKQlVRVA01GEFUO7Kp/iBqMIJ0LK/YSLqrRGzj/sdwynlKM2NYxezyjZBQNcLtfcQGvEhx8TIhB/z/23vTIDnO887z9+ZR99n33WigcRPgLV4gRYqXKEqmZGpIHZY80ki2Zz6sd2ImYiZm58PO7szuehzrjRnv2hPjsWRZtnVQFGWJpEiKFC+J4AES99VA4+r7qOqqrjPPdz9ko4FCV6MBEAC7wfxFAAhU5Zv5ZlZ31j+f93n+D/kju+fzOTs/9UXGt/+S0vDRud7fAjUYQo+liXauwciOL5nHeSkoepD2bZ9j9LVnqGbGkNJFiyRou/1hgulmnGqZmcM7asZUs+MgJXosRax7Hfljez37IiEIpppov+uz6PH0FfEMPh9mPsPwqz+h95GvE+9ZR/udn2Hk9Wf8zkB1URVPYIUCqLEw0ZvWIy0bvSVN45P3Iw2L1OfvRqgq0nUJreum6fcfYeR/+64X8ZxDBDRCa7vRGuKgKijBAEJTQVXIv/j2og/qQteIfmITiq5jTc6QfuxulEgIZ7aIky+iJqKENvYSWteN3pom88OXa4qLAt0txLdtpXp0mJmfvVG3ul1NRInduQVpWOSf3w5zQjO0uoPW//kJlGAAhEAEvEse6GhCb0nX7MOtGChPv4ZLrdAUoQBNv/cw8ftuQgDObBlnpoASCRHeuMr7s7mP6e88h1tZ/gUTPj7XOtVylrGT2xk7uXRf6eVGUIRp1LwlxmHjIGPm4IJtVHSU84hM8Ja+x81B4uEGWvU+RowBmvVuNBFg3DpGpY7t0dmElRgCpW5UMqomQYDhlGsKjYpzuZunuyEVnZUnNJGS6Q9eI9LaQ6Stl+4HvoQxM4ldLaNH4wQSjcwe308g1Uy4qWPB8GjHatq3fQ5FDyJUDUXT0CJeBXjzTffRsPl2pGPjOjbVzDgjv37qrDxQQax7LYm+jdiVEnapgF0tzi/lBxKNKHqA8thxZg4sdMNwqiVGXv0J3Q98iXBrNz0PfRWrlMcxqwghUPQgWjiG0HRGX/vp5b90rkNmz1sk+7fS94U/xMhNIy3TM3ePp5COw9TO1xYYzheHjlAeP0mkfRVd9z9Bw+bbsMoF9EicUHMn0rGZ3PEyLTfff9nnvBSViVOMvv4MXQ9+mfSGWzFnZ5h6/9crylfzqi6daw0Jgms6Gf1P38MtV2n5518gvGUNjV99iNKOQ2R+8CvUVIyOf/N7BLpaCPS0Uj1wYn68PZVj5H/9a5RQACUWpvErDxG9ad0FH1+EAzR++QHMkWmyf/kM1cERcBy0hgQNT9xP9BObSNx/M8W39mEcH70s52ycGGfsP/+DZy4b1Gn+5qMEulqYfW0nhdd31ohj6bo4M+f0dBaC1CO3k7j/ZuyZAtkfvEx57yDSsBDBANFPbKDxyQeIb9uKNTrNzM/eXDGFUdcqqhqgved2hKIxdmr7ov6WPj7LEVVo85HKklO/cj6ltaCx0Cz7XCask/SGriOhNZLQmmjSu5G4jJvHzrtkLYTneRlSolTc2nuiik6D1gESZp0M9lkFT3lnCkOWCYoIbfpqjjofsLwyMC8Mq5hj6KW/p+UTDxHvWUewsZ2glNilPNO73mBq1+s0XncHuA72ud17hEBKr7MOtoljgFWqL+rF2VXYANIlP/ABajBEMNlEMNVISG31bM1sE6uYY/b4fjK7f1PXFB3AyE5w4rm/oXHLHSTXbEGPpdFjSW9OloE5m6E0erxmWd11bCpTw7imURPlt8qzVCaHMGfP2IRJ6WJkJ0HKmuVvY2ZyXkjmj+6m5dYH5/qyB5GOTTUzQWbPm8wc3LHgO9Kplhh+5Ue03vYw0a5+ol39XlDHqFKeHGLy3V9h5KeINHfhGJW6kU27UqQyOYRVLly076VVmqU8cQpjZrLu9/fsiQOMv/UcDZs+Qayrn8Kpw1QmLtyu76Pm6uZoKoLiuwcwhyYBKL6938tZVBXyL72Lkyvi5EsYJ8aI3LSeQFtjjdBEStxyFbdchZkCTnHhEvP5EELgVE2m/vs/Yhw7IyTN8hSZv3+J0Lpu1HSc0MbeyyY03XIV46j39CRCAdy53FM7k6c6sNA/7lz0tgaSD9+GdFwyf/sCxbf3n3mzYjD7qx2oiRgNX7yXxP23UHhj1/yy/UpB08Kkm9ejKCqua5OdOly3S0s82UMk5pkZVys58tmFkZblQCLVy6q1DyEUlUppiunxhT6CPj7LFVta2NJEF0GSWjMT1omzooqClNrMqtDWC9pXxS2QsUZoD/TTEVhLTElTcvPM2Es7bgRFhL7Q9QxU3p1fAldQ6QquJ6E24uIwbnrtCs8cr8iIMcDq0PX0BDdRdYuMmcfOygUVKCgElDAxJUXWHlu0sv2jxsxnGH75R+ixJFo4BlJilfLY5SIgmXzvZSZ3vLJAmJRGBhn8yZ9f2EHk/F/z5AZ2kj+6By0SRw2GEZo2LzTt0qy3DL4EdinPxNsvMLXzdfRoAkUPeu0rTQO7XFhQbGPNZjn2079YMJ/coffJHf6g5hylbTH8yo/nzALmXpeSkVefnj8pqzDDiWf/mkA8jRIMIW0bq5g779yNmUlOvfT3BGIp1FAUAMcoYxVy89HDE89+9+wLV8Ps4F5mj+2re02XIrtv+1zLykXGSnlmG6grRpczV70YyBo+U2VoT+XAlbilCtb4nJO+lNi5OXuHaOiyHltKSWXfMYw6tkL2TAFzdJpIQwKtKblsHC8i1/ejpmKYpyYo76kjrKSk/MFh0o/djZaOE1jVvuKEZjCcpH/zY+h6BMcx2bfju+SztbYnQqh0r7mXptbNAEyN71u2QtOyyl7vbCGwjJWXuO3z8caUFaasIXqCm+gKbkAVOnl7EoFCQmuiRe+h4hapuLOElcR593W6KKg1sJpWfRWa0Jk0Ti6wWFowTkpm7HHaA2uIqWlm7DEcaZNQm2jSOxEojJlHyNpjC8aeNPYRVVO06qvYELmDzuA6Cs4MrrRRhU5YiRNTU5hulfeKz9XkeC47pItVmMEqLNKXfTHB8SGFiHQdrGLO67LzIXCNCsYFCFPvoBdzLrLO93PtC9Kx5zoBXQSu6xU7LdrZZ4nr+mGu+4WMXWEC8zRXV2hKiVM4E4WUttdA3i1VkdZZldPOXPKxpl72KRiDI3UtF6Trzle6KwGdZaE0FUGov8tbCrEdIjeuRdYxplcT3tMXmoredOUtGK4UUrooik403rZAaKpqgGis9aonY18KxdlR9rz7V4C4YtY1Pj6LIaVn1u6c4yEpcXCwawuQpMTB2/bs361j1V0ElCDNei9dgXV0BdYh8Qzep61hBirv0h3cSHdw45JV2zl7koKTIaW1YLkGE9aJxecOuNKm6pY4VNlOo9ZJb2gLyWDzfHW5g8WweZijlR11o5GWNNhffpNScIaOwDoSahNJteWsY7hY0iDnTOAsZ5Hp43ONcNWFpnScs/7r3aC8Ku6rICBciVOnont+Pld+BheHEGiNSYTwBGfbHz+x9JDQ1fNWu9xUyhmCoRSxROeC90KRNHogilHNo+vL3YdU+gLT5yOj5ObYXngGOSciT3PKOMioOVhTiW1jsav4CgrKfL9y8KKa+0pvklAPEtca0NCxMSnYWWadDC4Og5WdnDIOYLrnj1g5WOTtSZJqMzlnkpKTO8/Wkj2l1xAIDFmh4GSZtE6S0loJijC2tMg7UxSc7Hmti2xpcrT6AUPGYZJaE2EljiIUbGlRcYoU3RkMt7yk/ZGPj8+H5yoLTT5aNXeO0L2sXAl7LSEQuhfVNUemqJydr7oI5omFS0krBcsseRHNWCuKouO6Z5L8w9EWVC1IfuYEyYbVS+5rvj8teJ/7srSRWQxx1vzl3Pwv7Jdnfpz0Ijcrdanlo2bdrQke+qcdmFWXH/yn4xSyF2YnkmzWefLf9hGKqjz7l0Mc210/daJrQ4Tf+RfdADz1pyeZGvJy1gJhhSf/bR/p1jPWPlKCZbjkJk1O7i9yZMcsmVFj8ZVG3LptHW1p1rX7OVtgno2LQ86ZIOfUf2hysHDcpa+LJgKktXZAMmYOLpkTee58yu4sZfPS0oEMWWLSWngtfHx8rh4rujPQckIJ6Jd/sV1K3Ir3xWCcGGfqO8+unJ7ul4B0barlDJFYC3owhlE5k5cUS3SClJQK46Qa+xfdh65HaWrfQkPTekKRNCCoVrJkpwaYHt+DZdZ+6Qih0Nx+A8FQEqOaY2psD7LOclowlKK5fStCKMxMH6E4W9teLdXYTzzZXXs+0mFqbDfGeXpen42qBkg1raWheT3RWBuaHsJ1XSyzQKkwQS5zlOz0QN2KxlCkgeb2G0g1rCYQTCClTbk4xfTEXrJTA7gr0eblIyTVGmDrvWkqRYdA+Dy9k88hGFa57u4UkYTGG08tHtWOp3W2fDINEp79b2esVlRNsOG2JK2rwtQvCoDsuMlL3x3htR+OY5vL/0GiRe8hpqYpubNkFuk/7nNpCF1HiYRxSmWw/cYdPssTX2heCKcjSopgsdCl2pAA5cK/kC4IV2KNZ5Bb16C3N6AE9GvbJ1MolGZHSaRXEY40zgtNIRRiiQ4sq4xRmVm0O0M03kb/5i+QmBN8jmOCEERiLTQ0baCl40aO7PsJ5eLk/Bgv0inpWfMpQOK69oIqcUXV6Vv/aZrbb6A4O8rk6K4Fx0419tPVd7fXmUQACFzHZDZ36oKEZjjSxOqNnyXdtBYhVMD7mfN21UqqcS3haDMzmaMLorMNLRtZs+GzhCINnoWIYyKEQjTeTlPrZqYn9jN48BdYpl+YtHKQvPPsNDtfziAEhKIanesibL03TUtPiMf/VS+2JXntB0tXb19tBAI5929Ka2F16EbA8+Q05UI3CZ9LRFVI/e4jRG+9nuJv3iP3sxc+6hn5+NTFF5oXgFs2QErUeBQlqOMY50SHVIXwht45IXq+HUnvDxdY6CQl5X3HSHzqZgIdzQTXdlGpV3l+jSCEQrEwBkhiiQ5ymaMAaHqUUKQBo5rDNIo1LedOEwjGWXfdF4klO8llBhk58VsqpSkQXjS0Z819JFI99G/6Agd2fh/bOpOrOzW2h3iyi47eO+lb92lKs2NUytPz77d13kpT2xYsq8SxQ89iVHMLjj9y4k2mxnaj6xGi8Tb6Nnzmgs9bD8RYt/WfkEj14thVpsY/IDt5kGo1hyJUIrEWkg19zEwNIN3aqEU81cPazb9LIBgjOzXA6MnfUilnUBSNZMNqulffQ3P79YBkYN/TuHXaq/ksT0YGyux4oba38ct/O8of/tl6+rbGuP/32tnxy2mKueUTyQqIMFui9wACTQSIqkk0AkxaJxkxr347wmsZoesEV/egxGME1/TOpcws/wj3h0IIIls2o6VSNS+7hkFp526k6a/cLEd8oXkBmCNTSNtBS8WI3LiWwhu7zqxqCQhvXEX05vVL7kdaNs5sCSklwTWdCF2rrbavQ2XfcaqDI4TW99D45ANMTOexxqYXrKop0TBqIuLZRK3Qm41AUC6M47o20Xg7QihI6RIMJQgE4+Smj3jL2nUimm3dtxFLdlLID3N4z48wjTMmz5XSNNVylutu+QaJdC9NrZsZH35v/n0pHU4Nvkos0UkivYpV6x7i8N6f4Dom8WQX3WvuAwRDg6+Rzx5fcGzw8ktPL8ubZoFVF5ET2t59G4lUL65jcvTAz+aW78+ML+SHmBjdybkfuqJodK++j0AwTj57jIG9P65JDSgXJ6iUptl441doar2O6Yn9vqfnCmd62OC1H47Tt7Wf5u4QjZ3BZSU0BYKwkiCsxAAwZZUJ6zhHKx+syHaQyxlpmBR/8x6RSpXiG++s2Pv+RSEE8TtuJ7xhXc33gJPPUzk0gOMLzWXJihGaIqATuXmdF1UMB1GjIUKrverk4Ko2Gr/0oGfmXqniFKuUdx+paV/5YTCOjWAMjhDa0EvT1z5NoLOZ6uAoQlMIrukkvm0rTr60dMW3lJTeP0zkxrVErltN87c+R3nXEaTtoIQCiKBO4c09yLMipm6pQubvXqT1j58g2N9Jx7//fcofDGAOTSAdFzURJdDVTLCvA+PkOBP/79N1W2SuFIxqAaOSJxpvQ1F1HNsgluhACIXC7HBdeyNND9Pc5hlIjw/vqBGZpykWRinkhkg3r6ehZSPjwzs4W7hZZpHBQ8+y+cav0di6mfbcKSZHd9G34VECwThTY7sYG3qby5yF68293Zv79MS+BSJznjqvhWMtpBr6kNJh9ORbC/JPAfLZY8xMDdDcfj2tnTeTmdi/wgqjfM5l8mQVx5aomiCSWF63cENWeL/4wnyfcktWqbpllqGnx8pHSopvvkPxzYWtIK9ZXJfM08+gxuOosSjhDeuJ33XH1Z+HEKiJBG61UvN97VOfq3iXOs+N5ty36myqxsM0f+OzqMlo7aZSorc1knps2/xrbsVg9D9+b74jz8XMsZ6QccsG099/gZY/+DyBnhZSj93tFeXMPVFVD54g88OXafmjz6OmYuc9QnH7XkIbeojduYX4vTcSv+d673wVgZMrUnrv0IKl+erAEBP/5cc0fka50kYAABq+SURBVPlBQuu6STx468KZV01KOwfml+ZXKo5dpVya9IpaAnEqtkE00YF0HUqzY2h1rI1CkUaC4SSu6+A6JpFY24JtBODOLTuHI40oilZT1Q5QzA9z8ujL9G96jO7V95FM95FMr6JcGOfEwItXZNk5GEoRCqeR0iUzefCiRGAs3o6qBbHMMoV8/Z91KR3yMydobr+eaLwNPRCtK8R9Vg6BsIKiCFxXUi0tt4dKuaBlpI/P5cSezmBPeyklQteJ33n7VZ+D3txE6x/8M2Ze/BWl996/6sdfaVwVoSlNm4m/eAYR0DFHzjj1myfGGfmP30NWDaR15oaZe/4titv3Yk2eqTp28iXG/+8fINSlC26kK2uO45arjP/pD0BVsEYW6RTgOGR/8DL5Z9/CniksWIYwjo4w+n98j8jWfoL9najRME6xQvXQScp7juKWDcb/609QgnrNvM/FLRtM/dUvKP52L+HNfajpONgOdq6IcWwEp1Q/ClsdGGL0T/6O0Loewht60JpTIARuoYw5PEV1cARrZLKuGf1KQkqXYn6ExpaNhGPNGFUvumkaBaqVLDF9ocdmMJREUTSEUFm/5YlFDaSF8H52FEVDURcKTYCJ0Q+IJ7tp676VxtbN2HaFwUPPUq0s/pl+GIKhJEJRcR2LanmxbhSLjA2nEULBMks4zuJFYtVKFpBoWgg9EPOF5lUiEFIIRurfr/SQwqU0hVAU2HxXCqFAdsQgM3oNFwdeDYRAiUfRW5tRYlGEELhVAyeXx87kvJy/c4MPikDoOrjufOqTEgmjd7SixmNI18XJzWJNTCOrtcVPIuhFeqVlLRoUELoOitekg3Pt+IRABBb2mJeOe+FV50KgJmJoLU3eOQNu1cCeyeFk80vnOaoKWkMarbUJJRDANQzsiWnsbG7Ff/9cKMHeHrSGtNee02dJrs5VktLryHMObrlK9eCJBa9bYxmssdokeGk7VA9fYhN5x6U6sMRYyXwP9kV3kytSeGOXl6NZhwv1sJSmRXnXEcq7Li45XlZNKnuOUtlz9KLGrTSKs8MIBLF4O8X8COFII8XZUWyrfsWqquhetbd0KcyOLBl5NKr5RSOH0nXITh+mtfMmFFWnWs4usDK6nCiq14VKSterkr+YsYr36yvP6epyLqevhxAKQrn83bZ86vOVf7+aL/7rVXXf04MCPSiwqhcmNFVNEE1p3PJwE9seb8V14DdPTzKb8Yu7LhW1IU3igW1EbtiMEo8hdM1bpXIcXMPEns6S/f7TmKdqf/+D/X00fv2LVA8Pkv37Zwhft57k5x5Eb2vxRCISaZiUdx8g8zc/mheUaipB67/8Nigqmb99CuPIwnxvoWs0fvNJgqu6Kbz6FrMvvV7zvt7eQtO3v4pyTppWacduck8/v+Q5K7EoiQfvIXrr9SiJ2JxQOn3OBvZ0lsIrv6X07s66OZ9aSyPJR+4nvGU9SiQMqgqOg1MsU9m9n/zzv8aZuTA7t5VMqH/N5XeZuYb5eMhxVa2xxJFI78lrhS8zX6tUShksq0ws2Ul45iSaFqaYH6nrbwnekricsyY6uv8ZSoWluvIsbuAeDCXp7X8AoWg4jkUs3kFH710MDf76iuQ2esv5EiEEqrowUnHesY6JlBJF0eejtfVQ1bkoinQXVK37XBmEEITjKqFofWF/no9rnju/0Mzam+MIIdBDCo0dQRraA9im5JW/G+OV74/5qY+XiN7ZRtM3nkTv7gDHxc5ksTMzXt57NILW0ogSCuHMLoz+C11HS6fQ21qI3LyFhi895vXIPjGENEyUaASttQl7KlP7HaMoqOkUqMqcIK2HQE3E0RrTKJHQgnddw8QcGkFNJlBCQfS2FpRwCCUaWfKcha6RfvJ3iN56PW6linHkOPZMHqEoqA0p9NZmAp3tiKBeV2Tq7S00fesr6J1tODN5KnsP4ZTKaMkEgb4eYvfcjt7eyvT/+Aec3KUZ7F9xFAWtoYFQXy+B7i60VBIQOMUi5tAwlcNHsLN1VpaEQAQCaKkkelOTJzSBQEc74c0bazaVlk118NjCaPQcWmMD4U0bCfZ0o4TDuOUyxomTlA8cwsnlLvMJLw+ufaEpBE1f+iLB1X3e/6XErVaxxicp79tP5eAhP5l3mWEasxjVPOFoM4l0L4qiUsgPLbq9Uc3jujaKohIMpSjOjl7ScRVFp3ftw0Tj7RRyp5gYeZ++DY/StWobxfww2alDl3pKi2JW80jXQVEDBMMNFzV3b6ldogeiqFqwxrLpbEKRBkBg21XMOgVDPpcfKSU/+dOTHHq3fnRn9dYYX/5fztfhStDWF6at70xOshCC8eMVfvR/Hmf/Wzkcy1eZl4IIh2h48nfQuztw8gXyP3+R8u4DuKUKuC4iEEBNxlETcZz84mkmensL6ccfpbL7APkXXvWWjm0HEdBRU8krYrXjZGbIfOdHoCiIgE7zt79KeMuGCxqrt7cSuWET0jDJfPdHVPYfPtMARFVREzECvV0YR08sGCsCOqnHH0Xvaqey5yAzP/o5dmbGE6SKQnBNL43/9AmCa/tIPPxJZp56dvkFcoQg/chDxG6/DTU+V0vheD7KqN4DoZ3Nkv3pP1Led6BmaHhdPw2/+xhqPI4SCnnXXwiS92wjec+2mm3tfJ6RP/kz3NI591pFIXbLTaQeeQgtnfauneuCqhL7xC0kp6bJ/vxZ79jXmIPAtS80ATWZRE0lqR48hLRslHiM0Ia1RG/cSnn/QbJP/wwnv0yfwD6GuK5DcXaU5vatNLZsxLIrVErTi25fKU9jVGaIxFpJN68jM3XwEn5RBW1dt9LSfj2WWeTY4ecp5IYIR5voXLWNvg2foVyaolrOLL2ri6BayVGt5IjEWmhs2Uh28sAFR04LsyNYZhlND5NI9TJVJ49UCJXUXMvOUmEc2xeaV42p4SpDB+tf71hKO2+6A0je+cU0H/zK+3nbdFeKe55oJRRTmZkwfJH5IQhvXEtwbR/YDrmfPk/pndplYmma2FMZLyJ5HpRohOqJAbJPPYusnEnrkaaFPbn4/eqy4LpIw7yolspKPIoIBHCyOcyTw7Vd5hzHi1IusuwdXNtHaGM/7myRmaefx54+K+rnuhhHjlN8bTupxx8lcuMWZn/1Jk42d4knd4WQEqdcBtehtHM31aOD2FlPLAe6u0hsuxOtoYH073wWY2i4RhM4hSLl3fvm83Njt96CEglTOXgYc6Q2tcKtGkhr4UNG9MbraXz88yAUiu/uoHzgIG6lgpZIEvvEzYT619D4xOM4pTLGsfo2eiuVj4XQBJCVKtmfPetVqykKWmMDyQc/RezWm0FKpv/uh16Cts8yQFLMD9PWdQvxZBfF2bHzdtexrQqToztZte5hmtuvZ2ZqgMzkQRauKwqvraNjzVegnyaR7qWn/34QgqHBV5mdOQlIhgZfI57sJpFeRd/6RxjY8+OLzqU8H7ZVZnp8Dz39D9DUtoWZ6SNMj9e3ODq3/3u1nCU7dYjWzpvp7L2TfPYYplH7wJRuXkeqcS3StZkY+cC3NlpBjBwp8/5Lntg5unOWtTfF6Vgb4TN/2M1f/5uBFdF+cjkSvn4TKArm8AjlnfsvPXrkuhS376gRmcsZJzeLrFRRUwni997J7K9/i1u4gG5hQhC+bgNC06ieGFpUgFePHgfpoibj6K3Ny09oAoXt71DauQcnn6/53CuHDmNNTNL8tS+jNzYQ7O2lvOeM57A5OoY56tVgKJEIkU0bUSJhSnv2Uty+tL2UmkyS/vRDCF1n5tnnyb/2Zk3hVHn/AZq//lXCG9aReuh+Jv/H3yCvoZaiHxuhWYPrYk9NM/PML9BSKSLXbSK0fi2VfQdACGJ33Eags4Pc8y/gls5ajhSC+N13oTc3kXv+RdxKpeY9vb2NyKYNqA1pZLVKdfA41SNHkaYvYC+WUmEc17FQ1ACl2dEFwvBcxobeJdXYT6qxn3Vbvsjk6C5mpo9gWSVUNUAonCae7CKW6OTI/mdqCnwCwTirNzyKHogyPbGPseH3OC1SLavEscPPs/mmr9PYson2ntsZPv4mZ4vYQDBOMJRC1YJoWohwrNnLmRTQ0LyBQDCOY1exbQPLKCyoYB8beod007q5Lj+fJ93UT3byMKYxi1DUubl3A5LBQ8/N51lK6TB07DUSqR7iqR42XP/luc5A014ks3ENnau2oWpBpsZ2kZ06eFk+G5+rT37K4qW/GeVr/2ENN9yXZss9aXa+fHEuBT5e1bfe0oQQAvPkMNK49Kp96ThYI8uvBehiWONTFN/ZSfye20h8+l7CN2ym/N4uyrv2Y01M1UY4z0JoKnpHq5cr3NpE49ceryvOlUjYK6aaq2pfjsiqgVOt/5lXjx7Fzs6gt7agpVOX9biRzRvRmhqxJiYpvP3egup8t1ym+O57hNev9Sram5uwxlbOz9ZSfDyF5hxupUJh+zuE+lcT2XodlQMHAUFo7Roimzcy+8qrtUJTEYTX9xNc3Uf+5VfhtNBUFBJ330XyoU951hazBZRwmPjdd1Hed5Ds08/gFv0ly4uhUvYKgoKqPpefef6og22VGdj7FH3rP0Nj6yY6eu+ko/fOuXFnCsFMo1AT1VMUjd61DxFPdlMpTXHi8Au450QsC7khTg3+mtUbHqV79b0UZ0fn22OC19mnZ82n6lZ4dK++96z/SabG9nBo9w9rzsc0Chze+2NWb/gc6cZ+WjtvobXzlgVzr5cjWilNcXjvU/RveoxkwyqSDatwHWuuwtyzcJoc3cmxQ8/57SdXODteyHDbZ5vZeEeSz/5RN4M7C37V+UUiNG2+yOZDV0c7Lu5Kyu93HHLP/BJ7KkP8vjvR21tIPvYw8QfvwRg4RuGNt6kePrbQJklR5nMatZYmtKbGxY/hSkDChbRYXmZI28EtlRBweW2LFMUrHhICa3wCFAUlsrB4y5ktgCtRwmG0hgZfaF5LmMMjSMsi0NaG0PUlW0LWI7xhHclHHqK8ew/5F1/GKZURAZ34HbeRevgB7EyG3HMvXHMJvpcLozrL8UO/xLYq85XltlXm2KHn0ANRslOH57ctFyc5euDnc96Q5+4nz8Den5Ac6aOhZSPRWBuaFsRxLYxKjmJ+mJnMIOXimap0RQ1QyJ2iNDtKYXa0psf5GSTjw+9hWxU0LVzjYACQnR6Y68qzRK97PAFdTzRXStMc2vUPpBrXkG5eRyTWiqYGcV0bw5ilODtCdvJQ3arxQu4U+3Z8l+a2LaSa1hIMJXFdm3JxkszEAXKZo3U9Q31WFtWSwy//api+rTF6NkW558lWnvvLYf+2cjGI+b+WyJH9iBDUbbF7uZBVg8LLb1J+fw+RGzYTufUGAj0dhG/YTGjjWkrv7iT301/ils/xc56bUmXPwQU5rfUwT15ss5SriKKgpVLoLc1ojQ0okTBKIIAIBtEaG+eispfvcELT0BobPCeKzRvp6l+kCFBRYM4nXI0sbEyykvnYC023auBWDZRoBKGoSC5SaCoK8bvuQFar5H75Ek7Oe0qWhkHhzbeI3nIT0Ru2Mvvq67XRUZ95bKvM+PC7Na9J6dbty21Uc4yd2r7ovlzXYmZ6gJnpAUDM3bTlnLZbeHP0jv3egtcX7NfxIoP1KOROUchdosfrWTiOQWbyAJnJA1zI3M/GMouMntrO6Km3L2qcz/kRQhBPa1jVRXxXgVLOwr1KDXoOvzfLjhcybHu8hU99pZ1dr2QZPuzfVy4UaTu4c0unajLxEc+mDqpa15D9cuPM5Cm8+hbF375HoK+b+L13Etm6idi2T+AWSuR+/tIZMelKryIfcEtlyjv3Lr+K8gsk0NVJ8v77CK9dgxKJIF3XW8aWEqREBAKX/6BCzPueSts+bxTcrVQBiXuNpdt97IUmMNegYy7kf5GosSh6R7u3fP7Ju2tzL4RACYUQuoYai/lC86ojV3AU+VLnvpLPefkRjqv88X/ftKiQdCyX/+fbBxgbrN/R63LjWJKXvjvClnvSJJt1PvMHXXz33x3FMvwirwtBGib2dIbAqi6CPZ2IgH51c+hdF6GpiEWWlpVwCDURv2rTkaaFcfgY5rEhnC8+SvzeOwjfeB35F19DzglyadtYYxME1/ahd7UjAoH591YSgc4OWr7xdbTGBqzJKWZ/sx1jaAgnP4tbNRCKoPnrXyXY3XWZjyzn819LH+xi5pcvLXmP/jC5w8uRj73QVEJBlGAQcybntfE6L4JzY+pC1z0xqalErtu04AdIVqs4eXN5LtP4+PjUxXXArLqomljUdB3ANr2+46eR0hunB13keaI+risx56KkNUYAEmzTxay6OE798WODFV77wTiPfLuTzXel2HhHkj2vXZkWqdccUlLZe5jIzVvRu9sJbVpHZdf+q3Noy8YtV1DDIfTWJuo9moTW9qHGo1dlPrVzszAGTxC/5zaUYAChiDNhFymp7DlI7M5b0NtbCW9eR/n9hatNyxpFIXHPtnmROflX38Waqm1HLYLBK/OQ7rjY+Tx6R7sXcCqXPzatOk/zsReawd5uhB7AGpvw7I3Okx8jhFiQJCxdFxwHc2KCqe98H+nUWXqX1Fao+/j4LGv2vp7lP3y+wNLJWpLs+JmlsOyYwf/15b0IRZCfXnyJ7NiuIv/747sByE2c2a5acvgvf3gAVVMo5etH2qSEF78zwtu/mAQE5dlrxwblalDZexDzxDCBvm4anvgcM6pC5cARL0onJcwVDOmtzVjjk7iFy1PI6VYqmCPjhBvTRO+8lcpez1IHCagKgd4uEo98aunWhkJ43YU0bT6nT6gqIhjw+qOfXgo+h+D6NQhVxRwamTen9wZ7aQTRm7aAomCNTy6oVageHqRyYIDw1k2kH38U6bhUDw/OXzOha4hQiEBHKyIYoLL34LLK2hG6RqCjHSEE1cMDWNMLc/GVUAg1cSHpFHI+cHRuvn7drW0b49QQ4Y0bCHR3ocZjHzvf7o+10FTCYWK33wbSpbzvLD81x/FaTp3TJkwEA14LsbNwyxXsXM6zQxDCXx738bkGMMouU+WLX75ybMn0yNLjLMNlenjhdlJCdmzpSmaz6jI1dG0tr10t3FKZ7I9/QdM3nkBraaLpm1/CGp/yOvu4Lko4hNbUgAgGmPzz72BeJqGJ7VB8421C61ajd7TS8j99E2PwJG6litqQItjXg1MoYBw9QbB/1YLhSiJG6vOfRo1FEcEASiCA3t4CQPi6DbT88beQpoU0TZxCkfwvflXTCjK0fg3Jhz+JPZPHGp/yfDVtGzUeI9DdgdbSiFsqM/vKbxYITWlazDz1nLftqh6avvVlrLFJr3JfSpRIGLUhhZZKUHpnJ5W9h1hWSlPOBYWgvpAXguhNN5zpGHS+XVn2vOe21pC+oMOXd+8lse0utHSKxN13MfPCS2DXyccRwpvfRRjxrwQ+nkJTUdAa0qQ+/SChNX2U9+yjeviI956UWJksQtMIre3Hmpyab7MVuW4zemMjrnnmBi8Ng9LO3aQffYTEvXeTe/FlZHXOwPesHM16PXN9fHx8fK4+5rGTTP5/3yP56XsJbV6H3taM3tXuvek4uIaJNTKOW1wYOJC2jZP3RNrFLoFW9g+Q/eE/kvj0vWiNaSK3Xu/t0zQxjp0i99PnCazuRWtuwK3UPkgogQCh/lWIucISANc0cedaXWpNZ0SPW6nWbAdeJbg1MY3WkELblAJF9QL2jusd/8hx8i+8SvXAkbpztyenmfpv3yfx4D1EbtqC3t5KoKdz7k0H1zQxh0apHBi4YnniSjg8L7JFMIje2uIFhTSNYG83diaKaxhI08KtVufbgErLwhweIdjbQ2TzJkq79mAODSORqOEI0ZtvIHnfvd5nukREWVoW5tAwgc4OojffSPXYcYxTQyBBzPWxP91x6DTm2Dizb/yG1EP3k7j3HtR4nOKO97GmsyAlajSK3uL1UHdmC+ReevmayrUX58sdFEKs/DMVgtZ//m2CfasovrcDadlo6TTB3m7UeIzKocNknnoGZyY3PyTQ1UnrH30LFEHpg904uTx6awvBVT1Ix0FNJBj9kz/zugvgdQpo/NIXCW/eiHlqGHN4eH67QHsb5b37yT3/4kd0AXx8fHx86qIqqKkkelsLaizipTlVq9iZGezpLLJehbCiIIJedfL8cvtFosRjBDrbUOIxkBI7M4M1Ou4d73TluW3XRhaFWCAez0e9uYm5aK2WTiJCQYQQuIaJPZ3FnsxcWH92RaAm4ujtrajxKBKQ5Sp2JoudzdW/ZpcDRaH5K08S3rwRoSje9VDV+T7l0rK9hGdXIl2H3IsvM/v6m/PDAx3ttPyz30drbEQaBlYmC66DmkyihEIU33sfaZgk7rvH697z8quLTiXQ3UXLN7+Olk4jLQunWPQ8MINB7FyOsT//ywUFPSIQIPXQAyTuvstLc7AscBzPLVmZS4VQFAq/3U7mJ8+sSKEppaybS/CxEJrN3/iaZ5gK4Lpersz4BOU9+yjv2Ve3wiu8cT3xe+8m0NYKgDU+Sf7V1wm0tRLfdifj//UvaqKUIhQi9olbiN6wFa2xARQFt1TCHBml8JvtGMdPXI2z9fHx8fHxufYQgvi2Owm0t13Q5uV9+6kcqG1yEehoJ/HJuwn29aJGIriWhT01TfH9nZQ+2IXe3ER8252U9+yjcujwInue21dXJ4lPbiPY0+N1RbIdnFKJ6vETzPzjs/VbSCoKodV9xG69eT5fE+ZS8DIZqoPHKO3ag51ZmV2/Pr5CE+8pTsw99SDxxKZpLpkHIXQNoQdAeEsb0rK91wIBz9C2zrUTAX0ut1MgHcd7QvyYVZj5+Pj4+PgsSxQFJRjwUgekRFrWfM7lh96X43j7Wuo7XxGIQPCMLnEdXMuqn7e5gvhYC00fHx8fHx8fH58rx2JCcwkfBR8fHx8fHx8fH59LwxeaPj4+Pj4+Pj4+VwRfaPr4+Pj4+Pj4+FwRfKHp4+Pj4+Pj4+NzRfCFpo+Pj4+Pj4+PzxXBF5o+Pj4+Pj4+Pj5XBF9o+vj4+Pj4+Pj4XBHO66Pp4+Pj4+Pj4+Pjc6n4EU0fHx8fHx8fH58rgi80fXx8fHx8fHx8rgi+0PTx8fHx8fHx8bki+ELTx8fHx8fHx8fniuALTR8fHx8fHx8fnyuCLzR9fHx8fHx8fHyuCP8/B1Xhl7+xGGsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stopwords = set(STOPWORDS)\n",
    "\n",
    "# Function to generate stopwords\n",
    "def show_wordcloud(data):\n",
    "    wordcloud = WordCloud(max_words=150,\n",
    "                          background_color='black',\n",
    "                          max_font_size=30,\n",
    "                          stopwords=stopwords,\n",
    "                          scale=4,\n",
    "                          random_state=1)\n",
    "    wordcloud = wordcloud.generate(str(data))\n",
    "    \n",
    "    plt.figure(figsize=(12,6))\n",
    "    plt.axis('off')\n",
    "    plt.title('StopWords')\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.show()\n",
    "    \n",
    "show_wordcloud(data.lemma_rev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d04acfcd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}