{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4f65a551",
   "metadata": {},
   "source": [
    "# What will I get next month if nothing changes, and how far can I trust that number?\n",
    "\n",
    "Someone asks how many conversions the account will bring in next month. The honest answer\n",
    "is a number with a range around it, and the range is the part that matters: a forecast\n",
    "with no range is a guess wearing a suit.\n",
    "\n",
    "This notebook builds the forecast the platform's own forecaster is modelled on: a\n",
    "simplified version of LinkedIn's Silverkite, written out in full so every step can be\n",
    "read, and then does the thing most forecasting write-ups skip: it checks the range. A\n",
    "synthetic advertiser is built with its future decided in advance, the forecast is made\n",
    "without looking, and the two are compared on hundreds of accounts built by the same rules,\n",
    "so every claim below is scored against a truth nobody has to guess at.\n",
    "\n",
    "Six things are worth knowing before the working.\n",
    "\n",
    "**The anchored shortcuts lean low on a growing account; the ruler does not, and cannot say\n",
    "how wrong it is.** Last month's total and the recent average times thirty are anchored on a\n",
    "past that was smaller than the month ahead. A ruler through the last eight weeks is as close\n",
    "a guess as the forecast whenever those eight weeks are the whole story, and the notebook\n",
    "measures on nine account shapes where that holds and where it does not. No shortcut carries\n",
    "a range, so none can say how wrong it might be.\n",
    "\n",
    "**The range runs narrow, and more history does not fix it.** The band claims nine in ten\n",
    "and delivers about three in four for the month's total and seven in eight for a single day,\n",
    "at every history length tested. The leftovers that size the band are what remained after\n",
    "the fit had already eaten some noise as signal, and the notebook measures how much wider\n",
    "the band would need to be to keep its word.\n",
    "\n",
    "**The month's number is limited by the trend, not the noise.** Day-to-day wobble mostly\n",
    "averages out over thirty days; how well the growth rate is known does not. For a single\n",
    "day the noise dominates the band; for the month's total the trend's uncertainty is most of\n",
    "it.\n",
    "\n",
    "**A month is where the forecast stops being read, and the band's honesty is one reason.**\n",
    "Even on accounts whose rules never change, the band's catch on the total falls from about\n",
    "six in seven a week out to three in four at a month and two in three at three months. The\n",
    "condition is the other reason: \"nothing changes\" is a bet against every week of the horizon.\n",
    "\n",
    "**The forecast cannot see a change you already know about.** A budget increase you have\n",
    "already approved lands inside the month, and a forecast built on history alone will be\n",
    "wrong by that increase. That is not a flaw to fix in the maths; it is a fact to\n",
    "tell the model.\n",
    "\n",
    "**A change in growth is hard to see, even two months on.** The bend search\n",
    "is a detector with two failure rates, and both are measured here: it invents a bend on\n",
    "about a third of flat accounts, and it sees a change of this account's size on about half\n",
    "of the accounts two months after the change landed. Where it does not, the forecast leans\n",
    "the old way.\n",
    "\n",
    "**The data in this notebook is synthetic.** It is generated here with the answer built in.\n",
    "Run it on your own figures in the last section; nothing leaves your machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0828294f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:14:50.711446Z",
     "iopub.status.busy": "2026-10-24T12:14:50.710969Z",
     "iopub.status.idle": "2026-10-24T12:14:52.073620Z",
     "shell.execute_reply": "2026-10-24T12:14:52.070315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed 20260913\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "SEED = 20260913\n",
    "rng = np.random.default_rng(SEED)\n",
    "\n",
    "PAPER = \"#fdfcf9\"\n",
    "INK = \"#1f2124\"\n",
    "MUTED = \"#565c66\"\n",
    "LINE = \"#e6e2da\"\n",
    "SIGNAL = \"#0f6e56\"\n",
    "FLAG = \"#b06a2c\"\n",
    "NAIVE = \"#2a78d6\"\n",
    "BAND = \"#a9d6c5\"\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"figure.facecolor\": PAPER, \"axes.facecolor\": PAPER, \"savefig.facecolor\": PAPER,\n",
    "    \"axes.edgecolor\": LINE, \"axes.labelcolor\": INK, \"text.color\": INK,\n",
    "    \"xtick.color\": MUTED, \"ytick.color\": MUTED, \"axes.grid\": True,\n",
    "    \"grid.color\": LINE, \"grid.linewidth\": 0.8, \"axes.spines.top\": False,\n",
    "    \"axes.spines.right\": False, \"font.size\": 11, \"figure.dpi\": 140,\n",
    "})\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "HERE = Path.cwd()\n",
    "FIGURES = HERE / \"figures\"\n",
    "DATA = HERE / \"data\"\n",
    "FIGURES.mkdir(exist_ok=True)\n",
    "DATA.mkdir(exist_ok=True)\n",
    "\n",
    "DAY_NAMES = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n",
    "DAY_SHORT = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\n",
    "\n",
    "print(f\"seed {SEED}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5034c00a",
   "metadata": {},
   "source": [
    "## The advertiser I build, and the future I hide from the forecast\n",
    "\n",
    "One synthetic advertiser, one number per day: conversions. The generator gives it four\n",
    "properties that real accounts have and toy examples usually do not.\n",
    "\n",
    "**A level that grows, and grows faster after a bend.** The account gains about fifteen per\n",
    "cent over its first three months, then a creative refresh lands and the growth\n",
    "more than doubles. A straight line through the whole history describes neither half.\n",
    "\n",
    "**A weekly rhythm.** Weekdays convert above the account's average, the weekend below. The\n",
    "rhythm is a multiplier, so it scales with the level: a bigger account has a bigger\n",
    "weekend dip in conversions, which is how volume metrics behave.\n",
    "\n",
    "**Noise that clusters.** Today looks like yesterday. Runs of good and bad days happen with\n",
    "nothing behind them, and a forecast that forgets this draws its band too narrow.\n",
    "\n",
    "**A future that continues the same way.** The forecast is scored against thirty more days\n",
    "generated by the same rules, thousands of times over. \"If nothing changes\" is a condition\n",
    "the generator can honour exactly; a real account never quite does, and the last sections\n",
    "measure what happens when it does not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9be6a848",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:14:52.081597Z",
     "iopub.status.busy": "2026-10-24T12:14:52.081008Z",
     "iopub.status.idle": "2026-10-24T12:14:52.109112Z",
     "shell.execute_reply": "2026-10-24T12:14:52.104140Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180 days of history; the account started near 70 conversions a day\n",
      "underlying level on the last day: 109.4\n",
      "growth before the bend 14% per quarter, after it 37%\n",
      "  Monday     runs at 1.060 of the account's own average\n",
      "  Tuesday    runs at 1.100 of the account's own average\n",
      "  Wednesday  runs at 1.080 of the account's own average\n",
      "  Thursday   runs at 1.040 of the account's own average\n",
      "  Friday     runs at 0.980 of the account's own average\n",
      "  Saturday   runs at 0.840 of the account's own average\n",
      "  Sunday     runs at 0.900 of the account's own average\n"
     ]
    }
   ],
   "source": [
    "HISTORY_DAYS = 180\n",
    "HORIZON = 30\n",
    "\n",
    "BASE_LEVEL = 70.0            # conversions per day at the start of the history\n",
    "GROWTH_BEFORE = 0.0015       # per-day growth in log terms before the bend (~14% over 90 days)\n",
    "GROWTH_AFTER = 0.0035        # after it (~37% over 90 days)\n",
    "BEND_DAY = 90                # the creative refresh\n",
    "DOW_TRUTH_RAW = np.array([1.06, 1.10, 1.08, 1.04, 0.98, 0.84, 0.90])\n",
    "DOW_TRUTH = DOW_TRUTH_RAW / DOW_TRUTH_RAW.mean()\n",
    "NOISE_SIGMA_LOG = 0.12       # day-to-day wobble, multiplicative\n",
    "RHO = 0.40                   # how much today resembles yesterday\n",
    "\n",
    "\n",
    "def true_log_level(t):\n",
    "    \"\"\"The account's underlying level on day t (0-based), before rhythm and noise, in logs.\"\"\"\n",
    "    t = np.asarray(t, dtype=float)\n",
    "    return (np.log(BASE_LEVEL) + GROWTH_BEFORE * np.minimum(t, BEND_DAY)\n",
    "            + GROWTH_AFTER * np.maximum(t - BEND_DAY, 0.0))\n",
    "\n",
    "\n",
    "def ar1_noise(n_reps, n_days, generator, sigma=NOISE_SIGMA_LOG, rho=RHO, start=None):\n",
    "    \"\"\"Clustered log-scale noise, one row per run. `start` continues an existing run's last value.\"\"\"\n",
    "    z = generator.normal(0.0, sigma, (n_reps, n_days))\n",
    "    e = np.empty((n_reps, n_days))\n",
    "    k = np.sqrt(1.0 - rho ** 2)\n",
    "    e[:, 0] = z[:, 0] if start is None else rho * start + k * z[:, 0]\n",
    "    for t in range(1, n_days):\n",
    "        e[:, t] = rho * e[:, t - 1] + k * z[:, t]\n",
    "    return e\n",
    "\n",
    "\n",
    "def make_history(n_days, generator, level_fn=true_log_level, dow=DOW_TRUTH):\n",
    "    \"\"\"One run of daily conversions. Returns (values, log-noise) so the future can continue it.\"\"\"\n",
    "    t = np.arange(n_days)\n",
    "    e = ar1_noise(1, n_days, generator)[0]\n",
    "    values = np.exp(level_fn(t) + np.log(dow[t % 7]) + e)\n",
    "    return values, e\n",
    "\n",
    "\n",
    "def make_futures(n_reps, n_hist, horizon, generator, last_noise, level_fn=true_log_level,\n",
    "                 dow=DOW_TRUTH):\n",
    "    \"\"\"`n_reps` continuations of one history: the next `horizon` days, one row per run.\"\"\"\n",
    "    t = np.arange(n_hist, n_hist + horizon)\n",
    "    e = ar1_noise(n_reps, horizon, generator, start=last_noise)\n",
    "    return np.exp(level_fn(t)[None, :] + np.log(dow[t % 7])[None, :] + e)\n",
    "\n",
    "\n",
    "history, history_noise = make_history(HISTORY_DAYS, np.random.default_rng(SEED))\n",
    "t_hist = np.arange(HISTORY_DAYS)\n",
    "\n",
    "print(f\"{HISTORY_DAYS} days of history; the account started near {BASE_LEVEL:.0f} conversions a day\")\n",
    "print(f\"underlying level on the last day: {np.exp(true_log_level(HISTORY_DAYS - 1)):.1f}\")\n",
    "print(f\"growth before the bend {(np.exp(GROWTH_BEFORE * 90) - 1) * 100:.0f}% per quarter, \"\n",
    "      f\"after it {(np.exp(GROWTH_AFTER * 90) - 1) * 100:.0f}%\")\n",
    "for name, mult in zip(DAY_NAMES, DOW_TRUTH):\n",
    "    print(f\"  {name:<10} runs at {mult:.3f} of the account's own average\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "154aad8f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:14:52.117788Z",
     "iopub.status.busy": "2026-10-24T12:14:52.116630Z",
     "iopub.status.idle": "2026-10-24T12:14:52.871448Z",
     "shell.execute_reply": "2026-10-24T12:14:52.869147Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x616 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 4.4))\n",
    "ax.plot(t_hist, history, color=INK, lw=1.4, label=\"daily conversions (synthetic)\")\n",
    "ax.plot(t_hist, np.exp(true_log_level(t_hist)), color=SIGNAL, lw=2.0, ls=\"--\",\n",
    "        label=\"the underlying level we built in\")\n",
    "ax.axvline(BEND_DAY, color=MUTED, lw=1.0, ls=\":\")\n",
    "ax.text(BEND_DAY + 2, ax.get_ylim()[1] * 0.97, \"creative refresh: growth more than doubles\", fontsize=9.5,\n",
    "        color=MUTED, va=\"top\")\n",
    "ax.set_xlabel(\"day of history\")\n",
    "ax.set_ylabel(\"conversions per day\")\n",
    "ax.set_title(\"Six months of a growing account, with the rhythm and the noise real accounts have\")\n",
    "ax.legend(frameon=False, loc=\"upper left\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_history.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12985b9c",
   "metadata": {},
   "source": [
    "## What a reader does today\n",
    "\n",
    "Three shortcuts cover most of what happens in practice when someone needs next month's\n",
    "number by Friday.\n",
    "\n",
    "- **Same as last month.** Add up the last thirty days.\n",
    "- **The recent average.** Take the last four weeks' daily average and multiply by thirty.\n",
    "- **The ruler.** Draw a straight line through the last eight weeks and read it forward.\n",
    "\n",
    "Each produces a number. None produces a range, so none can say how wrong it might be. Below,\n",
    "all three against the forecast built later in this notebook, scored on the same future.\n",
    "The scoring cell lives after the method; this cell only sets the shortcuts up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f39c6bdd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:14:52.906552Z",
     "iopub.status.busy": "2026-10-24T12:14:52.905826Z",
     "iopub.status.idle": "2026-10-24T12:14:52.917281Z",
     "shell.execute_reply": "2026-10-24T12:14:52.916097Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  same as last month     says next month brings     3006 conversions\n",
      "  recent average x 30    says next month brings     2977 conversions\n",
      "  the ruler              says next month brings     3159 conversions\n"
     ]
    }
   ],
   "source": [
    "def rule_last_month(values):\n",
    "    return float(np.sum(values[-30:]))\n",
    "\n",
    "\n",
    "def rule_recent_average(values):\n",
    "    return float(np.mean(values[-28:]) * 30)\n",
    "\n",
    "\n",
    "def rule_ruler(values):\n",
    "    n = len(values)\n",
    "    t = np.arange(n - 56, n)\n",
    "    slope, intercept = np.polyfit(t, values[-56:], 1)\n",
    "    return float(np.sum(intercept + slope * np.arange(n, n + 30)))\n",
    "\n",
    "\n",
    "SHORTCUTS = {\n",
    "    \"same as last month\": rule_last_month,\n",
    "    \"recent average x 30\": rule_recent_average,\n",
    "    \"the ruler\": rule_ruler,\n",
    "}\n",
    "for name, fn in SHORTCUTS.items():\n",
    "    print(f\"  {name:<22} says next month brings {fn(history):8.0f} conversions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b103a83",
   "metadata": {},
   "source": [
    "## The method, in one formula\n",
    "\n",
    "The forecast writes the logarithm of each day's conversions as a sum of parts, and fits\n",
    "them together in one regression:\n",
    "\n",
    "$$\\log y_t = g(t) + s(t) + r_t$$\n",
    "\n",
    "- $g(t)$ is the **trend**, a straight line that is allowed to bend. Candidate bends are laid\n",
    "  down every two weeks of history and judged on the series averaged by week (daily noise\n",
    "  clusters, and judged day by day it reads as trend), keeping only the bends that explain\n",
    "  enough to pay for themselves; the kept ones are then fitted on the daily data without shrinkage.\n",
    "- $s(t)$ is the **weekly rhythm**, three sine-and-cosine pairs with a seven-day period. That\n",
    "  sounds fancier than it is: three pairs can draw any weekday shape exactly, so it is the\n",
    "  same as one adjustment per weekday, written in a form that fits neatly into the regression.\n",
    "- $r_t$ is **what is left**, and it is not treated as random. Today's leftover resembles\n",
    "  yesterday's, so the model measures how strongly (a number between zero and one, called\n",
    "  $\\phi$ below) and carries the last leftover forward, fading it by $\\phi$ each day.\n",
    "\n",
    "The **range** has two parts, and the second is the one most write-ups leave out.\n",
    "\n",
    "$$\\text{width}_h \\propto \\sqrt{\\;\\sigma^2\\,\\frac{1-\\phi^{2h}}{1-\\phi^2}\\;+\\;\\text{trend uncertainty}_h\\;}$$\n",
    "\n",
    "- The first part is the **noise ahead**: $\\sigma$ is the size of a single day's genuine\n",
    "  surprise once yesterday's carry-over is removed, and the fraction makes the misses pile up\n",
    "  when days resemble each other. It grows with $h$ and then levels off, because the memory\n",
    "  of the last known day fades.\n",
    "- The second part is **how unsure the fitted trend itself is**. A line through six months\n",
    "  of data has a slope known only so well, and the further out you read it the more that\n",
    "  matters. It is the ordinary regression prediction-interval term, scaled up for the fact that\n",
    "  clustered days carry less information than independent ones would. It grows with $h$ and\n",
    "  does not level off.\n",
    "\n",
    "**Why logs.** Conversions are a volume, and volumes swing in proportion to their level: a\n",
    "weekend dip of fifteen per cent is fifteen conversions on a hundred-a-day account and fifty\n",
    "on a three-hundred-a-day one. Fitting on the log scale makes those proportional effects\n",
    "additive, which is what a regression can handle, and turns a symmetric band on the log\n",
    "scale into an honestly lopsided one on the real scale, with more room above than below.\n",
    "\n",
    "**What the real thing does on top of this.** Silverkite picks its bends with an adaptive\n",
    "lasso rather than a stepwise search, adds yearly and holiday effects when the history can\n",
    "carry them, lets the band vary by weekday, and can take future-known inputs such as a\n",
    "planned budget. The shape (decompose, select the bends, fit, carry the leftover, size the\n",
    "band from the leftovers and from the fit's own uncertainty) is the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8f0cb2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:14:52.927490Z",
     "iopub.status.busy": "2026-10-24T12:14:52.926910Z",
     "iopub.status.idle": "2026-10-24T12:14:52.966488Z",
     "shell.execute_reply": "2026-10-24T12:14:52.965210Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "engine ready: candidate bends every two weeks on the weekly series, none in the last four; bends kept on the worked example (day index): []\n"
     ]
    }
   ],
   "source": [
    "Z_FOR_LEVEL = {0.80: 1.2815515655446004, 0.90: 1.6448536269514722, 0.95: 1.959963984540054}\n",
    "COVERAGE = 0.90\n",
    "CP_EVERY = 14        # a candidate bend every two weeks of history\n",
    "CP_TAIL = 28         # but none inside the last four weeks, where a bend cannot be told from noise\n",
    "MAX_BENDS = 3        # the search stops after three, or earlier when no bend pays for itself\n",
    "FOURIER_PAIRS = 3    # three pairs = any weekday shape, exactly\n",
    "PHI_CAP = 0.95\n",
    "MIN_HISTORY = 14     # two full weekly cycles - below this a weekly rhythm cannot be estimated\n",
    "MAX_HORIZON = 30\n",
    "RIDGE_EPS = 1e-8     # numerical safety only; the fit is ordinary least squares\n",
    "\n",
    "\n",
    "def design(t, bends):\n",
    "    \"\"\"One row per day: the trend, one hinge per bend, and the weekly rhythm terms.\"\"\"\n",
    "    t = np.asarray(t, dtype=float)\n",
    "    cols = [t]\n",
    "    for c in bends:\n",
    "        cols.append(np.maximum(0.0, t - c))\n",
    "    for k in range(1, FOURIER_PAIRS + 1):\n",
    "        w = 2.0 * np.pi * k * t / 7.0\n",
    "        cols.append(np.sin(w))\n",
    "        cols.append(np.cos(w))\n",
    "    return np.column_stack(cols)\n",
    "\n",
    "\n",
    "def ls_fit(X, y):\n",
    "    \"\"\"Least squares on standardised columns with an unpenalised intercept: one linear solve.\n",
    "\n",
    "    Standardising is for numerical conditioning only - the fit is ordinary least squares.\n",
    "    The inverse of the (scaled) normal matrix is kept because the band needs it.\n",
    "    \"\"\"\n",
    "    mu = X.mean(axis=0)\n",
    "    sd = X.std(axis=0)\n",
    "    sd = np.where(sd == 0, 1.0, sd)\n",
    "    Xs = (X - mu) / sd\n",
    "    ybar = float(y.mean())\n",
    "    A = Xs.T @ Xs + RIDGE_EPS * np.eye(Xs.shape[1])\n",
    "    Ainv = np.linalg.inv(A)\n",
    "    beta = Ainv @ (Xs.T @ (y - ybar))\n",
    "    fitted = ybar + Xs @ beta\n",
    "    return {\"mu\": mu, \"sd\": sd, \"ybar\": ybar, \"beta\": beta, \"Ainv\": Ainv, \"fitted\": fitted}\n",
    "\n",
    "\n",
    "def ls_predict(fit, X):\n",
    "    return fit[\"ybar\"] + ((X - fit[\"mu\"]) / fit[\"sd\"]) @ fit[\"beta\"]\n",
    "\n",
    "\n",
    "def select_bends(y):\n",
    "    \"\"\"Forward stepwise search for the trend's bends, run on the series aggregated to weeks.\n",
    "\n",
    "    Judged on daily points, clustered day-to-day noise reads as trend and the search keeps bends\n",
    "    that are not there - measured on a flat account, three bends and eleven per cent of invented\n",
    "    growth. The paper's own detector aggregates to weeks first, which removes the weekday rhythm\n",
    "    and most of the clustering before any bend is judged; this does the same. Candidates sit every\n",
    "    two weeks, none in the last four, and each is kept only if it lowers the BIC.\n",
    "    \"\"\"\n",
    "    n = len(y)\n",
    "    nw = n // 7\n",
    "    start = n - 7 * nw                      # drop a partial leading week; the newest data stays whole\n",
    "    yw = np.asarray(y[start:], dtype=float).reshape(nw, 7).mean(axis=1)\n",
    "    tw = np.arange(nw, dtype=float)\n",
    "    candidates = [w for w in range(CP_EVERY // 7, nw - CP_TAIL // 7, CP_EVERY // 7)]\n",
    "\n",
    "    def bic(bends):\n",
    "        cols = [np.ones(nw), tw] + [np.maximum(0.0, tw - b) for b in bends]\n",
    "        X = np.column_stack(cols)\n",
    "        beta, *_ = np.linalg.lstsq(X, yw, rcond=None)\n",
    "        rss = float(np.sum((yw - X @ beta) ** 2))\n",
    "        return nw * np.log(rss / nw) + X.shape[1] * np.log(nw)\n",
    "\n",
    "    chosen = []\n",
    "    best = bic(chosen)\n",
    "    while len(chosen) < MAX_BENDS:\n",
    "        trial = None\n",
    "        for c in candidates:\n",
    "            if c in chosen:\n",
    "                continue\n",
    "            b = bic(sorted(chosen + [c]))\n",
    "            if trial is None or b < trial[0]:\n",
    "                trial = (b, c)\n",
    "        if trial is None or trial[0] >= best:\n",
    "            break\n",
    "        best = trial[0]\n",
    "        chosen = sorted(chosen + [trial[1]])\n",
    "    return [start + 7 * w for w in chosen]     # back to day indices for the daily fit\n",
    "\n",
    "\n",
    "class NotEnoughHistory(Exception):\n",
    "    \"\"\"Raised with the plain-words reason.\"\"\"\n",
    "\n",
    "\n",
    "def forecast(values, horizon=HORIZON, level=COVERAGE, ceiling=MAX_HORIZON, find_bends=True):\n",
    "    \"\"\"The whole method. Daily conversions in, next `horizon` days out, with a band and a total.\n",
    "\n",
    "    Every number the article, the widget and the workbook quote comes through this function.\n",
    "    `find_bends=False` is for one experiment only - measuring what the bend search is worth.\n",
    "    \"\"\"\n",
    "    values = np.asarray(values, dtype=float)\n",
    "    n = len(values)\n",
    "    if n < MIN_HISTORY:\n",
    "        raise NotEnoughHistory(f\"{n} days of history; the weekly rhythm needs at least \"\n",
    "                               f\"{MIN_HISTORY} (two full weeks) before anything can be said.\")\n",
    "    if horizon > ceiling:\n",
    "        raise NotEnoughHistory(f\"a {horizon}-day horizon is past the {ceiling}-day ceiling \"\n",
    "                               \"this method is read at.\")\n",
    "    z = Z_FOR_LEVEL[level]\n",
    "    y = np.log(values)\n",
    "    t = np.arange(n)\n",
    "    tf = np.arange(n, n + horizon)\n",
    "    bends = select_bends(y) if find_bends else []\n",
    "    X, Xf = design(t, bends), design(tf, bends)\n",
    "    fit = ls_fit(X, y)\n",
    "    resid = y - fit[\"fitted\"]\n",
    "\n",
    "    # The leftover's day-to-day resemblance, and the size of a genuine daily surprise.\n",
    "    r0, r1 = resid[:-1], resid[1:]\n",
    "    phi = float(np.dot(r1, r0) / np.dot(r0, r0))\n",
    "    phi = min(max(phi, 0.0), PHI_CAP)\n",
    "    innov = r1 - phi * r0\n",
    "    sigma = float(np.sqrt(np.mean(innov ** 2)))\n",
    "    sigma_resid = float(np.sqrt(np.mean(resid ** 2)))\n",
    "\n",
    "    h = np.arange(1, horizon + 1, dtype=float)\n",
    "    mu = ls_predict(fit, Xf) + resid[-1] * phi ** h\n",
    "\n",
    "    # Part one of the band: the noise ahead, piling up while the memory of the last day fades.\n",
    "    i = np.arange(1, horizon + 1, dtype=float)\n",
    "    mn = np.minimum(i[:, None], i[None, :])\n",
    "    cov_noise = sigma ** 2 * phi ** np.abs(i[:, None] - i[None, :]) * (1.0 - phi ** (2 * mn)) / (1.0 - phi ** 2)\n",
    "    # Part two: how unsure the fitted trend is where it is being read - the regression's own\n",
    "    # prediction-variance term, scaled up because clustered days carry less information.\n",
    "    Xfs = (Xf - fit[\"mu\"]) / fit[\"sd\"]\n",
    "    info_scale = (1.0 + phi) / (1.0 - phi)\n",
    "    cov_param = sigma_resid ** 2 * info_scale * (1.0 / n + Xfs @ fit[\"Ainv\"] @ Xfs.T)\n",
    "    cov_log = cov_noise + cov_param\n",
    "    var = np.diag(cov_log)\n",
    "    sd = np.sqrt(var)\n",
    "\n",
    "    # The month's total: the days' misses are related, so the total's band is NOT the daily\n",
    "    # bands stacked. Each day's expected value on the real scale is exp(mu + var/2), and the\n",
    "    # covariance between two days' misses is the log-scale covariance above.\n",
    "    mean_day = np.exp(mu + var / 2.0)\n",
    "    outer = mean_day[:, None] * mean_day[None, :]\n",
    "    var_total = float(np.sum(outer * (np.exp(cov_log) - 1.0)))\n",
    "    # the same sum with each part alone, so the article can say which part the month's band is\n",
    "    var_total_noise = float(np.sum(outer * (np.exp(cov_noise) - 1.0)))\n",
    "    var_total_param = float(np.sum(outer * (np.exp(cov_param) - 1.0)))\n",
    "    total = float(mean_day.sum())\n",
    "    sd_total = float(np.sqrt(var_total))\n",
    "\n",
    "    k_trend = 1 + len(bends)\n",
    "    return {\n",
    "        \"point\": np.exp(mu),\n",
    "        \"lower\": np.exp(mu - z * sd),\n",
    "        \"upper\": np.exp(mu + z * sd),\n",
    "        \"mean_day\": mean_day,\n",
    "        \"total\": total,\n",
    "        \"total_lower\": total - z * sd_total,\n",
    "        \"total_upper\": total + z * sd_total,\n",
    "        \"phi\": phi,\n",
    "        \"sigma\": sigma,\n",
    "        \"sigma_resid\": sigma_resid,\n",
    "        \"noise_sd\": np.sqrt(np.diag(cov_noise)),\n",
    "        \"param_sd\": np.sqrt(np.diag(cov_param)),\n",
    "        \"total_trend_share_pct\": var_total_param / (var_total_noise + var_total_param) * 100,\n",
    "        \"fitted\": np.exp(fit[\"fitted\"]),\n",
    "        \"trend\": np.exp(fit[\"ybar\"] + ((X[:, :k_trend] - fit[\"mu\"][:k_trend]) / fit[\"sd\"][:k_trend]) @ fit[\"beta\"][:k_trend]),\n",
    "        \"bends\": bends,\n",
    "        \"resid_last\": float(resid[-1]),\n",
    "    }\n",
    "\n",
    "\n",
    "def forecast_batch(histories, horizon=HORIZON):\n",
    "    \"\"\"`forecast` over many histories at once (one per row); only the totals and bands come back.\"\"\"\n",
    "    out = np.empty((histories.shape[0], 3))\n",
    "    for k, row in enumerate(histories):\n",
    "        f = forecast(row, horizon=horizon)\n",
    "        out[k] = (f[\"total\"], f[\"total_lower\"], f[\"total_upper\"])\n",
    "    return out\n",
    "\n",
    "\n",
    "print(\"engine ready: candidate bends every two weeks on the weekly series, none in the last four;\",\n",
    "      \"bends kept on the worked example (day index):\", select_bends(np.log(history)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f674efe1",
   "metadata": {},
   "source": [
    "### Checked against the account's own past first\n",
    "\n",
    "Before trusting a forecast of the future, ask how it would have done on months that have\n",
    "already happened: pretend the history ended earlier, forecast the month that followed, and\n",
    "compare. Repeated at several cut-offs, this is rolling-origin backtesting, and it is the one\n",
    "check a reader can run on their own account without a synthetic truth. The same routine\n",
    "runs on the reader's file in the last section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0bd2210",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:14:52.971807Z",
     "iopub.status.busy": "2026-10-24T12:14:52.971386Z",
     "iopub.status.idle": "2026-10-24T12:14:52.998462Z",
     "shell.execute_reply": "2026-10-24T12:14:52.997124Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  history cut at day  90: forecast   2409, actual   2542, off by  -5.2%, band caught it   bends kept at [20]\n",
      "  history cut at day 105: forecast   2757, actual   2696, off by  +2.3%, band caught it   bends kept at [28, 42, 56]\n",
      "  history cut at day 120: forecast   2817, actual   2892, off by  -2.6%, band caught it   bends kept at [29, 43, 57]\n",
      "  history cut at day 135: forecast   2856, actual   3000, off by  -4.8%, band caught it   bends kept at []\n",
      "  history cut at day 150: forecast   3033, actual   3006, off by  +0.9%, band caught it   bends kept at []\n",
      "\n",
      "5 past months: typical miss 2.6%, band caught 5 of 5\n",
      "A handful of months is a thin check - it can flag a forecast that is clearly off, and it\n",
      "cannot certify one. The hundreds of synthetic accounts below are what certify it here.\n"
     ]
    }
   ],
   "source": [
    "ORIGINS = list(range(90, HISTORY_DAYS - 30 + 1, 15))\n",
    "\n",
    "\n",
    "def backtest(values, origins, horizon=30):\n",
    "    \"\"\"At each cut-off: forecast the next month from what came before, score it against what came.\"\"\"\n",
    "    rows = []\n",
    "    for cut in origins:\n",
    "        f = forecast(values[:cut], horizon=horizon)\n",
    "        truth = float(np.sum(values[cut:cut + horizon]))\n",
    "        rows.append({\n",
    "            \"cut\": int(cut),\n",
    "            \"forecast\": f[\"total\"],\n",
    "            \"actual\": truth,\n",
    "            \"miss_pct\": (f[\"total\"] - truth) / truth * 100,\n",
    "            \"caught\": bool(f[\"total_lower\"] <= truth <= f[\"total_upper\"]),\n",
    "            \"bends\": f[\"bends\"],\n",
    "        })\n",
    "    return rows\n",
    "\n",
    "\n",
    "bt_rows = backtest(history, ORIGINS)\n",
    "for r in bt_rows:\n",
    "    print(f\"  history cut at day {r['cut']:3d}: forecast {r['forecast']:6.0f}, actual {r['actual']:6.0f}, \"\n",
    "          f\"off by {r['miss_pct']:+5.1f}%, band {'caught it' if r['caught'] else 'MISSED'}   bends kept at {r['bends']}\")\n",
    "bt_caught = sum(r[\"caught\"] for r in bt_rows)\n",
    "bt_miss = float(np.median([abs(r[\"miss_pct\"]) for r in bt_rows]))\n",
    "print(f\"\\n{len(bt_rows)} past months: typical miss {bt_miss:.1f}%, band caught {bt_caught} of {len(bt_rows)}\")\n",
    "print(\"A handful of months is a thin check - it can flag a forecast that is clearly off, and it\")\n",
    "print(\"cannot certify one. The hundreds of synthetic accounts below are what certify it here.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cc2f5ae",
   "metadata": {},
   "source": [
    "## The forecast, and the shortcuts, against a future that actually happened\n",
    "\n",
    "The account's next thirty days are generated by the same rules as its history: the\n",
    "\"if nothing changes\" condition, honoured exactly. One future first, so the reader can see\n",
    "the shapes; hundreds of accounts next, so the numbers mean something."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44c3b000",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:14:53.009386Z",
     "iopub.status.busy": "2026-10-24T12:14:53.008877Z",
     "iopub.status.idle": "2026-10-24T12:14:53.032265Z",
     "shell.execute_reply": "2026-10-24T12:14:53.031446Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bends kept: none   (the one we built in sits at day 90)\n",
      "day-to-day resemblance of the leftovers  phi = 0.391   (built in: 0.4)\n",
      "size of a genuine daily surprise      sigma = 0.093   (built in: 0.110)\n",
      "\n",
      "forecast for the next 30 days: 3212 conversions, range 3011 to 3413\n",
      "what then happened (one run):    3113\n",
      "\n",
      "  same as last month         3006   off by   -3.4%\n",
      "  recent average x 30        2977   off by   -4.4%\n",
      "  the ruler                  3159   off by   +1.5%\n",
      "  this forecast              3212   off by   +3.2%\n",
      "\n",
      "the account's last four weeks ran at 95% of its underlying level -\n",
      "a run of ordinary days, and nothing reading the history can tell it from a lower level.\n",
      "One account is one account. The next cell repeats this on five hundred of them.\n"
     ]
    }
   ],
   "source": [
    "fc = forecast(history)\n",
    "g_future = np.random.default_rng(SEED + 1)\n",
    "one_future = make_futures(1, HISTORY_DAYS, HORIZON, g_future, history_noise[-1])[0]\n",
    "actual_total = float(one_future.sum())\n",
    "# how the account's last four weeks ran against its own underlying level - the part of any\n",
    "# miss that no method reading the history can know about\n",
    "recent_vs_level = float(history[-28:].mean() / np.exp(true_log_level(t_hist[-28:])).mean())\n",
    "\n",
    "print(f\"bends kept: {fc['bends'] if fc['bends'] else 'none'}   (the one we built in sits at day {BEND_DAY})\")\n",
    "print(f\"day-to-day resemblance of the leftovers  phi = {fc['phi']:.3f}   (built in: {RHO})\")\n",
    "print(f\"size of a genuine daily surprise      sigma = {fc['sigma']:.3f}   (built in: {NOISE_SIGMA_LOG * np.sqrt(1 - RHO**2):.3f})\")\n",
    "print()\n",
    "print(f\"forecast for the next {HORIZON} days: {fc['total']:.0f} conversions,\"\n",
    "      f\" range {fc['total_lower']:.0f} to {fc['total_upper']:.0f}\")\n",
    "print(f\"what then happened (one run):    {actual_total:.0f}\")\n",
    "print()\n",
    "one_future_miss = {}\n",
    "for name, fn in SHORTCUTS.items():\n",
    "    guess = fn(history)\n",
    "    one_future_miss[name] = (guess - actual_total) / actual_total * 100\n",
    "    print(f\"  {name:<22} {guess:8.0f}   off by {one_future_miss[name]:+6.1f}%\")\n",
    "one_future_miss[\"this forecast\"] = (fc[\"total\"] - actual_total) / actual_total * 100\n",
    "print(f\"  {'this forecast':<22} {fc['total']:8.0f}   off by {one_future_miss['this forecast']:+6.1f}%\")\n",
    "print()\n",
    "print(f\"the account's last four weeks ran at {recent_vs_level * 100:.0f}% of its underlying level -\")\n",
    "print(\"a run of ordinary days, and nothing reading the history can tell it from a lower level.\")\n",
    "print(\"One account is one account. The next cell repeats this on five hundred of them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cbed3b88",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-10-24T12:14:53.745804Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1470x644 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10.5, 4.6))\n",
    "tf = np.arange(HISTORY_DAYS, HISTORY_DAYS + HORIZON)\n",
    "show_from = HISTORY_DAYS - 84\n",
    "ax.plot(t_hist[show_from:], history[show_from:], color=INK, lw=1.4, label=\"history\")\n",
    "ax.fill_between(tf, fc[\"lower\"], fc[\"upper\"], color=BAND, alpha=0.7,\n",
    "                label=f\"{int(COVERAGE * 100)}% range, day by day\")\n",
    "ax.plot(tf, fc[\"point\"], color=SIGNAL, lw=2.2, label=\"forecast\")\n",
    "ax.plot(tf, one_future, color=FLAG, lw=1.4, ls=\"--\", label=\"what then happened (one run)\")\n",
    "ax.axvline(HISTORY_DAYS - 0.5, color=MUTED, lw=1.0, ls=\":\")\n",
    "ax.set_xlabel(\"day\")\n",
    "ax.set_ylabel(\"conversions per day\")\n",
    "ax.set_title(\"Twelve weeks of history, the next month's forecast, and one future that came true\")\n",
    "ax.legend(frameon=False, loc=\"upper left\", fontsize=9.5, ncol=2)\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_forecast.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd9033a",
   "metadata": {},
   "source": [
    "### Five hundred accounts like this one, each with a future that came true\n",
    "\n",
    "One account and one future say little: how its last weeks happened to run decides most of\n",
    "the miss, and no method can see that. So everything is repeated on five hundred accounts\n",
    "built by the same rules (the same growth, the same bend twelve weeks before the end, the\n",
    "same rhythm and noise), each with its own history and its own next month. Every method\n",
    "guesses each account's month from that account's history alone, and that account's future\n",
    "scores it. Two numbers per method: the typical miss (the median of the absolute error, as a\n",
    "percentage of the true total) and how often the guess landed within ten per cent of what\n",
    "happened. For the forecast, a third: how often its range caught the truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4bb2358",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:14:53.758174Z",
     "iopub.status.busy": "2026-10-24T12:14:53.756932Z",
     "iopub.status.idle": "2026-10-24T12:14:55.378002Z",
     "shell.execute_reply": "2026-10-24T12:14:55.377137Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method                  typical miss  within 10%    leans   low on\n",
      "same as last month              8.7%         61%    -8.7%      98%\n",
      "recent average x 30             8.5%         62%    -8.5%      97%\n",
      "the ruler                       4.6%         82%    +1.4%      43%\n",
      "this forecast                   5.3%         82%    -0.4%      52%\n",
      "\n",
      "the forecast's 90% range for the total caught the true total on 77% of the 500 accounts\n",
      "the search kept a bend on 75% of them, and one within three weeks of the real bend on 39%\n"
     ]
    }
   ],
   "source": [
    "ACCOUNTS = 500\n",
    "LONG_HORIZON = 90            # the futures run long enough for the horizon experiment further down\n",
    "g_acc = np.random.default_rng(SEED + 2)\n",
    "_t = np.arange(HISTORY_DAYS + LONG_HORIZON)\n",
    "_series = np.exp(true_log_level(_t)[None, :] + np.log(DOW_TRUTH[_t % 7])[None, :]\n",
    "                 + ar1_noise(ACCOUNTS, HISTORY_DAYS + LONG_HORIZON, g_acc))\n",
    "acc_hist, acc_fut = _series[:, :HISTORY_DAYS], _series[:, HISTORY_DAYS:]\n",
    "acc_truth = acc_fut[:, :HORIZON].sum(axis=1)\n",
    "\n",
    "acc_fc = [forecast(h) for h in acc_hist]\n",
    "scores = {}\n",
    "for name, fn in SHORTCUTS.items():\n",
    "    guesses = np.array([fn(h) for h in acc_hist])\n",
    "    err = (guesses - acc_truth) / acc_truth * 100\n",
    "    scores[name] = {\n",
    "        \"typical_miss_pct\": float(np.median(np.abs(err))),\n",
    "        \"within_10_pct\": float(np.mean(np.abs(err) <= 10)) * 100,\n",
    "        \"bias_pct\": float(np.median(err)),\n",
    "        \"low_pct\": float(np.mean(err < 0)) * 100,\n",
    "    }\n",
    "acc_totals = np.array([[f[\"total\"], f[\"total_lower\"], f[\"total_upper\"]] for f in acc_fc])\n",
    "err_model = (acc_totals[:, 0] - acc_truth) / acc_truth * 100\n",
    "scores[\"this forecast\"] = {\n",
    "    \"typical_miss_pct\": float(np.median(np.abs(err_model))),\n",
    "    \"within_10_pct\": float(np.mean(np.abs(err_model) <= 10)) * 100,\n",
    "    \"bias_pct\": float(np.median(err_model)),\n",
    "    \"low_pct\": float(np.mean(err_model < 0)) * 100,\n",
    "}\n",
    "inside_band = float(np.mean((acc_truth >= acc_totals[:, 1]) & (acc_truth <= acc_totals[:, 2])) * 100)\n",
    "bends_any_pct = float(np.mean([len(f[\"bends\"]) > 0 for f in acc_fc]) * 100)\n",
    "bends_near_pct = float(np.mean([any(abs(b - BEND_DAY) <= 21 for b in f[\"bends\"]) for f in acc_fc]) * 100)\n",
    "\n",
    "print(f\"{'method':<22} {'typical miss':>13} {'within 10%':>11} {'leans':>8} {'low on':>8}\")\n",
    "for name, s in scores.items():\n",
    "    print(f\"{name:<22} {s['typical_miss_pct']:12.1f}% {s['within_10_pct']:10.0f}% {s['bias_pct']:+7.1f}% {s['low_pct']:7.0f}%\")\n",
    "print()\n",
    "print(f\"the forecast's {int(COVERAGE * 100)}% range for the total caught the true total on \"\n",
    "      f\"{inside_band:.0f}% of the {ACCOUNTS} accounts\")\n",
    "print(f\"the search kept a bend on {bends_any_pct:.0f}% of them, and one within three weeks of the real \"\n",
    "      f\"bend on {bends_near_pct:.0f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4a7f277",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:14:55.383851Z",
     "iopub.status.busy": "2026-10-24T12:14:55.382237Z",
     "iopub.status.idle": "2026-10-24T12:14:55.895938Z",
     "shell.execute_reply": "2026-10-24T12:14:55.893079Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1330x616 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9.5, 4.4))\n",
    "names = list(scores)\n",
    "miss = [scores[n][\"typical_miss_pct\"] for n in names]\n",
    "colors = [NAIVE, NAIVE, NAIVE, SIGNAL]\n",
    "ax.barh(names, miss, color=colors)\n",
    "for k, v in enumerate(miss):\n",
    "    ax.text(v + 0.2, k, f\"{v:.1f}%\", va=\"center\", fontsize=10)\n",
    "ax.set_xlabel(\"typical miss on the month's total, per cent (lower is better)\")\n",
    "ax.set_title(\"Four ways to guess next month, scored on five hundred accounts\")\n",
    "ax.invert_yaxis()\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_shortcuts.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db52ced0",
   "metadata": {},
   "source": [
    "### The same contest on accounts of other shapes\n",
    "\n",
    "The ruler is the strongest of the three shortcuts, and on the account above it is a close\n",
    "match for the forecast. Whether that holds depends on the account's shape: the ruler reads\n",
    "the last eight weeks and nothing else, so it wins exactly when those eight weeks are the\n",
    "whole story. Below, the ruler and the forecast on nine shapes an account can have, three\n",
    "hundred accounts of each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f47124fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:14:55.905265Z",
     "iopub.status.busy": "2026-10-24T12:14:55.904821Z",
     "iopub.status.idle": "2026-10-24T12:15:08.320195Z",
     "shell.execute_reply": "2026-10-24T12:15:08.313657Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape                                                 last month  the ruler  forecast  caught   closer\n",
      "flat                                                        3.5%       6.2%      3.8%     75%   the forecast\n",
      "steady growth                                               9.0%       4.5%      3.9%     82%   the forecast\n",
      "steady decline                                              6.9%       5.4%      3.9%     78%   the forecast\n",
      "growth changed twelve weeks ago (the worked account)        8.9%       5.7%      5.6%     72%   the forecast\n",
      "growth changed six weeks ago                                9.5%       4.5%      6.8%     61%   the ruler\n",
      "growth changed three weeks ago                              9.2%       5.4%      6.0%     59%   the ruler\n",
      "growth stopped eight weeks ago                              3.3%       5.5%      6.0%     72%   the ruler\n",
      "growth turned to decline twelve weeks ago                   5.7%       5.3%      4.4%     78%   the forecast\n",
      "a step down six weeks ago                                   8.9%      15.3%      8.4%     65%   the forecast\n",
      "\n",
      "the ruler is the closer guess on 3 of the 9 shapes.\n"
     ]
    }
   ],
   "source": [
    "SHAPE_REPS = 300\n",
    "\n",
    "\n",
    "def _hinge(bend_at, before, after):\n",
    "    def level(t):\n",
    "        t = np.asarray(t, dtype=float)\n",
    "        return np.log(BASE_LEVEL) + before * np.minimum(t, bend_at) + after * np.maximum(t - bend_at, 0.0)\n",
    "    return level\n",
    "\n",
    "\n",
    "SHAPES = {\n",
    "    \"flat\": lambda t: np.full(np.shape(t), np.log(BASE_LEVEL)),\n",
    "    \"steady growth\": _hinge(0, 0.0, GROWTH_AFTER),\n",
    "    \"steady decline\": _hinge(0, 0.0, -0.002),\n",
    "    \"growth changed twelve weeks ago (the worked account)\": true_log_level,\n",
    "    \"growth changed six weeks ago\": _hinge(HISTORY_DAYS - 42, GROWTH_BEFORE, GROWTH_AFTER),\n",
    "    \"growth changed three weeks ago\": _hinge(HISTORY_DAYS - 21, GROWTH_BEFORE, GROWTH_AFTER),\n",
    "    \"growth stopped eight weeks ago\": _hinge(HISTORY_DAYS - 56, GROWTH_AFTER, 0.0),\n",
    "    \"growth turned to decline twelve weeks ago\": _hinge(BEND_DAY, GROWTH_AFTER, -GROWTH_BEFORE),\n",
    "    \"a step down six weeks ago\": lambda t: true_log_level(t) + np.log(0.75) * (np.asarray(t, dtype=float) >= HISTORY_DAYS - 40),\n",
    "}\n",
    "shape_rows = []\n",
    "for k, (label, level_fn) in enumerate(SHAPES.items()):\n",
    "    g = np.random.default_rng(SEED + 900 + k)\n",
    "    t = np.arange(HISTORY_DAYS + HORIZON)\n",
    "    s = np.exp(level_fn(t)[None, :] + np.log(DOW_TRUTH[t % 7])[None, :] + ar1_noise(SHAPE_REPS, HISTORY_DAYS + HORIZON, g))\n",
    "    hs, truth = s[:, :HISTORY_DAYS], s[:, HISTORY_DAYS:].sum(axis=1)\n",
    "    row = {\"shape\": label}\n",
    "    for name, fn in ((\"same as last month\", rule_last_month), (\"the ruler\", rule_ruler)):\n",
    "        err = (np.array([fn(h) for h in hs]) - truth) / truth * 100\n",
    "        row[name] = {\"typical_miss_pct\": float(np.median(np.abs(err))), \"bias_pct\": float(np.median(err))}\n",
    "    res = np.array([[f[\"total\"], f[\"total_lower\"], f[\"total_upper\"]] for f in (forecast(h) for h in hs)])\n",
    "    err = (res[:, 0] - truth) / truth * 100\n",
    "    row[\"this forecast\"] = {\"typical_miss_pct\": float(np.median(np.abs(err))), \"bias_pct\": float(np.median(err)),\n",
    "                            \"band_caught_pct\": float(((truth >= res[:, 1]) & (truth <= res[:, 2])).mean() * 100)}\n",
    "    row[\"closer\"] = \"the ruler\" if row[\"the ruler\"][\"typical_miss_pct\"] < row[\"this forecast\"][\"typical_miss_pct\"] else \"the forecast\"\n",
    "    shape_rows.append(row)\n",
    "\n",
    "print(f\"{'shape':<52} {'last month':>11} {'the ruler':>10} {'forecast':>9} {'caught':>7}   closer\")\n",
    "for r in shape_rows:\n",
    "    print(f\"{r['shape']:<52} {r['same as last month']['typical_miss_pct']:10.1f}% {r['the ruler']['typical_miss_pct']:9.1f}% \"\n",
    "          f\"{r['this forecast']['typical_miss_pct']:8.1f}% {r['this forecast']['band_caught_pct']:6.0f}%   {r['closer']}\")\n",
    "ruler_wins = [r[\"shape\"] for r in shape_rows if r[\"closer\"] == \"the ruler\"]\n",
    "print()\n",
    "print(f\"the ruler is the closer guess on {len(ruler_wins)} of the {len(shape_rows)} shapes.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468ad7a2",
   "metadata": {},
   "source": [
    "## How far can you trust the range?\n",
    "\n",
    "A band that says \"nine times in ten\" is only worth reading if it catches the truth nine\n",
    "times in ten. Nothing about the formula guarantees that. The band is sized from the\n",
    "history's leftovers, and the history is finite, so the only honest way to know is to\n",
    "count. Below, each account's daily band checked against that account's future at every\n",
    "horizon from one day out to thirty, and the two parts of the band's width shown separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ef1bdb09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:08.339782Z",
     "iopub.status.busy": "2026-10-24T12:15:08.339255Z",
     "iopub.status.idle": "2026-10-24T12:15:08.536172Z",
     "shell.execute_reply": "2026-10-24T12:15:08.531526Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "horizon   band catches the day   typical width   of which: noise ahead / trend uncertainty\n",
      "  day  1          89.4%             38.4%         10.6%  /    4.6%\n",
      "  day  2          89.8%             40.7%         11.3%  /    4.6%\n",
      "  day  3          91.2%             41.0%         11.4%  /    4.6%\n",
      "  day  7          87.2%             41.2%         11.5%  /    4.7%\n",
      "  day 14          88.6%             41.6%         11.5%  /    4.9%\n",
      "  day 21          87.2%             42.1%         11.5%  /    5.1%\n",
      "  day 30          84.4%             42.9%         11.5%  /    5.6%\n",
      "\n",
      "nominal: 90%. Averaged over the month the daily band caught 87.1%,\n",
      "and the month's total was caught 77% of the time. To keep its promise the daily band\n",
      "would need to be 1.15 times as wide at day 30, and the total's band 1.36 times.\n",
      "\n",
      "Why it runs narrow: the leftovers that size the band are what remained after the fit chose\n",
      "its bends and its rhythm, and every term kept ate a little noise and called it signal. The\n",
      "size of a daily surprise came out at 10.6% on the typical account, against the 11.0% built in;\n",
      "the day-to-day resemblance at 0.37 against the 0.4 built in.\n",
      "\n",
      "Which part of the band matters depends on what you are reading. For a single day the noise\n",
      "ahead dominates all month. For the month's total the daily noise partly averages out and\n",
      "the trend's uncertainty does not: it is 62% of the total's band on the typical account.\n",
      "The memory of the last known day is spent within a week (0.1% of it left after seven days).\n"
     ]
    }
   ],
   "source": [
    "acc_point = np.array([f[\"point\"] for f in acc_fc])\n",
    "acc_lower = np.array([f[\"lower\"] for f in acc_fc])\n",
    "acc_upper = np.array([f[\"upper\"] for f in acc_fc])\n",
    "fut30 = acc_fut[:, :HORIZON]\n",
    "inside_daily = ((fut30 >= acc_lower) & (fut30 <= acc_upper)).mean(axis=0) * 100\n",
    "rel_width = np.median((acc_upper - acc_lower) / acc_point * 100, axis=0)\n",
    "noise_sd_typ = np.median([f[\"noise_sd\"] for f in acc_fc], axis=0)\n",
    "param_sd_typ = np.median([f[\"param_sd\"] for f in acc_fc], axis=0)\n",
    "sigma_typ = float(np.median([f[\"sigma\"] for f in acc_fc]))\n",
    "phi_typ = float(np.median([f[\"phi\"] for f in acc_fc]))\n",
    "trend_share_typ = float(np.median([f[\"total_trend_share_pct\"] for f in acc_fc]))\n",
    "\n",
    "print(\"horizon   band catches the day   typical width   of which: noise ahead / trend uncertainty\")\n",
    "for h in (1, 2, 3, 7, 14, 21, 30):\n",
    "    print(f\"  day {h:2d}         {inside_daily[h - 1]:5.1f}%            {rel_width[h - 1]:5.1f}%\"\n",
    "          f\"        {noise_sd_typ[h - 1] * 100:5.1f}%  /  {param_sd_typ[h - 1] * 100:5.1f}%\")\n",
    "# How much wider would the band have to be to keep its promise? Measured directly: the\n",
    "# multiple of each band's half-width that would catch the claimed share of the accounts.\n",
    "z_used = Z_FOR_LEVEL[COVERAGE]\n",
    "log_sd = np.log(acc_upper / acc_point) / z_used\n",
    "daily_needed = np.quantile(np.abs(np.log(fut30 / acc_point)) / log_sd, COVERAGE, axis=0) / z_used\n",
    "total_needed = float(np.quantile(np.abs(acc_truth - acc_totals[:, 0]) / (acc_totals[:, 2] - acc_totals[:, 0]), COVERAGE))\n",
    "inflation = {\n",
    "    \"daily_widening_needed_day1\": float(daily_needed[0]),\n",
    "    \"daily_widening_needed_day30\": float(daily_needed[-1]),\n",
    "    \"total_widening_needed\": total_needed,\n",
    "}\n",
    "\n",
    "print()\n",
    "print(f\"nominal: {int(COVERAGE * 100)}%. Averaged over the month the daily band caught {inside_daily.mean():.1f}%,\")\n",
    "print(f\"and the month's total was caught {inside_band:.0f}% of the time. To keep its promise the daily band\")\n",
    "print(f\"would need to be {daily_needed[-1]:.2f} times as wide at day 30, and the total's band {total_needed:.2f} times.\")\n",
    "print()\n",
    "print(\"Why it runs narrow: the leftovers that size the band are what remained after the fit chose\")\n",
    "print(\"its bends and its rhythm, and every term kept ate a little noise and called it signal. The\")\n",
    "print(f\"size of a daily surprise came out at {sigma_typ * 100:.1f}% on the typical account, against the \"\n",
    "      f\"{NOISE_SIGMA_LOG * np.sqrt(1 - RHO ** 2) * 100:.1f}% built in;\")\n",
    "print(f\"the day-to-day resemblance at {phi_typ:.2f} against the {RHO} built in.\")\n",
    "print()\n",
    "print(\"Which part of the band matters depends on what you are reading. For a single day the noise\")\n",
    "print(\"ahead dominates all month. For the month's total the daily noise partly averages out and\")\n",
    "print(f\"the trend's uncertainty does not: it is {trend_share_typ:.0f}% of the total's band on the typical account.\")\n",
    "print(f\"The memory of the last known day is spent within a week ({phi_typ ** 7 * 100:.1f}% of it left after seven days).\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "41a38108",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:08.553391Z",
     "iopub.status.busy": "2026-10-24T12:15:08.552954Z",
     "iopub.status.idle": "2026-10-24T12:15:09.688595Z",
     "shell.execute_reply": "2026-10-24T12:15:09.676457Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1610x616 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.4))\n",
    "hh = np.arange(1, HORIZON + 1)\n",
    "axes[0].plot(hh, inside_daily, marker=\"o\", color=SIGNAL, lw=2.0)\n",
    "axes[0].axhline(COVERAGE * 100, color=MUTED, lw=1.2, ls=\"--\")\n",
    "axes[0].text(1, COVERAGE * 100 + 0.6, \"what the band claims\", fontsize=9.5, color=MUTED)\n",
    "axes[0].set_ylim(60, 100)\n",
    "axes[0].set_xlabel(\"days ahead\")\n",
    "axes[0].set_ylabel(\"share of accounts the daily band caught, %\")\n",
    "axes[0].set_title(\"Does the band keep its promise?\")\n",
    "axes[1].plot(hh, noise_sd_typ * 100, color=NAIVE, lw=2.2, label=\"noise ahead\")\n",
    "axes[1].plot(hh, param_sd_typ * 100, color=FLAG, lw=2.2, label=\"trend uncertainty\")\n",
    "axes[1].plot(hh, np.sqrt(noise_sd_typ ** 2 + param_sd_typ ** 2) * 100, color=SIGNAL, lw=2.4,\n",
