18_ADAS-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "(chap:18-adas-2)=\n",
    "# 定量的マクロ経済分析：Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div name=\"html-admonition\" style=\"font-size: 0.8em\">\n",
    "<input type=\"button\" onclick=\"location.href='https://translate.google.com/translate?hl=&sl=ja&tl=en&u='+window.location;\" value=\"Google translation\" style=\"color:#ffffff;background-color:#008080; height:25px\" onmouseover=\"this.style.background='#99ccff'\" onmouseout=\"this.style.background='#008080'\"/> in English or the language of your choice.\n",
    "</div><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import japanize_matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import py4macro\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# numpy v1の表示を使用\n",
    "np.set_printoptions(legacy='1.21')\n",
    "# 警告メッセージを非表示\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## はじめに"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "前章で展開したADASモデルは次の２つの均衡式で与えられる。\n",
    "\n",
    "$$\n",
    "p_{t}=hp_{t-1}+h\\left(a u_{t}+v_{t}\\right)\n",
    "$$ (eq:18-eq-p-2)\n",
    "\n",
    "$$\n",
    "y_t = -chp_{t-1} + h(u_t-cv_t)\n",
    "$$ (eq:18-eq-y-2)\n",
    "\n",
    "ここで\n",
    "\n",
    "$$\n",
    "h\\equiv\\frac{1}{1+a c}<1,\n",
    "$$ (eq:18-h-2)\n",
    "\n",
    "* $p_t$：価格水準のトレンドからの乖離率\n",
    "* $y_t$：産出量のトレンドからの乖離率\n",
    "* $v_t\\sim N\\left(0,\\sigma_v^2\\right)$：総供給ショック\n",
    "* $u_t\\sim N\\left(0,\\sigma_u^2\\right)$：総需要ショック\n",
    "\n",
    "またカリブレーションの手法を使い，次の値を設定した。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true,
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "ahat = 0.82274785974591313487\n",
    "chat = 0.26898029723198069174\n",
    "v_std = 0.01254675097186065853\n",
    "u_std = 0.01463393441548518220"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aの値：0.823\n",
      "cの値：0.269\n",
      "vの標準偏差：0.012547\n",
      "uの標準偏差：0.014634\n"
     ]
    }
   ],
   "source": [
    "print(f'aの値：{ahat:.3f}')\n",
    "print(f'cの値：{chat:.3f}')\n",
    "print(f'vの標準偏差：{v_std:.6f}')\n",
    "print(f'uの標準偏差：{u_std:.6f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "これらの値を使い，次の定量的マクロ分析をおこなう。第一に，ADASモデルの安定性を検討する。ADASモデルの定性的分析をとおして長期的な均衡は安定的だと直感的に理解できるが，以下では，差分方程式を使い結果をプロットし均衡の安定性を確認する。これにより，均衡への収束の速さを視覚的に確認することも可能となる。第二に，インパルス反応関数を使い，需要ショックもしくは供給ショックが発生した際，産出量と物価水準（トレンドからの乖離率）がどのように変化するかを示す時系列的なプロットに基づき検討する。このような定量分析は，ADASモデルの定性的な理解を補完することになる。第三に，確率的シミュレーションをおこない，ADASモデルが景気循環データをどの程度再現できるかが焦点となる。確率的シミュレーションでは，毎期毎期，総需要・総供給ショックが絶え間なく発生する状況を考え，生成されるデータと実際の景気循環データの特徴を比較することにより，ADASモデルのデータ再現力を評価することができる。第四に，確率的シミュレーションの結果を踏まえて，次の定量的な問いを検討する。\n",
    "> GDPと価格水準の長期トレンドからの乖離（％）の変動は，何パーセントが需要ショックにより引き起こされ，何パーセントが供給ショックに起因するのか？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## コードの基本形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "以下ではシミュレーションをおこなうが，コードは関数にまとめて書くことにする。\n",
    "コードは次のようなパターンになっている。\n",
    "\n",
    "関数の中身\n",
    "1. アップデート用の変数を用意\n",
    "1. 空のリストを作成\n",
    "1. `for`ループに`.append()`を使う\n",
    "1. `DataFrame`を返す\n",
    "\n",
    "簡単な例として次の差分方程式を考えてみよう。\n",
    "\n",
    "$$\n",
    "x_t = x_{t-1} + a\n",
    "$$\n",
    "\n",
    "`my_func()`は，この差分方程式を使った計算結果を`DataFrame`として返す。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def my_func(x0, a, n):\n",
    "    \"\"\"\n",
    "    引数：\n",
    "        x0: 初期値\n",
    "        a: 差分方程式のパラメーター\n",
    "        n: ループ計算の回数\n",
    "    戻り値：\n",
    "        DataFrame\n",
    "    \"\"\"\n",
    "\n",
    "    x = x0      # アップデート用の変数\n",
    "\n",
    "    lst = [x]   # 空のリスト\n",
    "\n",
    "    for i in range(n):\n",
    "\n",
    "        x = x + a      # 差分方程式\n",
    "\n",
    "        lst.append(x)  # リストに結果を追加\n",
    "        \n",
    "    return pd.DataFrame({'col': lst})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col\n",
       "0    0\n",
       "1   10\n",
       "2   20\n",
       "3   30\n",
       "4   40\n",
       "5   50"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_func(0, 10, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "本章のコードは少し長くなるが，コードのパターンとしては上の例と同じになるので，コードの理解に不安な場合は上のコードで復習してはどうだろうか。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## ADASモデルの安定性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "まずADASモデルの安定性を考える。すなわち，任意の初期値（$p_0$と$y_0$）にある経済が長期均衡に戻るかどうかを確認する。\n",
    "そのために，ここでは総需要・総供給ショックがない経済を考える。即ち，$u_t=v_t=0$，$t=0,1,2,\\cdots$とする。\n",
    "従って，均衡式[](eq:18-eq-p)と[](eq:18-eq-y)は\n",
    "\n",
    "$$\n",
    "p_{t}=hp_{t-1}\n",
    "$$ (eq:18-eq-p-v0)\n",
    "\n",
    "$$\n",
    "y_t = -chp_{t-1}\n",
    "$$ (eq:18-eq-y-u0)\n",
    "\n",
    "となる。ここから次の２つの特徴が浮かび上がる。\n",
    "1. この連立差分方程式を解くには初期値$p_0$があれば十分である。\n",
    "1. 初期値$y_0$は$u_t=0$となる式[](eq:18-ad_small2)から計算することができる。\n",
    "\n",
    "    $$y_0=-cp_0$$\n",
    "   \n",
    "1. 安定性は式[](eq:18-eq-p-v0)のみに依存する。\n",
    "\n",
    "また，特徴2から次のことも導き出せる。\n",
    "式[](eq:18-eq-p-v0)の$h$の値は正であり絶対値は1よりも小さい。従って，差分方程式である式[](eq:18-eq-p-v0)は安定的であり，ADASモデル自体が安定的だということが確認できる。以下では，シミュレーションを使いこの結果を確認する。まずそのための関数を作成する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def adas_model(p0=0, n=20, a=ahat, c=chat):\n",
    "    \"\"\"引数\n",
    "        p0: pの初期値（デフォルト：0）\n",
    "        n: ループの回数（デフォルト：20）\n",
    "        a: aの値（デフォルト：ahat）\n",
    "        c: cの値（デフォルト：chat）\n",
    "    戻り値：\n",
    "        yとpのDataFrame\"\"\"\n",
    "\n",
    "    # === hの定義 ==========\n",
    "    h = 1 / ( 1+a*c )\n",
    "        \n",
    "    # === 初期値，forループのアップデート用変数 ==========\n",
    "    p = p0\n",
    "    y = -c*p0\n",
    "    \n",
    "    # === yとpの値を格納するリスト ==========\n",
    "    y_lst = [y]   # 初期値を格納\n",
    "    p_lst = [p]   # 初期値を格納\n",
    "\n",
    "    # === ショック後のループ計算 ==========\n",
    "    for _ in range(n):\n",
    "        \n",
    "        (p, y) = ( h*p, -c*h*p )\n",
    "\n",
    "        y_lst.append(y)\n",
    "        p_lst.append(p)\n",
    "    \n",
    "    # === 辞書の作成 ==========\n",
    "    dic = {'y':y_lst, 'p':p_lst}\n",
    "    \n",
    "    # === 戻り値としてのDataFrame ==========\n",
    "    return pd.DataFrame(dic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "デフレーターの乖離率の初期値を1（即ち，１％）として結果をプロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adas_model(p0=1).plot(marker='.')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "このプロットの横軸には返された`DataFrame`の行インデックスが使われているが，時系列的なプロットと考えれば良いだろう。$p_t$は初期値である１から始まっており，$y_t$の初期値は式[](eq:18-eq-y-u0)で計算した値である。このプロットから次のことが確認できる。\n",
    "* 定常状態である0に両変数は近づいている。即ち，長期均衡が安定的だということである。\n",
    "* 長期均衡に近づく速度は，最初は速いが，そのスピードが時間と共に落ちているのが伺える。\n",
    "\n",
    "下のプロットは，横軸に$y_t$，縦軸に$p_t$を置いた図となる。点はAD曲線とAS曲線の交点を表している。右下がりの直線はAD曲線と等しく，AD曲線に沿ってAS曲線が下方シフトしていることになる。そういう意味では，{numref}`fig:18-adas1`で描かれている状況と同じである。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adas_model(p0=1).plot(x='y', y='p', marker='.')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "また，このプロットから長期均衡$(0,0)$に近づくにつれて，経済の動きが小刻みになっているのも確認できる。$p_t$と$y_t$の変化が小さくなり，動くスピードが遅くなっている。実は，式[](eq:18-eq-p-v0)を使うと，$p_t$が`0`に近づく変化率を簡単に計算することができる。式[](eq:18-eq-p-v0)を次のように書き直そう。\n",
    "\n",
    "$$\n",
    "p_t\\text{の変化率}=\n",
    "\\frac{p_t}{p_{t-1}}-1=h-1=-\\frac{a}{1/c+a}<0\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "負の符号は，時間と共に$p_t$の絶対値が小さくなることを意味している（$p_t$が負の値でも同じ）。また$p_t$の変化率の絶対値は，$a$の増加関数であり，$1/c$の現象関数となっている。この結果の含意を理解するために，$a$はAS曲線の傾きであり，$1/c$はAD曲線の傾きであることを思い出そう。このことから次のことが言える。\n",
    "* AS曲線の傾きが急になると，長期均衡への収束速度が速くなる。\n",
    "* AD曲線の傾きが急になると，長期均衡への収束速度が遅くなる。\n",
    "\n",
    "同様に，式[](eq:18-eq-p-v0)と[](eq:18-eq-y-u0)を使うと次の結果を導出できる。\n",
    "式[](eq:18-eq-p-v0)を式[](eq:18-eq-y-u0)に代入すると\n",
    "\n",
    "$$y_t=-cp_t$$\n",
    "\n",
    "となる。従って，\n",
    "\n",
    "$$\n",
    "y_t\\text{の変化率}=\n",
    "\\frac{y_t}{y_{t-1}}-1=\n",
    "\\frac{-cp_t}{-cp_{t-1}}-1=\n",
    "\\frac{p_t}{p_{t-1}}-1=\n",
    "p_t\\text{の変化率}\n",
    "$$\n",
    "\n",
    "$p_t$と$y_t$の変化率は同じとなることが確認できる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "下のプロットでは，$p_t$の初期値が負の場合を図示している。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adas_model(p0=-1).plot(marker='.')\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adas_model(p0=-1).plot(x='y', y='p', marker='.')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## インパルス反応関数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "インパルス反応関数とは，ある特定のショック（$u_t$もしくは$v_t$）が発生した場合，`y_t`と`p_t`がどのように変化するかを示す時系列のグラフをである。インパルス反応関数により，ショックの波及効果を可視化できる。また，後で考察する確率的シミュレーションの結果を理解する上でも有用な情報となる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### コード"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "均衡式[](eq:18-as_small)と式[](eq:18-ad_small)を再掲する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "$$\n",
    "p_{t}=hp_{t-1}+h\\left(a u_{t}+v_{t}\\right)\n",
    "$$\n",
    "\n",
    "$$\n",
    "y_t = -chp_{t-1} + h(u_t-cv_t)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "次のコードでは，ショック項である$u_t$と$v_t$は１期間だけ変化すると仮定する。\n",
    "従って，`u_std`と`v_std`は無関係となるため，コードには使っていない。\n",
    "一方，`a`，`c`は上で決めた値を使うこととする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def one_period_impulse(u=0, v=0, n_after=20, a=ahat, c=chat):\n",
    "    \"\"\"引数\n",
    "        u: AD曲線へのショックの大きさ（デフォルト：0）\n",
    "        v: AS曲線へのショックの大きさ（デフォルト：0）\n",
    "        n_after: ショック発生後0に戻った後の計算回数（デフォルト：20）\n",
    "        a: aの値（デフォルト：ahat）\n",
    "        c: cの値（デフォルト：chat）\n",
    "    戻り値：\n",
    "        y, p, u, vのDataFrame\n",
    "\n",
    "    * 最初の３期間はショックゼロ\"\"\"\n",
    "\n",
    "    # === hの定義 ==========\n",
    "    h = 1 / ( 1+a*c )\n",
    "    \n",
    "    # === 初期値，forループのアップデート用変数 ==========\n",
    "    p = 0       # 最初は定常状態に設定\n",
    "    y = 0       # 最初は定常状態に設定\n",
    "    \n",
    "    # === 結果を格納するリストの作成 ==========\n",
    "    # 最初の３つの要素は定常状態に設定\n",
    "    y_lst = [0]*3\n",
    "    p_lst = [0]*3\n",
    "    u_lst = [0]*3\n",
    "    v_lst = [0]*3\n",
    "\n",
    "    # === ショック発生時 ==========\n",
    "    (p, y) = ( h*p + h*( a*u+v ),     # 引数のuとvが使われる\n",
    "               -c*h*p + h*( u-c*v )   # 引数のuとvが使われる\n",
    "             )\n",
    "        \n",
    "    y_lst.append(y)\n",
    "    p_lst.append(p)\n",
    "    u_lst.append(u)          # 引数のuが使われる\n",
    "    v_lst.append(v)          # 引数のvが使われる\n",
    "        \n",
    "    # === ショック後 ==========\n",
    "    u, v = 0, 0     # ショックを0に戻す\n",
    "\n",
    "    for _ in range(n_after):\n",
    "        (p, y) = ( h*p + h*( a*u+v ),\n",
    "                   -c*h*p + h*( u-c*v )\n",
    "                 )\n",
    "\n",
    "        y_lst.append(y)\n",
    "        p_lst.append(p)\n",
    "        u_lst.append(u)\n",
    "        v_lst.append(v)\n",
    "    \n",
    "    # === 変数の辞書 ==========\n",
    "    dic = {'y':y_lst, 'p':p_lst, 'u':u_lst, 'v':v_lst}\n",
    "    \n",
    "    # === DataFrameを返す ==========\n",
    "    return pd.DataFrame(dic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### プロット"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### 総供給ショック"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "\n",
    "0から数えて3期目に$p_t$の１乖離率を引き起こす供給ショック（$v_t=1$）を考えよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_period_impulse(v=1).plot(subplots=True, marker='.')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "{numref}`fig:18-adas_as_shock`を使って説明しよう。\n",
    "* $t=2$期以前は定常状態である0に経済は位置する。\n",
    "* $t=3$期にAS曲線へのショックが発生し，AS曲線が上方シフトする（①）。経済は点Aにジャンプする。\n",
    "    * $p_t$は上昇し$y_t$は減少する。\n",
    "* $t=4$期では，ショックはゼロになる。しかしAS曲線は元の位置にすぐには戻らない。適応的期待により$p_3$が高かったため$p_4$も高い傾向にあるためである。従って，$p_t$は少ししか減少しない。その結果$y_t$も少ししか縮小しない。\n",
    "* $t=5$期以降，AS曲線が徐々に下方シフトし，経済はAD曲線に沿って0に向かって動いていく（②）。\n",
    "    * $p_t$は減少し$y_t$は増加する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{figure} /images/asas_as_shock.jpeg\n",
    "---\n",
    "scale: 35%\n",
    "name: fig:18-adas_as_shock\n",
    "---\n",
    "総供給ショックによるインパルス反応\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### 総需要ショック"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_period_impulse(u=1).plot(subplots=True, marker='.')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "{numref}`fig:18-adas_ad_shock`を使って説明しよう。\n",
    "* $t=2$期以前は定常状態である0に経済は位置する。\n",
    "* $t=3$期にAD曲線へのショックが発生し，AD曲線が右シフトする（①）。経済は点Aにジャンプする。\n",
    "    * $p_t$と$y_t$はともに上昇する。\n",
    "* $t=4$期ではショックはゼロになり，AD曲線は元の位置に戻る（②）。一方，$p_3$が高かったため，その影響によりAS曲線は上方シフト（③），経済は点Bにジャンプする。$p_t$は減少するが高止まりする一方，$y_t$は0を下回る。\n",
    "* $t=5$期以降，AS曲線が徐々に下方シフトし，経済はAD曲線に沿って0に向かって動いていく（④）。\n",
    "    * $p_t$は減少し$y_t$は増加する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{figure} /images/asas_ad_shock.jpeg\n",
    "---\n",
    "scale: 35%\n",
    "name: fig:18-adas_ad_shock\n",
    "---\n",
    "総需要ショックによるインパルス反応\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### 総供給・総需要ショックの同時発生"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGcCAYAAADknMuyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABVjUlEQVR4nO3de3gU9b0/8PfsNbu5kvsVCAQQiYBAuAlq1aoIsWrlWLSltVY91VOs1FKx51dKrQePRejp0VZrFZQWVLxfQOUUtSAkQAQNihAgIfeEEMgm2WSv8/tjMpv7ZZPdndnd9+t55tnd2Z3vfDZDmE++V0EURRFERERECtAoHQARERGFLyYiREREpBgmIkRERKQYJiJERESkGCYiREREpBgmIkRERKQYJiJERESkGCYiREREpBid0gEMxu12o7q6GtHR0RAEQelwiIiIaAhEUURzczPS09Oh0fRf76H6RKS6uhpZWVlKh0FERETDUFFRgczMzH7fV30iEh0dDUD6IjExMQpHE3xeL6rA2ne/hlsENAKwJv9ifHcmEzsiIvIvi8WCrKwsz328P6pPROTmmJiYGCYiXqppasOjH5UBBrOnM9DvPzqD62eMQ1qsScnQiIgoTAzWrYKdVUNYaUMr3D2WNHSJIsoarMoERERE1AMTkRCWnRiJnomoVhAwNtGsTEBEREQ9MBEJYWmxJlyU0r1tbu13prBZhoiIVGNYfUQ2b96M9evX48KFC0hLS8PGjRuxYMGCPj+bn5+Pffv2wWTqvPllZ2djz549w4uYhqyi0Ypv6poBACa9Bm0ON7ITIxWOiogovIiiCKfTCZfLpXQoPqXVaqHT6UY8tYbXiciWLVuwevVq7N69G5MnT8b27duxePFiHD58GOPGjev1+crKSmzbtg3XXnvtiAIl7/2jsByiCCyckIikKCPeOFyFPSUNuCwnUenQiIjCgt1uR01NDazW0OybZzabkZaWBoPBMOwyvE5E1q5di4ceegiTJ08GACxduhQvvfQSnnrqKWzYsKHX5ysrKzkPiALaHS68crAcAPCDuWPQYnPijcNV2HvyLICLlA2OiCgMuN1ulJaWQqvVIj09HQaDIWQm5hRFEXa7HWfPnkVpaSkmTJgw4KRlA/EqESkvL8epU6eQn5/fbX9+fj42btzYKxGx2WxoaGgYcCIT8o8dxTU4b3UgI86Eqyen4FyLDQDwVbUFja12xEcOP3slIqLB2e12uN1uZGVlwWwOvUECJpMJer0eZ86cgd1uR0RExLDK8Sp9qa6uBgCkp6d325+RkYGqqqpen6+srITZbMYzzzyDSy+9FOPGjcMdd9yB8vLyfs9hs9lgsVi6beS9l/afAQDcPmc0tBoByTERmJgSBVEE9p1qUDg6IqLwMdyagmDgi+/mVQl6vb7PE/dX1dTU1ITExESkp6dj3759KC4uRmJiIq666iq0trb2ecy6desQGxvr2dis473iyiYcqbgAvVbAbXmdP78FOUkAgL0lTESIiEgdvEpE5CYWuWZEVlNTg4yMjF6fnzFjBs6cOYM77rgDJpMJkZGR2LBhA2pra/sdNbN69Wo0NTV5toqKCm9CJABbCsoAADdckobEKKNn/8IJUifVPSUNEEWxr0OJiIgCyqtEJCUlBdOnT8eOHTu67d+1axcWLVrU5zFut7vba1EU4Xa7+61FMRqNnuncOa2795qsDrx9REoUl88b0+292dnx0GsFVF1ow5lzodmDm4iIgovXjTurVq3CE088gRMnTgAA3n77bezcuRP33Xdfr8/u2bMHF110EQ4ePAgAaG9vxwMPPIDMzExceeWVI4uc+rS9qAI2pxuT02IwY/Sobu9FGnW4tGPfnpNsniEiCiY1TW3Yd6oBNU1tSofiU14P3122bBksFguWLFmClpYWZGZm4r333kNOTg4qKysxd+5cbNy4EUuXLsXChQvxyCOP4N5770VdXR1sNhsWLlyIjz76CEajcfCTkVfcbhF/L5A6qf5g7pg+a50W5iTiQGkj9pacxQ/mjun1PhER+Y8oimhzeD+x2etFlVjzzleeldTX3jgF35059BGpJr1WtUOHBVHlnQUsFgtiY2PR1NTEZppB/OvEWSx/4QCijToU/vpqmA2988zD5edx85/3ITpChyO/uRZajTr/YRIRBbv29naUlpYiOzvbM7TVanfi4t98GPBYvv7ddX3eE/pyww03ICUlBZs2bfLsu/HGGzF69Gg89dRT3T7b13eUDfX+HbpjisKQPGT3uzMz+/0Hd0lGLKIjdGhud+LLygsBjI6IiILBz372M7z22mue0a2NjY348MMPcc899/jlfMNaa4bUp/K8Fbu/qQMA/GBe/00uOq0G88cn4MOv6rC3pMHTZ4SIiPzPpNfi699d59UxtU3tuGbDp3B3ab/QCMD/rbwCqbFDm0TMpNcO+XzXX3890tLS8Prrr2P58uXYvn07ZsyYgalTp3oV91CxRiREbC0sh1sELstJwPikqAE/u2BCx3wi7LBKRBRQgiDAbNB5tY1LisK6Wy6BtqOPh1YQsO6WSzAuKWrIZXjTP0QQBNx3333YvHkzAGDr1q1+qw0BWCMSEmxOF145KM238oO5Ywf9/IKORe8+Lz+PVpsTkUb+MyAiUrPb8kbj8olJKGuwYmyiGWmxpsEPGoE777wTv/nNb7B//34UFxfjtttu89u5WCMSAnYW1+Jcqx1psRG4ZnLyoJ8fm2BGRpwJDpeIA6WNAYiQiIhGKi3WhHnjE/yehABAbGwsvv/97+OHP/whbr/9dr+ulcNEJARs6Riye/vs0dBpB7+kgiB4Zlll8wwREfXlnnvuQUlJiV+bZQAmIkHvq+omFJ05D51GwG2zh74uz2UdzTNcd4aIiPpy7NgxzJ0712+dVGVMRIKcPIHZ9bmpSI4e+hLMl+UkQhCA43XNqLe0+ys8IiIKMm63G8ePH8ejjz6KFStW+P18TESCWFObA28dlteVGevVsfGRBkxJlyaYYfMMERHJampqMHPmTFx77bX43ve+5/fzcbhEEHu9qBJtDhcmpUQjb6z384EsyEnC0SoL9p5swC0zhj5VMBERha6MjAy0tLQE7HysEQlSothlXZl5fa8rM5gFXfqJqHymfyIiClFMRILUZyfP4XRDK6KMOtx0acawypg1dhSMOg3qm20oqQ9c9ktEFE5C+Q89X3w3JiJBaktBGQDguzMyEDXMCcki9FrMzo4HwNEzRES+ptfrAQBWq1XhSPxH/m7ydx0O9hEJQtUX2rDra2ldme/P7X9dmaG4LCcRe0oasPdkA368INsX4REREQCtVou4uDjU19cDAMxm87Ca0dVIFEVYrVbU19cjLi4OWu3Q17LpiYlIENp2QFpXZt64BExIiR5RWXI/kYLT52B3umHQsZKMiMhXUlNTAcCTjISauLg4z3ccLiYiQcbudGPbgY51ZQZYZXeoLk6LQUKkAeda7ThSccHTVENERCMnCALS0tKQnJwMh8OhdDg+pdfrR1QTImMiEmQ++KoWDS02pMQY8e2LU0ZcnkYjYH5OIt79ohp7S84yESEi8gOtVuuTm3YoYj18kNmyvwwAsGz2aOiHsK7MUCzISQAA7OHEZkREFGBMRILIsRoLDpZJ68osmz3aZ+UumJAEAPii4gKa2kKr6pCIiNSNiUgQkScwu25KKlJihr6uzGAy4kwYlxgJtyh1WiUiIgoUJiJBwtLuwJuHqwCMfMhuX7gaLxERKYGJSJB48/MqWO0uTEiOwtxxvu9QumBCRyLCfiJERBRATESCgCiK2DLCdWUGM298ArQaAaUNrag8H7qzABIRkbowEQkC+0+fw8n6FkQatLh5mOvKDCYmQo9pmbEAgM9YK0JERAHCRCQIbNkv1YbcPCMD0RHDn89/MPIsq3vYT4SIiAKEiYjK1Ta146OOdWV+MHesX88lD+Pdd+oc3O7QXS2SiIjUg4mIym09UA6XW8Ts7HhMSh3ZujKDuXR0HCINWjS22vF1jcWv5yIiIgKYiKiaw+XGtgPlAIAf+GHIbk96rQZzxkmzrHL0DBERBQITERX78KtanG22ISnaiOumjGx1w6FawPlEiIgogIaViGzevBm5ubnIzMxEXl4e9u7d2+9nq6qqcNttt2Hs2LHIyMjAgw8+CJvNNuyAw4ncSXVZXhYMusDkjAs75hM5UNaIdocrIOckIqLw5fXdbcuWLVi9ejW2b9+OyspKrFq1CosXL8bp06d7fdZut+Pb3/42MjMzcfLkSXz11VcoKirCgw8+6JPgR6qmqQ37TjWgpqlNVWUB0hDawtJGaARg2RzfrSszmJzkKKTEGGF3unGo7HzAzktEROHJ60Rk7dq1eOihhzB58mQAwNKlS3H55Zfjqaee6vXZV199FXV1dVi3bh10Oh3i4uKwceNGPP/882hoULbq/5WD5bjs8d24/blCXPb4brxysFwVZcnl3fG3QgCAWwT+deLsiMrzhiAInune95wM3HmJiEKVr/9QVfMf0cOh8+bD5eXlOHXqFPLz87vtz8/Px8aNG7Fhw4Zu+3fv3o3rrrsOBoPBs2/mzJlISEjAP//5T9x22229zmGz2bo13Vgsvh+9UdPUhoffKIbYMULVLQIPv16MojPnEWn06keCVpsT2w9VQh7sOpKyupbX1SNvHMXlE5OQFmvyurzhWDghEW98XsWJzYiom5qmNpQ2tCI7MXLE/x/5siw1l/fKwXKsfqMYbhHQCMC6Wy7BbXnDr+X2ZXm+jm24vLpTVldXAwDS09O77c/IyEBVVVWfn8/Nze21v7/PA8C6deuwdu1ab8LyWmlDqycJkYkAXu2RAAyXL8sCAJcooqzBGrBERK4R+aragsZWO+IjDYMcQUS+otabvT9vgL+/KRe3zMiEyy3C6Rbh7niUXrvhdgNOtxsutwiXKMLpEjs/K4rY9VUdntt7GqIICII0ynD++ES4Relz8mPnc+n/Vbe7x/uiCJdLxBeVF/DPY/UQAQgALp+YiEmpMZ7Put0i3KJUhug5Bzr2i3CJgFsU0Wpz4pPjnTXLbhH41evFeL2oEgadVipLlI4VOx5dbtHzvPt7IuxON8rOWXuVt3FXCQQBELscA3SWIfbx6HK7YXeJ3coK9B+9Mq8SEb1emtVTo+neotPf2id6vb7XZwf6PACsXr0aK1eu9Ly2WCzIysryJsxBZSdGei6aJyZI/3ijIryrxWhpd2JLwRl0zWuGW1Z/5WkFAWMTzV6XNVzJ0RGYlBKN43XN+OxkA/KnpQ9+EFGYUvpm73KLcLjcsDndsDvdsLukx3eOVOGP/yzx3Jzvuiwbl01IhMPphrPjGIdLfux87nRJNyhnl/1NVjvePFLtOad8A3zrcBV0Wo0nMXB0JAsOlwiX2w2nq2OfS4Sj46Zvc7rQanN1K+uRN4/ikTePjuhnJxNF4KX9Z/BSR2f/EZcH4NMTDfj0hO9qiA/4uP9draXdJ+UE+o9emVd3yszMTABSTUdOTo5nf01NDTIyeq+BkpmZ6alF6aq/zwOA0WiE0Wj0JiyvpcWa8Pgtl+CRN47CJYrQCgL+65bcYWf3UzJifFZWf+UF+h/GggmJOF7XjL0lTEQotPgycdh2oBy/frMzcfjFtZNwzeQU2JwutDvcsDldsDmkJKHd4YLN2bGv6+uOzzW22rHzaK2nbPlmv+mzMogiPAmG57HjuWsIsyCLIvC3vaX4297SEX3fnvafbvRpeT3pNAK0GsHzKG2abq8dTjdq+rgRT0yJQpzJAI0G0GoEaISOYwQBmo5HrUZ+Ds++c6027P6md/+4/KlpyBhlhlYDaATBs2k10h/X0jnQZb+A5nYHnvzoRPc/VAXgN0smIz7SCEHoegw6Xnfs03R5LggQBOB8qx3/se1wtz+iNQLwtx/OQlJUBIQun+0ss3u5AqT3G1ps+O5f9qHrP59A/9Er8yoRSUlJwfTp07Fjxw6sWLHCs3/Xrl1YtGhRr89ff/31uPvuu+F0OqHTSaf65ptvUF9fj6uvvnqEoY/MbXmjcfnEJJQ1WDE20Tyi/5B8WZY/yhuOBRMS8fzeUuw92QBRFP2y4i/RUA03eXC7RbQ5XGi1O9Fqc+HNzyvxvx+flGoJACydlYmpmXFod7jQZnehzSFt7Q4XrPbOfe0d+9vsUoJhtTthtTthc3av2v7Dh8fxhw+P+/S7f1Pb7NXnDVoNNBqg3eHu9d7YRDPiTAYYtBrodQJ0Gg30Wg30WqHjsfO5TitIn9Nq0OZw4oW9Zb1uqKsXXYTEKCN0Wg30HYmBXquRkgetVL702Pn8fKsdy54r6HYD1AjARw9egcxRJmgE6fMazdD+z6lpasNlj+/udUN98cezh/V/Z3/lPbJ48rDKS4o2+vQP1Rabs1d5V12U4nU5WfFmrOvjD3Il7jeCKPbsLTGwbdu24Ze//CV2796NiRMn4u2338by5ctRVFTUrZYEAJxOJ6ZPn478/Hz8/ve/R0tLC7773e8iJycHzzzzzJDOZ7FYEBsbi6amJsTExHgTKo2A1e7EtLUfweES8fFDVyI7MVLpkCiIjKTWwe50o7ndgeZ2JyztDrz7RTX+tqfU015/XW4qcpKi0Gp3wmpzocXuhNUmJRqtdiesdhdabNI+q8PVqz+Yv8VE6BBl1CFCr4VBp4FRr4VRp0FEx2P351oY9RpE6LSwOV34yyenut3sNQLwxK1TkRpjgkGnkTatBgadAINW27lPJyUQBq0GgiD0ezPd+/C3hn2jeeVguc9uqL4sKxjKq2lq8+kflr4sz9exdTXU+7fXiQgAPPvss3jyySfR0tKCzMxMPPnkk1i4cCEqKysxd+5cbNy4EUuXLgUAVFZW4v7778fBgweh0WiwdOlSPP7440NufmEiopzbnt2PwtJGPPqdKfjBvLFKh0N+5osmC1EU8eL+Mvzu3a/h7uib8P05YzA1MxbN7U5PciEnGtImJx3Sc5uz91/yIyUIgFGn6bOWIG/sKGTEmWAyaBGh18Ikb4aOreN1RJfnZoMWlnYHlj6zP2xu9oC6b4BqLy8c+TURCSQmIsp5ancJ1n90AtdNScGzP5ildDjkRz07Sf7XzZcgf1o6zlvtuGB1SFubHeetDjRZpUdpvx0X2hw4b7WjySo9+mrh5iijDgadgMZWR6/3rpuSgvFJUYg06hBp0MJs1CHSoEOkUYtIow5mgxZRRh3MBrl2QoNaS7uqawlkar7ZE3mDiQiN2JGKC7jp6c8QHaHD4f/3bei0XJpITbypwRBFES02Jxpa7DjXYkNDiw0NLXY0tNhQfs6KNw73PZzeV6ZlxiIr3ozoCD1iInSIjtAhOkKP6AgdYjoeu76OitBBq/F9E4PaEweiUDLU+7f340spbFySEYuYCB0s7U58WdWEGaNHKR0SdehagyEIwE8WZCM3I9aTXJzrSDTOdUk4htPkYdBqEGfWY5TZgFizHnEm6XmcWY+4jsdRZj1iTdJzh9ONm/78Wa/E4ZkfzBzWTTot1uTTDnX+6AieFmtiAkI0AkxEqF9ajYD54xPxwVe1+KykgYnICHlTg2FzulBvsaGmqR21lnbUNrWhpqkddZZ2nDlnxVfVnTMOiyLw3J6hDcuMNGiREGVEYpQBiVFGJEQZEaHTYPO+sl6dJP9v5RUdc+54N2LK1z3xfZ08MHEgUhcmIjSgBROkRGTPyQb87OoJSocTtHr2wXjwmgmYPnoUapvaUdvUjhpLO+qa2j3JxrlWu9fnuDgtBuOSIpEYZURStBEJkXKyIT0mRhlhMmj7PPaitOheycO4pKhhfVfWOhCRN5iI0IAWTpCmez9cfh6tNuew1s8JN212FyrPW1HeaEVFoxXHaprxyqEKz/tuEXhyV8mg5Rh1GqTGRiA1JkJ67HgeodPgkbeOdhuWqhUEPP+jWappsmDiQERDxbsKDWh0vBmZo0yoPN+GwtJzw5o4J5j11Zzicouos7R7Eo2Kxo6k43wbyhutONtsG6RUSeaoCIxLikZaTARSYiOQ1jXpiIlAnFnfb7OIRiP4fCIiJg9EpAQmIjQgQRCwcEIith2owN6S8ElE7E43nvn0JDbuKvH0nZiQEgWnS0TV+TbYXQN3/Iw26jA6wYysUWbERxmwrbC81/pB2/99vmpqMIiIlMJEhAa1ICdJSkRO9l5/Idi12pw4dbYFJ+u7bGdbcKahFa4eA9tL6lo8z3UaARmjTBgdb0ZWvJRwjI43d7w2IdbUvTZjWmYsazCIiPrARIQGNX98AgQBOFHXgjpLO1JiIpQOqV/9jUxpbLX3SjZO1jWjusm7VSv/c/FkXJ+birRYE7RDXAsDYA0GEVF/mIjQoEZFGpCbHoviqiZ8drIBt8zIVDqkPnWbWwNAXnY8IAInz7agcYBRKAmRBoxPjkJOchRykqTHmAgdbuljZcrFU9OGnUSwBoOIqDcmIjQkCyYkoriqCXtL1JGIiKKI6qZ2FFc24avqJhwqa+y2JLkI4EBp9yXKM+JMUrLRdUuKwqhIQ5/nUMvKlEREoYyJCA3JwpxE/OWTU9h7sgGiKHo9ydVIiKKI8kYriquacLTKgq+qm3C0qgnnrb3XIOnp/m+Nx/VT0jA+ORJmg3f/3NmcQkTkf0xEaEhmjBkFo06D+mYbSupbMDEl2mdld+3XkRwdgdKGVhytkpKNo9VN+KraguZ2Z6/jdBoBE1KicUlGDLLizdiw60SvuTW+P3fMiBIINqcQEfkXExEakgi9FrOz47GnpAF7Shp8koiIooinPzmFJz887hnaatAKsPccrgLAoNNgcmo0pmTEIjc9FrkZMZiYEo0IfedMocnRRjalEBEFGSYiNGQLJyRiT0kD9pacxV0Lsr0+3uUW8U2tBQdLG3Gw7Dz2nz7XqxOp3SUiQqfpSDhiPInHhJQo6AdZ/ZdNKUREwYeJCA3ZZTnSdO+FpY2wO90w6AZODGxOF4orm3CgrBEHSxtx6Mz5PptYenr+R3mec3mLTSlERMGFiQgN2eTUGCREGnCu1Y4tBWW44ZLuQ1lbbE58fuY8DpY14kBpI45UXOi19HyUUYcZY0Zh9thRGJ8Uhfu3ft5riOy4pMhAfSUiIlIYExEaMo1GQOYoE8612vHoe8fw2PvHcMecMTDoNDhY1oivqi1wubv370iINCBvbDzysuMxJzseF6VGQ9eliYVDZImIwhsTERqymqY2fFnZ5HntFoEtBWe6fSYjzoQ52VLikTc2HuOTIgcc6st+HURE4Y2JCA1ZaUMreo9nAa6ZnIz8aenIGxuP9DjvEwn26yAiCl9MRGjIshMjoRHQq0/HozexOYWIiIZn4GEPRF2kxZqw7pZLoO1oamGfDiIiGinV14iIHVNlWiwWhSMhAFg0KQ6X3jcT5efaMDrBhNRYE68NERH1It8bRLGvRv1Oqk9EmpubAQBZWVkKR0JERETeam5uRmxsbL/vC+JgqYrC3G43qqurER0d7fOF1iwWC7KyslBRUYGYmBiflk1Dx+ugDrwO6sDroA68DiMniiKam5uRnp4Ojab/niCqrxHRaDTIzPTvsvMxMTH8h6YCvA7qwOugDrwO6sDrMDID1YTI2FmViIiIFMNEhIiIiBQT1omI0WjEmjVrYDQalQ4lrPE6qAOvgzrwOqgDr0PgqL6zKhEREYWusK4RISIiImUxESEiIiLFMBEhIiIixTARISIiIsUwESEiIiLFMBEhIiIixTARISIiIsUwESEiIiLFqH7RO3+uvktERET+ETKr71ZXVyMrK0vpMIiIiGgYKioqkJmZ2e/7qk9EoqOjAUhfhEsxD1NTNXC+FBiVDcSmKx0NERGFAYvFgqysLM99vD+qT0Tk5piYmBgmIsPx+UvAuw8AohsQNED+/wAzlisdFRERhYnBulWws2ooa6rqTEIA6fHdB6T9REREKsBEJJQ1nupMQmSiGzjwV4CLLhMRkQowEQll8eOl5piePvsjsG0Z0Fwb8JCIiIi6Un0fERqB2Axg4vXA8R3Sa0ELXHQDcOJD4MRO4M8FwA3rgdzvAhwaTUTkcy6XCw6HQ+kw/EKv10Or1Y64HCYioa7tgvQ49z5g3n9IyUnd18Cb9wK1XwKv3wV8/TaweAMQlaRoqEREoUIURdTW1uLChQtKh+JXcXFxSE1NHdE8X0xEQpmtGag8ID2ffbeUhABAysXA3buBPU8C//oDcOwd4Mw+YMkG4OLvKBcvEVGIkJOQ5ORkmM3mkJuQUxRFWK1W1NfXAwDS0tKGXRYTkVBW9hngdgJxY4D4cd3f0+qBKx+Wmm7eug+o/wp4dTmQeytwwx8Ac7wyMRMRBTmXy+VJQhISEpQOx29MJhMAoL6+HsnJycNupmFn1VB2+hPpcfy3+v9M+nTgno+Bhb+QOrYefQ3481zg+AeBiJCIKOTIfULMZrPCkfif/B1H0g+GiUgoO/2x9DjuyoE/pzMCV/8GuOv/gMSJQEsdsO02qaakvcnvYRIRhaJQa47piy++IxORUGWpBs5+A0AAsq8Y2jGZM4F7/yV1aoUAHPkH8Od5wMl/+jNSIiIKY0xEQtXpT6XH9One9ffQm4DrHgPu3CmtTWOpAv5+C/Duz6XOr0RERD7ERCRUDbVZpj9j5gE//QyYfa/0umgT8Jf5QOm/pCni5UciIqIR4KiZUCSKnR1Vxw3QUXUwhkjghieAyUuAt+8HLpQDL+YDEACIXESPiCgQmqqkJTvix3dOwxBCmIiEovqvpQ6nOhMweu7Iy8u+HPjpPuC9B4Hi7QA61qkR3VKTzfirQ/KXg4jIZ0QRcFi9P+7IVmDnqs4V1Bc9AUy/3bsy9OYhz5595ZVX4vrrr8fx48fxwQcfwGQy4Ve/+hXuvfde72MfIiYioUiuDRkzXxoR4wvGaKnmo3h79/2iC/j6HWDeT31zHiKiUOSwAv+VPrIyRDew4yFp88Yj1VIN9xA98cQTePbZZ7Fp0yYcPHgQ11xzDdLT05Gfn+9lwEPDPiKh6NQI+4f0p79F9D58GNh2O9BQ4tvzERFRwN18881YunQpACAvLw8//vGP8cwzz/jtfKwRCTVOG3DmM+n5QBOZDUdshtQn5N2fSzUhggYYPR8o3w8cfx848QEw607gioe5bg0RUVd6s1Qz4Q1LNfD0bKkmRCZogfsLgRgvalf03k2slpWV1e31xIkT8dFHH3lVhjeYiISayoNSFWBkEpA8xfflz1gu9QlpPC1NGx+bAZw9DuxaI63oe/BvwBevAAt+Li20Zwj9mQWJiAYlCF41jwAAEif0+ONPC+T/UdrvR+fOnev2urS0FGPGjPHb+dg0E2rkZpnsKwCNny5vbAaQvbCzg2rSJOD2l4EfvgekTQfszcDuR4GnZkkdrdzuAYsjIqJ+zFgO/LxY+v/158UBGaX40ksvYffu3QCA4uJiPPvss7j77rv9dj4mIqFGnj/E180yQ5G9ELj7Y+CW54DYLGkytLd+Cvz18s4EiYiIvNPzjz8/W758Of76178iMzMT3/nOd7Bu3TrcfPPNfjuf3xMRt9uNgoICrFy5EvHx8di8ebO/Txm+2s4D1Yel577uqDpUGg0w9d+A/zgEXLMWMMYAtcXAlpuAv98K1H2tTFxERDQkCQkJePnll1FZWYnTp0/jvvvu8+v5/J6IbNq0CStWrIDZbB72EsE0RKV7pE5NCROA2ExlY9FHSP1EVhwB5vw7oNEBJ3cBz1wGvPMzoLlW2fiIiEgV/J6I3HXXXThw4AB+//vfIzLSy4465B0lm2X6E5kALPpv4P4DwOQbpUTp85eAP10KfLwOsLVwyngiojDGUTOhxBfTuvtLwnjgti1AeQHw0X9Ko3s+fRwo+HPHYnqcMp6ISGmffPJJwM+pus6qNpsNFoul20ZDcP6MNKRW0AJjFygdTf9GzwXu2gUs3Sw1H9ks6DVlPGtGiIjChuoSkXXr1iE2Ntaz9ZxYhfohN8tkzgIiYpSNZTCCAEy5Gcj/U+/3RBew6/9JE/kQEQUxURSVDsHvfPEdVZeIrF69Gk1NTZ6toqJC6ZCCg2dadxU2y/Qn6aK+p4w/+jrwx0uAN/8dqD0a+LiIiEZAr9cDAKzWYSxyF2Tk7yh/5+FQXR8Ro9EIo9FHC7WFC7cbKP1Ueq6mjqqD6TVlvBbIuwuo+0qapv6LbdI2/ipg/s+kJGuIK0gSESlFq9UiLi4O9fX1AACz2QwhxP7vEkURVqsV9fX1iIuLG9GoWNUlIjQMtV9Ic4gYooGMmUpH452+powHgMoiYP//Al+/DZzaLW0puVJCMuUWQGdQNm4iogGkpqYCgCcZCVVxcXGe7zpcTERCgdwsM3YBoB1+9ZhiYjN6zxiYOVPq0Hq+DCj4C/D5FqDuKPDmvcD/rQXm/jsw80dARKwCARMRDUwQBKSlpSE5ORkOh0PpcPxCr9f7ZH4wQVR5bxqLxYLY2Fg0NTUhJkblnTCV8uKNUtPMoieAOfcqHY1/tJ0HDm0CCp8FWjomQzNEATN+KCUlcaOVjY+IiLoZ6v1bdZ1VyUuONmluDiC4Oqp6yzQKWLgS+PmXwHf+DCRNBuwtQMHTwP9MB167C6g+wsnRiIiCDJtmgt2ZfYDLBkSn+31paFXQGYFL7wCm3w6c/Cew709SbdDR16RNxsnRiIiCAmtEgp08m+r4MBtRIgjAhGuAH74D3LsHuCi/+/uiG3hnBVf9JSJSOSYiwe50EM4f4mtpU4E59/Txhiit+vvMQqnDa2tDoCMjIqJBMBEJZi1ngdpi6fm4K5SNRWnx4/uYHE0ABB1Q+yXwwcPAk5OArd+ThgQ7bYqESURE3TERCWbyJGYpuUBUsrKxKE2eHE3oGEomaIEb/wT8sgS4YT2QPgNwO4ETO4FXl0tJyfu/kOYrUffAMSKikMbOqsHM0yxzpaJhqEZ/k6PNvlvazh4HjmwFvnwVaK4GDv5N2hInAtOWAVNv6z2fCRER+RXnEQlWoghszAUslcD3XwdyrlE6ouDhdkm1SUe2AcfeBZxtHW8IUhPXtNuByUsAQ6Q0DLjxlNT0wySFiGjIhnr/Zo1IsDp3UkpCtAZg9HylowkuGq20fs34q4B2i9Rn5Itt0vo2pz+RtvejpCavykIp6eNwYCIiv2AfkWAlD9vNmgMYzIqGEtQiYoAZPwDu3AE88AVw5SPAqLHSZGkVBZ39R0Q38O4DwLlTioZLRBRqmIgEK3l+jGBabVftRo0FrvwVsOIIcP1/935fdANPzwW2LQM+fwloCe3FrIiIAoFNM8HI5QTK9kjP2VHV9wQBmJwPfLhaSj66ctuB4zukDYK02vGkRcCkG4DkyeE1qRwRkQ8wEQlG1Z8DNgsQEQekTVc6mtAkDwd+9+eA6JKGAy/ZCGTMAI7vlLbqz4GqQ9K2+1Fp4b1JN0iJyej5gM6g9LcgIlI9JiLBSG6WGXeF1PGS/KO/4cCplwBXrAIsNcCJD6SkpPRT4EI5UPiMtBljpJFMk26QpqI3jeIIHCKiPjARCUac1j1wYjP6Txpi0oBZd0qbvVXqQHx8B3DiQ6D1LPDVG9ImaKVE5txJAByBQ0TUFRORYGNrBioPSs/ZP0Q9DJHARYulze0Gqoo6+pLsBM4eA86VdH5WXpCv7Txw0RIpSWHfEiIKU5zQLNgc/wDYdps0wuOBL5SOhobii1eBN+/u//3odGDsAmDsZcDYhUxMiCgkcEKzUMVmmeAz9jKpOabbCBxB6mhcd1Sabr74VWkDgKjUjsSkY0vIYWJCRCGLiUiwOcX1ZYJOXyNw8v8o9RGxW4HKA0DZZ0DZXmkETkstcPQ1aQOAqBRgzGUdiclCIHGClJiw8ysRhQA2zQQTSzWwYTIAAVh1GjDHKx0ReaOpqvcInJ4cbVIfoLK9UnJSeQBw2bt/JjJZOr76CNj5lYjUik0zoUie1j39UiYhwWigETgyvQnIvlzagI7E5JC0Dk7ZXqDiANBaL20y0Q288zOgvBDIXij9+0jI4dBuIgoKTESCCad1Dz96k5RcZC+UXjvagUMvSLO+9nTk79IGAIYoIG2alJTIGzvBEpEKMREJFqLYWSPC/iHhSx8BXPwd4KNfd+/8KmiAaXdIw4RrvpAW7TvzmbTJjLFA+vTuyUnc6M7khH1OiEgBTESCRf3XUnW83iytuEvha6DOr4C0FlHDCaD6cOdWWwzYmqQZYEs/7SzLFC8lJBodUPIR2OeEiAKNiUiwkJtlxswHdEZlYyHl9Tf9PABodUDKxdJ26R3SPpdDSma7Jid1XwFtjcCpf3YvW+5zcuIjaW2dpIuA5IuAuLGAhgt2E5FvMREJFqc5bJd6GErnV5lWL/UZSZsGzPyRtM/RDtR/BXy5HSj8S+9jvnlX2mQ6kzR0OHmylJwMlKCwmYeIhoiJSDBw2oAz+6TnnMiMfEUfAWTMlCZQO/Bs7z4n8/4DaK4B6r+RmnqcbUDtl9LWVc8ExVIDHPqbVB6beYhoEExEgkHFAcBhleaPSJmidDQUagbrcwIAbhdwvgyoPwac/UbaBktQgO5Di1MvAeKzgVHZwKgxbGIkIgBMRIJD12YZDr8kfxiozwkgzUmSMF7aJi/p3C8nKGe/kZKU0n917wwrk4cVewhATEZHYjK283FUtvTcNKr7x9nUQxSymIgEAw7bpUDwps+JrGuCctFiYNoy4I+5PZp5BGDGjwBrA9BYBpwvlYYXWyqlrWxP73Ij4jprT+wtQMkueEb0LPoDMPsnw/+eRKQqnOJd7drOA0+Mk/5jX3kMiElXOiKigX3+0sDNPKIItDZICcn5MqCxVHouP7bUDX6OiFggdrT0+xCbIT3GZHa87njUm/o+lrUrRAHBKd5DRem/pCQkcRKTEAoOgzXzCAIQlSRtWbN7H29v7UxQTv1Tmkm2p/YmoL0YqCvuPw5TvNT840lUMqRyj/xdSoYEDbDkj8DMH47gyxLRSDERUTs2y1AwGk4zj8wQKXXKTpkiTbZWtLlHU48W+P4bgNsBWKqkGg5LdUdTT7X02tEqzZHS1th/siK6gXdXAJ/+t5SoRKUAkUnSY1THY2Ry53NDZN/lsIaFaESYiKgd15ehcNbfiJ7xV/Z/jCgC7Rc6kxJLx1b1ee/J24DO9wejjwSikju3yGRpePPxnZD6rwjAFauleVpMowCdYTjfmIkNhR32EVGz82XA/0yTpt/+VRlgjFY6IiJlNFX139TjTRm9OtJqgH/bIu1rrQdazkp9VFo7Hlvqpc3Z5v35DNGAeZTURGSOB8wJnc89j6O6v3f0deC9n/tuDhYmNaQg9hEJBXKzTGYekxAKbyNp6ulaRl+1K12HI/dFFKWRO3JS0trxWHkQ+PKVPg4QAIiAvVnaLpQPL155DpbjO4HoVKmDrrwZY6SRRZ59MdKjLqJziP/nLwHvPuDbieWY2JAfMBFRs1Nd5g8hopEbrCNtXwRB+kPAGC0NU5ZNugEo3t67/8oDX0j9SdrOA9ZGwHpO6qtibez92PW5y9b3+Y/vGPr30xqkJMUQCVw407lfdAPvrJAWP4xOk76LIRIwRAHGKKn2xhjV5XWUtCxAV75ObJjUUAcmImrldnVODMVp3Yl8xxe1K3I5fdWwxGVJ75vjuycuAxFF4NxJ4OnZvZuOLl8lPW9vAmyWjhFDPTabRTrOZZfma7E29HUS4MBfh/79tMbOpEQXATQc71JUR2JTWST1lzGYpZXB9WZp2LQhUnrUdzx2e98MfLFV/bU1TJQCJiB9RDZv3oz169fjwoULSEtLw8aNG7FgwYIhHRu2fUSqPgee+5b0l8qvyqQVVYlIfXzRf0U22Bws/ZGbj+TE5Nwp4NXlALr+9y4A0ztWY7Y3A7YW6Rh7K2Brlp7bWvqvmfG3lFypNkcfISU+uggpidEZpfWMuu7XRXS87nj/zD7gwDOdw7KveBi45FbpPa2xowyjVGM0lNmp1V7748vy/JhwDfX+7fdEZMuWLVi1ahV2796NyZMnY/v27fjJT36Cw4cPY9y4cYMeH7aJyJ4NwD/XStW/y7YpHQ0RBYqvEpvhJjUuR0di0tqZnJwvBd64B70Sm1l3SmU72qQh03artC6Wwyrts7d2vGeVnkMFYyO0ho7kpJ9HAKg61OMgAci9ReqXozVIzVaeR/l5x2uNvvtnyvYAhc/CMzPwgpXAxTdKgxA0eumPTI1eeq3t8ajR917Z2pdJkj/6EXWhmkQkJycHP/3pT/GLX/zCsy8/Px8TJkzAhg0bBj3er4mImrPK568FKgqBK38NXLlq5OURUfhRQ22NTBSllcQbTwHPLOjdBHXjU1KTjrNdSl6cNmm0kqNd2idvjnZpv9Mmfa6lHjh7rPf5dBFSE7fbMbLvrTRB05moaDRSkthT/Hjp+2q0HZ+TN20fzzsenXbgm3d7nEsL/LzYZzUjqkhEysvLMWbMGBw/fhwTJ0707P/rX/+KjRs34tixPv7x9OC3RGTPBmD37zqq8gRg3s+ACdcOr6ySj4D9/+ubsgDgxAfA/qek51xGnYjUQunamv5i6jUsu8sN1d3Rd8Zlk26+LpuUxLjsPR473m+uAd7/BXrV/sxfITUVuexScuNydJRr73ju6PLcDrid0jDws9/0jtkULyUELof0OflRdA3vZ+BLP3wPyF7ok6JUMXy3uroaAJCe3n1q8oyMDFRV9T2BkM1mg83W2UZpsVh8H1hTFfDP38HzD00UgX1/kraR8mVZQMfsjz+XevqzwxQRKclXHX2HM3ppoJj66jQsl6nRAJqOPiVDpdX7JlHqL0n69719f2e3W0pI3M6OZKfjUU5WLFXAi/m9a5O++wJgiuuoAepIaDzldH0uv3ZJo7n+9Qd0S7gErXQ9AsyviYheLw3/0vRo4xIG6Cy0bt06rF271p9hSVWDfbVVxmb1P41zf+ytQFOFb8rqrzzRJf3CMhEholDhq6QG8G1i48vyBkuSetJoAI0BQD+z8sZn911e7s3Diy8ua+ix+ZFfm2bq6uqQmpqKkpIS5OTkePY///zzWL9+fZ9NM33ViGRlZfm2aWawqjylyvJHeUREpCxf9tXxdXm+jq0LVTTNpKSkYPr06dixYwdWrFjh2b9r1y4sWrSoz2OMRiOMRqPntZwn+bSJRogGvvU4sPNhAC4AWmDROmm/t+fxZVn+KI+IiJQlRAMJ06Tnvvh/3Jfl+Tq2LuT79qD1HaKfbd26VczIyBCPHz8uiqIovvXWW2JMTIxYUlIypOMrKipESO0o3Lhx48aNG7cg2yoqKga8z/t9lqxly5bBYrFgyZIlaGlpQWZmJt57771uTTUDSU9PR0VFBaKjowfsWzIccrNPRUVFeM1RojK8DurA66AOvA7qwOswcqIoorm5udeAlZ5Uv/quP4XtZGkqw+ugDrwO6sDroA68DoGjGfwjRERERP7BRISIiIgUE9aJiNFoxJo1a7qN0qHA43VQB14HdeB1UAdeh8AJ6z4iREREpKywrhEhIiIiZTERISIiIsUwESEiIiLFMBEhIiIixTARISIiIsUwESEiIiLFMBEhIiIixTARISIiIsX4PRFxu90oKCjAypUrER8fj82bN/v7lERERBQkdP4+waZNm/Dss8/i2muvhVar9fp4t9uN6upqREdHQxAEP0RIREREviaKIpqbm5Geng6Npv96j4BO8T527Fj89re/xY9+9KMhH1NZWYmsrCz/BUVERER+U1FRgczMzH7f93uNyEhFR0cDkL5ITEyMwtEEn3dOvYPHDzwOt+iGRtDg4dkP48bxNyodFhERhTiLxYKsrCzPfbw/qktEbDYbbDab53VzczMAICYmhomIl2pba/GH4j9AiBCghdQs9ofiP+CaidcgNTJV4eiIiCgcDNatQnWjZtatW4fY2FjPxmaZ4Su3lMMturvtc4tuVDRXKBQRERFRd6pLRFavXo2mpibPVlHBm+ZwjY4ZDU2PS6wRNMiKZnJHRETqoLqmGaPRCKPRqHQYISE1MhX54/Px9qm3Pft+lfcrNssQEQWI2+2G3W5XOgy/0Ov1wxoN25PqEhHyLbu7+y8AkxAiosCw2+0oLS2F2+0e/MNBKi4uDqmpqSOaXoOJSAhzi24U1hQCACaMmoCS8yUoqCnAVaOvUjgyIqLQJooiampqoNVqkZWVNeA8GsFIFEVYrVbU19cDANLS0oZdVkATkbKyskCeLuyVnC9BY3sjTDoT7rnkHvzyX7/E/ur9SodFRBTynE4nrFYr0tPTYTablQ7HL0wmEwCgvr4eycnJw26mCa0UjbqRk46ZKTMxP2M+NIIGZZYy1LbWKhwZEVFoc7lcAACDwaBwJP4lJ1kOh2PYZTARCWEFNQUAgHlp8xBjiEFuQi4AsFaEiChAQn1pEl98PyYiIcrmsqGorggAMC99HgBgbvpcAMD+GiYiRESkDkxEQtSR+iNod7Uj0ZSInLgcAMDcNCkRKawp7DXRGRERkRKYiIQouVlmbtpcT9XZ9KTpMOlMaGxvRMn5EiXDIyIiAsBEJGTJ/UDkZhkA0Gv1mJUyq9v7RESkbrWttThQcyBkBxowEQlBF9ov4OtzXwPobI6RyYkJ+4kQEQWOKIqwOqxeby9/8zKue+063PXRXbjutevw8jcve12GKIpDjnPs2LHYvHmz53VZWRkEQfDr9Buc0CwEFdYWQoSI8bHjkWxO7vaenJgU1RXB5rLBqOV0+kRE/tbmbMOcrXNGVIYbbjxW+BgeK3zMq+MKby+EWa/euUxYIxKCPMN2uzTLyHLicpBkSoLNZcOR+iMBjoyIiKg71oiEoL76h8gEQcDctLl49/S72F+9H3PSRpahExHR4Ew6EwpvL/TqmDprHW566ya40TnKUSNo8NZ33kKKOcWrc6sZa0RCTIWlAlUtVdAJOsxMmdnnZzifCBFRYAmCALPe7NWWHZuNNfPXQCNIt2qNoMGaeWuQHZvtVTneTDoWERHRbZbUM2fO+Pxn0RNrREKMnFxMTZqKSH1kn5+R+4kcO3cMF9ovIC4iLlDhERGRF26ZcAvmp89HRXMFsqKz/L6C+rRp07Bz507cddddqK+vx+rVq/16PoA1IiFnoP4hsmRzMnLiciBCRGGtd1WFREQUWKmRqchLzfN7EgIA//3f/41z584hPT0dN910Ex57zLuOscPBGpEQ4nK7UFgjJRYDJSKAVCty8sJJ7K/ej+vGXheI8IiISOXGjh2LTz/9tNs+b4b/DgdrRELIscZjsNgtiNZHY0rClAE/Kycqcg0KERGREpiIhBB5tExeah50moEru2alzIJOo0NVSxUqLBWBCI+IiKgXJiIhRO6oOlizDACY9WZMS5rW7TgiIqJAYyISIqwOq2eCsp7TuvdH/hybZ4iISClMRELE5/Wfw+F2IC0yDWNixgzpGLnmpLCmEC63y5/hERER9YmJSIjoOpvqUCevmZIwBdH6aFjsFs8ieURE5Dv+HnGiNF98PyYiIULu5zHUZhkA0Gl0yEvN63Y8ERGNnFarBQDY7XaFI/Evq9UKANDr9cMug/OIhICGtgaUnC8BAK/XjpmXPg+7