{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a13328f",
   "metadata": {},
   "source": [
    "# Why do some days always look worse - and when is it actually a problem?\n",
    "\n",
    "Sunday's cost per sale comes in at 29 euros. Tuesday's was 24. Somebody asks what went\n",
    "wrong on Sunday.\n",
    "\n",
    "Usually nothing went wrong on Sunday. Most advertising accounts have a weekly rhythm that\n",
    "has nothing to do with how well the campaign is run: people shop differently at the\n",
    "weekend, business buyers are at their desks on weekdays, payday lands at the same point\n",
    "each month. That rhythm is worth roughly the same amount every week, and if you do not\n",
    "measure it you will keep mistaking it for news.\n",
    "\n",
    "This notebook measures it. It builds a synthetic advertiser whose weekly rhythm I\n",
    "decided in advance, hides that rhythm under realistic noise, and then tries to recover it -\n",
    "so every reading can be scored against a truth nobody has to guess at.\n",
    "\n",
    "Three things are worth knowing before the working.\n",
    "\n",
    "**A normal Sunday and a normal Tuesday are about five euros apart here, with nothing\n",
    "wrong.** Once you know that number you can subtract it, and what is left is the part\n",
    "worth asking about.\n",
    "\n",
    "**How much history you need depends entirely on how fine a distinction you want.** There\n",
    "is no week count at which the pattern becomes correct - the error just keeps shrinking,\n",
    "and slowly. One week is already enough to know the weekend is the dearer end, and nowhere\n",
    "near enough to say by how much. Telling Monday from Tuesday, where the real gap is three\n",
    "per cent, is still wrong about one time in six after three months. A single \"you need six\n",
    "weeks\" answers a question nobody asked.\n",
    "\n",
    "**The verification everyone recommends is much weaker than it sounds.** The standard advice\n",
    "is to work the pattern out on one stretch and check it repeats on the next. Measured here,\n",
    "with six weeks on each side, that check misses four out of five advertisers whose weekend\n",
    "premium really did collapse. It is worth running because it costs nothing, and it is not\n",
    "evidence of much when it passes.\n",
    "\n",
    "**If the account is trending, this method quietly lies to you.** A metric drifting up or\n",
    "down through the window leaks that drift into the weekday pattern, and because Monday sits\n",
    "earlier in every week than Sunday does, the drift shows up as a tilt across the week that\n",
    "looks exactly like a real weekend effect. That is measured here too, and it is the main\n",
    "reason to check before trusting the answer.\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."
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed 20260911\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 = 20260911\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",
    "\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": "d6e8ee5b",
   "metadata": {},
   "source": [
    "## The advertiser I build, and the rhythm I hide in it\n",
    "\n",
    "One synthetic advertiser, one number per day: cost per sale. The generator gives it three\n",
    "properties that real accounts have and toy examples usually do not.\n",
    "\n",
    "**A weekly rhythm I choose.** Weekdays convert efficiently, the weekend costs more. This\n",
    "is the thing I am trying to recover, so I write it down first and never look at it\n",
    "again until it is time to score.\n",
    "\n",
    "**Noise that clusters.** Today looks like yesterday. Marketing days are not independent\n",
    "draws, and that clustering is exactly what makes eyeballing a chart unreliable - runs of\n",
    "four or five expensive days happen constantly with nothing wrong at all.\n",
    "\n",
    "**A level that can drift.** Most of this notebook holds the level flat, because that is\n",
    "the case the method is built for. The section on trend turns the drift on and measures\n",
    "what it costs.\n",
    "\n",
    "The rhythm is written as a multiplier per weekday, scaled so the seven multipliers average\n",
    "to exactly one. That scaling is what makes the numbers readable: a multiplier of 1.10 means\n",
    "that weekday runs ten per cent above the account's own average, whatever the account's\n",
    "average happens to be."
   ]
  },
  {
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   "id": "bda77b11",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Monday     0.946  -> a normal Monday costs 23.66 euros per sale\n",
      "  Tuesday    0.917  -> a normal Tuesday costs 22.92 euros per sale\n",
      "  Wednesday  0.927  -> a normal Wednesday costs 23.17 euros per sale\n",
      "  Thursday   0.956  -> a normal Thursday costs 23.91 euros per sale\n",
      "  Friday     1.015  -> a normal Friday costs 25.39 euros per sale\n",
      "  Saturday   1.104  -> a normal Saturday costs 27.61 euros per sale\n",
      "  Sunday     1.134  -> a normal Sunday costs 28.35 euros per sale\n"
     ]
    }
   ],
   "source": [
    "# The truth. Mon..Sun, as multipliers on cost per sale - higher is worse.\n",
    "DOW_TRUTH_RAW = np.array([0.96, 0.93, 0.94, 0.97, 1.03, 1.12, 1.15])\n",
    "DOW_TRUTH = DOW_TRUTH_RAW / DOW_TRUTH_RAW.mean()\n",
    "\n",
    "BASE_CPA = 25.0          # euros per sale, the account's own average\n",
    "NOISE_SIGMA_LOG = 0.10   # day-to-day wobble, multiplicative\n",
    "RHO = 0.35               # how much today resembles yesterday\n",
    "\n",
    "\n",
    "def make_series(n_days, *, base=BASE_CPA, dow=DOW_TRUTH, sigma=NOISE_SIGMA_LOG,\n",
    "                rho=RHO, trend_per_day=0.0, start_dow=0, generator=None):\n",
    "    \"\"\"Daily cost per sale with a known weekday rhythm, clustered noise and optional drift.\n",
    "\n",
    "    Returns (weekday index 0=Monday, value) arrays of length n_days.\n",
    "    \"\"\"\n",
    "    g = np.random.default_rng(SEED) if generator is None else generator\n",
    "    z = g.normal(0.0, sigma, n_days)\n",
    "    e = np.empty(n_days)\n",
    "    e[0] = z[0]\n",
    "    for t in range(1, n_days):\n",
    "        e[t] = rho * e[t - 1] + np.sqrt(1.0 - rho ** 2) * z[t]\n",
    "    days = (np.arange(n_days) + start_dow) % 7\n",
    "    level = base * (1.0 + trend_per_day) ** np.arange(n_days)\n",
    "    return days, level * dow[days] * np.exp(e)\n",
    "\n",
    "\n",
    "def make_batch(n_reps, n_days, *, generator, dow=DOW_TRUTH, sigma=NOISE_SIGMA_LOG,\n",
    "               rho=RHO, trend_per_day=0.0, base=BASE_CPA):\n",
    "    \"\"\"The same advertiser, n_reps independent times over. Used for every measurement below.\n",
    "\n",
    "    Batched because the measurements need thousands of runs and a per-run Python loop makes\n",
    "    that minutes instead of seconds. The arithmetic is identical to `make_series`.\n",
    "    \"\"\"\n",
    "    z = generator.normal(0.0, sigma, (n_reps, n_days))\n",
    "    e = np.empty((n_reps, n_days))\n",
    "    e[:, 0] = z[:, 0]\n",
    "    k = np.sqrt(1.0 - rho ** 2)\n",
    "    for t in range(1, n_days):\n",
    "        e[:, t] = rho * e[:, t - 1] + k * z[:, t]\n",
    "    days = np.arange(n_days) % 7\n",
    "    level = base * (1.0 + trend_per_day) ** np.arange(n_days)\n",
    "    return days, level * dow[days] * np.exp(e)\n",
    "\n",
    "\n",
    "for name, mult in zip(DAY_NAMES, DOW_TRUTH):\n",
    "    print(f\"  {name:<10} {mult:.3f}  -> a normal {name} costs {BASE_CPA * mult:5.2f} euros per sale\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae68b8f4",
   "metadata": {},
   "source": [
    "## What a reader does today, and why it misleads\n",
    "\n",
    "Faced with an expensive Sunday, the two usual moves are to compare it with a recent good\n",
    "day, or to compare it with the account average. Both produce a number. Neither produces a\n",
    "number that means anything, and the reason is that both are comparing a Sunday with\n",
    "something that is not a Sunday.\n",
    "\n",
    "Below, the same quiet stretch of data - nothing wrong anywhere in it - read three ways."
   ]
  },
  {
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   "execution_count": 3,
   "id": "4c0bffb6",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the Sunday in question              30.72 euros per sale\n",
      "the most recent Tuesday             25.79\n",
      "the account's own six-week average 25.31\n",
      "what a normal Sunday costs here     29.55\n",
      "\n",
      "read against that Tuesday   +19.1%  <- compares a Sunday with a Tuesday\n",
      "read against the average    +21.4%  <- compares a Sunday with a week\n",
      "read against its own Sundays   +4.0%  <- the only comparison that holds\n",
      "\n",
      "Nothing is wrong in this data. The first two readings invent a problem;\n",
      "the third one correctly reports that this Sunday is an ordinary Sunday.\n"
     ]
    }
   ],
   "source": [
    "NARRATIVE_WEEKS = 6\n",
    "days_n, cpa_n = make_series(NARRATIVE_WEEKS * 7, generator=np.random.default_rng(SEED))\n",
    "frame = pd.DataFrame({\"weekday\": days_n, \"cpa\": cpa_n})\n",
    "\n",
    "last_sunday = float(frame[frame.weekday == 6].cpa.iloc[-1])\n",
    "last_tuesday = float(frame[frame.weekday == 1].cpa.iloc[-1])\n",
    "account_mean = float(frame.cpa.mean())\n",
    "sunday_mean = float(frame[frame.weekday == 6].cpa.mean())\n",
    "\n",
    "naive_vs_tuesday = (last_sunday - last_tuesday) / last_tuesday * 100\n",
    "naive_vs_average = (last_sunday - account_mean) / account_mean * 100\n",
    "against_own_sundays = (last_sunday - sunday_mean) / sunday_mean * 100\n",
    "\n",
    "print(f\"the Sunday in question              {last_sunday:.2f} euros per sale\")\n",
    "print(f\"the most recent Tuesday             {last_tuesday:.2f}\")\n",
    "print(f\"the account's own six-week average {account_mean:.2f}\")\n",
    "print(f\"what a normal Sunday costs here     {sunday_mean:.2f}\")\n",
    "print()\n",
    "print(f\"read against that Tuesday   {naive_vs_tuesday:+.1f}%  <- compares a Sunday with a Tuesday\")\n",
    "print(f\"read against the average    {naive_vs_average:+.1f}%  <- compares a Sunday with a week\")\n",
    "print(f\"read against its own Sundays{against_own_sundays:+7.1f}%  <- the only comparison that holds\")\n",
    "print()\n",
    "print(\"Nothing is wrong in this data. The first two readings invent a problem;\")\n",
    "print(\"the third one correctly reports that this Sunday is an ordinary Sunday.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42fca88d",
   "metadata": {},
   "source": [
    "## The pattern, in one formula\n",
    "\n",
    "For each weekday, take the average of every observation that landed on that weekday, and\n",
    "divide it by the average across the whole window.\n",
    "\n",
    "$$\\text{index}_d = \\frac{\\bar{X}_d}{\\bar{X}_{\\text{all}}}$$\n",
    "\n",
    "- $\\bar{X}_d$ is the average across every day that fell on weekday $d$ - so for Tuesday,\n",
    "  the mean of all the Tuesdays in the window.\n",
    "- $\\bar{X}_{\\text{all}}$ is the average across every day in the window, with no grouping.\n",
    "- $\\text{index}_d$ is the ratio of the two. A value of 1.10 means that weekday runs ten per\n",
    "  cent above the account's own average; 0.85 means fifteen per cent below; 1.00 means it is\n",
    "  exactly ordinary.\n",
    "\n",
    "**Why a ratio and not a difference.** A difference says \"Sunday costs four euros more than\n",
    "average\", and that four euros is useless the moment the account's costs move - it is a\n",
    "different four euros next quarter, and it cannot be compared with another campaign at all.\n",
    "A ratio says \"Sunday runs fourteen per cent above average\", which stays true when the level\n",
    "moves and means the same thing in every account.\n",
    "\n",
    "The whole method is the cell below. Everything after it is about when to believe it."