    "             ls=\"--\", label=\"the band (both together)\")\n",
    "axes[1].set_xlabel(\"days ahead\")\n",
    "axes[1].set_ylabel(\"size of the miss the band allows for, %\")\n",
    "axes[1].set_title(\"Two parts: one levels off, one keeps growing\")\n",
    "axes[1].legend(frameon=False, fontsize=9.5)\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_coverage.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c704e9",
   "metadata": {},
   "source": [
    "## How much history do you need?\n",
    "\n",
    "The weekly rhythm needs two full weeks before it can be estimated at all, and the growth\n",
    "needs long enough to be told from a run of good days. Below, an advertiser growing at a\n",
    "steady rate (no bend, so the only thing that changes between rows is how much history the\n",
    "forecast was given), built five hundred times at each length and scored on next month's\n",
    "total: the typical miss, and how often the band caught the truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9bf82ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:09.697816Z",
     "iopub.status.busy": "2026-10-24T12:15:09.696222Z",
     "iopub.status.idle": "2026-10-24T12:15:20.085121Z",
     "shell.execute_reply": "2026-10-24T12:15:20.084172Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  28 days: typical miss  9.1%   within 10%  53.6%   band caught  80.0%   band width  33.5%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  42 days: typical miss  6.2%   within 10%  72.4%   band caught  80.6%   band width  26.5%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  56 days: typical miss  5.9%   within 10%  77.6%   band caught  79.6%   band width  22.2%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  84 days: typical miss  5.1%   within 10%  80.8%   band caught  78.6%   band width  19.0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 120 days: typical miss  4.5%   within 10%  83.2%   band caught  80.4%   band width  16.6%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 180 days: typical miss  3.9%   within 10%  84.8%   band caught  78.4%   band width  14.7%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 270 days: typical miss  3.3%   within 10%  91.0%   band caught  85.0%   band width  13.5%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 365 days: typical miss  3.0%   within 10%  93.2%   band caught  82.2%   band width  12.7%\n",
      "\n",
      "The miss shrinks as history grows, and is still shrinking at a year. The band's width shrinks\n",
      "in step with it - and its honesty barely moves: at every length it catches the truth 78 to 85\n",
      "times in a hundred, against the ninety it claims. More history buys a sharper forecast, not a more truthful band.\n"
     ]
    }
   ],
   "source": [
    "HISTORY_GRID = [28, 42, 56, 84, 120, 180, 270, 365]\n",
    "HIST_REPS = 500\n",
    "\n",
    "\n",
    "def steady_log_level_ending_like(n_days):\n",
    "    \"\"\"Steady growth at the post-bend rate, arranged to end at the worked example's last-day level.\"\"\"\n",
    "    end = true_log_level(HISTORY_DAYS - 1)\n",
    "\n",
    "    def level(t):\n",
    "        t = np.asarray(t, dtype=float)\n",
    "        return end + GROWTH_AFTER * (t - (n_days - 1))\n",
    "    return level\n",
    "\n",
    "\n",
    "history_sweep = {}\n",
    "for L in HISTORY_GRID:\n",
    "    g = np.random.default_rng(SEED + 100 + L)\n",
    "    level_fn = steady_log_level_ending_like(L)\n",
    "    e = ar1_noise(HIST_REPS, L + HORIZON, g)\n",
    "    t_all = np.arange(L + HORIZON)\n",
    "    series = np.exp(level_fn(t_all)[None, :] + np.log(DOW_TRUTH[t_all % 7])[None, :] + e)\n",
    "    hist_part, fut_part = series[:, :L], series[:, L:]\n",
    "    truth = fut_part.sum(axis=1)\n",
    "    res = forecast_batch(hist_part)\n",
    "    err = (res[:, 0] - truth) / truth * 100\n",
    "    caught = ((truth >= res[:, 1]) & (truth <= res[:, 2])).mean() * 100\n",
    "    history_sweep[L] = {\n",
    "        \"days\": L,\n",
    "        \"typical_miss_pct\": float(np.median(np.abs(err))),\n",
    "        \"within_10_pct\": float(np.mean(np.abs(err) <= 10)) * 100,\n",
    "        \"band_caught_pct\": float(caught),\n",
    "        \"typical_width_pct\": float(np.median((res[:, 2] - res[:, 1]) / res[:, 0] * 100)),\n",
    "    }\n",
    "    print(f\"{L:4d} days: typical miss {history_sweep[L]['typical_miss_pct']:4.1f}%   \"\n",
    "          f\"within 10% {history_sweep[L]['within_10_pct']:5.1f}%   \"\n",
    "          f\"band caught {caught:5.1f}%   band width {history_sweep[L]['typical_width_pct']:5.1f}%\")\n",
    "print()\n",
    "print(\"The miss shrinks as history grows, and is still shrinking at a year. The band's width shrinks\")\n",
    "print(\"in step with it - and its honesty barely moves: at every length it catches the truth 78 to 85\")\n",
    "print(\"times in a hundred, against the ninety it claims. More history buys a sharper forecast, not a more truthful band.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "98a21916",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:20.188149Z",
     "iopub.status.busy": "2026-10-24T12:15:20.187732Z",
     "iopub.status.idle": "2026-10-24T12:15:21.233722Z",
     "shell.execute_reply": "2026-10-24T12:15:21.229698Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1610x616 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.4))\n",
    "L = np.array(HISTORY_GRID)\n",
    "axes[0].plot(L, [history_sweep[k][\"typical_miss_pct\"] for k in HISTORY_GRID], marker=\"o\",\n",
    "             color=SIGNAL, lw=2.2)\n",
    "axes[0].set_xlabel(\"days of history\")\n",
    "axes[0].set_ylabel(\"typical miss on next month's total, %\")\n",
    "axes[0].set_title(\"More history, smaller miss\")\n",
    "axes[1].plot(L, [history_sweep[k][\"band_caught_pct\"] for k in HISTORY_GRID], marker=\"o\",\n",
    "             color=FLAG, lw=2.2)\n",
    "axes[1].axhline(COVERAGE * 100, color=MUTED, lw=1.2, ls=\"--\")\n",
    "axes[1].text(L[0], COVERAGE * 100 + 0.6, \"what the band claims\", fontsize=9.5, color=MUTED)\n",
    "axes[1].set_ylim(50, 100)\n",
    "axes[1].set_xlabel(\"days of history\")\n",
    "axes[1].set_ylabel(\"share of runs the band caught, %\")\n",
    "axes[1].set_title(\"What the band delivers, by history length\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_history_length.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d78104f9",
   "metadata": {},
   "source": [
    "## Why I still stop at a month\n",
    "\n",
    "Nothing stops the formula from reading ninety days out. Below, the same five hundred\n",
    "accounts read out to a week, a fortnight, a month, two months and three, with the ceiling\n",
    "lifted for the experiment only. Their rules genuinely do not change, so this measures the\n",
    "arithmetic alone; the last sections measure what one ordinary change does to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cdd69927",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:21.244788Z",
     "iopub.status.busy": "2026-10-24T12:15:21.243985Z",
     "iopub.status.idle": "2026-10-24T12:15:31.417317Z",
     "shell.execute_reply": "2026-10-24T12:15:31.403466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  7 days ahead: typical miss  5.1%   leans  +0.2%   band width  22.9% of the total   caught  86.0%   trend uncertainty is  14% of the last day's band\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 14 days ahead: typical miss  5.1%   leans  -0.0%   band width  18.9% of the total   caught  83.0%   trend uncertainty is  15% of the last day's band\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 30 days ahead: typical miss  5.3%   leans  -0.4%   band width  16.3% of the total   caught  76.6%   trend uncertainty is  19% of the last day's band\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 60 days ahead: typical miss  5.9%   leans  -1.1%   band width  16.7% of the total   caught  71.0%   trend uncertainty is  25% of the last day's band\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 90 days ahead: typical miss  7.3%   leans  -1.2%   band width  18.5% of the total   caught  64.6%   trend uncertainty is  32% of the last day's band\n",
      "\n",
      "Even with nothing changing, the band's catch falls from 86% at a week to 77% at a month and 65% at three:\n",
      "the further out the trend is read, the more of the band is the trend's own uncertainty, and that\n",
      "is the part the band under-counts. A month is where the arithmetic is still honest enough to plan\n",
      "on, and it is also where the 'nothing changes' condition has had the least time to break.\n"
     ]
    }
   ],
   "source": [
    "HORIZON_GRID = [7, 14, 30, 60, 90]\n",
    "horizon_sweep = {}\n",
    "for H in HORIZON_GRID:\n",
    "    # the experiment lifts the ceiling on purpose and says so; the default refuses past it\n",
    "    res = np.array([[f[\"total\"], f[\"total_lower\"], f[\"total_upper\"], f[\"phi\"],\n",
    "                     f[\"param_sd\"][-1] ** 2 / (f[\"param_sd\"][-1] ** 2 + f[\"noise_sd\"][-1] ** 2) * 100]\n",
    "                    for f in (forecast(h, horizon=H, ceiling=LONG_HORIZON) for h in acc_hist)])\n",
    "    truth = acc_fut[:, :H].sum(axis=1)\n",
    "    err = (res[:, 0] - truth) / truth * 100\n",
    "    horizon_sweep[H] = {\n",
    "        \"days\": H,\n",
    "        \"typical_miss_pct\": float(np.median(np.abs(err))),\n",
    "        \"bias_pct\": float(np.median(err)),\n",
    "        \"band_width_pct\": float(np.median((res[:, 2] - res[:, 1]) / res[:, 0] * 100)),\n",
    "        \"band_caught_pct\": float(((truth >= res[:, 1]) & (truth <= res[:, 2])).mean() * 100),\n",
    "        \"memory_left_pct\": float(np.median(res[:, 3] ** H) * 100),\n",
    "        \"trend_share_of_band_pct\": float(np.median(res[:, 4])),\n",
    "    }\n",
    "    print(f\"{H:3d} days ahead: typical miss {horizon_sweep[H]['typical_miss_pct']:4.1f}%   \"\n",
    "          f\"leans {horizon_sweep[H]['bias_pct']:+5.1f}%   \"\n",
    "          f\"band width {horizon_sweep[H]['band_width_pct']:5.1f}% of the total   \"\n",
    "          f\"caught {horizon_sweep[H]['band_caught_pct']:5.1f}%   \"\n",
    "          f\"trend uncertainty is {horizon_sweep[H]['trend_share_of_band_pct']:3.0f}% of the last day's band\")\n",
    "print()\n",
    "print(f\"Even with nothing changing, the band's catch falls from {horizon_sweep[7]['band_caught_pct']:.0f}% at a week to \"\n",
    "      f\"{horizon_sweep[30]['band_caught_pct']:.0f}% at a month and {horizon_sweep[90]['band_caught_pct']:.0f}% at three:\")\n",
    "print(\"the further out the trend is read, the more of the band is the trend's own uncertainty, and that\")\n",
    "print(\"is the part the band under-counts. A month is where the arithmetic is still honest enough to plan\")\n",
    "print(\"on, and it is also where the 'nothing changes' condition has had the least time to break.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8e02ab",
   "metadata": {},
   "source": [
    "## Caveats to watch out for\n",
    "\n",
    "Everything so far honoured \"if nothing changes\" to the letter. Real accounts do not. Four\n",
    "ordinary ways the condition breaks, each measured on the same five hundred accounts, each\n",
    "with the repair that works and the one that does not."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17de813f",
   "metadata": {},
   "source": [
    "### A change you already know about\n",
    "\n",
    "The most common one. A budget increase is approved on the first of the month and lands on\n",
    "the tenth. The forecast was built on history alone, and history does not contain a budget\n",
    "increase that has not happened yet. Below, every account's future with the level lifted by\n",
    "thirty per cent from the tenth day onwards, the simplest way an increase might land."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "17fd9a55",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:31.438846Z",
     "iopub.status.busy": "2026-10-24T12:15:31.430830Z",
     "iopub.status.idle": "2026-10-24T12:15:31.524402Z",
     "shell.execute_reply": "2026-10-24T12:15:31.523189Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 30% lift from day 10 adds about 22% to the month\n",
      "the forecast, told nothing, runs -18% against what happens\n",
      "its range catches the truth 11% of the time - the band was never\n",
      "built to cover a change the model was not told about.\n",
      "\n",
      "The repair is not a wider band. It is to tell the model: a known future change is an\n",
      "input, and a forecast that takes it becomes a forecast conditional on the change landing\n",
      "as planned - which is the honest thing to say about it.\n"
     ]
    }
   ],
   "source": [
    "PLANNED_LIFT = 0.30\n",
    "PLANNED_FROM = 10          # the tenth day of the month\n",
    "lifted = fut30.copy()\n",
    "lifted[:, PLANNED_FROM - 1:] *= 1.0 + PLANNED_LIFT\n",
    "lifted_totals = lifted.sum(axis=1)\n",
    "err_planned = (acc_totals[:, 0] - lifted_totals) / lifted_totals * 100\n",
    "planned = {\n",
    "    \"lift_pct\": PLANNED_LIFT * 100,\n",
    "    \"from_day\": PLANNED_FROM,\n",
    "    \"typical_miss_pct\": float(np.median(np.abs(err_planned))),\n",
    "    \"bias_pct\": float(np.median(err_planned)),\n",
    "    \"band_caught_pct\": float(((lifted_totals >= acc_totals[:, 1]) & (lifted_totals <= acc_totals[:, 2])).mean() * 100),\n",
    "    \"expected_change_pct\": float(np.median((lifted_totals - acc_truth) / acc_truth * 100)),\n",
    "}\n",
    "print(f\"a {PLANNED_LIFT * 100:.0f}% lift from day {PLANNED_FROM} adds about \"\n",
    "      f\"{planned['expected_change_pct']:.0f}% to the month\")\n",
    "print(f\"the forecast, told nothing, runs {planned['bias_pct']:+.0f}% against what happens\")\n",
    "print(f\"its range catches the truth {planned['band_caught_pct']:.0f}% of the time - the band was never\")\n",
    "print(\"built to cover a change the model was not told about.\")\n",
    "print()\n",
    "print(\"The repair is not a wider band. It is to tell the model: a known future change is an\")\n",
    "print(\"input, and a forecast that takes it becomes a forecast conditional on the change landing\")\n",
    "print(\"as planned - which is the honest thing to say about it.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0487c08b",
   "metadata": {},
   "source": [
    "### The last few days are not finished yet\n",
    "\n",
    "Conversions arrive late. A sale attributed to a click lands in the report days after the\n",
    "click, so the most recent days in any export are under-reported and fill in over the\n",
    "following week or two. A forecast fit on those days sees a dip that is not there and\n",
    "projects it forward. Below: every account's history with its last week under-reported the\n",
    "way a typical platform reports it, fit as-is, and fit after dropping the unfinished days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "78d799df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:31.536664Z",
     "iopub.status.busy": "2026-10-24T12:15:31.536279Z",
     "iopub.status.idle": "2026-10-24T12:15:34.502426Z",
     "shell.execute_reply": "2026-10-24T12:15:34.501476Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the newest day shows 40% of what it will finally report; a week-old day shows 92%\n",
      "fit on the unfinished data:   the month comes out -24.3% against the clean forecast, on the typical account\n",
      "drop the unfinished week:     -0.2%\n",
      "on the worked account: clean 3212, as-is 1978, trimmed 3216\n",
      "\n",
      "Dropping a week costs a little precision and removes most of the bias. Waiting for the\n",
      "days to mature is better still, and knowing your platform's lag is the whole trick.\n"
     ]
    }
   ],
   "source": [
    "LAG_DAYS = 7\n",
    "# share of a day's conversions already reported when the day is `age` days old\n",
    "report_share = 1.0 - 0.60 * np.exp(-np.arange(LAG_DAYS)[::-1] / 3.0)\n",
    "\n",
    "\n",
    "def with_late_arrivals(values):\n",
    "    out = values.copy()\n",
    "    out[-LAG_DAYS:] *= report_share\n",
    "    return out\n",
    "\n",
    "\n",
    "def forecast_trimmed(values_lagged):\n",
    "    # the trimmed history ends a week earlier, so reaching the same calendar month needs a week\n",
    "    # past the ceiling - lifted here for the comparison only, and said so\n",
    "    f = forecast(values_lagged[:-LAG_DAYS], horizon=HORIZON + LAG_DAYS, ceiling=HORIZON + LAG_DAYS)\n",
    "    return float(f[\"mean_day\"][LAG_DAYS:].sum())          # the same calendar month\n",
    "\n",
    "\n",
    "as_is_bias, trimmed_bias = [], []\n",
    "for h, f in zip(acc_hist, acc_fc):\n",
    "    hl = with_late_arrivals(h)\n",
    "    as_is_bias.append((forecast(hl)[\"total\"] - f[\"total\"]) / f[\"total\"] * 100)\n",
    "    trimmed_bias.append((forecast_trimmed(hl) - f[\"total\"]) / f[\"total\"] * 100)\n",
    "# the worked account, for the picture\n",
    "history_lagged = with_late_arrivals(history)\n",
    "fc_lagged = forecast(history_lagged)\n",
    "\n",
    "late = {\n",
    "    \"lag_days\": LAG_DAYS,\n",
    "    \"reported_share_last_day_pct\": float(report_share[-1] * 100),\n",
    "    \"reported_share_week_ago_pct\": float(report_share[0] * 100),\n",
    "    \"as_is_bias_pct\": float(np.median(as_is_bias)),\n",
    "    \"trimmed_bias_pct\": float(np.median(trimmed_bias)),\n",
    "    \"worked_as_is_total\": fc_lagged[\"total\"],\n",
    "    \"worked_trimmed_total\": forecast_trimmed(history_lagged),\n",
    "    \"worked_clean_total\": fc[\"total\"],\n",
    "}\n",
    "print(f\"the newest day shows {late['reported_share_last_day_pct']:.0f}% of what it will finally report;\"\n",
    "      f\" a week-old day shows {late['reported_share_week_ago_pct']:.0f}%\")\n",
    "print(f\"fit on the unfinished data:   the month comes out {late['as_is_bias_pct']:+.1f}% against the clean forecast, on the typical account\")\n",
    "print(f\"drop the unfinished week:     {late['trimmed_bias_pct']:+.1f}%\")\n",
    "print(f\"on the worked account: clean {fc['total']:.0f}, as-is {late['worked_as_is_total']:.0f}, trimmed {late['worked_trimmed_total']:.0f}\")\n",
    "print()\n",
    "print(\"Dropping a week costs a little precision and removes most of the bias. Waiting for the\")\n",
    "print(\"days to mature is better still, and knowing your platform's lag is the whole trick.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac6beb11",
   "metadata": {},
   "source": [
    "### The account changed character, and the history still carries the old one\n",
    "\n",
    "A pricing change, a tracking fix, a lost placement: something moved the level six weeks\n",
    "ago and it stayed moved. The trend can bend, but a bend is not a step, and the weekly\n",
    "rhythm and the band are fitted across the whole history as if it were one account.\n",
    "Below: a twenty-five per cent drop in level forty days before the end, on five hundred\n",
    "accounts, fit on everything and fit on the forty days since."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d1ad1077",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:34.558674Z",
     "iopub.status.busy": "2026-10-24T12:15:34.556448Z",
     "iopub.status.idle": "2026-10-24T12:15:37.220075Z",
     "shell.execute_reply": "2026-10-24T12:15:37.217313Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit on all 180 days        leans  -5.9%   typical miss  8.7%   band caught  68.0%   band width  28.2%   kept a bend on  94% of accounts\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit on the 40 days since   leans  +0.3%   typical miss  6.3%   band caught  82.4%   band width  26.5%   kept a bend on   0% of accounts\n",
      "on the worked account: fit on all of it 2310 (bends [103, 131, 145]), fit since the step 2352 (bends none)\n",
      "\n",
      "Read the bends the full fit kept: on nearly every account it explained the step as a bend, and a\n",
      "bend is a slope, so it extrapolated the drop as if it were still falling. The fit on the forty days\n",
      "since is centred and its band is honest again, and no wider than the full fit's. The\n",
      "platform's answer is a separate detector that finds the step and clips the\n",
      "history at it - the forecast does not find it on its own, and when it tries, it reads a step as a slope.\n"
     ]
    }
   ],
   "source": [
    "SHIFT_DAY = HISTORY_DAYS - 40\n",
    "SHIFT_MULT = 0.75\n",
    "\n",
    "\n",
    "def shifted_log_level(t):\n",
    "    t = np.asarray(t, dtype=float)\n",
    "    return true_log_level(t) + np.log(SHIFT_MULT) * (t >= SHIFT_DAY)\n",
    "\n",
    "\n",
    "history_shifted, noise_shifted = make_history(HISTORY_DAYS, np.random.default_rng(SEED + 4),\n",
    "                                              level_fn=shifted_log_level)\n",
    "g_shift = np.random.default_rng(SEED + 5)\n",
    "_t2 = np.arange(HISTORY_DAYS + HORIZON)\n",
    "_series2 = np.exp(shifted_log_level(_t2)[None, :] + np.log(DOW_TRUTH[_t2 % 7])[None, :]\n",
    "                  + ar1_noise(ACCOUNTS, HISTORY_DAYS + HORIZON, g_shift))\n",
    "sh_hist, sh_truth = _series2[:, :HISTORY_DAYS], _series2[:, HISTORY_DAYS:].sum(axis=1)\n",
    "\n",
    "fc_all = forecast(history_shifted)\n",
    "fc_clip = forecast(history_shifted[SHIFT_DAY:])\n",
    "regime = {}\n",
    "for label, cut in ((\"fit on all 180 days\", 0), (\"fit on the 40 days since\", SHIFT_DAY)):\n",
    "    fits = [forecast(h[cut:]) for h in sh_hist]\n",
    "    res = np.array([[f[\"total\"], f[\"total_lower\"], f[\"total_upper\"]] for f in fits])\n",
    "    err = (res[:, 0] - sh_truth) / sh_truth * 100\n",
    "    regime[label] = {\n",
    "        \"bias_pct\": float(np.median(err)),\n",
    "        \"typical_miss_pct\": float(np.median(np.abs(err))),\n",
    "        \"band_caught_pct\": float(((sh_truth >= res[:, 1]) & (sh_truth <= res[:, 2])).mean() * 100),\n",
    "        \"band_width_pct\": float(np.median((res[:, 2] - res[:, 1]) / res[:, 0] * 100)),\n",
    "        \"kept_a_bend_pct\": float(np.mean([len(f[\"bends\"]) > 0 for f in fits]) * 100),\n",
    "        \"worked_total\": (fc_all if cut == 0 else fc_clip)[\"total\"],\n",
    "        \"worked_bends\": (fc_all if cut == 0 else fc_clip)[\"bends\"],\n",
    "    }\n",
    "    print(f\"{label:<26} leans {regime[label]['bias_pct']:+5.1f}%   typical miss {regime[label]['typical_miss_pct']:4.1f}%   \"\n",
    "          f\"band caught {regime[label]['band_caught_pct']:5.1f}%   band width {regime[label]['band_width_pct']:5.1f}%   \"\n",
    "          f\"kept a bend on {regime[label]['kept_a_bend_pct']:3.0f}% of accounts\")\n",
    "print(f\"on the worked account: fit on all of it {fc_all['total']:.0f} (bends {fc_all['bends'] or 'none'}), \"\n",
    "      f\"fit since the step {fc_clip['total']:.0f} (bends {fc_clip['bends'] or 'none'})\")\n",
    "print()\n",
    "print(\"Read the bends the full fit kept: on nearly every account it explained the step as a bend, and a\")\n",
    "print(\"bend is a slope, so it extrapolated the drop as if it were still falling. The fit on the forty days\")\n",
    "print(\"since is centred and its band is honest again, and no wider than the full fit's. The\")\n",
    "print(\"platform's answer is a separate detector that finds the step and clips the\")\n",
    "print(\"history at it - the forecast does not find it on its own, and when it tries, it reads a step as a slope.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "77a8ba36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:37.239705Z",
     "iopub.status.busy": "2026-10-24T12:15:37.239295Z",
     "iopub.status.idle": "2026-10-24T12:15:39.501648Z",
     "shell.execute_reply": "2026-10-24T12:15:39.497817Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iNmoFEWl1j3q8f0/f/oNxcX2rto7jJ05x8dJltm3ZoPax3aZ1C/6YO5/Q0FDOnrugs4A2MjKS48dPAWgNkxbve29vH9p36sabNy5a5+/Ze4BJv01TSwtBTPrSzc2dmw63UCgUjP5hhM5taNvmwCEjePRIPVPE4/17Dh85yhH7Y/xvzA/8OHpkguu5cvU6I38Yo3ZNubu/Y+PmrVy6cpX9e7ZrfC88efKU3v0G4e8foDb9/YcPvP/wgUePHsdU4ty0Lsn7AzHfMJOn/M6u3fvUpivf69eu30xyZZ2jx07w8y+/apxXpdS+2x49fkKffoMJCPh8DDw83rNr9z5OnDzNxnWrkhTPtJTS9NTX8P2TFmm6T5/C6Nl7AHfv3dcZJjX3e0REBFOmzdR41n369Al//wCePXvOiZOnNTK54xdgxqwriMdPnvL4yVP27jvAlo1rtV7b6fHsSI6AwED69Bus9pwJCwvj4sXLXL58ld+nTU50HSn5prOzq4K+vr7qGqhmV0VteYc4mcUOt25rZIg63r5DVFQURkaGVKlcUWu8UprnIASkb55eQGAgvXoP4MnTZ2rTvX188Pbx4fmLlxw8bK8qPE2JtHjuKxQKRo/5meMnTqlNv3HzFjdu3mLY0EFMnDAuxXFMjqfPnOg3YKja/nz48JG9+w5w4eIl9u3ervX9ERkZyZRpM9m9Rz3dER4ejqPjHRwd73Do8FFWr1r6RXpPeOXszPhff1O1aAb18cHTKr4pPV5pkc+WmFOnzzJu/ESNbx0X17e4uL7lwsVL5M6di2ZNG6vmZXRedFzfQ95Wau7ttMqLTmle+L79B5k4eRpRUVGqac7Or1m2YjUnT51JVVkLpC5NF1di3yn3Hzxk8JCR+Pj6qk1XfpPv2r2XZUv+pn69uinfmSRIyXcipH8e5ZcihacZ6MzZ86oLr327mJZ35lmz0rBBPU6eOsPhI8dSdUMvW76KpctjPtirVK7I8GGDKV+uHEZGRri5u3Pt2g327Dugc/nlK9bw4cMHxo75gbatW2JhkYPnL14x+495PHr8hLnz/qJF86bkzpVLbbkcObLTr28vatWsQaGCBcidJzcRERG4ur5l1+59HDpsz6w//qRixQqUL1dW5/Znzp6Hr58fUyb/StMmjTAzM+P+g4dMn/EHrm/fMnnqdC7Xra2WiAD4c/5CVcFp547tGTigLwUK5MfF9S0rV61l+YrVFC6k+0Xj4uJKvwFDCAoOxrZMaUaOGErlShUxMTHhxYuXLF62gmvXbjBu/ESKFrVMcq2qUSOGMnTwAJq3as+7dx6MHDGUUSOGqoXRlpl485Yj+w8epl3b1gwc0BfLIoXx9PRi2YrVHD5ylKPHTtChXRuNl7efnx99+g7C4/17LIsUZvQPI6hezY6sWc144+LKuvWbsD96nDlzF2BlaZnij8I3Li5Mn/kHLZo3ZfiwwRQuVJDXr12YNn0Wj5885c8Ff1O4cGF+nzGbVi2bM3TIQFWYKdNm8PSZEwsW/kObNi01rqVdu/eqPrKrVq3MT2NHU7JECXx9fdmzdz9r12+KLaT9ia2b16u1oI6OjmbkD2N56+aGkZEhP4wcTvt2bTAzM+Pe/Qf8OX+hqgWnLkePneC3qTMAaNG8Kf369qK4jQ3R0VE43LrNXwv/wcX1LUOH/YD94X1kzWqWomMYl56eHnZVq3Dq9FluOjjSsYN64alDbOGpkZEhL16+UtXcUVIWrubJkxurouq1eKyKWtKpY3uqVK5Evrx5yJUrF/4B/jx96sT6DZu4feceP44dx5mT9mTJkkW1XFRUFBMnTSM8PBxrayt+Gjua8uXKki2bOYGBQTx/8ZLzFy5q3IvJceDAYdzc3RnQrw9du3Yif768uLm/48/5i7h67Tpr1m2kfbs2lC5dSm25K1ev89vUGSgUCkoUt2H8Lz9RsUJ5QkJCsD92nMVLVuDi4srgoSM5cmgvJiYmAGxYt5K3b91o3qo9AOvXrsSuavJrls+eNZ3pv/9GuYoxBRSzZ/5O+3at1cLErQ35z79LVZltTZs0YsSwIRQtasn79+/ZuHkbe/bu59z5i0ydNlNnbVptgoODGTpiNL6+fphlycLPP42hWbPGGBkacv2GA/P+XMiYn7Qn0JMqX758DBs6iOrV7MifPx95cucmODiYV86v2bxlOxcuXuKXCZMoV86WAvnz61zP5Cm/ExERwdw/ZtCgfj0Azl+4yPy//sbPz59hI0dz5qS9RuIrvY5dYtLqWhHfP30DfRo2qE/TJg0pXtyGvHnykDlzZj58/Mj5C5dYv34TN27eYsFf/zBt6iTVcslNH9y+c5eRo/9HZGQkNWtWZ8igAZS1LY2BgSH3Hzzk73+W8OjxE0aMGssx+/0J3o/aHDh4mGnTZwFQorgNo0YOo2rVymQ2NeX9h484ONxi7/5DGsul1T36y/jJhIWHMW/OLOrWqYWxsTHXb9xk+ow/8PH1Zeq0mRw+uEcVPnfu3NSoUY1r125wxP6YzsLTa9dvqCoXtW+r/pxOq/f97Dl/EhwUrEq7mmY25eHDx5iYmODn58f0mX8QHR1NxQrlGf3DCEqWLE5WMzN8ff14+syJM2fPqd5TSREdHc0PP/7Eo0ePMTAwYPCg/nTt3BELCwucnj9n4aJ/uX3nHn//u5T8+fNptDZRCggI5MexP1OsmDU/jR1NWVtbPoV94tDhoyz6ezHOzq9Zsmwlv02aoLbc1N9n4e8fQJ48ufn5f2OoVq0KObJnJzT0E6+cX3P58hXef/iY5P1R2rR5m6rgtG6dWoz5cRTWVla8f/+eDZu2snffgSSN/+rvH8DEyVMpVaok434ag22ZMoSEhODm/rkiWWrebUFBwQwbPpqAgACyZs3KL+PG0rhRA4yMjHBwcGTun38x9qcJ8aOVJMlNXyilND2V3t8/hw4fZcq0mHvs4T0H1XwDAwPV/ylN08W3bMUqfHx8GPPjKNq1bU32bNl4/uIFeWJb46f2fp/6+yzVs65hg/oMHNCXkiWLo6+nj4urKxcvXeHkSc1WnbZlStOmdUvKly9L3jx5yJEjBz4+Ptx/8JDVa9bz/MVLfhw7DvvD+9S+aVLy7Dh1/DA3HRwZNCSmMPXksUMUKPD5+tXXT97oTRN+naIqOB3Qrw89e3QlZ86cPH/xgr8W/svsP+ZhYWGhc/mUftOZm5tTqmQJnjx9hoPDLbXC0/DwcO7ee4CBgQHR0dGqgtK415SyJU5ZW1udlZZSmucgBKRvnt7KVWt58vQZhoaG/PjDCJo1a0Ke3LmJio7C7a07DrccORTb6hWgQ/u2tGzRjIFDRuDoeIf27dpo9FIV9zmRVs/9/QcO4e7+jp7du9KvX2/y5M7NixcvWfj3YlXPY2VKl6JdvLSXLgULFuDhPQeWr1zDipVrKFAgPyePqac7DQ21j5U9YuQYzM2zMuP337CrWhmFIuYczZ7zJ15e3syZt4AVy/7VWG7m7Lns3rMPIyNDBg3sT9vWLSlYsAABAYGcOnOWv/9ZgsMtRyZOmsrypf8kaT8g5n2n6/0H6u/AuCZOnoaenh5/zp1Nndo1MTAw4OHDzwV5aRXflByvtMhnS8yNmw788ONPREVFUaBAfn4YOYzatWqSLZs53t4+ON6+y6HDRzR6UMzovGilbzVvK76U3ttplRed0rzwx4+fqgpOLS2LMOnXX6hcuSKhIaEcOXqcxUuWM2feglQdm5Sm6eJK7DvF08tLVXCaPXs2xv00loYN62FkZMT16zf5c/4iPN6/Z8Sosdgf2psmPZdqk9LvxC+RR/nFREWGKeQvY/4GDhqssLSyUTRo1ERtur39EYWllY3C0spGcevWzRSt2/nVC4W1TUmFpZWNYviIkYrwsBCt4SLCQxWREZ/Upim3bWllozhy5LDGMu/fuytKlSmnsLSyUWzYuCHZcZs/f77C0spGMfrHMRrzXN44q7ZdrHgpheMtB40wjx8/VIU5ffqU2jxXl9cKq2IlFJZWNooJv/6qsWxkxCfF0GEjVMv//PM4jTB9+/VXWFrZKFq3ba8ICQ7Uesx69e6rsLSyUfTp2y/Z+1+rTj2FpZWNYuGiRQmG69a9pyqeU6ZO1bovbdq1V1ha2ShGjBylMX/ipMkKSysbRc3adRXeXh+0buOX8RMUllY2ikaNm2lcB4n9/fzzOFX8fhj9o8Z8V5fXqmuwWPFSih/HjNUI8+b1K9X5in8tBQf5KypUqqKwtLJRdOrSVREaonkuVq9ZrYqDvf0RtXn79+9TzduxY4fGsp4f3ytq1KqjCrNr1061+YEBvqrtT502Tesx+PDhnaJqtRoKSysbxZKlS3Veyy5vnJN1bJX7Va9BI7Xpbm9dFJZWNooqdtVVz4/4+/3D6B91npOE/sLDQhRdu/VQWFrZKLZu3aI279GjB6p9efLkUbKv+aTcD5ZWNoqVK1dqzA8O8lfUqBlznmbP/kNjfpOmzRWWVjaK+g0bK3x9PDXmHzt2VLX+VatW6TxHV69eTtV+6LqO4v65u7kqihUvpbC0slGM+mG01nvu99+nq9Z19+5ttXlXr15WzYu/3OIlSxSWVjaKotbFFZcuXdSY7/zqhaJM2fJptr/a/saM/Z/C0spGMe/PPzXmxY27tU1JxZ07jhph7ty+pXpm/Dl/fpoeu4WLFiksrWwU3br3TNJ5jH980vJakb+v/095vWi711Lz5/TsiaJY8VKKkqXLKnx9vTTmJyV9EBnxSdG4STOFpZWNYtDgIVrvhaBAP0XT5i0VllY2iokTJyUrjn6+3oqy5SvFvHs7d1EEBvjqDBs3bZmWzzfbchUVzq9eaCx/6tRJVZinTx+rzdu+Y7vC0spGUbxEaa3vgqjIMMVPP/8ck75r005telq+74taF1dcu3pF6zpOnjyuShPpiqO2v4TSE4cPH1LN05YmDwkOVLRr31FhaWWjqFTZThESHKDzWm/Trr3G/KjIMMWs2bMVllY2ispVqmlcK8plT506mWb3SVCgn+oa7NGzlyLsU7BGmEmTf1NtW9tzPe5+NW3eUhEU6Jfi+CT0bvv3338VllY2CqtiJRTXr1/TmO/q8lq1Lyl9fyQlfREVmfr0VHp//+zatTPR52pq0nRxj5WllY1i7949WreR2vv92tUrqm38Pn26zn3Rdt0mdt3Xb9hYYWllo7hw/pzavJQ+O+I+V5P7LRL3z8Hhhmo9f/31l9Zrq1Xrtqow8d9hqf2mU747evXuqzb9xo1rCksrG0W79h0VzVq0Ulha2Sju3bujFqZDx84KSysbxZy5c9WmpzbPQf7kT/mXnnl6yvtq1uzZyVpOmY+kLa8r7l9qn/tx3zvTpv2uMT8kOFCVX1WjVh1FRHhosvZD+S6vVadeguHivl9q1q6r8PL8oBFmw4b1Op+jjrccVMtry/+Miox59he1Lp6i85mU919UpPozu2TpsooXz59pDZfa+Kb2eKU2ny2xv/CwEEW9+g1V6YGPHzx0hk3uuzY986Lj3xPfct5Wau/t1OZFpzYvvHeffjHfLlWrKzzeuWnMP3TooFqaMTXHSttfQmm6qMikf6dM/u031fftg/v3NOa/ef1K9Z0xcNBgjflJuR4SyidLzXdiVOTXkUeZVn/Jq3Io0oyvrx+Xr1wF0Kil0ahhA7LGdq0QtyZZcuzYuZuoqChMTU2ZM3u6zhpN+vr6OmtBVK1aWW28SqXcuXJRt04tAB4+TH6/6cr9vX7jZoLh2rRuSaVKFTSmlypZgjKxNXQexuui7NCRo0RHR2NsZMSEX37WWFZPT49Jv+ruMuT1GxcuXY45L3NmT9cYVwVijtnYMaOAmJpBfn5+Ce5HamXObMovP/9PY7qenh4d2rUFNI9DUFAwBw4eBmDK5F919rX+vzE/ADHdgjx58kxrmMTo6ekx4ZefNKYXLFgAW9uY7vyioqIY/4vmPhQuXIiyZWNqMMa/ls6cPY+fn3/MPkyaoLVW18D+fbGKHYNjz171VtTK/S9VqgTdunbSWNbCIgc/jByuc7+O2B/Dz88fixw5mPTreK1hcuXMSb8+vQCwP6o5dlpKVY/tbtfFxZUPcVpsKFuVVrOrquqK0cFBveveW463VWGSw8DAgNatY7r8uX5DvUZk3K4u8uTOnaz1JlXBggUYPKi/xvRMmTLRokVTAB4+eqQ27969B7x4+QqAcT+N0dpNRPNmTahdK2Z8o/jdq31pBw8dITIyEgMDA6ZM/lXrs/eXcWNVz/+9CfQMEJ/yem/UsD61a2mOO2VpWYS+sddqelE9268n/mzXNr5FhQrlVd1OHThwWG1eeh47Ib4UG5tilClTik+fPnH37r0UrePqtRu8fOWMgYEB8+bM0novmJqaMmJYTNeFR4+f1Bi6ISEHDx8hMDAQPT095s2ZleD4oXHTlml5jw7o1xtLyyIa0xs2qEf27NkA1GrfA7Rs3hRjY2PCIyI4rqXFV1hYGKdOnwM0091p+b5v1LA+1avbaZ0XGRnzLs2UKRNmZqnvqQI+H8fiNsW0DqVhYmLMb5NjagH7+Ppy9twFneua8MvPWlvydWwfk9b09vFRG0MxKupz91J58qRd2uDM2fMEBgYCMGnieK1jGU345X9JbqH7vzE/pKqb7ITebQcOHQFiWlrH704UYtI2A/ol3DVcWktJeupr+P5JyzRdqVIlNHpuUUrt/b5l2w4AChTIzyQdXe4ByR4Xz9TUlGaxY4ddi/eNnB7PjuTYH5sms8iRg1Favp0yZcqk9TtPKbXfdNWqxXzT3Ll7j4iICNV05TdQ9WpVqR4bJu6Yb6GhoTx6/Dg2jPbnMqQsz0EISP88vcjYb/D0+P5Oy+e+qamp1ta1JibGqnwsD4/3GnkM6UHX2HsdYtMykZGRPH3qpDZv89btADRu1EBr/idA9ep2qrGd7e2Pp2GMtevetbPO7j7TMr4pOV6pzWdLzKXLV1VDd0yfNlmtl7X4kvuuTc+86Li+l7ytlNzbaZkXnZK88I8fPbl67ToAw4cN0vqN0qZ1SypV1Dy/aSWhNF18ur5TIiMjOXQo5t3RtWsnVd56XIULF2LYkJjxbS9cvIynl5dGmLSS3O9E+DryKNOKFJ5mEPtjx1V9WsdPaJmYGKvG1Tl27KRaX+dJdS324dWgfl2dD6vE1KtTW+e8orGZWl7xxkZQ+vjRk7//WUKnLj2pVLUmxUuVx7q4LdbFbVVdCXh5eRMYGKR7+3UT2H5sd6Re8R4OyszISpUq6hyw2NKyCMVtimmddyP2wWaRIwdWRS0JDg7W+mdVtCgQ0wf8o8dPdcYzLVSsWEFn93Cfj4P6ebh79x6fPn3CwMCAShUr6NwPc/Os5IztYkn5cZlcRYoU1tl1mrJ75CJFCuscbF4ZJv61dOfOPQDy5c1LhQqa4x9CTIJeea/ciZMRrVAouHvvAQBNG+sexLxpU93zrsVmklWtWpnIyAidx1DZNcLz5y81xgNNqTKlS6k+tm6qjeUT83+1alWxi82ku3nr8/w3Li6qwlZlRkN8jx8/5bepM2jRqj3lK1ajWImyqnvz9+mzgZgx8+KytiqqelH+OmmKxripaaF2rZo6uxDTdZ3fvnsXiEk0N27UQOe6W7aIGY/g5Stn/P390yC2KaO8RitWLE/e2K7j4sucOTP169UB4Pbtu0lar5+fH87OrwFo2qSxznDNEpiXVK/fuDB7zp+0bd+FCpVrYFOynOr6GTx0ZGyYNwmuo2mTBO7J2Hke72PG0lVKr2MnRFqLioriwMHDDB46ilp1GlHKtpLqHrEubqsaO+f1a+1jYiZG+bFfulRJMmUySeDdFJPhEhAQgIura0KrVF9/7LuvbNkyyer6Jy3v0bo60n/6+voUKVIY0HwfmJub06B+zFgvR+w1CzfPnb9IUFAQ+vr6GuN2peX7vl7s/mlTqlQJ9PT0CAoKYsq0Gan+wFUoFNy5GzOmY7OmjXVXhqxSWZVx4Hj7jtYwxsbGqsKH+IrGGQIgbro7e/bs5M+XD4CZs+bw/MXL5O+EFsr0X/78+XR2EWhubk4NHYXUcenp6akqfCYkJe82X18/VXopoTRIowTmpYeUpKe+hu+ftEzTJfQNm9r7/UbsN3brli0wMtLebWRCbt68xbjxk2jUtBVlK1RVez+sXrsB0Hw/pPWzI7mUz/f69etqLWCBmOsuc2btlRRS800HMRVC9fT0CA0NVcskVVUqrWanqjQat1Lp7Tv3iIiIqdRTpXIlnfuXkjwHISD98/RKlyoJwOq167l0+YrGmMepkZbPfWV3nNrUrlVD9WxIacXB5ND1/Dc3N1fle8XPd1KmratXt9N5HIKDgylRImYM7IcpzDdLjoTSk2kZ3+Qer7TIZ0uMcv9y5sxJ3QTe57pkVF50XN9L3lZK7u20zItOSV74nbv3VBWHE8r/SihPKqlSkqaLK6HvFCenFwQFBwPQvFlTnetQXg/R0dHcjf0uTGsp+U78knmUX4KMeZpBDsfWPitfvqzGuIQQM17C3n0H8Pbx4fKVazRsUC9Z63/7NqamjjLBlRK5E6hFnim2ZkTYp08a867fuMmIUWNVtcYTEhQUpPNhmDuBGnamsWN/ffoUpjbd/V1MTQcrK81jGpeVVVFVrZ64Xr+JebD5+PqqxhhKjE/s+FnpJaGahqamyuOgfh6UGTxRUVHUrJO0sUy9fXwTD6RF7ty5dM7LFFvglpQw8a8l5blMLPNWWRAeEBBAYGDM9RTzf8z1l9AA3blz5cLc3JyAgACNecpr4dTps0m6FqKjo/Hz80+T1hf6+vpUqVyJCxcvcdPBUfUxpswkqFG9GsWsrTDLkgUnpxf4+fmRPXt21XyLHDm0VhBYs3YDfy5YlOiHV1CQekIyc+bM/Dh6JH8t/IczZ89z5ux5SpcqSfVqdlSvbked2jXVxkhNiTwJXCOq+z0s3v0eW7OpYMECWscLVrKJPRYKhYJ3Hu/Jli1bquKaUsr42iTxmn7n4ZHE9X4Opyww0cY6gXlJceiwPb9OmppoJYGgoOAE5ytbFmhjHWee+7t35M8fkzGfXsdOiLQUGBjEoKEjklR4H/85m1TKwppHj58kI53iS1HLhNNFSm/fugFQulSpREKqS8t7NMF0T6aY9Gf89wFA2zatYsYLv3kLT09PtXSkskC1ml0V8uXLq7ZcWr7vlRXCtClqaUmPbl3YsWsPO3ftZc/eA5Qra0s1uyrUqFGdmjWq6yyY0CYwMFCV1ilWTHulQKVixaz5+NGTd+/ea52f08JCZ+39uLWh46e7f53wM//7eQK379yjRav2WFkVpUZs2qBundo6KzImRJX+s074WrK2tuLipSsJhrGwsEg0fZLSd9u7d5+v4YTeawnNSw8pSU99Dd8/aZmmK5TAfZia+z0wMAgf35jvpdKlk/+NPXPWXDZu3ppouKBg9fdDWj87kkt5bhL6pjIwMKCopSVPnmq2HknNNx3EtGKyKWbNi5evuOlwi8qVKhIZGcmdu3cxMDDArmoVPoXFfEfecrxNdHQ0+vr6OMRWQC1dupTO/AZIWZ6DEJD+eXo/jh7JqdNn8PLyZsCg4eTMmZMa1e2oVq0q9erU1tpLR1Kl5XPf2rqozmX09fVVzwblsyA9JZQXk0lL3llwcDAfP3oCMGfuAubMTXwcRJ8U5pslR2EdjRPSOr7JPV5pkc+WGFfXmG+RkiWL66wYqEtG5kXH9b3kbaXk3k7LvOiU5IUr42JsZEThwrrTg6kdHzSlabq4EvpOiXtME/q+trIqiqGhIZGRkWrfJmkpJd+JXyqP8kuRwtMM8PatG7dja2BWrliRJ080a3Bly2aOmZkZQUFBHDpsn+yElnLQ+tQUaBjoqCkTV/ye4AICAvjhx58IDAzE0rIIQwYNoFLF8uTJm4dMJpnQ0wP3dx60iK3xE7c7UI3tGyShYXS8CISGhAIk2j2Xrm7okvKSjS+tWhvqkpTzEF9Ctah0Sel+GOhr7xI6uWHiX0vBIcprWHeXgTHzP1/jwcHBZM1qRkjsdQC6z7Vqvqmp1kRdRl8L1atV5cLFS6oa1J6enrx+/UZVMKqnp0eVKpW4eOkKtxzv0LRJI27diglrZ1dFI6Hp4ODI3D//AqBmjWr07tWDkiWKY2FhgbFxTM35g4fsmfr7TFUXQXGNGjGUAvnzs3rtOp49e87TZ048febExs1bMTU1pWePrvzy89gEE3oJ0dfRtXhc8bueVJ7nLImcY7M411BwcMIFe+kpJPaaTuyaVF7Tyud4ousN/RwucwLPvoTmJcbFxZVfJ04hPCIC2zKlGTigL2XL2pIrZ05Vhp3DrdsMHjoywed6YvEwjdNqQXm84v6f1sdOiLQ0e86f3L59FyMjQ/r360OjhvUpUqQwZlmyqLq4HTB4OLdv39X6nE2K9H6/K2u4JvbujS8t71FdQ03Epa0r4saNGpAlS2aCg0M4euwkA/r3AWKO2fkLlwDNliEx89PufZ8pU8Jdyc6aOY3ixW3YtHkrLq5vuXf/AffuP2D12g1ky2bOkMEDGTFscJKOQdxjmNj5MlMdd+3vQP2kpLkBBerHvV3b1mTLZs7SZSu5c/c+r1+/4fXrN+zYtQcjI0PatWnN5EkTklWIGhIa827X1YpNKSnvtMTOR2rebUl99yaWRklrKUlPZXSaF9I2TZfQeU/NvsbdXnK/sQ8cPKzKZGvRvCmdOrajuI0N2bKZq1qwLluxmpWr1hIVqfl+SMtnR3KFKu/JRO45Ux33bGq+6ZSqVavKi5evcHBwZOTwoTx8+JiQkFDKlrUla1YzsmY1o5i1Na+cnXFyek7p0qVwuBUzlImu1hJKKclzEOJL5OlZFbXk0P7dLPpnCWfPnsfb25ujx05w9NgJAGpUt2PqlEkpajCRls/9pD4bvsT3WXLTkF8y3yw5dOWppHV8k3u80iKfLTHKd61ZMt+zGZ0XHdf3kreVkns7La/RlOSFK8sEMpmaJlj4npohPVKbplNKKL0aNy8scwLpJz09PUxNTQkMDEy36yEl34lfIo/yS5LC0wxw+MhR1f8bN29NtLbCmbPnCAkJSfTlFFfmzJkJCAj44gUFx0+cws/Pn6xZs7J31zat/dNHxhmrJK0pH97Kjzxd4j6I1JaPvXFLFLfhxLFDaRu5L0i5H5kzm/LovmMiob9OykRDYonsuOdS+dGdWUcBjNbldVwryod43z49mfH7lMQjnMaUY/O8cnbG08tL1ao0bsFotWpVuXjpCjcdbtG0SSO1MVHj27FrDxDTTdmWTeu0diMSpqUlT1wd2rehQ/s2vPPwwNHxDjdv3uLMufN4enqxfsNmXr1yZsO6VSnf6WRSnufgRM5xsJZrJCMon+GJXpNJzGRSrdf0czhd13Ni8xKzd98BwiMiKFK4MHt3b9M63kFEEp/tCcUjVMcHWWqPXVJqraakOy0hlEJCQjgUO/7hjN+n0KN7V+3hUplxpHzuNWxQn3VrlqdqXdok9d2rGa/0eb4lR6ZMmWjapDEHDx3hiP0xVeHpqdNnCAsLw8jIkBbNNbs9+pLve319fQb078OA/n14/caF27fvcP2GA2fPXcDfP4CFi/7Fw+M9s2dOS3RdWdQyT5L2HkyPd2D9enWpX68uXt7eODreweGWI2fPXuCtmxv7Dhzi0ZOnHD6wO8ldnCrPR9wMOm1S805TSs27Lanv3sTSKF+Dr+H750ul6VJzv2dORYbl9p27gZgKB/8smq81TPzWE3Gl5bMjuUxNTQkKCkr0ngvVcc+m5ptOqXo1O7Zt38Xt23eJiopSDWsSt2C0WrWqvHJ25qaDI9bW1ty7H9O1pLbvIiFS60vk6UFM66hlS/4mJCSEu/ce4Hj7DhcuXOL+g4fcuHmL7j36cvjQniT3MKKUls/9pD4b0iPtl1px842WLl6kcwzRr0VGxzct8tkSo7xOgpL5ns3ovOjU+hrztlJyb2d0XrSyTOBTaCgKhUJnPlBiZQYJSW2aLinivitCgkMwjx3WLT6FQqHal5RcD1HplAf2JfIovyQZ8zQDJHfA+JCQUE6dPpesZZRjQj1zep6s5VLr2bOY7dWsUU3nwN5pNSaSNspxNRMbSyz+mI5Kyu7W3N+9S7T11NdM2T1BSEjoFx8XJ60oz+WrV84JhlN2v5wtm7mqhrK5ublqzFBlP+vaeHp56awNpzyGrrFdGH5pZcuWUSWgbt26HSeT4PMYX8r/HRwccX/3TtXVh7Ya1spnQcvmzXSOv5DUe7NA/vy0a9uaP2ZP58rFM/Ts0Q2Ai5eu8OjxkyStIy0ULBhzjbi7v0swcfIy9hrR09OjQGw3sBlBGd+XSbymdY0TrLne/Kr/X73Sfb07JzAvMcrrp3HjBlozlwGeP3+RpHXpev4COMeZF3f/U3vsTIxjWhAldJ18+PgxwXULkRDn128Ij/0gb9WqhdYwERERqm7SUko5xrhyeIa09jn96JSs5dLr+ZZcypald+/dV3VBrOyyt17dOmTPnl1jmYx631sVtaRL544sXDCXa5fPqsY32rlrD97eiXeJmjVrVlVa59UrzaEo4lK+B+O+L9Jarpw5adG8KdOmTOLCuRP8/L8fAXByes7ZcxeSvB5V+s854WspofRdUqXm3VagwOdjmdB7LaF5X4uv4fvnS6XpUnO/m2fNikWOHAA8e5a8Z6RT7Ddyax3vB0h6Ojy1z47kUp6bhO65qKgo3rhof7+l5ptOSVkAGhQczKPHT7gZ29tO3G8e5f83HW5x//4DwsPD0dPTw65q5QS3K0RKfIk8vbgyZ85M7Vo1GPvjKA7s28nmjWsxNjIiKDiYTZu2JXt9afncd3Z+o3NedHQ0Lq6uQPql/VLD3NycbNnMgc9DV3zNMjq+aZHPlhjlt4iT0wutPc3oktF50an1NeZtpeTezui8aGVcwiMiErxHEkuTJCQt03S6xD2mCX1fv37jomoEoLyGlJTfNgl1Mf0hthvwtPYl8ii/JCk8/cIePX6iuvDn/jED5xePE/xT9iN/6EjyEme1asSMW3Dh4qV0H0Q6LmVz+4QSYIfi1NJLa5UqVQRiBqn28/PTGsbV9a3W8U4BateuCcTUjE1sHKWUUvYVHp2OmRPV7KpgZBSzneMnTqXbdtJTlSqVAHj/4QP3Ygelj0+hUHDy1BkAKleqpJqup6dHpYrlATh9VvdHyukEPmBq1aoBxAwCnh4ZEYkxNDRU7ZPDLUduael6qlxZW0xNTXn6zImzZ88DMQnaUlq67lHdm9Har7vQ0FBOnzmb7HgaGRkxetRw1e+0yMxMqqqVY45PZGQkZ2L3X5vjJ04DMWMpxR0TIm6//VFRCY8DmxjluqISGE+2Smx87917wIcP2gvqQkNDVc8e5T2QmOzZs6veFQmdw1MpOL9KyusnWsdxUigU2B89nqR1JXSuTp+JuSfz58unGu8UUn/slOMuu7i66hzz9/LlqzrjlZbXivg+xe1uSNf7/dTpswl+DCclfVC7Vkw65eUrZ5ySWGEhOWrWrA7Aw4ePk/U8T6/nW3LVqV1TVbhx5OhxvL19uHb9JqC9y17I+Pc9xGSKDhs6CFDPjEiInp4elStXBGKuLV0ZTLfv3FWNj6U8T+lNT0+PkSOGYhzb2tQ5GQWIyn3y8Hivs0JWQEAAN27eSm00U/Vuy5Eju2o803PnL+rcRkLzkiIp6YvU+hLfP4lJbZouqVJ7v9eI/cY+evxkknvcgMS/kd+//6AaqiM5Enp2qKddUv7dWTn2+/rixcs6u9a7eu26ztbiqfmmU8qTJzdFY8eUvHHDgdu376Kvr49d1SqqMMoCVkfHO9y46QDEjJmnrdKMEKnxpfL0ElKndk2q2sVc/86v1dNrSXlvpOVz/6bDLZ1ddV69dkPV6lyZV5dURqp0cfp+e9WqGfNeOHbiZLqs39Dwc88baVFBKb3jm5C0yGdLTM0aMd8i3t7eXLl6PcnLZXRedGp9TXlbSim5tzM6L7pypYqq1qYJ5X8p851SIr3SdHGVLFlc1XX1iZOndYZTHmN9fX0qxt6bSso8MF0VOaOiorh2/Uaq4qnLl8ij/JKk8PQLU9ZQMzIypHmzJomGV9ZkuHr1erI+8Hr17I6BgQEhIaH8NnWGzps6Ojo6WbV5ElMwtkXEHR2Fl/ZHj3Px4uU021587dq0Ql9fn/CICBYs/FdjvkKhYN78hTqXL1HcRvVRPXPWXDw9E66FkZKCohyxH3AfPdOvFk62bNno2CGmL/9//l2qqp2ky5cs8Eqqxo0akD17TGLgj3nztWZQbNq8TRX3Lp07qM3r2KEdEFMDbc/eAxrL+vr6sWyF7i5mO7ZvR7Zs5nz69InJU35PMIMkKioKF5fEMzqTS1lQeubseV68fEW2bOaULFlCNd/IyIhKlSoQFRXF6rUbAKhapZLWlqWFYmshndeRiTdn3gJ8ff20znv//kOC3bK4un5uAaU8Z19ChQrlKW5TDIBF/ywhQMvYLWfOnuPK1WsAdOncUW1etmzmqoRVYvd6YpT77ZlAza327dpgaGhIVFQUs+f8qTXMwr8Xq2ppxo9vQpTX+7nzF7UmgFxcXNmydXuS1xdfoYIxz/bLV69pzThbtXpdkmvXHbE/xv0HDzWm37//QDWGT8eO7dTmpfbYlS9XFgA/P3/V2IdxeXv7sGTZSp1xTstrRXyflPcIoLWVnZe3d4LpD0ha+qBB/bqqApuJk6YSFJRwl1bJKbQC6NCuLVmzZkWhUDDxt2kJFvbGTVum5/MtOQwNDWnZshkQ86w5evwEkZGRZMmSmSaNG2pd5ku979++dUtw3Wrv0iQWCnXp1AGIabG1dftOjflhYeHMmbsAAIscOWjcSPsxSAkfH1+dFRUhJu2gbI2d1P0BaNK4oapVw9w//9L6DbNg4T+JDjWQFKl9t3Vo1waIKby+feeuxvx3Hh5s2pz8FkFxJSV9kVpf4vsnMalN0yVVau/3vr17AjEtQ+b/9bfOZeMPBaD8Rtb2foiKimLKtBk6hw9I6bMjR5xCw4+pSLt0ik2T+fj6smzFao35YWFh/KXlu1sptd90Ssrvoq3bdxIUFETJEsXVMo7z5cuLZZHCePv4sHvvfkC67BXp40vl6SXW2lvZ61T8iiTKccYTuu/T8rkfGhrK3/8s0ZgeFhbOX4v+ASB//nzUjK18klTKig8+vr7pOryKcpiHhw8fs2r1ugTDBgUFqyqkJVWOHJ/PT3KX1Sa945uY1OazJaZ+vTpYxrY+nT7zD3x8fHWGjXtdZHRedGp9TXlbSim5tzM6LzpPntyqysarVq/XeiyOHjvB3Xv3U7yN1KTpksrQ0JD27WO+M/bu3a91XG1393esXReTD9ygfl1y58qlNl+ZB3boiL3Wcp/Va9fz7p1HquKZkPTOo/ySpPD0C4qOjlbVXq5dq1aSakG2iU1oRUZGcvRY0lr1QExXB2N/HAXAseMn6dlnAGfPnsfT0xM/Pz+ePHnK2vUbad6yfYoGi9elRbMm6Ovr4+vrx8DBI7h+4ybe3j68euXMwkX/Mm78RGyKWafZ9uIrVKggvXt1B2DHzt1MmDiFp8+c8PPz48HDR/zw40+cOHla1U2JNrNnTiNr1qy4vn1L63adWbt+Iy9evMTf3x9PT0/u3XvAug2b6NSlJ6N+/CnZcbS1LQ3AmTPnuHrtBoGBQURGRhIZGZmmBdm/jv+ZggUL4OfnT6cuPfl3yXIeP36Kn58fXt7ePH78lG3bd9Gn/2A6dOqeZttNK5kyZeLXCeMAuH37Ln37D+HmzVv4+vrh/PoNfy5YxB9zY/qXr1mjmsY4Zm1at6RcOVsApkybzuIly3F1fYuPjy/nzl+ke8++BAeHYG5urnX7WbOa8ces6UBMraSOnXtw8JA9bm7uBAQE4OHxnqvXbvDXon9p2KQl6zduTvNjYBf7wf/unQcKhQK7qlU0CkarxwkD6t36xtWyRUxm8vUbDvz8y0SePHmKr68f9+8/4Mex49i2fZfOe/PK1WvUrteYKdNmcvbsedzc3PH398fV9S179h7gf+MmAJArV84vnkkxdcok9PT0ePPGhe49+3H+wiW8vX1wc3Nn1ep1jPnfeABsilnTt09PtWVNTU0pFlsbavOW7Txzes6nT59U92Ny2NqWAWDfgUPcu/eAkJAQjfXky5eXEcMGAzEJthGjxnL//gP8/Px4+syJSb9NY/2GmOuoc6cOlCtrm+TtD+jXm/z586FQKBgxcgybNm/Dw+M9nl5eHD5ylJ69B5AjtjVWSrSIvX6cnV8zfOSP3Lv3AB8fX54+c2La9FnM/+vvJD/b8+XLy8DBw9m1Zx8fP3ry4cNHdu3ey8AhI4iKiiJXrpwMGzJQY5nUHLvixW1Uz4NfJ07h8JGjeHv78PGjJ0fsj9GlWy+MDHWPx5eW14r4tty9ez/Rv7CwcPLkyU2V2BZzM2bNZeu2nbi7v8PT0xP7o8fp0rU33t4+Gt3pxJWU9IG+vj7z583GyMiQ+w8e0rZ9Z3bs3M3rNy4EBATw4cNHHG7dZtnyVbRo1Z4/5mgfh0WXrFnNmD5tMhDTeqdD5x4csT+Gh8d7/P39efHiJdu276JD5+44xRkaIj2fb8mlbGHq5PScNbEVi5o0bqQag0fbPn+J9/2+A4eo26Apc+Yt4PLlq7zz8MDf3x/n12/YuGkrM2bNBaBUqRKqmrqJadmimeq9O3PWXOb/9TfOzq/x9fXj5s1b9B0wWJU5MGniL5iYGKco7to8f/GC2vWaMG78JI4dP8kbFxf8/f1xd3/HseMnGTg4pleKTJky0ahh/SSv19TUlJ/Gjgbg+vWbDBoyQpUR9vSZE79Omsq27btUXVinRmrfbQP69yVv3jxER0czeOgotm3fxfv3H/Dy9ubY8ZP06NU/WQXH2iQlfZEW0vv7JylSk6ZLqtTe79Wr29Gta2cA1q3fxLARo7l67QZe3t74+Phy//4DlixdQdsO6uNeK9Ph+/Yf5I+583n58hW+vn7cuOlA/0HDOHf+os5rLaXPDkvLIpiZxXR/u2r1Ot64uBAWFk5kZGSyWj9VqVyJZk1jCoiWLlvJzNlz1eLfp98gnJ4/J2/ePFqXT+03nZLyWfd5mBLNb55qsdOU30VSeCrS2pfM02vaoi0DBg1j5649PH3mhK+vHx8/enLjpgNDh49WVe6I33WkbZmY9KSj4x1OnDyNv7+/1vymtHruFyxYgI2bt/Lb1Bk8f/ESPz8/HG7dpv/AoTx8+BiIyZvSNXSQLmVj08VhYWEsWboCT09P1X7o6kkoJeyqVlG9U/5csIgRo8Zw6fIVPn70xN/fHxcXV06dPsvkKb9Tu15jHG/fSdb6y5Qupdr3f5cs552HBxEREcl+Fn+p+CYmtflsidHX12funJkYGBjw+vUb2nfqxq7de1Xv6ddvXNh/4BB9+g9WqxSd0XnRaeFrydtSSum9ndF50RPG/4SBgQHe3t5069mP02fOqY7jilVr+GXC5FR9R6QmTZccY34chUWOHIRHRNB3wBB27NzN+/cf8PTy4oj9Mbr17Iu/fwAmJiZM+vUXjeU7dYwpxH7w4BFjfxrP89jn+5MnT5k2fRYL/vqHIoULpzqeuqR3HuWXZJh4EJFWrt9wUHVl1qa17r6x4ype3IYSJYrz/PkLDh0+Sr++vZO8vR9GDSc4JITVa9bj6HgHR8e0fWlqY21txegfRrB4yXLuP3hI776D1OYXLWrJvLmz6NIt6fuRXJMnjuflK2euX7/J3n0H2LtPvTbUyBFD+fjhI2/d3DAw1LwFilpasn3LBkb8MAZ393fMmbtAVWs/PmXCNDl69ezO7j378PH1pW//wWrztm/dQI3qyauRp0uOHNnZvmUDw0f9yLNnz/l38TL+XbxMa1jluAlfm+5dO/PO/R1Llq3E4ZYjPfsM0AhToXw5li7+W2MgcAMDA5Yv/YdevQfy1s2NfxYv4584+29sZMTifxcy6495OsdjaNWyOYv+msfkKdN58vQZP//yq864GhnpLnhJqQrly2FiYqJqXVFNy1im8TMOtIWBmJpp9sdOcO3aDQ4eOsLBQ0fU5jdp3JDGjRsyafI0rcv7+wewfccutu/YpXW+WZYsLP7nLzJlypTofqWlOrVr8ses35k2fTZOTs8ZPHSkRhhLyyKsW7NC63hm/fv1YervM7l3/wGt2qjX3nN+8TjJ8ejftzcXL17GxcWVTl3VE7KXzp9SJc7GjvmB9x8+snffAU6dPsOp02c01tWoYX1mzZia5G1DzODwa1cto0//wfj6+jFj1hxmzJqjmm9mZsam9avp3K1XstarVLdOLTp36sC+/Qe5eOmKRvdOFSuUZ/QPIxgybFSi65ozazrjJkzSeq2ZmZmxavkSrR9bqT12s2f+Tu8+A/Hx9eV/P09Qm5c7dy42b1xLy9YddMY7ra4V8W1Jyj2jvMdnzphG9579CAoKYtr0WWphjIwMmT/vD3bu2qvK+I0vqemDKpUrsXb1Csb+9Asurm/5beoMnXErWrRoovGPr2OHdgQGBjJ7znyeP3/B2J/GJ2m59Hq+JVfVKpUpUCA/7955qI61ri57lb7U+/7jR0/WrtvI2nUbtc7Pkyc3/yzSnubURl9fn2VL/mbgkBE8evSYlavWsnLVWrUwenp6jP1xFJ1jW6mmpdDQUA4cPMyBg4e1zjc2MuLPubPIly9vstbbv19vnJ4/Z9fufVy+co3LV66pzW/dqgXW1lYsWboixXGH1L/bsmY1Y+XyxfQbMJSAgACm/j6Tqb/PVM03Nzdn47pVqnSBgYFBsuOY1PRFaqX3909SpDZNl1Spvd9nTp9CREQEBw4e5szZ81q711O2nlYaMWwIZ89dwMnpOevWb2Ld+k1q8/v26UmOHDlYvGS51nik5NlhaGhI717dWbV6HefOX1TrQrpgwQJcvqC7G7j45s+bxTsPDx49eszGTVvZuGmrap6enh6/T53E8ROndXbbnppvOqX43zjav4uqsie21amuMEKkxpfM01MoFFy6fJVLCQwt0r9vb43Wrx07tGPFqrUEBAQwavT/1ObNnzdb1WItrZ77nTq25/nzF+zYuZsdO3drzB82ZGCi6TBtKlQoT6WKFbh77z5Llq1U6yWoc8f2LJg/J4Glk2falEkYGhqyYeMWTp0+y6nTuruSNDZOXjowd+7ctGrZHPujx9m9Zx+79+xTzatezY4d2zZ+VfFNTFrksyWmRvVqLPl3IePGT8Ld/R2Tfvtda7h+fT5/p30NedGp9bXkbSml9N7O6LzosrZlmDdnJhMnT8PFxZXhI39Um1/M2ppxP4/ReD4mVWrTdEmVO1cu1q1ZzuCho/Dx9dX6zZ85synLlvxNMS2FtY0a1qdd29YcPnIU+6PHNYYiSat46pLeeZRfkhSefkHK7j2MjY1p0qRRkpdr06oFi56/4O69+7i4uGJpWSRJy+np6fHr+J9p2aIZW7bu4KbDLTw9vTAxMSFf3jxUqFCedm1ba3zcpdb/xvyATTFrNm7aytNnTujpxQx23LxZE4YOGZTuY7CamJiwaf1qtmzdwb79B3F+/QYTE2NKlSxJv769aNmimeohmSVLZq3rsLUtzekTR9i9dz+nTp/FyekF/v7+GBsbkS9fPsqULkWD+nWT1E1LfKVLlWT71o2sXrOee/cf4Ovrl24tlwoXLsThA3tiuq47doJHj5/g6+uLgYEheXLnplSpktStU4tWLZuny/bTwk//+5H69euycdNWbt++i7e3N6aZTSlZogRt27Sie7fOav37x1WwQAGOHN7L8hWrOXnyNB4e7zHPZk7VKpUZPmwwFcqXY9Yf8xLcfof2balVswZbtm7n0pWruLi8JTg4mMyZM1OkcCEqVixPk8YNqVO7Vprvu4mJMZUqlleN66WthnWFCuVVBaxZsmSmbGwLhfgMDQ1Zv2YFa9Zu4OAhe96+fYtpZlOKWVvTuVN7enTvyr79B7Uu26plc3Jkz87lq9e4dz9mPDsfHx9MjE2wtCxCnTq1GNCvj84a5+mtR/euVKlciXUbNnP9+k0+fPyIsbEx1tZFadm8Gf379dZZqNu7V3cyZzZl5669OD1/QVBQUIpqsjaoX5e1q5ezafNWHj9+in9AgNaapAYGBsyfN5uWLZqxc9ce7t67j7+/P1nNsmJrW5ounTvSpnVLnRlHCSlduhTH7Q+yeOkKzl+4iLeXNxYWFtSqVZ1RI4djHdvVZ0rNnzeb8uXLsmvXXl45v8bIyAjLIoVp07olAwf05c7de0laj5VVUQ7t383S5au4eOkyXp5e5LDIQd06tRn74yidLfNSe+zKlbXlwL6d/LtkOddvOBAYEECevHlo1LA+P4wcRu7cuROMd1pdK+L7VbpUSQ7u38XiJctjWo4GBJAzZ06qVKnEkMEDKF+uLDt37U1w+aSmD+rWqcWFsyfYtmMX5y9c4tUrZwIDg8hkYkKBggUoV9aWRg3r07hRgxTtS7++valTpzYbN23h6rUbvH//AQN9ffLmzUvp0iVp26YVJUoUV1smPZ9vyaGnp0eb1i1ZvWY9ENNdbd06ib+j0/t9P2hAX4rbFOPylWs8fvyEDx8/4ufnr6op3qhhffr17ZXsmvo5c1qwb/c2du3exxH7Yzx/8YLQkFBy5syJnV0VBvTrozEGTlqoXKkimzas4cqVa9y+cxeP9+/x9vLG0MiQggUKULNmdfr17Z2id4+enh5z/5hJzRrV2bJtB07PYlo5W1kXpWvnTvTu1Z1/0+hDP7Xvtgrly3H86AGWLlvJhQuX8fHxIWfOnNSuXZNRI4eqdaGlHLsoOZKavkgL6fn9k1SpSdMlR2rud2NjYxYumEvHDu3YtXsvd+7cw9vbmyxmWciXNy92dlXo0K6t2jJmZlnYtWMzy5at4sTJ07z/8J6sZlkpVbokvXp0o1XL5mqZz3Gl5tkxftz/yJM7N4eO2OP86jXBISEp6unI3NycPTu3snb9Rg4ftsf1rRuZM5tSrqwtgwcNoG6dWqpx2HRJzTcdxHzXFSpUEDc3d/T09LS2Ko37rVTM2ppcOXMme1+FSMiXzNM7dGA3l69c48YNB96+fctHTy+iIiPJnSc3lSpWoEf3LqrxL+PKkyc3e3ZuYdmK1dy6dRsvby8iIrSnJ9Piua+np8eyJX+r8t9ev3mDnp4+tmVKM3BAX5o1bZzk4xTf+rUrWLp8FRcuXMbN3T1NuuzXxsDAgKm/TaRTh/Zs37GLm7ccef/+PeHhEWQzN8fKuijV7KrSsnkzVU8xybHgzz8oVsyak6dO4+LyltBQ7WNEfy3xTUxa5LMlpkXzplSqWIENGzdz4dIV3N3diY5WkCdPborbFKN1qxbUq1tbbZmMzotOC19D3pZSau7tjM6L7typAzY2xVixcg2OjncIDgkhf/58tGjelJHDh/L4yZMUrzs1abrkqlChPKdP2rNuwybOnrvA27dviY5WUKBAfurVrc3QwQPJnz+fzuUXLphLpYoV2LN3P86v32BkZETpUjFlI2kZT13SO4/yS9GLigxLu35ChfhGtGnXmSdPnzHp118YGq97SCGEEEIIIYRIradPn9G6XUw3r443r2Bh8W10TyWEEEIkpG6Dpri7v2PMj6P435gfMjo6QgghRLqQMU/Ff847Dw+exY7TVSadup0SQgghhBBC/Lcpu0rNny+fFJwKIYQQQgghxDdECk/Fd+fTp086u/OIjo5m9h9/Eh0dTU4LC6rZVfnCsRNCCCGEEEJ8D/z8/HTOe/vWjbXrNwLQokXTLxMhIYQQQgghhBBpQsY8Fd8dd/d39Bs4lF49u1Ovbm0KFSxIZGQEj588Y83a9Vy/4QDAqJHDMDJK28HThRBCCCGEEP8Nq9du4MHDR3Tt3JFKFSuQPXs2vH18uXzlKkuXrcTfPwCzLFkYMmhARkdVCCGEEEIIIUQySOGp+C55eLxn4aJ/WbjoX63zu3XtTP9+vb9wrIQQQgghhBDfC4VCwbVrN7h27YbW+aampiz+dyH58+f7wjETQgghhBBCCJEaUngqvjsFCxZg7h8zuHDxMs9fvMTLy5uwT5/IYZGDihXK071bFxrUr5vR0RRCCCGEEEJ8w7p17UymTJm4eu067u7v8Pb2wcjIkAL581OnTi0GDexHwQIFMjqaQgghhBBCCCGSSS8qMkyR0ZEQQgghhBBCCCGEEEIIIYQQQoiMpp/RERBCCCGEEEIIIYQQQgghhBBCiK+BFJ4KIYQQQgghhBBCCCGEEEIIIQRSeCqEEEIIIYQQQgghhBBCCCGEEIAUnqaZAD9vAvy8MzoaQgghhBAimSQdJ4QQQgjx7ZK0nBBCCCHSmmFGR+B7ERkRntFR+Kr5eHoAYJE7fwbH5Osnxyp55HglXckyFQFwenIvQ+PxLZDrKnnkeIlvXWLpOLnG044cy7QjxzLtyLFMO3Is044cy7Qlx/P7lt55cnL9fPvkHH4f5Dx+OX/9uxqAX8YOS9P1yjn8PvxXzqMUngohhBBCCCGEEEIIIYQQQog0LzQV4lsk3fYKIYQQQgghhBBCCCGEEEIIIQTS8lQIIYQQQgghhBBCiP+U12/e8vCJE87OLrx67YqXtw8AixdMJ3funBrh/fwDuHv/MffuP+bVa1f8/AMwNjKiUMF81K5ZlSYN62BgYKBze9cd7nDy9EVc374DoEjhArRo2oAa1Sqlzw4KIYRIsYCAQADMzbNmcEyEyDhSeCqEEEIIIYQQQgghxH/I/kPHcbz7MMnht+48wNXrjujr62NlWYgSNlb4BwTy4uVrXrx6w41b95j480hMTIw1lt219wgH7U9hZGhIOdtSADx8/Ix/l6/nrVsLunZqnWb7JYQQIvV++HkaAFvW/p3BMREi40jhqRBCCCGEEEIIIYQQ/yHFbawoXKgA1lZFsLYqwuTf5+Mf29JIG7MsWejaqTWN6tUie3Zz1XSP9x+Zs2Apz5xecvDISbp3aau23DOnlxy0P0WWzKbMmPIzBQvkA8D93Xt+n72I/YdPUKF8GUrYWKXPjgohhEi2vHlyZXQUhMhwMuapEEIIIYQQQgghhBD/Ie1aN6Vb5zZUrVweixzZEw0/oE8XOrVroVZwCpA/Xx56dG0HwNUbjhrLHTl+FoAObZurCk4BChbIR/s2zWLCHDuT0t0QQgiRDv6a8xt/zfkto6MhRIaSwlMhhBBCCCGEEEIIIUSKWBYuCICvX4Da9PDwCB4+dgLQOrZpzeqVAXjw6CkRERHpHEshhBBCiKSTwlMhhBBCCCGEEEIIIUSKvP/oBUD2bFnVpnu8/0BERARZs5qRK6eFxnK5clpgZpaF8PAIPN57fpG4CiGEEEIkhYx5KoQQQgghhBBCCCGESJHjJ88DUKVSebXpnt6+AAl2C5wzR3aCgoLx8vahSOECCW5n2qyFWqd3btuQ/Hlz4ePpkYxYJ11kZEyr2PRav0h/cg6/D3Iev5yJM/4FYN7vY9N0vXIOvw/f4nm0yJ0/2ctI4akQQgghhBBCCCGEECLZTpy+wJNnLzDLkpkOsWOYKoV9CgMgk4mxzuUzZTIBIPTTp/SLpBBCiGQJDZVnshBSeCqEEEIIIYQQQgghhEiWB4+esXXnAfT09BgxpA/Zs5un6/ZmTh2ndbqy5UtKWpUkRXqvX6Q/OYffBzmPX87G1TEt/Q0MDNJ0vXIOvw//lfMohadCCCGEEEIIIYQQQogke+n8hr+XriUqKpqhA3tSpVI5jTAmsa1KP4WF61zPp9jWqaaZMqVPRIUQQiRbWheaCvEt0s/oCAghhBBCCCGEEEIIIb4Nb93e8efCFXz6FEbv7h1oVL+W1nC5c+YAwMfXT+e6vGPn5cppkdbRFEIIIYRIMSk8FUIIIYQQQgghhBBCJOr9B0/mLFhGUHAIndq3oE3LxjrD5s+XFyMjIwIDg/Dy9tGY7+XtQ1BQMMbGRuTPlzs9oy2EECIZZsz5hxlz/snoaAiRoaTwVAghhBBCCCGEEEIIkSBvb1/+mL8EP/8AWjVvSNeOrRMMb2xsRDnbkgDccLirMf/6zTsAlC9bGiMjo7SPsBBCiBR56ezCS2eXjI6GEBlKCk/FN2/8hMlYF7dNUth/Fi/Durgtbm7u6Ryr1LMubsv4CZMzOhpf1Ld0foQQQgghhBBCiP+KgIBA5ixYipe3L40b1KZvz05JWq5tbMvUg/ancH/3XjXd/d17DtmfignTqknaR/g/QpmPEhAQkGhY6+K2/LN42ReI1dcrOcfAurgtM2fPTecYfX/qNmj6n8vP/B7NnTGBuTMmZHQ0hMhQhhkdARHjp93rMzoKKn93G5TRUdBw46YDN27eol+fXlhY5Mjo6AghhBBCCCGEEEJ8s+7ce8SBwydVv4OCQwBYuGQtRoYx2YWVKtjSqX0LANZs3Mm79x8xMjQkPCKCFWu2aF1v7x4dMc9qpvpdqqQN7ds05ZD9aSb9Pl/VEvXhYyciIiLo1K4FJWys0mUfRept274LExNjunTumNFRSRd3797n4uUrDBrQF3Nz84yOTpJ5enqybcdumjVpRJkypTM6OuI7VKhg/oyOghAZTgpPxTfhxs1bLF6ynLatW6aq8HT0qOGMHD4EY2PjNIydEEIIIYQQQgjxfbn71pmtNy9SOl8hBtZqjIG+dF72PQkMDOKl8xuN6S6ubqr/C+TPo/o/OLZwNSIykstXHXSut0uHVhCn8BSgR5d2WBYuyInTF3ny7AUAVpaFaNmsITWqVUrNbohkeProDgYGBslaZvuOXZibm383hafxj8Hde/dZvGQ5XTp1+MYKT71YvGQ5hQoWkMJTIYRIJ1J4Kv5TDA0NMTSUy14IIYQQQgghhNDFxfsj7ZfPJSjsEwAG+voMrNU4g2Ml0lL9ujWoX7dGksNPmzQ2VdurWb0KNatXSdU6ROqYmJhkdBQACAkJIXPmzBmy7a/lGAiRUvcfPGTqtJkMGTyAdm0THnc6NVav3w7AsEG90m0bQnztpNqgSFN79x3AurgtV6/dYNmK1dSp34RStpXoM3Akd+4+0AgfGBjE7Dl/Uqd+E0qWqUDteo2ZOXsuAYGBqjDjJ0xm8ZLlADRt0Rbr4rZYF7dl774Dauvy9/dnwsQpVKpaE9vyVRkybBTvPDzUwmgbU1M5ZmpSlgd4+OgxPXsPoHTZythVr8u06bNwev4iWeMmuL97x8TJ06hVpxGlylSkZp2G/Dh2HC4urhphHRwc6dy1F6XLVqZG7Qb8u2Q5CoVCLcwR+2MMHjqKWnUaUbJMBWrUbsCEiVPw9PJSC+fm5q6K54mTp2nVtiOlylSkXsNmbN22U2PbkZGR/P3PEmrVaUTpspVp36kbly5f0TnO7PMXLxk95meqVqtDqTIVadysNatWryM6Oloj7JZtO2jYpCWlbCvRvGU7Dhw8nKRjJ4QQQgghhBAifc0+tldVcAqw49aVDIyNECIx/v4BjBs/ifKVqlO+UnXG//oboaGhamHi51sFBQUzc/Zc6jZoSqkyFbGrXpe+/Yfw6PETIGbsyqfPnLjpcEuVF9ez9wDV8i4urowa/T8qVqlJmXJV6NqjDzduqrdKVuYT3nK8zW9TplOlWm1atO7Anr0x0x8/fqqxL38t+pdStpXw9/fXuq+PHj/Burgt5y9cUk1zcHDEurgtvfsOVAvbvlM3Rv7wueA/7jH4Z/EyZs/5E4B6DZup9jFuniHA8ROnaN6yHaXKVKR5y3ZcvHRZa7ziCg8P5+9/ltCuQ1fKV6qObfmqdOvZl+s3bia6LMCDh4/oP3AoVarVpnTZytRr2IwJE6cAMUObtWnfBYAJE6dozSe9fecuffsPoXzFatiWr0qf/oN5+Oix2jbGT5hMhco1eOPiQt/+Q7AtX5XadRuzavW6JMVRoVCwdNlKatVpRJlyVejVZwDPX7zUGjaxa8XX149iJcqyZdsO1TT3d++wLm5L/YbN1db1628zadehq+p33QZNGT7yR27evEX7Tt0oZVuJ+g2bs//AoSTtx7fm8JGjPHr8hE2bt6Xrdi5fu8Xla7fSdRtCfO2kCZ5IFwsW/g3AwAF9CQsLZ82adfz861Su1Kip6gYjPDycvgMG8+DBIzp3bE/5CuV49Cjm4e/oeIc9u7ZhYmJMz57dCAwK5tTpM0ycMI48eXIDULlSRbVt9h80jEIFCzLup7E4v37Nlq07GPfLJHZs25ikOCdleWfn1/TuMxADQwOGDh6AhYUFx06cTNZA6K/fuNC1W2+CgoPp3q0zJUuUwNfXl0uXr/L8xUuqVPjc3cbjp0+58ONPdO/ehQ4d2nL06An+XbyMggXyq3WZsnXbTnLmtKBv355kz5aNx0+esXfvfu7evY/94X2YmKh3U3z+wkV27d5Lrx7dCHx5nTOOr5g2fRZ6rtcoa5VXFW75oZucdnxF/Xp1aNigHu/eeTB6zDgKFiygsV93796n74DB5MuXj0GD+pPN3JwbNx34c8EiXFxdmTN7hirsmrUbmPvnX1SsUJ5+fXrh5+fHzNlzKVSwYJKPoxBCiIz1+s1bHj5xwtnZhVevXfHy9gFg8YLp5M6dUyO8n38Ad+8/5t79x7x67YqffwDGRkYUKpiP2jWr0qRhnQS7EbvucIeTpy/i+vYdAEUKF6BF0wbS1ZsQQgiRxu69fc2+u9fVpt1xfYV/aDDZTLNkUKyESHthYWG4uL5NNJy/jycAXn7B6RofyyKFU9wyctTo/1GkSGEm/PITj588YdfufeTMacHECeN0LjNl2gzOnb9Iv769KFK4MD4+PtxyvMPLl68oa1uGqb9NZObsuZhmMuWHUcMAyJkzJp3v6eVF1x59CA+PoH+/3mQ1M2P3nn30HziUTRvWUKN6NfVtTZ1B7jy5GTvmByIjImnRvCm/z5jN4SNHsbX9nA+mUCg4fOQoDerXI1u2bFrjXbpUScyyZOGW420aNqgHwC3H2+jr63Pv/kMiIiIwMjIiODiYJ0+e6Wyd17xZE1xd33Lw0BGmTP5VNUxY3OHCHBwcOX7iFH169SBz5sxs2ryNUaN/4srFM+TIkV3nsQ0KCmbXnn20bdOK7t26EBwczO69+xkwaBgH9u5MsKtdL29v+g8cSuFChfhh5HAyZzblrZs7p06dBaBYMWvG/TyWhYv+pWf3rtjZxbToVuaTXrl6ncFDR1CxQgX+N3Y00Ypodu7aS49e/Tm4byfFi9uothUZGcnAQcOpWrUKv074mfPnL/LngkUADB82WGccAf7+ZwlLl6+iUcP61KtXh8ePn9J/4FAiwiPUwiXlWsmRIzvFrK24des2fXv3BODWrTvo6+vz1s2NDx8+kjdvTDfi9+4/pG0b9XP6+vUbRo8dR49unencqQN79uxn/K+/UbasLSXi7O/3IDw8HICAgMBEQqbOmJED0nX9QnwLpPBUpAuFQsG+3dtVXeTmy5WDXyb9zuEjx+jTuwcAu/fs58GDR4z7aQw/jBquWra4TTHmzFvA9h27GDigL5UrVaRUqRKcOn2Gxo0aUKyYtdZtVqlciam/TVSbtnHTVl6+fIWNTbFE45yU5Rf+vZjgkBCOHNyjSuj06d2DXvFqtiVk2u+z8A8IYO+urVSoUF41/YdRw1EoFPh6vVdNe/78JQf376KsbRkAunXpTJ36Tdi2fZda4enG9aswNTVV206liuUZ/+tvnDx1WiOh+OqVM2dOHiVfvrzc3+lD7bJFGPrXIU44vFAVnrp88OO04yuaN2vC8qX/oKenB0CtWjUYMGi42voUCgWTpvyOVdGi7N29XVVY27tXd/Lnz8fadRsZ0L8vJYrb4O/vz9//LqVcOVt2bt+kGn+2RfOmtI1Tc0wIIcTXbf+h4zjefZjk8Ft3HuDqdUf09fWxsixECRsr/AMCefHyNS9eveHGrXtM/HmkRoUfgF17j3DQ/hRGhoaUsy0FwMPHz/h3+XreurWga6f0665ICCGE+C9RKBRMt9fslShaoeDyi6e0KV81A2IlRPpwcX1Li1btMzoaKieOHUpxQU/5cmX5Y/Z01W9fX3/27N2fYOHp+QuXGD1qOMOGDlJNGxEnu6dZ08b8u3gZ5ubmdGjfVm3ZlavW4uXlzb7d26lUqQIA3bp2ommLtsyZu4DDB/eohbewsGDzhjXoxxk7uUnjhtgfPc7EX8ep8pxu37mLm5s7kyeO1xlvAwMDKlWqwC3H26pptxxv07xZE46fOMXjx0+pWLE8d+7eJyoqCruq2ruLLl2qJGVty3Dw0BGaNW1MoUKaFfpfvnLm9IkjFC5cCIAaNarRum0njtgfpV/f3jrjmC2bOZfOn1LleQH06N6FJs3bsGnLdv6cO0vnsnfu3MPfP4AzJ1eRM6eFavr4cf8DIHeuXDSsX5eFi/6lUqUKaucmOjqaqb/PpG6d2qxdvVw1vVvXzjRt3oYly1ay+J+/VNNDQ0Np0qQRv02aAECfXj3oN3AoS5evpE/vHmTJor3CjLe3D6vXrKdpk0asXL5Ydf7+WvQvy1esVgub1GvFrmoVtdbEtxxvU7dOLW7fvsstx9u0ad0S17duePv4UrVqZbVtvHzlzJ5dW6lSOaZibeuWLahdrzF79x1I8Fr6FkVHxfTuFxiUvoWndlUqpOv6hfgWSLe9Il306tldbWzRypViCgldXD93S3v23HlMTEwYNLCf2rJ9+/TCLEsWzpw9l6xt9uuj3gd79Wp2MdvU0hVuSpaPioriwsXL1KhRTa2GmKGhIQP69UnSNnx9/bh2/QYtWzRTKzhVUiY2lCpXrqgqOAUwMTGmUsXyascRUBWcRkdHExAYiI+PL9XsYj5qHzx8pLGdZk2bkC/f5xam2c1MKZTLHA+fzy9eR6eYbkoGDuinFq96detQPF5h9DOn5zx//oL27doQHByMj4+v6q9+vboAXL0aU3P58pVrfPr0if59e6slIkuXLkWd2jU14iqEEOLrVNzGio5tmzNuzFCW/T2LbOZZEwxvliULXTu1ZtmiWcz+fTxjRg1k6sQx/Dl7Erly5uCZ00sOHjmpsdwzp5cctD9FlsymzJ35K+N/Gs74n4Yzd+avZMlsyv7DJ3j+8nV67aYQQgjxn3L22UMuvXiidd6F55rflkKIr0Ovnt3VftvZVcbX14/AwCCdy5ibZ+XGzVv4+vole3sXLl6mSuWKqsKwmPWZ07lTBx49foKnp6da+B7du6gVnAJ06tgej/fvuenwuWvQQ4ftMTc3p2GD+gluv2qVyjx88IiwsDCio6O5c/c+LVs0w8qqKI637wDg6HibLFkyU6Z0qWTvn1K9urVVBacQ2+rVzAzXt24JLmdgYKDK84qOjsbPz4/IyCjKlS3L48fan7FK5rHfVadOn9E6DFZCnj51wsXFlbZtWqnlzUVGRGJXtYpGt8qAqqUngL6+Pr17dic4OER1HLW5eu064RER9OvbWy3PcGD/vhphk3qtVK1amfcfPvA29tjevn2H6tXsqFSpgioud+/HVN61i1d4WqpUCVXBKUDOnBZYWxfF1TXh8/QtioyKAiAoSPe9LYRIG9LyVKSL+N2vKl/8fn6fxyt46+ZOoYIFNVpMmpgYU7hwId7GG2Mg0W3GqyGWLVtM98B+OsZISO7yPj4+hIaGYm1VVGNZq6KWSdqGi4srCoWCUqVKJi1OWrqxzWZurnYcAe7ff8BfixbjePsOYWFhavO0deNQSEu3u1lMjfGM0wXNx9j/ra2LaoS1sirKi5evVL+dnWMyrefMW8CceQu07ou3T0x3jm/dYhIuxaytNMIUK2bNpctXtS4vhBDi69KuddNkhR/Qp4vW6fnz5aFH13YsXbmJqzcc6d5FvVb7keMx3VN1aNucggXyqaYXLJCP9m2asX33IY4cO8O4MUOTuQdCCCGEiCsqOpoZWlqdKknhqfjeWBYpzIljiY+LqOy2N5tF7nSPT0oVKJBf7Xe22CGzAgICyJrVTOsyEyeM45cJk6leqz4VypelQf16dGjfVutQTfG5u79TK6xSUub1uLt7kDv35+MVtwBSqW6dWuTKlZPDR45So3o1IiIiOHb8JC1bNNXaG01cdnZVCI+I4P79h2TJkoWgoCDsqlahapXK3HK8zZDBA7jleJsKFcqrNe5IrvjHFWLyC/39AxJddt/+g6xdvwlnZ2ciIiJV0wsX0jwWcVWvZkeL5k35beoMFiz8h5o1qtOkcUNat2qh1ghBmzcuLgD8NO5XrfPjF2AbGhpq5IcWjc3jdHN7p3M77u/exYYtojY9Z04LVX6qKmwSrxVlC+FbjrfJmtWMFy9fYWdXhYiICE6cPA3AvXuPKFSwgNq1BVAgv5bzZJ6NgICk5Ql/S6JjC09DQkKJjIxM1fWdkMPHzgDQrlWTdFm/EN8CKTwV6cLAQEejZoUiHbepfZw0RRK3mdrl04OBfuKNw93d39Gr7yBy5MjOuJ/GULRoETJlMiU6OooBg4aj0FJLTV/XvqYwnsqacKNHDadabIvd+AoVSjzxLYQQ4r/JsnDMB7uvn3omRHh4BA8fOwFoHdu0ZvXKbN99iAePnqrGNhJCCCFEyuy+fZXHHp/HfzQ1MsbU2Bif4JjWLc5eH3D18aRIOhcgCfGlmJiYJKmbXB/PmK5LLXJrFtB8LXTlwyWUp9W6VQvsqlbh1OmzXLl6jRWr1rB85RpWLPuHenXrpGn8MmkZy9XAwIB2bVuzb/9Bpk/7jctXruHr60eHdm21rEFdxQrlMTIy5JbjbczMzChSuDB58+bBrmpl5v25kPDwcO7df5jouJ2JiV/YqJJIXuHBQ0cY/+tvNGvamGFDBpIzpwX6+gasWLUG10TG2dXT02P50n+4d+8BZ8+d59KVq4wbP4k16zawZ+dWnV3pwuf8uSmTf6VEieIJ79xXplChguTLm5dbjrfJZm6OsbEx5craEhERwb9LlhMQGMjd+w+pVKGcxrK683TTO9ZfXlScfN6goCCyZ8+eLtvZs/8oIIWn4r9NCk9FhilcqCA3HRwJDQ1Va30aFhbO27dulCtfVjUtfne2GcHCwgJTU1OcX7/RmPf6jUuS1mFpWQQ9PT2ePXNKs3idOn2W0NBQ1q9ZQfXqnwsutcUzOfJkj0mMOTu/IVfOnGrzXsdbt7JWmrGxcaJd7ypr2L1yfq3RdfGrV86pibIQQohv1PuPXgBkz6be9a/H+w9ERESQNasZueKM96OUK6cFZmZZCAoKxuO9J0UKS0UdIYQQIiVCw8OZc3yf2rRR9Vvw1teb3bc/9w504flj+tVo8IVjJ4RIL3ny5KZP7x706d0DHx9f2nbowtLlq1SFp7ry4woWLMDr15pDZyjzogoWTFpBc8f27Vi/YTMXL13hiP0xChTIT7VqiY+tnClTJmzLlFEVniq7ca1atTI+vr4cPGTPp0+fqFqlcoLrSa/8xuMnTlGkcGFWLPtXbRv/Ll6a5HVUrFieihXLM+7nsRw9doIfx47D/tgJunftrDPeRWJbL5ubZ03S0FiRkZG4ubmrlgN4E5vHmVAL5IIFCsSGdVX9DzFjocZvlZuca6Vq1crccrxDNnNzKlYoj7GxMRUrlMfAQJ8TJ07j5v6OAf16aqzrvyQy8nMr5qCg4HQrPO3aqXW6rFeIb4kUnooM06RxIy5eusLGzVsZOfxzV3tbt+8gKDiYpo0bqaZlyZwZAP+AxLvFSC8GBgbUr1eHk6fO8OTJU9W4p5GRkWzavC1J68iRIzu1atbg+IlTPHj4iPLlyqrNT0krV2XtwmiFegvTNWvXJ3tdcVUtUZCtp++zYeNm7KpWViXMLl2+otZlL0BZ2zLYFLNm0+ZtdOvamTx51GsiBwUFY2hoQKZMmahbpxYmJiZs2rKNNq1bqrocefr0GVdix0UVQgjx33L85HkAqlRSr1Tj6e0LgEWO7DqXzZkjO0FBwXh5+yRaeDpt1kKt0zu3bUj+vLnw8fTQOj8yMgJA53yRdHIs044cy7QjxzLtyLFMO1/6WK6+cQF3P2/V7xymWehTvipnnj9md5xwpx7eok2xpA1D8zVJ7fH8mlscCpESUVFRBIeEYJ71c+VFC4sc5M+XT204KNPMpgRoyYtrUL8uGzZu4f79B6qK8QGBgezbf5CytmU0ulXVxda2NCVKFGfHzj3cdLhFv769klygaVe1Cjt27sY0syk//zQGgKKWluTOnYtVa9ZhaGhIpYrlE1xH5swxjTli9lFz6KyUUrZYVSgUqv25d+8Bd+7e19oVcFz+/v6Ym5urHYfSseO2Ks+NqSre6kN1lbUtQ5HChVm7biOtW7XQGCrN29uHnPEqpW7ZtoPfJk0AYlqubtuxi8yZTTXGFY2rdq2aGBkZsnnLNmrXqqGavmHTFo2wyblWqlatjP3R45xWKGjdsjkQU1Be1taWVWvWAWhtefpfEncc3MBAzaHa0oq0OBVCCk9FBurapSN79u1nwV//4Oz8hgrly/H4yRN279lPWdsy9OzRTRW2QuyLceHfi+nUoZ2q5pG2MRPS08//+5FLl6/Qu98g+vXpRY4cOTh+4hShnz4BSauxNuP33+jaow/de/aje7fOlCheHP8Afy5fvsrAAf2oUqF0suJUr14dTExM+GX8ZPr17YWpaSbOnb+Ij49vivZRyTJfdhpXtubkqTMMGjKChg3r8+6dB9t37KZUqRI8e/ZcFVZPT48F8+fQt/8QmrdqR7cunbGyssTfP4CXL19x4tRpDu7bRbFi1mTLlo2xP45i/l9/06NXf9q2aYWfvz+bt2yjVMkSPHn6LFXxFkII8W05cfoCT569wCxLZjq0aaY2L+xTTOZApgTGPMqUKaYLMOW7WAghhEhvTh89cHj7mrpWJShqkSujo5NqfqEhrLx+Xm3aj3WakNUkE7WKqndpev3NS6IV0ejrJT7EjBDi6xUcHEytuo1o0bwppUqVxCxLFq5dv8mdu/eYPGm8Klw5W1s2bdnG0mUrsbQsQs6cFtSqWYMRw4dwxP4YAwYPp3+/PpiZZWHPnv34+Pjyz6L5yYpLx/Zt+XPBIgA6tE+8y16lqlUrs2bdBoKCg1XjZQJUrVKZ4ydOUb58WTLHNsbQpaytLQB/LVpM2zYtMTQ0pHGjBokul5hGDRtw8tQZRowaQ8MG9Xjr5s72HbspblOM4JCQBJfdt/8QW7fvpFmTRhQpUoSQ0BB2796HmZkZDevXA6BQwYJkz56N7Tt3k8UsC5lNTVX5pH/Mns7goSNp2bojnTq2I0+e3Hi8/8CVK9ewtCzCor/mqbZlamrKmTPn8PcPoGzZMpw/f5Fr124w/pf/Jdg9cM6cFgwZPJAVK9cwZNgo6tevy+PHT7l46TIWOXKohU3OtWIX21L49es3Gud0zboNWOTIjmWRL5sX/LWJjIxS/R8YGJSBMRHi+yeFpyLDGBsbs2XjOv5dsozjJ05x+Ig9uXLmon/f3vzvf6PVBoe3q1qFcT+PZeeuPUycPI2oqCjmz5v9xQtPbWyKsW3LBubO+4vVazdgliULLVs2o2P7dnTu1kvrGA7xWVtbcXDfLhYvWc7xE6fYsXM3OS1yUrVqZUqmYDyCopaWrF29nL8W/sPipSswzZSJhg3qsXDBPOxq1E3JbqqMaFeNUtWbsHf/Qa7fcKBkyeIsW7yIHbv28OaNq1rYCuXLceTgHpYuX8UR+2N4+3iTLVs2iha1ZPSoEeTPn+/zeocPIXNmUzZs3MKf8xdSpEhhpk2ZhIvrWyk8FUKI/5AHj56xdecB9PT0GDGkD9mzm6fr9mZOHad1urIViq5WJYnNF0knxzLtyLFMO3Is085/4VjecXWmy+ZlhEaEk8XYhGsT5lE4HQpQv+Sx/PvwdgI+hap+W+XMw6imHTA2NMQid35K5i2I0wd3AHxDQ3ALC6diYat0j1da+i9cm0IkR6ZMmejTqweXr1zj1KmzRCuisSxShJnTp9Kndw9VuFGjhvHWzY3Va9YTFBxM9Wp21KpZg9y5crFn51bmzV/Iho1bCA8Pp2zZMmzasJoa1aslKy7t27VhwcJ/KFmieLLyxapWieklzcLCAmuroqrpdlWrcPzEqUS77IWYlq+/jPsfW7fu4NLlK0RHR3Pp/KlUF5526dwBTy8vduzczaXLVyluU4xFf83j+PGT3HC4leCy1avZcf/BQ+yPncDLy5usWbNSoXxZFv41T5UPamhoyF/z5zJ/wSKmTptJZGSkKp+0dq0a7N29jSVLV7Bx81ZCQkLJmycPlStXpFfPbmrbMjQ0ZMP6VUydNgv7o8fJls2c8b/8T62HQF3G/TQGE2Njtu/czbXrN6lYoRybNqxh8NCRauGSc62ULFmCrFmzEhISQqVKFVXT7exiCk8rlFfvwe+/KDo6TuFpUPoVnt66fR8AuyoV0m0bQnzt9KIiw77DoZO/PEmIJ+x7Pz4nTp5m1Oj/8c+i+bRrm7o+4b/0sbq/848E51fo8ZvGtJZtOhIeHs7ZU0fTK1pJ9r1fW2mpZJmKADg9uZeh8fgWyHWVPHK8BMCIMZPxDwhk8YLp5M6dM9HwL53f8Mf8pXz6FMbQgT1pVL+WRhjHuw9Z+O9qLIsUYt7MX7WuZ+LUebi8dWf8/4ZTuWLKPqal8PTLkWOZdr6HY+nq+paRP4ylenU7pk2ZlGHx+B6O5dfiez+WCoWC5otn4ujyUjVtQrOOTGzRKc239aWOpauPJ9XmTiA86vMYauv6jaZjxeqq35MObGXV5ZOq39Nad+N/jZPeOuxr8L1fm/916X1+5fpJX17e3tSs3ZAJv/zE0CED02Ubcg41jZ8wmVNnznH/zo2MjkqSyXmEwUNHcf7CRQAW/fUnHdq3SZft9B3yEwBb1v6dpuuVc/h9+K+cR+lnRYhk+hSvW8DIyEg2bNyCoaFhsmvXfe3CIiI1pl26fAUnp+dJGnheCCGE0Oat2zv+XLiCT5/C6N29g9aCU4DcOWO6fPLx9dO5Lu/Yebnijd0jhPj67TtwiKfPnNi4aat0Oya+CYfuO6gVnAKcf/4wg2KTNuYc36dWcFq5sDUdKqh/1zYsqV456cLzR18kbkKI/4Y9e/cDpLoxghD/BVFx3tmBQek35mndWnbUrWWXbusX4lsg3fYKkUyNm7WmaZNGFLexISAwgGPHTvL4yVOGDh5Injy5E1/BN+SEwwsWHe9P3bq1yZE9O8+cnNi9ex/Zs2dj2NBBGR09IYQQ36D3HzyZs2AZQcEhdGrfgjYtG+sMmz9fXoyMjAgMDMLL20ejgNTL24egoGCMjY3In+/7egcL8V/wzv2d6v+AgACyZjXLwNgIkbCwyAhmHt2tMf22yyv8Q4PJZqp7bLiv1QO3N+y+fVVt2vS2PdDT01ObVqtYKYwMDIiIiukq8Ibzc0LCw8hsnPiwNUIIocu16zd48eIVK1aupWWLZuTNmyejoyTEVy8qKlr1f3pWPhw2qFe6rVuIb4UUngqRTE2bNOLS5avs2XuAqKgorKyK8vvUyfTr+/29VIoXzImTfyAbNmwmIDCQbObmNG3amJ/+9yMFCxTI6OgJIYT4xnh7+/LH/CX4+QfQqnlDunZMuHa5sbER5WxLcufeI2443NUoaL1+8w4A5cuWxsjIKN3iLYRIH+/ev1f9HxAYSMEMjIsQiVl39QxvvD9qTI9WKLj04glty397rTOm2+9S+92sTEXq2JTWCGdmkgk7SxuuOTsBEB4VyXVnJxqXKv9F4imE+D4tWbqSO3fvUrVKZaZM1j5EhxBCXdwxT4PSccxTIYQUngqRbNOnaY4B+r0qUzQPPSf+m9HREEII8R0ICAhkzoKleHn70rhBbfr2TNr4cG1bNubOvUcctD9FpQq2FCyQDwD3d+85ZH8qJkyrJukWbyFE+nn3zkP1v3TbK75mvsFB/HXqkM75550efXOFp5dfPFHrfldfT49prbvpDN+gZFlV4SnAxeePpfBUCJEqO7ZtzOgo/KctmD+HBRkdCZFskZGfC08DA9Ov214395h0eqGC3/eYlkIkRApPhRBCCCFEst2594gDh0+qfgcFhwCwcMlajAxjkpiVKtjSqX0LANZs3Mm79x8xMjQkPCKCFWu2aF1v7x4dMY/TdWepkja0b9OUQ/anmfT7fMrZlgTg4WMnIiIi6NSuBSVsrNJlH4UQ6UehUODh8bnlaXpm/giRWgvPHMYvNFj1O0dmM3xDPhf4n3N6gEKh0Oju9mt24sldtd897epSJn9hneEblCjLnOP7VL9l3FMhhBDiy4vb8jQ9Kx9O+n0+AFvW/p1u2xDiayeFp0IIIYQQItkCA4N46fxGY7qLq5vq/wL5P49bFBxbuBoRGcnlqw4619ulQyuIN+5hjy7tsCxckBOnL/Lk2QsArCwL0bJZQ2pUq5Sa3RBCZBBvHx/Cw8NVv6XlqfhavfH+yJorp9Smze/Uj9/td/LOzwcAVx8vXnt9xDp33oyIYop8CPBT+921Sq0Ew1csZIV5pswEfIp5nz9658rHQH/yZM2WXlEUQgghRDxxxzxNz257bawt023dQnwrvrnC07MXrvLoiROub9/hHxDIp0+fyJIlC8WsitCkYR0qVyyrsUzPAT8muM6ZU36muLRYEEIIIYRIsvp1a1C/bo0kh582aWyqtlezehVqVq+SqnUIIb4ecbvsBQgMkpan4us08+huIqI+t/KoXMSaTpVqcOH5I7Y5XFJNP//84TdVeOod757LZWaeYHhDAwPqFS+D/UNH1bRLzx/TJZFCVyGEEEKkncioL9Py9PfJ/0u3dQvxrfjmCk+PnTjHB08vChcqQEkba4xNjPjo6c3d+4+5e/8xrVs0ok+PjhrLmZgYU71qRa3rNDc30zpdCCGEEEIIIUTa0yg8lZan34Wbrq9w8fWmZ+0mZDPNktHRSbVbb15y8N5NtWmz2vVCT0+PhiXLqReeOj1kcO1vZwxur6AAtd+JFZ4CNChhq1Z4ev75Iyk8FUIIIb6g6LiFp1L5UIh09c0Vng4f0ocihQqQKZOJ2vRnz18xb+Fyjp44Rw27StgUK6o2P6uZGSOH9v2CMRVCCCGEEEIIoU3c8U4hfbsdE1/GlhsXGLt7HQBb797k/M8zMTL45rIcVBQKBdOO7FCb1qZcVWpax4y9Xb+4LXp6eigUCgAuvXhCRFTkN7PPPsHq95xF5sQrlTcoUU7t94Xnj765sV6FEEKIb1nclqdBQcEJhEydqNjtGBgYpNs2hPja6Wd0BJKrhI2VRsEpQKkSxahZrTIAj544feloCSGEEEIIIYRIonce8VueSs35b5l3UCBTD38uaHzi8ZYrL59mYIxSz/6hIzdfP1f9NtQ3YFrrbqrfOc2yUrFQUdXvoLBP3HZ59SWjmGIKhQKv4M8tT3NkNsMwCZmjVrnyUMQil+q3h78vzz++S5c4CiGEEEKTWsvTdEw/Dxj+CwOG/5Ju6xfiW/DNFZ4mRFkTwtDw26jpKYQQQgghhBD/RdJt7/dlwakDBHwKUZt29tnDDIpN6oVHRjLDfpfatEG1GmGTJ7/atIYl1VtinnX6NvY58FOo2jiuucyyJmk5PT09GpQoqzbtgtOjNI2bEEIIIXSLio5W/R8UFKzqASOtZclsSpbMpumybiG+Fd9N4ekbFzduONxBX1+fCuXLaMwPCw/n4JGTrN24k03b9nL2wlX8/AO0rEkIIYQQQgjxLXK9f5mDM3px58i6jI6KSET8bnul8PTb9fzDO9ZdO6sx/ZzTgwyITdrYcO0szl4fVL+zZjJlfLOOGuEaxitIPP+NFJ56xRsjLWeWpBWeApqFp8+l8FSIjHbv3gM6delJmXJVsC5ui5ubO+MnTKZug6YZHbU088/iZVgXt1WbZl3cln8WL0uzbXz86MmIUWOoVLUm1sVt2bvvgNZwbm7uCc4XIj1FRX6u/BQVFUVoaGi6bGfl4jmsXDwnXdYtxLfim22ieeHyDZ4+e0FkVBSeXj68fPUGAwMDBvXrRuGC+TXCBwYGsWufvdq0Tdv20aNLG1o1b5Tk7U6btVDr9M5tG5I/by58PD20zv+vi4yMAPgqj8+wH37Gw+MDR/ZvU01r26k3+fPnZfWyRappkZFRrFi9npOnz/PhoyfFbazZvmlVirZ5/cYtlq5cxxuXt4SFhbFt40pKlrCJ3c6XPVYWFZsnOD8p8dB2DL+Ur/na+vrE1EaTY5U4ua6SR45X4ixya6ZNhBBp6+mFvbzcPxcD4O0pJ0rVbkVmC7n3vlYa3fYGSbe936rfj+xQawWh9Oy9O+5+PhTMbpEBsUo5/9Bg5p86qDbt58btyKmldaZd0eJkMTYhODwMgLtvX+MbHESOLImPH5qR4nbZC5DLzDzJy9YrXkZtrNerr559U2O9CvG9iYiI4IcxP5HVzIxpUyaSKVMmLCxyaIQLCwtjxaq11KhuR43q1TIgpl+/2XP+5PoNB34cPYJcOXNSuVLFJC978dJl7t57wP/G/JB+ERQCiIqOUvsdGBhE5syZMyg2QnzfvtnU7fMXzly66qD6bWJsTN9enWhYr6ZG2Lq17KhVowpFChckS+bMfPjoxdkLVzlz/gpbdhzA2NiYJg3rfMnoa3h5dGmGbj8um9ajU7zs+/cfWLZyPU+ePcfLy5toRTT58+WlYf069OrekWzZsmssc+DQUXbsPoCbuzvZs2WjaeMGjBjaH1NT9a4BNm7ewd4DRwgKDsauaiV+HTeGXDnVP8R37j7Ahs3b2btjA1mzpu0H65GjJ9i0dRedOrShfLkyZM+WjcDAILbv2kfFCmWpblclSevx8/dn4pRZFCyYn/E//YCxsTH58+VN07j+1xw4dIy79x/x9Nlz3NzfkS9vngQLcu/ee8iK1Rt48uw5RkaGVKtamTE/DKVggS+TwfrG2zPRMEVz5v4CMRFCCCG+Dw+Ob+TNsSXo6cX81teD4I+uUnj6lQoPD8fT00ttmrQ8/TZdeP6Ik0/u6Zx/3ukhfarX/3IRSgNLLxzHN+Tz9VgoR06G1W2mNayxoSF1bEqrjoFCoeDii8d0qFj9S0Q1xbxT0fLUIktWKhQsyj2318DnsV5rWJdM0zgKIZLG1fUtHh7v+fnPP+jcqYNq+pw/ZhAd/bk7z7CwMBYvWQ6MksJTHW7cdKBZ08YMGTQgwXAFCxbg6aM7asPGXbx0hY2btkrhqUh30VHqFdYCAwPJmzdPBsVGiO/bN1t4OmxQL4YN6sWnsDDev/fk5JmLrN24k1u37/Pzj0MwNjZWhR01rJ/askUKF2Bg364UKpif9Zt3sWvvEerXqY6RkVGi2505dZzW6crWNiltWWKS6eupIZKa1jFvPbzw8QugZcvm5M+fDwN9Ax4+esSmrbu4cu0mhw7sUTs3q1av488Ff9O4UQOGDhnIi5ev2LxlO29c3dm8cQ16sTlgh48cZenKdQzo34cihQuzYtUa5i5YzIZ1n1t+fvzoycq1m5g5fSqW1sWTHGcjIxP0DQzU9vvcmRPo6aEW1/uPnmFubs6C+fNU8XJzc2fN+i0MHzaYlq3aJGl7D5++JDgkhJ9/Gkuzpo015qf2Wkqut2fXJzi/cI/fEl2HtmP4pfh4erB5224Cg4IpV7YMISGhCcbF8fYdRo0dj7W1NRN/HUdwcDDrN2xh2A/jOHxwD7lz5Ur3OM86fzzRMH93G5QOW465bqUFXOK+9H34rZPjJYTISI77l+JxYYOq4FQpwOcDUhXp6/Thw0eN8ZkCA6Xl6bcmKjqaqYe2q03LnSUrnsGfz+W5b7Dw9Nwz9e6Gp7bqimmc78L4GpYsp1aAfN7p0VdfeOoVFL/ladILTwEalLBVFZ4CnH/+SApPhcgg3t4+AGTNqn4fJyWPU6jz9vZJUkMMPT09TExMvkCMhNAUGRWp9ju9KiD+MvkPAP6ak3i+sBDfq29+zNNMJiYUtSzE8MG9aVivJvcfPuXoifNJWrZxg1qYZzUjKDiEF6/epG9E/yMqlC/Hjm0bmfDLT/Tt3ZNePbsx94+Z/DBiEE7PX3Ll6nVVWG9vH/5dspyGDeqzZtUyunfrwpTJv/LbpAlcvXadEydPq8KeOXuemjWqMW3KJAb078P4cf/j8pVrfPr0SRVm9pw/KV++LB3aJ60QMyEmJsZqBacx8fUma1YzVcFpSikTtubmiX+gfvr0icjIyETD/detWraQ+3dusG3LBgoXLpRg2Jmz55EjRw527dhMvz69GDl8KJvWr8LT04tly1PWDbMQQgghvjyFQsG1rfPwuLBBbXpElILnXlF8kiTUV+vdu89d9iprykvL02/PNodLPPZ4q/qdydCIFZ37q4W54PRIa5e+X7MPgf5qv9tXSLggtGHJcmq/zz9/qFE54GvjHRyv5Wkyuu0FaFBSxj0V4mswfsJkevSOee6OGDUG6+K29Ow9QDVPOeapm5s7FavE9NS3eMlyrIvbJmm8UBcXV0aN/h8Vq9SkTLkqdO3Rhxs3HdTC7N13AOvitty9e5+Zs+dStVodbMtXZcSoMar8r4Q8febE+AmTqd+wOaVsK1GtZj0mTJyCr69fMo+Gbl7e3sz4YwFNWnWmlG0l2rbvwrHjJzX2QaFQsHHTVtXx0SX+mKfjJ0xm46atAKplE1pefBv27T/I1m07MzoaGuK3PA0KSp809IePXnz46JV4QCG+Y99sy1Nt6tauxvlL17l99wEd2yU8jiOAvr4+efPmJiAwCF8//0TDi5RTdksbEPD5I+30mbN8+vSJAf37qIXt0b0L8//6m8NHjtKyRUz3SJ8+fSJr1s8fdNmzZyM6Oprw8HAyZcrElavXOXX6LMfs02aw9roNmlKoYEF2bNvIjZsO9OozUDVPmQCaP282EyZOAWJa0K5avQ6A6tXs2LFto871uru/A1CtUxn+n8XLWLxkOTu3rGbfwaNcuHQVLy9vLp47SaFCBXn9xoVFfy/m2vUbBAeHUNSyCN27dWFA/z5qBbo9ew/Azd2drZvWMX3WHBwdb5Mpkym9enTlf2NH4+vrx6w/5nHh4iWiIqOoUSofw9pUxcjQINHjEhoayl+L/uXwkWMEBwdTrqwtkyeN1xr2iP0xDh6y5+nTZ3j7eJMjRw7q1a3D+F/+p2rd6efnR43aDenQvi3z5szUWEenLj356OnJpfOn0NfXXdejQP58SSrUfv3GhUePHjNq5DDM49TKLFOmNNWr22Fvf5zfp05OdQG5EEIIIdKXQqHg0rqpBDz43JtDmIEZmWqP4JdJswFoOsk6o6InEhF3vNMSxW348OEjQUFBREdHJ5jmE1+PwE+hzDm+V23aDw1bUbFgEUrkzsdzz/cA+IUGc8fVGbuiNhkRzWRTKBR4BX5ulWmeKTPGhglnm9jkzkehHDlx8/UGwM3Xm5ee7yme5+vtkcMrXre9uZLRbS9AdasSmBoZExoRDsAdV2cCQkMwN/16etQSIilOT21B5KeQRMMpFDGFFXp66fuOMsyUmaazTiQ5fM+e3cibLy/LV6xm4IC+lCtrS86cOTXCWVjk4I9Zv/Pb1Bk0a9qEFs2bAFCyZAmd6/b08qJrjz6Eh0fQv19vspqZsXvPPvoPHMqmDWs0uv6dNn0W2bNnZ8yYUbi5ubNh4xaMjP5gyb8LE9yHq1ev4/rWjS6dO5A7dy6ev3jJzl17efHiJfv37kh1/synT5/o1XsArm/f0r1LB6ysbThif4zRY35m0V/z6NC+LdXsqrLor3n8/MtE6tWtTYf2bZO1jZ49u+Hp5cWly1dZ9Ne8VMVXfB08vbyYMHEKCoWCWrVqYG1VNKOjpKIx5mk6FZ4uW6SZTyvEf813VXhqHtu1QkAyai0HB8ckkqS7hbQVFhZGcHAIn8I+8fTJM5auWIeJiQnVq1VVhXn48DEAlSpWUFvWxMSEMqVL8ujxE9W0SpUqsHTZKi5fvkqhwoVYu24T1tZWmJubExYWzrTpsxg+bHC6vMyKFbNm0V/zWLZ8Nb6+vkz57VcAilpaMmXyr8ye8yeNGtanTeuWAFoTqkpTf5vIxYuX2bFrD6NGDsOmmLVG+Kkz5pHVzIwRw4YQHh5O5syZcXFxpXPXnoSHh9O3Ty/y5c3L6bPnmPXHPN64uDDj9ylq6wgNCaXvgCHUr1eHxg0bcOr0WZYsW4mZmRmHDttTvLgN434ai8MtR+yPHidHVlN6NS6f6LH4cew4zp2/SOtWLahWrSrPnj2n/8Bh5MieTSPs1m07yZnTgr59e5I9WzYeP3nG3r37uXv3PvaH92FiYkz27Nlp1rQxx46f5Pepk9TGuX31ypl79x/w4+iRaZaJ9vBhTI3o+Necctr16zdxd39HoUIF02R7QgghhEgfl/euVis4DTfOTuspO7n3zFk1zdfXNyOiJpLgncd71f8lS5bg8pVrKBQKgoNDktRd3rcmOjqak0/uoUBBs9IVMTRIvNLi127xOXs+xmmhmTdrNsY2akN4gC/1rEuoCk8Bzjk9+GYKTwPDPhEepzu83FkTb5Gpp6dHwxLl2HLzgmraeaeHX3XhqXewere9OZPZba+JoRE1rUtyzukhENOF8+WXT2hdrmoiSwrxdYn8FEJkWHBGRyPFKleqSHh4OMtXrKZ6NTutQ0MBZM6cmdatWvDb1BmUKlUiSYWDK1etxcvLm327t1OpUkweSreunWjaoi1z5i7g8ME9auFz5rRgw7pVqsLO6OhoNm3eRkBgoFrl9fj69O7BkMED1KZVqliBsT+N55bjHarZVUk0rgnZsXMPL185M2fmbzRr0hCL3Pnp2aMbnbr0YM68BbRu1YIiRQpTpEhhfv5lItbWVskuPK1cqSLW1lZcunw12cuKr1NgQKCqFwlfX1/4igpPIyPjFZ6m09AXSekxUYjv3XdVePrk2QsA8uZN2uhGb93e8c7jAwDWRQunW7z+i3bt3sv0mXNUv62tLPl7wSzy58+nmvbh40fMzMwwM8uisXzevHm5/+Bz1z8D+vXh2rWb9B80DIgpoFy+9G8AVq1ei0KhYNSIoemyL7lz5aJD+7bs2r2PT2Gf1BJCuXLlZPacPyle3CZJCaRmTRsTEBDAjl17qFO7pkZNPQCLHDlY8vdccuX9XID3+4zZ+Pn5s3vnFqpWqQxA3z49GTFqDFu27qBHty6ULl1KFd7H15cxY0bRr08vALp360zdBk2ZN38hQ4cMZOKEmLF7+/TuwbN7Nzl162WihacXL13m3PmLDOjXh2lTJ6mmlyhuw4xZcyhYsIBa+I3rV6kVhgJUqlie8b/+xslTp2nXtjUA3bp2xv7ocU6eOqN2DPfuP4ienh5dOnVIMF7J8fGjJwD58ubVmKfsMu79hw9SeCqEEEJ8xS5fvsrImavpWDyS6oUMiTDNQ9tpOzDOkp0cOT4XmKZld28ibSm77c2c2ZRCcdKQgUGB32Xh6fj9m9hw7RwAA2o2YlHXgYks8XVz8/Vi2YXjatN+a9UVM5NM+AB1rUqy9uYl1bxzTg/5tXmnLxzLlPGONxZoziS2yGxYsqxG4emwus3SMmppSqPlaTK77YWYrnuVhacQ03WvFJ4K8f24cPEyVSpXVBWcApibm9O5UwdWrlqLp6cnuXN/zn/t2aObWitRu6pVWL9hM+7u7zAvpXtM5EyZMqn+VzbEUFZ4f/z4SaoLTy9cvES+vHlp2riBapqJiTG9e/VgyrQZPHr0RG0fhQCIiDOEWnh4eAbGRFP0F2p5KoT4xgpPnd+44u3tS+WKZTGIV1v3zr1H7NpnD0CjejVV0y9euUmRQgWwilc4+vrNW5au3AhAtaoVsciRPV3j/l/TtEljrK2tCQwM5M7de1y+fIWAAPWHeeinT5jEG1dUycTEhMjISCIjIzE0NMTU1JTNG9fw+o0LgYGBlChug6mpKS4urqxYtZbVK5diaGjIv0uWc+iwPQqFgnZtWzNm9EiNa+Vr161Le7WWllFRUZy/cJFqdlVVBacQ0+308GGDOXP2PKfPnFMrPDUwMKBHty6q30ZGRlQoX47TZ87Ru2d3te2VKpKbl+5OhIZFYGpipDNep8/EjCU8eLD6OEY9e3Rl4d+LNcIrC06jo6MJCg4mMiKSav9n77zj2yjvP/45be89E2fb2ZPsvRNGCJCQhlnKKIUCLeXH3rOMsgtt2XsTNgRIIIwQEkL2dJYd7yFbtiVr3t3vj9OddNrLtuR8369Xy93pee6ek2Tl7j7P5/OdJNxM79q9RxJPZ0yfir59++D9Dz+SxFOWZfHxJ59hyuSJQWuYhoPZbAYAaDTe56nVCO5zi8Uas+MRBEEQBBFb1n+3AX+96u+w2e14dy+DcdPmYvlV90OdJAhu2VlZUtuWFnKexit1TudpcVER0tzcKB0dRiB+zXoRsflYuSScAsDrmzfgrtP+kNDxpvd88R4sDru0Pqq4H86ZNEtan1gyQBbp+nvlERg6TchM9p40G280eYinuSE6MueUjgTDMJJL5efD+2FzOIJG/vYUeg/xNFSR2J15ZfK6p98f3AOe56kECpFQqHSh/RZ3Z2xvvFBTU4uTJoz32j540EDn63Uy8bS4WP4PeEaGMCmjvU3+u+qJwWDAU0//B5998RX0er3stVg46mpq6zBwYH+v3ybpPGprSTwlvGDjWDxlPWqedoSRwBkO/3ryOQDA//3tz12yf4JIBOLzSt4Pen0rHnv6BaQkJ2HggBJkpKfD1NmJuvpGqYDxqUvnY9oU16ykrb/vxH9feAPFhfno06cQKqUK9Y1NqKisBs/zGDigBJddtLqnTqnXUlRUKLlMT166GO8N6oebbrsHxX37YeYMQdxO0ulg9fMPkNVqhUqlgsrtZpNhGK9Y3rvufQCLFs7HrJnT8d//vYDXXn8T/3r4n2AYBtddfxOSk5Jw+Z8v6ZqT7CJKPFyPLS0t6Ow0Y8iQwV5tS53bqqprZNvz8/Kg8RCmxZgUzwvaFJ0gJHaYbQHF0+rqauh0OvQpljtMNRoN+pX0RVu7/IJ4585d+NdjT2Hr79tgtcoFSffat6K79Mmnn0VNbS36FBfj542/oKGhEddf93e/44kEUdC12exer1ltwhh1OorwJgiCIIh45d77HoTNbodSqcQjDz2AM5afJns9NUmDPukMUtUMrDV7/OyF6GnEmqfFxUUyp2lXxY71FBzH4dZP3pRtYzkOG8r34vSxk3poVNGxtfIwPti2Sbbt3tPPhdJt8qdWpcaMwcOw7sAuAADH89hQvgdnjJvSrWONBG9RMTRHZlZKKsaXDMS240J0uMlmxW8VhzBjyPCYjzEWNEcZ2wsAwwv7Ij8tQ4pvPtrcgP111RhRTKleROIQan3Rlibh363svF42wyeGKP2UW+LBB+x31d+uw7ZtO3DZpX/CiOHDkJycDJ7ncNHFl4PjA/cliK5C7jz1fobYk7BsdLG9P/20EU889Qxu+L9rMWWK/+vRnbv3RzQ+guhNJJR4OnjQAKxYfjL2HzyM2vpGHCw/CkbBICszA7OmT8L8uTMwrEwuMM2aMRkarQYVldXYd+AwLBYLknQ6DB86BFMnj8e82dNkAh3RNcybMxNajQYffPiRJJ4W5OfDaDTCaDR5Rfc2NDRIMar++GrtN/j99+1Y97XgOH7vgzU4d/UqzJs7GwBw7upVeO+DNQknnsZCvFMo/c+G9OvEjeFFaU1NLc694GJkZWXiumuvwYAB/aDTJYHjWFx08eXgOfksqRUrzsBT//4P1qz5BFdfdQU+XPMJUlNScPLS2EZd5ecLsyLrGxowcqT8QUZDQyMA35G+BEEQBEH0PCzLoqq6GgBwwXnneAmnAGBuPIobZgjXUtamX7p1fEToiLG9RUWFSEv1cJ72Ij7asVkS09xZf2BXQoqnPM/jtk/ekm1bOnI85pSN9Gq7YNgYSTwFhOjeRBBPmz2cp6HUPBWZN3S07PP+vnxP3Iqn7iJxqlYHrcr/JFp/KBQKnDLqJLyyyeWs/njnFhJPCSJeCdMV3qdPMY4dO+a1/eixCufr0QvJbW1t+OWXX/H3a/6Ka66+Utp+rKIy6n2L9CkuwuHDR72c8Ued5+ZpEIgUct33Lhxx7TyVi6dGY3h1m998+z1s37ET777/YUDx9LYbr45ofATRm0go1TA7KwMrzzwlrD6TJ47D5InjumZARMiwLAsH65C5E0eNGgG8C2zfsROzZk6XtlutVuzbfxCzZ83wuz+TyYT77n8I//ePa1yCWH0DiopcF29FRUVSJFhX0R0XR9nZ2UhOTsLhw0e8Xjt8RLg57xfDaFt/9O3bFz/+tFFyh4rYbDYcr6qWIlkA4Jtv18NsNuOl5/8j+4dYvMj2pE9xMWbOmIY1H32CCy84F9+u+w7LTz/Nq2ZqtIwaJTzY2b5jJxbMnyt7bfuOncjOyvKq3UoQBEEQRHxQW74TU/oo0Gnn0Sfd90QxTUqmtKziKIo/Hmnv6IDRWZtJiO3tnc5Ts82Gu7941+dr6w7sTMh40092bsGWikPSukqhxN3LzvHZdv6wMbL17w7uTohz1psij7OdP3Q0Hv32E2n9+4O7cdspZ8dsbLHCZLVIkcpAZPVORZaPnSwTTz/ZuRk3Lz0r7j9ngjgR0Wm1YBgG7e2BY3RF5s6ZhZdfeR07d+7C2LHCb3p7Rwc+XPMxRo0cIYvsjRSFQpjc7zmV/+VXXot63yJz58zGTz//gvXf/4iF8+cAEJ5jvfnWu8jNzRGeTcaAZOfzq/b2dqSnR/67SsQHrMMlUMaTeMpxnFQiQKFQgOO4sK+frVYLgODnNbR0UGSDJIheRNeG9RMnHE3NzT63r/n4C7Ash3FjXTfRixYtgFarxSuvviFr+867H8BsNmPZaf6F8ieeega5eTk4/zzXzXpBfh4OlpdL6wfLy1GQH/3FXCCSk4V6FMFqOESDUqnE/HlzseW3rdi2fYe0ned5PPf8SwCAhQvmddnxRcRjvPjiq7Ltb7/zvvQATETpdL5yvNxh+vwLL/nd/6qzV6DyeBXuvPt+WK1WrFxxRgxGLWfQwAEYNXIEPvjwI7S7XVzs338Amzf/hlNOWUI3+gRBEAQRp9Tt/RnnjFbjkgkaZBt2+GzjLp4qGQ4Om7l7BkeEjPvkRiG2t3c6T//741pUt+p9vlbX1or9ddXdPKLoeejrj2TrF89YgNJ8386jIXmFKMnKldZrDS042FDbpeOLBZHWPAWAif0HI1Wrk9Z3VFd4xQDHA9EIxJ7MGDxM9h4daqxLyO82QZwIaDQaDB1aii++XIvX33wbn33+JQ6WH/Lb/i+XX4rc3BxcdMnleOKpZ/DCS6/g7FXnoaWlFbfcfH1MxpSWlorJkybiuedfwqOPPYk33nwHf7nyGvz++/aY7B8Azll9NoYMHoQ77nkQTz7zHF597U2ce/6fsG//Adxy0/VQq8N33vtCnKx/973/xMeffI7PPv8yJvslegZ7nDpP3V2noomlwxje9bN4Pg4HG6QlQRAJ5Twl4p+HH34Mh44cwcwZ09GnuAimzk5s2bIV67/bgP79SvCnP54vtc3NycE1V1+BR/71BC67/K9YuGAeDh85ildfexPTpk72G9l64GA5Xn/jbbz/7htQuNVUWL58Gf773+eRmZkJhmHw7rsf4IoruraodVZWJvr3K8Hab75FaekQ5ORkIycnG9OnTY3pca679hr89PNG/PFPl+GC889FYUE+1q3fgJ83/oLzz1uN4cOHxfR4vpg7ZxbmzJmFV157A816PSZNOgkHDx7C5198hf79SuBw+wd89uyZ0Gq1+L/rb8GFF5yLpCQdvvv+B7S0tPrd/8IF85CVlYlPP/sCAwcOwMSTJoQ8th9/3oTqWiF2t6GhEabOTvz7mf8CEKJmzjzjdKntbbfciAsuugR/OOdCnLP6bHSaOvHiy68hNzcHV135l3DfFoIgCIIgugmToUladhdJ3VEnp4HnXcl0dlMbVJrYJlkQ0SFG9gLO2N5e6Dxt7GjD4+s/k20rSMtAg7M2JACsO7AroeJNOyxmHGyokdbTdEm4YfEZftszDIP5Q0fj1V+/l7Z9d3AXhhX26cphRo2n2BmOK1OtVGHWkBH4au82AMJk1x8O7cVZ42N7bxgtzV7nGLl4qlIqcdroSTL36Uc7NyfUd5sgTiQeuPdu3HnPfbj/gYdhs9lwzdVXYmhZqc+2ebm5eP+dN/Dgw4/i5Vdeh81mw6hRI/Dqy89h6pTJMRvTE489jLvueQCvv/kOeJ7HrJnT8fKL/8PUGXNjsn+dToc333gZ9937AD79fC3MnWYMGTIY/37qMZxy8pKYHAMAFi2cj4v+eD4+++xLfPzJZ+B5PqAphIhvWNYlnlrjSDzl3MqgZWRkoLXV4GVoCYbNLtRwdT9HX7z5zscAgPNWnxHW/gmiN0HiaZwwdvWtPT2EmLBkyUK0vmvAmjWfoKWlBUqVCv37l+CSi87D+eee7RVdccXllyEjPQOvvvYG7rzrPmRlZ+GC88/BP/5+tU8HIM/zuP3Oe3DOH87G6FHy2jpX/uUymEwmvP3OewCA888/B1dcfmnXnayTR//1IO5/4GE8/K/HYbFYMGXypJiLp/3798OH77+Nxx5/Cu+8+z46TZ3o378fbrvlRvzpogtieqxAPPv043jkX0/gs8+/xLr132P0qJF49eXn8OBDj6K6xvUwZUD//njhuWfxr0efwFP//g+SdDrMmzsbjz7yICZNneVz3xqNBmcsX4aXX3k9bNfpdxt+wudffiPb9tgTTwMApkyeJBNPJ0+eiNdefgGPPfE0HnzoUajVakyfNgU33/h/UgQ0QRAEQRDxh6W9RVrWpWX7bKNQquBgVFBDeBhgMxqQlFXYLeMjQsPTeZqa6iaehvnwJ17551cfwuiMRAMEF+btp67CH195Stq2/sBOXDP/1J4YXkR41gIdXtgX2UEci/OHycXT9Qd248o5J3fJ+GKF53mGG2k7b+goSTwFhOjeeBNP9R7nmBNFbC8AnDFOHt376c4tuGXpCkr0IYhuYuqUyTh6aK/X9kcefsBr27hxY/DJmvdC3nf//v3wn2eeDNhm5YozsXLFmSGPy5PCwgL891nvY3j2/fs1f8Xfr/lrwDb+yMvNxZ23CW7Z7Dz/tVpD3V/fvn282ioUCtxx282447abQ9oHEd/Y7fHpPHWvxZqZkQEg/OQWu1M8dbCBnadr1/0AgMRT4sSGxFMipixcMB8LF8z32t7SVOejtcC556zCueesCmn/DMPg/Xfe8PmaRqPBrTffgFtvviG0wTp5+81XvLb9tOHbkNoBwITx4/Dh+2+FfDx/F5bihaC/92rQwAH491OPBd2/v3E+8vADPi+eV88fg9Xzx/jo4U1SUhLuuP1m3HG7/GLQ1zFnTJ+KGdPf8doe6GJUqVRCqVTirDOWhzQekbtuuwFPPfl4yO2nTJmEd9+OXQ0NgiAIgiC6HquxDUrnckpmjt92rFIHNWt09vGfekH0DLV1bs7TwkKoVCokJyehs9PcK5yn+2qr8PrmDbJtdy87B7NKR0CjVMHmnOX/67FydFjMSNMlhjPa062YF4LgNqd0JJQKBVinS2LTkQMw22xI0mi6ZIyxwFM8DTfSdt7Q0bL178v3xF2tV71J/pA1N4rYXgCYPkiI7hW/I2J0L7lPCYIgiETFIYvttffgSOSwbs7TzMxMAOEnt4hiMBsktvfiC0N7Vk8QvRmqeUoQRFxgsViwZs0nmD1rJgoK8nt6OARBEARBxBmsxfVgIC27wG87RpMiLbfrG7p0TET4iLG9OTk50Gq1ACDVPU30mqc8z+O2T98Cx/PStllDhmPpyPFI1eowbdBQabudZfHT4X09McyI8BIVQ4h6TU9KxqT+Q6R1i8OOX44eiPnYYkmzKfLYXgAYlFuAftnyWq/ljfFV67XZFP5nGQiVUollYybJtn20c3NU+yQIgiCInsS9tmg8OU85HzVPjUZTWPuw2wRhOFhs77zZ0zBv9rQwR0gQvQsSTwmC6FGqqqrx8Sef45q//x9aWlvxl8sv6ekhEQRBEAQRh3BW14OBjFz/UbxKnUsIaAuQfkL0DGJsb3Gx6zNMc0b3JrrzdN2BXdhQvkdaZxgG9y0/T3IdLhguT3tZt39Xt44vGpo8xNNQnKeAEN3rzncHd8dsTLGG53lZzdN0XTI0qvDCuhiGwbwyj3M+EF/nHE1dV38sHyuvf/jpzi3g3SYREARBEEQiIUbbAvElnrpH7YqxvRaLRTbeYEixvUGcpwRBkHhKEEQPs3nLb/jH/92Inbt24/Zbb8KkiSf19JAIgiAIgohDGIdZWk5K913zFADUKRnSstHQ2KVjIsJHdJ4WF7lqjvUG56mDZXH7p/JSHudOmoXRffpL6wuGeYinB3YmjMDkWSczVMFt/lD5OcebkOhOh9UCq8P18DE3QkemZ3TvN/t3RDOsmONV1zXK2F7AFd0rcqixDvvqqqLeL0EQBEH0BPHrPHXF9iYlu0o/RCKeskFqnq7fsBHrN2wMc4QE0bsg8ZQgiB5l5YozcfTQXmz+5Qf86aILeno4BEEQBEHEKSrO9eBCk5Lpt11SmktYNbfpu3JIRJhwHIf6BsF5WlTk5jxNczpPjYnrPH3t1+9R3uCKZ03RaHHLyStlbYYV9EEft3q91a36uIt09Yen8zRU8XRs3wHITkmV1g821KC6NT7/LiMViD2ZUzYCKoVSWv/58H4vwbIn0XtEE+fEwHnqK7r3451bot4vQRAEQfQEdlnN0/gRT90FT41bDXlHECHUHfF8HEFie1954wO88sYHYY6QIHoXJJ4SBEEQBEEQBBHXOGxmKBnXTGuNm7vUk+SsPGnZajR05bCIMGlu1sNuFx7UFBf3Hudpu7kT/1y7RrbtmvmnoSgjS7aNYRgs9HSfJkh0r5dbMUTBTalQeMfYxml0b7NXnG1kjsyMpBTMGzpKWmc5Dp/v2hrV2GJJrM7TE4ruJQiCIHoLrMPdeRq6q7Or8SeecuGIp6LzNEhs7ymL5+KUxXPDGyBB9DLCK+BBEARBEARBEATRzdhNbbJ1dbJ/4abvhEW498lXYLTxuOjPU7p6aEQYiJG9gGdsr1jzNDHF08fWfSpz8xVlZOGvc0/22Xbh8DF49dfvpfV1B3b6bRtPeApuodY8BYS6px9u3yStf3dwFy6cOjdWQ4sZngJxTkrkjswzx03Ft/t3Susf7fgVF02fH/H+YomnwzYnBrG9gBDdm5eaLrmUxejekcX9YrJ/IvYcq6jC7n0HcfRoJY4cO45mfQsA4KlH7kJeXo5Xe47jsGXrThw5Vomjx47jWEUVzBYLhg8dgjtu/lvQ423asg1ff/sDjlcJjvt+JcVYumgupk4eH9sTIwiCiBKHIz5rnsrFU7W0HI7zVKp5GqTPOauWhzk6guh9kHhKEARBEARBEERcY3MTT+1QQaH0fxtTOHA4DjQLLlW9MX5mihNAbZ0f8dTpfDMajeB5HgzDdPvYIqXVZMR/f/xatu2OU1chWaP12X5W6UioFEo4OOGB1aYjB2G0WpCq1XX5WKPBS1gMw63oWQP0h/K9cLAsVEqlnx49g7e7NnJR8ZRRE6BRqmBzRuJtPHIADe0GFKRnRjPEmNDsJvQnqTVIidF3T6VU4rQxE/HyL99J2z7euYXE0zhmzSdfYev20J3gZrMFTz77UkTHeveDz/Dx599ArVJh9MhhAIDdew/gyWdfQlX1Upx91qkR7ZcgCKIrSLTYXvdaqMGQap46Asf2EgRBsb0EQRAEQRAEQcQ5aUVDcOePHO7/0YrDmbMDtlWr1VIMbGtra3cMjwgRmXha7O085TgOJlNnt48rGrZVHZUEMkCo8Xn2hOl+26frkjB1YJm0bmMd2Hh4f5eOMRZ4uzJDFxYL0zMxsqhEWm8zd2Jb1dGYjS1WeNYCjbTmKQCkJyVjgVtEM8fz+HTnbxHvL1ZYHXZ0WMzSejgieCh4Rvd+smMzRffGMaVDBuLMZUtw3TWX4ZnH70VGeuDvg1KlxMxpk3DhuStw5y1/x7VXXRLScQ4cPIyPP/8GKclJ+Oc9N+L6ay/H9ddejn/ecyNSkpOw5tO1KD98LBanRBAEERPksb3xKZ5qZTVPQxNCWZaV9hHMebrvwCHsO3AoglESRO+BxFOCIAiCIAiCIOIai9UKg8mGRhMPbd7AoO2zszIBAK2thq4dGBEWYmyvWq1Cbq4rElIUuwGgo6PDq1884ykqzhoyAgpF4NvshcM96p4e2OmnZXzA87wstjc7JTVs1+gCj1qv6w/EX63XWNcCPXOcPDb8ox2/RrW/WKD3PMcoool9IUb3ihxuqse+uqqYHoOIHaefugirVpyGiRPGSP9uBkKn1eKvl1+IkxfPxbCywTLXUyA++2o9AOCMZUvQp7hQ2t6nuBDLT1sstPlyXfgnQBAE0UXErfOUczlM1WpXbG+ozlPRdQoEr3n6z389i3/+69kwR0gQvQuK7SWIGLLznfsDvj529a1B+2aPWwIAqFovj8MJ1JcgCIIgCKI302owSMtZmRlB22dlZaHyeBWJp3FGXV09AKCwoFAmMKampkjLHUYjirx6xi9NXlGvwcWoBcPG4K7P35XW1+3fFddxxW3mTilmGIisFuj8YaPx1PdfSOvfHdiNm5euiMn4YoV3NHF0wuKSkeOhU6lhcdZN+/VYOWoMLeiTmR3VfqPB012bnZIa0/37jO7dsZmie09gbDY7du89CAA+a5tOmzIBb733CXbt2Q+73S4TAwiCIHoKNl7FU4fv2N5Qnac2N/E0WJ/xY0eEOTqC6H2Q85QgCIIgCIIgiLjG0OqqeZqZmRmwLc/zOKWgAdfP0OD0jP3obKkL2J7oPsTYXvfIXiDBnacdcsEtLy244DaiqARFGVnSemVLE4401cd8bLHCUyDOi8CROWVgGZLdHvJtrzqKVpMx6rHFEk/xNC9K8TRNl4RFI8bJtn26c0tU+4wW77qusXWeAsAZY+WO2092bqHo3hOYuvoG2O12pKWlIjfHe+JAbk42UlNTYLPZUVff1AMj7B3s2LELZ608ByNGn4RBpSNRXV2D62+4BbPmLurS455z3kU457yLuvQYXcGsuYtw+RVX9/QwiDjGIRNP7QFadi8sF13NU3chmA0S2/uPqy/DP66+LMwREkTvgpynBEEQBEEQBEHENS1utUuzgoinDMMgW9kJdboCAAubsRXJ2YnkZey91NYKAmFRUaFsu1jzFAA6OuJLUAtGJM5ThmGwYNgYvLH5B2nbugO7MCQ/Pr+nes9zDEEg9kSrUmPmkBH4Zt8OAEIN0O/L9+Cs8VNjMcSY4OnKDKeuqz/OHDcFn+1y1Tr9aMdmXDFnadT7jRTP2N5Y1zwFgOmDhehe8W9DjO4l9+mJSZNe+Pc7UCxwTlYmjEYTmvUt6FdSHHB/d9z7qM/tK5bNQ1FBLlqaumbClMPpIO+q/UeDw+HAFVddg5TkFFz3tyug1WrBcFZYrWZwLCuN2Wq14ZXX38ZJE8Zi4oRxMTm23W4FEJ/vS0XFcXy97nssO3UJiosKZZ8hx7KwWS09Pm7PMRLB6a6/RWOHa+JmZ6epx78rIgZ9o7RsNbuumfVN9chM0wXt39TomqTisNt75Lzi+feUCJ1E/Byz88K/1yLnKUEQBEEQBEEQcY3h55fwz4Va3DZbA2XDjqDtWaXr4YGlo6ULR0aEitVqhV6vBxDYeWo0JpZ4Gqlb0bMG6Lr98Vv31EsgjrBO5vyho2Xr7qJiPOB5nrEQFhePGIcUjVZa31p5GFUtzVHvN1KaTZ41T2MvnioVCiwbM0m27eMdm2N+HCIxsFoEcU2n9V8fVacT/kbMFku3jKm3UV1di4aGJlx43iqcufxUnLJ0IZKSknDbTf/Ah++8LLWz2Wx4/qXX8fu22P1788wTD+GZJx6K2f5iSUVlFZ5/6XXU1sVvskMijPFExeEWjxtPzlOHm1tU417zlIug5mkQ52lbuxFt7Yl1XU4QsYacpwRBEARBEARBxDX2TgOS1QyS1QySNMHroTGaFMAsiATt+gbQXP6ep67e9WCwuMhDPE11d54mWGxvhDGoc8tGQqlQgHU+7PrlyAGYbTYkafwLDD1FrKJeTxk1ATd99Lq0/s2+HeiwmJGmS4pqfLGA53mZKzNdlwytKvrai8kaLZaMHI8123+Vtn20YzOumX9q1PuOBE8XcbR1Xf2xfOxkvPTLemn9k51bcMvJK+O2ri+RONxz+3U+t4vOl0hcJaHQ1fuPhsPHagAAhcV9A45PpRX+/pNS0uLyPGJNqjMePz0zB9l5RbLPUKFUQqPV9fj74DlGIjjd9beoUrsmPrEcFzefT2pajbSck++6w0lJzwppjK3tZmmZ5fiAff5287UAgNdfeDySofolnn9PidA5UT5Hcp4SBEEQBEEQYXOsogqffrkOT/z7RVx93Z0456Krcc5FV6OpSe+zPcdx+HXLdrz57se498GncPFfrsc5F12Ne/75ZEjH27RlG+66/3Fc/JfrcfFfrsdd9z+OX7dsj+UpEXEMZ3HNek7PCS6FKpNcbqq2ZprNHw/U1roinbxje91rnibWDPdmjxjU3BDdihlJKZg8oFRatzjs+PnI/piOLVZ4nmModV190TcrF1MHlknrZrsNX++Nj99xo9UCq8Plxgj1cwyFM8fJa4D2pAsz0u9ruIjRvSKHm+qxt/Z4lxyLiG+0TlepxWrz28bidKcm6YJHThJyrr/hFqw+748AgL9ceQ0GlY6UapC61zytrq7BuJOmAQCeevpZDCodiUGlI/HEU8/43bfdbseTTz+LeQtPxrCR4zFh0nScvfp8/PTzL1Ibz5qnv27egkGlI7H262/x9L//g2kz52HYyPE478KLUVFZ6XWMbdt34E+XXI6xE6Zi1NiJOO30FXj3vQ9kbX7fth0X/PFSjBk3GSPHTMT5f7wEu/fsDfi+fPDhR/jLldcAAM49/08YVDoSE6cvxNZtO2TtNm/+DcvPWoVhI8djzrwlWPPRJ177MhgMuOue+zF95nwMGzEOCxafildefSPg8UV++vkXnL36fOn8Fiw+FY88+oTfMQ4qHYlfN7tqY6//bgNWrjoPI8dMxJjxU/CXK6/xeh/POe8inLrsLOzctRsrzj4Xw0dNwLwFS/HBhx+FNEbCNw7Wveap/9+v7sZfzdNgLlKRcGqeFhbkobAgL8wREkTvgpynBEEQBEEQRNis+eQrbN2+O+T2ZrMFTz77UkTHeveDz/Dx599ArVJh9MhhAIDdew/gyWdfQlX1Upx9Vs84eIjug7e7ZkmnZQe/idekZIBzpvWaWhsDNya6hTq3SDrv2N7EdJ7yPC9zZabpkqBTh+4cXTBsDDYdPSitr9+/E4uGj43pGGOBp/M0mlqgZ42fil+PlUvrH27fhJUnTY94f7HC6xxjKCouGDYGabokdFiE37Ed1cdwtKkBg/IKYnaMUOmKuq6+EKN73d2na7b/iqyUNFS3NqPaoEdNqx5Vrc2obtWjpdOIKQPKcOspK2Pi+CXih7wcwVnX0mrw20bvfC03J7sbRiRn5F3XoMNqDtqO53kA6HL3dJo2CXvveirk9uecswoFhQV49j/P4U8XXYDRo0YiJyfHq112dhbuv/dO3Hr73Vi8aCGWLlkIABg6tMyrrciTTz+L5194Ceefdw7KykrR0d6BXbv3YO/efZg1M/Dv9jPP/g8KpQJ/vvRitLW34/kXXsa1/7gRH334jtTmhx9/wp//chUKCwpx8Z8uRG5ODg4cPIjvN/yIP6xaCQD4eeMmXHLZXzBu7Fj8/W9XgeM5vPPuB1h97h/x8YfvoLR0iM/jT540EX+66AK8/MrruPKKP2PI4EEwthswcEB/qc2xYxW46m/XYfWqFVhx1hl4//01uP7GWzFq1EiUOffb2dmJ1eddBH2zHueeswoFBQX4dfMW3HPfP9HW3o6/XX2l3/eg/NBhXPbnK3HSSeNx3T+ugVKhREVlJbZu3eZ3jAAw2PnfDz78CDfefDvmz5uDG2/4B0wmE157/S2sWn0BvvhsDfJyc6VjGQxtuPSyK7Fs2SlYdtop+OzzL3HDTbchKSkJp57Sc3W2ExmHw108jaPYXkeU4ql7bK/bOfrikftvCXN0BNH7IPGUIAiCIAiCCJvSIQNR0rcYgwb2w6CB/XDLnQ+jrd2/6KFUKTFz2iQMGtgPAweUoL29A4//+8Wgxzlw8DA+/vwbpCQn4e7b/oE+xYJjraa2Hnfe9xjWfLoWY8eMQNmQgTE7NyL+ULKuOmia1Myg7XXpOeh0LpvbfLuhie7F3XnqGdurVquh0+lgsVjQkUA1T002K8x21wz+UOudiiwcNgb3ffm+tL7+YOgTUroTz1qg4Z6nO6ePnYybPnodnFOI+O7gbrSajMhKSQ3Ss2vxcmRGWNfVFzq1BqeMmoB3t26Utn28czP+sfD0mB0jVGIVwRwKntG9T3z3OZ747nO/7X+rOIy+WTn486zFXTYmovspKiyAWq1GR4cRzfoWL4G0Wd8Co9EEjUaNosLudzh1WM0wWhO31uqE8eNgs9nw7H+ew5TJk7B40QKf7ZKTk3HqKUtx6+13Y9iwMpyxfFnQfX///Q/4w6qVuP3Wm8Iel93hwGcfvA+1syZjZkYG7rnvnzhYfghDy0rBsixuv/NeFBUW4dNP3ke6WwKFKFRzHIfb77wHs2bOwAvPPSu9vursFVi05DQ8/cx/8dQT//J5/H79SjBl8iS8/MrrmDljGqZOmSxFTIocPnIU77/7Bk6aMB4AcOrJSzFj9gJ88OFHuOWm6wEAL770KmpravHFZ2tQUtIXAHDuOauQnp6O//7vBVx04XnIyMjwOYaff/4FGq0Wr73yApRKZUhjFDGZTLj3/odwwfnn4K47bpW2n3bKyVhyynK89PJruPH6f0jb6+rrceftt+CPF54HADhn9dlYtnwlHn7kcZxy8hKKTI8Aec3T+HGecpzvmqehiqfuNU8dIfYhiBMZiu0lCIIgCIIgwub0Uxdh1YrTMHHCGGRnZQZtr9Nq8dfLL8TJi+diWNlg2UzZQHz2lfDg9YxlSyThFAD6FBdi+WnCA9bPvlwX/gkQCQPPsVDxrpnRmpTMoH1SM10PYK1GQxeMigiX2jrhoWVqaqrMaSoibkuk2N5ohajRffqjIM310PVIUz2ONTfEZGyxxLNOZm6Esb0AkJ+WgdmlI6V1O8vi891bI95frNCbulZUPHPcVNl6T0X3ejpPuyq2F/CO7g2F3yoOd9FoiJ5Co1Fj9MihAOCz3MKmzYILb8yo4ZLQRsQH6enp2LFztyw5IlTOXnmm7POcNPEkAEBVVRUAYM/efaiursHFf7pAJpwCLnfv/v0HUVl5HMtOOwUtLa3S/xx2ByZNPEkWbxsJw4aVScIpAOTkZGPQoAE4frxa2vbV199g8uSJSElJkY1h1szpsFqt2LFzl9/9p6enwWw244cffw57bD9v3ISOjg6ccvJS2XFTUlIwfNhQbN78m6y9RqPBH1atkNa1Wi1Wnb0CVdXVOHr0WNjHJzydp/EjnkbrPHUXTzmOA8dxsRscQfRCyHlKEARBEARBxCU2mx279wqRllMnj/d6fdqUCXjrvU+wa89+2O12eujWS7F1tsN9wrwmxfcMf3fScgoghvU6zO0B2xLdg/jw1TOyVyQtLQ1NTc0JFdvb1OEpuIUnRDEMg/nDRuPt31wPVtft34XLZi2KyfhihafzNFpX5lnjp2JD+R5pfc32X3HB1LlR7TNauroW6NyyUchISkabWfDE76k9jkONdSjN9/330FW4i6dqpRLpuuQuO5ZSocCZ46fiuZ++CblPfXtrl42H6DmWnbwA23bswceff4PxY0fKUkQ++Vz4fiw7ZWGPjC1NmxRSu+6M7Y0Xrv3bVfjzFVdj5pyFGDliOGbPnonly071G5XrjmfCREaG8O9GW5vw70lVlSBQlpWVwh9ibc9rr7vR5+sKRXR+IM8xAkBGegba29tcY6g4jgMHyjFxykyf+2hp8f+bddqpJ+Pd9z/EpX++Evn5eZgxfRoWL1qAxYsWBP0eVVQI57763At9vt6vpES2XlhQAJ1HzeABA/oBAKpraqQoYCJ0PMVTnufjwsHrLnbKxdPQRFBPIZhlWb9/S5ddJbjOn//3g+EOkyB6DSSeEgRBEARBEHFJXX0D7HY70tJSfdbBys3JRmpqCoxGE+rqm9CvpLgHRkl0NXaT6yEWyzNQaoI/WMzIcz0Q422mLhkXER5ibK+vh5UAkJZ64jlPAWDBsLEy8XT9wfgTT93PU8EwyEpOiWp/y8ZMxHUfvAy70yXx0+F9aGg3oCA9M6r9RkNX1jwFAI1KhVNHT8RbW36Utn20/VfcsOTMmB4nECzHobXT9XuYk5LW5Q+Cbzl5JfbWHsfmY4eQnpSEvpk56JuVg76ZOeiTlYPc1HT89e3npPb17YYuHQ8hZ9uOPfjo06+ldaNJEPcfffoFqFXC48LxY0firOWumokvvvYuKioE4avTLNQKraisxu33PCq1ufjCVRg4wCXuDBs6BMtPW4RPPv8WN9/5sORE3b33IOx2O846fWmPlV8Itb6oGPmande9Ex56ksmTJ2LD+q+wbv33+OnnX/DmW+/guedfwv333oWzVwb+7fIVUwu4ROhQEEWi2265MaDIGin+xygfw5zZM3HJxRf5bFsWQEjW6XR4963XsOnXLdjww4/48cef8dHHn2LmjOl4+cX/+j0+AHC8cO5PPPYwsrO974F0Oq3fvkRscBdPeZ6Hw+GIi4m67g5TjcY9tjdw/VIRd+epuD9/52WxWCMYIUH0Lkg8JQiCIAiCIOKSJr0wmztQLHBOViaMRhOa9S1BxdM77n3U5/YVy+ahqCDXqxaSiMMh3GT6e50InUjey/aao67+jBqtzcHj4+yc68kXY+/slZ9dIn0veZ5HTW0tACA7K93nmHVaYfZ8a2tLt59TpO9lRd1x2XqqQhH2Psbl5EHBMFIN0B/L96Ku7ji0qp5/QAcIgluLySVoZyenwKD3Hy0c6ns5e9BQrD+0DwDA8Tze3rgOF06cEYMRR0a1x3iTOC7m38OFA0tl4ukHv2/EpROm+m0f679xvckoEy4ydUnd8rf26qqLwXIclD6cLTzPI0mthtn5MLfO0AJ9Y22XiLrRvp+9UTTr6DDi8NEKr+2VbrGlxUX5stdqauq9+pgtFtk2s9m7hujqlaejf0kfrP32B+w7cAgAMLB/X5y8eJ7PdBGiC4jg7yozMxMrV5yJlSvORGdnJ845/yI88eS/g4qnwejXTxDXy8sPyWp9+mqTnp6GmTOmhX2MWPyO9O9XAovFGtHxAcEdO2P6VMyYPhW33nwD/vfci3jokcfw6+bfMGP6VL9j7N9PcI3m5+f5fX/cqW9ogMVikblPKyqEa5Q+xTS5NBLcxVNAcGz2lHiq17fAarOiuKhIJp6qZTVPQ3WeysVT9xhgT15/4fEwR0oQvQ8STwmiF7DznfsDvj529a0BXyfij2vfeylom8dXXdwNIyEIgug5rM7ZrqKo4gtx5rXZ4v2gjugduNcs5ZShzbTXpOag0qiCvsOK9NzMrhkYETIdHUbpYXphQb7PNqmpgpvRZEocp7C+Uz7WnGTvWq7ByExKxrjifthWI0T0WRx2bDx2CPNLR8RkjNFisHRKwi4AZEdwjr44bfhYSTwFgM/37ehR8bTF47OM1Xm6M63/EGQlJaPVGd17uLkB5U31KMsrDNIzNug75a7u7CgdxOHgSzgFBHEjPzUdla16AECn3QajzYo0rc5neyK2zJk1FXNm+RfwfXHHzX+L+HjTppyEaVNOirg/ER06rRYMw6C9PbRyBq2tBmS5TWBMTk7GgP79UVNdG/VYRo4Yjj59ivHSy6/jjDNOl9U9FeNRR40cgX4lJXjhxVdw6ilLkZQkTx7R61uQ4yOZRkRs394eeTmApUsX49/P/Be/bt7iJWK2tLQiKyvTrwDq+f4BwIjhwwAAVqs14BhnzpyO1NRUPPuf5zHxpAlQqeSP71taWpGdnSWt22w2vPveh/jjhec592/De+9/iL59+1Bkb4R4ioo2mx0p3ffPpoTVasPSU5ajvaMdP/+wThJPFQqF7HsRqvPUUzwNtR9BnKiQeEoQBEEQBEGcENxz+3U+tweLYjsRo9q6ikjey4ZdrsgohS4ttL55RdhgHIJtO3Zg4sRcXNsLP7tE+l42NLuil4eUlvkcc05uHgDA1Gnu9nOK9L00cfL4wX5FfSMa+6ljJ0viKQB8c/QgVk5fEPZ+uoKG+mrZemFmdsBzDPW9XDktC7eu/RCdztpb22oqYVKqUZKdG+WII6PdI8ZuYJ/+XfI9PH3sFLz66/fS+vrKI5g6wrfrLtZ/4442g2y9MCs3Ln4/+mTlSuIpAFjVWvTvgnEl0m8mQXQFGo0GQ4eW4osv12LgwAHIzMhAWVkphvqJxF188umYMnkiRo8aiczMDOzesw9ffLkWF5x3TtRjUSqVuOeu23H5FVdj2ekrseKs5cjNzcWhQ4dRV9+A/z77JJRKJe6/7y5cctkVOPnUM3HWmacjPz8PdfUN+PnnX9C/fz889i//tRiHDxsKlUqF/z33Ijo6OmCzdGLSSePC+g24/LKLsW7dd7joT3/GypVnYcTwYTAajdh/4CDWfv0tdu/Y4iVsijz9zH/w22+/Y+6cWejbtw/0La148813UFRYiIkTJ/gco0ajwbRpU5Cbk4O777wV/3fDLVh+5iqceurJyMrMQHVNLdav/x6LFs7Hdf9wTWQoLCjAv5/5LyqPH0f/fv3w6Wdf4PCRo3jisYfjok5nIuLLedoTNOuboW9pAQAcO1YpxVkrlQpZ9HOozlPP2N5AzlOCIIDoqmsTBEEQBEEQRBehdbpKLVb/N6tiLZYkHblUeiusLhvrjzrwa5UDiryhIfcTZ/u3thi6ZmBEyNTWuaKWiwp9u+zS0lw1T8OpidaTeNbJzIug5ikAnDVe7vz6cs/vMFnjw02vN8rdMJHUdfVFilaHJR6i4Uc7fo3JviNBb+qa8/TkzPFTZOsf79jcbd93rxq9KbGt6xopRRly51hdW2sPjYQgej8P3Hs3CgsLcP8DD+Nv116Pr9Z+47ftRReeh5qaWvznfy/g7nv/ic2bf8O1f78at9x8fUzGMm/ubLzx6ovoW9IHz7/4Mv750CPY+vs2zJ83R2ozY/pUfPDemxg6tBSvvPYG7rrnAXz88Wfo168E556zKuD+c3Kycf+9d0Gvb8FNt9yBW++8H0crKgP28SQ5ORnvvPUaLrroAvz88y+4+9778fyLr6Curh43Xv+PgHVLFy6Yh+LiInyw5mPcedd9eP2NtzBx4gS88fpLktPWc4x/u/Z6HD58BABw5hmn47VXXkB2djb++78XcO/9D+Grtd9gwoRxOO20U2THysrKxAvPP4sdO3bhnw/9C3p9Cx584F6cvuzUsM6XcOHpyOwp8dRd7LQ7HJKoq1SqPMTT0ERQXzVP/XHHfY/hjvseC2e4BNHrIOcpQRAEQRAEEZfk5QhxVC2tBr9t9M7XcgPEdhGJjUWXh08PCg8K5v71lCCtXWRlCd+f1lZ6EN/T1Na5agwWF/t2fKSlCuIpy7Iwm81ITk7ulrFFg5cYFaHg1j8nH5MGDMFvFYcBAJ02G9bu3Y4VEyKrsRZLmrpQcDtr/FR8tGOztL5m+6+4Zv5pMdt/ODR7iMQ5qV0jLM4YPBz5aRlo7BDc2Eea6rGn9jhG9+kPjuPQ0NGG6lY9agx6lFcfg83hwFlT5mBEUUnUx/YUiHO6SCAOl8KMTNl6fTv9ZhNEOEydMhlHD+312v7Iww94bRs3bgw+WfNeSPv965WX469XXh6wzdtvvhLSWPr27eNz++TJE/Hma4FLFo0aOQL/+8/TwQfsg7NXninVZ3Wvd/zThm99tvc8H0CY3HXTDdfhpht8J9j4Y/q0qZg+LXgstvsYPRHrpYbC2DGjseaDt8MaI+Efe5w4T+1uMbsOhx2sm/OUYRgoFApwHBexeOoIENt7rKIqghETRO+CxFOCIAiCIAgiLikqLIBarUZHhxHN+hYvgbRZ3wKj0QSNRo2iwrweGiXR1RgMBmk5MzMj5H5ZmZlC/7Y2sCwb0B1AdC21tcIDS4ZhUOCn5mmaW72zjg5jQoinnsJipM5TAFg5frokngLAB9t+iQvxtLnDQzxNi53gtnD4WKTrktFuEWqA7qqpxKHGOpTmd2+sKs/zMiE8TZcErUrdJcdSKhQ4fcwkvLBxnbTtwpefBA8etYZWODjvh5//2fQ9frv5ERRnRjdJyFvsjw/naWF6lmy93iNemCAIgiC6G4c9PsRT9xqldrsdnFMkVSpVzv+GJ556ngcbILb34ftuDne4BNHroNhegiAIgiAIIi7RaNQYPVKIaf11y3av1zdt3gYAGDNqONTqrnnQTfQ8ra2uepmiIBoK+TobJhQpMKOEQW35jtgPjAiZOmdsb15eLjQajc82YmwvAHR0dPhsE2/o3cQohmGQHYUrc/m4yVAqXLfn6w/sRoup59+HZlNs3LW+0KrUOHX0SbJta7Zvitn+Q8VotcDqcD2cjEYED4UzPWKaK1uacLyl2adwCgBmuw3rDuyK+rieEczRfF9jiafztI6cpwRBEEQP4/AQI+MittfukMalVCqc/xVEVM8arf6weTlP/YunRYX5KCr0PemRIE4USDwlCIIgCIIg4pZlJy8AAHz8+TeoqXXVTayprccnnwt1mpadsrBHxkZ0DzLnqbOOaShkt+3GH8dpsHKEGnV7N8Z+YETIiLG9xUX+HYWeztN4h+M4NLuJmzkpqTLxM1zy0zIwp3SktO7gWHy687eoxhgLvOtkxlZY9Kz3umZ799UAFfGOs+1aUXHKgFIUZWQFb+hGXVtL1Mdt9qzrGifiaZGn85TEU4IgCKKHcTjkIqPVZvfTsmtxFzsdDgc4VojtVSiERB1RRBXjfIPhVfM0RNGVIE5UKLaXIAiCIAiCCJttO/bgo0+/ltaNJiF28dGnX4BaJVxijh87EmctXyq1efG1d1FRUQ0A6DSbAQAVldW4/Z5HpTYXX7gKAwe4arsNGzoEy09bhE8+/xY33/mw5ETdvfcg7HY7zjp9KcqGDOyisyTiAeb4L1g4SAmTHdAxoT+40KZmgdULyyZDUxeNjggFMbbXX71TwMN5aux5x2UwDGaT7EFVLByZKyZMw3cHd0vrH2z7BRdNnx/1fqOhqQtjewFgTulI5KSkSQLmocZaqQZod9HVArEnCoUC9y8/Dxe/9m/Z9sykFPTJzEbfrBw4OA7r3dymdW3RC4p6j/OMl5qnnkJyLM6VIAjiRMFXnVYiejzjbOPBeepwOKQapSqVKJ4K992B4nf97Q8ILLr+5/nXAQBXXHZB6AMmiF4GiacEQRAEQRBE2HR0GHH4aIXX9srj1dJycZE85qempt6rj9likW0zmy1e+1y98nT0L+mDtd/+gH0HDgEABvbvi5MXz8PUyeMjPwkiIchq24tlQ4VYZpuhAcguDKmfLj0bJueyuU3fRaMjgsGyLBoaGgEARUX+P7tEc57Gst6pyKmjJ+If778sRcj+cvQgqlv16JuVE/W+I8XTlRnrSFuVUonlYyfjpV/WS9vWbP+1R8XTrnaeAsAZ46Zg8oBSHG6qQ0F6Jvpk5iBVq5Ne/73ySMzF02aPSQnxUvO0ID1Ttk41TwmCIIiexu6Ij5qnnrG9LuepGNsrOk8jrXnq33n6i7NEDomnxIkMiacEQRAEQRBE2MyZNRVzZk0N3tCNO27+W8THmzblJEybclLwhkSvgud5KDkbwAjrmpSMkPumZuVL4qnNaIj52IjQaGxsAuuspxQ4tjexap42d8TexZeuS8KSkeNkcb1rtv+Ka+afGvW+I8XLldkFgtuKCdO8xNM7Tl0FhmFifixfeIqKXV3zVKQ4MxvFmdk+X/N0Y9a3G6I+nrsQzjAMspJTA7TuPpI1WmQkJaPNLCRY1Le3guf5bvv8CYIgCMITzxqi8SCeOhwO6ZpaqVTK/hup8zRQzdNrr7okrLESRG+Eap4SBEEQBEEQRC+HY+1orz0MPsRZyfECa7NAybjqH6rDEE/Tc1wuR4c5/sW43opY7xQIEtubSs5TAFg5frps/cPtm2Ky30hxj+1VK5VI1yXH/BieNUCrWpvxW+XhmB/HH3pj99Y8DYX8tAwo3MTDaGuechwnE0+zk6Or0RtrCt3qntpZFi2m+P8NIAiCIHov8Rjba7fb/YunId7j2W2eNU/995swbhQmjBsV1ngJorcRP1fLBEEQBEEQBEHEHI514JNbTscPD/4BXz58WU8PJyxsJoO0zPOAJjl0gSoj1yWe8jZ6EN9T1NXVS8uBY3sTzHnq5ciMjXi6cPgYmUC5u6YSB+prYrLvcLGzDhjMJmk9NzW9S9yACoUCZ46TJxms2f5rzI/jD08hvKtrnoaCSqlEborrb6LZ2AFbgGi9YLSZO2V1zeJBIHanMCNTtl7fTnVPCYIgiJ7D7pCLjD0lntrcxE67I4B4GqLz1PM8xBqqBEH4hmJ7CYIgEpRr33spaJvHV12csMcjCIIgYkPLsV1QmYWak1ztTnTqa5Cc06eHRxUadlObtOxgVGAUypD76tJdcZgKh3ctXaJ7qK0NzXmq0Wig1WphtVpPaOepTq3BsjET8eaWH6VtH27fhFtPXhmT/YeD3sP9FyuB2BcrJkzFsz98Ja1/smMz7l9+nuSOtDkcqGtrQY2hBTUGPdRKFRaPGIdkjTbqY+tNHuJpWs+LpwBQkJqBRjdXbEO7ASXZuRHtq9mjdm1uSnyJp8UeMcW1ba0YWdyvh0ZDEARBnOiwrKfz1O6nZdcii+21O6SJUOL1kVIRnvPU5hHb63me7nz8+TcAgDNOWxz6gAmil0HiKUEQBEEQBEH0Yg5v/V5aNtl4VOk7MTSnBwcUBlY35ymr1IXVV5PsivhV8baQa+g1Hd6BmoO/Y+S8VVAnx5fAkIiIsb0ajQY52b7rO4qkpaUK4qnxxHWeAsDKCdPl4um2Tbhl6YpurwHZ3NEmW+9K8XRc34EYmJOPY3phokdDRxvOfu4RtFs6UdOqR6OxHTzPy/pMGzQUn//11qjfF8+ap/EiLOanpQMu4zZq21oiFk/1xtjX6I0l7rG9AFDfRs5TgiAIouew2+O85qlKKftvxDVPA/T78GNhUhuJp0Qk9Jb69RTbSxAEQRAEQRC9mLp9m6XlHysd+PLLtT04mvAwtja6VtRJYfXVpGZKywoGcJiDuxnNrQ3Y+OSlqPr6WXz91N/DOh7hGzG2t6ioMOgNdFqaIFolhPO0o+vcijOHDEdBmkv8r9A34vfjR2K2/1DxdCvGyl3rC4ZhcNZ4eXTvhvI92Hb8KBo62ryEUwDYdPQgKvSNXtvDxVMIjxdhsSBVXuO5vt0Q8b68BOJ4i+31FE+jOFeCIAiCiBZv52nPi6d2d/HU6Th1OU85785B9gcAbIDY3tUrl2H1ymVhjZcgAOBgSx3KWxt8Xr8nGiSeEgRBEARBEEQvhec48IZKaf1IC4/Pv1ybMDcy7c0N0rJSmxqgpTcqXSo43iXWuddP9cf+r1+CkhHeG752R1jHI3wjxvYGiuwVEeueJmLN01gKi0qFAmd6CIkfbNsUs/2HSnOHp6jYtYLbWROmhd2nqrU56uPGq7BY6CHI10Xhxmz2iCbOiYO6ru5QzVOCIAginnDESc1TT+cpxzpje72cp6HVLvWqeRrAeXrq0vk4den8sMZLEDzPo8NmRZu1E2ZHz8Rdx5KEjO1dv2Ej9uw7iONVtWhr74DFYkFKSgoGD+yHhfNmYsK4UT77NTQ24YOPvsKefQdhMnUiOzsTUyaOw5mnL4VOF32tFIIgCIIgCIKIJzrqjkAN4Waa5XhUtnGwtVRg//4DGDFieA+PLjgdbs5TdXJ4D/sZhoFDoYGGtwIAbEYDUvJKAvZprTse/iCJgIjO0+LCwqBt01ITx3mq72JX5soJ0/HfH7+W1j/esRn3nX4uVMrQ6/5GS1fVdfXH8MK+WDlhmk+huCAtA30yc9DaaZSifYHoBEVAeMDjHmmbpkuCVqWOap+xIt9DPI0myrbFq35tfAjEIt6xvYaeGQhBEARxwsPzfNzE9to8ap46nE5Rl/NU8MVF7jwNLe6XIEKFhzAR2co60GbtRLJa08Mjio6EFE+/XPsdGpqaUdK3GEOHDIJGq0Zjkx7bd+7F9p17cerS+Th/9ZmyPscqqnDvg0/BbLFgYP8SDBs6GIePVOLTL9dh+669uOuWa5GcHF4UGEEQBEEQBEHEM0e3/yAtm5QZsLGNYACs++gNDB9+X9zXIels00vL2tSsAC19czx/Ad54Zw06HQx2PTosaHtTWwvc3xGOdUChTMhbprjAbDajpVUQfMJznsa/eOoe26tRqpCmi+295PiSgRiUW4CjTvd1Y0cbfjq8D/OGjo7pcQLhKRB3Zc1TkadXX4ZzJ89GU0cb+mTmoE9mNooysqFRCX+Hz2z4Crd/+pbUPlrx1GSzwuI2Kz5e6p0C3rG9UTlPvaKJ4+c8AaDIw3ka7edKEARBEJHC+RAi48F5arfbpfQghVIQTZXO66NQnafeNU/999v82w4AwJRJ40IdLkFA+IrysHEOGKxmFLmV0klEEvJJwOWXno9+fYu93KIHyo/gwUefxRdrv8PUSeMxZPAAAMKP3tP/fQVmiwWrVy7DcmehY4fDgceffhHbdu7BW+99gksvWt3dp0IQBEEQBEEQXcbxnT9JYmDB0Em42LAV/TQGZDWsRXvNH5HRt6xHxxcMa6cJPM9DwTBIyswJu39K/kC0WgCAR3t7B3JysgMfz9QKnXP5m/psLCPhNCpE1ykQqnjqdJ4a4zu21+ZwwGA2Seu5aekxn4jAMAxWTJiGR775WNr2wbZN3SqeetV17QbBTatSY26Z7yQpACjKkE+iiFZki9d6pwBQEMPYXr1nNHGcxfYWpGfK1im2lyAIgugpfAmKNlvPxI/Ka57apetNlfMeJVznqS0M5+m///cqABJPifDgnd5TB8fBaLfAxjqgSeB76oSseVo2ZKDPmN1hZYMxbfIEAMCefQel7Vu37UJdfSNK+hbh9FMXSdtVKhUu/dNqKJUKbPhpEzqMJq99EgRBEARBEEQiwvM87I2HpfWyyQsxrFCHrCThpnvH12/21NBCxlA8G/9Ya8XN6yzoP+PssPtnZbmEltbWwA/jeZ6HymqQ1o/rO8M+HiGn1k08LSoKIbbXzXkaz3V5uzqyV2TFeHkN0M92/QZzNzof9CZP8bTnBbfiLhZPuzqaOBwKPMYSjaDY7OUiji/nqVqpkr33jR1tcFCUIEEQBNED+BJPrT3lPLXJa56yzhqlovNUJdU8De3fTE8ROJB4OmfmFMyZOSWs8RIEz4vBvYCNdaDNau7R8URLQoqngVA6a8CoVC5Fe9uOPQCAKZPGe80IzsrMwNCywWBZDtt37um+gRIEQRAEQRBEF2JuqYOGt0jrBcMmYdC0ZdJ6054NcS1QAUCroQ08gE47kFNQHHb/7KxMt30ZAra1GVuhgusBwtGGDp+xXUTo1NbWScvFRcGdp6mpgnjqcDhgsViCtO45vNyKXRT1WlZQjLF9B0jrRqsF3+7f2SXH8kV31zwNBU/naW1bS1T783RkxlOcbbouCTq3+qt1ba0R/2br49hhK1Lo9tlyPO/1/SMIgiCI7sBdPFU4nZ3xENvrsDskh6lY81Th/C/LhSaehhPbe+lFqymlkwgbIbVXuF61sSzarIk9IblXiacVldX4dcs2KBQKjB0zwrX9eA0AYGD/Ep/9xO2VznYEQRAEQRAEkehU79koLVtU6dCmZmHEvJXivQy0rBGG4/t6aHShYWg1AAA0ajWSk5PD7p+V7XoY36LXB2gJGBsqpeUOKw+TlUVHR3zHx8Y7tXUu8TQc5ykQ33VPvUTFtK4TolZMkLtPP9j2i2y93dyJfbVV+HbfDrz26wZ8s29HzCZFNHfEn+BWGGPnqednGU81TxmGQb7be26yWdFhiWz2vqfzNDs51U/LnqOQonsJgiCIOMDuJiiK9x89JZ7aZLG9Dqm2qVIlmseE/zpCdJ7aw3CeEkQkcM77ECXDwM450G4zhxwrHY8kbuAwgA0//Yr9Bw7BwbJoam7B4SMVUCqVuPjCVSjp45rZ3KwXZqPmZGf63I84I71ZH/zi/I57H/W5fcWyeSgqyEVLU53P1090HA7hx7m3vz/Z45YEfD3Q+Yt9VcmZPvcVSt9ojhtJ33ggEb9bF4yZ2G3Hkr8vvI9toRPKuBPpcwhEIn6vehJ6v4KTnRfccUUQsea4PRv/2mjF4GwFVq1eDADQZeTBntYHGqMwaXDbV29gwV/+2ZPDDIjoFs3MyoyopiRTtwt3zdUiRQN0bnoZWLLYb9uGiv3ScpqWwdS+SlTt24KMaYv89iECI9Y8zchIR0pKStD2aW6uP6PRiPz8vC4bWzR4iopdGWd75ripuPOzdyRB9Jt9O7Dyfw+jxtCCGoMeRqu3Q/fqeafg7mXnRH1sd8EtSa1Bisa7dE53o1WpkZuahmanY1SMd1U5E6jCxasWaBcK4ZFQkJaO4wbXxI+69lakJ4U3kYTnedl5puuSoVHF36OgwnS5MF7fZgB8z38nCIIgiC7DPQI3JSUZRqMxPpynDofkhBVrnYrOUy5E56nNLj+PQKLr8apaAEC/kvDTf4gTF7HmKQMGKoUCNpZFp8OGNI2up4cWEfF3xRwG5YeO4seNW6R1rUaDC849C/Nmy2fnWixW4XWt75s9sX5qPEdDEb0Dk57czYlKhb4paJsBOfH5gI8gCII4Mdm6bQeq2nlUtbN4bMUV0vYhM87A8a+fAQC0HvgJPM9HJEx2NTzHwdzWBAUDZGZmRrSPlJQkqcar1RLYRdpitGJfI4sR+cJDiHNGq9G050eAxNOIEWN7Q4nsBTydp/Hr+u3OONs+mdmYMWgYfj4iiPs21oHvDu4O2Oft336KWjy12G0yl2Nuanrc/E4UZWRL4inLcWjsaENxZnZE+2r2rOuaEl/iaWFahmy9rq0VQwv6hLUPo9UCq8P18DXe6p2KeEYy10UZyUwQBEEQkeAeZZuUlATAu1Zod+EpnorlCl1lC6N1nvqP7b317kcAAK+/8HjoAyZOeKQAHEZwn7I8B1uA71m8k9Di6Z8vPhd/vvhcWKxW1Nc34et1P+CFV97Bb7/vxD+uvhQajSbmx7zn9ut8bhfdNuQs8c2J8v5UrX8p6n2ITtCWHV/LtpesvjXi43ZV33igu75b937/VdA2j6+6OGb7ihXyMQkPvCJ9r2L5HsQ7J8pvVqyg94sg4pOtW7cBAMrKSmXi47DZZ6Ji7TNQMICWM0N/dAdyB4/voVH6x2Yy4LSUXThtqQ42rgaszQJlmDNW03NcUbG8zRSwbT2bif/9bseppTwWDxFuk8ztgaN+icBUVApRyH37hib2pKW5RJ32OI7t9ax52pXOU0CI7hXF01BoNnbAbLMhKYr74WZPR2YcCW5FGVnYXeOK2a5ra41cPPWqBRo/5wlAFtsLON2YYaI3xW9dV3cKMzJl63Xthh4ZB0EQBBFbjhw5iptvuxN/OHsFVpx1Rk8PJyju4mlKT8f2uh3XZrND5UyOEMVTl/M0tFhUm1fNU/+ia9mQgWGNlSAAp/OU58GAgYJRgON52EJ0RscjvaLmqU6rxYD+fXH5Jedh3uxp2Ll7P75Y+73rdaez1Gq1+uwvOlN1usS0DxMEQRAEQRCEOxaLBbv37AEATDxpguw1bVoW2Iz+0vr2r97o1rGFis3UJi0rGYQtnAKALs0lqCgcgVNmKiqPAwBMdle9SKsxcWvucawDts724A27iI4OI2pqhLivstIhIfUh56lvVk6YhhFF3vmlWpUaA3PyMXPwcC9xM9p6kZ6CW1cLxOFQlB67uqeeInFXf5bhUuARIxyJG9NL7I8zd62I5+daH2U9W4IgCCI++PzLtdi6dRtefPnVnh5KSMicp8mi8zQ+YnvFGqXeztPQnH12D/E0kOh6+03X4PabrglrvATB8657aQXDgOM52Ml5Gj/MmjEZ3/+4Cb9v34UzTxccfLk52TCZOqFvMaB/v75efVpaDc52WV6vEQRBEARBEESisX3LJtjtwk3KpIkTvF4fOmcFjn72GACg/dAm8BwLRhFZzcCuwmYySMsORWQOOk1qprSs5m3gOQ6Mwvf80WPHKgAAdkYDsU44a+458TEabJ3t+PzW06B0mFB65o0YMX9V2Ptoa2vDbXfcg5EjhuMvl18adv9Dhw9Ly0OHloXUx9152mFMIOdpF9fJTNHq8OVVt2HT0YNwcCz6ZOagT2a2LEr3olefwqc7f5P61BpaMDC3IOJjNnVjXddw8Y53jVxk86x5mhNH5wkABT5ie8PF+xzj1XnqIZ6S85QgCKJXIJqZrNaeESDDxcG61TztaeepTDy1S2Kny3kq3NeE4jzled7rPEIVXYkTixaLCVaHHYUpGWGX7eCd/2MgiKcszyd0bG+vcJ66k+6crewe8zSgnxATdayyymcfcXv/fuHVDiEIgiAIgiCIeKTm84dw/wItLhmvxsh+3hMES2csA8sLN0Ia3orG8q3dPcSgmNtdDitelRTRPjQpmdIywwB2s383Y0WFEAOa38flyg0W9Ruv7Prsf1CzJigY4MjHD0W0j/c//AhffLkWD//rcdTW1YXdv7z8kLRcVlYaUp9EcZ42d3Sv8xQA0pOSsWTkeJw6eiLGlQxEXpr8YUZxhjy2NhpBEfAVTRw/glssa2N61TyNo/MEgALP2N4IBMVmTxdxSnydo0hheqZsnZynBEEQvQPWGQ3LhliXs6dx2H3VPA1fPHU4HDIXXiS4O0XtDocUs+tynqqkYwWDZVmv8bCs/8/Ebrd7OVWJE4NaYyuqja0wO8L//IXvmPA9U1Jsb/yx74Bwk15QkCdtmzBuFABg82/bvX4kWg1tOFh+BEqlAuPGjOy+gRIEQRAEQRBEF+CwmqE2NyFVw2BMoRL5+flebTTJ6eCzBwEA2q08dm/b0t3DDEpbc720rNCmRLQPpTZZEokBuZvVnY6GShTYK1GarUBhvus+Qhkk6jdeaTywWbbOh1gHyZ0DBw5Ky/v2hV5vU6S8XHCeqtUqDOjfL6Q+6e7O03iueeohuOXEQQyqp6BYG4WgCPgSFXv+HEWKPIXiCCOKeZ6XCeFpuiRoVeqoxhZrPJ2nkXyueq+6rvHzWbqTl5oOpVsyQKSfK0EQBBFf2J0CjD0CIaYncLiN0+U8DW/s7e3tmD13Mc7+w/lRCah2t+M67A5wThFKdJyG4zz1JYQ6AoinF19xAy6+4oawxkv0Djieh5V1wGDtDLsvD15wnjKMFNtLztNu5GjFcfz2+06fMyO27diDdz/8HAAwf/Y0aftJ40ejqDAfVdV1+PSLb6XtDocDL7zyDliWw9xZ0yTXKkEQBEEQBEEkKvqjO6Bw6oV2qJBWONhnuwkr/o5nt7K44zsrvt5Z77NNT9LR0iAtK3WROaUYhgHrFvlrMxp8tjv2+3c4swy4aooGQ1iXaKji7eATcKas2WyWrXe21Ia9D1H8BIB9+w6E3f/gIWFS68CBA6HRhBa7rNVqoVEL4lVcO0/dxKhUrQ5JIZ5fV1KcKRcUa6N1nnq5azP8tOx+imMU22uyWWFxe0Aaj47M/FTPmqfhn6tnXdd4c9eKKBQKFLi5T1tMRlgT5EE7QRAE4R/xGX4gl2M84S4oJqdEFtu7e88+1Dc0YNv2HWhubo54LN41TwWRVKx16nKeBn9vfYmnbADHalpqCtJSI5vASiQ2PA/YIhRPObe5AkJKDgMHx4KNYDJvPJBwNU/1+lY89vQLSElOwsABJchIT4epsxN19Y1oaBR+jE5dOh/Tppwk9VEqlbjqL3/EvQ8+hXc++Aybt+5AYX4eDh2pQLO+BSV9i3DuquU9dUoEQRAEQRAEETPKt6yXlpnM/n5rfPYfMx2FI6bi4A8/4Ztv1+O+e+6EVtvzIpCIyaCXlrVRCDecKgmwC7WWzO16n20aK13ioDZ/CNjqbQCEqF+bqQ3atGyf/eKWTrk7reX4QaTk9g25O8uyOHT4iLS+382FGiqi+FpWOiSsfmlpadC3tMSt89RktaDT7QFad0T2hkIfDzdmrSE652mTl1sxfgS3WNU8TQRHplalQnZKKlpMwt9DY0cbWI6TOTSDoY9Dp7Q/CtMzZd/dhnYD+mXnBehBEARBxDuisOcehxvP+IzttYcnnnrG7UaKbD92O1ing0+h8Kx5Glw8tfoQgAM5T5994r6wxkr0HnjwcHAsTHYrrKwdWmXoySw8eDG1FwCgFOuecg4kKeLnWUOoJJzzdPCgAVix/GT079cXtfWN2LJ1hxTVO2v6JNx5y99x/uozvfoNGtAP/7z7RsyYNhEtLQb8tm0XFAoFlp2yEPfcdh2SkyOro0QQBEEQBEEQ8YR7/dLCEVMCtj3tlKUABJffzz9v7NJxhYulw/UAXZceuXip0LrSZQzNvmt3djRUSMs5A0bAwbtuk/y5VeMVa0crNHA9HHlvjx0GhCd8Ha+qhtVqldYPhCmeNuv10OsFoXpoiPVORVKdaUDxKp561gKNF8Et1rG9eo86mfEiEgNAZnIKdG7xupGKp54Ccbw6MovSXZ8ty3Fe4w5GojhPAfm5AtHX7iUIgiB6HrEeZyChLp5wH6crtjcK8TTMyF+/+3FzniqVwr2K6ECN3HmaGJ8J0b3wvBC9a2UdMFjMQdt79+UhFs5xRfcK3zU76wDHJ44LNeGcp9lZGVh55ikR9S0syMNVl/8xxiMiCIIgCIIgiPiAY+1QtNdAvFspnTg/YPtFixZAc9tdsNnt+PzLtViwYF43jDI0bCaDdLOSkhm580iZlAY4dbgOfYPPNo72RulYxYNHo2qjFipeuFG0GlvDlB57lvZaV9xuu5XHxioWR6sbMXpC6PsoLz8kW688XoWODiPSQixzcuiQawxlYYqnac66px3G+Izt9RSu4kVULIyRG1PEM7Y3nmqeMgyDoowsHNM3AgCMVgvaLWak68KbEK33EhXj5xzdKczIwt66Kmm9vq0VhW7xtsHwPs/4/UUrzMiUrde3G3pkHARBEETsEN2SbILUPXSveZrkNFtFJZ76EC1DxWaX1zwV30OVUrhzER2ooThP3UVcjVoNm90OR4J8JkT3wfM8OGedXhvrQJu1EwVhpJbwku1UeCChYBSC85R1wMrasa+5Flm6FAzIyI310LuEhHOeEgRBEARBEAThm7aqg1AxwkxOlmeQ1X9kwPbpaWmYM2cWilIZ4PA61O7d1B3DDAnW7HIepmcXRLyfpL6j8VOlA2sPOeBIKfZ6nedYqO0ucSGzeBB4dbK03mloivjYPUH1wW3Scn2H8F0odxMzQ8FTPAWAg+XlYfR3E0/Dju1NLOdpvAhuWpVaJoo1tBuicnjEuyvTO7o3fKdts4e7Nh5rngLRxxQ3J1RsLzlPCYIgeht2ZwyuPQFje2PiPI1RbK97zVNFBM5T93PQOeOIA9WhvfbGe3DtjfeEP2gi4eEhSJ8OjkO7zQxHCOK81NeZ2ss4J3MLzlMedo5Fu9UCG8fCwiZOTXsSTwmCIAiCIAiil3D49++lZUdqEZTq4HVFTh0M3DRLi/n9gW2fvwieY2HtaEVHwzHoj2xH9bZ12PvN69j09r9Q/sP74MO4eYoG3maSltNzIxdPC8afjA/2OfDVYQeM6hyv182t9VAywgxZngeSc/vCMuQU3P+jFbess0BVPDriY/cEzWYFNlU5UGHgUNkmnJcvMTQQotiqVCqlbfv2H/DX3Lu/83g6nQ4lJaHXWgWANKdI19ERp85TD0dmXlr8CFHFbnVPOZ5HY0dbxPtyr5OZqtVBF8JvSXdS5FHjNRKRzdNdGy8RzJ5EKxSL9VIBIEWjRZImvj5LdzzPtZ7EU4IgiIRHFOgCCXXxhPvks6QkQTy12x3guNCjRmPlPJWLsHawzvswpdNxqgzHeeq2r6QkHYDAomuzvhXNevp3+ERDcI7yYMBArVDAxrFot1nC68+7ip4qpdheB4x2C2ysA7zb6/FOwsX2EkRPs/Od+3t6CESccu17L/X0ECImkcdOEARBuKjZswmi3JU7eHxIfcbMPgW73/gFAMBVb8dnf5sszRT1pBmAsbUJE864MvrBBqFSOQgH9mxBdqoW84sGR7yfrKxMabm11fsBQHt9hbRsUyZDqdYgo08pGk3CTV2roR3FffpEfPzu5oiBwTt7hBnuo0aOALBP5gQNBVH8nDRxAnbs3A2LxRJW3dODzv6lpYOhUIQ3X1d0nhrJeRo2RRlZ2FVTKa3XtrWgODP8esEmqwWdbu6EeIkmdidaNybg7ciMx/MEfNQBDSPK1mK3wWh1PfDKiTMHsSee8dMU20sQBJH4iAIdy7LgeR6MvxuNOMHh5hRNTnaVBLDb7dBqtSHtoyvEU4fDIdUoVTodp8oIa54mSc5T/67Y/zxxX/gDJhIe0TkKBlApFGA5QfgMvT8vOVcBQKFQgGMdsHEOWBwO2DkWiSOdkvOUIAiCIAiCIHoFPM+D1R+T1ksnLwipX8n4+XA4JVeGgU/h1Gjj8e/NNty8zoIKzjv6tis4bErGd8dY/Naei5S8koj3k5Xtehjf0mrwer3uyB5pmUkRaq9kZbn3SawZ14cOHwEApKen45RxhTh3tAqrShpRsWVtSP2tVhuOVQgC3LBhQzHUWbN0//7QxFOe5yXxdWhZWbjDd4vtjVPnaZzWPAXgJZRGGnnqKRDHoyPT81xrDeHH9nrWAo1XYTEaobjZs95pHEf2AvCq5Vrfnli/vwRBEIQ37jVEHVFE2HYX4hiVSiW0WldagzWM6F73qN6oxFObR81Tp/tV6ZycKP6XDcEV614/NUkX3HmampqC1NSU8AdNJDScm7SpYBTgnPVKQ8XVW6x5yoDlOXTabbCwNrA8J9VUTQRIPCUIgiAIgiCIXoCxsQIaCDf1PA/kl04IqZ9SrUX+pDO8trO8AlZGB4smC6rMvhiWp4DFATQ162M5bL+0OoXOrMzMqPaTlpoKlUrl3Kf3g/jGSpcomJLXDwCQ4y64toQvygTj+RdexkUX/xmHwqxFGgriPktLB6NI3YEpfVXol6lA5Z7NIfU/VlEhPTQaWlaK4cOHAhDcpKHErdXW1cFoEiKXy8rCq3cKAGlpgoBls9thtVrD7u9JU1MT3nt/DVpaYiPCxLvz1J1IBEXAW3CLJ4FYJCbO0zj+LN2JJspW71HXNTslNSZj6iq8XLZthp4ZCEEQBBEz3K8fEyG6V7wOVqlU0LhF3YdT91TuPI1cMHYXPG12OzhWdJ6qZP9lQxClbTZfztP4/zyI8LGyDrRbzRH1FZyjPBiI9Uo52MIo28M5naciCgg1T1mOg4PlAF6MBk4MKLaXIAiCIAiCIHoBZiThkY1WDM5WYN7UcVAnhe6imn7+zTAtOQ8Oayc0KZnQpGRCqdFJsVocx+GWEePA8UCzvnvEU4PBAADIjFI8tZs7sKgsGbB1IqvJW0A0NR6H2rmc119wSmZnu1xtrTES3UQ6Oox46JHHwHEcDh/5Cz768B3k5ebGZN8sy0rO07LSUuT216GlYTcAoLUqNOeoe33UstJS6aGNxWJBRUUlBg8eFKS/SxAeWloa1vgBl/MUANo7OpAXYjyaP776+lvcdff9UCgU+OyTDzB82NCo9hfXztMY1AEFEkNUjI146uHKjMPzBLyjbMNznsb/Z+lOZnIKtCo1rE6XEjlPCYIgEh93d2Mo8bI9jVjzVKVSRi6e2qKP7eV53iu2VxxbJM5T933ppJqn/kXXhx//LwDghmv/EubIiZ6muqMFrZZODMsuQqomvHspoWYpADBQMgqwYTpPRe+pmGbFMIwgwoKHnXMIFVXJeUoQBEEQBEF0NzU1tT09BKIH2bptJ6rbefxQwWLgkvBuchmGQWp+f2SWDEdydhFU2iRZPSKFQoHc3BwAQFNTc0zH7Y9Wp3jqXrM0EhwWE5b2t2FpqQoljkrwHjNn7SbXw/miwaMAAHzLMdw6W4N/LtQiZc9rUR3fk8rjx8E5H3DU1tbhyesuQP2B32Ky770/fIwF/ViML1JgWL9c9BtxkvSaw1AT0j7K3dywQ4YMlomN+0OoeyoTX8siEU9don9HDOqefv3NOgBAdnYWykrDd8J64hn1mpsWP2KUl/O0LTLnqadAnBuHcbbFXVDzNB7PEwByU9KgUiil9XDONVGiiUUYhpF9jzssZlnN1hMBupYjCKK34S7QuUf4xisOe6ydp5Gds1gjVoTjOGlsCqVY81R0noZX8zQ5BOfp7r0HsXtvaJMvifjCzrGwcQ4YrKaw+4pfOXfnqT0Mh7LQ373qqTO6l+Nh5zgAPBJIO+0e8bS11UA2cIIgCIIgiC5m7oKluPjSK7B+/fcJNZuPiA1bt24DIAid48eNjfn+c3ME8VTf3Ag+hNnN0WCo2o+lfTpwxjAVBmuiE2s1KZnSMsMITlR31rYPwa3rLXi3Mht5Q4SoY41Wi/wUBZLVDBh7ZJFH/jh2YDfUCkDBAGePVGFKRiM2Pns1TM2hiZuBqN62DkuGqHDROA0KbRXILnEJnxqHEawtuAghip99+hQjLS0Vw4a69rFv/4Hg/Z3ia0ZGOvLz88I9BaSlupyn0dY9bWlpxZYtWwEAixYugFKpDNIjOO5OPoZhkJ0cPzGosap56iUQx6FbsSA9UzbBoy5MoZjnedl5pmp10KrUAXr0HAqFAgVutUANZhPMIT7A9RKI47zmKeCj7mmE3+NEha7lCILobTjcXGuOBNAHpNhepad4GroI6ohBzVNf/aw2q3NsTvFUcp6GJ57qnDVPA+k1d9x0De646ZrQB0zEDTzPw+ZwoNXaGfa1BC+G6jJwXmszsHOOkNzNgFAzlefdpVNAySjg4FlnJHBixfbGRDzds3cfnvnPczjsjIgSWf/dBkydMReTps7CSZNn4o0334nF4QiCIAiCIAgf5ORk44cff8LlV16DmXMW4ul//weNjU09PSwiCEe3fI0vHrwEP7/2AJoP/Q4uwhnZW38XxNMRI4YjNTUllkMEz3H4Q0k9HligxarM3TAbGmK6f0+aj5djUrEC8waqkG+vjmpfSo0OLO+6fbMZDbLXKyqOw2gDdAWlUCcLrix3wVXFWWP6ANu6+RU8vFiLx5fqMLOfMFtcAzvWPnihl7AbLp2NFdJyn6HjkJzTBywv3PIxDNDRcCzoPsTY3TJn5G5qagr69ysBEJ7ztKy0VCZuhUosnafr1n8vPRRaunhhVPsChBn/7lGv2cmp0sOreKDII7Y30pqn8RxNLKJWqmTjajS2wx5GpJjJZoXZ7hIg41Egdser7mmIcbaJ5jwFvGOK69sNPTOQHoKu5QiC6G24uyIdUdT/7C5EsVeljtx5aouB89SneGoVxFOl5DwV/huK81Qcv1qtgsrpWA0U21s6ZCBKhwwMb9BEXMADcPAczHYbzGE+W+B5ufipZBghupcL7W/X132zgmFgZe1QOcV+LoEmh8VEPH3t9bfw1NPPINMtUquurh5X/+06NDU1Q6vVoqOjA3fdcz+2bd8Ri0MSBEEQBEEQHmz8cT2eefpxzJg+FQ0NjXjy6Wcxa+5CXPHXv+Gnnzb29PAIHzisZux843ZwtTvQuvVDbHr6z/jk2ulYc9tZ2Pr5yyGLdgZ9k+QKnDRxQszHySgUSFKwSNEIt1HW9q6N7m1vrpeWFbronH0Mw8ChcNV6sZkM0rLdbkd1teD4HDCgv7Rdk5opLSsZLiTHZqgoLK1QOEXFgkmnu45pM+Crhy8FF1ZNGRc8z0NpcdWjLSodC0ahgF3nEtQajuwJuI/Ozk4cr6oCAJSVuSJuhzmjew8EEU9lNVfLIovITU+LnfNUjOxNT0/HlCmTotoXALSZO+Fwm9kfb0JUui4JqVqdtF7X1hqR8O9ZJzPezlPEXVDkeR4N7W0h99UnWC3QSGu8etd1jc/P0p0T3XlK13IEQfQ23AW6REimFAVepVIJjcaVSiGKjwaDAcvPWoW773nA7z7ksb2RXdvbfIinFosgniqkmqdO8TQE56nonNVoNJL4mgifBxE+gsOTh5V1wGDtDKsv5xG7K0b32kL8rojOUvcptGqFEg6Og06lBgNBPE2UdA1VLHayY8dODBs2VIryAoAP13wMq9WKq/76F1z7t6uw6dfNuPCiy/DKq29gwvhxsTgsQRAEQRAE4YZSqcTSJYuwdMkiVFVV4+1338eHaz7GN9+ux7frvkNJ3z5Y/YezcfbKs5CdnRV8h0E4VlGF3fsO4ujRShw5dhzNesHl9NQjdyEvL8dvv4bGJnzw0VfYs+8gTKZOZGdnYsrEcTjz9KXQ6bQ++/A8j3Xfb8T3P/yC2roGqNQqDBrQD6efuhCjRgz12ScR6GyuhgryGxEVwwHtlXjl2SdwxX0vY+qUSZg2dQpmzpiGPkUFUKo1XvvZ/Mb9uHeeGkdbOYzrF1vXqQinTgacTq3O1kZkDeiSwwAAjK2N0rI6OSPq/fGqJMAuCKCdhmaIcmJVdY300GBAfzfxNDldmHHrvOuzGVuh0iZFPQ5bZzvUcD0IGXvmNdjKq9CydQ0AgNEfxobnbsH8Kx4Oe98WQ4Psu5RWOAgAkJTbH3yNIHZXH9yOoXPP9ruPQ25JQkPd6pUOHzYUX3+zDg0NjdDrW5CTk+2rOyqPV0kPliKpdwrEznna0WHExo2/AAAWzJ8rcw5ESiI4MosysnGoUaiZaHHY0dppRHZKeIKZp3gaj+cJCILizuoKab2urRV9s/z/2+NOoomKRR6CYl2ozlOP2N6chIjt9RCKQzzX3kJ3X8sRBEF0NbLY3gBOx3hBjBZW+6l5uuGHn7B7917s3r0Xt95yg+TidCcWNU/tPmKCReepSuXhPA1B2BLHoVarpf4s6z+K9bW3PgQAXHjuijBGTcQDHHiAB2xO8bTYbWJwMMRIXVH8VDAKwXnq/Dt2cKw0Edhnf6loqquNRqlCji5Flkokr4oav8REPG1qbsbUKZNl237euAlarRZ/+fMlAIBpU6dg3Lgx2L1nbywOSRAEQRAEQQSgpKQvbvi/a/GPv1+Nb75dj7ffeR+bft2MRx59Ao8/+W8sWbwQ553zB0yePDHiY6z55Cts3b47rD7HKqpw74NPwWyxYGD/EgwbOhiHj1Ti0y/XYfuuvbjrlmuRnCwXqXiexzPPvYaNm7YiSafD2NHDYbZYsXd/OfbsO4jLLjoH8+ZMi/g8ehKjW51LBy/UFFExwk3sIT0HfYseX3y5Fl98uRYAcPHUbAzLVSB98ESMmnMGCoZNhlKtRUfVHqRqGIwpUGJAYdc8TFXo0gG7AQCgr69Cny45ioC53RU3qk2L/nwU2lTALjyANzTVoa9z+1Fn/VE7J3eeMgol7IwaGqfQaTO2IjmnOOpxmJqOS8t2qKBJycT0C27B2vpjcFRvF9rsX4/tnz6H8af/Oax9G6rLpWWrIhkqbTIAIH/QSDTU/A4AaKs5FHAfYmQvAJSVupyjI0YMk5b3HziImTN8/72Jkb0AMLQ0UvHUzXlqjNx5uuGHH6UZ+0uXLIp4P+54iorx6FYszsySxFNAiO6NVjzNTYt+AkNXEKkbE/Dhrg3zPepuPCOZe7PztCgjU7Yeae3e3kBXX8vRJDiCILoDWWxvIoinYs1TlRpaH+JpQ0OjrK0v8dQ9njhS8TRQTLBCIdY8DV08dcX2qqFUqpz9/H8e3373MwASTxMRXtBOwfIcTHYrrA47tCp10H6AW81TJ0qGAcfzUt3Tvc21SFKrkRugP3hvYVQUThkwQiv3mcpxTEzEU6vVBrXa9QE4HA7s3rMX48aORlKS6+FXn+Ji7Nt3IBaHJAiCIAiCIEJApVLhlJOXYM7smXjy6Wfx4kuvwm634/MvvsIXX67FsKFluOH6azF71syw9106ZCBK+hZj0MB+GDSwH26582G0tfsXOziOw9P/fQVmiwWrVy7D8tMWAxCuHR9/+kVs27kHb733CS69aLWs38+//IaNm7YiPy8Hd95yLbKzhAf5+w8exv0P/xsvvf4eRo0cirxc3264eKap2uX04zL64cy73kVLxR4c3LQWq8bm49ct27B5y1YYjYIDr6/WCC2rgLV8A34v3wAHrwByBkNjbZXuUPqPndUlY9WkZQMdgvjX1lgTpHV0WI2t0g1XSnpoTrJAqJLTAaeJsaPFVa+17acX8a8lOrSaeWTZa2V9OFUS4KwRY+mIrHakJ81VLnGR1WZKN5GL//5vfHLnCqhNQlzx8W//h6w+gzHgpAUh7/v4/m3SsiK9UFouGX4SGn56DQDAtdcF3Ef5IWF8CoUCgwcPkrYPH+Ymnu4/4Fc8PegmnkYa2xsr56kY2ZuUlIRZM6dHvB93EsF5WuxDZBvVp7+f1r7xPM+clOiis7sKb/E09L/TZpP836p4/Czd8ap52mYIqZ/e5FnzNL7PE/AWik+0mqe+6KprOZoERxBEd2B3E0wdCRAT63CKnUqV0qfztLHJVYfaZrdDp9PBk66qeSqiVCqkMQKRO08dAWqlXvan1X5fI7oWB8fCZLchXaOTuTVDhXeG52qUKlhZB9psZuSHKp6Ksb3O4yoUCjhYB2wsC6PdCgtrd77kW1YUhVu/MK5o30QgJuJpYWEBDh9xPfj5bevvsFgsmDxZXlfGarUiyccPCkEQRE9y7Xsv9fQQugz3cxOjE3rz+RIE4c3evfvx1jvv4bPPv0RnZycUCgXmzpmFGdOn4fMvvsL2HTtx8aVX4KF/3osVZ50R1r5PPzU8N9fWbbtQV9+Ikr5Fsr4qlQqX/mk1rr7uDmz4aRP+sHIZ0lJd0bOffbUeAHDuquWScAoAw4cOwfw50/Htdz/hq2++T8hZsfqaY9KyLiMfCpUauUPGI3fIeMwAcMkll8DhcGDPnn34ef0XyKn8QNZfxXBAyyFJOLVBg5S8fl0y1qSMXJid+qLRTYDsCuyd7RAfVaRm50W9P21qFuzOSeImg+uBB98pxNlmJTHIK5A7SxlNCuAQRKS25joUInrqDrtqjmozi6RlpUaHk296BV/ceSa0nBkKBtj2ys3IKHgDWX3LQtp307F90nJGH5frM9Otv5Yzw242Qp3kWwwTnaMDBvSHVutyDxUXFyE9PR3t7e3YH6Duqdi/oCAfmZmZIY3bE61WC7VaBbvdEXHNU4vFgg0//AgAmDtnls+HWpGQEM5TD5GtJgxBERCuF/VubsXMpBSolTF5bBBzInVjAt41T+NdVCz0cmOG9rm6f2c1ShXStPH/POZEr3nqi666lqNJcARBdAfuzlM2EZynfmN7BfGxqalZ2uYrWheQC5++apeGQmDxVLg2czlP/cfveu4v1Jqns2dMCXmsRGypM7WhwdSOIZn5yNQlh91ffP6rUijBchysARzGXn2dsqYrtldwntpYB4w2C2ysAzql2m/mLu/lXZUj+E6FuqeJgCIWO5l40gSUlx/GCy+9gv0HDuKxx58CwzCYN3e2rN2hw0dQUJAfi0MSBEEQBEEQfrBYLHj3/Q9xxoo/YPlZq/DOu+9Dq9Xi8j9fgg3frcXz/3sGF/3xfHzw3pt49eXnodNp8d//vdjl49q2QxCOpkwa7zWDMiszA0PLBoNlOWzf6RKYmpr0qKquhVqtxknjR3vtc9qUCQCA38N0TsQL7rG96fklPtuoVCqMGzcGV113MxbduxaDll0LLncoHFB6tVXnl0Y0OzUU0nNd4qK1vTlAy+jhrCZpOSM3etkyKcPlXrU4I4FZmwUa1ixtTy0YIOujTHKJKW3N9VGPAQAMtUel5YzigR5jzMP8a5+DnRdu0dQMi+8e+zM4NrQHLubmSmm579Bx0rIuIw9GpGBfE4uNtUpwDv/7Kz8kxPa6R/YCQszS8GFCrGJg8dR3/3BgGAZpzmjRSJ2nP/38Czo7hc92yeKFEY/Fk6YOD+dpWvwJbsWZkQuKANBhMUs1jYD4PEeR6GJ7EyvONhKh2M460GbulNZzUtO67N+HWFLgKZ4GcZ4aOk14fN2n2F9f7aqz1Qvojmu5009dhFUrTsPECWOQnZUZtH2wSXBKpQIbftqEDqNJ1i/YJDiHw4Gvvvk+rLETBJE4uNc8tSeCeOoco1KlhEKhkGJ5JedpY5NXW0/chc9Io4oDia7eztPgxxD3p1GroXKKr44wRDWi+7CzLOwcizZbZ/DGPuB5Qb5Uugmf4fR11z6VDAOW42DjHOiwW2Dn2IDiqHgtxvitaMoAfKL4TmPkPP3Lny/BF1+uxYMPPQpAeJNmzZyOsWNcD7gqK4/j2LEKrP7D2bE4JEEQBEEQBOHBgYPlePud9/DJp1/AaDSC53lMmDAO55+7GqecvERWZkFk5oxpmDd3Dr75dl2Xj6/iuCAUDuzvWyQc2L8E+/YfQuXxGmCGsK2ySuhT0rfIZz0ZcV+NTXp0ms1ITkryahPP2NqbIPr7cvoMDNgWEISwkYvOx8hF54Nz2NFYvhV7NnwMw5HfwCgUWPDHW7psrFkFfSH6TR2dhi47DgAwdteNYmpW9M7TtOwCiLKXrbMNAGBqrpbKrLA8g6TMAlkfbWomWL2wbGxpRCywttZB9H0VDRrp9Xp2/xEYf+F92PO683O0dqCt5giy+g3zausOz3FQjci7rgABAABJREFUWV1OtIJBrvswhmFgGHkB/vevJwDY8Q+zA/k+dCKDwSDVcCor865XOnzYUGze8huOHDkKq9UGrVYje91qtaKistJv/3BIS0tFS2trxM7Tr7/5FoDwcGje3DlRjcWdRHCeegqKtYbwnKfekb3xKyp6nWs4sb0J8Fm64+XGDCHKVm+STz6I93MUSdMlIVWrg9FqASA4T3me9yv8rj+wC/d++T7u/fJ99M/Ow98WnIaLps3vziHHlHi+lgtlEty+/YewfeceybEUyiS4b7/7Cb9v352QCSIEQQTHXTwMJV62pxGdsmrnvadGo4bD4YDVR2yvP3eo+zl3TWyv6DwVRFTReWq323Hk6DEMLfOeUOte81ThFF/ZALG93373EwBg0fyuKQlD+IeHIHi2Wy0Br4H8wTmlSQWjAMtzYYun7s5TBoxzPCysrAMOjnXWKw18bH+vi5sTZcJbTMTTQYMG4r23X8crr76OllYDRo0agcsvu1jW5ueNmzB82FAsmD83FockCIIgCIIgPDh12VkAgOTkJPxh1Uqcf95qyS0WiJSUlID1TmJFs154sJ2TnenzddH10Kx3OWrEPv4cETqdFslJSeg0m6HXtyK5b2KJpwc0Y7Hhuwrkp2nw/B2zg3dwQ6FSo3DENBSO6J46YRn5faRl3hp5LcpQUHJWaVmdkhn1/jJyC1EDwM7ysNuFm0d3F6hdnQ5GIQ/lScrIFcukwtyuj3oMAKC0ur7bef18/20OnLQEv2/8Hi+9+wUOt3B44yITJgdJYja31kEF4aEJzwOphQNkr5eVusTMg+WHkJ/vLUiXHzri1t7bOTrM+VvicDhw+PARjBw5XPb60aPHpAdi7seLBLHuaYcx/O+Z3W7HuvUbAAAzZkxHWlrs6nUmas3TcEgkUdEzojg852ninCfgLSjWBREUAR/RxHEshHtSlJGFQ41CjWaLww5DpwlZfmrvrt27XVqubGkCywWPLoxn4vlajibBEQQRCe4CncMe/05HuzOlRRQoNRoNOjvNbs5T16RKf+5QWc1TP9G+QccRgvNU/F0Vnad33X0/3n73fdx912244LxzfO5PrVZD5YztDVSD9rW31gAg8bQn4HkeLM/B7LDBwtqRpNIE7+TWV9Q2BecpB1sYkxacd5QQZU6GYcAwDFieA8sJ0ijrIbDKjy+Iv36vUMWapyeSeAoAI0cOxyMPP+D39fPO/QPOO/cPsTocQRAEQRAE4cHgQQNx/nmrcdaZZyDVrWZoMB584B48+MA9XTgyAYtFEMTcaym6o9Npne0sPvr4v2HQ6bToNJthtlj9tgGAO+591Of2FcvmoaggFy1NdT5fdzhvoP29Hg2HqxpwtJWHIzUXVl4JaxccI1ZYWdcNjoo1R/R+hPJecqwDKrhu8DotVrBRvi/pA8fi5vUOdNocOGfVSCxqqsORXb9KrzPJ2V5j0uX2w/pqB4w2YOIA79fDxW7ugBquB0Y2hcbvPkctPg8HnvkMAPDlF19gyIBirzbu76Whqhx1Rh75yYBdmYL2NgMAg9S2INcVk7hj++8YOdTb5bxj21ZpuTA302tsfYtypeWtv21GUX6m7PVtv7v1z/PuHw4659+7oaUl7P38uuV3tLcLotHM6RND6h/q33h9qzyuWm2zdsnvQjQkecQyV+kbwxpjRW2lbD1dpQqrf1f+XnrC8zyS1GqYnQ8Daw0t0DfWhjQ7v8HDkau0Rvab1pV4vpcFqemSeGp12HGs6igyk/zXwTpWUyFbD/ez7Elyk1JwyG39YOUhlOV5R7jbWRbf7t8h2zaloNjneUb73czOKwreKAbE87UcTYIjCCIS3KN6EyEmVpwMqFK7xFNAcG4ajSapNAQQWmxv1zhPBfFT4eE8PVgu/Ou5b99+v/vTaDRQiqJrgEjh005eEMGoiVgg3nXbOBbtNkt44qnUm5Guie0cC5bjJKdywP6iMOp2OS1G99q54CIsD3nsrycMGPA873KoxjkxEU+fevpZjBgxDAsXBI5GWf/dBuzduw/XXH1lLA5LEARBEARBuPHN2s96eghEmNQ7Y1IL8vN7eCTB0aS4XF4qhgNrM0Opif1DTs5mgcGmggp2JKsZqJKid0sp1Vokp2egs1kPg0GI7W2vr4B4+5iS29erT9Ho2Xhr9+MAgJJ52V6vh4ulpVZatvMqqJP9O93y83JRVjoY5YeO4OdfNuOav14WcN/NNi0e/MkKBQPc+vcLvV4vLMhHcnISOjvNOHK0wuc+xO1qtRp9+/bxen3ggP5QKhVgWU7mUnX1PyYtDxrYP+B4gyEKBkaTKUhLb77/QYgYUygUmDNzelTj8KSlUz6eHD9OuJ4kKzkZaqUSdueDvwYP92EwPKNes5NDF2+6G4ZhUJiWgWMtgqhtcdjRbjEjI4CgKKLvlJ9nThyfp0h+ajqO6F1ul4aOtoDiqef3NTs5/r6v/sj3cAI3dLT7FE9/r65Au8X1EHt4fpGXIznRiOdruZ6eBAdEPhEuWrpzYgjRNdBn2HM43CZ2GfRNUX0G3fE5msSyERyLlqY6aJxx6c2NDTh0cJ+sbXNDLXIzva8hzG7XsB0dhojG29Lsv2yI2dSOlqY6WDqFsXIcB31jLczOWuetPiYgdrQZnEscbBahnS3ARMBFs08SxhHj95r+FoOT7LBjoFILgIGjzYCWMO6JeAD5HJCtSoKKBbJVyVBwQGtzPRQhTDDkOBZ9oQIYQOHUSrOVSVAA4BgGvEoFFQc4eDsYeH+OaQ47Bil1UPIKwIfWmq3UQgEFOlv1sIYZRxwtkUyEi4l4+uTTz2LFWWcEFU/Xrf8O73/wEYmnBEEQBEEQJyA6nRYmUyesVt8Px8QHbDqdTtYHAKxWm9/9iv2SdL4f5oncc/t1PreLF/z+LqaDvR4NjU3CQ/9+/ft3m6slUvicAlSxuSivrIc6NQsn5xZCpQ0uUrgT6nv5/MEMVFfX4OSli7C8eECkQ5aRk5OD5mY9Oi1WZOcVwe5Wb7Zk+ASvMWXlFkKpVIJlWVhsbNSfD8eyeHGbDbnJDGbNOCno/hYvWojyQ0dw9FgFOq2cl6Dp/l42bBZiKzkeGDttrte+eZ7HzJF9Yao/iqK27UhNToLGIw65skoQd4cMHoT8Im8xGQAGDx6M8vJDOFZZ5XWM49XCePqVlKBPv0EBzy0YOTmCy9XUaQnrfWdZFj/8tAkAMGXyRAwqDVwrViTU72WL2VWLV61Uon/fgWHXIOoOijKycNwpKLZbzNCmZyFFqwvSS8DMyGekl+T3Cesz6MrfS1/0zc6TxFMAMKs1GBjCsVvdPstUrQ5FRUGysXsAz/eyX14BNlUell43KVQB32eLYrdsvU9eYdz/OyPSP78I2OtaNzGMz7Fv/OU72fopYyf3yL/lBEEQhH/c65z6c2rGE+IYRXdncVEBqmtqUVVdg2a93mdbf/sQliOLVA/kPBUdp+IYAcF9KvYxm81efUQHsFqlkmJ7E6EG7QmLM/6WCxCRGxKMsDMOPBQR7oWBM443BLNoYvhJQydmsb2hwLJcXN5cEgRBEARB9DY6OztRWXkcHUaT36vcyZMnduuYcnOyYTJ1Qt9iQP9+3uJMS6vB2S5L1sf9NU8sFis6nTeHOTmJ5Tbp7OyUXJDFxfH/MJdRKFCdNRWffvMB0tIseCRM4TQcDM7POzMzdp+pGBnY0irECTJm18OPwkEjvNozDIOsrEw0N+vR0hJe3Uhf1DS0YFeDEKl1+tXLg7afN3cO/v3s/5CdxOCnj17EOVff4bftoUMuQWXIkMFerzMMg0V5jdDkqgG0oa36EPKGTpJe53ke5YeEmK+yMv/1SocPG4ry8kPYf+CgV73FcucYysq866WGS2qq4JAzhlnzdPuOnWhuFj7XJYsXRj0Od+ysA61ubsXclPS4vbctzsiWxFNAqI85JD+03xjPuq65afFdC7TIR43XEUW+6zGKmKwWmO2uCTnxXu9UpChd/ntY3x74d0lv6pCt56QmTs3TwgzPczV4teF5Hl+71TsFgKUjxnflsLqdeLuW6+lJcEDkE+GihcT3xIc+w57DXTxMSs2I6jPojs9RqRac8knJqcjOK0LZ0KHYsnU7amobYLHJ62rrUnyfj3vFR4VSHdF4tUn+EyMysnKRnVeEjKwc17bsPHDOfyrsDu+Jn4xCkIFSUtOQ5vx3luP9v5d79h0EAIwaEbzudjjQ32Jw9utrUWM0gud5pGl0GJGej3RtaIlPNtaBI03H0W43I0udAqPNDqVCgaGpuchODn4tWG9qQ1VrAwAg2RkXbHE4YHbYoWAAjueRrkrGIKUKCnhPbqtrrkGtrQMZqiQoGe+Y4HarFTqVGiMyCpAR4jn1JN0qnh4/XoX09MS5YCcIgiAIgkg0jlVU4p77/omNGzeB47iAbQ8f3B3w9VgzoF8fVB6vxrHKKkwYN8rr9WOVVQCA/v1cDrv+JcJyVXUdHA4HVCqVzz75eTlITor/i2939nz1Em6fo0GrGejHV/X0cEIiL1dwBHZ0dMBiscgekMYKm80mxbVm+amRFglZWZnQKAHWqIe1oxUa3vXwNzXfd8xsdna2IJ62Ri+eVlQel5YHDAgea1takodb5yQhP5kHW/4pHNb/8+v0FWN0iwoLkZ7m534rJR8wCt+z6oPbZOJpU1OzJOQHEj+HDxuKTz79HG1t7aitq0OfYqEWq9FoQnV1jbO/f/E1VNLShIdFVqsVNptNqjUVjLVffystL14UW/HUM842nkXFIg/hqbatJWTxtNlTPE2J7/t3z3Otawv+t+p5jjlxfo4inoJisHPVGz3E0wQ5TwAoTM+Urdf7ONdDjXU42twgrRekZWB8iXc950QkXq/laBIcQRDhwnGc7HcsEZynontTpRLcmYMGCv+2HD1WgYZGeZSuw+HbHWqLYc1ThhFqRLojjk2pdN0bsywLu03oY+rshCeymqchOE8feuy/AIDXX3g8ovETkSN+2hqlEjaORYfNErJ4ykv/Jwj4SgUDjudh54S/PYezbqlKofTd38dkLbVSCYvDDp1Kg067LaAFVax56m+KKcMw4OFemzW+iVg8ferpZ2Xr+/cf8NomwrIsDh85iq2/b8PsWTMjPSRBEARBEAQRgJqaWqxcdS4MhjZMmDAOzU3NqKquwWmnnozKyuPYt38/WJbDwgXzJXGiO5kwbhR++HkzNv+2HWedvlTm2mo1tOFg+REolQqMGzNS2p6Xl4OSvsWoqq7F79t3Y8okuatk0+ZtAICTxo/unpOIIa21R5GbrEBuMqBRRHZT3d3k5uVKy83Nep+1MaOlra1NWs7MzIjZfmcrfseSxToABtT8/pW03QEFtOm5Xu15nsfEQg79HEoMYMth7+yAOoTZuv4IVzxNyS5AVrIKgB1KhkfNrp/Qf9ISr3ZWYyvsdXuQncSgrMzbdSqSVjQY1kOCeFp/WP6w/WD5IWm5rDSA83S4Kwb3wP6Dknjq7nwtK43eeZrmJgB3dBiRkxO85izP8/j6m3UAgPHjxqKwsCDqcbjT3NEmW49nt2JxprcbM1Q8Bbd4FomBSMVT+Tnmxfk5ioR7rs0mDyE8jr+znoRyrms9XKeLRoyTYgwTmXi+lqNJcARBhIunOJcI4qk4ZvH3atCgAQCEyaMHDhyUtfUnjLpvtzkFzabmZnz3/Q84eckipKcH/zdZ3EdychJMJrkYqlCI4qnr3z0Hy0p9Ok3esb3iONRqtSS+sgEihRPx/rq3IET18lAplLCxLKxs6H83ovgpPmlRMAo4WAdsLAue53GgpQ48D4zK7eMzRYd3ypoKt5eUjAKZOmESr5Te4idLmOdF8TdAQg/vW6SNRyIWT598+llp5gPDMNi3/wD27T8QsE9ubg6uv+5vkR6SIAiCIAiCCMCz/3kOBkMb7r/3Tqz+w9m4/sZbUVVdgyceexgAcPjwEfzfjbfg6NGj+PD9t7t9fCeNH42iwnxUVdfh0y++xfLTFgMQbqJfeOUdsCyHBXNnIN3jYeCykxfg2edfx1vvfYLSIYOQnSUIavsPHsZ3P/wClUqFkxfP6/bziZbOljrpYjyrKLiYFg/k5riioZqamrtEPG2sPorCVAYmG4+sGKbWMCodYBMcrVaWwfO/25CfwmDG1Ik+bxwZhsFJKXVQDRWibi1tTdGJpxWVAICc7Gz/7lD34yuUUBeNAOp2AgD2/viJT/G08eBvOKPEgDNKtDCrj/rdX3HZWBw7tAEAYGo8JntNjOwFgKGBYnuHu2K79u0/gAUL5nn3H1oW4KxCw10Q6DB2hCSe7t23HzU1Qt3WWEf2At5xtnlxLER5OU8NLSH3TaTzBCITT73ibBPEkel5rkFjexPYeer1ufo416/3eUT2juwdkb3xfC1Hk+AIgggXT7E0EWpsirVBRYFx8KBB0mu/bv5N3taPeOqwO7zaPPTwo1jz0ac4dqwCN93gO4LcHdG9qtPqYDZbZA5eUTR1d55yLAubTRC2OgM4T9VqtdTPEUCU+/tfLw46RqJrEN2bSkYBjrfDxoUhnoqeTuc/0QqGAQsOVlaI3u2026DwEacr9Zesq37ETwZO56hvOOerfp2nzr69Xjx9+MH7AAgneuPNt2PiSROw6uyzfLZVq9UoyM/H+PFjQ45cIgiCIAiCIMLj542bMHDgAKz+w9k+Xx8yZDBeeO5ZzF9wMp5+5j+45abrozreth178NGnX0vrRueM2EeffgFq50zd8WNH4qzlSwEASqUSV/3lj7j3wafwzgefYfPWHSjMz8OhIxVo1regpG8Rzl3lXQty5vRJ2Ll7Pzb+uhX/d8t9GD1iKCxWK/bsKwfP87j0otXIyw0ursQbDqNeuhjPK/HvGIwnMhUmzB+oRLqWQf22L4HxY2N+jPrNH+PmWULdM1XVBgArYrJfhTYFsAn1MI9XVmJPo/AA4pQr/NcfZRVaqDhh5rbV2IpIpQersRVjTD8hc6waqvQkcKwDCmXwW7HhM5dh//uCeGo+vtOrzigAHN+3TVrWZPh3WxaXjoUomSo6m2X7Ki8XnKMpKckB6+/m5uQgLy8XTU3N2O828150rqpUKgwMwVUbDE/naSh87R7Zu3hB1GPwxCvONo5FxWKPOqC1baGLp+7nyTAMspK7P6UgHLzF0+DnmkifpTuF6eEJxV71axOo5mmBZ2yvR83TFlMHNh8rl9a1KjXmlI5Eb6C7r+XCgSbBEQQRLg5H4jlPReFTpVIDAIqKCqHT6WCxWFBbWydrK7o5PXEXVcVo37q6egBCWcNQcBc7VSqVJIwCLtHU03kqCq6BY3tDc54SPQfPO92fCgYsz8MWpvOUd4vNVTIMOI6HjXPAaLfAxrHQKYUoX6U/5ykPn5OLAaf4GSi2N6goKsingYsSxA8Ri6crzjpDWl7z0SeYM3uWbBtBELFl5zv39/QQCIIgiDinobER8+fNkdbFmyKr1QatVpjAlpuTg8mTJ+Kbb9ZH/cCto8OIw0crvLZXHq+WlouL8mWvDRrQD/+8+0Z88PGX2LP3IKqq65CdlYllpyzEWacvhU6n9dofwzD46+UXYmjZIHz3wy/YsXsfVEoVRgwvxfJTF2HUiKFefRIBhdXlCsosSAznqcZUi+XDhAcJluM7uuQYlg6XIJCUHjtRXJWcATjf8toKV8xsoAhdXp0CWAXx1GRohHe4b2iYmqqQq7Eit1gJO9pDEk4BoN+E+dj73n1QMICGt6Ct+gAyS4bL2jQf3w/xsUlWif+/hbQil0CvhgOWtiYkZQp/n+VO8bN0yJCgsZcjhg/DD00/y8TTcmds76CBA2IyWVbmPO3oCNDShRjZO2xYGQb0j/3fUyI5MiON7eU4TubKzElJhTLOY1CLMsI/10QVTwvSM2R1z3zVARVpNrbjcKPrAW+yRoPMpJQuH2Os0KrUyE5JRYuz1nBDuwEcx0m/T+v27wLn9nBudukIpGhjX4O7J+jOazmaBEcQRFfDeog+iSCeimMWY3sVCgUGDOiHAwfKvdr6Ox+7j5qnVqsgfra1t/vs47UPt5hdtdpTPBWdp666lRzLSccymUxeky7lzlOhnyOAE1jfIlxn5GRTPeruRnR/KpxCo41lwfFcQMeo1Ff6f+GzZ8CAhyDAttsssLMstEqV35qjXEjiZyhj8NObOUGcp+689cYrsdgNQRAEQRAEEQWpKSmyi1DRvdXU1CSLV9XpdGhsaor6eHNmTcWcWVPD7ldYkIerLv9jWH0YhsGi+bOwaP6ssI8Xj7A2CzRw3QDrsmJbn7GryCosQa1z2dFp6JJj2Ext0k1KSmZezParS8uCrUFYbq53CfyBhDaFNhWwNgMADI21iFSSa6xwlTfhdKE/gNCkZMCRWgyNSXjX9/7wMWacLxdPbS3VECWDkmH+YyvVSamwKpKgdTppDdXlSMrMB8dxOHzkCACgLEBkr8iwYUPxw48/o7LyOIxGE1JTUyTxNZT+oZCWGp7z9PDhIzh8RIgsXrJ4UUzG4Elzh4fgFsd1Mou9YntDE08NZhNYt0i4RBAVC9IyoGAY6UFPJDVPE8WRqVaqkJ+ajgZn/d1GYzvsrANqH5Mxvt2/00NcHJlw9UAL07Mk8ZTlODSbOpCfJjgW1/bSyF6ge6/laBIcQRBdjd3hKZ7Gv9PRM7YXEKJ7fYmn/mJ7bT7FUysAoKM9tImBktipUXvVi1YpxZqnrjE6WIfUh3VG+Gq1rt9kUXwVnaxCuwCxvTfcAwB4/YXHQxovETvchU0FowDHc7CxLHSq4NdynuInwzBCdC/Pod1qhp1jneKlv2MLxw8Wu+t37GLNVT/OVWcjv+JtvBET8ZQgCIIgCILoeQoLC1Bf3yCtDxksOM02/boFZ688E4BwE7Z9x07k5UbqoSNigdnQKC1zPANdek6A1vFDem6xtMzYTV1yDNbSId2kpOfETlROzsiV5OrONuGBc0ZGOrKyMv320aRkAE7NrEPf4LddMBqO7pOWtVn+Y3F90WfsXDT98hYAoG7XjwBull7jWAfUtjZpAnDegBEB96VILwIMgshYtf93FI2aidraOpicbqOy0iFBxzN8mOsh98Hycgzo3x/NzfqQ+4dCuM5T0XUKAEuXdI14mkjO04L0TJlDMZQoW8BHzGtK/J6jiEqpRH5ahhTr2mRsh83hgEbl/1GH3iQ/z5wEOE+RwowsSTzleR4N7W3om+X978dXe7bJ1k8eOaFbxhdLCtMzsa/OFW1Y19aK/LQM2BwOrD+wS9Z28YjeI55257UcTYIjCKKr8YyFTQjnqXPM7oLloIEDfLb1W/PU7TxtHuJpe7jiqVotRQiLKHyIp3abXVYX1WTqlIunYmyvm/OUZf2Hp/YpSozJvb0RdwFSyTijezkHdFAH6emqeeouXSoYBTiOh51hnfVUeWdtUv/HRiDx0+dRhL4s7194BUQn7AnmPAWEP+jPv/gKv27+DY2NjTIruQyGwZuvvRSrwxIEQRAEQRBOJk08Ce+89wHaOzqQnpaGBfPnQqPR4L4HHoLVakVRYQHefvd9NDQ0Sg/giJ7B3FovLduVSWAUygCt4wetm8irYi0+a3BGC29zibKxFE/TsvNhcC5PKAAwTg1FSgp4zgFG6ftGVJuWDasz+dJkaI742Ia6o1K0bmaf8OrbDp91uiSeasyNsHa0QpsmOAstLXVQMsKNJ8czSMnrF3BfGX1K0ekUTxuP7gHgqlcKhOYcHTF8mLS8f/9B2UOjmDlPw6x5KoqnAwb0j5mA60kiRb2G41B0R+/pyIxjd607RRlZspqY9e2t6Jft37Xe5OEizkuQ8wSEc91ZXSGt17e3eomnFrsN3x10iYsMw2BJAjozPevZ1re1YmzfAdh09CA6LGZp+5g+/dEns/dEvtK1HEEQvQnWIxbWEUbtxp5CFD5VbsLkwIEDfbb1dNZK291rnjprqLpie9tCGofNTTxVe0wKE8squIunZotF1qazsxPZbpG7rpqnGqkfx3GyWHx3Hrz3ppDGScQWnudl7lF352lo/QGAl4mfYkoLx3Nu7fyIp+CF7n72zzBMTDyjvb7mqTvNej3Ov/ASHD58JKhqHOuHKwRBEARBEITAyScvwbYdO7Bt2w7MnTMLOTnZuPmm/8Pd9zyAu+4RamfzPI/CwgJc94+/9fBoT2yaq4+4VnQZPTeQMNGluVwuSoaHw2yEOjm2kZcK1nXjr0uLXY2djNxCVLmtTyhSwo4OMAr/t0QpmXmwOpetHaG593xha6uXonWLBo0Mq2968RBYFcnQcp1gGODYb99g2Pw/AAAMNS7h06ZOh0IVeDZy36HjUb5XqG9nbj4OACg/5NrH0LLgwuOAAf2h1WphtVqxf/8BcJzrRr4shP6hEI7ztLq6Bnv2Cs7eJYsXdtn9pmfUazw7TwGh7qncoWhA36zALrVEcte6U5SRhe1Vx6T12rbA4ql7XVcAyElJjNheQIiydcdXTPFPh/ah020y+0n9Bktxt4lEoYd4WtcunOvavfLI3kQUhgNB13IEQfQm7A65M9PTidodWCwW7N6zF+PHjfWKv/WFKPCq1K7r6kGDBsjaiAkfYl1SdziOkzlPpdhemzO2t8PoV7B0x+7mFFWpPcRT53m4i6dWD/HU1NkpW/cV2wsIAneiRfv3dtzVNVH4tIU48cCXJ1TJKMDyHBxOZ7IYzeuzPx84ltdtB14KK+98gQngPWUYgONPMOfpQw8/hkOHDmPM6FG47NI/YfDgQUhNSYnFrgmCIAiCIIgQmTzpJHyy5j3ZtgvOOwdjx4zG2q+/RZuhDYMGD8TZK85EenpiPBTvrTTXHJWWNemxq+vZ1aiSUsHyjOR2tHQ0x1Q85XkeKs4u3YhpUjJjtu/UrHyvbZw2M6DYlp5TCFEytXeGNkvcE57nobIapPXcfmVh9WcYBmkDJ8B25GcAwMFfPpfE07rDe6R2muy+QfdVOHg0ygF02nmYeOHhVXn5YQBAVlYmckOIgFSpVCgrG4Ldu/di/4GD0nadToeSvsHHEApJSUlQKpVgWRYdxsDO06+/dYvsXbwwJsf3hafzNCfO62R6Coo1hpag4qnXOSaIqOjpUAxW9zRRa54CvtyYBq82X3pE9p4yKvEiewGgKD1Ttl7fZgDP8/i6F9c7BehajiCI3kU8xPbefe8DePe9D3HLTdfj0ksuCtpedIq6C5ODPJyneXm5aGxs8hnba7c7PNblsb0cx8Fk6pRNFvSFe2yvWi2fIBmS89QkF0/d9+fej2VZr/0TPYfgOnUJkEqGgY1jYedCFE99qJ8KhoGDY2HnWCgZhZe7VdYfgeuRMoDfFhzv3B50Lit/YomnP/z4Ewry8/HGay8ihURTgiAIgiCIuGLM6FEYM3pUTw+DcKO9sVpaTnWrIxrvMAwDh1IHJSdEJlrampFW4DvGKhIcFhMUjOtGSp0SO7eUxoeLVZMV+L3PyHPVJ+WsweNjfWEztkIF14OjlNySsPcxcs4Z2H7kZ9hYHvpGvbS9ve4oNM7lnH7DfHd2I614MD43jsK3P23FoEE6XAyg3BnbW1ZaGrJrc8TwYdi9e68s8nfIkMGyBzHRwDAM0tJSYTC0BY3t/eab9QCAosJCjO7C3zl3YTFFo0WyRhugdc9TnCGPMQ0mKALe4mmixNkWeZ1rYJe4+3mmanXQqTUBWscX3kKx/Fw5jvMhLiameOrpPK1vb8XBhlpU6F01w4vSszC2z4BuHlnPQNdyBBEfNOv1+PTTL3DKyUtQWEg1IYPh8Irt7X7n6ZGjx5z/PRqkpYAYxeselZuamoKCgnw0NDQiMzMDycnJQlsf4qnDw23rqnnqSoVob28PKp7abP5je8Wap+7RwhaLVdbG03kqiacaNVQqVz+HHzfwpVfeAAB44dmHA46TiC2SMOm8LRNiex1SbG+HzQKLw448PxOYBQGT93KedrI2KBgGCuf9nj/tUqq36j+4Vxqp99iDw0BwbQcSaOOJmHiyOzqMGD9+LAmnBEEQBEEQBBECbYpsbKpyYH8Ti7zBY3p6OGHBq103+u3NdTHdt7u7k+cBtS7wQ4Vw8OVizSweFLBPSpbLFczYOwO09I+xsVJatkEdkVO3aMQ0bHEMxy3rrHh2Y6s0a59td9XOLRkeXCBRKNXoVyrEBldUVKKzsxNHjggPksKJ3B0+bCgAwGw2Y+eu3QCAoTGuNZrmdAMGiu1ta2vD79sEoWjBgrldFjlmslpgsrkeSMVzvVMRT5GtNoigCCRWXVd3wnGemqwWmO2uh5eJco4iXrG97fJz3VFdIav/OjAnH0MLEmeCjju+Ioq/9ojsXTxiHEUNEgTRrTz77HO474GH8NgTT/f0UBICh4cLk+0B56kYrWvzEbHrC7FOq2fE76CBAwAA+Xl50Didmr5qnnoKqna7HTzPS85TQBBPg47bzSnqORZRNFUoXf8Gms1mWZtOT/HU5ooBVirdY3v91G11OPzWdCW6DlHUFCVKhYJx1jx1gOd5HGtrQmW7HnY/n5tLknSJnyqFAlqVCilqjdM56j+2lwPv2V0Gw/gXSXkfwq0/EsR4Ghvnaf9+Jej0+AMlCIIgCIIgupaa2tqo+vcpTswHqr2BSlsW3tnjgEKhwD/mrOjp4YSFKjkDsDYBAFobqoK0Dg+bySWe2hk1mBg+FNekZGKPpQCjdA3StuIhowP3Sc0E4JzBy3MRHbepslxa5pOyA7T0j1Kjw8g5y2H/djvs7e3Ytm0HBvUrgIY1STe2oThPAcFhCggOte++/0GaDS9uD4Vhw1zHEmcnl5WF3j8UxNn4gZynG3/5FZyzds+c2bNienx3PGtkJoLgVpwZvvPUs+ZpIpwnEJ7Ldk/tcdl6orhrRYqDxPZ+ted32frJo07qsjrAXY1XRHG7AWt7YWQvXcsRRGJR3yBcRzY1N/fwSBIDh4fA0xNinChC2tzqgQdCFHw9BcvRo0Zi069bMGjQQFRVVcv27et40v4cDklAFWlv9z850HM/al81TyXnqWu7uzgLeIunNpkYG9x5+upzjwUdIxF7eI9IWwWEmqdW1gELa4fF6WxmeR6+wpZ5Z3Su++UfwzBIUQupOUL/ALG9PIILoAEif4Pl9oqxv1yCOE9jIp6uOnsFHn38KdTV1aOoqDAWuyQIgiAIgiCCMHvu4ogfijIMg0MHdsV4RESo1NYJjs2C/HyvG/N4R5ueA7ZVqJPZ3hTdQ19PrEaDtMwpdTHdt1KtQV3ScAy21SNJLfzdBKs/mpJbgjfrBuG3HfswadJonB3BceuP7pWWddmRP+SeO2e2tPzdhh8w5E/n4vGdKWA69Vg4ZSROzQ2t3qi7w/TzL77yuT0Yw4Z5v29Dh8ZaPA3uPP3p540AhBn0U6dMiunx3WnqSLw4Wy/nqSG481TvVQs0/s8TCM95+pVHPdCpA8OrQdzTeDuK5ef6lYcz8+QEFhfzUtOhYBjp4d6x5gZ0ujnAk9QazHY66RMZupYjiMRCrCvp6agkfONZ45TtgdheUYT0JXT6QhR8Pe/Rrrziz+jbty8WzJ+LK6/6u9992nw4T90jewGgLQTnqeiU1Wh8xPY6J5gGcp4ajSavcQCCeKpQyGueEvGDS9QUrg0YhgHDMGB5Dq2WTtg4FmqF0m/NUEHADFCzlGECOk+DCaAMxP4++gY2rYoDcLY9gcTTP110AXbv2YvzLrwYd91xC2bOmE7RKQRBEARBEF3M5EkT4fm8zW63Y9v2nQCAjIx0yZFQU1uLtrZ2MAyD8ePGQK32NU+R6C7q6oS41eLixJt4mJJVgPZKwGjjwZgtMd23MrsEz/9uQ4qaweIlsRfD8jKTkNTq+qNJyQtcf1ShVCElMw88gNbW4M49X7Ta1djXwCIvmcHIkqER7QMACgsLMHLEcOzdtx/ff/8Dzl21HFV1gushafB0KJSh3dqVDhkMtQLIT2XQtPdHaXuZj9hdu7kDSrUOCpX89yI9LQ0lffuiqtpVuzcc52ooSM5To2/nKc/z+PFHQTydOOmkLi0hk4hxtpHUPPV0nuYlwHkC4YmnX3o4M08ZdVKXjKmryExOgValhtXpOnCveVqpb8S+OlcaQFZyKqYkmDjsjkqpRH5ahhRDbLTK/72ZUzYSSZrEqVfrD7qWI4jEwuoUT/1FnRJyWA9XY0+IzlLN0VCdp07BV+nmzgSA9PR0nH/eagBC3VDAT81Tj3O02+yw2uSu0PBje+W/96Jz1N156lnz1N15yrKsJFapVCq585S+y3GFWA1UXrOUAcvx0JuNsLMsVIzSr3OTd2qfgZ2j/sVLybkaxdgDdWbgcrcmAjERT+fOXwpAuJC7+NIroFIqkZefBwXjLaAyDIMN362NxWEJgugGdr5zv9/Xxq6+tRtHQhAEQXjy9puvyNY7Oztx3gUXY8jgQbjpxv/DvLmzZa9v+OEnPPjwo3A4WLz68vPdOFLCk9pawXlaVFTUwyMJn0HzL8S8B98DxwN3zYyt8NBpA/Y0CjGsp/WfGNN9A0BeMgM4dRUbNFAnBa8/mp2VCQBoaYlMPD1mycCL24SHH7uevjKifYjMmzsb1Uf3I89agV+/eFvaXhpGvVHepMfDi3VQMADL8dhVB+TlFyAjI0PWrn73j/jtheugS8/F7BvegjZNLlANHz5MEk/T09NRUJAfxZl5E8x5eujwEdTVC5MQZs+cEdNje5KIomK0NU9VCiUykpJjPq6uIE2XhFStThLX6tpawfO8l5uvvKEWh5tcdYLzUtMxsX9sa/V2NQzDoCgjCxX6RgCCoNhhMSNNl+TlOl08YqxUEy1RKUzPktVwdWfJiMR11bpD13IEkViIAhXVggwNT2GuJ4S6sJ2nTvHT0+3pjjh5xe5DDPY8js1uh9XiKZ6GF9vrOVlGdI7KnKcW+SQjk5t46u4AVqtVUuwv4C1wi9x2z78AAPfd8X9Bx0rEDsm96XYZ+//snXWc5OThxp/MTEbXfffcOVc49HA9pGgpLS4tFUqhLrSFtlSgtLSl/VFKcSs1irvDGSec69qty+zs+GSS3x+ZJG8yyUxmd2z33u/n0zISeSeTnUvy5HkeC2MBD7H3NMZzcIFN7TwVl6D7vhybazh/ahgG4A0mEgQBgmBOeDVaf7GRFXto+8GDaD94MLGBBMQ4Dh0dnfLr5P/Iu5MpFAqFQqFQKNnjd/f+CQeaW/D4Y39PutgGACccfxwee+Rv2H+gGb/93R8KMEIKAPQ3b8PSsn6snGjFjKqxFdkLAHWNTfIJU19vdvueBr1e+XFlQrTMJuWscuHAbP9oVZU43aDXK/drZkJzi9ixWFtbg5KS0bkjV9RGcPuJTnx2AYvejWTkrnnXp6uqQW6xsVoY1HoY3ched3UTShumITzUg46NryW9P3eu4qKdPXtm1nsV03Wevvvu+/LjVauOzeq6tYxF56nH4VSJn51Dgyn3Xy4ex2BQiXerKSkdU12ZjYTTNsLFMBhM3m+0rtMz5i+DdQwmZul1gQLAy9vUkcRnzF+WryHljIbyCsP3Th/DkcSpoMdyFEpxE47Q2N5MSIrtNRDqcknGnaeJGFtrilQXSVg103kKqIVMwFxsr7Qcu51NihBWnKeKCBrRiKek81SKAAYSTlbis3EGsb0trQfR0now7Tgp2UXpPFWOwy0MA57nwQm8HJlrJD3yQjpPZyI212j9gugfTX0aIOguQJD/P1XnKaM4VMcAWblas2/31mwshkKhUCgUCoUyCl56+VUcdeQRqKmuNpymproaRx+1Ei+/8hp++P3v5HF0FIm2Tz/EGTOlE+49BR5N5jgcDpSWlmJ4eBi9fdkVT72EeFpRUW484QjxeHfKjx0xr/GEBFVVlbAwgNvGo7+7A7WN5rpFJSTxdOqUKRnNp8e8I0/BR2ufAgBMdkdgtwJxWDFt6lTTy7BYWXD2clgTn7+xhNEVX1vefxbDnfsAAIHelqT35x5GiKdZjuwFgNISUTwNh8OIxWJJd9y/kxBP6+vrMCcD8XgkjEXnKSCKbEMh8cJZLB5Hf2AYtaX6f1cDQb/qDvaxIBCTNJVXYk+P0sHcOTSIKo/aWf7Cp2rxdPXCsRXZK9FQVqF63jU0iNqSMnywT/l9s1ttOGnOwjyPLPtohWKJpZOmJW2H8QI9lqNQiptQSIrtpT2RZuA0YmkhHLuSaBqLmnSeJsbIsmacp+k7TwEgoOkfHc7YeartPJWcp4p4qnWeBgP6zlObjVVFEscNvpPf/Pz7acdIyT56jlILYwHP84jJzm1j56gkYRqJnwyTEF8N5ufNdJ4K+vKnKTdpYrFjpfN07N1mSaFQKBQKhULRpbe3D2aOQQUB6O9PH+FIyQ39Hc3yY2dFfeEGMgpqa2sAAH19/Vld7uCgV35cWVGR1WUDQNPSU+TH0840F6Fb1/Me7jnDiZ+f7MSB9/+Z0foEQUBzsyg8Tp06evG0evoixKCIiL85zYlvHOMBE8+se9ZepXS9NpZacFiVgH1vPqaaxlM3WX4c6G2DlgUL5snOxAXz52a0fjNIsb1Asvs0GAxi3br1AIBVxx2bc4dk37DGeVo6NoRFbe9pR4ouUO1nrC5JH2ldTCTHFKs/a5fPiw2t++TnHrsDq2bNy8vYsk2jTp/t6zs2I044i4+dORelTle+h5Z1Gsr0xdPxEtmrBz2Wo1CKmwiN7c0IrchcCNE5E+epIAiy0Kh1e5JI4qnWWWv0mt+vPpb1Dad3nkZVnafqsVgTcb2k8zScMraXcJ7abKr5jJynDfW1aKivTTtOSnbhjTpPBQFRPp6I3TXuDBUTotK0lgpC2s7U1BhHBqfrS5Vig8eKeDr2csIoFAqFQqFQKLo0Njbgo4/XoK+/39Cx0NvXhw8/+hgNWe4npJgn0HdQPqEoq5uUctpipbamGj3tB8B5DyIei8LK2rOy3OiW/+CG5SwCMcDm7wBwWFaWK7F09bXg43HYWAfmn3ypqXmcbg+kSyD+gZ6M1te85QMsqw6h12nBjCZ9ESATGIsVbOM8oHOz/FqjmwPrLMloOdVT52Kg+1MAwKopVrBbn8aO7VZUz1yOismiEOqpVcRTf0+y87SpsRG/+NlPsHvPXnzmvHNG8nFSIsX2AmLvaVWVsv3WrF0vX1A6PseRvQDQF1C7A8aK87SpQiuyDWDxxKm60/YFxqa7ViJJPPWqRaWXt6ojbU86bBGcWfrdyjfaKNvOoUFsOdiseu2sBWM/shdIdtlKnDFOI3sBeixHoRQ7NLY3M7TOTD1hMV9jiJpwnpLirjVFb3gq56nea8Ma8XRoKIPYXl3x1JY0RskVLaGK7Y2pY3utxPIKEaVMMUaQxE9GHdsbF3jwggDWahNTc1N0ngpCCucpmJTOUyEhzRoJoJJ4a3bsBisxFG+LjTElnnJcHNt37sYnm7Zh+8496OnpA8/zqKqqwKIFc3HOWaegtia5u+hzV30t5XJv/+EtmDVzWq6GTaFQKBQKhZIXzj/vHPz+D/fh81+4Gt/+1i04+aQTVO+/+dY7+PVvfotgMIhrr7myMIOkIDrcC0ficc2E6QUdy0i5uHoHLjvZCaANgd5WlDUld2aOBGaoDfPrxIsAdiGSlWWSWO1OHHHRTRnN4yqvgSSdhYYzc9oeWPMyLpwnXlxhgjsymteIucedix3PKOJp3FkJJsPexslzV2BgzTMAABeb6L3h4/j0H3fi2FseBsMwKKlTnLKhgU7wXAwWmzo697OXXDTSj5EWlfNUc8FJ6ju1WCw45ugjczYGibHYeQqkFxRJdnapO63GymeU0H7WTo3zVNt3unrB2IzsBZI/a+tgL17fuUX12unjoO8UABp0YnubKqqwcMLonfzFCj2Wo1CKm3DCecrFx7Z4Go/HceBAM2bMmJ7TBA+t0zTf4qkgCIglhG49UVMLOd7Usb3ie3qCrF48sF8T2+sbZWyv5DwlxdNIRH3upHKeEmK/jdU6T/W/kz/93yMAgK988Yq0Y6VkDz33ppWxgAHgSHxvQorYXlEUNeEdTTl/ChK5v3pTmXOephZvi42siKczM+jSYBgGezQH9mbZsWsP7rzrPgBAdVUlFi4Q70Tft78Fr735Ht7/aB2+e+uXMVtHCHU47Fi5YonucsvKMrtTm0KhUCgUCqUY+dIXr8O69Z/gw48+xhdv/BrcLhcaGxvAMAw6OjoRDIUgCAKOOmolbvzidYUe7qFLeEh+WD1xbIqnvMUJ8OJFgLCvL3viKReSH3sqarKyzNFSUlUni6dRv3HsqR7D3c1yT0rlhBlZGc/kZSdhxzN3yM8ddjbF1PpUTZ6T9Frl9MVY8rnb5AtorqpGMBYrBD4OgY8j2H8QJfVTRzzuTNE6T0neeU8UT5csXoTy8ux342rRRtpWecbG+aM2tlcrKJJoxcXlk7Ozv+aLVOKpLxzCu3u2y8+tFgtOnbs4b2PLNtrv9X9b1mE4rPx2Lp44FRMqkm8qH4s06jhPT5+3JOdR3YWEHstRKMWLIAhyNGohHJTZ5Oe/+DUeeuQx/OgH38XVV12es/UUOraXFEzNxPbGCJFRcnfqkWnnaXJsrwnxNEqKp+rjfUk0VTtPQ6ppAgFFsFV3ntrUnacG38nH6zYCGJ142trahlu//T2cd87Z+MLnzaX+HOoIOrm5DMOgwukGAPijokhuFNubTsBkGDHa13B+ITG/wbFWauepqczfxDjHBlkRT6UDOS08z6O3t1cuh54woWlU62EYBitXLMFZZ5ykEkij0RgefORpvPP+Gvzhzw/hnl/dBptNba0vLSnBjdfn7h8DCoVCoVAolEJjt9vx0IP/h78//CgeefQJHDzYgb379svvT5jQhMs//zlcc/UVKWOIKLlD4ONg4yH5bMZd2VDYAY0URwkQEk/IvT0HUZeldF0rr9wxbfdUZGeho6S8phGdicfxUPoLHSRRbxecicdNMxdkZTx2dxniNhesCaG5bsEJGS/DXdWEqKMK9sgAeMaKheffjGmrPgvGovwuWKw2uGsmIpCI7A30tuZZPNXvPG1tbZN7ZFcdd0zOxyEIAnoJ52mlu0SMyxoDNCX1gOo7TwcCw/hg3075OWu1jjlxMbkHVPmsb+zcgijhqjhmxmGoHCMCuB7aKNs+v/p36cxx4joF9J2nZ4zjvlOAHstRKMUM6ewb6+Lp+g1inP3WbdvTTDk6Ypp4Y+3zXEOKm2acp2Q3aDY7T0khEwB8GcT2snZ1bK/FYpF1GIZhYLFYwPO87IqWCAb0Y3vtLAsbcSzLGcT23nrT6G/QefmV17Bhw0b09fZR8dQkvCDKn6nET9G5qf++bDw1vNGMAcAbzm8uTlff3SqvOsWc4viNnbPFRlbO+t57+zXD9+LxOD748CP89I47sWjhAtxz969GvJ4F8+Zgwbzku6TtdhbXXHEJ1m3Ygr7+Aezeux/zDps14vVQKBQKhUKhjFWsViuuu+YqXHfNVejs7EJ3t9jRWF9fh8bGMSrUjSPCQ32wEGcTzor6wg1mFNjcFUCoGwAw2NWWlWXyXAw2EFFZntw7Cs1QXtOoPIkGjCfUIAgCbDHFZVw1ITvuXABYcfWvsPb+b4C3ubDknMwvbDAWC86+7Rm0bnoLExccA1el/n7oqZ0ki6f+nlbkc28tLdF3nr773gfy43z0nQ6FguB4Zb8cS3G22s7TDgPn6SvbNyHO8/LzVbPmo8zlzunYsk0q5+lLGlftWBcXG8pS9yefOU76TgGg2lOKEocT/kTHoNtux3Gz5hV4VLmHHstRKMWJ5DoFjAWnsUJnl3gcrxX1sk1cEwmrfZ5rSME0YsJ5ypmO7c2s8zQptnc4A/GUZcES1RlSZC/5XBRPNc5TMrZX6zy1ks5T/e9kyaL5aceYjmDCDWumb5YiokiKKSRIQTAUOdM6T8XZdecXhERfaorxSbG7eqtXIoNTLwEpnK/FRmblOCPAarVi1XHH4q9/+SNeefV1/O3vD+dkPXa7HY0NtQCAQe9QmqkpFAqFQqFQxj+NjQ1YsmQRlixZRC+2FQmhwS75cZSxw8raCziakeMsVyJ1h/s7U0xpnmhQfQxvLxLx1FGiCBWWeDjFlGoivj7YoAhSntpJWRvTxIXH4Pgf/gsnfu8J1fgywVFaiVnHXWAonAJASa3SKxjobR3RekaKkfP03URkb0VFORYsGP1FnXT0avpOa0tKDaYsPpI7T/XF0xc+Xa96vnrh2OsDrSsth5Xo/u30iZ81Fufw6vbNqmnPGsN9pwDgsttR6dZ3zk6srMaCpsl5HlHuYBgG1x5zivz81lPOg3OM/rs5UuixHIVSPJDOPtKhONaIRKLo7+8HkNwrrz99BH/801/wwYcfZ7wurcjMFTC215TzVBXba+zuZxNOUL2IXl3xVCNSBwLBtO5ladl2loWNJZ2n6nFJ8cJJztNUnaeq2F4euUKKSh7rHcH5RInN1X8/XWeoOUenoDu/KTmTMZ5WFm5TaKdS7O8h1XlqhunTp2HxooV4+ulnce3V2S+153kevX1iNE9FefLdwJFoFP/53yvo6x8Ey9owcUIjli9dqDsthUKhUCgUCoWSCwa7WuTHvH3sHod6qhrgTaQIhry9WVlmLKCIpxwssLKOrCx3tNhLKuTHLDjw8Rgs1vQ9o/4eRWyMMg7YnJ6sjoux5D6ukRR88y+eJjtPo9EoPvxIvHB37DFH5yWysk8jntaUjp2/20p3CZw2FuHEBd5OndjeQCSMN3d+Kj9nGGZMiotWiwV1peWy43Qg4Ec4FsXHB3bDF1YuHi6aMAWTqoqjT3k0NJRVYDCYfMH7zPnLxl0f6G2rL8Hp85bAwbJYPGFqoYdDoVAOYUIq5+nYFYN6e5VjdzPO09ffeAu//d0fUFFRjvVr3ofFYt6Lpd1OXJ5je6OazlNBEFL+O0n2f5JuTy2j7TwFAJ9vGFVVxjdBks5TMrZXW1UoOVHJ/RNQi6cxTWwv2edq5Dz9139fBgBccN4ZhmNMhySexse4UzufCBCMM3nJ6QymERLLYAzcnwzDyNPoL9N4XkASP1MIt6Y0UcHMRywK8lrWUllZgU2bt+Rk2e99sBa+YT/KSktUfagSw8N+PP3P51WvPfz4P3HpRWfjrNNPMr2e2+64W/f1C885EY31NRjozc6d9+MN6Y6s8bB9qpacntPl29wVWV9Pqu0+mvUUw/eZjX3r8kUrsjWcoubFxL99ufy8xbBPZIPx9JuVD+j2Sk9VbWP6iSiHBL1te+XHbEl1AUcyOsprm+BNPI769d1smRIlxNM4UxzCKZDcvRoL+OAoS//ddR9QOqQEV1WKKYsXTx3hPO1rz+u63W43rFYr4vG47IrY8MlGBINi/Fc+InsBoHdY6zwdO+IpwzBoLK/EgX4x8tMfCcMXDqHM6ZKneXPXp7K4CgBHTJ2FutLicH1nSmN5pSqut8vnxYufaiJ7x0mkbWN5JXZ0Jf9NnjF//PWBMgyDI6cn1zdRKBRKvhkvsb2dXUoSTsAfTDGlyMCg+G+r1zuEjo5OTJw4wfS6tI7DfMf2knGxgiCA4zhZ+NQjRhwTWW0pnKdS56mOGKz3mja2FxCje82Kp6zNjPNULZ7GYhyi0SjsdjtiqtheViXAGu3L//7fKwCyI57m23E8luEl56mh+AnwKWJvZVE11b10griM5JcTS015Hx5j3LdK/H8qjMTbYiRv4mkgEMDGTZtRVpb9mKPe3n489tS/AQCXXHh20o/gcUcfjqOPXI7JkybA43aju6cPb7z9AV5/6308+uS/YbfbccqJ+Tn5plAKQaD/YKGHQKFQKBQKBYBvsB8cL8BmYeCuGruienXDJEgeWj6cvrPHDAFvn/xYYF0ppswvVrsTHCxyBG/EP2hSPN0pP3ZWmb/IVExUTJqLI264B57ayXBX5/czMAyDkhIPhoZ8svOU7Ds97thj8jKOPr86TnosdZ4CYu+pJJ4Covu0zKl8ly9oxMWxGNkr0VRehU+wX37e4R3Ai9s+UU2zegy6avXQRjIDQKnThWNmzC3AaCgUCuXQIKKK7R27ztOuRN8pYM55GiW6Qnft2p2ZeKoREvMtOmudobFYLKV4SjokSbenFpuJzlM7y8ouVGk7s6wNscQ28fmGjccRj8suWLPO03AouV4kGAyK4ikxTpZlNZ2n+t/JZZecZzg+s0jidb5F87FMekmRAcDrip+AFPtrvJRUsbmScTRNY6nhOIU0wi8A2fltLl648GRFPD3Y0WH4XiAQxP79B3D/Xx9Eb28fLrrw/GysUiYYCuGue++HPxDEysOX4uQTkk+iv3zDFarnkyc14erLL8bECY148JGn8fSz/8Pxx65M+eMpcfuPbtV9XXLbUGeJPuNp+7S98WBOly85QQc2vZLT9WSDSZf+oNBDyMq+dcdbL2VrOEWN9O/So1vWp55wFNxzyTU5W3Y+GU+/WfmAbi8KxTwDlUvwg1f+gxI78I8bLi/0cEaMu0rpybTE0t+xboahPuUueIsjuxG3oyVuccDGi47HaMBrah5fdzOkyxJVE2fkZmA5hnWXon7BqoKtv7S0NCGeis7Td98V+07nHjYHdXW1eRlDcufp2BJPk3tPBzCnXrzoGYtzeGX7RtX7Zy8cu4ksTZrP+vK2jejwKlHFk6tqMH+c9IE26Iinpxy2CPYUF3opFAqFMjrCEUWcisfjaSNgi5XubuWmKm0Xpx4q8XTPXpx88omm16UV5vItOseianEzGo3C7XYbT086NFPUQ9hNiKdujxtRr3gTnhTbW1NdIzt/h4aMb0Alt5OdZVWaRbLzVHxO7p8SgWAQFRUVqo5elrWpxFOj7+TM004wHJ9ZIpFIYh3UeWoWSYC0GHaeJkROw9heUQFNbTzVF1iVyOA0paUJ76je2M3AIOGwHQO/oVk5sl51wmlpP6ggCJg9exa+/c1vZGOVAMS7F+763f1obevAgnmz8ZUML0CdfMLRePbfL8A37Meefc2Yd9isrI2NQqFQKBQKhULR0tHRCQHAcBSYNGPsxhA6ShXnJStEwcc5WKyjO7UYHlQu5NhcxSVQDVctwsvvv49ADDiurD79DABiQ92yeNo0Y0HuBjeOKS0Re0+Hh4fR3d2DHTt3AQBW5SmyFwC2d6qjUWvHWKRtU7k6MrqDiLV9f+8ODIWUmx/mN07C1Oq6vI0t2zRqPuvja99VPT9z/vKiv0BjFq1QDIh9pxQKhULJHSGNsy9dBGyxQsb2BoPBtAIGKUDu3r0no3Vp41rzHd+q5zxNBSkk2lJ8t3LnKZe8PMlt6nK54JXFU1Gkrq6plre/lKyiBxk3zNpZ2FjlPEtymirPxTMOaf90uVwIhcSbPoMB8TgvFiNje22wWCywWCzged7QeZoN5M5TGttrGkEWJg1ie2EsfgJSpK80pd78xs5PM25QSbzVQxq5kfCrN32xH5lnRTw94vAVMPqNZVkWdXV1OOrII3D26jNht9uzsUpwXBz3/PEB7Ni1F7NmTMWtX78h43+wLBYL6utr4Rv2Y9A7lH4GCoVCoVAoFAplFHR2iifLlZUVcLmKJ5o2UxylikjBMGLvqbN8dE7AKFjsG+DhsQOTZpsTKPOFddoxePPRdwAAgbgVFWmmFwQBQjQonw1WTpyZ0/GNVfr2rMfmJ25HScM0HH7tXbDY1OdzpaVi5cvwsB/vva9E9uar7zQci+LNnVtUr62YMra+S63ztHNIcWImR/aOXdcpkPxZB4N+1fOxHEmspaFM/VmtFgtOmbu4QKOhUCiUQwNtp+RYFU/J2F6e5xEKhVK6MaOE4Lh7T4biqUasjOfZeRqNaZ2nqcVTcnxsijQHNoXzVBJg3S5lm0rOU4/bjRKPB/5AAD6fsfOUdPuKnafKfma16jtPJddfRUW5LJ4GZPFUHKfFYpGnt1mtiKYQTz9aIx4nHrVy5MdPcucpx40Jl2ExICSycw23VZrYWyGldArZOaob2wvBhKDJGPhO0wu/yhIYZQxFvk9kRTx98vGHsrEY0/A8jz/d/zA2bdmOKZMm4Nu33AinwzGiZUk/Io4Rzk+hUCgUCoVSLJxz3kWYNGki7vvj7wo9FIoBHZ1izHVT49iOubayDuyxzMK6zdthcZbhTFfJqJcZqpyLe9eIJ9j/uqm4Io2rqhSxeHDQiwlNTSmnZxgGDzc3oHnvLpy4cgHOqpua4xHmHoHnwUUCYF2l2VmeIOCjP3wRABDsP4jenR8lRQSXlIjxzX6/X+479XjcWLZ0SVbGkI739u5AIKr0my2bPF23a7KYaarQOE+9ovOU53m8uFUtnp49xsXFVN9NpbsER06bncfR5JZpNeobTI6ePgcV7uKKO6eMDHosR6EUL1IEqUSM4zAWb4UkY3sBsY8zpXhKCHn79u1P2xtKonWaxvId26sVT9M4T8nxaUVKEqmDNGXnqV3sKuU4To5HdjjsKC0rhT8QSBnbq+0oJTtPk8RTTYxvZUWFfMNuICjqHpKgq1qOzQbEYoaxvff99TEA2RFPAfHYM9U2pYgIgr4wKSGl5ho5T0VR1Di3V4791Y3dJVeSYv2G6068lU4QZSSXavH3nlrST1JcCIKAv/79SXy8diOaGurwvW99BSUe4x/4VLS1d6CjU7zbZvrUSdkcJoVCoVAoFEre2bf/gOqEiFJccNEQAn0HYWWApqaxLZ4CQKh+Gda0x7H2gA8W2+hvRPR6vfLjisriikatrFREmYGBgRRTigiCgJbWVoQ4oGziYbCy2UnfKQS9u9bi7V9+Fi9+61isvT97FSze1m2q50MHdydNIzlPvUNDeP+DDwEARx25MmtpRul4edsnqudjMRZVG9srOU8/aduPLp9Xfn1KVe2Y7wNNJZ6ePm9Jyu6yscZhDRNw2rwlAACbxYofrb6ksAOiZA16LEehFC9a52l8jPY4krG9AOAPBA2mFCFje2MxDs3NLabXpXU15ju+NUk8JcQ8PcjvlIzK1WK3p+g8TWwv1q50lUrrdTgcKCsT60l8KWJ7k8RTMrbXoonttamPb8orlPOoYEI8lURhVmc5Rt/JiauOwomrjjIcoxlIpy/tPTUHn/B/pu4sTeU8FUVMo/mZhHOU15ldSLhBGTNhukbzI30UL4PUn6GYyMkRWXd3D7q6RVGyob4e9fXZ60157Kl/4+33PkZdbTV+8J2vobws9Z3P77y/BpMnNmGaRhw90NyGP/7lIQDAESuWoKqyImtjpFAoFAqFQikEkydNxLDfn35CSkHo37cJ187qBz/TgSC7s9DDGTW1NTUAxBPuwUEvqqur0syRmoFBr/y4qrK43H1VVaR46k07fW9vH4JBMS5r6pQpuRpWXrDYWAx37AUABHpas7bc9rUvqJ5b2WQBvrRUdDT39fXLr606Lj+RvYIg4OWtG1WvjUXxVCsoSp2nepG9Yz1KLZV4etaCse2q1ePJa2/BhtZ9mFpdh5qS4uqJpowceixHoRQvyZ2nqV2M2USKPR1tTHA8Hkdvb5/qtUDCFWmEVnDcvWcvZs0yV2PAxbiUz3NNpuIp6Ty1Wc3E9ibH0UrrZG02sCwrR+gConhaLomnKWJ7yXHbWVYVIWzV3GCjdZ563G7Z8SrH9kqCLrH/SKKrkah5zRWjvzGL3N7xOAdg7N5Qmi/kOF2D43JR/DT2bApC2uBecTpd56mJzlOGSbNuM4IoAwhjwXeaZfH00cefxIN/fwRtbe2q1ydPnoRrrroCX/j8paNa/vpPtuDFV94CANTUVOHpZ/+nO92KZYtw+HKx72P9hs34ywOPoamhDhMmNMBmtaGrpxfNLe0QBAHTpk7C9VeNblwUCoVCoVAoxcA556zGfX++Hx2dnWM+FnY80te+HwBgYRiwDmeBRzN6amtr5Me9fX2jFk8l56nVapUdh8WCMzqI8+bYUGJnEN76PHDe2SmnJ+/Inzp1bIunnlrlJtTIcD9iIT/YUcY0CzyPrq3vqF7jwskX7vT2g1XHHTOqdZtlU/sBdPoG5edTqmoxt3FiXtadTerLKmC1WBDneQCi81QQBLzw6XrVdGM9shcAPA4nypxu+MJqB43TxuLEOQsKNKrcwTDMmOvgpaSHHstRKMVLOKIWT/MVQRuNRnHG6s8gGo3itZf/B5dr5GHB/f0DSTGtAX8a8VQjQO7atRurzzrD1Pq4OJfyea7Riqd6TlESctukSgEgRUht922MU4RKbW+qw25HWcIIliq2N6pxnqpEzzTOU7vdDrfbDZ/PJztP9WJ7JXE4nsPvhBRPtRHOFH0ECKlSd+U3jIROqXfU0HnKKA7T5HnNNJZKztFk8dOcbKu8b0asLTRZie0VBAE33fxN/PT2X6C1tQ12ux3Tpk7FtKlT4XA40NLSip/c/nPcdPM3R7WeABEjsH3HHrz7wVrd/7W0HpSnO+6YI3D0kcsBhsH2nXuxdsMm9Pb2Y+6cmbjmiktw+w9vkbt0KBQKhUKhUMYyX7z+Ghx15BG47PNX48WXXkEkkvrOWkp+6T+4X37sKKst4EiyQ011tfxYewf7SLD07cKMKgazGkoh5PnCSjps3DBOmm7DEROtsA7sTTt9885NcCauTYx18dRRWg2bQzlfCvS2jXqZjMWCE777DGyECBvTE09L1CLttGlTMXlyfupWXtK4Ts+Yv2xMOjOtFgvqSpX4tj7/MD492IK9vUpkX21JGQ6fOqsQw8s6eu7TE+YsgGcc3LBCOTSgx3KU8cobb76NM88+H8/+89+q18fCxXOJcFjdeZqv2N6WllY0N7ego6MTe/ftTz9DCrSRvQDkPk4j9JynZtFuo3zH9pKxseLzdLG9pHiavvMU0BNopYhcNskpbHc4ZPF0OJPYXmJ9lqTOU7W8w7IsPIkOW614ytrMO08PtLThQMvojvvJnuCxGnOdb8x0ngoQDCNv00fhioWjvM5aeLm0dGRk3Hk6Bn7/s+I8/cez/8YLL76MpqZGfOvWm3HO2WfJJ5aCIOD5F17Cb+7+HV586RUcd+wxuPii80e0nuOPOxLHH3dkRvMcsWIJjlixZETro1AoFAqFQhlLnHzqavACj87OLvmmterqKjh1LhozDIO333w530M8pPH1KOksJbUTCjiS7OCO9uCyhTaU2hkMrHkaOPboES9L4HnMj27CgpUOAGGEh3rgri6ebeSpUMRuhgulmDIxzaYn8KtTnRiOCCiN9gAYu+4whmHgqZ2EoXYxajrQ24qKyXNHvVzWVYLpx38Ou1/+KwCACyXHVGqdp8fnKbIX0Ok7XbA0b+vONk3lVegcUly0D3zwuur9MxcsS7rwNlZpLK/Eru6DqtfGY2QvZfxCj+Uo45G33n4XX/7q1xGLcXjksSdx0YXideGv3nQLNm3egn/940nU1RX/jYWRcGGcpxHSvTfK2Nvu7h75McMwEAQh49jeXbv3mF6fdhvF8hzbqx17LJraeRobgfPUyN2qJ546HHY47GJ0rc9nUjy1s6quUm2HuzbGl2VZeDyieBpIiKfRmE5sb2I5RoL2bXf8FgDw6AP3GI4zHarO0yK7ObZYEVKG8soTpZwmvfiqL1zKnaVpxE+jTlReyKDzVBB0BdxiI0vi6T/hcjnx5GMPYeJE9UUOhmFwztlnYemSxTj9rPPwzD+eHbF4SqFQKBQKhUIxpv3gwaTXyK5ASmGJDPXILS/VTdMLOpZs4BJCWDlRPJ2I9B0Y1bJioWHVDap2T8WolpdtyPHY+EhStxGJwPNgY8MAA5Q6GHgqi/9iYDo8dVMU8bSnJc3U5rE5FUerfmyv2nl6XJ4ie1sHerG1Q+l3LXe5cdT0OXlZdy7QujGf3fCh6vnqhSvyOZycov2sDMPg9HlLCjMYCmUE0GM5ynjj4zVrceNXvi6LZr5EVGkoFMKLL70CAHjt9Tfx+cs+W7AxmiUU1nae5kk8Jdx76ZyT6ehKOE8rKsrBcXH4/f604qkk5DmdToTDYbS2tiEUCpmKD9YKc/l2nmbaeUqOL5V4alfF9GoF4lhifhasXSueOuBwOAAAQ6k6T6Na56myHItVE9ur4zx1J8TTYCLFk5PGxCbH9hqJmofNmWE4PrOoOk+p89QUknPUSKAUO0+NXZvy/CkFUAF6s5s2ghos2nyLqWK6LHayIp7u2r0XR648Ikk4JZk4cQKOOvIIrF23IRurpFByxuanfl7oIVAoY5ZvPPNg2mnuueSaPIyEQjk02bd7a6GHQEkBH1ScX7WTRn8yWmgq65X+RyFifOe0GaKBIfkxLzCwOtyjWl62sZcogoyVERCPBFXCH0l4qAdWhpefu2vGXk+mFrL31J+F2F4JlXgaSd15arfbsfKI/Ih8L29TR/aeOncxWGtWTp0LQlOFuo84zCkX5EocTqyaNS/fQ8oZTRrxdOXUWaglYosplGKHHstRxhu//d0fVALKkE885iP7Hnfs3JX3cY2E5NjeAoinaTo709HZ1Q0AaGioh3dwKCGeBlPOI7kHD5szG5s2b4EgCNi/vxnz56dPItEKzPnaZhJJ4mma7SeJ/FarNaX4RIqQWjertE67Xc956kBZaWadpw67XberVH6u7TwlYnsDKTpPpdheI1HzB9/6quH4zKIST2nnqSmkzlLj0lLxP3rxvIIgxvmmc34K0Bc6pb7VdMidpQbLNRJ+yflHFxCcP7JyBshxHNzu9Bc43G533u7KoVAoFAqFQqFQigVBEMByAflMw1PdVNgBZQEXEWVr5VJfdElHNOCVH3MWe9F1S7KuUvACYEkMK+IfNBRP/T2KYzHCOGGzp78rv9gpqVN6WwO9o3OeHtzwCmrmHAFHSSUmLDsd9QtWweb0wMomR1KSztMjDl9h6pwzG7yUFNk7tmNftYIiyWnzlsBhYw3fH2vMa5qsen72ovHjqqVQKJSxiOScnjVzBvbs3YfhYT94nle57nbs2Fmo4WVEuFCxvUT3sVYMzBQptre+vh6xqDh+s52n9fV18mt+f3Ldgh5aHSBf20xeX8bO02SRUQ/Tsb2a5TgcdlRUiDd1+XxDhmk22s5TVtV5qnaaWixq8ZS1s/Ixs+QqlsRY0jErxf9yORQ1ye1NNSFzCGmibxlimqR5TSxf2t+MxFcz4qeRfJqZk1Q4dJynkydNxNp16xGJRGTruZZIJIK169ZjUgp3KoVCoVAoFApl/NPd04t//+9VfLptJ3w+P0o8bsyfOxvnn3s6JjQ1GM7z7L9fwtbtuxAIBFFVVYGVK5bg/HPPgNOpf/xZTEQDXpUb0VWp/znHEs6yGvkxCw7xWBRW1p5iDmPCvgH5MW9LFtEKDWOxgGPssEO8ABANeOExcJR27tumzOeuzsv4cg3pPA30tKaMLU6Fv6cFnzz8fTAWK+rmHYtlV/1ctR9pqapURL9VeYrsHQoF8MFe5SIua7Xi5DkL87LuXNFUXmX43niK7AWAcxauwGnzluC1HZuxatY8XHfMqYUeEoVCoRzSRBJuzbq6OuzZuw88zyMQCMJHiKc7d+1GPB6XOxiLFdIBChQmtne04qkU29vYUI/BQTEVx2xsb1lZmfxaOBI2mlyF5DZkWRtiMU50xvE8LHnqWtc6TaNpOk8Vh2bqfZEUT5ME4lSdp3YHyhPbMRbjEAqFdG8O1IqnpNPVqhFLtWNlWUU8DaZ0noqPjdzA4cR+5zTQesxAnaeZI0byAqnkU8POUkH0c5pxfvKCkHROZ6JtVZ5fHqxq7Glcs9L8jPgZxkLnaVZ+qU479WT09vbhppu/iZ6e3qT3e3p6cdPN30RfXz/OOJ2evFAoFAqFQqHkkr179+Fnv/gVLv7s53Hyaavxy1/dJb+34ZONeOjhx+D1egsytp279+F7t/0a77z3MVibDcsWz0dlRTk++Hg9vv+TX2P7zj1J8xxobsP3bvs13v9oHSoryrF82ULE4zyee/F13PazuxEMhgrwSTIjPNgtP+ZgBesqSTH12MBeUqF6HvUP6E9ogqG+LvkxY9d3dBYaUtQNDxn3z/W0KMKbaxxE9gKAp1Zx88VCwyqncCa0r3sBACDwcYS93WldudOmTcUlF1+IVccdg0suuXBE68yU13duAccrF3eOmTEXZa7iipHOFG0PqITDxuKUwxbleTS5xWa14qnrbkXbnX/Fv7/0XdjTOEcolGKlmI/lKJRMkES2ujolscTn88ndp4DYf9rSmr1agFwRCqnPObg8dThmUzyVYnvr6+vh8YjH3OnEU0kAKytT6hS0EcZGSKKdw6EcR0vRuPnAyBVqPL055ynp4NQKtErnqU03tre8QqkT8A4NQQ9pGQzDwGq1qpZj1YilSc5TloXHo47tjel2niZie3keelz/le/i+q98V/c9MwiCoNo2uXS4jieUzlN9Em2husKjLH6avMdVK5VKemzae2QZfZerIIhdqqZWLxxCnafXX3cNXnz5Vbz+xlt47/0PsXLl4bLDtK39ID7+eC2i0ShmTJ+G66+jXXcUCoVCoVAoueKhhx/Dnb/6jXwyzzAMBga98vuCIOBnv/gVHA47PnfpJXkdWzQaxe/vexChcBhnn3kyPnfxufJdx+99uA733f8I/vDnh3DPr2+T73DleR5/+MtDCIXDuPSic3De2acBEE/E7/nD3/DJ5q144pn/4rqrLs3rZ8mU4d52+THHjn3hFAAsVhZRsLBDPCmO+PpH7Kj19Sviqc1ZlmLKwsHYSwBOvNA31NeFRp1pBEGAd+86SPdn100bH12Sdk85WE85Yolu2kBvGxwlxlGwegg8j/Z1L8rPJx6+Ou08DMPgl7+4PbPBjpKXt6r7Ts9asCyv688F2s5TieNnz0epc+zHSuvhthd/IgGFYkQxH8tRKJkiiWy1tUrShM/nU8X2AmJ07/RpU/M5tIzRCoYcNzoh0yzZiu31DQ+ji+g8LUmIp36/OfHU7XLBarUiHo8nRRgboYinDjnqV4zGHVlaTaZkGtsrvW+3px4fKa4mCbSJz2zXc5467KgoV8TTIe8QmhqTzypk96rNBoZhVLG9ZpynkjAeTPTZcglRWE+ENeo8rSgv1X3dLNptne++27GK5B5Np0DqO0/F/6bXPhlRaNXkA/Oiopl2jKk7T9OPQO48HQPiaVacp6WlJXjy8Ydw6iknIRqN4t1338fjTzyNx594Gu+++z5isRhOPeUkPP7Y31FSUpx3klMoFAqFQqGMdd59733c8fNfor6+Hvf98XdY9/F7SQekK5YvQ2VlJV57/a28j2/t+s3wen1oqK9VCacAcNzRh+OIFUvgHfLh3ffWyK+v/2QLOrt6MGliI85drSSY2Gw2XHf1pbBaLXj7vY8wnOakv9D0tO2TH1vdxhGaYw3epjjyAoS7NlMC3j75sb2kPMWUhcPmVkTdob5O3Wm8rTvgiCl3kM87/oKcjytfTDn6Asw89WosvuzH8FRnXsXSv28jQgPidmMsVkxYfrrq/XgsWvAT6Ficw2s7NqteO2P+0gKNJns0lOkL3avHeJcrhTIeKfZjOQolEwRBkEW2ulrFeTrk82FoSC2ebh8DvafaqNp8xfaS69W6HDPhl7+6C5FIBBaLBSuWLSWcp8GU88l9mXa7XJcSjph0nibchmTNSj4diJk6T82Kp6zdXOcp6fQEEs7TcuKcQnMTgdE4VHG7aTpP7Wxy56kk6JIirM0qPubi+vvxH+6+HX+4e+Q3MUY04il1nppDiu01dJ4yKWJ7zfaIJhauda8qfatmO08160/jmlXPL0Df81xcZC3DpramBn+5714cPNiBdes3qO5kOXzFckyY0JStVVEoFAqFQqFQdHjgbw/D6XTi4b//FdOmTjGcbv68w3DgwIE8jkxkf3MrAGDunJm6PTcL5s3G2vWbsG7jFpx2yioAwCebtgIAVh6+NKljsbKiHHNmz8D2HXuwcfNWrDpmZY4/wcgJl0zGQxujqHQxuPgL+eluzAcWZxngF8XCga52ZC6piYSHlchfZ1lx9oQ6SirAJTRe/2ByVQkArH/+QflxtGQC3NXj5xxo7jlfHdX8BxORvQBQO/coOMqqEQv58ebt54ELB8DHYzjjV+8UNNL6w3274AsrFxAXTpiCiZXGnaxjBZfdjipPCQYCfvk1C8PgzHHgqqVQxhvFfixHoWRCLBaTL6arY3uH4RseVk27Y8euvI5tJESSnKf5EYOipPM0TWenER9+9DGeevpZAMB1116FGTOmK9GuJmN77XY7nA4nAoEgImadpwnHo5OI7eXyGdsbzY3zVNV5qvk80jpZO6uK9wVE8bSsjIjt9erH9kpRzY5EGpONdIxa0ztPJbFaci2Tgq52ObnqIk12nlLx1AySgJlKgjTsLJWFUzPOz2Sh1extrEZLNz0/M3acp1kvAJkwoYkKpRQKhUKhUCgF4NOtW7FkyaKUF9sAoLKyEus3fJKnUSlIJ29GSSTS680tSsRtc+tBAMC0KZN055k2ZRK279iDltaDQBFrkj1+Dhu7xHsrb1lxRoFHkz3YkkrAL3ZUeYlo4kyJ+L3yiUlJZW3KaQuFq7wWXVEB/qiAJp1zfz4eg2/XB3II2WEnfjav4ytm4tEwOja+Lj+flIjstTlcqv5ULhwoqHj60jb17+KZ88ePuNhYVqkST1dOm42akuKMyKZQDmWK/ViOQskE0p1YVlYKh8OBSCQixvZquh537Cx+52koVBjn6Wg7TyORCL7/g58AAKZOnYKbb/oKAKCkRDzmSi+eKsKbIyHKabeFEfG4FNtrT3otH2gFvHTOXdJlm4qRd57a4XDY4XK5EAqFUjhP1eNQxfZatZ2n6puSWZZFnI8nliN+fmlfJR2skuiaq5sAtNveyOGaC/r6+3HwYAcWLVyQdAN2IXj7nffwm7vuwXe+fQtWHXdsymn5RPhtulHryY48JOdoOkT1MrnzVHzFYrK0VDu/IuiamT15/mIk6+IphUKhUCgUCqUwhMMRlJelvxg+ODgIC5OV9oaMKCsVT9B7evt13+9NvO73BxAOR+B0OtDXLzoSq6sqdOepqhRf7+sfTLv+2+64W/f1C885EY31NRjo1Y9ilfqMjN43w949u+XHLodlVMsqJmyuUkin297O1rSfy2hbxgKD8omJze4qyu1TsXQ1rvnlPwAA3z9qatIYBw/uRTAchd0JcAKD2rlH5vRzZGO/zBe9W98BFxEvzFkdHrANs+VxW1gn+Jh4Aa6/sxluLv8BThwnOmNe2LJO9frREyaNie1rhlq3+qaVE6fNyslnG0v7ZbFDt2V2Ge32rKrVa7rOPsV+LEehZALpTnQ6nCgvL0NPTy+GfD74EqKRnWURjcXQ3d2D/v4BVFcXb71FoWJ7Ryuerl23Aa1t4s2Od/7sp3A6RReo0nnqN5yXXKcY2yvOazq2l5Nie51Jr+UDrbBp3nnKppzOTOcpq9t5KorPFeXloniaxnkqjYNcn1YsJd+T1is5+iLRSGKMOp2nidheIzH769/6CQDg97/5ie776Sik8/Syz1+Fvfv245mnHsWK5YW/GfKpp5/Fjp278NTTz6YVT824MRkp9lYQYFU5T+UJ0swviq98kvNUai1N41w1UEcVMTSd89U4erjYyMqR1sOPPI6ZcxbizbfeMZzmzbfewcw5C/H4E09nY5UUCoVCoVAoFA1NTY3YuWt3ymni8Th2796LKVMm52lUCvPmzgIAbNq8DV6v+i7beDyOt9/7WH4eSlxsCYfVkUVa5N4dk9FRhaK7pwcA4HI5UVZaWuDRZA9PdRN6Ajz2DvAICKnv0E7FHsci3PNRBPdviKHusBVZHGH2kIR6APAOJd8l/vHWFvzkrTD+sCYKfsYpsDncSdMcqvR++qb8uGbesbCyyt+zldhO8Ujqzq1csru3GweHlJswGkrLMb9+pEHUxcex02bLjz12O848bFEBR0OhUIwo9mM5CiUTwkTMrcNhl4+Bfb5hufN0yZLF8jQ7dhZ3dG9YE9s7EiFzJETI2N4RrLO7W6zWs7MsjjhCOc6WOk/96TpPCUHR6cjs3EsSmB1k52meRGcgeXvlIrbXqPPUriee2sXtUJboPU3XeSqdA5PL0YqlWjHVbmfl8UvL0Y3tTeM8HRgcwsCgvrhrhqgmMjmfnacHmlsAAPv27c/bOlMx7Bdjyo2+bxJJvkyJFHurdY4monjTdpYm3tZql4IgJFZuwjqqM1B5dpMcMp2nr7z6Gmpra3DiCasMpznh+ONQU1ONl155FZ+/jEZYUSgUCoVCoWSb41cdi0cefQJPP/MsPnvJRbrTPPrYk+jp7cXFF1+Q59EBC+bNwawZU7FnXzN+cdefcM3lF2PqlEno7evHk/94TuVIzUW8zu0/ulX3dcmFYuQqSfe+GfoHxBOlpsYmVNeNn4qLxed+CVff8SgA4JvHLky7jYy2pY+zo9kroKKiBBNnL83NYEdJZY0AlrUhFuMQisSSPsMrr78DAUB3zIPVX/qJ6g77XJCN/TITBEHA/rceR6C3FYHeViy94mdwlqXvAw37+jC4b6P8fMaqi1VjtrtLEfOLDnOX05m3z0My0NuJtw+oxYrVi1aMq7/VG085D1GLFds723D10Sdh/oy5OVlPvvfL8QzdltllrGzPYj+Wo1AygRTYHE4nyhKu6mHfMHw+UUyYPXsm9uzdi8FBL3bs2IljjzmqIGM1g7bnM1ddkUnrjSqibbrYWT36+sRzrOqaatU5liSepovtJYU3l0s8vtX2vxohCWaqztM8imhGwqYRsnjKpnaeqjpPNWIwub1YjdApxRdXlIu9p4adpxoRl2Uzc55K37MkvEvpC6rY3jSdp//3h1/ovm6WJOdpnmJ7BUGQP5P2hod8ITkqpe9BcncP+4YN55HgBV41rx6SczSps9Skk5MBk4joTe48NRP7a9x5ai5ymGEAXhgbztOsiKf7DzRjzuxZKb9Ui8WCObNnYc+efdlYJYVCoVAoFApFw5duuA7/fe55/PC227F123acduopAMS7u9eu24BXX3sdjz72BGpqqnHVFV/I+/gYhsE3vnYd7vr9/dh/oBU/vfP38nssy+Lqyy/GAw89BYZh4HG7AIjO0kAgqIqrIpFOiHItVI2GkLcHK5lNmLyYhac6nugBKXz3SjYoLy+XBcW+vr4RL2dwUHT8VVZWZmtoWYdhGFRWVqKnpxeDg17Ve11d3fjgw48AAKvPOr2o98eRwjAM9r35KCI+8Xv2d7eYEk8Prn8ZSFwEcFdPQNX0Jar3WafSccqFU1+8yyWv796men7GOOo7BQCX3Y7vnkGFFgql2Cn2YzkKJRMihHjidDpk8XTI55MdWOVlZZgxYzrWr/9EjpYtRuLxeHK35RiJ7e3rF8XT2lr1cZvHI6Z/hMNhcByXJMIB4ueWhCi73Q6HHNtrsvM0sY2chPM0XlDnqUnxNI3zlGEY+RxIu0xV56ldP7a3PCGeart/5XFEJOepXV6WRDrnKcuy8mscxyEej+t2nkqxvUZOYLfLpfu6WbTn7/mKaya/81AolJd1koTDYXzmgs+itKwUTz3+MKxWKwIJd/fwcGrxVBAECILZzlIhyblpVvyUSIrtNd1Dysjr085vnkOo89TrHTJ1oaOyshIDg+n7qCgUCoVCoVAomVNXV4sH//pnfOkrX8cTTz6DJ5/6BxiGwetvvInX33gTgiCgrq4W9//lj6gkIkjzSWVFOe740a3YuHkbdu7ah1A4jJqaKhy9chnicfHwv76uRr6bt6a6CoFAEP0DXkyZPDFpeQMJEaumunhFt2D/QTS5omhyWRFlvONGOAXECwc11TXo7OqS72ofCYNeLwCgsqIiOwPLAYIgoKm6DEywDxZvCwQ+DsYi3rH93+eel08Wz//MuYUcZk7x1E6WxdNAbwtqZi1PO09J/RTUzD4cfXvWY+Lhq5P2f5tT6eIslHjaNTyET7va5eclDieOnZkbZyaFQqGkYiwcy1EoZglrOk/LyqTYXh98idje8vJyWajKl5NzJOjF1HKx/IiApHtuROJp4hi9prpa9XpJiXIMFgwGZXGbhHQP2u12IrbXnKNPEpjJCpZ8ic7ASJynSr9rOmw2FrEYl7RMab9g7cadp+VSbK9OFQiQ7DxN3XlqVT1nWVY1fTQalYV/uyr+N7XzdLQkO0/z8/dNisGhAlT77Ny5G7v37AUAtLS2Yfq0qbLz1JdOPDW5DulsSt95aqYz1cC5mpjXjHNUSMyhXb+A9ClisvP1UHGeVlZWoKWlNe10LS2t8j+UFAqFQqFQKJTss3jxIrz+yvP4x7P/xnvvf4j29nbwvIDGxgYcd+zR+Nyll6hOlAuBxWLB8qULsXzpQtXr7yQ6TxfMmyO/NnXyBLS0tuNASxuWLVmQtKwDLeId6lMmF2834UBns/yYtydflBjr1NRWo7OrC729I3eeehN9OhUV5dkaVtbhuSiunnoQmOoAsBsR/yCcZTXw97Si+71H0FTKwFYxESuWjy/HIklJ3WQM7PsEABDoNecOqZ9/HOrnH4fgQKeq61TC5iq88/StvTtUz08+bBEcttRxbRQKhZIrxsKxXHdPL/79v1fx6bad8Pn8KPG4MX/ubJx/7umY0NRgOM+z/34JW7fvQiAQRFVVBVauWILzzz1D5UqjjB9IgU10nhKdpwnnaVlZGawJMUi6kbIY0RML8xVDSjr40nV26iE5T2tqtM5T5XfEHwjoiqekMGhnWTldxWznqSSYqZyneRTJtW7h9J2n5sVTlmURCoXkSFztOnU7TzWxvcbOU/E7t+t0nlqtarHUYkkWT0nxKhKJyIKuTSf+1+j7+OXd9wEAvnvrl3XfT4d2W+er65ZcTziUf/E0RuwPktNUisYeHvanTKGSxE8znaV64icPc+IlGFE+1UqXvGxdHdnN3nyGTtIxoJ1mRzxdsngRXnv9TWz4ZCOWL9PvKNrwyUZs+XQrTjn5xGyskkKhUCgUCoVigMfjwVVXfgFXXTl24tx4nsfLr78DhmFw8gnHyK8vW7IA77y/BmvWbcQF556hOhEY9A5h1+59sFotWLJofiGGbYq+NqW2gi2tTjHl2GRqrQfBKgaVkXZEgz7Y3ZkJxENtO/GFKZ3w1dvh8qS/IbNQWFkHOFhhg3iBIer3wllWg09efAhLK/1YeqwDw6Vl48pZrMVTO1l+7O9pyWhed5V+zyCrcp76RzawUaKN7D1znEX2UiiUsUcxH8vt3L0Pv/7tXxAKh1FfV4Nli+ejt28AH3y8Hus+2Yzv3HIj5h02SzXPgeY23PHLexEKhzFtyiQcNmcG9u5rwXMvvo6NW7bhJ9//Btzu0UVEUooPsiPU4XCgPCHO9Q8MyHGaZWWlsCTEIL6Ynac6MbX5i+1VRKgROU8TNzjW1KjPQ0jxNODXv4EtonWeJkRQs46+OKfTeZonxy4wis5Tk+KpdpmCIKg7Tw2cp2UmnaeOxDgYhoHVakU8Hk8ST7XOUzvLgrEo5yPRaEw3tld6bCRqbtuxR/d1s2jjjPMlmsdipPM0/7G95PYcHvaD53k5tpfjOIRCIbjdbt155SDb9NopBEFIEivNipGS81Q/ttdM52lybK/5yGFp/erxx3kePUEfqlwlcFizIllmBUv6SdJzxeWXQRAEfPFLX8OLL72iUr0FQcCLL72CL33562AYBpd/4bJsrJJCoVAoFAqFMgZpa+9QXQAAgGAohPv++iiaW9pxyonHYuoUJZ53+dKFaGyoQ1t7J5574TX5dY7j8MBDTyEe53HCcUehrLQExcpQt+LQc1c3FXAkuWGVcxduWunAmU2D8HcdyHj+0FAvqpwCplZYUM4UrvPSDHGLctd81D8IgefRs0nZL+esOL4Qw8obnjpFPDXrPE0HGdsbi+T/+/dHwviwZa/83Gqx4NS5i/M+DgqFQhkLRKNR/P6+BxEKh3H2mSfjt7/8Eb7xtevwi59+G1++4QpEozH84c8PIUw45Xiexx/+8hBC4TAuvegc/OKn38bXv3wN7vnVj7Bs8QK0tXfiiWf+W8BPdejxxhtv4YEHHwLP59bpSe4HDofSedreflB+vbyccJ7meDyjQc/Bli8RMBIlYnvTdHbqIcX21mqcpyWkeJoQd7SQ7kHWzsKREEEjJsVTyYVndyhiJJcnxy6QvL0i6ZynMUk8TZ9AwibER9LdGo/HZV3EZrPJ00hI4qnkPPUaOU91HLCSEGu1quUcq47zlIxJjkQisqBr13GwGomaP/nBN/CTH3xD9z0zJDtP8yOekp8nVADnKfm74Pf7k/62hoeNbxblTYqXcueoXuyuSQFTnFQvtndkdtCM5mKSxz8YCaLD70VvMHW0cb7Jiox71JErceOXrsef//JX3HTzN+HxeDB5knjRq7WtHYFAAIIg4Ibrr8GxxxyVjVVSKBQKhUKhUAzo7e3FM//4F9auW4+urm4AQH19PVYesQIXXXg+6uvrCja25196A2s3bMbUKZNQWVGGUCiMXXv2IxQKY+XhS3HFZReqprdarfjql67EHb+8F089+z+sWb8JDXW12LOvGX39A5g0sRGXXXJegT6NOUKDXfJBd2XjlIKOJRfwrBtIXNTxD3Sjanpm8w/1dcmPGbv+XbjFgsC6gYh4Auwf7EF893o4ePE5LwALTr60kMPLOSWE8zTY26bqfdUSj4ZhtTt13yMpdOfpW7s+RYy4yHLUtDmo9BTvzRgUCuXQoFiP5dau3wyv14eG+lp87uJzVd17xx19ONZ/sgVr12/Cu++twWmnrAIArP9kCzq7ejBpYiPOXX2qPL3NZsN1V1+Kr916G95+7yN89qJzUFrgOOJDgXA4jK9+/VZEIhEsXrQQh69I318+mnUBogBksVjk2F5SVCkvK0sr4hQDerG9XJ7Gq4rtzdB5ynEcBr1eAKmdp/6A/jGYKraXcJ6a7TxVYnuVY8J4nkQ0INlpmj62d3TOU9J1qOc8lZYrVZUMDw/rukklwdxBjENyimrFUqu289Se3Hka03WeWhNj1v8+ZkybrPu6WZI7T/MjmpOxueTfTr4gfxeGh/1yZK+Ez+cz/DdcABL20XSdodCVOUXnqJn5GdHlmuQ8TaT2pptf523TkcOQnLNq8TbGxxOxw8WV5Zs1D+w3b/k65syehT/d93/Ys3cftu/YKb83a9ZMfOXGG3DO2Wdla3UUCoVCoVAoFB3++9zz+NFttyMYCqkOhvfu248PP/oYf7n/Adzx09vwmfPOKcj4VixbhCHfMFrbOrB3XzOcTgdmTp+Kk44/GkceoV//MH3qZNz50+/g2f+8iK3bdqGtvRNVlRU456xTcMEY6Mni/P3yQXfdpBkFHUsusLrKgeggAGCgqxWZnmb7BrqVZTlLsziy7GN1lgIRMfrM29uB7e89L7/HV82Ao2z8xTKTuGsmimfLggA+HkNosNvQTf3RH2+E1eHC9BMuQ93co8FY9EOPqqYtwfQTPg+b04OKKfNyOXxdXtr6ier5GQv0f4coFAolXxTzsdz+ZjFef+6cmSrhVGLBvNlYu34T1m3cIounn2zaCgBYefjSpGj7yopyzJk9A9t37MHGzVux6piVOf4ElP6BAVlQGBz05nRdUtqMJJzpdWqWl5fL+1KunbCjgYzt9XjcCASCeetwHE1s78DAoPw7UlOtFU+Vmxa1Ao8EGb0qiqeJzlOdGGM9ODm2Vzlfy1fcMZAs4GU1tjfhTiWdhlFNRywpntrtdvk3kPxb8PmGUVlZkXYcbKKvVCuWWjW/xSzLquYjnadk56k1EY2aK1EzyXmap5sNyO9DigfPJ+rY3mH4/Wqn6bDf2Hkq/a2aK4ERkmN35c7T1HMyjEFsryRcmm2hEciH5iKHScj1x/l4kphbDGQ1QPics8/COWefhd7eXnR0dAIAmpoaUVtbm83VUCgUCoVCoVB0+OjjNbj1W98DAJx4wip85rxzMHGCKGy0H+zAf597Hm++9Q6+9Z0foK6uFkcfdWTex3j48sU4fHnmkZgN9bX46hevzMGIco81ppwgldeN7g7eYsReUgVhqBkAMNR7MPXEOgQGe5VleSqyNKrcwHoqgES6lq+nHZGWDbAlThAXnvb5go0rX1hZB1yVDQgNiOd6/p4WXfF0sPlTDDZvAQD07VqD47/7FMqaZiVNBwB1845G3byjczfoFHDxOF7dsUn1Gu07pVAohaTYj+UkEafEwCEqvd7c0i6/1twqHhtMmzJJd55pUyZh+449aGk9CByjOwkliwx5lZjQXIt/kvNUutFRTzwtLSsdc87TkpISUTwdQf/oSIgS7rlMxdPeRN8pkOw8tdvtsLMsorFYCvGUiO1lWVkENes8lfYxMkY2Xw5EIL3z9Dd3/w5vvPk2Hnzgz2hqbBy185SMCbaxNllgBdTbQHKeAsCQbyhZPE185+Q8xs5TtbwjiqfKeiORqCwokmKu7Dw1+Lt7+PFnAQBXfv4i3ffToXV95stxTP6ume3mzSbk/iDG9mqdp8axtKY7Txkm4TxVi428ZB01g15sr2DO+al0npLVnZJrNT2y85V4LcbzSWJuMZCT9tXa2loqmFIoFAqFQqHkmT/+6S8AgN/99tc4e/WZqvcWL16E1WedgRdefBk33fxN/OnP9xdEPD3U4CIhsFBOoFyVDQUcTW5wVdQhmNBMA4M9Gc8fGuqXHzvLqrI1rJzgLK2EdAru3fYmbIx4ESAGG6YdfnrhBpZHPLWTZfE00NsGzE2uZdn/9pPy4+pZKwyF00KzvnUfBgLKzQ2HNUzAtJr6Ao6IQqEc6hT7sZzUMd/T26/7fm/idb8/gHA4AqfTgb7+AQBAdVWF7jxVCdGgr38w7fpvu+Nu3dcvPOdENNbXYKC3M+0yRgKXiIHM1fLzSWvLfvmxd6Avp59pcEC8QY61WcX1xNViisViQTToA5fomQyHgkX7HfZ2d8iPXQn3pX94KC/7BCm+BAP+jNZ5YP9u+bEVXNK8LrcL0aEYero7dZfb36O8FhoehBAXt2MoaO67koSseEwRsUaz32X6PUoOWZZlEYvFEA6FVPM+8sjjCASDeOH553HhZ85GKBhKjDeSdh1MQvrxDXnlaUmxOhTwIRpSvjs7a5OnEzhle7Q270eZWy3WBoNiLUg8HpXnsVhEWSoaUW/7aFjdqRkcHgQfVUTXvt5ORBMxwNGwMm8sKo4hotkmEq+/+T4A4JzTRnZXi3dA/e+Eb2gAA72dOf897SeW6/f58v677R1Q9oHenm50HGxVvd/R3mo4prggoEGwgLc6YU2hNVdZHLAIQNQ7iAGLckOMwMcxkRHlPkuK+asZcX8Thn0YIH5fPFwM0y1OWAQLmBTzC7DBDgYMz8ufhYeABp5BrdWVcuzi/FbwVgfsoTAGYuL8di6GRlhgCwQwYPLmjEypqm3MeB797KQMGRoawtq169HdbXyxpKurG2vXrofP58vGKikUCoVCoVAoGj79dBuWLF6UdLGNZPVZZ2DpksX4dMvWPI7s0CXkVfo84wIDe0llAUeTG8qIk5CIT/9iaioiAa/82FNRk40h5QxPpdJPw8SUCxWuqStM9XuOB8je00Bva9L7ocFudG56XX4+/YTL8jKukbBy6iy8fcsduOnYUzG/YQLOWpC73jcKhUIxQ7Efy82bK94Ms2nzNni96ut78Xgcb7/3sfxccvxIDjXSQUWidCjm3yF0KDI0pHxvXI4dgJJTWfruS0vVneKlJR5YLBY5djRexLG9pIvO7RKP+Yy6IrMN6ZaMxTL7zgYGvABE8VC7/QHA4xaje4OBYNJ7gDqGlrXb4XCIokvYZJeknvM0X3HHgBLhKn1nZAwxoHwO6fuVXIN2TVepHmzC8Ul2bKo6T222pNheiXJVbG+yViI7YEmnaCJmV9uPqn3O2mwq52k0YtB5msbx/YXPno0vfPZs3ffMoO3nzZ/zVFmP2f00m5Db0x8IIBhURwcb9QtnguTuTOo8zXA5o/F5Js2bBdNoERpPs+M8ffDvj+BPf74f//3XM4aFt339/bjs8qtx01dvxE1f+3I2VkuhUCgUCoVCIWAsFkyaNDHtdBMnTsCevfvyMCJKsF+5q5Szug17H8cylfWTIEnE8aA34/njoWFIp/yl1cXt+iurroeePLz87KvzPpZC4ambDEdZDUrqpsBdMyHp/eb3noHAixcN3DUTUT//2HwP0TQMw2DRxKmY6HDgpuNORUWR738UCmX8U+zHcgvmzcGsGVOxZ18zfnHXn3DN5Rdj6pRJ6O3rx5P/eE7lSNX2m2aD2390q+7rkvNlJK4SM+R6+fmE45VjUYerJKefibGIAo7bI67HwrpU71dUVKCqthEujxj3bLWyRfsd2uzi2B0OB1wJwdHGOvKyT5AdoQLDZLTOUESct6amGtV1yVULpWVlQGcXeMaqu1ynS3Eq1zVMQFUioSMajaUdB8/zcodhda1yjOXylI14u2X6PUqRtCWlpRjyDYPnBXlenudloctiE79Lafryquq063C5xf2W3A+8fkWsq6lrxKCPuNnS7Zanq6jmYbFYwPM8eNiS1sXFxRsJKiqVcbhc4j5YWl6pmr6ktFw1b23DRHg8SrS6w10if86Kyhp5Xk9iPsai/92fftro9m0rq75hxuHyoKq2Mee/p+6DSiVMjIvn/Xfb4VJuUojG4oBFLcTHBeO/4eFoGPv6DiIUj6LM4dKdBgD80ShsFgvmlNWgylUqv97p96LNK5ob3Tbj6OlQLAYewExPJapKKuTXt/d3oMPvR5nVmdSlSxKJx9EoWOEk9p0QF8W+3nb442FUsG7DeQGA4+PwxyKYUFqJCVXi/J/2tsMbCWOqpxyNpcWTRpUV8fStt9/FtKlTMX/+XMNpFsyfh6lTp+CNt96m4imFQqFQKBRKDlgwfy72mriQtm/ffixIcdxGyR6BcBTbeuKodDGoahqftRbltcSFmGjmd9LyEWWeiurijjWuqEsWCyNWD2pnHTqOxWnHf87QTcpFQmj58F/EtJeC0fQyaYkGvGj96L/gwgHw8Rjmnff1rI43Eyzj8OYGCoUytij2YzmGYfCNr12Hu35/P/YfaMVP7/y9/B7Lsrj68ovxwENPgWEYeNzihV+n04FAIJjUfychOVOdzkMjwaHQeL1e+TGXoYsxUyJR6buVnKelqvdLE+47S+JYgeeLv/PU6XTAZhPFkFw7dyUkBy8wgs7TPjFCVNt3KlHiEUWOgN+g85RYn91ul7/LUCgEQRBS3iRBujBtNhusVivi8XjGn2E0SOuSxETSxUs+ln6fpNdYM85Tvc5T4rFN4zx1EM5Ti8WCsrJSeL1D8A4psavasZFu1Us/ezEef/JpnHbKSapprTaN8zRl52my89So83S0aPtl8+U4Jv8uw6H8JxqQn1Ov83R42K+dRcZ05ygjGj21Tk0h8T9LunuXGHFmvc5TQEjfuUqsT5lX82bK+ROdrcQH4Pi46nmxkBXxtP3gQaxYviztdNOnTcUnn2zKxiopFMoYYPNTP0/5/uJLf5CnkVDGIt945sG009xzyTV5GAmFMnb48o1fxFXX3ICHH3kcV17xed1pHnn0cezYuQsPPXh/nkd3aBJ21uPvWwTEYjH84ffXF3o4OcFZpkTt2uLhtBdStFg44o7s8uKO7XWXJ194alh6ek7cNcVKqs/avu4FxIJi9JjN6cHkleemXR4XDmDHc/cmFm7B3HNvOqS2J4VCoZCMhWO5yopy3PGjW7Fx8zbs3LUPoXAYNTVVOHrlMsQTbqn6uhpZNKiprkIgEET/gBdTJie7agcGvYnpxl+1QTEySIin8RyLf7Lg6BCFcavVipKSEvj9onggRZdarYnY3njxxvaGwmL0ptPplKNP8yEGCYKguvEgFs1MeOzrE93gNdX6x9iSqGgUJUoKYKJ46lS9ZxTHDRiLp0YxsblAFk8TbmE9wRQAIonYcGl6Uug0wpYQIsnvJKaKOWZVsbvabVVeXg6vdwi+IZ3YXjnyWhnHNVdfgWuuviJpWqvmRkW7nQXDMLDb7YhGo4hEIrIITsb2WhOP4wb78auvvwsAOO2UVbrvp0Mrnubreyf3O+nvNp+Q4u3wsB9+TSS2zzdsOK8sZ5o5FxIE8EbiZxoFkwEjiqcasVISZM2diUnrkp6Jy2NMzi0tARAj2+OC9tMUB1kRT8US+PR3iDmdzqzkOlMoFAqFQqFQkmFtNlz++c/hjp//Ev97/kWcc/ZZmDhBdAUe7OjA/55/ERs3bcEVX7gMdpbF2rXrVfMfccSKQgx7XDNv3lzs2LoR/f39cLtTx9eMVRxErI6V4cGF/WBdpSnmUBAEAVY+Kp+h2T3lqWcoMCX1U/FqRynYyCDm1lpQ5bZi6VlXFnpYRYHA8zjwzpPy88lHfQY2pyfFHCI2J9G/JfCIR8OwpYipolAolPHMWDmWs1gsWL50IZYvXah6/Z1E5+mCeXPk16ZOnoCW1nYcaGnDsiULkpZ1oKUNADBlcnK6AyX7DHkVl1ssx+Kf1GMruRUBoKysVBFPy8eO8zQiC8EO2BJOv1w7dwFRCOKJLthMXZt9/aJ4WlubWjzVuuO067NYLLBarXASAmA4HE4tnhJimc1mhc1mRTSav65YQRDkjljZeUpsP7WQGlW9ZjchnkrCKLlMcp9gWVYWWAE98VTc//Wcp5EMxqF1nkoCqcPhQDQaVXVusqoO1dTO00ef+jeA7ImnuXK4aiF7gUOFcJ7GNOKpX+00HR42Fk/5hHqY3jia7NwEJAHTXGy+rnNVkLyoJsRXaSHEvGYRnbOKeMsJPPgidJ0CWRJP6+vr8OnWbWmn+3TrNtTVjc+4MgqFQqFQKJRC87kvXAWGYSAIAjZu2oxNm7eo3pcOTh957Ak88tgTSfPv3fVpXsZ5qGGxWFBbO36PgVl3GXiBgYUR96/I8IB58ZSP40CkAkFvH+oqPbCXFLfrxO6pQAfThI07e/GfncD3v30LPDXpu+kOBXp3fgx/d7P4hLFg2qrPmprP5lTfVMCF/VQ8pVAohyxj+ViO53m8/Po7YBgGJ59wjPz6siUL8M77a7Bm3UZccO4Zqou6g94h7Nq9D1arBUsWzS/EsA85BvMY2ys5T0nRqKysFB0dnfJjALAkMiaL2XkqC8Eul+I8zYMYREb2AiMQTyXnqUFsbzrxVCsmOgjzVDgcQXmK+x7jHCme2hJxx6G8xbeS28rjSXaeRgnHaDgSBs/zsvBmRrSUhEjy86hijllWHdvrUC+zIrHxhvScp5mIp4Tz1Gq1wpoQRaV5ye+WJZ2nieniBmL2uatPSbvuVGj3XSOHa7Yhv49IJAKe5/NazaGK7Q0kx/b6UoinAgRkYr/UCo5m9Ucmsa4k52rCTZpWehUXoI7tJd4yNQIB8vo5Pj6+xdOjjzoSTz/zLJ548hlc9rlLdKd58qln0NrahksuviAbq6RQKBQKhUKhaLjgM+eai3ihULIIwzDYXbISL73+DjirE6urzTtHLFYbXmn3YPeeThx//EJcX+TOUwCoqhIFXovFgnPPSx9LOx6JBYfh726Gv7cFnpqJqJq+BPvfflx+v2HRCXCb3A8sVhYW1gE+Jl5g5cIBoHz83mxAoVAoqRgLx3Jt7R2oq61RCQHBUAgPPvIMmlvacepJx2HqFOXGouVLF6KxoQ5t7Z147oXXcN7ZpwEQLzA/8NBTiMd5nHzCMSgrLUlaFyX7eAnnaa5FrEgkITgS4ml5mXKsVybH9iZEnBw6T//z3It4/Kln8du7f43Fixamn0FDOEI6T/MX2xuOqJ1z0UzF095E52m1UeepJJ4Gdd+XBEZJiCNdxJKgbATHEf2fVhvhdMyPiEaKo9LnjMVicsWIKrY3ElWJrWZES2k/MO481YqnybG9ADCkcZ4KgiCLp6mcvRKk81RPrFWJp0QXqnITgP73cfH5q9OuOxWFcp5q/y4jkQhcrvzdmEl+zuHhYfg1fcL+VOJpooc0XfQtw0guVbXgyEOaPw2J4wytW9SsgKm3fF7gRTHUxDFMQntVnKc8b6rrtRBkRTy94bqr8Z///g8//unPsHPXLlx04fmYNnUqAOBAczP++a//4Mmn/gGn04nrr6P9dBQKhUKhUCi54De//kWhh0A5RGEb56HZ+zaAEELhCDwe86cZkgOisqIiF0PLOqedegrefOsdXHLxBYdsqs7ul/8qi6VTjrkQFVMWwO6pAGOxQuDjmH7CZRktj3WWIEKKpxQKhXKIMhaO5Z5/6Q2s3bAZU6dMQmVFGUKhMHbt2Y9QKIyVhy/FFZddqJrearXiq1+6Enf88l489ez/sGb9JjTU1WLPvmb09Q9g0sRGXHbJeQX6NIceZEToaGN7H338Sbz88qv4za9/gabGxqT3JXGKdCtKblOAjO0VXWF8Dp2n//rP8zjQ3IoXX3xlROKpFP9Jdp7G8hDbSwp84jrNi6ccx8nH2cbOU9GRaVSzJ61PEuVcpPNUM7bk9SsiktVmlSNsjZyO2UblPC0RxVNBEMBxHFiWlaNxAVEIjqj6XRWR0QhpmxiJpyybrvNUP7aXFH0zdZ6S4qnsPA0qwrhNz3maI1EzqfO0AN87IP7t5lU8JdYfDIaSxHFTnadpYQCBl2N+5fkl8TONgMlAdKnqrc38+tWvxHgegiDAYrrzVJCdsuPeeTplymT87re/xjdu/TaeePIZPPHkM6r3BUGA0+nEb+/6JaZPm5qNVVIoFAqFQqFQKJQiobZG6VHq6+uXI8DSIQgCBge9AIDKyoocjCz7XHzR+TjzjNNQUmLuM45HPLWT5MeB3lZYrDYsu/LnmHvuTejc9Aaqpi/JaHk2pweRYTFWLkbFUwqFQilqVixbhCHfMFrbOrB3XzOcTgdmTp+Kk44/GkcesVR3nulTJ+POn34Hz/7nRWzdtgtt7Z2oqqzAOWedggvOPUPlZqPkFm/iuAsYnXNSEAT89p57MTTkw6uvvoGrrvxC0jRSbK+q87SUEE8TLtR8OE8lYUzr5DQ9P9HfKglQ8Tw4KLXRp6Swlo6BgUFZTDEST90J8TQYNHKeSvGxoijnVMX2pt6W8bg6tlf6ngsR2+t2u1Wvsyyb1HkaVYmn5jtPyZsQSLctq43ttevH9vo0sb2RqCJKa6N+9SCdp3piLekqFqOT1fOR8d1vvPk23njzLXzzlpvR3tEFAFi0YG7aMeiR7DzNf2wvAITCIQD5q4bRrr+7u0f1fHhY3YFKIgmI6cybWuemhBT7a1a+TI79ldafXnzVIgmgFpPpGWJAsBLbm0lnaj7JingKAKeechJeev4/eODBh/DBhx/L+fVNTY049pijcM1VV2Dy5ElplkKhUCgUCoVCoVDGGrW1hHja348pUyabms/vD8gnmGPFeQrgkBZOAcBTN0V+7O9tlR+7Kusx/cTMXKeAKJ5KcGHjCwoUCoVCKTyHL1+Mw5cvzni+hvpafPWLV+ZgRBSz8DyvcrmNRsQaHPTKXY1akURCEipJwa2UcJ5KLlRrHpynksCVaWeohOSydDiceY3tHY3ztK+vT35M3uhIwibENCNXoLZ7kxTCI+HUzlNSVBRje1PHxGYbdeepcqwZjUbhdrtVwnQ4Es5YPJUicGOEoE2K2yxrg410gmqcp2WGztPMxmEl+jzTxfaS4qrLKboxQ4QI/rNf/AotLa047LA5eO3tdQCARx+4J+0Y9NAK/blyuGrRriccGtkNEyNF6+jv6uoGIMaU+3w++IaTO24lxCTe9CKiYWepycpUBozociXWJQgCeMFE5K80AKidq1L0rhnxVBJneSK2V/tZioWsiacAMGnSRPz0xz/M5iIpFAqFQqFQKBRKkUPezd7b25diSjUHd6zD0ZOsCMQEVDvycyGFMnpI52l4sBvxaBhWuzPFHKmh4imFQqFQKLnH7/eD5xWBcjTiX3NLi/zYyDEqOU9Jx53U8wgA5YnOU0uO40MBxV1nJPSmQ3JZulxO2BKOvdHGHpshOhrxtL9ffmzkPJUik42+Q6ljVRLdyAhm0dFnTJzYPmRsL5eHuGNA3Q/rIZynkqhH7gvRSCRj0TJV56nVaoXFYtEVMyUq5M5TtZhGjsNhT+/KJ6N49WJ7yUhm6TsAlMjmUCiEeDwOq9UqJwL5/QEcvnxR2nWnIsl5mrfYXq3zNL/iqfZ3rH9gAADQ0FAPn8+HYDAEjuNU35uE2HmKtJ2nRp2lgtngX1n81Kw//Zzy7NppJeepjYiRTrcMXhAFXE6IJ4Tb4ut8z6p4SqFQKBQKhUKhUA49HL5m3HykHWUOIPDh34DTTzU1X/e29/DZBeJJPtO/OZdDpGQRV0U9LKwDfKKnNNDXhrKmWSNeHime0theCoVCoVBywyAR2QuMUjxtJsRTA1EkIsf2Ep2nRGyv5LyzygJe7pynkpCWSewtSUgvtjcPYpA2tpfjOPA8L4ueqZBuaLSzLMoSQrUWSQiOG7h+k5ynhHsynMZ5yhEiEmuzwSbF9ubJgWjkPI3FksXTcDiScddoqs5T6b3UnaeieBqJRBAOh+W/E/I7NzMOcl9g7WRMsLg+v1+5MZEU7Mgo41AoDI/HLcc3x2IxfP1r16dddyqSOk8LFdsbSi3yZ339BjcHNDY2YPfuPQDE6F69yhqz0bVGnaVy7G4aEVJ2rhLrEx+bFTCT5VM5tte0ACougxcE0bUqCGnjggtB+l9aCoVCoVAoFAqFQkmBx2nHtEoLqt0W8MEB0/OFfMq0ztL8ddFQRgdjscBTM1F+vuul+8FzI7sYCQCss0R+zFHxlEKhUCiUnKCNB9U6tDKhpUWJ7TcSPaXIWQfZeUrE9paXq52nQg7F09goxVO589ThlKNuuVEc+5hebyRZoDT7vfX1ic7T6upqQ1HCYpHEU/1lasVTlmVlsS5d5ymncZ5abbl3GJOQcbqSyxJQ+m/JbRsZgfNUv/NUfCyJp6wJ8RRQu0/V42CRjnTO0yDReUqKuaR4GgwGEY1G5fFzI4y3JtHuu/lynmr/LtOJ/NkmZvC70NTQID/2+fSje4XE/8xqiNrOUl5aQtr5GUAjvsqeVRPrliYh184JvOgeNTl4m4UBx/MIcVHEEsJrMZJV5+kLL76Ml195DQcONMPvD+iq5QzD4O03X87maikUCoVCoVAoFEoBKa1plB9bouZjVyP+QfluTle5fhcTpTjx1E7GcOc+AEDX5jex/bl7seCCW0e0LMl5amEdOb1wSqFQKBTKoYzX61U9H43z9ADhPOUNxDC589ShOE8lwRQAykrz5zwdfWyvIgRL4t9oY3ubW1owoalJJXglrVdXPI0lRcDqMZj4vquqjG9QTOc81TopGYaB0+lAMBjSHRtJXNt5KonOWRDmzKB2nrqTXlc5T5PE0/SipZ7zVBtzTAqbSbG9FYp46h0aQn19HQC16KgVXPWwpO08VcRTcjxklHEgGFS9F+M49PaK4nttrX7kczoK1Xmq/btMJ/JnG6PP2dioiKfDfv3zZUGQ5dOU61A6S7Xzm5lb8Y2qOk/TzKMLMVOMj0MQBFhMCr9WxgpO4BHkouO/8zQej+PGr3wdb771jqG9mGEY09ZjCoVCoVAoFAqFMnZwltfKj1k+Aj4eg8Wa/qIDF/RBuoxQVl2fo9FRcgEZtQsAk488b8TLmnfe1zH/gltM7TMUCoVCoVBGxuCg2nk6us5TxXmqF8MqCIIsODoJ5+kRRxyOWTNnYObMGXJspTUPnadybG9sdOKpy+WSxZjRxPa+/Mpr+PJXb8YZp5+K+/74O8Pp9J2n5sRH6ftN5aJM7zyNJS3D6XAiGAwhEg4jHA7jzbfewVFHrkyKISX3C1sBYnujBrG9kkhKinuRSETlVB1t56nULaoSMzX9peVElPKQV/nbzLx7VemYtOs4TwMGnaduQlAOBoJgCfGU4zjc8r2fAQAefeCetGPQI7nzNE+xvdrO0zzH9ho5w0nx1Mh5yicE0bTuTbmzNLnz1JR9NPE26fYU+1YFc6G9THJsb5znE/ObdZ5aEOPjCHFRcAnhtRjJinj68COP440338bxq47DbT/6Lv74p7/gP/99Hju2foKWllb857nn8eDfH8E1V1+Bb97y9WyskkKhFAGbn/p5oYdAoVAoFAqlCHBV1CdO9MT/hb29cFc3pZ2Pjyh33ZYT7lVK8VM/7xi0r30eAFAzZyXKmmaOeFlWuzP9RBQKhUKhUEbF0FB2xFNBENSdpzpiWCwWA59wkpLuubLSUrzy0nOqaS1W0TnHG7gfR4sgCLKoFRtx56kowDgdDtlRORrn6YZPNgIANm3aknK60YinkrhrJcQ1LdbEtk/feUqIgAkxPByO4G8PPoy777kX556zGr/77a9V85IiltVmlYW7fHTFAhrnqTvZeUpuW7HzlBAtU7iBJaR+UZV4GtV0ntpTxfYS4qlPP7bXnPNU+X71YoL9hHhKvl/iUTtPyf2E4zhMmjC6czPtjQr5cp4mdZ7m2Xlq9Luqcp4Op3Kepkevs1Sa37TzVFBLr6aFV+08AHiBB5f4vTcf22tBiIshEBOdp8UpnWZJPH3u+RdQWlqK39/zG5SWloBhxB9elmUxc+YMfPOWr+PwFctw7fVfxqyZM3DeuWdnY7UUCoVCoVAoFBPE43E8849/YtfuPWhqbMSll16MstLS9DNSKCax2FhELU44BPHkNDjQYUo8ZTjlTmBPBY3tHUs0LD4J00/4PELebsw//5ZCD4dCoVDGNfRYjpINBrMU29s/MAA/ETupJ4qQka6k81QPq+R+5HMjrpCfcySxvZFIFIODXgCig1H6bKNx0vX29gFI/k701q3FtPM04SaVHJ96WK0JQdNA2NLG9gKi+xYQ41D37T8AADh48KDO+gnnqVVxno427tgs5Het7zxV3o+Ewxk7PqVtQorE2s7TVLG9TqcTDocDkUgEXsJ5Sn7n5jpP9cVTaV5SJCbdpdrOU3I5sRiHX9zxg7TrToX2by1v4qlmPeFQccT2VlZUwOl0IhwOw+cb1p1GDO015/7UdpZK85uDSYrtjfNCRn2r8joFATGeBy/wsGQws5WxgBd4BGPRpM9RTFjST5Ke/fsOYMnihSgtLQGgKMzkznL8quOwaOECPPrYk9lYJYVCoVAoFApFw5/u+z/MmbcYa9ask18TBAFXXHUdfvTjO/DoY0/i13fdgwsu+hz8/kCKJVEoI8Cp9Pb0te8zNYuVV07m7Z7yFFNSig2L1Yb5F9yCFdf8Cq5KGrlMoVAo2YAey1FyCRkNCoxc/CNdp4C+WEAKNmTnqR65dp6SIs5IxNOP16yRoz9XHrFCFsTioxFP+0TxNBKJpIwV1XOeRs06TxPfiySQ6qE4T/UFH8V5Ssb2JpynkYgsKuuNieOU12w2GyHU5kc8jaWN7SX2i1hMJfibEk8T+0FUJ7ZXEjGtVqvsMK2qqkpahtR7OuQbeWxvOucpidQ7CygiOCCKp2Q3ajYidgsX26veF/PdeWp0c0NJSYmsnQ0PG4incmmpmc7TZKcqLwgQBCGtACq+Lah6Rjk+Dl4QYMnAeSouRUjMC1gStZ2+UDDtfAzDwMpYEBf4JAdtMZEV8TTO8ygvVy52uFziP4paFX3y5EnYvWdvNlZJoVAoFAqFQtHw/gcfoaqqCitXHi6/9sabb+HjNeswadJEfOdbt+CoI1eiubkFTz71TAFHShmPsGV18uPe1vTH/PFYBDYoF8lYKp4e0gg8j1hoGGFfX6GHQqFQKAWDHstRconW5WjUzZeOFqLvFIAcz0sSCReP81SvkzITXn/jLQDAlMmTMHPmDFk0y8RB+eZb7+CBvz0kb6u+XuV4x6sRtUlG13kqbk9bythe8T1BEHS/R0kYJGNsnU7xun84FMbg4KC4Lp19iePIzlMltpcrQGyv260IhVK0bkQj7kluaqvVKm+XVEhCpWr/4pLdvr+/5zf41jdvxqrjjklahqSnDKmcp+J3brFYVM5VI4ycp3riKfm+xWKRt0sgEEQwqAheI/k70SKJpyyb2t2cbbR/l8US2+vxeFBWKgrpPiPxFOZid/U6S6X5VROkgRRfpd5Rs7G7ErwgJGJ3eURiMdz8+AO48N47cesTf0OUS70fWS0WWbQtVrIS21tXV4ve3l75+YQmMaJr565dOOrIlfLr7e0HU/5gm4Hj4ti+czc+2bQN23fuQU9PH3ieR1VVBRYtmItzzjoFtTXJd3IAQHdPL57990vYun0XAoEgqqoqsHLFEpx/7hlp/yGnUCgUCoVCKXZaW9swc8YM1Wsvv/IaGIbBH35/NxbMn4err7ocx51wKp5/8SVcf93VBRopZTxSWjsRge5PAQBD3a1ppgaiAfWFIuo8PXTp37cRH/7+OgCAu3oCTv7xc2nmoFAolPEJPZaj5BJvlmJ7tc5TPTGMdHs5nKmdp9YcO09JISiaYeepIAh44423AQCnnHKS6JZKXNs2u/0ikQi+etMtCIfDOOywOTj2mKNk5ykgfi9kH6Jq3oQAZbPZ5PWZ7W2Vpk8lBJLvxeNxWCxqn5We81TuPI2EMZAQT/XENlIss9mU2N5sOBBv+8kd6OjoxH1//J2hOzNGCL/kNBEd5ymgmMDMuD0BIraX+DxSn6skFAPAquOOxarjjtVdRnmZKKbpdZ7a7XZTQhbpPCVjfvU+B8uqpSC3241gMIRgMCj/HUqf6eovfRMA8Pe/3JV2DFp4npdvznC53IjFfHkTzbX7VziFszsf65fweDxpnadmRUS9zlIAkI2r6eZnkp2rMSHz6F1pnZIAumbvLmw/KF4H2NLWjCc+ehdXHXey4bw2iwUcz4O1ZJgVnEey4jw9bM5s7D/QLD8/8sgjIAgCfn/vfXKMyH/++zw2btqMObNnj2pdO3btwZ133YdXXn8HwWAICxcchsWL5iEajeG1N9/Dd350J3bvPZA034HmNnzvtl/j/Y/WobKiHMuXLUQ8zuO5F1/HbT+7G8Fgfv+QKBQKhUKhULLNwOAgamqqVa+tW/cJmpoasWD+PADiSd6SxYvQcbCzEEOkjGNqJioXeyPe7rTTxwjxlIMVFmv6Th/K+MRmJ9wAIX+KKSkUCmV8Q4/lKLnEO6SN7R2Zu6w5yXmauvNUzwFHYsmx85QUTDON7d26bTu6usXj2lNPPgmAEtcaNykGdXV1y2Ly/gMHEIlEVW7TVL2nkgtREl2ATJynCRdkCveiTSOeatGP7U04T8MRDAwkxFMdwYgUkUQ3Z3YciL29fXjs8afw5lvvYNOmLYbTSd87a2dVblJp+2n3BUnQMtMzCqjFU8m1K/XMWi3mzGNSbK9e56m2I9UIQ+epRjy1WCxJ4rgn0XsaCAYRyKLzlNy27kQ8cL7imrXiZf6dp8n7t51l4XDYUVYmdpUbdp4KUudpWvlTN7ZXSLSmmoGBEvMLJARQwLR4Kk0lQEAsIZ5+2tasmubxD99OGiOJzWJFXBDnTf+ZC0NWnKcnnLAKr772Bj748GMcc/SRWLRwAU44fhXefuddLD/iaHg8Hvh8w2AYBjd97cZRrYthGKxcsQRnnXESZs+cJr8ejcbw4CNP45331+APf34I9/zqNvnHg+d5/OEvDyEUDuPSi87BeWefBkD8Y7rnD3/DJ5u34oln/ovrrrp0VGOjUCgUCoVCKSSlpSUYGlLuWu3u7kH7wYM4/zPnqKZzuVwI5vkOTMr4p27yLEg+BD40mHb6iF+ZJm5J7UigjG9sTqWHigv7RxQZRaFQKOMBeixHySXeQY14OkIRK7nzNNkxSjpPnWnEU6V3M0edp6OI7ZUieysqyrFs2RIASm+k2dje7u4e+XFHRyf6B/pV76eK7ZW2Y4mnJGW/qB5K56mxkGdJI57KHZ52MrZX/D4HBwdlcVdvu0oiliTaSa7H0QpzA4ntACDl76C2f9RutyMUCsmiqjYS2Tc8MuepuC4ODodd2eYmkzfLEs5Tn4Hz1AxWQhAlx6Sdn3xPwp0QT4PBoGo5HMfh7//3e1Pr10MlnnrEdYz09yZTtHHkoVDhY3s9JeK5TmmpKJ4OD+vfLCp7SUfQWQoki6npIKfmeNF5yscFbGjbi2m19agqKU27DF4QEOd5CBBQ4S5Jen9PdwdmN0zQndfGiM5Tm0VAsZpPs+I8/cy5Z+OlF/6DuYfNkV/7w+/vwhc+fykqKioQDAQxe/ZM3Pu7u1QxviNhwbw5uPmr16qEU0C8K+SaKy6B2+VCX/8Adu/dL7+3/pMt6OzqwaSJjTh39any6zabDdddfSmsVgvefu8jDCdcshQKhUKhUChjkcmTJmH9+g0YStxV/t/nngfDMDj2mKNV0/X09KC6Wr/mgEIZKaX1U3BgmMUHrRz2R2vSTu+eMA/ffS2Mn70TQc/E0/IwQkqxYnMqJ9oCHwcfS+73olAolEMBeixHSYVAuIRGguRwlDor9XoqzYyhuUUrniYvR3LPkeszQhLw9Bys2YAU67Q9l+l4/fU3AQAnnXi87OBUYnvNiYCdXV3y446OTvQSfacAZFFUD2k7lpQoN5qZdc+aEU9VzlO9zlM952ni++zsVD6XnmAkrV/ebol1jdZ5Sjqo9TphJbTiqbajVBvhLAlaIxNPxWXJn9lEZypAOE+Jz5SxeEo4i8lYXq3jm9VxIEvCZlDTeZouWjnd71BEz3map9he7f4VzrvzNPl3ocQjnuuUyeKpL2kaQIriNf8bn+Q8TUT5mrkJVXR6CnJUMMfHEY3F8NVH/oLvPvMwrn3gXrQP9KVaAAQICMdjsvNUb62vfrrReBEMAwvDgOPjGccF54usiKcOhwOzZ81EVVWl/Jrb7cZPf/xDrPnwHezcvgkv/u/fOOvM07OxOkPsdjsaG2oBAIPEXTufbNoKAFh5+NKknaeyohxzZs9APM5j4+atOR0fhUKhUCgUSi65+KLzEQgGce5nLsGXvvx1/Paee1FSUoKTTjxeniYajeLTT7dh+rSphRsoZVziqZ2Ej2OH4ZltHN5rSz+91+dDiAN6gwJK6qeln4EybiGdpwDAhelNrRQK5dCEHssVN/F4XBa28000GsV551+CSz53+YjEJ47j5FjS2poa+bVM6evvRyAgiiwuOY4zjfPUmcZ5asmt8zQ2wtje7u4e7Ni5CwBw8kknyq9LIpTZDkfSedrZ2Yk+jXiq7aIliSbEwZKS3MT2qpynOp9HEhjthFAoOYk7CPFUb0ySA1BKhsx0uxnh9SrCUyphLNl5Kv43ath56ktMl7l4KglmimBtLuyzolwUT4e8yYJwurhrCdPOU504YjK21x9Qjr9Tuarv+8tfsfyIY7B27XrDachtK/1OcHmL7VXvi/kXT8V9wO1Wakk8JeJ2VjpP9Z2nPMTS0pF0lgIwFDD1F4JEyK+4jBgfx6bWA+gYHAAA+CNh3PGfp1LMzkAQgGAsCk7gwQsCYjr/Nr25fQuiKfYn1mJFjI/DymRFpsw6xTmqEcLzPHr7xC+4orxMfr259SAAYNqUSbrzSa+3JKajUCgUCoVCGYtceMFnsPqsM9B+8CBee/0NsHYWv/jZT+Q4IAB44823EQgGceTKIwo4Usp4ZcIEMZLnYEdH2mnJ6LjKysoUU1LGO1bWruq8jVHxlEKhHKLQY7ni5qprvojDj1yF7dt35H3du3bvwdZt27Fhw0a0aDpHzUDGQUu9umZjZ0n6+pTI2abGRgD6TsJIBp2nkiORz1GsJynkxGIx0+5d8rMeRqQt2mQR0Nz26+rqlh93dHSht0/jPE3VeRqVnKeZi6dmnKdSZDKg3zkbi+rE9rpE56nf70+aTr3+RP+nVXLsZrbdjBgiIm5Jh7OWqIHzVNoftK7VbDhPJeHMZja2N6FfkJ9JGrfZ7lXSeUqK3NrOVD0RXYrtDQQCaudpjMP3f/xrfP/Hv06a57n/vQCvdwhvvvW24ZhIV68kIubLeVossb0VFRXya9Lfb6kc05yq8xSm+j+1naWS69QscmdpYiaO59ExqI4U39/bjUBEf/sxCfE1xEXB8XEIgqAbzTwcDuHDT9cajsPNOlDucMFmsic432Sl87RYeO+DtfAN+1FWWqKK9e3rFwXV6qoK3fmqKisS06XvZqJQKBQKhUIpVmw2G+793V34zrduQf/AAKZPm6aKeAKAKZMn4S/33YslSxYVaJSU8cyECU0AxAt0fn8gaf8jGRhUjr3Jk0vKoYnN6UE04AVAnacUCuXQhR7LFTdr160Dx3H48KM1mDdvLgRBwJ69+zChqREej/ExTzYghZ6ROKlId2NtbcJ5OoLY3jAhREj7pp54Ko3RbrfDYknt3WESjiO92NhsQIqNgiCA4zjd/kctpIONjDyVRCie58HzfNrP19WtiKfdPT3oJMRUILXzVPrePYl4VSAT52l6Ic+aznkaM47tJdET4iUhhU2K7R2deEp2xEaixrG9cvwt0XkKkLG9auF1WO48NSdakvtENKZ1npqM7ZWcp0M+eV+Snad2c85TMiKYVYmn6vn1xFPSeWrRdJ62HezUXV8oKPbMphSudZ2n+RFPteJ8OJzffnDpb6GyogIdHeI2lP59kJynPqPY3ozkT3VnaVwQe0cHA368vHE9ptbU4ZjZ81LMzUAQlN5Ujo/Dbkve99/e8SlWLzlcZ24GgsAjyEVlIZcziF5/e+3bOGHp0brvWRgGFsaas9//0TJuxNPe3n489tS/AQCXXHi26sciHE5td5fiI8wcfNx2x926r194zolorK/BQK/+D8uhjnTAMRa2T9WS3MZLp8PmriiKceSDbOwP2di3Ll+0YtTjGAu8mLitqNCf1+x3ZWacufpNGUu/WcUA3V7pqaptzOv6JkxokkUsLfPmzcW8eXPzOh7KocOEJmW/O9jRgTmzZxlO601EAgFAFXWeHvLYXCWEeKofZUWhUCiHCvRYrvjgOE52NEkXxN//4CNcefX1WL58Kf7x1GM5XT8pRoRT9DwaQXYqVleLztORxGiGCReSRxZP9WJ7zUePSu7H0XZhGhHViI3RaNSUeEoKgjYd8RQQ94t0TkUytpfneWzdul31/uCgcRS0JKS5XC6wrA2xGJeB81Tt/NRDJZ7qCB+6nac636nemCRxXuqIlfo4Rx3bO8LOUzubOrZ32J+h89Su5zxNH5VMUp5wngqCAL/fj7Kysow7Ty1Wc7G9dp19nuw8JeN/o7EY/nznD3XXFwwlxNMUwjX5veTbeaoVT/PtPJU+J5msVJpwnrqc4rYwEp7lzlMT2btkZ6mVYcDxPGJxDj94+hEM+MUbAb69+gKcumCpwfzKOkXhU5xfywtvPYfTJ0+ErUr/mlaEi8HCWCBAQMzgO24nzvvHGuMitjcYCuGue++HPxDEysOX4uQTjin0kCgUCoVCoVAolEOOhlILjppoxVmzbGj+8D8pp42t/Tt+eYoDPzreDqF7a34GSClayN5T6jylUCgUSrFBXoDv6BTF03XrxM6/HTt25nz9ZAxmKsHICNKtV1NdBWBk8amSKAoorjU9J6FkUEnXdwqoBTw+B+4jrUiWaWcoANhYY/E0HV0ap+mWLZ+qnqd2nopjdzjsROysyfHHR+88jel1nuo4T3meTxK/pec2q9p5OtrYXrLzNBJOL55KIqL0X0lM1wpYUpSqnsioh6rzNCEUS585nRtZojzhPAWUv1FpfzXbeWojxHFS0E2O7dURTxN/w8FgUO4yBsQb5Gtrq1FbW500TygkTpe58zQ/nadJsb157zwV96+KCuW7lZzj0ncSiUR048N5COjz+fC3t1/Fs2s/QJRL8beu01m6u6tDFk4B4Ncv/Ms4ppyYn+Pj4MHruj/3RIEdLz+kvwhGjPvlBWkM+t9xN+w