diff --git a/.ipynb_checkpoints/final-checkpoint.ipynb b/.ipynb_checkpoints/final-checkpoint.ipynb
index 250742b..7525253 100644
--- a/.ipynb_checkpoints/final-checkpoint.ipynb
+++ b/.ipynb_checkpoints/final-checkpoint.ipynb
@@ -4,17 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# CMSC320 Final Project: Chess Data Analysis\n",
+    "# Mastering the Chessboard: A Data-Driven Approach\n",
     "##### *Mohammad Durrani and Pranav Shah*\n",
     "\n",
     "### Contents\n",
     "1. [Introduction](##1.-Introduction)\n",
-    "2. [Required Tools](##2.-Required-Tools)\n",
-    "3. [About The Data](##3.-About-The-Data)\n",
-    "4. [Data Collection: Scraping](##4.-Data-Collection:-Scraping)\n",
-    "5. [Data Processing: Cleaning](##5.-Data-Processing:-Cleaning)\n",
-    "6. [Exploratory Data Analysis](##6.-Exploratory-Data-Analysis)\n",
-    "7. [Future Work and Conclusion](##7.-Future-Work-and-Conclusion)\n",
+    "2. [About The Data](##3.-About-The-Data)\n",
+    "3. [Data Collection: Scraping](##4.-Data-Collection:-Scraping)\n",
+    "4. [Data Processing: Cleaning](##5.-Data-Processing:-Cleaning)\n",
+    "5. [Exploratory Data Analysis](##6.-Exploratory-Data-Analysis)\n",
+    "6. [Machine Learning](##6.-Machine-Learning)\n",
+    "7. [Future Work and Conclusion](##7.-Insights,-Future-Work,-and-Considerations)\n",
     "8. [References and Additional Resources](##8.-References-and-Additional-Resources)"
    ]
   },
@@ -29,14 +29,37 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This project is designed to gain a greater insight into what charcteristics impact winning a chess game the greatest. We will go through the entire data science pipeline from scraping and cleaning the data all the way to training a machine learning model on it. As chess players ourselves, we have seen learning resources encouraging various qualities of ones game for success, but there has never been a consensus on the most impactful features that players should focus on. This study aims to answer this question to provide players with better direction in their play."
+    "In this data science project, we will navigate through the complete data science lifecycle, from data collection to training a machine learning model and uncovering valuable insights. Our main goal is to analyze the key characteristics that significantly impact the outcome of a chess game, identifying the most crucial factors contributing to victory. As chess enthusiasts, we have explored various learning resources that emphasize different aspects of the game. However, there has been a lack of a data-driven consensus regarding the most impactful features that players should prioritize to maximize their chances of success. This study aims to fill this gap by leveraging data science to provide players with evidence-based recommendations. By analyzing a vast array of chess game data, we will uncover patterns, strategies, and characteristics that distinguish winning players. Through machine learning techniques and statistical analysis, we will identify the key factors that greatly influence the outcome of a chess game. The findings of this research will serve as a valuable resource for chess players of all skill levels, helping them focus their efforts on the most essential aspects of their gameplay. By providing a data-driven perspective on the qualities that matter in chess, we aim to change how players approach their training and help them make informed descisions. Moreover, this study is relevant in a general data science context due to the lack of analysis on the subject currently done and the abundance of data avaliable (millions of games are played each month).\n",
+    "\n",
+    "Join us on this journey as we explore the world of chess analytics and uncover the secrets to success on the chessboard. Through this project, we will advance our understanding of the game and provide players with the tools and knowledge they need to improve their chess skills."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Required Libraries and Imports"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Required Libraries"
+    "We use a variety of libraries in order to help us with data cleaning, visualization, and cleaning. Use ``pip3 install [packageName]`` to install the package locally.\n",
+    "\n",
+    "* [Pandas](https://pandas.pydata.org/docs/): high performance data structures and analysis toolkit for data frames\n",
+    "* [NumPy](https://numpy.org/doc/stable/): scientific computing library, allowing for convenient and performant array / matrix operations\n",
+    "* [Matplotlib](https://matplotlib.org/): plotting library for data visualization\n",
+    "* [scikit-learn](https://scikit-learn.org/stable/): tool for machine learning pipelines\n",
+    "* [category-encoders](https://contrib.scikit-learn.org/category_encoders/): set of scikit-learn transformers to help with encoding categorical data\n",
+    "* [chess](https://python-chess.readthedocs.io/en/latest/): allows us to parse Portable Game Notation file (.pgn), which is how chess games are stored"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Import Libaries"
    ]
   },
   {
@@ -45,86 +68,85 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
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+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
+      "  from pandas.core import (\n"
      ]
     }
    ],
    "source": [
-    "import sys\n",
-    "!{sys.executable} -m pip install pandas\n",
-    "!{sys.executable} -m pip install matplotlib\n",
-    "!{sys.executable} -m pip install numpy\n",
-    "!{sys.executable} -m pip install scikit-learn\n",
-    "!{sys.executable} -m pip install chess\n",
-    "!{sys.executable} -m pip install category-encoders"
+    "import chess.pgn\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import re\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score, classification_report, precision_score\n",
+    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neural_network import MLPClassifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Background / Further Reading: Chess"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to understand some of the motivations and terminiology in this study, we reccomend a familitarity with the game of chess. Reccomended resources include: \n",
+    "* [Britannica: About Chess](https://www.britannica.com/topic/chess)\n",
+    "* [Chess.com: Chess Terms](https://www.chess.com/terms)\n",
+    "* [Chess.com: What Is The ELO Rating System?](https://www.chess.com/terms/elo-rating-chess#what-is-elo-rating)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3. About The Data"
+    "## 2. About The Data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This information was gathered from the lichess open database, showcasing the evaluated games played over a month's span. The data was acquired from the website: https://database.lichess.org. It was divided into two CSV files: one file encompassing the complete dataset, and another smaller file suitable for sharing on GitHub. These files are identified as evaluations.csv and miniEvaluations.csv respectively."
+    "There are a wide variety of chess databases online that allow you to download and analyze chess games. For this study, we selected the Lichess open database for two main reasons:\n",
+    "\n",
+    "1. Lichess is one of the largest online chess platforms, providing a vast collection of games played by players of various skill levels. This ensures a diverse and representative dataset for our analysis.\n",
+    "\n",
+    "2. The Lichess database is freely accessible and regularly updated, allowing for easy acquisition and use of the data for research purposes.\n",
+    "\n",
+    "The data was acquired from the website: https://database.lichess.org. Due to the size of the data (~30 GB of games / month), we restricted our analysis to March 2024. Once downloaded, it was divided into two CSV files: one file encompassing the complete dataset, and another smaller file suitable for sharing on GitHub. These files are identified as evaluations.csv and miniEvaluations.csv respectively.\n",
+    "\n",
+    "The evaluations.csv file contains the full dataset, which includes a comprehensive set of features and game information for a large number of chess games. This dataset will be used for the main analysis and model training.\n",
+    "\n",
+    "On the other hand, the miniEvaluations.csv file is a smaller subset of the full dataset, specifically curated for sharing on GitHub. This file contains a representative sample of chess games and their associated features, making it suitable for rapid iteration."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. Data Collection: Scraping"
+    "## 3. Data Collection: Scraping"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "At this stage, we need to take the data downloaded from the Lichess Database, filter out all of the games missing evaluations (approximately 90% of the database), and organize it into a csv with all the pertinent features. This csv will be organized in a way where it can be directly read into a pandas dataframe."
+    "At this stage, we need to take the data downloaded from the Lichess Database, filter out all of the games missing computer evaluations (approximately 90% of the database), and organize it into a csv with all the pertinent features. We required games with computer evaluations as this allows us to create deeper insights using blunders and other features. Note that 90% of the dataset being removed may sound significant, but considering ~90,000,000 million games are played monthly, this is ~9,000,000 games. This csv will be organized in a way where it can be directly read into a pandas dataframe."
    ]
   },
   {
@@ -133,17 +155,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import chess.pgn\n",
-    "import pandas as pd\n",
-    "import glob\n",
-    "import numpy as np\n",
-    "\n",
-    "#pgnFilePath =  r\"\\\\wsl.localhost\\Ubuntu\\home\\mdurrani\\lichess_db_standard_rated_2024-03.pgn\"\n",
     "pgnFilePath = r'./data/*.pgn'\n",
     "#outputFilePath = \"./evaluations.csv\"\n",
     "totalGameCount = 0\n",
     "totalEvalGameCount = 0\n",
-    "fileList = [] #glob.glob(pgnFilePath) # modify this line for the directory that has all the pgn files \n",
+    "fileList = [] \n",
+    "#glob.glob(pgnFilePath)\n",
     "rows_list = [] # list of dictionaries for each game \n",
     "for file in fileList:\n",
     "    pgn = open(fileList[0])\n",
@@ -184,7 +201,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 5. Data Processing: Cleaning"
+    "## 4. Data Processing: Cleaning"
    ]
   },
   {
@@ -196,55 +213,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
-      "  from pandas.core import (\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   ECO  Result  BlunderDifferential  MistakeDifferential  \\\n",
-      "0  B09     0.0                    0                    2   \n",
-      "1  C33     1.0                   -1                    0   \n",
-      "2  B12     1.0                   -1                   -2   \n",
-      "3  B13     1.0                    0                    1   \n",
-      "4  C40     1.0                    0                   -3   \n",
+      "   WhiteElo  BlackElo  ECO  Result  \\\n",
+      "0      2344      2247  B09     0.0   \n",
+      "1      1605      1733  C33     1.0   \n",
+      "2      1897      1491  B12     1.0   \n",
+      "3      2026      1684  B13     1.0   \n",
+      "4      1520      1079  C40     1.0   \n",
       "\n",
-      "   InaccuracyDifferential  TimeDifferential  TimeControl  Moves  \\\n",
-      "0                       1               -26          180     36   \n",
-      "1                      -2                33          600     48   \n",
-      "2                       0               495         1800     48   \n",
-      "3                       2               531         1797     90   \n",
-      "4                      -3              -121          600     48   \n",
+      "                                          Variations  BlunderDifferential  \\\n",
+      "0  1. e4 { [%eval 0.13] [%clk 0:03:00] } 1... d6 ...                    0   \n",
+      "1  1. e4 { [%eval 0.13] [%clk 0:10:00] } 1... e5 ...                   -1   \n",
+      "2  1. e4 { [%eval 0.13] [%clk 0:30:00] } 1... c6 ...                   -1   \n",
+      "3  1. e4 { [%eval 0.13] [%clk 0:29:57] } 1... c6 ...                    0   \n",
+      "4  1. e4 { [%eval 0.13] [%clk 0:10:00] } 1... e5 ...                    0   \n",
       "\n",
-      "   DevelopmentDifferential  KingSafetyDifferential  EloDifferential  \\\n",
-      "0                       -1                      -4               97   \n",
-      "1                       -2                       0             -128   \n",
-      "2                        2                       0              406   \n",
-      "3                        2                       0              342   \n",
-      "4                        1                       4              441   \n",
+      "   MistakeDifferential  InaccuracyDifferential  TimeDifferential  TimeControl  \\\n",
+      "0                    2                       1               -26          180   \n",
+      "1                    0                      -2                33          600   \n",
+      "2                   -2                       0               495         1800   \n",
+      "3                    1                       2               531         1797   \n",
+      "4                   -3                      -3              -121          600   \n",
       "\n",
-      "   AverageElo  \n",
-      "0      2295.5  \n",
-      "1      1669.0  \n",
-      "2      1694.0  \n",
-      "3      1855.0  \n",
-      "4      1299.5  \n"
+      "   Moves  EloDifferential  AverageElo  \n",
+      "0     36               97      2295.5  \n",
+      "1     48             -128      1669.0  \n",
+      "2     48              406      1694.0  \n",
+      "3     90              342      1855.0  \n",
+      "4     48              441      1299.5  \n"
      ]
     }
    ],
    "source": [
-    "import pandas as pd \n",
-    "import re\n",
-    "\n",
     "# Read data back in from csv and ignore the first column because it just contains the indicies.\n",
     "# We only want white and black elo, opening category, result, and variations\n",
     "smallDataset = \"miniEvaluations.csv\"\n",
@@ -332,29 +338,6 @@
     "        return len(moves) + 1\n",
     "    return len(moves)\n",
     "\n",
-    "def getDevelopmentDifferential(variation):\n",
-    "    moves = re.findall(r'\\d+\\.+\\s[\\w-]+',variation)\n",
-    "    moveList = []\n",
-    "    for move in moves:\n",
-    "        moveList.append(move.split(\" \")[1])\n",
-    "        \n",
-    "    whiteNonPawnMoves = 0\n",
-    "    whiteCastleTime = 0\n",
-    "    blackNonPawnMoves = 0\n",
-    "    blackCastleTime = 0\n",
-    "    for i in range(len(moveList)):\n",
-    "        if i<=24 and moveList[i][0] in [\"N\",\"B\",\"R\",\"Q\",\"K\",\"O\"]:\n",
-    "            if i % 2 == 0:\n",
-    "                if moveList[i][0] == \"O\":\n",
-    "                    whiteCastleTime = i//2\n",
-    "                whiteNonPawnMoves+=1\n",
-    "            else:\n",
-    "                if moveList[i][0] == \"O\":\n",
-    "                    blackCastleTime = i//2\n",
-    "                blackNonPawnMoves+=1\n",
-    "    nonPawnMoveDifferential = whiteNonPawnMoves - blackNonPawnMoves\n",
-    "    castleTimeDifferential = whiteCastleTime - blackCastleTime\n",
-    "    return pd.Series({\"DevelopmentDifferential\": nonPawnMoveDifferential, \"KingSafetyDifferential\": castleTimeDifferential}) \n",
     "# Turn string result into a number result \n",
     "def getResultForWhite(result):\n",
     "    if result == \"0-1\":\n",
@@ -370,15 +353,12 @@
     "\n",
     "df[\"Moves\"] = df[\"Variations\"].apply(getMoves)\n",
     "\n",
-    "df[[\"DevelopmentDifferential\",\"KingSafetyDifferential\"]] = df[\"Variations\"].apply(getDevelopmentDifferential)\n",
-    "\n",
     "df[\"EloDifferential\"] = df[\"WhiteElo\"] - df[\"BlackElo\"]\n",
     "\n",
     "df[\"AverageElo\"] = (df[\"WhiteElo\"] + df[\"BlackElo\"]) / 2\n",
     "\n",
     "df[\"Result\"] = df[\"Result\"].apply(getResultForWhite)\n",
     "\n",
-    "df = df.drop([\"Variations\", \"WhiteElo\", \"BlackElo\"], axis=1)\n",
     "\n",
     "pd.set_option('display.max_columns', None)\n",
     "print(df.head())"
@@ -388,7 +368,488 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 6. Exploratory Data Analysis"
+    "This next section will keep track of the board position and calculate 4 positional features at the end of the opening phase of the game."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   ECO  Result  BlunderDifferential  MistakeDifferential  \\\n",
+      "0  B09     0.0                    0                    2   \n",
+      "1  C33     1.0                   -1                    0   \n",
+      "2  B12     1.0                   -1                   -2   \n",
+      "3  B13     1.0                    0                    1   \n",
+      "4  C40     1.0                    0                   -3   \n",
+      "\n",
+      "   InaccuracyDifferential  TimeDifferential  TimeControl  Moves  \\\n",
+      "0                       1               -26          180     36   \n",
+      "1                      -2                33          600     48   \n",
+      "2                       0               495         1800     48   \n",
+      "3                       2               531         1797     90   \n",
+      "4                      -3              -121          600     48   \n",
+      "\n",
+      "   EloDifferential  AverageElo  KingSafetyDifferential  MobilityDifferential  \\\n",
+      "0               97      2295.5                      -2                    10   \n",
+      "1             -128      1669.0                       0                     3   \n",
+      "2              406      1694.0                      -1                    11   \n",
+      "3              342      1855.0                       0                     1   \n",
+      "4              441      1299.5                       3                     7   \n",
+      "\n",
+      "   MaterialDifferential  DevelopmentDifferential  \n",
+      "0                     1                       -1  \n",
+      "1                     3                       -2  \n",
+      "2                    -3                        1  \n",
+      "3                     0                        1  \n",
+      "4                     4                        0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import re\n",
+    "\n",
+    "def setupGame():\n",
+    "    gameArr = [[\"\" for i in range(8)] for i in range(8)]\n",
+    "    setupArr = [\"R\", \"N\", \"B\", \"Q\", \"K\", \"B\", \"N\", \"R\"]\n",
+    "    for i in range(8):\n",
+    "        gameArr[7][i] = \"B\" + setupArr[i]\n",
+    "        gameArr[0][i] = \"W\" + setupArr[i]\n",
+    "    for i in range(8):\n",
+    "        gameArr[6][i] = \"BP\"\n",
+    "        gameArr[1][i] = \"WP\"\n",
+    "    return gameArr\n",
+    "\n",
+    "def makeMove(gameArr, move, moveNum):\n",
+    "    piece, specifier, file, rank = breakMoveUp(move)\n",
+    "    color = \"W\" if moveNum % 2 == 0 else \"B\"\n",
+    "    if piece == \"Castle\":\n",
+    "        colorRank = 0 if color == \"W\" else 7\n",
+    "        gameArr[colorRank][4] = \"\"\n",
+    "        gameArr[colorRank][5] = color + \"R\"\n",
+    "        gameArr[colorRank][6] = color + \"K\"\n",
+    "        gameArr[colorRank][7] = \"\"\n",
+    "    elif piece == \"Queenside Castle\":\n",
+    "        colorRank = 0 if color == \"W\" else 7\n",
+    "        gameArr[colorRank][0] = \"\"\n",
+    "        gameArr[colorRank][2] = color + \"K\"\n",
+    "        gameArr[colorRank][3] = color + \"R\"\n",
+    "        gameArr[colorRank][4] = \"\"\n",
+    "    elif piece == \"Promotion\":\n",
+    "        firstMove = move.split(\"=\")[0]\n",
+    "        makeMove(gameArr, firstMove, moveNum)\n",
+    "        piece, specifier, file, rank = breakMoveUp(firstMove)\n",
+    "        col = ord(file) - ord(\"a\")\n",
+    "        row = int(rank) - 1\n",
+    "        gameArr[row][col] = color + move.split(\"=\")[1][0]\n",
+    "    else:\n",
+    "        piece = color + piece\n",
+    "        col = ord(file) - ord(\"a\")\n",
+    "        row = int(rank) - 1\n",
+    "        curRow,curCol = findPiecePos(piece, specifier, gameArr, col, row, color)\n",
+    "        #En Passant Condition\n",
+    "        if \"x\" in move and gameArr[row][col] == \"\":\n",
+    "            if color == \"W\":\n",
+    "                # Erasing black piece, so add 1\n",
+    "                gameArr[row-1][col] = \"\"\n",
+    "            else:\n",
+    "                gameArr[row+1][col] = \"\"\n",
+    "        gameArr[row][col] = piece\n",
+    "        gameArr[curRow][curCol] = \"\"\n",
+    "       \n",
+    "def findPiecePos(piece, specifier, gameArr, endCol, endRow, color):\n",
+    "    col = -1\n",
+    "    row = -1\n",
+    "    if specifier:\n",
+    "        # Specifier is file\n",
+    "        if 0 <= ord(specifier) - ord(\"a\") <= 8:\n",
+    "            col = ord(specifier) - ord(\"a\")\n",
+    "        # Specifier is rank\n",
+    "        else:\n",
+    "            row = int(specifier) - 1\n",
+    "    # Check all 64 squares brute force\n",
+    "    for i in range(len(gameArr)):\n",
+    "        for j in range(len(gameArr[i])):\n",
+    "            # Meets piece and specifier constraints\n",
+    "            if piece == gameArr[i][j] and (col==-1 or j==col) and (row==-1 or i==row):\n",
+    "                colDistance = abs(endCol-j)\n",
+    "                rowDistance = abs(endRow-i)\n",
+    "                if piece[1] == \"N\" and colDistance + rowDistance == 3 \\\n",
+    "                    and min(colDistance, rowDistance) == 1 and not kingChecked(gameArr, color, i, j, endRow, endCol):\n",
+    "                    \n",
+    "                    return (i,j)\n",
+    "                if piece[1] == \"R\" and min(colDistance, rowDistance) == 0 and not kingChecked(gameArr, color, i, j, endRow, endCol):\n",
+    "                    blockingPiece = False\n",
+    "                    if colDistance == 0:\n",
+    "                        multiplier = 1\n",
+    "                        if i < endRow: \n",
+    "                            multiplier = -1\n",
+    "                        for k in range(1,rowDistance):\n",
+    "                            if gameArr[endRow + multiplier*k][endCol] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    else:\n",
+    "                        multiplier = 1\n",
+    "                        if j < endCol:\n",
+    "                            multiplier = -1\n",
+    "                        for k in range(1,colDistance):\n",
+    "                            if gameArr[endRow][endCol + multiplier*k] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    if blockingPiece == False:\n",
+    "                        return (i,j)\n",
+    "                if piece[1] == \"B\" and colDistance == rowDistance and not kingChecked(gameArr, color, i, j, endRow, endCol):\n",
+    "                    blockingPiece = False\n",
+    "                    if colDistance == rowDistance:\n",
+    "                        rowM = 1\n",
+    "                        colM = 1\n",
+    "                        if i<endRow:\n",
+    "                            rowM = -1\n",
+    "                        if j<endCol:\n",
+    "                            colM = -1\n",
+    "                        for k in range(1,rowDistance):\n",
+    "                            if gameArr[endRow+rowM*k][endCol + colM*k] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    if blockingPiece == False:\n",
+    "                        return (i,j)\n",
+    "                if piece[1] == \"P\" and not kingChecked(gameArr, color, i, j, endRow, endCol):\n",
+    "                    if (((color == \"W\" and i==1) or (color == \"B\" and i==6)) and rowDistance == 2 and colDistance == 0):\n",
+    "                        if color == \"W\" and i<endRow:\n",
+    "                            if gameArr[endRow-1][endCol] == \"\":\n",
+    "                                return(i,j)\n",
+    "                        elif color == \"B\" and i>endRow:\n",
+    "                            if gameArr[endRow+1][endCol] == \"\":\n",
+    "                                return(i,j)\n",
+    "                    elif rowDistance == 1 and colDistance <= 1:\n",
+    "                        if color == \"W\" and i<endRow:\n",
+    "                            return (i,j)\n",
+    "                        elif color == \"B\" and i>endRow:\n",
+    "                            return(i,j)\n",
+    "                if piece[1] == \"K\":\n",
+    "                    return(i,j)\n",
+    "                if piece[1] == \"Q\" and (min(colDistance, rowDistance) == 0 or colDistance == rowDistance) and not kingChecked(gameArr, color, i, j, endRow, endCol):\n",
+    "                    blockingPiece = False\n",
+    "                    if colDistance == rowDistance:\n",
+    "                        rowM = 1\n",
+    "                        colM = 1\n",
+    "                        if i<endRow:\n",
+    "                            rowM = -1\n",
+    "                        if j<endCol:\n",
+    "                            colM = -1\n",
+    "                        for k in range(1,rowDistance):\n",
+    "                            if gameArr[endRow+rowM*k][endCol + colM*k] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    elif colDistance == 0:\n",
+    "                        multiplier = 1\n",
+    "                        if i < endRow: \n",
+    "                            multiplier = -1\n",
+    "                        for k in range(1,rowDistance):\n",
+    "                            if gameArr[endRow + multiplier*k][endCol] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    elif rowDistance == 0:\n",
+    "                        multiplier = 1\n",
+    "                        if j < endCol:\n",
+    "                            multiplier = -1\n",
+    "                        for k in range(1,colDistance):\n",
+    "                            if gameArr[endRow][endCol + multiplier*k] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    if blockingPiece == False:\n",
+    "                        return (i,j)\n",
+    "\n",
+    "def kingChecked(gameArr, color, startRow, startCol, endRow, endCol):\n",
+    "    startPiece = gameArr[startRow][startCol]\n",
+    "    gameArr[startRow][startCol] = \"\"\n",
+    "    endPiece = gameArr[endRow][endCol]\n",
+    "    gameArr[endRow][endCol] = startPiece\n",
+    "    for i in range(len(gameArr)):\n",
+    "        for j in range(len(gameArr[i])):\n",
+    "            if gameArr[i][j] == color + \"K\":\n",
+    "                kArr = [(0,1),(0,-1),(1,0),(-1,0),(1,1),(-1,-1),(1,-1),(-1,1)]\n",
+    "                row = i\n",
+    "                col = j\n",
+    "                for k in range(len(kArr)*8):\n",
+    "                    r,c = kArr[k%8]\n",
+    "                    r = r*((k//8)+1)\n",
+    "                    c = c*((k//8)+1)\n",
+    "                    modRow = row + r\n",
+    "                    modCol = col + c\n",
+    "                    if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                        if gameArr[modRow][modCol] != \"\":   \n",
+    "                            pieceColor = gameArr[modRow][modCol][0]\n",
+    "                            oppositeColor = \"B\" if color==\"W\" else \"W\"\n",
+    "                            piece = gameArr[modRow][modCol][1]\n",
+    "                            # Rook or Queen\n",
+    "                            if k%8 < 4:\n",
+    "                                if pieceColor == oppositeColor and piece in [\"R\",\"Q\"]: \n",
+    "                                    gameArr[startRow][startCol] = startPiece\n",
+    "                                    gameArr[endRow][endCol] = endPiece\n",
+    "                                    return True\n",
+    "                                else:\n",
+    "                                    kArr[k%8] = (0,0)\n",
+    "                            # Bishop and Queen\n",
+    "                            else:\n",
+    "                                if pieceColor == oppositeColor and piece in [\"B\",\"Q\"]:\n",
+    "                                    gameArr[startRow][startCol] = startPiece\n",
+    "                                    gameArr[endRow][endCol] = endPiece\n",
+    "                                    return True\n",
+    "                                else:\n",
+    "                                    kArr[k%8] = (0,0)\n",
+    "    gameArr[startRow][startCol] = startPiece\n",
+    "    gameArr[endRow][endCol] = endPiece\n",
+    "    return False\n",
+    "    \n",
+    "def breakMoveUp(move):\n",
+    "    # Get piece\n",
+    "    files = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']\n",
+    "    ranks = ['1', '2', '3', '4', '5', '6', '7', '8']\n",
+    "    piece = move[0]\n",
+    "    specifier = None\n",
+    "    file = None\n",
+    "    rank = None\n",
+    "    if move[0] not in [\"R\", \"N\", \"B\", \"Q\", \"K\"]:\n",
+    "        # Castle or Pawn move\n",
+    "        if move[0] == \"O\":\n",
+    "            if len(move) == 3:\n",
+    "                return \"Castle\", None, None, None\n",
+    "            else:\n",
+    "                return \"Queenside Castle\", None, None, None\n",
+    "        else:\n",
+    "            piece = \"P\"\n",
+    "    if \"=\" in move:\n",
+    "        return \"Promotion\", None, None, None\n",
+    "    \n",
+    "    # Get file, rank, specifier\n",
+    "    for i in range(0, len(move)):\n",
+    "        if move[i] in files or move[i] in ranks:\n",
+    "            if specifier == None:\n",
+    "                specifier = move[i]\n",
+    "            else:\n",
+    "                if move[i] in files:\n",
+    "                    file = move[i]\n",
+    "                else:\n",
+    "                    rank = move[i]\n",
+    "    if not file:\n",
+    "        file = specifier\n",
+    "        specifier = None\n",
+    "    if not rank:\n",
+    "        rank = specifier\n",
+    "        specifier = None\n",
+    "    \n",
+    "    if piece == \"P\" and specifier == None:\n",
+    "        specifier = file\n",
+    "    \n",
+    "    return piece, specifier, file, rank\n",
+    "\n",
+    "def playMoves(moves):\n",
+    "    gameArr = setupGame()\n",
+    "    for i in range(len(moves)):\n",
+    "        makeMove(gameArr, moves[i], i)\n",
+    "    return gameArr\n",
+    "\n",
+    "\n",
+    "def findKingSafetyDifferential(gameArr):\n",
+    "    # Using the Pawn Shield method for calculating King Safety\n",
+    "    # The lack of a shielding pawn within one or two squares of king gets a -1 penalty and an open file gets a -3 penalty\n",
+    "    blackKingSafetyPenalty = 0\n",
+    "    whiteKingSafetyPenalty = 0\n",
+    "    for i in range(len(gameArr)):\n",
+    "        for j in range(len(gameArr[i])):\n",
+    "            if gameArr[i][j] == \"WK\":\n",
+    "                minRow = i+1\n",
+    "                maxRow = min(7, i+2)\n",
+    "                minCol = max(0, j-1)\n",
+    "                maxCol = min(7, j+1)\n",
+    "                whiteKingSafetyPenalty = calculateKingSafetyPenalty(minRow, maxRow, minCol, maxCol, \"W\", gameArr)\n",
+    "            if gameArr[i][j] == \"BK\":\n",
+    "                minRow = max(0, i-2)\n",
+    "                maxRow = i-1\n",
+    "                minCol = max(0, j-1)\n",
+    "                maxCol = min(7, j+1)\n",
+    "                blackKingSafetyPenalty = calculateKingSafetyPenalty(minRow, maxRow, minCol, maxCol, \"B\", gameArr)\n",
+    "    return whiteKingSafetyPenalty - blackKingSafetyPenalty    \n",
+    "\n",
+    "def calculateKingSafetyPenalty(minRow, maxRow, minCol, maxCol, color, gameArr):\n",
+    "    totalPenalty = 0\n",
+    "    for b in range(minCol, maxCol+1):\n",
+    "        # Missing Pawn Check\n",
+    "        penalty = -1\n",
+    "        for a in range(minRow, maxRow+1):\n",
+    "            if gameArr[a][b] == color + \"P\":\n",
+    "                penalty = 0\n",
+    "        # Open File Check\n",
+    "        if penalty == -1:\n",
+    "            penalty = -3\n",
+    "            for a in range(0,7):\n",
+    "                if gameArr[a][b] != \"\" and gameArr[a][b][1] == \"P\":\n",
+    "                    penalty = -1\n",
+    "        totalPenalty += penalty\n",
+    "    return totalPenalty\n",
+    "\n",
+    "def findMobilityDifferential(gameArr):\n",
+    "    # Calculating mobility by looking at number of legal moves\n",
+    "    blackMobility = 0\n",
+    "    whiteMobility = 0\n",
+    "    for i in range(len(gameArr)):\n",
+    "        for j in range(len(gameArr[i])):\n",
+    "            if gameArr[i][j] != \"\":\n",
+    "                color = gameArr[i][j][0]\n",
+    "                piece = gameArr[i][j][1]\n",
+    "                if piece == \"P\":\n",
+    "                    if color == \"W\":\n",
+    "                        if gameArr[i+1][j] == \"\":\n",
+    "                            whiteMobility+=1\n",
+    "                            if i==1 and gameArr[i+2][j] == \"\":\n",
+    "                                whiteMobility+=1\n",
+    "                    else:\n",
+    "                        if gameArr[i-1][j] == \"\":\n",
+    "                            blackMobility+=1\n",
+    "                            if i==6 and gameArr[i-2][j] == \"\":\n",
+    "                                blackMobility+=1\n",
+    "                else:\n",
+    "                    if color == \"W\":\n",
+    "                        whiteMobility += generateMobilityCombos(piece, i, j, gameArr)\n",
+    "                    else:\n",
+    "                        blackMobility += generateMobilityCombos(piece, i, j, gameArr)\n",
+    "    return whiteMobility - blackMobility\n",
+    "                        \n",
+    "def generateMobilityCombos(piece, row, col, gameArr):\n",
+    "    mobility = 0\n",
+    "    if piece == \"N\":\n",
+    "        nArr = [(-1,-2), (-1,2), (1,-2), (1,2), (2, 1), (2, -1), (-2,1), (-2,-1)]\n",
+    "        for i in range(len(nArr)):\n",
+    "            r, c = nArr[i]\n",
+    "            modRow = row + r\n",
+    "            modCol = col + c\n",
+    "            if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                if gameArr[modRow][modCol] == \"\":\n",
+    "                    mobility += 1\n",
+    "    elif piece == \"B\":\n",
+    "        bArr = [(-1,-1),(1,1),(-1,1),(1,-1)]\n",
+    "        for i in range(len(bArr)*4):\n",
+    "            r,c = bArr[i%4]\n",
+    "            r = r*((i//4)+1)\n",
+    "            c = c*((i//4)+1)\n",
+    "            modRow = row + r\n",
+    "            modCol = col + c\n",
+    "            if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                if gameArr[modRow][modCol] == \"\":\n",
+    "                    mobility += 1\n",
+    "                else:\n",
+    "                    bArr[i%4] = (0,0)\n",
+    "    elif piece == \"R\":\n",
+    "        rArr = [(0,-1), (0,1), (1,0), (-1,0)]\n",
+    "        for i in range(len(rArr)*4):\n",
+    "            r,c = rArr[i%4]\n",
+    "            r = r*((i//4)+1)\n",
+    "            c = c*((i//4)+1)\n",
+    "            modRow = row + r\n",
+    "            modCol = col + c\n",
+    "            \n",
+    "            if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                if gameArr[modRow][modCol] == \"\":\n",
+    "                    mobility += 1\n",
+    "                else:\n",
+    "                    rArr[i%4] = (0,0)\n",
+    "    elif piece == \"K\":\n",
+    "        kArr = [(0,1),(0,-1),(1,-1),(1,0),(1,1),(-1,-1),(-1,0),(-1,1)]\n",
+    "        for i in range(len(kArr)):\n",
+    "            r,c = kArr[i]\n",
+    "            modRow = row + r\n",
+    "            modCol = col + c\n",
+    "            if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                if gameArr[modRow][modCol] == \"\":\n",
+    "                    mobility += 1\n",
+    "    elif piece == \"Q\":\n",
+    "        qArr = [(0,-1), (0,1), (1,0), (-1,0), (-1,-1),(1,1),(-1,1),(1,-1)]\n",
+    "        for i in range(len(qArr)*4):\n",
+    "            r,c = qArr[i%8]\n",
+    "            r = r*((i//8)+1)\n",
+    "            c = c*((i//8)+1)\n",
+    "            modRow = row + r\n",
+    "            modCol = col + c\n",
+    "            if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                if gameArr[modRow][modCol] == \"\":\n",
+    "                    mobility += 1\n",
+    "                else:\n",
+    "                    qArr[i%8] = (0,0)\n",
+    "    return mobility\n",
+    "\n",
+    "def findMaterialDifferential(gameArr):\n",
+    "    blackDict = {\"P\": 0, \"B\": 0, \"N\": 0, \"R\": 0, \"Q\": 0, \"K\": 0}\n",
+    "    whiteDict = {\"P\": 0, \"B\": 0, \"N\": 0, \"R\": 0, \"Q\": 0, \"K\": 0}\n",
+    "    # Calculating material imbalance\n",
+    "    # P-1, B and N- 3, R-5, Q-9\n",
+    "    # +1 for bishop pair\n",
+    "    # -1 for rook pair\n",
+    "    # -1 for knight pair\n",
+    "    # -1 for no pawns\n",
+    "    for i in range(len(gameArr)):\n",
+    "        for j in range(len(gameArr[i])):\n",
+    "            if gameArr[i][j] != \"\":\n",
+    "                color = gameArr[i][j][0]\n",
+    "                piece = gameArr[i][j][1]\n",
+    "                if color == \"B\":\n",
+    "                    blackDict[piece] = blackDict.get(piece,0) + 1\n",
+    "                else:\n",
+    "                    whiteDict[piece] = whiteDict.get(piece,0) + 1\n",
+    "    blackPoints = calcPoints(blackDict)\n",
+    "    whitePoints = calcPoints(whiteDict)\n",
+    "    return whitePoints - blackPoints\n",
+    "\n",
+    "def calcPoints(dict):\n",
+    "    points = dict[\"B\"] * 3 + dict[\"N\"] * 3 + dict[\"P\"] * 1 + dict[\"Q\"] * 9 + dict[\"R\"] * 5\n",
+    "    if dict[\"B\"] == 2:\n",
+    "        points += 1\n",
+    "    if dict[\"N\"] == 2:\n",
+    "        points -= 1\n",
+    "    if dict[\"R\"] == 2:\n",
+    "        points -= 1\n",
+    "    if dict[\"P\"] == 0:\n",
+    "        points -= 1\n",
+    "    return points\n",
+    "    \n",
+    "def findDevelopmentDifferential(moves):     \n",
+    "    whiteNonPawnMoves = 0\n",
+    "    blackNonPawnMoves = 0\n",
+    "    for i in range(len(moves)):\n",
+    "        if moves[i][0] in [\"N\",\"B\",\"R\",\"Q\",\"K\",\"O\"]:\n",
+    "            if i % 2 == 0:\n",
+    "                if moves[i][0] == \"O\":\n",
+    "                    whiteCastleTime = i//2\n",
+    "                whiteNonPawnMoves+=1\n",
+    "            else:\n",
+    "                if moves[i][0] == \"O\":\n",
+    "                    blackCastleTime = i//2\n",
+    "                blackNonPawnMoves+=1\n",
+    "    nonPawnMoveDifferential = whiteNonPawnMoves - blackNonPawnMoves\n",
+    "    return nonPawnMoveDifferential \n",
+    "def findStatistics(variation):\n",
+    "    # Match with a number followed by some .'s, a space, and then a combination of letters, dashes, and equals to match all moves\n",
+    "    moves = re.findall(r'\\d+\\.+\\s[\\w\\-\\=]+',variation)\n",
+    "    moves = [move.split(\" \")[1] for move in moves]\n",
+    "    modifiedMoves = moves[:24]\n",
+    "    gameArr = playMoves(modifiedMoves)\n",
+    "    safety = findKingSafetyDifferential(gameArr)\n",
+    "    mobility = findMobilityDifferential(gameArr)\n",
+    "    material = findMaterialDifferential(gameArr)\n",
+    "    development = findDevelopmentDifferential(modifiedMoves)\n",
+    "    \n",
+    "    return pd.Series({\"KingSafetyDifferential\": safety, \"MobilityDifferential\": mobility, \n",
+    "                      \"MaterialDifferential\": material, \"DevelopmentDifferential\": development}) \n",
+    "\n",
+    "df[[\"KingSafetyDifferential\",\"MobilityDifferential\",\"MaterialDifferential\", \"DevelopmentDifferential\"]] = df[\"Variations\"].apply(findStatistics)\n",
+    "\n",
+    "df = df.drop([\"Variations\", \"WhiteElo\", \"BlackElo\"], axis=1)\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Exploratory Data Analysis"
    ]
   },
   {
@@ -400,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -409,7 +870,7 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -425,7 +886,7 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
+    "\n",
     "\n",
     "df[\"Inaccuracy Differential Group\"] = pd.cut(df['InaccuracyDifferential'], bins=[-12,-8,-4,0,4,8,12], precision=0)\n",
     "df[\"Mistake Differential Group\"] = pd.cut(df['MistakeDifferential'], bins=[-9,-6,-3,0,3,6,9], precision=0)\n",
@@ -474,7 +935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
@@ -483,7 +944,7 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -524,7 +985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
@@ -533,7 +994,7 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -573,7 +1034,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
@@ -582,7 +1043,7 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -620,12 +1081,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We filtered the data to only include openings that accounted for at least 1% of games in our database to get results with a low variance. The results show that openings do have a small impact on winning. The least and most winning openings differed by about 20%, with most openings hovering close to the 50% win rate."
+    "We filtered the data to only include openings that accounted for at least 1% of games in our database to get results with a low variance. The results show that openings do have a small impact on winning. The least and most winning openings differed by about 20%, with most openings hovering close to the 50% win rate.\n",
+    "\n",
+    "Let's graph our positional features against the mean result!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
@@ -634,13 +1097,13 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -669,12 +1132,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we can see, like elo differential, development differential has a small but present impact on win rate. Players who spend less time moving their pawns and more developing their major pieces come out ahead after the opening."
+    "Development differential has a small but present impact on win rate. Players who spend less time moving their pawns and more developing their major pieces come out ahead after the opening."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
@@ -683,13 +1146,13 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -699,7 +1162,7 @@
     }
    ],
    "source": [
-    "bins = [-12,-8,-4,0,4,8,12]\n",
+    "bins = [-4,-2,0,2,4]\n",
     "df[\"KingSafetyGroup\"] = pd.cut(df['KingSafetyDifferential'],bins=bins, precision=0)\n",
     "\n",
     "# Drop na values caused by values that don't fit the bins\n",
@@ -718,7 +1181,105 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This graph has a similar trend as the development differential graph. Castling earlier in the game before the opportunity is lost generally increases one's king safety and win rate."
+    "This graph has a similar trend as the development differential graph. Having more pawns and less open files around the king slightly increases the chance of winning out of the opening."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Mean Result')"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = [-12,-8,-4,0,4,8,12]\n",
+    "df[\"MobilityGroup\"] = pd.cut(df['MobilityDifferential'],bins=bins, precision=0)\n",
+    "\n",
+    "# Drop na values caused by values that don't fit the bins\n",
+    "groups = df[\"MobilityGroup\"].dropna().unique().sort_values()\n",
+    "\n",
+    "x = range(len(groups))\n",
+    "y = df.groupby(\"MobilityGroup\", observed=True)[\"Result\"].mean()\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.bar(x, y)\n",
+    "plt.xticks(x, groups)\n",
+    "plt.xlabel('Mobility Differential Group')\n",
+    "plt.ylabel('Mean Result')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mobility out of the opening seems to have the greatest impact on winning out of all the positional features. Placing pieces where they control a substantial amount of space is important in the opening."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Mean Result')"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = [-4,-2,0,2,4]\n",
+    "df[\"MaterialGroup\"] = pd.cut(df['MaterialDifferential'],bins=bins, precision=0)\n",
+    "\n",
+    "# Drop na values caused by values that don't fit the bins\n",
+    "groups = df[\"MaterialGroup\"].dropna().unique().sort_values()\n",
+    "\n",
+    "x = range(len(groups))\n",
+    "y = df.groupby(\"MaterialGroup\", observed=True)[\"Result\"].mean()\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.bar(x, y)\n",
+    "plt.xticks(x, groups)\n",
+    "plt.xlabel('Material Differential Group')\n",
+    "plt.ylabel('Mean Result')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Material of course also has a significant impact on winning. However, out of the opening, players are usually still even in material, so this feature isn't very indicative of winning in the way it is currently presented."
    ]
   },
   {
@@ -730,7 +1291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
@@ -757,7 +1318,6 @@
     }
    ],
    "source": [
-    "import numpy as np\n",
     "# Filter dataframe to remove outliers to make axes smaller\n",
     "mincnt = 300\n",
     "# If using small dataset\n",
@@ -815,36 +1375,48 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 7. Machine Learning"
+    "## 6. Machine Learning"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 136,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "        Loss       0.81      0.81      0.81      1351\n",
-      "         Tie       0.04      0.75      0.07         4\n",
-      "         Win       0.84      0.79      0.82      1463\n",
-      "\n",
-      "    accuracy                           0.80      2818\n",
-      "   macro avg       0.56      0.79      0.57      2818\n",
-      "weighted avg       0.83      0.80      0.81      2818\n",
-      "\n"
-     ]
-    }
-   ],
    "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import classification_report\n",
+    "Now that we have completed the data cleaning and visualization parts of the data science pipeline, we want to train a machine learning model that will allow us to perdict the winner of a chess game given certain features like ELO difference, time difference, blunders, etc., and then can examine the feature importance in order to show players which ones are having the greatest impact on determining the winner. We will be training a classifier (as we want to classify as win, loss, etc.) rather than performing regression. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preliminary Reading: Classification and Categorical Encoding\n",
+    "\n",
+    "We reccomend a familiarity with classification and categorical encoding to help understand this section. Reccomended resources include:\n",
     "\n",
+    "* [IBM: What is random forest?](https://www.ibm.com/topics/random-forest)\n",
+    "* [scikit-learn course: Encoding of categorical variables](https://inria.github.io/scikit-learn-mooc/python_scripts/03_categorical_pipeline.html)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preparing Data For Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Originally, the data for winning and losing is stored as a numerical value with 1.0 for winning, 0.0 for losing, and 0.5 for a tie. This is converted to a categorical label (rather than numerical) for training to allow for classification. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "def convertWinToCategory(val):\n",
     "    res = \"\"\n",
     "    match val:\n",
@@ -854,34 +1426,189 @@
     "            res = \"Loss\" \n",
     "        case 0.5:\n",
     "            res = \"Tie\"\n",
-    "    return res\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We first pick out all of the features we will be using in order to train our model. This includes king safety differential, development differential, average ELO, time control, moves, inaccuracy differential, mistake differential, blunder differential, time differential, elo differential, opening, and the result. These features are all explained in the sections above. After that, we want to limit ourselves to openings that make up at least 2% of the database, which helps us avoid the curse of dimensionality (there are hundreds of thousands of chess openings, making encoding difficult)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract necessary features for training\n",
+    "train_feat_df = df.loc[:,[\"MobilityDifferential\", \"MaterialDifferential\", \"KingSafetyDifferential\",\"DevelopmentDifferential\",\"AverageElo\",\"TimeControl\", \"Moves\", \"InaccuracyDifferential\", \"MistakeDifferential\", \"BlunderDifferential\", \"TimeDifferential\", \"EloDifferential\", \"ECO\", \"Result\"]]\n",
     "\n",
-    "train_feat_df = df.loc[:,[\"KingSafetyDifferential\",\"DevelopmentDifferential\",\"AverageElo\",\"TimeControl\", \"Moves\", \"InaccuracyDifferential\", \"MistakeDifferential\", \"BlunderDifferential\", \"TimeDifferential\", \"EloDifferential\", \"ECO\", \"Result\"]]\n",
+    "# convert results to categorical values for training\n",
+    "train_feat_df[\"Result\"] = train_feat_df[\"Result\"].map(convertWinToCategory)\n",
     "\n",
+    "#retuns a df of the value counts of all the openings\n",
     "value_counts = train_feat_df[\"ECO\"].value_counts()\n",
     "\n",
+    "#filters all the values such that the count of their occurences needs to be greater than 2% of the dataset, and then cuts the others out of the training dataset\n",
     "filtered_values = value_counts[value_counts > len(train_feat_df.index)*.02].index\n",
-    "train_feat_df = train_feat_df[train_feat_df['ECO'].isin(filtered_values)]\n",
-    "\n",
-    "# convert results to categorical values for training\n",
-    "train_feat_df[\"Result\"] = train_feat_df[\"Result\"].map(convertWinToCategory)\n",
-    "\n",
+    "train_feat_df = train_feat_df[train_feat_df['ECO'].isin(filtered_values)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Encoding Openings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Encoding the chess openings posed a significant challenge due to the vast number of possibilities, which can reach hundreds of thousands. Using one-hot encoding without filtering would introduce n new features, where n represents the number of openings, leading to the curse of dimensionality. To overcome this obstacle, we implemented a filtering process that limited the openings to those that constitute at least 2% of the dataset. This decision was based on the understanding that openings with a lower representation would not provide a statistically significant number of games to derive meaningful insights. Once the filtering process was complete, we applied one-hot encoding to the remaining openings, as they are categorical variables. This approach allowed us to effectively manage the dimensionality of the dataset while preserving the most relevant information for analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Use pd.get_dummies to perform one-hot encoding\n",
     "one_hot_enc_df = pd.get_dummies(train_feat_df[\"ECO\"], prefix='ECO')\n",
     "train_feat_df = pd.concat([train_feat_df, one_hot_enc_df], axis=1)\n",
-    "train_feat_df = train_feat_df.drop(\"ECO\", axis = 1)\n",
-    "\n",
+    "train_feat_df = train_feat_df.drop(\"ECO\", axis = 1) #drop old opening column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choosing A Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the data has been prepared for training, a model has to be chosen. In order to do this, we trained a wide variety of classification models, did hyperparameter tuning and k-fold cross validation, and then saved the accuracy for each. This allows us to find the model with the highest accuracy and prescision for our needs. The accuracy of each model is then plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but GaussianNB was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but MLPClassifier was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set up all classifiers\n",
+    "classifiers = [\n",
+    "    RandomForestClassifier(),\n",
+    "    GradientBoostingClassifier(),\n",
+    "    AdaBoostClassifier(),\n",
+    "    DecisionTreeClassifier(),\n",
+    "    KNeighborsClassifier(),\n",
+    "    SVC(),\n",
+    "    GaussianNB(),\n",
+    "    LogisticRegression(),\n",
+    "    MLPClassifier()\n",
+    "]\n",
+    "\n",
+    "# seperate out data into testing and training dataset\n",
     "X = train_feat_df.drop(\"Result\", axis=1)\n",
     "y = train_feat_df[\"Result\"]\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=.25, random_state=42, shuffle=True)\n",
     "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True)\n",
+    "results = []\n",
     "\n",
-    "model = RandomForestClassifier()\n",
-    "model.fit(X_train, y_train)\n",
-    "\n",
-    "y_pred = model.predict(X_test)\n",
+    "#loop through all classifiers\n",
+    "for classifier in classifiers:\n",
+    "    clf_name = classifier.__class__.__name__\n",
+    "    clf = classifier\n",
+    "    \n",
+    "    #fit each classifier\n",
+    "    clf.fit(X_train, y_train)\n",
+    "    y_pred = clf.predict(X_test.values)\n",
+    "    \n",
+    "    # extract the accuracy and precsision\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    precision = precision_score(y_test, y_pred, average='weighted')\n",
+    "    \n",
+    "    #save the results in a dictionary\n",
+    "    results.append({'classifier': clf_name, 'accuracy': accuracy, 'precision': precision})\n",
+    "\n",
+    "# plot using matplot lib\n",
+    "accuracies = [result['accuracy'] for result in results]\n",
+    "precisions = [result['precision'] for result in results]\n",
+    "classifiers = [result['classifier'] for result in results]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 6))\n",
+    "ax.bar(classifiers, accuracies, label='Accuracy')\n",
+    "ax.bar(classifiers, precisions, label='Precision')\n",
+    "ax.set_xlabel('Classifier')\n",
+    "ax.set_ylabel('Score')\n",
+    "ax.set_title('Classifier Performance')\n",
+    "ax.legend()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the graph above, it is clear that the GradientBoostingClassifier performs the best with random forest being in close second, and KNeighbors performing the worst. This is also intuitive because gradient boosting and random forest are ensemble methods that combine multiple weak learners (descision trees in both cases) to create a strong learner. On the other hand, K-Nearest Neighbors (KNN) is a simpler algorithm that relies on the proximity of data points to make predictions. It may struggle with high-dimensional data and can be sensitive to the choice of the number of neighbors (k). \n",
     "\n",
-    "print(classification_report(y_pred, y_test))\n"
+    "Furthermore, gradient boosting is an iterative algorithm that progressively improves the model by focusing on the misclassified samples from previous iterations. This allows it to effectively handle difficult cases and achieve higher accuracy. This is what leads it to better performance over the random forest classifier. In contrast, KNN's performance heavily depends on the quality and relevance of the selected neighbors. If the neighbors are not representative of the true class distribution or if the features are not well-separated, KNN may struggle to make accurate predictions."
    ]
   },
   {
@@ -891,41 +1618,54 @@
     "### Hyperparameter Optimization"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After selecting the best model, we want to perform more rigorous hyperparameter optimization in order to get the best accuracy and prescision possible. We use GridSearchCV to perform an exhaustive search of the parameter space and then save the best perfoming model. For additional reading on hyperparamter optimization, please see:\n",
+    "* [Machine Learning Mastery: Guide to Hyperparameter Tuning](https://machinelearningmastery.com/hyperparameter-optimization-with-random-search-and-grid-search/)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RandomForestClassifier(max_depth=9, max_features=0.7000000000000001,\n",
-      "                       max_samples=0.5, n_estimators=140)\n"
+      "Fitting 5 folds for each of 27 candidates, totalling 135 fits\n",
+      "Best Model:\n",
+      "GradientBoostingClassifier(max_depth=5, random_state=42)\n"
      ]
     }
    ],
    "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
     "\n",
-    "param_grid = {'n_estimators':np.arange(50,200,15),\n",
-    "              'max_features':np.arange(0.1, 1, 0.1),\n",
-    "              'max_depth': [3, 5, 7, 9],\n",
-    "              'max_samples': [0.3, 0.5, 0.8]}\n",
     "\n",
+    "param_grid = {\n",
+    "    'n_estimators': [50, 100, 200],\n",
+    "    'learning_rate': [0.01, 0.1, 0.2],\n",
+    "    'max_depth': [3, 5, 7],\n",
+    "}\n",
+    "\n",
+    "# Create a Gradient Boosting classifier\n",
+    "gb_classifier = GradientBoostingClassifier(random_state=42)\n",
     "\n",
+    "# Perform grid search with cross-validation\n",
+    "grid_search = GridSearchCV(estimator=gb_classifier, param_grid=param_grid, cv=5, n_jobs=-1, verbose=True)\n",
+    "grid_search.fit(X_train, y_train)\n",
     "\n",
-    "random_search = RandomizedSearchCV(RandomForestClassifier(), \n",
-    "                                   param_grid) \n",
-    "random_search.fit(X_train, y_train)  \n",
-    "best_model = random_search.best_estimator_\n",
+    "# Print the best model and its hyperparameters\n",
+    "print(\"Best Model:\")\n",
+    "best_model = grid_search.best_estimator_\n",
     "print(best_model)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
@@ -934,13 +1674,13 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "        Loss       0.82      0.81      0.82      1361\n",
-      "         Tie       0.02      0.18      0.04        11\n",
-      "         Win       0.84      0.80      0.82      1446\n",
+      "        Loss       0.81      0.80      0.80      5054\n",
+      "         Tie       0.09      0.29      0.14        93\n",
+      "         Win       0.82      0.80      0.81      5419\n",
       "\n",
-      "    accuracy                           0.81      2818\n",
-      "   macro avg       0.56      0.60      0.56      2818\n",
-      "weighted avg       0.83      0.81      0.82      2818\n",
+      "    accuracy                           0.80     10566\n",
+      "   macro avg       0.57      0.63      0.58     10566\n",
+      "weighted avg       0.81      0.80      0.80     10566\n",
       "\n"
      ]
     }
@@ -951,9 +1691,23 @@
     "print(classification_report(y_pred_rand, y_test)) "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding the most impactful features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By looking at the most impactful features, we are able to see (generally) what matters the most when it comes to perdicting the winner of the game. This information is already a part of the scikit-learn model, so a plot is shown below."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -985,112 +1739,122 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>BlunderDifferential</td>\n",
-       "      <td>0.321084</td>\n",
+       "      <td>0.295608</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>MistakeDifferential</td>\n",
-       "      <td>0.202301</td>\n",
+       "      <td>0.161308</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>TimeDifferential</td>\n",
-       "      <td>0.112530</td>\n",
+       "      <td>0.114294</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Moves</td>\n",
-       "      <td>0.086889</td>\n",
+       "      <td>0.079396</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>TimeControl</td>\n",
-       "      <td>0.057776</td>\n",
+       "      <td>0.067113</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>AverageElo</td>\n",
-       "      <td>0.052524</td>\n",
+       "      <td>0.057234</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>InaccuracyDifferential</td>\n",
-       "      <td>0.046332</td>\n",
+       "      <td>EloDifferential</td>\n",
+       "      <td>0.054306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>EloDifferential</td>\n",
-       "      <td>0.041723</td>\n",
+       "      <td>InaccuracyDifferential</td>\n",
+       "      <td>0.051815</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>KingSafetyDifferential</td>\n",
-       "      <td>0.027419</td>\n",
+       "      <td>MobilityDifferential</td>\n",
+       "      <td>0.042024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>DevelopmentDifferential</td>\n",
-       "      <td>0.019580</td>\n",
+       "      <td>0.018609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>ECO_B01</td>\n",
-       "      <td>0.003085</td>\n",
+       "      <td>MaterialDifferential</td>\n",
+       "      <td>0.015957</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>ECO_A00</td>\n",
-       "      <td>0.003054</td>\n",
+       "      <td>KingSafetyDifferential</td>\n",
+       "      <td>0.014620</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>ECO_D00</td>\n",
-       "      <td>0.003018</td>\n",
+       "      <td>ECO_A00</td>\n",
+       "      <td>0.004130</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>ECO_C50</td>\n",
-       "      <td>0.002979</td>\n",
+       "      <td>ECO_B01</td>\n",
+       "      <td>0.003928</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>ECO_A40</td>\n",
-       "      <td>0.002971</td>\n",
+       "      <td>ECO_D02</td>\n",
+       "      <td>0.003024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>ECO_C00</td>\n",
-       "      <td>0.002941</td>\n",
+       "      <td>0.002849</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>ECO_C41</td>\n",
-       "      <td>0.002853</td>\n",
+       "      <td>ECO_B10</td>\n",
+       "      <td>0.002687</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>ECO_C20</td>\n",
-       "      <td>0.002523</td>\n",
+       "      <td>ECO_D00</td>\n",
+       "      <td>0.002647</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>ECO_B00</td>\n",
-       "      <td>0.002318</td>\n",
+       "      <td>ECO_A40</td>\n",
+       "      <td>0.002063</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>ECO_D02</td>\n",
-       "      <td>0.002242</td>\n",
+       "      <td>ECO_C41</td>\n",
+       "      <td>0.002011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>ECO_B10</td>\n",
-       "      <td>0.001957</td>\n",
+       "      <td>ECO_C50</td>\n",
+       "      <td>0.001611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>ECO_A45</td>\n",
-       "      <td>0.001903</td>\n",
+       "      <td>0.001292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>ECO_B00</td>\n",
+       "      <td>0.000737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>ECO_C20</td>\n",
+       "      <td>0.000737</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1098,41 +1862,46 @@
       ],
       "text/plain": [
        "                   features  importance\n",
-       "0       BlunderDifferential    0.321084\n",
-       "1       MistakeDifferential    0.202301\n",
-       "2          TimeDifferential    0.112530\n",
-       "3                     Moves    0.086889\n",
-       "4               TimeControl    0.057776\n",
-       "5                AverageElo    0.052524\n",
-       "6    InaccuracyDifferential    0.046332\n",
-       "7           EloDifferential    0.041723\n",
-       "8    KingSafetyDifferential    0.027419\n",
-       "9   DevelopmentDifferential    0.019580\n",
-       "10                  ECO_B01    0.003085\n",
-       "11                  ECO_A00    0.003054\n",
-       "12                  ECO_D00    0.003018\n",
-       "13                  ECO_C50    0.002979\n",
-       "14                  ECO_A40    0.002971\n",
-       "15                  ECO_C00    0.002941\n",
-       "16                  ECO_C41    0.002853\n",
-       "17                  ECO_C20    0.002523\n",
-       "18                  ECO_B00    0.002318\n",
-       "19                  ECO_D02    0.002242\n",
-       "20                  ECO_B10    0.001957\n",
-       "21                  ECO_A45    0.001903"
+       "0       BlunderDifferential    0.295608\n",
+       "1       MistakeDifferential    0.161308\n",
+       "2          TimeDifferential    0.114294\n",
+       "3                     Moves    0.079396\n",
+       "4               TimeControl    0.067113\n",
+       "5                AverageElo    0.057234\n",
+       "6           EloDifferential    0.054306\n",
+       "7    InaccuracyDifferential    0.051815\n",
+       "8      MobilityDifferential    0.042024\n",
+       "9   DevelopmentDifferential    0.018609\n",
+       "10     MaterialDifferential    0.015957\n",
+       "11   KingSafetyDifferential    0.014620\n",
+       "12                  ECO_A00    0.004130\n",
+       "13                  ECO_B01    0.003928\n",
+       "14                  ECO_D02    0.003024\n",
+       "15                  ECO_C00    0.002849\n",
+       "16                  ECO_B10    0.002687\n",
+       "17                  ECO_D00    0.002647\n",
+       "18                  ECO_A40    0.002063\n",
+       "19                  ECO_C41    0.002011\n",
+       "20                  ECO_C50    0.001611\n",
+       "21                  ECO_A45    0.001292\n",
+       "22                  ECO_B00    0.000737\n",
+       "23                  ECO_C20    0.000737"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# combine features and their imporances into a dataframe\n",
     "important_features = zip(X.columns, best_model.feature_importances_)\n",
     "imp_feat_df = pd.DataFrame(important_features)\n",
     "imp_feat_df.rename(columns={0:'features',\n",
     "                               1:'importance'},\n",
     "                      inplace=True)\n",
+    "\n",
+    "# sort by importance and reconfigure the index\n",
     "imp_feat_df.sort_values(by=['importance'], inplace=True, ascending=False)\n",
     "imp_feat_df.reset_index(inplace=True)\n",
     "imp_feat_df.drop(['index'], axis=1, inplace=True)\n",
@@ -1141,12 +1910,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1156,8 +1925,7 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
+    "# plot using matplotlib\n",
     "plt.figure(figsize=(10, 6))\n",
     "plt.barh(imp_feat_df['features'], imp_feat_df['importance'], color='darkblue')\n",
     "\n",
@@ -1169,14 +1937,35 @@
     "plt.show()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the plot above, we can see that the blunder differential is the most impactful feature to perdicting the winner of a chess game. This is intuitive as the player who makes blunders means that their position worsens considerable but is also suprising, that it is significantly more impactful than ELO differential. This is relevant and impactful players as it shows them that regrardless of your ELO differential, it is important to play calm, collected chess rather than trying to find a \"creative\" position as that will put you at a much higher risk of losing. Out of the positional features, it seems that mobility was the most important feature coming out of the opening."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Benchmark"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to do more benchmarking on the performance of our model, we will look at the confusion matrix in order to see prescision, accuracy, and recall in a more visual way. Additionally, we will compare against a baseline of random guessing and the heurisitic of \"the player with the higher ELO will win\"."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1186,9 +1975,7 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
-    "\n",
+    "# plot confusion matrix\n",
     "cm = confusion_matrix(y_test, y_pred_rand, labels=best_model.classes_)\n",
     "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
     "                               display_labels=best_model.classes_)\n",
@@ -1201,29 +1988,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Further Benchmarking"
+    "Now, we will compare our model against the baseline performance of guessing and the heuristic of \"the player with the higher ELO wins\"."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy of Greater ELO Wins Benchmark: 42.33%\n",
-      "Accuracy of Randomly Guessing Winner: 32.90%\n",
-      "Accuracy of Random Forest Predictions (w/o hyperparameter): 80.41%\n",
-      "Accuracy of Random Forest Predictions (w/ hyperparameter): 80.59%\n"
+      "Accuracy of Greater ELO Wins Benchmark: 42.99%\n",
+      "Accuracy of Randomly Guessing Winner: 32.55%\n",
+      "Accuracy of Random Forest Predictions (w/o hyperparameter): 68.50%\n",
+      "Accuracy of Random Forest Predictions (w/ hyperparameter): 79.57%\n"
      ]
     }
    ],
    "source": [
-    "\n",
-    "import pandas as pd\n",
-    "from sklearn.metrics import accuracy_score\n",
     "tolerance = 10\n",
     "\n",
     "bench1 = X_test['EloDifferential'].apply(lambda x: \"Win\" if x > tolerance else (\"Loss\" if x < -tolerance else \"Tie\"))\n",
@@ -1257,21 +2041,33 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 8. Future Work and Considerations"
+    "As we can see, our model performs significantly better than both benchmarks, meaning that we have found a meaningful model to predict the winner of a chess game. The hyperparameter tuning process has allowed us to find the optimal combination of parameters that maximizes the model's performance. Benchmark 1 follows the heursitic of a player who has a higher ELO will win (as they are a stronger player). Our model's performance surpasses this benchmark, indicating that it has learned valuable information from the features to make more accurate predictions. Benchmark 2, which is a random guess classifier, randomly assigns class labels to the instances. This benchmark represents the performance that can be achieved by chance alone. Our model's significantly higher accuracy compared to this benchmark demonstrates that it has captured meaningful patterns and relationships in the data, allowing it to make informed predictions. The gradient boosting classifier has proven to be effective in modeling the complexities of chess game outcomes. The use of cross-validation during the hyperparameter tuning process ensures that the model's performance is assessed on multiple splits of the data, providing a more robust estimate of its generalization ability."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Insights, Future Work, and Considerations"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Our current model could be improved upon with the analysis of additional positional characteristics, but doing so requires heavy processing as we would need to keep track of an 8x8 board for many moves. Other chess-related data science projects could be developed from the work done here. Projects like an ELO predictor could be developed with many of the same features used in this project."
+    "Our current model could be improved upon with the analysis of additional positional characteristics and better formulas for evaluating these characteristics. For example, with the mobility feature, instead of only counting the number of legal moves, we could instead count the number of safe moves that don't hang the piece. This is known as Safe Mobility. Unfortunately, calculating more positional characteristics with better formulas is incredibly time consuming and resource intensive, so for this project we decided to focus on the basic formulas and features. \n",
+    "\n",
+    "Our low accuracy of 80% is just a testament to how difficult it is to predict the winner of chess games. Chess is a complex game with many different qualitiative and quantitative characteristics to consider. From the results it is evident that reducing unforced errors and watching the time are crucial actions to take in the game. In addition,\n",
+    "from the results we can determine that mobility is the most important positional feature out of the opening. This is an interesting insight which highlights the important of early and precise development of pieces in the beginning stages of the game. From these results, players should take away from this study the importance of error reduction, time management, and early piece activity.\n",
+    "\n",
+    "This project, although it simply affirms many popular beliefs in the community, uncovers future chess-related data science projects. Projects like an ELO predictor could be developed with many of the same features used in this project."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 9. References and Additional Resources"
+    "## 8. References and Additional Resources"
    ]
   },
   {
@@ -1292,13 +2088,6 @@
     "\n",
     "Number of moves for opening: https://www.chessable.com/blog/opening-guide/#:~:text=Discover-,Introduction%20to%20Chess%20Openings,the%20main%20fight%20takes%20place\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/final.ipynb b/final.ipynb
index 4576e7c..7525253 100644
--- a/final.ipynb
+++ b/final.ipynb
@@ -64,9 +64,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
+      "  from pandas.core import (\n"
+     ]
+    }
+   ],
    "source": [
     "import chess.pgn\n",
     "import pandas as pd\n",
@@ -204,40 +213,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   ECO  Result  BlunderDifferential  MistakeDifferential  \\\n",
-      "0  B09     0.0                    0                    2   \n",
-      "1  C33     1.0                   -1                    0   \n",
-      "2  B12     1.0                   -1                   -2   \n",
-      "3  B13     1.0                    0                    1   \n",
-      "4  C40     1.0                    0                   -3   \n",
+      "   WhiteElo  BlackElo  ECO  Result  \\\n",
+      "0      2344      2247  B09     0.0   \n",
+      "1      1605      1733  C33     1.0   \n",
+      "2      1897      1491  B12     1.0   \n",
+      "3      2026      1684  B13     1.0   \n",
+      "4      1520      1079  C40     1.0   \n",
       "\n",
-      "   InaccuracyDifferential  TimeDifferential  TimeControl  Moves  \\\n",
-      "0                       1               -26          180     36   \n",
-      "1                      -2                33          600     48   \n",
-      "2                       0               495         1800     48   \n",
-      "3                       2               531         1797     90   \n",
-      "4                      -3              -121          600     48   \n",
+      "                                          Variations  BlunderDifferential  \\\n",
+      "0  1. e4 { [%eval 0.13] [%clk 0:03:00] } 1... d6 ...                    0   \n",
+      "1  1. e4 { [%eval 0.13] [%clk 0:10:00] } 1... e5 ...                   -1   \n",
+      "2  1. e4 { [%eval 0.13] [%clk 0:30:00] } 1... c6 ...                   -1   \n",
+      "3  1. e4 { [%eval 0.13] [%clk 0:29:57] } 1... c6 ...                    0   \n",
+      "4  1. e4 { [%eval 0.13] [%clk 0:10:00] } 1... e5 ...                    0   \n",
       "\n",
-      "   DevelopmentDifferential  KingSafetyDifferential  EloDifferential  \\\n",
-      "0                       -1                      -4               97   \n",
-      "1                       -2                       0             -128   \n",
-      "2                        2                       0              406   \n",
-      "3                        2                       0              342   \n",
-      "4                        1                       4              441   \n",
+      "   MistakeDifferential  InaccuracyDifferential  TimeDifferential  TimeControl  \\\n",
+      "0                    2                       1               -26          180   \n",
+      "1                    0                      -2                33          600   \n",
+      "2                   -2                       0               495         1800   \n",
+      "3                    1                       2               531         1797   \n",
+      "4                   -3                      -3              -121          600   \n",
       "\n",
-      "   AverageElo  \n",
-      "0      2295.5  \n",
-      "1      1669.0  \n",
-      "2      1694.0  \n",
-      "3      1855.0  \n",
-      "4      1299.5  \n"
+      "   Moves  EloDifferential  AverageElo  \n",
+      "0     36               97      2295.5  \n",
+      "1     48             -128      1669.0  \n",
+      "2     48              406      1694.0  \n",
+      "3     90              342      1855.0  \n",
+      "4     48              441      1299.5  \n"
      ]
     }
    ],
@@ -329,29 +338,6 @@
     "        return len(moves) + 1\n",
     "    return len(moves)\n",
     "\n",
-    "def getDevelopmentDifferential(variation):\n",
-    "    moves = re.findall(r'\\d+\\.+\\s[\\w-]+',variation)\n",
-    "    moveList = []\n",
-    "    for move in moves:\n",
-    "        moveList.append(move.split(\" \")[1])\n",
-    "        \n",
-    "    whiteNonPawnMoves = 0\n",
-    "    whiteCastleTime = 0\n",
-    "    blackNonPawnMoves = 0\n",
-    "    blackCastleTime = 0\n",
-    "    for i in range(len(moveList)):\n",
-    "        if i<=24 and moveList[i][0] in [\"N\",\"B\",\"R\",\"Q\",\"K\",\"O\"]:\n",
-    "            if i % 2 == 0:\n",
-    "                if moveList[i][0] == \"O\":\n",
-    "                    whiteCastleTime = i//2\n",
-    "                whiteNonPawnMoves+=1\n",
-    "            else:\n",
-    "                if moveList[i][0] == \"O\":\n",
-    "                    blackCastleTime = i//2\n",
-    "                blackNonPawnMoves+=1\n",
-    "    nonPawnMoveDifferential = whiteNonPawnMoves - blackNonPawnMoves\n",
-    "    castleTimeDifferential = whiteCastleTime - blackCastleTime\n",
-    "    return pd.Series({\"DevelopmentDifferential\": nonPawnMoveDifferential, \"KingSafetyDifferential\": castleTimeDifferential}) \n",
     "# Turn string result into a number result \n",
     "def getResultForWhite(result):\n",
     "    if result == \"0-1\":\n",
@@ -367,20 +353,498 @@
     "\n",
     "df[\"Moves\"] = df[\"Variations\"].apply(getMoves)\n",
     "\n",
-    "df[[\"DevelopmentDifferential\",\"KingSafetyDifferential\"]] = df[\"Variations\"].apply(getDevelopmentDifferential)\n",
-    "\n",
     "df[\"EloDifferential\"] = df[\"WhiteElo\"] - df[\"BlackElo\"]\n",
     "\n",
     "df[\"AverageElo\"] = (df[\"WhiteElo\"] + df[\"BlackElo\"]) / 2\n",
     "\n",
     "df[\"Result\"] = df[\"Result\"].apply(getResultForWhite)\n",
     "\n",
-    "df = df.drop([\"Variations\", \"WhiteElo\", \"BlackElo\"], axis=1)\n",
     "\n",
     "pd.set_option('display.max_columns', None)\n",
     "print(df.head())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This next section will keep track of the board position and calculate 4 positional features at the end of the opening phase of the game."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   ECO  Result  BlunderDifferential  MistakeDifferential  \\\n",
+      "0  B09     0.0                    0                    2   \n",
+      "1  C33     1.0                   -1                    0   \n",
+      "2  B12     1.0                   -1                   -2   \n",
+      "3  B13     1.0                    0                    1   \n",
+      "4  C40     1.0                    0                   -3   \n",
+      "\n",
+      "   InaccuracyDifferential  TimeDifferential  TimeControl  Moves  \\\n",
+      "0                       1               -26          180     36   \n",
+      "1                      -2                33          600     48   \n",
+      "2                       0               495         1800     48   \n",
+      "3                       2               531         1797     90   \n",
+      "4                      -3              -121          600     48   \n",
+      "\n",
+      "   EloDifferential  AverageElo  KingSafetyDifferential  MobilityDifferential  \\\n",
+      "0               97      2295.5                      -2                    10   \n",
+      "1             -128      1669.0                       0                     3   \n",
+      "2              406      1694.0                      -1                    11   \n",
+      "3              342      1855.0                       0                     1   \n",
+      "4              441      1299.5                       3                     7   \n",
+      "\n",
+      "   MaterialDifferential  DevelopmentDifferential  \n",
+      "0                     1                       -1  \n",
+      "1                     3                       -2  \n",
+      "2                    -3                        1  \n",
+      "3                     0                        1  \n",
+      "4                     4                        0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import re\n",
+    "\n",
+    "def setupGame():\n",
+    "    gameArr = [[\"\" for i in range(8)] for i in range(8)]\n",
+    "    setupArr = [\"R\", \"N\", \"B\", \"Q\", \"K\", \"B\", \"N\", \"R\"]\n",
+    "    for i in range(8):\n",
+    "        gameArr[7][i] = \"B\" + setupArr[i]\n",
+    "        gameArr[0][i] = \"W\" + setupArr[i]\n",
+    "    for i in range(8):\n",
+    "        gameArr[6][i] = \"BP\"\n",
+    "        gameArr[1][i] = \"WP\"\n",
+    "    return gameArr\n",
+    "\n",
+    "def makeMove(gameArr, move, moveNum):\n",
+    "    piece, specifier, file, rank = breakMoveUp(move)\n",
+    "    color = \"W\" if moveNum % 2 == 0 else \"B\"\n",
+    "    if piece == \"Castle\":\n",
+    "        colorRank = 0 if color == \"W\" else 7\n",
+    "        gameArr[colorRank][4] = \"\"\n",
+    "        gameArr[colorRank][5] = color + \"R\"\n",
+    "        gameArr[colorRank][6] = color + \"K\"\n",
+    "        gameArr[colorRank][7] = \"\"\n",
+    "    elif piece == \"Queenside Castle\":\n",
+    "        colorRank = 0 if color == \"W\" else 7\n",
+    "        gameArr[colorRank][0] = \"\"\n",
+    "        gameArr[colorRank][2] = color + \"K\"\n",
+    "        gameArr[colorRank][3] = color + \"R\"\n",
+    "        gameArr[colorRank][4] = \"\"\n",
+    "    elif piece == \"Promotion\":\n",
+    "        firstMove = move.split(\"=\")[0]\n",
+    "        makeMove(gameArr, firstMove, moveNum)\n",
+    "        piece, specifier, file, rank = breakMoveUp(firstMove)\n",
+    "        col = ord(file) - ord(\"a\")\n",
+    "        row = int(rank) - 1\n",
+    "        gameArr[row][col] = color + move.split(\"=\")[1][0]\n",
+    "    else:\n",
+    "        piece = color + piece\n",
+    "        col = ord(file) - ord(\"a\")\n",
+    "        row = int(rank) - 1\n",
+    "        curRow,curCol = findPiecePos(piece, specifier, gameArr, col, row, color)\n",
+    "        #En Passant Condition\n",
+    "        if \"x\" in move and gameArr[row][col] == \"\":\n",
+    "            if color == \"W\":\n",
+    "                # Erasing black piece, so add 1\n",
+    "                gameArr[row-1][col] = \"\"\n",
+    "            else:\n",
+    "                gameArr[row+1][col] = \"\"\n",
+    "        gameArr[row][col] = piece\n",
+    "        gameArr[curRow][curCol] = \"\"\n",
+    "       \n",
+    "def findPiecePos(piece, specifier, gameArr, endCol, endRow, color):\n",
+    "    col = -1\n",
+    "    row = -1\n",
+    "    if specifier:\n",
+    "        # Specifier is file\n",
+    "        if 0 <= ord(specifier) - ord(\"a\") <= 8:\n",
+    "            col = ord(specifier) - ord(\"a\")\n",
+    "        # Specifier is rank\n",
+    "        else:\n",
+    "            row = int(specifier) - 1\n",
+    "    # Check all 64 squares brute force\n",
+    "    for i in range(len(gameArr)):\n",
+    "        for j in range(len(gameArr[i])):\n",
+    "            # Meets piece and specifier constraints\n",
+    "            if piece == gameArr[i][j] and (col==-1 or j==col) and (row==-1 or i==row):\n",
+    "                colDistance = abs(endCol-j)\n",
+    "                rowDistance = abs(endRow-i)\n",
+    "                if piece[1] == \"N\" and colDistance + rowDistance == 3 \\\n",
+    "                    and min(colDistance, rowDistance) == 1 and not kingChecked(gameArr, color, i, j, endRow, endCol):\n",
+    "                    \n",
+    "                    return (i,j)\n",
+    "                if piece[1] == \"R\" and min(colDistance, rowDistance) == 0 and not kingChecked(gameArr, color, i, j, endRow, endCol):\n",
+    "                    blockingPiece = False\n",
+    "                    if colDistance == 0:\n",
+    "                        multiplier = 1\n",
+    "                        if i < endRow: \n",
+    "                            multiplier = -1\n",
+    "                        for k in range(1,rowDistance):\n",
+    "                            if gameArr[endRow + multiplier*k][endCol] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    else:\n",
+    "                        multiplier = 1\n",
+    "                        if j < endCol:\n",
+    "                            multiplier = -1\n",
+    "                        for k in range(1,colDistance):\n",
+    "                            if gameArr[endRow][endCol + multiplier*k] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    if blockingPiece == False:\n",
+    "                        return (i,j)\n",
+    "                if piece[1] == \"B\" and colDistance == rowDistance and not kingChecked(gameArr, color, i, j, endRow, endCol):\n",
+    "                    blockingPiece = False\n",
+    "                    if colDistance == rowDistance:\n",
+    "                        rowM = 1\n",
+    "                        colM = 1\n",
+    "                        if i<endRow:\n",
+    "                            rowM = -1\n",
+    "                        if j<endCol:\n",
+    "                            colM = -1\n",
+    "                        for k in range(1,rowDistance):\n",
+    "                            if gameArr[endRow+rowM*k][endCol + colM*k] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    if blockingPiece == False:\n",
+    "                        return (i,j)\n",
+    "                if piece[1] == \"P\" and not kingChecked(gameArr, color, i, j, endRow, endCol):\n",
+    "                    if (((color == \"W\" and i==1) or (color == \"B\" and i==6)) and rowDistance == 2 and colDistance == 0):\n",
+    "                        if color == \"W\" and i<endRow:\n",
+    "                            if gameArr[endRow-1][endCol] == \"\":\n",
+    "                                return(i,j)\n",
+    "                        elif color == \"B\" and i>endRow:\n",
+    "                            if gameArr[endRow+1][endCol] == \"\":\n",
+    "                                return(i,j)\n",
+    "                    elif rowDistance == 1 and colDistance <= 1:\n",
+    "                        if color == \"W\" and i<endRow:\n",
+    "                            return (i,j)\n",
+    "                        elif color == \"B\" and i>endRow:\n",
+    "                            return(i,j)\n",
+    "                if piece[1] == \"K\":\n",
+    "                    return(i,j)\n",
+    "                if piece[1] == \"Q\" and (min(colDistance, rowDistance) == 0 or colDistance == rowDistance) and not kingChecked(gameArr, color, i, j, endRow, endCol):\n",
+    "                    blockingPiece = False\n",
+    "                    if colDistance == rowDistance:\n",
+    "                        rowM = 1\n",
+    "                        colM = 1\n",
+    "                        if i<endRow:\n",
+    "                            rowM = -1\n",
+    "                        if j<endCol:\n",
+    "                            colM = -1\n",
+    "                        for k in range(1,rowDistance):\n",
+    "                            if gameArr[endRow+rowM*k][endCol + colM*k] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    elif colDistance == 0:\n",
+    "                        multiplier = 1\n",
+    "                        if i < endRow: \n",
+    "                            multiplier = -1\n",
+    "                        for k in range(1,rowDistance):\n",
+    "                            if gameArr[endRow + multiplier*k][endCol] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    elif rowDistance == 0:\n",
+    "                        multiplier = 1\n",
+    "                        if j < endCol:\n",
+    "                            multiplier = -1\n",
+    "                        for k in range(1,colDistance):\n",
+    "                            if gameArr[endRow][endCol + multiplier*k] != \"\":\n",
+    "                                blockingPiece = True\n",
+    "                    if blockingPiece == False:\n",
+    "                        return (i,j)\n",
+    "\n",
+    "def kingChecked(gameArr, color, startRow, startCol, endRow, endCol):\n",
+    "    startPiece = gameArr[startRow][startCol]\n",
+    "    gameArr[startRow][startCol] = \"\"\n",
+    "    endPiece = gameArr[endRow][endCol]\n",
+    "    gameArr[endRow][endCol] = startPiece\n",
+    "    for i in range(len(gameArr)):\n",
+    "        for j in range(len(gameArr[i])):\n",
+    "            if gameArr[i][j] == color + \"K\":\n",
+    "                kArr = [(0,1),(0,-1),(1,0),(-1,0),(1,1),(-1,-1),(1,-1),(-1,1)]\n",
+    "                row = i\n",
+    "                col = j\n",
+    "                for k in range(len(kArr)*8):\n",
+    "                    r,c = kArr[k%8]\n",
+    "                    r = r*((k//8)+1)\n",
+    "                    c = c*((k//8)+1)\n",
+    "                    modRow = row + r\n",
+    "                    modCol = col + c\n",
+    "                    if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                        if gameArr[modRow][modCol] != \"\":   \n",
+    "                            pieceColor = gameArr[modRow][modCol][0]\n",
+    "                            oppositeColor = \"B\" if color==\"W\" else \"W\"\n",
+    "                            piece = gameArr[modRow][modCol][1]\n",
+    "                            # Rook or Queen\n",
+    "                            if k%8 < 4:\n",
+    "                                if pieceColor == oppositeColor and piece in [\"R\",\"Q\"]: \n",
+    "                                    gameArr[startRow][startCol] = startPiece\n",
+    "                                    gameArr[endRow][endCol] = endPiece\n",
+    "                                    return True\n",
+    "                                else:\n",
+    "                                    kArr[k%8] = (0,0)\n",
+    "                            # Bishop and Queen\n",
+    "                            else:\n",
+    "                                if pieceColor == oppositeColor and piece in [\"B\",\"Q\"]:\n",
+    "                                    gameArr[startRow][startCol] = startPiece\n",
+    "                                    gameArr[endRow][endCol] = endPiece\n",
+    "                                    return True\n",
+    "                                else:\n",
+    "                                    kArr[k%8] = (0,0)\n",
+    "    gameArr[startRow][startCol] = startPiece\n",
+    "    gameArr[endRow][endCol] = endPiece\n",
+    "    return False\n",
+    "    \n",
+    "def breakMoveUp(move):\n",
+    "    # Get piece\n",
+    "    files = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']\n",
+    "    ranks = ['1', '2', '3', '4', '5', '6', '7', '8']\n",
+    "    piece = move[0]\n",
+    "    specifier = None\n",
+    "    file = None\n",
+    "    rank = None\n",
+    "    if move[0] not in [\"R\", \"N\", \"B\", \"Q\", \"K\"]:\n",
+    "        # Castle or Pawn move\n",
+    "        if move[0] == \"O\":\n",
+    "            if len(move) == 3:\n",
+    "                return \"Castle\", None, None, None\n",
+    "            else:\n",
+    "                return \"Queenside Castle\", None, None, None\n",
+    "        else:\n",
+    "            piece = \"P\"\n",
+    "    if \"=\" in move:\n",
+    "        return \"Promotion\", None, None, None\n",
+    "    \n",
+    "    # Get file, rank, specifier\n",
+    "    for i in range(0, len(move)):\n",
+    "        if move[i] in files or move[i] in ranks:\n",
+    "            if specifier == None:\n",
+    "                specifier = move[i]\n",
+    "            else:\n",
+    "                if move[i] in files:\n",
+    "                    file = move[i]\n",
+    "                else:\n",
+    "                    rank = move[i]\n",
+    "    if not file:\n",
+    "        file = specifier\n",
+    "        specifier = None\n",
+    "    if not rank:\n",
+    "        rank = specifier\n",
+    "        specifier = None\n",
+    "    \n",
+    "    if piece == \"P\" and specifier == None:\n",
+    "        specifier = file\n",
+    "    \n",
+    "    return piece, specifier, file, rank\n",
+    "\n",
+    "def playMoves(moves):\n",
+    "    gameArr = setupGame()\n",
+    "    for i in range(len(moves)):\n",
+    "        makeMove(gameArr, moves[i], i)\n",
+    "    return gameArr\n",
+    "\n",
+    "\n",
+    "def findKingSafetyDifferential(gameArr):\n",
+    "    # Using the Pawn Shield method for calculating King Safety\n",
+    "    # The lack of a shielding pawn within one or two squares of king gets a -1 penalty and an open file gets a -3 penalty\n",
+    "    blackKingSafetyPenalty = 0\n",
+    "    whiteKingSafetyPenalty = 0\n",
+    "    for i in range(len(gameArr)):\n",
+    "        for j in range(len(gameArr[i])):\n",
+    "            if gameArr[i][j] == \"WK\":\n",
+    "                minRow = i+1\n",
+    "                maxRow = min(7, i+2)\n",
+    "                minCol = max(0, j-1)\n",
+    "                maxCol = min(7, j+1)\n",
+    "                whiteKingSafetyPenalty = calculateKingSafetyPenalty(minRow, maxRow, minCol, maxCol, \"W\", gameArr)\n",
+    "            if gameArr[i][j] == \"BK\":\n",
+    "                minRow = max(0, i-2)\n",
+    "                maxRow = i-1\n",
+    "                minCol = max(0, j-1)\n",
+    "                maxCol = min(7, j+1)\n",
+    "                blackKingSafetyPenalty = calculateKingSafetyPenalty(minRow, maxRow, minCol, maxCol, \"B\", gameArr)\n",
+    "    return whiteKingSafetyPenalty - blackKingSafetyPenalty    \n",
+    "\n",
+    "def calculateKingSafetyPenalty(minRow, maxRow, minCol, maxCol, color, gameArr):\n",
+    "    totalPenalty = 0\n",
+    "    for b in range(minCol, maxCol+1):\n",
+    "        # Missing Pawn Check\n",
+    "        penalty = -1\n",
+    "        for a in range(minRow, maxRow+1):\n",
+    "            if gameArr[a][b] == color + \"P\":\n",
+    "                penalty = 0\n",
+    "        # Open File Check\n",
+    "        if penalty == -1:\n",
+    "            penalty = -3\n",
+    "            for a in range(0,7):\n",
+    "                if gameArr[a][b] != \"\" and gameArr[a][b][1] == \"P\":\n",
+    "                    penalty = -1\n",
+    "        totalPenalty += penalty\n",
+    "    return totalPenalty\n",
+    "\n",
+    "def findMobilityDifferential(gameArr):\n",
+    "    # Calculating mobility by looking at number of legal moves\n",
+    "    blackMobility = 0\n",
+    "    whiteMobility = 0\n",
+    "    for i in range(len(gameArr)):\n",
+    "        for j in range(len(gameArr[i])):\n",
+    "            if gameArr[i][j] != \"\":\n",
+    "                color = gameArr[i][j][0]\n",
+    "                piece = gameArr[i][j][1]\n",
+    "                if piece == \"P\":\n",
+    "                    if color == \"W\":\n",
+    "                        if gameArr[i+1][j] == \"\":\n",
+    "                            whiteMobility+=1\n",
+    "                            if i==1 and gameArr[i+2][j] == \"\":\n",
+    "                                whiteMobility+=1\n",
+    "                    else:\n",
+    "                        if gameArr[i-1][j] == \"\":\n",
+    "                            blackMobility+=1\n",
+    "                            if i==6 and gameArr[i-2][j] == \"\":\n",
+    "                                blackMobility+=1\n",
+    "                else:\n",
+    "                    if color == \"W\":\n",
+    "                        whiteMobility += generateMobilityCombos(piece, i, j, gameArr)\n",
+    "                    else:\n",
+    "                        blackMobility += generateMobilityCombos(piece, i, j, gameArr)\n",
+    "    return whiteMobility - blackMobility\n",
+    "                        \n",
+    "def generateMobilityCombos(piece, row, col, gameArr):\n",
+    "    mobility = 0\n",
+    "    if piece == \"N\":\n",
+    "        nArr = [(-1,-2), (-1,2), (1,-2), (1,2), (2, 1), (2, -1), (-2,1), (-2,-1)]\n",
+    "        for i in range(len(nArr)):\n",
+    "            r, c = nArr[i]\n",
+    "            modRow = row + r\n",
+    "            modCol = col + c\n",
+    "            if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                if gameArr[modRow][modCol] == \"\":\n",
+    "                    mobility += 1\n",
+    "    elif piece == \"B\":\n",
+    "        bArr = [(-1,-1),(1,1),(-1,1),(1,-1)]\n",
+    "        for i in range(len(bArr)*4):\n",
+    "            r,c = bArr[i%4]\n",
+    "            r = r*((i//4)+1)\n",
+    "            c = c*((i//4)+1)\n",
+    "            modRow = row + r\n",
+    "            modCol = col + c\n",
+    "            if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                if gameArr[modRow][modCol] == \"\":\n",
+    "                    mobility += 1\n",
+    "                else:\n",
+    "                    bArr[i%4] = (0,0)\n",
+    "    elif piece == \"R\":\n",
+    "        rArr = [(0,-1), (0,1), (1,0), (-1,0)]\n",
+    "        for i in range(len(rArr)*4):\n",
+    "            r,c = rArr[i%4]\n",
+    "            r = r*((i//4)+1)\n",
+    "            c = c*((i//4)+1)\n",
+    "            modRow = row + r\n",
+    "            modCol = col + c\n",
+    "            \n",
+    "            if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                if gameArr[modRow][modCol] == \"\":\n",
+    "                    mobility += 1\n",
+    "                else:\n",
+    "                    rArr[i%4] = (0,0)\n",
+    "    elif piece == \"K\":\n",
+    "        kArr = [(0,1),(0,-1),(1,-1),(1,0),(1,1),(-1,-1),(-1,0),(-1,1)]\n",
+    "        for i in range(len(kArr)):\n",
+    "            r,c = kArr[i]\n",
+    "            modRow = row + r\n",
+    "            modCol = col + c\n",
+    "            if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                if gameArr[modRow][modCol] == \"\":\n",
+    "                    mobility += 1\n",
+    "    elif piece == \"Q\":\n",
+    "        qArr = [(0,-1), (0,1), (1,0), (-1,0), (-1,-1),(1,1),(-1,1),(1,-1)]\n",
+    "        for i in range(len(qArr)*4):\n",
+    "            r,c = qArr[i%8]\n",
+    "            r = r*((i//8)+1)\n",
+    "            c = c*((i//8)+1)\n",
+    "            modRow = row + r\n",
+    "            modCol = col + c\n",
+    "            if 0 <= modRow <= 7 and 0 <= modCol <= 7:\n",
+    "                if gameArr[modRow][modCol] == \"\":\n",
+    "                    mobility += 1\n",
+    "                else:\n",
+    "                    qArr[i%8] = (0,0)\n",
+    "    return mobility\n",
+    "\n",
+    "def findMaterialDifferential(gameArr):\n",
+    "    blackDict = {\"P\": 0, \"B\": 0, \"N\": 0, \"R\": 0, \"Q\": 0, \"K\": 0}\n",
+    "    whiteDict = {\"P\": 0, \"B\": 0, \"N\": 0, \"R\": 0, \"Q\": 0, \"K\": 0}\n",
+    "    # Calculating material imbalance\n",
+    "    # P-1, B and N- 3, R-5, Q-9\n",
+    "    # +1 for bishop pair\n",
+    "    # -1 for rook pair\n",
+    "    # -1 for knight pair\n",
+    "    # -1 for no pawns\n",
+    "    for i in range(len(gameArr)):\n",
+    "        for j in range(len(gameArr[i])):\n",
+    "            if gameArr[i][j] != \"\":\n",
+    "                color = gameArr[i][j][0]\n",
+    "                piece = gameArr[i][j][1]\n",
+    "                if color == \"B\":\n",
+    "                    blackDict[piece] = blackDict.get(piece,0) + 1\n",
+    "                else:\n",
+    "                    whiteDict[piece] = whiteDict.get(piece,0) + 1\n",
+    "    blackPoints = calcPoints(blackDict)\n",
+    "    whitePoints = calcPoints(whiteDict)\n",
+    "    return whitePoints - blackPoints\n",
+    "\n",
+    "def calcPoints(dict):\n",
+    "    points = dict[\"B\"] * 3 + dict[\"N\"] * 3 + dict[\"P\"] * 1 + dict[\"Q\"] * 9 + dict[\"R\"] * 5\n",
+    "    if dict[\"B\"] == 2:\n",
+    "        points += 1\n",
+    "    if dict[\"N\"] == 2:\n",
+    "        points -= 1\n",
+    "    if dict[\"R\"] == 2:\n",
+    "        points -= 1\n",
+    "    if dict[\"P\"] == 0:\n",
+    "        points -= 1\n",
+    "    return points\n",
+    "    \n",
+    "def findDevelopmentDifferential(moves):     \n",
+    "    whiteNonPawnMoves = 0\n",
+    "    blackNonPawnMoves = 0\n",
+    "    for i in range(len(moves)):\n",
+    "        if moves[i][0] in [\"N\",\"B\",\"R\",\"Q\",\"K\",\"O\"]:\n",
+    "            if i % 2 == 0:\n",
+    "                if moves[i][0] == \"O\":\n",
+    "                    whiteCastleTime = i//2\n",
+    "                whiteNonPawnMoves+=1\n",
+    "            else:\n",
+    "                if moves[i][0] == \"O\":\n",
+    "                    blackCastleTime = i//2\n",
+    "                blackNonPawnMoves+=1\n",
+    "    nonPawnMoveDifferential = whiteNonPawnMoves - blackNonPawnMoves\n",
+    "    return nonPawnMoveDifferential \n",
+    "def findStatistics(variation):\n",
+    "    # Match with a number followed by some .'s, a space, and then a combination of letters, dashes, and equals to match all moves\n",
+    "    moves = re.findall(r'\\d+\\.+\\s[\\w\\-\\=]+',variation)\n",
+    "    moves = [move.split(\" \")[1] for move in moves]\n",
+    "    modifiedMoves = moves[:24]\n",
+    "    gameArr = playMoves(modifiedMoves)\n",
+    "    safety = findKingSafetyDifferential(gameArr)\n",
+    "    mobility = findMobilityDifferential(gameArr)\n",
+    "    material = findMaterialDifferential(gameArr)\n",
+    "    development = findDevelopmentDifferential(modifiedMoves)\n",
+    "    \n",
+    "    return pd.Series({\"KingSafetyDifferential\": safety, \"MobilityDifferential\": mobility, \n",
+    "                      \"MaterialDifferential\": material, \"DevelopmentDifferential\": development}) \n",
+    "\n",
+    "df[[\"KingSafetyDifferential\",\"MobilityDifferential\",\"MaterialDifferential\", \"DevelopmentDifferential\"]] = df[\"Variations\"].apply(findStatistics)\n",
+    "\n",
+    "df = df.drop([\"Variations\", \"WhiteElo\", \"BlackElo\"], axis=1)\n",
+    "print(df.head())"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -397,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -406,13 +870,13 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x2000 with 3 Axes>"
       ]
@@ -471,7 +935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
@@ -480,13 +944,13 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -521,7 +985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
@@ -530,13 +994,13 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -570,7 +1034,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
@@ -579,13 +1043,13 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -617,12 +1081,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We filtered the data to only include openings that accounted for at least 1% of games in our database to get results with a low variance. The results show that openings do have a small impact on winning. The least and most winning openings differed by about 20%, with most openings hovering close to the 50% win rate."