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 6 weeks:\n",
      "  Mon  true 0.946   recovered 0.867   off by 0.079\n",
      "  Tue  true 0.917   recovered 0.954   off by 0.037\n",
      "  Wed  true 0.927   recovered 0.887   off by 0.040\n",
      "  Thu  true 0.956   recovered 0.996   off by 0.040\n",
      "  Fri  true 1.015   recovered 1.009   off by 0.006\n",
      "  Sat  true 1.104   recovered 1.119   off by 0.014\n",
      "  Sun  true 1.134   recovered 1.168   off by 0.034\n",
      "\n",
      "worst weekday is off by 0.079\n"
     ]
    }
   ],
   "source": [
    "def weekday_index(weekday, value):\n",
    "    \"\"\"The seven multipliers, Monday first. The entire method.\n",
    "\n",
    "    A weekday with no observations in the window gets nan rather than a number, because an\n",
    "    index invented for a day nobody has data on is the failure this returns to prevent.\n",
    "    \"\"\"\n",
    "    weekday = np.asarray(weekday)\n",
    "    value = np.asarray(value, dtype=float)\n",
    "    overall = float(value.mean())\n",
    "    out = np.full(7, np.nan)\n",
    "    for d in range(7):\n",
    "        cell = value[weekday == d]\n",
    "        if cell.size:\n",
    "            out[d] = cell.mean() / overall\n",
    "    return out\n",
    "\n",
    "\n",
    "def weekday_counts(weekday):\n",
    "    \"\"\"How many observations each weekday cell actually has.\"\"\"\n",
    "    weekday = np.asarray(weekday)\n",
    "    return np.array([(weekday == d).sum() for d in range(7)])\n",
    "\n",
    "\n",
    "def weekday_index_batch(days, values):\n",
    "    \"\"\"`weekday_index` over a whole batch at once. Same arithmetic, one row per run.\"\"\"\n",
    "    out = np.empty((values.shape[0], 7))\n",
    "    for d in range(7):\n",
    "        out[:, d] = values[:, days == d].mean(axis=1)\n",
    "    return out / values.mean(axis=1, keepdims=True)\n",
    "\n",
    "\n",
    "recovered_n = weekday_index(days_n, cpa_n)\n",
    "print(f\"after {NARRATIVE_WEEKS} weeks:\")\n",
    "for name, truth, got in zip(DAY_SHORT, DOW_TRUTH, recovered_n):\n",
    "    print(f\"  {name}  true {truth:.3f}   recovered {got:.3f}   off by {abs(got - truth):.3f}\")\n",
    "print(f\"\\nworst weekday is off by {np.nanmax(np.abs(recovered_n - DOW_TRUTH)):.3f}\")"
   ]
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",
      "text/plain": [
       "<Figure size 1260x644 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 4.6))\n",
    "x = np.arange(7)\n",
    "ax.bar(x - 0.19, DOW_TRUTH, width=0.38, color=SIGNAL, label=\"the rhythm we hid\")\n",
    "ax.bar(x + 0.19, recovered_n, width=0.38, color=NAIVE,\n",
    "       label=f\"what {NARRATIVE_WEEKS} weeks recovered\")\n",
    "ax.axhline(1.0, color=MUTED, lw=1.2, ls=\"--\")\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(DAY_SHORT)\n",
    "ax.set_ylim(0.8, 1.25)\n",
    "ax.set_ylabel(\"cost per sale against this account's own average\\n(higher is worse)\")\n",
    "ax.set_xlabel(\"day of the week\")\n",
    "ax.set_title(\"Six weeks gets the shape, and still not the individual numbers\")\n",
    "ax.legend(frameon=False, loc=\"upper left\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_pattern.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c956d6",
   "metadata": {},
   "source": [
    "## How much history do you actually need?\n",
    "\n",
    "The usual answer is \"four to six weeks\". That answer is not wrong so much as incomplete,\n",
    "and measuring it shows why: **there is no week count at which the pattern becomes right.**\n",
    "The error falls steadily and keeps falling - it is still improving at three months - so\n",
    "any single number quoted as *the* requirement is a convention rather than a threshold the\n",
    "data hands you.\n",
    "\n",
    "The useful question is not \"is the pattern correct yet\" but \"is it sharp enough to answer\n",
    "the question I am asking\". Two questions, very different appetites:\n",
    "\n",
    "- **Is the weekend different from the working week?** Here that gap is large.\n",
    "- **Is Monday different from Tuesday?** Here those two genuinely differ, by about three\n",
    "  per cent - the kind of gap someone would use to justify shifting budget between two\n",
    "  weekdays.\n",
    "\n",
    "Below, the same advertiser built four thousand times at each window length, scored on\n",
    "whether the recovered pattern gets each of those two calls right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a18a0db3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:29.028829Z",
     "iopub.status.busy": "2026-10-24T13:13:29.028411Z",
     "iopub.status.idle": "2026-10-24T13:13:29.243825Z",
     "shell.execute_reply": "2026-10-24T13:13:29.242376Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the two calls being scored, as the generator actually built them:\n",
      "  weekend against the working week   +16.7%  (weekend dearer)\n",
      "  Monday against Tuesday             +3.0%  (Monday dearer)\n",
      "\n",
      " 1 wk: worst weekday off 0.139   weekend call right  95.8%   Mon-vs-Tue call right  61.6%\n",
      " 2 wk: worst weekday off 0.097   weekend call right  99.3%   Mon-vs-Tue call right  65.5%\n",
      " 3 wk: worst weekday off 0.079   weekend call right  99.9%   Mon-vs-Tue call right  67.8%\n",
      " 4 wk: worst weekday off 0.069   weekend call right 100.0%   Mon-vs-Tue call right  70.7%\n",
      " 5 wk: worst weekday off 0.061   weekend call right 100.0%   Mon-vs-Tue call right  72.8%\n",
      " 6 wk: worst weekday off 0.057   weekend call right 100.0%   Mon-vs-Tue call right  76.5%\n",
      " 7 wk: worst weekday off 0.052   weekend call right 100.0%   Mon-vs-Tue call right  76.9%\n",
      " 8 wk: worst weekday off 0.048   weekend call right 100.0%   Mon-vs-Tue call right  79.1%\n",
      " 9 wk: worst weekday off 0.046   weekend call right 100.0%   Mon-vs-Tue call right  79.4%\n",
      "10 wk: worst weekday off 0.044   weekend call right 100.0%   Mon-vs-Tue call right  80.8%\n",
      "11 wk: worst weekday off 0.041   weekend call right 100.0%   Mon-vs-Tue call right  82.3%\n",
      "12 wk: worst weekday off 0.039   weekend call right 100.0%   Mon-vs-Tue call right  82.9%\n",
      "13 wk: worst weekday off 0.037   weekend call right 100.0%   Mon-vs-Tue call right  84.3%\n",
      "14 wk: worst weekday off 0.037   weekend call right 100.0%   Mon-vs-Tue call right  84.7%\n",
      "\n",
      "weekend-vs-week, right 19 times in 20: 1 week(s)\n",