5+W3PB2PeeRqNxnDX7+5Ha1sHFsybja/ccHnSNE6nA4FA0PDgQvqHX+/HX8vtP7pV93XJbZNvZ8lYYSxtn7Y3Hizo+iXH6cCmVwo6jnww6dIfjHoZ2di37njrpVGPYywg/Xv56BbjUvd8cM8l15iazsz3YnZZmTKWfrOKAbq9io+16zZgzZq16O7pTbpYIMEwDH515x15HhllvOMOHsSlC8UT88C+NSmnjYf9cLEMXCyDEiKKjHJowjqJLi8qnlIolEMceixXfJAX4CXnaVv7QQBAMBhCPB43HdU5EkjBL5xCMDKCTNsrLS0FMDIRKxRSIjClKEo956nkSnM60l9rJYWmeDxuWngyi1YsNSs+kiKMOrZX+Z7TbcN4PI6e3l7Va/0DahdWqs5T6XtzOBwJsS5k3nkqx/Zm13nqMBDEOY5TLU/rwpREvtGKaCN2nkriqYHz1J+p85TYrsmdp5k5TwGl91QSJc3GB5t1npI3AEiQsb0MGdtrEOktCIJ8I0mqbU9uW+Umi0LF9uZXPJVjexM1kYDyWykJ4jzP68aH87yAn/37KXR6xd+IwYAf15+on0wp+k4hi6McH0dA57N+9NidOOKsq2CrbkqeX5B6U+MQBONo5TdaWrFQdwwMOJ6DxcLAwjCGztMwY8Vgbyeq6yfovl/MjGnxlOPiuOePD2DHrr2YNWMqbv36DbqxCzXVVQgEgugf8GLK5IlJ7w8MehPT0bgwCoVCoVAoY5dAIIAbv3IzPvzoY+M7DBPQC26UXFA/ZTY6Eo/jgf6U0zIx5e7mksq6HI6KMhagzlMKhUKhx3LFjMp5mhBP29vb5dcCgQDKysqS5ssWqtjeEYgBktBhZ1n52ulIOk/J5UgCkb7zVDKqFN55miyeGjvmjOYzju1NLQj19fUbikb19XXo7u5J7TyNKvHH0vdm2jmb+F5sKUT9VM5TQRBkl6Y6tldfEI/FOJVbUhKxpNheSbwj97tYLIYdO3dh/ry5pm8+UHWepnA/Sq5ZyY2p3X7a/UDa90yLp4QGIW0nnh9Z5ymQ7Dw1Ow7SeWpP0Xmqp5mQzlPypoUYx+HeP/8dAHDTjVfLr5OOydTiqbKPuiTxNE+xvdrfo3yLp5wsnio6U0kitpf8PQyHI6rvRBAEdPu8snAKAM+sfR9XrzrF4G+YAQQB0i8mx/PghORt/EZ7B2a/+GfUXK49XhDlV0EQwAkCeIE3FE8/ZUrAhwOwEOdr0hI4gQcriMckerG/Em3tew8d8fShhx/DzJkzcOwxR2V7PKbheR5/uv9hbNqyHVMmTcC3b7lRN3MdAKZOnoCW1nYcaGnDsiULkt4/0NIGAJgyeex9gRQKhUKhUCgSd99zLz748CNMnDABX/j8pZg+fRpKPJ70M1IoWaK0frL82Mb5IQgCGEa/tMXKKyfcdk9FrodGKXImrDgTlVMXwuYsQfVM/W4eCoVCGe/QY7nihbwAPzA4iGAwiNY2RTwdTvQV5gpS6EklGKWb3+5wyOJfbETO00Qcr8slOzD1oh4jkmPSRMqf1nmabbQimbYD1QjSwUaKHGT8aTrnaVe3EtlbU1ONvj7l5sJZM2eiu7sHQ0M+Q+eyHNtrt8tiWqbOU6tp56n6eyTXw6pie/Wvv2vHJccGJ9Yh/Zf8jn991z3424MP49Zv3ISvfPmLxh+GYGiIiO1N0bsZNXSe6sf2SoxEPJX2A9l5alI8LS0tBcMwEAQBQwlHrSRKmo3tNes8ZXX2Aw/ReWphCOcpx2Hdhi1J0weDivPcbGyvO9+xvYnvosTjgT8QKFjnKSmMl2icp4C475ZCSd7hBQEhnf35g6d/h6NP+yzYGrUhkOwsBcTY3ojODTFrmTJ8oXUHqrkYGOK3S54fomuVFwRD8bQLdsT6O+CYMEs9hkTXKi/wsIBBLMXvd3tXG5YYvlu8jEg8vePnv8SFF3xGFk+PP/F0nHnmafjut/UjbbONIAj469+fxMdrN6KpoQ7f+9ZXUkZ9LVuyAO+8vwZr1m3EBeeeobqAM+gdwq7d+2C1WrBk0fx8DJ9CoVAoFAolJ7zyyuuorKjAP599AjXVyf0kFEqucVcq8d028IgFh3SFUZ6LwQbl5MpekjwN5dBiwrLTCj0ECoVCKTj0WK540fbm7T/QjN7ePvm536+fmhCNRnHvH/+M+fPm4swzRv5vHSn4RSIjcZ4qIpzsAEzVp2cA2WVqsYgCEa9zwTwsC0DphSiVgKcTATxaRuo8NRZPlfHG0mxDsu902dIlePW1N+Tns2bNwPsffAhBEODzDatiPiVIIU3uPDXtPE0v5NlU2179PZLuQZXz1EAQ1+5Pcmwvq+k8Jdazc+duAMDeffsNx0giCILKeZoqwlruPGXVzlOj2F6JkYin0rok12MqwZrEarWitLQUPp9PFoUzd54q3yGrcp5qO0+Tx+QmYntJOI7Dt26+IWl6MrbbbGyvFPOcL+ep5GwuKSmBPxBAOBwGz/NZjwM3QhJv7XY7qiorMTA4iKoq0YXqsBPiqWb7xfg4Ijq/Jx+07Mfcf/4GtTfcA4YQuOXOUiK2NxpPnj/MWLGOKcNEb7dKgGUYBrwgIBrnwPG86EDVie4GgChjQW9nMyZqxFNAFH3jgiDG9qYQyA/29xi+V8yMSDy1Wq2qH9T2gwcxMDCYtUGl47Gn/o233/sYdbXV+MF3vobystKU0y9fuhCNDXVoa+/Ecy+8hvPOFg9WOI7DAw89hXicx8knHIOy0pKUy6FQKBQKhUIpZvoHBnDC8ccV/cW2js5uPPfi69i2fTe8Qz5YLRbU19fg8OWLcfYZJ+vezewd8uGf/30JGzdtw5BvGOVlpVi6ZD4u+sxZaY8FKfmDdZeCg1UWRoMDnbriaTQ4pJmvPGkaCoVCoVAONcbKsdyhSFjjXlq7dr3qudSXqOW99z/EfX++Hx6PG2ecfqphIkc61LG9mXeeKs5Tu+xASxc5q4ckIrucTllE1HMrybG9JjpPrYSowRtcvB8NWrEx09heq9Wq+t5IESqdINSdcJ5WVJRj2rSpqvdmz5opPx70eg3E04To7XDIImDGztMU4inpWtSKp+R6yP5NI+epdjvL4mkiVtamExctiXFmHcfhcBgRlQs7k85T9fYzcq2a7RpliQ5RKSJY+lvIpP+4orwcPp9P7nKVRGszNx4AgMVAPE1ynup8LqmLUxAE+APKDSBcjMOiBXOTpleJp6lcv4QALP/e5LnztKTEAyTuXYhEInAlHLC5X3/id8Nmxe0//RE++ngNTjv1FADq71Tr3I3xcYR1xNNPmDJE+3YjPtQLW0W9/LrYWSqAhySe8oga/B69z1TgnP6DKvHUZrGAE+IIcTEwAHgYO08BoL2zFcllmIDVIjpOrRYmZZpBh8+Ht7ZvQUt/L06ZvwQTq8bGcc6IxNPq6irs2Lkz22MxxfpPtuDFV94CANTUVOHpZ/+nO92KZYtw+PLFAMQfrK9+6Urc8ct78dSz/8Oa9ZvQUFeLPfua0dc/gEkTG3HZJefl7TNQKBQKhUKh5ILGxoa0/ViFZueuvbjz7vsQjcbQUF+L5UsWIBKNYtee/fjnf17Cx2s34ic/+IYqVaS3bwC3/exueL0+NDXWY8WyhWht68Drb76PTzZuxe0/vAXVtLu+aODYUthiXgCAr7sNFZOST7yjfq8yPSywsuYuDlAoFAqFMp4ZC8dyhypa5+nadWrxNBDQd576fL7E+0FEIhFD1146oiYFIyNIB6PsABxBbC8Zxys5uXgdt6i0PjOdpxZV52n29/9YVC1ImO4MlcQ/jWOPVXWept6GnQnnaUNDPRobG1TvzSLEU6PeU3I7Ztx5Kos4KWJ7Lamcp8o+p47t1RehtOOKa4REm+w8VbZZMBTUXbcRXq9P9Twz8VSK7c2O81TVfZsYvyJYm3c5lpWXAW2AL+E8lXuFR+s8tWs6T/Vie936SZ5G7uZgyFxsL/kZrFbjbuRcIAl4paXKDdbhcD7FU3EfYG02nHXm6TjrzNPl91SxvVrnaZxDVOf3ZIixYS9cqBnoUomnUvAuGdur5zwFgL2MC9H+DpBbwMZYEYxHEeQicFhtKWN7AaDDwDlqY6yIxGNgrY6Usb0fBuL48H//AAA8v3EtnvjyN2G3mbtRoZCMSDw99uij8K//PIfjTzwdEyc2AQDefe99fP7yq9PMCYBh8PgjD45ktQDEgw2J7Tv2GE5XW1Mti6cAMH3qZNz50+/g2f+8iK3bdqGtvRNVlRU456xTcMG5Z5j6x5xCoVAoFAqlmDn3nNV4+JHH4fP5ctq5NBr+9sgziEZjuODcM3DR+WfJd3H7/QH84jd/woGWNvzvxdfxuYvPlef5v789Dq/Xh1NOPBbXXHGJ3Avz4CPP4PW33sf9f38C3/vmVwr1kSgabCU1wKAXANDVsguTVyRH1EUDivM0bqHH4RQKhUKhAGPjWO5QRdubpxVPjWJ7Y4TLLhgMZUU8DYczj+1VOcESAgvP8xnHWZLOU6tOh6V2jGY+Lyk0xXPhPNV2nmYY26sVnUgxMl1vbHe3KDjU19djQqNSb8EwDGbOmC4/H0wcO2uJ6sT2mhZPTcT2Wm3mxFN1bK/+sTun6VuUnkuxvZLYSK5H+rsyG9csuTMlzMT2yuKpJvZ4tOKpJAoCijAo/ddmNS+5SN2YivM0s9helmVhZ1lEYzGVYKjtTNWN7TWoQeQ4Dv/49wsAgIvPXy2/Hgqaje1NuGftdsWhnufY3lIiYTQUDqESFflZv8FNF4D6O4lqtl/UILYXANZZynB4/0FguqJ1MUjE9krr5eOGv0cxxoKernaQWU9WiwUcH0coFoOFsYAXhJTi50HfkO7rbpaFhWHgtNpSxvaSDIWC2Nl5EIsmTTU1fSEZkXj6ve9+C96hIbzz7vtoP3gQDMOgt7dPlfVvxEjjKSSOP+5IHH/ckSOat6G+Fl/94pWjWj+FQqFQKBRKsfKVG2/AunXrcc11N+KXv7gdM2fOKPSQVAz7A2g/2Amr1Yrzzz1ddVxYUuLB6jNPwh//8jD27m+WXz/Q3IZtO3ajpMSDyz93gTwPwzC4/HMX4ON1G7Fl6060tLZjymS9IBlKvnFVNSI2uBcAMNjRrDtNcEg5bxBs+iftlEML38E9aF/3AmLhAFzltZh9ZnLPEoVCoYx3iv1Y7lBG6zwlexcBYNggtpd0JobCIQAjS0sh3ZPhETlPpfhXu+qifizGmY4HBdSiaErxVHJMOkw4Twn3o56LdbSMtPM0phH/JNSxvanFgq6uLgBAY0M9mpoU8bSqshKlpaWy6KXnPI3H47LQZ7fb5dhVrZPWCNkFaUshnqZwnqpje9N3nmq3M6cREmURTSe212xcs/bvzkx0rJHz1Mg5ad55SnTfxiTxVNx/U21zLRUVoqQ1JDtPlahmM7Asiztu/zF279mDo448Qn5dGz9MulIljJynHBfDcy+8DkAtngYz7DwVnafGvxMjwefzYdjvx4SmJt33Jce1ynkayvyGk5Eg3ZACGIinxI0HWuGf4+OIGPyebGBKEevv0HlHAC8Qsb0pO0e7QTaWWhgGDMMgwnNgYuLN6amcp12hCARBSNL2LIwF7kSKVFQzf60QRS+j//cUSvG3W0yMSDytqqrEX//vT4jFYujp7cWqE07DmWechu9955vZHh+FQqFQKBQKxSRXX/tFxDgOGzdtxplnn4+mxkY0NTXCYtG5eW2UaSAjIdVdzySlJcpdohs2fQpA7LDXngDa7SyWL12Id977GOs3fkrF0yKhsnEKeva9BwAI6p7kAd7eTvmxxeHJy7goxU2gvx373nwUAFA2cQ4VTykUyiFJsR/LHcponadajDpPOcJJRLq2MoWM0RxRbG9UidFUxY1ysQzFUyVGNpUoIsX72k0IQNYUvZvZQCvqZTO2N53ztItwnpLiaU1tDRiGQUVlBXp6ejGoI56SIi/pPDXtnDXhgkzlPCW7Re1kbK/Bd6rdFtLypO0nOTVVsb2JvwmzrkTvkDq2V+veU70nCc+JsbNE52k8Hjfc18yKp6RjW1rWSDpPpZSBoSTnqflI04svOj/pNZZl5cQmwMB5aiCexmIcvnDpZ8BA/W8P2XkaTRHbS3Ys2+SOZW7UsfQcx+Hs8y5CR0cn3nj1BUyZMjl5msR3oHKe5kk8JW+WSec81Qr/MT6OqIHztJ+xo6/voMo7yzBMwnkqQBAExPh46thdnzfpNRtjQZyPI8YwaZ2nHYIN9774T2zr7sLJ8xfjK8uPTRJSyd8AVuDRkEI8HSsNBSMSTyVYlpVVfrfbjQkT9BV/CoVCoVAoFEru+XjNOvkxz/NoP3gQ7QcP6k472jSQkeByOTF75jTs3nsA/37ulaTY3hdeehMAcNLxR8nztLSK458+ZZLuMqdNmYh33gNaW/U/JyX/1E2eDakRhfPrJ9MMDyidKTZ3ue40lEML1qlc4ODC+tGHFAqFMt4p9mO5Q5l0UbmGsb0q5+nIL+CrY3szF0+l+R0OR0adnVpUztOEeKQf22u+85R0P0qurWySHNubmXjKanr5bCa3nyAIcmxvQ0M9ysrK4Ha7EAyGUFtTAwCoqBDFU62jElCL5A6HIyl2Nh1y56hp56l62xvH9ppznsrO3cT6JQev5IgVBGH0ztMUAp4c22tPdp6Sn83pdKr+vs2KlgzDgGVtiMU4WfyNp4hsNUJynsqxvbHE36p9dNUmDMPAbrfL+5Ge89QothcATj7hmKTPEcrQeeognKfA6P++29oPor1d/Ddxz969+uJp4jsoIW7IFl3/uSeteGonO0/V+24sHk/pHO3o78VM7YuC+HfE8XHwSOMcjcbBhwOwOJUbl20WKzieh90qirCpYnfbGSfat24GADzw9qs4ZdIMHFav1gJJ8dUGAXVIIbAbCMXFxqjEU4l9u7dmYzEUCoVCOQT4xjPZuzvazLLuueSarK2PQil23n3r1UIPIS03XHMZfnn3ffjXcy/jwzUbMGXSBESiUezasx8Ohx1fvuEKLFowV56+r38AAFBVVaG7vKpK8fXexHSUwlPROAUAEI0LiMT0T+C4kka8to+Dxw6ctHxePodHKVJsVDylUCiUMXEsd6hCigZ6GDpPyYjSUThPyajWSCRzEVa6UG9n7aoY2kx7CFXiqdxhmSyISMKKmc5TS46dp1qx0XRsb+LivlYEIeNa4ym2n8/nk/ebhvo6MAyDpsZG7N23H7W1onhaWVEBQL/zlBRXHA575p2nXKadp2rhxDi211znaVzjwpTGId1QIDlAAeiKPpFIBB2dXZg2dYr8WnLnqfHfgvQ3I42ddO6Swl9ZWalGPDXvxLZaRfFU6TzN3HlannCe+uTYXsUlPlocDkdq8dTlMpyX47ikfV8V25sidjVCxPaSfy+j7T1tbm6RHxvdBCHthyUeRSQcyQ0nI4EUT7VdyYBamNeKz6mcpwDQFQhBiEXAsOLfn9h5Ksb2xng+EbtrLE53ww6uvwP2CUp4r81iQZjjZBeo9u/QKgiIG9yotaZ1v454SojHEFAvpBBPTfajFpqsiKckkUgUW7dtIwqx67Bg/vyMIiAoFAqFQqFQKJkzFlJAJjQ14Cc/+AZ+/6cHsWdfM7q6e+X3Fs6bg4lNDarppRMdo2NJ6eTdzAnRbXfcrfv6heeciMb6GgwQUbIkUtya0fsUNbyjDA+3TMAn2/dh6eI6nEVsN2lbdodZPL9bPGE66+bD6bYdAeNtvwwFFcE0FvLn9XONt21ZSOi2zB50W2aX0W7PqtrG9BNlgbFwLHeoIh1rWiwWlXvKarUiHo+niO0lIkrTCLCpIAW/VG67dPM7HHY5PlU7PjNI7lmXi3Ce6rgGM+k8Vbkfc+A8Te48NSk+GnaeKgJIqtheKbIXABoaxHOcI444HHv37ceypUsAEK5DndherfM0U/FUEvSsqWJ7VdGzxs5TUnhzOBRBvLS0FMPDw+L02s5T2YXJqsYhCYzkDQm8jnh6w5e+ivfe/xB/++ufceIJqwAki6emnKeaztNYlFPtA2WlpejpUc5JMxEtJUFY+qyS+GS2rgYAyss1zlNCeBwtpFin54S02WwqgZXk/Y/Ww+V04pijVsivkTeAiKJxXFcoVneeEh3BoxTMDhxoJtah/3cg/U2qOk/z5DyNETcQ6Dm+GYaRt7e+eGosLnczLLjBLrB1U+RlCQDiAi86TwU+pfO0m7Ej0t+OHlcl6soqYLfZYLVY5HktDJMU2zsRYbRAX2C3WdSfjxd41e83CwH1KZynsVEK6fkia+JpJBLFPb+7F088+UzSwYDb5cJll30W3/j616iISqFQKBQKhXIIs23Hbtzzx7+horwM3731y5g5fQoi0Rg2bt6KJ57+LzZu2Y5vfv16lfuUMrawWFlU1DUB2/ehs6tbdxqyr0i6aEQ5tLE6lNgwIR4Dz8VgsZnveqJQKBQKJZdIouGkSRPR0tIqvz5jxnTs3r3HXGxvlsTTdBHCepButtHE9kpdpk6HUxYH9NyXpEM1HaTzVE9EGy1akcV0Z6gc26sVT9WdsUZ0dyvHwfV1tQCAn/74B7j2misxNRE3WplI0UknnjqdzpE7T1PE9pKfRbvtye1GCnl2u9KlWVdbK4un2nEp4qm4fpZVd18GCSFOz728fccuAMDzL7ykiKdededpKvejVjx1JfbFYCio2gekzlH58+k4NI2Q/ga0sb2WDMRT6VwoFAohEonKgnA2NBSyY9Poc3ncbl3x9OHHngUYRiWeajWfaDQKl457NSq7flmVkJxK3CNpa2vHuvUbsPqsM1XbobmFdJ7q/x1Lf5Mej0feT/PVeUo6520G5zGKeKqMnxd4cDyfMjZXco5K4qmVEYXPEBeDlbGAF5By/oOMEze//QFaQ29icnUtfnvZtShzuWUB1sIwSW7QCUIELYy+eGpJ6jvVuFYhoI46T0Wi0SiuvPp6rN/wCQRBwNSpUzB50kQAYhb1gQPNeOBvD2Hz5k/xyEN/1bWJUygUCoVCoVCyQzQaxYsvvYq169ahq0vq2anDEYcfjrPOPC0rd7GOBL8/gN/98W/gYhy+c8uNqK2pAgB4PGKnitvlxL1/fgh/e/hp3POr22CxWGRnqdFdxZn0Kd3+o1t1X5dcKEauknTvU5KZNn068PZ76O3rR1llrXxhRtqW0ZhygWTq9NkoLS3RXQ7FmPG2X8bLK1XPS0tL4SipNJg6u4y3bVlI6LbMHnRbZpextj2L9VjuUEa6+D592jS0trZBEAQwDIPZs2YmxNP0ztPRiKeRrDlPHSonZSzD3jlpOzhdTtkxquc8jaRJjyEhnWv5cZ6aje3V7680G0NKCjZS/6LValXF0FZIsb164ikxToc989heMxGyFsJ5qhW21LG9yrV8hmHgcjkRDIZQV1+Lffv3i/NrhHjt+lXfczyu+nvQi2uWRLAPPvxI/nuT3JmSAzxV+pDkhJVEQ+l8Y3jYrxILS8tKVfNl5DxNCGSSo3JEzlNCvB0cHJS3RXacp8oytA5qCbfHjYHBwaTXjz5yOdxutXAW1vyGRSIRXfGUvFnDajLmmuSWb30XGzZsRDgcwWWfu0R+vblZuXHF6O9AcoyzrE3eT/MlnpK/p3qxvYDym0jug7F4PMm5qaWHsYMb6JCfi5G7MYS4KJxWFkLCgZqK1pC4ztb+Xry/eztWLzkcNosF0XgcDGNJctJPEFJEM2um1Qq3LATUwvi3KprhjTuFIivi6SOPPYF16zdg/ry5+MmPfyBHD0hs3LgZP77951i3fgMeeewJXHv1ldlYLYVCoVAoFApFw8aNm/G1m29FV1c3BKm8IsEz//gX7vrt7/DH3/8WS5Ysyv/YNm+DPxDEgnmzZeGU5IgVS2Cz2dDT24/unj40NtShproKzS3tGBjw6i5zINEPVFudvDxK4ZjQJMYOxuNxdHf3JMUQSheIWNaGkhKPdnbKIYiFdYCxWCEkTvq5kD9v4imFQqEUE8V8LHcoIzkpy8vLUFtbg56eXjTU16O6SjwG9Qf0nacccYE/OJrOU2I54VF0njocdrVzMpbZBWwyjldyjPI6rkFpjA4TzlN1dGz2nadakcW8c9Mottecc1daD8MwhgKm1HnqHRxKeo90GDscDlkE1Mbj6iEIgrwt9eJaJbSCJolRbC8gOo+DwRDq6+rk15Kdp/HEvOL6STFJFE/DqudapG3b09OLffv2Y+bMGfB6xe1UX1eHzq4uXcekdjysLJ6KImkgEFCJrmWloxFP1c5T6W9BL7LViHIihae3r09+7DAReZ0O8uYFo/3A7Xbrvn7u6pPlczoJrfPU6EYOMrbXRsaExzmY2TKtrW0AgN179qheN+M8JW96kPbTfMX2cmliewHxbwdQ/47H+LjYXZri968n4TyVl89YEBd4BGNRlLBO8BCSboAoFTgMM/rf+0d7d6rEU9Zi1YntTXFzQlzzu6qZ1wYedtaOWR439viDyfNneONOobCknyQ9zz33AsrKSvHw3/+aJJwCwNKli/Hwg/ejpKQE//3v89lYJYVCoVAoFApFQ1tbO6669ovo7OzC9GlTcestX8fv7/kNfn/Pb/DNW7+OGdOnobOzC1df90W0tbXnfXz9CaHTrXN3KiCevEsneIGAeIA9ZfIEAMD+ljbdeQ60iJ9jcmI6SnEwYUITbBag1s2gefsnSe9X9q7DqTOsOGmWB1xI36lBObRgGAY2p+JA5sL6F6HHKr6OvfB17EkSQigUCoWk2I/lDmUkocflcqGpUXQwT5w4Qb4JzExs70jidiXICNVUbjvj+RUxgxSxUnV26iG5BV0ulyyKcDrxi5KoYqbzlIw41RNiR4tWZDEd2xszE9ubXjxlWTHmVo+UzlNCmHI4HGDt5p2npBiZznkqjc1IPLXrjN+RSP2pqqqUt0dMI8RLzlGp89JK7ncxDsGQIqjoOe7I5X3w4ccAlOqPhoZ6AKJ7z+jYKlk8FY8zBUHAwMCAPF3ZKJyn1qTO04Rwl6JnVgvpPO0henKz4Tx12JW/P6MkUI+BeKp3Y0UomOw81YN0uo/EeerziVHQXUQFTCQSRUeH0luu93fM87y8P9hsNjhdolBZiNje9M5TZfySeJrq5pEAY8XHHZ347Uv/wYub1gMALInoXn8sDF5IFk8npBA/GxKpPw6rDQIE2K3WJPdo4yidp5bSKnxt9cWYoTOOTP/tKRRZEU/3HziAI1ceIee061FZWYGjjjwCB5qbs7FKCoVCoVAoFIqGP/35fvj9fnztqzfilZeew5e/dD3OXn0mzl59Jm784vV4+cX/4qavfRk+3zDu+8v9eR9fZbl4YnigpU33xKCzq0cWTWsSztTlSxYCADZs/FSnryiGDRs/BQCsWLowZ+OmZE5J32bcfboTPzzegb41T6veE/g4JsebcfZsFmdNiSAWpuIpRcTmVFzIsXEknu557e9455efxTu/vBS7X/q/Qg+HQqEUMcV+LHcoI3WeulxOzJ8/FwCwaNECOY7VTGxvMJjsvjELKRREU7jtjJBEDm1sb6ZOTzmO12nsPI3FYvJyzXSeqpynaWInR4LWqWk+tlecT9tdSDpJU4mn2thYPSorRddhJBJJinUmhSmHwy6LabFoZuKpkYgjIX0Wbe+oNH7Wnjz+hnpRvJw8eZJhnLA2wlbtcuXUsb0625Hcth9+9DEEQZCdp5J4CiR32mpfl8ZXVqqIlP0q8VTTearzeY1QbiCQOk8TUcUZOE8rCOdpT2+v/DgbnaekAGu0H5DOUzKmt6W1HfsOtKqmTXaeGomnUuepXRVhbOb3Rux9FZfbRfQGt7W1gSdEdr2bCEjBnWVtcDnFzxMewW/mSCAFQSOnr+QoVsX28mJsb7pO2N8OWfHSlg2455X/4q0dW2CzWMDxovtUz7maKnbXlohdt1msqHJ6YLfaVPOzAo+aDGJ3o5zWeSrAWlqFuVNn4jdHrcCX4uobviKHkvM0E+idthQKhUKhUCi54f0PPsSsmTNw801f0b27mWEYfP1rX8bMGdPx3vsf5n18ixfNg93OordvAE8881/VyZNv2I/7//4kAGDunJmoSAit06ZOwvy5s+H3B/Dok/+SjyUFQcCjT/4Lfn8AixYchimTJ+b981CMqWmaLD+O+XpV73FhP8jd0+6pyNOoKMUOS4in48V5Ggv5sfvlv8rPd7/yN/i7mws3IAqFUtQU+7HcoYzkXHK7XPj2N2/BX+67Fzff9JX04ilxMT80CuepKrZ3BM5TyeWUFKOZcWxvovPUoXSeai/4k+NzOk04TwnxNBfO01HH9uqIINJrKZ2nUWPxUaK8XBHOhhKuSglSXLFn2HlKjstqWjzViCGEW1nLr+68Az+57fu4+MLziW2hHpckJEpiPcuSjt24KsZa6zyNx+MqoezjNevg9Q7J54+NjQ3yexGDGGsj5ykA9PX1y49HFdub+Exx2Xmaeeep0+mU19nTq8T2ZsV5Sji/jcQ8j0cRT8vLlP3x/oeexk9+fo9qWq2DM2JwI4Ky77Cq/S/V34vEsH9Yfkw6Tw80t6im07sJgtwHrVax81Qct7KvRSKRUaUApEId25uBeBqPg0ey+FkhGP+tv7NzqyieCnEIECDoOU9TiKdRncQA0j1qgwCr1Qa3Xf83PBJP7Ty1CQKspdUAAM8Rq8Gy6v056vcajq2YyErn6fRp0/DxmrXw+XxJd2tIDA0N4eM1azF92rRsrJJCoVAoFAqFoqGvrx8rli9LO93cuYfh5Vdey8OI1FSUl+HKz1+EBx56Ci++8hbWrt+EqVMmIRqNYu++FgRDIZSXleL6qz+nmu+L134et/3sbrz+1vvYsWsvJk9sQmt7Bw52dKGyohw3XH1Z3j8LJTVVE6bLj5mI+kJQLKg8jwsWWO3pHQmUQwPWXQbWVQqb0wODdLsxR+em18HHiAsXAo/dL9+PZVf+onCDolAoRUuxH8sdykgX211uF0pLS3DaqScDgCq2VxCEJNGbdCJpnYWZQAoFI+k8VWI01Z2nsQzcP4IgEPHFTgiCKG7xmgv2kSjpmDQT25tb56nWqWnkVNQiCSHazlNAdPFFIhHTsb1GkNtHK4pKgrfNZoPNZstIPDUb2yu+b0nMo3EQE+5BLTNnzsDMmTPE942cp4ltI62fXE4kqhawtHGu2u06PDyMd997X35OOk/13I88z8vLkJykpYRI2kd0i5aORjzVOJAlAdqaQWwvwzCoqqxEV3e3KpY2G+Ip6aI1EvFJ52l5RRk6u7oAiPU5pSUlqmlTuaNVr0cVp3tyr25qZ+/wsHIjSm9vH2KxGFiWRbMp8VTt/JRie6V9LRQK4eRTV4MXeLz1+ktwGdQJjRQywnwksb0cnyx+ehn97fVpWwtsjBXBuLgcPefpxFTiqc5vl8o5CwHWkipctORoPPLBW0nTkr/zgH5sr7VUTBOzOD3wTJgJtCnXASLhkScx5JOsOE/POfss+HzDuPraL2Hbth1J72/dth3XXHcjhof9OPec1dlYJYVCoVAoFApFQ2lJieruTCO6u3tQ4vGknS4XnHT80bj9h7fgqJXLIAjAxs3bsHP3PlRWluPsM07Gr372PTQ21Knmqa2pwp0//Q5OOfFYhMJhrPtkC0LhME458Vjceft3UF1dWZDPQjHGXdUoP7YLEcQJ8YgLKXcTcxa7YQcU5dDj6Jvuxxm/ehun/PQF1C9YVejhZIW2tc8nvXbwk1fh69hbgNFQKJRiZywcyx2qKM5TdT+g5DyNx+O6biZ1bG92xFMjwSIV0oVuu92uEgPNOMGUMcTkFBin0wlLwnma5BokhDAjtxuJ5GAFkgW8bKAVWYzcclokIUFPBJFiWbkUHY5mxFPSoajtAJS+M6k3VlqONoZYD071HaQTTxPuSQPnaarxA4q4rB1/IBFTLYlzJYQQNzzs1zhP1dtR258KAE8+9Q/5cWMD6TxN/j7J+aV9kHSe9vdnufM0IVpJ291qzUxymTCxCQCwb99++TUzNx6kg1yGmdhe0nl68Xln4Lu3flk1LdlTC6TvPNXG9qaLpQUAn08R2ARBQG9C6E4ST/Vie4l9UIztFcVTaV87cKAZXd3d6OnpTVpeNtCKt3rYE99JNCm2V0j6PUnVWTp/4mRYE7G9ACBASBIwa2D8WxfV3DjDC7y8LEARPz975Crccvo5WGxVjyXo61c9j+nE9lpKlOs0DrtaqDbzO1YMZMV5euUVn8err72OTzZuxnkXXILp06Zi4kQxOq29vR37DzRDEAQsW7YEV1xOnQEUCoVCoVAouWDx4oV459338eFHH+Poo47UnebDjz7GuvUbcOIJhRMmZs6YiptuvDqjeSrKy3DtlZ/FtfhsjkZFySbO8lrwAgMLI15gCw12oaRuCgAgPDwoTyfYsnu3L4VSTAT62jGwb2PyG4KAXS/9Hw6/9jf5HxSFQilqxsqx3KGI0nmqPnYhnVl+vz/pffJi+miiIlXO0xHE9srOU7tDJaJkEtsbDitil9PphM0mOsS0fZWk+8pmwoFHOk/5XDhPcxLby6qmSbXelOIp8Z52WQG/WGHgSAhARg7PVGMH0rsgjZynqWJ7SWRHrMbRGwiI45diYUtV4umwSojTxjWT8asVFeXweoewdt16+bV0zlNyfml8LpcLNpsNHMehr18Ufmw2W9LfbEbiqU0d2yvtv5k4TwFg8qRJ2LBhI/bs3Se/5siK81RZhra7V8JDiqflSqKonrgVCmqdp/rinPS6w25XxdfqddtqIZ2ngBjd29TYiOYWrfM0eXycRjSXvlvptzdA9E5rxf5soFq/jmMdUG6GCOuIp1rxM1XsLgMGFoYBwyAhejJJzlPWYiziR8Lq71IrbNsgwFJaBbvNhjOXHIGa1i3YvEP5DsJR9b9nes5ThlUSpuya+N8oZ+4mlkKTFeep3W7How//DVdd+QW4XE7s238A77z7Ht559z3s238ATqcDV135BTz60ANZsZxTKBQKhUKhUJK59porwfM8rrvhK7j9jjuxefMW9PcPoL9/AJs3b8FPb/8FrrvhK/K0FEquYCxWxKzKhYhAf4f8eHhA6UBlHNQ1Qxm/tK970fC9rs1vwtuWnNo01gl7ezDUshVCDi5+UyiHAvRYrjgRBEG++O52q4UWKbYXAIb9yX3dpNAVDI48ppBcTiQSkR2gZlF1npLiqQknmIS2y1TqKk3lPLWmcT0CuXeeasVGvbjPVPPpiaeSAJ0q9lgSn+ypxFOVkK1elhSf2lAvpvJk1HlKCtgmnada4Vpajz1FZ6u4fJvuuCTxVHKckvG4w8N+lRDHafsTCWHrlptvwsojDpefl5eXobJScbTp3Uyg5wBkGEZ2n0qdp3Y7m+TwzES7kPYDrfM03TbXMnnyJACiqCyPw5HdzlMjEZ/sPCXrGIPBIIKamF5tb7NhbG9Eie0djfMUUHpPm5tbVa+ni+1lbTbZVSv99pKxw2ZvosgEVeytgYBu2Hkq8EniZ6MQhVFGkxS7a2MsiMU5WBgm6ffI4SnHyfMW6c4f1jhHtTG+NvBy7C4AOFwezfSam1KSxFceDNFzaneqUxv0YoOLkaw4TwHxjqMf/eC7+NatN+PTrdvQ3d0DAKivr8PCBfPhdNIuIwqFombzUz8f8byLL/1BFkdCGc9845kH005zzyXX5GEkFEruOerIlfj+976FX/36t3jksSfwyGNPqN4XBAEWiwXf/+63cOT/s3fWYZJU9/p/y9qnx2d2bF1gWWCxXdwtwGIhLkCA5MZv7OYmN3J/4d7ITXKTGyVGiBAhENzdWWBZQZd1GXdprequ3x/Vp+qUdvVMz0zP7vk8Dw/b1XWqTmnX1Hve97t2zSz1knGwwIVrgIT2h2r/3m1oPvQEAEBixKgxJIarnJoyGHMeVVWx3xLZG6lvQ3KwU/+89b4bsPZj/zfTXZsWUsO9ePPun6HzZU0w7lm/Fsd//GfgPEa8MxgMO+xZrjKhX3JbXWpRKj55YsLsmALML/OnVvPULgCWEutprnnq7nb0IkU5lcLhsB5ZmsvlTPVeaYFELFJvEzBHnFrrp5YDq8jiXzwtxPY6iE56bK+Hc9eP81Qy1Z81L4vUv2xrazUtx1fNU1rA9lnz1BoZSs65YrG9RBy2nkvkeiAR49FoBDzPI5/PY3x83CTE2Zyn1H495JDl+OAH3ot9+/bjzjvvwKErliMUMs59a+1FwB7fSqiKVWF4eESP7Q0Gg6ZlAcXFYhojvllBPp/XBzUU2+dW5ne026aVw3kapARYycUJGaHE05oaI7b3N3+4Fb/70z/xp9/+SJ9md556x/YGg0HTAAprbVsnxifsztNUKqUPJiDnkNN1YB40INnE00SCcp5Og3iay9lFeytW8TSvqpALAxes4nIEOUSDQUw47OdMSttPIi8gm89p4qllm4JVtXj32lOwYfcOjCTNg3uysnmZNteq1TkaNP/2ZSzXu9V5KkIFR/3W2GJ7S6i3PZuUTTwlhEIhHHfsMeVeLIPBYDAYDAbDB9dcfSWOX7MGN/3xz3j55Q3oKQxom9fchOOOOxYf/tD7seqwlbPcS8bBQLBmHtSE5jgd2G9EUKXGjBpDwRirV8swyOdkyMkJKOkJcIJoqp071xjauckklAbj9Tju2h/iyf95H1B4sdb3+jMY2rUFdYucR4TPBZRMCjse/QO2P/pH5KmXMANb12Nw+wY0LD/OozWDwXCCPctVHqTeKeDkPDXH9loxi6flie0FtBhKv+KpoijIFV6Ma85Tqs5mEQFh585duOUft+GDH3if2XkaDJoEonw+b4ipdGSsj5qnPO08zU9DzdMpxvY6iYdEGMl5iL2+YnvpSFPLsjq7tOfo1tYW03L8iL9mAbtIbG9h/+fzk4vtJduQNbmjs7r4TAYYcByHWCyGsbExm/PUWvPU6iAEgI6Odrz/Pe8EAKg8JZ46xfY61DwFjLqng0PEeRqYkvOUDA5QlJyvepdudDiIp+VI7zTH9jr3yRzbW01ND9kGi1idqMVie7Wap7TT3U9s77jpc29vH3bvMVynCxbMx65du52dp5bYXHK/Tjg5Tx1if6cKvX43sZoI2mQfKXnNdcpzHLK26Fs4CqcAkJkYAQCIPI+UkkVAEG2CZDBWg9amebj541/Aa+sfwpefeUH/Lmu53ziKn5RzNGg5F2ztFWvfVXAi7Tw1GyutYm2lUnbxlMFgMBgMBoMxuxx22KH4/vcm7+5nMMpBVVM7xjpfAQCM9+/Xp2cSIyCvyMLxOoeWjIOV3c/citdv+wEAYN4Rp+O4a384yz2aPPvX32363H7sBYi3LUPb0eehc8MD+vSt9/4SJ3zqlzPdvSmj5vPY/9K9eOuenyM92u84T/emR5l4ymBMEvYsV1nQDrlwyF08pV1NBPqFslV4KAWrAJhOZ0DpHN5tKZEhWBA9OY6DqqpFnac//snPcc+992N8IoF3X3GZPj0UCpnE01wup3+eivPUS4ycLJON7SV1M71je6da89S9/mxnp9l5ShyR/pyntIBdxHlKuSdp9NhhnzVP6fb0QAI62rqqioin46brwepINDtH7fuPFjyLxfbS7t54XEu9IcJuMBDUa1Dqyy6l5qlARHTFdO6W7jztsE0rh3jqJ7Y3QounVGzvOy8+B+ede7b+OZ/P2+o2O7l+ATq2N1C683TMLJ529/Rgx46d+udlS5dg167djteBOTZXMJynCe1co2ueOtV0nSr0+t3OAavzNKfmoUIbXGCN3Q1Eq3De8qPx4Kuv2JZDYm8lXkAsEILE8yZBUlLz4IPa9gdECa015r+7reJlVnFwntLOUUu5Hat4av0sWsTTYMga+8vEUwaDwWAwGAwGg3GQ0tC+FGMbtX9nRnr16UpyTBdPo7VNM98xRsUiUX9Uyym7e2euoGRT6Nr4iGla+5qLAADL3/FRdG18WK8JOvD2ixjY9jIalh074/2cLIM7NuL12/8Xo3vf8Jyve8tjWHXFl8Dxpb1AZDAYlU9Xdy/uuu8RvP7G2xgZHYPA82hubsBxxxyJi84/yxaDCQAjo2O47c77sXHT6xgdG0d1vApHrT4MV1x6AarjlR3jTztGwxbnaTAYQECSkJVlZ+cpJYily+g8dYvLdIJ2h5EX95IoIivLRcXT4eERAIXoTEo4CYXt4qnTv3mheHy71cFabkj8LIn7tEYgu0GEEMlBPCWOWq/9R5xtkkcMrFtsbzqdxuCg5o5sa7XG9iqmmGQnShGwBZfatbrztEhsr+Qg6pJ6p4A52po4P8cnJkwuQC/nqeiwflNsr8O1YBLRKAGIrrsKONc8LRZTTEPEb815SounpZUtaGxsQCgU0sVJSRL1msJTgT52rs5TU81TY/9Yz22n2HHX2N4p1DwdH7fE9vb2YuOmzQA04ZTUZS1W81QURd1Vm0wVnKd0nd1pqLlJ3/uKxvY6CM/WbQpF43jHEcc4i6eFa4bjOAQLIj4tiFrFy1AkZm5vicq2OU9Vi/hprXlqrZFsc816t5fzc0M8ZQVIGAwGg8FgMBgMRtlp6lhqfEiP6P+UZRm5vBZbWl3fPMO9YlQyYsj4o15JJzzmrGx6tjwBJWP0v7r9EMRbtesh1rRAF1IJW+/9pV4jq5LJjA/h5Rv/Dc/937WOwmlVyxIE4g3G/GODGNq1ZSa7yGAwZoC3tm7HV775PTz59AsQRQHHrF6FQw9Ziv6BIdx2x/342rd+gAmLA7N/YAhf+eb38MhjzyAYDODYow9HMBjAI489g69+838wODg8S1vjD1o0jFiiCwHDfWqt1QeYX9CTF/iloqqqPba3JPHUmJe4F4no4+WcBIwX/KOjo5bYXqt4aryIz5ncX35ie6map9MgnhJRLxwOmT4XgwjfokP8puRDPM36iu2l688a/SL1TgF7bC9QfBtMNU+LRMiS73NW56nP2F7dhUsNFKDF0yrKnV0V08S58fFxs3hqFXIogdtJhBJFUT//nGN7qfammqdmASkQCCBkiROdXGyvYqp3Kfg472k4jjPVPQ0G/Ncz9sLsPHWpeUo5T6PRqL6/ZYsTOpW2D/5wiu1VVdXkWqb3Rc5HbO/Y+Jjpc29PHzZu1MTT1auP1I+Pv9heS83T5PTWPCX7TBRF18ENId15au+/NXZXCoRwWPt8/ORDH8U1a443rytv/9tBpu6f1pqjgZBVvCwinhYRP7OW9VudrDbx1eo8nYZ7/XTAnKcMBoPBYDAYc5TTzjgPHMfhT3/4LTo62nHaGef5bstxHJ547IHiMzIYkyTeuhRP9VVhe+cgFh1yGK4oTH8zeDRu/ucORAI8XvmfC2a1j4zKQqT+qJ7L4un+F+81fW5fc6Hp8/LzrsX+l+6FWnhJMbRzE/rfegFNh54wY32cDC/97osY3rnZNj0Qq8WKCz+O+cdfgs23/gD7n/2H/l33xodRv+SomewmgzGnmIvPcr/74y3IZmVcfvH5uOKyC/QXxBMTCXz7+z/Hrj37cPd9j+B977pYb/Or392MkZExnH3GyfjIh9+tR8be+Mdb8Mjjz+DXv/8LvvLFT874tvgl7eE8BTTxdGh4GBMT9t8uOobRybnlB0VRbINsrPGZXlhjewEi2qWKxmiStqOjY6b+h0JB3bEIAHnKRaSUGF/q5mAtF0QkiUWjSCSSyGazyOVy+M9v/Tdefe11JCaSmDevGT//2Y8Qr7I779zEO3oer/V6OTfp+rO08NNJiadW5ylZtpfIR9eWnKzzdHR0FABM+8QJwxFrnOvjE0Wcp+MT5tjenNV5aizLyfkLaK7vZDLlKEK5xfZanafBYFCvQQlo52IpkbtGbG/OJACLRaKSnejoaMfb27YDKE9kr3U5biJ+1CSeRiCKIhRFwd0PPIFHn3oRP/3htwCYXZuErINwbb7fmGssK35iewvOU/I70dPbg95eLcXo6KOOxNa3twFwFj9Ncc+ipLtqk041T0sQT9/eth3f/M//QiQSwbJlS3DBO87DEYevss1Hzluvc4jcg53u4VYBM1AQHw9t7cDyWAQ3rX8eucJvrlW8zKt55KjfCclSs1QKR8GpKlTS3vKbIjvG9lI1S8MW56pVfFXs4ivo2F+L89VJ/K1EmHjKYDAYDAaDMUfZ39kJwHhQJZ8ZjEogGK9HX3gZNvf0I1U1qk8fHR2DCiAUq4EUirgvgHHQcSCIp+nRfvRvXa9/5ngBbce8wzRPpL4V80+4FHueuVWftvXeX6LxkOM9I/hmEzk5bhNOeUHCotPfj2XnXg0prL2MrD/0JLN4uvkxHHb5F8GVIXqOwTgQmWvPcuMTCezv7IYgCLjs4vNM96xYLIoL33EmfnbDH7B95259+q7d+/D6m28jFoviQ++7XG/DcRw+9L7L8cJLG7HltbewZ+9+LJjfbl1lRVDcear9fhWL7U2l0kXjVp1weslfWmwv7TzVXoYbzklvAYEIUyOjo/pyJEnUnH8uoggtyPoRkabTeZrP53UhLVIQvrPZLDZs2Iib//J3fb4dO3fiqaeewUUXGr/Z3rG9znVCaXzVPHWJ7e3q6gKg1Zatq6u1Laeo85QWsH3WPLUKmH39Wk3zxqZGz/aGU9FPbC/lPE26i6eyJX7ViVAwhGQyhXTGQYRyaV8Vt8b2mp2npYqWokg7Tydf8xTQxFOCNUp4stDLcY/tjZr+TeZLp7PIysY2JU2DJ7SIYaf7ED0tGAiWPDhirFDzdP78DuzZs9fkgF29+kjs2rVbW49D/DbtbBVFEeHC/VqWFWSzWVNd6lJqnv7s5zdg/YsvAQAef+JJ/OWvt+Dl9U/bzheyfW6CPwBdrM86iP5W9yZ9zXPBMCTkkSsUwJEt2qNV/LQ6R4VgBCJUyNB+fxRre5vzNA/Q7UNRiGoeCsc7r98W25s3ibdiKApBVV3F30qFiacMBoPBYDAYc5Qdb7/m+ZnBmG3a2rSR8p1dXbpjYmRMi2KqqamZrW4xKhRaPJXTc7Pm6f6X7gNU48Vv08qTEayqtc237NxrsO+Fu5BXCi+l976O3teexrzDT52xvpZCYnC/6XOseSHWfOz/EG0wCx3ReUsQrGnW6xynR/sxvPtV1C0+csb6ymDMJebas1wxBxuBjsbcsOlVAMAxRx2uR8YSAgEJxxx1OJ58+gW8vPHVyhVPLaKBFRLb6yieUiKOVm8zW7Iw4hRPSUfoFm9viARk3UQwKxbbS9Y9Njaq74dQSBMkBKqmNV2zkv633/hSQRAK7r3i4kpnVxca6ut97Uda0CNCSlaWMTg0ZFu31Qnm5Tw1xGf3/voRT2lhiT5XOjs18bSttVUX2wMliKd0v4pFJ5PjaN33/f1azdXGhgZbGxqyfXT/iXjKcZwuWgOlOE/N8atOkOPvJEK5tSfrJwQCkkkAs96jikHOjVwuZ3H7li65zO/omHQ/3KCXI4nOy1y0aCFOOGEtspkMDlmxXI/3Pev04/D+975bn48Wu2uqq9GTTju6fjMWpzt9/fipMzo+romny5ctxZ49e/XpsVgMy5YumVRsL6Ddx+l7uSIX7wugHdtnnn0OADCvuRk9vb2YKNTstYqnskfUN0Gveerk2qUFeFWFQMU3c4EQJKggdynrHaCY+MkFwpCg6u3s4qeT89Q4Z/hACCJUkLUUay9CBSeY1y9CRc5FvK1UyjL8818+8Vl8/ZvfKseiGAwGg8FgMBgMxgECiRlLJJIYGdHcp6OF/9fW1cxWtxgVikTVPM3LGeRz5a9FNJ2oqop9L95jmtax9iLHecM1TVhw8hWmaVvv+yXUCq3/kxwwu+Hqlx5jE04B7SVpw6Enm6Z1b3p0WvvGYDBmjnA4hOVLFyGXy+H2ux40RclOTCRw7/2PAQDOPM2IId+zV7t/LF7QAScWLdDuJXv3Vq7rlgiV4XDY5JIkGM5Tp9he8wvtpEP0ZTGyDg6rjIPbzg1StxSgap4S8a+IgEBe8MuygqEhrTZtKKS90OcFY1/QblOlROcpYETHFnOevvjiyzjltHNw5dUf9bVcet/FChGe2WxWjwYNhUIIFwRxm4CnCyF20YnUKp2q85TjOF2sMomnhdje1tZ5+jR6OcVcc+b6m/6cp3kqdlZRFAwVBOamIs5Tp9heMpAgGomYrhndeToxborDtjlPqfPSzcUXKDj4nGue+o/t5ThOF7Qm6zyVZcVSZ7Z05+n8+bR4Wp7YXnPNU+fzUBAE3PzHG/GPv98MSZKM+fKqXp8TAFJpSjytrQZgvrcQaDE7EAiA53l9AIAv52lBPF26dIlp+uojjwDP847nG0G2xDWT2F5Aq3eanETN0y1bXtP/jr3qyg86rovgNeCCoIunDvuOFiAl5E3OUY7jIVGhBQo45KkBm87iJ9VeECFxxm92FjD9hjvWPKWcoxADkEDNb+u71Xlqbs8FgpBA9Rdzg7I4T5948imcc/aZ5VgUg8FgMBgMBoPBOEAgzlNAcwnMq4tBTo5A4IBa5jxlWBBDZjeAkk4iEK2epd6Uzui+NzHRs1P/LEWq0bTyZNf5l519FfY+90/kstrLw7HOt9G9+TG0HnX2tPe1VJIDZudpxEE4JdSvPAmdz9+mf+7a/AhWXvqvLLqXwThA+OhH3o/v/vAX+OddD+C59RuwoKMNmWwWW7ftRDAYwCc++mEcsepQff6BQU2AqXMZNFVXq03vHxxy/J7mG9f/0HH6O9edgZbmBgz1dzt+P1WIiy4UDDquQxK1+9vQ4IDte2tNwO7OPVCV0gTU/p5e27TB/l7f2zvQZ8yXGh/BUD8HviBmjI0Oey4nQ7kx9+zeBUBzQA71dyM5PqJ/N9TfjYCgvRgfGerXp48ND0DJFI/iJ78RYyNDnv35+y23AABefe01X9tPRA9AE/8BbZt6ezSxPhqNQC4IrKPDg6ZlZgq/z3ImaVtXPq8JBYmJMdd+JAvnTT4ne/ZVEETIsoKRIeP82bN7NwCgoa5Gn5ZKGNsy0NuFsOQe/zw8aByDidFBcHl3p7JaEF0mJkb1dfX3D+jCSlDkPPtPBrslExP6fH29PQC0qGS6rVAQb0ZHRm3OwYHeTl1oHR7s06ePjwwiL2vXDImZHurvhlgQ74eH7Nfd4ECP0X50EELBL8flLYKXmsNQfzeCgQAymQxEQSjpPqLI2jakUwkMUu0SY97XlRPVMUOoFAS+LPczOW2IhYlxf30y7g3ma7Gvp0v/d6zg6BwbHbEts7fbeGZMJ7XrQxAEKIqC0eFB0zF0YmxUSymqjoURjUb0qN1Dli/GUH83FFk7l9OplG0ZI4P0vaffdO/p3r8HoyMj+ufRkUHXPvT09CKZSmPxogV44IH7AQA1NdVYstB4/h3o7QKfN5/DYyPa7xjPuV8z2cIxkWUF/T37wfE85qk88kIICjV4RISKsBBAI6WJShxA6ZeIp2WEC+5UJWsXP+O8hHqqPS3Jq+BQk80hUBB6Qw7t6/ggInp7DgHqlpMDBy6fQ2MhRliU7c7TRk4Cr082i78yODQoRoy9ChEqD4iJBIZKSFYohbrGlpLblOWvl5aWeY5WaQaDwWAwGAzGzLFnz17cedc92L/fPHJ/y6uv4Yp3fwCHrz4O519wCR599PFZ6iHjYKMBQ7hytYTPnRDAnod/i0TPDvzLiiH87/khnBXYNNvdY1QYQiAEcMafqHOt7ul+i+u07ZjzIEjuzoFgvB6LTn2PadrW+26Ami8+Kn+mSQyaf1ci9W2u88ZaliFcZ7ycSA/3YmTv69PWNwbjQGIuPMu1tc7Df/7H57BsyUL09PZj/cubsGnLG0il0li+ZBHaKaccYLg2SZ03K8TFWEoM7UxDap4S8c0KqRmYoFxNBKszkXbb+cXJZegUl+naPks7wSzO0yIxmnQEZ1//AADDOUU7CnPUS//J1H4UCkJYzsN5qqoq1r+4QetXJmtyTbmRdYjtlWVFdwlXxWJU3UprdKz22SmClURYeznp5IJI5FX/EDDci/Syeno18XDevGZ9mrnmqfdxK+UYEAcx7TwdoAYzNDTUebY3nID22F46NhWgXNqJhKmWsLXPio+ap3psr4Mz2+x+pp2nUdN8UuE5jbhYpRLjcgX9PMhb9nnpkktLi3HvDHi4lUuB3h4vBzQN2V9vbOvET3/9F306ibzlOA5VcW3Ao5MeZL7faPtV8HG9ANo1PlE4d2KxKJoajcjow1et1JZJzjcn56el5ikds55MmmN73a6hTCaLD33kk3jfh6/D+pc24NnnXwQAnLDmWNP+dLp3KoXt83KehqjfQtr9qqoqZOr+J0EFL5p/NwOWetnplPG3UtbBeWptL1naZzKUE9fJuSqYzxnreI005Z7N2pyrAMeb90PA0j4jV+7vPqEsztPzzj0bf/v7rRgdHUV19dwZGcxgMBgMBoNxIPHbG2/CX//2Dzz52IP6tJGREVx59XUYG9Pib7Zt34FPfvpzuPOOf2DF8mWz1VXGQUKVqODoFu2P5XT/TmQmRvTvrH+MMRgcx0EKRSGntPvVXBJP84qM/RseME1rX3Nh0XZLzvowdj99qz4yfqJ3V8F9es609HOyWJ2nUQ/xlOM4tKw+Czsf+7M+rXvTo6hdePi09Y/BOFCYC89yr7/5Nn70s9+hpjqOf//CJ7B08QJksjI2bn4Nf/n7ndi45Q188bPXmdyn5eJbX/+C43Ti8JmMq8QPRJyJRqOO62ho0GJNM1nF9n0ubxb4ApGqkvvZNzRumyZIId/LCYaNqNLm1gUIBgMIFkSFQDDsuRxaYBga0Rxh0Zi2H+oaRvTvYtX1+nLC0bg+vXFem6eQoG9PQaAMR2Ku/dm9Zw+6ujVHYT6fR1VNQ9F400TaEARILV5ZUSDntONSU1OjC/eBcMS0bhIhHK+ts/UpEtWWxQuSa39VVVMKqqprPPdxQAoASCAQ1vZrLpfTheply1fobUcThigViVV7LjNiOQZe9WFDIU3gFANBfZmZ197Wv1+6fKUuejoRjWnr4nheb58v+LXi1XFTP+e1aM8PxE1IU13XpItdoYhxzja1tOviG32tRwvHgBNE274IR6n289r15ba2LzDNF6/W9mMkHAEwiLDlHChGrIpsu4BYdb0+va5h3qTuR83NTejt7UM05n4dlEJD0zzq3y2+lhkMavtqIpnB9l379Ta8qE0Ph0OIx2sAACp4+77vMtyfTfPaUNfYAkkUkclkEIzE9Mhrp75MTCT0666lbT7a2tqxa7dW9/SUU09DTU0Nqmu1/awo9vttMKydE4IgoL6pFapAxRaHosjQNVEDzvfQnp5eDBccqv/1nR+hp1dz/p9z7rmobzD2ZzRuvy8EguHC/4Ou+7q+yZgejtUiVBXBjv79GMqa/+4RoSIlBdBPjX2QeA5U8i26shNQBG1wQ69qES9VFePBABRre0oj7cxMoD6uncOD1vZQMRIMYMKj/XhqAmOFaORxy+DPgCBgQDSrpRLHmZyzndkE4oVBSWlFgZLPY3G8Gi1V3gM2ZpKyOE8/86mPY/78Dlz5kY9i85ZXy7FIBoPBYDAYDEaJvLxhI1YsX2aKSr31n3dgbGwc73n3O/Hi80/hu9++HrKi4Mbf/3EWe8o4WGhasFz/dz45jIlh449pLuD+EoZx8CKGjPNCTk/MYk9Ko/eNZyBTcXqx5oWomX9Y0XaBaA0Wnf4+07Ttj/zBl5tmJklanacN7uIpALQeaY4e7tr0SMVtE4NRiVT6s9zERAI//tnvoMgKvvz5j+PIww9FNBpBXW01zjr9JFx71XsgyzJ+94e/6y/AibPUzSlJhCsyXyWSKrhFw5Gw4/exgihH6jzSkIhKwuRqntr3nVOdRzfoeYnzlLghndxbBFVVTW17CyJCOKTtB7rmaZ5yLU3JeZpzd54+88zzps9pH/uAdnZFCsePrnlaVRXT61Na678S4djJOerHueun5ikAiKTmaWF9vX19+nJbWwyhJWBynnpXDKSP62Scp/392jN7JBL2FE7pftF9SujuQXNJBlJzNJ/P2+rbOjlPeZ537b9eO9Lh3mKqmUrtN2vNUyK+E2d8YJLOU0VRkKP3uY8BA07M7+gw9Wuq1NVpIhTP86iqihWZW4Ocj4cubce3vv55fXq6UPM0HAp77nt6Gtmv5BrLKd7O0/FxY6BIVVVMd14vXrwINYWSL2TfON0XrTVHo5TzOZFIIkXdf92uXXp6d08PVFWLlj31lJP0fQMAikfNVdHjmqNTGOj7q2PNUNHi/LSUwMikjN+crGIXPznB4ly1tM9S7W3OU9VcMxUAJM7cPp2k21v677APJN7ifE1V/kDZsjhPr/vYJyGJIjZt3oJ3vuv9aGioR1trq/ODD8fh5j/eWI7VMhgMBoPBYDAo+vv6cfTRq03Tnn76WYiigC9/6fOorq7Gu664DH+6+a/Y8MrG2ekk46AiRtVFlJQExinxVAxXOTVhHOSsuOBfkM8pkEJRxJoWznZ3fGON7G1fc5Few6cYi09/H3Y+/me99unovjcx8PZLaFyxpuz9nAz5nIzUsFE7TIpWQypy/dYsOAyhmmakR7QX7amhbozuexM181dOa18ZjLlOpT/Lbdz8OiYSSaxauRyNDlGea45dDVEU0dc/iN6+AbTMa0JDfR1279mPoaERx2UODWvTG+srx2lihcSLRsKTEU/NL5RTKbvjrhi0KBWQJGRl2ZdwSCAv6AOBgP7bRIs+ftYLAAMDgwAMoZsWtei4XVr48vtbKPDasvIe0fXPPGsWTzOZDFDl/XtExw6T2N5cLoexMc1FG4/HXYVQqxBDU07x1HosOjuN2pL0QAp6OU5RzjSlCNhE6FGoNn192jN7Y0ODYxtTe8kuxJNY5FjULLx6CXhO4qmXa5mIUOm0PQqbtOc4zrT91vUTEVB3YpcoWurnQU4x7T8v8cyLw1Yeipde3mCK8J0Ky5ctxde++mXU1FTr4mMxyGABSeSxaEGHPp0M/AhHwvq+dxrEQU8j+5dEXys59+sFAMYo8TQej+Odl12Clze8go9dd40+nRwjpwEE5BiQbQiFQuA4DqqqIplMmqLVZYe4Z8B5QMkRh69CXV0tRqiaqU7XPjmHaZHVCu0Cp/eVYovNzYOzlP8ICAJo6yctPtrFV3t7SeAB2bhX07G/1u0WkbeJpwGBBxRjMGQqnQDZGlt7h7hzu/h7kIinL6x/Sf+3qqro7x9AfyFewIrfH00Gg8FgMBgMRmkkkkn9pQCgPZdt3LQFh69aZSqtsHDBfDz2+BOz0EPGwUa41vjDX+BUJPv36p8DsZpZ6BGj0ulYu262u1AymYlh9L7+jDGB49B+7AW+2weiNZh/wqXY9eTf9GnbH7mpYsTT1FCPqQ5rlBoU4QbH82g58kzsevKv+rTuTY8w8ZTBKEKlP8sNFoRONxFREAQEgwEoiqLHci6Y34aXX9mCnXv2ObbZtUeLBZ8/39vRPpvoztNwxPF7vY7jhP1FsLWuXmoyNU8pAbAqHsfg4CAyDoJRsfb0S3vd7egh/lldZSRBIFRwntICEe260wWEEgQkvojzVFEUPPf8C+b++aiTSwsstAttsFDTs6oqpgstVvHCEPDs4qcf8TTrUzyVRHMNx85OLZpWEAQ0NzcZ85XgPCX9EgSh6Lt4vnCcaPdw/4D2Xr+xqdGzLd0vWozSxdOYVTx1F7vpY28Izx61IwuCp5OAR7bf6hqOF3WeTk48zSk5k6uSOC1L5bOf+QRWrjwEZ55x+qTaW+E4Dh+5+sMltRFdXOmkXmgkTDlPs/Z9T9+vyHyTcp7GYli+bCkee/g+0zzEHSzLCvL5vKn2MnFvk/sbx3GIRMJIJJJIJpOmwStu15Bicasnkymcd66WqEKL+Y41Vwttne4ZBJN4Su0rR+eo1flpEU+z6SI1S23ipwA699fc3r5+WMVbXgBgzJeh2mctKQuSwzWgtaf6n7IPOKo0yiKePvX4Q+VYDIPBYDAYDAZjCjQ3NWHvXuPF1JYtryKRSODYY482zZfP54v+Ec9glAMxGEYWEgLQ/piSB/eCvL4JxSvXYcJglELXhgehUi8cGpavQbi2uaRlLD7jg9j99D90kXJg63qM7HsTNR3lrxlYKglLvdNIfXHxFABajzrbJJ52bXwEh6z7NBtQzWB4UOnPcrXVWm20XXv2IZfL2Rxt3T19umjaUHCmHrP6cNx2x/3YsPFVZLOyKRYzm5WxYaNW/uvYoyq3LjJxtkWKxPam02nIsuwpchEBohRM4mlVTBNPS3Keau1pYUiP7ZU9xD+HWEzAcJ7Ssb1m56n2W1ZKdClxnuZcnKdbXn3N5ux1chxaMcX2Ro3jNziouWirYlWOQrKqqoYA5yDgiQVhwCv2mKw74Du2V5u/q1sTT5ubm0xijcl56uKaIxAB209ssrPztCCe+nGe6ucSJZ4mtGMVLcF5SrsSyXnpx3nqFdtrdQAGg0EEAgH93CbHJlSo81nsWFkh+05WFFP/nVx3fqiursYV77xsUm3LBTnPOnuGcOMfb8FHPvxuAECycO8Kh8MIBtz3PS0IkmWR8zCX8xZPSW1vAIjHnYV2+jdElmWTGOnkWI5EIkgkkphIJEyx6W7ubfo+8Jtf/Ry9vX248ILzteXSsb0O177sItrTBKn7sLfz1E38NMikje2xO0cdYn8FAYCx3Zk07Vx1Wr9Te2M9Kbq9ZX8GHARkq/ibyZSexDDTlKXmaVtba0n/MRgMBoPBYDDKz6pVK/Hqa6/jgQcfxvj4BH72i1+B4ziccdqppvl27tqF5qYml6UwGOUlH4zr/+YSffq/YzXFR7IzGHOBfS/da/rcseaikpcRqWtB69HnmabteLQyalNb6536cZ4CQO3CIxCMGy9dk4OdGNu/tax9YzAONCr9We7II1YiEJDQPzCEv9xyp+lF+Nj4BH79e23AxKErlqKmILQuWtiBww5djomJBP7013/q7kVVVfGnv/4TExMJHLHqECyY7+/eMhukKNHACbquI6n1CGhCgbXe82Scp/RLaSI+kdjeYmIEQDtPjRfxxBnl1d7JVQYA4bAmNNECEe08JdG7fuudAgBXqIWXd3GePvPMcwBgcpmVWvPUzXmqR4pS89LCiFdsr5eTznfNU4uLlcT2trWa36GX5jwtuH99OCCdhK2BgvO0qbH487pe85TaZ4mC89Qmnsbcnaf0sfdy/RKCAXf3o1d72v2qx/ZO0nkq0M7TSdT6rUSIQDg6nsLjTxlR2YYDP0TVPHWP7Q1Ikn696teYx2ADwHCeCoKASMTZ6e81iEAuuB/pexNZztDQsHlel2uIPo9bW1pw6SXr9HXSoqi1RjJADVrwuO7cYnv91By1CpK0+OhUMxU28dV8L8tkjN8jm/OU58BZapwGLNuVydDirdV5ar/2bO0nMZhopimLeMpgMBgMBoPBmH2uu/Zq8DyPT33m8zjq2BPw2ONPYtWqlVi79jh9nr6+frz99nYcvuqwWewp42BCijmPWI/Xl6eWD4MxmyT692F07xv6ZyEYwbwjzpjUspaebY5V69r4CBL9zjGXM0nS5jz1F63J8TxaVp9lmta1+dGy9YvBOBCp9Ge5muo4rvzAFeA4Dvc9+Dj+9d/+H374k9/gOz/4OT73b9/CW1u3ozpeheuufp+p3ceu+QBqauJ45PFn8KX/+DZ+8ovf40v/8W088vgzqK2pxkevfv+Mb0sppIo4T6uoaFI6utdJKEhOouYpLRAQ4SedzuCft9+Jw1evwd//cZtne7rmKcEQ7NxFOFfnacGlV8x56ke4I+gCnovz9K2tbwMAVq40Ehn8OE/pbaCP30RB5I7Hq6i6lfaam8D01zyVLDGpRLikI3u15VCRoUVrnpLY3uIOSLLv89Qx7OvXap42NE7OeUoc6PTAAgCIRiOuCRT0sXeL3aXRBTyH+LloaqQAAQAASURBVGZyXju1px2N5JroaNcGb5Rq+jJcu4p+3gOAIMxdyYXss/rqMD5x3Qf16cYgkog/8ZQSCfXY3mLO04J4WlUVcz1P6PuY9R5lje0FjEET1hKTbteu6dq3OJdpMd7JdU4cz17nLXHuA9p9XG/rFJtbRDzN0uKlZd+KUO01U63t6dhey0AQibffvyXB3D7tId46OU8Dlv2SnQPO07LE9hJGRkZwx533YNPmLRgeHsaJJxyPj31UK+j79rbt2Lt3L0468QTXkVoMBoPBYDAYjMlz5BGH41e//Cl+/ZsbMTw8jFWrVuJLX/icaZ67770PsVgMJ5984iz1knGwEWloQ2bQ7jarbmDiKcNO95bH0fvqk5BTE2hYvgaLTn33bHfJk/GeXabP81adAjE4ub93463L0LTyJPS98aw2Qc1jx+N/xhHv/spUuzklEhbnacSn8xQAWlefjd1P/V3/3L3xERxy4SdYdC+D4cJceJY787QTMb+9Ffc99Dje3rYLGze/DkHg0dhQjzNPOxEXXXAWqi1xi40NdfjO//sybrvjfryy+TW89MoWxOMxnH3Gybjisgts81ca5AW32/tM2snW3dOD9nZtkIlTJG4qOfnYXkkSEabqPN5734NIp9N48MFH8J53vbNoe9rxRIQ4r9hep0hOAAgWXv4L1Mt1WhQpRbgjCAWHmpvzlPSFjpF16x8NXYfTyclWVeUc20sLgc6xvUTwdBcxJ+s8dTpegOa6FUURiqIUje0lQnApzlMi/qmqqgtNfpynZPvo/UcilmNR8z7neR6xWMxU25JAu3h1B6FHzVNyHjoJeETYcmpPRwcTIe5z//opHHXUkTj9tFNc1+eE7kDO5XTHNVDauV9pkG0KSDxOWHuMPj2Z1ISuSCSsO3WzDtegfv7SgzUcoqGdGB/Xzhsvh7KneOoQ2xsuDJoYGBg0zete89Q4j63uSXq5TgNPnNZvxa/zVETeLn5KVuept/jKCd7ts1ljAErW6lx1OIet7Wn3v7Vmq9N9zy7+lp7EMNOU7Up+4MGH8eWvfB2JRAKqqoLjODQ3G3Veuru78S+f+Cx+8D/fxqWXrCvXahkMBoPBYDAYFGecfirOOP1U1++vufpKXHP1lTPYI8bBTl3rQnQ7JHWGqljNU4adsc5t2Lf+bgCAFHavi1UpZMbMo9j91gN1Y+nZVxniKYB96+/Gind8DMFZvF6sztNogz/nKQDULT4Swap6ZMa1F1aJ/r0Y79qOeNuysvaRwTiQmAvPckuXLMRnPn51SW1qquO45sr34Bq8Z5p6NX0Q5ykRLq3Mn9+BxsYG9PcP4J5778dxx2qCg9PL9ZQPt6QVUkMwEAjoL97T6TT6C+7A8fExn+2NF9eGYFZ6zVMiItPRlDmTa7N05ylfpCYi6QstfPlynrrE9hK02N5C3UqZFk/LENtbEDjp/e4EEfjIsrIetVIlSYKiKMWdp6TurI/4WBKtSsS/8fFxXZRp9OE8lQrbZ6556hzbC2j73FE8pZ2nvmqeuounXu1pYY6IgPF4HBevu9B1XW6Qa0CRzc7TUs79SoO4K633Bjq+3Nt5WrjfUDHhRryxd2zv2Jh2L3Ordwp4x1c71Rwl1/3AoFk8dRuAIHu4zumBFE6xvWSfeYnnAZeap06xu9aaowGLmJqVqdhdq3PUyXnqJb5afq+s9VUBh9jgrHvsr2PNU4/2lUpZPOSbN2/BZz/3RfA8h6/8+xdx+21/s2X6n3zSiYjFYnjwoUfKsUoGg8FgMBgMhoV1l1yBT3zqX2e7GwyGieb5KxynS9HqGe4JYy4ghYyXonI64TFnZZC2iKd0jc/JULfkKNQuPFz/nJcz2PXk36a0zKmgqiqSA4bzlBckhKr911nkeAHzjjTHGHdtYu8EGAw32LNcZZJOkdhe5xp8oiji0os1o8jd99yniwdOsY6TcZ4SEU6SJD3yMZ3J6NGqY2N2IYrGycnoJ3bWSRgBgFDQyXmap/5des1T4jyl439piEhCx8C69Y+GbLsg8Kaar4R4Vdyx/qs5ttdJBPDef6qq6iKo/9hebX7dseogupJlFa15WhAyRB8OSCL0EfGPjjdt8iOe6rG9hnOW7HdrbC9gdmrTONU8dRKQCeR4OtW+9XIAOjlPJ4teyzOnHDA1T4lAOJHM4v6HntCnJ3XxlKp56lBvllyXpvtNqc7TKvt5Q/BynuZ0x7W95umgX+cpJSLaY3up6GyHa1+Pm/ZwTAuCoH9P99+6PE08NbvPA5L5c5ba/1bnqKjm7TVTvcRX2bwvnaKHrddL2iSeWsRbyX5t2dZ/sIinv7jhNwCAm278Na65+koccfgq2zyCIOCwww7F29u2l2OVDAaDwWAwGAwLO3bu8hydy2DMBtUtC9Gb5PF6Xw5jGRV3vCljy0QdQvH62e4aowIRQ4ZDQZkD4qnVeRqqnpp4ynEclpxtdpTtfvqWWdsX2YkRKBlj3eH6VnB8aa8RWi11T7s3sbqnDIYb7FmuMtGdpx5lyC6//BIAwMjIKB5/4kkAZmcSqbeZTKWQy+XQ1d3te/1Zk/NUc7+mkik9hnJ0rIjzlIgZATq2l4hwk3Gean2g6zrmqBf3pQh3BOI8zRdxnoZCQV1Q8+M8JQKJKEqOQlxVVcyx/istijqJCEQccxJQbO2LiKfEpUba0GK5Fb/iqe489eGA5HlS81RrQ0R5wKfz1NInUu8UcHeeOkELa7Kf+NNA8dhe55qncf3fUxVP6VqexerkzhVI31PZPP5yy536dDKIJBwO6/tNlhWbW9xpsIa+nzyc2gB0RzJ9jKzQx8x6HSjkeneoeTowaK156ie213wcaVHcaeCEn9hewPncdaxZahE/6Xs4AGQpwdMmXnIALHVLgwFzegJ9j89a9qVTbG/QIt5mvNbvSzwtPgBmtimLeLphw0asPvJIHHnE4Z7zNTY0oL+v33MeBoPBYDAYDMbkmN/RjvFCfRkGo1KIty7FA2PL8esNMr7+WAaP785hl7jMJJIxGASzeFr597P0aHmdpwAwb9VpiDUv1D/LqXHsee72KS93MiQHrZG9pccS1y05GoFYrf55oncXxrt3TLlvDMaBCHuWq0xSuvPUXTxdsXwZVh22EgBw+x13ATC/XI9XxQvLSuHfv/oNnHzq2bjn3vt9rd8snmovn7u7e5AvuDSLOU/1GE26BqFeq3EyNU+JeOriPC1BuCMUc56a9kFh/aQWrRdEXAkEJIiO4mmV7rw0xfZSwoqTA5Qsy00MMtdM9RfbS0Qbw73mUDOwMM1N2CboNU99OCB152nO7DzleR51dcXLBlhdzImEMeiqKuYgnrrUs3Ry/nrVPA3p9X/t+8KrPS3eWuvKlorhQM7p/ec4To9CnouQ807gcrhs3Xn6dOI8jVCxvYD9XDTuN8b5Szt0vSDiaZWDY5lAD4KwrtsQzY15IoW6u9aBIm4DELxiezmO012jXuJpMeexU+yx1ZXrGNtrFU9p56o19pcXwHGcZ3uz+GkRTx2uHdv6ZWfxl1NViJL92rK39x4EUgmUZRjERCLhayTKxMQE8qrzjyCDMVNs/tt/z3YXGGXgc7fcCAD40BHHAgCuf9zfHx0MBoNxILNu3YX4xS9/ja7ubrS2tMx2dxgMndbWFryycZP+ubamZtb6wqhs5rzztAziKcfzWHLmh7H5r9/Sp+18/GYsOvU94B2iA6eThKXeaWQS4ikviJh3xBnY+9w/9Wldmx7FipYlU+4fg3GgwZ7lKg9ZlvUX4l7OUwC47NKL8drrb+DxJ57C4OCQ6SV8VVUMPb29SKVSeP6FFwEAGzdtxkUXvsNXHwBNOCSCEe1cTafTyGSyjrG0AO0Es4unk6p56iCe0o5RIgIIJQhIgk/nKYkuputyekHqGkqi6Og8jcerKCHZXnMTcHaRWaN2rZQinhrLUkxtndqFCwI+qT/pBqktKfhwQOrO05zZeVpfX+crfpb0M5fTBER6AEgx56kgCPp+p2N7Zeq4uUHOZ0fnadZwHNvWTwlzU3aeUq5hpYQ6s5UM2edqLoPLLzlfn04ixyORiEk8zWQypnsjifI1OU+L1DQmGLG97jVPTTVDLXVLncRLt/u2e2xvsXrHEmRZcXTtk3Og2DUfDBHxlHJ+2mJ7HWJ3g2bnaIa6/9jaC/b7b8DiPKXvX9b9Ya1PqrW3iq9Ue6pmsVO9VcDB+SofJM7TpqZG7Ni5q+h827fvRFtrazlWyWAwGAwGg8Gw8LHrPoITjl+D93/gatx3/4Ouo8UZjJmmrc38N0BtXa3LnIyDHbrm6VwQT+01T8sTR9127DsQqm401jPah84NMz9YMDnYafocqW+b1HLs0b2s7imD4QR7lqs8aJHKy3kKABevuxCiKEJRFDz8yKNm52lcEwN6enoxODhoW7YXJuGwIEhYRYixcffoXiIu0aIDcRW5xc7S7ayEHMRT2jVVinBH4AV/NU8DgQBCBQEhnfEf2ytJkqOTKhqNOgrJ9H7xiu11E59pR5VX3U7ALmSTtk7tIgUhKJlM2r6jyU3FedqnPds0NTa6tqGhhSJFUUzO02LiaYxyptKuRCO216vmqeHeU1XV9B0RhZyOHS3MuQ048Iuox9Equot7rounVic0IZnSzrlwOGzab9bfKaeYcGtdXTfGChHkVXF38ZQ+3+yxvXbHcdSlVrWb65GO3rU6N8l0bT57ezKtaGxv4dylo8etzlNRtQuQwaD5N0g23bPM/RF5+3lobW8SPy3O1YDDtWdrT4uv1OAHESo4wUl8dRd/K5WyOE9PPOF43Hrb7Xj0sSdw1pmnO85z1933Yt/+/bj6qg+VY5UMBoPBYDAYDAtnnXMh8moe3d09+My/fhGANmI4ZBmhCGiRM0889sBMd5FxkNLeZhZcapjzlOEC7TyVU5UdXanm8ybnqRSJQ3CIqJoMghTA4tPfjzfu/D992vZH/oj24y4queboVEgOmMXTycT2AkD9smMgRashJ0YBAOPdOzDeuwtVzYum3EcG40CCPctVHsmkIXAWc57W19ehrbUFe/buw+DgkF5/DwCqCrG9W9/epk+ja0N6QdyTgYDkGjM6NjqGxgbn9APHGoRE/JtMzVPH2F6789SPcKf3R6+76Rbbq+2DIBVd7Ce2NysbYoa1Bl8sGoUgCLrQQTvJirrPCuJMWWJ7LeKpp/M0bNTO9UI/Br6cp9pzBXF+9g9ozzYNPlImrf2UZbN4GnOIX6XFy6qqKoyOjpnWD/irHWmNjqU/e7WPU8Lc1J2nhvBMoqvFEuKqKxHdia1y2LTldaw+4jAARnx5OByyOU9pjPuVsW+JQ9crJhyA7lp2q4urLddPbK9x3ElsrxXX2F7Z+9yzOsVpfNc8Ja5pr9hdp5qnIfO2ZGnnqWzeF5LDeWgTX6l1Wp2rAdGhZqnlWYRev0LduzXnqUNsb9DqfPU+HyqBsoinH/voNbj7nvvw6c9+AZ/99Cdw7rlnA4BeAP2hhx7F//7oJ4hGo/jI1R8uxyoZDAaDwWAwGBb2d3bapg0MDM5CTxgMMy11Ebz7MBEnzRfRn8gjpnQXb8Q4KKHF01w2BTWfA+cwcroSyCZGoVIRVeWod0oz/8TL8fZDv4NSEJEneneh9/VnMO/wU8u6Hi9ssb2TdJ7ygoSWw0/H3hfu1Kd1bXgIKy742JT6NxsM7dqC7Q//HiN7XkfL6rOw6p1fmlFBm3Fgw57lKg9apIqEnV/C0xBRIZvN2mJ7AbMoV8w9SKDrfRLXpxWvuqdONQglH7G9pB0drQoYNU/puo606ElEsFJqnhLnKS2g0Rj7QDJqXaaLO0/pdqIogOd5va9ExHNykpliex0cq14CCmDExgLONVNNyyqIj0S08RJPiRCUShYRT2Xi/vXjPDXXoySxvX6dp7RQJMsyJiZo56n9mqGFMdqZSjtPdQefV81Ti4DnJJ467XtavJ2qeEq2PZ/P68eNCIVzFXJui8Eq/PAnv8Wffvsj5HI5/VoKW2qeWsVT3XlKuVMF385T7T5GakQ7QR8zq3jqJF66OU/dBo4QgddNAHVz5mrrJ4MWitQ8DdhrnsoONU9hrXlqET+zVBvZIp4GHM5DKWQVPynnqnX9TjWXrc7Twr7K5XOg79wi8rZ6rYCTc/YgcZ4uWrgAP/nxD/C5z/8bvv/DH+P7P/wxOI7DnXfdgzvvugeAFivw0//7wZRrNuzavQ+vvrEVO3fuwY5dezEwOAQA+Mn3/xONjc4RSe+76tOey/zW1z6PZUvZiFcGg8FgMBhzmx1vvzbbXWAwHImMbsNJ87U/PRqjPLik/eUwgwGYxVMAUNJJSBH36K7ZZDrqndJI4RgWnvwubH/49/q07Y/eNKPiqS22t2Fy4ikAtBx1tkk83ffi3Vh+/nVzRngc2rUFb9//a/S/9bw+bffTt6B20RFoP7Z4zUIGww/sWa7ySFPiabhIbC8ABKgoUafYXhrf4qmp5qmz83R0zD2218l5KhLBzkfN09raGgwNDeuiI90HElNMiyJEBBNLEJGI8zSXdxZXspSgSOoFpn3UPLUKkZIk6YIFiQbVhWRaiDDF9tpFABIn6yY+T855Kjv2mYbE9iaKnDulHAOr83SgX3u+afTpPA2YYntlTBScp9FoxCSwE6pixrVAx/aaap7KdgehFS8Bz6s9Ld66Obn9QgtspA+lOK4rEf3ekE3ilBNPB2B24EciltjerEtsL32/8VHzVJZlPcrcy3lqFetpFIe451JrnuruVRfh3qtetH/nqZN4al6eyPPgOPP1E7TcE2TTPctSs9Th2g9a3KCm9tbYXoeapTbxtnDfz1pEcdHBNQsAgZC7+FuplG0oxFlnno7777sDv7/pT3jmmeewb38n8vk8Wlrm4ZSTT8R111yN9vbJ/6FF+Oed9+Plja+W3C4YDGDtsasdv4vH3S9IBoPBYDAYDAaDMTWaFyxH/7PG53AVq3nKcEYMmkeHy+mJihVP7fVOyyueAsDi096HnY/fjLyivZga3rkZO5/8Kxad8p5pFx1zcgbp0T79czBeDzFQXDhwo3HFWoSqG5Ee1RwtqaFuDG5/GQ3L10y5r9OJk2hKs//Fe5l4ymAcwNDO07CL65NGj2PMZE3OpHjc7qQqJoARdPek5BHb6yGeOtU81aM5fdQ8DYVCqK6OY3h4BIB5P+jCGyV6EoGklNqPAql5WtR5atQ8davJSiNTsb2kvS6eFgQa3RUn252PdFsa4qhMpVKQZdkmdNKOON/iaWG/Edeqk2uSiKfF6uXqx8CH81SwCFuDBaNSQ72/Ou709mVlGROF6FWneqeAWRijhVRaOPcX22ucz9YIZ6/2ixYuhCAICAYDrlHXfqHP8UxW60MpjutKhOyzsaF+fPJjWoIoHcUciURM9Uztsb1OMeHFne7j40a5Dqf7JYHjOAQCAWSzWVfnKS18OrmfAY+ap1OI7XWKDXaC/p3Q21qdn4L9Od8qiMr5PFQ1D47jbdsTcOiDtY5pNpeDqqrgOM6+fgdXdtDavnDNOkUOW12zABC0CLIHlXgKAG2trfjaV79czkXaWLZ0ETraW7F40XwsXjQfX/3m/2DUI5qCUBWL4ePXsXqrDAaDwWAwGAzGTFMzb4Hpc6TWXwwY4+CD43lEGzsAjtdcqKrzS9RKwOY8rS6/eBqM16Nj7TrsefY2fdrrt/0APZsfx5Hv+7q2r6aJ5FAXoKr658gk650SOF5A+3EXYvsjN+nT9r5wd8WKp8VEU8LA2y8iMzaIYNzfS2YGgzG3mNfcjOs+8iGkUmlUV7u/0CfojqJsxhzb61D7sVj0KoEWDt3EU2/nqWzqG1A8dpZebzAQhCAIunhKRwfrNR9p56nP6EoaXq95an+ZrqqqJbq44Dz1UfNUpurFkvYEItw5OclMNU8dHGg1NdX6v8fGxlFfX2deLyVkBIqIp5IlBlR3GnvE9iaLxfaSY+BDwLa6AokY5sdpDZj3jyIrSEwQ56mzeEoLY7TzNKfYxVMv4Tng4Tz1Ek/nzWvGP2/9K4LBoGn9k4E+x4kQVorjuhIh+5weQDA+bmgv8Xi8SGyvPSac7Ccv5+nYuHEPc3Lq0xQTT+njHnGJ7XVznurLcDmOXq5zss+EIuKpHj1O7TtbzVGHsiVWQVQGBzWbBheM2J2njuKptT0AJQtIQZt4GnCqWWppn82rUPM5W1vNeWpvbxN/c3ldvK1U5tzVfPGF58x2FxgMBoPBYDAqmu3bd+Bvt9yKzZu3YGh4BOecdQb+/ctfBABseGUjXn31dVx6yUWoqamZ3Y4yDhrCdebSHdFY8ZePjIOXM79+x2x3wRcz4TwFgCVnfRj7X7oPuazxonRw+wY88d334JCLPonFp713WurCJi31TqP1UxNPAaDj+ItN4mn35kchp/4NUrhy3MWZ8WFsuvmb6HvjWecZOB7BWC0y41odSjWfQ9emR7Do1PfMYC8ZBzrsWa5yaG9vw8euvRKA+0t4GqOWXdYS2zt556lsiu2dRM3TgiMu6OA89ax5StULjQWi2FOYHgrbxVOz87RQb7OU2F4P56miKFALg3kkyah5mi6h5imJ3qUFybge21sQQyj3lEy5UJ1EiFrq2hsZGbGJp9kSYnsNAVrbTq/YXhJBWizyuZRjwOviad7U92LuOQLdT1mWkUhofXMTJs3OU+PfubxDbK9XzVPqWqAdfOb2zvv+8FWHuS63FOh4WCKE8Q6OwbkEOe4qOPT09mNecyPGKPG0qipmju3NFI/ttbqbnRgeGtH/XVfrnVJErmOr21LWhU/judit5mmx2N7iNU/t7XP6wBF/sb30ABDr8iSHwSc28REc1EwKCEZsA2Gc4sbt7Xnks2kIUhAydf0Jqgreh3hK1m+LHHaL7XVqL6fBTSHZZropq3iayWRx/wMP4aWXXkZ3Ty8AYN68Jqw57li84/xzp5wjzmAwGAwGg8Hw5qY//Bnf+d739dHGHMdhqDBKHNBGbv/Xt7+HYDCA97333bPUS8bBRrDK7MgKzO00KwYDwPTXPCVEG9qx5mM/xsY/fQPpkV59el7O4I3b/xfdGx/Gke/7BqpaFpd1vckBa73TqYunsaYFqF10JIZ3bQagbUPXxoex4MTLp7zscvHGnT92Fk45Hu3HvQPLzr0WI3tfx8Y/fk3/qvPlB5h4yigb7FlubmPEMWZMzq0p1TwlAqAkmUQLmvESY3v1qFo/ztNg0OS6JbG5gLPoSc7dUuJLvZynJhdnIKDvAz81T41aqSS21xAUvGN7KeepgxBSTTlPR0ZHPftcTDylXcC5QoymWzsS25ssEttbivuXCE1Krrh469V/QNtuEtsbc43tpWueUuJpzu48FR0EIIIptjdjFtLJtVdMxJoqtEh3wNQ8LeyzeW2L8aX/+Db+9NsfmSN1q4o4T7N28dTPYI2h4WH937Sz2wlyL7M5T0nkLiW6h13EU7e+EBHTyXEOlCm2t9B/sq+0tsVrltrFRx75bAqCQ3sn57qz+JkEotUm8VQTP4uLrwo45LMpyJZdIan+2mvrTwMVLJ6WbSjEC+tfxJlnvwNf/Lev4G+33Ionn3oaTz71NP5+y2344r99FWec9Q489/wL5VpdyWSyWdxx94P47U1/wx9uvhWPPvEsRkbdHywYDAaDwWAw5hpPPf0Mrv/v76K5uRm/+NmP8dILT+t/fBOOPeZo1NbW4uFHHp+lXjIORjiOgyIaf7i2HnH67HWGwSgT6dGZcZ4CQMOyY3H6V27BgpPeaftuePereOp/3o9tD92IfM55FP1kSFicp5H6trIst2PtOtPnfS/cXZbllouhHRvNEzge7WsuxBn/cRuO+uC3EGuaj3mHnwYhYIgHw7u32PYXgzEZ2LPc3Ie81M9kMib3Iu22IyQTfsVTd+cpcXR5x/baaxDqbkvrW28KWnSlnbMkNhcwoi1zlPOIiGCliEhezlNaINHE00LkpQ/nqVUINMX2VhHnaZHYXgchpKaaEk9HpiaeilRMarF2JLa3WORzKXVniVMyn8sXFW+dMDlPFQUThfqYMYeoasB8LZhieynx1Fqr1gmvupt+aqaWA8EhtrdYZGulQ0TDTCaFBfO1Zz9SU5nneUSjEYiiqJ9b9pqn9thpX87TgngaCASKuvzJOWd1jzodd6vzlFz3JNLbilJEAPUSgv2edyRyOpuhxVOzEOxLPOUKzlPY64dKfp2fhWQbxSSe5n06R3mo2ZRzbK/kd/3+fgdni7JczW+88SauvuZfkM1msWLFMlyy7iK0t2sX1/79nbjr7nvx1ta3ce1HP4lb//5nrFx5aDlWWxLj4xP4+233mKb94ebb8N4rLsIF553peznfuP6HjtPfue4MtDQ3YKi/e0r9PFAhI34qYf/UrT5vtrvgiRipAVD5/ZxtPgTtJtwY0R60PnTEsbPZnTnBfYUIebav7FjvTZV0z5oLsP1VnLrGluIzlYHf/u4PCIVC+MPvf4NFCxe4znfYykOwa9euGekTzRtvbsP13/tJ0flWHroMX//yZ0zTevv6cevt9+O1N7YikUiirq4Ga49djcsuPt/0AodRuRz/0f/FM3/8NhqWHY1427LZ7g6DMWVmouYpjRSO4Yj3fBWtR52DzX+9HslBwxmaz8l4656fo2vjIzju2u+XReiklw9oDthy0HrUOXjttu8jL2svjIZ3b8F47y5UNS8qy/KnQl6RkRwynmfEcAynfOFPiDXNN80nBiOYd/hp6NzwoD6t65WHsOzcj8xYXxkHJpX+LMcoDhEos1lzbK+TkJSVNbGsmEiVpeJzacEIABYsmI+dO3d5x/ZmjHqhBNHDPWWsl9RKDehOMFEUTf3lHZ2nJDK2FPHUXVzJZs3OU/Lsb40Ldd4G4toVC/+3O091McQk3hkiiFM9vnA4jIAkISvLGC3iPKXdrk7o65eV4uJpWBOCikU+lyIeEgFcyRVfv2N7yqGnOU+9a56SWrOAl/O0eHSw2f1oie316QCcKnRdzOwB4jwl+2zntjdw+99vBADdeVpVFdOvh2AwgGQy5Su2V9Sd7u7iKRmEUFdbW7QGpuE8tYqn5Lwxzl2rEFtTXY3x8XFkZdmx3max2F6jJqz93uk/tpcMsjH2nVUItgqNABBwcm4WxEdrdK7kR7zkeOQzKa2udN4YqCX5jd0tiLeyJapa8i2+cshn/NX+ni3Kcgf50f/9DNlsFl/+0ufx0evsfyx87KPX4De//T2++z8/xI/+72f4za9+Xo7V+uaUE4/Diccfg/kdbYhGIujtG8CjTzyLRx5/Bn/66+0IBAI4+4yTZ7RPDAaDwZhb7B7s9zXfwvrGae4Jg+HOq6+9htWrj/B82QYAtbW1eHnDKzPUK4Pq6iqcetIa1+9femULUqk0Dl2x1DR91+59uP67P0EqncaiBR04ZMUSbN+xB3fd9wg2bnkd//nVzyESqdyoF4ZGyyHH4azP/WK2u8GYA2QmhpEdH4aSTiBY3YBI3cwMQCkVa83T6YrttdKw/Dic9u9/x9Z7f4mdT/4FoFxpY51bsfHP/4mTPvubKa/H5jxtKI/zVArH0HrU2dj/4r36tP3r78ahF3/Go9XMkBjcD6iGAFDVvMgmnBLajjnfJJ52bniAiaeMKVPpz3KM4tAvxXUHkyTqcauAJrylCrGrqVSqqEhlrnlqFk+XLlmMnTt3uTpPVVWlnKeUeCrZHaNWaOcpcVqGLCXZhELcbs5U89S/65HA8wX3Y97uPDUJkSXWPNWdp6LdeRqv0ty0Rg1D2nnq7XzkOA7VNdXo7x/A8MiI7Xta1CkmpNAxoFnLtlohf/OkisT2Tsp5ms/b9rUf6PNXkRUkEkQ8dXYPRqMRcBwHVVVNAqvJeUoELM+apx7OU4f41umAXj6JkS7lvK9ESFQyLQ6OF2qe0pHLwWBQE0+zlthez5qnxWN7a2trivaRDEiwxfYS8ZI6LlbxtLo6jn37yfyK7f6rFDn3yHQn56rsMy6a7JuMyXlavGapY2xvIbLa6v4MOImnjuJrCkrewTnqJH46tM9nU5AD5kQE3zVPOV53vlYqZbmDvPTyKzhkxXJH4ZRw3bVX444778ZLL8/8w90nPvph0+f5Ha24+kPvQntbC27849/x91vvxmknr/U1ouZbX/+C43TitpkpZ8lco5L2z75Hb5ztLnhCHKdDmx4sMufBzZ947VwiLso/bXl5NrszJyDv1di+svOjd5t/v5zuWdc/fv+klnUwUEn3+IOddDqDaipSy43h4WHwXNmqN/imrXUePn7dhxy/GxwcxtPPvQSO40wCaz6fx09vuAmpdBrvvWIdLrnoXADaHzU/+unv8Mrm1/CXW+7EtVe9d0a2gcFgTD9v3vVT7HvhTgDA8vM/ihUXfGyWe2RHVVWT81QIhCAEvSPGyokYDOOwyz+PlqPOxua/fAsTvYYDbXjXZuTkLASHlyZ+UVXV5DwVAiFb7eKp0LH2YpN4uu/Fe7Hiwk+Ad4gom0kSfftMn6MuwikANB5yAqRINeSk5pQY796Bsa5tiLcyZz1j8lT6sxyjOPRLccO9KCFEiaeHHXYoXi68H00kkqZIXCfomqd0bK8oipg/vwMAXJ2niqLogiQtHOqCnUdsLx33W9+g/QZUWWq3ktjSnDI18dTLeZoxxfZKuoDrp+apLp4GnGJ7C85T4rx0iO31EkFqa2rQ3z+AUY/YXkEQiu4HOgaUFmQkB8cqEYIURUE2mzVtD00px8DY/pxJvHVavxO0yJqVZV08dYvt5Xke5517Np5/YT3WrjkWoihCKdR7JShy8f1PomNzuRzS6VmK7XWoeTrXY3uJS5uu2TxGxFPqmBIXvFtsr2mwhu5uLh7bW1tXW7yPAf+xvYGApJ8nABCn6jc7iqeyt2vZq+ap3/OO3MfpfafYnKM+a5bqzlNLbK/P2Nx8JoW8xREs+Y7d5aFmUsjy5t+FksTXCneeluVJK5dTsGzZ0qLzLVu21HOEwUxz1uknIl4Vw0QiiW07ds92dxgMBoPBYDCmRGtrC97a+rbnPLlcDm+/vR0LFri/EJ4Nnn7uRaiqihXLl6Cp0XBvvfzKFnT39KGjvQUXX3iOPl0URVx79XshCDyeePp5jBfioRgMxtxHDBkuBCVdmde2kk4glzUcL8F4Q9GIsemgbtEROPXfbka8bbk+Tc3nkOjbM6XlZsYG9FhdQKt3Ws7tq19ytClaODM2gP63Xijb8idLot+836KN7r+VvCihZfVZpmmdLz8wLf1iHDzM5Wc5hoYunmYzphfptPP0iMNX6f9OFolfBejY3gAClCDR2NCA6oIjdGzU2XlKO7PMMZrudfsItPN03YUX4JKLL8K//5vZVGI4Tw3HqF7ztAQRifzGODlP7TVPC+KpD+epd2xvlamftHghU65hN8i+H/GI7fVj1NGdr0rx2NwwdR55nTuliIeG8zRnFm/9xvZS61AUBRMTWsRrzCW2FwB+/tMf4cXnn8KCBfMdXcfF6k4SdKe3xf04c7G9dvF0zsf2Fo77kcechCs/+nkAhvOUHujhFD2rfTbuGwSnQRZWhodHAGiDEophxPYWj2vmOM7kPqUHKFnFV3oZooPzU5vuVfOU3Pu8z4FgwL7vspblOcb2OomnBfHR5jwN2EsbudcsNa/bTfy01mElzlWbcAsV8OWc5SreeVoW8XT5smXo7OwsOl9nVxeWLS0uss4UPM+juVmLVxx2GCXEYDAYDAaDMZc47dSTsXfvPvz9lltd5/nTn/+Kvv5+nHnm6TPXMR88/eyLAGCL9X1l02sAgLXHHWV7cV9bU40Vy5cgl8tj4+bXZqajDAZj2pEo8VROT8xiT9yx1TudocheJwQpiJoFq0zTxrq3T2mZtsje+vLUOyVwPI+ONReZpu1bf1dZ1zEZEv0W56mHeApo0b00na88CNXhxT+D4Ze5/CzH0KBfitN1G+vqatHY2ABRFHHaqafo8yeLxK8C0N2AgUAAoaDhPG1satCFgLFxZ/GUfjnvVPM0n887CpZ022AwgNraGvzoh9/DuosuMM1jiCKUa7PMzlNaINHctwWBOl2C89QptrfgoqXFS30bfDgfSR3YEQ/nqZszlIYIPb5qnlKlSrzOnVKcp7oAnstPquYpPZ8sy5hIaKKuW81TQBO0SDvSR7oeZrG6kwRyPVjdj2Q7RJ/bMFlolym5XgRhbqcCkH2uqnn938TZTtzaAFXfmdr3qqo61zwlx7hMsb3k3LHH9jrXW45S4imJIAdgclpbl+F27jnFGvttS9AHgGSMASBWATMg2cVPgRfAU69EZPDIF8RH2XIflxzEU0fxM5O0iZ+aeOpP/MxPSXyt/NjeslzNH7n6w3hl42Y89PCjrvM8/MhjeOWVTfjI1R92nWc2SBRu6MGg/YRiMBgMBoPBmEv8y0evRXV1HF/7xrfw9W9+C08/8xwA7Y+dF1/agP/69vfwne99Hw0N9bjqwx+c5d4abN+xG109fQgGAjj+uKNM3+3eqw3QW7Sgw7Etmb5nb/GBfAwGY25gdp5WpnhqrXcanEXxFADiLUtMn8e7piaeJi3iabShvOIpALSvXQdQg2J6X30K2cRI2ddTCon+vabP0Ubn3x5C/ZKjEKpu0j+nhroxvHvLtPSNcXAwV5/lGAam2F6qdp4kSbj37n/i4QfuxiGHGGkByYR/52nQUvO0qbFRFwDHxsaLujZNMZrUS3C36F7a8eqGUHANmpynPt1XpuUI9uUY/aPqcAYCCJLIy2xx8TSrO0DFQnsH52lBaKH3gy6+eYggxHk6OkXnqaBHmvqpeWqIQKmku+jg1wEHUAJ4TjEJQpMRTxVFoWJ73cVT0/oL+z+ft8f2FosO1q83a2yvD/G7HEgm8ZTUPJ3bsb1kn21Y/zR+9sP/BwAYH9eex+NUbHfAoW4nXevXVPO0sEwv5+kIcZ7WFo/tJfcka91Ro9at+byJUPV36dhep7qler1jF9c5uZc4uVaVIq5VgvE7YdyfrZHGkoP4CAAB3rimaecmLZ6Kah6CaNe6OI6DRKmvbuKn5hx1Em95k5hIYntt9VrdxFPL/UiZA7G9k7qaO7u6TJ9Xrz4CV135QXzqM5/H+eedg0vWXYi2di2Cp7OzC3fdfS8eePAhXHXlB3HUUUdOvddlYt/+LnR19wIAFi/0/qOIwWAwGAwGo9JpamrEjb/5Jf7lk5/FX/56C/76t3+A4zg88uhjeOTRx6CqKpqaGvHrG37ma0TnTPFUwXV63DFHIhwOmb4bGBwCANTX1Ti2rStsx8DgcNH1fOP6HzpOf+e6M9DS3KDX77VC/oBy+57hH7Yvy8eBvC8z1MvL1NjQtG/jZPbl4H6LOBkIz+6xiJhfNA3ueX1K/Rncu9U8IRTztbxS92X1wiMwumszACCfk/H2E7egdc260jpbRsZ6dps+Z7lA0W2pW3kyup7/p/55xzP/BKqap9yXA/kanw2muj/rGlvK2R1X5uqzHMOAfiluFXAa6uvRUF9viltN+IrtNYQ4URTB8zzy+TyamppQXRAC8vk8EomkyRmm9cMQNpxiewEUSqzZX3QTcdLLcMIT4Yt68U9KtpUiIvG8fTkEa2yvXvO0FOdpQUwxxfYW6jcSoYN2vRrxn+4iCHGeOqUZ6m7hMsf20vHPXudOKcdAF8BzeXPNU5/CI8dxet3SVCqlxyl7OU9N6xeM9RMUH/sfgB5jbXWe0q7v6USgxCByvQglDBqoROioallREAYwXnC2V8UM8dQptjdrqU9MMJynxWN763yIp7rD3xbb63zc6eumaGxvkZqnbrG9qqpS4mmR2F6H8zZr2TdBl3uHJPBIF+ala4bKedXoo0vNUkATX+W8UmjPQ82mbZHBopp3FD85joMk8MgUrlW32F639fMcD5HjoKgq1b54/PpsMqk7yKmnn+tY70RVVdx3/4O47/4HHb/7wx9vxh//9Bdse2vmRmI++cx6zG9vxSKLOLpr9z787IabAABrjl2tv3hjMBgMBoPBmMsceeQReOTBe/CPW2/H0888h/379yOfV9HSMg+nnHwi3vfed/seBTwTKIqC59dvAACcevIa2/fkpYjbSxsy+t5PzSMGgzE3EILG6PBcpvhL5dkgOzFk+hyI1c1STzQiTQtMn5NTrHmaHukxfQ7VTo9w1HTk2bp4CgB9mx+ZNfE0J2eQHevXP0vRGojUuehG42GnmcTTgTeexqJzrwM/x50njNljrj3LMcyQl+LZbNa1bmMoFALHcVBVtcSapxI4jkMoFEQymUJTYwPiVYYQMDY2ZhdPs86xvXSf3Oqe+nGeOokipbgeCV7OU1Nsb0BCqOA8zeVykGXZ0yFJC8+AIboAhotO0sVLQ0hRKNewGzWF2oyjHrG9ftyb5FjIJcb2ejpPC8fDj3hIxKBcrvj6vZahKAoGB43nI+LsLQaJDTbXnC3u/AXMTm8ao2bt9Mb2igLtPM0Wps1t8dRUw7YgJBLnqVNsL+0Ap/9tcp4K3jVP8/k8hkdGABiDErwgx9UqfrrVWzbVPKWcp5OJ3nUTT+nBF6Wct2pBSLQ5T13efwQEEYC23TLHI1/4W0lRjXunm/MTACRBABQinjrH9kousb2AJr7S4mk+k3SO7eWdr4OAIOj7TgYHNWcXsCuJST3NrznuWDhopzPCK5tew+13GeIsyVH/4U9/q/8gHHXkYbj8Eq32yMsbNuOG3/4ZrfOa0NY2D6IgoqevH7v37Ieqqli0sAPXXfXemd8QBoPBYDAYjGkiGo3iqis/iKuurPw4tw0bX8NEIom6uhocdujy4g2mwLe+/gXH6cSF4uYqKfY9wz9sX5aPA3lfyk1GRKyak6d9GyezL3ty5pHutS2LZvlYtCAYr0dmbBAAkBntQ1WsClI4VqSdM8r4oOlz8+LDEfOxfaXuy/jJl2HXAzdASWsRf4meHRCy46hum97fAyfGLFHHVfP8HdPahnnY0bwQE727AQBKcgz5oX1oWHnilPpzIF/js8Fc259z6VmOYSYQMBxFskuEI8/ziETCSCSSvpyn1vqZ7e3tePvtbVi+fJlJCBgbG0NbW6uprWtsL+0uc4nt1Wueeoinzs7T0mueejlPaYHEGl2cTmc8BTISy0neGZN9KAiCLqgIVGyvqqrgOA6Kj9heUjtxxCm2N+tfPKXFmOLiqSECedU8JaKXHxckTzlPTesvEplLI0kS0uk0+vqNQUh+jUqkj3kH52kxEUoXni1Clt/2U4U+xw+c2F7tuK868jh8+/s/xw++8zUqtte43wQD9uhZk9OdqrlJYnvdap6OjY3pseN+nKfkOs66xvZ6iadTq3mqx/a6nHNA8UELtLBMrjlrzVKnmqeAve5oNp1EXs1DMYynEKECbuKnKAKF45TnOMhu4qeLc1USBUCmxNts2i6+etT9lQQeKKxOBg8oB6B4+tebbypzN/wzPj6B7Tt326bv2WvUZGltMWqOnHLSGgSCAezesx9vvLUd6XQa4VAIh65YiuPXHIUzTj1h2m+kDAaDwWAwGDPBq6+9jsNXHTbb3SiJp5/TIntPOfE4/Q93mlAoiEQiaRtNTCDOVDICncFgzH2kkCH4EVGt0qi0mqcAUNWyVBdPAWC8ewfqFk+ubE5ykKojzXEI17W6zzwFxEAYrUefh73PGc7NfS/chep3fnFa1ueFvd7pfF/tOI5D2zHnY+t9N+jTOjc8gKYpiqeMg5O5+CzHMENeimezWaNupoN7MRzWxFMv9yCBCKBETPvVL36CN958C+ecfSaGho3SFWNj47a29DM07SAVy+Q8JZGvJudp4UW8WIKI5BTdavTDLCjSwkM6k7a5bWl0B2hBCCT/j8WieqoivS/y+TwEQaBie72cp5oIMz4+DkVRLHVkyXr9Oz/z+bzpeDmJl2EqftTLtUyOhx8XJL1+k8u3BNcm2U/9/cbzUY1f8bQgnOeomqeypVatG4Zr1izeuIlo5YbuH6m7WorjuhIhxzIciaKnTzueYyS21+Q8dYieNdU8pe43xHnqEttLR1/X1vkRT6XC+qyxvc7XbTTqLJ461y0tuLZdzn99sINl0Ak9CKXYeRei7mFk/8nUvU9QVfBu4qnlvprJpm2uVdHDeWptn027xO4K/trLsmyveeriOgXMNVtzHAdFLh6/PpvMOdXwtFOOx2mnHO97/jXHrsaaY1dPX4cYDAaDwWAwKoRLL38Pli1bissuWYeL112IlpZ5s90lT8bGxrFpyxsAgFNPWus4T0N9HRKJJAaHRrBgfrvt+6FCbZSG+uJ/ZDEYjLmBGDLiKJX0xCz2xB1apASAUPXsi6fx1qUY2Lpe/zxZ8VTJJJGhnKehmiYILqPPy8H8tRebxNPOl+/Hyks+C75InbNyk+iziqcdLnPasYqn3Vsex+HZFMRA2KMVg2Fnrj3LMezQgkGykJbnJMBFIxEMYLCo81RRFN2RRUTMBQvmY8ECbYAHHds7OjZma29ynlJOMFrYdBNPyUt9r5qnooNrkERz8h7uIytEQMvn7eKKIR5r9V5DQWPQZDaTtc3v3Fb7TSH7kI6UpQUwRVEgULGSXgIiLcKMjo6hvt6I8C8ptpeaJ0mJ6U71UnmeRygUQjqdNs1rJacQ52nxV//0AFa6jqyfeq0EIvT2U87T2kKscdH1E+FcocVTf85Rcv7ZhCwf4nc5cHKe8h7C0VyAHMvNG57H3//6RyiKgkThXkZfN06RyW6DNYi72C22d5gaBOKnnrdbbK+ba5R2ntYUFU+9Xefusb3G52LuY9MAkMI1Z47dda45Ctivy2zGLn5KXjVPLcJuRs7axU+v9lbxVJGRtbhHvcRTyTKgw1pvtdLw/yvGYDAYDAaDwaholi9fhm3btuP7P/wxTj3jXHzoymtx+x13+aqlNBs8t34Dcrkcli1ZiNaWZsd5Fs5vAwDs2rPP8XsyfUFhPgaDMfcxiaeZJFSH+mezTaU6T2nGure7zOmNyXUKIFpvH7hSTmoWrkKseaH+OZsYQe/rT0/rOp2YrPNUm7cDNQsMt2Auk0TfazO/DYy5z1x7lmPYoQXKiYSWnuD0Ep68zC92bOmX+04O0GAwoCewjDmIp3Skpsl5Ssf2usQmEheZl/OUd3ANluJ61Jfj4TzNWATQoCm2N+25XBLLSUS0xgbt97q1xYjwpoUOItr5qXlaW0OLp+boXrJePwIkfX6kqCheN+E1Wjh3Uin3cydXkvPUmMfP+p0g8/YVnKehUMjkkvVcf2H/m84hn7G7JGLWPbZ3mmueUv0jQv1cd56SbcpmM4hGwpiYMAYyOounLrG9QftgDbfY3uEhSjz1Ibobsb3mwRNuwmeEOhfpqHM56xHb63LtSPo5Z24rlxTba9xTyTbIeSN319M5ahE1s9mMTYAUkfdwjlrEV0Vxdp66rd+ybRkli6zFPWoVSL3ayweT8zSdTmPLq6+jr6/PdvLSXH7ZJeVcLYPBYDAYDAYDwP333I4339qK2++4C3ffcx+ee/4FPP/CenzjP6/HueecjcsuvRgnnXi8HlE12zz1TCGy18V1CgBHr16FJ59Zj/UvbcTlF59v6vvwyCi2vr0DgsBj9REs4o7BOFCgY3uhqshlUyZBtRLIUOIpJ4gIRGtmrzMF4q1m8XS8a3LiaWJgv+lzpGF6xVOO49Cx9mK8eddP9Gn7XrgLLUeeOa3rtTLRbx6kEytBPAU09+nIntf1z/s3PIDWo88tS98YBw9z7VmOYYcWDBJEPHUQ4CKFGEniTnWDfr/qJmLG41VIp9PFnad0jCb1AtvNDWY4T93FU935Ry2jFNcjgTj4cg7OU9ki4tLlOminpBPW2N73vOcKAMDpp51CbQO1LwriDhFRvUSQako8tdY9LcV5Sq+fuEl5nnetGRuOhIEheLqWlRLqztJOSVNscAniKdmGvj7NeerXdQrYnaf5fF53Wxfrg5sLkIhoxWJ/pwq9f4lgXkqt30qEPudlRdHrnQJAvEhsLy2k0vfCYs5TkiYVDAZ9ie7kvKDvb6qqujqWyf2W4zhEo8bfFFbRnZ7mNnCCTLe6nZUSYnsDltjevCRAUS3iqZvz0zIgICvLtpql3s5Tq3jq3B4u7lnrPTErKzYHr1fNU1tssIP7t5Io2x3kZz+/Ab/+7e89R0yRottMPGUwGFNlxfbHAQDh5ctMn/2wdekZ09InBoPBqAQOPWQFDv33L+Hf/+0LePa5F3D7HXfh4UcexR133o0777oHTY2NuPjiC3HZpRdjReEeOhvs6+zGrj37IIkiTlh7tOt8xxx1OFrmNWHf/m7cde/DuOQi7UW0oij47U1/Qy6Xx1mnn2T6Q47BYMxtxFAEqz/4LUihKMRQFPw0RsZOhpycgZw0XlAH4/UVIWTE5i0COA4ovHwZ696u/w1eCskBs/M0Uj/9zv724y7AW/f8HGrhpXnfm88hPTaA0Aw6eq3O00hjaaJx61Hn4PXbfwQUYtf63ngW2eQYApF4kZYMhpm58izHcMYU25v0ju0FgGTKu+apSTx1EZKq43H09fUXrXlKC1FWgcSKqqq+ap7yDnG7uYLwVZLztBAdq1LuK4K1HyFLzVM36G0gbrF4VRWuu/Zq03ySZN8XfpyPdPznyIhVPC0e+0twcn56rZe46Lzq5ZYiYAsCHdtr7M9irk8acm4Sl6Kf6FV9Pbpwrp03ptqRPmN7c9Q57CWilRsnoXS61zndkP4vXXEYbvnnfXjXpefr31XFjWcaIo7S16B5sIdx7pNjbK3NSRgZGQEA1NXW+npuJfcCWrSj66m6xfZGIxFTvxxje4ucO+6xvdT6i9z7gibxNAslbL7HSlDBubimbeJnLoes4t85KlnFV0Wxiaciz7keB5v4qciQZbOJUvKILbaJr7mDQDz99W9uxI/+72cQRQFnnXk6Fi5YoCv6DAaDwWAwGIyZh+d5nHLyiTjl5BORSqXwwIMP444778bzL7yI3/7uJvzuxj9g21tbZq1/Tz2j1eU7+qjDEfN4bhQEAZ/6lytx/Xd/gr/dejfWv7wJ85oasW3HbgwMDqGjvQXvfzcbmMdgHEhwvICONRfOdjdcsdU7rYDIXgAQA2FEG9qRKDgo5cQoMmMDCFU3lrScpMV5Gp1m5ykAhKob0XjoCeh7/RkAgJrPYe9zt2P5+ddN+7qBQp1Xyk0cqm4quV5pqLoRDcuP0+vOqjkF3ZsexYITLytrXxkHD5X+LMdwJuDkPHV4CU/cVcVie7NZ79heQHOeAsDYqLvzNBgMml6Gi5Y6n1ZkWYZaGIzjVfNUcIjbJe4yoYT4Ut156iCuWOuWOtULdCKXy+nb4OVApAUwxRrb6xH7GolEIEkiZFmxxfZO1nlKxFOvuN+ID+G9lOhkOraY7M+AJJU0+Mp6jteUIJ7yAqmbq/VZoeJQ/TpP6QEAXiJaueE4DqIomq6hue48JfeG2jrtb+6xcWNQRhU1YLmuthYA0N9nPD+5xfaScyznEts7VKh56ld0d4rtpY+7VaBrbtKehRubGk3nlHPNU2/XueRwzlk/F4uLNsX2ZjKQc+ZzRvKKzbWKl7mcXfxUvWJ/zX2TFcUmvkq8h3PU1l5GVrGIpx7Xnd05q+DVfbsxkUkjHomiPhBBe8yfiD4TlOUO8pe/3oJIJIxb/vZnHHrIinIsksFgMBgMBoNRJsLhMC679GIcd+wx+PVvb8TNf/n7rPYnn8/j2edfBgCcetKaovMvXjgf3/l/X8atd9yH117fin37u1FXW4N1F5yNyy8+H6GQ+wsdBoPBKDeVWO+UUNWyVBdPAWC8e0fJ4mnCUvM00jAzNaU71l6si6cAsPX+XyEQrcHCU9417etO9FnqnTaVFtlLaDv6PF08BYCuVx5k4imjLFTasxzDHVowmChE8jq9SI8WBg8misX2mmqeOr+Qj8c1ByQtchBIjKZVAKXdR9b4ScCfaAvQoghd85TUDCwhtrfwsj7nUOecCBxGbK858tINU71YDxHOtC8UEttbPPaV4zhUV1djYGAQw4XYUeu6/Yin9PqJICq5HGugENsLb+G9FAHbyXnqtX4nrNtZSmwvOfaKLp6W4jy1uwCVEmpPlgNBECx9ntviKTkft76xGV/76pctsb1GzdP2du35sLdQvjEQCJiuR1ONZYd4bxpy/dQWBNliBPTYXuMalz1icy+84Hz09vXj+LXHFRVPjdhe52vANSqaWlax2N55zc1Y/9wTCAaD4IMSntm3zbwO5H3XHM3m7M5RT/HV8nsk53M2IbgU8TSby9n2o7Wuqtf6s4qMfzz7ODbt2alPe+aL38bK1g7XZcwkZbmD9Pb14cQTjmfCKYPBYDAYDEaFMTY+jvvuewC333E3Xtm4SR99ffiq2asRyvM8fvHj/yqpzbzmRnzqY1dOU48YDAbDPxmLeFopzlNAq3vas8UoZzHWtR2Nhxxf0jJmw3kKAPNWnYpQTTPSI73aBFXFq//4LrLJUSw795ppHYFujeyNlljvlNBy5Jl49ZbvIF+IIBvY9jLSo/0lC9gMBk0lPssx3AlSggFxnjoJcLp7sKjz1F/NUwA29yNgiItW4ZV2x8mKXUAwO8i8xFPiPKVie0uot0mwug9pbLG9ppqn7rG99L5zE0K07ygXbs5/bC+giYQDA4O2fU9EHV/OU2r9xHnq1S6iu5a9nKf+BWzaeZoq7M9SHZtWsaiU2F4SLUzOG1rIKVaz1BCyjPPGS0SbDkRRAK3hz3nnaWGfjo0Oo7WlEd3dPQC0648ehNHW2gpAi0nu7u7BggXzqfNeNO0HL2c5AAxP1nlKiXa0Y9k6YCUcDuOTH/+oaRsVRTGdK/pyisX2utU8pQX0IueAIAhobNSeDVNKtiTx0xq7K+fzyFru4SJUwC321ya+5iAr5kEoEu/e/4ClnIqsKLbfEK/r1vpdVlEwOG5OTWip9ieizwRluYPMm9fsOvqJwWAwGAwGgzGzKIqCJ558CrffcTcef+IpZLNZqKqK1tYWXHrxRbjs0ouxePGi2e4mg8FgzEnSo5Urnla1LDF9Hu/eXlJ7NZ9DcqhL/yyGopAi1R4tygcvSjjm6u9i/Q2fhpIyXA5b7/0lsolRHHbp58B5jISfChOUWxcAYo2TG+0uRarQdNjJhoCtqujc8CCWnPnBqXZx1lAySQhSaNr2PcMZ9iw3dwkE7eKp00t4P9GrgFkAdBPU9NheB+cpHdtLUyy2149oCziLIkTIKsWB5+U8JYIMeffM8zwCkoSsLHs6T/3UiwWKxfZ6vzqvrtF+I0dcYnv9vC+n10EEUU/x1IfwrgvYJTpPM8R56kP0pbHu38k4T/OFY6+UVPOUiKe0iOY/PrUcWPs418VT+tgriqI7T6so1ykAtLe36v/e39mFBQvmI5MlgzXM9wzaramqqm1AXOmxvcR5alzjcgmOY0mSCuKpU2yv7LkMIl5a3ZrKFOKiZYuo7O0cNS9bBg/Zch+UkAcn+Y/9pfcjAEiCh/PU0q9sPoesZV9Y+0gTtLSXczkMZozfroAooiYSdW0/05RFPF134Ttw81//jkQigWi0cjaOwWAwGAwG42Bi48bNuOOuu3HvvQ9gZHQUqqoiFovh4nUX4rJL1mHt2uNmu4sMBoPhi20P3YihnZugpBNYfOYH0XLEGbPdJR2r8zRYXUHiaetS0+ex7h0ltU+N9EGlRr9HGtpntOZQ3aIjcOJnfoP1v/gUMuNGbdldT/wFcnIUR77v6+A9osAmS7mcpwDQdsz5JvfvvhfvxuIzPlAxtZv8oObz6H39GWx/9CYM79yMSEM7TvjUDYjUtcx21w542LPc3IcWKRMTHuKpz5qnpuhZFxGzOh4H4Fzz1HCemtvSwoBTlCYRQYBiNU/t4impayiUENvry3lKiTrBUAhZWfZ0npLIYsBbxKT3BRFE5CJ1Dwk11QXxdGTyNU/pdfhynpLYXq+ap4r/mqf0PKlJiqfW+WtKEU8FEulqjkwGiu9/pwjVUtqXA6u7d+6Lp9r2tM9fjOfXb0IkpG0PGaRBaGhoQDAYRCaTQWenNvCO3G+CAfM9g94n+Xzeto9GhrXrx29sLznf6GOtlOA4liQJqVTKO7bXzXnqINiXun63dertVdW3+CmDQzZrEU85DhznLIBarwk5n7e39ziHHZ2rVuesi/AL2H+LJnI5JKn1N8TiFfXMXJY7yCc/8TE89/x6XHPdJ/Dt//5/WLxoYTkWy2AwGAwGg8EogSve8wEA2ijv0049BZddug7nnH2m5wsPBoPBqERG9r6BvjeeBQC0HHnWLPfGjK3maVXliKfRhg7wYgB5RXthPN69A2o+B84jfovGFtlbPzORvTTVbctx0r/+Ds///BNIUS7Y/S/eCzk5jmOu+g6EQMhjCaWTsDhPpyKeNh92MqRwFeSUNop+vGs7hndtQd3iI6fUx5kgn5PR+fID2P7oHzHRY9SeSg7sx5t3/QTHXPWdWezdwQF7lpv70KIBiZR0EnAihZqnyWI1T33F9hbE0zEH52mhD9boXdEknk6+5ilvcQ1qyyuz89RS8xTQ6p6Oj48jnfbpPPXYBlroICKwHt1ZRASp1sXTEdP0UsRTJ+epl1M2XBDeUx6xvaUI2DwllJD96bV+J6xCk18RDDCENXIOySU4R/VampToTkeIzkRsr9XdW0qt30qEHMuWtvnY8sY2rFym1TatqoqZ5uM4Dq2tLdi1azc6OzsBAFmXGsv0vSCXy9nEUxLbW1dqbC91jdP3sWICtpP4al2O27mjx/Za7ptm52tp10/WIbbXb+yuDA6ybL4Piry7+OjUPps130skj3PY7lzN22umetw/rDVP+yw/P01VM5N445eyXM3BYBB/+P2v8c53fwDnX3AJ2lpbMG/ePPBOB4rjcPMfbyzHahkMBoPBYDAYFIetPBSXXXox1q27AA319bPdHQaDwZg0Uth4QaOkJzzmnHlsNU8ryHnKCyJizYsw1rkVAJCXM0gMdCLW5E8MTFjE08gM1Tu1Em3swMmfuxEv/OKTGKfcs72vPYUXfvkprPnojyCFqzyWUBom5ynHIdLQNullCYEQ2tdchF1P/lWftufZ2ypaPFUySex97g7seOLPSA/3Os7Ts+UJZJNjCETiM9y7gwv2LDf3cXI4Or2EjxaiVxMl1DyVXNyTVXpsr3/nKS1KOYmnppqnXsKjg/OPCJC8z4E7gHdNRGvNU8AQZ7xiezM+Y3tpoaPU2N6aIrG901Hz1M+5U4qAbXKe+li/E9b5a2v9CyAkNpj02RS767fmKeX6KyX2txxYhTo/UcmVDNmePbu24d3vuhx9PdpAtqqY/bmrva0Vu3btxn7iPNWvVWuNZfsABfozuX78OpbJ+ZbP56EoCkRRLCm2N+AlnsrernMy3VovNZfzL95akS3ufxF5cKLzoCmn2N5s1uzAlzxKLdidqzxkm/PUo2apg3N13BL7Gwm4D/iy9r8vb9YPG6oq6zmzLHeQgcFBfPDD12D79h1QVRV79+3H3n37HeetJNstg8FgMBgMxoHEXXf8Y7a7wGAwGGVBDBnlYORMYhZ7Ysda8zRYQTVPASDeulQXTwGt7qlf8TQ52Gn6PBURcaqEqhtx4md+gxd/9a8Y3r1Fnz60YyOe+8nHcPzHf4pgfOrikpwcR3ZiWP8crm2BIE3N5bfgpHeaxNOujQ/jsMs/j0C0ZkrLLTeZiWHsfurv2PXULZCTo57z5pUsujY8iIWnvGuGendwwp7l5j4cx+lRlgTPmqfJpGMNQAItnrqJmLFCCbVkMoV8Pq+7QQEglUoX2prvaxIlSjkJCL5rnjo4RnO68OhfQOAFu4PV2hdaoAsVxNO0V81T6js34RkwCx3EtWjUPfQWEYnYMzpiFq6zpYinlFCRTCWLtgsXzh1v52mh5qkPEYd2npLzdiZje/XI5jxx/fqP3SXHhxZc6X+Xuh2TwXqO+IlKrmQ4joMkiejr6cTiBW3Y/vZbAOyxvQDQ3qY9J5LYXnLN2Zyngtl5SjM2Nq5f934dy/Q9SZZliKKo33cAQCxy3Mk9WVa8Ynudl+E0YAQwi/ZSqbG9Ds5T15qnjrG9JYinjs7TtOc85vWbz28ZHIaoCHFOVVEbini0Ny+7Py8A1M9f44HoPP3u936Abdu2Y+2aY3H1VR/GggXz9VEwDAaDwWAwGAwGg8FglAItnirpyhJPTc5TjkOwqm72OuNAVcsS0+fxru1oOfJMX21tsb2z5DwlBKLVOP6Tv8DLv/sS+t96Xp8+1rkVG276Ck749K+mPEDbXu+0Y0rLA4CqeYtQt+RoDO14BYAmPO578V4sOeMDU152uUj078OzP77GVFuWRgxFUbd4tR6fDWj1W5l4ymAUxyqeOglw0UJsby6XQzYr22J1CXR8rpsQFKHewaZSKUSjxm9od3cPAKC5ucnUhud5cBwHVVWLO089YqOdapUqJQh3BIF3d57KjrG9Wny7Z81Tat95umcdYnvlItGdBL3mqYvz1E/8LS2uE0HUs+Zp2Lvmqaqq+nb4EQ8FSmgh+9NLbHbCup66EmJ7Rd117BTbW8R5SiJUKfGpFAdiObA6TYUZWOd0I4oSZFmBLCsYH9cSYKqq7OJpa1srAGB/IbZXd7pb7hn0PlIs1/gwFXnt97yh7wXZbBbhcNjsWC4muhfObznrEdtbpOZpPp83DVYp5by14hTby7mItzbnJzhk0pbYXU/x1LxcGZzNeerVf+v6M+AxSu3HOHKQPO631vb9Fnmy8UB0nj751LPoaG/HTTf+2nM0EoPBYDAYDAZj+uns6sKLL76M3t4+06hxGo7j8OlPfXyGe8ZgMBj+EEN0bG/liKf5nILMxJD+ORCrBV9hta2qWpeaPo9RsbfFSFidp/Wz5zwliMEw1nz0R9j452+g65WH9OmD2zdgomenTSwulQmLeBqbQr1TmoUnv1MXTwEtunfx6e+vmDSuPc/d7iicBuMNWHz6+7HgpMvB8QIe+o9zkSvUwhrZ8zrGu3eiqmXxTHf3oIM9y81tbPVFHQS4cNgQPJPJZFHxVJJEk6OUhjawJJNJk3hKahG2t9vv55IoIivLU6p5SoQvIoioqqq7yEoREEjpt3zO3Xlqiu0NFWJ7fdY89RID6Zf5JIpTr3nqM7Z3bGxMjw/VluPfeUq71FI+xEvatexEKbUfAbPYR2qelurYtO6nkpynunBujkz20w+yfabY3hJif8uB1WkqeAhXcwVRFNHQOA+79uzX48CdnaeaeNrb2wdFUfT7hu0eaIrtNV/jpN4pANTW1fjqHz0ogVznpYjm3jVPjXuuV1utvaJvKz3ww0+tYRp7bC/AuSzD5hzleGQz5nuB5BGZ7hT7m7A4+MMl1Cwd5CTQR7QaMjjJ/TfD5jyFed4D0nmaTqdwwvFrmHDKqBg2/+2/Z7sLDAZjDvG5W8y1uD90xLEAgOsfv382usNgTJpcLoevfeP/4dbb7oCqqlBV1XVe9sKNwWBUMpXqPM1ODAPUvTVUYZG9ABBvMYun493bfbdNDhjiKccLCNc2l61fU4EXJRz94f+Ckkmi7/Vn9OmjnW9PWTxN9O8zfY6WSTydd8SZCERrkE2MaOvp24PBbS+jYflxZVn+VLGeF9GmBVhy5ofQftyFEKiXXq1HnYN96+/SP+978W6svOSzM9bPgw32LHdgYK8v6l7zFNBEsNraGsdlOUXWWolEjWUlkkk0Fv6tqir27S+Ip2128VSUiHhqd3uanafu73v1uN2CIGIWEEqI7SUCWt6p5qldiAwFC87TjIfzlNoGLwcoLXQQ4Y38v5gIU11tvOgfGxtHXZ3mnJMd+uxGyc7TiOY8TaXsMc2A+Rj4iZA1OU8L+9OPY5aG7q8oiojFoh5zmyHxzkRUM9UsLSJ+Sg4Rqqb41CKxy+XA7jyd27G9gHb8Fy09BJte22Y4T2Mx23xkUEYul0NPT69rjWV6n1jd5Sbx1KfobnKeyiRquwTnqad4SuoFeztPtXmN1AB6WVOO7eXdB9o5xe7Klvug5HEOWuuZyhyHIYtztS7kv2apVfysVRVwgn/nac4yqPCAdJ4esmI5BgYGis/IYDAYDAaDwZg2fvrzG3DLP/6JaDSKyy5dhyWLF5lGnjMYDMZcQaJrnqYmZrEnZtJj5r97K63eKQCEapoghmNQCvst0b8POTlTtI6nnBw31b0M184DL0z/S0e/cLyApkNPMomnY53bgGPfMaXl2mJ7m6Ye2wsAghRAx/GXYMejf9Cn7XnunxUjnk707jZ9PvlzNzrWZO1Yu84knu5/6V4cctEnK85xfaDAnuUODKwxt05CmFXwdMMpsta2LFqITRjLGhwaQqoQ7drh4DzVavqlXJynmmjLcZynEEEcZSQ2tVThjiAURFirK43uS4ByYxLBIpNxdmbT7QBA8nBCSabYXm07iCBTTIShxZ6R0VFDPCXOUx/xt/Q6SBSvl3gZLsT2AlrMbsRSOo8Ww/1EyNLCVrpQI7fkmqfUempra0pKWeAtkc2lOAj1+pPUeUfXsSw1PnUyWGtjigfA76Moiejt2Y81xx2LbW9uAuAc29vW2qr/e39nJzKF+FdrjWVTzVPLYI3BIU08DYVCpnPbC3Nsr3a8k1QNYK+occA4r2TLvS+Xy+mDlvyJp3bHM8/zrikBbmRk830s4BW761jz1OwclTzuvU7i65DlPloXcj8ONucoZ6l3DMXTeRoqcm85IJ2n115zNT71mc/jlY2bcPRRq8uxSAaDwWAwGAxGidxxx92IRiK4645bsHDBgtnuDoPBYEwas/O0csTTjEU8rUTnKcdxiLcsxdDOTQAANZ/DRO9uVLev8GyXGDTXO43Mcr1TJ+Jty0yfx7q2TXmZ9pqn5XGeAsCCEy8ziafdmx9DZmwQwXh92dYxGXLZNJJDXfrnQKzWUTgFgLolRyFS34ZkIdI5MzaI/reeR/Nhp8xEVw862LPcgYG1vqaTAEQ7T1Me4qlTZK3XsmghtnO/kSbQ3mG/pxuuPbv7ijjIgsGgpxBmdZ6aImNLie0ltVMdnKeTrXlK9p0gCLq70QlaDNFje33WPK2uNlxSoyPGAKTSYnuNefSaox7tohbh3Sqe5nIlxvZSEZ8ktrdU0ZEWif26B/X168K5duzNDsIisb3kHHaL7Z0J8dQa21vCoIFKRRRF7N21He+74mLcNeYe29vU1AhJEiHLCjo7u/TBDLbYXuo4Zi1uz56eXgD2usxe0OcbudZ6erT6zoFAwNXJr7d3cZ6aI6PdYnup+4WDeDqZc653dNj0uUYAxjIpxAJB8JxZSHWqWZrMmJ2jVoHT3N78XRY8hrLGdoTUHCIFZ7+f9n0W52kNFMDjup1X7V3XtqnCxNOyhHCvWrUSH7nqw/jwVdfif3/0E7z08gbs39+Jzq4ux/8YDAaDwWAwGOWnp7cXxx13LHvZxmAw5jyVWvM0PWpxnlZXnngKONQ97Soe3ZscMIun0UoUT62RxFMUT1VVRaLPEE85XkCkvtWjRWlEGzvQsGKtsb6cgr2Ui3O2SPTvM8VPx5oXus7LcRw61q4zTdu3/u7p6tpBD3uWOzCwup6cXqbTDisv56lbDCZNxBIBTCCRvRzHobWlxdaOCINW9xVguLmKlWgjQhGJ251sbC+JjvV2ntLiqbaP0541T/1tA+0S02N7ZRLb6y3emVygVHQmEYj8xN9yHGfbV17iKb3OVDJl+552nnqJxgTaeZpKF48NdoKev5R6pwB1DhWOfSnxp4aD0Ghjiu2dgZqn1pheP/u80iH3rEQioZ/LTs5TnufRWnCf7u/sch3sQcc4JxLm5/rubk30bG2Z57t/tAudrLOrq1tbTmtLUeezm3gqy/TAAx/OU5NoT+J+Sz/+3cPmGvSNIoecqiLlMLDFqWbpcMo8iKTOw/FuFVbHICJLVQiohQJO9KhZahVfLeJurSp7tl/Q4C6SS4KAKg/X62xQljvIqaefC47joKoqfnHDb/CLG37jOi/Hcdj21pZyrJbBYDAYDAaDQVFfX6e/SGAwGIy5TKXWPJ0LzlMAiFvqgPqpe0rXOwWASL094nG2kSJVCNe1IDWkvSBLj/YjMzGMYMx7FLsb2cQI5NS4/jlS31r2qOKFJ1+Bga3r9c97n7sdS8+6ElyJkW7lZKJ3l+mzl3gKAO1rLsLW+3+lC669rz6FbGLE1a3KmDzsWe7AwCaeOgg4tHsw6SGeEqHBK76Z1MDU5qfFU21QTHNTk2PdUiJa0QIAwXCe+hRPFeIaLE24sy7HyXmqCzKUQBcsOKMyPmqeFtsGjuN095w1OraYi4w+1nSEcCnOU7IeWnj2rHlKiack5pfGLGD7iO11cJ6WKp6KltjeUrAee1rQKrb/yfc5U2zvTDtPzevws88rHUkUEYvF0dXTr0+LO4inANDW1oo9e/Zi//5Ok2OdJh43HNrjE+ZEme5u7ZmupQTxlD4/dfGUiLCt9oEibu3tztPi555UJLa3mFvaie4Rs/O0WeIREiSkcllExIBJDHaK3R2zxO7WBiXIuZxjfK/NOWqN3VUVcB7bYK2ZaqW6iPhaE4kixgMT9nEyqOGB7oduRMuF/wIp7Hy+zTRluZrXHHcsSogyZzAYDAaDwWBMA+edczbuue9+ZDKZonU+GAwGo5IJROKoal0KMRStmD+egblR8xQAqlrN8bbj3TuKtpkLsb0AEG9dpounADDetR3BSdYRTfTvM30uZ2QvoXnVKQjGG3ThPTnYif6tL6Dp0BPLvi6/WOudxpoWes4fqWtBw7LjMPD2iwCAfE5G54YHsejU90xTDw9e2LPcgYFVrHNyLwYCAQiCgFwuZxI8rUxMaOIp7dyyIggCQqEQ0um0SYjdv19L/2vvcB4Mo8f25jwEy5Kdp8WdW07wXs5Tx9heP85Tf9sAaIKHLCtUbK+/mqe0A44IR8BkxFMBVHNv8bSI8K7kaPGwuIBNz0NigwM+arXS0MJ26eKpduwVxRzb6+TItUL6PquxvZZ1HBDOU0nEoYcfjc7eEX1aVVXMcd72Nu3+0tXVhXxeG2RlvQfSbcfHnZ2nLfNKcZ4ay5cLDnPdeergsre3185X4k7Xl+UjtpcWR82xvYVrfhLnXDcV28upKhoDIkSeB5/nIOdzJreoVbyUwWHIMgAmHghgLJtCVSBkc5paxdN+W+yut3O0ubrGc1tqVW/xlOM4tAcEvJW2/+7UZMfR99TfkD//Ws91zCRluYP89eabyrEYBoPBYDAYDMYU+Ny/fhrPPvc8Pvu5L+Hb//X/UFc3OScOg8FgzDbh2nk4/d//PtvdsDFXnKdVFuepv9hes/M0WoHOU0ATT3tfe0r/PNa5DQ2TFk+t9U47ptQ3J3hBwvwTLsG2B3+nT9vz7G2zK5727TZ9LuY8BYCOtet08RTQonuZeFp+Kv1Z7o03t+H67/2k6HwrD12Gr3/5M6ZpvX39uPX2+/HaG1uRSCRRV1eDtceuxmUXn3/AuW39xPZyHIdIJILx8XGkHNyDhImCS6sq5j2QKBqJOIin2qAYIm5YISKA4hDb6+Ygs6LH7ZbNeZqHqqomp5VzbK//mqd+xFPdhVsQQMg+kYqIiJIk6WmMZH25XE53QpbiPKXxivuNhGnx1MF5qpQWncxTzlN9u6fgPC09tldrS5yneh98iFDkHHaqPQmUvh2TwXqe0/tzriKKEkZHhkz1a51iewGgva0Q27u/Cw0NWk13p3tgLBrFRCKh39MArXxCd6FWaYsP0ZNA15U2nKdGbG8xyPlqvffRIryb8E4nCSiUc5Xc+6wxzsUYT6cwRt3H6iFDlAIAOIQECZmcYhJAnWJ7h5Q8AO2eKagqYoEAQmIAKUUuKp4mOXN/a6Egy/PIZTTx1RqB3FpTB4EDciocqSniPAWA9qDoLJ6qBfduMGL7braY+z5yBoPBYDAYDAYA4Pr//i4WLVqEhx95FM89vx6Hr1qJ1pYWx2hAjuPwve9cPwu9ZDAYjLmLteZpqEJrngYicYSqm5Ae7QMApEd6ISfHIUXcX74nBqzO00oVT631XCdf99QunpbfeQoA80+4DNse+j2gao6q3teeRmq4F+Ha5mlZXzHGrc5TH+LpvCPPgHhLFEpGc4yM7nsTY13bELe4nBlTo9Kf5aqrq3DqSWtcv3/plS1IpdI4dIX5Ot21ex+u/+5PkEqnsWhBBw5ZsQTbd+zBXfc9go1bXsd/fvVzpujZuY6f2F5AEzzHx8c9a56O+3CeAlrd08GhIdOySM3TjnYX8bTQL0W219Uz3J7e4pPhPNXub6bI2BLiyem6o/l83iT6EXcYLYQR8SSTNcdV0mRKEE+Jy4y4cGXZn4DHcRyCwSDS6bQuONOCjJ+ap4A96tNLtKWvFSfhnXYSWyNlnSDOT9P6p1DztK62tEEfuvPUGpnso16pkwhGXL+CIBStfVkOrAL1geA8lUQRr722GcuXGffyeNw9thcAunt6EC6cm07XXFW8ChOJhCm2d2xsTHfeTyW2V1XVkpyn7rG9lPA+6dje0uS2PUP9ps9NqgyIEXAcEOQlJOQs8qoKvnAuW8XPCU5AUjXO8xrI4KUAQoKIUUWGks9BpAT9YrG7NaoChRPAQUUmpyBkuTeJgoDWoIR9afvvBqeqiMM79hcA2sMBYNSeGlADGeB58EXE15mEiacMBoPBYDAYBwi3/fMO/d+JRAIvrH/JdV4mnjIYDEbpWJ2nlRrbCwBVrUt18RQAxrq3o37JUY7z5nMyUsM9+mcpWl1Rcck0VW1msW5q4qk1trf8zlNAi71tXnkSel9/GgCg5nPY+8KdWPGOj07L+rxQ83kk+vbon3kxgEidD5dGIIzWo8/B3ufv0KftW383Drvs89PRzYOWSn+Wa2udh49f9yHH7wYHh/H0cy+B4ziTwJrP5/HTG25CKp3Ge69Yh0suOheA9qL5Rz/9HV7Z/Br+csuduPaq987INswEVsHR7SU8ERqSHrG94xNaXeaqmHNkJoFEuZJl5fN5dHUWYnvbnWPYSb9kL+dpoIjzlIinBeGLjowVShARaAEql8tZxNOpOU+DvmJ7iZBMYnv9CyGBQKAgnmYL6zVEhWLOVX0+y3q8xMtikc85hT4GxYU8J3dqyc5TSuisqakuqa3uOibnkOzf/UqEypyDeDoTkb1O6/Hj9q10yL7v7TOeI2Mu96ClS7W0E0VRsG2blnTi5FiPx+Po7u7B+LghnpI6pYA/xyiBvhdkZRnDwyP6vcCPCOsmnprr5Tqff/TxpueXS3BM0+wZ7DN9bkQWEALgAESlACbkNLKUiGl1kg7A3M9aKICoOeLDBfdpVcA4J+uKpBjUQIEqSoiIASTkLIKCaBuE0B4JOYqn1VAgAEhxHLJyBlHJ+fejPRwCMG6bXqsq4B3crrNJWe4iL774cknzr1lzbDlWy2AwGAwGg8Gg+Muffj/bXWAwGIwDFlVVkRkb1D9L4SoILi8FKoF461L0v/mc/nm8y108TQ316K5IAIhWaL1TAIg1zgcvBZGXtRf74z07kc8p4EuorUdI9M2M8xQAFpz8Tl08BYC9z9+BZed+ZFL9ngrp0T7ksobYEG3sAOczYrBj7TqTeLr/5ftx6MWfBi9MfyziwcJcfpZ7+rkXoaoqDlmxFE2NxsCSl1/Zgu6ePnS0t+DiC8/Rp4uiiGuvfi8+/YVv4Imnn8d7rliHqiLuyrmCVXB0E3GiEU3w9HKekohLN+HCbVl9ff26e7S9vdWxjVt0JTCJmqcF4StP1SwtRUSi3Y/5vLnuqezggiVRz5ly1TwVjH2Rz+f1PvgR4Eh9R7I+WpDxHdsr+RdPOY5DOBzGxMQEUimnmqe083RmxFNTzdOSY3vJOaTtc0O49iOe2gcAlBL7Ww7sNU/nvldNlEQIooiJgji/8tBDXK/nVYetxLKlS7Bt+w6oaqHmqcM1Fy/E/tLiaTclnpbiPBUEQR9AkM1m9cheAGjzIcL6cZ66OZ9Nsb3U/KXcb2j2DJqdp81qVnNuchzqwjEMZ5KaeFoQSa3O03HO/LlGVYCCczMsShhMZ5FT8xA47R5bFQqjNiBhOGsXPwGgVpUBQUKAF5HksraaqwDQEQnj+SG7+FkNbX9keQFCLoeAkIPk8IzZEXVOmqiGAiFQWSkUZbma3/fBq0pShLdvfbUcq2UwGAwGg8FgUKxdO7mabwwGg1GJjHfvRGZiGEp6AjXzVyJU3Tir/ZGTo8jnjBcNlew6Bex1T8e7d7jOO7r/LdPnSH3liqccL6CqZQlG974BAMjLGST696Fq3qKSlqOqqsl5ygmiLwfmZGk69ESEa+fpDt/0SC/63ngW8w4/bdrW6cTEJCJ7CbWLjkS0cb4ed5wdH0LfG8/N+DYcyMzlZ7mnn9Vq4lpjfV/Z9BoAYO1xR9neHdbWVGPF8iV4481t2Lj5NZx60tqZ6ew046fmKaBF7QIw1Sm1MlFCbK+2LC3Gdd9+I4q9w8V5SpyvKYe6mcRFWbTmqc15WppwR6DrRNLRv4CzKBEkztOMu/OUuGd9iadESM4pemQv4E9EJPuIrG9S4mkJzlNAi+6dmJhA0iG211Tz1IeQxzvEK/uNGybQ/a0tsVYzWX9Oj+0txDT7qnmqzaOqqu5YLiX2txxYRcUDwnkqijj6uJMBAC89/wQuuOB813k5jsP73vtufOu/vqNPc7rmSOwvHdtLxNNYNKqLq777KEkF8VTWI3sBfyIsOb+zVvGUvnb9xPZS94pMwflK7k1+2W0RT5uQBcSC81QMQuR4JKkBJVbx1EotZKiCBA4c6sMxJJQMMoqCiGQck/mxKIaHRhzb10ABpCDAQXeuWsXT+VXOv0e1hZqlkEIIixJSchZS0C6G1ofDCKk5pB3qrfLBykq+Kctd5PJLLwYcxFNVzaOrqxuvvf4mEokEzjn7TNfiwgwGg8FgMBgMBoPBYBA2/vkbGN33JgDg2Gu+j5Yjz5zV/ljrnQbj9bPUE3/EWyy1Qbu3O86XzynYev+vTdOswmulEW9dpoungBbdW6p4mhkf1Ot3Aprb1q8DczJwvID5J16Grff+Up+259nb5pR4ynEcOtauw1v3/Fyftm/93Uw8ZWD7jt3o6ulDMBDA8ceZHe6792p1NxctcI7FXrSgA2+8uQ179nYCJ017V2eEQNAsHLgJYUTwdBIvCX6dp1YhtrMQ2SsIAubNc66vTKKAaTGDYAiW3iIaX3CMEqemOTK2lNhe2nmq6v8mTlDAvB9DBcEy7ek81YSQYgIwQNd/VaAohoDiN7ZXW9/knadWoaaYeKkfb4fYXjo62Y+AzXGc7uLT25XonqTnrynReUra5iyxvX7ET3r/KoomnurtZ8t5egCIp6IoIjNhDEy44B3nes5/2aXr8L3v/68R9+0S2wtYnKc9mnhaiuuUQOKyZdkQT+tqaxEOF3cu0tc7jTm218V5Sjmi6fnThW0nrni/2GJ7VRmqqImfYVGCwAvIq3moqgqO4xCSAggJAtKWQSaEGlWrOcoBiAfDECcEZPPm7eyIV2Gzh3jKBYIQOB5RMYiknLHVTW2viru2BQBOCiIoSEjKWeTyeVv9a14KoA0Z7EDE0ncZvFSa+DzdlOUu8v3/+bbn90NDw/jyV76GXbt249Zb/lKOVTIYjAOY53e+VXwmBoPBYHjy8oZXsP7Fl9Hb2wsAaG5uxto1x+LYY46e5Z4xGAyGP8SQMapZSdtf7M401nqnoQp3nsbmLQI4Xo/jHe/arr94odn7/B2Y6NmpfxYCIXSsXTejfS2VeKtFGO7ahrajvV/sWbHXO52+yF7C/OMvwdv3/xpqXnvh1ffmc0gOdiFS7xypOR3YxNOmhSW1bz/uQrx17y+AQjRf72tPITM+jGBVaU4jRnHm0rPcUwXX6XHHHIlw2Pzic2BwCABQX1fj2LautqYw33DR9Xzj+h86Tn/nujPQ0tyAof5ux++nChHT/C5fzZndTKnEmGNbUo5yeHjIddlEaOBUxXP9ZFkjhWW9vVV7r9Lc1Iix4X7HNpKovdAeGhq0LXt8bFRbL1TP9WZS2iAUOZvFUH83hgZ69e8mRgcx1O9PSEiMj+r/HuzrgpzWzDd0TdNsOqH3Rc5oomEul0Nf93699qXzNuSLHkMOaqHNCPp7OvXpbseORiwIv6Mj2r7v7+0y2k+M+jpvrJ4kOZPybBcoiD9Dg/0Y7Osy/bbTx2B8dBBDIT/RvbxJPFWy6ZKuJ3IeAABypbXNprXBA9lsBkP93Rgb1e4FPGc/XtbjmJowzpv+nv2IRMIYGx3St2m67gmmPslmAT/p45ypdPJ5BVteeQEAsHzZEsQjgaLbdM5Zp+Ge+x4CAChZ+/kbKNxvxsYnoCgyhvq7sXuX9vzZUF9b8j4j193I0AB6ejUBsqnJ3+9ArnDMUqmkaX7T/WtsCEP9dulsImGc68ODvXr7sRH389aL3f09ps9NyKJWCqMpD4wM9qI5z6FOjEDIk2ucw6J4HG8OO/9m1kJBczCOxjyH/Ngo2jgReY6HQGmty6uqAeyztY2qOQSgYkGoBrGcCpHjUC1GgDzAG2NasCJWBV5VkbfcuDTxM4hlUhRCHmiQ7G0BQOUDaFUz2MFZxFMoEMTi59pkqWssPWFmRoZg1NXV4oc/+C7OPOsd+N8f/QTf/MZXZ2K1DAaDwWAwGAcdu/fswec+/2W8+trrAKDXHSF/UB++6jD8+H//BwsWTP9LYgaDwZgKtHgqpxMec84MaYt4GqyubPFUkIKINnYg0bcHACCnxpEe7Ue4pkmfR06NY+t9N5jaLTnrStM8lUi8bbnp83jntpKXYa936uyKKyeh6kbMO/x0dG9+VJugqtjz/O049KJPTvu6CRN9u02fY82lOXbDtc1oXLEW/W9pL1XVfA6dG+7H4tPfX64uHvTMtWc5RVHw/PoNAIBTT15j+544A93cf8SlQ4tkcx2rW9PNwUQcUimH6FVAc9KR/VI0trewLBLj2llwYrV6OLpiUW2ZiYT9N5bEWRZzQBJHUY44TycZ20s7k+iap9mss4uTPp8ymQxE0fwSXmubtbVzg3Y/muoeOoiyVsjxJlHHZuepv1fv1qjXYq5LMkhh86uv46LL34+jjzwC1//nVwCYY3v9HgMtNply3JYY27t40QIAWr3JUuNXietYd54q/p2j9Dyknd5+hmqKW/tpddnNReh9d85Z/tIlLr/kIl089YrtnaCc7kT0bG4qvTSHVLjuZEVGT2GQ0bxmf8+v5J5grfes+HKe0uecca35jTqnyeYUdI2N6J9jqoII8uDFADhw4ADwHAdwHFSohSnAoppaV/G0Bgr4gHZ/4DhtKRbtEotd3OE1hXsAHwgV1s2DA5C3zBcMBNGMLLph3tZaKLpzlOM48ACUfF7bBgpOlNCqZm3rjyCv971SmLEKxvGqKhxxxOF46OFHmXjKYDAYDAaDMQ0MDA7i/R+4Gr19faipqcb5556Djg6txtG+/Z148MGHseXV1/D+D16NO++4BQ31lR05yWAwDm6kkBFRqFSAeJoZnVvOU0BzaBLxFNDcp7Qwuu2h3yM7Ybx8CVU3YcmZH5rRPk4GJ+dpqZC6nYSZcJ4CwIKTLjfEUwA7H78Z1e2HoHX1WTOyfnts74KSl9Gxdp0ungJadC8TT8vDXHyW27DxNUwkkqirq8Fhhy4v3mAKfOvrX3CcTlwqk3GV+KHU5dfUmX8f6hqaHNvWFebLKjnH70dHDVfdvNYOz/XX1ReWJWvLIk7eRYsXu7ZraNR+D9IZ2TaPqmovu6uqazzXG6+pA6BF7dY1tiBSZTiG6ptaUdfoT8yoqTNqtFbVNqCuQdueHPXqur6hWe9LY7PRp3BVDeqcrgVOE7Gq4tV61KbbtpA6haIUQqzaWFYdtU43IhHteYUXJdQ1tiDcO6R/19DU6uu8CYXMUaM1tQ3e+z1eDQDYtFmrKXz/Q4/iZz/7P3Ach0iV8bvf0NyGOh81SK1CUU1tXUnXU11jCx556F7U1daUHNsbq9K2BRyPusYWiAXxJBgM2fpgvRZrG4zzq6qmAXX1dZAC2r4Mhuztp4NozCwW19Q3zsh6pxN6m955xRW+tue0M+bhogvfgVdfex1nnnW27dpvmqctY2IiAbFwrQwMkPvUkpL3GblmRCmEgcERAMDChYt8LSderV0TucJ9ixCO7tb/3djchtpCMgINLbCGIlV6+3xB2IzHq31vy87+XuRVQ9psKoiXQ6KAsMijo7EFXRMj2D/SB6jQ65bOq60Ddu10XGatKqNH4MBLAjoaW9A72IWuiQTiQkgX9uvr6hzb1hRqlu4VOLQFJCyM12P3QCcm5DRqgsYAlVRAc452c2bxtAYK8oEodubSaI/UQuQF7B0bQpAXEaQE+YQkoR7mhAYAmmAbjlXU9TNj4ilhcGio+EwMBoPBYDAYjJL55Q2/QW9fH951xeX4xtf+Xa+FQ/iPr3wJ1//3d3HLP/6JG371W3ztq1+epZ4yGAxGccyxvbMvnqbHB02fg3NAPK1qWYruTYZQN969HU0rTwQAJAb2Y9cT5rI6h6z7JMRg8VpRs00gWoNQdRPSo5pjITXcg2xyDIGIcw0mJ2YjthcAGpav0RzBhfXn5Qw2/P7LSF3yWSw+44O2WOVyoqQTSI8a8Z2hmmaIQbtbqxjzDj8dYjgGJaW5R8Y638bo/q2obl9Rtr4erMzFZ7mnn9Mie0858TjwDo6rUCiIRCKp18KzQpypoVBluU2mQjBgfqHs5mCKRN3rVgLm2oBVxZynlpqnpAZgW6v7S2hSR3ViSjVPNWdjvhBHTrseBR+uTWM5lPM05+w8pd1soaBxvmRc6p4a22B3wVkhTjtZkU3iiLUWqRPBQo1bR+dpkf2nr99a87RIO+u9AdBEHUmSzM5Tn8eArjkL+K/VSrN40cKS2wDGOUT6rTtHfbh2BcHDeTpTNU8tDteZcrxOJ7FYFEcdexJEScTCBf4GWXEch5/8+Aeu38cLdTLHJ7Rn+nw+j56p1DwtnKPZbBZd3QWnvcf9joacG5NxntIucXp+w3la/H5D2DXYa/rcVHBjclJIfx7U6p7yyBZqGedVFR117gOnaqCAk0LgC4+TIUGCyPFQ1DwEFAaURGOoVmWMcubrvLZQsxRSEDzHISRKEDgeubxqKv3BCRJakcEG67pVBVxA6zvHcQiLAYg8ryUTULciTgxgoWpOXCCfK815OmM+8l279+Cll1+e1MXAYDAYDAaDwSjOY48/ifa2Nvz39d90/IM6Eongv771TbS3teHRx56Y+Q4yGAxGCVSaeDrXap4CDg7N7u36v9+866fIU3X5qjsORfuxF8xY36ZKvG2Z6fN413aXOZ2xOk9jTTMjnnI8j1VXfBkcT71FUlW8cceP8eo/vot8TnFvPEUmKBcyAMSaF05qOUIghLajzzNN27f+rsl2i0Ex157lxsbGsWnLGwCAU09a6zhPQ73mcBkcGnH8fmh4pDDfgVM31xrb6CbARfXYXBfxlBI1idDpRrQgrhIhluzXeg93MlkmLdISiNhdLIJSj+0tCJ7KZGN7qXlzeWMZWdlZPA2GjH65RT4TMaOYEAmYxRTZJKAUb0v2EdlnskvUsOf6LUJhsXbRqLN4CpiPgTUO2A2BN883GfF0shjRzwXxVNa2Q/Kx7yWH2F5y/PwI3+XAOkiglEEDlcqVH/oAAsFgWc8DEtubTqchyzIGh4b067ulpXSnIbkfTCQS6OvTBob5FU/JdtEDHQAgRw+ccBHvOY7TvzOLp9p9KBj0L/7tHuwzfW5CQTwNhPSoWyJgKoU487FsCvVx58GCQTWPMPKF9tp1RcRXEq2u5HPI8wLaHGJzayEDggReEMCBA8/xCIkSOA4mhyxECW2qfdBKDWRwknY/5MEb61bNwb+cKKEZMtbmtXQFXlWxLq/9ncUHKmsQZ1nuIv+8/U7X7yYSCezauRu333k3Uqk03nnZJeVYJYPBYDAYDAbDQk9PL8495yzPP5IFQcDq1UfgoYcfdZ2HwWAwKgFaPM0mRmavIwXSltjeSq95CmjOU5qxgsA4tHMTujc9YvrusMs+D24O1emKty5D3xvP6p/HurahfunRvtqq+TwmKPGUl4IIVc9cndemQ0/A8Z/4GV763Zd09yYA7HnmVqSGunHM1d+dlvXaInubSo/sJXSsXYc9z96mf97/0v04ZN2nIFbYS6+5xlx7lntu/QbkcjksW7IQrS3NjvMsnN+GPXv3Y9eefTh69Srb97v2aC7sBfPbprWvM4m95qmzABErCGBONUcBsyO0qHhaENsTySRkWcb4+DgAOMZOGsuM6uuhXUWAf9emUStUMf0fMByFfjDVPDU5T40X/PR+pZ3K6SLOUz81CIl4mVNyUGTZNt0Lso/I+kyCr0/xySr0FROtzjn7LDz00KM4/PDD8PwLmvtbVhSEYT4Gft2XVsFvRsXTwrrJcZcV2Xcf6OMza85T0eo8nfvi6ZIli/HH3/xvWZdJxFNAG7CRzI7pnydjtiODTx6jBhK1+hRh9XqpFvFU9uE81b6TIMsKZNmY30hR8F/zdNeARTxV7eJpUJAg8jzyah5KPgdVBRqraxFU88hw5uf2GshaeHAgpNdHDYkBCDyPTE6BqqoYy6SRz6toQxpvwJxoUKMq4IKhgnBqOF9FTkCOcq5qNUvt9904cpprttA+LAYgcjySeUvVVEHb/x/Pd+Kc/BCqkMO8gnB8QIqnX/ryf3hGy5Af4He/63J84uMfLccqGQwGg8FgMBgWQqEgRkZGis43MjJS0kM9g8FgzAbRhg7932Odpde0LDdz0XkabWgDLwWRl7UXHBM9u5DPyXj9n+YXYvOOPNO38FgpWJ2npdQ9TY/26/sEAKIN7TMuHDcsX4OT//X3WP+rzyA1ZNQI7HvjWTz7f9di+RVfRbCqvPUs7fVOF056WTULViHWvAgTvbsAAHJyFJ0v348FJ14+hR4y5tqz3FPPFCJ7XVynAHD06lV48pn1WP/SRlx+8fmm94fDI6PY+vYOCAKP1UccNu39nSmsYp2bAEde/o87xOYCWm1AQqyE2F76HPKqd1lVpYkZ+XweqVTK5HbOEOGxiHjKW52nyuScp7TQmqOck7R4SkfgRqm+uonPpcT2EvFSVpRJxPYS56lDbK9f52mJ4ul5556Nc84+Ey+sf1EXT4ljkz4Gc8N5qq1byZUufooOEapkP/gRvsuB9TwXZki0nW7KfQ6Q2F5Ai+7tGzRqOrfMcx5848W733U5XnzpZezZa5Rh8Os8JYMaaJc4YHaSel07hlPdaO/XrU+z2xbbW1ieZIinPMchJEjgOR5JOYugICITCKAVGeyCWWgksbt8IGgRPzUBM5tTIPICsoKIVgfnKYn8Jesl7QVec75KvIqxrOawbYG5Paeq4AFwgSA4aA7doCBC5AXk1bw59rcwoIgDsAzm+N5Ki+0ty9X86U993FU8lSQJzU2NOH7tGrS1tZZjdQwGg8FgMBgMB1auPBTr17+Et7a+jUNWLHec562tb2P9+pdw7HHHzHDvGAwGozRqFhgv0hP9e5FNjCIQrZ61/qQp8ZSXghBD3i6gSoDjBVTNW4zRfW8CAPJKFm8/8FuM7H3dmEcQsfLiz8xWFydNvNUinna+7butNbJ3puqdWqlqWYyTP38TXvz15zC69w19+tj+rdhy4xew8r3/ibrG0qPs3CineMpxHBad+m68+o/v6dN2Pfk3zD/hsmmt23qgM5ee5fZ1dmPXnn2QRBEnrHUffHHMUYejZV4T9u3vxl33PoxLLjoXgPaS+rc3/Q25XB5nnX4S4lWVf0/1S6mxvdlsFrIs28QK4jzled4xxtm0rML3uVwOPb2Gm6m21l08pd2sExMJ0zr8Co+CXvNUezmez9PCnf/XzrTzNEe5lGSX2N4q6nwhLlsrRmxvcfGU9NUe21tCzdNsxtZn/+KpeT4/7XieN7Ujwk9uMrG9Fuepn6jjcmF1npYiftLHR5612F7zeoQ5lOIxk5icpxMTel3m2toahMOluw0vu/RiPPb4k7jv/gcBaDG7DQ3+Bp2R6ysrO4unoih6PsvQgy0I6YJ4GipBPLU6Txv12N6g7hwFgLAYgMDxyOYVxAIhZIUA2tQMdnHm/Vajav3hpDDlXDUEzHROQUiUIIsBx9jdWlXR1007V8XCurP5HFRVhQIeAag4Mz+Ex3gtmv+KfGFbpBDAaeIrV6ibynM8cqoKURdP3e/JB6Tz9F8/88lyLIbBYDAYDAaDMQU++P734oUXXsQHP/QRfPzj1+GSiy9CQ6HO0cDgIO64827ccMNvoeRy+OD73zvLvWUwGAxvwrXzEKyqR2Z8EAAwsvd1NB164qz0RckkkcsYNelC8YY5IxDFW5fq4ikAbHvod6bvF532XkQbO6zNKp5o03zwgqTXbR3v3gE1nzPXEnUh0b/P9Hm2xFNAO5dO/PSv8cof/wO9rz6pT8+ODeDVm76E4DXfR9OhJ5RlXRN9u02fY00Lp7S89jUX4a17fgE5pQkX4907MPD2i2hc4e5CZHgzl57lnnpmPQDg6KMO1+NnnRAEAZ/6lytx/Xd/gr/dejfWv7wJ85oasW3HbgwMDqGjvQXvf/eBVeKLiGkENwGOiKeA5p6sqakxfU/E01gsVvQ3J0Idg/37O/V/13nE9lZRbtaJiQk0NTXqn33XPLU4Rk3O0xJqP3Km2F5n5yktgoZCIYiiCEVRXJ27JTlPqRqG8hRje03tfQp41vqKk4n7JXG3RAASBMH3s0olOE9JzdNSxE+R6meucO4ZAtjMbMOB6jy9/rs/AQB8/d/LM8Cuutpwnk6MT6C7pweA/6hdKxzH4b+v/yY2btyM7p4etMxr0Z3wxaBrHNOQGN5i1y25Lyim2F7NkUlHihfjgc98A2/1deL+P/83uns7UaM7Rw3xEyjUPeV5SBC06S41R2shAxwPCIb4y3EcwgUBU8nnEODD4EQJbXCrWRoCxzk4T5U8VGji63jh2vpwvgfHquOQ1DyWFxyknBS0xP4GIPI8cvkceI7DeDZl2jYrQoU5T9lQCAaDwWAwGIwDhHecfy6u/PAHMDwygu989wc4/sTTserIY7HqyGNx/Imn47vf+yGGR0Zw1ZUfxPnnnTPb3WUwGAxPOI5DzYJV5INN8JpJ5mK9U0JVyxLzBFXV/ylFq7H83GtnuEflgRckxFoW659z2TQSA50eLQzsztPZFY/FYBjHXfN9LDrtfabpuWwKG278ssn1PFnUfA6JPmO7hUAYoZqp1XkVgxHMP+FS07RdT/x1Sss82Jkrz3L5fB7PPv8yAODUk9YUnX/xwvn4zv/7Mk464VgMDY3gpVe2gOd5rLvgbHzra19AJFJZTpOp4je2t4pyfiYSSdv34+NEPPWO7AVgco12dnbp/66udk9soJ2nVvdmqc5TQIvupV2PfoUM23LyLjVPKaGM4zjdfTrm4jzNyqTmaXHxlBZTaEHEV2xvgMT2mp2nAUnyLV6WGturt6Nrfspm56lf1ylgd57OlPAIALxQiH62iZ+lxvbKpv+zmqdT4+3tu/D29l1lWx49CIR2nk6m3imhuroaP//pj7Bq1WH42Ec/4rsdub7yefM9y++54yS+Eqd7KbG9VaEwVrXOxxpJxgXqoC7UcVRsL6CJpyLHIyhImjDJi2jjZNvyalSlUC+VBw+6vSZgBgUJHAdADCCGnK19NRSgELvrVHNVzuUQFCS9ZikH4DA1oQunALR6qxz09YdFCQLHQ1HzyORk5FVA9rgv8sHKeh44MIZCMBgMBoPBYDAAAN/42ldwwvFr8fub/oiNm7YgldJGQAYCARx91JG46soP4Zyzz5zlXjIYDIY/lp59JRaf/l5UdxwKKVxVvME0MRfrnRKqWpa6frfiHf8CKTJ7+3WqxFuXYWz/Vv3zWNc2xJqKu0gnLOJprGlB2ftWKhwvYNU7v4hIQxte/+cPdZFbySTQtfFhLLYIq6WSHOzSXbqAFtlbDvf0wlPfgx2P3wyomtjR+8YzmOjb6+s4MJyZC89yPM/jFz/+r5LazGtuxKc+duU09aiy8B/bawieEw51O2nnaTHoGqD7Ozv1dl7ip0k8nTCv32/krVk8VXzHXtqW4+I8pWsSWvsSr6rC8PCILjJbIWKmv9jeQt1NW2xvcRGRRNySfUaiQKUSom+tgpvftrTIo9f8LIiQpTh/rVGzM+k8FQuRybrzVPYvfjrF9mYL54zVzTtdWPtZimhdyfz3N79U1uXxPI9YLIbx8XGMj09g3/79AID29rYpLXf16iNw1+23lNSGHoghy7J+zHTXc5Fzxym2N6M7T0uvR64W6ogaKzCLp2FREzAFjkd1MIyxbAptQh5Qzc1qoYmntHOUtBc4HhIvgAcHrrD9l+b6cIegDaQ7JT8MESjUPOVMNVfDYgA8x0PkeXCcUbPUCU4KAuD0+3+o0Pe0IkOGipAoISl4xPZKleU8LdtdRJZl3HPv/Xhh/Uvo6+szjQwywXG4+Y83lmu1DAaDwWAwGAwL55x9Js45+0woioLhkREAQG1NzYyNvmUwGIxyUbf4yNnuAgDYnH9zSTyNtzqLp7HmRVhw0uUz3JvyYq17Ot61DVh9VtF2tAMTmH3nKc3i094HDhxeu+37+jRaIJ4stsjeKdQ7pYnUtaDliDPQvflRbYKqYvdTf8OqK/6tLMs/WGHPcnObYMAa2+v8ojkaM8f2WpkoCJpVfpynDrG9dR71TgGzo3XCEn1L3usWc20KAlWrlHKeliog8S7O0wz1ftkq6FVVaYN/3GqeTi62N2dyk/mreRo0rU/WxbsSxFNpcs5TyUE8zOVIbG8JNWct2+k3NrgckHMoV6h5SqJT/ewDk/NWF4/9O1fLgfVcP1Du0/M7Wsu+zHi8CuPj4xgbn8C+fZp4umD+zA+2ogcnZGVZj9pV/Mb2Fu7pikPN02CwdPHPKp7ywZCp5mlIkNAUiSMkSsjmFHDgUC9wgDl1GHEogKQNiuEsztWAICIkSAhLEoYzIsDxuFQdwPJcCllwOFLVfgOI+GpqLxjOVwCAh/gJKWRyrpJ6rXI+Bw4cAgEBSY/9e0A6TwcGB/HBD1+D7dt3QFVVz3nnSl0YBoPBYDAYjLmOKIpobJg7L/gZDAajUslYY3vnkHgajDdAilRDTo6apq+89F/Bl/BitRKJt5nF09HOt4u2UfM5JAf265+FYKTijmejpcbp6L63przMid7dps/lEk8BYNHp7zPEUwB719+NFRd+fFbd4gcK7FlubmKL7XV5URyLuouXAPRanqU7T7XY3lqPeqeAJk6FQiGk02nT+hVFQb4gYBYTHnmqVmY+n5uU6xEwOx9zppqnmhApCIJNpCKxvW7OU9KWxOp6QYshJLoT8OdeJMfbGttbknhajtheIh7m5pbzlJxD5LiXIn5KJudt6e3LgTXy+EBxnk4H8aoqdALo7unByIj2XNrR0T7j/aDPb9rd7vfcMWqeGnWGSVs/MeFWVJkWTzlwYsDkHOU4DgsLJUP2jw9rzlJJAi+ryFPz1amyJn5SzlEAiAdCaAjHUBOMYCSTJBsBTs7gMNUycEcqtIfZuSoJgl471ct5CkmL/SUaYIAXIBWu8aCoxQ6T2F8nhMABKJ5+73/+F9u2bccRh6/CdddejSVLFpseABgMBoPBYDAY009XdzdeeOFFrF59JBYvWug4z86du7Bp8xaccPzaKdUXYTAYjIMJm/N0DtU85TgO8dalGNy+QZ/WeMjxaFp50iz2qjzYnafbi7ZJDfea4mujDR0VN8g72tABPhBGPqvVkBrv2YmcnIEglR4FR7CJp2WMKq5bvBrx9hW6QzaXSWLfC3dj8RnvL9s6DhbYs9yBgVVwdBPg6DqlExNOzlP/4mk4bLxw7izE9tYWcZ5qy44WxFNj/aY6o0XEU1qgU5TcpFyP2vyUCJuz1zx16oeX8zSXMxykAR8RuEZsr6w7HwF/AhzpGxFrJyOeShYxwq/zk26n1zxVSnf/8pOMDS4H5BwyxFP/sb30eUaOt9/o1XJhjeW2iqlzld/e9DcAwLVXvbdsy4zH4wCAt7Yaz2vzZ0E8dYq7Bqhzp0hcN7m2yfxk4AQA3cVaCibxNBDUapa6PJtq0zWB9ep8F34naLHHK/MJNEMGJwVNNUe1Nvz/Z++s45u4/zD+3MXrSoFSoTgt0NIWGe6uwx3GYBtzYc5v7gwGM8aGbMAYwxkyYIJsuA53t0LdY78/klzuIs0lTZPK5/167TXu8v3efe4iTe655/mgjrHPfXZxARjjfL26yGr7jPG7Jv+7sZ9cCYVEhjCVH7KLC8FIpDB0PLU2UTJyJcCYxVeGYaCSySEpYqGUSMGAgZ61/x5h5eUrtld85+4S2LFzFyKqVcOSH39A71490KB+PURG1rT7H0EQBEEQBOF+Fi76CdNfeaPEi8AMy+Kll1/H4p+WerAygiCI0lOUk4Gcu5e9s+/sius8BYBqjR7i/s2wEjQe+Gy5EwxdQeEfAkVAKLec/+Am1AW2HUgm8tLKb2SvCYZl4RdRm1vW67TIuX2xVNssS+cpwzBWPVkv71wOvU5rZwZhD/ouVzkQ6zyVSqXchfa8vHyrx02Cpp+I2F65XM4JbqZthYQEOZxnEmb5AqQz4qnE0nlqcj06G9vLd57yPjtMQqQtAdTfRu0m+MdgGaNsCy62VyuM7RUjQJqcZqVynroY2yvs+Wl0wRkFbKkTArZVz1WPOk9Nsb3Gnqcm0VtEDTKB89Z4/E7E/roDyxsFJCUIQxWJHbv3YcfufW7dZkCA4YaHCxcucetK2/PUFSx7nprgnKcie56axhcW8sVT52500+v1gtheRmYUH0sQTxkAkMjQXp+FNzSX8aT2Op7XXTPP5/UctYQBA4YBYO/zQaawcq76y5WID6uJSL9gw/4Zxr771Og8tey5qpDI4Gt8TF+COM1WRudpTk4uOnfqAF9ymxIEQRAEQXiNXbv/RYP69VA71r6bpHZsDBo0qIddu/7BK9Nf8GB1QvLzC7Bxy584cPg47qXdB8MwCAkKRIP6dTBkUG+EWMSM3b2XhpVrNuPEqbPIy8tHSEgQWqYkYlD/nk7/QCEIomJx79S/OL7iQxSk30JInSS0eeZ7j9dg6TytaOJpbIfhKMrLQO6dy4htP9zKsVmRCahZD2nZD7jlnNsXS+yVayWeVvN8ry0x+Naog+zrp7jlrBtnEBTd2OXtCcRThoFvuHuPu2bzHji1fg6Kc9IBGITsuyd2oXrTjm7dT2WnIn2XI+xjGdtYkgDn62twftrqeZqTaxAFTQ5LR/j4+qA40xzRHhQU5HCOSTwVOk/NYoJD8dTSeWp0PbIS5/w6pXOeWt80IxCAFWJie00xnOb4TblMJupGI67nqVE8LTYJvp6I7bXhoDOJh844ICUWz5dne55aOE/V4gQswPL4vRPbaxmP7GxkdXnlyanj3b7NAON71tTLuHpEhEtOzdLCf38V88VTtTjXs9RCPC0qNounljfPOEKnLgJ4bTAZY89Qfs9TPibx1CRe1kOB0AAqV1qJl9bzDeKnzeabMiXAWM9XSQ2fwSbxlZXKodUU25xvKb76yZRQSmQIU/njdl4mmBJie8ub89QtnyIx0VHILyhwx6YccvnKdfx36iwuXbqKi5ev4f4DwxfzOZ++hfDwULvz6IIbQRAEQRCVnVu3bqNtm9YOx8XGxGDPXvfeReoMN27exgeffoWMzCxUjwhHYpPG0Gi1uHM3DX/t3IP2bVsKxNPLV67j3Y/moKCwELVjotCwQR1cuHgV6zdtx5HjJ/HWa8/Bx6d83aFIEIT7kPn4oyDd0L8t6/pp6LQaj/fqtHSeKiuYeCqVqxA/8Dlvl1EmBNSsh7Qze7nl7FvnSxRPc9OuC5b9yqHzFAB8q9cRLJem72lRbgaK8zK5ZZ+QmqWKALaFRCZHbJshOLflO27dpR0/k3jqJBXluxxRMnyhTyaTlijA+fn54sGDBzbFU855KtKs4uPjw/URBMTF9vobXa38nqcC4dGBiCZ0nuo416gzrkdAKLbynaemWmyJieaep6V3nkq52F4NF9srRrwDeLG9ajX0er2Lsb2uiacC56UpttcF96+le9KTzlOBcK7TOSV+8udy4rHRgeooetVdWN4cUVl6nrZMTXT7NgMCAwTL3uh3Clj0POWJp+bYXnE9T02fFQLnqZPiqbZYqKkxxh7N9sRPBgzAMIATzk8+LMManae2PxcZucF5WrJ4a995ysgVMJTI68Wq9IWPVA6lVIY7+VmGxyQygNdGg9t+ZXSeDhv6MGbOmoPbt++Ueb+F1es24+CR/5yaQxfcCIIgCIKoCuhExuMxDIOiIht3CXqAvLx8fPjZ18jNzcOTU8ejTesUweN376VBxbv7VKfTYe63i1BQWIgRQ/phQN/uAAw/jmfN/QGHj53AshXr3NqLhSCI8kVAZAMwEin0Wg20xYXIuXMJgZH1PVpDYZZZPGVYCeS+QR7dP2EffwsXbfbNcyWOt47tdV/vT3fiZyme3nBdPM27d1W4bTdG9vKJafswzm9bAL0xMvLB+YPIvnkeAZGVx+lc1lSE73KEY6RSKaRSKTQaDaQOBByTMJprUzwV3/MUAHx5PVQBWCW52IJzb9oTTx30vhSInlqz89TZvo98EVbLc56aY3ttOE8D7DtP+e8PR+5ZAJAaxRSNVsMT78SJb3yncXFxsWuxvRZijZg+rZbzTMIPF53shHho6Tz1lniq0WhEC1iA4bPQ9F6z7HkqVvwuLZbnylOO14qIqeepCW/0OwWEPX01vB7HYoV7y9jeokJz7K6zTlptkYUhUa40ujsdx/bawiBelix+ogTxEzKlwVnK2E4P4MRTO/vXS61jf019TwXzpTLoLcVTVgLWQzc9iMUtPU8nThiL7t26YPS4Sdi5azd0Op3jSS5Sr25tDOrXAy88/Si+mvUuAgNKjq6wvOD2wdvT8cwTkzDr4zfRvFkCrt+4jWUr1pVZvQRBEARBEJ6iVmQkjh49zt1tbAutVosjR46V+Q1v9li1bjPSMzIxYmh/K+EUACKqhXO9UADg4OHjuH3nHqJq1UD/Pt249VKpFJMnjoBEwuLvXXuQk2t9wYkgiMqBRCZHYGQDbjnz6gmP7l+rLoY63+zkUfiHgmHd8lOacAOWQnr2rQsljs+7Zymels/YXlVYlODCVPatC9BpNSXMsE/uXc+Ip8qAMEQm9xCsu7Tj5zLZV2WlInyXI8RhEtQcXYQ3tUCz7Tx1Tjz1sRBPxThP/TjnqXn/RU70POW7GzVa13ue8sU7nQ3naYmxvbkOnKcKEeKpILbX5FwUJ4Ip5GanWVFREdf7VIxoa0JisS+xApyt2F6txpXY3orpPOWPs4wt9pSIWVmdpxu3/ImNW/506zYDLCLIveY8ldp2norteWoV21vE/7xx0nlaZNHv2qF4yRqE1ZLETziK7YX9+Q5jf1nD446cr/bEW2Psr03x1s2pKO7ALb/4OnbuicOHj+LateuYNPlxNE5ojnYdu6FDpx5W/3Xs3LNU++rfpxuGPdwXKc2birqDii64EQRBEARRVejQvh3upaVhztyv7Y6Z8+U3uJeWho4d2nmwMgPFxWrs2LUPCrkcXTq2ETXn8FGDSNIyNcnq7stgY49UrVaHI8c8K6YQBOFZgmLiuX9nXj3p0X0X5TwQLCsCK1Zkb2XHLyIWDM+xlHPrAvR2bujWqouQ/+AmtyxV+UHuF1TWJboEK5HCt1ost6xTFwn7ljpB7j3hvLISTwGgdoeRguWbBzejKCejzPZX2Sjv3+UI8ZgENUcCnEk8zbW4LqnVapGfb3AkmQROR/j4WjpPxYinxuhbngBZ7IR4ygp6lWp5wp2Tsb12nKeceGorttdYe15ePidimHBGAAbMYq+a53x0NrbXtN/8fIMY4isybhkQvk4kEoloAY4vcmosnadOxfZaOk89557kH6tWq+X1PBXpvuVFLvP/L1b8Li2W57myiKfLV27A8pUb3LrNAAsTXHS0d25gs9vz1OicdxjbaxT+TMJrYZHZeapwsj2kxjK2V2bt3OTjyHmK0s6XygEH88HArvPUEDvsYD4Y434s55avfqeAm2J7b9y8KVhWazS4deu2OzZdasRccDt1+jyOHDuB9m1aeqNEgiAIgiAItzD5kfFYuXoNvvrmO/x34hSGPDwQtWvHAgAuX76CVavXYcfOXQgMDMCUyZM8Xt/lq9eRX1CABvXjoFDIceLUWRz77zQKCgoRHhaC1ORmqFkjQjDnyjXD98zaMbZ70tWOicKp0+dx9dpNQJweSxBEBSQoJh7YZfi3x8XTCt7vtLLDSmXwq14bOUbHqaYoDwUZt+ETGmk19vzWBdDzHE1+1WJK7EXobXxr1EHu7fPcctaNswioWdfp7ViKrn4RtUtbml2CohohJC4R6ZeOAgB0mmJc/Xc16vd4pMz2WZko79/lCPGY3EeOBDhfo+Bp6TzlL/u7GNsbLMJ0YhJP+eKtM+KphOX3KtW5yXnKF09LiO3ludhyc3MRFBRknldk7kEoSjw1iiVarRaFBQYhRGz8Jt9pVlxUjLw8k3jqY2+K3f0Dzrk+GYaBRCKBVqvlhBxXopMtBT9PRs8KXkO84xArfnL9Jy1jez0lnsqEwnd5/l7hDA8P7OX2bVrF9kaXr56nYiO7ZVzPU6N4Kuh5WvrYXjAlODcd9hxVWvUcFTxu7JlqN7bX6Dx11POUtSF+AoBeanDO2o8dNvRctSW+MrJKKp5ePFd+7/SnC24EQRAEQVQVwsPD8f28rzH1iaewY+cu7Ny1W/C4Xq9HaGgIvvnqC0REVPN4fTduGm6uC/T3x+wvf8C+g0cFj/+y6jcM6tcDQwf34dbdf5AOAAgNCbK5TVMSyf0Hjl0tM96daXP9w/06oUZEGNLTbN/8Z4rusvc4IR46l+6jqp1Lxt/8mZV9+yLSbl6GxE13Jzs6lw+unxeukPtUmfPuLN56XSpDanHiKQDcPLUPoQ1bC8bk37uKC9sWCdYFN3io3D6XGo0aKot+rHfPH4Jv7eZObyvLIspYLVGW6XGHN+/FiacAcGnHcoQ06wbWnsvBA5T2tRkSXsOd5dilvH+XI8Rj6lnpSMDhYnMtxFN+H09XY3uDxIinnPOV3/PULCY4FE95x6fVaLjIaWfdd3znqUA85XqeWn9+BPibz0tOjlA8LbI6hpL7CZsEMI1aw/V/FSta82OBi4qKOOep5fMhZv+A85G5UqnU4NjknKdGAUgi/rI///lyxvnqDvgir1arg9r4ee1sbK9JNDY7Vz0V28sTTytRW4eBfbu7fZvlJrbXrngq7rVnHdtrFk/FxITzsYztZYyxtyX1PAUA2Ht/2+g5ajm/ZOeqHAxTwvySYnfBQC+ViYgNthM7XA5jeyt9B2N3XnADXL/oVtXx9I/okMQejgeVU6Q+QQAq9jGUlg4N24seGxBscCh16D1F9JwWykCna6oMbDL+3Rrb1LrHICEk3MfwA9KVc1UV/w5UtQv4ruCpC24AkJTUDH9s3YTlv/yKf/7dw6WB1KxZA23bPIRhwx62+tHiKUx3tB8yJoOMGjYAbVqngGVZ7N1/BMt+WYvV67cgLDQEnToYLnqb7uK01ztEaYzFKSwstPk4QRCVA1VoJCQKX2iL8gC9Drm3LyAwJsEj+y7OFf5WlPs5jkEkPItPRG3gxN/cct69KwLxVK/X4cLGL6HXmWMdfSNqo3pKH5RnfKvHCZbz7lx0ehs6jRqFGXe4ZanSDzKfsv09FNqgFRSB4SjKSgMAqHPT8eDUPwhv0rFM91tZKM/f5QjxmL67io3ttXSe5uTyxVNx8a+WztOgQMfvdZN7UyielsJ56mJsL995yu/5K6bnKQDk5Aj7nlr2PNWpLdxdFpiERrVGw/1mEXve7cb2OiGe8l8ntiKKHc0tKioy9zzVOu88ZXnPoyf7nQKAhOX3zdWI7jtpwvTcWcf2euY4pLzz7OzrvqrBj+318VEhLDTUK3XwY6lN17MAQM31yy35vWMpnpquRUgkEqffP1qL2F5RsbsMY1/8NPUstescLV3PVMbkfLXlHJWbXaesnW6hJvHW3vzyRqV/R9MFN4IgCIIgqhr+/n54dPJEPDp5ordLEaDX6wEYftA/PLAX+vXuyj3Ws1sHaLVaLFm+BqvXb+bEU3fyzpsv2FxvEv7tidyOHifEQ+fSfVTFcxkcm4D7Z/cBAHRZtxES3s0t23V0Lu/pigXLQTVqV6nz7gzeel1q6jfH1T8Wmpcz7whquLJ7JXJunDZPYFg0H/M2gqrbTqgqD6Sn3YZ/jbpgWAkXNZx/9zKCQyPAOOFsybl9CdCbXVz+1eMQWq2m2+u1JK7jKJxe9wW3fPfwJtTvOEzQn9aTVLTPzPL6XY4Qj9wU2+tAwOHE01yh+4gvZvqLFMv5PU8DAgJEXcTnnK+5edDr9WAYxkI8LXkbUoHzVMsJd87H9tpxnhprsXUs/hbOUz5FFrG9hY7EUy62V8Ode7GOX2FsbxHyTM5TD8T2ArzYWrVJPHT+OSjN/ksLy49s1uo456hYEdkc26sW/N9Tsb2Wrt3KgqkVYvNE992sGBBoju2NioryWsQx/7XBd9prREY+yyyioguNnzdKJ/udArbEU4OAWKLzEyjROeqo5ygDlCielrh/43pbsb2MXAm9xTjb822Lt5U2trcq4epFt6qOp8/P9T8WeGQ/ZYHJcZp+9HcvV+I99lw6I3qsyXG6Y9N3ouecrdvJ6ZoqA0bNAj8dP+jdQioAJsepK+dq1rCq13uI/gYSYuH/mOjcwbpfQpeObbBk+Rrcf5CBu/fuI6JaGJRKBfLy8gUXQPiYbpQT25OIIIiKS1BMPCeeZniw7yn1PC3/BNSsJ1jOvmWOWi7IvIfT6+cIHq/dYYShj245RyJTwC8iFjm3DY5TTWEe8h/chG+4eNE3994VwbJfRKwbK7RPdOuBOLd5HrTFhhvVs66dwv75z6P52Pcg8yHXJFH5MUU3OnLPmcRLS+cpvwepK7G9Yvqd8ret1WpRUFAAHx8fgXjqSEhjBc5TrUv9NgGjm4lhoNfrBc5TNRfb68B5mmvfeSqXy1EoPL1WyHixvc6Lp/zY3mLk5xnEEKdie3lCgiuxvQB4zlOj+9eJ2F7+8+is87W08OOFtTqt0z1LLWN7Ta8ZT4mnfIerI8diRWLWlz8AAH76fpbbthngbxZPo70U2QuYo6n5PXYB8eIp955TC2N7FXIXxFOrnqcKo3OzBPHRkXO0xJ6j9p2jAKA37t/hfFvip1xpuAkHDubb6XkKF85fWVN5grjtYLpIRxfcCIIgCIIgvEtYmCGWRyaVIiTYOkZMqVRwvYuysrINc0JDAAAP0jNtbjM9I9M4jmI0CaKyExxtFrsyr53y2H4LSTwt9ygCwiD3DeKW8+5fh8Z4MerEqk+h4V01VwVXR8M+j3u6RJcJjGooWM66If5GUwDIvXtFsOwp8VTuE4Colv0F6+6d3I1dn49Hzp3LHqmBILyJ2NheWz1H+csMw8DHRyVqn/yY2JBgcd+N+X09TYItPyrXkTOM77TTanVcv02JCy5z07a0WmvnqS3xVKFQcEKfpfPUNE8ikYgS0Uxj3BHbm+dKbC+/56kDt6/VXKOIYY6tNTpPnRDyyovzVKvRihawTMikQhcgF9vrqZ6n/NheJwTr8s5DLZvjoZbO91kvCV9fH06o91a/UxOm1zlfPDW5tx29B6x6npqST11wnmosxVOpssSep4wI56ijnqMAoLfnXJXIRMT+AqzE+jOZNTpHjd5S2/NN3tkK4jyt9OIpXXAjCIIgCIIoH9SONvxAUms03A1sfHQ6HfLyDT8eTD88YqMjAQCXr163uU3T+hjjOIIgKi/BtZuiYd9paDXtG3R4eZnH9luUJRRPFSSeljsYhkFAZH3zCr0eObcv4Pbxv3Dn2J+CsU2GvQKpQvwFbW8TGGkhnl53Ujz1kvMUABr2ecLKFZx37yp2zxyPO//t9FgdBOENTA4kRwIQF9ubn8+1uADM4qmvr6/AFVgSrjlPzQKhaZ8lCZaWCMVTDSd8Ous8BczuR53OuuepLTGDYRjOfWrZ87SoSPwxAGbRS6PRcNtyNbbX1PPUGecp/zy66jw1xdVyPU+diJD1Zs9TvvNUozVHP4utw/RaMwlZ5r6VnhEy+fHIlcl5+vijY/H4o2Pduk2WZREaYtBg6sTVduu2ncWWeCo6ttco/Jnec6Z2kK6Y87TFwsh2yBUAU5L4aRAf9faEepkccOBctdfzlJEpDa5Wh7G/tp2nkCmgh+F5duQ8tb1/cp56HLrgRhAEQRAEUT4IDQ1G7RhD1ODJ0+esHj9z9iK0Wi0Ucjkia0QAMPdY2XfgiOCCEgBkZGbh7LmLkEhYJDYt//GLBEGUDoV/COp1n4TwBi0gU3ku9lPgPGUYKAJCPLZvQjyWIl36paM48esngnU1k7ohIr6dJ8sqNdbO07NOzfeW8xQAZD7+aPPcAtRI7CJYrynKw4H5z+HclvnQ83obEkRlQmxsr0k81ev1nOgGmJ2U/iIFPMO2+OKpSOepIPrWJJ4aBAGFKPGU5xrU6lzueWrYli3nqf3YXgDwM6bWZGfbju111LPVhMmlqNVqufMgVjzl11ZQWIiCAsPNoL5O9DyVuaHnqUYtdF46Ix7yRT9nna+lhe885SdHio/tNTpvS3H8pUHC24/YGx2qMq+9/BxGDhuMAf37erUOS8cyAGhE9su1fM8VGT9v+BHeYrGK7ZUpwJQgXnKuTnvOUXnJzlWu56mN+aaepY5if8HApnjKGvdtT7gFV5ed2GA5OU89Dl1wIwiCIAiCKD8M6NsNALDsl7VIS3vArU/PyMLiZSsBAJ06tOZ+sCQnNUGN6tVw/cZtrN+4jRuv0Wjw/aLl0Gp16NiuNRf3S3iOuHrxeOe9D71dhhU3btxEXL14LFj4o1f2v3fffsTVi8feffu9sn93M3vOV4irJ/ytFFcvHrPnfFXiGHusXLUGnbv1Rv1GzdCuo+HzoF3Hbnhp+mvuK9oGI0dPwMjRE7hlsc+TXqdFUU46tyz3DQJr72IF4VUCIusKls9s/AaFWfe4ZanKD/EPv+jpskqNwFELQ2yv5bUNe+j1eoF4ykik8Amt6c7yHCJV+CB54sdo2Hea0c1g5uymb3Fw4cuCWGWCqCw4G9sLAHl5ZvHU3HdTXHQsULqep4Z9Wsf2OoK1dJ5qnO+3yW2LNXxG8J2n5p6ntv/2mp2ntmN7+a7QkuCLJZmZWQDEn3uWZTnx1TQXcLLnKU9kd7bnqMyq56krzlO+e9Kz0bP8OouMDj5n6uCOX6uBTqeDznhTjqcctPw6pZUotvf2nXu4feee44FO0q5NK7zw7BNOvT/KAlPSVUG+WbxUi4x8tozt5ZynChecp1biacmxuwzDQMKytp2fAPTSksVXFqaeqdbHyBjFSwljXzI0hu7a3r9cCZTQ7xTgibdS678v5TG2t/K8o+1gecFtQN/uAIQX3Lp0bEMX3AiCIAiCIDxAy9QkdO3cFtv/3I3pb36I+nVrg2VZnDt/GfkFBahXJxYjhph7pEkkEjz52Hi8+9EcLF+5AfsOHkX1auE4f/EK7j9IR1StGhg1bIAXj4hwFxcvXsKGjZsxZPBA1KrlOBVmx85dOHL0OJ59epoHqiPcwfnzF/DKazPQuVMHTHt8isDtUl4pys0A9GYHTGXpd7rht01IS7uPSRPHebsUt+Fv4TzVqYXx8I0HPFMhnz+Zyg++4VHISzOkZhXnZqAw8x5UwREO5xZl3xcIk75hUV4R/xmGQb3ukxAQWQ+HF78uqOnOsT+x+95VpE6eCd/wKI/XRhBlRYgxmjIoKKjEcb488TQ3Lw/VEA4ATrsfAdfEU7470hRX64xrU2rV89T5fpsmTAKezZ6ndoQwf+P13Jxci9jeYsPfALGxvfzjMAnXzrh+FXIF1GoNMjIyuHVOiac8IcL12F6h89IZ8ZT/fDkr3pYW/rk3xS0Djm88MMGP7TVF9gIeFE959bsSV11emf6G4SbZn76f5eVKyobgoCDcvn0HGZmZ3DqNyMhnS9eqqSWR0oWep9pia+cpDMG4ducwDGPfeSpTGN2pJcX+wrbzVFaya9Uw3xjba6vnqdG5ak+4Nc8HYOtGg3IY21vhxNPDR09gzfrfueVc411ZM+d+z71wk5rFY/CAngDoghtBEARBEER545Fxw9GwXh1s/WMnzl+4Ao1WixrVw/FQy67o1b2T1YWauNhofPj2y1i5dhNOnDyL6zduIyQ4CP16d8Xg/j1d+pFClD8uXrqMOXO/RquWqSLF091YtHgJiadlzJNPTMXjUye7ZVv7DxyCTqfD/958DZGRZvfb9t83cm4TZyjOy4TMJ7DEH/ilpbL2O/1t42acOn2mcomn1ePAsBLoeY4lEyF1khDdaqDni3ITAZENOPEUMET3ihFPrSN7Y9xdmlNExLdDuxd+xP75zyPv3lVufc7ti9g1czw6vvoLlIHhXqyQINzH5EkToFQqMWhAvxLH+fqZBba8PPONBSYXqL8TZg++EBoiMrZXLpdDoVCgqKio1D1PdTqtS65H87YMbietjZ6n9mrx97PnPC057tcSW/HKzgjXCoUCuXl5AiHG1wnx1C2xvZx4ahKwxV/2l7Cu91wtLfzY3sIinvPUgfvPBOc8VWu42FXAk7G9PNeuC6/78krt2Mp9Q1NIiKENR0ZGJrdObOSzpfPUFDct1unOR1tk3fNUwohwb9pybkrlAMua+5ragDGKn7Zjew3CbUmxu6wpdtfWOZIqwDD2Xa9c7XbEX6YcxvZWOPE0JycXFy5dsVp/9doN7t81a1QTPEYX3AiCIAiCIMoXbVqnoE3rFNHjq0eE48mp48uwIoIgbCGVSt128enBA0NUt+WFYGf6A+l1OhxZMgMZV/5D/v0b6Pzm2jJ1qwn6nQJQBISKnltQUACVSuXukkpFeazJXUhkCvhWi0HunUuC9axEhqbDXwdTgfuQBUY1xO2j27nlrBtnUL1Je4fzrMTTarFursx5/CJi0e6FxTjy45u4e3IXt16dn4UL2xch4eGXvFgdQbiPGjWq46UXnnU4ji/QCcXT0jpPxYmnpn0YxFPD/otcFE81Gm2p+k2aBDwd33mqLlkE5ZynObZ7norp2woInZ+W2xaDqT6+EOPjRM9TvlDorHjJFw8BQ3wy4GRsL0/A9LR4yhdu+c5T8T1PTbG95tefM/NLCz+qV+LhyOOy5J03nvd2CWWKyZ3Pd4tzsb3O9jzlxFPnxT+NRWwvI1M6vDGUBWMzNleMc9TwOKC3N7+EfqeA0bnKwKbzlBHlPGUNVdjZf3mjwv166NCuFX5eNLfE/x5/dKzVPNMFt2/nfICfvp+FLz79H0YNG0DCKUEQBEEQBFFlOXHyFOLqxeOvv3dy6/bvP4i4evEYPXaiYOyAwcPw+LRnrLaxectW9OjVHw0bJ6JHr/7YsXOX4PGbN2/hzf+9gy7d+6BNx97o0nMQpj31HG7cuMmNWblqDR574mkAwKgxExFXL77EfpQvTX8NixYvAQBurK2em0uX/YKOnXuiYeNEDBg8DMeO/2c15tz5C3jsiWeQlNIajRKaY/CQkdi1+197p0zA7dt3MPXxpxDfNAWpLdvh3fc/4i7W8dl/4BCmPfUc2rTvgoaNE9GmXRe8+/5HXH8cAJj1xZeo36gZHjxIt5r/7PPT0apNR87Ncfy/E3jy2ZfRpddgNEpojvadumP6K2+UWOujU6dh4MPDBetGjzWc6/37D3Lr/vxrB+LqxePM2XMAnOtnWhLtOnbj+qQmJrcW9E217Hm6ctUaxNWLx5Ejx/DOex8ipUVbxDdNwWNPPI30jExk3zyH/PuGm2fXr1qOSZMfR6s2HdGwcSI6du6JuV9+w50rsfzv3Y+R0qKt4KIbYIg9nb2nCDP/NcZx2XGejhw9AX36DcaxY8cxdMQYNEpojnnzFwAw9EGa+fkX6NCpBxo2TkTbDl0xa/Zcwb74vXrnf78Qbdp1QeMmyRgz/hFcunzFan///LsXQ4ePRuMmyUhMbo1pTz2H69dvCMa8NP01NGveCpevXMWESVOQ0CwFM/73LkaOnoBt2//EzZu3uPeOqf9sRSfAIroXAOp2nwT/6rW9UI37CKzVULCcdeOMqHm5PHcnYBAuywMylT9SH/0cdbsJ/86kXzrmpYoIwnvwe56aBFPDvw1CJj/W1xF8sS80NER8DcbenqV3nupK5Tw1CXj8v+EOnaeceGq756krsb0mnBGu5QqTeGoWYpxxnkpL4zw1ihBqo+vSlehk/vF7XDzl1cn/biw2PtjUX1ej0XDiF+C4b6W7EPY8rTzO08qOyZ3Pf89yN384eO3JuPecRc9TN8X2liQ+AkZx0pbz1NhztKT5DMNAwtjumcrIlSX2SzXNZwCbbSAYuVF8Lcm5CgYMA5viKeTlT6erPLdDEARBEARBEAThFI0aNoCfry8OHDyETh0NLqYDBw+BZVkcPfYf1Go1ZDIZ8vLycOrUGfTv10cwf//+g9i8ZSvGjBoBHx8fLP5xKZ548jns3rGdu5v32PH/cPjwUfTt0wsBvkrcun0Xq9f9hpFjJmDr5vVQqVRokZqCiRPGYuGin/DE41NQt04cAKCO8f+WjBw5DGn372Pnrn/w+Wcf2RyzZu165BcUYOSIoWAYBt/NX4Anpj2Lv//cwl0UOnP2HIaNGIvIyJp4fOqjUCgUWLf+N0ya/BgWLfgObR5qZffcFRYWYsz4R3Dr1m1MGD8G1cLDsWbdBuzZu89q7ObNv6OgoBCjR41AcFAgjh0/gR9/WoY7d+7iq7mGPkIDB/TD3C+/wcbNWzBuzChubn5+Prb/8SdGDB8KiUSC+w8eYPzER1GjegQemTAaYeERuH7jJrZu/cNurQCQktwcM2fNQX5+Pnx8fKBWq3H02H9gWRYHDh1GixYGJ/iBg4cQEBCABvWtRajS8Obrr2D1mvXYum073n/3f1CpVGjQoH6Jc2a89S6CgoLw9NNP4MaNm1i46CfIZO/jkRbxyLl9EQCwYfOfCI1tjEcmjoePjw/27N2HWV98idzcPLz6youi6+vdsys2bt6Gnbv+QedOHbj1Vy5dwuVMPQY3Mvx0VgTaj+1Nz8jA5CnT0L9/Hwwe2B81a9aATqfD5KnTcOzYcYwcMQy1Y2Nw4sQpfPXNd7h1+w4+/fh9wTZ+XbUGhQUFGD9uNPILCrBw0U8YPXYitmxci8DAQADA7n/2YNLkx1A7NgbPP/sUcnJzsWjxEgwdMQYb168WXDDXaDSYMHEKWrduiddfnY7AwED4+/sjLz8ft27ewhuvvwzAub5s5ZmAmnVx67C5zY9fRG3U7TrBewW5CZfFU6vY3lg3VVR6GJZFg96P4fKOn6EtNlx0zL51Hlp1MSQy8W50gqjoKBQKSCQSaLVa5Oaa4xvNTibx74eaNWpg1MjhyMzMRLOmTUTPM/UgN4un4iNvGWMEo16vh1ar5UXGuhDba3Ke6qx7ntoT9AICAgBYO09N5690sb3ihWtTXCc/tteZv618p5uYXrN8LCNEtS7E9rLeFE8FzlNzv3JbbmBbmERStVrNOQEN8z0lnlbOnqeVHdNv1XR+bK/Inqfm95zhs9Kdsb2inKeMHeep3NDv1JH4ajc21+hcFRW7a+v9KXMsvpbU87Q8Ok9JPCUIgiAIgiAIF3ljxjs4e+6ct8sAADSoXx/vvTPDqTkSiQRJSc1w4OAhbt2Bg4fQo3tXbN6yFSdPnkZiYlMcPnIMWq0WqSnJgvkXLl7Cti0bEBVVCwDQqlUL9Ok3GBt+24hxY0cDADp36oDevXoAANLTbgMA+vbti4eHjcKW37dh0MD+iI6OQssWqVi46Ce0bdMarVq2KLHu5kmJiIurjZ27/sFAO73E7ty5iz+2b0KA8YJgXFwspjz2FHbu+gddOncEALz7/keIjYnGql+XcReKRo8ajn4Dh2Lm51+UKJ7+vPxXXL58Bd989QV6dO8KABgxfAh69xtsNfbl6c9DqTT/GBw5YhhiYqLw2cwvcPPWLUTWrInasTFISmyGDRs2CsTTrdv+RH5+AXechw8fRVZWNlYuW4Dg4CCEhNcAAIfRgKmpydBoNDh85BjatmmNEydOoaCgAL16dsdB3vN/8OAhJDdPcnsf0e7duuDU6TPYum07+vTuyV3sLInQ0BAs/GEeV4tOp8PiH5diat+XuTGT24Sj8/SZ3PLoUcPx+ptvY8my5Xj+uadFX3ROTU5C9YgIrN+wUSCebt11ACwDNK9huBhmz3kKAHfv3sNHH7yLYUPNr4E1a9dj374D+HX5EiQmNuXWR0XVwiefzcJjUx4R3CRw/fp1/LltM6pVM/R9fKhVS4wYPR6LflyKZ556AgDw0cefITg4CL/+soQ7jx3bt8PDw0bh2+++x+uvTue2V1BQgIED+uK5Z58S1Lpk6c/IyMiw+/6pqESm9MK5zd9Bp1WDlSnQbOSblUKIU/gHQxkUgcLMuwCAwoy7KMrNgMKv5FjO3HtXBMvlIbaXDyuRIrBWQ6RfOgoA0Gs1yL51DsExCd4tjCA8CMMw8PX1RXZ2tiC211nnpGlbzn4XBAB/o0hocm+a9y1OvJJKJVCrNdBqtbzIWOcvOdt2noqM7c21dJ462fPUhljilPPURmyvSiVeCJC4o+epUfjRGJ8DqRPPAd8x6ax4W1oEPU8LeeKpSOeo6bnTWsT2OopedRf81zpfCK7ojJ38HADgp+9nebmSsiE4xPAdKj3dnPpjcm+Lju3lnKfGhBqXnKdmtzUkUrBSaYk9RwFjdK4t56lReC3J+WmYb7tnKuQKgEHJPU+NW2dt7l/Bbd8eJfZclZHzlCDcwrHl7zseRBAu0ODCXyU+frZuJw9VQhAEQVQEzp47h0OHjni7jFKRktwcX309D0VFRZDJZDh85Bg+eO8tnDl7DgcPHUZiYlMcPHgIvr4+aNxI6H5q364NJ5wCRiernx+u8eJD+aKhRqNBbl4eYmKiERAQgBMnT2HQwP5lclz9+vbmhFMAnPBrijbNzMzE3r378eILz1hFvbVr+xAWLPyxxP6Qf+/YiRo1qqN7ty7cOpVKhZHDh+KjT2YKxvLPQX5+PgoLi5CclAS9Xo9Tp84gsmZNAMCggf0x4613cePGTdSqFQkAWLfhN9StE4eE+MYAgIAAwzH9tWM3BvbvLfp8JMQ3hlKpxIGDh9C2TWscPHQYdeLi0KN7V7z5v3eh0+mgVqvx338n8cwz00RvtywZOWKYQMRNTUk2PC8yc9/RgjvnodOowUplyM3NQ3FxMVJTkvHz8hW4dOkSGlm8Zu3Bsiz69++DJUt/5ty5APD34fNoEMbCX2Gowzc82u42VCoVBg0UipGbt2xF/Xp1ER0dhfR0cyzYQ0Zhfu++AwLxtEf3bpxwCgAtWqSgfv162LFjF5556gncu5eGU6fP4InHpwgE6KSkZkhKbIa//t4pEE8BYNRIYVxzZcYnpAY6vLIc9079i7D6qQiIdK+D2psE1mrAiacAkH3jLMIb2r/BQ1NcgIL029yyIiAUMh9/u+O9RVB0PCeeAkDm1VMknhJVDl9fHyvxVO2g16c7MYmEJgHS2X2zrASAQTzVcLG9zneKMwlPWqPz1ODGNdRkr/+ov5/hcy07207PU5E3UdkSGv2dEE8VFrG9KpXKqeji0sT2moQetYXz1BkXpDedp/xzn5Nrfh59RPZoN51ny9hesc7V0lJZnaeVvdWhKbY3KysbGo0GUqmUu3HDkfOUe88Zb1goLDIIoKV1nhqclyU7NwGj+GknNtfweMmfvyX3TGUc9DxlAHvz5UroRThP7fU8hZycpwRBEARBEARRaWhQv+TYUU/iai2pqckoVqtx7Nh/8PX1RW5uLlJTkpGS3BwHDh7C5Ecm4MDBQ2jWrKnVD8maNWtYbS8wMABZWdnccmFhIb75dj5WrlqLO3fvQq/Xc49ZipbuxLI2U+RpVrahtqtXr0Gv1+PTz2bj089m29xGRmamXfH05q3biI2JtvpxGRcXa2PsLcye/SW2//mX4NwAwpi5vn164r33P8K6DRsx7fEpePAgHf/8s4dzHAJAyxap6NmjGz74ZDa++vYHPPRQa3Tt0gl9evcs8SKnXC5Hs6ZNOJfpgYOHkJraHCkpzZGTk4MzZ84iJycXxWo1UpKb292OJ7F+Dg1ioUYRCFamgE5dhJsZhXh08hQcOHZK0CsOcP71NWhgf3w3fwG2bf8TA/r3xcmTp3ErowBdmhp/3DMMfKvZF0+rV4+wuuB45cpVXLh4CSkt29qcw7/bHQBiY6y3HxsTg8NHDDdp3Lx5CwAQVzvWalxcXG38tnGzYJ1cLkdERDW7NVdG/CJiy1U8rbsIrNUQd0+Y+1NnXT9Toniad++aYLm8uU5NBMU0FixnXjsBYJh3iiEIL2Hqe1pa56nL+zeKhKY+q073C5VKUFRk6LWpc0PPU9M20jMyuAjf8HDbyQ8mUbWgoIBrNwGUPrZXJpM6de5NY/PyDEKIs3H4MoF46tzlesvYXq7nqTPirTdje3lCO/+7m9hzaKpXrdZAYxT+AfHO1dJSWXuezv/SdmuUyoIpthcAMrOyEBYaao7tdfDasXzPFRUZPjOVToqnOq0GOk0xt2zoOeo4dte+c9TQc5R1ECBkbz4jcxz7a+iZalu8hUxEz1PG2POUnKcEQRAEQRAEUblxJRqtvJHYrClkMikOHDwEPz8/REdFISKiGlJTmuOjj2eiuLgYR4/9h6lTHrGay7J27mrlCaRvvfMBVq5ag4kTxqJeXAz8/HwREBSKZ557EXpeTyt3Y682k3ir0xn+P3XKI2jzUGubY0NDQmyudwatVotxEx5FVmYWpj5qiGhVqVS4e/cuXnr5dUFfr6CgIHTs2AEbjOLpxk2bodVqMaB/X24MwzD4+svZ2Pn3n9i5ew8OHD6GF156FfN/WIhfly+Br6/9/lwpyUlYsOgnFBcX49DhI3jjtZdRs0YNREbWxMFDh5GdnQO5XI6mTcT3SStLJHaeQ4aVILBWQ9w8dxRz9xUjIPAcnnvmSURHR0GhUODkyVP4+NPPodM79/pqUL8e4hs3wvoNmzCgf1+sXr0KcgnQJMJQhyq4BqRy+y4IWxdMdHo94hs3wsvTX7A5J5rn3C4LXLkDniifBEZZ9D29ebbE8eW53ymfoOh4wXLm1VNeqoQgvIfpb3euQDz1pPPUtH/L2F5nnKcG0dMs3Dl/ydkkuJqcp/fT7nOPhYWG2pzjz0sZyc3N4wQRZ4/BMqbTz8/PqRYGCrnw762vk+JpaZynlkKOyTksqSg9T3n75t9U6Osr7hyaY3s1Xort5TlPXXjdE94hONjc+iAjPQNhoaGcc9nR55fpNafT6aDT6VBUaHSeKp1zTmqLCwTLjFzJ9ZEuCQYMGBvvU4brOerAecqwYGy8PwzOVcfOV4ZhbMb2QoT4yjLGUGA7ztfyBr2jCYIgCIIgCKIKo1QqEd+4MSeepqYYXIcpKc2RnpGBtet+Q2FhoctuxM1btmLwoAF4/dXpXM9T34AQq3g1Z3tslrYnZ1S0QbRSKBRo28a2eFoSkTVr4MLFS9Dr9YJaLl26Ihh39uw5XL58BZ998gEGDxrArd+1+1+b2x00sB8en/YMTp8+g3XrNyI5OYmL8OWTEN8ICfGNEBJeAxs3bcFTz7yA3zZtwfChD9utOSWlOb765jusXfcbMjIyuShjg8v4MLKzs9G0aYLoiDtvEhQTjx17DyNPDbzSNxGjJ4zlHrtx40YJM0tm4MB++OTTz3H/wQNs3PQ7mkawUEgNz68r4lN0VBTOX7gg+jV25eo1G+uuctHOkZGG/1+6fMVq3OXLV7jHHeHunrZE2RNYq4FgOev6mRLHW4mn5dR56hNWCzKfQKjzswAY+rRqCvMgVdq/EYQgKhsm56fJtQjwxD8PCFkmAdK656m47wMSrlepzqXIWG47rNB5ev/+A+6xsLCSnaeAQXizFE/F3kRk6ZT18xUf2WvYj/Bc+fiKi5w1wXe6Ofuc852XAO/YnRDe+TesOYosdTdC8dTwGmQYRvRzxxePi/nOUw8dB8MwkEgk0Gq1gghfonzDv0nW1KvYJL47jO3lvUfVag0KjU53Z39DaYosxFOZyNhdhgEjsd9z1NH3fHvzTc5Vxz1TWbuxvY7EV9YoDuttCdTl0HnqfAA9QRAEQRAEQRCVitSUZBw+fBQHDh5CSqpBUIuNiUF4eBjmzf8BUqkUSYlNXdq2RMIKonoBYPGPS7meMiZM8biWoqo9fLjx2Q5G2iYsNBQtUlOwdNkvePAg3epxW+v4dOzQHrdv38HWbX9w6woKCvDzL78Kxpnu5OefA71ej0WLl9jcbqeO7REUFIgvv56HI0ePYUC/voLHs7KyrM6nqa+nKaLOHs2TksCyLObN/wE1qlfnRNnUlGQcOHgIR44c4wTV8k5wTDz3YzbnziVufXFxMZYsXe7ydvv36wOdTo+33n4f9+6nI6Wm+SKYf0Rtp7fXq2d33Lx5C6tWr7V6LC8vz+o5+33rNty7l8Yt799/EOfOnUeHDu0AANWqhaNxo4ZYuWoNsnnujGPHjuPwkaPo1LG9qLpUKlWZxmYT7kcZFAG5bxC3nJd2DeoC+89h7r0rguXy6jxlGAZB0Y3MK/R6ZF4/7b2CCMILmBx2/Ph5s4BZ9uKp6TtYQX6Bcd8m16u4fZvcdoaepybnliuxvRbO0wcG8VQqlXLR/Zbwnaf8v2umGE2xx2AZ02ly44rFUmh21nkqc4vz1PC8uRL5zBe7PSHY8+GnxZieQ18fH9E3epkES7VG6Dz1VGwvYBaAXYmrLq+89PoHeOn1D7xdRpkRFBTI/Tvd2KvY9B5yFJ3NF1c1GjX3fV6pcNJ5yut3CphiexmwIsRLVmZDPBU9307PUZnC6Fx1recqZHKHzlnWJM3aiu0th87t8lcRQRAEQRAEQRAeJSWlOeb/sBC5eXkC8SwluTk2b9mKpk0TnO7dZKJzpw5Yu24D/P39UDMiDP+dOI2Dh48J+swAQKOGDSCVSjHvux+Qk2OIj23duqXdmLaEBEPU49vvfoh2bdtAImHRr29vp2p7+603MHzkOPTsMxDDhz2MqFqRuHcvDQcOHkZRURF++flHu3NHDB+CH5csw3MvvIwJ48cgPCwMa9ZtgEol/NFcJ642YqKj8OFHn+HO3Xvw9/PFlt+3WfU+NSGXy9Gnd08sXfYL5DIZ+vTuIXh81ep1WLJsOdq1aYlakTXBSGRYsWIV/Pz80KlDycKZv78fGjaoj1OnzwjOVUpKc06wKy/9Th0RFJOA2sEsfGTA9zuug503DxK5EmvXbrASl50hPCwMbds+hE2bf0eQvwoNwszRv66IT4MH9cfGTZsx/ZU3sPufPUhKaga1Wo0LFy5i46bfsX7tCsTGxHDjo6KiMHzkWIwaORz5BQVYsPBHVKsWjgnjRnNjXnn5RUx8ZCqGDR+DIUMGITc3D4sWL0G1auF4bMpkUXUlJDTGuvW/4b0PPkbTJgnw9fFBly6dnD4+wnMwDIOAWg1w/+w+bl32rfMIrZNkNVZbXIiMy8cF68qreAoYonvTzuzlljOvnUJYvRQvVkQQnsXbPU9VxqjJgkKTeGrYt1gRz+w81fKcpy7E9rLm7QBm52loaIjddgwBATzxNNd8U5Hzsb3CYzW5gcVi6ZJ09nuzlLf/0sb2uiSesrzYXg8I9nz4QpTpOfQRGdnLn6/VaLmelYDnYnsBg9hWXFzscdduWXLnbprjQRUYuVwOPz8/5ObmIiMjA3q9nnNvO3oeheKpBoWFRvFU6Zxz0tJ5Cpn4nqewGZvrRM9Um85V8fNtx/Ya5zvoeQowQDkUSm1RMaokCIIgCIIgCKLMSEluDoZhEBISgrjasdz61JRkbN6ytVSC2ow3XgXLSrB+/UYUFhWiWZN4/LT4e4yfNEUwLjQ0BO+/+xa++noeXnltBrRaLZYtWWhXPO3WtTMmjB+DDRs2Ye06g2DmrHjaoH49rF29HF/M+Rq/rFiFnOxshIaFoklCPCaMH1PiXJVKhSU//oC33/kAi39cCpVSif79+6Bjh3aYMGkqN04mk2H+vK/w9rsf4tt586GQK9C9exeMHTMKffoNtrntwQMHYOmyX9ChQzsEBQUJHmvZIhXHjv+Hbdv/RnpGBvz9A9CsaQJmfvYRokT0z0xNScap02eQkmJ+TuvVrYOgoEBkZ+cguXmiw22UB3xCIxEUHIQpyRlYe0aNWXO/RVBQEAb074s2rVtZvb6cYfDA/tixYxda1wsHy9zj1vtVd955KpFI8N23X2L+D4uwbt0GbN78O3x8fRATHY3Hpk5G9YgIwfihDw+CVqvFosVLkJ6RgebNE/HO/94QvA7atmmNhT/Mw+wvvsTMz+dALpejzUOt8OrLLyI0VFyf3lEjhuHkydNYtXotFiz8EZGRNUk8rQAERTUUiKdZN87YFE/P/PY1CjLucMsy30Cogqt7pEZXCIpuLFjOvHrCS5UQhHfw9bPR81TtuZ6nPj5G52mBoW+f07G9pp6nOh2v56nrzlO9yXlqFE/D7UT2AoC/n23naalje50WTy1ie511nvKcbs6Kl6a56tKIpzznqad7nvKFcVMCjckNLQaTSKrWaLhzAAgF6bLG5L62J/JXROZ89pa3SyhzQoKDkZubi/SMTEEqkuPYXvPjxWo1Ck09T0V+3pjQWDlPFQAjLnaXZa0/YxmZE/Ntvc9lCoAR6Ty16VxVco/bgzFt30b95RESTwmCIAiCIAiiihMcHISL56wvVk8YP8auiHjp/Emb63f9vU2wHBAQgE8+eg8AuJ6nIeE1rMYBwNAhgzB0yCBRNbMsixlvvIoZb7wqWF+rVqTd2mytj42JwayZH4vapyWRNWviu2+/dLifunXr4KfF34uqBzDHjA3o39fqsfj4Rvhi1qeCc+kM/5vxGv434zXBOoZhcPiA7R6szz49Dc8+Pa3Eum2NEbstwPo1M+ThQRjysPXroFXLFoJ9124/AnEsi5ExCQiObSLokWhZ489LF9ndlulcmjBdMEmurgd4JtaSekZabp+PXC7HtMenYNrj4gTdRydPxKOTJ5Y4pm2b1g77qH76yQf41M5jKpUKn3/2kah6iPJDQK2GgmVbfU/vnz+ESzuWCdbV7TwOTDm+oBsUEy9Yzrx2ykuVEIR38LVwnur1eo86T5VKk3haINi32J6ZJuFNo9GYnacuiKec89Qitjc0zPaNdIBBtJTL5SguLha0cjDH9op0nro7ttcJ5yQgPF8uO0+5nqfOC+8C56mHxVMAXM9QU3S1M7HHEl5sMT+215PHYbpZoDI5T4N5sbaVleDgIFy7fh3p6ekWrx3xzlNT3DkAKJVOxvYWW4inzsTm2nrAFNvraD5YMFIbQq/ROeqw5ynsxP7KDfWLEW+h15U4prxQed7RBEEQBEEQBEEQlYDlv6xEQEAAunTu6O1SyjUNernuLi2JX1asRJ242gjHXW6dzCcQcr+gMtkfQYgl0FI8vSEUTzWFeTi27G2AF10dHNsEcZ1LdtJ7G2VgOJSB1VCYZXB6F6TfRlFOBhT+wV6ujCA8Ayee5houpKuNrlPAs85TrVYLtVrtgvPULHqaep66FNtr6nlqdIClpd0HALspJCb8/f3x4MEDm85TscdgKXp5OraXL54623PUMrbX1H/RmdeO1IvOU8AsnpqeQ1dje/PzzWKUZSuNssR0A0Fl6nlaFQgONnzPyMjI5CJ7AceuZX4kND9uXem089RGbC9TcuwtYBQ3bQmUcoW4+QwDxsZrlZEb6nc5NpiVGvYvZj7K7019fCpGlQRBEARBEARBEJWcP/74C9/Mm49fV67GqJHDnI5+IkrHht824eNPP8fOXf9g5KCegjui/arHOryLmiDKGt+wWpAqzG6o3DuXoVUXccun1n2B/Ac3uWVWpkDimLfBVoC+UkExFtG912w78wmiMuJnFIpMF+FNzkHAM0KWiueWKigocNq5KOHEKw0nfLoW22u4TK3jnKdG8bQE5ykABAUaHHLp6encumK1KbbXNfHU30nnaWnFU4ZhOLebs885P7ZWo9Fw50/ssQMQRIA6K966A5PoaHKe+rgY21tQYBCj5HK5R4VM0+vHldd9eeXzufPx+dz53i6jTAkJCQIAZGRkCJynjnuemt8jOblm8dTZ325ai9heQ+ytOOemrRGM1In5jA1ZUKYwCKsO57NgJLY+J/RG52vJkiPLMGAjYsGozLHriqTuJc7xFiSeEgRBEARBEARBlAPeevcDzJn7DTp36ognn5jqcDzhXp557iUsWfozRgwfgm6p9QWP+ZcQ2UsQnoJhWQTUMr829Totcm5dAADcO/0vrv6zSjC+Uf+n4FctxqM1ukpQNEX3ElUXk/PUJByZXJOAZ5ynKh+zUFVQUFgq52npYnuFzlNTz1NH4mn16ob+4XfumvuUO+u+LK3zVG7Rp9SZ2FkTpv7mgYHOxaVKjWKnRq1x+bXjdeep8TWkNyYnOCM+82Oj843iqY+PePHVHUiNNym54rgurxw5dgpHjlXuv8UhISEAgPSMDGg0vJtWHImnvFhf0+c2ACiUTjpPi4XOU0auAAMxzk3WtsDJSkTOty2+6o2xva72PNUDhp6rjmJ/GRasVIagQS9AHpMAVUJ7qNo8XOIcb1F53tFEpePY8ve9XQJRSWlw4S9vl0AQbuG5FQtEjZs1bFIZV0IQBEG4A1t9YAnPwe+Tem6L8E57v4jYMt13Sb16CYJPYK2GSL94hFvOunEWPuFROLbsHcG40LrJqN1uuKfLc5mgaAvn6VXrPtwEUVkxCXXFxshcoQDmCeepWWjKLyhAESeeits3axRKdVottDrXxVPOearVQavVIj09AwAQHhZW4jxOPL1jjtt3VgBmWRYsy3KuTadje+UWzlMne54CwKcfv4/DR4453bZByuv56ap4yvL6YntFPJUKXy/OiM8yqVk8NvWfVDnhXHUHlTG299UXn/B2CWVOcHAQAGNsL9956qDnqd3YXmd7nlrG9sqdEC9trNezTKnmMzK5KOcoY3Suhib1wIMjvwMA/DuNgV6vF71/AJDFxCOsdhMAQKFGzX3+lidIPCUIgiAIgiAIgiAqJOe3LkDW9TMoyLyDxgOfQ2idJLdsN/fuFcGyX/XabtkuQZSWwFoNBMtZN87gwcUjKMxK49ZJFD5IHP0WGLbihI0FWoqn104ZLsJRXDZRBeC77PLz8z3e85TvPC0sKHC+X6hRMNJotWbnqbQUzlOdDhmZmdyFdEfO0xqceHqHW2eKHlY45b6UcsfuV8rYXlecp+3btUX7dm2dnsePreVHPjtz7HzRz5FwVBZIWOHrReWEc5QTj7U856nK+fNfGkzvAYmk4vzddUTjhvW8XUKZE8L1PM2ARu1MbK8d56kTUdkAoLGI7WVkCoBhRDg37YxhJaLng2EgiW0C7ZX/AACSkJrQS2SAiJ6pLMNAD6Bmt8lAo1aAVA5FVENkFxUYxVcH88GAYQC9watariHxlCAIgiAIgiAIgqiQ3Du9B+kXDwMA8u5dKzvxlGJ7iXJCYFRDwfLNw79DU5ArWBc/6Hn4hNb0ZFmlRu4TAN/wKOSlXQcAFOdmoCDjDnxCani5MoIoe5S8qMeiIkvnqQfEU55bKt8F8ZTvGNVoDQKE1IVeyyxruOCu1Wq5yF4ACHPgPK1RozoAs/NUq9Vy/Qudja41nXrnY3uF+3G252lpMDtPXY/t5YunXul5aiG2uxrbyzlPPR3bKzP1PCWppSIRbBRP8/LykZdvFjKdi+3lOU8VzjpPrXueOuPctITxCxE3HwaBVdlpDNTbFkKvKUZAt4koAsAAonqmagGAYaCo3ZRbr+c97mg+AwYVQDulnqcEQRAEQRAEQRBExUQVHMH9uyDzbgkjxaPX6ZB77wq3zErlFU6IIiovfhG1wcrMQoulcFqtcRtEtx7o4arcg1Xf06sUZU1UDfiuxaKiIoEA5ox70FVUKvMF/5wc82eKeOep2flXGucpa3Qf6nQ6oXga6qjnqUE8zc3LQ3ZOjsvnT8rr4ed0bK+F48wV56mrmIQcjVrD9XoFnIt85oun3ul5WprYXvPx5xcYxCgfD8f2mnueVp7Y3p9XrMPPK9Z5u4wyxRTbCwBpaeYED6mD9wBfXM0VxPY62fPUMrZXphDn3GQYMAwDefdHuHXy6MZgQ2uKms8wLBgGYMNqIWz8+wh/5FMojAkgYp2jttDDkBjiyPlqGlMRnKcknhIEQRAEQRAEQRAVElWQWTwtzLhTwkjxFGTehba4kFv2rRYNhq08F8OIig0rkSKgRl2bj8l8AtBs5JsVNurWSjy9dspLlRCEZ+H3ySu0EE89IWSpeBGnWVlZ3L9dc54axFOpC70fTZGnBufpfeM6CYKCAkucZ+p5Chjcp666L/mCiH8pY3td6XnqKsLYXp5wrBAv5HhbPGUt4m5di+3VoqDAO87T+PhGhv83buTR/ZYlm7b+jU1b//Z2GWWKKbYXAO7d44mnDmN7ze8RYWyvs+KphfNUrjTE5orsWSpp0hGho99C0IBnEDryTcODjDjnKANArxeKl844R22ih0jnrEF+Lf/SKcX2EgRBEARBEARBEBWUsnCeUmQvUd4JjGqIzGvWrsyEIdOhDAz3QkXuISjGou/p1RNeqoQgPAvfHVlUVISiYs/2PJXLZWBZFjqdDllZ2U7v29yrVAutMbZX4kJ8qUnA4ztPw8JCwTro31xDIJ7eQTBPbHVKQOS5BitWbK9ByHFXbK83xFPLuFtnnKf848/PN/U89ax4+t47/8PjUx9FdHSUR/dblkwYM8TbJZQ5wXzxNO0+92+Hsb1S27G9zoqn2mLbzlOH4qdBIQWghyK2Cbdeb+o5KrJnqqV4KdY5yjK2P5NNTlJR4qtBvS1xXHmAxFOCIAiCIAiCIAiiQqIMqs79uyDDXeLpZcGyf/XabtkuQbiLgFoNrNbVaNYFkck9vVCN+wio1QAMK4FeZ3CuZV0/A71OB8aBcEIQFZ2SYns9IZ4yDAMflQq5eXnIFDhPxYloJtFRq9FCY4ztlboU22vtPHUU2QsAgYGBUCqVKCwsxO07dxFX2/x327mep+bL5BUptldmiu3VqF0W3r0tnlo6T50Rn6X8nqcF3hFPJRJJpRJOAaBLxzbeLqHMCQwM4G4cSXPCeSrj9TzNM8b2yuVyhzd6WNJi6hfIK8zFf7cuIDcvy9yzVFTsrbVzUw+xzs/SOUdL3D8jMnbYhnhbHqFvoARBEARBEARBEESFxNJ5ahk/5QpWztOI2FJvkyDcSVC00KEp9wtGk2GvVti4XhNSuQr+1eO4ZU1RHnLvXfViRQThGfjiaWFhEdRqvgDmGSFLaex76kpsr8l5qtZouL/DEpdie40irFaH+w/MzlNHMAzDRfdax/aKP3+C2F5/Z8VT78X2moQetbo0zlOzRCAvBz1PnRNPzc+bqWevp2N7iYqJRCJBUKDBqX6P3/PUwc0fMpmM+7y6c8dw86azrlPA8NklkSnA+gSCDaoGRio1io8lS3aG2FvGxu8eg3wqKraXsZ7vlHMUNmJ3jeKrY+cqA4YB9TwlCIIgCIIgCKJyElcvHu+896G3y7Dixo2biKsXjwULf/R2KVWKdh27YerjT3l8v3zxVFuUD01BbgmjxeHO2N7Zc75CXL14xwMJwgkCazVEREJ7AIBErkLS2Heh8A92MKtiEBRj2ffUOp6YICobSqV3nacA4GPse5qd7UJsr1F449ftyLllC5NrS6fTmmN7RThPAXN07+3bd1Dkat9PY80sy0LlpHPR8lx50nnKP9cm56WhJvEiKD82V+YhwZ6PROq6eCoTiKc5hvkedp5WRv7auQd/7dzj7TLKHFN0r1A8Lfk9IJFI0KB+PQDAfycMLQb4n+OuYtIyHTo/jf9ZSad6Q5qv2J6ppXGO2tJH9Ubx1uF8GJ2n5V87pdhegiAIgiAIgiC8z8WLl7Bh42YMGTwQtWpFOhy/Y+cuHDl6HM8+Pc0D1VUu0tLSsPTnFejetTMaN27k7XJKhcwnEKxMAZ26CABQkHkHMh9/p7ez6MefkdC0Gbp362IlnvpGxLijVIJwGwzDIPXRz5F14wyUgeFQBoR5uyS3ERQdj2t71nLLmddOIapFX+8VRNglP78AG7f8iQOHj+Ne2n0wDIOQoEA0qF8HQwb1RkhwkGD83XtpWLlmM06cOou8vHyEhAShZUoiBvXv6ZaLzhUZuUXPU63WEH3LMIxLIqQrmJynmZlm56lCtHhqEL744qmz8ZX87Wi15p6noSKcpwBQo4Yhxv/OnTsoLiri1rsS2+vn5+e0k99SpHVWfC0NfPEwLy+f+7czx86PzfVGbK/E4vXijPgs4R1/tlE8VXlQvK6sLPhxBQCgU/vWXq6kbAkOCQIuAWn3zD1PxXzuJiY2w6nTZ6BWG/o8KxXKUtdiit119PnDMAwkLGtsG6rnxouO7QXL9Uy1LECsc9QwUge+iqqH2dXqeH7FgJynBEEQBEEQBEF4nYuXLmPO3K9x4+ZNUeN37NyNOXO/LuOqKidpafcxZ+7XOHX6jLdLKTUMw0AVzO97esel7Sxashzbtv2B4vxsFOU84NarQmpAKif3AlH+YBgGQVGNKpVwClhHEmdePeGlSoiSuHHzNl587X2sXr8FarUaiU0aI75RfTAsi7927sG9tAeC8ZevXMerMz7B7j0HEBwUiOTmTaDV6rB+03bMeG8m8vML7OypaiCRSLgeesXFxZwIKZPJPBbHbXLqZbnkPLUWT13peSrhO0+Nsb3h4eI+46pXN4mnd13u+ynjxFNf0XNM8IVmuUzmMccwAEh5/Rfz8w3iKcuyTgnv/Nhcr4inFjHPzsTu8o8zO5ucp+6iZ9cO6Nm1g7fLKHNCjM7Tu/fucev4PU3tkZTYVLBs2ffYFfQwCKGOep4C9gRWg/XU0XzOeWqlnYp1jlpLinq9XpRwCxh7pjJMhYjtJecpQRAEQRAEQRBEFaC4uNglJ0h5RxVcHXnGvogFGXdLtS13RvYSBOE8/jXrCNzk2TfOQadRgy0hQq8oNwMSuZJudPAQeXn5+PCzr5Gbm4cnp45Hm9Ypgsfv3kuDSml24Oh0Osz9dhEKCgsxYkg/DOjbHQCg0Wgwa+4POHzsBJatWIfJE0Z49DjKGwqFEmp1LgoLC7k+dJ4U4FSmnqc856mz4mlRUTFvnQuxvRJz79QHD9IBiI/trR5hjO216HnqjKBhio7183Ou3ykgPFee7HcKCCNGTeKpXC53Snjnx+bKPOR2FuzfQjx1xnkqsxFb7Ennb2Vl9IiB3i7BIzRs2ABbt/2BIp5jXcyNB0mJzQTLSmXpnacm56cYAdIggDKc29Q4XZRz1TzXsuepwZBaGueomM8dQ89Wa/G2PFL5fjkTBFGu2XPpjMP/CIIgCILwDCdOnkJcvXj89fdObt3+/QcRVy8eo8dOFIwdMHgYHp/2jNU2Nm/Zih69+qNh40T06NUfO3buEjx+8+YtvPm/d9Clex+06dgbXXoOwrSnnsONG2aH6cpVa/DYE08DAEaNmYi4evGIqxePvfv226z7pemvYdHiJQDAjbXVV3Lpsl/QsXNPNGyciAGDh+HY8f+sxpw7fwGPPfEMklJao1FCcwweMhK7dv9r75QJKCwsxMzPv0CHTj3QsHEi2nboilmz50KjMcQ36fV6jBw9AS0f6oDMzExuXkFBATp17YWefQZyF/hemv4amjVvhStXr2Ls+MmIb5qCNu26YN53P1jtNy8vDzO/+Bq9+g9Hw8aJ6N6zH5YsXS4YY+r9+sPCxfj+h0Vo36k7GjdJxtp1G9B3wBAAwPRX3uDO3cpVa+weZ25uHt5570O069gNDRsnIrVlO4wdPxknTp6yGrtv3wEMGDwMDeOT0KFTD6xes85qzNWr1/DEk88iMbk1GjdJxtARYwTPdUZGJurUT8BPS3/m1t28dQtx9eLRoVMPwbamPfUcZvx6AgG1GiAioQMupxVg/MRHkdyiDRolNEf7Tt0x/ZU37B4bAKQ81BW5uXlYtWYdUrqPxDObC7H0uOF58ateGydOnsL4iY+iSWIqmiSmYsKkKThtw7V74OAh7tg7du6JZT+vsLm/X1euweixE5Hasp3d52/EqPHo3W+Q1Vy9Xo827brgyaefL/GYCKKiwkpkCIyszy3rtGpk37pgd7xep8PhRa9h12fjShxHuI9V6zYjPSMTI4b2txJOASCiWjgCAszx6QcPH8ftO/cQVasG+vfpxq2XSqWYPHEEJBIWf+/ag5zcPI/UX14xxb4WFRWj2OicFBub6w5UNp2n4hyIbnOeGqNj09PTuejisDBxzlNTz9OcnBxkZGRw611xnvq74jzlxfY606/THQhie3niqTPUioyEXC6Hn68vIiIiHE9wM5biqTPn0LJfqmE+iaeEOKZMnoiWLVIF6yxfj7aIjY1BYGAAt6woZfy8Xm9wfkpZiTjxFAwYBgIBVK83RO467JnKmOby929wjkoYx3KhybnK34BBuHW8b/N8cp4SBEEQBEEQRKVHqy6GTlPseKARqdJXcEemXq+HplD8BUNWKodEJrwgwu914gyNGjaAn68vDhw8hE4d2wMwCEAsy+Losf+gVqshk8mQl5eHU6fOoH+/PoL5+/cfxOYtWzFm1Aj4+Phg8Y9L8cSTz2H3ju0INvY6O3b8Pxw+fBR9+/RCgK8St27fxep1v2HkmAnYunk9VCoVWqSmYOKEsVi46Cc88fgU1K0TBwCoY/y/JSNHDkPa/fvYuesffP7ZRzbHrFm7HvkFBRg5YigYhsF38xfgiWnP4u8/t3BxZGfOnsOwEWMRGVkTj099FAqFAuvW/4ZJkx/DogXfoc1DreyeO51Oh8lTp+HYseMYOWIYasfG4MSJU/jqm+9w6/YdfPrx+2AYBp989B569R2I/739Pr6Y9SkA4JNPZ+HmzVtYs3K54OKWRqPBxElTkZKSjJenP4+//tqBjz/9HAAwdcojAAzP9ZTHnsTefQcwaEAfNG3aFH/+tQMz3noXWVlZmPbEVEGdK1asglqjwZhRI8CyLDp0aIcXnn8GMz//AiOHD0VqajIAoHlSot1jfWPG2/jzrx0YN3YUoqOikJ6ejgMHD+PChYtIiDdHbF6+fAVPPvMCRgx7GA8PHohff12Nl15+HQkJ8ahfry4AIO3+fQwdMQbFxWqMHzca/n5+WPHrKoyf+CgWL5yPVi1bIDg4CHXiauPAgUMYO3okAODAgcNgWRbXb9zA3bv3EBFRzbD+4CH07d0LHaa/ivsPHqBbj76IqlUL0x6fCh8fFa7fuImtW/+we2wA8M6MV/DBJ7PRJCEe7esF4O7J3QjzMbyf0jQ+eGrUOAQGBOKxqY8CAJYt+wXDR47D6pU/o27dOgAMr6XxE6cgNDQEzz49DRqNBrPnfIkwG73alv68HPXq1kWXzp0glUrwx59/Y8Zb70Kn12HcmFEAgEED++HV1/+H8+cvoJ7x3AHAvv0HcPvOHbw94PUSj4kgKjJB0Y2RccV8s0vmtZMIirbdn/nCH4tx/5zh5otdM8chftDziGnzsMeiTqsaxcVq7Ni1Dwq5HF06thE15/BRQ/Ryy9Qkq+cl2Ngj9dTp8zhy7ATat2np9porCiaHZFFREZcS4VnnqVE8dcF5aqqXL56KER+st2OYc+eOOUXC1t9RW5hiewHg5KnT3L+d63lq+H7oivOU73B1xjXpDgSxvXkm8dS56N2QkGBs27IBEqnEpdji0mItnooXP205Zcl5WnrOnr8EAGhQz/bvscqCj48Pvv/uK0yc/BgOHjyMoKBAce5JlkWzpk2wc9c/AKz7HjuLVq+DjJXARybONc71HeVbT41ipKOepYBBJLXsmQqIdY5ajzFEDkNU5DDLMBAxrFxA4ilBEARBEARBlIIL2xbi3JbvRI/vO3s/wPAuEOh12PKy+H4y9XtOQYPeQoFMr9WAKSHS0B4SiQRJSc1w4OAhbt2Bg4fQo3tXbN6yFSdPnkZiYlMcPnIMWq0WqSnJgvkXLl7Cti0bEBVVCwDQqlUL9Ok3GBt+24hxY0cDADp36oDevQxuwfS02wCAvn374uFho7Dl920YNLA/oqOj0LJFKhYu+glt27RGq5YtSqy7eVIi4uJqY+eufzBwQD+bY+7cuYs/tm9CgL/B/RIXF4spjz2Fnbv+QZfOHQEA777/EWJjorHq12WcoDp61HD0GzgUMz//okTxdN3637Bv3wH8unwJEnk9b6KiauGTz2bhsSmPoE6dOERF1cLL01/A/956Dz17dENIcDB+XLIMT057DPHxQjGgoKAAXbt2xuuvTgcAjBk1AuMmPoovv/4WY0aPgK+vL7b/8Rf27N2PJx+fjAljRyAkvAbGjhmFSY8+jrlffYuRI4YhJCSY2+bde2n4a/tmTswGgE4d2mHm518gKamZ3fPH56+/d+LJJ6ZiyqOTuHWPTbUed+HiJfz6yxIkN08CAPTp1RNt2nfBylVr8NorLwEAvp33Pe7ff4BVK5YhKckQdzVs6GB069kPH3z4Kdav/RUAkJqSLHBEHzh4CO3aPoRDh44YBNM+vXD5ylXcv/8AKSnNAQCHDx9FVlY2tv8+D6GhIdzcl154tsTj692zKz75fC6io2ohtVYB7maa358//rYHWq0OK5b/hMjImgCAAf36oGuPvpg5aw6++eoLAMDsL74EwzD4dfkSVDe6X3r26IZefa3do8uXLhZEe40bOxoTJk3BggU/cuJpr1498NY7H2Ddho148Xmz43vd+t8QHByEDu3blXhMBFGRCYpJAPALt5x5zdrlDgAZl//D2Y3fcMs6dREyr55EbNshZV1ileXy1evILyhAg/pxUCjkOHHqLI79dxoFBYUIDwtBanIz1KwhdK1duWZImqgdE2Vzm7VjonDq9HlcvXYTEKfHVkpMF94LCwshMYpBzgpgpcH0d6lY7Xy/UFPEZSE/9tKF2F6T8zTTKOBKpVLUMv7tdYTpby8ALDEmVzRPSnSu76ebYnud6dfpDqRucJ4C4L7PewPTcw8YRClnhCipjd9A5DwtPe99PBcA8NP3s7xcSdnj6+uLBfO/xfcLFqFpkwTR8xITm3HiqbKU4ikASFkWPlJx22EZG85TABJjP1Ex8/lwzlGx4ieEzlXDgpPO0wqQ20viKUEQBEEQBEFUYVKSm+Orr+ehqKgIMpkMh48cwwfvvYUzZ8/h4KHDSExsioMHD8HX1weNGzUUzG3fro3gQkujhg3g5+eHa9dvcOv4IpFGo0FuXh5iYqIREBCAEydPYdDA/mVyXP369uaEUwCc8HvdWFtmZib27t2PF194Bjk5uYK57do+hAULf0RBQYHdO9c3b9mK+vXqIjo6Cunp5ni4h4yC6959Bzjn7JhRI/D71u2Y8b934ePjg0YNG+DJJ2yojwDntAQMF49GjxyOf//di4OHDqND+3b4+++dkMmkGD5kADeOYRhMHD8GO3bswj//7kG/vr25x3r36i4QTl0hIMAfe/cdwNAhg0vcVsOG9TnhFABCQ0MQFxeLa9fMr4e/d+xCcvNETjg1bD8ADw8eiG/nfY+0tDSEh4cjJaU5fv7lV1y/fgNRUbVw6NBhDBzQDzqdDgcPHUbfPr1w4IBB9E81iqemmMit27Zj+LAhLvV35fc81en12H/kJHp078oJpwBQq1Ykunfrgj//+puLFdy56x/07NFNcPG2bt06aNe2Df7eYRaBAeF7IjsnBxq1Bi1apGLnrn+QnZODAH9/BPj7o2uXTtiwYRMnnhYVFWPzlm3o17cXJ/YTRGUkKLqxYDnz6gmrMeqCHBxa/Br0Oi23zrdaDBKGTC/z+qoyN24aboIK9PfH7C9/wL6DRwWP/7LqNwzq1wNDB5uTKu4be1eGhgTZ3GaI8e/K/QcZNh/nM+PdmTbXP9yvE2pEhHE3abkbjcYgKJbV9gFAZhTusjLTOTFMImHLdJ98WEZ4EZtlWWRnpImaq1QY6r116xa3Li8nw+na1UWFguXGDeujKD8bRfnZdmbw0Oshl8tQXKxGfr6h7+XQwX25GsQ8h+GhhpvPIsJDnK5dr9eDZVnodDrIpVKPPW8AkJ+Tyf0748F9AIDUg68dd6DTarh/q1RKZNy/Y3OcrecxL8f6s0NTVFChjr880qh+bQDu/9zzxOepq4wbORiA+Nrq1jb/DmYZvUvHpNPrUV3PIlzqAwnDQJ6fj/TCQofz/DRqxEmUYPUsGONXoWCpD2RacfWHaHTwk/pAqjOJnQzCpCrINFqH8/UAZACkDItwrWkdi3CJEvJijcP5Wr0eNSGBTqKAhJsvhZ4FpHl5SC8sKnG+q4SE13B6DomnBEEQBEEQBFGFSU1NRrFajWPH/oOvry9yc3ORmpKMlOTmOHDwECY/MgEHDh5Cs2ZNre7er1nT+gdIYGAAsrLMF7kKCwvxzbfzsXLVWty5e1dwh6mlaOlOLGsLDAwEYO7ldfXqNej1enz62Wx8+tlsm9vIyMy0K55euXIVFy5eQkrLtjYfT09P5/7NMAw+/uBddOraCxmZmdiw9leb4pdUKkWtWpGCdbGxMQCAGzcMFyRv3rqF6hHVreqqE2cQam/evCVYX6tW6V0Er0x/AS9Ofw0tH+qAZk0T0LFDewwc0E8gKAJAzRo2Xg8BgcjONkcA3rx5SyCwmuuvbXz8NsLDwzmx+8DBQ/D398P5CxeRmpoMtVqNLb9v4x6LiY5CeHg4AKBli1T07NENr7/5Nj6dORutW7VE1y6d0Kd3T1EODJ1eh7z7ZqG3UOKHgsIHiKsda11vnTj8tnEz0jMyAL0ehYWFiI2JthoXFxdrJZ4ePHQYs+d8hSNHjqGgoEDwWI5RPAWAgQP6YeOmLTh0+AiSmyfh7x07kZ2djQH9+zo8FoKoyPiGR0Oq9OUi7XPuXIamqABSheFzT6/X4/gvH6Ag3fx5x0pkSJ7wITeGKBtyjX1JDxmjeEcNG4A2rVPAsiz27j+CZb+sxer1WxAWGoJOHVoDAAqNF0HtOcmUSrPjsipjcpkWFRVDp9MBgFOuydKitOjX58xNOjHGG+n43+tci+0V3vSU3LyZnZHWMAyDauHhuGH8HlQ9oho6dXAupeGZaVPQumUqHmqV6niwjf3L5XIUFhZ61Xmab/xe4cnIZ3fAf704G7krtfFaU6mUNkYSzvDYxKHeLqHcE9/YfGOxO95zDCPOuWkYa/qXObeXEawXsQ0n1pZigxUaEk8JgiAIgiAIohTU7TYRcZ1Gi5/AsFbLPT/eIXo6K7X+Yca4EI1mIrFZU8hkUhw4eAh+fn6IjopCREQ1pKY0x0cfz0RxcTGOHvuP67kpqMWes48nkL71zgdYuWoNJk4Yi3pxMfDz80VAUCieee5F6I0XB8sCe7WZxFudzvD/qVMeQZuHWtscGxoSYnM9YLhLOL5xI7w8/QWbj0dbRJ/t/ncPNBrDXfUXLl5CIwsXb1nhjgipPr17IjUlGVu3/YHd//yLb+bNx9ffzsc3X81G+3Zm8djehVJXEplq1YpE9YgIHDh4CIEBAZDL5WiSEA+1Wo0v5n6N7JwcHDx4CCkpydDr9ci+cRYFmXfx0rCHMGnscOz4Zz927v4HL7z0Kub/sBC/Ll8CX9+Se3hpC/MAxvya9A2LAvDA+eJL4OrVaxgz7hHUiauN11+djho1qkMmk+HvHTuxYOGP0OvMJ6tD+7YIDQnB+g0bkdw8CevW/4aoWrWQktzcrTURRHmDYVkERTfG/XMHDCv0OmTdOIPQOoYbL67vXYdbh7cK5jQa+AwCazXwdKlVDtPfUK1Wi4cH9kK/3l25x3p26wCtVosly9dg9frNnHjqTt550/bfXJPLxRVXiRjKevsA4OtruHGGkUi573o+Pr5luk8+IaHhgmWFQiF6340TmlqtCwmv7nTtvn4BguWOHTs5tY3IyEhOPJ00cTyq1TB/FxPzHIaE10BsnfrOlCxAoTCIp0FBwR573gAgK8/ca1atNXyPUfn4eLSG0qJQmvvE+vn52a3d1vOYk6+2Gle9ZlSFOv6qhCc+Tz1FSHgNxMXVxqVLlxEYFOLSMRVoinEx7QayNPkIV/kjKryGqNjdy1lpuJL1AEqpFHKJFGqtFvm6YkT5hqBWcITD+Xcf3MbN3AwESJSQsCw3P9rXV9T867euQq3XIl1iiAlWa7XI1xYj2scXkQ7mF2rUuJx2HTnqQgQZ3/uFGg00Oh3iAgJRw9/+b3BPQ+IpQRAEQRAEQZQCiUwOicz1O00ZhoFM5XxvJcttuIpSqUR848aceGqKQE1JaY70jAysXfcbCgsLXRZsNm/ZisGDBuD1V6dzP5Z9A0KQnZ1TqmMozTEDQFS04YKaQqFA2zbOX+CNjorC+QsXRM29dfs23v/gE3Tv1gU5OTl465330bp1S4SFhgrGaTQa3LhxE9HR5r5wV65cBQDO5RlZsyb27N1nFSl86dJlwbiScOXcVasWjjGjR2DM6BFIT89Av4FD8OXX8wTiqRgiI2vi8uXLVusvXb5ifNx80SElpTkOHDyMwIAAJDZrCrlcjsRmTSGRsNiyZRuuXruOxx57FAzD4N8vp0JTYHC8tHtxCVKefwYvPP8MNm7agqeeeQG/bdqC4UMftlsXwzBQF+QC5mt3qBFbDyrVWa42Qb2XLsPHR4WQYEPEn1KpxJWr12yME87948+/UVxcjO/mfYnImubnau++/VZzpVIp+vXtjQ2/bcKzTz+Jv/7eiUcfmWD3GAiiMiEQTwFkXj2J0DpJyLlzGSdWfSoYG5HQHrXbj/B0iVUSvjuxcwfrBqVdOrbBkuVrcP9BBu7eu4+IamFQKhXIy8tHEa8fJh+TM5UfaV4VMTtwzefJk+5BlcX5d8Z5WtuYksHHlhvQESyv76VUKkXz5olOzTdF5/v6+mDYMPt/88sKk7va19fHwUj3IuM7T/Nc73nqTfg9T319nDt/Upm1vOGse5WwxvRbzdQWg7DNpAnj8OVX36JXz+6l2o6EZeEjk4v+ncbA1PPUgFavM8T+iryp2rJnqmE+K3q+JTrowYKBRETbFHPP1PLf89T5JjAEQRAEQRAE4SLfzP8JIyc8Zfe/7xcttznv7r00fDXvRzz+zOsYN/k5PDv9bfy8Yp3gAhPhOqkpyTh8+CgOHDyElFRDXGpsTAzCw8Mwb/4PkEqlSEq0dhWIQSJhBVG9ALD4x6Vcr0gTposclqKqPXy48SL6YNkgLDQULVJTsHTZL3jwIN3qcVvr+PTq2R03b97CqtVrrR7Ly8sTXCR+5dUZkMmkePedGfj4w/dQXFyMN2e8Y3O7Py39mfu3TqfD0p9/gY+PihO1O3ZsD7Vag19Xr+fG6fV6LPpxKeRyuV0XLR9TnJyYc63VapGdIxwXEhKMGtWr270QXhIdO7TDocNHcezYcW5ddk4OVq1ei4T4xlwEL2AQTy9duoxtf/zFHb9SqURCfDzmzf8BgLmXrSooAvlqPfR6PQoyzH12TA5fR7WqlEpkZghdpoE14tCu7UP4fet23OT1cbt56xa2bvsD7dq2gUQigUQiQft2bfD71u24c+cuN+7ChYvYtfsfwTY5dy7vLZGdk4OVq9bYrGvQoP54kJ6ON//3DoqKiiiyl6gyBEbHC5Yzr52CVl2Ew4tehbbYHO+qDAxHs1EzSn1DDSGOsDDDTT8yqRQhwYFWjyuVCgT4G24IM0X4h4UaHCQP0jNtbjM9I9M4LtjN1VYsTMJbUVER1GpDUoVHxVOLqFlTjLAYatSoblUr64J4yk+waNokwWFihCWDBw1AWFgoXn7pBUHfe09hOgc+Top/pUUQ25tfUcVT8zE4G3sstSH2+Hg4OrkyMu35GZj2/Axvl1HuGTVyGP7d/adLN+PykTIS+EjFJwaxDAMGjDlVSa8H64T4aZ4P3nzx4qspotf0k0an14Flxe3fINwyFUA6JecpQRAEQRAE4QWaJjRCUKD1RY16dWtbrbt85Tre/WgOCgoLUTsmCg0b1MGFi1exftN2HDl+Em+99hz9QC4lKSnNMf+HhcjNy+PEKABISW6OzVu2omnTBJcvBHXu1AFr122Av78fakaE4b8Tp3Hw8DEEBwcJxjVq2ABSqRTzvvsBOTk5kMvlNt2ZJhISDBfW3373Q6OIxaJf395O1fb2W29g+Mhx6NlnIIYPexhRtSJx714aDhw8jKKiIvzy84925w4e1B8bN23G9FfewO5/9iApqRnUajUuXLiIjZt+x/q1KxAbE4NlP6/A7n/+xRezPkV4WBgA4NWXX8IbM97Ght82CWpWqVTYvv1PZGVlIyGhMf76awf+/XcvXnrxWe4CYpfOHdG6VQt8+c0PuHXrDhKaNsVff+/Ejh278PyzTyEkxPHF51qRkQgKCsSy5Svg6+cLH5UKic2aIirKuj9qXl4eHmrXGT17dEPDhg3g5+uLf/fsw+EjR/Haqy85db4B4LGpk7Hht02Y8MhUjB83Bn5+vvj119VIT8/A7M8/EYxNNbqdL1++YvW6nP/DQoSGhnL9SFXB1bH/37PYfU2LTvplaNrmPvIL8rFixSr4+fmhU4f2JdbVsEE97N+3HxHFDAIVDEJ8GLSIiMXzzz2E3f/8i2EjxmL0KIOzbemy5ZBKJXjhuae5+c88PQ07du7G0BFjMHrkcGi1Wiz+aSnq1auDM2fOcePatX0IcpkMk6dOw8gRQ5Gfl49fVqxEaEgI7t1Ls6qrSUI86tWtg02bf0dCQjzq1Ilz7oQTRAUlOMZCPL16AqfWfYHsW+fNKxkGSWPfhcKvaotunqS2MbVBrdGgsLDIqk+mTqdDXr6h56LC+FhsdCSuXruBy1evo3ligtU2L1+9DgCIiY60eqwqYRK7iouLOTHMGQGztFg6T50R31iWRWxMNM6dv8Ctc8V5yncstWrVwun57do+hP17djoeWEb4+Rm+qwUGBDgY6V74zsu8CiuelsJ5aqM3MDlPS09EtTBvl1ClkLIsVE6kWclYCViGgU5vdo7KWSnkrLjPXk48hcV8keKp+ZY1Q89VrU4PCctCzooUTyFs7aKHHgzDQFLOboYj8ZQgCIIgCILwOAP6dEPjRvUcjtPpdJj77SIUFBZixJB+GNDXEIej0Wgwa+4POHzsBJatWIfJEyiurzSkJDcHwzAICQnhxCjA4OrbvGVrqXosznjjVbCsBOvXb0RhUSGaNYnHT4u/x/hJUwTjQkND8P67b+Grr+fhlddmQKvVYtmShXbF025dO2PC+DHYsGET1q7bAL1e77R42qB+PaxdvRxfzPkav6xYhZzsbISGhaJJQjwmjB9T4lyJRILvvv0S839YhHXrNmDz5t/h4+uDmOhoPDZ1MqpHRODmzVv46OPP0KN7V0Fto0YOw+9bt+Htdz4QCMRSqRQLF8zDmzPexW8bNyMwMAAvvfgsHp/6KDeXZVl89+2X+OCDj7Dtzx1Y99tmREdF4+233sDY0SNFHbdUKsVnn3yITz79HG/OeAcajQaffPSeTfFUqVRizKgR2LX7X2zd+gd0eh1ioqPxzltvYsxo59934WFh+HX5Enz0yUwsXPQTiouLkZDQGIsXfodWLYUXShs0qA9/f3/k5+cjKSmRW5+aahBPk5sncetUwdVRN4TF1Swd/tr3H9b+dQj+/v5o1jQBMz/7yOax8Xn2qal49eJ/2HguF2od0CKSxaSI2ogIj8LyZT/ik09n4etv5gEAkpsn4eWXnkfdunW4+Y0aNsDiBd/hvQ8/wewvvkT16tXx7NNP4l5amkA8jYurja/mzsLM2XPw4UefITw8DKNHDkdISAhefvUNm7UNHNgPn342GwPJdUpUIZRBEVD4h6Iox+AIz39wE1d2/iIYU6/7JITVT/VGeVWW0NBg1I6JwuWr13Hy9DkkJzURPH7m7EVotVoo5HJE1jBEqDZPTMCO3fuw78ARDO7fU+ASzsjMwtlzFyGRsEhsKhTMqxom52lhYSFPPPWcAGZ5k5zCyX3HxsYIxFOJ1JXYXvOcli0q3nv7maemYc3aDRg8eIBH98uP7c3LywPg/PPnbfiuY2dv2JRZxPZKpdIKJx6XRz774HVvl1ClkLIS+EjFv26VUhkkLItirSGpwOQcVYgUPxUSKSQMA61eJ5gvRvw0wBNAGYPzVM5IRO3fMJMBP4pHq9NDKtK56knKVzUEQRAEQRAEwePg4eO4feceomrVQP8+3bj1UqkUkyeOwFMvzMDfu/Zg+JB+8PdzLtqLMBMcHISL505YrZ8wfoxdEfHS+ZM21+/6e5tgOSAgAJ989B4AcD1PQ8JrWI0DgKFDBmHokEGiamZZFjPeeBUz3nhVsL5WrUi7tdlaHxsTg1kzPxa1T0vkcjmmPT4F0x6fYvPxyMiaOH7UupclACxeON/m+tiYGPy0+PsS9+vr64sXnn0CLzz7BELCa9gcU9J5AAyO4M6dOpS4H8BwjK+8/CJeebnkcbaeTwD4eekiq3UxMdH45qsvHO6bZVkcO7zXan3XLp2tjk0ZFIFagSzGJ8pRs3lnJE/40OH2+UTVqomnWsigLTY4b1iJDKoQw7lNiG+MHxfZfr74tGiRgvVrVlitf/bpaYLlLl06oUuXTlbj7L32ZVIZJBIJ+vVz7uYAgqjIMAyDoOjGuHtyl83Hg2s3Q/2etj97ibJlQN9umP3VAiz7ZS2ia9VEeLjhBqD0jCwsXrYSANCpQ2tOAExOaoIa1avh+o3bWL9xm+BGuO8XLYdWq0OXjm24uN+qisnFW1RUDLm8GIBzfUdLv3/XnacAEBsTLVi2FaXqCJPzVCqVItnJfqflgR7du6JH964e36+0UvQ85YmnTrpGJRYuZ3KdEhUNBgbxVCUV/5mvlMogYVhodSbxU3xsLgAoJDKwrHm+Vq+DhGWhEJkawBoL1+p1kICFVq8X3TPV5DDVw9B+hmEYQ/2M1Anx1jOUr2oIgiAIooLz3IoFDsfMGjbJA5UQROXg8FGDoNcyNcmqn1lwUCAa1K+DU6fP48ixE2jfpqU3SiQIohygCo7g/l2QcbeEkbYpzn4AbXEBt+xbLRpsObjzWa/XY8Wvq9C2TWsu9pkgqgr2xFOZyh/Nx79fLt6jVZGWqUno2rkttv+5G9Pf/BD169YGy7I4d/4y8gsKUK9OLEYM6c+Nl0gkePKx8Xj3ozlYvnID9h08iurVwnH+4hXcf5COqFo1MGqYZ5165RF+z1OFwiB8edZ5atnz1HnnKR9LQUsMiYnNwLIsevbo5vG+oRUZKU9wKVarAVRE56k5ttfH19nYXqHgRO1ciIoEy7CQMCx8ZXKn+rcrJDJIGRZavQ56vR46vR4ShoFMZGyvUmqYX6zXQK/XQ28UP6Ui5zPG2F8t13NVZ+y5KjY2mAX/aLXG+sl5ShAEQRAEQVR59h86hv2HjkGj0SA0JBhNEhqgblys1bgr124CAGrHRNncTu2YKJw6fR5Xr90E2pRlxQRBlGdUQTzxNPOO0/Pz718XLPtFxJa2pFKRn5+P7X/8hX/37MOFi5fwxusOrL8EUQkJjLEd49p05JvwCbHtuic8wyPjhqNhvTrY+sdOnL9wBRqtFjWqh+Ohll3Rq3snq16dcbHR+PDtl7Fy7SacOHkW12/cRkhwEPr17orB/Xta9U6tivDF0+Jiw789KZ5a9zx1zvVaOzZWsCx1Iba3bZvWOLB3FwIDPdsztKJjGVsLVETnqfkYShvbS85T9/DY068BAL6d84GXK6ncKCRS1AmqBqUTrlPA0DdUIZWBZRho9DowMET2ihVgTbG/Wr0OOmO/UbkT81njOK1OZ+y7ykAukYBl2JInGpFLpAbnrNG5ahZfy5dcWb6qIQiCIAiCIKoEv2/fIVhesfo3JDZtjGlTxsGPF797/0E6ACA0JMjmdkKCg4zjMhzuc8a7M22uf7hfJ9SICOMiZS3RaAx3cNt7nBAPnUv7FBUVQK/XiT43dC6FFOnNP9QLM9Pw4O4NMCLvnNZo1Mi7d1WwTuJn/zPBE9y6fQfPPj8dAf7+mDxxDBIa1qkQzzW9Lt0HnUsAvtY9ryOa94QysrHT56W059NeRHpVpk3rFLRpnSJ6fPWIcDw5dXwZVlSxUZp6nhYVwccL7kGlyt3OU9cuOQcbv9sT4mFZFizLQmeM3wQqonjKc546KX6yLAuGYaA3OuCcnU/YJi+/wPEgwi0EKV1z2puie9VaLSRO9guVMixkrAR6vR5anQ4ShhXdLxUwOkeNPVN1ep3TrlGzeKsHywAG8VXKibLlBRJPCYIgCIIgCI8RE10Lk+rEIr5RfYSGBCM7JxenzpzHLys34OjxU/h09jz877VnwRp7HhUWFgEw341vicmpUFhY6JkDIIgy4q03pnu7hAqNIoAXaavXoTg3Q7jOAQUPbgqWfcJsu909Rc0a1XHw3+1erYEgvI3MJxAB0fHIvmbocewTHo3a3Sd7uSqCKBv4ztOiYoN46qz7szRYCk7Oim/VqoXDx0eFfKPg4orzlHAdqVSK4uJibrmiiacs74Y3Z2N7AUAmlXKRxSqK7XULi+Z95u0SCAeoJAYBslinMfQbdaJfKMMwZvFVpwXrpPhpiO2FUTzVG1yjTuxfaXKe6gzCq4RlRPdb9SQknhIEQRAEQRAeo3ePToLlcEUIOrRtiSbxDfDyGx/i3IXL2H/wGFq1SHL7vt958wWb600uFHuuEkePE+Khc+k+6FxaI/cLRnGuwYWuYHSiz0162m0UpgvF04h6iQiic+s09Lp0H3QuDaRO+hjnty4Aw7Jo0GsqFP4hLm2HzidR3jGLp8WcCOZJAUypsoztdW7fDMMgNiYGp06fAeBaz1PCdWQyS/HUc8K7O+CL7a44R6Uys3hKzlP3QO/h8o/CKH4W6NSQS6VOR94qTeKrVguFRAq5yNQeAGAAMDA4vjU6nVH8dN55WqzVQKtnIUH5i+wFqpB4+s38n7Dzn/12H+/SsQ0mTxjhwYoIgiAIgiAIEyHBQejQrhU2bvkTR4+f5MRTpVKBvLx8FBUV2ZxncqYqLfo0EQRR9VAFV+fE08Kse07NLXhwQ7DsVy3WXWURBFEKfEJqoNmI171dBkGUOQqFQawsKizkRDCZrOI4TwFDdC+Jp95BKhVe4reX2lNeMaUOAc73PAUAqVQGwOB6Vqlci0AliIqGUmIQTwE47RwFzLG/RXqN0/1GGeM+Tc5VuUQKmROf+6batToddKwOrJOxw56i/FVUxjRNaISgQH+r9fXq1vZCNQRBEARBEISJGtWrAQAyMrO4dWGhIcjLy8eD9EzERNeympOekWkcF+yRGgmCKL80HfE6WIkMquAIyFTWv/nsoSnMhTrX3DdZFVwdUgW5FgiCIAjPYboRsKi4yDvOU2XpnKeAue8pwzACMYwoewzioZmKFtvLF9t9XYjtlfLm+1BXsVNeAAB75klEQVRsr1t4+4PZAID/vfasV+sg7KOUSiE1ftY627PUMN/g/jTMd158ZRkGEpZFkVYDlVQGhUT8DT8KqTG2V6+HVqc39Gx1IvbXU5S/isqYAX26oXGjet4ugyAIgiAIgrAgLy8fgPnOewCIjY7E1Ws3cPnqdTRPTLCac/nqdQBATHSkZ4okCKLcEhTVyKV5BfctXKcRsW6ohiAIgiDEYxK71GoNiozJKp4UwCQSCRQKBZf24krsa726dQAAfn5+bq2NcIzMwnlakcVTV5yjUpn5+FUU2+sWLly66u0SCAdIWQnkEikYMEbnqXOOf6VEBqnJuepk7C5guFHG7Hxlndo/y7BQSKVgAGh0WsglEqf37wnKX0UEQRAEQRBElUOv12P/waMAgLjYaG5988QE7Ni9D/sOHMHg/j3BMAz3WEZmFs6euwiJhEVi03hPl0wQRCUh3zKyN4JSiQiCIAjPwo9ZzcnNBeB5AUylUvLEU+f33atnd5w8dRqJzZq6uzTCAXzxEKjY4qmvS7G95uN3xblKWPPh29O9XQIhAqVEBinLQsI479xUSKSQsCwYGJyrMid6ngIAC0boXHVy/6aeq2qd1lB/ORRPKUOBIAiCIAiC8AiXr17HP3sOQqPRCNbnFxRg/sKfcfHyNSgUcnRs15p7LDmpCWpUr4brN25j/cZt3HqNRoPvFy2HVqtDx3atEeBPd7gTBOEaBWnXBcvkPCUIgiA8DT821xuxvYDQsefKvuVyOV575SX07tXDnWURIrDseeqKc9ibCGJ3XYrtJeepu6kVWQO1Imt4uwzCAUqpDDKTA5V3o7kYGIYxiq8G16ez8009TxkAMlbKCaliUUp5zlcnnaueovzJuWXM/kPHsP/QMWg0GoSGBKNJQgPUjYv1dlkEQRAEQRCVnvv30/HlvMVYuORXxMVGI8DfD5lZ2bhy7Qby8vKhVCrw9OMTERQUwM2RSCR48rHxePejOVi+cgP2HTyK6tXCcf7iFdx/kI6oWjUwatgALx4VQRDlEU1Rgei+pQVWztPYMqiIIAiCIOzDb1thwtMCWGnFU8J7VPTYXpYvnrogfvKdt67MJ4iKikIqg4yVuuzaVEplkLMSl/qNMgwDKcO67BpVSmRgWRaM1hA5zDLlz+dZ5cTT37fvECyvWP0bEps2xrQp4+Dn5+tw/ox3Z9pc/3C/TqgREYb0tNtuqbOyodGoAcCp8xOSWDXvVJP6BAGovMffoWF7t24vIDjCsN3eU9y6XXu0UAZ6ZD9lwSbjDURjm6Z4t5AKQLiP4e9BWZ0rd/2tEFtfWf9tcuUzvqoREk53bAJAdFQkenbrgIuXruL6jVvIzcuHRMIiPCwU7du0QK9uHREeHmo1Ly42Gh++/TJWrt2EEyfP4vqN2wgJDkK/3l0xuH9PKJUKG3sjnOHo0eN4570PcebsORQWFmLnX1tRqxb1kfU0K1etwdffzseNGzcREVENu/7ehnYdu6FVi1R8+skHTm/PlbkjR09AdnY2Nm5Y7fT+nOHGjZto36k7PvnoPQx5eJDT81euWoPpr7wheK0W5WZgz9ypKMi8C01BLnrP3AOJzPHFw3yLnqf+FNtLEARBeBh+bK8JTwtgPiSeVlgsnae2Xk/lGb5jzaeUsb0qHxJP3cF3C5YBAKZMGuXlSoiS8JMpIGNZ+Ir4zWMLpUQGmcQ18ZWBUbzVSFxyjZqcpxKGKZeRvUAVEk9jomthUp1YxDeqj9CQYGTn5OLUmfP4ZeUGHD1+Cp/Onof/vfYsWCftxQRBVC1UhVl2HyuowMIq4VmuPEjzdgkE4RUiqoVh/OghLs2tHhGOJ6eOd3NFBACo1WpMe/o5+Pv5YcYbr0CpVCIkJNjbZZUZaWlpWPrzCnTv2hmNGzfydjkc589fwCuvzUDnTh0w7fEp8Pf393ZJFQ6Zyh85dy4Deh0AoDDrHnzDalmN2/DbJqSl3cekieOg06hRmHFbsA25f4jHaiYIgiAIwI54KvOs81SpMkcHV7TY16pOhe95KuX1PHUptrd0zlXCml3/HgBA4ml5x1emQHxYpMuRtwEKFaR5WfCXKx0PtoFSYnC+KlxynhqifiUM69J8T1A+qyoDevfoJFgOV4SgQ9uWaBLfAC+/8SHOXbiM/QePoVWLpBK3886bL9hcb3LbkLPENq6cn+t/LCircso1Jsdp+tHfvVxJ2bDn0hm3bs/kON2x6Tu3btcVztbt5HiQF9HrDf//6fhB7xZSATA5OivLuZo1bFKZbp/+BhJExebateu4ffsOnv/4fTw8eKC3yylz0tLuY87cr1Ersma5Ek/3HzgEnU6H/735GiIja3Lrt/++ESzrXP8Zd8ytiLASKZQBYSjMugcAKMi4a1M8/W3jZpw6fQaTJo5DXtp1TmwFDJG9zvb7IQiCIIjSoiwHzlOVki+eVizxraojkwrF7or2/ElYg/DDsqxLtQtie11wrhLWPP34BG+XQIhEKXX9Zhd/uRJJEdFg4NrvH5VMBlmRBAqJ8zVIWQmkrAQSlnUpNtgTVHmbZUhwEDq0awUAOHr8pJerIQiCIAiCIAjP8uBBOgC41elYUFDgtm1VFR48eAAA8Pf3E6xXKOSQueg8Kc3ciooquDr378LMuw7H5967Ilj2o8hegiAIwguUh9helcosOikqmPhW1bGM7a1ozmFWYpAofHx8XLqJjd/zVUXOU7eQmtwMqcnNvF0G4QFYhnX55tHqPoGICQhFqMrP8WALGIYxOlcl5Ta2t8qLpwBQo3o1AEBGpv04ToIgCIIgCIKobLw0/TWMGG2IQ37siacRVy8eI0dP4B7/59+9GDp8NBo3SUZicmtMe+o5XL9+w2obzZq3wuUrVzFh0hQkNEvBjP+9CwDQ6XSY//1CdOvRDw0bJ6JHv2H45PO5yMvLs6pl1eq16D9oGBo3SUZSSmuMHjcJ+w8c4h7/deUajB47Eakt26Fh40R079kPS5Yut9rO8f9OYPzER5Hcog0aJTRH+07dMf2VNwAAe/ftR98Bhujo6a+8gbh68YirF4+Vq9aUeJ5OnDyF8RMfRZPEVDRJTMWESVNw+rQwTWP2nK8QVy8e16/fwAsvvYqmSS3RNKklXnr5dYdicruO3TB7zlcAgMTk1oirF88tt+vYDS9Nf40bu3LVGsTVi8d/J05h5uyvkdKiLeKbpuCxJ57mhHD+dvlz1Wo1vpj7NTp17YWG8UlonvoQho4Yg127/7Wq6ey58xg5egIaN0lG67adMO+7H6zGFBYWYubnX6BDpx5o2DgRbTt0xazZc6HRaATjsrOz8dL019A0qSWaNW+FF6e/iuzs7BLPCZ9z5y9g9NiJaJTQHA+17Ywvv/oWOp3eaty27X9i1tYrePPPQjy/pRDDn34Pc7/8BlqtlhszcvQEbNv+J27evIW4evFoM+QpvP13IQBAo9Nj1f7r6D9wKJomtUR80xQMGzkWe/buE10rQRAEQbiCQmEtVnpePCXnaUWlosf2Vo+IAADUqlXTwUjbSHjCC8X2EoTnkLAswn38wboovvrJFZCxEvi42LO1rCmfkq6HycvLB2D7iwpBEARBEARBVFZGjhyGiOoR+Pqb7zBxwlg0SYhHaGgoAGD3P3swafJjqB0bg+effQo5ublYtHgJho4Yg43rVyM01NwXUqPRYMLEKWjduiVef3U6AgMNfcBffvVNbNiwEUOGDMbECWNx5vQprFi1Ftdv3sGSxT9wd7h+PmsOvvx6HlJTkvH8c0+BYRgcOXIMBw4eQovUZADA0p+Xo17duujSuROkUgn++PNvzHjrXej0OowbY+jFc//BA4yf+CiiatXCtMenwsdHhes3bmLr1j8AAHXqxOGF55/BzM+/wMjhQ5Fq3HbzpES75+jc+QsYMWocAgMC8djURwEAy5b9guEjx2H1yp9Rt24dwfgnnnwW0dFRmP7iczh56hR+WbEKoaEheGW67fYfAPDm669g9Zr12LptO95/939QqVRo0KB+ic/dR5/NQWBgAJ5++gncuHETCxf9BJnsfcz9YqbdOV/M/Rrzv1+AMaNHon79esjJzsHx/07g5MlTaNf2IW5cZmYWJk6ail69uqNP757YvOV3fPzp52jQoD46dmgHwCCMT546DceOHcfIEcNQOzYGJ06cwlfffIdbt+/g04/fBwDo9XpMeewpHDx0GKNHDUeduNr4fdsfePHl12zWaElaWhpGj5kIrU6Lx6ZOho9KhZ9/+RVKpbVDZ9XqtfD180WnWCnkUuAmE4hZX3yJ3Nw8vPrKiwCAJx6fgrz8fNy6eQtvvP4yruxaibzrxwAAhRrg9/2nMGDQQAwfNgR5eXlYsXI1JkyagjUrl5ermGeCIAiicqFUWveb87QAxhedKpr4VtWRWTlPK9bz171bF3z2yQdo2iTBpfl88VjlQ+KpO1i/aTsAoH/vrl6uhKjMRPoFIUzlB5W0fH5mVXnxVK/XY//BowCAuNho7xZDEARBEARBVBji33oaOUXlJ57WX6HCybfmODWneVIiiouL8fU336Fli1R079aFe+yjjz9DcHAQfv1lCQICAgAAHdu3w8PDRuHb777H669O58YWFBRg4IC+eO7Zp7h1+w8cwqrVa/HV3Fno1bM7AEOP5MaN6uO1Ge9j567d6NC+HS5fuYqvv52P3r16YM7sz8CyxnCciYbv6iaWL10suLA4buxoTJg0BQsW/MiJp4cPH0VWVja2/z5PIO6+9MKzAIDwsDB06tAOMz//AklJzTBwQD+H5+jzWXOg1eqwYvlPXC/SAf36oGuPvpg5aw6++eoLwfimTRLw/ntvccsZGVn4deXqEsXT7t264NTpM9i6bTv69O7Jne+SCAkOwpzPP0RoNUNNOp0Oi39ciuycHATYiWD+668dGD5sCN58/ZUSt337zh3M/vwT9O/XBwAwbOhgtOvYDSt+XcWJp+vW/4Z9+w7g1+VLkJjYlJsbFVULn3w2C49NeQR16sRh+x9/Yf+Bg3j9tel4ZKLB5Tx61AiMGjPR4TECwLff/YD0jAysW7MCCfGNAQCDBw1A5269rcbO/vwT3Nq7GidXGwTkiPgGiElohSXLluP5556GQiFHu7YPYcnSn5GRkYGBA/ph57lfkFVs6LPlIwN+X/MTgiPNgviI4UPQtUdfLP5pGT7+8F1RNRMEQRCEs9gSuzwdvaok52mFxTK211YMdHlGLpdj8KABLs/ni8fkPHUPv67eCIDEU6JsYRm23AqnQBWJ7b189Tr+2XPQKj4qv6AA8xf+jIuXr0GhkKNju9ZeqpAgCIIgCIKoaOQUFSC3qLDc/OdOIffevTScOn0GQ4cMFgh5SUnNkJTYDH/9vdNqzqiRwwXLW7ZsRVBQIFq2SEV6egbS0zOQmZmF5olNIZFIsHffAQDAtm1/QKfT4aknHzcLp0b4vVf4wml2Tg7S0zPQokUqrl2/juycHABAQIBBNNy6bTt0Ol0pzwKg1Wqxa/e/6NG9KyecAkCtWpHo3q0Ldu3+RxAJa+s8pKY2R0ZGJnJycktdD5/BA/sKzk9qSjK0Wi1u3rxld05AQACOHvsPt2/fKXHb/v7+6NfXLE7K5XI0a9pEENm8ectW1K9XF9HRUdzzm56egYceagUA3PP79987IZfJMGrEMG6uRCLB+HGjRR3n3zt2ITk5iRNOASA0NAQD+vexGqtUKrmep4UaPe7dvoHUlGQUFBTg0qVLVuP1ej1y717hlqVSOQKrxwAwiNGZmZnQaLRokpCAkydPiaqXIAiCIFyBZVnILfqUyzwsngqdpxWrZ2ZVx0o8rWLiN//4yXnqHoYO7oOhg62/bxNEVaJKOE/v30/Hl/MWY+GSXxEXG40Afz9kZmXjyrUbyMvLh1KpwNOPT0RQkOM7vAmCIAiCIAiismMS4OJqx1o9FhdXG79t3CxYJ5fLERFRTbDu8tWryMzMQkrLtjb3kZ6eAQC4dv0GJBIJ6sTVLrGmg4cOY/acr3DkyDGrHqI5Rrdlyxap6NmjG15/8218OnM2Wrdqia5dOqFP754uOSjS09NRUFBg8zzUqROH3zZuRnpGBsLDwrj1NWvWEIwLNIrP2dnZ8Pf3c7oGe1S3ON+Bgcb9ZNnvJfrcM09iyuNPoW2Hrohv3Ajt27fFgH59UK9eXcG4GjWqC4RZwHAcZ86e45avXLmKCxcvlfD8Gvqv3rx1CxEREVBZuABq2zintrh58xaSmydZrY+rbf16OXf+Aj6e9SP27CtEoQYATgNLXwYAm+J1cW4GALO72bdaNNas+w3fL1iMS5cuQa0233wbVauWqHoJgiAIwlXkCgWK1WrzsocFMCXvb7VMRuJpRcLy+apq4rew56mPFyupPJDjlCCqiHgaHRWJnt064OKlq7h+4xZy8/IhkbAIDwtF+zYt0KtbR4SHh3q7TIIgCIIgCKIC4a8oX3c1e7MeW9Fgep0O1aqF47NPPuTW5WQ9AAD4B4Yiolq46O1fvXoNY8Y9gjpxtfH6q9NRo0Z1yGQy/L1jJxYs/BF6nUEAYxgGX385G0ePHscff/6Fnbv/wQsvvYr5PyzEr8uXwNfXt5RH6hiJxHa4Dz+C2B1YunS5/cD+flq0SMHff2zG9j/+wq7d/2LpsuX4bv4CvP/uWxg6ZBA3TmJv27xj0On1iG/cCC/biSOOjvKs2JidnY2Ro8fD10eFXvWkCPNhIGMB33aP4dPP50Knt3YiK/xD0OuTXbhz6T/k37+B3YdO4+2XX0f3bl0wZfJEhIaGgGUl+GbefFy7dt2jx0MQBEFUPZRKBXJzzTf7UM9TQiyWztOq9vzJeD1Pfch5ShCEm6gS4mlEtTCMHz3E22UQBEEQBEEQlQhn+4tWJEwRtZcuX7F67PLlK4IIW3tER0dh7979SE1JhkJhuICTnnYbABASbnZnxkRHQavV4uKly2hQv57Nbf3x598oLi7Gd/O+RGRN87737ttvc3xiYlMkJjbFC88/g42btuCpZ17Ab5u2YPjQh60clSUREhIClUpl8zxcunQZPj4qhAQHi95eeSAoKAhDHh6EIQ8PQn5+PkaOmYDZX3wpEE/FEB0VhfMXLqBtm5Jbn0TWrIm9e/ejoKBA4D69bOOc2pwfWRNXrl61Wn/p8mXB8t59B5CRkYmv585C2vKnoNcaXKMPpNZiMv81wLAsFIHVoAishn8Xb0F0VBS++eoLwZgv5nwpqlaCIAiCKA2WN6N5WgBTUc/TCktVF09Nx8+ybJU79rLiwKFjAIDU5GZeroQgvEeV6HlKEARBEARBEIR4qlULR+NGDbFy1RqunygAHDt2HIePHEWnju0dbqNXz+4oVqvx7XffWz1WVFTMxah27doZLMtiztyvrfqUmpyOEonEuML8WHZODlauWiMYn5WVZeXwbNSooXGfRQDMfZCys3PgCIlEgnZtH8LvW7fj5i1zL9Gbt25h67Y/0K5tG3NtFYCMjEzBso+PD2JjYrhz4wy9enbHzZu3sGr1WqvH8vLyuG127NgexWo1li1fwT2u1Wqx+MelovbTsUM7HDp0BCd4PUcfPEjHuvUbBePMblkGsW0eRp0u49BgwPP4dd3vVttUqVQ2Y3xNbl7+a+jo0eM4fOSYqFoJgiAIojRYiqee7lupIudphUVW1cVT4/dxH5XKqRslCfvM+WYR5nyzyNtlEIRXqRLOU6J8cmz5+94ugXAzey6d8XYJXqXBhb9KNf9s3U5uqoQgvMdzKxaIGjdr2KQyroQgiNLyyssvYuIjUzFs+BgMGTIIubl5WLR4CapVC8djUyY7nN+6VUsMH/YwvpjzFU6cOImHWrdCQX4Orl2/gT/+2o1ZMz9G2zatUTs2BlOnPIJvvp2PkaMnoFu3zpBKpDh85CgaNKiPaY9PQbu2D0Euk2Hy1GkYOWIo8vPy8cuKlQgNCcG9e2ncPletXocly5aje9fOiI6ORn5BPlasWAU/Pz906mAQfGtFRiIoKBDLlq+Ar58vfFQqJDZriig7MbPPP/c0dv/zL4aNGIvRo0YAAJYuWw6pVIIXnnvaDWfac3Tv1R8tW6SgSUI8goIC8d+JU9i4aQvGjh7p9LYGD+qPjZs2Y/orb2D3P3uQlNQMarUaFy5cxMZNv2P92hWIjYlBl84dkZychA8/+gzXrl1HnTpx+H3rduTkOhavAWDqo49g7boNGD/xUYwfOxoqlQo///IrIiNr4MwZ8zaaN09CYGAAXnz5NYwfNxoMw2DtgjUAY32/cEJCY6xb/xve++BjNG2SAJ2mCO3btkbnTh3x+9bteOyJp9GpY3tcv3ETy35egXp16yAvP9/pc0QQBEEQzqC0EE893XeUxNOKi5QXWyuVSivUzX3uwHT8KorsdRvtHkr1dgkE4XVIPCUIgiAIgiAIwoq2bVpj4Q/zMPuLLzHz8zmQy+Vo81ArvPryiwgNDRG1jQ/eexsJ8fFY/suv+OSzWZDJpKhZozpGDHsYjY2OUAB46YVnEVUrEj8t+RmfzfwCPj4qNG7cCC1bpAAA4uJq46u5szBz9hx8+NFnCA8Pw+iRwxESEoKXX32D207LFqk4dvw//LZpC+7ffwB/f380a5qAmZ99xImjUqkUn33yIT759HO8OeMdaDQafPLRe3bF0/r16mL5sh/xyaez8PU38wAAyc2T8PJLz6Nu3TounVtvMWHcaGz/4y/s/mcPiouLEVmzJp579ilMmTzR6W1JJBJ89+2XmP/DIqxbtwGbN/8OH18fxERH47Gpk1E9IgKAwc05/9sv8e77H2PN2g1gGAZdu3TC66+8hL4DHLdWqVYtHEt/Woi33/kA38z7HsFBQRg1chiqVauGV157kxsXHByE77/7Gh98+Ck+nzUXgYEBGNC/L9q0boXxk6YItjlqxDCcPHkaq1avxYKFP6JG9Qi0b9saQx4eiLT79/Hz8hXYuesf1KtbB59/9hE2b/4de/cfcPocEQRBEIQz8J2nUqnUbn/zskKlNMf2etr1SpQOfmyvXO5Z0b08YDp+ft9eonRMmTTK2yUQhNdhtJoi6yYwhNPY6t9EmLF1fsh5apuQxB4AgPSj1hFj5R1vOE879DZcDNux6TuP79vdlLXzdOP/vgAA9Hn7mTLdT2VgbFPDxfqfjh/0ciXuoaxdnvzPeHKeEhURR9/j6Hue+6Bz6T7oXLoPOpfug86l+6Bz6V7ofFZuyvr59dTrZ9SYCdi7z3Czjo+PCieOefb36OUrV9GlW2+oVCrs37MDvr6+Ht1/WVLZPwM++vgzfPf9QgBAUFAgDh/418sVlQ32nse33/kAi39aioYN62PThjW2phLlhMr+XqwqVJXnkZynBEEQBEEQBEEQBEEQBEEQXoTvPPVGbG7t2BgsWjAPAf4BlUo4rQpIeRHPVTFyOTKyJgAgOirKy5VUHm7cNIhjtSIrtzhGECVB4ilBEARBEARBEARRaSjMSsPNg5tRkHkX0OuRMGS6t0siCIIgCIcoFObYXG8JYO3btfXKfonSIYztrXri6ZjRIxARUQ2tWlKfTnfx6v8+AQD89P0sL1dCEN6DxFOCIAiCIAiCIAii0lCUm4FT6wztCiQKH8Q//BIYhvFyVQRBEARRMgqFWfSqigIY4TqyKi6eKpVK9Ovb29tlVCrqxsV4uwSC8DoknhIEQRAEQRAEQRCVBlVQBPdvbVE+NAW5kPn4e7EigiAIgnCMt2N7iYoL33mqoNcO4Qb+99qz3i6BILwO6+0CCIIgCIIgCIIgCMJdyHwCIJGbow8LMu94sRqCIAiCEAffeSrj9bAkCEdIZVXbeUoQBFEWkHhKEARBEARBEARBVBoYhoGS5z4tyCDxlCAIgij/KJX8nqcknhLiqeqxvYT70Wq10Gq13i6DILwKiacEQRAEQRAEQRBEpUIVXJ37d0HGXS9WQhAEQRDioNhewlWkUrPYTq8dwh1MmPoiJkx90dtlEIRXoZ6nBEEQBEEQBEEQRKVCIJ5mknhKEARBlH/4vSpJACOcQUrOU8LN+PqovF0CQXgdEk8JgiAIgiAIgiCISoWKF9tbSM5TgiAIogIgjO0lAYwQj0zQ85Qin4nS8+2cD7xdAkF4HRJPiTLl2PL3AQAhiT0AANf/WODNcohSsOfSGW+XUOlpcOEvu4+drdvJg5UQVZHnVjj+fJ41bJIHKiEIgiCI0qMKpp6nBEEQRMVCoTALpgoSTwkn4DtP+fHPBEEQhOtQz1OCIAiCIAiCIAiiUqHkOU8ptpcgCIKoCPBFL5mM3IOEeCi2lyAIwv2QeEoQBEEQBEEQBEFUKvg9Twsz70Gv13uxGoIgCIJwDF88JQGMcAZhbC+9dojS8+Jr7+PF1973dhkE4VVIPCUIgiAIgiCIKs7Ro8cxeMhING6SjLh68bhx46a3SyKM7N23H3H14rF12x/eLsUlLl26jNHjJqFpYgvE1YvH3n37MXvOV4irF+/S9sTONcX2yv1DcDQ7CHXqJ+DUqdMu7dMZRo6egJGjJ5T5fgiCIIjKh1A8JecpIR6p1Px6odcO4Q7u3ruPu/fue7sMgvAq1POUIAiCIAiCIKowarUa055+Dv5+fpjxxitQKpUICQn2dlllRlpaGpb+vALdu3ZG48aNvF0Ox4bfNiEt7T4mTRzn7VLcyovTX8OdO3fx0kuG11idOnHYu+9Ame9XqvBB75l7IJHJkbNqDbDjjTLfZ1Xhm3nzUScuDt27dfF2KQRBEJUKcp4SrkKxvYS7+erzd7xdAkF4HXKeEgRBEARBEEQV5tq167h9+w4enTwRI4YPxcAB/eDj4+PtssqMtLT7mDP3a5w6fcbbpQj4beNmLFz8k7fLcCuFhYU4euw4hg8fgrGjR2LggH4IDwvDk09MxekTh13apjNzJTK6eFgWfDvvB2yroE5ogiCI8gyJp4SryHjiqYJeO4QbCAjwR0CAv7fLIAivQuIpQRAEQRAEQVRhHjxIBwD4+7vvx3FBQYHbtkVUXB6kG19bfn6C9VKpVHCB2BlKM5dwHb1ej8LCQm+XQRAEUalRKkk8JVxDyu95St+TCIIg3AKJpwRBEARBEARRRXlp+msYMXo8AOCxJ55GXL14Qb/Gf/7di6HDR6Nxk2QkJrfGtKeew/XrN6y20ax5K1y+chUTJk1BQrMUzPjfuwAAnU6H+d8vRLce/dCwcSJ69BuGTz6fi7y8PKtaVq1ei/6DhqFxk2QkpbTG6HGTsP/AIe7xX1euweixE5Hash0aNk5E9579sGTpcqvtHP/vBMZPfBTJLdqgUUJztO/UHdNfMUS27t23H30HDAEATH/lDcTVi0dcvXisXLWmxPN04uQpjJ/4KJokpqJJYiomTJqC0xbOVVMvzuvXb+CFl15F06SWaJrUEi+9/LpDMXnk6AnYtv1P3Lx5i6upXcdugjE6nQ5zv/wGrdt2QsP4JDz+1Eu4bqM37aHDRzB2/GQ0TWyB+KYpGDP+Efx34mSJ++fXb8nKVWus+uC269gNUx9/Cvv2HcCAwcPQMD4JHTr1wOo16wTba9fBcAzvffCx4Jhs7SuuXjzeee9DbN6yFT169Te8Xnr1x46duxzWuWv3vxg6YgyaNW+FhGYp6NK9Dz6dOdvqWIqKivHOex8ipUVbxDdNwWNPPM3dPMDnjz//xpBhoxHfNAVNk1risSeexv/bu+/Ams4/juPv7IEgy0xCCELsvfcsVaVGlR9auuhStFR161CUVrXVUrQ2Var23jNWbEnEHgkig6zfH5Fb172RIUOSz+uv5MznfO859z7nfM/zPEHBwSbLzZk7n2Yt2uHrV4NnuvYwOl9TY8HCJXTq3A1fvxrUrNOQd4e/z7Xr/40tNWHiZMqWr8yevcbbffPtYVSqUstQpqTPaO++/bw/6kOq1axP1Rr1eG/kh9y5Y3qtzZz1h+GabNCoBZ+P/drkHE36jDdu2pJYxkrVWf7Pv3j7VCI8PJxFS5YaztVhw0em6bhFRMS8B18OsrHRuJWSetZqeSoZbNx3PzPuu5+zuxgi2UpjnoqIiIiIpMOa0e2IjY7M7mIYWNs70vrTlWlap1ev7hQpWoQpP/5M/359qOxXCRcXFwC2btvBgJdeoXQpL955awjhd+4w4/fZPNfzBf75ezEuLs6G7cTGxtKv/yDq16/LqPeHU7BgQQBGvD+aZcv+oVu3Z+nfrw/HjwUwf9FfhFy4zOzff8XCwgKA8RMm8f2Un6hdqybvvD0ECwsLDhw4yJ69+6hTuyYAf8yZi0/ZsrRs0RxrayvWrd/Ihx99SnxCPH1feB6A6zdu8L/+A/EoWZLXX30ZR0cHQs5fYPXqxC5Gy5TxZug7b/Lt+O/o1eM5at/fdo3q1ZKN0clTp+n5fF8KOhXklZcHAvDnn/Po0asvixfOoWzZMkbLvzb4LTw9PRj+7tscDQhg3vxFuLg4897wocnu47VXBxERGcnFCxf5YNQIAJOuk3+Y8hOWVpYMemkAt27f5udffuODj8aybOkiwzJbt+3gxYGvUK1qVd56czDxCfHMnbeQns//j78WzcXHp2yyZUirwMAgBr85lJ7du9L12WdYsGAxw0aMws+vEuV8ytK2TSucChTgsy++4pnOnWjSuGGK3UHv3r2Xf1eu5oXne+Lo6MjvM//gtcFvs3XTWgoXLmR2nZOnTjNw0GvUrFmdoe+8gZWlFUHBwezdu5+EhATu3Qkj8sYlAD786FMKFSrEG2+8xvnzF5g+YxY2Np/z8QfvGra3cNESRrw/mhbNmzJi+DtEREQwc9afdO/Zh3+WLcbN1RWAeQsWMWr0x9SqVYP+/fpwLiSEQa8MplDBghQrVjTF+H03eQqTv/+RTh070LPHc1y9do0Zv8/m0KEj/P3XAuzt7Rn8+iusXbeB4e99wIpli3FwcGDlqjUsW76CMaNHUsrLy2ibo8d8SuFCBXnnrcGcPHWGOXPnc/XqNX6b9qNhmYmTfmDS5Ck0adyQPi/05MTJ0/w2fSYnTpzi9+k/G65JgFOnz/DOuyPo3asHPXs8Rxlvb8aP+5JRoz/Gr1JFevV8DgBPT48Uj1dERFL2YNJLLU8lLWyMxjxV4l0e38HDx7K7CCLZTslTEREREZF0iI2OJPauaauunKRG9Wrcu3ePKT/+TN06tWnTuqVh3pdfjaNw4UIsmDcbJycnAJo1aUzX7s8z9edpjHp/uGHZqKgonunckbffGmKYtnvPPhYt/osfJk+gfbs2AIReu0RF33KM/PBzNm/ZStMmjQkMCmbK1F/o0L4tkyaOw9Lyfuc4/RO7Ck0y94/fsbe3N/zft09v+g0YxG+/zTQkT/fv9+fWrdusXfWTUXJ32NC3AHBzdaV508Z8O/47qlevyjOdO6UYo/ETJhEXF8/8ubMoUaI4AJ07PUWrth35dsIkfvzhO6Plq1T24/PPPjL8HxZ2iwULFz8yedq4UQNm/zGHsLCwZMsUExvLsoULDC1RbK0sGDfxB06cPEX5cj7Ex8czeswnNG7UkGk/TzGs1/25rrRu25HJP0xl0sRxKR5vap0+c5YF82ZTs0Z1AJ5q346GTVqycNESRr43DN8K5SmQPz+fffEVfpUqpirWp8+cZc3KZXh4lASgXr06PNXpWZYt/4e+fXqbXWfr1u3Y2tkxc8Y0rKysjOad37OCA7NGc/J8LAAuLs5M//UnQ4IwPj6e32f+wbC3XiF//vxERETw6edf0eeFXnz04SjDdjp2aE/bDp35bfpMRgx7h5iYGMZ9+x1+fpX4Y+Zvhs/Ep2wZRn7wUYrJ0/PnL/D9D1N5b/hQXnqxn2F6syaN6dajN4sWL6X38z2wsbFh3Ndf8EzXHnz1zXjeGPwao8d8Sr26tenb53mT7drfj0NS6xN3N1cmTvqBXbv2ULdubW7cCGXq1F9o1rQJv/4yxRCHkiWL8824iaxfv5GWLZsbthcUFMzsmb/SoH49w7Tq1asy5uPP8fQomarPVEREUs94zFMlwCT1rB9oqazEu2SED0YMSXkhkVxO3faKiIiIiIiRq1evEXDsOM91e9aQOIXExEn1alXZsHGzyTrP9+ph9P/KlaspVKggdevUJjQ0jNDQMG7evEWNalWwsrJi5649AKxZs474+HiGDH71v8TpfQ+2gnswcXo7PJzQ0DDq1KnNuZAQboeHA+DklDhu6+o1a4mPj3/MKEBcXBxbtm6nbZtWhsQpQMmSJWjTuiVbtm4jLi7ukXGoXbsGYWE3CQ+/81hlea5bF6Mu/KpV9QMgJCQEgGPHThAcfI5OHTsY4h0aGkZsTCy1a9Vk567dj7X/h1WoUM6QOIXExKS3dynOnTv/iLUerUnjhobEKYBvhfLkz5+fcyHJb9PJqQBRUVFs2rzVZJ59IXej/3v17G50TtWuVZO4uDguXb4KJLbcDQ8Pp0P7dkYxzJcvH74VyrPr/jl7+MhRbty4YUhwJun67DOpGjt41Zq1JCQk0LZNK6P9eHl54u7uxq7de/6LgW8FXn/1ZWbNnsOAga8QHR3FV2M/MzqOB4/vwW77+tx/qWDj/a6Pt23fwb2YGPr362O0ft8XnsfGxpr1D13XpUp5GSVORUQkcz1Y11ECTNLC2qjlqc4deXzlfbwp7+Od3cUQyVZqeSoiIiIikg7W9o/ugjSrZWR5Lly4CIB36VIm87y9S7P8n3+Nptna2lKkiHGiKjA4mJs3b1GrbiOz+wgNDQPgXMh5rKysKONd+pFl2rtvPxMn/cCBAwdNxmcMDw/HqUAB6tapTbu2rRk1+mO++XYi9evVpVXL5jzVoV26HiSFhoYSFRVlNg5lyniz/J9/CQ0LM3TlClC8eDGj5QreTz7fvn2bAgXyp7kMhu0WM95uUqL41q3bAIbxL98eOsLs+g8nph/Xw+UBKOhUkNu3b6V/m8XNbLOgk+EYzen4VHvmLVjES4New93djYYN6tOmdUvatG6JQ6EiRssWe+gcLVjw/mdzP/keFJQYw57P9zW7L0+PxO5pk66P0g91m2tjY4PnA8nf5AQFBRMfH0/TFm3Nzr8RajwO62uvDmL5P/9y6NARPvpwpFGC+UGlShmXp3DhQhQs6GQo74WL969r71JGy+XLl48iRYpw8f78JB4lS6R4LCKS9/z4yyw2b0v+hZyWzRryUr+eJtOvXL3GwiX/ciTgBBERkTg7F6JurWp0ebod9vZ2ZraU99jZ/VdX0biVkhY2Sp6KiGQ4JU9ziINzP3/k/Ko9Rz1yvmS+HWePp7hMfe8KWVASyY3Kn97w2Nv4J5ltnSjb3HRhEZEskpCQwGdfTybg2CkAxn0xihLFTbu8fBIfuKV1fNHc7MFu5pIkxMfj7u7GuK/HGqaF37oBQIGCLhRxd0v19oODz/FC3xcp412aUe8Pp1ixotjY2LBx02Z+mz6ThPjE7n0tLCyY8v1E/P0PsW79BjZv3cbQYe/zy6/TWTB3Nvny5XvMI02ZlZX5JOWDXRCnb7tWZqcnbTeppe0HI0dQrpxPmrdvriUjQFwyLXiTL0+ad22QbIL3ERu1t7dn3p8z2bFzNxs3bWbz5q0s+etvGjVswLSpk4yWjY02n4Q1xDAh8Vgnjv8aZ2dnk+Uy6rsmPj4BKysrfps21Wzck5K6SQIDgwytb0+cPJ0hZUiNB1tAiYg8rIqfL4UKmra29ylr+jJUYFAIn345iajoaEp7eVChfBlOnwnm7xVrOXDoKB+NfBtHR4esKPYTzbjbXiXAJPUebHlqrl4uklZ/zP0LgN49n8nWcohkJyVPRURERCTbrNu4jYBjp7CwsEg2uaQHblkvqYvas4FBJvMCA4OMurBNjqenBzt37qZ2rZqGlhSh1y4B4Oz2XwtDL08P4uLiOHM2kPLJJP3Wrd/IvXv3+Pmn7ylR/L99J9cVbbVqVahWrQpD33mTf1asZMibQ1m+YiU9nuuabJLQHGdnZxwcHMzG4ezZQBwdHXAuXDjV23uUtJTLHE/PxFaRTk4FaNSwfprXf7CF7INdNV+8cDG5VZ4YlpaWNGxQj4YN6jHq/eH89POvfPXNePbsP4htAWcgsVveu7dvPHI7Xp6eALi7u1Gvbp1kl0s6/wODg6lTp5ZhekxMDCHnL+BboXwK+0k850uX8qJkCq074+LieHfESFxdXejQvi3Tfp1Bh/ZtadjAtDvdoKBg6tSuafg/LOwmt27dNrToTbp2zp4NMrqOIiMjuXLlCo0bNXxkWZI87rkqIrlD56daU9E35Zd14uPjmTx1BlHR0fTs1onOHRPHQY+NjWXC5F/Zf/AIf85fara1al5jZ2eHnZ0dd+/efazeKiTvsbZ5sOWpxsuVx7dy7SZAyVPJ2zTmqYiIiIhkixuhYfw5fylVK/vi4mw+AfXwA7cvPh7Om68NYMJXo6lR1Y+Q85f4c/7SLC557ufu7kZF3wosXLTE0KUpwMGDh9h/wJ/mzZqkuI327dpwLyaGqT9PM5l39+49wxigrVq1wNLSkkmTp5iMU5qUUDe0cnwgv347PJyFi5YYLX/r1i2TJLyvb4X7+7wLgMP9RPvt2+GkxMrKisaNGrBq9VpDl6eQ2P3p6jXraNyoYbItMNPKwcHhscZF9atUEU8PD6b9OsOkW2OAGzdCzaz1n6Tk6+49ew3TIiMjWbTkyb6+wsJumkyr+MBnXu35MZRt3R+AAsUePW5To0YNyJ8/P1N+/IXY2FiT+UldTVf2q4SLszN/zplHTEyMYf6ixX9x+3byXQwnadOmZeI5//2PJvPi4+O5efO/Y5r60zQOHz7K2M8/YdjQt6hU0Zf3Ro7mzp0Ik3XnzJ1vVO5Zs/8EoFnTxgA0bFAfWxsbfp852+g6mf3HXGJiYmmRiusawNHBweh7QUTkUfbuP8Sly1fxKFmMp59qbZhubW3NS/17YmVlycYtOwg3872W11hYWPDpx6Pp80IvGjcyP+yBiDkFnZwoVKggAJ73XwYTeRwD+nZnQN/u2V0MkWyllqciIiIiki2mzZhLfHw8L/6vJ5+M/c7sMik9cBsy9EM2btlBj26dKJA/87tkzUveG/Eu/V98me49XqBbty7cuRPBjN9n4+7uxiuDXkpx/fr16tKje1e+m/QDR44cpUH9ekRFhnMu5DzrNmxlwrdf0ahhfUqX8uLlQS/y49Rf6NW7H61bt8Daypr9B/wpX74cr786iMaNGmBrY8NLL79Or57PERkRybz5C3Fxdubq1WuGfS5avJTZf86lTasWeHp6EhkVyfz5i8ifPz/NmyYmhkqWKEGhQgX5c+588uXPh6ODA9WqVkl2HMl33n6Drdu2071nH3o/n9gq5o8/52JtbcXQt9/IgEgn8vOryNK/l/PZF19RpbIf+Rwdadky9V3rW1lZ8flnH/HiwFdp/1QXnu3yNO7ubly6fIWtW7fj5eXJ+HFfJrt+40YNKF68GO+9/yEDXwrEysqKBQsX4+zszMWLlzLiEDPF5B9+ZM+efTRr2piSJUtwIzSMP/6YS7GiRalVqwZOBQqQ/3jiOWJp+ejbX6cCBfh4zCjeHT6Szl2689RT7SlcqCDnL1xk3boNtG7VgqHvvImNjQ3vvD2EUaM/pnffATzVvh0h58+zcNFfhnFRH6WUlxdvvzmYbydM4ty5EFq2aIaDgwPnQkJYuWoNr786iB7du3H8xEkmf/8jvXp2p3GjBgB88/UXPNOlO2O/Gsfnn44x2m703bv07fcS7dq25uSpM8yZO58mjRsaWtG6uDjzyisDmTR5CgMGvkrzZk04efI0c+ctoFHDBrRo0SxVMffzq8i27TuY9tsMiri741GyJNWqVUnVuiKS9+z3PwJA3drVTVquFy5UkPLlyhBw7BQHDh6hScO62VHEJ0q3rl3o1rVLdhdDchhbW1sWL5zDrZu3KP3QGOgi6dG8Sdp7shHJbZQ8FREREZEst2XbbvwPBdCn17O4uZqOLZhED9yyT6OG9Zn+609M/O57vh0/CVtbWxo2qMf7I97FxSX5z+xBX3z2MX6VKjF33gK+HjcBGxtrihcrSs/uXQ2tAwGGDX0Lj5IlmDV7DuO+/Q5HRwcqVvSl7v0uUb29S/PD5Al8O3ESY78ch5ubK7179cDZ2ZkR739g2E7dOrU5eOgwy1es5Pr1GxQoUICqVfz4dtyXhuSotbU1474ey9ffjGf0h58QGxvL119+lmzytJxPWeb+OZOvv5nAlB9/AqBmjeqMGPYOZcuWSVdszXm+Z3eOHj3GosV/8dv0mZQoUTxNyVOAhg3qsXD+H0z+/kdmzJxNZGQURdzdqVGjGs/3evSb4zY2NkydMokPx3zKhImTcXVzpX+/PhR0cmL4ex88ct3s1Kplcy5cuMjCxX8RFhpGYefC1Kldi7feHIxTAdOx+FLS5ZmncXd3Z+pP05j60zRiY2MpWrQI9erWpmPHDoblevXsTlxcPL9Mm87Yr8ZRvrwPP0/9ngkTJ6dqP6+/9jKlSnkxfcYsJk76AUtLC4oXK0brVi1o1LABsbGxDB8xCjd3N94f8a5hvQrlyzFkyGuMnzDJpPveTz76gEWLlzJ+4vfEx8fT9dlnGD3qPaP9vvXG6xQuVIhZf8zh8y++wtnZmX7/e4F33hqS6u54339vGCNHjWH8hMlER0fTtUtnJU9F8qDd+w6ye99BYmNjcXEuTGW/8pT1LmWyXNC5CwCU9jL/cklpLw8Cjp0i+NwFSF3v4SJiRikvL1DeVEQkw1jExd41P7iUpIm58Zsy0sG5nz9yftWeozJlv48rqdzO1doCEOq/KjuLk6l2nD2e4jL1vSukuMyTGqvUHF92aNphEACbVvyczSV58o2fdQiAd/oYP9w6UTZtD2bzgj5VEh/Wzzq0N4Ulc4YJ3QekuMzb839L93Ye/A1MzXZSWybJvW7eus2wkZ9TxN2VT0YPxdLSkiFDx3D9RijjvhhFieJFDcu+9+FXBJ87z7C3XqZGNT+Tbc2eu4R/Vq6nQ9vm9On1bLrKk1I9LrPreXmJYplxFMuMk9NjuXDREoa/9wHLly6kYkXfbC1LTo/lk0SxzFiKZ8b58ZdZbN6WzJjjVSry+qC+5H+gN5CXXh9BREQkX34yAi9P0xeVVqzawKw5i6lTqxpvD37xkfv+8NNvzU7v2qk5xYq4Ym2dOWM9xsYmdtGeWduXzKfPMHfQ55h1tu06AEDDutUzdLv6DHOHnPg5pqcOqJanIiIiIpKlps9aQFR0NAP798LS0vKRy16/P06ji3Mhs/OdCxe6v1xYivtN6YFb0oPVhyXdGCQ3X1JPscw4imXGyemxjAi/BcCtsOvZfgw5PZZPEsUyYz1uPJV0/Y+XZ0kGlClFJd9yuDgX5nb4HQKOn2LewmX4Hwrgm4k/MWbkW4Y6XnR04pjjdnZ2Zrdnb293f7norDkAERFJ0fwlq4GMT56K5CRKnoqIiIhIltm1x5/de/15+qnWZlsfPEwP3ERERESeHB3aGvdc5GbnTNNGdalcqTwjPhjLydOB7N57kHp1Mv6B+yejh5qdntkti9VyOefTZ5g76HPMOknf9Rkda32GuUNe+RyVPBURERGRLHHnTgTTZ82naBE3unZun+X7T+8Dt7xyY5AVFMuMo1hmnJwey3wFCgJQsLBrth9DTo/lk0SxzFiKZ+ZzLlyIpo3r8c/K9fgfOmpIntrb2xEREcndu3fNrpf0opy9vX2WlVVERB6tV/fO2V0EkWz36H7SREREREQyyMw5i7l1O5wX/9cDW9vUjY2R1LJUD9xERMzr1rULZ08dzfbxTkVEihV1ByDs5i3DNFcXZwBuhN40u05o2M37yxXO1LKJiIiIpIVanoqIiIhIlti3/zA2NjYsXrqSxUtXGs27des2AD/+MgtbW1vatmpC3drVcXVxJiIikhuhN81286sHbiIiIiJPhoiISADs7GwN00p5liD43HkCg0OoUc3PZJ3A4BAAvDxLZE0hRUQkRQHHTwFQsYJPNpdEJPsoeZoHHJz7+WOtX7XnqAwqiaTWnegodpw9/tjbqe9dIcVlMmI/kneVP70h2XknyjZPdp5krbfn/5ap2+lTpRYAn274N0P2k9FSc/wTug/IgpIIQExMDMdOnE52/pnAcwDUqlEF0AM3ERERkZwgISGB3Xv9AfAu5WmYXqOaH5u27mLXngM8+3Q7LCwsDPPCbt7ixMkzWFlZUq1KpawusoiIJGPsuCkAzJo2IZtLIpJ9lDwVERERkSzx649fJztvyNAxXL8RyrgvRlGieFHDdD1wExEREXkyBAaHcPHiFerWroa19X+PFCOjopg9ZwlnAs9hZ2dLs8b1DfNqVq9MsaLuhJy/xN//rKFzxzYAxMbGMm3GXOLi4mnZrCFOBfJn+fGIiIh51atWzO4iiGQ7JU9FRERE5ImlB24iIiIiT4br10P5/qffmT57Ad6lPHEqkJ+bt24TdO48ERGR2Nvb8car/SlUyMmwjpWVFYNf+R+ffjmJuQuXsWuvP0Xd3Th1JojrN0LxKFmM57t3zsajEhGRh70zZGB2F0Ek2yl5KiIiIiJPLD1wExEREXkyeHqUoF3rppw5G0zI+YvciYjEysoSN1cXmjSsQ/vWzXBzczFZz7uUJ2M/HsHCv1Zw5OgJQs5fwrlwITp1aMWzT7fD3t4uG45GREREJHl5KnkaGxvL8pXr2bp9D1ev3cDe3o4K5bzp0qkdpUt5ZHfxRERERMQMPXATERERyX5F3F35X+9u6Vq3aBE3Br/8vwwukYiIZIawm7cAKFyoYDaXRCT75JnkaWxsLGPHTSHg+CmcnApQo5ofN2/dYs++Q+z3P8qwt16mamXf7C6miIiISJ40+duPHzlfD9xEREREREREMt8b734EwKxpE7K3ICLZKM8kT//+Zy0Bx09RprQnI4cPxtHBAYBtO/fy/dTf+eHnmXz39RgcHOyzuaQiIiIiIiIiIiIiIiJZr2gRt+wugki2yxPJ07i4OP5dvQGA/n17GBKnAA3r1WLr9j34Hwpg45adtG/TLJtKKSIiIiIiIiIiIiIikn2++XxkdhdBJNtZZncBssKJU2e5ExGJu5sLZUp7msyvX7cGAHsPHMrqoomIiIiIiIiIiIiIiIjIEyJPJE+Dz10AoLSXh9n5SdPP3V9ORERERERERERERERERPKePJE8vX4jFABn50Jm5zsXTpx+JyKS6Oi7WVQqERERERERERERERGRJ8fAwe8xcPB72V0MkWxlERd7NyG7C5HZfpk+h/WbtvNMp7b06NrRZH5cXBwvvPgWAFMmfkbhQgWT3daHn35rdnr/5zvi6GCPhYVFhpT5YXEx9x4538rGNt3rpuRR205J0r4trKwASIiLe6yyPMniE+JTXMbSIuX3FSysrCAB4uJiHrtMqdlfasr9JLOysgEyJl653fFTgQBU8CltND3ewird27RMSP6afpztZjfr+99Zsbn4OysjpSdedjY2mVUcE3djUv5+yOzy2Dvkw6mQS6buQ9Lv6qUQIAFra/PnQWxs4jmU3HxJPcUy4yiWGUexzDiKZcZRLDPW48bT2sZWdbknWEp1ucel6zHn02eYO+hzzDqnzgYD4OPtlaHb1WeYO+TEzzE9dTnrTCpLnnP37j3y5XPE2ipzQvo4J2J2nsRJ+w65cAkAjxLFsq0sOYVilTaKV+q5uroB4OBYIJtL8uTTeZU2T3q8clJlTrKHpaXlI18munTlOvDknuM5iWKZcRTLjKNYZhzFMuMolhlL8czdUqrLPS6dPzmfPsPcQZ9j1snopGkSfYa5Q175HPNE8tTe3g6Au3fNd8n7YFe9Dvb2j9zWJ6OHZlzB8pCJU/8EFL/UUKzSRvFKPcUq9RSrtFG8JKdzLVLikfN1jmccxTLjKJYZR7HMOIplxlEsM5bimbulVJd7XDp/cj59hrmDPsecT59h7pBXPsc8Meapq4szAKGhN83ODw1LnJ4/n6Mh0SoiIiIiIiIiIiIiIiIieUueSJ56eSa+gRYYHGJ2ftJ0T8/MfVNNRERERERERERERERERJ5ceSJ5Wt7Hm/z5HLl67QZnAs+ZzN+xaz8AtapXyeqiiYiIiIiIiIiIiIiIiMgTIk8kT62srGjfpjkA02fOIzIqyjBv2869+B8KoECB/DRrXC+7iigiIiIiIiIiIiIiIiIi2cw6uwuQVZ5+qhVHj50k4Pgp3h7xKb7ly3Lr1m2OnzyDlZUVrw3sg4ODfXYXU0RERERERERERERERESyiUVc7N2E7C5EVomNjWX5v+vYumMvV69ex87ejgo+3jzbuT2lS3lkd/FEREREREREREREREREJBvlqeSpiIiIiIiIiIiIiIiIiEhy8sSYpyIiIiIiIiIiIiIiIiIiKVHyVEREREREREREREREREQEJU9FRERERERERERERERERAAlT0VEREREREREREREREREACVPRUREREREREREREREREQAsM7uAkju9tfy1Zw5E8T5i5e5ffsOd+/do6BTAXzKlKJdm2ZUKFcm2XXPBp1jyd+rOHHqLNHRd3F3c6Fxg9o81a4l1tZWWXgUme/u3XscPnqc/f5HOHHyDNduhGFhAW6uLtSs5sdT7VviVCB/suvnpVglOXbiNMeOn+ZMYDBnA89x89ZtAObMmJziunktXrGxsSxfuZ6t2/dw9doN7O3tqFDOmy6d2lG6lEd2Fy9LBQaFcDjgBGfPBnMm8BzXb4QCMOmbj3Bzc0l2vStXr7Fwyb8cCThBREQkzs6FqFurGl2eboe9vV1WFT9LxcbGEXD8JPv9jxJw/BRXr14nPj4eZ+dCVPHzpVOHVri5OptdNy/GS55M6bnme/Ub8shtfvLBO/iULW123o7d+1m1ZhPnQi4C4OlRnHatm1GvTvXHOIonR3q/QwHOBJ7j39UbOHbiNLdv38HRwZ6iRdyoVaMKnTq0MrtObo5nWmP54y+z2Lxtd4rbfXXgCzRpWNdkumJpaseufazbuJ3gc+eJio7G0dGR0l4ladW8EbVrVk12vcNHj7NsxTrOBp0jNiaW4sWK0KJZA1o2a4iFhUWGH19WSm8sd+3xZ/W6zQQFnycmNhZ3Nxca1K1Jx/YtsbW1SXa93HxeZnU9KiEhgbUbtrFh03YuXrqCtY013qU8efqpVvhVLJ+Zh5rp0hPL+Ph4du89aLhXDAwKISo6Gt/yZfnw/TdT3GduPjclbXQvnTOkVE9q2awhL/XraTJd961ZKyufx+Tm38XspPvrnE911LRR8lQy1eK//gULCzw9ilOyRDEsLS25eOkKu/b6s3vfQfo+35V2rZuarLff/wjjJ08jLi6O8uW8KVSwIMdOnGbuwmUcCTjBiHdey1VJrm079/LL9DkAFC3iRvWqlYiJieHUmSD+XrGWLdv3MPq9NyhW1N1k3bwWqyS/z15IcMiFNK+X1+IVGxvL2HFTCDh+CienAtSo5sfNW7fYs+8Q+/2PMuytl6la2Te7i5llFi/9l70HDqdpncCgED79chJR0dGU9vKgQvkynD4TzN8r1nLg0FE+Gvk2jo4OmVTi7HPsxCnGjpsCgItzYSr7VQDgzNlg1qzfwtYde3hv6GuUe6iSm1fjJU+m9FzzAHZ2ttStVc3sPCcn8y8zzVu4jL+Wr8bG2prKlRKvl8NHj/PdlN8IOd+O5559Ks3leNKkN57/rFzPH/P+wtLSgrJlSlHex5vbt+8QcuES6zZuM5s8ze3xTGssyz/ihcPb4XfwPxSAhYUFvuXKmsxXLE1NnzWf1eu2YGFhQflyZShc0Ilr10M5dOQ4h44c5+kOrejVvbPJems3bOW3mfOxsLCgkm85HOztOHz0BL/+Po+TpwN5bWCfjDqsbJGeWM6as5gVqzZgZWVJmdJeOBXIz+nAYBYs+Yf9/of54L03sLczfZiS28/LrKxHJSQk8MPPM9m2Yy8O9vZUrexLVPRdjh47yZGAEwzs14vmTetnzYFngvTEMioqmu+m/Jau/eX2c1NST/fSOU8VP18KFSxgMt1cYkb3rVkvq57H5Pbfxeyk++ucT3XUtFHyVDLV+8Nep0xpL5M3jnftOcCkH2cwe+4SatesgotzYcO8yMgofvxlFnFxcQx+5X80rFcrcXpUFF98/T1HAk6yfOU6nunYJkuPJTNZWVnRvEl92rdphkfJ4obpkZFRfDdlOoeOHGPqtNl8/ME7RuvlxVglqexXgTq1q+FdygOPksUZ/M6HKa6TF+P19z9rCTh+ijKlPRk5fDCODok/ZNt27uX7qb/zw88z+e7rMTg42GdzSbOGT9nSeJQsjndpT7xLezJyzNfcuh2e7PLx8fFMnjqDqOhoenbrROf750ZsbCwTJv/K/oNH+HP+UrNvseZ0FhYW1K1VjQ7tWhhVmu7di+G3mfPYtHUXk3+cwYSvPjS8cJCX4yVPprRe80kK5M/Pq2lIghw/cZq/lq8mn6MDH3/wDiWKFwXgwsXLjPlsPIv/XknVKhVNbkBymvTEc+fuA8yeuwQvz5K8PXgARdzdDPPi4+MJDAoxWScvxDOtsWzRtAEtmjYwO2/J36vwPxRAhXJlTN74VixNnTkbzOp1W7C3t+PD99+ktNd/LYcOHTnO1xOmsuzfdTRpVNcQL4ArV68zY/ZCLC0tGTV8ML7lExPVoWG3+PiLCWzZtpuqlX0N9cucKK2x3Lv/ECtWbcDBwZ733nmVcj7eQGKvOt9PncHeA4eZu2AZ/V7oZrReXjgvs7IetXX7Hrbt2Iu7mwtjRr6Nc+GCQGJPPZ9//T2/zZqPX6XyybYieNKlJ5ZW1lY0ql8b79KelC7lwe3b4Uz4/tcU95UXzk1JPd1L5zydn2pNRV+fFJfTfWv2yKrnMbn9dzE76f4651MdNW005qlkKt/yZc121VS3dnV8y5clLi6O4yfOGM3bsHkHdyIiqV61ktHDB0cHB/r36Q7AilUbiI+Pz9zCZ6GmjeoyaMDzRolTAEdHB15+sTcAJ08Hcu16qNH8vBirJL17PMOzT7ejWpVKFMiffJfGD8pr8YqLi+Pf1RsA6N+3h+FmD6BhvVpUq1KR8PA7bNyyM7uKmOWefqo13bt2pFaNKjgXLpTi8nv3H+LS5at4lCzG00+1Nky3trbmpf49sbKyZOOWHYTficjEUmcPv4rleWvwiyaVUVtbGwb07Y6jgwPXb4Ry8vRZw7y8HC95MqX1mk+vZf+uA+CZTm2Nki0lihc13FgsW7E20/afVdIaz5iYGGbMXoCdnS3D3hpklDgFsLS0pIy3l8l6eSGeGXlubtme2E1dk0am3fUqlqaOnTgNQJ2aVY0SpwBV/CpQydeHhIQEzgQGG837d/VG4uLiaNmsgSFxCuBcuCC9nktspbpsxbrHPJrsldZYrlq3GYCn2rUwJE4hsXXBS/17YWdry7qN27gTEWm0Xl44L7OyHpUUz+e7dzY8lILEe/EWTRsQGxtruCfIidITS3s7O15/uS/t7w8VZGtrm6p95YVzU1JH99K5m+5bs0dWPY/J7b+L2Un31zmf6qhpo+SpZBsrq8S3F6ytjRtA7/c/AmC2//Iy3l64u7kQHn6HE6fOmszPjZwLFzSMdxp285bRPMUqbfJavE6cOsudiEjc3VwoU9rTZH79ujUA2HvgUFYXLcdIOmfq1q5uMo5Z4UIFKV+uDHFx8Rw4eCQ7ipdtbG1tKVY0MQny4PeS4iV50b17MRw+egIw//uS9F176MgxYmJisrRs2W3PvkPcuh1OvdrVjXoZeRTFM21OnQ7k0uWr2NmadoWlWJpnY5O6zpcefjlvv39iF2X16tQwWbZWjcrY2NgQfO68YeynvCAw8ByA2fGKCjoVoGTJYsTGxuJ/8Khhus7LjK1HXbt2g5DzF7GxsaFm9com+0qK5750dLGXEyQXy/TQuSkP0r107qb71pxBv4t5k36Ps4/qqKaUPJVscejIMQKOn8LB3h7f8sZjOAWfOw9A6VKmFVSAUvffEA8+l/bxLnOiOxGRhre1Hx67QbFKm7wWr6TjeLhVRZKk6edyyfFmhqBUxjC3nDOpFR8fb2gJX6igk2G64iW5xd179/hr2SqmzZjL738sZN3Gbdy8ddvsspcuXyEmJoYCBfLj6mLa3YyrizP58+fj3r0YLl2+ltlFf6IcCUi86S3vU4aoqGjWbdzGbzPn8/sfC9m0ZSfRd++arKN4ps3mbYmtTuvUqmrSbaBiaZ5fxfJYWlqye99BAoONu40+fPQ4R4+dwt3NhUq+5QzTIyOjDL975n7jrK2tKVki8a34vPQbF333HgD58zmanV8gfz4Agu7XwUHnJWRsPSo4JPFvj5LFTF5KfnCdq9duEBkVlQGlf7IkF8v00LkpD9K9dM60e99BZsxeyLQZc1ny9ypOnw0yu5zuW3MG/S7mHrq/zhlURzWlMU8lSyxbsZbzFy5xLyaGK1euExgcgoODPYNf/h9OTv8lBCOjooiITLxgXJJp/p/ULUBeeat75eqNxMfH41GyOO5urobpilXa5MV4JR2Hs3Mhs/OTjvdORCTR0Xext7fLopLlHEkxdEkhhtdvhGVRiZ4MW7bt5nb4HZwK5Dfq6kPxktwiPPwO8xYtN5r2+x+L6NmtIx3atjCafu3++fyobotcChfizp0Irt8IxdOjeLLL5TbnL14G4E5EBO+O+pzQ0JtG8+cuXMY7Q17C54HvEcUz9WJiYtixez9gvstexdK8EsWL0qdXF2b+uZgPPh5HeR9vChV04vqNUE6fDaZ8uTK88mJvo6FHrt3/fcvn6JBsfcmlcCECg0JyTT0yNZwK5Cfs5i2uXrth1KVakqvXbgAYDT2i8zJj61GG+n4y8bS3t8PRwYHIqChu3AjDsaSD2eVyquRimR46N+VBupfOmVat3WT0//zFy6lWpSKvD+pL/vsv9IDuW3MK/S7mHrq/zhlURzWl5KlkiUNHjnEk4KTh//z58zGwX09qVPMzWi46+p7hbzs78+OSJFVKo6NNWyvkNmfOBvP3P2sAeKHnM0bzFKu0yYvxSjoOOzvzN3IP3uBFRUfrhs+M1MYwOjo6y8qU3a5du8HsuUsA6N61IzY2/z1cVrwkN2jcoDYN6tXE06ME+RwduXL1Ous2bmPthq3MmrMEW1tbWjVvZFj+7v3z3j6Z3xb479yPymPnfsT9MU/mLVqOq0thRg57nbLepbgeGsb8RcvZu/8QX0/8iW8+H2l4s1XxTL39/keIiIjExbkwFSv4mMxXLJPXrnUznAsXYuqvfxjGQIXE5KhvuTKGITOS3E3h9w3AzhDL3FGPTI2Kvj5s27GXjZt3UL1qJaN5x0+c5uKlK4Dx735ePy8zuh713zqPjmdkVFSuOzcfFcv0yOvnphjTvXTO4uVZkgFlSlHJtxwuzoW5HX6HgOOnmLdwGf6HAvhm4k+MGfkWlpaJHTDqvjVn0O9i7qD765xBdVTzlDyVZE35eSZnzganaZ1aNavS67mnTaaPGj4ESGz9d+HCZZb+s4YJ3/9K00Z1efnF3ib9Zec0GRmrJDdCwxg/eRoxsbF0bN+SKn6+j1vMJ0ZmxEtEMl9kVBTjJv3MnYhI6tauTstmDbO7SCIZ7rVBfY3+9/QoTv8+z1GyRDF+mzmPeQuX0bRR3cd+SJsXxCck3P8rgfeGvkbRIonjp3iUKMbbg19k5JivCQ65wJp1W3ju2aeyr6A51OatiV32Nm5Q2/AwUFKWkJDA7LlLWLFqA00b1aVj+5a4ubpw7foN/l6xliXLVrHP/zAfjXzbpCtkMdapQyt27fFn976DTJ+1gA5tm1Egf34Cjp/i19/nYWVlRVxcXI6/18soqkdlHMVSRB7UoW1zo//d7Jxp2qgulSuVZ8QHYzl5OpDdew+aHT9RRDKX7q+ffKpXJU/JU0nW9RthXLx8NU3r3HxgMGFzHB0c8ClbmqFvDGTsuCls2roLv4rladSgNgD29v+9jXD37j0cHU2bbCe9ufAkvdmX0bEKvxPB2HFTCA27SeOGdXi+e2eTZXJqrCBzzq2U5OR4pVfScdw1M6YcGLewdbDXw0Fz7O3tiIiITDGG9nkgfvfuxTBu4s+cC7mIX8VyvD6oj8kyipfkZi2bNWDhkn+4HX6HU2eCDC39klqbJY39Z07SuZ/XvmuTfocqVihnSJwmsbS0pEXTBkyfvYCA46cM0xXP1Ll1O5yDRwIA8132gmKZnM1bd7Fi1QaqV63EKy+9YJheskQxXhvYh/DwO/gfCmD5ynU81yUxqW+XQp0K/ntL3iGX1CNTw8ujBENe+R8/TpvN6nWbWb1us2Fe8WJFaNq4LkuXryHfA2Oi5tXzMrPqUf/V91MTz9xxbqYmlumRV89NMU/30rmDc+FCNG1cj39Wrsf/0FFD8lT3rTmDfhdzN91fPxlUR300JU8lWR++/2ambdvCwoJGDWpx+Ohx9h44bEieOjo4kM/RgYjIKG6E3TSb4AoNuwlgdtDo7JKRsYqMimLsuB+4cPEytWpU4ZVkWubm1FhB5p5bycnJ8UqvpON4eIy5JEnHmz+fY65JGGc0VxdnIiIiuRF6Ey/Pkibz/ztnCmdxybJWbGwcE76fxrETp/EpU4qhbw4y+1ag4iW5maWlJUWKuHE7/A5hD7zQ43b/fE46v825kct+X1LLzdWZoODzuLu5mJ9/f/rNW7f/m6Z4psq2HXuJi4vHp0wpihV1N7uMYmnelu17AKhft6bZ+fXr1sD/UABHjp4wJE/d7scnIjIq2bHt8mIsAerUqkaF8mXZuXs/IRcuYWlhSRlvT+rVrsGSv1cCia3Nk+TF8zIz61GG+n4y8YyOvktkVBQALrmg/pXaWKZHXjw3JXm6l849kupJD9bfdd+aM+h3MXfT/XX2Ux01ZerfSbJN0lhC4eF3jKYnXYSBQefMrhcUHHJ/uRKZWLrscffuPb6Z8BOBQSFUrlSBN17t98hu2PJyrNIjr8Ur6TgC7x/Xw5Kme+aS480MpVIZw9xyzpgTHx/PDz//jv+hALw8SjD8nVexT2acA8VLcruIiEjAeKyPYkWLYGNjQ3j4Ha7fCDVZ5/qNUO7cicDW1oZiRd1M5udmpbw8ALhzf+zThyVNf/Cho+KZOlu2J3bZm1yrU1Ask5N0A++YTJe8jo6JrSTv3L/eE6c54Oaa+ADA3G9cbGws5y9cBvLmb5xTgfy0admEF/v2oH+f52jSsC62tjacOHUWAL+K5Q3L5rXzMrPrUV4eiX+HnL9EbGxssuu4u7ng6GD68mhOkpZYpkdeOzfl0XQvnXv8V3//rzcy3bfmDPpdzP10f519VEdNHSVPJdsEHD8NQBF3V6PpNar5AbBz9wGTdc6cDebqtRsUKJCf8j7emV/ILBQbG8v4ydM4fvIM5ct5M/SNgSm+RZtXY5VeeS1e5X28yZ/PkavXbnAm0DRhvGPXfgBqVa+S1UXLMZLOmV17DpBgGLsvUdjNW5w4eQYrK0uqVamUHcXLdAkJCfwyfQ47dx+geFF33h/2Ovkf6HrvYXk9XpK7hZy/yMVLVwDwLuVhmG5ra0PlSomJAXO/L0nftVX8fPPcOC5Jvy8nTwdy716MyfwjAScAKO2leKbFuZCLBAWfx8bamvp1aiS7nGJpXqGCTgCcPhtsdv6ZM0EAhmRpkhrVKgOwc/d+k3X27j9MTEwMXp4l9Qb8fWeDznH85Bm8PEtStkwpw/S8dF5mRT3Kzc0Fj5LFiYmJYd+BwybbTIpnzeqVM+KQsk1aY5keeenclJTpXjp3SEhIYPdefwC8S3kapuu+NWfQ72Lupvvr7KM6auopeSqZ5kjACY4EnDC5qOLj49m0ZScrV2/EwsKC5k3qG81v3qQ++fM5cuDgUbbv3GeYHhkVxfRZ84HEweAf1SIzp4mPj+f7qb9z6MgxvEt7MvztV4zeiktOXozV48hr8bKysqJ9m+YATJ85z9AdAsC2nXvxPxRAgQL5ada4XnYV8YlXs3plihV1J+T8Jf7+Z41hemxsLNNmzCUuLp5mjesbWtLnNrPnLmHjlp24u7kwasQQCjoVeOTyeT1ekvNt2rqLwCDTtykDg0KY+P2vQGIXlc6FCxnN79S+JQB/LV/NhYuXDdMvXLzM0uWrE5fp0CqTSv3k8vQoTvWqlQi7eYvZc5cQHx9vmLd3/yG27tiLhYUFrVo0MlpP8Xy0zdt2AVCzRmWjsSTNUSxN1alVFYB/V23g+InTRvMCjp1ixeoNACaJ6fZtmmJlZcW6jds59sB6oWG3mLNgKQCdOrTMzKI/kU6fDTKZFhgcwoTJ07CwsGBA3+4m8/PKeZlV9aikeP45fymhYf91e3fsxGnWb9qOtbW14Z4gp0prLNMrr5ybkjLdS+ccgcEhbNux16RlU2RUFL9Mn8OZwHPY2dnSrPF/zx5135oz6Hcx59P99ZNJddTUs4iLvZuQ8mIiabdi1QZmzVmMk1MBSnuVJH/+fISHR3D+4iVCQ29iaWnJCz270L5NM5N19/sfYfzkX4iLi6dCuTIULOjEsROnuX07nEq+5Xhv6GtYW1tl/UFlkn9Xb2Tmn4uAxLc5knvbo3nTBlQoV8ZoWl6LVZL1m7azYdMOABJI4Mz91gNlvUsZlmnetD4tmjYwWi+vxSs2Npax46YQcPwUTk4F8C1fllu3bnP85BmsrKx4981BVKtSMbuLmWX2+x9hyd+rDP8HBocQFxeHl2dJbKwThwGvXrUSz3ZuZ1jmbNA5Pv1yEtHRdyldyoOi7m6cOhPE9RuheJQsxkcj3zY7hm5Ot3f/Ib6d9AsAFX19cHU2PwZBrRpVqF2zquH/vBoveTKl9Zr/9ruf2XvgMMWLulOiRFGsray5fPUaQcHnSUhIoHQpD0a++zr58+cz2dfchX+zdPkabGz+e1P28NETxMTE8OzT7Xju2aey4IgzV3q+Q2/evM2Yzydw9dp13N1cKOVZkhuhYYZWHL17PEPH9qYJp9wez/TEEhJfuHv9ndHcvHmb4W+/QvWqKbeIUCyNYxkbG8tXE6Zy5OgJLCwsKFPaC1fXwly7HmqoT9apVY03X+tv8kLd2g1b+W3mfCwsLPCrWA57OzsOB5wgKiqaxg1q89qgvll01JkjPedlr35DcHN1pkTxojg6OnDl6nXOBp7D2sqKVwe+kOzYsrn9vMzKelRCQgI//DSTbTv34uBgT+WK5Ym+e5cjASdJSEjgpX49Te6JcpL0xvLXmfMICjoPJCZRLl66goO9PSWKFzUsM6Bvd0o/0NoFcv+5Kamne+mcYc++g4yfPI18+RzxLuWJU4H83Lx1m6Bz54mIiMTe3o43Xu1vUmfSfWvWy6rnMbn9dzE76f4651MdNW2ss7sAkntVq1LRULEMDrlAeHgE1lZWuLo607JZQ1q3bGzo//phNar58cnooSxeupITp85y5mww7u6utG/TjI7tWuaq5Bb818c7JP4QJadiBR+T5Glei1WS0NCbZt90f3Ba1cq+JvPzWrysra15/93XWP7vOrbu2Mv+A4exs7ejVvXKPNu5vcnDgtwuPPyO2fMm+Nx5w9/Fi7kbzfMu5cnYj0ew8K8VHDl6gpDzl3AuXIhOHVrx7NPtjMbqy00e/F4KOHYq2eXcXF2MKlR5NV7yZErrNd+4YR1s7WwJCj5PwPHTREdH42Bvj2/5stSrU53mTepjbW2++tyz29N4eZRg5ZpNBBxPvGZKe5WkfZvm1KtTPWMPLJuk5zu0UCEnvvhoGH8tW82e/QfZf/Aodna2VK3sy1PtWlC5UgWz+8rt8UxPLAEOHTnGzZu3KVTQyWw9xxzF0jiW1tbWvD/0NdZt3M72nXsJOX+Rs0HncHCwp2IFH5o0rEOTRnWxsLAw2War5o0o4u7K3yvWcvpMMLFxsRQvVoQWTRvSqnnDTDm+rJSe87JD2+YcO36aU2eCuHv3HoUKOtG8SX06tm9JsaKm53CS3H5eZmU9ysLCgtdf7kv5ct6s37Qd/8MBWFtZU9HXh85PtTYaczYnSm8sL1y4bHI+R0VHG02Lioo22U5uPzcl9XQvnTN4epSgXeumnDkbTMj5i9yJiMTKyhI3VxeaNKxD+9bNcHNzMVlP961ZL6uex+T238XspPvrnE911LRRy1MRERERERERERERERERETTmqYiIiIiIiIiIiIiIiIgIoOSpiIiIiIiIiIiIiIiIiAig5KmIiIiIiIiIiIiIiIiICKDkqYiIiIiIiIiIiIiIiIgIoOSpiIiIiIiIiIiIiIiIiAig5KmIiIiIiIiIiIiIiIiICKDkqYiIiIiIiIiIiIiIiIgIoOSpiIiIiIiIiIiIiIiIiAig5KmIiIiIiIiIiIiIiIiICKDkqYiIiIiIiIiIiIiIiIgIoOSpiIiIiIiIiIiIiIiIiAig5KlksGXLV9D52e5UrFwTb59KVK1RL7uLJGnk7VOJxs1aZ3cxcr1evfvh7VOJ8+cvZPq+7t69xyefjaV23cb4+tXghf+9yMlTp5NdfsBLr1K7XhNu376d6WVLcu5cCK++/ia16jSibPnKePtUYvWadY+9XZ3PmW/Y8JF4+1Ri567d2V0UERGRFC1ctARvn0pMnPRDdhdFRERERNJIdTkRySpKnkqGOXjoMG8PHcGpU2do1LA+Xbt05ulOHbK7WE+08+cv4O1TiV69+2V3UXKl7EqcPWmf62dffMmM32fj7u5Gg/r12L17Ly/0HUBERITJsitXrWHjps28P2IoTk5OWVK++Ph4Xhv8FqtWr8XT04POT3eka5fOFC9e7JHrqcKcNZSAFhERERERERERkbzEOrsLILnH+vUbiY+PZ8yHI+nxXNfsLo6k05qVy7C21ldDbnHt2jXmzV9Ek8YN+W3aVCwtLVm0+C+GjRjFnLkLeOnFfoZlIyMj+fTzL6lTuxbPdumcZWU8f/4CAceOU7NmdRbMnZ1l+xUREREREREREREReZhankqGuXT5CgBenh7ZXBJ5HGXKeOPl5ZndxZAMcvzEKWJjY+n67DNYWiZ+5Xd55mns7OwIOHbcaNlJ3//I9evX+eTj0Vlaxsv3vztKeeq8ExEREREREREREZHspeSpPLaJk37A26cSCxctAeD5F/rj7VPJaBokjrv4y7TpdOrcjUpValGpSi06de7GtF9ncPfuvUdu98jRAF5+dQi16zamTDk/o7EQzwYG8d7ID2ncrDUVKlajZp2GDHplMP7+h5Itc1BwMKNGf0zzlu3w9atB9Vr16dS5G+MnTCIs7KZhuVu3bjFz1h/0f/FlmjRvQ4VK1alaox7P9XzB6NgetmXrdga89CoNm7SkQsVq1K7bmE6du/HZF19x7fp1w/E1ad4GgF279xhilpbuXpO604yLi+Onn3+lddtO+PrVoHGz1kz47ntiY2MBuHDhIsOGj6Rug6ZUqFSdTp27sX7Dpkdu80EPdkN79+49Jnz3Pc1btqNCxWo0aNSCTz4dy507pl3ApjQWYnLjfl66dJlPPhtL67ad8KtaiyrV6tC8VXveeOtdtm7bkWJckrpzTTr2B2P74LE9WL6t23bQt99Aqteqj7dPJQICjgHQuFlrw7ZSE6/0fK4bNm6me68+VK5WmyrV6vC//gM5dPhIiseZGknjljo5FTBMs7S0pECB/Ny8ecsw7dSp00yfMZP+/+tDOZ+yj7fP8HDGT5hE2/ZP4+tXgyrV6tCte2/mLVhEfHy8Ybmk86pn7/8BsGjJ0lRfA71692P4ex8AMGnyFKM4m+vGNz4+nt+mz6Rt+6epUKk6tes1YdjwkYbr0Zyt23Yw8OXXqV23MeUrVqVhk5aMGv2xIdmbWg+eZ7t27eGF/71Ilep1qVazPi+/OoTAoGBDGaf9OsMQt/qNmvPl199y757p9yOkPs7myrH/gD/9Bgyiao16VKxck+d69GbL1u1Gy6f2OnpQarYrIiKSFTZv2Ur3Xn2oVKUW1WvVp/9Lr3D4yFGzy16+fIWffv6V3n36G+rvNWo3oM//XmLdug0my0/9aRrePpX49PMvk93/2C/HaXgBERERkXRSXU5Espv65pTHVtG3Al27dGbvvv0EnwuhSeOGuLm6AhhaMEZFRdG330vs2+9PgQIFaNyoAQA7du7miy+/YfXadcyc/gv29vYm29+77wAfjP6YEiVLUL9+XcLCbhq6lV23fiND3hxKdHQ05XzKUrlFM65dv86GjZvZuGkLE8d/TYf2bY22t2btet58exjR0dF4enjQskUzoqOjCQwK5vspP9GgQT3q1a1j2PdHn3xBsWJFKVXKi2pVq3Djxg327fdn374DHDp8hE8+Mm6l9+ec+Xzw4cdYWlpSo0Y1ataoTvidO5w7F5KYvGnTCjdXVyr6VqBd29asXLUGV1cXmjZuZNiGd5nSafoM3njrXTZt3kLdOnUoXdqL3Xv2Mfn7H7l69SovD3qR53r0wdHBgbp1anP5yhX27t3Py68OYeaMX6hfr26q9xMTE0O/AYM4GnCMunVq4eNTlr379jNj5mxOnj7NrBnTsLCwSFPZH3bp0mU6de5GaFgYXp4eNGrYECsrSy5duszqNetwdHSkUcP6j9yGl5cnXbt0ZtGSpTg6OtC+bRvDvMLOhU2WX7b8X+bOW4BvhfI0adyIS5cvY2GZvndL0vq5/jl3Pj/9/CvVq1WlebOmnDhxki1bt7N33wH+XjKfMmW801WOJMWLJY4beuZMIE2bNAbg5s2bhIaGUaLEf2OKfvjRZ7i6uvLGkNcea3/Xrl2j1wv9OXs2EFdXF1q2aEZkZBQ7d+3m/ZEfsmXLNiZ/9y0WFhY4OjrStUtnrl2/zuYt2/Dy9KBWzRpAytdAkyYNiY2LZd++A/hWKE9F3wqGeQ/+neSdd99j1eq11K1TC2/v0hzwP8iiJUs5dPgIf/+1EDs7W6Plx0+YxPdTfsLa2poqlf0oUsSdM2cDmTN3PmvWrmPuH7/j7Z2263TN2vXMnPUnfpUq0qxpY44dO8GatevxP3iIFcuWMHrMJ2zavJW6dWrj5eXJ7j37+PmX3wgNDePrLz9Ld5wftmHDZqb/PotyPmVp2qQRwefOsW+/PwNeesXoOyGt11FqtysiIpLZFi5awoj3R5OQkEDNGtUoVqwYAceO81yPF+jWtYvJ8qvXrOWrb8bj5eVJGW9vataozuUrV9i5azfbtu/gveFDGTRwgGH57s91ZeKkH1jy198Mf/dt7OzsjLZ37949Fi3+CysrK3p275bpxysiIiKSm6guJyJPAiVP5bG1ad2SNq1bMmz4SILPhfDKyy8Zko9Jxo3/jn37/alSxY/p036icOFCANy4EUq/AYPYu3c/4ydOZuR7w0y2P3/BIt5643WGDH7VKBFw4cJF3npnGPHx8fz042Rat2phmOfvf4h+L77Me++Ppl7dOjjff9B/7lwIb70znLt37/LxRx/wwvM9jbZ5+MhRXF1cDP+XLePNgnmzqVmjulGZrl27Rv8XX2H2H3Pp0vlpqlevapj340+/YGFhwaL5f1C1ahWj9U6cPGUoS5vWLanoW4GVq9ZQxtubb77+IlXxftiFCxexs7Nj3eoVFCniDsDFS5fo1LkbCxYuYd++A3Tq2J5R7w/HysoKgJmz/+Sjjz9n8vc/pimhsf+AP1WrVGbT+lWGz/DatWt07d6b7dt3snv3XurWrZ2u40gyb8EiQsPC6PNCLz4e84HRvNu3bxN8LiTFbdSuVZPatWqyaMlSChcunGJs58ydz9dffma2ApZWaf1cf5s+k5kzptGwQT0AEhISGD3mU/6cM4+pP//KN199/ljlqVjRl6JFivDr9N+pW6c2xYsX49PPvyQ+Pp4WzZsBsOSvv9m1ew9Tvp+Io6PjY+1v9JhPOXs2kBbNmzL5u29xcHAAEq+95/v0Z8W/q6hTuyZ9+/TG2Tnxs9m5azebt2yjVs0aqb4OXn15IG6uruzbd4DWrVvy1huvJ7vshQsXsbayYu2q5ZQoURyA8PA79On3IocOHWH5Pyvo+uwzhuVXrlrD91N+wsvLk6lTJlG+nI9hXtLLEcNGjGLRgj/TFJsZv89m8nffGl7oiI+P5+2hI1i2fAW9+/QnNi6Odav/oWjRIonlvniRTp27sWjxX7wx+FVKlixh2FZa4vywab/NYNzXX9DlmacN03786Re+GTfR6DshrddRarcrIiKSmS5dusyYjxPrTz9MnkD7dokv/yQkJDD2q3FM+3WGyTq1a9Xkn2WL8a1Q3mh6YFAwL/QdwLjx39GxY3vDS2nOzoXp0L4tfy1dxop/Vxn99kFiXSI0LIzWrVoYftdFREREJGWqy4nIk0Ld9kqmi4qKYt78hQB88tFoQ9INwMXFmY/GjALgzznziI6ONlm/bBlvBr/+ikkLqum/zyIiIpI3hrxmlDgFqFatCoNff5k7ERH8tXSZYfqv038nKiqKnj2eo0/vXibbrOxXiWLFihr+9/LyNEmcAri5uTFi+FAg8Qf1QaGhYRQoUMAkcQpQvpyPoVVuRhrzwfuGxCkktjbs/HRH4uPjib57l/eGDzUkTgGe79mdQoUKsm//AWJiYlK9HwsLC74c+6nRZ+jm5sYLz/cEYOfuPY99LKGhoQA0bGDautTJyYnKfsl3oZteTRo3zJDEaXr069vbkDiFxBi//eZgILHb38dlZ2fLyPeHcfnyFTo9042adRry19JltGrZnObNmnA7PJyxX42jadPGtGv7X1es9+7dM3T7nFrnz19gzdr12Nra8tknYwwJPQBPTw+GDX0LgN+mz3rs40qrD0ePNCROAQoUyM+glxLfOty5yzjOP0z5CYBvvxlrlDgFeL5Xd1q2aMYB/4OGrp1Tq+NT7Y1awltaWvLy/TcfT546zZgP3jeqlJcoXpxnnu5EQkKC0bnwuHFu366NyY3Bi/3/h5OTE/v3+6fpOyErtisiIpIW8xcuJioqitatWhgetkFiHevdd96kaBHTB2C+vhVMHrYBlC7lxZDXXyE2Npa1D3X59kLvxPrvn3Pmmaw3Z+4CILHeICIiIiKpp7qciDwp1PJUMt3hIwFERkbh7V2aKpX9TObXrFGdUqW8CAoK5vCRo9SuVdNofsuWzbE004Xqli3bAGjXppXZ/dapXQsA/4P/jX2aNF5m926pT5QlJCSwe/de9uzdx5WrV4mOvgsJCdyJSBzjMzAoyGh5v0oV2bN3H8NGjOLFAf+jQvlyqd5XetjYWNPggeRbklL3u0yuV7cOtrbGXZJaW1tTsmRJjhw5SljYTdzd3VK1r+LFi5kkkwDK3O++9OqVq2ktvgm/SonJ0W/GTcTK0pKGDeub7c45I7Vu1TJTt/8ozZo1MZnm4uJMoUIFMySekJi08/AoyT8rVhIZGUWN6lXp/HRHAL4d/x3h4Xf4+MPElxhOnDzF6DGfsG/fASwtLWnUqAFffPqR0UsFydmzdx8JCQnUq1vb7Jt5nTp24L2RH3IuJIRLly6napsZwdra2tBV+IOSut29evW/OF+/cYOjAcdwd3ejRvVqZrdXp3Yt1q3fiP/Bw1Ss6JvqcjRt0shkWlLX5slex6US51+5es0w7XHj3KxpY5N1bG1t8fQoyZGjAWn6TsiK7YqIiKTF7j17gcTfw4fZ2trSvn0bps8wfcEoJiaGbdt3csD/INev30gcczwhgavXEn+DgwKDjZavUb0alSr6sm+/PydOnjLUkc+cOcuu3XvwKFmSJo1Nf/tFREREJHmqy4nIk0LJU8l0V65cAcDjgS4nH+bpUZKgoGCumEkWlShe3MwaEHL+AgCt2nZ85P7Dwm4a/r548RIApUuXeuQ6Sa5du8bLr75hlIB92J07d4z+/+Tj0bzy2hssWvwXixb/ReHChahRvRrNmjahyzOdHrtb1Ie5uroatSpNkrSfYsl0L5Hv/vx79+6lel9J3VuYbCt/vjRvKzldn+3Mjp27WPr3cga+MhgbG2sq+vpSv35dunbp/NhjgJrzYIvErJZsTPPl4+bNWxm2n6pVKlO1SmWjaUeOBvDHn/N4Y/CreHp6EB0dzYCXXsHKyooJ335FVHQ0X38znlcHv8mShXNTHM/28v1rvWTJkmbnW1paUqJEcc6eDeTKlatZljx1c3M1jJP8oPz5ks7b/1pEXrj/vXL16jW8fR7dyjksLCxN5TCX6Mx3vwwpXccPXluPG+fixZM/5x7eV1pk1nZFRETSIul+Irl7iJIlTO9Jzpw5y6BXhxAYGJTsdsMfqvMD9H6+ByM/+Ig5c+fz0f0X0ebMS2yp0LNntxTrTiIiIiJiTHU5EXlSKHkqTzx7ezuz0+Pj4wHo/HRHrM0kHZJ4lymd7n2/N3IM/gcP0aplcwYNHEDZMt4UKFAAKysrzgYG0arNUyQkGK9TvpwPq1YsZeu2HWzespU9e/axfsMm1q3fyA9TfmLenJl4eJhPeqSHuVa5D7JIYX5G7iutkj7DByUl7l55+SXWrd/Izp272X/gAAcPHebnX37j4zEf0Pv5HhlajuTOsZSYK39aZXRMUys+Pp7RH36Cp6cHLw96CYClfy/n0qXL/DHrN8P4lBZY8N7I0Wzfscuoe+GcJC0xjo9PvKALFy5Ei2ZNH7msj0/ZDCtHVp4HmbWv7DqXRUREzEnLs67Xh7xNYGAQ3Z/rSu/ne+Dl5Un+fPmwtLRky5Zt/G/AIEwq/STeh4z96luW/LWMEcPewdLSisVLlmJjY81z3Z7NwKMRERERyVtUlxOR7KbkqWS6Ivf7ok9qKWpO0rwHx+1MSbFiRQkOPse777yZ6paDxYsXIzAwiMDAILNjkj4oMjKSTZu34OLiwo8/fGfSKiw4+Fyy69ra2tKieVNaNE9Mvly6dJkPPvyEDRs38c23E5k0cVyqypvT2djaABBxJ8Ls/IuXLiW7bvlyPpQv58Nrrwzk7t17zF+4iI8+/pxPP/+STp064FSgQKaU+WE2NonHcOdOBPnvt7BNktSSOSeaO28hBw8d5vfffsbOLrFb51OnzwBQ7YFro1q1xL9Pnz6TYvI0adyJ8+fPm50fHx9viFlarvWslNRKM1++fHzz9RfZXBrzckOcRUREMkuRIu6cPRvIhYuXzNb3z18wvic5c+YsJ0+dxs+vEl9+8YnJ8kHnkq/zOzg40LVLZ2bMnM2y5f9ia2vLzZu3eKpDO1xdXB7/YERERETyGNXlRORJoWYikukq+1XEwcGBs2cDOXT4iMn8AwcOEhgYhKOjA5X9Ht1N5oOSxjBctXptqtdp1LA+AAsX/5XisuHhd4iPj6eIu5vZ7jSX/r081fstVqwobwx+BYATJ04apicl5mLjYlO9rZzE3T0xcXPWTLcZp0+f4dKly6najp2dLX1696KMd2nu3bv3yG44HmRjY01cbFxqi2tWEcMxBJrM23x/3F3T/T7Zn2toaBjjxk+kQ/u2NG7c0DA9qTuSyMgow7SoqKj781Lebu1aNbGwsGDnzt1cvnzFZP4/K1YSHR2Np4dHhnTZmxTnuNiMi3PRokXwKVuG8+cvEBBwLMO2m5GyPs6Pfx2JiIhkldq1agKwbPkKk3kxMTGsXLXGaNrNW4nDJBRP5jfz72X/PHJ/ST2izJk7nzlz5wPwfK/uaSu0iIiIiACqy4nIk0PJU8l0Dg4O9OjeFYAxH33GzZs3DfPCwm4y5pPPAXi+Vw/s7e1Tvd2BL/YnXz5Hxk+cxNK/l5PwUPcLsbGxbNy0hRMnTxmmDejXF3t7e+bMXcCfc+abrHP4yFFDQs/V1QUnJydOnDzFzl27jZZbsHCJ2R/xqKgops+YZXSMSTZs2gIYjwvo7FwYGxtrgoPPEZuBCaAnRf26dQCY9cccrt0foB0Sx5Mc/t4HZru9XbxkKceOHTeZfvr0GULOX8DCwoJiRVOXEHJ3d+f6jRvcupX+sUPr1Us8hu9/mGo0ZqO//yEmTJxsdp3M/FyHDR+Jt08lhg0fme5tjP1qHLExsXwwcoTR9HI+PgAs+etvw7Skv8uV80lxuyVLlqBVy+bci4lh9JhPiI6ONsw7f/4C33w7EYAB/fuku+wPSmpVefrM2QzZXpI333gdgCFvvYu/v+l4x3fuRDBvwSKj48tKWR3njLiOREREsspz3bpgb2/PmrXrjV6yTEhI4NsJk0xe3ivl5YWlpSXbt+/k1KnThunx8fFMmjyFffsOPHJ/Zcp4U79+XQ4eOsyevfvw9i5tGP5ARERERNJGdTkReVKo217JEsOGvsWhQ0fYf8CfZi3bU+9+Um3Hzl2Eh4dTq1YN3nlrSJq26eFRksnfjWfIm+/w9tARjJ8wmbJly5A/fz6uX7/OkaPHCA8PZ+qUSZS/n/jx8vJk/LgveXvoCD748GN+mfYbfn6ViI6O5uz97nz/nD2dYsWKYmVlxWuvDOTLr7/lhb4vUrdOLVxdXTlx8hQnT57ilZdfYupP04zKFBMTw6eff8nYr8bh61seL09P4uPjOXnyFKfPnMXR0YE3h7xuWN7GxoZmTZuwZu16OnTsgp9fJWxtbfEuXYpBAwc8ZtSzX506tWjUsAFbt22nbYfO1KpZg8ioKA4dPEzFir7UrFGNffv9jdZZtXot7w4fSYkSxalQvhz58uXj2rVr7N23n5iYWF56sR/u7m6p2n/rVi2Y8ftsOnbuRs0a1bG3t6dw4UKMGPZOqo+hT+9ezJk7n7XrNtCm3dNUquTL5ctXOHT4CIMGDuDHqb+YrJOZn2v8/YS/tU36vr737tvP4iVLef+9dylatIjRvKc7dWDS91MY+9U4tmzdRnT0Xfbs3UfNmtUN12xKPvvkQ86cDWTd+o00bdGWOrVrERkZxY6du4iOjqZD+7a80LtXusr+sOrVquHq6sKq1Wvp0asvnp4eWFlZ0aplM1q1bJHu7XZo35a33xzMxEk/8OxzvfCtUB4vL0+srKw4f/4Cx44d515MDO3btk7TCx8ZKSvjnBHXkYiISFYpUbw4Y0a/z/ujxvDq629Ss2Z1ihcrRkDAcUJCQujVs7uhVQGAi4szz/fqzuw/5tKpczfq1auDk5MThw4d4cLFiwx8sT+//Dr9kft84fme7NixC4BePZ/L1OMTERERyc1UlxORJ4WSp5IlHBwc+GPWb/w+8w/+XvYPm7dsxcLCgtKlvOj8dEf69ultGHcxLZo1bczKf5by6/Tf2bJlOzt27sLS0gI3Nzfq16tD61YtTMZpbNe2NT5ly/DztOls276D1WvWki9fPkqWKMGQwa9SoXw5w7KDBg6gePFiTPttBocPH8XC0pJKFSvw6y8/4lO2jEny1NHRkU8+Gs3OXbsJOHacDRs3AVCsaFH69nmeAf0SEzwP+uKzj3FycmLrtu0sW76CuLg46tapnSuSpxYWFkyd8h3fTpjEv/+uZvPmrbgXcadv394Mef0V+g142WSdF/v/j+LFirFv/wH8/Q8RHh6Oq5srDRrUp0/vXoZxZFNj2NC3AFizdj0r/l1FbGwsJUoUT1PSx9m5MPP+nMnX4yawfcdO1m/YRBnv0nzx2cc8162L2eQpZN7nevR+V7Ldu3VN87qxsbGMHvMp5cr50K/vCybz7ezs+H36L3z6+Zfs3r0XK0tLOj7VntEfvJfqfbi5ubF44Rx++eU3Vq5ay5q167Gxsca3Qnm6P/csz3V7FkvLjOn0wM7OlunTfmLc+In4HzzE3n37SUhIoGjRIo+VPAUYMvhVGjasz+8z/2Dv3v2cObMRB0cHihQpwjPPPE3bNq0okEXj7pqTlXHOiOtIREQkK/Xo3o0iRYrww48/cfToMU6ePE31alUY980XnDp12uiBG8BHH47Cx6csc+bOZ8/e/djb21GtalXGf/sl9+7dS/GBW8MG9bCwsMDW1pZuzz6TiUcmIiIikvupLiciTwKLuNi7CSkvJiIi2e3a9evUrd+UNq1bMXXKd9ldHBEREREhcYysUaM/5tkuTzPu67HZXRwRERERSQPV5UTEHI15KiKSQ2zfvhMrKyuGDX0zu4siIiIiIkB0dDQ/T0tszfA/Mz17iIiIiMiTS3U5EUmOWp6KiIiIiIiIpMGChUvYvXsP+w74ExQUTIf2bfl+0vjsLpaIiIiIpILqciKSErU8FREREREREUmD3bv3sGjJUm7dukW3rl0Y+/kn2V0kEREREUkl1eVEJCVqeSoiIiIiIiIiIiIiIiIiglqeioiIiIiIiIiIiIiIiIgASp6KiIiIiIiIiIiIiIiIiABKnoqIiIiIiIiIiIiIiIiIAEqeioiIiIiIiIiIiIiIiIgASp6KiIiIiIiIiIiIiIiIiABKnoqIiIiIiIiIiIiIiIiIAEqeioiIiIiIiIiIiIiIiIgASp6KiIiIiIiIiIiIiIiIiABKnoqIiIiIiIiIiIiIiIiIAEqeioiIiIiIiIiIiIiIiIgASp6KiIiIiIiIiIiIiIiIiABKnoqIiIiIiIiIiIiIiIiIAPB/ah6V/YP+2Q4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1890x588 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(13.5, 4.2))\n",
    "# planned change\n",
    "axes[0].hist(err_model, bins=40, color=SIGNAL, alpha=0.6, label=\"nothing changed\")\n",
    "axes[0].hist(err_planned, bins=40, color=FLAG, alpha=0.6, label=f\"{PLANNED_LIFT * 100:.0f}% lift from day {PLANNED_FROM}\")\n",
    "axes[0].axvline(0, color=INK, lw=1.0)\n",
    "axes[0].set_xlabel(\"forecast minus truth, % of the month\")\n",
    "axes[0].set_ylabel(\"number of accounts\")\n",
    "axes[0].set_title(\"A change the model was not told about\")\n",
    "axes[0].legend(frameon=False, fontsize=9)\n",
    "# late arrivals\n",
    "tf = np.arange(HISTORY_DAYS, HISTORY_DAYS + HORIZON)\n",
    "axes[1].plot(t_hist[-28:], history[-28:], color=INK, lw=1.3, label=\"what the days will finally report\")\n",
    "axes[1].plot(t_hist[-28:], history_lagged[-28:], color=FLAG, lw=1.6, ls=\"--\", label=\"what the export shows today\")\n",
    "axes[1].plot(tf, fc[\"point\"], color=SIGNAL, lw=2.0, label=\"forecast on finished data\")\n",
    "axes[1].plot(tf, fc_lagged[\"point\"], color=FLAG, lw=2.0, label=\"forecast on the unfinished export\")\n",
    "axes[1].set_xlabel(\"day\")\n",
    "axes[1].set_ylabel(\"conversions per day\")\n",
    "axes[1].set_title(\"Late conversions drag the forecast down\")\n",
    "axes[1].legend(frameon=False, fontsize=8.5, loc=\"lower left\")\n",
    "# regime shift\n",
    "axes[2].plot(t_hist, history_shifted, color=INK, lw=1.2, label=\"history with a step down\")\n",
    "axes[2].plot(tf, fc_all[\"point\"], color=FLAG, lw=2.0, label=\"fit on all of it\")\n",
    "axes[2].plot(tf, fc_clip[\"point\"], color=SIGNAL, lw=2.0, label=\"fit since the step\")\n",
    "axes[2].fill_between(tf, fc_clip[\"lower\"], fc_clip[\"upper\"], color=BAND, alpha=0.6)\n",
    "axes[2].axvline(SHIFT_DAY, color=MUTED, lw=1.0, ls=\":\")\n",
    "axes[2].set_xlabel(\"day\")\n",
    "axes[2].set_ylabel(\"conversions per day\")\n",
    "axes[2].set_title(\"A step the trend cannot bend around\")\n",
    "axes[2].legend(frameon=False, fontsize=8.5, loc=\"upper left\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_lies.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e1e8d6",
   "metadata": {},
   "source": [
    "### A change in growth that is too fresh to see\n",
    "\n",
    "The bend search is a detector, and a detector has two failure rates: the bends it invents\n",
    "on an account whose growth never changed, and the bends it misses on an account whose\n",
    "growth did. Both are measured here. First the price of running the search at all, on flat\n",
    "accounts and on accounts like the worked one, against the same forecast with the search\n",
    "switched off. Then how often it sees a change of this account's size, by how many weeks\n",
    "before the end of the history the change landed, and what the forecast does when it does\n",
    "not see it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7c698a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:39.507515Z",
     "iopub.status.busy": "2026-10-24T12:15:39.507121Z",
     "iopub.status.idle": "2026-10-24T12:15:47.650077Z",
     "shell.execute_reply": "2026-10-24T12:15:47.647145Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a flat account:\n",
      "   with the search    leans  +0.2%   typical miss  4.1%   band caught  79.3%   kept a bend on  35%\n",
      "   without            leans  +0.1%   typical miss  3.1%   band caught  88.0%   kept a bend on   0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "an account like the worked one:\n",
      "   with the search    leans  +0.2%   typical miss  4.9%   band caught  73.3%   kept a bend on  74%\n",
      "   without            leans  -5.5%   typical miss  5.6%   band caught  63.3%   kept a bend on   0%\n",
      "\n",
      "weeks since the growth change -> how often the search sees it, and what the forecast does\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   4 weeks: seen within three weeks  20%   any bend  45%   leans  -5.1% (without the search  -7.1%)   band caught   51% (without   47%)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   8 weeks: seen within three weeks  53%   any bend  72%   leans  -0.5% (without the search  -7.1%)   band caught   70% (without   49%)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  12 weeks: seen within three weeks  49%   any bend  77%   leans  +1.0% (without the search  -5.9%)   band caught   75% (without   61%)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  16 weeks: seen within three weeks  41%   any bend  69%   leans  -0.2% (without the search  -3.7%)   band caught   79% (without   77%)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  24 weeks: seen within three weeks  13%   any bend  37%   leans  -0.4% (without the search  -0.4%)   band caught   77% (without   87%)\n"
