notebook.ipynb

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    "<h1 align=\"center\">Solving the N-Queens Problem with Evolutionary Algorithms</h1>\n",
    "\n",
    "In this project, we apply an unconventional approach to find a single solution to the generalized version of the [Eight-queens puzzle](https://en.wikipedia.org/wiki/Eight_queens_puzzle) - the _n-queens puzzle_. That is, placing $n$ queens on an $n \\times n$ chessboard such that no two queens are threatening each other. This problem has been previously solved using [backtracking depth-first searches](https://en.wikipedia.org/wiki/Depth-first_search) or efficient analytically-derived algorithms. The results of this notebook show it is possible to find a single solution to the n-queens problem for moderate values of $n$ (up to $n = 64$, a $10^{90}$ search space) by using evolutionary algorithms and genetic programming.\n",
    "\n",
    "These results are two-fold:\n",
    "- Whereas optimized backtracking depth-first searches becomes computationally intractable, evolutionary algorithms still have a high probability of finding a valid solution within a reasonable amount of time.\n",
    "- Specialized knowledge or deep analysis of the problem, although preferred for higher efficiency, was not necessary in finding a valid solution to the problem for moderate values of $n$.\n",
    "\n",
    "We experiment with two different numerical encodings for chessboard representations and their implications about the search space and the rate of convergence for finding a solution:\n",
    "- **position indexed queen positions** (search space: $O(n^{2n})$): an $n$-element array where each queen's position $p$ ranges $p\\in[0, n^2)$, derived from $p = n \\times row + column $.\n",
    "- **row indexed queen positions** (search space: $O(n^n)$): an $n$-element array where each queen's index indicates its row and its value indicates the column.\n",
    "\n",
    "> Note: It is possible to reduce the search spaces using encoding representations without duplicate queens, with the new bounds of $O({{n^2} \\choose n})$ and $O(n!)$ respectively. However, this requires significantly more programming and reduces the performance of the evolutionary algorithm's fitness evaluation function."
   ],
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   "cell_type": "markdown",
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    "<hr style=\"border-top: 2px solid #444\">"
   ],
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   "cell_type": "markdown",
   "source": [
    "## Development Environment\n",
    "In this section, the development environment for this Jupyter notebook project is prepared by collecting Python dependencies, importing libraries, and configuring the runtime environment."
   ],
   "metadata": {
    "collapsed": false
   }
  },
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   "cell_type": "markdown",
   "source": [
    "### Python Dependencies\n",
    "External Python dependencies are collected through the [Pipenv](https://pypi.org/project/pipenv/) package manager since it supports dependency lock-files. In the following script, `pipenv` and its transitive dependencies are installed from `pipenv-requirements.txt` and then the notebook's dependencies are installed from `Pipfile` and `Pipfile.lock`."
   ],
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  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "!pip install -r pipenv-requirements.txt\n",
    "!pipenv install"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Library Imports\n",
    "All internal standard libraries and external libraries are imported here, with some dependency versions."
   ],
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  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "text": [
      "Python version: 3.8.5 (tags/v3.8.5:580fbb0, Jul 20 2020, 15:57:54) [MSC v.1924 64 bit (AMD64)]\n",
      "DEAP version: 1.3\n",
      "Numpy version: 1.19.2\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import random\n",
    "import sys\n",
    "from time import time\n",
    "from typing import List, Tuple, Callable, TypeVar\n",
    "\n",
    "import deap\n",
    "import matplotlib.pyplot as pyplot\n",
    "import numpy\n",
    "import pandas\n",
    "from IPython.display import display\n",
    "from deap import algorithms, base, creator, tools\n",
    "\n",
    "print(f\"Python version: {sys.version}\")\n",
    "print(f\"DEAP version: {deap.__version__}\")\n",
    "print(f\"Numpy version: {numpy.__version__}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
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   "cell_type": "markdown",
   "source": [
    "### Configure PRNG Seed\n",
    "DEAP relies on Python's default pseudorandom number generator (PRNG) for its internal computations, located within the `random` module. To guarantee deterministic behaviour and reproducible results during this notebook's execution, an arbitrary seed is configured."
   ],
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  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "random.seed(43)"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Configure Pandas Table Truncation\n",
    "Pandas is used to render HTML chessboards using dataframes. By default, Pandas truncates rendered dataframes; this is disabled so that the full chessboard is rendered without any truncated rows or columns."
   ],
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   "execution_count": 4,
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   "source": [
    "pandas.set_option('display.max_rows', None)\n",
    "pandas.set_option('display.max_columns', None)\n",
    "pandas.set_option('display.width', None)\n",
    "pandas.set_option('display.max_colwidth', None)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr style=\"border-top: 2px solid #444\">"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## I. DEAP Toolkit\n",
    "In this section, a few DEAP utility functions are defined for re-use throughout the notebook. Most of the heavy lifting and core functionality is implemented here for running an evolutionary algorithms simulation."
   ],
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    "collapsed": false
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  },
  {
   "cell_type": "markdown",
   "source": [
    "### Evolutionary Algorithm Function\n",
    "The following method simulates selection in a randomly created population with DEAP's simple evolutionary algorithm; the configured genetic operators are two-point [crossovers](https://en.wikipedia.org/wiki/Crossover_(genetic_algorithm)) and a parameterized [mutation](https://en.wikipedia.org/wiki/Mutation_(genetic_algorithm)) function (typically, a uniform int range). The fitness evaluation function used throughout the notebook is a count of the number of distinct queen pairs that are threatening each other; thus, the problem is to minimize the fitness of individuals.\n",
    "\n",
    "Several parameters for the evolutionary algorithm simulation are parameterized for re-use:\n",
    "- `individual_generator`: a function to generate a genetic encoding for an individual\n",
    "- `fitness_evaluation`: a fitness evaluation function for an individual's genetic encoding\n",
    "- `population_size`: the population size to generate for the simulation\n",
    "- `generations`: the number of generations to run the simulation\n",
    "- `crossover_rate`: the probability of applying a two-point crossover to generate a new individual\n",
    "- `mutation_rate`: the probability of applying the mutation function to generate a new individual\n",
    "- `mutation_function` the mutation function for creating new mutated individuals"
   ],
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   "execution_count": 5,
   "outputs": [],
   "source": [
    "# minimize fitness\n",
    "creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0,))\n",
    "creator.create(\"Individual\", list, fitness=creator.FitnessMin)\n",
    "\n",
    "T = TypeVar('T')\n",
    "def evolution(*,\n",
    "      individual_generator: Callable[[], T],\n",
    "      fitness_evaluation: Callable[[T], float],\n",
    "      population_size: int,\n",
    "      generations: int,\n",
    "      crossover_rate: float,\n",
    "      mutation_rate: float,\n",
    "      mutation_function: Tuple[Callable, dict],\n",
    "):\n",
    "    # track start time\n",
    "    start_time = time()\n",
    "\n",
    "    toolbox = base.Toolbox()\n",
    "\n",
    "    # register population generators\n",
    "    toolbox.register(\"individual\", tools.initIterate, creator.Individual, individual_generator)\n",
    "    toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n",
    "\n",
    "    # register fitness evaluation function\n",
    "    toolbox.register(\"evaluate\", lambda individual: (fitness_evaluation(individual),))\n",
    "\n",
    "    # register mutators for individuals\n",
    "    toolbox.register(\"mate\", tools.cxTwoPoint)\n",
    "    toolbox.register(\"mutate\", mutation_function[0], **mutation_function[1])\n",
    "\n",
    "    # register next generation selection function\n",
    "    toolbox.register(\"select\", tools.selTournament, tournsize=4)\n",
    "\n",
    "    # generation statistics logging\n",
    "    stats = tools.Statistics(key=lambda individual: individual.fitness.values)\n",
    "    stats.register(\"min\", lambda population: numpy.min([fitness for fitness in population if fitness[0] != math.inf]))\n",
    "    stats.register(\"avg\", lambda population: numpy.mean([fitness for fitness in population if fitness[0] != math.inf]))\n",
    "    stats.register(\"max\", lambda population: numpy.max([fitness for fitness in population if fitness[0] != math.inf]))\n",
    "\n",
    "    # log fittest individual with a hall of fame record\n",
    "    hall_of_fame = tools.HallOfFame(maxsize=1)\n",
    "\n",
    "    # run genetic algorithm\n",
    "    _, log = algorithms.eaSimple(\n",
    "        # generate all individuals in population\n",
    "        toolbox.population(n=population_size),\n",
    "        toolbox,\n",
    "        ngen=generations,\n",
    "        cxpb=crossover_rate, mutpb=mutation_rate,\n",
    "        stats=stats, halloffame=hall_of_fame, verbose=False,\n",
    "    )\n",
    "\n",
    "    # return duration, statistics log, and the fittest individual\n",
    "    return time() - start_time, log, hall_of_fame[0]"
   ],
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   "cell_type": "markdown",
   "source": [
    "### Population Visualizer\n",
    "The following method renders a visualization displaying the average, minimum, and maximum population fitness values over each generation in an evolutionary algorithms simulation."
   ],
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   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "def plot_generations(generation: List[int], average: List[float], minimum: List[int], maximum: List[int]):\n",
    "    pyplot.figure(figsize=(16, 8))\n",
    "    pyplot.grid(True)\n",
    "    pyplot.plot(generation, average, label=\"average\")\n",
    "    pyplot.plot(generation, minimum, label=\"minimum\")\n",
    "    pyplot.plot(generation, maximum, label=\"maximum\")\n",
    "    pyplot.xlabel(\"Generation\")\n",
    "    pyplot.ylabel(\"Fitness\")\n",
    "    pyplot.ylim(-1)\n",
    "    pyplot.legend(loc=\"upper right\")\n",
    "    pyplot.show()"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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   "cell_type": "markdown",
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    "<hr style=\"border-top: 2px solid #444\">"
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   "source": [
    "## II. Position-indexed Board Encoding\n",
    "\n",
    "The position-indexed genetic encoding allows a queen to be in any position, with no restrictions. Individuals are genetically encoded as an integer list of $n$ elements. Each element $p$ is in the range $p\\in[0, n^2)$, derived from the queen's row and column position - $p = n \\times row + column $.\n",
    "\n",
    "The state-space complexity is $O(n^{2n})$:\n",
    "- for $n = 8$: there are $(8^2)^8=281,474,976,710,656$ possible states\n",
    "- for $n = 16$: there are $(16^2)^{16}=340,282,366,920,938,463,463,374,607,431,768,211,456$ possible states"
   ],
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   "cell_type": "markdown",
   "source": [
    "### Rendering Position-indexed Board\n",
    "Pandas is used to visualize chessboard representations by generating an HTML table with the following board-rendering method.\n",
    "\n",
    "**Legend**:\n",
    "- 🟥: drawn in a line to show a threatening conflict between a pair of queens\n",
    "- ♕: a queen that is being threatened by at least 1 other queen\n",
    "- 👑: a queen that is *not* being threatened by any other queen"
   ],
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   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "def display_positional_grid(individual: List[int]):\n",
    "    # unpack dimensions\n",
    "    dimension = len(individual)\n",
    "\n",
    "    # construct board using pandas\n",
    "    board = pandas.DataFrame(\"\", index = range(1, dimension + 1), columns = range(1, dimension + 1))\n",
    "\n",
    "    # draw all conflicts with a red line\n",
    "    for x in range(dimension):\n",
    "        x_row, x_column = individual[x] // dimension, individual[x] % dimension\n",
    "        for y in range(x + 1, dimension):\n",
    "            y_row, y_column = individual[y] // dimension, individual[y] % dimension\n",
    "\n",
    "            diff_row, diff_column = y_row - x_row, y_column - x_column\n",
    "            # check if queens are conflicting\n",
    "            if x_row == y_row or x_column == y_column or abs(diff_row) == abs(diff_column):\n",
    "                # draw a line of the conflict\n",
    "                for i in range(1 + max(abs(diff_row), abs(diff_column))):\n",
    "                    board[1 + x_column + i * numpy.sign(diff_column)][1 + x_row + i * numpy.sign(diff_row)] = \"🟥\"\n",
    "\n",
    "    # draw all queens\n",
    "    for queen in individual:\n",
    "        row, column = queen // dimension, queen % dimension\n",
    "        # use a crown if\n",
    "        board[1 + column][1 + row] = \"👑\" if board[1 + column][1 + row] == \"\" else \"♕\"\n",
    "\n",
    "    # render board with pandas\n",
    "    display(board)"
   ],
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   "source": [
    "### Example 8x8 Position-indexed Individual\n",
    "The following is an example of a randomly generated \\$8 \\times 8\\$ individual's position-indexed genetic encoding and board representation."
   ],
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   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positions: [4, 18, 44, 48, 9, 29, 23, 42]\n",
      "Duplicate positions: 0\n"
     ]
    },
    {
     "data": {
      "text/plain": "   1  2  3  4  5  6  7  8\n1              ♕         \n2     ♕     🟥  🟥         \n3        ♕  🟥  🟥  🟥  🟥  ♕\n4        🟥     🟥  👑  🟥   \n5        🟥     🟥  🟥      \n6        ♕  🟥  ♕         \n7  👑                     \n8                        ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>♕</td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td></td>\n      <td>♕</td>\n      <td></td>\n      <td>🟥</td>\n      <td>🟥</td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td></td>\n      <td></td>\n      <td>♕</td>\n      <td>🟥</td>\n      <td>🟥</td>\n      <td>🟥</td>\n      <td>🟥</td>\n      <td>♕</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td></td>\n      <td></td>\n      <td>🟥</td>\n      <td></td>\n      <td>🟥</td>\n      <td>👑</td>\n      <td>🟥</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td></td>\n      <td></td>\n      <td>🟥</td>\n      <td></td>\n      <td>🟥</td>\n      <td>🟥</td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td></td>\n      <td></td>\n      <td>♕</td>\n      <td>🟥</td>\n      <td>♕</td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "example_individual = random.sample(range(8**2), 8)\n",
    "print(f\"Positions: {example_individual}\")\n",
    "print(f\"Duplicate positions: {len(example_individual) - len(set(example_individual))}\")\n",
    "display_positional_grid(example_individual)"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
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   "cell_type": "markdown",
   "source": [
    "### Position-indexed Fitness Evaluation Function\n",
    "\n",
    "The following method is the fitness evaluation function for position-indexed individuals. Individual fitness is defined as the number of distinct pairs of queens that are threatening each other; boards with duplicate values are penalized with a value of positive infinity (since the problem is solved by minimizing fitness).\n",
    "\n",
    "Time complexity: $O(|queens|^2)$"
   ],
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   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "def evaluate_position_indexed_fitness(individual: List[int]) -> float:\n",
    "    # duplicate values should be removed with the severest penalty\n",
    "    if len(individual) != len(set(individual)):\n",
    "        return math.inf\n",
    "\n",
    "    # unpack dimensions\n",
    "    dimension = len(individual)\n",
    "    # count all pairs of conflicts\n",
    "    fitness: float = 0\n",
    "    for x in range(len(individual)):\n",
    "        x_row, x_column = individual[x] // dimension, individual[x] % dimension\n",
    "        for y in range(x + 1, len(individual)):\n",
    "            y_row, y_column = individual[y] // dimension, individual[y] % dimension\n",
    "            if x_row == y_row or x_column == y_column or abs(x_row - y_row) == abs(x_column - y_column):\n",
    "                fitness += 1\n",
    "    return fitness"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Evolutionary Algorithm on a 8x8 Board\n",
    "In the following section, the DEAP evolutionary algorithm is executed to solve for a single solution of the $n$-queens problem for an $8 \\times 8$ chessboard using a position-indexed genetic encoding."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Evolutionary Computation"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed in 11.706 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1152x576 with 1 Axes>",
      "image/png": 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TVmNnxU4MDQ9NOC5H9Q71Yl/lPmxI2oAgnyC722vVI+uRr7a5du2CK9QaaxETGIM5EXOm7HRpIiIiIpqZvvWtb+E73/kOFixYcMsI6TPPPIOvfvWryMjIwEsvvYTnnnsOzc3NE3qN559/Hg8++CCWL1+O2NhYV4XuELdNRRYE4TUADwJoFkVxzs3H/gbAsogyFECHKIrzbexbBcAIYBiASRTFHHfFKaXRBaQ0YRqn92/pbcGJ2hN4bPZjUMpGWgYpZApsTd+K1668hqaeJkQHRo+5vyiKKNIXYUHUAqSGplofL8gowNGaozheexz3Jt3rdFzOOFh1EL2mXhRkFDi0fVZ4FgCg1FCK5fFTa6pvXXcd4lXx0Kq1OFR9CMZBI1Q+KqnDIiIiIqJpLjk5GVeufNKZY/SI7OjnysrKrI//8Ic/BAC89tpr1scSEhJQXj7SteXxxx+3Viwe63i3P/fQQw/hoYceuiM+R/efDHeO2L4O4P7RD4ii+LAoivNvJrOFAMYbVlxzc9tpmdQCQFJwEvwV/hMe3dtZsRPD4jDy0/NveTwvPQ9m0YxdFbvG3f9i80VUdVVZR2stlsctR1RAlEeKSBXqC5ESkoL5kfMd2j7YJxgJQQlTckS0trsWCUEJ1irXrq6IR0REREREtrktsRVF8QQAmwshhZFVxZ8G8Ka7Xt8byGVyZIZlTqgysmW0dVH0ojva48wKnoUlMUtQpC+CWTSPeYwifREClYHYkLThlscVMgW2pW/DqbpTaOxpdDo2R5W3l+Nyy2UUaApuWWhujzZcO+XWsPYO9cLQb0CCKgFZ6pFR5amYfBMRERERTUdSrbFdCaBJFEX9GM+LAN4RBOGCIAhPeDAuj9OGa3HVcHXcBNSW803nUWOssRaNul2+Jh+13bU413jO5vNdg114p+odbErZhADlnX2q8tLzIEJ0unWQM4rKi6CQKZzumZsdno0aYw26BrvcFJnz6rpHKiLHB8Ujwj8CUQFRUy75JiIiIiKarqRq9/MIxh+tvUcUxTpBEKIAHBIE4erNEeA73Ex8nwBGqnCVlJS4PFhX6e7uviM+WbcMvaZebD+8HVHKKIeP9cfWP8Jf8IdvtS9KakrueN5H9EGALAAvn3wZfZF9dzz/rvFd9A/3I7krecz3LMsvC29+9CYyDZmQCa69BzIkDqGotgh3+d2FD8986Ny+fSNFrd46+hYy/DJcGtdEfdT7EQCgqawJJdUliBKjcL7m/JQ8H22dh0RS4LlIUwHPQ5oKeB56t5CQEHR1dTk1A3GqGh4ehtFolDoMiKKI/v5+p34uPJ7YCoKgAJAPYNFY24iiWHfz72ZBEIoBLAFgM7EVRfEVAK8AQE5Ojji6H9NUU1JSgtvjizXE4i+7/wJVugq5Kbm2drtD50AnvvH3byA/Ix8blm4Yc7vz75/H22VvY/7S+Qj1C73luV/v/jWy1Fl4dMOjY/4Q9lf149njz8Ivw8/lhZoOVB1Az40efHn5l50+9tz+ufj1334N30Rf5M7OdWlcE1VbWgu0AFtWb4HaT43SS6X4zeXfYMmKJTZHxKVk6zwkkgLPRZoKeB7SVMDz0Ltdv34dg4ODCA8P9/rk1mg0QqWStvipKIpoa2tDaGgoFixY4PB+UozY3gvgqiiKtbaeFAQhEIBMFEXjza83APi+JwP0pNTQVChlSujadNiYstGhffZW7sWgedBuJeF8TT7+evWv2Ht9Lz6n/Zz18dK2UugMOvzr3f867g/f2sS1CPUNRaG+0OWJbVFZEeIC47A0bqnT+6r91IgJjJnQ2mR3qeuuQ4AiAGG+YQBG2hKJEFHWXob5UfOlDY6IiIiI3CYhIQG1tbVoaWmROpRJ6+/vh5+fn9RhwM/PDwkJCU7t4852P28CyAUQIQhCLYD/EEXx9wA+g9umIQuCEAfgd6IobgIQDaD4ZsKlAPBXURQPuCtOqSllSmSEZaDU4FiSJooiCvWFyA7PthYpGkumOhNzwudge9l2fDbrs9YktkhfBF+5LzalbBp3fx+5DzanbcabV9+Eod8AtZ/asW/KjlpjLU43nMZX5n9lwlOcteqpVUCq1liLeFW89T3ODh9p5VTaVsrEloiIiGgaUyqVSElJkToMlygpKXFqlHQqcWdV5EdEUYwVRVEpimLCzaQWoig+Lorib27btv5mUgtRFCtFUZx3889sURT/210xThXacC10bTqIomh329K2UpS1l41ZNOp2+Rn5KO8ox5XWkV5RfaY+7Kvch/VJ6xHiG2J///R8mMwm7K7Y7dDrOWJH+Q4IEJCXnjfhY2jDtajqrELvUK/L4pqM2u5axAfFW/8dFRAFtZ96SiXfRERERETTlVRVkWkUrVqLrsEu1PfU2912u347/OR+Dk9b3pi8Ef4Kf2tP2kPVh2AcMt7Ru3Ys6WHpmBc5D4X6QocSb3uGzcMoLi/GivgViAmMmfBxstXZECHiWvu1Scc0WaIooq67DglBn0yXEATBesOCiIiIiIjci4ntFGCZtmovCeod6sW+yn3YkLwBKh/HFnUH+QThvuT7sO/6PvQM9aCwrBCzVLOQE53jcHwFmgJc77yOSy2XHN5nLKfqT6G5t9nhEeexaMO1ADAl1tka+g3oM/UhQXXrOoBsdTYqOiowMDwgUWRERERERDMDE9spQBOmgVyQ203SDlYdRK+p1+mksEBTgD5TH357+be42HwR+Zp8pyq23Zd8HwIUASgsK3TqdW0p0hdB7afG6oTVkzpOpH8kwv3Cp8SI6OgetqNpw7UwiSaUt5dLERYRERER0YzBxHYK8JX7Ii00ze56zCJ9EZKDk7EgyrkF3fMi5yE1JBV/+PgPkAtybE3f6tT+AcoAbEzZiHeq34FxcOJ9rVr7WnG85ji2pm2FUq6c8HGAUVN9p8AaVktiO3oqMjAyxRyAw4XBiIiIiIhoYqRo90M2aNVaHLlxBN8/bbuzkclswqWWS/jGom843R9LEATka/Lx0/M/xeqE1Yjwj3A6vgJNAQr1hdh/fT8+nflpp/cHgF0Vu2ASTcjTTLxo1GhatRan609jYHgAvnJflxxzImqNI52r4oLibnk8PigeKh/VlBhVnklqjbU4euMoHs0eu0fzVHeq7hQEQcDyONe22bJo7WvFvsp9Xv0eERER0SeGhofw2pXX8JmszzhUIHY6YmI7RdybdC9O15/G0RtHx9wmNSQVW9K3TOj4W9O24sD1A/jCnC9MaP85EXOgCdOgSF80ocRWFEUU6YuwMGohUkJcUw49Ozwbw+Iw9O16zImY45JjTkRddx3UfmoEKANueVwQBGSrs5nYetjfy/6OP1z5A+ZEzMHC6IVShzMhL557Eb5yX7cltrsrduPnF36OVQmrkByS7JbXICIiIs8513gOv7r0K5hFM56a/5TU4UiCie0UkZuYi9zEXLcdP9QvFG8++Kb9DccgCAIKNAV4/uzzuGq4areH7u0uNF1AdVc1npz75IRjuN3oAlJSJra13bV3FI6y0IZr8VfdXzFkHoJSNrnp1+QYy42EQn2hVya2vUO9uN553eECcRNhmWVQ113HxJaIiGgasCx9Ky4vxhNzn4BcJpc4Is/jGlty2IOpD8JH5jOhIlKF+kKolCrcm3Svy+KJC4xDsE+w5Otsa421dxSOstCqtRg0D6Kyo9LDUc1MoihCZ9BBgIB3qt5B12CX1CE5ray9DCJEdA12uS1+y7pwy99ERETk3XRtI9c/DT0NON1wWupwJMHElhwW4huCe5Puxd7Kveg39Tu8X+dAJw5VH8Km1E3wV/i7LJ6p0CvWZDahsafxjsJRFpZRZamT75mioacBnQOdyNPkoX+4H/sr90sdktNGV0evM7on8aztHhmxtYzcEhERkXfTGXRYnbAaYb5hKNIXSR2OJJjYklMKNAUwDhlxqPqQw/vsu74PA8MDk+5da0u2Ohtl7WUYMg+5/NiOaOxpxLA4POZU5KTgJAQoArjO1kMs73OBpgCZYZko1E++RZWnjb4J4o4RVbNoRn13PYBPElwiIiLyXl2DXagx1mBe1DxsTtuMYzeOoa2vTeqwPI6JLTklJyYHiapEh+8EiaKIwrJCaNVa6+ilK2nDtRgyD0k21XesHrYWMkGGLHUWR2w9pNRQCrkgR0ZYBvI1+dAZdHb7Q081ujYd5kbMBeCexLa5t9l6I4hTkYmIiLzfNcM1ACNL4Ao0BTCJJuyu2C1xVJ7HxJacIhNkyNfk43zTeVR1VtndvtRQimvt19wyWguM6hUrUfJi7WE7xogtMJJ8XzVcxbB52FNhzVi6Nh1SQ1Php/DDA6kPwFfu61XTcQaGB1DRUYG7Y++GSqlCjbHG5a9hmX6cqErkVGQiIqJpwHIdnKXOQmpoKhZELUChvhCiKEocmWcxsSWnbUnbArkgR3F5sd1ti8qK4Cf3w8bUjW6JZVbwLAQoAiRLbGuNtZALckQHRI+5jVatRZ+pD9Vd1R6MbGbSGXTWmx0hviFYn7Qe+yr3oc/UJ3FkjilvL4dJNEEbrkWCKsEtI6qWY94deze6BrtgHDS6/DWIiIjIc3QGHaIDohHuHw4AyNfko6qrCh80fyBxZJ7FxJacFhUQhZUJK7GzfOe4a1t7h3qx7/o+bEjegGCfYLfEIvVU39ruWsQExkAhG7tzlrUtkcG7psR6m5beFrT2tSI7PNv6WL4mH8YhIw5XH5YwMsdZzhGtWov4oHi3JbYCBCyOXmz9NxEREXkvXZvuliV/G5I2IFAZ6JW1RiaDiS1NSIGmAG39bThRe2LMbQ5VH0L3UDfyNflujSU7PBvXDNckmepb11037jRkAEgNSYWv3JcFpNzMcnPDMmILADnROZilmuU1H+y6Nh1UPirEB8WPJLbGOphFs0tfo9ZYi6iAKGv/Wk5HJiIi8l69Q7243nkd2epPbuwHKAOwKWUT3ql6Z0bNzGJiSxNyT/w9iPKPGnf9YpG+CMnByVgYtdCtsWjDtegf7kdVV5VbX8eWWmPtmK1+LBQyBTLCMlhAys1K20ohQECmOtP6mCAIyNfk40LTBYfWhEtN16ZDtjobgiAgQZWAQfMgWvtaXfoalpsxlhsyHLElIiLyXmXtZRAh3lGktUBTMNL68Lr3tT6cKCa2NCEKmQJb07fiZN1JNPY03vF8ZWclLjZfRIGmAIIguDUWqQpI9Q71wtBvGLMi8mha9Ui/XVePvtEndG06JAUnIVAZeMvjW9O3Qi7IUVQ+tYtIDZmHUNZeZp1KbTmvXD2iWmusRXxQPIJ9gqHycU+BKiIiIvIMy/Xv6BlrwMiMxsywTGwv2y5FWJJgYksTlqfJg1k0Y2f5zjueK9YXQyEosDlts9vjSAlJGZnq6+ERUUcqIltow7XoHupGnZGjY+5Saii12VIqwj8CqxNW210TLrXKjkoMmget30O8aiSxdeWI6sDwAJr7mq2zDBKC3FOgioiIiDxDZ9BB7adGVEDULY9bZq15Y+vDiWJiSxOWqErE3bF3o7i8+JaRyKHhIeyq2IU1s9ZYq7O5k0KmQGZYpsfXsNrrYTsaC0i5l6HfgMaexlvWl4xWkFEAQ78Bx2uOezgyx91+x9U6YtvtuhHb+u56AJ/cjHFX5WUiIiLyDEvhKFszJB9IfQA+Mh+van04GUxsaVIKNAWo667DmYYz1seO1RyDod/g9qJRo1l6xXpyqq8zia0mVAOFoGABKTe52nYVAGyO2ALA8rjliAqImtJFpHQGHQIUAZgVPAsA4Cv3RZR/lEunIluOZTln3VWgioiIiNxvYHgAFR0VY97YD/ENwfpk72p9OBlMbGlS1s5aixDfkFvuBBXpixATGINlscs8FodWPTLV15MVXmuNtfBX+EPtp7a7rY/cB+lh6Swg5SaWkfAsdZbN5xUyBbalb8OpulM214RPBbo2HbLUWZAJn3wsu3pE9fbp8/FB8W4pUEVERETup2/XwySaxryxD4wMQnlT68PJYGJLk+Ir98Xm1M04cuMI2vvbUd9dj/fq30Neeh7kMrnH4pBiqm9t90gRHkeLY1kKSImi6ObIZh5dmw4JQQkI8Q0Zc5u89DyIEFFcXuzByBwzbB7GtfZrt/TgBeDyXrZ13XXwkfkgwj8CAFgZmYiIyItZljHdfv0wmre1PpwMJrY0aXmaPJjMJuyu2I0d5TsAANvSt3k0hvTQdChknp3q60gP29G04Vq0D7SjqbfJjVHNTDqDbty7lcBIErc0dimK9cWS9DweT3VXNfpMfXd8D/GqeDT1NGFweNAlr1NrrEVcUJx1VNhdlZeJiIjI/XQGHYJ9ghEXGDfmNoIgIE+T5zWtDyeDiS1NWkZYBuZGzEWhvhDF5cVYHrcccUFj/4C5g4/cB5pQjccSW1EUHephO5pUbYmmu67BLtQYa8a9W2lRoClAQ08D3m943wOROc4y0+D2Uv0JQQkQIaKhp8Elr3P7zRjLz6krC1QRERGRZ4xXOGq0rWne0fpwspjYkksUZBSgsrMSjT2NKMgokCQGbbgWOoNnpvq2D7Sjz9TnUOEoi0x1JmSCjOtsXeya4RqAO5NCW9bOWotQ39ApNx1H16aDr9wXKSEptzxuOb9c1SbKMn3ewlfui6iAKLahIiIi8jJD5iGUtZeNWThqtMiASK9ofThZCqkDoOnh/uT78cLZF+Cn8ENuQq4kMWSrs1GkL3J6ivBEWBIBZ17HX+GP1JBUvFH6Bt6pemfM7eZFzsP3V3x/0jHaUmusxS+bfomsnizEBMY4vf+QeQj/fOSfXTaCOJYV8SvwrcXfcmhbywj4WIWjRvOR+2Bz2mb8VfdXbN2xdcztfOW++MnqnyApOMmxgEcxi2Z868S38GDqg8hNzHVoH51Bh8ywTChkt34kW84vV4yodg50wjhovGOWQUJQgstHbIfMQ/hGyTfwWPZjyInJmdAxfn7h54gPjMfDWQ+7NLbp5NUPX4Wv3BePzX5M6lCIZrQTtSewp2IPnl/1/C0FAKeS/zr9X7gn/h6sm7VO6lDIRSo7KjFkHrK7FMuiIKMAR2uO4njNcdybdK+bo5MGE1tyiQBlAP596b/DV+4LpVwpSQxL45YCAA5UHcA/3fVPbn0tSyLgzIgtADw570kcqjo05vNNvU0oLi/GY9mPIT0sfVIx2vLXq39FWX8Z/n7t7/jawq85vf/xmuM4VX8Ky+OWI0gZ5PL4gJHpsn/R/QWPZT/mUPKtM+gQHRDtcM/kx7Ifg6HfgKHhse9YHqs5hjevvonnljzncNwWZ+rP4GDVQdQYaxxKbM2iGbo2HR5IfeCO5yL9I6GUKV2SeN5eEdkiQZWAs41nJ3380So6KnCs5hj6TH0TSmzru+vx+pXXkaXOYmI7BlEU8YbuDah8VExsiSS2p3IP9lftxzbNNiyPWy51OHfoHOjE9rLt6BnqYWI7jVhu7DsyYw24tfUhE1siOzanbZb09ZOCk5ATnYMifRH+cc4/OlyteCKc6WE72v3J9+P+5PvHfN7Qb8C6t9ehUF+Iby/59qRivN3g8CB2V+wGAOwo34GvzP/KHSOE9hTqCxEVEIWX1r3k9L6OqjXWYmPRRhSXF+OpeU/Z3d6yvsRRMYExeH7l8+Nu8+zxZ7G7YjeeWfQMfOW+Dh8bgHWac2lbqUOx1Rnr0D3UbfMXk1wmR1xQnEuKO93ew9YiPuiTAlU+cp9Jvw4A61r3Mw1nRtaiOzmDYkf5DogQoe/QuzSu6aSlrwWGfgMM/QZ0D3YjyMc9N5qIyD7LZ16RvmhKJraWJVCsgD+96Aw6BCgCMCt4lkPbW1ofvvrhq2jsaZzQzL2pbmrOlyCaoHxNPmqMNTjXeM6tr1NrrIXaT40AZYBLj6v2U2Nt4lrsrtztskq4FkdrjqJjoAP3BN2Dlr4WnKw76dT+jT2NOFV3CtvSt7ktqQWcq17cO9SL653XHVpf4ox8TT66BrtwpPqIU/sZ+g04WnMUW9K2wEfmc0t/57FYC0eNkQAnBLmml+1YI7bxQfEuLVAFjCT1vnJfyASZ0+2Vhs3DKC4vhr/CHyazCeUd5S6LazoZXSjvquGqhJEQzWw9Qz2o7qqGv8Lf2vpwqrF8XrCewvSia9MhS53l1PT3qdz60BWY2NK0sj5pPVRKlduLA9V2O1cR2RkFmgJ0DnTi6I2jLj1uUVkRYgNjka/OR7hfuNPvUXF5MUSIyEvPc2lctjhavbisvQwiRKdGbB1xd+zdiA+KdygxHW13xW6YzCY8PvtxrE9ej72Ve9Fv6h93H12bDgqZAumhtqeeu6qXbV13HYJ9gqHyUd3yuLWXrQsveHQGHWaHz8aKuBXYUb4DJrPJ4X1PN5xGY08jnpz75MixPNjCy5uM7tnNgnRE0rlmuAYRIp6Y+4S19eFUY/kcbetvQ+9Qr8TRkCsMm4dxrf2aQx0hRpvKrQ9dgYktTSt+Cj88kPoADlcfRudAp9tep85Y5/Q0ZEctjVuKuMA4lybntcZanG44jbz0PCgFJbamb8W7te+iubfZof2HzcMo1hdjaexStxfmAkaqF4f4hth9D5xdX+IomSBDXnoe3m98HzVdNQ7tI4oiivRFmBs5F5owDQo0BTAOGXGoeuw11cBIUqIJ1Yw53TZeFW8t/DQZtcZam+estZetiwpIDZuHcc0w8su2QFOA5t5mvFf/nsP7F+mLEOYbhkezH4VKqWLSNgZdmw7JwcmI8o9i8k8kIctn1Na0rZgbMRdF+iKPdGdwhs6gg1I2Uv+kvrte4mjIFaq7qtFn6pvQjf2p2vrQFZjY0rRTkFGAQfMg9lTuccvxTWYTGnoa3JbgyQQZ8jR51vWJrrCjfAcEjDToBkam2g6Lw9hVscuh/d9veB8NPQ0o0HimlZOP3AebUzfjaM1RGPoNY26nM+ig9lMjKiDK5TFsTd/q1FTayy2XUdlZiYc0DwEAcqJzMEs1a9xRX1EU7a7DtcwMmOyo7VjVwqMColxWoAoAqrqq0D/cD224FqsSV0Htp0ZhmWM3adr62nDsxrGRqdxyH2SFZzFpG4POMHLeWNqcEZE0SttKEeEfgciASORr8lHRWYHLLZelDsuqe7AbVV1VWBa3DADX2U4X1mVME7ix7+jggTdiYkvTTpY6C9nh2SjUF7rlrmlTbxOGxWG3jdgCwLb0bRNan2iLZc3iivgV1kIBScFJWByzGEX6IphFs91jFOoLEeobirWz1k46HkcVaArsTutytDH5RMQExmBl/EqHp9IW6gsRoAjAfcn3AQAEQUC+Jh/nm86jqrPK5j5NvU1oH2gf9xdTvGryvWzNonkksbUxfV4myEamO7toKvLoUXSlbGR2wPHa42jta7W77+6K3TCJJuRr8q3HuNZ+zampzDOBod+Axp5GZKuzoQ3XorKzEn2mPqnDIpqRdAad9TP8/pT74a/wd3oZiztdax/p9b4+aT0A183OIWnp2nTwlfsiJSTF6X0dHTzwRkxsaVoq0BRA367Hx20fu/zYE+lh66yYwJgJrU+05VT9KTT3Nt8x2moptHW+8fy4+1sKIm1O2+zR6rTpYemYFzlvzGldA8MDqOiocHnhqNHyNfkOFdrqHuzGwaqD2Jiy8ZaCYlvTt0IuyFFUbvsix5oEOjBiO5mLkebeZgyZh8a8GRMfFO+yix2d4dZftvnpI7MDdpbvHHc/URRRqC/EgqgFSA1NBTDyvgwMD+B653WXxDZdXG0bKRalDddCq9bCLJpR1l4mcVREM0+/qR+VHZXWz/BAZSA2pmzEgaoD6B7slji6EZZZLyviVsBf4e+ymWAkLZ1Bh8ywzAkX83Rk8MAbMbGlaWlTyib4K/zdMs1ioj1snTWR9Ym2FOmLoPZTY3XC6lsev3fWvVD52C+0ZSmIlJ+eP6k4JqJAU4DKzkqb07rK28thEk0uLxw12sqElYjwj7D7Hu2v2o8+U98dNw8i/COwOmE1dpbvxJD5zr65OoMOMkGGjLCMMY8d7BMMlVI1qYuRsSoiW7iqQBUwchE1+pdtckgyFkUvsrvu7IPmD1DVVWUdrQVgvWnBqba3Gl1J21I4hFO2iTxP367HsDh8yw3WfE0++kx9OFB1QMLIPqEz6BDpH4nIgEiX3sQk6ZhFs9OtDm9nb/DAWzGxpWkpyCcIG5I2YF/lPpdXAKw11kIuyN3e/8vZ9Ym2tPa14njNcWxN2wqlXHnLc34KPzyY+uC4hbYsBZHmRc5Depjtqr3udF/yfQhQBNhMLCezvsRRSpkSW9PsF9oqKiuCJkyDORFz7niuIKMAhn4DTtScuOM5XZsOqSGp8Ff