K3ajoKYA90y9xx/hERGFHZ1OB7PZjLNnz0Kv10OjCa2/+0VRhNVqRX19PeLi4jyJ13AwEQkBcmfTyfGTER8R79Wxcj+Rw/WHYXVYVb1UNBFRsBAEAWlpaSgtLQ3Iei1KiYuLQ2rqyGZ8ZSISAuT+IfJidt4YHT0a6ZHpqG6tRlFdERZmLvR1eEREYclgMGDChAkh2zyj1+tHVBMiYyIS5ERR9NSIeNM/RCYIAuamz8UbJW+goKaAiQgRkQ9pNBpEREQoHYaqhVajVRgqbSpFvbUeBo0BM5JnDKuMeWlS8ww7rBIRUaAxEQlycvIwI2UGInTDy7rlDq4l50vQ0Nbgs9iIiIgGw0QkyBVUD79ZRjYqYhQmx0+WyuMsq0REFEBMRIKYw+3AwbqDAIa2vsxA5I6ucsdXIiKiQGAiEsSKzxaj1dGKUcZRuCj+ohGVJfcTKaguCPmZAImISD2YiAQxuX/InLQ50Agju5QzUmbAqDWivq0ep5tO+yI8IiKiQTERCWK+6B8iM2qNuDT5Uqlc9hMhIqIAYSISpJrtzShuKAYw8v4hMrkc9hMhIqJAYSISpA7WHoRLdGFMzBikR6X7pEy5n8jB2oNwuB0+KZOIiGggTESC1EhmU+3PpPhJGGUcBavTiuKzxT4rl4iIqD9MRIKU3Hwi12L4gkbQeCY34yyrREQUCExEglBtay3KLGXQCBrkpeX5tGz2EyEiokBiIhKE5CQhNzEXMYYYn5Yt17AcbTiKZnuzT8smIiLqiYlIEJKbTXzZP0SWFpWGMTFj4BJdOFh70OflExERdcVEJMi4RTcKawoB+LZ/SFdygsPmGSIi8jcmIkHmxPkTaGxvhElnwrSkaX45h9xPhBObERGRvzERCTLybKqzUmZBr9X75Rx5qXnQCBqUWcpQ01Ljl3MQEREBTESCjtw/xFezqfYlxhCD3MRcAKwVISIi/2IiEkRsLhuK6ooA+K9/iEwun/1EiIjIn5iIBJEj9Udgc9mQZErC+Ljxfj2X3GG1sLYQbtHt13MREVH4YiISROTaiblpcyEIgl/PNS1pGkw6ExrbG3Hi/Am/nouIiMIXE5EgEoj+ITK9Vo+8VGnWVjbPEBGRvzARCRIX2i/g2LljAPwzkVlf5H4i7LBKRET+wkQkSBTWFkKEiJy4HCSZkwJyTjnhKaorgs1lC8g5iYgovDARCRJd+4cEyvi48Ug2JcPmsuFw/eGAnZeIiMIHE5EgITePBKJ/iEwQBMxNlxIfeSI1IiIiX2IiEgQqLBWoaqmCTqPDrJRZAT23Z92ZGnZYJSIi32MiEgTkJGBa0jSY9eaAnltORI6dO4YL7RcCem4iIgp9TESCgNw/xN+zqfYlyZyEnLgciBBRUMvmGSIi8i0mIirncrtQWFsIILD9Q7ryrMbLfiJERORjTERU7utzX6PZ3oxofTQuTrhYkRjk5pmCmgKIoqhIDEREFJqYiKic3D9kdtps6DQ6RWKYlTILOo0OVS1VqGiuUCQGIiIKTUxEVM4zbFeB/iEys96M6UnTu8VDRETkC0xEVMzqsHomEpPn81CKZxgv150hIiIfYiKiYkV1RXC6nUiPTMfo6NGKxiJ3WC2sLYTL7VI0FiIiCh1MRFSs62yqgiAoGsuUhCmINkSj2d6Mr899rWgsREQUOpiIqJjcUVXpZhkA0Gq0mJM6BwBnWSUiIt9hIqJSDW0NKDlfAgGCJwFQGvuJEBGRrwUkEdm8eTNyc3ORmZmJvLw87N27NxCnDWryzf6i+IswKmKUwtFI5H4iR84egdVhVTgaIiIKBX5PRLZs2YLVq1dj+/btqKysxKpVq7B48WKcPn3a36ceVG1rLQ7UHEBta62qygKAjys+BgBMTZrqk/J8ISs6CxlRGXC6ndh6bKvPvquar0O4xObr8hibOspjbOooL5xiGxbRz8aPHy+uX7++274lS5aIDz744JCOb2pqEgGITU1NPo3r9ROvi1NfnCrmbs4Vp744VXz9xOuqKEsURfG146+JuZtzxdzNueIlmy8ZcXm+tHzHck9svviuar4O4RKbr8tjbOooj7Gpo7xwiq2nod6/BVH035zd5eXlGDNmDI4fP46JEyd69v/1r3/Fxo0bcezYsV7H2Gw22Gw2z2uLxYKsrCw0NTUhJibGJ3HVttbi2teuhYjuX31M9BivZy91up0403ym1/7hlNVfeRpBgw+/+yFSI1O9Ls+XfPlzA3z7swvEdQjF2HxdHmNTR3mMTR3lBVtsvr7XWCwWxMbGDnr/9uuc4dXV1QCA9PT0bvszMjJQVVXV5zHr1q3D2rVr/RkWyi3lvW6mAPq8wMPly7LcohsVzRWKJyKB+Ln5ujzGpo7yGJs6ymNs6ihPrbEpda/xayKi1+sBABpN964oA82JsXr1aqxcudLzWq4R8aXRMaMhQOh2U9VAgyeueALxEfFeldXY3ohffvpLn5TVb3mCBlnRvv0ZDMfomNHQCBq4Rbdnn8+/q5qvQwjG5uvyGJs6ymNs6igv6GJT6F7j10QkMzMTgFQzkpOT49lfU1ODjIyMPo8xGo0wGo3+DAupkan47fzfYu3+tXCLbmgEDdbMW4Prxl43rPJaHa0+K6u/8pSuDQGkn9uaeWv8/l3VfB1CMTZfl8fY1FEeY1NHecEWmxL3Gr/2EQGASy+9FHfeeSdWrFjh2fe9730P6enp2LBhw6DHNzU1IS4uDhUVFT7rIyKra61DZUslMqMykRKZopqy/FGeL6n5uzI2dZTH2NRRHmNTR3nhFFtXcovGhQsXEBsb2/8HfdpFtg9bt24VMzIyxOPHj4uiKIpvvfWWGBMTI5aUlAzp+IqKChEAN27cuHHjxi0It4qKigHv835tmgGAZcuWwWKxYMmSJWhpaUFmZibee++9bk01A0lPT0dFRQWio6N9vt6KnK35o7aFho7XQR14HdSB10EdeB1GThRFNDc39xqw0pPfm2bUbKhDi8i/eB3UgddBHXgd1IHXIXC41gwREREphokIERERKSasExGj0Yg1a9b4fbgwDYzXQR14HdSB10EdeB0CJ6z7iBAREZGywrpGhIiIiJTFRISIiIgUw0SEiIiIFMNEhIiIiBTDRISIiIgUw0SEiIiIFMNEhIiIiBTDRISIiIgU4/fVd91uNw4cOIBXX30VmzdvxoYNG/CjH/3Iq+Orq6v9svouERER+UfX1Xc1mv7rPfyeiGzatAnPPvssrr32Wmi1Wq+Pr66uRlZWlh8iIyIiIn+rqKhAZmZmv+/7PRG56667cNdddwEA/v73v3t9fHR0NADpi3Ap5uFx1NXBXl4Bw+gs6FNSlA6HiIjCgMViQVZWluc+3h+/JyIjJTfHxMTEMBEZhguvvYb636wB3G5Ao0Ha79Yi7tZblQ6LiIjCxGDdKlTXWdVms8FisXTbaHgctbWokZMQAHC7UfObNXDU1iobGBERUQfVJSLr1q1DbGysZ2P/kOGzl53pTEJkbjfsZ8qVCYiIiKgH1SUiq1evRlNTk2erqKhQOqSgZRg7BuhZJabRwDBmtDIBERER9aC6PiJGoxFGo1HpMEKCPjUVkQsuQ+uevZ59CffeC31qqoJRERGFH5fLBYfDoXQYPqXX64c1GrYn1SUi5FvOc+ekJ3o94HBAYzYpGxARURgRRRG1tbW4cOGC0qH4RVxcHFJTU0c0zxcTkRDmPH8etq+PAQASfvxjnHv2WVj37wfuvlvhyIiIwoOchCQnJ8NsNofMxJyiKMJqtaK+vh4AkJaWNuyyApqIlJWVBfJ0Yc+6fz8AwDhxImJvzJcSkUNFcLe3QxMRoXB0REShzeVyeZKQhIQEpcPxOZNJqmGvr69HcnLysJtpVNdZlXynZd8+AEDkvHkwjBsHXXIyRLsd1qIihSMjIgp9cp8Qs9mscCT+I3+3kfR/YSISokRRRKuciFw2H4IgIHL+fACdNSVEROR/odIc0xdffDcmIiHKceYMnNU1gF4P86xZAKSEBOisKSEiIlIaE5EQJScb5unToemoOoucOxcAYPv6GJyNjYrFRkREJGMiEqLk5he5FgQAdElJME6cKL1fUKBIXERERF0xEQlBotOJ1oJCAPD0C5HJr9k8Q0QUXBy1tWgtKAy59cI4j0gIaj96FO7mZmhiYhAxZUq39yIvm4/GzZvRum8fRFEM6U5URERqI4oixLY2r4+78NZbqPv9Y56V1FP+89eIu+kmr8oQTKYh/Z//7W9/G9OmTcP69es9+6655hrMmTMHjz32mLehD4qJSAjyDNudMwdCj3Hd5pkzIej1cFbXwF5WBmN2thIhEhGFJbGtDcdnzBxZIW436n73KOp+96hXh036vAjCEIYS33///bjnnnuwbt066PV6VFRU4F//+hdeeOGF4UY8IDbNhCDrvt79Q2QasxmmSy8FALRyGC8REfWQn58Pk8mEd999FwDw0ksvYfHixRg92j8LprJGJMS4W1th/eILAL37h8gi58+H9cABtO7bh/jbbw9keEREYU0wmTDpc+8mlXTU1eH04iVSs4xMo8G499+DPiXFq3MPhVarxb333osXXngBt9xyC1588UX85S9/8Spmb7BGJMS0HjwIOBzQZ2RAn5XV52ci588DAFgLCiE6nYEMj4gorAmCAI3Z7NVmzM5G2u/WApqOW7ZGg7TfrYUxO9urcrzpE/iTn/wEH3/8Md5//31otVpcffXVfvqJsEYk5HiG7c6f3+8/uogpU6CJjYW7qQntR4/CNH16ACMkIiJvxd16KyIXLID9TDkMY0ZDn5rq1/MlJyfj5ptvxr333ouHH37Yr+dijUiI6Tqte38ErRaRc+YA4DBeIqJgoU9NReSc2X5PQmT3338/mpqasHz5cr+eh4lICHHU1cNWchIQBJg7Eo3+yP1HWpmIEBFRH+bNm4fm5mbExMT49TxMREKItUBqlom4+GLoRo0a8LNyP5G2I1/A3drq99iIiIj6wkQkhHiaZfoZLdOVYfRo6DMzAadT6uBKRESkACYiIUIURbQOMH9IX9g8Q0RESmMiEiJsJSVwnj0LwWj0TFg2GLl5hokIEREphYlIiJCH7ZpnzYLGaBzSMeY5cwBBgP3kKTjq6v0ZHhFR2BJFUekQ/MYX342JSIho8aJ/iEw3apRnUbzW/awVISLyJb1eDwCwWq0KR+I/8neTv+twcEKzECDa7bAePASgs7llqCLnzUP70aNo3bfP65UciYiof1qtFnFxcaivl2qczV7ObqpmoijCarWivr4ecXFx0PZYYNUbTERCQNsXX0C0WqGNj4dx0iSvjo28bD7OPfccWvfvhyiKIfNLQkSkBqkdk4/JyUioiYuL83zH4WIiEgI8zTLz5kHQeNfaZpoxA0JEBFxnG2ArKUHExIn+CJGIKCwJgoC0tDQkJyfD4XAoHY5P6fX6EdWEyJiIhABv5g/pSWMwwDxrFlr37kXrvn1MRIiI/ECr1frkph2K2Fk1yLksFrQXHwXgff8QWeS8jmG8HSNviIiIAoWJSJBrLSwE3G4YsrOhT0sbVhnyBGjWg4cg2u2+DI+IiGhATESC3EiaZWTGiROhTUiAaLXCeuSIjyIjIiIaHBORINeZiAyvWQYABI0GkXPndiuPiIgoEJiIBDF7ZRUcZ8oBrRbm2bNHVJZn3Rn2EyEiogBiIhLE5NlQTVOnQhsdPaKy5H4i7cVH4WpqGnFsREREQ8FEJIj5on+ITJ+aCsO4cYDbLXWAJSIiCgAmIkFKdLth3V8AYGT9Q7riMF4iIgo0JiJBqv3YMbguXIAmMhKmqVN9UqbcPMMOq0REFChMRIKUnCyYZ8+GMIJVD7syz54NaLVwnCmHvbLKJ2USERENhIlIkLJ2NJ/IzSm+oI2K8tSuyB1hiYiI/ImJSBByt7fDeqgIQGdziq94hvGyeYaIiAKAiUgQshYVQbTboUtJkUa6+JBnuvf9BRDdbp+WTURE1BMTkSDkGbY7bx4EQfBp2aZLLoEmMhKuCxfQ/vUxn5ZNRETUExORICQPr/V1swwACHq9Z5ZW9hMhIiJ/YyISZJyNjbB11FT4sqNqV+wnQkREgcJEJMjItSHGSZOgS0z0yznkmpa2os/hbm/3yzmIiIgAJiJBp9UPw3Z7MmRnQ5eSAtFuh7WoyG/nISIiYiISRERR7Oyo6of+ITJBENg8Q0REAcFEJIjYy8rgrK6ROpTOnOnXc3UmIlx3hoiI/IeJSBCRm2VMl14Kjdns13NFzpsLALAdOwZnY6Nfz0VEROGLiUgQ8TTLzPdfs4xMl5gI46RJ0nm5Gi8REfkJE5EgITqdsBYUAvBv/5Cu2E+EiIj8jYlIkGgrLoa7pQWa2FhEXHxxQM7ZtZ+IKIoBOScREYUXJiJBwjNsd84cCFptQM5pnjUTgl4PZ00N7GVlATknERGFFyYiQSKQ/UNkGpMJphkzup2fiIjIl5iIBAFXSyvajnwBIHD9Q2QcxktERP7ERCQIWA8dBJxO6DMzYcjKCui5I+dLM7haCwshOp0BPTcREYU+JiJBQIlmGVnExRdDExsLd0sL2oqLA35+IiIKbUxEgoCSiYig1SJy7txucRAREfkKExGVc9TVwX7yFCAIiJw7R5EY2E+EiIj8hYmIysnDdiOmTIE2Lk6RGOR+Im1ffAFXS6siMRARUWhiIqJySjbLyAxZWdBnZQFOJ6wHDygWBxERhR4mIiomimLnRGYKJiJdz8/mGSIi8iUmIipmKymB62wDhIgImGZcqmgskfOk5pnW/eywSkREvsNERMXkZhnzrFnQGAyKxhI5dw4gCLCfPAVHXZ2isRARUehgIqJiaugfItPGxSEiNxcAm2eIiMh3mIiolGi3w3rwEIDOUStKY/MMERH5WkASkc2bNyM3NxeZmZnIy8vD3r17A3HaoGY9cgRiWxu0CQkwTpyodDgAunRY3b8foigqHA0REYUCvyciW7ZswerVq7F9+3ZUVlZi1apVWLx4MU6fPu3vUwc1T7PMvHkQNOqouDLNuBSCyQTX2QbYTpQoHQ4REYUAv9/h1q5di4ceegiTJ08GACxduhSXX345nnrqKX+felCO2lq0FhTCUVurqrIAoOWTTwFIE5mphcZggHnWLADA+W3bfPZd1XwdwiU2X5fH2NRRHmNTR3nhFNtw6PxZeHl5OU6dOoX8/Pxu+/Pz87Fx40Zs2LDBn6cfUN2GDWh87m+AKAKCgJjFN8B06fCGyLYdPgzL+zt8UhYAtBV9Dts33wAA6v/wB2ijoxB3663DLs+XNJGRAIALL7+MC6+8MvLv6sOfnc+vQ5jE5uvyGJs6ymNs6igvmGJLe/R3itxrBNGPjf0FBQWYN28empubERUV5dn//vvvY9myZbBYLL2OsdlssNlsntcWiwVZWVloampCTEyMT+Jy1Nbi5Leukn74wUCjQc7uf0KfmqpoGEH3cyMioqHz8b3GYrEgNjZ20Pu3X2tE9Ho9AEDTo4+DIAj9HrNu3TqsXbvWn2HBXnamz5upKS8Puvh4r8pynjuHtkOHfFJWv+W53bCfKVc8EfHlzw3w7c8uINchBGPzdXmMTR3lMTZ1lBd0sSl0r/FrjUhdXR1SU1NRUlKCnJwcz/7nn38e69evx7Fjx3odE7AakauuBtzuzp3DzAR9WZY/yvMlNX9XxqaO8hibOspjbOooL5xi64sqakRSUlIwffp07NixAytWrPDs37VrFxYtWtTnMUajEUaj0fNazpP6asYZNrMZkQ//CnWP/Zd0ETQapDz8K7SZzWjz9jy+LMsf5fmSmr8rY1NHeYxNHeUxNnWUF06x9UG+bw9a3yH62datW8WMjAzx+PHjoiiK4ltvvSXGxMSIJSUlQzq+oqJCBMCNGzdu3LhxC8KtoqJiwPu8X2tEAHg6pS5ZsgQtLS3IzMzEe++9162pZiDp6emoqKhAdHT0gH1LhkNu9qmoqPBZsw95j9dBHXgd1IHXQR14HUZOFEU0NzcjPT19wM/5tY+I2g21/Yr8i9dBHXgd1IHXQR14HQJHHVN2EhERUVhiIkJERESKCetExGg0Ys2aNd1G6VDg8TqoA6+DOvA6qAOvQ+CEdR8RIiIiUlZY14gQERGRspiIEBERkWKYiBAREZFiwjYR2bx5M3Jzc5GZmYm8vDzs3btX6ZDCTlFREfR6PTIzM7ttb775ptKhhSy3242CggKsXLkS8fHx2Lx5c7f3bTYbHn74YeTk5CA9PR033ngjqqqqlAk2hA12HZ588klERUX1+t2ora1VJuAQ9sILLyA3NxcZGRm46KKL8Je//KXb+/yd8L+wTES2bNmC1atXY/v27aisrMSqVauwePFinD59WunQwkplZSXy8vJQWVnZbbv55puVDi1kbdq0CStWrIDZbIZWq+31/n333Yf9+/fj0KFDKC8vR05ODhYtWgSXy6VAtKFrsOtQWVmJFStW9PrdSFV44ctQs2XLFqxZswavvPIKqqqq8NZbb+HRRx/FP/7xD89n+DsRACNZRyZYjR8/Xly/fn23fUuWLBEffPBBhSIKT0899ZT4b//2b0qHEbbGjBkjbtq0yfP6zJkzokajEQ8dOuTZZ7PZxISEBPHtt99WIMLw0PM6iKIo3nrrreKf//xnZQIKI/fdd5+4devWbvtWrlwp3nzzzaIo8nciUMKuRqS8vBynTp1Cfn5+t/35+fnYuXOnQlGFp8rKSmRmZiodBnX45JNPkJKSgpkzZ3r2GQwGXHvttfzdCDD+bgTG008/jWXLlnXbV1xc7JnSnb8TgRF2iUh1dTUA9FqEJyMjg+1+AVZZWYnGxkbcdNNNGDduHPLy8vDCCy8oHVbYqq6u7nNxKv5uBF5lZSWKioqwYMECZGdn45prrsFnn32mdFghzeFw4Gc/+xn279+Phx56CAB/JwLF76vvqo1erwcAaDTdczBfr+xLgxMEAfX19Xj66acxZswYHDp0CN/5znfgcDhw7733Kh1e2NHr9b1+LwD+bgSaKIowGo1ob2/HO++8g9jYWLz88sv49re/jYKCAkydOlXpEEPOmTNncNttt8FisWDv3r3Izc0FwN+JQAm7GhG5ulOuGZHV1NQgIyNDiZDC1ksvvYT3338fY8eOhSAIyMvLwwMPPIBNmzYpHVpYyszM7PV7AfB3I9AEQcDJkyfx+OOPIz4+HlqtFnfccQeuuOIKbN26VenwQk5RURHy8vKwYMECHD58GNOmTfO8x9+JwAi7RCQlJQXTp0/Hjh07uu3ftWsXFi1apFBU4cntdvfa53K5+NeGQq666irU19fjyy+/9OxzuVz4+OOP+bsRYPzdCIwzZ87ghhtuwNNPP43169f3WleGvxOBEXaJCACsWrUKTzzxBE6cOAEAePvtt7Fz507cd999CkcWXhYvXoxf/vKXsFqtAIBDhw7hj3/8I+6++26FIwtPSUlJuPPOO7Fy5UpYLBa4XC78+te/RlxcHG644QalwwsbjY2NyMnJwbZt2+B2uyGKIl588UXs2bMHy5cvVzq8kPLTn/4U9913H5YuXdrn+/ydCIyw6yMCAMuWLYPFYsGSJUvQ0tKCzMxMvPfee8jJyVE6tLDy3HPP4ZFHHsGkSZNgt9sRGxuL3/72t/jxj3+sdGhh609/+hMefvhhXHzxxXC5XJg9ezY++OAD6HRh+V+FIuLj4/GPf/wDv/nNb/DQQw/BZrNhwoQJ2LFjByZPnqx0eCFl586dKCoqwnPPPdfrvcrKSgD8nQgErr5LREREignLphkiIiJSByYiREREpBgmIkRERKQYJiJERESkGCYiREREpBgmIkRERKQYJiJERESkGCYiREREpBgmIkRERKQYJiJERESkGCYiREREpBgmIkRERKSY/w+V499J0jiX+gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_period_impulse(u=1, v=1).plot(subplots=True, marker='.')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "{numref}`fig:18-adas_adas_shock`を使って説明しよう。\n",
    "* $t=2$期以前は定常状態である0に経済は位置する。\n",
    "* $t=3$期にAD曲線とAS曲線へのショックが発生し，AD曲線は右シフトし，AS曲線は上方シフトする（①）。経済は点Aにジャンプする。\n",
    "    * $p_t$と$y_t$はともに上昇する。\n",
    "* $t=4$期ではショックはゼロになり，AD曲線は元の位置に戻る（②）。一方，$p_3$が高かったため，適応的期待によりAS曲線は大きくは下落せずオレンジ色の線に下方シフトし，経済は点Bに移る。\n",
    "    * $p_t$は減少するが高止まりする一方，$y_t$は0を下回る。\n",
    "* $t=5$期以降，AS曲線が徐々に下方シフトし，経済はAD曲線に沿って0に向かって動いていく（③）。\n",
    "    * $p_t$は減少し$y_t$は増加する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{figure} /images/asas_adas_shock.jpeg\n",
    "---\n",
    "scale: 35%\n",
    "name: fig:18-adas_adas_shock\n",
    "---\n",
    "総需要ショックと総供給ショックの同時発生によるインパルス反応\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## 確率的シミュレーション"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "インパルス反応関数の分析では，ショックが１期間のみ発生した場合を考え$p_t$と$y_t$の動きを考察した。\n",
    "ADASモデルの特徴を理解する上では非常に有用な分析方法である。\n",
    "一方で，現実経済では大小の総需要・総供給ショックが毎期毎期連続的に発生していると考えることができるとともに，その結果としてデータに景気循環という形で現れていると解釈できる。\n",
    "この節では，そのような状況を再現する確率的シミュレーションをおこなうことが一つの目的である。\n",
    "もう一つの目的は，確率的シミュレーションによって生成された$p_t$と$y_t$のデータが実際の景気循環のデータをどの程度再現できるかを考察することである。データの再現性の程度によって，理論モデルとしてのADASモデルの「精度」を考えてみようということである。\n",
    "\n",
    "ここでは総需要・総供給ショックに関して次の仮定を置く。毎期$u_t$と$v_t$は正規分布に従って発生する。\n",
    "\n",
    "$$\n",
    "u_t\\sim N(0,\\sigma_u^2)\n",
    "\\qquad\\quad\n",
    "v_t\\sim N(0,\\sigma_v^2)\n",
    "$$\n",
    "\n",
    "この仮定は次のことを意味する。\n",
    "* 正規分布により，大小の様々なショックを捉えることができる。また，小さなショックと比べて大きなショックは発生し難い。\n",
    "* ショックの幅は標準偏差である$\\sigma_u$と$\\sigma_v$で捉えることができる。\n",
    "* 平均を0とすることにより，$p_t$と$y_t$の長期的な値も0に設定することができる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### コード"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "$a$，$c$，$\\sigma_u$，$\\sigma_v$には，カリブレーションで得た値を使うこととする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def stochastic_sim(n=100, a=ahat, c=chat, ustd=u_std, vstd=v_std):\n",
    "    \"\"\"\n",
    "    引数：\n",
    "        n: シミュレーションの回数（デフォルト：100）\n",
    "        a: aの値（デフォルト：ahat）\n",
    "        c: cの値（デフォルト：chat）\n",
    "        ustd: 需要ショックの標準偏差（デフォルト：u_std）\n",
    "        vstd: 供給ショックの標準偏差（デフォルト：v_std）\n",
    "    戻り値：\n",
    "        y, p, u, vのDataFrame\"\"\"\n",
    "    \n",
    "    # === hの定義 ==========\n",
    "    h = 1/(1+a*c)\n",
    "    \n",
    "    # === 初期値，forループのアップデート用変数 ==========\n",
    "    p = 0   # 最初は定常状態に設定\n",
    "    y = 0   # 最初は定常状態に設定\n",
    "\n",
    "    # === 結果を格納するリストの作成 ==========\n",
    "    y_lst = []\n",
    "    p_lst = []\n",
    "    u_lst = []\n",
    "    v_lst = []\n",
    "    \n",
    "    # === ランダム変数の「種」==========\n",
    "    rng = np.random.default_rng()\n",
    "\n",
    "    # === ループ計算 ==========\n",
    "    for _ in range(n):\n",
    "        # ADのショック項\n",
    "        u = rng.normal(0, ustd)\n",
    "\n",
    "        # ASのショック項\n",
    "        v = rng.normal(0, vstd)\n",
    "        \n",
    "        (p, y) = ( h*p + h*( a*u+v ),\n",
    "                   -c*h*p + h*( u-c*v )\n",
    "                 )\n",
    "                    \n",
    "        y_lst.append(y)\n",
    "        p_lst.append(p)\n",
    "        u_lst.append(u)\n",
    "        v_lst.append(v)\n",
    "            \n",
    "    # === 変数の辞書 ==========\n",
    "    dic = {'y':y_lst, 'p':p_lst, 'u':u_lst, 'v':v_lst}\n",
    "\n",
    "    # === DataFrameを返す ==========\n",
    "    return pd.DataFrame(dic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{caution}\n",
    "統計学では変数$x$が正規分布に従う場合，$x\\sim N(\\mu,\\sigma^2)$と表す。$\\mu$は平均であり，$\\sigma^2$は分散を指す。一方で，上のコードの中の正規分布のランダム変数を生成するコードでは，例えば，`v=rng.normal(0, vstd)`となっており，`0`は平均であり，`vstd`は標準偏差が引数となっている。分散と標準偏差は似て異なる概念なので注意して使い分けするように。\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### プロット"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "まずループ計算を100回おこなうシミュレーションの結果をプロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = stochastic_sim(n=100)\n",
    "sim.plot(subplots=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "$u_t$と$v_t$が毎期毎期変動しており，その結果として$p_t$と$y_t$が変動していることになる。\n",
    "視覚的に$p_t$は持続性が強いように見えるが，適応的期待の役割が大きいと考えられる。\n",
    "\n",
    "このようなシミュレーションをおこなう上で注意する点がある。\n",
    "ランダム変数を生成させた結果となるため，実行する度に$p_t$，$y_t$，$u_t$，$v_t$の値は異なり，プロットは異なるものとなる。従って，シミュレーションの度に平均や標準偏差は異なる値になり，特に計算回数が少ない場合に問題となる。\n",
    "解決方法は簡単で，単純にループ計算の回数を大きな値にすることである。\n",
    "（例えば，サイコロを$n$回投げて平均を計算するとしよう。$n=10$だと理論上の平均である3.5にはならない場合が多い。しかし，$n=100,000$の場合の平均は何回試行ても3.5に非常に近い値になる。）\n",
    "\n",
    "では，10万回のシミュレーションをプロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGcCAYAAADUENqTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9eZgcV3ku/lbv09PLLD37vmrfJWuXLGxjCVk2BoQxu4EAsZ0LdrjKTzdxiAOOwSRxSAgBcgHHDrnBhoDBWMa25E2LLWu1rH02zb5v3T3Te/3+OHWqTlVX9/SMehZJ532eeaa7+lTVqbN83/st55QgiqIIDg4ODg4ODo7rCIbZrgAHBwcHBwcHR7rBCQ4HBwcHBwfHdQdOcDg4ODg4ODiuO3CCw8HBwcHBwXHdgRMcDg4ODg4OjusOnOBwcHBwcHBwXHfgBIeDg4ODg4PjugMnOBwcHBwcHBzXHUyzXYGZQiwWQ2dnJ5xOJwRBmO3qcHBwcHBwcKQAURTh9XpRXFwMgyF1v8wNQ3A6OztRVlY229Xg4ODg4ODgmALa2tpQWlqacvkbhuA4nU4ApIFcLtcs14aDg4ODg4MjFYyOjqKsrEzW46nihiE4NCzlcrk4weHg4ODg4LjGMNn0Ep5kzMHBwcFxQ+D9jpHZrgLHDIITHA4ODg6OGwKD/tBsV4FjBsEJDgcHBwcHB8d1hxsmB4eDg4ODg+NahiiKiEQiiEajs12VtMJoNMJkMqV9C5dpIzhPPfUU/v7v/x7Dw8MoKirCk08+iU2bNumW7ejowMMPP4x33nkH4XAYH//4x/Gd73wHVqtVLuNyueBwOFRr4D/5yU/iiSeemK5HuC4RjERhNRlnuxocHBwcHJNAKBRCV1cXxsbGZrsq0wK73Y6ioiJYLJa0XXNaCM4zzzyDvXv34sCBA1iwYAGee+457Ny5EydPnkR1dbWqbCgUwm233YYdO3bgF7/4BXw+H+6880489NBD+OEPfwgAGBkZgc/nQ19fn4r0cEwe7zQNYkt93mxXg4ODg2PGITKfj18ZxKqKnLRePxojdzAa0uuJiMViaG5uhtFoRHFxMSwWy3WzYa0oigiFQujr60NzczPq6uomtZlfMkwLwXn00UfxjW98AwsWLAAA7N69G08//TR+8IMf4B//8R9VZZ999ln09PTg8ccfh8lkQlZWFp588kls2LABf/u3fwuPx4P29nbk5eVxcsPBwcHBkRb4g+kP8zT3+5FhMaIkKyOt1w2FQojFYigrK4PBbIXNfH154TMyMmA2m3HlyhWEQiHYbLa0XDftScatra1obGzErl27VMd37dqFffv2xZU/cOAAbr/9dpVbatWqVcjNzcX+/fsBAO3t7ZPavZAjMcSJi3AkgT8Ywch4OOXygbC+EBVF3hMcHDONmfB5TOfcNhgMCEdj03b92US6vDaqa6b7gp2dnQCA4uJi1fGSkhJ0dHTolteW1ZZvb2+H1WrFAw88gPnz52PhwoXYu3dv0lhkMBjE6Oio6o+D42ox6A+hzxtIufzR5kHd429e7r+qelwZ8KNzePyqrsFxfcIbSJ2AzwQSkfzZvtZ0QBCAG9F2icXm5kOnneCYzWZyYQ0bSxQvNJvNusyNLR8MBuH1evHpT38aZ8+excsvv4wjR47gi1/8YsJ6PP7443C73fIffw9VcoyMJRaKqW6OdaptOE21meu4ejvwaq28SExE7EaUpDOEc53XrkF0snV4tqugwttNA3PyWtOBmcyKicwhT44/FJntKugi7QSHhpKoJ4eiq6sLJSUluuW1ZbXl77//fpw5cwbr16+H0WhEaWkpvvvd7+LZZ5+F3+/XrcfevXsxMjIi/7W1tV3to13XONk2lPC3iTbHGpMG9/DYjbGJ1mRy+8bDUbl90olUuc3xK0q/zlb/XOieHrIw5A+hIw1eLD3rs98XnPC8YOTqvQln2vnOutOB2QoBC4IwYx6c8TnuzZoLSDvBKSgowPLly/Hiiy+qjr/yyivYsWNHXPnt27fj5ZdfRiSiKIELFy6gt7cXt9xyi3wsFlOzVboPQCLPkNVqld87NZvvn5oO5TbXkCgMcz1issKrdzSAPu/EynK6wIYr3ptAmTb0+lTfG/t8cWVOJ/HSXRnQNza6R0hIL91KZzwchT8YP7+SeSP18Oblvind/52mwUnfS4sB/+yNjesZb11lCHiqEACIKWY6+nTG7lzC9ZAnOC07Ge/ZswdPPPEELl26BAB4/vnnsW/fPtx///1xZXfu3In8/Hw88sgjiEajGBkZwYMPPoj77rsPHo8HAPDd734X27dvlz09XV1d2LNnDz71qU/BbrdPxyOkDdej8n9rigoh3RgPpWbB6CnqiaANUbQOkHwvEWJa3dBTVZDTsUJUm9PTNhif4zaUxAt0ZSD5/hyn20cmJdQHUvCi6MngZN7IVEEvG4nGcFBSlnrJ5Vd7r/NdXsRiYkJyOF1IZdwdaUwcDjrUMDsEIlWkGr7tGZ04ny4VDyTtP0EAwtFYSjkpx1rUukGPrCfDdCcb+4KROZtbkyqmheDce++9eOSRR3DHHXeguLgYjz32GF544QXU1tbKK6Kee+45AIDJZMJLL72Ec+fOoaysDIsWLcKSJUvw/e9/X77e1772Ndx0003YsmULiouLsXr1aqxatQo//vGPp6P61wwms5pHi6vxLNG9HnpTSLY9mMCSosq0a0Q/xJDKtd9tmZg8joUiaB+afBjjYINC4vzBCI40kecQxUm+0XaCsoMawnC5x5vSZWfbuJpKsmc0JsYJdQA4nEBZnp0gD2YqJC/RuHonQW5HOCricCOpXzLvFYvJvO+o3xfEwYZ+NPfHExzq+ZoKJko0TkTMWKs9WQhOq1zZul7sJmP4ROvVE83Jgm37SzpzSTttaF31QA2j7pEAxkKRpGVp/wkQ8F77CLxT8M788PUGdV0nmOSBMCFSw2OhpH+D/uCEZfT+RsbC6Bwex5A/iCHpGkP+IMYn8Wwf+tCHcN9996mO3XnnnXjwwQdTb5irwLS9i+orX/kKLl26hM7OThw9ehSbN28GQHJu2tvbsXv3brlsaWkpnn/+eXR2dqK9vR1PPvmkas8bm82Gb3/722hoaEBnZyc6Ojrw/e9/HxkZ6d1rYDoRisTQm8RaCISjur/TQX78ylCcwNEK3FQtByA9nqVUEjHZHBAWVPhc7lF7V4IRkrNyoYv8frVu0pOtw4jGUrd0qBXVxQjs8XAU/T5FcB6/MhTnidCrZyLyluy8VHJKJktO9erxn29fUX3XesNYBamMQWXM6CV7nmobnlAp+4PROOs/NEVLdCpDg44rgLQjJevBiH4dRIgpESk2vylZUn6y8aztV+o5YNs0lTyq023DOH5lKM4Dk8pcoqGdYEQ95icCSyZaJc+fL6B+Hq2H4uQ0EKDfnuyQ5ajeWKRkNRWwHsxjLUO4MuCXr/3ESxdUZekCC3msaJpauwBDT04HwuoxeLChH0MJyPJYMIJIdOL+FEURY1ex348/HEUkKqrmhx55S2Tw/Nmf/Rl+9atfybmyg4OD+OMf/4gvf/nLU67TZMBftpkmjIyFEZXYtB5C0RjakngSfnaoGee6FMJABS9dTvzS+114p0lRMCwZopPlTIe+5UAtkV5vAN5AWDXxkynh81J9WHcvGzZg6wsQEtc6MIaGXiLsfnOyHaM6liR7fxGK18IbCOPlsz04zyihgw39kyY5RxoHVJ4CevrwWAj/fbQ16bl6XqHmfr+qDmOhCI61DKoE9kEdL8TFbq9882AkqruseyoeYCpsU4WWRF7s9soWZ0gSXO+2DKqEGEtM/3CmCwDgDSQnVt5AGJd6vBgeCyESjSV0ueu51vXCYbTsRN4iURR1V/D1jgbwPyfadc+52O3Fuc5RvHKuW3Wcjk1ZT4nEKgeAqGYcst/o/duHxlTX0UIvN4S2x7GWxMYANW5SWak4JIWfWIOobXAMb13ux2ggnNDQahsck+f68Fg4KRlIlhydaKWTdm6l4oE+1TY8YQ5brzeAJknG9XgD8jh+pzm+HqmQAj34ghH5792WQbx6vkf1O/ssYzqh81HNs7LyQk9neANhiCJwun0YAJHxrAyKiiKGx0MwGARk2S26fzazESajAW67OWEZ9s+doS7ntpvhtJriykV0hFY4KmJcx/Davn07ioqK8Otf/xoA8Nxzz2HlypVYunSpXjOnHZzgpAEXukdxsm0Ig/4QfvFOK0YDYZVw7x0NIJzAQmzo9WJkLIwByVoaHgvhROuQnOdCBzUd2/Q7Sy5okmQ4EkPb4Jhs+dGJc0JSVkN+Ui/W2tIqv8Y+n2xJ0vj0m5f65Zj9Gck6bR0Yk+sMEAY/Foqiqd8Hn2Qx/OtrjXHPe7ihPy6m3SkpA3Z5K7XyozER57u8k0rUDUdj8EnPKaiOixgaC+sq02Seh46hcaX9oVjoLAmYiIMdaRzAydbhuBDARHlErEITRUKgT7YOywnBo4GwTGATkVUqfN/vJPWm/do2OKZSYlQxsQrwyoAfvz8dv8qRhdZ1/56Ua3NZquOFbkVBCwLQognHiCJwVqrbr4+34732YYyMhdHvC2LIH8Lzp+L3z6LXAoA3LvXFJUgDhDyyY/SyjpeB7bcTrUP4zUlyLxHEaKEKPxYT8dYltcJ/9ZxayQFKojYd47Ttnz7SIhstgOJBFUXgUIM+IRiVCOWZjhGVccNCm6eUiDRQQnviypBKdhy83C+PQVrm4OV+vN00oBva9QbC6Bgex/MTjInjVwZVBJANDeoZTgCZ6yFGToajMfzhvU65fCKc6xzFlcExmbRSj6DWI9I9EtCdp2x4MNHeUkNjIVmxa8kNAPzhPWIE9PuC6BkNpJxofK5zVDf5/x9fuaT6Ph6JIiyRs2hMVMmNQDga13b+YAThaAyD/pBsmIiimNSbrfVK0+ulSgn1iI8gCLj//vvx1FNPAQD+67/+a8a8NwAnOGlB7ygRMiIIy37pTLfKWnmneRCvnO/BTw82xZ3rD0bR0KcI3vfaR2TXbiwm4pfvtqlCQf+8Xx2nZd3QESnHgR6jyvFFyQLX24SKDmKaV9I2OIZfvtsme4Wa+/0QBHXMPhAmRIYFFdjRCfZnaRtSyEVjny+BlS+qJgt7Pb0cCj2hFIrEZKXJVkeEqJvvMFEiIbW832sfxqB/YstTT+H+6I1GlcemY2gc5ye4LyWp77UP41DDABp6fRgaCyEUiaFnNICTrcPwBSIYD0XxkzeV8cV6sOg40Lq7nz3Wpkqy7JY+UwU4GojgUMOAXGda9V5vQKUkWWJFSR/b5lqle6ptWOWVOdjQj3cl78W5rlF0jwRwuLFfHvfPnyLKlA1pnOschTcQgQgRJ1qH45Rgc78/zuPChh3fvNSHE61DqpDggC+EF95TFPeRpgEEwjG0D43ht6c6IAjqcUI9BOz8HA1E0Nzvl+918HI/ItGYnKROwT6/ICghHpbQHrzchz4vIXl6od7hsVBcntLptmEih+JK6yMSi8V5OiKxGGKiCKMkE0RRlOfpvjPdaNSM7WhMuR8dY283DaKHaW82NEiNFa03a8AXVMmVREnO73eMoKXfrwoFiqKI353ukOVQU59fRSgBqOQvq7xZb6he3g4AXOr2IhyNQRAElRcoGInico8XvdIzXez2Jt2HiPY7JY/9viAONw7gvfZhRGNiSlsPiFCTjlCU9BdLXsIMyev3BWV5rudd0pPBkWhM9oRFYzGJVEXiztHKej0vzn333Ydjx47hyJEjOHPmDO65554JnzFd4AQnzTjUMICLPV50DiuT+1zXKF4914OO4QBOtw1jP2MB/OfbV9A2SAb76bYR/PpEO/7wXheOXxlCVBSx7/1u9HoDhJwAONoyoJoE4WhMViACgMY+ZYdbOolp4h1ZwqhMjlhMjBNuI+NhtA6O4c3LfQhFYnj6SEvcM74jCZF3mgdkRX6+axRXBsZwsccrW80NvT6Z6PWMBvD0kRb86A0iZLpGAnjhdJfsqWE9KG9d7sehhn786ng7TkjCQoSI023DuNDllZU3fb6GXp98TBQJgfGHImgfHAcExVqnBI91ob9+sRfHWgZxum0Er57rUSljKiwFQZnIvz1JFF2vNxinRGIxEQ29XgTC+uGoMx0jKk/UkaaBpHkIrHU+6A8hJiqK5GjLIMJREb892YHLvT5sfuI11bnaFRDDYyGIojq3prHPhwuM90XriTnZOqQSmocb+tHQ68OP32hCnzeomwt0vmtUHmPUe+YNhOUwz4VuL7pGAvAFIyoFxnpxBqT9beh1RJEQlsu9PpxuG0bXyDj6fUH87GAzhvxhlfvfF4zgUEM/2ofGIIqi7HHUIiaKCEdFlQfz3ZZBlcenR1rif7BhAP5gBIFwlIwpDVjSfbptGEdbBvHUIeKxaej1YTwcxbsagkIVIh2TlKiwyvZE67BMJLVt3e8L4hfvtEIEUcqtA2PqfD3GezHgC8reNIpWxovZNjiG96RQCEU0BhgMpM8/9/N35Xn8/OmOuFDlfxxuUZ5fus7J1iH87FAzTrYOIRiJ6SpWLbQel0gshl5vEMOa8E+/L4hv/+G8huCQOlOl3DUyHhfaDoRjstdv//keNPb58PrFXvn3h395SlWezUESBAHvNA1i0B+SycPxK0N4u2lQRZK7RwMYHidz7UL3aBxhOto8iFhMRDND5EIRIsMb+3w4zHjzjl0Zwsh4WEWg6ZymcysmivDrhI5HxsNxBC8RYqKIQDgqGzIRyQMOEKN5TCJlYYbYxSTSS+cF+T2m68Vxu9349Kc/jc997nP45Cc/OaMrnznBSQPEuA/Azw81AyBJo5e6vfKeHa9d7MXBhn6cbB3CydYhXO71IRyN4ZVzPWgZ8OP5U53oHiUKgM5PbyAiK8Yhfxhva9zVz5/qRJ83iHBMxJGmAfT7QjjTPoJ3mgfx0vvdspAXBAExUURTnw+RqBi3/8fIeBin20bQNRxA90gA+8+TyX9YUkRUyFHXJ/WSNPT60O8L4mTbEAQIOHZlUBb6YyGSPH2+axSHGwZk78nf/O4sLvd65WXHrCCIiWRine8alRWyL6C8A4oqscONA3LojE7IR39/Dmc7RyQBLUCAgBZNvko4GpOF1IVuL/7vW80YHg/hQvcoIlFRDvlocz9oaEuA4mbvGQ3I5Zoky50lUP2+EFoY6/1E65Aq7CiK6mc/1TYsn3+mYwSXerz4+aFmWeFTQiiK5I3Fw2MhHGkcQL8viJZ+P4KRKNoGx0joo7FftpJPtA4hEhNx/MqQPEwv6KwKoSSD4jcnOyCKxEtxpmNEFuaiqBBA1gskhzka+iEIhET959utsmK5ILWbKEIm+tGYiB5v/H5BdAXekaYBhCLEohwZD8ukxBuI4EzHsNwnAPEgXOrx4XDjAM53kfFFieKgPyQ/uwiJXEDEoYZ++IMRvHGxT/ZiUQz4g4TcCgLeaR7Ef76jTs6m16K40D0KiMDFHq9sPIgAAqGoas+bixrPHSU477WPYNAfksmRAIUEsSG29ztG0D0SwKA/iM7hcfz6RDtekUJmwXAM7MusozEypo9JJCsQjqKpT5kTkZiIzuFxlZeIKL0YQtEYOofH0dLvRygSQ783FLc6inplqUI93DiASEyEySjg9YvEC6XnNQUIETnaPIj2oTEcleQLJXmhiIiWgTGcah3GsZZBnO0ckWVQTBQx4A8hEI4iFIkhJgKDUvtSkkq90v2+IIKRKI42D0IUgaY+H14514P32kdkQnFlwC/LSTrGf3W8TXWdsRCZWy+fJe1M8wvZcNHPDjZDFAlJJzlepOy/v0UMu9Ntw3jzcp9q/lAPKA1tvd9BvPgnrgxhdDyMf3j5Ii50euV7BsJRmciFIjEyviQZNDwWwniI6I6xUFSWNTFRhCAIGA9FEZGM4nA0htHxMEQQ+RqMRAlxCUVkeToeIuGvsVBUIkHEyApLXiMfQ3CCGoLK4stf/jIuX748o+EpYJreJn4jonc0iKa+LhxpGsCCIhcu9/owHoriuWPtsJoMCISjaB0cwz+9ehn3bazEd/ZdwAPbanGqbRi1+Q60Do7JVhX1FtD8iNcu9OJCN8knCUai+NHrjfjUunI4bWa0D43hUEM//vy506jKtaN7JACDoCzRfLdFiYVTQdk9EpDedksmbvdIAP5gBK9f7MXPDjXDaTPhaAuxVj68vBjRWAznu7xYUOSUXLpjyHPYIIpEIDf3+2ExGjDkD+G7L12AzWxANEqVuIiTbcP44WsNMrs/fmVIsrhIcmixOwNb6vNwoXsUhe4MWVmdahvG0lI3ukYC0nLHEExGAywmA8qyM3C4sR8GgShqt92Mm+fl46nDLajNd2B1RY7sLteLmP3+dCc8Diua+/w42jKIu5YXQxAEhKMxfPDJN7FtXp5c9p/3X0aW3YL3O0YggqyqGvCFcFnK8XntYi/eaRpEvsuKe1aX4XKvD0VuG64MjCEQjsIbCOPNS4RM/ufbV9AzGkCVJ1Pul+6RAOoLnGjs88mWXLadvHz2f050oLnfjz+9uQal2XaMh6J4SrKYv7i5Cu80D2L7okIAxCt28HI/vvvSBXx5Sw0u93rx04PN+MLGKvz6RAcudXtR5LahvsCJhl6fvG9NjAkr9qtyVnw40TqMeQVOPHe8TbZWP7iwEIIAvHKuBxaTAZc0eVz/vP8yKnLtuHlePlw2ImKoAj/dPiw9u4hAJIr/ONyCty73yUqE7auLPV65XseuDOJwQz+MdXkIhBWy8NblfogiML/QifNdozJBOdo8iLJsYinue78bn15XgVNtwxgZD2NrfZ5EGHwwGgS82zKE4qwMBBjPaO8omUenWodVZKF1cAxvNw1gwBfCeDiK99pHcLHbi/oCJwpcNhxqGMCOxaQ/hsfCiMZiJMfOH8RPDzZjRVkWzneNqjwoACFm3aMBtPT7iRdEagcDs4SrcySAo82DKM3OwIkrQzjSNACLyYDNdR58f/9l/Mu9K9DrDeKiRIS6Rgj5/t3pTvzXOyS5/u/uXoInX72E8VAUm2o9MBkFtPT7ceBCL+oLnKrnP35lCMOSAfHPBxqwc2mxFIaH1FdKcvfoeBj/cbgFrYNjCEdjeP1iH+YXOvGbkx2wW4zIc1pRlqOsem0dGMPhhgGsr87Fe+0jWF2ZjcMN/Vhc4sKr53vxxU1V6B4Zx+m2Yawqz0bH8DhOt43g4OV+FLszcKihHwUuG063DeNfX2/EljqPPMZ+f7oTMVGE2WjAG5f6MDoeRpUnE62DY8jv9eLYlSH8z8kOLCt1y+N8ZDwse7nOd46iJCsD/b4QTmuSnAVBnWvybvOg7ME63zWK0UAEoUgMP3ytAZvqPDIhox7d4fGw7AF76Ww31lTlYN/73ci0muR5dLHbi5fe74Y3GMGiEhfahsbQNTKOOjsJ7zkzjXL4tXskAIvJgKGxMFw2E8ZCURgNgkw2aT6SPxiB3WpCJCbKxrZBINezW4wIRAgp9gcj6PeFYDMpL/XsGw0i12GBwSAgFIkhEo0hAMBsNJDxkcJi5vPnz2PdunUzllxMwQlOmjAaCOPbfzgPQNlC++kjLfAFIzjToY4H93mDeKd5EA9sI9+17uO3LvejNt+B1y4QD4o/FMGFbi/Od42isc+Pxj4/xsNR3LG0CD2jZNXAm5f6INZ54AtGEI6KaBnwo6nPj4ZeH0SRrMKqL3CiZzSAH7/ZhMfuXoySrAwcbBjA9sVFONTQLwtTbyACh5UMjd+e6kRdvgN///JFPHBzDeYVunCh24vVFTm43OtDrsOCt5sGYTQIWFRMdosOhBVXJVmBRDZ5q8i1QxBI3lEoGsO+98kKFlFqg18f78DmOg8ypXt7A2G8cq4HsZgIV4YZgXAUI+NhvPR+Nz6+mrxb7P8ebJbb7e8+rLiyL/d6cb57FLcuLABASGIwEpVdvOc6R2E0CHHvdLoyOIZoTMSBC7344EKiqAjp8GNpqRuiSPrnQ0sKyaq58TDeuNSHpj4/WgaMqC9wIhCO4sUzXShw2YiFE4ni2WNkNc+ptmHMK3SiyG2TravGPh/mFzrllSk/ebMJ75SSVVqD/hCCEeKqb+rzqbZn/+f9lzEWisptfWVgDO80D+JSjw/vd4yoPCuvnO1BKBrDhW4vHt+nXuLaMuDHY9LYHfQHkW23oKnPL6/0uNjjRVGWDQDkcCpAQpW3LSyII5DHrwyhPMeOP57tRo0nk/TNi+dxy/x8iKKktEVifb51uR+NfX7U5jtwrmsUZztH0OMNwGUj77R78QwZI75ARPYIXRkYQ6HbilNtw3BYTbjYQ8h3vy+I4y1DiMRi8En5OW9d7pcV93+904qKXDv+6dVLAET0egMocmfgVNswIbjMMzx95ArC0Rh2LC4CoHiImvr86BkN4DcnO9A2OI4fHGiAxWRArsMitzdtj7ebBvBuyxC6RwPo94XwxsU+nO/yYnmpG38824O3mwak8dQnK539F3qRaTWhLt9B7ivduKHPB7vVhGAkhlfO9eJlJsGZ9Y5Qa/r7+y/DYTXBnWGWvQgAWdnY5w0i02LEfxxpwRc3VWFoLCx7jjbXedDc78erkvf2kLSKcdAfQufwOCIxUd4j6sCFXpiNBpyWtgdoGRjD+a5R1Ep1Zz2EHocVx1qGcMdS8mLlP/vvk2gdHMNTh1twuHEAP79vDfp8QRxuGMC7zYP44qYqXJLkoiCQefPzQy0ASBgoHI3hWMsgBIF4RU63DWNxCZE/z7x9BVvq81DtycTrF3vR0OvDntvnIyaK6B4JyCGfPm8Qo4EIOkfGke8i25J4AxH85M0mOGwmtA+N4UzHCF56vxvdowE4bSacuDKEfl8Q/b4gXj7bDYfVJK9c/X/SCs3LvT6IAF4+14OcTAv+8jdn0Ch5zM51jsreMW8ggideughA2QvqdNsI/ni2W75mc58f/mAU57tHUVdtRkwUVStTozER3kAYkZgIm9kAXyCCDIsR/hD5T+ELRhATSQjPCeLVyrSaMB6OIhyLwSAIsJpIQIfKyGAkJntq6BSnnqNwNEbOlXJ1LCaDbt5XLBbD5cuX8a1vfQuPPPKITonpBSc4aYB2xYGyTLoDoqhe3gcgLiEy0QZivzpOlCIlQGyo5VTbMFaWZ6usS6qQmvv9qMi1441LSgiqaySAAX9Itrj+43ALHrq1Hue7RnHwcn/cXh9snUkCm4imfj9ek1zOVNFSd3A0JqqSD+l9YkzeRygSgyjGP//J1iH88Ww32ofGMTwewifWlKOxzycrijcv9yMUiWFjbS6OtQxhNBBRWUIUz0ku5a7hcVwZGMOhhgH0eYO4qSoHX/nP47hzWTG8gTDKsu34vwebsXNJETIsRnlCtw+NyXkhMRH45bE2rK/JxQCTnEstshfPdMsua+ruH9Mk+l7o9qLak4nuEcUCjERFnLgyhEA4hnynFUeaBmA1GfDrEx34h5cvYk1lDq4MjKFzeBwVuZlyrsZ4KIpBf0gOF5J2I21M+308HMXLZwkhON0+LOfUsPkbdJUQRevAGE61DeNyrw+1+Q782+uN2LtjAf72hXOqcmy+Q1OfDxW5mfIYGQ2E5dAW7ZO3Lveh3xfC/EJCLo42D2JBoRO93iAyLEb0eoM41DCgEsIXurxyfpdTIrl0blCXeevgGF4934O7V5QgGImhY1hRor2jQfzyWBtGxsIwGwW8cJok1//6RDs+ubYcoSjJwXj9Yh/6fUG0D43jIyvJ++7ah8blUOLhhn60DPjhDURw+6JCBMIxPHesDR3SHD/SOCB7NkJREsZ5+sgV1OQRxc56kS50e+UQWSAcxZuX+uQwY+vgGJ473oZClw1joSi+v/8yAGLt/05apURz016/2IchfwirKnLw+9OdGPSHkGk1odcblAk6a0Sd7RxFbqYFRxoHVPONJnP7Q1ElEbqhHyPjYWW1XceoPH+fO96O6jxCUh/9/VlVaOtw4wC21ufhROswnFYTSrKJKa+3A/XBhn50jwbQ3E9CQVcGiJFGPRtPH27BmY4RrKnMwTvNAzjVNowxmgcSjanG/YXuUcRE0meXe5VtH4aZ3ZnfvNSHak8mnj/ViSy7WRUGpqH5YIT0HSueH/vDeYyHozjRSubo86c68G7LEKo9mTjZOqxKyD1woRd3LiuWv7N7SwUjUXlZ+S8k71m/lEQ9v8iJ4bEQ+rxB2TtEV5KOjIdVHqLu0QA6hsfxfuco7qzOjWtX+pJLo4FkuQUiUd29ncJREe1D4zCbBDl3sWc0gJgoIhRRQuZ0ng34QzAbDfLYGh0PIzvTIud8Wk1GmI1Khks0JsKgs4ilq6sLq1atwpe+9CV84hOfiKv/dIMTnDTgfNcoXr+okAkqRPRyHFiwKza0+N2pTvil2G6XlLD8/47GvzCUXYnADi62PgBJft5S75HLR2KibAXGRBH/+XYrNtbGTyAAGBkng76hV0lKpUuHWS+Bn4lFs6tB/u0NslycCtQjmn0yTjCrDnpGg3HLOZv7/XBnmHGoYYCJj8fvb0JzhvyhqByiu9Tjw5rKHIQiMZxuG0bb0Bg+vJwotdPtRGB5gxH8+ng7SrPtqnyZ41eG8Mq5Hrku+8/3qpIltW2sh6Z+P5oYCzsUjeF0+whOt49gaakbVwbG8B9HFMFIkzTDmv06rgz40esN6O4JxB6j9WdXcvz3u20wG/V3q3vzcp8sqLyBMPp9Ibyts38IG7p67WIfqvPG0D5E8jZOtQ3jL379HtZX58rKnSaMsnOAyu0rA2P4/M+Pqq4ZE0VZqQPKZmL0yf7lAFk9mO8klvagP6R6Iea/v9WMz6yrwJA/RLyEoZi88m94TJ2o2T0SkOv5PycI4aMrDQGSwE2J2qA/BF8wgtPMUt4TrUOyh5PFd6XN3+jYp89OSSrtG6qATlwZgjcQgTfgk+sJQM6VAQghpSTwdPsInDazsoJNym3JthNv16GGfpUVPeAP4Z3mAXm5uR6+v/+yavfffl8I+y+ol0FTUtOi8yoOmrfD7r81lOA1EDFRxMXuUfzraw3yPKLXvNRDDJrDjf3yVg6UuPdpkqTpsnrtyyb1lrUP+klou88XlD3FFNRweepwC7pGiKeXjgsaUn5XszcRG47tGQ2qxsVEOb3/9nojekaDGBkPJ9zP6P2OEVV/0LBbaIIkbZbc0i1J2AUF4+EoRBAyQ4mPNscwGImizxuFRfLksPtVBSMxBMMx2MwGuawlInl8RBHjwSjMRgMyrepGKCkpgc83+VflpAuc4KQBqWarU1CBR8MWemC9BsneGnsxxa39Xz3fI7uOASK07JLgpGSA3Y+DJSuRWLyyokQjmfBk7zUZ6G3xPjIehisj+XA9oknupaBEjgq3/36XEMVeb1C2ZkYDkbiNCwGoPBmJ9siYKvT2v2AJFLta7kSSpaeJFAqLRCv3w1ERL0lenx5pu4MfvxG/nYEWtE9/+HqjPP6XlLjl3/XeOcV6ILU7BycaI1pvAJ07rHeS4mjzIKKiCFG6NLsq51tSP4oi4hKJAfUScnafHto2LIbGwrrtmcpKIRZ6K0600G4gqfcuMNr/3kAk7ndtfhSLhl4faiTvDMUzR1pUnpBkePlcN25bUCh/n8iga+rz4+9fvqTbTlSe0AUUv3y3TZ7DNEw5WbBtZxCg2idGC+3Sbm1ZdnsLFol2Gm7TWW1Hk+Zfu9CbcM4e06y2k1+L4wsiFI3BonsWAR1NfToeNJas9Ov8ziJRO3kDYURjprhyvmBEWsAShT8URdYcej0kJzizgIl2hE0VPzvUPHEhBj96Q73x3vsdRKFP9Obdqbx36GqgzUmiSCagkiHRJoGTvV4qCimd0BOScw0suU/2rh5APc5SnQOHk7zwUYtkZL83wRjQA2uV640dNrRwNUikHJMh2fu59MjYRGjUEMvOJBteat+x1TY4Pokddwj09ofSg97O4JPFGEOyf/luvPebhXZ8mAzqBcZajypFold86IGOz1QMEi1iIlkhle9O3N4Dk3i1xlQQianzf1j5KYoioiIxSMgClsljOt5ezpeJc0wI7f4UswXqYeCYPGaCnCXzMs0VJCLPLBItaU439l/onbjQDGOyxI0m/s5FsBs9aoncRDiawot8AcSthpsuDAdiCEdFiJHEJGa63y6uxWTJ7UQYGyNtaTab03ZN7sHh4ODg4LjucD0ZROMREfubfLjDYkR2DiCYLIhbGTJHEAgkf+GuFqIoYmxsDL29vcjKyoLRaJz4pBTBCQ4HBwcHB8ccx/+cJ16oW6qj0qKBuUlwLONTC1FlZWWhsLBw4oKTACc4acA0hA45ODg4ODhkiAB+fd6PP1weQ7bNoNoiZC5h/5/fPOlzzGZzWj03FJzgcHBwcHBwXCMIRER0+WZ24cdkYLPZZrsKMniSMQcHBwcHB8d1B05wODg4ODg4OK47cIKTBsR4Eg4HBwcHB8ecwrQRnKeeegqLFy9GaWkp1qxZg4MHDyYs29HRgXvuuQeVlZUoKSnBQw89hGBQvcTv7bffxubNm1FeXo66ujr85Cc/ma6qTxpzdLUeBwcHBwfHDYtpITjPPPMM9u7di+eeew7t7e3Ys2cPdu7ciaam+C3gQ6EQbrvtNpSWlqKhoQFnz57F8ePH8dBDD8llLly4gA9+8IP4+te/jtbWVjz//PP467/+azz77LPTUf1JQ5ijy/U4ODg4ODhuVEwLwXn00UfxjW98AwsWLAAA7N69G1u2bMEPfvCDuLLPPvssenp68Pjjj8NkMiErKwtPPvkkfvrTn6K/n2zX/fd///fYunUrPvrRjwIAFi5ciP/9v/83vvOd70xH9SeNC92Jt0/n4ODg4ODgmHmkneC0traisbERu3btUh3ftWsX9u3bF1f+wIEDuP3222GxKK8RW7VqFXJzc7F//365jN71Tp48iZ4e9ZtvZwP90/wOEA4ODg4ODo7JIe0Ep7OzEwBQXFysOl5SUoKOjg7d8tqy2vJ6ZUpKSgBA95oAEAwGMTo6qvrj4ODg4ODguDGQdoJDX5Rl0LyNVUiQiWs2m+PKasvrlUl0PYrHH38cbrdb/isrK0up/hwcHBwcHBzXPtJOcEpLSwEonhyKrq4u2euiLa8tqy2vV6arqwsAdK8JAHv37sXIyIj819bWNvmH4eDg4ODg4LgmkXaCU1BQgOXLl+PFF19UHX/llVewY8eOuPLbt2/Hyy+/jEgkIh+7cOECent7ccstt8hl9K63fPlyFBQU6NbDarXC5XKp/jg4ODg4ODhuDEzLKqo9e/bgiSeewKVLlwAAzz//PPbt24f7778/ruzOnTuRn5+PRx55BNFoFCMjI3jwwQdx3333wePxAAAeeOAB7N+/H7/73e8AAJcuXcK3v/1t/MVf/MV0VH/S4PvgcHBwcHBwzC1My8s27733XoyOjuKOO+6Az+dDaWkpXnjhBdTW1qK9vR3r1q3Dk08+id27d8NkMuGll17CAw88gLKyMhgMBuzevVu1BLy2thYvvPACHn74Yfzpn/4p7HY7/vqv/xqf+MQnpqP6HBwcHBwcHNc4BFG8Md4zMDo6CrfbjZGRkbSHq6r2/gE3RitycHBwcHAkRst3dqb9mlPV3/xdVGlAtt0ycSEODg4ODg6OGQMnOGlAVoZ5tqvAwcHBwcHBwYATnDSAR6c4ODg4ODjmFjjBSQP4IioODg4ODo65BU5w0gHOcDg4ODg4OOYUOMHh4JhDMBk4W+bg4OBIBzjBSQM8mdbZrkLakJvJV4TNJio9mbNdhWlBpsU421WYFZiNc4ewpos8V+fN7TGa57x+5PF0wmGdlm3w5hQ4wUkDFhZPfl+dye5+vKwsa9L3SIZit033+I4lhVO+pp78dNkSTyL7FJXeslL3lM6bCLX5jmm57rWGogRj42pgNSfv6zuWFl31PaZrXKQCq0lflBo1kyIROSh02fCxVaVpq0+1JxMeh1rRzyt0AgDWVedc1bUXFc9eO6eCe9ek9mLl+VJ7zDWwRDQVEmIxkrGXSKYD+uNzfU2ublmb+fqhBdfPk8wR3FSlCI/tiwhZ+PDy4rhyS0smFhIryrPkzyaDcNWCicWOJfoKxWI0Jp0oWiwoUshdZS4R3tWMFyLfZUuJyLBW17wCJz6/oVK33KJiFxan0Ha3LlDeUVbktiEngWfKbjGiRlI6Wn62qiI7rrxeX04Vn1lXEXdsYZELdy4rxuKS9GxGqedBSKSMKZZPgkynStRpMVZ4Oxnym8peUhMJ3pq8yRPUkqwM3DwvL2kZd4JtIFijQ0smUgGrvBw2k3wfqrCmgnxpHv2fDy3AuuocVOdlyjKJ1jcZQUmlPz+9tnzK9QPIGNeDtp0TzVk9sOO8NNuekhdnQ41H/rymMn6uJ8NEfWQ0CPjIihJk21PbQiSfqe/8IoV4sc9F5apWblCikpFEzq6t1icz8j0ZsremUl/PJHIAbqjJlcd/ug3xqwUnOGnGAmagrJYmjVbwlufYcVNVDj62qhQfW1UqK/MlJW4sKnbJ7vyt9WrBW5yVoWt10Mlcp+OBuG2h/stIE2F+kRM1+Y6E7uxH7liomtzsRtgfmJ8PgEw4Shrq8h0oycqIu47HYVURCtbT47ab40jMJyWh+uC2Woggk41OKlYQOq0mVOdl4itbq5k6AvclIExrKnNw87x8lGZn4OOry7C01A2zUcDqimxdYsYKRQD46MpSPHxbPQBMKMxY8gsAUantLBLhWF2RjXynFSaDgNsXqj1pU1V+ekr//3xoARYl8TpW5E4cgvA4rHj4tnrsTECUAaA0W+l3QdKcVZ5MmahnWkyoL3Ag32lNKDwBRfiW59gTlrGZDVghEVJWuVGF7bSZVGPsr3YugNkooDovE3dJpPW+jZXy706JfNTmO/DonYsAKGT+b3YtBACsZ5SGiVFErCdQYEY5O07r8h1YW5UjH6NtdeuCAl2S4WTJUBKrnp6bnWlGntMKgyDI85JOVVEEVjLGU7bdjA2SkqzPd8Z5mej5FKyy/KBGvlCjjO2D9Rrluq46VyWX6DlZ0vyh3rxVFdlJxymLXctIH26pz0Oh2wZzggGllUU7lxahJi8Ti4rdyM206Bo1FKyX0WkzJTUEN9TkojzXrpIXyTzED2yrBUDk2pY6Re4vL8uCIBCiU+CyYV11DlZWZKnus2MxkRVLS7OQDA6rSZYf1Z5MlfxlSQ1b5welerHQkvkidwY+u56Qrp1XEQGYDnCCkwYU6gx0lohsqM2VhYTLZkJdvgMGQcCGmlxk2834mzsXoTbfgS9sqsQnbirHPWuIMl9bFc+62VdC0Al2U2UOvrCxCpvqPKjS5HDYLUaVN4Mq4U9LVkC+xtLJkSzpTEaIskTLbjHK5wLAXctL5M8P3VaPz2+ohMVkkNvkFubeHocVG2pysaDIBXeGCSsZYcLW22wkaoG2WV2+QyaOgkCU/MrybJUioKEAg/R/ZblybRFiQsu0NDsDRW7iZaorcMBoELB9cRFuXVggK2WLyYB8pxVFbhuskhehMpco25LsDNTkOXDzvLw4q9FsFGSy+vkNlVgsWc5OqwnVnkyZHJbn2GE0CIr1I5DnLMvJkPtnx+JCFLisKuWzojwLX9hYRa6pCQWWZGXgIytK4gT6V7ZWo9KTKSs01hqkbcQK4tVSH62qyJavtboiGx9aUghRVJS+02pCTV6mihR+eHkJynLIOTmZZmyq9UAQgB2Li/D5DZX405trYDMb8Zc7F8DjsMYpDEqyzUYDlpdlYVVFDn72+dXy7zsWF+Kmyhx8/xPL4bKZ4bAaUZvvUHkCqLdg55IiWYhX5