      "Monday-vs-Tuesday, same standard:      not reached inside 14 weeks\n",
      "at 14 weeks the Monday-vs-Tuesday call is still only 85% right\n",
      "\n",
      "KNOWING WHICH WAY ROUND IS NOT KNOWING BY HOW MUCH, and the gap between those two\n",
      "is the whole reason a single week-count is misleading. After one week the direction\n",
      "of the weekend call is right 96% of the time,\n",
      "while the worst individual weekday is still 14 points out - so the shape is visible and not one of the seven numbers is usable.\n",
      "By 14 weeks that worst weekday is down to 4 points, and still falling.\n"
     ]
    }
   ],
   "source": [
    "REPS = 4000\n",
    "WEEK_RANGE = list(range(1, 15))\n",
    "\n",
    "TRUE_WEEKEND_GAP = float(DOW_TRUTH[5:7].mean() - DOW_TRUTH[0:5].mean())\n",
    "TRUE_MON_TUE_GAP = float(DOW_TRUTH[0] - DOW_TRUTH[1])\n",
    "print(\"the two calls being scored, as the generator actually built them:\")\n",
    "print(f\"  weekend against the working week   {TRUE_WEEKEND_GAP * 100:+.1f}%  (weekend dearer)\")\n",
    "print(f\"  Monday against Tuesday             {TRUE_MON_TUE_GAP * 100:+.1f}%  (Monday dearer)\")\n",
    "print()\n",
    "\n",
    "stability = {}\n",
    "for weeks in WEEK_RANGE:\n",
    "    g = np.random.default_rng(SEED + weeks)\n",
    "    days_w, vals = make_batch(REPS, weeks * 7, generator=g)\n",
    "    idx = weekday_index_batch(days_w, vals)\n",
    "    worst = np.abs(idx - DOW_TRUTH).max(axis=1)\n",
    "    weekend_call = (idx[:, 5:7].mean(axis=1) - idx[:, 0:5].mean(axis=1)) > 0\n",
    "    mon_tue_call = (idx[:, 0] - idx[:, 1]) > 0\n",
    "    stability[weeks] = {\n",
    "        \"weeks\": weeks,\n",
    "        \"observations_per_weekday\": weeks,\n",
    "        \"typical_worst_error\": float(np.median(worst)),\n",
    "        \"unlucky_worst_error\": float(np.quantile(worst, 0.90)),\n",
    "        \"weekend_call_right_pct\": float(weekend_call.mean()) * 100,\n",
    "        \"mon_tue_call_right_pct\": float(mon_tue_call.mean()) * 100,\n",
    "    }\n",
    "    print(f\"{weeks:2d} wk: worst weekday off {np.median(worst):.3f}\"\n",
    "          f\"   weekend call right {weekend_call.mean()*100:5.1f}%\"\n",
    "          f\"   Mon-vs-Tue call right {mon_tue_call.mean()*100:5.1f}%\")\n",
    "\n",
    "\n",
    "def first_week_reaching(key, target):\n",
    "    \"\"\"The first window length at which a call is right at least `target` per cent of the time.\"\"\"\n",
    "    for k in WEEK_RANGE:\n",
    "        if stability[k][key] >= target:\n",
    "            return k\n",
    "    return None\n",
    "\n",
    "\n",
    "weekend_ok = first_week_reaching(\"weekend_call_right_pct\", 95.0)\n",
    "mon_tue_ok = first_week_reaching(\"mon_tue_call_right_pct\", 95.0)\n",
    "print()\n",
    "print(f\"weekend-vs-week, right 19 times in 20: {weekend_ok} week(s)\")\n",
    "print(f\"Monday-vs-Tuesday, same standard:      \"\n",
    "      f\"{mon_tue_ok if mon_tue_ok else 'not reached inside ' + str(WEEK_RANGE[-1]) + ' weeks'}\")\n",
    "print(f\"at {WEEK_RANGE[-1]} weeks the Monday-vs-Tuesday call is still only \"\n",
    "      f\"{stability[WEEK_RANGE[-1]]['mon_tue_call_right_pct']:.0f}% right\")\n",
    "print()\n",
    "print(\"KNOWING WHICH WAY ROUND IS NOT KNOWING BY HOW MUCH, and the gap between those two\")\n",
    "print(\"is the whole reason a single week-count is misleading. After one week the direction\")\n",
    "print(f\"of the weekend call is right {stability[1]['weekend_call_right_pct']:.0f}% of the time,\")\n",
    "print(f\"while the worst individual weekday is still {stability[1]['typical_worst_error']*100:.0f}\"\n",
    "      f\" points out - so the shape is visible and not one of the seven numbers is usable.\")\n",
    "print(f\"By {WEEK_RANGE[-1]} weeks that worst weekday is down to \"\n",
    "      f\"{stability[WEEK_RANGE[-1]]['typical_worst_error']*100:.0f} points, and still falling.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dadec85c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:29.248128Z",
     "iopub.status.busy": "2026-10-24T13:13:29.247193Z",
     "iopub.status.idle": "2026-10-24T13:13:30.025288Z",
     "shell.execute_reply": "2026-10-24T13:13:30.024247Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1610x630 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.5))\n",
    "w = np.array(WEEK_RANGE)\n",
    "\n",
    "typical = np.array([stability[k][\"typical_worst_error\"] for k in WEEK_RANGE])\n",
    "unlucky = np.array([stability[k][\"unlucky_worst_error\"] for k in WEEK_RANGE])\n",
    "axes[0].plot(w, typical * 100, marker=\"o\", color=SIGNAL, lw=2.2, label=\"a typical run\")\n",
    "axes[0].plot(w, unlucky * 100, marker=\"o\", color=FLAG, lw=2.2, ls=\"--\", label=\"an unlucky one in ten\")\n",
    "axes[0].set_xlabel(\"weeks of daily figures\")\n",
    "axes[0].set_ylabel(\"worst weekday, percentage points out\\n(lower is better)\")\n",
    "axes[0].set_title(\"No point where it becomes right\")\n",
    "axes[0].legend(frameon=False)\n",
    "\n",
    "axes[1].plot(w, [stability[k][\"weekend_call_right_pct\"] for k in WEEK_RANGE],\n",
    "             marker=\"o\", color=SIGNAL, lw=2.2, label=\"is the weekend dearer?\")\n",
    "axes[1].plot(w, [stability[k][\"mon_tue_call_right_pct\"] for k in WEEK_RANGE],\n",
    "             marker=\"s\", color=NAIVE, lw=2.2, label=\"is Monday dearer than Tuesday?\")\n",
    "axes[1].axhline(95, color=MUTED, lw=1.2, ls=\":\")\n",
    "axes[1].text(w[7], 90, \"right 19 times in 20\", fontsize=9.5, color=MUTED)\n",
    "axes[1].axhline(50, color=LINE, lw=1.2)\n",
    "axes[1].text(w[0], 51.5, \"a coin flip\", fontsize=9.5, color=MUTED)\n",
    "axes[1].set_ylim(40, 104)\n",
    "axes[1].set_xlabel(\"weeks of daily figures\")\n",
    "axes[1].set_ylabel(\"share of runs that get the call right, %\")\n",
    "axes[1].set_title(\"How long depends on the question\")\n",
    "axes[1].legend(frameon=False, loc=\"center right\", fontsize=9.5)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_settling.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "458876eb",
   "metadata": {},
   "source": [
    "## Does the pattern hold next month?\n",
    "\n",
    "A weekday pattern is only useful if it repeats. This is the one check that settles it, and\n",
    "it costs nothing: work the pattern out on one stretch of history, then hold it against the\n",
    "next stretch it has never seen, and ask whether the same days are still the expensive ones.\n",
    "\n",
