14_practice_for_fb_mock_interview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "12326d04",
   "metadata": {},
   "source": [
    "# A) LeetCode List\n",
    "\n",
    "https://leetcode.com/discuss/interview-question/790428/Facebook-Phone-Interview-Questions\n",
    "\n",
    "# 1. Product of array except self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5a64486",
   "metadata": {},
   "outputs": [],
   "source": [
    "def productExceptSelf(nums):\n",
    "    \n",
    "    n = len(nums)\n",
    "    \n",
    "    L, R, ans = [0]*n, [0]*n, [0]*n\n",
    "    \n",
    "    L[0] = 1\n",
    "    \n",
    "    for i in range(1, n):\n",
    "        L[i] = L[i-1] * nums[i-1]\n",
    "    \n",
    "    R[n-1] = 1\n",
    "    \n",
    "    for i in reversed(range(n-1)):\n",
    "        R[i] = R[i+1] * nums[i+1]\n",
    "    \n",
    "    for i in range(len(nums)):\n",
    "        ans[i] = L[i] * R[i]\n",
    "    \n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72900b3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[24, 12, 8, 6]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums = [1,2,3,4]\n",
    "productExceptSelf(nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed2ad729",
   "metadata": {},
   "source": [
    "# 2. Left most column with at least a one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7251ed4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def leftMostColumnWithOne(binaryMatrix):\n",
    "    rows, cols = binaryMatrix.dimensions()\n",
    "    smallest_index = cols\n",
    "    \n",
    "    for row in range(rows):\n",
    "        low = 0\n",
    "        high = cols - 1\n",
    "        \n",
    "        while low < high:\n",
    "            mid = math.floor((low + high)/2)\n",
    "            if binaryMatrix.get(row, mid) == 0:\n",
    "                low = mid + 1\n",
    "            else:\n",
    "                high = mid\n",
    "        \n",
    "        if binaryMatrix.get(row, low) == 1:\n",
    "            smallest_index = min(smallest_index, low)\n",
    "    \n",
    "    if smallest_index == cols:\n",
    "        return -1\n",
    "    else:\n",
    "        return smallest_index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba0e8b62",
   "metadata": {},
   "source": [
    "# 3. Employee free time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c4bcd78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def employeeFreeTime(schedule):\n",
    "    ints = sorted([i for s in schedule for i in s], key=lambda x:x.start)\n",
    "    res, pre = [], ints[0]\n",
    "    \n",
    "    for i in ints[1:]:\n",
    "        if i.start <= pre.end and i.end > pre.end:\n",
    "            pre.end = i.end\n",
    "        elif i.start > pre.end:\n",
    "            res.append(Interval(pre.end, i.start))\n",
    "            pre = i\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "963eb5be",
   "metadata": {},
   "source": [
    "# 4. Subarray sum equals k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6d9e6b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Brute force --> O(n^2)\n",
    "def subarraySum(nums, k):\n",
    "    count = 0\n",
    "    \n",
    "    for start in range(len(nums)):\n",
    "        sums = 0\n",
    "        for end in range(start, len(nums)):\n",
    "            sums += nums[end]\n",
    "            if sums == k:\n",
    "                count += 1\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "168f5d22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hashmap: O(n)\n",
    "from collections import defaultdict\n",
    "def subarraySum(nums, k):\n",
    "    count = 0\n",
    "    dict_cumsum = defaultdict(int)\n",
    "    curr_sum = 0\n",
    "    \n",
    "    for i in range(len(nums)):\n",
    "        curr_sum += nums[i]\n",
    "        \n",
    "        if curr_sum == k:\n",
    "            count += 1\n",
    "        \n",
    "        if curr_sum - k in dict_cumsum:\n",
    "            count += dict_cumsum[curr_sum - k]\n",
    "        \n",
    "        dict_cumsum[curr_sum] += 1\n",
    "    \n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e57ed704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums = [1,1,1]\n",
    "k = 2\n",
    "subarraySum(nums,k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e30c170e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums = [1,2,3]\n",
    "k = 3\n",
    "subarraySum(nums,k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef8a818d",
   "metadata": {},
   "source": [
    "# 5. Copy list with a random pointer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "63ded615",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Solution:\n",
    "    def __init__(self):\n",
    "        self.visitedHash = {}\n",
    "    \n",
    "    def copyRandomList(self, head):\n",
    "        if head is None:\n",
    "            return None\n",
    "        \n",
    "        if head in self.visitedHash:\n",
    "            return self.visitedHash[head]\n",
    "        \n",
    "        node = Node(head.val, 0, 0)\n",
    "        \n",
    "        self.visitedHash[head] = node\n",
    "        \n",
    "        node.next = self.copyRandomList(head.next)\n",
    "        node.random = self.copyRandomList(head.random)\n",
    "        \n",
    "        return node\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e87a4fd0",
   "metadata": {},
   "source": [
    "# 6. Serialize and Deserialize binary tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "19e6c693",
   "metadata": {},
   "outputs": [],
   "source": [
    "def serialize(self, root):\n",
    "    \n",
    "    if not root:\n",
    "        return ''\n",
    "    \n",
    "    queue = [root]\n",
    "    \n",
    "    result = ''\n",
    "    \n",
    "    while len(queue) > 0:\n",
    "        currNode = queue.pop(0)\n",
    "        \n",
    "        if currNode is None:\n",
    "            result += 'None,'\n",
    "            continue\n",
    "        \n",
    "        result += str(currNode.val) + ','\n",
    "        \n",
    "        queue.append(currNode.left)\n",
    "        queue.append(currNode.right)\n",
    "    \n",
    "    return result\n",
    "\n",
    "def deserialize(data):\n",
    "    \n",
    "    if not data:\n",
    "        return None\n",
    "    \n",
    "    list_string = data.split(',')\n",
    "    \n",
    "    root = TreeNode(int(list_string[0]))\n",
    "    \n",
    "    queue = [root]\n",
    "    \n",
    "    counter = 1\n",
    "    \n",
    "    while len(queue) > 0 and counter < len(list_string):\n",
    "        currNode = queue.pop(0)\n",
    "        \n",
    "        if list_string[counter] is not None:\n",
    "            left = TreeNode(int(list_string[counter]))\n",
    "            currNode.left = left\n",
    "            queue.append(left)\n",
    "        counter += 1\n",
    "        \n",
    "        if list_string[counter] is not None:\n",
    "            right = TreeNode(int(list_string[counter]))\n",
    "            currNode.right = right\n",
    "            queue.append(right)\n",
    "        counter += 1\n",
    "    return root\n",
    "        "
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAABMMAAARECAIAAAD3PkEMAAAAA3NCSVQICAjb4U/gAAAgAElEQVR4XuzdDXgT550o+lnccVwp3oxJJVhPqQeClKynbAYSiawFB5mCSCOykXmK2CJybXOIOWD2EOcBcwve67oL5mC8cdxrJwfXG8PWokV0sbNBNIgUiwX5BCnFyuUZDpESEEvHC9IWhjrSdT1X5L4j+UO2JfkDY2z6n+Yp1uid9+P3zozmr/ed0Z99/fXXGCwgAAIgAAIgAAIgAAIgAAIgAAIgMGqBGaNOCQlBAARAAARAAARAAARAAARAAARAQBSASBL2AxAAARAAARAAARAAARAAARAAgbEJQCQ5Ni9IDQIgAAIgAAIgAAIgAAIgAAIgAJEk7AMgAAIgAAIgAAIgAAIgAAIgAAJjE3iASPIOa23ct3vbpvz8/K279jWcYvnw2Moec+orh998fX1+nUsY65ZhV/3G9eu3H2ajNQyHvGcd3jHW1nd893q0bK13d4+1+Nj0vG1//vrXd7feHHsm427+2IuCLUAABEAABEAABEAABEAABEAgicB4I8mAo75in/ks6wulZqTjwZusvblq3xF3KElRj/ItXPoUQaRLUsU6cLZ/3FHe7OTvj6VCYa/zU5+4Ad/h+OwRtTJVInmKyJDgY6k3pAUBEAABEAABEAABEAABEACBiRf4xriy5B1HmxwBTL60eGehhkzD+Evmqjqr71yL7WXGkDmuLB/qRilMQc27BdEiwqHAf/BjLu0Lp6sTw3AcF0Id7R2hxRrJmLN44A3mGyvrjA+cC2QAAiAAAiAAAiAAAiAAAiAAAg8qMK5IkneL43IS1doNYhiJFmKRsXCD9LpUsXBmpEIC5zphOdnO+rqwjDkLtXkmA0Og1aFzVVsOsXReIXnd2nbVnypjdBtWyz6ztJ73+HFKs6aocDmJhxxVW+rZ5wyF8zlrm9uPkcxKU2EeLW4/aAl5Tzc3f+T03cMyshauXGvSZxPCFfPuA1YujSnaW6olvOayvdZOqaqosiTHW/9GjSNdt+dtvW//XuttlJGrJj9fU7wNM9c4ulVFtSXadLSSay3bbblJmfZX6AfFwwL7iYsL44rVeumZVvdlh/OORtvbUlfN5hp3lqHwhUDbmQ5f8EnqJUNRvpZEA4cC5/j54dZPPVwXJplJLlxuKnyNjo0/uQ/Kdx/zyldXHFyvEM1c9VtqHKnLSms309xZs/mU0xPgsTQ5xazcsEGvQNVDs1v32+4uLmncpsLDAdcvmlo+8XD3BHwmSWuNhXkRYlhAAARAAARAAARAAARAAARA4OELjGt2602fT8Bwci4lRl/RBVcsN+gW0zIxsOQdh/bVfODyheVKhbzH57DU7Ds8MO9VcP9rK5epXZ1D9tx0WfaXm78kNa9oyG6vvflwW6A3O+FKa/OnGLMsl36Ccx2vqT/d90ZfeYGzdQea7VyqUrOYltx2mKtrrJ0Ynm0sWE5iXW7LrxzsyWbbTUEMcZfGRlhS8llajH5T5IocjeIvGM0iAutmOy5HJqzednfcFPB5avWQYVXB47rkx3BKvVSnfg5H6R2uQfURvrBaXPjCVTqGuOs922Q+L455+lrrGz5mg7PUumWMvNvn+NXPWr7oq33kX/IlNYVj3GcdPvGOTYG9xIZSCEZF419Y6pts19NoncGQq8B95801R4dOG+Z+XVd3ihWeydWv0dESv+t4XcO5sQ+0DqoOvAABEAABEAABEAABEAABEACB0QqMZ0xS6A4GUfCTJpHGLeWGzXqRxzJ1JeUFTDrGX6z/cZ2j7YM2PaOPpscXrd22QSPpknrON7gx2rC1QCcTZD5H/adc5y0MeyaSKU4bd5Xo0LhfDrG7zMLaHb4VBqq/uLDPccYdwpmiXSWadExwN+34qd1+3qdfR9FrC3TufbbzDVW4IBCMcYN28EidhFmTx7a7uXtZqzcXqXBMCKqJczYxistRBd1un4BTKrVscLsEFg1CYvi8hUwmIV1E45fcnk+c3Co92Z9shtKwvQjVlk/zuRvdnTc5NExLLMwrlPZQyzVUGuZL37n7A7/fL2DzY7KepdYoLN7P3e5OIzXb477CY+ladTYecvn9YSxjtoJZvIT+m1zNZV7yHWrIZNrALU5IkcooWp1LU7kadydGUjAkObjb4BUIgAAIgAAIgAAIgAAIgMBDExjPmCSehktTMKw7FIytVt+jUPkvfVwYkzMqFEaihVikyZZgwg1xGDO6yGfJxbgoTSpmIpWR4jRRXJqGY+Genv7nqcoUyqciyTMVFApAb3OB2AfkCNx1NEO1292wDT04Nn9TjR09NtaPgiuUXkIbX9fKUwRBwOnXCrVDgsLeKgz8gz+nUs/EQlecbDfPfuZBgaT6haGBpPt/uVH+JKNCoSPBMMo0TPjC4Yp9+Go6SUZqS6SLwXK0FcR8RjFHcP+qvqr8zb0nUWyJoToNLl+mfkmJh7mOS5xwze2+g6zUdBpqgZqZifnbD+/bsSn/rZqTn13nsaFP2VG+qJbP4N3Hq3Zv3bT1gMXpHZ5kWFNhBQiAAAiAAAiAAAiAAAiAAAhMkMB4xiTR+Bc5A2O56+g2SLJ3gmvAVlV+BmP0G4rUKD5MvqRE4qJoDItHHqeKliFb9fRHXQKW6Oc60lUFb62e27chni7enIiWu4HAV5H5opzXw6+SjTBUh9Maldx2mu246OSvCrhCrZ4VrVDf/4fcTndkturxneuP97/lc7T7DOuo3td4XzNS+mM+gW3eXXWKw7MY9aKVq2fbLef9vU2OyZ54UUMfZVm3y9mF7ggltC/SYhNmakp+Irefa3P+9ornhs91+rD7SqCs0iTeTNm3SJiiynLaZnd2XGa9N9z2GyhJ6cFNzNCIM2YT+BMEQAAEQAAEQAAEQAAEQAAEJkpgPGOS2Ey1ZoEEC7lamx2c+OOKAne2ufUKz32J5mdikjmUPAXzu11s5N5D/pLjSghNDaXQPYFjWP7T7fxCHMETPme9KJ9MMjN2c5wk0cBhKMDjCsV8hez3zpO/cbL+HjH/gM18gg1J5Giok7943OJK+Isd/eOfipfQYCPvPN7KduPKYVNbQ+4LHV1oqFNOZVG9/80iUF04V9JfpAyzjvOckK7ZXF5atFZPR0Ys4wAQDJrOKnxhM7dzomokkOSv2Fs/cvZkF5bufafx3VLtLEy45buO6jCwhHznrSfbA9SakoqaxsZyA4XAfT40JxYWEAABEAABEAABEAABEAABEJgEgXGNSaLRs/UbnL4G9/n6nS4zgQf5LhT1SRSvGjVoBJDI1T9vb7hkqyr1KEmMu+rjZ5D613LF0G8MDeKsb+/2PUfwV1kuLGG0moGbElEmKZRmOW07wra+XR5g5Cio83VT5Er0Cxm8vbnV3YUr1r1ZMtu6u85hP9qspouYJ2IKTpFIpTh6uM7x/ft8q0tN6F7JuRpVprX1JpogSqsWDZnayjvbUUSMKwx7Klb3vRVy1e+ocdx2Oa4YFc8lahJBIIpO74XTDizNe/IchwLJ0B8jse6ghVAvZpovuXgek69QKSOxZmq3x/5ru7/d51tKE13slf/E8CyaHni4EUqCC50O6ymfzcvlMjLhS/RcWYx8ToECeFhAAARAAARAAARAAARAAARAYBIExjUmieqVqS0pLzUuRc9BDQa7cSKL0W8u2/VadNxRpt22p3i1ikrxe9BPfVAa4449JjSGOaZllq7gNSro9fhTKNW6km0rht7vSK4q2bVBq8A553knP5MxbC8xzsP59ibLJR6fo9vwCkUsNhpQobft5uPs4NsTSc0qDXo8zd1bPC9E7vRModQvioGqeM/k0EDS7UQjqmm0JifmDRTYLpZjYT8KMofc+DjQxBRq9UajKvMr9wfoUa536RUadOsm92X/vaIDCSUvqMXfBkmRM+ieychqyaLCnVv0TDrnPGWxfhKQvGgo2W4gB0WJuOIHJSVrVSTfYTthveDD6VeK31wXGdCEBQRAAARAAARAAARAAARAAAQevsCfff311w+/lLGUEPk9SbfMUHHAqJikQbaQ670dNeeD9MbaPStGuK1yLC0ZXdrO1vIfWbyEbk91AcSCoyODVCAAAiAAAiAAAiAAAiAAAo9YYHyzWx9xpSeweO/pBttlX8dn6A5PlXbx5IaRd1yWYxfYq24vemLsEi2EkRPYrZAVCIAACIAACIAACIAACIDAQxX4U48k8ZCv4zKHzaINGwrRT1NO6pLWE7js9nVLqaWGopgfy5zUOkBhIAACIAACIAACIAACIAACIDB2gak3u3XsbYAtQAAEQAAEQAAEQAAEQAAEQAAEJlNgvE/cmcw6QlkgAAIgAAIgAAIgAAIgAAIgAAJTSQAiyanUG1AXEAABEAABEAABEAABEAABEJgOAhBJTodegjqCAAiAAAiAAAiAAAiAAAiAwFQSgEhyKvUG1AUEQAAEQAAEQAAEQAAEQAAEpoMARJLToZegjiAAAiAAAiAAAiAAAiAAAiAwlQQgkpxKvQF1AQEQAAEQAAEQAAEQAAEQAIHpIACR5HToJagjCIAACIAACIAACIAACIAACEwlAYgkp1JvQF1AAARAAARAAARAAARAAARAYDoIQCQ5HXoJ6ggCIAACIAACIAACIAACIAACU0kAIsmp1BtQFxAAARAAARAAARAAARAAARCYDgIQSU6HXoI6ggAIgAAIgAAIgAAIgAAIgMBUEoBIcir1BtQFBEAABEAABEAABEAABEAABKaDAESS06GXoI4gAAIgAAIgAAIgAAIgAAIgMJUEIJKcSr0BdQEBEAABEAABEAABEAABEACB6SAAkeR06CWoIwiAAAiAAAiAAAiAAAiAAAhMJQGIJKdSb0BdQAAEQAAEQAAEQAAEQAAEQGA6CEAkOR16CeoIAiAAAiAAAiAAAiAAAiAAAlNJACLJqdQbUBcQAAEQAAEQAAEQAAEQAAEQmA4CEElOh16COoIACIAACIAACIAACIAACIDAVBL4xiOvDH++vuqkl7/l5++ThoqDxnmRGoW9rXtrWr7gBQwnZtH6vyvVZz3ymkIFQGAUAl2uhkqzs9MfEjDJTJJ+9c2SFZj1H9+xXuX4brQ3y8mcgooNDM47asoaXHfEHZxaWVyxgcZHkTckAQEQAAEQAAEQAAEQAIEpIjCuMcmwu2Fzfk27MNo2hHn2vDuQIDWxtLiyfC09Wy7HuPbzbG+mKQpD+btlqxSaLbXvVg8LI131m3aYveEEOSZf3eW1uzgxCW+v2ri79Wby1PHfZd/furXRHf+9ca0VbjhcX0SajppWMt6m8W5zxdb8/PzdLb7YWgxk/gBN9ja/uek917gaF3+jgVrFf39QCwKXHCw/PB3XWp5fdTbOG8OTPpQ16EDYurXh8uC801VF+38sBoaEdlvtwZJVJJZC6ksPliyXY7jCWP5OJQoj0RaEpqRqm+ZZXWntu5VDw0iutSy/6lxoXHUWBqweZF8aV9lJNmLR/nNoIg+ZoWUl2PmHJnvA13F7PH6eY9s5J+QgHVSRgaqOVJP+UyI2Usr4LYW1IAACIAACIAACf6IC44okx2glfGqu+cDDJwn8fL7gS6aVz+L+i3b3wPVziOPlDEOMsbQRkrMn6i2fJYpqR9j2Yb0d5qz/s+HCf4w6Mk9Qj8CFFhtPm3aUFS0jB5JMUOay765cvSgzQcljXz2mWt1pa6iz+f44vJQMelmehpIOf+NRr5FKJRjWLQj3+ysS4rt6sPtCsHugaoH2DmKNaYJ38IRWj5rkIZcff+d/yIUmzX4sO+eYDoekpcZ7c4SaxJwSR0gZL3NYBwIgAAIgAAIg8KcrMEmzW5MHSZzXTz5rzP0W09LQ0ebiVcsi0WP4ui9M6dEV+eO/JOcZbfuDwRBGMbkLFIPnSU5M5gSjN4y2IqNJNyG1kiiWGxSjKW2y0+ASiRQLB4NBNPYYKbuzw/8EiWOBEFoTXbpc1n+nDStgTuvE9E2CnX9iMh9XLmPaOSfkcEhUzdHXZPQpE5UF60EABEAABEAABP6EBCY4kgx9YWs2n3Jeu4vNVKr1pg0rKMmVwzvrHEIYK9/oLajeo5MNxw15b6ZSK3HJvFz1MZfjvCOwTC+m+p337mw6TvJoBmjG7Imqho9ZP06qXzYVrqYjIafAnTMf/vCCJ4BlUAtXrjPpswcNaXItu/ed9mNY1XqvobJUjjbgL1tq6uzuQI/8OZ1pk5GZKeYSpxXDa923Jm5iNJfvcPdKTbfD7ua+klLq1woLl1Ni0NDtsx1pOuXyfZWuXPKqwn/Uy1QVY4fKLTcE7L1NW7173l2AYfd5z6/rmz5yckKGcvHagnwNmTKk+JDv42az1em5E21moT5b4m7cVHUWjefW5+fbY5x5W1VM5mvEfMbXZDS79UCXqXGLKkHT0ITMfdcX6DC3/UYIw0mVYYNRk4ljYZ9ldzm3+r2SpZH+uWbZuZczvFsYrImp1UZ6oHkCZ//5YeunHn+3lMzW5L1uVH3LXb/jMNuNsSX5nm2NOu9Os6BVcvYLAVnezgLh/d2eZbWly4kEtYoLvkcX6eW+ReDaLeZTLs9Nv5AmVy42FLyuRTGfOM34FGF8jjtzjvX3ZCiXri3aoJGJHcGzLU3m37DcfTmzikGv4w6JSqVodTDYO47Ku7+UqmnC8rEnGEQxA8pd8H7UkflyUZIBd4Frayi3Om72yBVLDPkmETNSeGuzGe1Ud1PldI7BtE4jVrV/CbtirUrQW/H3pREOEwzNcizbx6kMuPuU88bd1FmMrmCz4VkJFrDt2+Vg9lfoZ4lFBk6V77ikri7Ty6JWCu7MBdaPyZlXioyZHeZjdjaAkSpj8aaIJ1pCHmudxXqJw2S09odFxuhobDjgOtbUcoHlUI8v0Bo3GBl0zN+x7St1K17FXadZbNHmik2qmC+URr/zo7mau9mc6j2rUI4hR+1/b+DzqsvFc4vgqt9+Qla610gJnONX5tZ2VtzfEpauuB63xwMu85EWx1UumCpXvqg3rddSaTF9Ic4UHWnn7E0+YQdpgp1zoCboBOk4bra2s1xISi7SF+brJR/HnBIrNe6f9NYZ644ng6bH73BS6+XeU+jkM/icec1uOWZ1ev08JiWf0xgLTSoZJlys33KGWP00a3OHsv7qae//nrWtuoiJKAmXD+9s7ClAL+G7lNi9Bv4GARAAARAAgWklMKGzW2/b6t4+xTMFZVXVFa/TfGvVofM8ll1QuUWFZ+orDsUNI9E1hccTVijRpWIak5tDCp87HDdEQv5LLkNBJcIUbjtsN5SmssqK9bT/w3qxILTJxYaDxzhq7a7q6oo3cnrO1NZbOwdlQP5NRekKglhWeqTSSKGoIOxrOxegN5RW7i5U3LU2nHCLQwNxW5GoHokTc+ft3PPFlYca96+TX2k2226jLEKupipL59y1uysr3lAFT1kj5RG6HRWGLJzZ/G5tvhhTCQHHmauk8a1KdAHd095kaR96v1zgbF3VcY5as6tyf5kpm7e+fcB6E2MK36tYTeKq4sbGWOehmU9Ak1GcEadpqOJB13kPvfXgO7WVRXM9TbUWb8KBlmG16uMNnGlovkYZf1Rdu7dYk+I4dMTBp6iK9xfQaQpTdWNJDrrq7OHaHUHNGyUbDepvD+qVeLWKCz5oK+ELyzuNrPyVN/fXvFu5RYt92mxuF/clsSO+sDmE3NK3jzSWv4JfbGqOdETgdH3NmZB6Y1nlLuPcK3ZX16Dc+l9I0yX4/ZA4JonyucYKzzAyiVSaggXRc3jQctPW9o3c3KSThdmPHanLSyorSrRp7obaFhEz7GutqXFg2jfKq6t3GMkvmw4eiZkMjrIdahV/XxrxMIm0Iug4y1Kmysaf1RbTgZb3T/qSzE6PWmG6PbVHajdQ3mN7951O1f+otnG/UXa52eLq3YFDl2zOmXmleyuKXhTsdTWRY1Ngf1F16Ipct72yen+JLt1d93bfbiOw9iukcVuJaXn0S6JIpZD/GHZ+ksmWX7/qEYsPX/d6Q8INT+QF5rniTaUXUikh15GDTZcJ/bbK6ooiTbi9ptrcu9PGlB6M3+MhR/Mhd7q+dH9t9Vt64kpTw2mut4rx/om3c/anG3Y4jPe8NNLOKbA/P9jwGaH7O3SCLGJ+31JzxJEx5JTYW6kkMm7bRcKwu/ZI3S5Nj633nBlyN9c2X59n2nOgtra8kAm2Nf3KFT36hc/bOiT64u0FawwvLxTczqu9q9mLbux5DTxmKt7OAutAAARAAARAYNoITGQk6fs3u4cyFL3GULNk5CKD6WWZ+5wD3ZKIp6SK4zCJvnu+4QlmKqNjjwqtVjHDZxefuyP4rmHUkHmasaoSxrjJwMwhqRxT4XKp+5wzgAWcZ5xPrSowLVbIZCS9arNBcd1+3jeoK1JwsTIpqEqR1SkS9Q+KdAsocr4mT6sM3uTQeGWiVsTt0iSJccVK41JSkoLLFmuz065z6DrzzgWbS6rLL9DMI8lsXdF6DRGtxgxMBJqR2lurNMaAmjaPpFRGHS103hx8hRrm0CW+7JUi01IFmUmp1hUbnvGdOefFxKZh2AwMH+I8JPMHbjJyiNO0iA610qSbg4qXKAx56qDD0XvVGE9uSK36kvB8CJMSsqdlRCatLywt+QEtjvhFW5TS17AsrXEFTS+iIyOEA0ucWiUCj9lKwCltfqExh5LNJEhGp1UIgVu9kSSGM7o1Klkahs/J1SiiHcE5znvIV4oMiygyizFsNiS6FI6OSUbmsgbYW3IGBY3fRBNesWAXWsXbz/DqVUl2brF+5IrCgmUKcg6t37SW4e0IU7hitwUWGjfp6Dky2TyVyZSLf9LWMeR7hiFWcfalURwmER9ySZ5uvgRLIegcRh7w+YZ+oRGDiP5EVgaGQI8ZYhgFjtOr9DSB47OYhZQQuH03mhSfn7dtvYrKpJi1RXrKZ3f4sJDTdh7Lfb1A+ywpm6XQbjCq79nbep+6JWW+n6fKppl5MeORY9z5qQX0k9e811Hw8jvWI2UY2Q3vl2JAzl7tyf4uJR6PF/HcjYXabFI2h9FvNam72myRb3fQSHNf6XcT9PhdnhfwdEI+k5DN1xZuLy18KWOwyKBXcXbO2Pcn5iAdaecMue0XezQ/LNQ+K0PtNRZs0M6RBoecEqO1SiKTQixZradn4ugo1yyaG+Q48WjpkSpXFRauYUgZIctS6RaTwd+jwcnIgtO6tRo6m1GQjJrB3BcjvSuwTjfGvKRM9JkQawN/gwAIgAAIgAAITFmBCZzdKnC/40KXm7ZvbOprrSBI5vrDvXeKJSIIiGOPZO+7mZrcBS0N7Q52Taqnm9KkJ9oIw2crFX3vkvMo/Fynv4f0dQrea7vzP+jbKox+YiEgYJFppfEXOTm7901pGo71CEI4YSuGxC2R/BImFt8kMvrmLqZKn8DEBw7d6uRmULo5vVXB5ympGSjQHraky2W9TcOlElwQegaluN/J3ZLOndcnhhHKeXLzDXQ9pxiWUdwVD9jkSJ7DmyaulZJz+mqFk6Qs6O5EI9Jx65BwpWLpSuVF8+4tVopmmEVqzZI4fYfL5fEnhQ6v1SjAJVkanYR1nLT4uIC/0+u5JmTMisYSGJaeQTwRrSqemoYL93swgUf4ZFbfnOun5lIzsci449AFl0hxdJ9kSBA+92AKjbiTSVIlM4RQkA+5nZ5ndEUx8dHQjcXXONU/IC+h5sqCbCfPY1ywy1O31dmfXhBIDu1BSX4gZ/i+FPaP8jAhnu5jxlPF+icdkxywwtG3IhnEn0cPq1T01Qb2//V6ZlBUH5yMmpPh7+SEW1xniHMfyLcNNAlT/mcQ+w76loeQPz3swB3rzj+PVggtbCdGohFJhS4v3NTyhQ+Ts+w9ejWKYnyBAJapzeorRUJRmQL6+RbsmZjSBfFwi9fjpGaV2t64b8s5UskwzIua3NgZuMN7dPjOOTzNwJpxHaQJq9qXb4DjBFL7nb72om9kInsO+vps6HIrgYx4jiEI9IVBZMHROTN6eiLQd4AS9/lWy00/1+m74fVhWerePNPl8r6DiPlrBmt0st0MfdXpxlUlzw7r36H1gNcgAAIgAAIgAAJTWmAiI0nUUCKnaM8aJbqW7F1mSHuH3RIihDzXUilx1mJ0IdTLFlo+c7adJlJnLuyPluJsjcajYoekxLFVdMGKM+srCxcMlI890XfVEyeLuKvEq95RtyJhYg+qChr5HFJC8mvxvsT4DPFifKzLODbpKyJhKxLVIU7TxKQo4hW7ILIVCsrR/w8FQA8vTZRn7/o5utJqtdft7PjM7T5RY/2NvuwnRvHydWBBwcnQYdfom3FqNQrwkPtweZ1T8oJm4Tya0eqZD8vP9JU1io6IjAPHW3Cp9Ek0l7XL4w6TzLORFBIJ6qPAHfbkRaluU19IFW/b3nX9Y7C94+fom44eLFNX8paO7N9qBi4ddM/n0OziNUHso9EcJsM6b2jm2MCTadGI+ih2WnFuQu+SKh6/kZe4Im/3ttjvjPCnCEwc/0xoO6we4pB+/CWNZhSH7Fc50stlLaAVGOW/6PE9xXLz1JEps8P2xoEdJlHpA+uJnOKD2Xr3Jbf7sw5rne3MspLKjUyi7wfi7Jzxa5xo7ZgP0jiAY5iAkkQmXg3vOOorGryzNTnZlPr5XN1zzVX/T1+ymEJxWqNOqXdeCWEut1RVokhw7MQrANaBAAiAAAiAAAhMRYExXFyMVH2JfJY8iIZIviWTzRL/w65aLXZP3BGbgazQ/UvdVP/oIlovYXSab4VcH9rxxDdJomRCwI9GG6MLGtzAyEwylSRnYZ23eCJSumwW7vuNxXq5b6ZibO2TBRhjasWYEqP5gpnkfd/1m71VEW76OnuvxUe+aB+o/oxMcnbw+jWubw3vuebPmCVLdAkbJ5yLpRD/HmMrhm7e/1rgbvTVqst3PZBBzkGDWuJ4VijUO6wa+g9/70zH4UGmmI3gO2exsphCpTNuKq0sN1K33WhAafxLQvD+LEMdbW1BVVHZFpI1pHcAACAASURBVJNhlZaZj4fi7S8DFcAzSTLI9eOHfL7fJ6idVCLBhE4Xiy/oG1ZNk0pSMN7lEHL04j26IyyC/1bfeHV3J3cng8wkiExSGuB4vPf4kgU7Wn7lEKdujmlJGfVhMjxbcT/t/yGTmBoOTxlvzVcBf98MWZ7r/IokM3GZPHNGgLsrjZ4xZE/5HSdanOIdxQmWMe/8EnrB3OuXre4vZUqFhFAoSZ/besk3d0EkkJydKcPQCFpfWag3O3H5bPmgshP1eJhzHW91/ZFilhsKSiqqN9J3P3NfT3ZiSdCi3tUjngFGcZAmqmp/yU/LZDMCaCS4d8W11vJdh93Rn6UZUvPRyMQ0KPCJ3YnrikuLjK/pNAyF/eFe/NbiStWi1CuuFucVqUo9+Aui+BvAWhAAARAAARAAgSktMP5Ikr/pZi+z/f9xXRia4aS8dbKh2eG7HeAuWRqOXvB/UyYGE0/geJeX/czXH/v1kwifu65L5IOu3XCUDYWHSeqZpFdXvLOl1YsuTIUbNvPZ0MIVSwhMplnO9Jw93HTWGwgE2FNN5o99+F8MnQiZiuPBTtb1eSDRbV+JWhG3G8eUGJu5RLcoaDtidlzjuM/tTc1taOqvuKSk4mlY5+dOtjNRpWIKTyE1y2nuVIO5HU3O87mO17d+SWm14qN64i+jyHxsrYhfjLjW91GT2eULdLLWxuPsbK0WjcWlyElS6rG3Or7g0DV806nee+ASNBnH7rgt6NmkXwR4PsB+wgYkaJYsehSTFMfuedysL3mYN7xiicAHUuLSdGnwJusJhATe5zrWcPKGMHQ68aBsSe0KJnD6sMXFic18v5WN8yuXkQ0khHQGLnlOw/TP0MYJaRpGPG/QL0i6Y/cV5z1jcdwWsDDvPtbSIdOufBbdcZarncVaDllcNwKBG67D71vc6Imjg54XOhqrUR0mwy3FNelzqae49g9t7E0fe7bJcmmEr4mGZBJyWy1u1IVC4LzZ6lPqlpBYujr3pVTn0UPWy1zgttfedLjlKiZPMl471p0fzS/IpskrDiemVKLnzc6ilU+4HZcJOjtSBto9VELb4Qb75wGxNw+ZnUSu9vkhvZOgx1NS735x0nzUyt5Gu6r3grsTn0MNe8ZyfMU4ayfmIE1Q1f7yJAtzc7ALR5vQ8RXodLceswXm0Mo0LM4pcVQyA+0Qny/V5WOv80KI951rMp/z9856HdpUnH5JhV20OdJU6rnie6HP7a0n3fGm+A/dEl6DAAiAAAiAAAhMQYFxz24VvB/U7Ou/IzGF0Ja+W7RAX/wWZj5+fG/pXSGdZF4uKf5+ZCLeM5rc2fUtdQ09ZZXGeX0IAXv9260dv0PDFG3bb+qKy039Dy8hl2ppd4h+KhkXnrWQvte8exv3VQqpfu3NwshNSsTSzaV/RL9CcmDHEUE6m9ZsLjXNH3rVTqk1yostdf+3UFpBxS8gM0Er4qYeU2JMotpY4j/SZN5r63lKueS/aKgP+cgtaDJGw9h/2VQTxH+aE7eYQStly7ftEpqbj++13cMystT6t4z6JHfKxWaenyDzsbUiQSYYplhM8yeqdtzGyOd127YbIiNvuGpdka7R3FTZljpLrV+TFzyEZv4ObvJ2Tf+AKrW6uOgPh1sP7mjoxuXz1Po3N0Q6ltYstjYdrQlh+w0JC4/7RiLw/sQ4s6ZId8hcs8Mq/gTIopUbVgnNN9FNp/TQbyD6tkD7WEmoqbm53NqdQa9Yqcm0xi0Yk0qJ2bn6VZH9v3dBjxKi9Ws1iXIenA/OvCBzvrPTfKdH+pwWYYpRSorC+FYJdtTS9A/WIPr9lxfySvK1Q3OTDFjtSTDxdTSHSfxGpSjy3sjz/7y1qrxHnq0zrlEf+iR+wrhr5Yto/Nf7tjagnwhaqN++TStGcxImf2fRMXPre7stQTxDsaRwh0mcIRodKIuXyxh3fhQ9MrTMws1RRvZGSvmMxJrC0OgBSOIiURXuDB47bH17R1M4g1qgLdlhoFFkPvj7nAQ9LtNt2hw40lL/I3Nwhhz9gkjJpmF9Ea/+CdbFnAEe4CBNUNX+MiXM6zsLj4rHV5OQQakMJfni4TVwSqxd2Zc0gUyCfpHkGAu9Dcf/x/aW+1K5Qr1ynfbUB+gOWGzw8G4k7/lq1Uwb+6I6Oix/92pby9m5iu8z8e5CT0AFq0EABEAABEAABKaMwJ99/fXXU6Yyj3tFwgHvZzyxQCGLhrdXDr9Z11NQ+3j8ohr6BcLdnuXiTztOoV58nMGnEDNUBQRGKxBy1Ze2kLsqDXNGuwWkAwEQAAEQAAEQmLIC45/dOmWbNIUrxjuPHqg5hiaA8mh2ouXEhZ4FC+FB+A+zvwD8YepC3iAwBgHed8llP9rCkloNhJFjcIOkIAACIAACIDB1BcY9u3XqNmnq1gxNDvxvxqDZvHeHX0gjlSpj6frkPx0wdZsyPWoG4NOjn6CWfwIC4YDzWJ0tzBi35ya5E/ZPAAKaCAIgAAIgAAKPjwDMbn18+hJaAgIgAAIgAAIgAAIgAAIgAAKTIwCzWyfHGUoBARAAARAAARAAARAAARAAgcdHACLJx6cvoSUgAAIgAAIgAAIgAAIgAAIgMDkCEElOjjOUAgIgAAIgAAIgAAIgAAIgAAKPjwBEko9PX0JLQAAEQAAEQAAEQAAEQAAEQGByBCCSnBxnKAUEQAAEQAAEQAAEQAAEQAAEHh8BiCQfn76EloAACIAACIAACIAACIAACIDA5AhAJDk5zlAKCIAACIAACIAACIAACIAACDw+AhBJPj59CS0BARAAARAAARAAARAAARAAgckRgEhycpyhFBAAARAAARAAARAAARAAARB4fAQgknx8+hJaAgIgAAIgAAIgAAIgAAIgAAKTIwCR5OQ4QykgAAIgAAIgAAIgAAIgAAIg8PgIQCT5+PQltAQEQAAEQAAEQAAEQAAEQAAEJkcAIsnJcYZSQAAEQAAEQAAEQAAEQAAEQODxEYBI8vHpS2gJCIAACIAACIAACIAACIAACEyOAESSk+MMpYAACIAACIAACIAACIAACIDA4yMAkeTj05fQEhAAARAAARAAARAAARAAARCYHAGIJCfHGUoBARAAARAAARAAARAAARAAgcdHACLJx6cvoSUgAAIgAAIgAAIgAAIgAAIgMDkCEElOjvPjUkoXaznU6hXG3pzbrtbjrZbGqn01rWzX2Dcf6xY8azuy783X8/edDkQ2DbiOVe0s2ddw3BV9Pdb8JiB9mLMdOvzoip+AFkAWIAACIAACIAACIAACINAvkPLjH/8YOEBgVAIoFqr7l/uv/VfNzFElj0nE2395fvbav/2eWv3n7M/eZ2d/b9HslLHmMab0afLv3L/FfzO149PAXy7/q6dnSMlvC/w3v/d//M1z0jHlM4GJZ/z5M1l+S+Nvv6Win/7GBOYLWYEACIAACIAACIAACIDAIxCAS9pHgD4Fi+Q/t1o+8FE/NM31Wk+edbE372Iy5ZI1BaYcEu+rbuCc2S7TV2TFVl9wH9lrvhzwd/KCTFt6oIhJi7zbaas6YHEHQhgukVO6N/ao/d4Ljgt6ZoWMnP3U3d9yPMbIHlyh22c/ZmnzBgJ3BNmzOXnrjbGZ+m6m0mt1wbIG26U8hUoiXPOnPqN98DJRDsJl8973Hb7bvJCCE7OUuv+2xzDTXn/A0tHJh8K4ZBapXltalEMI7sM7f2rzd2N4OqnJ34PWYDJd3vzypl/7KvKoCakJZAICIAACIAACIAACIAACj0pgGo1JCq66jTvfOf4vQ5bW33RlSm3/V0Ng0feee2oCGQXO9eEv//mf/qnZbPngnOvLP6RlKb+T/lAG0oT2mo21N7/7PfrpMc01vlS/qcr7lyvRgJsQuPSJ75vfkUejuDEbhLwf1NT/rwx9wX/5o+Uf/7XnpR8YchdSKZzrE5fTm4pUiUiOYW9rwydz1/6Qjr7sLSVlNpOr+4uA29vV9fvr97NWqL8dCTzTn9HoXkj5PKD6Pw8Ur6blKYSSeYFWzJZ+g3f96790PvOa4btPJ62m4Lt4Wfj27KTjh7zjn/751l9v27ZOr//r2Tc++udfnL83dwkzuzfw5T7538KLz6tnf+WwfDrjr5c+8++fsH+upkceCQ1zLtcd8tuDGjmkqimz/ir3e/LOjz65xWz5ablpARqh/SalXsncv2Rnv6krO1iiyxJ7AtF8T9Ht/Vr3o7L/+l+o3r4h5EL7P3/yzWXqgQA9KQS8CQIgAAIgAAIgAAIgAAJTU2AajUni9Gule5aLjMJlS80Zad42vSIVvUolSNz3mpZIdvE/VnzB27L3wIchepW+6DUS7+FYW2vDXl+wrFSXOdasHlr62erVK3ECxbZ32hrqHMx+DT2eogQfCiOvMDtLcgPN5ZY/6is2aGQoz3kK8j63433W4fQZsijR/HOHC2Pe/HacMrhrfuXfGoj3G5znnKbF2t5+uB/wS+m8Wb3pcRmJBiF5l6UNMxavUcTJZdCqoM/Nyl5kxJokWni3o53txNjV2SrJTMa4mrnwXpu13cCsiJTPe4MSWoJhlFY79/SZts/npvaQuv7R1UR5ius7O9wYs5gaIe0MiTQdw7qDPRiGShEXIRTsFrCQgNb0LbzzE0H3Q40sNi/ZQkZmueAOqXJ6txtIDn+BAAiAAAiAAAiAAAiAwPQRGNMo2CNulmQOTWeL/ykzCRQ/UguiLxXkU5TmNR2Nruwnarlhbfrw3sLNe0rWaZlsBc1ojSVv6meyrR+4QhNVxIPnk6kyvPKgc0RDV8z1p3DDJr38Rou5Hdet0/YHb7LnGSWO3b3DR2vKXWZ75inJOKEd7/VJlc9rclWEwLY5bvc17KYnmKmMncIqXLOaP1MWbddR2Die2DPMKwWXSoWvusRYDi0SksxIETiOi6aLzGWNFC5bomOC9l9a/IRyQkM3qQRlFwwF++rFX+aw2RIUW4bu964SrthYUq8a+gWHTElleK94JoKgr2z4FwRAAARAAARAAARAAAQmXWAajUkmtumyV223KSsqDXO41rJ9nEqPXzrjvPnVk1lLTAXaux81nXL5vkpX6jaWGBeI0UToC1uz+ZTz2l1splKtN21YQQ2JMbwOBzcrp+jFmCAAp1a/UUJj0aGqkO/jZrPV6bmDZVALV64r1GeLGbDNbx7uXqkJOmyXuR5CqdtQQN+wmE+7ubCcebVo22oFzturdjjIVwmP3XvvPi5bgMrW9k177Gsdz7Y2m+1u7m6qnM4xmNZpSDzkPrS75pqqbK9JgWP8xfrdP+P1P9mjv1W/6efErgNK247DbDfGluR7tv49af0HVlNdsToawQVse3/kWrR/zypf/eY6/m9r90TH6/oh0RN0jrUJi0s1MoznZPodpdrYeyClBGpVKh4dUAv5bvplikGja73ZCD4PpjCm4YRWS55rtf+bT7+WQm/x1wIZCvGP3iXgsHxCGPNRUZztpG9JnuZB47p0TfHbC0O4GNChRfh94G4YJ/sGpn03MGp1tGQJs1yTesBNvD4BN2YONCdFSkhxgQ+GwmgaKxqc9LJhBSN70hoOBlFwiXYcZHs+NXdjnEJlmbLg+Qm6U3SgQvAXCIAACIAACIAACIAACEyqwHQakxwdTNBx1kNtrGx8t0x7v63uH+q92UUHD723SxW0Hj3pQ9f9t211b5/imYKyquqK12m+terQ+d5ht778Q9zv/Ph3ho6/SbIYOotAcVXgbF3VcY5as6tyf5kpm7e+fcB6s3dT7rzd/1JJ7aH3ShbwrT8tt/C5JbWNtZsUN05Y2qK/PoGG7S6TG/a98071myre8k6ze9AgZ9jXWlPjwLRvlFdX7zCSXzYdPIISSJh1JnW3rfkUh3W5zEc7yDUF+v5Jtimq4v0FdJrCVN1YsmS+Rk36fuvs/aGLgNPlm6tSybCUTPWreZp5w+46vOFw+PBscegRkzE6bfbg4bMunsdw8juk2LbwXfRCSmTE6YIbnuB3lOKW83O183Hugp0Vh9sErw+jUODbB2N9t8F6sn57/vr1+TtPBeUPGkZGs02TSHrHSEPui2wonc7NITH0GJ6j9c12t+MMG7XFs7Ur0fD1nDh1f4BVUiniDIlhI1q4z/xyhowOU/KRUvkLZ/jFugGAmJIIgsDvBPxoV4QFBEAABEAABEAABEAABKatwCMekzx37twvfvELpPfDH/5w2bJlE8JI5uTpslCoQtHPylswjWGp+HATxQt0xseBAJp5+G92D2Wofo0Rg59ZBlNnx+5zjsBSfczgURDNmHwyXRr/Trkw5zjLyl6pNC0VQyxyXfHda9tPnfPqNyjQS3yeNm+xOHBHP6+QnONz1zDiC2ahYoa5E4V3KPxLkeeuMSjEQIrUrdGeOdDWsZ5R9zVbuGK3BRYW/khHi89nkZlMHvf/EBNoCJVpHbP7yOH6a4EOWV7Fqkh0179EK5oi/kMuVlMfOp239fpZ6CcUndef0RSLDSNVrw3eJLItf83rvy9XPR2/oaEvPVyK0vh8NLwUYyZpWpyUAQ+XMTeauUyjpVsanXa3kV7EXe+mNAPzjUl9+RF9TJUn9k/hSovlklS7pUgrNpbSri/Wro8tgdTvKJjYEtF0WqkER3NZgyhsDrG+J2gNjgWkOH4/FESRZMht/ZLSb0wQL+Opqd09MLt1onsE8gMBEAABEAABEAABEJhUgUccSaIw8g9/+ANqMfpjoiJJ4unesTUczcxM7xtHS8FwDN1SJ3C/40KXm7ZvbOpjFgTJXDRAFPNwF6k0DfsqEiPEiZzud3K3pHPn9QdmhHKe3HwDTVYUI0nsaXlv2SmpqTghTg9FywwcT+nBomNQMzLJ/sGx2XKZ4OJuDfQ338kFuzx1W539qwSB5FAImoUROYXGCzsa3HLjXn28mxX7tsjUqLNanK6AfjXmdHHKperB44wDZaG/+C7URkGIH9Pwzgvsk4u3LemPsOPcIYnyCHlupCqW9joRi3XqY1XouTs8GQzMVMYJXgeVH/Mi7LM1tKA5un2LEPB14jV+8XlC0WWGfMnrJlXc37G842o4wio2lxYNuyUxUWmD1nfaDx/ruNu/KszfuInV1FwY6Pr0hcZN2uFtkUqfxMIobhR8V0PUYpE5MkzJBb8KeU87iZVFSeRHVTFIBAIgAAIgAAIgAAIgAAJTWOARR5IPQwZPNmNXDJuInKI9a5TiY1+jywzpQMQirpFQWaTwqYcLM1RM+BRqb6hxUabN8WZ4ovsJI1nhYrw66kV8Vgwu3mXXv4R7sExdyVu6gbhlBi6Nhk/dnM+PKu/3fB7A5sS5+64vD5n6JWXLJ05uEebsVOYuShbOSCVSPOzx+Xhs/tBk/EVz63+oTUVM37AaSopx6NmkQ9onXPd2U/r+scc0cX6p/Uyb7aIcV+hGDYGGaindlpKYDXj7Iatsk4mOH77GZNztaz1kk/6wtACNMXdz7A0p/ezQtoxQjUxtQYk2pgtcDY1Y4WbViP2IBiXFR+78u5sjaE2knniaFAsLQa+t7b6mMMlkWqGnJy11xPxHqDa8DQIgAAIgAAIgAAIgAAKPVCBZ1DUJFUOTWv88sqA/JqE4FCXKZ8mDaJjvWzLZLPE/7KrVYvdE73brrwD1kpoM2K2f8gNVEny2047rmFSOZ5Kzg9evcX1v8Z5r/oxZsgQTGYe16X6n72bvICB6VI0PJ+f2/U4GSkpkktIAx+O9dZMFO1p+5bguJhe8J8xtafqSfNp3otl+Z1i2MStkKtXcTrcdPbTnOXWyEUk0ffY5BZkisB+1ursGZci7zTXHe155q3BgkC8lg3gaC/IDQ3e9G3R6/bNiH9CKK5ZpKMxr/SiojHuPYLKKj+M93tFsCa4qFsNItNyw27xDenIceY52Eyma/3yf7/gMU0aet4QWXCKVpgjuCxy9ik4SKPLoltOZMvmIQfJoKwLpQAAEQAAEQAAEQAAEQOARCDziMUk0o3WiJrWOEk+h1SrPtjQ0ywpfVuJc2+GjF3peXTl0GGuOvnCFc++hfUGfPncBKQled3/cagvQps0a9IgXzXL65LEGs6xQp8B8dnPrl5RuPT3K0rGwv+3nZsUbeuV91vILh3RpKdP7k/ViBjidq52113LIIl2fS2E+6/sW99MbjGmYcK2l6WMhd0eeKpu/e/FHlp87mO2agTqnofHCex43Sy+mKbR2plrzjKXpY4wpLOmLbznXB67gAr123uAAJ0uXt6S95pytpuKu/pVc+jvS1K849qLDeU9h3FUy+AdGJGSmPHArIGDiTad9i+C71IHNHjz2OEero1ua/qBUTOCPssTHFXwtNWavlAlbGlwYdl8Ict7UV43x0z6EtZHn60iVqhgnNMiLnlG0yhjTN3EKDnQGnpxDJRlWjrMNrAIBEAABEAABEAABEACBKSbwiCPJR6CRqS9+CzMfP7639K6QTjIvlxR/f/hNcLhifVmZ3NJibz10yi/gcnKBtqgsTxN5Yqps+bZdQnPz8b22e1hGllr/llEf++MZyZuUQqqZHtvbOxruy+kl20rXioNXvWOUaENcYXyrBDtqafoHaxD9wsgLeSX5WgI90PWINbR4mzEbpZXp1q92VBw3uxYW9w9qSWjNYmvT0ZoQhn7wA0UoBLNYiX2Jq1/oCyTDnc4PW3ipVjtvSMhMqDbtKZ2NmtlhPeK2EeRcBa1aUlQReU7QkIXKVqT+YmDGb8h1eO/RC9ztkIDv2Ok17dncHz0R6mVqmxeNdg7NYYJf37aZW728gNlv9mWcQhriVHyCi+3PDk/PkL+o1oud0rekE7Isfd7K5EFiwOMLKlYpH1a1IF8QAAEQAAEQAAEQAAEQmBSBP/v6668npSAoBD3ixl71VvR3Lx+uBvdB+b7O1dVbVKOdczua6gjs4R+Zib+rNIw+bB5NtvHTjPo+yfibP8Da8GjvkxxnGQHbvkqffn9R7Fj0OLOCzUAABEAABEAABEAABEDg0Qk84vskH13DH8+SQ52su73VfPaeekn/w3ImqKU4rV8pdUZ/LXKCskycDcG8qpv7sEc14xafQutfTXaXY9yNRr/SZ3dg39NDGDl6MUgJAiAAAiAAAiAAAiAwNQUgkpya/TLOWvV4bXWNtqCqwLBg+ATVcebZv5lsRYEmYLX2zyZ90PySbU9kjvohRsmyGcd76I7QiRzKHVSDgL3l+kLTkJ8DHUcdYRMQAAEQAAEQAAEQAAEQeNQCMLv1UffA9CofPdf1mE+90TAZT2adXjIj1jbM2RrPZKwtiP+rmCNuDglAAARAAARAAARAAARAYCoJQCQ5lXoD6gICIAACIAACIAACIAACIAAC00EAZrdOh16COoIACIAACIAACIAACIAACIDAVBKASHIq9QbUBQRAAARAAARAAARAAARAAASmgwBEktOhl6COIAACIAACIAACIAACIAACIDCVBCCSnEq9AXUBARAAARAAARAAARAAARAAgekgAJHkdOglqCMIgAAIgAAIgAAIgAAIgAAITCUBiCSnUm9AXUAABEAABEAABEAABEAABEBgOghAJDkdegnqCAIgAAIgAAIgAAIgAAIgAAJTSQAiyanUG1AXEAABEAABEAABEAABEAABEJgOAhBJTodegjqCAAiAAAiAAAiAAAiAAAiAwFQSgEhyKvUG1AUEQAAEQAAEQAAEQAAEQAAEpoMARJLToZegjiAAAiAAAiAAAiAAAiAAAiAwlQQgkpxKvQF1AQEQAAEQAAEQAAEQAAEQAIHpIACR5HToJagjCIAACIAACIAACIAACIAACEwlAYgkp1JvQF1AAARAAARAAARAAARAAARAYDoIQCQ5HXoJ6ggCIAACIAACIAACIAACIAACU0kAIsmp1BtQFxAAARAAARAAARAAARAAARCYDgIQSU6HXoI6ggAIgAAIgAAIgAAIgAAIgMBUEoBIcir1BtQFBEAABEAABEAABEAABEAABKaDAESS06GXoI4gAAIgAAIgAAIgAAIgAAIgMJUEIJIcTW8Ijtr8TY3u0SQV04R59rw7EEnNNr+56T3XaDd8tOm6vHYXF6kC11qeX3WWf7TViVO6q35TidkbjvNO8lWT3QuonjvGUk/eba7Ymp+fv7vFl7whU/fdSyN0TcIuiDlYRmpd8t0y5t0x5DlSmb3vC4FLDjbZAZG8bqMsZTTJQo6a/J3HHs1+wr6/dWvvaXAAxDvSKS5mq1E08NGdhcRd9NCoT/IYJtxwuL4QRtGkCUgyAvLA0TfijjruynCtZRPyofAod+BxN37sG05EMwO2fRvLrbfHXvjD3iLMHt6+Ps6yscYlPJxrnnDAcWg3+ozctN8WvbKa4CbGnnbGv5+P4aw4wfWfkOzCPsuu/JrzoTFmFvPZN8A4xjwGkgu+0zU7N+fnb613xFbkYV5UC+01+Tss47iwHNTILnvVxt2tNyPr+h34mJXjJsGwEc7/D5DzRG0KkeRESQ7kI3xqrvnAw4894Jn4qowlR/ZEveWz6Fk6g16Wp6GkY9ka0o5fIHChxcbTph1lRcvI8efyaLecrV69kiZSxlyJsRwsyXfLgXfHkufoKnynraHO5vtjksTJ65Zkw+n5VgyI7LsrVy/KnKhmTJuzUJiz/s+GC/8xSZHkCMj9R9/IO+pEdRTk8ycskDJ35dY9e8rQf6VGRoI/aygV/96zZ4eRxh8Oy3Vb60VMvbl01wa17CGUEHPaeYDcH9pZ8QHqNAmbDnz2TQCjwNpOsJLl20rfMiyUDFR+4j/TJxwmjdK8plUQYr4T4DC4eiOc/ye8LWPP8Btj3wS2GFlgkq4vRq7I+FJIFMsNivFtCluNXSAYDGEUk7tA8ZA+hcdeo7FvkakyjDeaGPXBkny3HPTuqPMce0vjb5G8bvG3eTzWEoze8FBaMsVJJ3UXGwH5AY6+h9J1kOljLiAhn6Wj33oK7WgEhlRm0zGX/Q+h8f9vMChRqhbTirF/WfkQ3ekfKwAAIABJREFUajNyliMcsCNnMI1STOiJOhwMChm0SkXPGyowqSfcoYWP4jWOIklqFOnGk2Tq704QSY6xX9Fo9Q4ntV7uPeX03OmRP6czbTIyM2MyuXJ4Z51DCGPlG70F1XvEs203Z2s8eeoT31dSauGrhUUrKDFgCAdcx5paLrBct5RcoDVuMDLDv2rj2dZms93N3U2V0zkG0zoNiYfch3bXXFOV7TWhsIO/WL/7Z7z+J3v0mVjoC1uz+ZTz2l1splKtN21YQUXO7CHfWQtajaqakaU2FBRqs3A02az+ftG7mxjx/bC74e8asC3v6r/Yve+0H8Oq1nsNlZUa9092e5bVli4nUOUdvzK3trP+2HqOiBDxCFyyNB2zswGMfF6nkTrO4AXv5NPskTfNglbJ2S8EZHk79+ifYq3HWu2fefxBTJrJ6NYXGhYQmJi/g3yV8Ni99+7jsgWoPVoqLZLpfd7z6/qmj5yckKFcvLYgX0MO+WgJuMxHWhxXuWCqXPmi3rS+b8O4vRCndWiqxm42p3rPKtQfIUftf2/g86rL9eiF4KrffkJWukNu2ZV0B4hUU5zhfKKq4WPWj5Pql02Fq6MftAJ3znz4wwueAJZBLVy5zqTPJtyNm6rOomkc9fn5drTD6NLjgd+x7St1K17FXadZbNHmik0qLH53R8se9P9oXsSB36/96XYN2h/40/u2H8MLa0u16eiFiMzs36OTxanVoCyi3f23cq/1gqcrlXzBWPQKbv/58QvXvnoya4lpa4EK6aD5dT8ndlWb0Cd93H4XMxzeBYMPFl3fIcCfrdpxitx1QMwNLaGL9Tt+KS2uWund27tb8pdbxV2r8y72FHLcUPiKQiJOyY68O7s19gDsz1PMSOAcx83WdpYLSclF+sJ8vQI5xD0SY8GZpZmfnGa7MbYk37OtsWQx7z5haWnv8N0RcIJa+MqGwlUxpS8n0CTJw90rNd0OdOSiQ179WmHh8sgh378kPnz4K63mY/YO311MplS/YtqwPHoUY2inbvqVAx1K1GK9srs/o/gdFw9nWOlJexPN9tl0giw7YIz4o3l6W1ozKw6uo3pzCbvqdxzuB1l9beeBLlPjFhWGJnV/gK+eF3B8FhBmPKXo7ZeYolEHDNv/Y9/mWuKehYKtZfs4lR6/dMZ5M7LLFWjvftR0yuX7Kl2p21hiXBA51cU5W4o9bv/5YeunHvH0la3Je92ompX065qQx1pnsV7iMBmt/WGRkSEwNONrdzm3+r2SpZFSrll27uUM7xYGa8otNwTsvU1bvXve3Uj3tkLsWRv195XGLLRfoRmAVWxO2cH14pdy6DCs73nj4EYaj1vPfoWbrbvLOzR7K9ApXVzE/dBOl1fS597sRY7boujRd0Bpi+mXkpzYliaQDwfi7cwJjhS0+pbTvP9Mm9efOovRvb7ZkD00fEjwMZRoBw6xHxwyn2HRyZxevpK8auFffq94MR7/kOwniv4RcFuaLfbLXDCt/8MRE/fAU4TxOe7MOdbfk6FcurZog0YW2YeHfw6KjUy6N4rl8Ik+nuJ/BCQ4TmOrHvJ93Gy2ok/k6EdAoV40RDOH0R5uwN2nnDfuirYFmw3P9tsK7Ps7a+4Zflqija7yHdu5txO9FE/pA8vkgMSWmOTv4ad6lHjEM22CjzbsfM2W99C0WWzf620qdAZe5I9zTfIAH5FDTjuopvH38+RHbpKzItonFdyZC6wfkzOvFBkzO9BJXrwuUhmLN2lJdJjGlRnCG3C3Hm2xX/bdTSXpXGPRGkacAZSk05OWKH5IdeUwXS4XJ2CSrJx1heK5LnaJW6Wb1vKKFunr1aXLCEzwte4tt88pqdwks0U+eU33qvqvIf/+Ve4ffyndVl3ERC7bhMuHdzb2FKCXseek4ZW/Yy0vNXsFzFW23r4i5rw6cRfVoWt2yzGr0+vnMSn5nMZYaBKvXmKWRFcvcT+aB13NbtWw1TZlRaXq05hPsVI5ypu/bKmps7sDMfFC9EyVtI9iayVexUU/ZBNd3w5qxCN48Sc6u/UPf/iDNbKgP8asLrhtFwnD7tojdbs0PbaGE+5BX5ZkF1RuUeGZ+opD6BpdzDt0yeZK1xWXV2xbLmWPNtnE2x4E9hdVh67Iddsrq/eX6NLddW9b0PEzaAn7WmtqHJj2jfLq6h1G8sumg0fcIUzCrDOpu23Npzisy2U+2kGuKRCvOW7b6t4+xTMFZVXVFa/TfGvVofPiTV18+6GqY9epNaXVB8rWzvE1vWNG139xF/JvKkpXEMSy0iOVRmogNgu5jhxsukzot1VWVxRpwu011eJxLi7JEVCCm611de34suLKvaV5sg7rOa6nt+Aert0R1LxRstGg/jZvb6yxdqF619ZWlRmpzpb3rb2z1QW27TK5Yd8771S/qeIt7zSjtkeKDTjOXCWNb1WiaKqnvcnSPmROf8jRfMidri/dX1v9lp640tRwmosWG68X4raOZLLl1696xHzD173ekHDDE3mBea54U+mFIs6IbUdJbjtsN5SmssqK9bT/w/re7rjYcPAYR63dVV1d8UZOz5naemsnxhS+V7GaxFXFjY1oh4lbpWjLWfsV0ritxLScliTo7mhLh/w/9d3s1Gve62Kvhdir1wXB5/ky8uJz1jObpmXo+4g4tRqaFWqyizD+pLFxn4H4bVP5O87M9ZWN75atTLnQ9CE7aM9N2O/xDoRhB0u0XGKRWsm7ndejr0Ksi019QTUwb0pwWxrt2NLi/dXVZX87lztR33Ilpr4J8hQPup8fbPiM0P0d4i9ift9ScwTdiJH4SBT6wFesLdlfQKcpTNWN6Oqc+3VdTTuWW7y/tqay5BXCe7TZ1jlUiztv554vrjzUuH+d/EqzOXLID17i7kI3WmvetvHfNZVVVe56lfQdqzp0LnJr5jVLzXuu6KGkkzrbrvR6x++45DjRWoy+N4fWO/I6RVUcAxKbRLjmcKbo97z9zju7ddiZmt7696WIX+GY7ROchVCKoOOsh9oo7nLa+211/1DvzS46eOi9Xaqg9ehJH7qPIP7ZEgucaWi+Rhl/VF27t1iT4jh0xJHsXtfIudo5M690b0XRi4K9rgYdngkWQrejwpCFM5vfrc3vCyNRUoKm53Deq5F7BH7HeroEvxd9ayRWj73ao1igxBPUc6CUOSrNHJ/zUu+9YPwl1/XZatWcgfeTtShxvySST7Azxz1SxDqwZ+1CTkFFRamBvNHyfkQ+dkl0XkqwAwfO1tWcDjH5ZZVlJuXNU9bem04TH5L9ZXV7zdXoKNQUVVRXblspdTdEPhzFRfjC5hByS98+0lj+Cn6xqTnyARH3czCRSUyDknw8xbsGSNDMWCHU5KrjHLVmV+X+MlM2b337gPVm9H20h7OUqbLxZ7XFdGCwLa58iXmS7XB2RVKG0e5xl14cO/UPfUk3OSCxTUn2d7xP28Td2n+mTfDRJlla0rhDS8zUlr5/pCRHSHxNMs6PyOGnnTj7+YhHbuKjT9wnMd2e2iO1Gyjvsb37Tqfqf1TbuN8ou9xscaH9M7FMv7Hgs7xdY8fUb/wE7fAa7FxdfVtghE5PVqKYL9fezi8pfaf2nR+vy3C/N+Rcl6BKc/SFepI9YXF1CdypppN/0BSsZ/q/zohl/MtFSxYKbufV3otF9qIbe14zaOZz3D12lr6irkiVRhp+cuTd2PPqsM/0se1g/Ywhd3Nt8/V5pj0HamvLC5lgW9OvxG8oBpZEVy+JPpqxmKvZvjlZQ3ensK/tXIDeUFq5u1Bx19ofL4y0V8Q9vhJe38ZNPZkr/xQjSRQ9lpWV+SML+mPMwWQKsWS1np6JYxKFZtHcIMcNuTrBU1IxDEf/iy74PF3BOo1iDsWs1jNpHIdCm5DTdh7Lfb1A+ywpm6XQbjCq79nbBl+QC1fstsBC4yYdPUcmm6cymXLxT9o60GmHUJnWMYFTh+sbzR2yvIJVJCrF9292D2Uoeo2hZsnIRQbTyzL3OUcA453n3NLlhaYcSiajNOsLTS9lYgkiSSwFF6udguGxQ3x3Ltgu4rkbC7XZpGwOo99qUne12aKB80gI6FzMLVi7+RWazKRU67fp58d8GZWlNa6g6UW0LEWQMXlFr+vpTIJADitV8nt+f/RutBR57hoDGuvBcFK3Rou7Im1HSxpj2GRg5pGUyqijhc6bvYFi3zFzl+cFPJ2QzyRk87WF20sLX8pI2AsJWkctoJ+Mhl7oWlDKMLIb3i97rwWzv0uJuY3UdjGNhDGies4hqRxT4XKp+5wzgAWcZ5xPrSowLVbIZCS9arNBcd1+3heRx7AZGI6EElQp0gQp8/08VTbNzJMk6O5IqmELrlxId3tYdE2MAmMfySzAfF6EJlxnvRkLGDJRrYbkk0Kovy+O4OGzFtJzsIwXVuvmSbA0ilkgD97mgjGJk/R7nAMBde/gg6U3J0Ktpu+6f+sTX4ZY5xUpmu0yUEg3z3fh0pkyYqaMWmwq2fmGNuZSGyWLn2fIbb/Yo/lhofZZGdqZjQUbtHOkwWRH4gB477GcgnpIwGapTRtN2vmodJJekZstDfj9Q7AwXLHSuJSUpOCyxdrstOviIT9kibcLsXY7pzQWr1VRs0jFsoLilwn3GTva1Nve7s82RA8lVP/VWdFDKcHuNBKOWJFR9+bQave/jlZBBBm8pKvz1kW+LJdpjCtl7vNot+9fElQ4NoO4Z6FIAjInT5cl7nL0s3KM1BiWkniKRPECnXE7ELiPJTpb8nwIkxKyp2VEJq0vLC35AZ385m98ft629Soqk2LWFukpn93hG9y8mFczMPEsPyN10AkTDWX+FRn9HopH30LRDNUZ+R7qjocNKJjn8ET1jMmXVKso32+jbrzTdZ1UqcRTfN8yQovi90si+QQ7c9wjJVIBubawYBlNzqF1r+TI7/h80XNyX90SnZcS7MAcip3mri4yqihyDmPYbFA9Ecko2SHZW5Lwma2tS23aomfmyMhsXdHGXKzd5ox+DOOMbo1Klobhc3I1iugHRNzPwUQmfY0R/03y8RTnGiBBM2MyDItNlr1SZFqqED8W1xUbnvGdOeeNpiCX5OnmS9CxSecw8sAgW/xZjSqddV6KcN9wuroWLhm4gI9UdJJAYtqS9M84p/pk3TryR5v44YilPqSPyOEXP8P381EcueJFn7gMPyuifdLAEDhGMIwCx+lVeprA8VnMQkoI3L47mh0e89rbA8zajehKCe3w+sLXVyskPSN0epISI9XEFxhMOeJYB8EY9c9xg851iTuLWl2oT3dYGhsaPrynfn3DoN0w9uwtYdQM5r4YuagVWKcbY15Sxn5aJKy82Mfo4hlH16Kxy4RcVGM9UuWqwsI1DCkjZFkq3WIy+Hs0ODmwJLp6SfTRLG45cDXbl8+QT7EUifoHRboFFDlfk6dVBm9yvdcLyfeKQa3vf5Hw+jZ+8klc+6c4u/X8+fMLFy4sLCyMOqOXer1+LOYEgc4KkQVPwzFB6BttS5CHjJT3vpMqfULgBQG7xXWGOPeBfFv/FgKm/E90QT4wwYDv5IJdnrqtzoEkAsmh67IsjMgpNF7Y0eCWG/fqI3M7Be53XOhy0/aNTX2JBUEy19/Ddd7CqOV91yFpCt06BUrAJqhmnNW3AgEsE02I7X1LQlGZgrPTj4nZJEfguc678uzeuXnoupKiMtCFV3TB5fK+dsro3FzfJVvrbzq52xz3pYcL9wUMMzLJ/vBgtlwmuLhbka3T5TI0I1FccKkEH2ZPalap7Y37tpwjlQzDvKjJVfV9ZRanFxK0bhWtEFpQ6EWia0GFLi/c1PKFD5Oz7D16NToZip2dvO2Rys1WijMnIws5j8LPdfp7SF+n4L22O/+D3vVYWMDwgIBRfa8xLBH4M+LVv/zpaEck6O4wFpnHNWyRKOhn/A5xnMTrSaONL/rqLqLHQQnuzzE6n0Jz8BLVKva8j5rcVzqOp2JPEb0dmDr0UzNZv2PDu6D/65ahtZaoX6Sb/3/23ga6ievOG57aO45XqjcyiWTWU9djEik8nvqJoJXIa8GLzAbRjcgTwVPELiLHNg8xh5hdYl4wJ+Cu1y2YB+MTh6ydLF5OZE4sWkQOdk4Q24gEiwX5DVILYjnjl0jEHpeOC9IGixLpcT01fe/oy/qYkWUwhpB74xM0d+79///39/+4nzNz0kH9BJdccZACed2zcSXylJpl1vZ3Xnc8LSXkC5QqtSoKdTKZ+GsfTTOY+vvRZoEOABxBHDRze+L34wGPp4JiCq3wmt1ynGItlqKoO+OqVK6i/KiFA5dHuF6+lWpCfjCqkJTgsRCAleBCCz3C+AMjo/nFUVfKxvEioR1wnPByKy4jcDLUZmrDpshB5+KF4VPowOfnYujIiDe2bcUnMJJ49JeHg+ipCDAoGGTkiSLrQ2DlK+SQfNFStWS57IJp12YLTsjlC5WqxVPwysfx6FknMV6U7x0BZ7/i53E8wsVl46XEd23gCIAyeJUuXlQj+arLQzELfK4hXF6bxysniOqxJFYoZb2uSz6tJtvh/BJTViYIIJ1mi1iy/MhzGzOnpyA0oCSeG4UnVyhAmHF2nBhLfHHJ7+M04LER+kY+FsM7T1oyF2V5ZNA5em/QTJFaGrU0dJ60GHHR4Z3/vHxReEaKoDm5KHN3HJng6gcnSDtPNI6Le/zdE0cXwIY+Dj+NR+guaLKwZF5MoSLZPIlpGKxEhzrUqIUDwVkZ4vd7s6VKRf5+pyO4VE1/7gyUgdfcxNNFZguQBKbpLlJDPZ9aEyItnwkhkycQH0QXmdKSVDvnizDxnptCJpoRs0nQdyL5or8Km1gOWD5G/pTpaHBUTGCxscwinR5sKn6UgRdwcgzJJfkeFu1rBJKCxFjHpywwQEXxVa9qnHssvvK6nQsTrTCh/aj8/5Ijhx3kmJy46nChirrn4hwL4bfYuEjIiye4kbmBxQ2qEZFUrRa4zvWar3vpEWrYQyHFyjgufKMXvq4ZAe2PG83yySvB5kbaLgTzhXEGPPvGpvRWwU2Mf3zLXX72cr+NM8nZQzfECSxaJ/hQmD0qXbVrS/wIGH1ychrJFpkYRwo1dds0k+OILLAPE6o8RlNe0Id73V/4kCIQY9n+XFRes3u1LDZbQ7KEouwhZ5hXyv/H43uppBNKCYUTRgrsnXSFU9jcTclhM0AADa0wstRoa8te81f44kUEUa7QLnV1tNMcddixYmSNCs1iQ2OaJCqvPVCqdV10uS5fsrRbTy+ta94gZ+tzaIGndbmEXHrIdpXGPHRxGSFFcO8FN/UkSc9Tsg87TrFsEBUNTOri53Xs3j9gh8rXNVeXxbXgieiaRKQej0js3dDWJZv41B0hkfKPiPgB1n3VTWeTQVxLzBdiH3o8w0F3oHQV+1B7JlKlkEyfwa13ThXwEhL8UEl0Wxy/W1niJIUL68IPTEZLC+Trm99ZRjqcLtJl6/zU6qhprlvCSypyg+/4Bacnsqv/McDjKTOe43v2f8oQ5UpZWYXq5XHb/tjyzWQxLmObSjxwn9O5slLsfdIMOM0J5QInMbZkIEvYzMIFIz3ftGsxSILw92VpKJ/6wlLxRctsTX2r0uNyXLrscp1os3ymbfiZHjxhzpvYHfJIymFdOAX8uykemkRrHlE6YSaH3cEvxbI1BHZ13OyhyBEKKzMAHfj45IwnUgB2JXucF33KbOdQkbImemgqUqSIq0W87Qnf4EOex5j5oU6rBd64FLcvHZInGhjH7yLcW8ScLhnfxqQIE3c5ZQcRJcOHSRybDLunSI0UU+Fc14tvRfR3uGYaqwQFpS+AqeQlxy2MvhiQr0t43IwlMzuAcAnPmccdADnVmhBpeU0ojkuKA06GzXvuIpMbwWHnmXhuMpnI9dQ2yYkMD7XJ7PtUenz11LDGL5L/Oj2ahTBfkkNjCiK6mpMqLEqolNkdjoEg4nQJFUk9eDqLTSWVmjMNA4uvfMve0dTpmasqL8WVz1do5ne3/GcKbc7RC3fXDOrGjWZTKKXPmNoquOpzjm/TTOi5aDyQPP5O44GweySILlmy5NKlS8ZQAj/A5WyLJZYUZvnoUaG4QMz+Pem1n+hxJD5MJSrEhD7aj4YKgL/ApZ4P7aGn3RjPCVNfrraukqBOdNtugRywpCQJgP3KpyOFkasWs80dyMYK5yL0b6NzM4bsenOX+Qt2eMSMBSLx+JbXF3/eNclh5haKEbB0E4UnSFEjqGRudIc1HWoirDDf+9vY6ScffX2UYwo2bD/tAe/dqa9ao1UvIiSMfzQ2gL07Ql2PyAie7KNQrKQgHb/IvQnaebzX+UdcvkxXVdfUuoEYvewa4owCoAJv6wREWcnQFYsLjAWlApFUhlEuy0WqpGwar6djfF6w2xhOYGcDwQqxHAwrQEZu+EVhhRag1GdmyxV/Qqt4RYovxaNufnjExALJoAu82KJwvgwtkMkEnvOnwGk7Qga6gewMpOKnnHInM72nVOPIECxYXOYnP7eyR1tfkCYUAO8eOGbzFhDqVwy1jQfqFiOuy8lPGXMQfEoszvKxm0zhNNjbuLPLlTe1JyaQmiD7+ijZuoa6Sp12qYLIC/iB+3D2PRwSpM8SiYHLDFMxg6CHqMBcrBARAFcancwHG0ah08R8irs3cJJEAzOosehRiwmvN3kqwNsQhl3MjtylBykGmH1sPM0ncCoxPodNLRnN4YmWDHXWbCERqUKj31jf3KjHb7rYY9786WufNyo+WJ/+GsMKwUErsEMUDEaiV/D3XhCjQoln5A9Ok8/3kza7O1smexrF5+PeAev5qyKiFAN1eORMEkisfKFk6LLddnGoRJH0zYNpt4glzYc8nzFzegrfYxGTsvPFJR4DRgtLCkbp61HbukMN3Qj5Zgado6QQQ69TVFQkhvIMI2Lx5L5VIp6c/eC1DOJemu4pSWPsJU8z40uCgzZzA0ODsdVSv3vQm18gzmggWKxUzSEv/cpOMnJlabLtzRIgHK3OOCsDtfKOZOKZPJgukuWQNuxk5rkZoxFfMANkRAWY8NYQHf0Mle+Tlh3ttpxpeUGKaN7rQ9G+xuf+chT7ftx5jTQi3bJ1n6CIDXVatK/rBFe3G4MRlSkW5gw4e9geXJnYg4Md0fsTPqU1GcUN3+c2B6qpra/Rv6JRyXHkD7cT6fCNXvi6Zg4pJrPSmlPamvw3pzW+5SfzIO58G2eSf/VXf7Vnzx5JKIEf4HJmkUWfQNE7HvIyFZtLJNPPU1a8kOM4eshyhfbd9NiMXT1XEUliR4gSFeoC0nzI7Bz2+YadXe+bXeD1g7kIM9hj/JSpWLdKsWy9rog0f8C+QwLs2ctunOzstlM3ffRFc+fR896/FIsQkXKx3P+p0XSB8vko5zHzeUZGFCNYUSE6YO1xUvSgs/fI6aHoUA88hRAYIZ1f+KJjKQSZs1ijYPq6Om1f+HwjpOWQySGqUD+f3I0lty50TajVkivHD50i6ZuU63hn7xcpS4mgkBAckfK5r1DBsaBvwGr8pSMI3C88Zpvw9n1gsg/6fNdsnb+wC5eowy8B4+Q1mZmdM3rtpOmohbzp9/s8510jaBGe/HLXWGn+1olKCWzA7kBkMjB9LSBkT7jsV8BYkG+owiWU39HT6wFIMsNW05ngghcXi8CjY8vk42e6jGc8Pp+PPGU0fUqhf524WcQvUjwPHnWzb3Kz9Ng84RczxFf4HkFkOfousxNjJBuXPfO184IbPA4aGsFkIBVX+/jyMtJ7XGV+ZxEQCsJ3+uSlJxXKpBMvueCli92mEy76lt8/7HAMjuPApKekKVhQUY6cP2q0XQPGDOaiVl8RIRNP7Yks4Vwhitx2u0jKLxQBk71K+oJMEBA53EuOgcMqXLbNBxB/PoAOGzB3fuSigbv2mzp/5SWWsa8mli5bXuLu7TxJ0j7a1dNpuRYmwaO4qcDh5z95BwXP995xWE6S1HWP/Rem/q9SKk0CknjrjsP0vtUzAuTv6jzDKFmzjyUegRMJcEShFOapGTzREkVugTd8moDGQTggPyd9AgwDTgz6455eO3jzakoKuixmFwiojO+cyULJNIsxJFuCYUK3rdd+jQYLSsZT0cfZs3PQXGTkCwcZmzpHqAmkpTh9zu59RgbeziWaJ5NctTuziAUhG+aRM1kO0UKF7MuTJ90liqQXC4LpK2eLYgS49cKHPI8xc3oK/+ZDjDlfXOI24GxM9TeyoY+NvRdp33XS8v5x9uUjIGXSOT6vrhDYuw5ZyRGf7wub8UgfotAsjn+DegKinP0gHyZxNdN0T8kaY6+5mxlfEjR5GUGf6jT1h3rk4x29X+JqdfSBDi6ak3nZuOIFjPy0L1imTN0IQh8kIMEvbL0nXRmvJvE0IwO1shhyj2TiaD6YLnLKsJOR53J7Hw8gsexMkJmvVs9xHX8/ZPAD4IWLbtF8Qjw9pSeLwQz0sn3NTRoMzywjBBvrphbJb//ATOK69UsVq9ZVMJ8aewYTomgijCjxggK5YLXnKpQlydynabEI/zghSjkDGIV5AvQORQ75maCfOms0nfUmPZvGN3rh65qTWxW9ntKc+CpOkT+t8e0UtGb49rf0dCuYPU7z2cjp4P6MqmJuR09753hDM08vIZBX7qg5Zup9b5c5gOZLF1dvNyQ9+QDe2qHfVoccNRt/bgmAF4b/cFVdpVoEXiB2xBJctEXPrkqKNetW2puOm5wLahXa2m2I6fjxPfWjTB4m/3Fd7d+GlsCXbKr/Y7fxl3us4J3j0sXrtxrYTmiJoZrqOn6o0SLEVS/rNHdM4aUpXKmSXehp/xem/uDyaGsFiuodgWNdlre2Gyfy8TJ13XYdS2Hq9WnwPKeu7rVA5/G2XR/mYAs1qjK3M/WoWIGmeh3V2du48QNUVChT6VYpjtrA018IONCVjSnl49a3tnfelRCLt9SvyfCLx2LNxk2+Iz0db5oCWRLweZW6jWowlo0tAidqkady+l3AAAAgAElEQVR1oFCBnBCb6SJ2LIggYOolsGTLiaRjZmktAi1eQNzu3rWF/ho05JU3qkOPa4bVYTq1f/sRRjiXUG2qN8S/iIglyCPS5OQ+xLWQW91gJnm6dwhZpI49ohmRMRsnCKHlSomU3ddFpdIS9PNxgogM8jOQKkImo38y0Xs8oThn0bOnbSeT4HklkeOgFyrjXiYcupunMGyhjUe7Gk+OMkKMWLxpy0pg7XFK5qYpkL+6o/poV++B7UYmH1fo6ipZrXB7YpKFCwjVIovxaFsQ2Ve3YRV9xLh9UwCdgy9Q63UT3a7feRFFguT3eAGgq0NMx7oaT4zmgK+ArG3QLwstXgB1b2WMRzt2nUDAx0s0i2h7iAGP4jjBmaZEheClDrSxt6Xx43yZeo1uid+aRCAOEH38LbFcIXJ2/JMJfJ9j8do3DLGnlNMJnECaKwplIDxntAQQrayt+QOr8c4xVDJPqX1jPSvRxIjj457AHI0q9gR4lINkIYH++97XO8G3lBZot25Rs/CjirU1msMmY3NfToFSu3pV4JA7VFwsV8ltvzS2BdDwJ3ZiUoIXuGIImfNc6BRtoVQGvmpEEBEb5pEzuYVzlMpnTOSEYkHK4hXO2aJY/Ti9hL5jFEk8piJdyW3MGJen8ATReNH54hKPAYtfrK27Y+w6vKtnQkIsK1eIreDBB/YRgCk7x1zCsGML+kFPR4MpIMCI8podqxNNLRFQnn6Q7RzTReM03VOywkLXPM2MLytetmUn0919fI/1NgK+y6Xdptdm+GAYeN5eocRO3C5ZxNUVPkhARq/29Zwpkf6tnPs5fE4oODJ5xjxJkZbPhCYJPpAucuqwk4nn8kVFDjTis3iQiS+C4vptWxAwsAEGn4vJl9fVvgi8WzwtL0iSAi1V4h5T40ej3y1aoN1Wzca6yW00bpGCTtPxy+KVTaGPEpTpDYrt7Ucsyn+a7PziYKwiwNjpWaVijpX8UUoPDqpP02KRmRhUC8r11Z7O4/97a89doUSqXL5Wfeoj9hDN5Ck7vtELX9fMo9lJHJpwniL3ls09vr03WjNb6zt//vOfZ5YipAYRAAgEh8mhrBKiKHxyx2/dt9Ve1tq0MmVkxAkW+CzbNvbLPLoiztswkw8BxvV+p391LTt7fkjpvvT+kGSGbGcGAfCNrKORb4rODMEHRiV4rqM7p6YGfLoQpoeGAEMPkEyhPPKOqTHwuV6zaMcBQ/y7tR6abI8iY2bAtOvfkKoWA9dU8lEU+JGU6eF3kY8CLOB7km3h7xM+UGmCzo76HmznN2YgB0cv92wO38bTrfcMFqyYOQLjlKWt9ZBlwOe/5fN8ajr9Jb7g+cymkZnzgCWTELhptzPy6F7jw0EH6v3h4A65Zo7ABGW9KEx92CxzArDkjCDgO9e5571e1wg4ekzZj/ZcypMnn2OfETaPARFwJO+izXzCjpYnfpTvMWjaLDfhEegiZ7nFD4mdn7rotB3tITG16puzHwBHL/dsLd/S0633jBesmCECosXVm2hjT/t20x1EVCxXv74FbjBmCN29FytQ126699ozUhPqfUZghEQeIALZuG5LVdL3yh4gO0iaGwFUvrZ25RFT1z/1eO8Ksfmq6m1pX6vLTeTbkXuH7H3X7HtmVa0W/3Y0+IG18hHoIh9Y2x4lwhM+x7F264Rcv7XiG7SBAEcv92xD8HTrPUMHK0IEIAIQAYgARAAiABGACEAEIAIQgW8pAvB067dU8bDZEAGIAEQAIgARgAhABCACEAGIAETgnhGAM8l7hg5WhAhABCACEAGIAEQAIgARgAhABCAC31IE4EzyW6p42GyIAEQAIgARgAhABCACEAGIAEQAInDPCMCZ5D1DBytCBCACEAGIAEQAIgARgAhABCACEIFvKQJwJvktVTxsNkQAIgARgAhABCACEAGIAEQAIgARuGcE4EzynqGDFSECEAGIAEQAIgARgAhABCACEAGIwLcUATiTfLQVP+Enz7l8rIx0b2Nlyxn/TInL9LdVbjd7JpLoMb6LdjJzJvHiNcykePfVTL+tZUOjZeS+aKSvHBzo3Vu3sXLDG11X0hdMveu37qtcV7mr93rqrdnNYVHiEuNix8Y6U4phRGXjq8Un+4Sr8/XXO6eNEh+5zPJniemks5Ddb2x8z5mZcLNVatI374sjM2x3XmNYEukN476YIIjfZWp6vbKyclcPFU9pkrszrVmm4z7DkRNBphMk+bRwh8f70jVkRu8NmndsaLOPJdOcCcCTad73ddDeVrnjWIJhJNOcdPmp1P3AutRkkeA1RAAiABH4diAAZ5KPtJ6ZX5vaPnL72flePrF0lQoXPlhxb/V1tlupP2bKJE68TKs8FuWCjlM9NLaybucbWulj0aD4RsxVrlxOiLIfu3bNeIOm6Swzzj89wZnxzQna8q+d538fmkk+SMPwne+x+gnD9oaapdhku+K5p29turszHTmno3deLeTiqlfUUlE6uR/CvZkB/CEIHsdyCnXPdpf6cMGA3CECEAGIwINH4C8ePAvI4b4QCA3iQBJIl+kewWlLVLz7auM3rXJgfAzBFCr5c+JvmuQZyFuo0BVmUAwWeeQRmAnfjKPxIA0jEAgiuLyiTIomoDoTLXjYkZO7DSiYSeKPngVxC/voyZlGoqk7yke8S03TNngLIgARgAg8ggjAmWQGSgEn+rbbsZdFbpvn9l1UXKY1rFfjuQhzoWPzadHKp0irKyhdt7tuqZD6tNtkcbhvIfn4guVrq7WlAvZUasPeoTIN4rINBxEUU+jW61WFofGSz2XuNtuu0IFcCVGuM6xVYWgizfJnhj/9f5kJpHGDp6q1Kti+y730YP0ysI4d5GNEK3So65RjeDSnQK6p2qR7DgiABAdt5mMWh8frR4TYfJW+2qDgnAFNODu2d5FjCFlX6d5yuK4c9Q/0mo7ZLlGjiFimfMmwfhnOkoulga4d7faYeCCbueEw7Tvd5/Gy3F/dpGObD86tkb3dJpuLHs2ZbGY8GQScWzsl0s+nT58lveP5siVratarxNkI+f7rHXdr3t0oZwuDw0v/0IlsfremjMWTVmjRi6cd17/+bvFiQ5V69FfGU07q6zyZZkOdviwsI+Nzmvba+tx3vosrXqqu1AB9gRS8Zu02nXIMjiJzZEqgxhfZFpFH3jAxahltO+8Tr9qxW1scJ12qjrI85p2NveDo7MDWdWd0zc16PG77jkXsqO3S9RBiP47QB2w9p4zdpxwUIyGWLsfjTxQD+kaT9ao3p1CpWyG2H+6lXqg7slnBVvmku/tXDuo2kl+8YPkag7aU3b8IfmEx/vL0pUEvky2SSJXadQZ1ceLYe1J2cMprF1neunsFUHbQfvAfO/2rWhu14IJxdmw9Ia7fLgFl/VfMbe02l29cMl9j2KiXzwkdYvxAtLPVIAXt8rl6j/bYrlCjORhRoa9ZLQ9vonDUisOMVXmP0fQZSd+VyFew6ovupDP0WVPXx+fdvrCDhBvF7yCclsNjLXxM/Vd6jcds5Mgo8iTwyfXVL0kTbBgIN0bbPzT19pPeMSFWptav18sBRqzLO/B1Es8p4M5x4MSamegsmhAd6+GTpz6nvhbiC16urnkRZxUz4XMeM/acJ+l44glYcYEcQj41OPB5CgJs/UiP/SodyJHIfqQ1rFPjg/G+uRs7FWfh2wyBQ430yvfqloSQAKcc99C6d+tUwEGS1Y07WhrNwwzy3sbXPbvfldviDWMa4iW0lyN2uQ5vbDkTRJCOykpbVetuTSQ6+a3x3MsQ5K7f/e8dxl85aCZftmhNVaUKC7kep1PH8WQdIRw5wSHkrrHlqjE7CEdATcpXqquXATXxWKDPunenXb6vSVvAEvOdatx+Udn6psScGCQnGU1LC6+ryFarrKlZVxQOaBxxmxmxm470ngfhtEilW8j0fo7V79HFRxvgynS/2XTK6b7uZXIlskW6qlfVoBPhaSYbVKxHuk/9hvpaRGhUqfuhGQLO6cWTMHi639j/1Zp3tqqAefk/2bv1GFp9sF6dF/YpgCfQL4cNIBNUb+Pb9PPl/n4rLayobTRIXCbjh3bSh+CLtLKUU7ghfpxxZlLdoMHUGYAP68L5xUpdVbU6YIrvs2a2S00YD7ykCn7kwHcdMDwbkvSOvW17L7bzgH7eJFDwF0QAIgAReDwQgKdbM9MjQ/Zdwdbvffvt1jcUfvPb3S4w8AGJ+aLvkkBbu7VqZZnYd6a95TiNr97ZvK/BUOq3vLXfEnkWLuA85yZeP/D2weaaErfxoNkDFkXHPKbWtn5EVdPU2rxludDVeeBICs2X/6F5swIt1DYdio2uWKZpGNnPkLih+fC/HawlfD3vn6TApCXo6j7YPTTPsHv/wYON1fJAn/FDJ/fKc7aidl8VkSs1tLLTSGS4t+0tq/8HhoaW5p0vY9SxlkNnEx+gLK1KEo88Y2PKq5qa6nXYcIQ7GB+0tdkR9WuNra3b9diXxlgz43FnrlntTEX9W0cON76EXjB294fR5VNNwH7GjW9oPvxug/puX/vPOzylNQcOvbdTEbAcDTUZpAmq75xf8VpT8w4dNmiK6Oumtf2tU355VUNLa9OrhL+35dC5cIvG6X57QPVa3Qad8ntxTDl1lC3V7ztgeA4l1h88kjiNRK73trWa7bcERLlKlj1kOxJBzH/uUNsv7J4xCSHHmYum3oEo/ACc9rZelzenWEmI6V6jhYreASre322jc2SqRYTgph2YCvvY55ir+19Mdp9Y+ZJeuxhjBqyd/5L6pGtMfkxeKhm66mahnBjyeILMsDt0gbgHPDnEAnZIClA66yPW1zfvqpaOWjpPuBIMg6HMb7XZEOVrPwMmqkLOtnf0hZ7YTV8L2OcnHW2ng8oNDc079SUDNuediEj+C50HjtH4mp2trU2vlY+fPtgRfZaVy0H4LYfTWriZMi7zYRuypHZfa2vD35XQJzp6BuL0