4j3lsQRAQr5rcxchYPWwtElQJLilQZRbNuGq4escv2wJNAW4Yb+B809izAwr1hQhUBmJD0gbrY0nBSfBX+DNpu42uTYeEoAQE+wQjOiAaYb5hTP6JJGD5uRv9mTc3Yi7SQ9OnzHTk0rZSa3yuah9H0qoz1qF7qHvS1z+WwYNLLZdcE9gUwMSWpq2CjAL0mnpxsOqgS49b112HmMAYt/ZyBeD0+kRbdlXsgkk0WYtG3a5AM36hLUtBJE8VjbpdgDIAG1M24mDVwTumdenadFD5qNw+cp6nyRu30NY1wzVcabuCAk2BzbW+y+OWIyogymZyrmvTOfSLabIXI3XddRAgIC4ozubzlvdwshc8tcZam79s7026FyqlaswLPeOgEe9UvYNNKZtumcotl8mRGZZpnbJNIyyFo4CRGx/Z4dlM/okkUNpWihDfEMQGxlofEwQBBZoCfNT6Ea4ZrkkYHdBn6kNlZ6X1MzlBlYA6Y920GqGbiUbP2pkM6+DBJAZQphomtjRtzY+cj5SQFJffNXVnD9vbFWgKMCwOY0f5Dqf3tYy2LoxaOGZxgUx1JmaHzx6z0Nb2su23FESSQoGmAH2mPuyv2n/L47o2HbLV2W4pHDWapdBWYVmhzUJbhfpC+Mh88GDqgzb3V8gU2Ja+DafqT6Gxp9H6eGtfK5r7mh36xRQfFI/67nqHCn3ZUtddh6iAqHFbCgGT72U71i9bf4U/NqVuwqHqQzZnB+yr3If+4X6bN1C04VpcNVyd8Pc+3XQNdqHGWHNL70JtuBb6Dj0GhwcljIxo5rEUjrr999CDqQ9CKVNKPmqrb9fDLJqtn8nxQfHoNfWifaBd0rhocnRtOihkCqSHTm4mnWXw4J3qdyY9Y2uqYGJL05blrumllksoby932XHrjHXWRMDdkoKTkBOdM6E1EOebzqO6qxoFGeOPtuZr8qFv1+NK65VbHjcOGvFO9Tt3FETytDkRc6AJ06Co7JMLhCHzEMray9y6vna0fE0+artrca7x3C2P95v6sadyD9YlrUOIb8iY++el58Esmm+5QWEZYXNkxDZeFY+B4YEJj9yP1cPWwhUFqoCR0YuxftkWaAowMDyAvZV773iuUF+IzLBMm43mtWotek29qO6qnlRs04W1cNSo80ar1sJkNkHfoZcqLKIZZ2h4CPp2vc3fQ6F+obh31r3YXbkb/aZ+CaIbYZntYlkDbJ2d46Iq+CQNnUEHTajGJQU9rYMH1/fb39gLMLGlae3B1AehkCnGrErrrN6hXrT1t3lsxBYYVb14nPWJthTpixCkDLKW+B/LWIW29l+3XRDJ0yw3KK60XbFO66rsqMSgedCt62tHG6vQ1uEbh2EcNNp9jxJUCVgauxTF+mLryKPlgiNLnWX39Sfby7a2u3bcKt4hviGTLlAFjCTrY/2ytVTwvX12QGlbKXQGHfI1+TZH31kc6VaWNX2jzxvLhTXfIyLPKe8ox5B5aMzK/PkZ+TAOGnHkxhEPR/YJnUGHUN9Qa00Qy+8BrrP1XqIoTrpw1GjWwYMpsiZ8spjY0rQW7h+ONYlrsLtit0um6dV31wNwf0Xk0dYnrYdKab968WidA504VH0ID6Q+MG5hIuCTQlv7r++/pdBWkX7sgkie9mDqg/CR+VjfA0fa5LjS6EJbHf0d1seL9EVICErA4pjFdo9RoClAfU89zjScATBywZEUnIQgnyC7+1pmCEwk8RwYHkBLb4vdmzEJqsmt4xVFETqDzuaoq0WBpgBl7WW3rJkt0hfBR+aDB1IfsLlPamgqfGQ+LI50U2lbKaIDohHuH259LCEoASqlioktkQdZPpPG+sxbErME8UHxkiYMljoOlpuGlmsXVkb2Xk29TWgfaHfZjX3L4MHHbR/jquGqS44pJSa2NO0VaArQMdCBozVHJ30sy4W/p6YiAyNJ1QOpD+BQle31ibbsu74PA8MDt7ROGc/thbauGq7i47aPxyyI5GkhviFYl7QOeyr3oN/UD51BhwBFAJKCkzwWQ4GmAEPmIey9PjKVtrqrGucazyFfkw+ZYP+jdO2stQj1DbUWaXC0cBQwuYuR+u56iBDtnrOTbQPR0NOAzoHOcb+nTamb4Cf3s96gGDQPYl/lPqxPXj/mVG6lTImMsAwmbTeNLhxlIQgCtOFaJv9EHlTaVoogZdCYs2Fkggz5mnycbTyLG103PBwdMDg8CH3HrVOlA5QBUPup2cvWi7njxr5l8GA6jNoysaVpb2nsUsQGxt6yRnOiLBf+npyKDIwknoPmQZvrE28niiIKywqhVWvHHT0bbX7kfKSGpFoTDsso2lgFkaTwkOYhGAeNOHzjMHRtOmSpsxxKKF0lU52JOeFzsL1sO0RRRLG+GHJBjq3pWx3a30fug81pm3G05igqOytR31Pv8C8mX7kvovyjJrQuytrD1s45aylQNdFqmdY1w+N8TyofFTYkb8C+6yNtuC71XoJxyP5Ubm24FqWG0hlfybN3qBdVnVU2pz5q1VpcM1yz2S+ZiFxPZ7D/e2hr2lbIBBmKy4s9GNmI8o5ymMx39npnyx/vpjPoIBNkyAjLcNkxbx888GZuuyoUBOE1QRCaBUG4Muqx/xQEoU4QhEs3/2waY9/7BUG4JghCuSAIz7krRpoZ5DI58jR5ON1wetJ3KWuNtfBX+EPtp3ZRdI7JUmchOzx7zOrFo5UaSnGt/ZpTa2MFQUC+Jh+XWy7j47aPsadyD+5NunfcgkielhOTg0RVIt6+9jautV9zOGl3pfyMfJR3lONSyyXsrNiJlQkrERUQ5fj+6fkwmU34ybmfABh7Cpst8ar4CV2MWJJhe9PnE1QJkypQVWoohVyQ2/1lW6ApQM9QD96pfgenu09jlmoWcqJzxt1HG66FcdA44y/GrrVfgwjR5s0DbbgWg+ZBXO+8LkFkRDOLyWxCmcF+AcPowGisjF+JHeU7YDKbPBTdCMvNxttvhMUHxXPE1ovp2nRIDUm1u8zMWaMHD7yZO4c7Xgdwv43H/0cUxfk3/+y7/UlBEOQAXgKwEUA2gEcEQfD8FSxNK3npeRAgTKhtzmh13XWID4qXZHqurfWJthSVFcFP7oeNqRudOv7mtM1QyBT41vFvOVQQydMs07ouNl9En6nPY+trR9uYvBH+Cn/8+8l/R2tfq9PvUXpYOuZFzsPJupMAHKuIbJEQlDChqcK13bXwkfkgMiBy3O0mu/ZK16ZDSkgK/BR+4263IGoBkoOT8cqHr6B8oHzMolGjWS7MZvpUW+sUNBvnDQtIEXlOVWcV+of7HfoML9AUoLWvFe/WvuuByD6hM+hsTpWOV8WjsafR44k2uYaubfxaFhNlGTzw9p62bktsRVE8AcAwgV2XACgXRbFSFMVBAG8BcGyuH9EYYgJjsCJ+BYrLi9HW14b2/vYJ/akx1nh8GrKFpXrx3679bcz4mnqasO/6PmxI3oBgn2Cnjq/2U2Nt4lrcMN5AoioROTHjj6JJYUvaFsgFOQDnkkJXCfIJwn3J9+GG8QYi/SNxT/w9Th/DkgzHB8U7NSIer4pHU08Thoadm2pa112HuKA4u9O2J1OgCoDdwlEWlkIVNcYayCBzaCp3elg6FILC40nbsHnYo69nj65NB7Wf2uYsgSRVEvwV/h5P/if7Hk22P7EoipM6xmT3Byb/PbibWTRP+j2a7P+zcdA47u/W7sHuSR1/YHjA7u9vVy5lsFc4arSVCSsR6R+Jt8veHje+AfOAy+IDMOaSnYSgBJhEE5p6m1z6ep4w2Z81R84Te3+k/Hlv7WtFc1+zW65/LIMH55vOo3mo2eXH9xSFBK/5tCAIjwE4D+Aboije3iU6HkDNqH/XArjbU8HR9FWgKcAzJc8g9++5kzrOsrhlrgnISZbqxcXlxXbX6+Sl503oNQo0BXin+h3kped5dP2qo6ICorAyYSXeb3gfKSEpksRQoCnAjvId2Jq+FQqZ8x+h9yXfh+fPPu/0Hdf4oHiIEFHfU+9U0axaY61Dxc4mM2Lb0tuC1r5Wh3/Zbk7bjP/74P+g9dUiwj/C7va+cl+khaah1DD+bAVXOnbjGL797rdRtKVo3FZJnmQpHGVrhFsukyNLneXR5P9Mwxn885F/xpsPvIn0sDt7F9vT2teKrTu24nvLvof7ku+bUAxfOPgFpIak4nvLvjeh/X9y/ic43HAYuWLuhD7zDlUfwvdOfQ8HCg5MqaUbo33pnS8h3D8cL656cUL7v3TpJey7vg87t+2EUqZ0ev8TtSfw1SNftbvda/e95lCF+dv1mfqwqWiT3WUUn874NL677LtOH9+W0rZS+Mn9kBycbHdbhUyBrelb8buPfodVf1s15nYyyJBkSHKoBZw9JrMJ19qv4dOZn77jOcvvgzpjnUc7PExWRUcFHt7zMH634XeYHzXf6f17h3qxsWgjDP0TGXP7xKaUTXhh1QuTOsZEXW6+DMCxNoETsSVtC371wa9wuvs0Po07zx1v4OnE9mUAPwAg3vz7ZwC+OJkDCoLwBIAnACA6OholJSWTDNF9uru7p3R8051MlOEfwv8B/eaJL4wXBAGZnZmS/T8uGV4CH7XPuHeeg+RBMOqMKLlaYvP58c5DURTxpcgvIbktecqeq/ea78WC8AU4eeKkZDE8GfkkNB2aCb9HT0U8BZVJ5dT+rf0jF237Tu2D1t/xu7VVHVWIGIxw6LVC5CG4oL+AknbH4wKAK70jpRT6b/SjpNmxfb8a+VUEDAY4/B6EDYbhcudlHDt2zCNLAX7V9Cv0mfrwP4f/B5vDNrv99ewZEodQ3l6O5OGxfzaD+4Jxuvs0jh476pEbU680v4L+4X7877H/xUPqh5ze/3DnYXQNdqH4QjF8q3yd3n/QPIiLTRdxuekyFvUuQqA80Kn9B8wD+Hvt3zEgDuCVA68gy9/5i8VfNf4K3UPd2H5sO9L80pze391qB2txtvEsZJBhxeAKhCicS76HxCG8UfsGesw9+PWBX2NewDynY/h1068RIg/B+uCxe6rv69yHX777S3wx0vlLwrPdZ9Ha14oNwRsQLLc9U+lK3xXs0O9ATl8O/GWTX5t4pvEMYhWxePeEY9OLM8wZeFj9MIbFsUe+d7bvxP8e+198Jvwzk46vfrAeA8MDEJqEOz4vWodGfpccuXAEvUG9Nvaemk4YT2BgeAD/d+L/8PmIzzu9/2njaRj6Dbg/5H4Eyey32bPlYu9FnLxxUrLro983/x4qmQqdus4xr/EmKz8sHzHDMVP2GtAejya2oiha5z0IgvAqgD02NqsDkDjq3wk3HxvrmK8AeAUAcnJyxNzcXJfE6g4lJSWYyvHNBGuxVuoQJm0Ltkxqf3vn4RqsmdTxZ4Jc5Hr8NbN6svB/2/8P4anhyM107PU7BzrRV92HJZlLkDvH/j6p+1MxLAw7/Tl19fJVoAV4ZN0jCFQ6llzkItepz8R6XT3OnD2D7CXZiA6Mdio+Z9V01aCsuAxKmRIfDH2AF1a9MKHReVe60noF5htm3L/wfuQm5drcpqO8A8dPHUfKwhS3z2ho7m1G6fZSKGVKXBy4iJ/e81O766tHE0URP9vxMwBAh2/HhH43Xm65DLFGhAkmdMR14AGt7V7IYynWF2OgZgAKKFARUIEvr/6yU/tXdlaickclACBKE4XctFyn9veEH7//YyiaFDCZTWiJbsHWu5xb2XWg6gB6bvRAKVOizLcMX8/9ulP7N3Q34GrhVTwx9wk8veDpMbfzO+uHt669hblL5zpdnPH1A69jlmoWfrrtp2Pe9Pqo5SN8dt9nYYw3YmOmc/UnbmcWzXjuzeewOXUzcpfmOrzfRoz/uje238ClgUv4+YqfI0AZMKkYd1XsAhqAgnsKkBqaestzJrMJP3zjhwiMC0TuwtxJvY4nHXvvGGAAPuz7EAuWLXB6hsSr+15FakgqXtz64oRvjr5R+gZeOPcCZi+Zbbduhau19LagdHspHpv9GNYtWue213H2d/NU49G5hoIgxI76Zx6AKzY2OwdAIwhCiiAIPgA+A2CXJ+IjIpqqIv0joZApnJoq7Gzf5YkWqNK16ZAcnOxwUjsRlqnbnlhDWlxeDJkgw7cWfwstfS3WYl9SshSOGm8Ku2UquCemI++q2IVhcRjPLXkOxkEjjtw44tT+F5svoqqrCrGBsShrL5tQmyLL9xkbGOtQxfjbFeoLkRqSihWqFThy4wja+29fGTW+Yn0xFIICAoRJ9YB2l35TP3ZX7sb6WeuRE52DIn2R0+sDi8qKEBsYi8/P/jxO1p1EY0+jU/tbCjbmacZfHlOgKYDJbMLuit1OHb+qswoXmi4gT5M3brIyJ2IONGEal/TprDHWoGeox+UFfJYFLbNWjJ8sXZsO/gp/m8tWFDIFYgJjvK7KvK5Nh7jAOIdbH46mb9fjw5YPHSpWOB5P/h663c6KnRgWh5Gfnu/x1/Ym7mz38yaA0wAyBUGoFQThHwG8KAjCR4IgfAhgDYBnbm4bJwjCPgAQRdEE4GkABwHoAPxdFMWP3RUnEZE3kMvkiA+Kd6qXraM9bC0mWqBKZ9C5vZhXRlgGBAhuT9pMZhN2lO/AyviVeCjjIUT6R1r7O0tJZ9Ah2CcYcYFxY26TGpoKH5mP2y+6zKIZRfoiLI5ZjIcyHkJCUILTCUORvghByiB8ed6XMWQeQmVHpdNx6Aw6hPqG4p/u+ifo2/X4uM3xS4Xy9nJcbrmMAk0Blgctx5B5CHsqbU0is21oeAi7KnYhNzEXkQGRU7J9ypEbR0Yq3GcUoCBjpGDb+cbzDu9fa6zF6YbTyNPkoUBTALNoxs7ynQ7vP2weRnF5MZbFLbO7ltNSMb5IX+TUDYqi8iLIBTm2pW8bdztL0bqP2z7GVcNVh49viyM9uycizTcNycHJLkm+S9tKkRmWCblMbvN5b+tlOzg8CH2HHhtTNjrc+nC0In0RFDIFtqRNbsZbpjoTAgS73SlcTRRFFOmLsCh6EZJDkj362t7GnVWRHxFFMVYURaUoigmiKP5eFMVHRVG8SxTFuaIobhFFseHmtvWiKG4ate8+URQzRFFME0Xxv90VIxGRN4kPcq6XrbWHrRMjtiJENPQ0OPwa7f3taOhpcHv7pQBlAFJCUtxeQOpk3Um09LUgX5NvLfrybu27aO6Vtkqkrm3swlEWSpkSGWEZbk/+zzeeR42xBvmafGslzbONZ3Gj64ZD+3cNduGdqnewKWUTFkYtBIAJXSjq2kZuqFgqxjtzA6KofORCd3PaZsT5xGFuxFynkqqS2hIY+g3I1+RP2SShSF+EhKAELI5ZjHtn3QuVj8qp92hH+Q4IEJCXnocEVQKWxi5FcXmxw6O+ZxrOoKGnAfkax0aYCjQFqOysxOWWyw5tP2Qews7ynVidsNqhInQPpj4IH5nPpBPHUsPIFPy0ENeuqbYk3x80fzChGz0WZtGMq4ar434mx6u8q5dteUc5TGYTtOFah1sfWgwOD2J35W6sm7UOYX5hk4ojUBmIpOAkj1foP9808pk71dowTkVTr+wpERHZ5OxU4druWgT7BDvc+slaGdmJCx7L6KAn+gprw7Vuv6Ao1Bciwj8CKxNWAhipMD4sDo+sWZPIkHkIZe1l1n6+49GGa1FqKHVpa5PbFeoLofJR4d5Z9wIAtqZvhUyQ2a3WbrG/cj/6h/uRn5GPWcGzEKAIcHqU2TKCow3XWivG76vch94h+8VwBocHsbvi1gvdfE0+yjvK8WHrhw69fqG+ENEB0VgetxwJqoQplyTc6LqBs41nrTcf/BR+eDD1QRyuPozOgU67+1tGW1fEr0BMYAyAkcSzrrsO7ze871AMhfpChPmGYU2iY3Ub7ku+DwGKAIeT7xM1J2DoN6Agw7GL/RDfENybdC/2VO5Bv2niRSR1bTpowjRQyp2vEG3P5rTNUAiKSSXfN7puoNfUO+4smvigeLT1t6HP1Dfh1/Eky+d+tjobG1M2wk/u5/B5cvTGUXQOdDp8g8UebbjW41ORC/WFUClVWJ80dgE2GsHElojIS8Sr4tE50Olwz8fa7lqn2jlY2to4kzxbp+V5oK+wVq1FU28T2vra3HL85t5mvFv7LrambbW2NZkVPAtLYpZMaH2iq1R2VGLIPOTQzQNtuBbGQaPbRhA7BzpxuPowHkx90FosKiogCqviV2FH+Q6YzCa7xyjUFyJLnYVsdTZkgmxCbYpGj+AAQEFGAXpNvThYddDuvkdrjqJjoOOWC937U+6Hv8LfoYSiobsB79W9hzxNnnWJQHNvMwaHB536HtzJsk58dJ/oAk0BBs2DDk25PlV/Cs29zbeMEK2dtRYhviEOvUdtfW04VnMMm9M2w0fu41DMAcoAbEzZiINVBx36jCvUFyIqIArL45Y7dHxg5D0wDhpx+MZhh/cZTRRFty69CPcPx5pZa7CrYpfTS0IsHOmxa1meUt9dP6HX8DSdQQeVUoUEVQJUPipsSN6Afdcdu5FVqC9EXGAclsYudUks2epsNPQ0OL0mf6I6BzpxqOoQHkh9wKkCfTMVE1siIi9hSVIdTVrqjHVO9WCN9I+EUqZ0KinSGXSID4r3SA9Py4XaZNfIjcVSEOn2Qjf5mnzUGGtwrvGcW17XHsuUO0cupi2juu4aUdhTuQeD5sE7psTla/LR2teKd2vHb39S2lYKnUF3SxGX7PBsXGu/hmHz2K1Qbjd6BAcA5kfOR0pIikNJV1FZ0R0XuoHKQGxM2Yj91/ejZ6hn3P0tBZEs6zotPaadmcLvTkPmIewo34FV8asQFRBlfTxTnYnZ4bMdWp9YpC+C2k+N1QmrrY/5yH2wOXWzQ4W29lTugclscnqUrEBTgD5TH/ZX7R93u8aeRpyqP4Vt6ducqlieE5ODRFXihEdEG3oa0DnQ6fLCUaPla/LRPtCOYzXHJrS/rk0HpUx5RzXk0SzLU6baTIOxlLaVIis8y/qZUaApcKjQVq2xFmcaziBPk+eyFmiWm2memo68t3LvyGeugzMTZjomtkREXsI6ourAxYhZNKOuu87hwlHASIGquKA456Yit7m/cJRFpjpz5DXdkLSZRTMKywqxOGbxHZVE7026F8E+wZIVkdIZdAhQBGBW8Cy726aHpUMhKNxy0SWKIgr1hZgdPtv6f2GxMmGlQ4W2ivRF8JX7YlOKtawGtOFa9Jn6UNVV5XAspW2lCFIGWX8mLOsTL7VcQnl7+Zj7jS6IdPuFbr4mfySpuj52UjVsHkZRedEtBZGc+bn0hHdr30VrX6vNpDJfkw99ux5XWm01pRjR2teK4zXHR2Yu3DbdNl+TjyHz0LjVi0VRxPay7ZgfOR9poc6tQ7VULy4sG/88sqz1zUsfv9ry7Sxrws81nkN1V7VT+wKemaGyLHYZYgJjJvx5U2ooRUZYhnXWiS3WZSdTsJr37YbMQ7hmuHbLe74gagGSg5PtnidF+iLIBJnd4mLOyFKP9Lt2d70H4JPP3OzwbOvr0viY2BIReQlLkurIxUhLbwuGzENOTUUGnCtQZRw04obxhkfW1wJAsE8wElWJbqlIea7xHGq7a20mA75yX+v6xI7+Dpe/tj26Nh2y1FkOjTj4yn2RFprmlouuK61XoG/X23yPrIW26t5FU0+Tjb2BPlMf9lbuxYakDbeM8FsuWJ35f9UZ7nxPHkx9EAqZAkXlY4/GWabo2rrQnRsxF+mh6eOO5p1uOI3GnsZb3gNnZ1K4W5G+CJH+kdZ14qM5UmhrZ/lOmESTzRY9mjAN5kaOX2jrg+YPUNVVNaE1jY5ULx42D6NYX4ylsUudmpFisSVtC+SCfEKjtqWGUsgFOTRhGqf3dZRcJkdeeh5O1592+pwSRdFaaG484X7h8Ff4T5mbMeO53nkdg+bBW76n0TeyKjoqbO5nMpuws3wnVsR9sk7cFUJ8QxAfFO+REduP2z5GWXsZi0Y5gYktEZGXCPYJRpAyyKGLEUvy6+yFnzMFqiwXnp4asbW8ljsuKG4viHQ7y0jV3uvO9U+crGHzMK61X3Nq6qOlyJarC0gV6gvhr/C/ZbR1tLz0vJGWMBW2W8Icqj6E7qHuOxKelJAU+Mn9HB6Jt47g3HbxHu4fjjWJa7C7YrfN9a6WVk5jXegKgoB8TT4+av0I1wzXbL52kb7ojoJIUQFRUMqUUyJJaOppwrt172Jr+labU3Qthbb2X99vc32ipa3IwqiFSAlJsfkaBZoCVHRWjFm9uFBfiEBlIO5Lvm9C34O96sXvN7yPhp6GCV/sRwVEYWXCSuws3+l0/2Rdmw6poaluX+toufFimfbuqPqeenQNdtn9TBYEwekq+1K5fdmBhb1CW6fqTqG5r9ktSWF2eLZHCkgV6gvhJ/fDxpSNbn+t6YKJLRGRlxAEAQkqx1qLWLZxesTWiQJV7urnOB5tuBa13bUOVXZ1VEd/xx0FkW6Xqc7EnPA52F623a0Vh29X3VWNPlOfU++xVq2Fod/g0hZFvUO92H99PzYkbUCQT5DNbewV2iosK0RScBIWRS+65XGFTIEMteNtiiwjOLaS/QJNAToGOnC05ugdz71X/94dBZFu92Dqg1DKlDYrPLf1teHYjTsLIskEGeKD4qfEtM6dFTvtTtEdr9DW+abzuGG8Me56vvuT70eAIsBmQmEcNOKdqnewMWUjApQBE/oeQnxDsC5p3ZjViwv1hQjxDcHaWWsndHxg5Dxp62/DidoTTu3niZ7dABAXFIflcctRrC+e2NpzB26ETdU2VbfTGXTwV/jfsUTEUmhrrBtZhfpCqP3UWJW4yuUxadVa1Bhr0DXY5fJjW/QO9WJf5T5sSN4AlY/Kba8z3TCxJSLyIo7eZa8z1kGAgLigOKePDzg2rVJn0CHKP8qhHpKuYrlrP9aI2kTsvb4XQ+Yhu3f28zNGWsKMtz7R1SxTip25mLZc1LpyROFg1UH0mnrtFjDJ1+SjrrsOZxvP3vL49c7ruNh88ZaiUaNp1VpcNVx1qPL0WCM4ALA0diliA2NRVHZn0lVYZv9CN8wvDOtmrcPuit0YGB645bndFbthEk02z5OpMPplFs0o0hdhScyScddjWwpt2ZqOXKQvQpAyaNy2IpbqxQeqDtxxA2z/9ZFWTpMdJRurerGh34CjNUexOdXxasu23BN/DyL9I52ajtzS24LWvla3Fo4aLV+Tj6beJrxX/57D+5S2OT5V2tLL1pM36ibCshRDLpPf8dxYhbZaeltwovYEtqZvHXet8URZbjS68vfQ7ayfuZyG7BQmtkREXsRyl93exUhtdy2iAqKcvvhzphCOI2u5XC0rfKSAhquSNkuhG1sFkW63MXmj3fWJrqZr08FX7jvmtFBbMsIyIBNkLp2yXagvRGpIKuZHzh93O0uhrdsTy2J9MRSCAlvSttjcLzs8G91D3Y6dd2OM4AA31ydq8nC64fQtx2rta8Xx2uMOXejma/LRNdiFI9VHrI9ZirgsiFpgs9rsVOhle7bxLOq66+yubbWsT7zccvmWQludA504VD3SVsRf4T/uMSyFtg5UHbjl8UJ9ITLCMjA7fPbEvxEAi2MWIyEo4Y7Ec3fFbpjMtm8uOEMhU2Bb+jacrDuJxp5Gh/ax9uz20NKLNYlroPZTO5V86ww6pIWmwVfua3fb+KB49Jp60THQMYko3cssmscdJV8Wu2zkRtZt79HOip0YFoeRn+6a3rW3sxaQckO9B4sifRGSg5OxIGqB215jOmJiS0TkReJV8RgYHkBrX+u429Uanetha+FogareoV5c77ru8cRW7adGTGCMyy4orrReQXlHuUOtFIJ8gnB/8v1jrk90B51Bh8ywTKdamgQoA5ASnOKyAlLl7eW43HJ5zNHW0XzlvticthmHb3xSaGtoeAg7K3ZideLqMUf3rQWkHIhZ1zbyntgawQFG1voKEG5Zn7iz3PEL3btj70Z8UPwtF8v2CiLFB8Wja7ALxkGj3eO7S1FZEYJ9gnFvku114qNtTtt8R6Gtfdf3YWB4wKGk8a6Iu6AJ09zyHunadChtK0WBpsDueWKPTJChIKPglurFlvW/8yLnIT0sfVLHB4A8zc014eW214TfrrStFAIEj1WnVcqV2JK2BSU1JXY/74GR96e0rdThxNvyWS/1TIPx2FuKYavQluU8yYnOQXJIslviivCPQFRAlNvW2VZ0VOBSyyWX/CzNNExsiYi8iKNTheu6netha2EpUGXv+GXtZTCLZpvTQd0tW+26wh2Wgkgbkx0rzpGvyR9zfaKrmUXzhEfFLQWkXKGovAgKmQKb0zY7tH1eeh6GzEPYU7kHAHC89jgM/YZxRxLTQ9OhkNlvU2QdwRnnPYkJjMGK+BUoLh9Zn2i50F0UvcihC11LS5j3G99HTVcNgE8KIm1I2mBzH6krI3f0d+DwjcPYnLbZodE6tZ8aaxPXWtcniqKIwrJCaNVah843y6jv6EJbRfoi+Mh88EDqA5P+foBPqhcX60fWO19uuYzKzkqXTc1MVCXi7ti7ra2D7NG16ZAckjzhtcMTkafJg0k0jdteyaKlrwWGfoPDnxfe0MvWkfZKtxfaOt90HjXGmglV5XZGtjrbbZWRi/TOfebSJ5jYEhF5EUuyWmOsGXObweFBNPc2O9XD1sJSoMrexY51Wp6HR2wtr1nVWTXpUVNLQaT7ku8bsyDS7eZFzkNaSBq267dP6rUdUWesQ/dQ94SmPmrVWjT1NqGtr21SMQwOD2J3xW6sTVwLtZ/aoX0y1Zm4K+IuFOoLrVN4owOisSJuxZj7KOVKaEI1di8UrSM4dt6TAk0Bmnubcar+1CcFkZxIiLambYVMkKG4vNhaEGlTyqYxkxqpe9nurtyNIfOQU31dRxfaKjWU4lr7NafeI0v14uLyYvSb+rG3ci/WJ6+/pZXTZFirF1eMVC8u1BciQBEw4WrLthRoClDXXYf3G963u62nCkeNlhqSioVRC8dtr2ThTOEowLn2cVLRGXTwkfnYnP5vERsUi+XxnxTaKtQXQqVUjbtO3BW04Vpc77zu8tk7ls/cNYlrEO4f7tJjzwRMbImIvIgjI0P13fUQIVrvyE/kNeyNPOnadFD7qREdED2h15iM7PBsiBBxrX1yhTsmUpzD0hLmw5YPoW/XT+r17bFMy51IsRrLDYexeoE66mjNUXQMdDg9SpavGSm0daj6EE7VnUKeJm/MqcMWlhYa413AO3rxvjphtXV9YpG+CCqlyqEpuhbRgdFYGb8SO8p3YHfFbrsFkaQcsbWMSN8VcZfddeKjLY1birjAOBSVFaGorGikrUiq421FLNWLd1fsxt7KvTAOGV1e6KZAU4DWvlYcuH4AB6sOTqrasi1rZ61FiG+I3XWs7f3taOhp8FjhqNHyNfmo6qrCxeaL425XahiZKp0Z5tg5EKAMgNpPPeVHbDPCMuyuiy/QFKCptwkHqg7gUNXIOnF3t2TSqrUQIaKsvcylxz1WcwztA+0sGjVBTGyJiLyIr9wXUf5R415AW56byIitZT97BaosoxdSrP+xrsec5DpbS0GkeZHznNrPuj7RiaIuE1HaVgqFTIH0UOfXE1rWAU52ynZRWRHiAuOwNG6pU/ttTBkptPXdU98F8Ml0wfFo1Vp0DHSMW8xHZ9BBKVOOO4IDjIwAb03biuM1x0dGW1M32S2IdLt8TT5a+lrwfxf/D5lhmeMmNSG+IVApVePOpHCXD1s/RHlHudNTL2WCDNs023C64TR2V+7GhuQNCPYJduoYBZoCdA124cVzL2KWahZyonOc2t8eS/XiH575IfpMfS6/2PeV+2Jz6mYcuXEE7f3tY27n6cJRo61PWo8gZZDdzxtdmw4pISlOJf5ToZr3WERRRKmh1KFZQbkJuVD7qfH909/HoHnQoZoJk2WJy9UFpIr0RYgNjMXSWOc+c2mE49UoiIhoSohXxaO0rRSHqw/bfP5Mw5mR7SZQPMpy/IHhAeys2Ikg5Z1TdM2iGeXt5bhnzj0TOv5kRQZEIsI/AidqT0x4xLhjoAOXWy7jmznfdDo5t7aEqdyNZxY9M6m2I+PRtemgCdVAKXe+XYXKR4VZqll4t/ZdJAcnT+j1e029ON1wGl+Z/xXIBOfugwcqA3F/8v0oLi/G8rjlDp2L1gtFQylig2JtbuPoCA4wsj7xDx//ARAxoYRoZcJKRPhHoLWv1aHCWY72mHa1In3RyDrxFMdHWy3y0vPw8qWX0Wfqm9CaREv14truWuRp8lx+o8tSvfjVj16FJkyDORFzXHp8YOQ8eUP3Bn596de4O/Zum9scrz0O4JOq7J4UoAzAppRN2FmxE6sSVkEu2J758FHrR2PGP5b4oHh83PaxK8K0q8/UhxpjDTLCMhzavq67DsZBo0OJraXQ1usfv47s8GyPFPiKDoiG2k/t1M3DrsEunGs4BxG2bxr3mnpxuv40npr3lN0ZLmQbE1siIi+jCdXg72V/xzMlz4y5TYhvCCIDIid8fADW0baxSNmGYEHUAhyqPuRUj8fb+Sv8J1ycI1+Tj4NVB3HkxpEJJRT29Jn68GHrh5M69vyo+dhVscvuFMbxKGVKp9ZtjvbpzE9jR/kOPJz5sEPbZ4RlQC7IoWvTYd2sdXc8bxnBcXSNZUpICu6OvXvcqqrjUcqU+FTGp/Dn0j87VBApPigeFZ0VTr/OZPQM9VjXiQcqA53ePyYwBrmJuajtrsXCqIVO7y8TZHg482G8dOklbE3b6vT+jsjT5OH1j1/HZzI/45YZIhlhGVgQtQBvXXsLb117a8zt0kLSnB7RdpWHMh7C22Vv45vHvznudvbacd0uQZWAw9WHMWwednsi9efSP+PXl36Nvfl7HbrRZUkYHS1QmK/Jxxu6N/CZzM9MKk5HCYLgdJG+F8++iJ0V41fhVsqUDs1wIduY2BIReZlvL/k2Pp356XG3ifCPcHqUzWJR9CLszduLPlPfmNv4yn1t9hH1lP++57/x5NwnJ3WMML8whwsi3W5p7M31ifoityS2h6oPoWeoB5tSNk34GN9b9j08lv3YpOII8Q1BTGDMhPadEzEHxz59zOECKH4KP6SEpIw5AmIdwXFiOugv1vzC4W1teXLuk/hs1mcdKogUHxSPE7UnYBbNE/7Zc9aB6wcmPUX3xVUvYlgcnnDS+Njsx7AlfcuEf5bsSVQl4tBDh9x2fAB4+d6X7a41nejPgStow7XYX7Af3YPdY26jkCmc6ncNjJyzJtGEpt4mxAXFTTbMcX3U8hGGxWHsKN+Br87/qt3tdW06KASFw62dUkJScPihw249T26Xrc7GH+r/gIHhAbvVyI2DRhysOogHUh/AF2Z/YcztJvOZS0xsiYi8jo/cx6kiMc4SBAGzgme57fiu4K/wd+t7YI9MkCFPk4eXLr2EWmPthForjaewrHDSaxZ95b6SvkcAnK7qmR2ejdP1p20+Zx3BcaKAz2QLDcllcoT6hTq0bYIqAYPmQbT2tSIqIGpSr+uoIn0R0kLSnF4nPtpki+zIBJnbkwl3V4cNVAZK/rNiz0SXljhyzLruOrcntpZieMX6Ynx57pftjhCXGkqRFprmUPsqC09XEdaGa2ESTShvL8fsiNnjbrv/+n70D/fjUe2jU/5c82YsHkVERDQB29K3WVvCuNL1zuu42HzRoXWd041WrUVLXwtaelvueE7XpoNckEMTppEgMvs8XRm5rL0MH7Z+OCPPE3INT7Wpau1rRXNvM+ZHzkdTb5PdJSSiKE64h7cnWQsZGuwXkCrUF9otQkeTx8SWiIhoAmICY7AibgV2lO+AyWxy2XGL9cVQCApsTXfPmsWpzHIha2s68kRGcDzJ071si/XFUMqUE14nThQTGAOZIHN7L1tL27Gn5j1lbcM1nubeZhj6DZJUoXZGfFA8VD4qu+tsdW06lLaV8iaUBzCxJSIimqACTQGae5snVcRqtKHhIeys2InViasR4R/hkmN6E2ubotsuFK0jOFP4QtcyldPdSQIADAwPYHflbqybtQ5hfmFufz2anpQyJWIDY90+y8DSEmdO5BxsSduCkpoStPa1jrn9RJYdSEEQBGSrs+0mtkX6IvjIfBwqQkeTw8SWiIhoglYlrkK4Xzi2l213yfGO1x6Hod8wodYr00GgMhDJwcl3jNhaR3Cm8NRES49pT4zYHqk+gs6Bzhl7npDrxAfFu/2c1bXpkKhKRLBPMPI0eTCJJuyu2D3u9gIEh1sDSUkbrkVZexmGzEM2n+839WNv5V6sT17vUBE6mhwmtkRERBOklCmxNX0rTtSesLku1FmF+kJEBURhRdwKF0TnnbTqO1toeMsIjqd62RbpixAfFO9031Ki28UHxbv9nNUZdNaf3dSQVCyMWogifRFE0XY/11JDKVJCUiZd/M0TtGotBs2DqOyotPn8oepDMA4ZJ1W5nBzHxJaIiGgS8jX5GBaH7fYntKehuwGn6k4hLz3P7T0lpzJtuBb1PfXo6O+wPmYZwckMm9rVRD2RJNR01eD9xveRl57nsbZCNH0lqBLQ2tc6bnu3yegc6ERdd90tywjyNfmo6qrChaYLNvfxhsJRFuPVBQBGblZOtsI9OY6fiERERJOQFJyEnOiccUcgHLGjfAcAIE+T56LIvJOtC8VSQymSQ5Kn/AhOvCoeTT1NGBwedNtrFJcXQybIZmRxMXI9SzXv+u56txzf8nM8OlFdn7QeQcogm0Wk2vra0NTbNKXX04+WFJyEAEWAdR3xaFWdI8l7niaPRaM8hIktERHRJOVr8lFjrMH5pvMT2n/YPIzi8mIsjV3qln6V3sRyQTs6sZ3qhaMsEoISIEJEQ0+DW45vMpuwo3wH7om/BzGBMW55DZpZ3N2myrKsYPTPb4AyAJtSNuGd6nfQNdh16/ZesuzAQibIkKXOsllAqqi8CHJBjq1pvAnlKUxsiYiIJml90nqolCoU6gsntP+ZhjNo6GlAfgaLAYX4hiA+KN56oWgZwfGGC11LkuCuYjwn606ipa+FRaPIZSxtqmqMNW45vq5Nh9jA2DuqdxdkFGBgeAD7KvfdsT0AZKqn9rKD0bThWlxrv4Zh87D1sSHzEHaW78SqhFWIDIiUMLqZhYktERHRJPkp/PBA6gM4VHUInQOdTu9fqC9EqG8o1iaudUN03ker1lpHbiw9ML1ixPZmkuCu0a9CfSHC/cKxKmGVW45PM0+4Xzj8Ff7uG7E12J5tkR2eDa1ae8d0ZJ3hkwrK3kKr1qLP1IfqrmrrYydqTsDQb8BDGQ9JGNnMw8SWiIjIBQoyCjBoHsTeyr1O7dfW14ZjNcewJW0LfOQ+borOu2SHZ6O6qxrGQaN17VpWeJbEUdkXFRAFpUzpll62zb3NeLf2XWxL3walTOny49PMJAgC4gLjUGd0fWLbPdiNqq6qMQtB5WvyoTPoblmfWtpW6hWzM0azfH+lhk++D0uF++Vxy6UKa0ZiYktEROQCWeosZIdno1Bf6FQRqT2Ve2Aymzi9dBTLheJVw1WvGsGRCTLEBcW5ZSryropdGBaHZ3xxMXK9BFWCW27GXGu/BmDs9bKbUjfBV+5rHbW1VUHZG6SGpMJX7mudRt3Y04hT9aewLX0bFDKFxNHNLExsiYiIXKRAU4Cy9jKbFTJtEUURhfpCzI+cj7TQNDdH5z2y1COjs7q2kdEcb7rQTQhyfS9bs2hGkb4Ii2MWIyk4yaXHJrK0qZpMVXdbbBWOGi3YJxgbkjZgb+Ve9Jn6Pll24CWtfiwUMgUywzKtyyd2lO+AWTQjL503oTyNiS0REZGLbEzZCD+5n8NFpC61XML1zuscrb1NhH8EogKi8H7j+yMjOF50oeuOXrbnG8+jxljD84TcIkGVgJ6hngnVBxiPzqBDhH/EuMWT8jX56B7qxqHqQ3YT4alMG66Frk03UuFeP1Lh3rLmnjyHiS0REZGLqHxU2JC8Afuu70PvUK/d7QvLChGoDMR9yfd5IDrvkq3Oxsm6k9avvUW8Kh6dA50wDhpddszt+u1Q+ahw76x7XXZMIgtrNW8XT0d2ZLbFouhFSApOQmFZIUoNpTYrKHsDrVqL7qFuFOoLUd9TjwJNgdQhzUhMbImIiFyoQFOAnqEeHKw6OO52xkEjDlYdxMaUjQhQBngoOu+hDdfCLJoBeEfhKIuEINdWRu7o78Dh6sN4MPVB+Cn8XHJMotHckdj2mfpQ2Vlpd7aFIAjI1+TjYvNFnKo75ZWjtcAn06d/8cEvEOIbgrWzWOFeCkxsiYiIXGhB1AIkByff0cbidvuv70f/cD/v7I/BcoEbExgDtZ9a4mgcF69ybS/bvdf3Ysg8xPOE3MYyZdaVRc/07XqYRbNDsy22pG2BQlCga7DLq5YdjJYemg6FTIHOgU5sTt3MCvcSYakuIiIiFxIEAQWaAvzsws/wg9M/GPMCp6SmBBlhGZgdPtuzAXoJywWut43gODNiazKb8OpHr6JroGvMbY7eOIrZ4bORqc50WYxEowUqAxHmG4aDVQfR1tc25naLYxY7PBJpXS/rQKIa4R+B1YmrceTGEa9r9WPhI/eBJlQDnUHHtfASYmJLRETkYlvSt+DvZX/H/uv7x9xGEAR8dcFXIQiCByPzHtEB0VgRtwLrk9ZLHYpTgn2CoVKqHBr9OlZzDL++9GsEKgMhG2MSnUwmwzNznnF1mES3WJ24Gkeqj4zZz3ZgeAA7y3fiyKePwF/hb/d4OoMOob6hiA2Mdej1H5/9OG4Yb2Be5Dyn4p5K7k+5H8khydCEaaQOZcZiYktERORiaj819uXvkzoMryYIAn6z/jdSh+E0QRAQr4p3aL1iob4Q0QHROFhwEHKZ3APREdn2gxU/wA9W/GDM5881nsMXD34Rh6sPY3PaZrvHsxSOcvTG3fyo+SjaMv7yjanui3O+KHUIMx7X2BIRERG5kCO9bBu6G/Be3XvI0+QxqaUpLyc6B7NUsxxqZTY0PAR9h95r18uS92JiS0RERORC8UHxqO+ut1Z1tmVH+Q4AwLb0bZ4JimgSLNWLLzRdQFVn1bjblneUw2Q2MbElj2NiS0RERORC8ap4DAwPoLWv1ebzw+ZhFJUXYVncMmurFaKpbmv6VsgFOYrKx58yrDOMFI7ypv7TND0wsSUiIiJyIXuVkU83nEZjTyOrp5JXifCPwOqE1dhZvhNDw0NjblfaVoogZZC1jRCRpzCxJSIiInIhe71si/RFCPMNw5rENZ4Mi2jSCjIKYOg34Hjt8TG30bXpkKXOgkxgmkGe5bYzThCE1wRBaBYE4cqox34iCMJVQRA+FAShWBCE0DH2rRIE4SNBEC4JgnDeXTESERERuZplerGtyshtfW04duMYNqdtHrPHMdFUtTxuOaICosYsImUym3Ct/RrX15Ik3Hkr5XUA99/22CEAc0RRnAugDMB3xtl/jSiK80VRzHFTfEREREQu5yv3RZR/lM2eoLsrdsMkmjgNmbySQqbAtvRtOFV3Co09jXc8f73zOgaGB6BVM7Elz3NbYiuK4gkAhtsee0cURdPNf54BwMn3RERENO3Y6mUriiIK9YWYHzkfaaFpEkVGNDl56XkQIaK4vPiO56yFo8JZOIo8T8rJ718EsH+M50QA7wiCcEEQhCc8GBMRERHRpMUHxd9RPOqD5g9Q1VWFgowCiaIimrwEVQKWxS5Dsb4Yw+bhW57TtengJ/dDcnCyNMHRjKaQ4kUFQfg3ACYAfxljk3tEUawTBCEKwCFBEK7eHAG2dawnADwBANHR0SgpKXFHyC7R3d09peOjmYHnIU0VPBdpKnDXeWjqMKGppwmHjh2CUlACAP7c+mf4CX7wv+GPklrXvyZ5L2/7PMwaysLpntN45eAr0Pp/Mu34dONpxCpi8e6JdyWMjibD287F0Tye2AqC8DiABwGsE0VRtLWNKIp1N/9uFgShGMASADYTW1EUXwHwCgDk5OSIubm5bojaNUpKSjCV46OZgechTRU8F2kqcNd52FHegQOnDiBjUQaSgpNgHDTi2b8/i82azbhv2X0ufz3ybt72ebh8eDmK3y5GuX85nsp9CgBgFs349l+/jS1pW5C7NFfaAGnCvO1cHM2jU5EFQbgfwLcAbBFFsXeMbQIFQVBZvgawAcAVW9sSERERTUXWXrY3C0jtv74f/cP9KNBwGjJ5Px+5DzanbcbRmqMw9I+U1LnRdQO9pl6uryXJuLPdz5sATgPIFAShVhCEfwTwKwAqjEwvviQIwm9ubhsnCMK+m7tGAzgpCMJlAGcB7BVF8YC74iQiIiJytQTVSGJrKSBVqC9EZlgmL/pp2shPz4fJbMLuit0AWDiKpOe2qciiKD5i4+Hfj7FtPYBNN7+uBDDPXXERERERuVukfyQUMgVqu2uha9OhtK0U31nyHQiCIHVoRC6RHpaOeZHzUKQvwmPZj0HXpoNSpkRqaKrUodEMJWVVZCIiIqJpSS6Tj1RGNtahSF8EH5kPHkh9QOqwiFyqQFOAys5KXG65jFJDKTLCMqCUKaUOi2YoJrZEREREbhAfFI+KjgrsrdyL9cnrEeIbInVIRC51X/J9CFAEYHvZdujadNCGa+3vROQmTGyJiIiI3CA+KB4VnRUwDhlZNIqmpQBlADambMTeyr3oGuyCVs3ElqTDxJaIiIjIDSwFpGapZiEnOkfiaIjco0BTAJNoAsDCUSQtJrZEREREbhAfFA8AyNfks2gUTVtzIuYgIywDckEOTZhG6nBoBnNbVWQiIiKimWxp7FI8nPkwHsp4SOpQiNxGEAQ8u/hZlLaVwlfuK3U4NIMxsSUiIiJygxDfEPz70n+XOgwit1sauxRLY5dKHQbNcJyKTERERERERF6NiS0RERERERF5NSa2RERERERE5NWY2BIREREREZFXY2JLREREREREXo2JLREREREREXk1JrZERERERETk1ZjYEhERERERkVdjYktERERERERejYktEREREREReTUmtkREREREROTVmNgSERERERGRV2NiS0RERERERF6NiS0RERERERF5NSa2RERERERE5NWY2BIREREREZFXY2JLREREREREXo2JLREREREREXk1JrZERERERETk1ZjYEhERERERkVdjYktERERERERejYktEREREREReTUmtkREREREROTVmNgSERERERGRV2NiS0RERERERF6NiS0RERERERF5NSa2RERERERE5NWY2BIREREREZFXY2JLREREREREXo2JLREREREREXk1JrZERERERETk1ZjYEhERERERkVdjYktERERERERejYktEREREREReTUmth7UOyRKHQIREREREdG043RiKwhCmCAIc90RzHT23R1X8F+n+yCKTG6JiIiIiIhcyaHEVhCEEkEQggVBUAO4COBVQRB+7sB+rwmC0CwIwpVRj6kFQTgkCIL+5t9hY+z7+Zvb6AVB+Lyj39BUdVdCCJp6RVy80S51KERERERERNOKoyO2IaIodgHIB/AnURTvBnCvA/u9DuD+2x57DsARURQ1AI7c/PctbibQ/wHgbgBLAPzHWAmwt9h0Vyx85MD2C3VSh0JERERERDStOJrYKgRBiAXwaQB7HD24KIonABhue3grgD/e/PqPALbZ2PU+AIdEUTSIotgO4BDuTJC9SpCvAjnRCuz5sB79Q8NSh0NERERERDRtKBzc7vsADgI4KYriOUEQUgHoJ/ia0aIoNtz8uhFAtI1t4gHUjPp37c3H7iAIwhMAngCA6OholJSUTDAs91ukHsJ79QL+d/sxLI119K0ncq3u7u4p/XNCMwfPRZoKeB7SVMDzkKYKbz4XHcquRFF8G8Dbo/5dCaBgsi8uiqIoCMKkqimJovgKgFcAICcnR8zNzZ1sWG5jPnYMcTVm6PpUeC53idTh0AxVUlKCqfxzQjMHz0WaCnge0lTA85CmCm8+Fx0tHvXizeJRSkEQjgiC0CIIwj9M8DWbbk5rxs2/m21sUwcgcdS/E24+5tVkgoC8hfF4V9+Cpq5+qcMhIiIiIiKaFhxdY7vhZvGoBwFUAUgH8OwEX3MXAEuV488D2Gljm4MANtxsLRQGYMPNx7xewcIEmEVgxwden6cTERERERFNCQ4Xj7r59wMA3hZFsdORnQRBeBPAaQCZgiDUCoLwjwCeB7BeEAQ9RiorP39z2xxBEH4HAKIoGgD8AMC5m3++f/Mxr5caGYSFs0JReLGWPW2JiIiIiIhcwNEKRnsEQbgKoA/AU4IgRAKwO5dWFMVHxnhqnY1tzwP4p1H/fg3Aaw7G51UKFiXg34qv4KO6TsxNCJU6HCIiIiIiIq/m0IitKIrPAVgOIEcUxSEAvRhp20MT8ODcOPgoZCi8UCt1KERERERERF7P0eJRAQC+AuDlmw/FAchxV1DTXYi/Euuzo7Hrcj0GTWapwyEiIiIiIvJqjq6x/QOAQYyM2gIjFYp/6JaIZoiHFiWgvXcIR6/aKgpNREREREREjnI0sU0TRfFFAEMAIIpiLwDBbVHNACvTIxCp8sV2TkcmIiIiIiKaFEcT20FBEPwBiAAgCEIagAG3RTUDKOQy5C2IR8m1ZrR1860kIiIiIiKaKEcT2/8AcABAoiAIfwFwBMC33BbVDFGwMAEms4idl+qlDoWIiIiIiMhrOVoV+RCAfACPA3gTI9WRS9wX1syQGaPCXfEhKLzI6chEREREREQT5eiILQD4AWgH0AUgWxCEVe4JaWYpWBiPj+u7oGvokjoUIiIiIiIir+Rou58XAJwC8G8Anr3555tujGvG2DI/Hkq5wJ62REREREREE6RwcLttADJFUWSVIxdTB/pgTWYUdlyqx3Mbs6CQOzOITkRERERERI5mUZUAlO4MZCYrWJSA1u4BnNC3SB0KERERERGR13F0xLYXwCVBEI5gVJsfURS/5paoZpg1mVFQB/qg8EId1mZFSx0OERERERGRV3E0sd11889oootjmbF8FDJsmReHv75/A81d/YgK9pM6JCIiIiIiIq/h6FTkUFEU/zj6D4AwdwY20zy+PBkmsxm/O3ld6lCIiIiIiIi8iqOJ7edtPPa4C+OY8ZIjArFlXhzeOFMNQ8+g1OEQERERERF5jXETW0EQHhEEYTeAFEEQdo36cwyAwTMhzhxfXZOO3sFh/OEUR22JiIiIiIgcZW+N7XsAGgBEAPjZqMeNAD50V1AzlSZahftnx+D196rwpVWpCPZjIWoiIiIiIiJ7xh2xFUWxWhTFElEUl4mieHzUn4uiKJo8FeRM8vTadBj7Tfjz6WqpQyEiIiIiIvIK9qYin7z5t1EQhK5Rf4yCIHR5JsSZZU58CNZkRuJ371aid5D3DoiIiIiIiOyxVzzqcwAgiqJKFMXgUX9UoigGeyC+GenptRq09w7hr+/fkDoUIiIiIiKiKc9eYlts+UIQhEI3x0I3LUoKw/K0cPz2RCX6h4alDoeIiIiIiGhKs5fYCqO+TnVnIHSrp9emo8U4gLfP10gdChERERER0ZRmL7EVx/ia3GxZajgWJYXhN8crMWgySx0OERERERHRlGUvsZ1nKRYFYC6LR3mOIAh4em066jr6sOODOqnDISIiIiIimrLstfuRjyoWpWDxKM/KzYjEnPhg/LqkHKZhjtoSERERERHZYm/EliQkCAKeXqNBVVsv9n7UIHU4REREREREUxIT2yluQ3Y0MqKD8Kuj5TCbucyZiIiIiIjodkxspziZTMBX16RD39yNd0obpQ6HiIiIiIhoymFi6wUenBuHlIhA/PJoOUSRo7ZERERERESjMbH1AvKbo7Yf13fh1yUVUodDREREREQ0pTCx9RIFC+OxeV4cfvrONRwubZI6HCIiIiIioimDia2XEAQBLxbMxZy4EHz9rQ9Q1mSUOiQiIiIiIqIpgYmtF/H3keOVxxbB30eBf/rjebT3DEodEhERERERkeSY2HqZ2BB//PbRRWjs7MdX/3oRQ8NmqUMiIiIiIiKSFBNbL7QoKQw/yr8L71W04b/36qQOh4iIiIiISFIKqQOgiXloUQKuNnThdyevIzNGhUeWzJI6JCIiIiIiIklwxNaLPbcxC6syIvG9nVdw9rpB6nCIiIiIiIgkwcTWiynkMvzykQVIDAvAU29cQG17r9QhEREREREReRwTWy8X4q/Eq5/PweCwGV/60wXUd/RJHRIREREREZFHMbGdBtIig/DLRxagorkbuT8pwb/v+IgJLhERERERzRhMbKeJ3MwoHHs2F5/KScDfztVg9U+OMcElIiIiIqIZgYntNBIf6o//zrsLJc+uwadzEq0J7r8Vf4Q6JrhERERERDRNMbGdhkYnuA8vTsTfz9cg9yfH8K/FH6G9Z1Dq8IiIiIiIiFzK44mtIAiZgiBcGvWnSxCEf7ltm1xBEDpHbfM9T8c5HcSH+uOH2z5JcN8+X4NP/fY0pycTEREREdG04vHEVhTFa6IozhdFcT6ARQB6ARTb2PRdy3aiKH7fo0FOM5YE98//eDeaOvtR8PJ70DcZpQ6LiIiIiIjIJaSeirwOQIUoitUSxzEjLE0Nx1tPLsXQsIhP/fY0LlS3Sx0SERERERHRpAmiKEr34oLwGoCLoij+6rbHcwEUAqgFUA/gm6IofjzGMZ4A8AQAREdHL3rrrbfcGfKkdHd3IygoSOow0Nxrxk/P96OjX8TTC3wxN1IhdUjkQVPlPCTiuUhTAc9Dmgp4HtJUMdXPxTVr1lwQRTHH1nOSJbaCIPhgJGmdLYpi023PBQMwi6LYLQjCJgD/J4qixt4xc3JyxPPnz7snYBcoKSlBbm6u1GEAAFqMA3j8D2dxrdGIn3xqLvIWJEgdEnnIVDoPaWbjuUhTAc9Dmgp4HtJUMdXPRUEQxkxspZyKvBEjo7VNtz8himKXKIrdN7/eB0ApCEKEpwOcziJVvnjriaVYkqLGM3+7jN+9Wyl1SERERERERBMiZWL7CIA3bT0hCEKMIAjCza+XYCTONg/GNiOo/JT4wxcWY9NdMfjhXh1+vF8HKaemExERERERTYQkiysFQQgEsB7Ak6Me+zIAiKL4GwAPAXhKEAQTgD4AnxGZcbmFr0KOXz6yEOrAK/jt8UrsvlSPZWkRWJ4WjmVp4YgL9Zc6RCIiIiIionFJktiKotgDIPy2x34z6utfAfjV7fuRe8hlAn6wdQ4WJYXhUGkTjl5tQuHFWgBAcngAlqVFYFlaOJalhiNS5StxtERERERERLdiOVwCAAiCgLwFCchbkACzWcS1JiPeq2jD6YpW7LlcjzfP3gAALEsNx9fWabAsLdzOEYmIiIiIiDyDiS3dQSYToI0NhjY2GP94TwpMw2Z8XN+FE2Ut+NOZajzy6hksSVHj6+s0WJ4WjpvLoYmIiIiIiCTBxJbsUshlmJcYinmJofjSqlS8dfYGXj5egc/97n0sSgrD19ZpsEoTwQSXiIiIiIgkIWVVZPJCfko5Hl+RguPPrsEPts1BQ0cfPv/aWWz79Xs4erWJVZWJiIiIiMjjOGJLE+KnlOPRpUl4OCcRhRdr8dKxcnzx9fNIVPvjnvRI3JM+UnBKHegjdahERERERDTNMbGlSfFRyPDIkll4aFECdl6qx8GPG63FpgQByI4Nxj3pEVieHoElyWr4+8ilDpmIiIiIiKYZJrbkEkq5DA8tSsBDixJgGjbjw7pOvFfeipPlrfjDqSr89kQlfOQy3J2qxkOLEnDf7Bj4KZnkEhERERHR5DGxJZdTyGVYOCsMC2eF4em1GvQNDuNclQGnylux96MGfP2tS1D5KbB5Xhw+tSgB8xNDWXiKiIiIiIgmjIktuZ2/jxyrMiKxKiMS374/C2eut2H7+VoUXazFX9+/gfSoIHxqUQLyFsQjKthP6nCJiIiIiMjLMLElj5LJBCxPi8DytAj819bZ2PthA96+UIsf77+KFw9ew13xIfCR3yzWLdzyFwQBCPRRYH5iKHKS1ZifGMo1u0RERERExMSWpKPyU+IzS2bhM0tmoaKlG9sv1OLD2g6YzSPPixhpHSSKgHjz75r2Xhy91gxRBBQyAXPiQ7A4OQw5yWrkJIUhPMhXum+IiIiIiIgkwcSWpoS0yCB8+/4sh7bt7B3CxRvtOFdlwLkqA/74XjVefff6zeME4p70CKzKiMTS1HAE+vIUJyIiIiKa7njVT14nJECJNVlRWJMVBQDoHxrGlbpOnKtqx5nKNvztfA3+eLoaSrmAnCT1zfW9EdDGBEMmY5EqIiIiIqLphokteT0/pXxkKnKyGk/lpqF/aBjnq9pxQt+CE2UteOHAVbxwAIgI8sVKTQSWpKixKCkM6ZFBTHSJiIiIiKYBJrY07fgp5bhHE4F7NBH4101aNHf144S+Fe/eTHSLP6gDAAT7KbAwKQw5SWFYmBSG+YmhCPDhjwQRERERkbfhVTxNe1HBfnhoUQIeWpQAURRR1daLC9XtuFBtwIXqdvz0WgsAQC4TkB0bjAWzQjEvIRTzZ4UiJTyQo7pERERERFMcE1uaUQRBQEpEIFIiAvHQogQAN4tR1bTjQlU7zlcbUHihFn86XQ1gZFR3XmIo5o/6w8rLRERERERTCxNbmvFCApRYkxmFNZkjxaiGzSLKm7txqaYdl2o6cammAy8dK4d5pPsQ4kL8kB0XjOzYYGTHhWB2XDASwvwhCBzZJSIiIiKSAhNbotvIZQIyY1TIjFHh4cUjj/UOmvBRbScu13bg4/oulNZ34ejVZmuyq/JT3Ex0g5GTpMY9mgiE+Cul+yaIiIiIiGYQJrZEDgjwUeDu1HDcnRpufaxvcBjXmowore9CaUMnSuu78NbZGvzhVBXkMgGLZoUhNysSuRlR0MaqOKJLREREROQmTGyJJsjfR25dd2thGjbjcm0Hjl1twbFrzXjxwDW8eOAaYoL9sDojEmuyIrE8PQLBfhzNJSIiIiJyFSa2RC6kkMuwKEmNRUlqfPO+TDR39aOkrAUl15qx70oD/na+BjIByIoJxpIUNZakqLE4WY1IFQtSERERERFNFBNbIjeKCvbDp3MS8emcRAwNm3Gxuh2nK9twrsqAv52rwevvVQEAUiICsSRZjcUpasyJD0Z4oC/CApRQyGXSfgNERERERF6AiS2RhyjlslvW6Q4Nm3GlrhNnrxtwrsqAAx834m/na27ZJ8RfCXWgD9SBPggL8IE6UImIIF/EhvojLsQPsSH+iAv1Q4i/kmt4iYiIiGjGYmJLJBGlXIYFs8KwYFYYnlydBrNZRFmzEeXN3WjvGURbz6D1b0PPIGrbe/Fh7ci/hy3lmG8K8JEjNsQPcaH+SA4PxMa7YrA0JRwyGZNdIiIiIpr+mNgSTREymYCsmGBkxQSPu92wWURr9wDqO/pQ39GPhs5Rf3f2o+hiLf58phrxof7YtiAO+QsTkBYZ5KHvgoiIiIjI85jYEnkZuUxAdLAfooP9sGDWnc/3Dw3jndImFF2sxcslFXjpWAXmJ4aiYGE8Ns+L83zARERERERuxsSWaJrxU8qxZV4ctsyLQ3NXP3ZeqkfhxVp8d+fH+P6eUswJl+FM31XEBPsiJmQkQY4J8UNkkC+LVRERERGRV2JiSzSNRQX74UurUvGlVakore9C0cVa7LpYhd+frMTQ8K3rdGUCEBE0kuymRwUhOzYY2pt/1IE+En0HRERERET2MbElmiGy44KRHZeNe4KasWrVarT3DqKxqx9NXf1o7BwY+bqzH/WdfTipb0XRxTrrvlEqX2uSq41VYW1WFFR+Sgm/GyIiIiKiTzCxJZqBZDIB4UG+CA/yxey4EJvbtHUPQNdghK6ha+RPoxHvVYyM9EYE+eCbGzLxqZxEyFl5mYiIiIgkxsSWiGwKD/LFPRpf3KOJsD42aDLjUk0HXjxwFc8VfYQ/na7G9zZnY+nN3rxERERERFJgpRgicpiPQoYlKWq8/eVl+OUjC9DZN4TPvHIGX/7zBdxo65U6PCIiIiKaoThiS0ROEwQBm+fFYX12NF49UYlfl1Tg6NVmfPGeFHx1TRrX3xIRERGRR3HElogmzE8pxz+v0+DYN3Px4LxY/OZ4Bdb89DheLqlAfUef1OERERER0QzBxJaIJi0mxA8///R87PzqCqRHBeKFA1ex/PmjePi3p/Hm2Rvo7B2SOkQiIiIimsY4FZmIXGZeYijeemIZqtt6sPNSPXZcqsN3ij7Cf+z8GLmZkdi2IB5rs6Lgp5RLHSoRERERTSNMbInI5ZLCA/G1dRr889p0XKnrwo5Lddh1uR7vlDZB5avAOm0UcjOjsCojEupAH6nDJSIiIiIvx8SWiNxGEATclRCCuxJC8K+btHivohU7L9Xj6NVm7LhUD0EA5iaEIjcjEqszIzEvIZR9cYmIiIjIaUxsicgj5DIBKzWRWKmJhNks4qO6TpRca0FJWTN+cVSP/zuiR1iAEis1kbhvdgzWZ0fDR8EyAERERERkHxNbIvI4mUzAvMRQzEsMxdfv1aC9ZxAn9C04XtaCE2Ut2HW5HpEqXzyyOBGfvTsJMSF+UodMRERERFMYE1siklxYoA+2zo/H1vnxMJtFHC9rwZ9OV+GXx8rxUkkFNmRH49FlSViWGg5B4FRlIiIiIroVE1simlJkMgFrsqKwJisKN9p68cb71fj7+Rrsv9IITVQQHl2WhLwF8VD5KaUOlYiIiIimCMkWsAmCUCUIwkeCIFwSBOG8jecFQRB+IQhCuSAIHwqCsFCKOIlIOrPCA/Cvm7Q48511+MlDc+HvI8f3dn6MtT87jmuNRqnDIyIiIqIpQurKLGtEUZwvimKOjec2AtDc/PMEgJc9GhkRTRl+Sjk+lZOIXU/fg8KnlkEmAA+/chof1nZIHRoRERERTQFSJ7bj2QrgT+KIMwBCBUGIlTooIpLWoiQ13n5yOVR+Cnz21fdxrsogdUhEREREJDFBFEVpXlgQrgNoByAC+K0oiq/c9vweAM+Lonjy5r+PAPi2KIrnb9vuCYyM6CI6OnrRW2+95YnwJ6S7uxtBQUFSh0Ez3HQ5Dw39Zrx4rh+GPhFfW+iHORFyqUMiJ02Xc5G8G89Dmgp4HtJUMdXPxTVr1lwYY7avpMWj7hFFsU4QhCgAhwRBuCqK4glnD3IzIX4FAHJycsTc3FwXh+k6JSUlmMrx0cwwnc7DVfcM4B9+9z5+8UEPXvrcQqzPjpY6JHLCdDoXyXvxPKSpgOchTRXefC5KNhVZFMW6m383AygGsOS2TeoAJI76d8LNx4iIAAARQb5464ml0MYF48tvXMCuy/VSh0REREREEpAksRUEIVAQBJXlawAbAFy5bbNdAB67WR15KYBOURQbPBwqEU1xoQE++Ms/3Y1FSWH4+lsf4G/nbkgdEhERERF5mFQjttEATgqCcBnAWQB7RVE8IAjClwVB+PLNbfYBqARQDuBVAF+RJlQimuqCfBX44xeWYKUmEt8u/AivnbwudUhERERE5EGSrLEVRbESwDwbj/9m1NcigK96Mi4i8l7+PnK8+tgifO3ND/D9PaUoazLiP7fMhp+SRaWIiIiIprup3O6HiMgpvgo5XvrsQnx1TRreOleD/F+/h+q2HqnDIiIiIiI3Y2JLRNOKQi7Ds/dl4fefz0Ftey8e/OVJvPNxo9RhEREREZEbMbElomlpnTYae7+2EknhAXjizxfw4/06mIbNUodFRERERG7AxJaIpq1EdQC2f3k5Pnf3LPz2eCU++7v30dzVL3VYRERERORiTGyJaFrzU8rx33l34eefnocPazuw6RcncaayTeqwiIiIiMiFmNgS0YyQvzABO796D4L9FHjk1TP4yl8u4KPaTqnDIiIiIiIXYGJLRDNGZowKO59egadWp+HdslZs/tVJPPr79/FeRStGOowRERERkTdiYktEM4rKT4lv3Z+FU99Zi2/fnwVdgxGfffV9bPv1ezj4cSPMZia4RERERN6GiS0RzUjBfko8lZuGk99egx9smwNDzwCe/PMFbPjfE9h+oZYVlImIiIi8CBNbIprR/JRyPLo0Cce+kYv/+8x8KGQCvvn2ZWz43xPY91EDpygTEREReQEmtkREABRyGbbOj8f+r6/Ebx9dBLkg4Ct/uYitL53CqfJWqcMjIiIionEwsSUiGkUQBNw3OwYH/mUVfvLQXLQaB/C5372Pf/jd+/iwtkPq8IiIiIjIBia2REQ2yGUCPpWTiKPfzMV3H8xGaUMXtvzqFL76l4uoaOmWOjwiIiIiGkUhdQBERFOZn1KOf7wnBZ/OScDv3r2O371biQMfN+KhhQn453XpSAgLkDpEIiIiohmPI7ZTRc05YM//A8ysxEo0Fan8lHhmfQaOf2sNHluWhOIP6rDmpyX47o4raOrqlzo8IiIiohmNie1Ucey/gfO/B66XSB0JEY0jIsgX/7F5NkqezcWnchLx5tkbWPXiMfxwTylauwekDo+IiIhoRmJiOxW0VwGVx0a+vvgnSUMhIsfEhfrjR3l34eg3crF5XhxeO3Udq148hhcPXEVH76DU4RERERHNKExsp4IP/gJAALRbAN0eoKdN6oiIyEGzwgPw00/Nw6H/txrrtNF4+XgFVr5wDL85XoFBE5cWEBEREXkCE1upmYeBD94A0u8Fcr8DmIeAD9+SOioiclJaZBB++cgC7P/6SixJUeP5/Vex6Rfv4nQFb1QRERERuRsTW6mVHwGM9cDCx4DobCBh8ch0ZFGUOjIimoCsmGD8/vHF+P3nc9A/NIxHXj2Dr7/1AZpZYIqIiIjIbZjYSu3iH4HASCDj/pF/L3wMaLkK1J6TNi4impR12mgc/n+r8bW16dj/USPW/ew4Xjt5HaZhTk8mIiIicjUmtlIyNgFlB4B5jwAKn5HHZucDPkEjCS8ReTU/pRz/b0MmDj6zCguSwvD9PaXY/KtTuFBtkDo0IiIiommFia2ULv8VMJtGRmktfIOAOfnAlSKgv0u62IjIZVIiAvHHLyzGy59biI7eQRS8fBpP/vk8TpS1wGzmsgMiIiKiyWJiKxVRHFlLO2s5EKG59bmFnweGeoErhdLERkQuJwgCNt4Vi8P/bzWeXpOOc1XteOy1s1j902N46Vg51+ASERERTQITW6lUnwIMlbeO1lrELwKistnTlmgaCvRV4Jv3ZeL0d9biF48sQEJoAH5y8BqWPX8UT/75PI5zFJeIiIjIaQqpA5ixLv4J8A0Gsrfe+ZwgjCS8B54DGj8CYu7yfHxE5Fa+Cjm2zIvDlnlxqGzpxt/O1eDtC7U4+HETEsL88ciSWXh4cSIignylDpWIiIhoyuOIrRT62oHSncBdnwJ8AmxvM/dhQO4DXPyzZ2MjIo9LjQzCdzZpcfo7a/HLRxYgMezmKO6Pj+Drb32AC9UGiGwBRkRERDQmjthK4aPtgKkfWPT5sbcJUAPazcCHbwHr/wtQ+nsuPiKShK9Cjs3z4rB5XhzKm7vxxplqFF6oxc5L9ciODcZjy5KwdX48/H3kUodKRERENKVwxNbTRBG48Ecgdt7In/EsfAzo7wR0ezwTGxFNGelRQfjPLbNx5l/X4b/z5sAsiniu6CPc/aPD+P7uUpQ3G6UOkYiIiGjK4IitpzVcApo+Ah74mf1tk1cBYckjPW3nfsrdkRHRFBToq8Dn7k7CZ5fMwvnqdvzpdDX+fKYKr526Dk1UEDbOicF9c2KQHRsMQRCkDpeIiIhIEkxsPe3inwCFPzDnIfvbymTAgkeBoz8A2iqA8DT3x0dEU5IgCFicrMbiZDWajVrs/6gRB6404lfHyvGLo+VIVPvj/tkxuH9ODBYkhkEmY5JLREREMwcTWw+SDfcDH74NzN4G+Ic6ttP8zwHH/hv44A3g3v9wZ3hE5CWiVH74/PJkfH55Mtq6B3BY14QDVxrx+ntVePXd64hS+WLLvDh87V4Ngv2UUodLRERE5HZMbD0oqvkUMGi03bt2LMGxgOY+4NJfgDX/Bsj5X/b/27vzMLmq+8zj31/t1fsiqbu1tPYdoQUhi11siTEm2BMcg01EHBLHHoc4nswkTp6MnTjxM8kkA/FCvMTGxh7bOCYewDGYOAaBMUYgIYEkhGSppdbWknrfu6ur6swf96rVWpFEd90q9ft5nnpuLberf6rn6HS995x7rogcV10S5/2X1/P+y+vpGhji2TeP8pOth3noF3t4cksTf/ebl3LtvIlBlykiIiIyprR4VA7VNf0UqudA/RXn94Mr1kLPEfjVf4xNYSJyUShLRLl92RS+dPdl/NtHryQZC7P2oZf55L+9TvfAUNDliYiIiIwZBdtcad5Bedd2L6Se7wIvc38NSmq9RaRERM7B8vpKfvxH1/AH183iXzfs59cfeJ7ndzYHXZaIiIjImNC81lx59VtkLUxo6V3n/7PhCCz7ALxwP3ymevRrG03L74bbPhd0FSICJKJh/vyWhfz64lr+xw9eY+1DL3PXqmn8xbsWUqpzb0VEROQiomCbK7PWsPdwJ7NKJl3Yz195H0TikEmNbl2j6cAG2PQduP4voUTn9InkixX+6O0D/7mTf3m+ged3tvCZ2xdzzdyJxCKauCMiIiKFT8E2V+bezL6DUWZd6M8XVcGaT45mRaOveQc8uApef8QL4iKSN04evb334Q3EIiEumVzG0mkVLJtWwfJplUyrSup6uCIiIlJwFGxl9EycD9NWe9fqveIPz/9cYhEZc8dGb5958yib9rXz2v5OvvfyPr7xi70AVBXHWDq1nMumV/L+y+uZWBoPtmARERGRc6BgK6NrxVp4/L/Cvpdg+nmu/iwiOZGIhnnXkjretaQOgHQmy44j3Wze38Fr+zvYvL+DdTubefDZ3Xzoqhn8wbWzKS/SObkiIiKSvxRsZXQtfg889WfeqK2CrUhBiIRDLJ5czuLJ5XzwHdMBaGju4YH//BX/vG43336pkd+/Zha/e/VMSuL6syEiIiL5R6uGyOiKFcOSO2Db/4OBzqCrEZELNGtiCV+4azlPffwaVs+q5v6f7uTa//0s//J8AwNDmaDLExERETlBzoOtmU0zs2fN7A0z22ZmHz/NPmvMrNPMNvu3T+W6TnkbVqyFdD9seTToSkTkbVpYV8a/rF3JYx+7isWTy/jsk9u57h+e5dsvNZJKZ4MuT0RERAQIZsQ2DfyJc24RsBr4mJktOs1+P3fOLfNvn8ltifK2TF4ONUu86cgiclFYNq2Cb9/7Dh758GqmVRbxPx/byk33P8fjmw+SzbqgyxMREZFxLufB1jnX5Jx71b/fDWwHpuS6DhlDZt6obdNmaHot6GpEZBStnlXNDz5yBd/40OUUxyN8/JHN3PqFF3h2x1GcU8AVERGRYAR6jq2ZzQCWA+tP8/IVZvaamT1lZotzW5m8bZe+D8JxePXbQVciIqPMzLh+/iR+fN/VfO7OZfQOpvnQN17h/V99iY2N7UGXJyIiIuOQBXWE3cxKgOeAzzrnfnjSa2VA1jnXY2bvAj7nnJt7hvf5MPBhgJqamsseeeSRMa78wvX09FBSUhJ0GTmz8I37qW7dwItXfoNsWNfCzBfjrR3K2EtnHc8dSPP4riG6Uo7lk8LcMTfGlNKzHztVW5R8oHYo+UDtUPJFvrfF66+/fqNzbuXpXgsk2JpZFPh34Gnn3P3nsP9eYKVzruVs+61cudJt2LBhdIocA+vWrWPNmjVBl5E7e1+Ab94K7/0KLL0z6GrEN+7aoeRM72Cah17Yw1efb6A3lWZFfSVXzq7mitkTWF5fQSIaPmF/tUXJB2qHkg/UDiVf5HtbNLMzBtucX5DQzAz4OrD9TKHWzGqBI845Z2ar8KZMt+awTBkN06+CqlneIlIKtiIXveJ4hPtunMsHV0/n4Rf38tzOZr747C4+/8wu4pEQK2dUcuXsCVw5u5olU8qDLldEREQuIjkPtsBVwG8DW8xss//cXwD1AM65LwN3AB81szTQD9zptCpJ4Tm2iNR//hW07IIJc4KuSERyoKo4xidunscnbp5H18AQLze08eLuVl7c3cI/PL0DgJJ4hLJohuotL5CIhohHwt42GiYeCZGIhqksilJXnmRKRZK6igR15UnKEhG846P5xznH0e5Btjd1sb2pm4bmHqqKY9RXFzGjupjp1UXUlScJh/KzfhERkUKW82DrnHsBOOtfdefcF4Ev5qYiGVNLPwA/+xvY9C24WVdtEhlvyhJRblpUw02LagBo7RnkpYY2XmpoZfueA5SWxBhMZ+lLpWnrzTKYzjAw5G3b+4bInHQpoeJYmMkVSeoqklQko4RDhhmEzfz7RjgEITOKYhFqy+LUlieoKUtQW55gYkmcSPjEc3+zWS+QHmjv40B7Pwc7+jnQ3kdLT8oL4IkI5ckoZf6tPBmlLBElHg2x+2gPbx7uZntTF28e7qatNzX8vhNK4nT1D5HKHL/ebywcYmpVkulVRUyvLmZGdRHTJxQzo7qYqZVJouFA13QUETlvg+kML+9po6s/zXXzJ1ISD2LcTCSYEVsZT0prYP4tsPm7cMP/hHA06IpEJEDVJXFuvbSOWy+tY926FtasWXXGfTNZR3P3IAc7+mnq7KepY4BDI7b7WnvJOEc2C1nnyDpHZsT9vsHMCaESIGQwsTRObVmColiEQ539HOroZyhzYoCuLo4xoSRObypNV/8Q3YNpzjRvKBENMb+mlJsX1rCwrpQFdWUsqC2loihGJus43DVAY0svjW197G3tpbGlj8a2PtbvaaMvlRl+n3DImFqZPB54q4uZXJ6guiROdUmMCcVxypIXNmKdzmTZ19bH7uZedjf3sOtoD7ubexjKZLl27kRuXFjDsmkVOR1Nds7R1DlAOGSUJ6OnnIMtUgi6B4b41w0HONTRz+9dM5O68mTQJeXE4c4B1u04yjNvHuWFXS3DfVk8EuKGBZO4belkblgwadT+X/enMuw62sPE0jiTSuOExqCvGhjK4JzXp4/FzKCDHf0cbO9nyZRykjH1d2NBwVbG3oq18Oa/w86fwMLbgq5GRApEOGTUlnsjrVB53j/vnKOtN8XhrgGOdA3Q1DnAkc4BDvv3+1IZlkwp55ZL6phamWRKZZJplUkmVyQpip345zGbdXQPeiG3s3+IroEh+lMZZvijrWcKhOGQMaXCm0595Wnqa+4ZpLG1j70tvd621dtuamynezB9yvtFw0Z1sRd0q0viFMfChEJGyIyQ4W+P3+/sH2JXcw+Nrb0nhPdJpXFmTywhFg7xlecb+Od1u6kujrFm/iRuWjiJa+adftQlm/VqPja63dKTYkpFYvhzONuX2FQ6y7ZDnWxsbOeVvW1sbOygpWdw+PVkNExFUZSKohgVySiVxVHKkzEiISOddWSzjnTWkclmyTi8bdYxsTTOJZPLWTy5nHm1JcQjY/eF0TlHY2sfrx/sZOvBTl4/0MH+tn6mVCaZWV3sfw5Fw5+HvrxevA529PPNX+zhkZf30z2YJhwyvrt+H/fdOId7r545pu0wCJmsY/P+Dp590wuzbzR1ATC5PMF7l0/hhgWTKE1E+fHrh/jxliae2nqY4liYmxfVcNvSyVwzdyKxyLnPSOlPZdjY2M5LDa2s39PK5v0dw31YIhpiepV3esf06mOzX4qpryoiGQsTCRmhkBEJeTN5wiEjbN7snubuQfa19Z1w29/WR2NrH0e7vf4oHDJK4hFK4hFKE96tJB6hJBFlRnURV8yuZkV95TmF9qPdAzz5ehM/er1p+HJ4kZBx6dRyVs2s5h2zqlg5vZLShAZ+RkNgl/sZC1oVOU9l0vBPS6BmMdz9aNDVjHvjth1K3lFbPDPnHO19QxzpGqC1J0Vr7yDN3YO09qZo7RmkpSdFS88g/akMWedwzhupPjaC7Zwj66A4Hmb2xBJmTyphjr+dNbGYshFfojr7h3huZzPPbD/Cszua6ewfIho2Vs+qZnl9Jc0jp2m3958yCj7S5HIv5M70b5PKErzZ1MWGxnZe29/BYNr72fqqIlZOr2R5fQWhkNHRN0RHX4r2vqHh+x393jbrGP5ieuxL6rEvrmEzDnX0Dx8EiISMeTWlLJ5cxiVTyrlkShkL68pOOVAx0tnaYVNnP682drDlYCdbDnaw5UAnXQPe74pFQiysK2N6VRGHOvrZ29p3QlAHqC1LMLUyiRmkMo6hdJahzLGbI5XJks06asoSzJzgfVGfMSIgTyyNnzBy5JyjayBNe2+K9j7/1jtEKAQl8SjF8fDwF/KSeITieISiWJjBdJajXYMc6fYO8hzpGuRo18DwQZ9kNMzqWdVcOXsCiyaXjerIfV8qzaGOfu+AUH+aroEhuvqH6BpI+88N0TOYJhIy4pEw8WiIWDhE3D/3PhYJEY+EKEtEqSqOUVEUpbIoRmVRjNJE5JSRu/5Uhra+FO29Kdr8z6mtN8VgOotz4HDDsy+cO36/ojjG/JpS5teUUl505pCxeX8HX/t5A09tPQzArUvquPfqmVQVx/jbH7/B09uOMHNCMZ++bRFr5k86588pH/vDTNaxfk8rT25p4idbj9DSM0jI4LLplVy/YBI3LJjE/JrSU0Y3M1nH+oZWfvT6IZ7aepiOviHKEhGumTuRquIYxfEIJX5bLfbDY3E8Qjrr2LC3jfUNbbx2wAuy4ZBxyZRyVs+s4tKpFbT3pWhs7WVvax+N/oHAY/3K+TKDurIE06qKqK8qYlpVEdFwiJ7BIboH0vQMpOke9LY9g17b3d/WR9Z5//8v81f+v3JONZdOrRg+laStN8VPth7mR68d4qU9rTgHC2pLuW3pZObVlPLqvnbWN7Ty+oFO0llHyGDx5HJWzaxieX0FtWUJJpTEmVDqHbwc7dHjTNaRSme9W8a/pY/fdm7ZyG/ecsOo/s7RdLZVkRVscygfO62ceeZv4fl/hE9shfKpQVczro3rdih5RW0x/6QzWTY2tvOzN4/ys+1H2N3cS3VxjKmVSaZWFXnbSm87rTJJVXGcg+397GntZW9LL3v8297WXjr6hgAvbC6eXMZl06u4fEYll02vZFJZYtRqzmYd+9v72Hqwi62HOtl2qIttBztp9c93DoeMBbWlLK+vYEV9JcvrK5lRXTT8ZfFYO3TO0dDSyyt72nh5bxuv7G1jf1s/4I2UL6gtY8nUci6dUs6SqeXMqyk95Zzo7oEhGlv7vM+gxfvyfbCjj5AZ0XCIaDhELHL8fjQcwgya/GC8v62P9IjzyotiYeqrishknR9kTz3v/K2Ycdpp9LFIiJqyODWlCdr6UjQ09wJQloiwelY1V8z2gu68mpKzfrF2ztEzmKaxtW/ErIPjweNI1+AZfzYR9QJriR9qBtMZUuksg/7trf6tIWN4hH8wnaW1d5CBoQsLOSPVliWYV1vKgtpS5vlh92BHH1/7+R42NLZTmojwgVX1rL1yBlMqTpx6/NzOZv76iW00tPRy08IaPvXuRdRXF73l7zy5P8xmHfva+thxpJsdh7vZ29JLWTJKbXmCuvIEtWUJJlckmVQWP2F0eGAow4H2fvb7o5GNrd72QHsfFUVRFtZ5B3sW1ZUxZ1LJKaOO6UyW9Xva+PGWJv5j22FaelIkot704l9fXMt18yZSURQ7588ylc7yi10t/Oj1Q7yyt204JJ58+scx4ZCxZEo5q2ed22hmNus40j3A3hbv/89gOkPGn92Rde6E2R5Zf4bHsSA7pTJ53iPrXQNDvLKnjV/ubuXF3a3DI9dFsTCrZlbhHLywq4VM1jFrQjHvXjqZ2y6tY25N6Snv1ZdKs2lfB+v3tLG+oZVN+ztInRTSE9GQF3L9W3VxjEQ0RCzi38LHDwbFIiEiIaN7IE1bX8o7WNg7dPwgWN8QnX1DZz04CfDOGVG+/JFfO6/PJZcUbPPEuP4S174XPrcUVn0Ylrwv6GrOLBSG2qUQvnhn6Y/rdih5RW0x/6XS2fOaPjhSe2+Kps4BZk7I/ZRc57xzm7ce7OL1Ax1s2tfB5v0d9Pgju5VFUZZN84Luof17aQ9XsaGxjZYeLwxXF8e4fEYVl8/0vlgvqCvNydTSdCbLQT/k7vUPEOxv6yMaDlFZHKNyxGjlyNHLrHP0DmboGUzTO+gFh5H345EQNWWJEbc45cnoCYH1SNcALzW08uKuVl5saBkO9RNKYsyrKSWVzjKQztCf8hZ4GxjKMDCUoX8ow8n5c2JpfPg88ZkTvIXRKoti3gJsiQhlySilichbfqZpfzRpYChLZ7/3BX3kl/WOPn/bP0QiEqaqOEplcYwq//OpKo75n1uMpB/gjv2TzcCw4cfN3YPDIXLn4W7ePNzNruaeE4LGtKokv3vVTN63ctpZF0hKpbM89Is9fP5nvyKddXzkutl89LrZw/8PUuks/akMvak0fakMfak0z7+0kWTtLHYc7vJqONJD/9Dxc/DryhPDI4gnm1ASY2Jpgo4+79SLkV/tk1Hv4MjUyiStvSl2HO4eft9wyJg9sZgFtWUsqCtlf1sfT287QltvimQ0zA0LJ3HrkjrWzJ941lkPF2IwnaF3MEPvYJrugTS9qTSZrOOSKeUFtfhUW2+K9Q2twyv/p7OOWy6p47aldSyqKzuv0dbBtHcecXP38Vk5Ld2D3tZ/3NqbYnAoMzzKeqZjP5GQUVHk9xkj+o7yoijJaNgPxd5siJEhORYJ0dywjQ+8WyO2gVOwzXPffi/sfiboKt7azX8DV/1R0FWMmXHfDiVvqC1KLmWyjl1He9i0r51N+zp4dV87vzraA8DUyiSr/CB7+YwqZk8sztvLSuXK/rY+ftnQyi93t7K3tZdkNEwiGiYZ9UaIkv79RDRMSSIyvNL39OoiigsomJxNOpNlb2sfOw53k4iGWDN/0nlN0z7cOcD/emo7j28+REk8QsigfyhzxtFKOH4gYb4/Yjy/toy5k0qGP9PugaHhNQOaOgc47G+Pdg1QXhRlelUx9dVJ6quKqK8qZkJJ7IS2nMk6Glt72d7UzZuHu4YvT3awo5+iWJgbF9Zw65Jarps3SeeIF4D0SVOJ01k3fE7whfZh+f63+WzB9uLoeaQw/JevQdOmoKs4u2c+Cxu/CVfed/ywroiIFLxwyJhf6wWGO1fVA975xc8893Pe+878HZ0IyjT/nMPfWjkt6FICEwmHmDOphDmTSi7o52vLE3zuzuV8YFU9j792iFg4RDIWpjgWJhmL+NswxbEIu97cyh2/djUTSuJnfc/SRJTSRJQ5k06d2nouwiFj1sQSZk0s4dZL64af7+wfGr6GuBSOSDhEJBziPGaHX9QUbCV3iqthzk1BV3F2Pc3w2Eeg8UWYcVXQ1YiIyBgqT0apTOjawTK23jGrmnfMqj7rPpGj298y1I6l8qRW5ZXCp95cZKRFt0O8DF79VtCViIiIiIjIOVKwFRkpVuQtbvXGY9DfHnQ1IiIiIiJyDhRsRU62Yi2kB2CLrrkrIiIiIlIIFGxFTjZ5GdReChsfPv3F/0REREREJK8o2Iqczoq1cGQLNG0OuhIREREREXkLCrYip7PkfRBJaBEpEREREZECoGArcjrJClj0Hu8821Rv0NWIiIiIiMhZKNiKnMll98BgF7zxeNCViIiIiIjIWSjYipxJ/RVQPUfTkUVERERE8pyCrciZmHmLSO37JTTvDLoaERERERE5AwVbkbNZeheEIrBJo7YiIiIiIvlKwVbkbEomwfxbYPP3IJ0KuhoRERERETkNBVuRt7LiHuhrgR1PBl2JiIiIiIichoKtyFuZfQOUTdEiUiIiIiIieUrBVuSthMKw/G7Y/Qx07Au6GhEREREROYmCrci5WPZBb7vpO8HWISIiIiIip1CwFTkXldNh9vWw6f9CNhN0NSIiIiIiMkIk6AJECsaKtfCD34Gf/DmU1QVdzQWbtq8BXtgUdBlvz8QF3mrVIiIiIiIo2Iqcu/m3QuUMePkrQVfytswGaAi6ircpFIH/tt27HJOIiIiIjHsKtiLnKhKD+16FTGFfz/b555/n2muvDbqMC9fWAF+6EjZ/F67+46CrEREREZE8oGArcj5CYQglg67ibcmG4xAt4H9DzWKov9K7/NJVHwezoCsSERERkYBp8SgRKTwr1kLbbmh8MehKRERERCQPKNiKSOFZdDvEy7xRWxEREREZ9xRsRaTwxIpgyfvgjcegvyPoakREREQkYAq2IlKYVqyF9ABs+UHQlYiIiIhIwBRsRaQwTV4GdUs1HVlEREREFGxFpICtWAuHX4dDm4KuREREREQCpGArIoXrkjsgktSorYiIiMg4p2ArIoUrWQGL3wNbHoVUb9DViIiIiEhAFGxFpLCtWAuDXfDG40FXIiIiIiIBUbAVkcJWfwVUz9F0ZBEREZFxTMFWRAqbmTdqu++X0Lwz6GpEREREJAAKtiJS+JbeBaEIbNKorYiIiMh4pGArIoWvZBLMvwU2fw/SqaCrEREREZEcU7AVkYvDinugrwV2PBl0JSIiIiKSY4EEWzN7p5ntMLNdZvbJ07weN7Pv+6+vN7MZAZQpIoVk9g1QNlWLSImIiIiMQzkPtmYWBh4EbgEWAXeZ2aKTdrsXaHfOzQEeAP4+t1WKSMEJhWH53bD7GejYF3Q1IiIiIpJDQYzYrgJ2OecanHMp4BHg9pP2uR142L//KHCjmVkOaxSRQrT8g95203eCrUNEREREcioSwO+cAuwf8fgA8I4z7eOcS5tZJ1ANtOSkQhEpTBX13pTk9V+GQ68GXY28hSWtrXDwwaDLkHFO7VDygdqh5Iva0DxgTdBlXJAggu2oMrMPAx8GqKmpYd26dcEWdBY9PT15XZ+MDxd7OywvvZHZ0f1wuCHoUuQthDNZug53Bl2GjHNqh5IP1A4lX2Qq8jtPnU0QwfYgMG3E46n+c6fb54CZRYByoPV0b+ac+yrwVYCVK1e6NWvWjHa9o2bdunXkc30yPlz87XAN8LGgi5BzcPG3RSkEaoeSD9QOJV80F3BbDOIc21eAuWY208xiwJ3AEyft8wRwj3//DuAZ55zLYY0iIiIiIiJSIHI+YuufM/uHwNNAGHjIObfNzD4DbHDOPQF8Hfi2me0C2vDCr4iIiIiIiMgpAjnH1jn3JPDkSc99asT9AeB9ua5LRERERERECk8QU5FFRERERERERo2CrYiIiIiIiBQ0BVsREREREREpaAq2IiIiIiIiUtAUbEVERERERKSgKdiKiIiIiIhIQVOwFRERERERkYKmYCsiIiIiIiIFTcFWRERERERECpqCrYiIiIiIiBQ0BVsREREREREpaAq2IiIiIiIiUtAUbEVERERERKSgKdiKiIiIiIhIQVOwFRERERERkYKmYCsiIiIiIiIFzZxzQdcwasysGWgMuo6zmAC0BF2EjHtqh5Iv1BYlH6gdSj5QO5R8ke9tcbpzbuLpXriogm2+M7MNzrmVQdch45vaoeQLtUXJB2qHkg/UDiVfFHJb1FRkERERERERKWgKtiIiIiIiIlLQFGxz66tBFyCC2qHkD7VFyQdqh5IP1A4lXxRsW9Q5tiIiIiIiIlLQNGIrIiIiIiIiBU3BNkfM7J1mtsPMdpnZJ4OuR8YHM5tmZs+a2Rtmts3MPu4/X2VmPzWzX/nbyqBrlYufmYXNbJOZ/bv/eKaZrff7xe+bWSzoGuXiZ2YVZvaomb1pZtvN7Ar1iZJrZvYJ/+/yVjP7npkl1CfKWDOzh8zsqJltHfHcafs/83zeb4+vm9mK4Co/Nwq2OWBmYeBB4BZgEXCXmS0KtioZJ9LAnzjnFgGrgY/5be+TwM+cc3OBn/mPRcbax4HtIx7/PfCAc24O0A7cG0hVMt58DviJc24BsBSvTapPlJwxsynAHwErnXOXAGHgTtQnytj7JvDOk547U/93CzDXv30Y+FKOarxgCra5sQrY5ZxrcM6lgEeA2wOuScYB51yTc+5V/3433he4KXjt72F/t4eB9wRSoIwbZjYVuBX4mv/YgBuAR/1d1A5lzJlZOXAt8HUA51zKOdeB+kTJvQiQNLMIUAQ0oT5Rxphz7nmg7aSnz9T/3Q58y3leAirMrC4nhV4gBdvcmALsH/H4gP+cSM6Y2QxgObAeqHHONfkvHQZqgqpLxo1/Av4UyPqPq4EO51zaf6x+UXJhJtAMfMOfFv81MytGfaLkkHPuIPCPwD68QNsJbER9ogTjTP1fweUXBVuRccDMSoB/A/7YOdc18jXnLY2u5dFlzJjZu4GjzrmNQdci414EWAF8yTm3HOjlpGnH6hNlrPnnMN6Od6BlMlDMqdNDRXKu0Ps/BdvcOAhMG/F4qv+cyJgzsyheqP2Oc+6H/tNHjk0n8bdHg6pPxoWrgN8ws714p2LcgHeeY4U/DQ/UL0puHAAOOOfW+48fxQu66hMll24C9jjnmp1zQ8AP8fpJ9YkShDP1fwWXXxRsc+MVYK6/2l0Mb4GAJwKuScYB/zzGrwPbnXP3j3jpCeAe//49wOO5rk3GD+fcnzvnpjrnZuD1f8845z4IPAvc4e+mdihjzjl3GNhvZvP9p24E3kB9ouTWPmC1mRX5f6ePtUP1iRKEM/V/TwBr/dWRVwOdI6Ys5yXzRpxlrJnZu/DOMQsDDznnPhtsRTIemNnVwM+BLRw/t/Ev8M6z/VegHmgEfss5d/JiAiKjzszWAP/dOfduM5uFN4JbBWwC7nbODQZYnowDZrYMbxGzGNAAfAjvQL/6RMkZM/tr4P14Vy/YBPwe3vmL6hNlzJjZ94A1wATgCPBp4DFO0//5B12+iDdNvg/4kHNuQwBlnzMFWxERERERESlomoosIiIiIiIiBU3BVkRERERERAqagq2IiIiIiIgUNAVbERERERERKWgKtiIiIiIiIlLQFGxFRERGmZnVmNl3zazBzDaa2S/N7L0B1bLGzK4c8fgjZrY2iFpERETGSiToAkRERC4m/rX/HgMeds59wH9uOvAbY/g7I8659BleXgP0AC8COOe+PFZ1iIiIBEXXsRURERlFZnYj8Cnn3HWneS0M/B1e2IwDDzrnvmJma4C/AlqAS4CNwN3OOWdmlwH3AyX+67/jnGsys3XAZuBq4HvATuAvgRjQCnwQSAIvARmgGbgPuBHocc79o5ktA74MFAG7gd91zrX7770euB6oAO51zv18dD4hERGR0aepyCIiIqNrMfDqGV67F+h0zl0OXA78vpnN9F9bDvwxsAiYBVxlZlHgC8AdzrnLgIeAz454v5hzbqVz7v8ALwCrnXPLgUeAP3XO7cULrg8455adJpx+C/gz59ylwBbg0yNeizjnVvk1fRoREZE8pqnIIiIiY8jMHsQbVU0BjcClZnaH/3I5MNd/7WXn3AH/ZzYDM4AOvBHcn3oznAkDTSPe/vsj7k8Fvm9mdXijtnveoq5yoMI595z/1MPAD0bs8kN/u9GvRUREJG8p2IqIiIyubcBvHnvgnPuYmU0ANgD7gPucc0+P/AF/KvLgiKcyeH+jDdjmnLviDL+rd8T9LwD3O+eeGDG1+e04Vs+xWkRERPKWpiKLiIiMrmeAhJl9dMRzRf72aeCj/hRjzGyemRWf5b12ABPN7Ap//6iZLT7DvuXAQf/+PSOe7wZKT97ZOdcJtJvZNf5Tvw08d/J+IiIihUBHYEVEREaRv+DTe4AHzOxP8RZt6gX+DG+q7wzgVX/15GbgPWd5r5Q/bfnz/tThCPBPeKPCJ/sr4Adm1o4Xro+du/sj4FEzux1v8aiR7gG+bGZFQAPwofP854qIiOQFrYosIiIiIiIiBU1TkUVERERERKSgKdiKiIiIiIhIQVOwFRERERERkYKmYCsiIiIiIiIFTcFWRERERERECpqCrYiIiIiIiBQ0BVsREREREREpaAq2IiIiIiIiUtD+PxefbbTNYeS6AAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "duration, log, fittest_individual_p8 = evolution(\n",
    "    individual_generator=lambda: random.choices(range(8**2), k=8),\n",
    "    fitness_evaluation=evaluate_position_indexed_fitness,\n",
    "    population_size=2500,\n",
    "    generations=100,\n",
    "    crossover_rate=.5,\n",
    "    mutation_rate=.5,\n",
    "    mutation_function=(tools.mutUniformInt, {\"low\": 0, \"up\": 8**2 - 1, \"indpb\": 1/4})\n",
    ")\n",
    "print(f\"Computed in {duration:.3f} seconds\")\n",
    "plot_generations(*log.select(\"gen\", \"avg\", \"min\", \"max\"))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Fittest Individual"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positions: [33, 61, 31, 3, 8, 50, 20, 46]\n",
      "Duplicate queens: 0\n",
      "Fitness: 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": "   1  2  3  4  5  6  7  8\n1           👑            \n2  👑                     \n3              👑         \n4                       👑\n5     👑                  \n6                    👑   \n7        👑               \n8                 👑      ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Positions: {fittest_individual_p8}\")\n",
    "print(f\"Duplicate queens: {len(fittest_individual_p8) - len(set(fittest_individual_p8))}\")\n",
    "print(f\"Fitness: {abs(fittest_individual_p8.fitness.values[0])}\")\n",
    "display_positional_grid(fittest_individual_p8)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Evolutionary Algorithm on a 16x16 Board\n",
    "In the following section, the DEAP evolutionary algorithm is executed to solve for a single solution of the $n$-queens problem for an $16 \\times 16$ chessboard using a position-indexed genetic encoding."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Evolutionary Computation\n",
    "A larger population size and more generations are simulated, since there are expontentially more states in a $16 \\times 16$ chessboard."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed in 326.230 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1152x576 with 1 Axes>",
      "image/png": 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2DKtWrSr1MGYtflubcpDueZUk6aAsy5clX5cZ1gKym+0AwE7BaXhDXuXfbLxERERERETZYMBaQPWmegDAgDe/VPtCFh+wcmsbIiIiIiLKBgPWAqrUVUIjaTAaGC31UMqONzwVsPrCvhKOhIiIiIiI5gsGrAWkklSwGqwY9TNgTSY6BAPMsBIRERHR/DEfev7MJ7k+nwxYC8xmsGHYP1zqYZQdrmElIiIiovnGYDBgeHiYQWuByLKM4eFhGAyGrG+jKeJ4Lkg2g40Z1jTiS4K5rQ0RERERzQfNzc04c+YMBgcHSz2UWfH7/TkFicVkMBjQ3Jz9jiEMWAus2lCN/qH+Ug+j7CQErMywEhEREdE8oNVq0dbWVuphzNr+/fuxYcOGUg8jLywJLrAaQw0zrGkklAQzw0pERERERFlgwFpg1YZqTIYmEYwESz2UssI1rERERERElCsGrAVmM9gAACP+kRKPpLx4w17o1XoAzLASEREREVF2GLAWWLWhGgAD1mSekAdmrRkGtQGBMLe1ISIiIiKimbHpUoHVGGoAMGBN5g17YdKYEJEj8IV9pR4OERERERHNAwxYC0xkWNl4KZE35IVJa0IoGkIgwgwrERERERHNjAFrgXENa3rekBdmrRmBSIBNl4iIiIiIKCtcw1pgFdoKaFVaBqxJREmwQW2AL8KSYCIiIiIimhkD1gKTJAnVhmoGrElESbBeo2fTJSIiIiIiygoD1iKoMdRwDWsST9gDk8YEo9rIbW2IiIiIiCgrDFiLgBnWVCLDatAYuIaViIiIiIiywoC1CGwGGwPWJGINq16tZ4aViIiIiIiywoC1CNJlWId8Q3hz9M0Sjai0gpEgwtEwzFozM6xERERERJQ1BqxFYDPY4Av74AtPdcP9+sGv444/3XFB7kHqDXkBIFYSrDZckM8BERERERHljgFrEYi9WOMbL705+iYmQ5N4uv/pUg2rZLzh8wGrJraGNT6QJyIiIiIiyoQBaxGIgFWUBUeiEZwcOwkA6HJ0lWxcpeIJeQDEMqx6tR6BSACyLJd4VEREREREVO4YsBZBtaEawFTAembyDAKRACx6C/b37b/g1nDGZ1iNGiOichShaKjEoyIiIiIionLHgLUIkjOs3e5uAMCda+6EL+zDU/1PlWxspZCwhlVjAAB2CiYiIiIiohkxYC2CGkMNgKk1rD3uHgDAbStug81gu+DKgkXAataaoVfrAeCCyzITEREREVHuGLAWgVFjhF6tT8iwNpobUaWrwraWbXjyzJNKEHchSC4JBhiwEhERERHRzBiwFoEkSbAZbErA2uPuQbu1HQDQ2doJX9iHJ/ufLOUQ51R8SbCSYWVJMBERERERzYABa5FUG6ox4h9BOBrGqbFT6LB2AAAuqb8ENYYa7HLsKvEI507ytjYAM6xERERERDQzTakHsFCJDGvfRB9C0ZCSYVWr1NjWsg2/PfFbfGTXRwAAWrUWf3/Z32OpZWnCfTx15il0u7vxwbUfnPaxfvDqD7CiegWuab6mOH/MLHlCHkiQYNAYYFDHAtZAJFDiURERERERUbljhrVIbAYbRv2jSsOlZdZlyu/evfLduLjuYnjDXnjDXjx55kns6d2Tch+/PP5L3H/4fkSikYyPM+Qbwjde/gZ+1/27gv8NheINe2HUGKGSVEqG1Rf2lXhURERERERU7phhLRKRYRVb2rRZ2pTftVvb8eMdP1Z+3v7wduV68XrcPQhEAuif7MeSqiVpH2d3727IkJX1suXIG/LCrDUDgBKwMsNKREREREQzYYa1SKoN1QhEAnh16FU0VTTBpDVlvG6HtUPJxAqekAfnPOcAIG0wK+w8tRPA1BY65cgb8ip/vygJ5hpWIiIiIiKaCQPWIrEZbACAg66DSsOlTDqsHTg1dgrhaFi5LD6ATQ5mBZfHhUMDh6CRNOWdYQ17YdKcD1hZEkxERERERFliwFokImD1hDxKw6VM2q3tCEVD6JvoUy4TQapOpcuYYd1zeg9kyLhhyQ1wB9zTrnUtJbGGFYCyrQ1LgomIiIiIaCYMWItEBKwAssqwAomZ1G53N/RqPTbaN2bMsHY5urCsehkubbgUMmS4A+7ZD7wIPCGPsoZVBK4sCSYiIiIiopkwYC2S+IB1pgyraMgUn0ntcfdgqWUpllUvw6mxUynZU6fHiUMDh9DZ0gmbMfZY5VoWHL+GVavSQoIEf4QBKxERERERTY8Ba5FUG6oBABKkhA7B6Zi0JjRVNCUErCfcJ9Bh7UCHtQPBaDChXBiIdQcGgM7WTtj0sYC1XBsvxa9hlaTYfqzMsBIRERER0UwYsBaJUWOEUWNEc2WzUgY7nfhOwePBcQx4B9BubVfKhZPXse507MRK20q0WlqVbO5sMqxROZr3bWcSv60NEHtuFvoaVlmWSz0EIiIiIqJ5jwFrEdWb6rG8enlW1+2wdsAx7kAoGsJJ90nlsnTlwmcnz+LI4BF0tnYCmMrm5huwfu3g1/D+P76/KEGWLMsJTZeAWOOlhdwluHe8Fxt/sVF5HYmIiIiIKD+aUg9gIfvKtV+BRW/J6rrt1naEo2GcHj+tBKft1nalXDi+8ZIoB97esh0AYNVbIUHKO2B9c+RNHBk6ghPuE1kH2NkKRAKIytGEfWgXeknwsZFjCEQC6B3vxVLr0lIPh4iIiIho3mKGtYhW1azCoopFWV03vvS3x90Do8ao3LbD2pGQYe1ydGGVbRWWVC0BAKhValj11rzXsIpAt8vRldftp+MJeQAgoSTYoDYs6JJgl8cFAPCEPSUeCRERERHR/MaAtUy0WdqgklTocfeg292NpZalUEmxl6fd2q6UC/dP9uPVoVeVcmDBZrDlnWEVt9vl2FXwsmBv2AsAStMlYOFnWJ0eJ4DY2l0iIiIiIsofA9YyYdAY0FzRrGRY47fC6bB2IBwNo2+8T8mCJges1YbqvAJWWZYx4h+BzWCDY9yBN0ffnN0fkkQEbfElwXq1fkFva+PyxjKsC3mdLhERERHRXGDAWkbare14ZeAVDPoGlRJhcTkQKxfucnRhbc1aNFc2J9w23wyrJ+RBKBrCOzreAbWkxk7Hztn9EUlEhtWsiSsJvkAyrKIcmoiIiIiI8sOAtYx0WDsw4BsAgIQMa5ulDRIk7O/bj6PDR1Oyq0AsYB0N5L6GVQS5S61LcUXjFehydBW0LDhdhtWoXtjb2rAkmIiIiIioMBiwlpH4rOoy6zLl30aNEYsrF+P/Tv0fAGB76/aU29oMNowFxhCKhnJ6TBGw2gw2dLZ2om+iD8dGjuUz/LREhjVhWxvNwt3WJhQNYcg3BGDqbyciIiIiovwwYC0jIqtq1pphN9tTfheVo1hXty5t52GbwQYAGAuM5fSYImCtNlTjhsU3QCNpCtotOFOX4IW6hnXQOwgZsQw1S4KJiIiIiGaHAWsZabO0QS2p0W5phyRJCb8T2dfOltRyYCAWcALAsG84p8cUAWuNoQZWgxVXLCpsWXC6kmCDxoBAeGGWBItyYIAZViIiIiKi2WLAWkZ0ah0ubbgUVzRekfK7jfaNqNJVpV2/CkxlWHNtvCT2bhUBb2dLJ/on+/H68Os53U8mGbe1ifgLvoVOORABa5WuCr7Qwix7JiIiIiKaKwxYy8wPO3+Iey65J+XyTYs24el3P40Gc0Pa24mAVQSg2Rrxj8CsNUOv1gMAblhyAzSqwpUFe0NeqCW1cv8AlH8vxMZLYkubpZalzLASEREREc0SA9Z5JLlMOF6+GdYR/wiq9dXKzxa9BVctuqpgZcHesBcmjSlh7KIB00Lc2sbpcaJCW4E6Ux3XsBIRERERzRID1gWiSl8FtaTOK2C1GW0Jl3W2duKc5xxeHXp11uPyhrwwao0JlxnUBgBYkI2XnB4n7GY7TBoTM6xERERERLPEgHWBUEkqWPXWvNaw2vSJAeuWxVugVWkLUhbsDXsTOgQDsW1tgIWZYXV5XWgwN8CkNXEfViIiIiKiWWLAuoBUG6rzWsOanGGt0lVh86LN2NW7C1E5OqsxeUKehIZLAGBUny8JXqgZVtP5DGvIuyAbSxERERERzRUGrAtIjaEmpwyrLMuxDKvBlvK77a3b4fQ4cWTwyKzG5A15E7a0ARZuhjUYCWLYP4wGcwPMWjPCchihaKjUwyIiIiIimrcYsC4g1YZqjAayz7COB8cRlsMJTZeE6xdfD51KN+uyYF/YB7MmsSR4oa5hFR2C7Sa7EqSzLJiIiIiIKH8MWBcQm8GGEV/2GVaRjU0uCQaACl0Frm66GrscsysL9oQ8qU2XNLGANRBeWNvauDznA9bzTZcAwBNmp2AiIiIionwxYF1AbAYbJkITCEViZahvjLyBv9v3d/CFfWmvL9a7JjddEjpbOzHgG8Ctf7gVtz92O25/7HbsPLUz5XpHh4/iU098SnnceOnWsIoMqy+SflzxHjnxCH7w6g+mvY4sy/jCc1/Ak2eenPH+isnpdQKA0nQJYIaViIiIiGg2GLAuINWGWGmvyJz+8vgvsef0Hjxx5om0158uwwoA1y+5HjctvQmN5kbUGmsx6B3E91/9fsr1fnr0p+hydKHb3Z1wuS/sw4h/BPWm+oTLc8mwPvjGg/jp6z+d9jrd7m48+MaD+EPPH2a8v2JyemIBq2i6BIBb2xARERERzQID1gWkxlADABgNjCIUCWFP7x4AQNep9OtQRcCabg0rABg1Rnzxmi/i/q334/6t9+PDF30Yb46+iZNjJ5Xr+MN+7Du9DwBSAtZTY6cgQ0a7tT3hchGwztR0KSpHcXLsJEYDoxj2DWe8nlhnm/z4c83lcaFKVwWT1qRkWD0hlgQTEREREeWLAesComRYfSN47txzGA+Oo83Shqf6n0obOCkZ1jRdgtO5ccmNkCAlNGI60H9AySL2uHsSri9+7rB2JFyebdOls5NnlXLm5PsWZFlWxuMYd5S0K6/T60SDuQEAlL1nfaGZy56JiIiIiCg9BqwLiAg8RwIj6HJ0oVJbiX+84h8RiATwRF9qWfCofxSVukpo1dqs7r/B3IAN9Ruwy7FLuazL0QWr3orWqtaUoLLb3Q2NSoMlVUsSLs92W5v4+8uUPX1z9E04xh3YUL8B4WgYfeN9Wf0txeDyuGA32QGAJcFERERERAXAgHUBERlWp8eJx08/juuXXI+N9o2oN9Wn3Z5mxD+SdXZV2NG2A93ubnSPdsMX9mH/mf24seVGrLCtSAkqe9w9aK1qhVaVGBBrVVpoJM2MGVZxf0aNMWOGtcvRBbWkxsfWfSzhNqXg9DhhN58PWNl0iYiIiIho1hiwLiBVuipoJA3+7+T/YSI0gc7WTqgkFba3bMfT/U9jMjiZcP18AtZtLdsgQcKu3l14uv9p+MI+7GjdgXZrO/on+xMCtG53d0o5sGDQGLLKsDaYGrDStjJtICrKga9ovAIbGjZAgpQxsC22QCSA0cDoVMDKbW2IiIiIiGaNAesCIkkSqg3V6HZ3o0pXhU2NmwDEtqcJRoPY17cv4foj/pGMDZcyqTXW4jL7ZehydGHnqZ2wGWy4tOFSdFg7IEPGqbFTAGKZxf7J/pSGS4Jerc8qw9ph7UC7tR09Yz2QZTnh98dGjuH0xGl0tnbCqDGiubK5ZBlWsQdrgym2htWgMUCCxAwrEREREdEsMGBdYETGdOuSrcra1HV162A32xPWngLnM6wZtrSZTmdLJ06OncTjpx/HtpZt0Kg0SmAqAkYRuOabYY1EIzg5dhLt1nZ0WDswFhjDkG8o4Tpdji5oJA1uWHwDAKDd2l6ygFXZ0uZ8hlUlqWDUGLmGlYiIiIhoFhiwLjBiHWtna6dymSgLPnD2AMaD4wBiW8a4A+6cM6wAsLVlK1SSCmE5rDzOksol0Kq0SkmuCBwzZVgNagMCkcz7sPZP9iMQCSgZ1vj7BOLKgRddAavBCiAWHJ8eP41QJHOn4IngRNrLg5HgtOOZicsby7CKgBWIrWNlhpWIiIiIKH8MWBeYRnMjqvXVuLzx8oTLd7TuQCgawv6+/QCAscAYonIUNcaanB+j1liLy+2Xo85Yh0vqLwEAaFQatFpalaCyx90DrUqLxZWL096HQWNQtqxJJz7gFVna+PWpR4ePon+yH50tU4F5u7UdYTkMx7gj7X0eHT6Ka359DQ4PHE753T8/88/4m8f/JvMfPYOzk2cBAPWmeuUys9bMgJWIiIiIaBYYsC4wf3PJ3+Anb/lJSmfetbVrsci8SOkWPOofBYC8MqwA8MWrv4gfdf4IapVauazD0pGQYW2ztEGj0qS9vV6tnzajKe6n3dqOGkMNLHpLQoa1y9EFjUqDG5bcMPX4aQLbeI+dfAwROYIT7hMpv3tj5A0cHjycsk42WyfHTmKReRGMGqNymUljYkkwEREREdEsMGBdYGqMNWiztKVcLkkSOls78czZZzAWGMOwfxgA8lrDCgB1pjq0WloTLmu3tuOs5yy8IS963D0Zy4GB2FY1061h7XZ3o9HcCLPWDEmS0G5pVwJRUQ581aKrYNFblNu0WdqgklRp17FG5aiyhlesN43n8rjgCXnS/i4b3e7ulL/XqDHCE2KXYCIiIiKifDFgvYB0tnYiHA3j8dOPY8Q/AgA5b2szHZHhfHXoVZz1nM3YcAk433Rpmi7ByQFvhzWWvZVlWbn/+HW6QCxru7hycdoM6yuDryjrTJODUk/Ig4lQbG1ruuzrTMLRME6NnUr5e81aMzOsRERERESzwID1ArK6ZjWaKprQ1dullAQXMmAVAaYoO54uw6pX6zNmWCPRSEoA2G5tx0RoAgPeAXQ5uqBVabFl8ZbUMVjSdwrucnRBp9Khw9qhBK6C2JIGyFxOPJ2+iT6EoqGUv5dNl4iIiIiIZocB6wVElAU/f/Z5ZdsZq95asPtfXLkYOpUOe3r3AMi8pQ0wfUlw30QfgtFgSoYViJXe7urdhc2LNqNKV5Vy23Zre+z2kaBymSgHvrrpaiy1LE0IUIHEjGs+2+KIIDf57zVpGLASEREREc0GA9YLzI7WHQjLYfzh5B9g0VsyNkXKh1qlxlLrUowGRqFX69Fc0Zzxunq1PmNJsAgAl1mXKZd1VMeCwf898b9wepzobOtMe9tl1csQkSNKQA4AhwYOYdA3iB1tO2A32+H0OBOaKzm9sYA1UznxTESQm7x2mCXBRERERESzw4D1ArPSthJLKpdgIjhR0HJgQWRFl1qWJnQQTmbQGDJmWNMFgDaDDTaDDbt6d0Gn0mFL85ZpHz8+8OxydEGv1uO65utgN9vhj/gxFhhTfu/0OCFBwqbGTTg5dhJROZrdH3tej7sHTRVNMGlNCZcbNUZ4w968Ow8TEREREV3oGLBeYERZMJD/ljbTEWWx061fBQCD2oBQNIRINJLyu0wBoLjPq5uuRoWuIu39tla1Qi2plaA3Eo1gd+9uXNt8LUxaExpMDQCQsI7V5XWhxliDlTUr4Qv7lD1Vs9Xt7k5b/mzSmhCVo9M2lyIiIiIioswKVw9K80Znaye+/+r3UWOsKfh9t1tiQeWMAavGAAD4xqFvpJQlHxw4iFW2VWnv+0XniyndgePp1DosqVqCfX37IEkSRv2jGPINYXvrdgCA3WwHEMuqrrCtUP5tN9kT9nFtroyVM4eiITxy4hHc3H6zMuZ4oWgIjnEHrm2+NuV3Zq0ZAOANeRP2ZyUiIiIiouwwYL0ALa9ejk2Nm7ChfkPB73td3To0VzRjU+Omaa+3rHoZdCodHnj9gZTfSZBw1aKrUi6/uulqvOh8Edctvm7a+76m6Rr8/NjPcfLVkwCAReZFuLYpFlDGB6yC0+NEm6VNCbK73d3KY3Q5uvD55z4PALhtxW0pj3V6/DTC0XD6DKsmliH2hr2oQeEnB4iIiIiIFjoGrBcgSZLwve3fK8p91xhr8Kd3/WnG613ddDUOvv9gTvd93eLrZgxWAeDejffi3o33ph+foQYaSZNSErxp0SZU6apQb6xPWf8q/p8uYBWlx5lKggGwUzARERERUZ64hpUuKGqVGnWmOiXDOhGcgCfkgd0Uy7x2VHcoQehEcAIH+g/ArDXjJddLGPINpdxfj7sHKkmV0iEYAMya8yXB7BRMRERERJQXBqx0wbGb7cpWNiJwFaXC7dZ2nBo7hagcxf6+/QhFQ/j0xk8jKkeV/WXjdbu70VzRnHZ9KzOsRERERESzw4CVLjgNpgYlUBX/bzDHugd3WDvgj/jRP9GPLkcXGs2NeEfHO9BuacdOx86U++px92RsMCUaLTHDSkRERESUHwasdMGxm+1weVyQZVlZyypKgkXw+fLAyzhw9gC2t2yPbQXU1omXXS9jwDug3E8wEsTp8dNp168CUxlWT8hTzD+HiIiIiGjBKlrAKkmSQZKkFyRJekWSpNclSfr/zl/eJknS85IkdUuS9KAkSbpijYEoHbvZjmA0iNHAKJweJ1SSCnWmOgBT2/L86LUfIRwNK1vodLZ0QoaM3b27lftxjDsQlsMZM6zx29oQEREREVHuiplhDQC4QZbliwGsB7BDkqQrAdwH4L9kWe4AMArgw0UcA1EKkU11epxwepyoNdYqe8FW6CpgN9txcuwkmiqasLZ2LQBgqXUpllUvwy7HLuV+RDfhjBnWuG1tiIiIiIgod0ULWOWYyfM/as//JwO4AcDD5y//CYBbijUGonTEelWXxwWX16UEsILImG5vjZUDC50tnXh54GW4PLEy4m53N1SSCq2W1rSPo1froZJUzLASEREREeWpqGtYJUlSS5J0GMAAgN0AegC4ZVkOn7/KGQBNxRwDUTLREdjpjWVYRQArdFhiGVNRDiyIn2977Dbs+O0O/Pzoz7Gkcgn0an3ax5EkCWaNOasM66B3EB/c+UEMegdz/nuIiIiIiBYqTTHvXJblCID1kiRZATwCYGW2t5Uk6SMAPgIADQ0N2L9/fzGGWBCTk5NlPT5KFJWjUEONF469gLMTZ9EmtyW8fktCS3Cz9WYMHBnAoJQYQN5kvQmukAuQgSZ9E9bo1kz72qujavSc7sF+b+brAMBhz2G8NPQSfrHvF1hvXp/338ZjkcoBj0MqBzwOqVzwWKRyMJ+Pw6IGrIIsy25JkvYB2ATAKkmS5nyWtRlAf4bbfA/A9wDgsssuk7ds2TIXQ83L/v37Uc7jo1T239rhr/QjOB7EpSsuxZY1WxJ+fxtuS3u7LdiS9vJMrI9YUVVdNePx0Xe0DxgCatpqsGV1bo8Rj8cilQMeh1QOeBxSueCxSOVgPh+HxewSXHc+swpJkowAtgE4BmAfgFvPX+0OAL8v1hiIMmkwNeCVwVcATJUIF4NZm11JcPK+sEREREREVNw1rI0A9kmSdATAiwB2y7L8GIDPAPikJEndAGoA/LCIYyBKy262YyI4ofy7WExaU1ZNl8R+sOL/RERERERUxJJgWZaPANiQ5vKTAC4v1uMSZSM+SE3uElxIJo0JA96BGa/HDCsRERERUaqidgkmKlcNplhnYLWkRq2xtmiPY9Ka4Al5ZrweA1YiIiIiolQMWOmCJDKsdaY6qFXqoj2OSWOacQ1rOBrGoG8QakmNId8QwtHwtNcnIiIiIrpQMGClC5IIWItZDgxkt4Z1yDeEqBzFCtsKROQIhnxDRR0TEREREdF8wYCVLkiiJLjB3FDUxxFdgqNyFAAwHhzHeHA84TqiDPjiuosTfi6UYCTIIJioDA35hhCKhko9DCIiorLGgJUuSDaDDRa9BW2WtqI+jkljAgD4w34AwD2P34N7n7g34TpOb1LA6i1swPrzYz/Hux59F2RZLuj9ElH+gpEgbnrkJvz2zd+WeihERERlrWhdgonKmSRJeOhtD6HaUF3UxxEBqzfshTvgxkHXQVTpqiDLMiRJAgC4PLGtbNbXr0/4uVDOTJzBiH8EvrAPJq2poPdNRPlxeV2YDE2y0RoREdEMmGGlC9aiikUwaoxFfQwRIHpCHuzu3Q0gVhY86BtUruP0OGHSmLDIHBtPoU9gRQlycikyEZWOeJ/P1JSNiIjoQseAlaiIRMDqDXmx89ROGNQGAEC3u1u5jsvrQoO5AZIkwW62w+UtbIZ1PBALVMcCYwW9XyLKnwhYs9n2ioiI6ELGgJWoiERJ8An3Cbw2/BpuX3E7AKDH3aNcx+lxKt2KG0wNzLASXQDExJQv7CvxSIiIiMobA1aiIhIZ1kdOPAIAePfKd6NaX50asIptdsz2gq9hFZlVkWklotJTSoJn2PaKiIjoQseAlaiIzBozAOAl10u4qPYiNFc2o93arpQEh6IhDPmGEgLWQd9gQbe6YIaVqPyIiSmWBBMREU2PAStREcV35e1s7QQAtFvb0ePugSzLGPQOQoas7AtrN9khI3Z5IUTlKCaCEwAYsBKVE7F9FZsuERERTY8BK1ERiTWsALCtZRsAoMPagcnQJFxel1IWKDKsDeZY4FqodayToUnIiO2/yqZLROWDJcFERETZYcBKVERmbawkeF3dOiyqWAQglmEFYo2XkgNW0XypUJ2C44NUZliJyoM/7Ic74AbADCsRpXrojYe4R/M8N+Qbwq+O/6rUw1gwGLASFZFWrcXmps34wOoPKJd1WDsAxLa2EWWBSknw+cC1UF9U8UEqmy4RlQcxIVVjqGGGlYgSDPmG8PnnPq80a6T56bGex/DF57+IId9QqYeyIGhKPQCihe47N34n4edqQzVqDDXodnfDpDGhQluBCl0FAKBCV4EKbUXhAta4IJUZVqLyIN7fS61L8aLzRUSiEahV6hKPiojKgfh8EBPaND+N+EcAABPBCdQaa0s8mvmPGVaiEuiwdiglwSKrKtjN9oKVBIsgtd5Uz4CVqEyI9/dSy1IALAsmoimig3iht7ijuSUC1sngZIlHsjAwYCUqAdEp+JznnNJoSWgwNRQswyrWsDZXNDNgJSoT4v3dZmkDwMZLRDRFZFa5hnV+UwLWEAPWQmDASlQC7dZ2eMNenBg9oTRaEuxme8HXsC6uXMwuwURlwulxolpfjWp9NQBmWIloCkuCF4ZR/ygABqyFwoCVqARE46WwHE7NsJobMOwfRjASnPXjjAfHoVPpUG+qx0RwArIsz/o+iWh2xFIAsU8zA1YiEkTA6gl5WE46j7EkuLAYsBKVgNjaBkBqhvX8zwPegVk/znhgHFX6KlTpqhCRI/CEPLO+TyKaHZfXhQZTg7JPM0uCiUiI72HBsuD5azQQy7DyvKswGLASlYBFb0GdsQ4A0mZYgcJ8UY0Hx1Glq0KVvkr5mYhKy+lxosHcoOzTzICViASnx4nmiubYv1kWPC95Q174wj4AwERoosSjWRgYsBKViMiypusSDBTmi2o8EAtYLToLAHAdK1GJeUNejAfHYTfbYdQaY5exJJiIAESiEQx4B3Bx/cUAmGGdr0Q5MAB4gsywFgIDVqISEetYM5UE/8uBf8GmX27Cpl9uwn0v3JfXY4wHx2HRW5hhLSBvyIvbH7sdRwaPlHooc+pF54t43x/fl3Zt9T2P34Pfd/++BKOaf8RElN1sV0qCWTJGRAAw5BtCRI7gotqLIEEq2BZ36QQjQbzvj+/DS86XivYYFyrRcAlg06VCYcBKVCJ/sfov8O9X/7vSeEUwaU343BWfw20rbsMtHbfAbrZjX9++vB5DKQnWMWAtlFPjp3B0+CgODRwq9VDm1KGBQzgyeAT9k/0JlwcjQezr24dnzj5TopHNL2JvxQYTS4KJKJEIUJsrmlFnrCtqhnXAO4Ajg0dwZOjCmnydC/EZVgashaEp9QCILlRNFU1oqmhK+7vbV96u/Pu7r3wX/3P4f+ANeVOC25mMBcaUpktArESYZkecQMR/IV0IxLHj9DiV/UOBqRMslq5lRzxPdrMdRg1LgoloSvznQyG3uEtHTGCzi23hifODan01A9YCYYaVqMyJ0uGTYydzul0kGsFkaDK2hlV/fg1rkGtYZ0ucQMSX/FwIxMlN8gmU+LmYpWsLiSgJbjA1QKPSQK/WM8NKRACmPk8bTA1oMDcU9XNVCVgZUBWcCFgXVy3mGtYCYcBKVOZEc6Zud3dOt5sIxjrTWfQWGDVGaCQNM6wFIE4gLrgM6/mTm+QTqPiANSpH53xc843L40KNoQY6tQ4AYNaamWElIgCxCS2D2gCL3oIGUwOcHmfR9k8X5wNcQ194o/5RGDVG1BvrOSFQIAxYicrc4srF0Kl06B7NLWAVAUaVrgqSJKFKX8U1rAVwoWZYRYfp5AyrCGDD0fAFF8Tnw+l1JmxlZdQYmWElIgCxCS272Q5JkmA32+EL+4r2vS0qrsTkNhXOiH8E1fpqmLVmBqwFwoCVqMypVWq0WdrQPZZbwCoCDLF+tUrHgLUQRNOcYf9wiUcyt5SSYG/6kuDkf1N6Lo8roTO4SWtihoOIACROaBVyT/Z0mGEtnpHACGwGGyp1lVwjXCAMWInmgXZrO3rcPTndRsmwnt/SpkpfxZLgAhAZxQstw6qUBHsSS4JdHhc0qlj/PgasM3N6nAl7L5s1LAkmohinx4kGUyxQFRNbxVrHKj7TmWEtvBHfCKoNsQyrJ+ThcpkCYMBKNA90WDvg9DhzmqmLLwkW/2fTpdmJylG4vLEAzRv2whf2lXpIc0ZMdiQHrE6vE6ttq2O/Y+OlaU0GJzEZmkwIWE1aEwNWIkI4GsaQb0j5fBD/L1qGNcgMa7GMBkZhM9hQoa2ADPmCOlcoFgasRPOAaLzUMzaVZZVlGcdHjme8jQgwRIfgKl1+GVZZlvHGyBs5324hGvYNIxwNY5l1GYDULOt4cBznJs+VYmiKN0ffLPhsbigagjfshVlrxkRoIuEEx+VxYbltOfRqPTOsMxABvcigAIBJYyr7Nawuj+uCqyiYzlhgDGcnzxb9cdx+97x8T70x8kbRGgUtZEO+IUTlqBKo1hnroJbURTsGxLIhrrEsLFmWMeKLlQRX6CoAMItdCAxYieYBsbVNfFnwvr59+PM//DmODKbf9FtkU2e7hvW5c8/h1j/cOm1wfKEQAcfqmlhGMfkk/usHv467dt815+MSTo2dwrsefRf29e0r6P2KiY7l1csBTM34+8N+jAZGYTfZlY6WlJno9N1UObX/sklb/gHrJ5/4JP79+X8v9TDKxn8d/C+8/4/vRyQaKerj/Pvz/457Hr+nqI9RaM+fex63/uFWvOh8sdRDmXeUPVjPlwKrVWrUGmuLXhLMNZaF5Q17EYwGlQwrwCx2ITBgJZoHmiqaoFfrE7a2+eOpPwIAesd7095mPDAOg9qgbJ9h0VswEZzIOfsm7j/T41xIxAmFCFiTGy/1jveib6IP4Wh4zscGAMeGjwFAwbM/4sRGBKyiLFicSM3FJvcLwe7e3bAZbFhTs0a5zKQp/5Jgp8fJ93+c/sl+DPgG8JLrpaI+ztHho+if7C/qYxSa+F7i8oDcKXuwxnURt5vtKcswCkVMRAajQQQjwaI8xoVoxBfrli/WsALMYhcCA1aieUCtUmOpZamSYfWGvHjyzJMAMp8YjAfHlewqEMuwypBzLk0RX6IMRqaeg1W2VQBSM6xOjxNROYoh39Ccjw2YyuAVunwzOWAVnYKVjMD5gJUnqZmJ9+y2lm1KkypgfmRYJ4ITfP/HEe+vLkdX0R7DH/ajb6IP48FxhKKhoj1OIYWiIezp3QMA7Eifh/jPU8Futqd0Zi+U+NeIGcDCGQnEAlbRJRhgFrsQGLASzRMd1g4lIHmq/yllEX+mE8nx4LjSIRiY6hac64lEcnByIXN6nNCr9WiztAFAwr6jsiwrAVupnisxoVHo/VDFWqd2azskSMrfF59hbTA1YMA7UPQyyfnqyf4n4Qv70NnamXC5SWNCMBos26AkGAnCF/bBHXCzcch54v21p3dP0aopTo2dgozYOlC3312Uxyi05889r3y/MGDNncvrgkljQqW2UrnMbopVrhRjTfB4cFx5LAZUhSMyrDaDjRnWAmLASjRPtFvbMeAdwHhwHF2OLtQYapTuwemMBcZSMqxAHgFrUnByIXN5XWgwNcCsNUOv1idkMkcDowhEAgBKGLCOFSdgFcdMjaEmYU2VUsJmaoDdbEdEjpQsu1zudjl2odZYi0vqL0m4XJzQlGuWNf7zolilifOJLMsY9Y+izdKG0cBo0dZqxi//KPT7uVi6HF2o0FbAqDFyC7U8OD2xPVglSVIuazA3IBAJKJOGhRKJRjARnMCiikUAGFAV0mggdl7ADGthMWAlmidE46XXBl/DU2eewraWbVhUsWj6kuC4DKvoFpzriYSyXpEnq8oempIkodpQnbCGNf75KUVwH4gE0DfRB6AIAWtgak/f+OZKTo8TVr0VBo1BKWPjxEaq+HJgtUqd8DuT1gQAZZu9jP+84Gsb+1wNy2HctPQmmDSmopUFxzfYmw8BaygSwt7Te3HDkhtg1VuZYc2D0+NUGi4JytY2BS4LFgEqA9bCE+9XrmEtLAasRPOE2Nrmh6/9EP6IH52tnUq5UDrp1rACyGkvVrHvKMCSYCB20iC2JLEZbAknkvHPTymeq1NjpxCVozCoDUVbw1qpq0xoriQCeGBqqxYeJ6meOPMEApFASjkwECsJBsp3DVl84MHXdmr9amNFI7Ys3oK9p/cWpZy7x90Dg9qQ8Jjl7Nlzz2IiOIHO1k5Y9BZmWPPg8roS1q8CUx2DC/3eE6+PErAyA1gwI/4RmDQmGDSGsv98n08YsBLNE4sqFsGoMeIF5wuoM9ZhQ/0G2M32jGvLxgPjSlYViCsJzuFEYsQ/glA0BIvegkHfYNmus5sLkWgEg95B5YTCZrAlnEiKGXCL3lKSE3tRQri+fn1R1rCaNCZoVVolYBVrdsUJVbE3uZ/Puhxdyns2mciwzoeSYL62U9kTm96GHa074A648cK5Fwr+ON3ubqyvX5/wmOWsy9GFSl0lNjVuynsLtQtZKBLCkG8oJWAVHYMLHrCef30WmZlhLbQR/wiqDdUAYg0zzVoz92EtAAasRPOESlJhqWUpACilheLLbMA7kHDdUDQEb9ibmGHNo+mSKHO9uO5iyJAx6B2c1d8wnw35hhCRIwkBa3KGVaPSYJVtVUlO7HvcPdBIGqyvX4/J0GRBtymILy+3m+3whr2YDE0qa66A2ISIUWMsWkfL+WoyOImnzjyF7a3boZJSv3LFDHy5bm0Tv3aOr+1UttNmtOGqpqtQoa0oeFmwL+xD/2Q/Lqm/BGpJXfYBazASxOOnH8fWJVuhVWtRpasq+JrLhW7ANwAZslKpItQYaqCRNAUvxxevD0uCC2/UP4oaQ43ys1lrZoa1ABiwEs0joix4R9sOAJnLhZQ1h3EBq0FtgE6lUwJWWZbx7Ve+nbBWKpk4QV1ftx5A4dewHR44jE8/8Wnc+8S9uPeJe/Fvz/6b0rioFH7X/Ts8f+75tL8Tz0VyhlV0bxQNmRrNjSVZ69ft7kZLVQvqTfUA8s/K/Pr4r3Fk8EjCZePBcVh0sWy9OKE6NXYK48Fx5fmQJAkNpoairnX+xbFfKHvNzhf7z+xHMBpMWw4MTGVYi3VCI8syvvPKd+AYc+R1e/F5MZv9IA8PHMYvjv0ir9uWG7FuvVpfDb1aj+sXXx8rC44Urvrk5NhJyJDRUd0Bq95a9gHrM2efwWRoUjnGq/Szy7D++LUfl8373BPy4Gsvfa3oFRDptrQBYhm6elN90TKsjRWNABZGyWo4GsY3Xv5GTo3/wtEwvn7w6wVtFhifYQWACm0FJwQKgAEr0Tzylra3YEfrDlxcdzGAzGWY4ssovumSJEmxE4nzwexrQ6/hW4e/hf898b8ZH0/cr3i8Qn9p/vL4L7Hn9B4cHzmOw4OH8Zs3f4Ojw0cL+hi5+PrBr+OHr/4w7e/EyboI2KoN1fBH/AnbC4n9SId8QwU9gc1Gj7sH7dZ22Aw2APkFrKFICPe9cB8efOPBhMvHA4kZVgB4ZfAVAEjICBRzz8BINIL7XrgPvz3x26Lcf7G8NvQajBqj8h5KpnQJLlKGtX+yH/cfvh//2535fT4d8XmxzLos79f2Z0d/hvteuG9BNG5TSoLPv882LdqE8eC40vCsEMQkYru1HTajrewD1leHXoVKUuEK+xUAMKuSYH/Yj68d/Bq+dfhbhRxi3p4/9zx+/PqP8ey5Z4v6OMO+2ERIrbE25Xd1prqCVzeJ16fOWAeNSrMg1rAeHT6K77/6fTx++vGsb/PG6Bv44Ws/xM5TOws2jhH/iPL5AAAVuooF8fyWGgNWonnk6qar8ZXrvqKUFopsWnJGTwlY4zKs4mfxO1HGNl2G1eVxQavSYmXNSgCFD1h73D24ovEK/OEdf8C3t367KI+RrWAkiGH/cMbnI3kGXHwhiYyL0xNryNRgaoAMGQO+gbT3Uwy+sA9nJs6gw9qhjCufRi2OcQfCcjjtBIg4lpID1viMQHwH4UJzB9yQIc+7dZROjxON5sa05cBAXElwkTI44nie7n0+nfHgOMxaM5oqmvJ+7nvcPZAhY8/pPXndvpyM+kdRqa2EVq0FMFVSWcjjstvdDa1KiyWVS2DT28q+6ZLT40SdsU55Tix6CwKRAPxhf873Jb7Lnj77dFmsgxXPfb7vn2yJvzW+74RgM9gwEijOVmUWvQWV2soFkQHMZx9y8b6N30ZqNmRZTpthXQgZ7FJjwEo0jxk0BlTrq1NOlsT6lOQvvypdLMMqyzK6emMB63Qf1KIrbpWuCmatuaClruFoGKfGTinb9ZS6aY9YBzzgG0h7ouT0OmHUGJXALT4wjMpRDHgHlAwrMLd/hyghnG2GVXzhp0yABKYC1lpjLVSSKm3AKrLLETmS198xHfH3zLeA1eVJ7fwZr9hNl8T7ezYBa5WuCg3mBkwEJ3IeZygSQu94LwAUNItRKiP+EdiMU9mTYmw70j3ajTZLGzQqTcpa+XKU3N023z2/gan3dzgaxr7T+wozwFkQz32hAppMMk0yA6kN/gryeIFx6NV66NV6mLXmBRGwitcol/eLqPoo1ITERGgC4Wg4McPKkuCCYMBKNM/FbzMiZPrys+gtGA+O48jQETg9TnRYO+DyujJ2sIs/2Z5uC5189E30IRQNKetyK3QVBQ+KcxH/t6X78hIZVLGpe3xgKLopxwesc1n+KMbbYe1QZnbzOckVX/iiC7AwHpzqOK1RaVBrrFWer+SS4KgcxXik8JkRccI23/YCjd8KKR2jxgigeCXB4tjon+zPKygeC4zBorfkHZj1jvciLIfRYe3A4cHD827CIdmofxTV+qnsSb2xHhKkgr7fRXk/EFt6UO4ZVpfHlXCM59ORXrmv8+9vo8ZYtD1ucyE+R4udYR0LjEGj0iifB/FEwBqVo4V7vOCY8jotlJJV8Rrl8n4Rn2c97p6E77x8KU3ZWBJccAxYiea5BnNDyklkuqZL4ufx4Di6HF3QqrT40NoPAcj8ZZywz6a5sOWe4jGXWZcplxU6KM5F/HOYbjbd5XUpHXEBKIHhqH80YX1rsTZ6n063uxsalQaLqxajUlsJjUozqwxrIBKAO+BW/u2P+BOOpfiyaJ1ap1wuTlpHw4U/wRZ/T6ZtnMpRKBLCsG942gyrSlLBqDEWNcOqUWkAxDLxuRIZ1nz3g+wei72XPnbxxwAAuxy7ch5DORn2DyecjGrVWtQYawr2fveGvDjrOatUntgMNkyEJgra9buQZFlO+J4ACpNhvaXjFjx79tmSdxsWnzunxk4hHA0X7XHE+0xMiMazGWyIyJGCbo0Sv+3dQskA5pNhFcfbRGgiZbeFfCSvcQewYDLYpcaAlWieS9eZNV3TJfGzO+DGLscubG7arOzzly5gjUQjSpkrkD6TOxviy6XN0qZcVujHyIV4XJ1KlzHDKk7aAShZlmH/cML6VrPWjAptxZz+HT3uHrRWtUKr0kKSpLzXvXW7u6FX6wFMZTrSTX4k772qXH7+59FI4QNWsVYYmNvs9Wy4vC7IkKcNWIHYOtZiZFijchSnxk7hqkVXAcivrFGUg4vJmlyf+x53D1SSCtc1X4dVtlXKUoT5atQ/mrA+DSjsRFt8wyUgcWKsHI0FxuCP+BOOcREI5RuwWvVWvL397QjL4Zwa6BSDCEBC0VBBG2sli192kUwcA/GfgbN+vLi+BAthjeVEcEL5zsq1JFh85xUii54uYK3UVsIb9iISLfxSmQsJA1aiec5utmM8OJ6QoRkLjMGkMUGr0iZct0pXBU/IA5fXhR2tO9BU0QSjxpj2RHbEP4KwHFayZnaTHcP+4YJ1v+1x96CpoklZwyf+llKVfLo8LlTqKrG8ennK8xGOhlM2dTdpTTBqjBj1j6ZseTPXgXePu0fJyADIq7NoIBJA30QfLmu4DMBUAJ9u8iO+TDyeuNwdcef2B2Qh/oR9vpQFi3EmP0/JirVPX/9EP/wRP7Ys3pJxImYmYg9e8TmQayaxx92DxZWLYdAY0NnaiSODR3B28mzO4ygHUTkKd8CdcDIKFPb9Lj57xPtZ7OdYrutYlWM8TYY1n+yoWA+7umY1miuaS14WPOofRZ2xDkBxy4Lj97pONptGetM+XlxJcCGzt6UgXps6Y11uAavXhUsbLgVQmHXK4rHjJ7VEJ3hPeH5PCpQaA1aieS5dCWqmLz8x861T6bBl8RaoJBXaLG0ZM4rx96+szSxQsNDt7k4IsoBYtnjYV7igOBdOb6ysraO6A92jiV9cQ74hROVoQkkwAKUhisvjgk6lU7KuDeaGOQuqvCEv+if7lYwMEMv+5npy4xhzICJHsLlpM4A0AWvc7L8IXpKfj0pdJcxaM9xhd85/x0ziT0LmyzpIZZ2vOfMaViA2+VGMDKs4AVtRvQJtljaccJ/I+T7GAmOw6CzQqXWoMdTknGE9MXoC7ZbYsbm9dTuA+VsWPB4YR0SOpASshXy/97h7oFfr0VzRDKD8M6zp1rKL7558M6x2kx2SJKGztRPPnXsObr+7IGPNx4h/RJnEy+f9ky3xPktnNo30pns88ToVa8JsLonPuo32jXAH3Fmt943KUbi8LqyyrYLNYCtIwJppDSsAeILz+zkuNQasRPOcOFGIP5GMnz2NJy67pvkaZdavw9qR9oM6OWuoZFgKECyEoiE4xh0JQZZ4LBlySTJoLo8LdpMdHdYODPuHE06SlOA9KVMmAlanx4kG81RDprlYixuOhhGOhpWTqOQMa67lY+IYuNx+OTSSRhl/uo7TyZMY8RpMDRgJjyjjK1SjkFH/qHISP98C1mxKgn2hwq/LFa9pu7Ud7db2nDNE/rAfwWhQObHNdR17MBJE30Sf8j5fXLkYa2rWlDxrlq905X5A7P3uCXnyzlJF5ajyful2xzoEq1XqhMcqZDloIaU7xiu0sRP0fANWMcHT2dqJiBwp2XZIsixj1D+KRRWL0FTRVPIM64ivcAFrcknwZHCyIE2HCin+fTHT+uEedw+MGiPW1q5FVI5mld0f9g0jHA3HJqqtHQUrCa7QViT0dhDvh4nQ/M5ilxoDVqJ5Lt02KiO+kbT7uVn1VgCxEwGh3dqOQd9gygd8cpCWbzMhf9iPP/vfP0s4ST09fhrhaDg1w2rOLSh+feh13PibG5VN12dDnCiJk+v4IF6sXUoOPEQHT5GdFRrMDRjxjxStUcp/vvif2PCzDdjwsw34iz/+BYCkgDWPbRB63D3QSBostSxFval+ag1rmgyr2HtykXlRyv00VjTiiO+IMr5tv9mGQCSQ2x+Yxoh/BI0VjbAZbHPa0CreH3r+gHc++s6UtUiRaATv+P078IeePyRc7vK6UKmtVCaHMjFqjUXJcHS7u9FoboRZa0aHtQNOjzOnbpXJr/10EzHjwXF0PtyJPb1TwcWpsVOIyJGEY7OztROvDb+GMxNn8vmTUvzN43+D777y3bxv/4NXf4APdX0oq5N1JWA1ppYEA6mfWx/u+jC+88p3pr1Pp8eJzb/arLxfDpw9kDL5BBQ2wxqVo7jtD7fhJ6//ZNb35fQ6oZE0SukyAKhValTqKlO6BN/3wn349BOfznhf3pAX48Fx5flcaVuJJZVLsPf03lmPMx/jwXGE5dgWJekCmk8/+Wl86fkvFeyxMq1htRqsAJD3Xqy/Ov4rfOBPH1B+DkfD8IQ8SoBcoatAWA4nfE7/4tgv8OGuD+f1eABweOAwbvzNjXn3GxgLjOG6B69T3hcbfrYB337l2xmvLyZ6ao21ALLLRsdXB7Rb29Ezltgp+GsHv4ZP7P1ETuMe8Y2krHEXAet8z2KXGgNWonkueW3ZsG8Yrw2/hg31G1Kue2XjlfjC5i9gW8s25TJxcpT8ZSyaEYjAN9/tWnrGetA30YffvPEb5bL4zE+8XMuODw8ehsvrmnUpTyASwGhgVMmwAonPxxNnnoDNYEtoEAXEAsNh/7CSnVX+DlPxtrYJRUJ4pPsRrK9bj7vX342719+Nf930r2ipakkYlzfshT/sz/p+u93dWFK1BFq1NmFNXrqmS2tq1uCLV38RNyy5IeV+/vaSv8VbLW/F3evvRmdrJwZ8Azg3eS7fP1cx4h9Btb46bZOxuXLg7AGcGD2B/sn+hMvPTJ5Bt7sbz559NuHy+GzRdMwac1FKguO3RxH/7xnLPouQ/NpPt8b88dOP46znLH7z5tT7PLmBEDBVFlyILKssy3ju3HM4NHgo7/t4yfUSXnS+mFW5p7I+TZ94Qppuos0b8uIF5wt46I2Hpq0y+NOpP2EyNImPrPsI7l5/Nz6x4RP46LqPKr+fTdfvTA4NHMKxkWN48I0HZ51Vc3lcqDPVKRlhoUpXhbFg4iToi84X0dXbhSHfUPr78k51WwcASZKwumY1To+fntUY8yUmCaoN1Wi3tsMx7kAoGluuMuQbQpejCy+5Xpr140TlKCaDkxkDVq1KiypdVd4Z1oOugzg0cEiZlBaVAPEZVgAJnWxfcsbeF+LvzdWRwSNweV3Y6chv7+XjI8fhDrjxrmXvwt3r70ajuRGvDLyS8fqij0Mu27rFr7/usHbAE/Io7+FQJISH33wYB84eyKk7tMvrQr2pPuEyURLMrW1mhwEr0TyXvLZs7+m9iMrRhCyqoFVr8faOtyvbXABIm1EEptZ0ijJXk9aESl1lzuWY4qT1RdeLyolKj7sHEqSUADDXrTPE3zzbElFxP3azHQ2mBlRoK5Tnwxvy4skzT2Jby7aE5w2InciM+EcSuimL+wGKs7XNc+eew3hwHH950V/ioxd/FB+9+KN41/J3JWyHkE+TjvjgJr70U2TZKnWVynUlScJN7TcllD0JK20rscO6Ax+9+KO4bfltAArzPIz4R2Az2NJu4zRXxLGc/F4RP6e8h7IMWIuxhjUSjeDU2CllAibTxNR0khtu2c12TIYm0554iQD0+XPPK8ddt7sbakmd8D5vqmjCutp1BQlYJ0IT8Ia9s8o+ivd+NuMRj1NjrEm4XJmgigvmxRZCg75BHBrIHFB3ObqwtmZtLFC9+KP4yLqPoNXSqvxedP0uZMAq/ta+iT4cGzk2q/tKri4RqnRVKRlWp9eJqBxNyMIn/D5NebGYJClFuap4zmsMNeiwdiAcDSvB897e2PdsIZYnTAQnIENOWxUl2Aw2jAbyO87FGMXnkwhcRcAqKkDi39dOjxMyZAx6B/N7zPOf0fmuVxdjvXvD3fjoxR/F6prVGZ/rscAYBn2DaLe257TeN/54Sz4Pevbcs5gITiAUDeH0RPYTJqJpWLx0EwKUOwasRAtA/En8TsdOtFnaEvY3nU6juRFGjTHlRDZ5GxcgNvOda7DQ7e6GBCnhRKXb3Y3myuaUTdJzDYrF9WZ70hDfHEeSJCy1LlUyUU/1PwVf2Jd2AqDGUBNbXxPXTVncTyHGlU6XowuV2kpsWrQp43VEBijbk1x/2I++iT4lqLGbpk4SxwJjqNRWpmRQspGpVDJXoWgI48Fx2Ay2ku3VG4lGcNIdC0JSAtbzTbpOjp1MKBd2eV0zdggGYmtYC10u1jfRh2A0qJyINVU0Qa/W51SNoKxfPt8MJtM69rHAGJ47+xyuWnRVwppD0SE4eWJje+t2HBs5NuvMmRjHbII5cR+7HLtmDIrE4yQHFnWmOqgkVcLzIp5nCVLGYLhvog+vD7+e9rMlnlh6UAiRaAS7e3cra9VnO3GQ7nsCiE1yxK9h9YV9yvGU6TEzBayiAmauJWdYganXVWQOkzv05yPTvunxRL+EfCgB6/nPKfG6iONYTEbGfwaJ7/l8P2vF7Y4MHUmpSMlGt7sbFr1FKTW3m+0Zzz3EuUuHtSOnyVqnxwm9Wg+r3poyodfl6IKE2CRwchPGTEQTp+T3gzIhwIB1VhiwEi0AdpMdLo8L45FxvOR8CZ2tnWk3IE9HJanQbkltyJIuO2Q323Mux+we7UZHdQfaLe3KiUp8Ni/lb5nmiymZyGjMtkmT0mDq/BdN/HqlLkcXagw1uKT+kpTbxa9VSTjJSpNxKYRgJIjHTz+O65dcnza7KYh1b9me4JwaOwUZckKGNRQNYcQ/Mm0zkJnku3dnMtEAy2awwW62YyI4MeuTxFyJABBIDVjFsRKIBJSTs0AkgBH/yIwNl4DYCY0v5CtoFin+JA6IrStcalmaX4Y1riQYSM2Y7z29F2E5jHs23IOWqpap9/lYT8o6dQDY3nK+W3Dv7LoFi+Nq1D+a13M3GZzEZGgSLVUtcIw78MboG9Nef8Qf6w2QvF2YRqVBrbE24eS+x90DnUqH6xdfj929u9PuwSieJ1EmnclsgpVkLw+8jCHfEP58xZ/jikVXoMvRlfdxJ8tybDlEmmPcorMkBKzitWqpasFB10EMeAdSbiOOq/jJv1yrbgpJNLoSy0EkSOhx92DQO4iDroPKMozZfs6n6xOQLJ++BEBsveqgL5YlFZ9byY+XHFCFIiGlL0S+f5vL68KSyiUA8suy9rh70G5pT2hk6Al50lZ3xC8xEn06ssqwep1oMMUmqS16C2qNteh2dyvfs9tbtyuveTZG/LFmg+m65wPsEjxbDFiJFgCx5vCw9zBkyOhsmX7GPlm7tT3hJFzsOxp/4iAeJ9cvsB53DzosHehs7cRB10GcnTyL0+On057IAshpjWKhMqzi8cQXTbulHSP+EZyZOKOUA6fLMMZ3C03eo7VKV1Xwk6xnzz6LidDEjBkZmz63gDV578f4wGS6ZiAz0av1BWmSFL+3XTHLracjTlpqDDUpJzDdY91KJkA8lwOe2Al58nsoHZPWhLAcznu9WDpiHEstS5XLkt/nM0lXEgykTkB0ObrQXNGM1TWrsb1lO150vohzk+cSOgTHa6xoxMV1F2PnqfzWtwniGAhEAnmVVIvPsvesfA/UknrGbKNYR52O3ZQ40SaawLxl6Vsw5BvCywMvp9xml2MX1tWuU5qYZSKWHhRCl6MLRo0R1zZdi86WTvRP9uP14dfzuq/RwCiC0WDasvcqfVVCIz/x3Lx/1fshQ8bu3t0pt3F5XLAZbAmTcYWa9MpHfIbVqDGiubIZ3e5u7O7dDRky3r/q/QBm//0j1vpONzGY7zEgtmQDptavKxnd848nAioRDA74BiAjNokxmwzrhvoNWFuzNucsvizLKdveTVe11OPugUljQqO5ERqVBha9Jbs1rEmTLaKT+jNnn8FkaBJvb387miqasv7MzLSbgFFjhASJXYJniQHrLE0GwjgxGkE4UpitG4jy0WBuwGRoEs9OPIsOawc6qtMHg5kkb+Uy5BtCRI6kzJw3mGLdb7Pt+uoNeXHWcxbt1nZsb90OGTJ+8OoPEJZTOwQL2QbFkWhEmaWfbfDi9Dhh0VuUEmUxth+99iMEIgHsaNuR9nbxGda0wX2BT7K6HF2o0lVhU2PmcmAg9wxrj7sHGpUGS6piM+LxWY3xQP4BK3C+jHyWJ3Tx24kUcnulXIiTlhtbbsSpsVNKI45wNAzHmAM3ttwIYCqwTd4WajriuCtkWXCPuwdNFU0waU3KZe3Wdgx4B7LebmQ8OA4JkrIGq85UBwlSwvtt1D+K5889jx1tOyBJEna07UBUjuL7r34fUTma8X3e2dqJN0bfwKmxU3n/jQmd0fM4mRe3X2Vbhcvtl8+YbRTrqNNpMCdOtIkqkmubroVRY0w5ae8d78WxkWMzTj4BhcuwhqNh7O7djWubr4VJa8INS26ARpV/WXCmE3Tg/BrW4LjyfIrrXrXoKnRYO9Jm3dKthy3VBBUQe70rtZVKAC0qb7ocXeiwduCqpqtiY5vlZ5FSopthH1Ygdgy4A+60mfrpiLHVGeuUz6aZMqzxf08+f5uY8Lab7ehs7cTrw68rnfazMegbxERwImGya7rjQLzXVFIspMn2/SIyrEKHtQM9Yz3406k/oUpXhSsbr8xpu5tM25hJUuwzlF2CZ4cB6yx1vebEvz/vh2OYByKVjjhhOBM6M2N5WToiwBUn5Zk+eHPtFBxflthubUeHtQOPdD+iXJaO3WTPKige9g8jLIehU+lmv4bVm7gOSzwfj3Q/gjpjXdqOywCUrJpBbUhZ15bPet/pBCIB7Ovbh61LtkKr1k57XZPGBJ1Kl3UJWY+7B61VrUqpY3xWYyw4lndJsLiv2ZbMxW/Gnm+36tnqcfdgkXkRLqq9CKFoSDkBOz1xGqFoCOvq1qHR3DjjeygdccJYyMZL3WPdKdlN8Z4Ta3FnMhYYQ4WuQqku0Kq0KaWve0/vRUSOKIHXMusytFnaZnyfK2XBeTZlARKPgdkErA3mBuxo2zFjE6JR/2jGgDW+OdBkcBLnPOfQYe2ASWvCtc3XYnfv7oRuo+LvzubzOp+u3+kcdB3EiH9Eea0seguuWnRV3mXB0x3jVboqhKNh+MKx/YXFa1VvrseO1h14eeDllPdwcrd1IPa3a1SakpQEj/pHE7Yw6rB2wDHuwKGBQ+hs7ZzaA322JcFJGc90qg3Vsf1FgzPvLxpPfAdtbtqMEf8IRvwjKWvTk5sCidcl3+/WQe8gonIUdrNdOb5zeZ8nV/wA05eGd7sTP+uyKZ+ORCMY9A4mHLsd1g74wj7s6t2FG1tuhFatRbu1Hb3jvQhFZq5+ie86nKxCV5H3Ps0Uw4B1lpY3xEop3nRxMTWVTvwHZK7lwEBqB9FMH7y5bjuTvH1NZ2snwtEwVJIqoRNm2seYISARv19Tu2bWaxqTS4PqjHWo1FUiHA1je+t2ZeY2mciwxndTjv87CnmS9Ux/rEwpm4yMJEmwGW3KGqyZnHCfSNnHVavSxkqCZ5lhLUSTpLLIsJ4PAJPfK/Fbt4iSMiB1i47pmDSxLGih1uWGoiE4xhwpwaL4OZstXID0e0MmVw50ObrQUtWCFdUrAMSOPfE+10iahO2W4jWYG3BJ/SXo6s2/6Y/T64ROFct+5bO+z+V1QYKEelM9blh8w4xNiKbLsNpNdvjCPowHx5XSy/jPvRH/CA66DirX73J0YX3d+qwmNPLp+p2OKAe+uulq5bLO1k6c85zDq0Ov5nx/yjGeoSQYmMrmOb1O2Aw26NV6JYhJLgtO1zdBJakKUqWRj+QS8HZrO6JyFDJkbG/dDp1aF1vyUKAM63Sfs2JyNNetbcR7dXPTZgCxz6vx4DiMGqMy8akErOdLgkWQu7pmdV7BePxn36KKRVhXl1tX8HTbYdWaaiFBShnPqH8Uw/7hlO+vmSaw0lWRifsIR8PKeVS7tR1hOYze8d4Zxx3fxCmZWWtmhnWWNDNfJZEkSdUAFsuyfKQI45l3OuorIAE44ZoELir1aOhCJT50F2kXYal16QzXTiW2ctnXtw8GjQHPnXtOuTzhcc7Pcu48tRNnJ8+m3E9LVQvW169XfhaNRxZXLgYQyybcf/h+LKlcAr1an34s5qlZa1GietJ9EiatKeHLRXypXlx3MQ4NHILT60xYrxdv2DeMId8QVthWpP290+tMGLckSeiwdigz6Zno1XqYtea0J2x2sx3ugBu+sC+lG/Kbo2+iWl+NOlNdxvtOttOxExa9BZc3Xp7V9av1iZ1FXR4XPCFPyvHhDXnRP9mPWzpuUS6LP0mcTdMlIPZ6igmF+PLUTLwhL064T+DiuouVy0b8I1BLalTpq6CSVLFtnOJOXDwhD06Mnkh4DWcyEZxAj7sn7W2ODB5Bq6VVOYEUZb9XL7pa2aKl292NG1tuVLpgL7UsRYe1Ay+cewGRaAROjxNVuqqs/mZxHZFhHfWP4un+p5W1ZyatCVuXbM04cZKsb7wPoWgoJWBdVLEobUdwIJZxOOg6mHB8pZussJvtODJ4BL/v/j3C0TBecL6AD6/9cMKETWdLJ77zynfQUtUybTXA9tbt+PILX8bPj/5cWUe30b5xxjWdgsvjwvLq5Xht+LUZT1Bfcr6Ei+ouSvjccXqcqDXWQqvSwmqw4opFV+BPp/6kfI5oVBpsXbIVBo0BkWgE7oA7YRlAvPg1dskNr65uuhpGjRE/ef0ncHqcmAxN4o3RN/CZjZ/J6u9U9pYMjKCxojHl92+MvIE6U13GYBqIHcN7evdgS/OWhM+jLYu3QKvSosvRhXV167Iaj+D0OKFRadI+rsjejQXGlMk78X3SZmnDiuoV2OnYib9Y/RcAphpgpQvgG0ypVRpROYq9p/dmNcmzqGIRNto3Tnud0+OnIUmS8l0FxKp4ROMgYOr1XF69XDlGcmkSKB5Ho9IkHOPjgXHoVDoYNIaMtxPHQHy35FAkhL19exEIx6qR1Co1rl98vVKxAcReI5PGhPV16wHEPreSJ6K0ai30ar0SUDk9TlRqK9Fubcf+vv0pYznoOoi1tWszfocnZ947WzrxlZe+gt7x3owTWPF63D2o1lcnbB+lVWlRZ6xLmRxIt6e7zWDDi/4Xp32MdEs2xHejVW/FxsbY8SJe8+6x7hmXWoljPF3Dy0pdZcYuwecmz8EX9qV8N8uyjMf7HlcmEiRJwuZFm1O21ZrO4YHDCcF2n7cPW7Al69uXk6wCVkmS9gO4+fz1DwIYkCTpgCzLnyzi2OYFo06NWqOENweY6qfSqTPVoVpfjStNV+Z1e0mSsK5uHQ6cPYADZw8AAOpN9WlPViu0FXjozYfw0JsPpdyPTqXDvtv3KbfrHos1HhElhUstS7G+bn3K/qsJj5FU+hOJRnDXrruwunY1vnnDN5Xrid+LwMbpyRywfuPQN/D46cfx5O1PpnyZiO0WkoPzDfUbMOofTQic0umwdmCVbVXK5Y3m2Inl2cmzKaWZH9v9MayuWY3/2fo/09634A/7sb9vP97S9paUDqWZ2IyJs8z/+PQ/YsA3gEdveTThemKPueTnrsHcECuFioZml2GN29omm8mUXx7/Jb556Jt44rYnYDVYAcQCVqveqgRsydnrbx/+Nn569KfY+a6dWQc73zr8Lfzy+C+x6127EiYchnxD+MCfPoBbl9+Kz135OQBTZb/t1naYtCY0VTQlZFibKppg1BjRbm1HMBpE30RfbLuPLLJnwFRJsDhh/O+X/xu/PfHbhOt884ZvYsviLVndX7qTOCA2EbHUsjRtE5HH+x7HJ/d/Er9666+wtnYtgFjmJ7nUfZl1GXb37sbnDnxOuc+3Ln1rwnU6qjtwUe1FGSeIhO0t2/FfB/8L9714n3LZFfYr8IPOH8z4N8qyDKfHiZvbb54xYD06fBQf7Pog/unKf8JtK25TLk9+jW5eejM+89RnlL8NAD512adwx5o74A64IUOetiQYiE20dbu7oVfr0VTRBCC2Rnl7y3b8vuf3eKr/KQCxz8ptLdtm/DuBqQxruuxaVI7ig10fxNvb347PXJ45AD46fBSjgVHc0HJDwuVVuipc0XgFnu5/GvduvDer8QjiBD3dREpKhtXjTAgGO1s78Y1D38C5yXNorGicqupJsx7WbrbjlcFXEi57wfkCPrk/u1NQlaTC3j/fi1pjbdrfy7KMTzz+CVj1VvzkLT9RLk/+/G+1tMKqtyZM7tlN9pz26fzUE59CrbEW37rxW8pl6d5nycQxEF81s69vH+59IvE1+7tL/w4fWvsh5WeX14UGc4MyKd3j7olNRCVNQpq1ZqUpkMh0N5gaMOwfRjASVNbxOj1O3LnzTvz1+r/Gxy7+WNqxJges21u34ysvfQW7HLtw17q7pv07gdQSXyFd1VLy5BAQC+7HAmOxKg9V+jBHWQ4Q971fpatCh7UDVzZeqXzPtlnaoJJUWa1jne4z36w1Z6yQ+OxTn8WZyTPYfevuhPfSs+eexd/u+9uE696+4nble2km4WgYd+26C/7I1FKCJboluBt3Z3X7cpNthtUiy/K4JEl/CeCnsiz/iyRJzLCe11ShwgkXA1YqHa1Ki1237sKzTz2b931884ZvJmw1UG2oTgnuDBoDum7tStkQHoh9yXzi8U9gf99+3Nx+M4DYl8mlDZcmXO+HnT+cNlOU3A3w5YGXMeAbgGYk8ePK5XFBr9YrJ8XTlRAfHzkOd8CddlPvTOuw7tlwDz6+/uMzZrV+vOPHUKVZXSGCs+QtfIZ9wxj0DeLA2QMYC4zNeKICAAf6D8Ab9ua0Ptmmt+GUO9bQZsg3hBecL0Cr0kKW5YTXVfz9IsAW7Ga7sm/ubEuCgdiMdjYB6/GR44jKUZxwn1CyIiP+kYS1ZHazHY4xB4DYCftOx06l8+gda+6Y8TEi0Qi6HF2IylHs7t2tZHkAYE/vHkTk2F6Vn738s9CoNCknRR3WDiXo63H3JFwuLkt3rGUiSoJ9IR9C0RB29+7GtpZt+OSln4Qsy3jPH9+DnY6dWQesPe4eSJDSTgy1W9vxzNlnUi4/PnJc+b8IWNNN5Hz04o/i5o6blTWPJq0pbRD3wI4HZnzv1JnqsPfP9ypru351/Ff4+bGfY8g3lDG4EMaD4/BH/GipaoFRY5w2YBV7Zr45+mbC5S6vK2Gi5i1tb8ElDZcoa00/uf+T2HlqJ+5Yc0fCOup04ifaetw9WGpZmtBZ/F+v+teEE/wKbYUyITMTpSQ4zV6kZyfPYiI4MWPppjheV9tWp/xulW0Vnj37bEJgko3pTtDFZ4YIWF1eFy5ruEz5/fbW7fjGoW9gV+8u3LHmjmnXw9rNduzq3YWoHFWOqTdGYlsQPfS2h5TsfDpnJ8/iw7s+jN29u/Gele9Je503R9/EybGTqNBWKJ+PUTkKd8Cd8Hrr1XrsvnV3QmaxwdyAF53TZ/OEUDSEE+4TyrpeIZtO7EqGNS7oeWP0DaglNX739t9Bo9Lgjp13pBzjYp9cSZISuoQnP16lrlLZdkUEueK1GPAOoLmyGcDUe6jL0ZU5YPU6YdaaldfFbrZjfd16dDm6ZgxYZVlGj7sHb1v6tpTfNZgbUvfAdnejQluR8DllM9ggQ4Y74M74OZLpeHvwbQ9CLU29bw0aAxZXLs4uYPU6cbk9fQVUhbYCZybOpB2H6CB+aOBQwvlSl6MLZq1ZGdOnn/w0Toxmt5wDiE20+iN+3HvZvbhhSWyi6qXnX8r69uUm2zWsGkmSGgHcBuCxIo5nXmqqUOHUkAchdgqmEjJoDFnvvZqOTq1Dc2Wz8l98WVG8Kl1VwvXEf9c1X4dGc6OyVmUyOAmnx5lSlqhT6zLOegKxbIRVb1W+UMT9nfWcTSj/Eh0llTWNGcqyonJU6USa7ksn03pdtUqdseQpnlalTbvlzVLL0rR7uImfw9Ew9vXtm/H+gdhzUK2vzvhlmE78Oh6xDUMwGkw56c3YYMtkVxpfZRNUZ5Jvo674523UP6ps1SPuU7zeRwaPwOV15dTt9NDAIQz6BtPepsvRBY1KgxH/CF5yxb7cxUmSCADbre1wjDvgC/vgGHMoExIi+DnhPqGcKGZDWcMa9uL5c89jPDiOm9tvRnNlMxZXLcaNS27EvtP7sm660+3uRnNlc0opOhALqod8QwlbjgDpn/d05eAqSYWmiiblfZ8pgJvpfS5Y9Bblvt7R8Q5E5agyUTKd+ON2uiYrsiwrDV/iT3ZFhjb+uJckCXazXRnPW9regteGX8OZiTMJ66jTqTXWQi2p4fQ4U7bkAGLlxfGfl9kGq/GPmS7DKl4vsW9mJt3ubhjUBiXrG6/D2oGIHIFj3JH1mABMOymjBKyBcXhDXkwEJxKu21LVglW2VcprE98AK5ndbEc4Gk6YlOhx98BmsGFVzaq030fiv8sbL0eHtWPazwblOys0qXwfjAfGEZEjKa938ves3WzHRGgiq/WJfeN9CEfDSnMuYSwwc2M7q94KCVLKc7C4cjFaLa1ormzGMuuytPupi+dddLwdD46ndCQ2a80JXYLtJnvarWTEe6jb3Y3u0fTbvbg8rpSJrh1tO7LqCu7yujAZmkzbrE0sU4l/7nrcPVhqXZrwmqQL7tM9jlFjTAncdWpdyvd5u2Xm7cBEE6dMPQsqdBVpS4LFOu7k76JQNIQ9vXtw/eLr0VLVgubKZqywrUC3uzvrBmniWLjUfunU547GmtVty1G2Aeu/AegC0C3L8ouSJC0FkH2Yv8A1VaoQishwDHFBNV24RLOVZ84+g7HA2FTjEUtqac9MxJqlSDSW6RIBU/yXsfhS1al1sTWNGQKi/sl+ZUY73ZdOutKgQjBqjGn3cBM/W/SWrAIsX9iH/Wf248aWG7MKAASb0QZ/xA9vyJuw32VySZXT44RG0qSsi4k/cZxNhrXeVJ/2cdMJRUPKSXP885bc7KbB1KBsIt/l6IJOpcMH13wQrw69iv7J/hkfp8vRBYPagDvX3InDg4eVsQ16B3HQdRAfWP2BhK1Ietw9aK5oVtaadlg7EI6G8dSZp2JbNJ1f2yTKhV8ffh3ugDvtyXc64n49IQ92ntqJCm0Frlp0lfL77a3b4Q17lXL9mSRn9eOJy5OPS/HeEpfLsjyrPXjz0VHdgXZLe1bvi/gAZ7omK68Pv47+yX5Y9Bb0uHuUk72J0AS8Ye+0WXBR0dDl6MJIYPqAVa1So85UhxPuExjwDmR8/vNh1pqhU+nS/o3i9UqXfY3X4+5JWJ4RT4w12+07gNhEoMubGpgI4jN7PDiecVKss7UTR4aOoH+yP6EBVjKlG2+abYOysb11O152vZxQQSTIsowuR1fKd8xMExTCdN1rk4nXSjTnErJ5n2lUGlj11oQgLL66A4i9jqfGTilb34SiIWV7GSD2ueUOuHFm4kxKgFyhjQVUwUgQI/4R2M32tFvJ9Lh7UKmthAQJu3rTd/5Nl3m/ccmNkCDN+N7OtJwBiB0/yc9d8nMAxDWomqbqYrr1psnare04PX4awUgw43UybQUoVGgrlLWo8bocXVhpW4nrF1+P3b27lddOTFzG99DosHZgPDiOId/QjGMGkNBfYSHIKmCVZfk3siyvk2X54+d/PinL8ruKO7T5Y5E5dsCzUzBd6ER30H19+9KuLcmWWKsitmG4c82dABJPsuM7SjaYM3eRjD8JS3dCNt3M/myl28NNfOG/s+OdeO7scymZrmRPnXkKvrAvq+7A8UR3yzdG38ChgUPY0rwFQOqJlcvrQr2pPqV8Mz47OJumS8qEQhbdJk+Pn1bKMZMzrPHNbsRJwVnPWexy7MLVTVfjncveCWDm7RPEJMg1zdcoa9HEbUQm+ub2m7Fl8Rbs6d2DcDSc9sQQmMrMxP9ONF6KH+dMRMA6FhjD46cfxw1LbkgozbzcfjmsemtWgVwoEkLveG/G911yl2MgtmWSWIcnLveFfQhHw7PKruejs7UTB10HMegdnPZ68Wseqw3VGU9ORcb8vSvfC3fArawBzGaiqqmiCRfVXhQLWM9nNzM1XRJjEa99Pp97mUiSlPFvTA6wMkmX9RXaLG1QS+oZs0jxRvwjCEfD067ZU0kqjAXGlIAn+blWugU7dic0wEoWvw4eOF82OtaT9WRoZ0unsmQg2fGR4zg9cTrlO0Y8n9O93kDiFmAzSZ5wFcYDM69hFWMR4xLv2fjArsPagUAkgDOTsdLTQe8gZMjK8y6u6wv7UgJkEbDGd/hNF4x3u7uxtnYtLrNflnE7pHT76TaYG7ChfsOMn2HTnTckHwfDvmGMBkZTjgPx3TdthtXjyvo7P5sKhJn23TZrzfBH/AhFp7bHOTd5Dq8MvoLO1k5sb92OId+QUh7c5ehKmbjMNNmYSXx/hYUgq4BVkqT/kCSpSpIkrSRJeyVJGpQk6S9mvuWFobFCBUkCTrDxEl3g1tSsQVNFE3Y6duLE6IlYCVplagnaTETJ507HThg1Rrxn5XugV+sTymmHfEPKF7HdZM8YEIkP91W2VRkDVrHdQqGl28NNNJTobOtEWA5j7+m9095Hl6MLNoMtZS3wTETG9ME3HoQMGR9Y8wEAqVsSZVqHFn/ZbLNs2W7xo6yzq1mtvFbBSBAToYmETIcY285TOzHgG0BnayeaK5uxtmatsl4xk4Ougxj2D6OztVMpSxRbq3Q5upQ9gztbO+EOuPHM2WcSyn6B2Am+BAlPnnkSKkmVsFa03dqudPvNtiTYoDbEGsOc3ouJ0ETK5IRGpcGNLTdif9/+lPVvyXrHexGWwxmzT43mRpg0poSTHseYA1E5itU1qzHoG8RYYCyrrTaKYXvrdsiQM2ZvBFEZUGuszZhhFdmzzYs2K92glW2HPJn3TIzX2dqJYyPH8MrgK5Agpd2yQrCb7cprX8gMK5B5qw7xOoomM+mMB8enzfrq1Lqs1+kJStY0wzGuklSo1FViPDie8bleXLkYa2rWYKdj57TrYZMzfU6PE56QJ+tJgaXWpVhWvSxtsNTl6IJaUuPWZbfCZrDlnmFNk4XMJP49F/85nG0lg81gU0q/xXs23URapr2g46+bErDqKpRlPOI2Jq0JlbpK5fUTy2vare3obOnEybGTKVtkhSIhDPuG0x4Xna2d6HZ3T3ucdbu7UWOoSVsyn7zvbabgVvQ7mG5bt1yWbCjPa4YSaHF/8WNMJtbzinXCAJTPuO0t23Ft07UwqA3ocnTFuj+f3psycZlusnE66bLP81m2JcHbZVkeB/A2AA4AHQByaye3gOnVEpbYTLGtbYguYJIkYXvrdjx/9nm8PPCy0mEvV3azHWOBMXQ5unBd83Uwa81os7Sheyz2hZFcfjNdQNTj7kGDqQEX112MnrGelBnh6craZit5DzclM2Btx2rbajRXNE874+wNefHkmSexrWVbTuXAwNQsc5ejC8uql+HShkuhUWnSlgRnWjcmzDbLlm3A2uPugUpSYVvLNowGRmMz6OdnyRMyrOdPNB568yHo1Xpct/g6ALEToqPDR9E33pfxMcRelNc0XQMgFiAdGTyCwwOHE7Yxurrpapi1Znz3yHdTAkCjxojmymb4I34srlycMNmRsNl9lhlWSZJg0phwbOQYKnWV2NS4KeU6na2d8IV9eLr/6WnvS7xHMp2oiOYr8Sc94gRX/O097h4l8z/XAavY63amTLnT40SdqQ5qlVoJ5pLf246gA+c859DZ2jm1PYU4mZ8hIyKITr67enfBqremLakVxH0ZNcasu1VnK906XRFAiAyKO+BOe9uT7pMAps/6pqsGmU42AX+VrgrjgVhJsAQp7edsZ2snXh9+Ha8Pv57xc7haXw2dSqd8hkxXNppJZ0tnbPuzuM8hWZax07ETVzZeCavBmvC+mKnJllBvqocEKevPt9U1saZX4vrhaBiTocms3mfVhmql9Dvdc5Bc2p0csNYaa5XHSf5MFxnW5NvE9wsQy2s6rB3Y2hLbZiv5+2vANxDL6qb5TtnWsi1WSjzNe3u6ICs5w5rpOLDoLJAgZcywhqIhDPoGs/58zqYCYab3g+gJEr+OtcvRhVW2VVhStQQmrQnXNl+L3b278XT/05gIpk5c1hhqYNFbssqwhiKhlInW+S7rpkvn//9WAL+RZXn6GrYL0LL6SrzJTsFEsbJgOYyjw0fznt0TJy7xazjiTyaSv1QbzA2YDE2mXSMivgA7rB3whDxZB2yFEL+HGxCb8R0LjKHD2qGs+X3+3PMZv1if7H8S/og/53JgYGqWWWyCHr+3qiDWoaX7krXqrdCr9ZAgKRvL56vB1JB1BqK5olnpZNrj7lEyHWJdEjC1ifxYYAzXNF2jnAwoaw57008ChKNh7Dm9B9c2X6uU4Yrn9nMHPgcZsvKzXq3H9Yuvx5HBWEP85GNZnAgkl6PFnyCkW4+XiWi8tHXJ1rR7l17WcBlsBltWJXXJWd9k8d1CxW3Ukhpbl2wFMLVXIzC7cvB8dbZ24uWBl6cts3R6p/b1tBlsyol/vEOeQ9CqtNiyeAvqjHWo1FUmfIaoJNWM3YgXVSzCurp1CEfDMwYvYjxLLUvzmqibTroMa/9EP/wRPy5puARA5rLgbAK8dms7Tk+cVhqtzUQp853ms9Ois8TWsHqdqDHWpD2uxXt2PDie8WRfkiQ0mBuU4yGf5SbifR1fFnx0+Cj6J/unvmMs7cqkpnguZ2qOpVVpUWusnTFgFROXVzReoTTnAqB0yM7mfRZ/DPS4e6CRNGitalV+b9aasci8SHm948t7gan9xYHUiSiz1gxPyJNymwZT6vPebm1HrbEWGxs2YpdjV8JE0XSZ9zpTHS5tuDTW1T1NKbHoEJzpOK0z1iU8d93ublTqKlM+Z9Uq9bTLBIa8QxmD6nSyqUBwep1pmzgJ4jtUNOfqn+zHq0OvJny3d7Z2YsQ/gq8e/GraiUvx+mUzsSQqbWbaO3Y+yfYT9TFJko4DuBTAXkmS6gBk167wArG8oQKnhjwIhtkpmC5sq22rlf328p3dEycuJo0JVzddDSB2cuL0OBO2cIgvCQZS12dGohGcHDuJdmt7xvUfLo8r69KgXIkMsyglEo8tvkR2tO1ARI5kLAve5diFWmMtLqm/JOfHFhlWYOpkLTlgHfWPIhQNpc1sSFIsI1Kpq5z1ybfdbFeaJE1HnKzEv1bpMqxiE3kA6Gyb+sIXwUWmGfyXXC9hxD+CHa07lMtEWWLveC9WVK9ICPTE85YuABQnfsnHuCgXrtZXw6AxTPv3xksOoJNpVBpsa9mGJ888mdAtO1mPuwdLKpdMW+LeYe3AiH9EOaHrdndjSdUSLKlcApPGpHQSBZDSTXQupAsukrk8UxMtShfduBPUqBzFIe8hXN10NSp1lZAkCcusy6ZO5j0u1Bprs6pcEMfLTOsZxXiKkdWIz64J4m8R3cMznaD3uHtmzPp2WDsQlaPKdlEzcXqc0Kv1CZ8zyar0VUpJcKbsaVNFE9bVrgMwfbY2PtPX7e5GrbE2p07LrZZWrLStTJjwEeubxZYfy6qXKZOaI/4RVOmqstr3WjQJnM5gaBBhOYzl1ctRa6yd6kacQ+m9zWBTSr+73d1oqWpJmQRIntit0FagQleR8HsgNUCu0FUoGfsqXZXyeWQ3Ty23Ub6/zn/2bW/dDse4I2Ernem2JwJi76WTYyfTZgnPec7BG/ZmfP+oVeqE505MRqdrnFStr844EaxUV+Twvd9h7VAaSaa9zxmaOInXQExQiO+o+M/7a5qvgVFjRO94b8aJSxGwztQpeKZKm/ko26ZLnwVwFYDLZFkOAfACeHsxBzbfLG+oRDgqwzHMTsF0YROZQyD/D0vxRbJl8RblpD9+/Ua6siUgdX1m/2Q/ApGAkmEVtxc8IQ8mQhNZlwblKnkPt+TMwIrqFWipasG3Dn8Ld+26K+W//X37sa1l27RliJmYtCYYNUastK1Eq6UVQOLJBzBzWaTdbC9ISWhyKRcA/PfL/43nzz2v/ByMBHF6/DQ6rB2oN9Ur2TCxDik5u2U322FQG3Bt07UJl3e2xNYcijLseKIcWEyCKLc5f7wmB4tXLboKldpKNFc0pwSf4qQq+RgX5cK5HlNGjREWvQVXNF6R8TqiLPjDXR9WjpFHTjyScB2xRno6ye+F+BM/ccIr9louRYa1zdKGFdUr8INXf6D8nZ964lNKoC7LckJlQLptLI4MHoE74k54TUVmWZbltI1hMhFlwdmuZyzGSaLNYIMv7EuYrBAn0GJ/03Tb3gCxbZZmyvqKYyZ5TWIm2XRZrdJVKV2Cs+nGPF3Gy26aWlaQS4fgeJ2tnXhl8BX85a6/xF277sLDJx7GpsZNSnls/ERZcmfy6SQveRjyDeFzT38uoaHeudA5ALFjI/764n2WzbILMR53wJ3xOeiwduDU2CmEo+G0z7sSsKZpugTE/vb429hNdoz4RxCIBJTfieDrxpYboZbUCZMAMzUxzFRKLB5b/A2Z2M12uDyxbYGm+6yzGTN3Dp8pqE5HdAoWn0cf3/PxhKUn8RNo6Yjn974X78Ndu+7CA68/gLU1a5X9bYHYd4Bojphp4rLd2o6J0ETajtfxsqm0mW+ybbpkAvBxAN8+f9EiAJdlvsWFZ1lD7GBkWTAR8OfL/xydrZ05NwoSFlUswtvb3650bgQS1+c4PYnlN+kCIiCxFM5qsKLWWJtwQiY6eq6wrchrnNmI38Ot290Ni96ilLdKkoS/uviv0FTRBH/Yn/Lf+vr1uH3F7Xk/9vtXvx9/dfFfKT+LgDUqxypBZvrivnX5rXj3ynfn/fhC8l65J8dO4gev/gDffuXbynUc4w5lragofcqUYRVju3vD3UomQBDB6CuDr6SM4/DAYWy0b0wJPm9uvxnbWrYpXYMFnVqHT1zyCbx/9ftT7mvzos3Y0boDmxalrje9c82duG3FbWmfi0zeueyduGfDPdNmdC6pvwRvaXsLNCoN/GE/TrpP4r9f/m9lKwQR9M90Mh9/Yu4P+9E30ZeQMT7hPlGypkvCRy/+KJZULYE/7MdEcAJdji483vc4gNgWLoFIIKEkGEhssnJo4BCA2OsktFvbMRGcwKBvMKfKCrvZjo+s+wjeuvSt015vWfUyvG3p23Bjy43Z/6FZEq/PQddB5bIToydgN9uVipZMW9tkE+C1VrVCI2myKjeMRCN4yfUSllcvn/Z6VboqpUvwdCfzN7XfhB2tO5TAO50GcwMGvAOxrt1j+TWTubn9ZlzZeCUC4QD8YT+WWZclfMfET+SMBkZzDlhF1uvhNx/G73t+j/87+X/Kdc6FzikBRPzEYS7vM/EZeG7yXMJ7Nl67tR2haAh9E32xsvmkwHHrkq34s7Y/w7LqZQmXi4Dq1NipxIA1bh/t5OPIZrDhcvvlCSW+Lq8LldrKjPu4i1LidB2GD/QfgE6lw0rbyozPgci0D/mGMB4cz3gcTLfV1QvOF2DUGJX3TTa2tWzDpQ2XKt/Nz559Fg+9+ZDy+/glCukstSzFtc2xxkr+sB8tVS24a91dKde7Y+0duLn95owTl9k2XhJ79BajmWSpZNvF48cADiKWZQWAfgC/AfBYMQY1H7XXVUAlgY2XiBALOP/zuv/M+/ZqlRpfuPoLCZeJ9uzd7m6lUZKY3a8z1cUaX3gzB6zi//Ef9F29XajSVU2b1Zqtdms7njjzBIKRYOwL39KekJV469K3znginK9PbPhEws92sx3haBgj/pFYaZUnsbQ62Vva3lKQccSf9ABT28GIvRHrTfUp2ed2azt29+7GBv8GaCRNygndO5a9I+1jLalaAq1Km1JyJvZ4vbb52pTb1Bhr8LUtX0t7f+9Z+Z60l1cbqvGV676S9ne5BqsAspoYUKvU+I9r/0P5ucvRhU898SkcdB3E5Y2Xx/ZglCMznsw3mBpQoa1Aj7sHp8ZOQYackDH+Xffv0DveC5WkynjiWWzbWrYpmc2oHMW2h7ehy9GFty19W0qDExFYxGdYu93dqFJXJZSNxjdecnqcuKb5mqzHk/xeSkev1uNL13wp6/vMxaZFm1ChrUCXo0sZtwggLHoLVJJK6SAbbywwhiHf0IzHhFatRUtVS1YNXV4eeBlDvqEZ19Zb9BalEdR0J/M2gy3je0mwm+yIyBG8NvQafGFfXhnWelM9vr/9+9OOt9ZYG8uw+kayzk41mBrgDXsxGZpEpa5S+XzrcnThvaveCyAWsIoAwm6yY3/ffsiynFNzM3GcvzzwcsJ7Nl58QOPyuLDKtirh93azHfdde1/K7UTW1Bf2JbxWIuDtn+zHqbFTuMKe+F3Z2dqJf332X3Fs5BhW16zOqifE9tbt+Pxzn8ebo28qk8Xx240lT0ImjP/8czfTuuxqffo1rOFoGHt692BL85aclmyssK3Aj3f8WPn543s+ji5HFz556ScRkSMJ+92mY9KacP/W+2d8nDU1a/DvV/97xt/HV0Jc1XRVxut1u7uz3vZpvsh2YVK7LMv/ASAEALIsewHMvNvuBcSgVcc6BXNrG6KiELPT4os4/sshU+OLbnc3Gs2Nykl3h7UDJ8dOIipH4Q/7se/0PtzYcmNW65TyJfZwOzV2atq9EOeCsi3A+RN+p9cJrUqbdSYhX8kTCrscu9BU0ZSwN2K3uxsqSaWUL3dYOzAWGMMJ9wnYDLasNngHYms9Wy2tKTPQfeN9CEfDC2pNz7XN18KoMSonyPFNUaYjSn+73d0pZXjitocGDqFKV1Xw5kH5UEkqdLZ24kD/AUwEJ1IqA0TmKf4Etcfdg0ZtY8L9iL/tZdfL8Ef8RVu7Xgw6tQ43LLkBj59+HKFICJFo7DOlw9IBlaSCVW9Nm2HNpsxSSJ7Qy6TL0RUrx08z+RMvPgib7bILcfsDZw8AKN7aPPEc5JphBWIVKz3uHnS7u9FU0YRDA4eUz9pzwXNKANFgbkAgEoA74M6puZmozHnBmXmvXxFkHxs5hmH/cNaNheIb6yWXBAOxzH4gEkj5bNm6ZGtCWfBM5d9A+lLiQwOHMOgbnHESRDx3otIgY4bVaMN4cDxh31Mg9ty5A+68GhnG29G2A+c85/Dq0KsY9A4iKkeLtrQons1gS9h+KZ1sK23mm2y/iYKSJBkByAAgSVI7gOxayV1AljVU4k1mWImKRjQcSPelKNa2xEsuYWq3tsMX9uHs5FkcOHsA3rB31l9cMxGP/9y55zARnCjpl0hy6bRYh1bsoEQ0SXJ6nOgejQVJd6y5A8uqlynNJ5KbBYnn6aDr4IzNbpJ1WFI7KeazFUa5M2qMuK75Ouw5vUdpxKKW1AmdQzMR76Uedw80Kg2WVC1RLgfOZyhLVA6cTmdrJ0LREPb37U9Ze61X62HWmpUMa1SO4uTYyZSAtcZQA6veimfOPpNw+/mis7UTE6EJPHvuWfRN9CEYDSrHs81gS7uGNZeOuh3WDpyZODPtXr8iExbfaTuT+CCsYAFrfyxgLdb7WDTXGfWPZv25E/+5usuxCxIkfH7z5yFDxp7TexCMBDEUHlLGHH/9fEqCD7oOQqPSYHFVakmrSWtCU0UTnj37bOyxspyUia+kiH+tRMAr3jPJx5HVYMWVjVcqJb6Zus7HE6XE8WXBYhLkuubrpr2tuO9nzj6TsLwm5TH059f7+t0Jl+9y7Io1c2y+Os2tsrdl8RZoVVp0Obqy3iKrUGaaWHKMO7KqtJlvsj1L+RcAOwEsliTpFwD2Avh00UY1Ty1vqICDnYKJiqbd2o4B3wAGfYMpJWbJW6eEo+FYBiLuQzu+XKrL0QWr3qp02CwWsYebCMxK+SWSvMn9TI0iCqnBHOtQvKs3dkK3rWUbOlumti9JnlwQz5Mn5Mk5A9xubUf/ZH9igxp3DyRIC6oJBTC1FcJLrpdiQX/VkoTN5jNpt7bDHXDjReeLaK1qVaoMRLkwULr1q+msq12HRnNj7ATR44RGpUk4LmwGm7KG9ezkWfjCPti1ice2yCy/NvQagPkXsG5q3IRKXSV2ntqZEojaDLaMGVaz1pzV39pubYcMGafGTmW8jui0nc1EX0KGdZbZbPF5/9rQa6g31hft2BSTmjLk3DOsXie6HF24tOFSbLRvxPLq5ehydOHU2ClEEVVeK/FcuLwujAfGYdQYs3rPitJvT8iT8J5N1mHtUI7xbDOslbpK5d/x362iGZy4v3QTBZ2tneif7MehgUMY8Y9kta95Z2snTk+cxvGR41mXAwNTz91rQ6+lLK+JJ7Z1i6+6CEVD2HN6D65fcv2s13ZW6aqwedFm7OrdhXOTsYZaxdrPPVn89kvpiF0JFtLkLJB9l+DdAN4J4E4Av0KsW/D+4g1rfhKdgk8NsVMwUTGIL3wZctoMa3zji76JPoSiobQbq78+/Dr29+3HjS03ZrWtxWyIPdyODB1JGEMpVOuroVPplAyry+sq2h60ycTrs9OxE5fZL0OtsVbpDvrYycdweiKxhElskg7MvJ1IsnSNKU64T6C5shlGjXG2f0pZubrpahg1xlgQk0MzGvFcHxk6kvC8S5KEpdalAErTITgTSZKwvWU7Dpw9gBOjJ1IqA6oNU9tYiNc9OcMKxI4NOVYsNmcnmIWiVWuxdclW7Ovbh6MjRwEgMcOaZs1eunXzmWTT0EV02s5m/a8IKiVIqDVNv9/tTCx6CwxqQ8a1m4US//7JNmCtNdZCJalwoP8AesZ6ErqOHxo4pGQn02VYx4JjCcHidETpd/I4k4mJh/jHmkmmDCsQCxJlyFhkXpQ2oLxhyQ3QqDT42dGfZf2Y8aXELw+8jGH/cFaTIOL7SoY87XMgtluKf0+8cO4FjAXG0NlSmKqqzrZOOD1OZVnLXE2AZdpTXhCVNgttcjaXOjADgFEA4wBWS5I0/eKFC9Cy+tiHDjsFExVH/IlKuoDVF/YpJVbpSuGqdFWoN9bjwTcehC/sK3o5sCDGUK2vRo0xfQnTXJAkCQ3m2EbwUTkaK9+ao3V8DaYG9I734tTYKeWEQWxf8tOjP0VUjia8VpIkKWu+cs2win1u4xvIiK1bFhqDxoAti7dgd+9u9E30ZX0yH/9cJN9G/K4Ue7BOp7O1E+FoGE/3P50SbMYHbKITuF2XemyLv1UjaVBrnF0QVQqdrZ2YDE3i4TcfTgggqg3pm8xks82RsLhqMTQqTcbGS6JhzXXN12U18SMmnOqMdbPuEyBJUlH3uRWSu+BmQ6OKHUuPn34cKkmldIne3hKbkHvg9QegwtQWIzaDDRpJEysJDoznlC0WY5ruOYh/b+dTEpz83prpebfoLdjUuEnZTzybwE2UEu907MTOUztjkyBNM0+C1BhqoJE0044HSJ9h3enYiQptxbTNinKxpXkLdCod9p7eC7PWnPXEw2xl2