     ]
    }
   ],
   "source": [
    "SEARCH_REPS = 300\n",
    "AGE_GRID = [4, 8, 12, 16, 24]      # weeks between the growth change and the end of the history\n",
    "\n",
    "\n",
    "def score_batch(histories, truths, **kw):\n",
    "    fits = [forecast(h, **kw) for h in histories]\n",
    "    res = np.array([[f[\"total\"], f[\"total_lower\"], f[\"total_upper\"]] for f in fits])\n",
    "    err = (res[:, 0] - truths) / truths * 100\n",
    "    return fits, {\n",
    "        \"bias_pct\": float(np.median(err)),\n",
    "        \"typical_miss_pct\": float(np.median(np.abs(err))),\n",
    "        \"band_caught_pct\": float(((truths >= res[:, 1]) & (truths <= res[:, 2])).mean() * 100),\n",
    "        \"kept_a_bend_pct\": float(np.mean([len(f[\"bends\"]) > 0 for f in fits]) * 100),\n",
    "    }\n",
    "\n",
    "\n",
    "def accounts_with(level_fn, n_reps, generator, n_hist=HISTORY_DAYS, horizon=HORIZON):\n",
    "    t = np.arange(n_hist + horizon)\n",
    "    s = np.exp(level_fn(t)[None, :] + np.log(DOW_TRUTH[t % 7])[None, :] + ar1_noise(n_reps, n_hist + horizon, generator))\n",
    "    return s[:, :n_hist], s[:, n_hist:].sum(axis=1)\n",
    "\n",
    "\n",
    "search_cost = {}\n",
    "for label, level_fn in ((\"a flat account\", lambda t: np.full(np.shape(t), np.log(BASE_LEVEL))),\n",
    "                        (\"an account like the worked one\", true_log_level)):\n",
    "    hs, ts = accounts_with(level_fn, SEARCH_REPS, np.random.default_rng(SEED + 600 + len(label)))\n",
    "    _, with_search = score_batch(hs, ts)\n",
    "    _, without = score_batch(hs, ts, find_bends=False)\n",
    "    search_cost[label] = {\"with the search\": with_search, \"without\": without}\n",
    "    print(f\"{label}:\")\n",
    "    for k, r in search_cost[label].items():\n",
    "        print(f\"   {k:<18} leans {r['bias_pct']:+5.1f}%   typical miss {r['typical_miss_pct']:4.1f}%   \"\n",
    "              f\"band caught {r['band_caught_pct']:5.1f}%   kept a bend on {r['kept_a_bend_pct']:3.0f}%\")\n",
    "\n",
    "print()\n",
    "print(\"weeks since the growth change -> how often the search sees it, and what the forecast does\")\n",
    "age_sweep = []\n",
    "for w in AGE_GRID:\n",
    "    bend_at = HISTORY_DAYS - 7 * w\n",
    "\n",
    "    def level_bent(t, bend_at=bend_at):\n",
    "        t = np.asarray(t, dtype=float)\n",
    "        return (np.log(BASE_LEVEL) + GROWTH_BEFORE * np.minimum(t, bend_at)\n",
    "                + GROWTH_AFTER * np.maximum(t - bend_at, 0.0))\n",
    "\n",
    "    hs, ts = accounts_with(level_bent, SEARCH_REPS, np.random.default_rng(SEED + 700 + w))\n",
    "    fits, with_search = score_batch(hs, ts)\n",
    "    _, without = score_batch(hs, ts, find_bends=False)\n",
    "    near = float(np.mean([any(abs(b - bend_at) <= 21 for b in f[\"bends\"]) for f in fits]) * 100)\n",
    "    age_sweep.append({\n",
    "        \"weeks_since\": w,\n",
    "        \"seen_within_three_weeks_pct\": near,\n",
    "        \"kept_any_bend_pct\": with_search[\"kept_a_bend_pct\"],\n",
    "        \"bias_pct\": with_search[\"bias_pct\"],\n",
    "        \"typical_miss_pct\": with_search[\"typical_miss_pct\"],\n",
    "        \"band_caught_pct\": with_search[\"band_caught_pct\"],\n",
    "        \"bias_without_search_pct\": without[\"bias_pct\"],\n",
    "        \"band_caught_without_search_pct\": without[\"band_caught_pct\"],\n",
    "    })\n",
    "    r = age_sweep[-1]\n",
    "    print(f\"  {w:2d} weeks: seen within three weeks {r['seen_within_three_weeks_pct']:3.0f}%   any bend {r['kept_any_bend_pct']:3.0f}%   \"\n",
    "          f\"leans {r['bias_pct']:+5.1f}% (without the search {r['bias_without_search_pct']:+5.1f}%)   \"\n",
    "          f\"band caught {r['band_caught_pct']:4.0f}% (without {r['band_caught_without_search_pct']:4.0f}%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a308e6",
   "metadata": {},
   "source": [
    "## What it is worth, in money\n",
    "\n",
    "Next month's forecast is what a budget, a sales rota or a stock order is planned on. The\n",
    "difference between the shortcut and the forecast is a planning error, and a planning error\n",
    "has a price. Below, the account's cost per conversion is set at a round forty euros to make\n",
    "the arithmetic visible; substitute your own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "38716545",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:47.666201Z",
     "iopub.status.busy": "2026-10-24T12:15:47.664936Z",
     "iopub.status.idle": "2026-10-24T12:15:47.685712Z",
     "shell.execute_reply": "2026-10-24T12:15:47.682547Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a typical month on these accounts brings 3473 conversions; at 40 euros each:\n",
      "  same as last month     typically off by  303 conversions - about 12,120 euros\n",
      "  recent average x 30    typically off by  296 conversions - about 11,841 euros\n",
      "  the ruler              typically off by  158 conversions - about  6,335 euros\n",
      "  this forecast          typically off by  183 conversions - about  7,321 euros\n",
      "\n",
      "planning on last month's total costs about 4,800 euros a month more\n",
      "than planning on the forecast; on accounts of this shape the ruler is 985 euros cheaper than the forecast -\n",
      "the table of shapes above says where that holds and where it does not, and only one of the two\n",
      "says how far it might be off.\n"
     ]
    }
   ],
   "source": [
    "COST_PER_CONVERSION = 40.0\n",
    "best_shortcut = min(SHORTCUTS, key=lambda k: scores[k][\"typical_miss_pct\"])\n",
    "typical_month = float(np.median(acc_truth))\n",
    "money = {\"cost_per_conversion\": COST_PER_CONVERSION, \"typical_month_conversions\": typical_month,\n",
    "         \"best_shortcut\": best_shortcut, \"rows\": {}}\n",
    "for name, sc in scores.items():\n",
    "    conv = sc[\"typical_miss_pct\"] / 100 * typical_month\n",
    "    money[\"rows\"][name] = {\"typical_miss_conversions\": conv, \"typical_miss_eur\": conv * COST_PER_CONVERSION}\n",
    "money[\"anchored_shortcut_extra_eur\"] = (money[\"rows\"][\"same as last month\"][\"typical_miss_eur\"]\n",
    "                                       - money[\"rows\"][\"this forecast\"][\"typical_miss_eur\"])\n",
    "money[\"ruler_vs_forecast_eur\"] = (money[\"rows\"][\"the ruler\"][\"typical_miss_eur\"]\n",
    "                                  - money[\"rows\"][\"this forecast\"][\"typical_miss_eur\"])\n",
    "print(f\"a typical month on these accounts brings {typical_month:.0f} conversions; at {COST_PER_CONVERSION:.0f} euros each:\")\n",
    "for name, r in money[\"rows\"].items():\n",
    "    print(f\"  {name:<22} typically off by {r['typical_miss_conversions']:4.0f} conversions - about {r['typical_miss_eur']:6,.0f} euros\")\n",
    "print()\n",
    "print(f\"planning on last month's total costs about {money['anchored_shortcut_extra_eur']:,.0f} euros a month more\")\n",
    "print(f\"than planning on the forecast; on accounts of this shape the ruler is {abs(money['ruler_vs_forecast_eur']):,.0f} euros \"\n",
    "      f\"{'cheaper' if money['ruler_vs_forecast_eur'] < 0 else 'dearer'} than the forecast -\")\n",
    "print(\"the table of shapes above says where that holds and where it does not, and only one of the two\")\n",
    "print(\"says how far it might be off.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7266ba8",
   "metadata": {},
   "source": [
    "## Run it on your own figures\n",
    "\n",
    "Everything above is synthetic so the answers could be scored. This section runs the same\n",
    "forecast on a file you export yourself. It runs on your machine; nothing is uploaded.\n",
    "\n",
    "**The file this expects.** One line per day, two columns, any order:\n",
    "\n",
    "| column | what it holds | example |\n",
    "|---|---|---|\n",
    "| `date` | the calendar date, any ordinary format | `2026-03-17` |\n",
    "| `conversions` | that day's conversions (or leads, sales, sign-ups) | `84.5` |\n",
    "\n",
    "Any extra column is ignored. If you have several campaigns in one file, filter to one\n",
    "first, or add them up per day. A forecast of a mixture is fine, a forecast of a file with\n",
    "five lines per date is not.\n",
    "\n",
    "**Two ways to get there.** From Google Ads: Campaigns, segment by Day, download. The Day\n",
    "and Conversions columns are the two this needs. The download's title lines and its Total\n",
    "rows are set aside for you, and a file with one line per campaign per day is added up per\n",
    "day (the notebook says how many campaigns it summed). From GA4's Reports, the Date and Key\n",
    "events columns work as they come, \"#\" lines and 20261019-style dates included. From an\n",
    "events export or a warehouse: one line per conversion with a timestamp, and the cell below\n",
    "adds them up per day for you.\n",
    "\n",
    "**Put your file's name in the marked cell** a few cells down (`MY_FILE`), run every cell, and\n",
    "your forecast prints there. Leave the newest days out: they are still filling in, and a\n",
    "forecast fitted on them sees a dip that is not there. The marked cell sets the last seven\n",
    "aside by default; set `UNFINISHED_DAYS` to your own conversion lag if you know it.\n",
    "\n",
    "**What it refuses, and why.** Days with zero conversions. This method works on the\n",
    "logarithm of the daily count, and the logarithm of zero does not exist. If your account has\n",
    "zero days, the daily count is too small to forecast day by day: forecast a bigger number\n",
    "instead: weekly totals, or clicks, or conversions across more campaigns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4f898734",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-10-24T12:15:47.798558Z"
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   },
   "outputs": [],
   "source": [
    "# ---- export reader (shared across every Stochastic Strata notebook; do not edit here) ----------\n",
    "# One reader for the file you bring. Ad platforms and analytics tools do not hand over clean tables:\n",
    "# a Google Ads download opens with a title line and a date-range line and closes with \"Total:\" rows,\n",
    "# a GA4 download opens with \"#\" lines and writes dates as 20261019, a European download separates\n",
    "# with semicolons and writes 1.234,56, and Excel turns numbers into \"1,234.56\" or \"€12.30\". This\n",
    "# reader finds the real header row, sets the title, comment and total rows aside, turns formatted\n",
    "# numbers back into numbers, and reads dates in the platforms' own forms. Where a file is genuinely\n",
    "# ambiguous - 06/07/2026 could be the 6th of July or the 7th of June - it stops and asks rather than\n",
    "# guessing, because a guessed date silently moves a day to the wrong weekday.\n",
    "#\n",
    "#     table, notes = read_export(\"my_export.csv\", columns={\"date\": [\"day\", \"date\"], ...})\n",
    "#     for note in notes: print(note)\n",
    "#\n",
    "# `columns` maps the names this notebook uses to the names your file might use (case and spacing do\n",
    "# not matter, and a currency suffix such as \"(EUR)\" is ignored). `date_columns` are read as dates,\n",
    "# `id_columns` are kept as text. Everything else that reads as numbers becomes numbers; a share\n",
    "# written \"49.41%\" becomes 0.4941. A share the platform shows only as \"< 10%\" or \"> 90%\" is read as\n",
    "# 0.10 or 0.90 and the direction is kept in a column ending \"_bound\", because it is a limit, not a\n",
    "# measurement. Every row set aside, and why, comes back in `notes`.\n",
    "import csv as _csv\n",
    "import io as _io\n",
    "import re as _re\n",
    "from pathlib import Path as _Path\n",
    "\n",
    "import numpy as _np\n",
    "import pandas as _pd\n",
    "\n",
    "\n",
    "class ExportReadError(ValueError):\n",
    "    \"\"\"The file cannot be read without guessing; the message says what to change.\"\"\"\n",
    "\n",
    "\n",
    "_TOTAL_ROW = _re.compile(r\"^\\s*(grand\\s+)?totals?\\b\", _re.I)\n",
    "_CURRENCY = _re.compile(r\"[€$£¥₹]|\\b(EUR|USD|GBP|CHF|SEK|NOK|DKK|PLN|AUD|CAD)\\b\", _re.I)\n",
    "_EMPTY = {\"\", \"--\", \"-\", \"—\", \"–\", \"n/a\", \"na\", \"nan\", \"none\", \"null\", \"(not set)\"}\n",
    "_MONTHS = {m: i for i, m in enumerate(\n",
    "    [\"jan\", \"feb\", \"mar\", \"apr\", \"may\", \"jun\", \"jul\", \"aug\", \"sep\", \"oct\", \"nov\", \"dec\"], 1)}\n",
    "\n",
    "\n",
    "def _norm(name):\n",
    "    name = _re.sub(r\"\\((?:[A-Z]{3}|[€$£])\\)\", \"\", str(name))\n",
    "    return _re.sub(r\"[\\s_\\-./]+\", \" \", name).strip().lower()\n",
    "\n",
    "\n",
    "def _decode(raw):\n",
    "    if raw[:2] in (b\"\\xff\\xfe\", b\"\\xfe\\xff\"):\n",
    "        return raw.decode(\"utf-16\")\n",
    "    for encoding in (\"utf-8-sig\", \"cp1252\", \"latin-1\"):\n",
    "        try:\n",
    "            return raw.decode(encoding)\n",
    "        except UnicodeDecodeError:\n",
    "            continue\n",
    "    return raw.decode(\"utf-8\", errors=\"replace\")\n",
    "\n",
    "\n",
    "def _rows_from(source):\n",
    "    \"\"\"Every row of the file as a list of strings, whatever the container.\"\"\"\n",
    "    if isinstance(source, _pd.DataFrame):\n",
    "        return [list(map(str, source.columns))] + source.astype(str).values.tolist(), \",\"\n",
    "    path = _Path(source)\n",
    "    if not path.exists():\n",
    "        raise ExportReadError(f\"There is no file at {path}. Check the name and the folder.\")\n",
    "    if path.suffix.lower() in (\".xlsx\", \".xlsm\", \".xls\"):\n",
    "        sheet = _pd.read_excel(path, header=None, dtype=str).fillna(\"\")\n",
    "        return sheet.values.tolist(), \",\"\n",
    "    text = _decode(path.read_bytes())\n",
    "    lines = [ln for ln in text.splitlines() if not ln.lstrip().startswith(\"#\")]\n",
    "    counts = {d: sum(ln.count(d) for ln in lines[:60]) for d in (\",\", \";\", \"\\t\")}\n",
    "    delimiter = max(counts, key=counts.get) if any(counts.values()) else \",\"\n",
    "    return [row for row in _csv.reader(_io.StringIO(\"\\n\".join(lines)), delimiter=delimiter)], delimiter\n",
    "\n",
    "\n",
    "def _find_header(rows, wanted):\n",
    "    \"\"\"The first row that names the columns: the one matching most expected names, else the first\n",
    "    row as wide as the data below it that is mostly words rather than numbers.\"\"\"\n",
    "    widths = [len([c for c in r if str(c).strip()]) for r in rows]\n",
    "    if wanted:\n",
    "        best, best_hits = None, 0\n",
    "        for i, row in enumerate(rows[:40]):\n",
    "            names = {_norm(c) for c in row if str(c).strip()}\n",
    "            hits = sum(1 for options in wanted.values() if names & set(options))\n",
    "            if hits > best_hits:\n",
    "                best, best_hits = i, hits\n",
    "        if best is not None:\n",
    "            return best\n",
    "    body = [w for w in widths if w > 1]\n",
    "    width = max(set(body), key=body.count) if body else 1\n",
    "    for i, row in enumerate(rows[:40]):\n",
    "        cells = [str(c).strip() for c in row if str(c).strip()]\n",
    "        if len(cells) >= max(2, width - 1) and sum(_looks_number(c) for c in cells) <= len(cells) / 2:\n",
    "            return i\n",
    "    return 0\n",
    "\n",
    "\n",
    "def _looks_number(text):\n",
    "    if _re.fullmatch(r\"\\d{1,4}[./\\-]\\d{1,2}[./\\-]\\d{1,4}\", str(text).strip()):\n",
    "        return False  # a date such as 22.09.2026, not a number with separators\n",
    "    return bool(_re.fullmatch(r\"[<>]?\\s*[-+(]?[€$£]?\\s*[\\d.,\\s']+\\s*%?\\)?\", str(text).strip()))\n",
    "\n",
    "\n",
    "def _decimal_comma(values):\n",
    "    \"\"\"True when these values write 1.234,56 (a comma followed by one or two final digits, or dots\n",
    "    grouping thousands with a comma after them), False when they write 1,234.56, None when they do\n",
    "    not say (whole numbers only, or a lone '1.340').\"\"\"\n",
    "    comma_votes = dot_votes = 0\n",
    "    for v in values:\n",
    "        s = _re.sub(r\"[^\\d.,]\", \"\", v)\n",
    "        if \",\" in s and \".\" in s:\n",
    "            comma_votes += s.rfind(\",\") > s.rfind(\".\")\n",
    "            dot_votes += s.rfind(\".\") > s.rfind(\",\")\n",
    "        elif _re.search(r\",\\d{1,2}$\", s):\n",
    "            comma_votes += 1\n",
    "        elif _re.search(r\"\\.\\d{1,2}$\", s) or _re.search(r\"\\.\\d{4,}$\", s):\n",
    "            dot_votes += 1\n",
    "    if comma_votes == dot_votes:\n",
    "        return None\n",
    "    return comma_votes > dot_votes\n",
    "\n",
    "\n",
    "def _to_number(text, comma_decimal):\n",
    "    s = str(text).strip().replace(\" \", \" \")\n",
    "    if s.lower() in _EMPTY:\n",
    "        return _np.nan, None, True\n",
    "    bound = None\n",
    "    if s[:1] in \"<>\":\n",
    "        bound, s = s[0], s[1:].strip()\n",
    "    negative = s.startswith(\"(\") and s.endswith(\")\")\n",
    "    s = _CURRENCY.sub(\"\", s).strip(\"() \").replace(\" \", \"\").replace(\"'\", \"\")\n",
    "    percent = s.endswith(\"%\")\n",
    "    s = s.rstrip(\"%\")\n",
    "    if comma_decimal:\n",
    "        s = s.replace(\".\", \"\").replace(\",\", \".\")\n",
    "    else:\n",
    "        s = s.replace(\",\", \"\")\n",
    "    try:\n",
    "        value = float(s)\n",
    "    except ValueError:\n",
    "        return _np.nan, None, False\n",
    "    if negative:\n",
    "        value = -value\n",
    "    return (value / 100.0 if percent else value), bound, True\n",
    "\n",
    "\n",
    "def _parse_dates(values, column, dayfirst):\n",
    "    text = [str(v).strip() for v in values]\n",
    "    present = [t for t in text if t.lower() not in _EMPTY]\n",
    "    if not present:\n",
    "        return _pd.Series([_pd.NaT] * len(text)), []\n",
    "    notes = []\n",
    "    if all(_re.fullmatch(r\"\\d{8}\", t) for t in present):\n",
    "        return _pd.to_datetime(_pd.Series(text), format=\"%Y%m%d\", errors=\"coerce\"), notes\n",
    "    if all(_re.fullmatch(r\"\\d{10}(\\.\\d+)?|\\d{13}|\\d{16}\", t) for t in present):\n",
    "        digits = len(present[0].split(\".\")[0])\n",
    "        unit = {10: \"s\", 13: \"ms\", 16: \"us\"}[digits]\n",
    "        notes.append(f\"'{column}' holds epoch times in {dict(s='seconds', ms='milliseconds', us='microseconds')[unit]}; read as such.\")\n",
    "        return _pd.to_datetime(_pd.to_numeric(_pd.Series(text), errors=\"coerce\"), unit=unit), notes\n",
    "    cleaned = [_re.sub(r\"\\s+(UTC|GMT|Z)$\", \"\", t) for t in text]\n",
    "    slash = [t for t in cleaned if _re.fullmatch(r\"\\d{1,2}[/.\\-]\\d{1,2}[/.\\-]\\d{2,4}(\\s.*)?\", t)]\n",
    "    if slash and len(slash) >= len(present) * 0.9:\n",
    "        firsts = [int(_re.split(r\"[/.\\-]\", t)[0]) for t in slash]\n",
    "        seconds = [int(_re.split(r\"[/.\\-]\", t)[1]) for t in slash]\n",
    "        dotted = all(\".\" in t.split(\" \")[0] for t in slash)\n",
    "        if dayfirst is None:\n",
    "            if max(firsts) > 12 and max(seconds) <= 12:\n",
    "                dayfirst = True\n",
    "            elif max(seconds) > 12 and max(firsts) <= 12:\n",
    "                dayfirst = False\n",
    "            elif dotted:\n",
    "                dayfirst = True\n",
    "            else:\n",
    "                raise ExportReadError(\n",
    "                    f\"The dates in '{column}' (for example {slash[0]}) could be day-first or month-first, \"\n",
    "                    \"and nothing in the file says which. Re-run with dayfirst=True for 6 July written \"\n",
    "                    \"06/07, or dayfirst=False for June 7 written 06/07 - or export the dates as \"\n",
    "                    \"2026-07-06.\")\n",
    "        return _pd.to_datetime(_pd.Series(cleaned), dayfirst=dayfirst, errors=\"coerce\"), notes\n",
    "    parsed = _pd.to_datetime(_pd.Series(cleaned), errors=\"coerce\", format=\"mixed\")\n",
    "    return parsed, notes\n",
    "\n",
    "\n",
    "def read_export(source, columns=None, date_columns=(), id_columns=(), dayfirst=None,\n",
    "                max_unreadable_share=0.02):\n",
    "    \"\"\"Read a downloaded report into a table with this notebook's column names. Returns\n",
    "    (table, notes); raises ExportReadError with a plain-words fix when it cannot read without\n",
    "    guessing.\"\"\"\n",
    "    columns = columns or {}\n",
    "    # Each name's candidates in the caller's order of preference, the name itself first.\n",
    "    wanted = {name: list(dict.fromkeys([_norm(name)] + [_norm(a) for a in aliases]))\n",
    "              for name, aliases in columns.items()}\n",
    "    rows, delimiter = _rows_from(source)\n",
    "    rows = [r for r in rows if any(str(c).strip() for c in r)]\n",
    "    if not rows:\n",
    "        raise ExportReadError(\"The file is empty.\")\n",
    "    notes = []\n",
    "    h = _find_header(rows, wanted)\n",
    "    if h:\n",
    "        notes.append(f\"set aside {h} line(s) above the header (a report title or date range).\")\n",
    "    header = [str(c).strip() for c in rows[h]]\n",
    "    body, totals = [], 0\n",
    "    for r in rows[h + 1:]:\n",
    "        first = next((str(c) for c in r if str(c).strip()), \"\")\n",
    "        if _TOTAL_ROW.match(first):\n",
    "            totals += 1\n",
    "            continue\n",
    "        if [str(c).strip() for c in r] == header:\n",
    "            continue\n",
    "        body.append([str(c) for c in r] + [\"\"] * (len(header) - len(r)))\n",
    "    if totals:\n",
    "        notes.append(f\"set aside {totals} total row(s).\")\n",
    "    frame = _pd.DataFrame([r[:len(header)] for r in body], columns=header)\n",
    "    rename = {}\n",
    "    for name, options in wanted.items():\n",
    "        by_norm = {}\n",
    "        for c in frame.columns:\n",
    "            by_norm.setdefault(_norm(c), c)\n",
    "        match = next((by_norm[o] for o in options if o in by_norm and by_norm[o] not in rename), None)\n",
    "        if match is not None:\n",
    "            rename[match] = name\n",
    "    frame = frame.rename(columns=rename)\n",
    "    missing = [name for name in columns if name not in frame.columns]\n",
    "    if missing:\n",
    "        seen = \", \".join(f\"'{c}'\" for c in header[:12])\n",
    "        raise ExportReadError(\n",
    "            f\"Could not find the column(s) {', '.join(missing)}. The file's columns are {seen}. \"\n",
    "            \"Rename the column in the file, or add its name to the list this cell accepts.\")\n",
    "    dates = set(date_columns)\n",
    "    # The file's own convention, for a column that cannot tell by itself: a clicks column of whole\n",
    "    # numbers says nothing, but its \"1.340\" means one thousand three hundred and forty when the\n",
    "    # file's other columns write decimals with a comma.\n",
    "    every = [str(v) for c in frame.columns if c not in date_columns for v in frame[c].tolist()\n",
    "             if _looks_number(v)]\n",
    "    file_comma = _decimal_comma(every)\n",
    "    if file_comma is None:\n",
    "        file_comma = delimiter == \";\"\n",
    "    ids = set(id_columns) | {c for c in frame.columns if _re.search(r\"(^|[ _])(id|pseudo id)$\", _norm(c))}\n",
    "    for col in frame.columns:\n",
    "        values = frame[col].tolist()\n",
    "        if col in dates:\n",
    "            parsed, extra = _parse_dates(values, col, dayfirst)\n",
    "            notes += extra\n",
    "            bad = int(parsed.isna().sum() - sum(str(v).strip().lower() in _EMPTY for v in values))\n",
    "            if bad > max(1, max_unreadable_share * len(values)):\n",
    "                example = next(v for v, p in zip(values, parsed) if _pd.isna(p) and str(v).strip())\n",
    "                raise ExportReadError(f\"{bad} value(s) in '{col}' are not dates I can read, for example '{example}'.\")\n",
    "            frame[col] = parsed.values\n",
    "            continue\n",
    "        if col in ids:\n",
    "            continue\n",
    "        present = [v for v in values if str(v).strip().lower() not in _EMPTY]\n",
    "        if not present or sum(_looks_number(v) for v in present) < 0.5 * len(present):\n",
    "            continue\n",
    "        comma = _decimal_comma(present)\n",
    "        if comma is None:\n",
    "            comma = file_comma\n",
    "        parsed = [_to_number(v, comma) for v in values]\n",
    "        unreadable = [v for v, (_, _, ok) in zip(values, parsed) if not ok]\n",
    "        if unreadable and col not in columns:\n",
    "            notes.append(f\"'{col}' mixes numbers with labels such as '{unreadable[0]}'; kept as text.\")\n",
    "            continue\n",
    "        if unreadable:\n",
    "            notes.append(f\"{len(unreadable)} value(s) in '{col}' could not be read as numbers, for example \"\n",
    "                         f\"'{unreadable[0]}'; they are left empty.\")\n",
    "            if len(unreadable) > max(1, max_unreadable_share * len(values)):\n",
    "                raise ExportReadError(\n",
    "                    f\"{len(unreadable)} of {len(values)} values in '{col}' are not numbers I can read, for \"\n",
    "                    f\"example '{unreadable[0]}'. Fix them in the file, or export the column unformatted.\")\n",
    "        frame[col] = [p[0] for p in parsed]\n",
    "        bounds = [p[1] for p in parsed]\n",
    "        if any(bounds):\n",
    "            # An object column, so a day with no limit stays None: a newer pandas would infer a string\n",
    "            # column here and turn None into NaN, which is truthy and reads as a limit.\n",
    "            frame[f\"{col}_bound\"] = _pd.Series(bounds, index=frame.index, dtype=object)\n",
    "            notes.append(f\"'{col}' has values the platform shows only as a limit (for example \"\n",
    "                         f\"'{next(v for v, b in zip(values, bounds) if b)}'); kept as the limit, with the \"\n",
    "                         f\"direction in '{col}_bound'.\")\n",
    "    if dates:\n",
    "        undated = frame[list(dates)].isna().all(axis=1)\n",
    "        if undated.any():\n",
    "            frame = frame[~undated].reset_index(drop=True)\n",
    "            notes.append(f\"set aside {int(undated.sum())} row(s) with no date (GA4 writes its grand total \"\n",
    "                         \"this way, under the header).\")\n",
    "    return frame, notes\n",
    "# ---- end of export reader -------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e336a050",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:47.806707Z",
     "iopub.status.busy": "2026-10-24T12:15:47.806302Z",
     "iopub.status.idle": "2026-10-24T12:15:47.843206Z",
     "shell.execute_reply": "2026-10-24T12:15:47.838948Z"
    }
   },
   "outputs": [],
   "source": [
    "class DataProblem(Exception):\n",
    "    \"\"\"Raised with a plain-words explanation the reader can act on.\"\"\"\n",
    "\n",
    "\n",
    "def read_daily_csv(path, date_column=\"date\", value_column=\"conversions\", dayfirst=None):\n",
    "    \"\"\"Read the reader's export and hand back (dates, values), or refuse in plain words.\"\"\"\n",
    "    try:\n",
    "        frame, notes = read_export(\n",
    "            path, columns={\"date\": [date_column, \"date\", \"day\"],\n",
    "                           \"conversions\": [value_column, \"conversions\", \"key events\", \"conv.\",\n",
    "                                           \"leads\", \"results\", \"purchases\"]},\n",
    "            date_columns=[\"date\"], dayfirst=dayfirst)\n",
    "    except ExportReadError as problem:\n",
    "        raise DataProblem(str(problem)) from None\n",
    "    for note in notes:\n",
    "        print(\"  \" + note)\n",
    "    frame = frame.dropna(subset=[\"date\", \"conversions\"])\n",
    "    if frame[\"date\"].duplicated().any():\n",
    "        lines = len(frame)\n",
    "        frame = frame.groupby(\"date\", as_index=False)[\"conversions\"].sum()\n",
    "        print(f\"  {lines} lines added up to {len(frame)} days: the file has several lines per date \"\n",
    "              \"(one per campaign, usually), so the forecast is of their total. Filter to one \"\n",
    "              \"campaign first if that is the one you want.\")\n",
    "    frame = frame.sort_values(\"date\")\n",
    "    dates = frame[\"date\"].to_numpy()\n",
    "    values = frame[\"conversions\"].to_numpy(dtype=float)\n",
    "    if (values <= 0).any():\n",
    "        n = int((values <= 0).sum())\n",
    "        raise DataProblem(\n",
    "            f\"{n} day(s) have zero or negative conversions. This method forecasts the logarithm \"\n",
    "            \"of the daily count, which needs every day above zero. If you have zero days, \"\n",
    "            \"forecast a bigger number - weekly totals, clicks, or several campaigns added up.\"\n",
    "        )\n",
    "    gaps = np.diff(dates).astype(\"timedelta64[D]\").astype(int)\n",
    "    if (gaps != 1).any():\n",
    "        missing = int((gaps - 1).sum())\n",
    "        raise DataProblem(\n",
    "            f\"{missing} day(s) are missing between the first and last date. The weekly rhythm \"\n",
    "            \"is read off consecutive days, so fill the gaps from the source (a day with no \"\n",
    "            \"spend still has a date) rather than leaving them out.\"\n",
    "        )\n",
    "    return dates, values\n",
    "\n",
    "\n",
    "def consolidate_conversion_log(path, timestamp_column=\"timestamp\"):\n",
    "    \"\"\"One line per conversion with a timestamp -> one line per day with a count.\"\"\"\n",
    "    frame = pd.read_csv(path)\n",
    "    lowered = {c.lower().strip(): c for c in frame.columns}\n",
    "    col = lowered.get(timestamp_column.lower())\n",
    "    if col is None:\n",
    "        raise DataProblem(f\"This file has no '{timestamp_column}' column; it has {list(frame.columns)}.\")\n",
    "    stamps = pd.to_datetime(frame[col], errors=\"coerce\").dropna()\n",
    "    days = stamps.dt.floor(\"D\")\n",
    "    counts = days.value_counts().sort_index()\n",
    "    full = pd.date_range(counts.index.min(), counts.index.max(), freq=\"D\")\n",
    "    counts = counts.reindex(full, fill_value=0)\n",
    "    out = pd.DataFrame({\"date\": full, \"conversions\": counts.to_numpy()})\n",
    "    print(f\"  {len(stamps)} conversions across {len(out)} days; \"\n",
    "          f\"{int((out.conversions == 0).sum())} day(s) with none\")\n",
    "    return out\n",
    "\n",
    "\n",
    "def describe_sufficiency(n_days):\n",
    "    \"\"\"What the history can and cannot support, in words the reader can act on.\"\"\"\n",
    "    notes = []\n",
    "    if n_days < MIN_HISTORY:\n",
    "        notes.append(f\"REFUSED: {n_days} days. The weekly rhythm needs at least {MIN_HISTORY} \"\n",
    "                     \"(two full weeks) before anything can be said.\")\n",
    "    elif n_days < 56:\n",
    "        notes.append(f\"TREAT AS A ROUGH GUIDE: {n_days} days. The typical miss at this length is \"\n",
    "                     \"about double what six months of history would give - measured in this notebook.\")\n",
    "    elif n_days < 120:\n",
    "        notes.append(f\"USABLE: {n_days} days. The trend and rhythm are readable; the band keeps \"\n",
    "                     \"its promise to within a few points, as it does at every length.\")\n",
    "    else:\n",
    "        notes.append(f\"GOOD: {n_days} days. Enough to check the forecast against your own past \"\n",
    "                     \"months, which the cell below does.\")\n",
    "    return notes\n",
    "\n",
    "\n",
    "def report_forecast(dates, values, horizon=HORIZON):\n",
    "    \"\"\"The whole reader-facing routine: refuse, warn, or forecast.\"\"\"\n",
    "    n = len(values)\n",
    "    for note in describe_sufficiency(n):\n",
    "        print(\"  \" + note)\n",
    "    if n < MIN_HISTORY:\n",
    "        return None\n",
    "    if horizon > n // 3:\n",
    "        print(f\"  a {horizon}-day horizon is more than a third of your {n} days; the method reads at \"\n",
    "              f\"most a third of the history ahead, so this forecasts the next {n // 3} days instead\")\n",
    "        horizon = n // 3\n",
    "    if n >= 120:\n",
    "        origins = list(range(90, n - 30 + 1, 15))\n",
    "        rows = backtest(values, origins)\n",
    "        caught = sum(r[\"caught\"] for r in rows)\n",
    "        print(f\"  checked against your own past: {len(rows)} month(s), typical miss \"\n",
    "              f\"{np.median([abs(r['miss_pct']) for r in rows]):.1f}%, band caught {caught} of {len(rows)}\")\n",
    "    f = forecast(values, horizon=horizon)\n",
    "    last = pd.Timestamp(dates[-1])\n",
    "    print(f\"\\n  history ends {last.date()}; the next {horizon} days are forecast at\")\n",
    "    print(f\"    {f['total']:.0f} conversions, range {f['total_lower']:.0f} to {f['total_upper']:.0f}\"\n",
    "          f\" ({int(COVERAGE * 100)}% band)\")\n",
    "    print(f\"    bends kept at {f['bends'] if f['bends'] else 'none'}; day-to-day resemblance {f['phi']:.2f};\"\n",
    "          f\" daily surprise {f['sigma'] * 100:.0f}%\")\n",
    "    print(\"  assuming nothing changes - a budget move you already know about is not in this number.\")\n",
    "    return f"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48fb316a",
   "metadata": {},
   "source": [
    "### Your file\n",
    "\n",
    "Put your export's file name below and run every cell. Leave it as `None` to see the\n",
    "demonstrations that follow instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fa821751",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:47.850432Z",
     "iopub.status.busy": "2026-10-24T12:15:47.847817Z",
     "iopub.status.idle": "2026-10-24T12:15:47.869777Z",
     "shell.execute_reply": "2026-10-24T12:15:47.868305Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MY_FILE is empty - the demonstrations below run instead.\n"
     ]
    }
   ],
   "source": [
    "MY_FILE = None            # <- your export, for example \"google_ads_campaign_by_day.csv\"\n",
    "DATE_COLUMN = \"date\"      # the date column's name, if it is not Date or Day\n",
    "VALUE_COLUMN = \"conversions\"  # the count's column, if it is not Conversions or Key events\n",
    "DAYFIRST = None           # True for 06/07/2026 meaning 6 July, False for June 7; None asks if unsure\n",
    "UNFINISHED_DAYS = 7       # newest days left out because they are still filling in\n",
    "\n",
    "\n",
    "def forecast_my_file(path, unfinished_days=UNFINISHED_DAYS):\n",
    "    dates, values = read_daily_csv(path, DATE_COLUMN, VALUE_COLUMN, dayfirst=DAYFIRST)\n",
    "    if unfinished_days:\n",
    "        last_kept = pd.Timestamp(dates[-1]) - pd.Timedelta(days=unfinished_days)\n",
    "        print(f\"  left out the newest {unfinished_days} day(s), after {last_kept.date()}: they are \"\n",
    "              \"still filling in. The forecast starts the day after that, so its first days overlap \"\n",
    "              \"ones you have already partly seen.\")\n",
    "        keep = dates <= np.datetime64(last_kept)\n",
    "        dates, values = dates[keep], values[keep]\n",
    "    if len(values) >= 35:\n",
    "        recent, before = values[-7:].mean(), values[-35:-7].mean()\n",
    "        if recent < 0.8 * before:\n",
    "            print(f\"  WARNING: the last week kept averages {recent:.1f} a day against {before:.1f} \"\n",
    "                  \"in the four before it. If that is conversions still arriving rather than a real \"\n",
    "                  \"drop, raise UNFINISHED_DAYS and run this cell again.\")\n",
    "    return report_forecast(dates, values)\n",
    "\n",
    "\n",
    "if MY_FILE:\n",
    "    print(f\"YOUR FILE: {MY_FILE}\")\n",
    "    my_forecast = forecast_my_file(MY_FILE)\n",
    "else:\n",
    "    print(\"MY_FILE is empty - the demonstrations below run instead.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b775a3d7",
   "metadata": {},
   "source": [
    "### Exercised twice, on data whose answers are known\n",
    "\n",
    "A loader is only worth shipping if it fails honestly. Below it is run on a clean export it\n",
    "should read, on a raw conversion log it should add up, and then on three broken files it\n",
    "should refuse: the wrong columns, a metric with zero days, and a window too short."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5dcb32ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:47.890228Z",
     "iopub.status.busy": "2026-10-24T12:15:47.889781Z",
     "iopub.status.idle": "2026-10-24T12:15:48.058172Z",
     "shell.execute_reply": "2026-10-24T12:15:48.056796Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A CLEAN FIVE-MONTH EXPORT\n",
      "  GOOD: 150 days. Enough to check the forecast against your own past months, which the cell below does.\n",
      "  checked against your own past: 3 month(s), typical miss 8.5%, band caught 2 of 3\n",
      "\n",
      "  history ends 2026-06-03; the next 30 days are forecast at\n",
      "    3144 conversions, range 2936 to 3352 (90% band)\n",
      "    bends kept at [59]; day-to-day resemblance 0.31; daily surprise 9%\n",
      "  assuming nothing changes - a budget move you already know about is not in this number.\n",
      "\n",
      "A RAW CONVERSION LOG, ADDED UP PER DAY\n",
      "  4635 conversions across 60 days; 0 day(s) with none\n",
      "  USABLE: 60 days. The trend and rhythm are readable; the band keeps its promise to within a few points, as it does at every length.\n",
      "  a 30-day horizon is more than a third of your 60 days; the method reads at most a third of the history ahead, so this forecasts the next 20 days instead\n",
      "\n",
      "  history ends 2026-05-30; the next 20 days are forecast at\n",
      "    1714 conversions, range 1568 to 1860 (90% band)\n",
      "    bends kept at none; day-to-day resemblance 0.27; daily surprise 10%\n",
      "  assuming nothing changes - a budget move you already know about is not in this number.\n",
      "\n",
      "WRONG COLUMN NAMES\n",
      "  refused: Could not find the column(s) conversions. The file's columns are 'Day', 'Sales'. Rename the column in the file, or add its name to the list this cell accepts.\n",
      "\n",
      "A METRIC WITH ZERO DAYS\n",
      "  refused: 1 day(s) have zero or negative conversions. This method forecasts the logarithm of the daily count, which needs every day above zero. If you have zero days, forecast a bigger number - weekly totals, clicks, or several campaigns added up.\n",
      "\n",
      "TOO SHORT\n",
      "  REFUSED: 10 days. The weekly rhythm needs at least 14 (two full weeks) before anything can be said.\n",
      "  returned None - no forecast invented from ten days\n"
     ]
    }
   ],
   "source": [
    "SCRATCH = HERE / \"_your_data_demo\"\n",
    "SCRATCH.mkdir(exist_ok=True)\n",
    "\n",
    "g_demo = np.random.default_rng(SEED + 31337)\n",
    "v_clean, _ = make_history(150, g_demo)\n",
    "clean_path = SCRATCH / \"clean_export.csv\"\n",
    "pd.DataFrame({\n",
    "    \"date\": pd.date_range(\"2026-01-05\", periods=150, freq=\"D\"),\n",
    "    \"conversions\": np.round(v_clean, 2),\n",
    "    \"campaign\": \"Brand Search\",\n",
    "}).to_csv(clean_path, index=False)\n",
    "\n",
    "print(\"A CLEAN FIVE-MONTH EXPORT\")\n",
    "d_ok, v_ok = read_daily_csv(clean_path)\n",
    "reader_forecast = report_forecast(d_ok, v_ok)\n",
    "\n",
    "print(\"\\nA RAW CONVERSION LOG, ADDED UP PER DAY\")\n",
    "log_path = SCRATCH / \"conversion_log.csv\"\n",
    "g_log = np.random.default_rng(SEED + 4242)\n",
    "day_counts = np.round(make_history(60, g_log)[0]).astype(int)\n",
    "stamps = []\n",
    "for k, c in enumerate(day_counts):\n",
    "    base = pd.Timestamp(\"2026-04-01\") + pd.Timedelta(days=int(k))\n",
    "    stamps.extend(base + pd.to_timedelta(g_log.integers(0, 86400, int(c)), unit=\"s\"))\n",
    "pd.DataFrame({\"timestamp\": stamps, \"order_id\": range(len(stamps))}).to_csv(log_path, index=False)\n",
    "daily = consolidate_conversion_log(log_path)\n",
    "daily.to_csv(SCRATCH / \"consolidated.csv\", index=False)\n",
    "d_log, v_log = read_daily_csv(SCRATCH / \"consolidated.csv\")\n",
    "log_forecast = report_forecast(d_log, v_log)\n",
    "\n",
    "print(\"\\nWRONG COLUMN NAMES\")\n",
    "bad_cols = SCRATCH / \"wrong_columns.csv\"\n",
    "pd.DataFrame({\"Day\": [\"2026-01-05\"], \"Sales\": [24]}).to_csv(bad_cols, index=False)\n",
    "try:\n",
    "    read_daily_csv(bad_cols)\n",
    "except DataProblem as exc:\n",
    "    print(f\"  refused: {exc}\")\n",
    "\n",
    "print(\"\\nA METRIC WITH ZERO DAYS\")\n",
    "zeroes = SCRATCH / \"has_zeroes.csv\"\n",
    "pd.DataFrame({\n",
    "    \"date\": pd.date_range(\"2026-01-05\", periods=21, freq=\"D\"),\n",
    "    \"conversions\": [3.0] * 19 + [0.0, 2.0],\n",
    "}).to_csv(zeroes, index=False)\n",
    "try:\n",
    "    read_daily_csv(zeroes)\n",
    "except DataProblem as exc:\n",
    "    print(f\"  refused: {exc}\")\n",
    "\n",
    "print(\"\\nTOO SHORT\")\n",
    "short_path = SCRATCH / \"short.csv\"\n",
    "pd.DataFrame({\n",
    "    \"date\": pd.date_range(\"2026-01-05\", periods=10, freq=\"D\"),\n",
    "    \"conversions\": np.round(make_history(10, np.random.default_rng(SEED + 9))[0], 1),\n",
    "}).to_csv(short_path, index=False)\n",
    "d_short, v_short = read_daily_csv(short_path)\n",
    "short_result = report_forecast(d_short, v_short)\n",
    "print(f\"  returned {short_result} - no forecast invented from ten days\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5711d46e",
   "metadata": {},
   "source": [
    "## The numbers this bundle publishes\n",
    "\n",
    "Two files. `worked_example.json` carries every figure the article quotes, so the prose can\n",
    "never drift from the arithmetic. `checks.json` carries pinned cases with their own daily\n",
    "figures stored alongside them, so the interactive version on the page recomputes the\n",
    "forecast from the same numbers this notebook used rather than from a regenerated copy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eafda8ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T12:15:48.067654Z",
     "iopub.status.busy": "2026-10-24T12:15:48.066810Z",
     "iopub.status.idle": "2026-10-24T12:15:48.140143Z",
     "shell.execute_reply": "2026-10-24T12:15:48.137139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote checks.json          5 pinned cases\n",
      "wrote worked_example.json  17 blocks\n",
      "\n",
      "Every number the article, the workbook and the video quote comes from one of these two\n",
      "files. None of them is typed by hand anywhere.\n"
     ]
    }
   ],
   "source": [
    "TOLERANCE = 1e-4\n",
    "\n",
    "\n",
    "def pinned_case(name, note, values, horizon=HORIZON):\n",
    "    values = [round(float(v), 6) for v in values]        # the widget only ever sees these\n",
    "    f = forecast(np.array(values), horizon=horizon)\n",
    "    return {\n",
    "        \"name\": name,\n",
    "        \"note\": note,\n",
    "        \"horizon\": horizon,\n",
    "        \"values\": values,\n",
    "        \"expected\": {\n",
    "            \"point\": [round(float(v), 6) for v in f[\"point\"]],\n",
    "            \"lower\": [round(float(v), 6) for v in f[\"lower\"]],\n",
    "            \"upper\": [round(float(v), 6) for v in f[\"upper\"]],\n",
    "            \"total\": round(f[\"total\"], 6),\n",
    "            \"total_lower\": round(f[\"total_lower\"], 6),\n",
    "            \"total_upper\": round(f[\"total_upper\"], 6),\n",
    "            \"phi\": round(f[\"phi\"], 6),\n",
    "            \"sigma\": round(f[\"sigma\"], 6),\n",
    "            \"bends\": f[\"bends\"],\n",
    "        },\n",
    "    }\n",
    "\n",
    "\n",
    "g_pin = np.random.default_rng(SEED + 8080)\n",
    "flat_history, _ = make_history(120, g_pin, level_fn=lambda t: np.full(np.shape(t), np.log(BASE_LEVEL)))\n",
    "short_history, _ = make_history(42, g_pin)\n",
    "\n",
    "checks = {\n",
    "    \"method\": \"least-squares trend with stepwise-selected bends and a weekly rhythm on the log scale, \"\n",
    "              \"an AR(1) carry on the leftover, and a two-part band: noise ahead plus trend uncertainty\",\n",
    "    \"tolerance\": TOLERANCE,\n",
    "    \"constants\": {\n",
    "        \"cp_every\": CP_EVERY, \"cp_tail\": CP_TAIL, \"max_bends\": MAX_BENDS, \"fourier_pairs\": FOURIER_PAIRS,\n",
    "        \"phi_cap\": PHI_CAP, \"coverage\": COVERAGE, \"z\": Z_FOR_LEVEL[COVERAGE],\n",
    "        \"min_history\": MIN_HISTORY, \"max_horizon\": MAX_HORIZON, \"ridge_eps\": RIDGE_EPS,\n",
    "    },\n",
    "    \"cases\": [\n",
    "        pinned_case(\"the worked example\", \"six months of a growing account with a bend\", history),\n",
    "        pinned_case(\"a flat account\", \"no growth at all - the trend should stay level\", flat_history),\n",
    "        pinned_case(\"six weeks only\", \"the shortest history the article calls usable\", short_history),\n",
    "        pinned_case(\"a step down, fit on all of it\", \"the confidently wrong forecast\", history_shifted),\n",
    "        pinned_case(\"a step down, fit since the step\", \"better, still leaning low\", history_shifted[SHIFT_DAY:]),\n",
    "    ],\n",
    "}\n",
    "(DATA / \"checks.json\").write_text(json.dumps(checks, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "worked = {\n",
    "    \"label\": \"synthetic\",\n",
    "    \"seed\": SEED,\n",
    "    \"account\": {\n",
    "        \"history_days\": HISTORY_DAYS,\n",
    "        \"horizon_days\": HORIZON,\n",
    "        \"base_level\": BASE_LEVEL,\n",
    "        \"growth_before_pct_per_quarter\": round((np.exp(GROWTH_BEFORE * 90) - 1) * 100, 1),\n",
    "        \"growth_after_pct_per_quarter\": round((np.exp(GROWTH_AFTER * 90) - 1) * 100, 1),\n",
    "        \"bend_day\": BEND_DAY,\n",
    "        \"level_last_day\": round(float(np.exp(true_log_level(HISTORY_DAYS - 1))), 1),\n",
    "        \"noise_sigma_log\": NOISE_SIGMA_LOG,\n",
    "        \"day_to_day_correlation\": RHO,\n",
    "        \"dow_truth\": [round(float(v), 4) for v in DOW_TRUTH],\n",
    "        \"day_names\": DAY_NAMES,\n",
    "        # the advertiser's own daily figures, so the workbook recomputes the article's numbers\n",
    "        # from live formulas over exactly these rows\n",
    "        \"history\": [round(float(v), 4) for v in history],\n",
    "        \"one_future\": [round(float(v), 4) for v in one_future],\n",
    "    },\n",
    "    \"backtest\": {\n",
    "        \"origins\": ORIGINS,\n",
    "        \"rows\": [{k: (round(v, 1) if isinstance(v, float) else v) for k, v in r.items()} for r in bt_rows],\n",
    "        \"typical_miss_pct\": round(bt_miss, 1),\n",
    "        \"caught\": bt_caught,\n",
    "        \"months\": len(bt_rows),\n",
    "    },\n",
    "    \"forecast\": {\n",
    "        \"coverage_pct\": int(COVERAGE * 100),\n",
    "        \"candidate_bends\": len(range(CP_EVERY // 7, HISTORY_DAYS // 7 - CP_TAIL // 7, CP_EVERY // 7)),\n",
    "        \"bends_kept\": fc[\"bends\"],\n",
    "        \"phi\": round(fc[\"phi\"], 3),\n",
    "        \"sigma_pct\": round(fc[\"sigma\"] * 100, 1),\n",
    "        \"sigma_built_in_pct\": round(float(NOISE_SIGMA_LOG * np.sqrt(1 - RHO ** 2)) * 100, 1),\n",
    "        \"total\": round(fc[\"total\"], 1),\n",
    "        \"total_lower\": round(fc[\"total_lower\"], 1),\n",
    "        \"total_upper\": round(fc[\"total_upper\"], 1),\n",
    "        \"band_half_width_pct\": round((fc[\"total_upper\"] - fc[\"total_lower\"]) / 2 / fc[\"total\"] * 100, 1),\n",
    "        \"point\": [round(float(v), 3) for v in fc[\"point\"]],\n",
    "        \"lower\": [round(float(v), 3) for v in fc[\"lower\"]],\n",
    "        \"upper\": [round(float(v), 3) for v in fc[\"upper\"]],\n",
    "        \"noise_sd_pct\": [round(float(v) * 100, 2) for v in fc[\"noise_sd\"]],\n",
    "        \"param_sd_pct\": [round(float(v) * 100, 2) for v in fc[\"param_sd\"]],\n",
    "        \"memory_left_after_week_pct\": round(float(fc[\"phi\"] ** 7 * 100), 1),\n",
    "        \"total_trend_share_pct\": round(float(fc[\"total_trend_share_pct\"]), 1),\n",
    "        \"one_future_total\": round(actual_total, 1),\n",
    "        \"one_future_miss_pct\": round(one_future_miss[\"this forecast\"], 1),\n",
    "        \"recent_month_vs_level_pct\": round(recent_vs_level * 100, 1),\n",
    "        \"day1_width_pct\": round(float((fc[\"upper\"][0] - fc[\"lower\"][0]) / fc[\"point\"][0] * 100), 1),\n",
    "        \"day30_width_pct\": round(float((fc[\"upper\"][-1] - fc[\"lower\"][-1]) / fc[\"point\"][-1] * 100), 1),\n",
    "    },\n",
    "    \"shortcuts\": {\n",
    "        # each shortcut's guess on the worked account, and its miss against the one future that came true\n",
    "        \"guesses\": {name: {\"guess\": round(fn(history), 1), \"one_future_miss_pct\": round(one_future_miss[name], 1)}\n",
    "                    for name, fn in SHORTCUTS.items()},\n",
    "    },\n",
    "    \"accounts\": {\n",
    "        # the honest score: five hundred accounts like the worked one, each scored on its own future\n",
    "        \"reps\": ACCOUNTS,\n",
    "        \"rows\": {name: {k: round(v, 1) for k, v in sc.items()} for name, sc in scores.items()},\n",
    "        \"best_shortcut\": best_shortcut,\n",
    "        \"forecast_total_band_caught_pct\": round(inside_band, 1),\n",
    "        \"kept_a_bend_pct\": round(bends_any_pct, 1),\n",
    "        \"bend_near_the_real_one_pct\": round(bends_near_pct, 1),\n",
    "        \"typical_month_conversions\": round(typical_month, 1),\n",
    "        \"typical_phi\": round(phi_typ, 3),\n",
    "        \"typical_sigma_pct\": round(sigma_typ * 100, 1),\n",
    "        \"typical_trend_share_pct\": round(trend_share_typ, 1),\n",
    "        \"typical_memory_left_after_week_pct\": round(phi_typ ** 7 * 100, 1),\n",
    "        \"widening_needed\": {k: round(v, 2) for k, v in inflation.items()},\n",
    "    },\n",
    "    \"coverage\": {\n",
    "        \"claimed_pct\": int(COVERAGE * 100),\n",
    "        \"daily_caught_pct\": [round(float(v), 1) for v in inside_daily],\n",
    "        \"daily_caught_mean_pct\": round(float(inside_daily.mean()), 1),\n",
    "        \"daily_width_pct\": [round(float(v), 1) for v in rel_width],\n",
    "        \"noise_sd_pct\": [round(float(v) * 100, 2) for v in noise_sd_typ],\n",
    "        \"param_sd_pct\": [round(float(v) * 100, 2) for v in param_sd_typ],\n",
    "    },\n",
    "    \"history_length\": {\n",
    "        \"reps_per_length\": HIST_REPS,\n",
    "        \"rows\": [{k: (round(v, 1) if isinstance(v, float) else v) for k, v in history_sweep[L].items()}\n",
    "                 for L in HISTORY_GRID],\n",
    "    },\n",
    "    \"horizon\": {\n",
    "        \"reps\": ACCOUNTS,\n",
    "        \"rows\": [{k: (round(v, 2) if isinstance(v, float) else v) for k, v in horizon_sweep[H].items()}\n",
    "                 for H in HORIZON_GRID],\n",
    "    },\n",
    "    \"planned_change\": {k: round(v, 1) for k, v in planned.items()},\n",
    "    \"late_arrivals\": {k: (round(v, 1) if isinstance(v, float) else v) for k, v in late.items()},\n",
    "    \"regime_shift\": {\n",
    "        \"shift_pct\": round((1 - SHIFT_MULT) * 100, 0),\n",
    "        \"days_since_shift\": HISTORY_DAYS - SHIFT_DAY,\n",
    "        \"reps\": ACCOUNTS,\n",
    "        \"rows\": {label: {k: (round(v, 1) if isinstance(v, float) else v) for k, v in r.items()}\n",
    "                 for label, r in regime.items()},\n",
    "    },\n",
    "    \"bend_search\": {\n",
    "        \"reps\": SEARCH_REPS,\n",
    "        \"cost\": {label: {k: {kk: round(vv, 1) for kk, vv in r.items()} for k, r in rows.items()}\n",
    "                 for label, rows in search_cost.items()},\n",
    "        \"age_rows\": [{k: (round(v, 1) if isinstance(v, float) else v) for k, v in r.items()} for r in age_sweep],\n",
    "    },\n",
    "    \"money\": {\n",
    "        \"cost_per_conversion\": COST_PER_CONVERSION,\n",
    "        \"typical_month_conversions\": round(typical_month, 1),\n",
    "        \"best_shortcut\": best_shortcut,\n",
    "        \"rows\": {name: {k: round(v, 1) for k, v in r.items()} for name, r in money[\"rows\"].items()},\n",
    "        \"anchored_shortcut_extra_eur\": round(money[\"anchored_shortcut_extra_eur\"], 1),\n",
    "        \"ruler_vs_forecast_eur\": round(money[\"ruler_vs_forecast_eur\"], 1),\n",
    "    },\n",
    "    \"shapes\": {\n",
    "        \"reps_per_shape\": SHAPE_REPS,\n",
    "        \"rows\": [{k: ({kk: round(vv, 1) for kk, vv in v.items()} if isinstance(v, dict) else v) for k, v in r.items()}\n",
    "                 for r in shape_rows],\n",
    "        \"ruler_closer_on\": ruler_wins,\n",
    "    },\n",
    "    \"reader_demo\": {\n",
    "        \"clean_days\": 150,\n",
    "        \"clean_total\": None if reader_forecast is None else round(reader_forecast[\"total\"], 1),\n",
    "        \"log_days\": 60,\n",
    "        \"log_total\": None if log_forecast is None else round(log_forecast[\"total\"], 1),\n",
    "    },\n",
    "}\n",
    "(DATA / \"worked_example.json\").write_text(json.dumps(worked, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "print(f\"wrote checks.json          {len(checks['cases'])} pinned cases\")\n",
    "print(f\"wrote worked_example.json  {len(worked)} blocks\")\n",
    "print()\n",
    "print(\"Every number the article, the workbook and the video quote comes from one of these two\")\n",
    "print(\"files. None of them is typed by hand anywhere.\")"
   ]
  }
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