y/4MOo8cMF4Rabc0tzbVqCb621pNrHuCxLisF0S6XQePtO9UjVuTwUlyFkDootWZp6ltbNqyTEgeNVpvsiTIX7QcGpBotja37qvT5Lna3wr5fnziBJnT8MJCcXhK0N59yJWnrd93sHWbVjRg7PyERpJ9k8fCp5CE0Wxv0hWj8k3vHqwkJstOT7yE5nLGLnn1e00rMVRRe/hwfKATJXFnfPbTVzH9tuamjYrxfqM5HCV4nToR5+gVfc5GP1/bfOjwvrWSgW5TSE0gcVkgJ4EUvUdLTVMLCdv+IKClxG3GY37LSM7R7tzTXLcsx/axPZAiD3PN/PZhUvLSG/va3m3erEZ+3W3qj4Rormb67e+3mW/I1uxqbqok6P+w08kEMwKc34sj5PAflOYMeoZYOw+SV4cYhnJ/Gbr4gnTPJQgxf/81Meo4T8tfratZq5ZdN7e950SX1jbvqdcIHX2xmBknM7fLxxXw9x9qOTaEr65v3d+wpogyvm0i5yX3WeHinGYZusWlGv4udXI8sGS5Qup1OTxh+kGXg5yjUMavUcbJCX9CBCACEIFvNAJwJpmZ+rIlFat17HYGimlWq1Fn36XwZAclNGtURKlc+iQNRgPil2oMS6RYIa5YW6t7hjp9NtKR4MsNmiKwRSGQ6lYpA3b7VYa5bO27ozRs1sqLxFippmZDBdJvdYSHATGacxA0OwewBP9Npol0jLDFqzTPCpBsEVEul/goCgg5LpStqK5eLcfEInGxQrMIC3wFNid5UphRNvsPabPRMn3tGgVegEmXVtX+WOQ6bUsafCSJJ1FXVy0lsCJC81K55BbLnRmwWX0L9Bs1RJFYPE9hMFSgn0ehixcBlWtWK8S5CFpUoZIyI9dTBjmJ8mLlqzTFAiQXJ56TIJhKtwRDswXSHxL5N32+u6Gi2SLVuhpNKYY9p67+e9V4iCn1HzY3rqt5RY4XiLGFOsOPxa6z9tDcCEGK1foXCWIhAfZCY4lXR2gOAoplA+1MFga/wETaw4hU1Q1bNlTX7VwvR/2uc4C+33Xe5UdEqtea6jfX7m6olsc2xa7ZbYMMKl21u6G2dmtT3QosMsuYoOynXUFUvn5nXfWGLQ3VahHjsZ2jkNs0fQdB88QyuWplZf2ON3c3bdMm7lEkyIOXEd8ND+l+R7qFcrl42PMlOw8kr46X/gBni2YLlD+p0ZTh2LOqVWpZ4DrtjSfgsfX75Gs2aIlCYKLa6ldXSgXjU9dCaPs5N/ZSjW4hjhXLdZt0RMR6fY7TjidXVBkWScVijFixSScdYhsVShwOksZyOKyFh+mY338HFc4Ri+aI8UWGuh2vqYviW4ggt85bL6AVG6rVpZi4SK593aC802cNT6izRYtXaok5KPBa1cKSAE0ne02cswCi6DxN1VqVtAiXr9TKc2kamHDQYT2HVLxapX4OExdI1ev1ytu2PjJxKskFMq/hsWxSPWXU72fQPJFkjkj8rLp6a331C/lswaTQwWXhCVhwSYJkIWwAysqJN/VpihfHhC92ZaMs/SwEjQ90oF4S91y5bqNOPg/DFXoNEYkS6Zw6UdXhK1S6XL8EE2Sj4kXq0twhVk2hlGqBXLVDeYl6jxa7Ly2kxm3morWPURo2qKWFbPitXoalysOguLqyWl+Oi+eIMLlGLWV8NyJGytFMv8t+UVjxqkE1D8PKtJv+Tpm8OQ8YTA14Oi8OS4jKFhBjbhKsfIEFLAqTlyGUB6DMDJGe/DI5xmcDocqS8pXahYS8DKP6+72luk0vEaAzVf199UqOkxc8Lj8Jk99x1iVcVm0A+Ihx1bpqwwuFyFiKX4DyaUVKVU26LnWy7xYpFxGjFx2hV5cFL/2alChVaWL1pNTwF0QAIgAR+KYhAE+3ZqaxrEIsNgadKxEzTvpGqGKeRPJE6MfdEfqGsGRerL8XyeZJTMNg9CkFh/uwomg+imHigGvE7x2jmSK1NHTkEiR0nrQYcdHhBfIYzcjNxH/SMUJET0XPLKE57IAHdGMiqVotcJ3rNV/30iPUsIdCipWchBMz/b6bo5ISPEoOwUpwoYUeYRBweooviedGT83mCgUIM84g/hE6cMfd/rojVoVhMBrM3pJWZ/PyRWEYETQnF2XuhmYs/CnWTBSMPfNE7MAZJDC1Q6IVszB8XkRQFMexu6dpH4P8jg5eMW7dYIwSZhhBiTe0h4lKJLGWxth6b/DoKLUoW8dHg0OtE4y9baM9RuKGzzvuo70MkoUT4bO+IoIoQl2DbIngDXp0AsmXEqFDeqi0VCY6SbE7Dww9BCxhzNW5pbIzSioAhHlarpT2eAZsnU22ToFEKldU/LgEmBdvmkdImR4wpMPAjqRUs2rC2HONQiQkeZtYKQtDJcHmRlAS5oIcBpxVjiWgu1ExgUXHm+JFOj245weLI1y1YpNqhnUErDhqCU+W4HMQtlETXmqE8QzuqvwoymGCQVCgFbA7w+EgfoTHckDtVGth/NxM85SaZdb2d153PC0l5AuUKrUKnLKLTzd8PqRw8oSwAMcLGceIl/VaRCQSRU0IgMMwUxilGJNEKOcIn2D8DIPcoEeCtGt/pTXGkUFk/wXAmDQgTpDpj3gMj7PtiFS1Qmk7vHfzWUwml8t/pKpQcMwROC08HglOScC6QwJcoQtev+AWL45A2tiVyig5J08ijqgPFQrQkEIYmsep45eEEuiI8qPoAzUhoZeZgcRhgcjcZP5pr7H70UJq3KZHaAbThE5IswmX4mjKC5AFxSqNgLSfNFO0zzvicQ8y+QXRdYrUZt6kR5BCVfTMBbB0LMudtkVs75YMOK8XxyRl10uJZ7z2qyDKe9y5hP5HVPsF8NI4xvUFQlTiyF3QdXJ2lDgQRiwOxw0/PTKaXxx9mCIbx4uEk0E1LDRfnIk1aYIeuYHgsRl4rlSzlvXqxIWcUOm0ZpmqmnRdalzfLVIoCfBQyZBBWuBwXJUof8KxFhATFv6ACEAEIALfXATgTHL6umNHlCi7KwXSVHu6YEUfbAuG+q/woACM1kPVw1tnMebxl1PRjFWK/xFiFOGRUOCWvaOp0zNXVV6KK5+v0MzvbvlPTgIpmXGTisl7aWVDU+9OjCOFmrptmsleNAvsESXzQrPANh9HGo+XIe43B6Pk2mjO5IYhmFQD+izuovKa3atlk7yyhOAlpW6w75KVvB3C0kujo2R27DXLIFui3lhbEW0tmiWSZPtdYUmi8iftZCIAokhKnKrkKaq2rSyJtgLNY/ddtdv34ef7bE4X6aE8/RbPRXdgT5O2kEsakJdLyKWHbFdpzEMXlxFSBPdecFNPkvQ8JQHmGonceEjcf3Zou4lNrAvI1zVXl8Wp+gkwVwP7oFwOwmc5XrBFxm0tcbLGmArk65vfWUY6AGIuW+enVkdNc92S+JUAVmkJidPskwtxXINmRof98YJIV+3aEj99RZ/kXodIoMhveJxtF5XXHijVui66XJcvWdqtp5fWNW+QJwrDY+GA690UBDgal5g1TfGmpMfp+6m1uNrO69Sp1cM53GritMAkEkmtTmFwP1rgsJw/gcWXKVw06OpqbHcIfqhaMI+Qq7XyjxtPR6XiaWac0KHtx/SJB3BOL46nJCJ+gHVfddPZZBDXEvOF2Icez3DQHShdxf+UYEQYdiMdpBTRJoM5n8gxl+crMO38sBAcqknTpcb3gHlKZWl3r8OjxRzkXJW+aNoCwAoQAYgAROAbgUDq2P8bIfasC3l3hLoeGXKB5z4oFCsJvYZhMoFNy7mBoUE6muN3D3rzC8Sh3QGGHo7m36GGfPlYkUhSiKHXKSr6IgGG8gzHFmQTCSdfpWOUXBZc+z63OVBNbX2N/hWNSo4jf7jNUYgjSyQuzPcOU/7oLXqICszF+OYsHARCWaJCTOij/ahYXBD6C1zq+dAeeoQmg5Sdw4wFImVveX3cL13goXPX5408AYUgI/QI2AouEEgKJAGwH/p0RBjkqsVsc6c+fRSjOE0dibC5QmRi1Dcmkj4rlQrovl/12QF+2Ri773cXGE2oKUHKAzZ2Q0kwF8vPRrxXXaFn54Iu50Bkh4TduAYZPj8qBaTEXzlOfuYALyNCbrqs/251ZSlr32x+99C7NYsEyBgVM8uY2HE/BERZydAVi+tLsUwqEEllGOWyXKRKytiJ5JRJVIAJbw3R0U/C+D5p2dFui9kDb3W0EMMCdMwRghT1VagswKEAGbnhF4UtoQClPjNbroTpcTjI9CyHjyl4hcwxm7eAUL9iqG08ULcYcV1OfFBxbqEYATv10dYAaUdQydzo5iJvIzO7IZYUZvnoUWHE+J/02k/0OGJmGaLBCXLOtILDBO083uv8Iy5fpquqa2rdQIxedg2lmw+zxxWCwcgsJfh77yi/JH6u2fE0/SIOq2nGLs6VsUTop+3UPJrjsMBQ05lAJOww3huRg/DcFKatBW4ysVzs+xh6A+xLRjJoKvYMdaxI8FJfX0BR07DZoFuhlj+LBtM7ZwFWmAVMPdqLXadpjrkxx7wpQdB0XjxZUEwskAy6TpN04XwZWiCTCTznT5E+KSEDZ3AysgEBVpg/Otn7+Gg6JU7zuXxMimyscC5C/zba8zJk15u7zF9wwZ6RSJMVM+5SBQteWBC4YgchF1ukxLg4wzyIAEQAIvAYIABnkpkpccLb94HJPujzXbN1/sIuXKKWRw+mRupnY6plBH2q09RP+XyU83hH75e4Wh15TQX1K6PJSflGSMvh4+Rctfo5BH1eXSGwdx2ykiM+3xc245E+RKFZnLpZ9wSK3vGQlylwCjATRqmNEeYJ0DsUOeRngn7qrNF01pvunF6uEEVuu10kmAERajU2YO78yEWD9vSbOn/lJZZFXpYY44KmipcoAUpUqAtI8yGzc9jnG3Z2vW92gZdYJkGXKnQoBysqRAesPU6KHnT2Hjk9NPWydByhCdrWa6XBKPCOp/eEHQ3pCxzzld042dltp2766IvmzqPnvX8pTrNDlKGOolxRYhl4oy9DHtu791Bn28FuWz94WatIgAiUy1QS8DJQY0tXj7lzv9EefQMN8qxKDY7gDlv2v7m3pWlX+/nwkB5MunBgSwLwypm3GjsOd7S8b3H2kwGhEEH95GmL5cjbLe+be0+Y+r4IIgKCCB3iDQ5Ye0+BpzGTk6iUwAbsDkQmAwsfBYTsCZf9iogojR49TS6eeD1frZ7jOv5+yEQHrN2n3KL5RBq4opUx9Yty3yddZifNGvz7vWRkLipWLZOPn+kynvH4fD7ylNH0KYX+dYQeh4NMz3J4mOaC90x2m0646Ft+/7DDMTiOA6OKb+WcxRoF09fVafvCx0p7yOQQVaifn2o8HaYQ5yzcQOYpK17IcRw9ZLlC+256bMaunquIJAl7LpDFmQWHCNPsnNFrJ01HLeRNv9/nOe8aQYtwcF6a1zezJRgmdNt67ddosLBgPBV9cJNLElF2DpqLjHzhIEfCz4WzPKfpF3HYpA2SHBhycU8qNl2n5uASykq1QCSvBH+S7v/YSl6nyDNG88XoZIZT79PVAp8c0XwA8mL0fNf7Ns8IDcKv8ZPYpDJWExXmCQPXSbcvyPgp57HOk8NMuiPYIqXmBSbWixmP21PDBTI14Om8eLJN3yOILEffZXYBC0Qz2TNfOy+4wWPb7AJWZjYgXba8xN3beZKkfbSrp9NyLRUvHpefLChSLpb7PzWaLoR65GPm84yMKObyi8xEihHOvEsVPK8kbvdZSUwZflv6LZelx0py4J7aOpgDEYAIQAS+MQjA062ZqSobU8rHrW9t77wrIRZvqV/DvkYkNrkLkxAv27KT6e4+vsfKfrlBqd2mj31PQrqI8J9o2X4TwZ7XbNkaepN7NmHYsQX9oKejwRQQYER5zY7V7ONNSTSRZ1QVczt62jvHG2pjJ37SMEptjKBcX+3pPP6/t/bcFYLvRixfqz71EXh8C+HedhEQqkUW49G2ILJv9wpdXR1iOtbVeGI0B3zTYnNHLzEAACAASURBVG2DflnKDIRLvAQZUKl+Wx1y1Gz8uSUAPv3ww1V1leoMZiMsDdESQzXVdfxQo0WIq17Wae6YptEFo1JlsafjzV7vH4Ul5dX1a0OvfSnU1m5DTMeP76kfZfIw+Y/rav827UpxLreOUkEO56Dz9PX/mGP80Eb22wF91VqDgf0CByJYWP1GNWL8yN73K5+sXG8Qd5ucoRrZuG7LlsBhU59naES0eP26cfMR23gWKym2om7nRHf3aYfjHCUslOvWVOvZGaN603YkHzx7c77XhQgkxSrDGoM6pJPRL2w9/0EQK+TgsG5CKpATYjNdJAu97AEM6QSWbDmR4c4yiuu3bUGOhEw0F5Mvr6t9UYxkoAPRkk11QWN3d6NlLJ94cbmq0BIWCeTX/xF8hGX/9iOMcC6h2lRveDYyZ+NykOlZDjfTPIVhC2082tV4cpQRYsTiTVtWJmlcoKjeETjWZXlru3EiHy9T123XEWClI5MN8Dhn0SWiHr0SyCt31Bwz9b63yxxA86WLq7cbJt+3FC7ECTIi5gwO3EwQsWbjJh9Q05umQJYEfMikbmPIxSZ9sznuvavgBqpYW6M5bDI29+UUKLWrVwUOuVnK3JIgcpXc9ktjWwB9Z0mU/zT9Il7sacUu8NzcJPdyntZP16l5yHBYICJd9doq7we9LY3jklKNfrXy0OehyglBMhYSp6sFHjli2QDkreuN7/fuf3M0B1dplkipwaRXfKHy1TWaQ6a27Rb2EyALl69fwXRfB0/mJ2p7kg9rjes/6Dq+f/vXubLFKzTSE+BgeVKaGvA0XjxJKxsnCKHlSomUPbkDTlaUoJ+PE9FlKG4bSNpFB2rdyhiPduw6gWALtZpFtD1ZVNBBcMeZWMGwqMZf7rGC73JJF6/famBdm6vP4hYphWM4Yxpdaq5cVSZ0+BXKsJn4yNO9pPpHmkwW5HiYw2yIAEQAIvDIIfCdP//5z4+cUI+aQODjctvC3/66B8nA58J2uZeFvwN5D9VhlccXAT9p66eRp0rkP5KCGSD7ObKD9vyVTQfWSafd5gmPuZ1UbdGFv7A37eoPswJ0kIeJPuQd+uTvoxei/ZRrRCgrDT8fgdA9uxq/1LyzXZ3JuXSo00cGgdB3aX/UGvoKNEwQAYgARODxRADuST6eeoWt+gYgkBMkT3bZ/SgmVxFzmCGXPZgtWQyeZZ1+8v/a7pMu/wZOI6ffVFgDIvBtQCDo6mm1Y5U1q8rE47Td9JlXtjp5P/vbAMM3to2M7wuS+tJuoRboNsNp5DdWjVBwiABEIAME4EwyA5BgEYjAg0BAAM5e6pHjdvKq3TqBiOYSmtUGfWlmD+klyiP6UVXttJ4jfRDNgTQhAhCBmUKgUFtT6e/6eP/2www6B1/w47pquK81U9jOCh36XGe7U6SqrEv+8tCscIdMIAIQAYjArCEAT7fOGtSQEUQAIgARgAhABCACEAGIAEQAIgAReEwQgO9ufUwUCZsBEYAIQAQgAhABiABEACIAEYAIQARmDQE4k5w1qCEjiABEACIAEYAIQAQgAhABiABEACLwmCAAZ5KPiSJhMyACEAGIAEQAIgARgAhABCACEAGIwKwhAGeSswY1ZAQRgAhABCACEAGIAEQAIgARgAhABB4TBOBM8jFRJGwGRAAiABGACEAEIAIQAYgARAAiABGYNQTgTHLWoIaMIAIQAYgARAAiABGACEAEIAIQAYjAY4IAnEk+JoqEzYAIQAQgAhABiABEACIAEYAIQAQgArOGAJxJzhrUkBFEACIAEYAIQAQgAhABiABEACIAEXhMEIAzycdEkbAZEAGIAEQAIgARgAhABCACEAGIAERg1hCAM8lZgxoygghABCACEAGIAEQAIgARgAhABCACjwkCcCb5mCgSNgMiABGACEAEIAIQAYgARAAiABGACMwaAnAmOWtQQ0YQAYgARAAiABGACEAEIAIQAYgAROAxQQDOJB8TRcJmQAQgAhABiABEACIAEYAIQAQgAhCBWUMAziRnDWrICCIAEYAIQAQgAhABiABEACIAEYAIPCYIwJnkY6JI2AyIAEQAIgARgAhABCACEAGIAEQAIjBrCMCZ5KxBDRlBBCACEAGIAEQAIgARgAhABCACEIHHBAE4k3xMFAmbARGACEAEIAIQAYgARAAiABGACEAEZg2Bv5g1TpARRAAiABGACEAEIAIQAYgARAAiABF4xBFg/jRx989selhyfuc738n6znfQv8h+WAJkyPc7DxGjDEWExSACEAGIAEQAIgARgAhABCACEAGIwCwgAKaRE3fvzgKjKVlkZ2U94pNJuCc5pRJhAYjAbCPgP9fRctLjv+H138V0TQf080ICTHh697T1XPMzCCoqILT/UK8tnm3BID+IAEQAIgARgAhABCACjzcCYDfyEWngoyMJHyDwOUk+ZOLzGfvByo2HXZkUZctM+MlzLl+oNNn9xsb3nJlWnJ1yceLNDkNuLo84SklC+6x7NzRabiY3xXeqsXKPJazryXvOjo3bTZ6J5MKZX4uW1DY3riHmSiQI3X+OZMI1s6W6xncbVkhVmw++23of08gJyryzsu1cEFD1cNrnhKvz9dc7r2Qub+YlGd9FO+kPl2eoT9p2bKqsfL3j2Puz4SbMhY6Ndfell8zbyVfygQcEv8vU9HplZeWuns97GytbzkSw5pMnfX5woHdv3cbKDW90JRhDnBKBqd8jpPT9i5cofLxppW9WQohOKHrH1rJhV+/1qaqnvc/tU2mrzMzNWIzia8WgeceGNvvYzHDjpTJl9Jiget9cl5zu0Yp4pZiNGzydwpSs4yyE22gfmgmlEX3CZz+0C0SWjfusCf3dHY/NSYfq0b0N9xtw0vC/x1uT4t0jgdmsxmkYGXUZM6GFGbc6ZtjuvBYZvEwFI7cjgFozLtVUkkzef3QObM6kJA+mF4AzycztKtOSzK9NbR+5/fcxkciU0z2Ve0TEe0TEuCcIZ6USRQVeMCx/DvVesLnYSV84BWm/RC4XzZQE4h8sX7mwcKaoTU3nVl9nu5X6Y6ggQ1pPkIJlW+q36f7vhbMhBiMiVq5W4Y/6EwdTo5imhO98j9VPGLY31Cz9b8TSVSpcmKbwVLeCjlM9NLaybucbWmlc2XglTkWC/37+fYuXSHs6UvEGn1xc9Ypaen/uNds+lQrxTLQileqM5qDYi7W7G3ZP/tVqsMfaMePRm7QQHqN9+CaUquwha+8FRLmpfud6pTjuLnmiw3w5eSk1tfbDynnExUuCZUrD4INxRpo5w1Y3QVv+tfP87zObSfI4AmjvDEvFhyDMvz8E4OnW+8OPp3Zm3sNT+cFnPyLiPSJiPHi874UD7fFiz+krnpb3dF7qc/oVS0PD24khagLXCu6FIGcdkVyr47wxC5kTgQCTTygUBHt2F5sFMQTPqXXPzULDHiaLQCCI4PKKMikKpFimi58ATl+swPgYgilU8ufih47TJ8NdQyC9X/G46WaYyx18UDCTxDOkwFfsYfpUWKaZaAVf62YqXyCREaUPwq5mSsAHSGdKC5mywAMUjo/0/wkEBDLFIkL6rZnw8yHx4PIfrt5nmjt3iJ0uejMt1XT5w/IZIQBnkhnBNFnIb2vZ7sDXSTynHO5b45L5GsNGvXxOHJGBrh3tdmYCadzgqWrdjYE7Y7T18MlTn1NfC/EFL1fXvIizg7wJn/OYsec8SY8JsTK1fr1entCrgqNfu8jy1t0rQG7QfvAfO/2rWhu14IJxdmw9Ia7fo8dvuczdZtsVOpArIcp1hrUqDEXA+b3Np0UrnyKtrqB03e66Usp0pMd+lQ7kSGQ/0hrWqfHBBPE08UwZ2n7cZOkn6aAQW6itrtRK8xDET1qO9douu70BRFgo16yr1pWBKU3Q84mx+1ckdXs8v5Ao/0m1PrxL5id7u002Fz2aMykSN773g9It6956l/Rl1PkJiSzc1LRRgVyzdptOOQZHkTkypdaw/kU8ZaoVpM6YQRGgsvxipa6qWl0MlBCkPu02WUAmko8vWL62WlsqQMDJz12N9Mr36paEaICTAHto3bt1qrhmMNdtpm7Lec/od+ctVhdxt4894XyipfNT0otiyh8bqlcSIXIMfdbU9fF5ty/M0aAt5dv+CHqu5+DLUcG8CuUxp/2c3beU1T7yO8/oXIJj/HW9d1fjJdWeJm14f5GFyEY0NusKfa4T5p7+S9QtBhXhC15aX71CGg8OODqy/47h8GYFq+keo+kzkr4rka+QA1Ypm1ng8NLeoTIN4rINBxEUU+jW61WFrC0jPg5TBG21fdBl+bXbCyy8VLXqVb3iaVfH9i5yDCHrKt3rVvuPH/MwiLNhne3F3XU5/xYSg3Ad2tU2qGjYYwAzIf+Fjl3/5tf+bDdoVJBTxT5nsnnn8qgDZE94TNv3+9e9V6sIyRxLt1y9R3tsV6jRbEy+wrDplbCmuOlwiwGa2g+sy+m+7mVyJbJFuqpX1cAZWQPjsDqegBBjyEYYO/ayyG3z3L6LisuAQatx0K5ks5ePptiS6/DGljNg/7qjstJW1VoVbN/lXnqwXmpvbOoRvtpaDxYjGKp3T6OtqK55ozzBR8Zo+4em3n6SVVY4HM3xmHc29o4gyMDWdWd0zc36yF7uhHNSiVsO14Fm3vW7/73D+CsHzeTLFq2pqlSFNpemNHU2xLHiLQsAu6IVOtR1yjE8mlMg11Rt0j0nQHhwYPrbNp7AGvbrQyPaoL1tc29h04Gf+BKkKo9TcaqRxMfA/YZg+9v08+X+fistrKjdhlm2W2VNzbo83jjvu2g2HrORPgR7XqMS2k+jVW9XEvHmMulTqS5QkGh7E1O4p/9My/ZT2M79hvDwPXihY/svhbUtVUSAMyxHpQCnW7eGWgGiU9BjPdJ96jfU1yJCo0qNNjPU0SxFp4oe3A6Vkssdk3sb43TUaCDiUeTodII8YYTbT8Hpwa6x5aqA3XqFHhfJNOuriGGz6RMXPSGRv1yzZSUIRfyhb9JtM+77Qgf22HBXg/AZ7aQJpVpvcojjQiwkMIdPAWk54EpWgn+g13TMdokaRcQy5UuG9ctw5Fzb5vecYGaw99U+BfD6qH/RPbv2fuJFkJZ1HhAi2E6SueEw7Tvd5/GyjvzqJh3oUqdkynq6Ff9psx489j9Bdm1tIcsbDqyTgnoAh47x1w5sIAIpIgG65JE3TIxaRtvO+8SrduxW/aGXdcyRUeRJ0J2vr35JOpogXjSChZvri4b9HIyo0NeslouykeCgzXzM4vB4/YgQm6/SVxsU7JArpSMLeTF3X5BWX+zgrVtY21rFGvCwedc/2fCtB2sWggvgho1DK95ZObgr1TA0gBnnGDKqt5nRQswswWAAPLNwSqSfT58+S3rH82VL1tSsV4mz2We1uu6Uy+84nTSDCIrL14YGftxDpupAW6N5mEHe2/i6Z/e7G+IiZCqe8QODLYe3ZHfGD2JXXt8bHqJE/HTMDkaYYCytfKW6elloLD1GWY8YTzmpr/Nki1+Weo965C27NfFjcqCs6Wg2iuuj/G/aUJYK0RS9wAy09Ft6uvUPf/iDJZTAj2mjyLisF0S6XQePtO9UjVs7T7gS1l5Kq5o3K9BCbdOh3eF5WvCi1ZmnqW1s2rJMSB41Wtln7RjyFy2HBiSarc2t++o0ea72t8xgVB2XMHmpZOiqmz3VODHk8QSZYXfoAnEPeHKIBTjjMbW29SOqmqbW5i3Lha7OA0ciRyCZL/ouCbS1W6tWlgnt3Ydcedr6fQdbt2lFA8bOT2gkRbwoU4b84EDnZZHmH1pbm2rkX/W0HbEHwWjucJvljryq4eDBlgY9PtLzvoV9/G/wZOcJ/4INuw/ub6p6PmjtNDmBbODpl7Y2O6J+rbG1dbse+9IYE4kD4RQxpocSQ9oGMP2WOsMyQnDT2v7WKT+QsaW16VXC39ty6Jw/iaO//1DLsSF8dX3r/oY1RZTxbROYz/jOtLccp/HVO5v3NRhK/Za39lsyeT5qjDS9ZSTnaHfuaapdMu446+FoHVDwTbt1WGZoaG5aR3g/7giL5L/QeeAYja/ZCSB+rXz89MEOCxipcybG7Z6QykBvmSuvKMeYL+z2Ybac/0s6X4pz1ChSqIoox8XIKSP/RefQXKWiCKH/vR1YSUXtvoNtzXUviTxHu608HH2fdLSdDio3NDTv1JcM2Jx3OJggSMB5zk28fuDtg801JW7jwZDRjnGbou90Z/cgrn+z9eCeWlW2/dARuz9bUbuvisiVGloP1618pam9RpGL6X525N3JgbhAvtagHLN2n6KRO07T0UvY6ip2bsyt4iCHeXNKHc68PUTdxvDvJw7lg66u5k5SvPKN/e8d3KnJOddxqH/yJHEyMW4xEOaa+e3DpOSlN/a1vdu8WY38utvUz1ogp9WBfC5TT2TFkH1XsPV733679Q2F3/x2d/R0c5zZj3PZkrz6vaaVGKqoPXw4EnxYukXaai1GnjA77zD0KePJP6iq1iVOI5Gg88gB4xWRdksz8H3VRH9bq8lzV6rfd8DwHEqsP3gkNo0E1OKVGBpQMj776auYflszWNMZ7zeaQwBOw9RZEQP2MyRuaD78bwdrCV/P+yep8KMBfDgkosVepUgVLcJlJEnBZ2LUcZ6Wv1pXs1Yti7cOzjh/vbe9vR9dWtu8p36V+JLlLD2eKkw0h8MFEgtP6Z6ihUqZ3+UYClcLkk4y54cKAuUJyxyS+O3vt5lvyNbsam6qJOj/sNPJZWakoxFnFj0SeU8EmDEm8sdEOj/emMynI+5OhzuM8PkpEIs+Z/O+UHfw0Ht1Zf7edxrN/oq6g4cPbpQOnzD3RWIqV+iLNYhbjGSsk695jTZWkMt6E6nwIsbpU5nIOdzb9pbV/wNDQ0vzzpcx6ljLobN+wZK6w9vVojnq+vePxKaRQBDsfzTVvygSLa2PhQjyjI0pr2pqqtdhwxFHnpKpiCCKaM/VENC/I913GK8HrLWyIwry6ri0TIZyiRSCYZzutwdUr9Vt0CkLXObDNmRJ7b7W1oa/K6FPdPQMcIgXAY+hzG+12RDlaz8DwycVcra9A6g56Oo+2D00z7B7/8GDjdXyQJ/xQ3byzO3F3H3BFPpC58ulY24y1AX7r3rouwGPJ+SRt0jyhmxBeNYNLlMMI32XMTNaiEATReia1c5U1L915HDjS+gFY3e0W6T7+/2L698++PY/r813vdfGO4BBRJrtTbpiVL7p3YOJC20ceKa0N24Qm7jBcs5GP1/bfOjwvrWSgW5TaCwddBpbzCMlbIh7TRE4ZUkcjoeaMy3NJuIQfxUc7n+nrXVzw883/3PrTz9w8IykogAO9b/T3vaPDT/f2LDvzU7Lb76aIn+k/8OfH2CJb/zntp+bLw6HnwDiESZ9KEuBaMpegIfNdLK/jTNJMHtsaGjwhhL4Me3JZLZo8UotMQdFBFLVwpIATSdNXNDsHARBwX/hhM7TVK1VSYtw+UqtPJemQegIOqznkIpXq9TPYeICqXq9Xnnb1hd9r0q4Fl5GfHfQMwQiGYitQrlcPOz5MhJbS3+AM5etfXeUhs1aeZEYK9XUbKhA+q2OsBwooVmjIkrl0jmjfj+D5okkc0TiZ9XVW+urX8gHxJPEi0gZdNkujKv+vlr9nFhcJNdXrVcXCQMII5avqnlVSxSKREDO5QrJba/3jwjj949mC0QSiUiMyVfXNGzRgmVbZsBm9S3Qb9QQRWLxPIXBUIF+3neJf1h+fygJ5X+7SlFKyOcJqP+wuXFdzStyvECMLdQZfix2nbVHev9I2/yOsy7hsmpDOS4W46p11YYXCpEADQav4pdqDEukWCGuWFure4Y6zTMtjHco5ortPKMyVKqlhZh0abVhqYTb3QRy/UadvAjDyw3Vy4Susw4f4nOcdjy5osqwSCoWY8SKTTrpkO0cxV192B0olIWDqFStlmZRNva9Oww1iOChc4spCVMqcOo3gAtIfodzCFMoMIRBCpSGDQb1s2LRHIx4saJU6POCFWSORNvPubGXanQLcaxYrtukS1j1jyuPLzdoioAAAqlulTJgt19l+EzR7w8iQpH4KbGokNBW19f9hBACOmHZs0P/oOD/wE1QJP64lEhhWCv3nerqOGy6JF5VtQIDBXlUzG3eHI0LZ/2WogUY/nTCfepjkwMz1K5V4CKBqFitVYk8V9x8FHjEQBgUV1dW64F1zRFhco1ayvhuAFfksrrQy044AkISy2xJxWodu3eMYprVatQZ86OY2Qe4bSkbRQGYWSFo4xK+slqbZzcf7uz8+Lby1fVJ80jk1nnrBbRiQ7W6FAO+r33doLzTZwUdMprDqiYbRIxE+eKVCO7kynXA1OdhuEKvIZiR6yDATcfUQ7Sxxas0zwqQbBFRLpf4KCocN3hxSJQnfJUkVaQIt5EkBR9J+UrtQkJeFtpIjiWuOA8GynTZmk0vEWzQWLdF+2wi0IlycbvAZJkM3FOkVBKjrt9QbKUg6RgQgsPgYPLOGZYTmYeu/C77RWHFqwbVPAwr0276O2XCRnSoyEx0NJlGjzgJGc+xXZUbKqN/jexbjibSxWROHfF2OlxhhMdPWaHQeepVi8RotoB4HridrGK1XIyiIvkCaZZvJNqXpIa+WHN4xYhrMPdPbqONleW23klSaRFL9alM5CRtNlqmr12jwAtAB1dV+2OR67QNuHQopOQkBRaEDThslIiFCIm6umopgRURmpfKJbdYR86AqZj471h43dwPVs0JOT4SWje/5SZ9Uvl8lE8kFoditf5FglhIiMGg5A4qnAN6OjG+yFC34zX2uFCKeBHoPLZ+n3zNBjCwAcMnbfWrK6WCcWRcKFtRXb1ajolF4mKFZhEW+ApsTiKcXszTF0ylrzyCwL2hOTMD5pCy52WjoTlz8Co5VEgQ8ScGEg1jii4jpZn3pIVEC0XlmtUKcS6CFlWopOGozhZAy3SGcnZgIpLrtfNpm51KrBZ3lYWw4+CsnKTugxPPhIEByyY2iE0gj0qX65dggmxUvEhdmjvEjqVB5+UUaiqr2BAHRsLrVGBvOTlNR7PJdSev/Z9+9JlvvuGdPT99b/emjS/8NTueSZPu/oVM/Xc//+lPDzdu/slfud871h8JJDz5DCp+Ub/xnZ/99PD/88qz1y3/eub3aWinC2WpEGXQC6ThleGtb+Pp1nPnzi1YsKC6ujqMEbjUarUZ4hUqJhKJIgMINBdMoZg0y9JscTEWnW3kCJ9g/GD99QY9EqRd+yutMa4MIvuvAKA8KcY8Qsr0gOUrDMRWqWbVhLHnGoVISPI2sVKGek/TTJFaGj3ogs6TFiMuOvxm0TyJ5IkwGUy1Qmk7vHfzWUwml8t/pKpQpA4kogx9NM1g6tiODQjQoS9MiCsqqIvW3s9G6Js0/aWbngDjGAQtrVhZuL9z+8beeWDGqlQtVoFDBr4ROnDH3f66Y7JNDEYD78nwSxWZo/R90EOIJE+FVcDQv6ODV4xbNxijfBlGUOKdQMBhjEiaoEduIPgydk7CplypZq0UnBK23RCWzItmIiLZPIlpGCwK4NFq3P96R7zMXBUWQR4twQtRrp1MdK6MPRscStg8HD074h3HqBHGM7ir8qMo5QkwWPcxSOiERmLysXuPUdkKVRVlPZ39dnJ1jnsMV0XJJlURK5SyXtcln1aT7XB+iSkr2eqYQiu8Zrccp1j1URR1Z1yVzCp0zYzQN4RYcXT978kSfA4CzDElCbGiqFQohokDrhG/d4zbFNVLlssumHZttuCEXL4QGAlHM1Posxmi8mr9+e2dLol+jzZyTpJbxdMxb7Dt8Ds6gIUPXkbZTlAOJx3wm3ZtMUWyxgLjP2T3R1I1wh5y4hYDIYpVGgFpP2mmaJ93xOMeZPILGDAs5rA6cCILcEo19SSGWYVY7NT0XImYcdI3QrEhZvYTXj5b4oQUQfFVr2qc4CXD5XU7F6YEgRs+H1IYOu8dSgIcL2QcI2DJAYwEMkh5EnHEJlGhAGXDIb94XMCyLERPRUMfmsOWCe9JcuKQgURxRTIyErE4YeU7Wj01zvvpkVFJaezwvBjH89NgJJ3CBcAJ8SndU6D8EdF90kH9BJdccZACed2zQDoxwRWWOYC5SY8gharvRe4AxWJZKQsl99/RZBo94gVEsRU1NS9EO0YwNJwLjkmzUYgvJnPqyM/f6aSEEXAcj8tPw0I9JYnYX3ZODioShV0kC0yTgDGHS3CEPgTIHEppxIhv8/R/T2W9vIhJAa9Un8pATr/v5qikBI8NRLASXGihRxgk1lOmb4V4btSVcoUChBlnMgIHLyW+awPr5srgVbp4UY3kqy4PxSzwuYZweW2e38UjErvUBpazwwLlKTXLrO3vvO54WkrIFyhVar6OMqyvUTGBRQOheJFOHyKiVgtc53rN1730CDXsoZBiJcjk8mK+vmAqfSEiohQzg7Nmy0Tkl5i8Uj7+nocaq0CveCT/fRUADkxcudOUXUZitXvTQgKNvHxRZBiJ5uSizN3IIFfyPSxqGwJJQb53BJxzzdA0IuS58Exp9OQgNvGWKD/KHYylEfatljdG6CxcE+0u0XkyPCu69hOrKgKr8RlqNkWSuIycLOT/3P79yG1x8ZN5xeHh3d3BD1qOJR1jQov/5uf/S/nkM8ofR+qKfqiQCT4YGbmLiMEiL09+sWJpZKScN2/pD/I/+91/Mchf83WX6UJZKkSZ9ALp2p3RvW/jTDIjYGauEFiS4TAIVLpq15b4YIc+GYveId65hFx6yHaVxjx0cRkhRXDvBTf1JEnPU4IHuUaTPpcafxm3zSwqrz1QqnVddLkuX7K0W08vrWveIOcQBrDj3JyeoK0te81f4YsXEUS5QrvU1dFOh2ST6hreUw04HC6Xy96912I3/Gy3cmIcKdTUbdNMxpUssEaYKY7TQIndrwjtvbAJDPvB3KNm92rZ5JAuS8ixZ/pZUAAAIABJREFULpWZIMnjwrss/ZQ0mYlmccMZ3syZrMjCy85Q5Ouaq8vimDwRXZNI4BF0D+bgk896iZRLF5gvO/o+EeXMWcAbtgvArmSP86JPme0cKlLWFLIcPcf37P+UIcqVsrIK1cvjtv2x+XZKmxIyYvAmFQPdCduKUO546EgaivCZYpGmvlXpcTkuXXa5TrRZPtM2/EzPDnCmTGM05QWkve4vfEgR6F55VYxymXfKPCnMjxmh6PyikgQfu+ujb0kqtvyzjn3lTzjl5OQKeDTKKwY4IdvY7hD8ULVgHiFXa+UfN55O20ZuU+erwvbgsW3beLPP0JYidP3X6dEshPmSHBpTEMmPWrFNS0iR0XNyNuc1cIFkr5mGqUdI8mAexzABh0mBwRPp6VNGMZA9RZJxyvxT1ZwuMNnUjNxT8EMl0W1x/G5liZMULqyTgrjHF5anbEFolyA5zURHk0iTL3oklBI8JZMmvckpxQzDFSIyc+ooTaeTHEbAGTdeP83AJblCX6xBacRIhnt615zWyxPiJimHEePwqUzk5HQozuEBV1PQ1JKZMAVL0hNmctgd/FIsW0NgV8fNHoocobAyAxuxeUXKQcBsPyKGQL6++Z1lpMPpIl22zk+tjprmuiWJYyougSfzbtk7mjo9c1Xlpbjy+QrN/O6W/wzd5PDilSCbc9QxZZeElRH5Zzzu6yJ3Dq4iZP68026PG/EIib/B00iXgX0m1L5HLcTR4Irqodvx0Y9zdMSZGS8dB54pA4NUKwpR4MCB0zbi2YHf09CsnvvIF0tQ9Dd6XeCTC/960OK7m1+6SPO//lb2ZNb3/2ftlv+R1CP8xV8+CUYtX/1/H560/+a6n+24/vR/bv/pv/0JiAq6YJ7821fP/uLMlWu3xtjy43eQ7/GtaLOi3Fco4+wFkhCb/iWPxqZP6BtUY8mSJZcuXTKGEvgBLmdbeLGkMMtHjwrFBWL270mv/USPI/lbhQKirGToisUFYqtUIJLKMMpluUiVlLFvBJEUYuh1iop+GYyhPMNIyqLtBO083uv8Iy5fpquqa2rdQIxedg3xOd5TYjE4yQMWmMJpsLdxZ5fLbT/tAc+y11et0aoXERLGPxoa1wcHrOZPKVGpSruudve+ndqn3K4Bv6gQE/poPxpqEfgLXOr50M6ezr3nlBlKkgJJAGx9Ph3hi1y1mG3uhP20bKxwLkL/lo4IwpBdb+4yewqxuYGhwWgm4ncPevMLxAJwpA9FgsHIClzw917Q5PgkKZIIRyaRp66D5VqOxPi8YLcxnMC6HYIVYjkYVoCM3PCLwvgUoNRnZssVrlVI8GTsGB7b0gREBHKN6umg82MbyvmQZISPWPlCydBlu+3iUIki9KL2CbKvj5Kta6ir1GmXKoi8gB8YDOc4GC3EsAAdQyNIUdFj/YltY+jhKGJ3qCFfPlYk4jFFhjprtpCIVKHRb6xvbtTjN13h50M4wErIYjwnTH252rpKgjrRbbvFtp5bxdMy7wl6iEYKi7EEVmClI/frACMEe09sum3r2G+8xPu1PR4xkOClvr6AoqZhs0G3Qi1/Fg2GVcppdV9M1frw/bsj1PWI9TCUm0KxkoLEitkZ21K43i1b9wmK2FCnRfu6TiQ+kQ0KzC0UI2ARPsoCaH8ElczlObadSQumKx4fTU4cwIxiLHoMZMLrTVmATiA2LSPhE2MyX4QV5nt/Gzl7Cw7x0tdHI5GCo+5ULpChewoWLC7zk59b2aOtL0hZPsPcYZlDhAKsMAtoNmpL12maw/3vu6PJNHpwCJiQBbaguWMyb8X/n71zgYuySv/4CAyXAWRAQQVR1EBzNPEClljiDTOtNNMu2Jqtf1vT1mzNttbWrVzd3Da72FqsK7aBFyzFFEu8YYkppuJlTMELXgaVURgEhssA/X8zryLgjAK+wMy8v5OfaXjnfc97zveceef8zvOc51j+0bn9MWLhe2ox71ofmHn0VZ1huRh1zd38eXftvfUkVodyKn3Rw89lVf0sac5mFbUN8DdfvptHLQ0qTJ/X4aaQCCG9u+nUKakZjiEhreVB3YJyjifvPmG04CGDOhUJEXTWpOS0UUU+GT193j9nDZSlH775oLuteMo2Ae65ZzU3l6Jptyx6Y0nKqb0pafKo6XOmTngyKiI0SHY931R8s99iC78Fd20v5NdRpXJQp+1Q6zqrguRBIV3K1LtS1AZVaB1dt24iN/P/26pZ/Zw6tYKZTGsfyrlw9mbf0GaczgvoAFcjS0MmM1MZFnjWvks9/sbIqjLr7E2/MMOFLJj+aiVtPVq29rXV/5b79Rz3wpSFf33nX5O6F6UmbcYvZuX5bz9b8qd/flz935/jD+bLdNtXfXPE65F33pz96dzZn77wgNcNpWXheNHB/648KHto4kKcPHf2nyPM+shUlaWej7I6/QrcqeJ1+UyKSrJly5bz58/3MyW8wZ91IVX3c+QucnlBpvpwVpWWqH2tZ/jgB53TVn6ZdFSjvZKZErti/QmZ322dR9ldFXA8NU0WEoJxZBtViEt66lE8W43nyXtFDlakrvgyWZ2t1Z5Mif1qpywsamAtA6Cjc96pTfErk9RXdDpt5u70bHlgENwFzRdP0XvwANnulbEpp7TabDyUk7WBqhAfOKhoM45m6Uv02uPJsavT9HhalcFEkr1ndWz8vixdgU5zNC3jml9QoLtcNTiyjTrhy4T957Tac/tXLE9IRxxIWD/weF2fmHpzNFOFwnwxqpOqGyU4LoRc3hQTl5p1Ras5mBCzcneOm2/NqUhl+MBQ3TZjgbXarP1rEnYbQlSdAyKGqDSbY+L3mA6u/TzxdFBkpErm6BcQ4J6Rkph6SgPlHru55upVkO8ZFel1KCE2OfOKNmtffNyuKi1as5F1aesTM2E9NZxLjt+h7z1soFLmGzEktGzHitgdmVqtVr05Nn5blrydmUlTw8n9ZxV+NQbyctQySF4RENTF7NP5xq2VfcJCTm/alNEpzBhyDskdblraE2qt3qBHmy5LVCPEBVxqzaSAyGGh2i0rEvZrtNnqpOWJagsLvrN+iI3fn2U8Z9laddvIyK6WuqJcZowtHI/uhM6n3qvWKuANC2u2u1yWn5GuvjVUqVkYw5n1sdsMg58fGzZk4phAdcLXqfjdMt/EFro3AskmrU/JrBUxqFSj0foFdTDO5utPpSRu2m8UII4h4b080jbG77+g1RxP/vyzTUWqyN7GBtHu35CYcspo+66ezBdDJnf3dC+6oM4AZR16V8ymcwaTw7u5XlfH4UJFzs6v41PPaLWnUmJWpbo/HBla24pY175kKr8u9esEddCYiYPCxj4/2LAtdv2Zmn3AZ2BUmGHnipiUk/juq5O+jE9TDo7sZbmn3a0R4X5Zx65ei3DtP81xkGPtcUFa0iZ11oXM1FXxe6qmPMyWykInufvDp3ZRbvytioz0O7r2y81qzZWs9LUxiSfNfpuEky18BW7lXMevp0IVptJu3XTIKyxc6D/u5h/LZoqsDI960FDVl2LXGr9Nt6d7/aGRWXx66E/iu5Z+Z7F/qzyOFp7Jt5f45hFLPzrmHiOWvqeWc6/5ye2PvqrPLRXD0m/frYzNdtqqjy303gYTs1jOajVFDw84nhCzIV2D38U98TE/5KiG1FwUUBMLlk4WZav3n9TWflzePK0uNzUuvO8epPkpNadLCGJEKzuH+J1I3e+g6m3q8HUqkiuCRcfFr0vX5Op059LSzpQFBRoDi5svXrfISJ/0tctNw6fjCPCWoeym8vdUyAuy1Gd1Br0ua1ds/K4c08Il899i878Fd20vVMYxRBWSl/rT2aBgowYL7hqk2Zea001VI9YXTrtzx6jZBPjLfDWrnVa3Vrgt39sOGI4nGvvGFQ2efknZqqiBARaHTI7OcldZ9sk0dXb1rmGeZ33re6tc+PHqU5T8FX4uNRqMhON2YmVTreRen5atffGtv3XHD2VkF5TjgMIFniGmZRhGm+S0hX96tfq/957r41VZXFQsU/q188I5lQUH9iEMuykjS8fLS4rK3fwDTB5qRWe2H7EoHUy51PNRVrdfAVPODU8S9W6Feqzn2sj6IO4SMbjt5+uXxJTNXWBcVmgmKUInvTF1TXzi0rcTiuTewQMnz46uHQYDV7UJVfkmaAKNz1ZERgjpokhyDFUJ04Ouqug3Zsi/Xv/53PgiRYBqwNQ3njIugqw5rvGNmvKy9qv1n78VX+Tgh+D+s6ZEGsfJ1Yo34ZZfnyL0hTcmr1yR+M/ZsQbvoLAxsyaFKRSGyc9nxSTOm/K1XOkfEjFmbNjKFCzQiuw+4bXxcbGr58/MNbgjUPgLr03oiq9A8ITXZ8lWJsS+n1SETS76jp01yXQ7WXbaxvVFPlERVQuxBCANplTLauQ/avrrsvi1a+fPyTN4BoQ+Omv6yJqmJwzqH355TqmxwMnY8CN44MSZ0UYHvyEz3jTExa2dn5wvw9Ygo16fMMr4uyUPe2Zq1LL42AU7nduEj3pqbNGXGTUaUI5qvmyITfjgrXjnwIgxw0I15gxN8o69Vflxb8/QFDoGhD/52mTTClWhGPGbP5j9lcG9rSri5TnRtSJ2aFM+/yjx0MUcvWznzAuI93sr3n3Aw5GqdL0KbhN3SD7h4V3i1RVhvQUh6Rg8+qWxmq9iZ79cJPcJ6h05YUxFXPrFHFkfM1mgbLP0sXFx85JKvFXDhkf4J5k5CW3cX6Vbt2j2FeMWCDNmjjH2TEfzXTFo9PSp143dKaZE7tc5fNRrE00MVBH9k2JXLtbLFv5l0G13qMhK+ipJ33/GhO7oTr5Rz49OfXdt/P7e08PMN7H57q1N35p4VtY/srpRV3Yh65xjwECTZe9sasL6rFERRgcl9Pnp0V/Fxb87O881IHTojDlPmLYAyT2UvG6Tb+CoyFoFtNDTEHQq6sv4xbOTjFuA9Bk+cYQh7gIW3KrM9jr1bZU2cwB9JrQs+aPZMZV+qoEz5ow3E//o7n3pZr76/fFrD/uOftcUSbrnhOiw2Uu+Sgr/q6ntbiRF2OQ3itasSPpodmyFdxAeFLPH3OYBW62YimqNaMF9ve7FM1P9qkNmOfgjQoYmNnHRvI3eIZHjxzysSxbOr14q4+ZJQrrbM/CtqWYcPu9Qpo5jZv1fUczaxW9/4xzQJyqiZ8Z+s16XphwsfAVu5m7p64lQWTWTole4yjlN0yf8RpO1iTL7WJZVVfrW5cYfmolfr1j7wexC15CBI6KC1+WYqdy9/tAYn2xmnx55J3au39EpeGTorVXrZm5/65Cv2WfybUPDWxcYn8a3/eggWKjZx4iF7+kdS3TrQzOPvqoPzRbD+KmF376qC8132qqPLfTeaiU2T8xSlSyWs9oF6OGzZPFrVsxbl+eMH/dn5k4YYqZjVV0QFB4Rsm/9ks8Mcz4Zbv62dbkpfhxVqgCZ2rmrybvQPzhEKdMhPI3wjKpLkTzDomdoYleumLcpz+AeoBr48ozRxu9RteK9qKp64smDJrw+Q4ahEYZPePIPnzV9mK+iYsLkzJi1/5i5vtLdLzh8+DORmzcgnoUszOwPGcLjmxt1mP9JqsFFruqhku3VhgQbB0dGXzMHtW+P257w1TrGGPNYaxwVqxXueit59/CgzPh5G/I8AnuPen1ypLFrWBoy+YZGhKasjl1cJP90ZkSVS7aFp+Kt35Q5plFjnZMi7KVZOV/Fxs9PLvMKGfhIRNBGnWmp/a2kGFCflrV8Y+3JHcs3rNGVy+Suvr1GPDnSOBR3UnjdWFVd87p2I8dEfJG47K1fPBVObkHt22OFpDE5WDjuFf5c1Pn/Llu8v6Wns5tv90Bf+Z32lJBbGnJYKHvdfgUsXFzHwy1+++23Op7K00igYQT0P30e5zx1av+a3++G5cWrmo0A9ih7O2MINgCs35O+yctrSF8eo3tqujCN0eR3v+cbYne1129uBnjPmdlwBlbJQX9OfdahkypQGBfpkhfOTO354buj7zTUtuEmYNFvEGj4o4+/fexEdkMAOzouvrH7tNXUqUKbeVin7BnsK4wuj694bUnZi59MtRAOpH7FLkHAKKtJrrX0sdUUTCiIFL1brawJ7L04FVnJB93DjVYmJhJofAJXUlMNoTUiqjf+PXkHiRAoy0pa/OGXSce1ulxt5rb4raeDeveijJRI49e/mvztqz8zXkEC9SGgS1v5weI1WHKjw6KqhHW7y3r2ru0qXJ/seG7DCEjUu7VhsHhVQwg4Bo2Z8aIxlikTCTQBgTaR019ugtvwFlIkoBw4+WVN7Pols+MLZMqOoZGvzBgTKEUOrHOdCPC3r06YeBIJNJSAY/DYP0woio+fPzvH4BoQEjZhzvN32Oyufndp0cJafDZRkvoVvcnPthZSTV5x3pAESIAESIAESIAESIAESIAEahAwlFdUVN4WCrY5IDk6OMidrNoaQyXZHP2C9yQBEiABEiABEiABEiABErBKAhCTlYgl03zRZGCNdGjRwsplJJqOStIq+y8LRQIkQAIkQAIkQAIkQAIkQAJWTIARd6y4cVg0EiABEiABEiABEiABEiABErBKAoy4Y5XNwkLVn0Cz+yHUv8i8wt4I2Iovir1xZ31IgARIgARIgASagwC9W5uDOu8pNgHrWRstds2Yn+0RsP718bbHlCUmARIgARIgARKwPgL0brW+NmGJ6k8Aq6LrfxGvIIFGIcDe2ChYmSkJkAAJkAAJkICVEaCSrFODGM6l7j9lMJ6qS1n00tuJF+p0VWOfpN08b9L8JO0dblORHvPKKzFHcYYmcd6kRTt0Fs+t0Kl/SjdldbczLWbRnB80Y3Ct5qw2722VBNgbrbJZWCgSIAESIAESIAGRCVBJ1gFohSbpi5jdl0xK0laTt2rQ2Iggd0vFN/wSv3hDhq4Cn9/lTEs52Pnx4oMfzF32Q14dannsm2n/2HKucXYhOrX+w0lvL/zjgvhtdSlJHQrLUxpOIDPprfkfTntz3gd7ShqeCa8kARIgARIgARIgAZslQCVZl6azaQ0pVFARPGRMRGf5HWp7s5J3P/MOmUjgo/LDKxdOemPepDffnzb/i4/WHzxXXLPSAQ88O7y7b92+WOeSlkxb+Wt9oDkqHhj36dvRw7xvXXR49cK3vr9kyN7x1nsbDt9VwVZeWvXPD5dnluf/tGxK7NEG92zDgfgp/07LN5xZPn/Jqgab6K2qMJaaIW/P+3NXHzCU//zV+++nVLPqB49aOHfKk4EMWmYJHI+TAAmQAAmQAAnYOQEOg+7awLrkRfMSzhlkS6e8kvmXfz9lPF93NGHxkpR0bZlft6joKRNCfYwH9aeS4+I3p53Jk/mEhI+KnjgsSFEt78y41z64Nv7TmRE4qNvy95lr5JM/mRPpaXKXnZ0auvAvUb76rG1x8UlpGbky76Dew5+ZPKq7QlaRlTjvY02vAbo9yRr3wdPnRYdcTomPS9qdmefReWBkoNnC69TrY+O3qzWVfqEjQnGGyRAJn9W3MwZ9MmeIEiXN2pGAkmbklnl3DB/z4uTIovg3lqQaKmTzXsp88cMX9UuqnXl7kZDV3L9rwsbI0zennctzbhMa9eLLY7oa66o/k5KwJiktM0cncw/oFjFhcnSYr8yw7/NpW5WjW6mT0/XBj0XoN6QFvf3P6PtMJS9IXTw7MeDNf07oXK0iFdr0dQnr9xzKyjXIlUG9H5s4eUSwEdrRxNg1KersPJkX6Eyc/JjxYHMleY+nlz4frNee3fZd4j9iS955ZYB/VVG8Qwb1ba5y8b4kQAIkQAIkQAIkQAIk0EQEqCTvCloZNftd3V/nZT36yayBSllBCtTdzl0B0RPnTHDTJP03JmZdyCdTQuVXkpd8tFk24sW5fwiQa1Ljly/60mXBrIch226koB7dnWMzzxoiVHK9+sRZg8E547QhMlSuP6nOaKuK9pVpdyxZtLYoYuKbL3aRZ/8UH/vRB7J5746CQKnIS9utmTBp1ii5MqRCHf9RrLrb5DfnB8syk2K/ypR1Dq9VAe2WzxdvlY2eMjeslW7/ypiEAllkzTN0e75ctEYXMWnO1GBZxsaYmI/jfRe+uGBa3rR1fnPnRwe7ahJvnm++SEb5WpS6Q/3iqwumdipTr1q0aPmm0AUTgkrT4z6J0wyY8ZcpQc76zOTlX8Z+ExI6LQxnG07uPDRs8vSZ7s5tg84e37Q1LTP6vmAc16enqX3CxnasUT7N90sW7/GbPH1hqE+ZZn/Cf1bGJfd8d4xvesKyFNlj0xeGKYsyk2L+8/n6oI+ju9equunPygur/rGm+KnZL3W79ak2Zdlbp/t8+vs+ioKMb79J3nU6T+/g0fGBQb9/so+/YKatzPjivSTn4RGyQ2nHr5eUOQRNevXpvu4lx7d8+/XPZ7Qy7+5hgWXVzX0OTnK5q5f//eOeyTv+zz3bTw94oYtMlpf26Zep50sKta7h780Z0fGWWbJce2T711uPZuYVy5y9gwc++eqQQP3+1e9vvVRWotNXJs5e8INMJu87fsZzRiriJMPZPUuT9p26XKiXOfl2eODpcaP6thIn5zvkYummlo7fIat7/8jSTbP3fPPf1Kzs/GKDk0fH7oN+92Sfji7Gu1k6fu8lYQ4kQAIkQAIkQAIkYJcEqCTr0KwOMmeM9B2c5Y6mkx0V4U9PjeoJCRI0NjIldY8mRxZq+DElI2jMh0+GGrVjmzHR2Yfe3pWqfXiU783s5SG9VSXr1dkyVfuzmVkBoT21WZkaWWjAWXWmd89RARWaxB1q38cWRD8cgCsCnpmed2bm5l2Zo54zCh2/AaNH9TGKDMP+xN2GiBmTIoNdZTL/ydFZxxfVdizUpP6UEfDYh2P6GO8c8PIY9ewqYSgURZe2K919yLvRA4Lwt+/zk4s2nJWVyOSOxiriv1upQgO5aKZIE422wICBY6PuwxuFakCo346sLL0sqMI9ZMTkqOGhAcZMwqL6b0o+COOkzAhErooaH6Ey2RB9+6sSNqdlPhMc7Kg/9IvaL3xCkED1xo0Nsjbh0S9FRN5nvE45bHD3DTE5OTKZp05XIFf6+Cp9lL79o2cpw8puGQFvFdn4zqFdSKAh8ZxW1q2Kffn5C9p2HQMVsoJdCd/uchr653fC/cvOrFoW/1my78JRNw27lYX7D2j/MHnGS54yQ55W7yzTH9n0xc/lj01961Hfwp/Xxn5R4WmUxbWSV2CQoiDrgk7WRSnzDv/jn8NlWCe5qcZJhswtHyRc6PXslFd7KOXFlw5kluNjr7BnPwyTwbv1H/lDP3z+/trZ1ufvoP6jn3bylnvf//So8qAq+VrpFBL57KTgdl4OugPfxi5d473wlQFGIg7eA0aMMLR1Ung+8oKvX/UGr889ZfKgsBfcvBVy10dGDpWbbPLGZOmmlo43R2EMct9hE4b1C1TKi86sion/Yke7hSPboeyWjssUwU882dZf7iQbMErh6VEvSjyZBEiABEiABEiABOyYAJVkAxrXL6DtjRG4u6tcVmYwVBg0FzX6o7EzX4q9mZ3BoOiUUyHzrZJJimBVl5zUEwiPmpnhqprQL2vJPkS4MaSflKkmBckq92suu3fqbJSRpqQM6ewXf06jkwXhD1/fG6IoJzvH0DYiADLSmOSdgvzltZSkIRv5BHS8KaK8OgX5yIqE04XXCk32ZVnQkJs3cg2OesakUaufI7yvNGZlrkjG85Wtbppb5c5GFgjVowyOjFSk/5SYcCFHk511LjNL1vGmvdTTz89k9jFeGBauggfs2ejgNmlpJ/zCn66qsvC5PCBslPup1KS1WZorGk1WVlZBWQQ+8QyPGpK85NNX0loHq0J7h0dERsAx2Hxyuq+z/9UTF/Qy1/3/XfJD24kLR8oyNK4hA31lxQcPnHV96Pfh/iiMS+cn+3fY/uPxcyMDq4yHwRFDe5mylXv7esnKDx/LNISMG+qP74jyoche3x07Y+6GTgoXmb4YayVv2Z9rnlZ+ZO+Rsh7jnoWMxAdu7fo+YC6bG8fKD6z8eNmJmq3hFDzlraf7WtZ8Xp16mtxp2/Wt5lUr7xL+6I08lX3DQhRfZ2dXykyrN107PtDT+IlnyKC2dyjJ3T5qFTLIZOS8r+8tGWzpppaON0thOoYNumEF9+w8qIf39otXDbJ2oGvpuMzFtxe8tJGCTdMzTCRAAiRAAiRAAiRAAiYCVJKidATj0F85YOpfngqBae9GcnBX1rC2KVU9AuJOZGgc1fqgUapu7gHfZGae02cUdR9bfZVgzeLcyM1oMKxKt2SG3MGyvLhxuvyGHbVmtvfyl1AUMzfOTf383ZjMthEDugeF9xoc1S1u0ZGb96kefsYzPLx7XGJa5qiANHXbiAm1l3oaMtfO/2CbQTUgPKTn4IjHy1I+EMS5InTigk+HqNP2p6vTU2K2JadNreE8XL1GXp0ClTsvZBXJDxd5O53NPJfveqqk3WOwYepLiirdvFveOFehdJeXlMBqekNXOHj4+tzQ6KYzynXF5e5tPW/U1MvDglIs15fKFG5ulpEW5xWUe3XxNkPMzDVODzz58j9G1fpArqjbxdUvM1z79ZtNqQcu6MqMVSnOL7+/HGq/bnGAzJSrbocs3dTS8brl2sCzLN00/8SuVTuOnsotMZIpK5C1N+AbBcCWjjfw9ryMBEiABEiABEiABOydAJVkXVr4rgN5hV8bv6LjWlnrCMEIqd21IiEnNHq8ydn1ZvJV9fbbkr5VpvEPDZG3cQ9RbN29WacNjgyBfqnwD2hbdOiMRtZTsNHpMs7kePv71goq4xfo574lK6tE5muSPFkXsg2ykBoVkPsHBFTLR5+Vda2mscwxwL+tLOW8RtY/yHihQb3ir/GKFxeMrZGL6Q+HOhWp6jrt3pQ0edTcOdHBRv1sUB/Ovz1L0xFF7wd7xyemJl3JCug/uZZFUlah3rkzK+T5pbMGmaqem5yIymKBojY9cYcu9OnfSnoTAAAgAElEQVTIyCdVkU9OiPhy5uLDmYaHw8w3jG/74Modv+43FAcPGZbz8+HDLtl+He6DHbLS1d2hOO+6TGYypul1RQbXthb0IT53Uro7lRXdDMxaVFxoKn3tlH8hS+8ZFGg5G5mbt6dTfl6eQeZrprSQdsadV6pS+ZF1S5aeNLq/3kpO90/7y51skjVOvvGHbvuqb460f+ad50O8cNfTG/74VU07p7lr7vmYpZtaOn7PN7xTBhZuWnTwvysPKsZOXtjbaCLO3rLkr+dMuVg6fqdb8DMSIAESIAESIAESkDSBRjZS2AdbR2e5qyz7ZJo6W2+pQvDsDLm8KSYuNeuKVnMwIWbl7hw339ryor1K5ZC287BvCMKOOgaFdCncvy8jqKdp/aBjQMQQlWZzTPyeLK02a//azxNPB0VGqmrdTt4zKtLrUEJscuYVbda++LhdmtvKExA5LFS7ZUXCfo02W520PFFdWusUZfjAUN222Ph9phutSdhtCFF1lMld5PKCTPXhLG2V4qhbkapyd/dUyAuy1Gd1Br0ua1ds/K4cmcFgNPvclhS9wlX5O5PVAeGC02CNE9yVCpn2hFqrN+iz0xOXJapLjM7DMleDZldc/Lp0Ta5Ody4t7UxZUOCNWDm3ZQ8DU4cuAXm7dl/t0K1zaLDsxx8vKjt28sJ5bsF9O5X8/OPBbDApOLNh33ll9+7V4uLUyskpuEew7NTBw0bn4PJzB349X13yVZYbDOX52b9+u2ZXdvsBQxFux2JyeuDB7s7Hdqw+pjOiLdYeOHKhirHSw9Vw9RICA99MTg88NW3hn16t8W/m6AfMaFCL9zN+UFkMCaz0a2eUkZUFB/Zl5t91dxBcVXrhh282fHsEZtoGJUs3tXT8zjdppMKUlxSVu/kHmDyNi85sP3Kzv1s6fudC3vnT3PSk9cnqhuK8c978lARIgARIgARIgASanQBtknVpAt/QiNCU1bGLi+SfTrJwvv+o6a/L4teunT8nz+AZEProrOkja9vboB5VKveko52C2yATeXBwJ/neMpXqht70HTLjTUNc3Nr5yfkybM4x6vUJo+B2WcNgZbxowusvG2ITPngr3jkwYsywUM3J2uVRPvzyLH1sXNy8pBJv1bDhEf5Jtc7ACXNK42JXz0/GdiPBAyfOjFbBwtklYnDbz9cviSmbO73KldZ8kWrf8MbfigETJmfGrP3HzPWV7n7B4cOfidy8QQPp7Xf7+a6hET3d03Rh4bcvO3MMHv3SWM1XsbNfLpL7BPWOnDCmIi79Yo4sLCx6hiZ25Yp5m/IM7gGqgS/PGH0b3ls3cu3eyXu5pl2v9k5eymCvDdkBXYQ7eQ56etzVb5Lffz/J4ODWsceo10fUdq6tXlhFj9F/uPzt158t+Ubh6t1W2cHxllXScOybKXMRtMY7WDX0z4+GC9F/jicu+eJYCVxJ9cWyfy04KnPwfeylSY+2RaONen3c9tVblk1bWSx3NcZurVop6dXnkaFHk/7x/j65k2vYhFkvdJPJ3ZW+pm1b7ik5tBs5JuKLxGVv/eKpcHILat++Tvtbll06vP+gzj183AO150DqVBhLN7V0/M6ZNlJhvMKfizr/32WL97f0dHbz7R7oK4eNGsnS8TsX8s6fatVbE9WR/aJufsXvfDY/JQESIAESIAESIAEbI9Dit99+s7Eis7g2T8C4h2Zavw9Nm1uKk0rKmsB7U5yi3ksup5KW/OuAwd2t3eNTnh3kfS85mbnWcPybP67UjZs1ZVjj7xdi5vY1D1lVYcyXNnPLO98ezy+puO/xGX/sW32FrfF0V1MgKiYSIAESIAESIAESsGMCtEnaceNaYdUM2pPqrNOpSVm9x0wTTUZaYT0bqUj3jZqxtHY8HtFudS7jgrLf00JEVtEybWhGVlUY85UIHvH+n0eY/4hHSYAESIAESIAESEACBGiTlEAjW1EVDenLZi7er4yYNGuqaXdDsVKpoZzWdbFgMp97JNCiRQsX7D/JRAIkQAIkQAIkQAJ2TYBK0q6bVzKVM5RXVFTWJaqMZIiwos1HwNHBQe5UYwug5isL70wCJEACJEACJEACjUWASrKxyDLfJiYAMVkJuyTX/TYxd96uGgFYIx1atKCMZKcgARIgARIgARKQAgEqSSm0MutIAiRAAiRAAiRAAiRAAiRAAmIS4H6SYtJkXiRAAiRAAiRAAiRAAiRAAiQgBQJUklJoZdaRBEiABEiABEiABEiABEiABMQkQCUpJk3mRQIkQAIkQAIkQAIkQAIkQAJSIEAlKYVWZh1JgARIgARIgARIgARIgARIQEwCVJJi0mReJEACJEACJEACJEACJEACJCAFAlSSUmhl1pEESIAESIAESIAESIAESIAExCRAJSkmTeZFAiRAAiRAAiRAAiRAAiRAAlIgQCUphVZmHUmABEiABEiABEiABEiABEhATAJUkmLSZF4kQAIkQAIkQAIkQAIkQAIkIAUCVJJSaGXWkQRIgARIgARIgARIgARIgATEJEAlKSZN5kUCJEACJEACJEACJEACJEACUiBAJSmFVmYdSYAESIAESIAESIAESIAESEBMAlSSYtJkXiRAAiRAAiRAAiRAAiRAAiQgBQJUklJoZdaRBEiABEiABEiABEiABEiABMQkQCUpJk3mRQIkQAIkQAIkQAIkQAIkQAJSIEAlKYVWZh1JgARIgARIgARIgARIgARIQEwCVJJi0mReJEACJEACJEACJEACJEACJCAFAlSSUmhl1pEESIAESIAESIAESIAESIAExCRAJSkmTeZFAiRAAiRAAiRAAiRAAiRAAlIgQCUphVZmHUmABEiABEiABEiABEiABEhATAJUkmLSZF4kQAIkQAIkQAIkQAIkQAIkIAUCVJJSaGXWkQRIgARIgARIgARIgARIgATEJEAlKSZN5kUCJEACJEACJEACJEACJEACUiBAJSmFVmYdSYAESIAESIAESIAESIAESEBMAlSSYtJkXiRAAiRAAiRAAiRAAiRAAiQgBQJUklJoZdaRBEiABEiABEiABEiABEiABMQkQCUpJk3mRQIkQAIkQAIkQAIkQAIkQAJSIEAlKYVWZh1JgARIgARIgARIgARIgARIQEwCVJJi0mReJEACJEACJEACJEACJEACJCAFAlSSUmhl1pEESIAESIAESIAESIAESIAExCRAJSkmTeZFAiRAAiRAAiRAAiRAAiRAAlIgQCUphVZmHUmABEiABEiABEiABEiABEhATAJUkmLSZF4kQAIkQAIkQAIkQAIkQAIkIAUCVJJSaGXWkQRIgARIgARIgARIgARIgATEJEAlKSZN5kUCJEACJEACJEACJEACJEACUiBAJSmFVmYdSYAESIAESIAESIAESIAESEBMAlSSYtJkXiRAAiRAAiRAAiRAAiRAAiQgBQJUklJoZdaRBEiABEiABEiABEiABEiABMQkQCUpJk3mRQIkQAIkQAIkQAIkQAIkQAJSIEAlKYVWZh1JgARIgARIgARIgARIgARIQEwCVJJi0mReJEACJEACJEACJEACJEACJCAFAlSSUmhl1pEESIAESIAESIAESIAESIAExCRAJSkmTeZFAiRAAiRAAiRAAiRAAiRAAlIgQCUphVZmHUmABEiABEiABEiABEiABEhATAJUkmLSZF4kQAIkQAIkQAIkQAIkQAIkIAUCVJJSaGXWkQRIgARIgARIgARIgARIgATEJEAlKSZN5kUCJEACJEACJEACJEACJEACUiBAJSmFVmYdSYAESIAESIAESIAESIAESEBMAlSSYtJkXiRAAiRAAiRAAiRAAiRAAiQgBQJUklJoZdaRBEiABEiABEiABEiABEiABMQkQCUpJk3mRQIkQAIkQAIkQAIkQAIkQAJSIEAlKYVWZh1JgARIgARIgARIgARIgARIQEwCVJJi0mReJEACJEACJEACJEACJEACJCAFAlSSUmhl1pEESIAESIAESIAESIAESIAExCRAJSkmTeZFAiRAAiRAAiRAAiRAAiRAAlIgQCUphVZmHUmABEiABEiABEiABEiABEhATAJUkmLSZF4kQAIkQAIkQAIkQAIkQAIkIAUCVJJSaGXWkQRIgARIgARIgARIgARIgATEJEAlKSZN5kUCJEACJEACJEACJEACJEACUiBAJSmFVmYdSYAESIAESIAESIAESIAESEBMAlSSYtJkXiRAAiRAAiRAAiRAAiRAAiQgBQJUklJoZdaRBEiABEiABEiABEiABEiABMQkQCUpJk3mRQIkQAIkQAIkQAIkQAIkQAJSIEAlKYVWZh1JgARIgARIgARIgARIgARIQEwCVJJi0mReJEACJEACJEACJEACJEACJCAFAlSSUmhl1pEESIAESIAESIAESIAESIAExCRAJSkmTeZFAiRAAiRAAiRAAiRAAiRAAlIgQCUphVZmHUmABEiABEiABEiABEiABEhATAJUkmLSZF4kQAIkQAIkQAIkQAIkQAIkIAUCVJJSaGXWkQRIgARIgARIgARIgARIgATEJEAlKSZN5kUCJEACJEACJEACJEACJEACUiBAJSmFVmYdSYAESIAESIAESIAESIAESEBMAlSSYtJkXiRAAiRAAiRAAiRAAiRAAiQgBQJUklJoZdaRBEiABEiABEiABEiABEiABMQkQCUpJk3mRQIkQAIkQAIkQAIkQAIkQAJSIEAlKYVWZh1JgARIgARIgARIgARIgARIQEwCVJJi0mReJEACJEACJEACJEACJEACJCAFAlSSUmhl1pEESIAESIAESIAESIAESIAExCRAJSkmTeZFAiRAAiRAAiRAAiRAAiRAAlIgQCUphVZmHUmABEiABEiABEiABEiABEhATAJUkmLSZF4kQAIkQAIkQAIkQAIkQAIkIAUCVJJSaGXWkQRIgARIgARIgARIgARIgATEJEAlKSZN5kUCJEACJEACJEACJEACJEACUiBAJSmFVmYdSYAESIAESIAESIAESIAESEBMAlSSYtJkXiRAAiRAAiRAAiRAAiRAAiQgBQJUklJoZdaRBEiABEiABEiABEiABEiABMQkQCUpJk3mRQIkQAIkQAIkQAIkQAIkQAJSIEAlKYVWZh1JgARIgARIgARIgARIgARIQEwCVJJi0mReJEACJEACJEACJEACJEACJCAFAlSSUmhl1pEESIAESIAESIAESIAESIAExCRAJSkmTeZFAiRAAiRAAiRAAiRAAiRAAlIgQCUphVZmHUmABEiABEiABEiABEiABEhATAJUkmLSZF4kQAIkQAIkQAIkQAIkQAIkIAUCVJJSaGXWkQRIgARIgARIgARIgARIgATEJEAlKSZN5kUCJEACJEACJEACJEACJEACUiBAJSmFVmYdSYAESIAESIAESIAESIAESEBMAlSSYtJkXiRAAiRAAiRAAiRAAiRAAiQgBQJUklJoZdaRBEiABEiABEiABEiABEiABMQkQCUpJk3mRQIkQAIkQAIkQAIkQAIkQAJSIEAlKYVWZh1JgARIgARIgARIgARIgARIQEwCVJJi0mReJEACJEACJEACJEACJEACJCAFAlSSUmhl1pEESIAESIAESIAESIAESIAExCRAJSkmTeZFAiRAAiRAAiRAAiRAAiRAAlIgQCUphVZmHUmABEiABEiABEiABEiABEhATAJUkmLSZF4kQAIkQAIkQAIkQAIkQAIkIAUCVJJSaGXWkQRIgARIgARIgARIgARIgATEJEAlKSZN5kUCJEACJEACJEACJEACJEACUiBAJSmFVmYdSYAESIAESIAESIAESIAESEBMAlSSYtJkXiRAAiRAAiRAAiRAAiRAAiQgBQJUklJoZdaRBEiABEiABEiABEiABEiABMQkQCUpJk3mRQIkQAIkQAIkQAIkQAIkQAJSIOAkhUqyjiRAAiRAAnUnUFhYeP78eb1eX/0SBwcHLy8vPz8/T0/PumfFM0mABEiABEiABOyVAJWkvbYs60UCJEAC9SDw22+/qdXq3bt3//zzz0eOHMnNzTUYDNWvb9GihYuLi7u7e7t27VQqVY8ePfAaFhbm6OhYj9vwVBIgARIgARIgAXsh0AKjB3upC+tBAiRAAiRQDwIVFRV5eXlnzpxZs2ZNYmLixYsXW7ZsCcNj+/btH3zwwS5dulTPq6ysDCfgZJgrCwoKiouLYbSE2oSYHDJkCM739/dXmhK1ZT3agKeSAAmQAAmQgM0SoJK02aZjwUmABEigoQSgIQ8cOLB58+adO3eeOHECPqtQg6GhoZ07d+7YsWNAQIBcLreUN67Nz8+HBL127VpWVtavv/566NChY8eOYV4S1yKHBx54oLcpeXh4WMqEx0mABEiABEiABGydAJWkrbcgy08CJEAC9SOwf//+t956a/v27XBYHTp06MyZM6OiopydneuXy21nnzp1asOGDevWrduzZ4/w4SOPPPLUU0+NGTMGCvO203mABEiABEiABEjAtglQSdp2+7H0JEACJFBHAjAhwgK5cuVKvHbv3n3s2LGwQ8J+6OQk8oJ5WCxhpUTCesuzZ8/CbgkLJ9ZV9unTp1u3bp1MycfHBzq2jiXnaSRAAiRAAiRAAlZIgErSChuFRSIBEiABMQlgQSNMhYsWLTp37hyk4yuvvAJTJDxaxbyHubyEdZg6nU6j0aSmpkLBpqenQ1V6e3sHBwdHRkYOHjy4V69e5i7lMRIgARIgARIgAWsnQCVp7S3E8pEACZBAwwhg4aJWq92xY8eSJUtgHuzQocObb745fvx4V1fXhmV471eVlJTs27cPRUK6fPkyRCYsog8//DAkJWL2wFCJgD3YZQQ7jtz7vZgDCZAACZAACZBAoxKgkmxUvMycBEiABJqHAOKsfvfdd/BlhYaEBfK5556DWvP19W2e0pi7qxAJFsFgEa0HtkpE7oGGRMCe++67D4ZThP/BLiNubm7mLuUxEiABEiABEiCB5idAJdn8bcASkAAJkIC4BL744osFCxbAp3TgwIEffPBB//79rXlRImynSHCFPXz4MLxw169fn5GRASAKhQIrObGe88knn4RDrLiImBsJkAAJkAAJkMA9EqCSvEeAvJwESIAErIXAyZMnt2zZ8tVXX0FDYhVidHQ0rJHQY9ZSvjqXIzs7+6ApqdVqrO1EzB44viJgDxIWWAYFBSEYLLa+rHN+PJEESIAESIAESEB8AlSS4jNljiRAAiTQxASuXLny2WefwZe1sLDw8ccfR0ydrl272sF2jqWlpbm5udi7Eq6w2LYkJSUFe42gXq1atUL4WfjrDho0CA6xXFfZxP2NtyMBEiABEiABEKCSZDcgARIgAVslUF5eDpPdqlWr/vOf/yCYzfDhw2fNmgXDnTX7st4ja6jKn376Cary559/zsnJwY4jcHyFARbW1969e3uZkru7ux0TuEeAvJwESIAESIAExCJAJSkWSeZDAiRAAk1HABoSGzbCCLl582a8x0rCCRMmIPxp05Wgue9UVlZ2/vz506dPY8tKBOxBwpu2bdsKMXugKrG/CIL3uLi4NHdJeX8SIAESIAESsE8CVJL22a6sFQmQgB0TgIJ67733Vq9eXVxcPHPmzLlz58IuJ2UrHAL2VFZWGgwGuL8iYA/UNVZaAkjr1q2joqIQs2fYsGEIDGvHXYJVIwESIAESIIGmJ0Al2fTMeUcSIAESaAgBrIHElh7x8fHQkG3atMF6SMTUwYYZDcnLrq9BGFjYJ3/55ZcDBw4gChEcgLGONCAgoG/fvthcBDF7sLVmYGCgLcYisut2Y+VIgARIgARsjACVpI01GItLAiQgTQK7du366KOP9u3bhy0Wp02bNn78eEgjZ2dnadKoe62LioquXbuGsD3YrxIWS2CEqkQkWJgrISyxwBIbpfj7+9c9Q55JAiRAAiRAAiQgEKCSZE8gARIgAeslUFBQADvkkiVLsNEiBM/vTaldu3bWW2KrLxni9Pzwww/btm2DxVKn00Fqwj6JgD2IBHv//fcrlUr4wUKuW309WEASIAESIAESaGYCVJLN3AC8PQmQAAmYJaDX6yF4VqxYAS/NTp06wQgJd9YuXbqYPZkHG0AAGvLMmTOI2ZOZmXnYlCAysVMlIHfr1g0Be+A5jL0rucVIA9jyEhIgARIgASkQoJKUQiuzjiRAAjZGYMuWLe+++25aWhrMjwsXLnzqqadgJZNyTJ1GbT8hYA9WVyLB9puYmIiYPYgNCw2JYLCIi4uEuLjYXKRRi8HMSYAESIAESMC2CFBJ2lZ7sbQkQAL2TODSpUtwuVy6dCnWQ8Ig9qwptWzZ0p7rbJV1g6SEUzHa4uDBg6dOnUKwXPjBIlSPELMHJmKYLuFszHWqVtl6LBQJkAAJkEATEaCSbCLQvA0JkAAJ3IFASUnJV6Z07Nixhx566PXXX8eOiL6+vrRD3gFaE3wEc+X169evXr2KmD0QlgjYs3v3bjRWq1atsPPKww8/PGjQIJgr8WcTFIa3IAESIAESIAGrIkAlaVXNwcKQAAlIiwBsX5AoycnJH3zwATargMnr1Vdfxf6H0qJga7XFziJwP0bMHsSDhc7EessePXpgy0pEgsUaS9iQPTw8XFxcbK1aLC8JkAAJkAAJ1I8AlWT9ePFsEiABEhCLQFZW1tq1a7EwD29g3XrxxRcjIiK8vLzEyp/5NDaB/Px8qEq4v+IVAXvUajXMlfB97dy5s0qlgn8yXtu3b9/YxWD+JEACJEACJNAsBKgkmwU7b0oCJCBpAnl5eTExMYsWLcLqu9GjR7/zzjuhoaFOTk6ShmLLlYcTbHl5OSzMWOm6cePG77777qeffsJBtClWVz7xxBNo5X79+jk6OtpyLVl2EiABEiABEqhBgEqSHYIESIAEmogAxAaMV+vXr1+5ciU2ihwwYMCUKVOGDx/eRLfnbZqQAFTlnj179u/fj9WVFy5cQMye0tJSWCmhJ+EKC6MlbJVt2rTh9EETtglvRQIkQAIkIDIBKkmRgTI7EiABEjBLAGvq/vOf/2CHCeiKP/zhD/Bl7dq1KxbUmT2ZB+2GACyTCNiDdPnyZQjLlJQUxINFIKXWrVsHBgZiNmHgwIFhYWHcYsRuWpwVIQESIAHpEKCSlE5bs6YkQALNQAAL56AilixZ8uWXXyIKy6hRo958882QkJBmKApvaTUE9u7dizBLSBcvXkTMnuLiYuhJWKexUBb7i2B+AcJSLpdbTXlZEBIgARIgARIwQ4BK0gwUHiIBEiABUQhgsdyaNWu+//57LJAbZ0o9e/ZkVE9R2NpHJjBUCjF7MjIyYKs8fvw49qi87777EAMWXQWpW7du3AzGPtqatSABEiAB+yNAJWl/bcoakQAJND8BCIN58+ZhSST2hHjllVemTZsGPeDg4ND8JWMJrJJAZWWlwWDAStozZ858++23iNmDSLBwgsVCyj59+iBmz4gRIyAsrbLsLBQJkAAJkIBECVBJSrThWW0SIIHGIICtBWFZ+uqrr3744QfsXP/UU0+98MILMDE1xr2Yp30TwEajcIJNS0vDClvIS/jBwrIdHh4OYQlJ2aFDh4CAAB8fH8bsse9uwNqRAAmQgDUToJK05tZh2UiABGyGQFlZGbaqx2LIn3/+2c/Pb/r06Y8++ijWvNEOaTNNaK0FRRhYOMFqtdorV678+OOPu3btgrnS09MTMXuwdyV2IkXYHkSFdXV1tdYasFwkQAIkQAL2SYBK0j7blbUiARJoGgKIzFlYWHjs2LEFCxZgiN+uXTs4sr722mtNc3feRZoEEMYJkhLrb7EQFztYogdidSUk5dChQxG5B5uLIGCPm5sbzZXS7B6sNQmQAAk0GQEqySZDzRuRAAnYG4Fr167BixUxdbZv3w63w4kTJw4bNgx2SHurJ+tjrQQwkZGVlYWYPViXe+LEicOHDyNmD5RkcHAwzJW9e/fG3pVwroajtbXWgOUiARIgARKwYQJUkjbceCw6CZBAMxL45ptv/vKXv2Acr1Kp3nnnHfiywr0QIVKasUi8tZQJIFoPYvbAy/ro0aMJCQlJSUnYuRRbicA++eCDD44ePXrIkCEICcsuKuVOwrqTAAmQgLgEqCTF5cncSIAE7JwAAp9s3br166+/RmQdLE77v//7v8cff1ypVNp5tVk9GyQAx1e4vyJmD4yW586d02g0WFrZr1+/0NDQXr16IWAPnLFhrqS2tMG2ZZFJgARIwCoIUElaRTOwECRAAtZPACFP4uPjly9fjkE5zDsvvfTSQw89hKG59ZecJZQ4AayrRMyenJwcaMvk5GTIy7NnzyIuVKtWre6//34srezfv39ISAhj9ki8n7D6JEACJFBfAlSS9SXG80mABKRFABv95eXlwZf1k08+gUESezDMnTsX6yGlRYG1tS8CpaWliYmJiNmTmpqan5+P3WtgnETAHkyRwBUWClOhUEBYMvKwfTU7a0MCJEACIhOgkhQZKLMjARKwJwKIYrJx40bE1IENB16sEyZMeOSRRzw8POypjqyLlAlgXeWpU6eELSuPHDmCNZZYXRkYGIg4PQjbAz/Ybt26de7cGZuOSJkS604CJEACJGCWAJWkWSw8SAIkIHUCiF/yt7/9bdmyZdhiYezYse+//76/vz/2WpA6F9bffglAVQoJW4zACL9582a9Xi/E7ImKikJMKUyjtG/f3n4BsGYkQAIkQAL1I0AlWT9ePJsESMC+CSD6JewzGzZs+O9//4uaYgD9u9/9Dju/083PvtudtTNLAOZK7JKKmD2wyWN58NWrV2GuxKJKxJpCyGLE7MFiSy8vL7PX8iAJkAAJkIDdE6CStPsmZgVJgATqSiA9PX3p0qUISYILxo8f/8ILL3Tt2pV2yLri43n2SwCW+ezs7CtXrkBbYvdUrK68fv26r68vVlRi10pMtfTt2xfesJxwsd8uwJqRAAmQgBkCVJJmoP8zoYQAACAASURBVPAQCZCApAgg+sjp06ehIbG3h5OTU3R09Ouvv96xY0dJQWBlSaBeBHJzc7FrJWL2YP4FOhN+sHB8hQfsww8/jKhUUJgI2MMdVuuFlCeTAAmQgM0RoJK0uSZjgUmABEQjgOHv3r17ISBhh/Tx8cF6yHHjxmGrPdFuwIxIwN4JwCEcoXrgE56RkaFWqxGzB7GOEaSnS5cu8IDt0aMH9hfBny4uLvZOgvUjARIgAckRoJKUXJOzwiRAAgKBX375Zd68eSkpKRjjvv3221OnTsXOB7BJkg8JkEADCPz2228w7yPBRPnDDz+sW7cOkXsQuQr+4TBRPvbYY9g7JyIiAj6xDcicl5AACZAACVghASpJK2wUFokESKARCcAr79ChQ7GxsdjbAzYTrIecOHEifVkbkTizljCBgwcPYrIGlv/Lly+fP3/+2rVrPXv27NevH1ZXYn+Rdu3atW7dmtvqSLiDsOokQAK2TYBK0rbbj6UnARKoFwHsbRATE3P48OGgoCAYIWEkweIuR0fHemXCk0mABOpLAC6vFy9eRMwe7NEKZ3J4BMBc2aZNG0R/DQsLe/DBB7F3Jb6V9c2W55MACZAACTQjASrJZoTPW5MACTQRAbjbIdrkggULsJ9Bp06dZs2a9dxzz9ES0kT0eRsSMEcgMzNTiNlz5syZoqKi4uJiLKocPnw4dq1EzB53d3e4ncMztkWLFuau5jESIAESIIHmJ0Al2fxtwBKQAAk0HgGtVrtlyxbE1EFQELjVPf/88yNHjoQlpPHuyJxJgATqRaCgoAAO54jZA6MlbJWI2YMoPvB9xaQPtCX2rkTAHvify+XyemXLk0mABEiABBqbAJVkYxNm/iRAAs1DAINRCMiPP/4YISXhPofgOg899BBi6tDE0TztwbuSwN0IIGYPwimXlJRcvXp1w4YNiYmJ2GIEe1TCMglVOWLEiMjISMTsgbnybjnxcxIgARIggaYgQCXZFJR5DxIggSYjUFFRceHCBQT5+OKLL7CLOtZfTZo0CXt7cM/0JmsC3ogExCIAVYloPbt27Tp27BgC9uCrDU/18PBwuL9itx7YLbHMEjF73NzcxLoj8yEBEiABEqg7ASrJurPimSRAAtZOAPE8li1bhrA6586dwybpM2bMuP/++729va293CwfCZDA3Qgg+qtGo8ErDJXbt2+HH6ynpyeUJIJm9e/fH/Fg4Qfr7+9/t2z4OQmQAAmQgGgEqCRFQ8mMSIAEmosAgkBid4H4+Pj33nsPNsmhQ4fOmTNnwIABzVUe3pcESKCxCVRWViII88qVK7du3QqFiYA9OAI/dgRkHjhwIFQlAvYgcXVlYzcE8ycBEpAyASpJKbc+604C9kAAo8nVq1cnJSVhm4EhQ4b87ne/w0oqV1dXe6hbk9QBQ3AMxJHM3g1ewTDqwoHQx8cHy9XMnsODJNC8BOCMgI0rjx8/DvdXvFGr1YjMHBISglA98IPt2rUr3mDvSqrK5m0m3p0ESMD+CFBJ2l+bskYkIBUCZ8+e/eSTT/73v/8h9iM2h5w+fXqXLl1ghZBK/Rtaz7KyMuzph2iZSBh2YzUpjiCZzQ8BipxMCTISroP33XcfIOMVS9SwuTyH5mah8WBzEYBLgrChCGL2IOAWwvacPn0a80pYSIkeC493TDNhmSVnmpqrgXhfEiABOyNAJWlnDcrqkID9E4ANDTvRxcXFrVq1CkoGzmx/+MMfYHmw/5rXv4bw94OpFhabnJwcqMd9+/ZhdRmkIyyNSqUSxkbB3gijDZJZEY4QuLDzYHuG7Ozs69evI96JkPLz8/ERNmmAnkTC5bBbCslsPvUvO68gAREIoNMi/hYSdhlBH4btHf0TiypDQ0PxGhQUJNjb2WlFYM0sSIAEpEeASlJ6bc4ak4AtE9izZ8/y5cuxMgobBrz00ktPP/007GO0MNRqUohtKMa0tDSYHBF86NKlSxCTIIZVZEgqlSogIADeqkhQkl5eXnXZGQWiFNYeyEhYgPGq0+lgE8YunQh/ggSTpq8pIQIKVCWG6Qit2b17dw7QbfnbZldlx2pqyEjMieTm5v7000+I2QNvWGwti06Lr8OgQYPQabHlLISlXVWblSEBEiCBxiRAJdmYdJk3CZCASARKS0sxCnz33Xdhh4RWwWLIP/3pT61atRIpexvOBvoQQ2TwQYKxMdmUfv75Z1gdoeKQoOuioqKGDx/eeCGIUAZs0oCtGpD2798PKxAKA20Jn1h4EmKMjm0AIV8h+OEii+To6GjDxFl0OyKwY8cOIWYP5kcw/4LvC6L1oLs+8sgjwcHB+BPdFd3YjmrMqpAACZCAmASoJMWkybxIgAREJwAXSlgPEFNn27ZtMCA8++yzo0ePhjqS+P6QwIIFYBk3EzxXkWBsQZSRqoSFYYLTaV1MjmI1HAQk9v2DpkXxkM6cOQPTJV5RBmwuLyyzxEpLREDBnx06dGAUH7HIM58GE4C9HS7cMOPj+4S+Cks+XjFjhe6KjgozPt6gu7Zt27Ypv0oNrg4vJAESIIEmI0Al2WSoeSMSIIH6EUDwjB9//BEbexw4cACxXl577bXx48fDG1PKGhILvaCokeDli5WKsKJg63bAQdBarBeF9Q+RRZBg/bMGux9aEMUTEjwJ0ZrwKkxNTcVwHCVEgmNt3759Yf95+OGHseSSw/T6fUN4diMQwFSIELMHsyEwV3733XeYoMF3CvZJSEp80RCzB1uM0KO+EdgzSxIgAdsjQCVpe23GEpOA3RNA3EU4SS5btgzCA7H7J5sSVIfdV7x6BSHDsEmmVqvFEkfsarB3717Ey4GVD6YSrOyCeQT+otiQ/cEHH0TUEBtS17D/YHUlzD6IHAs7KiqI5kZNETwJYZMQvAfL1WAIEoL3YOKAvoWS6vZWWFnErNq0aRNi9mRlZcHH/vLly+iWcNtGR8WrsOQYnvbWMHdjhfRYJBIgAfsmQCVp3+3L2pGAjRFAHJeNGzeuWLECdkiIit///vewswUGBtpYNe6huCAALzsk7JOJYDkYtiLBJAKVBZMILHiwQEJdw9EXB+/hPlZxKcyVUJKIK4sEExAi90BkIsYmhDEEs5AQBAXBe2AFgnshNx2ximaTaiGw+hdfSfjBQk/CtA5tiVkeIdAU3Mgxp4O+ivkdqc15SbU7sN4kQAJGAlSS7AckQALNTwB2KogKRGT961//evLkSQzL3n777Weeeab5S9aYJRCC5cCbDiNURKzZYkqQ0NBLWD0I9zmMSrED3ogRI6CjpGOaQ0+AuXLnzp0YqcN0CT5YFIqEbUsQNAjRUOAKi3VrAiVgsSF7bGP2JubdDATwFf7mm2/gBAthiW8xui7md/CFxfwXfLY7duyIXopEc2UztA1vSQIk0CQEqCSbBDNvQgIkYJkA5viTkpLWrFkD783BgwcjLisWI2Gm3/IVtv0JhpvYD1OISYPVg/DwhHjGQbh0IlwkEiLl3H///Qicg106bLuq91x6jM4FVsCFIChC/B6E8GnZsiXEZFX8Hpgr4eILP0OqyntGzgwaSAC2dPjk4+sMuyVmQOBKAGcKBJfCdxkOBeifEJZ2/FhrIDVeRgIkYOMEqCRtvAFZfBKwZQKIbIHFkIsWLYJLJ2xN2NgDDpyenp62XCeLZYcWwpYDSNiiQ6/XI1gOEgaX2J8DwXLgtgp1BIOGQqFg4BmzELHZiQBN2C0Ta2h379599OhR4BKCDGH1GsyVSPAzhLakIcgsRh5sVAKwUmL6Q9hTBP4F2LUIoacxTySEmBL2F4FpHe4GnPVo1IZg5iRAAk1DgEqyaTjzLiRAAjUIwKwEOyRk5KVLl7AecsaMGRBUdrOLPYLlYPmfsAIQjpoIlgNjBWoKG6MQLwexOh566CHEy5HUEtDG+A4UFhZiaSWG7MLODULwHoRIAWqIc3QthISFOQgBUXAEWl06TsKNQZt5NowArJR43MFhG+uB4YKBRwEcEPr164f+iYSVz+ifcN6mtmwYXl5FAiTQjASoJJsRPm9NAlIkgOijX3/99bfffotB1RNPPPH888/DDunh4WEHLODPBj2DhGA5wngRdYSJVYiUg7WO0I0IloPEyDGN0dyw9KIJwByvcBgW4vfgDSKgIEARNDzcX4X4Pd27d4c1uDHKwDxJ4A4E0EXxAMTDAdv5wD0B++LALwOdEwnPB8x9oH8icZPVOzDkRyRAAlZFgErSqpqDhSEBuyUAMx1GUYsXL166dCk2QoSG/Nvf/oYFgbZbYcSAQTAY+K0hzioi5cCHDYse4WkpRILBoPCxxx5DvByIFtutox2UHG0EW5AQvwdr2NBq8JLFK1QlXA2xLhcbOUBVwlYpJLoW20Gj21AVsP8NYvbEx8fj6YGHCTxjscFPVFQUnLQHDhwozDoxrJQNNSiLSgJSI0AlKbUWZ31JoKkJYOAO69CGDRsSEhIwgoeGHDduHHw7bdHP8Pr16wj6AqsCdpaD4RExNvAelUJcDUR/gccalj9BOiLGhn1YWZu6rzTy/bB6DSbKjIwMrFmFfzUS2hGuhjBXItARGlFoR3jDtm/fHsFRuNKykRuE2d8igLk2eMKnpaWdP38eUx54ZsI9Hk8SPFjwVMFGOJjvQIITLKmRAAmQgPUQoJK0nrZgSUjADglAaC1YsOC7777DoHzSpEmvvfYaVgTZlu8WRnhQjDA5Ir4LhncwqArxcjDCQ7h/hJmFWxpcWN3d3RH3hRYtW+nEMP4gcg+aEtGeMHwX4vegu2KxLtoRCa7IMAohOArMy1zOaivNah/lxOpfTFoh7dmzB+ZKBLUWwkphygO2SkzDIWHuwz4qy1qQAAnYNAEqSZtuPhaeBKyUAAbo2CARYyAsiUSkkzFjxvz+97/H/LqVFrdasSAw4G+GhKgt2CMOcVZhKMDSOwjg1q1b4xXOkBERERjJMaC/9bdmfUsIVYnYSHBXRqNjMZvQEzB3gEiw0JOwC8Hg3KFDB3QDrL1E/B5OHNSXMM9vGAHEKN64cSMkJdYAazQaPJHQG7G5CAL2IHKPEFAK81mM2dMwvLyKBEigwQSoJBuMjheSAAmYIQBXT4SR+Pzzz2Hnwa4Mzz777NixY6EhrXnMXVlZicEZ4n8iCigSvMuEwC1wJEN4VUgIQT8gxCKWLdHj0Uyr2+MhWITg+Cr0BEyLCPF7IC8xg4D4KHiFMzPM0fA8xM6f6Bv2yIB1sjoCmOyAbzaCweKRlZycjIct9hdBh8SjSYgEi2DFXbt2ZUwvq2s5FogE7JQAlaSdNiyrRQJNTgChTSDD5s+fv2nTJgxu5s6d+8orr8Bu0+QFucsNseGbEHMFbqsIn4hgORiNYfmcECwHqzdhb3z88cdHjhwJqXCXvPixxAhAXm7dulXYFxRDeaEjYSYCsaMQvAfGapisETEFvQgzDkjWPIEisaaz2+oiDOyKFSvWrFmDdb9CGDD0RkSTGjRoEPokFmwzlJTdtj0rRgJWQIBK0goagUUgARsnkJubCy/Q//3vf1BlMD/CCDl+/Hg4BFpPtaAe4a2KVXCYy4doVKvVeMWfEJOYv0ecFSFYDqbzUX672dbSevjbZUmwdyUioyB+T2ZmJuzYCMWE+D3oZlhUiTVsCNuDlbQwV+JPxImF/yFVpV12A+upFKY5hJg90JZ4xGFeDzMd6IHoh8LmInDMRmIwMOtpMpaEBOyAAJWkHTQiq0ACzUkAQVn/9a9/wfcPcQXnzJkDUx6cWq3HBRRr3lJSUvbu3YuFRhjlYwEnErxVsT8HguVg8p7Bcpqz99jFvTFPgf0bsDEguhbkJaKkwMqNRbZXrlxRmBJM9OhyCN4jbEYPX0S7qDcrYaUEYCQXAvagWyLa2erVq/F8RkgwdEVMajzyyCNwu3jwwQcZR8pK24/FIgGbIkAlaVPNxcKSgNUQQMgHhH/AekiMmGHKQ1zWZ555BhqyGQuIYZMQKQduh7CRInSKEEkfS9pgEULYQ3geCqMohEtpxnLy1hIhgO8I+iEWDMM6BDMRTPdIEJz4vmDxrbC1A5a3oTciYaBPo6VEOkbTVxNer3hcY9YPRktMdqA34ggMlVhaia4YGhqKRzeWhWNarenLxjuSAAnYNAEqSZtuPhaeBJqBAAwvCMoKX1Y49SF44OTJk4UdtJuhKKZbIv4EFCM26oDVER6GMARhBA/1KATLgdVRcC+EkrTFHSybiyrvKyIBGC2hITF8Rwgf9E8IS/RYdFdEhUW3REJ3RUfFyB6+1nBHpCusiPCZVXUC6IrogXhmojfCN3vbtm