BILP8NshMkgYFU5OW5gBokgkGdbU5kNtzRn7r2pDBaTAVUSaWQJ3F9snw89CIIyxwQAdy0j8+Wjq0qxtjoHmVbSXnT86705J9NihI1p17/fvVT+vIWZm5vrPDKZsJqU8l/cRMZIgUsZn1pla5IUnyvDhFvm58vnAKTv9frGajJguUROavIykWU341/uXQEA+PJmYmBU52XKJIWSuZUVWbIX8c5lxTAbyGfa/jQPsDzHjkqPcv5dOt5TKmcpAcy2m7G2Ogei9LxLNIYS9XLU5jsgCMC2eflYUuLG/EIncjItchgPIHLmT2+uAQBsm5enUux1BQ45X+6TjEfrgwsLsKoiGw6rCeU5dnz91joAkOcBhcNqQpHbJl9zYy3pt92rypDrsGKpFG69bWEhit0ZuKkqByJEbKzxqMbpx1aV4s7lxXDaTMjJtMhRA4CQNy2WlLphMgjy9SG1E5XX2+bloS7fIYdLWbkpCCSKUK2jY9bX5MJmNuDjq1MLD84UOMFJAzbWeGSPBUAEktVkQKbVBIMAFLhs2FjrwbJSNwrdNsLsaz3IybSo3MWLit1YWZ6F2nwHvrq1ButrcrGhJhcGQbHcv7S5ClWeTNy5rFi2WF0ZZiwvz0Ke04pbFyjWltkoYHVlDr6wsVIWaHdKlk6VJxP/50PzsaDIhd1M7D/HQYTLLcx1qMi9dUE+zEaDPOjrCxxYILlTl5S4kWk1yaRkfiERSh6HBVvq81Cdl4kMiwEbaz24Y2kRavMduJ2ZjGU5dlkZZdstEKU6fmRlCTbX5WFrfb5s5W+bn4/afGL5b6zNRZ7DiipPJgSBCAqryQijQZCV7TLGsvnU2nJk282ysraYDKjNd2BTLbH8cjMtyLQY8ZUtREA//pEl8GRaUOi2wWElYYR7byqTvTFrJcFTm+fAn39wnnyfunwHKnIz8d2PEWW0tT4PFRIpumdNGe5cXoxYTBlDNXmZMkkTIGBjrQdmgwFZdjOq8zLxwUUFWF6WpfJirK3KxZZ6Ym1tm6e2sjOtRmRaTbh1YYGsBASBjFUBSgjvWx9ezFwvBxW5dty6IF/OZ6H3+5PNVXjkjgVw2kwodNviPDelEkm79yYi7O0WI7LsZtwyvwAfXVmKDTUe1BUQb15Frh0fmJ+PIrcNNrMR7gwzsjItqCtwwmQQYDMb4LSaZKX/mfUVeOi2ety+qADuDDI+V5RnoTQ7AzX5mci2W/DhFSUQIODDy4uxqdaD0uwM3DI/XxbUmVYTynOVtsvNtGJTnQfVEllh5w1pKwEGAfibOxepFMqHFhcij1F0a6tysLwsC7cvKsTHVpXKYbSFRS7kO60odNngkgiX3WrE5zZUSH1M+nlhkQsfWakYCUaDAIMgqIhsfaFT9pQsK8uC3WKS57MAJffIIAgwGw0odNlQk+eQZYtZkh3bFyvzbUONB/MKiLfGnWGGKIpYW5UjzxXqlQSIN2bX8mJ8/xPL5WMFLhvuWl6MfKcNJVkZMAgC1lXlojSbtPG8Qic+urIUn1tfCYAQ/DuXFcPjsOCW+QVYUZ6N2nwHvrK1Gh6nRWpzSPNYae91VTl4cFstLEYD7l5B2un2RYrRRMPiIoBNtWQuWE1GrKzIxuJiN1ZXZstG3a0L8mUCQ715dosRBoOAshw75hU6UZ5jx/xCJ5w2E5aWurGwyIVqT6bc3n+1cwEAkjNFz19Zni3X6SMrS7GiLAuLil2oyLXDlWGG1WTAZskrs7TUjXXVORCk+lbk2lGSlYHafFKvbfPzcVMl8ezV5juwqoK0kwABogjEGP67rjoHGWYj7BYTClw2rKrIxocYUsMarzuXFOKxuxcjN5PI1sUlbhgEAcVuG6o9DqyrzsWmWg+KsjIgIt5LU52XCZvJgEyLCTVSW1BvV4HLipKsDHxpUzWy5thrizjBSQMWl7hUCub/fm4N7t9WC3eGGfdtrEKxOwPVeZlYXZmDugInPA4rNtbkoibPIZ9X5LYhy26GQRBQmp2BD68gRGRzXR421nqwuMSFZaVZKM224541ZfjYqlKsr87F/EInMsxGuGwmLC52QxQJGXJnmOXJvLQsC9WeTJTn2CEIgjzwC90ZWF2RrVq5s7w0C1vq8nBTZQ7mFZDzFU9DZpyHCCACZ62k8KlFuaU+DzsWF8JpM8MgEM/Q7lVlyLQYsaoiG/esKcOK8ixVEmZ5jh0banLlibNtXj421HhgEIDyXDvuXlEKUSSeqwyLEUXuDJRkZWBhsQvuDDM+ISUX0hDImsoclOfYZYt+bVUOvnZrHe5aXoJMqxE7lxTJ99o6Lw/zC10oy7Ejw2KEIAiwmQwwCIRAVuQSRbCw2IXafKdMECiJspgMuG1BAe7bWIm6fIdsEZdm2/GtDy/GzfMIwfnHjy8DQBQgtSYdVhMyrSasr8lFUVaGymraWp+HBUUu1OU7sU7q73ynNS5f4VZJAd6+qAAry7NQnJUBo0HA1vo82eWcaSFCvTjLJnslKD66shQLilwwGw3IslvwkKTgqNVtkwTprQsKYDQIqM5zQGT28N5Qk4u9OxagIteOZaVuGA0CLCYDjAYBX91aLT/T12+tR2k2EaKi1CcAcc1vrM1FhtmIFWXZ+PiaMmysJX3/4eUljDud3HN1RTY21eXBLj0TVViLS9y4eV4eHFYTPr2+AtV5DtQXKGR6XXUO7ttQiZuqclDoysCCIhdyHRaVQJ9f6IRBIN5Sq8mITIsReU5Com9fVIiFxS6sq86BKIpYXOLGqopsGA0CsjLMuLk+Hzl2C3YsLoTDapI9ih9fXYqNjOtfkLx0m2o9yM20yOT+s+srUZKdgawMM1aWZ8FmNuBz6yuxQvJIfkEKo62tykG+0wpBAFZX5mBznUf2DHucFmRYjPA4LCoyRkm9IBBPzd/etQhOmxnLy7JQ5cmUczlKs+0qD64oivBkWpFtt8hz7LaFBfjgwkLkO63ItBohAvjmnSR0V+iy4aaqHOxaVoR71pRBBCFuRoOAnExSt9p8Bypy7CjNsmNBoQu1+Q4sLnbjtoUFsqdNFAlhKM+144MSgXDaTPiQRK4FQfGixUQRRW6bPE5KszOwvoYYO1+7pQ71BQ7cuqAABU4bavMdWFaWhXynFSvKs2QCW+S2YWGRCxtrPXj4tnqsrMhGaXYG6gocEEVyP5qvVZyVgTynFasqsuHOMOM2KaRMCe7S0ixkmE0QQLy8GWYjBBCZZDQIqPRkYkt9Hu5ZXSaT2TuWFmFznQfzCp2ozM3E1vo8VHmIzLWZDch1WFCWkyGP+WpN+PnmeURuUzitJtngIIS0BIuK3cTYLsvCklI3HDYT5hU6IQjAv35qJf5q5wKIIvF4zy90osBlw799aiW21ufhT7ZUQwTxrFtMBnx6Xbn8rBaTQUWg5wo4wUkTzCYD6gvIgFtfk4tsuwUCJIvMIMCTacWCIhc21XqwqNgFk9GAshy7rGjmFTgBUXEJ1kuM/gPz8yEIArbU56E4KwMOqwnzC51YVkYsWKoEAMiW4p9/sB63zM/HV7fWoMhFPA9b6vPw6F2LIAhKPgclLvdLbthdy4ohCIDNYoTJaMATH1uKeQVOzCtwyqGWVRXZEEXivbGYDFhc4paIk9IWNBRTnJWBVRXZWFGejRXlWVheloVKTybWVefCaDDAYTXJHiWAPLvDasLHVpXCnWHGpjqPamXJ5zZUQBCAugIncuwWGARga30+LEYy2Wi+CXWhVudl4pNryyFKbfPAtlrkO20wG4mVTL0ygJIEyyq6T64tR4HLhipPJorcNiwpdSPfacMXN1XJ3oAsu0VKAM+FIAD1BcQK/OrWahS4SNhl96pSCIKAlRXZ8DisEEEIQ2WuHYuKXVhZno1V5dkQANkTKEqV2lKfh3ynFWU5duxcUoTVlTmozXcQK1AgOTZb6vNgkTxrVR4Hvn5rPao8REBmWkxYKxE+an1Sa5GGn9ZUZicUTktK3fjGBwnZybQa8cVNVTAKAkSIMBsNcjjhzz9YL3vWttbnodidIXt/6goUt/+ysiz5/jmZFrmMANIn//zJFfjgogKZNNTmk3FGqZQoAt/72FLkOZUcrk21HmgjOzV5DuIJFEX81c6FuKkqB1vqCPG5c3kJLCYDttR7sG1eHjIlggkQb8i66lw5JwIgBOJDiwuxuS4PGyQvQZnkqfi0JuHzS5urcNvCAiwty8KCYpccIvmQFCKj/V7gskEQBDnfaffqMqypzMGmOg9MBgEiSBjlU2vJmKdz3J1hRml2BmxmIxaXuGE1GVDktsFpI3PcaTPh5nrF2KIrPNk2BIjnY211LiCKqC90wmE1YXlZFuyM5wwgnkiq3FdWZMuEtzjLJucd3b2iFLmZFhS5CdmoySfe1OVlWbCZjSjOypD7h5IsURRlA4zKjic+thQbaz2q/JxMqwmLit1y+LfQZcPmujyUZJHQMD0uiiRsRBPkS7IysKjYjdsXFWB9jQd/tXMhirMyUF/gkD098wqdcNmIN7fIbYMoknl3y4J8VHkysX1xIWryHdi+uFDK+yN+N4NAiK/FaIAo0nxACzbW5qI8x45NdR7dBOkPzM/HhppcxGKEIJbn2JHvsuJTUogrz2lFptUEi4nohk11HhgNAu5YWoQNNR5sqPEg12HFLfPz4/IDH76tHnaLCR7JA//3u5dhdWWOnJ9DsaI8Cz/5zCpZRq6pzJEJniAAdosJIoh831jrUaVa3L2iBDFRxOY6D7bW5+ED8yeX3zkb4AQnDRAEAeurc/H/7ZiPbXJCnyhbaAAk5WjFvTeVxyUPA8DGOg9EEEG/ojxLnvTzCp0QQDw5ZqOAJaXEtUhJijvDjM3S9ejknlfoxJ3Li5Ftt8hCqDQ7Q1KgDtldytb/CxurJItQwJ1LiSt5WVkWvnpzNb68tRoZFsWio7H8e28qh8dhxddurZctVQByXJ5aWvUFjoTJe9RyXVediwKXDXs/tABOm1m10oe2Rb5TyQNYUurGzqVFWFudg23z8wihFARU5maijAnjfGhxEQqlECGF0WDA7tVlsvegIjdTtqLuv7kGCwrViY2Fbhs21XpUyZ00BFflyYTdYiIKVxBQkpWB2xcXIstuwZ9srka+i4RhAMBlM8NkJAJydWU2qvMc+Ivt81HgssIjCeryHDvWVGYjx27Blro8leLOl9ziG2s9yLSYZOKzpc4Dq9mA8hy7LLicNjNKsjPgtpuRYTbCIJB4PbsEvED6/IWNVRBFEcvLslQ5C9/4YD0qcjPx4AfqUJZjx6qKHCwucaM4KwOZFhMWFbtQnJWBR+9cJFuVG2o82Fyfh3s0S3UNgqC6tgBgVXm2nBxK+5iOojWVOVKoSZ1YLIKsANyxuAiZUkK5wSDAI3kzACKki9w2eT5Qr6IgAH99xyJZ+Wytz0MuQ2iJ95B4HvJdNpkQAkqOCp13e7bPR1mOHVWeTPyvD5A8C4/TilyHFTdVkRBEoYus3svKMGNddS6Kswgx2SD1nyC1C60fVVofW1WKL2yskhOUS7PtMAiCPF6r8xyYX+jE4mLiOfY4rFgn5d5sqc+LS2YHSAhZFAlp31TrkQnd+hoPPrFGySFZzcxTp9WEh26rhwhCbhxWE74qGUNf3kL+5zos+OrWatWigKWlWRAgyOEKNnxPvQ6CIOAjKxXjpb7AQUhbsQuCQIiwXt4ckZGEwKyvJvl8VAlbjAZkS31b5cnEmspsVOZmwsHkpy2UQkcAmWuZVhNWlGepvCHU4ymK5F53ryhFTBSxpjIHHocFu5YVY0s9ySeLSXJeEAR88ibiMRQEAYtL3LCZiQeT5mdtrPVgXXUuirJsqC9wyjl3dO6w3peavEzZQF5dmSOHqQWQPENtH9OxQ8WFx6HIfjbXZ3VlNmxmI8wmA25fVIjbFhZAu27BKAgodBEiWuCyYUONB8XuDHk82sxGLJL6Kd9pleXbXAQnOGmEyWCQ2TAdaJvrFMVKlbygM3M31OTKk8VpM6vKJFq2KwgkWUyAonBZsEqdgk0uW1+TK5MRloC47WasKCPfs+3Eze3OMMkCZ0GRC3csLZZdyQ6rCV/apKxaqslzqFYk1OY75UlMIYoiClw2lOXYYRDIJLx9UaFsPbKThiZMehwWeT+V9TW5yLKT0ILdYsKmWmL5sjkq2XYLynPt+LNbalWrie5aXox5BU7kOcm5VZ5MWZEKgoB8icBt0RBBLUn7+GpFQLPL5QtdxFWutz/ShhqPHFpZXOKGxWTA/CIXFhS5UJKVgeo8BwpcNlR6MhMKjge21WJdTa7cVvUFTpgNBqytykFpdgasJgNWlmehnvGc/OQzq2EyGmRvSn2+E3dIBDRbisvftbxEzr0iq28K5LHHtp/HYVGFMOoK1K7yNZKXiYXWqq3Jd8BgEORnNEjKgt47z2lFaXaGnHdBiRtVOoRwZcueFLb9b6rKSZhUzubhsHOswGWFx2FFWU4GVpSRsMMGnflDQVcoAZC9sJSsK8uxXVhc4iJhGlHxwnztljoYjYKcM0YNDtqfX9hYJRNRg5SX4nFYVSSiwGXDyopsFLptWFTskpP52VVHgiDIhMVqIh4+l80Mg4GsyAFIqIuGVbTjO9dhQXVeJkRRSeJ12dShzUXFbgiCoLov9fiwKMnOwNJSt0r+0DFiNRnl5NRchxVmowEP31avCvsLEORwlEEQsL6GEEaAeNFKsjOwqkJR+qQOArIzLapEe7PRgLVVubh9UaFMarYyBDjBsAFAcpiyJHm4oMgFgyBI41GAKIpywjhFdZ4DuQ6rlIMjhYmtJiwrzZLbnN0eIdky72y7BflOm9xHH16uXoLO5nLSOUuJmt1ilMeOEtI1ozbfgZo8h0pOACQPiOZW0ijDXcuL4babZZlfnmOHzUTy7PQI9VzB9b+V4QxiWVkW+rwB5oigEqKsUtDCajLCZDDo7lFCLRTtioAtdR68cakPaypz5MlBE/Wa+/2qsg6rCb5gBEtLs9DY5yP1sZjkJL1Mq1FFDtgcDUEQ4kjZ8jK1AtWuIEq0Vw27MsxmNsJmNuCHn1oVt8SRnTT0s8lokAWQ1WSUP1d5MmEyGrC+JhcCgI7hcQCKwGZXlABqZZgMrFfKaTPFJdBNdB3W48SCXjUn04J11bl441IfAHUoByD5Ih6HFR9lLF2Km+vzcK5rVP6+qc6Dc10jAMgyXn8wIv9WV+DA5R5f3LPJYwZKIjb1Um2u8+gScQC4WVI8gkAsST0itrHWg7cu98kKVnslVulsqPHgt6c65ARKSkLqCpzoGiHzqSzHjsY+n+wZ1UIURZgMBllp7tZZzaFdacZi+6IilOfaZXKSTNElA6vk6CoWoyAgJkImFQUuGy50e0miMfMw1PNIx50gEC+oy0bys1ZXZuPtpgEAhLBYzUZkMG1vMgoqjxSVDwDwqbUVcNmU5eAURuZeTg15WVmRjSJ3BmLikG6bs/Of/SxCjGu/L2yshCAIeP1ir+q41WzArmXFuNA9qjougIxpik8xIWgIZHz6pDG+fXEhznSMQISo2uKArcPiErL9g8VkkJOx5XLSw81jjESDIGBBkVOWHdql+SYDIXWRmBg3WPSWg9NVswBZxUdlybKyLLx1uS+uvBZ0XFIdsrTUjdLsDDnMx8rftcx+aQuLXUmJU1mOHZ2SvKQwGoj3rZIxBqj3mBLZTXUeWEwGfHiFkiCvswBw1sEJThrhzjDLLuxVFdk41TY8qfPJII3foIoqUiq8qLuRTkx2ANdKq3e0BGd1ZY4sXHKkV0uwuQcAZNetHkqzM2RlNb/IiUh0aqNZO9lKszPiwhBaJNubAoAqJEWENJmweh6sqaDQZZOTPFlQC5Bd7n5TVQ6ONg/Ke9ukCvYVGey5iXbJznfZ4lY6sF48tl+L3BlxBIeFwIQ82WOJQJPStyQhQVRxJhOuFBaTAS6bCQZBQJUnE4P+kPyb9vlLsjNUyokuJXdnmFFf4JSfmypcdn8P1sLXojzJ2E+E5cxeMhQsqdUSazaZX0Dy13IYDQI21+Wpxp3RYJSfh92LpqXfD7NBiNujie2bQrcyVtjl7QBgMxl191DJtluYcRHfz8UMSZ1o803tOKHfbGYjCt1G+EOR+JMYaPdzotejRHxdVS4u93plpaz14qWyaWC+0yqTAy3B18ogk9GAe9aUYzzcnJS8U9QyCw8AtVd+UwI5pfUgs6jOc2DQPxi3U7ZRELBtXr5Mpj0OK766tSbhdQAylk62DsUdT7YfFh3n1HM/VYNgusEJTpqwQiPszEZDwh0hJwtWgQPJXw1BE/30MNFGUHquRmrVbV9UKCuPRJ6JqYAmnCZDMs8XC7qfTLoxT2dzRQCwSUSEte5o8udkdmGtyctUkTytokoEg+ZZN9Z64qyxVJDntMIwhWB1MhIEkP5oHRyTv69OMh+K3BkYGguhIjdTJVi1JE5LhmkYSbuihCLVsTMVaMM1k8WyJPMxy27RHUN6z6MlSnoeBJbcadvKYBDkkJjdYsRYKKr6vb7Aqbt6MhGWlmRNODa0skZLYIqzEssYMuyJgcUaefMKnfKeOlPJCyF7+1BP+MRyxGIyoL7ACYMgICaKcR52FiwZ1G5Ymehe2vmtRa7DGifvDNKCBxapjlOtB0ZPH2iPTdaQm2lMS+3Onz+PHTt2oKKiAhUVFXjsscd0N7CiEEUR3/ve9zBv3jyUlJTg5ptvxrlz51Rl/uzP/gxZWVkoLS2V/yorK6ej+lOC1r2bTiRT2towCY1xU+HIkiN6LNHVtHkygGK5FOtYJVeDVF63oAeWbGiFoM1sVFn3U4VWiSSKMSfa/nwy5AaIV9rJoA0xsDBK+3noQY+kVXuIUqnyZMZ5G9KB6jyH7EXLc1qT7sA717Z4X63Zuj+VPmXPScW/mZ3kmgUu65RfGsnOVa3nQSsvqDKkxg9LQmk44qaqnEkZDguKnBNa9BMRkGSGjyCF/LSwmowTkoJkSJZz5cpgQ/b6ZXIdqfVXIjI+WaQ6b1OZW3okS6+P9HQEQLx9iQzB2UTaCU5/fz+2bduG22+/HS0tLTh8+DB+8Ytf4Hvf+17Cc7797W/j5z//OQ4cOID29nbcdddduPXWWzE8PCyXaW9vxxNPPIH29nb5r6WlJd3Vv+ZAXZEUNLZLBZbeVv16Fl46vTKpYCrv7gHUHqpEQjCRMlo5QaiLgvW8OaymhJN6uqFnNZt0CFwqz6UdJ8DUwjKTBSWcc/0FjVpo+zyZdZ7onGTQC29pr5WOcac1vLSkKZkFPtU5Wp3nSEg0rjYhdV7BxORpOrBSJ0SdbrAh5nSuTEr0LrV0wiDtezXXkPYa/ehHP0JeXh6+/vWvk2WzJSX41re+hX/4h39AOByOKz8+Po7vfe97+Na3voWSkhIIgoCHHnoIHo8HTz/9tFyuvb0dZWVzaxvoaxV63pipvBF9MlhWlpXQu5BuJArFpfJmXi2ShVWmG6m8EwqY2nMlQrKwyWxjNt/2PlnFnDlB7tHVhrdmAsmSsqeKqyVtu1eXIsNsVK3GmmloUw+SvSMNSH3csq/80YaZZgRzMEn4apF2gnPgwAHs2rVLdeyOO+5AX18fTpw4EVf+3Xffhc/nw86dO+PO2bdvn/y9vb0dpaXxq0kSIRgMYnR0VPXHMXtwZ5gnFce/nlGZInGZDSQLm0w3XBNYmpMJ5aUbk7WoZ5MYpwt6ifVzAQYmV2Y2oB0LyZLFgdkdt6liriYJXy3STnA6OztRXKx+KZrVakVubi46Ojp0y+fk5MBmU7vQS0pK5PLhcBi9vb344x//iJtuuglVVVW46667cPbs2YT1ePzxx+F2u+U/7v25ekw1b4ZDjZkIDV2LmIkwAMfESPUN3rONmQ6r6yHZSqdrCSbjBJsAXaOYlL+wr68PK1asSPj71772NZjNZhh0lmQkyhRPpfzg4CBKSkpgNBqxf/9+WK1W/NM//RO2bNmCM2fOxBEqANi7dy8efvhh+fvo6CgnOVeB6rzMSSfPcnBwXHtINVl2NsASiukOq6cCmmu0MMW9teYqrrUcuVQxKYKTl5eH9vb2pGUOHDiAzs5O1bFgMIihoSGUlJTElS8tLUV/fz9CoRAsFkWBdnV1yeULCgrQ2tqqOm/Pnj34+c9/jt/97nf46le/Gnddq9UKq3XuTtRrDdeCm5WDg+P6xtWskppO5Osk8XPMPtIeotq+fTtefPFF1bHXXnsNOTk5WLlyZVz5lStXIi8vDy+99JLq+KuvvoodO3bI32OxWNy50Wg0pf0KODg4ODg4OG4spJ3gfPazn0VXVxd++MMfAiCemD179uChhx6C2RyfRGg2m/H1r38df/VXf4Xu7m4AwL/+67+ipaUFn/nMZwAADQ0NqKqqwssvvwyAEJu/+7u/w8DAAD7ykY+k+xE4ODg4ODh0wb6wlmNuI+3rALOzs/Hqq6/iwQcfxLe//W1YLBZ86Utfwp49e+Qyu3fvBgA899xzAIC/+Iu/QDQaxbp16xAKhTBv3jy8+uqryM6WXtZYW4t//dd/xaOPPor77rsPwWAQK1aswP79+5GXd30keXFwcHBcj9C+zPFah/adcRxzF4KYbIvh6wijo6Nwu90YGRmBy3VtJ4RxcHBwcHDcKJiq/p57Ww9ycHBwcHBwcFwlbpiXbVJHFd/wj4ODg4OD49oB1duTDTjdMATH6/UCAN8Lh4ODg4OD4xqE1+uF2536nj03TA5OLBZDZ2cnnE5n2peW000E29raeH7PNIK388yAt/PMgLfzzIC388xhutpaFEV4vV4UFxfrbgycCDeMB8dgMEzqXVZTgcvl4hNoBsDbeWbA23lmwNt5ZsDbeeYwHW09Gc8NBU8y5uDg4ODg4LjuwAkOBwcHBwcHx3UHTnDSAKvVim9+85v83VfTDN7OMwPezjMD3s4zA97OM4e51tY3TJIxBwcHBwcHx40D7sHh4ODg4ODguO7ACQ4HBwcHBwfHdQdOcDg4ODg4ODiuO3CCw8HBwcHBwXHdgRMcDg4ODg4OjusOnOBwcHBwcHBwXHfgBIeDg4ODg4PjugMnOBwcHBwcHBzXHW6Yl21O59vEOTg4ODg4OKYH/G3iE6CzsxNlZWWzXQ0ODg4ODg6OKaCtrQ2lpaUpl79hCI7T6QRAGijdr3Hn4ODg4ODgmB6Mjo6irKxM1uOp4oYhODQs5XK5OMHh4ODg4OC4xjDZ9BKeZHwjwj8w2zXg4ODg4OCYVswqwXnqqaewePFilJaWYs2aNTh48GDCsn19ffiP//gPbNmyBVVVVTNYy+sQnSdnuwYcU0U0PNs1mD00vTHbNeDg4LiGMGsE55lnnsHevXvx3HPPob29HXv27MHOnTvR1NSkW/62227DSy+9hPLycoiiOMO15eCYI7iRlfyNTO44ODgmjVnLwXn00UfxjW98AwsWLAAA7N69G08//TR+8IMf4B//8R/jyp86dQoA8fok8/RcLaLRKMLh61OQms1mGI3G2a4GBwcHBwfHtGNWCE5raysaGxuxa9cu1fFdu3bhySef1CU4k0UwGEQwGJS/j46OJi0viiK6u7sxPDx81feey8jKykKhCEx6J6C2d4GyNdNRJQ4ODg4OjrRjVghOZ2cnAKC4uFh1vKSkBB0dHWm5x+OPP45HH3005fKU3OTn58Nut193mwGKooixsTH09vYCURuKJnuBYHKCyMFxXePKYaBiw2zXgoODYxKYFYJjNpsBIG5HwnSSir179+Lhhx+Wv9N19HqIRqMyucnNzU1bHeYaMjIyAAC97WPIj0ZvjHDVSAfgLpntWnBc6wiPz3YNZgadp4Di5amVbT8GlK5O/dqxKGC4AWQOx5zBrCQZ050IqSeHoqurCyUl6VFGVqtV3vNmor1vaM6N3W5Py72TIugj/yPB5OWmCXa7HRCEKeQZXaOJ3b3nZ7sGHDOBkJ8o0GlDGsb/tZAkPTaJLSRG2iZ37cbXJld+JjGov7hlzmB8CAgHZrsW1xxmheAUFBRg+fLlePHFF1XHX3nlFezYsWM2qgQgvR6khBBj5P/40PTfSwfkGa+v8BvHDYJkw7bnLBAem7GqTApUMV1vK+D8/ZM/x9eb/nokQvux1MsONk9fPdKB4VaSJnAtkOQ5hFlbJr5nzx488cQTuHTpEgDg+eefx759+3D//ffPVpVmFkbL9Fx3NibALHmjOCR0nprtGswMkjlRpn3riKswClqmb9XnrGIqbd59Jv31SITx4em/R9A7/fegEEWg+c3UyvZdnN66XCOYNYJz77334pFHHsEdd9yB4uJiPPbYY3jhhRdQW1uL9vZ2lJaW4rnnnput6k0/DNOU/jRtuQJJBPxEAnygMb1V4VBjKpb01eDE0/rHY7Hpv3dCMidiej2T12iIdtKYxHOKM9Dfcx1tRxP/NtIefyzRHPF2T3AjOrYT9I82PKt376tByJ/e680QZnUn46985Su4dOkSOjs7cfToUWzevBkAydFpb2/H7t274875/Oc/j5aWlhmu6XWOSIj8TxqHvgoBP9fdv+lEYGS2azD90FqH1CpvPHD112YFqdYCFwD4+/TP0/MmDF25+vqkC3SOTQW9F5TPw63xv3t7pn7tq8IskL7JkOi2t6evHkBiDxY16Nh+o2hKkIfU/T75n8gb3nE8eV2aXif/Q2PTY1BeOaL+3nV6mnPe0gP+LqprAdMddmp5i/wf0CE4LYcmf73QDOZCjHZNT/tMdfKmGvfXtlHXe1O7X7rRdXry5+jlVYT8U/MmtjJKiRXqtL2aXk/d/T7QMPn7J8RVeodSDS0AQMcJ9fdRZuuM/svx5Xven1qddDGJ50zVg6MiYCmQomTJtE1JSPTYoPr7TMihbp15O9SS5ASd529nxvn53+ufFhjWP5eCEt9oUMrvTDP5HNSQJn8froVcTk5w5gqS5bEkUhThcdx88834zne+g/vuuw9FRUWoXrAUP/7xj9NXr0T3joaJhd2vo0SuHJ74uulatTDUDESTWMdaIZxqDsB0r/jQtpGvl1iEE+XT0OT0dCdriiIhi/S6zW8lrksquRejXeklGK1HlHtr3e+BESAWAQRhYi9S12n1mE2Z0Gmeebj16j1EiUitVlEnQiqW+nQaR9q5legdd6f/i/wXkJrevcIYVdrE7GTn6y3ciMXImOm7lMKN2fMSGDiswSeKicPDycaV1jMZYL5PJjGaBZ1rohSqbWDmAQ2j9Zyd2rWBeC+8CDLf5jg4wUmGWIwIm3T+jQ8p/0c6lOPebsX9mkxhs4RDCoc88cQT+NCO7ejq6sIvn/k59uzZg9//PoElkC4EvYTVD7Ukri+dFEGdsI12wiSzypMl8k2kbF9/XP09ncQgldwXtn7RCPlLhMYDE1+zU7Luu8+kl4Q1vQYMXFYEezRE6sIqLfosqQq2tnfU35Mt2dfLQWCVcyKFEw2TUEAkAECYOITh61Nb2D6dkFcqxCUWAUSdOundP5rAeEkUbgOUkEMyHPvZxGX0QlrpgnbuJSJm1IMzlTSpkD95CI6Vh6IYT/q6ThJZy5LtVPJTErU/vd9ECyuSzeP2Y8DbP5q4DqkgNAb4maX9jQfi52dA2qR1tCu1a6ZCiv29nOBwgCgKPUEIECGpezyJkKaDT4zJ5999993YfSdZXr9mcS2+8PnP4Uc/+pF6EkamcQ+F5rf0j1Nh066JHwd0dkXWCh3WkmnVKEotkiX6sTkdbe/q32uq0PVwSEJfFMlzszH3wIj+jtCxKND8ulphxGITC9F0xsBV944q1rlWeAKJPTMxRjB2niD9zCqgESbUog3Z9JyND02w+6wkUjhNb0Bu82SGQaoYalGHgUYS7Kzu7dHPq9HzICWaH4kQi6j7djgB4Zqo/wcaUx8j1KOoVczsXI173kmGQQToh8G1iASBS38kfdHzPnDut8pvvRovRMshxTgSY/HhIeppoONNFPVzYyhiMeJdTqbkIyGpTyd4fupR0vOcJA1jaeoDICEzvHJYbYTEooCQglpPlhPWnMJ2Bt1nromVWpzgJIPBANhzru7PbCdLws0Z5HtGtvKffrbnAGYbuR+LZDkMdMPAWFS9Q3M0iPq6OpKIzVqIkVDqCqBVk5yXaM8eba5AKug4DrS/O3E51o07JgndM7+KLycIQMindh1Toa4V1pRcaAWcNlymZ8F3niKCN5HCA0jIgXqbxBi5HyUOifIK/P2krFYohbyTy4cZZTbN1LPstQJNz/MliqQ9B5sTbOImkjKs940ln2w4QRTJMx39d/36asMZnSfjV+Ml9M5pjosxAIJ+nksq4TSWTPc3qPMN+qSxEvSR/o+GyTWHr6RuNPRL4ZHxFENPBzXv4qOEa7JEabg13rhKpNhovoqWgLczxkOL5v5TWUWlF8Zix64oknHQ+BoxTrQkn/VCBL1Epg3Tsarp6/FBZWxQNB4AvAk8GdEwCXk37CfP6u/XDwOy4yyZF4M+q9Y72Z7EIGMxNqg2kBIZxCzE2MSelZH2iXPCUtkgdbhN7bWbg5uqcoKTTiSzlhKFWeigZS1l2SKVrIiQL+ltBwbUu482t7SgoqIiviCrZEWR3DMciFcCQZ9aiWk9MFTgslZIMi8KoFgi3m510iSL1rf1XbuU7LQfi08ypRMsPE6szfFhxdrXEgTWEmavobUq+y8DHcfURIj2HzuJm19Xn+frJV6i8SGFLFBQ5a1t685TRCjpEadEylkv7EDrNdpJlLTWHa0VaJdeIv/1wlyCwISjoIzD7veByy+ry4Z8UCmW8WFJiYpKffRw9rfq8XPmV/FGqkqBipr/7E9SW2sFu5a4BkaVhNyxQSUXR5sTobfqr+0d0oft75KxRu959jdKmUThIHqc9fjFovphufd/rTYo2HlFlb2vT2NcSG2i55nQjiF2HOjtqyLG1EpdZWSJhNCGA6SNEq0YTOT9EKGEWNm2YOdUw36dk9hrh4j3QJTqon0eQPKIvknkRf9FQrTZMGHveXW7tB0lz9x3gcif3nNKHZN5WrRtmyz8zELrwabPr/Usv/es+ntKIWlR8Wqy4Xh2rOl5XrTPMtqpH3KUjQFprl1kNutNZvzNEjjBSScSvpBSSDzpqeWkFe5saEWeuBEdq1HE008/jQOvE+v5zLmL+PG//1/8yZ/8ibqY1hsUDQOHniQCu1WzBBDQt1xiMSLUqIeDFb500tLJQwUPJX3UEhElL4Ceohpp1/daiDHlXqOdiiDwdisCk9an/RgSKkP2mdgk354z8f1z4mkNEdNJILikUfa0XMcJ/fIQ9a1eUST5LxTNb5JzO47HE4SO41LCpObZXv+O5H6XhDNN7EyEloOkPEvKBYHsBiwI6npSwnj+dzrLVaV6UA/aYCMhgyzB05Lf5jeJV472x4i2nSXQ1wawBESbnN17jswJQYi3cIevKHkZbe8Sb0ZUIl+dJ4jy8ifIydLzuFFyJK+cEZSwJ6AkslLF0HuBzAc9T8/wFeLZGW6TSKnU/9q2om0QDQM+SUl1HCf30BK6IS0xo4ZShPFyQAqfHgOe/Wz8uI9FFYLnHwAuqHebJ21tIImxXe+Ra53/vdoISuQdEKDIMmrI0PlOE64FzQmiSDwh1EC6cogo7sb9OjtXS9dufE15ruFW0k5UZvS8H5+MHvQqRGb4CumP8Lj+6rTgiNIP2jmuCqOK8f2TKKRz6hfkv5agsoRZENQkjT6PnlHQKJFE2qbNryvL0BOlP+iFVuk4ZpPhtaS25VBqXtJZAic46YR2MySVwGUGQWgsngyxrtjgqL43SNQoyPA4EIvis5/9LH7y06dQWlqKuz7zVTz+7b/F3XffrZSR6yLVISJ5bQTpxXd00Op5NpvfAIZbpGuEgXd+nHxPBipIqYBpep3cV0441ZAbeXKIiVcQiDGFQNGJOtCofNaCWjrDbcSCi4bjrSvW7U8nMqtItV6mrveAMNO/0Qi5Nr2PlpjREE38w+gcYkJUjQeUthsfAnrOqcsONqnHlbcH6DtPFH3/JcidODaoJrWsF5Bes+l1afv3iHRNQfI0SYrF103KUMEtikT4h3war54AXPiD8vncb0j70XoONKjDT+3vqoV33wXSLtqmoeSVhjT9/VJOm2SdDl0hJLf5DahCVLKikNri/O+JYqJkpPOktPIqDJz+f8r9rhxRwlMXXkAcqEXsS5D0Skk8DU2MdpCxQcN9sTAZs+Fx4MyvybH+S8SCH24lfdZ4ACharlyTtlskKBkWIgmfnHiKyRWSnpMNEVKlRHP1BhoU0inGJKJ8kSj2WJSRR0wnBIbj5ZQYI6SLhuma3yDtNtoOvP1DaVxoOjIijcOQn/xG+36oRRpPfrW3gRpc1JM1PiSFlroVeSEinuyyhlMkQK4rxsi4aTxAPNOnf0naPBqKV/aiqLTp+d/re9hC/onf19X9niTmROa5oZCSrlP65539H/J/bJAQ7OEr6vanYyEwSsa63grWvgsg44ER5iy5bXpNkQWHf6CEPVmZQvfSoYtD/L3kLy6pfIA8ixibsySHE5x0gSUosSixChLtwxAeI2VoTgwNf9AljexYiUXJ4IuGlXJUWUuDMjcnB//9zM/R3t6OpuOv4f4vfFKqU4Dciw4+mrdDPQmsAh4fIvdtfI2sPAhJ9zr2czKROk4S6+mN7yqEKDhKhFfnCcaqYTDYpOyXMNRCFI/swZEm4LGfkv8954B3/i3+Gn2X4r0evh7g3PPEC6AKL0jPGRgGLu4D3niCfB9uJUpmpJ3UvUMiUt4e0m+0LfQEF00QvPgi6R9q6Ywy7uTjP1dW41CvRFwsnApmNpwjCfChFnL/WAR4+1/JMfZcqqz6G0h7th5Rlrs3vEo8ILRN246SewkGdV4S6xk49H3iZYoESZ+OtDLKUZCUjUiU8aU/KnVmFbcYlUinZu2vIABHfkieja5+6X6fCGx/r0SudJh0eIyMJXbstxwkobaOE8Sr0H1G8VB1n1EvjRUMSjuNtCv91vQaITFjg8qYphBj6nBQyE9IRGQceP9/4usosn2oEej+fjJXW96Mz1UBCEkbGyB9Fw1LJElzjY4TZPxketTHW4+QslQJtR4h82KkjeQ4hfzxnhg67+jYbjsq5aq9Rerf9DpR4A37yfgdaiGkRxQBr+Q1vHIofu6JMWXsjQ+TOlGvSsOrypJnOi99vcDxp8h1X36EkAt6fn9D/AonQCG2HQxZGuuXDBqmzXw95FqjHdL9qJwbIf3hlchTz/uknq1vk+uIIjFI2NWd/RfJMSoDWM9eeFxNdljv0NgAkTUAMTQGm4BT/yWtkm0jc6RNE0prPaKfstB4gMyroWZS7+Coviex7SgZ60Mt8UbByV+QOdgi3W9skISj2X6ke+2MtCt6iyXHQ81kvLLP3HmSzKVzz0sGhNSG1Bi6uI/UfarL3KcJnOCkC5GgovhDfqIMYxEyiEI+dW5JeIyUpYOLMvzwmDQQmVFLCU5wVDkeHmPcs6ImeZhRHtEQmZxBr/JH64qY4mruu6DE9GNR4ppufI0MVqoUxiWrIhYhdRcEct6p/yLnXvoj0xaSxXb0J4owC4xIXhGR+YOS/6K1iun34db436IhQjhO/icRqgCZfFQBXfoj+Y0KKWq19l0g5176I3D5j8A7mqWajQeI4qAWlq9HI3xF4v5llUnrO8C53ymJ0MOt5HfqJQOk1VSvS3UPKp6VjhNSqOYykJGjdnFTJXrlkGLZUYIxNkDOFUAUftvbkF3idAy2HiFC+8gPlXoc/Xci8DqOEwHc8hYRwHSXUpp82cfkKIgiGWu+XvJ6kT7GO0JJk39AGSdt75KxLhgURUVJSdd7pF/0kiCHW0n9wmNEWYkiacPec0RwssmpwVFlpUfIp3hj2HEy2kXu0/QaqfP//Am55rs/VRRcJEC8GD2avZEGmxVrueME6c+mN0jYLzxO7qNdMj8+TOp56WX9cHRgWPGgRALkebRWb+MBMk5ZJTHYRPpMFKVxIIUu+y+S5xpoJJ4MqkDPv6Dx/kr3pOGrE0+TfomGiYK/+AdCNMQYyZ0So8ARiWSf+n9KTsxwG7lXz1nlmcf6CYkNjytetta3SR3bj5F+oURHjCk5cC2HCPkY6yfygxpa+7+lbg957gkKec2pJuQzMk7aY7SdyJhIgLSTv1/tXew9D5VM9PXoJHuLpJ/HBwkZo6AyNuhjxpYg5QFeIG3u61Ge0dstyTOBPN+AFK719eqEAsNExsZiyvzwdhGjuOkNqY0FxUtMvT80fNh/SQoXghhs1KsZHiftSZPJO0+Qcif+Q/GM6e4FJjJGkGS0UGPuymEyDi/uI9cYaVfyhnrPkrl46J/IeQnTNGYHnOCkC6KGaMSkVUvRoDoRM+SVEnslIRQLJ04ipkQBiCcp1JqjzJwqTSqoKKjCE2PK9WgM3tdDBO1oJ6kfu+GUGFXOjUWIFUInG7sK4OKL5BnHBpXcnINPKs/b9LrkHYoRS2qwOd5LxVoP3WdI+e4ziqCi4Z9LL0lxcYEIVH8/EZp9kmdotJ0IJX8fCTHQthEMijJpO0om/UCDur9EkSTQNr2ukNHT/00Ee8hPhJUoEm9J0xuKMAj5JEX6hmQRisCr3yTW25nnyHeadCyKwKF/Jkqo/V0i9M49ryTlyspyiNQjGibtEPKTdqPEq/E1IuBEEEUc9CleDDZn4+0fERJHwzDH/0MTKgRw5lnSFrGIJPiod43pIPr8xSt0koZF0t6dJ8kzn/8daYfzvyPt33WKXJeOHeqhov3Khhm6TpE2oCEFev1jP1d7G8/+RlHibe8oHlFRVEgaDQs1vq6urq+bXNPbrXhRvQxhaT8qKSPp+xWJ6PSeJf0SHlOIOkvUxCgZA/4+QgLk7folTxKbINpyUJlbdLxDIpLsPBwfIvNr+IrUz16oJ44EQSDEDYjPp6PhSFqfsQFyXbY9o0FlxSVr6bMelP5LhFi88ggpwxpYtG8BMh/a3iH1oIsCAHUyNkRCDgIjJJwjGMiYufCCOmw01Kz0S2ScrA7MqSEK/aX/I9U9InkUpXo0vErueUrKQWt6TXlWmufGhmMouR8fVEgqlaW0Hy6/TML50TBp6/FhUvfOE+Ta+/ao25y2B/We9Zwlz9zzPnDiGeW3pteYFaWi8v/iPjKOm9+QwkhvEmNRFCWvjZRn03KQzLuglzxz13uknQQj4sbJhRfIc1HvohhVcsPO/Q6AAOz7CzLXT/4nmRuRALl32TpSruUQKTfQILWrALhKiBHW9g4ZW1ezmeA0YJre+HgDYozx0Mg5I5IApJ4aquToBItFpC3tEyw3jYYVzw1duRENAl7lGq8/92My6SlzDvkAg1FniSUz4MUoEawnnwHW/qm0PLKFCEF7rlL8vV+Sz12niLCjuQGRAPDG99R1DXmJMPbUkbKUtAw0krrk1hCruus04ComwjI4SoQIa934+8i1BQH4416gfJ1k1b5LBN5QM5Bdqb73SDtgcZA8EEeBuh8A4tp2FRNCx5I4g1EtnEURaD2skEhBICTHnquE4EJe4v7V5tc0vEIEjsVOCNeFF4kAzMgm1k/IT+7t7yPt03qE3P/KIVKm+32ypQBAlMHSe4hw6TwBrLufCD2q1Efbgbz5xGqKBCWvwDhph+73gKqtChlylZK8KX+/Elajnh5KrPsvEcJWtAzIriDHLvwBKFxClEJ4DMirlx5UUK4RHCHt3vY2GU/dZ8g5ABlPw61KH7B5AKckwvX2D8k9qXUaGCGWtMWh1K3zJPHEucsUl3nnaSCrQvFKyCFPkbTXvJ1EKbErPABFcY20EcVW8wHlWZpeI20W8isWNRC/qo/O4XPPk7oDZIzQz2P95Fqv/R2w7F6dzdWkeg5cBgbqyfNllQN1t6nJXnhMvW0/VSCJVi4NNJC+k3MwRMlr0hq/akcUydijCPmBc/8PyMyL967RVUvtx5T8pEt/VPpM64VqOwq4S5R5SPufEg5A8saIRNlTYv6rLwDucoXYth4h/eHrI/P9yhEi8+jcH5HyeKhREw1KYdmDgNWl9lLQZ6IG2PiQkmsTHiMhW0BRzjS5+PwLwMrPAc/fD3z0pwpppvLI6iCejOAoIWbv/RKo3sY2NCFflOSOdil7+tDl6+9K2yiclY7TRGTZuAyr5aMoQrX0/40ngHk7oAq3Br3EEItF1K+S6H6PEByznawQ63kfqL1Vyh0TyNyJSbmF/ReJ7Fh4t+K1pc9Bc6haDgK2LKUtL75IZNSGP8NcASc46QLrDaDxU5rEq13dIXtdxsh5WmUZDRHSYzSpBRq9TtgPQFDeSK5dpRHT5Baw9aMWElWAQ81STsvvyH48DfuJQs+pUgSCdsfV4db4lRJdp4HyDUq47eI+iWSNk3vM20EUGkAUUMdxYP5O4lY98ywRrgBRtrl15HNgVEoUFIlHgEK7j86ZXwGrPk+8SHZN/gKt/6HvkzZ5/9fKccFIFK9gUCbv2d8QhQMQYR0eB97/lVI3gFiNVHkONRPFo83faT1MhGzlZqIQLJmk3bxdgM1Nypx4hvSvt4sQKCpI5JwXKcTTegSoMKjbPBomwspTr4wnaqGyS1ujIdIuo6xCEkibUHc8DWU2vALU7wAKFpFrFywmv492kK3fWc9dNKTk2tDQg9muViwFixUBGwkoGwFSUunvAyAyYS8Q4UxDiYDaY0QVD1VuA02k3VveImRCjJEQybyd+m74/Y9KbSBdg1XOokjGkcEImSAEfaTttPtC9V8mCrfnLBn3NPmadc/3nEVc/gpACL9FIrIX/wBUbFSMC9nyF0moQrtMGIjfsgEg9/V1E5Jz5YjSPq9+kzxD0VLSD2eeow+rXgL83i8Bo5WQNkcBaf/uM8qqnZEOMk6pHDj3W6BktXQpjQe58yRpk7oPEmJPPTjs6xfG+kldBaNETCQCMtKqhB4pmR9sIqSmTwqJ0LZjMT5IwlsV68lc8nYB5kzld5rATO8z3Er+lt1L2oXOKzrf6FymhiAgrbjrJ/uZCdL8sToU0v3+r9VykiZEa0Pc8kfqQZX6hHqpvV2AszD+GQFJp5QqbR7ySSH3l4m8pos/WilRFoHLryjny15/Wg+BkDZvtyIztOHNYz+LzymjOonqEE8t+d/93iy++FUfnOAwEK8mE5ySjJBf+Ry3yVZQyt4Pq89hCU5wlAiL8BhRhEEvsWgjAYUwkYsrAz3oVfJMADLwzTalKBMCE6MhUo7mNEQCRMg0v0EEyVAL+U+VMKC/F4Q2bwEg16ETml32DCgudFr3iBQrHrgMNLQAKz9LfhobBOxDJN8hb4G0emlcvUKNWthU6MnW/gniqdFCMOhvrkWt2JCPWOFUANFJTt3wND+JKvIrhxTFe/nVCVZVCJKiyCTXb3tHeofXRYVI0dVg1CrWbu3feZJ4RthdXKk1LcbUybZ9F4i3g4L2N0W/lJzKHhtqUYR/7zmg/nbpeLNSRkvSO44rydp0XNpzgREmsZ6dT+3H9Dcq074jqOk14u3T7vnB5hexoEplqFlRbtGg/kaF2jAMFdCiSCzxsQGitOg4aHuH5FKwWxF0nQIuSUmlox0KSes+o7+BHE3YlZXrFcDqVNepX5NIH/KT+ai6nlQnOuYpGg9IuXQjpK8jARKm8fWS3ChvJzEUIgHgnZ+Qcw7/i/5LIunqKkC9NJiupqNgxzuVcdQAEaPkb7AJcDPKOM4IiwCQxoPeBqS0vWJh9WZ3kaCaEFO8/ys1+WFXPFJypTXKAsMkwZpC79UZ9NjZ3yjGymATkFWm1A9Q2o2Ou7d/KM0L+hyx+D2NWG8ZfSZqmL71D0wlaPj0GJFvVP7RcDOV/Vo55O9TzxkavmRTH+T7X1T/BxTZErf5o8bApPJyqAUw2TCXwAkOALPZDAAYGxtDRkbG5C/ACqdYhFmtpEFkXH/7fSr4I+PEJUsVXCQIQJxwoz85Ns+609ll0UxOzph3CIiGYA5Ik4Hdlp4SmaEWsvsyDVdR9GuUkRbHfhZ/jlwf5rmpK14wKPeU3wkkMnu4iFKSZT/gLEp+b+pR0XvXFF1NpQVdVn3utyRMVLJKv5w2KbHrFLF4AUXZJQJdGTY+qOz3Ii8vlyxtGhKjhKrxNSJMWVx+Vf/6rIVIlRBLJGIRdVju4D+SZ2U9I/2XiFsfIAqYEm52ozw2BAuQfB4KquS0pIIl+HqEGCAkibUyAUI42H2TAGBsSL0K6soh9dhlFTAbDtEDDf3QNhhpU/bHYZ+ZWtcsOWcVT+NrkqUei19SHBmHnGsCQPYKCQb1niN0TrG7Z3s7iYeO7Vv68kTtfiX9F4FcyYJuep3c592fEQJMScUJqa8oOWK9KRRRaTUhlWXsfTpPxb8igZLDRG3ddx4oW6OQYNVvGoLy+neVz1R5U++0GFOTIz1yQ6H3XjFACbXrEamJ3tVF69MjhZBH20nfDrcRbw4F9XKoCEJUOX72N2r5LMaUfBxaFlAWkLBki5J9MUbkQP588p0SC8FA2kW7tch7zybeqLDrNCFG1Lii96cLGgDFYNBCm4c30a7IswhOcAAYjUZkZWWht5coR7vdDmEyLxIL+oCIJIgHO4FIVL/cQAdJPk6EwS5AZIkJZdrUEo4ynwHZAqLHRvuleohAMKTUKTAOMSxiLAz0+vuRdWUfjFHp2ol2Fe6/BJSvVx/TW/6qxUR7RACKwlUJWqmurNXff1mZeIm2V6egiknPU8Mu6WZx7nklNMaG9BK1ibzXCxK/QFELlvwm2spca1GJURJeSFZG9zqSImDbSus1CYxIYTytt5IZ73Tss9afdldgrYdODwONJFTAWtNaNLyqthAHGtTjgip57RJv7Zb/7CaOE70jh16LWrQX9wGLPxpfjg1n6oHOh3d+FG+5UsUi7+UyqIyFRHk0LLR7TSV6nx2gyYMRFU8ORaL9orRgc4/YcaPNZQKUVUXJXrty4unU7qs3tlmvGYWjUH87CoqJXmWgNdBSeRUKu/le+1GScwKQOrPnJ+pTSjpaDyuhQ1pXVd6mZj6yZIyG9kM+8kf7lnpxqQGsnZMtBxE/zyWMD5IwqtWlNkwS7TzOQjsX2fk7x/bD4QRHQmEhiXtSkjMphAPJ3wxMIRj04/LTAp36REPI6j+JwssTWLgUejscpxPsS/ToqwNYJBPq6QLbb9pwiRZ6m78lgu57xK5i8qfi+qWeMXaMpfJmYECzJ4jUDok2tEsV0SAJGyQjOHqKgfXMaIlVIrBCOlXjRA5ZRaX9fjRzkyqZRC+7ZKG3W/Hr31E++3pIiKJ0jX5Zr0axaBUNSz60YN+dBcQrumAKhEoLNiR8teMgXbC5khOcyc6vZG/8ptD2S6KxRceftjz1RGo3Gm3QeC2TQTsu0/Hi5O4zygpQ1is2GRmnhzn2hnFOcCQIgoCioiLk5+cjHE5RKVBcOQK8/OfTU7F0QRRhDgwonpu5AoM5/i3UswXdF0xOEXpJrldDblOxjGiIi00g1SOJEwmhw/+Ser0mQqIXtSYDa2kn8qZpwYZxJwql6mEqJGAiaMeyt0udLJ8Ms60oJqOAZwp6OwuzaEgQxk2EuFdbpICJxrP2dQvUm5rIiyxjEuQsZYKT5Jp0vkzUppMGJzhzGkajEUajceKCqpOigC+NyvFGgrMwvcTiqpBG92rce3KQ/GWsEyGp5aqBNl9Gi6mQjhnF3HJzzwr0xs+NjnRvIjctHuprbOzqJZtfDWabmGvAN/pLB66xMT2nMGfIzQxgohyBmUIqeVIcHByTx1S8h9cTdN+/N3uYW7W5ZsEZDkcKGJnITc3BwcFxDWOOJRnPKsF56qmnsHjxYpSWlmLNmjU4ePBgwrIdHR245557UFlZiZKSEjz00EMIBlNcyTLdmLHEYY5rGtqVPxwcHBzXE3iIiuCZZ57B3r178dxzz6G9vR179uzBzp070dQUv2oiFArhtttuQ2lpKRoaGnD27FkcP34cDz300CzUXAdzjLVyzFHEveSPg4OD4zqCMMn81WnGrBGcRx99FN/4xjewYMECAMDu3buxZcsW/OAHP4gr++yzz6KnpwePP/44TCYTsrKy8OSTT+KnP/0p+vtTWOrHwcHBwcHBMb2YWw6c2SE4ra2taGxsxK5du1THd+3ahX374neGPXDgAG6//XZYLBb52KpVq5Cbm4v9+/dPe30nBvfgcHBwcHDc6JhbDGdWlol3dpLNkIqL1e8NKikpQUdH/L4XnZ2dWLx4cdzxROUBIBgMqnJ0RkfTvMSQhd7rATg4ODg4OG4ksK+FmQOYFQ8OffeTwaC+faLXI5jN5riyycoDwOOPPw632y3/lZWVJSx71Uhly28ODg4ODg6OGcOsEJzS0lIAiieHoqurCyUlJbrltWWTlQeAvXv3YmRkRP5ra5vO/VZ4iIqDg4ODg2MuYVYITkFBAZYvX44XX1S/xO2VV17Bjh074spv374dL7/8MiIRZaO0CxcuoLe3F7fccovuPaxWK1wul+qPg4ODg4ODY5pQsnq2a6DCrK2i2rNnD5544glcukR2fnz++eexb98+3H///XFld+7cifz8fDzyyCOIRqMYGRnBgw8+iPvuuw8ej2emq66DuZVYxcHBwcHBMeOYY/vgzNq7qO69916Mjo7ijjvugM/nQ2lpKV544QXU1taivb0d69atw5NPPondu3fDZDLhpZdewgMPPICysjIYDAbs3r0b3/nOdya+0Uxgjm1PzcHBwcHBMeMwzK3XWwqieGPsUjc6Ogq3242RkZH0h6v+8A3g3X9P7zU5ODg4ODiuJVRsAu77Q9ovO1X9zV0PHHMTrtLZrgHHtY6cmtmuAQfHjQWd1c6ziblVm2sVYnS2a3D9YcKw39yK9XLMQdR8YLZrwHGtwFk02zW4PpCRM9s1UIETnHSgJfFLQucUTBmzXQPAYE6tXHCCjRlzqq++LhzTA8+82a4BQcFC5XN25axVg4PBXPWqzdT4MNtn5j4srDO4gtjOCQ6HqxSo3z79g13rBaneOr33SwVla1MrlznB6rjZEBQzgao50EfXEuwJxklGtnr8z/RCAFvWzN5vNlC4dPLnVKy/unuy5NmlvwdaHFJ5AaQYU3+v3Jx6nSZDjmKRxL8VLEk8nq8GxcvTd60JPTRzy7POCU464O2ZuIxWGOTUkLeQZ2RP8maTGEBG5d1dsHuArIpJ3gtA3vzEvzmLAUfB5K638K7Ev1FLI6tCXyEt/hiw7F7yOZVmyF8I1N6q/5vNDdhzyWeLQ308GRyFE993xacT/5aZl/zcRR9O/rv7Knbkzsyf+rkstEJOr02opy5VJTRVFC1Tf6cKgo4Tiupt01sPLWZiy/rJ5Kklmvt61n2q3tHJkACKRP1QepP+cSp/8heR/zf9ifLbRKS1ZFXq9dISnFThKgFqb0utrMkGGK3Jy7DjOat8anWaLlRtBQoWJS8zx5aJc4KTDpgsE5dhWXTYL30QMWnGe/tj5H9uHeCpJ58TeTOiIeVz4WKgaAKLqyD+fV9ItsjO6oRu/evjN2uUUbYmSQWkaxnN+sLLaFbqQ5WqqwQwZ5LPWkGTvwAoW6dzHSuQW0sIGqC+14QbVSVoj+WfnrAIAGDBnckvTxVRIoVUvz35+Sy048KtUYhTJTxakqa3NNRkBT7wV4nd4xO1Q25tanVxaXInSlaS/zWaDUDZOk60lFXPis6fQLCnA3QcA8rcTobJKJPKTeT/sk+qj+dUpX6NdCARKSljCA7rpaEk5dZvkuOUANjcQG6K4S65nRK0V3YV0H9ZfawmRUI8NjDxnKTPULYWsDr0y9B2YXOBRtpTqwMA1H1Qkd9agqqVgZRkJTOW2PGXzYwRKpdMNvJ//h3kf26dVIATnOsPa76UelmDGRgfIp/FWHL3oZ4ScLFKWRpMLAlhFUPeAuWzGEssXDZ/g/yvvlnnxwTamk4asy3+NypQ9IhXZh6w5OPqY5RoLL5bqYe2rlkVRJhQLPsEse4sDmD1fURZl2rISfl6oHiFVF8mNFZ/O/mtYgOtsPLbWL/6Gtp8Ei3ho+5pD9PurDWYihWmp8y2/G/9sqYJLEAWVIDR60cC6t8TCVtAP18rb75iUWfmE6FqyojvK0c+ee7ilcqxnGpivWfmA3W3K96zRFh6j/q7th8SCWcVQZX61Z6rJjVsmIAlklS51Orsjp6ITFxtLpjFqSiLwsVAqWQAUM9j/kLJG6a5v9kOVGxUPmvhKCDzisoA6qXM0SiriTyWiaAloKynNxVypgX7DKyXxpZFrmcwqedV0bLk+Tz5TP4VJXd0LmpDVpl5gDkD2PoXyjH2XrLy1iAjB1j52fjjWsOBjj2bS03kWFASwa5CYutQtSW+zoBCnswZwFCLuiw1LubvVMZY+XogWxrzica0xUHGT7kUTjTZyBhcsEshfnTe0DJUBnAPznUIRz7gLifCioVesiWroERR32VZIU3IeR9SFEHRcsLQqes/r14RhpFx8t/mJr9TocYqV1GMDy3QybngDvXxktXMuQIR4vM1ZSiZMFoV9zIVUoKBCEBXsTr8AxAvwjKN8jJKz0TDXcUryTWol8ZkI9dmBY0tSxFiVVuAm/8/hcxokTc/nixWbyPkTDCoPXAsgXEUAr4e0vYUlRvV16F9TtvAaIFMCgWDWphWbAIKl6g9ZRYHZOVFlbZgJMpWS46yq8hYo1h4F7Dww/rPXLIKWLJb+iKQ/tCSszWSIqH9xip7PWKWW6vUcdHdinI3GNWhKEomWRJjMBH3tj2HXKdqS/zYZ8O1dEzQ9nUWaJSxoFyXoniF2ktlkTwieQsS55+xAtkq3UvPg6MXSq7fQYQ+C23IdiJlv+AORbk5CpR2pMZG2U2ERLOEIruK9INZIqHFK5W5UbhEqW8Fo6BoW7AhIrNdqR9tR5aoCAZlbFg1RKhqi9ogEkWpnW9OneDQMKKrRFHmjoLkoSc6pvIXKeXovKfjwzOP5PDRuWe0kH4ySN9Z8lOyCoBI5hF7X5o+YLTGj2fZYyMC2/5PfB3L12pCcdIYEwzJPZdWRhbQelMSV7BE7d2hRuL8nWSu5tYqz0f7khKZomVkDJntxCih87Z8A3RRspKkVNhzpXEvkvFTvk65B0DGHV1BnJGV+LlmEZzgpAP2XCJYCzUhHnbCUGFAXejla4GqzUTAeurV1q5FEiqZeeSPKk17jnLNFZ+Nt74y86TfRaI4LZlESWdXEgHiYQiCo1BRysUr1BaxYFCsgMUfIZN86ceJZUIVjiiS6xlMzMSSlFLpanKN8WEixAqXABv+F9swykerm7QBK0AdBeSaNB8lq4I8E6uMBCi5LgYTKWMwKt4gdrVWwSJ9y6JyC7DoI+RPBkMCMrKJxZtdCax7QPJWaKy/rDLSdvT6lkxFSbAeNICUcRaqlSBt99w6NUEE1CELgPS3NoyopwzsHmJZhXzke2Q83mNSsBhYeCcZBxUbSPn629VlaKhx9RclZSiFRT/2M3UY0WBSh6KoZ6x4uTLuRZG045KPSc/iImMLIGMjpwb4+DPKNahgD3mlZ8pVt70jH9j4dWDeTiUXpO52NQFd8jHJ0hXj+43WnfVUGa3QJU5WN1GEziJCdimJKFhExpBgIGRSL6x2xz/FH6NY94B6LBjNyhyl9bU6iUepZKVitedUEa8lHXOFi0mZvAXALd9Uzrd7yNzOqlDa2pyheB3ZZfRUIds9Su6eyUY8pQCwSuOpmL9TQxhEUof8RWpynJFN5oObek8ERW7lzVPq5CwkBteS3YScFCwhfZNVpsgiQVCHpQQD6Scarl/6CTKezRnkfOptcBaS36wu4hlkQ/WFS4hspKFzq5sQAFGU7l+uEN95HyL/aT+LIrm/2UbaedtfKcfXflW5h2yUCKTNcmuJwUjHEUDqVLpanTeZW6f0uSDoJzObbKSeRgsZo5n5ivyhBo0gAH0XSLsYTArBsWSqyR4FOy5KVimGscikVJgspL60rynJnGP7BnOCkw5k5hOPil5CJWX7BhP5vXSNpMwyyX9qianIkTSIZKtTULw91L2up9hoGKpkFVFWOdXA4o8SZeEsIsKBWle1tyjhLkCd3GYwKnWo2gpAJAru079SC6+CRaSOH/grWgFyz+WfAtbdT+5ZfTNQuEzteWBJhLOQ1PnWb6qVkNUF3PRl6VkF9cSxe0j9SlerJ2PNLcC87cDNexWSCJB6sO1F3bUAsPyTiS3GoRYSisvIIso6Mq6uO0C8KJS0ustIPall7S4hVs/Kz5J+pwpBRdQE0o43fVkRYlSgljOxc3e54g7OW0AUvi0rnrjl1pHnq9mmDhVWbSX1Wf5p0j8lK0k7eupIWzoLCUFgQetbdhM531VCCISWyGeVK55JbVsKBiJEBckTWLyCEHsAqJNypqq2kt9YglCyUp28fOujwG1/q7j/F95FFD3rfatYT+pIPaAAGVMVDOkpW0fIf9VW0o6lkos/M0+dd0GVQP4ioszpc9EyBUsUCzmnhlj5bC6GHtnR5lVZMslcrVhPnrVqqzosUfdBoOpmklvmLlWIY3YVsO5PlfkiGCVvITNP6HUysslxSuTMGcDmP5ee5QNKeSoX2o8q89lTrybpeiEhU4bSPrc+CkBU5iT1QtZ8QJmPZWuVdihYQrzFtbeSOlqcUtlM4O5/A+btIHXIriLXpWTLaCXfDUbSnzTsYzCRc6kcpkZj9TbSfgYTmV9U7lVvI20Xiyjt4CyQ2kVqv+wKIrty65T55y4jc8fqJOdVbgI2PayWjQYTmffZleT48k8pY8jikMhBhVLPwiUABEJUaPsVLSPjwzOPhKypXACIgcDCaAaiYTI3V36WtFnRcuBDf6+Useeq54JMkJcofVu8kvQL/V0UFfnGnvOBR4i8pQavYCQGJ0vQ5gA4wUkHDEaiwKgrk1p/gkAGf1Y5AEZ5UTe62Qbc9iiZaOyKiLI1RLAYzURQyl6bT6sFoKtYnUyZmUfqYssilmHZWiLMTFYl16dyM2H41OJe9wD5f8eTilCv3KRM+Ir1khUhECbPKreytURhUYZfsYE8iyOfWJh580h9cyr1yR8ld4KBKKuFd5HnrtmmVpS0XWm462M/I/+1HixBIMJv7VeADX+mHHcWKsKheIWShyAIimuVuo5ZIkUFqtmusWIYZHoUi8nigJw4nlsLbP3/SHsXLiWCXCZwkpAoW0eerWgpIZ/uMvLfUUCEyvydwKr7iLeFEgiTldR56SekMKGG4JSsIm1OxwUlg9U3E2Vnc5PxseBOMv4Kl5Df6B+9Xu0tincpGiZEQBtSbT9GyhktSp6Du1QZL7SNad0Ll5Lj83ZIXh+3Yp1XbICKPLpL1f2bXUEUJg0V5VSR9hMMpF5Z5QrBX/V55bycKiXcBRDPKa1Xbo1CRFZ/gVyf9ZBVbiZl8uZJXkCDYqlWrFe8hYCSM0THx+KPKb/d9BXyO5vbk7cAsldy3k7ibbK51CR/1ecBI83fyCIJwuvuVzwQMrmVxpyrRGnDTA/5XH2z5L1jjQqp3lVbSD9Qwu8qIWFLWgdHAeDIY5Lx2bEmfc6ukPLxBKWuEIhSrr1V8hAISm6U1UnuufarJEE8bx65X+WW+GvT+9H5RcPk9beTY/N2KMZE0XIyZtylyvymc9vmJuMjI5uM++WfIsfL1pKx4S6TxogAmexbncDmhwmxKVwqeYcLidz01EmybqvSrpZMhsBIntcVn1Xalo4fUSSyKbeWhMUchUSmyknsotLeNrd0Twd5FhqGrNxM9I2nnvGcCspcMphIOyz9uDpB2OKQCD0TMitcSgwCujpKtcpVUJ6vdI3yed6HSJ9RIrb4Y6SvChkjeY6AE5x0wJwhWQ5FZMBUbSWDjyqIJbuJknQVkwFOJxhABlnlZiCfxr4FMmipMMyqIIrIXUZCNnR/GJMV2PCgOmYeiygMmnVPVm1VrA95IoNMHgpHvjJBNn6d3J+yc+pKdZcqTF+QBr89l6xWchSSycR6R0rXEC+EIBFAOaQkKZylH5eEr0CUU/58cl+DkfxRAUfJSdkaIrC1xIZN9KxYTwTZ4o8qHpBVnyfhuKJlktUuWXw1H5BcsFnAys+pXcYAsX6cBcreNIs/Svo6bwFpI/ZZnUUk5CNKSkswApm5Sn/l1igCjj7XwjsVdzyguOwBJVfFnCEpbkEhzMUai4r1FjgLiYCj1pfVQYRWRjb5LofrGGVV8wFCXIwmhQxkV5JnzypXYvFiTPFaAIRI1t5CrnfzXpLnYLIRBexmcknK1pF7O5jVVzR0RVe+sWED6qWh3hI2/FV7KyEM5gzSdjY3qZPJJhkWgtqSLlqubNGwYBchXwWLSbn67VICspmMA0smIbUZ2aRNS1aR3x3U7S8kzvOSLWrqEalTvCfmDOKNsbmJwjdlSPlpmYQ4l0vtU7aWjHXPPLX3tmgZuY7BRJSUbAwJyn9nISENBhOpa8lKyZP7QXWdWZJiskghixoiTxbepSaDnjrS9tTTxvYFJRYUcvKsQDZYrL2VeJmoEixZKZFTSW5UbyOfy9cBudUkl0O7YEG1KlJQ5kfZTUQ+ZGQr52Tmkd9pyIUakc4iIovtOaR9ChYRAkTbUDASGVu1VWpjIzH6ChaqCWnhEtIP5evJNdxlpJ9YQ4wmCFNZW7ZGkXWCgfS5s5Dcv3AJuUemh7Rp/nzSLgWLSRNRQpi/UDHw1t8vJVwblWuWr1P6lCYDG4xqWUJB67XuT8n/BZL8MRjJXLC6FO8qoPYIUplLPbKA4j11FJBxM28SKzxnCJzgpAOFS4hgKr2JkATBQAZabi2Z6CWrJAEgTW7dvW9Yawzq5NzKzfHLOelALFikKOaxQTJRtELYZGE8PwJh8Vll8bkpnjoyWK0OMtjZEBYgkaXNSnIze3z+TvKZdfPn1SsemowsYOHd5DgV3sUrSbtYnfGLtWhOwO2PK9YPILniDerVCGzbUOsJIIqkYBG5viAQobTko6SPipcrz06fp+6DShjA6iIKv3qbIvSdRUQorPwsIZv3/IIcdxWTP1sWo1yZBxIEQnINJnKcejtcJYoAcZepXdD0uTx1Sj/o7ushKd1tf0lItMGoFlIAaav8BUS4sqSWni8YlHNoHs7ar0jHtxIln1UhhTg1id7UI2R1ALGYkke0+gvK9dnEXzaHomIDs68GMw6pMC1aRggKXaVYs40oTOohyvQQRVV9MyH2VVuIkqFeGoAQLepRXfEZQoaLlpJnW/kZ0qc51eSeJhsxUtZ+meQd0V1ZM/PJ70XLSP9WbVUreEpuym4i8+emrxBFtfoLZGxRIly5Ceg9R8ZOyUoSLqjcyKxmE8hng0nZEkEwSMRQah/22QSDkqeVv1AKGW9TfqPjqWoruZY2kRYg5G3x3cqYBMh4LVkl9ZtIlF/+QiKT5t8hreDaoFwbIAYCQPrAVUz6yeoi9f7AXxLC/JF/l4i6Qa0Ml3+a3Kt8AyGjdEzUbEu+f5FgUIcj6dyr2kqMB4Ccn8XmF0rtyBozBVIOEQBs+QYhe6KoGE6Fi6V8SA855iwk5+cv1MxZZgzLdZGMRlsWmZtFyxUvKwTyjM5CiXBJRJUafADRFVSeu0tJjhXVDZSoV99MZMnSezRL50W1g5fm8GSVEYOmYiO5dvEKKUVBUGSMPVtKahZJ39pcpE1qbiFjVvbWSZAjFXMLnOCkDYxHQ2DchfkLiICzucjAEIxEuLGDwWQhg4l6TIpXqpNCPXXKRBdF4j2gKF5OBFfhUnJdq0u9XbYgTQA2tGXLUlZ4aZfbssSBThY2P0cwEBcr9RTRe9FVVazlUL2NED2aKKf1kBjNhLxRywJglnJK3g6jmfxOLUF7LlFQVLDQiUYVt3b/E3mfFMlahEDam90pueYD0n02kPre/ncMCVuutIE5g5TLm6cmka5SIhSNZqIIq7Yov1sygUiIEDxqeRUsJsmnLIHMq1fakK1bVgUJR+UvVBPjqs3E+hcMRBnX3QZ51RYlTWyiLEtk2cTWfJpjISj3o96V8vXkecrXSsqOlZYS7B6ljQuXMHkSTCx+/oeUFT1soiTNy9Duz8QSi+IVylyg7VeiIfC5NeqwFAuaQ+IsIG1jtJI+psqL5q6UryPEY/FHFYJVvFJpN0FQ+sWWRfqEgt7b6iKEZv39RIlk5JAxYc8h59tzyVjJyCZty861PCkHpnQN471hvH3yayeYPhAMyorEjGzSDwYDsfidxQqZMRjItVzFkJUq9Y4ULFa2JGD7pmytkpdjspIxs/KzpG7sPKZ9QnOZjJoxJwjSYod8cm9Thlp+iSLxwtD57MhTk3C9lzfKYWJBaZ9l9xA5WbyCeK0oqWZX/bDzjZ1j7JYEC+6CKjQDqOtDZWLJSn0DkIL2p0zWPkDCUFQOx2JkflKvHkXhUmJoQSQ6o3SVOmxqsil5NNRzQr1bgqAk3MteUEYerv2y8nnzn5PytixlpRQ7vYtXKvXKq1cWiQgCmdtURhQvJx44CnnrjbkBTnDSDdb9v/F/qX+zOMhEtueQQZNdRYRu1RbiznQVkwGcmaveH4fda8FTrx5EBYuI8qzeSnI+2HBFThVkCz1Rdju7fwsArPy88vnuH5H/rDAoW0usRmrxydaFRJRY4iYIRHhRoeBkFCsNy1RuVG/+R+vPJrblVEmeIR0FW3Uz+U9zgxJt6kXzc/QgCOplxHnz45fFO/IkgiSqFYEIpQ2tLhKfzptHBIhBiu9TRVG8ghzLriTH8xnLkQVLKC2ZZLyw48FTK4VVpDaiybuuEtLG1AVdfTMZc3S8iCIpx1qeKz+jvnf1NkLwAHIu7bv8BfqbwlVvVVaX0AR6QL20Pqca2PmP8ecCZOzTBHQ6RtkQ3II71KS5ZCVUfWuxE2WeaMM66nEqXEL6UAAhC7KlKyoePL09huRXZwgKeStdRepECSnNmai+WVmlxK6mW3YvmTPFK8i5envPsF4uqtColQ8k3vcnu0pKYme8e+5S8nza975Vb2PCMH8af63cWiVxVh6D0v1dJcCaL5LnU6061MwzNueD/Y2SIoOBWS2oIRLaLRhYsOTenEGejfVG0fpqVwuyixDYd5PRkBSgeMucRYSgCUL8tKRjk3oT8+apr0ceKL7eNG/HWUC8SvTZxRgxtOjcESSvjSOPeFErNyl5gex9HNJKWer9zGYMEkAZw1ReJNqXpnS1+jc9/eAsVFISLA5Ft7FlMz1Sn0vXYsP2cwCc4KQLrGLPzCNCRmb+0oCo3KxWjsUr1BaPswDY8QQZMKyHgD3HWRC/6ZPFIS3HrCf1MGeQMjTvw2RT56mw78mJCw3dzJRjBDF1w+slGgLxYREttBMof4ESz2c34aOKNzNPfa+CRaTeRlNqSxG1wpNCLzat9xu7F4rcF1J9tDuo0v7JyFYLQHcJEUBUyVZuIp9zawjxM5g0VqB0fTbMR/OICpcoz0OFIkDOl71ZFqIIqUfEnEHK0nCKICjeETbUwcJTp1idgqBe+aN9bmq5UUXPjtnMXPU5Ro2ypVAdl8gGS1byFqjDQfYcqEimOSN+N2MWbM4QVYLOIqjGbqK9hAC1F4wlbYCSxErHPvUEaWFzKddxl+nvli2CPAtdIABIoVUqoqX6apcKL9jF5O9JWP1FTT2lPs9j8gL1SJazkHiVyteR8U+9e0azcg9zJrDuq0p57Y6/qmXcgjr0NRHYfs6uVJMa1uDJrlLkw0TvRkqk4K2O+HAt9Vio8pQ0pDfZRna0r9gVTjUfUBY8mKyKR4ouKqDJ5tDcs3pb/GpFgIzhTI+ytB5QcrNUu3Ub1eE7vV3qKWhyOvVk0flnMCmeWItdWcSi3SA0p0oJbeltkjmL4AQnXfDUScvFF0grL+bHl2E9GED8LsYbvkaIkSAogp9aIKxbWJvgp3WVVmxQBqbNJe1qytx7jUYAsqCESzuxWXe69v4UCV86pyMUlnxMUQZs/dlkRXOm8purmFip1TcnFzIURqs6TJKZR5Su9nUFLGisPu79RkmEKKugBYP+zs5UyZavU4RH5SZSds2XdNpzguejgs1RSMihvCReIyidhcr+JyySuZFZQjBRPbS7+Gbm6pRJ4FnRwmghhHf9A+rlyO4SfSWpJVvVH4gvQ8E+BuW8cq5Wgmd05APaPKpE446dK4leQElJo3aXWRYZWWQMUsJYtFwZw/TeuTVK/1du0m9fbT+UrFTyeeS8Mx2SRetI28ZVTOrAGlSsN6FgEQBBswUEC2GCl+IKiY0Vq1NpV62XueYDRB4KUr5VoldvGC2J35VHjQG9+QEoIVUa8tGb11pQYsPmKAqCessKCu1CCiCxEaAFNULZ0JanlsgpNtzPGs/aDQnZPqPklZKU3BppldjNShmLJIuN5vgcz5xqhezPsZ2ME4wMjinBYCCTLU7IpdjpesXoMS05YlG6BhjtSnJdzYVZha2nlIDkHpnKjUBoDGg9oj6e9P1BGkGmndxsrBwgvzkLlOTlRGCTillorXqbK3431kTXy/To90XxCqDzhPoYG9qa6tzWuuYTEhDNDeh4oxDFeNKsp0D0BK7u7abwQJOx2FnQ8bbobjK2gCQvotWplzZXI+k7z6B46wLD+uOiaCnQ/Kby3akZT4kUcyJCR8ejwUTaKNkrN6iyEJm8CJbYUMt+0nuOJAihJINWPmiNKSDxO+5YQgXEe7dSHV9aw8lgILIh6CUe60Qv1WS9PomgR/SAeLlSMcG4dpcS+Ve2Nn739lTA5m1OBJoyoOe5TDS3teF5ajDT8oVL1SHNREbsNQZOcOYSRADFGu9BKlqTLpkFFDe6nqtcD4mWvU5UB4tdnZuTDCaL/jVYaF3dE72riKJi/cRl5HokUSpyPZJ4ApJ5ctIJrXDJzAP8vfqWspwkDBLDZ1+E6SpJ8W3eCZR1si3ztV4uilQs3YlAhW5pAsWV9G2mEtgQjLZ4+XrlHja3kseVrD6sMqa5LFNBVkXq7xPLyFav0klE5lNCgjbTC4OkA468+PFKQxtGJlw3EcFKBmr0TfQS4XQgoXdaAiXMn/v95N4XByg5Z+ncBZhNQwASz1dqTAmCTk7RtQ8eoko3JppsiawNilRJgxZU0VGLK9mLFCeLigTJf9pJk+jtvsUr4nfenIjwTMe7TSbKEwIUwjQVWXO18inRi/jomNKuQgOU/WYAQgi0uySz37UvsKQwmBO4+ZP00eKPJv5trkNl5QrxS15Z5M2L96hdjRteG26h0ObRAMRTYZqslyYBTLbkipeSJ7rrd6J3d6UKunePXlvR0Ic54+p2vtVuqzBZaD0mybo1Ub9pMVlyQ/Pi4tppEsJEzwulPcbuQTUZ0NVg1yg4wUk3HPnJmfB0vZRMz3WsB6rA4whHEqRqlaeab8Ghj6sV2Lf8TfLfE5FvT61+CDSZda0NKc4oZijOn1WuWRU0SUzGWEmWGzZVsMo2pypJrgwUj6+njuRZpJoPMteQ0OunA/YZLZlAVmXaqzMpaPcXSxXT+aJL+g6xaxQ8RDUdcBWT+PBcBF2xlMhbwJE65E3qZgCpGHRTtdISIZ1ewLmKZBvJTRSWAJKHjbT5UDONVDyWV4tU8lxmEgnztiaAyTp570u6oTI651ay7rWKWfPgnD9/Hjt27EBFRQUqKirw2GOPQUwSgwyHwzhw4AD+5E/+BJmZmXj99ddnrrJTAbvkOKU8COi7hdP98rKriXlzQCV4tEl+V5UjMUehtxpwtpGMlEwWehvJTQaTyQFLN+YCAb2avLTZXHEzxzak45gezArB6e/vx7Zt23D77bejpaUFhw8fxi9+8Qt873vfS3jOY489hkcffRSVlZUYGxubwdqmAXrxdT3ouYVnwgrjiAf7SoJUMZPKbrqSQ7Vwp0jOZxJXS0quFyRbSJDqIoPZRtJl5NN53xRXCSVKzp1uTEX+TAeu8bSDWZEUP/rRj5CXl4evf/3rEAQBJSUl+Na3voV/+Id/QDgc1j3nb/7mb/DGG2/gL//yL2e4thw3JFT7wcxBcE9c6pjrfTkduFa8ibPpAUsF6Q77poq58l6nRAtHrhHMCsE5cOAAdu3apTp2xx13oK+vDydOnEhwFsd1haqrXKXBwZEq5oo1zMHBMaOYlSTjzs5OFBerV/1YrVbk5uaio6MjLfcIBoMIBoPy99HR0bRclyNNSLY0lyMeMxWSut5xjbvcOTg4UkfatUxfXx9WrEi8edzXvvY1mM1mGHTi6EIak84ef/xxPProo2m7HgcHQRo345oMeEgqPbjGXe4cHBypI+0EJy8vD+3t7UnLHDhwAJ2dnapjwWAQQ0NDKClJT1Lj3r178fDDD8vfR0dHUVaW4I28HBwcHBwcHNcVZiUHZ/v27XjxxRdVx1577TXk5ORg5cr07KtgtVrhcrlUfxwcVw29lyRycHBwcMw5zArB+exnP4uuri788Ic/BAB0dXVhz549eOihh2A2X6M7aHLcGOAbJHJwcHBcE5gVgpOdnY1XX30Vzz33HIqLi7F+/Xp8/OMfx549e+Qyu3fvxu7du2ejehwcHBwcHBzXOAQx2fbB1xFGRkaQlZWFtrY2Hq7i4ODg4OC4RkBzaIeHh+F2u1M+74ZZq+v1kndD8URjDg4ODg6Oaw9er3dSBOeG8eDEYjF0dnbC6XSmdTk6oLBL7h2aXvB2nhnwdp4Z8HaeGfB2njlMV1uLogiv14vi4mLdLWYS4Ybx4BgMBpSWlk7rPfhqrZkBb+eZAW/nmQFv55kBb+eZw3S09WQ8NxT8rXUcHBwcHBwc1x04weHg4ODg4OC47sAJThpgtVrxzW9+E1ardbarcl2Dt/PMgLfzzIC388yAt/PMYa619Q2TZMzBwcHBwcFx44B7cDg4ODg4ODiuO3CCw8HBwcHBwXHdgRMcDg4ODg4OjusOnOBwcHBwcHBwXHfgBIeDg4ODg4PjugMnOBwcHBwcHBzXHTjB4eDg4ODg4LjuwAkOBwcHBwcHx3UHTnA4ODg4ODg4rjvcMG8Tj8Vi6OzshNPphCAIs10dDg4ODg4OjhQgiiK8Xi+Ki4thMKTul7lhCE5nZyfKyspmuxocHBwcHBwcU0BbWxtKS0tTLn/DEByn0wmANJDL5Zrl2nBwcHBwcHCkgtHRUZSVlcl6PFXcMASHhqVcLhcnOBwcHBwcHNcYJptewpOMOTgmgd6xXrSOts52NTg4OKaAEz0nZrsKHDMITnA4OCaBaCyKcCw829Xg4OBIgne739U9HogEZrgmHLOJaSM4Tz31FBYvXozS0lKsWbMGBw8eTFi2o6MD99xzDyorK1FSUoKHHnoIwWBQVcblcqG4uBilpaXy3549e6ar+hwcHBwc1yiC0SD6x/tnuxocs4xpITjPPPMM9u7di+eeew7t7e3Ys2cPdu7ciaampriyoVAIt912G0pLS9HQ0ICzZ8/i+PHjeOihh+QyIyMj8Pl8aG5uRnt7u/z3xBNPTEf1OSaAKIqzXYUbHuFomHuSpgGnek/FHRuPjM98RTiuGv6wP+nvMTGW0nW4vLt2MS0E59FHH8U3vvENLFiwAACwe/dubNmyBT/4wQ/iyj777LPo6enB448/DpPJhKysLDz55JP46U9/iv5+wsDb29uRl5cHq9U6HdXlmCQOdR5Sfb88dDnt9whFQ2m/ZrqQDoF3tdZlm68N/WOTu8aV0StXdc8bAb6wL+7Y8Z7jAIChwNBMV4dDg5HgSErlBAgTztO3O99O6Vpaecdx7SDtBKe1tRWNjY3YtWuX6viuXbuwb9++uPIHDhzA7bffDovFIh9btWoVcnNzsX//fgCE4Exm7fuNgkgsMttVAAD0jPWk/ZrHuo+l/ZrpQLo2ibwweGFK510eukzyCKbAsTq8HVO6Z7oxEhxBMBqcuOAcw/mB87NdhTmDppF4b/xM4Ez/mbhjesaQCBExpOahuRroJS33jfXNiIE2MD4w7ffQQ9NwfN+fHTg7CzWZGGknOJ2dnQCA4uJi1fGSkhJ0dMQL2M7Ozriy2vLt7e2wWq144IEHMH/+fCxcuBB79+7F2NhYwnoEg0GMjo6q/uYyxsKJnyURjnYdnVT5QCSAcHR2wxpt3rZZvf9U0eNPP4mbCvxhP6JiFCLEa3ZH7g5fx5TG+2yjy9815XNbRlomfQ71VnT6Oid13pm+eBKQbrB1msgbOd2G2NHuBHIwBSOAeuemCr2k5U5/JwLR6U9mvjh4cdrvoYdOf/x4nKvezbQTHLPZTC6s2U45kTA2m826Wy+z5YPBILxeLz796U/j7NmzePnll3HkyBF88YtfTFiPxx9/HG63W/6b6V2MWXbd6etEt787afkTvdO7fLHb3402bxs6fFOz4gcDg1M6T0to2kZTIzjnBs/pHp+s52M8Mp6WkNJU8zBO9p6cVPmJxglABFtMjEHAzBKcUDSUlCCnSp7Fqbif5gAahhtU30/2nkx5bLX72uOOTXTu2X5iFU+WIHhD3kmVT4RzA/pzUAvtnNSuYHq7K7VQUCqYzLOlMs6CkcSeRLZ/xsJjqufU82IkOjcRJuMFC0VDsmw41n0s5XN7x3pxcfDiVeWRTUcKwkwh7QSHhpKoJ4eiq6sLJSUluuW1ZbXl77//fpw5cwbr16+H0WhEaWkpvvvd7+LZZ5+F36+fSLZ3716MjIzIf21tM+s5OD9I3Nnv9b2HcCw8oZA62n0U7/e/rzqWzoHVNNwEAcKUiVQqwk7PozTVvA+WUPX4e+SEwcHxwUkt9TzddxoRMb0WZCLBKYoiRkNqT+F4WBEsI8ERue60XbTWuVaJaiEIgmwxp9uD837/+0lJytHuo/hD8x8S/p5oaS6L3rFeQISKnMXEmO65emRY277ThVSs4/HwOA53Hp7yPY50HQGACcN1M0EI9RJy6RzUS7zWgvWEBKPBCRN8ganlsr3X917KZS8PX538PNihrPyNilGMBEcwHhlHIBKIM0TYeqVieHT6OiflmQtGg3IqQPdYN15ueTml8y4NXYI/7L8qI69nrGfOpENMFmknOAUFBVi+fDlefPFF1fFXXnkFO3bsiCu/fft2vPzyy4hElAa8cOECent7ccstt8jHYjF1PDUajQJILOStVqu8a/Fs7l6cqkAOR8MYDg6rNpGjA5odnH1jfXHnpmppCcLEiXfA1EiJP+xHKBaCP+yX3fFaIcDWfTIx6oHAgIrU0Lj3ZL1RvWO9kypPIYrqeL6ewjnZexIxMSb3RdNIU5yQ7/Z3wxf2YSw8hnZvu1xuMhAgIIYYCVGlIEiTKSete/503+kJScrVusUvDV2KC6+91PySLrHSC2eyioQNjbBjjSXabaNtusR7onbXy2/Qmz+JyEdKISkR8IV8ON6dPEwyFeWU7Bw94qjnafSGvPCGvLqJ11poPSGne09PeA6bvNs80jxh+cniasPKWvLaP96Pbn83jvXE5wdSOd/mbUsasjvcQa7JPm+y8A4lv4nm+9n+syryEY1F5c+UQD176dmUSLJWXlM90D/ej19e/GVc+XR5CqcT07KKas+ePXjiiSdw6dIlAMDzzz+Pffv24f77748ru3PnTuTn5+ORRx5BNBrFyMgIHnzwQdx3333weDwAgO9+97vYvn277Onp6urCnj178KlPfQp2u306HmHSoAJFL8v/1SuvIhQL6YYfWkdb5ckRiARkxcfiSOcRWSBfHFIrmGgsOqnwkQgRr7W+pjrmD/tVLkw9xaJnZVKh2OnrlD+Ho2FZIP6x5Y+qxFa27jRuLopiSmGZuOcQRVwZSZGISXP70tClSd8HIJYsTTAVIGA0OBpHKllPDQB0+bpwuu80BgIDssCjQuZqwpF0dYgoEpLgC/nwUvNLceXoPZMpp0Mdh1SKfCLCmMoOzo3DjROW0Srf9wfeT1AyOS4MXsDA+ADOD5xXhQzo8vlgNIiLQxcRisWT6d82/BZAanlsbH21ikJP6bR52+SQlCiKuu1GldJP3vsJAKBhqCGu/em9puLBSbbyJ9U8uKHAEIaDw5O+txaRWESXVLG5Qh2+DgQiAZX8ZBca6OUV6cna4cAworEoevw905LITvsi0cKKcDSMqEhIhp5cO9hJvEJHOo/Ix073KWRQm8hODQ5R1Cc4Q8EhVQiQHUNUZxxoPZASSabeY0oM27xtCEVDiMaicR6cocAQTvedntOrXYFpIjj33nsvHnnkEdxxxx0oLi7GY489hhdeeAG1tbXyiqjnnnsOAGAymfDSSy/h3LlzKCsrw6JFi7BkyRJ8//vfl6/3ta99DTfddBO2bNmC4uJirF69GqtWrcKPf/zj6aj+pBGMBmW2r5flf7L3JK6MXMGV0StxFrAv7JMHiVbxiaKI/vF+xBCTlfm5gXN4t/td2ZJ9p+udCetHJ5MAASLEOMu9YbgB+5r3Jd0Xgj1HgIBj3cdkpc5aI+z1u/3duOJVkxCtsI6JMTQNNyV1gbKTsz9AFPehzkMTEgVqwej1yWRXxNB6H+85jrMDZ1WkkhVWKgUvSuQxPI5oLKqr7LSx8dGg2uOn7ROtFyEcC+sqLDZfIJFwC0QDcihVC5oEPBIcUQm8iaAn1KlyGwoMIRKLQISoUk6pkCIg3ssgiiLe7XkXAwG1t0UEmTfvdL2T0INFx9trba/p/k4RjAbl+xpgULVlotwSto8HAgM40HpA/k6JMR2bETGCcCyMTn+niqQNjA/IBgEde76QTybpraOtKXvTxsJjspfgxaYXdcu83fk2fCGFDMt1EUk7/77x9wm90ayC1kM0FkXveLz3VHs9X9inGj+haAiRWATRWBQjIR0yo0O+/tD8B4RjYXT5u2S5SvvsbP9ZlfwRIcYZjHJdQr44OckSDBr+6h5Tj3c6FkVRVIWbk3kM2SRp2k9to21TSlGIiJG4eTIeGZ8USWbn49Huo/K57HG6aorWvW+sb05uQzFtOxl/5StfwaVLl9DZ2YmjR49i8+bNAP5/9r47To7iTPvpnpxnc5rNSVrlnBNJEpLABBFsjI2NjQ22AZ9PPu5sn33Ahw3nw9w5nCNgGxsD5ixykkjK0iqt8uacw4SdPNPfHzVV0z1hg7QrLaIffvtDO1vTXV1d9b7PG+otknPT1taGrVu3srY2mw3bt29HR0cH2tra8OSTT0pq3mi1WjzyyCOoq6tDR0cH2tvb8dRTT0Gn001W98eF6q5qyQRK5G597sxzONx9GK3OVolC5Dk+aW2HsBDGc6efAwAc6j6EQDiAQe8gfCFfUmGTiFGzicclVnaCIOBU/6kxuRzPDpyFAAHekJdNcnrP/Z370WBvwLHeYwljxHafPS5MJJCEDGZJH+05ysbjQOcBdA13YWfrThbSODcQ9cKMlDjn8rvwfuv7+H3N7xN6MUbL+o/d/knHbWer1Bo63nscISEET4j0pdMl3WlD+/hB6wf4oO0DidBzB9z4oPUDyfViyRglzoIgIBQOMSFLXdb1Q/VsLiTbmbS/a3QSHPuslDif7DuJD1o/AEBywvwhP073n2b5YslCMU6/kyXJPnn4SdLW0QR/yI8OV4eEWCXz4MUqztjSAT3uHjy892EJKWx3taPX3cuIQBjhONIISK3/l869lDT0JAgCwkIYbc428BwveT80tyvWA0rR6mhl163pq0FYCGPIOwRfyCfxLjx3+jl0u7slXkEBQlShCCQ/6mjvUVb7aF/nPvR64sPViTAcGGbrTtz/fZ37UDdYxwhE3VAdDnUdQk1vDdpcbeh2d0OAgH2d+9Dv6Yc/5EevuxeCIODd5ncl12f9pmRCtG040Y6iROuve7hbIkd9IR/eaXpHQqwp4W93tROCGxNSpWFwsWFA5a3dZ2ekoXu4G3s79saFIqkMPNp7VPJOkxGE2Jy50/2nExpWXS4yxxOFrxLJ5GZnc5yXKDYd43hffD7SnvY9cd5kcf+The3ERUMbHY0IhAKSOUo97cnCb7VDtVOmDIUY8llUE4TD3YfZoo1VckBEMXPEAxASQkwAHOs5hv899r8J3dGtzlYWU+XA4WCndDE3OhrR4iTWosvvgt1nxyv1r7C/08l4rPcYuW7EGqP3GA4MSyYstVjECzN28YmF6om+EzjcfZix+GA4yBYBVU6CIMAX8qHZ0QxP0IN+b78kue5A5wGmtO0+O471HkOjvRGCIMAf9uO/qv8rYX6G2EMhVpD1Q/U42nMU/rAfnqAHfZ4+9gzBcJCRgCO9R+AP+eEOuOMs4aM9R+ENepnwFAsWDhzq7VFLhlq9fz39V8k1GuwNqB2qxeGew8Sr1VON/Z37JdaVw+9Ar7sXezr24GDXwbh5IwgCE9iDvkGcGzyHVxteJTU+hDA4jkOPuwfHe4/D5XfhcM9h9Hv6mQeB3iscDiMshCWKdX/n/jgCIR4ncT/faX4HB7sOYn/XfhzoOoB+bz+znsW7g8R5AtXd1UxoUmXYMNSAQDiAent9Qnd7TW8NexeBcADOgJRwD3hJgrl4zjr8Dsl8bbY3S0KRezv2YtAXr0xP9Z9iz1s/VI99nftQ3V2dkCSGEWZjGuvx6XH34FdHf8XWjt1nZ88mVszvNL3D7tfh6kCPu4etw14PIQ1HesmY07Gn29LDQhg1fTWStS32CLgDbjZ3EhlL1NtIlTe9b5+nD13uLpbsDBAyYveTZPjtddvR4mxh7Y/1HEPtYC32dOxJ6hmjXq02Vxv8IT8GvYN4v/V9CIIgIUKJPKiNjkYJMQkLYTj9ThztPcr6Twn/wa6DECCw+Ux3tHlDXrZeab/F+XN07hzqPoSQEALHcezdBsNByZpocjRFn6tjH3rcPRjyDrH3KggCnH4n6ofqcWbgDHsH1MB1+Bxxnps3G6W14MSEY8A7gDODUbnWaG9kBCw2RGX32SWeH/pcdIyHA8MJiX3tUG3CUPT+zv3MqHuv+T0c6Dog8WDR93p24Cx7hhZHS5wxf771vSYLMsGZIHiCHtT01UgWsdjlPOgbBAeOWWlUQHzc/nGcq7XT1Ykh7xCZPJE5fXrgNPq8fcxad/qd6HR1os3Zxtyh4lwdAHjmxDMAiDB3BVx48RwJC1KB5vK7JKEWAQLODJxhOTqtjlYmUPwhP1tEJ/pO4JW6V9DsaMa7ze8yQeAJetDmbJO4Vk8PnMbPj/ycCYWdLTsl+QEn+0+ysdjTsYdZrfs696HZ0UwsNQhxoYEB7wBb8Lvad2HQO4hQOIRT/afwfuv7ONZzDBw4cBwHf9iP/Z37YffZ8Z+H/pP0NeDBgHcAncOd7LncATc8QQ+GfEPo8/ZJyICYeHYOdyIUDjHL8EDnAbS72uO8EmJv1aB3MOHuLyHyny/kkxAngCjnD1o/kIQOzg6cRSAcYAKPCvOjvUfROdyJYDjIQnfDgWE23/Z1EAV+sOsg/n7u7+hwdUg8dg32hmheVORZKbnu9/QzkslzPHrcPZKkUkowaX2MThfpR1gIs/BUq6MVz599Hn+v/TterX8VHMfhZP9JppCP9R2D3W9nuTG/r/l93Fid6DuB91reY+REbHXSsaPzv9HeKAkNHe05Gpdv8391/4fq7mq4Ai4IgoAh7xB+V/M7AES5UiVME0MD4QDqhurwUdtHONF3Ai6/C0d7jyIoBFnouKavhhGV4eBwwnpFHDiJFxeIhJojU4yun1A4hDZnG+mj34V2ZzsGfYM41X+Kff9g10Ec7TnKnv9k30kc64sq6ZdrX2ZKh+ax/Oeh/0QgFGDPRUGNJYCQ+n5vvyTc5vA74Aw42fygBkCzo5l5Cek63V63Hbvbd6Pd1c7m2YetH8ZtkqDtD3cfRpuzTaowObCxBICP2j4CQIjjucFzODt4Fr89/lsy1oFhtDpbUdNXI6lQ7A160eHqgDvgRnVPNYJCEK3OVvzLx/8Cp98JDhxanC1od7WPuJ3dF/Lh3OA57GrfxeZso70RJ/tP4u2mt3G05yh2d+yGM+DEib4TcPqd8AQ9o+6Uqh+qZ/OjdrCWjemRniM40HUA7iAh3IO+QYCLrslT/afQ5+lDKBxCh6sDbzS8gT0dexgp+cOJP6CmryaOjHmCHpwZOIO6wfjdmvQdeoIeifdGrNPEGylana148dyLqOmtYRsxxpKndzEhE5wJxIHOAxIL4K9n/gpfyMfyZThwjFC83/I+HH4HHH4H9nXuAwcOvqAPAgS83fQ2Xm98HXVDdUyJ97h7cKTnCCNNNX01LB+FLsxTA6dwvPc4zgycQZ+nD6cHTuM3x3+DsEB23Tj8DgggrsbqbhJWY14KcKjurmbfA4AXa19Em7MNZwfOosXRgh3NO9DubMcr9a+g0dEYJ7i9IS/eanoLroALPe4eVHdXY8g7JHHj1vTVsPCG0+9Et7ubhXf+cOIPqB+qR9dwF+qG6vB6w+ssqXZPxx680fAGQkIIQSEo2U3zesPrqB2sRUgI4V93/SsOdB7AkG+I5au82/Qus4QoyTvYTZ67fqge//zhPwMgZItaL8OBYdh9doSFMD5s+1CyOwEg8f9nTz0LgAiANlcb6obqWDgsLIRJnkdEaZ0ZOIOT/Sclbmf6bj1BDxMa/1f3f+hwdWDAOwBXwIU9HXsk9VY4jsOx3mNocjRJiOTfzvyNzY13m9/FkZ4j+NOpPzGF7Q664Q0RYd9ob8Q/6v4RDXcJAmp6a9gzChDweuPrjAj3e/vZO2ywN6C6u5p5SYLhIOqG6nCs9xjODZzDoe5D5Noc8XTRXIV+bz/ODJxhtZh63D3oc/cxEuL0O9Ew1IBD3Yfg8rvg8DvQ6eqEIAh4/szzAAhZPjtwFvs690GAwCxOf8iPzuFOhIUwE9J/r/073m99n4Q9BAGugEsitP0hP95oeAOnB07jeO/xuBCE0+9Eh6sDHMfBG/LCHXTjUPchHOw6iH2d+xASQggJIbxc+zI4cCyptaavBj3uHvS6e7G/c79kntJ7cByHs4NnJRY5x3Hs7y2OFrI7Rgjiveb34A662d/7PH3Y27GXPcvOlp2o6avBoHeQzSFKKA52HYyGZCLzzhvyom6oDv3efnzY9qHkmT9o/YBdl4YXxTuGPm7/mJGkBnsD82Z91PYRAuEAzg2eQ7e7Gyf7TiIkhJjnSRAEuINu1PTVoHawlnk6+r39bI7taNmBl869hH/U/YNttODA4fWG19Hj7oE36GVygxpEDp+DkfmwEMZPD/0U3qAXH7d/zAzB3e278U7zOzjccxjeoBf7OvbhnnfvYe+DyusmexPznB3oPCBZ776gDw32Bgz6BhmxdQfcONl/Ehw4/OrYr1h4/q3Gt3B28CzODZ3Dx+0fx737WOzv2o82Zxs+avsI+zr3oc3Zhp9V/wy723ez0DBAyD0HDv/04T+xz7qGu9DmasOx3mM40XeCEH8IONR9iJFasRfxzMAZ1A7Womu4C282ES9Ms6MZx3uPo8nehOO9xxEWwqSQqGjTzJGeI2y9H+09Co7j4Av5EAgH0GhvRKOjEW2uNrzT9I7swbkc4Q664fQ7mSej3l6PZkcz9nXuw6GuQ2yx0Ql/qv8U9nftx7GeY2xR9Xp6MeAdYAl5L5x9Acd6j6HP04enDj/Frk3DEp2uTrze8DoOdR/Cno49OD1wGh+1foRjvcSNfKj7EAa8A3jp3EtocjRhyDvEkoydfid+W/NbtDnb8HHbxxAgYNA3CH/Iz5TJ8d7jCIQC+NPpP6Hf04/aoVr8puY3aHI0scXf6+mVLFxPIFpU7/TAafbcR3qOYG/HXuYhoKTqw7YP8bezf0OjvRF7OvagbqgOnpAHf6/9O7le0AOH30E8FhDwVtNbaHO1seRrgOyGOdR9CJ6gh8XMG+wN6Pf240j3EXQPd+ODtg8keTg0n2B3+268XPsyC4VUd1ejfqgegiCgw9VBPAbN7yEcDktDE5FH3t2+G56gh7l8zwycwdtNb6N2sBaeoAeHew6juqeauPkj49Jkb0LXcBcGvANocbawrdnUGzEcGMYvjv4CLY4W1A7VwhvyslBFr6cXPHicGzyH+qF69Hv74fAR0vpB2wcAiJX9UdtHzJ3vCXrgDXnR7e5Gv6cfjfZGdAx34HDPYTTYGzDgHcCP9v4Iu9ujXrUzA2eYl6bb3Y0h35DEWyLePvvwvofxQesHTJB+3P4xnAEnePB44uAT7F1TRUKV+kvnXkIgHGDzf2fLTtj9dgx4BnC89zjcATfea3kP+7v249H9j+J7u74HX8jHdpCJcyfovHqy+knUD9UjLIRRO1iLId8QnAEnaodq0efpw1tNbyEUDuFE3wmIS/lTof6/x/+XjfWh7kN4p/kdNl9eqX8FHDg2x3jwjLy0u9oZEXir8S0MB4axp2MPs+Tpsx/oPAB/2A+7z44mR5M0vBd5ruv+cR0jsS2OFvzx1B9Z2MYT9KBzuBOdw53wBr043X8aAgT8/OjP8atjv2K//+nUn3Ck54jEg/VkNcmDou+j190Lh5/sBqRelW53N6q7qxEWwiypl8qdvZ178W7zu2h3tUtzhUSe1R53D2qHapnBdaj7EAs5inPa/l77d3S7u3Fu4BzaXe34r0P/BYCQ5X2d+xgpp97cswNnUd1dje3129HkaMKp/lPo9/SzMD/dTXes9xjODZ7D6w2vM+JzuOcw9nfuR1gIM8Iq9mp4gh5w4PD82efxdtPbON1/Gl9+58uMmAuCgJdqX4LdZ2fzps5eh6M9RyXGLM0zYmEtEZ852HUQPe4e5vmhXs1QOIRQOIQ+Tx/ebX4XbzS8gSZHE95sfJN5TWjeWbe7m+XcibeGc+AkocsWRwsLCXqCHrzX/B57jtcaXsPhnsNosjfB4XOgw9WBl2tfxpGeI5IdsXWDddjbuRd/OvUnPH/meRzoOoBeD0ki3tmyE432RuYNBiIV8sMBHOg6MOWqqysvdQcuBwz6BtHr6cXh7sPodncjLIQxI20GmhxNWJC1gG0bPNZ7DEXmInzcRojOS+deYqTj7aa3cVXBVUwRAWBWImXFDh/JN1hjW4P3WsjEPdl/EtmGbHzY9iFzU//6+K/jMtpfrn0ZFSkVkhj34Z7DON57HA1DDXi76W3cXHEzs+qO9ByB0+9Es6MZba429Hn6oObVOD1wGh3DHbBoLLD77MxadPldONR9CJn6TAAktuwNeVFqKcVwYDjq0QCxKjqHO/Fa/WusLbVWPAEP3ArilqXkgQePY73HcLL/JJbnLgfP8YxUUGH4P0f+B+Up5QAI4XzqMNmFp1Fo4Av58Nczf8VdM+8CECWaba425lGjuUlvNr6JjuEOrMhbAZ1ChyM9R7Cvcx8C4QAThif7TyLPRIpQHug6wEJuNX01cAVc+L+6/wNABC8HDs+dfo69m15PLz5s+xAOnwPHeo8xYqZX6pmVdKjrEArNhayWyEdtH2F66nT89cxf0WhvRI+nBw1DDfj3Zf8u2WJt99kZ+fjrmWhOUGwegJisHe89TjyD9gaoeTXCCLP3Ut1TjXwTqQAuFqKBcAD+sB9He46i39OPv539G9pcbSg0FeLMwBnolXpWFO35s4Qw0zAsJRCeoAcHuw6ibqgObc42dj2624YqKGrtUxIz5BvCC2dfwIq8Faxtk6MJxc5ivN/6PlK1qSxURud6t7ubeX5ea3gNepUerc5WNne73d1odbZiwDOAId8Q3ml6By+eexEmtQl1g8SLuqt9F3guag+eHjjNrn924CzbTdNgb4CCV6Dd2Y58Uz6ePfksnH4nzBoz+r394Dke7oAbXcNdaHe1o6a3hoUp0rRpaLQ3Yo1tDTFKImM25BtCu6sddUN1EAQBJdYSAGSNUm9rq7MV1d3VjKz3unthUBmY7KkbqsMHbR+w8Xz65NMASN7HoHcQ1d3VSNGmwO6z4y9n/sIIb+1gLZbmLGVJ5++1vMeSq2sHayWhtqM9R9HiaGHK+fWG11loviqtir37HS070GBvQJo2DU+feBrDgWFYNVY2vvs796PP08fCLYd7DmNB1gL2vg50HcC5wXMoMBWwZ6gdrGU7mFwBF1ocxKigCr7J3oSD3QclOVZhkDy2v539G5M/NJ+Lyo89HXtwrOcY9gT3sPe9u303yqxlCAthSfhGHPI90nOE5MV1H8Zva36LLSVboFGQjTN/OfMX1A3V4dTAKRSYCljqAp234qMQej296PP0oXawFkXmIrS52uDwO3C87zi0Ci0j1sf7jpOQ2HAHSi2lxMPZc5iNebe7Gy/XvowlOUvQ7mpHn6cP2+u348VzL8If8jPP3dMnnkZQCDIvWZ+njz3Xyf6TEASBhZ8pqHfQF/Jd9Orqo0EmOBMADhz6Pf3whrxod7bDGXAyZUW9ERTixLWdrTslf6PbzXe07JBcmyJZQb+u4S5k6bPY74m2650eOI3K1Er2++723UyIUWUiFlZ7O/Yyxf3jAz8GAPjD/ritk9SKjc3oj9058efTf5b8/h97/0PSN4o2VxtSdakAojUdxLkpezr2sEV7ZvAMe9azg2cTbvmkC1ecWPf93d9HmbUMzY5mdu//PvLfAKLCZU/7HhRbisFxHAZ9gzCoDKjuroY/7Icr4MLLtS+Tvom2Toq37lPhK0CQVEQFwCpK20yk6jfNO7m18lbWBw4cS47d27EXabo0uPwuptRdARd2te/C9vrt7LqvNryasPbHrvZdqO6uhk6pw4y0GZIt+eICXv4w8WpRb83rDa9jQdYCAFKSZPfZ8fSJp2HVWCUJqEXmIgDkHVJlQ+8Vu1MqEArgL2f+ApPaxOrU0NDED/b8IG4HCX3n9LpHe45K5iINxQx4B+LzACLWdI+7Bx+3f4wMXQaAKOmiYVA6tnSuOP1OQvRjkp3dATfsPjsjx3TOUC9hp6sT7qCbzUcOHMwaM3517FdYlL2I5TP5Q36WQNvubGeGTauzVSIDnjn5DNbmrwVA1kK9vR5p2jRJCITenypqWqekwd6AL838EgCSAxNbE2hvx15WaiBFk4JT/adgUpskbY71HmNECQC7x77OfdhUsgkA8VJQQ2xa6jTSh8j8pXNkR8sOzMucx8Kd4g0LFo2F/d7masP3dn+PzQF3wI23m95mbemuTXHO0BuN0u3vgXCA7NiLvBtxvpcYHDhJkrrYkwkQI9QZcErILUAIY+wOKrqOAeDZU89Co9Awb9arDa9CryI125x+J/q9/cybOFKl+5q+Gmz7aBvSdelocjQhGA4yj5s35GX5ee2udhhVRnL9gBPOAadkaz6VQSf7TmJe5jxGmmJ3zr5w7gUA0Zpl4ne0q30XrBor+VyUS0W9eH2evkkp2HghkAnOBIADxyZErDAcD2IFFgC81vAa+zd1accmowKj16KgbSpSKuI+p8pBTALEicDi8AQFDRFRwjbkHSLu21GOGhgLTvWNXJmZLqKx1ACi8AQ9EoU44B2Q9HVny06JEqfJv3QHUFgIo8neJFE8QOIt2DT0kwxU4Cs4heTz1xuixyBQwgUQ4nG4+7CEHAOQ7H6hoImYYtDn9gQ9CauwivFO8zsotZQCIGOU7DDCkBBiCdsAWQM0iTpRxWjxPAbAhK9YwFJlNJbT6X99XFoDSxzuoYmZFLs7dqPATAhn3WAdtAqt5O/uoFtSS+fZk8+yfyd6v3+v/TsqUyrjyg/QJObY2kXidSHOiRD3k+Oi+XmJqknH7jqKrf0DSHc/0pAl7S8g9QxQpTQcGGbjfaL/BApMBXF1VMZyPII49yJRHgY1gMRzRoxYxbi7fTeTVbHvM3YdvFr/atz1wkJYsnsqUamIj1o/ipPXsQSQesrpOxUTvdEQS56od3V7/XaUp5SPqYp5MBzEwa6DSNelM++TeE6KZQDVQYnW388O/wwAGYc+Tx/6PH0J64PFQmwwDXgHGMGh4VuAyGEa0htr6YKLBZngTADGe6DihSJ2q+FY0WhvTChgkhV7GwmxEzlZHZ3zqcI60WdHUVCvCxB/eGiiuhxD3iH2b+r+HQuC4eCI5dqppyx23oxUdThWqAOJBdmFnmLc4+6Bilex3xNtNU0EZ8A5atG888H5zJ9EdXWocqAekFiId1iJyYM40VOMRN5CmsAeq5CTQTwfxaGORDWuxkL6ks25hNXVI4pbfN2wEE44z0bCWIsNjuWcsliM5TwrIHHBv1gkIv7nY4yO5zli16KY3I710GGKPk8fe48jFWQdC6i8P58K8olImdh7f76HMk8W5CTjCcCFKpWLiQtdHMmQSGkA4z9r6VIhUa2URJ9NBKhVNF5lcrEg9uSNh2xOhnCbqF0Z4lBeIoz1+AIZUsSGnicS4z1rLhZTWfacj86g4cypjKlWzVgmOBMAXh7GTwXE9WhkXBxMFiGPxWSRWRmXDrH5NDI+fZA180RgaiWOy5gk0N1RMi4ezid8KkOGDBmATHAmBGNJwpMhQ8b4Ia8tGTJknC9kgjMBGOkkbBkyZMiQIUPGxYdMcCYAU616owwZMmTIkPFph0xwJgDns91OhgwZMmTIkDF5kAmODBkyZMiQIeOyg0xwZMiQIUOGDBmXHSaN4DzzzDOYOXMmbDYbFi1ahF27diVt297ejltvvRVFRUXIy8vDgw8+CJ9PeqbOvn37sGrVKhQUFKC8vBy/+c1vJqvrMmTIkCFDhoxPOCaF4PzpT3/CQw89hBdffBFtbW3Ytm0bNm3ahIaG+MqSfr8fV199NWw2G+rq6nDy5ElUV1fjwQcfZG3OnDmDa665Bg888ABaWlqwfft2/OAHP8ALL7wwGd2XIUOGDBkyZHzCMSkE50c/+hG+853vYPr06QCArVu3YvXq1fj5z38e1/aFF15Ad3c3HnvsMSiVSlitVjz55JP4/e9/j74+crbKf/7nf2LNmjW46aabAABVVVX453/+Z/z4xz+ejO7LkCFDhgwZMj7hmHCC09LSgvr6emzZskXy+ZYtW/Dmm/GHRO7cuRPr16+HWq1mny1YsABpaWnYsWMHa5PoekeOHEF39+iH0MmQIUOGDBkyPl2YcILT0UFOXM7NzZV8npeXh/b2+MPTOjo64trGtk/UJi8vDwASXhMAfD4fHA6H5EeGDBkyZMiQ8enAhBMclUpFLsxLL52sGJ5KpYprG9s+UZvRius99thjsFgs7Cc/P39M/ZchQ4YMGTJkfPIx4QTHZrMBiHpyKDo7O5nXJbZ9bNvY9onadHZ2AkDCawLAQw89BLvdzn5aW1vH/zAyZMiQIUOGjE8kJpzgZGVlYe7cuXjjjTckn7/77rvYuHFjXPsNGzbgnXfeQTAYPc/pzJkz6OnpwZVXXsnaJLre3LlzkZWVlbAfGo0GZrNZ8iNDhgwZMmTI+HRgUnZRbdu2DY8//jjOnTsHANi+fTvefPNN3HvvvXFtN23ahMzMTHz/+99HKBSC3W7HN77xDdx1111IT08HANx3333YsWMHXnnlFQDAuXPn8Mgjj+C73/3uZHRfhgwZMmTIkPEJh3IyLnr77bfD4XBg8+bNcLlcsNlseO2111BWVoa2tjYsXboUTz75JLZu3QqlUom33noL9913H/Lz88HzPLZu3SrZAl5WVobXXnsN3/72t/H1r38der0eP/jBD3DbbbdNRvdlyJAhQ4YMGZ9wcIIgCJe6ExcDDocDFosFdrt9wsNVs56dNaHXkyFDhgwZMj6JqPlCzYRf83z1t3wWlQwZMmTIkCHjsoNMcGTIkCFDhgwZlx1kgiNDhgwZMmTIuOwgExwZMmTIkCFDxmWHSdlFJUOGDBkyZMiYWGh5LSxKC/gp7Jvwer3j/o5KpYJCoZjwvsgER4YMGTJkyJjC4MBhc8ZmrE5bDSWvBIeRjyq6lGhsbDyv71mtVmRnZ496DNN4IBMcGTKmEBZkLUB1d/Wl7oaMKY7F2YtxoOtA0r+b1CY4/c5Jubc8Ry8+Nmdsxvqs9UhJSwGn5jCF+Q2KU4rH1V4QBLjdbvT09AAAcnJyJqwvMsH5BGMyhdilgJpXwx/2X+puXFIUmApQ3V192b3biUCmLhM9np5R26l4FQLhwIhtOHAQMLVLgBlVRrgCroR/q0ytHJHgqHn1ZHUL2YbscbVP06ah39s/Sb2ZGGQbstE13DXp98nQZaDX0zuu72h5LVanrUZKWgoUxgsP4yh4BULh0AVfRwye4xEWwgAArVY77u/rdDoAQE9PDzIzMycsXDV1A3mXEeZkzIn7bH7m/HFdY3bG7LjPvr3g25Lfiy3jY84AkKpNjftMr9SP+zojIdadOjdjbsJ2Cl6RsH0iWDVWmNWk4NOMtBkX1sFxwKAyTOr1eY4sSY1CM6n3uVBk6ROfATcWrMpbFfdZpj5z1O/R+ZEMJZYSAGRuxCJ2PKkbXDzXq9KqRu3D+YCu/+mp09lnNqNN0kbBxT/bSOtwtPqsJrWJ/XtB1oKEbZScMuF9Eo2fGMnWbzIsyl7E/h37ntO0aUm/l6mTtq1IqRjT/cYyl0a713hQmVIZ91kiuQoAOcbRvRM6pU7yu0VpIWEp9YW7bZS8ksnNiYRaceGEWq8n8zAQGNk4GQ9kgnMRoFXGM1rxYi0wFST83qaSTezfN5TdgPVF6yWKRSw4gMRCL8eQeEHRRVRkLpJ8XmopxcLshQm/Q0GVMCBdyOLPxff/3PTPsd9NahNmps+Ma7ckZwmW5iwFACzLXTbi/bP0WZLriMeSjsnt024HAKRoUka81niR6HoXSnrExJQ+S4GpABm6jLi2YxXEFo3lgvo0Gmalz8IV+VeMqa34+UosJSg0F8a1UfGqpN+nczWWpMQ+o0VjwWrbaiZsjSoj+9tPVv0k4bV/sOwH7N+Fpvh+jRXXlV6X9G+0PwIE1mcFr0CmPhNl1jIAZIxmpM0Yk+GTZ8wbtc2KvBXs32JiBUS9OyttKwEAVxZcKXmOFO3Ia8ZmsiX8XMWr4ubsjeU3St4tfScKToFUbSpKrCXsb7HrSIAgmTvzMueN2C+KORlzkKZNw8q8lXF/e2LNEwm/k2fKQ74pP+7zWDJAx04s6xLJq2QEZ0n2klHvIW6jU+rAR/7TKOONnliZq+RHDspkGbLYO1Apou9FTAqtWmvc98R5MYlyZBJdc7yYyNwbCpngTABGEkrlKeXMUgKiCljJKxnzv7LwSny+6vOsTYYuA6WWUqyxrWGfpWhSYFQZJRM4XZfO/q1T6uIsNQWnSLjIF2UvYkIqdlKpFeoRPSKZukxsLtnMfhdbezPTZ7JnmplGyMc/LfwnRqKuLLgSZdayhH0qt5YzS5cSHTGZWJazDLPTiRer1FrKvDy3Vd4GozqqyFK1qcjUZcKgMqDMWgar1or5mfMxLXVa9F4p5ZJ7l1nLErr0TSpiBWsVUYJ6XZlUkc3PnA+jyijx0iWykGK9FuI5IW5fYCZkd2H2Qkk/C0wFMKkSk8NEoO8oVgCKLXsKqgDp8yo5pYR05xpy477Dczw2FG8YsQ9XF16NUkspLGqi1DlwmJc5DxzHodRSisdXP87aJvJgsD5H+lVqLZV8viBTOt9npM1AkbmIKdS7Z93N/kaVg5JT4urCqyWK2Ga0YXrqdCzOWTzi84zkIaVzE4BkLQOJ58OMtBn46qyvYm7mXPZZui4d64vWs985jmOEyKgysjFaX7Q+zsqPRZY+CzeV3yT5jCqxOZlkrs5KJ0fMqBQqRpxTNClYmJXYwBETFZ7joVPqmNcMABbnLMZnyj4DIEqwkoWzFmQtQIomhYU1AGB57vK4+5RaStlnVNaMpsQBQsJuqbgl7nN6PfFzAGR9UZkFREnf/CypbKdjt74w+p4SGTi3Vd4Gk9qEb837luTz6WlkrU1Pnc7eZ4YuA4uzydwzq80SskGNXJVClfCdi+eWVqmVeL9jvSpapRYqXsVkvlgGjeaB4TkeZo0ZHDj2fuh1eI5nYzDVkp9lgjMBoIsaiLeuzGoz0nRpjNGLF9hq22osz12OxdmLMSNtBrZ/ZjsAoqQNaoPUIxOZN1cVXMU+opMqU5eJa4uvhVVjxZaSLezvCk7BlEKuITdKECylyDfmY2PxRlg1VmZFAsDKvJVJrQ8A+OLML2JJzhKUWkpRkVKBBxc8yP52VcFVuG3abUjTpmFG+gxJHwEidMXkRIxiSzETYBw4mNQmttA/U/YZlKWUsRBFvimfLa6K1ArJOC3OXoyK1AosyFqApTlLwYGDWW3GshxiZemUOpRZy5hSzzPmYWnOUkYsAKLw8k35uG0aOcxVHBqxGW2M8GQbsnFr5a2wmWwSAnVj+Y0otZRKBBL1WlDyeFMFUT4KTsEUjVi5aRQapGhTmMeO4zjMTJ+JXGM82UiGO6bfgUx9pkQxp+vSUWIpkRAdKrAy9BkoMBXg6qKr2RxYkrMEBrUhLiwhVjJUUNJxoe/39mm3Y27mXAgQoFVoUWwpZh4qjuOYUAeQMB+Gjh99N7nGXIlAr0qrYiSYA8fmRKY+EypeBYPKEOehXJm3ElqFlt1bo9BgTf4aZOmzJN5OGjpZlbeKha7o+1mSQyxscf9vqYxXpgDxkHLgsMa2Bjqljik1q8aK2RmzoeSUmJ46na0LjuMwP3M+isxFWJa7jBk5q22r2VpK16VLZM7sjNmYnTFb8k6X5S5Dhj5DMn5UNtG5SueWglNgSymRG0tzlyb10PxkddQLNjdjLqrSqhhBoNeixPqz0z4LgLwXKgsoiVLzaqTr0rE4Z7GEIFEyRNdDvimfhXQ4cIwMiuVHbPifyrgtJVsk40EJbazsoV7BBVkLkK6PGoxismXVWGFQGbA4ezHbnv2V2V9hbWMJl4pXIUWbglxDLq4uvBpF5qK4+6+2rZZ44L8444soNBfi5oqb2ZhuLtmMxdmLUZVWBRWvYiRd7MkxqAxI16UzAmRQkzmiUqigUWjYtbRKLdQKtZSMiYZCbGBw4GDWRNeZWqGGRW1Blj4LWpUWWqUWWYYsWNQWqBVqGFVGaBQacBw36SH88UImOBOADcUbmOuTTpTylHJcX3o91tjWgOd4PLziYSg4BRZkLUCJpQTLc5djac5SpvhSNCksFv6lmV9i16GKhgOH5bnLGdO+Z/Y9AMji+tKsL6EqrQqLshdJJpgAgS2oVbZVLI+H4zjkm/KxMm8lqtKq8J9r/hMAUcI3lN8Qp2xmZ8xGsaUYq22rUWwpZlb40pylUPJKrMpbhUJzIe6suhPZhmxcVXgVCkwF7NlMahOUnBJzMuYwb9cXZ3xRco9Yxb00ZymKLEX4fNXnmQdAr9Tjs9M+y/6fpk2DzWiLs7LoM8/LnIc1+WsALioIBUHA9aXX46uzvwqNQoNF2YuQbcgmAjeisDYUb4CKV7HrimtOcByH26bdBovGAr1SD7PGjHmZ85igzDflE2IVUbZXF14NgISeDCoDvjnvmwCAO6vuxNfmfA2F5kIoOAVuKLuBhTnmZMxBWAgjRZOCqrQqlFhKoOAU+OVVv8SM9BmSdywOwwDAzRU3s38XW4phVBnZeCg4BX53ze9QYilhij9Tlwlw5P9Lc5ai2FKMZTnLmIC7ufxmCIIAg9qAqwuvxnWl10HBKVBiKYEgCCxMlanPxGrbaizLWcaU1qLsRdCr9BAEAUa1EXnGPBSYC2DVWJnVtyBrAVK1qcjSZ2Fx9mL2nuZlzmNEYn3RetxWSchmnjEPs9Nno8RSgtun387Gmc7Z+ZnzWSgjEZHmOJJcfG3JtWxOcZDuStEpdcxy/u7i7yJbTxSvVWPFspxlrC9fmvkl3Fh+I26tvDWpe31t/lpMT5uOdF06TGqTJJzBcRysWiuW5i5Fhi4DqbpUpGnTUGguRIG5gPV/rW0tMnQZbO1/vurzKDAXMMKVpc9CsbkY1xZfy65tM9qYJ5SGXsSGzLTUaazPaoWaGRdl1jJUplTijul3sHlHUZFSgWsKrwFASMj01OmMCCzMXohrCq9JGN6iXqj1xesxN3Mu7p9/P2amzwQHDityV0jCaeL2lGjNzZgrmfPLc5fDoDKgxFIiCX8DJNS02rYaBeYCzM0k38sx5OALM74AgHgb6Dista3FFQVXYEvJFkZS1tjW4JaKW5BrzMWmkk0wq83YXLIZX5/zdUxLnYYiSxH0Sj0jY9mGbCbndEodOHC4vux6GFVGXF92PYosRQCITPnstM+yeVpkKYLNZGNENVWXipV5K/HgggdRnlKOMmsZNpVswoq8FVibvxYAMZYpoTCpTWw+GFQG1idqrGgVhNDQkJFaoZZ4c1W8SuLBoc+vUWok16ZkipJFi9rCvN1mjRk6pS763QjJmUqQCc4EQKfUwawyS0IKSk4JtUKNNflrMD9zPniOx78t/TesK1iHHEMOE7QPzH+AKXCALF6r1opbK2+FQWVgIZ8UbQoUvIJZtXSh0FwTg8oAAQJmZ8zG0pyl+OrsrwIgQnRJzhKJ23N66nTMTJ/JJjj18vAcj3xTPpbkLMGMtBkos5bhjul3IFWTiqq0KraQqcCk4ZKVeSsxP3M+83SoeBXKrGVYbVuNbH025mTOQY4xBxaNBanaVKTp0iAIAkotpbi68Oq42De9P1UsFI+ufBRLc5ZCq9TilspbMDN9JmwmGwxKIvzmZ84Hx3GMuOhVemZ1XVlIXM5U6GuVWqwvWg+jyohyazkqUypRZC7Ct+Z9CwuyFjBPS5m1jAna2RmzoeAUMKvNkvDIoqxF4MDhqoKr8NS6p5hiovUqsvRZSNelI1OfCQ4cpqdOh1qhZsp8We4y5BpzmQAqMhcxgWIz2VBgKsCXZ30ZSl6JFE0KFmURyy9dly7xHAFRK70ypRIWjQU2o40pBL1Sj0x9Jm4sv5GFITYUbwAH4h3iOR4F5gJYNBaJN4n2S8EpsL5oPa4vux4F5gKEEWYk8LbK2yBAwA3lN0hyuKinbHnucmaxLsxaiCU5SyBAwPzM+Si1lmJR9iIoOAUjbHqlnnkvbCYb1uWvg4JTYFrqNExPmw4BAsxq4jKnpM6sNkOj1OArs7+CUmsprFor7pt3nyT8qOAULCwyJ2MOSq2lzMNF39vcjLksvFNoLsQtlbew8biq8CpJ6HhFblQBpWnTGHGknqp8Uz5mpc/CgqwFCAthdg+O48CBw7TUaVBySnAch60VWzE/az5bV/QZBQiwmQhhET8L9QpMS50Go9oIvVKPtba14MDBqDbGhV2np01n8mNh1kLWF+p5KzIXIduQDQ4cFmQtIHO6MOoxFgQBN5bfSMZaY0ZVWhU4EPliVptxY/mNcQSHelC/Musr2FqxFVtKtqDEWoIby28Ez/FYkbcCuYZcVKRUYE7GHFSlVeGm8ptwZcGVxINjyIFRbZTk36Tp0nBb5W3IN+XDoraA53i2XudmzMW8zHnMuJuWOg03lt+Icms5sg3ZMKlNyDZkI8+YhxV5KzAnYw5mps9kc3Ne5jyWg7K1YiszXKiMzDflQ8EpwIFDiiYFmbpM6JV65BpycVXBVbiy4Eomj2JDmjSU/ssrf8kIZaY+E2m6NMxIm8FC92JjT6fQSaICBpUBHDjolDp2PSWvhJJXgud4aBUkDEV/1DwhNgpOISE0OcYcWDVWMk8iZEan1CFLnwW9Us/acuCgVxHDm+d48BwvCWfRNSdAIGSHGz18eDEhE5wJRL4xX2KNcOBQYilBlj4LuYZc5BhyoFFocEfVHayNildhVsYs9vvKvJWAQASpilfhizO+iPKUcraA52fNx5aSLXEJxdQ7k6ZNw+yM2cgz5jHlsiI32qevzfkaylPKsaF4AzQKDfMQiYU2Bw7XFl+LpTlLYVQbUWotxX1z7wMQJQgAGDGrSqtiC9aoMrI4c5Y+C2UpZeAQDUcIEFCZWgkBAipSKrAoexETLhzHoTylHAqeJCDOzZyLVG0qI3NpujSoFFHPCsdxgECS4spTyvGdhd8BBw7ZhmxJQqrY5b46fzUT7FQJUeGlVqhZ36mHhnrZisxFmJMxB2a1GZWplZKQFnXBU+sLIApkcfZi8ByP++ffTwitICDPlMfGnJIMDhy+NPNL7HoCBMxKnwWtUosCUwEqUytZHgwHDpWphPRm6DKYMKRWFJ1r6bp0VKZU4suzvgyO46DgFPj8jM+zcaP5Ww8ueBCl1lLcUH4De4dqhRqp2lSoeBXSdGlYmrMU2fpsbCjeACWnRKmlFDmGHOgUOkYmU7WpSNWmwqK2SHb8FZmLIECAWqFmYzM/az42Fm0kz6DPQGVKJXKNuViSswRpujRckX9FwnyXJTlLkK5Lx6LsRYwUL8tdBh480nXpjBhoFBoszl7MrHcaDqTPTufT3Iy5yDJkYWH2Qpbnk6JJwYbiDcg2ZLPwa6o2FWa1GQWmAszJmCNJpDSqjEwxpepSmWVP10mOIQfLcpdhtW010nXp4Dke6/LXRfOaBNKn5bnLMTN9JtJ16cg15mJd/joIApkHGoUGHDhYNVbJXC62FGN66nRGUGkfxM+71rYWACGYS7KXsPAPQJTTxqKNuLLgSqTqUiUkQq/UkzCXLgNrbWtxa+WtTNHRkDolM1+c8UVJmIa+Z7PazJTimvw1KLYUY2H2QhSaComi5jholVrolcQQsWqs2FyyGSpehRlpM3BN4TXYVLIJFo0FZSnE62JQGfCFGV9AliGLzYG7ZtyF68uux8KshbBoLJIk9ump05nsnJU+CzqlDteVXgeDyoBrS64FD14STtcoNFiXvw4ACVspeIWEYHDgEEYYHMeRfL+I4ViVVoVrS67FVYVXMS8GNdBiw6T07zSZOHZ3WL4pXxJ6EwSByTOT2gS9So+wEIbD74DD54Av5IMn4IHdZ4cn6IE35IUj4EAgFIA35AXP8XD6nXAH3LD77HD5XXD4HPCH/HD6nRAgwO6zw6Q2ISyEYffZ4Qv54PK74A64oeJUCIaD4Dle8p7VvJp4eCKqSKPQsDkCAEVFRXjmmWfY701NTeA4Dk1NTbhYmFp06xOOPFMe5mXOg5pX40jvEfa5AAEl1hJ0DHewzypSKmDRWNiEiQ0Lxe4q4cDBZrTB6XdKdmUpOcLcIRDC4fQ7UZlSCSWvxP9b9f+wp2MPbq64Ga/Uv4J8E8m7odApdcwFWWAugIJTQBAECIguqDkZc9DsaAYHDkXmIskEn5E+AzV9NVAr1Mz1PjdzLqvdQQWmVWONS5ATIGBF3gqEhTCz2nVKnUTR65Q6VKVW4VD3IWwujSY2CwKxznjwECCgKq0Kn5v2OehVeklYotRSinp7Pfss25CNJdlLMOgbhNPvRLm1HMWWYmQZsjA3cy4sGgsydBloc7WhxFqC+Vnz0WhvhMPvgM1kQ54xjxEoSjBnZ8xOSHr0Kj1m6Gegx90DjULDBGGhuRAVqUSgaZVa9t7FVtGy3GUQILCk8QJTgcQr8uVZX8bbTW/jutLrMD1tOl6pfwWlllKcHTyLNF0aIzVpujQM+YZQbi3HwqyFTGjOzpiNFE0KFmcvhpJXIteQi7kZc6HkldjTsYfdpyqtChUpFfiw9UPcPetupGpT0evpRe1QLbL0Wew9a5Qa5BhykGUgeRjp2nSWaDsrYxZW5a2S7JahnwuCgFsqbsGejj1I06Vhr2cvpqVOQ3V3NdblrwPHcXhq3VMAiEu/wd4AgKyNL8/8MgDikalIqWBKioIDh1xjLkosJVDxKig4BfM40KVGiViJpYSRzhJrCWxGG/QqPfPMcBxJjl6eu5x5Ke+YfkfCENhjqx7DB60fMC8RDVGoeBWmpU6DRWPBqf5TmJ42HRxH+tjoaJSEQfVKPRZlL8LRnqMACCEUk1KKXGMuU6h0Dc1Mnwlf0Mf6LVb2aoUat0+7HQpOgZAQwpr8NczAsvvtLJxKx2ZXxy6UWErQ6+lFZWolMvWZqBusg1qhBq0jtLl0M2xGm2TeUNBnB6S5MvlmQkwo2ZqXOQ+Hug9hSc4SBMNB1s6qtSJVm4rrSq6DM0BqQn2m7DPI1mej3FouyRXM1GXCpDZF30nkHXNcNDcrS58Fo9qIjcUbUdNXQzyAHMfyuuZlzIMz4MTM9JnY1b4LACEhicpXcOBQYi1huUeU2OYYctDn6QMARso2Fm/Eif4TcUSmIqUCRrUxbuwKzYXsvXEcB41SA54n84PWm9EpdfAEPYxQ6FREvqoVauiUOoQRhk6lgyPgADhAr9Azjw8NG9Pr0WTvRPNZq9RCpVBJQk80/KXgib7wK6K1y+QcnMsc1D25KGtR3C4TavEszl6MTH1m0pglVXpzMudgYfZCMhm5qMDQKDSMCCzOWcysCuoJmpY6jQkhgLD+QnOhhNwAkLBtgAh36hXhOE6yhZXjOEntDovGwmLlVWlVCbdKUgWiV+njyAdAPBA0EXJJzhKY1CZsLtkMJadElj4LGoUGeaY8LMxaKElGBKKCi44VDTWIQ3E0QZEq3lJLaVxoiIZfdEodFmUvYmNME06zDFkwq80othRjUzHJyyizljGvBBWOsfU8lmQvwfS06dhUsglqhRoVKRWMNCba/k2Rqc9kbl6L2hIXgqJxb61SiyJLEYwqI+6dcy+0Si3rM/XqGFQGzEibgUx9JvQqPROwZrWZ5N1Exko8jslgMxGlX2guRFgIs3Gj75QKYIAkfm9btI39/tXZX2WCTxyO5TgOCl6BpTlLmfLO0mdBEASUp5SjMrUSK/NWIk2bFpejJfbiVaZWMmtZ7KGgc6YqrQqVqZX4TNlnwHM8rim6BoWmwrj8JYB4I+huKvr9PGMe2cUkSjZPtK3doCQJn7mGXNiMNpRYSiThV4vGAq1Cy8J1HDhMT5vOyFrss9Gdkevy10HFq1CZWinxTFLvUVVqFfPgmNQm5uEDwL5DFdriHCJ7xLu3ALAwBru22ghBEHBzxc3I0GVItpIDURlFw2OxsGgs+PGqH0tITiw2FBH5KK4/REMiYg/e8rxozo34+Q0qg2Tn2x3T75DsMuQ4jpE2MdERo9BcKPEQixPHAWBeFjHSqLFmUBmg5tVQ8iSnMNG8LLeWMxIHgOWzXVVwFfRKPQsdFpoLMSNtBpv74mtRL5iSV6IytZIZlgaVAVqFloWTrForLBoLsg3ZTCab1Wbk6ElKQK6BJObnm/ORok1hbaxaK6xaK/JN+cjQZ7DPxT9p+jSY1Wb2O5OVKl2U9EXGlAM3IbVwJhoywZkgUKEkFoJU2FIlQF2liSYCbbM0ZymzTAwqA9u2reAULLeF4ziJUOE5ngmwitQK5kkQILBdJomEuVjIlVvLWW4HXfDUg7AsZ1ncVmEqlGIVMJB4KzLNWaGWqkTRgZMojBJrCZbnLSfu7AQ1UyhRFAvkuZlzwXN8QiFGd67F1g0aT0LcVQVXsdg8tV7FO1liYVQb2bZTGoq6Z849ce1it8xXpFRApVAhRZOCWRmz4qx2KsBZeIsjOShbK7ZiVd4qlncwK30WeI4n1lck5Ci+Fg+ehTuokC+1lGJR1iLWb6pkqCVKkWPMYWOxJn8N1trWSkKXsX1W8Ao2v8XhWApxyKc8pRwZ+gykaFNYUiUNG85KnxVXZ4MSTOoVo6Q7VRe/EzDflI8CMwkziRWQGAnLBahNcTV4aAK0GF+f8/WE16TINeRCr9SzNSDOx4kFz/EoTylHliELucZcZgj8zxX/w9rMzSQ7mVJ1qWz9092CAJEzi7MXY1H2IvYuzWozZqRHlSr1rIm/RyH2FI+0szIWJpUJs9JnId+UP+J2dvrcOcYcyfjOzZyL2RmzJeUZAEIAxOFsIBqaVfAKLMpeJPGiAoSYLMpeJJGrQLS8Rb4pn41dZWplXA4RnV8GlQHT06Yjy5CFTH0m1Ao1qtKqiPEHjiVbU4iJbbGlGFn6LCh4BQrMBYyUKXgFUrQpuGvGXQAQZ8Qlg0qhgkqhSmiUUIKYqCZZIiSS1eL5OFpxTXG70UoXXApMCsE5ffo0Nm7ciMLCQhQWFuLRRx8dsfKmIAh44oknUFlZiby8PKxduxanTp2StPnmN78Jq9UKm83GfoqKiiaj++cFrVILHjyW5Cxhwuh8wHM8pqdNl0xQk9rEYtlWrVWiFCtTKlmCMRCvXKhCjLXYYiHus0VjwZUFVzLrK5H1Qxd+7P0A4o4WW/QUAgTm6RGHFOjWxmSg7leKaanTwHM8cgw5CQlQssqtI1kYsd4lcd4SECWwZo2ZjYX43rEerNkZs2HVWiXeEWpNWjQWJgzEng4KnUInEdSJCpBR0Jo/czLmQK1Qo8RSgnRdelwRvNg6MzzHs9yCAlMBBEFAjjEH6wrWMfKSZ8yDIAhxxQXptlCAuOaphywR2aUYzUMkHv9kFWstGgu+NvtrEsU3P2s+9Cp93DxMVniP5gQBCQrWjWMDyEiCn5KtWK8HQNZytpF4m8RrKpa45ZvyJbkfVIHSsZZ2OzLHODLHqTexIqWCtY/dqURBQyuJSBY1eBIV2IstICpOSr971t1jVrAUNARLt2pTr2MsSi2lJNFVRETZrrkkBgv1lIvnJ91xZlKbEhbFlOTwRTzXdGcdLQkgNphGMpbyjHmSJGVAmsuY7LuJSGcsYuUWx3ES0qLkldApxkg8IpeiOxx58JKt5iMhNj8HIEc2iKsSNzc3j60fE4gJJzh9fX1Yt24d1q9fj6amJuzZswfPPfccnngicQVJAHjkkUfw9NNPY+fOnWhra8P111+Pq666CkNDQ6xNW1sbHn/8cbS1tbGfi5msNBZQq26kqqbJQIViojhobKhLbC1k6DPYroFYZOgyRrW8xIJiQTYhBum6dKTpkpdQBzBqwTnxAhaDLuYyaxlmZpBrJCrdL8ZYK5hSxFaOXpqbuC8jIZnQoc8t3nIb2556HIDEZx6la6UEZDRCnMjbMCOdJHlatVboVXoUWYqwKHtRQgWYCDTpmf07po/iUF+eaeTKudS7l4jsihFLGsUY67lQse/FZrQlJETJ+pKiTWHKS0wi1+SvGZNCoViasxTLcpdJlBxd//Te4vIFKl6FDH0GZmXMYpa6eK3HehdjMZqSoXlpSk4Zl+8EEE/haMdrFFoSV3IWEw1KzmLvIc6xid1aPh4ka0+94TnGHGiVWoknUKfUxRWBLLIUJQyJU1Bya1KbEs4V8fuIDbPFHrNBQb3SIxkkFGMpVjhW78lI0Cq1oxqQyaBX6S8op2bOnDl48803EQ6H0dXVhYceeui8r3W+mHCC87//+7/IyMjAAw88AI7jkJeXh4cffhg//elPE54x4fF48MQTT+Dhhx9GXl4eOI7Dgw8+iPT0dPzxj39k7dra2pCfP/rEuZSgAi1FmyIRxIkETizSdenQKrQJJ1Sis6zESNGmJCzhT3NfRgJ1l5ZZy0Y9/4jGo4Hxn6UFSAV6tiGbeYEoaRjJA0AxljL14grIYgskQ5/BQnWxu3TEzxZ7JlFsAitAQnpJ7y9yc9OdcyMhtrLyWGDVWOMqTifzUFHiKrmntZwJ9kSkWqxoYr1kseMzlvkNjC0kaDPZRlXE4vCTON9kLMjSZyW02BdlLxp3DQ+O4ySel5EUkkqhild8o9xurFVhKUG9d+69SUvlJwvfikHX1kjHfIwljCL2Zo71XKhEZ+2JMVKfLBpLXEmJXGNu3POOd64kwoKsBXHPRI0KSrqShT8vFUY6BoVCXDNNjFhineh8tETpDwDwk5/8BP39/cjNzcVnPvMZPProo2Ps8cRhwndR7dy5E1u2bJF8tnnzZmzduhWHDx/GkiXS2PXBgwfhcrmwadOmuO+8+eab+Na3SKnrtrY22GyJmXMi+Hw++Hw+9rvD4Rjvo4wbYnKyqXgTmxxjPX030RkgY8FExD7H0kfxfUYjTmOF+JqjeQCA0Q8UtRltEotLbImtL1zPhF4sURL3I9brleg8r1kZs+DyJz7pWYzxhCsT5XWMB8mqHGsUmjgrLjbsmCiE/IUZX0hIzuj4iPOoKEY7RywZqHBV8apRLdexEOHR7nMxMJoRwE+QfZljyIGSV07YAa2xhD7Z4ZrJDuU8n2JvF3IA5EQQl7FCp9RJctIydBkotZaic7hzxO9N1Lu+mEhEZhKtzWTvu6ioCB9++KHks9EOiZ1oTPiod3R0IDdXKmg1Gg3S0tLQ3t6esH1qamrcEet5eXmsfSAQQE9PD95++20sXrwYxcXFuP7663Hy5Mmk/XjsscdgsVjYz8X2/pSllE3JrPLx4kKUyViQLF/mfDHSro1EC3GksAlFsj4m2j1yIaCWlrhMejIk2ok1knWdyAsoPlMm2VEJI4VGEiUMn28lU7VCPSbX/kgYzQswFi9NsmuMNK8SYU7mnFGNgNH6kixkFAuT2jQpCZ7U4Eo2z8d6LtpkYSoktY6VXI2X+I/lkGC1Qj2plYMv5ODMqYJxeXB6e3sxb17yfIj7778fKpUqYYJpshcxlvYDAwPIy8uDQqHAjh07oNFo8LOf/QyrV69GTU1NHKECgIceegjf/va32e8Oh2PKh7gmEslyYMaLsXhVPskYTUAks1IvBLG7kmIxFsUxEVYr9RiladMmzPo/X1g0FlxTdM0FXWM0LwAtmDcSkinz8ZwBBoytHshoa2ss4djJRGwocqphoo2jycR4iUiisHgsxpLHcyGYCgTyQjGuEcrIyEBbW9uIbXbu3ImOjg7JZz6fD4ODg8jLi1+wNpsNfX198Pv9UKujHo/Ozk7WPisrCy0tLZLvbdu2DU8//TReeeUVfO1rX4u7rkajgUZzaYX2pcRkT/5PCybDSh1rXsLFwmgJ5Rcbk1osbAodlTPe5xwtEVnGpcNoeZIXG5cDOZkITHiIasOGDXjjjTckn73//vtITU3F/PnxMen58+cjIyMDb731luTz9957Dxs3RgvThcPhuO+GQqEpd7iXjMSoSK