+    "We filtered the data to only include openings that accounted for at least 1% of games in our database to get results with a low variance. The results show that openings do have a small impact on winning. The least and most winning openings differed by about 20%, with most openings hovering close to the 50% win rate.\n",
+    "\n",
+    "Let's graph our positional features against the mean result!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
@@ -631,13 +1097,13 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -666,12 +1132,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we can see, like elo differential, development differential has a small but present impact on win rate. Players who spend less time moving their pawns and more developing their major pieces come out ahead after the opening."
+    "Development differential has a small but present impact on win rate. Players who spend less time moving their pawns and more developing their major pieces come out ahead after the opening."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
@@ -680,13 +1146,13 @@
        "Text(0, 0.5, 'Mean Result')"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -696,7 +1162,7 @@
     }
    ],
    "source": [
-    "bins = [-12,-8,-4,0,4,8,12]\n",
+    "bins = [-4,-2,0,2,4]\n",
     "df[\"KingSafetyGroup\"] = pd.cut(df['KingSafetyDifferential'],bins=bins, precision=0)\n",
     "\n",
     "# Drop na values caused by values that don't fit the bins\n",
@@ -715,7 +1181,105 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This graph has a similar trend as the development differential graph. Castling earlier in the game before the opportunity is lost generally increases one's king safety and win rate."
+    "This graph has a similar trend as the development differential graph. Having more pawns and less open files around the king slightly increases the chance of winning out of the opening."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Mean Result')"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = [-12,-8,-4,0,4,8,12]\n",
+    "df[\"MobilityGroup\"] = pd.cut(df['MobilityDifferential'],bins=bins, precision=0)\n",
+    "\n",
+    "# Drop na values caused by values that don't fit the bins\n",
+    "groups = df[\"MobilityGroup\"].dropna().unique().sort_values()\n",
+    "\n",
+    "x = range(len(groups))\n",
+    "y = df.groupby(\"MobilityGroup\", observed=True)[\"Result\"].mean()\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.bar(x, y)\n",
+    "plt.xticks(x, groups)\n",
+    "plt.xlabel('Mobility Differential Group')\n",
+    "plt.ylabel('Mean Result')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mobility out of the opening seems to have the greatest impact on winning out of all the positional features. Placing pieces where they control a substantial amount of space is important in the opening."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Mean Result')"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = [-4,-2,0,2,4]\n",
+    "df[\"MaterialGroup\"] = pd.cut(df['MaterialDifferential'],bins=bins, precision=0)\n",
+    "\n",
+    "# Drop na values caused by values that don't fit the bins\n",
+    "groups = df[\"MaterialGroup\"].dropna().unique().sort_values()\n",
+    "\n",
+    "x = range(len(groups))\n",
+    "y = df.groupby(\"MaterialGroup\", observed=True)[\"Result\"].mean()\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.bar(x, y)\n",
+    "plt.xticks(x, groups)\n",
+    "plt.xlabel('Material Differential Group')\n",
+    "plt.ylabel('Mean Result')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Material of course also has a significant impact on winning. However, out of the opening, players are usually still even in material, so this feature isn't very indicative of winning in the way it is currently presented."
    ]
   },
   {
@@ -727,24 +1291,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Slope for EloDifferential vs MistakeDifferential: -0.0021435697933253747\n",
+      "Slope for EloDifferential vs MistakeDifferential: -0.00214356979332527\n",
       "R^2 value 0.0032011971202323686\n",
-      "Slope for EloDifferential vs TimeDifferential: 0.04576733042463111\n",
+      "Slope for EloDifferential vs TimeDifferential: 0.0457673304246289\n",
       "R^2 value 0.002036248672220742\n",
-      "Slope for TimeDifferential vs MistakeDifferential: -0.0010108434488335428\n",
+      "Slope for TimeDifferential vs MistakeDifferential: -0.0010108434488334914\n",
       "R^2 value 0.0007322937210155755\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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LKKVT5EZLDQlbp6uXvqCD/Ios/blCQ8LWVKrzblcu80Y0/b0jiX1+MBbCfAXdI3kGD/XNuHdyJqpBTjuK6+5azRf/71+SACaE+ECpNxuc+6d3hRAfSD/6y8foOzIAkFjoiucd954NHBvie//PI4nVOdMO7evlmc17auEnqUa7tZXeMwv97x2jXAziegmFoPHz7i+E5EZLAImFrvH1elVAf64Q10vo/Rw/71J/kf7ekXgZEqxXnXc4lGfgwIk4bFkSCV0wNl8TWV7csod9rx9KpI4QQpxpEryEEKJK+v8/2OSCeUIIIc5iEryEEEIIIYQQImESvIQQQgghhBAiYRK8hBBCCCGEECJhEryEEEIIIYQQImESvIQQQgghhBAiYRK8hBBCCCGEECJhEryEEEIIIYQQImHumV4AIT7IrLXsfeFdtn37Od58aT833ruWDQ/dxPJLFidW8+CbR9j27ed5afNurrz5YjY8dBOXXXchSp0bNzEaHS7w9CO7ONxfRi9YgM3lsIVC5a68CVAKOtqgu4PedAs/+u5L3LFxNe2d2UTKRZFh16sH2frYbt7bf5xb77iMjfesYeHijkTqWWt56+BxvvHEToZWt+ONhPh9JdxClEg9gDCjKSxKUbz2Qrz+Ipn9Q/jH8ondZitKORQvaKdweTs6hExvmdRAiEpolTEO5JZ7DF7mYHyLPwLeKCiTzCu0gHWg2KUpdWr8YUNqKEKHiZTDAsZXlBdnyC1bhn88T+roKE4+SKYgYH0X05bBZNP84Mc7yS5o5/LVSxPbr504NsQTP97JM4/tZsnKeWz46DquWX8xjuskUm90pMiTT7zBti17SKU8Nt2zhvW3Xko67SVSr1ws88KjO9n68A5G+ke565M3c/vP3kj7vLZE6kWR4fVn3mLbt5/nvb3vc+uHr+GuT9zEwhXzEqknxAeVsjap1sy5a3h4mI6ODoaGhmhvbz/TiyOazFrL/t2H2fbt59ny8LOcODKA42qi0KC1whjLRVct5+5P38KdD97IgmXdc6557GAf27/zPJu/uYMDbxxBOxoTmVrdhSvmcfenb2HDQzex6oqlDXiVzVUslHlu8x62fv9lXtz+BlFoUGosa1ljsPk8NpePQ1gjtLdCdweqqxMcDdaitMJa0Fpx/fqL2fShtdx65+Vksv6cSllreXPvEbY+tpvNj77O0GAex9FEkUEphbWW1WuWcfeH1nLnxtV0dbfM+eUd6Bngp8++wY+f2cP7vUM4WhEZG7+pSuEUI/wTJfz+Ek7JzLle5CsKi1MUlqYJ2t1xHx6gFboQktk3SObdIbz+4pxDmPE0xZVtFC7upLS4ZeLNk5VChZb08TKZ4wH+YDjnelZDfonLyCqP3HIP66ixG24rwII7Cv4weDlQdm4VLYAG44J1GXt9tvYHXs7iD0b4QwY9x4/QAsZThFlN2KLHvb7Ki1QKZ6RE6v0R/KMjOKW5B3frOpi2DLYji/W92rqpHYWJLAsXtXPv/WvZdM8aVl2wYM71hgdyPPXo62z5wSvsfvk9VOWD0zreFts6Mmz46DXc9ZF1rL52JVrPbVBQsRiw4+m32fL46zz/7D6iKD5GoMBEllTK5bY7LmfTvWu44caLcOcY+qIw4tVte9j6zR08+d3nKeZKOK7GGIs1Fu1orrvnKu7+9K3c8rHrybSm51TPWstbL++Pj4X/5zmGTozUjklKK6yxXHHDRWz61Hru/Nkb6Fwg7SVx7qo3G0jwmgUJXuenI/t72fbt59n8zzt4f9+xWviZlKLWoL7ypku4+1Pruf3j19Mxg7ONg8eHeeJ7L7L5mzt486X9lVBgxxp7J6kuz8rLl3D3p2/hroduYvHK+bN4pc0RBhEvP/kmW3/wCs88uotyKZz+PcUCKg5huVz8UyzNrGhrFro7Ud0d4Lq1ht5kqiHa8xxuufNyNt5/FTfeegm+X/9AgffePc7Wx17n8Udfp7dnqBa2JlNbDKW45rpV3H3/Wm6/63JaZtA4OtY/wmPPvcmPn9nDO4dO1F7DlKohLBeS6otDmA7qPyQYV1FY5FNYkqLcVT1zr5gy5RgLWuGMlMnsGyT77hDucLnuetZRFJe3Uriog+LydnDUtJ9hrSEfGNLHAjLHy3gjUd0hzCooLHQYXeUzstLDegpl4hA2+ROIX7sBbyQOYW6eSgO/jnowedia9kmVEDZiSA0a/GEzo54+40DYogmzDsZTY6/hNPXcwSKpIyP4PaPooP7UZx2Naa2ErbQ//efH2Ha46oL53HPfWjbefSWLl3TWXa+QK7Fj8x62/vBVXn7qLYyxtVAwmeo22r2wjbs/fh0bPrqOCy9fUnfPWxhGvPjCu2x5bDdPPfEm5XJYC5LT1cu2pNi46Uo23rOGq9etjANaHYwx7H32HbY+vINtD+9gZCBXCz+Tqe5jvZTL+o9cx6ZP38IN96/DT9Xf8/be3vdrJx57D/VNu99WemwbXXfHFWz61Hpu+8i1tHQkM6JAiDNFgleCJHidP/qODvLE915g8zd38M5rB0/fkJ1ENTBprbn2rtVs/Ln13PqRa8m2ndqgzg3lefpHr7Dl4Wd57ck3agesqRoJkxeshD5juey6C+Ket5+9ga6FyQxlmwljDK8//y7bfvgq23/4KvnR4rRhZErV9yWKsKM5TC4H5Ska8Jl0HLbmdcK4s+ozUV3GdMbnrnuuZOP9V7Hu+gtwnFNb4EePDLDt8T08/sguDr5XR/iZRPU5rqu56ZZL2HT/Vay/7VJSkzSOBkcKbH7hLX6yYy873z4yoadwRqoN6tEw7gkbKKMnaSwaB0oL4rBVnOdXGunThK1Ja1XqaYU7UCT7ziCZ/UM4+VPHzlkFpaWtFC7soHhBO9bVs/oMx3r6DOneMpneMl7+1PXOAqV5DiOrPEYv8IhSGgwz/0Z0JcCoCLxh8EfAKUwewqwaF7Y0pw8/09TD2Hgo4qDBG508hBlNrWfL+LN9P2t/4PXlSR0ZxT82ippknbFaYVszmPYMNpOKJ8603rj92uVXLOGe+9eyYeNqurpbT3louRzy4hNvsu2Hr/Lslj0E5Wh222ElUCxdNa8WwpauOvVkljGWna8dZOvju9m6ZQ/5XGlW+7Xqczq7stxz71o23nMll00S+qy1vLvzIFsf3sGWbzxN39HBacPWlPUqz0m3pLjrkzez4VO3sO6uKyfdr/UcOM62b8fHwkNvHT3NSbLJVQOo6znceN9aNn5yPTfffzWpzNxGFAhxNpDglSAJXue2kYFRnvrBy2x5+Fl27XirNhylEVtK9WDl+g43f2gdGz95M+tuv4JXtu9l67ee5YWf7iIMomnPkM5E7Wwjiqtvvzw+2/ix62ht4tlGay1v7TzE9h++ytbvv8xg3+jswtbUBeIQFobY0dE4hGkdh635nZBKza5hOYXqsrd3ZNh4/1o23reGxcs6eWLLGzz2yE7e2ns0buRN0zs5m3qplMvtG65g471XccXVy3j6tf08smMvz+85GAfzSg9rQ1R7UYYC/L4S3lCZcrdPfrFPcWGq0tPEzMPBpLUqfyiFfzxP5p1BUu8NE3WkKFzUQeHCDmzKqfWWNURlfXDzEZljZdLHA6IUjK7yGLnQJ8zqBtcjDg5hZSjiMOgy4CqsW+lBa9T7Ob5eZPGHDKnBCKdgiTJx2Ir8SqFGfX+qun0Zi9ebI3V0BO9EHjIpTHsW25JuaL3xs1l3zSruvu8qbr31UvbtOcK2f3mVJx/ZRTFfbuh+pjaMfPUS7v74ddzxwFr6BvJs3bybzY+9zuBAvqH1HEcRRZZFizu49/61bLx7DW4Use3hHTz+j09x5HSjLmZarxLC2rtb2fjpW9jwqVtYfOFCnvz+i2z+5x289cp7yezXMh63fex6Njx0E9dtvBLXk0sPiA8mCV5T+N3f/V1+7/d+b8K0RYsW0dPTU/c8JHidu1578g3+0yf+eMIY9aScfIaykQfRyYw/2/iHP/xNrrz5ksRqjfe7v/o3PLd5T2PD1lSsxbSksdnGhq2pOI4mNAYcXSuV5B7VcTRlx5JblcEqZnUWf0ZspcepgziENDIcTFpv3GupNOQbFn6mqVnqgrCFJtWLvwOWGmzC+1mpp0KLl7Nx71fSF+GxFhVZWg+VEru4yXhaq/hkVl8OFdnE9zOq0sMbdKSwKa8p+zWtFfZQL/rI9MP6GsVxNZEB5Xu13rYkm4rVY2HXwnb+7tX/Lj1g4gOp3mxwXl5Ofs2aNRw9erT2s2vXrjO9SOIsceTd3loYSjJ0AacMC0n6YFrtQQuDiKPvHU+01njvvXkUIPnQBXGvT3WYTBOu8jj+NVmbbOiq1jOepnrdhkRDF8ShQDMWRpJ+S5Ua+4HkQ1ClpqmeZG9KPVDB2N+bUs9Uhjg248qnSqFCmhK6YGwbqA5xTHo/E2/nFlu5EEYz9mvGWFQhHkqd9HECKsemcYEr6fPz1WPhQO8whdFiorWEONPOyz5d13VZvDi5y30LIYT44Dg3bsQgmqlyIUshhJiR87LH6+2332bp0qVceOGFfOYzn+Hdd9+d9vGlUonh4eEJP0IIIYQQQghRr/MueN188838wz/8A48++ih/9Vd/RU9PD7feeit9fX1TPuerX/0qHR0dtZ8VK1Y0cYmFEEIIIYQQH3TnXfB64IEHeOihh1i7di333HMPP/rRjwD4+7//+ymf8+Uvf5mhoaHaz6FDh5q1uEIIIYQQQohzwHn5Ha/xWlpaWLt2LW+//faUj0mlUqRSqSYulRBCCCGEEOJcct71eJ2sVCqxd+9elixZcqYXRQghhBBCCHGOOu+C1xe/+EW2b9/O/v37ee655/jkJz/J8PAwn//858/0ogkhhBBCCCHOUefdUMPDhw/z2c9+lhMnTrBgwQLWr1/Ps88+y6pVq870ognRNM28bfr5dYt28UEkq6iYKVlnhBCzcd4Fr3/+538+04sgzmJeKt4ktKNqNxw+p1RuPvP1//sx9g8U2HjPlVx8ySJUg2+sWsiX2LHtTTb/5DV6ewYbOu/Tqtzws9GvadqS2PgGtU2q1lTn4GZwClv9o0mfoWryOtPsG5Wpcb+bsf6oShnVvBs3Y20z1xhsZX/WzI+ymftRx9FEkcH1z7tmqTjPKJv0LcnPQcPDw3R0dDA0NER7e/uZXhzRQGEQsvVbz7Hl4ed49Ym98YEHRWKbieuC60BkIAiSqcG4tk9rBtPZiu1sQbsaE1mWLuvivg+tZePda1i2vHvWNcrlkBeffpstj+zi2e1vEAQRWitMEKFKISoIwSTzPlYbQCbtUu5KY9M+bjFCB8k0jmylgWc8RWG+g/E0qWGDO2oggXrVBqzRkF/gUprvgtI45bH/ToIFwhYotwAadCm5qGAra2nUYglaDDpU+EMaFSX56hSqNcQuKhNkIBz1MYFDMiGsMk/XoDMhXgHcEy5qNKl6laraUm63KGNJDSm8oXh6Up+kBcK0RQeG9EBEui+Kw1ACIaw6u1K3ppy2+ENlWg6V4u0+gRBWfRnlDo/CojT4DulBk9h+plZXKyIM+sQQTs8AOldKJtRW5+k5mK52SHnoXBHypURem3Y1JjSkW1Lc+eCN3PfZW7nq1ssSqCRE8urNBhK8ZkGC1/lh8PgwT37/JbY8vIO9L7yL0grbiODgueD74HkTziZaa+PwVS5DEM65TG1Js6k4bHW0xCFvElorjLFcfMki7r1/LRs2rWb+gtOv21EY8eqL77HtkZ088fgeioVy7czlqQtkITKocogqh3NvNFTDiKcpz8sQdGcwGRfGvaeqbPByIW4uRIdzLFit50BxvkdxvkvQ7kyop8uGVF9Epi/EzZu5NcYq9ayC4jyHwkKXYpcDetwcjcUtgVsE3YAQVn2HwiyU2yBoBRw14QG6DE6l3lwbY9VenyhjKXcagg6D9SYukJNT+IMab0ihjJpbg1NV3tBMhF5SQi0uoVrG1lVrwRQdysM+wVAKG2oaEoq0RbcGqNYQlTJM6EQoKJzjHs4xD1VoTD2rLEG7JeiwhC12wuxUGdJ9ivQJhTei5tzzVv0oojSEGQjTTFhHVWRJ90VkekNSA5UTE3P4DKtPC9o1uUUOxQUuxpu4TWSOlWk5UCD7fgkdMad1pvpphK0O+aUZCovTRNlx+1FrcXNxyEwNhHOuV5utAuNrjK+xWk3cr40WcXoGcY8OoIrBHLeJ+LnW0djudmx3O7RlJtQjCFGDOfTQKKpQnsvLQjsaExk83+WWD1/Dxp+7mevvvgo/5Z3+yUKcxSR4JUiC1/mn91Af27/zPJu/+Szv7X2/dvCom+uC78VhS5/+mjbWVHrAygGEMw9hNuNjOuKeLbz6h27Ex9q4h++qtSu4576ruGPDFXR0ZMfmbS17dh5i26O72PqTXYwMF6YOW1MuoIXQoMoBqhzV/7xqI8FVlLvjsBW1eBMbCVPU02WDmwvxRiNUvQG6Wk9DsduluMCj3HFS+JmCUzSk+kIyJ0KcUp1nxNXYd+JKXZrCQo/iPAfrnL6eiixOCbwi6KD+5nutYZmGcnsctqxbxzNNHL7cUtyYZ4b1TCoOW+VOg/XreKIBd1ThDWq8YYWydYaFatjyDWpJCb24BG1RPasMUd4lGPYJhnysmeG1qJRFtYbo1hCVPn09AJXT6F43DmFlPbbsdbDKErZayh3x73ounaVLkD6hSB9XuHk1o3UGwPgQZOPAVc82oQJL5kREtjfEG66/d7i6XEGrJr/QIb/QwaRO/wJVaMkcLdF6oEjmaAlsfaGv2lsWZhwKS9MUlqQJW+vYj1qLN2JIDYSkByOUYUahyKr4RJLxdbzN17GSquECbs8gTs9gfEKrnnq1/ZrCdrZh57VDe0tdnyGlADU4ih4cjevVoXrSUjua6zetYdPPrWf9A+vItKbrer4QHwQSvBIkwev8duito2z79vM8/s0dHDtwYuoQ5jrg+eDXF7amYo2Je8HKAURThxTre5jOFmxnKzTg7KFS8dlwrRTX33Ah1167iqHjI2x9ZBd9x0dmHramXHALQRQfxIPo1IbYuEZC0JUm6M4Qtvunb5RMU88pxiHMzYWTDkeqfl+k1OVQnO9R6nIn9vzMsJ6bN6T6I9InQpxJhkHVzuJ3aPILXArzXaw3y3rEIcwtglsAHZ0awqr/jvw4bJXbmFM9DDilSk9YOHXoM16lZ6vTYObS5jLgDcchzB2pfvdlfLdOJbC4BrW4HIetznAuqwxhziMY8gmG/anDkLKobCVsZesLW5MXBDWi0b0eTq+HCk/t6RsbmgnlDkPQZmHyDu26OPmxEOaU1KTrqAIiL+4RDTPUdUJgKrpk4hB2LMLLndo7PBZ+FPlFLvkFDlF29vtRVTa0vF+i5UCRdG98pmCyepGvKSxJU1iaJmh3Z7+fMRZ/OCI9EOEPRVMOe7TEQ5aN78QnPOawkuqBHE7PIM6xQVRoJllniKd1tGLmtUNHKzizfE+thWIZPZhDDY6iwmhCvdpJPCxrb7mMTZ9az+0/cx1tXa2zqyfEWU6CV4IkeAmIe37eee0A2779PI/+76cYHczHB7HqMEJnDq2gqWpGURzAgjJEBus62K5WTEcLpOcQRk5DawUjJbRK8PtuEB/My5UQVjmQh51pyt0Zwo5UfWdkZ1jPKURxCMvHCSXocCgs8Ch1u/X1/Mywnjca94Sl++JhSfFZfDf+rlgdZ/FnSoWVEFaMQ5jx4qBVbgfjJ7C+RONCWATGsQSdhqDTEmXs5IlsjvW8oXg4optToEEtKqEWl1HdAarBb6k1EI56lIdShCPxCQ6VjSphK2x4PSyoQQfnuIvu9VBGEWYsQYchaLfYRl+LwIKbq4SwXoUOFcYd69lq+DYBOAVD5njcE+YWLJGvyC9yyC90CVvmEEamoIuGlkNFWg8USfUHGFdRWBz3bJW76+hBnyEVWfyhiPTAWE+f9RSR58QnPBq93zYG3Tcah7Deofi7te1Z7Lx2bGfblEPOZ81ayJfiXrChHCoyXLJuJXd/+hbu/Nkbmbeks7H1hDgLSfBKkAQvcbJnfvo6//Xf/s2cerZmwmpFuKA1PoA26apTOldu6hW18ovTRGl39mdkZ6jUClFWJdKwnIypnAJPJPxMVk9bTAasQ1PWGYslmBfGwaBJK05n5yjaM6jGn/OYVLHoEhnd+LA1hXDUJRr1Gh+2puDkFOljek49WzNhrUWH8RDGZu3XUidMvM03+qTOFFoPB/FFcZp1tcCRMk4panzYmkplGPnjO/5Lc+oJcZaoNxvIdTuFaACtVdNCFxAftGfw3a0PJEc3LXQBoJsXugBQCtPM75Mr1bQGe5Vt8vfllWebFroAlG5apoxpmvsZqrkNJ5x5PYVJNa8cxBewaO5nqJq70ji6uS09pcBr4kYoxAdMQzfHT3ziE3U/9jvf+U4jSwshhBBCCCHEWauhwaujo6ORsxNCCCGEEEKIc0JDg9ff/u3fNnJ2QgghhBBCCHFOaOIXKIQQQgghhBDi/JToVy6/9a1v8fDDD3Pw4EHK5Yl3O3/55ZeTLC2EEEIIIYQQZ43Eerz+1//6X/zyL/8yCxcu5JVXXuGmm25i3rx5vPvuuzzwwANJlRVCCCGEEEKIs05iwevrX/86f/mXf8mf/umf4vs+v/Vbv8Vjjz3Gf/gP/4GhoaGkygqBtZa9L+zjye+/SH6k2JSax48NYR1Ns26KZx2NcRS2SZcltkCUcTFec16jVRCmFZFHc+oBxol/N+UzrBRSYbMKgtUWqyy2SQWtshABpinlAEsYKaJI0Zy7U1oyXpkWv0Sz1lKlDU4qBNWkzxAI0xarz9HbfdqxX81ZZeKtz+gm1lPEx4om1SOIUMWAkcF8Mypy9Mggj/90F8eONaddOdQ/ytbvvsh7bxxpSj1x7knsBsrZbJa9e/eyatUqFi5cyGOPPca6det4++23Wb9+PX19fUmUbQq5gfLZaf/uw2z79vNsfngHJ94fAMD1HG7+0Do2/tzN3HTv1fjpxt1YqPfIINt/spPNP3yV994+Fk+0tnIDyQgi09DbtVitiNpSRO1pTMar3YBTGYsKDCoyDW2PWcB4GuPr+P5W1XqhwcmHOIUAHTauoAXKHS7FeR7lef7Y/YOMxSmBU4xvrtqo9zR+fRBmFWHmpPsVjXtZDc221cBlT5qkwbjVmxs3sJyyRCkw6ZPu/2RBGcCAamBBi8V4FuPbiTdOVhbr2PhUX0NP91m0a9CeQbumdk9apSyuE+G6Bq1sA+9Va/GdiBavTNYL0JUPMjKKkVKKkWKafODR2LXGopSN7+9bma21EJUcwqJLVGr0SgNEoIyidmbHgi6DU1Q4RVANPONjIV782rpy8n82WOWEhw5BRSe9c9Xts6H1LE4ZnILFLY3b9q1FRaAjW9kOG1evuo9RZuJ0FVlUaOLfjaoHEBp0MUAXQ1QUF9WO5vrbL2Xjx65l/abVZFoad4O2/r5Rtm/dy2M/3cVbbxytTV+9Zhn33HsVd25cTVdXS8Pq5UaK7Hh0J1u/+yKvPPUW1sQf4vKLF7LpEzey4ePXsWTV/IbVEx9M9WaDxILXRRddxLe+9S2uu+46brzxRn7lV36FX/u1X+OnP/0pn/nMZ+jv70+ibFNI8Dp7HNnfy/ZvP8/mb+7g8DvH0I7GRBNPsVenpbM+t3/8ejY8dDPX3rUax535TR4H+0Z54tHX2fLDV3lj5yGUVtjKgW6C6mZlDERm1iHMKkXU6hN1pDFZP544WSuyUk+ZOBipcJb1AOsqIl9jPT15rWo9pVCBwckHOIUwbkDMol7Q6lCa71Oc72Hdyqngk8tWpikTBzCnaE9tNNXJuBBkFGGLioPOZPVOrl0xq8bKJGFr2oc6ld63WbanrbIYH0yKsRs0TzYfO/ZbmcrvWRS0WKwHkWfiGyafbhbVEDbrvGBRjsXxIrRnThuqqiHMcw16Vj03Fk9HZL2AFq+Mo2119Z/4qMq00CiGi2lGimmK4fj0ObOaShEHu6meXllvrYWw6BAVXaLybFca4sZ/NC5sTfU4QJcqIaw0uxB2StiC077OOYUwG4csHUwStqZipl+s6etZdABu0eIW6tj2LajQxvvQ2QY/a+P9fz09zDYOYDq0YGYZwiKDLoboYoAKJy+qtcIYi+c7rL/7SjZ+9Bquv/NyfH/mlxcYGSnw1PY3efynu9j52sHxL6VGVbYHpRXXXruKu++7itvuuJzW1vSM65UKZV7Yuoet332J5zfvJgwitKMwJx3nqq/xkquWs+mhG7nrY9fSvUhurXQ+OuPB61d+5VdYsWIFv/M7v8Of//mf84UvfIHbbruNF198kU984hP89V//dRJlm0KC15nVd3SQJ773Apu/uYN3XjtY2/HVw3E1UWho7cyy4aGb2fDQTVx588VoPfVp+NxIkacf38OWf3mV155/t7KnrwSuellbC2CY6UORVWBafML2NKYlRXyqewZqIazaEzb9gbUatqq9WzPuHqi0OHU5qvSEhahpPg8LhFmH0nyP4nw/rjlZS3a6GShQka2EMNCnaWwYB8IMhFkdh5HTha3paldM+/RxYWsupaxT6QnT08/EMi5s+fUs4DTLXEcIs8Q9WpFfZ9iaYh5Kx0MgTx/C4rClXYPjRSjNDN/Y+MFaWVw3wnUiptnkAXB1RNYr0+IFuNrMbBWtPDaINMPFNMPFNOXodI3Nk8LWTF5fNYQZCIsuYdHFBKddacaFrerEmW77oIvgFhW6dLp1ZlyJk0PXDOrVHcIqYavWuzWLcjX1hDAb98g7RYtXqDMATTmfyoml0wa2uLdMz2Ws5ExCmKmGrRCCCKj/fXUcTVQ5AXrnA1ez4aPruPrmi3GcqTfEQqHMs8+8zebHXueF5/YRRbbu4331ca6rufmWS7n73jXcfMslpFJTj3oJg4hXn3qLbd9/iad+/CqlQjDpydzJxPsGhcVy1U0Xs+kTN3D7A+toa2DPmzi7nfHgZYzBGIPrxgebhx9+mKeeeopLLrmEX//1X8f3/dPM4ewlwav5RgZGeeoHL7Pl4WfZteOtygHezum7HNUQ1r2og02fWs+GT97MxWtXoJSiVAx4bvsbbP2X13jhiTcJQzOjgDetWgiLagc6C5isR9SeJmpLgZ6i52eW9U4eYhI36seFrZmGu2lqAejyWE9YtfEQpjWleR7FBT5R2plZ2JqyHqBAhxZdrAzlqRwjja6GLYXxVePez/G1x89yBj1bsylVHYpYDWG1sFUJXA1/bSeFMIuNg6AfDyds3JDBajcKY8MRq0MGdTyMcHZha7p6Ckeb2nDE6mroKFMLW54TNWYVrcyjHDoMVXrCAlPtbY/DlqqsNKr2x1wKxvOwkSIoukRFBxPWVpq5h60p6mHAKcY9YbpcXWfGlZht2Dpd7ZP+Hff6V8JWo7ZFe+rfayMjQ4tbBLdg0VED66nKybPIosYHx0aEramYaggbN2zdWFSp0rNVnlnYmko1hLV3Zrnro+vY8JFrWH3tSpRSBEHEi8/vY8vju3n6ybcol8M5H3urPVWplMcdd13OxrvXcP2NF+K6DsYY9rywn23ff4nt33+Z0eFCbflmS2mFNTYebnnXFWz82etZf9/ahg63FGefMx68zmUSvJpn366D/N1XvstLW3ZjIlPboTVa9azWwgsW0HHhEg4c6KdcrP9s12xZawk7UnHYcmbY8zO7grUeLnQCYeSkWgChZwk6HMJsg8LWlPUqf5hKT1FtmF2C72el5GyHPc6G0ZZSF0QZmnInRmvjoGW9Sr0k15m4Iiob4mYilDOHLsM6awG0p4vMa82RdhsTtqasVpn3cDHFiVwLkY0/wMRW0cp7F5U0pf40nK4XrEH1iMAfVuhANW3D0KXKUMKkWzSV+bsFiz8a93Ilv4rGvWBOcQ5DEWdST4EqhXh9+fh7WwmWq4acrkVtdF60kMM9g+Tz5TmHn9PVy7b4rMj6DOw/zsDxkcTqVUOfl3K55b61/Ov/9DMsWt7d8DrizKs3GzT0Pl47d+7kqquuQmvNzp07p33s1Vdf3cjS4hz16P9+ihc3v14LW0mELqAWrnp7R+ktHztlelJsyiXqzIxNSDgkWKWw3rgaSZarvJbifDcOeuOmJVMv/sP4px+a11BJN4ROYlIQNXP0igY7/kRt4i9W4WbDuIcr8XrxzLuyBVJO5Wx+kqtoZd6lyMWgEj8nULsYR+BAMPPvtM62njKgw+aFLiw45dM/rCEqr8kfGevhSn4VVajANudiltXe33yQeOgCamGnbyBP77u9p0xPql5+qMC7rx9OvF71O2FBKeTJf3mFy69ZxSf+7cZEaokPhoYGr2uuuYaenh4WLlzINddcg1KTfw9GKUUUNapPXpzTbNwbFRlZXz6o1Gm+r99oNokhTaK5mvz5KZLvGD21ZpMu8S3OCWdibWnqJtHsffYZ2PiU1jP7brg4JzU0eO3fv58FCxbU/i6EEEIIIYQQosHBa9WqVbW/HzhwgFtvvbV2cY2qMAx55plnJjxWCCGEEEIIIc5liX01e+PGjZPeq2toaIiNG2V8qxBCCCGEEOL8kVjwstaiJhk039fXR0uL3NdACCGEEEIIcf5o6FBDgE984hNAfAGNX/qlXyKVGrscVhRF7Ny5k1tvvbXRZYUQQgghhBDirNXw4NXR0QHEPV5tbW1kMmOXyvZ9n/Xr1/Orv/qrjS4rhBBCCCGEEGethgevv/3bvwXgggsu4Itf/KIMKxRCCCGEEEKc9xoevKp+53d+J6lZi/OMTfgmxiJZctcSMWOWpt7X50yso7bZL1KIGZI1tLGskbaMSPDiGseOHeMXfuEXWLp0Ka7r4jjOhB8h6rH+gXUsWjkfiG+knJTqvD0MHe3peJpO7pCjtAIFTmToyvgopVCKSS9I0zCVGzeqcgSm0tRMssVZqZcaDFGBmTAtmXrxLx1aVNSM11f9bZvzflboEuhipVbS9SxgQAVNrAeYgoM1E6clWW8olyEM431AoqtoZd4ZHaCa+PqUF6K0mTAtsXoWrAarbfyCm7TOWGWT/fBOqhemky81vp5xFU3ZwVTewyjljKWuJryvqhyhSmFlnUmuXnycBVxN+9IuHDfe7pM83lfnfcEVS7n2jssTqyM+GJRN6DbaDzzwAAcPHuTf//t/z5IlS05pUH784x9PomxTDA8P09HRwdDQEO3t7Wd6cc551lr27TrE1v/zHFse3sFA7zCOq4nCuZ090lphjMVxNTfeu5ZNP7eem+6/mnQ2xdFD/Wz/yU4e/8ErHN5/Au1ozBx73uJNQGGxrLl2FZs+dg133HcV7Z1ZBgZzbH/qLR7bvJvdbxxBKdWYO9xbC0qhigHOYB5nqIAOIqxWRB1pwq4sUSVo0ojQV61XDnFPjOL2jaIKASgoL8pSWN5GcVkruLr22IYwFrdocYoWHVQm+RBkFWFWYR3V2NO31uLmLV7e4hTj2RoXwnQC9eypf1eAcSxhCwSt8WttaD0FKgIVVn6swipLlLKYjMV4jC1Io1hQRsVBj7iRqdIROhtCOopXlUa8xuo8IrCjLnbUhXLc+GppL9Ixf5T2eaM4rm3IKlqdh4kUQ30tDJ1oJTcUf/dZpQy6LcBpD1Cubdjrq50TGHExwx5mxIPKZ2hdsJ7Fahq+jrp58IcU3jDoSGEVROn4x3gNrqfAKVn8YYs3DE4IVsWBKMwooup1vRqyX4vrqdDi5eKf6n4GFQfNuEU/91ITGItTNDjFCB1UzyyBdXS8j2nU/rO2klr0UA63P4ceKaAsoDV4Hvhe/PdGfYjV45sFjKn922qFyfrYlhTWcxp2nKge7y+7fDH33LeWuzauZt78NkaH8jz9k51s/e6L7NzxTnXh5pz9qm2GJavmcfdDN3LXx69n+UUL5/w6xNmr3myQWPBqa2vjySef5Jprrkli9meUBK8zxxjDnmffYdu3n2fbt59ndCg/oxA2/gTA1bdfzqZPree2j11Ha0d2yufsf6uHbT/eyZYfvsrxniEcRxPNIIRVd/gXX7GETR+7hrseWMv8RR1TPv5Y7zBbtu/lp5t3s//Aidrz6zYu/NTCVimc+uGOIuzMEnVniVr8eOJMDnTVemGEc2I0PmiPlqY8NFtHUVrcQmFlG6XFLWPH8JkeXG0ctNyiRZenbgpYIEqNhTD0LEJRZTfpFMHLW9yCjRslU9QzXhzCoqzCzqbe+JmNC1tTMa4laIGwdZYN3GrDstK7VQ1bUz5cnxTC5vL6TCVwUQ1ck1AWlQ7jEJaKt70ZrS7V5TOVsJVzoainXGilLC2d+TiEdefReoahqPJYY2B0IMvgiTZGBzJYO1WvvUVlIpy2AN0WoBxmXK8WtvIuZsjDDHtgpv8MjRsHMWbTnq6GnyL4gwp/CHQ49QyMrgSwdHySYrb1dAD+kMUfsTjlaR6uIMxAmFVEHvGTZ7NNRHHQcnNxven2MxNCGLMMDNaiiwa3aNBlM309rTCOgtmGsMp+TQ8XcAZyOEN51HTHGqcSwrxqCJtxQeI3ycYbx2mOa9bVmIyPbfGx7sxDWPXYuWLlPO69fy0b7r6SpUu7pnx8f+8wT/7oVbZ8+wXeeu1g/PwZ9NpW2yLdC9vZ9IkbuOvj13PxmmXJjmQRZ40zHryuvPJK/vEf/5Frr702idmfURK8zg5hEPLq9jfY+u3neOr7L1EqlCfvmVKgVeVs13UXcPenb+GOj99A9zThZzLWWt7YeYhtP97J1h+9xvBAfsoQVl2OpavmcffHrmHDh69m2ar5M36NBw71sWVbHMKOHhvCcRRRNMkmOz78DOZxBguoYjDjtrBxNVFXlrA7i8n6Ux/oqtMjg9ufw+0bRQ8XZ1WvuKyF4sp2yguqV0CdooFUbSSUwC0anNLM2/qWscZYmDlNY6Uatsrg5ixewY4ND5tBPeNXesIyldBXj0qdmb8+G9drgaCFuFE9VQN3XBhRIehwLADNhNEWk7ZEGTt9vfF1TSXY2WnC1lT0WAhTKVPX67N5J+7ZKjinWbhTKW1o68rTOX+U1q58PK32x8R61S0zN5hh8EQrIwMtmGimDVSLykY4bWV0W4iaKhRViymwBYdosBK2ZljPYkHH4d16k9Q5uaaKt0F/KA5bTnkW64wz1hNmpwuZ43qa/GHwh+2stnujx7Z7401zIqS2zsS92V5urEd7JsZCmCI+QzNN6Kvt1yphqzR12Jq2Xr0hrFovV8LpH8UZzKNmM5rDccZ6wuoJFnWGrUmfCuA5mKwfH5ecqUdMVI/JCxa2c+/9V7Hx7jVcOIuepmOH+tj+g5fZ/O0XOPj2sSlHvVTDVmtHlg0fv467Pn4dV95wIXpWwVR8kJ3x4PXTn/6UP/qjP+Iv/uIvuOCCC5IoccZI8Dr7lAplXnhsF1v/z3M89+hrhEFUO9u18vIl3P3pW7jroZtYvHLm4WcyUWTY+cJ+tv34NZ74yS4K+XKt3ryF7bWwdeHlixtytstay1vvHGPztr08tmU3A4P5CeHHGSrgDObR+XLDRrsY34mHInZnMWlvwnAUZzCHe6JyhrRBe5Ao5VBc1kpxVTtBd3rCOH9dJh5KWJq6p2mm4jPiiiCriMZ/X0MpdLlyljtv0XMbYTpWj7jnLcwoonSlcTS+AVjp3WrU52excb2W+GdCA9fGPVs6ZNzQvrkzjiVKxz1hJ9erDSWcTdiaijaobDwcUXlmQvgh78Q9W3kn/rAbUc6JaJ+Xo3P+KNn2Ym26UpAfTjN4opXhvhaisEHfY1YW3RLitAWo1jBeZSqboS3pStjyIWhMI88Sf2622hNW/ewqPU3eYBy44vDTgP0a8cmBWggbFzJVZPFG4rDlFhq3XRinGsJ0peet+oZWhg/nGlsvDkXVEMZJ+zWLU4xwSqZx+zXiUQXWOelEj1LofDkOWwM5VBg1piCA64LnxkFswkpq46DVwItKWMD6DrYawsYFnI6ODHffexUb71nDFauXNqyn6cBbPWz//ks8/q0XOH5koHasT2U87vjINWz4+PVcc/tlOK5cv+B8dsaDV1dXF/l8njAMyWazeJ434f/7+/uTKNsUErzObrnhAjt+/ArHD/dz60evY9UVSxOtVy6HvPTU27y9+31uuP1Srli3ItGzXcZYtm/bw3/7zW+g8+Vph/U1ggVs2iNs89GlAGfgNMNRGiDKugzfsBST8nCLjQtbU7EagozC6ngooW5gm2TSekC5XRFmxtaTZD/DeChiuYP4u1tRA8PPFPWMawk67ex7tmbKNWgvhEhB3p12mF1Dynkh7fNyKGUZ6mslLCd2keCYtuhs/F0wM+xBOdlGnsWiQnDLFm9U4RSSXmfi4K5tfJLFzSW9TcRDHiM//n6Ym2/CfgbQ5Qgdxd/dakY9lSugSvF3fHV56iHnDZPy4wCW8DECKsemlItNufzG7z7I/R+7FifBi3BZa3nr1YO8sHUPF65eyo0br8RPe6d/ojgv1JsNEjtSfO1rX0tq1kJMq6U9wz2fubVp9Xzf5ZZNq7ll0+qm1NNaceVlS/B6R5pSTwGqGOCP5JtSD8DJh/iDIVF7wo3ZCmXAzyXfUKjVA5wAosxpH9qgegq3BNE034lpdD0dgk44/EwQasj7zSsXuPT3zGy48pwYhRn2ZzUcdDYUCqcMmePNqhcP600PN2c7VMSByys0d7t3iwYdNvE1jhRxhnJ1f09pzsIInCbttyG+EmIpZPUVSxMNXQBKKS6/dhWXX7sq0Tri3JbY1vH5z38+qVkLIYQQQgghxAdKoqcH9u3bx2//9m/z2c9+lt7eXgAeeeQRdu/enWRZIYQQQgghhDirJBa8tm/fztq1a3nuuef4zne+w+joKAA7d+7kd37nd5IqK4QQQgghhBBnncSC15e+9CV+//d/n8ceewzfHxt3v3HjRnbs2JFUWSGEEEIIIYQ46yQWvHbt2sWDDz54yvQFCxbQ19eXVFkhhBBCCCGEOOskFrw6Ozs5evToKdNfeeUVli1bllRZIYQQQgghhDjrJBa8Pve5z/Ef/+N/pKenB6UUxhiefvppvvjFL/KLv/iLSZUVQgghhBBCiLNOYjdQDoKAX/qlX+Kf//mfsdbiui5RFPG5z32Ov/u7v8NxPrh3+JYbKNdv/75etjz2OlsefZ0oMmy67yo23ruGSy5b3LC7yo/Xc2KYx59+g0ee3Etv/wgbb76Me2+7gmuvXI6TwE2NB0cLbHn5bX787BvsPXiMW9ZcwIdvvoLb1l5Iymv83RoK5YAn9rzLd5/exY43D+D1l8keGCXzfh4dmIbXswqKS9KMXtRCflUWbzig5e0RWt4dxc01/macFigtSjGyuo3hq9rBKjJ9kO6zuIWGlwMgzEJ+cfxjPcj0xD/+UDI3cI1SluJyQ/4CQ9hq8fo13nEXd1DHNxtuNNfgLS3irczjzitTHvQp9GYp9aWxpvHbhFKW9gWjdC0dpHPRKPl8iuMnOujraycMk7iDiWVZZz9XLDnCZYuPUCj77D28nDffX8pwviWBerCwfZCrlx/k6hUH0Mqw8/Aqdh1aydGhLpJYa1rTBS5ddpTLlx2hNV1k35HFvH14KUdOdGMTqOd6IQsWDrJk0QCt7QWGDrfRv6+LoUPt2Kjx64x1LCwsY5eVYUGAPuHgvuvjHPBQQQL1lKU0z1JYaigutrh5yB7SZA4r3GLj308LBK1QXKAozAcVWVoPGVrej/BHk7nBVpBV5JY65BcpIg/a3hyl7Y0R0keLyezXMg75C1rJXdxB0OGReb9Iy8E86WOlRG4UbRxFaWGa4pIs5S6f6y5ZxkMbr+Guqy8im278/fyCIOL5V/bz2Pa9PPPCPi5aNZ/7N65h422X09mRbXg9Ywx7XtjP1u+9xJP/8god3a1seuhGNnz8Opasmt/wegD7Xz/E1od3sOWfnyEKIzZ9+lY2fOoWLrlmVSLttd6DJ9j2f55l8z89zYkj/dz+4E1s/NR61t6xOvF7siWh3myQSPCy1nLw4EEWLFhAT08PL7/8MsYYrr32Wi699NJGl2s6CV7TO/r+AFse283jj+zi8ME+tFaYyl3sHUcTRYYlSzu550Nr2XjvVaxYNW9O9fqH8mx99k0eeXIvu98+Gu8grMUCjqOIIktne4b7blvNvbddwZWXzC305Ytltr22j5889wbP7jmAMRatFMZaHK2IjCXje9x9/SXcf9MV3HTFStw57ESCKGLHmwf48ctvsnnXO5SCsFYnvilm/DvdUyBzYJT00QI6mv1mbYHSwhS5C1vIXdyCSTlgLGg1Vg9IHStWQtgITmluoa/U7TF6RRvDa9oI272T6gEK3IIlfdyS7otvtDoXUSoOWoUlELQxoQ42/u0UIXMUsj3gjs6tOW08S3GZobDSUJ4/7rMZV0+F4B138I47OMMaNZeK2uIuLuKvyOMuLqKcsTrV39ZA6USawvEspf50nLJnzdI2L0/XkiG6lwzjeAZrQGmqqycAQ0NZTpzooH+gjSiay8k3y8K2Ya5Y8j6rl75PS6qEMQqtLbZSTyvoHexg7+FlvHlkKflSeg71oCs7ytrlB7lm5XvMbxup1QNqf+/PtfDawQvYeXglfaNzOzZk/BIXL+nh8uVHWNw1hLGVG8aqsXqFks9bh5fw9uGl9A50MJe11HEi5i8YYtHiAbrnj92cXVXWFaUhCjQD+zvo39fF8JG2Oa0zVllYEMRha0kZTl5HiX8777s4+32cQx4qmkM9LEGXpbA0DlzGBwzxuJ9xm6TfXwlhRxROeW6NzSBTCVsLIUqrk16fBaXwRgwthyJa3jdzvplzmIb8EpfcModyuwZb3blQ26e6oyFte4Zpe2ME/0R5bvs1X5Nf1UruojZKi6t3g6/Us/HrU2VDy6EC2YN5UsfnVs9qKM2Pw1ZpQXrsGKGotTM812HDuot54MYruHXNKvw5nACNIsOrrx9i85NvsOXJN8gXyrU2RdyGsCgU161byb13Xcmdt1xKSzY1+9dnLfteP8y277/Mlu+8wMDxkVqbCcZe4yVrl7PpEzdy18eupXvR3G7ifnR/L1sf3sHmf3qaw28dRTsaU6nnuJooNCy+cAH3fO52NnzqFlZctmRO9QZ7h3jiO8+z+RtP88bz+1BaYSs77Wq9jvltbPz0rWz81Houv/HiREJfEs5o8DLGkE6n2b179zkRtE4mwetUfSdG2L55D48/sou33+yZELamUn3MhRcv5J4PrWXDPVeysM6dyGi+xPbn3+bRp/by8uuHsJUj53RrczWsLJrfxv13XMm9t17BxSvrO3NUDkKefv09Hnn+Dba/9i5BGJ32NVbrtWdT3H/TFdx/4+Wsu3gpWp9+J2KM5aV3D/OTl9/kkVffZLRYHgtbU6kc6IgMmcN5ModypHsKdZ1ttEDQ5TF6USu5i1uIWtyx8DPdkyqt6sz7eVreHiF7YBQd1LdLCdpdRq5oZeTKdsrz/NPXq9ZU4I1a0icsqT5w6ux4izwoLoL8EiiPX82mK1mp5+Yhc6QSwurseTOOpbQkDlulRXasTh31VBm8424cwkZVfSFMWdyFJbzlebxlRZRrxxp6p6lnQkXxRIZib4byYOo0Txp7crajQPfSYbqXDuGlorGwNdUz7NjvgYFWTvR1MDjQirH1nZjoyo7WwlZnNj8h/ExXD+D9vnnsfX8Z7xxdTCmo74x4a6rA2uUHWbfiAEu7BjBWobBM1Q6wFqyNl+nYUAevHrqAXYdXMlyo74y47wZctPgYly07yrL5fWOrzDQfR/U9GMmneevQMt4+vIT+kba66illmDd/mEWLB5i/cLgWXKerZ41CaUtYcujf10X/vk5Gj7VQzzpjsTAvjMPW0jJ4dayj1XAUgnPQw93vo4+6KFNfvbANCksN+eUGkx43v6mfVPudPg6ZQ5pMj0KH9TX+whQUF0BhoSLMqvq2wUoISw1UQtjRCKdUVzkiD/JLHHLLXEqd43Yy076n8b7WGyjTtmeEtjdH8AeDuuoZV1FY0UL+ojYKy1qobyWN6+liRMuBPNmDBfyBoL69jIJydyoOW4vSWEdzupW0eqzMpjzuvf4yPnTj5dxw+Yq6Rr1Ya9n71lEef+INHt++h8HhwoTwM5lqW8B1NLfceDH33rWaW264iFTKq+MVwuF9x9j2/ZfZ/O0X6DnYNyH8TCZ+6QqLZe3Nl7DpEzdw2wPraOusbz/Td3SQ7d96ls3feJp3XnmvvvZaZZkuvGoFd3/2Nu76ufUsXFHfSfPcUJ6nf/AiW/75GV7dtmfcyb9p2k+VELZgxTzu/uxtbPzUei5Ys6KuemfKGQ1eAGvWrOGv//qvWb9+fRKzP6MkeMWGhws8tXUvjz/6OrtePVg7AzSbNUqp+KzHlVct454PXc0dG6+gs2viMKFSOeDpl97l0affYMfL7xJGpq4dxmSqz1u1tJsH7rySe267gqULJ4a+yBhefOMQj77wJj998S0KpeD04WcKjtZExjC/o4UP33wF9990BZevWDDhTI61lj2Hj/GTl9/kRy+9Qd9oftb1qgc6FRgyh3JkD+bwj586xCRoc8ld1ELuklaC8T1NM1ULfZbsgVFa3hkhcyh/Ss9bmHUYvbyVkSvbKC5Ox/XUaRoJk9ar/YE/TBzCBkBHEx9mHCgujHu3SuOPEbOqFz/PG4bsUcgc45TGkVWW0iJLYUVEcZmdeBZ/pqpncosKr7fSE1Y4ueFgceaV8ZYX8FfkUb49fcPyNPVMoCn0xiEsGPFPWfh0a5HupcPMWzaEnwlOG7amLGcVSlmiSNHf38aJvg6GhlqwJ/WitKYLXLH4CFcuPRz3NJ0m/ExXL94/Kd7rXcCb7y9j37FFhNHEM+IZr8SVyw6zbsUBVs07Xvv4Zl5vrOftUN88Xj10AbvfX06+PLHnzdERFyw6zmXLjrBq4XG0iiuqWYzPqoawgZEW3jy4jLffX8JI/uTGmKWre5SFiwdYuGgQ1zW1z2KmqiEsyLv0vdNN375OCn0Zxq8zFgsdURy2lpcgNYd1tPq8Mjjv+bj7PXSve8oQ3TAb92oVlhvCFua8TRBBpkeRPaxIH1OnhL7Ig+L8OGwFbRN76mdcr/Lk9AlLy+GI7NHolJNLxoH8IofcMofi/OoLm+V+1Mb7/FRvKe4Je3MENzdxR2o1FJe2kLuwlcKqVqx7+vAzpWrPWy4k+16eloMFvJGJL9ACQadPcXGG4pIs1pt9veoxtKMlzQOVE6BXX7jklF6Udw8cZ/MTb/Do1t30nhip9WzNtl4q5XLXLZdxz12ruWHdKlx3Yg//8SMDbP9BHLbee+Mo2lGYWdRTWmGNRTuaGzZcwcafvYH1911F+qSet+H+UZ763gts+cbT7Hr6zbGRQTMtqSrtNWNZffMl3P2527jjwZvoXDCxLVwqlHnux6+w9Zs7eP6RVwmDaNavsRr6Vly+hHt+/nY2/Nx6Fl+wcMbzSdoZD14/+tGP+O///b/zZ3/2Z1x11VVJlDhjzufgVSiU2fHkW2x+9HVeev5dojmEn8lUA5jSiutuuJAN915JujvLEy+9w7bn3qZYDmcfRqZQHSa4+uJF3Hf7apYt6+KZvQd45Lk3GMoVG16vOr8VCzr48PrVrLl4Ma8eOMoPX9zLkYHhhtcbf7Yxc3CUVG+BcrdP7pJWyt3+7MPPaeqpwJB9d5TsgVHCVs3I6jYKK04ajtII43reUkOQ6rNYJ+7dKixkbChRQ+vF/EHIHLW4kaW41FBcbrAes2/oTVdPgc4r/GMO6bIhtaiItyKPzpjG1mNcji45FI5lMMMe7V055i0bIt1annXYOl29MNSc6GtnZDDDstYBVi89wtLOgQnD7BrBWIVWljDS7OtZzL6ji8i4AWuXH+TihT1oZbGoSgiau1roA97tXcRrh1YyEmS4cPFxLl7cg+ea0/bezazeuOGWAx28eWgJPcNddM4bZdHiATz/9L2TM65ZmV9xyKfvnW5OHOmg1GlheRmyjV9Hq/NTBYWz34fDLuWMprDMEnTY2YefqVR7o0PIvK/IHFEYNMUFalwPejL7tWyvIft+BBEUljjkFzkThtk1tB6Qeb9I695h/KGQwrJW8he2YX2NMhY7mxNzU9WrhD5vKKDlQB6/LyDoTFFcmo2HuM823E2hemxd2NnKR25ezfUXLWPfm8d4dNtuDh7ub2hbBsa+WtHakuLuO65g/bpV9L/Ty5bvvMgbL783YZhdI1QDipdyueX+tdx2/1pKI3m2f+s5XnpsFyYytaDWCNXlV0px7cY1bPjULWTb0+z44cs89b0XKOXLtZ6rRql+Rpddf2Hc8/bJ9XTNcbhlo5zx4NXV1UU+nycMQ3zfJ5PJTPj//v7+JMo2xfkcvP6/v/VNnn3q7YbvoCajtaLY6hB0eLM++zQTSkHkQpDVjQ8/U3EgSFd2JslsihNYYwnbxn+xKOGCxhK0AtqiLI07aE/FQpQxWJ/GNkqmqYcfoTNR4xuWU9RLeQGLukca3nCeirKGJa3xd35U7Y/kWAvXtx/AV/FZ8KSH9xujaHcL+DqqBbJE61lF3vjkTLqhYWsq1sJwmOZIuas564yFfODy+sCSse+AJb3ORDA0mG1aPQzoIRcVKpqxH1W2Emxtg8PWVCzoosUrqNmPgphhPRVa/JxqeNiaimMhfTREV072Jl7P0fivvY8OooaGn+nqBf1DEASnHbrYCJN9NyxJ1R7LbHuG7/T8RaK16lVvNkjiElMAfO1rX0tq1uIMGh6Mv+CSdOiq1qg21JMOXTB2lhhoTugiPqahaEroAuIDaDNazxPqxQfSJC7Yd4pKO6j292bVsyQfuir1qg31ZoQuAK2b0g6qUQpcFTWtpta2Nswu6dBVrWEqK2fSoQsqF+Oo1GvKOqPAKE1TTuxUaZpeT1nVtA1jwr6zSfu12vdKkw5dJ9dr0ntqIlu5Bkhzjr1RZFCVYJJ06KrWq7Zokg5dJ9dIOnTB2OeWG8onXqvREgten//855OatRDiA6SaS4Q4WzUxVwohhDiPJXr+a9++ffz2b/82n/3sZ+nt7QXgkUceYffu3UmWFUIIIYQQQoizSmLBa/v27axdu5bnnnuO73znO4yOjgKwc+dOfud3fiepskIIIYQQQghx1kkseH3pS1/i93//93nsscfw/bF7pmzcuJEdO3YkVVYIIYQQQgghzjqJBa9du3bx4IMPnjJ9wYIF9PX1JVVWCCGEEEIIIc46iQWvzs5Ojh49esr0V155hWXLliVVVgghhBBCCCHOOokFr8997nP8x//4H+np6UEphTGGp59+mi9+8Yv84i/+YlJlhRBCCCGEEOKsk1jw+m//7b+xcuVKli1bxujoKFdeeSV33nknt956K7/927+dVFmRIGstxUL5DBRufknROPLxCSGEEE0mB9+zUkPv4zU8PFy7W7PnefzjP/4jX/nKV3j55ZcxxnDttddy6aWXNrKkaIJD+4+z7Sc72fyj13j/+Ai4TtNqK2MrN4Jqzt3sazuqyo2Nm1KvWbXG1bRNvNdo03f+Z6JeEz+/qHY31eYUNra6+TWnnsISWAdN2JRNXmExTd0gQFdWUoXFNqGwo0xT67kqYmxDbNYb29x6VluUOXfvANeUG96PU30rm3bo1WdgDXU1lCs3h0/6OKUYu/n1OXgzTaUV1ljaulvP9KLMmLINvG234zgcPXqUhQsXsmnTJr7zne/Q2dnZqNmfNYaHh+no6GBoaKgWNM81x3uG2PbILjb/y6u89/YxtFYYY+Nt19EYV4OjkwlDlVVS5YswWsB4imBBK8HCVnB1MiHMVnb31mI0GF9hUgqrVWJHAuNAlIbIByzoCFSCO0frQJC1hOn45ThlhYqSO9BZZYmylihjsFqhAwVh/H8qgYoWi/UtJmWxTpwu4/czkVcHgJcK8bJlnFREEDoEgYtNpMUSf0oZr0xnS4G2TAGLJogcTGKvT5HSAfPTOTr9PAZNIfIJrEOj15pqIPBVyBJ/kCWpQTK6TGBdotrAjEbWi1+Bg2GJO8wSZxhHGU6YFgZMJqFwEq8zLapMqyoQoTkRtXEibCdCJxCK4nopFdKii4RG0Rt0cKTcRcl6iYUwhcVREeVIc6LYRm+hjXxY2ckl9L5qZcFCueRSKnoEoZtoPcczaB2hSg4m52EL1ZORyezX8C3Gj7d0VdTxT1K1ABww2qIMuAWFmyfRkGk1GC8+DHtFcHPx8TBRBlRkcYsWr2DQYZLHQoh8hXEsXl+e1LE87kARGl2vsmOzWhF2Zgi70lAs4xwdxDk+grI2uRDmOuC6cdgLIgiCxtdQcdvBYrnq1su4+7O3cfvP3kj7vLbG15qFerNBQ4NXR0cHzz77LKtXr0ZrzbFjx1iwYEGjZn/WOFeD12B/jicfe50t//Iae3ceis8oWDvlRmqJz+BY1xl3ZmWWu5Fq2CqWUSN5VL4Y93aNf4iCsDtLeUEr4YIW0A0IYbZS2566A4xfX7zDjHw19hrnwGoI0xCl4nmfwlQCWINCmNUQZi1hBozPqS/SgC5XQphpQD1liTJx4DLpSd5UAyoYC2Hxf8/ufa02EqxXCVv+VB9ipdFQez9n+znGh2XXD/FbyviZAO2ctI5aCENNOXAJAmcOtcbqpdyQzpY8ndkCvjuxNWJt3AMWGIcgcubUkK42xD0VMS89yrxUjqwTnLJ5hUaTNx6FyCecQwir1nOIWJIaYok/SLebO6WesRBYd1wIm1vzSGFY5IyyzBtmoTOKc9KGFlpFv8lwImph0KRrz5qdeN4ZFdCmC7Tq0in1jIWBqIXjYRt9USt2Tt8AiN8bT4W06iJZXcI9acO2FgajLEfLnRwpdxFaZ84hLA5bBkdFk+4mc4FPb6GVY/l2SsZtQOiLw5ZW8VxOXmeiSFEsepRKHmHYiBMFFu0aXM+gHXNKPRuByXmYnIstzi2E1cKPZ7FpM+V+TZUUuuBAqboXndvxyWqLcW18XJqkni6Bm1O4BVANOLlkNRg3DlynrPIWdBAHMC9PQ45N1flW396TX4EK4xDmFkxDQl8tbPkK43LKSqpKEaneHKmeHO5wefZraDVsKQg7MoTdWaKO9KntlTDC6R3GPTKA7ovvqzvnnjfXQXkeeC7qpNdnrYUwwpYDCMM5FAHtaExkuHjdSu753O3c+dDNzF/WPad5JuGMBK+HHnqIp59+mtWrV7N9+3ZuvfXWCffwGm/Lli2NKtt051Lwyo0WeWbLXrb86DVeff5drLUoFXfhzsSEEObUGYgqj1GlMmqkgMoVUFF9e1irFcG8FoJFrYRd2coeq87hQtOEremeYryxHelMwp7VEKbGha26lxF0WAlhdVer7IAzljALJlVnPYAInDLooP4QVm0kmLQlbKmErXrbjQZUOQ5hKqpvIWuNEheiVKVRUm+9cSHM1j7709WND4eOF+Fny/jZMo5b37ZhLXEvWNkhCOsLYbXw40R0ZuOwlfbrO2jFIUwTRJqymVk9R0XMS+WZl8rR6pbqWr2thdBq8pFPPvIxdXwQ1XoawyJ/mCX+IPO90bjHog7GKsrWIbBuXfUqSwrAAifPMneQxe7oKWFkKoHV9EVZjkctjNgU9TXgK4FZBbRXwla99SKr6I9a6Q3bGIhaKlPrq+cS0eIUadElPFVfy9FY6AtbOVrqpCfoxMyo583iKoOjDApb9zozEqToLcQ9YYGZSeizccDTk4etqYShroWwKKo3uMfrjHYqYcs9NWxN+cxQYXIuZtTDlusNfZU9m2exM92vmbgXTBc1lGcWwqy2GKcStmawOTkFcPMKpzCzwGdVfBw1LlDvNxYsOCVw8/HPjE9IThO2Jn+8RYfgFA1ewc4o9FniE5yRr+NAWedKowsh/rEc6Z4cTi6IvwpwmtdZ/e+oPU3YnSHszMRtr3qUQ9xjQ3EIG8zPLPQ5Dspz47Cl66tn44MhNgjrDmHVsLX04kXc8/O3s+Hn1rPsksX1LuUZcUaCV6FQ4O///u/Zt28ff/RHf8Sv/uqvks1mJ33sn/zJnzSqbNOdC8HrtRf28/1/2sHzT7xJGJraUMJGsAqs42A9fWqvVDVslYOxsBXO7fSSdTXB/BbKi9riMz0w+Q7P2JntgKeqRyWEpabeuVoVB60oVTmjN8eCylQC2BQhzAJRJWxF6QbUi+IzjrqsTjkA1HqaUhC2GKLMDBoJU4kqPWFlNeWQFutYTMpgUg2oZ4kbLFZx6pcZ4sOQdgx+S5lUtozjze2Uq7UQBA7lwCUMJw6dq4UfbWphK+Of2tM003qh0XFPmNGMXyHGh5+uStjq8Ipzrle2DoXIpxB5E0JRfFI1bizP90ZY6g+ywB/GnWOXbmQVgXUpW3eShns87y5dYLk3xBJ3BL/OMDKVknU4UQlheXvy0Ln47z4hbU6BNl3Em+Np+tBqToStHA/bGTKZytSJ9RwMLbpIiy7iqWhOn2FkFceDNo6UuzgetE3R81bt2TLoOsPWVKyFwXKG3kIrxwttRHay0GfRWPQMw9ZU9cJQUyp5FIsexpwcwqphy+J4Ec4MwtaUNQMV94SNethgsnoK3Hi/Zn1TfxiZSjQWwtQp9SqUjXua3AbsR00cwry8Qhcrsz+pnlVx0LJefBJyrscmp1gJYYUpwomd+HtOH6G1cc9b0eAW7CTHwthsT8pOxhkt4x/Lk+4ZRRejCSGsVq/VJ+jOEnZl5vyde1Us4/RUQthIcfLQp3Uctnyv7rA1lTiEBZUQNnGf7LiaKDTMW9rF3Z+9jQ2fWs9Fa1ee0pt2tjojwWv8xTU2btzId7/7XfmO11nqX3/sT+g5PNCwsDUVq1TcE6YVWIsaLaBHC6hgbl3PUzGeQ7CwldLyTmzKmfnZrhmKz3ApgqzCOorIr4StyYb1NaigqoxNVyauE7RUwlYS1yithrBy3PtmXQizhihr595ImKJePPxRoYL4DayFraSu6VKtWRl2kWop42cDHG9uDdmpGBP3hEWBJjKa9kyRzmyBllQ5sa9MBkYTGUVkHdq9AvNSOTr9QiNGz05ar2RcCpFHYB3anCJLU4Ms8obwdKPGDE2sF6Hj755ZRUqHLHeHWOoOk9HJ7GcKxuWEyXIsbMUA7bpIq1MkNcdwN5WycTgRtXE06KBkPVp0iawuklLJXIAktJpj5XYOlOYzHGXQGFw997A1FWOhv5SlJ9dOX6kFBWNDCZPaJgKHUsmjUPTQalzYSmA/ai3YQGNGXaKReBSQTRtsqgFhayoRqIJG5+PjoHXBOJWwlcSxKYrDkDsaj2KwlZ4tO9cR11Mx4BbBG4l7xIBEj/VYi1O2OAWDW4pfV5RSRJ5qyNcQJqvnDpfxj8XDEa3rEM6Lw5b1G3pdvBqVK+EcHcQ7cCIefeR7KM9FOcmspDY+GGKDAAV87FfvZsOn1nPl+ks/MGFrvHqzQUM/va6urtrFNT6Ib9r5JApN4qELQFmLCiIYHEGVkr8UvQ4iUu8PgVaUVnbN+ezT6SjAKVsiz5Jb1ISrPap4h288KHclXw4VH7AjF8qtMxzzOMt6OGAyFttikq83rma6rUAmGyReU2tI+REtLXm6M4VkixFvAr5jSHsBq1r6m1Iv7YRknICrsoebUs/F4CrDjan3SScUtsbL6JAVeph2naecWMt5jK8jlupBWnSR4VrvV3JcZViWGiSlQ3YXlideTyuYn87T5pUZOZH861MKfD/C9yNwDUlv9EqB8g26u0yUBhMldiefMQ7YVkPkAkFz6oWtYDyLW2jCjlsTD6d3oKUn+XIoFQctXxElcN2IyeqFHan4Z3FH477nNg3bkiK8ZBGuAac/n3g9FR8MUSmf9q4W/t9/cn7c47ehW2Nrayt9fX0AbN++nSCJq5oIIYQQQgghxAdMQ3u87rnnHjZu3Mjq1aux1vLggw+ekxfXEEIIIYQQQoiZaGjw+t//+3/XLq6xfft21qxZM+XFNYQQQgghhBDifNHQ4JXJZPj1X/91AF588UX+x//4H+fkxTWEEEIIIYQQYiaSuTQKsHXr1qRmLYQQQgghhBAfKA0NXl/4whf4yle+QktLC1/4whemfewf//EfN7K0EEIIIYQQQpy1Ghq8XnnlldqVDF955ZUpHyeXmhdCCCGEEEKcTxoavMYPL5Shhmev/W8fY2Q0vkM5NvlbJVmAjA+eA4VSfGO+BBlXE7alsOMCfpKv0ar4rvUqAKtJ7gaV1XrE9y4hqtSp/iRWz4JjwTHxi7UkWxBQ2uCk4/sxRWUXa5O+z44FbQmNQmubyP0wJ7JoZShFDq4yid0otkphyTolNAaLqtxnNNGtgqwunf5hDZRRZZQyRCS+CQLgoJivXSIUA8aQ9F0KFZYOXaZVhwxGafLWJclXaS24KmKF38dQlGU4SidezwJLskPkQ5/hchqb8DrqKENHpkAYOeQDD2uTvd+VwuKnAqxRlMsu1iR9f63KfltZCDWY5I/2VkOYNuhAoSJQSa4zxMfcYhc4xfgGzsmuMcT3D0uBikCHTbjVZGTjtlqT2muqFKIKzbsVlFUK0j5Fx+XtvUe45Iol53znjLLWJnYXXWstfX19KKWYN29eUmWart67U59NjhzqZ9tPd7H5R69x+EAfSoEdfwNlYxu6Udfa5lrFd4+srmZKQRCicsU4hJnGhDCrFcH8FoKFrYRd1StpqrEXZBv/+sIMBFlFmD31zvXGiW903KhQZBm7cbLxT52nVbbhIcxqi00ZbMrELdnxewpLfBBvZAjTBjcV4aQitGsYv2eykSYsOYSBE4e/RlAW14/wUiHajddDpSxxU8GitcHRjQxEFk9H+G6Ip01lvmP1XG1wtUHTqJqWFqdMh1egwyvgKIu1Y5ujQTU4hFnSKqDTzdPp5PC0qdVLSkoFzHPyLHByZHQ4oZ6C+L2kcdu9RpFVLi3aI6UcqodPpRQ5Y+k3Ef3G0KhbOCssKWVIK0OKsX2lUlA0Dn0mTX+UoWgbcw61ul5EaEwlvlbf08BoToSt9AWt5EyKRryrtXpWE9mJ9SKrGCqlGShlGQkaUw9AK4PvhKTcCF3ZJqqKgUuunKJQ9hoY+iyOjn+UnlgvCjXlkkfQ0JNLdvJ/KuKTdaGOb6icRD01bpICDOiyQpcbF8Jq1dS435V6yoA7Cl4uDmKN2avF855wTLdjv3Vo0UGDQ5ix6DCed+3GyXbsg1QNbq+pIMLty+GeGMUZLdVeXlKsAlI+pFPge5WFiCsuXt7F3R+9hg0PrGXFBQsSXIrGqzcbJBK8enp6+K3f+i1+8IMfMDIyAkB7ezsPPvggX/3qV1m0aFGjSzbVByV49R0fZvtju9n8o9d4542jaK0wZoqPe/xqMMuNekLYqpqq1VWtVw5Q+VIcwma4KloFYXeW8sI2wnktcd16Wnpmdk1NC0QpCFoUQUslbJ1uD6XA6MoOe4YnN6tn84wfB65a+DnNgltlx2rN8EVabbF+JWw5ddSzzC2EKYuTCnFTEaoWfqYoVVk9TKgJyw5RuXoUnIk4bLmpEMebvt74UORog55VCIsDle+E+E5U1/M1FldHcQibRb2ME9DhFuj0Crh1hJ+5hjC/Era6nDz+SeEnCR4h85w88908rbocrxdqqqWufoazD2EKyCqXrPJIK6cy8dQmpIXaSjpqLf3GMGDiHriZsaSIw1ZaxQF9ss3QVv5QCvLGpS/K0B+lKePMrFrl8zdootP0FVY/25JxORG00he2UjCT36tzunnYcWFruoBTrRcaxUApy2ApQy6c5MzTaWhl8JyIlBPiaDv1Olp5o62FfNkjV/YpBt6M64FF63i/UTsRcJr9Whg4lEsuQXk2PZkzOHZWV6ZIQaDiIDbjEDZJ2DpNPRWNC2Ez7HmbELZmUM8bjX90eRbv6Mlhq46F1IHFCaiEzBmaLGxNWWtsRVWVY/CM64UGt78StoaLQBN6C1NeHLZS/rQHiWpb9YJLF8Uh7ENrWbC4I8Gla4wzFryGh4e55pprGB0d5ed//ue54oorsNayZ88evvGNb9DV1cXLL79Ma2trI8s21dkcvIaH8jy1eQ9bfrKTXa8cqDQ5Jp5lq0t13MdpeopmFLamqwVQClD5IhRL8c5kinphZ4ZgYSvBglZwdH1hayqnCWGWOPhUw5Z16ghbU6nuyKvDEaeqqcd6tmydYWvKedURwqyKwxYpi3Xt7OvVG8KUxfEjnFSIPm34maachSjQRGWXKJiuwWhxvAg3FeH69YWfyeYB8Wqu9emGBsYNLr/S0Jt9PVV3CEvpgA6vQJdXwNPRrDeJsUb49C0cT4V0OHm63DxpHSQethwi5jkF5js52qpDGKcMW9NT2Fq8mOr5CkgrlxblkZkmbE1lfAgbtpY+YxgyhqnbUxYfS1pFpJWp65zOqfXiz2DEePRFGQaiNOEUO5pa+KkjbE1Zs1KvEHm1nrCS9aZ8vLFUwpYzq96k8T1v/cU4hBWiqUORwtZOeLjOLHpfKx+AsZAv+eTKPqVwulBUf9iatqyFoOwQlD2CaU8uNaDpVp1FpOJesHC6rWIGYWu6egpUOC6ETRP6aoeROdbTwbiesGlG0VnioGWrx+k5HAudchykpg1h1qJCcEJL9QzNrA8X9YQwY3AGCngnRnEG87V9RiNWpakWC9+DdKV3a6YbhKqc+rSW1VevYNNH13HHvVfR2d2SxOLO2RkLXl/5ylf4h3/4B5555hkWLJjYTdjb28ttt93GL//yL/Of/tN/amTZpjrbglchX2LH9jfZ8pOdvPTsPkxkUFpNHEo4F9UAZk7a8So19ruRrI17wAolKMbfnIjaUgQL2wgWtmI9Z25ha0Kt6h+q1lBSQOTGYStsVRh3DmFrKiedTbNqXNiqp6dpFqy2tYOYVRbrWUiZOGzR4HoWMDD2nTDQlZ4m7ZvGrTLjPrqo7BCWHUwYNzYd1+CmwjhszTHAnloUtLKV74NZlLI4qtLQqwxhanQ9Z9xwRKXi8NPpxT1bKafxPU0nhzCHiI5Kz1bWOV1P09xpDF2VsNWp4zOyja43vhcsDltOJWy5aKUa8hFW52GsZdAY+o1h2FosFq8StjKzCFvT1atuc8PGj0OYSWHQcfjBwTB9T9Ns6ikFo1GKE0Er/WELgXWxFkKrMdaprEeNUet5i5xaCCsZD4XFcyJ8J8TVsz+pM1W9yChyZZ98yaccxYFcK4vjxNtkEvu1ai9YGMysJ3NWNSEOX2E1hI3T6A29etiJQJdU/J0wq8YyQKO//lYNYeVKT1guHh7YsLA1RT1l4p4wHcR/V5WwpUOLmkvYmq6uHes1U8biDBVwT4ziDuTjYYpJhy3PHQtbujFvqlIKi0UpxbU3X8Smj6zjlo2raWlNN2CpG+OMBa/169fza7/2a/zyL//ypP//N3/zN/zVX/0VO3bsaGTZpjqbgtef/V8/5kfffomgHE4/lLBRqrNPsk96nDDtUFyQxvoNDFvTiBwozlcYP4GwNQmjIOisDCUk+XoWi2kPsakEwtYUXD/ATUdjZ9aSqlmdt417oxobtqbmOSGdmcL0Q5gaxuLrkMvajtPqlhOvF799lmVeP63O3HqaZmKV188iZ7Tu0cNz1a48urWPo1Si9arzzpuAflNCqWTXmeq8RyOX3eWFlJO7dWdcr/JHaDWvja5k1KQq/5PcB1htwRwrtlKM4h1pottE5T0dLqYoBH7yG0NlH1YqOhRyjfuu22mFQHE2w7lnoXKizs3paXvAGloPSJ0Y1wOWZNnKZ5gajHDzTfsE8Y4MkT4wEF/QLMGwVWV9D9pbwEm2vVZt67qew90fWcf/5/ceTKTOTNWbDRq+F37rrbe49dZbp/z/W2+9lS9+8YuNLnt+spbgL/5//M8jz1N0PIrap6h9CtqnUPt3/LvgxNOr/y5W/l3Qfu25RtVxiqlZe4yKsNXDepXlasKVbqI0ceiC5rxWp3KxjCay6YT3vidxUuOG9yX5nlY/Nk1z6lX4TqWHi2asoooWJ6DVLTelniK+sl2b29wrFC50crWhlc24wFWbdnEqhRK9umRl3mWiyoVcmlNvxKYSD11Q2dwUFCKfUdOcM9HVi8WUjNeUdaVaoxS5zTlGVGqEQbJXsTxF4ldAHEdVe4OaVFNRGeJHUz/DpK+6eDLv+OjYVaSbcdhPeZUeLhLdsVU7GMIg4tHvvXzWBK96NXxPPDw8TGdn55T/39nZyfDwcKPLnp9ef53/8M53z/RSnFZZObUQWHDGwmFRe7UAWDwpLBYq4XDItjJaylJ0fQpuinzld8H1iXTCQy9EQ6hmnGoTQogmavI5yDPk/HiV56ozclX2c/xS8I3Q8OBlrUXrqXtOlFIkeAX788tll/Fax0WsG3r3TC/JtHwb4UcF2qMCNO/2ELMWaIe855P3UxQ8n4Lnk/dS8Y/vU3Ar0/zKtMpjCpX/rz527LnxY0Mn+TPOQgghhBDi7JRI8LrsssumvAGahK4GSqX4L1f9a4qF2d2607ER6SggY8pkTJmUKU/4dzoqkzYB6cq/M1H89/gnIBNVHlf5d7ryb9/O/ALKZxPPRHSUCnSUCmd6UeoSah0HPj9FLpWi4PuVf0+cVvB9cr7P6DyXXMqnkPLJp3xyKZ98OjVhWj7tU3JdOXslhBBCCNEgDQ9ef/u3f9voWYqERMoh5zrkOHuuCnOy4vw0QXv8BWbHRKSigGxYIhOWyVR+p6PyqdPCMtlx/85E8bRMWKo9Nl2dHn0AuuGm4RpDe7FIe7F4phelLpFWFFIe+bQ/4ac2LTN+mk8+PfGxhXGPzWXGHlfyJSgKIYQQ4uzV8OD1+c9/vtGzTMTXv/51/uf//J8cPXqUNWvW8LWvfY077rjjTC+WmEakHfLaIe8lFxTLLVDqbsx1bLUxZIJy5adENiiTDUpky6V4WiX4+W48LVsukymXaSmXyJbKE6ZlyyVaKtMywQc7KDrG0loo0zrLntpmM4pTAl9+XOALspqg1aGY8ShkfIppj0LapZjxKWQ8ChmPYjr+KVQeU8h4lFISFIUQQojzSaJfOhkcHORb3/oW+/bt4zd/8zfp7u7m5ZdfZtGiRSxbtizJ0tP65je/yW/8xm/w9a9/ndtuu42/+Iu/4IEHHmDPnj2sXLnyjC2XOLcYrcml0uRSUwdF40JpXvOWyWKJFk4e3JQxZMoBLaUymVI5Dn/FMi2lEtlimWx58mmZUpmWYvx/LcVKWCwF8d9LZVpKH4yANRVt+UAFRYBSxqGcdSlnXIKMQ6ny93LWmTit+pOJp5erz6s+PuNgWsDtiggzmmnv4iyEEEKIaSUWvHbu3Mk999xDR0cH7733Hr/6q79Kd3c33/3udzlw4AD/8A//kFTp0/rjP/5j/s2/+Tf8yq/8CgBf+9rXePTRR/mzP/szvvrVr56x5RLiTLJak0+nyKdTp3/wDKTbSiidwHc7rSVdDsgW4jCYLZbJloJaOKz+ZKrTC+P+XQzicFksj00vBZX/K5PE4jZTqhCRKkRAcy8DP1thWhNmNUHWIcxq0q0hNqswWYVpUdgWjcnE/7Yt46ZnVTy9RY/9Ozv2G0eCohBCiLNHYsHrC1/4Ar/0S7/EH/7hH9LW1lab/sADD/C5z30uqbKnVS6Xeemll/jSl740Yfp9993HM888M+lzSqUSpdJYA0Yuhy/EWUApiimfYsqnf8Jk29QRfC1+idZU6fQ1rcUvR6SLAZlCmUwhIFMISBcD0oWAzLjptWnjpqeLIelCQGuxRHupgF+I8AshXiHCiT7YSdEtGtyiId0fnulFqYtNK8goaNFjP1kFWQ0t1d8ashqbHfe40fieOn57RNRhMJ06/ulQmA4NngRFIYQ4lyUWvF544QX+4i/+4pTpy5Yto6enJ6myp3XixAmiKGLRokUTpi9atGjK5frqV7/K7/3e7zVj8WbsnP+KiLXUbvsuGqeJb6m1FtXMj7CaQc62VUYpyimXcspluCMz69l0enmuaO9t4IJNwVrcsiFbKHKp6cUtRLg5g5ePcPMm/nfe4OYjvLzBzUW4BRP/5CO83BSPy0foD/aFT1FFC0ULA+b0j51kWmvlp1lWADexb9L/izxFud2h3OHGP+0O5U6XcrtLuWNseqn693aXcmf8d+M35vuwDdPsQ8W5fGiy1T/O0Rd4jr6s8c6Hi4h/ENvAiQWvdDo9ac/Qm2++yYIFC5IqW7eTL3dvrZ3yEvhf/vKX+cIXvlD79/DwMCtWrEh0+er1737zw3z3Gzt4961jaK1qd/RupOp8HUdz6ZVLicKId944Wvv/RDZua7GAd2wEU/KI5reBo+NiSWxplfn6AyGqbCnN87BegvWMBa1whspkBsqUF6eIWr3a9IarHD91CO5RTdRuCFsAAyTRdqoerw2oHh/dEmI6TXKhaNw6qIYdUBY6xjWKk6qnoDCSwilCuq2MdmxibZXqqjgw2sqBomFJ5yC+H2KMQicwNtKgCVOKXt3OYJRl6bxBOtMFIqNwkqhnFVpZAuNgQ8WFqQFWpAeIrEJTZy9m2aJzFp23qLxB5+O/65xF5Sy6YFHj/50zqAKURnwyBUtbsQw5A3kDeRv/zhnUB/t6NtNyAkumLyTT1+jexicaPL9Y5CjybT75dp9cW4p8u89IW5rjfhtDbRnyXR7DbWlG29OMtqUYaU0x2pZmpD1NOTXHJk9l27YGvKIlNA4qE8XbJiSyn6luaX5gMQWIxn9lOMH9mlsElYcwC9YhuQw27tjklCzGBeuoxOup0KJCg7Ia4yVZL95xq9BggzJOoDAZL9m2DICFoM1HjxRRtfUzgXpq7P68l1y8EG9+O2+/0UMYRIm3SVddvJCPfebmhs8/aYkFr49//OP81//6X3n44YeB+IM5ePAgX/rSl3jooYeSKnta8+fPx3GcU3q3ent7T+kFq0qlUqRSjf3eS6Pc9zPXct/PXMvhAyfY9ujrbP7xaxw51I/jaKLo9GdjpzJ++1x73QXc/eGruW3Talrb4jP1/SdGeOKx3Wz+8Wu8tedIYzaw6o6oUMT0DcLgMCoISQPWdQiXdhGtmk+0sGPyBZ1lPV0K8foKeP1FnGLc+LAKiovS5JdnKCzNxN8VmeuOuRKqdDEk81Y/mXcG8HpyqGq9FS2Mru1mdG0XJuPOPYRVlzeypAYsmT6LPwzKKsAhaLPkl1oKyy1RhrmHsHEff6pPkT6qSZ1Q6EgBKUzWECwPCFaGRB0mrqeY/Xs6rpGghxx0j4dzzEUF8YuwnsEsDDFLy9jOcV0sc/kMqw2vksYMeNghH8qafkBpQ7q7SHZBjnRXMT4gzbFedZOIypp8bwv541mCnMf7LEMpy4L5g6xY1suKZSdwXYM1ak7fp6uFn8jhvf55vNc3j96Rdmy8lrKsdYCrFx5m3cJDtPqlOYcwaxVKWSKreHd0Pm+MLGF/fh6RdQBY4g9xa+e73Nm1j8WpYSKrcNQ09XyF8RWmC8CZpN7YarNndAlPDl7M80OryJt4/z7PLXBP1yE+3H2ANS0DRDbeJGa7m7EWDPEyv5Xv5CcDK3l8cDnHgywAHWGeO9S73GHfYXW5F2coQg1ZnCGDHjToQYseMuihyu9x09QHY1RmwzmRpW2wRNtgCRg504tTl8G2DMOtaYZbq7/TDFemDZ08rSXNUFuG4bY0hWIGczyFPe6jAk0GsI4lbDeEnRFRdvwZpzksYGW/5uYgc0STPqpxC/EMrbYUu6GwAEpd4+o0oJ4uQ/oEpE8o3ByVvQyELZZSO5Q7VLwBzvXYOy5sZY8Zsj0RqQFbqxelFaUOTanTwboNONaPO/ame/JkDudI9xRQlTZSlHYJ52UJ5rdg0u7cQ1itdxKcvlHc9wdxewZRYdwGtEphPRfte+A4DQl92tGYyHDRmuXc/cmbueNnrmPB0i4AcqNFdmzdy5YfvcYrz70bd2yg5nQf32qbdtHSTu7+6DXc9aG1rLp44Zxew5mibEJ3NB4eHubDH/4wu3fvZmRkhKVLl9LT08Mtt9zCj3/8Y1paWpIoW5ebb76Z66+/nq9//eu1aVdeeSUf//jH67q4xvDwMB0dHQwNDdHe3p7kos6YtZZ33+ph66O72PLjnfQdH5lRCKsGqMvXLOPuD1/NHfesoXt+27TP6Xl/gG2P7uLxH7/Gof0nZhbCqjuAUhnTPwgDw3Caq+BZ3yVcPo9w1XzMvLaxMzz17EiqZ5+CaCxs5YNp97HGURQXp8mvyFBclB5rTdez36qEJxVEpN8ZIPP2AKn3R5iu7Wg1FC5qZ2RtF7krO7G+gzIWW08Iq+6ALaSGIH3CkBoCNc3Hb7EEnZBfZikssxif+kPYuNfhD8ZhK92r0OH0yxq1RQTLQ4JVAabFzryeAjWqcY56OD0eqjT9k23KYBYFcQhrn2HPWzVslRV20McMelA8tVE/nnIMmXl5sgvzpDpKY6VmUM+EivzxLPnjLZSH/WmfrLVh8cJ+Viw/ztLFfTiOrYWa05arhh+jODjQzXt98zk63IGxU7+nCssFHSe4euEh1i44TNqtv+dt3AlZDubn8cbIYvblFlA2050HtKxK93Nb57vc0bWPbi9/+hA2rp4lDpTv5Ofz5MAl7Bi6gKEwO+3zlvqj3Nd1iI90H+DCTB2hb5zQKlxlOVRq5Sf9K3lsYAWHytPvR9udIrd0vMednfu4oqUXUzlLXc9uLbIaRxkGgwxb+i5hW/8lvJVfwHTrjKdCLm3r5arOI1zUegJdWfGmW2d0yeCPRLiDEemRANVvOf5+KwM9LYR9mvZ8iY7RPJ2jBdpzBTpyBTryBTpGC6TCD/j40nNIqDX5lE8+5VPwPQo6RZEURZWi6PoUKj95N0XBHZuW832Gun0GF6QY7vbJ+/E8cukU+ZRPyZ3mFhm18DMWtrwRUNOso1ZB0Arldii3zTCEjQs/meOGlh5Dus9Mf+wFwqyqDKvVM+t5Gxd+Ur1FModGSR/No8OpC1rAZD2CSgiz/gxDUfXk8WAe9/0B3KOD6NJpzsgoBb4XhzA9s1E91bC19MIFbPrkzWx48AaWXTR9+Bnsz/HU47vZ/C+vsve1QyhdCWB17EqrbdfO7hY2fngdGx5Yy2Vrlk05Ou1MqzcbJBa8qrZs2cLLL7+MMYbrrruOe+65Z9phfc3wzW9+k1/4hV/gz//8z7nlllv4y7/8S/7qr/6K3bt3s2rVqtM+/2wOXuMZY9i78zBbH93F1kd2MjpcnDSEVYPSigvnc+9HruGu+69iceXMxUy9t683DmE/eo3jPUOTh77qhh4E2P4h7MAQFGZ39TWT8YlWzCNctQDTkZ18J1LrNjD4/ZWwNVKe1Qkt4ykKSzPkV2YpzfMrU08KYePqpfcPkXm7n/SB4drZrhnVcxX5yzoYubqL/GUdYz1g41/juB2+PwzpvriHazbfo7FYSvOgsMxSWGqxLqceeMaFFm9YkT6qSB/TOOWZv6MWi+k0lFfEPWE2PUUIq0xTBYWuhC2dnz78TFkzE2EWB5ilAbbFTF6vGrZChR304rCVd5jNaVDtRWTm52lZmMdvK08+LKkatiJF4USG/PEspaF03PKYIdcNWbq4n5XLe1m4YKC2qkxYZcaFnyODnezvn8/hwS4iM/P31FGGi7uOsW7hIa6cdwTPMbWes5PrKQXvFzp4Y2QJb40spGj8KeY6NYXlsmwvt3a+y+2d+2h1y5OGouq0w8VOnhi4mGcGL+J4MH34mcpF6SHu7zrIR+YdYLGfrwWr8arTjgdpHulfyU8HV/J2oYPZrDPzvFFu69jPXZ37uCAz+XDLatjKhT7bBy5mW/8lvD6yGDOLbuu0DriivYerOo+wMjt2qZrx9aqfaWg0ewcWs7NvOW8NLaz1Ts6EVob5LTkWtw0zv3V0rBNlknUUYGCglZ5jXRw/3kEU1V/PjSLaCwXaC3kW6iEWOQMsYIiO0SKtI0VaR0u0jRRpGynRNlykdaRE62j8O326BqxoGoMi7/nkvVR8H8VUJfT5qfgnlSLv+RT8eHrOTxGUPUzeI8q5FHWKguNTcHyKlZ+C41PS3pShwyoIWjSlDk25vXIrjUmPhfHx3u8rkjmYI30kh1Oe+WgjC0StPsG8LOH8Fqw7RSiqhq3RIu7hAdwjA+jCLMdCa43yXFQ1hE3CcTVRaOhe1MGmT97Ehgdv4KI1y2fVhj/eM8T2R3ex+V9eZf8UX4+pthmzrSnuun8tGx64mquuW4XjnGXfJ53EGQ9eX/3qV/nyl798yvQoivhX/+pf8Y1vfCOJsnX7+te/zh/+4R9y9OhRrrrqKv7kT/6EO++8s67nflCC13hRGPHK8/vZ+uhOntq8h2JlQ12wuIN7PrKODfev5YIGdttaa3lz9/tse3QXW36yk6GBfPwfYYQdqIStXKFh9QBMa5qwEsJsa2UgvLF4A0W8/gLucGnas10zFaV0PBRxZZZyp1+rlzo8TObNftLvDaGD2Q/3nKxe7opORtd1U7iwrRbCvFFL+oQl3W/RDWwrWG0pLoDCUkNhMbWRW24O0j2adM/YcJSG1MMSzY8IVoQEywNstU1eUjg9lZ6tET3tGdKZ1rOtphLCypCOVw4bgR3ysIM+dnR2YWsqTiokOz9PdlEeLxtvg9ZAsT8OW4WBNJjGHWB8P2DZkhOsWtHLvO5hlIqP270j7bzbN59DA92Uo8aNOPd0yOXzeli34CCXdR+rDUM8Xmplz/AS3hpdxGjYuBugawxrW49wa+e7rO94j7QTbwAnyi08MXAJTw9exOHS7E4iTc6yJtvP/V0H+VD3Qbq9+ITRcOjx6MBKHhtcwa7cvMrQzMZY6g9xW+e7bOjcx+JUPLSuZByeHriQbf2X8PLwcsJZhJ+ptLpFVrf3sLbzCEsyQ0Acut4ZWsBrfcvZO7CYsvEaVs/REQtbR1ncNkx3Nl9rZw4PZTl6rIve3g6CoHH1UJZsR4G2eTlaugqoyuYWFFxGe1sYPd5CWGxgPSy6JcTpKuN2lVFOZT9TVthjKUyvD5X9jDaG1lKBjmKejmKB9gm/87QXC5XfedpLBToK8e/2Yp5s8MG5z+D5oKA8CsqjqDwKOv5dUi4F5df+Hf+4FLVfe3xJexSUS66tleGuNkbntVPw0xRdn1LZ4rw/hHtkAGe0wbcKcTTK81CeWwthrZ1ZNj54I3c9eAOrb7gQPUU4m41D7x1n+yO72PzD1zh6OD7Z46dcbrv7SjY8cDXX3XIxnpforYYb7owHr0WLFvGVr3yFf/tv/21tWhRFfOYzn+H1119n7969SZRtig9i8BqvXAp4+bl36exu4fImdNtGkeG3fvZ/suuJvdiRXKK1ID5zZJcvRM3rwB0pTzvMrlECJ6ScikgdHkGXkh9SE8xLM/TA5Xg5cJpwvDWOpbAEnKLGHZ1+eEgjWGUprjBxZ+Kgk3w9LMGSiKjVYkfdWfU0zZSbCXCzAaXBNDZK/mxeqrVAx6ohjo+2UQhm3tM0U2mnxLqLDnKi1MpgkPzQck+FrG09wlCYYV9hPsl8U37M/5+9/46X46zv/v/XNTNbT1evVrHVJcuSJduSm4orNmDcMCT8TBIgJDEJAXIn3EluSsIvJCGUwM3NnTvEIXzvL7bBBTDGTc2W1YttVRdZsno9fc+Wmbmu7x+zuzpHOkdnzzk7K8n+PB+P47I7u5/Z2SnXe65rZyw0c2uOE1GGLe1D8UO5Sk1nhjGRJhrsDFvbRpMtY/jpSY2VZqjTzjttQ0l74a8zEeMxyPVoa02SyYZfT1maqmQWN+PgdkQIe50BQ9TxwANanNDrGQy252N7we9uVZn3a8poYr5LwssFf26WqJMl4rjUtOSoyuZIeFmSXpZ4YRovR9LN5v872+W1SS9XnM4qZSyaKJs0DhkcMsohTQSqq5hw9VRUdTVUVUF19em/qqrSHksme+xJKzDGsGf3EU4ca2HO1ZcST4S/3Yel1GwQWpx85plnuOmmm6ivr+f+++/HdV0++tGPsnv3blasWBFWWVGCaCzCNTdMqVg927aoSzjQ3lGRegqw2rLYdjacq/h0w0l52AdbKlILwO7wiTdVrByWr4ifUBVbnsoo7FNOMNSxEvVQ0O5U9PK7XjqClw6/8VyQzsZoaRpcsXoZP8rb7d1fsCgMrnHY0nZJxeppLLa0DwvlIqTdU7yTHoJbxh7K3jTnkhxvq+t9wjJxfYcTJyp3oX2jLToaz/1bv/IKhi6rCv3cTaGItFlEQzrfaZRFxomRcWI0FR8j+D1yhb5GK6dJnCxxqIcxxLRL3M8R910SfpaEnyPh5/KP5fL/f47HvRzJdIqY8Uhol4TJETdu6KddwpbAI4F3+qcEbU3w4sHQ6yrgsvxfnyQSQbj7sz+Dv/qr4KIhF4nQ9uBXXnklTz75JB/+8IeJxWL8+Mc/Zs+ePaxYsaLHqwcKIYQQQghRdkqRtaNk7Sj9Pk2qDbG3K3QvWmOI4BNrOknCyxHHC/6MRwKXRP6/C48n8o/HOz2eMB7x4mN+8XX2xd6jmE4Hf3/zN3DDDXD99ed7jkoW6qmzRYsW8dOf/pR77rmHadOmsWrVKoYMGRJmSSGEEEIIIS5uSuHikFVxWq3KDsGbs2QG3/zNX/X9hbkctLef/kulgr+BPub2cAGRW26BefMG9mErrKzB6+677+728aFDh1JfX9/l915PPPFEOUsLIYQQQgghzpdoFAYNCv5Et8oavOrquh8Lfuutt5azjBBCCCGEEEJcVMoavB5++OFyvp0QQgghhBBCvCdc7BdiEUIIIYQQQogLXqgX1/jFL37BY489xv79+8nlut5saMuWLWGWFr1oOt5CoipOvCpWkXqeb4LLfXplvMNvb7q763tYpQATdSDnhX4nmEI9bYPyw7/zTIG2QJnK1TMq+JyVq2cwirLf66bHehiwQOmKfUIsS6O1olJL1VYa31SuXsxy8YyNbypzTjFmuViKitxTC8BSmrjtkvErdRsCQ8T2cP3y3kj8XPVwNHiVW2eMbcAP/96ExXqWAV3B/YwF2gGrQofewrHJqtAl8w3gR8ByK3iscGzw/MrVi9hgDEpX6EqESuF5FbgB6vtUaMHrX//1X/nrv/5rHnzwQX75y1/ye7/3e+zZs4eNGzfyJ3/yJ2GVFefQfLKNl5/axLJH17J70ztEYg4Lbr+CxfddzZVLZxKNlfdgnu7IsfalN1j+3DY2HujAzJ6KamvHnGqGplbQ5d+wVTSKisdQ0QhkMsHN+2wbHKfsIawQtnRVFJ2sB9uCTA7rZAvqVCsqU/47G7sNcdKTGkhPGYxfY6N8QyQV/Fm58h94tA1eQuFWKXRUgTHYWXAy4RzojAI3CW6Vwo8Fscvyglrh1DO4NZCr17j5+x0qD+yMwsqW/4ajBoOJGvy4QcfzwcsDK21hp1UIIcygEj52Qw57UBYVMWhfkctEyGUctGdR7qUajXg01KRoqEsRj3poo+hwI3S4UbIhNOCjlsfoZDNjq5oZFOvAN4ojHbUc7KjneLoGXeaBHRHlcWn1CabXHGV81SnAcCA9iN1tI9jTPpRcmW8+pzAMjqUYmWxheLwN2zI0ZRPsb2vgUKo+hBBmiDseNbEMtbEstmXIujZNqSRNqQQ5r/yhTymD7fjYcYOqdTGeQrdF0G0RTLb866ixDCauMQmNieSDUIeF3WGjMqrsIcwog46Ajhpy+Z/C22mDk1I46fKHMINBx8CLQ3qYAgyRlCJ+yhBrKn8oMoAfh1yVwq0CLIXlgpMyRDrCqWds8KOg6yzSw2NYriHa7BNr0dhZU/5jBQSroWORnTQcfI3dmsZuTaM6cuWvZ1v4tXF0XRKdGAVao061Yh9vQTW1o8K46WQsiopFIRJhx85j/PeP/5DFH5nHwtsup6omXv5671PKmHBuGTp16lS+8pWv8LGPfYyamhpee+01Jk6cyP/4H/+DxsZGfvCDH4RRtiJKvTv1hSDVmmbNb7aw/LH1vPrSLowxKKUw+TMntmPhe5pEdZwbPjKPRfdczeXXTcG2+9dYyeU8Nq3dw/LntrF21Ru4ro9lKXTxTE2+D0MbTEsrNDZDcxsDunNtJIIVj6FiMdS57pJuWUEAs+1+hzBDcPZJJ6Po6lgQts7sWSv8f0cG60QLqrEVlev/6UavJkr6sgbSUwbhDUqAzr9/oWR+kSrvdAize7jyakmf0QI3ofCqFH7hCrbdLS8NTtZgZ4Kzqf098BgFXiIIXF7iHPXM6QA2oHoYvGrI1mvcOoIB12d2reVXRysHVlZh5fofwgwGEwEd1/gJ03M9BSoHdsbCygwshKmYj92QxR6UQ8X0WfUKq6jvWeQyDm4mgvb7H1Ac26ehNkVDbYpk3A3en9M1i/W0IuVG6XAjuLr/IcxRPiOTLYxNNjM03l58XBXrKZQyuNriUEcdB1P1nMxW97uerTTjkyeZVnOUy6qP41gGbRSWMl3q+UaxNzWE3W3D2dsxBN/096aehoZoByOTrYxItBKxdJfdTOfd5clMFQfaGziUqsPV/Q19hqjtU5sPW46te9ytpXMOTakkzanEgG7mrJTBsjWOo1GW6XGbMG6nEJbr/01SjTojbNF9PXywOiyslI3K9T+EGU6HrWIW766eBjsNkZTCTve/581g0NEgbOk4Z6/qpvgPoq0Qb4RoM1j9PP9pCIKPWx2ELWN3M0Qh//92LghhTkf/60Ew6sKPgo4qjE3P9bKaaLMm1uIP7FhI/v07H2+7TBBsFMrzsVryISzj9v/YZCl0TRy/LoFO5kcjdbcRej7WyZagfdGcGljoi0aCsBWNos447hbabk7E5qql01l01zyuWjKdWKKyl7a/WJSaDUILXslkkl27djFu3DiGDRvGCy+8wOzZs3nrrbe45pprOHXqVBhlK+JCD17ZdI4Nz7/Oip+vZ8Pzr+O5Ppat0P65v+pCCKsdXM3ie69m0T1XMXXexLM2xjP5vua1zftY8dx2Xlq2k3RHDtu28P1e9rCnW2OYppYghLW1n75z+rk4ThC24vF82OrjoDTbArv0EGYcKwhbVbFit39J4a2wY27rQJ1sQTW2obzeT//5CYfMpQ10TBmEO7zq7LDVYz1AgeUaIu2GSIcpaYhJEH4UblLhF05slfT5gnrKBztrcDKlDX80BA0Etyqo2+dOiXwIs3Ol1jP4ScjVG3L1pvuDdm/1smBlrXzPYu8v1I5BxzU60cd6xdCnsDLBX0mhLxL0bDmDcqiEf3o76uWlxc3QtYo9YUb3/oXYlqaupoNBtSmqEtnTpUqs52mLVL4nzNO9N6gtpRkRb2VMVTMjEm1YypS0GRYCUta3OdhRz8FUPU25ZK8zqjCMTTYyreYoU2qOEbX8LmGrt3qutnirfRhvtg9nf8egEnreDLWRDCMTrYxMthCz/ZI+X+EIboBjHTUcaG/gSEdtSaEvYvnFnq2oU1o9ChlJQSoTpSmVoLkjgV/CdwgG29HYtsayS/v+ih9OgclZ+K1BCMPrfR01ymBiOghcsW7CVi/18MFK2VgpC+X2HsIMQcjyo8HJlr7sXwohzEmBk1LY2d73M4Vw58eDv25P6vRSL9ZsiDdBtCUYSt4bPxKMSHCrFdopsV6hJgY7q4h0BCGslHpGBWHLj/UQtnp8YbBy2WlNrCUIYSUdC+F02KIPP1coHOtzXjGEWSWccDUKdHU+bFXH+1wP18M63hyEsLZ0aatcxAlOVMeiJdezbAvta2KJCNfePptFd13JnOum4ET6fzLkvea8B6+JEyfyi1/8grlz5zJ//nw+9alP8Yd/+Ic8//zzPPDAAzQ2NoZRtiIuxODluR5bV+5i5ePrWf2rLWTTJYafHhRC2NDRDSy5fwGL7rmKCTPGFJ83xrBr20FWPL+DFc9to7UlPaB6nc/kmMbmIIS1d5wxU/bpsGX3Ifz0xrZP/3V6P2Or02Er6gy8Xn5TU60p1MlWVFMbqtPy0lGbzMQ60pMHkR1dc/p1/a1ZPPtngiEfKdPlbKMh6GHykioIPwNdlp1CmJMJhiR2HmJiAD8WhC03qUpvJPRGg10YitgphBlMMPyl3uDWBw2UstTLhzA7Y6Hcro0jY+eHESZ0cJZ7oPUKISwLVqEnrPMbOhq7PofdkMOq9s7qaepzuXw937WLPWGmU+izlKa2Os2g2hQ1VRmKpQZQTylwfbsYwjr/PkthGBpvY0xVM6MSLTiWKfYu9Yc2YClIexH2p+o51FFPq5voNIVhZLyFaTVHmVZ7lITtlhS2eq4XvDbjO7zZPpzdbcM5nKmn8wKrcrKMTLQyKtlCwnEHtJsp9rxpxeGOWg60N3CsowbTKfTZlk9NNEtdPEvM8Qa2WzOn8317JkZTKklLRxzd5Td2BtvW2IWeLQa4qymEsIyN3+qg2yPQqbfWYDAxg4n7mLgpy/YejChQWO0WVoeF8s6o54COBD1OZavnnw5hnU/2FMNdPmz1+STSOepFmwyJRoi0dX077UCuKujd0pFuerb6XDNYD5w0OB3B0PXOm5hRQcDTsT6Gu17qRToM0RafaKs+69gUhK1OrxnosV4pVNbFbs73hHU64WoAXRXDr02ga+PBaJyBKGzEWRfrWDPWiWasjmzXaRwHVRhKOMB6hfZhdW2CGz40l8V3Xcn0+ROwBvo5LnLnPXh96lOfYuzYsXzlK1/hRz/6EV/4whe49tpr2bRpE3fffTc//vGPwyhbERdS8Hrr1X08+18vs+qJjbS3dBQ3iHIqnOkYc9lwrrpjLl5VgtWr3uDk8baBha2eFFtjLuZkE6o1hRWJoJwyhJ/eSjs2ur4aXRMPwhaEUK8w3FJjmttxYz7psUmyl9Sd7vkpd838Zm5nwc4EPTBBT1NIy7LQ8+aBcoPQ4yX7eMayH/WUBnyDXx30bOlYuPXQoLLBf5pY/ix3GPU61bUyEI16RGpzqJrgjOpAwk+PpfJHBi9nE8WnNpGhrjqN1c3o2nLVUwqyno1tNMPibYyuaiFq+QMKWz0phLB2N8bJTJJ6p4Mp1ceoiWQHFLZ6rhe8Z8qLsqttOKe8GhpiaWoi2ZCW5+nhlgfa6zmWqcWxDPFIMPYqtN2agZZUnMaOJFnfwbLLELZ6qFf4hkzaxktF8LUKwla5Tup0UzMYEqxQKQs8KzihE3Y9D+x2sHyFjlGekzq91Is1GpxUEH78WBnC1rlocDqCk3XkLwYSzEgItQohrF0TP+kTSZc4mmQg9ZTCSudQqSzGttC1ie5/plDGeiqdxTrShN3SgRUthK3yf4mFNmDD0BqW3D2f2z6+gDETh5W1xsXivAcvrTVaaxwnaLw+9thjrF69mssuu4zPfvazRKMX7xjRCyl43Tfxz0i1ptHlDj890KOHQbJyP7JUja2o9o6KXT3Ir03ij6rcHdfdODTNSIQeKItM8KfCPIiewYtx9tnEEGUHa/xkGc50lyzf0KtQPSfqUTW4I9yGUCc2mrH1LZW8SCizaw9iK12ZTcLA6GgjCcut2Oc7mqulya/Kh6Pw6zXlEhzKNIRfKC/t2uxvrtx+1GhIp2JBV0mltvs2J6hXISoHVsWugAp2BwP6zVlfKRecbO/Tla+eoWHPAH4A1l8V3JFG3j2JFcKFP3pi2YoJ00bzg9/+RYUqXlhKzQahXdXw4MGDjB07tvj/999/P/fffz/GGA4cOMAll1wSVun3FTfnVSx0AeH1kJxDYah15YpVUGF5VrBuGL0jvRasaL1Khi7Oz+cr1K1gvUpuGkqZitVT+Y7fSm/6Kqwz+t0o/zXeelPxhUloPSQXiPPx0SoVuvLFKuq8rSqVPNZX+FCofYObPQ9h9iIT2oDMCRMmcOLEibMeb2xsZMKECWGVFUIIIYQQQogLTmjBq3DZ8jO1t7cTj8v9AIQQQgghhBDvH2UfaviFL3wBAKUUf/u3f0symSw+5/s+69ev54orrih3WSGEEEIIIYS4YJU9eG3duhUIery2bdvW5SIa0WiU2bNn86UvfancZYUQQgghhBDiglX24LVixQoAfu/3fo/vfe975/2qf0IIIYQQQghxvoX2G6+HH364S+hqbW3lqaeeYvfu3WGVFEIIIYQQQogLUmjB6/777+cHP/gBAOl0mnnz5nH//fcza9YsHn/88bDKCiGEEEIIIcQFJ7Tg9dJLL3H99dcD8OSTT2KMobm5mX/913/l7//+78MqK96LKnUPLxGO8/H9yTojLnBGVlIhRKgqv49pb8vQ1tJR8boXk9CCV0tLC4MGBXeuf/bZZ7nnnntIJpPccccdvPXWW2GVfd+ZOGMMALYT2lcZUKAsBZkcAFYlbqRsDEQcDMHFWipB5dygboVYrkF5ld05VnxXXMH7exvAyuVvpFqpD2o6/VWA9i1MBZepNhaeruRtOA0dfiT4r4osU0NG28X/Dr2agZjlVaweBPVUBbd8x9JYqoIrKaAqWs+ArU//dyUqFg/xFapnBycHKrYbVZU9NmkLipt9JZozVPjYaww6FqlkRQBONab46OJ/5G8f+ikrnnmNdEe24vNwoSv7xTUKxo4dy9q1axk0aBDPPvssjzzyCABNTU1yH68y+pdn/4o3t+5j5eMbWPHYOppPtmE7Fr5XnoOQZSm0Nlw6cyxLPrqAGz8yn4yrWfnCdl585nUOH2zCshXaL9MuxZjgzu7ZHOZUE6axBXI5VCyOlYijHOf0NOWQb6Ab20LXJtG1ifK8by+MAi9h4VbbxJoMOgZeFHRMlf0gUPxmFBgnqI0Bywflh3vMMQpsD4wOGg7GJrSCxgYvZtCOQmUAG4xtgtNLJqy65nTIU6cfCquWFfOxopp0LoJtGxzLx7KCb7hcm0TnejHbIxHxcLWFNgZLGWxlQqkFELc8qpwsaR3B9yxiyiNm+VjKlHmxBu+WtLIMcdoYbLdhK03WRMiaCLrM5yQLuyxtFK6xiCqPkZFmOnSUDh3DNQ5hrDjGgEYRsz0urT5Ouxen1Y3T4ReuNlz+ep5ReMZiRG0rGS9COhch7UbKXqtzM9ayIFmdRWsLz7VxcyHuaJRBKYOq9cD3MTkLk7UhrJMTyoBjMHGDb0DlFFbOAj+sHSmQ31+71WB5YLuAn5+dsGpa4CWD45LlhVwPwFE0T4rgdBhiLT7RNkO5s3thDTURhRe38KOgNDgZg53VZa9X2NEoz8dKe1hOBFNXBTkPlXVBh3RywrIwUQdiEbBttK/Z/MpbbHz5TSJRhwWLprL4A5dz5bWTiEZDix0XDWVC6kr44Q9/yJ/92Z9RXV3NuHHj2LJlC5Zl8f3vf58nnniiePXDi1Frayt1dXW0tLRcUFdt9H3N9rVvsvIXG1j1xEY62tL9CmGWbaF9zaiJw1j6wAIW3X0Voy8dftZ0xhjeeesYK57bzrLfvs6pk+3YtoXv93HjLrRKXA9zqhkam6Ej3cPMWah4DCs+gBBWCFtKoWsS6LokJhkLo+VaVNjIvISFV2XjJSzoptfQAH4U/Djowsmqfs6XgSBs2fmzpd29jQkOBMrL/7tflc54y8KbdPNmxXkqUwgzFvgxgx8PQmX382OCAGZTphBmTr/+XO9TltXJoCIaO+ajIrqHVcFgWxrH1ig1kBAWvDZq+yQiLjHH624VBQwWQQCzBhTCgi8ianlUO1mSdhbH6u5wZHCUJqY8olZP81R6vbhyGeK0MthpJ265Z09lwMciaxxyAwhhhdVMG/CMjWtsNIruVgzPWEEI82N42AxkJTWm0Ha28t9oN/W0yoewBGkd6Xe9QuvBNwpX2/im+8+nDWTcIIRlvMKG2t8v0qBQQQCi+3XdGPA9C9e18dxyhDCDsgzK6rkenjodwswA6ykDdhC4elz9fFBZK+jhH2joy4ctle+WUd0tLw2WG/yVJTB02tR72M2gvCCIlS2EnWu/rQ2R9iCERdoNqp8t4+J27yi8uMKPWWB3v9JYrsEuhLD+tsQLYcvXWGkXK+NhdXci3BjwNeTcYGSPHmDTXylMNJIPWz1sGHmFdmEiGeX6W2ay+AOXc/m8Cdh2yCO1KqzUbBBa8ALYtGkTBw4c4Oabb6a6uhqA3/zmN9TX13PttdeGVTZ0F2rw6szNeWxZsZOVv1jPK09vIZdxi4GqO4WANnhkPUvvv4ZF91zNhJljUCW2qrQ27Np2gBXP72D5c9tob82cO4QVApPvYxqbobEF2lJ9+5C2jRWPoeJxlG2fO4QVwxbo6gS6NompincbfsqlsGH5cQu3ysJL2n2qZ1QhhCm0k9+dl/JyFQyj6HOwyYcwyyM4CPftpfTQpuz1NecMht29ToEfN/ixfNgq9XX5wGRsE/T19+mrLzFs9aTk1+SbyhGNFQ16t/oWbE6HMMsyJZyXCNariBWErbjjFXvQSq1X6AUrNvNLqad8qpwMVU6OiNWXVpwhonxilkdU+b0uG4XBoIgor9izlbRyJS9TY8DDLoYwU+IXaQy4xsYzFj59WLkBV9vFnjC/xDMFpYStnutZtOVDWFb33vPWOWx52sLry8ZLcNI97UZI56Jk/dLHeik4Z9g61/x6noWXc/C8vs1rELb6dnKhGMKyNibbl3r5oFXope/Dy/BB5SysrCo99BVClqbnsNWT/oSwM3Yrfd79esGxqc+hrz/7bd8QbdfEmjVOqvQtStvB8d6PW5juwlZPjMHKGeyMxs6WsJcptp80dsbDyngoT/fhMGPA8/MhzKPkMd2KIGxFI+DY/TrDV2gX1tYnWfyBy1l0++VMnVV6W/NCdkEEr/eqiyF4dZbpyLLhuddZ/vN1bHxhG76nsSyFshS+p6muT7L43qtZfO/VTJ0/Ecsa2FkI39Ns3bSXFc9t5+XlO8mk3WDIoq/z4200pqklCFut7aVv9OfiOKdDWKf5L3b1V8XQtVXomngwLiUkhSaLH1O4VTZe0u7bDrin91XgxwohjLPbRv0NWz0WDIYhWj7F32id+bam84NlOOGKle8J66bREXz+YEimLsOoJUPQuCn2hPV4yjX/n+U8Jpz1XvkDu5MfShjxUWVYRRUG29bYVhDCTq8zwX84ncKW3aew1ZMggNnqdANAdapnK59qJ0uVkyNq+WWpF82HsIjyi/UKYcvGD8KW0061lRlwh7Yx4GKT1RFy3ST3YJidhWvsPoetHusZmw4do0NHu/S8FXaZQdhS+UA48JU0p21a3SCEueZ0z9vpdl4wjNDTVskh9Fx8rUi7ETpyUVy/+54+1Y+w1RNjKA5F9P3C8jyjXj5sUYaaxgCuhclamFx368QZPVsD75gDLx/Cct2EsIGErZ7qdQ5hZ+5GCl9n/vGy7Eb16Z6wHkNYGffbyjNE24IQZqfPDmHGIhhGGLcwTnlWUjsbhDAr16lepy50K+NiZzyU24ewdY56eD5kc0EI626SqBOErYhTng0xrxDChg6v5d5PXs+HP35N2d77fDgvwesLX/gCf/d3f0dVVRVf+MIXzjntt7/97XKVrbiLLXh11t7SwSu/3sLLT22iYVgti+69mitumIrt2L2/uB9yWY8Na97mf37tcU4eagzCVkvrwLu5z0FFIqgh9RCLoWsT6JoE2OF8vgLtgLYVfsLGrbLLswPuqZYFXgK8pOoaVMIqWQhhhVFZYYSRruWCIYQJg7GDwKmjYdbLh7CIASuksNUdW6MsgxUNerfKEbZ6opQh6rjEIh4x2ycecXsY1lcuwfDAKidLzPLzYcsLbTSvwhBVHiOiLcSVy2CnjTo7HVo9YyCHQ6ufwMfCy/duhbXSGANZ49DsJckZp6xhq8d62uFEtppWN45fxrDVE89XpHJR2rOxLkErrO9Q6yCEZbORILCrYNsPc50xOQuTzh+LznnSpxwFAVdhpSyUr8oXts5RT/lgZfOjJvJC3Y0WQp9HRfbbyg2GIsaaDCY/lNA4Ia40OghhsVM5VL53S+X88D6iMeB6kM0f7KMRiJY3bPWktj7JY6u+HHqdMJWaDcr6K7etW7fium7xv8WFp7ouya2/ex23/u51FakXjTlct3gqy/9XnNUvH6jI1QmN66IjFmbskIrsMAB0xCI9LNr7hGVgaXAykKuvSLlgeIFDcZx9BcqhNOSSQegKv54KfkxuzvF7ihBYjsap7v4MY7kZozBGMTjZw28ny07hG4uRibaKVDMosibChNgJklYu9HpKQQwPYxQZE/52rxTElUfM8kj7IZ6F6FzP9qhxMhzP1IRaq8CxDdWxHGm3Ahs9wcCHaMzH88NMP6cpBSqmg9BciauEKiBqIGOCi3FUoJ5xwOR7pCrCCkZA9Gm08gCYiCIz2K7A2pJnKfyEwkllsXIV+JBK5cNW5a+GWNlLPp5fZQ1enS+YcTFfPEMIIYQQQgghyqns13X8/d///V6nUUrx4x//uNylhRBCCCGEEOKCVPbg9Z//+Z+MGzeOOXPmVOymt0IIIYQQQghxISt78PrsZz/LI488wjvvvMPv//7v87u/+7sMGjSo3GWEEEIIIYQQ4qJR9l9c/vCHP+TIkSP85V/+Jb/+9a8ZO3Ys999/P88995z0gAkhhBBCCCHel0K51E0sFuNjH/sYL7zwAjt37mTGjBn88R//MePGjaO9vT2MkkIIIYQQQghxwQr9GqNKKZRSGGPQukLX/BRCCCGEEEKIC0jZf+MFkM1meeKJJ/iP//gPVq9ezZ133skPfvADbrvtNiyrgjfKEWdJt2dY+8yrrP7VZuqH1rLo3quYuWBSaN+L53psfuF13tjwduWGmkYjWFqhjjajq+Lo6lhw05aw+Bqn3ae6w8WtdsjVRTCREOtpg5Mz1Lxr8BKKbJ2FjoV4ZxFtiHRApD24T4sXD24YHd6dRoP7wMSPBvVydeBVEfoNoh03uCGtjht0zIRaDw202/jtNirpo6o8VIj3+DYaclmHg8frScZz1CQzRJzwToRprch4DttOjaQqkmNYoo0qJxfmfUbJ6ggvN0+i2s4yMXGCEZGWUOud8mrYnx2Mi02NnSFpZbFCvPlumx/naLaWDh0lbnvELTe0egDtbpQD7fU0p+NEHZ+Y7WGHeNNtz7doS8fJZCJYlsZxNFaI9YwGNx3Bb3dQNqioBtuEegNlMhakgvtAmagBJ9z9jJVWOK0Kyw3ud2Ucwt2PeuCkwMqBsUOuByjP4HQE97XUEfAjgB1eQeUZoi0+0WaDdhR+zMp/xpBq+ppocw4rlQNU8NlUeDdsNsZAWwrd0grGYNXXQnUVqhLt9ordHO38U6bMreE//uM/5pFHHuGSSy7h937v9/jd3/1dBg8eXM4S512pd6e+UOSyLpuXbWfFz9ez9plXcXMelm2hFPiepn5oLUvuv5rF917NZbPHoQa4UWut2fbyLlb87BVWPvoKqZYOLNtC+yH2eDoOVjKBSiZRtk3Qug0+hwFMMoZfE8ckY5SltaINyvWxPI3Spzehwn/5VTbZuihuXQRTjgOBNliuwXYNKn8jY8XpT+nHIFNnkatT6Eh56jlpiHSAkz5dr8BY4CaCEGacMtTLhx8rB5Z39j5YW5CrM+TqwE8w8J20CW7SrDywPIUyZz4dhC+dMOhomRpHPihXYbkKTJelGfwr4aOqvSCIleE4ZwxoX2E8C2MKa0uBIh51qUmmqUlmceyBb5vaQNZzyHgRcv7pFBlUViTsHMOTbQxLtJNw3AHXMway2iHtR8jqSKdK+XpWjonx41yaOM5gp33AbRVjoMmv4qhbxzG3Hg8bhckvVYVCU2NnqLEyJKyBh0xjoENHOenWcCJXQ844dN6vgSFmeSTsHHHLK0tbrMOLcLSjlsOpOtq9WKd6waeMWJqY4xJzvLLsRn2tSGVitKfjZNzCeeDTezZLaWxHY9t+Wc6dGQ1exsFLO3iZzvXyLIOK6uDPHnjTyBggZ0GHDVnrjO0eUAYTMZiohnLcx9mAlVXYbQqnzUL56synMU55Q5jygmOEnQbL71oLCG5y7ARBrCz1fIOdhkgaLLfrGqoAbYMfBR2lLMd65RsibZpYi8ZJFbb207QNftzKh7DyHHsjLTmipzJEmrNnHZsAjG2BbQUzMsAN3xgDqTSmpRXT3Apad97FgGWh6mpQ9bVQlRxw+7Az27bwfU394CruffA67n3wurK99/lQajYoe/CyLItLLrmEOXPmnPMLeuKJJ8pZtqIuhuDl+5rXXt7Nysc38PJTm0i3Z7AdC9/rvoFV2ACGjxvCTR9dwKJ7rmLs5JEl1zPG8Obmd1jxs9Us/39fpulYC7Zj43t+7y/uL9tGJRNYySTKcYKjXE/rXH7PbBT5XrA4Jhnt205LG5SnsTwf5fe+2RSm8GocsnUR3NpI3w4EplPY8k5/jHPVUwSBKFtnkatVfTsQGIOTgUgqOJB2t8Pvrp62wSuEsL6EzELYcvNhq8Q9kXbyIawW/Dh9O5j7Qa3uwlaPs6kMOm7w40EjqU/1dKewpUt9oQnCV7UXhLE+NDiNAeMrtG9hipt6b2sNJGNBCKtOZvvUq2EMZH2HjOuQ9btpyHZbT1EdyTI80crQRDsxu/R9hDGQ0zYZHSXjR/Ixq2dBMFJU2xkuix9jYuI49YUzCSXWa9UJjrp1HHUbyBmn+J7n+nwWmho7TY2dIa7cPu1m0n6Ek241J9xaMjoCXcJW9xSGmOWSsF1ifQxhWd8OwlZHHS25RAn1gvUjavvEHI+Y3bd6WitS2Sjt6TjpXKT4Cc5dT2FZQQBzHN2nesaAn7Fx0xG8dB/ShmVQUT8fwvpWD1dB2g7+zgxbPVEGEzWYSN9DmMqC02YFYcsr7YUGMJF8COtrPT8IWk462J/2vobm69mn//q2Hw1OBDrp4OQcvby8sAczTj6ERejbsVcbIu2GWItPpO3ssNVdPQVoR+HFg56wPvW8GYPT6hJtzBBtzAYnc888V9YdBcbKh7A+fD5jDKQzp8OW55+7XuE520bV16LqaiEZ71cIK7Q1k9UxFt12OYtun8XMuePeE6Phzlvw+uQnP1nSl/Hwww+Xs2xFXajByxjDro3vsPLx9az8xXpaG1PnDFs9KfROjZ82mqUfvYYb776KYWO777V8d9dBVvxsNS/+Py9xbN+JftXr28xZp8NWJHLusNWTQgizFLo6H8Like7fx+TDluuD3/sO+Jz1FLg1Drn6KG610/2O0pggGOR0tz0/pSps1G5VEMLcGtV9KDIGOxuErUhH6eGnu3rBgScIfn6shxCW72my3OBvoPX86OkQpmM9TKzzYcstPWz1WNcKApiO657PGGtQnkK5Kn/GuZRmSQ+UgSoPq8oPQlj3X2E+bClMMdz1rV4wl8HCqYpnqa3KUBXPdtvLYAzkfJuMFyHjDeS0eVCvLpphWKKNoYl2ItbZ+w5jwDU2GT9C2o9g+vnT5EJganBSXBo/xsTECartbLfTtvsxjrp1HHEbyJhoL2GrJ8H37uAXQ1jM8rqdMqttTuV7tlK6c09T3ykMCTtHwnaJqO7XGVdbHOuo4UhHHaeyyeIr+8cQtT3ijkfU7r6eNpDORmlPx0hlYwOqBWBZBsfxse3uQ5gx4OdsvA4HNx0pPfx0W0+hbA2FnrAetgm8TmGr5JMsPbAMOqohYoIQ1g3lgt1m4bRawYmdATAqH8AiwWiGbr8ePxjS55zR09SvepweithjPW2w8/UKm2l/6hVDWKRTCOthpYm0G6KtPtFWExwrSgk/PdZTeHELP6Z6PNbbKY/oqQzRxgyWV2LY6onKH+Ntq8c2kclkT4etXD9HHRTmMeIEIay+FhWPn/MlhTZlNOZw7dLpLP7AbOZecylOJMTx9efBeQte7wcXUvAyxrB3x0FWPr6B5Y+t4+ThpvKFH0VwYRRtmDpvIkvuv4Yb7ppHtiPLikde4cX/5yXe3XEg/GGElkLFE1hVSYgEZ0jL2d0NQde9ro6ja+KYiI3yDZbng9fPsNWTYuiDXF2EXF0ULxkMCbFdHYSRctXqxChwq/M9YVUKywuCViQF3bR1B1Yr/28dDXrBghFLQU3bDYJXuespgrCXrQ9CmLE79WyFtGoay+An8iHMDnq2lKugOBS0zN+kZYLfglV7wdAko9BeIWyVr1YxKipDdSJDTTJDMpbDMzYZNxhK2PcQ0lu9YK0ZFOtgWKKNwbEURql82IqisRhYU+9MwXsNjbRyafwYE+InQVEMWykdD6VeRHnU2mmqrQwKwym3mhNuDW1+oeFS3nXGQpOwXRJ2DmUMJ7LVHEnVcSJTXYgU/W7ndUdhgl4wx8NRPhk3Qns6Tiobyw93LTeDbQXDES1LYzwLtyPo2TK63GfQ88cCxwQhLKKDgJW2IO3AQE+y9MQ26IiGqAF9umfLyhZOB5T5WJgPYYXRu3YmCFzWAMLPOeuRH/5oA8pg58DpCOqGs8bkP18MtB30pEVbNdEWHeKxUAXDESNgZzWRUxlijRmsnMao/p+A7JGlMFYQwozrnQ5bme5PNA1YLFrsCVOxaDALlkJrg+1YzL9uMks+cDlX3TCFeCIazjxcACR4hehCCl5/+aFv8drLu0MPP8pSYAx+Oo3J5lBWcKXKsh61u6ubTGDV1wf/HdYPWDsxERtTXz3gcdMlUaCVIjMiEZylqgADeIk+DoMYYD0/QnnGvpdYL1djcOvBqLJHn25pq/A7sAp9RltjBrsVuCYtFDbwmrp0qNenKSg0JcfWNJGMeITSkO0i+Hz1kQ5qIrkK1Atqpv1I/jdUVKReyo3xTvMgNFY/e+/6Rmvo6IhhTLkDc/eMAd3qBD8GrdB3qHIKVcF6dgc4qaBOJfZsygt6miqzV8v/lqo9hBDSE21InPS6/C4tbJGjbdhZryJrDIB34iQ0t1SgUicNdThjRzF7/gSW3DGbhUumUV2bqOw8nCelZoNQrmooKueNLXsBwu1xAkz+AhIm/5stoyuzd1SRSLCDqlCjFtuuXC0TDEeoVOgqqlDognxvRpiXXuumXuEKjxWralW0WnAltIqtMgpl6YqELqAYCOJOYUhe2Ms1eP9osfVVie9R4emexleFUy/jOfleQ0IPXQDGqHzoCupXhK5kPYUq9uBVpl4wGqKC+1Jd0b1aUK+C3QBKU9HQBWDlgv1axZZrJlOpSkWDkg4/WvbfaBhcXfHaF4uL/9ds4r2vUkFIhOK8fH2Vrimr6EVOvkDRV7LOiAvb+VhDq2sTErp6IcFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwesi50RsrEregFdR0ZtDqArfBErlb/ZbqbLFG0ZW6saRBjAm+KtUyQrXU4bKLU/ytSpdr/O/32v1KNyAt4L1qOgqmi9qKrhMK/zhzss6WsnlCabC9c7HTZkqutZUevs7H5SqbJtGqYquN5ZtEYk5lSt4kZIldJH7+iN/ym9/8hIv/3ITmY4clm2hfV3WGoX3HDZ2MPOW3IDX0cGaX26k5UQrtmPhe+WtZzs2vudTM6iaa++eT3xwPZtffoMj754K9fNFYxGuuWk6dWMGs2vnYd7ceRjLUmgdwhEh38pTqSyxAy5eXQy/JhY8F8aO2RhQCrulA/tQGr8qgjeqDmyr+FwY9SxXE2/z0RGFW+tgbBUcYMv9EfPvqbQhecTgVkN2kEJHQ67nG+LNYCyDW6vwEoRaDwORkxZWi4Pf4OPWdVo3w6gJmJxFx/EEkYSLU+2dLhXOKoOfs9hzZCgNiQ4G1aWwbRPmKkrOs3mrYyjVkSyjqlqIOT7agFXmetooLGXI+TYn2qtJeTHqE2niES/Uz+drRS7tYDIK31Y4ER1Kvc45RKdt7A6FbwGx/KMhrZ8ocFIWqhW0AbeG4JRyiNuh5YLdEfy/H69AvRw47QYnDX5SoZ1w66ENdgbsrMFEFDoS7n4UbYi0eUTaNCZq4yXyJ5PDOhYCTsqFbA7LV+hEJLR6hTbEpROHMfvWy0kfaWHtizvJpENur42qZ949c3FPNbHmqQ0Vaa8t+dh13PGHN5f1/d+LlDEVP8930WttbaWuro6WlhZqa2vP9+wAkMu4bHxxGyt+sZ51v30VL+cPaKMubKD1Q2pYfP81LLrnKibPGV88W+P7Ptte2sWKn61m5WNr6GhND2ijthwL7WniVTFuuHcBiz92HXOWzMR2bCDoNdmz4xCrfrmZ5U9sovF4K7Zt4ffz8xV2hrZjMX/JdBbfNY+rbppBPBEtTnP0UBMrn9vGi8+8xoG9JwcewgqbmuujMjnIuqhOm59xLLz6BN7gJH51bOCBqBB+2jPYh5qwjzZjZdzTT9sW7sg63Esa8IbXnj6o9rdmIYx4wUHUafewXdPlabfaJldnk6t1gpZtOQ7m2uBkDXZWY7mn384AXhVkBllkBimMM8B6XRoJEGs1RFKdei0BLw6ZhiD0+TEG/vny7x1JQbQFom2gOq3yftSQG+6THaHxakyXxmi/6ynAVai0BVkbpTu9maVxalyitTms5MBDWGEV155FNhXBTUfQnl18XilNfU0Hg+vbaKhNYQ3wPEHhta5v0ZRO0JRO0uFG6LzWDE6kGF3dwuiaFiKWxhiFUv3b7gv1PG1xqLWOg631nOioxnSqVxXNMbSqnWHV7UQdf8CfL3hXaGyt4lhLLadaq9Hm9KgIy9ZE4y7RhIftDDCEdQ5bHQ5uSxSvLUKQuPKTWAYSPlT5QQgb6DZI8HorpXBO2ERO2ljZTvWUwa0x5OoMXnWn1w5wm1AeOG0Kp11hu6rT0wYdAy8BfoLiCZJy1IufMiROQqSt637Nj0GuWuHWMPCTWZ33aymIthucjq5vp23wEgovoTB2GT5f/ouMtvjET7jEmjwsv1M9R5GrdXDrHPy4XbZjodPuEj2ZIXYqi+We3pEapfCTEXR1FF3osRlAvUJbYfSoem6+aSZLFk1j7JhBxedzWZeNL73Byl+/yrrlu/Bcf0Dti0JbqG5wFUs+OIcb75jN5Fljzlt77f2q1GwgwasfLsTg1VlHW4a1z2xlxc/Xs2XlTrSvUZbC9LJRFzbEZE2CG++ez6J7r2LmgsnYvQxldHMum59/neU/e5lXntxALuOWFPoK0zhRhwUfmseSj13HVbfPIRqPnvN1Wmt2btrLql9uYeUvt9De0lFSCFOWyrdMFJcvvIwlH5nHwtsup7ouec7XAex7+xgrntvGsmde58TRltJDX7Hl5aPSOcjmUCXsXHXExhuUwBtcFZyNK/XAk59OpXPYh5pwjjZjpbKl1RtdH4SwIZ1aK73V7NTz47R7RNp9rKzu9ZhsFLg1Ntk6B7fG7vtBzhjsbBC4rJzpvR7g1igygxTZBtW3xkp+Fxlph2irCcJPL1+hAbzk6RCmI/ShXjCd05EPW610aZT0xE8YsvkQ5if70MAtTOflw1bmjLDVA2VrnNockdocdiIfGCihZr6e9hW5jghuRxTftXp9oWVpGmpTDKlvo66mg2Kp3lbRTj0/hbDVnov2+kKFZliyndE1LYysasW2TEkhrHO9o+21HGit51iqpkv46eGV1MayDKluZ2hVOxG7xFDUKfw0p5Ica67lZEs1nu69EWQ5PtG4RyzhYtklrjOFyVTQs+W2RPFaoxiv9yHvxjaQ9IO/aN/XUSujcI7bOCdt7HTv9bRlcGsNbp3BK+zm+1BP+eC0B2HLyoLq5cUGgx8PApgfL7FWp3r4EG80JE4F235J+5kEuNWKXDV9O5lV6PnpCMLWmSeReqqnnSCE+fE+hr78yhxpC8JW/JSH5fV+LPQjCrfOIVcXQUf7cOalMMoj7RE7kSF6KoOd7f2YbSyFXxVFV+VDWIn1bFvh+4bBg6u5eekMli6exqUTh/U6tLCjPcu6ZTtZ/uutbH3lLbQ2pbXX8m2QZHWMG++YzaI7ZjNj3oQLrr32fiLBK0QXevDqrOVUG6t/tYXlj61lx/q3UaguY9OLw+ziERbeOYfF917D3MXTiUT7Nwo1ncqw/unNLP9/V7Pht1vxva5ncgo7FMu2mHvz5Sz9+PUs/PB8kjWJftXzPZ9XV7/Jyl9uZvUzr5093FKBUkHNKVeMY8k987j+jitoGNq/780YwxvbD7HiuW0s/+3rtDZ3E/o6h61Cz9YAhhPomIM7KIk3OInp7kBQCFtZF/twM87RZlRrut8nJHXcwR3TgHvJIPyGZDf1OH2GtN0PerYyvYetHutZ4NY45Ood3KpzDDExQchyMga7hLDVE6MgV5sPYfWq+8ZK50ZJqwnCTz+/wqCnLx/CGhSmu2FC+f+3M/mw1QK21996Br/akB2uyY7w0d31vHVq6JG2URkL5ff/t6Iq4hOpcYnUZbFi3QSGfD2jg7CV64jg52xKb5l25dg+g+raGdLQRnUyE8xD8R+detIMNOfDVls2Rn/XGltphle1Maa6mWFVbWd1DnfuaTqRquZAawNH2mtLCj/dM9Qn0gytamdIVSof+jot007hp7UjzrHmWo631OB6/f31gMGOnO4Js6xuQlEhMGetIGy1RDFu/89wG0cHAaxKg9NzPZUD54RD5ISNlVK9hp+eaCcIYbk6E/RM9bRN6NNhy870HrZ6/HyqUwjLjyLvvp4h1gSJUxBr6j389FiP4GRPrlrhVtPjPhTAyUCkLQhbA9mv6Ui+JyxO92NzCz1NHT7x40HYsnP97D0GdMwiV+uQq3MwkW5CWGGUR9Yvhi0nXcJZqx5o20JXRfCrY5jI2T1vhWN/TXWcJYunsXTJdGZMG43Vz3HKLY0pXnl+O8t/tZUdW/b13F6LOSy8eQaLPziHOQsvu2jaa+91ErxCdDEFr85OHGrkpSc3suzRdbyz/UAwzO6mWSy+72quvm028WSs9zfpg7amdl55cgPL/u/LvLZyBwbDzGunsvR3buD6e66mbkh5l10u47JxxU5WPLmZ9S9ux3N9xk0ewZJ75nPjh+YyvFNXfzn4vmbb5n2seG4bK57dRjbjgtaQzgWBy+9/GOmOAXQiglcIYREbXB/nSHMwjLApVfYh+H5VNAhh4waha+JgDE7Kx2n3cTr8stfTNsGBtd7BSwYHOssFJ6ODsFXmvZW2IFenyAxW5GpV/gypIdYS9GxZ/Qw/PTFArvZ0CMMKfr8Raw56tuxcuesZvNrTPWEmAmiKYQuv/w3ZnlhRP+gJq8thRTRGg5sOwpaXdRj42NKuohGPQXVtDG1oI5nIYQy0ZuI0phO0ZOOYXnua+iZi+YyoamVsTTODEykAGtNJ9rc2cLitjpxf3p9OK6UZlEgztLqNQYkOLAtSmShHm2o53lJL1o2UtR4YnGjQExaNuygLjKtwW2J4LRF0tv+BuftqBiL5nrAqH2zAg8iJfM9Wq1X2ddSPBL1guTpTPDHhpMBpt7A7+h+2emIsgxcHPwk6ChhDtEWROBmErlJ6tPtUT4FbFYQwL0mwX8saom2GSHsI9QA/mu8JiwGWws7ooGfrpIuTKe+O2wB+Ighhbm0E4yiUq4mdzBA9mcFJeeU/NkUs/GQUXR3DOBaRiM2SRdNYsngaV84Z32tPU1+dONrCS8+8xvJfbuWd3UewHYt5N0xh8Qev4OrF07r8LKIcKt1eey+S4BWiizV4dXZs/ymq6hIlDbMrh6bjLWhfM3hkQ0XqpdoytDWlGHHJ4IrUW/3Ma/z9H/9n2cNWT7Tvo3MZVEe27GGkJ96Vk1FGVaxeZnAE41gVq+fGg4tx9Lenqa98B7ykwsqVO4p0z00YWicZ8Msftrpj0FDvo73ehxGWS9WQdrRS+GUOWz2x8VEGsn65w0/3lAe2b8i4lRneo3IQySlMrkLfoWdINAVDCpWpzDqTqzIoTcXqxRs1Tqb8J3V6ovNfXbnDVk/sNpdIu4ud6f+ohL4wng++h50p/4nAbusBxrH44tfv5pZ75lWgIhw71ERVTZzq2sr0NFW6vfZeUWo2kKsavk8Nr1AgKWgYVlfRelU1capq4hWr5zjWgIYT9pWCkn67VU6Wa8CuTOMEgiEwYVxQssd6ftCwrRRlyt/Ddc56KJRfue8PVJcLZVRCzndQduVWmpzvBN0LFeJrC3cAw/v6TmFylaunUNjpSq6jYHmqUucFALDdyoUu6P9Qwv5ShrL3cJ2zHmBnKpQq8/WUp3EqeCGJ4aMrG4Aq3V57v5H7eAkhhBBCCCFEyCR4CSGEEEIIIUTIJHgJIYQQQgghRMgkeAkhhBBCCCFEyCR4CSGEEEIIIUTIJHgJIYQQQgghRMgkeAkhhCg75Rqq9ro4rRW61LNvSLyZI3rYg0rcntIYYoc08QO6MvWA6Emfqn0uyqtMPafZJflGC6qCl+uuKE8T3deE3Zg+33MihHifkPt4CVEGDUNrUSq4H4wxJrjLYkiUAiyrWMKyFDrkG15ZtoV2XbBjWLZC++HWs20LfAOR4L/9kO+RZtsW2mgMCttS+GEvT0uh8411S53+77AopbC8YL1U+XsWhVFR+YbkQZeaPVmqDuSKN21ND7dpmRqlbVIUP1nG833GkNyfpXZHB7U7OrCzwadyh9q0zo/RPjeGN6SM99sxhuhxQ9V2Tc02jdMaPOxVQdtMm7bpNtkR6vRCLgOnWVOzzaXuNY/o8WA78KPQOiVG6/QoqbEOWOWrZ7V71L7aQu3GZhJ7OlCAsRQdU2ppu7yBjsl1mEj5z9kqACu4LZoyIW4X2hA52Ez8zZPE9pxCecEydYcmSU8bSnrKEHRtrPx1AdtSaBtswLYsfB3yfq1TjUrt14yjiv8d9nFJKYXJr/vBJqeC429oBfM1tWHQsJrw6oj3NGVCXUvfm0q9O7V4fzlxpJmXfrWF5U9s5J2dh8t+4Cm832Uzx7DknvnMvX4Ku9a+wbKfrWHby7uL05Vrk7YdC9/TjJgwlJs+fh0L75rP4RPtLH9+O+teeQvP02X9jIWAVVuXYOmts7hx6TTS2ufFl3ez4pU36EjnsG2FX6bQV2iIxGMOixZOYen1U6mpS7B83Zs8u3onjS0dZQ19hYakbVssmD2eW6+dxvgxQ3jp1T08s3Yn7x5tKnvjSCkFxnD5ZaO4fcE05swYwyuHDvDUrl28euQIlgoaKgOqqA2JIy417+So2ZfFck83nk/PyOlOoY6xDq1TorRdGkXH+hEYjCF+xKV2R4q67R04KY2xQJ3xNRXmITvGpm1enPY5Ufy6/oUwp9FQvV1T87om0mjO/nyd6rl1itZZFm3Tbdwh/QsodrumZodH7Wsu8YO6+3r5z+wlFC3To7ROi5Eeafcr9FkZn+rXW6nb2Exyd/vpgN65pgVo0BGL1PQ62mc10HFp7YBvql7YLmYMH8Zd06dz/bhxbHv7CL/ZuIvNbx88/XkHspIag3OkjfhbJ4m9dRIr6wVh9YxtrbCcc6NqSE8bSmbyYHQyMoDCp/czDVUJ7pgzldtmT8Jt9XhxzW6WrX2Djoxb1u2+835t8dWTuWnhVGpr4qxY9ybPvryTxuYQ92tXjOeW66Yxcfgg1q16gxd/+zoH3z1V9pN1SikMhhmzxrL09llcPmccr619m+W/3MLu1w6gLFXWE6CF+b/ksmEs/fBcbvzA5QwfM6g8by7eM0rNBhK8+kGCl+jNwXeOs+pXW1j2+EaO7DsZ9Bj140BXeN2YicNYcs98bvzQXEaNH3LWdI1Hm3np8fUs+9krvLl5b78DUSFsNQyvY+nHrmXR/Qu47IpxQQO+k1Qqy5qX3mDZc9vZsmkvRpvgYNjH3UmhAZBMRrlx6XQW3zyDy+eMC3q8Osm5Hhu27uOFl3axev1b5Fy/X5+x8BrHtrj2qsu4+YZpXHPlRGLRrp3/Whtee+MQL6zZzQtrd9Peke1X46jzYps7bSy3XjeNG+dNorY6fta0bx88wXPr3+CZNTs53tTe78ZY4TNOGjuUDyyYxs1XTWH4oLPPzh5qbeXp3W/w1K6dvHnyFLZS+KV+f8YQP+EFYWtPFjvbfRjpViGEWdA+LkLrlCjtEyKYyLkb8NGTLrXbO6jbniLa7Hcbtnqc3XyIyEyM0DYvRmp2FF117lBktxqqd2iqt2liR/vw+egU+oaqYk+YV3fuz2elDdW7grCV2Bt0FZ4Vfnqql18Wbo1Fy4woLdOiZIeee0CLcjVVO9uo3dhM9fa2oEc0H656r6dQ2uDHbdpnNdA+q4HMJVUl97wV1rUJDQ18ZMZ07pg6hXEN9WdNd7I1xfNb3uQ3G3exY/+xvvWCGYNzMkXsrZPE3zyJlcphlEKV8vrCOqogd0ldEMIuG4SJlTZIqLDtVsWi3DZ7Mh+YM4V5E8dgW2fv19a/to/nV+/m5U1vl2W/dt2Vl3LLddNYMGdC9/u13Yd4cc1uXnglv1/rx8msLvu16WO59fpp3DC/+/3a3rePseL57bz4zOucPN7W79BX+IwTJw3npg9czo03zWDo8LPbX8cONfHSM6+z7JdbePetY/0OfYX5HDaqPghbd85m3GXD+/w+4v1DgleIJHiJUhljeGfnIVb+cgvLH99I4/HWXg88heeHjKxn6d3zuPHDVzJh2qiSax7dd5yVP1/Hi//3FQ68cbjX0FcIW9X1SRbdv4BF9y1gxsJJWFZpZ+ubm1K8vGI3Lz63jZ3bDvYawAoH0EjU5tobprLklhnMu/pSIpHSeiPSmRyvbNjD86t2smHrXnxtztkgK8yPpRRXzh7HLTdO4/qrJ1GVLG04kef5bNy+n+fX7GLFhrfI5rxeQ1FhfqZNHM5t101n6TVTGFxfVVI9Ywzb3znCc+t28+z63bS0Z3qtV3h+zLB6PrBwGrdcNZVxIxpKqgfw9qlTPL37DZ7YuZNDra3dhzBjiDb51LyTpXZPLuhp6kMY6VahF8WBtksjtE6OkbrEKfaiOM0edTs7qNvWQeyEO/B6nRrUHVMjtF8ZIzUrhsn3vFkdhqpdwTDC2H4TDC0a4BGyMM+ZUYrWmTbt02z8qvzwqJyh6k2P2tddqt70QZcetnqrlx1k0TIjRsvUKG5DftvyDck326nd1Ezt1lasXPe9hX2qlw9hXrVD++WDaLu8gdzIxFk9b4V1dER1NXfNmM4Hp01hytChJdc5dLKFZ7e8wdMbdrH3WGOP24TdnCb25knib57AbsmUZZ3BBJ8zO7EhCGET6uGM/VVhm4k6NktnXsYdc6Zw7eTxRJxS92suL29+m+dX72b9q33br82bdQm3XjeNG+Zf1rf92rb9vPDKLlas79t+berE4dx2w3SWLujbfm339kMsf247K57bRmtLuuRj4agxDdz0gctZdPNMxowbXFI9gP17jrPqN6+x7KktHDvUVHK9usFVLPngHG68YzaTZ40568SjEN2R4BUiCV6iP7TW7Ny0l1W/3MLKX26hvdNQtkL4qW2oYvFHruTGD1/J1Dln9zT11b4dB1jx2DqW/ewVThw4VaxT+HcsGeX6j8xn0f0LmbN4Ok5kYD/7PH60hZXLdvLis9vYu+d4MWQV/m3bFvOunsiSW2ay4PrJJBLRAdVrbUuzat1bPL9yB6/uOHj690vmdMNk5tRR3HLjdBYtnEJDXXJA9TI5lzVb9/L8K7t4Zes7eL4+6zOOHzWI266fzs0LpjJqWN2A6nm+ZvPuAzy/fjcvbHyTdPb0sKTC7zeG1FfxgQXTuOXqqUweO3RA64wxhm3HjvH07jf45a5dnOzowFZQ83qaujcyRFvLELZ6qp0PAH5UkWnQOG054kfLELZ6kg99xoH0ZTFUNEbsEN0PsyuHTqEvMwKM0iT2+1geJfc09VUx9A0C100R39uG3eEXw1LZ6xV63hqitM4ZTPuNI/GUoT4e58PTp3HntClcMXLkgPdrbx8+yW83v8FvNu7iaFMbtqWIvH6ExPajOKc6QlxnguGJxrFITx5Mx4KxuPVxbEtx7ZTx3DlnKotmXEoyOrDhia3taVauf4vnVu9i686DxfXRcHq/NmvyKG69fhqLr5lMQ+3A9mvZnMsrW/by/OpdrNnSw35tdLBfu+nage/XfE/z2pZ9rHhuO6te3EkmnTt9LMz/e/CQapbefjmLb53JxEnDB7xfe2v7IVb95lWW/+pVmk+1n1UvWR3jxjtms+iO2cyYN+GsURdC9EaCV4gkeImB8j2fV1e/ycpfbua1NW8x5/opLLrrSi5fMCmUHb4xhjc27mHFY+tY+/RmLp09jiUPXMtVt80mNsDw05MD755ixQvbWblsJw2Dqlh66yyuWzSV2tpEKPVONraz4pU3eH7VTjxfc8uN01hy7VSGDw1nG23vyLJq09s8t3onR0+2svSaKdy8cCoTx5w9FLQccq7H2u37eHbdbra/c4TrLp/IbddMZdalo7DKeHGFAm0MGw8e5PG1r/H619aW/f3PqbWdSp5jNjVJGOBJhz5xffB1xT6jaU9hGpsrVO20qd+7k4/euoCrx549zK4cjDFsf/cov1m3k2Uf/1HZ3/+cFFTfNJXf+fr93DxrEnXJs4fZlcPJpnaWrX2T517eie9rbr1+GksXTGH4kPD2ay9tfJvnXt7J0ROtLFkwhZuvncrEsSHt13Iem9buYflz29i9/RBXXzeJJbfOZNqsseHs17Rm+6Z9rHr6NTa9/AbT545j8QfnMGfhZUSicr050X8SvEIkwUsI8X5x7HATD978rYrWVK3tFa1naqugxCFhZeH6qJCv1NnZ+QpeP9r4/2fCzLGh18llctyR/J3Q63TmRGzu/rM7+PQ/faKidYUQF6ZSs4H0pQohhBBCCCFEyCR4CSGEEEIIIUTIJHgJIYQQQgghRMgkeAkhhBBCCCFEyCR4CSGEEEIIIUTIJHgJIYQQQgghRMgkeAkhhBBCCCFEyORucUII0U/HD5xi1c/XcfJQIws+dCWzrpsayg2wC1pb06xesYs33zjKvKsnctU1lxGNhbcbz+Zc1mx5J7T374mBit5AOagoyu2V7XsZMWkEiVgktBqu67N23Z7Q3r8nnqfZsfswR442M3JEfai19u0+zEu/3orva66/cw6XzhiNUuFtIScONrLyiQ2cPNzEwjvmMHPh5FD3a21NKVb/Zitvv76fuYumM3/JDKLx8NYZIc6n990NlMePH8+7777b5bG//Mu/5Jvf/GbJ7yE3UBbi/av5RCsvP7GBZT97hV3r30ZZCstS+J6mbkgNSx5YyKL7FzBl3sSyNI7SHTnWrn6TZc9tZ/OGPfi+wbYtfF8Tj0e4Yck0Ft88gzlXTsB2Bt448jyfDdve5YXVu1m54S2yGZfk/jbsdg8INxAZBcqAyWVRWRcMKEVouahQz6+KoeIxVCHxhXVUzL+3NhrlayzPFOchrHrGgMm5mKZGVNYLtx6nQ7M7rIqWpZcRSUZZdMWl3D5/KgumjyMaGfiJAt/XvPb6AZav3MmKVbvpSGWxt78Dx5qAcNcZVLBQTTSCvnwienAtUyYN5+YlM1h8w1QGD6ouS5kj755k1a+2sOzxjRzccxwrH3y0rxk5bghL75nHjR++kjETh5WlXvPJNl5+ahPLH1vLro3vnL1fu+9qFt1zFZPnTijPfi2VYd1z21jx+AY2r9yF9vXp/VoyynUfnMuij8zjiuumYFfy5uZC9FOp2eB9Gbz+4A/+gE9/+tPFx6qrq6muLn1nKcFLiPeXVGuaNb/axPJH1rB1xQ6MMSilMPrs3aftWPieZtglg1n6sWtZdP8Cxk8f06d6uZzHpnV7WP7CDta8/AZuzseyFLq7evnGSk1tnMU3z2TxzTOYPnMMllV640hrw2u7D/LCK7t5cc0btHdksS2F36mecjWRlhyR5ix2xi9br1QhCOiIIj0iRnp4DK/GQbma+JEOEgdTRE9kgnkoQ71i+HEUqfFJUhOryYyIg4HEcZeqg1mSR93yhbB8+EFBeniE9rEx0sMjGAviJ1yq3s1QvT9T1hBWeAu3PkJmeJzMsDjGUdiNaeJ7m4i/04jV4WKUQpWhCVCYb68+TubSQWTHD0JXR4vPF9alZCzCLfOmcOv8KcybPAbbKv1EgTGGXbuPsHzlTl5cvpOWlnRx3S/KuajjTVhHGlEt7QP+XEWFsOXY6JGDMCMHY+qr8wmv+C8AZs8ay81LZnDDwsnU1MT7VKbxWAsvPb2V5U9s4q3XD/S4zQPF5yZMG8WSu+dz44fmMHRUQ5/qpVrTrPnNFpY/tp5XX9p1zv2aZVtoXzNs7GCW3H8Ni+65ivHTRvepXi7rsnnFTlY+sZG1z72Om/WK73umwn60pj7Joo/M58aPzGPavAlYfVhnhKgkCV49GD9+PJ///Of5/Oc/3+/3kOAlxHtfNp1jw29fZfmja9jw21fxXB/LVmi/9F1moVExdsoobvqda1l03zWMGN/9GWrf17y29V1WvLCDVct2ku7Ind2w7EVh+sFDqll66ywW3zyDSycN7/YMtTGGN/Ye54VXdvHc6l00NneUXE9lfSItOaLNWayc7nsIK4QfW5EZHiMzPEquPtK1BduJlfGJH06ROJAi2pTtdz1jQcfYJKmJVaRHJzA99BAqz5A8kqPqUJb4Ca9fPW+FSbNDHdrHxOgYGUFHe2g0+obE0RzV76ZJHspi+fQ59BWWiVfjkB4eJzM8jo710FNgDM7xFPG9jcTeacLK+cVwUXK9Qm9hdTQIWxMG4df1HjQKIay+OsHt86dw6/ypzJowosdelL37TrB8xS6ef3EHx0+0lr5NZHKoY41YR06h2tMlf66iQtiyLMzwBvTIwZjBNdBLw78QiGxLcdW8idy0eDoLr7mURDza7fRtzR288tvXWP7EJravLwyZNKV/FYpiWJp25QSW3D2P6++8groeet6y6Rwbnn+dFT9fz4bnXx/Yfm3ySJZ+dAGL7pnPiHFDu53W9zWvr3mTVU9u4qVfbiadyhZDVakK0w8aXseSe69i0UfmMXHGmFCHWwrRVxK8ejB+/Hiy2Sy5XI6xY8dy33338Rd/8RdEo93vFAGy2SzZbLb4/62trYwdO1aClxDvMZ7rsXX5DlY8tpbVT20k25Hr8YxsnyiwVNAgm3zlBG76+HXccM/V1A+rZdeOQ6x4YQfLn99Oa3dn8fup0JgaNaaBm26bxeKbZzJm7CD2HTrFi6/s5rcv7+TI8dazerb6xBisrE+kOUekOYd1rsZUp/CTGRolMzxOdlAE+tAzB2CnXOKHOkgeaMdpc3vuJerc0zQqQWpiFR2XJDGRvp0xt7Ka5JEc1QdzRBvPPdyyEH6yDTbtY2OkRkXR8b7VU54heThL9b40iSO5c4a+Yk9T0g56tobH8ZN9HMqnDZEjbcTfaST2bjPK0z2HvnwY0XGHzMRBZCcOwhuU6DEw96aw7g1vqOYDV0/jtvlTmTR6CEeONLN85S6eX7aD/ftPnbPnpySpzOkQls6e4/MFjxulMEPrgrA1tB76+fumwnxHIzbXLZzMTYumMf/KCfiux7rnt7PiqU1sWrkb7euBf0ZAWarYazXnusksuXs+C26dRTTmsHXlLlY+vp7Vv9pCNh3Cfm3OeJY+sIAb7ppH/dBadm/Zx6onN7LiiY20Nqb6HLZ6UpjvUROGsuTeq1n0kXmMLtNwSyEGQoJXD77zne8wd+5cGhoa2LBhA1/+8pf58Ic/zL//+7/3+JqvfvWrfO1rXzvrcQleQrx3vPLLTXz7s/+H9uaOsjUSulM4S2sSMSLTx5Pt9JutsFiWwsdgxlaR1uVp5J3FGOy0h9OcI9qSQ/nB0LlCGskOzg97GxIFuzxnqp3WHPFDKZIHUtgd3ul6QHZYjPZLq+kYl0THy/MbETutg1B0MEe0xQ/qEYSfXI1N+9goqTFR/GR56lk5TfJglup9GeLHc/li+Z6mmEVmRBC2vCqn3+GnC08TPdRC/J0mogdaUNp0Ggpqk53QQHZCA+7w6vLU68TO/55o8Cnw2tzQ1lHa0ljHTqGONga/I+zU0WcG1wZha1g9lOG3aJ3ZtsL3DYnmDuxjrfiuX57w04PCiRfLaCzfw824FdmvoSBanSSXdSuyX9PaMGn2JfzDz/+MqtpEaLWE6M37Knj1FIw627hxI/PmzTvr8ccff5x7772XkydPMnjw4G5fKz1eQrz3/c8//y+e/j/LQmsInckMrsNcMqIitQD8qCIzvG+/Oek3Y7BTHvguXpVDZmi0zz1Nfa0Xac4ROZnCT9qkxlfhV4V70V6n3Sd5OIcyhtSYGG5NuBcAsNM+NXuyRFt9skNjuLU9D80sB5Xzie5vxjnVgTuqltyo2j73Tva5pmuoPeCGWqPIGGhuR51shmQcPaIBouFfSS/2zgmsjlzodQpMNguuV7F6WBaqzKG1FD9c8ddM6ONvzoQop1KD13vicvIPPfQQDzzwwDmnGT9+fLePX3PNNQC8/fbbPQavWCxGLBYb0DwKIS58ylLgn++5eA9QCq86QnpYhfabSuE2xEiNi2Eq9Nt7r9qmZUqiYte99xM27ROSRFOVqWeiNtlLB5O9tPvj4kVPKWioQQ+urfS9C4QQ72PvieA1ZMgQhgwZ0q/Xbt26FYCRI0eWc5aEEEIIIYQQoug9EbxKtXbtWtatW8fixYupq6tj48aN/Pmf/zkf+tCHuOSSS8737AkhhBBCCCHeo95XwSsWi/Hoo4/yta99jWw2y7hx4/j0pz/Nf/tv/+18z5oQQgghhBDiPex9Fbzmzp3LunXrzvdsCCGEEEIIId5n5BbgQgghhBBCCBEyCV5CCCGEEEIIETIJXkIIIYQQQggRsvfVb7yEEKInlqUqdvNkIg6Wp+Hdo5iqOLo6AbFoODfE1RpaU9gtbVRtTeMPrcYd04A/tAasEM69GYPlGqyMT/yUxotbZOttcrU22CF8PmOwchBt09Ts1/gRyAy2yDZY6Eg4N2iycoZYiyHWrMFAtsEiU6/QsXDqKdeQOKlJHvVxsoZcjY1bY+HHVEjrjCHa5hNt9nDSGrfGJlvr4FVZ4dQzhkirR/xEjkizi3Es/JiDidqh1VOpLFZjG3ZTOzoWQQ+txR9UDU64N8IGgvuGmfDLBLUqe5OyCpcrss5XYSH6SBljKrX5v2eUendqIcTF49DbR3nsX55m1S/Wk27PYDsWvlfGIOY4qKgT/LubRoKxLUx1MghhUWdgLRhjoK0D1dSGam4LwlehsacUGINxLNzRDXij6/EHVw+4nvIMdkbjZHxUN4vNKMjVWGTrHdwaC6yBNZSsnCHSrom2aWw3/9EKtfL/dmsU6cEW2XqFcQZWT7lB2Io3a5z02fUU4CUg0xDUG2joU54h3qhJnPCJNQWf6Mx6OqLI1Vjkai10dIAhWhsiKU202SPa5qMKRTq1ELQNuTqHbK2NnxhgCDMGp90n3pgjdsrF8s1Z9QygozY65mAiAw99Kp3FOtWGfaoNlfM6bRPBJgOg66vwh9SgG6rBLs+JCctSaG2IxRzmTBwOx1rYsfZtPNcvPldOhfe0bIuZ8ycQtWD7mjfD2a8BKFBKYbRhypUTqB1Wx9vbDtB8oi2ceoBlW2hfM3bSCG55YAEf+exS7DJ9X0L0R6nZQIJXP0jwEuK9K5d12fT866x4dC1rn96Mm/WKB/k+c2xUJIKK9C1ImYiNrk5iqhMQKXFggjHQkUE1taIa28D3ez+zXghhUQd3TAPu6AZ0faLkeVWezoctjfJLOJTk58dYkK21ydXbuNWlN6iVZ4i2a6JtBjtruoSfnuqZ/ES52tMhrNTQp3xDtMUQbzY4qa7hpyeFpeBVKTINimxdH0KfLoQtTbxRdxt+uqunAD+qyNVa5GpsTKmhzxicDk20xSPWkg/MJdbTERV8h3UOfrzEBq8xOB0+sUaX+KkclmswiuBz9vZSBTrqoGM2xulDCMu62IWwlcn1Pn1hHVUKPagKf0gtuq6qzycKCuHHcSwWXHUpNy2ezjXzJxKLRQBItaZZ89w2Vjy5iVdfeTNYUfPhpT+CxaEwGGZdfRlL75nHwttmU1OfBIL92uZl21nx8/WsfeZV3NwA9muFz5h//YQZY7jpgQXc8JH5DB09CACtNTvW72Hlk5tY9dQmUq3pAYewwuuHjm5g6X1Xs+iueYybOqrf7ydEOUnwCpEELyHeH9LtGdY+vYXlj65h8wvb0L5GWb00jmwbFXFQ0UhZxt2YqHM6hHU3DCqdzYetVnC93sNWT/Kv08ko7pgGvNEN6Jr42ZP5BjvjY2c0lldC+Omtnk0wFLHOxkue3aBWviHSboKerUxp4edc9YyCTIMiM8giV9tNCNOGaGvQsxVpO/3S/jjd85bvCatVZw+3NIZYsyFxwid+UmOVEH7OVU8BXlyRqw1C7VmhzwQ9k7GWYCih5Q+8nh9VZOsccnV2tz1vdsYndioIW3ZW93+dKdS11OmeMLub4Zauh93YjnWqFSuVHfA6amwLf3A1enAturbnExNKKYwxWEoxd844bl48nesWTKKqKnbOMs0n21j9m1dZ9sQmdm/ZF+xjjCnpOykEvEmXj2XJ3fO44c45DBped87XpNszrPvtayz/+To2L99R2n6tUC8ftkaOH8rSBxaw6J6rGHPZiHO+xnN9tr60m5VPbmT101vJZdySQ18hbNUNrmbxPVex6K55TJ4zrttRA0KcTxK8QiTBS4j3n9bGdlY/tZHlP3uFba+8ERz4jQl6VCwLFc33bIXxuynybbB4FF2dwDg2tKawGlshW8JZ/L7WyvdC6Jp40BM2sh7LcnAyftBLwcAazmfVy7+fdiDb4JCtsbC0ItqmcToGELZ60in0ZQZZZBqCB2PNhlirKamnqT9MvuctUx+8deKUJnlCY3nlr1fseUsqcjU2fgRibT7RFh87xO/Qi1tk62y8uEW0zSV+ysXp8Mter1jXUuiYjW8rrNaOoGerLQ3lrlcIYY6NP6QGf3ANpjoOShUD14ypo7hl6QxuvG4K9fmepr46fqiRl369lWWPb2Tf7iNYtkKf0ZtcCC1jLh3G0nvmc+OH5jJy3JB+1Wtramf1r7aw/LF1bFv7Jir/QTu3DAvhZ9DwOpbcfw2L7r2aS2eN7Vf4yaZzbHhhOyue2MDGZTu6HW5p2xa+r0lWx7nhritZdNc8Zi6YJEMJxQVNgleIJHgJ8f526nATqx5fz7KfvcLb2w9hJeMQWtOyK+35kOoIvU6xnm2h507CWCrfKAuXAXTMHvBvwEqup4IAFsqFP7qjwMpo7Fzpw+wGymiD7Q+8p6lP9dIuVKgexsC7R0CboDMq7GWar+GOG8q4BZO5Zel0Ft8wjeHDytseOPD2MVb9agvLHt/I0f2nABgysp6l98xn0YfnMr7Mw+xOHWnmpac2suzRtbz92n4AquuTLLrnahbdcxXTr74Uq4wnllKtadY88yrLH9/Aa6+8idGGSMxh4e2zWXT3fK5cNJ1IVK4BJy4OErxCJMFLCFHwv//25/zqxyvL/gP5nhjXw3SkK1ILgqGO/pxJFasH4MdDuppdT/UiqqI3V7EyGtut4KFX6+DiFZXia+yMV7l62qD2Ha5cPYJep5v+f9fzhW9/IvRaxhj27DiE7/lMnn1JRYbZHd57nJOHm5hx9WXYFbjSY/OJNvbsOMCM+ZcS72VophAXolKzgZxKEEKIAagdVI2yLND++Z4VIUSFWJaitvrs30CGQSnFZTPHVKRWwagJwxg1YVjF6tUPreHKRdMrVk+I80UGzAohhBBCCCFEyCR4CSGEEEIIIUTIJHgJIYQQQgghRMgkeAkhhBBCCCFEyCR4CSGEEEIIIUTIJHgJIYQQQgghRMgkeAkhxIDJ7RCFeD+RLV4I0R8SvIQQYgCSNXF8T2M7ldmdWlZw81TLrkw9WykwpiI3bQXAmK7/rkS9zn+VKXq6dgUogu/OrtA6U1xHrcqsM5ZjYRQVW0ctx8J3fapqExWpJ4R475AbKAshxADc+Xs3MmrCMFY+uZHVT28ll3GxbAvt67LVKLzfyPFDWHrv1Vxy2TC2vbSLFY+upbWxHdux8L3y1Su8X92QGpY8sJDpS2ay50gLLzy/nWNHW7Bthe+XMTQYA0qBr1GNrTgtKUwiih5Rj6lOnH6+nPUADFiNrdgnWjERC39EPXpQVfBcuRvx+Zp2Wxb7ZDt21kfXJNA1SQghoFiWQmvDhPFDuOmmGYwYXsfWzXtZuWwnHR05bNvCL+M6Wni/IUNruOmWWUyZNJw3th1k2TOv0XiyPbR6VdVxFt06k9lX3MXhXQdZ9ug6Dr51NLRtMBJzWPCBOSy+92rm3zyzbO8vhHh/UMZU7BTfe0Zrayt1dXW0tLRQW1t7vmdHCHGByKZzbHhxOyuf2MiGF7fjuX6xAdxXhfAzaHgtS+65ihs/Mp9LZ47pclbf93xee2kXKx9by6pfrCeTyvY7hBVel6xNsOi+a7jxvmuYdd3ULr0kxhjefOMoy5fvYNkLO2hqSvW/QV0IU1qjmtpRp1pRze2oMw5JOhFFD6sPQlgiNrAQln9v1ZzCPtaMdbIFdcayMlEHb0Qd/qgGdF1y4PWUwkplcU6045xKYbl+10ksFQSw+ip0VTx4sJ/1CoF4xIg6brl5JosXT2PcuCFdpnFdn80b32HZizt45aU3yOW8/q+j+e++tjbBkptnsGTpDKbNGN1lHdVas/O1A6x8bhsrnttGe2um3+uMZSu0b4jFI1y/dDqLbp3FnKsm4kTsLtPt23mIlY+vZ9lj6zhxsLHf9ZSlMNpg2Rbzls5k8X1Xs+D2K4hXxfr8XkKI97ZSs4EEr36Q4CWE6E2qNc2a377Gisc38OrqN4qNcHOOBm6hgVhdn2TRXfNYdPd8ps2bgGX1PkQsl3XZ9PzrrHh0LWuf3oyb9Xo96194PhqPsPBD81jywELmLp1JJNr7YAitDdte38/yZTtZsXwnqVS2tAZuIfy0pFAnW4LQpXtvFBvAVMXRw+vwhzdALFJaKMpPo1o7grB1vAXler3WA9DxCP7I+iCEVcf7VM/KuEHYOtmOlS2tnrEtdG0Sv74Kk4gGD/ZSr7DMGxqS3HTTTJYumc6kScNLGnaXTudYt+Ztlr+wnQ3r9+D7utcQVgh3iUSEGxdPZ8nNM5h9xbiShjH6ns/WDXtZ8dzrrF62k0y6997hwvw4jsVV101myQdmc9W1k4jFI73WM8bwxua9rHx8A8t/vo7WU733DgeLTWEwzFo4mSX3X8N1H5xLTUN1r/WEEO9fErxCJMFLCNEXzSfaWP30Fpb9YgO7N+8tnkmH0+Ennoxy3Qfnsugj87jiuinYjt3Lu/Ys3Z5h7dNbWP7IGja/uA3t69Nn7/MNWduxmX/r5Sz+6EKuuWMO8WT/z+J7ns/mTXtZ9uIOVr/8BtnsGb0ohbDVng7CVmMbyvPP8Y7nZgBTm8QfVoceXg8Rp2soKoStVAbraBP28RZU1u13PQBdFcPLhzCTiHZfL+fle7basTpcBjKA0Dg2fl0SXV+FiXetVxxmVxVjyZLpLFkyjVkzxw7oN1XtbRlefmk3y57fzmuvvnt6Pszp8BOJ2Cy8bjJLbprB/KsvJVpCQO9JLuuy4ZW3WPHb11n38ptdeoeVCoKPUoor5k9g6e2zWbhoKlU18X7X833NtlfeYOXjG3jpyY10tGWwHAudD2GF2pOuGMfSjy7g+rvmMXhEfb/rCSHeXyR4hUiClxCiv44fbOSlX25m2S/Wc3DPMa655XIW3T2f+UtmEC3hLH5ftTa2s/qpjSz/2SvsXPcWs66bypIHFnLth+dRXV9V9nqZjMv6dW/zwvPbWLv6LVQ6G4StU60l9zT1hVFg6quDEDa0Dlwf+1gT1vEWrI5s+esBujZR7AkzShE5mR9G2J4dUNjqiY466EIIizjcdHPQs3XlleNxBhDQe9J4qp1VK3fx4nPbePutY1w5fwJLb57Jwmsnk0hGy14v1Z5h7ao3WP7b13h1w16mzBjNktsv5/qbZ1DfUP511M15bF62nRWPb2DN01sZfslgbvroAm685ypGjh9a9npCiPc+CV4hkuAlhCgHU8mrBVa4Xi6T486GPwgliPSkcDCrWM1kHBWJVKyeAWobqnhs1z9XqOJ7ex09H/WEEO9NpWYDuaqhEEKcJ5Vu8FW8XkWrnZ96laxZ6XrwPlhHJXQJISpI7uMlhBBCCCGEECGT4CWEEEIIIYQQIZPgJYQQQgghhBAhk+AlhBBCCCGEECGT4CWEEEIIIYQQIZPgJYQQQgghhBAhk+AlhBBCCCGEECGT4CWEEKLsnKjDpLkTALDscA81hfdXljr97xBvz6QsFdz/ydcUbgNVqB0WK//+MxdMCrWOEEKI8MgNlIUQQpSdZVl8f/XX2Lv9ACsfW8uyn73CyUNN2I6F7+kBv7+yFEYbLNti7k0zWfrRhVxz51zaGlOs/Pk6lv2/q3l31yEs20L7A6+HCm62a7Rh8pUTuenj13LD3VdhOTarn97K8sc3sGPDniCQGYMxAy9ZmPfRE4ex9P6rufGueYwaP3TgbyyEEOK8UMaU4/Dw/tLa2kpdXR0tLS3U1tae79kRQogLnjGGXevfZuVja1nx6FpaG9v7HMKC3iWFwTBz4RSWfmwh1901n9rBNd1Ov3/3IVY8FoSwY++e7FfoK4SfcdNHs/Rj17HovmsYPm5It9OePNLMS7/czLJfrOed7QexLIXWfTvE2raF72uGjKxnyb1Xsegj8xg/bXQQ6IQQQlyQSs0GErz6QYKXEEL0n+/5vPbSLlY+tpZVv1hPJpU9ZygqhJ9LZ48LepruuZohoweVXM8Yw1tb9rLisXUsf+QVmo+3nrNe4bnh44aw9OPXsfj+a7hk6ug+fcZD7xxn1VObWPbz9Rzee+KcPW+FerUNVSy6ez6LPjKPqVdOkLAlhBAXCQleIZLgJYQQ5ZHLumx6/nVWPLqWtU9vxs16xd9saV8z6tLh3PQ717Ho/gWMvnT4gOtprdn+yhuseGwdKx9bS0drGtsJ6vmepn5YLUseuJbF91/DpLkDDz/GGPbuPMTKJzex/BfrOXW0JahnwPc18aoYN3xoLos+Mp/LF07CduwBf0YhhBCVJcErRBK8hBCi/NLtGdb+ejNrnt7MyAnDWXT/NUycdUloPT9uzmPLsu289MR6Yokoi+67hpnXTsGywrkYiNaa3Zv3svLJTbS3dHD9B+dy5eLpRGORUOoJIYSoDAleIZLgJYQQQgghhIDSs4FcTl4IIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQvaeCl7f+MY3WLhwIclkkvr6+m6n2b9/Px/84AepqqpiyJAh/Omf/im5XK6yMyqEEEIIIYR4X3HO9wyUUy6X47777mPBggX8+Mc/Put53/e54447GDp0KKtXr+bUqVM8+OCDGGP4/ve/fx7mWAghhBBCCPF+8J4KXl/72tcA+M///M9un3/++efZuXMnBw4cYNSoUQD8y7/8C5/85Cf5xje+QW1tbaVmVQghhBBCCPE+8p4aatibtWvXMnPmzGLoArj11lvJZrNs3ry5x9dls1laW1u7/AkhhBBCCCFEqd5Xwevo0aMMHz68y2MNDQ1Eo1GOHj3a4+v+4R/+gbq6uuLf2LFjw55VIYQQQgghxHvIBR+8vvrVr6KUOuffpk2bSn4/pdRZjxljun284Mtf/jItLS3FvwMHDvTrswghhBBCCCHeny7433g99NBDPPDAA+ecZvz48SW914gRI1i/fn2Xx5qamnBd96yesM5isRixWKykGkIIIYQQQghxpgs+eA0ZMoQhQ4aU5b0WLFjAN77xDY4cOcLIkSOB4IIbsViMK6+8siw1hBBCCCGEEOJMF3zw6ov9+/fT2NjI/v378X2fV199FYDLLruM6upqbrnlFqZPn84nPvEJ/vmf/5nGxka+9KUv8elPf1quaCiEEEIIIYQIzXsqeP2P//E/+MlPflL8/zlz5gCwYsUKFi1ahG3b/OY3v+GP//iPufbaa0kkEnz84x/nW9/61vmaZSGEEEIIIcT7gDLGmPM9Exeb1tZW6urqaGlpkZ4yIYQQQggh3sdKzQYX/FUNhRBCCCGEEOJiJ8FLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFCJsFLCCGEEEIIIUImwUsIIYQQQgghQibBSwghhBBCCCFC5pzvGbgYGWMAaG1tPc9zIoQQQgghhDifCpmgkBF6IsGrH9ra2gAYO3bseZ4TIYQQQgghxIWgra2Nurq6Hp9XprdoJs6itebw4cPU1NSglDov89Da2srYsWM5cOAAtbW152Ue3stk+YZPlnH4ZBmHS5Zv+GQZh0uWb/hkGYfrQlm+xhja2toYNWoUltXzL7mkx6sfLMtizJgx53s2AKitrZUNOUSyfMMnyzh8sozDJcs3fLKMwyXLN3yyjMN1ISzfc/V0FcjFNYQQQgghhBAiZBK8hBBCCCGEECJkErwuUrFYjK985SvEYrHzPSvvSbJ8wyfLOHyyjMMlyzd8sozDJcs3fLKMw3WxLV+5uIYQQgghhBBChEx6vIQQQgghhBAiZBK8hBBCCCGEECJkEryEEEIIIYQQImQSvIQQQgghhBAiZBK8LmD79u3jD/7gD5gwYQKJRIJLL72Ur3zlK+RyuS7T7d+/nw9+8INUVVUxZMgQ/vRP//SsabZt28aNN95IIpFg9OjRfP3rX0euqxL4xje+wcKFC0kmk9TX13c7jVLqrL8f/ehHXaaRZdy9UpavrMPlNX78+LPW17/6q7/qMk0py1z07Ic//CETJkwgHo9z5ZVX8vLLL5/vWbpoffWrXz1rfR0xYkTxeWMMX/3qVxk1ahSJRIJFixaxY8eO8zjHF7aXXnqJD37wg4waNQqlFE899VSX50tZntlsls997nMMGTKEqqoqPvShD3Hw4MEKfooLW2/L+JOf/ORZ6/Q111zTZRpZxj37h3/4B+bPn09NTQ3Dhg3jrrvu4o033ugyzcW6HkvwuoDt3r0brTX/+3//b3bs2MF3vvMdfvSjH/Hf//t/L07j+z533HEHqVSK1atX88gjj/D444/zxS9+sThNa2srN998M6NGjWLjxo18//vf51vf+hbf/va3z8fHuuDkcjnuu+8+/uiP/uic0z388MMcOXKk+Pfggw8Wn5Nl3LPelq+sw+H4+te/3mV9/Zu/+Zvic6Usc9GzRx99lM9//vP89V//NVu3buX666/n9ttvZ//+/ed71i5aM2bM6LK+btu2rfjcP/3TP/Htb3+bH/zgB2zcuJERI0Zw880309bWdh7n+MKVSqWYPXs2P/jBD7p9vpTl+fnPf54nn3ySRx55hNWrV9Pe3s6dd96J7/uV+hgXtN6WMcBtt93WZZ1+5plnujwvy7hnq1at4k/+5E9Yt24dL7zwAp7nccstt5BKpYrTXLTrsREXlX/6p38yEyZMKP7/M888YyzLMocOHSo+9rOf/czEYjHT0tJijDHmhz/8oamrqzOZTKY4zT/8wz+YUaNGGa115Wb+Avfwww+burq6bp8DzJNPPtnja2UZ966n5SvrcPmNGzfOfOc73+nx+VKWuejZVVddZT772c92eWzq1Knmr/7qr87THF3cvvKVr5jZs2d3+5zW2owYMcJ885vfLD6WyWRMXV2d+dGPflShObx4nXnsKmV5Njc3m0gkYh555JHiNIcOHTKWZZlnn322YvN+seiuffDggw+aD3/4wz2+RpZx3xw/ftwAZtWqVcaYi3s9lh6vi0xLSwuDBg0q/v/atWuZOXMmo0aNKj526623ks1m2bx5c3GaG2+8scvN5W699VYOHz7Mvn37KjbvF7uHHnqIIUOGMH/+fH70ox+htS4+J8u4/2QdDsc//uM/MnjwYK644gq+8Y1vdBlGWMoyF93L5XJs3ryZW265pcvjt9xyC2vWrDlPc3Xxe+uttxg1ahQTJkzggQce4J133gFg7969HD16tMvyjsVi3HjjjbK8+6GU5bl582Zc1+0yzahRo5g5c6Ys8z5YuXIlw4YNY/LkyXz605/m+PHjxedkGfdNS0sLQLH9ezGvx855qyz6bM+ePXz/+9/nX/7lX4qPHT16lOHDh3eZrqGhgWg0ytGjR4vTjB8/vss0hdccPXqUCRMmhDvj7wF/93d/x9KlS0kkEixbtowvfvGLnDx5sjh8S5Zx/8k6XH5/9md/xty5c2loaGDDhg18+ctfZu/evfz7v/87UNoyF907efIkvu+ftfyGDx8uy66frr76av7rv/6LyZMnc+zYMf7+7/+ehQsXsmPHjuIy7W55v/vuu+djdi9qpSzPo0ePEo1GaWhoOGsaWcdLc/vtt3Pfffcxbtw49u7dy9/+7d+yZMkSNm/eTCwWk2XcB8YYvvCFL3Ddddcxc+ZM4OJej6XH6zzo7ofEZ/5t2rSpy2sOHz7Mbbfdxn333cenPvWpLs8ppc6qYYzp8viZ05j8RQm6e+17QX+W8bn8zd/8DQsWLOCKK67gi1/8Il//+tf553/+5y7TvJ+WcbmXr6zDvevLMv/zP/9zbrzxRi6//HI+9alP8aMf/Ygf//jHnDp1qvh+pSxz0bPu1kdZdv1z++23c8899zBr1ixuuukmfvOb3wDwk5/8pDiNLO/y6s/ylGVeuo9+9KPccccdzJw5kw9+8IP89re/5c033yyu2z2RZXy2hx56iNdff52f/exnZz13Ma7H0uN1Hjz00EM88MAD55ym89n9w4cPs3jxYhYsWMC//du/dZluxIgRrF+/vstjTU1NuK5bPBMwYsSIs9J9ocv7zLMF7xV9XcZ9dc0119Da2sqxY8cYPnz4+24Zl3P5yjpcmoEs88LVtN5++20GDx5c0jIX3RsyZAi2bXe7PsqyK4+qqipmzZrFW2+9xV133QUEZ69HjhxZnEaWd/8UrhZ5ruU5YsQIcrkcTU1NXXoLjh8/zsKFCys7w+8RI0eOZNy4cbz11luALONSfe5zn+NXv/oVL730EmPGjCk+flGvx+fjh2WidAcPHjSTJk0yDzzwgPE876znCz+SP3z4cPGxRx555KwLE9TX15tsNluc5pvf/KZcmOAM57q4xpm+//3vm3g8XrzYgyzj3vV2cQ1Zh8Pz61//2gDm3XffNcaUtsxFz6666irzR3/0R10emzZtmlxco0wymYwZPXq0+drXvlb8Ef0//uM/Fp/PZrNycY0S0cPFNc61PAsXJXj00UeL0xw+fPi8X5TgQnXmMu7OyZMnTSwWMz/5yU+MMbKMe6O1Nn/yJ39iRo0aZd58881un79Y12MJXhewQ4cOmcsuu8wsWbLEHDx40Bw5cqT4V+B5npk5c6ZZunSp2bJli3nxxRfNmDFjzEMPPVScprm52QwfPtx87GMfM9u2bTNPPPGEqa2tNd/61rfOx8e64Lz77rtm69at5mtf+5qprq42W7duNVu3bjVtbW3GGGN+9atfmX/7t38z27ZtM2+//bb5P//n/5ja2lrzp3/6p8X3kGXcs96Wr6zD5bVmzRrz7W9/22zdutW888475tFHHzWjRo0yH/rQh4rTlLLMRc8eeeQRE4lEzI9//GOzc+dO8/nPf95UVVWZffv2ne9Zuyh98YtfNCtXrjTvvPOOWbdunbnzzjtNTU1NcXl+85vfNHV1deaJJ54w27ZtMx/72MfMyJEjTWtr63me8wtTW1tbcT8LFPcHhRMvpSzPz372s2bMmDHmxRdfNFu2bDFLliwxs2fP7vYE8PvRuZZxW1ub+eIXv2jWrFlj9u7da1asWGEWLFhgRo8eLcu4RH/0R39k6urqzMqVK7u0fTs6OorTXKzrsQSvC9jDDz9sgG7/Onv33XfNHXfcYRKJhBk0aJB56KGHulx22xhjXn/9dXP99debWCxmRowYYb761a9KT0Hegw8+2O0yXrFihTHGmN/+9rfmiiuuMNXV1SaZTJqZM2ea7373u8Z13S7vI8u4e70tX2NkHS6nzZs3m6uvvtrU1dWZeDxupkyZYr7yla+YVCrVZbpSlrno2f/8n//TjBs3zkSjUTN37tziZY5F3330ox81I0eONJFIxIwaNcrcfffdZseOHcXntdbmK1/5ihkxYoSJxWLmhhtuMNu2bTuPc3xhW7FiRbf73AcffNAYU9ryTKfT5qGHHjKDBg0yiUTC3HnnnWb//v3n4dNcmM61jDs6Oswtt9xihg4daiKRiLnkkkvMgw8+eNbyk2Xcs57avg8//HBxmot1PVbG5H+hLoQQQgghhBAiFHJVQyGEEEIIIYQImQQvIYQQQgghhAiZBC8hhBBCCCGECJkELyGEEEIIIYQImQQvIYQQQgghhAiZBC8hhBBCCCGECJkELyGEEEIIIYQImQQvIYQQFyylFE899VQo7z1+/Hi++93vFv//6NGj3HzzzVRVVVFfX9/jYxealStXopSiubm55NcsWrSIz3/+86HNkxBCiLNJ8BJCCHHefPKTn0Qpddbfbbfd1q/3++pXv1p8D8dxGDJkCDfccAPf/e53yWazXabduHEjn/nMZ4r//53vfIcjR47w6quv8uabb/b42PnUXWBauHAhR44coa6u7vzMlBBCiJI453sGhBBCvL/ddtttPPzww10ei8Vi/X6/GTNm8OKLL6K15tSpU6xcuZK///u/56c//SkrV66kpqYGgKFDh3Z53Z49e7jyyiuZNGnSOR/rK9d1iUQi/X59b6LRKCNGjAjt/YUQQpSH9HgJIYQ4r2KxGCNGjOjy19DQ0O2027ZtY8mSJSQSCQYPHsxnPvMZ2tvbu0zjOA4jRoxg1KhRzJo1i8997nOsWrWK7du384//+I/F6ToPNRw/fjyPP/44//Vf/4VSik9+8pPdPgbQ0tLCZz7zGYYNG0ZtbS1LlizhtddeK77vV7/6Va644gr+4z/+g4kTJxKLxTDGlPy6n/70p4wfP566ujoeeOAB2tragKB3cNWqVXzve98r9urt27fvrKGGp06d4mMf+xhjxowhmUwya9Ysfvaznw30axJCCDFAEryEEEJcFDo6OrjttttoaGhg48aN/PznP+fFF1/koYce6vW1U6dO5fbbb+eJJ57o9vmNGzdy2223cf/993PkyBG+973vdfuYMYY77riDo0eP8swzz7B582bmzp3L0qVLaWxsLL7f22+/zWOPPcbjjz/Oq6++ClDS6/bs2cNTTz3F008/zdNPP82qVav45je/CcD3vvc9FixYwKc//WmOHDnCkSNHGDt27FmfJZPJcOWVV/L000+zfft2PvOZz/CJT3yC9evX92VxCyGEKDMJXkIIIc6rp59+murq6i5/f/d3f3fWdP/3//5f0uk0//Vf/8XMmTNZsmQJP/jBD/jpT3/KsWPHeq0zdepU9u3b1+1zQ4cOJRaLkUgkGDFiBHV1dd0+tmLFCrZt28bPf/5z5s2bx6RJk/jWt75FfX09v/jFL4rvl8vl+OlPf8qcOXO4/PLLS36d1pr//M//ZObMmVx//fV84hOfYNmyZQDU1dURjUZJJpPFnkHbts/6LKNHj+ZLX/oSV1xxBRMnTuRzn/sct956Kz//+c97XUZCCCHCI7/xEkIIcV4tXryY//W//leXxwYNGnTWdLt27WL27NlUVVUVH7v22mvRWvPGG28wfPjwc9YxxqCUGtC8bt68mfb2dgYPHtzl8XQ6zZ49e4r/P27cuC6/ISv1dePHjy/+Bg1g5MiRHD9+vE/z6Ps+3/zmN3n00Uc5dOgQ2WyWbDbbZbkJIYSoPAleQgghzquqqiouu+yyXqc7V3AqJVDt2rWLCRMm9Hn+OtNaM3LkSFauXHnWc50vN39myCn1dWdehEMphda6T/P4L//yL3znO9/hu9/9LrNmzaKqqorPf/7z5HK5Pr2PEEKI8pLgJYQQ4qIwffp0fvKTn5BKpYrB5pVXXsGyLCZPnnzO1+7evZtnn32WL3/5ywOah7lz53L06FEcx2H8+PGhv+5M0WgU3/fPOc3LL7/Mhz/8YX73d38XCELfW2+9xbRp0/pdVwghxMDJb7yEEEKcV9lslqNHj3b5O3ny5FnT/c7v/A7xeJwHH3yQ7du3s2LFCj73uc/xiU98osswQ8/zOHr0KIcPH2bbtm18//vf58Ybb+SKK67gL/7iLwY0rzfddBMLFizgrrvu4rnnnmPfvn2sWbOGv/mbv2HTpk1lf92Zxo8fz/r169m3bx8nT57stjfssssu44UXXmDNmjXs2rWLP/zDP+To0aP9+rxCCCHKR4KXEEKI8+rZZ59l5MiRXf6uu+66s6ZLJpM899xzNDY2Mn/+/140gAABAABJREFUfO69916WLl3KD37wgy7T7dixg5EjR3LJJZewaNEiHnvsMb785S/z8ssvU11dPaB5VUrxzDPPcMMNN/D7v//7TJ48mQceeIB9+/ad8zdm/X3dmb70pS9h2zbTp09n6NCh7N+//6xp/vZv/5a5c+dy6623smjRIkaMGMFdd93Vn48rhBCijJQxxpzvmRBCCCGEEEKI9zLp8RJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJBJ8BJCCCGEEEKIkEnwEkIIIYQQQoiQSfASQgghhBBCiJA553sGLkZaaw4fPkxNTQ1KqfM9O0IIIYQQQojzxBhDW1sbo0aNwrJ67teS4NUPhw8fZuzYsed7NoQQQgghhBAXiAMHDjBmzJgen5fg1Q81NTVAsHBra2vP89wIIYQQQgghzpfW1lbGjh1bzAg9keDVD4XhhbW1tRK8hBBCCCGEEL3+BEkuriGEEEIIIYQQIZPgJYQQQgghhBAhk+AlhBBCCCGEECGT4CWEEEIIIYQQIZPgJYQQQgghhBAhk+AlhBBCCCGEECGT4CWEEEIIIYQQIZP7eAkhhLgg+L7Payt2sOaXGxkxYdj/x957h0lynfe57zkVunvyzEYsCCITJAKRc9oE0BQDQIICBUq25EeSdS2Jj2mKpCzL9IVsifLltUXJ4n0k+T66oiiaSSRAUGJC2AAiEthFTiRALNIibZrUqarOd/+o6p6Z3emZ6ZmuXuzu9z4YzE53df2ququrzlvfqTqs/eglLD962cFeLEVRFEXpCCpeiqIoykFDRHjqvp+y+Wt3s+lrdzG2exzPtzgn/M2nv8zpl76TDb98BVd85CIGlvUf7MVVFEVRlEVjREQO9kIcaoyNjTE4OMjo6CgDAwMHe3EURVEOKUSE5x97kc1fu4vb//ed7Hp5D55vSWJ3wLTGGkQEay3nXn0mGz52OZdccx6lvtJBWHJFURRFOZCFuoFWvBRFUZSusPO519j8tbu57StbeeWnr2I9i0tS2ZpNugDEpecGXeLYdusjPPCDhwgKPhd/8DzW33A557/3bMJC0LV1UBRFUZTFohWvRaAVL0VRlIWxa+cetn7jHm7/yp08+9DzWGtxbnbJaodGhazYV+TKj1zEuhsu46x1p+P5XgeWWlEURVEWzkLdQMVrEah4KYqizI2I8J8+8Kf85AcPYYwBEfI62ni+RxIn9I/08ftf/jgX/sI5+QQpiqIoyiws1A0OmdvJ/+mf/innn38+/f39rFy5kmuvvZZnnnlmxjQiwo033siaNWsolUqsXbuWJ554YsY0tVqNj3/84yxfvpze3l4++MEP8vLLL3dzVRRFUQ57olrET77/EEjaXTDPU3xJnAAwOVrm0S1PzDO1oiiKohwcDhnx2rp1K7/zO7/Dfffdx2233UYcx1x99dVMTk42p/n85z/Pn/3Zn/HFL36RBx54gNWrV3PVVVcxPj7enOYTn/gEN998M1//+te56667mJiY4P3vfz9JkhyM1VIURVE6hLXmYC+CoiiKorTkkLm5xg9/+MMZf//d3/0dK1euZNu2bVxxxRWICH/+53/OH/7hH/LhD38YgL//+79n1apVfPWrX+W3fuu3GB0d5W//9m/5h3/4BzZu3AjAV77yFY455hhuv/123vOe93R9vRRFURRFURRFOfw5ZCpe+zM6OgrAyMgIAM8//zyvvfYaV199dXOaQqHAlVdeyT333APAtm3biKJoxjRr1qzh9NNPb04zG7VajbGxsRk/iqIoiqIoiqIoC+WQFC8R4ZOf/CSXXXYZp59+OgCvvfYaAKtWrZox7apVq5rPvfbaa4RhyPDwcMtpZuNP//RPGRwcbP4cc8wxnVwdRVEURVEURVEOcw5J8frd3/1dHn30Ub72ta8d8JwxM/v4i8gBj+3PfNP8wR/8AaOjo82fl156aXELriiKoiiKoijKEckhJ14f//jH+e53v8vmzZt529ve1nx89erVAAdUrt54441mFWz16tXU63X27t3bcprZKBQKDAwMzPhRFEVRFEVRFEVZKIeMeIkIv/u7v8tNN93Epk2bOP7442c8f/zxx7N69Wpuu+225mP1ep2tW7dyySWXAHDuuecSBMGMaV599VUef/zx5jSKoiiKoiiKoiid5pC5q+Hv/M7v8NWvfpVbbrmF/v7+ZmVrcHCQUqmEMYZPfOITfO5zn+Pkk0/m5JNP5nOf+xw9PT187GMfa07767/+6/ze7/0ey5YtY2RkhE996lOcccYZzbscKoqiKIqiKIqidJpDRrz+6q/+CoC1a9fOePzv/u7v+LVf+zUAPvOZz1CpVPjt3/5t9u7dy4UXXsitt95Kf39/c/ovfOEL+L7P9ddfT6VSYcOGDXzpS1/C87xurYqiKMphi3OOxx/cwaZbtuMd93ao1XHjE8jkJHmMoiwAvSVkxTD1lUPc9sTrrLjpQa5Y+05GRvo6ngfw+mujbNr0JHfc/jhJ7Niw8TTWbziNNUcPz//iRTC2r8yPb32cTf/0MG++uo/L33MGa3/h3Zx06pp5r2FeDNVKnftve5zN39nGTx9+gfPXn8raa8/l3RefjOd1vqNMEic8fNdP2XLLNh7c8jSnnncc6z50HuevO5VCKex4nojwzLbn2fLtn3DXd7dx1PErWH/9RVz2wXPpH+rteJ6iKEoDI5LDkfAwZ2xsjMHBQUZHR/V6L0VRjnhEhGefeIUt//wwm7/7EHt3TeB5liRxM6aRyUlkfAKZLC89sxjCymFk1QiUiqnUGcN0Dznr7GPZeNXpXHb5KfT1FZeUt2fPBHdueZrbbnucp5/aibEGcenh01qDc8JJJ6/iqqvPYO26d7F8ef88c5yb8mSNezc9xeZ/fpjt9zyLy24CJU6wnsUljlVHD7Pxg2ex9hfO5JgTViwpL6rHbL/zabZ8Zxv3/PAx6rWomeP5liR2DIz0su7ac7nymnN559nHLkn6nHM8+eDzbL1lO1tu2c7EaLm5zTRyC6WAy37hLNZecy5nXfYO/GBpJ0h3PPUKW779E+74xr28+fKe5no1Pj/rWc7bcDrrfvFCLn7vWRR7C0vKUxTlyGGhbqDitQhUvBRFUeCl595gyz8/zB23bOf1l/c2G8ytEcAgziETmYRVKgvOk9CHFZls9fU0ZasVjQa151kuvOhENl51OhddfBKFQrCgvInxKnfd9Qy33/Y4Dz/0wtRytDhqNhZFBM549zFsvOp0Lr/iFAYHexaUV69FPPDjn7L5e49w/+aniaIE6xlc0vow3VjH405exYYPnMWV7303K9cMLSgvSRyP3/8cW27Zxp3/9BDl8eoBwrw/jeeXHzXEhg+fx5XXnMvx71qzoDwR4bknXmHrLdvYdPOD7Hl9rCk/LfOy5/sGSlx5zTmsveZcTj3/eKxdWOXttRfeZMu3H+COb9zLSz99dd5ttPF8UPC55H1ns+4jF3LuhtMJwkOmg5CiKAcBFa8cUfFSFOVI5Y2de9n6vUe44zvbeeFnrzcb/m2TSZMkCTI+gZuYgGrtwMl8D5YPpbI1kHUDW0SlpbGchYLPZZefwoaNp3Huecfj+zOrKNVqxH33/ow7bn+C++97Lq3ALGIdm1UUazjv/BPYeNXpXHLJyZR6ZnadS+KEh+//OVu+/wg//tHjVCvRvPIzK4ZmReyd7z6GDR88i8vfcwZDIzO7zokIP33kRbbesp1NNz/I6O4Dq5MLXsdMUt52wkrWX3c+a685h6OOXX7AdK88/wZbbtnOHd9+gFd37FqAoM9OYzlHVg6w7kPnsfaaczjx9LcdUHnb8/ood37nATZ94z5++tCO9LMQyfqltpGXSV9Pf5ErPnQ+a6+7gDMuPSWX7paKohzaqHjliIqXoihHEvt2T/DjHzzKpu8+xNMPv5h2s1tEQ7YlDQmLY2R8gmSyDAMlZOUIDGdd9kzzf0um0YDv7S2wbv2pXLn2ndRqMZs3PcldP36GWi1evFDOQmNeQeBxyaXvYP2GU+kvBNx92xNs/t4jjI9WFi0/s5GKSPqennXhCax//1kcc9wyfnLb42y66UFef3lPR/NgWnfLM97G+g+fz+kXnMCj9z7Lppse4OdP7uzo+wlT0nfUsctY/+HzOX/du3j+8ZfY9M37eOzunzan61QTpyFhg8v6WfeLF7L2ugs45dzjc7nGTlGUQw8VrxxR8VIU5UhhfLTML1/6J8RRDJiONWRbIkJ9VT/43rxdCTvBdAHptIy0ypOxKl55kZWtNrHW4GoRplKdt9tiJzBmqiumMQahg4I+R6YbnwTouODNRuNz+80/vp7rfufqXLMURTk0WKgbaKdlRVEUpSXliRpRPc7+6sJ5OmOgcf1OF6oJ08UnbwlqZFgnYLqT55w0TShv6YKZ179167zudNHKW7og/dw832Pv66O5ZymKcnihHZUVRVEURVHaQHsYKoqyGFS8FEVRFEVRFEVRckbFS1EURVEURVEUJWdUvBRFURRFURRFUXJGxUtRFEVRFEVRFCVnVLwURVEURVEURVFyRsVLURRFURRFURQlZ1S8FEVRlJa4Low1dUTSnSGulJzQj09RlMWgAygriqIoMxARnnr8FTbf9gR3/PBRBOjqsEUiacu2i4MldXUdbZcHgdIxpzqK9S1JlNA7UDrYi6IoyiGGipeiKIoCwPPPvcGmWx/n9h8+xq43x/E8S5I4GOrF1iJMLcK4fM/1i2fw91VxJR8p+IhnUxHrsIRNFy1h6g+R/D3F9QSIb7HVGFNP8s2zBlcqQDHEVOvpj+T4GRoDnk1/i0CS5F4eMp6H9PdBFKU/OVRprWdxiSMo+Fz8C2ez7iMXcv5Vp3c8R1GUwxsVL0VRlCOYnS/vYfPtT3L7Dx/j5Rd3Y63BZXKVNBqwvsX5BegJIXaphNXjjkmYeAY8L60EGYMBvFoCtQSxBhd6SOh1RMKmCmkGMcyYlwCIpBE5y4kUfJKCD04w9SR7T9P3e8kiZgziW8S3YKeuKJBiiIhALcJUaqmELTWrgefN+Ayb+Ok6kiTpT6cwJl03M7XN4PtQKiFJAvU61CNwi5cwYw3iBOtZzttwOut+8UIufu9ZFHsLHVsNRVGOLFS8FEVRjjB2vTnOnZtS2frZM6/NkC03l0wZA4GHC7xUgOIEW4vTSli7nmJNKlKN6kirSCd41RipxuClEuZCf1Hd9cQYMNnvloHZNJhml8dcJcwapOiTFFNBsbUYW4shalPCDIjnZbJlWr+nxkAxTCXMCaZWx1RqUIvay4NMtuyUALXK87IqmPipCMXJ4oRoP9lqOZnnpQJWLKay15CwBXyOTSlHOP3ik1l//UVc9oFz6B/ua395FUVR9kPFS1EU5QjAOeHW7z/Crd9/lMcefhFjDI0+YHPKViuMgcDHBT70FjBRgqlmlbBZJheyCsICZGvWOIBE8CoxthIjnkVCDxd6c0pYQ7YWVSU7QMKkfcFsB2twpQBXCiBx2FomtnFrSUkrW7NUmhaYJ6UCUiqAc5hqHSqppLSck2ezylb7n2EqYVllTGRhErZA2Zr9pSatgvk+UhKI41TAovoB3R8bJx9OOvNYNnz0Yi6/9jyWrR5qb/0URVHmQcVLURTlCOC5n73G//jcP2fCld5Ao2MYg4Q+EvqpnNRjbC2CeoIxmWz5du5KUztxgEkcVBy2EiG+zboj+llFi0U11OcOTefXfN/yljDP4nosrifIunfG2Gqcdtvzsm6EixDYlliL9BSRniIkDlOtYco1TJyk4tOobnUqb38JS1xanXJu6rNbjNy1jDMQBBAEiJQgitNKWBQxtHKAa//NBq687gKOOm5FR/IURVFmQ8VLURTlCKBxvVZHhWs2jEEKAUno403WOiZbLeMAEzts7IgKPrKYyk9bgab5WxLXnRsG+hbnh0jgYSOX/90ePYv0lpAwwJuo5p9nDPiZ2MVJ7nnGGAgDCAOMNfzip9/Hdb+xNtdMRVEU0HG8FEVRlLzo4u3gD2pmt+h0FW+hmYdxnjmctxdFUd5yqHgpiqIoiqIoiqLkjIqXoiiKoiiKoihKzqh4KYqiKIqiKIqi5IyKl6IoiqIoiqIoSs6oeCmKoiiKoiiKouSMipeiKIqiKIqiKErOqHgpiqIc5jjnePahHZhd+6BcTQeszRMRmKzg3tiNjE0gLuc8wHkG5xnEQP5p6SDNScGSFGw6YHPugZIOntwtRCDKBjQWyX2bEUBsNhC3Z7vyGeIcrlbj+cdfolapdyNRUZQjHCO5j6Z5+DE2Nsbg4CCjo6MMDAwc7MVRFEU5ABHhZw+/wJZv38+mb97PvjfHpp70PdxgLzLQC4Wwc2MnVWuwax/s2pv+u4G1mKEBzGA/9PZ0bOwksYak6ONKPi70UjkwmXmJYBwgdGyQYwFcYHChQfyZczWx4NUctu4wnTqqZrJlYsEkkv9gzSIQJ5hahK3FB8qWMdDhAarFmHTgZN9mn51M/Y4SiOL0PehUoHOQJOmPc82HC6WQS993Fms/dD5nX/FO/MDrVKKiKEcAC3UDFa9FoOKlKMpblRef2cmWb/+EO75xH6+/uAvrWVziWr8g9HEDfchgL4RB+4H1CHbvS2VrstJ6OkNqLr6HGRzADA1Aqdi2hIkhk60AF2adNuaaxxIlTAAJDElgkGAe6cgOpzYSbEPC2sw7KLKVuEy2ooVV1QxTgzkvQsLEkMqW56UiNx9OUgGLE8xiqn4iEMcHyNb+eL4liR19gyWuuOY81n34fE694ASs1c5BiqLMjYpXjqh4KYryVuL1F3ez9aafcMc37uWFp3fOL1stkGKIDGSVsMBvPWEUw57RVLbGJxe/4EGQVsKGBjDFQuvlAlzRJyn5uEJWiVhM1UVIpUaYU8IEED+TrXAJFR4RbN2lEhbNIVEi4MAkDptId/pKJgmmFqeytYhtpckCJUxgqrJl7VRla6E0pncuq4QlmLmaLyKpaMXxnLLVCs+zJIljeOUA66+7gLUfOo8TzzimY9VaRVEOL1S8ckTFS1GUg82+N8e48zsPsumb9/H0gz/HWNOxa6kEoFRABnuR/l7wvbQRu3csla194+mEjSpWJyiEmKFBzFA/JgxTRyp4qWwV/Y52bwOmJGxam1w8cKHFLUW2WualVTCv5tJqFll+3E3Zyipb9QjiJchWK/aTsBmy1Xg/O/G+NiQscVOVsKyymXYjjJcmk/vROJGx+tjlbPjIBaz90Pm87aRVHZu/oiiHPipeOaLipSjKwWLP66P893/7tzy09SlEBGM6J1z7k7ZlBYnq6TVbIp2VrRYk73w77m3L08pIF3Beet3Wgrq9dQBbiSnsjTBJlw6/icOOV1JB6UJcKu5hKl3QeYmdEZbd+GNsElPL/wYZ1hqcE4571xo+/vkbOPX8E3PPVBTlrc9C3UA7LiuKohxCPLPtebZvfjKVLSHXOwYaSLtzVabdCbELruDWLOuadEF6w4xuSReAV3Xdky7ARDGmS9IFpO+l7y36GrC2yDK6IV0ALvu+vfjMq9z7g0e6kqkoyuGDipeiKIpyRHNQrtrRS4UOaaynzSdFUdpH9xyKoiiKoiiKoig5o+KlKIqiKIqiKIqSMypeiqIoiqIoiqIoOaPipSiKoiiKoiiKkjMqXoqiKIqiKIqiKDlzSInXnXfeyQc+8AHWrFmDMYbvfOc7M57/tV/7NYwxM34uuuiiGdPUajU+/vGPs3z5cnp7e/ngBz/Iyy+/3MW1UBRFURRFURTlSOOQEq/JyUnOPPNMvvjFL7ac5l/8i3/Bq6++2vz5/ve/P+P5T3ziE9x88818/etf56677mJiYoL3v//9JEmS9+IriqIoiqIoinKE4h/sBWiH9773vbz3ve+dc5pCocDq1atnfW50dJS//du/5R/+4R/YuHEjAF/5ylc45phjuP3223nPe97T8WVWFOXwJI5iHtr0BJu/eS9vvLiLy649nyuuu5CR1UO55IkIT93/LD/4/zbnMv8jme4NZTwtU3Qor0OZOEq479bHOO3Ckzh33akEYT7NqepkjXt/8DBbvv0Tkjhh7XUXcPEvnE3vQCmXPEVR8uWQEq+FsGXLFlauXMnQ0BBXXnklf/Inf8LKlSsB2LZtG1EUcfXVVzenX7NmDaeffjr33HOPipeiKHPinOOJe37K5m/ey5Zv3sfkaBnPt7hEePzun/LXn/7fnHH5O9lwwyVces359A/3LilPRHj+8ZfY8s17ueNrd7Prlb1Y3yLGYqwF04Wmu7WYnh6kVoXEpbaQh6kYg4iAMZjX9yBHLydvNWmshnGC8w2mSwYW93mYxOFVXJqfc54EPq7gY2pxd2QvcVCLIPS7s40CUipiqrXUaLvEzp+/wR/96l9T6i1wxTXncuWHzuPdl7wDb4mDK9drEdvueIIt376fe773EFEtbg7Y/ODtj+MHHhe+50zW/eKFnH/VGRRKYSdWR1GULmBEuriX6iDGGG6++Wauvfba5mPf+MY36Ovr49hjj+X555/ns5/9LHEcs23bNgqFAl/96lf51//6X1Or1WbM6+qrr+b444/nb/7mb2bNqtVqM14zNjbGMcccw+joKAMDA7msn6Iobw1EhGcf2sHmb97Lpq/fw97XR/F8SxK7Wae31uCc4PmW865+N+s/egkXve9sir3FBWfu/PnrbPnmvdz+1bt55WevYT2LS2bJsxa8VMKEdL/YMYoFKBXT3w0pKpeR0XFkfBycLF3CzFQ7WZYPkLxtOcmaYQj89AkBkwg2kY7JnqSxON8QFw1JwYKXvW9OsDHYWDouYWIh8cEFBsnyTOwIR2PC0Ri/4prL1rFMA9hsjsaAE0wtwlbrUIvThzsWJumPc80PVSD9LMOgsxLW2GicpJKXuHQ9RCBJIIohjrsqYY19wsBIL+uuu4C1HzqPU84+bsHfySRxPHrXM2z59v3cefODVCaqc+9nsn1CoRRy2TXnsu66Czl77bvwfK+Tq6UoygIZGxtjcHBwXjc4rMRrf1599VWOPfZYvv71r/PhD3+4pXhdddVVnHjiifz1X//1rPO58cYb+aM/+qMDHlfxUpTDlxef2ZlWmr56N6/teBPrW1yLRlArGo2jsBhwyQfPY91HL+bcjWfM2i1p9869bP32/dzxtbt59qEdTYFbeJjF+B5iTNoIXUwjtxBOyZZtfdZenIPJMm5sDMYnp/rNLXBxm/Iz3JvJ1jIoBnO8QMBNk7BFIh7ERUtSsIg/x/uT5dkkFbHFSpgYSIJMtixzfia27ghGYwqjMV5t8RImJstp/G6Fc5hqhK3UIUraz2oa80zZajk5pPIVBqmMMc/yzZUXT5OtuaZPEqhHqYR1Ec+zJIljxdHDrP/IBay99nyOe9eaWRZRePrBn7Pl2z9h87fuZ2z3xJyy1TIve03fYA9rP3Iha6+7gFMvPBE7x3dYUZTOouKVcfLJJ/Mbv/Eb/P7v/z6bNm1iw4YN7Nmzh+Hh4eY0Z555Jtdee+2scgVa8VKUI4U3XtzF1m/dz+1fvYsdT7zcutK0CBqNo57+Ilf+4kWsvf5ijjv1bdzzT9vY9LW7eezuZ9Kz4yJLP1HvWYznpfKUdd9rSRhkslWERXSREueQ8QlkdBwmJ9MHZ5Ow7DHpLxEfsxx39DKkp9B2XlPCYodx88uJ2OmyRftCKpLmxJmEzTc54AJIAoN4i8gDbNURjkYURmNsJPNK2IJlqxWJw1Tr2EqEiee40VRzw8wqTYvcUMUAQQCFABoVmtmWu7HtOrcw2ZprueMYogjmWr8caOxD3nbSKjb84oVcee15VCeqbPn2T9j0zXt585W9i5KtVjTmNbJqkPXXX8Taj1zIiWcc09lquKIoB6DiBezevZujjz6a//W//hf/6l/9K0ZHR1mxYgVf+cpXuP7664G0Kva2t72N73//+wu+xmuhb66iKIcO3/3r2/h//v2XMdakgpDjrnH/hpaxBmmnutVWmIfxvZkN28BPZatUBK9zXZMkTtJuiGPjSLky9XhPIa1sHT2CDPR0LA8RTJL+ME3CxEBStCRFg/NNR7u4GQc2EkwyLQ9wWTdCtxi5myPPq2bdEffFM6t9BmQpstWKOGlWwsz0kw6NypbrjCA0Z2tMKv/TJQyyboRJKlud/GqIZF0RD4KEWUNSi8C5jp7UaZmXZVzxofP4j//f/5FrlqIc6SzUDQ6pm2tMTEzw7LPPNv9+/vnnefjhhxkZGWFkZIQbb7yR6667jqOOOoodO3bwH//jf2T58uV86EMfAmBwcJBf//Vf5/d+7/dYtmwZIyMjfOpTn+KMM85o3uVQUZQjk5eeebWjZ57nYv+M3KQLIEmQJIFCiBkaSLsT+vns+o3vYYaHYHgIF0fUe8EN9CBDvfncZMEYxDdpJUuExAfxDS7ooGztn+dB4plm5Q1DZ2Vrv7yk5FEpeVRWhZRerROOxmlUXhUM30P6PJLeAmayirdvMtdrpYwI1OpQq6fXvpVK6bVoeWU2RC8MUomcmMwnZxack+Z7mbd0Tc/Y8eQruWcpirIwDinxevDBB1m3bl3z709+8pMA/Oqv/ip/9Vd/xWOPPcaXv/xl9u3bx1FHHcW6dev4xje+QX9/f/M1X/jCF/B9n+uvv55KpcKGDRv40pe+hNfBs76KohyiHO7dcXo7WG2aBxMEJMcOzz9hxwINrmjTa6m6lRfQvXvCG4Mr2KmbZXQhD8929QYVpnGzjG5xMK6ByuuuoIqiHBIcUuK1du3aObv//OhHP5p3HsVikb/8y7/kL//yLzu5aIqiKIqiKIqiKC3RW94oiqIoiqIoiqLkjIqXoiiKoiiKoihKzqh4KYqiKIqiKIqi5IyKl6IoiqIoiqIoSs6oeCmKoiiKoiiKouSMipeiKArgnDus7/IsdP8u1l3PFOnq7c+7vX5COjh0V/O87g6x0PX31JrD+nsP2fhhiqK8JTAy1/3ZlVlZ6OjUiqK8tUkSx6P3/oytt2znjm/9hChKIIqQah3q9XxCrcWEAQQ+GINxDlePIIpzkQYJAxjsg5HB5qCxJnYQJ5gc9v7OM8QDIdFQAVfys5a0YLLhmTrdjJdspmJBsnHYxMt+LB0fm03Ishrzzx40Lvvd0TSa751JwIvSD8yrObzJBL+S32foChZXsIhvseU6/u4y3r4yNur8OFtis0GNCwF4HjTG80ryWT8x4EoBrhQioQeJw46VsWMVTLWez9BsnpcOTm2zMUPrUbqPieM80tIx2Hwf46f7mQs3nsbaa8/joqtOo9hTyCdTUY5gFuoGKl6LQMVLUQ5dRISnH3qBrbdsY/N3tjG2ZxLPtySx239CpFZH6vW0kbQUrMUEfipc1pI236ead83dcJwgUSZhS0B8Dwb7YXgASoVU6KYLSONv5zBRArFbUmNTrCHuD4iGCiQ92fCQswmPZIPkLlFQmrJlspwWMxNAfHBeOv1iJawpWzarALWazXTJXMo6Svoe2RhswuzykW0zXsXhl2O8qluSpIiFpODhih7im9m3GcCbrOPtLuPvK2OSxQeKMRD6qWz5/oF5DZxAkkCyxPUz4AoBridACrNso438KMaOllMRqy9RimwqP3he620v288Q1SFOOpJnspM6M57yLC5xhAWfi9/zbtZeey7nXvlOgvCQGs5VUd6yqHjliIqXohx6PP/UTrbeso07bnqQXa/uw/MsSbLAs/ciSK2WNZAW2BgzBoIAG2YNr1YNy1njBOIYqccLPiMunoWBvlS2eopTyzB/WPrbCSZeuISJgbgvk62+YOF5zRm0J2ELla25lle8TMLs/C9u5DlvHtmaawZOUllYqIQ5wSapcLUtGU7wKgl+OUklbCGLaMAVLEnJQ3y78G20IWFjVfw9ZbzRavpZzvcymKps+VnlZ6F52YkCYpdK2PyvSj244JOUAqQULHz7zPJMPWpKmIkWKEXWZtWtA+Vn7iVN1695oidZaJ6ZJlsLu3qkse/r6StwxQfO4cprzuGMi07C8/TqE0VZLCpeOaLipSiHBq++sIstt2xn07cf4OWfv9E867sknEsrYbXagWeojYHAx4ZB27LVEhEkitNK2H55Yg3098FwP/T1TC3DErKAtGE7SwNXgKQvIBoIiQfCtNE3s3jXZl7jf2ZWCWscnJpdBjvUB0yaQsUBEiZLka2WgaQSNlt3y4ZsJTSfX1KOSefpl1MJs7X9PsNMtlzBwwXZM4vdZqblefsq+HvLeGPVGdIokHarLUx1r10S004UpN0RD9xGJfRwpRBXChYk2fPmGYOp1qckbP99iEnlpz3ZmjuPJEEa3RHd7Hkm8GetoLdDQ8IGl/Wx9tpzWXvNOZxy1rGYDnfRVZTDHRWvHFHxUpS3LuN7J7njpgfYdNOD/OzRl7DWdP7i8mln4KVaQ+IEE3ipbEF+jRYRXD1CCkFa3RroXXLDa748EofzDVF/QDwYpteO5BE3/VDkmOoamHP7TywkPrgwrRpKjm9nGggmcdg6eAnpuuYRl62DSQRvMsaLBPENLsyqGp3eRpvC4PD3VvD3VrCOtMKVZyNeBJwgCK4YpLKV8zZqKnXsvklsJcL4XlZpyiFwuoTVUgFrylYnTursR0PCVqwZZsN153PVL17AmuNXdDRDUQ5XVLxyRMVLUd66/PdPfIVNNz0ISHducJckB56RzhEphriVI7k0vGbNs1A+prdreQeDWp9BfLq2fl7F4dVzd8omJnZ4Od3DYda8Wkzp1XLX8gSIh9roXtsB/J37sPtfF5oncT4332mF9SzLVg3w5fv/qGuZinIos1A30A69iqIcVtSqEV2TroNBo2HZpQamdDnvYGCWcOONReXRPemaSuxm2kHYVozp+md4OOMSR62yxJsKKYpyACpeiqIoiqIoiqIoOaPipSiKoiiKoiiKkjMqXoqiKIqiKIqiKDmj4qUoiqIoiqIoipIzKl6KoiiKoiiKoig5o+KlKIqiKIqiKIqSMypeiqIoiqIoiqIoOaPipSjKYcfhOoSXkg+6vSjKgUT1iPJ45WAvhqIcVqh4KYpyWHH0cctBwHpd2r11c2BhA8QJiHRPFpykP11C6K4ICWCSLgYCYk0zuyuY7r6v4hmk2yMMO9fVOPG73Hw6CAOYl/dN8otv+23+6w1/wV3feYB6td71ZVCUww0jInqyr03GxsYYHBxkdHSUgYGBg704iqLsx46nd7Lllu1suulB3ty5F8+zJEmODTORtOEnkv50EgMIiIFkoIdkuBfXV8TGgq0nmFiak3WKxhokJUvU7xP3+GANxk092ek8AzgP4oIhCQTrDF5N8OqdzZqR50O9H+q9BvEMXh28GpicjoqSZYoPOMGvQlARbJxfnhgQDxDBJuBF6e/Ge9DJLEhnKibN88sx/niEV44bT+WGABJYXOAhoZe/qIhgajG2XMeW65hunJxo7GfylEwnSK2G1OsQpZ+b9S0udhR7Clz24fNZd/3FnL3uNDzfy285FOUQY6FuoOK1CFS8FOXQQER45uEX2HLLdjbf/CBjeybxfEsSd0PC3JJKDI2Xuv4iyXAvyWAPzFbFc6mAeTWHSRYf2GiIJ0VL1OcT9/mIP0vjNSudNCRsqc1bZyEpGJIwrZQcmCfYCPxa+nvJeR7U+yDqTzMPaKALmJhUwupLlzAhFS3ng1gOXAFJxcuvCEE5e1+XkAVphvOyrNkExAlelP4sJa+RaQw4M0de4vAnIoLxCFtNGouYG6mEebiCN1WZylPERDDVCDtZx1bquYn79LzmTyckTASp1ZFaHaJozkkb+8++oV7WffQi1v7ixZx68clYqx2olCMbFa8cUfFSlEOPJHE8dt+zbL1lO3f+03bKEzWsZ3F5VcIau9Y2zlA3dsbSWyAe7iUZ6oF2zionmYTVFyZhzcpPaIj6faI+HwnaaEAJ4MC22Y9NLMQhJKFJqzELbRSLZEKUShgsrAEvNpWtep8hKbaTBzZKBcy2UXkT0iqT87Nq04JfmElmQ8IW8J7OqDTZhtwtXDJMItiGhLXTGphPtlq9LHb44xHBRB1bcx2vvO2PmEzCwi5KWCXCm6xhKlGu69bIm1FxX9BLsumiCKnWob64LoQNCRtZPcSGGy5h7fUXc+KZx2IOQrdIRTnYqHjliIqXohzaRPWYbVufZst3tnHPjx4lqsVYa3B5dRea4wx1U35KAclwH/FQD4T+kiNN7LB1l3ZHnMX7nJ/KVtzv48IOnK2eR8LEQJLJlvNZeuPXpRLm1wWTddWbPkcxEPWmshX3dCCvIWE1DpC+pjBbcEGbstUyT/BqqYT51dklTBYpW63yjGNeCXONql0HGtemnkxVwqL8r9ESQypgoZ9KmEi+EuZkqitiNd1oclWSuSSscV1oHCPVGtSjjnaLbpzEOuqElVzzb6/mQ7/7no7NW1EOBVS8ckTFS1EOH6rlGvfd9gRf+m//xOsv78k/MGscOd+ChWQwvW5LCkFueSYRTOQwiSMpeUT9Pq5g82t0ZhLW6PKWFDokW63IJMzGgvPTboRRD2DzygMvAq8KyLTrtvJqVYvgVSGcSLsldky25sgzCXh1l15/ZvLPs3WHP1onHOvODRzEGlzo4Yp+d25ckThsuY63r5x/V0Ro7mekXgfn0m6EtXrnr0GdBWPgh+V/yD1HUd5KLNQNln5aVVEU5RCm2FNg7TXn8OSDP+f7X7k73+u/IG2VGIMb7CFaM5RvVpYnviEpWKLBLu3ysxs61Pu7dN2HTbsQVgZNKgi550FSSGVkqddILQhjSEoQCQTl7uSJDwkW0417IRqDK3hEw4WuiZdxgleNU/HqBp7F9RfxynWo5XQ3lelk+xmp1aFS6eqtQvV0vqK0Rq+GVBRFURRFURRFyRkVL0VRFEVRFEVRlJxR8VIURVEURVEURckZFS9FURRFURRFUZScUfFSFEVRFEVRFEXJGRUvRVEURVEURVGUnFHxUhRFURRFURRFyRkdx0tRlLcUSZLwyOYnuPMf7yUshaz9pUt514UnY3Ia5FREePahHTyx5QnifeMQBhAEueUBSODhegqYWLKBacl1ENfGsDpexSFeNphxXoMLA2IgLqRjXRkHJibXQWMFcEH3hioSBBeQDtIM+GXwamByG0E5HYi6PgD1fggm0vG88n5PxYOoZLCxYKP8xoduBhqorizh1RK8iQib5P+JepN1xLO40AMvx3PRIphyDSYr4AQ8D2x+g5gLpN/xwT7o74HxMpQruWQpirJwVLwURTnoiAhP3f8zNn/tLjZ99S7Gdo/j+Wkj6Ob/+X1WHLOMDb98BetvuJTjzzi2I5kvPrOTLd+8lzu+ejev7XgTaw3iBKq1dODRQogphOD7HZEw8SxJfwk3WEIKQdoQEzBJ9rzprIQJ2bysgfQ/vBgkFrwaiCe4IJOwDuUlBYiL6WDGGAOSNt4JMwGLMglbclomBj4kIbiQnK0glS3xISmmP+LRNL24P/0c/UnBL4Otd0bCxGbrV2jkpfOMBgEHwbgQjqfi16n3FMPUdigGMaTbSDH97LxIsB36DBvvX0MgxRqS3oCkN4CRYipg43X8yRjj8pEwGzmIHF41Tk9KhF4qYbYDEiaCqdaxYxXsWBmTTBttO47T74jnge93Jo/0PcT3IEjXwTRGMx7shyRBxidhfBIqtY7kNbCexSWOsBjwnl+9oqPzVpTDCSOiY4y3y9jYGIODg4yOjjIwMHCwF0dRDlmef+wFNn3tbu7433fy5ku78XxLErtZp20c2N92yho2/soVrL/hMo46YVVbeW+8uIst/3gfd3z1bnY8+XJznnNiDRQKqYR5XlsSJtbg+ou4gR5cMUgfXMDrZXrjt508mCFbC8lrVDaSwCBtSpiQSk/UkK35qmiNhnajCtZmA76xrC5MhQRLs1KSF86TKdny58nLnjMx+JMQTIKN21s4MdNka6F5CQRjqYR51fbf06ZsNZhvBpJWwGy8CAmTqd8Lel3WRPEqMf54hFeOcq/0GUB8iwsaEtbeZ2iqEXa8jB0tY+JkgS8yqYB57UufGJOKViZbiLT+Hjeei2NkLJOwWr2tvAbWGpwTPN/jgn9xJus+egkX/sJZFHsKi5qfohzKLNQNVLwWgYqXoiyeV3/+Opu+dhe3f+VOXn5m58LkZz8aB/yTzzmBjb9yBVd+9BKWHTU867T73hjlzpt+wh1fu5unf/IcxhpEZHH90jwLYYgpFDC+N+skYgyur4AbKOEaDZAlVJTEZoWOFhJ2gGwtJYu0uuECk1ZYWuS5IK1sxaUsdzHy05CwJBOwpPUsnE1ly4XTKk05ypZYIS6CK6bruuj1M2CjVML8SbDJ7DMRk+a4QlZdYvF5JoZwTAjHGpW32SfFTN+u2syaMbNUwrxIWn+G7crWXDjBK0f4ExFeOc610NlcbD/tiiih1/r7VY+xY2W8sTKmHi8t2NqpSliLPDFklS0/3S/NJVutaLwmilIJG5uAaO5ln37i6d1XvJMNN1zKpdecR99Qb3vZinKYoeKVIypeitIeu1/dy9Zv3MPtX7mTn23/+aJkazYajQBBePcVp7Lhl6/g8usuxFrL3d99kE1fv4eHtzzZbJRKJ7sr+R4mDKEQgp9es+UGSrjeYm7XT4ltiNg0ybLkcp1Io4uZ8w3OS2UsyWRLvEXK1pyBMyUMC0kwvZtdh/P2jzdTlS2XFSc7lteQsFomYWUwzjRlMre8eiphwXgqgB2TrVa4rCvidAnrhGy1zBP8iQh/so6tJHn3Nk0rroHFhT4SWEjclGxVo3w2UWublTAx07oRNq5H69R3vyFhtToyNpFWwhrVOgPWpCe7TjnvBDbccCmXf/gCRlYPdSZbUQ4DVLxyRMVLURbOn/8ff8P3/t/bU0mS9HquPLDW4ESwvo/1A5wTrGdwOV+g74Z6SU55++LPOreJGHDF/C7Kny1v7BgPV8hBtlqQeEAXZKtBvVdIGifsu9B6NzH4VbrzGQrYmtD7ar4345iehwjhuGC7lYdQ3DmBV1v6yZwFMzbRvE6qG99EKRXTm2RA/ttNYz82PoG8uou3v/NoNn7sUq68/iJWH7si32xFOURZqBscUreTv/POO/nABz7AmjVrMMbwne98Z8bzIsKNN97ImjVrKJVKrF27lieeeGLGNLVajY9//OMsX76c3t5ePvjBD/Lyyy93cS0U5cjiwVsfSYXLSW7SBeCcZGKXnpkFcpcuAOkrTVW4utGQzqnC1QrnkUoXdKeFCVNHpi7lSePmHN3IM+k1bl37DA149S5JV5ZnHN2RriwPobvSBZh61LVNBoAw63bYje0myygsG+R/bf9v/L8P/Tc++ukPqHQpSgc4pMRrcnKSM888ky9+8YuzPv/5z3+eP/uzP+OLX/wiDzzwAKtXr+aqq65ifHy8Oc0nPvEJbr75Zr7+9a9z1113MTExwfvf/36SZIEXwCqKoiiKohzmFEoFjn3X0Qd7MRTlsOKQup38e9/7Xt773vfO+pyI8Od//uf84R/+IR/+8IcB+Pu//3tWrVrFV7/6VX7rt36L0dFR/vZv/5Z/+Id/YOPGjQB85Stf4ZhjjuH222/nPe95T9fWRVEURVEURVGUI4dDquI1F88//zyvvfYaV199dfOxQqHAlVdeyT333APAtm3biKJoxjRr1qzh9NNPb04zG7VajbGxsRk/iqIoiqIoiqIoC+WwEa/XXnsNgFWrZo7rs2rVquZzr732GmEYMjw83HKa2fjTP/1TBgcHmz/HHHNMh5deURRFURRFUZTDmcNGvBrsP7ipiMw74Ol80/zBH/wBo6OjzZ+XXnqpI8uqKIqiKIqiKMqRwWEjXqtXrwY4oHL1xhtvNKtgq1evpl6vs3fv3pbTzEahUGBgYGDGj6IoiqIoiqIoykI5bMTr+OOPZ/Xq1dx2223Nx+r1Olu3buWSSy4B4NxzzyUIghnTvPrqqzz++OPNaRRFURRFURRFUTrNIXVXw4mJCZ599tnm388//zwPP/wwIyMjvP3tb+cTn/gEn/vc5zj55JM5+eST+dznPkdPTw8f+9jHABgcHOTXf/3X+b3f+z2WLVvGyMgIn/rUpzjjjDOadzlUFEVRFEVRFEXpNIeUeD344IOsW7eu+fcnP/lJAH71V3+VL33pS3zmM5+hUqnw27/92+zdu5cLL7yQW2+9lf7+/uZrvvCFL+D7Ptdffz2VSoUNGzbwpS99Cc/zur4+inK44pzjyfueZfO37ufNl/cc7MXJl24NFHuQ8ro3VPNBRujqynY57vDmILyRB+XzE+nq4Onjo2X+4g+/xZXvP4szLjgBz+t8J6mx3eP8+Nv3ccf//jE/3fZzzn/Pmaz/5Su48BfOJiyGHc+rluv85LbH2HzTAzx051OccvbxrP/IBVz6vrPoG+zpeJ6i7I8RkW43Gw55xsbGGBwcZHR0VK/3UpQMEeHZR19ky7d+wqZv3sveN8bwfEtcreNqNYjj/MKNSRsl1mL6ezF+AOUqOJe2jjq9l8vmKcYgI/24t61Aegq5N4oaqyGBQXyTa16jYRkVobLCEpcsSHcam2LAeUyF5RgqVoj6IC4JphtrJ0ACXgQ26VIj2gnhKIQTYKP8pKExX+cJJoGgIvi1/CVFLJhKRDhax59I9zP55hmcOMxkBTtRwTRWMMfWlAQB9JYg9POXL5F03xk7fGNIEsfgSC9rP3AWa99/Fqe8+5h5b1o2F5WJCvfc8iB3fPXHbLv1EZxzWGNwTvB8SxI7ir0FLr/uItbdcBnnbDgDz1/8yfE4Sti+5Uk23/wg93z/YerVCOtZXOKwdir3vPWnse7D53Ph1e+m2NN56VMObxbqBipei0DFS1GmeOlnr7Hl2/dzxzfu5bUdu5oHtP0RESSKkCjqjIQ1GjrGwEAfdmgA+vow1jTzKFdgbBImytnZYpbUOGq+tL8HGRmAwX7IzgJLtkziW8TrvBSJSec/fb5i0kYgnulIS7PRfkxCqA5ZakOGpGBmTGASsAlpYzNnhLRRjc3WHzqznkaIeyDuEVxh2jyF5joa6dDnJ1O/TWP+059zYFxnJazpAfu/ZyLYOgTjEI6nn2OnspwPSSH9EW9qXWwkBONCYVzw6p2TMDHgAnC+mZFnYkewL6Kwr44/GXc2r+DjCj7i26nvoXPY8QrevknMRDVdhk5JmOdN/WR5Auk+J/DA9zqzn2k0A51A4iBxs75nnmdJEseKo4ZYf83ZrH3/WRz3jtULiqjXIh74wUNs+tqPufe7DxLV4pbHimae75HECX3Dvaz7pctYf8OlnHrJKVg7f+XNOcfj9z7Llu88yNbvPEh5vNqUulY0licsBlzy3jNZ+6HzOWftuwjCQ6pzmHKQUPHKERUv5UjnzZf3sOWmn3DHN+5lx5OvzHsA3Z8pCatD3Ebrb3qDpr8PMzSAGejDzHMgFudgsgLjmYS1iQCmt4QbGYChPvDnPhALgE0bhEuRsNlkq2XeIiVsesO5OmyoDlqS4vyZU8JwECTM0nYLXhCSEsS9kq3fAgKzdWxscwuuiE17Pxqvn/eVjbwEjFt87W3BgiqCV4VgIpUws/Cv79QsPIgbsuXPv8S2JoQTQmFMsHH7EiYm3U5dYNJtYJ5t1NQd4b46hX11vErSfh7gCl4qW8ECJCdx2LEy3ugkZrKWLkMbeUAqVZ4/Q7bmWj58C4Gfvq6d/Uyj6ScCcWvZaoX1DC4R3nbCCjZeey5Xvu9MVh8zMmOaJEl4ZPMTbP7aXWz9x3upTMwvP61oSNjIUUNs/OUrWHfDZZx41nEzKm8iws8eeZGtNz/Ippt+wr43x5eQl76up7/IFdecy9prz+P0i0/Opbulcnig4pUjKl7Kkcjo7nHuvPlBNn3zXp564OcYa9Kq0hL3IOLcVCUsmUfCentS2RrsxyzyukxJHEyWYWwi7Y44F6UCbngAhvshDBaXB2ANzmtI0TyNqQXK1px5Jqs6tGqcZgLrPKgNpbIV9ywyM6vi2CRtvHdTwmR6d8QDpkkXxBUh6kmla9H38c2kyLp0m59Vi6Z1N1uwbM2RZzIJI2szzqViS64GiuBX0kpYMDH3Zyh2umyxyG1G8GoQjgvhuMxZeROmV7YWmQfYWkK4t064t45Xdy0lTAAJPZKCj4RLqChFCd5YGTs6ia3U55Y+a9OTOQuQrVakEuallbCGHOw/r8Y1Yk7SfW3cnmy1otFd7+Qz3sb6D57NUav6ePD729n0tbsY2714+WlFY35rTlrNxl++gnde+k6e2raDTd+6n9df2tOszHU6b3B5P+s+fD5rP3Qep5x9XMfmrxweqHjliIqXciTyL8/4DG++sgdjDOLy2W2Ic0i9nkqYyw6cpWImWwOYoLNdPiROYGIy7Y5YTc9QUwgy2RqADl/cLTBVBbNTEtaULehoF8VU+hqVMIC0UlAdMNSGDFFfh7tETpewpEvXg5EKWFoBSYXLFdJuhHGJbL07HJhJ2IxuhEuVrTnyGlUw3JT0NQS744FO8MtpFcyfnOqy2OhG6AI6vM2k0heOp9Uw47K3uFHZWoJstcrzqlMSZuNU0SXw0upW6KffzU5Sj/FGJ9NKWC3rZm1tKlp+56/ZEkMmYf6UhImkvQsSl9vJEWPAjU+QvPZGx2WrFTbwMUNDeJ7F5XRcmpGX9e74o6/8NhdsPD33POXQYaFuoB1XFUVZEON7J0Gya6dywliLKRahWESG+6EQYsL8LnI2vgdDAzA0gENSQSmGuV28bgCTCCRC4hukkFlBjnlpdzkhCg1jx3nERTrfsJweaMBZwAO/nk/MAZEJSALloxwuzCoxeQZ66Q0kbA1MkvMtOUy6PoJAPO26rLxCrSHug7gPTE0Ix5dQ2VoIxmTX2xkqyxy9r7OgboRLyUtKPpWST+WoEr0vVlMJyus7ARD6JCsGSVYMEry0G1tP0sycMAJECURJdpLHA5HcT4SIZD0KoCvSBWnxzs9uzNGVvGz9JvZNdiVPOfxQ8VIU5S2JCUPIUboOIAzSbjpdIq0kdKMmNBUY93Q3r5sYQIKcpatFblfDuhloDbK4HraLw2QVrm7mBV5Xusc28Tyw3QtMb+iiHZsU5a2CXiWoKIqiKIqiKIqSMypeiqIoiqIoiqIoOaPipSiKoiiKoiiKkjMqXoqiKIqiKIqiKDmj4qUoiqIoiqIoipIzKl6KoiiKoiiKoig5o+KlKIcgtUqdx+56msnRclfyksThIL+xdfZDSAf97eZNkMU0x//tTh5kA990J1EM4OjaCjYGwZUu3f5ckO5+gIBYSX+6FCqmy3lkn2FX0tJA53X3O5gUDK5Lt7AXyAZp7t4988WzuMB27z31LPT1dCmt+4gIUo8oj3Xn2KscfhjJczTUw5SFjk6tKJ0kjmIe2vwkW755Dz+++QFq5Tp+4HHBe89i/Ucv4YL3nkWh1Llxr0SEpx96ga23bGPzd7YxtmcSEcE4h0QxxHHHspqZgQ99pfTAHWQDMgngHEjnhzASAxJ4SOilDYZGnkg68HCn8wDxTJo1bZBYmT4+UwflVgzU+w21IUt9IB0kVrLHxaPj40KlwpzOOx10N33QxuDVwNY7+54KggtIB+DtZWoMKAPOSvYedzAwS8UC3rQNMgFbN5jIYFxnAyXLm/F5OTAx2Mhk22kHtxnSjOY2mX2Gjc/PRh1+SyUdANvG6W8D6WC/MXhxup6d3WZI1882BmpO87yaEEw4gkmH6fDYv2JJ5Scw6SDtgK1G+Hsq+Puq2KizgeIZXE+I6y0gYbYfTRy2XMdWImyHBzcWAxL6SDFAfC/dh8Ux7NoHu/fBeE6DDRswnocdHsZ6FslpEGURQaIIqdaQWi3dPq3h3KvPZMPHLueSa86np7+US7Zy6LBQN1DxWgQqXkq3cM7x5L0/Y/M372XLN+9lYl8Zz7ck0w6cjb8LPSGXXXs+666/mLPXn4YfLG4k2R1P72TLLdvZdNODvLlzL55nSZL9DtSN3UbikDiCOFnsKiKel8pWf086iLFIa/kQASdLkjAxIH5DtrK5tMxj6XkA1uB8A3aePJYuYQJEfZlsDdp0HYWWK+CmN0AXsZLp+mWVioZszTGxrYO3xAa88yWVrR5wAS3XTxCMMR2QsOz1jfnMlpc9ZhIwdZP+LLLcd4Bs0ToPByYCGy9e+maVrdkmyvK8WvoZLlqKBExDHuebh4CJBS+aJmaLidxftmadKN2v+ZVUwvyyW/TgymLABQYJ5vgOZvs6b7KOvzeVMJMsLlCswZVCXF84JVv77z+yPBMn2HKErdQXn0cqW67gwzx51CPYtTcVsXJlUXnTsZ7FJY5jT3sbG3/lSk67/F088cDz3PGP9/Pys683n18KIgJxjKvWkFo1PQ60WA4/9Ln4g+ex/obLuOC9ZxMWO3cCVDl0UPHKERUvJU9EhOceeYEt37yXO752D3te23eAbLWiMV3fUA9X/uJFrLv+Yk675B1YO3ev4ldf2MXW727njm8/wMvPvdH+gUsEiZP0LGcyv4SJtVOVrWI4t2zNktVkgVIkkDaAAg/xs/eiXalpQ8KasuUZ8Myiq1hTDeG5Xy9A3JPKVm3IIv7cstWKdiRMTNYNzWNxndYbElZbQOMbcJ4Ql9LKlgtpa/2aXfOyxvfCJGyabLWSnzleCtMkLJpfwpqyteDl2y+vIX2xwUQsII/5ZWu+PAe2ln6G80qRAA5so7q1mJaHgI1kZnVsnkjZX2DbyhP8ybQK5pdl/jwDzjdI2JCt9vdr3kQmYaM1zDwVHDGkstUbIsUgfbCdPGMwUZxJWDR/HlkPgUKAFPz29muN96Jagzf3wu69UK0v+OWNY9uq41aw8VeuYN0Nl3Hsu952wHQ7ntrJlu88wB3/+BN2tTpxONdiTpetNl7XWL5ib4ErPnIx6264jLPXn47nd69bqXJwUfHKERUvJQ9e/tmrbPnmvdz+v+/m1ecXIT/70TgQDK8aZP0vXcK66y/mpLOPw2QHyz2vj3LnPz/Eppse5GePvoS1BteJrhoiSJx1RZy2/GIM9JagvwTFQvpgJ7rVNXZh+0mRAOLbtLLl2w5lNf5nZs1LqxRZo6vD18PtL2ECJMVMtoYtLlicbLXCTZeAacvQ6EYorSo/iwpLKyj7V1HETslW0jiJ3KH1EyOzdLdcgmy1DMpmE0+TMBqfYZrTlNdOrNt0CYtMmitT2wwsrcI5V55XS0XMTt9tuVS0Fi1brXBgY0krp26/7/102VrqNtqs9AnBpCOYcHhVmZkXmLQrYbOrawfeWCd4YzWCfRW8sVrzvRNASgFJb4iUwo7uQ009wVbS7ojT8/A9XMFHCsFU1X6pecak1a8396bdEevRAZPNOIZ97HLW3XAZ7zj3hOYxbO4I4entO9h68wNsvukBxvZMtjyBKUmCVGu4WnVJvTf2X+7+kT7W/dKlrLvhMk69eP4ToMqhjYpXjqh4KZ0iiRNu+avbuPXLd/L84y91pIvEbFjf4mLH6uNWcNKF72B0rMqTD+7InpX87u8ggrMGegrQU+y4jMyWJ8Y0q1u55mVvWtrQs0uqbLUVa6C80qO2zCMpdFa2WuUlhbRLn3jklzetiiJOkEKaCznlTcP5DoK8rgnbDwFTBxPbzsnPHFmQSV+clbfyzjNpN9JgFIIqHb9earY848BEkmZ1SpjnykuEYMzh1SXf7/006fP3VfGqSefkp2VmJmG1GBM78D3IUxgaEjY+CW/sxts7ShI7egZ6Mmm5lDMuf9eSpCVJHI/e81O23vwgd96yjcpkDWsN8WQZqVbTa5ZzoiFhy9YMs+GXr+D6T3+QweXabjwcUfHKERUvpVP8bPvz/O6l/3nqzGwXsMtHMIb8ZGs/3Eg/eB2qOC0kr5DdKKNbd2C02QXzXcqrDVjGTgy6kgVpW70+2LU4AOKeqa6B3cAVE1hsd7RFYOoGk3QpDCAGG3XvbLutQ+9rXYsDEbyF91rrCIW9bmZlL2e8iQgv6mJzLRFMMn/3ys6RWublx/bxnn95Bede9e5FX6c8F/VaxPYtT/GDL/+Ye762pePznxMD//Z//Bof/sT7upurdIWFukHnt2pFURZMs+95l09/dPV0i+melAAI3c0Dup93JNDNtzTvytORxhFwOrfbm8vB2Dy7m5mmffz/+U2GR3pzSwkLARe9590c/86jui5e1rZ3vZlyeKIdThVFURRFURRFUXJGxUtRFEVRFEVRFCVnVLwURVEURVEURVFyRsVLURRFURRFURQlZ1S8FEVRFEVRFEVRckbFS1EURVEURVEUJWdUvBRFURRFURRFUXJGxUtRFEVRFKUNjoChyg4Kjz/6Uu5jXSVxwmPbduSaMRvOOZ554FnG9052PVt566ADKCvKQWT1cSs45pSjeOmZV7GexXVhcEWJIkwQ5J6DIW2d1COkFAKmKwNymsQhXncGURZARBC6M9ioAH7VYSJBgi4NbypgYpBuHi0SwJPuDUzdyAPy/yQFsWCSbmSleViDGMFId95P8SHxwYu7EgeAM2CFrn0XkxBMNf13V/ICi4kTTGMFczQ/Ic3o1nvZxMAfffZbDA73smHjaazbeBrvfNcaTAf2A845nnrkJbb84DG2/PBRxvZOQqkIlWoHFnyBCNx584Pc871HOO/qM1j/0Uu46H1nU+wtdm8ZlIOOERE9cdMmY2NjDA4OMjo6ysDAwMFeHOUQR0T4+WMvsuWb93HH1+5m9869eL4liTsgYY0DtLXQ3wsDvVAsYJxAPcLUI+ik7GV5YsD1FnB9JaQnzBrvDhsnmCT/XY4A4lnEt+BlB+0ONeIbjZG4aKn3e0R9HuKBVxGCyYSgLGnjqEM08pKCoTJiqQ1bksJUZ4W8GkZiIS5AUpiSLuPSn1zyECQACQTxmg+mG1MOaQAUHZTi9DcgkYXIIkkjs1PZaZ71HbaQ4IVJ2rCteriyj6t4HcyahhVcIOBLOvvIYKsGU7P5SVj21hoBG4FfhqDcEM3ORzkfxEu3V+Mkzaznl5eEkBTBBWlGOCEUxgS/0llRaexCkgLEJUNcTOccjieE+xKCiXSb7fSnKJ7BBQYXWDBgIsGrJtiay3Vfg2HGPtrzLEniWLlygI3vOZ11G07j+BNWtjdfEX7+zGts+cGjbPreI+x+c7w53+Y01RoyOobsG4N61KE12g/rYTwLxjYlsnGiNSwGXPLB81j30Ys5d+MZBKHWQw5VFuoGKl6LQMVLyQvnHE/d/yxbvnkfm79xD+N7J9uXsIZsGQN9PTDQBz3F1mcNkwRqmYS5xe0OGq+SnhDXX8L1FMC2yHOCiRNs5FIBzBkBxM8kzC5OwpryE5pUtvq9tGEyG07wMwnzy7KoxoqYtOGaBFAd8agNW+JSiyretLdwqQ0jMWlDLymkDctZZ9hoWHdAwgRB/Ey2/BZ5jcwlS1i24AUHpQSKCabFRygOJG5ImGVxTer0NdZz2EKMV5g7z1U93KSP1BrWuch1NZK+n4G0vphAwNQNpmoxNbP0WvR826CArU+TsCVsN+lJlUy4Gg32WTCJ4NXBqy8+r7FaLkhlKwlpue8wkVAYTyXMqy1+iwFwIUQN2fJa5CVCMJZQGE3wJ5cmYWLBBRYX2tb7bRFs3WGrCba+uP3ajNkZprbPefbH1hqcE455+zKues8ZrN1wKmvWDLecfueLu9n8g0e5458eZudLexbUm0REoFpD9o0h+0YhTpZWWbQWY7309zzr1zjG9/QXufIXL2Lt9RdzxmXvxPP0aqBDCRWvHFHxUrpBEic8vOVJNn/jXn5880+oTtawvsXNJ2F9PWl1q7eEsW3suEXS6ldDwubZNTRlqxTi+oq43gK0e6BwDhM5bNwlCTNZJSywaRVQWndnazScnG+oD3jU+zxcod31E4KyI5hweNV0/VoegrODvPOgOmKpDnvEvW12mVyEhAnTZCts44WNF0sqiQtp3EpjAT1w4TyyNU/mwiQs+xQDBz1xKlvevC+aOQeXVsIk8tL+bHM2qdPnjOfwwgRbSLBee9u1JOAqPq7sI/Vmy3TuFxlB/Gmy1eZnaGoGW7VQN1naAmawWOEX8GrgT4JfYUHVYSGTg6y61d76CSZhSsLmyWvu14Ks4hvSWkZaYOtCOJZJWLSQLSaVu6hkiEsm7SrdBiYSwrGEwmiMX1lY1+embAW2pdy1xKUS5lUTTDTPfm16Zhuy1YqGhL3jlKPYePXpXLnuXSxb3s+u18fY+qPH2PTPD/PcM681p1sMIgLlSiZhY+DcwiSsDdlqRUPCBpf3s/6XLmHt9RdzynkndKS7pZIvKl45ouKldJt6tc4DP3qUTd+4h/u+9xBxPU4PXI2vb08xrWz19qRdGpaKSHrGr9EdMYtpXl5Q8En6Sri+AvhttmRb5TlJBSxKOtpVr2WkaVTCvGbDqtkI8sgqWz5JoTPXi5lE8Ccd4aTDqzUVJH0/LVSHLNURj6i/Q9enNSpT0/6c3nZwIcTFRcjWXHmZgE3//GbIVqOy1YkTuTLt9wwJy9bUzypbpQTjd2aDkgQk8tIuiTL93TQYK2llK0ywHcszuHLaHVEij5lN+Kyy5Qt0qqeimyZh0SwS1sHqamN+XiWtgnmVmdsqLEG2WuYJNk6rb140tZ1OnWSZ6l7brmy1yvNqEGaVMBsz43vvPIh6MtnyO9OwtnVHOJpWwhr7meb7alLZksB07jpYJ9iaw6sl2OhA6ROTPbBfV8Kl0pyVEwasx+S+ciYnMt85w7YQEZiYxO0bg9HxA09IWpue4LRex+WoIWEr376M3/ub3+Sstad1dP5KZ1HxyhEVL+VgUh6v8MN/uIu/+aNvQ7EA/b0YrwPy0woRiGKcESTwcX1FCHLOSwSvGnVFwADEGOrDAUkxvWYrLtmONhL2x8SCV0urfLVBS31gji4+nUCmume5sHEWP784JG2wGAti02u38s4DoJCkN8koJZggv41HhLT6FYMxBi+MMb7kei8QiQ3xWICLvM7KVisc2AmLrUx913M95+7SKlg4PiVcuQY2JCxKvxtJgbYrTe3m+RUo7E276cUlk61jfple1VF6PcKr01nZakUi+JMxtu5yka1ZiR3+ZD3fjAxxDhkdR3a+kUqW13nZmg1rDdf9u/fyG5+7IfcsZfEs1A30Kj5FOcTo6S9x6bXn87/+5s7uBBoDYUAyVMz/INrI800qIl24EQeAESEuWarLw5xblyniG6q9WVfCbmDSxmzUrfNEWaPLlbpkzo1L9wbiaXcnzDHOAJ6k1a0uXYZhfMH0JEitS4EWJBRMpTtxWIh70u6AXcGY9NqtAl35zmMMcU9aQczjxh+zkRQt0YCPKcvir1VqB8/gChbbpf12tzHWwmA/5s29Xc21eq3XYYV+moqiKIqiKIqiKDmj4qUoiqIoiqIoipIzKl6KoiiKoiiKoig5o+KlKIqiKIqiKIqSMypeiqIoiqIoiqIoOaPipSiKoiiKoiiKkjOHlXjdeOONGGNm/Kxevbr5vIhw4403smbNGkqlEmvXruWJJ544iEusKIqiKIqiKMqRwGE3jtdpp53G7bff3vzbmzaw7Oc//3n+7M/+jC996Uu84x3v4I//+I+56qqreOaZZ+jv7z8Yi6sc4sRRwsN3/ZQtt2zj6Yde4IL1p7L22nM5+d3H5DKwoojw1BOv8P3vbCcpBZhEMHECTnIfisYfqyGewYU+EuQ3wLAYSAqWqK8ELh2Q0y9HGJdLHM5CNBhQ7w9mjnWT0xsqpGMHRT0gPuDAuPyGEhJIxysq5hRwQJ6AL7iSQ0oOEoOpG4gNJqe1tH5C0F8n7KvhMESRRxTnN8KwNY7+Uo2BUgVjYLIWMlEr4CSvc5lC4CWU+uvYgTLVSki1EpIkeQ1knn4RpCBEK+rYmsVULCbK5zMUJB1rzofqcrB18Krp77y2GTHpIM3iAQLWkX4Xc0lLM4wD5wEWTJLv9x5Jf6IeS1wEv+rwK5KuZ05xSQhJr4es8AkmEsLxBK+ez5hezbl6hmiggI0cpp5gEpfPeyqSHmejGJYvgzhCKlWo5T/wXBwl3HnTTzju9GO45APn0tNfyiVndM8Ed33vYTZ/ZxtJ7Fh77blc8f6zGF7RrQEgjwyMiMz7rfjkJz+54Bn+2Z/92ZIWaCnceOONfOc73+Hhhx8+4DkRYc2aNXziE5/g93//9wGo1WqsWrWK/+v/+r/4rd/6rQXnLHR0auXwxDnHkw88z5ZbtrH1lu1MjFXwfEsSOzzPkiSOVW8bYf1157P2mnN4+8mr55/pPDz/3Btsuu1x7vjh47z5xlgzp4mkAmZi1xUJE0ACiyv4iL90CRMDLrS4gsX5jdFws9/ZLsqrJPiTEV45xizxWC4GogGf2lBINODPzNtvuvS5JeYBLoS4CEnBzN7XIGuYdaLxJ6SN2KSQDRBrswdz3DDEE6TkcKUkPaXXyJv+u24wUWckzHiOoK9Oob+OV0wQyVZv2mYTx5Z67BPHlqWuvDGOvmKNgWKNnkLa2GrMsbE5VqOAiVpIuR52QMIE33OEfkzguanNMxsL1xiIIo9KOZUw55aeBzQHv57xdmWfoYkNpmKwFQ8TL31jEgSxpNvnLHk48CqZhEVLlzAxqWiJnw4ofsB3QtLMjklY9p02Lvv3fs81p+mUhDU+wtnkSgSMwasLfsXhV2XJJ7MaJ3XigiEpmpn70CzP1h3hWEIwnuBFS9txN19taH3McYKtx9h6AkkHjoXOQZQ0T3DO9rxUq6mE1aOlprXEWoNzgh/6XPQLZ7Puoxdzwb84k7AYLmm+5Ykq9/7oMTbf/CAP/fgZnMjUSWNJvyDvvvgk1n3oPC5977vpG+xZ+socpizUDRYkXuvWrVtQqDGGTZs2LXwpO8yNN97I//1//98MDg5SKBS48MIL+dznPscJJ5zAz3/+c0488US2b9/O2Wef3XzNNddcw9DQEH//93/fcr61Wo1ardb8e2xsjGOOOUbF6whCRHjuiVfYess2Nt30IHveGGvKViusZ3GJ49h3rGb9dedz5QfPYdXbRhac+eore9l8+xPc9oPHePnF3VjP4JIFHLhEMFEmYdIFCTPgAg8JvbYkLJWRTLaC/WRrzhcKXjnGn4jxKvGC10+AqN+nPhRQHwzAmrZkpF0Jm15piotmYfIzTVIWI2HOS0XLFabO5OcqWzaVLSk5JJCFr58jkzALycIb1MY6gt6IsL+OV4rTx5r/myNWIIo9osgjThYuYQaht1ijv1ilr1hPN8+51lGmGoeVesBErUC5HrLwb6HgW0fgJ4R+stCvAwBR5GcSFiALlr45ZGuulxjS6lfFYisWkyx8IxMEbCY+7eQlUxJm4oVvM42TEOK1+Z1oSFPCgdK0kNdlssUC4xYtfY0Nrq3XZCez6kxJ2AKdSEgrhUnREBdMuh9dSJ4xeFVHOBYTjCfYZKELm63iXLLVIs8kaRXM1hPMbNLUCicQx5jIpeK1oCU0qYSVK0i1ClG88Lw2sb7FxY5ib4HLP3QB6z56MWetPRXPX1gFvF6NeGDzk2y+eRv33/44cZTM2b5oSJ/nW85bdyrrrj2XC686nWJpadJ3uNFR8TpU+MEPfkC5XOYd73gHr7/+On/8x3/M008/zRNPPMEzzzzDpZdeyiuvvMKaNWuar/k3/+bf8MILL/CjH/2o5XxvvPFG/uiP/uiAx1W8Dn9e/vkbbL1lG3d8+0FefWFXU6bawqQnJcQJp5x1LOuvO4/L3zd7+X73rnG23vEUt//oMX729KvNHd6icY1KWLLkKtFCEGNwoYcLPfDMAQfKVEZMKlthB7orOsGfjPAnY2w1OaDhIUDc66WyNRQiXnuyNRvNRsD039OeEz+rbBXN0uRn+hnx2c6YN562U5UtmV5pygkx+8kWi8zbX8LqFuNmmZGRVLb6avi9C5etVnkiUM+6IiazSpjQE9bpL1XpL9awi60WNtpiAuWsK2IlCmbN86yklS0/Sduxi8xrbDL1mk+lElKrhojMutW0J1tzZGLA1A2mbLHV2T/DA2RrKd8Jk4pXsxI2i/QJ+1W2WGTetBk2qlKzfg9l2jTtSNpceXNJ3/R9Qyf26yJ4VSGopr9n24+Kl55ASooGafOk1f5ZkApfMJYQTCSzdn9srtZCxG6+vIaE1TIJm63ZK9KsbMlSuitmeSTJlITFbVhmmzROAPcP97L2+otZd/3FvOuik7B25smXJJ66LOKu7z9CtVxfVHum8ZqwEHDJe9/NumvP5ezLTyEID7srl9rmiBSv/ZmcnOTEE0/kM5/5DBdddBGXXnopO3fu5KijjmpO85u/+Zu89NJL/PCHP2w5H614HVm8uXMvW7/7EJtueoDnn9q5dPmZhrFmqnx/yUms/9B5nHnZKWzbtoM7fvQYjz38YnPajn0zGwcC5zCRSyWsQ7OeM9amEpaEHlL0SLLqVsevDWueERe8yQhvMkIsREMhtaEgvR6t8R50mIaEOQ+SUtowyUV+9pMwIa1quUJaVeuKbBVd+lNYgmy1DMjml4CpW4ggKMaE/XWC3gjT6a6SzRPU6fVg9dgSegkDpRr9pSqelY5uMlNfQcNELWSyXkgzfUfoJ1i7gGphW4E0JbNWDTIJCzojW3NlkkqYLVuo2tSxprttDtuMjVIJs1XAmKnKVl7fh4ZgNboHNqpUeTEtryFauZ5EE8GvSFoFiwRXNOl+rQMnrWbLAvAnHeF4KmEIS5etufKMwcQOU4ux9TjtHRIl0O4J1TbyiGNcuQKVyuzdFTtEQ8KWHTXE+hsu5cqPXEStnrD1u9vZcst2JkbL8/bUaSsvu+Shp7/IlR88h7XXnMNpF5yI5x1W9+1bMLmK1wMPPMA//uM/8uKLL1Kvz7yw8Kabbmp/aXPkqquu4qSTTuLTn/70orsa7o9e43X4sm3LU3z2X/01aSejaaeQc8B6hgRDctQIGNNRwWuJc9hK1BXxalBZ00PSE3QnTIS4aNPrqHKSrf2JSlAfyqFR0gJnIe7L/uhCnhghWRl17x64AgPDk/i+68p7KgKDxQqlIO7KJiMCtdinEofd2UQFanWfvft6QUxXthlisHt9TLfyHPiTpHndQMCv0FZ3uaVi62Dz6702EwEkuxFHl/Zrturoe7nevWNTkhDsHO1KTxAARHC7duda/ZqO9SxSKmGKhY7KVisaGRdsOI0/+tK/yTXrrcpC3aDtQ+nXv/51Lr30Up588kluvvlmoijiySefZNOmTQwODi5poTtNrVbjqaee4qijjuL4449n9erV3Hbbbc3n6/U6W7du5ZJLLjmIS6m8lXhj515EQCRf6QLS/tTeVAUod+mC9Gxf/ikzcEFed16bBWOmdS3qzpp241qqGTQqCN3M6+YJTAOe55r/zj3OgGel+e9u5LlMELqyiRqmulR2a5uR7OYp3cozXZSuRl6+7dgD6WbfJNPi3zliO3EjjHbIu3K4P8bkU1VrgUscJrvmK2/pmp7x2ou7cs861Gn7cPq5z32OL3zhC/zzP/8zYRjyF3/xFzz11FNcf/31vP3tb89jGRfMpz71KbZu3crzzz/P/fffz0c+8hHGxsb41V/9VYwxfOITn+Bzn/scN998M48//ji/9mu/Rk9PDx/72McO6nIriqIoB49un4xQFEVRjkzavhruueee433vex8AhUKByclJjDH8+3//71m/fv2sN6HoFi+//DI33HADu3btYsWKFVx00UXcd999HHvssQB85jOfoVKp8Nu//dvs3buXCy+8kFtvvVXH8FIURVEURVEUJVfaFq+RkRHGx8cBOProo3n88cc544wz2LdvH+VyueML2A5f//rX53zeGMONN97IjTfe2J0FUhRFURRFURRFYRHidfnll3PbbbdxxhlncP311/Pv/t2/Y9OmTdx2221s2LAhj2VUFEVRFEVRFEU5pGlbvL74xS9SrVYB+IM/+AOCIOCuu+7iwx/+MJ/97Gc7voCKoiiKoiiKoiiHOovqatjAWstnPvMZPvOZz3R0oRRFURRFURRFUQ4nFiReY2NjzXvSj42NzTmtjmulKIqiKIqiKIoykwWJ1/DwMK+++iorV65kaGgIM8vgIyKCMYYk6eKIgorSYbox3oWiKIqiKIpy5LEg8dq0aVOzi+HmzZtzXSBF6TZJ4njsvmfZest2Nt30QHfDpZsjOAJmahzOroxdZMA4SQek7tKAxgjp+9rNvG4OBNXlTabbeQYQMRika++rk+5uMunIrd3bcExXR4qlyyPTpghCN4eHFwNd3ETTTLqY1+WPsJvjXwNd/LJPz6Sr76s4B1lRJHdMeufwYm8h/6xDnAWJ15VXXtn89/HHH88xxxxzwAcpIrz00kudXTpFyQkR4ZmHX2DLLdvZ8p1tjO6ewPMsSTdGls92vmINSV+BxBOwHjZy+RxUG3kGaoM+8VEBfjmmuKuOVxfEdL6d1Jhd1O+T+I646OHHYHIqiDcaJM7PfjyDkfzbf84HF4CYbAmyvLwOc2KFuCQkgWDEYGKTb2PTCJQSTCKIn21E+a0dAKVijYAEz0+InYcTm2te0YsJTEJoYxLxcM3WUafXM52nZxzDhTJx4DEZFajEQfZ8Pu+rQejrqRL6EZOVIuVKIbesFIEAkuE6tuphqjb3VrUYiPvA1gUbmdy+99L4vw+1YfAi8MsGG+eT18y1EPWl+0+/BjbKWcIErOPAkBw/xrjHMn50QDieEI67fPfdIiAQD/diKxG2FuV2ErQ519BH3r4GJsuY0QmoR7nkzcgen4RCiCkWwPdzObtkrcE54bhTjmLDdeez/kPndXT+hyNGpL2tzfO8ZrfD6ezevZuVK1ceEV0Nx8bGGBwcZHR0VK9pO8TY8fROttyynU03PcibO/d2X7aMIRkokgyWcH2FmTtBEUwsqYDFsuRjXFN+Bjyqwz71AQ+8mXnBeEzxzTrFXXVsLEs6I9c4Rkd9PrVlIbXhAAlmNppNInh1wauD6dDb7jyIi4akYBBvv3dNBJNMa0R0KC/qTRt6LmC/zxBwYJPsGNeBPDFC3CMkPYLbv80sYGKwkYWYDkmYIKUE6U2g5GbkSVaoEWc6JGGZ/BQi+nqq9JZqWDv1QYmAE0M98YgSn858KwwFG9MX1ugLa/h25obhBGKxqfR1qKVpcYReQsHGePvlxc4wGRUYrxepJT6daVILnnX4xmGMzNhEnTOUqyETk0UqtTB7tEN7m2zdzH7bKDWLqaY/nWy9N31u/+9EArZORySs8WmIBy6Q9Du/37kAE4FfhqBsOnZySezUiaT988gEzKuCjTskYZLun23CAZU8IQ2YcQ6kAx+jmOmf4fSNVAgmHeFYTDDRoW+hCCSCTdLfZv/nogRTizD1qCPbDACBhxQCJAzA7rcjrdZg3zhm3zjESf7VMGuhEGKLBfC8JUmY9Swucax++zI2XHc+V37wHI45aVWHF/jQY6Fu0LZ4WWt5/fXXWbFixYzHX3jhBU499VQmJycXt8SHECpehxavvbibLbds445vP8DLz73R3Gl0CwFcf5F4qITrK87cAbd80UwJg4Ud5xpf5rjPprI16CP+wvLCfTHFN2sUd9dTKVrAgaBxwI97vFS2RkJcuIAKRSZETQlr84AjNpWtuGAWtn4ATjBucRImNpWtqJdMfhbyngKLlD4xQlJKhcsVWfCHbyKDjQxkjb+FiJg0Fq7oMtlKDmzozfa6RUtYJj9hQ7aqeN78b1DabrJEiUeUeG1IWJoX2IT+sEZfUCPw5v/+i4DDEDtLLLZt6TMIBS8mtDHefvLTishZJuoFJuoF6q5dCRM84/Cswy4wL0kMk5UCk+Ui1Xq7lbc5ZGuulzQErGaztIXlNQVoNtma40UmniZhC0qa9nILLpxdtlrl2UzC/LJp++SSmKkq+oLySNfPq4FfXUSPgjlka46XTEnYItrtwjSBW9BGKoQTCeFYgl9O39AFxTaati49lpr9ZWuu19XjTMLi9rcZP5Otgp/KzkLyytUpCXMufwnzPEyjEmYtC9nPNE5SD6/oZ/2Hz2ftNedw4ulv6043xkOEjovXJz/5SQD+4i/+gt/8zd+kp6en+VySJNx///14nsfdd9+9xEV/66Pi9danMlnj1m/cxx03PcDPHnkpLYdn3QvyphHhegskQyWS/iJ4S+guJYKJBC9ykBwoYVPyk8pWbcg7oNLUFk4o7I0ovlmjsCc7+zftQND4MylYqstCastCXNFbfJ4INk67CXn11rt/MVOVLeez+C4T2XZgsspUy8kMxD1pF5+kuIQ8mFfCGvLjihD3ujRvKT3sHJg4lTCTzNZ1Lk00BYdryNbSPkIQk0lY49EDt9LQj+ntTWUr8Bd/8kMEYpdJmPM4cKuZ6tbXH1bpC+sUvMWXIhYqYQYhtDGhl2TVpkVHUk88JuoFxqMCsfOYvXEkWOPw25CtVsSJZbJcYKJcoh7NJn3TNlwj2TUdi8/DAVWLrXpIvSFFs8+wLdlqhaSVKS9KT1C03M/YaZWtJXwnELA18CuZhLU49jRkS/zFy0wjb4aEtfp6ZbJlXDrNEt/SOSVs+iq3JVstMLEQjCcUxhL8qjvwG5E1Z40DYrdw2WqFSCpftTrU0/3HrPPzLS4MkEKw5GM9E+VUwkYnprpN5Nlu8f1UwgrhAaLYkK3egRJrrzmHtdecy6nnH49diFAegSzUDRY8jtdDDz0EgIjw2GOPEYZh87kwDDnzzDP51Kc+tYRFVpTO8c9fvov/73Pfbe7jncvXuMQajBNcT0g8WCIZKIK/lKP2NIxBQkMcWnCCjR0mSrtMxAVDbcSnNuTjCh3aGVqTVq+WhZhYKOypU3yzRrgvxgVTzyUlrzP9xY3BBeACQ9wj2CithNkIMBAXMtnav1vfEvIajYXEk2bXpMb9DuIeiHshLrGw6uSCMgE/bWuSNXjSOzqkFbS4N61wLUm2pmNBQiEJJc2LDDYC4wwEDtcbQ0+CtD2S4+wYAxjBWJkmYYCA77m0stVTJQw60w/LGAg8R+A5RCJiZ6knPrGzWCP0B7VMtuKObTIeguclhJKQiEm7I2atydAmhF5MsETZmk7oJYyUygwXy9QTj/GoyHitgMNgTdqV0Otgnu85BvsrDPZXiGKPiXKB8ckSSZJJXydkazoW6HG4HgcJSNViKx7E6XvaEdmajgEJIQ5JT/ZEaSXMxOlzLgAXZsvViUyTnkipF6E+JHjVtBLmVdKZN2VrtvMGi8yTAOIg3X/ZKJUwryLpR+ey/dwSZWu/yOb+s1nJygrgzeqkoWMbjfiG+rBPfdjHRI5wPKGwL8GLJKtsZbLVqcO9MVn1KkjnX4+gFkGUgJ32XAeP9fT3Qn8vcrSD8clUwsZy7EkWx0gcI5NlCHxMoYAphFjPsvbac1l7zbmcddk78IMOraOycPFq3M3wX//rf81f/MVfaKVHeUsT12M833bt9vCuJ6B+zMjCuhYsBWtwoYfrgfGjg5nXbOWA+IbqygLVlQW8issaJTlmGoMLwYUG12gA5ZzXkLBavxD35pwHqRRZiAtC3N9B2ZorryAkPQm2N849b0rCHMes2JO+xTl/hKmE1RkMK/hLrPwsJM83gk+CZ+pLrjQtJK/gJxT8SUIbMR4Vcu/eE/gJwwNlwiDmjb0D+d8AzgN6Ha7oMLsKnTODVhiy/cy0ylCemQaSUvoTjGVdAnPOa6yfrYM/2ZW3FOPA2VQmJeeNRgJLbcSSFCyDPy3nvn5YgxRDpBhOdWPMcx2thcH+9Ofp5yHK+Q4uAFGMRDEyMckZa0/lU3/+K/lnHoG0fb7z7/7u7/JYDkU5tDEmf+nan5yl66DnZVJ0+OaRv3TtT5fzuv2VsDlL3v7kLZVHWl4aegTkdXk/0/WrcLq80Rzu63cwbn1vO9XbQzmAtsVrcnKS//bf/ht33HEHb7zxBs7NrCj8/Oc/79jCKYqiKIqiKIqiHA60LV6/8Ru/wdatW/mX//JfctRRR+kdTRRFURRFURRFUeahbfH6wQ9+wPe+9z0uvfTSPJZHURRFURRFURTlsKPtHvjDw8OMjIzksSyKoiiKoiiKoiiHJW2L13/9r/+V//yf/zPlcjmP5VEURVEURVEURTnsaLur4f/4H/+D5557jlWrVnHccccRBMGM57dv396xhVMURVEURVEURTkcaFu8rr322hwWQzmcqU5Wuf8Hj5DECRe972x6+ku55sVxwss795EUQsTVMHkPngyI72Eih3imc4PuzkESpgNwpgPx5n87XTHpwKPGgY3zz3NWqA2lY7YG42Ak30QxQlxM31cbdSEPwQWC2GyA4/w/QbwgIQwjotjDufzv817wI/r8GpHzqLlOjRI7R56NeHtxN5F4vFnvR3K+d75vEk4ovoE1wku1EaJOjUbdAoOwPJxgeTDOzuoQNRfM/6IlIfQGNd4+vJtdE32Uo0LOeWA9RzhUIa4GJNX8txk8hykmEBukC3lihKTPYWKDLZuu7GeifiEpQmEv2CTvvHTg+aQIXgVsZ8ZLnzvTQmVFQDCe4FfzH7czCQ31AR+/4vAnk64ce5OVg5hKDbtnAiM5t2esQQb72B05yuUaPT35fu/jKOHh+57jzddHuWjtuxhe3pdr3lsBI5Lzp3gYMjY2xuDgIKOjozqQdAuiesy22x5l8zfu5e7vbiOqRQD4oc9Fv3A26z56MRf8izMJi2FH8pwTnnjkRTbf+gRbbn2cifFq+oQI1GNMpYap1ju605LAw/UVcX3FGSPXiwXn2Y5LWBJAfdBSG/JIitPm69IBOW2cikqnSIUyFTxpDGac5Xl1waunuZ1aQzFCfQBqwxANzMwLxqAwavAnOicpQjpgcn0gy7PNJzBxOvCojTqbJ6EQ9wiuxyHetCcdkJiOS5j1Ewq9dQp9dbxgqlGSJIZ63ace+R2VsMCLWdE7ycr+cfqLtak8Z5iMQybjgKiDEuabhLeX9nBiz5scVRxtft0iZ3m1NsQrtSF2R30dy7M4jins4eTSaxxf2oWfjb6biOHF2jKeq67k5doIScekTxjwqiwPx1kRTODbNE8E3qz380J5mFcqQx2VvoKNGAirDIZVAju1zYxVi7wyOsirY4PU4s5Jn7WOMIwphDGeP5XnIktttEB9LMTVOyi1VrA9MbY3xhSS5hBJkoCbDJBJH6lN3+EtDUEgFFzBQSgz9mt2wuBPeKmEdTDPhZCUHEmRGfu1cBSKuwzhXrCuU3mQFNJ9aL3fMH1T9GrgT4BfnjZQdSfZbxW8qqOwO6KwN8Krd+5g6HxDbdintiwg7pnacdvIUdgTU9gT4VVcB4+F4EJLUvCRYNpgenGC98Yo9vV92H2THc1joA+3rB+G+5sDMPq+x8WXnsz6jadx4UUnERY68z10zvHkQy+y5XuPsOX7jzbba8YY3n3+8az/wJlcuvE0+gbyPUnfaRbqBosSr3379vGtb32L5557jk9/+tOMjIywfft2Vq1axdFHH72kBT8UUPGanSRxPPbjp9j8zfu481v3UR6v4vmWJJ65x7W+xcWOYm+Byz50Puuuv5iz152G53st5jw7IsKzz7zG5lsfZ9MPHmPP7gk8z5Iks+zhG5t5LZqSsEWso/gW11tE+otI4KfznWVIheaXqiFh/uJGInVeQ7YscY9tmYeQDpTpUmkwyeIkTADxwAWZbMHs7Y9peXYJEpaekYXaENSHSBsJMktmIy+BYBTCUZMezNtMFISklMnWYLqus+ZNyzUR6frFi5Mi5wtJjyPpdWmjpFWeTPu9BAmzniPMZMsPk9abTPZ4HNtMwjxE2hcGzyYs751kZd8Eg6UKkK3efpnNPGeZiELKcUAs7X3nATwcR5f2ckLPLo4p7cUzgojB7LfBOzFYI9SczyvVIXbWhtgX9xy4YPNgENaEezmp9Donld4gtElz3rPlRc7yfG05P6+uZGd9GFnEt6LXq7EimGBFOE5oZ/8MRQxZs57XqoO8UBnm1eoQySI+w8DGDARVhsIqodcqb+rf+yo9vDI6yGvjA0RJ+40xYzLZKsT4vpt3G01qHrXRAtFYiIvb32Ywgi1lslVKyzCzbaON76bEBjfp4yZ8iNqXMEEgyGSrIHPuXzBAAt6ExRu32Gr7EpZW0CEpCUlJ5t2P4tIKWHG3Idy3uAp/EkK9H6IBgwta5DUyAa8K/mQmYUtxovkWNdto/HKSSViMjdsPdB7Uh3xqIwFRX7bNzbGRerVp0ldrP09oyJaHhLZ1e6HxpajHeK/vw3tjH2assoi9DNDfgywbQEb6wfNmbV94niFJhGIx4PK172T9xtM455zj8fz29jMiwrNP7WTL9x9l8z8/wp43x2dtr1lrcE7wfMv5l72Dde8/kwvXvpNiqTMn6fMkN/F69NFH2bhxI4ODg+zYsYNnnnmGE044gc9+9rO88MILfPnLX17ywr/VUfGaQkR45oHn2PzN+9j8jXsY3TU+q2y1ojFt33Av666/iHXXX8K7LjoJa1t/qV96YRebf/Q4t//gUV57ZR/WM7ikzR2dCFTr2EotlbG5JvUsrreA9BWRQtBaflq9vvEPC87PKmFzvN5ZqA9Y6kOWqDebrh1p21/C5uka2JQtPxORxezBMyny6pJWiub4+BuVptoQ1IcXID+t8mII96US5lVbS4oguEIqW/VBkLkaCfPkNqtg80iY86bJ1hLycICbX8KMdYQ9USpbhTh9rJ1NJttIk8RSq/tEkZ817GfHGsdIT5mV/eMM95SbS7bQzGbbIfGYjAPKcTinMBiEo4qjnNDzJseW9hBYN6v8tMJJWnyuJAEvV4d5pTbMRFKcawlZGYxxcul1Ti69TsmL2sybkr7nqit4rrqSN2aUcQ+kZOssD8dZGUxQ9KK2djMN8UzE8HJliBfLI7xem7u7pW8S+oMag2GFkh+3mTf1712TvewcHeL1iX4SN5cUSbOy5QeZ/LTxnZds+rjiUx8NqY8XkGSuxp9gSgleb4TpySpbi9jPSGRwEwFu0oe4dZ4g4AtSEKTgWsvPPHkmBm/c4k1YTK21hAmC+FOytej9aAKF3amEBWNz72cSH6JMtpJCm3nTcr0yBOW0O+KCvlLtfnbNrHTmwURCYU9MuC+as/ujmEy2hn3qg40TCqaN9zSTsMqU9HlR6xUUQIJUtlxhDtmaJ89U69jX9uG9vg9brrWeHKC3iIwMIMsGYI6Tx7PREKX+/iLrNpzG+qtO49TT3oado2fPS8+/yZbvP8od332I117ei/UsbraT47PQaNuFBZ9LNpzKuvefyTkXn0QQ5tute7HkJl4bN27knHPO4fOf/zz9/f088sgjnHDCCdxzzz187GMfY8eOHUtd9rc8Kl6w44mX2PzN+7jja3fz5ku7m1WspdCQsGVHDbH+hktZd/3FnPDut2OM4Y3XR9l62xPc/v3HeP7Z15tnRTqCc5hKHVOtpd0Syfo59xRw/ZlswaIqVrORio5p/mBMusPvN9QGPaKBTMwWc6CZLYz9KmGNZbCZbPkdyJmeZ9K+/baWVcJkqtJUG0q7Ei5aRlrl1adJWD2daRJI2v1lENxiGwkt8nCpgNlmpc8gNpOtHsEVpDN503P3lzAjhD11Cr11glImW83/LSEq22bi2MskLLVxgzDcU2ZF3wTLeifxrLR7DqJlnjFQSzwms0qYy1qtK8Nxju/ZxQm9uynYuC35mS9vPC7wcnWYnbUhKi69jmHEn+Ck0uu8o/Q6/X61I3mNeUwmIc9VV/JcdSV74l7AEJqI5eEEK8Nxer16h97PVMIi5/FiZZiXysO8WU+7W1rjGAiqDAZVSn7a/btTn1/iDG9M9PPq2CBvTvThJP0MgyChUIgIgqQzu1CZOpkVlwPqYyH18TA9Y4Vgigm2N8b2xJh25WeOTAxIzaaVsEkfErt02Zonz0SZhI172CidqfNkSrbmqqAvMq+YSVijW7fzUtmqD5i062KH96N+Oa2EpSfPprFY2WqZmW414VhCYU9EOBpjXCpbUb9HdSSgPuSnZ2g6so5Z5W0izSvsSytvAohvMtnyOnc5QkPCJmt4r+3FvrEPW02/41IKU9laPghh+yePZ6MhYcuW9bHx6jNYt/FUTjxpFcYY3nx1H1t/+Bh3fPdhnv/pax1przXyevoKXPned7P2F97N6eceh+flf73yQslNvAYHB9m+fTsnnnjiDPF64YUXOOWUU6hWq0te+Lc6R7J4/fimn/D3/+VbvPTMq22duWiXxryXn7SG4ISj2PnqGMYYcrskMdsRibi0i13jjEqHZKsVzsLYcSG1Ea9zO/xWZG+dCGkDId9Vy/KEuATRUHrdQe7rZ8DW0q4z3coT0kaXBA3LzSkvwxhH30CFsBS3fxZ/EYgIq0oTjJTK+B2SrdZZ6e814V6OLe2mp81K02KyjEmvER32ygz41WZ1LA8a67Ir6mVnPNLsRtjOSfXF5I3HIS9Ulze7Peb5+TUk+pndKym7MN9tdJqEVcshiRhMu5WfdiMb1eGKTxx7nZOtloHZvCsGE9n2K1uLzLMVCCayvLw20Gl5RFDaBV6UU87+eU6wdYcEWS+UXN/TdKMpvhkTlKULx/r0i2jHKnixdEy2WtGoTK0Y6qWUCDt37Erba9O/oB2kIWFDI7184IaL+OXfXt/5kEWwUDdoWxWLxSJjY2MHPP7MM8+wYsWKdmenHGLc9Jc/5KVnXgXITbqmz/vNyYidO0cB8pMumNoh+R4UgvTvnKULIClZasv9qZZenpEmPbtHF24W1sjDGOoj6fUHzcfyzCO9Ps2FMx/LM09CSaXL5JyXEYQJhZ54avPMOTP0HCt7J/Ft+v3L82vR+Nqd3Ps6PVkLLA/pmp4FsCoYo9+rZnm5xGXzTtclxiO0U11C84ps5EXip2fDc96tNeYdOY+KhPlvo9PWx9lMuvLMY2q7icVMtaC6sJ/BmKkb8nQjj+xGGXluoNPyvKgL0jUtD2twRS+VrumP55KZbjR+jS4d67MTLL6fSte0x/KgcanH7lf2snPHLiBrr+XUZGtcF7ZvzyT/8P/ckU9IjrQtXtdccw3/5b/8F6Ko0U3B8OKLL/If/sN/4Lrrruv4AipvLfQmmMqi6IboKcoiyc4RHIRU5VAl/yEg3gocCet4+GK6v1NTFkDb4vXf//t/580332TlypVUKhWuvPJKTjrpJPr7+/mTP/mTPJZRURRFURRFURTlkKbtW4MMDAxw1113sWnTJrZv345zjnPOOYeNGzfmsXyKoiiKoiiKoiiHPG2JVxzHFItFHn74YdavX8/69W+NC9oURVEURVEURVHeyrTV1dD3fY499liSZI6BEBRFURRFURRFUZQZtH2N13/6T/+JP/iDP2DPnj15LI+iKIqiKIqiKMphR9vXeP3P//k/efbZZ1mzZg3HHnssvb29M57fvn17xxZOURRFURRFURTlcKBt8br22mtzWAxFURRFURRFUZTDl7bF6//8P//PPJZDaZPxvRPcddP93HPLAxx1wirW3XAZ77zgpNzGbajXYu6771leflm7mCqKoiwdQcdJOnSRI+DzE+QIGa/s8EREDtqnZ8VRdHV6XJ3epEaPq9Hj6vS4GqWkTsnV6M3+7knS50oufbzH1bPH0n+H0vq+Ek8X1/CD/3khl/7yOgaW9XdxDRdP2+IFsG/fPr71rW/x3HPP8elPf5qRkRG2b9/OqlWrOProozu9jEpGtVzjvn96kE1fvYuf/OAhkjjBWIO1hpv/5/dZ+fblbPjly1n/scs57rRjlpyXxI7t23ew6Y4n2LrlaWq1CK+nBIUAU4vSY05O4ymLMRgRpFZB7ABWTL6HuWxdxAhJADaaejjPPFtz+JMJUa+X+w5Ssg/L+WASMJJvYuOg7VUg7u9GIyXNML7DiEESS76N23TenhPEOhLJN89k76iLDSYRxJt6Jr88SBzUY0PBTxBsjl/7qbnuq/ewsjCOo0vvKQE+MTG57taajHgVEgkZl7C5DHky4FWoeAF7k8alAfnm9Xh1VhbH2FXtw2FzXsf00yoFdWqxj5Nu5BkKhYi4HpC4vPczGYEDseAaW2jO++/AIYnFRIZuCFhSgKhH8Mvp33nmNb7fYsBId/RZgPqAJRx3GNfZeQdJTE9cpTeu0RtXKSU1euIavbUK/bUKvXH6d0+S/pSSWvpY9ndPXKUnqVNKani57/06yzurO/nrT3yBP/+9/805V72bDR+7nEuuOZ+e/tLBXrSWGBFp611+9NFH2bhxI4ODg+zYsYNnnnmGE044gc9+9rO88MILfPnLX85rWd8yjI2NMTg4yOjoKAMDA7lmRfWIbbc+yqav/Zi7b/4J9WqE9Swumf2b23ju7e86mo2/ciVrf+kSjjp+1YLznBOeePxlNm16gs13PMn4eBXPsyTT80SgXMXuGsPsHsVESWdaK8aACK7gUX/7CPVjh0lGesAYbN0RjMaEoxFevUPnGhuyZaGy3KeyMqA+7IExmEgo7okp7UoIJl3H8iSbUXXEUl7tU13uIZ4BARODjVMpaizeUmnKVhGifiHuzxrsAl4Z/HGDP2k6JmGN98kVhGjQEQ84JMyecEBsMJGhc4e6TLY8h98b4fdG2MBhDLi6JZ4MiMfDDkpYlmcdxYEaxYEqQU+EMZDEhkqlQKVcIIq8juQ1mzzGsax/kpWDY4z0lbFWSJxhLCowWitRjoPmK5ael87lqOIoJ/a/yXG9ewhtghPDvriHXfVe9sW9HWvYNtZxpT/GaT07OaX4Gn1eDREYdwX2ul72Jj2ZhHUur88knBDUOC6oMWgTRGBC4PUEXk8MEZ2VMB9hxCaM2JheIxgD485nR9THz6MBxiXoqDAYHIO2wpBXps9UMQaqzufF+jJ21FawO+mjcw34TH5sRK9Xo8fWsQYiZ3mlPMgLkyO8Xh1opi19HdO8gpfQG9ToDSI8KziByVqB0WqRsWqxqQtL/wzTJfdtQimMKAZ1fE8QgVo9YKJcYLJcxHX85ItgPMFYByY7RCYGqVmk7mUS1kkEfAFP0luvGSABW7Z4ZQ8Td1b6BBAvPSaJl87WJBCMQzhm8MrZ/rZDWQASQlxIRa9xXsfWwK+Crc+zZiL0RDV6ohq9UY3eepW+epXeqEpvvdb83RNV6Wv+Pf35xnNVSnHUgbU68oixlE1AGZ+KeHyPE/guJyLGNNu/QcHnog+cx/obLuOC955NWAy7smwLdYO2xWvjxo2cc845fP7zn6e/v59HHnmEE044gXvuuYePfexj7NixY6nL/pYnb/FKkoTH7nyKzV+7iy3fvIfyWAXPtyRxG6dJDFhjcE54x3knsvFXruDK6y9mZPXwAZOKCM8++zqb7niS2297nD27Jw6UrVaIwHgFu3sUs3sMk7j2WiuZbIlvqR8zTP3YYeIVfWBb7P5EsDVHOBoTjsbYuM1DarOyBbURj8qqgOqI3zqPtCpV3JNQ2hXjV9qXvsYS1ocsk6t9Kis8JJhjDpmEeTGwCAlrLF9SkFS2+gQJ5niBA3/S4E8YvMlGXvuHOhcI8aAjGnRIYZ4FTEglLF68hBkrqWz1RNhCQqtetiLg6h7xREAyESCLPUNthEJ/jdJglbC33jIPII4slUpIebJAkrQnYekmmm41I31lVgyOsbx/As9rva3HzjJaLzBWK1FJgrby0sy0wb+iMM5JfW9yfN9uSl7rhkEshr1RL7vrfYwmpeZc2s0b9iY5rbSTd5ZeZcivtJzeCYy5EnuSHkZdT9sN98YuqWgcJ/hVjgtqjNi5t5lRgTcyCUtob7fWwDZlK6HfuDnz9rmQHXEfz0f9lMVfpIQJAyaVrX5bmWu3xmQS8kJ9Gc/XVjDmehaRl74bRROnsuXV8Uzrd6iWeLxcHuKFyWW8WVtM5S3dpkOb0BvW6A3q+LZ1nhMYrxYZrRYZrxWyNNPGZ5hVtI2jFNYphhGB1/qYKAKVWshkucBkpYgs+mSWYKxgvCnZapXHdAlbQh5eJlwN2WpFBLaSSVjSfl5jv4ZNe140ZGs2vCShb7LG8K4qw3tqDExMic508WnITV8mPFOPTz0fuA6XmI4Qatan7BWY9IqU/QJlb9pPUGAyKKY/foGyP20av8CkX6TshZQlJKqBrSSYZHGnQEQExidwY+MwMZlt/HPTaDMXewtc8ZGLWXfDZZy9/nQ835v3tYslN/EaHBxk+/btnHjiiTPE64UXXuCUU06hWq0ueeHf6uQhXiLCMw88y+av3c0dX/0xo2+OtS9bLZh+3de7rzyVDb98OZd9+EL2TdTYfMeT3HbrY+zcuQ/PMySL/GIA4AQzNonZNYrZO45x0hSrmQtEVmkyREcPUj92hGh1P3htntEWwas4wrGYYDTCztE6asrPsEdlZUB1mY/47R84vIqjuDumtDvBq7WWsMbj9X5LebVHeaWPKyziwOjSKpiNWVD3BBekshX1S1ppapcklbBg3GCzdvBcEiZeVtkaFFxxEX02MgkzkYFkARJmBL8nrWx5xdYN55ZxAq7qEU+GxBPBPI2V7Kx6X53iYJVCXw3T/iZKHHmUyyGVcgE3p/SleYM9FVYOjrN8YJzAb//7X088RutpJazu/JZ5TfkJypzU/wbH9+6mP6i1nRc5y56ol11RPxNJsWWeRXAY+my1KVsrgom28xIxjLoSe5JexlyxuTb709gVBDiOD2oc59dY6cVtbzNOYK9LJexNZ3DMLWEGYcgkLPMSBoybU35mQwR2uQI7oj52RP3U8OaQonQp+kyNIW+SAVuZU35aMZoUeaG2nB31FUy6wjx5htDE9Hk1erwa/iLyynHAS+VhdkyMsC+aS/qmKk19QZ3eoD6n/LQicYaxTMIm640d4xwn24zLKlsRgdf+fsYJVKohE+Ui5UphzqwUAStYTyCrhraDCBAbpO4htfnsaUp+UtlyFOKY/kqVvkqNvmr6u7dao69So79ao6/xXKVKXzV9rr9co6+c/a6mFZ2+Wvv7DyVlPGyIS5GyDdN/ZzIz6c8uNxNhsSlA5SB9rm791rY+HRFMLNhago3mP+WSVidN82cxG6mpJ3jlGK8cpe3DlpNmz5XLyOgYMj4JS5Bnz/dI4oT+kT7W/dKlrLvhMk69+B1Y25leFA1yE69Vq1bxwx/+kLPPPnuGeN166638+q//Oi+99NKSF/6tTqfFyznHb531KXY8/lLHZKsV1qZVMHnXcciK4ebfHcc5zN6JtCvivompVoqBePUAtWOHiY4ehE6dfRDBn0wIxmLCsRjjpsnPgEdlpU91uY8LO/RFE8GfzCphu2NsNJUX9RjKq33KqzySUge/2A0Ji9J+6Y1+9+IJ9YG0G6EL6VgvFxODP2Hwxw1ebaqfv1ghHkirW0lPBzvIZ40HE5tmpa/RxPV74lS2Su03nFvGCSQVP62ElRsSlm6oYU+UylZ/LW0MdSivXveplEMq5RCZ1i2pr1hl5eAYKwYnKARxR/IAqrHHWL3IaK1EJFOVt36/ykl9b3JC3y6GwtaVpnapOY/dUR+76v1UXKOPqaFo6pxaepV3ll7lqGC0Y59hLJZ9SYk9SR8TMtWg9hDe7tc4PqhxlBe1LT+tSAR2O3gjMex2087gA4PGMWIThmyC16E8J/B6UmJH3McLUR/xtGveejLZGrQV/A5dNCICe5JeXqgv44XaCqoyVT0NTEKvV6XX1gls545R41GBFyeH2TE5wkQ8Je6ecfQGdXrDOuEc1cl2iRPLaLXIaKVEJZ5aP4NQDKP/n73/DpPkKPD08TciXZk246SRRsiMBMgLSTMyo5FGY2RAAiQ8EnCrg4WH37LH7nF7ewt7J1bcsXDrYHfvy5q7XeAOVouHRRiBxkgaeTcySEJC3s2MxrQrl5kR8fsjs6q7Z7q6q7orc4zifZ6ami6Tn8ysNPFGRGZQ9EJ8t3d5WgsqtYBKNaBRc5k/VmX+aJWFo2MsHBtjwViFBWMVFo5WmD9SZf5ohQWjVRaMJM9B1LvjgQVCx6HiF6gEAWN+wFhQoBIUGAsKjAVB8p4fpK+l73sFahSoEVCXAaN+gZoXoGR6RVRzW+nVuVAZvKrBG9M4DTNp0kYw3jLZw5OhiAxOqBDRHuc7B7QjZydb0+TJhkJWo0TCTCJbQghMrYYZGsGMjoFqf1ON2dIsYy85bjH/+5Ev4QfTdQfqjszE62Mf+xivvvoq3/72t1mwYAEPPfQQjuNw5ZVXsmrVKr785S/Pdd73e3otXmE95PLSB3owZ52jzz4JCtP1B+shsSJSNbSXtHCZYFb3dOkcbZLrzjDUF7roQm9rNfYilTAn1NQXOMR9WecBwqAKSSuTKpD51cEiSvrAa9+g+nooW+0w4LsR0tW4pbjrlqau4zSIhkACQX8Dx82gMmJingEnNvhCsbCvQjHItr+/MSCMpk82OLw4zEK/0rNzaDvqymG+qHGIN8qR/q6eyU87QuPgM0CfMBzhhsyiQbsrYgMvxzEawzypMs9TBh4N+9iufQZkDV/0vlAyEWPgqcah/CZcTFFG+DL7vFcbfWwZfh2BExPMoqVpIn4jZmB3jf7hOoNDNQaG6vQP1RgYrjMwVGdgd42+oQbl3XXmD9eYN9K7CghLQtX3E3kpFNJHkMhNkP5/wuujQYFKIUiEqFBgtJDKUCGg5vtTF/oNuBUIhgTKA+OSy7kwGJr4QsZ5saG8NUY0a3azPnAbk3QLNPRWtqbJc57djrNjNJGtOL+Khm9v/T/MP3SwZ9Pr1A26LgH/xV/8BZdddhmHHnootVqNCy+8kK1bt7JixQo+//nPz2mm8+QrX/kKf/7nf84rr7zCySefzJe//GUuuOCCfT1bOZJ1yXkCrkN8+HzicnZ9aychBeE8l7gk8llMIQgHHVQxp+UTSd/4aH62cjAR40FU0vltNgLccozj5dM3X0jw+0P8WXTtm1WegL5Sg0NL3Xe1m3WeG3Jq/8u55AEUnYjlpedyy/OF4g1uHT/rgkKKK+AQR6PIZ5txBBzqVtEqzCVPCFjgVhjUM1w+YAzBSExpd0hxd5g+R5R2hRSHwuR5d0hpd9T6269mK3GWcXb1ldjdV2bnQJld/WVeWTDIA8cdlYhNMWCsGDBaTKWnEBB6c6wYbQhkNePK1SYCVAHiUj5xkI/cTc4TGFeQcT3LOEJgPElOh7UkzyjM0HBH124dDHS9dwwMDLB582Y2bNjA/fffj9aaM888k4suuiiL+cuEb33rW/z+7/8+X/nKV1i5ciX/8A//wFve8hYeffRRjjrqqH09exaLxWKx7E1k8HbEeLtU+tAT/p8+dmu8nQp3d/K3nHMF8kO9mPODntBzGB4sth5Dg0WG5xUZHigyNK+U/D1Y5GmxiK2lQYb7isQZXujfREcyvaurxWLZH+hIvBYsWMATTzzBokWL+PCHP8xf//Vfs3btWtauXZv1/GXCX/3VX/GRj3yE3/7t3wbgy1/+MjfeeCN/93d/xxe+8IV9PHcWi8Vi2acYA3UDFQ1jGkbTx9iE5zGNNxrjjinEqEaMGcSohjGDGEv/HtOI0d7V4g4Ab+jZ1A5cKmWf0cECI/MKjMwvMjJYYGRekZF5hfT1IsPzkufReQWq5TZd1YBd9Rz6ak9g2+4BGnFOLUIWi2W/o6O9PwxDRkZGWLRoEV//+tf5n//zf9Lff2CMEL0nYRhy33338Ud/9EeTXr/kkku4/fbbp/xOo9GgMeFuPSMjI5nOo8Visbxm0AZZNcgxgzOqx58rBjmmccYmvycrGmd0wvPYhO/kfFO1fEaH2XcYAdUFPtX5PrX5HrV5PtUFfvrsUZvffC95rs/ziAuzb8VpaJctI0f2cAksFotl/6Ij8VqxYgVXXnkly5YtwxjDJz/5SYrFqUeF/ud//ueezmCv2bFjB0opFi+ePKjw4sWL2bp165Tf+cIXvsB1112Xx+xZLBZLZshI440p/EqMX1F4Y8mzP5b+f0zhj8V4FYVfTf+fvuaPKbzm9yoKp9sx9CzTYooCs0CmDwe9MP3/Qqf1+s55Ltvm+UQLHOJBSc9un9iGXXGZX4dLMs2wWCyW1xIdidc3vvENvvSlL/HUU08BMDw8fMCP1yX26HbQvJXlVHz605/mU5/6VOvvkZERjjzS1spZLJY2GEOhEVGqhpRqIeVqSLnSSJ6rDUq1kAWNKovCMYqViGI1TJ4rIYVqRKESUaxG6XvZ3vHwYMb0S+iX0Dfh0S+hT0CfA/0y+UxZTP7cnt8Lpr67V02Hud1cA6CuJDVlu6lZLBbLgUpHR/DFixfzxS9+EYClS5fy//7f/2PhwoWZzlhWLFq0CMdx9mrd2r59+16tYE2CICAIcrr1usVi6QqpNKV6IjR9tWRQz3K1QbkWpo/x1/pqyeul1mth8lo6GKgf27utzQblCcI+l7DsEPU5hGWXqM9hcLCG6hPoPonuE6iJz/1i8mvl5Hku92V/g1vM7a6GFovFYrF0S9c311izZg2+f+D2bPd9n2XLlvHLX/6Sd7zjHa3Xf/nLX3LFFVfswznLGWPSwX1yKqTskzzyy4PxEZQP4jw/iumr1+mrN+irNyjXG/TV6vTXGsnrteTv1nv1Bv3p3321evpaSF+tjvMauXVsr6kXXeolj1rZp1b20oefvua1fY9+w5GH7Cbuk4R9LnFRZrp/CDQrik/jiPx+Z53zbp/3YSbvw2gz82DPg4P4NzTke67IO6+ZmWvePthID+YD22uM19zNNQA+9alP8aEPfYjly5ezYsUK/vEf/5Hnn3+ej3/84/tkfoQUyUMItMq424qUCOngPPwMuA5m0QB64SCUgt7vZMYgYo2ox/TvrGIEhAt8wgUBcb+byU4tw+QalnJFIWNDY55DfaFDY55DliO4SgWikoyvpd0ejPVhDKUopNxo0NeoU27U6Ws+Kg3663XKjQZlVado6pRVPf3cnsJTpxTarmqzQUlBpeRTKQVUm89Fn0rJb/09+X2fanH8tUp5/DuxN37DAaUFYewQxQ6xlswL6iwsVZhXqOHI3kuK0oKx2KcWe9SUxyIzxlK9kyP1bgpO7werVEYwFJfYHZW4f/QY5jlVTiy+whsLWxl0et9FXRvYpcpsiwe4WfUTCM0JfpU3ehUWOVEGhzXDqIFXleFV5WBwmCc186WmLEwmZZVh5fF8XObpqEyIQ1nUGXBqlEUjk8NaRfk80ziEJ+qHMaoLlGREv1ujz2lkItIN5fDc6AJ+M3II2+t9FNyYgUKdgaCB5/T+nKg1jNQL7K6WGK4VcKSmXAgpBw28DMbyMwbqYwGVoSKN3cmgU6akoKjA073fZgyIukQMO7jDLmjQZY3q0+iiyUZSInCqEmfMAZWeCzMc1FjEyeDJ/ohAhkmO8pNMMrh7vlAGf8RQ2K3xRpO8qCSJisk4Wz1HG/zhmMLOkGBXBEIQl1xUyU3G2cqgvCarEe5QHXcoOU6r/gDVH6CLXraVdFLmOoaXdCRaaVwvp7FX90AYM/PSXnzxxWzbto1ly5bx9a9/nfe9730H7M01mnzlK1/hz/7sz3jllVc45ZRT+NKXvsSqVas6+m6no1N3w32/fJCf/fMGbv/RPUT1qLVh9AQhEU4iXHvtPAIwYAo+euEAZuEAFOfQrdIYiDWyoZCNGKH32LzSPO0KwoUBjQU+qjw3CRPNGwaMKWRkJld+NZdPQn2BQ32hSzgw+aDlaEU5bFAOU3lJ/996LWwk4hPWJ7zefC+Rnb70dU/brmqzoe65jBUKjAUFxoKAShAkfxeC1uuVok9t0KU6z6U6kIjOWDFgrBQkwlP0qRZ8tDPzWVcrQRQ5xJGDMVNve0IYCoWQYhDheWpO5x2tBY3YIYxdlJZMrKIVrb8M84tVFharDBZqcypQKyOoxh5jUUC9dU1QMy/ZRwAOC0Y4pryTI4u78eTsjzfawEhcZFdcZjguYfYoaSWZgsXuCCcUX+ENwTbKzuwHAjYGhnSJbfEA2+J+FE4rY2LeoIw53h/jjV6V+XOQTGMMFQM7lGG7gojx320iDob5qYQV5yhhY9rl+bjEc1Efo8abtHxNBJr+VMJKIpxTXl27PNtYxNONQ3k1HmDvZoRkacsyZMCtUXbmJn2RlrwwNp+nRhfxcmWwlTa+jEleyYsYKNTpDxq4c6iYMAZG60FLtrSRe/yGyRx4jqJcaFAKQtw5SJ8xEFZ9KkNFKrtLaDV5v28hDZRiKCqEN8fCZ0Mgh13ksIOIps4z0qBSCTOFOUpYnMpWRbbPA4xrEglz9nq7OxR4FfDGBE4tydp7C50gYf4c87TBHzUEuw3BiGHPOofm0iovkbC4KDBzueGNMXgjMYWdEcGuEKmY8kBjXEFc8lDFVMLmkCfrMc5QHW+ojoj15Lxm+ckRqP4CcX+AKfSm0tyRAqUNxaLPBWcfS/+uER668QGefeSF3pZ/UybK1rlvXcal/34t5751WU8zOnWDjsRr27ZtrZtrfP/73+fSSy9te83TD37wg9nP9QFCFuLVpFapc8e/3cuGf7mVe2/cglIaKQR6T4GZCSEQ0kE4U8jWDJhSgF40iFkwAIHX2ZdijWzEiWypNvNqDL6JKekGRR1S1CFl1aAoI7ySJghiSkSUogalOJWfqPloUIoarf/3RQf2zV32JaOFVGyKidiMNgUnKFB1C1TcgIpfmCBCheTzE57HgoCG62FEcuozvkEVNbqooZNKJANoEKFEhCK5b3VHJKc66Smc/gi3L0J6nR2gtYY4lS2t2xSC2uVJTSGIKBZCXLezGmptIIxdwihp2UqY/ovNwrUUmoXFKguKFQaCRsd51dhnLPKpqeZ+22EemiXFIY4p7WJJYaijAq4xMKoK7IrKDMUlNHsWZKf8Vut/r/OGOL74Cq8PtlPoYJRfY2BUF9gWD7A1HiDCnVJG2i3jIhlyvF/hjX6VPtlZBUlNG17VSetWvctDsDdBwgodCkNNO7wQl3guLrNbB3TTh0qiGZA1+mWNouispS/UDs+HC3m6cQivRPPSVzvLExj6nDr9bp2y7Ez6lBa8VJ3H0yMLeaEyH2VkR79hS/r8kMFCnX4/RHa4jVYaPrurRYaqpS7zBIEbU0olrJPWaGMgqrtUhkpUdpdQkUM3xxlcnbSCFRXC7XCDCwVyxEEOu4hGp8e1NNUxqL5UwvwOJUyBbMpW2Kzo6DAPMJ5JeoV0KmEa3Cp4o8lz93kTWsI6+ZoxeGOGwm5DMGwQmqlrWabIAtC+ICoJoqLorJeNMbgVRWFnRGFHiIxNx3kC0J5ElVziogtuZxImGnGrZUuG3VUWG1cSDxRQ/QEm6O5GP1ImZVnfc1h53hu4aPVJnLVsKb4/Pp3nHnuRjddv5qZv3MK2Z1/FcSUqnp2ECSlaN847c92prPvAKs678izKA6VZTW8meipeE1m6dCn33nvvAXtzjV6QpXhNytk1yubv3836b97CQ7c8mh5sTPsW2QmyJYACqciYiBIR5fS5aCKKRJQnvFea8F5pj+8F2Fac2RBJmQiMnwqLX0j+DgIqE1pyWq08foGqGyTyIwMqQfL5qhcQO7NrEjfCEJcN8YAhLjG5C4ZJumvIhkA2BELPrRar2Y5iAoMupl1a9jwPKCASyFDCHPNaUhQo3L4Qpy9C7lFY0Rri2CEOu5Gt6fMcR1MIQoqFCHePbknGQJi2bEWqM9lqR7OA6ErFwmKVhaUKZW9yAdcYqCmPscinGvsdFChnznOE4sjibo4p7eSwwihyQjWvMVBRAbvjErui8l4tTd3nJc/H+Ds5vriVY4NX8cTkdTqm/VS2Bmm0afnpnGRZDncSCXu9V6W4R0tfwxh2KNiuDNUe9X4JMMx3FPOkJthj1kMjeTEu8VxU5lXdrNCc2zbqoloSFoh40jYTG8lL4XyebhzCi+GCVJjnsk4T6et36/Q7dYpysvRpA1trAzwzspBnRhcSm9lvM00Ehr6gwWChTtkPJ5VvjYFa5LG7WmR3pUSs55qXbAQFL5UwP9pL+qKGQ2WoRHV3iajh0ovjDJ6GYtoStufhPwY5krZs1TqVuxlS3aaEKcyel/FrkLVUturdyVbbPAHaNRgv6ZGyZ9OVWwV3DLyK2KulaVZ5JPKlgim6PxqDW4XCbk0wZNq2NHWTBaAKEBUlcWFvCXOqisLOkMKOECc0SSXmHPOML1stYXsONSFChTOctGzJetyTy+K076CaEtam254UAm0MjhSctfxYLl5zEueteD3FwvT3ijDG8MR9T7Px+s1s+Jdb2b1tGMd1UDPdAEskdy032nDSijdy0QdXccG7z2XeIYOzXcyOyUy8LPmJ10R2vrKbm799Ozd94xaevO9ppBQUVMi/cgNFK0bUpEfVSaSl6vjUnICqE1BxA6pOIX0vea3q+JNeG3MC6k7AaKnArsVFqod6GD+DTuJ7kJx4kmehQcazP/B2lmeI+w1RObkwuBey1TarKWGFpCXMSJCRBJVVP/EkzykoZH8IvkYpB9XKy+AiCgSuoygEIa6viJVDqObal2ZqmgVH34lZUKwwWKgTGYexKJhTAXamPE/ESStYcQikYFfUR2R6U9CbKs9BcVzwKscVtuPJmG3xIFXTXctPZ3m0iuJHunXe4I0x4FTZpZLrt7KkKAwDQlEzBV6My2xVRZqdpnobnXadI6Zf1qgqn+fDhTzXWDQnYZ4pz0HT79aIYsmLY/N5enQhDTVXYd6bZrlYCkN/UKfoRtRDj93VEqGaq/y0I/mFin5E0Q1RFY/qUImw5meah68hUMgInFEXKsn5aa7yM3Ve0oMhLiuMa5B1iazJDLImpKYSJhR41aQroehtT7O983xAG4IRTWG3QcbZ/IKQTDcuCpRj8MYiCjsj3JrOcotBFxyULxFhjDvcwKlGmeUJQBdc4v4CaiBApC1vxsCbTj2Si9eezKrz38hA/9SXKM2E1pqHb32MjdffxqZv3UZluLpXS1izK+Gxpx3NRR9cxYXvO49Dj1zUgyXsnJ6K19/8zd/wsY99jEKhwN/8zd9M+9lPfvKT3c/tAca+EK+JvPzUVjb+6208/M8/4otP/79csxWCqgyoSZ+a41OVAVWZPjsBFekn78lUcGRAVaavO8n3mp+P5MzN1KroYUoddnfsAbUFDiOvz3HogJyrPQwQziObs0sb4oIBJ6tT2t6YvggKOrdllK7C9fMby8lzYuaV62RXTJiMKxTHDexEiHzyJIY3ll7Ba1U7Z4sAjvW2MSBrud3E69W4j126fy6V6l2xIyrzm9phGcjW1FQjly3bXpdbHsbQCDvrXtsLBKC3BxCLXPIAnCrIMAvZmhojTOYDdE9EhhAM5RaHUIa+l5JK67yWsvByBXQuewQAYrQGsc4tz5AI2NJ1J3LxupNZc8EJLFzY19OMOIq575cPsfH6zWz+/l00aiGHH7eYiz94Iavffx5HHn9ET/O6oafiNbF74dKlS9tPTAiefvrp2c3xAcS+Fq+JfHLgHZxYe5EqHlXhUcWlJrzW3xU86rho0ZsWHLFkMRTyExNV8jDF/MSreojD6NKDXLzm55sZT9XlMEPMQARBfiIkXYXr5Sd6vhszWMrvGkdXKF4/uDO3PICTyi/lehv6N/qv0CcbueVtiwcY0uX88sJ+nqkfmlveaOjz8PYlueUZA2GY33kCwGwrZNiCvzduBWSUX54RdHa9bo+QDQiG88sTsaH/5Xx7CxVeGsu0V8ueyFS88mTJ0kX80/o/yiWrXm2w46VdHPH6wxD7wa3vO3WDjq6Me+aZZ6b8v2Xf85y/kF9Hva1RsFh6SV61+haLxWKxWPYdbgd3Fe4VhVLA695weG55vSLHemiLxWKxWCwWi8VieW3SlXhVKhWuvfZaTjnlFPr6+ujv7+e0007jc5/7HNVqNat5tFgsFovFYrFYLJYDmo5vwh+GIRdeeCGPPPIIb3nLW3jb296GMYbHHnuMz3/+8/zsZz/jlltuwfPy7WdtsVgsFovFYrFYLPs7HYvX3/3d3/Hiiy/y4IMPcvzxx0967/HHH2f16tX8/d//Pf/hP/yHns+kxWKxWCwWi8VisRzIdNzV8Pvf/z7/7b/9t72kC+CEE07gj//4j/nud7/b05mzWCwWi8VisVgsloOBjsXr0UcfZfXq1W3fX7NmDY8++mgv5slisVgsFovFYrFYDio6Fq+hoSEWLlzY9v2FCxcyPJzjIAwWi+WAwN5K3mKxHHzYI5tl/2bmUXot+4KOxUtrjeO0H01PSolS+Q5GZ4FFS5LRcGUOYyc4rsTEcfL/nMZqEDo9cuR0BHHCfPPAJFl55RlAT/h/Hnlmwv/zyGsOaprb8olcBytTWuS6ySgj0DlupsZAbJz0/9kPimkMhGmeziFPG4ErVCs7jzw/xzxjwBUagcltn0iWK8dSpgGcfPNam2Zex7Wp/p9hnhEGk+c2IwxG5LjVGINxROv/eeQh88trlgkPScuklvYIYzr7RaSUnHLKKbju1PfjiOOYX/3qV68J+ep0dOo8aNRC7v75g2z81u3c9dMHiCOFdARa9WZHc1yJijUDC/tZ+/7zuPDdZ1M3kpt/9hC3/PxhapUQx5Eo1ePR0Y2BKMZEMTgCUw7Q/UVwZPJeL0cpT3cBESlEQ6FcQ/3wgOoRBeKyA3rCAayHeW4DvFGDUzWokiDsE6hCeiboZfkvnZ6MDU7N4DQgLkE4KIjK6Wd6uj6TPKHBqYNsGLSfZMYlkuqeXi9jmitDkKEB16D7NWpAgdPjvOa0DIiqRFYchAIxGCMGI/BMz5eveZQ2sURFEhSUSiHlcgPPV5nsEkJArCSNyCVWsKBY59DSGANBHUgXr0eZzbyGcthVLzHcKLAoGOPY8g5eVx7CEaany9haPiMZjooMx0UGnSrHBDs4xt+JJxXaCKTozXG0Oa3YSJ5rLOC5cBHDqsgCr8pCt4Ine/sbJsJq0AieqyzgiZHFvFQfZDBosKhYoeDGvd1mJpSXR+oFto3182qljELiSI3IYp9Pp6cjSVx1UTUXXBCBAi85H/Vyn2jNfywQow6MuRALjAPGIZPjGQLQ4FbArYKIwXgC7WWZZ/Aq4I8aZAzhgCAcBO1lcW5Kz4VVCIYN7hhoD3Qg0G5250InNHhjGm/MIPSEjF6v07TsIKp15As7kS/vQjouYsEgDPQjZLPGrtcHthizaxizewShDaJcQpTLCNnbHVE6Eq00QdHjgrecxuq3n8np570ex23fSHMw06kbdCxe1113XUfBn/3sZzubwwOY/Um8JlIZqXH7j+9jw/W38cDGX2GMQQiB0d0VHpqyVeovsOrd57Lmvedy6gUn7tXKFYYx921+kk0/fZDb1z9GFMZzkz5jIFYQRRCrvQ4NBjAFL5GwvgLIOUhYU7ZijWjEiFAxVRkr6nOoLgmoHRGgCnOQsOYBv2HwRg3+mEFOUUehXQjLgqhfoPw5nHia8qMMbg3cWps8B8L+RMLiIumXZp+HBqeRPGS896SMMMRFiMukecztHNCSrWae2ONtgy5pVL9C9+u5SV+6fYhaKls1idirhcRAQcNAhBiMwZ29hDU3bR0LVOigImdCtfc4rqsolRqUyw1cb/YF+Ob3lBY0IpdG5KLN3i3bnoxZVKpwaHmMPj9Mvkf3y9jMi5RkV73ErkaJWrx3idITMUeWd3Ns3w6WFMe7s3e7jK3lM4KRuMhwXKKi/L3yJJol/m6OCXZwlL8TR5hZSZgxAiEMygheCufzbLiIl8L56L06mhj6nAYL3AoLvAqu0LP6DSeeyV+qzePxkcU8PbaIht57iJeiG7KgUGVRsYrvzHKbaW7aAsYaPlvH+nl1rJ+Gmqpy1iClwXF0kjPH45qJBXHVI665mHiKQp4wCF8hChpcPftKguZ8KhBjbiJcDTnlcaYpYK1dZq7H0Rp4laTyau+8JEt5YOYqYemG41UT2fLG2OtcaABVgEYqYWYuUpR+z60Z/GGDP8LU5yYJKgAViKSlaI55Mkplq2Jw4qk/BnQ5uu1Uec1apBDnhZ3Il3cihqt7z7qUMK8fMX8Q+pMaUDGnA7fC7B7B7B6GsanH1RXFYiJg5dLssgApBVobXNfhnItOYs3bz+SsNSfgB3YoqZ6Ll2Wc/VW8JjL06gi3/uBuNlx/O4/e+SRCCoxp34zfrLnwAo+Vb1/GmvetYNnFp+H5nY04UK+G3LHxMTbe8CD33vYkWukkczrpa256SkEUQxR3fFw1gCn66HKAKRc6k6H0ANWSrUY8pWy1y4vmuVQPD6gtCdB+B9I34YCfnNAMTtRZHiQn1bBPEPWJzmobWy1NBqcpW1PITzu0C+EANAa7bHkzE2Qr6jzPCENcgqgvOcFCB3np7yWjVLaivQsl0+XpciphfXq8e+BM6xQQDYmsSETVQeguttKSgoEYMRB11vLWLFgqQRw6qHBq2Wr3Zc9TlMoNyuU6jjNzK1FL7rSgHrmEsYvSnZc8AidqSVjJizrOi7VgdypblWhv+WmbJyOOKe/k2P4dHFoYax1C2mW2ls+QylaRiipgOsxzUbzO38XS4FWW+LvTbyVC1S6vySvRIM82DuGFaD6x6ew4KjD0OzUWeBUWuFWkMC2Ba5dnSA5/2+v9PDaymN+MHkK1tUPNhKHshSwsVFlYrOLKmaWv+X4t8tg62s/2Sh+1yO8wL8l0HI2UpjPZm7hP1FxU1UNHko6PNDKRMFlQnVWETJAfxhzkmAu1vWWr/ddnIWHpz+vUktYft8YUlTrtv2rctKXI7SCrlWdw6+CPJLIlO+y0Ykh7TAwIwgE6k6IJFY/+UCJbU8lPu68aJxEwFYCRXZwL46SS06vors69XUtYqxYpRr60E+elnYhdY517ouvAvAHkgkEolzqoUG4292rM0GgiW6Njbct3eyEEolRElstQLKYvtc8TQmBIKvLPWPkG1lxxJisuPoVyf6HTJXxNkKl4xXHMpk2beOqpp7j66qvp7+/n5ZdfZmBggL6+vjnN+IHAgSBeE9n+/A5u/u5d3HT9bTz7yAstyWqKkXQkyy85jbXvO48Vbz2DQnluO9PoUJXNN/2KDT9+kIfvewZBKn0wqXaGMIK4c/lphxFgigG6L8CU0gJH8yDSlC2lEfW0ZavLFsCp8hoLPGqHB9QODybX/k084DdlK5xzow7Kh6hPEPbvUfs3oXuIWwe3bpBzzINU+gYTCdNTtbwZcMJUtnqQp6VJWsHKqYRNzGvKVjxBtuZ4LY4RBt2XdEXUpSlKHAJEKBBjDrLqINRcl9BAn0pawvrjyS1vEwqWKpKo0MF0IT/t8vwgTlrCSg2kY/bK0wYakUsYucS6i4JsG0peyKLSGIeWKgQTurJNbGkaqhfY3SgzEgZzzis7DY7p28lxfTuYH1TRZu8pjqkCQ3GR0biAmWNVti8ijvJ3sjR4lcXeyF7vCwGvRv0801jE8+FCGmZuNcACzTy3xgK3wqA7XmPeFEkpYFejxOMji3ly9FBGWk3Is8Uw4DdYUKiyoFDFkWav37ARO2wb62f7WD9jYefC3C5PCJByCglr7hMa4pqHqrrosAf9+aRGBDqRsCn2CQyIioMYc6DqzP0405QwN5WwKY5rTmNCV8I556UC5iWyAnvkNeVnxOCPTt3S1G1e1JdKWD/JRrnHOpWhwR+GYCQ5F841T7uphPlMrnBtnnuVwaukstWY+7nJkEx37+4bzVokhXxldyJbO0YQc23L8FyYP5hIWLEwQcLMeMXOyFgiW8OjycFgLkiJKJWQfWUIkvJTU8KaZcQTzzyatVcu4/y3nMa8hQd/GX+2ZCZezz33HG9+85t5/vnnaTQaPPHEExx77LH8/u//PvV6nb//+7+f88zv7xxo4jWRF554hU3fvoNN37mTBYsHWXvVSlZesZyBBdnsTDtfHeHWnz/CN/7XTYwNV8dbtjJqaDVi/How4zmIhkKGMaJH17ztlSehvsincmSBcL6HV01q2JLuIRnkkXT5CPsEUVkgw0S2enGCaZsXJCfWxjwQWiStW+He3VF6hXYMURmi/uRvJ+1KONdCSTuMNKh+hRpQGAdk1Ulat+KMbiAjDPTFmAURBAodJd0IjZrq7N4LDIVCRKlcp1CMiGKHRuwSq7nLVru8Pr/BIaUxFpaqVCOP3Y0Sw41ixy1N3TLg1Vjat4PjB7ahjWQoLjEaF1Fz7jc0NUURcnSwg6XBdqQwPNs4hOfChVR1py1N3eGgmedWKYs6nlT8ZvQQnhhdzK6wPPOXZ4HAMBjUmR9U6fNCdlbLbBvrY6RRIKttRgiQwiAw6NAhrnroRhYXTyV5OAYRKKSnIRRJV8LK3GWrbaJIJQwQE67bkh23oHebl0iKkcmB2h81+GN01fLTbV7Yl3RbVwH4o0lrWpbnQu0llYTaE0nXxYrBqWdzlGmd7oyBRogYruC8tBO5bWjOlbltCfykK+L8geSa993DMDQCvb6mvonjtCSsNFDk6k9dxoWXn84hS+Zlk3eQkZl4XXnllfT39/NP//RPLFy4kAcffJBjjz2Wm2++md/+7d/mySefnPPM7+8cyOK1r/j8x7/KbT/dkt/N+3wPCnOtke2cuOzROCK/miAjIC7ms2xNaovEDN0fektUSmqK80K7E2qJc0AVNfG8/G5GJB1NYaCRWx4YikGH/Yl6xKkLXyHotA9TDxDoPHcJXhwd5MWxeZkJ7J7EsaQR5nfthlECtSvf7kvesOi4G2Ev8Hcnrfe55Y1ovFp+eXkjlMGr5njFjNL4P783v7x9xJkXncr//MW1+3o2Dig6dYOuizWbN2/mtttuw/cn9+k++uijeemll7qfU4vFYrFYLBaLxWI5yOm6H4bWespbxr/44ov09/f3ZKYsFovFYrFYLBaL5WCia/G6+OKL+fKXv9z6WwjB2NgYn/3sZ7nssst6OW8Wi8VisVgsFovFclDQdVfDL33pS6xZs4aTTjqJer3O1VdfzZNPPsmiRYu4/vrrs5hHi8VisVgsFovFYjmg6Vq8lixZwpYtW7j++uu5//770VrzkY98hA984AMUi3O9na3FYrFYLBaLxWKxHHzM6p5hxWKRD3/4w3z4wx/u9fxYLBaLxWKxWCwWy0HHrMTrpZde4rbbbmP79u1oPXk8gU9+8pM9mTGLxWKxWCwWi8ViOVjoWry++tWv8vGPfxzf91m4cGFrhGtIbrRhxcvSjrzG8NoXiEjjb6ugSh6q7IHMcFwYbXBHG/gvh8T9HtGCAsbNaLBfAGMQGgq7DNoTRCWBcTNcPmMQCoKhZFytqAzaz3icHQ2ynv7XT8cPyzJSg1MBpy6J+wyqbDLPEw2JqhYQpRjRFyOy3WTQdYfKiI/jK/y+EOlkdwAwJnk8PzJIwYk5pFSh4GY7WFKoJdU4AARlt4EvVaZjejVih+FGAaVFKyfLPK0hCh1UKBGOQUiT7ZhlGsSYg1sRaNegPWZx+68uMODUk0GMjUz2+2zzkoHuvbFkDHXlg3GyPq4l+5zyQKhk4OZME5XBrWlErFEFiQ5kthupMnjDId5ojA5cVNHN9txrDLIWI5YshnoDMzwKU9zl22KZjq7F69prr+Xaa6/l05/+NFJmeZSyHEycc9HJbLn114yN1HAcicpo5HXpCLQyOAK8ok+9FmWa18oNFU6o8IbDZHDjPh814KNKbm9OPMbgjIa4u2u4w3WEAQP424Gnh4nmBYSLikTzC9CLk3kqW0Ilg30KwDQADMFugw4gLAvikuhZ4UHEBicEp5EUEJrF9GAIlGeI+hIJM16PTqwaRJwu38TNo54UxJQPOkgHVe5FpAYZJQ8Rk05U4u8GIw3RgCYeNKhijyTMgGgInLpEhMkgsQaDGfZBmES+BqJExHpwKDcGdMNB11xU1U1G+U5/xdqOIm4xxu8L8fvCnuUZA0pLtBYYBNviZEiT50YW0Oc1OKQ0xiGlCr7Tm8JRpCXV2KcS+0TGobl8w1ERVyj6vAZ9bgNP9uZ4EynJjlofr1b7GI0KaZ5oVWIZYxAi+aNHhxmi0CVquMRRczRxgVEAJhEwR/dOwgxQcRAjLmLMQZhkojId59t4oHyTSFGP8mQIbkXgVUBo0ZoNpw446X7fQ+mTDYM/Cv5osu83j2tuHYxjiP1EjHpy3AbQBhmDE6XHNSHQLuCRLH+cvE+v6nq0walr3KpChuOVK15VJ+fCokQVnaTyrBcbjTa4YxH+UIg7FrfOhe5YhAFUyUWVPHSxd+de2VA4lQinGiEMsGBesicuWQyVKmb3MGZkLKmt6CGOK1GxJigHqDAmjhTSkeiMyjPNvFJ/kfPfcU4mGRYQxnTXDrFw4ULuvvtujjvuuKzmab+n09GpLZOJI8WWzU+w8Yf3svmnDxLWo54cRIRICpRCCM684HjWvGM5515yKqW+gMcfeoFNP32IjTc8yMhQNRcJS2YKMGCkIO73iQd8dMHp7kRgDLIS4Q3VcIfqCGVa022fB+GCAuGiIvFg0F3tnzHpiRmkSmplp/14+qyKEJYSCeu6tlElsuU2EsmbKU8AKoCwD+LyLGqMTSpbEYl4dfKVtDCmAsCZ8eN75ckoKeyJtAFmpkztGOJBTTSg0YUOvrBHnggFsi6RjUS2ZkQaRH+UPErdtdoYAyaUqJqLqnqgO1qjeOUIvy/EK0VdS5g2oLVAKYmZcfmSrXTQr3NIeYxFxSpul1IUa0FVJbIVapfxLbFdnsCXMX1ug7IbzipvV63Mq7V+hhqF9NXO1qsQ3QuRMRBHDmHDJQ47bepNBEw6Jtl+utxGqUnEiIscdTvaZgyJfGk/bQnrMk9GiWy5FYHs4DgDSau3bkpYl+tURKlsjYATdrLFgG4eZ3y6P44ag4zAiYF0+dpOIQ0UOqns6uRYvxfa4DRS2WqY6fOasRLiokNclEnlWZcHGrcS4w2FeCOp/LQ7Fza/Imj1QNFB9+deESrcaoxTiRB6uhXUrAwxMDKKHhqF0bFZd/Fpyk/fvDJr3r+SNVedz8krj6dRC7nj3+5lw7/cyr03bkEpjRQCPe28zUyzDOYVPFZeeTZrrzqf5Ze+Cc/35jTd1yKdukHX4vWHf/iHLFiwgD/6oz+a80weqFjxmjuNWsjdGx5l0w/v5e71jyY1ObKLg4hIhUsbTly2lLXvXM75l5/OvIV9U35cKc1D9zzDxp88yC0/f5h6NcxPwlKMK4j6fVS/3/5EkHZlcIdqeLvriFjPeILZi6aEOYJwYZFwUYF4wG9/4tHJyVfGszgBT5x1IC5BVJbERabNcxpJgUTG0xdKpssCUAVaLWFtCytN2eqkUDJTrpMImApoXyPezAvT1q1ZZgFozxANauIBjQ6myYsEspEIV7PVYFY4BtEfIgdiKLSXMB1JVNVFV12MkszuVwSEwSuHBP0hbjFum5e0bAmUlphZLF+ySyRbzYJCjUNKYywoVHHk1Bu8MoJq7FGJfRranTCVbkjWSSAj+rxEwpw2O5gygt31Eq9W+9hdL2FSZZ7t7ijSJtxp12ecyFbUmGu/WoNwNdLR6XF5yo9AI5WtERfU7POMSCRM+WbaLsEiArcCXkUm+/5s80hb3rzkuW1ebPBGIRhNWs/mclwz7gQJm+ZHlGklkpjtca0bCTMG2dC4VY1T13M7rjkCVZSphLU5kBqDU0t6kfjD0fQVjzNgpECVPOKyi/HbS5gIFU41wq2ked0nAQKjNWZ4FDM0AmOVGb/VlJ+gFHDBu85h7VXnc8a6U3G9qTuljewaZfP372b9N2/hoVsebVWwdVqcl1KgjcFxJGe9+QzWXn0B575tGcVyYeYvW9qSmXgppXjrW99KrVbj1FNPxfMmW/Ff/dVfzW6ODyCsePWWykiN2298mI0/uIcttz2ZlApSqdqTppwtPXEJa995Fhe+/QwOWTK/q7wwjLlv8xNs/MmD3LHhMaJQtboo5oX2JPGAT9zvY3wHUY+Tlq3ddWTYwz7j6YlKe5JwUSJhquwltcAqbdnKwD2NgCiVMJX2kHLC5CGi8VnrSVb6HJcSCWtKn1BJllC9va5hUuEooNUNqtmSJsPe5wlAB4mERQMa46Z5DZnIVkctTV0muhoxECEHIkSg0bFAV5OWLRPPQbbaIKROWsH6ItxCUlJWWqC1RLe6Lc49L20fR6BZWKxySGmM+YUaADXlUYkDamq2stWOZKspOhHltCUMYLhR5NVqHzvrZbSRrXnrHeNdEQFULNOuhN6sBHZGhEE6Ou2OCDQEYjSVraj324wRJu0SbDBOsq+7VfAqAhmKHqclc6+98etAhTZ4Y0k3QqeafKbXxzXtTej+SLLfO3Hy3NNf0CT/CC2QcXKdrTAGGRrcmsKp6TlVzE0VJwDtilZ3ROOArCv84QhvOETGs5ettrmOIC57qJKXnHtj3epGKKMengzTcgxKoYdGEgmr1lpvN2XL9RzOuXwZ6z5wAWdfdgZBsV0N29TsfGU3N3/7dm76xi08ed/TbXsRNe/HYDCctuokLvrgKs5/5zn0z5+6strSPZmJ13//7/+dz372sxx//PEsXrx4r5trbNiwYfZzfYBgxSs7hnaMsvknW1j//Xt5/P5nEVIghEArzeFHL2Tdu87iwiuW8bpjD+1JXq3S4M6Nj7PhJ1u477bfZNZ3eioMyYmNKKld63UhYa+89ARWO24+qi+xhYwv7QYgCgQ6vS4rl7wCRH0il7CWhDn5LJvBgBSEgybbi8gnJybXncnk74y3UkDgLaziFpsly+zymqIzWKgyUGxkmjWRWuQyXCtmJFt7o2JBbSzAmN7Lz94YUAJvu4vMQLbaRTp107p2Mo9f0R0zeD2WrenQIr/jTNLapCltjejR5YrTxwFCaZxKiMzhXDgplxy20KaERTH6+ZegXueMtaey7gMXsPLKsygPlnsS88rT29j4r7dx0zdu5oXHX0Y6EoxBa8Mblh3LRR9cxYXvPY+Fh3dXWW3pjMzEa/78+XzpS1/immuumes8HrBY8cqH7S/t4pYfP8DYcI3zLz+d404+YpLo95rRoSp//8WfsP7HWzLL2AutEY0otzgjoHLaYbnlQXL9Vz6SkNAYABXkl2cE2d4NbQ+UB3GOhx0jDKqUX2swQOGI0Uzvurgnh/aPErj53Z1suFagEvbqjhEzEzYcGtXuatLngmgI/G05XiNikrsF5klhh8lFSppoSa7HmWBXTLArzmkLTbr5uZUwp7R9x3nnHsMn/8e7mXfIYKY5zzz8HDd/5w6CYsDq953H4ccuzjTP0rkbdH1XwyAIWLly5ZxmzmLphEOPWMC7P74ut7z+eSVOWXZMvuL1GiC5m57FYpmI3S8ObDK9tf5+wmtgEXNFOoKTzzshc+kCWHrq0Sw99ejMcyzd03X9ye/93u/xt3/7t1nMi8VisVgsFovFYrEclHTd4nX33XezYcMGbrjhBk4++eS9bq7x/e9/v2czZ7FYLBaLxWKxWCwHA12L17x583jnO9+ZxbxYLBaLxWKxWCwWy0FJ1+L11a9+NYv5sFgsFovFYrFYLJaDlhzvkWOxWCwWi8VisVgsr006avE688wzWb9+PfPnz+eMM86Y9pbe999/f89mzmKxWCwWi8VisVgOBjoSryuuuIIgSMYAufLKK7OcnzlxzDHH8Nxzz0167b/8l//CF7/4xdbfzz//PJ/4xCfYsGEDxWKRq6++mr/4i7/A9/28Z9disVgsFovFYrG8RuhIvD772c/y4Q9/mL/+67/ms5/9bNbzNCc+97nP8dGPfrT1d19fX+v/Sikuv/xyDjnkEDZv3szOnTv5rd/6LYwx9hb5FgBEjgP9vlYwGHIdEcakjzx/yrzzXgvkuU4NGJPv2Ey57xeWnmLMPvj19sGh1G6hvUNrY8sYls6v8fr6179OrVbLcl56Qn9/P4cddljrMVG8fvGLX/Doo4/yjW98gzPOOIOLLrqIv/zLv+R//+//zcjIyD6ca8v+wsp1J/Gua85n3sJku3GcjC6DNOnQqVGMDkOI48mvZ4UQ+NsqyFDnk2fArWlklJZss84D3BrI0JD56LQGMAYZpnk647wUGYGsMZ6Xwyi8IsonJ8EQDhVQkcxlkzEGdleLVENvPC+LzAm7gES3/s6S5vSlMAih89knAAQoP4cdIs0TGowg+32iOV1tUB7jP2gOP6Rb18iGzmejMQbtgJHkuJGCzkNKmnlhiB4Zg3pj8us9plmGGJhX5IqrzmXNZadlkmM5cBDGdLa1SSnZunUrhx56aNbzNGuOOeYYGo0GYRhy5JFH8p73vIf//J//c6sb4bXXXsuPfvQjHnzwwdZ3du/ezYIFC9iwYQNr1qyZcrqNRoNGo9H6e2RkhCOPPJLh4WEGBgayXSjLPkFrza/uf45NP32ITT99iMpoHceRKDWHwkSzSj2OMaMVGKtCFI2/77qIYgFRLIDj9KwK3giBKXrogg+e05qmkaAKDqroYFzZuyr/VEhEWoBtTtEIUJ5A+wLtpC/OMa918BJpwav5SN/THig/ee5FXrMKWMaJbDkhyXJOyIsD0IHoeVXxVMtnnCRT+yTVaD2sotaOSQpekzLTH1TS4+UzGAeMYyZNWwiNEyi8Qox0de820XTb1Eam5S3Ryiv7IX2FkIIXpa/1IAtoRC6jjYBqw0ebZoWOQQqDlMmjV8smBOhYEjZc4tDB6PE8YPy368VvmG5zIgZZkciqREYZ3reruY1rcOrgNJLKiInHmWRbaooDPVpOg1MDr2pwGhPyACQYKcbD5pqXbjQyNDh1gxOa8eOMABUI4pJABa0dZe55QuDUFP6Qwh+JkSp9C5LjqxTpvtm7jVQojQgVMlQInaHgTTj36t3DmKGRceEC8H3EvH7k/EHw/TmfC5tlhWLJZ9Wlp7L6Lady2vKl2VXkWvYLRkZGGBwcnNENurqd/HQ31dgf+L3f+z3OPPNM5s+fz913382nP/1pnnnmGf7P//k/AGzdupXFixdP+s78+fPxfZ+tW7e2ne4XvvAFrrvuukzn3bJ/IaXk1OVLOXX5Uv5/n34rD9z5Gzb95CE2//JXNOoR0pHoTiSseQBXCjOWylYjnPqzcYwZHcOMjoHnjUuY7L70YASYwMMUfYzvTnkSERrcqsKtKrQjUAUHXXQwjugubkLdjdBMkq1JeQbc0EBoksKDL9A+6G7zSD8+hWxNygOcKHkk0pcIina7LBw1P67AaRicKF3O6fLGDNpPJcyffUHMTFy2PaYhSOZJKjB1MO4ECZtlnpaJALVfp8lvZdR4LXW7z85MInbGNW1FzhhJXJfEdQ8hNW4Q4wYx0jVdbTOtTdSANgJjpp5pYyRjjQJjjQJSaMpBSF+hQcGLuyqLNT/biBzGGgGVRoDSUxW6BNoItAJUIl+O1K2cjk+56brQShA1XKKGO0G2Jue1Ps8e67Cb37D5PQVOKlsiFMn2kSVmgmyF7Y8zIgbiZP/RqYR1fQ/ndKNx6uDWDG59vJJlUh6ABqFNKmEmkZRuV0VTtmJwahqnYabOM+DWDW49OY7GBYEqSVTzMvUuN1KnofGHYrwRhRPtHSggOaYrg1HJDBgnXb7ZlAm1QTZiZKRAmey2mHT5jFKYoZFEtqptem2FIWb7TtT2nVAIEPMGkPMGwXM7ljApBVobPN9lxZoTWHPZaSw77w34ftejNlkOcrpq8RocHJxRvnbt2tWTGWvyJ3/yJzNKzz333MPy5cv3ev173/se7373u9mxYwcLFy7kYx/7GM899xw33njjpM/5vs///b//l/e///1TTt+2eFmaNOoRd938OBtveJB7bvk1caxbB9xxmqUgjRmrwlhlcu1at/h+ImGFIJWwqTGAKXjJI/BmdVI0gHFFqyUM2UaKmgU3RFJTOYfrHbQE7SW1t6ZdXhMBehrZ6oRE+hIh0i7tly+VrWbLlpxlY2czTwXNlrfpC2XTyVZHeSQSpnww3vTTMICRacvWLFuxTLMVZQYJm/g54xhw2n92JoSTSJgXxAinjYSlrxkDWreXrU5wpKIvlTDfVVOWxZqvRbHDaMOnUg+IW0273dKUMINIS9977c7N5dNN2XLQarZNkXu0hO35/wl5aJBViVORiEZOstVIBKidbHUwieTY4ST7RtuJNGUrBLdqcGtTy1bHmc2WsLZ5ybzI2CBrGrdhpqzU6QQtQRUFcVEmlT3TbKQy1PjDCn84xglnt4BTLl+7c442SatWqJJWrlkldjpXAqM1Zng0ka2xyuwnVyqmEjYwZS8UIQVGG6QjWX7e61lz+Zs498LjKZaCuS+K5YAjkxav6667jsHBwTnPXDf87u/+blshanLMMcdM+fq5554LwG9+8xsWLlzIYYcdxl133TXpM7t37yaKor1awiYSBEHrro6W1zZBwWPVpaey6tJTqYzWue2mR9lwwxYevOup5JytDaZaTVq22tWudUsYYsIQMwwEQdIKVggQQiQnP99FF/1EtubYR14AIjbIsRh3LEZ7El2QqIIzPm3DBNmae/cQqUE2DE4jaW1pdkc0E5ZFz6llZTLCgNsAGiaVvqTrjmmWkTU4zW6Eau6RzTy30ewmZFCBGO/+yNxla1IeSY2/jMe7P2o/bVkSotVdUDdbAeacl05AT+iKOOFhMIgeyNZEjJJEVZ+o6iHd8ZYwkdZL9EK2JqK0w3CtyHCtiOcoykGDvkIDz0lKybGSjNUDxho+kepFDbdAa4HWAImAOVKPL5+GKHSJGy4q7kW/zz1awvZ8y4CsCmTVQdYylK2m3JlEspxG8pit/DRptto4MZg46YLY7I7YXBQZmpZszbaSZa9MDaSVcknlxrikCGVaLVvNbn1zQWqQFYNXUWgH4mLSEqbdJFDEBn84xh9WOPW5y8/eLX3N9dnclsx4N8I4Q9lqtmwZAyNj6KERGB2b1BNj1lRrmGoN9fI26Csj5g0gBvsRaQXoKWcczdrL38T5F51E/2Bp7nmW1wQH1TVee3LDDTfwtre9jeeee46jjjqKn/3sZ7z1rW/lxRdf5PDDDwfgW9/6Fr/1W7/F9u3bO2696tRqLa8dbvrX2/nz3/lq0rKVww0kjO/C64/CeM60rWA9ywPChUkftozrt1t5jcHxQkMeoU2564VsdUJUgqgvv+VTriGcT88EdiYMBl3SGVwP1j4xGGyW0vPZSn03KTGHcY+McgZ0KDFK9ki2ZkbUwK04ScuWyWMjBX80vWYr+8MoBvDGNDLtsptHnltVyDi/4wyRQupE8nLJi9NWrSxlawKmWkNv25FUdOocbugiBJSKfOLPP8CVH78k+zzLAUPPW7z29+u77rjjDu68807WrFnD4OAg99xzD//xP/5H3v72t3PUUUcBcMkll3DSSSfxoQ99iD//8z9n165d/MEf/AEf/ehHrUBZ5kS5rwC1en6BJrmGKy8EOZZnJ8bkeNjpRS13N+S5PmlG5X1t92x72s2KpIUrv1OVIIzzvX7DGIGK81upAoGs57fRCJJufnnn5bXJCMhN8po4kcEJ8zu4CQMyzvlgOpfuhN1iDFSq9PUV8su0HFR0fNbosGFsnxEEAd/61re47rrraDQaHH300Xz0ox/lD//wD1ufcRyHn/zkJ/zO7/wOK1eunDSAssVisVgsFovFYrFkRcfipfNowp0DZ555JnfeeeeMnzvqqKO44YYbcpgji8VisVgsFovFYkmwgwpYLBaLxWKxWCwWS8ZY8bJYLBaLxWKxWCyWjLHiZbFYLBaLxWKxWCwZY8XLYrFYLBaLxWKxWDLGipfloGR09xgv/Pql3O7GGYUxOd7HOqEe5jNuCSSDYdYjyPE2wbKhEDneBhltWoOd5oJJBjruwRjUHefJRr55IiIZRDavSCUweeZpcs9Dk+tvaER+cZgJj1zyTDoYfL77fZ4Y0gHaLT0lbET7ehYsByj5DkJisWRIrVLnjn+7lw3/civ33rgFFWuWvP4wLvrgKtZcdT6ve8PhPc2Lo5gHNj3Gpu/dxS0/vBcR+GAMRilQOpuTuRDgSKSU8MzLGCEwAyXMQB+UC72XvyhGVhuIah031hhADxaI55VQg4XeD94ca9yxEGcsohgqDBD3uzQWBoTzfYzX4zxjkFEy1k1zQFMjDcoTKA+QvV2fBkCAkSCUIBhJ/q/85GF6fUQ2yUCtMgY3FgTDSV7UZ4j6QBXo7SBGBtBJnowFouImAykXDKpo0IHp/aBJaUFdaEE8HAAG4WlkoJC+QvR+k0HHEh1JjE4mLqRGusmj53kaTN3B1BxoOEgECIP2DHim99WnBkQoEHWJaAi0l76mkwe9/glNMmCyU08eE19v0eNtRkYGr6LxqgYZp5uQY9CuQLv0/jiqDU6U5EoEyHQQP2OyEbF0usIAnkT5DmiDjNKBjbOUv3TdOY5EqexrJaTnoAHpSHQOeU3+9nf+Nw/c9BBrr76A5Ze+Cc/Pb1xNy4GNMPv7AF37IZ2OTm3JniiMuOfnW9h4/WZu+9E9RPVorwNw8+9j33Q0F33wQla/7zwOed3CWeVprfnVHb9h0/fu4ubv38PYcBXHlaipWoJ6JWGpbAnHmbpAIEhO3o5ED5QxA2UoBrMvPMQKUW0kwhXFbfOMEKjBAvGCErp/DtKnNE4lwh2LEPW4FTExr7n6okGvJWE4s8wzSWHLiUzS4rRnHsnqFIB2QHlpYWyWEjZRthBi2kKkcSD2QfvJ/2cbKHTSmiZjpi1kaSeVsP4kc9YF3KZsRWLaPCMMqmDQRYP251CCnyBbM31Q+BoniBG+nvUm2pKtWGJUcyJTbTUgpEF6qYTNIc80HEzVwdSdKbImIFMJc+cgYQaIBLKRCtd0TSTN7UvNzYdEKltuPRW6jr84y7zY4FUT4XKi8X18TwzJfqE9keyDs/0R9YTjjGoz6xPPC3Nt6ZsoW2aaPCEQyiQC1iMJk1KgteGYYw9h3aWncNihAzx419Pc/POHqVYaPZew5vQWLR5g7eVv4g3HLuTxWx/lpm/ewq5Xhtqfk3tMM6fUX+TC957HmqtWctqFJ+E4uY4cb9lP6NQNrHjNAite+xalFA9uepSN12/m5u/cTm203tmBVoAQAmMMJ593POs+sIpV7z6XwUXT/4bGGH7z0PNs+u7dbPj2HezePtL9gd0YTKxAq85OrgKQDsJtI1sz4TrowXLSEhZ4M09D6VS26ohwCtmabj4NGEckrWDzS+iyP3OeNjiVCKcSIqtTyNZMeQLCeT6NRQHRoDezFKWyJaPkuZs12vy5TFPCPGZcvpZsCZJ56yJwkvQFSUtYRwVqnbQczCRbbb/uQtSftIRpv8O8ZsvWLMo4RiYSpooa4zHzOmrKlkn2YdF1CdwgAoUTKIQ3sxQZk3RdVJGTylb3W41wzHhLWCd5ocTU3KR1q6v+YSbZbhzQbocSZoAYZEMmsjWjxE49DaFSEevg4yIeb9mSqvu4vSc4w9vK4FYNflXjNNrLVjsMyX6hPTFecTLtF2au1JkxsJtWsAmy1fU+nxb9xiXMdDWvTfk57PBBLnrLaay+6GSOPmbRpM9EUcz9tz/Fxp89yO0bHiNsxC1J65Zm3sC8Iqvf8iZWv+VUTjztSMSE30RrzaO3/5oN19/Gxn/dzNjuSo4S5qBixeAhA6y96nzWXHU+J5z9+knzZzm4seKVIVa88scYw2N3PcnG6zez4frNjOwYndMBVcik8Cal5MyLT2Pd1Rdw3hVnUeovtj7zwpNb2fS9u1j/rTvY+uyOuXdlSGsb0Xq8JWxPHAfhyLQLX7fFhDb4LnqgDzNYhondIbRG1EJkpQ5pf/U5pTWlyJXE81MJK06QPmOQ1aRlS1aiuS9ZM09CY0FAuNAnGpicJ1RSCJJRb3orNQ+WzcLYxG5JLUGTdC1bM+UZd7w74qQCdUt+umw1mCZPkOQ0W8ImdX804y1byfVw3evPlLky6YrYkrCJM2T2XLYeJAqTdEUMFGKCFLVkK5aYWPYmK5kywtE4nkY4ZlIekUTXHEzNhdnIzxRZBsAB02wJmzjZGGRdIhoSoXq0kQpAj3dHnDRVNd6y1ezW1/Oi6MQJ6rRlq2pw6mavt2eLEel+7+4hYXOo1Jk+kKklbC6y1TYrnZBKuyOqqSWsKT/zF5RZd+mprLn4ZN5w/GEdyUW9GnLXLb9mw08f5N7NT6KUnlHCmnmFks+qS05hzWWncdrypTjOzDVRKlbcv/5hNl6/mVu/dyf1SiP3lrBDj1rEug9cwJqrzmfpKUdlnmvZt1jxyhArXvmx85Xd/Oh//Yz137iV7S/syOTA2RQq13c5+/Izmb9kEY/f/yzPPvpSdv3GTVpDbQxGa4SUGJEWYjOsITMFH1MuIoRIbpZBBoWgCWjfQQ0WwXVxqnF21xakEqZdQf2QAtH8AGmm7/bWCwygXIhLAuPKTFdmS/q8pAAo9hKS3ucJIC4kLWHabeb1Rrba5rqGqKTRwWxatWaBMIggRjgGrXopW+0wSKkhklB3oRfyM0OecQwogYwkIs5+IxUa3Aq41aQVFrJfq25N441p3Hr2eUaAlsk+2FPZahtIUlGmeihb0+YZRJxImCsESmnKfQFrLj6ZNRedzClvOgo5h+tfx0Zq3Lb+UTb85EEeuveZibEtGfM8hxVrTmTNZaexbOUb8P3ZXwAb1kPu+ukDbPiXW7nzhvuIwzipfM3hZkrNMsSRJyzhog9eyBWfuJTyYDnzXEv+WPHKECte+fF3//Fr/OBvf5rLARJABj6iUBy/bupgpFzKtfuDKZeggxrKXlFbUkb1d9C9skdEBUFcErnlGZHIV14YCfVD8ssDaCxU+d5z11OQ42UZoi4RYbfdF+dADDLKb4XKEErbcosDY+h/UeW1NhM67F7ZK0SkM61omTLTGC45/0TWXnIyZ561FNft/U6ya8cot/7iV6z/yRaeevwVzlzxetZedhrnrj6BYinoeV5lpMrtP7qHn//TBh665dGeT38mfufL/553fPKy3HMt2dOpG9i7Glr2a1SskI5E6V5cEDAzRqddLA5W6YL8+5zviy7ueS6jaP1zULJPdoW8V2feec2LAA9W9sFGc7BvMvuC/sESf/TZKzLNWLConyuuPpcrrj43uXYz42N3eaDExR+6kNNWncQHl/5Opll7Ih1JHOVTlrHsv9hxvCwWi8VisVgs+xR7IwrLawErXhaLxWKxWCwWi8WSMVa8LBaLxWKxWCwWiyVjrHhZLBaLxWKxWCwWS8ZY8bJYLBaLxWKxWCyWjLHiZbFYLBaLxWKxWCwZY8XLYrFYLBaLxWKxWDLGipdlvyevwZMtBxF2k7FYLJY5EdYjXn5uR+Y5Klbcu/4R/vUvf8KTW57FmGwP4MYYntrybKYZ7XItFjuAsmW/5vizXs9P/vGXaJUMPqiVzixLOhKtNVIKtDY4rkTF2eU1p59XXhOjFMJxMs9pESvw86vjcWoxqt9L5CuHcWFkbEjDMs+CNMqY3AaJFhpEDMYlt8UUIZggpzwDqAk5OeQZx0BkEHmszHQdGvLLMy4YaRBa5LbNxB64UfY5TZqLlcviGYMRIJoF96z3/fT4Eu6q8JE1X+S4k49g3ZXLWPXWN7Fw8WBPIrTWPHrXU2z63t1s+t5djA1VEULwtf/xAxYfvYiL3reC1e86myPfeHhP8gCe/dULbPz2nay//ja2P78jWY85yFCz7BIUfI47/ZjM8yz7N8JYBe+akZERBgcHGR4eZmBgYF/PzkFPZbjCbT+8h/XfvJUHNjzcGt2+Fy1hjuugYsWhRy3i4g9dyJqrVnL4cYdx3/pfsfG7d3HHTx8gasQ9k76mXPXNK7Hm3eew+l1nc/zypTx219Ns+t5dbPre3VRGatlIWCGAUhF8LznZRDEiint+4mlNLfAwxQAT+MnfsUIqDVm1YAqSE6kA40jiPo+430MVUsnsdWHFgNCmtcAqEMRFgfJFRnkGGSeiZ3RSuFW+QPsZZDUjBcn6FGA8UD6ogKSvRK9LnAbQIFXybFyDKhnisum99DU3QQ1CiXHx8jXGN+Ca3ueJRGBFXSIaEjQgwTj0XvqaeQpkHWRDIJTAOKDdVKJ7/dsJELGhuCN5uGOgfYiLgqgkQGYgYcbg1MGtG5yGQaT7pMyg/qq5ySTrMFmXGJAROLFB9DozlR8Ra2RdIUOdSq3EeBLTrMvq1b7fzAtjnNEGcrSOTM9BSYTAYDhl+VLWXLmM8998KgPzy11GGJ56+AU2ffcuNnznTnZtHW57rmuec4858QjWve9cLnzn2Rx65MKuF2vrs9sT2fqXzbzw61cmncuNMYnUagVKdT3t6WjmuJ7DuW9dxtqrL+Dsy84gKAY9zbHsP3TqBla8ZoEVr33H0KvD3PKdO7npm7fw2B1PIKRID56dT6MpW4OHDLDu6gtYc9VKjj/r9YgpTmC1sTp3/vxBNn7nLu5d/wha6SSzC3lonlgKJZ8LrljO6nefw+mrTsBx9251iqOYBzY9xqbv3cXmf7uPRi2am/QVfCgWIQiSgs+eGANKQzx3CTMAvpvIVsEH2aaVS2tEnDzmLH0TZKtdAUQ7grjfI+730YEzt9aitLWpWciaaipGQBwIVFGivOZ8zjYvkS2RFuymzAO0l0qYN4esJmJcuKYKNCSFdx2kEjbbODP+LGKmWT6D8SAuGVTZtAq8Xec2v6NBxIlstWsBMtKkEqZhrnkKZCpbQrXJw4xL2GyldsLyOU3ZiqdeRkMiX9pNM5l9nlCGwk5DcafBH26/jaoAolJSOTGX/QHAaTRlq81sp+eEuUpY6+gkQbkiFdY2864MTmyQEYjZHtaa8qNMKluqrdAZMVHCxOyOay25U8jRBs5oHRGqaTeF5vlPSsGZFxzPmivOZMXFJ1MstxeKF3+zlU3fu5sN37qTl5/Z3vU5rZl5wvJjWfvec1l15XLmHdK+7LXzlSFu+d5drL/+Np68/5mOygnJ+xqjNOjZSVgzRwjBmetOZd0HVnHelWdRHijNanqWAwsrXhlixWv/YPvzr7LpW7dz0zdu4ZmHn5/2YN6Un9JAkdXvPY81V53PqatOxOmiy93o7jE2/9v9bPj2nTx8xxNpgcZM6Q6t2i7f4dy3nM6ad5/DWRedil/w9v5wGxq1kLtufIiN372Te37xMHGkWt0SpyXwoVhIWrjayc9UGJPU+jVbwjr9mudgCgGm6EM3XRiNAW0SCVOqu+uyBIlIdlnQ0F7aEjbgoz3ZWWGlA9lqmyebLWES7XVYODIGEYNUBqG6yzMkrQzKF+hmR/IO15GRtJWtafO8pFCdtLx18IWm/ChmsXwmWb60JWxGSZmUl8qW6W6bMdIkAhbozvMUiIZENiS0kZ+2eRhw0t+ji+WTDXAaAhF1m5dKmMeEVpSZ8gyF3UnLVjDUnWgYIC5AVJKoQjNvmvltylYETs3gNroUmwkS1k7up/yaTFq2tMPUlVbT5AkNMkorTWaa1wkCKxoKJ9QI1V2xrClh2pMztyw2j0FK44zWccYaiHo8q/qT5nnO8x3OvfgU1rz9DJatOgE/cHn1pV3c8oN7WP+tO3j6kRc7O3fNwESxOf2CE1jz3nNZefkZlAdLjO6ucNuP7mH99bfz8K2PT1jc7jONMaB10hKmZxBEQasHzkkr3shFH1zFBe8+l3mH9KZLpuXAwYpXhljx2v944dcvsfH627jpGzfzytPbcVyZlOuVxi94rHzH2ay96gKWXXIant+5/LRj5ytD3PLD5KTymwefR0qRls8NUkrOXH0Sa95zDisuO4NSf2HG6c1EZbjKbT95gA3fvpMHb3086R4xcc/1vXHZ6sX1W8Yk12ZFESKeovbPlehCgCkGMEXL3azytEHEKmkJm4qmbMGcW3USQZGtljDj7iFhTdlKL9/qRWceLSEuJC1h2p1QOEp/SKGSboTdykg7jADlgQ7E5DxoXf42XctW13mk0hckhXhIpztH2WqfZ9AFiEsaVWRcUibmxolwdStb7fJwTNIV0dd75+lUtuoCYtGT66mMMIkMNbsj7rF8spG0bMmwO9lqnzfeFbHVspi8A0AwDIUdhsKu3nTnMyLtilgUSesp0No6RCIvblO2etGVL5UwqcyU+3Wy/GmlRTeyNU1ec7+W0YS8CQIrGwrZSGRrrokGQIJOW8ImXcMkBGiDM1ZHjjaQtainvT4dR6KUJggcyo5h58u7EiHpskdKp0hHoFVybfTgYJHdW4fQSvdE8CbSTsKaOUtPPYqLPriK1e87j0OPOqRnuZYDDyteGWLFa//FGMNvHniGjddvZvsLOzj/HedwzluXUSzPXX7a8fIz27n5e3fzxAPPsnzdKZx/xTIGF/Znljf06gg/+fot/L+/+Cn4aetWL+SnHcZAHGO0BtcZl62sLvBOuz+KMK2F7ZFstY0DdMGhsaiI9mVPZasd2oGwJNG+SFq24mzzjEhaGeKCTIShR7I1XZ7yIS4mNf5CzaH7VUd5BlWAcDBp0hCxSH/DrLYZk1wH5iXX3YiGRES9ka22ealsCQMyFMhGdssHyW8oVNJqEwwZiruS/2eFliQCVhCJcNXJ5FqtFq1W7ERutSPSfSO745pQ4I3ESAWyoZIuxNmkpRImUmEwOJUGshJmf++YRgOqtYxTJuRpDWE+d1VJJExhlGblO87iI5+/iiOPPyKXbMv+jxWvDLHiZdnXbHt5N7918V/kmqlLfnaFkimQYe9v/DEd4YIC8YCfW55yQRXyu9ujdqExP788A8TdXXs/Zxrzzfg1SzmQtVDuSbM7YV74I4aB57O/02qTpNtjfssHyX6R1w1JAYqvNHAa+a1TOVJF1sJMWp2m4mAWr4n84T9/nHVXrcw917L/0qkb2HG8LBaLxWKxWCwWiyVjrHhZLBaLxWKxWCwWS8ZY8bJYLBaLxWKxWCyWjLHiZbFYLBaLxWKxWCwZY8XLYrFYLBaLxWKxWDLGipfFYrFYLBaLxWKxZIwVL4vFYrFYLBaLxWLJGHdfz4DFYrFMQmtEtQ5jtWR8nUIAQfZjiDmjIU41Ii57qJILTkb1UulAqm5ocCsKFUhUUWKcrAZuTR4yhOJ2jQogLgm0l+H61CA0+EPJOEkqAJPDeEneKBiZ5Gk/wzwDMk7WKSZZNuNknBeCU01yjZN1nsFpgFtJBjWWcTJ4cpZjlmkHooJABQIZJ/tHpgOLpwMoO3EyOLV2RbpOM0rUBremECoZsJmsB2rXBlGrI0YqECtwXXCyG/jeGIOpNzAjI5gwRHhe8pC2ft9imYgVL4vlAGTR4kHedc35/PKH9zMyVMVxJEr1flBOKQVaGxxXcvTRixithex4dbT3ec1CQrUOtQYwoUDSSAbHNIEPxQB8L5PCgxNpiEDWFewEXXSIy34iYXKOecaABhkbRGwmFWBlpPHHNNoTxAVBXJDQCwkzifxIzaTBU2UE/phBu4aoJIiLPRq01qQDCu8xqLBU4DbAiESIVJCdNMg4magTpnm+QQegvR7kGRBxsv5kLCZPrjk+rTToXkmYARGB0wCnLiatUxGDiQGZiGZv8gwyAq8C3ljyuwEYR6CkQAUglOmphGkJcUGgigLtimQlinRw8aJIxKhucELTmp85kcqW0GYv6ZHKYADtGowrEjma63FGG5y6xq0qnHpyvBQwfjwxibD0TMKMQdQaiLEaolJHTByAPgyTZyl7JmHGGAhDdKWCGauAHj8nGKUw9To4TiYS5rgSFWvKgyUOWTzAtudepTZab73ea6Qj0UrjBR7nve1Mzlhzcs8zLK8NhDEmp/HMDx46HZ3aYskaFSsevOcZNv30IW75+cPUq+GcpUgIQbME9Kazl7L2badz3rqT6BsoYozhycdeYeMvHmHDzx9m967K7POMgXojKSDU6klBrhk9/QxiCn7SEua5mbeEGUCVXFSzJaybvLSguqdsTZcFoP1EwFRBdCd9bWRrujxBIiYtCetG+pqypUlauTr9mtxDwjImkT7TfctbunwyAhmJbr6GcUzSEia7zIvBqYPTSMSjkywgkTCnyzxARAavAn4lWc7mNjF9YCIwMkolrPM4jEhkKy6mra7GTL9PpTMklEklLN2+Ow6cLFvQ2fwakbTWakckF2V0ut+bybLVsaCa5LtdS5gx0AiRY7VEuLTp7DgKiXw1H10c10xTtippa1qnuO64hM3iuN081wRFj/PfvozV7zqHM1afiOu5RGHMfTc9zMZv3cHt/3YvYT1qydJsEVJgtEE6kuWXnMba953HireeQaFcmPU0LQcvnbqBFa9ZYMXLsj8ShjH3bX6CjT95kDs2PEYUKqQj0KqDXVwkwmW04fhTX8e6t53OBZeeyvxFfW2/orXhkS3PsfHGX7HxF49QrTRmlrC0kCCq9fEa2U4LCVMhBaYQJBLmZteNpokRoMoecdlDF9rk6US0ZGw6Kji3zUqfVSBQRYkKxNR5qWyJVLZmuwbGpW9cwqaUvmaeAtLlm8taNw7EASgfyEPC5B4SttcHUtmKU9ma4xnSAMY1aIe0AL/3ZybJ1hxbdgwTWsHa5iWy5VXS1sGpP9ZBWLJykvXVXsKMgDgQqIJIfmeY3b6azqiMDU4j6Q455e+TCoxUe7dszQaTdkXULm32CYNsaNxaIlxzbg1sSli7/csYCONUtqqIXvQ+cJykJUzKKX8bE0WYShVTqWCiaO55rov0/UTGptkWmucw13M4+5LTWPOeczj7ktMIin7b79Qrde786RY2/Ott3PuLh1CxbvXemIlkVgQGw2nnn8Daq1ay8orlDCxofy60WMCKV6ZY8bLs79QqDe7c+DgbfrKF+zY/idamVXs3kebJ6OjXH8q6t53BqrecymFHzO86L44V99/1NBtufITNGx8jbMTj0pcWEkS1hqzUJ3VH6SmOTCUsOZlnjZEiuR6s7KJ9mXRDm6NsTZsHqIIgLkq0JxBGtGQLettzb1z6kuvB4iBJaLZu9VpvWy1v6fVgyieXWz8l0pd0R4QJLVsZnRWNSFrCtAvopBuh2xCIOJs8SK6dSq4pMni1pBuhk/Tm7X1vz/T6RScyoEAHJC236frtacVIWnSRMamEpS1bKmldzuzyN5lKmEy6P7pVlchWRvv9RAkTUYwYqyHHqoioF30v25B2RTTGYKqpbDXC7PI8D+l5LQkTcrwl9PRVJ7L2vedw3mVnUB4sdT3p0d0VbvvRPay//nYevvXx1ut7Fn2b58I3nLmUdVetZNW7zmHh4fPmumSW1xBWvDLEipflQGJkqMrmXzzChhu28Mj9z7VK1YuXzGPd28/gwrecxtGvP7RnefV6xF23PsGGGx/m7tt+g3llJ6Le6Nn0Z8KUi9DX/Ql6tijfQfUHGNF5V7S5oB2IBrLvYtnECKjP67LL41zygHAAjJdLXNotkFxa25qIcI4tTV0iG4bC7jQ7hzwjSFpMc9pG0ZrSDt2b68A6wADuWJQIZk7IHSM4u0Zzy9P1OnokvzwAhED293PS2cex9r0ruOCKZcw7pHdlrF1bh7jle3ex/vrbeOK+Z1qvH/nGw1n3gfNZ/Z5zOXxp786FltcWVrwyxIqX5UBlx7Zh7rvtSY55w2G88ZQjZtXPvhvGRut8ZMVnGdmR3wncDPYlXQ9zQhU9VCmbG35MmecJ4v787oukJTTm53tnsvoC8jGElKQLWX55spG0ruWFWzUEI/nlaZncHCM3jKFva07WleINh9m1ck2B8/JO5Fg9tzxdqaAr1dzymnzlvj/jDWcszTxn67PbeWDjo5xw1nEsPeXIzPMsBz+dusEBc5/Pz3/+85x33nmUSiXmzZs35Weef/553va2t1Eul1m0aBGf/OQnCcPJzeMPP/wwF154IcVikSOOOILPfe5zezU5WywHK4sWD3LpO5dz/Kmvy1y6APr6CxSn6YtvsVgsFkuTRUcsyCXnsGMO5S3/frWVLkvuHDC3kw/DkPe85z2sWLGCf/qnf9rrfaUUl19+OYcccgibN29m586d/NZv/RbGGP72b/8WSGz04osvZs2aNdxzzz088cQTXHPNNZTLZf7Tf/pPeS+SxWKxWCwWi8VieY1wwIjXddddB8DXvva1Kd//xS9+waOPPsoLL7zAkiVLAPjLv/xLrrnmGj7/+c8zMDDAN7/5Ter1Ol/72tcIgoBTTjmFJ554gr/6q7/iU5/6VC4tABaLxWKxWCwWi+W1xwHT1XAm7rjjDk455ZSWdAFceumlNBoN7rvvvtZnLrzwQoIgmPSZl19+mWeffbbttBuNBiMjI5MeFovFYrFYLBaLxdIpB414bd26lcWLF096bf78+fi+z9atW9t+pvl38zNT8YUvfIHBwcHW48gjbZ9gi8VisVgsFovF0jn7VLz+5E/+JBmzYZrHvffe2/H0puoqaIyZ9Pqen2neWGO6boaf/vSnGR4ebj1eeOGFjufJYrFYLBaLxWKxWPbpNV6/+7u/y/vf//5pP3PMMcd0NK3DDjuMu+66a9Jru3fvJoqiVqvWYYcdtlfL1vbt2wH2agmbSBAEk7onWiwWi8VisVgsFks37FPxWrRoEYsWLerJtFasWMHnP/95XnnlFQ4//HAgueFGEAQsW7as9ZnPfOYzhGGI7/utzyxZsqRjwbNYLBaLxWKxWCyWbjlgrvF6/vnn2bJlC88//zxKKbZs2cKWLVsYGxsD4JJLLuGkk07iQx/6EA888ADr16/nD/7gD/joRz/aGsjs6quvJggCrrnmGh555BF+8IMf8Kd/+qf2joYWS4b4BQ/p7INDzUG6S+e9WKI5zGGewx2anPOamTlm5bp4OW80ue96ea9P8s9DiJwz8/0VHTc5R3j+AXOzbYtlVghzgIwefM011/D1r399r9c3btzI6tWrgUTOfud3focNGzZQLBa5+uqr+Yu/+ItJ3QQffvhhPvGJT3D33Xczf/58Pv7xj3Pttdd2JV6djk5tsVjgpae3c+O/3M6G797Fzq3DOI5EKd3TDOlItNIcceyhnPfWM5DlInfc/Guee2o7Ugq0zugwJ0A5DrroYjxnvKySYUWOFqB9gfYlxhXZ5pmkRGsciAoCVRAgs8pK/zECIw1xAVRRJNWDht6XA9NpCg3EyTIaP3nOMg8NsgEySv/OunxrQEYGtwZOCEIlL2cVa0i2UeOl61KIbMJM6x/8oZhgV4hUAlX2km00k9/QgBDIeowzGiIjgwnczPNEI0LsHEWMVJBCZHp8aebqWGHqNUwUQawyiXFciYo1xb4CF75nBZdcs4ZTLzgxkyyLJWs6dYMDRrz2J6x4WSzdY4zhsXufZtMP7mXT9+9hdKjaOvHOhqbALTxskHXvOYfV71jOMSceMakS5bmntrPpZw+x/oYtbH9luCVoc1oOwLgS4zpJgWtCXvKewDiyp4UxA+P9E/bI014qYc7e73cfZFoTFtog9OTZN4AqiHEJm2sBsLl+0jymyNM+iYQVxNz7aEyQLRGDjCe06KVvGyfJVD69kz6diJZMxWfPZUSAkT3ImTBREYNUe+ehDTIGJ07em+vitVZfugx7LseUy9eDbdQbjSm8GibCFY//iAbQRZe47KJK3twrCpqyFSqckRBnLEJOOGYZAFeifWdcwnqQRxQjdo8hh8egHk3+jQQgHYQjeydhxmC0BqXHjwPNt7TGRFEiYWpuEtY8BnuBy3lXnMXaqy5g+ZtPxw+8OU3XYtnXWPHKECteFsvcULHiwdueYNMP7uHWHz9AvdLoSIqaojYwv8zqd57F6ncs54RlS2dssTbG8MSvXmLTzx5m408fZGhXpauWt6RALjGuhA4LO0aAcUTynVkWjkyzRaTDvHEJE+MFuBm/mGYYg1B7y9Z0eXFBEBdlIil0Np8Tc6eSrelmUwWgmhLW7So1qfwoOspMJBqUl4hY19JnEtGSUSJBnS5jS1BmsXxCpTK5p2y1QxtklEpYl/URE4WqU2kc/45IjbfD3zHdlt1KKls7GzjhzEUXI0AVXVSfhyqmXdg62ifSlqZI44yGuKMhMpp5BRnAeBLju5jA6Xx/aO6rsUIMjSGHK1BtdPYbCgFSzk7CmrKlNXTYK8AoNS5hurONRkqBNgbHkSy75E2su/oCVrx9OcW+Ynfza7Hsx1jxyhArXhZL7wjrEfdu/BWbvn8vd974EFEYT5Kw5v8L5YAL3nYGq99xFm9a+UYc15lhylOjlOaR+59j088e4uYbH6Y61thLwloHRUegXadj2ZqKVmHTSbsGtpmOIXmr+fm51GSbid0Rp2t561K2psuLi00Jm0b6DDBFS9ps8loS1uxJ3iZPxqn4zCGzJWF+KmHtJmRARGmXvg5lq10ezCBh6YeEnrCMs8wDQBmcOBXFaUoFpkvZajsdANmUsKk+kGxDTk1ReLVBsDPErc++tdoIUGWPuOyhC+mxY+I205QtlciWMxohG2pu24znYAIH408hYc19RGnEcAUxPIYYq8/tNxQCHImQUx+vjDFpHYtJWq7m0AXbGANaY8IwkbA9ipJJvMBgOPWCE7noA6s4/13nMLCgf9aZFsv+jBWvDLHiZbFkQ3Wszp03PsSG797NA7c8huM4nHvpaax+53KWrz25591Roijm/jueSiXskUS+HIFxnDm1VLWjVdh00lapdPrdtGx1i5agPYn20+uzTCo+ymRy6Y2W4xKmPTEuW+kNM3p+GYxIBCwuCLSfGGbbbna9yAN02gqm081RxBNatzLIQ0zcRtLlanaV7HFec/uQ0YSumCL5XXvaHXJiJCT7hQCkQIY6ka0dIW519vLTNk+KpCtin48OHFAGdyztRliLM/kNTdoV0Xgy2Q9GK4ihCmK0Oq3ozhqZtIQhk6ZaYQxG6Y5bqbqhKXKtljBjeMOZS7nogxey6r0rWLRkQc8zLZb9DSteGWLFy2LJnrHhKq7rUCjnM4beX//ZT/jZj7egVD6HROWK7rojzREtQBfSQlgOeQaoLnJyywOIygIj88vT0HFvuZ7QFMrc8pJWsLwCRUMx7/FRZJRNpcBUGG2SioG88nbthkaEOEiLXsYYMIYbh/95X8+KxZIrnbqBvW+nxWLZL+kbLOWa57hNCcqnQCQAk+MwFrO5bKgXmTavt3m5ZuYcKAAnyvmm6Vnd8XS6vINUuoDkels7PI/F0pYDZhwvi8VisVgsFovFYjlQseJlsVgsFovFYrFYLBljxctisVgsFovFYrFYMsaKl8VisVgsFovFYrFkjBUvi8VisVgsFovFYskYK14Wi8VisVgsFovFkjFWvCwWy35HWI+485eP8MCtv0bFKvM8pTQ7XxmCRpzPrZ6NQTQi5HANVO8HNJ06E0ScjFmUT57BqWlEXrcHNwZZN8gwp9t1pwNCo8lnBAJjELFBRvndjlzEBqeR3zYjGjHUGpDDPg8kgwk3Qohz2u8tFstrHjuOl8Vi2S9QsWLLbU+y6Uf3sfknW6hXQwD655VYfeUyVl+xjBOXHZOME9MDjDE88chLbPrpQ2y4YQvDuypAUhtlPInxXHBlb8ekiWJEtYGsNhBp4dIAuhygBovo/gLIbOrDBLQkyEiDcQTGobfLZ0wieKmMFIYSqdQuxAVJXBQYt7d5QoGMmgMLN5cPoqJBFQXapXfLaMafhZk8xJURyaPXY1+J2ODWDW49Xa/pbBjXoPweLx8glMGtGbyaQcaT8+JAoDxA9jAvVARbxyi8NIq7qz6+6lwHUwig4IPj9CwPY6ARIuoNaETjr0uJCXzwfXB7mDcBKQXacYAIx5WoONtKl4kZ0hHojAeHb+YddvSiTHMslgMZYYyt5umWTkentlgs06O15rH7nuXmH93Pph/dx+hQFceRqD1agZon9IWHDbL2nctZfcUylp64ZFYS9txvtrPppw+y/t+2sP2VIaQj0W1anYwA4zkYzwFnlhIWq3HZiuKpP5OO22wE6L5CImHlQk8LuHvSOvBL5iZhqWyhExmZLk8A2oOoKFEFgXFmlycUyDh5zDQF7UBcZPbSt4dsdfQVkcgfMCsJS1qawK0b5AyNP4ZknSpPYGYpYUKlclczONH4bzVVFs08X6D82eURa4JtFQovjeK9Wk3moc3Y5QYQnjsuYbOpmDAGwghqDUQjnPnzjpNKmDdn6WsezxYfMZ+L3n46F77lNOojVTZ97y42fPsuhl4d6amENac1sKDMmvecy+p3nc3Agj42ff9u1v/rHbz89PZpj3ld56XLFxR9zr9iGWvedQ6nX3gCrmfr9S2vLTp1Aytes8CKl8Uye4wxPP3oS2z60f1s+P497No2MqVstaNZaFiy9BDWvfMsVl9xJkuWHjLtd7a+tJubf/oQ63/8AM8/9WpS89xt9ykB2nNTCRPTFziVRtQayGodGnF3Ze+mhEmB6i+iB4vokt/blqkpMAAStCuSZr/p8jqUrWmzAO0L4qIgLojpJdMYhJ4gW11mtqTPTQQsLjKz9KVdCWezfJMm05SwGeKEmiBbcXv5mSmrJWEzibROZMurGWTqIl3nAcpPJEx7M+Qpg/9qhcLLo/jbqghtWtt6V/heImGBP+M2QxRDvYGoh7PvSug6GN9P8zqTvubxbN7CPta97XRWX3Yarz9p74oirTWP3PEkm757Nzd//24qI7VZSVjzO4VywKp3nMWad5/Daecfj+NMnl9jDM888iKbvncX6799JztfGZpVXrP1zPUczr7kNNa+91zOuvhUgqLf1XQsloMJK14ZYsXLYumel57ZnsjW9+7h5Wd39KTWtSlQx558BOveeRar3nYGiw6fB8DuHWPccuPDrP+3LTzxyIsIKTBNYZgrUqAntoQBaI2ohchqA+pJSXbOqtSUMEeiBoqogSKm6OUiYUaStBA1Jay57ppdCXuYBaCDtCUsGJcwocysZWu6PAEoL5WwAolIN9/sgWy1QzcFrPnz6QmylfZ669Uva0SyjNoXqfglv6FTB6+mcRoZ5DUlrNnypg3ezhrBy6MUXhlDqFnKVrvMwE9awQJ/fBuNVSpbjZ5em9ZqefPTlrA9JKwpW+X+AqsvO43Vl53GyWcejexQ1uIo5v6Nj7Lxu3dx2w33E9aiaY+Rzfc832XFZaez5j3nsGzdKfiB19nyGMNjdz/Fpu/dzcbv3sXo7sq0EiZkun6F4PRVJ7L2vedw3mVnUB4sdZRnsRzsWPHKECteFkvnbPrR/XznKzfx9KMvza6lqQMSDxEYDMec9DpEqcAzT2yjWcLL8ihnjEGEITSiXl7a0xbtO0RL5mNyql020PtrwWbIi0sC48rMBGhiFkBUhrgke3pt1nQIZXCrvZetdhgSgXXCfBbRGI334i78bWPISPdUtqZEgHGcpBVNZ3vdVKsl0nOhv4wREr/gsfKik1hz+Zs4c8Xrcb25dU+sVxvc/YuH2fidO7n7Fw+hYo2UyRWMRhukI1m29mTWvudczn3Lmyj2FeaUp2LFg7c+zsbv3s2tP7qXeqWRiJ3WCCEw2nDiWcey9r0ruOCKZcw7xJZ7LJY9seKVIVa8LJbO+fcrP8fW53fmlmeKAXid1fr2hEbY2XUjPUL1BURHLswtL7mxQk5GkhL19fimJjNQnyfQfn55biVpccorUcQGN79NFDlWp/Twy/kF7iMOP34J13zmCs5edTyFjCpCKsNVbvvJA2z8zl2oWLHm3edw/tvPpH9+XyZ5YT3i3vWPsPE7d7HthR1ccMVyLnzHWRya4zHHYjkQ6dQN7NWPFoslU/Kv28lXEiwHAftgkzmot9LXQHWu6zmsXHcSqy49NdOc8mCJS65eySVXr8w0p4lf8Djv8jM47/IzcsmzWF5r2HG8LBaLxWKxWCwWiyVjrHhZLBaLxWKxWCwWS8ZY8bJYLBaLxWKxWCyWjLHiZbFYLBaLxWKxWCwZY8XLYrFYLBaLxWKxWDLGipfFYrFYLBaLxWKxZIwVL4vFYrFYLBaLxWLJGDuOl8VisVhe27wGxp2y9BatdD45WvPYnb/h5u/eiVKaVe86h1NWHo/jZFdv/uyvXmDj9Zt59aWdrLzibM56yxn4QY6D0lssBzFWvCwWS6Zc9sGVfPv/u4nKSA3Hlag44wJLGIGU4EgwBkTGQ9U6MsnIaaBoWY8Q1Qam6CcvZLx8BkCbtH9EPsP+ishgPLL/7VLcuiF0AZlxngEEaBdoTH4t69jkNxTZ7xPGoAseqhwgK42DbqBo6Ui00hx+zCGcfXE2gycbY3j6oefZ+O072HD9bex8ZQjHTUTrhn9cz7xDBlj7/vNY/d4VvHHZUkQPfs9XntnGpn+9nZu+cTPPP/YSMhW7X379Zgp9BS5897msuep8Tl9zCo7rzDnPYnmtIozJqbRwEDEyMsLg4CDDw8MMDAzs69mxWPZ74khx/62Ps+mH93Pbzx4krEetAkxPMAYTxxDFoNNpeh4UC1AqJCLWyxKuMUlOrEHrZKrN14zOpgVFiGQ5hAAhMI5E9Qeo/gImcHtfoBagRfKciwAJ0FIkgidFsgoFaCfNz2AWjAAjx/+vXdC+QAX0fpm1wa0a/KrBqScbiJGgXYH2erx86WndrSi80Ri3ohAGtCNQRQdVdjFuDysm0jynEuLuqODuriGUnrBPmGwqJqQAxwXHSfYNY0ApiCccB3qA40iU0iw8bJB17zmH1e9YzjEnHtET4ZnIS7/ZysZv38H6b97Gy09vm/YY2azEWnz0ItZdtZLV713B0Sce0VXerq27ufnbd3DTN27hiXufQkqBNmbK45fjOqhY0b+gj7VXnc+aq87npBVv7Pk6sFgOVDp1Aytes8CKl8Uye+q1kHvW/4qNP7iPuzf8ChXr5ISvuzwUNWUrVklhazoCP5GwQpBKWJc0D5Nag0oebYsbzUJms9A5F1LJaglXG7TnoPsC1EAB4zmzL1DnLVuAccBIMW2mIZGU5HOpQM9y9oxIHjQfbfK0B8oXaL/9fM0cZnBrBq+SPLebSnP5tCeS1rDZ5DXlp6bxRmO8sRgxzeanXYEquaiSi3FE9/US6TYmqyHuzgrurhoymmY/7JWECZGIlutOvy/PUcKaYjMwv8zqd57F6ncs54QetS5N5NUXd3Hzd+9k/fW38fRDzyMdgVbdrZ+moB114hFcdNVKLnzvuRx29CFTfnZ09xibv38X6795Kw/d/Gjr9W6Kgs11s+iIBaz7wAWsuep8jj3taCthltc0VrwyxIqXxdIbKiM1bv/5Q2z4wb08eNuTQFKYM1NImDFJwdUolbRszSRb7SgE4xI2XUGheWg0JpW7aWRruml0K2EdytaUcYDxHVR/Ad0fYNzOJKzV8pOXbEmSwv4sBK8lYc3vdyAMncjWdHnaTyWseZlLu3lutjTVDW7F4NVM4old5hkHlCswM0lY+ts6dYU3EuOOKWSXhXYDGE8SlxxUyU27IzL1emrKVj3C3VnF3VVFNuKu8lrT6aZ1uClbzZatbrdTrZPjhVLT7ofNlq1COeCCt53J6ncs500r39jzrnXDO0a59Qd3s+H62/nVnU8ghEjEZ66lMUEyLW04/qzjWHfVSla982wKfQF3/vg+NvzLrdzz8y2oWM2usmsKmtJ3xBsP5+IPXsjq95/HEa8/fM7TtVgONKx4ZYgVL4ul9wztGOXWG7aw/nv38OstzyUFP22SMqDWmChOaq97hRDjEhb4k19vdiNUqnc9wCYK2J6HXQEIObtCZbs4wBRcVF8B1R8k16JNfH9CN7s8hGsustV2mntOlwll1znIVts8AcoDHaQtU60sgdMweBWNWzXIXvWgJZUwT2CaXS6b8hNqvJEYbzRGxr05jRtABxJVdFFFZ9I1YSJUactWFVmLerNKJ7YkTyVErjt72WpHU8LieNJ+6Pku5156GqvfuZzla0/O7GYSX//cd/nXP/sxWuuWJGWBSH87rRTCKHSse9u9ewqklGitOX3tKfz5TZ/NLMdi2R+x4pUhVrwslmzZ9uIu/vOVf8X253aAirO/65wQMNAPvpfIVtZ5E1vCmt2lMpQfA8SDBeJD+yfISA435XDAiPS6rRzy4kCMi1bWN6wQYETSouVVDXKWDbAd5wHEChkb3EqME2a7kRpAS40xCne4jqyE2a7S5v7QbN3qpWxNl6cVq99+Jp/8n++jWC5kl5fy+6uv47G7fpN5ThMdRaAz3jin4Jf6O7lnWiz7kk7dwN7V0GKx7Hcsft0CFi4ss/2prfkEGgNhhJjN9V+zodmdMCcEIBtx0jKUI8bNb6jIZqOhyWkR8xKuVh7p9VtjPWz1nSmvGuPsHs0lr7VPeDndtjzNk57L608/OhfpslgsFjuAssVisVgsFovFYrFkjBUvi8VisVgsFovFYskYK14Wi8VisVgsFovFkjFWvCwWi8VisVgsFoslY6x4WSwWi8VisVgsFkvGWPGyWCwWi8VisVgsloyx4mWxWCwWi8VisVgsGXPAiNfnP/95zjvvPEqlEvPmzZvyM0KIvR5///d/P+kzDz/8MBdeeCHFYpEjjjiCz33uc9gxpC2W/Q/HkUiZ/aBMRil0vY569VXi519A7dyFqdezOy4YA0pBHEEUQRyD1snrWcQBxnUQjkPh5VH8HVWcagQ6u+UTscZpKLzdIe5IhKyrbPMijTsWUXqpTumVBt5IjIh1NnmAUAanoRF5jeEVa9zREG80TLad5vaS9bnLd1GLF6Dm92EKfubjmDdHaM56iDtjDLpWQ726g/DpZ/nuf/8W3/nLH/PqizuzDQYc18nluNYkx+ECAZBSIJ0DpmhpseSOMAeIdXz2s59l3rx5vPjii/zTP/0TQ0NDe31GCMFXv/pV3vzmN7deGxwcpFgsAsmo0m984xtZs2YNf/zHf8wTTzzBNddcw2c/+1n+03/6Tx3PS6ejU1ssltnz1MPP88O/X8+tP7qXeqWBdCRa9aYwbbTGRIn4GNWm9Ow4iHIZ2VcGz0PMpQTTLCTPJFhSjg8kO4c8A+BK8Fxw5JTTMoAuuMQlF11w51ZCMwahDSI2CGXYc0rNJTauQPsS7U89T13lKYNsaJxQIfbYLAxJGV55grjsEJecuQ8erQ0yBic2e+VlgtI4tRi3GiND3VqmPTETt5WsS9naQL2BrIXJgOM9mqyUAq0NS09awtHHL+HVrSP86r5nWtPvRSnFGAONBrpSxVQqyb4oAJOuNiEwxnDyecez7gOrWPXucxlc1Pvz+1MPPscP/78bueX7dyfHNVeie1xJ4LgSFWuCos/yiztgSmMAAQAASURBVE/FdQUPrH+Yoe3Drfd6iZQCbQyOI1l+6elc/rGLWfG25T3NsFj2dzp1gwNGvJp87Wtf4/d///fbitcPfvADrrzyyim/+3d/93d8+tOfZtu2bQRBAMAXv/hF/vZv/5YXX3yx44KVFS+LJT/CRsS9Nz3Cxu/cxZ0/20IUxrOSsKZsmTiCuMumCs9FlsuIchnheR0Gdihb7ehSwgwkkuU54DpdFcKNAFX0UEUXHXT4XWNAg1Q6ka0OF7ElYZ5A+Q7G61wyhdKJbDU0osMWtOandCCJSpK45ECnLQ55y5Y2OLUYpxojG8k22o3cmB5Ie8dojaiFiFoDorhrCWvuw0uWHsK6d57FhVecwRFLD229v3PbMLf85EE2/PA+fvPISy056wZjDEQReqySyFa7SpYJCJkImJSSMy8+jXVXX8B5V5xFqb/Y5RJOT9iIuPfGB9nwrTu48yf3EzVmd1xrIh2BVgbXczj7zaez5n0rOOeyMwiKPgBKKR659XE2Xr+Zjd+6jepIbU4SlmxiAoPh1AtO5KIPrOL8d53DwIL+WU3PYjnQec2K1xFHHEG9Xmfp0qV85CMf4WMf+xhSJs3e/+7f/TuGh4f50Y9+1PrOAw88wJlnnsnTTz/N0qVLp8xsNBo0Go3W3yMjIxx55JFWvCyWnKmO1rnzZ1vY8J07uX/jo2ilk4JSmwKZMSaRrWaXvh4gfD8RsHIJ4bp7Bs5NtqYMBIQcF7GJcZDIluskwtWDAreRAlV0UUVv6pYpbZBxd7I1bR6gPYEOHIw7hTQogxOqtGvf3AKb31aFRMDiotxbwkwiWzI2ra6EmWqMMciawq2mXTJ7lJeLhJm0uUhpRK2RSFis2s5/s6C/8LBB1r5zOauvWMbSE5fMWOn58nM7uPmGLaz//r289MyOGQXFRBGmUkFXKhDNfr9v5ri+y4q3L2ftVedz9lvOwC/4s57mVFRHa9x5w/2s/9bt3H/TIzMe15oIKVrNu6dfeBJrrzqPlW9fTnmwNO33ojDi/l8+xIbrN7P5+3cR1qOOpa/5uTecuZSLPnghq967gkVLFnSzuBbLQclrUrz+x//4H6xbt45iscj69eu59tpr+fSnP81//a//FYBLLrmEY445hn/8x39sfefll1/miCOO4Pbbb2fFihVTZv7Jn/wJ11133V6vW/GyWPYdI7vG2Pxv97Hh23fyyJ1PItJ+Q1obiGN0FM6p0NUJohAkElYsJoXNrA+naUHauA74Lrhu5603s8A4grjooYoOCJkIV4aLaARoT6JdgdAGJ9TI2LTtZjfnPCAuJhJmPIGMyU+26gqnGuPU40zXKeQsYbFqSZhQuiVb/fNKrL5yGauvWMaJy46ZVdddYwzPPv4Km368hQ0/vI8dW4dxHIlSGhPHmLQboQnDni9eczkK5YBV717BmqvO54x1p+A4Tk9zRnaOsvmH97D++tt55PZft45rrUOLSCqZjTaccPZxrLtqJaveeTbzDh2cVV692uCuG+5j/b/cyt0/fQAVq70krPn3645fwkUfXMWa969kyXGHzX1hLZaDiANCvNoJzUTuueceli8f7ys8nXjtyV/+5V/yuc99juHhYSARr6VLl/IP//APrc+89NJLvO51r+OOO+7g3HPPnXI6tsXLYtm/2fHybm754T2s/9adPHnXrzMpeLVDlEs4bW74kwXGdWCgPF7QzTpPQDxYyPcq/bSmP6/EqOxi/PxuCOAON/BGo9zykpbR3grC9IHJ71ccrXDh5W9i9RXLeNN5b8BxezcPxhgef+A5Nv14Cxt/eB+7H/p19hUfKY7roGLFR/70at7/R+/ILGfHS7u45Xt3cdO/3MZTDz4HwDEnvY51V6/kwnefy+KjF/U0b2yowuYf3M36b97Cgxt/hTGGRa9bwEUfvJC1V61k6alH9zTPYjmYOCDEa8eOHezYsWPazxxzzDEUCoXW392I12233cb555/P1q1bWbx48ay7Gu6JvcbLYtl/+czlf8q9P9+S291KRX8fTn9/bmJiPBf6p+9K1NM8KRLxypFOr9/qFVG/i3HzEy9vdwO3GpH9bQITjBBJV9Wc+V9f/QhvPPmIzHNqYzXePvDvMs+ZiOs5vPP3Luejf/ahXPJefnpb0ur0hsNzydu9bYidr+zmuDfNrnXSYnmt0akbuG3fyYFFixaxaFFva2wm8sADD1AoFFq3n1+xYgWf+cxnCMMQ30/6aP/iF79gyZIlHHPMMZnNh8ViyY9COdjXs2CxWAAvyKeI0cuWtP2VJccuzjVv/uJ5zF88L9dMi+W1wD4Vr254/vnn2bVrF88//zxKKbZs2QLA61//evr6+vjxj3/M1q1bWbFiBcVikY0bN/LHf/zHfOxjH2vdwfDqq6/muuuu45prruEzn/kMTz75JH/6p3/Ktddea2t0LBaLxWKxWCwWS2YcMOJ17bXX8vWvf7319xlnnAHAxo0bWb16NZ7n8ZWvfIVPfepTaK059thj+dznPscnPvGJ1ncGBwf55S9/ySc+8QmWL1/O/Pnz+dSnPsWnPvWp3JfHYrFYLBaLxWKxvHY4YMTra1/7Gl/72tfavv/mN7950sDJ7Tj11FO55ZZbejhnFovFYrFYLBaLxTI9+V9ta7FYLBaLxWKxWCyvMax4WSwWi8VisVgsFkvGWPGyWCwWi8VisVgsloyx4mWxWCwWS97ssxE086NRz2+Q6Lx5Dfx8FoslA6x4WSyWg4rBhf0YY3DyGhBX6dwGTxYCRFrikzKnITC0BmOSRx6YtFCbVx6Azi8KwDg5D1+S57qckPkHH/1nvvCZ73LnLb8miuLMohzXodhfRAiByGG/cFyJihQDi9oPkmqxWCxTIYzZF0fkA5tOR6e2WCz5E0cx99/0MBuuv5XN37uLRi1EOhKtel+6FkGAKBURhQJCZid6Ugq0NrzhtCNZ+87lHHfaUTxw9zOsv2EL214e6v3yGZNYXhzDWA0Thui+ImZBH6YYjL/fa7RBhgrCGKQE3xmXlAzyDGCkQDsCPIF2ZVIdmcWypadaoUBEGllXOJFChIqsVMGQLIpBpNaeh/AZ0ImoC8BxJEppSuWACy85hdWXnsqpZx6N4/R2fxl6dZhbvnMnN33jFh678wmEFBjdu+KN40pUrCkPlljz/pWsuep8Tjn/BGSG+73FYjlw6NQNrHjNAiteFsuBQaPW4M4b7mfDv9zK3T+9nzhSLYmZLcL3EMUSolhAOE4P53YyTZl63XGHsu5dZ3Hh28/k8KMXTfqMMYYnH32ZTT97iA0/eZChXZVWQbdrmjKlFFRqyWOKVgrju+h5fZgF/ZjAm7uEGYMIFTJUiFjvJSFGgPEcjO9gHDnnvKZsGUeA3FtGDGCctFWqWaaebV5TtjSIyCBis7dkGYOINTKMEdHeyz8r9rFstaO5bQ7OL7H2Laex+tJTOf7kIxA9nsftz7/Kpm/dzk3fuIVnHn5+1hUTze8FRZ/z33UOa6+6gDMvOhXXO2BG4rFYLDlhxStDrHhZLAceleEKm39wN+u/eStbNj6SNgfQWa245yKbsuVmV+hqFkwXHT6Pde86i9VXnMkxJyzp6LtKaR65/zk2/ewhbr7xYapjjQ4kLF0JWieiVa1Bo7PrcgxAwUfP70PP7wPP7VyKmrIVqa5kwwiB8WUiYh1KWOvXTWXLTCFb033XOGDcVNLS1TXjlwQIlYpWbFrdQ2cONIhoXELpIG7S18W+kS3obj5hfFs/9LBB1l3+JlZfeirHHHdoz+fy+cdfYtO/3sZN37iZV57ejnQlOm6/TzQrZlzP4ezLzmTt1Rdw7lvPJCgGPZ83i8Vy8GDFK0OseFksBza7tw9zy3fuYP03buGxu55MuiUZw6Qr5l0HUSwmwpVhDXezC9PAgjJr3rGc1VecyfGnHz2nVoAoivn/s3ff4W1cd7r43zMFhZ0UJZFUb7Z6t3ohVWxpbceOEye2k6x9d1N3ndzcxHc32c3+7E3P3hTnxjfJZm+u011jO4njokaqWrKq1SWrN1KFFAmSqDPn/P4YACIlFpDEgCrv53lgmcAA35nBlPPOGczseOcoqt7cjY2r9yMasaDpAtJuMYFSOUErGALC0R5NgwKgsrxQhTmQBTmAobcORYnGuSWhRWyIWM9Pr1OagIz3hKGN3/UoANAApWlO71UPw4gCoAxc6SlrGcISYUuqKz1bPd2zSieE6TEb6CCEqZZBy/XA1bOw1Z5E2Bk0rBhL75mMDz4yGx5Petc5pRSO7DyOyuc2YNXv1+NyTT10Q4dt2c7vwuLf4eSK8Vj8sfmY98EZyM7PTus4ENHNi8HLRQxeRDeP8yed05Je/MFfEKhrjIctP2CaaT8Fqi3LHp6NhfdNxYRZI9P+uxcACIeieHfdIfzluc3Ys/0EEI4AzWEgFE57LSAeUHL8kIU5UAU5ELZ0enBids/DSDv1oDshTHoNpxdTT0/YaremaHE6IgBhKWixNISt9kgFLWZDC1tXAm0mwlaieaDSH7Y68r2fPYrJM4a79vlSSuzdcBCVz23A+le2oGx4fyz++AIseHA2Cvvlu1aXiG5eDF4uYvAiuvn89N9exhu/2wi7g9OQ3PDm6R9npM6h7cfw35d8OyO1kgaXZrRctDjLOQUxU+yOf9OUbiJiQ4/amSuoFITM8CUfAXz7//wtps0akfG6RETdlWo24OV4iIiIiIiIXMbgRURERERE5DIGLyIiIiIiIpcxeBEREREREbmMwYuIiIiIiMhlDF5EREREREQuY/AiIiIiIiJyGYMXEVHczXpbw1jUwt7NRwBdB7QM3XnKYwK2BOSVm++6TW+OQgtGgQzce0oBUIaANARUJmZpfB4m6rk9RxUAJQSkrkEJ4Xo9KAUEw0BtA/ZtPIRoOOZ2RSKijDN6ewSIiK4H42eMwMqX3kW4OQJd12DbLjXeBSCEgJIKd1SMcacGANuW2L3pMNa+ug3r/rQdoeYIhKED0AGloKR0glE6Q5FpAH6f89CdOskWu1CAEICA868LtJDlHE1sjEJ5dNg+A9JrpC1sKgDQAKUL5zPj8055NEAqCCv+SEs1xOefgpDx+agBEBqU4bymbAVhK4g0fYUKuPL9JP5VCsqI/2vL+LikaRqVAiJRoCkE0RRKBuY/fPMVvPLD1zHvvmko/9BMTFk4Brqhp6MiEVGvEupmPcTrolTvTk1EN5ZoxMK2yv2oem07Nq/ci1jUgqZrkGkIYZouIG2FoaNLsfhDd2DBvVPQb0BRGsb6CqUUDu44gbWvbkXlK1sRqGuGbmiwrQ7GP96ghuxmCNN1IMsH+P2AEQ9bnQWrqxv3LklMjfTqkD4T0qt3uV6rsJV4a3ufEZ9/QgKw4qGoyyPthCwhpfNviu8RlgK6Ua912BKpFVQKsCWElF0PfUoB0Vg8bAWd8N+OxLKbU5CF8g/NRPmHZmDszBHQNJ6sQ0TXl1SzAYNXNzB4Ed38Qs0RvLNiDypf3YYd6w5C2gpCc3qqUpUIbSWD+2Dxh+5A+X1TMXBE/7SP64kD51D12lasfuldXDp3ufs9dskQZnd8LpumOb1aWX6nlyuVsNUegSs9Ui6GMMCZJOkznIen/RCWDFuauHJCflfHLbFrlXB6wToKRcmwpSC6u0tOfAfSqdVRCEtW0LoQtjqoK2yn97TDj4lZQFPQCVsxu8tlEiGsqH8+Fn1kFso/PBMjJgyCcHmZISJKBYOXixi8iG4tgcvN2PDGe6h8ZSv2bj0GAQFAtdlBlAg9hX1zsegBJ2yNGD8w7Q3E6pOXsPa1bVj90hacOXI+bT1zSUpB2faVHglNAL542PKYPQtb7clkCBNOCLN9JpTp/I6pVdhKd33lhCHNVoANJ2Ap51/RhTCfci3A+XxLOWEMiUCZhrDVVr1E6JMtQphlO2GrOQRE0vebrcSyXjqsLxZ/dDbKPzQDA0eWpO3ziYi6isHLRQxeRLeuS9X1WP/6Tqx+ZRuO7j0DTRMQmnBOicrzY+F9U1F+3zSMvWOYK6dEXTxbh2/+/X/h8K6T0DQBme5Ge0tKOYEkJ9sJW4DrgQiAEw4ydREQALFcD6wCb2amDYCI2dBDdtp+m9UpqZzTHzMhfhqiduYCEIqkNd+1JbEODB8/EF/79edQNjz9PcpERJ1JNRvw4hpERF1QXFqAD36qAh/8VAXOHb+ItX/egYvV9Zhz10RMnncbDNPdiwAc3XsGh3edBAB3QxfgBBFNA7wed+tcUzez5Wy/kbHQBcRPPbxZD3kKAcQsiFAkI+US68CxvWdwYOsxBi8iuq4xeBERdVPZsL54+L/f1dujQT0kMnB5diIiIl4aiIiIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERHdSDJ8c+Fb4Q5XSiX+kxkZ/wpvESKDN8EmIuoOBi8iohvIhNmj8IG/L0duQRYAQDfc2YwnPjevIBtjxpaib0m+87zuTj0t/rk+v4mxkwZj8PC+zvOae41ppRSUUjCq6yHqm53wlXi4IBEMRowpxdgJA2EYOgD3pjHxuQMH98HYiQORleUB4N53mPjcooFFGL1wLAr75TnPu7yMZuf58TePLcT0JeNdqUNElC5CqQwe5rtJBAIB5Ofno6GhAXl5eb09OkR0C7ItG+9tPIyqV7di/V92ItwcgaZrkLbs9mfqhgbbksjK8WHBfdOw8IPTMWH2KOi6BqUUDu89i6o3dqPy9V2or2uGrmuwe1BP0wSkVDBMHTPLR6Pi7kmYseA2eLwmAODMiUtY+9YerH59F86drutxPQCAUk4fXnMQqj4A1dAISOczldeELC2CHFAMVZDtBLAe9qIkpnHk8H64s2IsKuaNRr/iXABAc3MEG9cfwuqVe7Fj+wkoqSCEQE92y4l51L8kH0vvmoCKxWMxZKgTYmMxG9u2HMWaFfuwcd1BxKJ2cvx6Wi83z49Fd45DxdJxGDt+IIQQkFJi/+YjqPrju6j647toaggml7HuSizjHr+JufdMRcWHZ2JqxVgYptHtzyQi6qlUswGDVzcweBHR9SQajmFb5T5UvbINm9/ejVjUSjmEJYYzvQbmLJ+E8gfuwLTysTA97TdkbVti7/YTqHpjN9a+uRvBpkjKoUgIAQUnYEydPQIV90zG7EVjkJ3ja/c9SikcO1SDqjd3Y/Vf30PdxcauhbB4gFLBUDxsBQDL7vgtfg9kWR/IgcVQOf4uhTBdE7ClwoDSAtxZMQ5LFo7BwLLCDt9TX9+MdVUHsWrFXuzfe6ZLASwxLwoKs7B46XhULB6H20eXdnjqXSgUxeYN72PNir3YuvkobFumHMISy4zPb2LhojGoWDoek6cO7bBny4pZ2LX2ICr/uAUb/rQdkVA05e+wZUC/Y+kEVDw4CzPunABflrfT9xIRZQKDl4sYvIjoehVsCmPzW+9hzR+3Yue6A5CJXpQWDepEQ1bTNUxfNBYVH5qBWUsnwJfd9YZsLGphxztHUPn6e9i0ej+iEQuaLiDtFrsWAQg4QWLslMFYfO9kzLtzPPILs7tcT0qJA++dRtWbe1D55m40BUJtN+ATQSkSgbzcAFXfCMRiXa4HADLHD1lWBDmwGPB72wxhiXHoU5SDOyvGYvGC0Rg5rF+3fnd04XwAVWv2Y9WKPTh29EKbgShRLyvbi4pFY1GxZBwmTBzUrdMIGwMhrK86iNVv78HunaeSk9aydZAYB9PUMXv+bVi0dDzumD0Cng4CensioSjeXbEblS9twbsrdsOK2dcsM0IT8REQmLRgNBZ9ZBbm3j0F2flZXa5HROQ2Bi8XMXgR0Y2gobYJG/+6E2tefhf7th51rpMhgAmzRmHxgzMw52+mJH8rlg7hUBRbqg6i8vX3sHXD4eQpZcNuK8GS+6ZgwbIJyd+KpYNt2di55Riq3tyNDav2IRyKB6tYLB62AkAkmrZ6CoDKz3Z6wgb0AeKnRObm+LBk4RgsXjAa40YPSOtvtk6fqkXl6n1Y+fYeVJ+rBwB4PAbmLbgdi5aOw7Tpw2Gaetrq1V5qxNrVB7Dq7T14/2A1ACd0TZ0xHEvuGo/Z825DVjcCenuaG4LY9MZOrHlxC3atO5A8QHD7tGFY/NHZWHD/dBT05X6WiK5vDF4uYvAiohvNpep67H/3KMbPGomi/ukLP+1pCoSwa/NRDL2tBAOHFrteLxqJ4ePjn8Dlc3VAKOx6PQVAFeVi1LTheOaNf4Hh0gUrkvWUwpH3z+PihQCmTh8Gn890tR4AVJ+9jMOHqjFl2lDkZaCnqf5iALs3HsbtU4eh/+A+rtcjIkqXVLPBDXFVwxMnTuDv//7vMWzYMPj9fowYMQJPPvkkotHWRzJPnTqFe++9F9nZ2SguLsYXvvCFa4bZs2cPFi5cCL/fjwEDBuDrX/96j37ITER0IyguLcCC+6ZlJHQBQE6eH/PuHJ+R0AUAHq8Jjx3LSOgCnEvCa3WNyFfS9dAFOL+NG3VbCebMuy0joQsASgcUYuGisRkJXQBQ0DcPC+6fztBFRDetG+IyQAcPHoSUEv/5n/+JkSNHYu/evfjUpz6F5uZmfP/73wcA2LaNu+++G3379sWGDRtQW1uLRx99FEop/OQnPwHgpNGlS5eioqICW7duxeHDh/HYY48hOzsbX/7yl3tzEomIiIiI6CZ2QwSvZcuWYdmyZcm/hw8fjkOHDuFnP/tZMnitWLEC+/fvx+nTp1FWVgYA+MEPfoDHHnsM3/rWt5CXl4ff//73CIfD+NWvfgWv14vx48fj8OHD+OEPf4gvfelLvPkiERERERG54oY41bAtDQ0NKCoqSv79zjvvYPz48cnQBQB33XUXIpEItm/fnhxm4cKF8Hq9rYY5d+4cTpw4kbFxJyIiIiKiW8sNGbyOHj2Kn/zkJ/jsZz+bfK6mpgb9+/dvNVxhYSE8Hg9qamraHSbxd2KYtkQiEQQCgVYPIiIiIiKiVPVq8HrqqacghOjwsW3btlbvOXfuHJYtW4YHH3wQn/zkJ1u91tapgkqpVs9fPUziwhodnWb4ne98B/n5+cnHoEGDujytRERERER06+rV33g9/vjjeOihhzocZujQocn/P3fuHCoqKjB79mz84he/aDVcSUkJtmzZ0uq5y5cvIxaLJXu1SkpKrunZunDhAgBc0xPW0le/+lV86UtfSv4dCAQYvoiIiIiIKGW9GryKi4tRXJzapYbPnj2LiooKTJs2Dc8++yw0rXVn3ezZs/Gtb30L1dXVKC0tBeBccMPr9WLatGnJYf7lX/4F0WgUHo8nOUxZWVmrgHc1r9fb6ndhREREREREXXFD/Mbr3LlzKC8vx6BBg/D9738fFy9eRE1NTaveqzvvvBNjx47FJz7xCezcuROrV6/GE088gU996lPJG5k98sgj8Hq9eOyxx7B37168+uqr+Pa3v80rGhIR3QT8OT4ITTg32XJZ4nR4X7bP/WJERHRTuCEuJ79ixQocOXIER44cwcCBA1u9lviNlq7r+Otf/4p/+Id/wNy5c+H3+/HII48kLzcPAPn5+Vi5ciX+8R//EdOnT0dhYSG+9KUvtTqNkIiIbkzffuNfseq367Dqd2tx6sBZaLoGacv0FRBO4FJS4bbpI7Dk4wuw6JF56ft8IiK6qQmVSC6UskAggPz8fDQ0NCR704iI6PpxYt9pVD63Aat+tw4XTl2Cbmiwre6FsESAGzJ2IJZ8fAHKH5qLkqH90jzGRER0o0o1GzB4dQODFxHRjUEphUNbj6DyuY1Y/Yf1aLgYSCmEJYbpN6QYSz++EBUPz8WQsbyoEhERXYvBy0UMXkRENx7btrFn3QFUPrcBVS9uQjAQahXCEv9f0C8Pix+Zj4qH5+G26SP4G2AiIuoQg5eLGLyIiG5ssWgM21fsxprn1mPjq+/C8Bio+OhcVDw8D+Pnj4au6709ikREdINg8HIRgxcR0c0jGolB0wQM84a43hQREV1nUs0G3MsQEdEtzeM1e3sUiIjoFnBD3MeLiIiIiIjoRsbgRURERERE5DIGLyIiIiIiIpcxeBEREREREbmMwYuIiIiIiMhlDF5EREREREQuY/AiIiIiIiJyGe/j1Q2Je04HAoFeHhMiIiIiIupNiUyQyAjtYfDqhsbGRgDAoEGDenlMiIiIiIjoetDY2Ij8/Px2Xxeqs2hG15BS4ty5c8jNzYUQolfGIRAIYNCgQTh9+jTy8vJ6ZRxuZpy/7uM8dh/nsbs4f93Heewuzl/3cR6763qZv0opNDY2oqysDJrW/i+52OPVDZqmYeDAgb09GgCAvLw8rsgu4vx1H+ex+ziP3cX56z7OY3dx/rqP89hd18P87ainK4EX1yAiIiIiInIZgxcREREREZHLGLxuUF6vF08++SS8Xm9vj8pNifPXfZzH7uM8dhfnr/s4j93F+es+zmN33WjzlxfXICIiIiIichl7vIiIiIiIiFzG4EVEREREROQyBi8iIiIiIiKXMXgRERERERG5jMHrOnbixAn8/d//PYYNGwa/348RI0bgySefRDQabTXcqVOncO+99yI7OxvFxcX4whe+cM0we/bswcKFC+H3+zFgwAB8/etfB6+r4vjWt76FOXPmICsrCwUFBW0OI4S45vHzn/+81TCcx21LZf5yGU6voUOHXrO8fuUrX2k1TCrznNr305/+FMOGDYPP58O0adOwfv363h6lG9ZTTz11zfJaUlKSfF0phaeeegplZWXw+/0oLy/Hvn37enGMr2/r1q3Dvffei7KyMggh8Nprr7V6PZX5GYlE8PnPfx7FxcXIzs7GBz7wAZw5cyaDU3F962weP/bYY9cs07NmzWo1DOdx+77zne/gjjvuQG5uLvr164f7778fhw4dajXMjbocM3hdxw4ePAgpJf7zP/8T+/btw49+9CP8/Oc/x7/8y78kh7FtG3fffTeam5uxYcMGPP/88/jjH/+IL3/5y8lhAoEAli5dirKyMmzduhU/+clP8P3vfx8//OEPe2OyrjvRaBQPPvggPve5z3U43LPPPovq6urk49FHH02+xnncvs7mL5dhd3z9619vtbx+7WtfS76Wyjyn9r3wwgv44he/iH/913/Fzp07MX/+fCxfvhynTp3q7VG7YY0bN67V8rpnz57ka//xH/+BH/7wh3jmmWewdetWlJSUYOnSpWhsbOzFMb5+NTc3Y9KkSXjmmWfafD2V+fnFL34Rr776Kp5//nls2LABTU1NuOeee2DbdqYm47rW2TwGgGXLlrVapt94441Wr3Met2/t2rX4x3/8R2zevBkrV66EZVm488470dzcnBzmhl2OFd1Q/uM//kMNGzYs+fcbb7yhNE1TZ8+eTT733HPPKa/XqxoaGpRSSv30pz9V+fn5KhwOJ4f5zne+o8rKypSUMnMjf5179tlnVX5+fpuvAVCvvvpqu+/lPO5ce/OXy3D6DRkyRP3oRz9q9/VU5jm1b8aMGeqzn/1sq+dGjx6tvvKVr/TSGN3YnnzySTVp0qQ2X5NSqpKSEvXd7343+Vw4HFb5+fnq5z//eYbG8MZ19b4rlflZX1+vTNNUzz//fHKYs2fPKk3T1FtvvZWxcb9RtNU+ePTRR9V9993X7ns4j7vmwoULCoBau3atUurGXo7Z43WDaWhoQFFRUfLvd955B+PHj0dZWVnyubvuuguRSATbt29PDrNw4cJWN5e76667cO7cOZw4cSJj436je/zxx1FcXIw77rgDP//5zyGlTL7Gedx9XIbd8b3vfQ99+vTB5MmT8a1vfavVaYSpzHNqWzQaxfbt23HnnXe2ev7OO+/Epk2bemmsbnzvv/8+ysrKMGzYMDz00EM4duwYAOD48eOoqalpNb+9Xi8WLlzI+d0NqczP7du3IxaLtRqmrKwM48eP5zzvgqqqKvTr1w+33XYbPvWpT+HChQvJ1ziPu6ahoQEAku3fG3k5NnqtMnXZ0aNH8ZOf/AQ/+MEPks/V1NSgf//+rYYrLCyEx+NBTU1NcpihQ4e2GibxnpqaGgwbNszdEb8JfOMb38DixYvh9/uxevVqfPnLX8alS5eSp29xHncfl+H0++///b9j6tSpKCwsxLvvvouvfvWrOH78OP7v//2/AFKb59S2S5cuwbbta+Zf//79Oe+6aebMmfjNb36D2267DefPn8c3v/lNzJkzB/v27UvO07bm98mTJ3tjdG9oqczPmpoaeDweFBYWXjMMl/HULF++HA8++CCGDBmC48eP49/+7d+waNEibN++HV6vl/O4C5RS+NKXvoR58+Zh/PjxAG7s5Zg9Xr2grR8SX/3Ytm1bq/ecO3cOy5Ytw4MPPohPfvKTrV4TQlxTQynV6vmrh1HxixK09d6bQXfmcUe+9rWvYfbs2Zg8eTK+/OUv4+tf/zr+1//6X62GuZXmcbrnL5fhznVlnv+P//E/sHDhQkycOBGf/OQn8fOf/xy//OUvUVtbm/y8VOY5ta+t5ZHzrnuWL1+OD33oQ5gwYQKWLFmCv/71rwCAX//618lhOL/Tqzvzk/M8dR/96Edx9913Y/z48bj33nvx5ptv4vDhw8lluz2cx9d6/PHHsXv3bjz33HPXvHYjLsfs8eoFjz/+OB566KEOh2l5dP/cuXOoqKjA7Nmz8Ytf/KLVcCUlJdiyZUur5y5fvoxYLJY8ElBSUnJNuk90eV99tOBm0dV53FWzZs1CIBDA+fPn0b9//1tuHqdz/nIZTk1P5nnialpHjhxBnz59Uprn1Lbi4mLout7m8sh5lx7Z2dmYMGEC3n//fdx///0AnKPXpaWlyWE4v7sncbXIjuZnSUkJotEoLl++3Kq34MKFC5gzZ05mR/gmUVpaiiFDhuD9998HwHmcqs9//vP485//jHXr1mHgwIHJ52/o5bg3flhGqTtz5owaNWqUeuihh5RlWde8nviR/Llz55LPPf/889dcmKCgoEBFIpHkMN/97nd5YYKrdHRxjav95Cc/UT6fL3mxB87jznV2cQ0uw+75y1/+ogCokydPKqVSm+fUvhkzZqjPfe5zrZ4bM2YML66RJuFwWA0YMED9+7//e/JH9N/73veSr0ciEV5cI0Vo5+IaHc3PxEUJXnjhheQw586d6/WLElyvrp7Hbbl06ZLyer3q17/+tVKK87gzUkr1j//4j6qsrEwdPny4zddv1OWYwes6dvbsWTVy5Ei1aNEidebMGVVdXZ18JFiWpcaPH68WL16sduzYoVatWqUGDhyoHn/88eQw9fX1qn///urhhx9We/bsUa+88orKy8tT3//+93tjsq47J0+eVDt37lT//u//rnJyctTOnTvVzp07VWNjo1JKqT//+c/qF7/4hdqzZ486cuSI+q//+i+Vl5envvCFLyQ/g/O4fZ3NXy7D6bVp0yb1wx/+UO3cuVMdO3ZMvfDCC6qsrEx94AMfSA6Tyjyn9j3//PPKNE31y1/+Uu3fv1998YtfVNnZ2erEiRO9PWo3pC9/+cuqqqpKHTt2TG3evFndc889Kjc3Nzk/v/vd76r8/Hz1yiuvqD179qiHH35YlZaWqkAg0Mtjfn1qbGxMbmcBJLcHiQMvqczPz372s2rgwIFq1apVaseOHWrRokVq0qRJbR4AvhV1NI8bGxvVl7/8ZbVp0yZ1/PhxVVlZqWbPnq0GDBjAeZyiz33ucyo/P19VVVW1avsGg8HkMDfqcszgdR179tlnFYA2Hy2dPHlS3X333crv96uioiL1+OOPt7rstlJK7d69W82fP195vV5VUlKinnrqKfYUxD366KNtzuPKykqllFJvvvmmmjx5ssrJyVFZWVlq/Pjx6umnn1axWKzV53Aet62z+asUl+F02r59u5o5c6bKz89XPp9P3X777erJJ59Uzc3NrYZLZZ5T+/7P//k/asiQIcrj8aipU6cmL3NMXffRj35UlZaWKtM0VVlZmXrggQfUvn37kq9LKdWTTz6pSkpKlNfrVQsWLFB79uzpxTG+vlVWVra5zX300UeVUqnNz1AopB5//HFVVFSk/H6/uueee9SpU6d6YWquTx3N42AwqO68807Vt29fZZqmGjx4sHr00UevmX+cx+1rr+377LPPJoe5UZdjoVT8F+pERERERETkCl7VkIiIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERERERuYzBi4iIrltCCLz22muufPbQoUPx9NNPJ/+uqanB0qVLkZ2djYKCgnafu95UVVVBCIH6+vqU31NeXo4vfvGLro0TERFdi8GLiIh6zWOPPQYhxDWPZcuWdevznnrqqeRnGIaB4uJiLFiwAE8//TQikUirYbdu3YpPf/rTyb9/9KMfobq6Grt27cLhw4fbfa43tRWY5syZg+rqauTn5/fOSBERUUqM3h4BIiK6tS1btgzPPvtsq+e8Xm+3P2/cuHFYtWoVpJSora1FVVUVvvnNb+K3v/0tqqqqkJubCwDo27dvq/cdPXoU06ZNw6hRozp8rqtisRhM0+z2+zvj8XhQUlLi2ucTEVF6sMeLiIh6ldfrRUlJSatHYWFhm8Pu2bMHixYtgt/vR58+ffDpT38aTU1NrYYxDAMlJSUoKyvDhAkT8PnPfx5r167F3r178b3vfS85XMtTDYcOHYo//vGP+M1vfgMhBB577LE2nwOAhoYGfPrTn0a/fv2Ql5eHRYsW4b333kt+7lNPPYXJkyfj//2//4fhw4fD6/VCKZXy+377299i6NChyM/Px0MPPYTGxkYATu/g2rVr8eMf/zjZq3fixIlrTjWsra3Fww8/jIEDByIrKwsTJkzAc88919OviYiIeojBi4iIbgjBYBDLli1DYWEhtm7dipdeegmrVq3C448/3ul7R48ejeXLl+OVV15p8/WtW7di2bJl+MhHPoLq6mr8+Mc/bvM5pRTuvvtu1NTU4I033sD27dsxdepULF68GHV1dcnPO3LkCF588UX88Y9/xK5duwAgpfcdPXoUr732Gl5//XW8/vrrWLt2Lb773e8CAH784x9j9uzZ+NSnPoXq6mpUV1dj0KBB10xLOBzGtGnT8Prrr2Pv3r349Kc/jU984hPYsmVLV2Y3ERGlGYMXERH1qtdffx05OTmtHt/4xjeuGe73v/89QqEQfvOb32D8+PFYtGgRnnnmGfz2t7/F+fPnO60zevRonDhxos3X+vbtC6/XC7/fj5KSEuTn57f5XGVlJfbs2YOXXnoJ06dPx6hRo/D9738fBQUFePnll5OfF41G8dvf/hZTpkzBxIkTU36flBK/+tWvMH78eMyfPx+f+MQnsHr1agBAfn4+PB4PsrKykj2Duq5fMy0DBgzAE088gcmTJ2P48OH4/Oc/j7vuugsvvfRSp/OIiIjcw994ERFRr6qoqMDPfvazVs8VFRVdM9yBAwcwadIkZGdnJ5+bO3cupJQ4dOgQ+vfv32EdpRSEED0a1+3bt6OpqQl9+vRp9XwoFMLRo0eTfw8ZMqTVb8hSfd/QoUOTv0EDgNLSUly4cKFL42jbNr773e/ihRdewNmzZxGJRBCJRFrNNyIiyjwGLyIi6lXZ2dkYOXJkp8N1FJxSCVQHDhzAsGHDujx+LUkpUVpaiqqqqmtea3m5+atDTqrvu/oiHEIISCm7NI4/+MEP8KMf/QhPP/00JkyYgOzsbHzxi19ENBrt0ucQEVF6MXgREdENYezYsfj1r3+N5ubmZLDZuHEjNE3Dbbfd1uF7Dx48iLfeegtf/epXezQOU6dORU1NDQzDwNChQ11/39U8Hg9s2+5wmPXr1+O+++7Dxz/+cQBO6Hv//fcxZsyYbtclIqKe42+8iIioV0UiEdTU1LR6XLp06ZrhPvaxj8Hn8+HRRx/F3r17UVlZic9//vP4xCc+0eo0Q8uyUFNTg3PnzmHPnj34yU9+goULF2Ly5Mn4n//zf/ZoXJcsWYLZs2fj/vvvx9tvv40TJ05g06ZN+NrXvoZt27al/X1XGzp0KLZs2YITJ07g0qVLbfaGjRw5EitXrsSmTZtw4MABfOYzn0FNTU23ppeIiNKHwYuIiHrVW2+9hdLS0laPefPmXTNcVlYW3n77bdTV1eGOO+7Ahz/8YSxevBjPPPNMq+H27duH0tJSDB48GOXl5XjxxRfx1a9+FevXr0dOTk6PxlUIgTfeeAMLFizA3/3d3+G2227DQw89hBMnTnT4G7Puvu9qTzzxBHRdx9ixY9G3b1+cOnXqmmH+7d/+DVOnTsVdd92F8vJylJSU4P777+/O5BIRURoJpZTq7ZEgIiIiIiK6mbHHi4iIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERERERuYzBi4iIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERERERuYzBi4iIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERERERuYzBi4iIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERERERuYzBi4iIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERERERuYzBi4iIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERERERuczo7RG4EUkpce7cOeTm5kII0dujQ0REREREvUQphcbGRpSVlUHT2u/XYvDqhnPnzmHQoEG9PRpERERERHSdOH36NAYOHNju6wxe3ZCbmwvAmbl5eXm9PDZERERERNRbAoEABg0alMwI7WHw6obE6YV5eXkMXkRERERE1OlPkHhxDSIiIiIiIpcxeBEREREREbmMwYuIiIiIiMhlDF5EREREREQuY/AiIiIiIiJyGYMXERERERGRyxi8iIiIiIiIXMbgRURERERE5DIGLyIiIiIiIpcxeBEREREREbmMwYuIiIiIiMhlDF5EREREREQuY/AiIiIiIiJyGYMXERERERGRyxi8iIiIiIiIXMbgRURERERE5DIGLyIiIiIiIpcxeBEREREREbmMwYuIiIiIiMhlDF5EREREREQuY/AiIiIiIiJyGYMXERERERGRyxi8iIiIiIiIXMbgRURERERE5DIGLyIiIiIiIpcxeBEREREREbmMwYuIiIiIiMhlDF5EREREREQuY/AiIiIiIiJyGYMXERERERGRyxi8iIiIiIiIXMbgRURERERE5DIGLyIiIiIiIpcxeBEREREREbmMwYuIiIiIiMhlDF5EREREREQuY/AiIiIiIiJyGYMXERERERGRyxi8iIiIiIiIXMbgRURERERE5DIGLyIiIiIiIpcxeBEREREREbmMwYuIiIiIiMhlDF5EREREREQuY/AiIiIiIiJyGYMXERERERGRyxi8iIiIiIiIXMbgRURERERE5DIGLyIiIiIiIpcxeBEREREREbmMwYuIiIiIiMhlN13wWrduHe69916UlZVBCIHXXnut1etKKTz11FMoKyuD3+9HeXk59u3b1zsjS0REREREt4SbLng1Nzdj0qRJeOaZZ9p8/T/+4z/wwx/+EM888wy2bt2KkpISLF26FI2NjRkeUyIiIiIiulUYvT0C6bZ8+XIsX768zdeUUnj66afxr//6r3jggQcAAL/+9a/Rv39//OEPf8BnPvOZTI4qERERERHdIm66Hq+OHD9+HDU1NbjzzjuTz3m9XixcuBCbNm1q932RSASBQKDVg4iIiIiIKFW3VPCqqakBAPTv37/V8/3790++1pbvfOc7yM/PTz4GDRrk6ngSEREREdHN5ZYKXglCiFZ/K6Wuea6lr371q2hoaEg+Tp8+7fYoEhERERHRTeSm+41XR0pKSgA4PV+lpaXJ5y9cuHBNL1hLXq8XXq/X9fEjIiIiIqKb0y3V4zVs2DCUlJRg5cqVyeei0SjWrl2LOXPm9OKYERERERHRzeym6/FqamrCkSNHkn8fP34cu3btQlFREQYPHowvfvGL+Pa3v41Ro0Zh1KhR+Pa3v42srCw88sgjvTjWRERERER0M7vpgte2bdtQUVGR/PtLX/oSAODRRx/Fr371K/zTP/0TQqEQ/uEf/gGXL1/GzJkzsWLFCuTm5vbWKBMRERER0U1OKKVUb4/EjSYQCCA/Px8NDQ3Iy8vr7dEhIiIiIqJekmo2uKV+40VERERERNQbGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERERERuYzBi4iIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERERERuYzBi4iIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERERERuYzBi4iIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERERERuYzBi4iIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLjN4eASIioltBc2MIm1bsQ+VfdqKxPoiFd0/Cwnsmo29pQW+PGhERZYBQSqneHokbTSAQQH5+PhoaGpCXl9fbo0NERNepSDiGdysPoPLPO7G16iAsy4amCUilIISAkgqjJw/G4vunYt6yiSjok9Pbo0xERF2UajZg8OoGBi8iImqPFbOxY+NhVP1lFzau2ItoOAZN1yBt2ebwQhNQ8SA2efZIVHxgCuYsHY/sXF+Gx5yIiLqDwctFDF5ERNSSlBJ7tx7H2td3Ye1f30NzYxi6rsFuJ2y1R9MEpFQwDB0zFo1Bxb1TcEf5aHh9pktjTkREPZVqNuBvvIiIiLpBKYUj+86i6vVdqPzTTly+1NgqbHU1dAGAlM6xUMuysXn1fmxasRdev4l5d01A+b1TMHn2SBimntbpICKizGCPVzewx4uIiP7vd1/HH3+5rls9W12VqDFqwkD871e+4GotIiLqmlSzAS8nT0RE1A3nz14GRPd6troqUeP86TrXaxERkTsYvIiIiLpJ9PYIEBHRDYPBi4iIiIiIyGUMXkRERERERC5j8CIiIiIiInIZgxcREREREZHLGLyIiIiIiIhcxuBFRERERETkMgYvIiIiIiIilzF4ERERdYPX74ECIERm7ualaQK+LE9GahERUfoZvT0CREREN6LP/dsHMGbyYKz50w7s33ESQggoKEClr4ama5C2RNmQYiy+fyoW3z81fR9OREQZJZRSadxF3BoCgQDy8/PR0NCAvLy83h4dIiLqZRer67Hujfew+tUdOH6oGpomIGX3dq+6rsG2Jfr0z8Oi+6ai/J7JGDa6NGM9a0RE1DWpZgMGr25g8CIiovacOX4Ra1/fhdWv7UD1qdpkr1VHEmErNz8L5R+YjPJ7JmP05MHQNP4igIjoesfg5SIGLyIi6oxSCscOVKPq9V1Y86cdqLsQSAYs4MpphL4sD+Yvn4jyeyZj0qwR0A29l8eciIi6gsHLRQxeRETUFVJKHNh5ClV/2Ymq13chEopi1uJxKL93MqYvuB0er9nbo0hERN3E4OUiBi8iIuou27Jh2woeL69vRUR0M0g1G3CrT0RElEG6oUPn3peI6JbDX+0SERERERG5jMGLiIiIiIjIZQxeRERERERELmPwIiIiIiIichmDFxERERERkcsYvIiIiIiIiFzGC9oSEdF141JtI97ZchSlJQWYPGkwDN3d44NNwQjW7joKr8fAvInD4PO4eyPjaMzClu3H0dQcwdwZI5CX63e1npQSe985gtNHajBjyXj0HVDkaj2lFI7tP4d9245h4qyRGHp7qav1iIhuJAxeRETUqwKNIazbcBgr1uzD7r1nks/n5fqwuHwsFpePwbgxZRBCpKVeOBrDhveO483NB7Bhz3HYtgQAeE0Di6aNxF0zR2PW2CEwDD0t9SxbYufuU1i17gCqNh5CKBwDAOiawB1ThmFp+RjMnTESWX5PWuoppXB410msfXUb1vzxXTRcaky+Nnr6MCz+8EzMu3cqCopz01IPAM4ev4iqP+/A6le2ofrkpeTzA0f0w+IHpmPhvVNQOrg4bfWIiG5EQimlenskbjSp3p2aiIjaFgxFsWnzEayq3I+t24/DlgqaEJBX7ZJ0XcC2FYr75GDponFYXD4GI4b17XIIsywbm/efxNtbDmLN9iOIxCzomoAtr6oXfy7H78WdM27HXTNvx5RRA6FpXaunlMLeg+ewet0BrFp7AIGmcHJaWtI0ASkVPKaOuTNHYsmCMZg5bRg8ZtePi548VI21r27FmpffxfnTtdB0DTIeKhOEJqCUghACk+ffjooPzcCcv5mM7G70vF2srse613dizSvbcOzAueS0XC3x/KgJg7Dog9Ow4J7JKOqX3+V6RETXq1SzAYNXNzB4ERF1XTRq4d3tx7Gqcj82bj6CWMxut7HelsSwAwcU4s7F47B44RgMKCtsd3gpFXa+fwZvbzmEFe8eQlMo0mbYak9i2D55WVg+awzumjkao4f0azf0KaVw9MRFrFp3ACsq9+NSXVObYavdevFh/X4PKubejqULx2Dy+EHQOzjd8vypWqx9bRtWv7QFpw5Xtxm22pOYn4apY8bS8Sh/YAZmLBkPbwc9b4HLzdjwxntY89o27Nt2HAICCgpIYRKd2ebMu/EzhmPRB6dj7vKJyM3PSml8iYiuVwxeLmLwIiJKjR0/zW5N1QFUrj+EUCjapTDSnmQvyoh+uHPxOFQsGI3iPrlQSuHAyfN4e8shvLn5AOoCwS6FrfYkPqOsOA93zx6Lu2aOxtBS5/dSZ85dxur1B7Gich9On7vcpTDZbj1dg21L5Of5sWThGCyZPwZjby+FEAKXLwSw/i87sOblLTi040SyFyuV8NOeRGDz+j2Ye/dklD9wB6YsGAPD1BFsCmPzyr1Y89p27Fx/CFIqp2YPpjExjzRdw/SFo1Fx3zTMWjoOvixv9yeCiKiXMHi5iMGLiKhz//dX6/DnN3chEAgng0S6JTqflAL6jyxCwLRxsaE5LWGrPcmet9wceENAdXVDWsJWexLTUuw1UXixEdUHqwEoQPQs/LRbz9BgWxLZeT4UlPXBheoGxKKWa9OYCH2m18DcuybgH7/xIHLy3b3oCBFROqWaDXg5eSIiSrtI1MLvXtiMQCAMAK6ELsAJXInDh8cCDbjY0OzUcykEAUiGjwtnGlBd3dDqOTckpqXh2AWcO3AOSilnul2qaVvOd9XcFMXZk7WIRS0A7k1j4tTIWMRC1Z934vDuU67UISLqbQxeRER0c0jPRQ+vb7fCNBIR3aQYvIiIiIiIiFzG4EVEREREROQyBi8iIiIiIiKXMXgRERERERG5jMGLiIiIiIjIZQxeRERERERELmPwIiIiIiIicpnR2yNARNe3xstN2PDKFmz601aUDu+PiofnYfSMkRDi5rihkG1L7NlxAlUr9qKxIYT5S8Zh1oLb4PN5XKmnlML7R89jddUBnDxVizmzRmLhvNuRn+d3pR4AnKuux+qqA9i97zQmTxyMRQvHoLR/vmv1Ll9uxurV+6BFbShNQGnCuf+US8uM0oBorkA01/l8PQoIy71bXikAVjYQKhYQCvBfVPDVAcKleygrAJYPCI3KgRrpR9apJvhPN0GLuXNTagCws0zE+ufBzvPBaIjArAu6Wk/pGmSOF9LvwYrK/eg7pBiDBvdxrV5jIISNaw7gnbUHUTKwEBV3TcDt4wa4tl2LhqPY8uZ7WPfHLfD6PSj/yCxMqRgH3dBdqWfbEnu2HMXav+xA4+Ug5t89GTOXjIPP79527cjO46h8bgNOHTiLWfdOx4IPz0Jen1xX6hHdqIRSyqVdxc0rEAggPz8fDQ0NyMvL6+3RIUq7cDCCzX/ZhjV/2IB339wJ27IhNAFNE7AtiX6Di7H4Y/Ox6JH5GDpuUG+PbpcppXBo31lUvr0HlW/tQcPlIHRdg4KCtBU8XgNzykejYtkETJs1AqbZ82NUp87UYnXVAaxYvQ/VNQ3QNQGpFJQCNE1g+pShWLJoLObNGomsLG+P69XWNWHN2oNYuWYfDh85D00TUEpBwKk75vZSLF00DuXzb0dRYXaP6zU1hbFhw2GsXrUPO3eeBOBMW5IApCagdJGWAKYEEMsRiORrTuDShJNQ4rUgAT2qoEUBYfc8hCkAVhYQzQNieYDS0aqesBT8lxT8FxS89empZ3uBaK6GaL6ANASSM1QIwFbwn21C1rFG+M42Q7N7viuXXgPRfjmI9c+DzPYAVzUPjMYozNpmmHUhiDTUU5qAzPZC5vqgvKbzpHC2M1IqDB/eD0uXjkd5xRj069fzfW04HMXmdYdR+dZubN14BLYtne2aELBtib4l+Vi8fCIqlk3A0BH9elzPtmzsrNyHyhffwfpX3kUkGIVmaBAAbEsipzAbFR+ZhfIHZ2Ps7FHQtJ6dhKSUwqFdp1D15x2o+tN2NNQ1x7drgLSls127awLK75uGqfNvh+np+Xbt9KGzqHxuI1b9bi2qj12AbmiQUkEpBU3TMHXpRCx+ZD7m3HcHsnLdO7hE1NtSzQYMXt3A4EU3o1g0hu0rdmPNc+ux8dV3EQ3HoOkapN32Ue7Ea4PHDMCSjy9E+UNzUDqsf4bHumtOHDmPyrf3YvWbu3GxpgG6rsFuZ/oSr2Vle7HwznEov2sCJkwZAl1PvXF04WIAa9YewIrV+3HsxMVkg7ItiddMU8ecmSOwpHwsZtwxHN4uNI4aG8NYu/EQVq7Zj/f2nE7mm7a28i2zz5SJg7F00TjMmzMKuTm+lOtFIjFs3nwUa1bvw+bNR2BZssNpTFDCaXR3NYQpALFsgWi+hmhe/P0K7aec+GvCBrR4CNO60GmjANg+J2xF8wFlIKV6WswJYP4LCp7GroUw2wSieQKRPA3S0/H0CamgNAFhSfhPNiHreAC+6mCXet6kqSPWNwex/rmw83zOwtLRdxJfmIyGMMxLQZj1YYhOvu9WbxcCMtsDmeODSvS+dFBPCOeAwbhxA7BkyXgsWDgaBQVZKdeLxSxs33wUlW/twaaqg4hGLGi6gGwnOCaW30FDi7Hk7kkov3M8SgYUplxPSon977yPqpfeQeWLm9F0uRm6ocG22tnOxF/rU1qARQ/NQflHZmPEpCFd6nk7cagaVX/egTWvbsPFc/WpbddyfVhwz2SU3zsV42eO6Np27fQlVD2/Eat+tw7H95xKaT9heg3Munc6Fj08DzOWT4HHpTMKiHoLg5eLGLzoZmHbNvasO4DK5zag6sVNCAZCHTYS2iQATTiNldumj8CSjy/Awo/MRlFJ6o0VN9WcvYzKt/dg9Ru7cfrEpQ4bXe1JNFYKCrNRsWwCKpZNwG1jy9psHNXXB1G14RBWrtmHfQfOJRuOXasnYNsKPp+J8nm3Y3H5GEyZPARGG42jUDiKTZuPYGXlfmzddhy2VM730YWaicamrmuYdcdwLKkYi9kzRsDnM68Z1rJsbN9+AmvW7MP6dYcQiVjJ8e0qBQAaWpyOeO38VAAsv0A03+ndUkYnYaujYokQFlHO6YjtjLLtuRK2pAc9qqdHFPznFbIuKJjBtgeVBhDJFYjmabB93Zw+qQBNQERsZJ1oRPbxRnguhNr8GKVriBVnI9Y/F1ZBvBeiq72QiXGUCsblEDy1QRgN4TbnqRKA8ntg5/igsr3d6vFMrEdCCEybNhSLl4zD3Lm3ITv72t5h25bYs/Mkqt7eg7Ur9iHYHOkwjLRdsMV2bWwZFv/NRCxYMg5FxdeeOqeUwtH3TqLqxXew+rlNqKup7/p2FFdCStnw/lj8iBPCBo4qbXPYmtO1qPrzDqx+ZRvOHL3QYfhpT2Ke5PfJQcV9U1F+3zTcNnFQ29u1iw1Y99JmrP79Oux/5zBEvAcdXVjtE/PEl+3Fgg/PRsXD8zBl0XjXTrckyiQGLxcxeNGNTCmFQ1uPoPK5jVj9h/VouBjoViOhLS132BMXjsXij83HvAdmIrcwp8ef3RW1lxqxbuU+rH7jPbx/oDqlXphUJRor/UrynSPid41Hcf98rN/0PlZW7sPOXacg4w3EdGxeE/Vyc31YXD4GixeOwaiR/bF95wmsqjyAje+8j2jMTts0Jj7H6zEwf84oLK4Yi2mTh+DggXNYs2Y/1qzZj+buNGQ7kBzreAiTmoDti59GWKBBmt0MIx0VFM7vwBKnI0odiOU7Ycv2wpV6RlAh67yC/6JTM5rr9N5ZiU7GdP2+KB7C9JCFrGMBZB1vhFEfhdUnG7F+ObCKsuPTJtIzjYleMlvCrA3BUxeEFogAfg/sHC9Uts85FTRNEsuoYeiYNXsEFi8eh5kzR+D4++dR9fZerHlrd/L04XQsoy2/lglTh2Lx8omYu2gMGs7Xo+rFd7DqDxtRfewCNEODTMN2FEDyANHwCYOx+JG5WPjhWdA9Ota9vgtrXt2O9/ecdme7NqAQix+YjoUfmIri/nnY+Oq7WP379di5Zk8y+Ko01NQNHbZlI7coBxUPzUXFw/MwdvZtPT7dkqi3MHi146mnnsK///u/t3quf//+qKmpSfkzGLzoRiWlxGcmP4ETe0+nLWy1J9mLYmj4p189jkWPzHetVksv/GoDnv0/q5J/u7mF0zSBmKHBzvdCAWltCLUl0TgS8Z/7dLenKfV6zm/6DEtBxXvE0hW2OnJ5pIlYnp7e8NMeBURzFawcZKyeHgY8DfG/3b5IjXR697LOSwiIzk8l7CmlAAn46mNO75fL9ZLLaNACbOn6Mppcx8+cBxqautXT1CUi3tvn90HLyY4f3FLubtd0AasxCFVXByWV69OY2BeVjeiP/9r7I3i81/a0E13vUs0Gt+ShhXHjxqG6ujr52LNnT2+PElFGWFELJ/aeBgBXQxeAZABRCji664SrtVp6/8C5ZF23DytJqaCMKz1bboYuAMkGZWK63AxdLT8/cYQ7E6ELAKzs+K4pExfOFPHTCTNYT4vBCSOZuDKoJqDF4IQuwP2aQkBIdeWUQ5frJdeB+LLp9jKaXMeDYedvt9cJ5ax/wnB+66mUu6ELgHMqdiyWXO/dnsbEvujc0fNobmjnfFyim8QteTl5wzBQUlLS26NBdEvQ0niKEREREdGN6pbs8Xr//fdRVlaGYcOG4aGHHsKxY8c6HD4SiSAQCLR6EBERERERpeqWC14zZ87Eb37zG7z99tv4r//6L9TU1GDOnDmora1t9z3f+c53kJ+fn3wMGnTj3beIiIiIiIh6zy0XvJYvX44PfehDmDBhApYsWYK//vWvAIBf//rX7b7nq1/9KhoaGpKP06dPZ2p0iYiIiIjoJnBL/sarpezsbEyYMAHvv/9+u8N4vV54vdfeK4SIiIiIiCgVt1yP19UikQgOHDiA0tK2b1JIRERERETUU7dc8HriiSewdu1aHD9+HFu2bMGHP/xhBAIBPProo709akREREREdJO65U41PHPmDB5++GFcunQJffv2xaxZs7B582YMGTKkt0eN2iCldG7caugZqaeUcm7GaWam3q0ik3dpv7VuCU9ERNT7Mt1eu1HdcsHr+eef7+1RoE4opfD+juOofHEz1jy/EaGmMObdfwcqPjIbUxaNg2Gmf7E9d/ISqv76Hlb/eSfOn7mMafNGoeLeyZhVMRa+LE/nH3CdCzaFsfmt97Dqxc0Zr23FbLz+px0QQ8tQsWQcRozqD5Hmm6pKqbDn8Fms3HAQG7YfhVKZuRcugMymyltFpuepSvwnQ0uNABTUlZsaZ6BeRvXCrfsy+O05MnHz65aUglIq7dvOdvXS7Re/9bc/xZ0fn4c590xDTkFW74wEpUwphfd3nkDVH7dgzYubnfbavdNQ/uBMTCkf60p77UYnlOLx4a4KBALIz89HQ0MD8vLyent0bhqnDp5F5YubsfoPG3D+5CXohpa8o71u6LAtGzkFWVj44CxUfGQ2xs25DZrW/bNlL51vwLo3d2P1n3bi2MFqaJqAlM7qoOkC0lYwPQZmLx6LinsmYdq822B6bpyNSDQcw9Y1+1D1yrvY/NZuWDEbmq7BCoWhwmGoSNT9kfB7gb6FQP9i6D4Ttq1QNqAQS+4aj4ol4zBwcJ9uf7RSCodPXMCqjQfx1roDqK1vhq4JyKgNMxCD2RiDZru7eVMCiOaYsLNNaAoQ0tVyUAAsPyANAT0G6GFn+txoIyXmnJUF2B4BI2zDE1CAS6FWCUAoIFIgEBiiIZqnwwgKCOVSC1Aop6jPBvpFYfmBWJMHytbgajNeU4DHghEGjHoDIuJuPQVAmhJmEPA1AEaze9USy4zSFbSYhBmS0EPuLqMCgOUTsA3ACFrwNFgQiRdcWv2VJiBjUYi6AERtAMKW7tSLf6YyDag+eRBZfmjhmFPPRUoA0jCggkGgPgCEwq7WAwAYOjTTA91rQtpOr8kdd07AogdnYcaySfBl8QJn15NTh86h6uUtWP38Jpw/VXtVe835/5z8LCz80AyUf3gmxs0e1aP22o0g1WzA4NUNDF7pc/7kJVS9tBmrn9uAk/vPQtM1yE52KomVurB/PhY9NAcVH5mNkVOGpnQkMHC5GRtW7MWaP+/Evp0n40ecVYenp+m6BtuW8Gd7sGDZRCy8exImzhgOXb/+NiK2ZWPXhkOoemUrNvxlB8LBaHL8r6aUgopEoSIRqGgaQlii4eExnbBVXAhk+9scNBFyh4/shyV3TUD54rHo2y+1denUuTqs3HgQb63bj7PnG6BrArZs4wtUClpEwmyKwWyyINoaphuUAKwsA7FcE3aW3vrIt62gxxQ0S6UlhCXG2PYLRLM1xLIFoF2pJywFs1nC06SgR1WPG9TJhqxfIFwgEMnXoMzW9bz1Nvy1EmajM4E9qhcPW7EcoHGohqbBGmK5LT5RAkYQMBoFjCbR8x6iRNgyJbSSCET/CESunfwKlQLsoIFogwexgBeQaYoLQgF+G8i2AJ+8ssgoQAQ16HU69FoTIiZ63IBX8TdLD2D7AOlFqy9Jiyj4agFfLWCEex7CroQtQJqANNB6GbWdZdRssmGE07eM2h6BSJ6OWK4OabZeB731MXjrYvA0WM449KBesq4AlKlDmhrQctsvFURDE8SlgBPEpOrZd5gIW7oG1ScPsk8+kOtHy4VURCxozRFozeH0bdcAKK8J5fM42/AW2zUVCgN1DUDtZSAaS1/I1HUI04QwjDb334n2gMdvYu49U1H+4CxMc+msF+rc+VOXsPaP72L1C5tw8sC5rrfXHpyF8g/PxMjJQzLXc5tBDF4uYvDqmcvnG7DulXex+rmNOLT1KIQmoJTq1oY8sVKXDOuLxQ/PRflHZmPw7WWthgk1R/DOmv2o/Msu7Nj4PqRUTs1u7LASISavMAsV90zGwr+ZhNGTBvXqRkRKiQPbjmPtq1tR9cpWNNYHWx19SoWS8koIi8VSL57YARv6lbCVk9Wl03CEcL7/cRMGYvFd47GgfAzyrzrF5PylAFZvOoS31u3HkVOXWvVOpkQp6CEbRrMFsynmHBFP9a3xf+0sHbEcE1a20aph2V49IQEtpqDFVJfrCQCWVyCWIxDL1qD0zuenFlMwmyQ8zRJaLPUGdbIh6wXCBRoiBRqkJ7V63job/jobZnPqDepE2LL8QOMQJ2xFC9D5MiMBo0nAbBTQg85TKQWxxDKqS2j9o07YKrA6LacUYDWZiDZ4YDV6nBHvkkTYsgG/3fkqoQDRpEGvM6DXGhB26iFMxf+rTAHbp2B7kdKls/Sggq/OCWF6tOshTGmAbTqBq9N1AvEDBU02PE2ySwcKEsNJUyCS6/SGSk/nEygsCe9lC766KIxG23kuhXrJuomwZWiALjpfRm0JUd8EcakB4nJj6j1vibClCajCXKjifKi87NS2M+FYPIRFILrQnEsO6TGgfF4or9np9CmlgGDICWF19UDM6noI03UnaBkGRBd6QBL7tKw8PxY+4PSiTJjbs7NeqHOXLzRg/WvbsPr5d3Bo+7EetdcSQa1kaF8s/uhslH94JgbddvNcUZzBy0UMXl0Xagpj/avvYs1zm7Craj+cVkb3wk97Eiv10HEDseihuSgY3A/bNx3B5sr9iEXtrjfWO5EIYX1L8rHoA1Ow6ANTMHhEv7R9fmdOHDiL1S+/izUvb0FdTUOXw1Z7lC2dABaJQFnWtQMI4bRKNQ0oLnDCVn5Oj3/zkAhgmiYwdfowzF54O0KGwpp3DmPP4XPJsj2mFIygBaPJgtFstdkQS5SRPh2xXAOxbNNpeHWznrBbhLB26jlH8YFojuaELaP79bQo4j1hEprdQT0TiMTDlu3r/venRSR8dRL+2rZ7NRJhy/YATYM1NA0WCBen0JBth7DjISwgoMXPgmoVwhKNQU1B9I1C6x+BKIpBdLONpiQQa/Qg1uCB1WS2KNJqKOcfn3R6tvx2t+tBAVpAh16rQ7tsQLTR85YMIwacsOVD969TrBSMZiRDmGah3QZ1V8NWe7SodA4UNEloVvshTOpANE9HNFeH7e3+MqNFJbyXY/DVxmAE7XbrKQDK1KBM3Tng0d3tmmVD1DVCu9QANDQDV9dLhC0BqIIcqD75UIU5zna1O5SCCEWhNYWhBaPtBkwFAKYB5fM4Yaub9ZRSQFPzlRDW0emWmnalZysNQSmxryvom4uKj8zG4o/OxshJvEBauoSawlj/p21Y8+JmvLf2AFxtr40dgMUPzcGij8xCn9LCtH1+b2DwchGDV9f94DP/hRW/WZf87ZSrBCDyciAKC9o9zS7dNF0DlMKLW/4/ZOf4XK9Xf6kRj4z/Jwit867+nlC2DRWOhzDbdhohRflO71ZBbvcbCZ3QNIHmAgPSpycDmSukE8LMphj0oB3v+dGcnq0cA8pI8/QpBWEBWkxCs640nBNhK5Wepq7W0yMKZrPTG6ZJpyHrhC0By9+DhmU79JCEr86Gv9aGHnXqNQ0SaBqsIdRf9Kix3hYRa9ETFnESniiKOacSFkch0nyBLWULxAImYg1eWMH4KU8e6fRsZVlprwcJaPU69DoD2mUdAgJKB6xE2Ep3PaVgNjoBzFfn/G5RifhphKYTvNK6zCgFLargaXJOR9Rsp0Y01wlbbiyjWtiGry4GX10Melg6YcvQnMBlaOm/cEbMgqgNQLvYANEUcrJJfjZknzyowlznjIF0kgpaMH4qYijm5CFTh/J6oLye1qdKpoGSCgg0OgHsciB5YE4YhhO4XOyVSoSwX733PZQM7etanVvJD//x/2HF7zZkrr0mBPoP6oNf7f4Pd2u5LNVswBNlKSNCjSEIkYGVGHCOIkJAKJWR0AUgGX5iEQvIcb9eJBSFUk7vlJuErkNkZwHZWbAG9AG8HkB3/1KxUqpkA93VY0OagJVjwsoxAVtBQKU/bLUkBJQJ2KaOiEfB9mnOb2LcOlVVCNg+AdsHBIs1QHdOKXTzimy2X0PzAA1NA3RE+0lYWUjpVMnuUiYQK1SIFSrkFzZB89oQpnvLjNAVPIVReAqjaG7ywLK19IetljRAFtmQRTZQa0A0mK7OTwiBWB4QywNCxQo5p51xcHMZlV6BsFdDqEiHUM4phW4uo9KnI1imI1jmQ8HBkFPKzdPFTQOqpAh2SREQjjq13PydkiYgc3yQOT7nVMSYSnvYakloAijIAwryoOoaoJ2vBYTIyCn4ibM8Qs0R12vdKoKN4cy215RCsDEDF3C5TjB4EVFqfF7XeriuC7qAyuRv9YRofVGADNSz3e+MbVWv1YUyMlHSJyGMzJ3EIfS2Tx11je5cxCJjhEh/j1on9TK6TgDOKcSZPO/HY3Y+TDppGqBncJ3QNFd7uIhudGkNXg888EDKw77yyivpLE1ERERERHTdSmvwys/PT+fHERERERER3RTSGryeffbZdH4cERERERHRTYEn4hIREREREbnM1YtrvPzyy3jxxRdx6tQpRKPRVq/t2LHDzdJERERERETXDdd6vP73//7f+G//7b+hX79+2LlzJ2bMmIE+ffrg2LFjWL58uVtliYiIiIiIrjuuBa+f/vSn+MUvfoFnnnkGHo8H//RP/4SVK1fiC1/4AhoaGtwqSymwYhZ2rN6DY7tPunuPpGtksJaUUJFoxqZPAVCaQDBD9xJpDkZg5/mh0n3jzXaoDF/hWSX+I5VzM07XC8brZKoenHmqkLm1QiHz9UQMQGZupQdAIWZpsCwtQ1+hQpYnghxvGJmaqwqANBRUBuvF/Jlb/xWcG25nbHujFCyfgG1kpqDz/QlIXWTkG0zUsz1a5tZ7TUDl+DN3aw4BQNfRFAhlpFyoOYJ3V+/D+TN1GalnxSzsrNyHY3tO3bzttVuMa6canjp1CnPmzAEA+P1+NDY2AgA+8YlPYNasWXjmmWfcKk1tkFJiz/oDqHxuI6pe2IjmhiAAYMBtpVj68YWoeHguykaUuFa/T1kRlLpyl3nXaBqErgGRqPMQAsrvBfw+wONJ6w0dVbwedM3Z+AuBTz3wE8ycfzsq/mYiZsy/DV5f+u7ZEg7HsGnzEayu3I8tW4/BHt4fUApacwRaXRP0hiBEGm+orACoLC9UbhZUjt/1e3gpODfalV4NtkeDpgAtqpyAoilIXSRvqpy+ogpCAtpVs01qCkpD8ntNWzkNsLyA7ROQicaeahE04yXTVg+ANJ2H0uMfnoF6theQHgG92TkwoHQF6VFQpkrz4b745+kSMBSam50blemahMcbg8drQU/rPYwUvLqFPG8Y+Z4wTD1+43Rbx6WmbFwKZiEY9SCdc1VJQMY0KEt3bpycJwEFiIiAFhUQloBI57eoAEhAkwLKVAiVCEApGEHAbAKMECDSOEtbLqPOzcSdJ4UENAsQVnqXUecgCyBsp0aonwcAoEUlzGYJs9mGZqexHOLbNVNAmtqVbZhU0CISWlRCk+lt5CoNkB4N0qNfudm2VNCDFvSQDS0q077eQ8CZtqI8yKI8QEog0AytoQloDqV1mQEAGAaEaUDpOoQQ+MpHn8GUBaOx6IPTMeuuCcjKSd9NC6ORGLZXHUDla9uxecUexCIWAOD2KUOw6IE7MP+eySjsm5e2elJK7N14CJUvbsbalzZfaa+NLMGSj81F+Udmo2x4/7TVu1pxWUFm2mu4UqN4QKGrda4nQrkUoYcPH46XX34ZU6dOxR133IFPfvKT+MxnPoMVK1bgoYceQl1dZo4WuCEQCCA/Px8NDQ3Iy0vfypZuSikc3n4Mlc9twJo/rMfl8w3QDR221XqvomkCUiqMnDIMSz6+AAs/OgfFZUVpHRcpJQ5sOYKqFzej8oVNaLzcnL6VOhG2RHyP3d4uRRNOAPP7ANPsVghTic9J1sM1DXNNd+747vWZmLd4LMqXT8CUmSNgmF3vnYrFbGzbfhyrK/dj/ab3EY1aye+r9Yg5f2uNIeh1zdACQYhu7MwVAPg8kLlZULlZznQqldbwcTWpC6eR4NWgNNHhV6iEM7zS0P0QlghbKXb/SA09CmFKOEHE8gpIE84HdfQxLcarO1OoACgDsE3n305rtfi3u/WkCUiPM53tfneJIgYgTQXlUd1sUbcOW+1/hrMg6boNr9eCx2tB07q3uzM1J2wVeMPw6Habq0TiuYhl4GJTNmqbsxG2unfgRUlAWhpUTIOSGtpcKRJPSTgBLKI5YaI7MzUetoR0/r/Nz2hRz2hWMJvjIazr1eI9MYnA1cH6kFgu7XgIs7u7yFwJW1cfZLlmOCGgRyTMJhtGUHY8fAekBkhTg/Ro7W+r4vNU2ApaNB6IullPiXjY8sbDVpsL6ZV6TgizIGLduwl4q7CF+B9Xf1BiHGwJ0dAE0dAEBMPdD32G7uy7db3NbbGma5C2hOkxMHPpeJTfPw13VIyFpxsHQG1bYvem91H12nasf30nQs0R6LoGu8XBTaHF5zMEJs4ZhUUPTMfc5ZOQnefvcj2lFN7fcRyVL27Gmuc3ov5CoM02UmL/P2LSECx5ZC4WfngW+pSlN7RIKXFw6zFUvbwFlS9tTm97Da2/p9l3T0HFg7Mwbcl4eLwZvrl4mqWaDVwLXp/85CcxaNAgPPnkk/j5z3+OL33pS5g7dy62bduGBx54AL/85S/dKJsR13vwOnngDCqf24DVv1uPmhMXoBkaZAorjIg3LJVSmDBvDBZ/bD7mf2gW8vrkpnX8bMvGrqr9qHzhHax/9V2EmyNdX6k1AaFpUCLeROhqg1jXkiFMmB2v7MmwpcV3oF2oldhQ5+T6sHDZBFQsn4CxkwdD66D3yLYldu85jdVVB1C19gCag9FrNvgdj61wjqY2NEO/3AytseOjjQoAvCZkThZUXpazc3M5bCkNsFs1EtDlFlUyhOnofFzjYUuoHhytFy1DWMf1FJwQYnsFbE/i/d1sEKcQipJhywCU2cnAHX1Ii39TqpcIW+1kg/bf3yKEJXrCOnuvUIAhAV11uV5iogwj3hPmsTrtwDU0G3meMPK9YfgMq0urRGLYYNTEpWYnhEXtjk8wUQpQlub0btktGrMpFcSVEBYR0KIahN3ZOoErPT9dPbcv2YCPh7AmQI+ksMzozjLTYdjqpG7KISwRtiS614MVbxrpYQWz2YYZlJ1uO5Lhx9P5QaQ26wkBYcV7waIp1jPjB62MLh4ka1FPD1rQgzY0u+OC14YtdH2lsGyI+kaIhmYgHOl89ug6YBoQhtGlbWiice/L8mDe3ZNR8cHpmDRnFPQOTs9XSuHgjhOoem07ql7bjsDl5pT3vYlApBsa7lg0DhUfnIYZS8bD5/d0+L5TB8+i8sXNWP2HjTh/8mJyvDvjzAoBBYXxc27H4ofnYN79d7jTXlt7AFUvb8H617YhHOxGew1OSFVSQdM1TFs8Dos+Mhuzlk+GP409k72t14OXlBJSShiGs7N58cUXsWHDBowcORKf/exn4fF0vDBez67H4HX+5EVUPr8Rq363Dif3nU555W2PpglIpaBpGqbdOQmLH5mPOfdNhz+n60dyOhINR7H17d1Y88ImbP7rTlhRq/1xj4ctCOf8+LSdNmjoV0JYfHl1djDxnq0uhq32JDbghX1ysOjuiShfNhEjx5RCxMPuwUPVWFN1ACvX7ENDQ6gLYasdLY426vXN0C43Q2u6crRRmQZUrh8yLxswDffDlkiErW40Etr7zMT/aIDUrgphSjlBS14ZMG1T10YIU0gEERHv+Un3aZGI90a0+FO/cppWes/HulIPSJ795TScvYD0xqe9G4G57VJO+FIe1bqXTignaBmyG2Gr/WoAYJo2PF4LHo+V/Kp0IZEbD1t+I+aMQk/qxU/rFAJoinhxqSkbtcEsWDJ+CmZPwlYHNROhSEQ0pzdMiiuvdTdsdVbPAsx4CNOirZcZaQK2KdJ7mqlyamqWs44np0a2OH04rWeZKhhBCTMoYYSuhKJk+PFo7fc0daMWEO8Ji0hosRb10CLcJU5XTkc9IaDFJLSgBSNoJ8+YSIYtIbrd699ePcSseAhrgojErryua4BhQphdC1vtSQSF3IIslN83DQvvn4Yx04ZC0zQopXDiwDlU/Wk71ryyDZeq63u87020YTw+E3OWT0TF/dMxdcHo5Fkv509eQtVLm7H6uQ04uf9sj9trLUPN1CXjsfijczD73mlpDzXRcAxbV+5G5UtbsPnNnbCidofj3ioczhqFRR+djXn3TUdeUU5ax+t60evB62Z2PQWvNc9twKs//isOvnvEWfniR/nSKdkt7DUw+wPT8ci/fAgjJg1NbxEAwcYQ3nl9B9Y8vwk7Vu+FlNIJJUJzuvQz8WNdrwkUFTr/ulgvMU/7lxWg+LZ+OFPbiAsXAtB1AbuTo47d0uJoo94YcU4l8piuhy3AaXDZPj19jYR2tAxhiR92u73EJE6ZiuQJ2L7MLKPJU/tMZOROjIneO+l1GtHpClvt11OQOTZUtg1koB6g0Ce3CQOKGpDjcS6O48rX2GLTfCGQg5MX+iAacSMxt64JAYgoYDTq0GIuLzDxeloUMJsUIEUaA3PHdY2ggh7tQY92V0gFs0nCiCmnZwtwb91PNNGsRC+xy9uZxGnrERtmwHICmNv1hABCEWi1DdBsCQj3ltNECCvsl4tBI/rjUnU9zp241OPw0269eIjLyvVh5O390Xj+Mo7uOpmR9tqsu6fi4X/+AEZMHJLeIoi3197YhcoX38GOyv1X2mvxAChtiRETB2PxQ3Ow4IN3oDjNp0Nej3oleO3evRvjx4+HpmnYvXt3h8NOnDgxXWUz7noKXvcV/C1CjeGMXe1GaAJLPrEA//Ts467WabjUiA2vbcXvv/dn1F0IuFqrlZwsoG96f9/WEWloiPVJby9ih5SCEUzjL8dTEMk3AT0DoTlOXX0qjMsiOQLRvAwdGIBz1Tc7k4uMAKIFmasHAFZJ/Ly1DH2N4weeRZ4/nKmvEEfPFaO6rgCZmkAtKGAGXL1tZyvCAjwZ3GxDKfguZ7AeAE+g+7//uhHozRaMJitTqyBEKAL9YkOGqvUOWXc57UGrI0ITWPLIXDzxX59xtU5DbSM2/Gk71rzwDhrrm1H+4Zkof2AGyka4dwGQ61Gq2SCtW+LJkyejpqYG/fr1w+TJk5OnUF1NCAHbzmzj72YlbZnRS4wmjmi4Lb84F3d/chFOHqrGX5+tgh3L0PKSqZbXLUSIjO5rMtZYb13vJl5uemvSMlhXEyrDq75w1gueb3LDuonXeEcKP7tMd72bXoanUYg2LsTlgvw+ubj778px99+Vu17rZpDW4HX8+HH07ds3+f9ERERERESU5uA1ZMiV80hPnjyJOXPmJC+ukWBZFjZt2tRqWCIiIiIiopuZa79grKioaPNeXQ0NDaioqHCrLBERERER0XXHteCllGrzct+1tbXIzs52qywREREREdF1J+2XOXrggQcAOD/qe+yxx+D1epOv2baN3bt3Y86cOekuS0REREREdN1Ke/DKz88H4PR45ebmwu+/ct1jj8eDWbNm4VOf+lS6yxIREREREV230h68nn32WQDA0KFD8cQTT/C0QiIiIiIiuuW5dkfFJ5980q2Ppl6UyXuGnT1SjUNbj8C2eM+3GxnvVUTXu95YRLle3OBuge9P4Ra4X9lNLJPtNUqdaxfXOH/+PD7xiU+grKwMhmFA1/VWD0qPxR+bD91w5qemu/Z1JmvkFGRjxvKprtW5dLYWL//wL/js1P+Jx277Ag5uPgRI6Vq9lpRSUKEIEI0lnnCvmHDuKi9siSwz/v1pLu/i4tMjRfz/M7RR1iJ2ZluZMnPTBgB6WEJY8Xpu11UKwlYt6rlbDgqABLQo4suMy/XiRFBzaiUebol/dm0gB7btrH9ufoWJz87LCkITGfgO4/NPGQoqE/Xin68055GpegAgdWRmuxb/eNtA5rdrMkPfIQBpiiuFMrBdU6YO5WIb5up6yboZWGb0+HT5i3KT7TR322vOZ+fkZ2HGXZNcq0PdI5RLkXj58uU4deoUHn/8cZSWll5zhcP77rvPjbIZEQgEkJ+fj4aGBuTl5fX26KCpvhkbXn0Xa36/Drsq9zlPCkD18I7lmqFBWhK+HB8WfngWKh6eh8kV45MhLF0CtY1Y9/JmrP79OuzdeNBZVpRqvS3UdQjDgDDNNq+W2W1KObsWKaFiMahYPHT5vBAFedAKCwDTcDbMaairac6d5IeO7IfFd0/CwmUT0K+0ANU19VhTdQArVu3DqdO1yeF6LD7ewpLQGsPQm6LQYjaUEFA+E8pvQqVx+lrVtSWEHd+pCcD26bD9BqQ3vvykuZ4WkdCizgMApEeD7dMhfVr6pw2AFpHQgxb0sA0owMrWEO5jItzHhDJF+g4Xxz9HjyoYQQUjpKBJwPYAkVyBaL5wGklpridsQA8r6BFAswFpANEcIJYnYPuQvnot6ypAJI6zCAWZJSGzbShvfF1Id72oAGIaYAloQqGwoBnFRQEUFTZB01RaVosWbTzUX87BxUv5qL+cA1sKQFdQpvNI+7QB0IMCZoMGMyAAKaB0QHoA2wvnsGval1HAbFQwmwDNAqQWr+dxaqe7nhZT8NZLeOsljIiC0gDLp8HK0mB7RPq2a4l1QioYzRJmsw09Ej+QZQpIU4My4nXSvK3Rg9aV7QwA6dNhZRmw/QaQzoN18QVVb47CqI/ACEScoGfokF4DyudJfz0hIKIxiKYwRDAC4epB1viXGN/XI2ZdWT4MA8I0AF1P2zKj6xpsWyK/Tw4WPTAdC++bhtsmDUZzQxAb/7wda57biPfWHnDGS4j0tdeyvVjwwAyUf3Q2Ji8cm/b2GrUv1WzgWvDKzc3F+vXrMXnyZDc+vlddb8Grpcvn67H2pXew+nfrcPDdIxCaiB/RSe39mq5B2hKm18DsD0zHoofn445lk+HxedI6nsHGEDb9aStW/2E9dqzcDSklRKobH113AphhdC+ExTesyradsGVZHR/xyvJDFORBFOZDdGPDnNgA9x9QgCV3T0b58gkYNKxvu8MfP3ERqysPYOXqfbhwMZB8f1enD7aE3hSB3hSFiFjttneU5oQw6fcARg92PMo5Iits6fzb3mACsP1Ow0F64kf9ulovEX5iElrYCVvt1gMgvfEQ5u1BPSGgRW3oQRt6yLoSENqoF8vTEe5jIFJkQundCEWJhqWlYDQrmCEFrZ0zbhWchnQ0TyCSJ5wGYDfrCRvQIwp62Pn/9j7CNoFYrlNTetD9BnUibCX+bW8w7aoQ1pN6sXjYigmIdj5E02wUFTahb58ACvKbgUS5VGu22OQGAlm4eDEfdbW5sO22G0EKCjAUpOn82+1pE4AeFjDqBcyABs1q+4MUAGU44V160L1zX5Lh50rY0mPtDy61eD2vcHrDuruMWvGw1SBhhNrfzkgNsPxOCHMOTHRzuyYVjKCEp9mGHu54uyYNAenRnHUe6PZ2TQ/b0Jst6GELiY7KturZPh1Wlgnp7+bBrPg80YMxGPVhGA1h52BZW4MCTq+U14Tymd3fRwgBEbMgmsPOoyv7tu5SEipqAZbV8Rk0QgCmc4AXWtcX0sS+OivXh4UfmIry+6Zh3MwRyR6vq10+34B1r7yL1c9txKGtR3vUXpt191Qs+uhsTL9zYtrba5SaXg9eY8eOxe9//3tMmTLFjY/vVddz8Gqp5sQFVL2wCat+txYn951JrqRXS6zsmqZh6tKJWPKxBZj9genIyvW38andFw1H8e6bO7HmuQ3Y/JdtiEWsdscpVSJ5pMr5uWL7QezK0S6Z6NnqzqKfk+2EsII8CE1rd2ee2AAX9MnG4rsnoXzZRIwcc23Pb0eUUjhwqBprKvdj1Zr9aAiEOg9hUkFvikBrikALtx+22q2pa1dCmN7+9LUYyZTCVrtv1wRsvw47y4A0Owl9ibBlKWhh2wlbXfwKlUiEMAPS00njKNlIiPdshWxo7TRKOqoXzTecEFYYP0Ld3r48GX4UzGanZ0u3ulQOCoDldwJRNFd0HPoSz8srYUuzut4Wlh4gmufUVAY6b6ukGLbafbuuILNsyBzp9BR1VC/xdVkCiHYcttpjGBb6FDaib3EAuTkhIFHu6o9JjIoAmhp9uHgxH7W1eYjFuvZTagXlnBpoyiu/wu5ks6ZF4PRsNWjQYl2bPqdB3SKEdfbdCUBYgNmk4Gl0TkPt6jKjdKee7REdh74W64SnwQlbZnPXtzNSbxHCUjkwoZywZQYljFA3tzOmdiWEpbJdi0oYzTHowfbDVkf1bL8BKzuFMwoSB5HCFozLIRiBCLRY1/bBCoDyGM5ZEx6j832EEIBlQzSHoQXDELEM/G5bKWc/b1lAd9oYmub0hHkMQLS/kCbaMB6viTl/MxEV90/HlPm3w/R0bb2vOXkRa1/agtV/2ICTB86m1l5bPB6LH5qDWfdMTXt7jbqu14PXihUr8IMf/AD/+Z//iaFDh7pRotfcKMGrpZP7T6Py+Y1Y9bt1OH/iInRDg5TO+c3j547G4o8twIIPz0Jen1xX6r/5y9X42f/4FUJN4WSXeLoJw4DwmIDm7HicxlGLsNXZ0a4uFRNAbg60wjwgN8cJYXHZOT6UL5+A8mUTMG7KYGhaz8/ltm2J9/acxprK/ahcdxDBYPTKi1JBC0adwBWMpe1MHhgapM8D5Y8f/Wu5qVAKwupe2GqP1IXTE5ZlQBkt6iVOlQxL6BG7y42S9iSOGEu/DmleWy8Ztqz0FJQaEC00EO5jIpqf6PkQyYalEVIwgwpaLH1nY8WygWiuE8JahT7VImylsZ7tA6K5zumIyVPLWgzQ3bDVbk1Dws6WUNkSyrjqSLEtkqcSCpWeih4zhj59GtGvTwOysyMtFxkEg15cvJiHS5fyEY2YaamnED8N0VRQeouJE4CItQhbEXQ5ULZdD5AmIL3Ov4llJdEbajbFe7bC6VtmpHHldESnXryhLhU8ASdseZpUWtb7RD0rS4PlbxGK4vSwgtlswwx2PWy1W1M4pz1LjwaliVbbGS3q9GwZQQsiHaeWwzmYZWXpsLNMJ4RdVc+4HILREIEWTU/4UQCUNx7CzBZhI37WRSJsIdr1A4Gpj0R8mVHK2c/HLMBOY7jTtCs9YS1Cpm5ouGPROFR8cBpmLBkPnz89PU0nD5xF1UubsfoPG3D+5KVW7bVxs2/D4ofnYv4H73CtvUbd0+vBq7CwEMFgEJZlISsrC6bZekdUV1fnRtmMuBGDV4JSCoe3HcX6P25GQb98lH90DooH9HG97tc/8gOs/+PmlLvQe0QICMMANM054uX2xTk0DSIvB8jy46s//yTm3zUBhuneedWxmI2VK/bgh994DVrYghaMpq2R0BYFAKYOO9fv7Djt9k/rSxdpCFjZzk5Mj9jtntaXLkoDbI8GaMI5jTCWvkDZFqkDwRIPYvmm07PVxV6DrlICiOQCkXwNeqzrvRRdrgcn9AVLRdrDVtv1FJRHwcqPN7ai6Qtb7fF5oyguDEAIoPZSHkIhr6v1lFBOb68NGI0CeqjrvXddqgfnFE8BwAgBRtD9ZUZpClrMObXW0yhdXe+d3loBywR0y+nh0lzezkjNqSxs57dbXe1B73I9XcCOn2ZpBLp3FkRXKAEoQ4fSBEQoAhFJz4HADmvathOybBvIxBWQdR0wdCz7xHx88v/7IHLys1wrpZTC4e3HseG1rSjom4eFH56J4gFFrtWjnkk1G7h2Ofmnn37arY+mHhBC4PY7RuL2O0ZmvjYEVCaSV+IUg0yREqo+ANQHMHnGMFdDFwCYpo5pk4fArA26WidBAEDMhmbZ6f3heAc0S8EIWhmrJyRgNHf9FJ/u0mzAd9mG7t4muBWhAE+z87uejNQDYAbhekP2Sj0BERXQQrhyNT2XhSMenDlb7HrASxBKwGgW8AQyVA9Oz5anOSPlIOAcEMg+n5nbhwg4F6wxmzO0kMJZH/TmWLu/o0p7Pds59VxEu3jOcjcJBYhQxOlxyqRItPNh0iUe8ibMGO5q6ALi7bXpw3H79OGu1qHMcm03/Oijj7r10URERERERDcUV48NHj16FF/72tfw8MMP48KFCwCAt956C/v27XOzLBERERER0XXFteC1du1aTJgwAVu2bMErr7yCpqYmAMDu3bvx5JNPulWWiIiIiIjouuNa8PrKV76Cb37zm1i5ciU8nitXeqmoqMA777zjVlkiIiIiIqLrjmvBa8+ePfjgBz94zfN9+/ZFbW2tW2WJiIiIiIiuO64Fr4KCAlRXV1/z/M6dOzFgwAC3yhIREREREV13XAtejzzyCP75n/8ZNTU1EEJASomNGzfiiSeewN/+7d+6VZaIiIiIiOi649rl5L/1rW/hsccew4ABA6CUwtixY2HbNh555BF87Wtfc6ssdUAphaPHL2J11QFUrjuIwoIsLF00DuXzb0dRYbYrNWsuNGDN2oPYErIRnTcO2rlaaNV1EMGIK/WUaUCWFsIeWAzl80CvroN2rhaiIejKjRyVrsEqK4Q1tA9kUQ6efn4d7lk0EVPHDoKupf+4hmVL7Nh1Eq+/uQvB4UXQwjEYgQj0ZnduoqwAxApMhMr8CJX5YYQkfBej8F2KQXPpdjuWT0O42ES4rweQCr46C946G3rMnXvfWF6B5gE6mgYakKZAzukYck5b8DS6c38f2wSahmsIjNYQKbKRVS2QdVbAUwdXboirNIVwf4XmwRKRfgpmnQbvBQ1mnVs3GVbQ+kVhDAkha0gYsWYToUtZCF/2QUk3jvUpFOQ1Y8jA8xg88ALCUQ+One+HExf6IhzzdP72bij0N2Fq6QlMKzsOTVPYcWYYdpwZhtpgriv1DMNCcZ8A+o+ph9drof50HupOFSB02QdXbmusKeh9IzD6hYF8C6j2AKe8wEXTuUtumimhECuUiPa3UbdQwnsByD6mwX8W0GwX6gGIFgBNAzQES3SYzQo5pyWyz0roLt0CMpYlECzREexnQrOAnNNRZJ+JwQi5s12zPQKhviZCxVmQpkDW2RCyzoZhNrpzjy2pC4T7+xAq9cLKMeE7F4T/TBPMuog7+14NiBZnIVSahVgfHzznm+E9WQ/P+SYImf55qgCoHB9UYS5kfjZe+Ot7UEW5mDv3Nviz0r+dUUrh2PvnUfX2HqxduQ8FRdlYvHwiFiwZh8I+OWmvBwA15xtQWXkAq9bsg21JLF48FosqxmJAWaEr9QKXm7FhxV6s+fNOXKxuwLxlE1B+9ySMHFsGkaF7ePYGoZRK+xKqlMKpU6fQt29f1NTUYMeOHZBSYsqUKRg1alS6y2Vcqnenvl6cOXcZa9YewIrV+3Dm7GVomoCUqtW9aadMHIyli8Zh/tzbkJPt7VG9usvNqNpwCCsq9+HAoWpoQkAmFjOlACEgAs3QzsZDWKRnezqla5AlhZAD+kAW519phwgBSAVoAiIYgXbmklOzOdyzepqAXZKP2OBi2IOKAF1LTpeuabClRH6uH3fNGY2lc0Zj3MjSHm1EpFTYe+AsVq89gNVrD6CxKQxd12DbLYKBVNAbIzAaI9CCsR7v6GI5BkJlPoQGZsH268n56Nz/2vkuvZct+C7E4K2L9fhGubZHINzHRLifB1aWnpyfAJL/bzbZ8NZa8F62ehz6bBMIlhpoGmQg3CcRBoSz7MSn1QxI5JyKIed0DGawZ5tJqQPNQzQEbtPQPEQD9Pi8FAAkAM25WW3WaYGsMwJmoGchTEEh0k8hOFAiNFBBmVfqJOoKC/Cc1+C9oMOoFz0MfQpaYQzG0BDMoSEIv7ymnpJA+LLPCWH1vh434HOygxgy4AKGDTqPvJwQpBTQNIWWe7Sa+gIcq+mHkxeLEbN7dpwxxxPC5NKTuGPAcQwpvASpnFvCA4CCgCYUztQXYdvp4dh5bigC4Z7dXFXXbRQVNaJf33oUFly5i7GIz0uhAZEmE3UnClB3qgCRxp5ttyEUtD5R6CUR6P0iEDquWUYREVAnvU4IqzPQk9CnoGDlK0RKbERLbGcZTSyb0mlYCwvIOgFkH9fgq0aPDxREc4DmARqaBwlYftF6GYUCFJB13glhWdWyx9sZywsE++sIluqI5cb3E8l55mzXvLUWck7FkH02Bj3aw+2MAYSKTYT6exDN1+PPxrdrie1oowX/6SCyzoVhBHs2gUoDwn19CJX5EOnru7KPaLEd1UIW/Keb4D/