lNeEB2Cs8nazyfZbmtzH4ADAP4RwL3n//tUEcc1L7XXm6FWSQxYiYqk0dyonCglfxGLmdfkTc6XWpYmXK/d2q7ssTfdNgqFFN+luNTEtgDDvmGEo+E5mxW2m2Oz9CpJha0tW5XLRUkrkJo1iC93zEVzRTP0ar0yaRGKhJQ9XheiztZOjAfHEZWjWR9jdcY65QQr0/NeThlWAFhbu1Zp1pV83NYYapTjqMfdg3pTPUyq1GyQ+NvqTHV57W9calc0XgGL3oIR/0hKcDURnEAoMtVkZsQ/knK96WhVWrRWtWY8EX7R+SJGA6PY0bojq/tL3nZstsRnfDHfx2JSE8itskN8vl3WcJkyEdJqacVK20qM+EdQp6lTAgi1So16Uz2c3tga1mz2YBXEZ+FMGVbxt8xUYitoVBoYNUZU66tTuudms79wZ2vnVFY3yyBZlBL/rvt3Wa2JBqaeu5nGI9awiqqLUCSEvaf34vrFsy8HFip0Fbi66erY59EcNnCbqRIi322fyl22GdZbAKyQZfmtsizfdP6/m4s4rnlJr1Fjaa0Zx84xYCUqBpWkSui0GE98YXSPdmPYN4zjI8fRVNGU8iUovsy3tWwrejmwkNyBtZQaTLG1pMoG73NUFiler432jQmZLZHl1kialGZB8eWOuVCrYuVQ3WOxE+/4PV4Xoqubrp7qhG3J7kRF7HULpB6X8Se85USSJKWMPPn9X22ohtvvRlSOTptNF3/bfCsHFrQqLW5cEsvgiUoCIG5rn7h1rLk0XBJEM650uhxdMGlM2Ny0Oe3vk4kJj0ItOxCvWfzfXQz5fO6IsSVX7YifG3WJ5emiSeB4ML8M63Sv6VLLUkiQcn7eK7QVaW8j/rbpPj+vX3L91Dr4LB9XlBIHo8GsJ0Hi73+646BKXwW1pEb/ZD+GfcPY27cXE8EJ7GjL/nGyIV7fuVpaA0DZU/7oyFEM+4YT/nN6nLE9eov8HimFbAPWkwCKt+/DArLCXonjzvFSD4NowVphW4EqXVVK+c2iikUAgM889RlseWgL9p7em7I5urg9kHpiUUzLbctj/69ePmePmYndbMegdxBnPWeVn+dCc0UzgNTnfUnVEqyyrUKrpRVadeLXjHit6kx1OT9efCfFfE7c5xO9Wo/rF18PnUqHlqqWrG+3vHo5DGoDllQuSbh8mXUZJEglLV/PRBw/4ngSbAYbwnIYY4ExnBw7mfHk2mawoc5Yh6bKpqKPtVhE0L7MOvX5JgKZ+BLIfDpjd1g70D/Zr+wPKoiGNVsWZ79/pUljglFjTHmt8tVc2Qy1pC76/pIrbCugVWmV9aLZaK6IjW3rkq0Jl4vlD8nrqUUTulwD1npTfew5rcz8nBo0BrRUtaCpIrdj3Gqwpn2txGNN9/0lmhDZDLas+wSIUmKTxoSrm7Lv2ttc0YxaY+20EwoqSYUaYw1+fuzn2PLQFtz7xL2o1FViU+OmrB8nG1sWb4FBbcj5uZ6tZdZl+NOpP2HLQ1sS/tv28DZE5WjCZ8NCkW16wQvgsCRJexG3nY0sy/cUZVTz2KrGKjx25Bwm/CFUGhjjExXaX6//a7xr2btSLq831ePr138dg95B5bLNi1IzAX/W9mdzWg4MxGa8v3Pjd3CZfe4eMxO7yY6wHMbR4VjTlrkKWNfUrME3b/hm2hOTr1z3FQQiqTulXVJ/Ce7fen/a13EmHdYO/N/J/8NkcDJlj9eF6FOXfQq3rbgtJeifzscu/hhuar8ppdKgzlSHH3b+EKtsqwo9zFlbU7MG373xu1hfvz7hclG++erQqwhEArHJiQz9D/9n6/8Ufe/hYtrUuAn3b70/4eQ7XcDa4+6JZc1yyCZvbtqM/zn8P9jXtw+3dNyiXK40rMlhok+SJHxv2/eULZNm670r34vL7ZejQlcx85Vn4cNrP4zrF1+fU8n4HWvuwJbFW1ImeRZXLcYPt/8QQ8eGEi63m+zY490DjUqTU+n9B9d+ENtbt89YHfTVLV/NucHcl6/5csJaVuHGJTfiW1u/hVU1038efO7Kz2HAO5DTY/7Tlf+EQd9g1pMgAHDPJffgL/x/MeP1vnrdV3F85Ljy80rbypw+H7Nh0prww84fznnH8X+44h/w/Lnn0/5Or9Zjy+ItczqeuZBtwPro+f/ipe+nfIFbaZ9qvHRpy/z9QiQqV3WmuowZt+TZ7XR0at2MG94XQ7ZldMUmvlgPDxyGXq3PKYswG5IkZfwSzZQVlCQp79dKZGF6xnpS9nhdiGqMNTlnRGuNtRkbD220byzEsIoiXdMUEbC96HwRQGzCYqQ/tQkRAKyuWV28wc2BdO8LEbAnZ1g7rB1ZdQgW1tSsQVNFE3Y6diYErF2OLlRoK3L+HEueWJgNi96CSxsuLdj9ZWI1WHGJ4ZKcbjPd++/yxsux/439CZc1mBsQioYQioZyam423Xs2Xj7VPJluo1Vrs+oK3WBuyLk0trGiEY0Vqd28p2M327MKENfXry/o8ZfJurp1RX+MZG2WtgXXBXgm2ZYEW2VZ/kn8fwBy22fgArHifMDKdaxEVI7ECcXrw6+jwdSQ08nsfBLfmCKXTqk0P4mA9QXnCwDKY734XFIyrL5YwCrLcsrextkQ64SfP/s83H43gOI0rLnQxQdc5dbcjKgcZRuw3pHmsjsLOI4Fo8lqRKVegzecDFiJqPyI5lSBSGDOy5jmUlNlEwxqA44OH81puxean0TAdnzkOBrNjWlLGxeyKl0VNJJGabo07B+GO+DOa912Z2snwnIYj/c9DgB49tyzGA+Oz+m6/4UuIWAts+ZmROVo2oBVkqT3SJL0BwBtkiQ9GvffPgDpa20ucJIkYWUjGy8RUXmy6C0wqGPrhRZywKqSVFhqXYp9p/chIkcWZBMKmlKtjxV95bK1z0IiSVLCXqyi0Vg+z8Vq22osrlyMLkcXgFg5cKW2EpsWFbZhzYUsfl0xA1aimc20hvUZAOcA1AL4atzlEwCOFGtQ890KeyV+f/gsZFlesOV2RDQ/SVJsu4Pe8d55u7VHtjqsHUpzqQsxiLmQaNVaVOoqMRGcWLDdoGcSH7CKDsH5PBeSJKGztRM/fu3HGPAOYN/pfbh+yfXKPqI0ezaDDVqVNraGNYd9WIkuVNNmWGVZ7pVleb8sy5tkWX4i7r+XZVkOT3dbSZIWS5K0T5Kko5IkvS5J0t+cv9wmSdJuSZJOnP//glsLu9JehQl/GGfH/KUeChFRClEWvJAzrMBUkJpuj1daeERZ8IU6OWEz2BIC1ipdVVYNetLpbO1ERI7g889+HhOhiZz2yaSZqSSVMmHIDCvRzGYqCX76/P8nJEkaj/tvQpKkmWpewwD+Xpbl1QCuBPDXkiStBvBZAHtlWV4GYO/5nxeUVY2xxkvHz7EsmIjKj2i8tNADVpFdWlK1pODbGVD5EQHrhZxhHfXH1rD2uHty7hAcb0X1CrRUtWD/mf2o0lXhysYrCzlUwtTnL5suEc1spqZL7wMAWZYrZVmuivuvUpblad9hsiyfk2X55fP/ngBwDEATgLcD+Mn5q/0EwC2z+QPK0fKG8wErGy8RURkSJ0oLvSRYZNou1IzbhUYErEstS0s8ktKoMdRgxD8CWZZn3RlbkiRsb9kOILZdGCd8Ck98DlfqKks8EqLyN9Ma1kcAXAIAkiT9Vpbld+XzIJIktQLYAOB5AA2yLJ87/ysngLRnTJIkfQTARwCgoaEB+/fvz+eh58Tk5GTK+GqNEp54pRtrpDOlGRRdkNIdi0TJJK+EKnUVHIcdOKc6N/MNclQux2FUjqJeUw/bhK0sxkPFZRw3Yql+KV44ENvaplyOw7niHnPDE/Lgt3t+i4ngBORBeVZ/f32oHnpJj5aJlgvqeSyGdMeiccKIJm0TDjx5oDSDogvOfP5MnClgja8lyWvKUpKkCgC/BfC3siyPx5enyLIsS5Ikp7udLMvfA/A9ALjsssvkLVu25PPwc2L//v1IHt/63hfRO+zFli3XlWZQdEFKdywSJduCLbgbdxft/svpOLwBN5R6CDRHtmBLws/ldBzOhaE3h/DYs49B3aoGzgI7Nu7AFY1XzOo+3413F2h0F7Z0x2Ly8UpUbPP5M3GmkmA5w7+zIkmSFrFg9ReyLP/v+YtdkiQ1nv99I4CBXO93Plhpr8LJIQ/8oUiph0JEREQLXLUh1sPyReeLAC7ctbxEtPDMFLBeLJosAViXS9MlKZZK/SGAY7Isfy3uV48CuOP8v+8A8Pt8B1/OVjZWIhKV0T0wWeqhEBER0QJXY6gBALzgfAHV+mrUGGtKPCIiosKYaVsbdVyTJU0uTZcAbAbwfgA3SJJ0+Px/fwbgywC2SZJ0AsCN539ecFbaY4vo32DjJSIiIioy0XTK5XWx0RgRLSgzrWHNmyzLTyNxDWy8rcV63HLRWmOGTqPCMW5tQ0REREUmSoIBdsYmooVlppJgypNGrcLaRVV4sXe01EMhIiKiBa5CWwGtKrb9DNevEtFCwoC1iLasqMeRM24MTQZKPRQiIiJawCRJUrKszLAS0ULCgLWIrl9RD1kGnnhjsNRDISIiogVONF5ihpWIFhIGrEW0ZlEV6ir1ePyNBblzDxEREZWRakM1bAZbwnpWIqL5rmhNlwhQqSRcv6IOf3rNiXAkCo2a8wNERERUHO9d+V4M+ljVRUQLCyOoIrt+RT0m/GEcZPMlIiIiKqLrFl+HW5ffWuphEBEVFAPWIrt6WS00KollwURERERERDliwFpklQYtNrbasP84S3SIiIiIiIhywYB1Dtywsh5vuCbQ7/aVeihERERERETzBgPWOXD9yjoAwL7jLAsmIiIiIiLKFgPWOdBeV4HFNiMDViIiIiIiohwwYJ0DkiThxlUNeOrEEIYmA6UeDhERERER0bzAgHWOvO+KJQhGovj1C6dLPRQiIiIiIqJ5gQHrHOmor8Q1y2rxs+d6EYpESz0cIiIiIiKisseAdQ7deVUrXOMB7HzNWeqhEBERERERlT0GrHPo+hX1aKkx4SfPOEo9FCIiIiIiorLHgHUOqVQSPrCpFS/1juK1/rFSD4eIiIiIiKisMWCdY39+WTNMOjUeYJaViIiIiIhoWgxY51iVQYt3XdKMRw+fhXPMX+rhEBERERERlS0GrCVw1zVLAQn44h+PlXooREREREREZYsBawksqTHhY9e149FXzuLZnuFSD4eIiIiIiKgsMWAtkY9vaUdztRH/8uhr3JeViIiIiIgoDQasJWLQqvHPb1uNN12T3OaGiIiIiIgoDQasJbRtdQOuX1GHr+85Adc4GzARERERERHFY8BaQpIk4V9uWoNQJIpP/eYVRKNyqYdERERERERUNhiwllhrrRn/fNNqPHViCN95sqfUwyEiIiIiIiobDFjLwHsvX4K3XtSIr+56Ewd7R0o9HCIiIiIiorLAgLUMSJKEL73rIiyyGnDPrw7D7Q2WekhEREREREQlx4C1TFQZtPjmey6Ba9yPT/zqEALhSKmHREREREREVFIMWMvI+sVWfOmdF+GpE0O4+5eHuD8rERERERFd0Biwlpk/v2wx/u3ta7D7qAuffOgVRNg5mIiIiIiILlCaUg+AUn1gUyu8wQi+/KfjMGpV+PI710Glkko9LCIiIiIiojnFgLVMfey6dniDEXxj7wkYtWr8681rIEkMWomIiIiI6MLBgLWM/d2Ny+ALhvH9p07BqNPgMztWMGglIiIiIqILBgPWMiZJEv7fn62CNxjBd57ogUmnxj1bl5V6WERERERERHOCAWuZkyQJn3/7WvhCEXxt95sYngzgn962Gho1+2UREREREdHCxoB1HlCpJHzl1otRY9bh+0+dQs+gB/e/9xJYTNpSD42IiIiIiKhomKabJ9QqCf/41tX4j3etw/OnhvGObx3Am66JUg+LiIiIiIioaBiwzjO3bVyMX/zllRjzhXDTN5/GT55xQJa5VysRERERES08DFjnocvbbNj5t9diU3sN/uXR1/HBB17EwIS/1MMiIiIiIvr/27vz+LjKQv/jn2f2SSaZ7Gmb7m26JC0tUJZSoK2UVaUUvFcREVf0qrgiLle9eK/6Q1FcEUVFcLkgKEvlsgiUlrLTQune0n1v2mbfZn1+f5xpCN2hzZxJ8n2/XvOa5Ewy883kmTPznfPMOSInlAprL1VeEOSPHzmN715aywvr93Hxzxby1KrdbscSERERERE5YVRYezFjDNecNZx/Xnc25QVBPn7XIr714DI64im3o4mIiIiIiBw3FdY+YExlAQ99bhqfPGcEf3lxC5f8YiEL1u5xO5aIiIiIiMhxUWHtI4I+L//57hr++okzsNZyzR0v8x9/Wcz2xg63o4mIiIiIiLwjKqx9zLTRZTz+pXO5/oIxPL2mjlk/WcCtT68jltQ0YRERERER6V1UWPugoM/L595VzZNfns65Y8q4+fE1XPyzhTyjacIiIiIiItKLqLD2YYOL8/jt1VP440dPI2UtH77jZS7++UJ+s2C9pgqLiIiIiEjOU2HtB2aOreDxL57Ldy+tJejzcNOjq5l20zx9xlVERERERHKaz+0Akh0hv5drzhrONWcNZ/O+Nv6+eBu/W7iBp9fUcd27qvnEOSMI+rxuxxQREREREemiLaz90LDSfL5ywVie/PJ0po8p5+bH1/CuHy/g1/PXsa815nY8ERERERERQIW1X9v/Gde7PnY6Q0vy+NFja5j6/+bxpb8tYfHmBqy1bkcUEREREZF+TFOCheljypk+ppw3drfw15e28I/F23jgte3UDCzk6qnDmHNyFSG/pguLiIiIiEh2aQurdKmuLODGS2t58Zvn8f05E0hbyzfuX8a0m+Zx69PraOpIuB1RRERERET6EW1hlYPkB31cdcYwPnj6UF7aWM9t89dz8+NruG3+eqaPKWdMZQFjB0Q4eWgxlYUht+OKiIiIiEgfpcIqh2WM4cyRpZw5spQVO5r4w8KNLN7SwCPLd2IteD2GC2oqueas4ZwxogRjjNuRRURERESkD1FhlWNSOyjKLe+fDEB7PMkbu1t5ZPlO/vbKVh5dvouR5fmcOrSYCVVRJlQVMnlIMV6PCqyIiIiIiLxzKqzytuUFfEwaUsSkIUV8adYYHlqynYeX7uSp1XXct3gbAIOiId5/2lDef9oQBkQ1bVhERERERN4+FVY5LiG/N1NMh2KtZVdzJ69sauC+RVv56ZNr+flTa5kyvISzRpUybXQZkwYXEfBpX18iIiIiInJ0KqxywhhjGBgNc+mkMJdOGsTmfW3ct2gbC9bu4edPvcHPnnyDSNDH9LHlXFBTyYwxFUTz/G7HFhERERGRHKXCKj1mWGk+1184lusvHEtje5wXN+xj/po9PLmqjv9buhNjYFR5hJMGR5k8pIipI0sZXRHRzptERERERARQYZUsKcoLcNGEgVw0YSDptGXJtkYWrt3L0m2NPLN2L/e/uh2AAYUhzq4u4/ThJUwcHKW6IoLPqynEIiIiIiL9kQqrZJ3HYzhlaDGnDC0GwFrLtoYOnl23l2ff2MsTK3fz98zOm0J+DxMGRTltRAmnDy/h1OHFFIY0jVhEREREpD9QYRXXGWMYUpLHlacP5crTh5JOWzbta2PZ9iZe39rEa1sb+N0zG7ht/nqCPg8fmTacz0wfrc+/ioiIiIj0cSqsknM8HsPI8ggjyyPMnlwFOMd+XbKlkb8v3sbtz2zg7pe28OkZo5hzchUDo2GXE4uIiIiISE9QYZVeIS/g46zRZZw1uoxPnjuSmx9fw48ec04jy/M5Z3QZM8ZWMHVUKSG/1+24IiIiIiJyAqiwSq8zfmAhd3zkNNbsamHhG3t4dt1e7l20jbte2ExewMu51eVcctJALqipVHkVEREREenFVFil1xo7oICxAwr4xDkjiSVTvLB+H0+s3M2Tq3bz2IpdFOX5ueKUwVx5+hBGVxS4HVdERERERN4mFVbpE4I+LzPGVjBjbAX/M3sCz63fyz0vb+VPL2ziD89u5LThxXzgtKG8+6SB2uoqIiIiItJLqLBKn+PxGM6pLuec6nL2tsb4x+Jt3PPKVr5y3+vcOHcFk4cWUTOokNpBUaZXl2tvwyIiIiIiOUqFVfq0skiQT00fxbXnjuTFDfXMfX0Hy7Y38sdnNxFPpckPePnQmcP4+DkjqCgIuR1XRERERES6UWGVfsEYw9RRpUwdVQpAPJlmxY4m/vjcJn63cAN/fH4THzpjGF86v5qCkLa4ioiIiIjkAo/bAUTcEPB5OHloMb+48mTmfWUGl00exB+f38isWxbw2PKdWGvdjigiIiIi0u+psEq/N7wsnx+9bxIPfGYaJflBPv2XV/nEXYvYuLfN7WgiIiIiIv2aCqtIxuQhRfzzc9P45iXjeHHDPi746QK+9/BKmjoSbkcTEREREemXVFhFuvF5PVx77iie/uoMLj95MH94biMzbn6aZ9bucTuaiIiIiEi/o8IqcggVBSF++L6TePi6s6ksDPGxO1/h3kVb3Y4lIiIiItKvqLCKHEHtoCj3fXoqU0eVcsPfl/LTJ9Zqh0wiIiIiIlmiwipyFAUhP3d85DTed+pgfv7UG3zrweWk0yqtIiIiIiI9TcdhFTkGfq+Hm993EmWRIL9ZsJ60he9fNgGPx7gdTURERESkz1JhFTlGxhi+dtFYvB649en1WGv5wZyJKq0iIiIiIj1EhVXkbTDGcP0FY/EYwy/nrcMYww/mTMAYlVYRERERkRNNhVXkbTLG8OXzx5BKW349fz2Di8N8duZot2OJiIiIiPQ5Kqwi74Axhq9eOJadTZ3c/PgaBheHmT25yu1YIiIiIiJ9igqryDtkjOGmKyays6mDr963lMrCEGeOLHU7loiIiIhIn6HD2ogch6DPy28/NIUhJWE+9efFbK1vdzuSiIiIiEifocIqcpyiec5xWtNpy3V3v0Y8mXY7koiIiIhIn6DCKnICDCvN54fvO4klWxv50WOr3Y4jIiIiItIn9FhhNcbcYYypM8Ys77asxBjzhDHmjcx5cU/dvki2XTJxINdMHcbvn93Iv1bscjuOiIiIiEiv15NbWO8ELjpg2deBp6y11cBTme9F+oxvvns8E6oKuf6+19nZ1OF2HBERERGRXq3HCqu19hmg/oDFs4G7Ml/fBVzWU7efNek0Jp2EVOLQJ+lXgj4vv7ryFDqTaX74qKYGi4iIiIgcj2wf1qbSWrsz8/UuoDLLt3/irbif6c98HJ45zOVTPwcXfj+rkcRdw8vy+eQ5I7j16fVcc9ZwTh6qme8iIiIiIu+Esdb23JUbMxx42Fo7IfN9o7W2qNvlDdbaQ76aN8ZcC1wLUFlZeeo999zTYzmPR17bFgq3P0MgGDjostJ9i8lr38bzZ92J9fhdSCdu6Uhavr6wg7KQ4VtnhjDGZOV2W1tbiUQiWbktkcPROJRcoHEouUJjUXJBbxiHM2fOXGytnXLg8mxvYd1tjBlord1pjBkI1B3uB621twO3A0yZMsXOmDEjSxHfvvnzh3LIfGsehbs/wPQhQPUhLpc+rSW6lRv+sZTm4jHMnlyVlducP3/+oceiSBZpHEou0DiUXKGxKLmgN4/DbB/WZi5wTebra4CHsnz72TXqXRCMwooH3E4iLrji1MHUDirkh4+upiOecjuOiIiIiEiv05OHtbkbeAEYa4zZZoz5OHATcL4x5g1gVub7vssXhHHvhlUPQzLudhrJMq/H8O331LCjqZPbn9ngdhwRERERkV6nJ/cSfKW1dqC11m+tHWyt/YO1dp+19jxrbbW1dpa19sC9CPc9tXMg1gQbnnY7ibjgzJGlvHviQH49fx1b69vdjiMiIiIi0qtke0pw/zNyBoSisPx+t5OIS771nvF4PYYb565wO4qIiIiISK+iwtrTfAEY915Y8wgkOt1OIy4YGA3zxVnVPLW6jidW7nY7joiIiIhIr6HCmg0T5kCsGdbPczuJuOSj00ZQXRHhxrkrtAMmEREREZFjpMKaDSOmQ7hYewvux/xeD/89ewLbGzv49fx1bscREREREekVVFizweuH8funBXe4nUZcMnVUKbMnD+K3z2xgW4N2wCQiIiIicjQqrNlSOwfirbDuSbeTiItuuGgcBvjRY2vcjiIiIiIikvNUWLNl+LmQV6ppwf1cVVGYa88dydzXd/Dqlga344iIiIiI5DQV1mzx+mD8pbDmMYhrOmh/9unpoygvCPK9h1dirXU7joiIiIhIzlJhzabaOZBogzf+5XYScVF+0MdXLxjLq1saeXjpTrfjiIiIiIjkLBXWbBo2DfLLNS1YuOLUwdQMLOSmR1fTFku6HUdEREREJCepsGbT/mnBax+HeJvbacRFXo/hu7Nr2dHUwQ8eWeV2HBERERGRnKTCmm0TLodkh1NapV87bXgJnzxnJH99aQtPr6lzO46IiIiISM5RYc22oVMhUgkr7nc7ieSAL58/hjGVEb7296U0tsfdjiMiIiIiklNUWLPN44Wa2c4W1rsudU5/uQLqDjEtdPUjsODm7GeUrAn5vdzy75Opb4vznYdWuB1HRERERCSnqLC64bRPwJAzIBlzThsXwku/Pfjn5v0PPP19aNmV/YySNROqonz+vGrmvr6DB1/b7nYcEREREZGcocLqhvKx8JGH4eOPO6fx74FVcyHVbW+xdauhbiVgYeVc16JKdnxmxihOG17Mfz6wjI17tUMuERERERFQYc0NtXOgfR9sWvjmspUPAgaiQ3QYnH7A5/Xw8w+cjN/n4bq7XyWWTLkdSURERETEdSqsuWD0LAhE3lpMVzzgHLf1lA/DlhegeYd7+SQrBhWFufl9k1i+vZn/98hqt+OIiIiIiLhOhTUX+MMw9uLMtOAE7F4Je1ZD7WVQcxnOtOCHXA4p2XB+TSUfnTacO5/fxGPL9dllEREREenfVFhzRe0c6GiAjQucravG4+xNuHwMVE7QtOB+5OsXj2PS4ChfuXcJq3c1ux1HRERERMQ1Kqy5YtR5ECyE5Q845XT42RCpcC6rnQNbX4Kmbe5mlKwI+rz89uop5Ad9fPJPi6hv0/FZRURERKR/UmHNFf4QjL0Elt0H+95wSup++7/WtOB+Y0A0xG+vPpXdzTE+89fFJFJptyOJiIiIiGSdCmsuqZ0DqZgzHXj8pW8uLx0FA06C5f+A1rpDn6x1L7f0iJOHFnPT5RN5cUM9X73vdToT2nOwiIiIiPQvPrcDSDejZkIoCoNOhvyyt15WOwee+i78uPrQv3v6p+CSH/V8Rsmqy08ZzI7GDn78r7Ws2d3KrR88mZHlEbdjiYiIiIhkhQprLvEF4cMPQV7pwZedfq1TYlOH+Dzj8gdg2b1w4ffB6+/5nJJVn3tXNbWDonz53iW895fP8oPLJzJ7cpXbsUREREREepwKa64ZdPKhlwcjzjFZD6VgENxzJWxYANWzei6buGbmuAr+7/PncN3dr/GFe5awYM0ebpxdS2FIb1CIiIiISN+lz7D2BaMzexjWoW/6tEFFYf527Zl84bxqHnp9Bxf/bCEvb6x3O5aIiIiISI9RYe0LfEEY925Y/U9I6hAofZnP6+FL54/hvk9Pxec1vP/2F/j9wg1Y7XRLRERERPogFda+onYOdDbBhvluJ5EsOGVoMY98/hwurBnA9/5vFd95aAVJHfpGRERERPoYFda+YuRMCEZhxf1uJ5EsyQ/6+PVVp/Cp6SP584ub+cSfFtGW0JZWEREREek7tNOlvsIXgPHvgVX/hGTMmSYsfZ7HY/jGxeMZXprPtx5czisbLFsD67lm6nDCAa/b8UREREREjou2sPYltXMg1gzr57mdRLLsytOH8tBnpzEq6uWmR1dz7s1P85cXN2uasIiIiIj0atrC2peMnAGhInju57BvndtpMoyzQ6iSEW4H6fMmVEX58pQQ+cNP4kePreZbDy7nzy9s5jvvrWHa6DK344mIiIiIvG0qrH2J1w+TPwgv/hq2vOB2mjdtfg6uvNvtFP3GacNLuPdTU3ls+S5+8Ogqrvr9S5w3roIvnT+GCVVRt+OJiIiIiBwzFda+5sIfwMxvup3iTU/9Nyy+EzqbIVTodpp+wxjDxRMHMnNcBX98bhO3zV/He375LLPGV/KF86qZOFjFVURERERynwprX2MMBAvcTvGmif8GL98Oax6BSR9wO02/E/J7+Y8Zo7jqzKHc+dwm/vDsRt77q2f5wGlD+MbF44nm+d2OKCIiIiJyWNrpkvSsqilQOBhWPOB2kn6tMOTn8+dV8+zXZvKpc0dy3+JtnHfLAv5v6U6s1aFwRERERCQ3qbBKz/J4oPYyWPcUdDS6nabfKwj5+cYl43nos9OoLAzy2f99lfff/iLPrdur4ioiIiIiOUeFVXpe7eWQTjjTgiUnTKiK8tBnp/HdS2vZvK+Nq37/Elfc9jzPrN2j4ioiIiIiOUOFVXpe1SkQHappwTnG5/VwzVnDWfDVmfzPZRPY3Rzjw3e8zAd/9xJLtja6HU9ERERERIVVssAYZ1rw+nnQ0eB2GjlAyO/l6jOHMe/66dz43hrW7m7hsluf4zN/Xcy2hna344mIiIhIP6bCKtlROwfSSVj1sNtJ5DCCPi8fmTaCBTfM5Iuzqnl69R5m3bKAW59eRyyZcjueiIiIiPRDOqyNZMegk6F4ODzxHecwN0diDJzzFaiZnZVo8laRoI8vzhrDv00ZwvceXsnNj6/hH4u38YVZ1bznpEF4PcbtiCIiIiLST2gLq2SHMTDruzDkDCisOvKpZRc8+1O3E/d7VUVhbvvQqdz1sdPxeQ1fuGcJs25ZwH2LttIWS7odT0RERET6AW1hleypvcw5Hc1zv4Anvg31G6FkRE+nkqOYPqacc0afy79W7uKX89bx1b8v5ev3L6NmYCGnDitmyvBipgwrYUA05HZUEREREeljVFgl99Re5hTWlQ/C2V9yO40AHo/hogkDubB2AC9s2McL6/exaFMD97yyhTuf3wQ4W2RPHVbMSYOjTBpSRO2gQvICWsWIiIiIyDunV5OSe4qGQtUUWH6/CmuOMcZw1qgyzhpVBkAilWbljmYWbW5g0aZ6Xt5Yz9zXdwDgMVBdUcDEwVEmDY4yeUgx4wYW4PfqkwgiIiIicmxUWCU31c6Bf/0n7FsPpaPcTiOH4fd6mDSkiElDivj42c707brmTpZua2Lp9iaWbmtk3uo6/r54GwBBn4eJVVFm1VTywTOGUhjyuxlfRERERHKcCqvkptrLnMK64gE493q308jbUFEYYlZNiFk1lQBYa9ne2MGSrY28tqWRRZsbuOnR1fxq3jquOmMoHzt7BJWF+vyriIiIiBxMhVVyU3Sws0fhFQ+qsPZyxhgGF+cxuDiP95w0CIDl25v47TMb+N3CDdz1wiaue1c1nzhnBEGf1+W0IiIiIpJL9GEyyV21c2D3Mtj7httJ5ASbUBXll1eezPzrZzJzbAU3P76Gi3+2kGff2Ot2NBERERHJIdrCKrmrZjY89nVYfCecfLV7OfwhKB5+8PJEB1gLgbysR+orhpbmcduHTmX+mjpunLuCD/3hJS4/pYpvv7uG4vyA2/FERERExGUqrJK7CgfB0LPghV85Jzdd/SCMmvnWZfd+GOJt8NFHXInUl8wYW8FjXyzl1qfXcdv89Tyzdg//PXsCl0wc6HY0EREREXGRCqvktit+B1tfdjGAhYe/BEvvfWthbdkN654Em4aGzVA8zL2IfUTI7+UrF4zl4gkD+do/lvKZv77K6IoIl0wYwMUTBzJuQAHGGLdjioiIiEgWqbBKbosOdk5uWvcUrHoYkjHwBZ1lq+Y6ZRVg5YMw7QuuxetragYV8sBnzuK+xduYu2QHv3p6Hb+Yt44hJWFmjKlg+phypo4qJT+o1ZeIiIhIX6dXfCJHUzsHlvwV1s+DsRc7y1Y8COXjwJ/nHHpHhfWE8nk9XHn6UK48fSh7WmL8a+Uu5q1yjuf65xc34zEwqjzCxKoo1ZUF+L1vbnkNB7xEgj7yAz48Hogl0sSSaYyB0vwgpZEApZEAJXkBfF7td05EREQkl6mwihzNiOkQKnKK6diLoWUXbH4OZnzdKaxPfBvqN0LJCLeT9knlBUGuOmMYV50xjFgyxSsbG3h5Uz3Ltzfx7Lq93P/a9nd0vcZAcV6A0vwAZRGnyJZFgpw1qpTzayo1/VhEREQkB6iwihyNLwDj3+tsVU10wsqHAAs1lzl7CH7i206ZPefLLgft+4I+L2dXl3F2dVnXstZYkrS1gLPT5s5EitZYktbOpPM7fg9Bn5dUOs2+1jj72uLsa42xtzXO3tZYZlmMlTuaqWuJcefzm5g0OMr1F47l7NFlKq4iIiIiLlJhFTkWtXPgtT87O1pa8QBU1EDFOOeyqikqrC6KHPBZ1mjYT+VhfnZ0xZGvK5lKc/9r2/n5k29w9R9e5syRJXz1wrGcOqzkxIQVERERkbdFH+ASORYjzoVwCbx4G2x5AWovf/OyCZfDrqWwb717+eSE8Hk9/PuUIcy7fjo3vreGdXWtXHHbC3zszldYsaPJ7XgiIiIi/Y4Kq8ix8PqdacGbn3W+r73szctqZjvnKx7IeizpGUGfl49MG8EzN8zkhovGsmhTPe/55bN84/5lNLTF3Y4nIiIi0m9oSrDIsZpwObx6F1ROhLLqN5dHB8OQM5zLEu0H/57xwOQPQsnI7GWVEyIv4OMzM0Zz1RnD+MVTb3Dn85t4dPlObrhwHO8/bQhejz7fKiIiItKTVFhFjtWws2HQKXDqNQdfNuVjMPfz8NzPD74snYSGzXDF73o+o/SIaNjPt99Tw79PGcJ3HlrONx9Yxm0L1vGxaSP4tylDDvocrYiIiLxzTR0JCkM+7fhQABVWkWPn9cG1Tx/6skkfcE6HMvc6WH4/JDrAH+65fNLjxg4o4J5rz+TxFbv5/cINfPefK7nlibWcU13GkJI8hpbkMTAaoiDkpyDk6zqPBHx4DrM1NplyjhMb9nsP+zOxZIrdTTHS1hIJ+YgEfQR9nqw8kVtrae5IsqOpg4b2OEGfs9flkN+LMc6emQFK8gMU5/nfUabORIrmzgRhv5e8gO+IW66TqTR7W+Psau6kPZYEAwZDXsDLmMoCwgHvUW+vNZZkZ2MHsWSa0RURQv6j/86xstbS0J6gM5Ei4PPg93rwGIglnf9zKmUpCPkoDPvf8ncmU2k6k2k64ik6EymSaYvfawh4PYQDXgpC/oNuqzORIpFKEwke+4u61liSeGa8BX0eUtaytzVGXXOMhvY4hWE/ZZnjFee/zTdi9rXGMMZQEPLhP8IxjjsTKXY2dbKjsYOdTZ2k0mmMMXiMobE9zraGDrY1tJNKWyZWRZk0pIiTBhdRXhA8aobORIo9LTHqWmK8vidJeMM+CsN+omE/5QXBI+baz1rLyp3NLHxjL42Z/2VnIkVxfoDqighjKgsYUZb/tu+fI93evrY4G/a0sXFvK36vh0FFYaqKwhSEfMT3j520JS/oJT/gPP7r2+PUNcfY0xLD5zWU5AcozQ8SDfsJ+Y9v/VDX0smybU2sq2ulujLCqcNKiIbfOgYTqTQNbXHq2+N0xFPOYcIigWMaj9ZaUmnbo8fCbulMsGhTA7ubOzllWDHVFZG35LLW0tieYHdLJ7uaOgn5vYwfWHjQ3wmQTls27Wtj+Y5mIkEvw0rzGVKcR8B34vJ3xFNde7w/UMjvPeR6MZ22rN7Vwosb9rFkayNjKiNcMnEgI8sjx3SbqbRld3Nn12OuKM/PqUNLiOb5D/q5/Y+DjkSK7jGL8vyHXD8dq1gyRWc8TUcixY6mDp5eXceTq+pYtbOZskiQc6vLOGdMGYOiYZJp27XOqxlUSF7g6I/BRCrNurpWttS3d43XWCLNqIoI4wc4j+W0hcb2OI0dCQJeDxWFwWO67p6UTlvneTHgJeg79HPU/v9ffeajSl6PIeDzMKI0/7CvJ3orFVaRnlY7B179k7OH4fHvdTuNHCdjDBdNGMBFEwbw2pYG7nx+E8u2NfHkyjriqfRhfgciAV9XUSkI+WiPp9jdHGNfW6zryT/o85AX8BL2ewkHvAR8Xva2Oi9ID1QQ8lEzsJDaQVHGDSigMhqiPBKkrCAAQCJlSSTTJFJp4qm0830qTSLpfB9POgWpM5EilkjRmXC+7kym2NsSZ0eTUyZ2NnbQFk8d031TEPQxpCQPG+vg5yufozORJpZMEctcdzJtGV6ax7gBhYwZUMDu5k5eyRxTN5Gyb7me8YMKmVgVpboiwtaGdlbtbGH1zmZ2NndymNd0eAyMLI8wtrIAgI5Eio648wJr/wut+rY4LZlDHgH4PIaxAwoYP7DwzWKZSJO2Fo8xeDwQS6Rp6kjQ3JmgI5HKFHanuFtrSVnnxUV9W5w9LbHDjoMDx0RB0OcciimZesvffyhlkSDjBhQwqjyfva1xVu9qZuPeNtIW/F5DNBygKM9PcZ6forwABSEf6bQlkbLEkil2ZV6UNrYnjuE/6SjJDzC8NI8RZRG8HqhrcYptIpWmqjjM4OIwhSE/a3a1sGx7E3XdxmnI76G8IMjQzBs5QZ+X9Xta2bCnje2NHUe83UjQx+DiMNbCgrV7SGfumqqiMJOGRKkZWEhDe4INe1rZsLeNxvYEiVSaZMoefN8vfrHrS4+BgVGnCHo80NieoKnDuT+GFOcxpMQpIM+s3dOVMeDzEPJ5CPm9NLTH3/J/iob9DIyGKC8I4vM4hdsYgzHObXmMIT/oozQ/QEl+gGTasmFPGxv2trKjsYNkypKylngyTfsxPsbejrDfS0HIuS+HleYzIBqisT3OzianoHk9hqLMePEYQ1OHc3/saupgd/Nb1znGwNjKgq774cDHUXc+j8Hnde4P5z7wUhQOEM3zk05b6lpi7G7uJJZMUxB68/7JD/rICzhvWiVSzn3SFktiDBSG/BSG/eQFvKTSTtlNvuXc+b97PR68Bjbua2f59iZS6Tf/XyX5AU4aHKUtlmRXcye7m2PEkwc/VquKwgwvy8Prcd5siifTLN/eRPMBf6/HQEHIj99r8Hk8+LwGv9eD1+OcO3+Lc9qxu5NfrHyOls4kHrP/fvdjMGxrbGfLvvaDrr87r8dQFglQURAiEvTRFncO3banNdb1f6goCDL39R38+F9rGTeggMlDiggHnDc40vbN+31va5zmzPqsNZY85Pp0bGUBQ0rC7G6OsbOpk72tBz8HdRcN+xlSEqYoHKA1lqQtliSeSlMWCVJREKSy0Hmc7D/f0djByxvreXlj/UHrA4+BKcNK+OKsajbsaWP+2j2HPN66x0B1RQGjKyPEk2naYkna46muN/nCfi87mjpYvavlkP/n7teTPsR9UBD0UV7o5K8oCFGSHyDg8+DzOI/xHY2dbNrXxpZ97cRTaUJ+53khlHlDN+R33rBMpS1p64zTdLdx68/kzAt48XlM1/L2eIpdTZ3UtXR2rW/Cfi9FeX5CfudnvR5DWzzJzsZOkocIXxYJMHNsBeeNr6Q0EqC1M0lzZ4INdUlmHPE/mbuMPdwzfw6ZMmWKXbRokdsxDmv+/PnMmDHD7RiSq1JJ+MkYGDkD3ndHj96UxqJ79r/Tubu5k5bOZOaU6DpvPmBZOOB1nggLQ+QFvE6hiqdoz5w6EyliyRSl+UEGFYUZWBTC5zG0xpzr2d7Ywcodzaze1Uxn4ugF6Vh5DJRGggyKhhgYdW53UOa8JD9AIvXmO+3d7WmJsbW+nc317eyo20dlWQlBn5dg1xO4B2Ngw542Vu1spqHdeSf7pMFRpgwvoao4TGemXNa1dLJ8ezOrdjYTS6bxeQyjyiOMH1jA0NJ8KguDDCgMdW3hstaZPrZyZzMrdzhbhbwe0/WiJeT3Zrbeep2SURRmYDSE3+thxY4mlm5rYu3uFjzGEPR5CPg8eIzBWkhbS8DnIRr2UxjyEw54nS0CmTK+v6R4DRTnBagoDFFZGCTs92beLHBepOwvuB6PoaUzQUN7gqb2OB6P6coY8nucLZ9+L36vIZF0ClhrLMn6ulbW7m5hXV0rpZnyOm5gIQVBHw3tcef6OuI0tCVoaHfKxP4X0Pu3GAwuDlNVlEdewNtV5gEqM5mL8vw0dyTZmzlO8Zb6djbubWXT3nZS1mZeuAXxeT3saOxge2MHTR0JRpdHmDg4Su2gKF6DM85jSXY1dbKlvp2t9e10JlKMLI8wqjyfkeURBheHGVQUZkBhiIDvzRd1haHMC/nMlrD2eJLl25tZuq2RJVsbWbqtiS317YT8HkaURRhZnk95JOiUBq+H/ICXioIQ5YVB1q9axrjaSbR0JmjsSLCzqZNtDe1sa3BeIBeFndtKpi3b6jvY2tBOc0eCs0aXcX5NJe8aV0FZ5M2tuolUms372nhjdysb97Wxs7GTnU0d7GmNk05bLJZ0mq6tZKm0pTWWZF9bvOsFc2VhkJFlEYaUhLvKjc/joao47Nw3ZRGS6TQ7Gp0t0K2xJEG/8z/0GENHIkV7PElHPE1Jvp/yghAVhUGSKUt9W4x9bXGaO5KZ/2+Spo4EW+s72FLfzq7mTorz/AyIhhhQGM7MBnC2KqXT1hnjYT/lkSC1VVFOGhxlVHmE1buaeWVjA4u3NGCtzcymcEpmcX6AkrwA4YCHhrYE9W1xGtrjXf/PVBraYkkaO5wx6jHOeKsoCJIf9NHYnmBfW5yGtjht8STtsRTtiSR+r4f8gI9wwHlTqKUzSXOH84aR1+PB6wGfZ//9Z7q2Ju0vBJWFQc4cWcrUkaUMiIZYtLmBlzbUs2JHE0V5fioLQwwoDFHRdR6kLZZk1c4WVu5sZntDO2nrbIU1xjB+oFMAJ1RF6Uw442DT3jaaOhIk0pZkKk0ybZ03IdLO47Yjnur6m+IdbVRVlFAQ8pFKWxo7EjRm7qfBxc6bOgOiIfzeg7eKWes8pupanJLdHk+SH/SRH/RRFPZz6rBizhhZSlVRmB2NHTy6fBePLtvJ5vr2zHNKEmMM5ZEglYVByguCFGbWZ4UhHwOiYaqKnTdy9rTEWLSpnpc31VPXHGNANMSgohDlBSHyA97M1j5nLAJYoL4tztb6drY2dNDSmSASdGYB+b0e9rXF2N0c63pe7K4sEuSMESWMG1BAXtDXVcqmjiylOD/Q9XPptDPjoakjgc9j8Ps81LfGWbq9iaXbGtm4t42w39v1hkc880ZsezxFeUGQCVVRagcVMqo80jV2PR5YX9fGmt3NrK9rI+jzUJSZJdSZSFPX0tk1e6GupZO6lhj1bfGuN8ZS1jIoGmZYaR7DSp035PY/L+x/fuxMOG8YezJj1Jt5U2v/eN3/pkxH/M0ZNV6PIeTzMiAaorIwRFkkQCyZdrb+tieIJdMk006GkN/b9cbh/vWUtZbmziQL39jL/DV1B93nwws9zP/mxcf6csAVxpjF1topBy1XYT1+KglyVP/8Iiy9F766DgJ5PXYzGov9Typt2d7Q0fWkun9aZsDrwe9zCsv+0rK/wOz/PuR3thwFM+dhv/eYpkwezdHGobWWva1xCkK+I07HTaTS7GjsYEA0dNgpUeKuZCrdo9M6D6U1liTvCFPo98uV9aG1TnHdv8XVzRz6PKA73ByL1lqsxfUpoh3xVFfpLo0EGFmW32vHY294LCVSaZZsbaQzkSISdGZ4rXhtEbMvnOl2tCM6XGHVlGCRbKidA4v/COueePMwOCIngNdjGFqax9DSnnsj5EQzxhzT5xH9Xg/DSvOzkEjeqWyXVaDX7eTM+VzvO/+M34nMIf3P/mnqbgsHnM/+9oV1em94LPm9Hk4bXvKWZduCuZ/7cHQcVpFsGDYN8sudnS+JiIiIiMgxUWEVyQavD8ZfCmsfh3ib22lERERERHoFFVaRbKmdA8kOp7SKiIiIiMhR9a4Pgoj0ZsPOgkglzP08PPGdE3Od/jy46j4oHnZirk9EREREJIeosIpki8cLl/wY1j52Yq7PWnj9blj6N5h+w4m5ThERERGRHKLCKpJNNZc6pxOlYROseECFVURERET6JH2GVaQ3q50DdSuhbrXbSURERERETjgVVpHerOZSwDhbWUVERERE+hgVVpHerGAADD/bKazWup1GREREROSEUmEV6e1qL4O9a6BuldtJREREREROKBVWkd5u/KVgPJoWLCIiIiJ9jgqrSG8XqchMC75f04JFREREpE/RYW1E+oLaOfDwl6ioWwibQ26neeeCBTBggtspRERERCRHqLCK9AXjL4VHv0bNqp/Aqp+4neb4fHIeVJ3qdgoRERERyQEqrCJ9QX4ZfGohrz/7GJMmTXI7zTuTTsI9H4Rl/1BhFRERERFAhVWk76gYR0PJLhg1w+0k79zoWbDyQbjge+DRR+xFRERE+ju9IhSR3FE7B5q3w7ZX3E4iIiIiIjlAhVVEcseYi8AbdPZ4LCIiIiL9ngqriOSOUCFUnw8rHoR02u00IiIiIuIyVwqrMeYiY8waY8w6Y8zX3cggIjmqdg607oKtL7qdRERERERclvXCaozxArcCFwM1wJXGmJps5xCRHDXmIvCFYMUDbicREREREZe5sYX1dGCdtXaDtTYO3APMdiGHiOSiYASqL4CVD0E65XYaEREREXGRG4e1qQK2dvt+G3CGCzlEJFfVzoFVc+Efn4BQ1O008jaN2bEDWh50O4b0cxqHkis0FiUXDK1PAjPcjvGO5OxxWI0x1wLXAlRWVjJ//nx3Ax1Ba2trTueT/qOvjEVPKo+TIyMJrp3ndhR5B0qsJb7XuB1D+jmNQ8kVGouSCyLhob32NaIbhXU7MKTb94Mzy97CWns7cDvAlClT7IwZM7IS7p2YP38+uZxP+o8+NRbPu8jtBPIO9alxKL2WxqHkCo1FyQUre/E4dOMzrK8A1caYEcaYAPABYK4LOURERERERCSHZX0Lq7U2aYz5HPA44AXusNauyHYOERERERERyW2ufIbVWvsI8Igbty0iIiIiIiK9gxtTgkVERERERESOSoVVREREREREcpIKq4iIiIiIiOQkFVYRERERERHJSSqsIiIiIiIikpOMtdbtDEdljNkDbHY7xxGUAXvdDiGCxqLkBo1DyQUah5IrNBYlF/SGcTjMWlt+4MJeUVhznTFmkbV2its5RDQWJRdoHEou0DiUXKGxKLmgN49DTQkWERERERGRnKTCKiIiIiIiIjlJhfXEuN3tACIZGouSCzQOJRdoHEqu0FiUXNBrx6E+wyoiIiIiIiI5SVtYRUREREREJCepsB4nY8xFxpg1xph1xpivu51H+g9jzCZjzDJjzBJjzKLMshJjzBPGmDcy58Vu55S+xxhzhzGmzhizvNuyQ4494/hFZh251BhzinvJpS85zDi80RizPbNeXGKMuaTbZd/IjMM1xpgL3UktfY0xZogx5mljzEpjzApjzBcyy7VOlKw5wjjsE+tEFdbjYIzxArcCFwM1wJXGmBp3U0k/M9NaO7nbbsq/Djxlra0Gnsp8L3Ki3QlcdMCyw429i4HqzOla4LYsZZS+704OHocAP82sFydbax8ByDw3fwCozfzOrzPP4SLHKwl8xVpbA5wJfDYz3rROlGw63DiEPrBOVGE9PqcD66y1G6y1ceAeYLbLmaR/mw3clfn6LuAy96JIX2WtfQaoP2Dx4cbebOBP1vEiUGSMGZiVoNKnHWYcHs5s4B5rbcxauxFYh/McLnJcrLU7rbWvZr5uAVYBVWidKFl0hHF4OL1qnajCenyqgK3dvt/GkQeHyIlkgX8ZYxYbY67NLKu01u7MfL0LqHQnmvRDhxt7Wk9Ktn0uM9Xyjm4fi9A4lB5njBkOnAy8hNaJ4pIDxiH0gXWiCqtI73W2tfYUnOlFnzXGnNv9QuvsAly7AZes09gTF90GjAImAzuBn7iaRvoNY0wE+AfwRWttc/fLtE6UbDnEOOwT60QV1uOzHRjS7fvBmWUiPc5auz1zXgc8gDOVY/f+qUWZ8zr3Eko/c7ixp/WkZI21dre1NmWtTQO/480pbhqH0mOMMX6ckvBXa+39mcVaJ0pWHWoc9pV1ogrr8XkFqDbGjDDGBHA+vDzX5UzSDxhj8o0xBfu/Bi4AluOMv2syP3YN8JA7CaUfOtzYmwt8OLNnzDOBpm7T5EROqAM+CzgHZ70Izjj8gDEmaIwZgbPDm5eznU/6HmOMAf4ArLLW3tLtIq0TJWsONw77yjrR53aA3sxamzTGfA54HPACd1hrV7gcS/qHSuABZ/2ED/hfa+1jxphXgHuNMR8HNgP/7mJG6aOMMXcDM4AyY8w24L+Amzj02HsEuARnhw7twEezHlj6pMOMwxnGmMk40y83AZ8CsNauMMbcC6zE2ZvmZ621KRdiS98zDbgaWGaMWZJZ9k20TpTsOtw4vLIvrBONM61eREREREREJLdoSrCIiIiIiIjkJBVWERERERERyUkqrCIiIiIiIpKTVFhFREREREQkJ6mwioiIiIiISE5SYRURETlGxphKY8z/GmM2GGMWG2NeMMbMcSnLDGPMWd2+/7Qx5sNuZBEREekpOg6riIjIMcgcmP1B4C5r7Qczy4YBl/bgbfqstcnDXDwDaAWeB7DW/qancoiIiLhFx2EVERE5BsaY84DvWGunH+IyL3ATTokMArdaa39rjJkB3AjsBSYAi4EPWWutMeZU4BYgkrn8I9bancaY+cAS4GzgbmAt8C0gAOwDrgLCwItACtgDXAecB7Raa3+cOVD8b4A8YD3wMWttQ+a6XwJmAkXAx621C0/MPSQiInLiaUqwiIjIsakFXj3MZR8Hmqy1pwGnAZ80xozIXHYy8EWgBhgJTDPG+IFfAu+z1p4K3AF8v9v1Bay1U6y1PwGeBc601p4M3APcYK3dhFNIf2qtnXyI0vkn4GvW2pOAZcB/dbvMZ609PZPpvxAREclhmhIsIiLyDhhjbsXZChoHNgMnGWPel7k4ClRnLnvZWrst8ztLgOFAI84W1yecmcZ4gZ3drv5v3b4eDPzNGDMQZyvrxqPkigJF1toFmUV3Afd1+5H7M+eLM1lERERylgqriIjIsVkBXLH/G2vtZ40xZcAiYAtwnbX28e6/kJkSHOu2KIXz3GuAFdbaqYe5rbZuX/8SuMVaO7fbFOPjsT/P/iwiIiI5S1OCRUREjs08IGSM+Y9uy/Iy548D/5GZ6osxZowxJv8I17UGKDfGTM38vN8YU3uYn40C2zNfX9NteQtQcOAPW2ubgAZjzDmZRVcDCw78ORERkd5A76yKiIgcg8yOki4DfmqMuQFnZ0dtwNdwptwOB17N7E14D3DZEa4rnpk+/IvMFF4f8DOcrbgHuhG4zxjTgFOa93829p/A340xs3F2utTdNcBvjDF5wAbgo2/zzxUREckJ2kuwiIiIiIiI5CRNCRYREREREZGcpMIqIiIiIiIiOUmFVURERERERHKSCquIiIiIiIjkJBVWERERERERyUkqrCIiIiIiIpKTVFhFREREREQkJ6mwioiIiIiISE76/9L1xKkMOB32AAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "duration, log, fittest_individual_p16 = evolution(\n",
    "    individual_generator=lambda: random.choices(range(16**2), k=16),\n",
    "    fitness_evaluation=evaluate_position_indexed_fitness,\n",
    "    population_size=15000,\n",
    "    generations=250,\n",
    "    crossover_rate=.5,\n",
    "    mutation_rate=.5,\n",
    "    mutation_function=(tools.mutUniformInt, {\"low\": 0, \"up\": 16**2 - 1, \"indpb\": 1/8})\n",
    ")\n",
    "print(f\"Computed in {duration:.3f} seconds\")\n",
    "plot_generations(*log.select(\"gen\", \"avg\", \"min\", \"max\"))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Fittest Individual"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positions: [53, 135, 208, 251, 126, 185, 204, 1, 40, 22, 146, 93, 106, 227, 79, 164]\n",
      "Duplicate queens: 0\n",
      "Fitness: 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": "   1  2  3  4  5  6  7  8  9  10 11 12 13 14 15 16\n1      👑                                          \n2                     👑                           \n3                           👑                     \n4                  👑                              \n5                                                👑\n6                                          👑      \n7                                 👑               \n8                                             👑   \n9                        👑                        \n10        👑                                       \n11              👑                                 \n12                             👑                  \n13                                      👑         \n14  👑                                             \n15           👑                                    \n16                                   👑            ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Positions: {fittest_individual_p16}\")\n",
    "print(f\"Duplicate queens: {len(fittest_individual_p16) - len(set(fittest_individual_p16))}\")\n",
    "print(f\"Fitness: {abs(fittest_individual_p16.fitness.values[0])}\")\n",
    "display_positional_grid(fittest_individual_p16)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr style=\"border-top: 2px solid #444\">"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## III. Row-index Board Encoding\n",
    "The row-indexed genetic encoding makes use of the [Pigeonhole principle](https://en.wikipedia.org/wiki/Pigeonhole_principle) so there is only a single queen per row.  Individuals are genetically encoded as an integer list of $n$ elements. Each element $p$ is in the range $p\\in[0, n)$, derived from the element's column; the element's index indicates the row.\n",
    "\n",
    "The state-space complexity is $O(n^{n})$:\n",
    "- for $n = 8$: there are $8^8=16,777,216$ possible states\n",
    "- for $n = 16$: there are $16^{16}=18,446,744,073,709,551,616$ possible states"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Rendering Row-indexed Board\n",
    "The position-indexed board renderer is reused by mapping values from row-indexed boards to position-indexed boards."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "def display_row_grid(individual: List[int]):\n",
    "    dimension = len(individual)\n",
    "    display_positional_grid([dimension * i + individual[i] for i in range(dimension)])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Example 8x8 Row-indexed Individual\n",
    "\n",
    "The following is an example of a randomly generated \\$8 \\times 8\\$ individual's row-indexed genetic encoding and board representation."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positions: [0, 7, 3, 6, 4, 5, 1, 2]\n"
     ]
    },
    {
     "data": {
      "text/plain": "   1  2  3  4  5  6  7  8\n1  ♕                     \n2     🟥                 ♕\n3        🟥  👑        🟥   \n4           🟥     🟥  ♕   \n5              ♕  🟥      \n6              🟥  ♕      \n7     ♕     🟥            \n8        ♕               ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>♕</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td></td>\n      <td>🟥</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>♕</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td></td>\n      <td></td>\n      <td>🟥</td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td>🟥</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>🟥</td>\n      <td></td>\n      <td>🟥</td>\n      <td>♕</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>♕</td>\n      <td>🟥</td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>🟥</td>\n      <td>♕</td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td></td>\n      <td>♕</td>\n      <td></td>\n      <td>🟥</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td></td>\n      <td></td>\n      <td>♕</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_individual = random.sample(range(8), 8)\n",
    "print(f\"Positions: {example_individual}\")\n",
    "display_row_grid(example_individual)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Row-indexed Fitness Evaluation Function\n",
    "The following method is the fitness evaluation function for row-indexed individuals. Individual fitness is defined as the number of distinct pairs of queens that are threatening each other.\n",
    "\n",
    "Time complexity: $O(|queens|^2)$"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "def evaluate_row_indexed_fitness(individual: List[int]) -> float:\n",
    "    fitness: float = 0\n",