2YcJfn+e4OkDG1Md5dc+PBxczBmWhcai+bjORIVE/mUmLcO7AubmrMRcOEE5w777wTnZ2d+OUvfwmAeGK2bduGBx98ECpVvIJTqVR44IEH8L3vfQ9dXV0AgF/84hdoamrC5z9Pyr3X1dWhuLgY77zzDgBCbP7f//t/6O/vx4033jjRjyBjEkDPEJpITDWrScbUxyeZ4Mj4dGNSE3SnwLKYjOeb8DhGSkoK3nvvPXzjG9/AI488ArVajbvvvhvbtkULmm3duhUA8OKLLwIAvvvd7yIUCmHp0qXw+/2orKzEe++9h5QUkmhVVlaGX/ziF/jRj36Eu+66Cz6fD/PmzcOOHTuQkXH+RfVkyJDx6YLs8ZXxSQN1DLjdbuh0kxN60iq0l3xtuN1uAEjoCDlfcMLF3rd1ieBwOGCxWGC322E2n/+WRBkyZIwfoXBoQgqXXSgGvYNx5fhlTC52te+KO5JExvjQ2dmJoaEhZGZmQq/XX3IyMpEQBAFutxs9PT2wWq3IyYkvy3G++lvORJUhQ8akYyqQGwAyuZHxiUR2NqlT1tPTc4l7MnmwWq3sOScKMsGRIUOGDBkypjA4jkNOTg4yMzMTngjwSYdKpYJCMfFGkExwZMiQIUOGjE8AFArFpBCByxWfvPrRMmTIkCHjEwN6UKYMGRcbMsGRIUOGDBmThpEOypQhYzIhExwZMmTIkCFDxmWHT00ODt0NfzFOFZchQ4YMGTJkTAyo3h5vVZtPDcFxOp0A8Kk6cFOGDBkyZMi4XOB0OmGxjD3k+akp9BcOh9HR0QGTyTThRZLoSeWtra1yEcFJhDzOFwfyOF8cyON8cSCP88XDZI21IAhwOp3Izc0Fz489s+ZT48HheR42m21S72E2m+UFdBEgj/PFgTzOFwfyOF8cyON88TAZYz0ezw2FnGQsQ4YMGTJkyLjsIBMcGTJkyJAhQ8ZlB5ngTAA0Gg3+/d//HRqN5lJ35bKGPM4XB/I4XxzI43xxII/zxcNUG+tPTZKxDBkyZMiQIePTA9mDI0OGDBkyZMi47CATHBkyZMiQIUPGZQeZ4MiQIUOGDBkyLjvIBEeGDBkyZMiQcdlBJjgyZMiQIUOGjMsOMsGRIUOGDBkyZFx2kAmODBkyZMiQIeOyg0xwZMiQIUOGDBmXHT41h21O5mniMmTIkCFDhozJgXya+Cjo6OhAfn7+pe6GDBkyZMiQIeM80NraCpvNNub2nxqCYzKZAJABmuhj3GXIkCFDhgwZkwOHw4H8/Hymx8eKTw3BoWEps9ksExwZMmTIkCHjE4bxppdMWpLxM888g5kzZ8Jms2HRokXYtWtX0rbt7e249dZbUVRUhLy8PDz44IPw+XySNmazGbm5ubDZbOxn27Ztk9V9GRcZgfb2S90FGTJkyJBxGWFSCM6f/vQnPPTQQ3jxxRfR1taGbdu2YdOmTWhoaIhr6/f7cfXVV8Nms6Gurg4nT55EdXU1HnzwQdbGbrfD5XKhsbERbW1t7Ofxxx+fjO7LuATwNTZd6i7IkCFDhozLCJNCcH70ox/hO9/5DqZPnw4A2Lp1K1avXo2f//zncW1feOEFdHd347HHHoNSqYTVasWTTz6J3//+9+jr6wMAtLW1ISMjAxqNZjK6K0OGDBkyZMi4zDDhBKelpQX19fXYsmWL5PMtW7bgzTffjGu/c+dOrF+/Hmq1mn22YMECpKWlYceOHQAIwRlP5rQMGTJkyJAh49ONCSc4HR0dAIDc3FzJ53l5eWhPkGfR0dER1za2fVtbGzQaDe677z5MmzYNVVVVeOihh+B2u5P2w+fzweFwSH5kyJDx6cTw3r2XugsyZMi4yJhwgqNSqciFY4rxJMt+VqlUCQv3iNv7fD44nU7ccccdOHnyJN555x3s3bsXX/7yl5P247HHHoPFYmE/cg2cKQ5BuNQ9kDEF4W9rg/fMGXhPnz7va4QcDgih8AT2SoYMGZ8ETDjBoaEk6smh6OzsRF5eXsL2sW1j2997772oqanBsmXLoFAoYLPZ8JOf/AQvvPAChoeHE/bjoYcegt1uZz+tra0X+mgyLgGEYPBSd2FKIdDZmfDzsM+HsMdzkXsz+fA3NSPY24tg/8B5X8NzvOaC+xFIIKNkTF0EBwfZv0MOB/wtLed9rUB3z0R06bwQ9vsBAMMHDlyU+wmBAITLyNiccIKTlZWFuXPn4o033pB8/u6772Ljxo1x7Tds2IB33nkHQZEiO3PmDHp6enDllVeyz8JhqQUWCoUAJPcMaTQaVvNmKta+8TU0XuouXDAmdNEleY/D+/ZP3D0uA/jq6hJ+HuztRbC7+yL35mLh0h+t4quvv9RdGBXJ5sZUgCAIF1Vxek+eit7b70fY5Trva/nOnZ2ILp0X3PuJ/BNiyqZM2v2qq4FAIOHfxKSRInyR+nW+mJRdVNu2bcPjjz+Oc+fOAQC2b9+ON998E/fee29c202bNiEzMxPf//73EQqFYLfb8Y1vfAN33XUX0tPTAQA/+clPsGHDBubp6ezsxLZt2/C5z30Oer1+Mh5h0jEei1AIBiGEx+ZiD3R3I+RK7NVyV1cDALyR93IhCLvd8Mtbu2VcFAhjCmEO79kzqb3wnjo1apuLlesjRAw8MQJdU5fgBtraEGhuvqR9oN6QTwMmwzD0njgR95n7InmWzheTQnBuv/12fP/738fmzZuRm5uLRx99FK+99hrKysrYjqgXX3wRAKBUKvHWW2/h1KlTyM/Px4wZMzBr1iw89dRT7Hr3338/Fi9ejNWrVyM3NxcLFy7EggUL8Otf/3oyuj/lMPTyywjZ7Un/Hh4ehqeGTL5Qfz8Eb+JQRTiSlE0tfe+ZM+fdp7DPh9Dg+YcNpjJCE5iQLgjCuAWr84MPJuz+sfCeHTu5Dfb3M1I8EQi73RDGq2RExEb8XhKF6hIp/YlEsL9/1DYXK6Q62WRuwjEGgkrzrMJ+P4K9vRPeBfeBgxN+zamK856HgpDUm/5JxKRVMr7nnntw7tw5dHR04MCBA1i1ahUAknPT1taGrVu3srY2mw3bt29HR0cH2tra8OSTT0pq3mi1WjzyyCOoq6tDR0cH2tvb8dRTT0Gn001W9ycM4RF2eo0VwUg9oGQQBEAIEreiv7Vt7NeNESKJlN9EeHsuBfwXkHOVKGfD19BwfhZgMAjPoUPjcs+79+4b/33G2p2esVn5w3v3wnvq1Ijz19/UNK57+5ubRyTqI4Lj4Dl2jP067nDMOMY/0bUnkvSOBn/bxFT1TjTnfLW147uGaM4LSUIXEwWaZyV4vUlzzcaCQHt73PueSnkl7sOHL3UXEiLQ1SUTHBnjw/lawKGhoWh8cyyLM9LE39g46gRNxu4TKb9gT3JLylcfX5n6UoMK5PEq32QIRLxdoYEBYJQwYUIlGBEYw7ujFre/rQ3+SXDXj0UB+ZvHlmw5FguQkkghGITnxMnE1xHnXpyPkomZy+6D52+Fj1VpJgr1eCcgUXmsGG3uxuZCJCPew7v3xIUqxAmz3rOj55YMvfxy9Hr7osR7vOsrNDQ0KslgBtd5zBNxf3wJKuYP79o97msCROnH5rbZX3t9fNfokSYph91TZzOAp4bM65DdjoGnn0n6jkaaK1M59CcTnIsIqoDEishz9GjCtp4TJyXxTY7jEk4+QRBiQkWJJ2jY62X/HmuewEgKk+M4BDrj84hGUtwjeqJinu18z6YajrihPcePj9guNDRE+jQwcpjNN4IHS0wC/M3NEg8Da4N4TxnCYfa8/japxy1RIl8iJEruGy3uHujsROACdxMmJD6CgLDLmfiezc3Se8YQlmT5YuJri+FKoqiCAwMQAklIWYRkUkI+FsU+HgjhMDw1NUlJnhiJ5kjCa4qeWwiFJOMejhBp6nGliahx3xXCI+bvBXt6ku4Qop42MVnw1NREDYhxziPvyZOSxF+atC1O3vaJQ+aieRLr8aBrF4jKiWT9udBQl6+uHr7aqEfPe/YsvDUjk93Yew78/g/nff9EYbWxGEex8jOZJzZkJ3OJpjhAQNwaDXR1IdibWHYLgeCUDv3JBOciglpAYkso5CSKIex2Y3j/yAlb7n2JQxfeU6chITYJPDjuQ4k9SSPlZIj7Q/N1JMJGiHf9jhQi854ZSbFIr5PICgMA+yuvkNYx92WhtMjno4VCqOXS/+vfjNhuJIiJYtLnFgT4ausQ9rgRSrCTw98o3U0n2f0RCEiIqRjug4ci26dHzgsR56Uk2okxUujFXV2NQAwBS0iiRvAYCuGoy1sQub/ptt1kBD/p9ZKMh7+5GYLfFyfIEymDYE+8UqfE0t/aCggC3IePkP6dpKRFAMAxa1xiMOzahfCwO+66vtpaQk6S5BGNBNf770v6K5knggBfXb1Ekbqrq9ncH/zLXyREeXj37qSWua+2Fu4jR+I+9xw/ztYIRdjuwHAMmRqPJzJkH2L/ppssEm22iDUqnG+/I+2bqF+xZ9jZX30t2u7o0THlGboPHhxzblqiuRMLx1tvjyp/Yg2FYG9v3Gf+tjaEh13w1dYSDzL9PMGWd+rBYoQvRn7S+TwqEoSofHXxOwgD7e3wnj0H5473AJB1MlbyfjEhE5xJwEhWQ7KJ3/r1eyH4pQpI8PmIxSRELFRRsTL3oUMj9iHWM5C0rz3dcLzzTpybMRDjlg32EUUajqk7RIT7MIb+8Q/2ma++ngnU2H4IoRCEcBjh4WFG7kZELJGJEABxyAfAuLdIj6UuBiWibGxE4Rbx+I+YZxUOY3jfPoQGBtm8CDmkz02VRCxZCQ4MJg0phQYH4XjrbYmwF/w+DEaS9wFCSodF+TxiJUcVtzjfKLaOjr++HmFv4m2ggiDAc+y4+APJ3+m1hneLPC4i69DfQixu96Go9ZeIAI5EnnyNjXAfPEiEu0AszVhB7m9pQWign/UvWWmD4Y8/RnBwEP6mJgzv3o3wMOlLaHCItQn29GDoBTK+NFTmPnw40keBhJVFc5q+777/+Z+4+1HPVVICG2PsiGWKEBYQ7OoS/a0PYU/0OoGWVrbjRQj4EfZ4YP/HdonXz7mTECh/SzNcH3woHYvIvA/ZHQiL5qoQCkVJMl3fSYg9Ja5RgghAiH/eYG9f3GeBrm7pvBaSh4YFn5fdw1dbK9npw+QLx0nmZ6x8Dvv8kvD8aKQ7Vqb529ola2fw+efhqTmRvBSIIKDvN7+Jek1ADM1AW5skRyrY1QUhGCKGjtuNYG+vxHsl6UPEgxUlfAIcMcRQTEbFoWPvmTNwHzpIxiscil9zQvwuRiEUgvvQwahMGxi4qHlqY4VMcCYBcZaPx8M8H+LQSUAspBJYf57jNRjevx9hp0Pivg3290sVj2g+uquJwI31DIjhr6tnAi40MADXBx/GuRm9p05JL6DthAAATRFJREFU+5TIAuQAz9FjcLz7roT4BDo6MLxrN7znzsF35gxCdjsCPT0IDQ3BV1sLf1MT+XddnSRHaVxJjAmEXtjni1ucgt8vcV8HunsgCAL8CfKHxIIrODjIBD0bG0FA96P/DwDgfPc91lasVMVhuL5f/So6bqLx6/vlL9m/fbV1cH30MYDINmRBQNgdGUsOAAQ2Lq6PPiLP0NMDhCNhC/F7EQSJRUyFD/PEiT0JIoswNDSEkNOJjof+VTIeiax+72lCMIdeeBHuakLyaMhHnAfBCKAQTkhSwsPD8J45A8EXJdaeI0firEBmidK+cBwLBTlefQ1hnx89T/4M7kOH4Kurg/f0qbiQBvMcCgKGnv9bXF/sr7xClPmwO9rnOHDwnj2DftHOzY7vfjc67wUB3hM1cB89Cs/x44QsNTcDHIfwcLx3tve/yS7RkXKKfA0N8Dc3I9jdjf7f/U70F4GN7/C+ffCeOQ33oYMIdHXGhS7p3A329EhCeO79ZG4HWtsg+P0Swi5uJ34fIbudKe3h3btHNBKG9+5FaGgIXhq24zh4a45Lntd7+jTpc1yBSgHuAwcl4atkRSyHd++GEPmbmNwDxEAUE3zP4UiZjFG8OuKwqedwtURGBdo74jzpof4+CH4/89iFPYSMxHqnqDc10NGB0NAQHDFnMwqCgKF//ANCIEDmi2jOD/z5OQzv3SuVs909CT1gVAZRb3fk4ux9+VtaiByOrK1gby8Enx++M2fY+6XeJLKWEsh+QQBCYbineJ0ymeBMIKigH963T7L4Xbt2IdCewBUbYetiokNdjMGuTnhPnkTY64UQCsN9qBr+5iZyn9NkgYoJwdCLLwEgLD22Pk2sogq5XEwZeU+dhrfmOIY/JsrTtXs38Rb5Axh8/nn2HbHVw5R45LKe6sNAmPziOU4FooBgTy96fvYz+OrqEHY4EHYPw33wEDxHjhKBd/w4wm4385B0/b//J9m1NXzgAMBxCHR0iGqQkPuEXS5GSIhlYyc5S4IgqVfS95vfwt/cgrDfD+cHH8BXW4u+X/wSgy+8ACAqBPp//3t4ReTTe/IUE5xiiLfnCn4/BEGAe180VOU9cxbugwdJbpTDSfpTUwNAgCeieKkV5qutxdALL0gUquD3sZCB7+w5QBAwfOAAwn4/HG++FVHEwwh090i8BdS7JHjiPQKC34eQ3Q7njh0QAlFCQQV5sL8fgc5OON96iwlMwecDhEiYRRAQ6O4mzxTx5PkbGticoP/3t7VjeN/+uHoxIbtdEkIBiJVMvYJ0fgbaOxByRr047kOHIv3hJPlo/oZ6QgQjwt759tvMVd770/+SkExm8XIcXB9/RJRqzFr0RjwA3prkeVv+5maE+gcka078nMN79xErvLkFrg8+wNCLL8Hf1oZgdzdCLqfEOzvqDjA2Hu3wt7bFeWvd1dUI+7zknQeJ0hQ8XgQ7u5Juw4/1HHtOngQEgZDKw4fhfPc9eGpOEC+U1wPB54PzvXch+HwI9PQQUikIzJjynjoNx+sk2dZXXw/P0aPw1dUx8hkcHIT7yBH46smzBjq7EBqS9kEc7hLLKPehaoQGBiTK233oEALt7Qj7fAh0dLJnEAQhad7a8IEDEIIB+OrrEXKICGwsIvcWgkGWiE69EWG3B94TJ1j/BL8fgt8P79mzCLmGI+vcQYyL7h4M792HYEcnfGdOsz6w/kS8mf6GBlKgVnRN8g8i04f37WPzxddQj7DHQ8LRAwNwReSP9/Rp+M6dlXiJAl1dCHvc6H3qvwFw8Dc1wVdfj6GXXmLP2f/0M0QuBYNxmyYCHR3w1dYi0NyM3qeewvD+AyQ8mGyTgBAm5Dhi6CTScZcaMsGZQAR7etiEc7z1tuRvrtjaJmEimBxvvMlinJ7jx+FraGCLaXj3bnAcB9/Zswh7vaRthBSF3cOSXB5fXR18DQ0ItLTAfUDKqof37CELMEKewu5huN5/n3mTQk4Xs3R8Z86SXTFHj0omtO/sWeINaWmB9/TpSIgiSmbCXg+EUIiQHfFjut0Y+NOfSd7AmTOs/+7qw5EE6UG49+9HMOLhERsLgbZ2BDo74G9uRqCrmwlpf3Mzwj4f2xlhf+11ovQ7OjH00ktwvPkWOI6D+/BhhJ1E+DTddhtL9gs57GT7dk0NQhHhGOjuhvvoUWJlRZ5bbA27Dx4EOA7B7m64Dx1CyOmE+8BB9P/61xBCYSak3IcOwrnzfTjffhthr4cp20BbG5zv7QAAeGtqIIQFDP395bidPfbtryAQ2Sbc8a8PAYKAwT8/h9DQEAIdHRh68UX4zp4lRE3kCZK8a0hzpdyHD2Pw+b+h77//h5CviHXmORJt43r/AwBA1yOPAgD6f/d7ACT01/fLX8L1/gcsL0QSdogg2NcP9/79cL3/PgtXhhwOCIEgXDvfZ3lizBoWKfHhXbsgBAIs94K67sMeD8BxcB88yLxXQjgE76nTEeUXK3CjFi9dh0N/f5n9bfCPfwJAlIY0TEGUjSREFhH+7upDpNR/a2vCQn+emppI3k4Y/oYG+FtaCBHt7wcEcn/P0WPo/93vGakOdHVLKrCHvV4yVoIAz3GplwOCEAkjcuj97/9G2OtFeHgY/rp6hAb64W9pjjQLk/kgWrN0vYeGhiSJsf6WFngOVSM0NBQhmn0IdLQjNDSI/t//Hp5D1eR9nD6DsM8H3+nTCA0OIuSws37729oQcjjhOX6MkJv6BgS6uuF49VUM7z8A98FDGHjmWXiP1yDQ1YX+X/+aJPSLlGqgowP+xia49++PENHIuouEfMmciYzzkaPEo9XUhIE//ZH0ob4eHMfBe+wYQg4HvMdrorW+RCQ30NEBb00NQi4nwm43fOdq44y+8LAbvro69P/u9wgNDWHohRcQ6O5hYZ3h3XuIoebxQAgE4G9qhhDww71vHzMuE9UfE/zxRigJnZFxHN63D44334zMrXhPiedQNTMQAaDr+z8g+V79A5KcnUBXFwnXNjQyue6PEDvn+x/Afbgaro93IdjTQ2Qiz8MdE4oLDQ1B8Pthf+MN9P/2d3C89irxilF9JPKGeU+dIvl1ADOqun74wym1HR+QCc6EQQiHSbXOiNXhb2mW7hBobJAkeQ7v2QvPseOSkJWvtg7Du/fAvXcv6AIIuVxwHzyIkMNOlFykVoGn+jBh+7t2E0teENiEpx4c5873yYKnnpZICMBfVw8hGMTgX5+H4623EOzqiro+OQ4QEuWoCHB++CEG/kiES8uddwLBIAJtrRh68SW4duyE4823MLx7N9wHCaP3HD+GYAfZuRP2eOD66GN4jhxGaHAAgs8LCEDHtu8CiBDASD+pR8d39iz8ImvXHrEYB/78HIRAgIxHTw+cb72F3v/5H9i3b4fz7bfZmDrffpsojZrjEHx+BPv6ELIPEQ8SINkZEIjkunT9x8MIRwQVfQ4AREgcOoTw8DBcH++C59gxhIYGSUgQQNu99yI0OAjXzvfhfPddknfTP4Cu/3g48v6bALoTjuMwvHs3Bp55hjz77t1MqTl37oTg9RKrPRghW+EwPIcPw3fmDLw1NfAcPSYJS/qbm+E+cBD++gb4zp0jz+lwsDnkeu89hAb6IQQCCPb2sZBYsK+f7G45cYLlLrh27iTvIJKDMrx7N9z79sPf2EiIaH8/uv79h+S+9Q3EoyEIhPTGeA86f/DvGHjmGXhPnSLeQEGA6733JO08NTVwvP02At3d8Le3IeywI2S3I+x2w/7Kq8RT4/XAR72je/bA39jIdkSFPVGrnCZ9cxyHnieeQOMtt8K9bx989XWwb98OgHim3IcOwXuGKG867iGnAxzHwf6P7eydkPF4P64uUqCzE8G+fgQHBuH68ENGzEKDgyQk5XYzMkS9Ne79xLNFwxS+2loSfurpgfvgQXiOHYPzrbdIKMnnJZ7L9nYE2lrZPcPDbrR+9R7YX/4/MnYnTyLQ0grfmbMRmQEgHGbeWX9jE7Haf/2baH4FRHlXEbIS9vkYIXS+t4OFkOnfKaEdjswbAAhGiHnY6YKvrh7hYReC3V3kGQMB+E6fhnv/frK78CgxhLynTsH14YcIuVwsRBpob4fnyBGS+xRTvNB39izzCAU6OshxC4IAf109vGfPYuC55xB2e+DcsROOV16F4PczL8nAH/5AxiEYIkm6wy54jh6Dp+YEAu3tGHr++aiHiOPgOXIE9ldexdCLL0Lw++E5dhz9v/8de3/Du3ej/+mnMfTy3wEAvT/7GVP8znfeIZ5ckXE3fOBgXCFUX2MDAh0dzDD0njgBwe/HwJ+fI4Q4Qv5i1xHrJ/WSHzmCQFsrev/rSdI+Mib2f2yHu/pQXD6i5/gxBHt6yBoXBGK88jzCdnvkGUTCJBwGJzr8WnC7EejuJjlKInLa9cij0d9FOTqDf/0rphKUl7oDlwtcH3wAf1MzVPkF8FQfRtjtRmD9ergPH0aofwDBnl64Dx6CYeUKhD0ehL2euFwdX20t3Hv3Ql1czD6jYRPvsePgdDoM79oNVUE+QnY7+n/3e6hsefCdq4Wvtha+SCVQZnXs3QtOqUBwYACh/n54TpyEORIC8NXWSs5nCbS3I9jXT6wFpwOuHTvAm0wAiEXtOXESIZcLgeYWhN0etnWSJouGvV62sMJeD1EgNEFREND7s6fYjpyB5/4C45rVUBcUsvs7330PvMWCtLu+CPvLL0ORkgoh4CdK+OxZqAsK0f3wIzBeeQWCXd1QZWdDCATgqa6Gr74eYZcLnkiSbrC3F8MDA9AvW4pgTy84joe/vh7q4mJ4j9cwa3bwuefY/f0tLVAVFmDob3+DYeUKyXthCaURd7Pz3XcJSTlwEMHOTmhKS8ljRsIXgbY2CMEgvCdOMOEU7OtF2OlC5799j4RLdkWVhffUKXT9x3+A1xui3iOPN7LFV4AAAWG3J7p199xZKDMy2NgO/uUvUBcXwx7xnNhffQ2Bzg7wGg30i5fAV1sHw3LyTKGhIQzv2wtNSSl8DcQC7n3qKWhnzpQ8s7+pGUHJVnoBA88ScqvKywMixyc4d+yE+8B+DO/ZC93cuQj29GB4zx6osrLgfOut6AW5aE2VYZbTFEawsxOu93bAvHEjwk4XPCdPwrBsOeyvvsYUgXHlysh77QMEwPXhh9DOng39vHnwRHYH+s6eiwpcjpPuQurqZso50NEBRN5J/29/h76f/xypX7gTg8/9Baq8PITdbnBqNUL2IVhvuhm+s2fhPnQQgt8PTknEZfdjP4YyOyvipRGgraqKPqcgINjdDd5oJDlqhw8z48H1/gcQ/AEoMzPgPlSNoZdegvvAQaR++UvgtVq4jxxBoKUVIacTwe5uGK9Yh0B7BziFguTUrV4lCdUFe3uhys6B+8ABtla9Z84g0NFOvB8iL5HvzBnYX3sNvnPn4I94jGleS6ivD6G+PkJEwmG4PvwQunlzAZ4nldFFYc/YBN1gTw+C/f1QWK3wN7fAffAgTFdFzxAMDQ5KvjO8/wCM1dUkQV2pIH0+dRrqsrJoCIsaAQcOwLx5E7nP4AC8J0+yzQ/Bnh54jx1H2OmCv7ERwb4+qPJtkr4Nf/Qx/DabZCeg7+wZAGSXXNjjhX7RQkAIw1dbC1VBPgS/H30//zkCXV2wXHcdK3Ew8PTTUBcXIxipkeRvbITn6DHwelJs1hMJwdJ3LQT88J46DcOKFfCcOEFkvsMJd/Vh+Ovr4S8oQHBwAM4dO5ncHPq//0OgpRXDAIyrVsXVUBuKeAABYhj4amvhPXmSFXmVJPTT8acbFwQS1tLNng3Xx7tgWLkq8jnJkaNh2rDPxzzXNFwcdrvBKVUQhDCG9x8AbzAQr6Aol4lGKPxT7Dw0meBMAIJ9fXDvP8DCDcHeXgR7ekjdCLdHMjG9J07AuWMnvCdOQF1SQrwQrS3wNzZCXVqKkN1OYqeRBDBKIKiQ8DU2sBBJ2OlEoKsTwrAbCIWYBUHi4fXwNzfBl52FwRdeQKC5BQqLhSUOJypr73jjDQheL3Olh51OhF1OhAYHEXbY4XznXQBAr+gYDQbR4gaIMKc5It5Tp6BfujQ6Xp2dcLzxJvTz50suEbbbMfDss1Ckp4PjmzH4l79CXVYK95HDMC5fEfEAkDCPwmoFADh37Ig7SE8IBuFvaCDPyHFQZWWRsWxshBBRlkC08FvY7UagrY25t32nT0M3fwH89fXEovZ64D1+HNrKStL/7m4o0tKYFUu9H/6WVkZQ7NtfkRxa543sOPI3NIDTaDD8IQm5gOMQ6u1DqLcP6tJSIPJeQkODCDudGPrb8wh2dEjzdMICq18y9MKLUBcWSHZmDTzzDILd3TCsWsWKiolzR9yRZMVgTy8RzOGwJP+o6bOfIx4ykYdLvFsm0N4OXySJ3d/UBPs/thMr8h//AKdWAwB6/zu6c8hz/DgUKVb4zp5DyOmA6ZprMLx7FxCxFMMeDzq++y8I9fWR3UFhgYRNIl5Eliy962OmrLzHj0M/b250TEQ7ccaaC0BJUGhoCKGBAQg+HxRpafBHPK8d3/kOAMB3+gwCra3gIwf2ek+eROAdskMlNDCAYHcP+MiZeJJaT4J080Cwuxv27duRescdzHIXPB54jh0Dr9fDU32YKRqAeKK8x49DM30aGcdj0hwh51tvwxqpCE/XWqCzi+S7RDwk/qYmtgsp0NKKoef/Bu2c2QDAvFUUw/v2MeMoNGRn/3Z99BEhPAAcb7+DlM9+FgBR+gDAqdUwrFjODIbYQondj5KwJ0Ih+Ovr0f3jn0CVmwt1aQkAYkAEursR7O2Fr6EBwx9/zPJahnftIsbV4CA4XgFEPHc0PE0TZcNOJwSvL+LpEiXixux4ch+qhr+1FerCQvjq6xDo7ASnUGB4zx5YC24l34kQ4Njt6mGfl81ZgBiS1OsUdrsx8MwzMEfePxfxChmWLYWnupoQmZ4e9P+BhMn9LS2E6Ii2YDteex280QhlRjp852rR+zOpnKWeudDQIKv67G9qQv+vfw3DmtWStrRfNMHe39AAweuF4803Ifj96PzXf4V+0UK4Dx+BYekSRpwDnR1w7SAylhoowc5OcHo9hv72Anynz8B09VVAKARnpB0A2F+nh2tPrSrIMsGZANBJ5HznHegXLmRCre+//weG5cskbSm5AaKLkyoLaoF6jh5lrD62YNPwRx9DM306E/4Q5deJw109P/kJAEA3ew4Lv4TsdnT+K9kpkyiBNtDWBmV6OlPcADD4/N+I9ajgmRAV72igu7XCTieG/i/iOj9yFN6aGihSU1m72J0HYYeDCIwYhIaGIAQCUOURa4wKgNj6G8O7dsV9lz0H3S3Q0AB1aSnxuNDv7Y0/w8f+6mtk22NkR0DfL3/FlEDnv30PluuvI+0iYY7w8LBEiFLPSv+vfw1FRnqkE2PbESZRzCJh3PHP29i9AGkdI/FY0jCHMkLigOiW+eGPP4YnonjFIUdfbR1CDkJcoYwXAZ7Ywmr9/fDFbOWluVYDTz8t2SZP3evicGyovx+8wQDv2bMQ3G4M/PnP8NXWQTtrNmsbirQPdHQwxUlB35/jDemuk4Fn/4iUOz8f13/3EWn/ExVrDHR1MUXGttPyfHRdIVoE0vUh2UZN10zsmnTt3AkucrQMXSP++nqEYrxiZDBCcEaIFb3e0Et/h2HZMgm5oc8HRAle3PZlQYjbhdX50EMAAPsbr0NTWkY8shEDwB8Jd1GyHfuePSKPgfP9ncz69509yzwZwa6uuFwkwe9H70//K/qIAyPXZgp0dECVmyvpAx337kcelSRE+xoaGfkOtLay7w08+2zcdf3NzWRMDh9Oevq7EMkp8p0+DWVODlHeEVIei9jnDHZ0gtfrWaaMt6YGwx8TzyBdp47XSB0eIWLw9f2G1Nmiib7Uyx5bbsN76hSC3d1Q63QYevElYuwk6UdoyM7kn7u6muUVicG2m9OwZGTuUN1Ed7QCgDIzQ/SQ8YbvwLN/hHnLFhLePXiQzTlBtO7D9J1NsWMeOGGqZQVNEhwOBywWC+x2O8wRS2yi4Nz5PtoiJ6WnfvGLLLcCAJRZWUzhGFauTKiYtbNmJa2OyRsMcYthsqAuLoa/sRG8xRKdsEolybgfD3h+1GMNAEBVUIBASwsyHniAxLQB8GYzwsPDSL3jjoRC7EKhKS+PO4/HuHZtfBK4+O9r1jAld7FB34Vh+fKEBywqs7PBadQI9vQmJK2jgdPpzut7FwJlZiaCPT3gNJqExQfHg/NdH4qMdImHajKgqaxkuUMjgbdYAIiUxBREyp2fZ0naxiuvZFb+hUCVl3feFctHgvnaa+F4441R2/F6vYScG1asSBjmOR+oCwvhb26GKiZElhQKBfPeAhj5ezF1fUbsR2kp80hORnsASPnsZzH4l78AAKy33oqcH/1wXN8fC85Xf8sEZwIw+Pzf0PXDHwIANBUVI5b3n8rgVCoIgQB4ozEu7DOZMKxaxSwhitFIhwwpzpeocFpt0urAMi4MYyU4MiYWitRUSZ2nTzPGOxbqoqJxnzMmJoaK9HRU7Pp4lG+MH+erv+VdVBOAQHe0js0nldwA0TyNi0luyI3jObZMbsaH8/XCyORm8jDek7tlTAxkchPFeMfifA4p9p6ObucPjXTe4CWATHAmAp8OJ9ikYaR8GhkyPrEYQ5hWhoxPOqYyoZQJzkRgvDkqMmTIkCFDhoxJhUxwJgCuBMmfMmTIkCFDhoxLB5ngTATkCJUMGTJkyJAxpSATnAnAVKveKEOGDBkyZHzaIROcCYAwxqJuMmTIkCFDhoyLA5ngyJAhQ4YMGTIuO0wawXnmmWcwc+ZM2Gw2LFq0CLtG2Arc3t6OW2+9FUVFRcjLy8ODDz4IX0yF03379mHVqlUoKChAeXk5fhMpgS1DhgwZMmTIkBGLSSE4f/rTn/DQQw/hxRdfRFtbG7Zt24ZNmzahIXL2khh+vx9XX301bDYb6urqcPLkSVRXV+PBBx9kbc6cOYNrrrkGDzzwAFpaWrB9+3b84Ac/wAuiQyxlyJAhQ4YMGTIoJuWohrKyMnz961/HP/3TP7HPtmzZgvLycvzXf/2XpO2f//xn3H///ejs7IQ6cuhZdXU1li9fjvb2dqSnp+Puu+9Gd3c3Xn31Vfa9n/70p3juuedwOObAuGSYzKMaTk+bPqHXkyFDhgwZMj6JmH7m9OiNxokpc1RDS0sL6uvrsWXLFsnnW7ZswZtvvhnXfufOnVi/fj0jNwCwYMECpKWlYUfkQLedO3cmvN6RI0fQHTnIUoYMGTJkyJAhg2LCCU5HRwcAIDdyrD1FXl4e2hOcHNvR0RHXNrZ9ojZ5eXkAkPCaAODz+eBwOCQ/MmTIkCFDhoxPByac4KhUKnJhXnppjuOSto9tG9s+UZtk16N47LHHYLFY2E9+fv6Y+i9DhgwZMmTI+ORjwgmOzWYDEPXkUHR2djKvS2z72Lax7RO16ezsBICE1wSAhx56CHa7nf20traO/2FkyJAhQ4YMGZ9ITDjBycrKwty5c/HGG29IPn/33XexcePGuPYbNmzAO++8g6DowMozZ86gp6cHV155JWuT6Hpz585FVlZWwn5oNBqYzWbJjwwZMmTIkCHj04FJ2Sa+bds2PP744zh37hwAYPv27XjzzTdx7733xrXdtGkTMjMz8f3vfx+hUAh2ux3f+MY3cNdddyE9PR0AcN9992HHjh145ZVXAADnzp3DI488gu9+97uT0X0ZMmTIkCFDxiccysm46O233w6Hw4HNmzfD5XLBZrPhtddeQ1lZGdra2rB06VI8+eST2Lp1K5RKJd566y3cd999yM/PB8/z2Lp1K3784x+z65WVleG1117Dt7/9bXz961+HXq/HD37wA9x2222T0X0ZMmTIkCFDxicck1IHZypCroMjQ4YMGTJkTC4u6zo4MmTIkCFDhgwZlxoywZEhQ4YMGTJkXHaYlBwcGTJkyJAhQ8bEQ9BqIVitwCi14C4VvF7vuL+jUqmgUCgmvC8ywZEhQ4YMGTKmOASOQ/C66yCsXQNOpZqyBKexsfG8vme1WpGdnT1qEd/xQCY4MmTIkCFDxhRH8LrrwG3ciIwUK7Qch6lJbwBtcfG42guCALfbjZ6eHgBATk7OhPVFJjgyZMiQIUPGFIag00FYuwYZKVZYJyGUM5HQarXj/o5OpwMA9PT0IDMzc8LCVXKSsYxPLdQlJaO24fT6cV+XNxjOpzsyphAyHnjgUndhSkORkX5R76ebN++i3u9iQJk7dk+FYLGAU6mgncyw1ARcW3EBJVj0EVkbCAQuuB8UMsGZABhWr7ro9zSuWXPR7ylB5FBVAOA0GunfLmJsmDeZxtyWE/UZAPQL5gPKUZyY4fC4+6RITR33dy4VlJmZceNyMcCp1ZEOTE0nsnZGleR3hdV6cW4csVzj1tTFQqzlnGRuTHSARLdgAfn//PkJ/85HLPyJgMJiOa/vcefhmRjxeiOsO8tnPhPTmAPOMyzFjXGNjSX3ZdRrJTg4e6yYyNwbCpngTACU6RkAxq7YdHPnJvw86cKLLATThg3sI8OK5SPeQ1MVX3ww5Y47xtS/ZFCkp7NFqauKKoCU22+XtDOsWDHidcZCSpIJE/OmTdJ7LV0y6rUoNFXTJV4bXm+APiJYk2KEOpgqetBrzKLWTpsW31jURlNZycZRO2c2eKORfD49+s4SzYWxKllldnbCz/ULF0q7ZDYj85+/k3BejDaX1YWFI/5dkZEOTWUlAEBTXhZPZCLCLJlQYwRonLDedisAQL9EOi/0y5ZG+zZOsmJcuxaZ/0KOheFHUI76RYvA6/XQzp49ruuLYd50LQDAdOUV4/qe9dZbE36uSEmR/G66+uroL5E5KCZThmXLot9NS2Pz1HT1VZLraGfNgio3FwCgKS+Pfif9/Dw75ohsU8XMXU152ajf5cfpNYglr2PGBSjgWA+UZvp0cMoowVFmZEj+rp1OZEiidcaNMXxDyUgyWcprNNJn4jgoR1j3nEotITiJSDinvkTEPAlkgjMBUJhM4PR6pH7+DvBGI7SzZsG4di04vR7KzMy49vokSlkZERgAoFsYVbxU2YkZv+maayTfVdlsEqWQ+vk7465vXLMG1q1bo/cTH1QaUcC8wYCUOz+fsH+6WbNg3rIFUCigys9nE1y/ZLGknbqkGJYbbohbtNH7xnsNYhVaMgWqnTGD/L20NO5vqshJ9smgX7Aw7jO6YNVl0uux/ghCVMjGCBaqKDXTKke8LwBk/vM/S4ScYTkhqBzHMy+RftFC9lx6kaKhUOXnk//bbFCLEvnEY2XevJn0LYGgogSKusatN3wmqbI3LFsmIVmqggJpgwSWGlWmKpsNxuUr2Pc1FZVMkFLLlFnkHAdOq4XxCqlCV6SnRW81jjAh7UPa3V9mn3F6PQxLlrLnV9lsUKSkSEhQymdvj1Ng5uu2kH8IAjNiRiK8yowMaMrLYVwV8ejGEt+ZMxN+z3T11eCNRmJAcOQ72tmzR3xu/dKl0g+S6d7IM9H5kP6N+6J/C4UAAOYtm6PPkB2VCen3fh3qyHs3rl0rMbDMGzeC0+mYDNEvWgQAsMQYIKy/CxdCYbGw+Zb2la8kNfRiHyz1C1FZloi8s/GOQFs1MoGhckm/aFFS5U/XGoV+4cK4+ZH2la9IZai41zHyTBdDejO//W0AhJAbr7wSluuvi7kAmQexBw3wWm3cvKAGkjJCLmmYaCSPE5V7CpGxKd6ZJe4/+zfHgUviSVOmpYHjeXDqi+8NHgkywZkAqGw2pH3pS9DOnAlldhYsWzbDeust0JaXQzt7FrNwqMUDAVBGMsXTvv41dh3D4sWsncJkZlZvLGM3XXMNVNnZMKxZDYBYy9abboxayRHykPrFL0oEgiIlhU1gTUUFIwTGtWuhLi4Gp1ZDv2gRrCL3aCzR0M2dg/R7v468/3wCmmmVZIELQpRcKJVQZWWBNxmTLjDdjBlMuVDXtGZ61OthuvoqGJYtgzI7GwqrFdoZM8BpNLDeditUEeWceicVelGho5szB+rSUqR99asACBmy3HQjsn/4QwBA5j99W9IP3mCAYTkhEgqDUeJlMG/cCIAImIwHH4S2qgqma4j1S59Lt4C401UZsSRWYM/F6/VQWCwwLF8ORRpRMuqCfJiuuQaGlSvB63VMiGlKSiUWNABoRN4g6403oOjvL4HTEmJJhbBBJOCVaRFiwPPxFmfkV4XBAE6nY/lFVDmJSaMyPZ09LwCY1q2FuqwUGQ8+SC5vMjHBZ1y3jhBaESkBx0E3exb5p1JJiIFKxZSLNvI3cBxSbr0Vlhs+A1VuLrSzyOe62XMAANk//GHSsAUQJW280Ui8JxGvkW7efKR8nhD1jG99E7zJCE2EFBqWL4dh2VKk3vVFcGo11KWlUOXmQpmZCXVRERtX0xXxXhTxWjSsWiW1zBUK8txzZkOZnY2ch/9DMh7JCIsqNweqnGxYNl0Ly2euJ89VUgLtjBnQzpoFZXY2OJ0O6qIiZhjoFy6E5aYbo31ZKvK8RMiM+botTPmpcnKgKiiAurAQCouFrOtwGJxKBVVmVElTggUQD6dly2bybjMziYxh48CTdR+RY9oZM6CZNo3N1/RvfVMyLppp06CdUQXjunWk/0uWIOt734Np4wZirIkUuenqq6CdMxuK9HQYVqyAIAhQFxWBN5uZPKLPqEhPZ+uQItb4ifU0UC9D6pfuAm8wQFtVxQxKRXo6sv/9B8j+/vdI24hCt96yNc7bqMzJhjIzMyHpojIHiHqhVIWELNL5rI14cfTz50FdStroly6F5eabwJtMUGZkgNdoGLHktFpApQYUCpHxwYHXEHnEG8i7FhNrXquFUuTF43genEYTZzRzKhV4g5F9zut0UGVlQWGxSOdtAi8Wp1IRgp6aOmbv0sWCTHAmAOriYmaNGJYvh7qkVBL6oBObt1iiJAdAyuc+x7wKhhUroF+2FJryctbGfM010M2bB01ZjJs2MoG1EeFuWLIU4BVM1SvT0wEOMF1zNUxXrIPl5puQ+8QT0M2cQQR9eRmMV6xD+tfuIfe5lihzCALzLlFLWC1yEad89nZYNm+Gbg551qzv/gt5TkEAr9NCU14G/aKF7Nmp9aewWlmeUtb3vgeFNQXGNWvIeOTkIOfRR5G1bRuz7szXXQdwHLL+5bswX7cFmrIyaKuqwGu04A0GqPLyiFIQKcSsf/1XKNPToZ1RhdSIB0q/dAksmzYx8sUpFFCmp0OZlQXN9OnQzSPPoa2qgvn666DMzGAufP2ihUQwCQJ0c+ZAt3ABrDfdDGVODvQLFkBdWAhDhKRZPnM9lFlZzCsjCALSv3YPFBYLjGtJrhTHc0BEhltvuQXKzAxoSktgWLGCKWXeYACnUEC/ZAkRGiYTFBYL9IsWQZmdjZTbb4duxgyo88m4pn3lbgCAcc1qQlIiwkc/fx44lQoZ93+L9C+imKiA1i9bBrUtj809Tq2GMjsb+sWLoh6kJYvB6w2RcVwKgIM6z4b0ewh51M2dw8aZNxqZ4uFNJhaC084k78a4bi3p7xfuhH4RuaemmIQKDRFPhGHxYijS0qCJSfw2rFxJ+hghlarCAqlCiShGZXYW9MuWQRlR1rxeB47nkfqFL4BTKCNjE3kBHBdxt6tguf56WLZshqZyGjQVFaTvEaJrWr8eUCohRL7H6fUS5aFMT5dY3ooUKzIeeACG5cuR9tWvsL6kfeVu5DzyMNK+8hXJs9GQoSAIMK67AuB46ObPJySQ42C+diPMG9ZDv3Ah1Pn5xPCxWiXeO+stt5C+qZTRECfPQ2GxQFNSAv1iQl6VmZnEWuc4WG64Abp586DMyoLp6qsY8SZ9isotTqWCYeVKaCormEFCCQpActh0c+eC02phvfkmpH3pruhYGE3RcDDHQV1YCE15BXiTkbXRzZwB09q1sN58EzNc1EWFMG/aDF5NlHBWJDSoysuD6ZqrocrOhraqCvoli6FfuhT6hQuhrayEbv58GFauhGH5cihTU5B+79eR88jDAABNaSkjrcYrroB2FvGkcUolUj9/B9Lu/jKsN90M/ZIlkbYFZI5oNEj7yt0Rj18qDKtXIe9nTzKvDcfz0FSUs5B0WkSemjZskJB9TqcnaywjA8qsLOjmzAE4DopUYnCqbPnMU6ydUQXz+vVQpqdBU1EBdWkpzBvWk+soFOBUZG7ykfXAa9TgDXpJaEi8yYFTayQET5GSCoUx+g4o1CUl4PU68Ho9eK0WnFIJRVoaVDk5LJSmzEiXOArZv5n3P/66lxoywZkAaGdUEYVYWQl1URH5UBAAjoOmtAyWTZugLi6GYfFiFDz7DJRpqdDPmwtVTjbUtjwijMrKCPvlOGiqpkO/cCFS77oLyvR0GFatBFQqqGwk54PjI4pqwQLwFgsUZjN0c+dCt3AB9EuXwrxxI5SpqdDNmwdVfgHMGzZCYSXMXL9oEbSzZkvCR5xWC4XJREjWggXEo8BxUBUWwLB4MTRV06HMzYFx1SoJm9fPnwdNeRl0s2dDv5QoF8OSJdDNnQtNSQlUBflQ2WzIuP9bMG+8FlAoIuMjQF1YgJRbbgF4DsrMDPB6PSNBvFYLTXk5W6iainLwRiOz1ExXX00Wrl4XjVVHhL5lyxaA42BYtQqqzEyoCqQeKO30aTBv2ID0e7/OPlOXlkKdnw9eq4NuzmwoMtLBWyzQzpkD3mBgXhGF0QBNaSk4rRb65cuYB0OZnQ3LddfBuHYN8bDwxIpRZmVBkZ5OPHtZWeA1Ure1ftkyqAoKRN4rAYYVy5H2la9AlZMDTVkZ9IsWIVWkOABAlW8jPzk50dAaz7N2vMEIZWYGIxh0HClZMK5eQ3KZBAHqCNFQ5+dDN2MGUm67jcwJtQbqkhKk3f1lRl5i8yFUEaIltr5VOTnQlJaC1+uJcl66FMYrroDlM9dDUzkNvCisoS4uJuEHjgNvMiH9a/eA0+tg2kgsVuqBgSAQMqpQwHzttdBOm8ZCrZqS4kjfyqGbO0fav9wcNraaklIo0tPJe+eiIR5Op2VhT04TdcWT/3GMFBqWL4Nu5kymWCg0lZXMgAEiOWEcB01REVOS6qJiEqrmOJaXpMrPh6ainK3DtC/dBcPyZeA1Gpg3bYIqz0aIOcdDmZUZUYDlMKxZjZQ7PofUL9wJ3mAAr9MCPA9Oo0XGffeyfli33gztrFkwLF0Gw8qV0M6aScJ+kefhTSbk/MePoEhPR+rnPgdOrSYhpLRImCMjnfweUYZ8ZK4rzCSkoczKihLgBQugKSsja1anJeuY45By6y1kXXIcVDnZSP3Sl6BftAiq3Nyo5zQtHbzRxNavpnIajOvWJgw362bOgnnTtTBv2kQ8rIIA6803Q5mZidQ774Rly2aYN22CwmqF5YYboC4qQtZD/wLLjTfCct11LITI8TwLw6vybFAVFEK/cAF0s2bCeuMN7H7GtWuhmz0H6lKi/FWZWeB1OkI4VCrwej1MV17JcvGoYScO9ShziNzUL14E3YyZMK2/BoZIPljKrbeC43loq6aDUyoAXgHDsmhupTIzk6wlEXHgVCpwSiW4SKiK02hYOItTKcFpNOCUSrbmqbdXmZUFjufB63XRPiIa3uJVqri8P47nI9fSQ2G1EtkfydPhTSZGbEZKDr766qvxne98R/LZVVddhX/7t39L+p2JhExwJgDKlBTwGg1UmZlQixSqpqKCKJqqKhbyUOfnQ11cDG1VFVLvvBPqoiJpPD2iLDiVCooUK/SLFoLX62G5bgtUWdnEkhK5kfXz5sG4bi14owGqrCxk/tO3oZ1RBWVGBjiOi0vC5RQKGFevhrqggAlb4laPWBWR+K0yK4tYYcXFyPjGN6Etr0j47IZlRNFrSoqhqahgScD6RYugLixE9g++D+ttt0E/by5rR+/JG/RQUWtIpYIxooABwBLJf+ANBmgqKmDdejPM11zDXNMGUd6P2CtGr61fuBDqklLmURW7rbWzZzFFpZk2DQpqVXIcOK0OmsIicBwH3ezZsFwXtdB1c+cSi51DNIzH89DNmUO8HyWlxMMSIQRUkOU+8TgUZjOy/u3fkCEKk+kXLiTWkkpF3NCCAPAKqHJzkXrn51ncnuN56fNm58C8fgPLY+FUaqJQbrmFKNTSqBdEW1UFdX4B1EVFzKMAiHKZIqRZU1kJIRyGcfUq5r3TVlYwAmRYtQppd98tGUtOrSZeCY6L5J7NhmH5cugXR/uqnz8PHMdBYTBCYSXeKE1pKfFQbNkMlc1Gxiky3obly4nHKiMD4KIeccOSpTCvv4a8l/nzmVLRTJsO3cIF4HgFLNdeC95oIDsMOQ7m666DYckSqAvyoV+8CJxCCd28eeD1BqgL8iOvnCPEj+OQ+tnPQjd3riQUY75uCwnp8Tw4rQaaigqkfPZ2qEtLo8SI46HKzY3OX54n/RcEqPJtUGZGjYmUOz4H6223QlNRAUEQmFdHYbWyfD3DqpVQFxWyOQmBeOlM69fDdMUVMK5cSfL+InKAWvi6OXNgXLcOCrOZeQFNV14BTWkpdLNmwbLlOmZ16+bOgX7JEijMEcNn6RIWVgWAnP/4DyIP0tOhF4XhtLNmQ11aCt28edDOnBHxhhGvgnb6dChSU6EpLiFevZIS6ObPg2HpUhhWrIAqKxOmNWvAG43gIspRmZYK/fzo9RUpVvAaDTHaZpLx1c2eQwwZBQm7KlJSoJszB9pplcxo4I1GJs/S7r4b6ojHRjdnDqw33wTD0iVI+dxnAZ6T5LXwOi0JBen14M0WWK6/nv3NdAXxVpnWro165HkexnVXkPAlr4jOX0QT2zO/808AyDpRWCzQzZ0ryQuimyw0paVQ5eVCnZ8PRUqKRFZTQgwAhpXRTRu8ThfNqzKZwZtMCNntCDmdELxehL1ehOx2CMEgQg4Hwm4P+d3vR8jrRTgQIJ97veQ7gQCEcBihoSH2Ew4EEbI7op/ZHQgPD0PweAiZ1unAq9XM2KXzltNq4vIU77vvPvzxj39kW79bW1vx0Ucf4Z577sHFgExwJguCQBSvACK8581llqth5Uqk3X038wBYb74JmgpCILRVVRGrTgDAAYIARUoKUr/wBQAkUVgrylcxXrGOLB6BxIsVFgt0M2aw8JVWtDOHQmExQzdvPiFkxcVRZR8JhSlTUqCbOZO50DmVUmKlihPJtLNnk9h4xAKjgkVTWgqO58k2ZKqlOC5CRjg2RprKaeze4t1XNEyQcvPNAKJJqboZMyQKHACMV10JcBzbHaFISYF2WiVUeVHiE/VYcVBYrNHvrlghce+aN26A9ZatEQJmYO5nPuLmVaSkktBYhMikfv4OQkBE5IyGBTSV06CdXsV2JijT0pBy+2ej78FoJFZlaRm006ZBN38B1Pk2aEqKSQ7MhvUsP0aZFQ3L0BCXfuFC6GbPgWHpEihTU8g8ufPzUOXksmgMr9cTq5vjoK2sSJoUCT5CbjmOkTrd3LmMCBhXrmAhRP3CheA4Dsr0NOiXLIFx9Wro5s2F+dpryTiIFIj4dxrCs95yCwvNKbOzAYWSXC8jA4alS6HOz4/mEoHsQCEWJweAA6/TscR2VW4udDNmRoWsQsHmojIlBaqcHGhnzmQKVVNSQtpEPK36JUvZ/FRmZUFhMsJyQ9SKz3roIVhviexQ4jgYVq4gYRoO0TAEx5HriTw/bGeRKEdUlZMNVXY2TFddDbXNFpn/lWxc6VwzLF/O+guO5Gzply5joUdKoM0bN0A7c2Z05xLHRQjaTQDHx+eMiPqrMJvBa7XQzSEkWpxsrszOJqQZZN7r5hDPGKfWQF1YwPLE6HeMq1ez79I8QhqK5A0GGNeuiZLxmORbJp8ico5+X5mdzXasKSxm6BYuZEm0ukiIybB8OSHjdL7R8acbGCLvgVMqoSoohKayEtryciAsEBKdmQVOq4MqK5PkyUXWLYUiNZV4iWJ2qBmWLoF2WiXAAdqKCnAaDdkNqVaDU6tZHorpqqtYGFuZlgbeoIc6L09CGNVJqv5qKyuJcVdeLnmP9NqcVishFJToKQzRhGNOoSS5UuQbhJRE5Cqv0wMqFTiej9u5ximSJwvzGg3AR0mMOj8fdKMAr1JBEZNntmXLFuh0Orz66qsAgD/+8Y/YtGkTCmI3LUwSZIIziTAsX0aUHUcEM3XxJ3LpqSMEwrB6FbTTpkFbVQWOIwnMpjVrSMJXWmrEFT+XfU+Vm0fciNqxbc9TpqWRHTLG+GJ01GpQ5eaOuIPDILLQOZ6PeIqWJmjJMaGtstmQ++gjkr+SUJi0b4r0dOae1s2ZE92KLQLNQdFGSKGmrIyEllKJAOc4jrhPI6E23dy5sG4lREm3YD5UebnSZF7R+1CmpRFPB0eIENslInpm5o0rKGBJiBzHsRCJMj2dxNszM2GMqZEUO+7aadOgys2BMj0NyhSrJKlbP28eeL0OitRUlrBLr6GbN5/1FyBhO4XZzJQAa5uaCt5sZkRJlW9LWGJAP28edLNmSYQXAKgKCqHKlpIiThRq082ZnfB6NJFSP38+UWocx4QzbyCubk6tZqEP1t+IdUt3HClzcpAuSsRHhAjp580j+SOUdEUUaqJdTmKypMrLI57VyGfU+wCQ96nMyJBslVUYjcyytt54E9SFRZHnWkBIjUDGOHYXHgAWUgbIzhZNxPOltuVFc3k4sjtRDLF80E6vSrqjUDd3LhTWlOjuQI4DhDALVWrKy8GpVNEt8pH1kAjpX4uOsSorC6qc+MRZw5LFAMeTPB1BYCSIzi0K3mgkBC4CdYwyExsDYohrv2jKy5FKQ5QgITJ9ZE6pS0qgzMggnjqRx0BTURG3BVwQiLGkysokazsnB5qyUvBaLbSVFWzecEolk6viPCQAUbJJfgNAjCgxWbNGDDHDihVsDWY8cD8006dFS2vMmxdXXFQ8FrzBIAljczxP1goA04b10VwXvR68SgVOq4XSbCayIzOTeAEz0qHKzoYyJQXK1BSobDby3KkpRL5arUQ+paVClZ4BhdUKVeS77CciR9jvkTQI+rtkPGifktT0USgUuOeee/CHP/wBAPDss8/iG9/4RsK2kwGZ4EwSOL2ebKVWKpMKFTGot4R6F6jg5jRaogiMRiiMRigsZtZWU1oK7YwqlsOTDJqYkIUYKZ/7HLn/ooXJt8By5zlNlFHhwymV4E0k/EUTgyEI4HheIuAL//RHttslWVVMqnCoV0OVkyPZ7ghIlZrCaID5WlJfxLh2LTTFxSSJLmIhaqdVxmznF8ApFFDl5iQlA9TDJE4mp/kK2unTGbET92MkKLNzElZA1s2aBd2sWXE7QShRot4icY0bTXkZ659x3TqY1q1jCks3YybzMFDhCRClpMrLYyErCk6pABTSGjZqWz4EQYB+wQKWuAqBjCP5EgeFRfruYmsjaWdUJfcmRaCfP58QaIUi+h5EycIqG8ljAYh3Ybx1Sgwrlks8nJxKxZ41UeVc0zVXM48Wr9MxbyXJwVsUV/9IU1yM9Hu+SgjqnGh+kLqoCBkP3A9OpY5Lqo6FMiODkHy2NuOfkeZVUFJP36/CaiX5GgoFDMuXQwiHI0Qz0XrmoF+0CIZlS6GbO4eRsVhop1WSEIsgJK1BFVvCIhbUkyeBIEBdEvVmqG15bHwp2M4fhUKqZEWeM8k7SGRIFhQQYhghsXyCWi7iPCtllnSHJA0XqfLyiBckAlV2Drs+ACisKREvZ0bCcaJeeEpmdfPmQTdvHvSLFrHnVEQ8kACQef/9LLeHygJKKvhIDmXkoaP1bzQakkemVJK5ERkzVkIiyXLhYuvjQJq4HCs3RsPdd9+N999/H6+//joUCgWuvPLKUb8zUZiaZUQvA/AR6zQ0MADG+kcpSKWbNw9hlwthj4fIcHE8M7LNVFxPIS73JAlUOTnw1dUl/Bv1HHFaHUicKzqxFelpxDOjVEIIjl4+WyygAMQJKOrhoMpPU1oGdXGRxD2uSeKyja0jAYC516lQ0y9eBPf+/eSzqir46uslwg+AxGNAr2lYvVqylVI7cyZJ4BMJUeoxmQzoFy6E59jxC7qGOGlcYbXCsGwZ1Pk2QCA1dtK/TpKqaXKnKjs7zjWdCAqzOc4LZd50Lfp+/es4L58yPR3qIh/8jY1xHj3DMunvypQUNub6yG42dk+LBSGHU0r4WJgTkurSqsxM8BYzdHNmg1MoRhS4qnxp4ipRQMTaFod9ASTcacJxHNQFBfA3NzNiCQjQlBCPQKK6T4qUFOgiuTpi8BoNjCuJtR9blFAMdVEhOJ6Hv7mFdkJ6HYMe6vx8eM+eAxAtuqcSKWZNeXl0+y7HQb90mSSni8J09VVkp2F28iMElOnp0Pj9rB/aBAVFtZXEs6pfuBDB/v6k14qF2HPFNmvEQGFNkfzOcdyItYkkMUJEyJFCkfCdJAL1EgMkdy+R912VnY1Ad4/kMxpGE4PIuUg4NMbwEc83Kpe0M2fGGa4cz8d5PYFoCJ/XRclZ3PqMzE8mC5PoI0KQYktM8KJ/8qSkRjA4YoIxRWZmJm644Qbcc889+Jd/+ZdR208kZIIzwdDPm4tAZ6fkMzoHxFZcIigiXhoxfLWEmHCRZNaRkPGtb0UI1XkgJo4NANYbb4zWSRhDfQNVzsiEiz6bKjcXgY4O4k7NG7k4H/tuggqyyqwscCo1W7ixC19dWDgmq54q2rS7vijpp7Tvhsg1xx47ThQGTARepxtXRWbJd5PUGjKuWwd1YSH8LS0xXxjdGycussepVHGeCWVaGnht4oJfalse/I2NcZ+LK6DGhmRiPVe62bMR6OqCEAyKrxD5HxdXKVg3a5bEg6cqkBJr1reCxKEeIKpwNAlCTXHXyc+HuqgIYa8XnFrDcnLGC3rcSiKFRRHNxUl8Axq2MVitEIJBaKbFEw5GZEW6fqRjAlIi1aA1pYm9S2LDihaWTARep0vowUxEXmIL2iVDIuIAJC4GGeeFG+M9ABL2FmLORBIbqJrKSoRdw+Tf5eWM4MTu5JP0J2bex0I7Cukay5Eq4nUWJxti5lBsvsx4wGu1ovEZffLfd9992L59O+5k9csuDmSCM8HgDQYp62aLaoQKqAmqHZ8PFEYjwsNuIByK+9toxycApFqtWNmMNbxCQT0qo0FTWopAR0fEnT56+9i8Egp9gjCC+MyasZ7Bwu4zguVKEeuVGmlxJ6vUqsrORqCraxw9S36f2PwHgCRyKkyju44p1AX58Dc3s9+100cvZZ8hKuRGtpOOvcDXWI5KiPcuCdDOngXB42GKJlGeCBDZ2TfixBrh+I0xeLWogua1WrbTZ6xIVs04EcZTNI1TqUgCsi0+Z40hkoOjFxXGo8aBIiWaW0HXEA2PjHjfcYYFAYzcx3GC1reJzfMBkvRtjP0d7dlVmZlAArEdW5F7PIg9JkF8BAYwvvkQhws4I4rX6+LGjTcY2Gf8GAy5ZcuWwel0nncfzhcywZlsRITKSJOTFnqbCKiyEpMlbrQJznEwrV17QfeOzYMZCcr0dIRdrjEJnEQ7wZIh9rylqQhNefm4CY54q+hoSOQJHAk07CKGeGtzIojJo8JiOe8DDMcDhckMRUEhC0PRCs6xyihZmJOG587L3TJBUMacDTUSxLkqtBDeeLwQCcFxEiOA5YqNo1+Tgshu0/FiPEpfXVqaMOdmIkA9f7HXT+YFGwuSyfLzQbIk4LEg0RiL9clkHJI5UZiUJOPTp09j48aNKCwsRGFhIR599NERXZCCIOCJJ55AZWUl8vLysHbtWpw6dUrS5pvf/CasVitsNhv7KUoSp51KUGZkgDdbEifWjQVTePJcCLTTp0NdWnrehypOHZyfwhkp7yIRzleIxIYAk50PFpvsKs49mBqI5FKp1dEt37Q43AgJ9pIrXIAVe74YLewwVoyWjDw2XCA5Og+MlkguReI5rqmYGANQYTSOKcyTDCMdJJnM8zcWL9hUBDfGXblTHRO+4vv6+rBu3TqsX78eTU1N2LNnD5577jk88cQTSb/zyCOP4Omnn8bOnTvR1taG66+/HldddRWGhoZYm7a2Njz++ONoa2tjP01NTRPd/QmHwmodcy7GpcRouSXibdITBWVq6rjDSJOJ8z5l+DwwUt7FREITk/id8KRzIOF2/PPGJJHyi/l+Jgojnc58XrjAsb3Y1ra2omJMskNdWCA5MkKMifRkXAgSJWZfrrgQj89UwoQTnP/93/9FRkYGHnjgAXAch7y8PDz88MP46U9/yqoZiuHxePDEE0/g4YcfRl5eHjiOw4MPPoj09HT88Y9/ZO3a2tqQH5f/IONCoczKgrq4JC63JHZ3xGS5dqcSLrmbfoogdsfTeDHevJSxQG3Li6sh8mkEq3D8CcJYZAc5/+jyUKqfdPBJTgz/JGLCCc7OnTuxZcsWyWebN29Gb28vDh8+HNf+4MGDcLlc2BQp8S/+zptvvsl+b2trgy3B+STJ4PP54HA4JD+fRIx1K/j5QltRkTDpTzHRludlCnqUweWECz0ReCxJxOMFbzBMnNfrExz2VY9DBsqQcT6YSl71C8WEE5yOjg7kxihljUaDtLQ0tLe3J2yfmpoKbcyWtry8PNY+EAigp6cHb7/9NhYvXozi4mJcf/31OHnyZNJ+PPbYY7BYLOznk+r9iQ0xfJIwWt2fywETuSNExugYz24tGTIuN4RFNaAuN0zGs42LqvX29mJegq25FPfffz9UKhX4BMl8yWK/Y2k/MDCAvLw8KBQK7NixAxqNBj/72c+wevVq1NTUxBEqAHjooYfw7W9HDzZ0OByfWJLzScVodXtkyBgvEh8JImMsGClJVsbUhlqtBs/z6OjoQEZGBtRq9ZTevTQeCIIAv9+P3t5e8DwP9QTmJ46L4GRkZKCtrW3ENjt37kRHR4fkM5/Ph8HBQeQlSGS02Wzo6+uD3++XPFhnZydrn5WVhZaYgmXbtm3D008/jVdeeQVfE52jQqHRaKD5FOSNTBbi673IkCHjk4xPU5Ls5Qae51FcXIzOzs44/Xq5QK/Xo6CgIKHD43wx4cG2DRs24Nlnn8Ujj0QPVnz//feRmpqK+fPjy93Pnz8fGRkZeOutt3Bd5BRjAHjvvfdw++23s9/D4XDcg4dCocuGxU41JCuVLkOGDBmTgamyW2qqQq1Wo6CgAMFgEKFQfDHXTzIUCgWUSuWE6/MJJzh33nknfvzjH+OXv/wl7r33XnR2dmLbtm148MEHoUqw9UylUuGBBx7A9773PSxevBjZ2dn4xS9+gaamJnw+cppsXV0drrzySvz2t7/FNddcg1AohJ/85Cfo7+/Hjf+/vXsPafL74wD+nqH1T7OsnNtjdrMbmiW6KHAV2b00+0JFRVQWRWGgUilU2PWf8I8i7EZ0sSi6/FGEBaklEXShKLppmJGJSXZVi5ybfb5/9NtDy76/1nqc69n7BUGec9hO72Pbh+15zvnnH63/CURE5GM/7txLbRkMBgQHB//0vZTa0rzA6d69O0pKSpCRkYFt27YhJCQEy5Ytw7p169Qxs2fPBgCcOXMGAJCTk4PW1laMGjUKLS0tGDx4MEpKStD9f7ftRkdHo6CgAJs3b8aSJUtgt9sRHx+P0tJS9PqPjcuIiH7mvzY7JCJ9MYinp5z95RobGxEaGoqGhgYYA+DuHiIiIj3w9v3b93uXExEREbUz/ezo8wuuD6r+1g3/iIiIApHrfft3v3AKmALHdVQ798IhIiL6+zQ1NSE0NNTj8QFzDc7Xr1/x6tUrdO3aVfNb0VybCNbU1PD6nnbEnH2DOfsGc/YN5uw77ZW1iKCpqQkWi+W39skJmE9wgoKCfussK28YjUb+B/IB5uwbzNk3mLNvMGffaY+sf+eTGxdeZExERES6wwKHiIiIdIcFjgY6d+6MvLw8nn3VzpizbzBn32DOvsGcfcffsg6Yi4yJiIgocPATHCIiItIdFjhERESkOyxwiIiISHdY4PyhI0eOIDY2FpGRkbBarbh+/XpHT8mvHDp0CLGxsVAUBUOGDMHevXvd+u12O3JzcxEdHQ2LxYLU1FTU1ta6jamtrcXcuXPRt29fKIqCrKws2O12tzE3b96EzWZDVFQUBg4ciAMHDrSZS6CsVXV1Nbp164bFixerbcxZG8+fP8fMmTNhNpthsVgwd+5c1NXVqf3MWRufPn3C2rVr0a9fP/Tu3RuxsbHYt2+f2s+cvfP161fcvHkT2dnZCAsLw5EjR9z6/SlXT+byS0JeKywslIiICHny5ImIiJw+fVqMRqNUVVV18Mz8Q2FhoURGRsqjR49ERKS8vFzMZrMcP35cHZOeni5jxoyRDx8+iMPhkKysLBk2bJg4nU4REbHb7TJ06FDJzs4Wh8MhHz58EJvNJitXrlQfo7y8XLp27Spnz54VEZHHjx+LyWSSU6dOuc0lENaqtbVVbDabxMXFyaJFi9R25vzn3r9/L5GRkbJjxw5xOp3y5csXWbhwoeTm5qpjmLM20tLSJDk5Wd68eSMiIvfv35eIiAjZvXu3iDBnbx08eFCsVqusX79eevbsKYcPH3br96dcfzUXT7DA+QMDBgyQ/Px8t7YZM2ZIVlZWB83Iv6xatUpOnDjh1padnS2zZs0SEZHq6moJCgqSO3fuqP12u1169Ogh58+fFxGRY8eOSVhYmNjtdnXMnTt3JCQkRH3xW7p0qcyYMcPtefLz8yU+Pl79OVDWauvWrTJ9+nTJy8tTCxzmrI0NGzbImDFj3Nq+f7Flztrp0qWLmplLZmampKSkMGeN9OnTx63A8adcPZmLJ/gVlZdevnyJqqoqpKSkuLWnpKTg0qVLHTQr/1JQUIB58+a5tT18+FDdwrusrAwmkwkJCQlqf0hICCZNmqRmeOXKFUyePBkhISHqmISEBPTo0QOlpaXqmJ+tw7179/D69euAWavbt29j165d2LNnj1s7c9bGhQsXMGvWLLe2Tp06qX9nztpJSEjAhQsX1NOjP3/+jLKyMiQlJTHnduJPuXoyF0+wwPHSq1evAAAWi8WtXVGU3/+eMAA4HA6sXr0aN27cwJo1awB8y/DH/AD3DL0doygKgG/fFwfCWn369Anz58/Hzp07ERUV5dbHnLXx7NkzhIeHIz09Hf369UNcXBy2b98Op9MJgDlr6fTp06ivr0d8fDwyMjIwduxYLF26FGvXrmXO7cSfcvXkeTzBAsdLwcHBANDmZFOtTyrXg+rqathsNpSWluL69euIjY0F8C3Dn50M+32G3o75sR/Q91plZGQgMTERCxYsaNPHnLXR2tqKvLw8zJ8/H8+fP8fZs2dx8uRJ5OTkAGDOWnrz5g3evXuH0aNHw2q1IjQ0FEVFRairq2PO7cSfcvXkeTzBAsdLrpPJXdWoS11dnVqtEnD37l1YrVYkJSXh3r17GD58uNoXGRnZJj/APUNvx7jubFEURfdrdebMGZSUlLS5Q82FOWsjKioKixcvxoQJE2AwGDBo0CBs3LgRhYWFAJizVhobGzFhwgSsWbMGe/fuxaJFi1BaWoro6GgsWLCAObcTf8rVk+fxBAscL5lMJowYMQIXL150ay8uLsbUqVM7aFb+pbq6GtOmTUNBQQHy8/PbnE8yfvx41NfX48GDB2pba2srrl69qmY4ZcoUXL58Wf0aAAAqKipQX1+P5ORkdczP1mHEiBEwmUy6X6uioiLU1tYiLCwMBoMBBoMBmzdvxtGjR2EwGBAUFMScNWCz2dDS0tKm3fV7zd9nbVRUVODt27cYN26cW/vEiRNx69Yt5txO/ClXT+biEY8vR6Y2Tpw4IYqiyNOnT0VE5Ny5c2I0GqWysrKDZ+Yfpk6dKps2bfq/Y5YvXy7JycnS0NAgTqdTcnJyJCYmRhwOh4iIOBwOiYmJkdzcXHE6nfLx40dJTk6WFStWqI9RWVkpRqNRvbr+6dOnoiiKnDx5Uh0TaGv1/V1UIsxZC5WVlWIymaS4uFhERF6+fCkxMTGyceNGdQxz/nNNTU0SHh4uq1evls+fP4uIyIsXL2TUqFGSlpYmIsxZCz/eRSXiX7n+ai6eYIHzh/bt2ycDBw4Us9ksVqtVrl271tFT8hsAJDw8XBRFafPHpbm5WTIzM0VRFImIiJDU1FSpqalxe5yamhpJTU0Vs9ksiqJIZmamNDc3u425du2aJCYmisVikejoaNm/f3+b+QTSWv1Y4DBnbZSVlcnIkSOlV69e0r9/f9myZYvbCy5z1kZ5ebnMmTNHFEURs9ks/fv3l5ycHGlqahIR5qyFnxU4/pSrJ3P5FZ4mTkRERLrDa3CIiIhId1jgEBERke6wwCEiIiLdYYFDREREusMCh4iIiHSHBQ4RERHpDgscIiIi0h0WOERERKQ7LHCIiIhId1jgEBERke6wwCEiIiLdYYFDREREuvMv3dEe+OQelWgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = stochastic_sim(n=100_000)\n",
    "sim.plot(subplots=True, linewidth=0.1)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "このプロットから次の点を確認できる。\n",
    "* $u_t$と$v_t$は概ね$(-0.05,0.05)$の間に収まっている。これは`u_std`と`v_std`の値の反映と言える。\n",
    "* $y_t$も概ね$(-0.05,0.05)$の間に収まっているが，$p_t$の変動幅は$y_t$よりも若干大きいようにも見える。\n",
    "* ４つの変数全てにおいて0を中心に左右対称に分布している。\n",
    "\n",
    "このシミュレーション結果を用いて統計的な特徴を計算し，実際のデータの特徴１〜３をどの程度再現できるかを考察しよう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### GDPと価格水準の乖離率の標準偏差"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**データの値（再掲）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true,
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "df = py4macro.data('jpn-q')\n",
    "for c in ['gdp','deflator']:\n",
    "    df[c+'_cycle'] = np.log(df[c]) - py4macro.trend( np.log(df[c]) )\n",
    "y_std = df.loc[:,'gdp_cycle'].std()\n",
    "p_std = df.loc[:,'deflator_cycle'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDPのトレンドからの乖離率の標準偏差：0.01483\n",
      "デフレータのトレンドからの乖離率の標準偏差：0.00749\n"
     ]
    }
   ],
   "source": [
    "print(f'GDPのトレンドからの乖離率の標準偏差：{y_std:.5f}')\n",
    "print(f'デフレータのトレンドからの乖離率の標準偏差：{p_std:.5f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01483302010531571"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "y_std_data_glued"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.007490182102289268"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "p_std_data_glued"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from myst_nb import glue\n",
    "glue(\"y_std_data_glued\", y_std)\n",
    "glue(\"p_std_data_glued\", p_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**シミュレーションの結果**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yの標準偏差:0.01342\n",
      "pの標準偏差:0.02471\n"
     ]
    }
   ],
   "source": [
    "for value in ['y','p']:\n",
    "    print(f'{value}の標準偏差:{sim[value].std():.5f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013424787155401612"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "y_std_sim_glued"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.024710518539247484"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "p_std_sim_glued"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_std_sim = sim['y'].std()\n",
    "p_std_sim = sim['p'].std()\n",
    "glue(\"y_std_sim_glued\", y_std_sim)\n",
    "glue(\"p_std_sim_glued\", p_std_sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* GDPの標準偏差はデータと近い値になっている。\n",
    "* 価格水準の標準偏差は大きすぎる結果となている。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 自己相関係数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**データの値（再掲）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true,
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "y_autocorr = df.loc[:,'gdp_cycle'].autocorr()\n",
    "p_autocorr = df.loc[:,'deflator_cycle'].autocorr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDPのトレンドからの乖離率の自己相関係数：0.697\n",
      "デフレータのトレンドからの乖離率の自己相関係数：0.830\n"
     ]
    }
   ],
   "source": [
    "print(f'GDPのトレンドからの乖離率の自己相関係数：{y_autocorr:.3f}')\n",
    "print(f'デフレータのトレンドからの乖離率の自己相関係数：{p_autocorr:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6974503788764271"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "y_autocorr_data_glued"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8303258249321758"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "p_autocorr_data_glued"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glue(\"y_autocorr_data_glued\", y_autocorr)\n",
    "glue(\"p_autocorr_data_glued\", p_autocorr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**シミュレーションの結果**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y:0.023\n",
      "p:0.818\n"
     ]
    }
   ],
   "source": [
    "for value in ['y','p']:\n",
    "    print(f'{value}:{sim[value].autocorr():.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.022750288940115854"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "y_autocorr_sim_glued"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.818445014235782"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "p_autocorr_sim_glued"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_autocorr_sim = sim['y'].autocorr()\n",
    "p_autocorr_sim = sim['p'].autocorr()\n",
    "glue(\"y_autocorr_sim_glued\", y_autocorr_sim)\n",
    "glue(\"p_autocorr_sim_glued\", p_autocorr_sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* `y`の自己相関係数は低すぎる。持続性が捉えられていない。\n",
    "* `p`の自己相関係数はデータと近い値になっており，`p`の持続性が再現されている。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### `p`と`y`の乖離率の相関係数：同時期"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**データの値（再掲）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true,
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "yp_corr = df.loc[:,['gdp_cycle', 'deflator_cycle']].corr().iloc[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDPとデフレータの乖離率の相関係数：-0.153\n"
     ]
    }
   ],
   "source": [
    "print(f'GDPとデフレータの乖離率の相関係数：{yp_corr:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.15298375930457533"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "yp_corr_data_glued"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glue(\"yp_corr_data_glued\", yp_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**シミュレーションの結果**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true,
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.06145276780475122"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "yp_corr_sim_glued"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yp_corr_sim = sim[['y', 'p']].corr().iloc[0,1]\n",
    "glue(\"yp_corr_sim_glued\", yp_corr_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.06145276780475122"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim[['y', 'p']].corr().iloc[0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* 符号は合っているが，絶対値で考えるとシミュレーションの結果は小さすぎる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 結果のまとめと拡張"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "データと`100,000`回のシミュレーション結果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "|             | GDP標準偏差 | デフレータの標準偏差 | GDP自己相関係数 | デフレータの自己相関係数| GDPとデフレータの相関係数|\n",
    "|-------------|-----------|--------------|-------------- |----------------|---------|\n",
    "|データ        |{glue:text}`y_std_data_glued:.5f`|{glue:text}`p_std_data_glued:.5f`|{glue:text}`y_autocorr_data_glued:.5f`|{glue:text}`p_autocorr_data_glued:.5f`   | {glue:text}`yp_corr_data_glued:.5f`  |\n",
    "|WNショック    |{glue:text}`y_std_sim_glued:.5f` |{glue:text}`p_std_sim_glued:.5f` |{glue:text}`y_autocorr_sim_glued:.5f`|{glue:text}`p_autocorr_sim_glued:.5f`|  {glue:text}`yp_corr_sim_glued:.5f` |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "WNショックのWNはホワイト・ノイズを表しており，上のシミュレーションの結果を示している。データの数値と比べてみよう。\n",
    "* GDP標準偏差の自己相関係数は概ね近い値（◯）\n",
    "* デフレータの標準偏差の値は高すぎる（X）\n",
    "* GDPの自己相関係数は低すぎる（X）\n",
    "* デフレータの自己相関係数は近い値（◯）\n",
    "* 同期のGDPと価格の相関係数は的外れではない（X）\n",
    "\n",
    "捉えることができていないデータの特徴もあり，100点満点換算だと40点しか取れていない。\n",
    "しかし，全く的外れな結果ではないことも事実である。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "次に，モデルの簡単な拡張に触れる。ホワイト・ノイズは前期のショックと今期のショックは何の関係もない独立を意味する。総需要ショックと総供給ショックは，そうなのだろうか。例えば，政府支出を考えてみよう。政府は，増税を実施すると，１四半期後に減税するような行動は取らない。少なくともある期間，政策を一貫して実施することになる。投資はどうだろうか。１四半期毎に企業は投資をコロコロと変えるような行動はしない。消費者の嗜好の変化も需要ショックとして現れるが，ファッションで今季は赤が流行り，１四半期後には青が流行るということではない。すなわち，ショックはホワイト・ノイズではなく，ある程度の**持続性**が存在すると想定できる。総供給ショックも同じように考えることができる。\n",
    "\n",
    "この特徴を捉えるために，総需要ショックと総供給ショックに次の自己回帰モデルを想定することができる。\n",
    "\n",
    "$$\n",
    "v_t=\\rho_v v_{t-1}+e^v_t,\\qquad e^v_t\\sim\\text{WN}(0,\\sigma_{ev}^2)\n",
    "$$ (eq:18-v_ar1)\n",
    "\n",
    "$$\n",
    "u_t=\\rho_u u_{t-1}+e^u_t,\\qquad e^u_t\\sim\\text{WN}(0,\\sigma_{eu}^2)\n",
    "$$ (eq:18-u_ar1)\n",
    "\n",
    "新たなパラメーターとして$\\rho_u$，$\\rho_v$，$\\sigma_{eu}^2$，$\\sigma_{ev}^2$の４つがあるが，パラメータを推定した際にコクラン＝オーカット推定法を使ったが，そこで推定した残差と自己相関係数を使い推定することが可能である。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````{note}\n",
    "ここではカリブレーションに最小二乗法を使いパラメータを推定したが、次の論文では状態空間モデルと呼ばれる手法を用いて推定をおこない、\n",
    "確率的シミュレーションを含む様々な分析を行っている。論文で使った`Python`コードも公開しているので、参考にしてはどうだろう。\n",
    "```\n",
    "「需要ショックと供給ショックについて：AD-ASモデルの状態空間分析」、『国民経済雑誌』第227巻第5号(2023年9月) 頁1〜14\n",
    "```\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### ADASモデルの評価"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "ADASモデルは，経済を理解する上では非常に有用だが，データの一部の特徴の説明力に欠ける。\n",
    "どう評価すれば良いだろうか。\n",
    "\n",
    "経済学研究で用いる推論方法を考えてみよう。\n",
    "帰納的推論（inductive inference）とは，真のモデルが存在するという前提の下でデータを使い推定するモデルの蓋然性を検討する手法である。\n",
    "典型的な例として，計量経済分析が当てはまる。\n",
    "推定されたモデルは，帰納的推論の**アウトプット**ということになる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{figure} /images/inductive.png\n",
    "---\n",
    "scale: 35%\n",
    "name: fig:18-inductive\n",
    "---\n",
    "帰納的推論\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "一方，演繹的推論（deductive inference）では，モデルは**インプット**になる。\n",
    "モデルを「装置」として使い，理論の含意に関して推論しようということである。\n",
    "例えば，ADASモデルで比較静学をおこない，外生変数の定性的な効果を検討する場合が当てはまる。\n",
    "政府支出が増加すると実質利子率は上昇するとい結果は，ADASモデルを使った演繹的推論の結果である。\n",
    "しかし，経済モデルは複雑な経済を抽象化した考え方に過ぎず，ADASモデルを含めてどのような経済モデルであっても複雑な経済の全ての側面を捉えることはできない「間違った」モデルである。\n",
    "また，この章の目的はADASモデルが真のモデルかどうかを判断しようということではない。\n",
    "従って，データの特徴の一部を説明できないのは，単にADASモデルの限界を表していると解釈できる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{figure} /images/deductive.png\n",
    "---\n",
    "scale: 35%\n",
    "name: fig:18-deductive\n",
    "---\n",
    "演繹的推論\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "では，ADASモデルのどこに有用性があるのだろうか。\n",
    "カリブレーションを説明する際，手作りの体重計の例を使ったがもう一度考えみよう。\n",
    "カリブレーション後，60kg以上の体重は誤差が大きく使いものにならないが，60kg未満であれば誤差は許容範囲だとしてみよう。\n",
    "この場合，「普通の小学生やそれより幼い子どもの体重を測る」ことが目的であれば，手作り体重計の成功と考えることができる。\n",
    "要するに，完璧な「装置」でなくとも，目的に沿ったものであれば十分だということだ。\n",
    "「完璧でなくても良い」という考え方は，地図の有用性を考えれば直ぐに理解できるのではないだろうか。\n",
    "地図は，実際の距離を縮尺にしており，多くの詳細が省かれている。\n",
    "三宮のレストランを探す場合，世界地図は使いものにならない。\n",
    "しかし，石油を運ぶタンカーの運航や宇宙衛星を落下させる際には重要な役割を果たす（政治的な目的にも有用だという主張もある）。\n",
    "経済モデルは地図のようなものであり，AD-ASモデルは不完全だが，ある目的のためであれば有用になり得るということだ。\n",
    "\n",
    "このような演繹的推論に基づき，再現性の精度には多少欠けることも念頭に，次のセクションではADASモデルを「装置」として使い**定量的**な問を検討する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## 定量的な問い"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ここでは上で使ったADASモデルを使い次の定量的な問いを考察する。\n",
    "\n",
    "> GDPと価格水準の長期トレンドからの乖離（％）の変動は，何パーセントが需要ショックにより引き起こされ，何パーセントが供給ショックに起因するのか？\n",
    "\n",
    "この問を検討するには，定性的な比較静学では不可能であり，定量的なアプローチが要求される。\n",
    "ここでは分散分解分析をおこなう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### $p_t$に対する影響：分散分解分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "均衡式[](eq:18-eq-p)を再掲する。\n",
    "\n",
    "$$p_{t}=hp_{t-1} + e_{pt},\\qquad\\qquad e_{pt}=ahu_{t}+hv_{t}$$\n",
    "\n",
    "$p_{t}$の分散$\\text{Var}(p_{t})$を次のように書き換えよう。\n",
    "（分散と共分散の性質については[付録Ａ](sec:18-2-appendix_A)を参照）\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\text{Var}(p_{t})\n",
    "&=\\text{Var}(hp_{t-1} + e_{pt})\\qquad\\cdots\\cdots [1]\\\\\n",
    "&=\\text{Var}(hp_{t-1})+\\text{Var}(e_{pt})+2\\text{Cov}(hp_{t-1},e_{pt})\\qquad\\cdots [2]\\\\\n",
    "&=\\text{Var}(hp_{t-1})+\\text{Var}(e_{pt})\\qquad\\cdots [3]\\\\\n",
    "&=h^2\\text{Var}(p_{t-1})+\\text{Var}(e_{pt})\\qquad\\cdots [4]\\\\\n",
    "&=h^2\\text{Var}(p_{t})+\\text{Var}(e_{pt})\\qquad\\cdots [5]\\\\\n",
    "(1-h^2)\\text{Var}(p_{t})\n",
    "&=\\text{Var}(e_{pt})\\qquad\\cdots\\cdots\\cdots [6]\\\\\n",
    "\\text{Var}(p_{t})\n",
    "&=\\frac{\\text{Var}(e_{pt})}{1-h^2}\\qquad\\cdots\\cdots\\cdots [7]\n",
    "\\end{align}\n",
    "$$ (eq:18-eq-varp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [1]：$p_t$に式[](eq:18-eq-ppp)を代入。\n",
    "* [2]：右辺を展開。\n",
    "* [3]：$p_{t-1}$と$e_{pt}$の間で時間がズレているため$\\text{Cov}(hp_{t-1},e_{pt})=0$。\n",
    "* [4]：定数$h$を$\\text{Var}()$の外に出す。\n",
    "* [5]：$t$が十分に大きい場合（$t\\rightarrow\\infty$）は$\\text{Var}(p_{t})=\\text{Var}(p_{t-1})$。\n",
    "* [6]：$h^2\\text{Var}(p_{t})$を左辺い移動。\n",
    "* [7]：両辺を$(1-h^2)$で除する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\text{Var}(p_t)$は`h`の増加関数となっている。即ち、$p_t$の持続性が高いと、それだけ分散も大きくなる。\n",
    "持続性が高い場合，一旦総供給曲線が定常状態の位置から離れると元の位置に戻るには時間が掛かることになる。即ち，定常状態から離れた状態が長くなる。そのような状況下で，更にショックが発生すると，定常状態から更に乖離する結果につながることになり，変動が増幅されることになる。\n",
    "その効果が右辺の分母$(1-h^2)$で捉えられている。また、`h`が`1`に近づいていくと、分散はものすごい勢いで大きくなることが分かる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "式[](eq:18-eq-varp)は，$p_t$の分散は$e_{pt}$の分散に依存していることを示している。\n",
    "$e_{pt}$は需要ショックと供給ショックの両方から構成されているため，それを分解する必要がある。\n",
    "$e_{pt}$は次の式で与えられる。\n",
    "\n",
    "$$\n",
    "e_{pt}=ahu_{t}+hv_{t}\n",
    "$$ (eq:18-eq-ept)\n",
    "\n",
    "$e_{pt}$の分散を次のように計算する。\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\text{Var}(e_{pt})\n",
    "&=\\text{Var}(ahu_{t}+hv_{t})\\qquad\\cdots\\cdots [1]\\\\\n",
    "&=\\text{Var}(ahu_{t})+\\text{Var}(hv_{t})\\qquad\\cdots [2]\\\\\n",
    "&=(ah)^2\\text{Var}(u_{t})+h^2\\text{Var}(v_{t})\\qquad\\cdots [3]\n",
    "\\end{align}\n",
    "$$ (eq:18-eq-varep)\n",
    "\n",
    "* [1]：$e_{pt}$に式[](eq:18-eq-ept)を代入。\n",
    "* [2]：右辺を展開。\n",
    "* [3]：定数$ah$と$h$を$\\text{Var}()$の外に出す。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最後に式[](eq:18-eq-varep)を式[](eq:18-eq-varp)に代入し，次のように整理する。\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\text{Var}(p_{t})\n",
    "&=\\frac{(ah)^2\\text{Var}(u_{t})+h^2\\text{Var}(v_{t})}{1-h^2}\\\\\n",
    "&=\\frac{(ah)^2}{1-h^2}\\text{Var}(u_{t})\n",
    "+\\frac{h^2}{1-h^2}\\text{Var}(v_{t})\\\\\n",
    "&=\\left(\\text{需要ショックに起因する変動}\\right)+\\left(\\text{供給ショックに起因する変動}\\right)\n",
    "\\end{align}\n",
    "$$ (eq:18-eq-varp2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "＜この式の解釈＞\n",
    "* 左辺は$p_t$の総変動であり，それを需要ショック（$u_t$）と供給ショック（$v_t$）に分解したのが右辺となる。\n",
    "* 右辺の解釈\n",
    "    * まず総需要・総供給ショックが全くない場合を考えてみよう。$u_t=v_t=0$となるため$\\text{Var}(u_{t})=\\text{Var}(v_{t})=0$となり，右辺は`0`であり$p_t$は変動しない。\n",
    "    * 次に，$u_t=0$に固定し，供給ショック$v_{t}$だけを変動させてみよう。需要曲線は動かず，供給曲線だけが上下にシフトする場合である。$p_t$の変動は$v_t$の変動にのみ依存し，$\\text{Var}(p_{t})=\\dfrac{h^2}{1-h^2}\\text{Var}(v_{t})$となる。同様に，$v_t=0$に固定し，需要ショック$u_{t}$だけを変動させてみよう。供給曲線を一定として需要曲線だけが左右にシフトする。$p_t$の変動は$u_t$の変動にのみ依存し，$\\text{Var}(p_{t})=\\dfrac{(ah)^2}{1-h^2}\\text{Var}(u_{t})$となる。\n",
    "    * 最後に，$u_t$と$v_t$を同時に変動させると，$p_t$の総変動は式[](eq:18-eq-varp2)の右辺の２つの項から構成されることになる。第１項が需要ショックに起因する$p_t$の変動であり，第２項が供給ショックにより発生した$p_t$の変動となる。そういう意味で，$p_t$の総変動は需要ショックと供給ショックに分解されたと解釈できる。\n",
    "    * $p_t$の総変動の大きさ（水準）を決定する上で，$h$ が重要な役割を果たしている。$h$はは約`0.8`であり$p_t$の高い持続性を意味する。上で説明した、持続性の影響は$\\dfrac{h^2}{1-h^2}$で捉えられている。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "需要ショックと供給ショックに起因する$p_t$の変動の**割合**を計算するために，式[](eq:18-eq-varp2)の両辺を$\\text{Var}(p_{t})$で割ると次式となる。\n",
    "\n",
    "\\begin{align}\n",
    "1&=\n",
    "\\frac{(ah)^2}{1-h^2}\\cdot\\frac{\\text{Var}(u_{t})}{\\text{Var}(p_{t})}\n",
    "+\\frac{h^2}{1-h^2}\\cdot\\frac{\\text{Var}(v_{t})}{\\text{Var}(p_{t})}\n",
    "\\end{align}\n",
    "\n",
    "この結果を使い次のように定義する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{align*}\n",
    "\\left[\n",
    "\\begin{split}\n",
    "&p_t\\text{の変動に対する}\\\\\n",
    "&u_t\\text{(需要ショック)}\\\\\n",
    "&\\text{の寄与度}\n",
    "\\end{split}\n",
    "\\right]\n",
    "&=\\frac{(ah)^2}{1-h^2}\\cdot\\frac{\\text{Var}(u_{t})}{\\text{Var}(p_{t})}\n",
    "=\\frac{a^2\\text{Var}(u_{t})}{a^2\\text{Var}(u_{t})+\\text{Var}(v_{t})}\n",
    "\\tag{u-p}\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{align*}\n",
    "\\left[\n",
    "\\begin{split}\n",
    "&p_t\\text{の変動に対する}\\\\\n",
    "&v_t\\text{(供給ショック)}\\\\\n",
    "&\\text{の寄与度}\n",
    "\\end{split}\n",
    "\\right]\n",
    "&=\\frac{h^2}{1-h^2}\\cdot\\frac{\\text{Var}(v_{t})}{\\text{Var}(p_{t})}\n",
    "=\\frac{\\text{Var}(v_{t})}{a^2\\text{Var}(u_{t})+\\text{Var}(v_{t})}\n",
    "\\tag{v-p}\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "それぞれの式の最後の等号には式[](eq:18-eq-varp2)を$\\text{Var}(p_{t})$に代入して整理している。\n",
    "式[](eq:18-eq-varp2)の$\\dfrac{h^2}{1-h^2}$は$p_t$の持続性の影響を捉えていると説明したが，その影響は式(u-p)と(v-p)を計算する上でキャンセルされており，ショックの相対的な寄与度はもっぱら供給曲線の傾きである$a$に影響されることが分かる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### $y_t$に対する影響：分散分解分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同様の手法を使い$y_t$の総変動の分散分解分析をおこなう。均衡式[](eq:18-eq-y)を再掲する。\n",
    "\n",
    "$$\n",
    "y_t = -chp_{t-1} + e_{yt},\n",
    "\\qquad\n",
    "e_{yt}=h(u_t-cv_t)\n",
    "$$ (eq:18-eq-yyy)\n",
    "\n",
    "$y_{t}$の分散$\\text{Var}(y_{t})$を次のように書き換えよう。\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\text{Var}(y_{t})\n",
    "&=\\text{Var}(-chp_{t-1} + e_{yt})\\qquad\\cdots\\cdots [1]\\\\\n",
    "&=\\text{Var}(-chp_{t-1})+\\text{Var}(e_{yt})+2\\text{Cov}(-chp_{t-1},e_{yt})\\qquad\\cdots [2]\\\\\n",
    "&=\\text{Var}(-chp_{t-1})+\\text{Var}(e_{yt})\\qquad\\cdots [3]\\\\\n",
    "&=(-ch)^2\\text{Var}(p_{t-1})+\\text{Var}(e_{yt})\\qquad\\cdots [4]\\\\\n",
    "&=(ch)^2\\text{Var}(p_{t})+\\text{Var}(e_{yt})\\qquad\\cdots [5]\\\\\n",
    "\\end{align}\n",
    "$$ (eq:18-eq-vary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [1]：$y_t$に式[](eq:18-eq-yyy)を代入。\n",
    "* [2]：右辺を展開。\n",
    "* [3]：$p_{t-1}$と$e_{yt}$の間で時間がズレているため$\\text{Cov}(-chp_{t-1},e_{yt})=0$。\n",
    "* [4]：定数$(-ch)$を$\\text{Var}()$の外に出す。\n",
    "* [5]：$t$が十分に大きい場合（$t\\rightarrow\\infty$）は$\\text{Var}(p_{t})=\\text{Var}(p_{t-1})$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "式[](eq:18-eq-vary)は，$p_t$の分散と$e_{yt}$の分散に依存していることを示している。\n",
    "$p_t$の分散は式[](eq:18-eq-varp2)で与えられているが，$e_{yt}$は需要ショックと供給ショックの両方から構成されているため，それを分解する必要がある。\n",
    "$e_{pt}$を分解した同じ方法で$e_{yt}$を分解しよう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$e_{yt}$は次の式で与えられる。\n",
    "\n",
    "$$\n",
    "e_{yt}=hu_t-chv_t\n",
    "$$ (eq:18-eq-eyt)\n",
    "\n",
    "$e_{yt}$の分散を次のように計算する。\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\text{Var}(e_{yt})\n",
    "&=\\text{Var}(hu_t-chv_t)\\qquad\\cdots\\cdots [1]\\\\\n",
    "&=\\text{Var}(hu_t)+\\text{Var}(-chv_t)\\qquad\\cdots [2]\\\\\n",
    "&=h^2\\text{Var}(u_{t})+(-ch)^2\\text{Var}(v_{t})\\qquad\\cdots [3]\\\\\n",
    "&=h^2\\text{Var}(u_{t})+(ch)^2\\text{Var}(v_{t})\\qquad\\cdots [4]\n",
    "\\end{align}\n",
    "$$ (eq:18-eq-varyp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [1]：$e_{yt}$に式[](eq:18-eq-eyt)を代入。\n",
    "* [2]：右辺を展開。\n",
    "* [3]：定数$h$と$(-ch)$を$\\text{Var}()$の外に出す。\n",
    "* [4]：$(-ch)^2=(ch)^2$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最後に，式[](eq:18-eq-varyp)と式[](eq:18-eq-varp2)を式[](eq:18-eq-vary)に代入すると次式を導出できる（詳細は[付録Ｂ](sec:18-2-appendix_B)を参照）。\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\text{Var}(y_{t})\n",
    "&=\\frac{h^2}{1-h^2}2ach\\text{Var}(u_{t})\n",
    "+\\frac{h^2}{1-h^2}c^2\\text{Var}(v_{t})\\\\\n",
    "&=\\left(\\text{需要ショックに起因する変動}\\right)+\\left(\\text{供給ショックに起因する変動}\\right)\n",
    "\\end{align}\n",
    "$$ (eq:18-eq-vary2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "＜この式の解釈＞\n",
    "* 左辺は$y_t$の総変動であり，それを需要ショック（$u_t$）と供給ショック（$v_t$）に分解したのが右辺となる。\n",
    "* $p_t$の総変動の大きさ（水準）を決定する上で，$h$ が重要な役割を果たしている。$h$はは約`0.8`であり$p_t$の高い持続性を意味する。持続性が高い場合，一旦総供給曲線が定常状態の位置から離れると元の位置に戻るには時間が掛かることになる。即ち，定常状態から離れた状態が長くなる。そのような状況下で，更にショックが発生すると，定常状態から更に乖離する結果につながることになり，変動が増幅されることになる。その効果が$\\dfrac{h^2}{1-h^2}$で捉えられている。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "需要ショックと供給ショックに起因する$y_t$の変動の**割合**を計算するために，式[](eq:18-eq-vary2)の両辺を$\\text{Var}(y_{t})$で割ると次式となる。\n",
    "\n",
    "\\begin{align}\n",
    "1\n",
    "&=\\frac{h^2}{1-h^2}2ach\\frac{\\text{Var}(u_{t})}{\\text{Var}(y_{t})}\n",
    "+\\frac{h^2}{1-h^2}c^2\\frac{\\text{Var}(v_{t})}{\\text{Var}(y_{t})}\\\\\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "この結果を使い次のように定義する。\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\left[\n",
    "\\begin{split}\n",
    "&y_t\\text{の変動に対する}\\\\\n",
    "&u_t\\text{(需要ショック)}\\\\\n",
    "&\\text{の寄与度}\n",
    "\\end{split}\n",
    "\\right]\n",
    "&=\\frac{h^2}{1-h^2}2ach\\frac{\\text{Var}(u_{t})}{\\text{Var}(y_{t})}\n",
    "=\\frac{2a\\text{Var}(u_{t})}{2a\\text{Var}(u_{t})+c(1+ac)\\text{Var}(v_{t})}\n",
    "\\tag{u-y}\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{align*}\n",
    "\\left[\n",
    "\\begin{split}\n",
    "&p_t\\text{の変動に対する}\\\\\n",
    "&v_t\\text{(供給ショック)}\\\\\n",
    "&\\text{の寄与度}\n",
    "\\end{split}\n",
    "\\right]\n",
    "&=\\frac{h^2}{1-h^2}c^2\\frac{\\text{Var}(v_{t})}{\\text{Var}(y_{t})}\n",
    "=\\frac{c(1+ac)\\text{Var}(v_{t})}{2a\\text{Var}(u_{t})+c(1+ac)\\text{Var}(v_{t})}\n",
    "\\tag{v-y}\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "それぞれの式の最後の等号には式[](eq:18-2-eq-vary2)を$\\text{Var}(y_{t})$に代入して整理している（詳細は[付録Ｃ](sec:18-appendix_c)を参照）。式[](eq:18-eq-vary2)の$\\dfrac{h^2}{1-h^2}$は$p_t$の持続性の影響を捉えていると説明したが，その影響は式(u-y)と(v-y)を計算する上でキャンセルされている。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 産出量水準の乖離率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 計算結果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "変数の推定値を使い，式(u-y)と(v-y)を計算してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDPの乖離率の87.2％は総需要ショックに起因する。\n"
     ]
    }
   ],
   "source": [
    "# 式(u-y)の分子\n",
    "numerator_y = 2 * ahat * u_std**2\n",
    "\n",
    "# 式(u-y)の分母\n",
    "denominator_y = 2 * ahat * u_std**2 + chat * (1+ahat*chat) * v_std**2\n",
    "\n",
    "ad_shock_on_y = numerator_y / denominator_y\n",
    "print(f'GDPの乖離率の{100*ad_shock_on_y:.1f}％は総需要ショックに起因する。'  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDPの乖離率の12.8％は総供給ショックに起因する。\n"
     ]
    }
   ],
   "source": [
    "print(f'GDPの乖離率の{100*(1-ad_shock_on_y):.1f}％は総供給ショックに起因する。'  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### 解釈"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "GDPの乖離率に対しては総需要ショックが相対的に大きな影響を及ぼしていることがわかる。\n",
    "2つ理由がある。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "第一に，総需要曲線の傾きが大きいためである。\n",
    "総需要曲線の傾きは$1/c$で与えられ，$c$が大きくなるとより緩やかな傾きとなる。\n",
    "実際，式(u-y)は $c$ の減少関数であり，式(u-p)は $c$ の増加関数である。\n",
    "この関係を理解するために，{numref}`fig:18-y-uv`では，AD曲線の傾きが急な場合と緩やかな場合の２つのケースを示している。\n",
    "* $1/c$ が小さいケース（$c$が大きい）：\n",
    "    * 総需要曲線の傾きは緩やかになる。\n",
    "    * 総供給曲線が動くと$y_t$は大きく動くが，$p_t$は大きく動かない。\n",
    "* $1/c$ が大きいケース（$c$が小さい）：\n",
    "    * 総需要曲線の傾きは急になる。\n",
    "    * 総供給曲線が動くと$y_t$の動きは小さくなる一方，$p_t$は大きく動くことになる。\n",
    "\n",
    "$1/c$ の値は`3`よりも大きく，$1/c$ が大きいケースが当てはまる。この場合，$y_t$に対する総供給ショックのインパクトは小さな値となり，相対的に総需要ショックの寄与度が高くなっていると考えられる。また，需要ショックは総需要曲線を左右にシフトするが，総需要曲線の傾きが大きい場合，そのインパクトはより大きくなる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{figure} /images/adas-y-uv.jpeg\n",
    "---\n",
    "scale: 35%\n",
    "name: fig:18-y-uv\n",
    "---\n",
    "総需要曲線の傾きが大きい場合，GDPの乖離率の変動における総供給ショックの影響は小さくなる。\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第二に，総供給曲線の傾きが緩やかなためである。総供給曲線の傾きは $a$ で与えられ，低い $a$ は総供給曲線をより緩やかにする\n",
    "式(u-y)は $a$ の増加関数であり，式(u-p)は $c$ の減少関数であることは簡単に確認できる。\n",
    "{numref}`fig:18-p-uv`では，AS曲線の傾きが急な場合と緩やかな場合の２つのケースを示している。\n",
    "* $a$ が小さい場合，総供給曲線の傾きは緩やかになる。その場合，需要曲線が動くと$y_t$は大きく動くが，$p_t$は大きく動かない。\n",
    "* $a$ が大きい場合，総供給曲線の傾きは急になる。その場合，需要曲線が動くと$y_t$の動きは小さくなる一方，$p_t$は大きく動くことになる。\n",
    "\n",
    "$a$ の値は`1`よりも小さく，$a$ が小さいケースが当てはまる。この場合，$p_t$に対する総需要ショックのインパクトは小さな値となり，相対的に総供給ショックの寄与度が高くなっていると考えられる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "```{figure} /images/adas-p-uv.jpeg\n",
    "---\n",
    "scale: 35%\n",
    "name: fig:18-p-uv\n",
    "---\n",
    "物価水準の乖離率の変動において総供給ショックの影響が大きい理由\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 価格水準の乖離率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### 計算結果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "変数の推定値を使い，式(u-p)と(v-p)を計算してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "価格水準の乖離率の47.9％は需要ショックに起因する。\n"
     ]
    }
   ],
   "source": [
    "# 式(u-p)の分子\n",
    "numerator_p = ahat**2 * u_std**2\n",
    "# 式(*p)の分母\n",
    "denominator_p = ahat**2 * u_std**2 + v_std**2\n",
    "\n",
    "ad_shock_on_p = numerator_p / denominator_p\n",
    "print(f'価格水準の乖離率の{100*ad_shock_on_p:.1f}％は需要ショックに起因する。'  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "価格水準の乖離率の52.1％は供給ショックに起因する。\n"
     ]
    }
   ],
   "source": [
    "print(f'価格水準の乖離率の{100*(1-ad_shock_on_p):.1f}％は供給ショックに起因する。'  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### 解釈"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "価格水準の乖離率に対しては総需要ショックと総供給ショックが概ね同程度の影響を及ぼしていることがわかる。\n",
    "総需要ショックの変動は総供給ショックの`4`倍近くあることを思い出そう。\n",
    "この大きな違いを減衰させているのが，総需要曲線の比較的に大きな傾きであり，総供給曲線の比較的に小さな傾きである。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## まとめ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "GDPと価格水準のトレンドからの乖離率のプロットを再掲する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjUAAAGcCAYAAAAh7lEjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAADD8klEQVR4nOydd3gc5fW2n9mu3nt178aV4oJtqo1Nb4EEQgskhASbEAMJ5AsBfiQBQgohkEIN1YEAAZtibNMM7rhX2bLVu7Rq2+f74513ZrZXWVrtua9L17bZ3dHu7MyZ5zznHEEURREEQRAEQRBxjmawV4AgCIIgCCIWUFBDEARBEMSwgIIagiAIgiCGBRTUEARBEAQxLKCghiAIgiCIYQEFNQRBEARBDAsoqCEIgiAIYlhAQQ1BEARBEMMC3WCvwMnC5XKhvr4eaWlpEARhsFeHIAiCIIgQEEUR3d3dKC4uhkYTWItJmKCmvr4eZWVlg70aBEEQBEFEQE1NDUpLSwMukzBBTVpaGgD2oaSnpw/y2hAEQRAEEQpmsxllZWXycTwQCRPU8JRTeno6BTUEQRAEEWeEYh0hozBBEARBEMMCCmoIgiAIghgWUFBDEARBEMSwgIIagiAIgiCGBRTUEARBEAQxLKCghiAIgiCIYQEFNQRBEARBDAsoqCEIgiAIYlhAQQ1BEARBEMMCCmoIgiAIghgWUFBDEARBEMSwgIIagiAIgiCGBRTUEARBDEN6rA48+1kV2nqsg70qBHHSoKCGIAhiGPK3DUfw6JoD+NOnhwd7VQjipEFBDUEQxDBk87F2AMDO2q5BXhOCOHlQUEMQBDHMsDtd2CUFMwcbzXC6xEFeI4I4OVBQQxAEMczY32CG1eECAFjsLhxr7R3kNSKIkwMFNQRBEMOM7cc73G7vbzAP0poQxMmFghqCIIhhxvYTnQAArUYAAOyjoIZIECioIQiCGGZsP8GUmnMnFAAgpYZIHCioIQiCGEY0d1tQ29EPQQCuOa0cAAU1ROJAQQ1BEMQwYoeUehqbn4ZZFVkQBKDJbKUmfERCQEENEfc88sE+XPG3jbDYnYO9KgQx6PDU04yKTKQYdajMSQEA7G/oHszVIoiTAgU1RNzz2uYabD3egb311GSMSBwcThdE0bv/zI7jnQCA6WVZAIAJRWkAgH0N9Psghj+6wV4BgogGi92JHqsDANDYRfI6Mbx5Y8sJPP9VNVq6rWjvs2F8YTreu2Mu9Fp2fmp3urCrrhMAU2oAYEJhOlbvbiSlhkgISKkh4pr2Xpt8vaGrfxDXhCAGnj98cggHGrvR1muDKDID8KEmJVjZ32CGxe5CukmHkbmpAICJxenyYwQx3KGghohr1EFNk9kyiGtCEAOL2WJHk5mpkf+9fQ5mVrD00r56JVjZWdMJAJhWngWN1KNmQhELao4098DqIN8ZMbyhoIaIa9rclBoKaojhy5HmHgBAQboR08uzcEppJgD3xnp83tMppRnyfUUZJmQm6+FwiTjc1HPyVpggBgEKaoi4pr1X8dE0UlBDDGN4UDM63z2tpFZqdtexoGZKiRLUCIKACYWUgiISAwpqiLimrUdRahop/UQMY6p4UJPHghqlqskMURTRb3PK/pqpkorDKctOAgA0d5OZnhjeUPUTEde0eXhqXC5R9hIQxHDCU6kZk58GvVZAt8WB2o5+tv2LQH6aEYUZJrfn5qQaAQCt1ICPGOaQUkPENe0qpcbuFNHeZwuwNEHEL0daWFAzSgpqDDoNRucztWZ/g1n200xV+Wk4OSkGAO7KJkEMRyioIeIatVIDkK+GGJ5Y7E7UtPcBUJQaAJgoVTbtazCr/DSZXs/PSWVBTXsvBTXE8IbST0Rc09brLqc3dFkwucT7TDVR2VPXha+OtKKhy4KWbiuWTi3CBVOKBnu1iDA51toLlwikm3TIk1JJADMLv7WdmYWrJCXHt1JD6SciMaCghohr+JlnVrIeHX12n2bhbcc7cPsr23DHWWNw3ekVJ3sVBw2L3Ymrnv0afTalN8m3NZ0U1MQhaj+NICieMa7UbD/RIauWU3wFNZJS46lsEsRwg9JPRFzDPTWTitmOvNGjq7DTJeKX/92NJrMV731bd9LXbzCp6+xHn80Jo06DG+ZUAmAVYi6X97wgYmjjaRLm8KCmtYd1GC7JTEKuSsnhcKWmvddG3z8xrKGghohbrA4nuqW5T5Oknh2e85/e2FKDA42szPVYa9/JXcFBhvuLyrKTcf/SCRAEFuTR2Xr8wU3CnkFNRrIeJZlJ8u0pflKv2ZJR2OkSYbbYB2gtCWLwGbCg5oUXXsDkyZNRWlqK2bNn48svv/S7bF1dHa6++mpUVlaipKQEK1asgNXqfnD66quvcOaZZ6K0tBQjRozAnXfeid7e3oFafSIO4KknnUbA2AJWBdJoVpQas8WOJz4+KN9u7bGiO4F26LzDclGGCTqtRj6Db+4mM3W8UeVHqQGUJnyA79QTwCql0k3MbdBKFVDEMGZAgpqXX34Z9913H1atWoXa2lqsXLkSS5cuxdGjR72WtdlsOPfcc1FaWoojR45g79692LZtG1asWCEvc+DAASxduhR33nknamtrsWPHDuzevRvLly8fiNUn4gRenpqVYkBRJuvLoR6V8Nd1R9DWa8PIvBT5TLU6gdQanoorTGefTX6aFNSYySwaTzhdIo62shO40XlpXo/zFBTg2yTM4b1q2sgsTMQb9d+GvOiABDUPPvgg7r77bkyYMAEAcOWVV+LMM8/EU0895bXsm2++iaamJjz66KPQ6XTIzMzEk08+iX/9619obW0FAGzevBk33ngjLr/8cgBAZmYmVqxYgbfeemsgVp+IE7hSk5NikA/cjV0WiKKI+s5+PP9VNQDggaUTMSovBQBwtDVxZt/ISo2UniiQPiNSauKLmvY+2BwuGHUalGQleT0+QR3U+Cjn5si9aij9SMQTogj897aQF495UHPixAlUVVXhwgsvdLv/wgsvxJo1a7yWX7duHc4//3wYDAb5vpkzZyInJweffvopAOD666/Hk08+6fa83bt3Iz09Hf6wWq0wm81uf8TwQg5qUg1yB9U+G/PZfLinETanCzMrsrBwXB4qc1hQk0hKjTr9BChKTRMpNXEFNwmPzEuF1ke37NmVWchM1uOMkTnISNb7fR2qgEpMRFHEPz4/ivd31Q/2qkRG+1HAHHqRR8xLuuvr2QdXXFzsdn9JSQnq6rxXrL6+HpMnT/a639/yAPDSSy/hwQcfxLPPPut3PR599FE8+OCD4aw6EWfwnhvZKUYkG3TISNKjq9+Opi4LPj3QBABYMrkQgiBghKTUVLcljg+LBzWFHkENKTXxhT+TMCcn1Ygv7zkLuiDjQbJTKP2UiGw41IJHVu9HukmHZVOLgz9hqHF0Q1iLx1yp0evZmYJG4/7S6t4Knst7Lutv+f7+ftx0002488478fLLL+OGG27wux733Xcfurq65L+ampow/gsiHlCnnwDFO3KoqQebjrYDAM6ZUAAAGJHD00+JE9RwT42s1EifDyk18YXnIEtfpBp1MOm1AV8nN5VGJSQiz315DABgtjgginFYzh9mUBNzpaa0tBQAU2BGjx4t39/Q0ICSkhKfy3N1R43n8m1tbTj//PORl5eHvXv3eilBnhiNRhiN3v0aiOEDD2q4Cbgww4SDTd14Y2sNHC4Ro/JSUJnLghlZqUmQoMZid6Kjj1V6FaUzH4ai1FBQE0/whpK+/DThwIN/GpWQOBxq6sYXh1vl2zanC0Zd4OB3MBFFEa09NuRJ+yq4nMCxz8N6jZgrNQUFBZg2bRpWr17tdv8nn3yCJUuWeC2/ePFifPzxx3A4HPJ9Bw4cQHNzM84++2wAgN1ux7JlyzBv3jysXr06aEBDJAZtKk8NoCg1nx9qAaCoNABQkc2Cmq5+OzoSYKfOe9Qk6bVIT2LnLrJR2EfXZWLo0ikFp9kp/v0yoUCTuhOP57865nbbYncN0pqExjOfHcXsR9biwz0N7I7GXYClE9D7Vyk9GZDqp5UrV+L3v/89Dh06BAB49913sWbNGtx+++1eyy5duhT5+fl44IEH4HQ60dXVhTvuuAM33ngjcnNzAQBPPvkkkpOT8eSTT/pNYxGJB/cG5KiUGjVnq4KaJINWTsMkQgqqnqeeMk3ybyY/nR3UWrqt1FU2juDKSmayIciSgaHqp/jG5RLx0tfV2N8QWtFLe68Nb29396VaHU4/Sw8NNh1rAwB54ryceqqYE/JrDEhQc8011+CBBx7AsmXLUFxcjEceeQTvv/8+Ro8ejdraWpSWlmLVqlUAAJ1Ohw8//BD79u1DWVkZJk2ahClTpuBPf/qT/Hpr1qzBtm3bUFZWhtLSUre/r7/+eiD+BSIOUNJP7GBdpApqMpP1mFGe6bb8iNzESUE1elQ+AUBuqhGCADhcItr76MAWL3RK31V2tEEN9amJa1bvacCv3t2Le9/eHdLyr246DqvDhckl6TDp2aHeOsSVGj6JvoPvn3hQM2JeyK8xYAMtb7vtNtx2m3dteWlpKWpra73ue/fdd/2+1vr162O+fkT80+bhqSlQHcAXjcuHTuses1fmpmBjVRuOJUBQI1c+pSs+DL1Wg5wUA1p7bGg2W33OCCKGFlaHE73SQNKsqIMa9vzOfjscTpfX74MY2mw4yNLq+xvMQb8/URTxyqYTAICb543Ag//bB4vdNaSVGlEUUdvBFOb2XhtgtwAnvmEPVoQe1NBWTcQlNocL3Rbmw+JVHWpVQu2n4YyUlJpjUln3J/ua8ONXtg/LWTi+lBoAyE+TKqCorDsu4H4arUZAmim6c9CsZAMEgfUy4yZyIj4QRRFfHGZBjc3hQnVb4H5btR39aOiyQK8VsGRyEUySOXgoe2pauq2wOtj6dfTagZpNgMMCpBYCuWNCfh0Kaoi4hKeetBoB6SZmoCzJTEKSXotkgxZnjs31eo7SgK8X7b023PXGt/hgdwPW7G44eSt+kvDsUcORfTVU1h0XyH6aJD00QfrQBEOrEWS1p63X4/t32oH37wI2/yOq9yAGhkNNPW6tGA41dQdc/tuaTgCs27RJr4WRp5+GsFJT06EEam29ViX1NHIhEIaXdsDSTwQxkPCdclayQd7Zp5n0ePUHp0Gv1SDN5F0pwsu6j7X24s+fHpYnfAc764lHGiSjcHGme1BTwJUaqoCKC7i3ICslutQTJyfFgPZem3evmkMfAlv/BehMwMwbAS0dGoYSXKXhHGjsxgVTivwuv6u2EwBwSmkmAMSFUlPTrgwj7uizuwc1YUBKDTGoPPnJIdz4/GY4nOH92Dwb73Gml2dhconvoX5lWcnQCGyUwktfV8v3nxiGQU2jD08NoCg11KsmPuDpp6wA4w/Cwe+ohD3SHD2HBWg7EpP3ImLH51KvmVKpV9HBxsAVUDtrWPXQKWWZABAfSk27sh929bVDrN/BboxcENbrUFBDDCrPfXkM6w+24GAQOdWTdo8eNaFg0GlQlp0MAHCJrEIKAI63Dy/jsMXulA9aXp6adFJq4olYlXNzcnyNSrD2AAc/VG437YnJexGxwWJ3YtNRVup887wRAICDjf73lw6nC7vrWFAzrYyd4MWFUqNKP50m7IcAEcgdC6SH15eOghpi0LA5XHIKyGIP7wyitce98ilUuK9GEIDfXMxmjh1v64vP9uF+aJZy70adRg7cOPHWVbi1x4q99V2DvRqDRqzKuTk5vkYlHFwDOBTpH427YvJeRGzYUt0Oq8OFwnQTlk5lKafj7X3ot/neZx5u7kG/3YlUow4jc1nTuvhQapRtcK5GCqzDTD0BFNQQg0inqldKn58fqD/ae90b74XKpGI22f2KGaU4byKrkOq2OGSZfzjQoJr55NmsMt66Ct/84lYs+8uXOJ5Ag0jVtPey7TIzym7CHFmpURuF9/yHXWaUsctGUmqGEnzMwfwxuchLNSI7xQBRBA43+1Zrdkom4amlGbLf0BhnSg0FNURcom4A5++sw+pw4s0tNbJHRH6uR+O9UPnhwlF48upT8NAlk2HSa1EgeUzifXr3h3sacN2/NuF4W6/fyidAUWpaeoZ+V+F+mxO7azshisARaahjohFrpSbbU6npaweOfMquL7yPXTaG1tyNiC0WuxPf1nTiUFM3Grr60WS24EhzN9YfaAYAzB+bB0EQMK4gDQAzC/tiJzcJS34aQKXUhKmInywcTpe835qW3oNRmgaI0AAVc8N+LbK4E4OGerBev58f2+rdDVj51i5cNqMEf7hqmnw/Tz+F46kBgHSTHpdOL5VvV2SnoMlsxYn2Pkwvz2JNPFbfDeiTgPMeDuu1B5Pnv6rGpmPtWP7Gt3KPnuIM7wGIfFCc3Smio88md5kdihxq6gaPu+IlXRZreOAfbeM9Tq7nqIT9/wNcdqBgMjDpEuDdHwO9zUB3E5Dm3euJGDjufH0HPtrb5PMxQQDmjWZtKsYVpuHro2045Ceo+ZabhKXKJ0DlqXEMTaWmocsCp0uEQafBsrRDQBvQnjkZOUmZYb8WKTXEoNHRq6R8/KWfGrus0qW7UtMZo519eQ4zDh/nFVBNe4At/wQ2/gXobozqtU8m/KC/40Qn/vnFUQC+lRreVVj9nKGKesZNyxBf14GCN8nz9EZFiteoBF71NPkywJAC5Ixmt5tIrTnZbDveCQBIM+mg0wjQCEBGkh6lWUn44YJRsn9wfCFTanwVV/TZHHIPm2k+lZqhGdSckCqfSrOSMN2xEwBwLH12RK9FSg0xaISSfuqWuv16dv019zODcUZSdDv7imyPoIb3RgCAhl1AWmFUr3+yUB/0+YHQs/KJk5dmRFuvDU1mCyYUpZ+U9YsEtbzenKAdkOX0U4z61GSrlRqXE6j+kj0w8RJ2WTgZaDvMfDWjz4nJexLB6bc55enpX6xcJO/XfA1wHlfoP/20t94Mp0tEQbrR7aRGUWqGZvqJl3OXZSZhXMM2AMD+pBmYFcFrkVJDDBodIaSfeqTqKB7EcHiQk54UXVzOlZoTvKz76GfKgw07o3rtk0WfzSF/TnNG5cj3F/pIPwEqs/AQVz/2kVIT85JuPlKk2+KAtbcTEKXfHTcJF05hl8PQV9PVb8czn1XJRvqYE0UFZa1kkk0z6pCRpIcgCD4DGgAYI3lqWrqtbil8QG0SznS7f6grNdwkPDO5EamOdvSLBuwWxkX0WhTUEIOGm6fGr1IjBTVeSg27HbVSI5V4H2/rAxw24PhG5cHG+AhqeAl3skGLP149TU5VjM5P9bm8XNY9hCugRFHEAVVQM9QDsIHA7lTmm8VKqUk36aGTKmK6OlhVDXQmQCe9foEU1AzDXjUvf12N3645gGc/OxrbFxZF4J3bgT9MADprInoJflAvzU72G8xwUo06lGXzJnzuag0fj6BOPQHxoNSwQHOmk7UT2OIah9YId0+UfiIGjY4QSrr5Tr3b4oAoihAEAXanS55cnO5jHEI48PRTc7cV1uNbYLSrqqDiRKnhB/z8NCPy00145/a5qOvsxwhpgKcnXKn5tqYLH+9thFYjYP6YPBh0Q+ccp6HLArNFUecSUanhbQYEIfrgnaPRCMhOMaC524quzg7kA4BRlYLkSk3rIcDezwzzw4Q9dSxI9lQ3giKKwN7/sv1B+1HA0gnMvgWYeDF7fONfgG9fYde3vwSc9cuw140f1MuyQvu8xxWko6a9HwcbzThDUmctdqdc/j2jPMtt+XhRaipshwEAm13jw/+eJIbOXoxIOEKpfuKeGqdLlAOfbtXBLtrJxZnJevk1uvevZXeOOotddp4A+juiev2TAT/g88qmytwUzB3tPdCTw8vY1+5vwq0vb8PNL27FA+8MrTNzbhLm301Lt3VYNUgMBe6nSTfpoY1ymKUaruRZeqRt25imPJhWCCTnAKILaN4fs/c82ThdIpweLQu4gTbcnlg49CHwnxuBr/4I7H8POPY58Ob1wJdPAtVfAWt/rSy7+82I0lCyp0Q6yQoGNwtvqVb2Tx/tbURXvx3FGSacOiLbbXmTdMIy1JWaDBf73Tchy+2kNxwoqCEGjQ43o7DD5zLcKwIoKSieekoxaKHTRrEJ2y0Q2o+iQvLVaKslP82Ei4DMCnY9DrwF3ESbn+bbGOzJeZMKMWdUDiaXpGNaWSYEAXhjaw02HGweyNUMC26CnDuKBWdWh8tNuUkElF5MsUk9cZL0LBXh7Jc6NZtUSo0gxL2vpttix5zfforrn9sk32