2YcYOMxOJemMphNsc0B+Y4sDaBMXvYc0iABOpCgEqyLpR4DgmQgAxDEOy/t379+vfeew+hHTCNjTfDhw9vSkdWIVgOFjdii3lEPRH2eMR4CGWASsT6Rmy2hh1H4GHLWJrsstZPAKN5xO8RQvhAVWJVG/o2RvCwFCFcCkzoSFCV6N7Y3aEpv2jWj44lFJEAHqExMTGrVq2CTyxslbCNIygxYvYgIYgU9lYVHrAi3pFZkQAJ2A0BKkm7aUpWhAQaiwBW3UA6IuJ8XFwcYopggU10dPTo0aOdnZ0b65Y184WNEffFBgwY8cCYgwl1BMvBcEcIJoF4OViEhqEPAktgQVrTFIl3IQFxCUBVHj9+HD0cdnXEhcI3Dn0e9n90coSDQvwedHK8wRQJEhwRxb07cyMBEBA20cV+SFgggN6IzW+wtBJ2cjxaMamBJy38O9q3bw9tSVwkQAIkIBCgkmRPIAESuBMBrDxcunQpfFkxukVQ1jfeeAND2yZYToOpcYTJQfASLDODeoQREqNqGEWxH/eIESOGDh0K/ytsMYI4hBzW3Kn9+JmtEcDEDfp5YWEhVlRiSdvOnTt37dqFbwHsRVhLid6OFBYWhpG9sH8gVaWttbANlBePX/Q37F0JbQk/lISEBEQnFmL24MGL1eaYT8TyAThm20BlWEQSIIHGJEAl2Zh0mTcJ2CwBbA558eLFdevWLVmypLS0FEOHadOmwdmpkSoElYiFZHDwgykG6hFhIaAeIWKxfqx169ZYw4NRC+6O0JdWuEFlIzFhtiRQnQCmcvDVQNyUw4cPC8F7MMqH1yus8cIeD3A4x5cFXxAkqE3G72H/EZEAVhZ8//33UJUwm+PJDBM6OhgeyFgqj4czVvliRgMJNkwRb8qsSIAErJ8AlaT1txFLSAJNTQBOTcuXL//hhx8wYhg3btwLL7yAoarou5BhaIIwOdj3DEMTSEfMeQvBcgTdiDEKJr/h1IcN0KAnMWJuagq8HwlYKwF8UzDRg9E8XvENwtcH31msscR3B18WxIMVdkbFtpbwS6TR0lqb0SbLhW6GXof9eBE+ClGjsF4dodd8fHzQ9+D7ClslAvZgV15sCsW5DJtsYBaaBOpJgEqynsB4OgnYLwG41WFs+vHHH3/00UcI+wEB+eabb2IwKkqNoRsxBMEtsOgRgw9suPfTTz9hV3fEckCCTIXZE6oV2zzCriLKHZkJCUiKAOz5mzdvxgQQth6Bc6zwdRPCUCFuCgxHMB9huxFMyghJUnBY2UYlgL2gELMHe1fCMRuBo/CcR/CzwYMHw5EEvU6I2cPZwEZtAmZOAs1FgEqyucjzviRgRQSwKuaXX37BBtabNm1CEFREZP3d736HtVj3PqkMaYpIOYjfAJsJJrARLAcJMlWwliB4A9Z6YekjLCcMOm9FHYJFsWUC0JCw8MNWCZs/3kBhIuENvtr4osHUjwRXWETugfUSqQmWPdsyTpa9HgTwqMcUIZb1wgEbu93AXImZQTztEbYHPyjoeELMHm7IVA+mPJUErJsAlaR1tw9LRwKNTwD7iX3yyScIzYqJ5JkzZz733HNwKL2XuKwI1YA8hQS/OwQOQcJENRZ0weSIYDkBAQHCai6GWm385uUdJE0AvgCIVoWE7yDmdBC8BwnfTRwX4vdglxEM8TGhA22JxHVuku4u4lUeK+0hJhGzB1MYWF25YcMGLOtFl0OC6/VDDz0EP9i+fftiK0vx7smcSIAEmoEAlWQzQOctScAaCMD/DZYKaMjExESMJp9++unp06c3YBtG5INIOUgwNsKtDtIRCX9iKhrZ4hVuq1CP8HHiINUa2p1lIAEQwOJkeCTiCwvTJb6tGOUjwTgJBwEhfg+G+/j+YsYHBznjwz5z7wTQzZKSkiApscwSC32xzBK9C+vhoSfheo0tRuB6jTW98Me+93sxBxIggSYjQCXZZKh5IxKwFgJwfsN6qpUrV2K9IvxLJ0+eDHdW/JDX3eOopKQESxwxGEWCI9NlU0J4HghRjAkwOIBVE8FyEHQB8T+spdosBwmQwG0E4CyALy8G9/BLREJgWIRNhlss1CP2eEDClxoWS7jFImFDS7rC3oaQB+pHAPZw/HwgZg/0JFZV4GcI3Q/dDDF7sMUU5hyxdSpi9jRgWrN+5eDZJEACYhCgkhSDIvMgARshAA2JXacRRwcBb6D0/vSnP02dOvWuAhI//HB8RcKIc/v27bgWCfunC3E7EE0hKipq/PjxkKN0VbKRjsBiksCdCODLDlsl5puQ4GKAmSPhCQArJSKpCHsJYs4Ijw4spcZz4N4XVN+pNPzM3glgo6ktW7YgZg9cr/Eev1NwYBk4cCAC9iDB6Ro97d67GX7I2FHtvSuxfs1AgEqyGaDzliTQ9ARgMIQIXLVqFbakw7zvxIkTn3jiCQTbsFQSwVIhmCkgPjF/jNgJUI9wPaoKloPJY/zGixXc1VJJeJwESKAZCWBYj+8+ImZBW+KBkJWVBa94GJHgiIgAKrBSwp0BDrFC8B68YmrpHkuLVZ2wVsEEeo/58HJbJIDfGsTsETobgrThT/QEBOyBlRK2cXQ5+NGgmyHid31rt3Tp0meeeQYbltT3Qp5PAiRwBwJUkneAw49IwB4I6PX6devWLV68GMNBxLxBTB3M8lpyOsVSFnirwuMIr/gVxxpIJER2hcPqiBEjsOIRHkcYQWJBC1ez2EPnYB1IoD4EoCoRT0sI4YPnieCecPDgQViQkBBMBaN8TDBhsSUShv4NWxqNYC0jR4788MMPoRzqUzqea1cE8MuFCVAs3z19+nRCQgLWWKL7Yb8o/PpgBhOG8fDwcASLqvsv0ZQpU2Dw/Pe//00PbbvqKKxMcxOgkmzuFuD9SaBxCMAbTavVpqSkfPbZZ8ePH8e07quvvvr8889XTeXC1QcqEeNCJEwAQz1iRIglUhgmwocNE7d4HTJkCHxW8ZvdOGVkriRAAjZPAP4L2O8B8Xt2794NcyXCdQrxe2A4wtQVzJVQlYjfIwTvgRKoS1zop556Chtjfv3114j2DIFq84xYATEIIPjwxo0bv//+e2wuhV83GK7RxyAmMdEJX2sszkcfg860tKFUfHw8nHEwkYrfRJVKRU9XMdqEeZCAjEqSnYAE7JAAnNC+/fZb7A8J0yK8WPHziTUnCJ6BqmLnaCHaAeQlpCN+krH68erVq4iOg59k2BPwBkso4URELyA77BmsEgk0JgHYfPBIwcYPcH9FQvweJMhLCEghfg+G+8L+sZjbglusJVX56aefwnsCUX9eeumlhQsXQiE0ZqmZt40REH7FEO8N854I2IOETW6E/VG7deuGYLCwhyNhMrR6xRAtFr0Opk6cAycdTFLYWLVZXBKwSgJUklbZLCwUCTSIgBAaB3EL/va3v8EvCOpxwYIFsCjiOFY3IZgBvNHweu7cOUzHIiGMAeb+J0yY8Nhjj1ka0jWoILyIBEiABG4QgFMiprTgnYiE9ZbCYwqvmLES9hUcMGAATJcwJQlmIrziSTVu3DjMcCELiM+dO3diQSaBkoAlAvCvWbt27RdffIHpUaGDYQMq/AIOHjwYnjWQjuhUCBw1aNAgeN8gE/hd42TMsQpdzlK2PE4CJHBXAlSSd0XEE0jANghggnbTpk2IqQMXIERRx5pGjL1gfkTCiiYsNcFvp2AHwAAOZgFM3OINf0dto3VZShKwCwJwhYWYRIJPBJ5UsF5iYguvWGCJ5xUSBv1IcM5HZGmE+BIqjYAr8+bNg7bkhJdd9IJGrAQW2aJ3YcIUP3mYP8X7nJwc9Ct0IQSHQywfxJwTbo+JVPSxP//5z1j534gFYtYkYO8EqCTtvYVZPwkQgNMOFn7ExcUhXg48fDAIgwMPwuRgmhY+P5iFxS4dmJqFqw9+MpE4GpNAp2AVScDaCWDQL6zTho8ilmoL8Xsw7YXor0hYb4kTquoAB9fo6Oi///3v3GrI2tvVasqH3gWzNnoXDJVr1qzBRiNQj5jLqCogovUMHTr0f//7Xy0/WKupAQtCAjZAgErSBhqJRZQagUqDobLcgD0cf6uoNL7e/Ce79b5COFhhKJ/3ySerEhLgy4pQOtjcraWbm5e7u3+7doMiI6NGjOjbr58D9nzDhm+mf47Ozi3qHzxdavwlW9/KisqK8gr0st9M//BS7Y3p72oHy8sr0OUcHE1+0qb+hVfj2xtvcMj03vSpo5ODo1O9o/ZLtiGkVnF0q/JKA6bA0MMK9YXYdmh1/OrVKxOw6rI6CuwoOHbsmL+8+5avv4+jg5PQ3VrIjI82U78zHqj23tj7HFs4GZ+LshZSQ8r6ojthOlXYB1V4hYs1zNoQlrXgIEQwAgUjQEDVrpXCVsnVX01PthsJzz3OxrKDkUAVASpJdgYSaGYCFaWlxbnXSq/lluTi3zW8YqK+vLi4srwc/34zvVaWV/xWbqisqKh2xPhReUXFq1u3lZSXq1q16uLt3drdPcDHu32rVi09PCAgjRrS9Fr1Rq5QuCiVrt4+rq18XHxaueFV6e0glzczAt6+OQhANxblFxfl6wuv64t0xjfFhSWlxehmxo6GLldZ8RtWuBlfje8rTTrT+CqcgHE6Rv4OjpifcICivPGK/cOFP6vemP6Uu8jdPFzcvRTuXm6mV+MbubNTc9Sb92xmApghKyotsAlJHwAAIABJREFUKCjNLyjRCf8KS66XlBebxKTxGYf1bJ/+6Wv1oRO1Cor5iPZd2zw46oEHIoMhDtGz0NuMWtHYxSAsjRMbeOOIT/Bq/MhR7uiicHb3dPXydFV6uig93fDq5SrHjpfUls3cDcS9PbQiJKIQNxivSPDQgUsOfiRNv5MVcKWOjY2Fn06t+0ITYk8shOcR9i+FmIRQNE7Lmt6YfcUlWCoC27jg4yO8YYRhcRuUudkQASpJG2osFtX2CZhCARRcuFBw/lzB+fPGf+fOFefm1qqYW0tPNx8fJxdXJxcXRxcXvN5643zzvavxI0dTjArkCjmKfwa8lpSWm97jtbwM/8pMf5bg1aAv1kOm6vXVb4fhlsLPzwPx0zt0ML12dGvjh/GZrAVHWrbf36rVACOt69cKcy/lX7usy72ku5aty79ae27e2dXZQ6mA6nNydnSSO8mdHeXO+J+jk7MT/sRBxEQxfmT6U+gg5Yby8rKKG69lhrIb728cNJSZxnFl5ZCsJUWl6KjVmborFa3aKVu19fJpp8Q/b9+WGPjbFXRWRiZDoxeU6nKuZ2sLs7XXs3MKNdeKtOUVhupsnBydPF29XZwUckdnJ0fnf878d/rPx6pOwCOue69uY58fO3LMo8qW3jAO4SMITkNFqQFeGZWlhvIyQ2VZeUWZ8U2F6c3Nj/SlhUWl12HzrH47N2d3P492vi0DjK+eAX4t2zk7ulBb2lBvRaeCUESIcgQKxisSIpBX94VGXRD4F+GCETQYbzA3gQUgWDkp1BE9CstAEOcJJkoEFIAIhGLER7Bh4kwEhsUr7OHCG+EI/qz6CDIVG5DUeppBWyLPgJsJ7+GhjRvZEFUWlQQaRoBKsmHceBUJ3J1AWUFByTXYGK8V52AQpSm8eLEoO7skL8/J2dnJzVXu6ubk5iZ3c8P0pounh4uHp4unp3NLT7zBgP3uuTf0jPLSktKCwrLrBaWFBTAMlBQWGvR6mEDxE2p6LcZo38Xb26N9e8/27d3xc9i6tatPK9dWrVDaht6T1zUpgbJSQ5FOD/FWqNPn5eTnXbmO16I8PTz/oBKdXeR4lbs4OaMPuru4Koz/XPDqjs8a0UgIw2aJvrS0qKxYX1KqL4OwLC0pM5QaDMYZD7waYPBUeLgq/Voq27T0aePl4e0Ou6WHlwIFa1J8vFlDCcB3oqAkD2bG6yW63CI89S5dK7iSX3INZmxINWcnV2cnzFLg1dVVrnCTu7s5e+DV1dndxenGswVTEvHL4z9891/ePt6dQzoHd7uvd1hv/GvXvl1DCyWDCbTEoC8xFBWXFRaXFRUbikoNxWUVJZhjM5SXCG9QDB93v9YebVp7tMP8BgyYLV2VHi5eDb4pLxSLAMyJwvakeEXsHAQFELatwnGIt6oENShsJon9JIWEI/BGRTEg/ObMmbN3714h7g58WRFtDu9hcmxwIQUpK1g+oWmRYBFFeIKqBBUKBQtrJ3a8REIgWZgukVBIoVQNvjUvJAFrI0AlaW0twvLYPIGiS9naw4evHTlceOlyub64vFgPN1R3eJwGBLT091f4+BhtiTecTuUOciwga/jvmQiwYCU1Oc1WGBdnGj1pDcXFRVevXtdormdn66F73YyK18lN4dUpqNUDvXx79YJ/rAj3ZRZiE4B01Jy+fOHE5WuX8sqKDWUlBig095ZuSl8vpW9LT293CEijG6qxx8Fty+iGKnYR6pcfzKRGd9lydECj9yxsTIX5RbBe6bQFBbkFsHwada+r3Ku1Z2DXtu1D2qIW9bsBz24SAnh2nLn261ntiYu6s9BspZBn5SWQiz7ubfDPS9HaRa6Ao6nR79T0Dz3P0sJFzXlN3LK4fg/16xzcGWJS6dMojxrj0nOT9zY6HZYN4BV2y7yinNzCK/nFxn1HXExyFz6xnVp369y6Wzuvjk0Ckje5RQAGRkQd//XXX0+ePFllDIRuxGpGJFj8IBfhIlGVhCWOZgnCrxVKD8ZJCDlPT89GEnLCskxYNYWE8kNnCnGJEZoYn0JYIqEMISEhcKbFjs2NVBKzEHiQBBqPAJVk47FlzlIhYCgqKrueD5Oj9siRa4cP63NyjHPubq7urVpDOrb0D/Bs2wbq0RZxQFUKkjJfc7Ek/7pgt/Tq3MkoKR/o5ebr6+zlhcraYtXsoMyw5hUXlRRcK7p46vLFk5euXcqHPoQ/qgImFVj2TAISSswWawoDJpxv84yqMh+WVaPpsqzc08c9MKRdICRlm5ZuHq6oGp3HmqVxsaARXqP6ssKz106c0f56Pu+UYwvYs11c5e7e7n4+HkYBqXD2bJay3eNNMblxHUvVCy/nFl3R6a8ZTKZLGO87te5ukpSBqBcMmOx498j59sshwLCsEYZH+KBihxgISIhDSEeYFiG6sKco9qzy8fGxRfIwYMILF3oSu5LgDdZqwnSJsLHYmAT7daF2ULmQuLZYtdvbkUckSIBKUoKNziqLQwBrEXNPnMg9rtadOgUZWV5U5NmmjWe7dp5t27oplW4+3nI3hTh3soZcfvuttLCwOC8PKy0LLl8uuHTp+uXLWGDpGRiIKVaf7iq8cmll0zRUhaFCm52XferKlXNXcy9f1xcUGw2PrVu2bO0JX1CFpxtijNjToATGVdQRAYGu5xZCXl7PLYCDrndbL7/AVv5d/Np0aO3sZpNquWl6i7h3gZv++WunzuWeugrP1cIr7i5e3u6+Sne/lq7e7i4t3Zw9ETpV3Ds2Y25YbwlbZVFpfn7xNVgs8/RaB1kLuL/6tfQPahXSwSdY4ezRjMWzm1vDWxVbvyBhuSPWHyKMDXRjYGBgmzZtsDkHkj3Z7rDeMjc3F3uTwE0XxlIkWC/h/or6YrtLmCtha7WblmVFJEKASlIiDc1qiklAf+XymY0bL+zYgSjjyFcZ2L59n76+3e5HyEAxb2PdecEPNvvQoeyDBwuvXUNJXZVegVEjgkaOdPawSVuEdcO+Ubqi68XH9546uf9MQW4RDnl4Kzp0DfDv0sbF1dkmyi9KIWGuvHL+6rlfNXlX8jHZj0WeXUI79BgQ4tOOIzBRAJvJBEbIIxf3/XI+RXv9Mj5G2KUurXt08uuJ0DVmzrbfQ9rrF8/kHNPozghV7Na2V1inwR2877PfGjduzVJTU3ft2gUXUNwG5sf+/fsjCg6WFDbuXa0pdzzBYKgEh/T0dDj5YwawR48eQ4YM6datmz3pZ2tCzrKIT4BKUnymzNEuCcACCcNjXsbJy2lpucePe/j6umMN/f+z9x7wdVRn+v/tTbrqvViyZFm25SZ33DAGA6YYTMCEpSYhsIHkE8im/EJ2A/lvEjZssmw2QEJC6BADwTTb2MZgG/de5aZi9d5v7//namwh1OWrMvfOM3Euo7kzZ875nvfOnPe85WRmRmVkwAIZkk0eZKPMdfWt5WWtFeWWhkanzRY7bWrCrNlREyeGJ6dw7cpBMuz/NKhMdeVNpQWV1UX1iB6MiAmPToyMTY42RoUhiU7/14bwtzBUNtW0Nte2mpqxBqE9IT0mY0pqclZ8XGoMVigJ4YaPWtNgi6tuLStuOF3ceBqBXhHIKB2WhJSn+Awlo/dQeSIKtMFU1WCqbLM2tVkb48KTchKnZ8TmJBpTkUloqKVJ6nwoTogehJ/nqVOnjh8/jrbDHIe4R6zAAedVIYGqpIB0Nhb+rnDrFQyzMFcinHLGjBnQJ2Gr5Poi0hSJIGo1Nckg6ixWdcwIVO38smrHDiza4YGmlJ2dNH061EhNGMbxEjJC9k8f+VKcZkt7VXXNyeOtVdUapKjD0GDlyrhp0/u/kN/2RcDn9VUV1xXsLmyobIY1MjYpKj03JTIuQqvHQhwUvIvYoOHA/RWxlJWFNUg4BOderCYyaX521rQ0aN19seXx/gkg7erB0u1lTYVIxAq31cy4yQkR6Uicg1U6+r9QUt8iB6zVZapoOl/ZXKSQy6MN8ZOS82elL0bIqKQ4DLKx8GLdvn07YiCR/wYurAsWLEDiGcQHYrWMQZYQ8qdB00awaFNT08mTJ48cOQLHV6iUc+bMueaaa5BbKOSbzwYGKQFqkkHacaz2iBNAej9nW1vT6YLide9bauv00dEJublJU6fBjXPE7x3kN4BxsvrokcbiYqwyEjNlStbNqyKzszVGer0Otl+xQga0x+M7zlYX1yPoMSEtdtykVCRfHez1Uj3PaXOWn6uuLqm3me1RCcYZV05Oy0nUG3VSNqANSRaQdrXRXHukfOfJygNYnCPOmJQZlxcbnhxK0Y9DAjLIk2GlrGoprmg+325rUcoVi7Kvg0oZro1AotpBlhDCpwmRgV988cX+/fuRvBQTjPBihQ4Zwk0elqaBG8y2gIa1T6BhQpmESomcQ1K23A4LWBYy7ASoSQ47UhYYCgSsdXWV27fVHzmCFSDjsrMTp0yJSEvD2o+h0LbRaoO9vR1uTHWnTpnq68PHjUtesCB16ZXqMKpD/XUAbI8lJytKjpcjm05MUlRyVmJcSoyeCyr2x6z7d0j0CpfX6gv1HQwjM/NSJ8zMQDLb7ufx7y4EsBLG2dpjBdWHq1ovwPaYFj0hMWqcURfd13IdhNeTAKJJWy31VS0l0CoNmrCsuMlTU+elRmX2PFM6R2BYO3ToEBKxIpEMFCFkK01ISAhkIUfpoBNaCp8LxJGe6NgQOYmUPAglzc7OZhSl1CRBzO2lJinm3mHdxoAAEslUfP55wct/h00yYfKkrKVXYgVIZiW97J7AZKqpqqpo69bWmhp9bGzegw8mzp5z2aWF9oUV52t3vn+wrcmERKx5V0zEOoo0pl12j/t8MpfDeWrP+dqyBvgDz7122tTFEy+7tNC+sLa9YtOpdxASCTvk9PRFyVHjaYQMoMd9Dpe9sO5ocd1Jr8w7N3PZkgkrJRg/CV/WN954A7F/MKNdd911s2fPpvJz2UKF16jNZvvyyy83b96Ml8LixYtvv/12GHgvu0BeSALDSICa5DDCZFHBTcBlMbecOVP8ySeW8vKo9HHp8+YaU1I4lB+uTm0qKqo6cqStqip2xvTM61YiJY9Sw5iri3QrC2tP7y26cKoSeXQyp6TFp8ZIOZXOcImcUE5rQ3vZ2aq60oa41Oi8RTmZeWlY+nB4bxGkpfl83jpT1bGKvaeqD2I9DwRDZsTmykNoGY+x7ResIFJUd7ymrdSgDpuXuWxCwlSs8zq2VRqFu8OGhoQxe/fuPXjwIJZJRDBkfn4+8rKOwq2lcAuEmO7atQuJXsH52muvRVYeKOpSaDjbKGYC1CTF3Dus2+gRsDU0FLzyMuLcI5FKbtEiLAupZID7cON32+3t1dWFW7e6HI74/PwpDzygMUrd5xDurAc+PYG8rBqdZsKMDOQdpZ4z3HIn83q80CeLj5e1NLRh/cmFq2bFpoT+mL5/jHDF3Fuy9Vj5HqfHOSl5dkp0lgTtZv0jCvxbr89rsrcU150obzqXEpWxbOLNGbEhbhj/7LPPEBKJBK2rVq2aOXMmV0cMXIq6lYDFQlpaWrB6ytGjR5G46NZbb502bRqnvIedMwscPAFqkoNnxTNDk4DH6azdv//03/+mVGsyFy5MnjkzNNspmla5HY7KAwcqDh82JCXlffvbkVnZCkkq7VBvkFZn+7v7kXc0Y3Jq1rQMlVopml4KwYrAQ6yqqLbwaKlMLrvyG3PTJiZJM7lrh3rTuuX0eyWNZ9OisyelzDVomA1rZAUeS4YUVO1vsTQsy70JyV1DUmmvq6v7+OOPkSRm+vTpUCORvXtkmUq+9JKSknXr1jU3N1911VVXX301LMCSR0IAY0OAmuTYcOddRUIAmXWKP/qweteuuKzsjIVXYG0PhkSOTte0lJaW7d1ramjIuO66iWvulNp6KlaT/cSXZ88fLtUZtBNnjUdyndHBzruYWszFMBHVtKRPTJpz3fSoeMkpUYfLd+4v+cLjc09MmpUek0N31tH5UdhdlqK6E2WNZ1Kjxi/JWZkWnTU69x2du+zbt2/Tpk1wuVy+fDk0SYbwjQ52LBmyZ88e2IGxIOfKlSuR0Gh07su7kEBXAtQkKQ/SJWCqKN/31FM+lytn+dWJ0+kfMtqSgJxGF7bvKNu/L27q1Fk/+alKMqlxYRzb/NrOCycrs6aPm5g/XqmiKXJUZc9vnCysPb7zTHxazM0PL8c6K6N6+zG92daz7x+4sD0lKmtu1gqm1RnlrvDJfK2WhoMln1mdpjVzHs6OzxvlCozE7eBsCR1y/fr148ePv//++7E+5EjchWX2RQBPs+rq6jfffBOLhfzrv/4rIif7OpPHSWCECCifeuqpESqaxZKAmAk0nzl94oUXFD7flJtuistFngm5mGsbknUD86iMDCzUWXv0aNPp0xGZ47VRoW+aQ8De1jf21JU1Tb0iN3vaOOYzHH3ZhuBFxBoRkoqVJwsPX4hJjgqLNIT8E8DiNH1x9kPk18lOnD49bSGXOhwDwZPJ9ZqwhIj0NlvTmZojRi0s4imjX41hvKPJZPrkk0927959xRVXIJtoGBd5Gka4gyvK/zSLiECoJNxckdlVr9ePG8fXyuDY8axhIkBNcphAspigIlCzZ8+Jv/xZrVbn3XILkusEVd1DqrJ4C4YlJMRmZ9ccOlS9d0/0xFxdSGeia2syf/7WnsaqlhlLpyRlxIe89iJmYdWH67DaZF15U9GxssjY8OjESDHXNsC6mR3tHx1/taypcMa4xePj81RKdYAF8vLLJqBV65OiMtptLScq98BKCTfXIH0OIK3OK6+8UlBQcNNNNy1btkzDXNyXLRMBXwh34pycHLvdjnRHTqdz0qRJARfJAkhgsASoSQ6WFM8LGQIt586e/OuLaq126q236kNabwmKLsMoShMWFpWe3nD2bM2+/Qn5+ZoQ9Y+yWxxbXt1pMznm35AfnRARpMPHoBCqQVYS60ymTkhqqmktOlo6bnIKdMtBXhhcpznctk0FayuaS2ZlLEuJzqZT65h3n0qhjo9IszjbYZmM1EcnGFPHvEpDrYDb7X7//fdPnz69evXqefPm0bdiqACH/Xxo8oiTROQklEkolhkZGeyUYYfMAnslQE2yVyw8GLIEzFWVx597TqVUTl29WicBX8pg6Ugok5EpKQ2nC0xVVUkLFoReAh5Lm3XbO/uhtExfMjkyjqFEYhFMrNsZkxjVUNVcdKw8eXyCwRiCyuSuok1nao5OH7cYaqRYuEu+HkqFMs6Yana0nq0+kmBMiTbEBReSjRs3YsVILEExd+7c4Kp5CNcWqiMUSLPZDH/juLi41NTgm6EI4d4J4aYpQrhtbBoJdCPgbG8/+PTTbpNp0sqVushQdmYLxq6Hm/GUW25pPnnyxHN/Csb691dnn+zQZwXlZ6unLpwUmxz6saD9oRDfd7ow7bRFuW0Npt0fHXbaXeKrYEA12n/h873Fn01Iyk+LyQmoIF483ATUSs20tEUatWHtwT/XtFUMd/EjWN7OnTs3bNgAU+SsWbNG8DYseugEECd5xx13IJXrG2+8gXVZhl4AryCBIROgJjlkZLwgSAkgU2jltm2Olpaca64Ji48P0laEdrWNSUlp8+ZV7d5ds3dPKLW0sbql5Hh53oLcpIwgszyEUi/005bwqLB5182or2g+c6Con9OC7qu69qovCzemRmdnxk0OuspLocJalX5G+hK1SnOwdJvL4wyKJiOzy7Zt27Kzs7GMIf0nRdhl6JSbb745Njb27bffttlsIqwhqxRiBKhJhliHsjl9EnBbrWWfbU7Ky0N+lz5P4hdjSgBOrWlz5hgTE4o3bLC3tIxpXYbt5naz49OXdyA7aGp2wrAVyoKGmwCW9EydkHhoy6nasobhLntsynN7XYfLdnh9nskp82D+GptK8K4DEYg0xE5NW3C29lhp4/mBzhXF919++SX0k3vuuQfmL1FUiJXoQSAmJmbJkiVFRUVHjx7t8SUPkMAwE6AmOcxAWZxoCZx+/TW524151NCLwRMt88uomEqrzV250lxScmHHdqyUdRkljOYliEjBYtz93BEtOHuoxNJmGz81Xa1lwsx+UI3xVwiYHDcxFdloDn9+yuUSu48r8p1gHb/+kbXZms/UHpuWtjBcR0/+/lGN8bcZsZNjw5M/O/m+0+UYw6pA7Ac0YVVUVGCpifnz52PliTGsKm/dPwGkc1uwYAF8XLds2dLe3t7/yfyWBAIkQE0yQIC8PDgI2Joaa/fuSZg8RanVBkeNJVzLiOSUhEmTyr/4orGxUeQY4OX161//+sKFC33V02FzIjwyKiEiPi2mr3N4XCQEjDFhaTnJdWWNZYViD1rDgP73v//98ePH+0F3sHSHXhOeHpvbzzn8SiQEMuImmdyt+0/vxBzBWFUJaT+ffPJJZP7spwKIkIyOjp45c2Y/5/ArkRDA6ixYqWXv3r0DzjqJpMKsRpASoCYZpB3Hag+NQN3+Az6vLyojg0svDA3cGJ0dO2GCt6Gh6MB+h2MsJ+kHbP3KlSv/8pe/zJkz5+mnn+51Ot/abmuqas2exoTsA7IUxQkZU1IdZtfZ44VYk00UFeqjEikpKXv27Fm0aNEjjzyCReR6nuXyOI6W74oLT1HKVT2/5RGxEYgOS9BpDOcbjmHoP1Z1i4yMxEMMz7Rrr722uLi4ZzVQNyz7gYyg8Uw00JOO+I4I6VsPHz5cW1srvtqxRqFDgJpk6PQlW9IXAa/LVX/sqC7CGJGc3Nc5PC4qAuEJCdrwcMvx4yLPPqdSqf7zP/8T7kNPPPEE0hj+6U9/QmhKV3/XinM1CqU8kYl2RCVefVdGZ9DGp8e219rOnTsn5ol8rVb7+OOPQ93985//nJaW9tRTT504caJrs87XnVTIlfERqZw767u3RfSNQWOMDUtqdTcWlp0dK8GDqKxZswZuq5999ll+fj4mKbCYRNcplcrKSvjzY+KMQiUi0em7KlhhMisrC71WWlra60Rn35fyGxIYAgFqkkOAxVODlIDLbHa2tUWmpio1QZZ2wmF32W0OWd/xUB63y2K1ul19n+F2W2x2h1vsAYfdRAtrtKj1enddXU1NjcijJTGFP336dNT/7NmzGNwjaR6G9RhvCS1qqm6NiAmF1SNddovdZu+nL1wOnOF09xc0GhzPj+iESIfJBfPLGFqHBkMKGTW+8Y1v4MympiZMZ6xaterBBx/sNCU1mKqVSpVRF3JLzng8NovV7vSIPoZ6MH34tXPijCken7upvaG+vn7IFw/TBVjYY+nSpSjMZDJhkmL16tXQLffv3y8Uj18EnESQtXWY7sZiRpYAFP6kpCSkcoVNEk+Jkb0ZS5cwAfq9SLjzJdN0t93mttv148YFS4t9bse544f/9+Xfvb63Uqjzt77zxCO3Ls9LjRb+9Hrc1ZXn3n799f/7YGuz/1DYtStWP/6tNfMnpOhUcv8Bn6e+uvjtf7z+1DubnR7/6H7+Nfc+cdeqZTPGqxUdJ4h7U6hUaoOhtbraUVl5PioKmehEW1+1Wo1s+EeOHEENYUyAPolh/R//+Ef4u2Ic1tbUrjUEcWiu22E+eXjr8//1X58Vl6GBclnmv/7osW/evDIz4WK+DbfTfO74jr/98b8+OOpfP0Mfmbjm3p8+eMuNaSmRQTpPqQ/XtbeasdQBtDJ0qJjNLz/4wQ8+/vhjq9UKM3hZWdnf//739957D9MZ3/3ud5tM9cgepFGFTHZNr7W5bs+Gj//vD68V1bRB0tLyl3/30fuuXTYj0hhk84N9PcoM2giXy9lsazx58qRSqRwrwXv00Uc/+ugjwS7a0NCA/U8//fSuu+5CCCX+1Ol0BoOhrybwuNgIhIeHQ5NEBCz6Ds4LYqse6xMaBOT9zDGHRgvZChJov3Dh4H89nXPlsvjJk8RPw21p/PXzf/rH2vXdkn6kZN24553/L0Hpb8Hnm157+A+vVTf5R1Sdm94Y/+dnX10zK0nmdex87+0n33xhX9XXLEQKXdo7b7114/hw8UNADc9v2vSDv/61Xq1Ra7UYvoi5zrBAQoHsVkOMBWGrnJO5+I7bvpk7K0vM9e+rbpbmC8+/8Nvn/7G+2wmTlj224fmfYRrS52x85al/e+HzLQ0XTbAXTzQmXP3Jx6+PNwalLtlQ2fzLX/1HcVMBehDrHIzVgL6vTul6HMP9wsLCTgN451fjx4+ftXLi0tvnrsi7azDliP+cmsO7/veX//HJoa8Z6+RK/V3/79dP/OD6YJgcG5ix1dH+4tpnvnjpmEquCQsLG8OlGhEMiemJbjWOioqCA//ChQt//vOfD9wYniEOAtXV1c8999ykSZMmT548e/ZsKJbiqBdrEVIEaJMMqe5kY3olIMf8rlLpdoo6d8vFmnudr/79+f95Zz3y9ylk8996+cdLxoV/+tr/++4bx+tKbBiuY0XC9nMf3vHk83aXR26I/N6PfvuLq/IKdqz9zn//pcrrrbf621h06PPHnv/zGbNfjfz5v//pu1dNrTvx8RWPP+u1VzY6xL68QWcPepzOnPiE3Lw8uVqNyVSY/nrtXDEchDmopyYJJQQOh1ma6Uj1JIZKDrUOPq/7rVeffmHtBlyom7X67Sf+PSvG+fYv1zyzq6LcZOl4czg//MVdv9l4CrKq0eqf+9uWBdnR617+6VN/3+g07bP6/VyDUpOEfS8tLiM1LxYtFLngYbj/0ksv9dQkYSSfdl2ax+P0+bxyeVD2wtfEVW7+0w9+uL7Yn1ho6Tfu/vcnvy9vPP397/78XHG92VwdlL+u3n6NLq/LEK5bsGSeQREBtQ0pUns7azSOQf3oqUliXuyaa67pKWyjUSHe43IJYGUXmIsQz48CkHSAmuTlguR1/RGgJtkfHX4XGgSUep1Kr7M2d/iBintrqTn35ufrEdU4buaqt377o9lJCLHzRTj95snkVbOzlDKnqfzR7/4P1Ehj8sTnf/PH22f6F7s36o1emyw6XrcgO0nms619++3jD8prAAAgAElEQVQzZo9en/67Xz7z7WsnYsLe0+EApgpfsjwtOKYk8fKzm9rvmD1Lv+ZOmUKBpPPIKyjOrkPg0Pe///3OusGSgDii6667Dp6HEydO3Pj3HQ6rqLOA9kW1tfyLd//+CYbp066697mnnsqMM8h87Z4qv0382tkTIZbVhzY9s/uCW6bKnXL3q6/+V0qYv6Qovd93Ojzxp1lRwfpysZvtc6fNz1mRhIZMmzZNzJ7Vr776atesjEjoev3110MakS7l01NrT9cccbrtWnWQ+yL6XIee/uWHfjUy7Ob7fvUfv1kZrpFZXEa1UiZXxeSkzw8NgySEDTbJ5Myk5blXRCoT0JU5OTl9/TZH9PjatWuhSXbeAgotIicfeuihG2644fPPP//ggw9gCYe5fkTrwMKHiwD8WjE1Jjgk95wdGK67sByJEwjWl73Eu43NHxIBlU6Pf9YmsS9OiEadObT+QIXflvjEo9/P96uR/k2tQcrZ5sxwhDz5Lpz8fJPThhH7Iw/+m6BG4gSFzG93gOXVoFP5Gg6++OUZHLzunh/ds8KvRmLTKPy/dEVsuF6IouwoVswb4lpdNrs8PAxqJOop5rVAkBZvwwa/4Q4btI4f//jHyIMCHVI4EhETVlPSIGbUfdTNs/f9fy/0i9b0n//8l341smPTIFISiiLG8j7v9r076tssxoScf/vFo4Iaia/UCr/pWJ2ZpA6CaNzem24x2dT6iwNlMSc8bGlp+dnPfia0AfMs3/nOd+6//34InuAKHqmP9vq8dpc16DXJ9oqn/rodzcxZee9//HZleIdrAuZrEAUq16g0MYagFbTu4me2tylkSri24guYkqAAjL6DK5bw/cMf/iDUDH7dd9xxB8ImMTFhNPpfRvC5xRwfQoi5Ckj3zhPr38icBM1f0CTF/DQTKz/Wa1AEgt/vZVDN5EmSJqAxGsNT01orKh1t7eIG4Wtv9gcC6TIfu2N27KUfp9fa1jFD3OHFZWpo83m9KTmTblswpbMtFq8HyiW+xz+LuRk52uSyqT+/b6nuUhE2e4cWHTx5Na1NTU6LRZkxXmgjxlWdjRXbzosvvgivoYSEBAy5kKMFQUSdaiSqmjIhsaWh3dNPcl2xtedifdyN5/1Sd8W3Hl+UetGO7fN6qn1+GULaTAwoWxxm+LWOT7t+3tT0zkbYHR0Ll/WdS1ikzb1ULbgi11c0GWIvJkkS7RQG+CP8CUk+sWbD7bfffuDAASgA8D/sjCgeF5Pj8bhaLGOWBXS4OtpptyB9s1yWcPc91wtqJEp2u6weT0e0Qqj4tkLtr2ktUcu1OoXfuI/Rf9fFhIYL5oDlYP0PBElCb4SD9L59+9555x0YJAU1EtciESic9o8fPz5gOTxBDAQgRRcuXMCKQYKnNP7EJoaKsQ4hRoCaZIh1KJvTO4GkKxZ4XM7Wym5ZbHo/eeyOeioK9+DuytTwLt4CirDYzpRrnopSk9stUyABq/KruXiDXq27FEXYdKGgo/7qMP/U9sVNrxVv7tOvatllr62y0u31qiZMEI5hXaxeTxvzg2fOnNm8efMvfvGLrVu3YmSP6KZuVYpPjTFGhxWfKB/zqg6pAtATj+7yK43qzsE7picUqqTO+Q2fx9JYiRNUclVX86NeGz+kG4nt5IbqZrvVHhZ/UZMUbaonhOZu3Ljx3nvvRbJWuCN2nbwQkKZGjY8LT2owVXl9wT12tDZUybwQRaVe/dVwRQWrsVKkz4TLE+k2a6PZYYpTpSll/kc5AttG3yAJ3RXOq7feeuvbb7+NnMBYEaRbW+Bzm5iYWFBQYLf7Y1a5iZwAJsJKSko6Hw4QKroli7zLgrR69G4N0o5jtYdGIH7GTG1UVGNRUXxuLlaYGNrFo3e2KmPSUtmmL3ywMH61yTMm5slkJzsGGMrM7Ah/9QX746VzoqIxayyH2U6lkKVlz5DJ1uKbrla82ORx/nODZ+jVcP68IjNTrr+4hoFoNUnkn8DMPXyH+npDGyJ0cSlRxSfLMvNSNbqg6QC5PGbuMuW67W4ZzI6dm1yfM1ktOwwXVplcroyMh1AdwRi/q6YSnZzlP70jZjIYt7KCSrVBqb00kyPaBQ8Q8oQgSeRo7eunAdfEWRlLtp372ON1K4I5qi0iMV3e4eLe9YGmM2K9WYNMYel4KobC1tBeCVfkREOm0Jgx0STb29ufeOKJKVOm9CVUOD537lwomQikxJL3ocA9pNsA8zL6dPHixUIrYU8O6eaycWNGgDbJMUPPG48mAYxFcu/6l5ayMlNth/edWDeD1q9s2I68s6fy0roKXkfjuVIcPLivoM0t02nDkIyxuqRs39mv7Ktt7WabU2a2Os+UNns0GqSm8cmOvvFZ8aWxl6+xuQ4luIrPHWoOgrnkst172hsb1ZMvuu9iTCxa0xDGVVDi+1IjwVypUk6alw1fxNIzyK0bRK54SmN4HOq/5+21JW0XMwZ5XUj+69csjx2vcMllOhWSnsiqGz8/V9568ffkczc312Df/OWGKlvwmcKqS+qaalsj0wwqnT9OEkYh0Qoehvu5ubl9jfiF7piclB+hizpcug3K5MUOCsL/IO9krEzuk9Vs+fyo49IPyG5pdzkcXrO5/HRxMP2q+uBvd1lKG0+nqLM18otj/TERPLhUILdZ/0I1f/58PPG2b9/eR1N4WCwEWltb169fn5qa2tmhTNwqlr4JuXoon3rqqZBrFBtEAr0QCEtKbjl3rnLfvqRp05RiXVJCrdZuen9zk6xpb1F9Svo4hbn6hddeeebtbS1er6epImvRrUsmRP7p/U/ttpbjp0/Ik7PjfbadO/7507+sLWowO5zOmMSM65csLt37xrF63/HDpxSa5PQY+ZcbP/jFc6+WtVpkspYCTf5353fYJ8W6maprzm76VJWb69ckO2wRSGMDryqx1nfgesG71Wqyl5+pTsqMV2uDxYaiiIxTv7TuC4/jfMGFqrSMLE9T6cuv/O7vGw/b3J76ipKl3/jeeHnRpzt2NTa3HT3cmjtzgr2x6B+/e/LFDZvbrPC/Lk/PX50/rruv78Cwxu4Mj9tzen+hTO1JyotEMhdUBCGICAwbfSfD4WKg6sh+dKJyb7g2KtLgX9QkGDeF1qgvfGPbGWd5wZmS82FZk8JrTh987t9+d/B8hdvjbFBF33XrYlUXV/9gbOOh0s9tVut4Tb66I90ONlibEd4mwrZgNSak24EmiSRPyclIBcdNjASQWeDDDz+srKycM2cO8iQJVcTck5gX0xIjR9ZpcATkmCwf3Jk8iwSCnkDDsWNH/ucPqTOmZ125DCtMirA9Hqf5t3/4j9/9c6e3yw9To0OwmsNid2Wl/mjfx3efeffnq/60o61j6UhhU6rUBrXPZHMnz7r12PNP7Nr69nf+8/lWZ1d3MG1EuLLdbJXLMvfv+OdUo0jzHbpsttMffdRcUaG/805F1MXl1JBHZAyXVhsWIcFjdu0zG7R6bf6yKbBSDkuZI12I1+3475/d+OKWM0j90XkvXRi8qE0wgM/97rvvPZxz10P3HT1ysquZW6UKl8vtSDx5452/+MMvHrmUA3WkKxto+V6Pt/DohZKC8szF8bqIi9p+eno6BvQwiQda+thd73Q7XtnzjNPjmp99nVE3ZusTBgjA13jiysUPtbSYupi5VWHhOosZjhtxLxzYvCxDF+AtxupyPBnKm87CbpylmZGinihUA0N/KABjVaUB74sgyVdeeeXkyZOPPfYYFlwd8HyeMPoEzp079+abb+LxhXWMhLvD4DxjBiJfuJHA8BOgTXL4mbJE0RLQx8XBJ6py506VVhshSjOXQqm5YuacKKM+UqNQ6yLSUzKWLFjy0H0P3T43o6axNnZC3h1XTxs/7ar5ExIVPhhLlCmp4+ZOn3/3bbd9b9U8S12TQpdx1/UL8nImLUlJ1BpUCpU6KSEtf9qc1Svu/vG915iaG03yiDtX3ZDYseif2DbkpC3Zsb2hvExz9Qpl0sXZbgyqEJAT1KN5cEb91VrluUMlME7GJkUFhTKJ/Doz514ZG2nQKBQaY1RWes6iJSu//fCPlk8x1lc2x6Vef9NVU267YUU6sj9hbRltWGZG3sIlK765+gdXzU2pqGrMmHLFVfOmIzOU2MSsZ32gRl4oqCg8VhqfYzQm6QRhg8dyRkZGsEcWKRWq9Ojs01VHGixVSREZKmWwmMS/1ktyQ+L1S6aHa5BmTGGMTczKnbb85tUP/eCeaLW5yapasnpVZnRQtguNrG+vOFG5O1KekK6erJRfDODHSpKddqSe4jrmRxDDCWvkiRMnkPZp0qRJ4rSdjjmlMawA8rUi6S6ckLF8i+BSgWfauHHjOnPwjmHdeOuQJECbZEh2KxvVJwGvx7P/T//Xum9f3i23xk/K7fO8Mf7Ch6xrLrcX4Z1wR9GolFiAwWq3+ZS6cG3HaMPng/uKA2tjKBQYHWrUKoXcZ7fZ3DJlmF4rDN7dLieK8GH5bpWq4wQZ5pIdbrkxTCfO4X3prp2le/dpFi9R5SHDkH/D+w+DqtDwoUJexF0bDpzecSEnPzMn/+LqJmMsZYO4vc/ndTrsLq8Py/dBEtUqpdfrtlttCq1B5w+ThCR6nX5B82BeQzjB53VbrDalWqcPElfehqrmo9sKwhI1ydMv+rWiXchRCWewYJ/CEHq4qKbgw+OvRkckzM+6DrrlILpdjKd4PZgGxCqLMiQQ0mg1SoUcB2x2jz7MoAzOhA/ttubtZ/5plMdO1i3CSpICdPhfwI4kfsFD0p3f//73sHphIVMxiotU61RTU4OFqSA/WL6lMzASO1OnTqXOL1WhGPF20yY54oh5A1ERgG4WkTm+vqiodv8+ndEYFh8vynd2h/qn6Ri5d8QKIssO/tIgN6uwIXUmhlPY1P4zOvzvsBqD//xOG5B/vOU/ghOUHW1EmWqdRiVCZz2o95WHDpXt3aecOFE9Kx9DRaGVCFSDXaiffDaiEq3+K4MuSB6f0FDfUHG6FusyRMSEKzt7s/8rx/RbVBti45dEtbpTFNUaLaTukiR2nOCXxK9O0Gi0ELsxrfigbo5F++rKGk7tOaeLUabOiu58FGDIhZQ2+A0OqhTRnwS/VrvVWdJ0yuo2RYclBqtl0j9VAUnDB+bF/M85hVIFQRPnvNiAQlFvqjx0YavKo5ugnd2ZaAciN2HChKCwhEM5we8FmauhumRmZo5JiqABIUvtBARGvvvuu0jsfMUVV3Rdkio7OxtxrVKjwfaOGgFqkqOGmjcSCwGNweCNi2+pqW4+dgyKZSQjPca0ZxAbWbRlS9Xx48pp09Rz58nVF3NOwC0HRiExe3kNFRtU4piUyMbGprKTNU67KzY5ShGkxpShtlyU5/u8vpKT5WcPFuvj1Miyo7ik2EPwMJoPpYEXBC8uLNHUYK0wna0zl8eFp2hUwRpYKEpRGnKlqltLjpbtULu1E7XzDIqIzusTEhKQXUyUk5vd24hKxsXFOZ3O48ePl5aWQlcJCgW4ezNC6G8sbvyPf/wDPQI1Mjb2qwxbyJAE19agEKoQ6g1pNYWapLT6m60FATxSY5KSrDEx5traxkOHvC5XRHKyiBeZDOVOc5hM5zZtqj9/Xj13rjp/FsxbQmsxmsfQBK/AEGu8Tq/TRCqsDnPt+SZLmy0uJRrKJN/xo9/Lbpen+Hhp4fHSyHR9yszoTjUSNcFoHrl2gjdla68wdVpdTFiircVZbS2uM5XHGpO1akRLB0EUa6/NCd6DWJGlouncwZKt4bKoSdoFWsVXS6/CiJSXlxdETwPYIeEEjl8KdJg9e/YgoB3zL0FU/+CVom41d7vd6IK1a9diZ8mSJV2tkdiHe0Vo+PWETH+FXkOoSYZen7JFgyIQHRNrMRqdblfrqYL2sjJdZCT+DepKnjQcBDwuV1Nh4bnNm9taW7VXXqmekgcVv7NgpATEQlghNppH6zDM8qc90HmcPkdjWWvdhUaVWgVP1+EgyjIGS6CppuXM/sKq4tr4icaESRFdx75YcgahuSGZK99gMOgVRp9Z1eKorWg7D1jhuqjgDZscbGeL6bx2W9Opyr0X6gtilSlZ2vxOp1Z/X4SHI3tN0AkefEagPaLa9fX1hw8fRkMwEdP/ipRi6pBQqEtTUxPWjfz8889BHtbIrml1YCWGXw8dj0Ohm8XdBmqS4u4f1m7ECGCWLjwyslmlliclmwtO1Z88iQElErpySnXEkH+t4MItW0p27vSEh2mvvU6Z+rVU8ngjwr0wVKdR/a6GcXFuuUMTI2urs1adq7OZ7fR0HR2pw13g0Xpq9zmXx5k2LyYiRS8sHSncHWPiyZMnh+o42P98i4hQuNVqm9HuNpe0nWq3NaZEZyGX0qjBl/KNalpLD134vM3SlK2elaqZ1Ll0pMAEhqMgXTge1UZcMUQL4XkHDhyAcQxP71CKShCz0BYVFWG1D6z5gTSt3XLqIOYWR5ivVczdFzJ1oyYZMl3JhgyZAN5/cfHxVpnMk5qGzKeNx46bqqq0RiMCKcW52uSQWyi+C9x2e8O582c3fNJUVa2eMUN79Qq5wdBZTRghYYqEUSj0rJFduwJjejgdOd1OVSSWDfU1lrfWlTZp9RqNThMUC4SIT6wGrpHb7Wmtby/Ye76ysCYsUZuaH60NV3edNkKPhPz8PdqLACqlTKWxYdFMbYOtoqTxFA7q1WFq5cX45IFR8oyhEPD6vDBFnqk+cLb6kN4XgcDIKGVip18x4GOsjyVzg3fELzzNYJaESgm7N5K+HDlyxOPxYF4G1jDOzA5FWIZwLnLnwg75z3/+Ezr8woUL4cXT9aWJvsCkGL4aQok8lQQulwBXAblccrwuVAhgbQysv1RfW+suveA6cljR1h6blZU+f74xKSlUmiiOdvh8zQC9e7e5rk6elqaZNUuRkIhVTLpWDpE2SDgRqtbIbt2AmJby8vLKyipbi6Ox0GRtckXFRWRNS49P+ypZgjh6LuhrYWoxY7nIpuoWpV4enxthiNEovkpyDBlUJCUlIf9k0PkWXl7HYJRfV1d3vvC83WuucRc1eMoMusgJCdPSYnLo7Hp5SPu6yuo0FdedqGop9ri96arJcao0lfxrGju8qYMlWWtfbew8DqGCiaylpaW4uPj06dNYvQkazqJFi6hMDohuSCe0tbXt2rUL6jpWAps4cSLSm3fzX8WkGI4zAdKQqPLkQAhQkwyEHq8NHQKFhYW1tbVY9M917Kjr2DGsvThu3rzUOXPUej2W0widdo5FS7zQmRyO4i8+rzt7TmYwaBYuVGVnI2awa13wLsTSZPBrHYsKjtk9fT4fppaR+RCE2qtt9afbXTZ36oRk6JOGCIOSmV0D6xlkZ7XbHJXnapBZB6pj3ARjTFZYV3dWFI9pC+TXCb0UOwOSa25uxrjfZrOZvS1FjsNWb3uMMWF6+mIET6oU6gEv5wn9EMAiqy6PCwpkQdU+r8cbr0rP1uR3e+Jh/gKPO5jB+ykn6L6CGglPS6QPhbaDsEkET0KfXL16NTxN4DROlTKQDsXLArPeBQUFm5FcoK0Nj6wZM2Z0UxchVJibQMCtRGZjA+HJa4eRADXJYYTJooKYAB7TDQ0NFRUVZjNGVs3uM2fdRUUauSxuwoT43NzozMwgbtvYVR0KZEtpacPZs41FRd6ICHVurnJCjiL8awlmMLxA3CAmViUbWoNhQVlZWWtrq8vuaauwtFXZPHZZXGpMSlZCXHK0ShMiqxqOphhi+N5S31ZTUl9f2eTxuY1JuqiMMG3410hC8IQFS7EW/GjWTTz3ghoJwcNzz+l1NLur69yl7d6GBGN6anRWUlSGTv1VWlHx1Fn8NWm1NlS3lNS0XrDazdHK5CRVllEZ2y1NLrxAoQlAkww9N35oO3BwraqqgsUMS03CPgn1EqtQTOvYQmlxndEURZA8derUiRMnYPiFIyucd5A1t1sFBKHCyzRkFsIdTcK8VyAEqEkGQo/XhhoBmIYwT4+HtaxjAtB95rTr6FG5xxORlJi5ZGkUF2UaSofXFRSU7t5ta21FXkIs8qHKHC/XaLomaEVhmDqFc1dIjqiGgkoGh0MMv2CclPlkcIQz19nrz5i8Tm94dFjW1HFJ4zHi5IINgyWK1KzFJ8pa6to8MAflGqPGGZSaXpZawVAe8xcSn7yHFwZyP8IjA+N+qNwmb3O585TZ14o1QiYkTM9OnM58PIMVO5nMZG89V3MImXU8Xk+iKjNFlYNFPhSy7gmNMNaHKTKEh/sQKsyLQaigVeKVCus3tCBMWMDxZOnSpXB5DdLcQoOXhGE8E/S2bt0KHRKWXkEhR36Hnk8teLQivwCUyWG8NYsigUESoCY5SFA8TSoEhKEVjJMWiwX7PpvNdboA+qWvtTU8NjYxLy86I0MfHa3SaqVCZCjtdFmtlqam5uLiutMFdrtDER2tnjxZmT1B3gMXYtLgh4PpVY4qOgHDOAl9ElP4UCzhmdlWaWurtNrbXXAMS8lOTEyPC4s0aHT0POxFIrE+pKXdikjI6pI6c5tFE6aCHTI6Mww6ZLezMQiDYQSCJ1lTZE98eNZB8DBmxWjV6/O0eutqXcVmb5tP6U2Lzk6JyorQx8BESe/EnuiwPqTV0d5sqatsLmo01Wjl+mhlUpIqGwuudDsZ5kcM9CF4WCY39EyRPclAjUQcOITK4XBgYhbBI9At8SfajkS1sE/C5ZW/wZ7ccERQv4XcRYg4RTYmBHJjgeWeuPCThFAhuQC8iPnz7BUmD44CAWqSowCZtwg+AhhR4Z2HFyEcwFB7n8XsbWxESh5PSalaLjPExcVPmBCbkwOVMvjaNjI1NtXUNhUVNpeWWhobPWq1cuJEdUamPCZartX1vCESSGJE5V+T4OsZd3qeKbUjGEOYTCb4HEKrRNs98Ds0u8319vYqm88tD480xKfGJoyLjYgN57hBkA1Lu62hsqm2tMHSZoVhDQt7GJP1ugiVStdLeDPGZLBDQvAkklxn8D8fjPXb29sRtYvYNlzllXkQOdnmqW/wVDhkljAtJC45JXo8PhXyXsAO/kYhc6bDbatrK4cja5utyemyRyoSYlWpRkWMTt7L8rCwI8EGDmskdkKGwIANgVDhaQah8rv54Gnm8UDGYJwUnm+ggdh4rFSBuL4Bi5LICQiuOX/+PIIhMZeNVwC8dWCHxOsS8609H/iw8UKHxMQEV4yUiHiItpnUJEXbNayYKAgg2ygmUzFCxUsRFfI5ne5zZ90njnvNFp/bHZWakjRtRnzuRAV0J5Wqm+umKBowYpXwwWLrdjstlvqzZ2tPnLC0tMhVKkVcnHrGdFVmFvxWe94ZeiMGUhg94OXX81se6SQAtBh7YbwFwcO+cBwpeZqKzQ6T2+fxhkeGpU5AvtF4rUGrVPXiuhnCMPFLRBiky+GGAllZVNNc16ZQytU6ZfT4sJjxvQzigQKDMNh1MXmBgRcnL/qXDcyglZSUYAbtK8HzNFa7zrd6G7w+t1qlHRebmx6bE6aNRKJXqfm+wgKJf43mmrKmszUtpfBcVclViarxyeoJGrm+J1gIHuYsoA/AoNTzW+kcgVKENynUJCiTQqshZlh5EuF/OAJH33nz5s2dOxe6JX6nPV03QxsUHvLYENpw8OBBMMFkIt6SmPCCt2qvDjsQKiCCUOGcUF38NrR7PPRaR00y9PqULRpmAvDSQSgRZlIxnyrokzKv14NEr/V1npoab3290uUyJifDASU8MSEsLj4sNjaEl6NEEh1rU5OlscFcX2+qrmmvrZUZjcqEREVykjIxURET26sOiTcfJlYxUIBHq9QGCpctjhhSCIKHcRj2UQ5mMxxI79ritLY47a1Oj0NmjA6Lio8wRodjB/9CeDlKKDaWVqup1WJqsbQ1tLc1mXxyrz5Ko49W66M1uigN9MmeqAVfVggeNtohe/Lp9QgG94hzwxMP4ndR8GQ+LBmCKEqTpwmfVl+7UR8VY0iMNMRF6KONuhjEVfZaVGgcNDvaTLbmdltzi7WhxVLndrnCFdHIo2NU4F9Mt4U9OpsM0zekDs89Rq+BCYQKzzEIleBELVCCbik84nAQG7LIwHKLGR84c2IL3jU2BxR7PM0QxYBJaujSyE4E7yd4mMN5VRAYyExfZkbolsLTTLIJ6gZkyxNGnwA1ydFnzjsGJQGMqKBJ4okvuB1ebIPb7UOmirpa99mz7pISmDtgmVTqtHHZ2XETJkZljFPAUBkSm8tma7lwobGwsLWyEsqk1+WSadSKrAnqSZMQDImJ914VSDQdTDA+wOwpxvE0B12GLGAEJoSxYRDWeTmiKL1uH+yT7ZXW9lq71+WDZRJZXqMToxLHxSWOiw0ZlRJzN43VLfXljY1VzQ67y+P2yBWysHhtZLrBEKWRq+S9KpACKExbYGCK8SgnLy5D8Dp8DvxLnsI8LuiTKMQn8yIxj1vmRKLXBne5w2eDZRKiF2WIS44anxSRodf2bha+jAqM7SUQvFZbPQyPyKBjc1kEa2SkMj5BlYGMrEqZSinv89kOkYNTIpKghHBancvrHQgVIkfg7yp4+giFADUEDGZwCBtMc1Av8bLwB4enpOR1bPghX97txHYVmi9kYUX0I1RowRoJzRCL2SJqFK2GwPT0YhVagfkIvEaFqdi+zhFbe1kfiRCgJimRjmYzh4cA3nl4zyGUCHP2ePN1+ur4S8cftTWeqioYKn1Wq89ikXvc4fEJxsREmCsNMTFqgwGrUyqReA16l1g3v+ugy+W226E6Oq02S0O9ua7OVFdrbWn1qdXysDBFRAQySihTUxXxCf1482IcgDcf5lbhyCqp0KCR61jIGwQPM/fYwRCk80b+VRPbXdYmp7XZ6bK63Xavx+kJjwqLjDVGxBnhB6vVa5CnB3qmSt2Ly/HIVXioJUNLdDs9TofLaXda2mztTSYYHtubzVAdEfSo1ithfjTEavVRarhV9lU4hmJYYw3jLRWsYBUAACAASURBVHh/0RbUF6UhHcfQv7Fjw4wG9rtea/MhlrKx3dNo85qcPrtL5tBrwqBVIp4Xn1qVXquC5OmgasrRi2Ld4GLigoLjtuOf1WnCMh6tloZWayPG/fBZ1ch1sEBCh4xQxKvkfT66MU0GwYP2iCcerJEc6/ff23iCQZXCA81qtSIlT9eTEVqJWTN8halbPOvgEwSeMFRC14JuiXRZMMfhp43XipinJqEbo134yWDDaEFYGQXmRzQHcoINFkg8o7D1834UhAoTE5iNhWj1j5TfksBYEaAmOVbked/gJoCXBF6BeBdiw7vha43BAMRsRpIeX7vJ09iAVD34p3C7oUlqwsKQ9BX6pC4yUhcZoYvAp/+fEstjjNEGjdHR1mZra8Onvb0d/1xI3W7HWN6CRKwygwGhj/5/sXFyo1ERFgZlsh8FEo3Amx4KJD7xsueU/LD3KkZgEDx4RkHwMEa56G7dcRv4vnocHpfNg3UpHW0uqJf2NpfH4dPo1RqdRq1VqTUqfZhOb9QJn4ZwnVrb58h42GverUC3y20zO2xmu81it5ns2IEO6Xa6HXan0+aSK33aCLUO/yLVar0KaqRKh6C8XvxXhWIxcIe8QYGE7GGUxvChYe8+TJRB8OCRAcHDyPjr5ftciCD32RyYQvO2W7yt+GfzmTUqHZRJfGqUWp0mzKAON2iNBm2EQQPJG7PFKqE3Wl0mq8Nsc5osDhM+7W6by+N0umxIooMQ3DBFZJgiKkwZhdw5yMUKZbIf8yM4QBOA4EGBhOBhnzrk4GUPr0oIFTRGzFNAqLo+zVAIXrLQJAWpwxMPJ2AaF26f8PCEMokdvGWgjwE+PrH1lcLtwIED0EuR2gemv8HXbUhnoiGY40MlOzc0CgMDVBgb5l+gBwqVhFqIJxVEpX9newgSzhdy7VCohtQXPHn0CVCTHH3mvGOoEcDoCr46GGBhsIU3Sq/NQ1SEp7rSW13jbW5Cth5EWuKf/8WJHZ8X70bY7wyxcf4wy7hY5IaVYwFBece4Gf/p3On4U1Dkuh7syAUkfPi+2untIMZLVtgWOv5Zm/DZ7HLY/QXidhim4/9KlTwCsT8xirQ0VWqqPGxgXzXUBFOnGLtj3hTBLf3MsPZKhgcvmwDGWBA8zN9DvYTgdRuHCcW67R6YKy1NDuiWDosb7on+0zrEBDtqjRr5YLFqpTHKADMmVhmBtgkh6/if/78d8tdRkvDfrkf8X/ulzl9ax//8uxflsOOSi3/499xur7XdZsZQsNVqbrOaWywOGwwREDxBov33g8aoNaoMsRpDjFYbjvxVHYX0vQmChwEZxlsQvF6zU/R9Nb+5fAKQNzjpwesVY/2+BM/jc7V5G9o8DRZvi81r9sg8HRKHx6Nf9PDEMOqihRhLoz/SMho6Z6eQCQ++TjHsedxf9c6nXlc56yqLlyTdgihHe7PJ1oJ/7fYWWB1xFiTP/yT1C7dcI9fq8dRTxkYp46FD+oVyoA31xwYdBoKHEf9Ap/P7gQlAqKDvQa6ggPUlVIJ5HKdBYYOqhksgUn6p6nj64VEAT1H0CN5Ewga1zd/Hcvnq1at37doFzXP58uVXX301PqF2YqJTSPAjnOOXqY5tMDvQG/HsxU9A2PAQFh5HQlHCC1EIlIXVcZAZ5gShgs6JJkC0OoSTGwkEAQFqkkHQSaxiUBDAWw1uOdhgKcIQH5vwTuql8oiuxNy9zYpPGXZwps3ms9p8dhyx+fftdrVOp1AKIUiqizvd/vRHJymh+OFbLxaz9+cUxKfbC222y45/CC8cgZoLt1WXCz5YMr1efvGfQWHQw5oj1xvkYQb/jk7fV8Rjt4bgNYy5VWwYweOFjbdmLy3loZEngI7FtDcED5+C4H3N6bpLBSCQHofX7f/n8TvBwoDp8LvC4oj/uNPrdXkRUqhQIt7XL3RwIsXm3/n6P+FIeX1JbVN1Suy4uIgEJLFE/BwkzT+XcnHHn2G18x/We0RYmUqrUGqUHZ8KOKxiR4XUs5c+5b2lzOnJDzXEjH6n4EH8aPruSWl0jkDkMKAXBA/Pvc5wym53h+qIqF6nz+GCB6zP7veD9f/p/+z8h7kspVKNJxrEruOzQxBxzJ8h9qJQKmWCIKr8uoMPi64KEuaGxCGo0wNpw0G/zHUc8fmPuD1OhUyplsP4rvN/KrT4hMOqWq7TyHTQIdUKLBczWMu88MSDQUx44vVvVhqdLgixu+AZJVgpIVTCmxQv1l7biDOhWELtFDxgBQMm/sSGfeETjy/MbOL5AHvmRx99BN1PKErIfYogauRHhccs1DaooHiFQYCx4Y7CTtf9rgdxR5QMoygKx6ewg4eSsC/sDFI2oC7iKshVp1DRn6LX7uZBMROgJinm3mHdgpIABjUYUOMlBCul4PEycDMw7OmwUiLY0v8J9c9slrmc/sQ28BrDp8vpc17aF474P50yHET+CwReatRytebip1qDRQ9kGDLhCIIbNRpkxME+RlCysHAM0mQKZYcREkMspWDhHLiGl87Ae07wusF71691dGyDv5xnjhwBCB42jHhgJBcEr6+RfWcd/PPvGJH57eL+yXivRwYlE/ok0vl43MiDgZH4xU8c8bo7tETsuPyBZRt3f7h+9zpdRwhmXva0hbOX5OVO12BkrpYjLM6vfGKWoyMjDo5AY+yIleuYZ8en3xQ5tBl3CBtm62F+FBKZCLI3cjBZ8uAJ+GcoOjaM/oWHXrfIt96K8gsdJK7DK8P/KaiUEECvz+WG5OG55nPBkgnbJg76P6EZYkfm34E5ESu/+NPeyKB8+pPfYAcLcmBHgZ2OdDg4QSFXQV1EcGOHBRLPKb/daDAmx24VFjz2IXtC2jAmcOqtQ4f5mCBUeKAJQgW5GlCo/I+wDvtk5wZ9EtomHonYsELjb3/7256FQCbQrfB9XbRokZDzpvOzrz+h+wmSILz+LuMliJtiMgISBSUW6ihKoFANswCxuFEkQE1yFGHzVpIkgLeaYC/CG1HIlYK3Woftpnc/WPFAwrsNs7l4ZWLDjKlge8SE61B1APG0SFI1gYBhdl8wV2JHGE5BtxwuwUOBJ0+e/PLLLzFEQ5gTvLwwbpsxY8aCBQsw049BEny6Ls/jFOMqQfAEuzeGXEJkFOcsgkKAMZqHJAgPPZiVhKE8BA8PPZHXH0+2TsHDgw5SJzz0OMoXQ8fh7YmnmbBBqASD4eCFCs/A/fv3P/30092yRuHtdtNNNz300EPo8ZFopiBUglxBacTTTHig0ZNiJGizzDEhQE1yTLDzphIlgNde5xQpBuJd/XCEiKMx5ILRUqejjuCuI+iQwicH8WPYNYHfGtoj5K1T9gTBE3zAsB+gbgmphhqJtIRwIUNyi3379mG0hzil5ORkxAjNnDlz9uzZ2OmrFRAtQd6ETwy2YPcWpA47FLy+uAXFcWiVnYKHoX/XJx72B7SZj2gbMcQXvBM7PyFvGN8L4seB/ojCD6RwQaiEqTF84jnW+SjrKVSY0Th69Ojhw4cx4YXVRxBj2fVxhxnShx9+eMWKFcOlRkKoID9d36SCUOGBJuwE0nBeSwLiJEBNUpz9wlpJjoAwkS/EueETc/l4KQruOp0s8Kew37mDP7vuC9922gy7Gg+77UNvFGJ+Oj/xnpMcdDa4gwA0TCEkSfjE3D8GW33JWK9C2E3wUM66devWr1/fGcKEMCQkusCGbP5CGkNYe4ToIKZokqwYQg3ofOIJDz3MSgQieCDZ19MPx4WAtM4nHv7kPEXoyR60SrjCIiRy8+bN//jHP+A3AS0OiiIsgdAYcXzTpk2CnEAAHn30URgkBZnp9ibt9e3Z82BXoYJeSp+d0JMotmhAAtQkB0TEE0hgbAgIdiR89rrhtdftOF5y2ISIC2EH+902HIcOiQ0v17FpFe8qegIQLQzxBQdsQcZwRBji9xRFfIWtu5xd+hsGgYMHD8IggNyGcC07deoUXF7nzJkDJ9jc3Fx8Tp48GUMx0SNhBUeJAKyUgh9sz+dbT1HsJnh9PfQEYcQTDzuj1AzeZtQJYNIKz5kjR46cP39+7969x44dQwji/Pnzp0yZgocMYiAxh4Vz7rrrrs8++wy1y87O/vWvf33VVVd1e6YJQiW8KPuXKIgThWrU+5k3FCMBapJi7BXWiQRIgARCiQDMnsgDhDyfGO299NJLGMwJIUOYxb/iiivuvPPOG264gVbxUOpxtoUERoEA9MBPPvnktddegwur8IRBKrg77rjjgQceQGpWpEqC7wMUQqEmsHvn5+dD1YQauXbtWkxpjUINeQsSCHkC1CRDvovZQBIgARIQFwGM/w4dOvTee+9h/IfIpbKyMiiZMCBAn4T1AEGVMCBwmT5x9RlrQwIiIAB3CUQ8YoPb6gcffPDFF19gNiozMxNPjLlz5959992TJk3qq5qIk4R6OW/evL/97W9I1trXaTxOAiQwJALUJIeEiyeTAAmQAAkMGwH4klVWVp49exbLlmzbtu3jjz9GIBNGe+PHj8foEOZKhFZOmDBh2O7HgkiABIKTwI4dOxDieObMmcLCwqKiIqREQtzj6tWrkdwL2iMeFwM2C3ZIzF49++yz48aNG/BknkACJDBIAtQkBwmKp5EACZAACYw4AfjBwv31j3/8I/LBClkZEVp5++2333PPPXl5efCAxcaAtxHvBt6ABMaUANwW8CjAEwBBjy+++OLGjRsRqi1k2b3++ut/8pOfICn0UCv47rvvrlq1ioHZQ+XG80mgfwLUJPvnw29JgARIgATGgAASwO7Zs2fnzp1wf0V05YkTJxDyNGvWLEQ3wUo5bdo0+KchD+cY1Iy3JAESGBkCcF6F1RFO7+fOncN6QsjXhTQ5cFvFKrXwX0WCHDwBRubOLJUESOAyCVCTvExwvIwESIAESGAUCGBwKeTSwODylVde2bBhA9J7Iq8G0vpPnz4d2Xrg4UY7wyh0BG9BAiNHALmd33jjDbi4NzY24vcOayScV7FKByaMhHWD6IkwcvBZMgkEQoCaZCD0eC0JkAAJkMBoE0BcJRzV4PYGD1hk62lpaYFKeeONNy5fvhyusCkpKbGxsZ0JG0e7crwfCZDAQAQwGYQfb1VVFaaHMDeEZM5YMSg1NRWGx5ycHCTOWbp0KZeqGogivycBURCgJimKbmAlSIAESIAEhkoAg1H4wiFJD7zg1q9fj1QcycnJ8H1Fwp7Zs2dDscRScjRlDJUqzyeBESKAWMfTp09v3rwZP9gLFy7gB4ukzXBeveWWW7DALH65EydOpH/BCMFnsSQwQgSoSY4QWBZLAiRAAiQwqgRg6ICtEtl6MFqFTyw2LCiHQep9992H8CqMULmS+Kj2B28meQJIzoxfJXLnlJSUIHEOsqciSzN+hsibBT+Cxx9//Oabb0YeHclzIgASCGIC1CSDuPNYdRIgARIggZ4E7HY71qv88ssvy8vLCwoKjh8/jnOgTML6Ad85RF4hDSyUzJ4X8ggJkEDgBKBAwu0ciXPgMoBfIhzREf2YnZ0t/ACxciwW+EGoc+A3YgkkQAJjToCa5Jh3AStAAiRAAiQwIgRgD0H2jtbWVphEsJTcunXr8Cd0SGxQKdesWQOLJYIqR+TeLJQEpEegtLQUiXPef//9pqYmBDBbrdYpU6Y8+OCDCGMOCwuLiYmh86r0hIItDnEC1CRDvIPZPBIgARIggU4CiM768MMPt27dWlNTU11djSVGEJqFYe7VV1+NhB8Is4RiqVQqSYwESGAwBKAx4neET2iPH3/8cWVlZVxc3LiODYs33nbbbcixPJhyeA4JkECQEqAmGaQdx2qTAAmQAAlcPgGMfZE3Ej54J0+eRAoQeOIlJCTAAQ/ZevLz85ctWwYnWEZwXT5fXhnSBJB2dcuWLVjuFdZ+JM6BMol8OTfccMPChQvT09Nhh8TSHSENgI0jARK4SICaJEWBBEiABEhA6gTgB/vpp5++8MILiK5Ehklk6zEYDCtXrrz33nsXL16MfZVKRVul1KVEwu0XfhSYf3n99ddffvnloqIi/CKQOycpKenhhx/+8Y9/zF+HhKWDTZc0AWqSku5+Np4ESIAESKArASyJfurUKeiTsFhiuHzs2DHEes2cORPJQrBQAQyVWFkEq1YSGglIgQDcv0+cOAGrI9bt2LNnD34RiYmJSF6FMOM5c+bAJxx/SoED20gCJNAXAWqSfZHhcRIgARIgAekSgJUSq6Vj0QJ4wG7atOntt9/GwpXw2UO2HsSAfeMb38ACBhkZGdIFxJaHLoGGhgaEE3/wwQeYT0HiHKz6iNkTZKi65557sIPEOcy8Grqdz5aRwNAIUJMcGi+eTQIkQAIkIE0CyCaCiEoMrxEVVldXB8UyLS3tpptugmUmKysLbn4YYWOhPGnCYauDmoDZbEYOKmw7duz46KOPsHAOUq2mpKRAqlesWHH33Xdj9iSoG8jKkwAJjBABapIjBJbFkgAJkAAJhCYBWGng5ne+Y9u2bduuXbugQyLjSGZmJvxglyxZMmPGjPDw8NBsPFsVQgSwQA5Ux927d58+fRrmR6y/Gh0dvXTpUmScQvYpJM5BQmOFQhFCLWZTSIAEhpkANclhBsriSIAESIAEJEUAyUgwHH/ppZdgrsQ+NiQjgSUHroD4hB8gkpFwOC4pkRBtY71eL+TTYrHA8IjEOYgHhmRCXLH4Dby1f/jDH0J7FG3lWTESIAEREqAmKcJOYZVIgARIgASCj4DT6Tx79iwsPMjTg+hKuAg2NjbCPjlv3jwM0LFNmjQJHoPB1zDWOMgJ2O12JM6B4RHZpLB0B+QT6Yjz8vKmTZuGzyuvvBKfnOwI8k5m9UlgbAhQkxwb7rwrCZAACZBAqBKA2UfI1lNRUfHFF1+89dZbWHYP2XqwIdcllmu/8cYbafwJ1d4XVbs++eST999//8CBA1jAA17ZiH685pprYC2H+MGRFRtX7xBVf7EyJBB0BKhJBl2XscIkQAIkQAJBRgCrKWzduvW9996DrRKGSuTsQWglsvVgWI9lRRISEuBeqNPpgqxVrK7ICGAdVCSCgrAdOnQIyVeRc1iv12PyIj09HYt2IOHwokWLRFZlVocESCC4CVCTDO7+Y+1JgARIgASCiAAWVCguLoafIXL27N27FxZL+BlidT5k64Gr4eLFi2fPno2FRoKoRazqmBNA9CMWe4TbKlxY4V+N5R9hFb/iiiuuv/56iNbEiRPhVk3b45h3EytAAiFJgJpkSHYrG0UCJEACJBAEBDDi379/P9xf33jjDavV6vP5oBUgcyb8D2+55RY4H8rlcgawBUFHjm4VBTnBJ1LmINUT0ufYbDbICbYbbrjhe9/73nXXXTe6NeLdSIAEJEqAmqREO57NJgESIAESEA8B+CXCSgmt8uDBgwiqhGUJSzIgDwqy9SBnD2xKsCxlZGSIp8KsyegTQEon2BuxXEdBQQESO0FaMPsA2RAkZOHChZiDGP1a8Y4kQAJSJkBNUsq9z7aTAAmQAAmIiwBskkK2HsRSYrFKRLsdPXoUS4nAPhkXF4fQShidEPMmrkqzNiNJALZHpMzBGjPbt2+vqqpC4hwokFAa16xZA3doCEZ8fLxGoxnJKrBsEiABEuidADXJ3rnwKAmQAAmQAAmIgQCWj0c4JRQJWKKQgbOurg5pVFauXInFKvPz86FeIlsPjoihqqzDsBBwu93Nzc0NDQ1nzpxB8tUtW7bgT/QycucgmBZuz5hNYI8PC2oWQgIkECABapIBAuTlJEACJEACJDAaBLCgvOD4ev78eRipECOHMMvs7OysrCz4wSLDClxhoW+MRlV4jxEggN6EV/OuXbvQuXBhRS+3tbVBdYT5cdasWRMmTECaX6PROAJ3ZpEkQAIkcJkEqEleJjheRgIkQAIkQAJjSAB+sFhifu3atW+++SYMlfCBxDZ37ty77777m9/8Jjweka0H2xjWkLcekIDQawiR/dvf/oaurKysFHoNkwIPPPDAnXfeiTVIByyEJ5AACZDAWBGgJjlW5HlfEiABEiABEhgGArBlXbhwAYYsZOtBRhbYskpLS2GohFaJiEqk6sFSEOPHj+c6EMPAepiKQDolmB/hvLqvY0P0I9IpweQ4ffp0dBly52CJ0WG6FYshARIggREkQE1yBOGyaBIgARIgARIYNQKwUprNZsTUYXl6+L5u2LABGT7Dw8NjYmJgorz66quxwCCcYNVq9ahViTfqSgAaPuJd169fX1ZWhpBXpFaaOnUqsihhQwwkQl7pvEqBIQESCC4C1CSDq79YWxIgARIgARIYLAEsM7h169aNGzfC8AUNE9oLlhxcvnw51huE/yTUS8RVGgwGOsEOFugQz0O2JDBH7px169ZBgUT0o5CDF9Gt0B5XrVqVmpo6xCJ5OgmQAAmIiAA1SRF1BqtCAiRAAiRAAiNBwOFwIBgPvq9wqoQTLJYihJIDl1eoNFOmTIEfLBTLlJQUqpTDAh/eqrAG7927F/6rcDaG7zF8jGENXrp0KZjn5uYC9bDciIWQAAmQwNgSoCY5tvx5dxIgARIgARIYbQJI9AIlBylesEG9FG4PT0uk6rnrrrsQYznaFQqJ+zU2Nr711luvvfYalgAVGpSZmXn//fd/73vfg/NqSDSRjSABEiCBrxGgJkmBIAESIAESIAGJEoBKiZg9ZOs5dOgQdMvijg0WM5goYaicNGmSsMqIVquVKKB+mw3VESZHqOKw8cICiQU/kSkH+Y1gdYQFctGiRVi6o98C+CUJkAAJBDcBapLB3X+sPQmQAAmQAAkETgAqpZCtB9rRzp07N2/e/NlnnyGEUsjWs3jx4muvvXbJkiXI3xP4vYK9BMQ9Ag7iHo8fPw5cLS0tSUlJN9544+23356eno7QU67qGexdzPqTAAkMkgA1yUGC4mkkQAIkQAIkICECyASLbD2bNm3atm0bsvVAX8JyI7CzIVsPPpOTk5E8BrlGpRBaabVakThHyIj7ySefwAKp0WigY0NvhIJ9yy23TJs2TUKSwaaSAAmQwCUC1CQpCyRAAiRAAiRAAn0ScLvdXbP1wA+2pqYGEYBw3cQSiLNnz8YSiAitDD2Vsr29HX6/0BuPHTuGTEUlJSWRkZHzOzbElMKFFelzVCpVn+D4BQmQAAmEOgFqkqHew2wfCZAACZAACQwTATjBYoNOJWTrQWQgFEhsUCzXrFlz5513zpw5c5huNWbF2O12LMX5+uuvwx4LLRq2WUQ/3nbbbd/61regNmMZldDTmceMNW9MAiQQ5ASoSQZ5B7L6JEACJEACJDBGBCorK7GmCJLNCCY7mC7h84lsPdjy8vJgqITVTvyhlVAdkTUHG3KuYvUOtEitVguJc6A6wpUXzqvMOTRGIsbbkgAJiJoANUlRdw8rRwIkQAIkQALiJ4BsPYgkRPqZffv2bdmyZePGjVC9kHgmPj4eOWBXrFiBpRTj4uLE1hAEgsL8+OWXX6LmyKOj0+lQ1RtuuGHWrFmoLeqPeEix1Zn1IQESIAHxEKAmKZ6+YE1IgARIgARIIEQIbN++/dNPP4VWiWw9ra2tTqcT4ZTI1nPVVVelpqYK2XrgKTqarXW5XMgbhA1Wx48//hi5c+CniprAefXKK69ctWoV1F3GPY5mj/BeJEACwU6AmmSw9yDrTwIkQAIkQAIiJYB0r1igEo6jcH89cuQIsvWUl5dDkxR8R2H6g3qJtD0jGnmIWEcs1wFj6eHDh1ENbFBrhcQ5iOrEmplIHYT1TkRKkNUiARIgAREToCYp4s5h1UiABEiABEggVAggdQ22ioqKdevWvfPOO1AskbwHZkk4kd5xxx1I2IP1Koe3rQjgfOONN3CvtrY23Bq3W7169X333Ye1H3Ff5s4ZXtosjQRIQIIEqElKsNPZZBIgARIgARIYYwJYnlFYY+PUqVMXOjbkuYGpUFhTJDExEU6nCLPENhiDIXxWsTYJtqqqKphAUSbUSCzjkZ2dDZNjfn7+4sWLkTuHiXPGuNd5exIggdAiQE0ytPqTrSEBEiABEiCBoCJgsViQ8AYbfF+RAgduqNXV1WFhYcZLW3p6OtZvxEIjvTartLQUF+ISGB6xmUwmaKHIuYrQR2iP2B+kLtpr4TxIAiRAAiTQDwFqkv3A4VckQAIkQAIkQAKjTQAZek5c2hDiiJw9NpsN+XJ6rQcsmVA7MzIysPQI8sTCqpmUlNTrmTxIAiRAAiQwvASoSQ4vT5ZGAiRAAiRAAiQwnASgScIVFsbGXgvFepVpaWmRkZG9fsuDJEACJEACI0eAmuTIsWXJJEACJEACJEACJEACJEACJBCaBEZ1KafQRMhWkQAJkAAJkAAJkAAJkAAJkIDECFCTlFiHs7kkQAIkQAIkQAIkQAIkQAIkEDABapIBI2QBJEACJEACJEACJEACJEACJCAxAtQkJdbhbC4JkAAJkAAJkAAJkAAJkAAJBEyAmmTACFkACZAACZAACZAACZAACZAACUiMADVJiXU4m0sCJEACJEACJEACJEACJEACAROgJhkwQhZAAiRAAiRAAiRAAiRAAiRAAhIjQE1SYh3O5pIACZAACZAACZAACZAACZBAwASoSQaMkAWQAAmQAAmQAAmQAAmQAAmQgMQIUJOUWIezuSRAAiRAAiRAAiRAAiRAAiQQMAFqkgEjZAEkQAIkQAIkQAIkQAIkQAIkIDEC1CQl1uFsLgmQAAmQAAmQAAmQAAmQAAkETICaZMAIWQAJkAAJkAAJkAAJkAAJkAAJSIwANUmJdTibSwIkQAIkQAIkQAIkQAIkQAIBE6AmGTBCFkACJEACJEACJEACJEACJEACEiNATVJiHc7mkgAJkAAJkAAJkAAJkAAJkEDABKhJBoyQBZAACZAACZAACZAACZAACZCAxAhQk5RYh7O5JEACJEACJEACJEACJEACJBAwAWqSASNkASRAAiRAAiRAAiRAAiRAAiQgkuocZAAAIABJREFUMQLUJCXW4WwuCZAACZAACZAACZAACZAACQRMgJpkwAhZAAmQAAmQAAmQAAmQAAmQAAlIjAA1SYl1OJtLAiRAAiRAAiRAAiRAAiRAAgEToCYZMEIWQAIkQAIkQAIkQAIkQAIkQAISI0BNUmIdzuaSAAmQAAmQAAmQAAmQAAmQQMAEqEkGjJAFkAAJkAAJkAAJkAAJkAAJkIDECFCTlFiHs7kkQAIkQAIkQAIkQAIkQAIkEDABapIBI2QBJEACJEACJEACJEACJEACJCAxAtQkJdbhbC4JkAAJkAAJkAAJkAAJkAAJBEyAmmTACFkACZAACZAACZAACZAACZAACUiMADVJiXU4m0sCJEACJEACJEACJEACJEACAROgJhkwQhZAAiRAAiRAAiRAAiRAAiRAAhIjQE1SYh3O5pIACZAACZAACZAACZAACZBAwASoSQaMkAWQAAmQAAmQAAmQAAmQAAmQgMQIUJOUWIezuSRAAiRAAiRAAiRAAiRAAiQQMAFqkgEjZAEkQAIkQAIkQAIkQAIkQAIkIDEC1CQl1uFsLgmQAAmQAAmQAAmQAAmQAAkETICaZMAIWQAJkAAJkAAJkAAJkAAJkAAJSIwANUmJdTibSwIkQAIkQAIkQAIkQAIkQAIBE6AmGTBCFkACJEACJEACJEACJEACJEACEiNATVJiHc7mkgAJkAAJkAAJkAAJkAAJkEDABKhJBoyQBZAACZAACZAACZAACZAACZCAxAhQk5RYh7O5JEACJEACJEACJEACJEACJBAwAWqSASNkASRAAiRAAiRAAiRAAiRAAiQgMQLUJCXW4WwuCZAACZAACZAACZAACZAACQRMgJpkwAhZAAmQAAmQAAmQAAmQAAmQAAlIjAA1SYl1OJtLAiRAAiRAAiRAAiRAAiRAAgEToCYZMEIWQAIkQAIkQAIkQAIkQAIkQAISI0BNUmIdzuaSAAmQAAmQAAmQAAmQAAmQQMAEqEkGjJAFkAAJkAAJkAAJkAAJkAAJkIDECFCTlFiHs7kkQAIkQAIkQAIkQAIkQAIkEDABapIBI2QBJEACJEACJEACJEACJEACJCAxAtQkJdbhbC4JkAAJkAAJkAAJkAAJkAAJBEyAmmTACFkACZAACZAACZAACZAACZAACUiMADVJiXU4m0sCJEACJEACJEACJEACJEACAROgJhkwQhZAAiRAAiRAAiRAAiRAAiRAAhIjQE1SYh3O5pIACZAACZAACZAACZAACZBAwASoSQaMkAWQAAmQAAmQAAmQAAmQAAmQgMQIUJOUWIezuSRAAiRAAiRAAiRAAiRAAiQQMAFqkgEjZAEkQAIkQAIkQAIkQAIkQAIkIDEC1CQl1uFsLgmQAAmQAAmQAAmQAAmQAAkETICaZMAIWQAJkAAJkAAJkAAJkAAJkAAJSIwANUmJdTibSwIkQAIkQAIkQAIkQAIkQAIBE6AmGTBCFkACJEACJEACJEACJEACJEACEiNATVJiHc7mkgAJkAAJkAAJkAAJkAAJkEDABKhJBoyQBZAACZAACZAACZAACZAACZCAxAhQk5RYh7O5JEACJEACJEACJEACJEACJBAwAWqSASNkASRAAiRAAiRAAiRAAiRAAiQgMQLUJCXW4WwuCZAACZAACZAACZAACZAACQRMgJpkwAhZAAmQAAmQAAmQAAmQAAmQAAlIjAA1SYl1OJtLAiRAAiRAAiRAAiRAAiRAAgEToCYZMEIWQAIkQAIkQAIkQAIkQAIkQAISI0BNUmIdzuaSAAmQAAmQAAmQAAmQAAmQQMAEqEkGjJAFkAAJkAAJkAAJkAAJkAAJkIDECFCTlFiHs7kkQAIkQAIkQAIkQAIkQAIkEDABapIBI2QBJEACJEACJEACJEACJEACJCAxAtQkJdbhbC4JkAAJkAAJkAAJkAAJkAAJBEyAmmTACFkACZAACZAACZAACZAACZAACUiMADVJiXU4m0sCJEACJEACJEACJEACJEACAROgJhkwQhZAAiRAAiRAAiRAAiRAAiRAAhIjQE1SYh3O5pIACZAACZAACZAACZAACZBAwASoSQaMkAWQAAmQAAmQAAmQAAmQAAmQgMQIUJOUWIezuSRAAiRAAiRAAiRAAiRAAiQQMAFqkgEjZAEkQAIkQAIkQAIkQAIkQAIkIDEC1CQl1uFsLgmQAAmQAAmQAAmQAAmQAAkETICaZMAIWQAJkAAJkAAJkAAJkAAJkAAJSIwANUmJdTibSwIkQAIkQAIkQAIkQAIkQAIBE6AmGTBCFkACJEACJEACJEACJEACJEACEiNATVJiHc7mkgAJkAAJkAAJkAAJkAAJkEDABKhJBoyQBZAACZAACZAACZAACZAACZCAxAhQk5RYh7O5JEACJEACJEACJEACJEACJBAwAWqSASNkASRAAiRAAiRAAiRAAiRAAiQgMQLUJCXW4WwuCZAACZAACZAACZAACZAACQRMgJpkwAhZAAmQAAmQAAmQAAmQAAmQAAlIjAA1SYl1OJtLAiRAAiRAAiRAAiRAAiRAAgEToCYZMEIWQAIkQAIkQAIkQAIkQAIkQAISI0BNUmIdzuaSAAmQAAmQAAmQAAmQAAmQQMAEqEkGjJAFkAAJkAAJkAAJkAAJkAAJkIDECFCTlFiHh3BzK7b85vuPvXrMFcJNDO2mte58/rEfPru7NbRbydaRAAmQwIgR4HtwxNBeZsGuwneffOSJ9wov83JeRgJDI+A69tqTP/nxI/f/y7/8yw//esx+6eLqLc/88EEc+5f7H3zsyXcLPEMrtP+zqUn2z6fjW0/phz/38//a9vhbhcPaE4Oox1BOMRVuP1glXGA9/eFvHn/w/m8/9uq+7c98+4kPK4ZSTvdzXaWbn/3Jw/ff/8jzu61ffVfw8iOPvHSs+7kB/O0q232wqEMnPPj8g4ND7bK2yuKn5CSr+75t1YdP3v/MFx2aShc+fZ9/Od/0gcK6+9n7f/JO6eWU2Os1RwaLpderezk4YkC+di9Pa8HOYw293N5/yGKyRGblpYZ9/WtT4ELbx/0GPNyltoVvPvbgnw8OeMVlndD7b0rW5e4FI3j3y6py/xcFLpyeht0vPvHI/fc/+PSWvqSl/yp0+/ar58lIiFPrlt98+8kPq4dUo1E8ueTdn3z72d2d44lA7tylZ4f2iP5KmKs+/PdLD+FAajL0a4P7Pdg62MfgIN6DQ2fX7YoGv8BvqOteTsPGJ+//9YbuP9hBv8G7Fzc8f7sajuwuGPnZya9+Dj2r7bJYFal52fE9vxnSkf5u0W9BI/ny+tqNexeAfut2OV+6Go598PyT0JTwgvjhE8+8tr300lh01Fo65GqP6q9APfP+X/33/fMyExPUzQd2H79EJ+Xan/7Pr9ZMmfnAMy/976/W5CmH3Ih+LlD18x2/6kJAnXrNQw8siPrqiCYhdVh7YnhpF6x7/l3XA8vmpspk1gMbP6hKXf34v85MTZGdv2VZVJdGDPmmroIt6woMy7//09kp4w1DvnqwF3iqNvzlrxduyJ87oR+1sHth6tw1v3iy+8Gv/x2dd+Xq+Ey/ptKFT/+XSOXb0QHiOvTWsx/F/2zhzPjefjupN/z0Vz156zIXBSi0Pcsc3JGutY2fuuImR8rgrhviWX38pvpnNcR7jO7pSfNuWqGO6q2LB1uPC1s+3C+b9/BPF6WnBjr+wi27Pk9GQpy8qXNvuykvcbCNC+LzOnt2iI9oEQgz34NBLHeXX/XmbX99bvfMpxflXX4Rg7iy/5+DYeYDT84cRCn9ntL/Lfq9dARfXv3ed2S+dBW888yz+6Kuvflbq1PUrubSXZ+88utq569+ei0G5KHV0oD4VZXUT/zmrVEv//XAjgN3z192cdTvbagPy1s9Aq8qapKD7S1DwsS8KcMwqhns/YbtPIvTLkudu2hmrr/y8bdkBlSwx2JxRefNnZuXFVAxA108Qh6qhpzlt+YMdG9+P6IEhty1amiSgQltAO3prG3UzBtvDaCc/i7t+zc1ZFb93WYUv0uZe2uASrfNYjFMnDs/LycQdfSrFncBORLiFJN37U2jiHcMb/VVzw5ZNod8wTA3k+/BYQbK4roQGAXpvvxbjODLa/SFwHP+4L76mXf+6u4lgilj5txM2RO/+mx32bVrsmQh1dKA2LYWloZNvG5R3tx3D+7ctrtu2Y2C9lhx3pKSPxJqDDXJgLoLFr/SrW++teHA+WZZdGb+iju/deMUg98b9sn/rZqxsHXPlqqwqx598u68Lqa1/5+994Fu4roTf2fxjuuVSiOTSrCeehlIpPA89UaQSuRZsMhsEC0iW8EpYheZY5tDzI+YXeIcY06Mt153wSzGL467dni4PGSfWDSI/rDygrxFTrEoyC9ILSjPZ/yIRGx56XhB2uBJHem4np/puyPJtv7MyDIYQsq95TTWzNx7v/dzv9/vvXfunwkN2s1nrU6vn0aE2AqVvtSgiK9YsJKtfaxAPuZyUQwiWFqwvVQvD79QGKccvzBZ+kj/uBDLV+uL9PJwRLrfYjxrJ0dGkWeABEWlm6SjXdVHLvoRpGGHZ7Vq8prjvxBkYP+OS7r6tyTmN22yunr1YFN1B62tq9PmIshdW8OPLWhRfcXa+MnKgNvcabb3U8EsCVGgM2xXYfestVUmL4O4anbYXzn07i7O13wMddnU/uFVTyACxKDNY5NlCzW+QTXusLupL4W48oelpetxlsq4z9Zh7Hb5vlwoW/Oq1H/GK28oR07WmocZ5MTu172H3s1HkPu05z9ajb90Uky2bPW2kmJV4mzwPbfpPZsvEMRerShZnWLKFaxurfasazZ80RDl49XV1+vx2N5qwG0502Xv941mYkShvmyrnJ1aSUaBIkxf0+7zWM0xfbizCxav7rXk1B3fjrNVMhUCLpPxFw4ygOCrtTKu1WVRLEGHrZ+aEMk0RSXEsNl00U1NSuSvlu3bLAWIZlMY2n36SMunxL63SoCahG7ZOk3dzsFRZJFMqTUUvYInzhxPBtznzV19N3z3GFSEr9xUVLoxRmESgIAlVZVO/O8lXutVz1gm9pK+bBNqf+/c1cEvv7l0jeH1kojq0gMW01n7Dd8oIpYpNxmK1oczBYXv6HLcpIKZEtn3tIYdanyw/UCLg5lEand5SxoPaWLUHiw8M3aTFC3SvF6uThiEgOWI+1ml1eWCdXFHKIUOdXc7h0czF8s1JXt0L6CuE/uNSFnzXnnYyBiy40DrmKFxn0LAVWvgCXrAajrXc2OYRVS4tURfgIHKpPrMpm6X57afyZLIVutKdqoxb5y0yy6+cWzMcGqvgtvkEU7ZEtlzKBKfTQ3E5Y4Buccp26kL3R/7gPmsfLW07JWw+UwGXGeNXVdJKt4nTGsgcs92pMotfRV1XSSRVXvqdstHucwTAHCcM1n7SCokxFZpS4u1UoHPXF1LbT5REWmwwQrJw5Tu3QoVGu/cqtWB99utv/GwTilPtWWnXrEYRcAayPdEBxv1dBtP1dCkpdMEXMFo5pRvifWQV5r2nnCBftORnb2Kfac0N/e33i97d3f4pf6ku+0f25C975bl8wNPNGHc2RDjT7YFptSJ267Z5fTdIv0Kqucy6Z/Ilq3dVlak4pxCn4LMuE7sNS2seKco3h8ylOscMDRQNd/EX9pUWqxJMsWZWprNxsOL/Lml4mqAQMIhr62js/u3vi9FhEYV4xJTkkdYe7fh/1yvXwpQk+37G8iCmuM72JdvYNlY68Rrx+X2vWzNaodikc7qouOUuQSkxtxxmo729Hr9rBXv3KMDjSZbubMpM4DwAbp5ecDxSYBZ8Iw03NiBmGTHGyZGLaPsVwPiLQcOab+TlM4ir/lgePnx42sHuV3xg7eDW1lCdL+5qcXuDkxIVmgMu/XyRezFmcDfDoLqO/b5tp/uVwFc9MUj+8+ipc1V6oXgB6hxMGUHvDGXIk3yOIGYPJnbdlOn9ap39JvL16hBd4IzcLbgXC3RxKWGym7s4DFD5BVS6Fpr5fvC8oYSYkFSnSZ1hzmMaJGrtbKdHEfIimLPvlMVBTNeJp2Wl6fHFfJeNHb+kvR9MZGdQxT8CHTPEFusOcT2i8Aq/c5Ohy/ArDBUbCeS2oP0mZcGm2Kc2C6CGXGYOixXgQXlqnSrGMvHWNVhHejJcLbFbO1HGi9+N0J+cNLUQ4IuFrF+A3bTTH//RPnqGKcMqpXHPPkUIHDdzPZLAwj2okYldPSgJe8UAw/J3T9M7sQmsZpWrEwURej/9jNIuAUEYamm/KA8M9xtmC4pr6Elt3TACniKNpUl228kCxoPbQQ6F3I0/1MbvaWxVgt+MK7W/efFVYf1+FiqFi2azmOxgmhejM+DSPVZqEitxi5b7L/2abfh4BY9GMiWsn/Me3hK90n+/ve/t4YD+CNdppNBZpyJ/mOi74cCl1oazlH41oP1R2sMebT17WPW2+H0JkedVyn5zoqy7WpZrD2G3J3NnUPLDYeONTfXlsqDvcZfsH2mhED19dFrqt5pfudftme7TzRZ2R04IVfHcWO/SLuvvrGuTDXZ19TIDuoQxm0+ZUfWlh9tbKz5+2XU+dauAQT7u7qqV0SidVUdR/eX/9txwwsoUdTcETNkEq0r0q2gus7a6cmA/T2Lb4W+NGEYOe41NTb1Iaqyusb6fRuE7rbjHe7QYm1dS5kiC9P9pONd1ilwBPpa2/GzFL7tYGNj3WsFEz3NrWHh2UBdsVMvltefPHV0u2Sg02Rjd1mEXMYG88iybdX1da8pgt3W8HE5Ik1lnW4pKt/zbnM4Fybg6LmJ6d+sr9utmOgzmvtiNmiyCdOO8w5sR4UWD/T2kQn3wjknhhk+CcNIxmd+u8mOKF/7CSi1Crnc0tobQDhRJCbJ9XvQ3HTCha4rrz9cpRE6eweS65mNBbD4X65oPnmiIp+2/LTWTBdWNJ9q3i0dPm8GmSOzKAzjPdfU0o+XVrLDSPBSoOXtblpeUtPQWLeToC0NJ6/QCZJR/9EC6rWw/GhzU33FJpH3TKdtJEZhEoCAyIzb5hLpf3Lq1BGd6LfG2necOTvqT71bsyHjqvFDki3SsKXpbRv9XUNNQ/3BVzHf2YaTl0GmIUfnSfdCbdXR5sY3taIBY9tFCskrqd+rQHO0dSfjhpHIuLvrEqrbZ5AzTgfvJspIOYKOSyRuqD/1s+ZyItB1+oJvEpWvliP9TtBdYAPjcbkniNWEgK/W7tpa37bSf11Sd6zx4CaR61SrdRhhbpnfOUVKNr1xtOnd+r1q5Dedpj6aT1pek0eSZYtnzykSn00lsQpdt7kWaspr6/atF5JnjGHzYcifN5wckGj21zcerdAsdLe8bWZ9QkJgSPsApt9XYVhPTHCbJ0O+d7ztE5HmH4HVlsk/72rqiN0EnZherHPLtrd1DuL6txqbD5erMhwnO2LPS+KpGvCiranJgahfq21srNRjnxlZ3xKTiWBtxalKtWiRuup0R2znL0kOLuAcJswk+JNoOpzVEb7H3LI5mMKqtztO1W5Crxk7Ex1OvCCT1NB/Ijm5y+Kv0o53j5tGlumrG080lCuD3e+8HzYWzjCLjUfjcErFo42