TDKMh2sOtDBDr40O4LAeR0mwo48q+N7mvj9nwnqqH92QDzEvNPTogqQDA74UszIEsygVMI1kv0Y4yTR1z5o7CosXjMGPmCHg8PdvOnD1di7Vv78WqN3bj7KlaaLqAtFu31yZNH4ZFyydi3qIxyM7x9ahe3eVmrF17ECtX78XBg9UQQiARCxLTOGpkfyxZMg4VC8eguLhnB5eCzRFsXr0fa17fhZ0b34dUyqkpFTRdg7Ql+g8oxOL7pqD87kkYNLxfj+plUqrZwJXgJaWEz+fDvn37boqgdbUbIXhdqm1E5bqDWLF6H94/eiG5ArUn8bqua5h1x3AsXTQWs2eMgNdrplSvsSmM9e+8j5WV+7Bz96nk8+0vXSrRfoe43OT0hNXUQcRS2xEoTUD2K4AsK4LsX9gq/LT/pnjoawxCO3MJ+tk6iHBqd7xXArD75sEa0gfWoD6Ax7gSRtqhawK2VOhXlItl88Zg6ZzRGDm4b2r1lML7xy5g9doDWFm5H7V1TdeGrXamD7aE0RiB3hiBFrJS3tFZfj0ZtqwcI4X5ieQO1lsbg/9iDN7LVso7OmnEw1ZfE7HcFOolphGA2Sjhq7XgqbdSDn1SB4IlOpoHGgj20+PtH9F+2zHRGBMC3ss2ck7GkH3GghFJbQKVBgQHagiM0tA0XIMyRbJB2f5IAtAAoxnIOuX0hJnNqX2DCgrRIoXQIIXgIAnpvfJ5ndUTUcB7Xof3vAa9MfUQJvJiMIeEYAwLQcuxO68XX2akLRCu9SFUm4VIgxepNuD9vggGlzlhq6igCVI572xvsVFKAFBQSuBMbSGOne+PM7VFsKXe9huurmdEMaHkFKYPOI6RfWpaHdNpu96V1eJYXT9sPz0c750bjGAstcaREBJFhU3o27cBfYoak0Gy3dVCxVcbDQg1eOMhLB+xYKpH4J3ArJdEoJdEIAyV8jKDoHYlhDWk1thUULBzFSL9bURL7S4to1oEyDoukH1cwHsh9QMTMT/QPECgeaCGWK7otJ5QzvZe2ApZ56QTws6rlLdrtgmE+jlhK1LQImx1tp0B4L9gI/tUFNnVMWgpdkxJDQj3MRHqbyJSaHRpu+apj8J/OoSsc2HokdQ2pEoAkT5ehEp9iJT4oHTtykLf7kg6+0q9KQb/qSb4zzTBaE5tAhUAq8CLcFk2wmU5UB69831FIvSFLXhP1sN7qh5GXSjlfaHympCFuVB9cqE8Zqf1Evtmv9/EgoVjsGjJOEyZPAS6kVoPf+3FANau2IdVb7yHo4dqutRemzFvFBYtn4iZ826D15dae62pKYz1Gw5j1Zp92LXr5JXpbqdky2OgEyYMxNLF4zF//u3Iz/OnVC8aiWHrusOo+usubKk8gFjUTgbKzqZxyKj+WPyBKVj4N5PQr6wgpXq9pVeDFwCMGzcOv/zlLzFr1iw3Pr5XXa/BqyEQwroNh7Cycj927z3TamXpisQC7/UYmD/3NiwpH4PpU4fCMFo3VsLhGDZtPYpVlfuxedsx2LbsdIPRphYjKC42OCHsQj3EVSFDCUD1yYM9oA9kaRFgpLAB7qimEBCXm6CfueT0vEVb7wgUAFmUDWtwMayhxVA+s9Ow1Z7EfBlcWojl88di6ezRGNC/4Jrhzpytw6q1B7BizT6cra5PhrduT58loQfCMBojEBH7mh2P7dUQKvUhNMCPWIGn80ZCu/Xib7MUfJdi8F2MwtNwbT2pA5FC5zTCa47IdqMepIKnwYavznLqXTWrlHCOODcPMBAs1aFa9jR1tV68ceS7FA9h56xrTktSAEKlAo2jdDSO0iC9KYStdus542k2xHvCzgoY4WtHPJbnBK3gYAk7C503ZDuqKQAtJOA9r8FzXoMRvPaDRJYFY0gI5rAQtALL6X0R6PZ3KGMCwdoshC75EWvyXPNBHjOGQWUXMWzQefQtargyHl2sJ5XTK2XZGk5eLMbx8/1w7nIB1FVfjqlZGNf/DKaVHcfYfmehCZXs0epqPREPfQcvlGL7meHYWzMIUfvqxpFCQUEz+hbXo29xALreSdhqTyL0CaD5kh+1JwtQfzofVuTqUKQg8iwYJRHopWEITwphq92JjL8voF8JYc3XhlrbLxEpkYiW2rCzVPfWwRb19CCQfUwg63jbp+haXiBYJtA0UEO0QHR7u5bseYsp5JyRyDkj4buo2tyuhfppCJYYCBcl43n3t2u2QlaNhZzTUfhrrj24pAQQKTIQ7GciXGy27mnqar3EGQy1UWSdDsFfHYZmqWsGixWaCJX6ESr1Q5kpHOhst6bzPqMhCv+pRvjPNEMPX3vA1co1ES7NQXhADqS/8wOd7UqEsOYofCfiISxw7U8dlGlAFuZAFeVC+b3dnr5ECMvN9WHxknFYtHgcxo4bcM1ZL4H6IDas2Y/Vb+7G3l2n4suw6np7LR5gvF4DcxeNQcVdEzB11og222ubtxzBqtX7sOXd7rfXEu/RNIHp04Zh6ZJxmDN7FPz+1gd7bMvGri1HUfX6e9jw9l6EQ9HODx63RSDZIzZ60iAs/sAUzFs2AQVF7pxu2RO9Hrz++te/4rvf/S5+9rOfYfz48W6U6DXXU/AKhqLY+M77WFW5H1t3nHBWiJan9vWQrgvYtkJ2theLFo5G+bzbEY5aWLPuANZveh+RqNX9cNCWFr024sJlaOfqAMuGLC2CHBDvaeruBr+9enHiUgD62VqgOQhrYBGsoX2hsr3d3+C3I/H93D60H5bNH4spowZg1+5TeHvNPhw9frF74bUjiRAWs6EHItCCUUSKTYQG+BEtSmwsu9FI6KSeFpPOqYiXYpCmQKTY4xyR7W4jod16SDZWvJcteC/bsD0KzQMMNA8wnJ4mlcafprQ8LanGQvZpC3rMRvNwDYHbddhZnR9V73o9h7cOzqmI9UCkv0JwiISVC3fqCUBvFvDWaPA2AN5+ERjDgtCLY85XHB8mLSUTq31UQ/BiFmL1XpTkN2DowPMo6VuXXN3TtdonQljU0nH8fD+cvNgHhWYzppadwMSS0/DodvLUxXTWi9ka9lYPwo6zQ3EmWIQ+fZzeLdO0oZSASNcPNa+cUIDG89moO1mAhrpsiL4xGKXh1qeCpqkeAGd5uGxAnfDCPu9BtEAhWmrDylWth0mHRO9wI5B9VMB/WiCaraF5oIZwn8RAadyuJUJfRCH7tET2GQlpCIRKdISKNde2a8JSyDobQ/bpGPSYQrivB6G+JpThQr34guO7EIH/TAhG0EKkv/P7XuntwYHOjuoB8NRF4DvVBM/lCKLFfoQH5MLO6bynqcsSPW8NYfhO1MNzNgDh9TlhK9uX9nqJoFFcnIMlSydgztxROH+6Dqvf3I0dm486P/nQnFCRznrZOV4svHM8FiwZh5Bto7LqADZsPIxIxEpr+6Ll6ZZz54zCoooxyDVNbFyxF1Wv70JjQ6h7YasdTnh1vqPJs0ag4t7JmLNkXI9Pt0yXXg9ehYWFCAaDsCwLHo8Hfn/rLsm6ujo3ymbE9RS8Hvm7X+BcdX36G+tt0HUNlpROgyweyFylFJQm4ju0NG+A26kndcDO0tMettoi4mHBaJTJSXSbUgoNI8x4gyuNjZL2CwIyfjw6nY2EdusBkUIgloP0NizbIRRgZ9mQeSmcZpcOyikqDBcasu3U04TEgJLLSHZqub8aYmbxCWQZsfSGkXZIJTDcewGFRhC2FNDTFLbarScFDof7Y39wQKY2a2iMenC2uTBj66BtC1wOZF052uHyMqokgIDHWQczsV2TgB4SzoU/MjFPJWCEnN+BZmahcXr7NBsZ2Rc6p5co6Fam9vWAsCXyjkecC3Fk4EIOui6AQARCIWPttYhPh/LoaQ0/HdXD5aBzsa8M1EvMw/yibDy/8fq4YF+q2cC1qxo+/fTTbn00tVB32fnRtdsrMQBnRdIS/5+BlNByY5iJK9wIcaXh7PaOBvH9i5NjMxK6ADjTpWdgXiaIFicBZaKscH7zAMD9EIR4uzLx4/wM1LuSfpCx+Sl0lYnV4UpJAXjjP3BxO3QBgCYUTOGc6uR26AIATVMIS9O5MEcGtmtCALaKn3aUqXUwsaBmqJ6zDmaoHpx6ItGuzERNDfFQiQztC1vWzlS9NHdrd1ZPOQfOMlIPTptJj29eMtdeM678fwbqGbZzKkQm6iXmYUNdcydDXn9cC16PPvqoWx9NRESUNhnMlXSTcOPWD0R083P1GO3Ro0fxta99DQ8//DAuXLgAAHjrrbewb98+N8sSERERERFdV1wLXmvXrsWECROwZcsWvPLKK2hqagIA7N69G08++aRbZYmIiIiIiK47rgWvr3zlK/jmN7+JlStXwuO5cpnJiooKvPPOO26VJSIiIiIiuu64Frz27NmDD37wg9c837dvX9TW1rpVloiIiIiI6LrjWvAqKChAdXX1Nc/v3LkTAwYMcKssERERERHRdce14PXII4/gn//5n1FTUwMhBKSU2LhxI5544gn87d/+rVtliYiIiIiIrjuuBa9vfetbGDx4MAYMGICmpiaMHTsWCxYswJw5c/C1r10fNzu70Sml0nbHcyIiIkoN97xE1weVsRuhpkda7+MVCASSd2s2TRO///3v8Y1vfAM7duyAlBJTpkzBqFGj0lnylnTqcDWqXt2GNS9vQSQvCzD1jN0EECqz96nMtEyvvkpcqZuReaoAyPiNWzO3yADI3DKTgXvutiZVZleIDO9klIzf7BvIyHQKKFhKg6bsDN1LVcGCcKYxQ9+jKWwoCAgoqAzMVD1+t99M1bty4+uMbdmQ6S2NEsq5CW+mpi/DO92MN2Uz3ajQRMb3TRAttqWZIBWUUhAZ2rCp+I2pM0op/O3Cb2PxfVOx8J7JGHZ7aYZHoOuESmNU1HUd1dXV6NevHxYtWoRXXnkFBQUF6fr460YgEEB+fj4aGhqSQdNtF87UYe1r27D6pS04efAcNF1A2grK0GEVZUMW5UJmeeBG6yGxgEgTsE0AQkCPKQjLeT7dq3Ryl20KWB5AaU49LebuBkvqgO0VsA1nVyqku/WUBsR8gNQAT1DBE5DQbHfrRbM1RHMFbJ8ANHfDlwJg+wDLBwgbMIOAHnWnKabiS42dBUTyFGw/IGwBOO1NFxpH8akwJewsC8onnfVOOdXSz6knNAnDY0PTJaQUsCz9Snp3oZ6mSWT7I8jxheHxSBfqOBL7aw0Shd4Qir2NKPSEYGo2dKFcCkUKAgqlRgMGm3XI1UNoVl6ElNlirNJbDwBM2DBg4XwsD0fD/XEyUgwJzbVQJKAgIBG2TNRHsnA56odU7tbz6BaUBIJhLxpDPthSg3shTEFozpSosA4VNqAsd+spzQleWkzACGsQVmJupnvf6ywzygCk4ewDzSYBPei87lboU3p8Xw9AjzgPV/eFApCG03zRLWc/4eoBNAUIW0EPSXiabRjNEprtXkElgFi2BssrYEQkvPUx6GHp0r7QYWfpiOWYUIYWb685r7iwGb1S1ZLQwpbziFjuBkylnIOe8Qij6RqkLTFwWF8sun8qyu+ZjNLBfdwcg2ukmg3SGrzy8/OxefNmjBkzBpqm4fz58+jbt2+6Pv66kangVX8xgPV/2YE1L7+Lg9uPQ2jC6VJt5xuTXgN2YQ7sPjlQXrNHISwZfox4GDHjDfVWBRW0mIIeVdDsnu3mEu+VBmB5NdgeAehXfZpS0KLxemkKfUoDLK+A7RFQxrX1hMSVRw9rAc4G2PIBlk9AxkNsy3p6WMETUPA0SmhpaOc6G3yBWI4Gyy+uWR6SOSHx6Emt+L+2F7D8Tui6+mRmYQFG0AlhWiw9y4ztU4jmKcRynOX16oGEDQjLCWE9a6jEKxoSMsuG8kvAaL0yKhUfTLas07MZK4SC4bGge2xourp6kYG0nQBmx7Qe1UqEHyEksv1RZPsi8Hos13qBkvWgUOAJocgbRL4ndM1mRkDCEBKmsKH1OIQ531c/vRFDPLUYYNbDI1of7bCUQJP0IiD9iMBAOpZSHTZ8IgaviEG/qkUZlTpORIpxJNwfZ6JFyWo9CUUCChoUNCHb2myjMebD5bAfDVF/Mi70pCEgoGDqNkzdhi7kNctoOGqgKeRDU8ibptAXD1uagtCuXR5UTECGDMiQcdW62J1K8TmjAUpXzjbt6o+0AT0soEecENbTegICylCwPQrSVNf+KMR2ApjZJKCFnKd6GsKUBtge5wAr9GtGCloEMMKAFk3U6xklnIOd0sC1+x8FaJZTS4+mKSzEw5aw29ifK6ctYzTbMIM2RDr2vQCsLA2xHB2WX7um/aRFbHjqLSeERXsWwpIHx/06ojkmrGzj2vaTVNCjMt5+UukJfYnw01b7SCpoEQtaOAYRdbaxaamXqNkBTROQUmHkuAFYdN9ULLx7Eor6ud9J0ivB60Mf+hA2btyIMWPGYO3atZgzZ06re3i1tGbNmnSVzTg3g1dzIIRNb+zCmpffxXsbDyXDU1d+y6UAKL8HdmG2E8JMI6UQ1jL82B4njFyz126PraDHQ1hXN1pOT5MG2yugrt5YtPumFvW6GMKUiIctr4DSkVorLh7CNIkWvShdqRcPW57U6xnxXjBPk+rS0T9ngy8QzdFgZaX2HSY/voshLLnB9zhhy/Ij5V+OipgTwIwgkkE6lXoCgO2Jh61cINlBkcKbhQVotuh6Y0xX8bBlA2ZqX0aPQphQMDx2sncrxUUGtqUlH6nUahl+/L4osv0R+L0xl0+5c+ZfnhlGH28QBZ4QdC2VeeqECUPYMISElvJK4Uxhkd6MIeYlDDIv///s/Xe8Hcd93w+/Z2Z3T7sNnegEK8DexU6ABClRxbJki7Zs5bFlW7F/zvMkDh+XOL8kjqPkUeL8YjmOH8WO4zjF+alYzSoUJZIASFHsJFgB9gaiX1zccuruzszvj91z7gVwyzn3nj0AyXm/Xge4p+x+dnZnZ+Yz39kZ8m1muMhKJkyOCZMnbNuENc2WISdC8tOYrZmoGZ836st4pX4aB6Jm3dJ2SYNKzZagvWJGW8F4mJiw8SjfsZ6vNIHUKNl+Hq01fCZqeSr1HLajaG3S6SCUmdZszaRnI4mtKUy9ExOWNkwlGGkTI9LurRuDrEtUQyRR9w6wKjVbwTRmaxY9ryzwJwSqITpqUFuZGC0TJFGutjBJBMxrgOjQhFnSyJZKtNva0CaddCqk81Evs5mtGbexyIbFr2q8qm677m11PBYSsxUVTzRbM+mpuiEYi8gdjZBxm/ULaV2Yk0T9idmyXnuZRmiLDA1eaBCdjrKxgDHTm62ZMEkkTNVjiDo0YW2arelIyoikm+eCKzZw88cv4/oPXkj/ULHjfbXDSTFetVqN//E//gevvfYa/+E//Ac+97nPUSxOn8AvfvGL3ZLtOVkYr8fufZ67/+ZBHr/3eeJItxz7QrGAKeUSE7a4Dzw1rQmzCuKm2WrX/MxAclMnxmgmE9aKNOVk++ZnJkxiwJqRt2n1RGomcyLtYVuAXtOEaVql7fF7s6SRn7xA5xaoZyx+JYmC+eUT+4knC/zEbEWlDgzzNMxlwloG3YeoALrQQaU9g6CM0khYhRPyzFS9cMAQ9ScNhQVhkkiYjOXMXf3SYgoaW0zN1sKyzBQTNtOOLCo1W8prryE7m56OJXEkMbpZGR/XpQwUclFitvLhQrJMO0cEQJ8XsiRXYShXw19QSHfShPnixGfBmhGVAVljfXCEdf4IpWZX/TxpWJVGwvLEKKYzYRJDTkSJ2WJh17Csc7xeX87L9dM4EvfNECVKzoPqwGzNRGwEY2GBo40i5ah5g52o50lNoDRem2ZrJoyFaj2gXMtTqQfTagGp2UqjWwu8J2woMTUPW59liK6wGNWZ2ZpeMDVhDYmqC8QMps/KKWZrIeUoaWfWRGLCZDS9CbMiKT+Nn5bbC0mjAVVPI2Hx9N0SlkTHeB2YrZk4zoRx/O7S5qxIy3ehFxhlSU2RV9V4NXOCCWt1POYEYZ8iLqn2O49n0POqmmAsIhiNk+GPU8LRrbowkER9XjKU0F/YHHlCW1TDoEIzc6e5JY1sdWG4om6asAgRz9RAnL/ZmgkhkwCGVJLLrz+HD37qSq774IVd2z+cJOM1dXKNLVu28K1vfcs949UGURjz8XX/H4RMxqhmhQVMfx69qI94aR9WCkwgOos0dSSY9DQ1hyNCOmwxJxdufmagZfrSyJsOkh6hE4b1dYvjhiOaAKKm2cqiJastQSWJhHlVi85B1CeJ+mQm13CqCbMyGcrXjGydMKyvS4IyTAyYV7PJUMkBCPstJkeXxpwch0mGIgqT6NuCxhYNBNk85NcyYVaABeWnZstfWEN2Nj0dpZEwLckHMaVCg2I+RLYVaZq3MgIoqIjF+QqLgxqByuIhRovC4AlNXkbkRMzpqdkaUPXuq1loWI8Jm5gwC+kwwhiPbCYEGYsLvFpfzvPVNdStnw4jTExXFnqRkYw2Chypl6hrD0+aZCihzCZ9xggq9YDxap566IMgGVa7QLM1E9aCbShMTWHqKinfmsMIs5jr2SamSDUksp4kyKRma8HmZwZkI4mEBWNJhL9ltppD+7qM0IkJU/Xkb6uS0SxZpQ8DKkr0pLbdM1sz6iUmzK9oVN1gApFEtkrqxMcUuoG1eGVNbjQkGIuxSiSRrT4PEyzQoc+gJ3TaiV03CJsMISR9vCWTcxobZD1CVUNE83mtWR6n6RbNwMbXHv+XXY1+tesNutp0WrRoUWtyjV7NovJewJrkuQWboemC5MZRE3XURJ3amhKmkPFsiEIkz4h5gqho08kcss0XVgl0QRDnm2PxM86HIhmuGOcgLvVATwnCAUHYL5MhCRnrtfZuobKCBffItiNoctDIQS2wC+8hbQcJNrDYfAz5jGdUIb1kApQfE+Sye4Zqqp4XGPL5iKFCrWcz9wngsiXv9EBPoFFoq/hY6Vn6VT3rYo28iMkTU6BBhMo8jYNejcv73kJheKa6HpuxoC8NywoVin7IwVp/5umT0tJfTIa5vnN0qCf3hMhrZF7TGMn1oJ4AG0AcGERetDdidYGYHIQ5iwmS58+y1rMqqQN1kJihXpTbOgdWWArD2cshBbqoiIsqMek9yKRxv0fc79FYkkbbMm+vCWIPZMMgoh7Mf+pJTF8OtMErN7JWa9EcTaYzbnPPRFf7dvr6+jhy5AgA999/P1EUdXP3jm6y0HEpHetlb7pOql5T8z2t11u5bkz4cUrr8T7IMidB771czLhiLQvBk6D3Hi/Xel6O9lbufVCw9ficvs8CNV2NeG3dupUtW7awadMmrLV84hOfeE9OruFwOBwOh8PhcDgcndBV4/U3f/M3rck17r//fs4///wZJ9dwOBwOh8PhcDgcjvcLXTVehUKB3/iN3wDgiSee4N/9u3/3npxcw+FwOBwOh8PhcDg6IYt5yQDYvn17Vrt2OBwOh8PhcDgcjncVXTVed955J5///OcplUrceeeds/72j//4j7sp7XA4HA6Hw+FwOBynLF01Xjt37mzNZLhz584Zf+emmnc4HA6Hw+FwOBzvJ7pqvKYOL3RDDU9dTC5ZQVHo5jpJWS/wkSxqiGkuFpnxWl4ki/0iSRbHzVQtXetPgIiZXHcqQ9Fm+owHUie6WafRqHRRTEu6Plq2ehab9RqKJ+ghJxduzL5vyCKExZhezRRsCVSMJzXGCozNeo5pS15FxEYgBJkt9DuVvAipW4k0PgURZbJ++VQksFRKDIIRY8l68RRrYUBV2VjYy9uNJZRNIWNFUMIw6NeoaZ/QZLUSbhOLwDJYqNKIfepRRiv9HofKxVgjMVEvFg20oNJyRvdgnneb1BU6sMgIhO2BnkgWahYxydpTGesBREWQIciM60I7Kdk7rKV12bJayHiqHBDnJcYTqJpGZrzUlQXiPh9dkHijDVSosxU8BcjsGS8Aay1HjhxBCMGSJUuylHpX4wWKsy9exyvPvI1UEpPBom7GV8RLS8TL+9H9eaS1EDUb8Ykh6qoJS0so0fy/+XkMSIuRdNWEWQAJRpIu1Jx+qJIGizC2qyasabasShZtRkwW+lakxkjR1ZXymmbLeOl+bVp3G1BRUvEI3b00mnSBSpOfNHnoSeNnm+nrWpZJjblKOwQyxmLBsxCY5P/mxSM1mGmroXuGwSKkRXkGqRIjYq3ApppCmC6bMEugNMUgpBSEKJks1J7oQmwk2si0IdEd0UDGDAY1FgV1ciomNs3FhS1SWJRIGtfdSmMgIlZ4Y6z0xxj0qtSsT934gKUgYooipCC6t0i1BPqEYkD6lMRkJj0dGLcwrA0jxtKtpoNNW3km7c1ZlzvK2uAol5feYiQu8Xp9GW+GS6mZXJcUjyWvNDlZY7GoERvJRBxQiXJEtpsrqdvWspLSMyxSNYSooY2g3MhRaQQ04uxMmCpohNBYCyaUmFBh4m6asKRsEfK4ziQ/qZPQorsmrFnnGsAKrA/Ws5gCiNgiItFdE3a8E1FpPRwkxyDiLpuwKW0LaUgWGi4CpaT+Uw2Lqne3Lmy2L+x02SILJ2Ztcu5Met5kKmPT9szx7aqFypHW6Wn6jJ/e34s8VMPiVTVeVXftGlrABDIxeDk5WSmt7kdVI/yROv7ROjLuflu42cZee+Zyin35ru+/HYS1tuvZ5sCBA/zu7/4u3/nOd5iYmABgYGCAT3ziE3zhC19gxYoV3ZbsKePj4wwODjI2NsbAwEBX9mmt5c1de9nxrSfY9vVHGd4/ivIkegEZzypJvKREtLwfPZhmsFlaIFMb1PMyYVMKxLlu0NbXqUGZj94x5qdptmb9saUZ6ZtPoTWd2ZpzG5Gmb56dxVYkpsf66TmyM+wn/VyYtOcvZF49VVYkZkvnk17LGfWmcEz6Os4yqelXdtKkZhotTM2Wb5KGz5xazRM7XxOWmC2pEsPVzvYivYnma8I8qSmlZstTpmW2pjm0JD8fY8I6F/SEbpmtghfNrDdFWAmLFGZe67h7aJb7Y6z0R1mkKq3PZ9qPwFIQESURkZuHCRNASSgGpEefUEghpr0tkiImaVyPGhg2hlFj6fQ2bNbIqUWd83cAh6IB3mgs461wCaH1O1Ts7NiEgFArynFAOQ7Q8zJhycHPFZVs6sVaUm7kKDcCIp1hf3HznjBgIoUOFTaejylKzZaYUsbMVT9pQMvEjHVekLbM1pyGqhnV1yBDgYhAzEevk9+K1AjF6f/zaXWm6ZuzXpuip+oW1Ug7DTuXm9lszbrRPJlitlrXaC6tBZiwVntGttN+Sg5I1Q1eRePVTcfX0ALGF+i8Qufk7Dd/qudVEhPmjdaRev4nVymJ1oahpX3c/PHL2PzRSzjr/NVdf+ypXW/QdeM1Pj7OJZdcQrlc5hd/8RfZuHEj1lp27drFl7/8ZRYtWsRTTz1FX19fN2V7ShbGayrWWl584g12fOtxdnzzccaPVto2YVYK4sVF4mV9xItK6c3Unkk4Zj8kjenW6JKZMmgHZmtOvTZM3zGFRZvm58SdtFo2rUJu1jKnqefNU2/KftoxYS2z5aW/bcP8HLsDWhWPDJNomJgl61gBOphituhQbwrtmLBkWB8YZSHr0UtNPWUToxUsNOw5mVlmK7OFsEjPoJRBzGaY59BKbt+5TZiSmqIf0ZcL8ZVuw/wcp5T+3liBNoLYzN7aUMIw4NdZFNQoeiEwP5Mo0kiYFGbWelhiWOaNc5o/ylJvYrIt26GmxFIUIUUZEaBn3b4oFANSMSC8xGx1cE6bv9XWMmIsR4xlzMw8fLZdszWzniDN6eyPFvFGYyl7GkuI6WZk6njNJI11rSjHOSpxgJk1VJ2aLZhfJ1SqF8WKiUZApZEjNtmlb6oJ06HChAo7a2QqLRvS+3Xe5YxlMgo2mwmbWveahVRMiSGSoUiHrc9WOc1fZur2x0TC5jo2k4wCn5d2eg1lDLJu8Rpz1IXQ6lxtsZDrOOdvOjRbs2nZybTNkkNBHDcyaD4Yi6oZ/KpG1c2setYTSWQrrxKD13F7JjVhE2ESCRtrJCOY5qBptkr9eW766CVs/uglnH/F6UiZ3XCak2a8Pv/5z/M//+f/5KGHHmLZsmXHfHfo0CGuu+46PvvZz/JP/+k/7aZsT8naeE1Fx5pnf/IyO771BA9850nqlUYrQzWxAvRQkWhZH/GSEihJxy2vWTjBhKV3TSsM3mWmM30t8zNfszWnoD3GhDWPASVmjzTNQwpoDYtsmpSkgEqMj+1m5Od4ExYm1ywJ9UOcT/7v+jkVU9InJ82WVXbe0b9OaOoRmCS61cVrOFXlGBMmLEoZlJdEubqnN3mTiSkmTArTGkaY8zo3WzOqNU2AFcRWoFMTJjEMBA2G/Bp9fiM9noXrNUlMmEmGI4rk/RJvgtO8UZb74yhhsVYkUYQuIDGUUhPmk0QjC0IyIDwGpIfq0GzNRCtqYxMDNqwNE3byu/marZkwViCFRVvBnnAxbzSWsS9chOnmuOfjaOaZuvZSE+ZjWzfdlNuvS8lsntNGrCjXc1QaOXSW45ObJkwLdDoc0Zo0Vd0wW7PpagFxs5KYUvcuxGzNoicikNEUE5ZBHd/UguMiYc1jWIjZmlXPImPRioQ168JmZ25b0cl5azf/TkSbkb+uX8XjTBjQfmSrUx0BmGQool/VyEZSkhkl0HmJzqukzdaNunCq3liD4Ggdb7xxTBu0OYwwyPtc/8EL2fyxS7j02rPx/Aw7aKZw0ozX1Vdfza//+q/z2c9+dtrv/9t/+2/85V/+JQ8//HA3ZXtKL43XVMJGxJPbdrH9m4/zyN3PEIUx4emLaZw2CF53zdZMtHpKeoRWEBdFzzQnJ49oOpZs9YyAsB/sAiNNbdGsZKYYvl6g8xadz6KGmR7rGcjrJJrWdbN1IkIa/JxGNh+az1hPCc2y/jJ5P070M9SzaWNlea5Mv99Aip4UM5yb38fp+cP4wnTVbJ1IcsHWKM2ZviHoktmaUS3d97A2vBYlj7xmSdOEjURFdkxsomJ680yDNnCo3k9ovK4arumwNnkdnuijGmXRi3S8YCIR1xUm6kGDrlmmhALRkNlPkNEkBq8ieqenwa8mhgQyvoppZ6tfSaJhvaqbRGy7+uzZXDRNZS8mUEOAiCwytmReUbSGFBhyR+rk907geYqrtmxk88cu5aotm8jlsxtyPRPteoOuD5Z++eWXufbaa2f8/tprr+W3f/u3uy37viDI+Vxz+8Vcc/vF1Cp1Hv7Bs/zhlx+Y/EEvpunvnecCkjHBPUlXk+ZsiL1KqAIb9EaqmSSb6ZQ6J6KD3pkuIIly9eB5sSZSpRGuHunlfE0hyLrJniAEeMIwGDSO+SxrNuQP46VdttmZLmhesJXKEqQJyzJ9zX2Pm+xNF4BMz90R3d8z0wUQW0VovV70XSXP5QPVKJsJRk4UTP5LJuDonZ6Ie2iCSCeL6qWemd+zWPMiHdYiNT2tm3ppuhJBet8ubHaS96IgVZLG8iL/9B/ezrW3nk+pP/uZXrtB10uO8fFxhoaGZvx+aGiI8fHxbsu+7yiU8tz8s1cR9NzV99h5nSRJR/c4Kcv29VjTZdF3O72/gi7PvLtx18/RKT3PM++TNXNv/eQV7xrTBRkYL2vtrA+vCSHIYCJFh8PhcDgcDofD4Thl6fqgI2st55xzzozTNDrT5XA4HA6Hw+FwON5vdN14/fVf/3W3d+mYhcF6mbOH9xJLj1hIYqmIpDrmfeslku/eL+Fnh8PhcDgcDofjVKHrxuuXfumXur3LTPjSl77Ev//3/579+/dz/vnn8yd/8ifccMMNJ/uwOuOtt/j6N//ZyT6KnhKlJjJqGUpJJD0ipY4xmdHUv9Vx76ea0eO+C5UiVh6xappYb/J7JY99n+77mP0er5V+r4V0htfhcDgcDofjfUym85uNjo7y9a9/nddee43f+Z3fYfHixTz11FOsWLGC1atXZyk9K1/96lf5rd/6Lb70pS9x3XXX8Rd/8Rfcfvvt7Nq1i3Xr1p204+qYUulkH0HP8Y3GN5p3z2OU7360EMRq0phGSrXeN/9umtvjv4uUR5iXRJ7k0MAAj5+5AS0kVgi0lGgpMFJihEALiZECIyY/12LK9/K474VMPhMCI0Xrb+0btAQtndl1OBwOh8Nx6pCZ8Xr22WfZunUrg4ODvPnmm3zuc59j8eLFfOtb3+Ktt97if/7P/5mV9Jz88R//Mb/6q7/Kr/3arwHwJ3/yJ/zwhz/kP//n/8wXvvCFk3ZcHbN0Kbf+wp9Qq0fHfCytwTO69fKNxrPHvT/us+bfx35n8E18zHvFidv7U7Rm/Nye+Lvj9xOYXs0l6+gEZS0qjskRs1Cr/3/cu70rx9QJ8fEGr2nqjjdtUkwxeMf9Nv1d8neyP9vcTiULe7e+l6JlLI0UaDW9jpl6XHLqcYljjKmdsp2WEs8zlEqN1nbHvmT6e4FRyX6MFBg1uV8z5VhP3H7K5yo5Tqksi/oq2Ob36fY2fU2+p/W5M7wOh8PhcJxIZsbrzjvv5Jd/+Zf5oz/6I/r7+1uf33777fzCL/xCVrJzEoYhTz75JP/kn/yTYz6/7bbbeOihh6bdptFo0GhMrmNzqk+Hb4QkVJJQdX+qeaN6sDDLFKK8wOR6J2gF85vr01o8Y/BMPGkotW5F6FpmUx/7XqEhH+Mbk/xex+l+0t/qGF+nJrj1d7rv9NU0sa33elLLm/J5a9sp+060zNzp6xLPrFuDMhZpLdIYlDEoaxHWJn8bg0y/V9YgjUl/m35vDcJYVHq+2yFJI4Az9r3CSIEVHGPSjBQtg2qnGD0VGwaP1E/2IbewgQAf8AV4AgKR1JS+OPblkXw34/v0/4DW39ZP9j0oQSiB8QXGB+sl3xlfYKe+DwTWS94nfyfvTZB8n/wuXe9QObPrcDgcpzqZGa/HH3+cv/iLvzjh89WrV3PgwIGsZOdkeHgYrTUrVqw45vMVK1bMeFxf+MIX+MM//MNeHN67gHSJcsexNIfjKUUnTUjjQWMoq4M6EQvYHi/9Fg6kCxpnkG1EaswSM5cYN5GLkUqjrEGZqaZuBoOX/q1aJi/5bcvkpe+lbf5u6t8GT2p8qVFTzKQ0Nn2l2049zqaOtjMeh5jyW3XcvgJiCipqfTaplbyaxyxNoiGaxlUnn4lUu6VlmHY/Uz9TxqT7Amnmnpm2+Rul332z2IrQQgjJ3dLlfaf/L0tfveIGXuNXmL5j8VRAS0HsSWI/eZa29bcniT2F9pLhys3vtSeJfIVOfxt5ilFTTLZVitBTRF66vUr+jrx0KHT6++Rz2fouKb+T96GniFu/ab6O3ZeNBZH03rvRXdv85z2avpNAz8+mtSchf7o8MxeZGa98Pj9tZOill15i2bJeVjnTc/x099baGafA//3f/33uvPPO1vvx8XHWrl2b6fG1y6//4g185TtPcODwOEoKdBuNonmRLgOgKhFWSUzBz/SmllJgjGXd4kFUv8/rB48cfyjdxViQAlmLEJFG9+eSHuSs0piWTbJuyB0xhAMq6Q3PqsxK9yu0xasadF4mkURDBqv5NTWTCxWMgi6CLtD19Fkp0YCWJDsXAq+mIW+IhphsO2d4TmVkCZSGPjOpk6VebFniVykONpCeze4apnpGw6AJWVSqEAQaq0GSmjjDpMFLTd8xhk9PmsGp3039zIsMG54dRsYGFRtUZOiP6iwVZQo2RIQWGScvEVlkZBFRch6Sv9P/m+9Di4w57n36fZzBeXIsGGUsKtTkQheV7iVh02wqjyg1rZFSRJ5HjESjCP3JyasiNfk6/lneqDkh1XHfhVMnq/Km7ivVTSel0kZCTRL5HnH6eeQdN0lWNyarataFBoh0srasJzOs623rfzleA8/DFoNWm6PbiPQfa2HDqsXExrLn4GirTZUJrfZTAxEabF9+ygF1P41KCbS2LFvcx89+6NKu7z9rMjNeH//4x/lX/+pf8bWvfQ1IjM7bb7/NP/kn/4Sf+ZmfyUp2TpYuXYpS6oTo1qFDh06IgjXJ5XLkcrleHF7H/OyHL+Nnbr+Ul147yD0P7uZHD+zm6Fi1OyYsLYhkJcQfruAdqSKjpGLUeY9oaYloealrJqx5zCuXD3D7Deex9dqNnL5mCQBHxivc88TL3PXIbl548yAyXYh7QSlMj1mEmvyecfJ7J/CO1hGA8QSNlX3U1w0QLitOlmYLSWLTdGhLbiQkPxwSjMUImwxxrC3zqaz2qa0MsKoLJqzZY2khP6IpHojJH9ZIk3zVGJJUV3pUV3nJUKVumKJ0H17Vkhu1BGMg07ZUVITaMqivAJ2jO3qp6fDLlr49htI+g19NvoqLUD4dymdAuCj9rVigZtP8hBAcVgTDElUVCATWs+gVEfHKGDM0pQHZjTwTgxj2kYc9xJhilAFGpaFwWo3imgr506qT2bMLetZA5UiR8uES1ZEC1kqEsCxaPMHKlSOctnIEzzdYKxBi/ndhs03y+kXLOFIt8c7YEAcmBoiNAizrSiNcuewNPrD0DQaCOtoK1AL1LAIpLPvrgzwzsYYXJlYxoZPpetYEZbYO7eFDi/awNlcmtgJvAXpAax/7wyL3jK1h+9gq3g6T4fclVeeC0j4u7tvDmvwoxqZZdLpraFLDmZpNEU953zKlYEOLii31usdLw6fx6shyjpT78LQhp2MWqwqL/QoDsoGKNF5k8LRBxRYV6eT/2OBFOjHCscFLv/Oi1BzHFhU339vUMGtEA2RqdKcOfVZu/c5TkkBrAq2BaM7fOrqDBjRTjKj0pp+RWSRGU4spszdPmc05Pu69Vh6hUAwtHuCsM1dx1lmr6B+sge9zZF2eZ98a5slXDnBwvI72PBrI43S9dL9TliSaMnN0LCVWTOnha5qtSgP/rRGCPUdRE8kjOSbwiFcNEa9djB4qdrV9ONif57brN3HrdRs5/+yVMwZMTmWEzWhF4/HxcT784Q/zwgsvMDExwapVqzhw4ADXXHMNd911F6WTOCPfBz7wAS6//HK+9KUvtT4777zz+PjHP97W5Brj4+MMDg4yNjbGwMBAlofaMcZYntn9Dvc++CL3PribSjXszIQ1zVY9wjtcwT9SRTZm7ia2gCn6RMtKhMtK2JzX0U2mlERrw+LBIrddv4nbrt/EuRuWz3oz7Rse44ePv8Rdj+zm9f0j80qfiA25vRPk3xnHH67N2lY1gaS+up/6ugGixc2enDZN2BTzExyNyA+H5EajpLdtJj0FtRU+ldUBteV+Z6avNTwEcqOG4oGYwqF41p5+K6C+RFFZqaid5nVu+tLfqrold9QSjIOapR63QNSfmrDlYHw600vNllezlPYY+vYagonZNwkHJk1Y3E9nUaJmD2kMwbAkGFZ4E4nZmvEQcwa9IkavjDADprPIW8uggzjiIQ/7iFGFsDNvLDxDYWWV4toyuWX1Sak29ZqHVztaYOJQicpIEatnPkFCGpYuHWPlyhFWrBhFKtv2bd+scYSAo7UCe8eG2Dc+SKhn7gcUWM4aOMSVS9/gyqVvUvCijkyYsYnZOhKWeGZ8Lc+XVzES9c12lJyVH2Pr0Dt8aNHbLPPrHZmw5m9H4hz3ja3mvrHVvFIfZLYLMuRVubD0Dhf3v8PyYKJ1zO3QPBd17fHo8AYeGT6T3WOnYWfJ5L6MWd5fZmX/OIOFGtB+npnsxBcMH+nn4OFFDI8MYMzseaY0WKN/cYXi4PzzaH0ix8SREuWjBYxWs29U0IhSjCzp5H5vt5yxk3/44xAcEOQOC4KaTSac0uaY52iTv2OUFyOKMaIQ49sYP9Z42iQmNE6fq9Um/Vu3/vdafxv8UJOrGHJVTRCZyYmopj7/a3Rr4itFOnnV1Gd+Y43fw2d3HY6Txk//NHz5y5DPz/nTrGnXG2RmvJps27aNp556CmMMl112GVu3bp11WF8v+OpXv8rf+3t/jz//8z/nmmuu4b/8l//CX/7lX/LCCy+wfv36Obc/lY3XVOJY8/gzb3HPj3ez49FXCMN4epPSivzEk5GtatRxx7kFdH+OaGmRaFkJ66tpTVjzGPqKObZeey63XreRizeuQc4j7P76viPc/fhL3PXwLvaPTMyaPrQhd6BM/p0JgkNVxDwigrrgJSZs/QDxYG7m4QLNYXZjMbnhkNxI2Ir8dKTnC6qn+VTWBDSWNBum05iwNI3BuKZwIKZ4UKPCztNnJdSWKSorPWrLVZK26RorUyI/udEkuqXCztNngXAwNWHLwHpMr5caJdmw9L1jKO1NNOeTR8PFkyZMF5jehE0xP8GRxGz5Y7ObrZkwBYM+LYmE2ZKZWS/9XxxVidka8RCmcz0ZaAqrK5TWVggWN5KsAcee0ykN2cZ4jolDfZSHi5h4tobs9CilWb58lJWrjrB02eTwcjGNnhAw0cjxTmq2alHQefqE4bzB/Vy59A0uXfI2gdLTmpSmGRmP8zw7vobnJlZzMByg85Cg5YLiCLcO7eHWoXcY9MJpTVhTr6w9to+tZtv4Kp6rLqHzXArL/HEu7NvLJX17GPJr05rMZpojI3lqZB0PHz6T50bXENvOr2HOi1jRP8HK/nH689PnmakthaOjfew/tIjDw4PoWc3P9EhlKA1VGVhSId+X9JLPlEeFgEYlYPxIkfJICR11rodITJjsi6Gok7w5S7km6gJ5yEMO+8ha5+N4LRZdssRDhmjIgJpDL0o6dXLDElXuvJyxWHQeokFLODRLOdrqRLIU91v69hryw8mzm50S55O6orpcERdnGJY/ddTFUUPhkCE/YpBzeEJhDV5zduZ01mMpNKbPYvpBBgYvjvFtEq31TXzcxFIx+XJEcbRBfjwiiGN8mzz369l0nzaZrMlvvdfHvFcYpDIoZVFicqbo4yfMar3SibNUBs+GOubggQfgFFiH96Qbry984Qv8/u///gmfa635zGc+w5e//OUsZNvmS1/6En/0R3/E/v37ueCCC/jiF7/IjTfe2Na27xbjNZV6I+InT7zGPT/ezcNPvYHWScknYo03XMU/UkGWw649mmKBeDBPtKxEtLQIKqm8coHH5qvO5tbrN3LVhevxvHlUotPpWcuutw7yw8de4q5Hd3N0IunBxViCQ5XEbB0oI7v4sH/c51Nfk0TCdN9kA9KfiMkdDskfCZFx9/R0TlBZFVBZExAOTUYHvIqhuD+meDDGq3dPzyiorkiGItaXTI6pl7ElOAq5MYuqd+9xJiugsSg1YUtpmRMRWUr7pjQSuqVHEnGrnA7lDWCal9CAf1SQG1b4R+WskaZOMX2a+LQkEmbzzbABiLHUbA17CN09PVWIExO2row/MBmGbJQDJg6VKB8uocPujTj3/ZgVpx1l1aojDC0qt9ph1dBvma1y2L2eSV/GXLToHa5c9gYXDu1FyeScVrXPcxNreH5iNXvqi+hWLlUYLus7zNahPdw8uJeiSnpT6kby4/GVbBtfzZPlZeiuPXRnWZUb5aLSXi7qf4c+lZgUbQXPHV3Nw8NnsnNkHQ3TvRlzin7Iiv5xVg6MUwwm88zYeJH9BxdxaHiQKOqenvI0fYsSE5YrTfbehDWPiSMlJkZKxI0uPhUhLKIUI/piyJtJrxAK1GEvGc5bkfPqZJkOKyy63xINaeIBO1muxc1OHYk3Pr9OnWn1sOgihIOWaBBaPtxYigctpXcMhUN2TvPTCVFRUF0mqa1Q6OYsxNaSG7MUDmkKw2ZeHY8zoX1oDCrCRZI4P3mv+ROawoEGhUMhqtHNulAQDvmESwKikpqsC8sN/FcPE7wxjBqrdU3PKpkM1Tt9KfGqIZBJGgdKeW67biO3XbORC89e1bUARqwNT77wNvf8ZBcPPLqbuFZPzGUUkd83SnHfCLnhcXxr8dB4pMaYKe+xKExihtGT70nf28n3CoMKJLLooQoKT4AymsBqlpZ8lvXnGcx7qDiCMIQoSl7H/x2G8A//IXz+8+BluixxW5x047VixQo+//nP8/f//t9vfaa15ud//ud5/vnn2b17dxayPeHdaLymMlGp88sf+78YPzCOGq9nPv+MFRAvKnD6eav4s//xm+Rz2U6rp43h//o33+SH33kCf/8EMsp2yIUFomV9xKsGCUajeUWaOiUqSmqr8gRjGr+SvZ72YfScAioCr5r9nEVGgs4nz5sUDs2vR7YTrIDRcyXhgCQYk101P9PqYdGLDHrAII4qRJTVDCeTyMEQb02D+mieqJ791JZBvsHic0cYb+QYqxfIOtcUVMi1a1+lZn3erC7FZDZrTEIgNNcO7MOXhsfKy2nYbCt+gWWJnSBnYp45uo5KnPVzx5Y+IgaJOHK0n0aj8+hkp3h+TLG/Tr0SENay10NaglAj6xIx3j2zNRNWWGzeImKSCHoXO3Wm1cMidYyKLMVD2U8ukwwjF0RFQX7EzDrkvFtoaTDKkDsa49WyH16plaUxcQR1tIo6Usm+/eQrzNrF3Pjhy/lnX/wsnsq2XGuEMf/fX/4zdj32Kt6BcUTGM9NaQC/pI7+0ny8/+IcM9BUy1cuSdr1BZjXFXXfdxdatWxkaGuKOO+4giiJ+7ud+jhdffJHt27dnJetog/5Snv5aTHW8N2vnCAv+SI1lQmVuugCUlKwrFSnuK6Pj7AtiAfjjIb43j7F288SrGUr7ejdFm4ogf3SGYZUZIA3kD9quRgxnQ1jIDUtEpLJ3lYBAIMcVtt6diG87xJWA2oEeNGZTGo2APWOLeqZX0wHPjq9Fqd482xJaxU8mVraibFljEbxaWcFYOXsTmyAo1/NURnvXuRiHHuPDsz1712WMQB32M+/YaSKswB8Rsz7j21U9BPmjkJuYMrY4Uz0IJizBRO+G23kNiz/au7pXRpbcq4d7piciTW7PCKvzQeamC5JRSacheXXfOBk/iQQkecY7UqZoeVebrk7IzHhdfvnlfOtb3+LjH/84uVyOv/qrv+K1115j+/btM84e6HA4HA6Hw+FwOBzvRTK1z5s3b+Z//a//xc/+7M/y5ptvcv/99zvT5XA4HA6Hw+FwON53dDXi9clPfnLaz5ctW8bQ0NAxz3t985vf7Ka0w+FwOBwOh8PhcJyydNV4DQ4OTvv5Bz/4wW7KOBwOh8PhcDgcDse7iq4ar7/+67/u5u4cDofD4XA4HA6H4z1B9lOkOBwOh8PhcDgcDsf7nEwXHvn617/O1772Nd5++23C8NjpPp966qkspR1zYJh+YfvMsBajezSHLmCMxXZpccF2aE662jvF3mv2XE8kr15N9dzS640clh6nD4uVIMx7N5eK5Kz2TM8TGiUsse3VsgAWJQy6h3pIkyys1yukTSqoHl1DK0gWMe+J2skpt3tbzgA91jOSri4IPaemryDSvbuGUmBM76boN8Ymi0T3YDr5Jr1sr51sMltA+U//9E/5P//P/5Nf+qVf4i//8i/57Gc/y2uvvcbjjz/OP/gH/4B/82/+TRayPeHduoByrRby4MOvcO99L/DY46+DsaixGnKsiqyG3S9ErE1exoC1CCm4/iOXsvmTV3LFLecTdHlNL2Msu55/h+33vsAPv/c0jUaMqDUQ41VEpYbIoOCynsIW89BXBCXT9NrMKnIrBSanMHkP60kwFhFbRGwyqeishLioiPsUOicBkdZ0yffdTqMFdB6iokDnkw9yY4bcmMYv20z0GosE1eWK6goJUqDq4FXAq3e/8WAB40NcgLiYnF8RgwpBhtk0VkzOYJbG6OUxNm+hIWDcgwkPEWfQoFYGMRAjhkJEwWANaC3RscRaQbdzjRSGRaUqy/rKDBZrWASVKKASBdS113U9heac0kEuHXibi/reQQC7qyt5ZmINL1dXoOm2KbIEUlPyQgpehBKWsVqBgxN9HKmU0Kb7JkxKQxDEBL5GKouuK8KxHNFEgM0oz1DUyGIMgYVIYCsKW/EgC73Un0sj0vI6ue9VI7kfsyhnrALrJf8DyDp4jfS+z0jPeGBVUhkFE4bcUUNuvPt1RVKuCeKCJC5IrAQVWbyqwaubTNYtMwp0ThLnBdaTyIYmGI3wx0JU1P2C1ChB3O8TDfjogoeohXhvDaPeHkaOVbtf30sJQwOIJUPYgT4CX3Hrxy5hy20XcOGl65FdXlMzimKeevhVdnz/Ge7/wbNJ53WlChNVqNS6qtWikIP+PsRACaTkkqvP5OYPX8x1N2+i1J/PRjND2vUGmRmvjRs38gd/8Ad8+tOfpr+/n2eeeYYzzjiDf/Ev/gUjIyP82Z/9WRayPeHdZLzCMOaxJ17n3m27eOiRV4kijZzae2LTno1Yo0arqLEaoh7NvxBpmq2m4ToOpSRaG/KlHDd87DI2f/JKLr7uHJQ3v8aDtZbXXj3I9nt2cd+PnuPIcLmlccwxAVTqyInUhC0g11sloZjHlgrge5PnsPWD1j9dMWFWgMl5mLxKetqm0xOkJswkRmwh6ROp2SopdCFt9EzXG9UMLpgFpg/QOYiLgjjPiYs0p+kT2pIbTUyYV52/CbNAOCCoLZdUVyhMIKYPklhQVfCroOoLS6PxIGqaLY8T9dLrJWOQXWiMWd+gl2rMshhTMid2szf163LShOkFKEqL6I8QQxEU9aTUcXrWCOJYYnTThM0PgWWoWGVpf5lFxSpSHntbNP/WRlCOclSigNAo5ntWBYYzCsNcOvA2l/TvoaAitBWo9EYzViCFpWEUz5dX80x5LW/UlmLmPZrf4ktNyYsoeSFK2hPSl/wKRipFDk/0M1ItYuz8TYqUBt/XBEGMUsfqMWX9XV3zCMcC4okAu5BImLRQiJElDTkz/T0hgFBgKh5UFOgF6KXllWgO9yBZYPh4PaETU6QaIPUC5dSk4ZqufEnKbVqmT0bzv+8tSUeO9ZLOnZn1bNKZNWoIygszYUm5phKz5U1TjqYZVTUsXi01YQvRk4nZ0gWJ8cQ0dWHyXtXi1IRFSD1/QSsh6vOJBgN0IW2jTNUzFqRAlOt4bx7Ge3sYWWnMWw8hYLAfFg8ihgYS8zXlpDbbNkOLStxy+4Vsue0Czt60EjHPaJHWhuefepMddz3L/Xc/S7XcOK79lGobgy2nJqxWn3/6AHIB9JcQg33gHdt+arZNPU9y5Q3nsOX2i/jAjeeSy3e3kz4rTrrxKhaL7N69m/Xr17N8+XLuueceLr74Yl555RWuvvpqjhw5koVsTzjVjZfWhp1Pv8V9O3Zz/wMvUqtHJ5qR6UhvABHGyNSEyTCeW3AOszUTypPo2NC/qMTmT1zJ5k9ewaYrzmirEHlnzwjb732Be+9+jn17jyKVwLRbwBqDKNcQEzVEtd5WRWelhGIuMVuBf2KBP+OGU46pAxNmBZggjWz5s5if6fSEAD3FhLWjB+iCJCopdEm1p3X8DtIGTbt6JoCoIIiLJGarnRFi6W9kZMmNanJjBlVvL41RSVBdkZgtne9MDwNeNXmpRntpNGoysmV82tOboiuizhpj1rPoxXFitvqn3IezbTz1lqmlJqzstTccUVjoi5GDEfTHk1JtnlNjBDqWaC3T8V5zYRks1FnSV2ZJX+UEMzLjVs2+JSMpRwGVKEfUVpTIsjZ/lEv73+aygbfo9xrHmK2ZaP6mqgOeKa/h2Yk1vN1YTDtX0ROaohfR54d40nSUPm0Ew+USh8t9jFaLtHNXCGHx/ZhcoFFee3pTTVhc8YnGA6KJoL1rKCwUNKIYQ960l19STQTQkJiygqoH7eTRKWZLdGL0myYsTu/BentD2SyATCNNnQRbp5i+TiNvNtUzHu0/tX98Z9aoxq+0V44m5ZokLiiMP435mVXXouoWv6bbLretAJ0TxHmZ6EFHda9X1fijIf541N41FBCXPKLBgLjkdaYnBHK0gvfmYdSeI8h6NPd2AAN9idlaNAhqms7VaWi26VasHGTrhy9iy20XsG7DsjYO0/LyC3vZcdezbP/+04yOVDpqH6I1dqIK5QrUw9m3aeJ7MNCHGOhru/0klcRoQ5DzuH7reWz+0EVcdvWZeH6vhll3zkk3XmeccQZf//rXueyyy7jyyiv5tV/7NX7913+dH/3oR/z8z/88IyMjWcj2hFPReFlreWHXXrbt2M2923cxMVFv72aaeYeJCWtEk5GwSJ/4G2PATulRnydNE7Zk5RA3/8xVbP7klWw4b/UxJuzwoXF23LeL+374HK+9eujYyF2nTLZWEOUqcqIKtWOHW1ohjjVb0LkhmU7XTv5/fAevDRQ6r7DBPMzPdFqQRKSmMWEW0HlJXEqiW22bnzl1mdaEJcNRIE7NllUL1Eu3VaElGNXkRg1eeGx+iPNQXaGonqaIix02EmbQE3rShB0fmTIyMVu6CDqga+dThslwRHGcCbPSYhZr9NIIM9Sm2ZpFp0VFJSasoo5rsCZmSwxEyXDCYztkO5dsRm6MaA1HPD7X9OUaLO2vsLSvjKfaNAez6AkBoVapCQtOeF5qRTDGpf1vc/ngWyz2q22ZrZlobjsW53l6Yi3PltdyIBw4Jo1KGIpeSMmLCJTuSvpiLTlc7uPwRB9j9fwxeonZ0gR+jPJM+tn89CYjmRCVfaLxHHHFP86ETZotUUjD4wu87y1AXSZDEavqWL35mq1Z9BCTHSGqwTFD51pmS4GdLtI0X72mCaufGHmzIjV3fmK8ulGOitiSP5qWozV7YrmWl+iiRAdyYeVoa6cWr27wagbVOK5uEqCD1GwFHZitmWiasImYYCzCn4iOibxZUrPV7xP3+yeOupiHFoA8MoH31jDeOyOI4zuxS8XEbC0ZOiHy0ynNttD6M5ax9faL2Hzb+axYOXTMb9567RA77nqGbd97moP7RrvSPiSOsROVJBIWHmcyPZVGtvqTKNcC0tc81lJfnps+dAGbP3QhF1y2HilPrfkBT7rx+rVf+zXWrl3LH/zBH/Dnf/7n3HnnnVx33XU88cQTfPKTn+Sv/uqvspDtCaeS8Xp7zxHu/tFz3HPfCwwfmWaYXTdo9uTUQuSRMupoBZE+t5UFzZ6O1Wcs58ZPXEGwYhGPPvo6zz+7ByEEXc+yU4ZbiokqohFBzod8Lvk+q4c+U/NllEgiW7kumK259LRtDSXUJbVw8zOj1uSfRkwxW9MNR+mKXjrEpG7wxw0mB/UVkrA/bSQguqs5tUe8kvSG61zyArqfviYGRGiRBY1ZHGMW66SXu9vndEqkj7KChkDmTBLdUhnoMaWfwAh8NEtKFZb1Vwi8hZmR2fSEgEaswFjOK+7jsoG3OS03jrECge2qZnM44nDYx5Pj69hVW41SyfNbkF36wlhxcLyP4VoJKwXeQs3WjIIkJkxDNBHQqPpYD0Qh2zxqLVCV2AkPUW0zWrsQTZIotGwk5sg2I01ZlaMiHYJcS/5vPSeWpV5oyR3V+FWL8SU61wXzM4ueMBZVM8jIYj2R6GVRF7bKNYs/HqFqGutLov4AsqgLp4wJVofG8N45ihcJxOKhzkbOtIsgaSMZy8bzV3P19Wej6xE//tHzvP36AjurZ6KZhihKTJgViP4iNp9LI9rdvY7NNu6iJX3c/JGLuPWnLuX0s1Z0VWO+nHTjZYzBGIPnJaHar33tazz44IOcddZZ/MZv/AZBEGQh2xNOJeP103f8KRMT9Z7NeOO/egBZbm94XjcwSwcxi/qzMVzTEWtk3LvpkawUxIO5uX/YLT2gvrSD4RNdoLpYpMNueqMX9llMHrputmaih7NpAdiChnX1bBpe0wpaPH+aZ3Ay5Pzl+/Gk6VWW4eOLnmRN7iiC7LOptfBI+Uyera6lVyf0aL3AO+WhnmgBaC2o1nO9yzMGeL3YI7EEVaNnM/fBpNHrVQq9qsWv9kgMwNiezkyISYePZtGrMx3WUny7mjxz1gM9IYB6mEwq1owy90JUZGSap0EqiVKS7z72L3qiNxfteoPMppN/5513WLt2bev9HXfcwR133IG1lj179rBu3bqspN9XNBpxT6cZFbb7M8vNRjLFqO3hrKa9TB3tPdrSbXo9bavskQE6WXq9qtSaSDup2wvEcf/3ACm6G22aC1+aBY0u6oTkEUyJpHeevZfZ8xh6WtT0tlw7KUX3SdB8z9Iq13p34wvbOz1rpzzy18sCoIcFt9Gmp8sUdYvMBkhu2LCBw4cPn/D5yMgIGzZsyErW4XA4HA6Hw+FwOE45MjNe1tppZ6crl8vk8++++fkdDofD4XA4HA6HY750fajhnXfeCSQP+P3zf/7PKRaLre+01jz66KNccskl3ZZ1OBwOh8PhcDgcjlOWrhuvnTt3AknE67nnnjtmEo0gCLj44ov57d/+7W7LOhwOh8PhcDgcDscpS9eN1/bt2wH47Gc/y3/8j//xpM/653A4HA6Hw+FwOBwnm8ye8frrv/7rY0zX+Pg43/72t3nxxRezknQ4HA6Hw+FwOByOU5LMjNcdd9zBn/3ZnwFQq9W44ooruOOOO7jwwgv5xje+kZWsw+FwOBwOh8PhcJxyZGa8HnjgAW644QYAvvWtb2GtZXR0lD/90z/lX//rf52V7PuKylgVHeveip6MBWFO2iI0ju7gLqDDcTzurnh3466f41TH5dFTk8yM19jYGIsXLwbg7rvv5md+5mcoFot85CMf4ZVXXslK9j1Pvdrg/q8/yr/81Bf51JrfJB6tJF/0YoVhazG5zNbcPhEBIop7u+CvSAqrnhRYIlmQuoerQyfp0r3VE3HP5AAQPe6L6Dlxkkl7l216uYB5otfQqvlnT/SOxsnsuz0qRhn0atgerrwdSE0PSzaE6J0WqZJVvV1I1fZ6NeMe69le3oLQ27qX3mo1MV5mTe7p6WXbqccolZzL01YvOslH0jmZtaLXrl3Lww8/zOLFi7n77rv5yle+AsDRo0fdOl4dEkcxT977PNu/+hA/+c6ThLUQqSRGG7ynXsMOlTDLhzDLBkCppGbv1g2X7kvEBlkJkV4AAwLCCBph91sqaVvEKokeKmGGSthAIAxIbTMrKS1gJdjAg4JCRgYZakQGJqW5x7ikCAcDopJChRa/bpCN5NtuFpc23V9UEDQGFWGfQFiBV7eosPv1eVPPeBAVBTqXVOLCgsiwbWQl6CDRRZN0K2VY71jAehbrpWnWICORWRotFhtYdAB2JIfIaVTOgJcIdr+ObV5JgdbJ/qWcvB+6q5do+UpTDEImohyR9cjJmJyKu542gcUiyMuIFcEY47bAq40VDKoKQ6qKL0wWxSjaCiZMnkV+hWsHXuZAOMT+cIiaCVrH1E2sTTQ9ZVg/MEIlyjMR5mhoj8nr232khL5ig1hLokihTbPB2UW99PCtARNLzIoQUVfImkTUJCKrm19YjGfR+aQ8U3WBbIDI0IlZAboAWoOMQUZJeZqJFmA90DlB2GfxahBUEs0sckxLzxNYaVGxQIW21WHX7bqQpp4vMJ5FxgKvYZFh9/WOF6+tyqPqBq8S41Xi7K6hFFhfYEoFhLXIWoSsxwiTndW0SmGbxtKCNKbr7TWlJFobcnmf67eex5bbL+KSq87orkgPENZm08f3pS99iX/0j/4RfX19rF+/nqeeegopJf/pP/0nvvnNb7ZmP3w3Mj4+zuDgIGNjY5nN2miM4bkHX2LH1x5mx98+QnW8hvIkOp65VWeFwC7uQy8fxC4ZSGq/+bQeWq0Eg6qEqEqEiPSJBZK1EGsIw8SIzTcrNc2WFJjBInqohO3Ln3jcNjFeQtsFm7DWpjLthZJMf5606YoJa26pi4pw0Cca8LFqGj1j8WoGr2aQ0fxNWLOC1DlBfVASDiiMP82ebGK+vLpFRsxbr4lREBUhLooZ9JKXMN1pOFiRmC2dSyrTGQ9ezPJdJ3qAVanZUjPs06QNo0gsKI02zTXWB5OzmMBOP0ZBWGROI3Ma4dkutY7SvDftfixCNKMazPK7djQEnkzMVjGI8KaNWlgCqcmpmEDqeZuhprHxRcyKYJwVwTh9qjHN/iwl0WBQVRlSVZSw8ytGSTsgrKBscpRNnpr1Of7iWAtlneNAOMiBcIiG9edtwppFsLYCbRXaTp/xIy0pRwHlME9oFF3KNDNiLMSxIo4V2jR15qE31WxpidFi+tCTAVGXiQmry1RtAekTFuMlHS3T3oMWRDTFhHXhXFrBpNbxu7NJR4/okglLyrWkDLUzdFiJyOJXwK8m5m8hWqR6xk87yqa7uUxSN6nQIvX8c2jrPlSgA4HxATmzntewiC7UhS3xGb+zqJrGL8eo6jTtq06lBFhfYgKZpG+a9pOINLIWI+tRd+pfJbFKzaiX1PVmQYZPSoExFs+TXHXDudz8kYu48vpzyOX9BR5992nXG2RmvACeeOIJ9uzZw6233kpfXx8A3//+9xkaGuK6667LSjZzsjJe1lpefvINdnztYbZ95SFGD4/PabZm3JeUmCX9mBVD2EXJuU/u7Dlub2MTs1UNEY0OCgNrIYonTdhcNM2WADNQxAyVMP2F6QvEmfTMFBPWzibNPyRYJRLj00lrSicGTIbtFSTNAj/OK8JBLzFbfvtDDYS2qJrBrxlkbOeseFpmyxc0BiWNAYnJdTC0YUrF04kJszIxW1ExrdTaPadTTBi2/UrOAiY1W+bEduzcdNDua5ofVNJAmNFszXigacMobs+EtfQ80DmLyc3Q0JsJaZA5k5gw1akJm81szbxN04SJ5si5WbdPfqCEoZgLKfghvjIdaFpyUpNTEb6cO9LXNDBKaFb4EywPxhn0ah3p9cs6g6rKoKwhhcVacYzpPDF1idGopGaragPavQjWwpgucCAc5GA4RGTVnCasWYMbK4itRM/Ucp6BUCvKYcBEmCO2PTBhBmKtiGKFMXJuvXbN1oyCIGoSVVUQJtu1Z4yOM1sd3PcyTEyYCDszYS2f3ElHkW1G2xPz117KUuR8yrWkfvAr4FVBtjG0u6WnQHup2Wq3riet50PwQtvRiIJkFIRAB53pYSyqkdaF8byK0c4wFq+q8SoRqpaWa+1INc2WL6GT9oy1iFBPRsLaPEwLoCRWyaRjvwO9TkyYkKIVALjkqjO4+cMXce2WTZT6T+3RcqeE8XqvkoXx+tp/+B7f+8v7OPjWMNKTmHmYrZmwnsIsHUhM2EBx8ovmTWOSULSqhB3dhDMLWojSoYjRsV1jrd6u/jx6qA8zUAC1wHHPLRNmkl7A6X4yX7M1g57QacEV6mMa1C3zE8gksjXoJz1QC0TEzUiYnraiMx40BhSNQYnOdSGNxuLNUvFYkUa2CslQwoWfU2Y1YZbEZOlcYrq61i6cxoTZ5r8yGYoyayStXSyQDhMS0fRNMassOp9GttQC9QCUQQYamTOzmLD5mK2ZSMyXFNO7aCkMxSCiEIQEav6RqyYCS6Bi8jLGm2LCmkZFYljqT7AiN8Eir9JRu2t6PUO/rDOkqgzI2tQvWgVb1QaUTZ6KCbALfITaWDgalzgQDnIoHEQjW2lrRuCMFcRGEndotqbDWmhoRTnKUQ5zqYHL2oQJolgSxx7GHncTpm0vEwuMluk5XuCxaJA1haxKRDRd+uZptmbCTDFhrc6sE3faunxdKGdaUbD4+JSlzMdszaiXGpRqEgmbzhRZCdrv3GzNhNAWFabD5GfTC8T0o0rmo9cA1bAzm8xutqK1xavG+JUYWT/RhFnA+gLrK6zXpfZMI0bVIkRDH6M3mWdEEtlSHZitWfRabajj7IeQAmssmy5aw80fuYQbbj2PocV9C9PrISfFeN155518/vOfp1Qqceedd8762z/+4z/ulmzP6bbxCushH1v0q104srmxgYdZNohZvRRhLKoSdS3sPC3GQhRijAYpkue2BovgdaNlOQ1NExYbhJ1itrpQ4M+kJ2IDOunJifo8wkEfk88ufTK2qHoyFDFOn9uKC10ogGegVfFEdvK5rTyZ6TUjRYi0Em1GtjJ+LtmqxDC0GiVZ6U0ZJoRJhxHmumS2ZhAUnkXmY2Tu2JZDds9eW4Sw5Pw4fW4rIud1/1mtJhJDoDQrcuPkVcSKYJwlfgWVUcEmMQyoGotVGYFNzVYOk1Gm0VZwJOrjleoKyiaHNgptZdefCWtiLdRjj6ONArU4yETjeD1jBGGkiCIPYwRWS6yBzIxfnJqwskoiB156D2Z1TxiQDfAqacSuW2ZrJpomLEyGIpp0KGF2ehZVB38CVJiUozMO6+uSntCpIYrTYYu+SA1lRnVhbFENi1/NZPfT6Bm8isYfT0KZNuiS2ZoJY5H1GFkJEdZ2z2zNxBQTppTgs//vrdz0wQtYvnIoG72MadcbdHVyjZ07dxJFUetvx6mHCGPU3iP4wgevBzMUSgG5HHpxEVPswZhcIUCRPOiZvRqIpOcp7vNoLO5N+owv0IGkMZhdT/RUrBLERWjkRJad35MIIH1WzPZwEk3jgc2+jZlUoB6YwGL9XuRSgY0Fpu6h8r2a8jGJyqwYKPdEzSCpa8l5pf2UvLAneqO6RNUEeFnOGJOihGV5MMFoVGSk2peZ4WoiBBT8GEO9J8ZLCFDKEqCplXs0nMgD069BZ+l+piDBFMBG9GbmVQHWBy3JrmP1GD2BLiSdZT0xJkJgPYhVZtOonID1BLECv9qbgWLWk0SDMvHovZi0U4qknWZBxj1IYzJOHSuhOFjgU798ffaapwBdbdZMnTDj3Tx5hsPhcDgcDofD4XB0k673J//Kr/zKnL8RQvBXf/VX3ZZ2OBwOh8PhcDgcjlOSrhuv//7f/zvr16/n0ksvxc3b4XA4HA6Hw+FwOBwZGK/f+I3f4Ctf+Qqvv/46v/Irv8JnPvMZFi9e3G0Zh8PhcDgcDofD4XjX0PUpmL70pS+xf/9+fu/3fo/vfve7rF27ljvuuIMf/vCHLgLmcDgcDofD4XA43pdkMvdtLpfj05/+NPfccw+7du3i/PPP5zd/8zdZv3495XJvZrlyOBwOh8PhcDgcjlOFjFfGSSbSEEJgrcWYXsyH6XA4HA6Hw+FwOBynFpkYr0ajwZe//GVuvfVWzj33XJ577jn+7M/+jLfffpu+vnfPKtTvWQp5kJl77mOQlRDvaA1Rj5NF8zJERBo10UCN1hD1KHM9jMWrRBTfqRKMhqB7oFc1lPbF5EY0Iuv1NqxF1S35YUNuNFm4OWs92bAUDlsKhyyqZrO9hhZEBMEo5A6DqiafZYoGVQN/TCDrIns9C4QCPZzDlD1sxusIWQs6luw/MsDIeJEozra8sTZZq33n2FqeHl3D0bCQbZax0DAew3E/B6MBqjrIvJgZjQrsqS2iHPuERmauF8aKkUqRas0njFT2xagWhOUAagpCmSyiniUWaIhkHS9NT+5BWU/KF1lP1/LKUjMt17xK8pJhxnqAiCz+hMWrWmSYcblNsqBxblRTPBATjGlED+pev2xQNYNsmKTQyVjPG6ni7T2KOjSefXvGWkQ9QpbriGodouzba01q1ZCf7HiRMIx7oncyEbbLD1795m/+Jl/5yldYt24dn/3sZ/nMZz7DkiVLuilx0ml3dep2sdbyxd/8K7Z9+SdEjRipJEZ3udbJ5xClAqJUTE2XpTer4Z6IlQJd9DFFH+ur7qyKHhtkPULVY0R87LmzAkzOwxR8bNAlPWORkUaGifFpns3mzRT1e0SDPlGflywivVBS8+PVTVKhHacXFwThgCTsl1jVJb2QVHMypzTTqX2ISoKolCwq2Q09GYFXB682ueBnU8940BiAsB9Mrjv5VsSgGonm8YtTWglRnyUugc7TnVvFJI0fFQmEPvYOtFhMzqILFhN0Sc8CGqQWWNPc5WSuEXmDKMaIQozogi+yFqwRmFhiTHNR2mYOFeT8iIFSjb5iA08tvHyzNtm7tQJjJ/Wa//apOhtKR1hfOEK/3+iKXmQVVRNQNTkMzXIUQKAw9KsqA6pOTkRdKWYm4hxv1pbwenU5Y3EBgZ2yeLLFF5pAGTxhulOMasnRWoGRSpFKmOP4lrqnDIEf43nd0bMGwqpPWA6Ias0F6KfkG2XBM+Db7t0TkUA0ZPKaZqdWplpd0pMheBWBVwVhjt2pFWD8pHxDdUGPtFyrg9c4caFmK0AHyct6dCWNIrb4FfAroMLjy7UkbSZI09iNTKMtQdkQTBi82jR1YVEQ9kmiPtmdutdYvFqi6VXsCXWhVaA9gfFE9+r68Qb+cIVguHKCmbSBQg8UMYMFbNCF+fGsRYQaWQlRlRBxfNtTCEzgJVpKducaToMQSRmbLwTceMsmtnzwAi65fAPK622QYCG06w26bryklKxbt45LL70UMcsF+uY3v9lN2Z7SbePVpFau8/D3nmLbVx/iyXuew2iDkAI7316VwEeUioi+IiiV5OqMbpr5YpU81oR1gjbIeoyqR4jItGUlrRCYgofJ+1i/w0LEWkRoUrOVFE4zbp3WBFYkJiwc9In7vI71VGhRNYNq2LnTlv4flVIT1mnF0zI/qdmaI9u1TFgOwpIgLtKx6RNRouXXTjQ/M+oFiQEL+8EEHerpxGypBsg2O9aMgrjPEvXRuSkyICOQkUgjk9M19Y7FCovOW0zeYvwO9WyiKWKBMM1zNnfOEQWdmjDdaRbFGoHWEqvbaa0mmaqQi+gvJiZMyfbLt+nN1sw0jcoiv8rpxWHWFUcoqqhtPYDISmomoKJzaBSzd1ol33lCMyCr9Ks6uXYzWkpN+6nZWsaRqO84szU9AosvNYHUKGE7uobaCEZTszXRyLX2ODsW39P4vsZTnZkwayGq+TTKAVHVTwrJObSAxIT5BrwOTZgF4sRsyYZsQ2/KpvM0YSIEryrwKyB0extbmZoUn47HIgmdmK1OyjUrkrJbB4l56CSNQlu8KvjlRJM2NrckaZvU60DQWPyKJZjQ+JVJszWXXlwShP2KqNihKbKJ2fLLBr9i26oLIUmX8VMT1mldXw7xhyv4wxVkZI51kzNg8h5moIgeKECH7ScRpWar3Dihs3pGZNOE+YkJywilJFob+gcK3PzBC9j8wQs478I1s3qKU4GTZrx++Zd/ua2T89d//dfdlO0pWRmvYzRGyjz47cfZ9uWf8NxPXkrOqbVzR319b9Jsed4pabZmwnqJCdPFAGbq5TAWWY+Q9RgRJt15802dlQJT8DF5D+vNYMKsRUSp2YrMgjoIrYRw0Ccc8NHFGSJv1iKjJLKl6nMX+LPqCYj6BI1+RVSaoeKxFhknkS1/mshP21rp/zqfRsKKzFjRidi2IltynkPeppq+ZiRsxsibmYxsyXhhsV7jJZGwqA9sMPPBySjp6RZpI2hu8zPDrkQSBdN5O3MP9XFma0Hd2MImEbCiRuSnN2GtyJYWGC3npZe0KZJcU8o36C/VKeUbM46AToYStme2ZibRWxaUOb14hLWFEXJq+gwYW0HN5KiaHJGdy2zNpicIRMyAqtIv6/gzZPiGUbxdW8Lr1aUcDJt1yvzSKbAEqQmTM5gwY2GsludotchYrYBNc+h8ihuBxfc1vqdRM5gwayGue4TlgLASYDswP9PsLTFf3iwmzAI6NVv1zszW9DsTc5owETeH9QlkvLA618opkbCZ2rcmjWx1oVyzMo2E5VJTNK1eYraCSjJMmoXoiWTUhPFnMWE2GbIYTGiCcloXzjOTNuvCsE8RF2cwRdaiGha/nES3FloXtmPCZDXEH64SHK4gG/G80tfSKwTowQKmvzBz+yk2qEoDWQmR4cLGm1slsYGH9b2emLAly/rZevuFbL7tAs44e8UpacJOmvF6P9AL4zWVI/uOcv83HuW+L/+EV3e+iZQCMzUK5ikoFZF9RfD9d5XZOp7W0LJAJZGwgg9CIBsxsh4hGgszWzPqehKdTyJhKIGIDbKxcLM1E8YThAM+0aCPzgmkpjWUcL4F/mxYCWG/pNEviYvJULdmZGu+5mdWPSAuJCYsLiTRM5VGthbaSJhOC0AXEhMW9SWRzWSoZGKC6LJekkch7LPEfUklKyJQEYhovjZrDl05xYQpErOlRTqcKANFaRHFGFmMsX6SKY2WmHh+Zmsmmm0NgaVUqKcmLEz0rMBaMWfEpzO9yaHBp+XGWV88wprCUaSw1NJhhGGr9dndXJMXIf2qRr+qYy28U1/EG7Vl7K0PTjE/3UurFIZAanxpkFgm6jlGqkVGawWMlW1F0zpBCIvvxfh+YvpMqGhUAsJygDVZDHG3yTBEzyQRMQ2ioZLIlsmqDrTY9BYQBlQF/IpARiKTcg0F2gPrJx+2IvbJLdL9ulBBnA5HRKaRrQrJUMkua8Hk8EfjJ/WUV7MEE2ZB5mdWvbQuDPskOpeMRvDLiV5WdWESyZRJPdHQBMMV/MMVVC3KJM/YUi4xYX15gMlhhI2kJ7D7eSY1YYGX6fwBUgmMtqxet5itt1/E5tsuYPXaU2edYGe8MqTXxmsq+14/yI6/fYR7//eD7H3lAHL5EigW3tVmayYsgEhu6l6lzAqB9VVPelOa6YuGcj2b7MQCYZ8Cr7uNhNnQ3uQQxKz1LEmvbVRM1HqhZ/3E7PUqk1pp04hij/Sw2KEIuvA4wVw0jcDyRWMUC50NCVyI3vrSMGuLYwjRi7vCMtwo8Xp5KYbum5/p9OqRz4GjA5mYrWkVDZjRADIxW9MJJh0swk7a+KzxRxMzAtmXNJakw6rbnUiz69nMzNZ0JBNlxMgeTX5tSUyYoHd1of/WMN54PXu99DawvpeMfMpaj2Zd6GFLuZ60RZsBiEuuOJ0/+tL/K3O9dmjXG7x7nlpzALDqjBX8wu99nL965o/4z4/9G7yBUvLFe8x0QVpQiOwbz8eL9iqELUgNSY9nmMTrjQlqIef1mMS8ECS9i6IHpqul16WH4tvFSnpbckt6YrpgMtqTz2VvuqbqDfn1tAjtTa6ZiPM0Y+lZmyAQhJHCWNkjPZKZAk0zk/aoPG0NJ+yNnmo0y5ns9QTJc1y9LEeber1Catsz09Wkee16lU5VbvRGLw2nCCl7mmeY6ZGNDGiO+nr6iTd7otdNnPF6lyKE4IwL1+F1Y1YbhyNT3nudAg7HQnF3xbubHncJOhyO9wjOeDkcDofD4XA4HA5Hxjjj5XA4HA6Hw+FwOBwZ44yXw+FwOBwOh8PhcGSMM14Oh8PhcDgcDofDkTHOeDkcDofD4XA4HA5Hxjjj5XA4HA6Hw+FwOBwZ44yXw+FwOBwOh8PhcGSMM17vcpSSSPkeX0/E2p5J9Wrx5Ba9S9rJ07W2p9cw0eyt3PuCnp5T0dMsY3qsJ6zLopnQw5Nq3+tX0PY2jSftbPa6bnqv08PzKaVAqndf+9cZr3c5v//FT/OBLZvwvORSZmHClEr2PTBU5KYPX8R1t51PvhgkeirDLGQt1BtQD8E0l7Tv/k0t0nMmleTyq89kywcvYNXaxelnGd3UaeEkJmqovUcQE9VjPs9KT0aa3KEaXjnM1hA109eICN4exds/Dtoc810mGEvuqCEY1wid6mRZD1hLMGbJHbXIKGM9m+j545A/DF5tyudZ6QGqBsE+hTch0mPISLO5Ty04vH+A2ngOa9LskqGeMYLnR1by5sQiIpOUZ9Z2/75v7jM2AmsscSwxRqTfdV0uaTinLxEJvNBCLFrfZaHX3K9oCLzIIMMp32VBM4+GoCrpPZFVfgFIizBVt/gVjT8ag8nwvm+eT23xqgavnHG51tynsXijdYIjdWRDp99lWFdYiww1RDEizrieaNZN5RrywFHEWLU3dWGtQeRbTKOOtRabkZ5K24IDi0rcdPuFXPfBC3rWXhMTVUS5Cjq7PNNsryklueKas/iDP7qj6xpZI2xWV/89zPj4OIODg4yNjTEwMHCyDweAykSdh+59gW3f3ckzj76eZngx75tbKYnWhnwx4MYPXcTmj1zMRVdtQHkKgDCMeeKBl9jxvWd4ZPsuolAjpcCYBWSn5rFGETSi5P/mVwC+B/kAcjlYoMEUU9ofF152Ojd/9BKuv/V8+geL6aFYXn/lIDt++Bz33fUsRw5PtM7JvLE2Ea43YLQM42WI4smvcz5m+SBmxRC2mJ/8/QL1RGyQlRBVDZHR5PEbJYgGc0SLcuiS3z29SOPtG8PfP4YardLco1WC6LQBwjVDRMv7aH2x0CijtciGxWtYZGgn9YC4KGj0S6J+keSZ5LZYkBakDb2axWsk0Yumni5AY0AQDoL1uqGXbK/q4E9Y/DJIPfl1nIPG4uSlc3RNT4bgT4BfBhVN7tD4lnCJJlxm0KUpYZv5ajaP14CoS0RDQiwQ6Q6FNBQG65QW1cj1NSalFqhnLNRCn1oUEMaqtUMpDCuL46wfGGFVaQwl7YJuC9vKG4KD1X72lIc4UBvA2GYDyBIoTd6PyPtRUqwt5BpOuST1SsDEaJHKWBGjJ/XwLOQ1Iq9BLVCPye1FXSCPeqhRhYgmG3hGWXQBdMFi/O7pyQiCUQjGBKoxJY9KS9wHUX9yPy5YzwASZGgp7dWU9mpyI1PKGQmNQUV9iSIcbKZbdOGesATjltyYwa/YY8qZuCCI+iRRqQvlmm39Q3AkpLC/Ru5QHZkaPAvoPp/Gkjzh0gLWl12rK1Q1JhgNCcYiZDzZdjBKYPIepuBhVff0RK2BPDiKPDSGqIeTXwceevkgZsUibH+ha3o0IuT+I4j9RxDjk3UhQiDyeWSxAEGQfjR/vVZ7rZTjxo9eyuafvpyLrjlrsr3WiHnigRfZ8d2neeS+F7rbXovjpB0TT2nLAOQCbCmPLRVAShaSSY9pr12yjltuv4jrt2xiYLAw/+PPgHa9gTNe8+BUNF5TGT1S5sd3P8e27+7kxWf2IGRqwOa40lIJjLb4geLqm89jy0cv4fIbziEIvFm3q1YaPLJtF9u/+zRP/eQVjLGJZjs39dSbtxFBFM55nBYg8FMTFnRUQDYLm7M2reKWj13CjR+8kCXLZ7+Gxhh2P/cOO374PNvvfo6J8Vr7JqxZAIcRjE7AWDn5e67Nijn0skHMaUNJGtutCJq/0wZViVDVEBHqOYs748tJE1bw5qXnHRgj2D+GOlJpNRJm1Vs5SLh2kHhJKf20zcZKM3oXWlQjec21mRUQlQRhvyTqE51VqmnDREbg1SxenTnTZ4G4BOGAoDEAqA4aR1PNz7glKIOM594kLkyasI4auM2GcwzBRGK4ZEjL/MyEzk2aMFOwHethQDRSsxWJOfWkpykO1iktqhKUoiTr0aYmaQA98qiFAfXYm3NDT2hW942xfmCEFcXxtvsJptaoh+t9vFMeYl9lkNiquY6QnBeT9yLyftx+Fm2eegGNms/E0SLl0SI6nlsPf4oJ66Rt1MwzoUAeVchRD9mYuzfdeJa4YNFFsJ2Yvql5dCw1W7W586hRlqgf4v4OOyZaepbiPkPfXk1+2MxdriloLFI0lijCvvR8tHsh04zjly25UUMwYecuZ0TSuRSWJHGpg3JtitnyxyIK+2rkD9Ymo/azbBYPBIRL8zSW5KETU9Q0W3VNMBrij0aoaPY61ALWk5i8Qhf8zkzmVPNzcBR5eAxRqc9dV+SDxISdtghbzHVeF8Yxcv8IYv8I4ujE3IcqZcuEiaD9ul4qidEGP/C4+oMXsuWnL+fyzZsIcv6s21XLdR65bxfbv7OTpx58eX7tNa2TjvFojoqJNJcVcphiAYr5jjrNW+21jSvZevuF3LT1fJYs6297+17jjFeGnOrGayoH9x7lgR88y33f2clbrxw8oZejecNJKbj8+nPY8rFLuHrLJgql3Lz0xo9WePCeF9j+nZ08/9SbacVoj404TymgaISJEZlnNmz2rJAPEjMGJxRazQJqzelLueVjl3DT7Rexau2SeenpWPP0E2+w/e7n+fF9L1CvRSeasKnpGy3D2EQyXHKe6bP9BcyyJBKGP4spMjaJalUiZCOedweoDiTRUI5oUR6TUyfqNd8bi3doHH//GN7hMmKevWcm5xGuHiBcuwg9NENvY2uoJKiGSczWPEsuKyEsCcIBSVxMdU7QI+1Vt3g1i6qDnGewMzF90BgURP1M33iY0ovvT1j8CVBz+/Pp9YCoDxqLkpf1mFFP6CmRrfrcDdnp9Sy6aImWGMKlGjNbA9cCDYFsKAjnNlszofyY4lCN0qIafiGe1oQ1i5RG7FELfeqRz9wWfXoCGbOmf5TT+0dYWii3Pm/1xNrJ9yP1InvKQ+ytDBGa2TutZsaS92LyfkTOi4/Rav0ivU2iusf4aGK24nD+egQmNWEGpou8Nd9HAjWamC1Rm981tCTRL11IomHTmrCpeXQ8MVteZX55FED7aSRsYIaOiSmRpuIBQ2mvpnDQzPu+1x40FivqSzzi0gwGpWm2qpZg1BCM2wWWM0kkLC7MVK4lx+BNpGbrQA3VmJ+gFRAN5WgsyRMtzs9QriV6MjRJZGs0nL8eYH2JznuYvDd9A751U8TIQ2PIw6OI8dq8cowFbCmfjEI5bRE2N82okCkdj+LgSGK4jowj5tusVgpRyCMLBYR/ol6rvaYEl9+0iS2fuIKrb7twYe21Hz7H9r/byfNPvjF7e03rpK0Wx/NvrwmwhTQKVkiP+YT2WhIAWLN+CVtvv4jNt53PqjWL56XXa5zxypB3k/GaytuvHeL+u57hvr97ioN7R0HABZedzpafupTrbz2fgUWlOffRCcMHx/jxD57jvu/s5LXd+5IPtU7NVjg5Nr5LWCEg50M+lxgUIVi6YoBbPnYpm2+/iA3nnNZVvbAR8fhDr7Lt7ud4eMeLifnSOolqjZWhWu+qngXsYCmpCJYNgpeYIllNIluyNn+zNZOeySvC1IQ1h5ioIxX8faP4BycQCxl6OQ266BOuHiRatwjdlxTMMraoukHV52+2ZsIoiPpkYsLSxoqIbSuyNXVYXzewAsJ+CAcFUR/JkJM46eX2J0A1FjYq6gQ9IByYNGFIwCRGy59InomZb0N2ej2L7rOEqQmzzQZuKFrRrW7qAXi5KDFhi2t4gcZaiLSiGgbUIg9ru/tcQ8ELWdt3lNMHRliUTx60Gw/zvJ2arVocdFVPYMn7EQU/wlc66dMJFROp2Qrrs/dwd46FnEmiYLnUhGkmhxFWu3sNLRYTkETCCkzm0dRs+RUQXXzmrqkX9SevpGPCkj9s6XsnpnjAzBlh7hQdCOqLFfWlCp1P8qNXS8xWbrz7ekZCVJJE/RKdT86dqsYts+VVu1uwWSkIF+UIl+aJhnI0h7gnZitC1eYeddGRHmAChckrTC41YdogDo+hDo0iRitd17MDRczyQXSzA9QYxPAYct8RxOExhOluXYjnIQt5RLGIUEn0+oKrzmTLJ6/g+o9c0v322oExfvyDZ7jv20/x2q4p7bUoTqJbXbYKVghsMY/tK7RGLi1d3s/WD1/EltsuYMNZK7qq1wuc8cqQd6vxamKt5c2XD9A/VGTpisGeaP6zX/wST27fNfnQZcZYIegbKvG3u/+oJzMV7vjGY3zhc/8Fao3MtQCspxBnrEFEuutmZFo9QJfHEbUQGfXmGtYv24Ap5ubdA9wpYUlgchIRd9f8zETsgS6IrputmdAqGYaowu42ZGfCCMPEORa06IkeWHLryhjJlGeosmUwV0VJSyWeX49zp8TjHraqCOsBvcg1omHxGyBq3TfM02G1xYvS6GsP8ozFUtrXwK9a1PwGJXRMOJA8UzjfiHanyLEKshbhlbvbMTcTOudh+nNdN1szYeoN7MhRRLk+/0hTJ3pYTM5DVOuIuDd1oZcPuP1XbuYf/tmv9kTvn/3i/58nt+2aMqlZtlgpKS0q8fUX/l3vZ5buIu16g/mOS3C8ixFCsOHclT3VLBR8hDGZTTZ1PMJaFLZnN3Eu5/XMdEHyjJEMe1PoQ9LEUxP1rvd6zYaMTGsWsZ7oaaDLPc+z6hkQvcsyCAteo3eVmkAg4l5WooJYq7kfwOsilTiHkr3TM0YS9ch0QWJ+ZK13kx8LxORsnT3Sy41bRA/LGdWgp/NJq9DiVXpXsEltkbUe1k0WxETvMo2wIMcrPdMDQGsKxe5G0mejUAgQtneLCQhj8Ix5V5uuTnDTyTscDofD4XA4HA5Hxjjj5XA4HA6Hw+FwOBwZ44yXw+FwOBwOh8PhcGSMM14Oh8PhcDgcDofDkTHOeDkcDofD4XA4HA5Hxjjj5XA4HA6Hw+FwOBwZ44yXw+FwAKJSRx6t9HTKfEcXsZbcOzHeSO+mslajAjki6dW8y2okJPdWueuLz8+ErMUE75Qh7uF8673G3e6OUxyXRd9buHW8HD1hYHEJay1KSbTOthKXUmCMZXBpf6Y6UxlY3JdoK4nJOH0AUiYLYUspkkI545JZKUksBWjbOr+Z6nkSwmTtGSUFOiu9eog8MILcfwR/vAqALQZE65YSr1uKHSxmIiuEaK2Tkmn6mnqARIBN748emEslRGIQZKKfiaK15IYj+l+uMvBiFa+SmK7GOsX4ZTkqlwTo/u72L6qyIP+GpPCawhtL9q0HDOHZEeEZMWawuykV4zH5h8Yp7BjDf7GGAHS/z8TlSylftoTG2hJ0cf0bEWpKu8bpf/oopZfGEQaML6mes4jqxkU01vaDzGa9HZl6WJlmmKxzqZIC4wlU1KNyTUm0tYDoyX0vpcCq5KT2ou4VUmDTsqUndaEA4StsU7sHHRJSSXRTW2SvqTyJjjQDS2dekLfbvNfbaycbYa3r3u2UdlendkwSR5qdD+xmxzcf58Hv7SSsR10tmJsFvhCCS2/YyJafvYprbr+YUn+hK/tvh7df3Mv2rz3Cff/3gxx8axjpSUwXe4qb+1u5YTlbf/E6Lr7lQl558QD3/d1OXn9xf9cbDs1Cd3BRiS0fu4SrbjyHg68dYPvfPsKzP36p9btuFSHN/JAv5rjhp6/gup++gnohx7YHXuShR18ljk130hjGiINHUfuPwNEJ4MTlaK1IFso0/Xni9cuI1y3BlvIL0yVpH1sLF29czQdvOI+1q4Z46Jk3+cFDuzkyWul6Y6x5vs5YvYTbr9/EBeesYufr+/jeY7t48+DRrus197d8qI+PXLWJqy9Yz8sTR/j27t3s3L8fKbpj+vzRSbMVjMat69UiPc8IqJ/pMXF5jsqFPqYwPxMma5B/U1F4XeEPS6ywCHtsrml+ppdoGmfFhGfE2NL80ipqmtwjExQeGCN4pgIm9VZTdmclCAPR4hwTVyylfNlSotPmWd7FhtIrE/TvPErphTFknBjmqQuYWykQxqLziurGxVQ3LiJcuXDT18wzaxcP8lOXbOLqDWvZ9cZBvv/obl5653DX8szxeov6Cnz4qo3cuGkDw28e5b57X+CZp99q/a5bkkoJtLbk8z43bd7IDTdupBHr7pdrKc19KSW5+sozuPmmjQxIxUM/eo4HvvcM1Uqju6ZIJAtRWyznX346N3/8MlafsZzHf/IK277/DCPD5a434JtpXH/mcm756MVs2rSS5x/YxX3/+0H2vLy/66ZPeRIdG5auXsQtn76Oy7deyFsv7OHev3mA3Y+83HXT19QrDRbZ8vPXseXT13PB9RuRsjeD1N4P7bUsaNcbOOM1D5zxWhiNWshj9zzH9m88zuP3PU8c6flVPFN6nDZesYFbPvUBrv/oZQyd5J4Tay2vPPUG27/2CNu+/BNGD4+3CtJOaW63+LQhbvn0tWy+4xrOvHj9CSu8731zmPvveob7vrOTfW8dmXch2awgi6UcN374IjZ/5GIuuGIDSh1b4I8cHOPH336c+77yMC8/9ca8Gw7N4/QCxdW3X8KWT13NlbdeRJD3j/ldpdLgJ4+8yj3bX+DJp97CpIV228VXrBGHRpH7jyCOjIE9sSE7E2n7HbOoRLR+KXrNEmwhaD+N6bk5+/Rl3H7j+dxyzTksW3xsHjXG8uwr+/jRwy/yo4dfZKLamLcpam63cukAt1+/iduu3siG1UtO+N2r+4a5+4mX+N6juzg4Wl6AnkQbw2Apz+1XbORDV5zLRRtWnpBH942P872XXuLbu3fz0vBwxw1qrxzT93KNwZcq5A5HJ5qtmZBgTfJ/dZPPxGUB1fMCbDC7YRAh5N9KzdaB5LfiBIs+PTbNWHqFoXFWRLQhxs7l20ND7skyhQfGyD1eRkxjfmbUS01YeFqhZcLixbnZNzKWwhtl+p8epf+Zo8iGaZmrdvV0n09lU2LCoqWFtk1YM68t6y/xsUs28uGLNrJp5bIT8sxbh47ywyde4vuP7ubtw6MLvidK+YAPXn4OH7piI5edvRp1XEN25EiZ++/fzT0/ep6XFtCZ1dzO8xTXXns2t9x6PldddSZB7thBRgsu11KmnrZLLlrHbTefz/XXnk1/37GZLgpjnvzxy+z43k4e+tELRGGMVAKj55/GMzatYusnLuOG2y9m6WmDx/zGGMOuZ/aw4wfPseMHz1KeqM/bhDW3W7FqiFs+ejGbP3QR685YdsLv3nxhT6sD9PA7IwuuewcW97Hl565h8x3XsOkDZ52QRw+9fZgdX32Ie//mAd547u15173N7XKFgOt/5gPc/OkbuGzrhXj+yR2Y9l5vr3UTZ7wyxBmv7lEZr/HQXU+z7euP8cxPXkq6GecI3zcLqNM3reKWT32AGz9+BcvXLO7hUbeP1obnf/ISO772MDv+9hGq47U5K4Lm932LSmy542o2f+oazrvm7LZ6u6y1vPHSAXZ8/xm2fWcnRw6Nz1nRNSveIOdx7dbz2fyRi7nsurPxg/YK/ANvDXP/Nx7lvq88zNsv7Zuz4mn2dkkpuXTzeWy542qu/cilFNvs7Rodq3L/gy9xz7ZdPL9r78wNFWMQw2OJ2To0mjQomyGnedLc0iwbIF6/lHj1YpjmPDUbemtOG+L2G89j67UbWbtyUVsacax5fNfb/OihF9n2+CvUw3jOBmfz+8UDRT503SZuu/pcNm5YcUIjYdo0Wctzbx7g7ide5AePv8Ropda2XiHnc9tl5/ChK87lirPX4qn2emRfGxnhey++yLd272bP2BhKiHQI1rHImqb/1RoDL1XI7wuBE6OTHZEaGeND5YKA8mUB1XN9UOleY8jvkeRfV+T2ytSgi/mPeRNpRFhAvFoTnhUTro+h2a+gLcGzFQoPjJF/aBzRsKCABTym1jSk9fV9TFyxlMolS9D9qaC15N6p0v/0KAM7j6Iqcdtmay69aFGO6qbFVDYuRg+daPqaeWagkOMjF23kwxedy6XrViHbGLZoreXlvcPc/cSLfP/R3Rwemzs63Gwc5nzFlovP4vYrN3LNpvX4nmorXQf2j7J92y7u+dHzvPXW8JyNzWY5JKXgsss3sPXW87nu+nMoFucwwCltl2vTaG485zRuu/l8brrhXJakw97nol4NeWTbLrZ/ZydPPPASRs8deWuW7avWL+WWn76Mmz56MatPP9H8TEccaZ5+7HW2/+BZHrx3F416NKfpa9ZdQ4tL3PyRi9n8oQs5+7xVbZdrLz72Gju+9jDbvvow40cm5qx7m6NK8qUcN/7MB9jyc9dw8Y2bUG3mmbdf3MuOr/yEe//mfva/fmjOUS8tg+4rrvrwZdz8Czdw9UcvI1doL8/0mvd6e22hOOOVIc54ZcPo4XF+/N2n2Pb1x3jxyTcmx4vbyZv3tPVLueVTH2DzJ65gzVmnnexD7ogojHnqvufZ/tWH+MnfPXFM+L5ZQOeKATd88iq23HENl245v+0CfzqMMbz4zB52fP8ZdnzvaSbGaq2KrDUcxZNcccO5bPnoJXxg80byxfYjOdPx1ot72fH1R7n3yw8d29soJhsJ5111Frf83DVc/9NXMLhkYb1dhw5PsP2B3fxo2wu89vrhJF3jVdSbBxAHRhDaLNhsTcuUoWz6tCHis06DVYuSXvxFfXzwxk3ceu1Gzlp/Yi9+J9TDiIeeeYMfPvQiD+58nVibVqSo1YtfCLjtmo3cdvW5XHLumrYasjOhjeGJl9/h7idf4kdPvkS1EbV0mnnG9xSbLzqT26/YyHXnrSdYQI+stZYXDh3iuy++yLd372a4WkUJQf71KkPPlCnuqXcUnexIO43amLxg4sICenEfwSGF0CKbh9Kazy1JSzxUQb11gNyTY8iyTozfPKIOc+lNDrfsxwwUyO+t44+GrbR3m6YJC5cXqJy/hNqly9FA3vf44AVn85GLN3L1GevaNujTYYzl2Tf2c/cTL3H34y8yVq1P5tHWvSG57vzT+fCVG7nxwjMo5Py5dzwLb755mO337eJHP3yOQ8d1ZjXLtfMvWMOtt17AjTdtZHBoYc+GTluupY3b1jC7dUu47ZbzufmmTaxcMTjHHmdnYqzKQz96nm1/9xTPPfYGzcxvpzwbtmTFADd//DI2f/QSNmw8MaLdCY16xOMPvsz2u57l0QdeOma4ZWvURV+OzR+6kM0fupALLlu/oGF2WhuefWA3O772MPd//VFq5XqrbmrWwX7O45qPXs6Wn7uGK267iGABecZay6s732D7lx/k3v/9Y44eGEV5Ch1rhBStYRSXbLmAW37xBq7/xFWUBkvz1jsZvNfba/PBGa8MccYrew7uOcIDf/ck9/3to5THqmz+xBVs/sSVnHnh2gUV+KcK9WqDR+96mu1ffYhnf/wil2w+j5t//lqu+tDFBPmFmZ/p0LHm6UdfY8f3nuHR7bvZcO5Kbv6pS7l26/n0D3Z/XLW1lpefeoMdX3+U+7/xGEPLBrjl56/hxk9exbLV2fR27XlnhG337+Zv/8VXaLxxIBONaRFA3uf2//Sr3HrdRi44p70e2U4p1xo88ORr3P2T3ex6/QDXXryBD167kQ9csB5vAQZ9JsIo5qHdb3H34y/y0O63uGjDSj5y1SZuuvAMihnkUWMtT+zdy3dffJHH/sEPkKHp2XRetTOWYPoCFhhPaxv1wluoNw72SA1QCuktzHzMh9P/1Qf52Q9/gBvP2UAugyFTsTY8/tIefvDEizzw7OucvWYpH7lqEzdfchYDxYU/k3k81lpeenE/2+57ge3bdrFoUYmtt13Ili2bWLY8m7ZAs1z70bZdaG249ebzuOWmTZy+fmkmeiOHJ/jxXc9w3989xeF9o9zw4YvY/NFL2HTpiUPcu0GlXOfh7S+y7a5nePn5vVx5/dls+fDFXHb1mXh+BuVaI+LJe55j+1cf4sl7n2PjB87ilp+/jms+ehmFvu7nGWMMzz/4Itu//CA//uajrDpjBbd85kZu/NQ1LFq+MMN8qvBeb6+1izNeGeKMl8Nx6vKv7vgPPPiNR7s26Uc7DCzp5xuH/1vP9N7rfOK836NeDXumVztrKWaB0d5O8J5/E/X24Z5NC49USL/3xuvPH///seGCtT3XdTgcjl7Trjdw63g5HA6Hw+FwOBwOR8Y44+VwOBwOh8PhcDgcGeOMl8PhcDgcDofD4XBkjDNeDofD4XA4HA6Hw5Exzng5HA6Hw+FwOBwOR8Y44+VwOBwOh8PhcDgcGeOMl8PhcDgcDofD4XBkTPdXNHQ4HI6TjO3VyrvvcbQ2PPX0Wzy5803O27iKq686kyDIrtqw1rLrrYNEsc5MYwbh3uqdLM0e88yud1i7cVUmC3w3Mcby/Avv8Mijr3HmGcu49pqzKRSyXZPtjZcP8JMfPc/gohI3fOhChpb0Zap3aO9RHvj+05hYc8NHL2HlumwWT24yfmSCB7/9OIf3HuXaj13OWZdks3iyw/F+5H23gPLpp5/OW2+9dcxnv/d7v8e//bf/tu19uAWUHY5Tl+/++Y/48zv/O2E9QiqJ0SYTHSEFTX936y/dxO/8t3+QiU6vMcbywu69bNu+m/t27GJioo6UAmP6CBt0AAAwTElEQVQs+bzPTdefy81bNnH5paejVHcGTby+/wh3P/4S3390F/tHJhh8epjc/ioAmTb3BGChsaKfaEUfILLVS+SQ+47gP/sGIjYgRaYLKVtACIHI5RCWVpoz0xMgLJj+PLWrz6JvsMgt129k6/UbuXjTGqRc+Bm21vLKqwfZtn0X9977AiNHK6086vuK6649m1tuPo8rrzijax0F+94+wv13Pcu27zzFO28MI6VIFmkXgos/cAY3f+xSrt16PqX+fFf0Rocn+PFdz7DtW0/w4s63EDLJm8ZYzrpgDTd/4gpu+tglLF4+2BW9WrnOw999kvu++hBP3fs8RptW+XnahmVs/YXr2XzHNaw9Z2VX9ByO9xrteoP3pfH61V/9VT73uc+1Puvr66Ovr/0eK2e8HI5Tm1qlziPffZJt//ePefzunWhtkCJpmC0IkTRirbGce+VZbP3Mjdx0xzUsWjHUleM+WVhree31Q9y3fTf3bHuBI0fKKCXR05jW5uf9fXlu3rKJWzafx/nnre64Qb3vyBg/fOIlvv/obl7fP9JqODeR9Zj8/ir5fRX88YimZ1gwIg02SaiuKVA+o0RtTR6EIDdqyY9Y/Anb/GlXaKYqGhBUlwnqiwVWWIovH6X09DDF3SMIbbtmwprmRy8qEp65lPD0pdhSgHe4TPDWUXJvHUVEXTR9qZmzeZ9o9SLiVUPY/kLr62aeWTRY5LYbN3Hr9Zs498wVHUdR3t5zhG3bd3HPvS+wf/8oSgm0PvH4m3qFQsDmmzZy85bzuOTidR13FBw5NM4DP3iWbd/Zyau79p2QR5s0P/c8yVWbN7L5o5dw1U0byeX9jvQq4zUe+uFzbPv2Ezzz8KtpRk0N3hSS0yawWC646gxu/ukruP72i+gfKnWkFzYinvjhM2z76sM88v2niBrxjJ1Vzc9Pv2AtWz99HTd96mqWr13SkZ7D8V7GGa8ZOP300/mt3/otfuu3fmve+3DGy+F49zBxtMyD33yU+/73j3n2/l2tzzsp+pqNjvXnrWHrZ25k889fx2mnL8/icHvK3r1HuW/HLn507/Ps3TeKkgLdQUO82cBdvLjErbeczy2bz+OsM5fP2KA+Ml7hnidf5vuP7uaFtw4iRdKonEtRVaKWCfMqcctYtI2YHNlXX5WnfEaRyroiNpi+IS4iS37Ukh8x+BXmZfqa20QlqC6T1JcKTDD9XkRDU9w9QunpwxReGQWbNq47SKMVAmEtpj9HeMYywjOWYgYL0//YWPwD4wRvHiV4ZzQxfUJ0Nvwx/b0NFPGqxGyZwWLTFcxIM4+dtmyAD910Hltv2MTpa2ZuwB86NM62Hbu5597neeONwzOanxn10jw6MFDglpvP4+Yt53HeplUz5tGJ0SoP/uh5tn33aZ5/8o00BmrbPjXNsiKX97nutgvY/JGLufSas/D86YdbNuohj923i+1/9ySPb9tNHOuO0ihk0hEkleTyG89ly09fztVbL6BQyk37ex1rnr5/Fzu+9ggPfONR6pUGypPouM2RAVM6nzZ94Cxu+fR13PDJqxha5tpCjvc3znjNwOmnn06j0SAMQ9auXcunPvUpfud3focgaH9MuDNeDse7k5EDR7n/aw9z7988wMtPvDZrA6fZGFm+fim3fuYmtnz6Otaft7bHR9x9Dg9PsP3+3dxz3wu8+tqhjhuyM9Hcz6qVQ9y29Xxu3nwea9csZqJa576nX+WuR1/kyVf2tBzJfGsebyIkvy8xYaquZzVhTfNTXxZQPrNEZX0RU+jseSPZsOSPJibMq89uwprfxQWoLZPUlgp0vjPLJqsRxeeP0Pf0MLk3x5OG7kznKjU/puATnrGU6Iyl6MWlOc3PMcSGYN8YwZtH8feNzW76mmbLk8Qrh4hXDmGW9HWmN4VmntmwdgkfvOk8tl63kdOWDzI6WuX+B17knnufZ9fufUlDvwtNlaYJW7q0n1u3ns/NW87jjA3LqFdDHtm+m+3ffZonf/IKRpuWoemGXqk/z00fvpjNH7mY8y9fj9GWpx58iR1/9xQ/ufvZrg2Lbu7Dz3lcc+sFbPn45Vx240b8QLH70VfZ/tWH2f61h5gYqXRmtmZApMMthRBcuuV8bv70dVz7scspDcxg+B2O9zDOeM3AF7/4RS677DIWLVrEY489xu///u/z8Y9/nP/6X//rjNs0Gg0ajUbr/fj4OGvXrnXGy+F4F7P/jYPs+MpD3Ps39/P27r1IJRECdGwYWj7ALb9wA1s+fT3nXHHme+LB8nK5zj//w2/y9LN7Wu3krEp/mUY11BklxmWMNjYZ6tlNQWvxR0Py+yvk91WRkcGm6RIWwiGf8pklyhuK6L7uPOejapb8UUN+xKLC1Js0h/UFUFsmqC2VxKXu5Bc11qD03BH6nj5MsK+Spi+JbNlAEW5YSnjGUvTy/nmbn6mIMMZ/Z4zcm0fxDk4knzWHEUqBXjFIvGoIvawfZPcmRRapkDWGIelTG2+0GvRZNVGapm8otkRHKsSRRiqBmWboYjdoDX/MKUwjolELZxzO2009X1i8Wo3KWLUrZmsmmqbPCzxu+fS13Pnnn5t7I4fjPcT7ynj9y3/5L/nDP/zDWX/z+OOPc8UVV5zw+Te+8Q1+9md/luHhYZYsmX64w0z7d8bL4Xhv8OYLe9j+5Qcpj1a48Wev4YIbNqJUdjOxnQx2v7iP3/xH/6tnelbA+IZsZ5ebFLMERxqosSq6IKlsKBENdfZ8Tad6Xg2CsaQRW18qifroivmZCe9IjcFHhgkON4jWLSZeNdhV83M8ohaRe/Mo/uEqelk/evkAZDg7IQDGEpR7O6NlsHess2GrC6XRyHQyleMx42Wo1Xum1+SHtd6VNQ7HqcD7yngNDw8zPDw8629OP/108vkTZxvau3cva9as4ZFHHuEDH/jAtNu6iJfD4Xi38542Xim1pWB76Je1D/RQL3fEUjjSOz0ZWfxqD5sIznh1HTNehno905ksp8MZL8f7jXaN13tiHa+lS5eydOn81rXYuXMnACtXzjxFai6XI5eb/kFVh8PhcDgcDofD4ZiL94TxapeHH36YRx55hC1btjA4OMjjjz/OP/7H/5if+qmfYt26dSf78BwOh8PhcDgcDsd7lPeV8crlcnz1q1/lD//wD2k0Gqxfv57Pfe5z/O7v/u7JPjSHw+FwOBwOh8PxHuZ9Zbwuu+wyHnnkkZN9GA6Hw+FwOBwOh+N9RnZTIjkcDofD4XA4HA6HA3DGy+FwOBwOh8PhcDgyxxkvh8PhcDgcDofD4ciY99UzXg6Hw/F+RWS4uO8xWIuoR6ixGkvebBANBNTX9BEuK4DM5hhkaAjGNX1va3QgqK5Q1JcqrJdRmg3ICFQleasLoHNktqaXiC2Fw4b+t2O8KkQlSVRSWD+r9Fn8kRr5vRW8sQZ6qEC0pIgpBdksEm0tcryGt38Mb7iM7StglvRjB4qZLkr9XsYag63WMOUyaI2QCpTqSTngLpnDMTPOeDkcDsf7gDM2LOPTd3yAH97zPCNHKygl0dp0bf+iESPHqnhjVUQ4uQiuV4ko7KtgPEF9VR/11SWiJfkFt85EbAnGNcFYjFe3WEAAftWSP2qwIqK+RFJd4VFfLEEtsDVoE7MlIxBxegzpV3IC/AmwAcT51IQtdDyJsRSGDcUDmuIhgzCpoAU5qsmPJiazZcIWajKtxRttkNtfIXeggoxNS08dnCA4MIHJeURLi8RLSpiCv8AEgizX8Q6M4R0YR9ajlh6NCDU8jvUkZskAZnE/tm/heaaJUgKtLfm8zznXnUV1/zh7XjuElAKTweLGzf0qT3L2BWeg6yGvPv9O+q3FdknSGout1zCVKtTqx36nY9AxVkqElCC7a8KkkhhtWL9pNR/7ja1d26/D8V5DWNutW/79Q7urUzscDsephjGW5194h/t27Gbbjl2Uy435m7BIo8aqqLEash61zM9MWAHCggkktdV91Ff3EQ+1H0UR2uJPaIIxjVdNjnfWLdOGvJFQW6aorVA0hmT7kTebmCwVJv+3s1WzQjXBlEhYu+1ba8mNGEoHEsMl9WQa5tTLCcI+RVyU2HZNprWoiZDcgQr5/RVkQ7eu0Wx6AjAFn2hpiWhJEZtrvw9XVEO8g2P4+8eQ1XBOvWb6beBhlvRjlgxgC51H3prmx/MU11x9FrfcfB5Xf+BMgiA59nfeOMz9dz3Lfd/Zyf63jyy4Y2Ly8AQXXbWBLR+7lOtuPZ++gQIAI4fG+fH3n+a+bz3BK8/umbfps9Zi63VspYqt1cHaOfNMCymTSJiU8zJhypPo2LBi/VJu+YXr2fypq1m/aXXH+3E43gu06w2c8ZoHzng5HI73AnGseWrnW9y3Yxf3//glGo24FQ2YeSONGq+jxqqIagi07yum0mxw64JHbU2J+uo+dH9w4g+NxS9rcmMar9yG2ZqJpgnzoLo8MWHhgDyxAZ+arVZ0az5ax+4OnUtMmAmmOXhrCcYsxQOa0n6NipjbjMyiBaALgrCUmLDpTKasRInZ2ldG1eIF6QlA9wVES0rES4pY/8TxlqIR4R0cxzswhhqvz2nQ59TN++glA5gl/ZCfJs80dYXAWosQgisuP51bbj6f6649m1IpN/O+reX1F/ez4/vPsO27Oxk5NNGRCWsaqHMuXMPNP3UpN3zwQhYv6591mwN7jnD/d3Zy37eeYM+rB1vRo9mOkUaIqVax1Sp0I0rXpglrmq2hZQPc/PPXsvmOazjn8g29G8rscJyiOOOVIc54ORyO9xqNRsQjj73Gvdt28chjrxHHZrIXXhvkRGK2ZLkBLNyQTKXZ8I/7fGpr+qivKqKMwh+PCSZMYgra7cXvQE8HUF3hUVumiAsCGYvEbGVUK1oBOp+8VMO0zJZXn7/ZmlUPiIuSqCQx0pA7WCW3v4w3MXd0cj5aAHogR7S0RNyXwxsp4x8YQx6tQpf1WrrFHHppMhyRNHrVNFwXnL+arbdcwE03nsvgYLHjfRtj2P3029z//WfY/r1nKI/XpjVhUgmMtqw9Yxlbf/oybvrwxaxYvWhe6Xnr5f3s+M5O7vvm4xzeN9rSs9ZCFGEqqdnq4jDhE5ASoRSIxIQ1zVZxoMDmT13N5juu4YLrzkUpNz+bw9HEGa8MccbL4XC8lylXGjz40Mvcu20XTz3xBsFL+xGmgyFMC8AC0dolkPN7oydgYp1PY3HvHnkuHIrp2z//SFOniEoDb+9I8nfmYmC1gUo1MXc9yjMA8cY1bLhkPbdtvYAtmzexbFn36mcda55+5DV2fP8ZHvzh89RrSbR32cpBbvmpy9j8kYtZf/aKrulZa3n5mbfZ8Z2n2P7tJxl5bS92oty1/beFEOQH+rju41ew5eeu5bJbLsAP3NQADsd0OOOVIc54ORyO9wsv73qHf3TbH/VUMzxjOcje9aYfPSdHXOqdXt87EYURnbkhaSJHq3iHxnojBlitoVrrmV6TX/23n+ZT/8etmeuEjYidD73K0JI+zrlwTebD7LQ2/O5tn+fZHc/3LM8AFPoLfG3/X5Ivzjw00+FwJLTrDVzXhcPhcDhmZHCg8yFa7zbc0ynvDRYv7uuJTpDz+cCWTT3RAlBKMrikD4HA9tB5+YHnTJfD0WXcAF2Hw+FwOBwOh8PhyBhnvBwOh8PhcDgcDocjY5zxcjgcDofD4XA4HI6MccbL4XA4HA6Hw+FwODLGGS+Hw+FwOBwOh8PhyBhnvBwOh8PhcDgcDocjY5zxcjgcDofD4XA4HI6MccbL4XA4HDOSy/tAspZQL5BKgrFkvCbtsWgL1vZscVorE7me0eOFypp5RfYozygv0ckVgp7onQzyxRwWi5C9uZhSSfIlt4aXw9Ft3ALKDofD4ZiRoaX9/Mfv/zbbvvE427/1BONHKyhPomPTNQ0pBcZYPF9x5c3ncfGWTYw0Yu699wUO7B9DKYHWXXYqqfMRjYhFT5WpL/GorS0RDQXpd6K7hsVaEAKvrFGVCFnXoCQ2NQ1dd5qpHmEElRomihCA8Lzua5GYLa0NfYMFbvr45Zx+9gpee/pN7v/mY1THa93PM0pitMHP+Vz70UvZ8qmrueqDF3Vt/6ca/+A/fpZNV5/Dff/7AV74yUsIKbBd7ixontPVZ5/G1s/cxNa/d2P3du5wOAAQ1va03+09wfj4OIODg4yNjTEwMHCyD8fhcDh6gtaGZx9+hfu//SQPfHcntUqj1VjrFCFFyxxcfO3Z3PzJK7n2QxdRGii0fmOt5ZWXD7Dtvl3ce8/zHD1aaTXw50WqJ8IYWf5/2rv3qKrq/P/jr3MOcOQiKGJcRMXGa0Ghpol5Qc3bz+vU11FnpqW/phqbtJzR1sqmUpucrExtbPo6TWXlml9Yk/VtyppSATUvo2hFlnlJRAX06w0I5QDnfH5/KKeOgBzRLaDPx1pnrdj7s/fns9/shNf5nP2hVPYSl2znnasiNECn40N0pk2oKsICJY+R6jrLcK4/xxm3ggvKFHy0TIGnffszNskT5JBxBpwNYZWB6RL6U4Vb9uNFsp0olu20q2p+DAiQLTBAcjjOfl3H/iq/987gIPUZcbNSx3RXcp9OCgh0eNuUl1Voe/pOZbyzWZ//a7vKSsvrfM9UBnS7w64eg5M0YFwv9RqefM3NzPzvoePKWLFRq5dn6vuvDtS5npK8gbhFq0jd/qu+GjCxj66/qa1sV3TKGWj8/M0GBK86IHgBuNaVucq1PXOX0t/fpk3/zla5q0J2h02eWmambHabjMeoc7cEDbzjFvUd2VXNoprW2p/HY/R19kGtXbNTa9d8o5ISl38hrDJslVfI/oNL9pJS2fyYeTGSKsIDvSHMHRzgXwg715+9zHM2bB0pU+APbr8mz4zddi6EOWQcfoawyjZuj2wnimU/WSxb8Rn/J+sCA2QLDJSx288eU0t/P52d7Hn7jRow9hb1GHiDXx/zKz3t0n8++VJr39msrZ9my13h9p6vJj8NAEm3ddTA8Sm6bVR3NW0e6u8VXtUO7c5TetrnWr18nfL2Ffg1s+gIcMhd4VbTyDANnNhHAyb2UZdeHWS38/QJUFcELwsRvADgR6WnXdr86ddKf2+btmV8K4/b4w1YkryBrG2nWA26s4f6je6m6PjIOvdXUeFWVlaO1q7eqfXrdsnlqvD9Bf6nMz/nwpa93F3n/oykssggnYkP1ZnWIfIEOXxDUeUnE8s9Cj5apuAj5QoqrLikTyoau00ep0MmqDKE6cePPlb27fHIdqpE9hPFshWVyHYpP81tNt+ZsJ9cn81mO/t8kc2mrn06acAdtyhlaJJCmwbXctKa/XDqtDZ+mKU1Kzbrq/W7JJ3tz3vPnPt+duiaoEETUtTv5z0VGR1xCRd4dTPGaN+XOUp/63Ot+cc6Hc876RPC7AF2eSo8ahLWRP3/q5cGTOyj5AGJcgQ4ajkzAH8QvCxE8AKA6hWfLNHnH3+ptSu3KXvLXkXHR2rQnT2VOra7WrePvuz9uVzl2rxpr9au3qkNG3ZLbs/ZoFXiks11aeGnOkaSq6XzbAiLD5FsNjU5evZjhM6TFZcWfmroT46zM2GeILtkt8tWeC5sFZbIdoHZojqz2bwzYbLb1TG5jQb/4lb1GZGsZi1qn528WCePFmr9+9u0Jm2Tvsv6Xq07xWrQ+N5KvbOnYhJaXvb+rnYej0ffbNqttf9vg9LTNsh12qWU0bdo4MS+6jEsWUFNrt5FSID6QvCyEMELAGpXeqZMziaBV+x5kUl9/qQjh05csUX8PKFNpKDAyx62amJOFkmFxbJd4R/baXsXq1nLK/OzrrTEdc09s2Uld4VbbrdHQc7A+h4KcFXzNxuwqiEAwBJNrvDy3jZd2ZXTbUZXbAl66dy1XeXvlRK6Li9HgIOPEwINCE9SAgAAAIDFCF4AAAAAYDGCFwAAAABYjOAFAAAAABYjeAEAAACAxQheAAAAAGAxghcAAAAAWIzgBQC4KrRPipck2R3W/2iz222S2yNJstltlv8BMbvDLgUFevux2a3t0BFwtoYtW0UqpGmwpX0BwLWCP6AMALgqPPbf/1f7d+Up84MdWrNyq44VFMrhsMt9LiBdKrvDLo/bo/ifXadBd9yi/qO6yhEYoMxPsrXmoy+Vs/eo7HabPJ7L80eOK8ceGRWmgSNuVurwm9SyZZg+/yBLa1ds0teb98gmmyRzWf6usiPALneFRyFNm6jfz3sq9b9uVVKfTnJcgSALANcCmzGX45/ra0tRUZEiIiJUWFio8PDw+h4OAOA8xhjt2nFAmR9sV/r/ZKno5GlvsLgYleEnKraZBv38FvUf3VXtOsdV2/bg/v9VxsfZWv3RFzpy+FSdQl/lMWFNmyh1WJJShyfphuQ2sturhp9jeSe17r2tWpO2Ufu+yq1T6KsMk4HOQPUe2VUDxvVS90GJCgzifVkA8Je/2YDgVQcELwBoPNxuj77avFeZH2zXug+/0JkS1wVDUeW+8MhQDRjdTf1Hd1Pnrm1ls/n38T5jjPZ+m6+MT77Smo++1KnjJRfsz+6wyeM2cjYJVJ/bb9CA4Tcpuef1Cgh0+H2NefuOKGPlf7QmbaMO7z3iDVTV9ncuoNkddvUYnKQB43qp1/BkNQl1+t0fAOBHBC8LEbwAoHEqc1Vo+7pdSv+fLG369GuVl1XI7rDJJpvcbo+CQ53qNzJZ/Ud300292l/yx+w8Ho927shVxifZyvg4WyU/lMrhsMsYI4/HKCDArlv7ddKA/3OTevTpKGeTwEvqzxij/TsPKeOfW7R2xSYdyzspR4BdHvePP+qTbuuogeNTdNuo7mraPPSS+gMAELwsRfACgMbvTIlLm1d/rXX/2iFncJBSR3dTt36dFeS05mN2FeVu7diyTxkfZ6u46Iz6DUlUyoDOCg1rYkl/xhh9u3WfMv65RQe+PayUEV3V7+c9FRkdYUl/AHCtInhZiOAFAAAAQPI/G7BUEQAAAABYjOAFAAAAABYjeAEAAACAxQheAAAAAGAxghcAAAAAWIzgBQAAAAAWI3gBAAAAgMUIXgAAAABgMYIXAAAAAFiM4AUAAAAAFiN4AQAAAIDFCF4AAAAAYDGCFwAAAABYjOAFAAAAABYjeAEAAACAxQheAAAAAGAxghcAAAAAWIzgBQAAAAAWI3gBAAAAgMUIXgAAAABgMYIXAAAAAFiM4AUAAAAAFiN4AQAAAIDFCF4AAAAAYDGCFwAAAABYjOAFAAAAABYjeAEAAACAxQheAAAAAGAxghcAAAAAWIzgBQAAAAAWI3gBAAAAgMUIXgAAAABgMYIXAAAAAFiM4AUAAAAAFiN4AQAAAIDFCF4AAAAAYDGCFwAAAABYjOAFAAAAABYjeAEAAACAxQheAAAAAGAxghcAAAAAWIzgBQAAAAAWI3gBAAAAgMWuquA1b9489e7dWyEhIWrWrFm1bXJzczVq1CiFhoYqKipKDz74oMrKyq7sQAEAAABcUwLqewCXU1lZmcaNG6eUlBS9+uqrVfa73W6NGDFCLVu21IYNG3T8+HFNmjRJxhgtWbKkHkYMAAAA4FpwVQWvuXPnSpJef/31avd/+umn+uabb3Tw4EHFxcVJkp5//nlNnjxZ8+bNU3h4+JUaKgAAAIBryFX1UcPabNq0SYmJid7QJUlDhw6Vy+VSVlZWjce5XC4VFRX5vAAAAADAX9dU8CooKFB0dLTPtubNmysoKEgFBQU1Hvf0008rIiLC+2rdurXVQwUAAABwFWnwwWvOnDmy2WwXfG3bts3v89lstirbjDHVbq80a9YsFRYWel8HDx6s07UAAAAAuDY1+Ge8pk6dqgkTJlywTUJCgl/niomJ0ZYtW3y2nTx5UuXl5VVmwn7K6XTK6XT61QcAAAAAnK/BB6+oqChFRUVdlnOlpKRo3rx5ys/PV2xsrKSzC244nU517979svQBAAAAAOdr8MHrYuTm5urEiRPKzc2V2+3WF198IUlq3769wsLCNGTIEN1www2666679Nxzz+nEiROaOXOm7r33XlY0BAAAAGCZqyp4PfHEE3rjjTe8X3ft2lWSlJ6ertTUVDkcDn300Uf63e9+p9tuu03BwcH65S9/qQULFtTXkAEAAABcA2zGGFPfg2hsioqKFBERocLCQmbKAAAAgGuYv9mgwa9qCAAAAACNHcELAAAAACxG8AIAAAAAixG8AAAAAMBiBC8AAAAAsBjBCwAAAAAsRvACAAAAAIsRvAAAAADAYgQvAAAAALAYwQsAAAAALEbwAgAAAACLEbwAAAAAwGIELwAAAACwWEB9D6AxMsZIkoqKiup5JAAAAADqU2UmqMwINSF41UFxcbEkqXXr1vU8EgAAAAANQXFxsSIiImrcbzO1RTNU4fF4lJeXp6ZNm8pms9XLGIqKitS6dWsdPHhQ4eHh9TKGqxn1tR41th41thb1tR41thb1tR41tlZDqa8xRsXFxYqLi5PdXvOTXMx41YHdbld8fHx9D0OSFB4ezv/IFqK+1qPG1qPG1qK+1qPG1qK+1qPG1moI9b3QTFclFtcAAAAAAIsRvAAAAADAYgSvRsrpdGr27NlyOp31PZSrEvW1HjW2HjW2FvW1HjW2FvW1HjW2VmOrL4trAAAAAIDFmPECAAAAAIsRvAAAAADAYgQvAAAAALAYwQsAAAAALEbwasBycnL0m9/8Ru3atVNwcLB+9rOfafbs2SorK/Npl5ubq1GjRik0NFRRUVF68MEHq7TJzs5W//79FRwcrFatWunJJ58U66qcNW/ePPXu3VshISFq1qxZtW1sNluV19KlS33aUOPq+VNf7uHLKyEhocr9+sgjj/i08afmqNlLL72kdu3aqUmTJurevbvWr19f30NqtObMmVPlfo2JifHuN8Zozpw5iouLU3BwsFJTU7Vz5856HHHDtm7dOo0aNUpxcXGy2Wx6//33ffb7U0+Xy6Vp06YpKipKoaGhGj16tA4dOnQFr6Jhq63GkydPrnJP9+rVy6cNNa7Z008/rR49eqhp06a67rrrNHbsWH333Xc+bRrrfUzwasB27dolj8ejv/3tb9q5c6cWLVqkpUuX6tFHH/W2cbvdGjFihEpKSrRhwwalpaXp3Xff1YwZM7xtioqKNHjwYMXFxWnr1q1asmSJFixYoIULF9bHZTU4ZWVlGjdunO6///4Ltlu2bJny8/O9r0mTJnn3UeOa1VZf7mFrPPnkkz7362OPPebd50/NUbMVK1Zo+vTp+uMf/6gdO3aob9++Gj58uHJzc+t7aI3WjTfe6HO/Zmdne/c9++yzWrhwoV588UVt3bpVMTExGjx4sIqLi+txxA1XSUmJbr75Zr344ovV7venntOnT9d7772ntLQ0bdiwQT/88INGjhwpt9t9pS6jQautxpI0bNgwn3t61apVPvupcc0yMzP1wAMPaPPmzfrss89UUVGhIUOGqKSkxNum0d7HBo3Ks88+a9q1a+f9etWqVcZut5vDhw97t7311lvG6XSawsJCY4wxL730komIiDClpaXeNk8//bSJi4szHo/nyg2+gVu2bJmJiIiodp8k895779V4LDWuXU315R6+/Nq2bWsWLVpU435/ao6a9ezZ00yZMsVnW+fOnc0jjzxSTyNq3GbPnm1uvvnmavd5PB4TExNj5s+f791WWlpqIiIizNKlS6/QCBuv8392+VPPU6dOmcDAQJOWluZtc/jwYWO3280nn3xyxcbeWFT3+8GkSZPMmDFjajyGGl+co0ePGkkmMzPTGNO472NmvBqZwsJCRUZGer/etGmTEhMTFRcX5902dOhQuVwuZWVledv079/f54/LDR06VHl5ecrJybliY2/spk6dqqioKPXo0UNLly6Vx+Px7qPGdcc9bI1nnnlGLVq0UHJysubNm+fzMUJ/ao7qlZWVKSsrS0OGDPHZPmTIEG3cuLGeRtX47dmzR3FxcWrXrp0mTJig77//XpK0f/9+FRQU+NTb6XSqf//+1LsO/KlnVlaWysvLfdrExcUpMTGRml+EjIwMXXfdderYsaPuvfdeHT161LuPGl+cwsJCSfL+/tuY7+OAeusZF23fvn1asmSJnn/+ee+2goICRUdH+7Rr3ry5goKCVFBQ4G2TkJDg06bymIKCArVr187agV8F/vSnP2nQoEEKDg7WmjVrNGPGDB07dsz78S1qXHfcw5ffQw89pG7duql58+b6z3/+o1mzZmn//v165ZVXJPlXc1Tv2LFjcrvdVeoXHR1N7ero1ltv1ZtvvqmOHTvqyJEjeuqpp9S7d2/t3LnTW9Pq6n3gwIH6GG6j5k89CwoKFBQUpObNm1dpwz3un+HDh2vcuHFq27at9u/fr8cff1wDBw5UVlaWnE4nNb4Ixhj94Q9/UJ8+fZSYmCipcd/HzHjVg+oeJD7/tW3bNp9j8vLyNGzYMI0bN0733HOPzz6bzValD2OMz/bz25hzixJUd+zVoC41vpDHHntMKSkpSk5O1owZM/Tkk0/queee82lzLdX4cteXe7h2F1Pz3//+9+rfv79uuukm3XPPPVq6dKleffVVHT9+3Hs+f2qOmlV3P1K7uhk+fLjuvPNOJSUl6fbbb9dHH30kSXrjjTe8baj35VWXelJz/40fP14jRoxQYmKiRo0apY8//li7d+/23ts1ocZVTZ06VV999ZXeeuutKvsa433MjFc9mDp1qiZMmHDBNj99dz8vL08DBgxQSkqKXn75ZZ92MTEx2rJli8+2kydPqry83PtOQExMTJV0Xznlff67BVeLi63xxerVq5eKiop05MgRRUdHX3M1vpz15R72z6XUvHI1rb1796pFixZ+1RzVi4qKksPhqPZ+pHaXR2hoqJKSkrRnzx6NHTtW0tl3r2NjY71tqHfdVK4WeaF6xsTEqKysTCdPnvSZLTh69Kh69+59ZQd8lYiNjVXbtm21Z88eSdTYX9OmTdMHH3ygdevWKT4+3ru9Ud/H9fFgGfx36NAh06FDBzNhwgRTUVFRZX/lQ/J5eXnebWlpaVUWJmjWrJlxuVzeNvPnz2dhgvNcaHGN8y1ZssQ0adLEu9gDNa5dbYtrcA9b51//+peRZA4cOGCM8a/mqFnPnj3N/fff77OtS5cuLK5xmZSWlppWrVqZuXPneh+if+aZZ7z7XS4Xi2v4STUsrnGhelYuSrBixQpvm7y8vHpflKChOr/G1Tl27JhxOp3mjTfeMMZQ49p4PB7zwAMPmLi4OLN79+5q9zfW+5jg1YAdPnzYtG/f3gwcONAcOnTI5Ofne1+VKioqTGJiohk0aJDZvn27Wb16tYmPjzdTp071tjl16pSJjo42EydONNnZ2WblypUmPDzcLFiwoD4uq8E5cOCA2bFjh5k7d64JCwszO3bsMDt27DDFxcXGGGM++OAD8/LLL5vs7Gyzd+9e8/e//92Eh4ebBx980HsOalyz2urLPXx5bdy40SxcuNDs2LHDfP/992bFihUmLi7OjB492tvGn5qjZmlpaSYwMNC8+uqr5ptvvjHTp083oaGhJicnp76H1ijNmDHDZGRkmO+//95s3rzZjBw50jRt2tRbz/nz55uIiAizcuVKk52dbSZOnGhiY2NNUVFRPY+8YSouLvb+OyvJ++9B5Rsv/tRzypQpJj4+3qxevdps377dDBw40Nx8883VvgF8LbpQjYuLi82MGTPMxo0bzf79+016erpJSUkxrVq1osZ+uv/++01ERITJyMjw+d339OnT3jaN9T4meDVgy5YtM5Kqff3UgQMHzIgRI0xwcLCJjIw0U6dO9Vl22xhjvvrqK9O3b1/jdDpNTEyMmTNnDjMF50yaNKnaGqenpxtjjPn4449NcnKyCQsLMyEhISYxMdEsXrzYlJeX+5yHGlevtvoawz18OWVlZZlbb73VREREmCZNmphOnTqZ2bNnm5KSEp92/tQcNfvrX/9q2rZta4KCgky3bt28yxzj4o0fP97ExsaawMBAExcXZ+644w6zc+dO736Px2Nmz55tYmJijNPpNP369TPZ2dn1OOKGLT09vdp/cydNmmSM8a+eZ86cMVOnTjWRkZEmODjYjBw50uTm5tbD1TRMF6rx6dOnzZAhQ0zLli1NYGCgadOmjZk0aVKV+lHjmtX0u++yZcu8bRrrfWwz5twT6gAAAAAAS7CqIQAAAABYjOAFAAAAABYjeAEAAACAxQheAAAAAGAxghcAAAAAWIzgBQAAAAAWI3gBAAAAgMUIXgCAejVnzhwlJyfX9zB8JCQkaPHixd6vCwoKNHjwYIWGhqpZs2Y1bmtoMjIyZLPZdOrUKb+PSU1N1fTp0y0bEwBcqwheAADL2Gy2C74mT56smTNnas2aNZaPZc6cOd5+AwICFBUVpX79+mnx4sVyuVw+bbdu3ar77rvP+/WiRYuUn5+vL774Qrt3765xW32qLjD17t1b+fn5ioiIqJ9BAQC8Aup7AACAq1d+fr73v1esWKEnnnhC3333nXdbcHCwwsLCFBYWdkXGc+ONN2r16tXyeDw6fvy4MjIy9NRTT2n58uXKyMhQ06ZNJUktW7b0OW7fvn3q3r27OnTocMFtF6u8vFyBgYF1Pr42QUFBiomJsez8AAD/MeMFALBMTEyM9xURESGbzVZl2/kfNZw8ebLGjh2rP//5z4qOjlazZs00d+5cVVRU6OGHH1ZkZKTi4+P12muv+fR1+PBhjR8/Xs2bN1eLFi00ZswY5eTk+LQJCAhQTEyM4uLilJSUpGnTpikzM1Nff/21nnnmGW+7n37UMCEhQe+++67efPNN7yxdddskqbCwUPfdd5+uu+46hYeHa+DAgfryyy+956281tdee03XX3+9nE6njDF+H7d8+XIlJCQoIiJCEyZMUHFxsbdmmZmZeuGFF7yzejk5OVU+anj8+HFNnDhR8fHxCgkJUVJSkt56661L/C4DAPxB8AIANDhr165VXl6e1q1bp4ULF2rOnDkaOXKkmjdvri1btmjKlCmaMmWKDh48KEk6ffq0BgwYoLCwMK1bt04bNmxQWFiYhg0bprKysgv21blzZw0fPlwrV66sdv/WrVs1bNgw/eIXv1B+fr5eeOGFarcZYzRixAgVFBRo1apVysrKUrdu3TRo0CCdOHHCe769e/fq7bff1rvvvqsvvvhCkvw6bt++fXr//ff14Ycf6sMPP1RmZqbmz58vSXrhhReUkpKie++9V/n5+crPz1fr1q2rXEtpaam6d++uDz/8UF9//bXuu+8+3XXXXdqyZctFfX8AABeP4AUAaHAiIyP1l7/8RZ06ddLdd9+tTp066fTp03r00UfVoUMHzZo1S0FBQfr8888lSWlpabLb7XrllVeUlJSkLl26aNmyZcrNzVVGRkat/XXu3LnK7Filli1byul0Kjg42DtLV9229PR0ZWdn65133tEtt9yiDh06aMGCBWrWrJn++c9/es9XVlam5cuXq2vXrrrpppv8Ps7j8ej1119XYmKi+vbtq7vuusv7bFxERISCgoIUEhLinU10OBxVrqVVq1aaOXOmkpOTdf3112vatGkaOnSo3nnnnYv47gAA6oJnvAAADc6NN94ou/3H9wajo6OVmJjo/drhcKhFixY6evSoJCkrK0t79+71PqNVqbS0VPv27au1P2OMbDbbJY05KytLP/zwg1q0aOGz/cyZMz5jaNu2rc8zZP4el5CQ4HN9sbGx3uv3l9vt1vz587VixQodPnxYLpdLLpdLoaGhF3UeAMDFI3gBABqc8xecsNls1W7zeDySzs4Gde/eXf/4xz+qnOv8hTKq8+2336pdu3aXMOKzY4iNja12hu2ny82fH3L8Pe5C1++v559/XosWLdLixYuVlJSk0NBQTZ8+vdaPYwIALh3BCwDQ6HXr1k0rVqzwLk5xMXbt2qVPPvlEs2bNuuQxFBQUKCAgQAkJCZYfd76goCC53e4Ltlm/fr3GjBmjX//615LOhr49e/aoS5cude4XAOAfnvECADR6v/rVrxQVFaUxY8Zo/fr12r9/vzIzM/XQQw/p0KFD3nYVFRUqKChQXl6esrOztWTJEvXv31/Jycl6+OGHL2kMt99+u1JSUjR27Fj9+9//Vk5OjjZu3KjHHntM27Ztu+zHnS8hIUFbtmxRTk6Ojh07Vu1sWPv27fXZZ59p48aN+vbbb/Xb3/5WBQUFdbpeAMDFIXgBABq9kJAQrVu3Tm3atNEdd9yhLl266O6779aZM2d8ZsB27typ2NhYtWnTRqmpqXr77bc1a9YsrV+//pL/lpjNZtOqVavUr18/3X333erYsaMmTJignJwcRUdHX/bjzjdz5kw5HA7dcMMNatmypXJzc6u0efzxx9WtWzcNHTpUqampiomJ0dixY+tyuQCAi2Qzxpj6HgQAAAAAXM2Y8QIAAAAAixG8AAAAAMBiBC8AAAAAsBjBCwAAAAAsRvACAAAAAIsRvAAAAADAYgQvAAAAALAYwQsAAAAALEbwAgAAAACLEbwAAAAAwGIELwAAAACwGMELAAAAACz2/wEr9KwQjFy/6wAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1000x2000 with 3 Axes>"
       ]
@@ -849,7 +1413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -874,12 +1438,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
     "# extract necessary features for training\n",
-    "train_feat_df = df.loc[:,[\"KingSafetyDifferential\",\"DevelopmentDifferential\",\"AverageElo\",\"TimeControl\", \"Moves\", \"InaccuracyDifferential\", \"MistakeDifferential\", \"BlunderDifferential\", \"TimeDifferential\", \"EloDifferential\", \"ECO\", \"Result\"]]\n",
+    "train_feat_df = df.loc[:,[\"MobilityDifferential\", \"MaterialDifferential\", \"KingSafetyDifferential\",\"DevelopmentDifferential\",\"AverageElo\",\"TimeControl\", \"Moves\", \"InaccuracyDifferential\", \"MistakeDifferential\", \"BlunderDifferential\", \"TimeDifferential\", \"EloDifferential\", \"ECO\", \"Result\"]]\n",
     "\n",
     "# convert results to categorical values for training\n",
     "train_feat_df[\"Result\"] = train_feat_df[\"Result\"].map(convertWinToCategory)\n",
@@ -908,7 +1472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -934,30 +1498,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but AdaBoostClassifier was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
       "  warnings.warn(\n",
-      "c:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
-      "c:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, msg_start, len(result))\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but GaussianNB was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
       "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
       "\n",
       "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
       "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
       "Please also refer to the documentation for alternative solver options:\n",
       "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
-      "  n_iter_i = _check_optimize_result(\n"
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+      "  warnings.warn(\n",
+      "/Users/pranavshah/anaconda3/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but MLPClassifier was fitted with feature names\n",
+      "  warnings.warn(\n"
      ]
     },
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -1046,33 +1628,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Fitting 5 folds for each of 84672 candidates, totalling 423360 fits\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[16], line 17\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;66;03m# Perform grid search with cross-validation\u001b[39;00m\n\u001b[0;32m     16\u001b[0m grid_search \u001b[38;5;241m=\u001b[39m GridSearchCV(estimator\u001b[38;5;241m=\u001b[39mgb_classifier, param_grid\u001b[38;5;241m=\u001b[39mparameters, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m---> 17\u001b[0m \u001b[43mgrid_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     19\u001b[0m \u001b[38;5;66;03m# Print the best model and its hyperparameters\u001b[39;00m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBest Model:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1474\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1467\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1469\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1470\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1471\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1472\u001b[0m     )\n\u001b[0;32m   1473\u001b[0m ):\n\u001b[1;32m-> 1474\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:970\u001b[0m, in \u001b[0;36mBaseSearchCV.fit\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m    964\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_results(\n\u001b[0;32m    965\u001b[0m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[0;32m    966\u001b[0m     )\n\u001b[0;32m    968\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[1;32m--> 970\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    972\u001b[0m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[0;32m    973\u001b[0m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[0;32m    974\u001b[0m first_test_score \u001b[38;5;241m=\u001b[39m all_out[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_scores\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
-      "File \u001b[1;32mc:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1527\u001b[0m, in \u001b[0;36mGridSearchCV._run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1525\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[0;32m   1526\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Search all candidates in param_grid\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1527\u001b[0m     \u001b[43mevaluate_candidates\u001b[49m\u001b[43m(\u001b[49m\u001b[43mParameterGrid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparam_grid\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:916\u001b[0m, in \u001b[0;36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    908\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    909\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[0;32m    910\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFitting \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m folds for each of \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m candidates,\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    911\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m totalling \u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[38;5;124m fits\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m    912\u001b[0m             n_splits, n_candidates, n_candidates \u001b[38;5;241m*\u001b[39m n_splits\n\u001b[0;32m    913\u001b[0m         )\n\u001b[0;32m    914\u001b[0m     )\n\u001b[1;32m--> 916\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    917\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_and_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    918\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_estimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    919\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    920\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    921\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    922\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    923\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    924\u001b[0m \u001b[43m        \u001b[49m\u001b[43msplit_progress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_splits\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    925\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcandidate_progress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_candidates\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    926\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_and_score_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    927\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    928\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mproduct\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    929\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcandidate_params\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    930\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrouted_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplitter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    931\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    932\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    934\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    935\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    936\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo fits were performed. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    937\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWas the CV iterator empty? \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    938\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWere there no candidates?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    939\u001b[0m     )\n",
-      "File \u001b[1;32mc:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\parallel.py:67\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m     62\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[0;32m     63\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     64\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[0;32m     65\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[0;32m     66\u001b[0m )\n\u001b[1;32m---> 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\joblib\\parallel.py:2007\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   2001\u001b[0m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[0;32m   2002\u001b[0m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[0;32m   2003\u001b[0m \u001b[38;5;66;03m# reach the first `yield` statement. This starts the aynchronous\u001b[39;00m\n\u001b[0;32m   2004\u001b[0m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[0;32m   2005\u001b[0m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[1;32m-> 2007\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\joblib\\parallel.py:1650\u001b[0m, in \u001b[0;36mParallel._get_outputs\u001b[1;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[0;32m   1647\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[0;32m   1649\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mretrieval_context():\n\u001b[1;32m-> 1650\u001b[0m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_retrieve()\n\u001b[0;32m   1652\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[0;32m   1653\u001b[0m     \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[0;32m   1654\u001b[0m     \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[0;32m   1655\u001b[0m     \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[0;32m   1656\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\joblib\\parallel.py:1762\u001b[0m, in \u001b[0;36mParallel._retrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1757\u001b[0m \u001b[38;5;66;03m# If the next job is not ready for retrieval yet, we just wait for\u001b[39;00m\n\u001b[0;32m   1758\u001b[0m \u001b[38;5;66;03m# async callbacks to progress.\u001b[39;00m\n\u001b[0;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ((\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[0;32m   1760\u001b[0m     (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mget_status(\n\u001b[0;32m   1761\u001b[0m         timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout) \u001b[38;5;241m==\u001b[39m TASK_PENDING)):\n\u001b[1;32m-> 1762\u001b[0m     time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m0.01\u001b[39m)\n\u001b[0;32m   1763\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m   1765\u001b[0m \u001b[38;5;66;03m# We need to be careful: the job list can be filling up as\u001b[39;00m\n\u001b[0;32m   1766\u001b[0m \u001b[38;5;66;03m# we empty it and Python list are not thread-safe by\u001b[39;00m\n\u001b[0;32m   1767\u001b[0m \u001b[38;5;66;03m# default hence the use of the lock\u001b[39;00m\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+      "Fitting 5 folds for each of 27 candidates, totalling 135 fits\n",
+      "Best Model:\n",
+      "GradientBoostingClassifier(max_depth=5, random_state=42)\n"
      ]
     }
    ],
@@ -1089,7 +1654,7 @@
     "gb_classifier = GradientBoostingClassifier(random_state=42)\n",
     "\n",
     "# Perform grid search with cross-validation\n",
-    "grid_search = GridSearchCV(estimator=gb_classifier, param_grid=parameters, cv=5, n_jobs=-1, verbose=True)\n",
+    "grid_search = GridSearchCV(estimator=gb_classifier, param_grid=param_grid, cv=5, n_jobs=-1, verbose=True)\n",
     "grid_search.fit(X_train, y_train)\n",
     "\n",
     "# Print the best model and its hyperparameters\n",
@@ -1100,7 +1665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
@@ -1109,27 +1674,15 @@
      "text": [
       "              precision    recall  f1-score   support\n",
       "\n",
-      "        Loss       0.71      0.74      0.72      4776\n",
-      "         Tie       0.00      0.00      0.00         0\n",
-      "         Win       0.79      0.72      0.75      5790\n",
+      "        Loss       0.81      0.80      0.80      5054\n",
+      "         Tie       0.09      0.29      0.14        93\n",
+      "         Win       0.82      0.80      0.81      5419\n",
       "\n",
-      "    accuracy                           0.73     10566\n",
-      "   macro avg       0.50      0.49      0.49     10566\n",
-      "weighted avg       0.75      0.73      0.74     10566\n",
+      "    accuracy                           0.80     10566\n",
+      "   macro avg       0.57      0.63      0.58     10566\n",
+      "weighted avg       0.81      0.80      0.80     10566\n",
       "\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
-      "c:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
-      "c:\\Users\\mdurr\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
-     ]
     }
    ],
    "source": [
@@ -1154,7 +1707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -1186,112 +1739,122 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>BlunderDifferential</td>\n",
-       "      <td>0.343769</td>\n",
+       "      <td>0.295608</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>MistakeDifferential</td>\n",
-       "      <td>0.210394</td>\n",
+       "      <td>0.161308</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>TimeDifferential</td>\n",
-       "      <td>0.117267</td>\n",
+       "      <td>0.114294</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Moves</td>\n",
-       "      <td>0.080014</td>\n",
+       "      <td>0.079396</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>TimeControl</td>\n",
-       "      <td>0.056936</td>\n",
+       "      <td>0.067113</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>InaccuracyDifferential</td>\n",
-       "      <td>0.044305</td>\n",
+       "      <td>AverageElo</td>\n",
+       "      <td>0.057234</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>AverageElo</td>\n",
-       "      <td>0.044162</td>\n",
+       "      <td>EloDifferential</td>\n",
+       "      <td>0.054306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>EloDifferential</td>\n",
-       "      <td>0.038189</td>\n",
+       "      <td>InaccuracyDifferential</td>\n",
+       "      <td>0.051815</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>KingSafetyDifferential</td>\n",
-       "      <td>0.020215</td>\n",
+       "      <td>MobilityDifferential</td>\n",
+       "      <td>0.042024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>DevelopmentDifferential</td>\n",
-       "      <td>0.016496</td>\n",
+       "      <td>0.018609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>ECO_9</td>\n",
-       "      <td>0.002964</td>\n",
+       "      <td>MaterialDifferential</td>\n",
+       "      <td>0.015957</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>ECO_4</td>\n",
-       "      <td>0.002961</td>\n",
+       "      <td>KingSafetyDifferential</td>\n",
+       "      <td>0.014620</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>ECO_8</td>\n",
-       "      <td>0.002893</td>\n",
+       "      <td>ECO_A00</td>\n",
+       "      <td>0.004130</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>ECO_3</td>\n",
-       "      <td>0.002859</td>\n",
+       "      <td>ECO_B01</td>\n",
+       "      <td>0.003928</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>ECO_0</td>\n",
-       "      <td>0.002809</td>\n",
+       "      <td>ECO_D02</td>\n",
+       "      <td>0.003024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>ECO_10</td>\n",
-       "      <td>0.002533</td>\n",
+       "      <td>ECO_C00</td>\n",
+       "      <td>0.002849</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>ECO_2</td>\n",
-       "      <td>0.002531</td>\n",
+       "      <td>ECO_B10</td>\n",
+       "      <td>0.002687</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>ECO_1</td>\n",
-       "      <td>0.002452</td>\n",
+       "      <td>ECO_D00</td>\n",
+       "      <td>0.002647</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>ECO_7</td>\n",
-       "      <td>0.002414</td>\n",
+       "      <td>ECO_A40</td>\n",
+       "      <td>0.002063</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>ECO_6</td>\n",
-       "      <td>0.002109</td>\n",
+       "      <td>ECO_C41</td>\n",
+       "      <td>0.002011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>ECO_5</td>\n",
-       "      <td>0.001729</td>\n",
+       "      <td>ECO_C50</td>\n",
+       "      <td>0.001611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>intercept</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>ECO_A45</td>\n",
+       "      <td>0.001292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>ECO_B00</td>\n",
+       "      <td>0.000737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>ECO_C20</td>\n",
+       "      <td>0.000737</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1299,31 +1862,33 @@
       ],
       "text/plain": [
        "                   features  importance\n",
-       "0       BlunderDifferential    0.343769\n",
-       "1       MistakeDifferential    0.210394\n",
-       "2          TimeDifferential    0.117267\n",
-       "3                     Moves    0.080014\n",
-       "4               TimeControl    0.056936\n",
-       "5    InaccuracyDifferential    0.044305\n",
-       "6                AverageElo    0.044162\n",
-       "7           EloDifferential    0.038189\n",
-       "8    KingSafetyDifferential    0.020215\n",
-       "9   DevelopmentDifferential    0.016496\n",
-       "10                    ECO_9    0.002964\n",
-       "11                    ECO_4    0.002961\n",
-       "12                    ECO_8    0.002893\n",
-       "13                    ECO_3    0.002859\n",
-       "14                    ECO_0    0.002809\n",
-       "15                   ECO_10    0.002533\n",
-       "16                    ECO_2    0.002531\n",
-       "17                    ECO_1    0.002452\n",
-       "18                    ECO_7    0.002414\n",
-       "19                    ECO_6    0.002109\n",
-       "20                    ECO_5    0.001729\n",
-       "21                intercept    0.000000"
+       "0       BlunderDifferential    0.295608\n",
+       "1       MistakeDifferential    0.161308\n",
+       "2          TimeDifferential    0.114294\n",
+       "3                     Moves    0.079396\n",
+       "4               TimeControl    0.067113\n",
+       "5                AverageElo    0.057234\n",
+       "6           EloDifferential    0.054306\n",
+       "7    InaccuracyDifferential    0.051815\n",
+       "8      MobilityDifferential    0.042024\n",
+       "9   DevelopmentDifferential    0.018609\n",
+       "10     MaterialDifferential    0.015957\n",
+       "11   KingSafetyDifferential    0.014620\n",
+       "12                  ECO_A00    0.004130\n",
+       "13                  ECO_B01    0.003928\n",
+       "14                  ECO_D02    0.003024\n",
+       "15                  ECO_C00    0.002849\n",
+       "16                  ECO_B10    0.002687\n",
+       "17                  ECO_D00    0.002647\n",
+       "18                  ECO_A40    0.002063\n",
+       "19                  ECO_C41    0.002011\n",
+       "20                  ECO_C50    0.001611\n",
+       "21                  ECO_A45    0.001292\n",
+       "22                  ECO_B00    0.000737\n",
+       "23                  ECO_C20    0.000737"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1345,12 +1910,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1376,7 +1941,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using the plot above, we can see that the blunder differential is the most impactful feature to perdicting the winner of a chess game. This is intuitive as the player who makes blunders means that their position worsens considerable but is also suprising, that it is significantly more impactful than ELO differential. This is relevant and impactful players as it shows them that regrardless of your ELO differential, it is important to play calm, collected chess rather than trying to find a \"creative\" position as that will put you at a much higher risk of losing. "
+    "Using the plot above, we can see that the blunder differential is the most impactful feature to perdicting the winner of a chess game. This is intuitive as the player who makes blunders means that their position worsens considerable but is also suprising, that it is significantly more impactful than ELO differential. This is relevant and impactful players as it shows them that regrardless of your ELO differential, it is important to play calm, collected chess rather than trying to find a \"creative\" position as that will put you at a much higher risk of losing. Out of the positional features, it seems that mobility was the most important feature coming out of the opening."
    ]
   },
   {
@@ -1395,12 +1960,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1428,17 +1993,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy of Greater ELO Wins Benchmark: 42.33%\n",
-      "Accuracy of Randomly Guessing Winner: 34.46%\n",
-      "Accuracy of Random Forest Predictions (w/o hyperparameter): 80.23%\n",
-      "Accuracy of Random Forest Predictions (w/ hyperparameter): 80.45%\n"
+      "Accuracy of Greater ELO Wins Benchmark: 42.99%\n",
+      "Accuracy of Randomly Guessing Winner: 32.55%\n",
+      "Accuracy of Random Forest Predictions (w/o hyperparameter): 68.50%\n",
+      "Accuracy of Random Forest Predictions (w/ hyperparameter): 79.57%\n"
      ]
     }
    ],
@@ -1490,7 +2055,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Our current model could be improved upon with the analysis of additional positional characteristics, but doing so requires heavy processing as we would need to keep track of an 8x8 board for many moves. Other chess-related data science projects could be developed from the work done here. Projects like an ELO predictor could be developed with many of the same features used in this project."
+    "Our current model could be improved upon with the analysis of additional positional characteristics and better formulas for evaluating these characteristics. For example, with the mobility feature, instead of only counting the number of legal moves, we could instead count the number of safe moves that don't hang the piece. This is known as Safe Mobility. Unfortunately, calculating more positional characteristics with better formulas is incredibly time consuming and resource intensive, so for this project we decided to focus on the basic formulas and features. \n",
+    "\n",
+    "Our low accuracy of 80% is just a testament to how difficult it is to predict the winner of chess games. Chess is a complex game with many different qualitiative and quantitative characteristics to consider. From the results it is evident that reducing unforced errors and watching the time are crucial actions to take in the game. In addition,\n",
+    "from the results we can determine that mobility is the most important positional feature out of the opening. This is an interesting insight which highlights the important of early and precise development of pieces in the beginning stages of the game. From these results, players should take away from this study the importance of error reduction, time management, and early piece activity.\n",
+    "\n",
+    "This project, although it simply affirms many popular beliefs in the community, uncovers future chess-related data science projects. Projects like an ELO predictor could be developed with many of the same features used in this project."
    ]
   },
   {
@@ -1536,7 +2106,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,