exO+VeVH1+9jV+2f8/dlk+B1j8W2DWTez22l8DL1/KfEkTLwH0KUBHNVC7Nex15kpFeYhBzeLJrIBhzZ4GuTHlm1tZ6uuKWWVeQbBR+s6HolKjNkknOdk22SGmkVJDxB/qkm7/So0qqOl399dELcm/vxz4ywzcKHyAZFiQ0Salm0YuAIqmsutxkIJq9lBqglGQbsKrPzgd7/9kPt758VzcMKcSAHDf27u9vEuDBTcJTy/PdFNrEolYl3NzTNIBzmWRPEtGjwq4AjY+JBJfzfYTHYM+IHZPnRlNZiu+OtImbzNHmnvknkdhKTUuJ1NqAGDRfcDpPwKWPcmCGwiA0wrkTQAueRoYv5Qtt3tV2Ossp5+yQ0s/TS7JwMJxeXCJwN82VOFEWx++OtIGQQCunFnqtbxpCI9JUJuktZZ2AEC7mIZuiwO2CPrqUFBDDBrqSNzfjsa3UsPuS48mqHG52MwbAJe3/g0P6F6GRnQAGeVA1gig6BS2XMPQn4PDjcJ8Ane4rDx/PCpyktHQZcHD7++L5apFDDcJjy9Kl4O1RCvrjnU3YU6SgQU1Ildq1OknQFFqmvaG9brNZguu+NtG3PD85mhXMSrU3cF3nGDpGXX5s7+iBJ/UbgX62gBjBlB+hnL/6T8Crn0DmPod4JpXWY+fKVeyx/a+DThDV4NEUVTST1mhKTUAcMci1lPore21+PM65kWZNzrXZwqLj0kYikoNV6nKspMh9LGgpktgRQ6dEaSgKKghBoV+m9NNnfG1oxFFUfbUAEraqUu6jMok3HqQGf4krtGtZ1dGnilJ8DyoGfpKTYsk3eZF2B04yaDFY1ecAkEA3txai+0nBtdHZLE7cayVHZgmFKUpox3iXKn5ZF8TfvO/ffL/FgzexylW5dwcnn4SrJJSY8pwXyB3LLtsPRTW69Z3WeASgaOtvbA7B+/gWa36fHdI1UCHVI3qesNJPx1iJz4Ycy6g9djfjD0fuOxZIHskuz1qEfMj9ba497sKQle/XR7sWxpGUDOrMhunjciG3SniP9tqAQBXzy7zuSxXaoaip4arVBVZekDaJl0mZn5up6BmkOhtBeyJdRYZLZ4mMF/pJ6vDBbtTMd2ZPRrxRdWj5sTX7LJiLtrKzlPuH7GQXXKlpu0wYBvac6F4aXZ+emieGl+cOiIbi8blA2ANvAYTPh4hJ8WAvFQj8iSvULwHNQ+9vw/PfXUM5/zhM9y9amfQNA2vfsqO0TBLjhzU2KQDvWf6KXcMu+xtCcso36NKFXOPxGAQU6VGUnMxbknwZbV6YNJl7PruN0N+C35Qz001yipaqNxx1mj5elayHudO9D3aYigrNY3S/mtUinRMEDTQJbNAOxJfDQU1KiKqrmg5BDw5CXjnR7FfoWGM58bqa0fT7WEMlT01sVBqTnzDLivmwnLxs9jgPAUnxHw4Rp7N7k8rAFILWBVImDL8yaZFVdIdDVzp6Yqw6iBW8MqnCUXpEARhWCg1DqcLdZ3s4OV0sTPrRU9swD3/2SWnHjyJdeM9jkk6cGps0pBQz/STMQ1IK2LXW4+E/Lo9VkVV5SnRSKhp73NrzBku1a3K57mrtgsOp8ttTlLISk37MaDlACBogdFnh/acqVexy/3vA7bQvEVK+iX88vl5o3PlwZWXTC+RgxdPhrJSw7eVEqP0eSVlITOVfRZq32WoUFAjseNEB2Y+vBZ/XBue5IpDawCHBahaF1VHyUQjFKVG7acBlGBGUWqiCWokpab8dBRmZ+FW17040/okjverDiCFQ98sbHe65OGE0QY1GZIhlaf3Bov9DewAxMtWFU9N/AY18sA+rQZv3z4HC8bmwekS8cbWGix6fANueXEL/rT2MD7d3ySbOWM138wTrtRouVJj8jEqg6s1bYdDfl31SUik31V7rw1n/+EzXPOPbyJ6vsslykqNRmBevS3VHbIaAAAWuyu0CfXcIFwxB0jKCrwsp3Q2kF4C2HuVfUwQIvHTcARBwB+vnoYfLRyF5eeM9bvcUFZquFeuSCcpbEnZ8jZP6aco+PxQK9p7bfjj2sN49rOq0J94QiobtHSyNBQREu0eB2JfRuFuzy7CFg9PTaRBTVcd60EjaIDS2dBqBMysyAYg4OWvjyvL8RRU49A1C7dJ08p1GiHqgx+vJhvsoOZIM1MQxvKgJjX+lZraDqbSlGQlYUZ5Fl686VS89aMzMG90LhwuEWv3N+PJtYdw84tb8fNVbHtTSroHJv2kd3ClxkdQkyMFNa2hBzXqk5BITd3Vbb2wOVw43NwTWuDhQaPZAqvDBZ1GkHu1vLHlBADIg1wB/9WWbhxczS5DST1xBAEYuZBdP/ZZwEU50Sg1ADAiNwX3LB4fsBp0KCs1cp8tnaTUJOfIbQwiUewoqJFQ/wgfXXMAb24Jod21KAI1Si8EtB4cgDWLHZuOtuHh9/eFl1MeIPjGWiJ10PSp1PhNP0nVT5E23quRzgILp8hnqT+WKgle23xCGR/Ay7prNg9ZFY5vt7mpRmiibNDGd4rcyzFYyN1FpSoOXtUVz9VPvGy1VNUxdmZFNv59y2n43x3z8MCyiVg6laV8PtnXBIvdKX8PMTcKS+knvZ17atK8F+JKTRhmYfXvNdL0U7sUpDtdYkTtBbhJuCw7GbMrWVCzek8jAGBqaQb4BIKgKSiLWRmZMnZxeCvBg5oQzcLcUxNqj5pIGNpKDdtWsgVpe1QFNeSpiQIeLY6UWsvf+/YuLH99Bz7d3+S/Vr6tCuhTqTNhVgucbH7z/j7888tjWO+ryZooshzyoY+BnoFvwtYu7bCLM9lO3uZweXUA9Wy25m0UjvAMlvtpVCWac0fnYGZFFqwOF/7GlbrK+YAuieXVq9ZF9l4DTLTl3GqGglLjdImol7wnpdJOPm8YeGq4UuOrumVKaQZunjcCT10zHUUZJvTbnfj6aJucoo118z3ep8bglJSagOmncDw1MUg/qdINbREc0Kol83VlTrI8KoDvv8cVpssqVdATu6a9gMsBpJcCOaPCW4kRZ7LLhl2AVKIcCFmpiSD9FCpGlVIzlDpz2xwuOXDJEKUCheQsCmpiAS+LXbl4HK45tQwuEXjn23rc/OJWzPntOt9mvhqPvG8YUu3JptfqkA2YboMMXS7g7duA35YDf54GvHolsOqGAV8frtSUZipnrp5qjZenxqOjcMTN91R+Go4gCLjzbLYjf3WTpNYkZwOzbmQLfP54ZO81wDTHyCQMDI2gpslsgd0pQqcRUChVc/FOyR199oiacQ0FlKDGf4pBEAScNZ5VoH2yrwmd/QPTfI8f2I1OaZ9mzPBeiKef2qpC7rnSrQ5qIhyWqj6IRXJA436aytwUr6GO4wpTkWxg6m6vNUhQw71Eef59Kn5JK2QN+SCykQoBcLlEedsIdURCJHClRhThVlE62PAqOZ1GQJJd6puUnCOn0iMZlUBBjYQyP8eE/7t0Ct760RzcOLcSmcl6tPZYfbeQ5wfHFLYjGspKzc7aTrmjptsZUN1WYNfrrD+ARkrn1G5lnTQHEH5GVphhkiVhz/bl3FOTLMnlPO0UVZ8aS5dSzVR2uttD88fkYkZ5JqwOF579XJrkO+cngNYAnNjI5rwMMTznPkXDUAhq+A6+ODNJbvWemaRMlh7MUuFo8JV+8sXZE9i+5INdDXLGMzMp1ukntts3ufxUPwFARhmb3u2yA53HvR/3QU8MjMIdUQY1vAdQZU4KslIMbkNdxxWky/uSfnuQQI2foPLgLlxCTEE1d1thc7ig1Qgoyoi8JUMwuKcGGFq+GnU3dKFfUrUo/RQ9oii6nfEKgoCZFVn4fxdOwnWnsxlAfMKrG9wkPO0adtkydIOaHSc65eutPaoNhZvhJlwI/KKepVucVpaKGkA6VHNt+JmjxeZ+Fs53kiWSmqOkn3hH4Qg8NbVbWJl2ViWQXuT2kCAIuFOqIHhhYzWe+/IYxLQiYPr32AKfPxb++w0w3GeSF+Lcp0BkDoHqJ2Wwn3Lw12iEuK+ACpR+UjNnVC5Meo38HaQZdTGfns5+byKSXVK1ia/0k0YD5Eg9UEJMQcXCKBy1UtOqKDUAMF1Sa7QaASPzUuSgJuiohDYpBZ0zOvBy/hi5gF0GMQvz1FNRhim6OXZBMGg18snjUPLVyD220oysczPAlBoyCkeHWTVjwvOMd1Ixk2Z313W5P6m3TZEop1/HLrtOhNyb4GSzQ9Ultr1XdWA4KJUtTrwE0BkVubVlYCf0qof1yTsaj7MnLmdz341c0h2uUuNyAXvfAd76AfDWLew+dctzFWeOycXVs8rgdIn4zfv7cPeqXbCe9hPWq+LoemD9/wEf/RJ4fwWrohpkBiL91GdzBk/z9LWzScWH10b9vmrkg3+m+8E/nn01DqdLLikuC6LUmPRaeYgnAGTGuPIJYMMNTbBBC+k79lX9BCgH9BAVaPfmezYvj1woRBPUuFwijktB8YgcKagpzwTAPDYmvVbe14ScfsqNMKipmMv2Ge1HgQ7/Slc05dzhIAgCjHxSdyiVXyeJZlWGRA5qkrLl0SBU0h0hfEeZZtLJJjrOlFIW1Bxq6nYfBsarnnLHMVNdkjTqPQxj3clCFEVsVyk1vAwY7UdZ8KLRKc2l8iawy+YDA7pOHaoeHPwz9zx74n0veIWU2eKAxe6EVTrgZoTqNdi9Clj1fdbls7+DfVc8EPVAEAT89vIpeGDZRGgENlfl3nVmYOrVbIHPfgd8/RSw9Tngqz+G8y8PCLEMatJUQWJQtWbb88C+d4GP74/6fdX4K2+N5wZ8co8anQa5IYyyOEtKQQGxn/sEMKUmDdLJl6Bhc4t8IY9LCM0rqPbUOF1iREqL+iAW7vMbzBbYHC7otQKKM5lyedEpJThrfD5+chZLI3FPTcD0k9OhKNWRpp9M6UDJTHY9gFoT7iDLaJAroIaQL43vvwrSjYAq/ZQlBfMWuyvsal0KahDYl1CcYUJWsh4Ol4iDqq6Uskm4/DR2GeG8lJPB8bY+tx2E7KnhKo26uVT+eHY5gEqNKIo+lRqLV1DDDqw8/eR0iWiSzngFAUg1hJh+OvIJuxxzPnDjGuDuw0DlXL+LC4KAm+eNwF+umQEA+OJwK3D2A8C4C4AJFwHjl7EFecnnINIaQ0+NViPIZfJBgxpugGzZD3RUR/3eHMV74lupiceybh6olWYmhVR2z83CQOzLuQEW1KQL3CScBjkv4Ylc1h1aUKPuKAxE9l1F46lRl3PzVE5Gsh7P3TAbl0wvAYDQlJrO48xLpEtijfQiRfbV+A9qDjezY8pAKzWAqleNL6VGFAelbUVLN08/mZRKseQcpBp10GvZdhmuWkNBDQIPBBQEAZNLmFrj5qvhZcHcbJoX3lnNyWRHDUs9ZUnKhmy2lJtLXaAszJWaloHrudNjdcgO/KxkxVPjqdTwHH1emlE2ivIzmzSjLrS+LKIIHPuCXT/jxyyA04YWDM2uZIFee68VztQi4JrXgKtfBpY9yRZo2gv0d4b0WgOBKIrKiIQo5j6pCamrsMOq+MkA4NBHMXlvwP+ZazzPf1I33guFoowkTCxiKaFYl3MDrE9NGtg6+ax84siemhCDGklZ5R6gSPxPbVEENdwkzFNPvpCNwoHO/mWT8GjmLYoU7qs5stbnDK1eqwPrDrAClDNG5UT+PiESUKnZ8k/gN9nKvvIkwVtSFKZq5GGWSM6GICjNRMP11VBQA8Ws5O9slwc1sq/GYQXqd7DrvCxYVmqGXgO+7cc7AQBnjWfDzrotDli723w3l+JKTeuhkEs5w4XP80jSa5Fk0MrNwDxLunn6Kd2kk3vS8DP5kHvUtB0BehpZBVPZqWGtZ3aKAYIAuESP0sLUfCB7FACP5osnmc4+O2zSNORIJ3R7wn015kBBTe1WwNGv3Obt5KNE7T3xr9TEb1ATzgTmC08pBgCMzk+N+fok6bVIFXhQ46PyiRPGYEtRFOWTEB5UhFvWbXe63EYtRKrUVAQKaozshCagUThaPw2n7HR2XLB0Ap/8P6+HP9rbiD6bE5U5yZhZEeIYhijgSo3Vl1Kz9TlWQLH/fwO+HmqaJKWm2CBtj4JGnhrPA3r++wkVCmqgUmr8BTWSWXhvvRTUNO8DnDbmzeBj58PMP59Mtksm4YXj8mTFo3/fR4DoZMpM9ghl4YxyQJ/M/r+OgamAavdoKuavIRY/80sz6eW0CN/AQ+5Rw9MkpacC+vDy1jqtRvY0eCkEFZLReBBTUPwAn5Wsj1mFjNxVuD/AAYV/pnw2VvWXgLXb//IhovaeeAZp8eypCbWcW82tZ47Eq7echpvnjQi+cJiYVJ4a0VflEyeMwZZWh0tWX0fm8aAmvO/K84w87KBG6lEzItd/8Jgsq8IBTtiiLefmaHXAhX9i17e/6LWveHs7KzS4bEYpBH8pwBjiV6kx17NjGqBcniT4NlLARyQkZQEatp68I/TfPqsKa2QGBTVQTzn2LeFPkZSaAw3drCqkcTd7oHCKko9Wd+Ac4B4v4dBnc+CA5AWaWaF0asShNezSc66JRqMEaM0D46vhOy9uBlPMe749NalGH0pNqJVP1ZKcOmJ+ROvKjZ1e/VHK57DLEIfWDQSx7FHD4T1RuviohLrtwN/mAf86H7BLZ978M511E5A1ggXAIbaED0Qg70k8Vz+F0njPE61GwJzRuV6FC7EgyaBFmuSpcRkCKDVAyIMt1eXcvDdMuKqap3ci/KBG6iacG61SIwVwuVEGNQBLd8+4nl3/33Km8gNo6OrHV1WsG/2l06Pw7YSBX0/NkU+V6wO0z/eF0yXK+9VcrTIigfOTs0cjxaDFzppOrNnTEPLrUlADZUdZYuz3aZYqy05CukkHm9PFjF3qoIaTWQFojWxid1cIc6NOErtqu+B0iShMN6E4Mwk5qUYAIlJqpQOTr7km+dxXMzAVUHxnxXOmSX56R/BqijSTTg5iaqQDREg9akSRqQgAG3kQAblp/pQaKaip2w7YPeTR7ibg2QXAixeycvIBolltsosRPHjs6rMBG/8C/Os8oGk3M8Zv/RdrWVCzmS084kxl+zkYfQpKPvj76KyqHmo5lNq8h0JdBOmngcSk08ieGqc+SHqLqxVB9gVcVU016lAoNZEL1yjM9wu8k3S/3Rly5YvTJeKEPCIhuKcm4OwntacmFpzzIJCSx6wJG/8MAHhnRz1EETh1RPaAdhJW41epqVIFNX2tQE/LSVmftl4rXCLTBTJckp+GVxGD7dd+tJCNqHjyk9ALcCioAdtRnqnZiYs+mgtseNTrcXezcBfQuIc9wOV3gElmcl+HoZOC4qmnGRWZANik2hyYobd2ABCUoY1q8iRfzUApNf7ST6ozCJdLydGnmnRyEBOWUtNygPkBdCagdFZE65rnT6nJqmTSvMsO1G1T7u/vBP59GdDwLUvT1G93f54zdo3tYlnOzclI0mOScAwX7fwhK9d22YH8iezBzx9nlWQuO5uJkz0SGCcFNYc/ijqAq233n6bhSo3N6ZI7S8cDdqcLDV0nr2w3FHRaDTI1bJ3suiBBTfF0dulpIG3cDXz7qnwSKP9WjTp5ewxbqZGCmrLsJBik6qVQK19q2vtgc7pg1GnkaklfBDUKW7qAXql7fKyCmuRs4NyH2PWtL0B0ufD29loAwOUzTo5KA/hRapwOoGo9u847yg9wjzIOTz3lpBihtUqeLZVSAwC3zB+J4gwTGsNIZVJQAxbULNDsYje+ecZnAz0e1Oyt7VQpNZPdF+Jy5bYXgP/cDDy3ZECriELhm6OsTI4Pd8tJNWCUUM8ezCz37TMZAKVma3U7rvn7N/h4b6OXUqPsaJSDVZ/dKYtm6Sa9HMQ0SRt3SEZhviMuO401FoyA3FQ/aQ9BUBr48Vy5rRd49SqgaY+y3IEPlOvHvwb+rxjY8LuI1sUTPvgxVpVP6GnGZdUP4gPjLzGiZzsLBpf+AbjtC5aS7G9nTQcBls4TBJaGM6Sx4JGb5yNEnoHjQ9Ew6bVy8BvQ7zNA5vZIaeyywCUCRh8+ocEkU8s+a5s+SPppzLnssn67MujW5QJeuxZ450fA/vcAKKb+VJNOrlSL1FOTnWJQ2uT3hBbUVLWwkQ8j81IDVkXyVLff9BP3DqUWwqZLhd0ZI6V14sVMyTfX4tDebTjc3AOjToMlU4qCPzdG+FRq6rczI7MpAxgl9So7SSmoFvVJmdxNONttGZNei5WLx4f1ugkf1NidLrT32ZQDvbUL2PeO13I8qGmqOQTYulk1DfeecPjtA+8De/7D5gVtfX4A1z4wFrsTm4+xjWX+mDwALCoepZH+V8/153ClpvVwzA4Sr246ga+PtuHWl7dh1TZ2lsJ3XL6a73E/jU7DOmF6BjEhGYWrJUNrhH4aQFEIWn3tXHkK6vhGoLcVePVqVg1lygDmSQd/XjYPMOnZaWPbRgw40MDy0OMKY1Qh89bNGNvE1veblLOBH28GZt/MDI9n/4otw3c+fBKxzgCMPotd3/LPsN6uq8+On6/aia+OMG9BTRBDLR/j0NnnR+3a9y7wUA6wOzafbyzg/1NJVtJJMYOGSoaGpYZsGv+pGgBsOGPRKez6Eal7dM03rHs6wE7g4FupCTdV2CYHNUa5TX5bb2iB0ZFmFtQEqxZTxiT42a9Jfhpn9ijM/d06XPb0xrCbv/nEkCwXFzRuZ37GcyYUuCvOzQeAHf8esH4xRl9KDffTjFyo2ClOkllYTp+nG1U9arK9lrvolGJMLg5gaPcg4YOa9l4bRBHKgR4Atr3otRz/UHUt0jDEvPGA1uPAOuVKIH8S20B4g7aGb2O/0mpObAI2PgWsexj44G52BvW3ecAfJuH4hhdhsbuQn2bE2AL2Y3dTavwFNRllrALKZWddh2PAUancElAidL7jSvZR0q1UPukgCEpTOI7nbS9cLpWf5syI19uvUgMoSk3NJuCZ+cxAq08Grl0FzF3O5NyWA2yOTHej0s+l9ZDyI44Ql0vEPmnq+sSiAL1GQqV+B3Dsc7gEHS6x/gaPp94NZFUoj49fBpTOVm6rPUqn3gpAAHa+Cux6M+S3/NeXR7FqWy3ufP1b9FodQacV80Z0nf7KzXe+wS4Prgl5HQaaSMq5Q0IUWZnwmnsjSvtlSCXdFm2QoAZgTSsBZftVB41V64COarnxXppJ55YqDGeOmKLU6JHDZ/+EmH7iQc2ovMD/T9DZT5Ihuiu5Ai3dVuyu68Jv18RIuZCUkNxmtl/i3eoBMJX3pYuBd388YNuvT6WGB6qjz1EU+pOk1HAljyk1SuM9TzQaAY9feUrIr5vwQU1LtxUmWFEitEr3COxMxOOLrcxJQapRh7FiNbuj0JcXZSxw+0bg+neBs6T28Q27Bq4aylwPvLAU+PiXbNjiln8ABz9gxk5zLZJ3vQCAqTT8LDEnRR3U+HH3azRA3jh2PUb5Vd4Ya8U5Y5EqVSCUSwevJB95brNKzga8001B00/121lvDX0KUDIj4vVWlBofQU3+RKbK2PuA7nqWg7/lU9ZlOimTzX8B2E5q52ushJ5Tt9379cKgpqMPPVYHDDqNXEIbFRufAgC0Vi7Dt+Jo74ORIDDDIwSgYAqQWaY8VjkPWLCSXf/f8pA8ZS6XiLekktbWHiue/axK1aPGj1LDy819HehEEaiVDMwD1IogEgL5hKJi3ztsTMemvwUdmuiLNCmo6Q8pqDmPXVatY6Z4rmSnSJ2Pt7/kZhQ26bWyktoURgqqXVLgspINilITZvopuFLD9ieZllq2z7R4zPSTtt3O5Er5rhe/Po7PDsXAPCuNohnd+y2MsGGkukrrqz+zflqAu7obQ7w8NX3tiudv1NmKd655/0npLtysrjpWDbP0ha/iAX9QUNNtxQihERqIrEaed9fd/pLbchqNgInF6ZggSMPJJKnO6nDi/nd248M9je4vnDuWnbXbewduHtSBD5iaklEGzL4FmH83sPQJYNkf2Sp0H4QAF+aPUQbk5aQaMVKQyuMClSzGcAZUR69NPkjeeuZIfLTiTDx73UzMH83Wy5dRmMvZaUa2c/Q0Bgc1CvOJ2uOXeitqYeC3pBtgwd/oc9j1SZcCt24ACiYqj49fyi4PfABsf5ldN2WyS34AjpB99UylGVeQBn0k03372pUdV2cNsPe/AADz9NsA+OkoXDkX+OEXwPd8pHcW3MPUG3sv8Ob3vSvCPNh0rB11nf1yR4SnN1RBFNm2kKPuottRDVR/BSDIFPGOaubrAWKmLsaCSMq5g2LtYUNVOVv/FfZLpEp9avqFEA4WJTPYwcZqBjb8lh2AUvKAxVJRxY5X0NvP/k9+wpIfwVgLPmg3J9UQllIjiqJKqQkt/fQdy5tM3V5zr/sC0r66xVjmdvfPV+2MaGK0G/kTIaYWwggrZmkOKicj5ga5KgoAcPiTAQkq3JQaazcbziu62L4+o0TqoKxj37M5dsN6u/rs8ngbNe7pp8BBTTgkXFDjmUtt7ra4p2Nm3sCu73xN6cshMaUkAxM1PKhhJuEP9zTi39+cwC//u9u9QZBGq+QoG3bG+t9g8O6Pp93GgpmzH2DBzfTrIOqSkAQLRgoNmDtaCWpyTS6UCdLO31/6CVA6C4eZX7XYnbj3rV1491vlR8FTT8UZJiQZtCjJTML5kwplQ5+vkm65R42s1LinmwIOs6zZwrrcChp2sI0CrtS09drg8GUavPDPwA/WA1c8792dlfcAOrERaK8CDKnA/LvYfbVbolqvvVJQMymMXLPMiU3AY6OAf57NAoBNzzAVacQCGEunAQiQ4imcwnwWnmi0wOX/ZGfvzXuDpqHekqo/rpxZiglF6XBIv51ST+/Ja9cwNbJpnxzU8I7Ubqg/z/6OqNN7seJgE/M9jQzQOyVsvniCHXRSmE8OB1Yz1TYMeFDTK4SwXhotMFoyDG/8C7ucdCmbg5acC/Q0Ir+R+df47zU/XQpqwlFqehWlRjYKhxBItPbYYLY4oBGUHjn+4EFNmquT3bHzVcXg3rxfLuyo17GgZsHYPIzKS0FztxX3v7vH8+XCQxBgKWejExZod6M8W1rXdQ8ztbdkFlOWexoH5JjBlBoRkxvfAf48gyn7ADD9u+xSZ1BK+GOYgrrmH9/g3D985nVi2OzLKJzk7akJl4QLajzlzJZuq3s6ZvTZrFy1vwP45znsjEiqopmeJ6KUp6kKWFCz/TgrRWvrtWFnbaf7mxVNY5f130a93ha7Ux5TD4DttLlnhPt3OFodOtJZ+uj8rEa35myF9npoBBFdYoqyU/RFhAHZ6t0NeH1LDR56f79sEpRnsvhJk8gDLX14arh3JiylZv3D7PKUa6NudZ6dYoBGYCdOPstLjansTNaXCTSznKVqOJMvU4bc1W6LqgRa9tNEEtTU72BnaHXbgGfOVMzsc34qB4s2h8v34LtApBWyhnyA0qDPB302B9bsZmrhlbPKsHLxOPkxNz+N3OlUBGq+QUYS99T4+B5qPJSvIZCCsjlcOCQFNZOKY+B7Alh1Dg8sLvwTqz4TnV7KsheHPgZevkwOfpJFKahBiLL+WCkFxVOok69gB0HpgDi58W0AbCYbABTwCqgwyrq5EpKjNgqHkH7iKk1ZdnLQZoU8/WRyqU5YP/wFU7/e/D5TvkefgxqRjZQpyjDhyaunQasR8MGuBnzopwlcn80hVyMGoiabjdU5S7+HdQFv2Al8+wp7cPFvlf3D4Y+Dvla4GHVanK7Zj0trf8fK1rNHAle9BJxxh7KQ7KuJjVnY6nBiX4MZZosDXxx2T+HxgDcvzaSM4SClJny6+r2DmtEaSVXIHcvOSubeyW437Qa+fgp4cRmw+R+YZmDL1Yl5cEqD4LadUGaifLq/2f3NiqexS24WFkXg9e8Cf5mplEeGyAPv7MHCxzdgmxRE4dCHbAdTMNl9zIHEPrDxDQvT3WXErP5qAECVWIS+QActHpB1HAtraCMf0NbaY5Xz6Xwmi7+mWEl67zLLblWOHvDlqfFjFD72Betuq9ErPo8o0GoEZKdE0c12vGpY6PTrmZFcn8yq7KKY6M7TT3zwYVjwsyKNnlXy2XuZBD36bKQZddBKClo4Jk+ZynnssvpLvxL6h3sa0WtzoiInGbMqsrBwbB5OHcHO0MrVQQ0fGgsADbvkgaxdvqqf5BlcUnDZPvhBzaGmbtidIjKS9LFJPzXvB1bdIB14z2Wp8tk3s8e2vei/UrG/A/jvrazJ2g52AE1ysaCmWwxxvUadDQhSwJBRrsxRm/F9AMBo8yZkwSwrNXnp4aWfRFFUWj2EaRQ+0hJa6gkAko3sf0iCar1ObASeW8ya46UVAZc8gw5p289MNmBqaSZuO5PtT+9/Z69PT9ddb+zEgsfWY3+D2esxNbuMM+ASBYx2VbPg9IVlAERg0mVA2WwleIzhkFiOSa/BaEE6HlTMA27fxErN1Sdkal9NDFBXjX55uE2+rh7GW5AiuA2zjJaEC2rMFvcffkuPFaNkj4mUjjntVmDFPuCyfzCZFQA+vBfFR14DAOx1leNYaw/6bA7sb1Bm3nx6wCNQ4YFBwy52Vt6wk5V7tx0B1oSXFtlR0wmnS8T7uyRVaf/77NJTpQHbYNZ1sf4H41zu/gJjJ8sZV7mKA58FJWeznRcANO4KaR3tTpeboY4PAJWVGj/SsC+jsNJNOExPzfpH2OXM77tX70RBbirbwfos6w7GpMtY8FAykzUA1OqAYsm4HGEKqq3HikazBYIAjI8mqJn7U2DhfayR4PmPAILgVmkWUVBTOpv14+hu8Ott4amny6azmTeCIOCJK0/Bd08rx01zVQG6Oqhp3K2UdHuul62XTUwHlFLzGAc1//ziKG55cQt6rX4CBx+oA8+oyrkdNuZleWY+O9EyZQJLfscORhMuZCmg7nr/g0U/+71yJty8F3DaYRTZQd0cqlKTlKkM7518mXIgzBkF5IyGBiJO0VQhVfLA5Yeg1Lz0dTWuf24zui129Nqc8nDW7BSD3MOqLYT0U1WI5dyAMvsphQc1vIqvaTdLV1/+LyA1Tw6ceSD907PHYHR+Klp7rPjN++4qhssl4ovDLbA7RXyyryng++/vMmCPWMlufHw/O5iXzGIqDaCYsuu2sTYRAPO/OKIfD2LUaZVMQ+EUprR5EmOlRn0i+NWRVlm97+pXDePVSpWxgkbxHEZB4gU1HjvEVnM/Rvoqcc4oAaZexbwSky4FXA5o9jKJdZ9Ygd11XdhZw0YQZCXroRGA/Q1mdwkydyygS2Jnw+1ViswIAHvfDqu1PN84PjvUwnbivLX1hAu9lj3c3IOv+lhOOL1zn1uaQ5BKFqvEYt/mVzXFUhldiCmordUdblN2eVDDPTX+qnR8lXQH8tToNIL8HDe6G9ksJkEDzP9ZSOscClHNHcofD9yxBfje28qBgHc3jtAszFNPvCIvbPolv0lKPrDwXuDOnXJlBqAaaumvH0wg9Cal9NtHCqrZbMHGKhZUXabqplqWnYxHLp2C8hy1UqOaq9W0Fxkm9p17nb3XbWeqZXqJ0pMohmZhh9OFJz85hLX7m/HxPveCgKMtPX5/R3wA7uSSCAJPNZ8+yDqdu+zA2CXA7V+zYAJgTSWnf49dX/trYOfrLJXCaT0MbP67crtpr9vwUbMrDAVp8aOsfH/ecvf7S2YCAE4RjiqeGv6bCeCpeXFjNT4/1IKP9zbJTfZMeg2SDTrkSCcSoZhzq2SlJrg/SKfVwKDVIFmQgppFvwDSipXrlaxikW9jPJA26bX4/RVTIQhsEOWGg8oJbG1HP3qlE7KvqxQ1whdHW3qwwSXtV7UG4Oz/B9z0EZDG0l1IL5ZS/yIzDO//H/DEeOZ/i9I8bNJrUCpI651Z7nshufHqwZhU7ar3mY1mC6pa2LGAB7uZyXoYbVIFWlIWK76IkoQLaros7jtqwVwLk2CHS2Ng85s8EQTgoqeUaiAA+10V2F1rlkcQzBmdK3fsdVNrtDrFm1KzSTFPlklnPB/cFdJ0Y6vDKZ81H23pReu3H7AZU1mVQMEkr+U3HWvHEbEEVsEIwdbDAiqOVLJ4VCwKbsLjTbdC9AStl37oRmli9J66LoiiGEL6ybshlrpPDV+GTxhPT9L7PvPtqGaXGaVs5xAj/I5KCJXsEexMl8Ol+9qtEb1cVKknIGilAQ9qIlJqAPcUlAfVbX0QRaAyJznwzBuLWenMrDUAjn4U2dlMNa/0Ew8OS2cznwAQ06Bmf0O3fND67KCiRFa19GDxn77ADc9vZl6Xx8eyURISe2QzdxR+GlEE9rzFri95DLjmNe9te/bNgDGD9Vj5723A42OAt25hPpqPfgm4HEwNAJhKLKW++0UD+pxhKEhFpwAXPMYOPmok5XGq5qjsqeHpo0BjDni6+eujbfJy2ZJCw43Cnf12OINMaA5HqQFYCkpWalLygevfYQrNPOVEqKNPST9xZpRn4ftnVAIAXt+szPfb36iknLad6AjoRTva2ot/Oi7AiWk/A279jBUOaD1OTHhfoHUPA298D7D1sC72UaonTKmRtl9/QU1WJTsRd1iU/WkUeJ4I8kabh5vYd+beTTh6Pw2QgEGNp1KT0VsNAHBkjvDeuDjGVOA7rwDGDDg1enzrGoU99V2yv2VmeRbOmsB6Nqzb7yE/cl/N54+xdtTppawkNquSVTB8+lDQdfbcMLq/fYddGb/Mp0G1urUXTmjRkiw52bnSIopyUFMlBkk/AUDRdPfnB+FT6X//7mksONxd14UmsxX9die0GsHvQSxJNgq75AoyrvjwnaQgCLKvxm/jPf4j9BWcRkFurCdE8wNM837WJ8Npdz+7DgKvfIrIJAwE7N4JABnSjjwmQY3H2SX//WUk+5C+1dRuZmbmrEp5/lBeL/MgeaWfaqQ0XtmpbGo4EFOj8JZqpZLqi8Ot8jb6v531sDlc2FNnhmX760BPE/NJONmBmPsrolJqWg+xVJ7WCMy4zr8h/UdfAgt/AWSPYpU0u1cBr17JZnJpdMClz7CDhuiSg8BuJIdvBveF1AeKpZ/Ybzk7NXj1Up9K3eiQ/TTsebwnkSgG9tX0Wh2o72IBSiieGgBI1mmQDOm3bEhhPbmmXOGmEnDfTJbHdrp4Mqv8UxeFHFBZEGwOF76t6YQvrA5W7GFGKkxnrXRv/6CGp6DMLE0rp2T8pRdDhCk1UvrJX1Cj0SpqjVopjRC+z+Q+vS+lFNTfP2cn2ovG58dPUPPCCy9g8uTJKC0txezZs/Hll95nbZy6ujpcffXVqKysRElJCVasWAGr1f0A8s0332D+/PkoLy/HmDFj8Pe//93PqwVG7anptTpQ6mQbjiYvQHkzwOTeH36BE5f9D03Ixr56RamZWZGFs8cz+fCrqjb3snHZcFvNLqddy0p/pV4y2PovdlYaAPXBNB09KGpYx274SD0BkKukerIlFYeXLJrrATsLeI6LBWgN1oKcKzVtR4IqSsfbelHV0gudRsAPF4yERmDr/c1RtsGWZyf77aeSpKpYsDjYjq7Hw1MDqCqh/DXe65DK7WPkpeFErdR4klYg7VREVnHx+Bjgt2UBZyetP9CMg43sO4iq8gkIHtQEanIXCgF8NWZJKQ3aEZr7acrPkBtdZnTul9dLbp+gbrpXeqpimu9pCitQDIQ6qGnrtcmf/5rdSirKWiWl2iydwNHPUN3Wiz6bE0l6LUbkRjHGgg8brDjD95w2TmY5sPAe4CfbgFvWAafexrw2AHDGj1llJ1d1j7ODVbeYFJsRAIVT4IAWuYIZmXZ2YsMVl84+m1+lhb93XWe/HCRwhUan1ahK+P1vhzz1lJtqcFNVApFhBPSC9H8bfKvHHR6eGs6UkgxoBKChyyL3XjkgKTX8wO0vBVXT3geXyIof8gINoS2dxfyMGh2rcDvn/7H7ozQPJwtW5ArSscZfUAMovdpiMG6kpYd9RmdKvdK+qWrDp/ubsbO2C0l6LX4wf2RMy7mBAQpqXn75Zdx3331YtWoVamtrsXLlSixduhRHj3pLwjabDeeeey5KS0tx5MgR7N27F9u2bcOKFSvkZQ4cOIDzzjsPy5cvx4kTJ/Duu+/iV7/6Fd58M/SW7ByzSrpWl3Pr8sf5e4pCVgXKJpwGo06DHqsDnX12mPQaTCxOx9iCVJRmJcHmcOGrI6qNmis1nGnXsstRi9hZlcvBpjn7Q+USTzFocaP2I5jEfoj5E9lO3Ac1UsMvUTYqS0qLVG3TaSqFA7rgSk1qHvMpQFSGePqBVz3NrsxGfrpJloLf28k+38oc/6kGdVDDd3Syp0blGVGUGj9BTacU1GRWBlzXcMlN40bhGAU1gPLdHV3PTJyiy6/H6lBTN258YQsu+PMX+N2HB3BU2pFPiiT9JIohpJ/YZ+6paoaMp69GFIG97wB735FfM2hHaDmoOV2eJG9qY2Zgl6gYydF+lP0/WgNbLilL2TnGQK0RRRFbqtnJS4FU0fPZoRYcae6Re9AYYEdKiyog3fdf7JH8ZOOL0uSDXUQclYIaXuobDEEASmcCF/we+NkBVuFy9q/ZY1IbCpzYCADoRpKbjy1i9Ek4KDIPX1YHSxnyAMMl+lb8HE6XbBQFWCsIQAlqACUwCmQWrgqj8kl+Xb3q9XwENU6XKAffnoFSilGHsQWsH9WOE50AgAPSycbiSUzF4Sdy3uuqeAsDGsc1WuDW9cBPv2V903g6qmYz0BvYsxOINAv7jLuR4p4O92TKFezy2GdAd2DjczD4sWvB2DxkJOnRbXXg3rfZseR7p5ez5qY90nukBmgxEgYRuAyD8+CDD+Luu+/GhAlMxrryyivx0ksv4amnnsIf/vAHt2XffPNNNDU14dFHH4VOp0NmZiaefPJJzJkzB7/5zW+Qm5uLxx9/HAsWLMDll18OAJg4cSJ+/vOf47e//S2uuuqqsNatv7Mb6GUbV1tzO8Y6TrBfXkq5fH8gdACmZeuxq5bttGYVZ0FvYUHE4spUvNLUga92VuPcCulHllQKuEyAo58NQDTmK+9TeibQeATYswYoX+T9ZrtXAR8/gLQxdyHJVo7zKpJxc81qwCaiquImjO737osgiiJaG9uRZHMgNX8iYBOB4zuA7m6gZg9gE9GTVoYkmwXdrZ3B/+esyUBrLVC1Ccj1P3/jq53HkWSz4LyKVKC3FzNyDKipsWDLnhokuUSMTdX4fS8NgEyXDVaHC/0dZkBIht3cjSSbBRkuq/y8PMGBJJsFeYLD92s1VrH/11gQ0ncZKgUaZ+ifV6icciP7XAsmsvTT9peAo1uAU71f/1h1M5Js7IznhY/3wgggJ1WPPI2fzyEQtl6gX/ITuIw+n88/576Orsj/34LZwOEvgL0fs/9rB+uofErxNUiynYsc2P2/tsPGnuMQgZxpgKMPsInQ1uxElmiFxS7C3NKJjJxkYPOr7DsvmQJYHewvpQLoagNq9wFpIyNbf4njrb3obe9Chk6DH82qwO8+PIhvdp+Atq9X/k5mCAehs1iYQV10Abv+hyr79UiyWTA9Wx/5Z+i0A4c+Z/9f4emRvU5KGcD3E+mj2Ws1sWCv12mEq6cn6m3a7nRhT385JumOIbVuC9C7FAYA+RoHui0OdDR3INvD79JnscufHwDU1FiQBKBAq2zTRTonGmwWmFs6gQLfk+iPn2C/jfHp/vcvnhQ6uwCbCKfWBG2/xetxc68NJiu7P1O0Ab3uFW+n5htw/IQFew/X48ySJDQ1tCFJBG6anod1247hwJFGWDrNXj1zTpxoQZLNgnGpWSGsaxJT5np7AV0mkDWRmbx3/4/NGIyAtHa2f2wQcpAW6P2N+UDeTKBuK7DlVVYNHAKf7mvCk2sP4beXT5UHQJtbO5Fks6BE78LCkmR8vK8Jve0WZOo0uHVmIfv/Qtlvh7ONijHm+PHjIgDx4MGDbvc/++yz4vjx472Wv/HGG8VrrrnG6/6ioiLx9ddfF0VRFEeMGCE+++yzbo8fPHhQBCA2Njb6XA+LxSJ2dXXJfzU1NSIAsYudN9If/dEf/dEf/dFfHPx1ASIAsaurK2gMEvP0U309SzcUF7s79EtKSlBX5z1Por6+3mtZz+V9LVNSwspBfb0mADz66KPIyMiQ/8rKynwuRxAEQRDE8CDm6Se9nuXKNR715v5yiHq93mtZz+V9LROsmdV9992Hu+66S75tNptRVlaGi37xJjb8ghmh/vPuO7hiz4/Qqc9D5srQGswBwL6GLlz+NDPbfXXvWXIe2OZwYeZDn8DhErHuZwtQlBlCD4hXrmAD+857WOkOCgBfPAl8/nugYg6ON7agwsqqlhwZFZjW9ACc0OLLexYhJ9XdcLZmdwPuenMnppdn4tUfnM6qq755mj2oSwLKT8P+U3+Ly17Yj/x0Az77+VmB16+7CfjzNCat333IZw56d10nrnrmGxRmGLH+bpZG67M5MPvhteAewU9/tgDFAT6PpX/6HEdb+/DiTbMxvTwLU3/N2oRvvPcsuSJi3f4mPPTBPvz2sqk4baSHH6TtKPDMXPY/rqzyXSUSIS6XiKkPfgynS8SGny9EQbpvKTwq/r4IaDkAXPmi0lVUYsXr3+LDvY24b8l4XD+nMrr3qVoPvH4t6xz6g099LvLJ3ib89PUdOKUsA6/fekbk7/XpQ8xTs/QJubXBs//8G25qfIQZNb/zKvOWAWwA5rYXmPeGN3s86wHgjNvZ9fd+CuxehbdSr8H9refhzVkHMGXP79j2eOtnrK8UZ/cqtnzFHOB7b7mvU28rO/cLMX+/5E+fo7q1D898bwYWjMvHQ+/vxaubWDnv3NE5+Mf3pqHvt2ORAguqLn4Xo1JtwCtXokNMwVn2P+GbBxbLgwTD5sWLWIPGCx5X5vNEyzNnstJvAC86zsN/c2/FOz+eG9VLHmg044q/foGtptthgg34wQYgfxx+9O9t2HCwBQ9ePAlXzXI/qeT70Nw0AyqzU7BVqiT909XTcJ5UYfTkJwfx98+P4Xunl+OXS31XCs373ado67Hj7dvnYEKIHrPn/v0ibjp+L1pTRiJ3+Vdej68/0ITbX9mBScXp+M+P5ng97nSJOPWRteizOXHaiGxsOtaOG+dWYOXiCVi1tQa/encvppSk47YzR6GqtQcjclJwzsQCzP3dOnT02vHW7WdgYlFGSOsq43IBf5rK/GPfXaVUGIaB5fWbYapajUcd1+DeXz0RvCHka98Fjq4D5t0FLPh50Ne/8M9f4EhLLyYWp+GtH81Ft8WOUx9h+5htD5yDZIMOXxxuQZpJh2llUlsApwP4XSUgOoGfbAfSi3y/uNkM+BA/fBHzoKa0tBQAU1dGj1bm7jQ0NMjqiufyXN1Ro17e1zINDcz05Os1AcBoNMJo9HaYN7u0QAo7MGsttYBBQGfGSGSmhD5wbvzIZFw6fyxyUwzIzld6NhgAFBTn4EhzDw71iigqYa/598+r8J9ttXjuhtkozfIwzE48H6jfCNR9ASz8qXL/0Q8AgwCceh0e/DINj7TeiSKhHbrz7kH2J3k40d6HI70icgrc17vaIqDfYEJ+YTb7P8+/Hxgzj7nd8ycAWj0yOvvRbziGOpsAMTk58MadMhLILmJD1rqPAeWneS3Sjj70G0wwZqTJn21yClBckovDzT0w6jQoLMoBAhgmNWlp6De70KszoVdnRL+BBQ6pORmAVDV11qyROGuWH49EYwv7vHIrgNQoqk18rRuA5Kx0NHdb0eLSoSCMbSVkKmcCXQeBzgNAyqVuD1Vbwb7Tomz5842cfvY5ZeX5fa203Az0G0xoduqie7+Lfut112fJ86DXnoebdB8CXzwMTFrMHvjv9cCRtey6UceaAZ5+g/L+lTOBg//BON1RfDfpU4yveov9Hxf8Bij2qFwsmcge661xX//uRuCFRSxA/+m3rFVDAJq7LdhvdkEwmjBtQimQpMcZUyrwrx2s18dZM0dC6K5CisGKLjEF2zUjMWpCKaxpuciytuGCnAYYMyKsULOYgZYd7P+YdH4MvneJilOAbtZVvFOThi6NIerX7tZa0WNIwSHTGEx17QO6DgAjZiA1OwP9hm60uLy3o369Ff0GEzSpyZg+sQRfNDDfT3peprxsWk4m+g0mNPnZDl0uEfV2LVwGLXLys4CU0E42kpMAGARYjak+X7cNBvQbTEjKSvf5uBbA6BEF2HSsHRvq+gCDCaMqC4GUFJw6uQz9a6qwucWGzW8powZO2dyAersWMGhRWZ4PGCI49E5ewhq41n7OtolA2PpYd3VbL7D0D4BGA529HjAIqEIJ7KZkNnsqEKdeC9SuB468Cyz5f0FPFOscWvQbTDjcIwIpKWjp60G/wYRUow7JWSyImz/N4/PsrAH0LkBjAApG+m++5wzd0B7z9FNBQQGmTZuG1atXu93/ySefYMmSJV7LL168GB9//DEcDsWMdeDAATQ3N+Pss8+Wl/H1etOmTUNBQUFY69djccolhindrBqrN0wzoUYj4P8unYK7zvOumBpbwHaUh6XKCFEU8a8vj+FQUw9e3XTC+8VGn8Muq79UWmE3H2CNljR6YPwFONSXisusD+LooqeBad+V5+OcUA+4lKjpYPfJM3SMqcCEZawyRMtUNK4sOVwizP0htH3npd1++tX46+kwRTKLVeakyBO5/cEroPrtTrlHjUmv8VsG7sUAlXNzciVFrKXHCrPFjv/uqJXLzmOC55wwFQ2dzLRYlBGD+UG8m3CA8smom+8FoKvfjj86LoPdkAm07Ae2vwB88gALaHRJTJW4+xA7G1WrKZLSM7lvM+7XvwK90wKMOguYdbP3m/AGfOY6wK4ygq7+OTvT7W1hlR1B2CZVPY0rSJM/kzNG5SDNqEOSXovzJhUAx1kl0WbXeOxr7AE0WuxOY6MartSsD+/DUXNkLTt7zR4Z221a1azTHKOSbt4o86hBCi6PrAU+fxx3Vd+Gn2jfRruPqkHeoyZJr8UZKtU1R139FGRSd2e/XVaCs1IMPpfxRarA1sci+A6C/O3P1Ewrz3S7Pb6IVUSVZydj/phcGLQaTChKxwVTCpFs0GKnVFhSlGGSh2qGzVgpkDm4JvByLQdZB+KvnwK2PS+3PNB2MYWxVsyD1RHC9z7uAjY1vP1o0AGbDqdL7idktjjQ2WeTK58Clq9L64SMkph0EwYGqKR75cqV+P3vf49Dh1gJ8bvvvos1a9bg9ttv91p26dKlyM/PxwMPPACn04muri7ccccduPHGG5Gby2rbf/zjH+PTTz/Fe++9BwA4dOgQHn74YdxzT3jzkzi8rDRbGu7ozI5ukrOaMfls4+bTeeu7LPJgx/d3NUAURfcnFEwCUgtZwyze7GjfO+xy1FkQTZlo7raiATnQT7kUEASUZbODGy/dVsN71JQG6NZq0mvlpnYtoZQpBzjgAko7/UyPng4zK5mKNUH6wQeCN+DrsylBTVqgSdxeK8GDmsrQnxMGvAFfbXsfrvvXZqx4Yyde/vp47N7Az0R3h9MlDwUsyoxB2iuERlfqoMZre5VwuUR8fqgl7F42ZosdZqSiYYaUGv7ol0p69NK/Aaf+AEjJ9X5i4RR5oGK9mI13K34JfPc/vneEyTmAMR2AqGwXBz4A9r+nLKNuZGbtZpOat7/sNhRys9Sfhg/bBFhJ71u3z8Hbt89hgW41S19sdo3Hvnozuvrt+L8WlrI4xbyBdRoOhMsFfPMM8PVf2bwqpwP44g+sMzDABlfGEl7WDdZ8LxYl3by8vsY0nt2x5y1g3UMo6t2Pn+n/g8VHHmSVXCp4UJNs0GJ6eSbSTDoYtBoUZCjbeKF0vbbD++QNYDPQALa9hnzyAyBFGpHQL/g+Sej006NGzfSyTPm6ViPILSwEQcDLN5+GAw8txpo75+Pp787E+rsXyiNBTvdMm4fDyEXsRLe9yv92dfBD4O8L3bsPH/4YsHZDkE5o6sRcWOwu389XY0xVLBGfPuQ2bseT9j4b1LuKE+198rElLzVAUNMpnegH6psTJgMS1FxzzTV44IEHsGzZMhQXF+ORRx7B+++/j9GjR6O2thalpaVYtWoVAECn0+HDDz/Evn37UFZWhkmTJmHKlCn405/+JL/e6NGj8f777+Ohhx5CSUkJli5dil/96lf4zne+E9H68TPQAhuLEoVgjffCgPcwOCS1gd7Op2qDfdF8HpKMILAzTgDY9y7bqe39L7s96VKYLQ7YHNLgL+nAyjvz1vhSaqT7yjzTXB7k8ym65hCm6AZVanz3dLh6Vhl+d/kU3HfBBF9Pc0NWamwOOeIP2qBNzQB1E+bwH+bvPzyInVLH0Bo/O9uIKJzC0iI9jSxNItHcbYVLBPRaAbkpAXYOoRJCUMODU4dLdJucrmbt/iZc/9xm/OZ/4bVu58qgbdr1QN541o4dYIM1J13q/4lJmcBlf8cXY+/FIusf8FnKeayfhy8EQQluv/kb67H0wd3sNh9RcuhjyHvhjU8B3/wVeO8O4G9z2LBYUZQ7xZ5Smun28mML0ph/w+WSe75sck3AvgYzXtl0HNutJfhGNxsCROCrPwb+QHa/CXx4D/DRL5h37YmxbNaT0waMXQwsui/w88NFpdT0iLHpU8OVmhPp09gEegComIsDo26CQ9RgdtdHwKtXuTXw7JeDGh1Mei1e+8HpeOUHp7n1oBopNS2s6eiH3el9QOX9a/icqFBJlpSaPvg+SeD7n0Bdr2VPCNjMKU/flFqZLkg34Q9XTcPmX56Nx66YGta6umFKl2dT+e0u/MkD7AR55ELg3N+w+w5/wtI8ADrFFHQjOTSlBgDmrWAnCE272bxCP3j2PDve1ofWUJSaTq7UDPGgBgBuu+02HDp0CPX19di8eTPmz2eD5kpLS1FbW4srr1Rq7UtLS/Huu++ivr4etbW1ePLJJ738MPPnz8eWLVtQV1eHw4cP49ZbQ6ud90VXvx1w2FDkYr4cY+H4iF/LE55+OtLcA1EU5a7DnPd3NXg/iQ8T3Poc8PuRzDCqNQDjlsgSXppJJ/c98Jd+crpE1EkDNcsDNLsDlFRGQ1coQc00dtm8nxk6PfAc/sbRaTW4enZ5SMZa9VBLPtuIq14hMdDpJ6kBX7cq5RTKsL2QMSQDuVI6U6XWNHSxz7sg3RQ0hRcSQboJAyzA1GvZe3mNJJDgwXk4gZ3LJcpNFdOTk9gcIY0emPodYEEIquuUK1A3+ruwwhB82CZv/rfteeCZeWyKddYI4No3mKTe08iCdKeD9QgCWBfk1oPAG98Ftr0g/28V/n5LNZsASxdEQyoOa0ai2+LA0+tZ+/e+06XmoTtfB7pqfT/f1gesfZBdzx3LlKi+NjbH6ZK/Ade87j1nKVrSSwATSwt3Ixk2hyvobKVg9FjZd+FMKWLDNu/cBdy4GnWz7sUt9p/BAiNQtY4pUBJy+kn63U8uycDsSvdtsiDdiCS9Fk6X6PMEjh9Ic8JIPQFAijShvFf0fbANRakpzDDJzRjHF4bmm8pPM0EXhqLkk7GSB81XUGMxyw1Wcfm/gFOuYdcbd8kpqAaBjfMJSakB2H5ijuT1XP+Il+LG8Qxq3JSagOknrtTErjo54WY/ASyocbVVQQcXusUkZOTHLkqsyEmBTiOgx+pAQ5dFVmqWSI7+D3yloMYvg+OU70FMygKskpIz+lwgKdNnXpKrMJ4/9CazBXanCL1WQGGQQIJLu42hKDXpxazluugEmrzPzLnyFWgnEAx1+mmPNN14SmkYFQJyN+GBVWoA4KzxbMcQdCBouPhI89VLfpriWPhpgJCUGkEQgo5K4JPX1VPZg9Frc8geiPQkPTDiTOC+GjaXKMRqNR44B017Lf4tcOnfpU68AlPBLvwTU3x4xdWhj5g0313PPo8Ve+WJ1+K3r8oBv5e5n7PxLwAAYfJlGFnAttUeqwNFGSbMW7gEqJzPJmtLy3nx9VPsvTPLgdu+AH5+hFVr/WQr6zwewwo+GUEAZv8ArrwJ2Olik76jnf/ElZpUk44pZNKJRVaKARtc0/FHnZTC4F2ioQyvTdL7UdvAtsPKXGYsrW7zbr7WJo15yQlTwTSBnSj0uAIrNYE8NQAwq4IFYVFPYQ8H7qs58TXQ3+n+GK8aTC9lKdzUfHluGrY+B0AJakJWagDg9B8BKXnMW7Pj3z4XafMYuXOirS80T028pJ+GOl39dvTWM2d6lViErFjI+hIGnQYjpB/i7rouefjgz84bixSDFnWd/dgutdfmVHc5ccqOC/GzireAmz8BlvyelcECPvOSXKlp7ra67ZC4clOcmRS0NXuRFNRwJSAggqA64O7welhWapLCO2NSozYKcxVgUqDZRm1VwNbn2Zm2tVs5WA+QUrNwXB5KMpPw8/PH4aa5bL5QoEF7ESH7apTPuLErhn4aQFFqgsxZ4UqerzNkAPLk9XBGKfC5awatRp7kDn1SWAdvnuL0pyDJ6AzAKVcD178L3LUP+PFmYOQC9hg/MBz+iCk5AAsiUvOABfey23XbkOLqgUGnYZOEPWk5CBz8AIAAzPmp28T0m+eNYJUl86Wpz9teBHpa3J9vbgC+fJJdP+fXbLREcjYrHEjND+3DiJSzHwB+9DXMYPupaFNQXL1UjzQBFAVlk036TTbtlVN+/SpPTSBG5LJ93dEWH0FNT2TpJ5OL/aa6/Sg1HX48gp7cd8F4/Pz8cbju9Mqw3j8qskcyRdflYOqXGq7wqkfzcE+WZB1o0bLCGq7UhKTSGVOB+VL6dt1DrJDFAx7A6KTjzvH2XiWoCeip4eknUmqiorPfDlsj+2JqNKXBS9vChPtq3t5eC4dLRF6aEaPyUnHORLZBvb/LvTz9fzvr0Wtz4r1dTTDnTQdOu02u1/cV7WYm62Wjr9pEF6qfBlApNaGkn4CAvhp/RuFw4Du31m4bjkkHTN5q2wunA3j1auD95cxkylNPpkxZWo81o/PT8NW9Z+HHi0YjK4X9n+29Ma4O4jsjVfqpXgo6CzNiFNT0B08/AZCNj0eavYdCiqIof0fmMJQaZe6TLniPDD/wbawrWPpJTXoxG+jI4VOQ67YxvwEAzLxReoMyIHcsBNGJOZq9KM1M8p322/hndjl+KZA7Rh4umm7S4TunSmedIxcCxTPYiJS3b1Gke1Fkvhl7H0uTTbos9P8lRmg0Akx6tt+LtgLKTalRwSuS9tiKIGr0TIWWql367O7pJ3+MCEmpCS+oMbrYb8rs8n2w7QpRqSnNSsaPF40O+j/EHB6Uew645AovPzkCgDHuRvMWHcsYWB1ObDzSikn/70O8vtlHVa4ns25kx4C+NuDFZV6KPfc38RPRE20hpJ9cLqX6iZSa6DD32+FqYbnHJkPsPkzOGMlX8+l+NuRxRnkmBEHAsqmsedDq3Q3KlGEA6w6y5RwuEV8ebnV7LV9BjSAIcnWT2lfDq6HKAlQ+cRSlJtSgZhq79KjOAZRUQKhTcn1hknYM2463QxTZ+uX6i/B3vSE3EMNXf2JeH2DAKp884aWmHX02v9VBEaE2C5tZ4NvgmX5q2Ak8fwGwx79pLyAhpJ+AwEFNc7dV9kT0WB0hezLkoCacqjYPuBrYGaAyKyhphaodvwiMWADkjFIeH8U8bmdqdvquIjTXAzvfYNfnLgcAXHRKMeaOzsFDl0xWFAtBYCkvfQpwdAPw/gpWYv7O7cDO19gy5//fwKSZQoCro1GnnySlJs1DqUkz6qDXCrBDB0e2FFQ2soGXoSo1lTksqOFBtBpFqQlPade72D6z0+F7f6VM6I58fzagcF/N4Y8Bl+q786XUlMx082W166Wgxu7CR3sbYbG7sPmYMoXeLzojcN07QOFU1hLhxQvdAhteiTa9nL1Xg9mCOul45Deo6W1mhnhBw048YkRCBjVd/XboO1hJXEdSZcxfnys1DmlnP0P6os8cm4t0kw5NZiu+qmLBS1uPFd9K1TSAMu2a4y8vWS6VdZ9o86HUZAf3XxSms2XCVmqa9yv9dCQ6Y+CpSZZ2sNXS/+NXpXHYgM+kpm6CFuhrBTb8H7s9QKknT/jOjk3zjWGvGkMKO7MHlBy4pNQUZZhYGefLlwHHvwpeVeMLW59SbRREqeFTj4+0eAc1nqmAnhA/A/5ZpQWb0B0ArtQ4XaKbaTts+NkuwM5C1UjG/TO1u1HqmfZzOVkg7bID5XOAMmZIzkk14pVbTsfF0zyagRZNBa54ju24d7wM/GUGsPNVdnvJ74GyUyP/H6JECWpCNI36gQc1nkqNIAjyb6UnUyrGaGJBDffUBOvZIis1rT6MwhFWPxmc7LW6nN4HW4vdKafjMqLYnw0oZacxRbq/Hajdyu6zdgNtUpm3WqnRaOUgHQA69YpSs1+aLu4M9eQgOZulc4umsf3u+yvkh1qlAHNcYRqSDVqIohIc+g1qeOoprVjuoRYLEjOo6bUh2cwa7/WkjYj56/MKKM6MChbUGHVaeaf3+mb2hX52qAWiqOxgNhxsdlNxeI8Sz7wk99Woe9WEk37iSk1bry20M7XMchbxu+xuPRCcLlE2CkezE/CUcCcX+wlqtr/IzGWphcCS37H72o9K63hyghqTXiufYca0AgoA5t7JLjc9C/R3ykpaub4LePlStjMB2Bmv1TvgCAhXabQGwBC4my5Xaqqae922R8D7rNlsCS0VpCg1kTcyN+m1ctokrBSUJ+OXscu0ImDcUvfHKubCLhhQKrRiilE6yTj0MfDSJcBvK4BNz7D75i0P7b3GLQYWS9uquY6lSb/3NkszDyImVcVhNHCzeKrR+/cvN9BLk9pmNO4G4N58LxA8qKnr7PfaT3F1IDvM9JPWwfaTHQ7v9eWpdK1GiGo7HVC0OsUrc/ADdtmwC4DIqts8R4CoUlBmE1NE+u1O7G9gfs+wqt+Ss4GrXmTXa7ewjsVQvovcVKPS+BVMhPT7/cjFHbHNliRkUCP0NMDg7IVD1MCZGV434VCoyEmRS2L1WkHurAsA3zmVGaI+3teIth6rrMxcP6cCqUYdWntsbr1s/Ck1ZT7TT31ujwUiM1kvmzWbzSE04BMEn76abotdbvcRlVHY44zNZ0WBvR/4/HF2/cy7gZk3uKecTpJSAyhqTczNwuOXsblMVjMcXz+Dlh4rctGF0R9dx8ofs0exgE50Mk9IOPCgJik7aMqjIicZOo2AfrtT9vVwPP0NoXYe5sFPehRKDaBKQUUT1BRNBa5/D/j+/5ipWI0hGfv0rJ/LVOtWoGYLm5d1dD1g6wYMacBpPwyvMd5ptwLnPsQCqFvXKxVYg4janB8NPX6MwoByQGtKkhqcNoWXfspOMSBNCi6Ot7mrNbz60G+a2g88qOlyGrwO6ErRgz5i39dJYeLF7HLHK0w59+Wn4Yw5j1UvFU2DKJ3MHGvtk4NRV7hp3KxKFjyp9kFcqclNNbgFNdnJBv+NEWU/TWyHTSdkUJPacwwAcFwsQGZa7Of46LVKBdTE4gy5vwwATCrOwNTSDNidIt7YWoPPD7GqiPMmFmL+GNZJVZ2CavVjtvJswGexO+XOxeUhBDWCIIRXAQX49NVwiTHFoI3KcO15xjbFV/ppw6PMb5JRDsy4nkmWZ6oGrWVWRvz+4aL21cQUjYYFbACETX/DWJzAf4wPQtd2kKkK1/1XacBVsym815ZNwsG7mqq3YU9fjWf6KdSybt54LxpPDaCkoKL+7EcucDcQq/jMycYyVDZ+BKy6gSmU4y4AfvglcO9xphKG29Z97k+Ba15VxjgMMia54WVsjMJpPpQNbhau1kn/c/sxwNrj1afGH4IgYGSut6+GteWXOsOHq9TY2ev0iiavgM5fz60hx7gLWGDR18r8dbKfZrr3ssnZwB1bgBvXyN+52vIQUZ+iMmkG4IlNEEVRNm3nphrd+jqF1niPgpqoyeqrBgAcFYvDzseGCvfVzPCYEQIA35nN5Lan1h2B2eJAdooB08oysUjqf7KeG4edLjlv7O2pUYIaURRRK6WhUgzakL0tYfWqAXwqNbEwCQPuZ2x5aUbke/bZ+eIJ5mUAgHP+HzOuAcDUq4H8ScyMWRRFt84wyZLn0sR+PhImXgLkjoXW2on3Db9EpdDIJNobPmBqFO+Kq+r7ERIhNN5T488sfKyV3eYnsiGnn2SlJjpZX+6hMwCzqQB2gvBBH5sKndK8HTDXAjmjgUufZWZuf52M44xYG4V9KTW8MqnBkcoURohA8z65+imUOUiVPoKadmm/IwgRGHp5UAMT+jx8WV1D3STM0eqUEQabnlGUGrVJWE1SFmBIltX5b1VNYX00aw5OubQPqvkGvTan7MvK8VBqTnaPGiBBg5oCG/swq8TisKP8UPnhglFYPKkQN8/z9uxcNK0YyQatfLayYGwetBoBC8exXOiu2i40d1vQ3svmaWgE7wZTJZnM6Ntrc6K914at0pyaytyUkGXTsLoKA8oPpmmvXJ4qd99Mie7MRn3G5qXSbHwK+FRq+X3Or4EpVyiPafXATR8CP90x8P09VGRztSDWnhqAHTSlvhB6wYkaXQVw08dKhQ6flF67xb36IRgRBjVVKrOww+mSU56jJTNxqL1qYlH9BKjLugfgswdQ39mPg2IZmkSpakSXBFz1EmtTP4wwxSD95HSJfo3CgBIctPXagEJp7lTjbvTLRuHgAaJiFlYFNdLvLjvZELQnlyeC5APpE01eY0A6/Ix8GZLMuIF1wW74Vukk7Cv9pMIofefqAoew00+AotTUbEFbNzuhTjZokWzQoTxHyX4E7FFD6afYUeJgH2aVWBR2PjZUJpdk4JnrZvrsRppq1OHCqUoJG1do8tNMmCp10d1wsAXNkp8mJ9Xo9cM16bVy1+Cajn68JvUauPCU0Evjwu5VkzWCtXB3WoH/LQe++hO0tWwIZzR+GsA9/TRZ3XRv+8vAx79k1xf+gs0i8cSUDqSFN609WvhOr32ADqyYfDkO55+PD5yn4pmRT8l9iwAwZcqQCljNSjl7KIRYzs3xpdTUd7Ku1UadBmMLmRoZcvopRp4afqCMylMTAGa+F7DBdBarsLvoz24zk4YL/EQimvSTOgWY6eN75Up4R69NGabZtMd/+qm7iQ1kfO8n8l0jfCg1vJw77JNSh42VEQPohRG9NvdtN27STwCQkgNMVcYN+TQJe2D0YRGIKP1UMJmp49Yu9NQwnxT/ritCUWpEUaXUxNYLmZBBTQXqAABVroFTaoLBDcNajYAFY5QNcdE4FuA8s6FKPpj4i3a5zLdmTwN21nZBrxVwxczSkNchbE+NICiy47f/Bj75FeZ/+X2UC01R7wTUZ2xyOfeRT4H/SdVAc5cDC1ZG9R6xRPbUDIRSAwBaHf5d8iv82L4cGdn5Xo+hdBa7XhNGCkptFA4BuaxbFdQclVJPlTkpchoo9Oon7qmJMv2UPLDpJ+5T+7jwh8DKo8DUqwbkfQabJN58Lwqlhismmcl6n3ON3JUa5lNC4x7fRmGnHVj1fdZRe/vLgIUVTMhBjcqgHmk5N089AUA/TF4BXafceC8OghoAOFVVQRdEpQHg5u/khKvU1HX249TfbsCxJDaoWJD2QTybUJyZBH4O7jeo6WtnzScBFozFkIQLapJgQbHAZPiqAfTUBGN6eRYevWwK/nLNdLdS6OvOqEBxhglHW3tx/zssAva3YZRK/Whe+KoaAHD+pMKwlCeu9ISs1ADAxU+xEQ7zVgAZ5RAg4jTN/qiDGjelpiSDlSy/+X3msJ96NUs7DaFqBMVTM0BBDYB6eUSCj75Dsq8mDLNwGEZhgAU1gsAkeV6yyc+WR+SmyGkkHqwEI9bVTzE3aUvIVYQ5KWxW1DAlFp6aYIpJToovpWYv+m1sW0jWqwLcj37JZhoBAESgbjsAxVPT0m2VB6Ly7THcxnu8BNkOHezQxXf6CWA+wvI57HrJjKCLx0Kp+XBPI5q7rfjIXAkASGrcAoBVPgFsVFCxtM/yG9TwQZapBWxESAxJuKCm3Mid70Z0IXVQDWHXnFqOC6YUud2Xm2rEv26YjVSjTs5V+9swuFJjdTCT1rWnhme4CttTAzDfyuxbJG/L5QCAWcKhqD/H3FQjTh+ZjXMnFqDI0A+8ehUrn62cD1z01JAKaACWywcG7sAKKMFmsa8RCdxXE4lSE2JQk2TQyt4trtbIQU1eimz47Q7XKBwzT83AKDXcdF+aFaMhokMUUwzSTzyo9zeqwC34zxnNPCD2XuQ5GgCo0k87Xwc2P8uuZ0vesVp2sEw36eXX52XdkU7o5kGNRWDfbZ9H+qkzxBEJQ4pL/8Y8eKfeGnRRX0pNuEHNtuPs5OgrGyvTz25js+rUJ9TnTChAikGL6WV+psyb2fcfa5UGSMCgJs/ANuIeJCEzWe+/hn4QmVCUjr9+d4bso/Gnvqhd5pU5yThjVGgHKw731LT0WGGPxAIvqQWzNAflVESkaDQCXr/1DPzj+lkQtj7HmpRljwKuftm7j8gQgBujOwbowAooaUGfc59KZ7OutJ0nWK+Itb8GXrkS+PppoKfZe3kgbKMwoPLVtHgENbkpSDNFln7KiLL6KWuA00+1UvrJ73TuYUIs+tS0BwkCclStD1yCFshnKYupAmuYmWzQAh3VSnfaM1cqTQlrNsuvw1NQfDq8nH4KdxixjW3HVg0PajzTT9F3Rz/pZFWyIaUhGNnVSg0P2sNJP4miiK3VrHJqh2s0XNAgw1qPfHS4ZT1+fdEk7PjVeSjP8fMbClM1Doehd0QfYHIMkklMNA2anyYUFozNw+NXTsX4wjRcMKXQ5zLqJnvXnFoedrOonBQD9FoBogjZlBwWUov3UZoGFGhVvUtOfAN01YX/egDLq2/5F7u+YKXb3JKhxEB7aix2p9zQSp77pMaYpphX/3EWm/h8+GPgo/uAJ8YDb1wHWMzuz4kkqPHw1fCgZmRuiuyNCSX95HKJsqITrVKTITffG6j0E5+hNryVmpgENUEmZfM0jkuUgt8K1mPpbt2bSEUfknQa4P27mL+iYh6w8D4WsANMqZEOuJUeFVByN+Fw7QOSUmOTgppeqz+j8NA9NkSDe880FgSFo9TUdvTLx4oeJKPRxPoPzdIc9AowA/Yt4/uiAdi/J2BQwzbiXpjCly5PMpdOL8WHy8/E1NJMn4+PzE2BTiPAoNOEZRDmaDQCCmRfTYhmYTXJ2ajRsvct72P+Hxz7AnjufOCtm8N/PQDY9y7QXQ+k5AOTLo3sNU4C6vST5xiBWMDLptNMOv9+pcr5yvWKecDZvwJKZjEf0v73gP/cxCaac8I0CgPuFVAWuxN1nWw7qcxNkb0x3dbgikmvzQH+MUXrqclLY599Y5cltgNFwQ5yPKUSSmfueIanfqLx1LT3Bh5VYNBp5EGXbb02YMFKONJKUaFpxkOGl6DZ+x+g6lOWlrrwT6yhYcFkQGcCLJ3yPCO+He6Ruq3L3YQjTD+JBhYkfbS3ye3hWLWoGKqolRo+isYZxk9o23Gm0vAswlYXG38xS3MoPH9qf/gnWKGScEFNlo79GPpgCl+6HGLkpBrx3A2z8eotp4VvmJMoksu6I1BqAOwEG1SX3yU15Nv2PLts2CWfZYUFn6sz+2alwd4QxOsMNMZUScrIyLxU/wrcgnuA8x8FbvscuPEDYP7PgB98Ctz0EeutcuQT4MN72fdg6wMcUuAahuTLDybbjnfgrMc3QBRZoJWTYghLqeF9MQxajU+zYjiUZSdDqxHQa1O6aMcK7qfJSNJHrSgNdWLRUZingbID7Euz1WXdSZmoP+vPcIoCLtV8rlQ3Lvg5kCuNUtAZlM64Ugrq9JFsm/36aBucLlFV/RSZUTgnOwc6jYAvj7Riu9SIbmdNp9LsdIBafQw2/DvXaQS5JUM4J2VbJT/NsqnMC7q2lyk1MzWHwvvMZKWGgpqoyZCCmh4xKXzpcghy5tg8zKqMfMMolM3CESg1ADY52Y4os3U721D3v88esPcqykCo1G5jkrPWAMy6KaL1OVmoz0AHogKKewdG5QUY45GUCZxxu9LpmVN+OnDZ3wEIwJZ/AB/fD+z9L3tMo2epqxAZk58GrUZAn82J+i4L9FoB159RAUEQlOqnEII6ufFeki7qmTpGnVb2kx31MUU8XPpsDvz6vb14esMRfHOUbbPD3SQMxCb9xNM1gVRvt7JuAG05M/CU8xL2oL2PzTqbc6f7k9QpKLCGnGkmHbotDuyu65LHx4RtIbCyydSm5HRcOp2ZVP/y6WHYnS7c+zYbtnnxtOKITxKHOnyUxZiCNDnACSf9xP00iycVoiw7CVudTKmZJFQj1xjGdjSASs0QHUM6cGRq2c61D8bwpcthSFG4DfhUOJwufGUdDRgBU8tOYOdrrDEfp+M4