043ST+Y6M9avFBPVrBxVJYzbyiIggcinvTeB3EOR3pGeM8XvBK0HQnPnImxPS/OmmbI4uOkmZyUt2pqCkrq5Khw2HrRjkkZYyM4MOZ4b20NvvvFOtQXqawq4GhAmqzxFUvVaxS6f8Dlc696X6o4+1HUzhih+4HQS10Hs5QBRV1VeXSketbecTDpdL1Q7i383LHPQOsSoYIm8OMaCj+Vn4x6ekZwlBiBEeRQrTTRHGSdPbRnKR9uDhuvK1E87LXs5nOVtwzpZItEopo93OoUgyIdJFZr6kIFCuOk0wJ04jylCUHy0hsqSGxrhhZCT1WVpevh7X4IW28/TKXYeaj9WVvBiytZlcoURzmIYQ+Mjszduzb73Yc9nhSTpWYy7M47MAR/hEsddXrM+0f+gIRrLkbovj6oTPjTRdDMmLa+prDLLb3dbIERXx8bjbGj4FuG1paemLdH62iG9YL1MT4dS4+4dcndi4zGN/ZEjVWiJwvvZAXavpA7v7VoCZFGAv4OKsxBhchsbZ0s2qXZg8TzJ008M2UpNDXm+IGfaEfyCeAW8msRJHZmnRIpI9DiuYZjDsCf6VjH2D+Hyh+nmUumoPNz+M14fg7PTE/IencSQJRo81NTX+cAB/pDeYZLxnq4t3FU/9q2XPrZmkQL9WvKnMsFaK5eCK7eW653w9U85UUrBZu4qQ54MZj5gwAWacS0u3yjGxSLxUoVmNBT8Hk5OJAc3XGQrYd24iuV67grI7fMi9q7ZraOGuUnUeJs6Va183KMd6baApGafpMVS4SCxaJMZXGyoOvMa+F8xA0YxMJANBwXQZyv4R/XsmH7GmSL/Ma2470WYelOqLp1ZRTz3AfGLrHVMa9mrluWIsT1O2qxDpszmBoOB1EIKy/8+96izg7HE+s7HEsFoqFmPExj066ZD9ii+SKirdoF+LCTJQ8Wp1XtYQBXo3oFAuoaa4RLUcY3PZoYrurVqAZIJsFmSy8oOQJdft1smXY7hCrwEv425H+0VRYce8vmfUKpHH3R/EpSne+88UPo5PzGXEa+8LyLft0hI5oNTa0p2bpYIJXhSxEbn+9vb1+fN0ezYRQDdU/1C6eSm3AaPL1VtWi9EMAfEieL8OZsnkYhQVyVdKFwRGQCculcIwQx81NV0W6g+UqcIvaH2/tntwXdkP5fhiMbZKZ/i+2H3ZEe4VTgcGWaw07DKonwcKgxGvFOYJA35/vMIklCVDpPyBVroQQRevJHKR7Jc2a5YLkCxcni8J3qVAA0ba7ZRMX75NgS/GpOtKyr8vcvfYKWSUphl0oUiySCR+Xl26v6r05WyQMKuWrP7Ey+T1IIpCLOB235HgsqRXzfHyYGu2aJ4XIBkiokAuCfjAVns0Tylf4HbejHhKl/sPhDJfwFdrLKJcbdkWYIBi6XpDCZhznmQYFFcXl+oLcPEiESbXqKVM4A5rlBzSpjT5ZNliZecTic+mEnJHl2tKtqukubh8s1aeRbHmE3LariCFO0vUL2DixVJ1kV75hb2XY8AilP9giyKPkC8PcptnyG2/NgFUVP2CGPgWfUmROlcY7ZokKMPUz2nnFqRDiFAkflYsyiG0pVUVPyKEMVG4q2bAbgus1O/WELli8XKFwVCIftx7I/4NEOtmEPbdc+rAAZzLhJEEfxJOlLc6wD1UrtmqAF0TNLdQJU1yOAky/YGiAtl4btw7dNA1OufLK9mnk+eIBIuk2k1yZMAdHi9xhVQ2HvN8slR82ki7HdeFhTsNrF/N1+75e2VEOGZ28mLir7FIn4kGPSZCjo+E+0z3PGRAKl8RUx9zctFJpiRRl5asI7BcQrOpQHKPteJ0lXmhcsv28DoRsUq/Qey+4oz6t6Vq/SsEsYoQ/4HHKB5rO5jKFT94O5ghUP6oTJOPY8+rtqhlwdsU8NwzIWU7iMpWEuMeErzVBV1hHybPR3xe4EGYIdKbnS/H+BSJS1tjrzH99quMylCsluYA519qWCfhjsHRgvO0RCKlkhh1/9bHphMinQNCsJMmLd3gM6KIzmZwuJJZWl6eHhdD06MZApFEIhJj8q1lNfu0bM+cy8OANQ9kAFevQshPSHS5LG7pEyjdXJnHZMFct/UyoB2PYC8pXY9FsPO0xfF1kuxGELYfu2xzmV6BY7ly3R6d4htJ1cjT1vApAHhVROVvi3R+FDv2aaMHXvD0Dzk7sUkiTF/AXqmqf6u0QEK7u40NP96/+80j7VfA+r3EwGFonC0dT9Fik8PziW9GXsSA92tCuVw87P0s+n4t77t4Gn41nNhjsIIpoQMeKntZRCvEKjUhuOe0g7e1k76hcRz05R5FeBpXt165cmXlypWlpaURoOCnVqudDS6KbSwre3nKUYLh0BKw6nKEuiNctjxqxmDcJ1suMQ1TNIKD1MRirj6xSKpWC9xXLObbfmrENwxeESxVJmct+Q7Gvk5gg0CyONs/QjF3AgEkRz09GhHgeA7jHPEjCqVmva3lp687vy0l5CuVKrUqTUXJ0ZRsclSfHSJ2HVUnLJJBEP8dislVS6de86DLpUsRNwWmQcCqpxRh0u8bYbyD1cUfTD00ySBoAKxDYH+LsqcKlSn8BkKDftWdEWoBrplaEsN62wXxA59IMgsl4mihUKEAZZjI662pLBYqDNvAzFmbc0ymZU8YevBAj1CjYgKb6hOKV+v0YM7wAx4Us+RDUyOj2UunRrYZOJ4rdHBGeVYSxZKRmYmKRJHcF4B3ARMIQJRCYe71ms4wjEgtEUWaSYb6HRXqN+7fZZzKh2EEy/yTSMzaPBRTaIW3HNZzPuouRfl8vrEJFadUMxdFkmcj6aOgM/bM1HlNmdG2mQ7cHZUsw6dqFsGW4UIrNcLoVBuV9lNH9l7GZHK5/HuqQgXvchU0X28Aw+CzDmqJoixhaidJNtGzU1mhmaxYANFCQinPbL/uYeQydkbyxRJ5Fp8CM8K7lBDHpyxTJN8MahgElUZAOi6YfVTAP+L1DDLZi5MbpvCDvCYvBTc5ZAtHioQHtKnp+GJsyvsA82FosCziDjUSotzHim3TzzCI7L/BGHC6NsI3MqZqkM88AxTFYOq/mupsgU45u7jRN51q8h/Tzk26doPsmql6rxUn5PJVStWaqRVHkThZHFUDrCw45ml53TkjNYNRwO5T+5ZkIbiAc5owZ1l4qwNktDBbFO1LoZlZKHM/3uEkSHLbN5yBrwmvrZoKDPkbt//eRFulO3qFAds+NQnxZn6msPHYOMlS3ae5G6C71AiSo/pONDJoK7AFHvAjHfJ4HvFNO5i8UoZuUktXl0k+b/f6mJUB9xAuL0/RsqR20UklFy+ZMsEsoQBhJoC1BdJSZnQJnjPVKomXYOjmgrO5AAAgAElEQVTICPBvIKCgZx/JhdcokoSIXHgU7SBYwsfjitk8H7gdRCTYkqiRCrNQZIIBOwVmXummbgcFUuI5v4OdbfZ6sgj993wt1zz0JOP+FCGKceS+i1uRIg03Dzlw2T/iZ5aosGiNoMvwHDSyJishCod68LVEAuX3iM4LTt+PcAlYaSKQVzwPlr2koRtpGlGsYKlbXp4eF7qxcHPOsbbK3ZblRJ4ceDwV6/K4XxKJ1UVaZMzR1Y8QRcp4p5yiKcH5eUfvUKBDiGmmXS14gY72g1t8bXF8e5DsRsaBG8nGplvFhdJlS1AqQQges/IHORWA7fxI8qZf64txPBu8Qkb4GqCFc+7EivLUevAPYehBt+PSha5TxxHR8ZL8qSYsInyyoXG2dIPm2ZvR5YSU6QIvYjDwfk2q2TJp7LrlQyQk+QWxWYbSV9Jr0R6DFURrLeQZzpSujdIQrdYozzaAc3doLBhYJHuoLjK/aj6NI0l+GqnuCJ6VScOH1swEnt4mazMgsNMvSeGeo7WuzbtEVZCHK18s1KzobPh/k54BF+7HXLwfySYps6jzEsiL6n+6nnS63KTb3vaRzVmWtOORKwe2XwHmvDIYn9dHvyJOcnPxcWLl4U4tchUIicp31Jfmx5T9G9GBDphgjDd0Pv+bmAG6AAxhUgfG/f+4g1Kd8hGcSRVXF0CKOBQzlcI26nEhSWTuWVx23jgRS2w6KRQmA9Ps06PnmkznVUQR2IrLCiMqKDu0VTaT9wJh/BGaYDXs4WMfMUSBUpZfqHp1wn5setiZmjD/3cSCh59cACQpP56ndV93uz+5YW2x9ayrqN8V2crIldSk1+HyY6uViS9uk57lYoUSYIHrKReYPAEDSXlxeFdygsbG/EyqGLCCuL22xSl4SbVyOSFXa+Uf1vYk5Zv6QiRNLtli4vGLlDrxyF1uPUGlW6r3xb48Qp9JNGVgktG5fVZDuMxzLLHbwCFP1AtN3Zl2brmaqkal1+288Ynbfb7J+ittzU/00pn4XFUzOYHkaCre1GDTjy0Aqyo48py+NBGrYzF/zwI8VZKpNCQNhzOTNE35RpcQ08Ob8I2gPxDEN9VUsftqoiEzUyjg8QBIChuPKUL6UiVqeHg2gw3pkAf940kzOewJfSaWbSOwmxNmr48c8WH5hmTFmpYufdkiUVDOtVBpKXOsy2WQBZGSZSLg1duMNJxGwfWOko3yiNpBblcMBvQchszpQqeLM4c/UrSDIuK7WOdND5VBhnAtsUKI/cLrHQ55gnlb+A/PS1SkBCcQFWymRoAacArLpR68LZHgJSXRaXX+bvMyFylcVSGNWM2supGeEcWKx1ERcdLPlCt6OVJNWVJdzQnVgNPpdrsdnUesDsNPDkWPM+EqPO1ykAhR+hLvi9SESGkx/19hW04OnIoUb2tcdYFM3Edi15IkJ8xe4aqC4EVwg0cBODqNPA0QaJXS78QO2tp6EM1uTbirgIqWK7TLcfROZfcnPiQ/puXhNDROt8NTtDgIWYRcetJ+k8K81NJ8Qorg/mse3zMktVwJNr8G0/GrQB6Ofux8W0FEaGbIO45rp1/8ZRGFBZi9p9d2TYJKNdyV+9BX+dA+dMJPcAJr1669ceOGMRzAH+DnAwq7IAdbEhwanO6H0Z5Bf/ZicQqfEfjY7kQ15VVl+h9qVHIc+f0XnFn7bw/R0RsBz2ej2F/h6JIcMQImMaceD/l8I6hkiQSc4WE5a/cvJtQ/NJTXHq9Yg7g/mdoqxelWphO4bja5MX2FHvutyXw9lCCGJAdDb/t8U4fEgOHmMM8ka1zEDAxbjIzcoUWLxWL2H+r7ldnaP1WU5KJiOdh939DUW0zmtm8k6n24G6TkBKJXQsCtB4nVSvFdmwnsx0szJPERLcaE94aoP0TjBy42HGixZ/KhAJ3p8anp0Um/P7GjIsByskeHfdP1SLFLQeccUinMM4TyRfnm7SrmkqlrEPhoMH0tCYLJnW9H4IuRm1az3ROX6yTZ2+uT7aipKNZp1ymIhUEaVPG0x08Ckoa4InFOtn+mmAg15AsuwXImwXEjFtcfcPl6XUlFXeMuYvQT9xB/+synDlcAU76Mh/pMlk/TyDb+EXSFQr6AdPW43PflyvBAkkeBUfFibPQ2OwEWDiHHiQNHum/d6O0NKspq9hp0G9Xy51HwXVLeMHeTn06KRyTerGa/IZbkLAhQo8KwrYnFz/gd57ucSR95m0mHzzyfFYvBUurp9UGDltqD7e5xds43FIr2V0L/5R/lEIjxXTZbSUSq0Oh3V9XX6vG7bnYFXUxIrhpRDiYMUDQa1VJx8EbXLxzhHVw8ISOTGQ9G79/zB7hOrpqOyWnCNNe7mvmqDspHCXPwqeniiCCZwiwkND4hioQsytrc0MVOCnKHVDbOHWPqKp82LsZyFoDGIsqMuU1RYRtPizxYB7mCJu0OT4ZM9m0UX4H7B2xXb4qIPCxeljm66NQFAXfTU2aGnRuJpkUN+hjQgiSMz9NLZ1qc0KNoB9NxxbFAHks7KCZWSgbd4FCVnBUydLFMJvBe7QZrlgkZmFHkUySwNyalE5DkSoQjM/0E3+2RFHYcpwIpWiLByjX5NPmxjV3a+nJ4bJBGnT64EfFpJk+PKzRgM3/kE+WptDvKDx09qH3W4x4ADQafOQSc18hM+ZqV3/BZOmP3kD8A85kssL/CULCea4o1WFsU/pOnLeYr4PR1NGfZ4lHq9lSrOOYbupNUjTxVwKMAItD58f/n9FceA9TtUbYh4WuAUnRik4VHQ76+blt/TJd1ciI0jgiEfFUQkwRnS7cwnWZUQOQvG+q3usH7NalAJJVhPrf1um9ZPnuKUlp+Nbkg4Mp8W0E0kxGvf3HsNiFUuk6FI17rL4OyR7NJEuT7NI4kv/Wtbx0+fFgSDuAP8JOzlme/mIGp1hNUd5upzxcI+FznWi2f4Wo1kSKicKEAHfORQzT4fLDvstF02Y8kLtZkYzMDlrYPwGpSyn2uzTpCaNZgyKI1GgXT295m/zQQGCGtJ01OUaH6RRTJAmdhdZrOu6l7ND3sdA5O4Lk5wKTAHqPgCOn6NJA4RowIFyLNZxzPfN+gXaUxbBQ43jMnnFSDvqguFDjaT9rIkUDgU7uxoxdRaNaknDcIJyxWrZdPXGo3XvIGAgGy22j6yIf+Jf+7bFCoVUFbh8kxSFEgl87eyFIlMJ2LZiEjnzrJ6V5DCqbg1m2flwGLKDNJu08sD/d4Am5rl907xhuNm88KtXqR+9zpcKkHbJ3dHtEKQsyDAs3F8DGn9QLpu+11/NzU93liXtL1G5Z5LG0XSCpAubvarLcSH0jn96wKI5Drt+XTtjM2ahIBK6dldy60gbPi7gao6+a2M1f9f5Ew2ywEq2cDN8lAiAmNuC2nLCQ4RAqsQJ5VYfhlJcDhYANmVl2BDfSZ2n7pJ9arsMzM0VsXTGes5F2aDnivukfQXBz0+dBvoOiYl/wEHGYXl6J/cGg0B2w/8PX2C+VgOdNcAypTyJmrH15FXlRFzknmU2D8b9Sy2xZjWFrv5U7Lb1GCWCpcKAzeJj0ACu1znW27MMxEjJJD2rmb/HRR+ETiKytH7gmPLlQWvpzpPHPS2k8F7nrtxvaum4iEa039VDwe8xSsLCxArp4x2m8B3wLeTNkCuYRMKMEwocducdyiQKMJTtZN6lyAVFHkHjjh2QQigmomPyYDAizx+4/JVUMUqheT5pNm13AgMOxqP212g3Nfk85LmC4rBhzagK3L5aMGXZaOniG+mb1IBC4TFnH5k7lWB9jX5OqyOKaGZ1PiBXy3gxjOboYP3bJbLrjC3TEBsUr+ZZ/FDLTsttvS8rNeRBnWS8bXZ7H0JW7pmdXGE6p95iefNoqUmpeZ3veAXw0EbtmN56JdWDQt8gJpHk5dcfifY3d2iZbLJDcdrgXEyoS1x1xIeeUEisJj+DNR0lTmMafptM07AlxNe9slRvnKmsTWJc10Ihk/qnYwHVccQ+vRtYOxVfIdgljg7P2E7QojGbjsuS9d1zxgAxj74ptPkTJmcQJovkb9zA2z0ea9G/BdM3VeplLoQPwt3paINR8FEei5cOMZhTKidWnUKa8RZYERxhceNzn9TjddCXl6XCgy0ve+0XTNR4/RVL/T87kE7Fvh7bFMUmBDnfRFYuI3dno5Eaerc2UeY3HAd61Br7aftntHKNDmGi9GXQp3W5zaYUZq/29lQx8aLdeBvyKtp8+5knuNPFXApwBAEkn/uZPgPPa7PtCJtXwaaT14GiCeTmzoU+BRk47gy1VveRmxt9Q2nbU5XG53n83U9E7XPdBJxmevWc6WTpxWMyrKI7ABhxORycDCt8WE7BtuRz94v8Y2t+n5VU7p5tkKwnkwvus3kCXhbvB0yFVrQAMETD/FDoW4CHP+8ZSubgWjxzT2Rs5OU7x+30Gms/PcYdsXSPZSpfZNvZbdYsQbUVCgL/W2nfu3/V33hRKpcsN2dfcH7EvW6b15kZjgpArca6r9YPSbuSu1b5aqWXUVKEoPBM+2W9+uNE5m4/nqikodAbpfWQrDPsp4pr32wigjxIg1e/ZtZnUIV6pk17pa/p2patYmScOQ59t7MzQ1P8DZJ181aK41tJ9X1LMrJKdCFmE4sA99r6u1xhQUYERB2YGt/BvdYjIQrd1T9QfwHYpjlR2McAmh2lNliG62TpKCvSBQ7KrwdxhNh20Tz8jW/I0K/5AO734Ty1Vy+/vGpiD60wLOiPEXlyrVUtJx2igr0BsiC1wD7h7LELJazWc5MXxKiGlXi+L6N/chHeFSZ2HyDRXlrwD0Ym4UOeBIHspoaaj9MFum3qZbS9sSJM3Rlu9njGdaq88j4MsKmtWUI42iJDzCqzAzz4lU27X2H3eZLiur1mvL30RM584drhplFmLy71eU/yDeoWRIN+/aQnUYK/cE0UX4SrVeN9np/h3YbYtxA0lH4KW6igrEdLa99vxoJvgKyPYa/XpWXzW79wQAybdMwQUS8NGait3hU52eUxUuae1qaZuoqQdff5oO2IsqucNhPhmS6wyzLnDlEgolXlZ885JLrpg6XpJPgRdryveHTMBezo8iS4jCPRXglGBka5nmpKmp0sp+AmTVhqKNTOdtsNuZEMVIu3IqV26T55Ip8RqfSInPTf2OyZ3n7ZRAXnyg7KzJcqLaHESzpWtKKw3yFCsiwNtTbvNE5TsPlJ5ptxyvNDLZuEJXUcwau2J7meaUyVjfm7lYqd26JXiSY1oN31xe9ns2Yts4Klmu1L5RlOQmkqoGlerfrEDOmI3/ag2CTwW9tKUi6cSvWCSitYZSX/u5k7VWIa56VacZM6WYM0a4TRiZ8Se7ptKea3UgI84Pu4KLNKrYo7MYsDYkG8tlFX7IYe7yaVXhD0uK1pVW0EbTqWrrOCjghoN7tOHNVJT7wy5HPqGL92m8Np6yKiPF4NFGVjGK3ms/d6zyyyzZmo0a6Xk/+3x65MEBrhhCZr4Qfn2dIwUnANIEkWSVc3TRM8pcnrh+L1oh6SmzWK4QuVp/bGK/GrX9DUOStoE2JW2jeITtIAKcf2pXHC115D9ptIPFcRG4fyS3g7HPZeAEIbT2L5OyTSQqlS5DP54gpkY3PIqEzuIEWI3awxjNx94ysZ+jeEVOpbmihL8lAsIJXlQSmU5q1fRmh9nrlN+ICNVqq/FMUwg5Gv6KQ/qBp8eVp39jW6fx/cP77zFC0N7tfEP/AjCUGHMIf2olGjJkKjVuvnTSJC00bE986TFH5nFZGPYXGU9bjr01momrNGulvsHw8Yo8bfGsZRa/Ul4xZmw/Vd01KSHWFyjENrBcPD4WXxXwKACQ5LVg27mm6l9kYqs0qnyPK7whgqcB4u7Ejt7s7bq0TPoDefxHmESK3YeqcszWPktbN41kSfAVirKD+sihg7OVVMDZ0qXlMRbLCbGZyo2cnARexAisGXIiJ5xhen6VQ7b5toKQq/3wmavU3RCDVh7wGg7tmf4AlEi5TmnzShNXcHDI9ICX/uyPf/zjA0aF0R4NAfAlnKbol+seTQZPTqqTAe8ntChfKo54rYH2N1omSprL+LfTpS864z7dRm8tTzyUNv0E4JOQACQACcQQCF1p7cwsK0v4zBpE9NgIgK/hnQFfs4x+bPCxZfvIM3qE7eAjlx1m8BUQoH3uEaEsL7qRiuqqrv1M89NKdRqvnjiFZagBksmRR8/NGwcfSTSLDhw3PMDioKnkQ8Pk0IJlRPREa9p2dL8jv7Fu85xG8pyiwotPKIGncXXrE1oVT6NYtPPMsaazLl+ABuvczOevTuSvnJpUejgcdx0ORh6/muThEoSxIQFI4GkmMOmzXRcq8xJe1T/NRGDZ54vAI2sH50tAmM4TRSDk7mps6LzsDdwDK2ytpl/5ZatSL0aZRfrAlbbDJyzuEbBHwec403VjoTy6rniWeLy3J3zWpsaT1oEAfS/g/cjU8xm+8kU4jOTF9SdwA85JPnGV+BTNSbKbi2xgLaxz0M9kYTLFBsMOTXpfhHziag0KBAlAAn/iBMC2hVk3Hf2JI/hKi/enOicJ28GvVK2+hpmD0zFM7R9e9dxl2F0qG/Slm8P7XR840KSlw2Tvp/z3hdgKla5Ir2JP23iIMBlwnTV2XSV9Y4hoqVy9tUi/Co4kH4LnEx8VjiSf+CqCAkICkAAkAAlAApAAJAAJQAKQACTwhBGAq1ufsAqB4kACkAAkAAlAApAAJAAJQAKQACTwxBOAI8knvoqggJAAJAAJQAKQACQACUACkAAkAAk8YQSe0q+APGG1AMWBBOZAAHy5rstitfWj2trwtzRo0tpp6glgivVa/Tr2cwMwQAKQACQACUACkAAkAAlAAo+aQMa//Mu/POo8YPqQACQwjwTAJvu/CAQyvjns8i352+8tyciSLPvzzxllqX6V6Mk4EITxXfqQ/MaKv4p8BnecIn33JYv4vz0fgwYMks2nW/+Pk3bmxb8lssOD5FPvvGu9+fkCyf/2Ld8vez+XSCWcCVF95vfebXjnwy9WFMolf44wt2z/17//+/+88fnkouef+zYcXM+j9sGkIAFIABKABCABSAASiBKAI0moCpDA144A9fH/h2hUIsf//G2WSoX/BTL4MSlUEkuejHFk6Pr7Vkbzd/lZ4FPsrg8v2bvPfPiF9Pt/LUmgzIyF7n+D/ZxybEg1SP6LJd8J9vx84C9fWiZMrrBv5YoCn99/9s7Vm8K/+d/xrIxFz31r9Peyon9QSeAwMpkWvAIJQAKQACQACUACkMA8EID7JOcBIkwCEnisBGgvI5CJni/ckOux/coHBmyeCWx+vsM5D8WgbJcYRYGITSkDU/xQp37hGc5UPVeu+sFnFRID5bmP675fgF63Oe6x93zDCL40+pAgXyW5biWZxDjsb8DkWa12/TLykp1ik6U8/+vJYcIlMLwGCUACkAAkAAlAApDA15wA3Cf5Na9AKP7XlUDIfabFOsygWSJh7LTZ5MQEKBHDhMZjBkzPqkr2qrGpkjKDNPIc+3WmNetXWs7a3C/jQdHKh/qc1DwyvO0ihYT2gScC2UEyIXpetiH3LTBIVm9FwSC5cDq1DJxYMuTwIkReosQsk+ViTKwmPjjXM6AtwbzBbz3cJ7YSc4C/IQFIABKABCABSAASgATiCMCRJFQISOArISCQ5UlMF22jK0rq/kmDJS9MnWTouz5vv7O3p9f9adCxSa2fmprz3UbwTazMAoVaea6pq5NZuV0z9zKE3B21bSMbDr2lmR6jzj2RxBjMHWpikTxxIMkx95gYMfJ71kGyeInQP0IjeSKwhdLWx6zcKBeH0bFM8hEEVWpWn2v6yLGhwJ/5nJo7D3gVEoAEIAFIABKABCABSGA+CMDVrfNBMXUatNtU93pxcXH1mfcbdlVbbqd++uHuXm/dXWHyptlxn6TJK+5A6gzHvHYXleqRgO3Irlrr3VSPfPX3Jn3mg8VNV0KJkgyaD+xqcozHX6btj7yawhkK5IayTTjTb2r7v30cCzYzUFGOVLHRUHW0rqyAcYGZuGgAa1nF0bWsKLFBLfHdRvHcxJKl8XsiGBLgK5Yl7l9MI2aKR5jQBJKVOJCceT5A2rqt1vA/16ek/T+ifztuRauGHRCGB8zsIHmBs6vTmyllZ1+nQyaKMuNB9ucYRX7i8UfBTTNBib9VS/pt7X3M9JrY2Ohz+HvS3fb66239c4iR7qMPaKSUpaa44RKdbi6P57kZH0JZap888QCEx2XOj4c3zAUSgAQgAUgAEniiCMA5yUdeHYGrXTaaMFRqZJI/o4SLROEdZI8qLFFu3oCmeYIn8xtT0wfigwXRWR1OkcjzrWamRK2Yx1krznyezouodGvZlpuHzZZW8wt1hjyeBaoopt5dTrc4SEZKBFzmblufV8A8J9OtYodY2FqNfAR9oE2SItXeOtV8g0ezMpHYdbkJ6YsJzSYico1EUNHGhMlYdkAYXcsaHiRXX0S3xA+SJxiwHjh84k6upuKtqdTpmLWsS9WaFV1GRFvBP56d70LPMb35NtI5Zj+fj8f4kGxi3RYxznEY0nzmB9OCBCABSAASgAQggSeJABxJPvLaCAZDCC4vzJeCni3+w+cfbX45Cl3OHHLgmAqbQ2z46EMTQHHd/9B7ftxu/Vmn7CdlishnM5JTRXHtbhEDFhDkKPS7FfrYBxapK/YmR/jKrqA5WOb1AINEPmsZcHc73P2BL8ft5gu0cqMq1ccuR9IaJAf8DCaPexkTcINvazpIhMLyDSpW+UXKQtWNezKecflXRmYm4z8tI53yIQLpep30CaALRYAEIAFIABKABCCBx0YAjiTTQA3WR1U6sFdFHrv3i/uoOF9rKFLjWQhzrXVvj2jzs6TNHZLuOFSxTkRdNrV/eNUTQLLxlRu2G7R5Ivep3Q2XwMq91uJie8m/qNz/apPV1esQa21dl3BnY9U6EcL4LIdr7bkV9bvlgsmA66yx6ypJjQuxfLW+SC9np51C3ovGzl+Svi8msnOIgh+V6uN70nEFAAvn3hMdbDRIx4DMTnyHxNvt9NybkKzQGHbr5Ytinh1oP9DiYCaR2l3eksZDGnHI91GnyQoejghfqs0TUF3VRy76EaRhh1dXX6/Hx0jrWYsdLCkMIsIcuWZHqS5/tglWFp0N/+d6do/fJNm+v4EsqDm+g+1wejvfaJ147fguAqVJS6fJ7qZGMyVEgc6wXYUlziaFfJfMpnBBspcqdSWl6qXgCQ6BQbLk6ddb75e9u1vOFhUsUPzHNmTvu2WxB7SEvLaOzu7f+r4UEeBDGnzVT/ebm1rs7kAsOgZ8tNDU7fLc9jNZEtlqXclONXbHUl17Q3W4ThsZwN+zHamyE7X1ulwmWRm481qsKSsiq9vsxtOEdD+vQOhCUSIV7uTSucr4Lhq7rlOBDGVZhTbV6C6dxBKeyZETY3YPoyBYccXyTTrwrySddNIZJE9S5F1cHj9eEct1JXJdbA4CRVnFbDmGbtk6gUoNjiKLZEpg0K/g4ZEnTXYZTb8iqfsS+UZWhaJTbOM+W4ex2+X7cqFszatS/xmvvOGQBlgTp+oylP29dutvPH5gxXmqLTv1isXxVTdtpOCPbpF+BdVzmfRPZMvWbisrUkW2fUbFjzNSliJzx2k62tPr9Wculmt27tElz2NziBRyn6xuGlTUHDaAt1n0tdbqn9HanxwCGssDgcPcuM0qI9aHlIRaqj3rmqvWA5vitE2wOvcIpdCh7m7n8Cgrf8ke3QuJ4/3AdbPxrJ0MINiLGpXQ0YOWvFNMJHraF/0cjojHS0dIcpnzbCoC70MCkAAkAAlAApDAbATgPsnZCEXuM2RvP1Z05J13Gt9Q0OZ3Ot2RfV3Mp703BNry/SWb88X0tbbjZyl828HGxrrXCiZ6mlutI4i89ETdZgxVlJ86dUjz7am8crWlWow8b3aNMVS38cLvVSU75AKEIX/ecHJAotlf33i0QrPQ3fK22Qte+A9eaDtPr9x1qPlYXcmLIVubyZW03Y+7DIzbdk2kq27uaDmomrC1nXfHzUDmldTvVaA52rqTYBiJBC61NJyj8K0H64/WGPJo69vHrLcR7O/qql4RidZVdYBhZAZtP9VkHZOX1DQ3N9To8ZGu09bZN2SKCCKX8t4Mb8b8HekZY/xeMNAGYzwfeXNCmi9DJ32WpiYHon6ttrGxUo99ZjzeEWU7XSi672TD2SF8a1XjsZptuT7jOyZynFtgbg5xV2nH6SbzHdm26vq6YoL6tYPijDPp670cIIqq6qtLpaPWCDrmlvmdU6Rk0xtHm96t36tGftNp6qORXIUq1+e8Ht1tSl93DS1RKnJBf51DGTizAhdFa/eUrZPQvzG2XuQWhy/iA14f7OqiC/fsUqI37c5537WbgasLEQcgM1uQqQs5zhlKGSvUb6dXacJj1IcLd20tb3fTQJcbGut2ErSl4eQVVuDAxdamnpByV039Qf2yAbtrLJJLyGVsMI8sY3XmNUWw2xo1JB7VDfS0dQ7i+rcamw+XqzIcJzscKVgwt2wOprDq7Y5TtZvQa8bOvnjbjjdSIA34xglTUFJXV6XDhrtOX/Al7IjmFkkg325Qjts6uylkzGU6cwPbWsK++OCBwGlu3LiTxIs8xulMwreCjkskbqg/9bPmciLAIf9tS0tLH7quvP5w1RbxDetlij3HOBxiPC3K64h4vDTwNsnmPJUw/C8kAAlAApAAJAAJPDgBOCeZHrsMSeFWnZR9gY5ptqp7jvXe2CFXgl8oodmmCn9tIGDrcT6zsd6wmt1SKN64R9f/T9YrPu12nP34+gIEBd3fmJNd8M2l2t/Wmk8xAvIL5esHwTgSCTltV5DCN0vUL7AiqYv0njfbeskt+H16NEMgkkhEYlS+tayGCKY7OZUhWrNZSywCGUtVq5Z1uSgaCc9xTgU0IxMUAPwPfJAQ9NQq/+AAACAASURBVPDEm+oNa1nhse3lo4P7uy97tUVS9pn7SPj78YxYvqUsX0ssBn+L1BsU1muU/w9I/PxQTOrRP8XEX2NdNz2hjeIJrzdEyPHPPJ6QVhz0kAGpegXKDNhtgZWlb2mILBBBbDB43P/GslXNzFXQzstu4fo6QwHOPrGjNPjBEBLkFThZgrgrtNtxXVhYbVAtB5exPX/vI09Od1ZjHswQKH9UpskHaPAtarujj/Ij8mwUVxcrCwvCk1eLNGppl+UOGCPgSgVu/q0zsFkrRminawhTlGD8ysAjnkBe9Jr2swbr2TaLtEa3/OGHSjz5hC/7BpiV66UTn5h9WdItXGuh6Wsm4xUqxcpn4YotZZvZ1dqcQaTQF16yOUZ04bWmvAEV8CXAE+Weu3dErtsUdwAPz6OzXPb92u7BdY0/DE/uL9YZRm5UX3YE1sodVzzYpsboBtQ9OrLSwiZ076rNJdTUlER0pmyHj2xhXxzwqa6EDiFCkfhZsUgg1pZW4feEqfYOonLNVoUYKH9uoUpqstymgLXGSj9jpOGrEnVpyTr2AWxTQc9hnw8snI9ZEc0nkkqkMGyXV3e0tw4Gboi31G1kzZwHgupGsrklHEkVI1+CeOwdXmcS9p5rtmieB38IiAK55FKi/Oy3QPO3HdzEOlRsxz6/t9I6nVeMp53gc0RcXloFUuAyZxYBDJAAJAAJQAKQACTwcATgSDI9fgtysOmTP5ZIxIyLuhOOuFAi+Ub4j0m/b4TxDlYXfzCV4CSDoOyGsanf8f9F8S07Na7D1kBBxcFV4WHTHWokRLmPFdumH2QQ2X8H0TWFm3OOtVXutiwn8uRK1ZqUm83iMhGJpgadKDhOk2G4xkzhCPdHqDvCZcunO1ci2XKJaRiMPGM7tWKisNB33Wb51Qh1l6I+81CTRHyRuH/hecQ37d4hRhm6SS1dXSb5vN3rY1YG3EO4vHwhQo9QwTFPy+vOmUIzGAU66lNfvAAd05E7CL5+SrYsqWa7FGFc9tkF5pLnLjWC5Ki+E70lwHFsgYfrOQm2JDrOEQJ04JiXSUSwVKURkI4LZh8V8I94PYNM9mJ2tCVWKGUW942AVpPhdH2GKYsxsI6XTxl4B09ZhP61LZ7D5q73bIpa7SPt5uKbDTgSsH7sEa7ShgfwiUG02lCxOvFiOr937NgR85i5NZ04c3zG1DnHCAhy5syZ+DgM9Tsq1G/cv8s4dZ1hBMv846wVYEunRqrPLMMXIewpsXdGqAW4Zsr80eUyfAE7kuRTXdXaDbJrpuq9VpyQy1cBg025fHhhtijiQBA0Mwtl7vPaaERU8ZIp8bKEYBXDRPxwn08kYE2iglL91co2t0R/WBueCuaBMMFlbmAuNH3qKZ2J6Fl28M4GNJO1hbg5VZoaGZXkRZYZg3tiHM8G77qiYdrTIvyOiNNLs0v6OcwZSf7uznRe8A9IABKABCABSAASSI8AHEmmxyn2Kbazh0Y7IjOrg0GfDpXvqC/Nn+n8IN9INX1I36ZGFyDMZ+TQuCLaoUelW6r3qWImGdBnREiWSFdzQjXgdLrdbkfnEavDEN7j9BhCTEnYqQZbwxHz5/ia1QRRoNCuc7e2gPmTNAIYAE+ayWFP6DOxbBuB3Zwwe33kiA+cjwI6lYHJCSRHU/FmzCcNF6DC2P2caeQw/UhE4InY7mnqD6IsQOLKmDKvkLu9tsUpeEm1cjkhV2vlH9b2RJ5fDGYlu1zXA8oM11CusgxUDXiJMEdlACmhy9WaF53ul1WPdBgZLeKIw+nNVv5wHhaKxjJLGrOlBPqV3WSHX6KCskNbZTO1v0AoyiDtcSKh4an4hNFOzBN8qpuhqWpUet3OG5+43eebrL/S1vxEzzeBiy5A09dAkDeaejsCn0gg5jjlYz+Z4vd8GkBywXCUD8KQKw7CzI85mBVXCpFi8r5JmY5ynysyuDZd8DQdUayX5kkSXoYEIAFIABKABCCBhySQumPykIn/CUW/P+K7HX3/z/g8PhRbxi7yjAkZGLYYGblDixaLxew/1Pcrs7Wff4fUPXvneR+xq0KL9rafZ7dDImJJzoIANSoMRxeLn/E7znc57yKhAZv5I58oT6XdUX7o6EHtsx73AH+yD4YcvMtfEhwanB4Z0p5Bf/ZicXSFaWQwNuzo8Yq3HKgq2aZVryYkDD0a6Y3OmiMqW7mCJu0OT4ZM9m0UX4H7B2xXb4qIPHbEJMrBhAGKRiPQxOLgja5fOIaipMNJZ2A5SxDqP6dkY8j2t6rNXn6BMzLB9wajCdzzBxIW5i3GchaM+IanqvI2RfH1XBPLFbrR2xtUlNXsNeg2quXPo6GZShArX1429InDfn1omULJzhnNVRnCeVGXTE68rGz11KRNogAzvwMfNVSf8fLfn7lDf0pyrlL1XXP6FilUKxD3WbM7ae0iWN3a1NjQwP+v9UJYY7+uQSBZLAmCie9vR7UOuWk12z1BcOwsFqSmrSDk830eLiGWg933DU1tKGVu+0bCOsOjuozvstlKIlKFRr+7qr5Wj991kyOPiRSPSCB3xnve1JulrSgmfOc77ffAFR4InOb2KVDplGYVW77UziQVCRGWk+3/T7BiNxIC1O1RjinaFI5oVi+dKnd4DxKABCABSAASgATmTACOJNNDNunvfc/kGAwEbtnbfu4QrlXLE5cFilXr5ROX2o2XvIFAgOw2mj7yoX/JNyqgHe+ZSVxXtE6xZUch85Gxa5BBFioLX850njlp7acCd712Y3vXTUQiBrOfI33vG03XfPQYTfU7PZ9L8Fyw8Qp0WC0WV3qzgjxFRL+BomNe8hNf4D6mWk9Q3W2mPl8g4HOda7V8hqvV7OJV8CH44Ajp+jQQEoLVdAFPvy80HgoM2IzvO0NghBnX0eMTSSDNw6krDv9zMjwDES2XSW46XAuIleH1qyhRqF5Mmk+aXcOBwLCr/TQY2AixOLYi5Ro5/RFLgJXtrPkqIyOW8wqM5eagA7Yul48adFk6eoYS1rCJlJqXmemqNJ5LdRpKPDZUuFAYvE16AiGGBmK0XRhmptcLi1YpZJ9duOBZplBEFh/OSRnYfEL9JpNPWfrDlCshpwQSr9lXA5b4pgz0gN3S1dpw3OplF2gmhJDvM0qYL8dph3OSSF7gyq5urayq4v9Xzr9JMjGruf+m+63mLnN705GGM2469ZTy3BOPxJCq1bI7F9o6Hb67Aeq6ue3MVf9fiEUIpn5FHrjYbnZRgRHSetpC/iH8+KI1mlVBWwcwf4r61G7s7PWHpeJRXRS55zaDs4hvBWg6QH5MBgQYNrUi9QHknTHSNMbuPCIhzGCX8SOmcMcWxfoiXS5pfo9Vex4IXOa2FOEzKw7xMnhtc9biE2q1pP/cyW6Suutzn2uzfMpV5hSOaHYvHSsC5frAYgeOFwZIABKABCABSAASeFACcHVreuQyMKV8wvZ2Zdt9CbFmX9U2dllgQh8EnMBZ9QfwZYFjlR2McAmh2lNleJ57MVfIZTr3iXhzHTgzFUHy9QZFZUuHVfljnbz4QNlZk+VEtTmIZkvXlFYa2JN48vRvbOs0vn94/z1GKJYpd76hfwEkS3v6ui4swnUKLL0CcD31nKpwSWtXS9tETb1+/b6DTGfnucO2LxDwpQ3tm3pteKSHK1Wya10t/85UNRtKd/jaLLW730NFOTKVbovijB3sBkRmeslBPpHAAa4YQma+EF7ilyOViRCaIMCokg2oVP9mBXLGbPxXaxB8f+SlLRXF6oTxdwQsIGADXyiRrinab2AHPzwCi9YaSn3t507WWoW46lWdZswUP4ErAJCL3ms/d6zyyyzZmo0a6Xk/F5rka+xxR5qTpqZKK/sJkFUbijYynbfBVlKClXaRUvmciZxUrJyikb4ygNjMbZvxkkj/uoLvxQP7DB1gFooFEWhZguh0cbKYU1dEeWrdC0Lql71cjwiI1UrRRUvLefnmnWpuHeWK9jiujbstfULdHq2I8Zprjhlzf1qxdtayzl2uHG35m4jp3LnDVaPMQkz+/YryH7B2BGqtImTs7Ky1jmcTr2xQ5VjDSQsUuyr8HUbTYdvEM7I1f6PCP6TZTX48qivaXF72+3bL8cq2cVSyXKl9o0jxMCWYMdLy2dfBcooEDnTtsIZW79PnAaHFmh2bHXXnTK6V5QpeCMCPJZobn1lxiSfmsc3Z62mpruK1YNu5pupfZGKrNKp8j4s9FSw+LNZwOyKwqpzLSydGn/49OeL8sIsWqtXLU5gdb2x4AxKABCABSAASgAQAgT/74x//CEHMQgB8qezN8Hcgp07dmOX5x3P7rrWpB68oSuvYm8cjEfIEivSYSv4Q2Yy520+65XtK5DH7YxOTAx8dbXFI9xmISZ/jIzc54F9ZVpZq3BmJP+lqfb2XOFaVODRPTP1J+h2wHTlol9aCD5CGHI17Lbk1x2ebfX3k0k8GvJ/QonypODLmHmh/o2WipLlM/mQNwR85hseQQWiYHFqwjMiNjLxp29H9jvzGus3pTek+mV76MVCDWUACkAAkAAlAAl8dATgn+dWxf6icGfJXHkyheag05jnyEyjSPJdw/pNjfNbTjpztZamGkZMBV0erY1GJFkVCJCVcLc28MsQuWZ2k3L1kZKVlnGCLiEIF9nUd5ogLy+tWZoJXNuMe8na29BV8/pnPOUXaeeYYKd9TtlEqDHl7z1+dyN8j+7rynXPhH2eECZ+16Ty6ZU+RagkSuG7u+QxXFaU3jHycUsK8IAFIABKABCABSGCKABxJfk11ASW2VxBP1kH2T6BIT3jl0q73zPT6fYZcrnHJJEP/t8/Xf6P3VzbX776peYsdvKBylczVasLX6NkVeZj8lVnXNvN/+uUJZYOK2JNFQ+QvugJryiuehIm/DOmW/6EPmkyHK/1MFiZT6Kt2PNR61ScU/BMglmhN6R7K2NVSaRpDREvl6tf3PVnLQJ4ARFAESAASgAQgAUjgiSIAV7c+UdUBhXl6CDC+Dw4fPu9DhMKkrWATE+ALln9gP2IZDWLNocaS8Cc7aHvTEd8rVQTtJwpEntRzkuzqVpv06CHNg35V5auqjMDltq6gpnQTDvYio1yj7K9KMJgvJAAJQAKQACQACUACkMA0ATgnCZUBEvhKCKDYhorGtdGsGfYTlFMhI2n0hAqnv0yKZoknbjknVmkEGWiKOcnQoMP2W6ePCQS7LciLak3+1+ZYkZDbbGM0pRtxlHZYrsl0G+H6xq9EP2GmkAAkAAlAApAAJAAJzEIAzknOAgjehgQggcdHgHY0HWx1jUUyFKgqT5SvgpOSjw8/zAkSgAQgAUgAEoAEIIH0CcCRZPqs4JOQACQACUACkAAkAAlAApAAJAAJQAIsgQUQAyQACUACkAAkAAlAApAAJAAJQAKQACQwJwJwJDknXPBhSAASgAQgAUgAEoAEIAFIABKABCABOCcJdQASgAQgAUgAEoAEIAFIABKABCABSGCOBOCc5ByBwcchAUgAEoAEIAFIABKABCABSAASeOoJwJHkU68CEAAkAAlAApAAJAAJQAKQACQACUACcyQAR5JzBAYfhwQgAUgAEoAEIAFIABKABCABSOCpJwBHkk+9CkAAkAAkAAlAApAAJAAJQAKQACQACcyRABxJzhEYfBwSgAQgAUgAEoAEIAFIABKABCCBp54AHEk+9SoAAUACkAAkAAlAApAAJAAJQAKQACQwRwJwJDlHYPBxSAASgAQgAUgAEoAEIAFIABKABJ56AnAk+dSrAAQACUACkAAkAAlAApAAJAAJQAKQwBwJwJHkHIHBxyEBSAASgAQgAUgAEoAEIAFIABJ46gnAkeRTrwIQACQACUACkAAkAAlAApAAJAAJQAJzJABHknMEBh+HBCABSAASgAQgAUgAEoAEIAFI4KknAEeST70KQACQACQACUACkAAkAAlAApAAJAAJzJEAHEnOERh8HBKABCABSAASgAQgAUgAEoAEIIGnngAcST71KgABQAKQACQACUACkAAkAAlAAk8hgdu2I/veaHczT2HR56XIcCSZDkbG0Vy8+5Q7nUfTfWbQfGBXk2M86fHp62P2hl3VltvsA8yww3UrrOIxF5NizuuFMa/dRc1ritHEyM43dp9wzT1lylJb3HCJ5okYc3eSJq+4AzzPJV8ODViOVOwu3vVGe3/sTSZw3UFGcnO17q4weSeTo87PlUB3bfFha/oCz0+ujzkVbm2PgZyGPN5pzZljFfOnPTcBkGmjoGdskz/xebqTTl4xQB7UvuZJ2rhkKEtNccPl0KNIOpImj/HOIUOmr6m40pzKumPYzmjgHHJI69EZD59OdccmyW0L6ZG//vCeLb2MUjOYVQzggSsfyAPPwAnLydt8pJYvjbuT7rbXX2+La0HSiJXikUmyfV9x05W0bWemvZ7/krIu5eS8dn5SFPxR3HpknZlHIWyqNOfqHFKllfJeDLEZH/vxw7d6jO9i04E9xcWvtzpiVJs8/frr89q7nnGnaXfemBCNiPOkf4mm5PJwNwO2I7tqrXcTE+HuAaYteWJyX9FvOJL8isDPmm0WrvqhWipCkEnK+n+2Xf2v8Ehy+uKs0R/uAfJ8q/mTJ2p0k02s26LChTzFmrnL/MbU9IGHTnfgF3J2d1HY5oqDb2ilMWnf621rsfn+wJMbvDwvBOYIWfzdDZtX5YCc51jF/LLOUYAnzyiiRZs3IPyonsg7PMY7r7LGsp3WwHnNId7DzzHph6r6JcrNGwhRxhyz/Po8/lBwvj7FBJI+sa7pSaAI4cy1FmKIxfhYYqpHOtfkpp9nSNt5UrB+X9WbupWCB05ltoixHebZnp2+j76gP1RbplqcdgT4YDyBP4dAnlACKLBbnJVtMmbCffriEyr0oxNLIF2vix3rxecUd3cuCxSCE+MIplDJXxA/OtFhyvNCQCTX6qYSmksVz0vmT3oiTyWQx2S802xjNXBeFeKhau/BI+codOybmT/l8OBw/pSpwLJBAmkSiPOx4kiPNM2oyY9NBoNMNqFQEMuT783jFWj08wgz3aTgSDJdUtHnwAKDSie+Q+LtdnruTUhWaAy79fJF8YlMBtznzV19N3z3GFSEr9xUVLpRyr6CCXltHZ3dv/V9KSI0KjDbOBU4r4OFrPttsroqQWeteZhBTux+3Xvo3W2B8MV6XS6CBNzmTrO9nwpmSYgCnWG7CgMz82BOvFukX0H1XCb9E9mytdvKilTiFG+duUQd7ao+ctGPIA07vLr6ej0eG50mLZ0mu5sazYzJFGGoPrOp2+W57WeyJLLVupKdalYYJOS7BK6zoLKXKnUlpeql4cUD45Tt1IXuj31fCvGVr5aWvYLHLimgLzVUdmMHjxmk4XxD11or3xeWN2zwHq72rGuuWi+i+y3Gs3ZyZBR5Bl+5oah0E2ALVreG7y6xHGhxMJNI7S5vSeMhTezwcJxy/MJk6SP940IsX60vArXmNR+stYwgyMD+HZdiSjrpaq1sJ8cRsqLYs+9UBRDuPu35j1bjL50Uky1bva2kWIWxsjHUZVP7h1c9ASQbCLLdoM2LqdNI3XLjQpjbdlOn9ap39JvL16hBVU6FwHUzW7QAgr2oUQkdPWjJO8VEWhlFU2A5kAWNhzaCkocczf/URm9prNWCH4yrdf95cdVhPY4EXGeNXVdJappDwiCaV4X4ystznVOrp4uaALkApQcsprP2G75RRCxTbjIUrccT3lqCtYXHxgyn1pHxVcyV+6TPUvsO9WIB3WejhIXltXqRK0k/F8TXcgGSujapWKOokoBy0P3mpha7OxDjBO7ZjlS5pa+irosksmpP3W7C/1GnyQr0P6Ihpdo8ATLpM1fXUptPVKwNlw+s+z1M6d6tUGUBu/DZOozdLt+XC2VrXpX6z3jlDYc04VUjHHlNkxxojwWCgeuc9jU5W73zeDaw/nP3eazmmD5sjyFH015LTt3x7ThY89Y+vkEVdNj6qQmRTFNUQgybTRfd1KRE/mrZvs3SiFEzVG9brdVxe0IiXaMrNqhywpe57IK51rq3R7T5WdLmDkl3HKpYF6OX6RsvSJyh7O+1W3/jYS09T7Vlp16xmM00tYJxFzPfHst22cWwBu5VsJ4tuWaBF6o5Qil0qLvbOTyauViuKdmje4GtZS6XNV1/tK0hxsNvDeNJVi1OH+uNq/o4d8diSCKfoJ+rnP/0nuhgo0EK1pdytxoh8oOTph4S+D1i/Qbsppn+/ony1bHempWWIyMAaNBuPmt1ev00IsRWqPSlBkW4PukBq+lcz43hUWSRrHBrib4g3FBEA+0+faTlU2LfWyXyBFcK1qmeb2j7iPSjmPL7htLNhAAsFTvYg1cfNzwfjjzmaKq0YAeP66c7qXF2UQIeYe44TUd7er1+tmp27tEBYwSBSxWj4kz9J3TL1gmasUFWZqXWUPRKxDXRZJfR9CuSui+Rb5SDZ6MLZjitGPQQZjXAhFynfkYNbdwBml3QYip/WFq6Pq7FjHNN9aoHLylXZyDqhEMea4vZep1CxIT6H8r0kepJtkox4zqx34iUNe+Vh6uVITsOtI4ZGvcpBKk5s87Hhv9zvX4pAEW2728gC2qO75CCJIDbb5147fguIsjVQJAdb5gYtYyyXw2Itxw4pPp9Yt8gVWcm4Lac6bL3+0YzMaJQX7ZVDubnufWWx6XwKAZPRfIoAHeOII1k8cIJczmHmRxZbRkrkI+5XBSDCJYWbC/lr6xIavzEPKtVk9cc/zXVQXpLYn6T7XyqB5uqO2htXZ0WdF3u2hp+bEGL6ivWxltscu/0nrW2yuRlEFfNDvsrh97dBTo2yYGrNQdT7pG2JtkEOGytHDkZ407z+TpvMVnfc5ves/kCQezVipLVcaVguxyfb/vpfhWwAvrikf1n0dLmKvVC8AOoq0N+FPQwuRoCviY+Jk++HmAcEc5uJ5eRTnD3mUuIBbM1+sk18KBXntLVrb///e+t4QD+mDM6xm27JtJVN3e0HFRN2NrOJ+7Spf6jpakPKSw/2txUX7FJ5D3TaQPDFYR2nG4y35Ftq66vKyaoXzuoaMZ81yO3RZrKOt1SVL7n3WZ2UDEVxr2mRpCJqqyusX7fBqG77XiHO7LynLllczCFVW93nKrdhF4zdval2mvBKSr2d3VVr4hE66o6EoaRoIPe1ORA1K/VNjZW6rHPjJFMmVvmd06Rkk1vHG16t36vGvlNp6mPZgvcd7Lh7BC+tarxWM22XJ/xHRMYnoEQum5zLdSU19btWy8kzxht8QvHRauUMtrtHIqUNES6yMyXFMR0d4Nxm0/ZkbXlRxsba/5+GXW+tWtghgqSV1K/V4HmaOtOxg8jkZCr47ixX6TdV99YV6aa7GtqNHnvS/VHjxteQImi5riSZijKj5YQWVJD46mKAjZjJuDouYnp36yv262Y6DOaw0jpa23Hz1L4toONjXWvFUz0NLda2VqOCTy4kHHS9LaRXKQ9eLiufO2E87I3Gue2paWlD11XXn+4aov4hvUyNRG+MXtGM3li8jzJ0E0PK9/kkNcbYoY94R+IZ8CbSazEMxjy5w0nBySa/fWNRys0C90tb5uBc08InCrEJwbP9dRajSAJkIctTW/b6O8aahrqD76K+c42nLzMqhBHiK9intxB8UedVyn5zoqy7eplw1z6GS8AbzpTEiQaxaSv93KAKKqqry6VjlpnnABD2gcw/b4Kw3oieKml4RyFbz1Yf7TGkEdb3z5mDW975gkhl7HBPLKM9Q+vKYLd1hm3wpdXJKEkneeyr7TqHZnNsyVITl2x+1+uaD55oiKftvy01kwXVjSfat4tHT5v7p1aGk9+5MhcX1FfV6HOcrc1d7HKxmcXwNA+7b0h0JbvL9mcH/t6Yy7GCzpgPW2dg7j+rcbmw+WqDMfJDgerSekrWGwhk9hGbgZ4azbouETihvpTP2suJwJdpy/4wDL71C4LSfLwXNXN7WN5xIsIyUEe3IjRz9g3NZwmD4rZdDEkL66przHIbndbI3v1k9SXI6OQu7O5c2i54dCx5ubaUnmw1/gLF+tm7tpa37bSf11Sd6zx4CaR61SrdXg6OcZ7rqmlHy+tTBpGAqnvOmzDMkNNfd0Owv9h68krYFOTUiH1u51R5xlyO8lFCiUYh0yHJDjkJTtTUFJXV6XDhqNVw6+KM+nctbW83U3LS2oaGut2ErSlgc0dqNnF1qaekHJXTf1B/bIBu2ssEoPPitMzwBjxY/8Ehka9WF5/8tTR7ZKBTlNCi5nomkDP+4FKytkZiJbqus25aEvV4bqy7zH2lqZwS8dllQwqXy1H+p2Rhh5hPC73BLGaEMzKWUQQuZT3Zthr/I70jDF+L3hDy/oK8uaENF+G8trvBNXnCKpeq9ilUy7m6Bskw4mCZXzmt5vsiPK1n4AelAq53NIKfBaP3nK7FB7F4KlDHgXgyRHhFC8MhLvd+f/Zex/4Jq47X3QW33G8UmlkUsmsZ10GEik8T13GpBJZCxbZCyJF7kbmU0QXkWebR8wm9n3E+YDzKfhdll5wFocXhy6ExeVGcGOxQfRh5QbRRKRYFMQGuQXl8hkeWAke1x0vaG7wZB3puZ41fWf0x/o3I8tgEkjO2XSxzpz5nd/5nt/vd36/82+SamUuXOAWt7yx941/WFPoP5Cls8RNUwKxVzc1/qOIg6RYus48n+k+5uHGWc/bTnq+pT4tjBT1TotMO/Y1aAsw88+OvJnsyiZxnmUUFlMBUV1LN6eizltynd4TXmxtswlney5Qab4y/r3S/BuBfsFyhalr/TxP930a+XGd6ptNEEpEeiCQkIJotpQHmPqSKOeiSirhM0uIXFbW7vrhNzGSBNFja2trMJLAH1MOJvMUi6tNxCwUkan1C+eGGCbV5+WRIp11vdXwhFIxCyOWVZbK2SBY4eP83kvyyues+nkYVmba+BNdbCCXyp/o0hlIPoKgM/LRx9PhjAAAIABJREFUpLVB/mN3z4jO+oKJLFFipcaG9ZXIBbcvygdKGldplQUIWlKpV/NDg/GIVURGJFjNQ9G8fCQPSa4RvM1f9bjZcssGI1GiVM7TWq2V6Ec9l8MIj+KG2npLBa6cpcBIo0HNszcBK5zvrF9eVW8F+Upcv7be+nQxEokk0XnGujV6dQlOVpvIAoZJY1Ch0xHD/t/RQtEw5bsqB7shEryPctwIKp8FsFXii6zNW55PXtMTiAPOERT8X0q6fd59Ea1cX28oxZQlpOlFq26kxw1cdVRoZmZLY6/nxakUkOYNZnIehmstRiIKKes77Xt0RZ11kVqpxIgVG83qfs+5CM/xJAnXFc95Xm+tNaiLMfXSeutSYYELJDD8M2WrN64ksGJcu7bJ9ES09skrSqoTwcuIb0VtHxiP5SSpHAh8GhuPS7+HI2Gf+xxS+Vyd4UlMWaQ2rLPoPvf0UBmhpIgISbEhkT+pVAOmo+2LgEx5PIzG0rhaixcBTOoan1H4T3ukBDepiyVqjyCiqqg2LSTIMgwRl89kBrLRicGbphR5Mt2PG4xlOPaEvsagCQ0yQMUjSU7+sEZbSpBzhkFQoVzZYF2iFjp0TaP5cfr0xKxBrHDSP0BEe+XG2jrBPgCNXqtPHGCTrCv2eprMi+hXjv0+iWVLZxqdZ6hZpETzZMQCsC8ALDGRShRVkOXqGexQPJLEltXXLVVjJYRpw2qS83iv8VJ6IVBHCeNqPVFKqpN3eUxReTkujMgVyseUimLCVN/S/GNCPkUBS26niD0ZZ7L0LLa4xviEDMlTEBWkiqVp4JtMZrKQNAsv1t0SNlbC3EUakIl8JDsun/OSA0mAfOaoITRzbnWDRYtjJaR5o1n7SDIwib9FKhqTa1bU168iMaVCOUdrXISFPgOLkwj9G09fiamhBuQr1VXWOrAKFDu7wfd/2NFxVm7Z0qBP2yIRrUdGWoAFLsHwCmt9ldx/1sciCt0iYviSL3JbUvjybymVTp+yfSZjLFAZgCgSQBSNKytUt4WuySaK8fYJPOPmhmdJvEiJLTRbn1H6z3pZhPGe68NWNpgX4tgcAZzYXKeUFueogOIAI6h6uWUJJstDlYsMpQX96SNmxnh9Vy2VcAYiLKFP1DSt1eLFOLm6wYTTHi+NSGglWqojZ/h914QxhQ/0+v9I6MpkOeCsJL6PRedAOTADSpD4UGQO9HYfxarJ+Wi2AWKOwbKMIBYSSl7MN8gAJ4ZxwHOBJVevNxHFwIMy1T9XrZaNIRJyK2pSJARDogulBECiRkSUPUBbzDikVYmWma0VghYpSItpPpOls8RNUzJi4g6S0rjOMjfg6DzQ6bihttQaFKkcSHqnKBjrgW+GCk6XSMo2CouogJSupZlTEectqe6RAP2oQa/o818J4er0bVCoppwY7aPAvAmYl6cxsgyhA8Ar4fupQGEZiWUdCETaF8/iJTzA9FdEOJdQUlGfWUrk0quZnt/fxN2t586dKy8vr6+vj0IIfppMpqnAqVAoYjEGWoAiPB9dO4pTQDGtSf6J13WcZm4xDE3TI2N68OwWM4QU6/8yVkqG49iMPuGHVH5WhoI3Gb7EoAY74iIJnaeeg/iZ6OLezEJFbMhH8wtQ/k4qdylkJViVqJobYkIjffte9E0853mMYRH9HL1RRnlPOmiGDQ4F+m7whUU8uCho6CaCV2GxwgVq4xo1+JsC/1NiseAJyZc/wnM8GHVieEYKy3Q/ILpO+ugf4yowuykjm6P7l6KEZuqMVe59P3/R9x01QZbr9AY92GwwabrJskhxbG+tUAOOF/O+IeD8g7AzhzRTpYzVgsplqNDh40F6iA/c2Fr7bvx14BKhLI8k9h1JwRUcCvKz9Vis79C5eDEqLFVxzNCwqnTClilxvFBgLoeKUhowj1Dz3cD2YWA8Vhtrxm3dn9CIiqI+J6o1KHKTGQoz/t217ol3eETzv0Jg0EkhkilCUmyMh8RxmJpUc+ytYdVcfIIJbC4udzFDPJK89U2kn6S4QoRxVKmM+aQyUflMJidJJ9GbGbWrsNkxoZUDIzDGgz3VQspTqB6L5N8ZYm7K586Lyz+i0MxT2QfArBOeQSqScXOImYEb41ud0XkafEY8GkPE6hIfjCOkMvUrx35Hslu2DMYfU8W6LC8/H1UoorHJDDAPBRQkWhgFw3PsNRk+VxmihjgOETcjQrGZKlVmuDJF5VUvWa65aN/6ggsnSHKhTr8YdKKkgMUNUUbTsmRI9qxg3xSPxaUYzRfkAOCgmKrJEunuyWU4nWER5BEgjBPymVY+U+VHOeZmIYbHA7uZ6rmzUeBGZSSxisrUBoPMf87pGAwyQ/RAgEbm6IAHxt5i5PgERQVZbRGoXQIBQ4/9KM8rDKr4wJpWCzpbo47beWwejp4dCo4jhFZHgA20/VZ1kc93TaX78YSuZfAYyVDOjrelQC5D+DEekTLRSGJtk2f+wISv2Datt8WJ8rxsbnBU0G5sTpzgo3PxWQgwo5JanKsCinOOKArjUgVGTGTS++TuqqXZnIHCRK8p8ZLC4BDDS2mlltCR+Ycv9fGkRliRXFBHFuSCM4KXEt/ygPUfXfgaM2dRg+qzwwGaL2f9/TjZOJPzSwwQwOSgqrgVmopvALp+WElg8ekU5SJzRBYRMblFxEyKhGCMI+KHiaQEgBTVFAExEfa4AJggFRl3UscC1V9icWmRqYqyd9ZUTVNcPouNdSu9W4/1E+tfNaSd7UIQSe80ectApqRLj8JC2UwVyDZiJlHPdN6Sq56pta4Gu0Y6fSMakzbDgMjUxONBr7BUHugrICw/oPddBLc58v7rCFGLI3d6pzbEx+uV8AAzEBHhXEpJxXzmgdycvYxq7y7jmxhJ3h1SOb8Fduns3P0hT1ToNGWV+h+NeXZPDEJJNCITJyJJKj+t6J3U30k/0RlgHinHlBurE8TGx5BiY/PLxoTCzQDLg2BLyOHt+3yyp/Tl8wjSYCLf2346a/1gqTM5cMwsK3tKR3S5fH+onttLyRc2Rw9MxovJyHVtP6+ifL1+yu/p/NDtawB79DNppOXw6SVinm56tuhvMUgBQZRc21ZflgT2I6l+kARcw35QSYIfQDxRaVq3Cg9yqCiZ6QKCVB/0XGOwADOnjFAjePBiH/0oxczTEdFRE1XXbG1KDr/RR9OmFMECeKYISbEBZgbEcLgVmSWZSJNKtWh3TL5hQoqriMcrrE4LKQf5lKKTaEHOf4E4KlvZdN28E5cEUQSyUZJ8Jq5fOfS7JMUkcY0FzJGi4hWlUZlY2I/CAn6OiZsR5CYCVufE0hSVt8TYskcX8Psuf+z3n+hw/drU+jNjPLJNJZ9eXaKi5GaKsSSSF+1ZMcsmarLSlU6EYlJWDjKcQSATeaGIuHyKqTwydid+9i+DdkpGZkW3vft3dAZm6ytKcd2CSuP8rvb/GXsjXf6j2XmYscmCHu+wn9AT6xJHGRK1RHaOJFK042bqdKVdTl/AhPmo2XpLfBZGilk0vbtBnC8higkSgkgoKhq2rdIkOJ8hV+RRnpRq4qhm0eJ7UMCcFC2Jn7traTa/JW5LQSX5Ql8AMKS0EiXABtdDvWBFEQSSZG2kNyfHGUHmEaXjDmqgL/ypUrOawK6NOQI0NURjZVZBVUSBFTo0HwFTV7G237OiScmtiEmpBnWKCUaKWKT8EBUAqRqlyUz+JNmLiI0vUp1114hxQ2DbSR5PB2humTLdlkl7p1mZzzYKi6iAqEhkVCBq2VJL8f5/9YfUYHd0xsuIgvge1nWtj8mjwriJmC/HfhkIDIT7QqU18zILx3LSTdzEEJ/yRqJHUjzApDJinEt67OI+s6jISTJ+Tw8yjes9kXsoXl6yZMnly5dtkQT+AD+nk+1xqqeH1qxtba41m5ZqiZkhDmzpBKpVhBXPANOzMQHiBxkmqm9S+QmeRJwTVTGGDtJ0/HOUQKEHEmswObdGitUogQxFVRRjcpbhUKWyKPJf6HL3L739fPhyT09I29D6gtW8wkA+gYIP8wgpDyuejTC/j89i89Thn251XM+NN1n54jKO+sgtbG19Wp3yDjiGfswTLCIMz1obt7/WvBjxf5x50C+jltnFSgSAH88P0/QQqpp9NwsSMRJ5GFaEDN3kFFEoilD61w7XlWjLY0Uk4EJUJSr5UKLv6EGw9gaSAisuDP5e2A0XSSwzOCysJudQUeyN2D8yomxu/xWXH4zHaplCrcFov+sSPbcsEkgqVcUzWGZYHuvBR4PeE92+jA8cpRKM/JJiQyp/cqlOrkShBG0foCfgY/rp0GysWISP1Cyp2lNKSchncpmc6EReyFAKSR5nFGOzQ/034vKPcH03goVFShnYUY0i4XBsp0D434LDURJYMXaH7hdWp4XED9JDItMKkrVN8uCu+x3QBV7jaHzbxXgwOLFQOkmVsXYEb8ZfAMs4twuxYoWUXkjSm5ry8vRZh4tC1FqjZUNL23YLfstPDeUgYLk3U7JnJVowuckSsfCptHKQ4fTK+Uzk04tk/40Wzy0aZgbj3TdC99/M8EcFCiIVsR95fKixsaXB8qxRT+LIv38eqQpVFmHDg2AXSzSFvQe27DoVUZBHCd0CsnqNnj9j774hUgvPBsF+j2gCC2IIUBYhsJSVP10euuIF9g1bpMNiz6fwTw6iCFZ1VCHA8ndiox5yzeXw9IXQYgwLMRPaDQaUzyL1SmnxvShgjg3Kapomb2lWZ+ALNhgfmMDemS8wrBiV1kp0vpacQfWe7vXfIXWRQHLy2kEhsJVwPkd5vH15Gs13UHw+HrzqPn9NQZSCjs1BfwGFLIqWAY6iCJPf7mfiH/piP2jfss/zibjcipoUCcGQ6iwJAZDQFESUvRT3QqoisCQ42B8vyfZ9Oox9F5fsrKkgllxh+JLD7scszRbsd3bHpbhoxEvcpXea+ygcrUhK1yZZp8gALuzz+UPEIp3yltv+wcR4HSumJMpVN/zg0rHi+Rq0SKORBc6fAhuuCQ3YUyY5EEgM8RP4iHuAGYxlZmRR0kyfWULkMqlOS843MZL89re/vXPnTlUkgT/Az2mBMk5EDnZ5sdcoNsyHh/zOQ05qFOx844U9Tk/zPW/bvTdY9hOP7XjkHgiQpPIneMrLRwuQoes+aiihsegCQ6XMe/igmxpi2ese25EeRGtcnLHNINEu1u/q9gRitwJMZEuwCib6UDQ0RPVeZ5ONBEpUGooox0FH7wDLDvQefsvhB7cjFqDymfLQINUHGszRvcc6Tw7wkQ2/Ct1ikvvQZr9IsyzId5znNUT27Q0JdmWElmBPn7z8aOolCqBAAbjdq8t+ws/c5rgBn+/GGF4SvQ4y9jL6CIqOBKiP6QnPQ3gwa7FRy/cc7vRcZ9khynXQ7lNUGhZIO3AFchT5vM9PJYKbBG/Rv5T6KnLszGHbmQDLstQpm/1DGv2LlLk5CbgQtMxoePSyw+YO3GLpi/auszHjRRgMqivHD56imFu0/3in83rUdZKsKHzV7TzlzxxdFKUEdtXrQzQaMMdWRGge8XuvgPE4shFrpq7y6Xzf0YOuKwx7K+CxHe6+hqjiW7TSm5jyW4oNifxJpRoQTwIZtB276uh8188AWblg73w/SFRFL8gVYSqpiyVqT3lJSj6TGciFjrhSiPAXzcrD9FUEc6rTfiEi/8f3Oz/FDQYCyVNhmLzP4/R+woAY33YqfkoViOjCkPsIsA8MAzS6qwfs38sxict88st33+/guDWGj/hcJyl6MOD9F/uFqMecI2dgV9Bph/cW2OvO+Y91X1Yalj8JzkKKmhFpilNTXhS5DS61tns+YTmOpT6iWBmGKZFJBUyqmSLYSvWsVAsmM1kgVs+08KnEJGVYhL34m5nISzEong+a+Tea/vdszksMO0i53jrem+4xxt7LrEg+U4aO0FQ/B773TZ+12c8Go2dA8L82aAadtoiaB852OX+HEkQiAARnIVeXce6jbiZT8jlftzMA6ucH3PYz4fJli6OmVrZAR3ze46YwXfRm2DTURMeCpDK5iCLYfai5ebKzy0vfYplLjs6j54N/DhZhMMMykv3gsKOXEQaUt5xUNCaR0mJJBeTpc07nxXT/VbxHpHNFx+vk4jm0VNIZAHTCfpdDGGp49pzdRWuMi7FsQyqq0ZL8+ffOIwv00fXlHGoHlcjUpThzzht8XAPOuyrmaVTXvL0ziPKIzzCp/gqFJBRNHJz5BsMs//G3Ih7UVXfXqT7FfKJYXG7FTYqEYAgBrYivJSEAUpqCiLGX4l5ICwN/1SmMpLcY4EW4hohsnTUlxCZqDFOOo95Hn7GaFhqtK2Tetx1pN9VM2TuNUc5pFE60W0rXJjenqdgN0gFeQ5L5lIdWkhnzUX9JEDN8PR8L8/JIHq55/Ivei33gNgphXl5qIJAa4uPVSnmA0l068SSLkmb4zBIil0Mtd1PkG7q7FUSPUzwbmTO4eerq9TXMEdvmjSF0Fl5usJjHu/x/CCJajKzdsu7tw8d3b/6iQLN4hVF9ApzTA0kmkT9Ro5LUk553bB0h9Ofr45kFhHVLE/p29/5We0iGERUNW1Zp43v+xVhl/aed/cgiw8RpE6GQNKu4Tq+52L3vn/iWvXXExLYiVG15uRk56rD9V1cIfNfgqZrmyGFrclWD8aC9Y7NL+ATIwuXrVvBdg+A8GKFYsrHlj122d3a6wVcQ1IvXbbISBZFzkjkkwUXI9zELdWmXKCBgX3sTYzt6ePvJYV6OEYs3NlUD5U8aiR/XV87e372vc6y1LXEjPCLT1m8JHTvsen2zbbwQLzM0bzYDZiSTjNAvctmOdoSRV7dJxOfR1tlP7d58hJfPJvQbW6yxO3LiVCXgAvcnWF7eyNscu39qzy/Rm5eRTHSpdo65+flQ5/GOrb/MxxYa9WV9vZE9RVIVDV/3dP+GIFYId5enpCKSUDqYEmE8RhBg+2SuPJKILfAJwtZwzO48sNURQkGn1G+2ktnkJkFYig2JfHQyqQaCnwTyCnNzM2I/dnj7ieF88BWQNa2WKukAN7mLIzKW3gspnigqKZ8pDIjRScU1oRQ78NQn4r+UVU2v8F1dx3e6P0fAV3BML1tMgleEatc0GA/ZbW09+UU606qa0MHoTmCZdn1z8IjNvtM99qhm8V/r8fc44aRdpledWVsSIETmUyHn7vsdKQbXUTA2Z/v29wo1htXmJZxbvArRXJR8Sul7Y4v99ph8vqFpk1lYSsoTNyMiS1ExklNTXry6seHfDztf29w5iqrm6UwvrRMsI1Cu7AIm1cwkbMvjTZToWVEEwPSNqMlKLpxk4WtFiUjLsLi5A0TEkBelLZ2pXNbYPGI7fGhr97iKqKrQKt1gJ2FGcbGKKiz1gc7j/7ip+45cpdYtX2M49S44tINgRcbGTWE7sN4nhpHZROXGZjPQiMTchEK/xuT5L932s7qWVPVH55QTn3dtbWK+yMN0z75UPzHWFZD6MrmP0+pErUUCnMb0LWfRZkiZ6ORGFpsaX0bsx4/vbBnmZ2LkM82NPxTcTWD3msO2rq7trtFCYtlyfbEr8pKEFksqIE//a3e3DDMtmuRIeAbsKRlJ4/Vy8ZKTtlTaGQAEVQsJ9Fe7XuwE30EpN21qMghoZ9FKlHha+60zvaQWHM2PpElrj5QCF7hiCJX/ZOQDQsVqjQLhCCLmAEyqv+AVCUWTcGZwy8tNyJGIB1WAkcubG5cpZePicqsVNSnglmkxwQCRpIivJSUAUpoiE2EPyZw2juCWlsClR3jAvv3d4W+VlJters/WWQXipikJscwLRHjqxOGePGPrD3FQL/4jq/Fi++ET2rbkTelT9U7jDZDwJdIbGP8tpWtJ5rRC6t2k/Dk6g5ryvmXTVFismRtc83CCkLuuzFULj1C1ei760RhBxIJ6iYFAaoiPVyrlAU7KbFYlzfCZ72HQn5STjAJ/9qc//SkjE2Z8/RDg/W91cqsa06/Z+vo19KFtUXiA6p8xlyiJBnac+9VN3rI9O6pFXaRII8cDjn2UvinincP0sCMwzgY+5hRlamXU+bp6+KV9Y3V7G6LfZXvYGwf5fwgR4JmrFF9Mxi7CGgVfpHUotsS/3/igtCfyFdQf7AHfGX4gOIJa/EB0w1fLxFfpa4FPL3bEvnn71YJw/2uHunb/Mc69hm/i7tbc0fn6lLzl9fJkfCbl69Osr1NLxmhXx56Drqssd5sNfGg//SlevkA6jASXvf7Wy6q1MIz8usgA5zu6u+NYL81yYOu448T5sbLy+JT+16WJsB0PFQLsuc6dB5x+cN0uS3uPdl+eSaZ8sPErbgvPXvf3ngKbLcv12gcjjBQAgVr8FYvFV1899LW+pD6AuvYlAZ1LNXBNMheUYBmIwP1HYJztPWbrPk/RI4hiDmlYtc6yMFskKex7hKuR979bvrQawp+4wT5d340gX4BptMuta43p37f60liBFUEEAAIc5Txi91xhgnfk2Hy9eZ1Fn3Im/avFiPcf2tTRq9DXNjdEvp73gCSoxQ9IR3wz2fgGrUmC47twxHxgpBxGkg9MV0BGIAIQAYgARAAiABGACEAEIAIQAYjAQ4IA3N36kHQUZBMiABGACEAEIAIQAYgARAAiABGACDwwCMBI8oHpCsgIRAAiABGACEAEIAIQAYgARAAiABF4SBCAkeRD0lGQTYgARAAiABGACEAEIAIQAYgARAAi8MAg8A39nuQDgz9kBCIAEYAIQAQgAhABiABE4EtCgLngcL7n8oYrt+2uA1+W5j9x2+yn6G9rDStrjE/m9oXlL4lTWA1E4CFAAEaSD0EnQRYhAhABiABEACIAEYAIQATuHQGsQo8PhtHe8+6LZmKpAn3CWPl9LlRtgd/vvXdsIYVvIAJwd+s9d/oN5/ZXnUwOZHiWCrA5lINFIAIQgQcDAfbD9q1HA7nwwl2nGD6XgrAMRAAiABGACHylCHAB/jGTqWoudcbDgO9pIUzff2DT9f3er8OoMUI5DjoDk45oHOU+suul52p3fRB1bcGXzNq3NO/qPN4b+c0Huvc7roS/0p6GlX8ZCMA1yXtGeY7xlU2ySfZDsH7Xhb6+c+78NYfUD9C3r+657ZAAROBrjYBycVMrOolyc1c9nuuUzxUy7iGwB+cb6V/rfoGNgwhABL6hCIAg52igfL1ZjQScex0Uj6IyuTzp08pj42PIHYTnw/xYAiEZaW2uxid+8zc4ZJ4SUxqId4+fvmqqwwKhbxOTGPqc4b7rUSN8yW4b0jVUq9Gc67ovBccZ90FX/uqWyflQEJVlFDuO9pxxB5ZZ1XlK7Qpdv0JtWYFFGEPV1SZqzz73Yy3G4vvCKST6gCAAI8ncOmKU9hxz9ARY9javfLKiZq2FnAgI82STxZEIoiRNz2rk191UbrXBUhABiMCXhgB/q9dx9CR183MuJMOfNll/rMcK4pUXTK7cilKD+Uk5837Pl8YwrAgiABGACHzNEOCuuxzv0vjfWecGXCfP9FKDw4hSs3hVnbUCS0RWKUEOTswJdXczWE1r82qxqGeUY/9AU7/rcQNqrIdaUUfECdGDCF4GIh2dcdHxjg+9yyuC+Y8bpg3Pux01ZAsthqvtnecaG5fcrynJXEBmz9o9StOOOXE8sni/CEIP5hOrjaHWTvelGrVWxt9IRRLFTdWq7V3u8hYjXEOZNgF78Ajl/cM//MODx9WDxhHn/W///eZfNTWtMZn+avbA+//9X859PncxORsNB864fu36H/4/132/KGlOTJx9nvb+ip37t7roZI14GZgLEYAIfLkIhKl3/ptfU9/8v//tM5X4Zz32t9//w2z9098tGKW973ved/7mDvFUIrCUYu1PQ70uWrVcj0+EoFIlYT5EACIAEYAIpCAQDrzbsf9fC011f/1Hx//9P8ae/rG5shzPY3o/6vUF8hf+zfx4YMV6/vmd/zA1LFVF/K28x+bPz7/+m3MfXQ0/sZgsyljJ+08F8lmz8e/91d88/d2Ry6fpx/7mqb+I+mnMR1f4H5R9F0XyVLNGP3L85urYYzrj92dP6sQl8QzmH9/5Rec7znf/n3d/Q90uwDXf/TZYl7nXUSNPNTev99DpP3tahz0y7SKSG8jjAWfnR3NX/x0Rw1zK+42yx3z0//I/WKCb/YXX8dsZf7Xk8d9/RH1bRyQjmfcdxWcfvBOYXfl95VTwnfbWQ4L3EwF4TjIHdDm/9wJ14RwlbPeeRVqqSXSox3WBQ25Twdl6DGFDk+4mz6GSaS7Su3/DZntAOABwf9I46z249cXa2g2vuu/y7Oc47XiltuNctj30/IC395MIuCOe9vVbnYP3py3JVDn3rvXbnUP3vyLpGqiulzYc6JV+nusT9tT22p2uu+ydWCWMs7W2/Wy2PhIKXtq/ofl+CFtutWfHYzLeeMrTc8XX4xdwkpXW1FQouN+6PANI+BojX6TO/ywUyk7/q3gqSMhB/32smfPbdwDlrt3aTd9lLaygR65bWd7m2UteiosUmKyPslBJPLp3nrNXMxLw9EaOw3P3YIvGOepcRNTuU7qP9JP6C+HpDzq2bKytfXH/sbembKy4D3fVrhVJW4/TCBi2pt+SJLj1TmbJ7qZbpiS9OXRQYtST4Ia/0FG72XEfB3eJeu8tO+ztqN1yjBYjwtMgjLxKNDYZ+Xf3O/5oalqnV89Tk1V1TT8hUDAD74u/NR5wf8DpDBMriyCWxEwb15Fyxn3I1hs1JmIVIEpt3X9eGbrkj/U/B/ayamJ7WecYjPOZPgSf2iHJMOV4m9LU7mjb/caeF8nQmf27DnpB/dMwaszUGZ4MnDyTy80bok2VyswVZP66txchyb+M05HyfqPPAZIyAUncYJj76eme64HAWMZx0zxcV4b0ftQn4iYndCfZvEg1Iaf8hPpMSTFzoh0vlIMWT4neXRaeHjamB3m4uzWHTsxD5XL+i5EQ2HUPdEaGYYV5vQxumAeFAAAgAElEQVTDIMu0esTTzpA1ZcJkGH/D677CpmkLWqw1aZNWIe+A5xkzZzmw8MAV6Xc7LyK6jS36Eux+bVoYZ1z/3Nm/slz7BIoU4PpnDYr7teMjCd07mHZVNVH0wOH9QDM0W1e9HFU8pBOOBfJv3QmFRqJHalCsWImO08xNXrZIr+ndb8cXWyJSx/S6eofSlVtJGvVzJtQ5+VTOA91dkzLHnu92c4R1s1GD3bcdFLd7Ovd5yVf1BOBmOuTnfvNMndjv4OsMycZ8UhwzCvC/tXe8q3ylgrxPs/P3kX5yf/GU+wQlq2pqeap41hf+R/44tSNQioXWluLI/MxnvfZDfuy55w0RAvlKDKEzILv3jCRu507XSbhkrqYivZN3UPKod+9tfxgohK/a959CzTtNqgH7GxdQ43bDhHYoF5AalOq/HYsRo0HOSxNBTrR1SkNDLfUP+7w2G4E3GyS9kSLjxlUcMNas3+lyeymEwcqsekHwFLpK/eXb8cAyN8Qi848U4zdpq5SR+UdP+1kw/6g3kymjRg4+YeaogWoWlrLHvHS1BZ++ITV3kJkr1Ng8KzZRtZT3iwjjYmIvq3KxkXTa3nGQTzdkKhmmxseOUcw4kd6iCd1JNi+5dYF4qWT1mYpiilOTyJ1ciyVenN7s6WFjmpCHkWQOnTtT3/h6eRiNnZjiP2OHx1EsEtYwF7whnVXW6w1owSya3jQvB2pfjyL/XwjMRWkXEerpM3YZwCQ57iiIJPGMAvchYxZhrL4PZL/eJIu15qk5kw8QHGhZ3WtvWpCZseGPu/05n1eoUACXg/Odp4llFuocRSwhMK1p8qDqzgPUrnthJRQKIzhZWSZ29Ohe6Eq9Ox3y82XzLNWWyfJFJuYne2VKz+83fYGZ8VCILyS0WkIY7zDzlPgDhWfhxKzIO7doNA9VziMidCKJjv8xjf+mcDuNdOOkpii9k3XQZM/vQwu+SpLg3OOxHn5Ri16JcIzStLnFMHE8D7AlVwC7nI/GZuvSg5w434pF9XUU3d7T1XlKvW2lpJ2WzRJ8NiVpriNTZFambWieKgQS84/InFDyqCHLxSfMGDXQxzU466VuI7hkWDxFdqcAcpgeDCrVysRyh7T3C5igBxA85i/JyCp9/m6/4jkRptHZmPKzPhqMKjNTOZ+i7uTQ7CT1mX7iifofEC19QNgAuMBIMgfhBEUSR6jD/otUeCZRWRExWCgq/8zv/0u9KftCI0d5zvp9/8azH3U7R8uNS9Vp0zZgu9rh0eX6Ua/Hz3whx3XP1tdX4WBtxLF1O1N9oHlJpPgNx5adjPnNZv2op32zD/+JKuA63zeSjz1laViJet4+fv7GF9+as9j6Yp02qstg7ftEe+eHVBDFdM9Y66ujV5PxzFn74ffO97FIIV6+fI3VVKpAxmnn9jeYBRXcBTcjr2zcbk3aPoJwV532Y57LtHD8XbfSuq4KR851vHCgFwjxrud6tE2HmisSjeeuOG3HPNTQMPIooL6ufmWkpaOM95d25wUqOCrHygyWdUn3FYGn4s2sD3VsdwzwyIENLwa2vbmabd/k1uxoM5cIk4qOLofnChMqUBEVZusavXAeH2yLOqWwzGdOn6WCY4WaJasb1umFqU22136k23uNCeWrND8wWdcasp5k43sPvGCf2fzGOmGlJJF4pve4o/sCxYx+C39qZX2tEc+cdpsozVHOLjvox+H8JPYQHnwK2X6qt28wyBeoNIvMdc8ZItcIhOkzIN/Xd3uscI7OXFdviK5xjYLtOidPfUQDYSj/UX3DMjxdvsbBddu27vOApRRI+UGPvct1PjD8rXmLDQCreGIvOYR+YRFsgVEv955G696oBW0UE4bkhkf+5pmezu0u7+CYSr3YXAvmcVHktntXi1/9I7T3AwpZuHHHQt//+bbilT1WNdhPItoLSJh696D9NPhORiFRtRy75uCeOdC4CM2ld0RqB6jd8DiOuXyBIIfIsfl6S701KvPcVZf9+OnLA8PILE3lqjpL8lUNCOd/a9e+60TTT+vI1MVtNB5GIjzl/V0QfcJieEJoOFqgHPvEN7bQmKW3QbEw2IzwOx/Ns6FTTmSBwViWSh0Be3R3MVoz6j/lGxjOLyKNdRvN4OPXYP/nK2BRbocpsgAOtiJvvqTb02pSRiVZzZw+TwURFbmywVJ8GSig0HdaS+OGqNiAWvtc+xyuSwyiJAx/12CJNklUKtI6a4M2qTlh+sMuuwuIX9Qa1JtKZf5DG9rPgF1g+2trPXV7tiXdkxAOfGDrep+iPx8rLCYqflwfq1RUHyOSE2mYWDN/qnJsPkyNIlRzbR8wIAWdG6LykydibQC34uZxogoESeF5tzW8L8WaYYF0C5agGQIbSZgxhca4ro4YcNg/8DPjKvJHDU2p1ycy3Vt3fRBEkPa1AXNbiwrUzF1xdOzz+Nkx1XyjdYOFjIRG4U/cXUCZbwjipzNZ1y1LtRNXD2/Z5+XHke3rAwKws1j/CWBVLtO3eVSBl69cV79CLcswxaorOWtuGv2ERydIYH+ZEfF7BsIIimnN6yyCFgu9I2ZLecbz9mHXb/sEc12qr3nOov2Of/9Ef61dxR0/Bj4P0Nu61rNsW3P+L3aPWA+9QPgPbu24oW3daQXTD9zF/Vt/wZl+ts10FxNMd7i+X+23ve8DtkKzaHVdrT6yPCJmqTKwIkIZtve2a3uLfYLbN9cTmcOZIAxHXrLzBg3jOc8qa7ZsMz2aQSfd/iYJHzB6k1i/eOG0DpqZOSxy7vakUW89IWXokqrP+BPsvt7sxv+vNgsIycapw5vaqYrW19aqQblA10v7x55/bT3BZxfULBonamEAafFRTzDwtl96ge3CF5k0oxmsgowBr5dGS58V9paCLR6GtCIjHAcE9rvR4DAjyEkUlpFrG8yBnc5fdjrmt1rmZektMR6mnic9/zgdowbY9jaTYQZ5JCmgmzqPSW/kDvL4MEBcrihMqU7U+wXX8Jxw9VxkVI8QmpWCb4mWGpaX8XiS15EgMlMhv8MBykhaJBnVnd0ad/JwUIGKGtJMJXUdc3o+7guGEHkxaVxbby5DUtSH9EwMK+KG7u6cxsm1OKOvwDr40W7PFXo4HyMqLQ2rSGEDl6jtzZ2lGymjCXYqyYL9H1r/P4m5FkBN0xykRUOdqchnsJ5rBjwnmStS0XL81W7HJblhQ0N0FwW2oqWl1myKhW7SpBSE4VlrS8eR1zZZzBlhZPQ15pyHWdDYdvDQq2tUV7vs7mxHjMDA6nf3Kiw/O3Rol1nxO9v2N3zFa9sOvdm6PO+87T0qOlHB3/K6BzTW1rYda4nge/sPngN6DMb4zteOMfjqV/bs2fF8xdjpvftd0TOB48O+8wz5XHPDGkPKgYEBZ8frbu571tb2tld+hNHH2g+e5WRLmg9tNihmGVreOpIcRgKuHIc8yJLGV/fsaf3JXObE/u6roM5w75HXbFcUpqa2PTsa9OMXOvYIQ/tkSWHcvMM8ByU3vrlXiHniaTRg39NxAdE37NjT1rRc7u987Ujs8AMYHb18ZcvrRw5tX4letHVdAN5w2Nt10D/T1PLq3j0vmxRXbZ0fMNnqHWf6f48Ul8xNLcN533zNPjTXsnXPgfZGXejUG+/EEBYhBZybjg4vYnh++549my3Yp7Yoe/wnjjcOUaqVL73a8WbbCwbkt112cM4WdMeFg+3H+vFVLXt2t64uoW1v2IF7DVL4krt3prFx+46mKjl11JYhDDz1L+0Hr6qMm9r2vNpsnOnf97pDgHSUsr9uo2aZXtm5o3HJmO9sIMbhoHPfvgvo0sa2nS01ysuus0x0K6ekMKQ2jPrQm1/V3Laj2VDg79zbHes7sL/nKmZparZWpVyeLtYLCHtmX8cHYbK2ta3Vqhk85Yqefc2td0RqD/u79nb1z7Nu27137/Z6MtRj+6Uwr4Hccu9/3cV9v27H7j2vrFT0HtrvGphoCR843rHvCl6/OT2MTGorT59ynL+jrf97c8R5VehfaGmoMSXtX03FJf4LTDybVze/9tYbLevMGWFktFDIe4bCrW2HfrG3kWC73zpJZz3ALGCIGLftPbJ3HR44tnPXB/mmn+499KpFeaXL0Rs76QMkxDerpmXnjoYf8J59HREtlpCKyBPRzgL90n6cwVe90vZqq7WUc72+2zWIkPUHdlRjqLbx0KHkMBLMZJ3sPMGVr9+2d/eOugVhd6dd4EVaH8XBiubmaRtfrSMK1NY9KfNQiJi1ib6R3Tym85xszYZELNgEzeDTzXsPHmgu45w/3+7gKpv3Htq7QT1wwtGTepYR+9sdLcsUiqUtR9oiW87G6Z6zLLGupW1rvXrY1XnCHxW/fa+f4si61vY9O54jOGd71OQmcCita3tBixabdhwUgGV+tQ8YssrGV/d2tDWvVASOdrkzTfHNqWhuKv1U/EO95/qIF197Y29bw9w+296ouRC3pezpzq4buOWne/bubNTneQ8e8XLJ/VX97I59DdoCzPyzI28mLLOMXGPVjbq7TjHISK/96GVsVd3dhJFAVFnv6WuY5eW2HRu0YxdsDsGM5zZszRCzvUWmFG4lBWxM2F6kf755vVn3F2J0sklz4pmo9ZMQANFhMXXUkzJ02ZlREEQJE7gWkeA/UH0jfDAA5o0FoaWujanLNOgt9ySCGqEvpnESFkZi1ANz3x0HeqPjjlHu67kqMupzNwLBOyrFY+KxX/jTPiZPo10QmZsTDXIirAqpQF3z9zXqGQHXQUfkQov7nsD8Y4zp2PyjITL/OB2jRp5M8e0Qx03bCf0pgIyEwcUA8gLx7kjxfgtww9rGHXvbGiNhZCRhps2J23FTOqBAjiJ86I8SnZI2HEjKZ5KS/iXnOdThGgHWdu/e9lYLPtT9lisw/qU4jSlmVlSLU5vJ047XOzyI7vmfAZdVj5zdtx+MLtLjppgNEfNj0619sgWTwDnTQfqDxEAsQSBLNowks4CT8eh2b+cRSr2xpUGbtuyQUXLqGah6uWUJJgO7fRYZSgv6wTHMbClPofuhST0TQYvKiRKk8Klq4zwZOExIlqlCt5iYBZKRlg1msgTDK6z1VXL/WR+LsL7TvkdX1FkXqZVKjFix0azu95yjoxWpKqpNCwmyLOnGbTA36fEwGkvjai1ehKmX1jU+o/Cf9gDWIvtNJnadxDkd5bgRVD5LqZilxBdZm7c8LyyL3T7vvohWrq83lGLKEtL0olU30uOOuF6TpBlIPqhoRj6atIGW/9jdM6KzvmAiS5RYqbFhfSVywe0TgjJQlDSu0ioLELSkUq/mhwYBm2CCjUdnKlSzFMonDPWbWuqfTp1sS+PgjwzDFuIlKUtQIDo+TpfWNZnJYoVsltq0kkSu+qUiAf6qx82WWzYYiRKlcp7Waq1EP+q5HEZ4FDfU1lsqcOUsBQZmXtU8exMwzfnO+uVV9VaQr8T1a+utTxcjkUgSnWesW6NXl+BktYksEM7kpqSwz30OqXyuzvAkpixSG9ZZdJ97eiiev+I5z+uttQZ1MeiseutSYfEEJOHjy2WrN64ksGJcu7bJBM6dCimbMCRXhy2rr1uqxkoI04bVJOfxXov2nZz8YY22lCCB4CUnkV5gQBw1t7rBosWxEtK80ayN3UqXU++I1D4m16yor19FYkqFco7WuAgLfQYWJxH6N56+ElNDDchXqqusdWDmbzzKKt//YUfHWbllSwPYQyWVwlccnecU615pMtyHU7LY4hrjEzIkT0FUkCqWBvt8siWAoZkEG2wVJKlGUWKFiVCgaBFZjvPsreHoi+gTNU1rtXgxTq5uMOG0x0sjElIRKS/WWeNCvyhXNliXqAXBWNNofpw+DWYf8lBB42ZEdTzBJs9xw8DFUakUSoxc1dDaZAKrT9n0MVsL46fF81JcFilrAyhNYh4zeJ6wZn0SFkygOc9Qs0iJ5smIBWDrBFjEJpUoqiDL1TPYodRIMoJJPpKHxGxRnkz34wZjGY49oa8xaEKDDFivFMQPNzc8S+JFSmyh2fqM0n/Wm0ZGIALOyQuN5pEinXW91fAEsJYYsayyVM4GAZVISjA/Rc1Noh8jNfEPvtxqLAEVy9TmGl3IC7RYqu84LgxWJZSPKRXFhKm+pfnHhBxQiXZUtL8E6w9aAW6+TKpFobWuIdlTh/cfsl9W1tTFPiiXzsbkvwtIMxi25mG41mIkomY8m6WawAqRsL0ROY5xm0XAkDkGyzKCWEgorovb8Mk5F1DKHINS3kt0kNSwmDzqSRi6yThREt/H+q/1ARvDBQJhgsSH+oQft/soVk3OR3MRVKEpmQ6JhIWRGvUCFy4ES83RcUf/d/XViSPliRZwI+C+Qp4XdwfA+QLqW4tMi2NGO1uQIzA8x9z0E3AbouuwMz6FOhlS0/E8bf5xOkgicrQACYXFQBnydO5pb8/y30HBPUtLUwEZXGKU/nrs9/30fpPNSzb5jCupMo9XkjUNz5mIYoUCuEDLtarPg0EQqX4pTuPkWpwMYcBzgSVXrwesApfVVP9ctVo2JmV7hfdEbIi4p5Ru7RPgiPeguIOUbNjF38spF+5uzQkmodAo7Tzolv9di7CoMcpQA3LiyWmNJxWFcXL58kcQLuuqBZj6UsWm8VA0H3k0fhdNfpJnhs7WgFAzmrB5OHp2KDiG0UN84MbW2nfjrQauNgpuCRJMtVKZ6WVzwHNVzcUn2onNxeUuBtw8gonCNlNnrHLv+/mLvu+oCbJcpzfoAQNXWRYpjm3aBG/JcLyY9w0FkYWiJCbJDN5k+BKDOv6tBXSeeg7iZ6LrtzMLFbEQBc0vQPk7YOFNrV+h8xza9cJZTEOS5A/0lck7+zKrGqQH8vDFKTuyeOq3/uDtsc7N/lhxHhwQNWa+Gs3hhpjQSN++F30TBXgeY1hEP0dvlFHekw6aYYNDgb4bfGERj4wzQzcRvCqOZYHauEYNXqTA/5RYLApEgDDwnDDSRjU+QvgmMxRm/Ltr3YlqEM3/CgVDQR5cJhwDB52LF6ODoATHDA2rSic22ilxvBD4s8h4UEIY8KSaQDkUV+OxemT4XGWIGuKE7s+bkMAJJiJ/ZPbCKMfcLMQmznzMVM+djUZGOyyH3hGrvUxtMMj855yOwSAzRA8EaGSOTljMuMXI8YlqFGS1RajkEnCheuxHeV5hUAmnHyXSkPvgCc60pVkPwshbFIUQ03vrkuKxuA6h+QIT2bV7AkOg20ih4ttRtvPBMIn8R8y9KEy0VImXFAaHGF5CKpDvSnTWnSHmpnzuvAlVVmjmqewDDIcIQpiZ0NLK6uLdnZs3OOcRpaROv1gPBIWR0sfoQbhMKtlyJK2NMFcxNfM4Yc2y0nxMFeuVvPx8VCEcyQJpBggaxybpIESFzY7JkjB/P8bz4zzzByZ8xbZpvS3eRJ6XzQ2OIxKX64BNpib5J17XcZq5xTA0TY+M6eNvxk3xlDVXGl05VhLvaBTDlCH/EBccFbelhiXLNRftW19w4QRJLgQdnWYQJCtRVNRbzm/u9KssO02JGzski0s8mKlSxoYtVC5DeXAjiaSlShm2pGwvkpgYyiYMKJgiiXAkSSf5/J4E7yLWT6rkzRyGRYWooZOimMjHS4lveQL9vA5cJTpnUYPqs8MBmi9n/f042TiTp3IU1EyNk7Aw3LjoqMfxQ8OFc+LjTh6Ol8i9GbzLZXJ0vI+mOeSJdG+Ku2h3/pvO2kAmpiqlgpw4WXDti84D7hEVt2AZlU9Dxn2dfxThr9jQsNkgkp81ayogg/5AmNFUfwMQv0fvdxTMF6DArc0hSRpS8O6EkgIPiaispC+5nb8eEuwnWLseT9q5llrNfXQapbQ4yUEG9mRYSWBxIVYuMgO/hHlX3PYKjGd6ULn5sUngiMIs4SDdFi085UwYSeYIGeftcoRWNMb2xg143AHD9EaSYLZb2s+NMCnc+zqVBMxusuUVlp8FA0GubasvE0KJWHoE+NeM8LcwX56RRF1eyZVsGbmu7edVlK/XT/k9nR+6fQ1tzY9lsC1Kc6Lm7M1MO56e9BOdIfjdaUlR0fhaqcl/ye//+LJrn/v00ua29UkjU2ppjqGHZxPFKZ8EDAXZEL6ytWVFIszOz5fLpIa08TGk2Nj8snHCPQeOqXwWEvYf3r7PJ3tKXz6PIA0m8r3tp9M5Tfk9uTCg6pqtTUKgHk/oo4rQB+BHAm0ASOJxxrF+aWHI4GxieiLa6tjPyMpVRhLthbE7iLCskZFy6p3M2m979+/oDMzWV5TiugWVxvld7f8zRjpTAIQHeZixyYIe77Cf0BPrko8AxxkaoexHad3fNwphpHBNq9uvnuZIchLVBrVOJsnp4CVpa76g6ZGmi0kFIqx/indWOs3Ib3EMwYMCtbn1gP6qz+f3+71du1xe68+2kdL6KEo8uZkiBUQtQ8TaTK4RaeQm8JlGmiIcT2QJeqeoaNi2SpMAcIZc+kJjsN165+4PeaJCpymr1P9ozLN7IgRNNcVT0dwMUzvBHphWE4x/5DcIfMH/R9P7YqKiEmPLHl3A77v8sd9/osP1a1Przyw5+eajDB0EpIN911mkJHNeMht8E8/EDIjA+eTDloTtTbKIEjM4goCBaZr4vS5SdHJgX4x5qdcy+ipTUKUNnRTRWD6Y6xl3UAN94U+VmtUEdm3MEaCpIRrcVqqIwJGLoIprnJiF4c+Ijnr5ifnUKFti44VyvhrLo6j3nf5FdWTScAa+6dNxfGzly8nbvySCnAQWXG9Xj3xtk3Hy07mc90A79YMdDdpJDXOYvhpMmodNBf5+zT+G+FEETKNM0ss5P54CyOC2uceQEAd2viTidzC7cq/e7wgXmgG2OeTCsaR89qUoKeNu3+X4DF+8iCAqtKal/v37Ik6saJIepEQVNie3JFZRDlp831hKlY8kC5ZWY