    "    for x in range(len(individual)):\n",
    "        for y in range(x + 1, len(individual)):\n",
    "            if individual[x] == individual[y] or abs(x - y) == abs(individual[x] - individual[y]):\n",
    "                fitness += 1\n",
    "    return fitness"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Evolutionary Algorithm on a 8x8 Board\n",
    "\n",
    "In the following section, the DEAP evolutionary algorithm is executed to solve for a single solution of the $n$-queens problem for an $8 \\times 8$ chessboard using a row-indexed genetic encoding."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Evolutionary Computation\n",
    "\n",
    "A significantly smaller population size and generation count is simulated with the row-index board encoding, compared to the position-indexed simulation."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed in 1.238 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1152x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "duration, log, fittest_individual_r8 = evolution(\n",
    "    individual_generator=lambda: random.choices(range(8), k=8),\n",
    "    fitness_evaluation=evaluate_row_indexed_fitness,\n",
    "    population_size=500,\n",
    "    generations=50,\n",
    "    crossover_rate=.5,\n",
    "    mutation_rate=.5,\n",
    "    mutation_function=(tools.mutUniformInt, {\"low\": 0, \"up\": 8 - 1, \"indpb\": 1/4})\n",
    ")\n",
    "print(f\"Computed in {duration:.3f} seconds\")\n",
    "plot_generations(*log.select(\"gen\", \"avg\", \"min\", \"max\"))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Fittest Individual"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positions: [5, 3, 1, 7, 4, 6, 0, 2]\n",
      "Fitness: 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": "   1  2  3  4  5  6  7  8\n1                 👑      \n2           👑            \n3     👑                  \n4                       👑\n5              👑         \n6                    👑   \n7  👑                     \n8        👑               ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Positions: {fittest_individual_r8}\")\n",
    "print(f\"Fitness: {abs(fittest_individual_r8.fitness.values[0])}\")\n",
    "display_row_grid(fittest_individual_r8)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Evolutionary Algorithm on a 16x16 Board\n",
    "In the following section, the DEAP evolutionary algorithm is executed to solve for a single solution of the $n$-queens problem for an $16 \\times 16$ chessboard using a row-indexed genetic encoding."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Evolutionary Computation\n",
    "A significantly smaller population size is simulated with the row-index board encoding, compared to the position-indexed simulation."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed in 107.635 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1152x576 with 1 Axes>",
      "image/png": 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tT/ncW97yFrzlLW8pGZ/e7WuFIqx1wmEQRSpVCq4fTLA6TU743X5k+SwmkhN1fx+KsBIEQRAEQRBEc0KCtU5IgjVDgrVexLNiDqvT7ITf5QfQmF6sFGElCIIgCIIgiOaEBGudYIJ1Nju7xCNZOSTyCViNVpiNZvjdc4K1AYWXKMJKEARBEARBEM0JCdY6IeWwUoS1biSyCTjNTgCAz+mDgTNQhJUgCIIgCIJYVKgLSH2p9vskwVonHEbKYa038VxcEqxmoxndju6GRlhtRhtFWAmCIAiCIAgJm82GSCRCorVOCIKASCQCm82me5uGVQnmOM4G4FcArHPvc78gCP/Acdx6AD8E0A7gMIDfEwQh26hxLBY2zgYOHAnWOpLMJeEyu6S/G9WLlbUiarG2kGAlCIIgCIIgJPx+P0KhECYm6l/4czFJp9NVicRGYrPZ4Pf7db++kW1tMgBuFQQhznGcGcBzHMc9AuCvAXxeEIQfchz3FQB/AODLDRzHomDgDHBb3GQJriPxXLyoRZDf7cevQr+q+/tkChkAQJutjfroEgRBEARBEBJmsxnr169f6mEsmIGBAezatWuph1ETDbMECyLxuT/Nc/8JAG4FcP/c498GcHejxrDYeCwe3RHW2cwsxhJjGEuMYSI5UbXNoMAXVrw1IZFLFEVYe929mExN1l1Usqhqq7VVEq/LHUEQwAv8Ug+DIAiCIAiCIBZEQ3NYOY4zchx3DMA4gMcBvAxgRhCE/NxLQgBWN3IMi4nHqk+wnpk6g5t/eDNee/9r8dr7X4tbf3wrvnXyW7rfhxd43PHAHXjg3AMLGW7Tk8gliiKsq13ioTIcG67r+6TyKSlCvlIswZ8/8nn80WN/tNTDIAiCIAiCIIgF0UhLMARBKADYyXFcK4CfANiid1uO4/4YwB8DQFdXFwYGBhoxxLoRj8dRSBQQioUqjvXRmUcBAL/j/R0YYcTT0afx0xd/ig2TG3S9V4pPYSw5hqdefAodIx0LHXrTMp2YRlSISt/ncFoUqk8Gn0TIXr9c1vNT52GGGTOTM5hOTzf9saZFPB7HwMAAjkwcwaXMpWX9WYjlCzsOCWKpoWORaAboOCSageV8HDZUsDIEQZjhOO5pAH0AWjmOM81FWf0AVMNlgiB8FcBXAWD37t1Cf3//Ygy1ZgYGBrDWtRYvTb+ESmO999F7sdW0FX//W38PAHAfceObL34Tu2/cDZfFpbktAAzHh4EhwOa1VXyv5UzuuzlcvfZq9O/uBwCsmV2DLzz4Bfg3+9G/ob9u7/Ps4LNwXnZi3ep1ePnyy8v6Ox0YGEB/fz/ue+I+CJPCsv4sxPKFHYcEsdTQsUg0A3QcEs3Acj4OG2YJ5jhu1VxkFRzH2QH8BoDTAJ4G8Ja5l70TwE8bNYbFRo8lOJlL4oWJFxDwBaTHAr4ACkIBh8cO63qf2cwsAGAqPVX7YJucHJ9DupAusgS329oBAJFUpK7vlS6kYTPaYDVaV0wOa7aQXTH2ZoIgCIIgCOLKpZE5rD4AT3McdxzAQQCPC4LwMIC/BfDXHMedh9ja5hsNHMOiwoouaRVDOjR2CHk+XyRYr+u8DjajDcFwUNf7MFFcb+HWTCRzSQAoKrrksXhgMpjqLtRT+RRsJhvsJjvS+fSKKGaVLqSRKWSo8BJBEARBEASxrGmYJVgQhOMASmonC4LwCoC9jXrfpcRj8SDP55HKp4oig3KC4SAsBgt2dc5/NVajFdd3XY/BkUFd78Na50TSK1ewJnIJAIDT7JQe4zgOXpu37p87U8jAZrLBZrKhIBSQ5/MwG811fY/FJlsQWxtnChnYTfYlHg1BEARBEARB1EZDqwRfaXisHgDQtAUHw0Hs6toFm6m4cW/AF8DLsy9jPDle8X3Y/mPZGHKF3AJG3LzEc2JHJLlgBURbcN0twXnREmwzir9JqrD8e7EyOzDZggmCIAiCIIjlDAnWOuKxaAvWydQkzk2fK7IDM9hj+8P7K76PfP8rNcrKIqxySzAAtNvb624JTufTUoQVADL55Z/HyiKsJFgJgiAIgiCI5QwJ1joiCdaMumBlYrTP11fy3GbvZrRZ23Tlscr3v9IFq9Ja3W5rr/tnThVSYoR1TrCuBJGXLqSL/k8QBEEQBEEQyxESrHWkkiU4GA6ixdqCLd7SdrQGzoB9vn0IjgQrFv0pirCu0MJLzBKsjLB67V5EUpG6FkbK5OdyWFeQJZgirARBEARBEMRKgARrHdGyBAuCgGA4iL3de2E0GFW3D/gCGE+N48LsBc33iWajsBgsAFZua5tEds4SrOhL225rR47PIZaL1e290vk07Cb7irIEU4SVIAiCIAiCWAmQYK0jWpbgi9GLGE2MquavMgI94nODYe1qwdFMFGtb1gJYuRHWspZgu9iLdSpVP6GeKqRgNVqlCOtyF3kFXqx0DIgtewiCIAiCIAhiuUKCtY64LW5w4DCTmSl5juWmquWvMla7VqPX3VsxjzWajaLT0Qm7ya6ZzzkUG8JvPvCb6L+vH/339ePWH92KJy89qbnvy9HLuOfhezCRnNB8XdH7RIfwfx7+P5hMTerephJSWxtTcZVgr80LoL65u5IleC7CutxFXqYwHyGuFC3+6HMfxUMvP1T3MUSzUbz9F2/Hpeiluu+bIAiCIFYyH3n2I/j5Kz9f6mEQRNNAgrWOGDgDrmq9CicmT5Q8FxwJYrVrNfxuv+Y+Ar4ADo4eRI4v364mmo3CY/Gg3aZdMfeFiRcQioewz7cPt665FQWhgJ++/FPN93/04qM4FTmF4xPHNV8n59DYIbwYeREnJko/d60kcgnYTfYS+3S7TYyw1iuyXOALyPLZIsG63PM+Wf4qoB0tFgQBj154FAdHD9Z9DJejl3F84jhORU7Vfd8EQRAEsZJ58vKTDbk3E8RyhQRrnQn4AjgydqRI9OT5PA6OHkTAFwDHcRW3T+QSODl5suxrJMFq1+5JGoqFAACfuPET+Pu+v8ctvbfg4OhByS6qBovuhuIhzXHKGYoNVb1NJeK5eEkPVkBmCa5T7i6LRsr7sMojlMsRuUjVEt/xXBxZPtsQgc6+w+Uu/gmCIAhiMREEAelCumjxmSCudEiw1pm+nj5k+SyOTRyTHjsVOYVYLqaZv8rY270XHLiyeay8wCOWjcFj8cBr82paY0OxEDrtnVLkMNATQDwXx8mIuhhO5pI4Ni6Om4lQPTChygRyPUjkEiUVggGg1doKDlzdLMHM/ruSLMHym5zWZ2GLHQ0RrHNW5OX+XRIEQRDEYpLjc+AFftnX0yCIekKCtc7c0HUDTJwJwZH5PFQWtdzr21tx+1ZbK7a1byvaXk4ilwAv8GixtlSMsA7FhoosyPu694EDV3bfR8ePIsfnYOJMVYlP9tpqRG4lErlEScElADAZTGiztdXNElwUYV0hlmD5TU4rWsyi1I1o4yNFWOmGSxAEQRC6YQu9FGEliHlIsNYZp9mJHat2FBVOCoaD2OrdKhUMqkTAF8DxieNI5pIlz7GWOSyHdSYzgwJfUN1PKB4qEqxttjZs8W4pW9QpGA7CbDCjr6evKnsvE6z1tASXi7ACYuGleglWJk7tJjvsRrv42DIXWfJCS1rim0WpG9HGhwnWldAiiCAIgiAWC1rwJYhSSLA2gEBPAKcipzCbmUUyl8TR8aO67MDy7fNCHofGDpU8x1rmMEswL/CqVYkzhQzGk+MlRZ4CPQEcmzimKoaD4SB2du7EprZNGI4PlxXCcuLZOKYz0zAbzBiODYMXeJ2fssJ+y+SwAqhYbKoaWHTRarTCZDDBwBmWfYRVHlXVip5KluAG3BTZGBoRvSUIgiCIlQqbg1CElSDmIcHaAPp8fRAg4MDoARwZP4I8n69KsO7q3AWr0aoaCZUirFaPVIBILZ9zOD4MAPC7FILVF0Cez+PI+JGix6fSUzgzdQZ9vj70unuR5/MYT45XHCt7n+s7r0eWz1bVDkeLRC5RVrB67dq5u9XAbgw2kw0cx8FmtC37VU29bW2Y6KeiSwRBEATRHDBLMN0/CWIeEqwNYHvHdjjNTgRHggiOiDbbXV27dG9vNVqxq3OXtmCdswQD6i1emE23191b9Pj1ndfDYrCU5LEeCB8AIApaFpXVY/Fl7xPoCejeRg9agrXdpp27Ww1M0NlNoh3YZrIt+5uEXLBqie/FiLAu94rLBEEQBLGYsPsmRVgJYh4SrA3AbDBjT9ceBMNBBMNB7OrcJQkivQR8AZybPofJ1GTR40WWYLuYE6tmj2VCUmkJtpls2NW5q6QK8WB4EG6zG9vat0lRWT2Fl5hAZRHkelUK1rQE29uRzCfrUoGWWVZZwSW7yb6iBKtmleB0A6sEF6hKMEEQBEFUC7snL3e3F0HUExKsDSLQE8Dl2GWcnT5blR1Yvj2AkihrkSVYK8IaD8FmtEmvUe77pemXJDEsCAIGRwax17cXRoMR3c5uGDmjrqq/Q7EheCwebG7bDA5cXSKs2UIWeT5ftugS+0z1yGNlNwar0Sr9f7nfJJhYdJqd2kWXGtnWhizBBEEQBFE1bA5CEVaCmIcEa4OQi9RaBOuWti1osbaUWHdnM7MwckY4TA54LB6YDWbVfM5QTKwQzHFc2bExG/BQbAjhRFh63GQwwef06bMEz1UiNhvN6HZ21yXCGs/FAUAzwgqoC/VqYcJqRVmC52zOrdZWXW1t0oU0BEFoyBiW+3dJEARBEIuJlMO6zBfPCaKemJZ6ACuVDS0b0GnvRKqQwrb2bVVvbzQYsa97H4LhIARBkIRnNBuFx+KR/vbavOqW4HiopOASY6t3KzwWD7509Ev42Ss/k4SfXFj73X4Mx4YrjnM4Noyr266WttErWKfSU/ivo/+FD+z5QIldOpFLANAQrBqR5WphNwab0Sb9f7nfJJhI9Vg8utrasG2YLbqeY6AcVoJoLgaGBhBJRfDmq9+81EMhCEIFymEliFIowtogOI7DH+34I/zJjj+B0WCsaR87Vu3AWHIMs5lZ6bFoNgqP1SP9rdaTVBAEKcKqhtFgxHuueQ9ara2YSc/AyBlx54Y7sdazVnqN3+2vGGEt8AUMx4el9/G7Km/DeDb0LH700o9wbPxYyXNMsGr1YQXqawleiTmsLdaWsjmk6XwaiVwCHfYO6e9GjIFyWAmiufjBmR/g3pP3LvUwCIIoA7sfZwqZurufCGK5QhHWBvK2LW9b0Paswm8oHkKrrRWAWHTJY5kXrO329hJL8FR6Cql8qqxgBYA/uPYP8AfX/kHZ5/0uP6bSU5rVeidSE8jxuXnB6vZjMjWJVD5VscgUE7ZqAjeenbMEW8q3tQHU2/lUC4umrrQcVg4c3BY3xpJjqq9hYn+1azUmU5N1/8zU+JwgmpNIKkLOB4JoYuQLvVk+K81PCOJKhiKsTYzUXkZms2WWYIZaixcmAstZgmt9byWsKBN7H/Z/PVZitl+1/SfzSQCA06QuWK1GK9xmd31yWPMZ2Iw2yWK9InJYCxlYjVbYTfayfVjZd7fatRpA/SOhVHSJIJqTqfQUnZcE0cTIz086VwlChARrEyO1l4mXF6xeu5jDKreNlGtpU9V76+jFqnyfWvq3qgnWShFWYP5zL5RUPlWUu7lSLMEWo0X8LGUinCw6zQRrvSMulMNKEM0HL/CiYCXnA0E0LfL7JuWxEoQICdYmxmF2wGvzlkZYrcUR1hyfQywXkx5jr+9x9dT83np6sYbiIRg5sQ0OoC8qK99W/n85UpXgMhFWYC6yXCdLsNxus1IswTajDVajtWzklEVY2TFS9xzWPOWwEkSzMZuZRUEoIJ2vf2VwgiDqg/y+udznIwRRL0iwNjm97l5JAPICj1g2VpLDChRXzB2KDWGVfVXFPFItWqwtcFvcmr1Yh2JD6HZ2w2wwAwDarG1wmBwV+7em8ilMpibFvq1qluCcaAl2WdSLLgFzubt1sASn8+mi72mlWIItRov0WdQmpiw6zQRrIy3BNDEmiOaAnfcCBGR5itwQRDMiF6kUYSUIERKsTY68Wm8ilwAv8GixtkjPs4q5cvEWioekgk0Leu8KVX+HY8NFtmOO43RVF2Y5rtvatyGajRZVQQbECCsHTlNwe23eukVY5ZZgm8mGHJ9Dns8veN9LRSYvtqixm+wQICDH50peE0lH4DQ7pWOpUVWCaWJMEM2D/D6x3BfmCGKlUpTDShFWggBAgrXp8bv8CCfCyPE5RLNRACgpugQUt3jRamlTDb3uXs0CSmq9XuURYa3tgPm+r8Px4vdI5BJwmB0wcOUPz3ZbO2Yzs6pirBrS+bTUgxUA7EZRJC/n3EsWYWVWZ7XoaSQVQbutvWGfV74/mhgTRHMgX+Qjuz5BNCeUw0oQpZBgbXL8bj94gcdofBTRjIpgZZbguYlIppDBeHJ8QRWC5e89HB9GgS+UPJfIJTCVnioRxn6XuA0v8GX3ywRtoCdQ9Ld83+Va6TDY555OT1f+IBqk82lYTbIc1rl/L2eRxXJYWeRY7bNMpafQbm+XXtMoS3C59ycIYvGRL2wu50U5gljJFOWw0v2TIACQYG16mPAcig/NR1hlRZfarG3gwElWr5H4CAQIdYmw+t1+5PgcJlITJc+Vq0Tsd/uRKWQwmZosu99QPASn2Ylr2q+R/pYTz8UrC1Zbae5uLWQKGSnKCECKti5nG46Uwzr3WdQmpizCKonaBvRhXQnfJUGsJMgSTBDNj9z5RRFWghAhwdrkyCvvqlmCjQYj2mxt0sp5PVraSO/NxLJKESUmMntdxbmyeioFh2KildhlcaHN2qYaYXWZyxdcAsS2NgAW3NpGra0NsLwnc0wsss+iaglOR+C1eedFZQOqBDcqP5YgiNogSzBBND/pfHr+/kkLvgQBADAt9QAIbTodnTAbzFILGaBYsAJiAaLDY4fxpaNfwtmpswBQN0swAHz31HcRDAeLnjsZOVn0Gmmbuff9n1P/g+dHngcA3Lz6Zuzs3Cm9JhQLYV3LOmn7mizBcxHWH7/0YxwZPwIAuHXNrdjevr3odfFsHN8/830pymgymPDWq98qWYrVii4BxSLr4Vcexs2rby4qdrVUxLIxPHn5SbzxqjeC4zjV1yhzWJU3vDyfx0xmBu329rKvWSiZQgY+lw9jybGmuOFOpiZxZOwIbl93+1IP5YrmkQuPoM/Xh1Zba03bHxw9iFZrKza1barvwK4QplLzC3zVnJdnp84ikUvg+q7ra3rfydQkDoQP4PUbXl/T9gTRLPx65NfocfZIc5hGkC6IgnUsOdYUEVZe4PHg+Qdx54Y7YTaadW1T4At48PyDuGvjXVInCYJYCCRYmxwDZ8Bq12qEYiG0WdsAFFuCAeD6zutx/7n78fUTXwcAXNVyFTrsHQt+b5/Th7WetRgIDWAgNFDy/BbvlhLxvNq1Gn6XH08NPYWnhp4CL/B4NvQsfnTnjwCIF75QPIRXrX4VAFHgnpg8IW0vCAJCsRD6evo0x9bl7EKPswdPDz2Np4eeBi/wODt1Fv95238Wve6Z0DP40tEvgQMHjuPACzxsRhvefc27AczlsCr6sALzk7mh6BD+7tm/w8cCH8NbN79Vz9fWUH5w5gf40tEv4YbOG9DrUa8EzaoEl8thZXm/7bZ2GA1GWAyWukZBeYFHls82VYT1J+d+gi8e/SJ+3fNruC3upR7OFUkkFcGHfvUhfHjvh/H2rW+vaR//OPiP2OzdjH9/zb/XeXRXBpF0BC3WFsxmZqVeyXr4wpEvYDQ5iv+9639ret9vvfgtfOfUd7DPt09aLCSI5chHnv0I+nv78fEbP96w90jn0/C5fOK/m2DB98TkCfzDr/8BHfYOvNr/al3bHJ88jo8Pfhydjk7c7L+5wSMkrgRIsC4DWBRyrWctjJwRDpOj6PmP9X0MH+v7WN3f12Qw4eE3PVzVNmajGY+8+RHp76+88BX8v2P/DzPpGbTaWjGZmkSmkJEis363H49degx5Pg+TwYTzM+cRSUewt3uv5vtYjVb88i2/lP7+0yf+VDVvlj323P95Dh6LBzf/8OaiiG6mkClqn6O0BDM7dDwXr+p7aBSDI4MAgMn0ZHnBOhdhZZ9FmcPKbIFs4ljv3rPs/VoszSNYpzOiSI+kIiRYlwjWviqerf1cms5MS32aieqJpCLocfZgNjOLVEG/JXgyNYnZ9GzlF5ZhMCxet0LxEAlWYtkiCAJmMjMNvwalC2np/lnNwlKjiGVjACClpVWzzUxmphFDIq5AKId1GcD6oUYzUXgsnrJW0GYk4AtAgID9o/sBlObY+t1+FIQCRhOjACBZj1nLG72029pV+7JG0hGYDWa4zaJIkfeWFQShJIdVGZVkr22GfK9kLoljE8cAaBebYjms5drasG1ZD1+b0VbXVVxmYWqmHBxWYbsevXuJ2mCTnWS+tskeL/CIZWNNcTwtRwRBQCQdka691SwkRVKRqiarciZTkzg3fQ6Adm0Dgmh2kvkkCkKhqsWeWpDnsDZDNW+2YJ/IJvRvM7cwWet1gyCUkGBdBvjdfsSyMYTioRI7cLNzTcc1cJldkhCVijW5e4v+zx4fHBnEWs9ayQ6jl3ZbO6ZSUxAEoejxqdQUvDavJPLlObPsRqBmCWY3JPbaVG7pBeuR8SPI83kA2sWmpCrBZSzBbNtGRVjZvprJEsxumgst0kXUDvsNal38SeQS4AW+KY6n5Ugyn0SmkMFq12oA+iM3giBgKj2FdCFdUz7d/vB+6d8kWInlDFv4bPQ1iOWwAs0hWJlQrcZpxl5LgpWoFyRYlwFM1J2KnCrJGW12TAYT9nTvQXBkTrDGQuDAocfZA2C+SFMoFkKukMOhsUNVR1cBUXxl+WzJBTWSjhRZ0PxuP0biIyjwBelGoGYJZpM5JqRrjQrVk+BIUCpeUC7Cygs8cnyuqEqw8ubKtmWFq+otWEsirE0gMNhNc6FtkIjaYZbgWu107DekCGttsGO/xyVee/V+j9FsFHkhL/27WoLhIDwWDzrsHSUtzAhiOcGO/0aKyByfQ57Pw26yw2wwN4dgzSWK/q8Hdp1nIp8gFgoJ1mUAs3DNZGaWnWAFRHtvKB7CUGwIoVgI3c5uqdJcp6MTJoMJoVgIxyePI5VPoc+nXXBJDWZvVQoS1m+U4Xf5kRfyGEuOSZEe1tpF/m82mZMirE1gCQ6Gg9jVuQut1tay1lY2sZT3YVVOTCPpCCwGi1SJ2WaqryWY7UvKYW0CgSEJVrIELxkLjbAuVnRjpcKOfRZh1fs7yK+p1U4+BUFAMBzEPt8+rHGvoQgrsayRFs0aeA1ii+V2kx1Wo3XZClaKsBL1hgTrMkDeomZZCtYeMWIaDAcRioeKWuEYDUaxCnI8hGA4CANnwO7u3VW/BxOlSkHC+o0y5H1i2U1HLYc1lU9BEASp6NJSC9ZIKoKz02fR19MHr81bNlKYE3IAxM9hNc1VPFaxBLfb2yWbtM3YmAhrq7VV9f2XAjbRlrf1IBaXBQvWRZgsrmTYsb/Kvgpmg1n39yi/ps5mqyu8dCl6CaOJUQR8ATEdgyKsxDKG3UcaOR9gC7ysDsVyFazstSRYiXpBgnUZ4DA7JNG13HJYAWC9Zz06HZ0IjgQRioVKesT6XWJeaXAkiO3t22vqd8psv/IcRZZ7pbQEA2L1X/mNgSG1tcmnEc1GpVXCpa5MemD0AAAxWt1uby+bi8kEq8VogdlghokzlUZYFVHnRkVYHWaH6vsvBRRhXXrYZK9Wez0J1oUhrw5uM9p0T4Tl50y1EVZWu6DP1we/y4+xRHP0lSSIWliMtAT5QrrNZGuKKsFS0aVaBCtZgok6QYJ1mcCE1nKMsHIch4AvgMHwICZSE0URVkD8bBdmL+DE5Ima8leBecFaZF/LRpHn80XirMvRBRNnQiiuHmHlOE6azMnta0sdYQ2Gg3Bb3Njq3Vq2IjIgi7DOiXC1/FRlXm+9I6zyYlb1zo+thVwhJ/1+lMO6dNTNEtwECyDLEXbst9naYDPZarMEVxktCYaDYm9utx9+tx8CBIzER6raB0E0C1IOawNFJLtfWk1WWIyWpoqwUtElYikhwbpMYFHJ5ShYATEyyPpyqUVYWbn4WgVrq7UVHLgiIafsNwqIRaB8Lp9oCS6UClb2dyqfwlB8SBrfUgpWQRAwODKIfd37YDQY0W5vr2gJthgtANSjp5FUsU267n1Y527mVpO17tHbWpDbGCnCunQs1E7HfkdWlISojkg6ghZrC8wGc1XnZa2CtcAXcCB8AAFfABzHzadjkC2YWKawwnENjbDO7dtutFflhGgkTLBW4zSjCCtRb0iwLhOkCOsytAQDxX1V1SKsgBjp29m5s6b9mwwmtFpbi3IU2b/l4gwQqy6Xy2Flf6fzaSnCuqlt05JWCb4cu4xwIix9h16bF/FcXPVGpoywWo3WIjHKCzym09OlEdY63oAz/JxgNVjrHr2tBTbJ9tq81NZmCZH6sNZaJVg28WmGSdxyYyo9VVNl8Kn0FNqsbQCqm3yejJxELBeTrlvyivAEsRyRu0SULfTqhXxespwjrJTDStQbEqzLhOUeYV3lWIWNrRsBlBesN3TdIEUGa6Hd3l4xwgrM5czKLcFGhWCVWYK9Ni/a7e2qUaHZzKxUlKneXJi9gCcvP4knLz+JH5z5AYD54lVs0qlWQEgZYbWb7EUT02hGbFFRksNahagcig1J0XI1SiKsGvvOFDI4P31e93urcSpySnPywCbZ6zzrkMglllxAX6nUq+jSQvaxWMxmZpvO+ip3VlSzSBVJR7DKsQous6uqySfLX93r2wsA6LB3wGq0NuyaWQ2TqUlMJCeWehjEMkN+/FcjJIdiQ0jy+hbq5M6vRkVYT0dOVyW4mVCtRbDW2r+ZIJSQYF0mbG3fCg6c1JN1OXJL7y3otHdKq/WMNe41cJgc6O/tX9D+ldVzlf1GGX63HzOZGUymJgFoRFjnKhrbTXbVCfKXX/gy/uCXf7CgMZfjfU++D3/59F/iL5/+S3zv9Pew1rMWa9xrxM9jV6+IDBRXCQZKJ6ZsmxJLcCGt+wb27kffja8e/2rZ54tyWI02pArlxcVPz/8Uv/Pw72gKYC2OjB3BPQ/fg/2j+8u+hk0y1resB0C24KVioZbgWieLS8EXjnwB733yvUs9jCLkBeiqirCmxMisx+KpWrBu8W6RrjUcx0kF9paajz3/MXzs+Y8t9TCIZUat16D3/PI9+OXsL3W9Vr6Q3ogI67npc3jrw2/VvGcqWYglGKAoK1EfTEs9AEIfW7xb8Mw9z6DN1lb5xU3Kn+38M7xz+zuldioMh9mBR9/8aE3VgeW029rxYuRF6e9IOgIDZ5DaqzBYRPf8jBjZU4uwpgophGIhXLfqOthNdiRzSQiCUDT2ieQEwokwkrkkHGbHgsauZCI1gTs33Inf3/77AIBuR7f03uV6zgLzfVhZtWOrqdgSzCyxSkswL/DI8bmKEW5e4DGeHMdEqnx0Qll0SatAxWhiFHk+j4nUBNwWt+Z7q/Hc8HMAgPHkeNnXKAXrVGpK6kVJLB7sd8jxOeT4HMwGc3Xby+yozR4lj6QiTWc/l1cHtxltuscXSUfQ6+nFdGZatyU4mUvi2PgxvGPrO4oeb5bWNmPJsYZZOomVSywzv7Cayqd0zVlyfA6jiVH0OvUFG6T+8A2qEsycH9U4DORtbZTzoHLEc3G0Wduk60aHvaO2ARPEHBRhXUYsZ7EKAGaDuewFvs3WBgO3sMNR2e5lKj2FVmsrjAZj0euYvVoSrIoIq9VkRSKbwGhiFH63Hw6TAwKEkpXORF68iNd7Apbjxaq2azxrsMW7BVu8W9Bqa5WeV2vhw8gKovWGCVZlJEUt6sw+vx6LYDwXhwBBs7x9NVWCpXYzNVbv3R8WV4m1JtLsOYqwLh3ZQhbpQlpaPKolyipfpW92wZrMJ5e8FZacTCGDWC5WW4Q1XX2E9ej4UeT4XEkRPb9bjLAutViMZqJUDIaoGnkBP73nz0x6BsB8bYdKsPtnoyKs7P5XbbTUxJkgQNB17RYEcY7gc/kAUISVqA8kWIkVg9fmLcpRjKQiJfmrwHyE9eWZlwHMizuG3WjHxehFFIQC/C7REgyU9o9MZOcEa50tbmwiVS5fmYlNLUsw+0x2o13VElwUYWWCVccNmI1Nt2CtkCu3kP6o0WxUiqhr3RClCKtnTrBSa5tFh/0GXY4uAEAqV5tgZe6CZs9hTeVTSBfS4AV+qYcCoLQAnd4c1mQuiVQ+hXZ7dYI1GA7CbDBjV9euosdZRfjpzHSVn6C+RLNRmkQTVRPNzF+Dqu1jzBaTK1HUh7UBOazs/qc3HzVbyCLH57DKsUr3duza1+PsAUCClagPJFiJFYMytzOSjpTkrwKA2+JGi7UF8VwcZoMZJkOxM95mskkXZZbDCpROktlr6i5Y5y7u5SpC20w2OM1OXZZgtQirkTMWRbqZJVqXYJ0bWzxb/qaVKWRgMVjEnrYV+j0uJMJ6cPSgJAgqCVa7yY5OZycA9cg00VjYQke3sxtA6eKPrn1ko5LgbfYcVnbMN0skWEoFqLJKMDsvvTYvPFaP7qhkMBzErs5d0rWTIbW2WcI8VuZgoWIwRDUIglB0Daq2j3FG0HfNkhddamSEVWvRWQ6b57DPrUewsn2z6z1rB0QQC4EEK7FiUFbPnUpNlbS0YTBbsNIOrHys190r5acqo0LMUlNvS7AkWDUqQnttXtWopGQJNs3lsBqtRZGUqfRUif2afV49N2CpNYmG4MjkM9L720zaK8QsJ6gWETk4MigKUXtnRUuwx+KB1WiFy+wiS/ASUBJhrTJCygs8YtkYOh3iokOzCMFysGvDUrbDkqN0VujtwyptV4UleCo9hTNTZ1R7ajdDaxv5tYIiP4ReWK946Rqks8o2u7fptQSn8ilw4GAxWBoSYWXzI72Clb2OfW49VmK2oN3joggrUT9IsBIrBtUIq4olGCju/aqERSfNBjNW2VeVtQQ3LMJawRIMiBNItbY2ygirsq2NvPAKg30Hem6MbGyVIqxShLdCH9aFRFj3h/djT/ceeO3eihFWFq1ut7eTJXgJkASrszbBmsglwAu8NGnSqjzdDLDPV4v1uRHII6VA5fNS2k4mdD0Wj66oJMsrVxOsq91isbOlLLwkv1ZQHiuhF3asVLtoxs49vZbgTD4Dm8kGjuOkCGs9c77ZOa3XEqwUrLoirHP1PXxOymEl6gcJVmLFIK+eK+VeqViCAe0IKxOoq12rYTQYVS3BgiA0PMKqVYFQ2XOWIfVhNYjVfksswSoivqocVj0RVrlg1Vt0qcqoZzgexsXoRQR8gYqRn2g2Kol/r81LluAlgFnCJEtwlQWJ2O/L8qjqXTmz3rBrRTNHWAtCATk+p71dqjjCClSefAbDQbgtbmxr31bynN1kR4e9Y2kjrFmKsBLVo7wGVdPHGKgih7WQluYc7N6c5etnXWfntG5LcLbYEsxqd2jBXtNibYHD5KCFIaIukGAlVgzyCKtav1E5UoRVwxLMogGSJVgmWDOFDPJCHibOhOHYcF2Lq+i2BKtECnNCDlajVSo7bzPakBfy0sR0Kl1qk2Y3Rz03YDa2VD6FPJ9XfY0ywip/fzmCIEg3smpFZDAcBIB5waplCZYJ1nYbRViXgoVagtnvy7bXO1lcCgRhvpJmsxSHiqQicJgc8xNhnXnr8usocylonWuCIGBwZBD7uveVVGdn+F1L29qGLMFELSivYdVGWKuxBLP7J1t4rqctmN1r9QpWtujGIqwseqoF27fD7BBz3+k8I+pAwwQrx3G9HMc9zXHcKY7jTnIc9xdzj3+c47hhjuOOzf33+kaNgbiyYDmKU+kp1X6jcphgtRvtJc+xyRyLwkqWYFlUiNli1reuR5bPavYBrRbJElym6BIgfq6ZzEyJaGSClcHEdyYv2orULMHs9bpyWGWTvXI3PGWElb2/klQ+JVmYqxWRwXAQ7bZ2bGzdWPGGyHJYgfKRaaKxLFiwZovteM0iBNVIF9IQIFr4minCKl+o0uuqmEpNwWPxwGw064qwDsWGEE6EVe3ADNbaZqmQj5+KwRB6qdUSzOYiWSGry9qbzqel81Pr/lkLeT6PmcwMgBoirHPpHFrpQNI2c/Mjl9lVVXVxgtCikRHWPIC/EQRhG4AAgPdyHMc8Qp8XBGHn3H+/aOAYiCsMFnmUrGzlBOucGGXFgeSwm0SvW2z07TCVRliZeN3cthlAffNYWVVbs8Fc9jXttnYIEKSbD6NEsLJISiGNZD6JdCFd1hKsK4dVduMpZ+tURljZ+5fbl9PsrCrCKggCguEgAj0BcBynzxJsnbcEz2RmKlohifoSzUThMDngtrgBVC/kJDuefRU4cE1ddEl+nWgWYT2VnqqplZU8hUCPYJWcDz3agnU0MYpcYWnOQbIEE7WgXDTTHWGdWyBV6+WuRqaQke6bFmN9I6zT6WlpMU130aW8ouiSjms327fT7KzogCIIvTRMsAqCEBYE4cjcv2MATgNY3aj3IwhgPoImr26pRrezG0bOqFp0qVyEVT75ZCuIW7xbANQ3j1VuYS2HPF9XTrkIayqfKunFyJAswVXksALliy8oqwSz91fCohvrPeuRyqdKBHCOz6larV+afglT6SkpiuOxeJApZFRv6qyFhdwSDMw3c68HOT6H0cSo6n/T6cr9JgVBKGuvXgoKfAEFvlDXbdiiQbkWUZWQFyKrVHl6qZEfx9Xm6uqhwBd0pSDMZmal43A8OV50LdRaSJITSc1HZtmij1ZUMhgOwuf0YY17TdnX+F1+CBAwkhip+BkaQT0swbzAL5ngbhZ4gV/wdauZrnuVKLEE681hld2j9Yi9dD5dYt2v1/WOLQy7zW79RZfm8lHbrG0wGUy6IqwlglXjPBME4Yo/l5Qs9PfO8bm6FupqFhYlh5XjuHUAdgHYP/fQ+ziOO85x3Dc5jmtbjDEQVwasem45ccYwGUzodfeq2m5ZsaN1LesAzEdY5TcbdkHe2LoRBs5Q3whrJqppBwZKKyIz8kK+SLAy4ZjJZ0oKrzDYTXGxLcHsJra+ZX3JZxEEAW988I342vGvlWwnz18FZJEflVVcZcXlct/bQvi7Z/8Ov3H/b6j+9+r7Xo0XJ1/U3P5HZ3+E1z3wuqa5wfzl03+Jfwr+U1XbvO+p9+GfD/xz2efZIozNZAMHrmZLsMfqgc2o3dt3qWl0hPX//Pz/4KvHv6r5mqHoEPrv65eOwwuzF6QICVCFJTg9JQndShFWQRBwcPQg9vn2STn0aqx2ievWw7FhzfduFMzB4jQ7a478fPfUd/G6B15X19oFy42vn/g67nn4npq3PzR6CH3f78NkarKOo2ocs5lZGDgD3BY3zAazrgVeXuAxnZ5Gh70DgL7rQaogy2Gtc4SViec1njVV9WHlwMFhdsBldunuw8oCApVSdv733P/itfe/tqldM4vJwdGD6Pt+Hy7MXqhp+2Quidfc9xo8dumxOo9s6TE1+g04jnMBeADAXwqCEOU47ssA/gmAMPf/zwJ4j8p2fwzgjwGgq6sLAwMDjR7qgojH400/xiuB9FQao8lRHH/5OOwGO3797K/LvvbtrrfDmrGW/G6cwOH/dv1fDB0bwhCGAABGGHHm5TMYmBJfeyJ5AgDw8osvo9XQisMvH8bAbPF+auXy+GUA0DyexnNizuzzR55H9qX5CoKpXArZfFba9nzyvPi6A89jpjADALhw8gLy5+ZXtguCGBk7c/4MBia0P8NwZBh2zo6UkMLg4UHM2GdKXjMdm4Y1LX6v55LnpPcPWYtF/QvJFwAAwpQo1J749RNYbxXFa6KQwFBsCA+efBCbpzcXbfeLsV+gy9SFMwfP4AzOIJQQ9/vEc0/AZ/EVvXYsNyaO++VhDIwN4FL6EgDg6f1PY9Q+qvlZ9XJ85Dh6Lb242XVz0eM8eNw3dR++/dy38YbWN5Td/ldTv8JYcgy/eOoXcBqddRnTQjg1egomzoSB7EBV2wybhjGQFrdRXg+HxofAgcOvnvkVzJwZZ185i4EZ/fs/MX0CBhhw4LkDQB64GLrYtNfbi5mL0r9PnD2BznBn+RdXiSAIODd1Dva0HVumt5R93TPRZ5AX8vjttt+GjbMBHLAtvk36zs6lxfMyeDiIiK384s1YfAxrhDUYGBiQrhPHzx7H6rFSs1SikMBMZgZchNP8bUZz4nn362O/RvZc/aqflkN5LL40+RKsghWcwOGlyy9hIDlQdttyPDD6AMYz4/jpkz9Fm+nKXHN/buI5nE+ex9NPP625QFGOZ2PPIl1I4+FfPYx11nX1H2CdORM5Axtnw6+e+RVMggnnL53HQGxAc5tEIYG8kIeH92ASk/jVr39Vco9SMjkzCcEkYGBgAGdTZwEAwYNBjNsWXifjQPwAAMCasiKZS+Kpp58q6smuxtmps7ByVvzqmV/BkDfgldArFa+9bJtnnnkGs1OzmEnNlN3m6amnMZWewr2P34st9vLXtCuFB6YeQI7P4VvPfAu3em6tevvR3Chi2RgeOfoIrBdLU96Ws1ZpqGDlOM4MUax+TxCE/wUAQRDGZM9/DcDDatsKgvBVAF8FgN27dwv9/f2NHOqCGRgYQLOP8Urg1LFTeO6F52BuM6OL66r5N7kNtxX97fiBA6t6VqF/n7i/+CtxYAJ4deDV+FXwV0gX0nX7/b/00Jew2rVac3+xbAz/9IN/Quf6TvRvn3/dl+77Ejo8HdK29rAd//3Yf2PbddvEFbsJ4I5X3VEUbQEA03dM8PX60H+99mf41//9V/idfpybPocNWzegf13p6z/zwGfg7/Sj/+b+ovff072n6HXT56aBCeDWnbfi4YGHsWbrGvSvEfd3cvIkEAJCuRB2BHZIkfJcIYcP/fBDuHvj3dJvYR42494n7sWWnVuwq3NX0Xu8MPECMALs27kPr/a/Guuj6/EfP/kP+K/2o/8q7c+ql3+47x9w05qb8Hd9f1fy3Kmfn0KYC2v+lo8++ygQA7besBUbWjfUZUwL4RM/+gSmc9N4zWteo3simv1+FnlLXvqcyuvhF376Baxxr0F/fz9c97nQ4etAf1+/6r7UeHbwWbRmW3HLLbfgcz/5HFq9reh/jf7tF5NgOAjMrYX0rO1B/3X9ddt3Kp9C/nt52FvtmsfUA089AH/Oj0/c9QnV570TXuAXwJZrtuBm/82qr8kWskh9N4UdV+2QPoPz+054e7zo31v63uyc7d/ZL53HakymJvGpH30Kq69ajf4t5V9XL5TH4k+e+gk6Yh0wckY4XI6qr9vxbByXfigufPVe04vd3bvrONrlw72P3gs+yWPfq/ZJlfSr4dzxc8AUsPGajXjV6lc1YIT15Re/+gXaJ9vFa9iPXOjo7kD/jf2a27w88zIQArav3o5XXnkF23dtx45VOzS3+fef/Dv87X70v7ofrlEX8Etg23XbNAuZ6eXSyUtABNizcQ+OHD+Cfa/aB6dZe5H0qeefgqfgQX9/Pzoe6oDL5ap4zjz+3ONoGW1Bf38/Xjr+Ep4++jRuuvkmmI2ldTl+/szPgRiQ7Eyi/wbt/V4J/MeD/wEAGHeM1zSnPDh6EBgBuFZOdfvlrFUaWSWYA/ANAKcFQfic7HH58tKbAGj75QiiCph97fzM+bIFl2rBbrKrFl1ymp11r3qpJ4fVZXbBbDDrzmHNFOYtwW220oiAzaTPZhnNRtHj7AGgs+iShvVQsgR7xKiqvPDSUHxI+veB8AHp3y9MvIBUPlV0867KEjx3fNSrtU2BL2A6PV02V3qfbx9enHwRsWys7D7YGJulenEilxBznnUWwirwBcRzcc3XywtfOUyOmizB7De0m+xN3Yc1lWucJZgdK1oWuzyfx8HRg5qFj6TzUiMPj/2eXvt8WoVWPho7Z1kF9nK0WMSUi6UqxMKOJY+1tmIwh8YOSdHmpWzPs9Sw46PWPGC23XIpyCO/BtlMNl05rOw+w84JXZbgfEpK06l3leBIKgKLwVJVxd9ELiGJWqfZqSsvX74N+85ms+q57+w4Co4EK3+AFc54chwvz74Mh8mBI2NHkC1U70Bh3+dKvDY1Mof1JgC/B+BWRQubf+U47gTHcccB3ALgrxo4BuIKg02uLkcvl81frQXlJJvlcTDBGklH6lZgRU8OK8dxqi1aylYJzqcRSUXQYm1RrT5sNVor3oB5gUcsG4PPKa45lS26pFIlWC0HJ5qNwsAZsMYjFmiRi0i2AOAwOaScVUCMXhk4Q1G0VuoPqTJxkuc+AuLvZTVa6yYOpzNi1cVyiyN9PX0oCAUcGj1Udh9sjM0gWAt8QTrO9d7wmBhP5BJl85Bi2di84DTbqz5XlJPFVGF55LDWu+iSNMnXEAkvTr6IRC6BPl9f2dewdl5aeWNqheu0Kn6yc5YVqyuH2WiG3WRfsgq9bPGk1nYbwXAQNqOt7rULlhvsel1rayA9x3IzEcvMX8OsRquunEsmHphg1XM9kLe1qXsOazoCr90Ll9kFQH9PVfZ6p9mpK4c1notL21TKfWfH0ZmpM7qKFK5k9ofFMj/v2v4upPIp0SFWJez7DMVCTVMXo140skrwc4IgcIIg7JC3sBEE4fcEQbh27vG7BEEIN2oMxJUHm1wJEMpGvWrBbrIXFV2KZ+MwcAbYTXZpgjYcX3gRkRyfQzKfrBhhBcTPWiJYeY0qwbICKkpsJlvFVdxELgFe4NHt7Aaw8CrB0UwUbosbFqMFbou7RLB6bV709fRhcGRQuvAGw0Fc03GN1B4F0L4hSoJ17jUcx8Fr81bVRkcLqX1Sme/1ulXXwW6yF4nucmOsV9R3IciP8aHYkMYr55F/72qiO1cortSsdCvoeo9MFG6r+JvrnSwuFew7NHLG+kdYdUSlguEgOHDY27237GvY+ak1PrXWYFoFVNg5q8ce6ra4l1awWhYgWEeCuKHrBnQ7uldkFEMPuUJuwYJTj1ugmZC7ROwmu65rELsesjmCnutBppCZ78Na5yrBrA87i36yCsBaxHNx6fUus0tXsaZkLjkfYbWWd0AB4ne01bsVAgQcGD2g+porhWA4iDZrG3536+/CwBk05w3lYMdcPBcvaXu43FmUKsEEsVjIJ1cNtQTnk3CanOA4Tlo9rcdqu9LCqoXX5pWqITO0+rDKW1QosZvsFSOsbGLhtXlhM9pUV4t5gUeWz+ruwyq36srFTigegt/lR8AXwEhiBEOxIcSyMbw4+WJJLg8Tr1qWYGZDlN6rTuKQjbnc92oxWnB91/XagjXTPIJVPhnRezwXCVaVz8CsYHJLcC19WIvseE0sWNl1wmvzNtQSXG71PBgOYmv7VrTaWsvuR0/vZfZbyo9tLZEXioUq2oGL9rNUluDMvGDVsuqrwSx7AV+g7qkgywn5gh9ZgssTSUVg5IySK6nS9YAXeGQKGckBwSKselvoVIL1Y2ZiUm/FX7klWI9glYtcrQXlHJ/DTGYGN/tvhtvsrkmgrRQEQUBwJIh9vn1osbbgmo5rahOsKk61lQIJVmJFIY901dUSbHYU5abFs3E4LeIFma2e1mO1XWlh1aLdXiq8ckJOip4AxTkw7Galhp5WIXIxXc4axHIu9OawytvNyCdBbPLLxGkwHMTB0YMoCIUSq6PJYILL7CobYbWb7EXFHtSs1LXCxqy1ONLn68Mrs69gLDFW8pwgCNK46xX1XQjynCbdglU24VT7DMood00RVnkOq9He1H1Y5YK1WmFeCfZdFoSC6r6TuSRemHihYoGWhliC5xaZ9FBrdHOhSA4Wq5jDmsqnquoBySx7gR5RsOp1Iaw05NfPWgXncrIEC4IgLXQA4v1SryW4zdYmuQ4qXQ/YPiWH0tyCby25jGqwRWtm1602H1WvJVgth1Xtd2b90DvtndjTveeKzmO9MHsB46lx6drd5+urWP9Cjan0FCwGcaFjpTlASLASKwqn2SmdrPWOsCr7sLKLfou1BS6zqz4R1qz+CGu7TRR58kiLsg+rvLgKswOpYTVVtlnKxbTLom4NYkKCjYE1QC+Xw8o+p9fmlcR3js9hNDEKv9uPtZ616HZ2IxgOIhgOwm6y47pV15Xsq9wEWK2AlVpkulbUbJNK2A1o/+j+kufShTRyvDhhboYcVnlOk96bXaUIq9I1UK1gFQShKAdWb4GwpSKZS8JkMImCKNcYSzCgLhQOjx1Gns9XFKwmg6miZXkqPQW7yV5k8S13nuX4HMKJMHrdvXo+RsXejI2CTf5YhBUoXwxGDWbZu7rtavS6ezGVnqp7nvJyQH6eXwmW4FQ+JbansVbn8mD3XNbLvdJ1i0VSmVCVIqx1cJTwAi+lBbFzWm8+qlywpvIpFPiC5jZFglXDEizvDR/oCSAUD12xi0CD4UEAkIrlBXwB8AIvVv2tgkg6gm3t2wBQhJUgmhpWjAgon1dYC8pJdiKXkC76zBZclwhrFZbgdns78kK+6IavtARbDBZw4BDNRBHLxcpHWE22ilEruZh2mBy6BKvZYAYHrmwOK7uZyS3Bo/FRFIQC/C4/OI5DwBfA/vB+DI4M4oauG1RL45er+KlWwIpFc3mB1/y8eoikIzAbzHCb3WVfs6ltE7w2LwZHBlXHx6iXiF4ILKdpjXtNbZZgFdGtdA04zI6qJvmJXAIFoYAWq2jr1lMgbClJ5VOwm+w1RZIrUSRYVSb6g+FBWAyWkvZOSjiOq/g9qi1weawepAvpkojPaHwUvMBXZwleAqHCCgTJBaveCKEgCBgcGcQ+3z4YOENdnTXLjaII60ItwctAsCoXkm1GnZbgdATt9naYjWYYYKh4PWB1JNhCL7uP1iPCGsvGkBfyaLe3zxddqmDvFQQByVyyqOgSoB0p5gVeXNC3iNtIKTsqv7N8wVfuproSCY4Esca9BqtdYo9rPfUv1IikIljtXo0Oe8eKuzaRYCVWHGySVc8Iq7JKsDzCCoi24HqsDFZjCWaWZ3bR5wUeeRRHWDmOg81kw0hipGgbJXZj5cm1XEy7LC7VkvjshsvGwN5fjyU4lo0hV8iVtMcI+AKIZqO4GL1YNnJUTYS13TYn9OuQOzWVmoLX5tXsV2rgDNjXvQ/BcLAk75CNuZ6VixcCi7Bu9m7GeHJcl/VW/hkaYQlW276Zc1iT+WTjBGtGW7AGw0Hs6tolOSu0qFRojVUUlVPO3ieds9VYgpcgd5GNu8XaolldXI1XZl/BRGoCfT1iSkI9axcsN9h5bjVaa/odWSssYHnksMoXOoDqI6wAYDVYKy7Usern7Pw1GowwGUx1WaCTFwiUii5VEKzpQhoFoVBUdKnSduya5zSJ25gNZjhMDtXzTGqdZfNinWcduhxdV6QtOMfncHDsYNH8xmw044auG1QXurVgUXS/a+Xl2JNgJVYcbJJV1wirohWH3PICiJOX4djwgqN2VVmC5wQ5EzrK6CbDZrRhOCZWMNaqElzpBiwvnuM0OVVXWaUxyPJo1QSGMidI/lnYRZbZC/f59knbVStYZ7OzqpZg9l4Lha2gV6Kvpw+TqUmxkbwMNua1nrVNlcO6uW0zBAgYiY9U3CaaicJisKDb2a1edEkx2WNCTm/J/ZLoxpwboB4R8kaQyqfgMDlqKi5VCS1L8GRqEuemz2m2s5FTqdCaWlXxclFJqaWN3gir1YNkPinZ4RcL+aJbpXYbSlikg12DpAjrCpsU6iGSisBusmOVfVVNEVJ5Xt6yirDKLcEVRKQgCJhKT0n3GytnrWwJZjmsisKJ9YiwSgUC7V5YjBaYDeaKlmAmTOWWYPnjarB7CKvxAYjfm1r7I7mIltxUo/ub9treKE5OnkQilyjpnR3wBXAxehGjiVFd+0nmkkjlU/DavCuyKBwJVmLF0WHvKMm9Wih2kx1ZPivlbsjzOgBx8pLls+i/rx+vue81uO3Ht+Ho+NGq30etqm052GSSXfSVBY8YNpNNarlTTlwp7YFHx4/inY+8s0hoRjNRGDkjHCYHnBaneoR1TrCyHBz2b+XNXZkTJBeRoXgIZoMZq+yrAIi/59VtV8Nr82JT2ybV8WtagpUR1rnvYCI1obqvatDKC5ZTLo+VjXmdZx1S+dSi5cO9MvsK7nn4nhKBySYiW7xbAOibjLN2D16bV58l2OSAAEF31EASGbLJIlC+wm2mkME7H3knjk8c17X/etNoS3CrtVX6txyW61Qpf5VRqdCaWlXxclFJds52Ojp1vTc7J6stKLJQ5MeilMOqs49oMBxEr7sXPa4eALLaBRq2uy8c+QK+fOzLVY3xc4c+h68d/1pV2yw2kbR4bNSai8y2abW21ixYY9kYfvfnv4tj48dq2r4aVC3B+bTmolsyn0S6kJbuNxbOoluwyh0S9UqBUBZR09Oippxg1RK6bBu5A63cgnIkHYHVaJX2G+gJYDYzizNTZ3R9pkbz+KXH8X+f+r8Nf5/94f2qrciqtUnLc4L9bj9Gk6NVFZVrdkiwEiuOd2x9Bz7e9/G67lNZNEFpCX7t2tfiHVvfgdvX3Y7XrnktxpPjOBCuvqeYWlXbcrB+qOGE2MpYWWGQYTPZMJ0RG3KXE6x2k73IHvjc8HM4Mn4El6KXisbmsXjAcVzZmx0TEaxYBBuPMsKqnADIxXcoFsJq12oYDUbp9R/e+2F84sZPwMCpX7I0LcEKe/XVbVcDQF0EDZu4VcLn8sFhcpQIQDbm9S3rxf0tUmubX174JU5FTpVEfNlvutm7GYC+/Dx2XJRrFxTNzB3TBvGYZvlZesWc8lhhCzLlHAHDsWEcGT+CQ2OHdO2/3iRzSTjMjpLK4vUgmolKkb0S0Th3bG1s26hrX1qF1ljfZtaOg1EuKsnO2XLnp5Jq80frhfxYqtoSPPOKVMwEkNUu0FjUeeryU3js0mNVjfGRi4/gmdAzVW2z2EylxOh7rbnIbBu/y49ELoE8n696H/vD+3Fi8gQefuXhqretFmVtCZvJhoJQ0By3siCf1WCtXCV4TpiyayQgXu/qEmFVjMdhVq9DIYcJUzbXYXmpWv1blSIXKJ8CwBbFWEpNs+WxDgwNYGBooOFOkFA8hFWOVVKdBgarf6FbsMoi1n6XH7zAS/PDlQAJVmLFsaltE16/4fV13Se7gSTzSQiCUFR0CRBvAn+792/x/wX+P3ys72PotHfWlPAezUalIgWVcFvcaLG2SBOmshFWWbSznLiymWxS/gwwP/mVT8ai2ah0QS13s1NWOWT/Vq4QK22i7CY6lZ4SJ7/u1UWv39O9B/29/apjZ/tRFoPJ8Tmk8qmSCGubrQ1bvVsXfFNkli+9udJq7XRKBOsi5bGyz66cbCZyCViNVnQ5umAz2vRFWOei2MrWRNLzsuMGgHTe1CpY2blYTmyx73Cp+trKI6x5IV/XFe5oNopuZzeMnLEkMhhJR+Ayu0rO/3LYjOULrTEruNLiWy4qWU0PVgDS8bDYdlC5g0WrGIySAl/ASHykJEfX79IuthdJRzAcH9Ztf88WshhLjDVFT2YtWH5zrbnIbBt2zNQSaWfXsMUQNyWWYI3+4gxlj+6qIqyy+6eeCv56iKQiMHAGyb3lMrsqWoKZ44eJT7ZwL68mr4Tts0SwlslhlTuUOuwd2NS2qeq8zUbB7n+NXlgr59QycAbs8+1DcKS0/oUaUk6w3bsic+xJsBKEDuST7FQ+BQFCUYRVSa35A2oWVi3kEyZ281SzBAPizUa+clv0GqMNeT4vrSSyfconY/KxucwuZPlsyWRcTTQro7dA6QSgKMJaRT9HhlrkR6vicsAXwLHxYwuy4EazUeT5vO5c6XZbe0klYHkOK7A4gjWRS0jRZeUkglnd9USPGCyK3W5rx0xmpmQ1Wln4Slr80fndl1iCK0wW2WR/qYpYyQUrULn3YjUw8e+2uEt+u0hKXz41Q6t4VbmcVC1LcDXnbLX5o/VC7mCRisHomIyOJceQF/Il34dW7YIcn8NsZhapfEr3sTgSH4EAoSkKsGnBJtgLtQSz77OWfQTDQRg5Iy5FL+nKtV8I0WwUHDjpnq/VX5zBrvXs/mDhLBUdF2ouqXpFWKfSU2iztknOJafZWfEarMxHZRFWtXQghmqEtcxxolYDIuAL4MjYkaYorMfmP42+TmnVwujz9SGSjuD8zHld+wHmI6zAyqpiToKVIHQgn2SrrSAqqbXNjVpVWy163b26I6xak1kpL3BOWLJ9yisfR7NRuK1iVKJc8QU10Ww1Wouit2xfwPzE1WEWxfSF2QuIZWO6+zky1Hq9aVVcDvgCyPG5mvKMGfJ8ET2o5XhGM1G4zW502DvEfS5CZOXw2GHkhbz0/nLkxcQqRY8Y7JhlkYTp9HTx8xl1wVpNhJXlTgOVJ4tLHWFN5pNS0SVA/+fUA/su1SIWeu3pDKWrQg773ZXnoVpUcjYzi1g2VlWEdSktwXIHi17BVU7As9oFE8nSfHj54pTexUv2vS9mPnu18AKP6cy0mMM6dxzqjSAz5JZgoPrjYCQ+gkvRS3jTpjcBEO3BjSSaEY8bZnnXI1iV94eqLMHGYktwvXJY5fcqp9lZuejSXCSVVfxl/9f6HOUswWpRdLVFtoAvgCyfxbGJY5pjazSZQgbjyXEAiyBYVeoFMKqxScstwascq2AxWCjCShBXGvJJttoFWYnf5cdYYqzqlVG1nEst/G4/RuIjKPCFihFWrcmsPGqVzCUla0lRhFUmpssVX1ATzWoViNWin16bF8cnxchfXSKsGhWXd3XtgtlgXpCdjE1I9YoENcusPDoJLE5UcHBkEFajFUbOqGoJZlEEFmGtNBmVW4IBqH9G2W9QrZCT504D+iOsS1V1uVER1nQ+jSyflQoGKX87lleoF6uxvNUwFAvBYXKgzdpW9LhaVJJdI6oSrFXmj9YL5eKJ3hzMcgJest2pLOzIjz+9i5fyyWUzVA1XYyYzA17g0W4Xc1jzfL7qRZmFRljZdft3t/wuVtlXNdxCqryGsWtQuQUfYP461GYTz6GFFF2qS4RVcX3QVXRpLleVRVal+75GhFWZ9wqI51kqnypyZPECj+n0dMn9c3fXbpg405K3t2GFKoHGLqxVSi3yuXxY51mnW7B6LB6x7y9nwGr3aoqwEsSVhnySrVYFT4nf7dfdFkROtRFWv8uPvJDHWHKsfFubuZuf1mRWvmLMLnBmg1lqh6McW7l+bGqWJlXBqhL9bLe148LsBfFzVTH5Baq3BNtNduzq3LUgwVpthLXd3o7p9HRRoQ72nZqNZngsnhLLcCMIhoO4vvN61cm6PDfb7/YjmU9KBbvUKPAFxHIxUXTb523dcpSLMHZz9ZZg+faVohtsor9kOay5FOzmecFar8JLyoJBsUxxxEJviyWGmlWfwXJS1foLK6OSUvRxmViCSwSrjsloKBaCiTOhy9FV9LhWnph88Ul3hFX2uma1BcuL99S68BDNRGE1WiVnSdWCdSSIDnsHNrZuXJRWKMprmNKRpEYkHUGLtUUqNqerrU1BXbDWK8Iq76tcTdElJlTNRjOsRqvmdsq8V2D+Ps9a4wHiMZAXSlNqHGYHdqzaseSFl5T1OxpFLBdDjs9pzs/2+fbh4OjBivUQ5G2UAKy4XqwkWAlCB/JoiV5LMFB9/kDVOayyCRO7eZZUCdZhCWbbpPNp6QJ3fef1GI4Po8AXwAs8YtlYkYUXKBWs5XJYlTdcZU4QgKKbadWCld0QZcVgtCzBgGi1OTN1pmZhI7ff6MFr80KAgJnMzPwYFb1oGz1JnUhO4PzMeQR6AqqtgIoirHMCRG4LV8LOBbklWPkZ5McNUJsluCi6UckSPPe7TGempTZUi0WezyPLZ0VL8Nw5Uq8Iq7KHqHwSleNzmMnMVBVhrWQJLidAle/Njo9qzlmL0QKb0bYklmD59UB3hDUWgs/lg8lgKnq8x9kDDpzqdZ4dhwbOUJUlmNlOm7XwElsQYlWCgRoEa3be2g5UF8HiBR77R/cj4AuIvTt7AphKT+Hc9LmqxlAN5SKs1fQxthqsFRfp1Pqw1jOHteoIay4BA2coKgLlNDu1+7Dm4jAbzEWdAtSOE60F30BPAKcip3S3nGoEiyVYldWb1ejz9SGVT0kOtLL7Uixa+t1+DMWGqrbsNyskWAlCB2oR1kqWYKC6Cm05PodkPlm1JRgQJzoZvrQHKiCLsGpcEFnOTKaQkcYc6BHzPCdSE0jkEuAFXqruyUSN0hJcLodVzRIszwkC5oWf1+bV/G7VqNYSDMznhhwYrb79ECDeHAycQeqLWQll31w2Rnkv2kZPUtmqdcAXgMfiKVrxBor7CzP7o9YxLBdRap8vVyit1FytJXg2M6suWMtMFtmEmhf4ks/XaNhnkluC65XDWhRhVQgtljdcTQ5rOUuwIAiaVX+VUclQLFTzObvYv09JhFVvDmsZAW82mtHt7FY9R9hxeHXb1VVZglnbrWa1BMsX6mrNRZYEaw0R2nPT5zCVnpKu3/u69wFobLVg5UIyuwZV6mMsv+daOAuyfFazFU46n4bVaC26L9ajSnAyl0QqnyrOYbU4kcqnNBf1WE0DudOiUu6rsuUfoJ6zrrXg2+frgwCh4bnJWoTiIWke08iFNamyr8a1e3f3bhg4Q8VjXFltuNfdi3guvqTCv56QYCUIHTAbYyqnzxLcYe+A1WitSrCyogTVRFi7HF0wcaaiCKt8ZRPQmcMquwGH4iG4zC6p5+BQbKhE/JWzBLOVYPkYylmClZ+Tja/a/FVAvRiMvIWFGtvat8Ftcdc80ZlKT6HV2lrUL1YLyTKbVghWWS/aRk9Sg+EgWqwt2OLdomqHlBdd6nH1AKggWGXHhdPshNVoLfoMTJAUWYKrzO0sG93QKLrEXrPYUSo1wVq3CKvMMcCi42zlXIp6VWEJtplsyPG5kgnrZGoS6UJaW7DKLcHx6lraSPtRifA3GrUcVj0tVbQEfLlq2pFUBFajVRSsOu4DgiAgFA/h2o5rpe2bEXlkbCGWYI/VA6vRCqvRWtX28kU3AOhydmFDywYMhhuXx6pszSVZgsu0hQJKi6BZDaL40RK56UK6yA4M1CfCyo4l+XhYAaVKLWqU85xKkVllyz9APWddS6ht79gOp9m5pLbgUCyEXncv7Cb74kRYNdwxLdYWbG/fXjGvV80SDKycSsEkWAlCBywqlMwnpYu18qIsh+M43VVWGVo5l+UwGUzwuXyiYC2UibAyS7CeHNZCWpqcySNsyrGVswSnC2lYDJaiFWK70Y4sny2aGKsVl2KTbWUPVj2YDCY4zc6SKsGshYUaRoMR+7r3YXBksCbLTLVtRFQjrItoCRYEAcFwEPu698HAGcrmsMpbN1TqJywXpBzHlUSJ1aLcNRVdUsthVYmwCoKASCqCja0bASx+HiCz/DnM85bghuSwWjzIC/PFbvTYypTIXRVypCJK5SzB1tIIay2LTHrtuPVCzcGiVgxGSTwbx3Rmuqxg7XX3qluC02K0w+/2Yzw5riluALGYUSKXwIaWDXBb3E2dw2riTHBb3Au2BAPVHweD4UFsaNmALud8PjFrhVIP66wSQRBKW3MZtXtBA6VFjiycuIirKVjz6ZL7dz1yWOXtThiskBIrrKRGMpcscU5Uyn1VE7nVWoLNBjP2dO9ZWsE6txCn1kKsnuithRHwBXBi8kTZgle5Qg7RbLTEEgysnF6sJFgJQgfyCKQUYbWUj7AC1fdiZRdF+UquHpgwZhOichFWzbY2sqgVs791O7th5IwIxUMl+aBaEdZyRZ/kEza1CCsbXy2TX6B04qNsYaFGwBdAOBHWzNMsB5uQ6oXl6LKVZXnVV0CcTMSysYZMugDgQvQCxpPjCPSIkQmlHTLH55ApZIoWYiodw0pB2m4rFt1qizBmoxkmg0mXYBUEoSQHVivCmswnkS6ksaltE4DmiLDWzRKsyGEF5r9/tQlpJcrZGsu1cGHIz7Mcn8NoYrS2COsiC1Y1B4taMRglrFpoueuS3+XHZGqy5HtkC1p+l74CfPLvvd3W3rQRVhbFYYteQO2WYEB/4StAvL8cGTsiRVcZAV8AqXwKL0y8UNU49JDKp5Dn80ULHazmQ7lzO1PIIJaLFd1zrZyOCGu+QRFWFVFUrjWdHHmKCENPhFW5TTlLsJEzlp3vBHwBDMWGlkRsSWkRLr+YutBAS20kFQEHrqQiu5K+nj4UhAIOjR1S34/Kb7zaJS7+r5QIq6nySwiCMHAG2E12pPIpZAtZmAwmWAwWzW38bj8OjR2CIAiq1TaVVMq51HqfJy49MR9hNalHWPVagodjw3iN/zUwG+bzs7Z4txSNrWyENZ8uKfrEBGy6kJa2i2ai6HZ0F72OTbar7cHKKBGsOgpYMfH2gWc+UFT0SQ270Y7/L/D/FVXD9a/SP1F3m90wG8zSjUX5e8sFbbezW30ncxyfOI5nh5/Fe3e+t+hxQRDwucOfw+vXvx5b27cWPcfsRH2+Pul9Y9mYdHyy6KB8ddzv9uPxS4/jT5/4U+kzfPzGj0sTEqUgbbe3YzQxCswFtcsVvrKb7GULkAzHh/EvB/4FWT4LQRBQEAq6iy6xKstMsCot1oMjg3hp+iW8c/s7ix4v8AX8y8F/we9v+/2axBejXpbgy9HL+N7p7+GDez4oFfph36Xb4i4qMtbt7K66xRJQfF7KCcVC4MBJlnAlHosH6UIaf/r4n4qWYqFQW4TV6sGZ6TO6XhvLxvDxX39c077I8Lv8+Oi+j5Zcc9UWT+TCn1WsVVJJwLPHh2PD2Ni2UXqcncdyp8r6lvUAxGvHl45+CX+z+2+kRTV5ZNtr85Ycu1PpKXzxyBfxgd0f0FwsPTR6CIfGDuFPr/vTsq/RYiwxhi+/8GV8aM+HVF1E8sIubosbHLiaLcGA/jxiAHhh4gWk8qkSwbq7ezeMnBGDI4PY071H9zi+ffLb2NCyATf7by4/VpX7sto16JXZV/D5Q59HTsghXxDzVOXnI5svyK97F2Yv4L6z9+EDuz8Ak8EkWoLLRFj1ziPUULUEq7Sme/Tio4hmonjr5rdKY1Uea8oI689f+TlS+RTecvVbAIhzAuXCmZolOJKOoM3WVuTGksPuU3898Neq9+b1nvX4271/q/WxayaSjiCVT8Hv9sMTKT0+2X32DRveIM2NGIfHDmNwZBDv2/W+osd5gce/H/p33L3xbilPHRDP6zZbW8XUoutWXQeb0YZgOIj+3n7VMQPFi5YOswPttnb8+OyPJaGbmk6hH6XbLwcowkoQOmGTbLVCBGr4XX4kcomiqrBa1GIJBsQJ03RmWropKYV0wBfAXVfdpTkRZzfJodgQsnxWmoD63WL0Vjk2A2eAw+RQ7cOqjLCyibv85q5mCd7Wvg13rLsDN/bcqO+DK1BaFc9On60ofte41+CNV70RJoMJ0Uy07H8z6Rk8cfkJPH7pcWlbrd5panAcJ9p+536nErFXRS/WB88/iK+88JWSBYOJ1ATuPXkvvnf6eyXbBMNB+F1+6TjwWDwoCAVpH2rVr1+37nXY1LoJ0UwUY4kxPHrxURyfmK9UqBSkyon2S9MvASiNTrHFHzUefvlhPD30NKKZKGLZGHZ17sI+3z7peQNngMVgUa1wy767dZ51MBlMJVGqH539Eb56/Ksl2w3FhvCDMz8o+n1rgYlTh8kBm9EGDlxNEdbHLj2G75/5Pi7OXpQei2ajcJldMBqMqhFWq9FaVeEjdl4qW3OE4iF0OjpLzmPGTatvws5VOxHNRpHKp7C7azf2+vZW8/EAVBdZOzp+FI9degxjiTHN83Q4Noz7zt6Hl2deLtmHmoOF/VtrHJX6zJbLE2PCjm0nd3E8evFRPHDuATx1+SnpMfb8avdq1fSA54efxwPnHsDTQ0+XHSsAfPPFb+IrL3yl5jYvP3vlZ3jg3AN4dvhZ1ecjqfncTANngMviqkqwSq2warAEn506CwDYsWpH0eNuixsbWjdI1xu9fPX4V1WvB3ImkhMAioWAmp3+2dCzGAgNYCY9g2Q+iRu6bigSz2oR1qcuP4Xvnf4eTkyeAFA+wgqIboZaOTN1Bk6zs2hRRs0l9T+n/gf/ceQ/pPSdchFWdq8QBAFfOvol/MeR/5CON7WiS2aDGT6nr+j3qdQ3en3Lety54U7Ve/MrM6/gu6e/q7s1WrWwRaped6/qgsp0Zrrsffbek/fiv4//t7hwK+P01Gn8z6n/waMXHi16XH4+aWExWnBD1w1l81jLLVres/ketNvbpe8uyTfmO1sMKMJKEDqRT7K1Ci4x5BMV1jxci0ptWMq+z9yE6eWZl2HmzCVCekPrBnzqVZ/S3Ae7SZ6fOV80dr/LL4oHlbGpWYPShXRZSzATrGo5QYAolP79Nf9e4dOWx2Px4FL0EgDxOx+OD+P3t/2+5jYcx+GTr/pkxX0LgoDXPfA6DI4M4m1b3jZfdbEKCyZQLOiU32m5PqZqsBtqKBbCZu/mkseD4WDRinyez+Pg6EG8bv3rpNfKV71dFpdq9eub/TdL0YdwPIzbH7i9aGIezUZhMViKWidNpaekyUtwJIir264uEfYOk6OskAuGg9jq3Yrvv+H7ZT+/zWRT7YHIvrsOe4eYT6uY9IfiIcSyMfACX7Syz36LhdrP5BFWjuM0hbkW0u8bD0lRO7ljoESwzlWHrCYCI7kqCqWWYK3FrWs6rsH/vP5/dL9POTwWD5L5JHJ8TupVWQ4m5r5++9c1F4lG4iO444E7EAwHi6KdgHqkTE8O5lBsqMiGrUQtT4wXeEynp9Fua0e7rR02o63ovGGTzmA4iDdufKO0fYe9A3aTHe22duxPFVdIlZ/bd151p+pYcoUcDo0dQkEoYCYzU1XEXW1sd6y7o+T5qfQUrmq9Svq7xdJSlWVSac32WDy6hWYoHoLD5FD9XL2uXun6rweW88fyAstFrdnvJl/8NBlMMHCGonM7korAbDDjB2/4gep5qJbDyq5PgyOD2NW5q2zRJWCuPoRR29VVjmA4iD3de4raMqm5pEIx8fp4KnIK1666tmLRpVAsJFnmT0+dxvb27Yhn46qR+X2+fXjq8lMo8AUYDcaKfaM5jsOnb/606nMPnn8QH3v+Y4ikIpq1RGpFvkjlsXhwOnO66Hl2n1HeZ3N8DgdHD0rP3b3xbmmbwRGxKJjyHlNN/+yAL4DPHv4sxhJjRTncbD9AaerXn+38M/zZzj+T/h4YGND1Xs0IRVgJQids8qm26qiGnrYgchZiCQaAl2dfhomrbQ2K3SRZLztJsLr9mEpPYTQxCiNnlArmAOrFF9QirErroZQTVOXnrIQ8YiNVkuwJaG2iG9bv7+DoQeT5vO5CCUrkuWlSxGeuirHUx1SPYI2Hiv6vfHwsOYaL0YvS4ycjJxHPxYusdMrJeqXq152OTpgMpuL+dHPWPnbDbre1oyAUkORFQX9kvDTfDJhzK6hYZZO5JI5NHFPdRo7NZFMtRMJ+F6/NW1J1meUlCRBKnAGSYF1grg+bjLLzRMv6rIV8QUI+RrmNEpiPDCorkuqhnLW61iJK1cI+g94qvXaTveJn7HH1YI17jWqxlkqWYK331hLwrdZWOM3OogjqbGYWBaGAdru4iCDPBc/xORwcm5/UsoJv8tY57fZ2RLPRomJQ7NiUb6Pk+ORx6RhkEZdqYOcsANVIDitqJl+oq8bSC5Qu1FWzPfst1AQhK36lt4AeuzZo5QWy9wSKiwFyHAebsfgaxIRHuUUjViVYft1jY2DHa7miSwBqzmMNxUIYig2VXFOVEdZkLlkynnJFlzKFDHJ8rqgyMztekvmk6j0k4Asgmo3izJSYBqA8jqqhGjdSLUi/uWu1qgOAfU+jidGiRZKTkyel71N5DWJ/K+8xysq+WrD5zP7R0nY/1faFX46QYCUInThMDiTzSdWLuBpSWxCdk+BoJgqb0Vb1KiqbTI0nx2HmtCMV5bAYLODAYSg2BANnQI+zp2jfp6dOw2PxFN2M5dYghp4Ia62R5ErIbyzBkSA6HZ1Y71lft/33+foQy4mrz3p6p6kht/qxqITSElyptU2ezyMcDwMoXQyR/y2/YQZHguDAYW/3vHVTWQiDVR90WtSPbaPBiNWu1aUiSiYAmICPFWI4On4UOT5XVrCqRR6PjB9Bns9XFqxGm+r2kmC1e+G1F1csns3MSser0gLK/l5ohJWJU9YGy2EuH0nWQm1BQlmohj0GVG9PB9SLV6XzaYynxheUx6uXagr2sIqdeiLIAZ+4sKS0UKpdd5TCv+x7awh4tYrwypxB+fMvTr6IRC6Bm1ffjMnUpGRflgtjtp38WsCOzfHkOC5EL6iORX7O1zKZZ+fszatvRigeKilGF8/FkeWzRcdatcWzlAuzHosHiVxCsz8pg7UaUcPv9iNTyGAyNalrHPLvR6sabSg+H/mWo2zXVqkIn2qEde44OT5xHPFsXD3COlcTotZerOyzsZxQhjKHtcgBEA6CF3jVAkpMjCZzSQTDQXQ7u7GxdSOC4SDyvFi5XO0ewtI6BsNiVf5qhJoStRZx9SQUm0+L8FjnKonLrify+4r82BkMD4IDh5t6bkJwZH5hKZ1P4+jYUWnfcqoR7le3XY02a5vqYlIkHYHdZG9IxLlZIMFKEDqpNsJqN9mxyr6qqghrLSLOY/FIuVi1ClaO42Az2VAQCuh2dEutYHpd4uTgzNSZkrE5zc6S6JGeHNZaI8mV8FjFYjDpfBr7R/ejz9dXc5EKNVieXjAcrKmNCDBvCWa2aDZuQBQ3dpO94k14LDmGvCBO7tQEa7ezG6tdq4tuaoPhQWzxbimypisLYbCCNqw/nxrKiblSsLIJSIyPIRgOwmQw4YauG0r24zA7VCOPwZEgzAYzdnXtKjsGQL23LyDe/FusLTAbzCUVi5XjlsP+HomPlPQlrQa1CGu1gjXH5xBOlC5IyC3BTrMTBs4gLXpU22IJUG8PxCrZ1lr4rBqk/FEdYqeaqG9fTx+S+SRenHyx6HG1645a/2Y5Bb6A4fhwxe9DWU1bWQCFPS8IgrR49P7r3w9AvJ7kCmK1ZfY+ahPyUCyE3V27xW3K5LEFR4LS91pLlWF2zrJibvvDxZEctYW6anKRgdJIN/t/pUg761Nbtlozs2brXCBm30+LtUWzv2W5Y89ushflsE6ltBeNpD6suWJLcIu1BQWhgMNjhxsSYQ2G5xZvW4oXb5VVgtnxu7trN46OHxXvUxBK5jpSwb1sFAdGDyDgC0hthabT0+JrVO4hHfYOXN12NYLhoFTNvdprFqMaN1ItyI8z1QrHadmxo1gY3tq+FXesuwORdERKsTo6fhRZPovdXbsxnZmWFodT+RSS+aTu78HAGbDPt0/VZbGQBYDlAglWgtCJ3TxfdElPDiswX7RID2p5nXphF9daBSswLyzl0RX271Q+pZpzWhJhVakSLEVy5ibGtRaXqgTb34HRA5jNzNbNDszw2rzY4t0iCtYa2oiw1+f5PKLZqDRJlh9LetpZsIkFB07VEtzr7pWiTHk+j2QuiRcmXij5PkoswdnK7ZqUE3N5tU82fkCMsAZHgti5aqfqim85IRcMB3F95/Ul0QwlNpNNta/lVHq+kAcrcCVZLmOVBWteyGMsOab53lqwz8TEYDnrsxaj8VHwAi/+vmUswQbOIPUH5AW+6HPrRS3CWqnAUD3R28NTEAQMx4d1j2lP9x5w4EpEiJqDxWwww2FylB3DeHIceT5f8b39Lj+G48NS7rZyQcvv9iOVT2EqPYVgOIht7duwxbsFaz1rEQwHMZIYgQBBeh9lz2YW+Q74AuJilEpEMJ6N48TkCdy+9nYAlZ0aarBzdlv7NnTaO0veR812WLMlmAlWlQqyakymJpEpZCoXv9K5QMyu4bevvR0vz76M8eS46uvKWcKtRmtJPqqWYGBFl4oswakpvMb/GliNVgTDQc2iS7X0YuUFHvvD+xHwBUoWb00GE2xGW4lgfcvVb0GOz+HZkFh0q5xgPTR6SLzP+gLo6+lDls/iueHnAJS/hwR8ARwdOyrlvdYqWPW6kWpF/purXaem0lMwcSbc0nsLDoQPoMAXkMwlcXziuCTggfnoK1sIYvnq7PPXYuPt6+nDRGoCr8y+UvR4LYuWyw0SrAShEzbJVrPJlMPv0t+LdUGC1b1wwcpujPKbs8figdvslv4tx2VxlTQdV81hVViaau03Wwm2v19e/CUAVLSV1kLAF8Cx8WMYjok3nFoswYA4uYlmonCb3UXl7L320kJBStjxtK19m2qE1e/yI9ATQCwXw8nISRweO4w8ny+xhClXjtnERctS5Hf5Ec1GpcheOUvwaG4UZ6bOlP0N1ATrZGoSZ6fP6lposBvtZSOs7Ddpt7Ujx+dULW/lLMHAwmzByVwSdpNdKuikVVyqHENx0Ya5rX1bUU5eNBuV8p2BeSsmy5esRw4rs4AuSg6rTkuw1GJC55harC3Y3r69RGyVu74qq4vL0SvglXZUZSSSjf3s9FlpUgvM25dZNWgph1UxIWeRb7/bj76ePmkxSg4rtnTHujtg4kxV2yWn09PSOcty9veH9xdVG1bL3WfHod7cUaWzhB3TlY6DSr9Fj6unZJFHC/bd/taG3wJQGk0GxMJMo0n1PsNylwezuGoJD3ZvZteDAl/AdGYa3c5uXN95vShYy7S1AWqLsJ6dOouZzEzZ67DT7JwXrPEQXGYXbum9BWaDGU9efhJAaU0D9jd7fp9vH27ougEmziQ9Vm5+FPAFkOWzeOLSEwCqv38yzEYzPBZPQyKsmUIG48n5tAg1Jwi7z9zYc6N0nz00dgh5QUxn8bl8WOdZJxVaCoaDuG7VdVI7G3aMsmOwGqGpFMPSmKrsC78cIcFKEDphk0+9lmBAvLmOJkaLimeUQxmtqoZ6RFjZBFY+MWQFQ4BSweowOUr6IqrlsLIWAFKEtVGW4Ln9PT30NDa2bizbV3EhBHwB5Pgcnrj8BNwWd9X5xnIrk5oFXFkoSI1QPAQTJ1pt5VGdVD6FidQE/G4/9nWL+ULBkSCC4SAsBgt2dRbbbJ1mJ4ycsaTokqYlmPWcnFshVoqAFmsLjJwRhxOHIUAoKz5ZPricA+EDAPQtNFhNVlUhKM/lVNrGQrEQjJxRGrecaDYqPbeQwkupfKooOlxL0SU2mQn4ApIIyhQyyBQyxfmXc0KhlkkPUOp8YO+tp7hRPdAbWavUB1WNQE8AxyeOFxWFK5dyoZWDKb13BbGsrBQcSUdg5IzSZJc9/9PzP0VeyKOvR1w8CvgCSOaT+MWFXxS9TmkJlou1gC+AeC6Ok5GTRWMIhoOwGW3Y1blLrJBd5WR+/+j+onM24AtgJjMjtZIB1Pt5eiweKXdRD7VGWCv9FhajBV3OrqoswXaTHTs7d6LN2iaJCznhRBi8wKu+p7zoUjQbRZ7Pa56D8l7uADCTmQEv8Gi3taOvpw/nZ84jlU/VNcIqFR/UEKzSgt5cVNFhdmBn507p+1ArugSIVW83tW1Ch70DTrMTO1btKLsN44auG2AymKTjfSECS631Uz0Yic+5HSpYgtvt7VJebjAs3metRiuu77oegCjkD40dwmRqEqcjpxHwBUps67VEWKXCcgoHyVSKLMEEQcxhN9kRz8XFogJVCFYBAkYSIxVfW48Ia61VgoH5CaxyYigJVqt6hFW+sq4WYZXaZ8zdqCVLcAOKLgFiLlQjoqsAcH3X9TAbzLgUvVTTzZZNaKbSU6q/t7xPazlCsRB6XD1Y61mLHJ+TrGxSFMblR5utDVu9W6Ub6a6uXSUTIY7jiibr8VwcdpNds4G5fGLOCzzi2XjR72jgDGiztWEiPwGX2YXt7dtV92M324tyuQDxpu+2uLHVu1Xz8wMoqdDJkBewUE76h2JD2NC6AYC6YF3jWQMTZ1pQhFUpWGspuhSKh2A2mKWJj1ofZPbvWCZWc3VItQhrNcWNFopeS3AtNuWAL4C8kMeh0fnqr2UjrBqCdSg2BCNnRLezW/P9lL1YWQSGRdpZAb7HLz0Oq9GKnZ07Aczbl9njbJFNymdPzR+77H32du8FB65EYA2ODOKGrhtgMVpqmswHR4Jwm93SOcuuofJKsFPpKXDgNHPhKxHNzLXCmjv+dB8HsRA4cNJ3qUY1jiZm4dXKC5S+d5Vjz26yS6211IS8GvIFLHmBOPn9qpxgrSXCGgwHsbF1I1Y5Vqk+r4ywsuOYRULZa+SwCGuWzxaNO9Azv025lCmH2YHrVl0nVdZdiIW1lkUZPch7sALqxzcTh/I0oWA4iF2du6Tfq8/Xh1Q+ha+f+Lq4EOQLSO2x2HFVa7eBgC+Ag2PzheVYtJ4swQRBACi2MVZjCQb02QxnM7MLFqysEmEtqEVY5X+r5bDmhXxRLqFWlWD2umg2Cg6c7jxgvciFE4tg1Bu7yS5FKmu5Ochz0+RFdOTPz2RmNAv/sJVw5bGljEQx+/JL0y+VFfByO6Qeq/tql9jagfUzFSCofgYAJX3/5NhNdmT5rGRrFAQBg+FB7OvepymYGWp9WDOFDGK5WJElGJi3XYViIVzddjVMnKnUEpyNos3aBp/LtzBLcD5ZEmGtWrDGQljtWo017jXS36o9ROdyB+WtfKpBLXKzWC1tADEiZjPaKlpB2eSOHXt62Nm5U8oLZJRzsGhGWOMh+Jy+sscxQ2lHVVZtZgX4cnwO13deL333zL6c43NY7Vpd1BtY3rNZHvlus7VJk2TGWGIMr8y+Ip3nXpu36rY2yl6dqxyrxOqvskhOJBVBq7W16PvQKzgZyki3Xmt4KB5Cl7NL09WizLHXYio1b+EN+AKqeYFaUV2r0Sr1MNYrPOTXA8kZYWvHZu9mtFpbAaDUElxjleBMIYPDY4c1F2+ZYOUFHsOx4aJ7B0OZjyr/W/46ecqJ1n1Evo2e/vTl0ONGqgXlApna8S3vncrycs9Nnyv6bLu7d8PAGXDf2fvgNDtxTcc10n6VluBqv4dATwCJXEIqLCeP1q9kSLAShE7kuX3VFF0CKgvWHJ9DMp9csCW4kRFWZc6pssogAGTymfJ9WGU5rG6Lu2hyVg/YjYXZZRuFfFJYLa3WVhg4g5jDqmJR9Nq84AUeM5mZsvsYig/B7/KX2IuU0QAWZQJKWxow5JN1PcXE3BY3Wq2tZUUU+wzs/cvBquiyydvl2GWMJkZ1R8btJntJhJVVqGQTCSnCmopIuWi97l7VIjFMzCirIFdLKp8q6lVcs2B1ry4SQaqCde63q7VitdRLUpaHV01xo3qgpyWKvMWEXqxGq5QXyKglh1U+iddCsqPGiiOscqTzUmGTZ38r30degE3ZfzTQE8ALEy9I0TrWl5Htq9oI61BsCMPx4dKx+QI4Mn5EWmxUKyxUTXsioPR3qMYSXNGa7fJjPDWuS9xF0hF47XPXqh71vMBQPASLwaIaoZTnsOotwie/HsjPWxblZfuVw457tSJzWhwbP4ZMIaN5TXWZXUjkEphITiDLZ6Xvd1v7Nql2hTJFhM2DTJxJqloNANs7tktzAj2ClVVzr5VGWYJDsRBsRpv0WypzrJW9iOX3Wfn5wxaj8ny+aCFIfo+JpCJwm91VXdsASC4Ltpgkj9avZGqf3RLEFYY8cqI3wtph74DVaMXPL/xcqj66vX07blt7W9HrWEn/WiOs3c5uGDnjgnNYnWantNLLKBdhlTceb7e3QxAEZPlsSZVgA2eA1WjF8yPPI8/ncWjsUN3zV4H5FhU7Vu3Q/fvUQsAXwBePfrGm1UyjwYhWa6umJRgoXsGVw4rs+N1++Jw+GDjDfIQ1HoLD5ECbVVyt3dW1C2aDGXaTHVu8W1THoxSser63XnevpmCVVp41iiexcymVT8FtcUs3Xr2VnZUVOoHSfKBWays4cIikI0W5aGoiKZqNYrNlM6x2q1QQhHFu+hxGE6O42X9zxXElc0mpByswbwnmBR4GzgBBEPA/p/6naEHidetfJxXjAMTfcceqHUU5eWo2etZORJkvWQ0203w/22qLG9UD5eLB8YnjyBay2N09PxHW6r2pRaAngM8f/jx+yv8Ux48cx1R6qqwleCo9hS8e+WLJcy/PvozfXP+but6vaCKajpS0EfG7/Dg6frREQAR8AXz9xNdLvnev3SvZ/Fn1b/k233rxW/jU/k+hy9GFX4/8Gm3WNuk4YmJXEIQie/fPXv4Zbuy5seTaUi7XMeAL4Lunv4tPBT+FDnsHTkVOlfwWSsEpCAJ+cOYHRf1Qb1tzG7Z3iFZjpbPEarTCarQWHQfPDz+PDnsHNns3S4+FYiHcuPpGaCHPsb+q9SrN10ZSEVzbcS0AMXrf6+5FcCSIt299e9F7+t1+1cVVu2m+8JveRSN57r7yehXwBfDLi7+sKFin0lN4bvg53HXVXSX7f/TCo3hp+iUA4rlk5IxF55ISp8WJ+Ey8JKpoMpiw17cXT15+sqSnKhOwO1btKFrENxvM2NO1BwOhAc37yDUd18Bpdi44Gui1eRHLxpAtZIui7g+efxCXo5elv2/subHkO8gWsvjJuZ/gt6/+7RLRPBQbKlocMhvFe6g8dUbei3hXp3ifdZgd2NJWfJ8N+AI4MXmi6Lzyu/14augpFPhC2ft8JVqsLdjWvg0Pv/JwURu0lR5hJcFKEDqRR070CiIDZ0DAF8Dzw8/jxMQJFIQCbCYbnvM/V3SRZRfYLkdXTWMzGUx4tf/VaI/XfsHasWoH7CZ7Sf7alnax/cLW9uLcQnazYhFWdkNVWy3c3r4dxyeO49TkKQDA7etur3mc5TAZxBVfVvWxUWxr34ZrO64tKWKkF5anqmYJZrbHV2ZeKRIxDFad2O/2w2w0w+f0SZMNZRTGbrLjDRveAJfZVdZm67F4pO31Cla/y48TkyfK5iLf0HUDzgyfwXrPerXNpbEB8xHWwfAgfE6fZIOtBItuyCfkSlueyWBCm60NU6mpIru0Wt9ItniwyrFK6pPHrG+fPfRZHBk/guff9rzUn7gcqXwKrbbWks+ZzqfhMDvw0vRL+LdD/wYDZ4ABBuSFPM5MncH/e+3/AyCmBcSyMUkU+F1+DMWGylqC80Iew7HhonzJapC3BzozdQYASoRWI1EuHnxi8BOIZqN47M2PSb9rKB4q6xDQ4tbeW/H141/HU9GnYHjRAI7jJNEk55qOa8CBw7de/FbJcwbOUBRF0qLX3Yvnhp+brxirmIgGegK4FL1Usni0s3MnNrdtxt7uvUWPt9va8eLki2L/0VioKM3h+s7r4Xf58YtXfiE99rYtb5OOAa/NiyyfFV0Tc8fxRHICH3nuI3j39nfjr3f/ddF7BUeC6HJ0YZ1nXdHje7r3wOf04Wcv/0x6TCmUlJbJs9Nn8c8H/rnoGD88dhjf/s1vS69TRizlx0Gez+MDz3wAW9u34pt3fBPAfFufaopfaQlWlvMnjxYHfAH84sIvkONzkohhOd1qWI1WyeUxlZ6CgTMUVfFWQ2kJNhlM0vf3av+rsca9BhtbN5a8DzB/f/32yW/jmy9+E3u79xblVhf4Aj763EeR43NSAbnb1tymeU13mpxI5pOqhc3u3HAnQrFQyT3KaDDihq4bVAXznVfdibHkmKZTx2Qw4e6Ndy+o3zVQXA+CfQ/xbBwfe/5j4MDByBlREAp44vITeOjuh4q2ffTio/jk/k+i1daKO9bdIT0uCALOTp0tuU6wFmLs/YB5J5HD7MDr178eHqun5D57x7o78IsLv8AtvbdIj/ndfuT5PMaT4wvqnfpbG34Lnz38Wem65bV5F/XavRSQYCUIncgjJ1q9KpX8523/Kf376ctP4/1Pvx8vTLyAPd17pMeDYbGZvN7JkRpfvPWLGBgYqHn7P7z2D1Uf99q8ePhND5c8zm5KrMqglmBlk5VG863XlU46643RYMT33/D9mrdvt7UjnAgjy2dLxN4W7xa4zC4Ew0G8bv3rSraVVsLnJm7yIiOhWAhrPWuLXv9PN/2T5lg8Vo/Uoiaei+vKE/S7/Xjs0mPSjVs5oXnL1W9Bx0iHZuEettiRzCVR4As4ED6A31j3G7qL/dhNdggQkONz0sKPWuETr01sEyT/3txWN2bSM9Jr8nweiVxCsgQDYoRms3czsoUsDo8dRrqQxgsTL2hGKwD1KsGAmNvqMDuk3+oHb/gBtrVvwyeDn8RDLz8kTZKVkQ6/24/nh5+XfiOlJRgALkQv1DzpkVuCgyPq1aQbicfikaIDk6lJKTp0OXYZaz1rS1pMVMO6lnX49e/+GgMDA+jv7y/7ut9c/5u6o6ha+N1+TKQmEElHkClkSn6Tu666S3WSbzVacf9d95c87rV5MZ2exkRqAulCukis2Uw2PPLmR8qORe7UYPcqljKgtL3yAo/9o/vR7+8vOf8cZgcee8tjWh+7xBLM3BKPv+VxdDo68fnDn8d3Tn4HyZx4DkSz0RIxKV9EOhk5iXgujmPjx6TzSd7WRwtl8atyzGZnxZw/2aJCwBfAj1/6MU5OnsTOzp3SQsH1nder7qPIEpyKoM3aVjH/3m62YyY+I24zZ69m33mnoxM//+2fl2yjFKzs9wvFQkWCdSIl2nr/vu/v8TtX/47mOBhOixPxrBhhNXAG9DjnC1rdtva2EicY497X3av6+O3rbte1GP3hvR/WNT4tpHoQ6Yj0PbDq9f/2mn/DHevuwLdPfhv/fujfMZYYQ5dzPhjAjtH94f1FgnUoNoSRxAjefc27i95LfnyqFbn75Ks+qTrGzd7NePTNjxY9xhYjQ/EQIqlIRSdAOd6x7R14x7Z31LTtcoVyWAlCJ/IIq/zf1bC7ezeMnLFk0hAMB7G1fWtRdKbZkVuCAW3BSszjtXmlvotKsWcymLCne0/J8cFQroSzAg6CIGhGA8rhsXjE4kmCoD/C6vajIBQkcVGLvVseYT0VOYVYrrrKzizfWm4LVit8wqyRodh8Lpoyqie34ytzzl+YeEGKopT7TeQk80nV6wQbp1KQskqSJyZOFL2vfEFiIjWB8ZRYCZrZ3tl4AeDi7MWaq0PKJ92syqXSkthI5JZg1tYImJ9QsgnoYubV1gr7zY5PHAewsAqobPuCUJAKq1TzHciLuzHYsXdm6oyU7w0Ap6dOYzYzq9uOr8RtcYMDJ/2OwXAQV7VchU5HJwBZxeYxsWKzmrNEfhyw3z7H53B07GjR2Ct9B16bFw6To2LNCDULr7L68mxmFvFcvOx72kw25PhcVdZOZQ6rHgunJFjzGcykZ3A6chpAqSivpYey0+REls/iwuwFdDu6KzpImgll2zJAvfAgMJ/jDYhRVHYtV5uHybdjyO8ZtVb2ZciLJarlhBPlIcFKEDqRR06qibDKcVvcuKbjmqILZTKXxAsTLzSsFUujqMYSTMzTbm+XRJCa2Av4AhiOD0sTEDmhWAgt1hZJuPjdfkTSEQzFhpApZGoSrAWhgEQuUZUlGABORUR7dy2FwuRCjp0LSkukFmqVMyOpCBwmR9F56rXPR1hXu8UqrErBKrfbKgtZDY4MwsgZxWqpOgRruQgrm6QOxYak1gbAfCVJ1jpEbUECAE5HTpdYu9n3ni6ka85dshltSBVSiKQiODt9tmbRUivy3yIYDsJj8cDn9JV+H4uYV1sr7Ld6YeIFAAvPJ2MTYra/qgSroqUTMP9dChCKJvBS/niN9x8DZ4DL4kI0G5UcCfLjaFfnLlgMFgTDQRT4AmK5WMk1Q3kcbGjZALPBLB0HesUY6xteUbCqFElqtbViW/u2+QhmXPvYY/3FM4WMaAHX8XuzXu5sDHqK5BgNRpgMJmQKGRwYPQABYusd5WespV8xm8ecnTq7LBaF5MiL6jGUv9mmtk3w2rxFLaBemX0FE6kJbPFuwVBsqOh7DIaD6HZ2lziV5AsqrPp2rUKT1Ru5EL2A2czsim9FU09IsBKETuSRk4W0ZAn4Anhx8kUpsnNo7BDyfH7ZCdaSCGueBKse5BMbVcFapmIlUNwrD5i/MbPXVjuxlxdM0VMlGJCJqKnTMBvMJW0Y9CC3ygbDQWxu21zVjZu9p7xyptpqNWt9IK8wyqLKvMADmLcytlhbSvrkBcNBXNtxLW5dc2vROauGIAjlLcFz1VyVUXBWSZKJhlA8BK/NKy0cyL/rkqiU7O+aLcFz7YEOjIrRzcW+BnksHiRyCeT5vNjWyLcPfT19OBA+gAJfqGkSvlSwMdYtwjp3nWD7q6atDzse5K1tQrEQOu2dcJvdRa1qWK9O1gO2FpjgZI4EZV/RXV27EAwHpfQRtWM5mokimUvi2MQxvKb3NdjZubPI/sra+lRCT6Vv9r0oRWbAF8DxieNI5BIVjz22aJbKiws+esSn3WSXii7pFbmAeE/NFDIIhoNwmp3ocnSVfMZQPKSrZ7Acdp25FL20LM4xOcq2ZYC4sOG2uKUCdAbOgH3dxT122TH1F9f/RdHfBb6A/eH9CPgCJdZ4ZYRV2Yu4GkwGE3xOn+SqWemFkuoJCVaC0Il8IiqvjlctAV8AvMDj4OhBAOIFc7Fzx+oBu9lJOaw8CVY9yCddatHJ9Z716HR0Fk0qGaywEoP9WxKsNURYATF/MMfndEVYuxxdMHEmqW+w3rxTOWzxZzo9rVo5tRLKyCVQ2vsSEEVDIpfAxejFovZMvMBLCy2z2eL8UL9bnPDOZmZxMnISgZ4A+nx9KAgF6ZxVI1PIgBf4omsD+zcb53BsuGRRgVWSjGfjJa072L9nM7OqUSn556wFm8mGdCGNYDgIt8WNrd6tlTeqI+wzvTj5otTWqM/Xh1guhlORUwjFRaGyHCZ1bdY2OEwOnIycBFD7IgKDfeaTkZNVt/Vps7VJFbIZoXgIa1vWFqUcpPNpHBk7suCFCiY4mSNBWYuhz9eHc9PncGH2gvT6ou3nIlhHxo8gz+fR5+tDwBfAmakz4oJTvLignBbyNIlylLN1BnoCUpEoJgjLLRSwRbN0Ia1bfDJLsNQaRed5Kxese7r3YJ1nnWqEtdvZXVWrGHa9FyAsCxeDHIdZdNMoj/GS62tPAJOpSbw88zIA0VGwxr0GN/XcJN5n586FM1NnEM1GVc8FZQ6rshdxtfjdfuk6sRyubc0CCVaC0AmbJFuN1gX1D7tu1XWwm+xFeRS7uhY3d6we2E12cOBKI6wmEqxayCcpahFWjuPQ5+vD/tH9RZUUC3wBI/ERVUFzYPQAOHDocfWU7E8L9v6s8I0ewWo0GKX3qbVvMCtg9tzwc8jxuaqtqOxckfdiVcsJY3/L27Uoq5pK1Y6ZYHX5MRwbxqHRQ+AFHgFfoOScVYOJ0nKW4AJfwHB8uKQtSMAXQEEo4NDYIakHK8Nr80r7UJvkS5+zVsFqFNvaDI4MYl/3vopFY+oN+0yPXRIL+wR8Aez1idbwYDgofh+u1TUtiiw2zI7KjoNaIzAM9pvW0mrIZDCh1dpaZJccion9mwM98ykHxyaOIctniyoQ1wITnPvD+3Ftx7UlKTPs/Ga/s1qENZ6L4/nh56XFWyYcDoQP6OrByvC7/UgX0po9OiOpCEycqWQcuzp3wWq0YnBkEKFYCO229rKL0+y8nE5PI5VP6ToHHWYHeIFHJB1Bjs/pFis2ow2vzL6CodgQAr6Aqu1Z2fpID/Lr/XKLsAJzRfVkx/hwTP36CojXkxyfw8Gxg1IUNeALYH94P3iBl+znrB+uHI/Vg2Q+iRyfW1BlX4b8OkGWYP2QYCUInbAb10J7fJqNZtzQdQOC4SAmU5M4N31u2dmBAXGCxhqPA5TDqpdKghUQJ3izmVmcmT4jPTaWHENeyJdYSl1mF2Yzs1VHYYB50ROO6xeswPzkptZ+uizCOjgyCJPBVLYSZznY55TnsJaLsJYbMxOqUg6rdT7COhwfxvMjz8NusmNHx46ic7YcbAIiTx2QW58nUhPI8bmSieHOzp2wGW14bvg5hBPhook5E0HycTNcZhc4iEKu5hxWkw3D8WGEE+EluQaxz/T4pcfR4+xBr7sXXpsXW7xbRMFaQyGxpYT9dq3W1gUtagLid2PixChOLd+B1+aV7JKpfAqTqUn43f6iCXxwJAgTZ8INXTcseKwj8RG8GHlRdfFpS9sWtFhb8Pilx8XXl3ELPHH5CWnxdlv7NrjNbgyGBzEcH9b9HciL2pRDWaGXYTVasatzl7RYovWebNGMFQbTI2LY9YCNTa/wsRgtODZ+DIAYrWa1C1iqAdtntceJPAVkuUVYAfH6zo5xtiCo/A56XD1Y416DYDiIk5MnkcglpGM04AtgJjODs1NnEQwHsaltk6o1nh2fsWys5t6pcuTfNUVY9UOClSB0wm42CxWsgHjTuTB7AQ+9/JD093LEYXaUCNZachqvJOQ3KHnVVznSpFJmC1bLqZILmlomtexGPJIQ20bozc1WRiurRR4h3blqZ9UWe3YusmMuz+cxnZ4umQDK/5aEn1URYVX0OPW7/cjxOTx68VHs7totVc4M+AK4MHsBo4lR1TGxyaO8/ZW8uJRUOEbxO1mMFtzQdQN+ceEXKAiFkgiB9F0rJvkGziAdPwtpa5Pn8+LnW+SCS8D8ZxpNjCLQM587FvAFcHT8KIaiQ8tqIs1+23pU/uQ4TtpPLed2u71dijJK/ZtdfqzzrEOXowvBkSCC4SB2rNqx4Huax+LBRGoCvMCr3suMBiP2du+Vzp1ybgFmCwfmK6Y/funxqqLM7LtSK1rHUFvcYgR8AZyfOY9TU6c0v3e2aMYEq96iS8B8cSC9wsdmsqEgFNBp78T6lvUl7XsSuQSm0lNVnyvLPcLabps/xsstCAJAX08fDo4exLPDz4IDJxX4Y8faQGgAR8fKp6bIFzn1VnfWQj5GPbnPhAgJVoLQidlghokzLajgEoNNDr9x4htosbaUNJNfLqhFWFlfTEIdNglVVn2V02HvKKlMW65qpbwFSrWUWIItVUZYa7QEGziDJDprsSMq29rMZGYgQCiNsMomFlqWYJvRJh237HWxbHGrHalFQni+wqoc1Qireb7oklbF24AvIBV0Uk64tKLZ7LFaV/zZb9DjFKMQi438Mym/6xyfE/uPLqOJNBtrvWx+bD+1nNuspRMwf+3odfdKVshfj/wapyKn6rJQwa4DDpMD1666VvU18t9Xq4CYXPAGesqfF+XocfWAA6dZeEmrSBL7PmLZmOb3zs4dthigq63N3PWAiWm9woddm9iijtTLU9aDG6hedDLB6jQ70WptrWrbZkBuCdaqJB3wBZDMJ/HDMz/EtvZtUlGmVY5V2Ni6Ed899V3RGl8mcMBeH81GRUvwAkUm+51sRlvNLRKvREiwEoROOI6D3WRfUMElxqZWsdx6NBvF3u69i547Vi+cZrHxOEARVr1YjBa4Le6K0cmAL4AjY0ck22sopl4FciERVqfZCSNnnLcEmxbHEgzMT/hqsaIq29qoNXMH5levvTavdN6qWYLln0P+PcrFNGuRUM4WXCmHdSg2VLaKp1w0lFuQUBWsc0Kh1nxJ9j3Ko5uLifwzyXPHdnXukiy11eblLSXst6qXzY8dv7V8B/IIa0l/yp4A4rk4BAh1cffI2zSVs0LLz6VylmCPxVO0eCsfm97rm9VoRaejs6IluNxvtNW7VRIouizBiRoirLEqI6xz91R2rVT2i9bbp1aJVI3cpa+gVbPRbm/HTGamYkXxPd17pF7BygXSgC+AaDYKk6G8NZ4dnxOpCcRz8YVHWF3zC1vL8XtfKkiwEkQV2E32ukRY2So3sPitJOqJ0+xEOBHGE5eewAvjYr9AirBWpt3WXjE62dfThyyfxXdPfxdPXHoCR8ePwuf0lVQnlCKsNQhWjuPgtrirj7Au0BIMiOeS2+zGtvZtNW0LyARrmaqfVqMVbrO76LtRswTLfwvWJ6/d1o6NrRulxw2cAft8+zA4MognLj2BJy49gWeGnkGukAMAqV2FfMXcbDDDbDAjlU8hFA+p/n4AcHXb1WiztsFkMKHT0Vn0XKUI60LyJZUT4cWGfe9bvFuKbLQOswM7O3cCWF65dXWPsM5NjGvNYU3kEkjn0wjFQ0VRNPZ7O81ObO/YvuBxsmNT6zjqdfditWu1aisstv0+X3Hhr7WetdICTzVtffxuP05FTknn6aHRQ9JzgiBgKlW+qi9rhQJoH3vMEjwSF9Mpqs1h5cDpjmpKEda579dj8cBtdktCtdZ+xexatZxcDHLabe3gBR4zmRnNtj6sfRhQeoyyv69bdV3ZYAQ7Pi/OXhTfd4HnN+ulTvmr1VF7XWaCuAJZ7V5d1Y1Ti1t6b8Fjlx7DTatvqsv+loJuZzcGw4P4q4G/AgBYDJayeZnEPBtaNlQsi7+7azccJge+cOQL0mO39N5S8rqt7VvBgcPmts01jcVj8eBy7DIA/Tmsazxr4DQ7scZTu43U5/RhZ+fOmtoDyFtKAPN5ZJ32zpLXrm9ZX9SuxWFywMgZiwWrTAyaDWZsatuE7e3bS1a/X+1/NR658Ih0vAPAxwIfw1s3v1U1wsr+TuaSYkubMhNDA2dAf28/Xpp+qcRtsal1E0wGE3o9pVG2tZ61Rb1oq6Xb2Q27ya5aGXMxYNEwteP6lt5bcCZypurK10vJatdqeCwerPesr8v+1resR6e9s6aJLZtUy/sQs+O5w96BazuulQTkQlnjWQMTZ8KrVr9K83W39N6CX4/8uuS8WuVYBavRWnIccByHW3pvwYHwgaoKyl3ddjV+cOYHRefpg298EFe1XoV4Lo4sn9UUHbesuQUDQwNY31L+d2Tn+Uh8BB6LR8p110Kew1pNa5QuRxeu7bgWqxyrAMzXLpBbguX9R/ViNBjR4+xZ9HZW9YI5ECLpSMW2Pv29/RiKD0kLYYzd3bvhMrtUr0EMtrDG2jLVI0d9m3cbupxdC97PlQQJVoKogq+89isL6r8l5451d2Cvb29dLn5LxUcDH8Xbt75d+rvN1lYXy/RK5zOv/oxU4bUcDrMDD7/p4aLG6GrWwB2rduBX9/wKrbbWmsYiF2t6i684zU788s2/XNDixH/d9l8wcLWZfJSW4APhA+i0d6oKwq/d/rWic5bjuKK+etFMFD6nr2ibe193r+rE5w3r34Bt7dukqOr7n3o/nh9+vqJgZRHWW9fcWvYzfTTwUakAkhyfy4enf+dp1cnoh/Z8SHUbvfzm+t/Eq1a/quqJbj158I0PlnxnAPD2rW/HXVfdtazafVmMFjzy5kd0W+sr8a7t78I9m++pyTbIRG4kJU7m17WsK3r+67d/vebzT8m+7n14+q1PV7wG/fUNf4337nxvyeMt1hY88ZYnVI/DD+z+ALKFbFXj+eDuD+LNm94MQMxv/8PH/hCDI4O4qvUqKX1A6777hvVvwE09N2la7dmiWaaQ0b2owo7z8eR4kXujEh/Z95GS89zv9uP8zHkA6v1H9fLAXQ8s21Z0Rcd4hYrif3DtH+BtW95WsvDhNDvx6Jsf1VysZffIC9ELRe+7EL546xeXbSrYUkGClSCqoJ5iTF4FcrliNVqx2VtbZO9KRm2CrsYqxyppVV2LWsUqML96zIHTPS4ACxY5CzmXWAG0dCENXuCxP7wfN/tvVp3Yq70P6xsJiBFW5TFcTrhzHIcNLRukv/t6+vDYxceQ5/NSlWDl+znMDkymJytW8bQarWWjSOV+X4vRsiALvoEzLKlYBcpXym6GsdXCQmzySkwGU82LQiyCGEmLk3ll9LPe9zI91yCz0Vw2ElnPY9xsNBed02s9axEMB/GObe8omz4gh+O4innh8oUUvfdx+XdejehR+w78Lj+eGXoGvMAjFAthU9sm3fuTo+yZu5xQugi0oqQmg6ns9aTSdcZitMBmtEkR1npY/mlhv3ooh5UgCOIKhk2wHWZH3SIui4HNZEM6n8bZqbOYzkxXlYfZYmkpawmuhoAvgFguhlORU5oR1nPT5wAs31wxYvnBRNTZqbPIFDJX9LEX8AVwcPQgcnxOcqwsNEomX1zSuy/5taEelWazfBbjyfGq+tSuJNj3PhQbEhcEG/gdeKweqWL1cg80LFeWz+yEIAiCqDtMrNWjv/BiYjPZkC6kpaq91eRhuq1uRDNR5Pk8ErlEzYKVvWcwHEQqn4KJM5VYiR0mB8aT4wBIsBKLB4sCvTAhFsO7ko+9Pl8fkvkkTkycmK8ovsAoGcdxki1Y777kgrVelWaPjB0R+48uo+Jk9cJj8cBkMOH4xHEAjT3G5ffJ5ZSmsJIgwUoQBHEFwyzB9ah+vZjYjGKENRgOYmPrxpLqulp4LKIlmK2Y19pPts3Whq3erRgcGUQyn4TdZC+xJcsnqVfipJJYGqxGK1xmF45Pzk3mr+Bjb3f3bhg4A4LhICLpSFUVerVgwkWv+GSpDMDCBTMTZ2zB7kpckGBpVewY73U1rgWW1POaKvsuGSRYCYIgrmCWc4R1NjOLI2NHqm7LwgQrswUvJO8w4Avg2MQxTKWmVHOA2WO1VPEkiIXgtXkxm5kFB25ZVVuuN6ytSTAcRCQVqapCrxbMFqzX3st6uQMLFz4+pw8GzoDB8CCAxoq1Zqbd1o7ZzCyABkdY5xY169WyiqgeEqwEQRBXMMtWsBptODJ+BOlCuibBGsvGpInOQgVrns9jMDyoWkiDPXYlR7iIpYFNrrucXVd8f+yAL4DjE8dxOXa5bqKjFvHJtlloHqTZaEa3oxujiVEYOAO6XaX9R68E2GKB2+yua8EzJWzflL+6dJBgJQiCuIJZtpZgkw2JXAJGzojd3bur2rbF2oKCUMBoYhRA7ZZgANjVtQtmgxkzmRnNCOuVaNkjlhYmpGixRBSsBaGAQ6OH6mbrlCzBVQhgtoBVD9HMrik+p68u/XSXI9Ix7vbX1P5JL2QJXnoaJlg5juvlOO5pjuNOcRx3kuO4v5h73Mtx3OMcx52b+7927XCCIAiiYcirBC8n2GRxx6odVUeH2WcOxUNFf9eC3WTHrs5d0r/VngfUe+gSRCNhoogWS4DrOq+DzWhDQSgsuEIvQyq6VEOEtR7Ch/2uV/Lvu1jHOFmCl55GRljzAP5GEIRtAAIA3stx3DYAHwbwpCAImwA8Ofc3QRAEsQQwsbbsIqxzk8Vq7cCATLDGFi5Y5WOwm0sFq8M0Zwm+gieVxNLA7Iu0WCLmm17fdT2A+kXJrKa5HNYqbKKSJbgOopn9rldyBF0eYW0kZAleehomWAVBCAuCcGTu3zEApwGsBvBGAN+ee9m3AdzdqDEQBEEQ2rCV42WXw2pagGC1KgTrAizB8jEwcSpHsgRfwZNKYmkgS3Ax7DytWw6r0Q67yV6VO8VutsNldhX1ca0V9rteyYthTEA2+hiXLMEUYV0yFiWHleO4dQB2AdgPoEsQhPDcU6MAuhZjDARBEEQpbdY2mDgT2mzLKzujxdoCl9mFa1ddW/W2ckuw1Whd8ORxW/s2tFhbVKsAt9paAQBrPWsX9B4EUS0+lw8AsK5l3dIOpEm4sedGAECXoz7TTo/Vg25ndcWO2qxtVW9TDva7rvOsq8v+liM+p3iMr29Z39D3WeVYBQDodlyZxa2aAU4QhMa+Ace5ADwD4FOCIPwvx3EzgiC0yp6fFgShZKbEcdwfA/hjAOjq6rrhhz/8YUPHuVDi8ThcruVlqSNWHnQcErVwMXMRPrMPVsPCV/2BxTkOo4UoooUo/JbqV9an8lP4h+F/gAEGuIwufMr/qQWPZyQ7AqfBiRZTsWjNCTlczlzGVbarFvweRPVcyddEXuBxLn0Om+2bl3ooTcP59Hmsta6FmVt4kaLZ/CxSQgrd5soihh2HM/kZpIW0rm308FL6JWy0boSBuzJrqAqCgJfSL+Fq29UNLbrEzqVGv0+jWQ7Xw1tuueWwIAgllRQbKlg5jjMDeBjALwVB+NzcY2cB9AuCEOY4zgdgQBAEzavp7t27hUOHDjVsnPVgYGAA/f39Sz0M4gqHjkOiGWj24zCRSyDwfdEeeFXLVXjw7geXdkBEw2j2Y5G4MqDjkGgGlsNxyHGcqmBtZJVgDsA3AJxmYnWOhwC8c+7f7wTw00aNgSAIgiCUOEwOGDkjgIXnrxIEQRAE0Vga6SG4CcDvAbiV47hjc/+9HsBnAPwGx3HnALx27m+CIAiCWBQ4jpPyWBvZbJ4gCIIgiIVjatSOBUF4DkA5o/dtjXpfgiAIgqiEx+rBdGaaBCtBEARBNDlXZpY2QRAEcUUjRVjJEkwQBEEQTQ0JVoIgCOKKgyzBBEEQBLE8IMFKEARBXHGQYCUIgiCI5QEJVoIgCOKKg1mByRJMEARBEM0NCVaCIAjiioNFVlssLUs8EoIgCIIgtCDBShAEQVxxtFhFoUoRVoIgCIJobkiwEgRBEFccFGElCIIgiOVBw/qwEgRBEESzctva2xDPxbG+Zf1SD4UgCIIgCA1IsBIEQRBXHB6LB7+37feWehgEQRAEQVSALMEEQRAEQRAEQRBEU0KClSAIgiAIgiAIgmhKSLASBEEQBEEQBEEQTQkJVoIgCIIgCIIgCKIpIcFKEARBEARBEARBNCUkWAmCIAiCIAiCIIimhAQrQRAEQRAEQRAE0ZSQYCUIgiAIgiAIgiCaEhKsBEEQBEEQBEEQRFNStWDlOK6N47gdjRgMQRAEQRAEQRAEQTB0CVaO4wY4jvNwHOcFcATA1ziO+1xjh0YQBEEQBEEQBEFcyeiNsLYIghAF8NsAviMIwj4Ar23csAiCIAiCIAiCIIgrHb2C1cRxnA/AWwE83MDxEARBEARBEARBEAQA/YL1HwH8EsB5QRAOchy3AcC5xg2LIAiCIAiCIAiCuNIx6XmRIAg/BvBj2d+vAHhzowZFEARBEARBEARBEHqLLv3rXNElM8dxT3IcN8Fx3DsaPTiCIAiCIAiCIAjiykWvJfj2uaJLvwXgIoCNAD7YqEERBEEQBEEQBEEQhO6iS3P/fwOAHwuCMNug8RAEQRAEQRAEQRAEAJ05rAAe5jjuDIAUgD/jOG4VgHTjhkUQBEEQBEEQBEFc6eiKsAqC8GEANwLYLQhCDkASwBsbObDlwssTcfz1j45hOM4v9VAIgiAIgiAIgiBWFHqLLjkA/DmAL8891ANgd6MGtZzI5Hj875FhjJBgJQiCIAiCIAiCqCt6c1i/BSALMcoKAMMAPtmQES0zfC02AMBUWljikRAEQRAEQRAEQaws9ArWqwRB+FcAOQAQBCEJgGvYqJYRrQ4zrCYDptMUYSUIgiAIgiAIgqgnegVrluM4OwABADiOuwpApmGjWkZwHIeeVjtFWAmCIAiCIAiCIOqM3irB/wDgUQC9HMd9D8BNAN7VqEEtN7o9NkxMpZZ6GARBEARBEARBECsKXYJVEITHOY47AiAA0Qr8F4IgTDZ0ZMsIX4sNL41QhJUgCIIgCIIgCKKe6I2wAoANwPTcNts4joMgCL9qzLCWF75WG6YzAgq8AKOBUnsJgiAIgiAIgiDqgS7BynHcvwC4B8BJAKy6kACABCuA7hY7eAGYjGfQ5bEt9XAIgiAIgiAIgiBWBHojrHcD2CwIAhVaUsE3J1LDs2kSrARBEARBEARB/P/t3XmcXFWd9/HPqbX3fU13es1OCCEJCQESOoAKCqKijMLggooLrs8zKs9sMouj4+ioMzqjzMDgIAqCoCgIAiZsCVlJSMjenXS6O53et+q9qs7zR1Xazr519a3u/r5fr35V1b236/6q+/Tt+tY591wZI2c7S3AN4I1lIRNZYUY0sHZq4iUREREREZGxcrY9rH3AVmPMi4y6nI219gsxqWqCKUxPBCI9rCIiIiIiIjI2zjawPhX9Gk3T4kZlJnnxuOBItwKriIiIiIjIWDnbwJphrf3B6AXGmC/GoJ4JyRhDVoLhsIYEi4iIiIiIjJmzPYf1IydZ9tExrGPCy0owHNGQYBERERERkTFz2h5WY8yHgNuAcmPM6CHBqUB7LAubaDITDIcUWEVERERERMbMmYYErwUagRzgu6OW9wBvxqqoiSjL72LjkQFCYYvbZZwuR0REREREZMI7bWC11tYCtcDy8Sln4spKNATDlrbAIHm6FquIiIiIiMgFO+05rMaYV6O3PcaY7lFfPcaY7vEpcWLISoj0qurSNiIiIiIiImPjTEOCbwew1qaOQy0TWqb/T4H1kukOFyMiIiIiIjIJnGmW4CeP3jHG/CrGtUxoWYmRH2Vjly5tIyIiIiIiMhbOFFhHzx5UEctCJrpUL/jcLl3aRkREREREZIycKbDaU9yX4xhjKEhP0DmsIiIiIiIiY+RM57BeEp1cyQCJoyZaMoC11qbFtLoJpjA9QUOCRURERERExshpe1ittW5rbZq1NtVa64neP/r4tGHVGPOAMabZGLNj1LJ7jTENxpit0a93jtULiQeF6mEVEREREREZM2caEnwhHgSuP8ny71lrF0a/nonh/sddQXoiTd0DhMMaPS0iIiIiInKhYhZYrbUvA+2xev54VJiewHDI0to76HQpIiIiIiIiE14se1hP5XPGmDejQ4YzHdh/zBSmJwBopmAREREREZExYKyN3fBVY0wZ8Dtr7fzo43yglciMw/8AFFpr7zzF994F3AWQn5+/+JFHHolZnWMhEAjQGkrk3nUDfP5SP4vzzzSflcjYCwQCpKSkOF2GTHFqhxIv1BYlHqgdSjyYCO1w1apVm621S45fPq6pylrbdPS+Mea/gN+dZtv7gPsAlixZYquqqmJe34VYs2YNyxYv5951L5BdXEnVleVOlyRT0Jo1a4j3vxWZ/NQOJV6oLUo8UDuUeDCR2+G4Dgk2xhSOevheYMeptp2IspN9JHhd1Lb3OV2KiIiIiIjIhBezHlZjzC+AKiDHGFMPfB2oMsYsJDIk+CDwqVjt3wkul2FBUQZvHOp0uhQREREREZEJL2aB1Vr7oZMsvj9W+4sXi0ozuf/VGgaGQyR43U6XIyIiIiIiMmE5MUvwpLakNJPhkGVbXafTpYiIiIiIiExoCqxjbFFp5Eo9mw91OFyJiIiIiIjIxKbAOsaykn1U5Caz+aACq4iIiIiIyIVQYI2BxSWZbD7UQSyvcSsiIiIiIjLZKbDGwJKyTDr7hqlu6XW6FBERERERkQlLgTUGFpdmAbC5tt3hSkRERERERCYuBdYYqMhJJiPJy+ZanccqIiIiIiJyvhRYY8DlMiwuyWSTAquIiIiIiMh5U2CNkUWlmdS09NLeO+R0KSIiIiIiIhOSAmuMLIlej3WLellFRERERETOiwJrjCwozsDjMhoWLCIiIiIicp4UWGMk0efmoqJ01tW0OV2KiIiIiIjIhKTAGkM3zC9gW10n1S0Bp0sRERERERGZcBRYY+h9i4pwuwyPbap3uhQREREREZEJR4E1hvJSE1g1O49fbaknGAo7XY6IiIiIiMiEosAaY7cuKaalZ5CX9rY4XYqIiIiIiMiEosAaY6vm5JGT4uOXm+qcLkVERERERGRCUWCNMa/bxfsWFfPirmZaA4NOlyMiIiIiIjJhKLCOgw8sLiYYtvz6jQanSxEREREREZkwFFjHwcz8VC4tyeDRjXVYa50uR0REREREZEJQYB0nH15eyr7mAI9v1iVuREREREREzoYC6zi5+ZIiFpdm8s3f76ajd8jpckREREREROKeAus4cbkM//ie+XT1D/Pt53Y7XY6IiIiIiEjcU2AdR3ML07jzyjJ+saGOzbUdTpcjIiIiIiIS1xRYx9mXrptFQVoCf/XkdoKhsNPliIiIiIiIxC0F1nGW7Pdw77vnsftID99+bo/T5YiIiIiIiMQtBVYHXD+/kA8vL+W+l2v4lWYNFhEREREROSkFVof8zY3zWF6Rzf97cjtvHNL5rCIiIiIiIsdTYHWI1+3iR7cvIi/Vz6ce2syRrgGnSxIREREREYkrCqwOykr28d8fWULvYJA/u28dh9r6nC5JREREREQkbiiwOmxOQRoPfWIZXf3D3PLjtexq7Ha6JBERERERkbigwBoHFpVk8tinluNxGW79yTrW17Q5XZKIiIiIiIjjFFjjxMz8VB7/zBXkpfq54/4NPLXtsNMliYiIiIiIOEqBNY4UZSTy+KevYOH0DL7wizf40er9WGudLktERERERMQRCqxxJjPZx0OfWMrNC6fxL8/t4auPv8nAcMjpskRERERERMadx+kC5ER+j5vv/9lCSrOS+Lc/7mdbfSf/9qFLmVOQ5nRpIiIiIiIi40Y9rHHKGMP/eftsfnrnUtp7h3n3D1/jwdcOEA5riLCIiIiIiEwNCqxx7upZuTz7pRVcWZnNvb/dyU0/fJVX9rU4XZaIiIiIiEjMKbBOADkpfh746GX84IML6eof5o77N/Dn/72e1bubGQzq/FYREREREZmcdA7rBGGM4eaFRVw/v4CHXz/ED1fv52MPbiTF7+GaOXlcVp5FboqPnBQ/bpehpqWX6pYALT2DvHvhNK6akYMxxumXISIiIiIictYUWCcYv8fNnVeVc/vlJaytbuPZ7Ud4flfTSa/b6nEZEn1uHttcz4LidD5bNYO3z8vH5VJwFRERERGR+KfAOkH5PW5Wzc5j1ew8/ilsaQ0M0tIzSGtgkOGQpTwnmdLsJMLW8sSWBn78UjWf/tlmijIS+cCSYt6/uJjizCSnX4aIiIiIiMgpKbBOAm6XIT8tgfy0hJOu/9DSEj6wuJhn3zrCoxvr+MGL+/jBi/u4akYOH1gynbfPyyfB6x7nqkVERERERE5PgXWK8Lhd3LhgGjcumEZ9Rx+Pb67nsU31fOEXb5Ce6OV9i4r45IoKpmUkOl2qiIiIiIgIoMA6JRVnJvGl62bxhWtmsq6mjUc31vGz12v52eu13LKomM9WzaAkW8OFRURERETEWQqsU5jLZbhyRg5XzsihoXMOP3mpmkc21vHY5npuXjiNz1bNYEZeCgDWWg53DTA4HKIoMxG/R0OIRUREREQkthRYBYCijET+/ub5fG7VDO57uYaH1x/iyTcauP6iAlzGsKm2nabuQQCMgfzUBGbkpbCsPIvlldlcMj0Dr1uX9RURERERkbGjwCrHyEtL4K9vnMdnqiq5/9UDPLSulrREL8vKs1lSlkmSz0Ndex91HX3sauzhu8/vhechweuiIieFitxkKnJTWFyaybLyLE3mJCIiIiIi502BVU4qO8XPV6+fw1feMRtjTn3d1o7eIdYfaGPjwQ5qWgJsb+jime2NhC34PC6WlWdxcVE6BekJ5KUmMLsglfKc5HF8JSIiIiIiMlEpsMppnS6sAmQm+7h+fiHXzy8cWdY/FGLDwXZe2dvCK/taua+6hmDYjqy//qICvnjdTOYWpsWsbhERERERmfgUWGXMJfrcXD0rl6tn5QIQDlvaeodo6h7g+Z1NPPDqAZ596wjXX1TALYuLWTEzR0OHRURERETkBAqsEnMulyE31U9uqp/5RenceWU59792gJ+uPcizbx0h2efmmrn5LCvP4qJpacwtTFOAFRERERGR2AVWY8wDwI1As7V2fnRZFvAoUAYcBG611nbEqgaJT+lJXv7P22bx+WtmsK66jd/vOMLzO4/w222HAXAZWDg9gxsXTONdCwrJT0twuGIREREREXFCLHtYHwR+CPzvqGX3AC9aa79ljLkn+vhrMaxB4pjX7WLlrFxWzsrln947n4bOfnY0dLOjoYsXdzfz97/byT88vZPLy7O59bJibphfeEzP63AorEvpiIiIiIhMYjELrNbal40xZcctvhmoit7/KbAGBVYhMrlTcWYSxZlJXD+/gL94x2z2Nwf43ZuHefKNBr786Db+9jdvsWp2Hu29Q1S3BGjsGmBJaSZ3razgurn5uFynnyBKREREREQmlvE+hzXfWtsYvX8EyB/n/csEMiMvhS9dN4svXDOT1w+08cuNdbxW3ca09ASWV2STl5bAb7cd5q6HNlORm8yXr5vFjQsKzzizsYiIiIiITAzGWnvmrc73ySM9rL8bdQ5rp7U2Y9T6Dmtt5im+9y7gLoD8/PzFjzzySMzqHAuBQICUlBSny5hyQmHLxiMhnj4wTF1PmEV5bj58kY8M/9QcKqx2KPFA7VDihdqixAO1Q4kHE6Edrlq1arO1dsnxy8c7sO4Bqqy1jcaYQmCNtXb2mZ5nyZIldtOmTTGrcyysWbOGqqoqp8uYskJhy/2v1vCdP+wlyefm7959ETcvLHK6rHGndijxQO1Q4oXaosQDtUOJBxOhHRpjThpYx7sb6ingI9H7HwF+M877l0nK7TLctbKSZ76wgoqcZL74yFbu+dWbDAyHnC5NRERERETOU8wCqzHmF8A6YLYxpt4Y83HgW8DbjDH7gOuij0XGzIy8FH75qeXcvaqSRzbW8Z4fvcaB1l6nyxIRERERkfMQy1mCP3SKVdfGap8iAB63i6+8Yw5LyrL48qNbuenfX+Wfb1nAuxYUOl2aiIiIiIicg6k5M41MCatm5/H0F1YwMz+Fu3++hXufeovBoIYIi4iIiIhMFAqsMqkVZSTy6F3L+fhV5Ty49iC3/ngdzd0DTpclIiIiIiJnQYFVJj2fx8Xf3DiPH//5YvY2Bfi/j20jlrNji4iIiIjI2FBglSnj+vkF/OU75/DKvlYe21TvdDkiIiIiInIGCqwypdy+rJRl5Vn8w9M7OdKlocEiIiIiIvFMgVWmFJfL8M+3LGA4FOavntyuocEiIiIiInFMgVWmnLKcZL7yjjm8uLuZxzdraLCIiIiISLxSYJUp6aNXlLG0PIu/fHI7L+xscrocERERERE5CQVWmZLcLsN/fXgJcwvT+OzDW1i9u9npkkRERERE5DgKrDJlpSd6eejOZcwqSOFTD21mzR6FVhERERGReKLAKlNaepKXn318GTPyUrj74S00dPY7XZKIiIiIiEQpsMqUl5Hk4yd3LCZs4a81c7CIiIiISNxQYBUBpmcl8RfvmM3qPS08te2w0+WIiIiIiAgKrCIjPnpFGQunZ/B3v91Je++Q0+WIiIiIiEx5CqwiUW6X4Z9vWUDPwDD/8LudTpcjIiIiIjLlKbCKjDK7IJXPVM3gyTcaWFfd5nQ5IiIiIiJTmgKryHE+W1VJUUYi33hmJ+GwJmASEREREXGKAqvIcRK8br7yjtnsaOjm11sbnC5HRERERGTKUmAVOYl3XzKNBcXp/Mtze+gfCjldjoiIiIjIlKTAKnISLpfhr945l8auAR547YDT5YiIiIiITEkKrCKnsKwim7fPy+c/Vu+npWfQ6XJERERERKYcBVaR07jnhjkMBMP8aPV+p0sREREREZlyFFhFTqMiN4X3Lyrm5+sP0djV73Q5IiIiIiJTigKryBl8/toZWCw//KN6WUVERERExpMCq8gZFGcm8WeXTeeXm+qoa+9zuhwRERERkSlDgVXkLNy9agbGGPWyioiIiIiMIwVWkbNQmJ7IbUtLeHxLPQdbe50uR0RERERkSlBgFTlLn11Viddt+P4Le50uRURERERkSlBgFTlLeakJfOzKcn699TA7GrqcLkdEREREZNJTYBU5B5+pqiQzycs/PbMLa63T5YiIiIiITGoKrCLnIC3Byxeuncna6jbW7G1xuhwRERERkUlNgVXkHN2+rJSy7CS+9cxuQmH1soqIiIiIxIoCq8g58nlcfPX6Oexp6uHxzXVOlyMiIiIiMmkpsIqchxvmF3BpSQb//Oweatt0mRsRERERkVhQYBU5D8YYvvOBS7DW8uEHNtAaGHS6JBERERGRSUeBVeQ8VeamcP9HL6Ope4A7H9xI72DQ6ZJERERERCYVBVaRC7CoJJMf3baItw538+mfbaazb8jpkkREREREJg0FVpELdO3cfL753ot5bX8rK7+9mv9+pYbBYMjpskREREREJjwFVpExcOtl0/n9F1eyqDSTf3x6F9f960tsq+t0uiwRERERkQlNgVVkjMwuSOXBjy3loY8vBeCO+9ezo6HL4apERERERCYuBVaRMbZiZi6/+OTlpCZ4ueP+9ext6nG6JBERERGRCUmBVSQGijOTePgTy/C6Xdz2X+vZ1djtdEkiIiIiIhOOAuuF6m2DHb8it/kV2PEreOtJ6O9wuiqJA2U5yfz8k8uw1nLDD17hzgc38uq+Vqy1TpcmIiIiIjIheJwuYMJrr4HH7+QigJ3RZUs/Be/8toNFSbyYkZfKc19eyc9er+Vnr9fy5/ev59KSDH5yx2LyUhOcLk9EREREJK6ph/VCFcyHuzew4bIfwt0boHwlVP/R6aokjuSk+PnSdbN47Z5r+Nb7LmZ3Yw/v+4+1VLcEnC5NRERERCSuKbBeKG8i5M6mL3k65M6GWddD2z7oqne6Mokzfo+bDy4t4dFPXc7AcIhb/nMtGw60a4iwiIiIiMgpaEjwWKuoitzWvASX3u5oKRKfFhRn8MRnruQj/7OBW3+yjgSvi5wUP7mpfm6YX8CfXVZCeqLX6TJFRERERBynwDrW8uZBci7UrFFglVMqyU7iic9cwa+21NPUPUBrYIgDrb380zO7+cEL+/jAkulcUZlNks9Dos9FSVYyuan+0z5n31CQsHprRURERGQSUWAda8ZEellr1oC1kcciJ5GZ7OMTKyqOWbajoYsHXj3Aw+treXDtwZHlPreLP7+8lLtXVZKd4icUtqyrbuO5t46wt6mHA629NPcMkuSBqsObWTEzl0UlmWSn+MhM8mGApp4BDrT0crhrgCsqs5mWkTi+L1hERERE5BwpsMZCRRVsfwyad0H+PKerkQlkflE6//pnC/nrG+dxuLOfvqEQfUNBnt1xhAfXHuCXm+q4bm4er1W30dIzSLLPzdzCNK6elUtpdhLr36phS20nz2w/MvKcxoDX7WIoGB5Z5nYZrr+ogI9eWcaS0kyMPlgRERERkTikwBoL5VdHbmtWK7DKeclK9pGV7Bt5XDU7j0+sqOC7f9jDH3Y2sWJmDu9ZWMSqOXkkeN0j2813NXD11VezrznA7iM9dPQO0dY7xMBwiJKsJCpykklP8vKbrYd5ZMMhnt7eyDVz8vjuBy4hc9T+RERERETigQJrLGRMh+wZkWHBy+92uhqZJGbkpfCff774jNsZY5iVn8qs/NRTbnPRtHS+dN1MHn79EP/y3B5u/PdX+eFtl3JpSeZYliwiIiIickEcuayNMeagMWa7MWarMWaTEzXEXMUqOPgaBIecrkTkpJJ8Hj65soLHP7McY+DWn6zjvperGQ6Fz/zNIiIiIiLjwMnrsK6y1i601i5xsIbYqaiC4V5omJx5XCaPBcUZPP35FVTNzuOfntnN9d9/mdV7mp0uS0REREREQ4JjpuwqMC7Y9VtIyT+3700rAm9CbOoSOYn0JC/33bGYF3c1841ndvGx/9nIipk5fObqSpZXZmtSJhERERFxhFOB1QJ/MMZY4CfW2vscqiN2EjOgaAm8/h+Rr3NReQ3c8WRMyhI5FWMM183LZ+WsXP533UH+Y001t/33euYWpvGxK8uomp1LXqo+SBERERGR8WOsteO/U2OKrLUNxpg84Hng89bal4/b5i7gLoD8/PzFjzzyyLjXeS4CgQApKSnHLEvobyS9a885PU9O6+tkt23ktSt/Rsij62TKuTlZOzxfQyHLusYgzx8cpj4QOU5kJxjK010sK/SwJN99TM/rYNDS2BtmWooLnzu2PbKDIcvRQ5fLcNL9DQYtYSDRo97h8TaW7VDkQqgtSjxQO5R4MBHa4apVqzaf7HRRRwLrMQUYcy8QsNZ+51TbLFmyxG7aFN/ngq5Zs4aqqqoLf6Lq1fDQe+C2x2DW2y/8+WRKGbN2OIq1ljfqOtlS28G2+i42HWynsWuABcXpfO36OZRmJ/HQuloe2VhHV/8wPreLBcXpLC3P4qNXlJGXdmKvrLX2rIcZW2vZ1djDa/tbeaOugy21nRzpHjhmm8rcZJZVZHNZWSaHOwd4eW8LWw51EAxb5hSksbQsk+WVOayak4vf4z7FnmSsxKIdipwPtUWJB2qHEg8mQjs0xpw0sI77kGBjTDLgstb2RO+/Hfj78a4jbpVcDm5/5JI4CqwSB4wxLCrJZFH0kjehsOWJLfV8/4V93P7f6zEGXMZw/fwC3jY3n12N3Ww42M59L9fw8PpD/N27L+LmhdMAeHFXM//6/F4OtPZSlpNMRU4ycwtTuemSaZRmJ4/s01rLgdZenn6zkd9sO8z+5gAA07MSWVqexeyCVLzRXtX+oTBv1HXw1NbD/Hz9IQDmFaZx51XlJHrdbDrYwWOb6/npuloyk7y899Jibl44jcFgmEPtfTR09HNxcRpVs/JwudQbKyIiIhJPnDiHNR94Mtq74gF+bq191oE64pM3MRJaa9Y4XYnISbldhg8smc5Nl0zj0Y11dPQNceuS6UzLiAxhf8+lRQBUtwT4ymPb+NKjW/nttsO09Q6xta6Tsuwk/uyy6dS29bLjcBfP7GjkO3/Yy9LyLG6YX8D+5gAv72uhrr0fgKXlWXzjvfN527z8055DGwyF2dccICfFT26q/5h1w6Ew66rbeHRTHQ+9fpAHXjtwwveXZCXx4eWlfGDJdNITvWP14xIRERGRCzDugdVaWwNcMt77nVAqV8EL90JPE6Se4wzDIuMkwevmI1eUnXJ9ZW4Kj336Ch549QDf+cMespJ9fOt9F3PL4mK87j9dUauxq58ntjTw2KY6/u63O0n2uVlemc0nV1Rw7dx8ijLO7lxuj9vF3MK0k67zul2snJXLylm5tAUGeXV/K5lJPkqykshL8/Pirmb+d91B/vHpXfzghX3ceVU5d15VruAqIiIi4jBd1iYeVVRFbg+8BAtudbQUkQvhdhk+ubKC2y8vweNy4fOceOnnwvRE7l41g89WVVLb1se0jMSTbjdWslP83Lyw6JhlN10yjZsumcaOhi5++Mf9/ODFffzPawf4+FUV3Las5IQeWxEREREZH7F7Vyjnr2ABJGZqWLBMGkk+zxlDqDGGspzkmIbVM5lflM6P71jM7z5/FUvLs/jeC3u54lsv8rmfb2FtdSsDwyHHahMRERGZitTDGo9cbihfGQms1sJZzqYqImNjflE6//2Ry6huCfDw64d4fHMdv3uzEZeBspxk5hSkcsuiYq6Zk3fWsx2LiIiIyLlTYI1XFVWw8zfQth9yZjpdjciUVJmbwt/eNI+vvGM2a/Y0s6uxmz1NPWyp7eSZ7Ueomp3L12+6iPKc5DM/mYgAkQnSPG4N8BIRGWsDwyH8Htek+zBdgTVeHT2PtWaNAquIwxJ9bm64uJAbLi4EIrMO/3TtQb7/wj7e/r2XuGnBNC4tyWB+UTpFGYk0dg1Q39FP98AwNy4oJDXh1JM3BQaD1LX3UZ6TTIJX14iNZ4HBIIGBIAXpp56t+lwNh8L0D4cYGArR3DNIdUuA/c0B+oZC3HlV+VlPOjaatZaB4TCJvmPbUzAUZuPBDlwmMvv2eL2hCYUt62vaeGlvCy/tbWFvUw8fXFrCX75zLin+c3sbEg5b9jT1kOB1Mz0zccyDb1ffMGmJnrh8s3e216/uHwoRthaIzCNwvseV4VCYrXWdVOamkJXsO6/niBfVLQEefO0gT29v5OKidD542XSunZsfs1NQwmGry6RdgK7+YV7b38rFRelMz0qK6b56B4P4PK5jJoN0irWWwWD4pH+ztW29tAaGSE3wkJrgwed2MRgMMzAcoqVnkFf2tbJmbzM7GrpZWpbF126Yw+LSTAdeRWwosMarzHLIKIHq1bD0k05XIyKjeN0uPrGigncvnMZ3n9vL87uaeOKNhpNu+8M/7ue7t17C5RXZxyzfXt/Fzzcc4qmtDfQOhUaGG88rTOPGBdO4dm5eTP6B9g0F+e22w6ze3UJaooeCtARy0xIIDARp6h6gsasfgyEn1UduSgI+j4um7gGaugfo6h9memYSlXnJVOamUJKVxLSMRJKjoePoP86OgfBJ993ZN8T+5gAHWns52NZLYXoiNy2YRnrSiYG+Z2CYX7/RwNPbGynKSOKqmdlcWZmD1+1ib1MPe5t6aA0MkeRzk+Rzk5LgISfFT15qAnmpfpL8bnzuP33KfPSNwOHOft6s72JbfSd17X2UZSczpzCNOQWpzClIPSYAdfQO8ast9by6v5V9TQEaOiOXWnr3JdO454Y5I5dyOlvWWho6+1m7v42X97WwtrqN9t6hE7ZzmUjQeHh9LZ+/ZiafWFGO33Pq0GGt5TdbD/OLDYc4Ev1dDQyHmZaewMKSDBYUZ1DTEuD5nU109A0DsLg0ky9fN4srZ2SfMQRtrm2nq3/4nK9VbK1lzd4WvvnMLvY2BfC6DZeVZfGeS4v4xYZDvLy3hW+/fwFXVOac8jn6h0JUtwTYfaSHV/a18Oq+VtqiPzOf20VZThKVuSlU5qYwIy+F2QWpzM5PPabOg629/H7HEbKSvVw5I4fizMgb4FDYcqi9j211naytbmVtdRv1Hf0k+dxU5CZTkZNCXqqf9EQv6Ule8lITqMhNpiQr6YQ3lMOhMPuaAuxr7uHionQqclNO+Fm0BAbZeyTAnqYeDnf2U56TzEXT0phbmIbf42JgOEzfUBC/1z0S5AeDIZ57q4lHNhxi48F23rOwiC9cO/OEN/GBwSC/fqOBh9cfYldj9zHrZuSlsKgkg8WlketpV+amjPx8hkNhdjV2c7iznySfh2S/h4HhEE9vb+T32xvp6BvG6zZcNzefDywpJtnnYcOBdtYfaKelZ5CS7CTKc5LJSfFR3dzLriPdHGzt5V0LCrnn+rkn/du+UNZa+oZC9A4FSUvwjvwurLUc6R5g5+FuDrX3ERgI0jMYZPeRHl7e24LP7eKaOXlsrevkMw9vISfFx4LiDJL9HlL8HoozE7l6Vi4XTUvDGENn3xDP7jjC6j3NeNwuspJ8ZCb7yErykpXiJyvJR36an5LsJPweN9Za1tW08fP1h/jDW02kJXqZET1WdrcOsT20D5/HxYLiDJZXZp/hVZ75Z9DVP8zhzgE21bazvqadTbXt+D1uZuSlUJmbjMftoqYlQHVLL32DQd42L5+bLy3i0ukZtPUOsba6jc0H21k1J4+q2Xmn3FdgMMgre1vY2xRgf0uAg629ZCR5mVOQyuyCNOYXpTEzLxW36+i10UP8cXczbxzq4P1LiplTcOzs/f1DIQ539TMcCjMctAyHwwwHwwyHLG29gzyzvZHVu1sYCoXxe1x8/poZfHJlxWmPgecjHLY88NoBvv3cHgxw0bQ0Fk7P5F0LCk8IetZGjhXDIYvXbXC7DP1DIbr6h+keGGYoaEeO2163i5QED2kJHlL8XlISPCR53RgDh7sG2Hqok231nSPHfgP0DgWpbeujtq2P3qEgC6dn8PZ5BayYmcMbdZ08saWeNw51nvb1uAwsKsnkE1eV8+uth7nlP9fytnn53DC/YOTMwv1HglSN6U9x/Bgb/RQuni1ZssRu2rTJ6TJOa82aNVRVVY3tkz71edjyEPhTx+b5jIEbvwfzbzn1Ni174Zd3wG2/hMzSsdmvjJuYtEM5I2sth7sG2F7fRVP3ANMyEinOTKSzb5j/98Sb1Lb3ceeV5ZTnJLPlUAdbajs42NZHgtfFjQumcdWMHGpae9nd2M2WQ520BgbJSfFzy6IiCtIT6B0MEhgMMRgMEQpbhkMWj8uQm+onL9VPfnoC0zOTKM5MPOkns72DQbbVdfLsW0d4cksDPYNBijISGQ6FaQkMcvTfQGpCJMAaQyR4RsNNit9DfpqftEQvde19tAaODVnpiV6stXQPBEeWrZiZw4eWlnDVzBxW727miS0NvLKvhXB0Xy4DYQs+j4u3zcvnqhk5DAXD9A4FOdDSy9PbG+kbCjEjL4W2wJ9qORfGgN/jImxhKHhsiE7wuijOTOJQe9/IumSfm8vKs1hansWeIz38fvsRhkJhZualMLcwjVn5KQQGQ/zPawcwBu5aUcGM/FT6h4L0DYVo7BrgQGsvB1p7GQyGKMtOpjwnmYxELzsbu9lW30VLzyAAual+VszIoTwnmUSfG7/XTVaSjxl5KZTlJNHSM8g//m4Xz751hJKsJOYWpuJ1u/C5XVTmpbByZuSN9YG2Xv7m1ztYW902Umd+WiRk7W0K8EZdB3Xt/aT6PVw7N4/r5xfQEhjiP1bvp7FrgHmFaUzLSCDZ7yE90csVldlUzc4jwevmSNcA33hmF7/ddhiAuYVp/N+3zeLauXnsbOzmhZ3NbKptZ3Z+Kitn5bK0PAu3y1DX3kd1Sy8/XXuQV/e3UpqdxP952yyum5s/8uHGxoPt/MVj26ht62NeYRql2UmUZCfhc7to6OzncGc/de39Ix8SAGQn+1g5K5erZuQQtpb9LQGqm3upaQlQ295HKNq4spN9XDkjh7mFaTy5fi97O4793ZdmJ5GW4GVfcw8Dw5F1aQkelldmc8n0DFp6Bqlp6aWmNUBbYIi+oWMnWzMGclL8JPncJETfRB9o7WUo9Kf9rJiZwx2Xl5KR5OP5nUd4YVczB1p7R9b7PK6RdmcMHP9WLMnnJj8tgY6+ITr7hinOTOSysiye3t6ItZYPLJlOUUYiLT2DHOka4NX9rQQGg8wrTOOG+QX4PC6Mgd7BEG/Wd/JGXSed0b+htAQPC0syGRwOsa2+c+RnMFqi183b5uXztnn5bK3r5NdvNIx8UGAMzM5PpTgzMfImO/o3lJXsY25hKlnJfp5+8zBZyX7uffc83nVx4ciHIh29Q2yq7WDjwXZqWnopz0liTkEaswtSyUr2RX6mXvcJQxqrWwLc/+oBnt1xhK7+4ZHfNUSOP3mpftp6h074ACjB6yI31c/7F00fmfE9FLa8vLeFxzbXRQLCYJDAYHDkuFaQFvlgYuPBdoZDlqKMRPweF+3R38XxXAaKMiMfXtW195OW4OGmS6YxHApT3dJLdUuArr5hRv+Kv/KO2Xy2qnLkNT731hG+9/xe/B4XhemJFKQnkJXsI8Uf6U3rGwqxr7mH/c0Batv6aA0MMhz60zMWpiewtDyLUNhS3RL5mwhbS2l2MhU5yRgDa/a0MBgMk53sG/ldul0Gay1/9+6LuGN52cjzDYfCrN7dzG+2HeaFnU0MBsMYA8WZiZRlJ9PZN8zeph4Go2041e9hYUkGaQleVu9pHvmb8bgMn766ks9dM4PB4TAPro1cB72r/9TH89xUPzctmMY1c/L4xYZDPL29kcrcZD53zQwuLkqnPCdlJByfzHAozJv1nby2P/KBYLLfTZLPQ3ayj5n5KczMT6VnIMhf/HIb62rauHZOHuU5yWyr72R7QxeDwTCfrarkS9fNwut20djVz9/8egcv7Go+5T7PxBhI8Ljpj07c6PO4yBk1aiHB66YkO4my7GSS/W5e3tvK9oaukfWz81O5ZXERs6K1BwaDDA6HSPBG/l5SEzwsKc0a+YCodzDIA68e4Ccv1xAY/NP/5VQvbP+Hd5336xgPxpjN1tolJyxXYB0bMQkKbdWw6QGwJ++tOGfbH4eixXDbI6fe5qVvw+pvwDu+Ccs/Ozb7lXGjwBp/+oaCfPOZ3Tz0ei0QeTN9aUkmK2flcPPCohOu9RoMhXlpbwuPbKzjj7ubR96Y+dwu/B4XHrfB7XIxFAwdExCPKkhLIDPZFxk25PfQ2DXA7iPdI+HwXRcXcvuyEhaXZmKMIRgK09Y7NNLLMNpQMMxwKDwSMo7q7BuiuiVAfUc/jV0DHI6GirxUP3lpCazbuosNrZ5jwkZRRiLvuXQaS0qzKMtJpjgzkT1Henh8cz2/2dpwTCBN8rm5acE0bltWwoLidKyFnY3drKtuw2KZlZ/K7IJU8lIT6B8O0TcUpGcgSEvPIM09g7T0DDIwHBr5chmD3+smwesiJ9nPxcXpzMxLweN2EQyFOdjWx87GbjYeaOf1mjb2NQdIS/DwvkXFfHDp9BN6COo7+vjm73fz9JuNxyz3eVyUZkV6nPxeN7VtvRxo6SUwFKQiJ5lLijNYUJzO8socZuWnnNXwzpf2tvCj1fvp6htmOBRmMBge+blmJfsIDATxe1187fo5fGhpyUnfyHX0DpHkdx/TQzEYDPHIhjp+v6Nx5A1QW2CIwGCQJJ+bKypzWFfdynDY8tmqSkqzk/jBC/s42NZHaoKHnoEgxsCsvNSRsObzuAiHLcFom81I8vLFa2dy+7LSkw697BsK8pOXathW38mh9j7q2/sJhsMUpCVQmJFIUUZitLcohZn5KcwY1TN4vMFgiENtfWyr7+LVfS28ur+V1sAQhcmGO1bM4r2XFtEzEOS1/a28tr+NwWCI2fmpzCpIZV5hpJfzVG+Ch4JhuvqHaezqH/lA4kjXAAPDIfqHIx8iVealMK8wjcrcFFbvbubnGw7R2DUAgNdtWF6Zw8qZOcyblsas/FSyk300dPazo6GbXY3dWGtJ9HlI9LoYCIZp7h6kqWcAn9vF+xYVcWVlDi6XobGrnx+t3s+jG+sYDllS/R5y0/xcOj2T2y8v4dLpGSdtV9Zaalp72VLbwZZDnbxxqAOfx8Xi0kwWl2ZSlp3MwHCIwGAQayNDxkf/3Q8FwyMfOF1WlklG0p/ebIfClsBA8Jih1DsaurjniTfZ0dBNss+NBcLRoeoQOZ6VZB/7gdFomUleZuVHRj0c7hrghV1NeN0u3jm/gOLMJFITPCT53HT1D9PUPUhzzwAZiT4uKkpjXmEa5TnJpCZ4z2nIb0vPIGv2NLN6TzPVzb1cPTuXmxZMY35R2sjrCoYibaGjb4j23mEOd/ZTE20TgYFhblwwjXctKDzhg8PVq1dz1cqr6RsK8Te/3sFT2w7z/sXF3HPDHP7pmV08saWBWfkp5KclcKRrgMaugWOCBkQ+UJyZl0J5Tgp5af7oiBI/C6dnUJyZeMzvPRS2WGuPGTHSMzDMszuO8Or+Vmblp3LljBwqc5P58qNbeWFXM5++upI7ryrjF+vreHh9Lc09g2Ql+7hxQSE3XTKNi4vSj3ldkWNnL2/Wd7G5toPNtR209Q5x3dx8blpQyKyCVL75zG5+taWe6VmJdPYO0zMY5Lq5+bxrQQEJHjcetwuv2+Bzu/C4XST53Cf8La7e08zf/mYHde2R416C10VZdjIpfg9J/sjfTCgMwXBkeOyOhm4Cg5HjU4o/EvZHf8ABkSDt87j4+k3zuHXJ9JGfXe9gkHufeovHNtdzaUkG119UwL//cT/BcJi7q2ZQkp1EMGQJhS2JPjfpiV7SEr343C7C1hK2lqFgmJ7o6SOBwSA9A8PR+yFKs5NYOD2DuYVpZ2ybjV39vLa/jTkFqSO9/ucqMBiktWdw5MOSjRvWc+s7rznn5xlPCqwxNiGCwu++DG8+Bl87AO5TDNP5n3dC7Wsw8x1w+y/Htz65YBOiHU5R+5p68HvcTM9KPOt/PN0Dw4RClmT/yS8LdHQIblP3AHUdfRxq6+dQex9d/UP0DERCXGayl8WlWSwuzeTS6CfgsbZmzRpWrLyal/e1sL6mnZWzcri8PPuUYWMoGKape4Akn5tkv8fxCSPae4dGenpOp669j8Fg5FzRRG/kzcvxocdaGx3aNnbD2Zp7Bnhtfyuv7G0l0efmi9fNJC/1ws+rDYbCvF7TztPbG1m9u5mLpqXxtzfNozQ7MqnYcCjMrzbXs+FAO5dXZnPNnDxyUvz0D4V4/UAba/e34vO4KM9JoSI3Mpt2ku/szzw62Zvs8xUOR4bg7ty8jlWrVl3w852rYCjM6j0tDIfCrJiZc9rz2M9Hz8AwHpfrhPOU40kwFObRTXVUN/eODJdMT/KypDSLBcWR8HM09OxtCtDdPxz9ACpEfUcfe470sLcpgMdt+PDlpdyxvGzCXhN79P9may3ff2EfP3hxHx6XwQJ3V1XyuWtmHnOcHw6FR0KP3xPpKY7FcTEYCvP1p97i4fWHRnr7r56Vy4eXl7JyVu4Fn5ryyr4WvvOHvRRnJnJ31QzmTUs78zcd5+iQ+12N3exs7Ka2rY++oSC9Q5Hz/10ug9dt8LgMcwrTuGpGDssrsslM9o2cDtLSM8jeph72NPXQ2jPER64oHTm2He+32w7zl09sp2cwyIqZOXzjPRdTkh3bc2nHw0R4j6jAGmMToRGw8zfwyw/DnX+AkmUnrh8MwD+XARbcfrin9tTBVuLShGiHMumpHUq8UFuc2Ky1WMuEn8DoZO3wN1sbeHxzPX/x9tlcMj3DkbqOstby4NqD1Hf0c9uyEiqPOwd7Kmro7GfvkR6qZufG5SRs52MiHA9PFVg16dJUUrYCMJGZh08WWA+tg/AwLPk4bLof6jdB6fLxrlJEREQEY8ykvRT9zQuLuHlhkdNlAJGf88euLHe6jLhSFD0tQeKD83M4y/hJyoJpCyOB9WRq1kR6Vq/+KhjXqbcTEREREREZBwqsU01FFdRvgMGeE9dVr4aSyyG1AKZdCjWrx708ERERERGRoxRYp5qKKggHoXbtsct7mqD5rcj6o9vVb4KBbkRERERERJygwDrVTL8cPAknDvc98HLkdnRgtaHIjMEiIiIiIiIOUGCdarwJkWG/xwfWmjWQkAGFl0QeFy8FT6LOYxUREREREccosE5FFaugeWdkGDBELrpVswbKV4Irek03b0JkhmAFVhERERERcYgC61R0dNjvgZcit23V0F0PlcddXL1iFbTshu7GcS1PREREREQEdB3WqalgASRmwgt/B1v+F3pbI8uPBtmjjj5++P2R7ceaPw3e+2NISDu77bsb4cW/h3d+G/ypY1+PiIiIiIjEFfWwTkUuF6z8KmSWgg1Hrs+6+KOQedxFo/PnwyUfgoT0yHZj+TXQBXuehoZNZ1/3vj/Atp9HbkVEREREZNJTD+tUtfyzka/TcbkiPaCx0NUA35sHHQfP/nuOblu9GubfEouqREREREQkjqiHVZyRWghu3/kF1po1kYmiRERERERkUlNgFWe4XJBRen6BtasO2mtiUZWIiIiIiMQRBVZxTmbZuQfW8pWR+7rcjoiIiIjIpKfAKs45l8A60AX97VB5LaQVK7CKiIiIiEwBCqzinMyyaBDtOPO2HbWR26zyyOV2DrwM4VAsqxMREREREYcpsIpzMssit2fTy3p0m8wyqFwFA53QuC0mZYmIiIiISHxQYBXnnG9g1XmsIiIiIiJTggKrOCezNHJ7toE1MRMS0iElD/LnQ83qWFYnIiIiIiIOU2AV5/hTITn37APr0R5ZiJzHeuh1GOqLTW0iIiIiIuI4j9MFyBSXWQbtB868XccBKLzkT48rqmDdD+F3X4qE3ng3/31QtNjpKkREREREJhQFVnFWZhnUbTj9NuEQdB6CeTf/aVnpFZA9A3b9LqbljYlgf2SCqI9OgFpFREREROKIAqs4K7MMdjwBoWFwe0++TXcDhIPHDgn2JcPnN49HhRfuub+CDfdFhi/7kpyuRkRERERkwtA5rOKszDKwIeiqP/U2o2cInogqVkFoCA6tc7oSEREREZEJRYFVnHU2l7aZ6IG1dDm4vLoMj4iIiIjIOVJgFWedbWA1bkgrHoeCYsCXDNOXKbCKiIiIiJwjBVZxVmohuH1nDqwZ08E9gU+5rqyCI29Cb5vTlYiIiIiITBgKrOIslxsySs4cWCfqcOCjKlZFbg+scbQMEREREZGJRIFVnJdZNvkDa+FC8KdrWLCIiIiIyDlQYBXnnS6wDnRDX9vED6xuD5SvgOo1YK3T1YiIiIiITAgKrOK8zDIY6IT+jhPXddb+aZuJrqIKug5BxwGnKxERERERmRAUWMV5IzMF1564bqJf0ma0o+exaliwiIiIiMhZmcDTrsqkcTSMbn8M2vYfu27/C8duM5FlV0YuzfPmY+BPi8ku8pp2wvbWmDz3MSqqIDkn9vsRERERkSlNgVWcl1kO3iRY98OTr08rhsTM8a0pFoyBWe+ATffDobUx2cU8gF0xeepjXXIbvPc/x2FHIiIiIjKVKbCK8/wp8KUd0N9+8vXJueNbTyzd8M9w+Wdi9vTrN6xn2dJlMXt+AJ7/W6j+Y2TyKGNiuy8RERERmdIUWCU+JGdHviY7txdyZsbs6fuTGmL6/ADMuh72PAMteyBvTmz3JSIiIiJTmiZdEpFzU1EVudXkUSIiIiISYwqsInJuMksj5x0rsIqIiIhIjCmwisi5q1wFB1+F0LDTlYiIiIjIJKbAKiLnrqIKhnqgYbPTlYiIiIjIJKbAKiLnrmwFYDQsWERERERiSoFVRM5dUhZMW6jAKiIiIiIx5UhgNcZcb4zZY4zZb4y5x4kaROQCVVRB/UYY7HG6EhERERGZpMY9sBpj3MCPgBuAecCHjDHzxrsOEblAFVUQDkLtWqcrEREREZFJyuPAPpcC+621NQDGmEeAm4GdDtQiIudr+uXgSYBdv4WsSqerkTiS2NcArfudLkNEbVHigtqhxIOE/kanSzhvTgTWIqBu1ON6YJkDdYjIhfAmQOkV8MZDkS+RqGUAG5yuQkRtUeKD2qHEg0XeNLjhQ06XcV6cCKxnxRhzF3AXQH5+PmvWrHG2oDMIBAJxX6NMfuPdDv25t5HuWzhu+5OJYWBggISEBKfLEFFblLigdijxoG8oRGCCZhUnAmsDMH3U4+LosmNYa+8D7gNYsmSJraqqGpfizteaNWuI9xpl8lM7lHiwZs0aFqkdShxQW5R4oHYo8WAiv0d0YpbgjcBMY0y5McYHfBB4yoE6REREREREJI6New+rtTZojPkc8BzgBh6w1r413nWIiIiIiIhIfHPkHFZr7TPAM07sW0RERERERCYGJ4YEi4iIiIiIiJyRAquIiIiIiIjEJQVWERERERERiUsKrCIiIiIiIhKXFFhFREREREQkLimwioiIiIiISFxSYBUREREREZG4ZKy1TtdwRsaYFqDW6TrOIAdodboImfLUDiUeqB1KvFBblHigdijxYCK0w1Jrbe7xCydEYJ0IjDGbrLVLnK5Dpja1Q4kHaocSL9QWJR6oHUo8mMjtUEOCRUREREREJC4psIqIiIiIiEhcUmAdO/c5XYAIaocSH9QOJV6oLUo8UDuUeDBh26HOYRUREREREZG4pB5WERERERERiUsKrBfIGHO9MWaPMWa/MeYep+uRqcMYc9AYs90Ys9UYsym6LMsY87wxZl/0NtPpOmXyMcY8YIxpNsbsGLXspG3PRPxb9Bj5pjFmkXOVy2RyinZ4rzGmIXpc3GqMeeeodf8v2g73GGPe4UzVMtkYY6YbY1YbY3YaY94yxnwxulzHRBk3p2mHk+KYqMB6AYwxbuBHwA3APOBDxph5zlYlU8wqa+3CUdOU3wO8aK2dCbwYfSwy1h4Erj9u2ana3g3AzOjXXcB/jlONMvk9yIntEOB70ePiQmvtMwDR/80fBC6Kfs9/RP+Hi1yoIPB/rbXzgMuBu6PtTcdEGU+naocwCY6JCqwXZimw31pbY60dAh4Bbna4JpnabgZ+Gr3/U+A9zpUik5W19mWg/bjFp2p7NwP/ayNeBzKMMYXjUqhMaqdoh6dyM/CItXbQWnsA2E/kf7jIBbHWNlprt0Tv9wC7gCJ0TJRxdJp2eCoT6piowHphioC6UY/rOX3jEBlLFviDMWazMeau6LJ8a21j9P4RIN+Z0mQKOlXb03FSxtvnokMtHxh1WoTaocScMaYMuBRYj46J4pDj2iFMgmOiAqvIxHWVtXYRkeFFdxtjVo5eaSNTgGsacBl3anvioP8EKoGFQCPwXUerkSnDGJMC/Ar4krW2e/Q6HRNlvJykHU6KY6IC64VpAKaPelwcXSYSc9bahuhtM/AkkaEcTUeHFkVvm52rUKaYU7U9HSdl3Fhrm6y1IWttGPgv/jTETe1QYsYY4yUSEh621j4RXaxjooyrk7XDyXJMVGC9MBuBmcaYcmOMj8jJy085XJNMAcaYZGNM6tH7wNuBHUTa30eim30E+I0zFcoUdKq29xTw4ejMmJcDXaOGyYmMqePOBXwvkeMiRNrhB40xfmNMOZEJbzaMd30y+RhjDHA/sMta+6+jVumYKOPmVO1wshwTPU4XMJFZa4PGmM8BzwFu4AFr7VsOlyVTQz7wZOT4hAf4ubX2WWPMRuCXxpiPA7XArQ7WKJOUMeYXQBWQY4ypB74OfIuTt71ngHcSmdChD/jYuBcsk9Ip2mGVMWYhkeGXB4FPAVhr3zLG/BLYSWQ2zbuttSEHypbJ50rgDmC7MWZrdNlfomOijK9TtcMPTYZjookMqxcRERERERGJLxoSLCIiIiIiInFJgVVERERERETikgKriIiIiIiIxCUFVhEREREREYlLCqwiIiIiIiISlxRYRUREzpIxJt8Y83NjTI0xZrMxZp0x5r0O1VJljLli1ONPG2M+7EQtIiIisaLrsIqIiJyF6IXZfw381Fp7W3RZKfDuGO7TY60NnmJ1FRAA1gJYa38cqzpEREScouuwioiInAVjzLXA31prrz7JOjfwLSIh0g/8yFr7E2NMFXAv0ArMBzYDf26ttcaYxcC/AinR9R+11jYaY9YAW4GrgF8Ae4G/BnxAG3A7kAi8DoSAFuDzwLVAwFr7neiF4n8MJAHVwJ3W2o7oc68HVgEZwMetta+MzU9IRERk7GlIsIiIyNm5CNhyinUfB7qstZcBlwGfNMaUR9ddCnwJmAdUAFcaY7zAvwPvt9YuBh4AvjHq+XzW2iXW2u8CrwKXW2svBR4BvmqtPUgkkH7PWrvwJKHzf4GvWWsXANuBr49a57HWLo3W9HVERETimIYEi4iInAdjzI+I9IIOAbXAAmPM+6Or04GZ0XUbrLX10e/ZCpQBnUR6XJ+PjDTGDTSOevpHR90vBh41xhQS6WU9cIa60oEMa+1L0UU/BR4btckT0dvN0VpERETilgKriIjI2XkLuOXoA2vt3caYHGATcAj4vLX2udHfEB0SPDhqUYjI/14DvGWtXX6KffWOuv/vwL9aa58aNcT4Qhyt52gtIiIicUtDgkVERM7OH4EEY8xnRi1Lit4+B3wmOtQXY8wsY0zyaZ5rD5BrjFke3d5rjLnoFNumAw3R+x8ZtbwHSD1+Y2ttF9BhjFkRXXQH8NLx24mIiEwE+mRVRETkLEQnSnoP8D1jzFeJTHbUC3yNyJDbMmBLdDbhFuA9p3muoejw4X+LDuH1AN8n0ot7vHuBx4wxHURC89FzY38LPG6MuZnIpEujfQT4sTEmCagBPnaOL1dERCQuaJZgERERERERiUsaEiwiIiIiIiJxSYFVRERERERE4pICq4iIiIiIiMQlBVYRERERERGJSwqsIiIiIiIiEpcUWEVERERERCQuKbCKiIiIiIhIXFJgFRERERERkbj0/wEyAooTIRLgMAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "duration, log, fittest_individual_r16 = evolution(\n",
    "    individual_generator=lambda: random.choices(range(16), k=16),\n",
    "    fitness_evaluation=evaluate_row_indexed_fitness,\n",
    "    population_size=5000,\n",
    "    generations=250,\n",
    "    crossover_rate=.5,\n",
    "    mutation_rate=.5,\n",
    "    mutation_function=(tools.mutUniformInt, {\"low\": 0, \"up\": 16 - 1, \"indpb\": 1/4})\n",
    ")\n",
    "print(f\"Computed in {duration:.3f} seconds\")\n",
    "plot_generations(*log.select(\"gen\", \"avg\", \"min\", \"max\"))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Fittest Individual"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positions: [15, 6, 9, 11, 8, 14, 0, 3, 1, 4, 7, 12, 10, 13, 2, 5]\n",
      "Fitness: 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": "   1  2  3  4  5  6  7  8  9  10 11 12 13 14 15 16\n1                                                👑\n2                     👑                           \n3                              👑                  \n4                                    👑            \n5                           👑                     \n6                                             👑   \n7   👑                                             \n8            👑                                    \n9      👑                                          \n10              👑                                 \n11                       👑                        \n12                                      👑         \n13                                👑               \n14                                         👑      \n15        👑                                       \n16                 👑                              ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Positions: {fittest_individual_r16}\")\n",
    "print(f\"Fitness: {abs(fittest_individual_r16.fitness.values[0])}\")\n",
    "display_row_grid(fittest_individual_r16)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Evolutionary Algorithm on a 32x32 Board\n",
    "In the following section, the DEAP evolutionary algorithm is executed to solve for a single solution of the $n$-queens problem for an $32 \\times 32$ chessboard using a row-indexed genetic encoding."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Evolutionary Computation"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed in 1269.433 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1152x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "duration, log, fittest_individual_r32 = evolution(\n",
    "    individual_generator=lambda: random.choices(range(32), k=32),\n",
    "    fitness_evaluation=evaluate_row_indexed_fitness,\n",
    "    population_size=25000,\n",
    "    generations=250,\n",
    "    crossover_rate=.5,\n",
    "    mutation_rate=.5,\n",
    "    mutation_function=(tools.mutUniformInt, {\"low\": 0, \"up\": 32 - 1, \"indpb\": 1/16})\n",
    ")\n",
    "print(f\"Computed in {duration:.3f} seconds\")\n",
    "plot_generations(*log.select(\"gen\", \"avg\", \"min\", \"max\"))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Fittest Individual"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positions: [28, 9, 3, 16, 22, 25, 2, 18, 31, 12, 4, 23, 29, 10, 1, 30, 0, 15, 20, 8, 26, 14, 27, 6, 24, 17, 11, 13, 5, 7, 21, 19]\n",
      "Fitness: 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": "   1  2  3  4  5  6  7  8  9  10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  \\\n1                                                                               \n2                              👑                                                \n3            👑                                                                  \n4                                                   👑                           \n5                                                                     👑         \n6                                                                               \n7         👑                                                                     \n8                                                         👑                     \n9                                                                               \n10                                      👑                                       \n11              👑                                                               \n12                                                                       👑      \n13                                                                              \n14                                👑                                             \n15     👑                                                                        \n16                                                                              \n17  👑                                                                           \n18                                               👑                              \n19                                                              👑               \n20                          👑                                                   \n21                                                                              \n22                                            👑                                 \n23                                                                              \n24                    👑                                                         \n25                                                                          👑   \n26                                                     👑                        \n27                                   👑                                          \n28                                         👑                                    \n29                 👑                                                            \n30                       👑                                                      \n31                                                                 👑            \n32                                                           👑                  \n\n   26 27 28 29 30 31 32  \n1            👑           \n2                        \n3                        \n4                        \n5                        \n6   👑                    \n7                        \n8                        \n9                     👑  \n10                       \n11                       \n12                       \n13              👑        \n14                       \n15                       \n16                 👑     \n17                       \n18                       \n19                       \n20                       \n21     👑                 \n22                       \n23        👑              \n24                       \n25                       \n26                       \n27                       \n28                       \n29                       \n30                       \n31                       \n32                       ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n      <th>23</th>\n      <th>24</th>\n      <th>25</th>\n      <th>26</th>\n      <th>27</th>\n      <th>28</th>\n      <th>29</th>\n      <th>30</th>\n      <th>31</th>\n      <th>32</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>👑</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Positions: {fittest_individual_r32}\")\n",
    "print(f\"Fitness: {abs(fittest_individual_r32.fitness.values[0])}\")\n",
    "display_row_grid(fittest_individual_r32)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Evolutionary Algorithm on a 64x64 Board\n",
    "In the following section, the DEAP evolutionary algorithm is executed to solve for a single solution of the $n$-queens problem for an $64 \\times 64$ chessboard using a row-indexed genetic encoding."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Evolutionary Computation"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed in 18581.660 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1152x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "duration, log, fittest_individual_r64 = evolution(\n",
    "    individual_generator=lambda: random.choices(range(64), k=64),\n",
    "    fitness_evaluation=evaluate_row_indexed_fitness,\n",
    "    population_size=150000,\n",
    "    generations=200,\n",
    "    crossover_rate=.5,\n",
    "    mutation_rate=.5,\n",
    "    mutation_function=(tools.mutUniformInt, {\"low\": 0, \"up\": 64 - 1, \"indpb\": 1/16})\n",
    ")\n",
    "print(f\"Computed in {duration:.3f} seconds\")\n",
    "plot_generations(*log.select(\"gen\", \"avg\", \"min\", \"max\"))"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Fittest Individual"
   ],
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    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positions: [58, 24, 16, 36, 25, 2, 7, 26, 35, 37, 32, 62, 57, 54, 21, 49, 12, 10, 34, 19, 30, 63, 60, 1, 39, 56, 8, 22, 52, 46, 48, 6, 38, 3, 20, 61, 9, 50, 17, 27, 45, 15, 23, 28, 0, 47, 5, 41, 31, 4, 53, 55, 43, 40, 44, 13, 18, 33, 42, 51, 59, 11, 29, 14]\n",
      "Fitness: 0.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Positions: {fittest_individual_r64}\")\n",
    "print(f\"Fitness: {abs(fittest_individual_r64.fitness.values[0])}\")"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr style=\"border-top: 2px solid #444\">"
   ],
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   "source": [
    "## IV. Conclusion\n",
    "\n",
    "The results of this notebook indicate that it is possible to solve the $n$-queens problem for moderate values of $n$ using evolutionary algorithms and genetic programming. However, finding a solution with this method is a computationally expensive process that is not on par with the performance of traditionally-designed algorithms - in some cases, several order magnitudes slower. As far as naive brute-force solutions are concerned, evolutionary algorithms are able to find solutions in less time than a backtracking depth-first search.\n",
    "\n",
    "A few observations:\n",
    "- Reducing the state space for genetic encodings can significantly reduce the population size and generation count required for finding a valid solution. As a consequence, the time to find a solution is also reduced.\n",
    "- Genetic diversity, through a large enough sample size, plays the most significant role for finding a valid solution. Adjusting other parameters generally does not improve the ability to find a solution; however, extreme values of parameters will certainly prevent a solution from being found.\n",
    "- A significant bottleneck is the efficiency of computing the fitness for the fitness evaluation function.\n"
   ],
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