kJIb+gtu+hu7nHz5Se0OHCmZKXp0Wx0DUgHFlRreKyZsJl4EnPsg8MmvgK+fUu5PDj7MUk1Gsh5PXj0NR5q6ceqIHMyoyJRb28vpJ4sDoigGDFZi1U2YMyovBcdae1HV0oM5owNvY139dvTbnL4N1wBe31yDFzZWu90XyqT7eEcJaiLpk8/gVUhZAfalOR7+s36bE392XIZFxkOYKlQBF/7ZuxjAI6jRagScMTIHH+9rwoaDzXLDx9wIPTUwpOD2BaPx1vZarD/Ygnv+swv7G8zITNbjgWUTw3vNOOL0kTn46VmjMX9sHqzS9x6qUdhsseNgEwsKZ1ZmYUZ5Ft5t70e9mI1ioR0F3XsAhNgEtU8a00CemuhJ07CDd4+YNKSNwicL3qumIdT5Tyq6+u04KhahXUyF4LAAn/3OfYHO6tBfzNoN7H2HXT/th2Gvy2DAfTUDMf+pKhSlJhhzfgos/QMbflc0jZVPzrwh7Je56JRi3HXeOMwbk+s2q4cHKE6X6FVF4glPP6VF6afhjMzjIxx6fT7eb3Pitc0ncP1zmzHzoU9w+qOf4upnv8YHuxq8Kv0+P8yGypZmKU3DppT6GKg6zOCeGmsMmu8FVGpS3JWaPpsTTmjxYOYjwF37gbLZ3k/iQU3zPlldmSsFr+/trAfAUihhB8mqoGZEborcgf3tHayw4RcXTBiwLvNDAYNOg7vOG4fZldnyPNZQlZodJzohiqzqNj/NhBnlLCDZJvlq0pq3KQu3VQG7Vvm3IPRLQQ0pNdGTIjAloRcmFAzjjTdUolFqWDmzgF3COCzENtYBVKMHRi5kfo6O46G/WMshNgk5tcD/ULYhRtYAVUCJooijKk9NxAgC8ybNjtC0HQSTXgOdRoDDJcJssSPFx0BDjqLUxGaXw4O9Kj/pp1+9uwerttXKtwUB2HSsHZuOteOU0gys+uEcGHQaWB1OOeX0z+/PQm6qEQcbuzGzYmhW3cWSaGc/iaIoq5SBThBzPBpV8vfTG03+D2rpRWx6c1cNa8I3coEc1PAp8VkpBmjCnPvES7ohGYXvWDQa7+2shygCp4/MxpURFFzEK1pJWXWGqNRsk+YL8t8GD2q2usbhQu030NRKjUBFEXj9u0DLfpYiH3Ou94v1k6cmZiSDHbzjofrpZBD2/CcVXf1sJ3VAr5qLM/4CxeTHZ3uEQtthdpkzJuz1GCwGqgFfS7cV3VYHNAJQ4a/PwxBAEAS3FFQgYtVNmMPTckf9KDVbpSqNG+dWYt3PFmDjvWfhJ2eNRopBi521XfjsEFNntlZ3wGJ3IS/NiHEFachNNWLu6FyfTcqGG0lRGoXNFgfsUulMwKAm1SOokUckBAlw5RQUMwuPyktBQbpyIhrR/ltWatj2M6YgDTfOGYHy7GQ8etnUqP1e8QSvYArVKMx/UzyoGV+UBpNeI1dAoUYasFu3nQU0AHB8o/cLiSKVdMeSJB7UiKZhawYLB95VuNFsgc0RXm69Q0q7VCdPVu6cfh2QJQ0o6wxDqWmVgprc+Alq5G6pMQ5qeEqlLDsZRt3QPrgqFVCBU3DK3KfYBjV1nf1eXWF7rQ5US3OCfrxoNEbmpaIoIwk/O28crj2Ndd1+S1JxeOpp/pjchDqgAaqBlhEqNVyhTDZoAwaBvCN6i9TfhH9fXsMsPZH6YOHIOkDybM0dnYsM9GCZ5mvkpUTw21Clnzi/unAiPl+5SG7wlyhwlSsUpcbhdOHbmk4AwKxKFojotRpMLc3EAbEcFsEEWLvYgN2drypPrNvm/WL2PsV7SSXd0WN0sSqfPgzt5nsni4J0IzKS9HC6RBySTGChwju6NqdPAvLGAyUzgVFnKVNXw0k/tcVfUDNQDfh4SiVik/BJJNSuwopSE5v0U1aKQe76qp7eDAAHGrshiuxg6umPuFxKL3x6oAmdfTZ8cagVAGt2mWhEO/tJKecOvB/NS5U6l/OgRnq/5GBq2NjFrBLyxEZg3zsAgPmVaXjF8H94yvAXXGT/KPyV9kg/JTI8/eQK4Vz2q6o29NmcyEzWY2y+Uj05syILTmhxPEkyV1d/Aex5S3li/Q7vN+AqjUYvK2axJGGDml4kxVcr7AFCEARMLmGdJXljq1DhHV3TUlKAH28Cbl4LaLRApjSDqqsmtF8MALSyJlvxlH7KGiCjME+pjIyDM0dl/lPg9FNXjKufACXo8zQL729gnZQnFnm3jR9fmI6JRemwO0U8/1U19knLzg1SQTUc4UGN3SlGNCYlFJMwAORKzRJbpDJsnxO6fZE9Aph3F7u+5h6gvxPn1T2FyZpqAMDM/q/CXmfP9FMiw9NPjhD20a9tYlaCS6aVuPmYvnd6BZZOLUL62Hnsji+fZCbgtCLWK8tqlhsoyqjLuQdAHU24oMbkYumnWWPLoRuCc58GA95Zck99uEGNNNGW+yS4nT69BBC0gNMGdDcEfyGXC2ivYtd5A644IFue/zRASk3+0N/xKpO6vQO7JrNFDpSVPjUDENQ0u5uFeVAzwUdQAyhqzdMb2M52UnH6sK548YdRr+z/IlFrgnUT5vCmbO29NtidLrlSLimYpwYA5q1ggzB7moB/X4aUnc/JD1X0fCtXRoWMj/RTosKDmmDxbLPZgrX7WeflazyGJpdkJuGv185A0eSF7I4eqUPz1KuUgg/PFBSvfBoAkzCQgEGNIMmP350/YZDXZOgwuYQFNbvrzEGWdMfvnBStDsiQqghC8dV01QAOC5OaeeoqDuBKzUCln+JBqeHNvMw+lJobnt+CZX/5Ep8dalFN6I5dweWofPchh5x9clDju8ngRacUQ6sRZJPr/DGJl3oC3FvmWyLoVcPTT4F61ADsd8IPoG09NjmoCarUAIDeBCz7I7suHRx3VdyAY2IhtKIDqFof3kpTUCMjG4WDeGpWbauFwyViZkUWxhX6adzJB+xyTrmW2REA76Amghl04ZBwQQ3sbKYODKF3VR3u8KBmf4M5LBmae2p8ziaSzcIhVEBxP032SJa+ihOyB8AorJ6vFM9KTU17n6yY3P/ObnkIXiyVmpG53kqNyyXiYCM7e+cD+zzJSzNiocpDc+bYxEs9ASz1rAsjBeFJR4jpJ41GkJvktfZY0c+NwqFWmI2YD0z7LrtedhqmXPcYKk+X5sIdCtNXI3tqhv5va6DR8JLuANVPLpeI17ewffh3Zpf5fzFTOpAvVcEWTwfyxwMlM9htL6Vm4CqfgEQMasgo5kVFdjLSjDrYHC55GnMocE9Nlq+JttxXE4pZWPbTxE/qCVAUqq5+e1itxgNxrLUXosgUjXhoOcCDFE+lZmNVq3y9pr1f9gnF1FMjBX1HW3vkstTj7X3oszlh1GlQmeP/N37ZDKYkJum1CdGTxh86rRTUhDPVUCKUuU8cdQWUkn4K4wRm6RPAxU8D174BQWeAMPZ8dv/hj0P37QGk1KgIpaR7Y1Ubatr7kWbSYdnU4sAvOOFCdsmbp3KlpnE34FB1mh/AbsJAQgY10kZtpEido9EImFgcvlmYe2oyfCk1mZXSQqEENYfYZRxVPgGKQiWKihE2WvjBf1R+gEGWQwgl/eT+/391hDW0m1aW6XZ/RgyVmrKsJOi1Aix2l9wRm6tD4wrTAnrmzp9UgNvOHIlHL5sy5MvmBxK95IMbSKMwoPhqWrqtcgl5SOknjj4JmP5d5UBYMZepLb3NQMO3ob8OBTUyoTTfe20zU2kunV4SPAid/zPgzp3AKd9htzMr2PBclx1o3KMsN4Bzn4BEDGpc0s6X5Ec3eAoqkqDGp1KTFUZZt1zOPTbk9x4K6LUa2SMSaKjltuPtITc3VPw08bF9+ko/iaIoKzX3LRmPcycq82BiVdINADqtosbwFNS+eslPU+g79aR+7n0XTMAl0lDDREVWaiJQGttDLOkGVEpNjzU8T40/dAZg1CJ2PdQUlNOhTKqn/X9IYxI2HGwGAFwRSqdlrQ7IqlRuC4JvX80ATugGEjGo4dBG7cYUHtTUh2YWtjtdaO5mB2q+w3IjM4wGfHFYzs2Re9X48dUcae7B5X/7Gj/8t48mVD44Klc+xceZpK+OwgebutHaY0OSXovp5Vl48KJJSDfpkJtq9B0AR8FIj3EJcjm3Hz8N4Q5XsyJRakIZZsnxnX6KMsAdw1NQIQY1dpWhnJSaoEZhu9OFXum7Ks+OsLO5r6BmAOc+AQk4+wkAoDOxqJKQ4b1q9tWb4XSJ8gbvj+NtfbA7RSQbtCj2Nf2YKzXmOsBpB7R+0g7WHqCbDaiLp3JuTlaKAdVtffIO3hPe0PBQU3fQSdaA0nMlfpQa7/TTl4eZSnPqiGwYdBoUZyZh7V0LAIGpW7GElXU3eQU1/sq5CXf0msg9NTyQDzv9JBmFo1JqAGDMeeyyfgew97/AhItYoYG1Gzi6gZUXu1xMMRh7PqCR9vkaHaBLvBJ+T7RBjMI9qhOV1ABz3QLiM6gZWKUmMY/sFKV7MSI3FckGLfpsThxt6cGYgsDVYdxQPCrPj/cjJR/QGlk77K5aQHQB374CzLwRyFS56HljpuTcATOODSS8vwlvLOZJvVTJ1GdzotvqkNM1Gw424/539uB3l091a/zG2/uPjGY690lE7ijcr+wAN1YxP83c0TnyffnpPgLfGMCDl1VbazGjPAv1UppvvJ9ybsIdrtSEW/1ksTtlxSU7NXhQk+tLqYl2vlZaARuee3QDsOoGIGsE8+Ud/Uxpw8/Z8W/gsn+w64aUAWn6Fm9oZKUGPk+4eqxKlVrEPd2KpQqotsNAfycbcDmAc5+ARE0/UerJC61GkDuw7g7BV8PPjEf7KzvWaJQKqJrNwIsXAl88Aby4DOhuVJbjQU2cmYQ5JZlsdhYPXjyp71S8NE0qX83q3Q2o7ejH6t1Kc8KufrucxinNShqI1Y05SkdhptTYnS5sOsqDmoEvlT5/UiHOHp8Pq8OFu97cCQAoy06KaZXVcIZ7auxhKjW88kmvFZAWwlk8V2pYSXcMPDWcK54H5t8NmDKBjmOsGsppZe0hJlwITLyEnVw1fAtUf86eQ/t/AIpSA7DAxhO+L0qNprdUSg6btg4AzfvYJRmFBwDaqH2imIWD+2q4UuM3qAGUFNT/fsrSUADQUQ28fJmSV+WDLOOsnJtTJKXe/Ac1yv2NZovqfnb9RHuffF9tB7uek2IIPsF4iMA9NVaHCxa7EztrOtFrcyI7xRDUrBsLDDoNnrluJi6ZppSbnoz3HS7w6qdw00/tPUo7h1Cq9Linprnbqsx+isU2npwNnP0AcNc+1qTvnAeBH30N/GQ7cPW/gateBCZJPW2+/iu7JKUeANzGHfhKQXGlJpSgNSB549ll8342xbu/k90mo3AMoXJun/Cg5p1v6/DUusNoMvuv2AkpqOFmYYeFbcDX/RdILQSa9wKvXMm6gTbvZcvEqVJTLCk1DZ2+P6uGrn7VdXVQw+6vUQU1Ne3svnhRaQAg1aCTlfxuiwNfHmF+mjNG5bjtNAcSvVaDP1w1DTfMqQQAnD0h/6S873BAVmrCTD+1hTgigcODmh6rQz6AhtWnJhiGFGDWjcC85UDBRPf00qyb2GX7UWVZws036csszNXXqJQaAMiXuve3HAAsXQCk96L0UwwhpcYn88fkIifFgPZeGx7/+BDOePRTvLHFuyOwyyUGTz8BilKj0QPfeYVN8L7ubcCUAdRuAV6+BNj/P7ZMnJVzc3hQU+dHqanzkX4SRVFevq6zX97Jc6WmNCvCSoNBQKMRZBOh2WKX02nzTvKASI1GwK8vmoTdvz4PV88uD/4EAoDKUxOmUiObhEPw0wDMaGrSux9uYpJ+CoWyU5VutwDt/yXU6aeASk2sgprm/YqfxpDGyvIHgAQNaihS90VBuglf3nMWHr/yFEwuSYdLBFbvbvRarr6rH302J/RaARWBSv2mXAmMXcIk4Io50ptMAm78kLU9Ty1k92mNQNEpA/AfDTzcU9NotnjtGKwOJ1pVBmKefmrvtcHqYGfGdqco31/bISk12fGj1ABKr5oP9zTiUFMPkg1aXDClaFDWJY28NGGhVD+FqdT0hN5NGGAjGdStH/RaIeaVcAHeHJh9k3Kb9v8AlD41gO8GfLKnJpbpJ7mce+CKQuIjcR9rjFQZ4Y8kgxZXzCxFfpoR1z+32WfTOJ56qsxJCeyKTy8Grn3d+/6CicAlT7NWvE17WXllepAW3EOUvDQjdBoBDpeIlm4rClXl7U1d7hUY/LOs90hV1bT3oSQzSQlq4kipAZQzuX98weT9K2eWxrRzMDFwKOmnMD01YXQT5uSlGuUUa9SVT+Ey5Srg41+xXjUU1ADwMAoHUGpSjVH+lvPGscu+VqV7/AD5aQBSagg/yAbYLu+0Skh+mlAQBKBwctz6aQCWly6QypU9U1Cet7ki43k/Nwsr6ac4U2qkAIZ3mP6+5G0hhj56Of0UnlLDg5pwmimqlZqTboQ3pQNTr5TePDEHmHqiDWIU5p6aqNNPhhTFX3l8I7scwPYdCRrUUE41GEVSWqXb4pAjdk5IfpoEwl9ZNzcJczNlk5+gpra9j/lsJKWmLN6CGlXKZ9G4PIzMo+0iXtBF2HxPHmYZoqcG8AxqBmHe1jkPAgt/Acy54+S/9xBEEATZT+0r/cSb70Ud1ABA/kR2efwrdjlA5dxAogY1VP0UlFSjTi7l80xBxUypGSYUZfou6+a3p0tDHVt7bLA6nPL9ekn6P9HeB3O/A91S8FiSGV/pp3TVTu+meSMGcU2IcJHHJIRZ/dQRUfpJSc3GtPIpVJIygYX3KP2zCDkF5evr77bGyFMDAPmSr6bjGLuk9FOMIaUmJLg/hIKawMhl3R6fE+9uO6k4HQYd+6k1m61yUMMnWNd09KNGSj3lphoHZ4cfBTz9NCY/9aRXPRHRwQPriJWaMIKa3DRl2UFRaggveNuFQEpN1CXdAJA3wf02KTUxhoKakOBBjbrXSluPFR19dggCn7tD+CvrbpBuF2cmoSCdSe9NZosc1Jw+ko0RONHeF7d+GgA4d2IBijJMuO+C8SE1YiOGDjpNZAMtW7uZCd7nMFs/8K7CQAyGWRIxQVFqAhmFY6jUcEipiTFkFA6JIh9KzWFJpSnNSoLpZFcwDFGK/XQV5lVOxZlJKEpX1Bzeu4YHNS3dVhxuUj7XeGPu6Fx8fd/ZOGt8wWCvChEmvPrJEUb1k8XulFMTualhBDVqTw3tO4YE3Czs2ygcQ09N7lhAUIUbpNTEGPLUhERhhnQgVnUWllNPpNLI+E8/caXGhAIp8DnR3if3rplQlC77lr45xuYlxVs5NxHfKGMSQldqWiSVxqDTuPmpgjHoRmHCC00go7DcfC8G7Rn0SWzgKIeqn2IMpZ9CwpdSQ34ab3hQ095rk4f1dVuU4ZRFGUkolNJPO050AmB9OrKS9SiTmhdurWZNqeJRqSHil0gGWvKJ9HmpxrDSjblu6ScKaoYCXKnxlX6KWfM9Tr7KVxNv6af9+/djyZIlqKioQEVFBR555BGIPiJBjiiKeOyxxzBu3DiUlJRg4cKF2Ldvn9synZ2duPXWW1FeXo6SkhKcffbZ2LVrV2QrSEFNSCieGiWooXJub9JNOvmHz9UZ/pllJOmRYtTJqteOEyx4Kc40QRAElEndg3mH4bJAHZoJIsbIfWrCqH7iSk1uGH4aADDptbKyQ0rN0EAbyChslWY/xSqoyVP5agawo3DMg5rW1lYsWrQI559/Pqqrq7Fx40a88soreOyxx/w+5+GHH8bzzz+PdevWoba2FhdffDHOOeccdHZ2ystcfvnl6O/vx/79+1FXV4eLL74Y5557Ljo6OsJfSUo/hYSi1LADtSiK2FfPJniPp0nIMoIgyJ8VH2zJTcP8/kKpQR+vGuHqTrlHEENKDXEyiaRPTatKqQkXnoIio/DQQCP4/v7tThcsdhboxsRTA8SvUvPMM88gLy8Py5cvhyAIKCkpwUMPPYQnnngCdrvda/n+/n489thjeOihh1BSUgJBELBixQrk5ubipZdeAgA0NTWhq6sLzz77LFJSmMn3pz/9KRwOB7744ovwV5KUmpDg5taOPjssdidauq1o67VBIwDjCmnUhJpijwZ8PLjhjfnU4xPU93sqM/x+gjgZyH1qwkk/RVD5xOHPIaVmaMCDWs8p3bycGxiA9JNGx4YaDxAxD2rWrVuHCy+80O2+ZcuWoaWlBdu3b/dafsuWLejp6cHSpUu9nrNmzRoAQEFBAbZu3YrkZOUAcOLECXR1dSE93bdiYLVaYTab3f5kKKgJifQknTyjpbHLgn0N7DMcmZdKlU8eeJZ18zJ43pjPM6gp9hHU5KUZ6XMlTipyn5oI0k+RBDUjctlJKW9xQAwuGj/VT9wknKTXBp7vFw75E4GZNwIL7wUGsPVDzIOa+vp6FBe7Dyc0Go3IyclBXV2dz+Wzs7NhMnmcyZaU+FweAKqqqrBkyRKceeaZWLBggc9lHn30UWRkZMh/ZWVl0iNaNkCRCIpbWkUV1EwsotSTJ55l3Ur6iQUv+WlGt9+xr/QTpZ6Ikw3vUxNOSbeSfgq98R7n7vPG4S/XTB+0Ke6EO1o/Sk13LBvvcQQBuPCPwJk/j91r+iCsoKalpQWlpaV+/x577DHo9XpoNN4v688lH+7yb775JmbMmIEzzjgDH3zwgd/l7rvvPnR1dcl/NTU17AFjyoBGicMNuauwuV/200wspqDGE8+ybs/0k16rcav+KJYUHHW6qYzKuYmTjNynJoKS7kiUmpxUIy48pRhGHSmSQwHefM/z65fLuWOVejqJhLXGeXl5qK2tDbjMunXrUF9f73af1WpFR0cHSkpKvJYvLS1Fa2srbDYbDAYl8m9oaPBa/te//jWeffZZvPbaa7jgggsCrofRaITR6ONHR6mnsFBXQO2XlJoJpNR44emp4VVQRaq0U2G6ST4g8GDGpNeiMN2ERrOFlBripBPJmAS5pDuCoIYYWvhLP/EJ3TFVak4SMU8/LV68GKtXr3a7b/369cjOzsaMGTO8lp8xYwby8vLw4Ycfut2/du1aLFmyRL79l7/8BW+88Qa2bdsWNKAJiJ66CYcDPygfbenF0dZeAJR+8kWJylOzp65LVmyKVUpMQboqwFEFOzwFRY33iJONPCYhxPSTKIpo7WYVfOF0EyaGJvKYBE+jsDWG3YRPMjEPaq6//no0NDTg6aefBsAUl5UrV2LFihXQ6707E+r1eixfvhz3338/GhsbAQB//etfUV1djeuuuw4AMwX/8pe/xHvvvefl1wkbIwU14cD7q3x+qAWiyM7O6AzNm4IM9plYHS4s+8uXsDlcSDFo3YIXHiDmpRnd5PfbF43C0qlFWDK58OSuNJHw6MNMP/XanOi3swaTFNTEP/6Vmhg33juJxHyNs7KysHbtWtxxxx14+OGHYTAYcMstt2DlypXyMldeeSUAYNWqVQCAe+65B06nE6effjpsNhvGjRuHtWvXIiuLNehZv349LBYLFi1a5PV+V111Ff7whz+EvoIGOhsOhyJJXWjuVlr7E94YdVqUZyfjRHsf9FoBF0wpwg/mj5SbmwGKOlPsUba9cFw+Fo7LP6nrSxBA+CXdPH2aYtAiJQ4PeIQ7fPfk2XxPGWYZgxEJJ5kB2SonTZqE9evX+32cBzMcjUaD+++/H/fff7/P5b///e/j+9//fmxWTk/9VcLBsxSZUk/+eera6dhd14XzJhb6VLO4wXpKCX2GxNBAbr4XYkk3r3wKt5swMTTxN6Wbe2riMf0Uf2scLZR+Cosiz6CGKp/8MrU0E1NLM/0+vnBsHj5afiYqc0ktJIYG8piEMJWaSLoJE0MPv31qYjmh+yQTf2scLZR+CovsFAMMOg1s0mwiUmoiRxAE6sRMDCmUgZahKTXRlHMTQw9/RuFua/x6ahJvSreeSrrDQd2Az6TXyB1BCYKIf8JtvtdK5dzDCkWpcb+/ZyCa750kEi+oMdBBOVz4MMZxhelyB0qCIOKfcKuf5AndlH4aFsjN9/wahSmoGfqQpyZsuFJDqSeCGF5EWv1ESs3wQB6T4KekOx49NYkX1FDzvbA5fxKr5rlwKs1rIYjhhD7C6icyCg8Pgg20TDNRSffQh9JPYbNkShGW0AA6ghh26CKsfqKS7uGBlH30Sj/Fc/O9xFNqjGQUJgiCAFTVTyEoNaIoorWHjUig9NPwwF/6qccqzX6ioCYOoPQTQRAEAECvCV2pMfc7YJMMxbmphiBLE/GAxodR2O50wWJn3zN5auIBSj8RBEEAUPepCR7UtPSwIa3pJp3b7DIifvGl1PByboCUmviAghqCIAgAqpLuENJPzVT5NOzwZRTmJuEkvVb2XMUT8bfG0WLKGuw1IAiCGBLowkg/kZ9m+KH0qVHu647jxntAIgY1KdmDvQYEQRBDgnDGJCg9akxBliTiBZ/pJ17OHYepJyARgxqCIAgCgGqgZQhjEpRuwmQSHi74MgrzCd2k1BAEQRBxhU7lqRDFwIENzX0afnDLjC9PTTxWPgEU1BAEQSQsaiNosAqoNimoyU2hoGa4oPVhFI7nxnsABTUEQRAJC69+AoJXQLX3sbREdgqln4YLvoIaZZhl/I1IACioIQiCSFh49RMQXKlp72VKTRYFNcMGXv3k8uGpofQTQRAEEVe4KTVBKqA6ekmpGW747FMTxxO6AQpqCIIgEhZBEOQURKAKKKvDKaclspMpqBkuaH1VP1nJU0MQBEHEKbwCKlCvmk7JT6PVCHF7Bk94E2hMApV0EwRBEHGH3KsmgKemvZd1E85K1sspCyL+UdJPyn09pNQQBEEQ8YouhPlPPKghP83wwpdRuJeCGoIgCCJe4RVQgaqfFKWGgprhhC+jsE3aDvRxOMwSoKCGIAgioZEndQcIajr6SKkZjvgyCjslxU4Xp2lGCmoIgiASGHmoZQjpJ+pRM7zgYozaKMyDWx0pNQRBEES8odcENwp3cE8NpZ+GFb7ST7y0X0tKDUEQBBFvyEbhACXdbWQUHpb4Sj/x7UDdmDGeoKCGIAgigZGNwgGa75GnZnjiq08N3w7UIzTiifhca4IgCCIm6ENQatqlEQnkqRleaGSlRrmPp6J0pNQQBEEQ8QY3hAYq6SZPzfDEp1LjpOongiAIIk7RaQI33xNFEe19vPpJf9LWixh4fBmFnZR+IgiCIOKVYGMSem1O2Bws4CFPzfDCt1GY0k8EQRBEnCL3qfHjqeGpJ5Neg2RDfLbOJ3zjs08NNd8jCIIg4hWeZnD4qX5qJz/NsEXjodS4XCL4ZkDN9wiCIIi4I1j1k+KnoaBmuKH18NSoA1tKPxEEQRBxR7Dqp/Ye6lEzXJGrn0Qe1CiBLaWfCIIgiLhDH6T6iTfeowndww85/SQpNOrAlqqfCIIgiLhDMQoH8dSQUjPsUPrUsNvq0m5SagiCIIi4QxekpJtGJAxfuFLDVTruq9IISg+beIOCGoIgiAQmWPqJKzVkFB5+yEZhKZ51xHnjPYCCGoIgiIQmqFGYSrqHLZ59auK98R5AQQ1BEERCowtW0t1LIxKGK1pJkVFKuuO78R5AQQ1BEERCow/SfK+jj03oJk/N8IOPSVBKurlSE7+hQfyuOUEQBBE1gcYkOF0iOskoPGzh1hmlpJuUGoIgCCKOCTTQ0txvl9vmU5+a4YfnQEtlQjcFNQRBEEQcwg9gdh/VT22SnybNpJODH2L4oPSpcW++R+kngiAIIi4J1KeGetQMbzQaUmoIgiCIYYQ80NKHUiNXPlHqaVgiG4Wlr55XwFFJtwf79+/HkiVLUFFRgYqKCjzyyCMQRd/OegAQRRGPPfYYxo0bh5KSEixcuBD79u3zu/wf//hHCIKADRs2DMDaEwRBJA680ZqvPjUdUlCTQ0rNsMTflG5qvqeitbUVixYtwvnnn4/q6mps3LgRr7zyCh577DG/z3n44Yfx/PPPY926daitrcXFF1+Mc845B52dnV7L7t69G3/+85+Rm5sb61UnCIJIOHz1qdlXb8bjHx3E0xuqAFA34eGKxsMoLPepIaVG4ZlnnkFeXh6WL18OQRBQUlKChx56CE888QTsdrvX8v39/Xjsscfw0EMPoaSkBIIgYMWKFcjNzcVLL73ktqzFYsG1116L3//+90hJSYn1qhMEQSQcSvqJHdh2nOjABX/+Ak+tP4IT7X3QaQQsGpc/mKtIDBB+jcLkqVFYt24dLrzwQrf7li1bhpaWFmzfvt1r+S1btqCnpwdLly71es6aNWvc7vv5z3+OMWPG4Iorrgi6HlarFWaz2e2PIAiCcEdJP7Gz9KMtvQCA0qwkPHn1Kdh6/zlYOrVo0NaPGDh4kZO3UZjSTzL19fUoLi52u89oNCInJwd1dXU+l8/OzobJZHK7v6SkxG351atX46233sLf//73kNbj0UcfRUZGhvxXVlYWwX9DEAQxvJGVGuksvc/mAABMKcnApdNLkUkm4WGLnH7ybL4Xx+knXTgLt7S0YPr06X4fv/POO6HX66HxEeUJgu8PKZTlm5ubcdNNN+H5558P2Utz33334a677pJvm81mCmwIgiA8kJUa6cDWa3MCAJINYR0eiDjEM/3EgxttHKefwtpq8/LyUFtbG3CZdevWob6+3u0+q9WKjo4OlJSUeC1fWlqK1tZW2Gw2GAzKGUFDQ4O8/I033ojLL78cS5YsCXldjUYjjEZjyMsTBEEkIp5G4T4rU2pSjNpBWyfi5OBlFJbUunhutBjzNV+8eDFWr17tdt/69euRnZ2NGTNmeC0/Y8YM5OXl4cMPP3S7f+3atXIQs3r1ajz99NMQBEH+O378OBYtWoTS0tJY/wsEQRAJg+eYBFJqEgdFqWG3HcNAqYl5UHP99dejoaEBTz/9NACmuKxcuRIrVqyAXu89ul6v12P58uW4//770djYCAD461//iurqalx33XUAWB8bz7+KigqsX78+qHJEEARB+MdzTAL31KQYSKkZ7mg1vku69YniqQmFrKwsrF27FnfccQcefvhhGAwG3HLLLVi5cqW8zJVXXgkAWLVqFQDgnnvugdPpxOmnnw6bzYZx48Zh7dq1yMrKivXqEQRBECo8xyT0WiWlxkhKzXDH2ygc/9VPA7LVTpo0CevXr/f7OA9mOBqNBvfffz/uv//+kN+juro60tUjCIIgJPSenhpSahIGdZrJ5RLh5M33KP1EEARBxCNe1U+k1CQMWlWVsVMUVVO6KaghCIIg4hBSahIXdZbJ6RJVJd3xGxrE75oTBEEQUePlqaHqp4TBLf0kinJgG89GYQpqCIIgEhiv6ifqU5MwaFTpJ4dLpJJugiAIIr6hPjWJi6dRmAc11HyPIAiCiEt0qindoigqnhpSaoY9bkZhlygHtlT9RBAEQcQlepUptN/ulCtgSKkZ/mg07tVPDirpJgiCIOIZdfluV79dvp5M1U8JgU41KkEp6Y7f0CB+15wgCIKIGl9BjUGniWtfBRE6GtWoBN58j4zCBEEQRFyiTj919bGghnrUJA7cV+NSeWqopJsgCIKISzQaAfzEnCs15KdJHOShlm4l3fEbGsTvmhMEQRAxgXsoeFBDlU+JAw9o1UZhUmoIgiCIuEUvHdlIqUk8tBrv9FM8T+mO3zUnCIIgYgJXasyk1CQcWpVRmKefqKSbIAiCiFt4uoGUmsSDj0pwukTYpdlPNKWbIAiCiFu0Huknqn5KHLSqPjVOmv1EEARBxDvcQyErNUZSahIFWakR1SXd8RsaxO+aEwRBEDHBM/1ESk3i4F7STc33CIIgiDjHs6SbPDWJg5x+EtVTuimoIQiCIOIUneypYRO6ae5T4iD3qVGVdFPzPYIgCCJu0XuUdJOnJnFw61PDm+9R+okgCIKIV3gJr00q6SVPTeLgyyhMU7oJgiCIuEXvkW4gT03i4Hv2Eyk1BEEQRJzi2WyNOgonDm5GYSfNfiIIgiDiHM90Ayk1iYPSURik1BAEQRDxj6cxlJSaxMFX+oma7xEEQRBxi1f6iZSahEEreKefSKkhCIIg4hbv9BMpNYkC94i7KTXUp4YgCIKIV7zTT6TUJAq+0k80pZsgCIKIW9RKjUYAjDo6NCQKilFYST/pKP1EEARBxCvqEt4Ugw6CEL8HNSI8uFLjcLkgCTXUfI8gCIKIX3QqD0UyVT4lFNwobHO4lPtIqSEIgiDiFZ2HUkMkDjyAsaqCGmq+RxAEQcQt6r4kpNQkFr6CGlJqCIIgiLhFbQylbsKJhUbjnX6ikm6CIAgiblEbQ2lCd2LBPTVcqdEISqATj1BQQxAEkeCo+9QkU4+ahELrodTo4lilASioIQiCSHhIqUlcNLJS4wQQ3433AApqCIIgEh51tQt5ahILHs/y9FM8m4QBCmoIgiASHrVRmCZ0Jxae6ad4ntANUFBDEASR8KjTT6TUJBae6SdSagiCIIi4xn1MAik1iYSXUkNBDUEQBBHPuI9JIKUmkdB4lHTH89wngIIagiCIhIfGJCQu3iXdpNQQBEEQcQyNSUhcPMckUEk3QRAEEde4VT+RUpNQeBuF4zssiO+1JwiCIKLGTakho3BCwb96paSblBqCIAgijnHz1JBROKHwnP1EJd0+2L9/P5YsWYKKigpUVFTgkUcegSiKfpcXRRGPPfYYxo0bh5KSEixcuBD79u3zWu6NN97A5MmTUVJSgtGjR+P3v//9QKw+QRBEQqGufqKS7sTCc0p3PE/oBgYgqGltbcWiRYtw/vnno7q6Ghs3bsQrr7yCxx57zO9zHn74YTz//PNYt24damtrcfHFF+Occ85BZ2envMybb76J5cuX49///jfq6uqwZs0a/Otf/8L27dtj/S8QBEEkFG5jEkipSShIqQnCM888g7y8PCxfvhyCIKCkpAQPPfQQnnjiCdjtdq/l+/v78dhjj+Ghhx5CSUkJBEHAihUrkJubi5deegkA4HK5sHLlSvzf//0fpk2bBgAYM2YM9u3bhxkzZsT6XyAIgkgo1L1JkvSk1CQSnkoNVT95sG7dOlx44YVu9y1btgwtLS0+VZUtW7agp6cHS5cu9XrOmjVrAAC7du3C8ePHcckll7gto9XSj48gCCJaePVTkl4b92fqRHjIfWqcNPvJJ/X19SguLna7z2g0IicnB3V1dT6Xz87Ohslkcru/pKREXv7IkSMoKirCzp07MW/ePFRUVGDRokX47LPP/K6H1WqF2Wx2+yMIgiC8yUoxAADy0oyDvCbEyYann+TbcR7UhpU8bWlpwfTp0/0+fuedd0Kv10Pjw2gkCL4/qFCWdzqd6OnpweOPP45Vq1YhPz8fb775Js477zx8/fXXPlNQjz76KB588MFQ/i2CIIiEpiQzCc98byZKMpMGe1WIk4zGI4iJ95LusIKavLw81NbWBlxm3bp1qK+vd7vParWio6MDJSUlXsuXlpaitbUVNpsNBoNBvr+hoUFevry8HN3d3Xj22WdRVFQEALjmmmvw0ksv4bXXXvMZ1Nx3332466675NtmsxllZWWh/7MEQRAJxOLJhYO9CsQg4BnDUPM9DxYvXozVq1e73bd+/XpkZ2f7DD5mzJiBvLw8fPjhh273r127FkuWLAEATJkyBZmZmbDZbF7PNxp9y6VGoxHp6elufwRBEARBKHimm2hKtwfXX389Ghoa8PTTTwNgisvKlSuxYsUK6PV6r+X1ej2WL1+O+++/H42NjQCAv/71r6iursZ1110HAEhNTcXPfvYz3HTTTWhuboYoivjPf/6Dzz//HNdee22s/wWCIAiCSAg8008J5akJhaysLKxduxZ33HEHHn74YRgMBtxyyy1YuXKlvMyVV14JAFi1ahUA4J577oHT6cTpp58Om82GcePGYe3atcjKypKf84tf/AIulwuzZs2CzWZDZWUlVq9ejYkTJ8b6XyAIgiCIhMBzKrcuzqufBDFQq99hhNlsRkZGBrq6uigVRRAEQRAAVm2twc//s0u+ff0ZFfjNxZMHcY28Cef4Hd8hGUEQBEEQEeOZbor39BMFNQRBEASRoHgZheM8/RTfa08QBEEQRMRohlnzPQpqCIIgCCJBoZJugiAIgiCGBd5KTXyHBfG99gRBEARBRIynUkNTugmCIAiCiEs8fcHxPvuJghqCIAiCSFAo/UQQBEEQxLDAu6SblBqCIAiCIOIQLZV0EwRBEAQxHPAcaKmn9BNBEARBEPEIjUkgCIIgCGJY4GkUppJugiAIgiDiEq8+NZR+IgiCIAgiHvE0CpNSQxAEQRBEXOIpzFBJN0EQBEEQcYm3UTi+w4L4XnuCIAiCICLGM/1EU7oJgiAIgohLPPvUUEk3QRAEQRBxibdROL7Dgvhee4IgCIIgIsa7pJuUGoIgCIIg4hDP9BOVdBMEQRAEEZd4GYUp/UQQBEEQRDxCs58IgiAIghgWeAYxNKWbIAiCIIi4xDP9pCVPDUEQBEEQ8YjXmIQ4Tz/pBnsFhhpOpxN2u32wV4MYwuj1emi12sFeDYIgiKgZbp4aCmokRFFEY2MjOjs7B3tViDggMzMThYWFEIT43gEQBJHYaIZZ8z0KaiR4QJOfn4/k5GQ6WBE+EUURfX19aG5uBgAUFRUN8hoRBEFEjpdROM49NRTUgKWceECTk5Mz2KtDDHGSkpIAAM3NzcjPz6dUFEEQcYuXUTjO00/xrTPFCO6hSU5OHuQ1IeIFvq2Q/4ogiHjGs6MwlXQPIyjlRIQKbSsEQQwXuDojCN5BTrxBQQ1BEARBJDA8BRXvKg1AQU1CsWHDhrhQGARBwIYNGwZ7NQiCIBICHsvEu58GoKCGIAiCIBIartTE+4RugIIagiAIgkhouI8m3id0AxTU/P/27j0upvz/A/hrYhqkWhSamS6jqJRrsaxYq6x7KiwSocTXNfF1+fGwuVuWpe+377rskmXxXa02tO651HfLqrXuYpmicilKQtPM9Pn90bfzNaY00cw04/18POax25n3OefzPu+p+ficzzmnSowxvCxV6PzFGKtRO3NycjBkyBCIxWK4u7vD398fbm5ukMlkiI+Ph7u7O4RCIXr37o3Lly+rrBsZGYnhw4fjq6++QqtWrdCyZUvMmjULJSUlGu07KysLw4YNg1gshlgsRkhICIqKivDTTz/B3NwcL1684GIPHDgAKysryGQyKBQKrFq1Ck5OThCJROjevTtSUlKq3E98fDw6duwIoVCILl264Ny5czU6RoQQQqpWcdrJGE4/0X1qqvBKrkTbJcd0vt/ry/qhkanmZRk3bhw8PDxw8OBBZGZm4pNPPkF8fDyuX7+OkSNH4pdffkH//v0hlUrh5+entv7Ro0chkUiQkZGBp0+fon///li0aBHWr1//1v2+ePECvXr1wuDBgyGVSqFUKhEaGoo1a9Zg2bJliIiIQGxsLIKDgwEAe/bswbhx4yAQCDBnzhwcOnQIJ06cgEQiQXx8PIKCgnD9+nUIBAKV/Rw+fBiBgYE4ePAgvL29kZCQgCFDhuDKlSuws7PT+DgRQgip3P8mCht+p4ZGagxcSkoKgoODwePxIJFI4OXlhaSkJGzbtg1DhgxB//79AQASiQTLly9XW9/GxgZr164Fn89HixYtEBkZia1bt1Y7YhQXF4fi4mJ888034PP5aNCgAbZv345Vq1ahfv36mDJlCnbu3AkAKCoqQkJCAsLCwlBaWopvv/0Wq1evhkQiAQAMHToUN27cUOvQAMDGjRsxfvx4eHt7AwAGDRqEPn36YPv27e913AghhJSrOP1k6E/oBmikpkoN+fVwfVk/vey3Jjw8PLBz506sXr0ad+/exZkzZzB58mScOHECHTp0UIm1sLBQW18kEqlcEdWmTRsUFxcjPz8f1tbWVe43KysLjo6OMDU15Za9/v9hYWFYuXIlsrKykJiYiK5du8LFxQU5OTl49eoVXF1dVbb3+rqvy87Oxp9//omEhARuWUlJCd35mRBCaokxXdJNnZoq8Hi8Gp0G0pd58+Zh0qRJiI2NRYMGDfDll1/Cx8cHP/74I27duqUS+/DhQ7X1nzx5ovKzVCqFmZkZrKys3rpfe3t7SKVSyOVy8Pl8brlCoUD9+vXRvHlzDB8+HLt378a5c+cwefJkAIC1tTUaNmyIjIwMtG3bllvvze1UcHJyQmBgIJYsWVL9wSCEEFJjxjSnxvC7ZR+48ePHY/fu3bh79y6uX7+O6dOnAwAmTZqEhIQEHDp0CABw8eJFREZGqq1/7do1rF+/HmVlZXjy5AmWLFmC0NDQau9n4+/vDzMzM8yaNQsymQxKpRJLly5FUFAQFzNjxgx8//33uHTpEoYNGwagfERm2rRpWLBgAf766y8AQFpaGtq0acM9JPJ14eHh2LRpE86ePQugfJRm4cKF2LVrV80PFiGEEDUVAzSG/oRugEZqDN6qVavg5+eHxo0bQyAQwMbGBjNmzMCYMWMQFxeHBQsWICQkBG5ubli7di2GDh2qsr6XlxcKCgrg5OSEkpISBAQEYPXq1dXu18zMDElJSZgzZw5atWoFExMTeHl5ISoqiovp2rUrLCws4O/vrzJfZs2aNbC2tsaAAQPw6tUrtGzZEps3b0bz5s3V9uPj44OYmBjMnTsXOTk5aNCgAfz8/LhOEiGEkPfD3afGCEZqeKym1xAbqKKiIlhaWuLZs2dqc0tKSkoglUohkUjQoEEDPbWw5goLC+Hp6YnVq1fD19cXpaWlOHz4MIKCglBYWAhzc/O3rh8ZGYkzZ85o7e69JSUlsLGxQWpqKpydnbWyD30x1M8MIYS8qc/6M7ib9wKd7D5C3NQe+m6Omrd9f7+JRmoM2IMHD3Dnzh0IhUIIBAIIBAIwxmBlZYWGDRu+17azs7PRrVu3St9zcXHByZMn37p+QUEBli1bhq5duxpdh4YQQowJTRQmdYKrqyu2bNmCKVOmoLCwEDweD+3bt8fJkydRv/77lVYsFiM7O/ud1w8MDMTTp0+xb9++92oHIYQQ7TKmicLUqTFwYWFhCAsLe6d1K5s4XFuOHDmitW0TQgipPSb07CdCCCGEGIOKERpjmCislU7NjRs3MGDAANjb28Pe3h4rV6586x1qGWNYt24dnJ2dIRKJ0Lt3b1y/fl0l5o8//sCgQYNga2sLW1tbDBgwABcvXtRG8wkhhJAPBtepMYJLums9g/z8fHz22Wfo168fMjMz8dtvv+HHH3/EunXrqlxnxYoV2LFjBxITE5GdnY2hQ4fCx8cHhYWFAMof2vjpp5+iX79+yMrKQmZmJnx8fNC7d2/k5OTUdgqEEELIB4NGat5i8+bNsLa2Rnh4OHg8HkQiEZYvX47169dDLperxb969Qrr1q3D8uXLuVv2z549G1ZWVvjhhx8AAMnJyeDz+Zg5cyZMTExQr149zJkzByYmJjh//nxtp0AIIYR8MLj71NBIjbrExEQMGTJEZdngwYORl5eHP/74Qy3+woULKC4uxqBBg9TWqZhs2rFjRzx//hznzp3j3v/Pf/4DmUwGDw+P2k6BEEII+WBUXMltDE/prvWrn3JzcyEUClWWCQQCNGvWrNJTRbm5uWjatKnaDcxEIhEOHz4MAHB2dsbPP/+MSZMmoVOnTmCMoaCgAGfOnIG9vX2l7ZDJZJDJZNzPRUVF75saIYQQYnSM6ZLuGo3U5OXlQSwWV/lat24d+Hw+TCq5gU9VzxLSNP7evXuwsLBA165d4enpiczMTBw/frzKCcirV6+GpaUl97K1ta1JqgbNwcEBMTExGsXGxcWhdevWEIvFmD17Nng8ntbuMFyXxMTEwMHBQd/NIIQQvTMxotNPNRqpsba2rvaGbImJicjNzVVZJpPJUFBQAJFIpBYvFouRn5+P0tJSmJqacssfPHjAxe/Zswfr1q3D5cuXYWlpCQAIDQ1Fhw4dYGtri+DgYLXtLly4EBEREdzPRUVFH1THRhOlpaUICgrCzp07MXz4cBQWFmLjxo0arx8fH4+zZ89iw4YN2mskIYQQraKJwm/Rv39//PrrryrLTp8+jaZNm6Jz585q8Z07d4a1tTWOHj2qsvzkyZMYMGAAgPKJwp06deI6NADQpEkTeHp6VjlRWCAQwMLCQuVFVD148AAvX76Ep6cnAOCjjz6q0foXL17E06dPtdAyQgghulKPbr5XtXHjxuHBgwf417/+BaD8i3PevHmYPXs2+Hy+Wjyfz0d4eDgWL16Mhw8fAgCio6ORmZmJsWPHAgA+++wznDp1iuv4MMZw8OBBHDlyBP369avtFPDfnQClL3T/quHzRbOzs+Hn54eWLVvCxcUF0dHRKu8nJSXhk08+gVAohLu7Ow4cOAAASElJ4Z7t1L17d4jFYiiVSrXtx8TEwMXFBTY2NujRoweuXr0KAPj++++xYcMG7N+/H2KxGCtWrAAAPHr0CGPHjoW9vT0cHBwwbtw4PHr0iNteZGQkRo0ahb1796JVq1ZISUmpNse4uDh06tQJIpEIrq6u2L59OwBg4MCBmDBhgkqsr68vpk+fDgDIysrCsGHDuNOjISEhVc6tevnyJebOnQuJRAJbW1uMHTsWT548qbZthBBi6EyMaKSm1icKN2nSBCdPnsT06dOxYsUKmJqaIjQ0FPPmzeNiRowYAQDYv38/AGD+/PlQKpXo1q0bSktL4ezsjJMnT6JJkyZcvEwmw5dffomQkBAolUo4Ojpi165dGDp0aG2nUE7+ElglrD6utv1fLmBqplGoUqlEQEAA3N3dce/ePTDGsGjRIty7dw9A+UhK3759sX37dgQGBiItLQ0+Pj6wtbVF9+7dkZKSAolEgpSUlErnl9y8eROLFy/GsWPH4ObmhoiICEyYMAEXLlxASEgI7t+/j8zMTG7+zqtXr+Dl5QVvb2/cvn0bADBz5kx4eXnh8uXL3EM279+/j/Pnz+P27duoV6/eW3M8dOgQAgMDkZCQgD59+iAjIwOff/45unbtihkzZuCLL77AP//5T5iZmeHp06c4duwYLly4gBcvXqBXr14YPHgwpFIplEolQkNDsWbNGqxatUptP8HBwcjJyUFaWhrMzc0xadIkjB8/HocOHdKoFoQQYqiM6ZJurTz7yc3NDadPn67y/YrOTAUTExMsXrwYixcvrnKdoKAgBAUF1VobjUF6ejrS09Nx6tQpbj7SunXruE5GdHQ0vL29ERgYCADw9PTEhAkTEB0drdFEYhcXF0ilUm6ELTg4GP/4xz+qjD9w4AAKCgoQFRXFtScqKgo2NjaIi4vj2nHt2jUcO3as2g4NAGzatAmhoaHo06cPgPIr4W7fvg1TU1O4ubnBxsYGP//8M8aNG4f9+/ejc+fOaN++PXbv3o3i4mJ888034PP54PP52L59u8q8rQrZ2dmIjY3F77//jmbNmgEANm7cCCsrK2RmZtKEYkKIUauYU0OXdBszfqPyURN97FdDd+/ehZWVFczNzbllPB4PjRs3BlD+ZZ2amqrypVxaWgp3d3eNtv/ixQssXboUx48fR0FBAZRKJRQKRZXxmZmZaNWqlUrHwdTUFK1atUJmZia3rF27dlwbq5OVlYWAgACVZRXb5/F4mDp1KmJiYjBu3Djs2bOHe7hnVlYWHB0d1dpSmYrJ7xUjiBUsLS1x9+5d6tQQQoyaCXdJN43UGC8eT+PTQPpiY2OD/Px8FBQUcKfqZDIZ93gJJycnCIVCbg5KTc2dOxfp6emIjY2Fk5MTbty4gbZt21YZb2dnB6lUCoVCgfr1yz9acrkcUqkUdnZ2XJwmIzQV7O3tkZGRobJMLpdzo0cTJkzAkiVLkJKSgitXrmDkyJHcelKpVCUWgErbKjg5OQEAzp07p9JOQgj5EFTMD6aJwkSvvLy80LZtW8ycORMymQzFxcUIDg7mRlOmTp2K2NhYxMbGgjEGhUKBTZs2YfXq1Rptv6ioCHZ2dpBIJCguLsZXX30FoHxSLQA0atQIjx8/BmMMhYWFCAgIQOPGjREREQG5XA65XI6IiAiYm5vD39//nXIMDw/Htm3bcPz4cQDlE5F79OjB3W3a0tISQUFBCA4ORmBgIBo1Kh/p8vf3h5mZGWbNmgWZTAalUomlS5dWegrTysoKQUFBmDx5Mjep+c6dO9ydsAkhxJhZNuSr/NeQUafGgNWrVw/Hjh3DixcvYGdnh06dOqF///7c6aW2bdviyJEjiIqKglAohKOjIy5dusSdoqnOqlWr8OTJE4hEInz88ccYMWIEWrdujStXrgAAAgICkJGRAYlEggMHDsDMzAzJycnIy8uDk5MTHB0dkZ+fj+TkZJiZvduo1+DBg7Fv3z4sXLgQQqEQPXv2xOjRo7nL/QEgLCwMt2/fVsnLzMwMSUlJyM/PR6tWreDg4ICbN28iKiqq0v1s2bIFHTp0QPfu3WFra4vhw4djwoQJsLa2fqd2E0KIoZj6mROWDXWDfyf1e8kZGh6r6pa8RqaoqAiWlpZ49uyZ2j1rSkpKIJVKIZFI1B7XQOq+vXv3IioqSqPLw2sLfWYIIUQ33vb9/SYaqSF6tWHDhiofu7Fly5a3rltWVoaMjAwsX74cM2fO1FGLCSGE1FU0UZjoVUREhMrjLGriwYMH8PDwQGhoKEaNGlXLLSOEEGJoqFNDDJZIJEJxcbG+m0EIIaSOoNNPhBBCCDEK1KkhhBBCiFGgTs1rysrK9N0EYiDos0IIIXUPzalB+e3zTUxMkJubC2tra5iamoLHM/w7K5LaxxhDaWkp8vLyYGJiUuWjFwghhOgedWpQ/kBNiUSCBw8eIDdXD897IganUaNGsLOzg4kRPCuFEEKMBXVq/svU1BR2dnZQKBRQKpX6bg6pw+rVq4f69evTaB4hhNQx1Kl5DY/HA5/PV3kAIiGEEEIMA42dE0IIIcQoUKeGEEIIIUaBOjWEEEIIMQofzJyaioeRFxUV6bklhBBCCNFUxfd2xff423wwnZonT54AAGxtbfXcEkIIIYTU1PPnz2FpafnWmA+mU9O0aVMAwL1796o9KADQpUsXXLhwQePt1yReW7FFRUWwtbXF/fv3YWFhobd2aGvbNc1PW+2oaWxN4rVZw5rGU47vHku/i7ppR01jaxJPn1PdtaO6eMYYnj9/DqFQWO12PphOTcVN0iwtLTUqXr169TT+Za1pvLZiK1hYWNR6jnXleACa56fNdmjzeADaqWFN4ynH92sHQL+LumgHfU7frx2AYXxONRmMAGiicJWmTZumtXhtxdaUNtth7Dlq83hoc7uUo25ia6qu1MUQc6TP6fu1oyYMoS48psnMGyNQVFQES0tLPHv2rMa9WENh7Dkae34A5WgsjD1HY88PoBwN1QczUiMQCPDll19CIBDouylaY+w5Gnt+AOVoLIw9R2PPD6AcDdUHM1JDCCGEEOP2wYzUEEIIIcS4UaeGEEIIIUaBOjWEEEIIMQoG1akpKytDamoqIiIi0LRpU8TExKi8/+eff6JPnz4QCoUQiUSIiIhASUmJSsypU6fw6aefQiwWw97eHl988QXu3LmjEpOamoqePXvCzs4OrVu3xtatW7WdGkdXOVpYWEAoFEIsFnOvefPmaTu9Wslv//79aN++PWxsbNCmTRts3rxZbT+GXkNNctRXDQFg+/btcHd3h0gkgouLC7799luV92UyGRYsWAAnJycIhUL4+voiJydHJSYnJwcjR46Eg4MDRCIRZs+eDZlMphKjzzrqKkdDr2NeXh527tyJXr16QSKRVLoffdVRV/kZeg2zs7MxcuRI2NraQiwWw8/PD5mZmSox+vxdrBFmQL777jvWpUsXtmjRImZlZcV27NjBvffo0SPWrFkzFh0dzcrKylhhYSHz9fVl48eP52LS09OZQCBgP/30E2OMsZKSEhYeHs7EYjF7+fIlY4yxGzduMHNzcxYbG8sYY+zatWusRYsW7N///rfR5FhYWMh4PB4rKSnRSU6ve9/8Tpw4wSwsLNhvv/3GGGPs5s2brE2bNiwmJoaLMfQaapKjPmv4ww8/MLFYzK5evcoYKz/eNjY2bPfu3VzMxIkTWa9evVhBQQGTy+Vs9uzZrF27dkyhUDDGGJPJZMzV1ZVFREQwuVzOCgoKWM+ePdnf/vY3bhv6rKOucjT0OjLGWIcOHdioUaPYmDFjmL29vdp+9FVHXeVn6DUsLS1lzs7ObN68eay0tJQpFAo2d+5c5urqyuRyObddff5NrQmD6tS8zt7eXuXLYtu2bczFxUUl5smTJ4zP57OHDx8yxhhbs2YN69Spk0pMYWEhA8DS09MZY4yFhISwwYMHq8R8/fXXauvpgrZyvHr1KmvevLl2G6+Bd8lvzJgxbMqUKSoxcXFxzN3dnfvZ0GuoSY76rOHUqVPZnj17VJZFREQwf39/xhhjWVlZzMTEhKWlpXHvy2Qy1qxZMxYfH88YY2zXrl2sadOmTCaTcTFpaWnM1NSU5eXlMcb0W0dd5WjodXzdjh07Kv3S11cddZWfodfw8uXLrHfv3qysrIyLKSoqYgDYpUuXGGN1629qdQzq9NPbPHv2jHsUQgWBQADGGFJSUgAAHh4euHXrFm7evMnFJCQkoHnz5mjTpg0AIDExEUOGDFHZzpAhQ3Dx4kU8evRIy1m8XW3lmJ2dDbFYrLuGa0iT/CqLadiwIa5evYrCwkIAhl9DTXLUZw2jo6MxevRolWVXrlzhbt515swZtGjRAh4eHtz7pqam+Pzzz3HkyBEA5TXq168fTE1NuRgPDw80a9YMp06d4mL0VUdd5WjoddSEvuqoq/wMvYbt2rXD6dOnwePxVLYBAObm5gDq9t/UNxlNp8bPzw93797Fxo0bIZfL8fTpU0yaNAmNGjXCw4cPAQA+Pj7YtGkTBg4ciIkTJ2Lw4MGIjY1FUlISGjduDADIzc1Ve2iWSCQCALXzkLpWWzlmZ2dDIBBg2rRpcHFxQdu2bbFw4UK8fPlSn+lplN/o0aOxa9cuJCYmgjGGjIwMrFy5EgC4GEOvoSY51pUayuVyzJgxAykpKZg7dy6Ayo8/UF6DiuP/rjH6qKM2czT0OmqiLtRRm/kZWw3T09MxYsQIjB8/nptDVBdqqCmj6dQ4Ojri7NmzOHr0KBwdHdG3b18EBATA3t4e9euXP7dTqVTi3r17aNmyJbp06YLOnTvj8uXLOHv2LLcdPp+v9q/k13uw+lRbOcpkMjx//hxBQUG4du0ajh8/jpSUFISEhOgrNQCa5RcYGIjNmzdj/vz5sLe3x4wZM7Bs2TIA4GIMvYaa5FgXapiVlYWePXvi1KlTSE5Ohru7O4DKjz+gWoN3jdF1HbWdo6HXURP6rqO28zOmGkZFRaFnz54YP348vvvuO265vmtYI/o89/U+3pyrUBm5XM4aNmzIEhMTGWOMrVy5knl6erLS0lIuRiqVMnNzc3bmzBnGGGMuLi5s27ZtKtv566+/GABuzoOuaCvHyqSmpjITExNWXFxcK23XxLvkV5nk5GTG5/O5iXqGXsPKvJljZXRZw7S0NGZtbc3mzJmj1qZ9+/YxkUiktk5QUBA3V2jKlClszJgxajFisZjt27ePMab/Ouoix8oYUh1fV9WcE33WURf5VcbQaqhUKtnEiRNZ69atWWpqqlq8vn8Xa8JoRmoAqA33HTt2DAKBAN27dwcAJCcno0ePHuDz+VyMg4MDnJyccP78eQBA//798euvv6ps58SJE+jYsSNatGih5QyqVxs5AuWXHb9OqVQC0H/vu7r8Kov55Zdf4O3tzT2/xNBrWFnMmzkC+qthVlYWBg4ciOjoaHz99ddqz43p06cPHj9+jMuXL6u07fTp0xgwYACA8hodP34cCoWCi7l58yYeP34Mb29vLkZfddRVjoBh11ET+qqjrvIDDL+Gf//733Hr1i2kpaXh448/VttPXf+bqkLfvap39ea/gK9cucKaN2/Ofv/9d8ZYeS+ydevWLDo6motZu3Yta9myJRejUCjY1q1bGZ/P52aH3759m1lYWHAzwzMyMphIJGJ79+7VUWb/o60c16xZw/r27ctycnIYY4zl5uayHj16sLFjx+oos3Lvkt/WrVtZ586duX8dHD16lH300UfclV2MGX4NNclRnzUcMGAAi4yMfGtMWFgY8/b2Zs+ePWMKhYLNnz+fubm5cZeIyuVy5ubmxhYsWMAUCgUrLCxk3t7ebPLkydw29FlHXeVo6HV8XVUjGfqqo67yM/QapqamMisrK5afn1/lNurS39TqGE2nhrHyD52rqysTCoXM2dmZbd26VeV9pVLJNm7cyNq1a8eEQiFr0aIF8/HxYadOnVKJO3fuHPP09GRCoZA5OTmxLVu2aDudSmkrx1evXrFFixYxR0dHZmNjw4RCIZs5cyZ3HxtdeZf8ZDIZmz9/PrOzs2NCoZB169aNJSUlqW3bkGuoSY76rCEA1rx5cyYSidReFSrujyQSiVjLli2Zr68vu3//vsp27t+/z3x9fZmNjQ0TiUQsPDxcbfhcX3XUVY7GUMcKbzs9o4866io/Q69hZGQka9CgQaXbWL9+PRdXV/6mVoee0k0IIYQQo2BUc2oIIYQQ8uGiTg0hhBBCjAJ1agghhBBiFKhTQwghhBCjQJ0aQgghhBgF6tQQQgghxChQp4YQQgghRoE6NYQQQggxCtSpIYQQQohRoE4NIYQQQowCdWoIIYQQYhSoU0MIIYQQo/D/XqeUoyAsLj8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_ = df.plot(y=['gdp_cycle','deflator_cycle'])\n",
    "ax_.axhline(0, color='red')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "このプロットを眺めることにより，総需要ショックと総供給ショックがどれだけ景気循環に影響を及ぼしているかが分かるだろうか。もちろん否である。ADASモデルを「装置」として用いて，「スイッチ」を調整することにより，結果が数字として現れてくることになる。\n",
    "「調整済み装置」があってこその分析結果であり，体重計がないと体重を測れないことと同じである。\n",
    "\n",
    "体重計の例をもう一度考えてみよう。\n",
    "今度は，AさんとBさんが作る体重計である。\n",
    "Aさんの体重計は単純な装置だが，一方，Bさんの技術は高く，設計図は洗練され使う材料もより質が高いとしてみよう。\n",
    "その差は精度の違いとして現れることになるだろう。\n",
    "マクロ経済モデルも同じである。\n",
    "ADASモデルは，研究や政策議論で使われるモデルと比べると単純なモデルであり，それが故に定量的な問に対しての答えも精度が低いものとならざるを得ない。\n",
    "大学院で学ぶDSGEモデルや構造VARモデルなどはより洗練された「装置」であり，ADASモデルよりも精度が高い結果を返すことになるだろう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## 付録"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "(sec:18-2-appendix_A)=\n",
    "### 付録Ａ：分散と共分散の性質"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "$X$，$Y$，$Z$，$W$をランダム変数，$a$，$b$，$c$，$d$を定数とする。\n",
    "分散に関して次の結果が成立する。\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\text{Var}(k)&=0\\\\\n",
    "\\text{Var}(X+k)&=\\text{Var}(X)\\\\\n",
    "\\text{Var}(aX)&=a^2\\text{Var}(X)\\\\\n",
    "\\text{Var}(aX\\pm bY)&=a^2\\text{Var}(X)+b^2\\text{Var}(Y)\\pm 2ab\\text{Cov}(X,Y) \\\\\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "共分散に関して次の結果が成立する。\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\text{Cov}(X,a)&=0\\\\\n",
    "\\text{Cov}(X,X)&=\\text{Var}(X)\\\\\n",
    "\\text{Cov}(aX,bY)&=ab\\text{Cov}(Y,X)\\\\\n",
    "\\text{Cov}(X+a,Y+b)&=\\text{Cov}(Y,X)\\\\\n",
    "\\text{Cov}(aX+bY,cZ+dW)&=ac\\text{Cov}(X,Z)+ad\\text{Cov}(X,W)+bc\\text{Cov}(Y,Z)+bd\\text{Cov}(Y,W)\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "(sec:18-2-appendix_B)=\n",
    "### 付録Ｂ：式[](eq:18-eq-vary2)の導出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "式[](eq:18-eq-varp2)と式[](eq:18-eq-varyp)を式[](eq:18-eq-vary)に代入し整理する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{align}\n",
    "\\text{Var}(y_{t})\n",
    "&=(ch)^2\\text{Var}(p_{t})+\\text{Var}(e_{yt})\\\\\n",
    "&=(ch)^2\\left[\n",
    "    \\frac{(ah)^2}{1-h^2}\\text{Var}(u_{t})\n",
    "    +\\frac{h^2}{1-h^2}\\text{Var}(v_{t})\n",
    "\\right]\n",
    "+h^2\\text{Var}(u_{t})+(ch)^2\\text{Var}(v_{t})\\\\\n",
    "&=\\left[(ch)^2\n",
    "    \\frac{(ah)^2}{1-h^2}+h^2\n",
    "\\right]\\text{Var}(u_{t})\n",
    "+\\left[\n",
    "    (ch)^2\\frac{h^2}{1-h^2}+(ch)^2\n",
    "\\right]\\text{Var}(v_{t})\\\\\n",
    "&=\\frac{h^2}{1-h^2}\\left[\n",
    "    (ach)^2+1-h^2\n",
    "\\right]\\text{Var}(u_{t})\n",
    "+\\frac{(ch)^2}{1-h^2}\\left[h^2+1-h^2\\right]\\text{Var}(v_{t})\\\\\n",
    "&=\\frac{h^2}{1-h^2}\\left[\n",
    "    1+(ach)^2-h^2\n",
    "\\right]\\text{Var}(u_{t})\n",
    "+\\frac{(ch)^2}{1-h^2}\\text{Var}(v_{t})\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ここで\n",
    "\n",
    "\\begin{align}\n",
    "1+(ach)^2-h^2\n",
    "&=1+h^2[(ac)^2-1]=1+\\frac{(ac)^2-1}{(1+ac)^2}\\\\\n",
    "&=1+\\frac{(ac+1)(ac-1)}{(1+ac)^2}=1+\\frac{ac-1}{1+ac}\\\\\n",
    "&=\\frac{1+ac+ac-1}{1+ac}=\\frac{2ac}{1+ac}\\\\\n",
    "&=2ach\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "この式を上の式に代入すると式[](eq:18-eq-vary2)となる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "(sec:18-2-appendix_c)=\n",
    "### 付録Ｃ：式(u-y)と(v-y)の導出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "式(u-y)に式[](eq:18-eq-vary2)を代入して整理する。\n",
    "\n",
    "\\begin{align}\n",
    "\\left[\n",
    "\\begin{split}\n",
    "&y_t\\text{の変動に対する}\\\\\n",
    "&u_t\\text{(需要ショック)}\\\\\n",
    "&\\text{の寄与度}\n",
    "\\end{split}\n",
    "\\right]\n",
    "&=\\frac{h^2}{1-h^2}2ach\\frac{\\text{Var}(u_{t})}{\\text{Var}(y_{t})}\\\\\n",
    "&=\\dfrac{h^2}{1-h^2}2ach\\frac{\\text{Var}(u_{t})}{\n",
    "\\left(\n",
    "\\dfrac{h^2}{1-h^2}2ach\\text{Var}(u_{t})\n",
    "+\\dfrac{h^2}{1-h^2}c^2\\text{Var}(v_{t})\n",
    "\\right)\n",
    "}\\\\\n",
    "&=\\frac{2ach\\text{Var}(u_{t})}{\n",
    "2ach\\text{Var}(u_{t})\n",
    "+c^2\\text{Var}(v_{t})\n",
    "}\n",
    "=\\frac{2a\\text{Var}(u_{t})}{\n",
    "2a\\text{Var}(u_{t})\n",
    "+(c/h)\\text{Var}(v_{t})\n",
    "}\\\\\n",
    "&=\\frac{2a\\text{Var}(u_{t})}{2a\\text{Var}(u_{t})+c(1+ac)\\text{Var}(v_{t})}\\\\\n",
    "\\tag{u-y}\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同様に式(v-y)に式[](eq:18-eq-vary2)を代入して整理する。\n",
    "\n",
    "\\begin{align}\n",
    "\\left[\n",
    "\\begin{split}\n",
    "&p_t\\text{の変動に対する}\\\\\n",
    "&v_t\\text{(供給ショック)}\\\\\n",
    "&\\text{の寄与度}\n",
    "\\end{split}\n",
    "\\right]\n",
    "&=\\frac{h^2}{1-h^2}c^2\\frac{\\text{Var}(v_{t})}{\\text{Var}(y_{t})}\\\\\n",
    "&=\\frac{h^2}{1-h^2}c^2\\frac{\\text{Var}(v_{t})}{\n",
    "\\left(\n",
    "\\dfrac{h^2}{1-h^2}2ach\\text{Var}(u_{t})\n",
    "+\\dfrac{h^2}{1-h^2}c^2\\text{Var}(v_{t})\n",
    "\\right)\n",
    "}\\\\\n",
    "&=\\frac{c^2\\text{Var}(v_{t})}{\n",
    "2ach\\text{Var}(u_{t})\n",
    "+c^2\\text{Var}(v_{t})\n",
    "}\n",
    "=\\frac{(c/h)\\text{Var}(v_{t})}{\n",
    "2a\\text{Var}(u_{t})\n",
    "+(c/h)\\text{Var}(v_{t})\n",
    "}\\\\\n",
    "&=\\frac{c(1+ac)\\text{Var}(v_{t})}{2a\\text{Var}(u_{t})+c(1+ac)\\text{Var}(v_{t})}\\\\\n",
    "\\end{align}"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernel_info": {
   "name": "py4etrics"
  },
  "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": "3.12.7"
  },
  "nteract": {
   "version": "0.15.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "7638eee3e7b130da5496a984d6d52bfbc9bf510a87a483ea7c2368031c9591e0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}