1LzJQd3USankikZE0yFyNe+LE93d9gDSKjX0Xmws/PA/o63e/OV8UWj+9h6Yf0iHI4eakPC/xaM7WzLrUaeDU58kwQsWSBYMZaPYUXI0E1OUaRUCv+h9K8driucND2FsrgwOAAmDGOJ6adDszFJQw0OFh/zBIuEQ6GN219rXoz4Pw7ws4uVCFg5ipMI0/QQqpqdjJ5UM0UsqaoYQwdpOn52n6cDA6KLqdHaxsFNOc7eP+Jklbmuecee9cTwx/7+zAF7onU0Iy/GU/s1X16AhEfHFNFUwLj2tnf3SQKmKMbkLMOhUXiVytDl7l96+/nw5Z6ekLah9QWreYWBfAINRwHNw4pnI8zv4xaQpw7/dKvjuiTxxAOlqngGywzLI52oVD4a9J7o9t1CVCUq+VACHHow+tkKBQY68fcTGypZZnBYEKm8HIWBD96M79EbHWJuF2LF6ZPHk3CMFs8tGmYG40RG6P6bEfubU++I1M5+5PGhxsaWBsuzRj2JI//+eYQBVFmEDQ+CBeBoCnsPbNkFTm2B9CihW0BWr9HzZ+zdNzJNP+M++ItenqecnRHt7uj8NS+yPB8jO33/CNLNh2KSnNTM3Gr4gg0KEaKQwMrVFxhWjEpIhSS9GcXY7FD/jYkBmOu7ESwsUiZ7EMnvhq+6HR/SilK9aW3jtldfMT3W57/KTa6PU2jmFK2NZMOSH0wrTWnTARwvVZEqBKTvOzHdR665HJ6+kBST41RPD61Z29oMjtkv1RIzQxyQhPSg8R41N7lunhmIdzRQQLYQK1FI9B1Pn3W4KEStNVo2tLRtt+C3/NSQVDNSqgicsPcUmJprCfpEl2eaprqFCnKzVBK2d8rCkAOdXNCYrIzksJgY9SQM3WSUwXNUUz4f3PLn7cvTaL6D4vPx4FX3+WsKohSbsqAm1yZhYSQQk4FxZzjhPLBM/NhNSgPmGGsWq5Ahd8eODscZP/VJIOD3OA/uanejNa80R/Zjx1MiyBFFgA9023zqddZSKQOW/JZCV9taP0kYydPnnM5Du9uP+sX9rsz5xyyelCjLUpnjYe7f5YpZYrOvd7W7FckdZPCRu2IVezP5a3bT4P3yN4OsEsdyWpPMzZAOeE8HwOVYLXWrTYZFhIoXYt/I0J4kMHF4JQydBPo5uSXxdyW1OEFcUYTJb/cz8TOi7AftW/Z58u+nHxvBINO1kHaQJJCYUjaMJHOA65bb7gxwg37PWY/w3zlv7wCIFURENgdaUymSp8IweZ/H6f2Eof0u2ynpi15EqXK+bmcAuJv8gNt+Jly+bLECUeqryLEzh21nAizLUqds9g9p9C+yBQaEwYBddXS+62dYlr5g73w/SFRFL9MTq7IA3LDXZT/hZ25z3IDPd2MMLylGZy02avmew52e6yw7RLkO2n2KSsOC5BFCopl5+eC0wNB1HzUU95mBnVhgqJR5Dx90U0Mse91jO9KDaI2LpbbS5eUPf3LSftRF3eI4NnDeP4SW4MK2K9bv6vYERtKawNKDIQwXjomGP/E4T/ZGYhIZsZD84oLTAQAY9Dv3/aIH0emFZS2evuB0XmDS4hKUqDQUUY6Djt4Blh3oPfyWww/uPyxA5TPloUGqjw3zHN17rPPkADgZAqI5hW4xyX1os1+kAbq9xxzneQ2R0x4qXeXT+b6jB11XGPZWwGM73H0NUSkRtMxoePSyw+YO3GLpi/aus0y0haATVVeOHzxFMbdo//FO5/Uo17kKQ+C0w3sL7MXl/Me6LysNy58U6/oseXmY/m80/e/ZnJcAhpTrreOxW2OkeieVVGbtcnDVwQhN9XN8mKPP2uxng+CgDUAT/2uDZtBpi8hq4GyX83coQQDPKZbAqeHVZZz7qJtJjQfC4E7dS8HgFW9cu3sDf1Rgj2ZpzzQ9mjkXf5S58J6bGqSpMzbHJcmgQ7S+sN/l8APPhWfP2V20xrgYQ2aKS4Xo60Im6JcqgjnVab8QEb/j+52fgu9LS+4RQpGhC+8IssqNcMwVX99nKrBdbXJ9lGqmcAfD531+KjFNhSBTszaSDRIUuWQAACAASURBVEt5MF00waHw0BDVe51NGKNUBsBORM3Nk51dXvoWy1xydB49H/xzZZptRR9B0ZEA9THN8sL3DdhrFDAJ4SG/85CTGhW2yKa1aaqam0Q/HR36fZu9lxYs8KHj1GyD4UkpWwpuZgYXuto9n7DAaFIfUSy4SRKsL4r1V3Id/I1u24d85doabdU6cwn4brvwoXZhgf9dpydz+iadu+y/c7JUErY3hXIuwiBJB7iY3U4vuE78HlKig2ZKDItJo56UoUuqH4T9TmdvzM4n5cvUpThzzhsE35PIQxTzNKpr3t4ZRHlkcMlFUMWbKGFhpBBTVy2f2+fsPEkxLOPv7nR9IkpVod2wrWWNHh+nXEc6Ov7J5jjPyBY17GhJveBdeDUzyEkQZC/YTo5V1y/NthIeZrmJzgPfdJvMgUPxJWbzEo1cbCkVTIPex/nHMMOMRD2RDMSE3a0tLVn+22hIjHmJt3MHGcFL1fk3wGbR+MvT4f0ygYCcKE//mGRy45LMS07yKZcD89l3hQ6Phtmrbts7vjBgGDgB989pTOJ2ci1Obtp8g2GW//hbEZf1KriTrE8xn1BOhx8rae0lxlxxB2kyw54hguIZcHerOC4puUWmbUdMOZSb9iKodk2D8ZDd1taTX6QzraoJHZReDsuoHJ1TTnzetbWJ+SIP0z37Un3kiKBiycaWP4Kb6ndvPsLLZxP6jS1WcP9Klrn2OebmZsR+7PD2E8P54Csga1otVdLGeqbW2sTYjh7efnKYl2PE4o1N1YJZ09ZvCR077Hp9s228EC8zNG82E2AHaaJSqWYqST3pecfWEUJ/vj7evALCuqUJfbt7f6s9JMOIioYtq7KcfVQaN2xkj3Tv/6k9NEMFPkDSvMEg+Has/7SzH1lkmDhHKlDnwWIpmKoXWtfvdXTTJn3kQ0mKpfXNnM1+aKtrtBB/avkrG03CsaFxxv9et7eMMFfEGYv+i6otLzcjRx22/+oKgQ8rPFXTXCvUCG4oMR60d2x2CZ8AWbh83Qq+axAcSCOi3WF7Z6cbfIZBvXjdJitAhkolKfZLRtZuaThmdx7Y6gih4MX6zdbInl1Q+0be5tj9U3t+id68jGSiK5ygE58PdR7v2PrLfGyhUV/W1xvZ+ycuDOn1oeRTSt8bW+y3x+TzDU2boveaphfK/lu5rLF5xHb40NbucRVRVaFVusFeMnDUQbx3UmiJ1V5hqQ90Hv/HTd135Cq1bvkaw6l3wcFRBCsyNm4K24H4nRhGZhOVG5vNwHP6bIKcQr/G5Pkv3fazupYkGQYXER9Zkp39+/M0T13zfE3wbWf79jFVqdGySnfwoylUpFpIoL/a9WIn+OBEuWlTU+QqaQmpEL18P1KVsqrpFb6r6/hO9+cI+AiN6WWLSXoiAy21vLS6C8jqptu8HJiC516yPAk6cTJ9lGqmjNAvctmOdoSRV7dNWJQpWZsc0ZommrhOr7nYve+f+JYduHjNxabGlxH78eM7W4b5mRj5THPjDzOcusf1lbP3d+/rHGttq15fwxyxbd4YQmfh5QaLebzL/4eM0+NT1dwk+pZ5KWyqFxHcifbNt4TvAAEtFry6PPG+w6sbG/79sPO1zZ2jqGqezvTSuoiFTeqvpRkAjNOuI67woiZLaUSv11Z7dxy395Y3LhzyvdfNyQ2GednmKzPIpWeIW6q0YUvC9qbQykUYJOkIbQnNMk56mXM698m/kztIdFgE38KYGPWaxA1d0q6ZUN+F7pOzcLM2XdLABa4YQuU/CW7FQpBitUaBcET8u/C5CKp4GyQsjDDuiIx6CKhoE287un/rCQRbaDIuYryiZPOU5LON5LOiz1IyhSDnX0CQQ6bFJPx1R9cVYt2G9I+rpbzMgpPw8o0b9ehQr6uXom4U1zcnfeFo8spTSkTnH3lk4pIsMJoaLNM0/8h/2jeEEVZpP2uKzEaK5wwySujJcbv/DxY8Ohbcu/c7TvuvoNr16mxsJw8HK3IwpEXG+rV0p3P7hrdRRbFGb67RHvWAex8QRZL6TAzr0+I0JnM/uRYnlUZxy8tNCPBCgctagJHLmxuXga5VToMfm8RGygSw1Jgr6iAlG/akM1zZOkvs2Z/96U9/EsuHeRCBrzECvP+tTm5VYySs/Pqn8ADVP2MuEbuTlnO/uslbtmdH9fSOVFlg5JmrFF9Mxi5uGu3dv8mh2PKaVbgzHSaIAERAEoFp0lzwPcmtfVV7W6q+GfZOEs57fRA+t78rv6EBfAj3wUm3XB2n8ea0DyA/OOxNLyfCARC74j+3CVOEE+mWZ/9RzviCeeIePpE6Ocre8QtuZRv4iHHgnF/2XabzbbQJfLz3NuXpjX0TK/ktuXqxPnon+XX7lqPyl3aY0yN1kTqmK4unjmx1Klu2rfzSBuh0ztkPdnXcNO+oFbtTIL3s5L/5K4e3f4i3NH9DvK3JAflaloBrkl/LboWNyorALa+XJy3fGLdqjHZ1nEBrNq7Tz0bYS47Tn+L6dV/qKMWe69x329hUa8BRjnqv+/JM8hXpta+sPQcfQgS+QQh85Zr7DcJ60qaO0+5Lct36BymMBB+K/XUfpjVOyvvXpABKmJbLOzyUaSLICYN91H3k+gbxMJIPs4P91MeentPewLi+uSyyb2cJSXV1yytahCFwFmFYIbmfPwYaWPoG/4nucb0fsI74PNfUptVf6gCd1g7lsjp9h901SJhL7rmFPO361bChtu4b423dM2IPJwG4Jvlw9hvkGiKQOwLjbO8xW/d5ih5BFHNIw6p1loVf7kDFUc4jds8VJnhHjs3Xm9dZ9MUPlEOWO5SwJETgS0RgejQXrklOU5d9mRFFjiw/gCzlyPndFRtnXB12fk2LEOSMs56O7Z1Xx2QF+emXUo6PgRuKw8LXLGJJVtH886bINu1Rf+d2L/GyAfk9pscZz6XJ1iTfjqxJfkmRJE91tXvUjY2LvurIC1yee4zWrTdHtkffdQLXIHVefqLeEonhYfoaIwAjya9x58KmQQQgAhABiABEACIAEfi6IJAIcsCdayHwialIAhdWJTUwL/5Nl3hevkwhi37fC3ySa59X9iRRvtyQ9WAlwlx09fp9Jz9GdMt1ugoTmfgw6f1CMnzJ3jWkq6++t/DtfnEH6UIEJBGAkaQkNPABRAAiABGACEAEIAIQAYgARAAiABGACIgiAL8CIgoLzIQIQAQgAhABiABEACIAEYAIQAQgAhABSQRgJCkJDXwAEYAIQAQgAhABiABEACIAEYAIQAQgAqIIwEhSFBaYCRGACEAEIAIQAYgARAAiABGACEAEIAKSCMBIUhIa+AAiABGACEAEIAIQAYgARAAiABGACEAERBGAkaQoLOmZ/IC395PIjdKcp339VudgeoGcfoM7x3a8WFtbu7X7I+f22vYzXE5vJRdi3bvWb3fdmvJ7Ey/wFzpqNzsCybecpREb56hzfjaSGeh6acOB3ruvDL4JEYAIQAQgAhABiABEACIAEYAIfE0RgJFkDh0LPmH0z53n/y3xbaIc3hEpwp7vdnOEdXNrw9L/jVhao8flIoW+6iz+t/aOd/u4SKip/N7y6oXFXzVHsH6IAEQAIgARgAhABCACEAGIAETggUPgPz1wHD2IDN1rDBltUygURnCysizysaAqs/pBbKnA00RrFaTJ/KAyCfmCCEAEIAIQAYgARAAiABGACEAEvkIEYCQ5Kficu327Y4BHDmx4MbDtzVVCee6Ko2Ofx8+OqeYbrRss5CwhM/yJu8t+yndjGJml0Zms65bhsiTa/kMb2s+EEWR/ba2nbk9deN/WvqV7W6pCztZdjNaM+k/5Bobzi0hj3Ubzk5H3OMp1zOn5uC8YQuTFpHFtvblMIcFrOPCBret9iv58rLCYqPhxvYWMlGT9ji6H5woTKlARFWbrGj0mhLCxBLa5bjiBte62qPNATtjb8YKzeMdrZZ4t+7zgC7/b1wfq9myb+8FLu0esh17QggL0h112l6/vNlKIly9fU28qFZikul46PLpcP+r1+Jkv5Lju2fr6KhxUwl1x2o55qKFh5FFQel39yuxfAE5wBf+CCEAEIAIQAYgARAAiABGACEAEHgoE4O7WSbtJYdy8wzwHJTe+ubeWEEqP0z1nWWJdS9vWevWwq/OEX1jEu+Xe9/opjqxrbd+z4zmCc7YfPJdyDJKsP7CjGkO1jYcObTMqkysNec9QuLXt0C/2NhJs91snaWFnKec51OEaAeT27m1vteBD3W+5JA833jjZeYIrX79t7+4ddQvC7k57L4hYRwP2PR0XEH3Djj1tTcvl/s7XjvhB9iSptK7tBS1abNpxMIVJ9sy+9uMMvuqVtldbraWc6/XdrvhJUeach1nQ2Hbw0KtrVFe77G5whpP3Ow55kCWNr+7Z0/qTucyJ/d1XJ6kWPoYIQAQgAhABiABEACIAEYAIQAQeLgRgJJlDf81A8hEEnZGPCst3CJIn0/24wViGY0/oawya0CATRBD6N54+3NzwLIkXKbGFZuszSv9Zb/TemlgFeajw+gwETVoYjD7CFtcYn5AheQqiglSxNC0EfLySrGl4zkQUKxRFasNyrerzYPCP4qzyHDecJ1OoVAolRq5qaG0yge2z/MfunhGd9QUTWaLESo0N6yuRC25fDlf8oHlCW8H/JdI4A2Jd5coG6xI1Voxr1zSaH6dPnw1EC6Dq5ZYlmCwPVS4ylBb0MwwIYjluBJXPUipmKfFF1uYtzxtKxDmHuRABiABEACIAEYAIQAQgAhABiMBDigDc3XoXHafCZsciLXkBiozx/DjP/IEJX7FtWm+Lk+N52dzgOKKMBp9ZK1E8Ft+2iuYLdIU1SSVRWUlfcjt/PcTcYphP+5jxyHKoWEJLK6uLd3du3uCcR5SSOv1iPdhgytxk+BKDuiD2AjpPPQfxM3d36eudIeamfO48LF65QjNPZR9gOCRy0lNRGOc+X/4IIlzVM1NnrHLv+/mLvu+oCbJcpzfoZ4rxDfMgAhABiABEACIAEYAIQAQgAhCBhxYBGElOS9cJ+1sVFQ3bVmnAil4szZArcggjQeGMRUoQTDLu9l2Oz/DFiwiiQmta6t+/Dyz2SaQCtbn1gP6qz+f3+71du1xe68+2kXdSC6f9jD1M3CQEzkZONUVbChZaM/iXkevafl5F+Xr9lN/T+aHb19DWvETqkOdUq4XlIQIQAYgARAAiABGACEAEIAIQga8eAbi7NZc+yIiV0l+SqYpUIYZFvqNUFgn/IddcDk9fKL1Yzr8HvKcDypotLXWrTYZFhIrnhpOuVE2jEr7qdnxIK0r1prWN2159xfRYn/8qpyrG0EGaHo2V5enAAKJUJp/PBLtYR/mx6PPxYDBlJ25qDTOKsdmh/hsToSzXdyNYWKRMvk8o5QXW7zzmCRYRhmetjdtfa16M+D8OTM/ttznjBwtCBCACEAGIAEQAIgARgAhABCAC9xUBGEnmAG9ePlqADF33UUOSd9aoDQbNzZOdXV76FstccnQePR/8c+XdL8PJ5TKE7btCh0fD7FW37R1fGKwZxsK+dIZRZOjCOzb7RZob4Zgrvr7PVHiJHF1gqJR5Dx90U0Mse91jO9KDaI2LI3fMRhNaguEjPtdJih4MeP/FfuGzeP4jKDoSoD6m2YngLw/TVxHMqU77BZpl6d7j+52f4gaD5G5bpIBnznbZT/iZ2xw34PPdGMNLikEsHr7ucZ70Z4lY0xsGf0MEIAIQAYgARAAiABGACEAEIAIPKgJwd2suPaMk9aTnHVtHCP15rUT5YlPjy4j9+PGdLcP8TIx8prnxhxMHCyVeyZJdZKxfS3c6t294G1UUa/TmGu1RDz3EIymXvsbeR0stL63usr2zc9NtXq7U6J57yfIkCNwI65Ym9O3u/a32kAwjKhq2rNKCVcTE2mCxqf45xuZs3/5eocaw2ryEc0fpPa6vnL2/e1/nWGtbeZxDZVXTK3xX1/Gd7s+Rwjk608sW0xxp7mdqrU2M7ejh7SeHeTlGLN7YVC1AMXytp/vMXPUPyVzOjkpTh08gAhABiABEACIAEYAIQAQgAhCBrx6BP/vTn/701XMBOYAIQAQgAhABiABEACIAEYAIQAQgAhCBhwcBuLv14ekryClEACIAEYAIQAQgAhABiABEACIAEXgwEICR5IPRD5ALiABEACIAEYAIQAQgAhABiABEACLw8CAAI8mHp68gpxABiABEACIAEYAIQAQgAhABiABE4MFAAEaSD0Y/QC4gAhABiABEACIAEYAIQAQgAv8/e+8D3sR1JnrP4h3XlUIik0pmPesykEjh89TLmFRyiqDINIguIhuZr4gWkWubS8xN7L1UecA8AX/XdRecC3jjkmuHxcvGZmORRfRicxPRIlIsFssXpBYrl0c8RCIwXjpekG7xJI50Xc81/c7ojy3Z8j/AYMN7Hp5Emjl/3vN7j8bnPe97zgABIDBzCIAlOXN0BZICASAABIAAEAACQAAIAAEgAASmBwGwJKeHHkAKIAAEgAAQAAJAAAgAASAABIDAzCEAluTM0RVICgSAABAAAkAACAABIAAEgAAQmB4EwJKcHnoAKYAAEAACQAAIAAEgAASAABAAAjOHAFiSM0dXICkQAAJAAAgAASAABIAAEAACQGB6EABLcnroAaQAAkAACAABIAAEgAAQAAJAAAjMHAJgSc4cXYGkQAAIAAEgAASAABAAAkAACACB6UEALMnpoQeQAggAASAABIAAEAACQAAIAAEgMHMIgCU5c3QFkgIBIAAEgAAQAAJAAAgAASAABKYHAbAkp4ceQAogAASAABAAAkAACAABIAAEgMDMIQCW5MzRFUgKBIAAEAACQAAIAAEgAASAABCYHgTAkpweegApgAAQAAJAAAgAASAABIAAEAACM4cAWJIzR1cgKRAAAkAACAABIAAEgAAQAAJAYHoQAEtyeugBpAACQAAIAAEgAASAABAAAkAACMwcAmBJzhxdgaRAAAgAASAABIAAEAACQAAIAIHpQQAsyemhB5ACCAABIAAEgAAQAAJAAAgAASAwcwiAJTlzdAWSAgEgAASAwGNOgGfPNjR1cPfeywHWdqjJFbj3CqAkEAACQAAIAIEJEgBLcoKgIBsQAAJAAAgAgaklELpsbvLlGpZI7r2ZFEK7NrP9sMXXd+91QEkgAASAABAAAhMh8OcTyQR5gAAQAAJAAAgAgXsn0Mc6TlgcWH6JhreftDncXj8vJhbpijfp5LNjtfKelqOs8q0iUfgCc3JP/Wkvy/FYmkSWqS6qMCquNO0+0s4EQhguks2lVm81aTMx37Gdez9hQgOYSEobdpZrMzBMqi14vrLxV0xVAXnvAkNJIAAEgAAQAALjEQCf5HiE4u6HOuo3v1a453Ri2FCvz+5iR9bCdzlc13jhOmfft2ln682RWca/4mn+6eZD7vHzjZ3jUv1mk9k3MHamJHd9qPWDriQ3HsAlnjldu31LYeGb9Y5QfHV84JLDEw7sEvr+QFvnO2oLt1nugUNid9nWysJ9Z8MiDqmeba2IXXwAcKZ5FUM6uh9Bh34g91PLuGVH+XmOWy4xwyS7zLnNVW8WFhbubGHi63kgz4QEwa5btm+qdQiup7hhmbRv8WN1cAAnzTkdLo72zOwd81k6WqmEHo2ryvFI3huf266mvQ2eLENxtqf2QDv+PWPp1rKCnFT2orn2iGvwEch1WF2EZqk02gb56q79ZfmyFJz6UdUvdhvpNEy0uKi6aiONY6IlZb/YK5iRKMnXV1f9iFIW7j94IGxGhhOpUafarUNVRy+H/zfAec67w3/Gpqaz8W3BZyAABIAAEHisCYAlOXH1hjpdnU9lpHs7nPGGo+dEveWzEVtSBljrPzS0/3vYknzkaa5qzUpKkjJpOaTfWblmcXiq8sAT77Gd8IhWlJW/pc+NLL9HmrjT1lBnY/74wNt7gBWmU8sL1KQY1Zhc9Q+wqelZ1QPR0cP6gTwYHU2yy4H2FhtHGbdVlCwnhnQ4tV0eGpZJR00ch3FyJi0+XS6mkepXNfL7CPzEJqnKB9PxO676/Zb+V0zFpLuhwa0oLNPRJJmt1L/xen4Gxl1q74yakpyzg5m3KOGJiM2WiDA++Mf+IUlCXBDD+L7g0F+XPo/9lnLjKgKPF1eaS0s7290JC3WR+/xvzbUnvZywtjiTB8OD0Q3UAgSAABAAAvdFACzJCePrdTovP0X/UEPedDi6xi01PWzIiJiZSv1qOrbMPa7kQxkktE6fFzcVnkTR8bIOBIN8OqVUUs8T8YbkeMWmw32RfIVevSBhzjYdxJppMkynH8iDZhcMhjCSzs+Rk3Pix8mUdnniw3LiOR80l/uvD0eWpJYajAW9/wofRg0B2+FGhi4qXow5PmphcwwF2bFRkUbRlAR5CLnI8Tohj4eRKhSJT0SxSDwLC6ERFU0hjy8om4OhS8iejCTmtEO8Mn/EE16qINN9V7xJh13s4kweDA9Dd9AGEAACQAAIjEMA9kmOA2jwNnfJ6UnNLltCi37T4nL4DPPk6BbbsnPPaT+G7dvg01dXG8io34+z7au0dPHYwc1v+na9v1aog7tsqa2zuwP9soVa42YDjaYCaDJwzdZsPuW83oPNUah0xo0vk0nMqpDXWmexXmIxKaX5SYmBDi/IDwRcxxpb2j1sn5jI0Rg2GiKWIt/tMB9pbff5U7PU+sV86wWifLee/Kx+84eSHTVGOYoN2+YkN8h8p5zeO3GSuOo3n5IYFrJnznn8/emKZetKNqqlKRiKbt3bazz8hhIFmjb1rVT3Oexu9msxqXq1uHgFGZ4NhTwnD5nPeFhkFq5YSVy1cD88WJqXaGUF3JZmi/0yG0yTUUv0xvVq4o61stzs4zFXxQb7y7ve30QJNQmdctVva/L0YR5TobfssBZd6WNthz85dYFBjea+UlzycrjRUfoerST8v9B1u+WY1enzc5iYWKg2FBuVifOs5KBSMO5Kq/mYvZPpwaQK1WrjxhWCRjxHfmrmNQrW3h6QFmwv4j/Y6V1+wPjlvjjVq1Gj/C2n+Z0zbQh+Bq19bYs+WyQENiPgP5b5rO3e3lTiRUPJatz+4fH2618/NW+p8c2iYVINdkEg/4d1721Vo9a503u2HsOLD5Rr0ARaqNBBv7NLK+XZc+amj9u9ASydzF253qjLHuGp4VnHcbO1w8OGxMRiXXFheDsW2qz1S3Nrh8cfP3Iico4cGDxr/7DJ+luvkDlbXfCaQfktd4KOfNuHyLxlDB6qZNccNC0Lj2IUeLmb1b9vUqdhWMDderTFfpnpSSWofEPJWtIZ/wMZHABCMZ7tsJhPubw3/XyaTJGnL3pNk+hqQbW5zEdaHFfZYKpM8V2dcYOGRE2ggiOAjPLzDGMeOSxxDMU/bz5BVOw1yIUfcshR+0ZrZtX+HwXiu2xaEj+8Q8ynzWYr+jVFtFCsyxa5D2/edxbN++sLC+1FNUhT4eawB/dMCPlsR5pP/Y75WkJp1YNKR2GKwrAsXyFBJIbpXfRp/GNK7f55LOekBsMoqon/daxeITp1VlxWU4LiMFHiLzdtP9xfhL4OX3gRpPUsqdm1CtEJOQ785wauoKZSh77wrvqtJ6Tl22QCspHPTPQE22pTVFXrs0YOqsiTMVmpiAbQfxOfMKY8zn3C0tLRydzhcQmZu3pj8Sp5/BOY77Lu22vF1pSXrybxUR473OXWxmN2T3cP9gz6HW4sXp1QA2oz5LK0XiX0xRTOM/h3istXKOObEM9G0Q2peBgX1s10Y7L88J+GoSQSi1KwrwfNxq5OLktNiG1YMIQsSUH9AbstqNqYbG1LmikNnmc5LHEZ8UrT9joHP4BVbvIV1RSF6qKDIfqQDzpsl9l+iUK7sYjqsphPu9kBGf1KSdkauaBDztPabEZ/BXpSYw/z4ZpNFB6+AQEgAASAwONO4An1SX711VfWcEIfJqZizn3Rg+XkUiJSRcv8Fx2e8KIu8TdV5S9LJMvLjwyZkeiyRLutSj8Pp7e8f6AwbCYNMG3nAtTG8uqdxfIea8MJt1D6tq3u3VMcXVSxr6bqNYpr3XfofGRpOkGi0CWbc05B+e6qku/y9rpaaze6y3s+2nfoiky7tbrmHZN2trvuXQsyzDDeZ3m30TNHt2N3tWlFqv1jx+Ci9VCNvNt2UaLfeeBI3Q51vy0qCSp6zebg88vfPXK4cjV+sbG5Y3hMFHvezi4qrT50+J31sivNZtttocrA2bra0yG6sKIaHQVx85Q1si80Xvw+n7mmtgNTl1TVVJetFLsb9h9xhzJ0VXUlyjRC//Mj70f4RIqkKEvfKaLS5Maaw5H5Ouq7a7a2tLKqbIXYc7Qx3OgofY9vNORuPtB8Y4Fx194DByqL6WBb4y9dCQvzo4Hqaq1918Z9x1ixr3rHKwRzbN+hcxGN9LMdjqD6ddMmveovoy2NVL3nrJ1fUlRVVa4nulo++ISJbExFwF0Sw88PH96jl/yusfIXzswN1Yffr1iZ0t74cWQQxYse/Ux+Jzv1uu+GIHTIc/UGzzPeL8JfPvd451KUFOMuNuw/xpLrdtTUVL2+pP/MgfrwwIhPvOfD/Q2fSbR/i7KU0H9oqT2CdqSGXEf2N16W6Mqq0UX1QEdtjWDSCynZwAicaWi+Threrjmwu1Sd4jh0xMEN11ESMsPkwHjG8m6tHVO9/nM0BtTYubr6Nn74DyRWhr9m+cVhj2z1T9+pfb/6DQ3222bz8NchhBzNh9yzdeXvHKh5Sye50thwmkWlkwIZqaNoO0mH5XC5Y9+Hd3koHxr/+46z5Nod1e9UGLM567t7rTcxuvhg1RoCV5YePjxoRqIiD+qZwDk+qLXcUqzbWV1VSLH/6hA6n5CS6D09+WNqcoNhdNUMjYFlK5fm8m7n1eiQ8lx0Y4vUVBJjg6CzZTeueoWnzMANny/Ed3nDXzDvFV8qlSssYYTPeAAAIABJREFUySV9Zg72NMmgCm8xGLtUoirZX9WhZ1N+6TsHaqtNqyW+o822+B9RwNHwi5b+fJMJmZGjPXZ4t+WwHVtW+k5NTcWP57Mn6luuJGoDC3W2dwazaPSbxZBDdbU6vOox1I1gbxCTEMQzwhX+D1zPbAnyQCYkXCxGzUedkpz73yS5C8SiNBTwGkRH7KBHgvs0Q/2QTrIEicacRILfCfiH7ZDPLqp+Q4ln6qoOxY9PoU30kPe/ZDpw6KAph2t9r9LC5ZsOHD6wWd51wtKG6A4wrbW1DkzzemVNzTYD8UWj8DBPFBa+AQEgAASAwJNG4Em0JJH1WFFR4Q8n9GFCxuQdp+MqTi0SJkXki7SMczouh2dLKTiekoqlYPiwXYizsFQ0c5iVGr2eIlL9qESbQxLPqws0iuBNFvkxmX+1e0l9yas0mSElFuuNP5S6zzlGbLjE8OcLyjYoyUySXleiIxm7g8FCTtt5LP+1Is0LhDRDrtloUH1pb/Pw/CVbG68ybtLIMwn58qLiFUSS0ZwiWbpGR6GgO5FcvXh+kEUr1uGE09q1Smkahmflq+V8983hE1RcvtKwjBCl4NI8TXbaDVa4zzrOeuavKTEoSSKL1m/RK78xvEH+M1tbr8r4ho7OkhLZ2pJN+ViHzYmaxBFIXPjvMG6RSWdKdO6JL9AWrVfLs0h6jY5OY4VGR+l7QsP9YsWq4uK1NCGVSOcptXlE8A/IOTmURgPlsdtZhaF0nZLMEACW/lDiPmOPgpinMbxMUYsp5KqNphGql2mKi5ZTRBalXb1EdodhIpOsFInqrwVnIJ6RS2Vh6S+u0S4QYWkknSML3maTmPrh2nFFLtXn9aB5LZpnMwSdgzE+JAh/w+NLz6EJLOA843xmVZExTy6VEtSqLXr5Dft5Jq6LwgzTfrFf/ZNizQtSaRZtKNqoyRIH77TbLuL5m4o12QS6qHvTqOpts4WXNbBkA4PjQphYIn1WKsmkdMXlph9RwvbQRB1hI8kkyIFhPntHgF63SUdlojGgK35tjVzUjw37gcSK8DipKSw2LCGlcyQErdXI+cCtYcsrPRzH47MlsjkS6fOa4q3lxS+lozWN5EBG6CjSzqjDcpjkg1+HdTlyfUAY/9LVJcZlciKTVK4v1T/HnDnnCz8TMNRBYYzHpwfyTODcjkvi/NeM6gUEkaPb8mPVcBMiqd6TcpjkYBhLNYNjYDatojG06CYMKd7jdGP0S4phGCJIyBzqqchaye89XjFNS7t8Xwi2iudqf/Z3SCFPsmfmEM6kg2rcUijDkCp5LAM9LY2a56WSOQT1cn62OOBHz+VI6nU3vdvIvmjasS7sYxztsdPHcb24eA6qQUrmGU3bX9cgZ2l8Qr/fL0LYHJngYx2ZBljvF0HZIqUi/FQRtj7iyGwclsRiFEoQFJyS/DUPr6BEmFiMfofh6Fb+2ifOOSvVw9yYgxXgqal9/RGzPr5S4W+W8Pgd3hK+QFOQJ8VTRNQi1GtF/lpaiuMSOlc+K9AdwPgrdlsg17BZS2VJpQuURmM+fqEttsNzeFXwHQgAASAABJ4QAk9idOv58+dzc3OLi4sjOkZfdTrd2PoOuFzeWVRJTnjaNl9Fz7G2dbg3Lk6IUxqzBhkxN/p3W5yGY/08P8Czv2dDlxu3bmqMFeR50Xy0fjxkq4RvpJMkWs4OJymZle7vZvlbbHeIde8ttEWvowkFpvjfQbaX5QltOABUSKScxC8P5hj8IKxTR77gSBKej57kMDtdErUD8dQ0nL8bd8BDJLckPRZIlyr+Biac1tDXzd5KJwalmy2fPxdnExv032L5LI08tgyPL5DPw9wsci3OGylYsitSIjYDQ43yHM9jo/Q9EucVrUIi12hE7vOtlpt+tpvp8jHYPFV87SximAQUF7jdI5tPxrqJEfNJsZXt5jGkdVwmG7yeTFDhmnRuTFFpYnRIhjCDExZqJLJnI8BxPBV7RhKtJjVmLSevTSSnnvM7rqKFBZ83jTJ8l6m7iE7I4N2fY1QhiQ14mG7ed31n4clY6QEewwM8Nqh8ZFuxqJOab8dGA5rrI+ZXzAEsUzMvdlFEkpm8s9uPyQU5Rw4M+bKViovmnW9YSYqmF6vUS+Pqj7U8Lhmum+2RUoPbYaV5egMqO8Ak7bhonlor8jg+sTBswN/t817n0zOGzYQJ9SqV/fCeN84RCpqmv6vOR7/CgRujAUnayqjDMmnu0S7eReNfPH/B4HqNRLFAZu5CSzMCzQmke3om3Ga7sUx1zDGOFEjM8ia0lVTvGDZoIg1lvhWY1GCQjq6auDGA09+jscNOTx9NXXW6caXphdhgG0ZkASXnW9BaCYE8knJtwUBjyzUGk3k8X1JrkO0pPH5G8hmqIvmg4nzJSw1bropWgxNKnfiaw3qcYW+zLMMwvf3qWAuek41ogYVSo9Nuwmm0xw6t0q6w1b33pvNbcorOVak16mF7OO+GnYm8cDrOSBD85w5XgFz5t8m8toN9TREhpySPYln5AOOX0c+jGyLhj4jglGTbfoPlbxocgYNl7vXDs7GnXEpqKi6Jdn4WWhnpxwYwxDzY66170zlYO88TLHpETfBhfq9CQTkgAASAABCYzgSeREty8voIOC94+RBf/+aG+khhZEf1tjk5pWZc82LUxoT5sWRJya61CrQ+HE2zxElOWBXWj6MpFU2JIl9xecHOsvhZC/6MhPsYTdBHWICjCpBwA5+FzJyxEnKzjJwJ9d/FBCfVGOlu4r1hX8coGL6VtFEsWd8TarrjqK9q8M1VL8kmVYvytQub9/2vxJb+7yighoWBRQoJ1mAqhqZT40r7IB38Euo7RPNVL5viCZE6aqGY+KXP1xXyBrMLFiA5hHkpvaG6OCdOad+IrRAMiT1SYmHUJaSkXR7MkaUtr1H53M7Oz9zuE7XW3+gqfm5ItJNGJ3N3RFvD207yPeRuqqxzil5U5y6gaI2O/rjyzIhckiWl+7N17ktu92ed1jrbmeWm6kKUaTwg8fWMNSyHxEZ7ye4hjf07GrPCCT8TBmsJ+zkT0iQG4QgFjdnf0VWTMAZwSq1KqXdeCWEut1hpCu84TZbQYTPyQ/arLOFj5+VQcoz0X/Qyz3jYBSrkdAtbklOdeN/x3Xs/5aklKkVOvvqVfvvewUU9TJxTXLqos/6Y2Z5XroksEI3y2KE3Vr+3wuN0uT1ue8OnNmdJtWlZ3F+FWcjqw/ibDIpUH24v8oz1mEOiL4+8yQP1Fk8TTMYRWhFL0ErcV1zA48Wz1ZGnEC56ChtAe3StPL1RO8aDie/vT0sd4/4wxMmft4OZ0B+XTK3pLS0xeGUW8sdOtZqgfiAABIAAEJjWBCYx75jW/ZiMcMuWLevs7GwMJ/QBfR2ndLfTeR2nC6v2V++P/tumI/7ocfw2FneXZAY27p9vkSxDFkQrut+SSjOEf9hVq8XuHRnu+HXAH9uLwrHdXxNEJi6VZc4KsD3iSEHpM37HiRbnbYz4NoHfQu62aG/QKvuISck4HZ3cbTxzfkYPezMWkNvL3Lg1vEFZJoHfZBjhfXdC4hlfFyaVxlx3k2suknuUvsdXFbhgd+La0vISw6taNU1iX305rKFRQEmkmen+LmYwmJK9wQTnEpljS5lE9WMXmOhdKZUru+5GhxllLlTgGehAR1/7KU9ATinQtDKFIDKw7lucJDxypBk48xuL9fKg4OEmnpVKUUza4Gi43lq5o8mdnilFx3oMnjwcYphuXDY3eeSdoK5zFqsHkyu1hs3l1ZUG8rZbCLgdNeFo0hoKRY2A0L/7e8I5JRmE+M4NNvZml8Dpfdvr7FySdQmUN9TZ1hZUllS8YdSv0tDP46HEPgnVDbCu462uP5L0Cn2RqapmE9XzmfsGNiaQEToadViiZZq+mJd+wO8fGWse3/dZmcTc4I3rbOwa573uT8+QRr1Y8Tmjnx/EMyGDyJyFNBj9ofE3WXaYVZxU75Ef4DAOcyc1GCagmkg3cYVyceoVV4vzilipSlx2SGAionLm37hsdX8hVchFErmCYNzWS8z8HMGQHDeNMqjGLReXYcDT1sYoNlSYCvW65UpqdpBDlGIwyUU0tdygy/JajjmEMTjaYwcdJXXM7s+gNK8aSyv3m5Zi7s+i+46jLaXMl78gwgLtrWcGf4rhO+gssbr6TnlJ6ZohPz/yAj7VywWHL7eJUYQD9qXX2UtEzxTCMLEIBT14HT5SlzcWLeFM2DlS2WjG/GRoobySTEIcYDk8+gdLGuxs+aUjvJcbEhAAAkAACDy5BJ5ES/Lpp5/evXu3LJzQB/R1bP2zLgcjVmmXy4ksIvpvsXblC5jXIWxrTMXxYLfH9XkgZu+FK0sRjuPr/tzp6U64HN8QCsFU3PqkodnB3A6wlywNR9v935SO9HGG3FaLG01m+MB5s5VRaJcS2GxV/kupzqOHrJfZwG2fvbGp5Somk2L4Is1SvL3pA7uvm2U6zI2nE+cuY3fyHu6mEOofKG583Nh6iQ3c9Fg/OD7yLdhIpHyRo+mQzdMdCHxubzzShim1S8dYxkar8mjS5PYM2XPDBBul7/G5xLNFeC/jucHxIY4512g+5x8K4g3nGw0UpdEQVywNJ91sIIAANvzaT61QE6PPw5KrfrIkA25ri93XO6LYX1LULGfbZ8I8G0shFc997broRbvLwjNHqXoF3X+2qfGsLxAIeE41mj9l8L9IHDui3PwlWPvRRvu1QKAbzXdtgSxK8RdLtUq+ranB/jm66LEeMjsl+ZpFo1k4OHYHnbtrRjVwyCNywRMQEQRaBRhNRykyghB77a2OaywyChpPxc4TWqjRzHEf/yA8Bq7Ymk95JQvR202T/kBwdJRl8KbHGwjxHOM61vBJFz8Yfx0FlJLac+0T81Gr5zYSytfu7sazSCJlVCBJdTTasMSzCLLXaf3Ew9z0OT4yd/whppSkXUbjfwXFnmowdzBovLiO17d+QWo01AhFxi4k73JC9vGfCRKV9iW+7UOz43ogcM3eeDxs58SnpHpPS/aYmjO5wTC+aqJi4NRLSuyizZGmVM0XLoWu2FpPCY+wYUmSTRFXHE5MocjAsAxK8Q2347KEyp7YOlPSQTW8hWTfh1QpRtGbgaseNNZC6AdyuNXTJ2w6GCqTQmh/rJVcOm5Bx8qM9thJQycGN5tPuNk7HNeFFhz7yazMxJ+TSKVHG4NDno/27D5sdVz2Mdc8rk/NtbsbmJzSHRsHbcNws5lE5oCfvTNMbFyEzncVESq0KBZLaKMkjhOaH41880dCWbS78amswe0HQ7fwb+B4r8/zGYMC4ieecCpfk+GxHLK4ugKBLlfTBxY3OtJZ2LnAuk622q9Ppq6Jtwo5gQAQAAJAYHoTeEKjW5H1OO7eyKjikA/kApueZ6QSztyTLl1OWw47HDd1OpVacbGl7r/x5QeKqCGrQ0qrafu/NNYG8feE0LtkKVNX+hZmPn58d3kPP5ugf2gq/WtiZD7ZYgr/1Z43G9CbQnJ1W8vCoVYiunB7yTFz68GdliCeLl9avM0YPryPMm7d2PhB6963e1JJtXaZnLk+4iigkQ3cxxXpy6Wm3samwztbBmTUiiVKqQ1FgSbUl0YZt5fhH7bUV5iDIoJaUrJ97ZibS0WUOs/aeLQ2hL2jTy7YaH0fyi1aYij2NRz/r1tb7oplctXK9ZpTJ9HOUmzI9YakSgpqnt5kwszHmipP9KSit4CsrzCsGGteSw6pfmVyYSdyNeA+03oDy9MIr+iITykkRYmtl+fL0TwbRfTK5+MX+in09rlwkizbUv5H9AaZvduO8OK5lHpLufH5YQahiH5te/HRptb92xr5dFKpNxUK5JXF24PHmqzvbmscSCdzNKZtemFgx5zGiRJg5JrSkq+EGhr6cNkCle6nG8PKG01HuHJ9ifawubG6LTVDpVtbEDwU3sKHk4a3yrAj4TGQRtArTaUvC1SHfiDhl52EE06vLdEeMtduswqvAFm8cuMqvvkm2nkY67aQR6rdvCWAanvbHJwlQ6/AMW0Ox5iPAiROR3E/z9GGZSY6EIhtbN1X+XG6QrNOv4yzReSKG5bhF1dEk3RF2Q6+ufn4btuXWPo8le4tg26sPWMP5JkgjP+NHzYd37vt6zTF0lVa+Qn/oDzhD8n1nmysiiYzGEZVTWLr4W/Pq5RzbJ7vqiJvRer53N7yrxS1ih4eup+BTjS1sFmKcDa0ViKyptDUODEAsdaSDqqR1upI4eJUadpUwB5p3LYliM8hczUG/UCz+/d+TDlUBn9+jWFJR+1HLfkLjaM8cpXGMrbxaFPlJz28mKCWbilbM/wZjs/T79gpsZywOS9YGi6I0c7y7MXqgh1GcuRbMWdTFNmMDrHVZSR4GsWzpdRqvTIuP9rTLFumW5PszR9xPQ54maB8lWIkA+w5df7c+pa6hv6K0uHR0UlyDzKXG94yYUctjX9nDaL9+y8WmArDP72BbufHLZxYo1mQuJg1RlVwCwgAASAABB4XAn/2pz/96XHpyxPfD45xd4sV2dH4OvQyvcovtO9t04wV/3RfzHj2iofPpKNn1PShN8FZJNv3G4UzIaZ3etigxqbBuz9o4NaW3see27Hrh7tA4OESCLnqy1uIHeG3PqI04LPUedRl+jHc+w9XvunbGvfpnp9d1VaXjbncNkHxA7Y91Yzunei7PSdYCLIBASAABIAAEJgUgScxunVSgGZS5pC7pWZf8zlf4A7HXraaf+NXLE7+nrEH1anA+YbdB1vd3SjOkHEcbelE7wAYyyfzoJq973oeOqixJL7tcPB0vNNtrMxwDwhMawIcc8llP9riITTqiBmJXvX5W0dArgQzciJ6kyzV06ytfewNuhOpCL1lyu7AfoBenjSx3JALCAABIAAEgMA9EQCf5D1hm6aF0KYdc9PH7d7bvBCvtdJQvGZCx1fce284T+sRs/0y678rJhaq9RsN6mG7hO696ikt+dBBTWlvoHIgME0IIPfjzt22AdqwtUybFQu3Rif9jL7ZeJoIPn3ECF1qqnVRpi3355YM2GsbuQKTPsl7e6ZPV0ESIAAEgAAQmPkEwJKc+TqEHgABIAAEgMBjQoBnP208IzIULbnXbYcDrO3wmfR1RcoxzjZ7TFhBN4AAEAACQOAREwBL8hErAJoHAkAACAABIAAEgAAQAAJAAAjMOAKwT3LGqQwEBgJAAAgAASAABIAAEAACQAAIPGICYEk+YgVA80AACAABIAAEgAAQAAJAAAgAgRlHACzJGacyEBgIAAEgAASAABAAAkAACAABIPCICYAl+YgVAM0DASAABIAAEAACQAAIAAEgAARmHAGwJGecykBgIAAEgAAQAAJAAAgAASAABIDAIyYAluQjVgA0DwSAABAAAkAACAABIAAEgAAQmHEEwJKccSoDgYEAEAACQAAIAAEgAASAABAAAo+YAFiSj1gB0DwQAAJAAAgAASAABIAAEAACQGDGEQBLcsapDAQGAkAACAABIAAEgAAQAAJAAAg8YgJgST5iBUDzQAAIAAEgAASAABAAAkAACACBGUcALMkZpzIQGAgAASAABIAAEAACQAAIAAEg8IgJgCX5iBUAzQMBIAAEgAAQAAJAAAgAASAABGYcAbAkZ5zKQGAgAASAABAAAkAACAABIAAEgMAjJgCW5CNWADQPBIAAEAACQAAIAAEgAASAABCYcQTAkpxxKgOBgQAQAAJAAAgAASAABIAAEAACj5gAWJKPWAHQPBAAAkAACAABIAAEgAAQAAJAYMYRAEtyxqkMBAYCQAAIAAEgAASAABAAAkAACDxiAmBJPmIFQPNAAAjMWAI8e7ahqYObvPwh39nW1hZz7Tt7ms6z/OTLT7YE22Gpf7tww9YmT59QlL9ma6j66c5as+3z0GSremD5A46mw/aH0fkHJjFUBASAABAAAkAACCQQSPnZz34GSIAAEAACQGCyBEKXP/yHz/5qc8Fz+GRLXmv5l9vf/w9/89L3FL0t7/66L+/7iqcmW8Xk8j+dJQn84e6zt9qvir//PTItZc5zT/d8pdj4E7Vs0rJPruExcou/TfK2f2xL+S4999EJMYZ8cAsIAAEgAASAABAYh8Cfj3MfbgMBIAAEnjQCfazjhMWB5ZdoePtJm8Pt9fNiYpGueJNOPjvGgve0HGWVbxWJ4uHctNW+f6brDuvvxamNNbtWS8M3eXfzzrozbIjHRHOI3HXlxTjbecrqWVVEfYsgUlrYf8ewjAeAmHO3mn/VyfoDXAqhWm00rCCHZON8/LM63Qq28qydXaonUljv/yXyH4wBF7Af2Ge5zHIhDJ8tky1et2uLOnC8sv5Txt/LY2kSYoG25G29PIW1vlNpvhzCUnDJ8wXlFXoyBZPkFSjP1lquUEXZD0aUBwARqgACQAAIAAEgAAQmTACiWyeCinccKNx82D2RrEKeAc5z3h0YO3evz+5ix8oSsO3ZVGm9PUYWPnDJ4YkE1rnqN5vMvoExMj/qWwOMZUdh7fkQNvhhmERDQNjWysJ9Z+8hYvBR9pHvqC3cZpm0Ci4NKi5Om/fcj4GA49DONwsLN79jSxh+g2w5+75NO1tv3nMDIwqOps0RGce5kJTDAxnVvZPv8m1X094GT5ahONtTe6Ad/56xdGtZQU4qe9Fce8Q1GAzKdVhdhGZpxFQc7F6W1vTOrtVyQibhPecdTPQnidMb9x8sU5Mv73qvbn/pcqkor7hqp55KwfgbHl8KRZHj4MECbte1cWJg+eutDf9Tot9WVV17oHwF3t64u/b00BOGv85hC6TEMg11y37mCo/d8QWfViTYwKOIELriij5kRsmAYVLN1v27XiHxFEK34xf7t6glGCZfV7V/s0qE4XRhzf4KZEaiwoTu7ZoiJaWvOPh+pWBGhpN06XLC/Yljhv3aR0UBN4AAEAACQAAIPFkEwJJ88Prmf2uuPenlxrTrPCfqLZ+NY2yOI9mdtoY6G/PHcXLNlNtxQNKp5QVqUjxTJL8vOeeq1qykJGhW/UC0ecPWehFTbSnfsVEVb+A8gMF2X52cQOEHyyG+wTRS/apGjoybCaY7rvr9lv5XTMWku6HBrSgs09Ekma3Uv/F6fgbGXWrvjJqSnLODmbcoN4kx1udj8JVFywns93ZHnPnHBfoVi2PGW4qEyJRgPGM97qU2Fwu219jpjqfz34JjZ/Get3t+1+bqRrlwcpVBmxnynDrjiT2FmJsYmYVhs1XaPKz9Uwfr86c+N8wITl59z/VOX2/yW/FXxWL0g+VD4U2YkRTsFQTm++LEvm73LTAaXkhgJqLpeV84nPf3LBxfPsgBBIAAEAACQAAITAEBsCSnACqaP01JrU9IpSL5Cr16wZMR7Zap1K+mJzSjn4jy/08wKFIo8yh51rimyUSqe4h5HiyHeMFxZElqqcGQ1HH6FLAdbmToouLFmOOjFjbHUDAYdZlG0ZQEhRtwEfdZyONhpIqkXr0ub5CkaI2GmuV32D2xR0HI25UqT9hQybmOWftf3VG0SMyPueokiDxuBgwTi0QY/2UwYsulyAi095Bj2S8jHWa9/VKF8JPCqR9oZJdtTR08OW8cFtHbE2ga5RRaH/g6GIx1l/d5vpTIcD4Y6o81E7CfDWpWkcNbFZGKuTc8Vx/dwT/DBYLvQAAIAAEgAASAwEQJwD7JiZKK5kPxgduc5AaZ75TTe6dftlBr3Gyg58RVcqVpe50DTQ0rN/mKanZppSHm02azFWXG0sncleuLddkitmXnntN+DNu3waevrjaQvR7rsVb7Z15/EBNn0toNxfqc8SyBAVf9NuEYRo+p0Ft22ITmiHc576/qG3/tZPl0Rd66okI1IcSP8ew5c9PH7d5ApHWjLntEzWhL2C/NrR0ef5+YyNEYNhpoKe86uLURKznwBh2253jPke31vcaaMqWI87Q2m+1utidVRi3RG9erCRzjL9a/cUay5lmPzR2Sb9hlWoq5T1haOjqZOzwuIXNXbyxeJU/iuoljlghE7f75Tu9yFKEXbK3Ywyp1+KUzzptfPzVvqbFI0/PrxlMu5uvZCu0mkyEnXGsykeLqDme5YjUfP9PZ1YPNUeSvLTIsQVKji63mY/ZOpgeTKtCmso3hTWWe5p829a1UBx22y2y/RKHdWER1Wcyn3eyAjH6lpGyNHBcGgIN4ReK1+768i0tzdMaNGjItscEkIoXch3bWXldW7DbK0Qz/Yv3Of+R0P9+lu1W/+UPJjr0K26A23/z/COvfedQ1VWsiBmbAtvtt12IUMZlgb44UHjtf+8ZBF5rI73mtTYmGxJKoKZ7Atlwm0Lhsqa2zuwOJozeJzImdGgiMp9aQ5+Qh8xkPGoHUipXEVQv3w4OleTiWZIAhH6xtT7lb/gruOu3BFm+pWuz8z8M4jD6qx9dRvOAounWrTVFVrc9CHW9tPGb3dPdgz6Cf4sbi1cOHZchlab1K6IspnGfw7xSXr1DGj1vxbOR2S8Ujuu5mujFZfvwPP9YocvfJ5ASWIdbQLfWX2py9tBrZsQM3mH5SO2TQ8r5PWv2Li5GDLnS5te0pve75YWN20l/JdVUHf4iJok30BJDFmyZJj3SAQ7GsVLQv8zTahS2NmE54aDy4hM8Wi1OQ3YgGoFBvwO2X5ZGSVqw7iOQg0JWQy8a8oNMM+6UIAshkczH7TRaFxD44caAmIAAEgAAQAAJA4GEQeEJ9kl999ZU1nNCHSWPm3baLEv3OA0fqdqj7bQ0n3AkeyOyi6jeUeKau6hAyI7HA2bp9x1ly7Y7qdyqM2Zz13b3WmxjxN1XlL0sky8uPIDMyhbMfrrX20kUVBw7sqzCQ3S0fWMffbpeiLH2niEqTG2uiNgMfcJy5Shjeqq7arOzvaLR0CGv83MWG/cdYct2Ompqq15f0nzlQbxWC3+JTyHVkf+Nlia6suqaqRD3QUVtj9vE4nUdjl52RFwZgvNfl7qcg09PBAAAgAElEQVTyKNEA01pb68A0r1fW1GwzEF807j/ijrgS+M/bOkW60q1Fa3Kk7K/qajuw/NJ3DtRWm1ZLfEebbcMbHY48EUj83aDjrJfcVH34/QrN3ba6v6v3ZZfsP3RwhzJoPfqJsAltdJGGarltq3/Xyv1VUdXemh2rJa7D9dYuDOtqrX3Xxn3HWLGvescrBHNs36FzEWcTxp63+18yHTh00JTDtb5XaeHyTQcOH9gs7zphaYvE4PGetsvExj2/+EXNT5Wc5RfNUQjRFpOLJKLXG1V9tuZTLNbrMh/tJNYW6TJjMsZrc+nzahXB/C4W7hdwupj5SmWi2zKZ8KJlpsPbNJI5mvIPjgyakaiB4WwHmLZzAWpjefXOYnmPNTp6k8ucoKZx1YqGeu3pEF1YUV1hVNw8ZY0GdiYdYOGaeY/9CmEoMxlXxIyciY1qVHR8HSXIHmnObTlsx5aVvlNTU/Hj+eyJ+pYrwzKFOts7g1k0hWAjT+ZqdeICAS+Ea0oI4pmw7H/gemZLxEkenyEfk0qSKItEtVwlCbntF8ODptvnn6sY1CLvbqw9ZjPv3rzhtQ2ba73iB3HcDhJaNDtmHd50ORmMWK6l05BR19rUYHV9ZnNEf4YSVb6azk7qTh0GZDJfvylBdnYwGI5lDXm8KZR8jhhPwb5Gh+6ghAznzyQrl4xYxhLu4ZJnnuq5w0Ecx2RwQ14gAASAABAAAtOCQJKp0LSQayqFQNZjRUWFP5zQh0kbkymSpWt01BwcE8nVi+cHWTZqgsRkxlNShYV5NKkbYB1nPdLVJcZlciKTVK4v1T/HnDnnQ6cXCnlSMDTTQpMsKV1Q8pqOypRIMuSalUrZl37/RHY/RiaNKbG5Yxqt36ynFxCk0qCl+G5hjT/gPON8ZlWRMU8ulRLUqi16+Q37eSYB7Z1220U8f1OxJpuQZtG6N42q3jabm8ezVfQst/OqMLvjfS73HylVjoi/YrcFcg2btVSWVLpAaTTm4xfaotvGcEq7Tk1l0/I5PJahMm4yap6XSuYQ1Mv52eKAH/lfx04JQBKyEksKtPNEWBpJvSDDCLV+GYGniOQvUum3A4G72Fgixaph/tXuzdKVFNCEVCpfYSxaS0sGkBFjZxWG0nVKMoOQLy8q/aHEfcaOkKGEL9AU5ElRK9Qi5LNCPkxaiuMSOlc+K9AdsSRTZPlr9YI/Cye0azW4KwYhXHxUkSRK43o6cKqp/rC5U1pQtEpw1AylOG0SeSqyy+kMH7YUcDlvPKdM2PWIHKejCI8LlaSG/xuXhrFNEal+VKLNIYnn1QUaRfAmi5QzqsxD1YyrVmGoz19TYlCSRBat36JXfiNceJQBFr4npv+6QJlN0QviPH/jj2qh5Pg6CjeQkPo4rhcXz0HDUkrmGU3bX9egfYPxaeCG74sQNkcm+G1HpgHW+0VQtkipCB8Vg7b/8bh4GOnwDS8zII9YafiifHUG77U70LgKXWfFcnKwVpwuef/Do9F0pFwz0eDbkWIlvRKwH/uEW4iGN/J/Y1JaX1S+f3+5UR1buRApS0yrHlhIdVSC2WLRLJ4PW5LsZY5AURW4GAHig+hIV4w904ZptOEoiSQJx0UCzyR34BIQAAJAAAgAASAwrQk8idGt58+fz83NLS4ujmgGfdXpdJPRkkQiic4h8TQ0V+IHdwINr+RuN3tLPH/BoM0gUSyQmbuQ5SmPyyml8vOZS7bW33Szt1n2Cy87QA2vZyLfZ8uk0fkoLhbhglADfqab913fWXgyVn6Ax/AAj5FDM+BbgQCWqZkXuyAiyUze2e3HlJSKTm265OVpheCRXFSEnBtcNxvs9da96RwUh+cJNmJcodP/I5YDsq6UOvE1h/U4I3SHYZjefvVE5B8lj+TZqB8DRxbSbEl6JBsywjGB+qgiDe0B4wO3WTFJxibOEnqNAZWz/7JHNp8cdJEQ80mxle1GL2lAlT4ri15PSU3FJZKImTMLGf8Iabj5WZnEoBEyV4ZigdlbQ9KPIZJkSbGhfVuDW2bYrRttVi1UlKlWzWtxugK6NZjTxSqWqRJdOVzgdnLhE23TIZESP4V30IWTGI3efh5FYo8hc6zseGrtQ0M9nRjEPFs+fy6OLChstAH2HIalSGTPDo3E5OKOHNWRfOPqaGR16LCZFba69950fktO0bkqtUYIOo1Pd0NBZPTwgkkzUiz+c4crQK78W2rkrYRKbnp75uZGB1uKXKOR24457Ne01BeYYu04RePr4VzmxvahBRj+q67uPrb2s6EaUl8oKF1Njuwl6gDTUtfKF5hMurjfebKMya+hd5ugg7wG7/GhW93c5X03hqzm1Pkvo/D7OOM/klckHLnTHQrynJsR5aqRpANiMcqF3JR3HGfu5Bqen0T3k4sGV4EAEAACQAAIAIFpRuBJtCQfuQqQy3IoDbC2fXssfyCX5lHUEqVuubu+TpiBTzbhs/CEaoXywpSY3lBdnBN35xsxIzjawAhPQPSADZxCAa6HXd4QhgxJujA8gR7oxzK1pre0QxbLLOTkQdYCMq4G5eV9x3fv/ZSnlqgUOfnqV/rtexsn25f4/PjYXvPRREpscgSZUY4wCbeFHMWTmPMK9iyO3MtDaQyR+ljGj4D7vZ8HsKwxnEJS1UuKlgtOdjHm7FbkLx4RE5j0EJSxQY2tgzFkjhYcX639dzFkS4xIow0wgVvYJz9WSjaqhfyT01G0BRG9sfq9FR6ny+1x2xs+tTlLqk3L4tjOEonTMP4mc4PHhtuL6JDVYw6Jvlwbc+vhaWJ0msyIvmEBH/I9Fgx2iViaT7U2uH5jw+6SmhFqHKPnyIFtUsbdv2JuuKUrWTF+FdzFxkZGvX2bFm0F5j73BBdQwp7gSSScKjDFL2WxJxtci0vQLtNxUhraJ4n1hDjmSqoiL2JnCsYlHwo4PmbJ1SUjTM+h+ng+JPAcpwG4DQSAABAAAkAACEw7Avcz/Zx2nZmgQMuWLevs7GwMJ/QBfZ1gwUlnQ86rucEb1wctQ8573Z+eIY1OqiL2QJfjjE9asL28aJ1Ok0fJeK4nbAI+gJRCEBlY9y1OkiGVCv9w5jcW62Uuoea5mVKsm0H7BiMpxDDduGyuEN+HL1TSszyuMy73XVoVnllLMglxgOXwSG1SabCz5ZcONO1OSAOetjZGsaHCVKjXLVdSs4McOkzybmKe0b4lNZBGyxy+PgGRcGkG0XMz6jpFYYaOg9v3nApKM9P9XcwgC/YGE5xLDO5bHLNN1J1u5ma02zzjZXBiftw+t9FF4n0nzG1pOlMhxZxott8ZqxGpUjm/221HRxstVA3zSKJO34vwY7IdXeaYkOOqFc+cn9HD3ox4qDGsl7lxK4xo9AE2Vv+n4h7aL3jM7s+gNK8aSyv3C+dCfeZLGLwp8+XoBRWB9tYzbML1PtZWV98pLyldM+TkQ77qp3q54PCBHfJeT1UIIaWxhPYkvijxX2xxzyaJqehUYp38tdbGC2RxmWBGoqHuPhPbGjn1TWPIDkdnxzLOG8/S0sgCQYpYhJzet9o7n9Zqxlg2wXjuy6/T5wxb4XoIEkMTQAAIAAEgAASAwP0SeBItyaeffnr37t2ycEIf0Nf7pZhYHv8Gjvf6PJ8xgbuEegXFnmowdzCBAOM6Xt/6BYneDoCyo91swW6P6/NASAgBC3gvM6G+UOCKrfFfnCE06U+Il+WZc62trkFzNNaYsIr/pdftGbKHhndDql5B959tajzrCwQCnlON5k8Z/C8S3RpzlmqVfFtTg/3zQKDbYz1kdkryNYvCU2FcoaT59o/bsUXqiIsGp/I1GR7LIYurKxDocjV9YHGj416HH8YoRuGggaueQIgPdbtbD7d6+lD85Pim8RCQ4b0Y6/tERCK/r1HcbG086WYDAd+55tbf4RRFUBoNccXSEL7IdJgbfu2nVkROux2ruei9AX/bh2bH9UDgmr3hI4d4mQaF/g6m0UTir7c0fsrnbyhQrtioz/JYPkx8G/swbc5RqZ+7Yfv0BvVd9Hr34Wmywo/LdjSZ4xoeT60phPoHihsfN7ZeYgM3PdYPjrsiZzGNMcCGdyv8ffxRnbTYBC6moXOMm80n3OwdjkPbUK/3k1mZiX4wkUq/Ri4KeT7as/uw1XHZx1zzuD411+5uYHJKd2ykE345mUTmgJ8dthzQ63H7JUSCwkT0crWMx0m5YgIi3l+WO66GejuXwtg+aGg41FBfV2v9gyzz4T3gxSIxLnqWyn1hEGr4HZNzVAWryDE75vffwjJjhnboc3vrJ+7YgsSY5eAmEAACQAAIAAEg8KgJPKHRrch6nOTeyMko6jl1/tz6lrqG/opqw4qyHXxz8/Hdti+x9Hkq3VsGXXgLH6lSKy621P03vvyAsXgD09BauflDXJKpUOsLlEftaH8jNrSKH/R2tHwyh9QrE70aIkqdZ208WhvC3tmV7G0EqBXJsi3lf2w2n9q77Qgvnkupt5Qbh+9WEimLtwePNVnf3dY4kE7maEzb9FTULsKpl5RPnXXRyvCL6FDC5Ya3TNhRS+PfWYPorSIvFpgKhZC9RMeOfM2mAvZI47YtQXwOmasx6Aea3b/3Y4vHARgHZOU4WeNvjyJSQg0Z2tKtIfPRpsoTPdhcKn+LSS+oQG8yYeZjwsVU9BaQ9RWGFWP5TRIqTCFUdL/t3W0Nd2XU0rLydYKhPQQhqUjocNQj1lBemUF4P6FUu2GNo+q42ZVbOhjeGa9N4TQUCZ2nwL7AVS+ONCQxbN7khB9iW0UmZ5tU5visKeOrVfpyqam3senwzpYBGbViiVJqw2ahzuLJB1jEzhwpzQRG9chCE7oyW2ksYxvRMPikhxcT1NItZWuGuwnxefodOyWWEzbnBUvDBTHa95m9WF2ww0iOPBFnNkWRzV5vSJcRVtAAY/37eutVFnngO99i1mzdpV8QFQp/QaN5nhXFOyonJO6kM7lPNDpuc9htvy9WFFdqZOPFD0+6mdEKpIglz5LatflxvyJ0KKtMudZAJxvCQ9WEGG9AkZsdzdRzta3l7Hz5X8ccm6M1B9eBABAAAkAACACBaUDgz/70pz9NAzFAhDEJ3LbWniFNG+O3L42ZH25OHQH0Psm3ou8nnLpGUM3syco93Wtq3kh4peGUtnh/lfPsFQ+fSUdPMepz1W+1SLbvN973axLvT6opLM19uudnV7XV6CWrU9hIrOoJ75N84LJMdJ/kvTYc6qjfeZH+mUk9/h7Qe20CygEBIAAEgAAQAAJTRODhBT9NUQeegGp5z2+8hHLqo+OeAJQzoouhbo+7o9V89kvV0nHcOdOqO4HzDbsPtrq7OS7AOI62dM6mVUMn6E4rSR+MMJKlepq1tT+cQMwFWl1idO2D6cMEapEt0annTiDfPWYJtJ9jlatH7AW+x9qgGBAAAkAACAABIPBQCTyh0a0PlfH9NoZT603UQ4tSu19pofz9Euj32eqO+IgVJfqcxH1891vxlJbH6fWla46Ym/5Li/+umFioLn7LMPURnVPao/EqT6MM61y1J1xLt0y9WzJNOmJD8njiPaD7uJSYcNj3pJvkLra4soymoa2Vk64BCgABIAAEgAAQAAKPkABEtz5C+NA0EAACM5oAz37aeEZkKFoCsZmT12PA0XSSX1momeR7SibfEJQAAkAACAABIAAEpoYAWJJTwxVqBQJAAAgAASAABIAAEAACQAAIPL4EYJ/k46tb6BkQAAJAAAgAASAABIAAEAACQGBqCIAlOTVcoVYgAASAABAAAkAACAABIAAEgMDjSwAsycdXt9AzIAAEgAAQAAJAAAgAASAABIDA1BAAS3JquEKtQAAIAAEgAASAABAAAkAACACBx5cAWJKPr26hZ0AACAABIAAEgAAQAAJAAAgAgakhAJbk1HCFWoEAEAACQAAIAAEgAASAABAAAo8vAbAkH1/dQs+AABAAAkAACAABIAAEgAAQAAJTQwAsyanhCrUCASAABIAAEAACQAAIAAEgAAQeXwJgST6+uoWeAQEgAASAABAAAkAACAABIAAEpoYAWJJTwxVqBQJAAAgAASAABIAAEAACQAAIPL4EwJJ8fHULPQMCQAAIAAEgAASAABAAAkAACEwNAbAkp4Yr1AoEgAAQAAJAAAgAASAABIAAEHh8CYAl+fjqFnoGBIAAEAACQAAIAAEgAASAABCYGgJgSU4NV6gVCAABIAAEgAAQAAJAAAgAASDw+BIAS/Lx1S30DAgAASAABIAAEAACQAAIAAEgMDUEwJKcGq5QKxAAAkAACAABIAAEgAAQAAJA4PElAJbk46tb6BkQAAJAAAgAASAABIAAEAACQGBqCIAlOTVcoVYgAASAABAAAkAACAABIAAEgMDjSwAsycdXt9AzIAAEgAAQAAJAAAgAASAABIDA1BAAS3JquEKtQAAIAAEgAASAABAAAkAACACBx5cAWJKPr26hZ0AACAABIAAEgAAQAAJAAAgAgakhAJbk1HCFWoEAEAACQAAIAAEgAASAABAAAo8vAbAkH1/dPuSe9Xosh1p9/LitBtwnGyq3bNh8wBEK52XPN+0xbd/3gdV9Z9yyMyfDAGs71OQKzByBQVIgAASAABAAAkAACAABIDAZAik/+9nPJpMf8gKBZASQ4VT33++++h/Vc5LdTbgmnju3NxB6ts/1Oz4vX/EU9vQ8ae+Xc9e/9oOsb45bduZkmPX0c/P8lsO/+5aSevbPZ47YICkQAAJAAAgAASAABIAAEJgYAZjkTozTE5yL+9xqOcmQPzHO91k/Oevy3OzBpIqla4uMSwg8hiVwzmyX6qrmDWIK+U41Wy4ygQCHZVAr1xl12ZLBe/x1LlWt197Z1nrGp90oxzlfUEKJ7oHwTVvt+2e67rD+XpzaWLNrtTRcB+9u3ll3hg3xmGgOkbuuvHR55Po9NBCu7rJ59wcO5jbHp+CSDIX2P+3Sz7HX77V0dnOhAVyUQajWlZcskfDupu3v2fx9GD6bUBfuQlcwqbbg+crGXzFVBeQ9tg3FgAAQAAJAAAgAASAABIDAdCUw03ySfMD9P5oO/lNTk9litbuu+lMIBSkZNGgeEmWedX38L//8T//UbLacPOf64qu0eYpvz06ZksYDtj1/+8+9Lwm+u0mkS/Wb9/n+n5V/9ewsjO9yXPrff0HMuTfxQr6TtfX/M11X9P0/Wv7+f/S/9CN9fi6ZwrouuJy+1MU/WBgxDwd8rQ0X5q/7CRWzFgOn6345sMa05f/VaVVpV1qb/rtzgPo+FZPhuvOqWEnnPn3n1y1XJd9X/YXXeT1TtXB8Z+YIAM88972XVbM+d3f/H465/fSL+QslQrB2yty/0v71X95yP/Uf3tllVJPiEcXiLgywLtcd4i+HrNyRmVMy/ir/B7LuX1+4Rb/xXqUxB8n5TVK1kr57ye75prZiv0k7Ly3cKv0DeZ/vT9q3K/7j90nhCkoSGd/xzxe+uVw1ZHOPbACuAAEgAASAABAAAkAACACBGUhgZu2T5D3H9tX+hlOsLDZtM21Zq8IvN+4+YGMHHiZ43teyu/KgI7hAV7K1orxMT/MdDbtrbd0PU4bx2pqrWrOSkiDjcYC1/kND+7+Pv3kxWY08g8zIK1RpmZY/WW/5o65so1q+QE6vKCr7MYXzjMPJRErxnztcGE3/5WAdrOO8233e4e3DMFyqWbdSPsDYfu2O7IpE+yK9/VIFjuHZmiXPOG3nWS+DkUPOzGSCjHGtz8fgK4uWE9jv7Y5rQ93kAv2KxYoJ+Dm7O92B8enMEolnY1hfsH9QEj4U7OOxED90BeOcF3jtT9TS+HUNaS4t7Wwf7PoYHYFbQAAIAAEgAASAABAAAkBgRhGYUZbkgNd1wU+vNxlXKekcWrlcb/rbApnvjKPrISLvsjZ+/GXull2m9Ro6W07RGoPpp7o5ntaTrpil9BCFGa2pTKV+NR2O6RzfShqtjtAVc/0pXL9ZJ+tqMXfg2vUaacyvKV1EI1Ow5w4XKcte9vQvUBBDXs9UkRjje7nQ3fD9bxFEGhZiWX/E4EexrE+HbbwUMv8HCvZMkzVEoNruMXV5gyRSgoaa5XfYPbHehrxdqfLn7rnSkbKIRUjiYCgYu8NdZrG5ImRbRvuIQF+xeQidcrh3U6og031XvPeuhpGywBUgAASAABAAAkAACAABIDANCMysfZKpOI5x/9vPY2TUSpinLd1Bp2YikGxrxZ4bOVrMbe8KYTih1G80qDOFXKHrdssxq9Pn5zAxsVBtKDYqI/vmAu7Woy32y0xPKkHlG0rW0oITj/O0NpvtbrYnVUYt0RvXq4fFJfocDjZjScl34ywGnFzzuomKicRdaTUfs3cywmZC1WrjxhUkskE8zT9t6lupDjpsl9l+iUK7sYjqsphPu9kBGf1KSdkaOT66/EODJIlsIfehnbXXlRW7jagK7mL9zn/kdD/fpbtVv/lDyY4a3Y19lZYuHju4+c3PizRfmD3qmqo10c7bdr/tWvzOrlVM/ZY67scHdr2caAOhE3SOtfF55WopxrFS3bZyTbzbUCxBnRKUIaQQc9Mvlce74qTa8oNLeZEoEuMZ4ji0ezBdIuBFFtd1f+pzmkinpEu0uSfq/NKSCTgPhzDEf2J9fpmcwDLEGrql/lKbs5dWI+fhwA2mn9SiDw8qpYglYpzngiFkDKNe9Pk8A3Ja+pR1IBhExiUih3CdT83flGRDpjRTGjzPcljEsH9QAkE9QAAIAAEgAASAABAAAkDgEROYUT7JFLlGRwVOVG6vqjeftLuvBfgBEfECKY3uSgu6znupN/f/4kB1yXxv4wGL8EaKkLv5QPONBcZdew8cqCymg22Nv3QJDiKesbxba8dUr/+8prpMjZ2rq28LYANMa22tA9O8XllTs81AfNG4/8iwwMQQ+3s//u14/5ugP9E8mpoX3q3Z1Vr7ro37jrFiX/WOVwjm2L5D52KOu/N2/0umA4cOmnK41vcqLVy+6cDhA5vlXScsqOVwSiZ/5A76b3LZRPR6o6rP1nyKxXpd5qOdxNoinWBXR5JEu61KPw+nt7x/oFirVhHM75zRpgJOFzNfiUzqlEzVKwXqBSM2E3Y5HAyeLbgeMSmt1cSdlyNU3ctxyFr/NiF8HuhBX8SS9MFWhQ8pMTMSwwIXnZ67MnW+SoKFPKfNdcc6Ou12BgW+oiSitUspamESAyyhtlG/hHxMKkmi2xLVcpUk5LZfDPev2+efq7jnSpO1JhYjQiHBbESJ/cwvo4mIm5ILe6K59jNcnhYZ8yOTRCLB7wSi/tiRt+EKEAACQAAIAAEgAASAABCYmQQesSV57ty5/xRO6MNEABIvl1e/XbxExrlPNe77L1s3v7Wn6Tw7GDpIrjRqs9B0XiTXF6iCDsdVHusXK1YVF6+lCalEOk+pzSOCf0DOSQzz2TsC9LpNOipTSmTril9bIxf181fstkCuYbOWypJKFyiNxnz8QltnQtBqEG2Ve2q2OJnJIIjvsdtZhaF0nZLMIOTLi0p/KHGfsbPhjuELNAV5UjxFRC2SizBF/lpaiuMSOlc+K9Ade+tgEvljUEaVTaI0rqcDp5rqD5s7pQVFq8LW3WCahaWipmel4ikYkaciu5zO28K9gMt54zmlSjC2COWres2C4R3irvv8d2WSZ4dfj1Qc+sLLpiiUiyJuTMHAEqclz4nsW8vHXuS03ahEfkcRtcpo2vuLqs2a2JE0uHxDuWFBvMSJnwdCXO/okaG8lxmQR3ZD4ovy1Rm81+5AtEPXWbGcHL3Se7gjEotwFMsaRLL0ephvUMhoFItx/G4oiIZHyG39gtTRozhW8dTUvv7R+3APwkARIAAEgAAQAAJAAAgAASDw6Ak84ujWjz766KuvvkIY0Ifly5dPhIckW2NA/zCeu+52nP2k5fB+TLK/KAcVFRNZMTsKJwhp0N3NYTlyjUbkPt9quelnu5kuH4PNU6GsXDfbI6WI2ORfmqc3IPvqNBvs9da96RwUg+cJFpl5Q4GdYnEa9nXYoEhmOXGB2z2y+eRgnCgxnxRb2W70OgpU47Oy6PWU1FRcIoSHojQLx1P6seiJQcnknxuVBQk8mmySJcWG9m0Nbplhty5us+JgJ2IfMtWqeS1OV0C3BnO6WMUy5CQcNXG9qI88n9wA4pztnqfyypYOev1GOxcWxXwesvjzTDvW39NLPjDe1bCt9oLUsLtKn5VM1Jvenrm5USmQv1ojtx1z2K9pqS8wxdpk+kF1dNubjnX2DFY2wHXdxGpr24dyz0ZLCZpEc1zILRY/hQ0gu5FnrobIPIFc2E3JBr8O+U47JStLxoCZTHS4BgSAABAAAkAACAABIAAEZjaBR2xJTg7edVvDGUy7WUsKpgsuWaDULSDxW9tOfcZgOcgyQ+doDpp4/WErCMfuOOqrGnxz1UuySdWifO3C5n3/a/Q2B/qxTK3pLe2QITELFye8nUJEziP433rZATosQzSFOhpqXaSxTBWzCRObCPt9kVdwFONmMHMy+QdvjiFbH8ugraOY3/t5AMsaI6hTqnpJ0XLByS7GnN2K/MVj2T5ikRgf8DIMhz0/PBt30dz67ypjyaAPDmXFWHSQ6fD+hTwfNbmeK9lRgHywIeayX5YjbBmdTMIzF8jlnIwcZcdjwId8jwWDFRJL86nWBtdvbNhdUjNc6liuTE2RSTMkw4Cr4TBWvEU5nmow5JQUjtz5NzcrodRh1eNpYmyAD/psbXfVxUkN3UgzfH9/Wuq49U8GC+QFAkAACAABIAAEgAAQAAKPnsAjjm79yU9+8nQ4oQ/jw8BDTMcp2+W4eNOB/lAfmuVHJuo824ViG8Opl7kRSCeyJIELdieuLS0vMbyqVdMk9tWXkfuSDEJ85wb7x2j2wOl92+vsWCYhDrAcLpVmhP8FO1t+6biR6JcjX1IRAbv1t9yQtDxjO+24gYllKRJpZrq/C5lf0cTeYIJziSTpRi8AACAASURBVKF9i0Nlkn5KIv9gPsmosvG+E+a2NJ2pkGJONNvvDKs5wYSRKpXzu912dKDQQtVYHkm0N3KhnEjhPb9udfcmVMi5zbXH+1e/VTx0SGlKuuRZLMgN+fkiBdhPG21ig0kwI5GB77WdY5L2eeyLxCpT1dtGOrlZGPJeT1XE702UqPJflPgvtrhnkyOdimM3NO5dMQppvst1foYpsqPmMC4Si1N4dztLraLGMBQ5tIt0jlQ2mtt23IYhAxAAAkAACAABIAAEgAAQmJYEHrFPEkW0TjCoVaCXpSl4yV5bVxlcuVK1QCbm/Z6OM7Y7VPFSEsUZovvMrxvNf1msJYLOY8c9czUVL2DiP4jwXsZzgyMzMdZlMZ/zY5nhdwAu1GjmVB7/wCZZmyvjOptPeSWvFKNwV03Gbsshi3hDPokx1g8s7mc3GqLH+cS0l6Urftm5+9CeIKPLzyFEwRvuT1ttAcq4RY0sDEqjIaosDSclxiUE77M1/tpPodNfU7A4u3OsUTBSfixmGeJUflLZ+OstjZ/y+dsKlNlcz8W3LR866K3qIcsrJRVPw7o/d3qeU1GZImyOSv2cpfFTjC42xdyDrOukK5ijG75Vcp62YGlH7TlbbVWPbnU+9W1x6tes56LD+aXcsMOUeA6piMiUBW6htzIOHXMbutxU38pKc9qaD7UhO5L/kmUyjJN0SI4FSrjX63H7JdqESkX0crWso42UK8YrPOn74fN1xAplXNeR3xYdO7TKEIc7SbVoF+xTWeQYnuIkZeASEAACQAAIAAEgAASAABCY9gQesSU5ST4S5eZd5ZkWa0drwykOS5ORC5UlOwzoTRWRJM+juBP7tt3GiEXasq16IQB1iaHY13D8v25tuSuWyVUr12tOnWS7QxghIg1vlWFHWuorzME0gl5pKn0Z1SI1vGXCjloa/84axNLJFwtMhSPDJNEhMRUVMkuLvfXQKT+Py4gcTUlFgTrieZynN5kw87GmyhM9qegtIOsrDCsmYUQkkX8QEC5PIhs60PWINZRXZshGXjGpdsMaR9Vxsyu3dMgDJqXVtP1fGmuD+Htbka0rofMU2Be46sWYBTbQ7fy4hRNrNAuGOf7CqOeibnZaj7htEmK+nFIuLalC5wSN0BmZLU/9KC7id4D5pNnG3MGYIT8kThXKRpS71wsDjPXv661XWfRykc63mDVbd+ljZ/bgL2g0z7OipIeo3mtrkXL47HTZd1U6gXMszUZnOOkKVo6t34CXCcpXPXjL9v56A6WBABAAAkAACAABIAAEgMD9EvizP/3pT/dbx7Qoj94n+f+z9zbQTVxpmnAN3nIzUtORyUhmXOOhTCLBcTUTmW6JrAWD7AaRRmRbcBoxg+DY5hCzieklzgFzAv7a41kwC3jj0IvD4uZE5otFN6IPVr4gpiPSWAyICVY3KB+nOEQiWF53ecDaYKUd6aNda/q7pf+fW/qxTZqfe+MTVLfufe97n/u+733vb+12Vx9uqoZvhXwseMzExDfEP/Nhy77h1e2vc1epTltg6e63TaKftOkSvzk5bdQfJaGcz0lOkgmfbV+bV7u/Xp6ysj1JcigbQgAhgBBACCAEEAIIAYQAQuBxQeDPfE7ycYHhGeAjOEy7rlhMF75SLuH7YMVkUcAp7Qphv51OPlI6WWrfZL4CSvtqplOOU+TFa3dgP9CiYeQUYUTZEQIIAYQAQgAhgBBACCAEHkME0EjyMWyUR8LSuMd25LgtoKjVLUzfoDrVEsXLa1U+q3VoqnS+8fzgkOd0rs4m8e+z9w5UGFK+8PmN1xAViBBACCAEEAIIAYQAQgAhgBB4FAg8NbtbHwU4iGY+CIB7XU95lZt1j+CUYj5sPCZpwbc0j58vWlerSPqKzGPCHGIDIYAQQAggBBACCAGEAEIAITBVBNBIcqoIovwIAYQAQgAhgBBACCAEEAIIAYQAQuBZQwDtbn3WWhzVFyGAEEAIIAQQAggBhABCACGAEEAITBUBNJKcKoIoP0IAIYAQQAggBBACCAGEAEIAIYAQeNYQQCPJZ63FUX0RAggBhABCACGAEEAIIAQQAggBhMBUEUAjyakiiPIjBBACCAGEAEIAIYAQQAggBBACCIFnDQE0knzWWhzVFyGAEEAIIAQQAggBhABCACGAEEAITBUBNJKcKoIoP0IAIYAQQAggBBACCAGEAEIAIYAQeNYQQCPJZ63FUX0RAggBhABCACGAEEAIIAQQAggBhMBUEUAjyakiiPIjBBACCAGEAEIAIYAQQAggBBACCIFnDQE0knzWWhzVFyGAEEAIIAQQAggBhABCACGAEEAITBUBNJKcKoIoP0IAIYAQQAggBBACCAGEAEIAIYAQeNYQQCPJZ63FUX0RAggBhABCACGAEEAIIAQQAggBhMBUEUAjyakiiPIjBBACCAGEAEIAIYAQQAggBBACCIFnDQE0knzWWhzVFyGAEEAIIAQQAggBhABCACGAEEAITBUBNJKcKoIoP0IAIYAQQAggBBACCAGEAEIAIYAQeNYQQCPJZ63FUX0RAggBhABCACGAEEAIIAQQAggBhMBUEUAjyakiiPIjBBACCAGEAEIAIYAQQAggBBACCIFnDQE0knzWWhzVFyGAEEAIIAQQAggBhMATg0Dwtt3UvrOmZrf5TohnP209svvNlk7TRS+bvRJB701vMHsylAIhgBCYDAIF//RP/zSZfCgPQgAhgBBACCAEEAIIAYQAQuARI4DPJv/S5yv49qDTO+cH359TMFNS9h++ZJV1+kWigkxFs95LH9nP/+r/vvQXiuoF38mUEr1DCCAEJonAf5hkPpQNIYAQQAggBBACCAGEAEIAITAdCLD3nOaTZ+m7X/kDAvJlreHHKmJmjC7jfkjqXsHpQzbHfbl6NuYdxMjVWUvFyaU6UhJwDGVNiRIgBBACk0QA7W7NBTjWcbhmy3FXLkm5NBN++pLLF0pN97y55agz14zfTLoE9r6ZAqGleOLIsL5rDtoPTTW1SJ9t3+YW6700InfMOzd3OB4kxzs7t+wweSbSEv8ZI8bsBzfvtoAu0A9+tFiHU1lhr3TU7DDnynN+7c5YWmoOXngUrZKkIKlVyvzMg0PmTFnexkDmS5evYOSHM1+pT1H8hKvrjTe6boAaMZbmmoMXH/UuszzsyTdon/PgKqe2v9a5pTF3exUvfdqrzA46nLchGwwTLHxOFcqUaILu3r4BEjZ3ONmQUE27pfK7TK1v1IDNlL3eTIxN+t2Yx+5kuNycTQsZ+cc40O+/8QbE/wk6Omp2nkrFhxOwYzk7S4m1DtLmD2hZTWvbgXfb35AHLnTuO+aI90B+DyuQiV6sWlHqtv3Gi00w7nFChj++qMXln6cH4UH1G6kRD0uT19mYPGftTydfv7xM6KPoa7I7RXFjOAW9nnwr8GDLZ6J5kk8mGo0kJ4Na5jzsb00dH7r9j9WYJIHjx4Q98XdXrF5UwvF1v6/riM37x8ygPpNvZ5KqH6mloumpe57tXkQtW6MihdNTdjKVPDl5FCw8QppPd+2mBlyRdOkaZekj9v4eT3sy7VzNUa5eQWXe2BdvrGkvPUZ6grH+z67L/w4ZScYt/NSEhstdULbijT17msFfk14uwOfrmrjfe/bs0FOPRpp8l3ttfsqwo7l+GTF19tMp0Gc6zZ+FZ5vTXz6jMSxt77vR3xeahBeUr1lTKfL/1mofjKDB3vFjL4gxTLykusJ/2eYapgMimeAxhmo65f+bquakeY7L87Q6LUn1fnRGbLrg5TeGeZUw6VaAlzJNXMGJR2PR7tbM+EzyLaRfnSSlR5LtcWBPJNfqHknlniKiOBhJklx9pmlpMJ92F0irddJHhmU+nDwyJh4Z4ae7dlOATUCtREo/BfwSs5YodKGJuD934BX2abXwAmI+FR7SsVcwbIyQlVPRUURoZW+6QyAQxEh51ULpoxmoTje7Twe9mcJvPwwExsZDtcGJEjE+4WXusthcrhG8Qxi5inshUKiVpzt6e9iK9ZpYvRmn1TmcIoq4WK5RhfJyAczsg7+MRypj1Kblx7TK/7RwlJ3INPAcc1qyl/b0peA1hnlVdRpaIam86eEqcxXQSDIzPmlvwZr1jn5yg8Rzrt99f1yyQGPYopfPTkh2s3vnEQc7gbVs9tS27+E6vweM7fjZc596vxaSFa/W1S8nOds24XOeMvZeppkHQmKhWr9RLwfTbfEAltF305Xte1aC2KDj8H/p8q9pb9GCB9bZuf2MuGmvnrzvMveY7TeYwEwJVakzrFcROMZe7Xz9vGj187TNFZRu2NNY7jWd6HXcYgKFEtn3tYYNavJOEnuaxEJZxnHaZL1CM0EhsUhbV6OVzgrdkHbKYv/MPRLAhCVyzYY63UKwRhb0fGzs+TXt/Wq8qISq/HGdXh5aOPPTlh6T3cWMFsZZSqxV4m+wgn9gzHC8Huvc0U0/wOjGGve2442ViR130HvBbArhXDRXqautU3O9QtD7SY/JCiKxIrJixfo6bbkAm/Cad7cwq482Lg15F2Dz6l5G916jKrG8oMd2oufc77xfiyiNimeZD+xLPHOw6xN6BCeUrxjqVkecleBtWw/g484oNlum1Bo2LieTp0LBPop9Aws1mMs+GAQdoEK3Ua8qCVXEB2kjHvRgkIKNItttstY2HWgIjPU5Tfvsfe6xb5OKVXU1GjJ+gCRUz8zgJ4ulRgyDMREusBGxZbd72eGm6gCoHaPQ4a5z/YOjhcVyTe1W3XwAAIxhHjaSxLLyhcFP/i2mIEkSCBe2JLagOKSK/TIRc9HU/dFlty8sJAZteUQ+YcIcp++71LGvx1+5fZceCFViyF0wEnBe8bLPMb7uZ9tVgJb/433bT+F1h5vUnE4BG+KQ79+jEbNwPqHGIavlCTEMkdUhy+6W66q9rdrwwOO+bV+TnWpp05XCSp/wWlreZV6q9F+xMcK/Xzn32kdf637WqA7D4T21c+8weORqFA0scwUoqdM9NMLOlMgW62o3qYEh4sSx12j6Dc08lMhXysFzaHUbaMpu94qjTcuAzvpcZ8y9V65777O4iKxYtbFupZQjC7VCuQPynDNPe8JrnyHsJYFT1dCiFzmhdcf8N62m0+evD3IWo2ptrX7xcFeSlcMg7Z5KfE3hBZiNjQEPdrd+INrVbpACK5G5S5pIwoTzvifTJYFs6SqP2Q62mAdZ7OiWNzx73tMx+5pc0ldx58c0tmjrppmmnwEL/7oCA1vpzon0C5jzF+mR8SLZ0nX1G1VizqcP0h8eM52nGbaIql5B3DL7XznasHgyYzf2br9p//k+zwhnoDZt1YX1F2oSOfmPM9m6RT6aZitcx7ccvAD2YHfW1NhBJ66ZxTh+ZbJcoUcSO+tUOgosSzcRazmM6d297+MRDDu4waNra5KAF/4b5o4jdpcv2aOA8Z9q65ZgGR2JUKE+l+Vkr/2Gd7SQoKr09Wvl3FI2rG8CxyW2nCGaD+ilodZxdLxuKWk9tJ6Msw7yOU3GXzloH0Yu1spSTojE0gXd1iNm6zUGE1Pqf6yPuAdQLU4gjS+sPfSeHpsVsS7++1+xBUUiUVgewF5WcVX4J06tUEt2f4yvKY1nJhTa8ERDIqs5/Q5ZY+JVkdvu+eohLl4IOnh1uG/137SYTtmve0cxsUy5yrCxOtTvg/qn+FSgI4ZFRjwcIP8g8PQgUQ5hpjiFe6hhfACTzHBP8Q8Sj/Wye6yQ+J6+fhVu/+D05Ttff3vuEsMbtYqw1wdjKcYz2KLc/WCF6oEDuHPAcVX+qK6uOuK4phvG0UR5fltifivktIDWycBeugvNMvYPuq2/dXNaVq5as0mvKE4wBclGrLGShfiBae3NMn1dLVbH0LhEukRXY+C8shzVlq97ijXYoPXgASu2uqlpVQgWLviTjOHaUBRMryF9dJRs+N9srcDjcMLd4LpAR4KJ3kwlFzVtT8/o7tY//OEP1lAAP/LGknXZrop0uw+fOLJLNW7rOuNKGvKX17a9rsBLtK3HgKfI0Q5eszlnaRpaWrdVC+mTRht3bI+lf3Hw2E2JZntb+/5GzSzXkXfMniQqhLxcMnDLzZ0omhjweILsoDv0gLlvegqpCpL1mNo7rmCq+tb2tm0rhK6uQydc4eNH7Od91wXahu21qxcKHT3HXLO0TfsPt7+lFd00dn3MYGnsRavP0h8c6vpMpPlJe3trvfzL3o4TjiDweY93WMfktc2HDx9s1pPDve9buVN5d852nfFXbN5z+EBr7UtBW5fJCcoGnlBHhwNTv9bS3r5DT3xhjLGUCeECRcP+Wmqm1NCeMozE/FeOHTw1QK5taj/QvK7Ua3zXBAacvgtHDp5myLW72vY3G8r91ncOWHM6YeJ3vN9hvitbt7uttYZi/tXBwHhi7zlsgzJDc1vrBmrko85jl0JLgfdsR9455wcYHGxv3UT5LQcj8UkUAs5LbuqNQ+8ebqsvcxsPh1rzAU8bQdGDRiYWMeHtu+RXvNbatlNH3DG92xNp7kiSrOAnt3ueMAYcF2jS0Hb854cbKF/v+2e9+ctAXCxf/UmKgkRrySNsKS3Fg0OC2Iv9V7sOnWLIdbuAKL9WOX7+cGfoiGkW+kFX98Een/z1xtRhJFDX3AUjAeeN/7G88I5ngNPrIH1rgGW97i9CD5/T7jkUJcZ4+OQ3DpktDyANldVSharU238tspXOf805MEepKOUrHSjyaP9lRr6psX79D1ao5N+mr/ePhdpgAhAZpRZXJA6y2dvmd4/TklVv7u94r+11NfbbHtMVTmt8H3d2nA8qNze37dKX3bQ7wxRCZMKB+ZcjwH5VNew/3NHWuErkOdlj49oIaoXyASRPewKKhNlnjIe9RHDUZYPwuoNW6HzH6v+72tYD7btWiZzHO62/T7JyPO2eRFw2BLOxCQAm/cwsGGmYwKrMD3KsJIiNEml2tOrm4vKt7x2uCfkoYIfiTUK/rdFQTf1lAovsbZuDrWp658TxllX4VWPPFa6zAlao4+OgvKa5rdkgGzpnhR225KtxSjx9wc5W1ra2NumIwYiBymASE5gch9kKed3R1tUErmg4fhx04kHniUPGGyLttjbQM6omrnS0myKddQIdAVT1eLgn/lNr03KRaFnTiTY9CcZswKZd9FEbm9p210lHrRGPgp//BFsnyuZIgBbxmt/psGPK1/4ZuAoq7OKRzj4fb9/Ew3A8+o6546gTX9bQtrdJI+zvu5nktcSSAQHrn72maW9r/fdZ+5GOkPnNQcDAQuQsQcQvZ2nH70bwF9XqFzFs2Gk+3n3+t/22qBEjlmrkC6kcD0kyV60Wu3v0/nXr/2N1pV+aEGKt7waxcd+777a/qfCbI33roKXjHZv/u4bmg227XiW8pw4euwgsWxDiU8Ejk6CE9yDRJLwGIU4DahgzSKbL5hTp//n48X060e+MLe/2l2xoO/5e84qCy8aP6HCbZWYJlMxcsjMvNbQdO75/veRmjynkuMINY6o8R9jOyF6aC+0739Vzh9S/3X54b4OqwHHsRMIRWUAw2Yjl6MDQnzgKqxvbWhvVM11dh3tzV1te+x+ums/R9W7veFVjY3wYCV6kGUOoXudjKPhaAcNgDmcE9pR/0riCJ5tq7LM4kgSjx+bm5pFQAD/yHkwWiJas1lKzcUwgVS0qCzBMaMARD3hBITCJ4L9wwOdpaterpKWkfLVWPpNhwCAm2G+7hFVtqlXPJ8TFUvVGvfIre19EwSO5yIXUt8Nu6O9pt1AuFw96vuC6HPrWePl3SfYzW9+Y0vC6Vl4qJso19ZursCu2/jAfOKVZp6LK5dLZo34/i88SSWaLxC+q67Y31b1cBKinsBcpL+iyXx1X/WOder5YXCrX125UlwoDGCuWr6nfpKVKRCLA5wqF5KuRkT9irN8/WiAQSSQiMSFfW9+8TQv2ALE37TZfhX6LhioVi+cpDIYq/NO+6+HRbSI66b/DQBUkzD9xafz9F13C6jpDJSkWk6oNdYaXS7AAA4Y04lX1hqVSooRUrG/QveA9f9GTTjI1xu9yXBNWbTKo5hHEQu3Wf1AmesPxxAK5fotOXkqQlYa6aqHrYj/wvr3/aneTuvofycliMbFIZ3hF7LroSD/gQq4waLgDYAKpbo0y4HDcYvnaCI4eDNKkWhSIVBvqNeUEMV9d94+q8WRscwE/3u4TecNILFmjeVGAFYioSrnE5wVf5oLXIoMMxMWSRwJ5hC21KflwiNP39Z/vf25lrWGxVCwmqJVbddIB+yUv8BegwhymH7hjPtBFkzWNteHV9ZRS8xGMGM64rIJ64KaBFwUmg7yEfCHm9QDlZwdoT9FCOYHx8JnBOGSzPDyySigVpPd3nDBzauUcIBQK3tJDFZdUrtYuouQLCcF8lWIW3X8tpMaD/c6xiiXyJNVhcVJdU6cHSjpbRMg1ainruwvMEOO45CZW1esWkcRcuW6rLu0wG4sVKw2bDeoXxaLZBLW8qlzoGwGLNFArlC8guduT0IoKxD5jPOwlg4PB6x6yGKXa+jVyQiyWVhtqwRLQBBvpDjgrx9PuKcSzGoRQ+kjIJhgJpXM5IFXOAHK0EKjKYzMwrrebUYhH9g0K5T9coyin5POSTSwu16xViGdieGmVSsoODwFF4KxQ2ep6vYIkSjkhUXwroUZ5/pSo62qXUUQppVlVKbkfMlAZbBEWYzIAtxUFOFedGRgOmuv+ZdtVvGpznbqcAD2j9g2DcqzPFpk9jleWR/V4qsHRLwQ7LSOgFQiUP67XLCSJF1Vr1LLAEANUIRP/MVs3M7sjgXnsV3zydZtBJw5cBW3dptVSwThf38TDbjzac+XKSLlu6yoK9L/AW1gd2zuanBN/cc22DQqyhJSvq9eSXrvDm4vPk0CD9Z4zX36oqPvPOgI0RIlCv2XPuwcadYuie6hmqxtfT9wZkYlxYrFWt7X1+Hut9Wu08mJYygJJ1VodtyMCJzRr1biT81tou52R6RvWKchiQrqstuEVkeu8ncGgPhU0MrkgWA8STZHJIETSQA1jBsksECl/yO0pw4srqFKs6HurNUAfZ5LyhZLAPSYQJpqJJS4FLl2hX0oICnDxYnX5zAHOceUzjCnyHKafkb10F9rvD2JCkfh5saiE0tY1Nf6YCm1jSQgxw56zA0MsB2ZBCsyCdss6ud8OvLIQuaxqm8n+Y2Ou7neMzPcad60LbaJJDCnGEKbX+RkKeCtwRaY7nCm8xB9TTTRvwqm8eBZ3t166dKmioqKuri4MHHjUarX5gCiK7rjA8JlgCMWG9/XzUhATksi7QuG3WD/LYneZ4SDjOlBji+VhMdn/Bgoe2oMXDvMoKdsL3FACrEhKNWsmjL23vZiEpr+iVsvwkfMMW6qWRvc34vOkczEXE55vmyWRRLpkQrVSaT++7/WLhEwul39fVaVIlfx4cT6GYQn134aVFcPmqvVzuZfiqirvNZvlN8PMPYb5ws1McBPPeHnV6pIDXTu2WOaBEatStUQFFvh9w0xgzH3kjf4YTZYlGOC9hujkHSaY4bsYWU1EMs6UatZLwdZe+11h2bxoJCaSzZOYBsFInsxC/x4zjJWo/iaSSkCSxAx3ehZ8jozb0BsKxDwSvzg8MsH6f88Ebxi3bzZG07OsoGxkAgvtzooFIVEa5QonCHHANewfeQBvIyh6GAxSLHHv0AyCnBdpHRzw//A8h200+PMC/+EwA4eR93If0fNRycQLOSYm8pQBkCUulgmwJf0UUzBhS03Nh0OM/sSId5j13Nld82E0K+fH+1iM5KfP2N4Hn7cmKUmCAiYUnJdgxEkIpNQLI45boJ087pmU/vveI1fBRVys63OMqiGxCRrOJ59xIAFDmS0Py/DIKqVQyiyu6z6tpqDf+QWhrCF4S8c4X00sjnpsBVKlouiAsz+4TM186gwsBNedJDWIYK5KI6AdZ81exjcy7HHfYYuKWbARGwgYMTdK5LkycjYWcV8iucEecK3wtsN62ssZFq/XOzauAq+gVuiOGW4tswOSwCrUnoC7tbnaptlnbos6jL0QvRg48LqDyt9jhCQZrbxIvlrPZbsfZYZXPpOQh1uJhAol/8wsGGnZ0qvMJ3UJXRKcpZSL5QpEkuejnUhisbOKRJFeCS+cibMPx7EHfuZuERHDaZa0bA7OOauTCuI5UbxnCgUYO85ivCYRWPgYk7xtQca5uOvzYSWhgxWhADqPErZ/eAR7IYEO2JPNo3rJ3QRf3STEnAh9IfAoxlmw+Z+Xf0AjZutyaDVAZ1RMEVG1FS/WAXFkPoT3TXz8ReP9zPBo0dzo4Y4CkiwVOmB5iuLyLyZLi0aGGTYHVmOUgjfMXZdEG3dtU0MHfrASpxQ3o4SIbZSdIxGzTuaun703KikjY8acKCOFVmaY1cF8KgEsMokjWA8STcAvhHFFghrGmya4ZEoB5Zga4ngh9pwoUo/ChPl6KEtgVigeREXR6gPHFQtdIZnJMKY2AZ/ihNhLd6GlS1fIrpp2v24lKbl8EfAqY7tGUwljuTowOCklI5kFZJk4QA/7MeCmxW0Un9riFL/9pz80gnkkSiXid6ZjDKfrNV+JKf5kQpUhrQDeQhxOrDwNqG8w4lkcSX6D8HJFgXlHSNeKS9fs3qaKjlu4ZM8le7EzKbn0mP0WQ3iYuQspKUaOXHV7n6OZeUpwem/0YXIlEh8TlplFlQ2HyrWuay7XZ9etR2znlzW2bZZDmAHEoIvTE4zt4D7zl+SSxRRVqdAuc3UeYbiCZ0p1zUdVN/v7XS6Xo2ef1WH45z3KiXGsRNP4liY2zsNm4MLEE6SPDPck8wdKeRieeeIvLzRJAwlgcJg4PuQw4UiJKuv3rJXFs8wQpl2ZCCYTQMowtMARAJlwjK+NYOhpSyCQahPEAxAsjPMGhnOgg0gI0wE+HJNQIRCZgdWCVwbu8ghYUhV4hC0xTYgXOA5xjEVSigAAIABJREFUAeYaQr6hrW5hQoW+JcL5hDlEU7a2SeP7+ZEPrKqfhmbBU8IkBUNEfZfoueVmCuggqaUWCIlfeTyDQXegfM08UAAPn/cHwGwwxDjwHUmKs8ovq8VgVbLXec2nLHAOlCrrwYFJMLqGooSFFJzbVREJ0pfBUPJ6/32CuRaQb0i1HmBLcMuRfsH3VBXzKLlaK/+o5XwKdNxjaIUnKbCe03sPfMJSlUrZwirVq+P2A6GZGqgV4ghMDpCUQuGPMPvMw16YQBScDHXPoEq87Z6CPEy/Iidd4fXIIxZWZR6QE6lCWUr19dPbmiOBz0i2VyGy4w/Dp2fz4JwvKZ4uOXwm8SuOnahA8mlBYjmcWiWF+OA5kU4u3UQqpUzPfPyn2FKoamSiG3rH1zdx7+L1BQPa5JAm16l6HU2eYEA4cx1+zJHVYduxM37tzkYVEK17NI1RVKqMpXA1rY/csgDOuQGpdQ+VMgO0MsSngkYmsQXpQWLvcxDCdPHmcmeQzKTC4Q+ZWOJywKxERsOYWkye7JVqmtqVHlf/9c9crjMd1t9om/9Zn9eFV2nSCQQv6rmEBTXymFVtM1VTuLCu4aXrnadM9sVN6uj8VWrVeZ85THLwJ+P5Ya0A3sIczpRCs7rBvExO5gVcQidD6cnJs3Tp0uvXrxtDAfwAj98072JJyQwfMyoUF4u5v+dGHGd6+1N38AuohWUDN6yuL8QyqUAklRFel/Wat2whdw2MpITAh7zeqHPJej2DCQsJkepMMM7TFucfSXm1rraxtX0zNfqZawBqH0GG58XiGWBZMar8dywtu7pdbsd5j3jNzqbadVr1YkrC+kdD1it402b+xCsqV2k3NOzZv0v7vNt10y8qIYQ+xo+HagT+Atd7f+UIHRKbVCggSuZgzP8K+bUgsHT327vNnhJiTmDgTjQS87vvjBQViwVgdIFjwWBkbTj47yOAz6RQTJTMGPaCmyHCxIYYJqUrDcf7RsDSVTiAaVSMKCEKBJJiSQAs//1VpF7YLavZ7k5eYAE5WGYwytWYd8BXRJSK+NoIih40MqkKD30jMQkZZobBymdC/5of+GAKFg5jUoGZH6AM58dGSgGDcGFLZSMjDlziAg6Z4bt+UVgOi3Hvb8zWG34sE30JtYhSrF0n953t/iR95zJYZpqkYIipCskdF7hSpGSBDC+WyQSey+don5SSgd0EfHzmZBxSUQk9Z5BVsfLlsoHPHPZrA2UKJdf98ZWeTniuUjWbvv5rB83KleUpUwrB6319AUV98+sG3Uq1/EU86A/lx0sIIsDE9DTo9X6ZTHeC7uvzyjY0N9botMsU1KyAH5gyoJJQKzQrF2uZzndyDNSefM6Ti4+9pOQ8dcdwcTExOhTbMRB0HN2571zMZOWKPFS/eNidjugcpI6HJcgsU04M4SVlxaPMUFTdgNkEt3ROX8jJFuWiBXNKxBjoO6KcAWEexiVzotuMItEZVI+/Snx9cShHTvzn0GqiYkJ4f4CJfmTL9/HBnUfshXz+AxjyPYjusZoYGUm1hQKipGh00BvW8tAWgrSeMMT8176R6LkWsIz5NUGU4DmwyuUco00nvcr/3MANI8HaqdPmihYWIvxowsNh71DUN/C6vThRViwSlxSNxGuKMQPewByiBOpTjWd3tGA9SLQuuQgh1DAW5SKZvIhlYokvU2bDmCLPOSlOrCTWe9FspTGpQqPf0tTWoifvubiDIdCQqwPDjtyNSvCDYeZ+EVGSvFrD12NmrCb5kpxapteWus2nkk9ycqxmNYaTMhQQECAOJyidxw3OyhWkgHyjnsWR5He+8529e/dKQgH8AI/5opY5Pf4tHB/z0J95Y8OS1PSzlFUvF/afPGa9wfjueezG7t5bmCRtekNUThE3Hf2YTAasajEl+5bLcUNElXPp8JfUVQJH9zEbPezzfW43nujDFJolKQuABYWjt8+aTlrpe36/z3PZNYyXkmC9Bc6eoKKqErt80mi/7fMNuyynbL5SSjYb7BTyuW94gw+Cvps24y/7g8BYjAN1Gb7yS6Ppqtc/5mdu9Lu/lIBdLjhVpS6mzcfMzkGfb9DZ/b7ZBe7gAh4zML69Fkd0FJcKBXieKcSxr9wuOtZBhdKIlEvk/k+4Unw+r/OU+TIro+YRqmqKOddluhKKPN1p+YJUqymsQEIQQrfd4rjNgOG28VzykdMQMc3LbN8HJscdn++23Xg63QSEyvT391o8oAtkB22mC8GK5UuA4ZGq1bK7Z7t6HN57Puaauevk5ZG/FKcYJJDV+2ujyen1DdPW46fpOWr1fN42gqMHgzTEUzRMMHaLjQEO95jHcsaBL1XLE+5u5QU/gUC83R/ywJhUXpYHeC34ZCCZGFwChXBhS+UjIw6hxGJVtXz8Qrfxgsfn89HnjKZPvPhfi7Cs9Ger9KvLBj7sccQ2IsbKzkcwkmr3NxQ1o7/vM24yCCsgZS987bzqBkegQxtjePjMzTikwhJ6ziCrokUK2Rdnz7rLFJGb+3hKT6dbQCpeJuhP+oILlVTKdcEYLpwlDAzRbl+Q9QMl7To7yIY2+xPq5XLfx91mJ8NpxPsWOvVrsUKwN8h3iwb5gsDaHLfQD8COPhaDWiFxTtYyznju9oR34z0Pe0ng8NUdI/9eLRuyGD90MT6f52KP5Xc4BT5gEecqJ+Sh+pXeOLnGwDFJyJ2D1MFZKijEZ2LDn/fTw7mciU8osYBQ/UA28JHRco3xDQEhOc1d2xYKwc/tlrOhrwpOIeRiEnl1MLHc2Us0Cravu8v+OegZaesxU7+oSv1SqnPGq3o+l7XX7km7caoQxwPDtPNzHx9qOfGfQ6thC9Tq2a7T74dchZu2nnNu0QJKzOM/4OCOgLF+61naO+Rx/MJ0JWUCCFiY6hVlbkvXWZrxMa7eLutteAsFXVYzNwRkfZdMVq9Ms4TAcmEVbBs59nMny9KWrq5jXV1HO7p+w8Y22sNLmpbYiZGYb9D1C4cw1LdSajVx09wV0mLvFVPXr0eoahVRCPOpoJEpq7WwHiTKew4GAWoY/zonyeRFKBNLfJl4DSNEnnNTnGhJ4FZVcNG9CfifwFulP6V9AnBKKJmNmBEby9WB8Zw3O+6x4OJc16ne62L1ivmp9eJRW95qRvIXEJp/0IiunTYn33sI1t6zGkOeElMZy/qc7nDyusE5cJW1uKwJntHdrWD0mOfZyKxIJiR4QVU1p7P3SNd4cxsFzyeQ1+ysP2WyHN1tDuBF0iV1OwwpB5C4fMVySmxmSmXc9W4YcEMF1gI5BXamgTCTMuzchn/Q29lsCggIqrJ+51ruEGTypK5Ys2Wr70Rv59umwAwJ+NZI4xY1NwRKYE/P7bILB4F80866k92WQzuMbBGp0DXWKAQCtm6Dt8vSsuUDXFQiU+nWKE7aweEudbn+zXU9xl/u3X6fFYILsje9qZ8Pelap/q1G7KTZ+F+tAfDxhe+taawJFYcN93/UG5id8GWnWJmRkinVYqvxZEcQ2x/66kkkiJZubfojV4oNfPBDumTjdgPnyFZv28X29Jzea/sKA58G0b6l13LuIK5YX685bjK29RUWK7Vr1wSOuZML4QDf+EH36QM7vp4pW7JSIz0zksIFR2VuBfVVz+5tzNcFhPJHb9aFj5WWaBvewkynT+9tGmVnEfJXGht+SKTnlS6m/GcO7riHES9ptm3XcU1WAG8jDIYejsEgTfQ/cKlyrqfzbcvIH4VllXVN67lLTOLNjfOBn8BpYrvDYUyvFm8MDqsFnwykrjVAJbBYAxU2LHILfJSTzDiEUoUlx3TuwI4TrHAOpdraZHgRoMVDvzReR2KFXnNp7+nTzoqtSUeK8xOMpNqRFCW03iiTclPsuFRahn86TlGRiQgePnG4cYisuPO2CPcig6zOVipfMNETioqohsFLh62TEAolcearssVp9+aA/bFr6zXHTB07rNwnQBat2LiS7RkC55YpQLwxaOzpabE+KKKWr1CVWJP4LpCu3ryGOWHcsTWAzyYr1HrdRI/r9yOYgoBYIQzLDxBBHvaEhsLJx96ixNT8dS/WNGwPmk52t5wZxeZQVVsbdZyNSuSKs2yp8pmMPI9+QdnNITIBEx08efYuiYclsVwlt//S2BHAf7YJTpovVry8oXHM2H18d++EhKquVIht4EAESDx6q6/3Qpn0h/LcDhnykM/FJPLaikSaAkXdzsCpbus7O4wTRSToRnfouJ4oZQjIp3o+13nLALZYHTuBHyZNKlWyq71H/gfb1ErCK8DDf7Itzd5q4F4o/VvbMOAGAFdhJiFf0diwHJgAMdR/AAakbhNjtBxs+ahIpl6nW+q3pTAHqrmdNZ7s3H0GA58K0yxmHDDuJYso/F/2vdEFvoJTod2+LbQJMDurQfA9oWsjLMbdvRUJIrX+OVgB0xsHunv5uO2dHV0PJdSSbU3rQoZurq6xETOd4rS4EDg565v11Vw1oD4VNDJxKgTeg8RqGXJ1Ug1CUh1h7hmGwSUz+zkIjnRmluAA8xlGBRGX571kNC+P4vCwR65uqP8D5392PcAl85TaNzemXuuRZNihfmAK17j8e+L+d3ea7o8LF6iBVwY5twJV2wKMp3uK08dfXK2vvNLxi96qBYbYfSVAreLGsAYOYaY+micHNBricPK6wQlchb5J9ijCX/zpT396FHQRTYRADIHgpc6ewvr6SX0o7EmAMfSVvGrw6cX0pcongX3EI0IgIwLsTdPun2O1Bw3pQ8mM+dJegg8bNLe4Vx1tCn/3Ne09iniWEGCZmzRbIo/cavIAfCjZLNp5yAC++vBUBdb1fpd/bUN4VvWpqtnTURnw9cXY9w+fjhqhWjzlCDyODuezuLv1KRezx616E17bNWHaCavHjUvED0IAIZCGwJiXvmY3g93UlaqpDiPBXZ23afd9oSj65fG0wlDEs4WA71LX3qMWcM213+d1nOy9Pkuu5N1v/MQic8/hYOXRXQhPbC0Q4wgBhABCgB+BZ3R3Kz8g6M10I1BA6rbVJt2JOt0lIHoIAYTAI0FgjLa8Z/a9sKZBS06RPuuxHOrowxbo9Tw7/qdIH2V/0hDA5esbVp8wdf+0d+ShkFigqnsrv6san4z6Fqsbtj4ZnCIuEQIIAYTA5BBAu1snhxvKhRBACCAEEAIIAYQAQgAhgBBACCAEnl0E0O7WZ7ftUc0RAggBhABCACGAEEAIIAQQAggBhMDkEEAjycnhhnIhBBACCAGEAEIAIYAQQAggBBACCIFnFwE0knx22x7VHCGAEEAIIAQQAggBhABCACGAEEAITA4BNJKcHG4oF0IAIYAQQAggBBACCAGEAECAZS50dV/xZ8WCuWLufLtmw/ZuOvRpQfa2rav1zd0dJtvnKd/ohFF6wNC3sxcBy5lrXPCaqfOsJ/UbyLnmRukQAs8iAmgk+Sy2OqozQgAhgBBACCAEEAIIgWlBIHjD1O2p0Fdm/6gyUaki5VVq/LLtKjcmxF/UVP1dpX6bQTM/41fTJxjnhxbTzw79/FNmWhjmIyJYpFf7zV2XHu14la90FI8QeBIRQF8BeRJbDfGMEEAITB8CQ7aO984P3mdGxnBqY/ueVeIQadbVs/vIeSbIYoLZRMW6poZl4fjpKxdRQgggBBACjzkCDxjHGbMDq6pXs/YPbQ6Xe4QVEi9p6zZrpbOirLN070lG8VZtbCzod1lM/3KdGfH5CwjlKoO+mowPE/0e9nmttpppuWBnluiIAsb9f4gqPBsKBYTiR0TJh9edY9lSwt+zrhN7TTd8I8N+VqxuOlAvnxlKN2w7eMDs8gUxXCAhNa/9VE8V4NSP1Pa9Jqe8QRGrIJwmikUIIAQ4BNCa5OTlgB10OG9DNkF4et7cctQ5ebog55j94ObdlqFMNPhKz5RnGt/dMe/c3OEI7U7JIwxbW2o2pIaafTZfHjS4pM7OLY0mz0SeuaaS3A9apMU6nEbi2mQ5mfDTl1yhejOWlpqDFx6jGVD6/TfeOO5Kq2rQ0VGz85Q3Lf7RRGRTgfzkf8LnOLb7jZqaLfthslaqady/Z5WUkIhY+pLDG5ErXL7x0NFtKnL5np8dOZTTMPKbE0vWd81BT6vIBG9a9jVuqdn8ZveNR9GgCQxPHqVp15R8YIwrbDI+2QR1Umiy9Ptvbti02zw4qdzfYCb2SkfNDnPYFGdXybysJR/gCbXLWmIie98gKvkUNeE176rpuJS2sXNyPWw+JUfSTri63niji0/r7zm7D3TRpfq6crrj8GX8Pxoatm9bs7CQuWrqOOGMMe2/YnUS6iXRqTb2jqXr30S6Ha1tHYebqvHLxr0dH8cXEtk7fmyemFiqpu7az99ksfuewHdkGZcjJ1Gr9Cy4vKb1UI2SLJbg9/sdn0V5L9E0vdOqL5fXHjz+bisYRoYyzlKq53vOXni0i5/pLGaPGfPYnTlzlaelhff7fOIR44RzjbI4q9nrlTUFn5pkzZg1QX4GPN5l0FP387Py9uQkQCPJybbVBGP9n12X/x0ykhR/d8XqRSWTpRvKN5NU/UgtzbBPhL/0KZX7DWQukKi27NnTnPC3y6DMUNNvgKWpFDFHuXoFJQp3P/nQYX9r6vjQ7ecGLUXUsjUqUphP7mcgbWYVyFf+B2yWq5hya9OujUr4wuIDjxdfUbuMwH5vdyRMD/l947JF34CXk2eD3u/rOmLz/jHPXJmSB/vP9TLE6sZdb2qlmdJN8t30MDzdmpIPVwkKm4xBZkGdHF6s2/kZJvmrEafDMzkCf4ZcuahkPtaSF/BY3XIp8c8AxFNU5H1n5yHz+KuNdaSrq8slq9mmlZNkuUL3+mtVxZj/2uXrkeGYv/+Kd+5LFbHRoPuSnf5dn5ObeMXJlXpNSZA+d56Ozvx6hzCylButaRZjlz9xMJ6RwhfgVhkO5RRmkJk7I7J/0MkFwf6L/fGJuIe+ESFVUZxYGi5bVO77NDarCGfkm4+lz3SaP8t33v2RsPn4cDLV6uVlwPPpMqbK2BOVH+1unXRzQcaQYVoiuVY3aarhjDgYSZIZafCWnjHX4/BSIH6BouY+DpxMBw8lCt1kJw2iTSiQVusehfc+HdX789HIogJ5yv//FwgIZIrFlJRvzD/oDpBq+cssdc7ksNP6+fLQZquge7BQujTrvqs/H0rTVnJg/AFGKFTy+fm4dNNWeo6E/syaApe5LIKaY9WSkrG0o/+BVPejgOljB71OSj0ZAgiHJ6lieVrLbBSzvZ8M9ihPDAGf7bjRK29oW4Q5DvYyC7c2lkcFcSYlp0S2S34/GI2B4WOQpr1i2eb4sqJQIMDYrwLhLUsFEmIOjrkY5iuMmg2IM+5xcWgvK079QC1ptXVjlHZbvFDXxw5vyl6nAhG1TB3fSTv5JvJ7vELZShWlMDsv9TnuqbXh0eOQO1BSkWL48BdkpM9B38fIx9kiTh4KlDOKwCMw4M8guGgkmUujBz0fG3t+TXu/Gi8qoSp/XKeXY7aDLeZBFju65Q3Pnvd0zL4ml/RV3PkxjS3aummm6WdjhuOvK7hNmOdE+gXM+Yv0yHiRbOm6+o0qMefLBukPj5nO0wxbRFWvIG6Z/a8cbVic4C+ABfftNllrm66UsTTvYxQ63HWuf3C0sFiuqd2qmz+eVPpmCvO5zD1m+w0mMFNCVeoM61VEAjHW2bm9R9jQXst5JIPm3T+1k9sP1y8CD2C3WMvAyp81Vgr8Ny2mU/br3lFMLAOnGjaGTjXQJ940sWoZY7/sE6/ZuUcr9thO9Jz7nfdrEaVRxZYR08GZ5Apj8I7dfMra7xnxY0JigUpfZ1CEjLj/ptV0+vz1wVFstqxqba2+MlS5h373v3Qaf90PMJQtXldboyJSBwlB7wWz6Vy/+/540VylrrZOPRfkC3o/6TFZQSRWRFasWF+nLQcVDYOsxa+d7x/6+ttzlxhq1aO/Np5zer+eJdNsbtQvDPeRrM9p2mfvc499m1SsqqvRkOCgBdiv9YFoV7tBCn7A25oFV9WZzjndQyPsTIlssa52k5rwdO884mAnsJbNntr22uCR3e5lYBcQwC0DeykykLgbCLTjbrqyfc9KgFfQcfi/dPnXtLdowQPX9GfETXv15H2IhICtX1vOEM0H9KHxFdi8+rqlpPXQejJRJUCVjb9y0D6MXKyVQTczs4zjtMl6hWaCQmKRtq4mdHgGHK35lclyhR55ICQWqvUb9XKuKaHCzHkiafol4jZ4R1Qg/W2y9gH5Twjpkoxd6nj9qBN4nfs29Sm2HW+sTNCNaEYwLy6RElixUC3v7bzW1z8mV4FaTAx4x0kNz1GZPMRywuc6Y+69ct17n8VFZMWqjXUrpYIJr6XlXealSv8VGyOsamgxlA3lJv+Lh7t2cNce0o017lB1grdtPUDQ73AKotQaNi5PV95G2a0UC5agpBMe864WC1hAuLl9wwXdP+t8/+03otXP0zZXULphT+My4RRUJoTvhLMzkWEAP1x5Weaiqfujy25fWDcN2vIUS8LJeUhTAjCrKMDAPqsdDuJVkdvu+eohLl4IsFBzSuqz7dvlkO9vDTuOvnMtO64p29+WmJNhjAsREPoTvY5bTKBQIvu+1rBBTd5JUNgDhuCRhIZ7i7DuyGCrOT1lhx2mE5bLYO2lVKVbxFo+JZr26shUexUrn6WvusaleqVi3Pkri+OGgeJsNQjpipBqaflMaIx0qkn/G5/zlLH3Ms0k6SmsICiGzdooB/7ULimOZsKv7NYymvhmooXco5mVbk9SS8xa9whpTkhs5P/VpgezmRN09/aDdGXzoQ1S8BYcS+kcf+3QZgr305Yek93FjBZOa3/6/hudD+vf2yLnOAGbBn/Shb3+Xn15Aj5BaA+bkIBjvp/cIPGE+jXJAo1hi17ODc8wqBHg7lOF65Sf7jWafkMzDyXylRw/6Vtigk6z5Rahq6Nw1ot/t66pWpHY5QhngRyFeOSooXcYk1SF2AgHcl3r0VcwQcRyjvr8YJuVqCic3w/2slIRUnPVmgW9RkzbGDfJYvlKXQigRxBYrxuT6mfiIrWauGix/6tXu44Exfjv+Iqk3I+kICCIWQwzxGLilP4Cph18lgfgn977c/SgzglfY0X4Ynp37/t4BMMObvDo2tr0JOh5IZ1sci3glhbqZiRmzCIeSZw0SUBO/w1zxxG7y5eLTEYLGrLsbrmu2tuqDc/F37fta7JTLW26Elh3mcAd2IUL0aOFQKdytmaJdY15GrN4lSuSPLkj04DYB4zt+Nlzn3q/FpIVr9bVLye5toWzkVjkU/gb7W7NoVHvnO0646/YvOfwgdbal4K2LpMzKNLsaNXNxeVb3ztcE3JkWdp+k9BvazRUU3+ZQBLccO1gq5reOXG8ZRV+1dhzhdsO4rtwpOPjoLymua3ZIBs6Z4UdtkygEXBcoElD2/GfH26gfL3vn/VOJJf+wGNq77iCqepb29u2rRC6ug6dcCWeusAXyKUP3HTojJ//lod5GPB4GO7hPk3flVWAodSgpeMdm/+7huaDbbteJbynDh67CGw/COPMFUdA9VrjZp3yb/yO9zvMd2Xrdre11lDMvzpCJDAMAk74Bd//WYxNClh4s0rQ1XO4Z2CeYc+Bw4db6uSBPuOvOO8fu2frfMfq/7va1gPtu1aJnMc7raGzQ6zPcf4WoX+rrXWLYvyK0RwCNjH4rxw7eGqAXNvUfqB5XanX+K4JON8A+YOnGXLtrrb9zYZyv/WdA9bIYVQAspvc3Hb8vWb1w74j/7XTU15/6NjRXYqA9SQAPER4wtt3ya94rbVtp464Y3q3JwlkjiVYW7O3ze8epyWr3tzf8V7b62rstz0mcE96eW3b6wq8RNt6bI8mYcozI3spMpBYV0JeLhm45eYgmBjweILsoDv0gLlvegqpCpLNIiGJtJJ+3zF3HHXiyxra9jZphP194ExLamDpDw51fSbS/KS9vbVe/mVvxwlHEAs6Txwy3hBpt7WBSNXElY52U/RW9XRhziZCEAFL074YVzBJFixtPL5DLZqtbnr/BHQYyfno3kKSBFREymVKUdBlvxraQTTsGZkjg09J5yOWzL8cAfpZ1bD/cEdb4yqR52SPLXzgdmK0/zIj39RYv14tA/f75Cj/v1c07K+lZkoN7aFR8T3bkXfO+eW1zQfbWzdRfsvBY5FbBxOUdzzdgiU0ZIFUv/+QYT64bejwCeBgz8DYz/uuC7QN22tXLxRnlMlsKhMupCCZYR7l9V/tOnSKIdftApL0WuX4+cOdkGPJca5hggTesnTfDWLjvnffbX9T4TenK2mcQBpX0VdBR88x1yxt0/7D7W9pRTeNXeCIV4rCJjZckp8J44r1mN8x0rO1u/a2NVYX2j9yBBKwh/wMuhzXArJFcpG4Qj430H+lP2LaIIqQnJvPhCalSjTpLP2Lg8duSjTb29r3N2pmuY68Y+b0NGtBEKb5VRKSmIuCWst42iTAofYkucSc6h4iL6KoUsZzK6Tgv6fdY+yIB8xdcOadvjUuXSjDwRRPR4cDU7/W0t6+Q098YZy+/jReP9gvnh42JSnrsl0V6XYfPnFkl2rc1nXGFe4loUaAT6d8H3d2nA8qNze37dKX3bTD7rAJXr98PVAqp4D5A4s2q1TcjEw8sIGxACYiiOe4KPZL/+gskTDJl8QFs6KKMeTs92LEMg244cbnsnR3WZ2f2RyRGwdEyiqVvDzH4wM+1znL+Ru+rz1281mHN70vgmGaFAc2nvytjJv7eLFK/SLOXLbTHBHW48VIacpwEcMKBKLvBPz+NGXl0w6Y5YH3/sANgzknfI0VqwLxn1qblotEy5qAlSYLoEqRCgHUTeI36ZHsWcUjmZOQa3TRR21sattdJx21ZpbJOIulClWpt/9aSBMBJtecA3OUilKMt7tMrVzK83RYM6hyxcpJ6zKC12zOWZqGltZt1UL6pNF2DyTlYSML80/8azR91YXTAAAgAElEQVSSzN6ErN8/CsyKRCISE/K19c3btJzZmYEVAhs7oxCPTC0L5T9coyin5PMSZ+5ACrlmrUI8E8NLq1RSdngIjL8YMDIsW12vV5BEqVy3Vaf4VhYeiCVrNC8KMLDHo1Iu8Xm9wLNIKJ39zNY3pjS8rpWXiolyTf3mKuyKLeEQADiQQFHkSKjvZMEYUvaSbDTUdwZv0QMlFCXCaLudkekb1inIYkK6rLbhFZHrvD0yUJyr1i+nqEWUeAz4N8KqTQbVPIJYqN36D8pwPeHgZKjQBGP5aU1S+O92zlUaB9tO6urWygmxSDxXoVlMBL4Ei5OY91/t7lJt/RoQL5ZWG2rXykUToT5kply3RSefR5AKvYYKA5tYqr//oktYXWeoJMViUrWhzvByCRbgkBevqjcslRIlpGJ9g+4F7/mLnnA2onKNZq4AnFCl5kswQqVbSuAFAun3qKJ7Pt/DUJICkWpDvaacIOar6/5RNf5pX/SUSLRcSFtjLE6qa+r0gI3ZIkKuUUtZ311ulI4XcOID/ouHiYzspctAQlZyIfXtO54BAAzwjYRyuXjQ80XENyr/LpldQhJIJf70XLkyUq7buooCcKn+sW41t6ibHLhB1zh4pZ4vFpfK9bUb1aXCwH1wvTtetblOXU6ASO0bBuVYny3k8ICQLsyZRQj+NlX7IlzxSTLOMV4Y+j8ssG7vhDTszuAvVamKWbedmygJ3mGE6XPVIQL5iCWLFSsNmw3qF8Wi2QS1vKpc6BsBM8uhIKlcrV1EyRcSeF7yH65IAfcPxwmpq/+RnCwWE4t0hlfErouOSOccVV7RGMyCRVgI/YMXYsCOFQCxDD9SmnUqqlwufS6jTGZVmVgRCQxzcRDl9fWf739uZa1hsVQsJqiVW3XSAfslb4xA+o90QeLSFEiq1uqkwDbhhGatGnemKWkioRSuIq9G/X4WnyWSzBaJX1TXbW+qe7kIvElR2HjDJXOWzhV7zdbHAgFQS0s461pXTaTXJTEmeKP/OiuTy4HHK1Z+j2RdjrAxhytCYk4eEUotLmbS/9hvu4RVbapVzyfExVL1Rr3yK3sfzWYvKJVi6JlHJaFpuUiYtUxMHAecz54klphj3bkCxNTfEeF5Nz+YdaPk5HBo3u2+m/ZJ5Qtw9qbd5qvQb9FQpWLxPIXBUIWnmPqp9Ke8cABXGt7DpuYoEC1ZraVm45hAqlpUFmCYSC8JMQJ8OsU4LrmJVfW6RSQxl/NAINunwYzkF0FstkSSWnzoeYJxfxGQvKSQhcwF+yDA4kIe4+qznzrrXwC8C85pEst1tU2HDjUZVNEjIQJFfSO3lSaXIJav0tU2v3d8b4N+tSq0+pMa2DF/kP8Upc/NFJWFtU+sUlOC+/12MBs84R14QMK2zgrBimsA3NydHHi1A2Z5eHp/mHPygK+xEoovwDmlCFtpPqVIYTfd0mZ0M0K5cxCPRE5AjgKB8sf1moUk8aJqjVoWGGJA/5apY4owSSgVpPd3/aHeyt/vHCAUCgLL1F2mVC7pMTgd1gymXEmlJHcZ+DxN7XqVtJSUr9bKZzIMA5wGOBuZOH8q3qHdrdmbES+vWl1yoGvHFss84FwpVUtCVizFYBWIJM/DbNusIlFkoIgXzsTZh+PYAz9zt4iI7b6fJS2bgwMJzBBEz3PzaFzAC7kykoseucuwpWppdMoQnyedCw4lgNmRaCZuGracMIM1q2oR/QUhr5GPH/V4H1ThNzySv1sjxvz0vVFJGRlLTpSRQiszDD5+AAoE4+dw0feYYaxE9TcRRgQkScxwcxxBwYmkgv1TIFFvaaj66/grXEBwg1KRVK0WuC5ZzEMjzLB3EMwTzlVyqxf3GCEZA0skX63ncn4JhscScWTzDC4U4Cw7nlTYBDN8FyNjTttMqWa9FOz1tN8Vls2LeXIi2TyJaRD0xFKu/CjIOBhwzBJxziMIwGpjUcozCHJepIlxUP2H55mIwx4tOb2twSmSuSqNgHacNXsZcP+4x32HLSpO7Zwi+R8OMzmwB5UBbB4lZXvBsjMBfCOpZs2Esfe2F5PQ9FfUahk+cp5HQpIgS3/wM8OjRXOjt7cXkGSp0JGSyscwLKH+26jkAycV7Bm7afJhJaG9xKEAZKWE7R+ODJ7ShRkuQtGdtPC3cHfB7+OR5CzO+5B7dE70nEwBkEOp7ZTDfltDfYHJ1sKUOj+xxAmFVnjbYT3tZe4xjNfrHRtXRWEUi6NeVF7yfz/WDCzzeyZ4w7h9szEaxbKCspEQPjHlhWMYo5H+Y5ZEErZauckkr8qkUw7HpCvvxIh3mPXc2V3zYTQPmDDCfSwG9Ri5NOmCxMXOKCFKoxTmSMSsk7kLbAUfH9B4QrVSaT++7/WLBBjPyb+vqkra1hfJEm+4ZBrpXDHDQEU0sWqQUhLnuyeTIxW8/ul1dt6aitBeQfGiCvJ0r+O3PvVycfZGhItQah3jJv0uMxxkXAdqbLEkLCb73wF8Cay/SyUz5WeYtYQTveuD25NFCclzq3s4A1lOfdsO5t2UwVvM3MX1ki+7PV62wucaIOUNszD/MBMYcx95oz+OCktwpj5+tn/y/Sm8guFYnh42LYtIJIp2QzNxsMFnHGyhhBqBcR6dYjHQ0RBzo5bnuTJyNpa69PYwGACjazYA+qp0C8h+7nD6yBU/gYxAk7llvb1HLOyaxkYtrxqnVW/yEUOWvS1mn6Kx/XWYxiYfehct1ihPHeTu3SECvtmyLB1EAk+8agizPKp5sN4f6pxM0I68DCCfUmDJVUm3tA+B/8nrBXEVZTk/JIt4pDZS6ChsKAiBTI6z7ASPTE5goeNdkSBWKGUW13WfVlPQ7/yCUNZwzGfoLuM5039NjzVLV670khJixER0qqVQ+C3Wz7IYDxuJ7nhGik/qSzSSzKHlZkp1zUdVN/v7XS6Xo2ef1WH45z2Rs9rx3GC6CEIKnwFm+1PD+EPIsYTURAnP6aY8KXF4xSwWlfIYiicWUkUXPO4hkbuQVFEy/6zzbo8b8wipH5BgNih1VBwmxS1XF2IzeBZyQvPBXICCk+kSGoHob6VgsJsa7js6W7s8c1SV5aTypSrNgp6D/28kSTqA4AUU2FSauT2H6ePZl+fxwngTgyF9astCWQq6uluO9Au+p6qYR8nVWvlHLedz4yqWKsJe5lzgCgTpMfsthvAwcxdSUowcuer2Pkcz85TgSMpoJgkBrkIkgHObySEN+HQJh4MWpxkhmEAZIsxQEYp5/9C3SffsJXCdWoXQKziT8Vw+D1h7XBN7JpZUUZYu529s2ENSHZ+OScaG0w1IgMkA6zm998AnLFWplC2sUr06bj8QG/WBqYoomTzlP1o2B7Wosn7PWlmcnxlCcJmwO1F5oRhmUNJsiOWsMhCIQBQUJRAt39BWtzAB129FXWYYGYggpSTjpoBwbq01JcAsZGISUWXDoXKt65rL9dl16xHb+WWNbZvDNzAlpIo1XDJxCFf/B8z9Jc9zpTKU8Dx2vf9GkA2atm8yRWInMNzR71uuFWdtRH4RSigg2aTj0jW7t3FHgqMBf04EjrRB+ruUimXDMEMVw69gMsCXKZM9ieTJqe5R+mBGeMJMD7qDX4hl6yji1rjZ46WHvcRCA1B3H2isEk3jW5q4Sz4DFyYcAgRUJtufYuOJBgpqrGIYxHpYPlTi8XxGwO2A65QrmSTMdZkhEM7E2CEv2OeSOl5kvdZTDpGuSRM1IPhMIR4ac6YE/1Wj0avauUMDLjbwf04H5lGJ1zekpp76s4iUSaWieeLwbqlUeuyA5wGpjXcrVFUlYT/fZ7sqwaWa1MTcc4B9gIFJ6tRXUDVMKTJqefLp/QF+eRnANLxh4pS7lkG7s1DdYeKRCkr6M59MJqcsBquSvc5rPmWBc6BUWc9JVMbuMpobrke5W7MMfV96VfhjgM+fJh8AMJhR5SfydLzJ5jI8HbWcWi2CN23mT7yicpV2Q8Oe/bu0z7tdN8GOEogI5VQOXlJWPMoMRdezxrwDd9OMQnZC8dIlJQQ+5I1dd8Z6PYNY2oz5XIqaQfdfoP3zKBInZS+M0xfBIQFKzo3oROKSopFBL6hSODAD3sAcIlXXiomSGWCtMMIqO8QwIWeCB5zsFUhJ4fvU3o9rGprq9T/SqOQk9oevQglwcTExOhRb/As6ju7cd47JTr2AKJmDMf8rmpKlu9/ebfaUEHMCA3di2f3uOyNFxTwdT3oZ4K5wbh98KAwzwzhB8I1n4nmD1/v6Aor65tcNupVq+Yt4MIZyOn0wqTl59gTUwrKBG1YX8I2kApFURnhd1mvesoXc3Qa8EgK84QfRxdyJkZGUJVZMQJQUjcYFA6w/ps5cY8+LxTN8YNElUps7lpZd3a6iEjEGRCVaw6DXO4xL5sD3SYFEmUWI5y1U+3KT5FTkg+47hbLEczLg8M73RCNXe12zSCI1cfg5H7GcoPv6vLINzY01Ou0yBTUr4AfLrWmO+GTlXyAplgSAfvyVWFzM/WG3rGa7O6WdeDCE1y0pdkoymQP9WJICTpuG7/pFoVqIi3Hvb8zWGxm0hYf4w2EvuCQjFMD9Gl6cKANKygkLG7lJEmNH7qYKehKtCcZ52uL8Iymv1tU2trZvpkY/cw3AvDQeDlKjib8l8LtxFQGL0hnMvf+aw/VQqm8+dKgt8rdnnRS743AMZVETUCqPCKXyE38WS0pm+JhRYVhyxM+NOM709t/jKSg7hlCV5C899zdz+OxJvMT86o7LKhb4abvDXSCT/RVOLiBHbtou3+JWGgFTohJC6GP8eEShxIHrvb9ycAcHEsPk+tOCQm4jaJjO/RFfygVmPD1sDjjxGAE+ncJLCCLAxPpBYJ/BHp+UUFAmnS/AfJctYD9L4itwxciRzuvS+obV8VVGXCT69pg/kGzT2NsW46dk3TZuGAlsvOt89GhkaknT9zxLbni7tXElCacIDr0XJx56x6XLVCTmsf46kGT8Y5kngv4/CEWzhSnUeG0pxPLw9P5Q5+R2zgYwbIt4lQJe+3hsVpOei3iEyWWyijwymcqdWPly2cBnDvu1gTJF6ANduXSXUD3Ky5qlsjF9zzxsTF8BjyklNJLM3jA4Nnzll0bTVa9/zM/c6Hd/KQHb/MBiAthGP/x5Pz3MnfLLIxQQqh/IBj4yWq4xviHa+v7p+Pd9c6eSUDr+krpK4Og+ZqOHfb7P7cYTfZhCsyR5DhUrkFGyUcelAW5vFZgzmU8yVx0jCyhZqC+mwFVmN81dH7oYn897xdT16xGqOu0qVJFS8zLb94HJccfnu203nnaEvTw4OMCLu2SxXI2N2RIrFvR5XPQNOvEPjGKFswT4mJce8LNBv/ei0XRxJLRvByP/Xi0bshhDvHku9lh+h1MUj3ufhJ5IuUTu/4RrNVAn5ynzZVZGzSNU1RRzrst0JRR5utPyBalWU7miPsHYLTYGdP9jHssZB75UDa4QyBZwcMddYIh2+4KsH7DRdXYQ3DYUmrH8Fo6PeejPvGADXyQAwZgCe6Jyirjp6MdkMuA6F1Oyb7kcN4BvxBlnPgnBSwlyrN96lvYOeRy/MF1J8yek1SvK3JauszTjY1y9XdbbadUVVFRVYpdPGu23fb5hl+WUzVdKyf56iUbB9nV32T8HkbT1mKlfVKV+idfR5BGhSFnwtzzal5Mkp1RijHaNiEIbrGNBIF+mkoAzLlJZWoUjEfmIpVAEXLJbNBCBIIDouIV+wG3+SaGcn/yDRQDsK7eLBooDNuPK7p7t6nF47/mYa+auk5dH/lKcspIKx5CvbonxU5PJOKUEhnmKFauq5eMXuo0XPD6fjz5nNH3ixf86pR48WROjJ0ZiNqrrFw5hWElnlZHPMVc+stFDXvqC0XwtOtCGclVQOHr7rOmklb7n9/s8l13DeCkJ7oWGKGwO7IAkQPuW4Je737d7hhlgXY0fx1xzxvmhxX4nURL8rqs02D4HDhgTpZE/ajm4qsTruOTJ2oh8IsTL5ixl1cuF/SePWW8wvnseu7G79xYmEYNxN6y/48MwRj1RJcFovNfiiE478jKQ8UUc8Fk89iShxDzrLpCWk8wlxwj40kMBJponk9xyOGdQFaGdMjhVpS6mzcfMzkGfb9DZ/b7ZBS62TTH1k+pPidIS/Kat1+ll7jgtJ84PpKyW8/SwGUGKvOQxAnw6RaiXy30fd5udDGef37fQkC/TCpS61VJBkP7Fvr3HrY4bHu9t2vmJqWNvl3dhw66N3CneeCghSiZGmPiWe3Do1NnVafcXeG3vd3Ud6+o80mH9Ekxb5FKVR5SG9V67js1J9hxK1Rqw3gqmXqG734MMM0aQaauocO0AXEMsD1/vD3NO5vI1VhIg4Kx/YJh2fu4LzuZRiqz4ZTfpuYgHd+tAhBOeEnlkMjW1aJFC9sXZs+4yRfimfix7dwnXo7ysWSoXOT9Du4zE3Dxs5FzAk5oQ7W7N3nJ4uf7NdT3GX+7dfp8Vgo9kbHpTPx+4xWK5Sm7/pbEjgP9sU3YiiSnEyxsax4zdx3f3Tkio6kqF2AY2keZHIrH07SrDzm34B72dzaaAgKAq63euTT8ngFPfpbBPfTIp1wVwa1YzaPF3o/tW5uoaGzHTqe6WM6OFoILrm/XV0UMUcbYE8pqdGz/oPn1gx9czZUtWaqRnuJNvPOAEvf/W2ysgtIvTLPHEiOP9g46k2uKq7ccbKvV1nq7T/21770OhRKpcsV597kNwjAcjijUN24Omkxxv2ByqamujDvT3kTN3mTATLd3a9Eeu1Wzggx/SJRu3GyjgDVRv28X29Jzea/sKA58G0b6l16bvs+WjikuVcz2db1tG/igsq6xrWp+66weWDwdXNGmOmTp2WLlPgCxasXEl2zMETmZSohdUVXM6e490jTc3xDaWiKfCXjG4Z8/MlHK+ERiAy14QWAvkVHhleSYFl5ASbd0mxmg52PJRkUy9TrfUb0upQwm4wJM1nuzcfQYDX/jQLGaSGw6kFsg37aw72W05tMPIFpEKXWMNJ3uKup2BU93Wd3YYJ4rIherGHToOfJ4AF6GxSGr422T5jw8Dc5LkKB8TXut/77TeYsAi4fW3vKu379HNixY6X61+kREkLlSmMJ+7WBZIV29ew5ww7tgawGeTFWq9bqLH9fsRLPGUF8AxL/nHKNViq/FkRxDbv2eltuEtzHT69N6mUXYWIX+lseGHqVMtPBjytEdy9JRkMkZKkMBwyiRXNE1YYU3nDuw4wQrnUKqtTYYX87WKYLcwoZSP297Z0fVQQi3Z1rQupKQF0jWvrRn5AAj6uKRco1+rPPZpqNREruLXfog1W7b6TvR2vm0KzJCAb9g0bgntcY4p7Nv1/DvBkrELPwHt277R+L7lwNuj4GyBZqnUeyd0s9HEcP9HvX6hWj0v6pbf73fcwiu2KpPcdOCaKE53OB3uddBuKF4irwjBd/tx9Qcmvf6UyXJ0tzmAAyNZt8MgB4nh/R0PhvHyEzrEbYWgaoHZGlX6HV0whOBxcQvZpofbk8QS4d0H30YIcIErgdGF80MaXiIFF3r6wbV04aEdLtW/1YidNBv/qzUAvkfzvTWNNemb3CfTn4qWGuq83aePtViFpOpVnWbMFJ6NjVYf3sPCwUmJBYYaZgT4dArENwaNPT0t1gdF1PIVqhJrein4XN2u3SLzGVv/p+auT4XgaofyRao1uwxk+riLu4KoB1xbpC2OiJrrjNFxz4/dG/FE6eIKtSR9n3l6qY8gJujs3nvyMnMvyOI7dnoMe7bGvmDG3dRt80jTvh/GMcF+4R4mKEOaHwS3paAhYZaHt/eHOiehyMwGkFSqZFd7j/wPtulwbV6dbCKuWU16LuIR56SVhDcaj0ymJp6tVL5goicUkYsKcuguefQoL2uWykWuzwldhg6eh4cNeOKnJ/Yv/vSnPz09tXkyasIyN2m2RB654uYB+OSfWbTzkOHFJ4N7xCVCACGAEHgcEQBfdXsr/GnHx4k7v9c1LJSVR3bRg0+xtXyh+dkONe/47nHifdK8BC919hTW1yd+JHnStFDGxx4B/yf7/umWpm1b+hT2Y886nEGWPrHbIm7asyptKAlN/3haHiirKBIh8AgQ+HNuOHgE1XkySPoude09anENg91TXsfJ3uuz5MrcV8aejCoiLhECCAGEAEKAOyPW236w56LHdx8cjrCafjMCvhX5dA8jwZcVbNeEyvL8l5SRvDyZCIiW6OSM7XLGA8hPUs3G+u23pFp1bsPIJ6liiFeEwCNBAO1ufSSwZiSKy9c3rD5h6v5p78hDIbFAVfeWPsM2uoyk0EuEAEIAIYAQeIwRKNHW1/i7Pzqw4zjLbW9+pbGuOmn76mPM+mRZKyB122oht+ZOlh7K97gjMJPSr3N2nHEu2foULEuy9Id2TNfwtM/3PO4yhfh7ghBAu1ufoMZCrCIEEAIIAYQAQgAhgBB43BBgmU+M5wX62sone6IkeM3UM6ysW42m9x83AUP8PL4IoJHk49s2iDOEAEIAIYAQQAggBBACCAGEAEIAIfB4IoDOST6e7YK4QgggBBACCAGEAEIAIYAQQAggBBACjy8CaCT5+LYN4gwhgBBACCAEEAIIAYQAQgAhgBBACDyeCKCR5OPZLogrhABCACGAEEAIIAQQAggBhABCACHw+CKARpKPb9sgzhACCAGEAELgKURgwvf/s/c2wE1c6YJ2L972eKVwI5MrmeseLw2JBOWOJ21mJPJZcJHZoMwgckfmC2Iucso2RUyBmUucAlMD3vH1XDCL8Ydx1g6LxxWbisVcxBR2dhAT5AyIC3IFaQaUpdpFpASLZdoX1Bsshkgf4/7MfKcl/+in5R9+Jji8J6nEfXR+3vOc95w+b5/3dLuPttoHpt8ynnV2WbuPtezd12K/zo/k55wdbQ529Gr6hUIOIAAEgAAQAAIPSQAsyYcEB9mAABAAAkAACEyfAO//n60Xc4r02dPOyvZ0s3km4/qKioIh65Eu/3CkBLnWmOftOOYJT7s8yAAEgAAQAAJA4JEIwPckHwkfZAYCM4VA8EJL/Slf8FYg+IAw1h4wLYgIPuzr3tPY9UWQx3BZFmX4aZVh3kxpEMgJBJ4+Apyn+7g9uLjMMNtl+63DdS0QylBQy83lb9KytFFpb9raPcqyn8d/9/ymvfH9nht32MA9nCpu2L0q+ivv6dzV3MOGeUwyh8hfW6UbYOzX3au3qmWEIpNj2T9jpEQoVrakSH220dpHlebiTx8UkAgIAAEgAAS+tQRgT3IKXRt01G/Y1X1zCilTJ2E639l42J369yf+C3dmb8l2qy/6DPvx1BZ2NpbsOO5PLGzY07plS+tVFM1215TUnw0KCYaDzAUPJ/wVE5mYc+ZfD/utO0saLyTtDVy37tjQ6Lwf38DHoVdTRyZbVlFXs5aaq1BgbO8FZsQVLk1prHm/+nWldnPT+w2PbEbe8zncrIhIl1s2VloeSfeGOeeRXVtKSjbus0e0aFqB5y47mYgaYu6HleTeY5gEpiX0RIlTtMI38SSTItdEFT3Eb5x974Ya2+3EnNzpmpI9tun3XWI5j/l6fF6KLziVJk9YfdBj2dvslKwq03Kt9adC1JqKys0mzeyA56PmlnNjTQ97TjvSlxWSY4ZltMwcfeW+3auUhELGMxecI5uNGE4XHzi8VUu+tvu95gMVy+XUut2164VPprPXvIMLVFEzMlKAfOlywnPKGVXzCcWEH4EAEAACQAAIPDYCYEk+NpRQUBKBTGp5kZaUonj+95bGj7xBwY4dj0xKDxFPmIDfH3rVvHIhHrjkiPGEC7NBBU0/hs9JMydbrJ+JGQtzNatXUuN7Mg/Ryn579yVMs6lqZ7EmfitnCmXdOdfabPf/eQopZ3gS+csrVy+evsfkDG/1o4gfMy/FFZNSk1NXFr5qqT8a0G4tz2c7G8/KTFtN6pdIpdpQsaWITAt7P70yMjDCjPMzOf2KmBbf9/nxlaXLCeyPDucX46ceg9yQarEqsvWIYbMJYg6O3XZY3Yqytw1EjDwSmp73pdMlNv5SSw2/AAEgAASAABB4JAJgST4SPsg8IQGJcoVRu2DE22p0ZRQXOWF2+PExE2B9AWIhXbiclty7cs49unsx3O8fJkcXqo+5xpHistXGVbTY2nnK1f2/oZBEpV5CKXMeg8U75VpnWEIZbTAuiTUuZpj834i4j+c9NUF3e5ub+MkmndRjPc6Qb5jUs0db812azsb4IBd5jobx3iuMhFTNEWvrDW+IpGidjpoVcDpGvQawsPdGuvLFGJ/VMGM9yS7dVqGdw8cJj4qd289cS3KIEKsK4oAAEAACQAAIPBYCcE5yqhiDV62NzQ4PN6RYpDdvNNHCUoBne62W027vzQCfoVAtMZa+pSPQHZ9zW452Oa+xoXSF6gcG83odmRGp5T5rbzt1+lP/11Iy/42y8tdIHLl6Vu9l1Qb8co/r5tfPzVtqLtUNftx+2u3/erZKv6HSlCc8iQ5fd1iP21y+QBCTEou0pjKzGq3Kka/aR/jqBZzzM46f9bxyZXHZKuXIc+tJ2hSt1Ih7TrtuDKZn0frSTcaFiVnFK400rv3XTobDyCUG1bi7ZpDparf8jmEfKOjXaVS/sBEpOLLu8i5vqprbvaPZyQ9jNRt8pQ2l4eZI5ApkEoT9n3RabC7vHSyTzF+5rsyQK/htRZhMIt5IE5GD6HY7+V/rTOh03zDTsa2eKag+sF6JfkWefi1Dbx/YQPFf2Dstp13XB7E5Ko3BXPxajEcY6kJ3y7ZOaUVDKYU67oZ1188d5Lam8sXoAglf0//6e5UFkmBft+W444p/EJOrNKvMxSuEEpgPtrQ8KH9/o9BYDDn0/rQV2/x+eW4M+rDPfrTz9B/8X8sovTal/TNVvbrVvavminZPrSG653THvrfKQdXUGXN49ryl4zcXvVyUodmQm6qusO9mOrkSlywo1KnRResAACAASURBVBx3Oy84ueUGwbr7o29wLpXCzBMvHLlqd9xfqb3vdHhYpMyaH5eVrSADXbv2nglgWP16n7Fuu8zyM4/yDdx9hsEWb6pd7PqnD2U7G8zKNCws2h086/iww/Z7b+C+lMjVFr1lUmeNL53DFxo3H3ajRfPet86pt7ZVFvAiajPs7645xL5SEOy1s9LCihqz0KFC17hbtncw9zGmssSL8qLIB0Hvb1vaP3axfKZqydrSEi0h+BmKtzSmO8f/5C407u0MFmzbaULqGmS6Oy2Iw2C6giowmtdpCZxnPtjReNf4XqUuOqj8x3fsGUCX+eyZ9s6PGf/docxsquDNMlOqfeBJtTpPvBVI5/ffM7dtViNZg302y4meKzcEtS9cU2oqEGamFG2PaSV6NegJi62XYcNSYrGhrMSgRBbRfdb5a0t3LyP0Tp7OVGwSngogR+5dNezqw5XLIq1E/tt7WOP7ldrYwm46LJ22i77B5xYs1eWIshSfQkcULOS0X2WHZCp9cSl1w2o542GHFfQb5VtXK4W2iEqFfGt3Oul9tYYsoTrkUrv9sqah2iBHs+VpmWkR23OeCQxlqpatLS/Wyj/viJmXdutHxwAbq8l1JhIxSW5+XGt43ymrS1JQ+wMJdhsj1+4sXB4zntKkUgmO3cej+hj4IzukKIyoXGJAD3oUSgLLkurorpbL51z3aC2Cjx70DJH6MbsUjZSTjGqdmX4eVWoLrTLS4wNFoZiLOW6yGKaMLzrsS1Y8QcecxBsyr8N39wEuz0Mz48h9KtXML6pR4sM5sWVwDQSAABAAAt9aArAnObWuHfafO89RxVV1u8qUg7bWkx60ruW/sB5qYxSr3tnX+H7dZh32+05LL9rnCTs7j3hmG6r2NTW8a5D1tbeeQbd2IYQv292z9RU1tVtXSJlj7faRo0Qh51kvuaGu7f1q3YNzzf/S4sstP3Dk8E51yHbslHBaJuzpbOrsX2Devb+pqaaMDp1r/7WwqkaBv+50pRl2Hzx0aJce62k8cn50l2nyNqFKGdJc1/bLpgqK6/ogUlFsSFXpdWvjYTe+vKJuT5Ve6jrXN/JMnDvT0tgT1myorttpmt/ncN+LLy23tG6zGs821B4ZX66hFNzZ5voTLLlmZ92+anNu0HZwv23kMOpk4o0VL6OoHNZ3LeLR9UfGe48P+JBJJSxzmWtDyjwVftvefPB0kC6trm+ofYsKdtcfuRBHCV9EK+97mcjr+IPXfOyDkM8X6a87DHNLlY9MhRvdjQftwZfN1fV1O98g/Mfrp8Y56Pyg0XpLtXZXXW0Jxf6bc0QJxiSP/jF1vcpRa3P8rssjvmvBy+7+uRp1Dha81HrgOIsWrw0NtW8XDPU0tdhSfVqA93qHlcLeYwZdWEDwnzudNyKt/pLNVJIJckUvJyicveBgX6moO9K2b52ir9OClJn4h9qq12Sy5VVH0eIbLZR5xtFHmLZWmldQ408pUnQH19PaeZ00/ayhaU+FNs155GjccS/Jssq27TrZHF3VB0crC/CUajM86LrI0m9Vlq/TqcaW12nqin2lVIbS3IBMUCGW55w91wjTu3W1G9VDve3WXmEPZ4KWJpAJezrqOzl6c6VgRiLztbHRienermlo2G4ivmw/cBR5DeOqV+nnmCuu6CgYRr02SC3Jl1w/1XoymL9hd9P+2tJXwvZWizvV7tGkWp2iFeOi3ra3HLQFv1dau79h5yqZu63FFulr0bbHNJBnPjzQ+plM/1OkTeX0V12NR51hLOw+eqD9qsywtQ5Faod7Gxssvqls5N1nLAfbmTmGnXtqK5YNuc77EkgK8ohPoUJCpGCBVyubjhyuzAt2v1djDRZWNrU1bVTeOGmNHDmctlToiZKTL6w6eLStZhV+qb0T9XuKeSlek6dQEc+c+zSQmZcvqH222rA8/onecDB0j5cQ86PWY/Cru9jzsvERMQ4l7POnkyS6lmmWa2Rhj+NSZLAP+AJzVWOGqe/kofbT3fVb169/q2TPZUw+bkaipLjs+ecG76C3Z8WHVIqHxL5KFO89dKjhHXXQeqgz4vCeauYX1agUwzmhfrgEAkAACACBbzEBsCSn1rlpEs2b5fo8knhJW6RThW6yaPOFx0ldSZmpgJTPkRG0XqfkuVvIShkMBnl8tkwxRyZ/SVe2rars1cxoHfgCfek6rTKHpFcb6AyWHbUtiIIi/TwJlkFSCxUYoTUuI/A0ifL7VOZtjnuAYUNS1etlZWtoQi6Tz1PrlxChr9DmZCTM1hSti7wSUK41rZR7LkzjjAyxtEj/kgRLk1EFtILz+xMWtSkq9fX2BnKNm1ZRRDap/cey1fOiCxnWecFLrCo3LiaJebRxk3FkOygGLZ6WjhY66J/xMMwia1a+qty8TIlKU6+rML7o7xldbk4i3ngpcup7RP81LxI/6POFKZoc8AoXd7wMp6QX4f5/c3hJY/mPaTJLTiw2mn8o95x3xp0kmk1RZCBii/LIhlS9ohqM2KLha0x/NkXJMMbhYFWmirVqMotQLi+t+KHM0+MQNwtjGocFPc7L0sK3zNoFBJFn2PQTjdjaEcOmoVeERk36/xDt4qDL3U+o1QTGuXpcz79eal6ilMsJ6vVNRmW/44I/VpDxv5HvXPbIklSp0yln+R3Ce3d4/3WMjOzxJIWJCseVK03LCEkaLl+iy83oF5Q5DRd6OQ3DR/ZbpPSPitS5FL1gvOmpuiMYDGNSmfwFuSybMpRVVb5JSeOliWzopAv/nVBtFAWrDYspOi+y/zYWohdpo3EZtHGjkV5AkGqTnuIHhD2ciVoaW1LounV/K0OWVJZGthP5Poedyzdt1FM5cvkCtdlciH967goyJRdq1bMZ1+XIoLrhct/LX0pL+GBwME0iUyhkcoJeU1691SBOXcgziVYLSURaMS6pwDnHUF6EJg25coW5dA0tG47YFxPmQlaE49IQGte6hXJ5Dm0qLdblSEN3Ltov4YUbynS5BIo0bDFr7p2zC0/SJgn8VcdFXmsu0Smz0cApMy9XJGdIMYUKCfEFuqIlcjQTUq8gwwxtq9JyHJfR+cpZ3AAanw8hFU7r16jlGRieU6hVRvsd6WrSvITqjtXkqVQ04PXfxeSKFPv6d/p9nIRaPDIp8uGvn8sQG2681x990IPa/kqhNov3OoTHT+HrrDTmQY9y3YGjx0bC0VojEc8UxyX8/VBC36RUvDRF4RqjYPXihH6NDncLqpvqdiOqUamGc3JHQwwQAAJAAAh8WwmAd+sUe1ZBzB25/UvROmCIR76aknlavYRxnrL6WS4w4PNe5zOz0E2c1L6ucbTt3XyeUNE0/QNtoXp0JS0nRhdT6dLv8MHRQy6yF0bcEXG0Up4tG7E70YocGxKEk6Flv8Rzodt6M8AO+G/4/Ng8TVRofC6ZHfWbRWvPuQQ+MBAYxuRiflPJjRyrFMPThYYl7EmKVxpkBwYz5416h6aRZI7UifLyA+wtKTFvdCH1/HxyDhZKrjIh5oGQa/6CsbWQTLVAYbnBBiOuWZOIF1MUmUs95/D185rwNXbeknLFVx0+P5/PefpJumI2z/yRDV9t37ahfTQHz0vmx1OSUbmEFdmiK2TMlwRdQg8d9vnvF+JXfYrvFcmR8+LtQcV8csxhlJhPSm3sAHop/8QNvM0OYNna744kkpAkMcsrlmPqeoXJ1RpVt+cKZ9CnudxfEpoSAjn0+gd43/VdJR+Nlo0MBpzjMeQ4nRg4Ye9xlHa2tjCvq7XXyaxJ994nBSe65DAcSFW4kFaWOcoEKTMWPQMWV0aaTPFCghQ8m6I7qGUrVZcsuzbb0DExerFGu1RE/pHCU6oNiRLI5SlW87GSzVbIR9qLI7dDnh/CUrc0vgGs/QM/YkspRpoeHGBD97zNW1xjxfM8wSI7Z55So87c73aFl+vYT92hPBN62yaeW7g6e3/r9o3dC6hcGrVRm7qR2IRaHaktuRXjbeS526yUJEdZyOjVJuHHrybOhQxqluUJ3X8ebfQ8neA03mfhsGzdyDMjDEOqnM27BgLY4limIn8HBgL8XC0xMkHh88lsfMTjYDxxiik0kuCFUcpp6em4bGQXbxZ6WoH6C8NuceJSkSKSjETNzpR9J/onnp6B8w8is+ukIVVFWIwRFwqjR3v4fXHrmu11eucWVo/dBVI9vL3pHZybP9JlacKjHvtxp+MLPfUlplqTPJonlXs8QUrFm5VNjLkcz1XIeTd7C8MWiN5uRDWKd3406ew6DTkhKRAAAkAACMxEAmBJPnyvIT+3mmaX5Pva/AXoNQkG+jc1PZHCZAUVB3INnssez2dXbM32nuWVdRtoFI/2akRXBHiqtUVUtDvOltpW31xtQS6peaVQv6iz/n+NyTy+duGRCTELPV+fahCVZDyzeKVJ5YtbrWi1N1UxktNF65hEvNhsaF0+bGVueMNfylVrKeLakNXnZwb8RJ5Zhgl8ZAXlu9eoxkWfJU14iSiRR2We9XlvyrzppJZSBWf3eH1ezCel/guJdjoTbexo1ZEuG4o1vxNM8YSGzcKS2CU3fSQmlV5hWWhXsst9mdOkuftzNOXowKSw0YTT6+vK8mKK/45MjF7Yez2djLh3RgLyoMu3fuY6d0aWPic/ZlEcK9VEhadS5pj8yWqQujty9FUNGp/HdeUzj+dko+13hupfmFJv2cUKOfL3SPuFLaZJAj4LT0o0UUtjW6RaU6Xnftn8oU37c6PgrDg8hGXrK9/VjwOchUsjL1NRvopMySuuOwR7OUSvj5xly1Aaqw9r+1wuj8fj7Nxrc5p/sXvk1GuyyBNptZBarBVxpSS1cQq5xCei8UlmpAJRVX+QlExIPR6JBE5uZUpVTz1bjhaSVJ24VON1TkosWbxIzBQqkkqk6Jyt4NghT3zAdNtu6eH1W8aVGc947msxm5Pzob3HojEZiKWFVHer+3d27AGpG3uOlULEsWieD+MZ0kTQooqXIKhgVuPIoQATn/mFGpI0KvVwnkxO+B0IAAEgAAS+NQTE1w7fmuY9yYaEr5w7F1KXV282G1/X0S/hwnNpFIZZ94lu959JeoWxtLK2YQM1+JmnX3SVMzXhuE8dLlxfUVVu+rFeS5PYn+6O5eNvsQMRBzoU2Ot+nsgWfZHD1OqJS5WiUgmRnTl4wx9taGQLI7L1iGcTRIi9zo4UEfb70e7HpAE9EZ8b6h/LhQW91wOZWUlLsUnLwVX5i4KMw+lNU6n+FicXkYE++8Vrwk4j2kBRZClCaJPob+XyLOFf7JrN6vAm7pfOo6hZjOssE1xAkTipenGIOY/enEjRaEMGk8mzMwPjTcbYfn9oLiG89SYtfdyR7E6AS/hWZBaRPQvtII8sQ/mbLIsclacUUuiVkFeueXV+/2dOx+X++erIxzDSCCILG7gVlEVaJ8/C/b+z2q6O9k9sdcP9vvuk8PaU0SCh9dq/Dbt/48BTHJKcRuFxFcVeJPydqjt4/3mrjcGUar1pY1VdjYm87YmeXBUp63GpTWzRU8WoQD6K6jVrae5UxyeCi7Qsm5BybBAf0S556ErXr5390T6fp9HOYa587GR4WhP5WHy4z279xC/L1RrWV+zet9PwgtfTJ9ZTUcEm0moRKvFRuDyLGLwpbI5GQth5eMfe06PDc4LcL8jlyHeUHbWdrnfX7OzwZGbLMaTJo9nQ6B7AFXORd4XgzBAOj+zshf89MBhfsiJHIR3w+0fHhf8m2shPCBOoemLSxOu5KaQSSPOhkUr5wK04T/bEQia+jk7aqSqKzYvc4OegY/Bdp2I+3SH8zjlbDzmk6yvNEQWIBtkLz/N3kTN3QhAe9Khin53INIXflwUudXlmk2gim1rgg3e/zpyT+CAppeI9GPDfHJ2g/F4/TszPwlLM/KIaNTil2XVqokMqIAAEgAAQmKEEwJJ86I7DpbOloZuMlwvzQb/7eOupG7zgKZeWPvjFKcsxG3M7GOR8Fz0DeA75KAaedLYEv+dn+oN8OOg/3245H8CEaiLhnsvygd03wPl7O1rP8prXlgoPr+94bF32ke+wP2zjUlWqXLFyvre79RTDcqynq9X2RbQCQvcazZ3psLpZboCxfdDNJH27D/8Ojt/zMZ/5keflSEgjtCso9nSrpdfPcX73iZbuL0n0AvzUIiN7o7tb5MP3EmUuyV5wBl5UoTdeyBaoFNec7llUvmAHYshVS3XrVGun03+bYy9bW49dDPwneeIj/jQVpRp0XugnlcjfEFcuJNlLzsAiKvrWFkqnI/qsrR95WCRlr6X14wC1QnjbJ5GTjffZu9x+9rq7+2hPf8I2rEyjf5U/96HFeZ3jvnC0n5j6F8NT6FWEi2yxWvXlqVPe+Wrh7b0oyLUr6KGzHe1nfRzHMafbLZ/48b9LbB9Kx3/u7pco4k6q4YgNiQ8TZOwHBiKFjoapFj6WAR1kDA0w7s+5pIXySJIU3YEjpbWiN6B+waExw3zKcBKCSOWmOm21QZuBaJfmrtfDjD0CiWulcDGdls7RmlbP7/+o03kHw6lCXRZjPWJ13+C4G+6OD6we9HbTqD9nGql+lWA+ORfO01CRGBwb6P3Xdsslf/BekL3q8n6lQM7hqGceQquT5E+MIP9ep7rZ3R5RWt/5zu4/4BQ1BXtEkl9YgF081o46ghvwdB+3czmU6u+W6tX8uY5Wx+cokrEdsbhkhbpX0P6VgiCkXke38wvW77G1nx77asWIMHieXvf8FWu73Xeb81+ydJ5PNmUnUvXEJiVcz0kh1ez55PNs72/szE0/c7bdejnxqVFCMSLzUiTFuCanqii2IJwy/JiW8f7uAzUtXQ7P5z5fn8dxoqWmyUW8VV1REKfKiu8S0gDLJjxbvMd4AjIibp9QQi/XKtBBUqVqEhTjPwcCt7DsJMMzheKhh56BsQmq9VdO6TIdnYGlmvlFNSrFcH4896AptxoSAgEgAASAwDdJALxbH5o+jl6boT9iadxuEz4Bsnhl8et85010zI/Sb9zEHe1q+ZklNEuBXppfuVHwTkpeRk2xYkmBqczXeuK/bet6IFUoNSvX6U5/JGxFCgtDOa2WuVt+bkGfDFm67h1z9CgOx/R0M7of6NHbYh46pKw021CxjW8/1rLrJIY+EqBfwjojdciWbaoMt3d21tjuZ1KvrdRm2xKrflFbOLelq7l1qLpizEtKvmLrTr6z88Qe+10sc57G8K7JEDH/UoSQt7fr1BzSiF40Ex/Qqy4JjElfGHmkn61UybAgeo1O1LRDAr+LWU6c2FM1yM8m6B9WVvwoMTta5FMvU9innEopIJMpVcQsRv7y6GuD5hkrKzHL8Y6ak4Pp6Csg66pNK4SloWyZuczfceJIjU1Kat8w6u9Z4jeYJHTJjuIPO07s3/51hmrp63rlyUCKdiVEp9QrQbg5Gs2LFmZYPXqgSiBf9Wf0lZP924/y0rmUdlOV+aXxDRChaM7RcrD7yh8DYezctpvo1cGjn8fAMGKZjvKEqedTyjV54fFZSY1Wdamr+b/zVTXCrq1ISNEd5OqK8j91dB/Y3nofVyzQGN4pHjtWllyIuNpMsO0vobRLbO3HGsPYvt2i3/GLKPAkGGPkIFaa9Bf2nDjhzt+kNr1biR2ztv+LLYQ+wvL9osqScVdEQq0hTt6dv2REkfBc0ztrO9v/dc+2O7wUKdJb75gWop4KPoxWJ0NJiMnSV2wLW44JSovNpQo3VRrRyJpcASX0WzvKjgkd0c5nkmpjZYnQD+qyHaHjHbaD29uHM0k0oW03RmxjXL2uXN9maa87l56lMawpCh2JPwmMK03vbuLbrft/ZknP0Rpfo9nPE6RMqeqTtg+5G6SQSln0dlHgw+76miFFrt60RnPk0wkLG5+X6kwLxlOOa3JTaYqK4oqVr6jcndFlPdN7pbvd9XEm8WIuvURfWaNM3B9E1F6klPcc6LtHyqiBOey3/T8ttmts8D525V3/6m27jaNi4At1updYydSdvMN+Lxd53XR8EFc8NFulERp6yH5we+sDBbV0a9VaQVHxVLcbUY3CUsyuj+MeNGG3wY9AAAgAASDwtBD4D3/5y1+eFllAjmkRQF9IOzbyjb5p5ZvBiW/bGnvIyuIJ9i1ncONA9G8ZAb7PsuuXWGn9uOku3kDQanEu39bYsLt5l33RP+9+7REe9YmxCfe27LpE/3Nl6m/XxuZC35N8166qRR+kFSsL4oAAEAACQAAITI0AeLdOjROk+uYJ8MzvvIR66r5e37zEIMEzSgC5o192WE868QJt8hdx4pmAVj9rOiJRryoYuuBI/ITvo2LgLp5n1as0j9k8fVSpID8QAAJAAAh8ywmAJfkt7+BvUfNwal1lxCEQAhB4ugncY7rfb3en6csM5GSCglZPRujb9/uCouJcpuv8I7wNKIlJ8FKXO8dcBNNjEhmIAAJAAAgAgSdKALxbnyheKBwIAAEgAASAQDyBYc7Z2RVaWa5PcaB4erw4Z8dH/MoSHQHP2aYHDlIDASAABIDAoxIAS/JRCUJ+IAAEgAAQAAJAAAgAASAABIDAs0YAvFuftR6H9gIBIAAEgAAQAAJAAAgAASAABB6VAFiSj0oQ8gMBIAAEgAAQAAJAAAgAASAABJ41AmBJPms9Du0FAkAACAABIAAEgAAQAAJAAAg8KgGwJB+VIOQHAkAACAABIAAEgAAQAAJAAAg8awTAknzWehzaCwSAABAAAkAACAABIAAEgAAQeFQCYEk+KkHIDwSAABAAAkAACAABIAAEgAAQeNYIgCX5rPU4tBcIAAEgAASAABAAAkAACAABIPCoBMCSfFSCkB8IAAEgAASAABAAAkAACAABIPCsEQBL8lnrcWgvEAACQAAIAAEgAASAABAAAkDgUQmAJfmoBCE/EAACQAAIAAEgAASAABAAAkDgWSMAluSz1uPQXiAABIAAEAACQAAIAAEgAASAwKMSAEvyUQlCfiAABIAAEAACQAAIAAEgAASAwLNGACzJZ63Hob1AAAgAASAABIAAEAACQAAIAIFHJQCW5KMShPxAAAgAASAABIAAEAACQAAIAIFnjQBYks9aj0N7gQAQAAJAAAgAASAABIAAEAACj0oALMlHJQj5gQAQAAJAAAgAASAABIAAEAACzxoBsCSftR6H9gIBIAAEgEAqAjx7trWjN5jq58njOWdHm4PlJ08IKYAAEAACQAAIzHQCYEnO9B4E+YEAEAACQODxEAhftXT48k0FsocvTq415nk7jnnCD18E5AQCQAAIAAEgMDMI/MeZISZICQSAABAAAkDgoQncZ50nrU6ssFzHOz6yOz3eAC8lXjGUbTAoZ48WyjNdx1j1u6WSSIT/o70tZ7xskMcyZIpsbWm1WdXXsefoRT8XxnCJYi61alulPhvzHd+1/5Q/PIxJ5LRpV5U+C5MtKVKfbbT2UaW5+EPLCxmBABAAAkAACDz9BGBPctI+CjsbSzYe8Yw5K/Ge1i1vrX+n0zeWM3y+vmRrBzM8aVFPfYLhIHPBwyWLec/ncLPJ0eIx9xz1G3Z13xT/8YnHcva9G2pstzFs7I/4KvkbTvcXkc4MfqNyxkvFna4p2WMTIS/Ci/efadyxqaRkS4vzSex6xOgA0/nOxsNuERGmGTXeusstGystvpQjhecuO5moX+EkKacpwRNK7hZvjm9Cbo+L6hNq01+n2PFhOIX6BGJHPFNImDrJbXfH/lYmx1SWyzQ2XcT/L3PFtq1FeensJUvjUffYMAr22tyEbql8pBzyx7sPbC1UpOHUm7WH9pjpDEyyuLSutpjGMUnB1kP7BTMSBeW6uto3KXXJgcNNghkZCfKlywnPKecj+Mimbgv8AgSAABAAAkDgqSEAluSkXSFRKUne72VHl7/s5z5sjuxrHzNqWvH9Pj+uVM5Pm7Sopz0B/3tL40feYNJCnznZYv1sambOU97EYdb2P1ov/vtMPsPEM/aTjGTF1qp3jfnRrZPHyjyVDjyeSuZqVq+kZKlGyp1zrc12/58jVU2c8vFI86RKkb+8cvXiiJEBQZTAX3kY3nG3HLAOvVFZRnpaWz2qkq0GmiRz1cbNbxdmYcHLF6+MmJJBV69/3ivxo2q2TILxoT8PjbcjHAxhGH8/ND6J3Gcct9TFrxOx+48Smp73pdP1rZg1RfsQIoEAEAACQAAIIAJgSU6uBvJFKsVtv39ktcEx10LUSv181uuLPnAeDvj8ofmLVE9gVT+5bI89xUy2saYCY+a3bzgU4jMptZp6iXhCKvcEGWWrjavo0S2fCftr6iknLOYb+VFGG4xLiG+k6hlS6RNUsSQCnL2t3U+Xli3GnL/qYvNMRWMepxkUTcmw4WAwOpOHGcYvVyVM5FKJdBYWDo1tW4YZX0gxB0NRyJ6MBv8Zp3RlYaJWS0jV3H7m2pNwG0hqIkQAASAABIAAEPiGCMA5ySmA/66KxJ1+P4blYdg9hhkg6C100HmK+ZzXLcGxsM97i1Auiiwkgkx3p8XhYQfTFVSB0bxOG/eYGj3J7m3ceJKo3m9SCtsyyG92c3d27YF1JMa5LUe7nNfYULpC9QODeb2OzEAJePa8peM3F70clknmr1xnNuQmvgcifN1hPW5z+QJBTEos0prKzOqEFQ3POj7ssP3eG7gvJXK1RW+Z1FnCo/PwF/ZOy2nX9UFsjkpjMBe/Rkr6OnY0O/lhrGaDr7Rht360HLZr194zAQyrX+8z1tWZSJ51/trS3csIBebpTMWmCSwD7kLj3s5gwbadplxJKjgikmAY8mfruFdA33O70TsQJfMK1pWZ6MS2owJtx7sdn3kDIUyaTevXlxnzktLEdW/QXl9jvcFjhzdu8e1+f02kx65aG5sdHm5IsUhv3mii0RpRFE5MOby7ZVuntKKhlEIgb1h3/dxBbmsqX4wu2O6amv7X36sswPyfdFpsLu+daMeVGVDzh/3dNYfYVwqCvXZWWlhRfZJbogAAIABJREFUY1bdclg6bRd9g88tWKrLGa0gRX+N/HzbVlNl8fGYu3q947XdFWm/tPA6Feu4yMmLduw2ZIl1DXLi3e4if6Lw2S5676UT3zeVr8IdH564eP3r5+YtNW8pjVOYeB0QjKH7rL3t1OlP/V9Lyfw3yspfIwXtGebcx9u7LjJsah3gb4q1Dvmsfijb2WBG+h/ss1lO9Fy5IWhg4ZpS05KB1u0dzH2MqSzxbm2rzGiNSdltOe644h/E5CrNKnPxChKZ0IKG3F+pve9Eww3JpvlxWdmKiGyiWhELX/LyS//flbvLGmpXR1Wcs+/5mXvxvt2rEm2ByTsa1fcg6P1tS/vHLpbPVC1ZW1qiJdIw5N26/565bbMaqZL/rBWNM++docx5GmNpmW5eZONKlOqoCgjZv1r73jYtambwzN5tx/GypiodOssndKWT3rdbP2dy/uIzg6h2iUWmmqlGsIec9qvskEylLy6lblgtZzzssIJ+o3zrauUU1CN+GG6ggn0i/TsKY/T/Ya+t2Wq7zGJySveP5SOzwRT0MOy2dl8jjGUUzvvxl8uqVqhjn79IZ0sxLB0XJlsMG/APYIrCyAwwHiRSSRr29ZjZeONKMEdLSO1YKIwsSWG64Rz2kKZ4QaRb44JCMRdz3GSR92vCD8Gr3e3HHczAIPY8mtaLy1YpJWjqqN7bn6fHPI4b6AwmoTYWm7TZQpni/SjU6+k+1uW46h9MJ6hCU/kaWtjqn+wGlCgjXAMBIAAEgAAQeDQCz+ie5J/+9CdbJKA/JgeYoaReHOq/jtYEGO/z+uUUNYeklM/5GK+Q1+/3S1XUd9Hy2t/d2OjEdG/XNDRsNxFfth84OsXX94WdnUc8sw1V+5oa3jXI+tpbzwh1BS+1HjjOkmt3NjTUvl0w1NPUYhuIFzbs6Wzq7F9g3r2/qammjA6da/+1O+FpP9fT2nmdNP2soWlPhTbNeeRo5OjObXvzwdNBurS6vqH2LSrYXX/kQhDLLa3brMazDbVHxs1IlJb4h9qq12Sy5VVHkRmZFnYfPdB+VWbYWtdQW64d7m1sEAwb0RD2dNR3cvTmSsGMTAVHVJJIcWxvb3Bp1aGmQ/+8LtNzuDGx7WhZ3dZou4fa0NRUX20iB7o+sKU+gBcVUKbfXmuch9Ob3m8qoYSoYf+58xxVXFW3q0w5aGs9GTkNm1qkaCn4Ilp538tE+iJ4zcc+CPl8Qn9hdxjmlio/V8Kdba4/wZJrdtbtqzbnBm0H99uip0aHB10XWfqtyvJ1OtUwYznYzswx7NxTW7FsyHXeFy1cvL+iv6H/Zhlqm8vVGYTxF0ffF5owxPY6Q9q3KzcYNd9N3TW8x+6WmX7R1rbXKPtDe80hV/b6urb3q1emXWz/DRPXe0k6EL5sd8/WV9TUbl0hZY6129EBVIxnflV/pE+h31bXsK9SP9vTfNCaqAP3xVs31g4EueWgLfi90tr9DTtXydxtLbY/qiv2lVIZSnNDW2VBzLr8RnfjQXvwZXN1fd3ONwj/8foj50dOn7EXHOwrFXVH2vatU/R1WiKypdaKMfg/Wft/v0r4/zDqeMi53P756sQHMIKkk3a0wIJz9lwjTO/W1W5UD/W2W3vj9qCCvUfqj/eTa6oa9levzfG3H7IgUxkFMarjbMiXc9Ov+/qFjgkz1/p55Fv/ZeTic8Y7l6LkU+CfYmYQ1S7RyHFpkv5C2AOvVjYdOVyZF+x+r8YaLKxsamvaqLxx0npOcOacVLz4YZi6f2NrRsRcc4qq9tSW/4B3NEdng0krEphduXgllENT6CkBTmpXaSNP6MYCH7oXwmQE8bwQw38VHJwtQzuQcQGXSnGMH9mUDHr+tyx/gVSSgRxeQ+gVO6h8zxk/9UNazDsAlz3/3OAd9Lqe+MB7rG0ObFnFvoaG6p/MZ0+2dPVFE4TcF7zUlgOHmurK53vbmyJjKkU/YrzferDRgWne/kVD3VYtdr65BaFPNccmCACXQAAIAAEgAAQeH4Fn0ZJE1mN1dXUgEtAfUzAmZUqlgo34t/r7fOlKJdp2mL9oHjoq6R/G2OveoQXCIUm+z2Hn8k0b9VSOXL5AbTYX4p+eGz2BM3GPDQaDPD5bppgjk7+kK9tWVfZqJnrm7OpxPf96qXmJUi4nqNc3GZX9jgv+uIKGpKrXy8rW0IRcJp+n1i8hQl+hzcm4EAyGMalM/oJclk0Zyqoq36TQQ3j/vzm8pLH8xzSZJScWG80/lHvOO9EiEE9LR/9B/8SFNFyIT0O/ImPpov0SXrihTJdLyHNowxaz5t45+/jbiMbzha5b0RsuyJLK0sheYio4qSRBWfA8o7lA2CaS0SbDItbh9MeLxcvpovK3DFS2TJal1K1UK+4GAtEjdvHp4q5mYUILZ6ULbUEhTaJ5s1yfRxIvaYt0qtBNFu29TiDSSFGzKYoM+K4hYDyyIVWvqAZ9aNsYC19j+rMpajbrPMvIV5WblymJbFK9rsL4or9n1FBUFKw2LKboPAK76rjIa80lOmU2oVxeZl6uiBYu2l9xTcBR96A+wlGPCGGezvQaRS2m5HdTd02aTPMj4QWVeFY+lYNlfn+1foEEyyDpPEXoNou2VmJDgg7gC/Sl67TKHJJebaAzWBaZzGGX/QJW+FapbiEhR+SLTZq7jnPxBimfonVjFQmQcwzlRUh15coV5lK0ozLMjyheWpz+MQ4HqzJVrFWTWQhUacUPZZ4eR8Rwx3DlStMyQpKGy5focjP6BdmwibRiDP68JRryhsslWJ7IG8DV/6Jak7gfGalg4o6O7n9n0MaNRnoBQapNeoofEPagxkLQdd4jXVFmLiDlclK7vsz8ajYWsSRFqMZkw1X5VPRRxTA6gk3QeZhfeFTB9zO+zDyamAJ/LMXMIKpdopEx4iT+iS/QFS2R42kS6hW0mYb2k2k5jsvofOUsbkAYBpOrBzpUMTYMJ+jf2Irxl4q2rleT2SS9ttxA+oXZYCoVIYBfhrE5ipHRldCUYdb7ZUjxiloVGUrC0UccmY0JQSpFu8EhYVOS/4LhVZQEk0rRHBrxbuW/OOWas1KbsI05WgCOS+KOU0bj7weD93DpHLlsjpxcYq7c8faYPwK50qzPQfVLlMYiTcjpvMan6kfM5+jl6LUb0OwnJ3INZW+tVkqGUs2xiQ2CayAABIAAEAACj4/As+jdeuHChfz8/LKysihGdGkwGCZGSixUPfdvfpYn0SEZ5WoVSixZRM3/wO29G073BebnCWdruAE2dM/bvMU1VhTPEyxaWs2buGz0K6F9XeNo27v5PKGiafoH2kLkgTXc7x/gfdd3lXw0ml1YanM8FnHhiwaZUqeTeC50W28G2AH/DZ8fm6dJqEy5bKXqkmXXZhtJ0fRijXYpys6zf2TDV9u3bWgfTczzkvmB4Yiz1sThFsdh2SMeeiilhCSzedcAsr+I+Hys/QM/EpVSjBQZFIfDYykkQaUpvkuMyiNRZGUGBpCfa0zbkZdbYaH/sr37dwPsbZb9Er0SiZpYdrFfFcTcEZzSDBwb4vnhlHDk4++JkVG5hPWaN7xCxnxJ0CX00GGf/34hftWn+F6R/MEAe0s6f8EYEJlqgcJygw1iJBJALh8xWQIDAX6ulhjZIcHnk9l4ZN9SrL/EBB+NwxWjiFN1jRIllSleiDYTx9Ox52UjXNPjbTbxauTE6Co8XfodPsjz2C12IMx69pfYxzLwmOr/jPj6ReNStW40B8/dZqUkOWq+yejVJuGnO8kiBLnbg4r55JhmEvNJqY0dQC7PQrMyR+ORbFjkTVETacUYfCxbq5nX5XJzhtWYy82qlmlSaP6EHR0VdrZCPvINCVwqwXk+5tUsw+zALYxcMaoJGUr9OqEzGEHMJKqxz28kyAki4BQeVfi8GZTpB/7mS+g9WLznc4wqIbFbrkn5YylmBlHtEo1M3EmL7ZkXRlUuLT0dl6E30ghhFnreNIShLpiCesQUlrJ/E84FZI5ri5zMicwGU6noQWQzkRfejpNkImL85043R678qeClnjKkSdCmJI98WXnOH1DQL6GEEmGuEDYl2XO/wwo3JEx9KUsa+WG2Rr/C3vzeFtffKik6X6PVaUf0R0rkjBaFE4Q85BkIYnniMzyaTgfl1Ng5afkSIxo/3JmHvgFNJjP8DgSAABAAAkAgBYFn0ZJMgWLCaFI5L+zwXme8t0hN5CgQ9rxKKe/2+7zpNxXKtZEl8fAQlq2vfFc/vrKYhZ49Jxc7vkhDhxKjQVZQcSDX4Lns8Xx2xdZs71leWVeCfsHp9XVleejx/Wj4jixu0XPH2VLb6purLcglNa8U6hd11v+vpOpy9FUNGp/HdeUzj+dko+13hupfrEaJZAXlu9eoxoueJU35Rs24IpNWmEkveo0kx1VrqvTcL5s/tGl/bkRbuCng8FdSSCL4DT+IqfhBcr2svX6v9Sty6RKKKlAblntamtmkxj9EhFDRpHCIPCrzrM97U+ZNJ7WUKji7x+vzYj4p9V9ItKITrXUEtbDrOxbGG4XPGu1Ykf4yRTVOrNh0YQU/8kMyIrEc04xDm7ci62xcWbRr6+gKWCgRfz7ZFhNrXUztsSAmEkpUwSK+FCKyDU+oFePw5ZpXVV2futjFmGtAVbg4WfgRiSbs6IgMs5BtPu0gInlcGTLqZaLzmpdNY8KkgVokJX7t890Ie0O5RQvQcT5hN3YS/qlmBlHtEoskBXlEZiqhyaIqESv/pOLFJk7dv3FIYgZOOppPopeTVjQLWX0Yf9OPXIUT7UXebzvulBmrol/yENqVIZiMSaNIKkOPe/4U5BgvnquNjgVc8hw2HPZ/YuPpYr3I8BgRnOfDQplxzUAXErq47r0VjMvtYTyO1k/srvK6ymUoHj2DQJVHk6NnWoJEWKp+TCwzcj3VG5BoZogEAkAACAABIPAwBJ5F79Zly5ZduXKlPRLQH+hycnISFZXD+i/4/HNVyuiyMw0dlUz3X3Z6scghSWR+ZBNSjg3icnlW5N/Qla5fOyOHnWICWgDdH922GA4EokbHMOs+0e3+M0mvMJZW1jZsoAY/8/RjBJGFDdwKyqKlZeH+31ltV4OxhXGfOly4vqKq3PRjvZYmsT/dTWoI7z9vtTGYUq03bayqqzGRtz3MANriU4TQbunfjoiKXbNZHV60qTRRiC745mbL0YspbowmDPv9A7hibrLvmAL5W6rXrKW5Ux2fCI1MAWciSQI3+0dby3m/HCT+c+yGJHrVjbPHh14zU1W61qBbQin44GDsyjdlS5LWdYkpJxJpPO08iprFuM4ywQUUiZOqF4eY8w6Gp2i0/zwrm5gbip6qjaQPeq8HMrPkCSepFDkK6YDfH/F1RMF/E22zoSDaXyNpJvnfVLtmkmIm/1muyJ7FsYPSET1/PuA82RX1FB3Lm6J1Y7/j8ixi8KawYR8JYefhHXtPs2JVy+TZmYEb/jG9Z/v9oblEtlhSIW7KWiFXq+cPeBzodT2LNKl2JIUCJ+joVDKMxacR2XMx9n+PtotnOn62y/r5pNmEBHIqX3Hd08Ow2YtUeBZ6n6jv4mmGU1IqZNVMgX+KmUFUu0QjI6Za8kw1FdmnIN6ovYSKm2r/fs0F0OZiJATZga8JIhufSkVp85ULJRh3sbsHeTTEBPTGo+aWK8ryitXjswraXn3uXjAU+wBLyCGVoqF71+u6R4y980sqQV8G8Tp9pGFJwrCOrYMP3v06c078sz/0O3pTznFHIIvS/dhcUXOgcinm+Sx6yphnb4yqyj1/P5dJ5MhS9CMmyyKkd/rZUWd+7kz9jmYHNpUbUKyA8DcQAAJAAAgAgUcm8Cxakn/zN3+zZ88eRSSgP9DlFDDKVUrplU+vpCspYjT1fEoZdLsCkUOSKOBUoS6LsR6xum9w3A13xwdWD3qzZdwLHjA8hyDvuWynGP9Nn/NXlt6vImWlpQ9+ccpyzMbcDgY530XPAJ5DEmly7Qp66GxH+1kfx3HM6XbLJ3787+I2T6SzJfg9P9Mf5MNB//l2y/kAFuddJwiF3fFY0etkv+BQ0cynDCdBflMYcplS3TrV2un03+bYy9bWYxcD/0mOisa/g+P3fMxnfuREGxvScTw0wLg/58JzlurV/LmOVsfnHDfA2I5YXLJC3SspzLM5WtPq+f0fdTrvpISTShJUO9/X3fqRB3muek602gYo/dIx8BHRhAUe573qD98Pc3329n91CS/AiHEtjGvA2EWa8J7Ggc9dzMDoujQp3QQijadNU1GqQeeFflKJVqK4ciHJXnIGFlEqRCKN0K6g2NOtll4/x/ndJ1q6vyR1ukTPWzxPr3v+irXd7rvN+S9ZOs9HF5Hi/ZUko1jEtLpGrIBoXCodGM8xW1P4arrr2BHbVZa77XO0d3RdwxSjjqojhYi3brwM8u91qpvd7ah/Oc53vrP7DziFBpawgXPX62HGDUcMo3Q6os8qaAKi2Wtp/ThArRDejyoepq4VczTaF/vtn/RTP9AI1gB6mtPV7UTv9U0IE3R0YtLka5lmKR38pN1yKaIJx60X0TG7yX3dI+V8Fz2qcJ37DE07EiwNPar42n3JS+ahQ3ro9Obk/FPMDKLaJRqZYqZKbmJyzBTEQzPe2DCcYv+GPTarBz1P4LkLFptfJcwGU6kIk2iM6AxhmPnV3j1tNudVn/8Lxv2JpXFPqz+vYmdx/Pugs4ns4QCb6GKNS9D7XSWEBj2qGw3ooCSOE7o3k778EUcjELiFZZPxsxZKkIHeyN1pOelh7wSD6LDu9SEyJ/KSVvQ46eN2i9svzKttJ5i5Ot1CLEU/YtginW6O58QHdmaAQ7Nf52mvbBGFHLtT3IBY90fdjutJuh0nLVwAASAABIAAEHgYAs+odyuyHic9G5mAk1ioTD/lUuaSY/ES9OqdYRc29iVJXGl6txI7Zm3/F1sIff3h+0WVJbpEt7ls9HYEtr27vuY3mSrdWuOyYOSwmVy/cRN3tKvlZ5bQLAX6tEblxkjGZZuq/ow+1bF/+1FeOpfSbqoyvxRns0kKTGW+1hP/bVvXA6lCqVm5Tnf6I3SGDYv9ziC5uqL8Tx3dB7a33scVCzSGd4qFt+BLDBXvYpYTJ/ZUDfKzCfqHlRU/iqx4XtQWzm3pam4dqq4zIT+60UBqtKpLXc3/na9qKlWX7Qgd77Ad3N4+nEkiUbcbqXhreTwbOj250qS/sOfECXf+JrU4nOwUkiCzNldD+iw1Hw0+l5NveLdMF2+rYFn6svX+1u6ajR/ismyV1likPuZAJ0uxhGSx0gh/y2kt7fjX9sYQ/p7gPywWUosUkxqnXqawT7noDrVMqSJmMfKXRxzo5Cu27uQ7O0/ssd/F0LcfDO+aDMh+SPDiE7RlE99u3f8zS3qO1vgazUZ2q8T7S0zMpDiJeNeMbnsmpU8REaMDiebvSA4JXbKj/Lil+/AuawjPVC4t225OfHllitaNV5mlr9gWthzrqDk5iM2lCjdVGgUTi9IusbUfawxj+3aP9eM8Y2UlZjkupExHXwFZV21akbqPU2nFwuTGyuglKuxLXPP96LbSgOs3XaE5em30Kx3jySfq6ORCE2JkkSHc/q977Oh7MMqlxdvMaLAI5yQnDcjlgZLars5XZqGkuFI5H/90iEIfPxTC5PxTzQxq0dlANFIiOlNNKveUxIsbhtum1L+KxRT+271bWtE3Y/IN27ZGZoPJOQjs5hl37pJZT9pdn1pbP5VmEmTuYm3RTjM5cjoxpkXCC5Y6vd6wIStup1E6W06tMqpj0stkMsUyw2qRL3/ElBb2eznhTc6JyGarzVvZdqT5pwZ5KUEt3bR1NZp7hQdJyiVU8GT99tsY8Yp+6zYjiR6XpJzhSdO7WzF0y6i2hDIIemVlxWuIiFx8jh0WdDso1ekWJN6OEmWDayAABIAAEAAC0yTwH/7yl79MMwskBwJPnAD6bF3jyBf5nnhdUMGzSYD9qGbvwOqGzSMfGAxfaOlMLy9HX4iF8KwSCH6y95+v6eu2xn1z8uFghHtbdl2i/7lSOzXrDX1Pcpd3RVPViqklfziZIBcQAAJAAAgAgcdN4Fn0bn3cDKE8IAAEZhKB8ADj6e22nL2LvE9H9oyG/fbLUk0umJEzqR8fu6yypUaatV8Uf2fWtGrjLp5n1asmOoE7reIgMRAAAkAACACBp5MAWJJPZ7+AVEAACDwpAkM+e3ObPaQuNeaNmo5ppHFrKZ3s8fikRIByn0oC6Jsrawn3SXfKU9RTkzp4qcudYy5aCA8mpsYLUgEBIAAEgMCMJQDerTO260BwIAAEgAAQeMwEePaT9h6JqbTgYR1NOWfHR/zKEl3CJzEfs5hQHBAAAkAACACBp4AAWJJPQSeACEAACAABIAAEgAAQAAJAAAgAgRlFALxbZ1R3gbBAAAgAASAABIAAEAACQAAIAIGngABYkk9BJ4AIQAAIAAEgAASAABAAAkAACACBGUUALMkZ1V0gLBAAAkAACAABIAAEgAAQAAJA4CkgAJbkU9AJIAIQAAJAAAgAASAABIAAEAACQGBGEQBLckZ1FwgLBIAAEAACQAAIAAEgAASAABB4CgiAJfkUdAKIAASAABAAAkAACAABIAAEgAAQmFEEwJKcUd0FwgIBIAAEgAAQAAJAAAgAASAABJ4CAmBJPgWdACIAASAABIAAEAACQAAIAAEgAARmFAGwJGdUd4GwQAAIAAEgAASAABAAAkAACACBp4AAWJJPQSeACEAACAABIAAEgAAQAAJAAAgAgRlFACzJGdVdICwQAAJAAAgAASAABIAAEAACQOApIACW5FPQCSACEAACQAAIAAEgAASAABAAAkBgRhEAS3JGdRcICwSAABAAAkAACAABIAAEgAAQeAoIgCX5FHQCiAAEgAAQAAJAAAgAASAABIAAEJhRBMCSnFHdBcICASAABIAAEAACQAAIAAEgAASeAgJgST4FnQAiAAEgAASAABAAAkAACAABIAAEZhQBsCRnVHeBsEAACAABIAAEgAAQAAJAAAgAgaeAAFiST0EngAhAAAgAASAABIAAEAACQAAIAIEZRQAsyRnVXSAsEAACQAAIAAEgAASAABAAAkDgKSAAluRUOoF3NpVsbPNEk/I3nO4v+MRsl1s2Vlp8w4nRT+r6ns/hZoXCg476Dbu6bz6pelKVOw7h3kMK4Ot8Z+Nhd6rypxr/8Bx47rKTCSbVMxxkLng4IZrtri6pP5ucIinLUxLxV9bASVvN2fduqLHdTkzHna4p2WOLEI4JT5HwYWdjyY7j/kS5hz2tW7a0XkXRbHfNqGLEastYZGLOh7nmextLtlv/evPJw8j41OaJGdrfsF7FSOJ++BtEuK97b+XGkg3vdAjq98hhXGmxxzMJT1mica1OMTmIliR+wxVNGhM5naaFHQ0l68fCho079rQ6rifd4hNqjME4mSwxM8ZkSaf5++NRsGlWOv3kKbpb/F4w3eLH1gCpMz6Oih6+E6enwDF6xTyWNVIclnGFeQKFp+6AGfPLE1j1ja8cZgyFhxAULMlpQhtmbf+j9eK/J91m5mpWr6RkadMs7WGTMydbrJ8lrsYftrDp54uFkEFqf6xTyqZdiPzllasXZ087W3yGh+dw51xrs93/58T6+d9bGj/yBv9qTwQS63+E67+uBj6CoGJZZ5LwmdTyIi0pRc2I0ZbxSLHmQdxfkUDs0P5m9SrFJDNNFmHX6S6WWF258x2DcppZxZLHTnGPZRIWq+TxxaW64T6+GqIlSRabd1fvRv9WbS5WZ/S1N7V7whPVMZ07xRObHB6Pgk3UzKf/t4dfA0yvbQ/bidNU4Ono1fQaIKQGhZk+M8gxFQL/cSqJIE0MgSQbMvpbttr4qGbRDMIcAwFHliT5EKLLaIPxIbI9+SwpOvjJV/yINcxoDZxJwkuUK4xjS/pRbYmLfMSehOyPjcBM0qtUjQ4N3ccItZZeKE+VYrrxY1PcUzsJx7TorzQfp88hqVwqWi+9CPNv63RdK6MX4xOwnbJkMDlMQHGm/PTQnThlNRklMe0MMwUhyPntJQCW5LT6Nmivr7He4LHDG7f4dr+/YeTGIxSB3Kg+lO1sMCsfsI4PO2y/9wbuS4lcbdFbJnVWzN0IOaNut5P/tc40D8OGmY5t9UxB9YH1wroUeeO0DL19YAMV6uu2HHdc8Q9icpVmlbl4BSnBMOboOxZep2IdFzn5338v9LEjgGH1633GuioFyhu8am1sdni4IcUivXmjiZ4T3yjkWHVaZlKyPReZAKagV5Wbsq+gKhgOLVBMFRt1hCAgz563dPzmopfDMsn8levMhly0z4j2+veyaiPuOe26MZieRetLNxkXDsVBWMvVb7OrauuMORjGeaydVsdVNpShoAqM5nVaoeRo7YvYnvNMYChTtWxtebFWnia0d/89c9tmdaoEGBZmPjpi6WFYPpNasZK4Zg3+8HDFknGYbNeuvWcm5cCzvVbLabf3ZoDPUKiWGEvf0hGz3C3bO5j7GFNZ4t3aVlkwWmZfx45mJz+M1WzwlTaUClBuuSz7es75AkLb39pkzEVdgXAz3Z0Wh4cdTI9pZgxy/lLL5h7Z6hcYuyesXL+7crlMjK2A3HnCYutl2LCUWGwoKzEoJX7rrhp29eHKZZGKrlt37GGN71dqw/a9VR7lG7j7DIMt3lRbku1K1rExDUzDgqIq1PlOx/2V2vtOJPnXUlLz47KyFWTsQknoka/WvrdNi+oOntm77The1lSlmx3xoN7upPft1s9mnb+2dPcygm7n6UzFJhotbodTyBwL5KbD0mm76Bt8bsFSHdKT5DAmPPoD6WqStiBl8J9F/ejy3hnKnKcxlpbp5uHMB1taHpS/v5EWykM+JD9txTa/X54XN1iKduw2oLE2GsLXHdbjNpcvEMSkxCKtqcysjqzPObel/ddONCLIJQbV/bHkQaar3fI7hn2goF8XahFyL7rFAAAgAElEQVQ2IgXv1l3e5U1Vc7tjtSXcHIlcgUZN2P9Jp8WGRI0OpTKDoDaiQ0no5VQijQkx/gcvNreg8fURvnoB5/yM42c9r1xZXLZKKZQ7zHlOWrt6r/jv8LiMzF9VXPZ6JD5Z61AXD3Pu4+1dFxk2tmcTJOA83ce6HFf9g+kEVWgqX0MLLhjTGfIC5aNdzmtsKF2h+oHBvF5HZmApOjGKy4Bf7nHd/Pq5eUvNpbrBj9tPu/1fz1bpN1Sa8iRRtSTekHkdvrsPcHmewVysI/H4oZ3ROjItP+ygEBjcF1N7YVC4yPUKX0QnRSbe4XhJ0Eh7EPT+tqX9Yxea0FRL1paWaAnBh0V04h1FP+yz7qzpHsCwvm3rzxrrKmWWXWgk1hqyhATIZ2/7ZU1DtUGeYo4VGTUhS4zS7p5/ZnQSnqbSxqmGmGJMQ6vHywr7zrR3fsz47w5lZlMFb5aZaCz+hqsSmcyFKUxkcogt1bp/f++c4t1bdei+M6WQIZUib62IwxbyyN14kqjeb1IKeZHr++bu7NoDeY5YjPoYGz94tbsd3VsHBrHn0X00OhhHZ4wVMuRSKD4JP8SofAQFC/c2/tMx6daGcjoj0sarHTvahkrR5ej94CHvBciLdaeYfsZA5ye9F0ygzEnzatwaoM5ExvRvqoomujmGnPar7JBMpS8upW5YLWc87LCCfqN862olPjbtrwiJLYpGFga2492Oz7yBECbNpvXry4x5CQpMhb+wd6Lb2PVBbI5Kg2as14TV3XiIW4HsJtAP91l726nTn/rRLTv/jbLy1yK37ClM1yLNjFcY/bQKvxO/AtmoHhf7m74rJa45Z/ks2/ezxoaq5RFPuWE0+moYXcPu12Si8GOX1kU7SlGO6az6xJaXQg+JrhwEcZKCMDkwBQ27X0eTSNjZ9E+twaKGGgO64N0t207Kq/aYSHTjnOzuHOyzWU70XLkh6FXhmlJTAVp6p5At5fhKEm06Ec+od+uf/vQnWySgP6aDS6bfXmuch9Ob3m8qiTEjY4rgelo7r5OmnzU07anQpjmPHHXGnbSTUVQO67sWcUz9I+O9xwd8yHYTluPMtSFlngq/0d140B582VxdX7fzDcJ/vP7I+WgBQ2yvM6R9u3KD8UeltVWvyWTLq45Gp85h/7nzHFVcVberTDloaz3pSX6mxX9hd2L63U1Hm4pJ3/E9e8+kG37W1LbPJL/aaXULTjzBS60HjrPk2p0NDbVvFwz1NLXY0PJFCCHnWYY017X9sqmC4ro+OOUfTgHhvs/S0NiLactrG+q2rpR6Wg8cHfEPEmrnC6sOHm2rWYVfau/sTXQbEk3AnW1uPBOmS6rrqs2qm6dtSWdTiX+YnAP/hfVQG6NY9c6+xvfrNuuw33daeoNYmrpiXymVoTQ3xJiRqK25pXWb1Xi2ofbI7ujigDnr4AtKa2urjMSNSNuFnupubHRiurdrGhq2m4gv28eaGeUV/S//+bkrEkPFttLVefIUbHnmwwOtn8n0P0XIy+mvuhqPOhO5xJXIOPoI09ZK8woqNLGOpVQhjL3gYF+pqDvStm+doq/TYo8/xEi+nJt+3dcvaE+YudbP837vl5GLzxnvXIqSh91HD7RflRm21iGBtcO9jQ0WX7Kqxcoc/fs+YznYzswx7NxTW7FsyHXel5wkrqFi2hLsPVJ/vJ9cU9Wwv3ptjr/9kAU9CEgdxgeL5rsxqcKezqbO/gXm3fubmmrK6NC59l+7hRZctzYeduPLK+r2VOmlrnN9I63izrQ09oQ1G6rrdprm9znc9+IrTNKW6M9Ib+tPsOSanXX7qs25QdvB/baRY8zJQwnBTSGSWNtSzS38dacrzbD74KFDu/RYT2N0xmB/24xGY2HFvqbGuspVMt+xTrswokW1jmd+VX+kT6HfVtewr1I/29N80JrYs7zferDRgWne/gUa3VrsfHPLOQ6b3pAPOzuPeGYbqvY1NbxrkPW1t55hxVo5FodweckNdW3vV+senGv+lxZfbvmBI4d3qkO2Y5GRGGnNuatE8d5DhxreUQethzo94VRD+2EHBeqhlGrPe+yXZMZdTUebd2qH7IkTb5IkPOfsuUaY3q2r3age6m23RqbBFJPDKIQ0pWnfAfNCnCpuGpntUyATnUJFRs2CxCkuWt70lDZWBlHFmI5Wjxd2/VTryWD+ht1N+2tLXwnbWy3ucNy9RnwyRwxTTw48z9qbGx1S044tk5mRPB++z/Po33usp9vOyJZqF8U+Z4vnnmLsY7zH2ubAllXsa2io/sl89mRLV19ih4lNwg81Kh9BwST00nze47oWneh45pIHe0VLxTT3m70XiCqzqIomrgHGYKe66Uw4DwRerWw6crgyL9j9Xo01WFjZ1Na0UXnjpBVNdfFBbCZHT7baGm336NLqpqb6ahM50PWBzZewWLptbz54OoiS1DfUvkUFu+uPXIhbHiavQMKX7e7Z+oqa2q0rpMyx9sgtewrTtWgzkxRmeoXz4yuQWOv3m7wrid6A0pQadabXPeqcPuB23VJpF8uwlPATVwtTX/WlmpEmWTnEKRNB5yr6r3mFm8Fwv88X5m94IxeYt8+XTuWTaVPo7tv2loO24PdKa/c37Fwlc7e12G5gqWQTFEhsiZWg4tO9fBYtSWQ9VldXByIB/TE9Y3IWlo5h+Kx0PMXTzWAwjEll8hfksmzKUFZV+SYV2ccYC3Lqe0RUb4JIayiaHIjozR0vwynpRTjjcLAqU8VaNZlFKJeXVvxQ5ulxjCy45ulMr1HUYkqejuNp6VgaNiJDmkTzZrk+jyRe0hbpVKGbbCBZC3Bab6RlOCajaSWOU68bKBmOZ9H5JM/dHkRPt109rudfLzUvUcrlBPX6JqOy33HBHy2GWFqkf0mCpcmoAlrB+f1IWjEI/Gf2c/c05s0GOkdO5OrLNxRivXZXdJ5Eta9RyzMwPKdQq+QHbiatIEUSsMiCnb+63KQmiRzauMmo/k5Sq9Im58DjpK6kzFRAyufICFqvU/LcrYhM0btmWuJaQQCLfhuNVujKSpdTRA6lX1WguCO0ne9z2Ll800Y9lSOXL1CbzYX4p+euJJuAOKVfq6VyaeWcFGzDHselIe0/lukWyuU5tKm0WJcjDSU1MSZCSv+oSJ1L0QskE+vYBCqEK1ealhGSNFy+RJeb0c/G9wOuyqfuexlkb6AZzU/QeZjfh1Lw/YwvM48m7ly0X8ILN5TpcgkksGGLWXPvnF3kqUViG/irjou81lyiU2YjlS4zL1ckpki4FlGGoOu8R7qizIz6UU5q15eZX83GJrIkMWxssMSO0yGp6vWysjU0IZfJ56n1S4jQV2hzEvP19gZyjZtWUUQ2iXpk9bxo97POC15iVblxMUnMEzQwdqUVFTlBW4TIYUFv5avKzcuUqDT1ugrji/6eUeNZZCilEEkUUcp+n60pWhfZIZRrTSvlngsuDuOxLI15g1n3klw2h6BeK8yVcgE0L4hqXdhlv4AVvlWqW0jIs5S6YpPmruMcE/+QwOfo5ei1GwxUNhrdhrK3VislQ9Mc8oPBII/PlinmyOQv6cq2VZW9minazLFIoqBIP0+CZZDUQgVGaI3LCDxNovw+lXmb4x5EUqUpCtcYhZ1WnNCv0eHuyEgUG9oPPSiwCdQ+TbZ0tYGa8/+39z7gTVxnoves3XFcKSYDVLLrCWEMkUI9pRmTSqQWNDIB0SLS2DyLaBFZbC4xX2L2EucB5wb4lnULzkLYUrq4ufhyI7OxaBC92GwQbQTFYkFckBqsLJ+4ICUwLh0vSBusxJUu8azod0ayZFl/LMtgkpj3lCe1zpy/v/POmfPO+54zOCaSqWaVBDhu6KowqSX5TOXqSmYaSSl0GjoyDaaZHOK54MJUPzjbp0M24rvmHghtfBtSCQaWjVTHCuP9/t5cESGVEhKSWVK7ea0WWYLinzVpJvP0k0Oo177vzY5+Tf1/1YT9boYL/lM7Vq9auRL9W7Nhx79wskUL5GF7XbqQAiNKetvv78PFk9BNJ6Fm6+s3vJjsgpFiEh71XZkg6iMXMBGjZDDn+fBNzrvsTox5Wh5P6At+FiQLc7p5NWENEB2tdA+d4eaBaeqq2RI0w9BPohkFGXYYCY4TTJksx9eTqEliKWZyjJcwVbUvoBmSINAsukAh/dTrRQcxxC2W2H+1uqnK2ucZqlBCzqrU/0DiPGVLKDtBrvBpmuplKtkUilmsZfI54ZE9guk6bTeHCkyWhQ+uQOJvii/wqZTuASRTMhM9XV3hN7/cH+xeuRK56Q0Hf+hqYeSrvjQzUuaVQzxAaib9cOQNPrItiRlG0u35eMC2VPptaiTDLXRtira2Ci1sJLJ5+mrkLhTi07QtXHPy/RXfoFH9/SB6t54+fbqsrKympiZCDP3UarWjopcik2zuAvl548aXzBTNMLOUqjlDHAhRBqqUftiKLD/K4GVu6uxa6SetHpYv8zmvUUxdgd95s1daQoWt8kIgSyixmevhMWGxhB6xKSpEUVKyaOApIM7HsX4e+WcKi4/4UDCRiGhiaF2CTSQmRNLnCQ48/8ljIT/bw3uublx5JJonxGO4j8eErZ/E5Gi1eJ6QLc1pNN4bHD9FLYs+ffFpsqmYk4tYvWK1Y3hePs7f6U/sR3KC237uxkSSiroNFchKinA0iw4bUnAQTVVpRC7bURPL+bw9HvdVfmLhSOxoA/VIiqINyBeLML6fx/w9XKDPvedle6wlPE9y6GkQ50IpXCqQSiPAQ97UbH0cx5Pqx6KPbzSXCQ7PbNoO5hLSyQOJh5UxP3o1kE6EMGJidCzzxA9hiQcLiWT0dK9NMJh73Pm07rvsnvPo8CHeeQWjV1LYDfTMK0Y+pQMtFFFUMW/v8WKz0jY5csHb4+WLVOSAYOAlVDE+YKNLkzFZGEJczw2MmkcOZMiXaZbJ0N+uNAWg6NQ3CyFTq0XO0x2m616uh+32sNhUJTJpcD29E6dGvYxyKWqK2IaK4Hu4G2JyalQAHimhJmHDqvrh1twRcpVMizYVI+TTpMZupGMIDU5xK6VuUuqOpRt3vIgqjt53kiIS7+nxhnBaoRV/ZDMfYrmbHMeybF+/CpWaUuqumnqCnHP7SkusWh6T/wfq6+B8g8S+V0KT0XfRktmVOvSQPpLVLS9TLVRa92176RQpZxjmu6qKOA+plB2O4cJxHCsgBvRO9AYNi04gOcVkzFm6SCrhHdyN+FbHSr2Lm+KGL7XYh8eTQC/nwgFHEy/PJ81rQ7tVIJUgR+JwcrEIF5KnmxywxKfG0IJS/RrxXZNi+stWaOPqTykY6HqqGy1Vs+Pi8NKKxcXbW9av7piGXsGhR6dKoBD3uEk9maeZHDyCNdnYcocXzyLQS8yMQaTQ1y+So6cjFgp4r3R2tG3bgzfVz03zyE1XXIFSM8+y55cv278ho5kypUqtGhjxuAzJk/Bo78rEVmQjYMz3GGyf3XWboS/bnbii/onoxB4pdHTPAiqxRQm/R/osSBbmO2g9kHZeTa41TUXDzgOTo+ur3Lw8nCAic10OeluN7tPEGlLM5JiErqhgL1g6ft8jTLkfu7kQPTQbz/2JC140rFtliMbzvKjEG8KGc7qWkNE3r+iRzft5HrvBZZqu03Yz9lgaaMDIC38MLSkHVyDx/foCn0pp15yPqxTEVrszqCrvdXzglS9gCPS+JA181JeE1cLIV32qlMtL3pfdymEaLePb0Rt8EtmWZJqqkKH9IxaTulyf0ovR653Mw42sQZyYiq2VCWYxejijkH7pm3x/DZXUUfx6EDXJUWDKIssUTcNOpcdp7/rQ6Ty8y/x77eaf6oR3q7GAHpMhk6vbHfxYIl9Kk5f7TR7W1cOSM/XCUytpzhLyCZZjpPWh9dQoA54jaJDpA1pd4MzyppqZcakeQqskwbo50kojVoJYHXE/M9WObLwpmtd/J7ItLX2rR3Al6GzdsscuekpVNo1m1FrmvS3HR5ArlgRPttmH+rFiTf2rmsFJOQe9hE4qdDBjGrZ9XFKepIg78as+9EiLJkgpY7HcaUVIMGIPO5oE/W2y7bKby3UFKS09Q0z+xuPpDroDpVXTkAtofGPClaWsaEibY20azIvGOqmfQyJSCkO6LP3xbRj8O83NcsvW3NjiKVKVl1LKJys0M9p2/BsqOOnOSO1uEMc/XWvSx0fqSNHz1E1KU1DacR/Ey6N3QDmoNt5zaOv2EzxdrpTPrFA912/dHl6+JMtzpCpcVrVxbfyqF39kBGvoLG95orzuzVKt84LT+WGXeY/l+DP1TasYVH+aQUTTQhoOKaMFNQ5PfIMWS5lSVsPlZ7opRib2KZs0NDKVYKeZHEZQmpAkmzl2hEXGJ0srtBnLykqqY6Xlyyo3v6W6ZLc7nU5b2zazTf/TTZEdoZEk2U7muES1diVp2d1hdCjrMr22yHsEmb5lkYpkpbTYu24XshfN1YZvg/j7a/jOi5gVTb+c57I7nC6nteWExV6L1NEhWVLIWzo5z/KuzE7AaJUyt9l+KYg5nGJFfXgXaHy4J8+ClKwyPwtSdSRlUclzd3yyVBWNfh4YOojJzQlxlh3bTJ9Qc2bTdLlC+4yzeU/CI15oD1Feu2lJ+IVFJOSIhz/wP4W0oIwZBSN9N+MbnkXhgtdVmifgF/hUSvcAyqWU35VuvdAVpHx2H61Brq2Y0IGU8N1JS+sUz500q76RzUhpuMVGIp9mZHutlznSw02dScswynvezT7i4qYp6cjrjIzDnWIRg9yP0i59R35/xUvL8H+nm8OGz/XVvjp37tyuri5DOKA/0M9s+pNiNRiXnWdPmcwuTKbQ6FY3NG3RUTedgrtgfECOIzP8LqvNnSuXfwOnZlDeS5Yzlwm6lESiLime6O1m/dH03DU2UEQKlsHkkHKySE42kphckizEem74iUKJRPiHs783mS/GWpFcRAoI0mISv86yUZ9DnvV0YxJJ1KKTXESGGLy4pLCXux51/ehjr91IWtJFihiOQ7CrszOgqN38kr5yoZp5HA8O06cMDRq4TBSTYh/nxyOgJJJAV/tvbOG9hWlCOraTJRLkNsNFc17t2PJaq/O2YPYNBgdsG8F/9yLP46QwvIxlI0JJRUvoMulVJzrkqHiGHC+Uy0WeM8eQ2zUtOHoVFUswZMiL5gmybA8uLUIvTDO0WTpFKu4ZFAz2OjKxZxlyyeIijPtj9MHMu1pf32i6gt6S5vG3AwOl3fL6hvd3RQa5c1Y7rqlrqNU9r1ExFPbZp+F2iMjiib2DNx0yEIRNj3gxSQa4q9FKUX8/GUGzkZWsKHAtlgvzu696JxZKosa8xBLSNCkxWfh32nHnhdeWA1m4qyxPFpOYq7OTlS/fXL+yUvuMgi4I+BEc9NxNKXUF0uIcH9crDt/7EskjXtvhdvvQPbREISm+dY2LfjXH9/6ODXuseVnd8iHOcajD8TnFzKusrm/cuYru/dB5Dd28WQ7iEDR3etjrA+OPdvWyOFkSPoomKdzFTZFW7JMqGUVEuskhXVHCvMsHBuSc995I8rqLz5jurkkuPEuhjS8gpWB8lPpGS654SEzwksV0giVKVdrldZveeE072e28hObr2LMmzWSevpv49DLFTI1ugch+0DTstup0DQtXjfY73I5am0Ne77DI0QFUHQet3kJa/by+bsub9XMw54eJO45TVDbauzJFUQlRwwgYLlfMyrvkaLdfEiuUsuSiRvMsyCSfo38WDC+iSWuANBXdxTyQDCghptt23COp2tBQvVSrnk1LeT96dofnppgAi6SF0gDyX/rGwOIBu2w2Wd2Z/VwSKpJknK7vopuZC09ozRf5VBpmzUkpldIrTsvZLu8MpVLwC7gr+GlWfWlmpKxXDiJ6Zsm1i2Ynsi3JRIRMTrJO8wW2ZGZYkcw8IrikkOy9LjjGhUPQ9taGbcc+uudL34SBT/j5IGqSEyZM2Lp1qzQc0B/o5/CMhlzNzcPzsZ4rdlds7TbkMo7dQueXGq0f+fx+n+ucyyciyURtSiQrpbjTNu90OTpqjJgml162OXLosrB7JK1Wk5dMLUecnM/HnjW2/M5Lz4sc8TekGrRTMtDjclzxRReQWfQgVVKJah7Tf7LVcNLj8/lcxwzGEyz+zfRGiVQQ8CfVFSJb616Lq8fnu2I17O/EFJo5yca6VNWniMslVc/Kr71n6LjA+a67zG8fCh8MlBgyccDFBeLAdZfbF+T9rONgy9FufsAHLV+MY5+6na5BrT1cNv4Qjvd5XB+yyLU3ZcDpCnWhy7TX5Oj2+bodrW+bnOi4y+G8p9KwFZVVlGNnDhiQqPh60PrD4ptCy8VSkhS7rR22jzg0mRiOJWxWi7Qog4yNUIRS9g57lKZz7J0fCjMalkvJp//Zcd6N/PiFGW3SHI2C72xtsV5BDXaZ9xrtRIX6SWQFytBmfKZG/UiXyWDx3PSx541tp6K6WeoWpIwllHMY/wmD8TyLbgvHQdMZXk5PxcgpxfglS7uD5a46OvYfv5b4Wj2xKHGBCO9jXdf8fNDPnjIYT3kjHomyeQtK3B0tR12cj3O2t5g/imQk1fMZ3/utJgcn9PftDlfS10dTSAuS23k0d6zFeDbc1EPNHR9TanWCm9Ngw9I1KbHpwu/0495nN75t8fSgGaO15SSvnD+HQHu1RZjvsgsJfhBJ174OFzpKBJkrU0qdRFnxdJ79wF7zRc5302M1tLZfxqQJs9YMtXqS89Db4bv7kqXtmJuYQUuyuuVz83o/Omo8YHbdRFOj54yzB59CocNLsx3EIWRC3s53jLarPt9H1pZf28Rz1cJZlKlu7dHfFOnEPtUIJcalasnQNGkmh8SCor8LSqhHuLPvWVzXWddJg+nC8KvQ1HfN3QvtkNalEoziNDdaum5F4nGs5+y7wj3u7/NzF+3uT6TIzxwpctEHLp9mMk/dzWhduOw5nSrUaTwS1uhuOc3tFleq94n9tzzOiy4X+ud0WNv3GM8H5LPK0E2ATyGpPrv5qIu97rH92ng2+jopBUZUfj46ibfNeNjJ3fL7u+32q/0UmqOG7za6Ouq78q4EDKefVmDnLbZ8hbIkVRNH8SzIJJ+jfxakn1dTrgHSVTT6eSAVoSFxYrT/xee+yAZvB9EMaXjXHkT6LXonHLdYQpsr5DeOtrTZ2Js+7oKp5cAZ79clCcus1HIVX1NB5uk6bTczCswICh9K4ot8Kg235pyqUE7qOnqco2eXRV7jjgR+ukFOs+pLt7xMu3JAL8s6jjmTpx+ilCYv2eyYXI7ehBbS8oectovIthR+Bo9gRKjvq+XXOwxhrcFzqq3jA5ymp6aZLdN18W7jH1DvVqQ9jnZvpIRRMdZ3DbsCeOR7CQkjQC2uq/2stePN9S23cek0pfaVFcluNegAV2Q0yHsi7PRaLJMTmJ+mBw6wnlpZX48ZD7ZuOdybh74Csmyzbl6iJooyUUqV/Hz7nn/iGxqpuxWBcH5i7pqGz9Hx1NvX7+fFRbRqTYP+8WGegHEQVkXrz6f1G9bi77Q3bzYGRCRdXrthSXLXs2isZH5dfZ+hdd/G9pCUnleukFiQf29C/kwccHRyg2avcdd6s/AJkFkLVizk266jTWs0IaJVs82GA7uC2Bvh85ejYbqqoqi5fU9L/+a6JK/HcBpcpnu1HjtgMvzMHEBfeXiqqn6lOr3OLeRIwxZnXthQc0AQFQM/kVJU1q8UcCmW1Wr2GQ1NnXmFSu2SqsBe92Dbon9lkLGRiVBysUJMLkXTYvPFEplg28FlshL8XD9NR/onUtRsCBxsNf98vSE0kZqprl9fSQsqNJ6hzQKxNbzBtP11Y94UVeV8hkPmxCxDhKHh3a0W9GkN2ZwV6/RC1XP1NWzrob1bzGJK9Vylps+YPEfH1yMq19V4Wg79w7r2O2KpTLlgmfrYEcGaRxajU3Z5w4HmjYcx9DkWzWzOFs6GKq0PGtratphvT6TnL1AVmxNbnUpaJPPWvsa3tR3aavkUQx8s0b6qi/8MSUIJ6ZoU3RszJHnacZcwCsLR/HdG9IWMOcte0QtyJFu8qorbb1i/JoBPosrUuspQm/NPXkxBppQ6ZuWG2oPGjrc2mgI4wluzXs8kWFFxSvfqWmx/+O7OJ5kF9XXz0V0jyeaWl2hWr/GhEl43BnKk6Csy9avDN06WgziESC6pZPotP1/fckdKz1nbsDR8KFL8rR27s0d/U6QR+0wGcKGd8S1J804tzeSQKGgDv3NlVS9Wed/p2LGlX1qq0S1R7j2XJmU4OvVdMyi0TWXR3FkJ7ZAqUwmGKJT6Rksp1bHS8FLdK0vb0D2+7hYvRs++F17RCZv34p41L6SZzMMPr4TJwRMrV8ToljAbDW3m8sbKoOt4h0v9Xc3AlBbXk+AF044L4d+4iJCU0M836CPPhWJ0vhRnQMjfmyhXL62c6x/YThyHUYec/yOhQKFfyxkOtG452suLSXrOmrWLSbShOK6elH+KRnlX3qWAPa5UTLK4vquM/3jGYPtG8yzIJJ938SxIJ6KDa4Dd1XTsZWK6ikY/D6QcuLjIQk3NcralY8vqd3CiWK6qrFIcsKIjEpCfWdyKUVv3KmY8dGhrQy9fQDI/qK/7IRKPoSFOrtK8gBRlnq7TdTNOYCoTK478TlN4+unui3wqDbfmJBUKsv2YVBl7kqGnfEb4qZmkXfWlXV6mWTn0XrG2/ytNLwwfjxcfChlaYuKmCLYltLqXTxeZcxl6wBcxzYgMya6pWxc0omnncC9WRFesqUcfmMDSLX3T9fHu4v/qL3/5y92VALmBwFgQ4LlLLr6YGTh96Db6to6J2PCm/vGxqAvKBAJfWQLo81AHwl+yzWSS/cr2ME3D0RcdX41+yTZNEogGAkAgLYGgo7mhnXwt/C1oCEDgHhJ4YJ9KwzNE3wfe41KtrQx/SXhchQfRu3VcDeD47YzvdMvWtzqcPcgXjlxB3rMAACAASURBVLUdaO8qYJQJ56OO375Dz4AAEAACQAAIjA0BP3vBYT3Q7iLVKlAjxwYxlAoEEgj4/2DzyRTjT41E3XxAvVtBxL/0BHBmWd3i/cbWv2v33hGTM1Q1rw49AvdL3wFoIBAAAkAACACBLx2BkM9+cI8lxOjWVaTYPPOlay40CAiMBwLEd6vrxp01MjIw4N06HgQU+gAEgAAQAAJAAAgAASAABIAAELifBMC79X7ShrqAABAAAkAACAABIAAEgAAQAALjgQBokuNhFKEPQAAIAAEgAASAABAAAkAACACB+0kANMn7SRvqAgJAAAgAASAABIAAEAACQAAIjAcCoEmOh1GEPgABIAAEgAAQAAJAAAgAASAABO4nAdAk7ydtqAsIAAEgAASAABAAAkAACAABIDAeCIAmOR5GEfoABIAAEAACQAAIAAEgAASAABC4nwRAkxxb2p62V1a/5RDqCPldp52+5Nr6PFYHF47mOjav3HHSn5zkXsSMaeH3ooFQBhAAAkAACAABIAAEgAAQAAJfHQKgSY7tWEm+vWDxrGJUB/8H464jbn8osTrX4WbThykUzMR08BsIAAEgAASAABAAAkAACAABIPClIfC1L01LxmdDCEZbGe0ZPz67CL0CAkAACAABIAAEgAAQAAJA4IEjAJrkyIbc5zS1mawXuUC+lC6v1C9TkTiGOZpXHyN0Mu74GZcXkzKLanXFXcaDVpcPIxW6utVqlAZ5t27v0+97xrVhj40PYVtWeap3btJIBirl2jdue9+LYTuWeyqbmlQolr9hN75xvNPjzStkNC+sqSwVCUn9ro42o9XJ9eZlV3tC31IXHvI5Dhraz7i422Jyplq3Qseg5t2ybGtwyp7DHe+7sFlrGlczvaeMre+dcfuwiVTZgmV6bSkxMnCQCggAASAABIAAEAACQAAIAIFxSAC8W0cwqLc9xp27zmKq2sadTWsXiJ0tb+53BsP5+I8sNkyzaff+3Ssoz8Gt297P076+e98bOsnFNpMjkiScrrS66SUFXqxt3DuoRqJo8keNDfMJ4pmG/U06KldI6Dpp5curGxsbKsnu9rePssgbNsR27Nplw9Qvbtm5c72O/NiQde3hJqQuHONdv96x95JUs65p5xv1mgLnnp+bPBHjKe+yXiJ1a+v18+j+8y1vHuSopa/t3Nn4Ynn/8d3N5p5oofD/QAAIAAEgAASAABAAAkAACDx4BECTzDzm/IeWzj6l/iUtM0VClmpqV1VgZy32yMk4OKOpZAgcIxhGhuP0Qi1N4HghU0bxvpu98UXjuXkYhqP/DQm5uBCfi+FhNRIFqbqm+hmanEJrFpVLb7FsEOMvWS2+Mt1qDT1FIpmm0Osr8HOdXREtdcS1pyscC9otp7GKF6rVT5CSQpl6hU75qbXTFVElxcwPqxSlNDMtYD9uf2RhtX62TCIh6YVrKmXXrKfZIR2BH0AACAABIAAEgAAQAAJAAAg8SATAuzXzaHtvcPwUtSx/ICU+TTYVc3I3wz8LJhIPhf/AkaI4kZgQ0RTzMKSh/+do9kVKiqKer/liEcb385i/hwv0ufe8bI81lOdJLnJGT5a1JxeO+bieIOfcvtIyWDom/48A9hiG5RLSyeHuhLxsD++5unHlkWiiEI/hPh6jEvTiWBnwBxAAAkAACAABIAAEgAAQAALjmwBokiMY3ztD08T9xHMEDfIeBjzZSBzqx4o19a9qyFg1Obh4EoZ5sWxrT1E4KhOXVW1cqyoY7AT+CIEJNk9kL41EIpUYZ5Y31cyM6+tDyBALAQgAASAABIAAEAACQAAIAIEHlECy4vKAghim29JiEr/OsrcHkvCspxuTSKK2w2EyjvRS0qdB4jMSxaTYx/lxiaQw/C/Q1f4b27XR2DtTNUciLc7xcb3igcIf8doOt9sj5tZY8lySLMR6bviJSAMKcfb3JvPFMfruZapGQhwQAAJAAAgAASAABIAAEAACXzICoElmHhD8SXWFyNa61+Lq8fmuWA37OzGFZg6yCmYT8IdwvM/j+pBFXqHxIQ/HAz0uxxVf3Pk8QxLgdIW60GXaa3J0+3zdjta3TU50yGrU1TabJqRKW6CseDrPfmCv+SLnu+mxGlrbL2PSRCVZoprH9J9sNZz0+Hw+1zGD8QSLf1M4uzV4xdpx1Alfw0xFFuKAABAAAkAACAABIAAEgMB4JgDerSMY3Xxav2Et/k5782ZjQETS5bUblijCX+fIJkxXVRQ1t+9p6d/cpJs2mJFSquTn2/f8E9+we0Hq4nCZ7tV67IDJ8DNzAH2D46mq+pXqe/cJDhGzckPtQWPHWxtNAXyibE7Nej2D+hY1wEaaRMxd0/B5m/HY9vX7eXERrVrToH9ccG7tvdzZfrJE9kNGEj0xKHUXIBYIAAEgAASAABAAAkAACACB8UXgr/7yl7+Mrx5Bb4AAEAACQAAIAAEgAASAABAAAkBgbAmAd+vY8oXSgQAQAAJAAAgAASAABIAAEAAC448AaJLjb0yhR0AACAABIAAEgAAQAAJAAAgAgbElAJrk2PKF0oEAEAACQAAIAAEgAASAABAAAuOPAGiS429MoUdAAAgAASAABIAAEAACQAAIAIGxJQCa5NjyhdKBABAAAkAACAABIAAEgAAQAALjjwBokuNvTKFHQAAIAAEgAASAABAAAkAACACBsSUAmuTY8oXSgQAQAAJAAAgAASAABIAAEAAC448AaJLjb0yhR0AACAABIAAEgAAQAAJAAAgAgbElAJrk2PKF0oEAEAACQAAIAAEgAASAABAAAuOPAGiS429MoUdAAAgAASAABIAAEAACQAAIAIGxJQCa5NjyhdKBABAAAkAACAABIAAEgAAQAALjjwBokuNvTKFHQAAIAAEgAASAABAAAkAACACBsSUAmuTY8oXSgQAQAAJAAAgAASAABIAAEAAC448AaJLjb0yhR0AACAABIAAEgAAQAAJAAAgAgbElAJrk2PKF0oEAEAACQAAIAAEgAASAABAAAuOPAGiS429MoUdAAAgAASAABIAAEAACQAAIAIGxJQCa5NjyhdKBABAAAkAACAABIAAEgAAQAALjjwBokuNvTKFHQAAIAAEgAASAABAAAkAACACBsSUAmuTY8oXSgQAQAAJAAAgAASAABIAAEAAC448AaJLjb0yhR0AACAABIAAEgAAQAAJAAAgAgbElAJrk2PKF0oEAEHiACfDcyZbWs/6MBLizpubXVy5f1+q6LaTlP7K0NL6ycZfRciWYMS92m3N9lLmKzOV8SVLcdHQc6jDt27FtV4er7wtsE+9pbzZdHAH/+9DGPpdpb4eHz1ST32XZv+2VF1Zue98XTupzHNyxoX5byyFH5Hem/F+R6yHOsrd1fHXpK0IemgkEgAAQSCKQ+/d///dJkRABBIAAEAACd0sgePGd//7hd1ZXTcczlTRhCuH75M7kG2cui7//PSo/d9L0Cb2fyVf8RCUdNmuIc7x30nrswHufyn7wHWmmSr4S1/3Wd08XLf3xs0rlBNf/eNtV9OysotwvpuG5kx+XeloNf5yuml7wxbRgoFakOO35X3ee/y+qSZmakS997M4N/9fzuv7g+9a870zOEZOP8v6vP/s3P5ohzpT1q3Q9Z8L0qV7Tvg++oaAnf+2r1HBoKxAAAkBg/BGAaXj8jSn0CAgAgTEmcJuzHTbZsIpaNW89YrE53V5eTD6prVmllcW0Dt7VfoBTvFotirbF7+ww/raL8/r8uaRykV43j4pdwvwefrJWO4/bctLKzakkczn3f5IVw2qRQqm5pOJ5svhIl2OUtjveuX+r8aLP2+PnJeqG7bVMfritPZYd201OXxDDRVJK8+Lf6eh7oMyNrK5NSq/njO2MlpkvIYse6f2A82OM5C4Hs8/R0mS093iDPCaaRNLPvVI/HzP/4y/Mlzn/bQwnpGR5deMKBvfbdm1ucdziURS1oK5xBY3jlHaxdEubpaxBc7dtSNMF/xWz6QhL/URf4jEfPelwXe/FJPI5S6r15WRs8H2njFaJtnFqtIjbrPWgqdPj893iJU+UVy3XxQNir+fRSzWBzS2WC1UyhYi/6s2brk5T+bDR1y27fnW8+xbn7cPpFTs3LYoA4J1tG/cc5yIky5Y21D1zV2DYI9ua33dzfh7LJ6TFqurNevml1q37z7AR2SuiF62r1xRjnoMbtx9lgyFMJGF0Gxs0hRgm0VQ9vsXwW7axihq2G3ARCAABIAAExpYA2CRHwpe37V710/+v5EezikaSGgv5XbbLfzW1aLjXwH0e67/dpsgJaQv0Wbb97T/3PV0hfzhdEt534Rz79cekaPHnaF690/OtBegldLrEX3R8iDW9vs6Sv/B7j3IDf0zNuEz+ott8T+vnz+5atfv6t5+lsxujC82rd0RGNm64R90wv9O4/af/aDA5sLJnv0XEiuG7bRf+45vkpFzMb93xcotv1rMzHhl1HUMzxsb97obb0/bKa7bJP1KQyPEzJvautlc2DkTeRWv7rDteyrLLNx2tu399g/mbnzzq+OW+//PYD3Va1bcn9l2y2899cOuxCuWADuA/03rg/36/puKxiKDzVzuarYRubU3VIg39n/a2d9ovi5/6/uMDMwB/yen55lPKb+V+fPhYT8mzDO50fvIt5vHhppBYn/uuWM/206OySeYWMRWab/qcnr6+T67dmTpf+Wi4sQXTVZqncq/4FP9te91iWjrsrBK85HDjpDALZQgjqyuXkDNP0bIi8df8jn/5Xz3Tn6/89uRhC+bZ8xf5R4edbB8in5r/9ASP5YPb36//5etVsglYzgS5SiPvO21li3+ybftL5WGzZ/5j31M/euPKhJ/8/Sa9UhrRnXO/QXzy/rueoorvSO6BMj20I0HPkV3N/3uitvr7n5v+8V/6n/7ryooyKpdznHPYPXnoHozcoCFPR8u5kqU/oQfuV7/tf/7zje+tXbtMq/1eUffv/vnXpz8tmcMUDcym3Ln/w3/3SWXRn22mP+R8b+70P55zTVDSo7HqPjL9e/OVOVecPf/Xz96c8FTFDEIQg9yi72h++OgN58N/88YmvYoaVj6Rwdxxi3x0cJ5JHkdixvc1JZ+etv2RWvZG05qni76G4d9knv3ehI8tH3xWXv+r/1YVsQZP/vazT+Ve9n+34adrfxh7IBJS/uw/n/v6M9H7Lbl0iAECQAAIAIGxJzDsGmHsqx+XNfB/MO464vaHhuuc63Cz6cO727pyq7Nlj4X9fLha4NpXnkCRcvECmkCL2Hsx3L4z7RY/rV+/ufYZpJJFQ4gz//eWM/+ecQ/WF8lS8u0Fi2cVCy24FxyG9CSfUj2vlg233B3a8VuO5jdN/c/V11DOlhanfOVaLUNRpYrKl16sKMT8F850DWys89vPslOfLItZHd2nra4POh09qDScWqjTFAddx467orMEex2jpiAVTqmZjZ05YeM8yJSUjbVn2Nlm+JHjrnrlP65kREH7Kfvgbss7Pq+YLkPGn0yh92qXZ8QW0ZHUhUtIST7md5g6MV3dElmm+gOs0+XN3H2xGI3EbZ6/Eysv6O/rx+7wgfDG1Ejwne0iluiZeGHIpZQzMcc5972+PXgWqZGX6Lq1Gv5Is+lz7doVKtk0GTOveu2PaZxnbXY20iT+is2BMcyj0Sb6nbazrrOnXYKUTWJ0ixm8p9Mc24jr9wREctRRSq0u+fh45xWPp5+Uj/qV3W0Piy+oRnPFn6y2jwYB+H398llCLZlCT5fTl5lbASHC+MDn/YOlBf0B9MbodmAw722X9YZixcJBO62QWFLGSLrOOL8cG1kzsYDrQAAIAIHxSgA0yTEZ2cyPzzGpFgoddwSKFZWL7tq7L0olEAhiFFMxU0ZNil9dfgWklWC0lbPjtN97OM440iQ19Eg3wvks+wwsU10zC7P9up2bqasqjZLMpxlkNgr5/RFtLOhysRJ53HpbLEIL5k8H9JZcKYmsSH6O+zTSE87dLwmv+HH6WbX0oqX1LE/FvBn9LuuRjo6Efydcg1rfXdHwe1ix/ElVhYLgXZ22m9GyrrsDxfIR6bKZtbhY+0ZaF3/VbPxQXrtOQ2H3SjhxkUiMhQIBpKNEQk+X9yGkmQSDsZg+h/mPtHZmouJFyqj+Ky4ui25mHo/gJWPzMbxytVba3W48i2uWqWMmT8mTDJKE3lsDw8tddPVPk5Mxg2guLhbzf+4LRBQvEUlOzOU5jotUGfZlDQ+aZI6GCVjfNXmJkah8aRrc7Q5QNKNW0zlem9UVHYmguztPlnnnb5oyk6PFInEOFkRT00AIujwBKdoRGhwcK/Z9m3hBRZI0SuTURM+le67kJzcRYoAAEAACQCAtAdgnmRZN6gvI/W+9nVou9Ryzu2/1S2do9Kt1TPxBCJdaN+yx8SFsyypP9c5NGkmQPdFmNKPE2ESqbMGyGm2piGvfuO19L4btWO6pbGrSUX0u88EO64dubwATFzOa5TWVMzOZSEKO5vXCMY+u+pXutfvq0eLnjt/922bD7+wcP1E+e2n1SlV48cFzp4yt751x+yK167WlSSWjHV+/MXacdXlvi8mZat0KtOuGd7y1zoDV7n6JCa+qeNf+Dc19+p1rFSK/q6PNaHVyvXlSurxSv0yF1mL8+eaXjhOLJ7sszqBs+ab6OZjzsKn9bBd7i0cbjsoWrahZKMv4Att/scNw0Orq6cUeQZhW1CxCWbiOzduuzdRgTms32rFFKipX6FTFQouCV62mg2a7x+vHxOQMla5GrwivMvyXzMZDx7u6e7FJ8ool1brwXqPgR5Y24zH7VSFSqdWvmB+3OW1gjIPsSRNKggZ04lRlZXWNWnDFTDFwWLhJnEKLXzhuv/7nh6fO0Vere39nOOZg/1wg16yq180UCQ6i623kc4Tb6vn0Di6ZiapUUwm+fykwBp17N+66qti8VS9DKsb55o3/w6/96SbtjebV7xCvbZdbYsP98v9Lmn/mUu1sXBxZWfksW193zHojupEp0qUUjXfuW73jJFquNa9caQ1LZiSl37Jji6mbx95a/bJn06+WhDFeNO3aY3X6hoh3ZowhX4Zxv81a9g+wmvOczHvAw+zYpEH3TgoJRLZHy7YGp+w53PG+C5u15oV84y/79PtqsXix16C23uYs+44eO8f+WUyVPVdTO59CI4e8XltvL1AFbJaLXD8h16yoprtNxvedXEjKPFe7djECHBeQd+s6i7yxqXJK0PO+oe13LvbT/onFdPlf1+iGGKeELEGHqeMyWVkjWI3wb9c0zFPEC7a4ADn75eEDWw3ZHkxaETczUEsb3/oBJhpQWXt9SFPIJyZG8iNT0gR6oKipas2MdgOmFW7qSCBo9fN0fJPv5d8868ZkunycUKvJUx3Wf2W1SylUvv+qb6JM+ONehhHW5bOZzhG6lSoJxlmOsnOqVBlnj5E0UixGoxMIDDhx+J0fi5U0YTrhDgimL8Sa9/yuq/gHtUnzI4YXkZJP3Cx6CTPS1w2ZmoNO0DnYyc9uQD30cxLt+gZ17K0ByipGNjokRpHhD7LXvRKZZFBiC1R1Py8LIr04XAn/ia83hJPEQKvZboxaHKldxMxT5W13Ei8k6V+ZWhe7jgzjUhmJFYrVTHvzhU57H6NCBELX2H5Kc69QoMpEYlEu9ueY2tjd5Z+iIsUWLCDo+ELHfFZLQLli2pC7NtJISbEkcPpe7KQdMRNICASAABAAAgkEHlCb5GeffWYOB/RH1jLBOy3nicqNu/fveU3Vb2k57Bzy2ry0uuklBV6sbdwrLNZ9J/fsOMRRS15remOzvtRv/vl283WM/FFjw3yCeKZhP1Ijc/3WfbvMfUz15t27d2zWUT3tb5s9Gd9/5yrq3qim82X6nfvqy4VHLO+zHb9M6l5talyt6D9rMJ0VXvH6z7e8eZCjlr62c2fji+X9x3c3mwXnuvgQdOx/03CR0K5t2tlYqwqd3bXT6OFxZjaDXbRHPkiA8W6Hs5+eTYtCbMeuXTZM/eKWnTvX68iPDW/uH3At4q90dom0deuqF8+UcL/ds+ssVlH3xu5dTfWLCM+BNktipUnIeadpnxWbW/fGzp2bf1zCHW5uvxRJE3CcdtMvv/mL3U21JW7DbpNwDn7Q2ba77do0/abtu3dvqWECnYbfOIQhuGlp/rnZ/53qxu07X1tEOPY1m7uFyD0/P+ZHdHfsbHyB9nfs2Hs60ZbjP7t3x8Fr1JKGnds3L53CGn5hRB1POXDhNgVsJ93UqqZ9v9qsvtO552fNntLaN/e+9ZoiYD5wlI0MHDLuXCRXbPvFL3a+ovCbftE21AErNUYRs0yvvG1pO8ZhfQ7jgS5ySbU27M4phPjhnvO4SkmyH9gHfKN9dgdbooho0tHkKRvP1LzVuJjEFXX79sXUSJSB0KxvrJyKM2t+tXtlWF0JsZ2nfPSKhqaNNbJe84B4jwBjpnEPOgw7TD0lSzc2Nb6oCBwzR2+blBIY7gnvsl4idWvr9fPor0e6liT2wQsWR4Gmbkvj2nli1wGDJWpS405bvU/X7977Vv1Mf8cvt5j8FfW79+1eLes+bOpM51R+9WjLYX/Zqk27tzdWPxm0tBgdiX5zwa4zXYEpDI0W58iSuUg19AUBH+gLYARJhreY8p/4ewsIZGyJC7ioILoavu5A3ovkMxp0wo3P2dHaYnZ8aLEN3CaEskLFlI7QlORzHus4ftH3Z4/VdNTGjsKAh+xOj8mF9frjFerHce5MxPbEe1iMGqpxx/dklH+PqC7O/KsW89HmdSuXL1+54VhAek/USNRgcYEIvzNg5+KvuvjpjEQkFucihSVM7bql82sVFbE7Lr6HaBzvRE3No+z50GzdNhuLlwqmR0zCaNQJb/f6kFUbJx8jhTyhXvRDTEwckj9fhFSvcAg6z7uCBXRFOYmhY3gONLdZnbbjYcdXJKGl6gUzaRq5TI8yBD1sHkWhzITyGSURdFrPh++cHo+3aGTG6hHWi4ys6HXkgFHS7/wjUTZNLMpHDq8BdMSO0Mf3WfoHTEoxIAgCv+UbgW/zCJsCyYAAEAACQCBrAg+iTRJpj5s3by4rK0O00B9bt26dMCH9yTfJSHOJOYu1tOAfKFPNKml3JL4TxXPzhJfc6HqIs510SRY16ecKywJyWV3v1XXHTnm0K2RCmjsYHjYbSpiq2plaWtiSRKgXKMznOe/nmCy53oSYyKI0N7o2zWcqV1cKRpRpOs0ZS8d1DpVmP25/ZGGTPuwWKFm4pvLifzWfZrXLqMGSbp2xnMcrXqtRP4HKIbUv69n1ey1OXd1MJZPTYr/MMwzOexzOz2n9TBF/yWrxldW8rqEFq4tEr3c7/6GzazmjRL9wWrNUFbaq8FyhUr9KpX5cWJ0S8ytKj7R4vRg2/Iar235/H05MkhCTCMlsfT2h6I8u6agFes0U1DZcVlmlPNViu6yTTRHLF9ZoFjDhTTMKzeyjlgvIOIkF/tXqnqLdWRX2BZ2nrw5a/CGeRZFU5c7nw9alwkp9T9fGUzbfXG3ci3q//ZRTPK9RX04JvVpeEzhyDQukGzihh2R5lWYq+oOin5C2Y6rKuUJDZE/RE0/4fHcwoZRcacWSyrAdltQsUR/fHqWEIhCgNBhVhEK/jNm4v7X5qq9LUtW4MLyUjIW44SZnK6n37PabWm0h+mCc/dp0VV284WE4qcOwHGzA2hErOQcT5DUnLyyNqPEi5V/XagQfP6pKbbWd5bwYw2fGyGPDjzuSNIdYs7laNQ3VQdYuZ117wgvTNBIom46uiZkfVinC7qOes+G2oRDHQfg1TVONDOPor2It8/4uwcsvLGn4NHXVbMGSQz8pE53yVyxhhB9MmSzH2IOqTWWn4f3+3lwRIZUSEpxZUruZDhCRuiL1ov+Grnk+DmIyqTQWE/9HiHN/HJA+qZCHMQq7vPCh27oGE/usB4/6Z+galgq6moSprGYq40sSKWrrU1aRIlLCLKpE/6pTXBqI4vv8vIiI6h6J6XxubmJJRNIkKjXdvs9uderoWdy125RggLqnYWR1kdot+7X3tN5IYWGbpC/sy+pz3ZAy5UhJQQ6vmKD/Y/3W437lj9OozvmCphM1Zg5tWY+15UDc5tLkZhco9WvUQ+9kZO/1eO9IFZMTxGsgc/BjN5cr1z0pzFioichWJ85PnZK/1G66IFa/VKsW5JlSL69TL49vAaldX53cosGYUNAfxInY242EpLybDcm0YQUOf7JCVWi1WG3c/MqJVzmx7N6Oj1iMJC3sd4x/5OLlyAQdFMbquhDDf3TUPmlBbbrPn+B5ebf7+eE6CdeAABAAAkBgbAk8iJrk6dOnkRpZU1MTQYt+arVZPRqFN6GRvDh6xvN83FkBQ0frTg93Q1wyLbaQIOTTpMZupHnK4tJJ6IoK9oKl4/c93E2OQ8uIED2aMS+QSgZWfrhYhAuNCnnZHt5zdePKI9HyQuiAe3QEguABOBBu+HxYcdiZMxxEFFXMo+PyMQWtZPJaL7h5Ri5YJJ+sRsYTfw8X6HPvedkey83zJIfW5SgUSKUPRaKRF6pW/JHNfIgVusOybF+/KpYh3R/ooJF5lj2/fNn+DRnNlClV6ugqVkxOidLDSVIScPb4sZkytVrkPN1huu7lethuZD2ZipRZ3neTE1NUVEcgmMU6FGk7wgUvGtatMkRr5nlRCXqHPXgQY4jruYFR86K15Ms0y2QY77CmHzhicmSRh1QyHCuI2gtykY4TFYScYjJmCiiSImdh7sZgz9NinIoR5TW6M+tbnFLdVu3gzqhkaMUq5dR2u8OnXYzZHZx8rnKgQZGUI5K65EJjMeEtfOEgLGH7eT7Ec3/KhFHwPh523G/0cDmUJooFnyancsKik04CkSaZS0jTrLYHWy8ho3pdnvgh3s+jVWW48ZOlA0xy8/JwQvAXRCEHx3PRfTGYO/4vvLRicfH2lvWrO6bRABEKcAAAEjpJREFUpYxSNUcVd5+EEyKLFjL38APekAmlCCej+KgFf0unXvUPpubZ9j0dfFV9vTax/NTturvY6x1bt5h8ivqdLw1xxI0WGt7zNnegycRsjfLgDuHcHTLgmyRP0H+iWdC3TdBJX7FW8cEbPf6LO64hbWsg5JXMR/75EeLxjR9FXan6HmItLe0D7hLCdd7H9uC7vMKpVJGQI53zgl6RpHvgIjGO9kkGef4K8udVCc0V5YlykDHMH3Ta3dM1tclNTlX/kLhide16dcZUCQn8fUiEeEFUUwS//Yzr4dlr58RedsT6lZD4lqNlv0u2pqFWMeTuT1Fk6ije0bJ+1zmJbmtjZUq75XV3b1HZQCty0ZQrsxy0WT/S0B9j8iVpZLzH2nqwqzdWXcjffR3btevMYOqCMt3qRL0avbpCRkke+bKikfRKmcdRfpEw8whGSa7z91jFqjSSmLpfEAsEgAAQAAL3lcCDqEneV8CpKkMmoMGA9szs2Gb6hJozm6bLFdpnnM17uFSZMsThOfiQYoXkwqqaWd5UMzPuykMJhpak5czAOhunkYPrPoc7iCFFklkZXh+H+rFiTf2rmsEHew4uRis2pCYNevHxnkNbt5/g6XKlfGaF6rl+6/aYFjdMF0TMiqZfznPZHU6X09pywmKvbaqfi9Ij3SyqGwg6DYrBsVu25sYWT5GqvJRSPlmhmdG2498GSk5FAKlntZuWyAcv5YgHF53DtCjVpUgheFYu4YJ2iWPxy8F0GFHC2xzrRZ30uq/4sCmxtWRyUyTKp+Xt5+zcLMzeI6+YNaKlZBKc5GLTxQjcM2HMNO5p9Lfw+A4NgymR4pd4MeE3SpByVZsufrji8mWVm99SoW95OJ1OW9s2s02P9qnGuzvmoAUuxl9nr/FYor7Is+aDNqKyAX37LhJwZMgK65wJwX/eYGBVG9ZrkL3Sf8UVmEanM1wm5hzdb4KSy2TENElqLYm/5rlNaWO2x3zBT9J6vNNyXorLNGkqxOmq+vh3XdyRFses2tTaSHwRo6krVRNyKc1L9XGN81v3miWr9Rm/eImcKB9Gvqx9bmeIZJ4IlywSoZvCd8t19LxYszr97SYcIoqLB96UpWpSlnHoiBk85GZZPxZ23IgP/vPGjn9X6mtjzpwoKcbdjs2B0bS32Y69FvFPGqqRs8VtztUtpp9ILCpTo/DiaTKZX5pu86fPg2yPVbFCyDkVdEeL4/cW7A6lTldVsbq6Xj1Yb8jRsg+rWaNIeYfGNU9MICeXz/w+lxsvDWv4aMYUPYyF0GZvM8+s0AyTn+/vz88b5nomCHAdCAABIAAE7pZAVoviu63sS5J/7ty5XV1dhnBAf6CfY9UwZJsqCly7GtMM/e6r3omF0VVdZMXcbTvukVRtaKheqlXPpqW8H73TTV6AjqaFuSRZiPXc8BOFEonwD2d/bzJf9A8pqqhYgiG7XjQuyKL3+9IiwcyDz1AwOS7HcYfzDqMML5yJYlLs4/x4pDSJJNDV/hsbWlUPCSFXZycrX765fmWl9hkFXRBA3/5GfrwZAtordtDqLUQni+jrtrwpnNnzobAjEpHguqP0+thrvonkFMJ3zmrHNXUNtbrnNSqGwj6LHH+JSwrJ3usDJlLkFWZ7a8O2Y73SQmkAxX1joM3YZbPJ6kZOU4Mhlywuwrg/RmvhXa2vbzR5hh24DJ1B/e1hrw9wQYeMsDhZEufcmx4j7zls7MzX1q+k2cNt1lvDVSNRKEp6nFZ09tEM5VCLJNLqs218xpWYKDPGjONOFpN32GvXBzqF9LGeiFSkl8Dh+j8G14KXLKYTLFGq0i6v2/TGa9rJbueloTdLbonsCRHmO9NxnBsi9ejUnz3NXbLausWDVkZkB324zx8YKvn8Rx2Gc1TNWkGNFDaAHY9ujRyD7gwUWcDoX2+sX0ilrgHteSuM3/OGy55RUZjH/LuAPI2nZ+pyRhJ7P+tK2Z7w4bk9Dhc+MzpM+cJZL36HjS/XUsO8s0DjmENIUn7VEnm37tyxY5h/e62x2T/WKMkMGZnLu37X4Rz69RT0tdddh/oXvVozaGXMnUhMxgLCMyE++G1tpsDCOkGNRKHbavEMmdJS9j45klxY3/j60E+eDCYKuq/mDZEBtHv3KcJ7vt1ZQJHJZd1VTPgDLZ+67X1k7Iir8Fi5bR5KOzv1O5BIhcLu1UmSgU9/3lUbIDMQAAJAAAiMksCDaJNEuyLR3kjk1IqYZb1JcgSc8YdwvM/j+pAlZlKqefTRgy1GSY1GhrFWY8fHlGa58EIfHc0XuOpyXCFpAj1Gfe6LbMUj0sDVM6Z37UHk3TrEX5ZnT5mdIkWl8Fn2uCDs3vnU7XShs3Co1K2SqOYxZkOrobi2aibhdbQZT/QoNg59nzxpjkZxbG9rC1ldRRd47QeNdqKi4cmwaoHLFQy/670zD5fXRywwOF2hLtxq2msSL6+gMNb8tsk5eYUu8YvkwsmDnssun4IR+12Wtg7XbV6FvGqHD/nojNk2Vw5R/Swl7nPZr/ZT5eFDWjGM/Z3B+GiNhgzYDx5yFak3P4GJPxHhfazrmp8qxjiHyXjKixULDsbU99XyEx2GI1J9ORm81N7xAa5aRMpwtfxke0ubpOYHcpzrbD1wpv+5BUMREMo5TEebwfhYjWYaxr5vOsPL66eR0tQDl7wsTNWxkLfzHaPsRa38jsv0a5t4bgPyDY6FdBj5q+2GE3zFerQz0N97/nXTOzZmnWqwqfHDjWInKVXTTYYTGFNTn7jUyiXTSV2qtiInUuG40Z4rdtd0ZfT80MSEyLktE8ZM444kbVbH3v1GcqWa4j3mtk5vSC5Uk04CkR9pyhDHIeX1UUfiWM/Zd63BgtrKUiLgsbs/kcqnoN1a8UGkrFxsuWhy/Xrb1h6tZrac/Hq/j3WesbrF6rrXEs4ELiaLQ27uFsbEXiIgd8Rmq396qeXtFlQoj3z5PqFf+SJf5fHshS6saKjtcYpaQ7cbPpPL7vEmyftZVxoREKEDkHBshoqJdQ0nkJGZeKIy+csf8UXwN7w+CUWmtEmOyrsVm6qpmnN21ynLrsZe7aIK+jFx3p8513mb/VOZ7rX6od/8EZHFUt8NtCUhZrpG3tG7jB4xEzK1ONBLKz7AefKeQ5789zT0uZxeQjNkZhExz6ikZzspWfi2vZcBnUQlxkSkEr0WjAa0qRXHSfVfJ3/5Y0jFvh7fw1OiOxpuOc2nvFSFBn2LBwIQAAJAAAjcNwIPoiaJ4CJlMsu9kdmMyHRVRVFz+56W/s1NunlrX+Pb2g5ttXyKoS9MaF/VacMHvlNKlfx8+55/4ht262uWsy0dW1a/gxPFclVlleKAFe1vjDsUJOA+2350EpWoSYpo1Wyz4cCuIPbGpqRNQZHmEnPXNHyOPoGxff1+XlxEq9Y06B9PMECJFDUbAgdbzT9fbwhNpGaq69dXhg/UQQGnn1Y8fNLBKKKftsZlulfrsQMmw8/MAfRVkaeq6lcKjk5D1MRc2eJVVdx+w/o1AXwSVabWVYbanH/yYrOGBVig0K/lDAdatxzt5cUkPWfN2sVIbRbUNtls2n94x/qbGPmkZu26SsF0UK6r8bQc+od17XfEUplywTL1sSNcTxAjCzV164JGVMjhXqyIrlhTXymg1ta9ihkPHdra0MsXkMwP6ut+mPhKPULJ8O5WC/pSi2zOinV6gUCagRu2G9GLuWhV1G/5+fqWO1J6ztqGpYImPkgpJUZ0oOt+c3D2Wp1wwIxEs3yxrfGQ0VFWFzOVxA/3QuSJRzCz5djHuPKpREUSNUKSXeMljIqxvmvYFcB/uTJN/4ozYcw87iLFqnrvfoNxq6X/Efmc76uo9/xhv7Q0EphOk4zjUJmmsaOLxkt1ryxtQ2Kw7hYvlsiVL7yiE46hGhLwqZWvbSRMhy32c6aWc+KJJFU6S1X1mj6Fi2ABTVNtbndQWzgwQM7DBttNP3bT64kWiSvUX5Q5Jeho3XrgDHczyOPrN3j0m9bE3lkIB3VaPMhoNjqKKXLdz7pSVB+LEouJogrtkIOsxISE1i6Ne1+TKj/n8Yjp2uGMlqlyDRtHKFZvaigytVu7zPudFoIskdGKObWN4WOhEgJVKsv7Ndo8zww04KbF2OHx85g1at7HcsnKFPkSyxnp7xBr/sdm82UO+ZJ0vcouXrepUjgiSwj4E2r145zonhurhWN1JfSiSkXcywt0FIF0rnZxqi9/xHXE52YDsoVRzdbnOt7hUn8XNMmRDjWkAwJAAAjcEwJ/9Ze//OWeFASFjCGBm+Zdx6n6FaM6iWcMmzXWRaOPN250z9vdMO8r9ZIZfU/y1cj3CceWD3dky7aexWlOUhnbqkdTesjn+dBPzIx+Gu9S6yt7+qt310a+WDqaAr/0efwntv39ZU0T+grrl76pd9PAke6TvJs6hss70n2Sw5UxzDX0imdLS/+qJl1UoRom7ZhcEvztjcTfNoXfi32lwkj3SY62Uz7LtiZW+0ZtvMfHaMuCfEAACAABIDBKAl+kc9Uom/zAZeNdv3eTinvuU/TAcRw3HQ72uJxnO4wnP0V+uV8dLcVvP7B910EH6/P7uh2mw2f6Z5ZFjd3jZmSGdISYU8lwljORw43HZxeFXknLtaqiL7B7BPOcpuTeWVATeoK+2WOfrEF+719YwGntArE98pnPL6wRo6o4l9Y+l3g01agKSp2JtdqwZ7WgRqamA7FAAAgAgftFADTJ+0V69PXg9LL6ZEe70ZcHOb/iBPo9lj37LAFFdaXw1cevSMiVVf0/Oupj49b1L69vMnmKdA0rx7mxDsundUtJx2FHOkfdr8jIZWgmLiHvoXNlhspSXSaK05xMmypxdnHoSN7f9qqXpz2sNLvSRptaMr9a5TObY+6soy3nvudDmzzH7E2Xz9p+rUyf8NHd+95DqBAIAAEgAATAuxVkAAgAASAwRgR47oThuEhXXf6V8tAeIxhfsWJ5T3tL1+M1uhTfxrzvPUHnuh5klasqx2CX4n3vy91XiD6dte/4xKXVyV8NvfuyoQQgAASAABDIigBoklnhgsRAAAgAASAABIAAEAACQAAIAAEgEPdFeYABBIAAEAACQAAIAAEgAASAABAAAkBgJARgn+RIKEEaIAAEgAAQAAJAAAgAASAABIAAEBgkAJokSAMQAAJAAAgAASAABIAAEAACQAAIZEcANMnseEFqIAAEgAAQAAJAAAgAASAABIAAEABNEmQACAABIAAEgAAQAAJAAAgAASAABLIjAJpkdrwgNRAAAkAACAABIAAEgAAQAAJAAAiAJgkyAASAABAAAkAACAABIAAEgAAQAALZEQBNMjtekBoIAAEgAASAABAAAkAACAABIAAEQJMEGQACQAAIAAEgAASAABAAAkAACACB7AiAJpkdL0gNBIAAEAACQAAIAAEgAASAABAAAqBJggwAASAABIAAEAACQAAIAAEgAASAQHYEQJPMjhekBgJAAAgAASAABIAAEAACQAAIAAHQJEEGgAAQAAJAAAgAASAABIAAEAACQCA7AqBJZscLUgMBIAAEgAAQAAJAAAgAASAABIAAaJIgA0AACAABIAAEgAAQAAJAAAgAASCQHQHQJLPjBamBABAAAkAACAABIAAEgAAQAAJAADRJkAEgAASAABAAAkAACAABIAAEgAAQyI4AaJLZ8YLUQAAIAAEgAASAABAAAkAACAABIACaJMgAEAACQAAIAAEgAASAABAAAkAACGRHADTJ7HhBaiAABIAAEAACQAAIAAEgAASAABAATRJkAAgAASAABIAAEAACQAAIAAEgAASyIwCaZHa8IDUQAAJAAAgAASAABIAAEAACQAAIgCYJMgAEgAAQAAJAAAgAASAABIAAEAAC2REATTI7XpAaCAABIAAEgAAQAAJAAAgAASAABECTBBkAAkAACAABIAAEgAAQAAJAAAgAgewIgCaZHS9IDQSAABAAAkAACAABIAAEgAAQAAKgSYIMAAEgAASAABAAAkAACAABIAAEgEB2BECTzI4XpAYCQAAIAAEgAASAABAAAkAACAAB0CRBBoAAEAACQAAIAAEgAASAABAAAkAgOwKgSWbHC1IDASAABIAAEAACQAAIAAEgAASAAGiSIANAAAgAASAABIAAEAACQAAIAAEgkB0B0CSz4wWpgQAQAAJAAAgAASAABIAAEAACQAA0SZABIAAEgAAQAAJAAAgAASAABIAAEMiOwP8PeZj53BNJIjEAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "82027051",
   "metadata": {},
   "source": [
    "# 7. Word break\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9a26feac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def wordBreak(s, wordDict):\n",
    "    wordSet = set(wordDict)\n",
    "    memo = defaultdict(list)\n",
    "    \n",
    "    def wordbreak_topdown(s):\n",
    "        if not s:\n",
    "            return [[]]\n",
    "        \n",
    "        if s in memo:\n",
    "            return memo[s]\n",
    "        \n",
    "        for endIndex in range(1, len(s) + 1):\n",
    "            word = s[:endIndex]\n",
    "            \n",
    "            if word in wordSet:\n",
    "                for subSentence in wordbreak_topdown(s[endIndex:]):\n",
    "                    memo[s].append([word] + subSentence)\n",
    "        return memo[s]\n",
    "    \n",
    "    wordbreak_topdown(s)\n",
    "    \n",
    "    return [\" \".join(words) for words in memo[s]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a9d3132d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cat sand dog', 'cats and dog']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"catsanddog\"\n",
    "wordDict = [\"cat\",\"cats\",\"and\",\"sand\",\"dog\"]\n",
    "\n",
    "wordBreak(s, wordDict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576f14ad",
   "metadata": {},
   "source": [
    "# 8. Generate parenthesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fa02d955",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateParenthesis(n):\n",
    "    \n",
    "    result = []\n",
    "    stack = []\n",
    "    \n",
    "    left_bracket_count = 0\n",
    "    right_bracket_count = 0\n",
    "    \n",
    "    def backtrack(stack, left_bracket_count, right_bracket_count):\n",
    "        if len(stack) == 2 * n:\n",
    "            result.append(\"\".join(stack))\n",
    "            return\n",
    "        \n",
    "        if left_bracket_count < n:\n",
    "            stack.append(\"(\")\n",
    "            backtrack(stack, left_bracket_count + 1, right_bracket_count)\n",
    "            stack.pop()\n",
    "        \n",
    "        if right_bracket_count < left_bracket_count:\n",
    "            stack.append(')')\n",
    "            backtrack(stack, left_bracket_count, right_bracket_count + 1)\n",
    "            stack.pop()\n",
    "    \n",
    "    backtrack(stack, left_bracket_count, right_bracket_count)\n",
    "    return result\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "47eda79c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['((()))', '(()())', '(())()', '()(())', '()()()']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generateParenthesis(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1f43b2c",
   "metadata": {},
   "source": [
    "# 9. nth digit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1ba5d390",
   "metadata": {},
   "outputs": [],
   "source": [
    "def findNthDigit(n):\n",
    "    \n",
    "    digit = base = 1\n",
    "    \n",
    "    while n > 9 * base * digit:\n",
    "        n -= 9 * base * digit\n",
    "        digit += 1\n",
    "        base *= 10\n",
    "    \n",
    "    q = (n - 1)//digit\n",
    "    r = (n - 1)% digit\n",
    "    \n",
    "    return int(str(base + q)[r])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a7f73ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "findNthDigit(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b03542de",
   "metadata": {},
   "source": [
    "# 10. Accounts merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "652f21c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accountsMerge(accounts):\n",
    "    visited = [False] * len(accounts)\n",
    "    result = []\n",
    "    email_accounts_map = defaultdict(list)\n",
    "    \n",
    "    for i, account in enumerate(accounts):\n",
    "        for j in range(1, len(account)):\n",
    "            email = account[j]\n",
    "            email_accounts_map[email].append(i)\n",
    "    \n",
    "    def dfs(i, emails):\n",
    "        if visited[i]:\n",
    "            return \n",
    "        \n",
    "        visited[i] = True\n",
    "        \n",
    "        for j in range(1, len(accounts[i])):\n",
    "            email = accounts[i][j]\n",
    "            emails.add(email)\n",
    "            for neighbor in email_accounts_map[email]:\n",
    "                dfs(neighbor, emails)\n",
    "    \n",
    "    for i, account in enumerate(accounts):\n",
    "        if visited[i]:\n",
    "            continue\n",
    "        name, emails = account[0], set()\n",
    "        dfs(i, emails)\n",
    "        result.append([name] + sorted(emails))\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bb3fc1af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['John', 'john00@mail.com', 'john_newyork@mail.com', 'johnsmith@mail.com'],\n",
       " ['Mary', 'mary@mail.com'],\n",
       " ['John', 'johnnybravo@mail.com']]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accounts = [[\"John\",\"johnsmith@mail.com\",\"john_newyork@mail.com\"],\n",
    "            [\"John\",\"johnsmith@mail.com\",\"john00@mail.com\"],\n",
    "            [\"Mary\",\"mary@mail.com\"],\n",
    "            [\"John\",\"johnnybravo@mail.com\"]]\n",
    "\n",
    "accountsMerge(accounts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "041f58c9",
   "metadata": {},
   "source": [
    "# 11. Valid Word abbreviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4e1d5e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def validWordAbbreviation(word, abbr):\n",
    "    i = j = 0\n",
    "    \n",
    "    while i < len(word) and j < len(abbr):\n",
    "        if abbr[j].isalpha():\n",
    "            if word[i] != abbr[j]:\n",
    "                return False\n",
    "            i += 1\n",
    "            j += 1\n",
    "        else:\n",
    "            if abbr[j] == '0':\n",
    "                return False\n",
    "            \n",
    "            temp = 0\n",
    "            \n",
    "            while j < len(abbr) and abbr[j].isdigit():\n",
    "                temp = 10 * temp + int(abbr[j])\n",
    "                j += 1\n",
    "            i += temp\n",
    "    return j == len(abbr) and i == len(word)\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3360bd42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word = \"internationalization\"\n",
    "abbr = \"i12iz4n\"\n",
    "\n",
    "validWordAbbreviation(word, abbr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6827a75",
   "metadata": {},
   "source": [
    "# 12. Koko eating bananas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4865d619",
   "metadata": {},
   "outputs": [],
   "source": [
    "def minEatingSpeed(piles, h):\n",
    "    \n",
    "    left = 1\n",
    "    right = max(piles)\n",
    "    \n",
    "    while left < right:\n",
    "        mid = math.floor((left + right)/2)\n",
    "        hours_spent = 0\n",
    "        \n",
    "        for pile in piles:\n",
    "            hours_spent += math.ceil(pile/mid)\n",
    "        \n",
    "        if hours_spent <= h:\n",
    "            right = mid\n",
    "        else:\n",
    "            left = mid + 1\n",
    "    \n",
    "    return right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "adab0805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piles = [3,6,7,11]\n",
    "h = 8\n",
    "\n",
    "minEatingSpeed(piles, h)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "329e4957",
   "metadata": {},
   "source": [
    "# 13. Binary tree right side view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2123c373",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rightSideView(root):\n",
    "    if not root:\n",
    "        return\n",
    "    \n",
    "    result = []\n",
    "    level = 0\n",
    "    \n",
    "    return traversePreOrder(root, result, level)\n",
    "\n",
    "def traversePreOrder(node, result, level):\n",
    "    if not node:\n",
    "        return\n",
    "    \n",
    "    if level >= len(result):\n",
    "        result.append(node.val)\n",
    "        \n",
    "    if node.right is not None:\n",
    "        traversePreOrder(node.right, result, level + 1)\n",
    "    \n",
    "    if node.left is not None:\n",
    "        traversePreOrder(node.left, result, level + 1)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10795484",
   "metadata": {},
   "source": [
    "# 14. Restore IP addresses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "986b22b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def restoreIpAddresses(s):\n",
    "    \n",
    "    def valid(segment):\n",
    "        return int(segment) <= 255 if segment[0] != '0' else len(segment) == 1\n",
    "    \n",
    "    def update_output(curr_pos):\n",
    "        segment = s[curr_pos + 1:n]\n",
    "        if valid(segment):\n",
    "            segments.append(segment)\n",
    "            output.append('.'.join(segments))\n",
    "            segments.pop()\n",
    "    \n",
    "    def backtrack(prev_pos=-1, dots=3):\n",
    "        for curr_pos in range(prev_pos + 1, min(n-1, prev_pos + 4)):\n",
    "            segment = s[prev_pos + 1: curr_pos + 1]\n",
    "            if valid(segment):\n",
    "                segments.append(segment)\n",
    "                if dots - 1 == 0:\n",
    "                    update_output(curr_pos)\n",
    "                else:\n",
    "                    backtrack(curr_pos, dots - 1)\n",
    "                segments.pop()\n",
    "    n = len(s)\n",
    "    segments, output = [], []\n",
    "    backtrack()\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e3a167be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['255.255.11.135', '255.255.111.35']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"25525511135\"\n",
    "restoreIpAddresses(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cd37490",
   "metadata": {},
   "source": [
    "# 15. Pow (x, n)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e07cde99",
   "metadata": {},
   "outputs": [],
   "source": [
    "def myPow(x, n):\n",
    "    if n == 0:\n",
    "        return 1\n",
    "    \n",
    "    if n < 0:\n",
    "        return myPow(1/x, -n)\n",
    "    \n",
    "    lower = myPow(x, n//2)\n",
    "    \n",
    "    if n % 2 == 0:\n",
    "        return lower * lower\n",
    "    else:\n",
    "        return lower * lower * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "561c0816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myPow(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc78d44f",
   "metadata": {},
   "source": [
    "# 16. Walls and Gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "73132b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def wallsAndGates(rooms):\n",
    "    \n",
    "    WALL = -1\n",
    "    GATE = 0\n",
    "    EMPTY = 2147483647\n",
    "    \n",
    "    rows, cols = len(rooms), len(rooms[0])\n",
    "    \n",
    "    for row in range(rows):\n",
    "        for col in range(cols):\n",
    "            if rooms[row][col] == GATE:\n",
    "                dfs(rooms, row, col, 0)\n",
    "\n",
    "def dfs(matrix, row, col, current_step):\n",
    "    directions = [[-1, 0], [0, 1], [1, 0], [0, -1]]\n",
    "    \n",
    "    if row < 0 or row >= len(matrix) or col < 0 or col >= len(matrix[0]) or current_step > matrix[row][col]:\n",
    "        return\n",
    "    \n",
    "    if matrix[row][col] == -1:\n",
    "        return\n",
    "    \n",
    "    matrix[row][col] = current_step\n",
    "    \n",
    "    for i in range(len(directions)):\n",
    "        currentDir = directions[i]\n",
    "        nextRow = row + currentDir[0]\n",
    "        nextCol = col + currentDir[1]\n",
    "        dfs(matrix, nextRow, nextCol, current_step + 1)\n",
    "    \n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2db432a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "rooms = [[2147483647,-1,0,2147483647],[2147483647,2147483647,2147483647,-1],[2147483647,-1,2147483647,-1],[0,-1,2147483647,2147483647]]\n",
    "wallsAndGates(rooms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b3f555d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, -1, 0, 1], [2, 2, 1, -1], [1, -1, 2, -1], [0, -1, 3, 4]]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rooms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31488358",
   "metadata": {},
   "source": [
    "# 17. Best time to buy and sell stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c8247ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def maxProfit(prices):\n",
    "    min_price = float('inf')\n",
    "    max_profit = 0\n",
    "    \n",
    "    for i in range(len(prices)):\n",
    "        if prices[i] < min_price:\n",
    "            min_price = prices[i]\n",
    "        elif prices[i] - min_price > max_profit:\n",
    "            max_profit = prices[i] - min_price\n",
    "    return max_profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9caa5985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = [7,1,5,3,6,4]\n",
    "\n",
    "maxProfit(prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d701d562",
   "metadata": {},
   "source": [
    "# 18. Find largest value in each tree row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2b867479",
   "metadata": {},
   "outputs": [],
   "source": [
    "def largestValues(root):\n",
    "    result = []\n",
    "    \n",
    "    if root is None:\n",
    "        return result\n",
    "    \n",
    "    queue = [root]\n",
    "    \n",
    "    while len(queue) > 0:\n",
    "        result.append(max(x.val for x in queue))\n",
    "        \n",
    "        for i in range(len(queue)):\n",
    "            currNode = queue.pop(0)\n",
    "            if currNode.left is not None:\n",
    "                queue.append(currNode.left)\n",
    "            if currNode.right is not None:\n",
    "                queue.append(currNode.right)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d311d318",
   "metadata": {},
   "source": [
    "# 19. Add Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d23d7516",
   "metadata": {},
   "outputs": [],
   "source": [
    "def addStrings(num1, num2):\n",
    "    result = []\n",
    "    carry = 0\n",
    "    \n",
    "    p1 = len(num1) - 1\n",
    "    p2 = len(num2) - 1\n",
    "    \n",
    "    while p1 >= 0 or p2 >= 0:\n",
    "        x1 = int(num1[p1]) if p1 >= 0 else 0\n",
    "        x2 = int(num2[p2]) if p2 >= 0 else 0\n",
    "        \n",
    "        value = (x1 + x2 + carry) % 10\n",
    "        carry = (x1 + x2 + carry) // 10\n",
    "        result.append(value)\n",
    "        \n",
    "        p1 -= 1\n",
    "        p2 -=1\n",
    "    \n",
    "    if carry != 0:\n",
    "        result.append(carry)\n",
    "        \n",
    "    return \"\".join(str(x) for x in result[::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "58699e77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'134'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num1 = \"11\"\n",
    "num2 = \"123\"\n",
    "\n",
    "addStrings(num1, num2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "478f1338",
   "metadata": {},
   "source": [
    "# 20. Add binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4cd8b977",
   "metadata": {},
   "outputs": [],
   "source": [
    "def addBinary(a,b):\n",
    "    return bin(int(a,2) + int(b,2)).replace('0b', \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "58fa5f9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'100'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = \"11\"\n",
    "b = \"1\"\n",
    "\n",
    "addBinary(a,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e237c0f7",
   "metadata": {},
   "source": [
    "# 21. Sum of two Integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e9ed98e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSum(a,b):\n",
    "    \n",
    "    p1 = len(str(a)) - 1\n",
    "    p2 = len(str(b)) - 1\n",
    "    \n",
    "    result = []\n",
    "    carry = 0\n",
    "    \n",
    "    while p1 >= 0 or p2 >= 0:\n",
    "        x1 = int(str(a)[p1]) if p1 >= 0 else 0\n",
    "        x2 = int(str(b)[p2]) if p2 >= 0 else 0\n",
    "        value = (x1 + x2 + carry) % 10\n",
    "        carry = (x1 + x2 + carry) // 10\n",
    "        result.append(value)\n",
    "        \n",
    "        p1 -= 1\n",
    "        p2 -= 1\n",
    "    \n",
    "    if carry != 0:\n",
    "        result.append(carry)\n",
    "    \n",
    "    return int(\"\".join(str(x) for x in result[::-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e3b70232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getSum(3,12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26db95c1",
   "metadata": {},
   "source": [
    "# 22. Add to Array-Form of Integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "2264988b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def addToArrayForm(num, k):\n",
    "        \n",
    "    num1 = \"\".join(str(i) for i in num)\n",
    "    num2 = str(k)\n",
    "\n",
    "    result = []\n",
    "    carry = 0\n",
    "\n",
    "    p1 = len(num1) - 1\n",
    "    p2 = len(num2) - 1\n",
    "\n",
    "    while p1 >= 0 or p2 >= 0:\n",
    "        x1 = int(num1[p1]) if p1 >= 0 else 0\n",
    "        x2 = int(num2[p2]) if p2 >= 0 else 0\n",
    "\n",
    "        value = (x1 + x2 + carry) % 10\n",
    "        carry = (x1 + x2 + carry) // 10\n",
    "        result.append(value)\n",
    "\n",
    "        p1 -= 1\n",
    "        p2 -=1\n",
    "\n",
    "    if carry != 0:\n",
    "        result.append(carry)\n",
    "\n",
    "    return result[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "834aec74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = [1,2,0,0]\n",
    "k = 34\n",
    "\n",
    "addToArrayForm(num, k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beb4245d",
   "metadata": {},
   "source": [
    "# 23. Plus One"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "09a5dabb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plusOne(digits):\n",
    "    str_number = \"\".join(str(i) for i in digits)\n",
    "    int_number = int(str_number)\n",
    "    add_one = int_number + 1\n",
    "    result = []\n",
    "    \n",
    "    for i in str(add_one):\n",
    "        result.append(int(i))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "138a8a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 4]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plusOne([1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690fe94c",
   "metadata": {},
   "source": [
    "# 24. Add two numbers LinkedList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3588e917",
   "metadata": {},
   "outputs": [],
   "source": [
    "def addTwoNumbers(l1, l2):\n",
    "    dummyHead = ListNode(0)\n",
    "    curr = dummyHead\n",
    "    carry = 0\n",
    "    \n",
    "    while l1 != None or l2 != None or carry != 0:\n",
    "        l1val = l1.val if l1 else 0\n",
    "        l2val = l2.val if l2 else 0\n",
    "        columnSum = (l1val + l2val + carry) % 10\n",
    "        carry = (l1val + l2val + carry) // 10\n",
    "        newNode = ListNode(columnSum)\n",
    "        curr.next = newNode\n",
    "        curr = newNode\n",
    "        l1 = l1.next if l1 else None\n",
    "        l2 = l2.next if l2 else None\n",
    "    return dummyHead.next"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8382d49",
   "metadata": {},
   "source": [
    "# 25. Add two numbers LinkedList II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "14324c1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ListNode:\n",
    "    def __init__(self, val=0, next=None):\n",
    "        self.val = val\n",
    "        self.next = next\n",
    "\n",
    "def reverseList(head) -> ListNode:\n",
    "    last = None\n",
    "    while head:\n",
    "        # keep the next node\n",
    "        tmp = head.next\n",
    "        # reverse the link\n",
    "        head.next = last\n",
    "        # update the last node and the current node\n",
    "        last = head\n",
    "        head = tmp\n",
    "\n",
    "    return last\n",
    "\n",
    "def addTwoNumbers(l1, l2):\n",
    "    l1 = self.reverseList(l1)\n",
    "    l2 = self.reverseList(l2)\n",
    "\n",
    "    head = None\n",
    "    carry = 0\n",
    "    while l1 or l2:\n",
    "        # get the current values \n",
    "        x1 = l1.val if l1 else 0\n",
    "        x2 = l2.val if l2 else 0\n",
    "\n",
    "        # current sum and carry\n",
    "        val = (carry + x1 + x2) % 10\n",
    "        carry = (carry + x1 + x2) // 10\n",
    "\n",
    "        # update the result: add to front\n",
    "        curr = ListNode(val)\n",
    "        curr.next = head\n",
    "        head = curr\n",
    "\n",
    "        # move to the next elements in the lists\n",
    "        l1 = l1.next if l1 else None\n",
    "        l2 = l2.next if l2 else None\n",
    "\n",
    "    if carry:\n",
    "        curr = ListNode(carry)\n",
    "        curr.next = head\n",
    "        head = curr\n",
    "\n",
    "    return head"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "447661ce",
   "metadata": {},
   "source": [
    "# 26. Combination sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "d7a783f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combinationSum(candidates, target):\n",
    "    \n",
    "    result = []\n",
    "    comb = []\n",
    "    \n",
    "    def backtrack(remain, comb, start):\n",
    "        if remain == 0:\n",
    "            result.append(list(comb))\n",
    "            return\n",
    "        \n",
    "        if remain < 0:\n",
    "            return \n",
    "        \n",
    "        for i in range(start, len(candidates)):\n",
    "            comb.append(candidates[i])\n",
    "            backtrack(remain - candidates[i], comb, i)\n",
    "            comb.pop()\n",
    "        \n",
    "    backtrack(target, comb, 0)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "7f0bf38c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2, 2, 3], [7]]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates = [2,3,6,7]\n",
    "target = 7\n",
    "\n",
    "combinationSum(candidates, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "7ed81ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "num = [str(d) for d in str(1542)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "caae049e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num\n",
    "\n",
    "num.index(max(num))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7249d698",
   "metadata": {},
   "source": [
    "# 27. Maximum swap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "388abd3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def maximumSwap(num):\n",
    "    num = [str(d) for d in str(num)]\n",
    "    \n",
    "    for i in range(len(num)):\n",
    "        max_ind = len(num) - 1\n",
    "        for j in range(i+1, len(num)):\n",
    "            if num[j] >= num[max_ind]:\n",
    "                max_ind = j\n",
    "\n",
    "        if num[max_ind] > num[i]:\n",
    "            num[i], num[max_ind] = num[max_ind], num[i]\n",
    "            break\n",
    "    return int(\"\".join(num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "cd715e16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7726"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = 2776\n",
    "maximumSwap(num)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a150a95",
   "metadata": {},
   "source": [
    "# 28. Random pick with weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "cec3974e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import math\n",
    "\n",
    "class Solution:\n",
    "\n",
    "    def __init__(self, w):\n",
    "        self.prefix_sums = []\n",
    "        prefix_sum = 0\n",
    "        \n",
    "        for weight in w:\n",
    "            prefix_sum += weight\n",
    "            self.prefix_sums.append(prefix_sum)\n",
    "            \n",
    "        self.total_sum = prefix_sum\n",
    "    \n",
    "    def pickIndex(self):\n",
    "        target = self.total_sum * random.random()\n",
    "        \n",
    "        low = 0\n",
    "        high = len(self.prefix_sums)\n",
    "\n",
    "        while low < high:\n",
    "            mid = math.floor((low + high)/2)\n",
    "            if target > self.prefix_sums[mid]:\n",
    "                low = mid + 1\n",
    "            else:\n",
    "                high = mid\n",
    "        return low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "e64fb036",
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = Solution([1, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "1fbf1f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solution.pickIndex()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c86503ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solution.pickIndex()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "9bb014e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solution.pickIndex()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56ce5b2",
   "metadata": {},
   "source": [
    "# B) Leetcode list 2\n",
    "\n",
    "https://leetcode.com/list/5jahjufm/\n",
    "\n",
    "# 1. Add two linked list reversed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b457a417",
   "metadata": {},
   "outputs": [],
   "source": [
    "def addTwoNumbers(l1, l2):\n",
    "    dummyHead = ListNode(0)\n",
    "    curr = dummyHead\n",
    "    carry = 0\n",
    "    \n",
    "    while l1 is not None or l2 is not None or carry != 0:\n",
    "        l1val = l1.val if l1 is not None else 0\n",
    "        l2val = l2.val if l2 is not None else 0\n",
    "        \n",
    "        columnSum = (l1val + l2val + carry) % 10\n",
    "        carry = (l1val + l2val + carry) // 10\n",
    "        \n",
    "        newNode = ListNode(columnSum)\n",
    "        \n",
    "        curr.next = newNode\n",
    "        curr = newNode\n",
    "        \n",
    "        l1 = l1.next if l1 else None\n",
    "        l2 = l2.next if l2 else None\n",
    "    return dummyHead.next"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b0ce83",
   "metadata": {},
   "source": [
    "# 2. Longest Substring Without Repeating Characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd00d2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lengthOfLongestSubstring(s):\n",
    "    \n",
    "    if len(s) <= 1:\n",
    "        return len(s)\n",
    "    \n",
    "    longest = 0\n",
    "    left = 0\n",
    "    seen_chars = {}\n",
    "    \n",
    "    for right in range(len(s)):\n",
    "        currentChar = s[right]\n",
    "        prevSeenChar = -1\n",
    "        \n",
    "        if currentChar in seen_chars.keys():\n",
    "            prevSeenChar = seen_chars[currentChar]\n",
    "        \n",
    "        if prevSeenChar >= left:\n",
    "            left = prevSeenChar + 1\n",
    "        \n",
    "        seen_chars[currentChar] = right\n",
    "    \n",
    "        longest = max(longest, right - left + 1)\n",
    "    \n",
    "    return longest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d04937ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"pwwkew\"\n",
    "\n",
    "lengthOfLongestSubstring(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "630de779",
   "metadata": {},
   "source": [
    "# 3. Letter Combinations of a Phone Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e399fa76",
   "metadata": {},
   "outputs": [],
   "source": [
    "def letterCombinations(digits):\n",
    "    letter_map = {2: ['a', 'b', 'c'],\n",
    "                  3: ['d', 'e', 'f'],\n",
    "                  4: ['g', 'h', 'i'],\n",
    "                  5: ['j', 'k', 'l'],\n",
    "                  6: ['m', 'n', 'o'],\n",
    "                  7: ['p', 'q', 'r', 's'],\n",
    "                  8: ['t', 'u', 'v'],\n",
    "                  9: ['w', 'x', 'y', 'z']}\n",
    "\n",
    "    if digits == \"\":\n",
    "        return []\n",
    "    \n",
    "    queue = letter_map[int(digits[0])]\n",
    "    \n",
    "    for i in range(1, len(digits)):\n",
    "        s = len(queue)\n",
    "        while s > 0:\n",
    "            currLetter = queue.pop(0)\n",
    "            for j in letter_map[int(digits[i])]:\n",
    "                queue.append(currLetter + j)\n",
    "            s -= 1\n",
    "    return queue\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "000db2ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ad', 'ae', 'af', 'bd', 'be', 'bf', 'cd', 'ce', 'cf']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits = \"23\"\n",
    "letterCombinations(digits)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6e193cd",
   "metadata": {},
   "source": [
    "# 4. Remove Nth Node From End of List\n",
    "\n",
    "https://leetcode.com/problems/remove-nth-node-from-end-of-list/solution/\n",
    "\n",
    "We notice that the problem could be simply reduced to another one : Remove the (L - n + 1)th node from the beginning in the list , where L is the list length. This problem is easy to solve once we found list length L."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be37fbe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def removeNthFromEnd(head):\n",
    "    dummy = ListNode(0)\n",
    "    dummy.next = head\n",
    "    length = 0\n",
    "    \n",
    "    #setting the first pointer\n",
    "    first = dummy\n",
    "    \n",
    "    while first != None:\n",
    "        length += 1\n",
    "        first = first.next\n",
    "    \n",
    "    length -= n\n",
    "    \n",
    "    while length > 0:\n",
    "        length -= 1\n",
    "        first = first.next\n",
    "    \n",
    "    first.next = first.next.next\n",
    "    \n",
    "    return dummy.next"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba844f31",
   "metadata": {},
   "source": [
    "# 5. Merge two sorted lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a54872a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mergeTwoLists(l1, l2):\n",
    "    if l1 is None:\n",
    "        return l2\n",
    "    elif l2 is None:\n",
    "        return l1\n",
    "    elif l1.val < l2.val:\n",
    "        l1.next = mergeTwoLists(l1.next, l2)\n",
    "        return l1        \n",
    "    else:\n",
    "        l2.next = mergeTwoLists(l1, l2.next)\n",
    "        return l2\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a83a64b3",
   "metadata": {},
   "source": [
    "# 6. Divide Two Integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dd26959e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def divide(dividend: int, divisor: int) -> int:\n",
    "    negatives = 0\n",
    "    MAX_INT = 2147483647        # 2**31 - 1\n",
    "    MIN_INT = -2147483648       # -2**31\n",
    "\n",
    "    if dividend == MIN_INT and divisor == -1:\n",
    "        return MAX_INT\n",
    "\n",
    "    if dividend == MAX_INT and divisor == -1:\n",
    "        return MIN_INT\n",
    "\n",
    "    if dividend == MIN_INT and divisor == 1:\n",
    "        return MIN_INT\n",
    "\n",
    "    if dividend == MAX_INT and divisor == 1:\n",
    "        return MAX_INT\n",
    "\n",
    "    if dividend < 0:\n",
    "        negatives += 1\n",
    "        dividend = -dividend\n",
    "\n",
    "    if divisor < 0:\n",
    "        negatives += 1\n",
    "        divisor = -divisor\n",
    "\n",
    "    subtractions = 0\n",
    "\n",
    "    while dividend - divisor >= 0:\n",
    "        subtractions += 1\n",
    "        dividend -= divisor\n",
    "\n",
    "    if negatives == 1:\n",
    "        subtractions = -subtractions\n",
    "\n",
    "    return subtractions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3780e760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "divide(125,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2804d397",
   "metadata": {},
   "source": [
    "# 7. Find First and Last Position of Element in Sorted Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0debccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def searchRange(nums, target):\n",
    "    if len(nums) == 0:\n",
    "        return [-1, -1]\n",
    "    \n",
    "    firstPosition = binary_search(nums, target, 0, len(nums) - 1)\n",
    "    \n",
    "    if firstPosition == -1:\n",
    "        return [-1, -1]\n",
    "    \n",
    "    startPosition = firstPosition\n",
    "    endPosition = firstPosition\n",
    "    temp1 = 0\n",
    "    temp2 = 0\n",
    "    \n",
    "    while startPosition != -1:\n",
    "        temp1 = startPosition\n",
    "        startPosition = binary_search(nums, target, 0, startPosition - 1)\n",
    "    startPosition = temp1\n",
    "    \n",
    "    while endPosition != -1:\n",
    "        temp2 = endPosition\n",
    "        endPosition = binary_search(nums, target, endPosition + 1, len(nums) - 1)\n",
    "    endPosition = temp2\n",
    "    \n",
    "    return [startPosition, endPosition]\n",
    "\n",
    "def binary_search(nums, target, left, right):\n",
    "    while left <= right:\n",
    "        mid = math.floor((left + right)/2)\n",
    "        if nums[mid] == target:\n",
    "            return mid\n",
    "        elif nums[mid] < target:\n",
    "            left = mid + 1\n",
    "        else:\n",
    "            right = mid - 1\n",
    "    return -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c0e2dd6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums = [5,7,7,8,8,10]\n",
    "target = 8\n",
    "\n",
    "searchRange(nums, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a708578",
   "metadata": {},
   "source": [
    "# 8. Combination Sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4c0c26df",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combinationSum(candidates, target):\n",
    "    result = []\n",
    "    \n",
    "    def backtrack(remain, comb, start):\n",
    "        if remain == 0:\n",
    "            result.append(list(comb))\n",
    "            return\n",
    "        elif remain < 0:\n",
    "            return\n",
    "        \n",
    "        for i in range(start, len(candidates)):\n",
    "            comb.append(candidates[i])\n",
    "            backtrack(remain - candidates[i], comb, i)\n",
    "            comb.pop()\n",
    "    \n",
    "    backtrack(target, [], 0)\n",
    "    return result\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9c25e2c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2, 2, 3], [7]]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates = [2,3,6,7]\n",
    "target = 7\n",
    "\n",
    "combinationSum(candidates, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d5c6ac",
   "metadata": {},
   "source": [
    "# 9. Permutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ea8e3c43",
   "metadata": {},
   "outputs": [],
   "source": [
    "def permute(nums):\n",
    "    result = []\n",
    "    path = []\n",
    "    dfs(nums, path, result)\n",
    "    return result\n",
    "\n",
    "def dfs(nums, path, result):\n",
    "    if not nums:\n",
    "        result.append(path)\n",
    "    \n",
    "    for i in range(len(nums)):\n",
    "        dfs(nums[:i] + nums[i+1:], path + [nums[i]], result)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fa97ed1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [1, 3, 2], [2, 1, 3], [2, 3, 1], [3, 1, 2], [3, 2, 1]]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums = [1,2,3]\n",
    "\n",
    "permute(nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cea56de8",
   "metadata": {},
   "source": [
    "# 10. Merge Intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "77675461",
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge(intervals):\n",
    "    intervals.sort(key = lambda x: x[0])\n",
    "    \n",
    "    merged = []\n",
    "    \n",
    "    for interval in intervals:\n",
    "        if not merged or merged[-1][1] < interval[0]:\n",
    "            merged.append(interval)\n",
    "        else:\n",
    "            merged[-1][1] = max(merged[-1][1], interval[1])\n",
    "    \n",
    "    return merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "00660fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 6], [8, 10], [15, 18]]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intervals = [[1,3],[2,6],[8,10],[15,18]]\n",
    "\n",
    "merge(intervals)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a3b5599",
   "metadata": {},
   "source": [
    "# 11. Simplify paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b566340d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simplifyPath(path):\n",
    "    \n",
    "    stack = []\n",
    "    \n",
    "    for portion in path.split('/'):\n",
    "        if portion == '..':\n",
    "            if len(stack) > 0:\n",
    "                stack.pop()\n",
    "        elif portion == '.' or len(portion) == 0:\n",
    "            continue\n",
    "        else:\n",
    "            stack.append(portion)\n",
    "            \n",
    "    return '/' + '/'.join(stack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f46a4864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/foo'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"/home//foo/\"\n",
    "simplifyPath(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d45a4f45",
   "metadata": {},
   "source": [
    "# 12. Merge sorted array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2288fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge(nums1, m, nums2, n):\n",
    "    nums1_copy = nums1[:m]\n",
    "    \n",
    "    p1 = 0\n",
    "    p2 = 0\n",
    "    \n",
    "    for i in range(n+m):\n",
    "        if p2 >= n or (p1 < m and nums1_copy[p1] <= nums2[p2]):\n",
    "            nums1[i] = nums1_copy[p1]\n",
    "            p1 += 1\n",
    "        else:\n",
    "            nums1[i] = nums2[p2]\n",
    "            p2 += 1\n",
    "            \n",
    "    return nums1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8bd627e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 2, 3, 5, 6]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums1 = [1,2,3,0,0,0]\n",
    "m = 3\n",
    "nums2 = [2,5,6]\n",
    "n = 3\n",
    "\n",
    "merge(nums1, m, nums2, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9287f23",
   "metadata": {},
   "source": [
    "# 14. Validate binary search tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ff1a8da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def isValidBST(root):\n",
    "    def validate(node, low=-math.inf, high=math.inf):\n",
    "        if not node:\n",
    "            return True\n",
    "        if node.val <= low or node.val >= high:\n",
    "            return False\n",
    "        return validate(node.right, node.val, high) or validate(node.left, low, node.val)\n",
    "    return validate(root)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0e20a1",
   "metadata": {},
   "source": [
    "# 15. Binary tree level order traversal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fdb831b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def levelOrder(root):\n",
    "    if root is None:\n",
    "        return None\n",
    "    \n",
    "    result = []\n",
    "    \n",
    "    queue = []\n",
    "    \n",
    "    while len(queue) > 0:\n",
    "        queue_length = len(queue)\n",
    "        currentLevelValues = []\n",
    "        counter = 0\n",
    "        \n",
    "        while counter < queue_length:\n",
    "            currNode = queue.pop(0)\n",
    "            currentLevelValues.append(currNode.val)\n",
    "            \n",
    "            if currNode.left is not None:\n",
    "                queue.append(currNode.left)\n",
    "            \n",
    "            if currNode.right is not None:\n",
    "                queue.append(currNode.right)\n",
    "            \n",
    "            counter += 1\n",
    "        \n",
    "        result.append(currentLevelValues)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15673a28",
   "metadata": {},
   "source": [
    "# 16. Median Stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c645932c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# brute force --> O(N^2logN)\n",
    "\n",
    "def findMedian(arr):\n",
    "    \n",
    "    result = []\n",
    "    \n",
    "    for i in range(len(arr)):\n",
    "        if i == 0:\n",
    "            result.append(arr[i])\n",
    "        elif i % 2 == 1:\n",
    "            tmp = sorted(arr[:i+1])\n",
    "            mid = math.floor(len(tmp)/2)\n",
    "            median = int((tmp[mid] + tmp[mid-1])/2)\n",
    "            result.append(median)\n",
    "        else:\n",
    "            tmp = sorted(arr[:i+1])\n",
    "            mid = math.floor(len(tmp)/2)\n",
    "            median = int(tmp[mid])\n",
    "            result.append(median)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "47aa2b22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 10, 5, 4]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [5, 15, 1, 3]\n",
    "findMedian(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e02a0351",
   "metadata": {},
   "outputs": [],
   "source": [
    "# better --> O(nlogn)\n",
    "\n",
    "import heapq\n",
    "\n",
    "def findMedian(arr):\n",
    "    \n",
    "    heap1 = []\n",
    "    heap2 = []\n",
    "    result = []\n",
    "    \n",
    "    for i in range(len(arr)):\n",
    "        heapq.heappush(heap1, -arr[i])\n",
    "        heapq.heappush(heap2, -heapq.heappop(heap1))\n",
    "        \n",
    "        if len(heap2) > len(heap1):\n",
    "            heapq.heappush(heap1, -heapq.heappop(heap2))\n",
    "            \n",
    "        \n",
    "        if len(heap2) != len(heap1):\n",
    "            result.append(int(-heap1[0]))\n",
    "        else:\n",
    "            result.append(int((heap2[0] - heap1[0])/2))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab67077e",
   "metadata": {},
   "source": [
    "# 17. Magical candy bags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "40c0196a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from queue import PriorityQueue\n",
    "\n",
    "def maxCandies(arr, k):\n",
    "    \n",
    "    queue = PriorityQueue()\n",
    "    \n",
    "    count = 0\n",
    "    \n",
    "    for i in range(len(arr)):\n",
    "        queue.put(-arr[i])\n",
    "    \n",
    "    for i in range(k):\n",
    "        max_candy = -queue.get()\n",
    "        count += max_candy\n",
    "        max_candy = math.floor(max_candy/2)\n",
    "        queue.put(-max_candy)\n",
    "    \n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1caeb4d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [2, 1, 7, 4, 2]\n",
    "k = 3\n",
    "\n",
    "maxCandies(arr, k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4be5e861",
   "metadata": {},
   "source": [
    "# 18. Largest triple products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "99fd5769",
   "metadata": {},
   "outputs": [],
   "source": [
    "def findMaxProduct(arr):\n",
    "    \n",
    "    queue = PriorityQueue()\n",
    "    result = []\n",
    "    \n",
    "    for i in range(len(arr)):\n",
    "        \n",
    "        queue.put(-arr[i])\n",
    "        \n",
    "        if queue.qsize() < 3:\n",
    "            result.append(-1)\n",
    "        \n",
    "        else:\n",
    "            x = queue.get()\n",
    "            y = queue.get()\n",
    "            z = queue.get()\n",
    "            \n",
    "            answer = x * y * z\n",
    "            \n",
    "            result.append(-answer)\n",
    "            \n",
    "            queue.put(x)\n",
    "            queue.put(y)\n",
    "            queue.put(z)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7b9eb7ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-1, -1, 4, 4, 8]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [2, 1, 2, 1, 2]\n",
    "findMaxProduct(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98c5966",
   "metadata": {},
   "source": [
    "# 19. Nodes in a subtree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f328d7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_of_nodes(root, queries, s):\n",
    "    \n",
    "    hashmap = {}\n",
    "    \n",
    "    def dfs(root):\n",
    "        \n",
    "        if not root:\n",
    "            return\n",
    "        \n",
    "        hashmap[root.val] = s[root.val - 1]\n",
    "        \n",
    "        if not root.children:\n",
    "            return\n",
    "        \n",
    "        for c in root.children:\n",
    "            dfs(c)\n",
    "            hashmap[root.val] += hashmap[c.val]\n",
    "            \n",
    "    dfs(root)\n",
    "    result = []\n",
    "    \n",
    "    for q in queries:\n",
    "        result.append(hashmap[q[0]].count(q[1]))\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6b2b1309",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node: \n",
    "  def __init__(self, data): \n",
    "    self.val = data \n",
    "    self.children = []\n",
    "    \n",
    "n_1 ,q_1 = 3, 1 \n",
    "s_1 = \"aba\"\n",
    "root_1 = Node(1) \n",
    "root_1.children.append(Node(2)) \n",
    "root_1.children.append(Node(3)) \n",
    "queries_1 = [(1, 'a')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7fa80e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_of_nodes(root_1, queries_1, s_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6606c03d",
   "metadata": {},
   "source": [
    "# 20. Number of visible nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cc778db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visible_nodes(root):\n",
    "    result = []\n",
    "    level = 0\n",
    "    return traversePreOrder(root, result, level)\n",
    "\n",
    "def traversePreOrder(node, result, level):\n",
    "    if not node:\n",
    "        return\n",
    "    \n",
    "    if level >= len(result):\n",
    "        result.append(node.val)\n",
    "    \n",
    "    if node.left is not None:\n",
    "        traversePreOrder(node.left, result, level + 1)\n",
    "    \n",
    "    if node.right is not None:\n",
    "        traversePreOrder(node.right, result, level + 1)\n",
    "    \n",
    "    return len(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c851b4",
   "metadata": {},
   "source": [
    "# 21. Queue Removals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bf1b3f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "from operator import itemgetter\n",
    "\n",
    "def findPositions(arr, x):\n",
    "    \n",
    "    queue = [(v, i) for i, v in enumerate(arr, start=1)]\n",
    "    result = []\n",
    "    \n",
    "    for _ in range(x):\n",
    "        tmp = queue[:x]\n",
    "        max_value = max(tmp, key=itemgetter(0))\n",
    "        result.append(max_value[1])\n",
    "        tmp.remove(max_value)\n",
    "        tmp = [(v-1, i) if v > 0 else (v,i) for v,i in tmp]\n",
    "        queue = queue[x:] + tmp\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ec791edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 6, 4, 1, 2]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [1, 2, 2, 3, 4, 5]\n",
    "x = 5\n",
    "findPositions(arr, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "616a9cba",
   "metadata": {},
   "source": [
    "# 22. Balance brackets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5cc2d813",
   "metadata": {},
   "outputs": [],
   "source": [
    "def isBalanced(s):\n",
    "    if len(s) == 0:\n",
    "        return True\n",
    "    \n",
    "    parens = {\n",
    "        '(': ')',\n",
    "        '{': '}',\n",
    "        '[': ']'\n",
    "      }\n",
    "    \n",
    "    stack = []\n",
    "    \n",
    "    for i in s:\n",
    "        if i in parens.keys():\n",
    "            stack.append(i)\n",
    "        else:\n",
    "            if len(stack) > 0:\n",
    "                leftBracket = stack.pop()\n",
    "            else:\n",
    "                return False\n",
    "            correctBracket = parens[leftBracket]\n",
    "            if i != correctBracket:\n",
    "                return False\n",
    "    return len(stack) == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "29fe73b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = '{[()]}'\n",
    "isBalanced(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40922351",
   "metadata": {},
   "source": [
    "# 23. Change in foreign currency\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "09bb8c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def canGetExactChange(targetMoney, denominations):\n",
    "    result = []\n",
    "    comb = []\n",
    "    \n",
    "    def backtrack(remain, comb, start):\n",
    "        if remain == 0:\n",
    "            result.append(list(comb))\n",
    "            #return True\n",
    "        \n",
    "        if remain < 0:\n",
    "            return\n",
    "        \n",
    "        for i in range(start, len(denominations)):\n",
    "            comb.append(denominations[i])\n",
    "            backtrack(remain - denominations[i], comb, i)\n",
    "            comb.pop()\n",
    "        \n",
    "    backtrack(targetMoney, comb, 0)\n",
    "    #return result\n",
    "    if len(result) > 0:\n",
    "        return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c6024999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targetMoney = 75\n",
    "denominations = [4, 17, 29]\n",
    "\n",
    "canGetExactChange(targetMoney, denominations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d5bd839a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targetMoney = 94\n",
    "denominations =  [5, 10, 25, 100, 200]\n",
    "\n",
    "canGetExactChange(targetMoney, denominations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b0d9672",
   "metadata": {},
   "source": [
    "# 24. Encrypted words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2b56d483",
   "metadata": {},
   "outputs": [],
   "source": [
    "def findEncryptedWord(s):\n",
    "    \n",
    "    if not s:\n",
    "        return \"\"\n",
    "    \n",
    "    cur_length = len(s)\n",
    "    \n",
    "    if cur_length == 1 or cur_length == 2:\n",
    "        return s\n",
    "    if cur_length % 2 == 0:\n",
    "        mid = cur_length // 2 -1\n",
    "    else:\n",
    "        mid = cur_length // 2\n",
    "    \n",
    "    return s[mid] + findEncryptedWord(s[:mid]) + findEncryptedWord(s[mid+1:])\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2b99536e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'eafcobok'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S = \"facebook\"\n",
    "findEncryptedWord(S)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3027af54",
   "metadata": {},
   "source": [
    "# 25. Balanced split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f33e17ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def balancedSplitExists(arr):\n",
    "    arr.sort()\n",
    "    \n",
    "    running_sum = 0\n",
    "    prefix_sums = []\n",
    "    \n",
    "    for i in arr:\n",
    "        running_sum += i\n",
    "        prefix_sums.append(running_sum)\n",
    "    \n",
    "    max_val = prefix_sums[-1]\n",
    "    \n",
    "    if max_val % 2 != 0:\n",
    "        return False\n",
    "    \n",
    "    target = max_val // 2\n",
    "    \n",
    "    index_to_find = binary_search(prefix_sums, target)\n",
    "    \n",
    "    if index_to_find == -1:\n",
    "        return False\n",
    "    \n",
    "    return arr[index_to_find] < arr[index_to_find + 1]\n",
    "\n",
    "def binary_search(arr, target):\n",
    "    left = 0\n",
    "    right = len(arr) - 1\n",
    "    \n",
    "    while left < right:\n",
    "        mid = math.floor((left + right)/2)\n",
    "        \n",
    "        if arr[mid] == target:\n",
    "            return mid\n",
    "        elif arr[mid] < target:\n",
    "            left = mid + 1\n",
    "        else:\n",
    "            right = mid\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "68ecdf41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [1, 5, 7, 1]\n",
    "balancedSplitExists(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4ff54c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [12, 7, 6, 7, 6]\n",
    "balancedSplitExists(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d1bd5e",
   "metadata": {},
   "source": [
    "# 26. Counting triangles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2dfff487",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def countDistinctTriangles(arr):\n",
    "    \n",
    "    hashmap = defaultdict(list)\n",
    "    \n",
    "    for i in arr:\n",
    "        hashmap[tuple(sorted(i))].append(i)\n",
    "    \n",
    "    return len(hashmap.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "13053957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [[2, 2, 3], [3, 2, 2], [2, 5, 6]]\n",
    "countDistinctTriangles(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1f83a1c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [[8, 4, 6], [100, 101, 102], [84, 93, 173]]\n",
    "countDistinctTriangles(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "309682ad",
   "metadata": {},
   "source": [
    "# 27. Revenue Milestones\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "28129db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getMilestoneDays(revenues, milestones):\n",
    "    prefix_sums = []\n",
    "    running_sum = 0\n",
    "    \n",
    "    for i in revenues:\n",
    "        running_sum += i\n",
    "        prefix_sums.append(running_sum)\n",
    "    \n",
    "    result = []\n",
    "    \n",
    "    for milestone in milestones:\n",
    "        result.append(binary_search(prefix_sums, milestone))\n",
    "    \n",
    "    return result\n",
    "\n",
    "def binary_search(prefix_sums, target):\n",
    "    left = 0\n",
    "    right = len(prefix_sums)\n",
    "    \n",
    "    while left < right:\n",
    "        mid = math.floor((left + right)/2)\n",
    "        \n",
    "        if prefix_sums[mid] < target:\n",
    "            left = mid + 1\n",
    "        else:\n",
    "            right = mid\n",
    "    \n",
    "    return left + 1 if left < len(prefix_sums) else -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ee910a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 6, 10]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenues = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
    "milestones = [100, 200, 500]\n",
    "\n",
    "getMilestoneDays(revenues, milestones)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e13b21",
   "metadata": {},
   "source": [
    "# 28. 1 Billion users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "61a8824a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sumDay(arr, t):\n",
    "    running = 0\n",
    "    \n",
    "    for i in arr:\n",
    "        running += i ** t\n",
    "    return running\n",
    "\n",
    "def binary_search(arr, low, high):\n",
    "    while low < high:\n",
    "        mid = math.floor((low + high)/2)\n",
    "        if sumDay(arr, mid) < 1000000000:\n",
    "            low = mid + 1\n",
    "        else:\n",
    "            high = mid\n",
    "    \n",
    "    return low\n",
    "\n",
    "def getBillionUsersDay(growthRates):\n",
    "    \n",
    "    i = 1\n",
    "    \n",
    "    users = sumDay(growthRates, i)\n",
    "    \n",
    "    if users >= 1000000000:\n",
    "        return 1\n",
    "    \n",
    "    while users < 1000000000:\n",
    "        i *= 2\n",
    "        users = sumDay(growthRates, i)\n",
    "    \n",
    "    return binary_search(growthRates, i//2, i)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a99c22d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "growthRates = [1.5]\n",
    "getBillionUsersDay(growthRates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9ff507b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "growthRates = [1.1, 1.2, 1.3]\n",
    "getBillionUsersDay(growthRates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a064f05",
   "metadata": {},
   "source": [
    "# 29. Pair sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5cef0446",
   "metadata": {},
   "outputs": [],
   "source": [
    "def numberOfWays(arr, k):\n",
    "    c = 0\n",
    "    \n",
    "    while len(arr) > 0:\n",
    "        s = arr.pop(0)\n",
    "        for i in range(len(arr)):\n",
    "            if s + arr[i] == k:\n",
    "                c += 1\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "559f0e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "arr = [1, 2, 3, 4, 3]\n",
    "\n",
    "numberOfWays(arr, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2b5ca5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "arr = [1, 5, 3, 3, 3]\n",
    "\n",
    "numberOfWays(arr, k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625ffce2",
   "metadata": {},
   "source": [
    "# 30. Rotational Cipher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "b5f3e90b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rotationalCipher(input_str, rotation_factor):\n",
    "    \n",
    "    cap_alpha = [chr(c) for c in range(ord('A'), ord('Z') + 1)]\n",
    "    alpha = [chr(c) for c in range(ord('a'), ord('z') + 1)]\n",
    "    \n",
    "    result = []\n",
    "    \n",
    "    for c in input_str:\n",
    "        if not c.isalnum():\n",
    "            result.append(c)\n",
    "        elif c.isalpha():\n",
    "            if c.isupper():\n",
    "                idx = cap_alpha.index(c) + rotation_factor\n",
    "                if idx > 25:\n",
    "                    idx %= 26\n",
    "                result.append(cap_alpha[idx])\n",
    "            else:\n",
    "                idx = alpha.index(c) + rotation_factor\n",
    "                if idx > 25:\n",
    "                    idx %= 26\n",
    "                result.append(alpha[idx])\n",
    "        elif c.isdigit():\n",
    "            result.append(str(int(c) + rotation_factor)[-1])\n",
    "    \n",
    "    return \"\".join(result)\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0f2d69e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Cheud-726?'"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = 'Zebra-493?'\n",
    "rotationFactor = 3\n",
    "\n",
    "rotationalCipher(inputs, rotationFactor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ab3ac2",
   "metadata": {},
   "source": [
    "# 31. Reverse to make equal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "9d40c2e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def are_they_equal(array_a, array_b):\n",
    "    \n",
    "    quicksort(array_a, 0, len(array_a) - 1)\n",
    "    quicksort(array_b, 0, len(array_b) - 1)\n",
    "    return array_a == array_b\n",
    "\n",
    "def partition(nums, left, right):\n",
    "    \n",
    "    pivotElement = nums[right]\n",
    "    partitionIndex = left\n",
    "    \n",
    "    for j in range(left, right):\n",
    "        if nums[j] <= pivotElement:\n",
    "            nums[partitionIndex], nums[j] = nums[j], nums[partitionIndex]\n",
    "            partitionIndex += 1\n",
    "    nums[partitionIndex], nums[right] = nums[right], nums[partitionIndex]\n",
    "    return partitionIndex\n",
    "\n",
    "def quicksort(nums, left, right):\n",
    "    \n",
    "    if left < right:\n",
    "    \n",
    "        partitionIndex = partition(nums, left, right)\n",
    "\n",
    "        quicksort(nums, left, partitionIndex - 1)\n",
    "        quicksort(nums, partitionIndex + 1, right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "91cb4d58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = [1, 2, 3, 4]\n",
    "B = [1, 4, 3, 2]\n",
    "\n",
    "are_they_equal(A, B)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8614f92a",
   "metadata": {},
   "source": [
    "# 32. Contiguous Subarrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "808eb536",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_subarrays(arr):\n",
    "    \n",
    "    n = len(arr)\n",
    "    result = [n] * n\n",
    "    stack = []\n",
    "    \n",
    "    for i, x in enumerate(arr):\n",
    "        while len(stack) > 0 and x >= arr[stack[-1]]:\n",
    "            result[stack.pop()] -= n - i\n",
    "        stack.append(i)\n",
    "    stack.clear()\n",
    "    \n",
    "    for i, x in reversed(list(enumerate(arr))):\n",
    "        while len(stack) > 0 and x >= arr[stack[-1]]:\n",
    "            result[stack.pop()] -= i + 1\n",
    "        stack.append(i)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "dee3e38d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 1, 5, 1]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [3, 4, 1, 6, 2]\n",
    "\n",
    "count_subarrays(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fabcb791",
   "metadata": {},
   "source": [
    "# 33. Passing yearbooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "dcb25586",
   "metadata": {},
   "outputs": [],
   "source": [
    "def findSignatureCounts(arr):\n",
    "    \n",
    "    output = [1 for i in range(len(arr))]\n",
    "    \n",
    "    for i in range(len(arr)):\n",
    "        next_s = i\n",
    "        while arr[next_s] != i + 1:\n",
    "            output[i] += 1\n",
    "            next_s = arr[next_s] - 1\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b02ee6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 2]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = [2, 1]\n",
    "findSignatureCounts(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5c7c7ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('f', 'f', 'f')\n",
      "('l', 'l', 'l')\n",
      "('o', 'o', 'i')\n",
      "('w', 'w', 'g')\n"
     ]
    }
   ],
   "source": [
    "strs = [\"flower\",\"flow\",\"flight\"]\n",
    "\n",
    "for x in zip(*strs):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e23e1f5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}