    "If they are, the pattern is real and you can subtract it. If they are not, the account's\n",
    "rhythm has changed, and every conclusion you drew from the old pattern is stale.\n",
    "\n",
    "The trap is grading that comparison by eye. Two windows of a perfectly stable advertiser\n",
    "will *also* disagree, because both are noisy, so \"the numbers moved\" proves nothing until\n",
    "you know how much they move when nothing has happened. That has to be measured first and\n",
    "it is what the cell below does: run thousands of stable advertisers, watch how far their\n",
    "two windows drift apart on their own, and put the alarm line where only one run in twenty\n",
    "lands. Only then is it fair to ask how often a genuinely changed advertiser clears it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3758b0ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:30.028390Z",
     "iopub.status.busy": "2026-10-24T13:13:30.028018Z",
     "iopub.status.idle": "2026-10-24T13:13:30.075533Z",
     "shell.execute_reply": "2026-10-24T13:13:30.074359Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "two windows of a STABLE advertiser disagree by 0.078 on their worst weekday, typically - from noise alone\n",
      "so the line for 'this moved more than noise explains' sits at 0.131\n",
      "\n",
      "advertisers whose rhythm did NOT move, flagged anyway: 5.0%  (by construction, 5%)\n",
      "advertisers whose weekend premium FELL BY 7 POINTS, caught: 19.8%\n",
      "\n",
      "Read that second number honestly: with six weeks either side, a shift this size\n",
      "is missed more often than it is caught. The check is worth running because it\n",
      "costs nothing, but it is not sensitive enough to be the only thing you rely on.\n"
     ]
    }
   ],
   "source": [
    "# The same advertiser after its weekend premium halves (each weekend day keeps 0.3 of its lift, and\n",
    "# the week is renormalised, so Sunday goes from 13.4 points above the average to 6.6) - a consumer\n",
    "# shop drifting towards trade buyers, which is the ordinary way a weekly rhythm dies.\n",
    "DOW_SHIFTED_RAW = 1.0 + (DOW_TRUTH_RAW - DOW_TRUTH_RAW.mean()) * np.array([1, 1, 1, 1, 1, 0.3, 0.3])\n",
    "DOW_SHIFTED = DOW_SHIFTED_RAW / DOW_SHIFTED_RAW.mean()\n",
    "\n",
    "HOLDOUT_WEEKS = 6\n",
    "HOLDOUT_REPS = 4000\n",
    "\n",
    "\n",
    "def holdout_gaps(n_reps, weeks, test_dow, generator):\n",
    "    \"\"\"Worst weekday movement between a pattern fitted on one window and the next window's.\"\"\"\n",
    "    days_w, fit_v = make_batch(n_reps, weeks * 7, generator=generator)\n",
    "    _, test_v = make_batch(n_reps, weeks * 7, dow=test_dow, generator=generator)\n",
    "    fitted = weekday_index_batch(days_w, fit_v)\n",
    "    actual = weekday_index_batch(days_w, test_v)\n",
    "    return np.abs(fitted - actual).max(axis=1)\n",
    "\n",
    "\n",
    "# **Calibrate on advertisers whose rhythm did NOT move.** Two windows of the same stable\n",
    "# advertiser still disagree, purely from noise, and until that ordinary disagreement is\n",
    "# measured there is no way to say what an unusual one looks like.\n",
    "g_cal = np.random.default_rng(SEED + 500)\n",
    "stable_gaps = holdout_gaps(HOLDOUT_REPS, HOLDOUT_WEEKS, DOW_TRUTH, g_cal)\n",
    "ALARM_LINE = float(np.quantile(stable_gaps, 0.95))\n",
    "\n",
    "g_pow = np.random.default_rng(SEED + 900)\n",
    "shifted_gaps = holdout_gaps(HOLDOUT_REPS, HOLDOUT_WEEKS, DOW_SHIFTED, g_pow)\n",
    "\n",
    "false_alarm = float((stable_gaps > ALARM_LINE).mean()) * 100\n",
    "caught = float((shifted_gaps > ALARM_LINE).mean()) * 100\n",
    "\n",
    "holdout = {\n",
    "    \"weeks_each_side\": HOLDOUT_WEEKS,\n",
    "    \"typical_stable_gap\": float(np.median(stable_gaps)),\n",
    "    \"alarm_line\": ALARM_LINE,\n",
    "    \"false_alarm_pct\": false_alarm,\n",
    "    \"caught_pct\": caught,\n",
    "    \"typical_shifted_gap\": float(np.median(shifted_gaps)),\n",
    "    \"shift_size_pct\": float(abs(DOW_SHIFTED[6] - DOW_TRUTH[6])) * 100,\n",
    "}\n",
    "\n",
    "print(f\"two windows of a STABLE advertiser disagree by {holdout['typical_stable_gap']:.3f}\"\n",
    "      f\" on their worst weekday, typically - from noise alone\")\n",
    "print(f\"so the line for 'this moved more than noise explains' sits at {ALARM_LINE:.3f}\")\n",
    "print()\n",
    "print(f\"advertisers whose rhythm did NOT move, flagged anyway: {false_alarm:.1f}%  (by construction, 5%)\")\n",
    "print(f\"advertisers whose weekend premium FELL BY {holdout['shift_size_pct']:.0f} POINTS, caught: {caught:.1f}%\")\n",
    "print()\n",
    "if caught < 60:\n",
    "    print(\"Read that second number honestly: with six weeks either side, a shift this size\")\n",
    "    print(\"is missed more often than it is caught. The check is worth running because it\")\n",
    "    print(\"costs nothing, but it is not sensitive enough to be the only thing you rely on.\")\n",
    "else:\n",
    "    print(\"A shift this size is caught most of the time at six weeks either side.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91547df4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:30.079015Z",
     "iopub.status.busy": "2026-10-24T13:13:30.078658Z",
     "iopub.status.idle": "2026-10-24T13:13:31.271897Z",
     "shell.execute_reply": "2026-10-24T13:13:31.270772Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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79+5n/doVypfC0Vm7biMTJk1VqZc8fvyUyVNmcOrUWdasWvrTjbCievvuHU2bt+Ht23cqy728PuDl9YFHj59w6dJlzpw6qrVjRieh9dcP3t507tpTpb4E8OGDNydOnubEydO0b9eaMaOG/1T+Ll66Qo9efVXqJu7ub1m7fiPnL15i947NmJurN4Zyc3tFuw5defX6tcryV69f82rba3bu2sukCWNjbRiRFJ/PvAWL1e7VISGhfAoMxO3Vay5cvExKa2vq16sTY96Tir9/AI2atuLJk6fKZe/eebBl63acnK+xY+tGwsPDad+xKw8ePlJu8+XLF3bt3su1azfYu3urWsA4KUiwQwihNbdu3SYsLAzgpycbNzUxoXGjBpQrW4ZMmTKSysYGHR0d3ri7s3ffAbZu28nylWsoXLhQjBWTxUtW4OnpSb++vahTqwZWVpY8ffaCiZOmcv/BQ6ZMnUn1alViLUhF1ahhfU6fOcflK068f++lMXr95csXjhw9DkDDBqqFuafPntOzd3+Cg4NJmzYNQwf/j5Il7AkNDeP0mXPMmDUXb29vOnbqzpHDe9UqyomleLGi3LvtzKgx49m3/yDFihVhzUrVSnfUAoGfnx+t23TknYcHmTNlpHev7jjYF8fMLDkv3V6xavU6Dh46wuQpM8iaObPGl8zaVL9eHWpUr0qHzt25fv0m9erWZuL4MSrbGBkZqe330s2N4SP/o2QJB3p270qOHNn56P+RDRu3sGbtBm7cuMXmLdtp17ZVnPIRFhbGsOFjCA4OxtY2KwP69aZA/nxYWJgTEPCJp8+ec+bsuRgr9NGZO2+hMhBQpXJFunftTJYsmfHw8GDt+k3s2Lmb02fOMXrM+BhbYA4aPIKvwV+ZOnkCjmVKYWhoyJWrTowdNwkfX19GjxnP/r071PZbvmI1CxYuQUdHh+bNGtO0cSMyZ87Ely9fOH/xEjNnzePZ8xf07N2f7Vs3xLs11ej/JijPr0L5cnRo34ZcuXKgq6OL26tXnDt/kWPH1HvRKIyfOBVfPz9GjRhKlcoVSZ48OXfu3mPsuEm8ev2aEaPHcsGxtNpnnyZNGrp26YiDfXHSpk1DKhsbAgMDeeHiyvoNmzl77jyDhgwnf3470qVVn4D3Zz/XwUNH8ujxE3R0dOjUsR3NmjTCysqKZ8+eM3vuAsZPmIyVlVW0x71x8xY9evcnNDSUkiUd6NyxPfns8qCnp8+du/eYM3cB9x88pHvPfhw+uFvtHIYMHaUMdLRv25oWzZtgbW3N02fPmDlrHhMnTY3x+JocPXaCed9aRtepXZPChQvGa38hxN9rz579vHF3p33b1jRp0pC0aVLzxv0t06bP5tLlK6xYtZZ6dWuTJ09ulf3c3F7Rtn1nPgUGYpc3Dz26d6FI4UIYGRnx7Nlz5i9awuXLVxk4eBhZsmQmn13eOOWnZ/cudOnUnmo16/H27Tt6dO9Cz+6qw7AqXpY52BeLDHY4q784d3K+DoCBgT4hIaE4O1+nZo1qyvXPn79Qvly0L64a7Ni2facy0FGsWBEG9OtNrpw58fX1ZcfO3axcvY47d+/Ru+8ANq5frTIXXlyMGDVWGeho1rQRbVq1IG3atLi5vWLx0uWcPHWGLdt2YJPKRqV1ep06NZk+cw4BAQGcPXc+2jK34sVq4UIFVV4wR0RE0KffIM6eO09yU1O6d+9ClUoVSJXKBm9vH/YdOMSSpSs4cvQ46dOnY8SwwRrT//jRn2EjRpM7dy4GDuiLXd68BAUF8cY9bkMkaus5P2LUf4SEhDBl0jjKlysLwJmz55g+cw5+fh/p2qM3J48d1PgC72ckdb1o3fpNykCHY5lS9O3TE9usWfHw8GDNuo3s3LUn2t7mMUmfPh33bjuzeOkKlixdQbp0aTl2WLVVu76+5sZG3Xv0xdzcjHH/jaR4sSJERMDJU2eYOHkaHz54M3nqDJYsmqe2n13ePNSuVYMCBfKROlUqLC0t8fHx4c7deyxfsZqnz57Tp99ADu7fpfydxbeOFBuvDx+UgY4UKSwYOKAfFSqUxcDAgCtXnJg2fTbvPDzo3rMfB/ftVGnFXrdOLWbPXcDRY8cZO2aExuP6f/u9KraPSlv30e07d/P+vRft27amadNGpE6VilevXmGRQn1kAk3sixejWtXK5MuXl1Q2Npibm+P14QNXnZxZsXINT548ZeSosSxfujDaNF66uTFl2gyKFyvK/wb0JUf2bHi+f8+GDZvZsm0HTs7XmDBxKpMmjo1TnuJi6rRZvH37DlNTEwYO6EdZx9JYWVkSEhKC68tXXLnqxLXrN+Kc3q+uv379GkyHjt148PARKVNa07tXd8o6lsYyRQreeXiyZet2Nmzcwtp1G8mSORNt28St/vsjf/8A+vT7H9my2TKgX2/y2dnx5esX9u0/xOw583FxcWXBoqWMHD5EZb/Pnz/TsUsPXr1+TbJkyejTuzu1alTH1NSU23fuMn3GbJ49f8GwEaPJkCFdtL39k+Lzef78BQsWLgGgXDlHunRqj23WrBgbJ8Pb24d79x9w9NgJ9PT0lPvcu+3Mvv2HGDVmnPLvqKJu+ytMnDyNz0GfmTZlImUdSwORQaMZs+bi4uLK0mUrcX35Eg/P9yrb7N67j5mz5vHq9WsWLVmudl2jk5jnnyjBDteXbvj6+pIiRYp4dyMWQvy5olY4sv1ka5jOndprXJ4qlQ1FChfCLm8eho/8jxUr18RYqH/95g0L589WqWzaFy/KqpVLKF+xOp8/f+bI0eO0bd0yznmrVLE8lpYp8PX1Y+++A3Tt0lFtmxMnT/Pp0yf09fWpV1e198eUaTMJDg4mRQoLtm/doNLNulXLZuSzy0OT5m3w9vFh4aKljB0zMs55+xl6enqYmpqi/+2hoqerF+NkozNmzeOdhwfp0qVlzy7VCH7BAvmZP3cmxsbG7Ni5m6nTZ1OxYvl4V87jQ19fH319ffR0I/Ovrxdz/hU8Pd9TuVIFli6er2zNaGmZgtEjh/HunQdHj51g9959cQ52PH32HA9PTwAWLZijMnSRubk56dOno0L5svE9PTw8PFm2YjUAtWpWZ/7cmcrP09IyBdOmTMDUxIS16zeya88+2rRpSYH8+TSm5evnx4G9O8icOZNyWe1aNTA2NqZLt17cf/CQp8+ekzNHduX6t+/eMWtOZKVx+NBBar/RZk0aUaxIYeo2aMrtO3c5fOSYWgUrJk5O19i+I7Iiram1jLW1FUUKF6JPr+7RpvHOw4Ntm9ervFgvX86RpUvmU7N2A7y8PnDx0hUqViinst/smVPV0rKysiRjxgyUL+fIgIFD2bf/IJs2b2PwwP7RHj8hn+u16zc4eeoMAP/r30dl6A97+2KsW7OC5i3bcufuPY3HjIiIYPiIMYSGhlKpYnmWL12o8jurUL4sJRyK06BxC54+fcaiRctUKn03bt5Stsrr06s7A/r3Ua4r4WDPxvWraNKstUrrndhcunyV/gMGEx4eTj67vEyZNC7O+woh/n6v37xh+NBBdOn8fViYFClSsGLZQipVqcU7Dw/27juoFuz4b9xEPgUGki+fHTu2blTpZWBvX4z1xVbQrkNXLl2+woyZc1i3ZkWc8mNoaIihoaHy3mlgYBBt+UERpHB79RoPD0+VXoyKAEiD+vXYvmMXztdUgx2KXh0pU1qrtBr/8uUL02bMBqBo0cJsXLdKGZS3srJk2NBB2KSyYdLk6Vy56szRYyeoUV29dXp07ty5y779BwHo0qkDw4d9b/1vaZmCZUsW0KNXP46fOMWSpctp2bypsiFPurRpKVq0MNev3+TAwcMay9xBQUGcOn0WUH+xevDQEc6cPYeBgT5r1yynSOFCynUWFhYM6NebLJkzMXDwcNau20j7dq01Bhs+ffpEzpw52LppnXLoEWtrK40t9zXR1nPew8OTnds3qfTSbN6sCXly56Jxs9Z8+ODN8pVrGPS/fnHKV1wlZb3o8+fPzJkX+cK5ZAl7Vq1YomzMYmmZgulTJ2JoaMjmLdvifV46OjqYmpoqX9gr/o6LsPAw9m7bptLTqWWLpoSGhjB2/GROnT6Lv7+/WuDpwL6damlZWqYgWzZbalSvSq26jXj0+AkXL17G8duLvPjWkWIzf8FifHx9MTQwYMPaVdjZfR8BoG6dWhQuVJDa9RoTEBDAlGkzWbn8+7B6dWrXZPbcBfj6+nHx0hWNdYpjx04SHByMgYE+NWuo3iu0dR/19HzPgH696dO7h3JZXHudpU+fjq2b16ktt7RMQc4c2alRvSrVatTl5KkzuLi+jPadoqfnewoUyMe6NSuU52FpmYJJE8diaGjIug2b2Lp9J506tdfae8kLFy8BMHhgf7VAgI2NDfbFi8YrvV9df129dh0PHj4iefLk7Ni6UaXOYmFhwbj/RpHS2po58xYyZ95CmjRuGONwT9EJCAggf347tm1erxKs6dm9C35+fqxctZa9ew+ovRRfv2Ezrq4vAVg4f7ZKna1SxfIULVKIug2a8uaNO+MmTOHoob2/zedz8dIVwsPDsbFJyfIlC1QCkRYWFtjaZlV7L2RqaoqhoYHK30npved79uzaqlL+6t6tMy/dXrF9xy7WrNuArq4ue3dvUxmOu0e3Lri6urFz1x6N1zU6iXn+urFvEjdfvwYzc/Y8itmXoUq12jRt3oaly1Yq1+/Zu5869Rrz6NFjbR1SCPGbUXRfh8g5OxKTojJ15+49goKCot2uWLEiKgV6BZuUKZVjLd6790BtfUwMDQ2pWzvy+Hv2qY/HDN+HsCpTupRK66j37704f/4iAN26dNI4nmzBggVo1DCyN8juPfuVvWV+J58+BbLn2zmOGjE02q6KitaBL1xcePjw973/jxw+RGXYBoUG9esC8PjxE0JCQuKUVtTrlcpGvddPQu3dd4DQ0FD09PQYNWKoxsDRoIH9lL+9nbv2RJtW+7atVApvChXKlyXFtxZZ9+6pdt3dsmU7ISGh5M6dk04d22lMN1s2W+rWiZwz5OChI3E7sW82bNoCQLp0aRkewzAMMfUWqV2rhsYeBLlz5STvt0Lbvfvx+73D9/vNlStOMW6XkM91797Ie0jKlNYaA6dGRoYMHtQ/2mNeunyV5y9c0NPTY+rkCRq/F8bGxnTv2hmAQ0eOqXT1370n8ndsZWlJzx7qY6wnS5YsxuP/yMX1Jb36DCA4JARb26ysXrX0p4Y0FEL8fdKnT6fxOZIsWTKqV4+c8+LefdV7petLN85fiHzJNHniWI3DKenq6tKvb08g8qWDn5+flnMOefLkUj5now5b5eHhidur11hbW9OyRdNv66+r7KvY3sG+uMryk6fO4Of3EYBRw4do7PnZoV0bsn57WbdjZ/TPd0127NoLQIoUFgzo31ttvY6ODqNHDUNXV5eQkFD2/lC2VZR5T585R2BgoNr+J06e5vPnz+jp6VGrlurcG+s3bgagZfNmKoGOqBrUr0uWLJkJDQ1V9orWpH/fXgl66RYXcX3O165VQ+NwlAULFlDOO7Ln23P1V0rMetHJU2cICAgAYPiwwRrLYUMG9dfY+jwxRTfmv2J4mNDQUB49eqK2PibGxsZUrRw57OblqzF/FxIqNDSUffsig49NmjRUCXQoZMyYga7fgsFnz13A68P3YdMyZ86k/A4eOHhY4zEUyx3LlFapo2nzPmppmYJuXTvFdKoJZpMypXK+uNh+k4P+11/jeQwY0IdkyZIRERHB3r3a+02GhUbW82y0WMdLqITUXzdu3ApA3z49NNZZALp26YipqQkfP/orvy8JMWTQ/zTeFxp8+416+/jg7v5WZd3Ob8+rcuUc1RqnQWTDiP/1j5wX9unTZ9E2BoNf//mEhkUOl2VpaRmvnl6/k7oaetUCyiB6aGgo9erW1jjvqKIRhqbrmhS0Euz4+vUrrdt1ZOmylejp61G+nKNKRRqgZAkHHj56zOEjx7RxSCHEP8Df359ly1fRrEVbitmXIWeegtjmsMM2hx12BSJb1oWFhamMj/+jsmVKR7suy7cH2IcfxiuNC8UkYU+ePOXBA9UWz14fPnDx4mUAZdBC4dbtO8r7Y7Vq0be8UjwsPn36pDJm4u/i1q3bfPnyBT09PQoXKkhgYKDG/8zNzbD+NvzN/Qfxf8n8K2TOlDHawoziOxISEspHf3+N2/zINmsWZcFu6PBRvHkTtyEWYnPz1m0AChUqEO1E0yYmJpQrGzkh3o0bt6JNS9FS7Ue6urpkypQRiByXNKorVyO7lZZwsCcoKCjaa54je2Svhfv3H8b95ICr39KvVaN6gguIZaM5L4AsWTID8CFKhTEq15duTJw8jTr1GlOwSAmy58qvvN906tLj2zYvYzx+Qj7Xm7fvAFDOsUy0513CwR5TU80BgyvfKuN5cuciWTKjaK9LtmyRrYj9/f1xe/V9jh3F96pcOcdox2IvXaokJiZxe8E0YdJU/P39sbK0ZN3q5b9sGD4hxJ+jdKmSGl9AQNR7teq98uq3e52VpSVZs2SO9l6XNUsWILLX2/0Hce+RFle6uroULxY5VKtzlGCGIpBhX7wodnnzkDx5cp4+fYavr59ym2vfenYU/6Hl782btwFIkzo1BQtqnu9BV1dX+bJBcd+Oq5u3IssD5co6Rjt2ffp06ZQvUG/cVC0/1KxRDX19fb58+cKJk6fV9lW8WC1V0kHlnh8UFMSdO5Evouzti0V7zQIDA5W9YKMrO+jo6Pz0hLnaeM5XqRz9/FOKde88Iudn07akqhcpvp9p06aJdkgjc3NzSjgU17gusUR3Lubm5sq6R3R1PCenawwcPJyKVWqSr2Ax5edom8OO5SvXAODq6pYo+X7y5BmfvgUNq1WtEu12irpgeHg4t27dUVmnCG6dPHmaL1++qKzz+vBBWTb8saeVNu+jJUs4JGhI3qhOnjpNrz4DKFuhKnnzF1W5Dor3hjH9Jk1NTShVUvMwRuZmZjh8+04qytrakDtPLgBmzZkf73uxNiWk/uri+pJ3HpH3puLFikZ7/UNDQ8maNbLecD8BjcQgsnGoprmp4PtzHlTrZX5+frxwcQGgegw91KpVraQc3ii6+m5SfD55ckcGCZ4+fcacuQvw/xYk/pMogow/yhhlmMLohg6Luk109e1fSSvDWK1ctZabN2/TqGF9xo8dRbJkyciWU3XojDRpUpM9my0XL11hoJa7dQohfg8WFt+7CQf85M39ydNntO/YFU/P97Fu++nTp2jX2WiYT0Mh2bfWYV9/KCTGhZ1dHvLkzsWjx0/YvXefSquc/QcOERYWhrm5OZV/mKdCEeU2MNAncybND2CA7NmzKf/99t07tQnOk5qi4BkWFkbJMnGbi8Pbxzf2jZJATN+RqC0Iv375Gqf0TExM6NO7BzNnzeXkqTOcPHWGPLlz4WBfHAeH4pQpXTJBXTQV353sUcbt1STHt+/O23fvot0mph4nxskiz/nLV9XzVVzztes2snbdxljz6+PjE+s2CgEBn5QTNOb5VolIiJhaWRl/e8HzRcN13Lf/IEOHj1abUP5Hnz6pt2iNKiGfq+K6Zomhe72uri6ZM2XioYbesYqu3vcfPCR/oegnqozKx8eXLJkzqxw/pok49fT0yJI5s8bjRxUUFKQM9Hbq1I706dV7rgkhRCqb6OdJU96r1Z5BkS8efXx943Gvi/tzKD7sixfj9JlzOF37HuxQBD4cHIqjp6dH0aKFOXfuAteu36BqlUq4ub1SDnH5Y88O97eR9+FscXy++/v7ExDwCTOz5HHKr7t7ZHkgtvJD9uzZuHX7jtrEu1ZWlpQuXZJz5y6w/8BhlYlV/fz8lMO6/Phi9fUbd0JDI1u69uozIE55je6aWVlZ/dTwFtp6zmeN4VkddZgc97dv1SaF/hlJWS9Sfj9tY/7+2Npm5dy33uu/gqY5ExWSGSvKfOp1vPETprB2fezl2E+B0X+OP0PxeULMv8msWbOgr69PaGio2m+ydq3qTJoynU+BgZw+c06lt86RI8cICwvDxMRYLTinzftoQuZoUQgJCaHfgMEcjWEePoWYfpNZMmeONnAOkb/Jc+cuaLWV+aD/9ad12464uLjSuGkr0qVLSwmH4tgXL065smWibZCmbQmpvyrqDAANGjWP03F8Elh/t7ayirY3ftT8Ra2XRf2ex/Q8NDY2Jn26dLx6/Tra+m5SfD6lS5WgrGNpzl+4xIJFS1m2fBWFCxfCvnhRSpZ0oHixor98Do74sommfBa1oURcttFU3/7VtNKz48DBw6RNm4ZJE/6LtrUIRD4AFYU8IcTfJ0P674WeFy6uCU4nNDSUnr374+n5HhublIwYNpg9u7bidPksd246ce+2M3dvfZ+8KKZhnvRiKAAp/NARLc4aNawPRN4DFZU5+N59vVbN6mpdNxVdy42NTWKcvyJ5lArdp0/Rd0dPKgEB8a8AxFbBTCqKMVJj82OPxZj07N6F2TOnkTt3ZBfPR4+fsHb9Rnr06od9yXJMnDxNYwUsJorvTmxDAileBgQGRv+9iUtB68fzje81D47jsF+AyrAYP/MyQ08vDsWaH87Lze0VQ4eNIjg4GLu8eZg5fTJHD+/jutNF7t125t5tZ1atiJxsLrYh5RLyuX7+/BkAk1iG5jCJpmfHz/4W43p84zj07Hjv5aX8jPLljdvEwEKIf49ugp5B8W9Ek1jlDkVrVRcXV+XwMorAhyKQ4fBtbg9Fjw/FfB2KMemjCvz2fI+uB59C1OejpuGkohP38kPk+k8a0q73LZBx6fJlld4qR44eJyQkFCMjI6pWUW2Jm7BrprnskCxZwodI0uZzPqZnZdTnZExDScVXUteLghTlhFjKAbGVI7QtIWWuPXv3KwMd1atVYfnSBZw5eYSb1y4pvwvdu0UO+6kYrkjbon43oivbQWRvJsVL2R9/7zY2NpQoERms2H9AdSgrxd+VK1VUG/ZNm/fRmN77xWbpspUcPXYCHR0dmjVtxNrVyzh/5ji3rl9WXgdF8DQsSh37R7GVTRXfSW3+Hu2LF2X7lg2UdSyNnp4eb9++Y/ee/QwbMZrSZSvRrUefXzKET0Lqr7+y/q4blzoZEMH3/AVGuU6mcXxeRfcsTKrPZ9mSBfTv24tUqWwIDgnByfkaCxYtpWXrDpQpV5nNW7bH+xi/Ulzq0nHZJup1TSpa6dnx6vUbypWNfvgFBUNDQ5XCkRDi71KoUEF0dXUJDw/n+o2bNGxQL/adNLjqdA1X15fo6emxecMajZF9/zgOKZSY6tWtzbQZs/jwwZvzFy5RsUI5njx9pmz9/OMQVvC9ovn5cxARERHRBjyivqhOnvz7wz6uE3yHJvI8H4rCs4mJMffvXI9l639T/Xq1qV+vNm/fveP69Zs4OV3j5OkzeHl9YPWa9bx44cKaVcvinJ7iuxNbgT0oji9N4svY2JiAgAC1SWW1IWplLz4vcLRh5649BIeEkCljRnZu36RxbNm4zteSEMbGxnz69En5MiE6QdEErxQvHyqUL8eqFYs1bqON438Oink9RM7fpmBgGHOZUAgh4kNR7siZIztHD+9L0rzY2eXF1NSEwMAgnJ2vY29fDBcXV5VAhiLooejxoZi8vHixomplOcVLnZgaKYDq8z8+DQNMTEwICAiIQ/kh8j6fXEPaVSpXJFmyZHz58oUjR48r5yVRDGFVoXxZtZ4mUV9+7929jQL5VUd++FW0+ZyP6VkZ9Tmpzbmqkrpe9P2FcSzllFjKEb+DzVsjXzTWrVOLubOna9wmvo2R4ivqdyMoMAjzaOa5jIiIUDZI0fR7r1O7JpcvX+XsufP4BwRgbmbGmzfuyqGV6tSuqbbP73If3bJ1BwA9undhUDSjvnyOw/cptrLp90CddutEhQoVYO3q5fj7+3Pj5m2uXb/B6dNnefrsOSdOnubuvfscObgn2jktk0rUgOV1p4tYWVkmYW7URQ1wBMa5vqu9Ca218fkYGRnRt09P+vTuwZOnz7hx4xaXr1zl7LkLeHq+Z9SYcfj7+yuDqiLxaKVnh6GhYayFM4C3b98m+qTFQoikY2aWXNnK5NDhYwluRfH4SeRkcrly5oi2C+OTp88Slkktsra2okL5yImzFJN179kTWXDMkiWzxokYFcO6hISEqoyb/6Pnz1983yfKJOaGUcbUj6kw/v597N3cf0bGjBmAyIqP128wJuPvLF3atNStU4tJE8dy8dxJWjSPfEFw7vxF7j+I+7wWiu/O8xcuMW737Nt3J+r3RhsU43C+eh39WNAJZW5mhpVlZIHy8eP4TSb5sx5/mxOnUqXy0U6u+TQR7zeK6/oyStfpH4WHh0d7v1AMI/A6gddFcXyXGHrjhYWF8dItccauFkKIuMiYIbLc4f72bayt7xObvr6+sozn5HxNGdAoXqyYMpCRP78dJibGPHr8BP+AAGXPDvvi6mOYK57XL+L4fLewMI/zEFYA6dOnBWIvPyjKnpqGIDQ1NaVSxfLA9wCHp+d75Xn9OISVIh3F5/H69Zs451fbtPmcd43hWe0SZZ02y2BJXS9Sfj9dYv7+xFSO+F08eRz5XVBMJq/J02fPEzUPUb8bMf0mXV+6KUcO0PSbrFGtCoaGhgQHB3P8+EkADhyK/G1aWqbQOI/d73Af9fPzU472UivK8Fs/istv8qWbG+Hh4dGuV/wmE2tYVXNzcyqUL8uQQQM4engfM6dPBiLvjTt3702UY/6MqHMqJLTekJjSpUur/HdMz8PPnz/z5lvvGW3ea7X5+ejo6JA7V05atWzGogVzuHjuJAUKRAb8ly5fleTlmH+BVoIdOXJk4979+3z8+DHabTw8PHnw8DH5ooxrL4T4+3Tq0A6I7CY7a878OO8X9YGm6MIeFkPhZf+BQwnMoXY1blQfgJOnzuDn58e+b/lqWL+uxu2LFC6orPgdPRr9OKVHjh0HIHny5OT8NmkjQAoLCwwMIjvlKcZd/ZF/QAC3v00ImRD639IPC4/+IWxfvKgyH0eOHk/wsRKDYnzQmL4/ScXAwIDePbsp/45PxbBokcIA3L59N9oxmz9//qwcL7lo0cI/kVN1pUuVBCK/64kxNIgyUHrkWKL2pPiR4lzCwzR/XyIiIjh46EiiHb9IoYIAnL94SWU4vKicnK9F26hEcV2ev3BJ0MsOxQu7c+cuRHtdL12+EmuLToh8EePy7AEuzx5QwiFuY0ELIURclC4dea8LDAxKtHkBFOWH8Di8hLBX9Ny4dgNn5RBW3wMZ+vr6FC5UiPDwcA4cOMzrN2++baM+ibPiee3h6cnt23c1Hi8iIoJj315oFikcv+e7ovxw7vyFaBvKvHvnwe07d1W2/5EioHHt+g08PDw5ePgI4eHhmJmZKRv/RGVhYUG+fJFDGiomG04K2nzOnzx1Jtp1isnb06ZJo9X5OpK6XlSkSCEg8jsSXSMdf39/rjpdS/AxDJS/vcQtuyu+C9G9aPTw8FQGLzWJSx0pNrly5VD2noppzgpF/UpXV5dChQqorTc3N6d8OUfgewBS8f/q1apqHHXlV9xHYxN1qLrovtN37tzF7VXsL5sDA4O4ctVZ47qAgE84fftOKsraia1hg3pkzpQRAFeXl/Ha91fUX3PnzoW1tTUAh4/8XvV3gBQpUijnsYnpt3H8xCnlb1ib9d3E/HwsLVPQvm1rIPJ+6e39fS4cff3vv9V/MQiSWOevlWBHowb18PcPYMiwURqHn/j6NZhRY8YRHBysHONeCPF3qlC+LDWqVwVgzdoNLF22Msa5Dnx8fOn/vyFs2rJNuSyDsqWxi8bWxNdv3GTb9l1aznnClC9XlpQprfn69SujxozH0/M9Ojo6NIgm2GFjY0PZsmUAWL5ytcZJte7df8Cub61BGjaoqzImrYGBAXny5Aa+9yb50fQZc+LU9Tc6ii63Xu+9ot3GwsKCBvUjh+maO2+hSk8UTX5lay9LyxRA5PwBScHDwzPGXk2vohTeU6SwiHO69erWRl9fn7CwMCZOnqZxm1lz5iuHMmjcqEGc046L1q2ao6+vj6fneyZOnhbj7/rr1+B4j1fbplULIHLC7Okz50S7XXQBgYRSzDV04dJljS/7ly1flait/Op9m+jVy+sDK1atUVsfHBzMzFnzot2/fDlH5YSpw4aPjnVyVZcfWqU2bBB5r/Lx9WXRkuVq23/9+jXG4wshxK+QM0d2SpUqAUROMOwVyzM+IeUOy2/ln/desfdYVQQ2nj17rnwBHjXYEfXvJctWAGBmZkaePLnU0qpUsbyyPDBp6nSNAf916zcpz0nR0CauGn+rf/v5fWTuvIVq6yMiIhg/cQrh4eEYGOhTv15tjemUK1sGc3NzwsPDOXj4iPIFe7WqlTGK0vM4qg7t2gCRL64UL2Kj88HbO8bGkwmlzef8gYOHuXNXvUHRnTt3OXT4KAANGmiuAyRUUteLKleqoBydY8q0mRpfRs2YNZevXxM+Ia2i7uHj66v1cl5U6b+13D51+qzaurCwMEaNGRfj8eNSR4qNvr4+9b79xnbu3M3Dh4/UtnF3f8vKb2XC8uUcsUmpeUJgxVBVl684ceWqE4+/9Vypp6GnFfya+2hsrKwslcMFaboOnz9/ZvTYiXFOb8asOSrDqCrMnjOfL1++oKOjQ/1o6uXxFRQUxLt3HjGu/+DtDYBFPOp48Gvqrzo6OrRr0xKAdRs2xRjYg8jvoabPNjEp3hefO3+RM2fPq63/+PEjc+ZGPsdy5cqp1eERf/bzia2nkSKAp6enp9I709Ly+3fl/U/cW/5UiXX+Wgl2NG3SiNKlSnLy1BkqVqlJ//8NASJf2A0dPprylapx5ux5KpQvS+1aNbRxSCHEb2zK5PEULJAfgOkz59CwcQu279iNi4srfn5+uL99y+UrV5k4eRrlK1VXa41UrmwZTE1NCAkJpVOXnpw6fRYvLy9ev37DipVr6NCpG1kyZ0qKU1Ojr69PvbqRBVZFq7USDsVj7C47fOggDA0N8fP7SLMWbTl46AheHz7g4eHJlq3badehCyEhoVhbWdGnVw+1/RVzoRw9doLxE6fg+tINPz8/bt26Q59+A9mydbuym3JC5LOLbIXn9uo1mzZvw9vbh9DQUEJDQ1Ue4EMH/4/06dPh5/eRho1bMG/BYh48eISfnx8fvL158OARmzZvo3W7TtRv2CzB+Ykvu7yRPQivX7/J0WMn+PjxozL/8ZlkPKEuXrpM6bKVGDVmPKdOneHNG3c+fvzIq1ev2bFzD/0HRj4jU6a01jicRXTSpElN966dADh0+Cjde/bjzp27+Pn58ejxE4aPHMPqNeuByIJi/nx2Wj2vjBkzMHhgfwA2btpKy9YdOHb8JO/eeeDv78+bN+6cOXueCZOm4li+crxbcTo4FKdpk0YArFq9jq7de3Pp8lU+eHvj4+PLnTt3WbBwCXXqN9HqeVX/Fpx1cXGlW48+3L59Fx8fXx49fsKYsROYPnOOspVRYrAvXlTZInbW7PlMnTZTea90vnaD9h278uDBQ9KkTq1xf11dXaZPnYiBgT537t6jTr1GbNm6HdeXbvj7+yuHGVm0eBnVa9Zj0mTVMaqLFimsnFR24aKljJ84hefPX+Dr68dVJ2dat+3Ik6dPSZ06VaznctXJGdscdtjmsOOqk+aWdkIIkVATx4/BzMyMV69fU6tuI1auXsuzZ8/5+PEjXl5e3L59l1Vr1tGwcQt69hkQ7/Ttvo1AcPLkaS5dvkpAwKdoyw8F8ucjWbJkRERE8O6dB+bm5uTOrRrIUPT+ePs2smFLsaJF0NUwQXSyZMkYOmQgADdu3KJNu844OV3D19cPF9eXTJsxm0lTIu/dJUvYU71alXidV8GCBaj/LbC+fOUaRo4ay6PHT/Dz8+POnbt079lX2WukZ49u2NjYaEzH0NBQeey16zZy794DQPMQVgr16tamYoVyRERE0P9/Qxg6fDROTtf44O2Nn58fLi6uHDh4mP7/G4JjuSpxas0dX9p8zqdJk5oOnbqxbccu3r/3wtPzPdu276RD5+6EhYWRMqU1XTXMaxb1+bhz15545T+p60XGxsYM6NcbgCtXnOjYuTs3b91Wlj+HDh/Nps3blMNqJoRi9I+vX7+yYOESvLy8NNY9fpaiQd6u3XuZNGW6SnmnXceunD5zLsbvQlzrSLHp26cnVpaWBIeE0KZ9Z7Zs3Y6HhydeHz5w4OBhmrZow8eP/hgZGTF86KBo06lUsTympiaEhYUxZNgoANKmTUPx4kWj3Sex76Ox0dfXp3KligAsWbKcRUuW89LNDW9vH86cPU/T5m14+PCRsiFPTFKnTsXDh49p16ELztdu4Ofnx5Onzxg5aizrNmwCoHnTxtjGIa248PHxpXylavTo1Y+9+w7w/PkL/Pz8ePfOgzNnz9O2fRcCA4PQ0dFRftfi6lfVX7t07kC+fHYEBwfTtn1nJk6eprwn+vj48uTpM3bu2kO3Hn2oULnGL59LsW2blspr37vv/1i2fBWvX7/Bx8eXM2fP06xlO169fo2Ojg5jRg3T+vF/5vNZtGgZlarUZM68hVx1csbT8z1+fn48ffachYuWsnhJ5FydFcqXVc6fA5A3T25l2WDegsW8ffeOkJAQQkND/4meHol1/lqZoFxXV5cVyxYxddpMtmzboWy18fTpM54+fYa+vh6tWzVn5PAh2jicEOI3Z25mxuaNaxg3YQo7d+3hzt17GltBKeTJnUulBYqFhQVjRo1g2IjRuLq+pEu3Xirbp0xpzYL5s6lRq35inUK8NG7UgFWr1yn/jm1i9pw5srNowRz69h+Eu/tb+vZXL8RaW1uzeuUSrK2t1Na1bN6Uw4eP4XztOmvXbWTtuo3KdTo6Ogwd/D+ePXuuHDIhvipVLE+GDOl588ad0f+NZ/R/45Xr+vbpSf++kdfD0jIFmzesoVvPPjx+/JR58xcxb/4ijWlaWJgnKC8J0aB+XZYsW4m/vz89e/dXWTd96kSt93jQ5ONHfzZv2cbmKD2Wokpuasr8uTNJlixZvNLt17cXHp7v2blrD8dPnOT4iZNq21SsUI4J40YnKN+x6dK5A+Hh4cyaMx8n52s4OUc/ZIGm7vOxGT92FCEhIezZu5+Tp85oHC5C23N/OZYpRaOG9dm1ey/nzl9U69ZfqGABevfqTueuPbV63KhmTp9MyzYdePLkKctXrmH5yu89PHR1dRn33ygOHjqCh6cn+lF6eikULVKYlcuX0G/AINxevWbk6HHRHitLlixqy6ZPncDbd++4f/+BxnvKf6OHc+ToiWiHTxNCiF8hS+bMbN6whu69+uLu/pbJU2YwecoMjdsqXhzFR8sWzdi+Yxc+vr60addJZd3mjWtUhuczNDSkcKECyiFUihdTD2QULJAfIyMjZYv3H3t+RNWsSSPeur9lwaKlOF+7TovW7dW2KVggPwvnz1Gb4DwuJk34D18/P86du8CWbTvYsm2H2jbNmzWmT6/uMaZTp3ZNtu/YpQzg2NikpFRJh2i319HRYcG8WQwdPpqDh46wY+duduzcHe32iuGMtEmbz/nJE8YycMhwho8Yo7YuefLkLFu8AHNz7ZZ5f4d6Ubu2rXjy9Cnbtu/iwsXLXLh4WWV9rZrVsbXNyoKFSxKUfsGCBShcqCC3bt9hwaKlLFi0VLmuUYN6zPg2F8LP6t61M6dOn+XJk6esWr1Opf4G0KZ1CywtLZm/YLHG/eNaR4qNTcqUrFqxmE5deuLj66ux3GZiYsyiBXOinacFIgOlVSpXYu++A8oe1XVq1YjxHpHY99G4GDZkIM7O1/Hw9GTW7HnMmv29B3HUumxMc+RA5Ll07dyRCZOm0rxlW7X1DvbFGa3lF+IhIaEcO35SGSD+kY6ODsOGDIx3o7NfVX81MjJi7apl9On3P65cdWb1mvXKxnI/0tXVRU9PK+3j48zY2JjVK5bQrkNXXr1+zbQZs5k2Y7bKNvr6+kyaMJaSJaJ/9iTUz34+bq9es2DhkmjvhdlsbRk/VrWebmNjQ80a1Th46Ajbd+xi+47vPfUc7IuzZdPanzup31xinb/WShNGRob8N2YEfXr34KqTM2/euBMeHk7atGkoVdIh2hYiQoi/k7GxMVMnj6dTh7bs3X+QS5ev4O7+Dn//jxgZJSNd2jQUKlSQWjWqUaZMKbVCWZPGDUiTJjXLVqzi7p17hISGkiZNaiqUL0v3rp1Jler3uafkypmDfPnsuH//ASYmxnFqcVepYnlOHDvAypVrOXfhIu/eeaCrq0OmTBmpXLECnTq2w8JCc/dXfX191q5exoqVazhw8DCvXr/B1NSEAvnz0blTB0qXKsHgISMSfD7JkiVj2+b1LFi0hMuXnfDw9Ix2LP+MGTOwf09kkPvQ4aPcf/AQX19f9PT0SWVjQ+7cuXAsU4qaMUxAp22pUtmwY+sGFi1ZzrVrN/jg/YGQkMTrEv+jmjWqYZkiBRcuXeb2ncj5NXx8fDAyNCJz5kyUKVOK9m1bx6ml/I/09PSYPnUiNapXZeu2Hdy6fYePHz9iltwMO7s8NG7UgNqxVHJ+VreunahWrTIbN23l0uWruLu/5fPnz5iZJSdL5swUKVKIqlUqYx9Dq7LoGBoaMmvGFBrUr8u27Tu5efM23t7emCY3JU3q1BQvXpT6deto/ZymT51IgQL52LZtJy9cXDEwMCBzpozUrlWDDu3bcPPWba0fMypLyxTs2bmF5StWc+DgEV6/eUNyU1PyF8hHl07tKVWyBNu+FfxMv43z/CPHMqU4e+oom7Zs48zZ87x44UJAwCeSGRmRLn068uezo2KFcsoJZqMyNzdnx9aNrFy9lv37D/Lq9RtMTIzJn8+OTh3b41imFEdimGNICCF+FTu7PJw4eoDtO3dz/MQpnjx5xsePHzE0NCBNmjTkzZOb8uUcqVa1crzTzpM7F5s3rmX5itXcvnMXX1+/GIe0sbcvrgx2aJqLw8goMiCimMsgtt6cA/r3oVw5R9au28iNG7fw9vbG2MSYXDlzUqd2TZo1baQc1z2+FC+Q9h84xK7d+3j48BEBnwJIkSIFhQsVpGWLppR1LBNrOiVL2JMqlY1yqIlaNatr7K3y47Hnz51JyxZN2blrD9eu3cTrwwfCw8JIYZmCbNlsKVXCgRo1qmmtBfaPtPWcz5o1C/t2b2fh4mWcO3+BD14fsLSyxLFMafr16Rltz24Pj8gJmXV1dSnwrfd7fCR1vUhHR4cpk8ZTsoQDGzZtUU70ndU2C00aNaRVy2bMiyZAEFerVy5h4eJlnD17gTfu7j81LFZ0kic3ZduW9SxatIyjx07g4emBWXIzcufJRcvmTalZoxpzo2m4BfGrI8WmYMECnDh2kFVr1nHq9Flev35NeHgE6dKlpaxjabp06hCnuV/q1qnF3n0HVP6OTWLeR+MiTZrU7N29jXnzF3Hm7Hm8fbyxsLCgYIH8tGvbmjKlS8a5LtuhfRuyZs3CqtXrePDwIZ8/fyFr1sw0rF+P9u1aJ/ieqUm6dGnZtmU9Fy9exvnaDdzfvsXr27CHadOmoVjRIrRp3SJBvet/Zf3VysqSjetXc/LUGfbuO8DtO3eVc0hYW1uRM2cOSpcqQe2aNaJ9H5GYMmfOxOGDu1m3YRNHj53A1dWN4OBgUqdKRalSDnTu2D7GIODPSujnM2TI/yhRwp6Ll67w+MlT3r9/T0DAJ8zMkpMje3aqVa1MyxZNMTIyUjvmjGmTyJbNlmPHT+Dm9vqnhiT/EyXG+euEhX5N/DE9hBDiL9ekeWtu3LhFg/p1mTVjSlJnRwjxlylUtCT+/v4snD/7lwYPhRBCiH/ZVSdnWraOHJrq/JnjCRquafDQkezavZf69eowe+ZUbWdRiH/K4CEj2LVn3z/R6l0IkTC/tk+SEEL8hV66uXHjxi0g9iGshBAivq7fuKmceD6xhhQQQgghROK4fOUqBgb6cR7mSAghhBAJp5U+Xbv37IvTdgYGBqRIkYK8eXJrHIdeCCH+RBs3bQUgY4YMMY5bLIQQmgQEfMLExBg9DfNxfP36lanTZgGRE7hlTsRJSIUQQgihXS6uL3n3zoOWLZqRKVPGpM6OEEII8dfTSrBj8NCR8RofXEdHhzJlSjF29AiptAsh/kihoaEEBwdz5ux5Nm7cAkDHDm0Tda4EIcTf6eatW0ycNJ1WLZtRsqQDaVKnIijoM7fv3GXxkuU8ePgIiJygXgghhBB/DtusWXB59iCpsyGEEEL8M7QS7OjTuwfub9zZvXc/piYmlClTinTp0gLw9u07Ll68TGBQEA3r10XfQJ+bN29z/vxFmrVsy/49O36riYaFECIucuYpqPJ3rlw5adG8SRLlRgjxp3vh4sL4idHP99Ovby+qVK74C3MkhBBCCCGEEEL8WbQS7GjUoB71GzWjYf26jBo5VGVGegB/f38mTJrG6TNn2bNzKxkypGfi5GmsW7+JZStWMXrkMG1kQwghfjkbm5Q4linF0MH/w9DQMKmzI4T4AxUqWID/Ro/g3PmLuLq68sHbm9DQMFKmtKZY0SK0bd2SwoULxp6QEEIIIYQQQgjxD9MJC/0a8bOJ9O0/iDt37nH65GGN400DhIWFUbFyTQoWzM/8uTP5+jWYshWqkDx5ck4dP/SzWRBCCCGEEEIIIYQQQgghxD9KVxuJXLniRKFCBaINdADo6elRqFABrlxxAsDIyJA8uXPh4eGpjSwIIYQQQgghhBBCCCGEEOIfpZVgR2BQEL6+frFu5+vrR2BQkPJvc3NzZC5fIYQQQgghhBBCCCGEEEL8DK0EO7Jns8XJ2Zm79+5Hu83de/dxcnYmezZb5TIPT08sLS21kQUhhBBCCCGEEEIIIYQQQvyjtBLsaNe2FaGhYbRu05FpM2Zz/8FDfHx88fHx5f6Dh0yfOYfWbTsRFhZO+3atAQgKCuLBg0cUyJ9PG1kQv4C/nzf+ft5JnQ0hhBBCCCHELyZ1ASGEEEII8bvTygTlADNmzWXZ8lUxbtOtaycGD+wPgIvrSzZt3krVypVwcCiujSyIRObj9Q4AK5u0/8RxRfTkmvx+5Jr8fuSa/H7kmvx+5Jr8fuSaiOgk5XdDvpd/J7mufye5rjFr0ao9Ts7XAHCwL86WTWuTNkNxJNf17yTX9e/0r19XfW0lNHhgf6pUqsiGTVu4ceMWnu/fA5A6VSqKFStM65YtKFSogHJ726xZGD1ymLYOL4QQQgghhBBCCCGEEEKIf5TWgh0AhQoVUAloCCGEEEIIIYQQQgghhBBCJDatBjuEEEIIIYQQQgghhBDqRo8cin9AAADmZmZJnBshhPj7SLAjCteXr7n38AkuLm68cH3FB28fAObPGIuNjXW80vIP+MSNW/dwcX3FC1c3Xr1+S1hYGI3q1aBxg5ox7hsaGsrBo6e5ePka7728SZbMiNw5bWlQpzpZs2SMdj+Xl6/Ys/8YT5658OXLV1LZWONYqji1qldCX18vXvkXQgghhBBCCCGEENqTN2+epM6CEEL81bQa7Dh0+ChHj53A1fUlnz4FEhGhPve5jo4OZ08f1eZhtWb3viNcv3VPK2k9efqC5as3x3u/0NBQpsxczMPHzzA3N6NIoXz4ffzItRt3uXn7AYP7d6NgfvWH483b95m9YCVhYWHkymlLCgsLHj15ztadB7j/8AlD/9dTAh5CCCGEEEIIIYQQQggh/kpaCXaEhYXRo1c/Tp85pzHAAZFBjujW/S5yZM9KxgzpsM2aCdusmRjx33Q++gckKC0LC3MqVyyDbZZM2GbJyIXL1zh09HSs++0/dJKHj5+RLWsmRgzpjYmxMQCXrl5n4dJ1LFq+nnnT/8PYOJlyn6CgzyxZsYGwsDB6d29H6RLFIpd//szk6Qu5//ApB4+eon7tqgk6FyGEEEIIIYQQQgghhBDid6arjUTWrd/EqdNnKetYhlMnDtGgfh10dHR4/OAWxw7vo0f3LhgaGtKjexdePL2vjUMmirq1qtC0UW2KFSmAlWWKn0orZ/asdGrbjAplS5I5Uwb0dGP/qMPCwjhy/AwAHdo2UwY6AEqXKEahAnkJCPjE2QtXVfY7c/4KnwKDKFzQThnoADAxNqZDm6YAHD52hvDw8J86JyGEEEIIIYQQQgghhBDid6SVYMf+g4cwMzNj3pwZZMmcGR2dyGQNDAzInj0bg/7XjyWL5rJ02Ur27T+ojUP+lZ48c+FTYBCpbKzJljWT2vqSDkUAuH7rrsrym7cjA0gl7Aur7ZPNNjOpbKwJCPjEk2cuiZBrIYQQQgghhBBCCBGbt+/e4eL6EhfXl7x99y6psyOEEH8drQQ7XF64UqhgfszMkgORQ1ZBZE8FhXJlHSmQPx8bNm7RxiH/Sm6v3AHImlnzJOSK5a++bfd9vzeR67OoB0gAsnzbz+2H/YQQQgghhBBCCCHErzFw0HAqV61F5aq1GDhoeFJnRwgh/jrambMjPBwLCwvl34r5JPz9A7CMMhxUpkwZOX3mnDYO+Vf64O0DgJVVCo3rFUNrfQoM4suXryRLZkTQ588EBn0GwDqaobcU+ynSj82YCbM0Lm9UpwJpU6fEx+vXtj4IDQ0B+OXHFdGTa/L7kWvy+5Fr8vuRa/L7kWvy+0nKa2Jlk/aXH1MIIYQQQgjx99BKz45UqWzw8vJS/p0+XToAHj95orLdmzfu6OvraeOQf6UvX74CYGRkpHF9smTfl3/+8uXbPsHKZUZGhjHup0hfCCGEEEIIIYQQQgghhPibaKVnR+5cObl1+47y7xIl7ImIiGDe/MXkz5eP5MlN2bvvILdu38G+eLEYUhK/g/GjB2pcrmjh96tb3SXVcUX05Jr8fuSa/H7kmvx+5Jr8fuSa/H7kmgghhBBCCCH+VFoJdpQvX5bjJ05x6fJVSpcqQYH8+Shfrixnz52nqH0pTE1N8fcPQEdHh759emjjkH8lRQ+Mr18198CI2jPDOFmyb/t8783x9WswJibG0e4XtWeIEEIIIYQQQgghhBBCCPG30MowVvXr1ubIob3kyZ1LuWzBvJm0btWcFClSEBQYRM6c2Zk/dyYlSzho45B/pZTWVgD4+PhpXO/jG7k8uamJMnBhYmyM6bcAh7dvzPsp0hdCCCGESGqfg4O59tqVM88fccXlCZ+Dg2PfSQghhBBCCCGEiIZWgh1GRkbkzJEdKytL5TITExPG/TcKp8vnePzwNocP7KFmjWraONxfK3Om9AC4ur3WuF6xPNO37b7vlyFy/ctXGvd7+W2/zD/sJ4QQQgjxq73182H0/s3YjetLi41L6LJjDbUWTsRuXF9G79/Mu4++SZ1FIYQQQog/km0OOwYPGZHU2UiQnbv2YJvDjqtOzsplV52csc1hx85de5IwZ0KIP4lWgh3de/Zj9H/jtZHUPy1XDluSm5rw3subF67qgYsrTjcBKFa4gMryIoXyAXDV+ZbaPi9c3Hjv5Y2ZWXJy5bBNhFz/Op+Dg7ni8oTjD2//8y1AHctXoUWr9r/kWH9yYSmptWjVHsfyVX7Z8YKCPjNy1FhKlC6PbQ47uvXoA/xZ1/BPymtC/QvnKER07rm7UWH2aBadPYLf50CVdX6fA1l09gjlZ43inrtbEuVQCCGEEEIIIcSfSitzdpw9d54qlStqI6k/io+vH5OmLQBg5NA+WFmm+Kn09PT0qFG1Ajv2HGLN+m2MGNIbE+PIIaouXb3O7bsPMTNLTnnHEir7VShbkr0HjnHrzgMuX71BqRJFAQj6/Jk1G7YDULNaBXR1tRLb+uU8Aj6yxvkCu+7dUHkxksLYlFYOZelZrgZpLSxjSEH8yY6fOMXDR4/p3bMb+vpauWVp1e+UvzXrt7Bl2w56dO9CjuzZSJsmTaId63c6byHEn+Gtnw+Nl03H65N/jNt5ffKn8bLpnB04UZ7vQgghhBD/MPvixXh0/6bUOYUQcaaVu0XatGkI/gta2d+8fZ89+48p//4UGATArAUrMfh2Yy1c0I6G9aoDEBYaxluP98p//2j0+FnKf3v7RA7JcOb8Fe7ce6RcPmHMQJV96taqzINHT3n4+BkDhk4gT67sfPzoz+OnL9DT06NnlzYYGydT2cfExJgeXdowe8EKFixdy4nTF7CwMOfRk+f4+wdglycntatXSvDnkpTuubvRaM08PgR+UlunaAG6/foldnYbQv70mZMghyKxnThxil179tGjW+ffsoDzO+Xv2o2b5Mmdi8ED+6ssf3T/ptaDnYl13omRVyHE72HJ+aOxBjoUvD75s+TcUcbXbZHIuRJCCCGEEDH5+jWYkJAQDAwMfvmxdXV1MTIy+uXHFUL8ubTyRqla1co4OV/n48eP2kguyQQEfOK5y0vlf2FhkQEMt1dvlMs833vFOb2oafn6RX42Pr5+Kst/pK+vz/BBPWnWqDZmyU25eeseb956UKxwfiaMHkihAnk1HqtIoXyMHz2QooXz8+atBzdv3cMsuSnNGtdh2MCe6Ovrxf8DSWKKFqCaAh1RKVqA/u1jfIeFhfH58+ekzob4jfn6+mFubq623MjIKNaCaUREBIGBgTFu8yvEJa9CiD/P5+BgNjmdj9c+m5zP/dNDVgohhBBCfPoUyOw582nYuAXF7MuQK29BHMtX4b9xE/Hz84tTGgcPHaFbjz6UKVeZ3HaFKVysJO07duXa9Rtq2yqGYnZ3f0u/AYMp7uBI3vxFcHF1Y9nKddjmsOPFCxemzZhN6bKVyJOvCHXrN+HCxcsAPH/+gi7delGoaEnyFypOz979+eDtneDz1zRnR9Rl+w8conbdRuS2K4x9ybL8N24iX758UUsnKCiIufMXUaVaHXLbFaZgkRK079iV6zduJjhvQojfk1aa4/bt3YPLV5xo17Er4/4bRcEC+bWR7C9XzrEE5X4YIiomNjbWbFm7INr1Ma2Lib6+PvXrVKN+nfhN6G6bJROD+nVN0DETS3BoKG98E/Zgm3vqQLxagE49uot+FevE6xgZLK0xjEer9JdublSsXJNRI4bSsUNb5fJSZSri4enJ3t3bKJA/cg6VN2/cKVuhKsOHDqJL5w5A5Avl9Rs2s2XbDl6+dMPExJiSJRwYOKAvtrZZlelddXKmZesOTJsykYCAADZu3sqbN+5MnjiWxo0aaMzbgwePaN+pG6lsUrJ29TJsbGwICwtj7fqN7Ny1F1fXlxgZGeFgX4xBA/uTM0d2lf3d3d8yYdJULl2+gp6ePmUdSzN61LA4fzYAri/dmD1nPpevXCUwMIgsmTPRrGlj2rdrjY6OjnI7x/JVyJA+PVs2rVXZf+78RcxfsJjzZ46TIUN6WrRqj5PzNQDy5Cui3G7zxjWUcLCPNh/BwcEsXbaSvfsP8tb9LeYW5pQvV5aBA/qSOnUq5XY7d+1hyLBRbFi3itt37rJl63Y+fPAmZ47sjBoxFHv7YjGeb1zz9/bdO8ZPmMrly1dAR4eKFcoxftxozM3MVNJzf/uW+QuWcO78BXx9fUmVKhV1atekX5+eMbZmUZxHZBoe2OawU8mHbQ47GjWox4zpk4Hv382+fXqSJXMmlq1YjauLK927d6F/3148evyEefMXcev2HT5+9MfSMgV5cudm4IC+2NnlSfB12bZjFxs3bsHN7RURRJAqVSrsixdlyqTvcz79mNfxE6awbsMmVq9cQrmyjsrt9u47yP8GDaVvn57079sr2mOWdqxEvnx5Wbbk+/14wMCh7Nt/UOW3CZHfy5w5crBqxWLlsqfPnjN/wWKuXnXm06dPpM+QnqaNG9Klcwe1Hig3bt5i4eJl3Lp1hy9fvpA5UwaaN21Ahw4do82fwoULl+jRux+FChVk2eL5mJqaxnodhPiT3H7jqjZHR2x8gwK588aVEra5EilXQgghhBC/N09PT7Zs20m1qpWpVas6RoZG3Ll7jy1bd3D9+k327t4Wa2Ox9Rs3Y2FuTrMmjUiVyoa37zzYsWM3rdp0ZMumtRQtUlhl+6CgIJq2aEM+u7z06d2dr1+DsTD/XncdNGQEenp6dOzQlrDQMNau20jnrj1YvHAeQ4aNpHrVKgwe2J8nT5+yect2Pn/5wpqVS7X+2WzbvpNXr9/QvFkTWjRvyoWLl9mwcQsRERGMHztauV1gYCAtWrXn6bPnNKhfh3ZtWxEUFMTuPfto2boDy5cupHw5xxiOJIT4k2gl2NGlWy8M9PW5fecujZq0JGVKa9KnS0eyZBpezunosGn9am0cVvwB3vh6U2zKoF9yrA1O59jgdC5e+1wfPhNbm9Rx3j5L5sykTZMGJ+drymCH60s3PDw90dXVxcnpmjLYceWqMwAODsWV+0+YOJW16zdSwqE4zZs2xuvDBzZs3MKly1fZs2srWbOoDsW1Zt16Aj8F0bRJQywsLFQCIlFddXKma/c+5MmdixXLFylfovftP4gTJ09Tv14dWrdsjp+fHxs3b6Vxk5bs2bWVbNkiJ63/+PEjTVu0wdvbhzatW5AxYwbOnD1Px07d4/zZuLm9olGTFgQHB9OmdUvSpE7NiVOnmTBpKi/d3Bj336g4p6XQs0dXwsPDuXb9BtOmTMTAIPKWpch3dPr0G8iJk6epUrki7du15uVLNzZt3srlK1c5sHcnlj/MrzNj1hwAOrRvw9evwaxctYYu3Xtz4exxjT0l4pO/z0GfadW6Aw4O9gwdMpDbt++wa88+DPT1lS/0AV69ek2jpq0w0NenebMmpE5lw91791mxcg0PHz5izaplKgGjqOyLF2P8mGHMnr8EaytrevXsGqfP6djxk3i996JVq+akSZ2KtGnT4uPjS5u2nUiePDnt2rYmpbU1Xh8+cO3adZ49f46dXZ4EXZedu/YwfMQYKlUsT9OmjdDV0eX169ccP3k6xjwOHfI/Ll91YvDQkRw5uBdrayvc3d/y37iJFC1SiD69Yv6O2tsX4+y584SHhyuDE05O19DV1eWq0zVlsOP16ze4u7+lbeuWyn1v3bpDm/adSJMmDR07tsPC3JyrTs5MmzEbt1evmDxxnHLb4ydO0bvv/7DLm4ee3buQzDgZR48cZcLkWQR9CaVXj+gD0QcPHWHg4GFUrlSRObOmYWhoGKfrIMSfJOBLwnom+idwPyGEEEKI31HWrJmVPeqzZo19KO6MGTNw+cIplYBG61bNKVa0MMNH/seJk6epWSPmhrJrVy3DxMREZVnrls2oXqs+ixYvZ/XKJSrrfH396NKpA8OHfX+X4+P1TvlvCwtzVq9cqqxflSpVgrr1m9C1e2/mzJpG3Tq1lNvq6eqxbsMmXF+6qb3v+FkvX77i6OF9WFtbAdCqZTPatu/Ctu07GTZkoPKc5y1YzOMnT9m0YTXFixVV7t++XWsaNm7B2HGTOHv6qFbzJoRIOloJdlx1uqb8d0REBF5eH/Dy+qBx2+he1gnxp/jx5amTkzMmJsaULlWSK1edlS9PnZydMTMzwy5v5EvJZ8+es3b9Rso6lmbViiXo6UUOLVa1SiUaNm7BtOmzWLp4vsqx3nt6cfL4QVKkSBFtfk6cPE3f/oMoXaoEC+fPJlmyyDldDh85xpGjx1kwbxa1alZXbt+oYX2q1qjL7LkLWLQg8iX/0uWrePfOgyWL5lGtamUA2rRqQa8+A3jw8JH6QTWYOXsefn4f2b51A8WKRrb2b9O6Bd179mXDxi00b9qYPHlyxyktBccypdi//yDXrt+gbp2acRqr8+y5C5w4eZpmTRup9BgoXqwovfoMYOHipYweqdpjJSIigl3bNyvnnsiezZbuPfuy/8BhWrdq/lP58/H1pVfPbnRo3waILID5BwSwd/9B/hszkuTJTQEYO34yhgYGHNy/SxmMadG8KXly52Ls+MmcOXueihXKacxHpkwZqVm9MkuWryFlSmvq14tbDydXF1eOHtlHlszfC50nTp7Gx9eXVSsWU7Bgge8bR3lZn5DrcvLUGbJns2XFskUqy4cNjTkYamRkxNzZ06nfsBlDho1i+dIFDBg0lIiICGbPnKb8HUWnhENx9u0/yKNHT7Czy6MMTtatU4vTZ84RFhaGnp6e8jmmCE5GREQwfNR/ZM2ShZ3bN2NkZAhEXr+0adOwctVa2rdrQ84c2fny5QsjRv6HY5nSrFy+SPmcq12tAkNGjGPhoqW0bN5ULcgGsHHTVsaOn0SzJo2YMH6MssJw4+atWK+DEH8Ss2TGCdrPPIH7CSGEEL+D0fs3x3nbCXVbxr6R+ONFbTAVF4aGhsp/h4aGEhQURGhoGCVLOABw+87dWIMdUQMdnz4FEhISgp6ePgULFODO3bsa9+nRvXO06XXq0E6ll3s+u7wkNzXFNLmpSqADIutX6zZswsXFVevBjiZNGioDHQqOZUpx8dJl3ri/JWeO7ERERLBnz34KFshHNltbfHxUhz+vXLki8xcsTpRgjBAiaWgl2HH+zHFtJCPEH+HHl6dXna5RpHBhSpcuycxZ85QvT52cr1OsaBHly9hTp88C0K1rJ5UXtAUL5Kd0qZKcPXeB4OBglcJM/fp1Ygx07Ni5hxGj/qNe3VpMnTxBZaLo/QcOY21tTckSDioPdENDQwoXKsCly1eVy06ePEOmjBmVgQ6IDEx26dSBo8dOxPqZhIWFcebsOeyLF1MGOiByMrFuXTtx8tQZTpw8He9gR0KcPHUGgJ7dVV8K16helaxZs3Dy5Bm1YEfLFs1UPjuHb8NXub169dP50dPTo2WLZirL7O2LceLkad64u5M7V078/f05f+Eibdu0JCIiQuV6lSlTGoBLl69EG+xIqPLly6kEOgBlr6ATp86QO3curU0GZ2Zmhofne67fuKnyHYmL3LlyMnTw/5gwaSrNWrTl5q3bzJ45lYwZM8S6ryJ4cdXZGTu7PMrgZNfOHdh/4BD3HzykYIH8asHJx0+e8vTpM0YMG0xgYKDKfCblyjqyctVaLl26Qs4c2bl46TI+vr40algPX18/5XZ+fh8pU8qB02cvcP3GTapUrqiSt/kLFjN3/iJ69ujKoP/1U1mXWNdBiKRSKENWUhibxmsoKz1dXSyMTRMxV0IIIYQQv7+t23awcdNWnj57TmhoqMo6P7/Y58599PgJc+ct5MoVJz79ME+jpgbJVpaWMb6HyJQpo9oyixQWpE2bRm15ihQW3/LpF2s+4yuzhnwoGpj5fqtT+/j44u3jg7ePD8UcykSblpfXBwl2CPGX0EqwI336dNpIRog/guLl6RUnJ+zs8uDsfJ3WrVtQ0sGeT58+cf/+A6ysrNSGxHn9xh2A7NmzqaWZPbstFy9dxsvrg8rvKXPmTNHm4/6DBzg5X6NG9arMmDZZrZDi4uKCt7d3jA/0kJAQDAwMeP3mDSVLOqitz5Y95mGQFHx8fAgK+qzx3HJ8W6Y4/8T25s0bkiVLpvFFePZstpw6fZaIiAiVzytD+vQq21lYKApksRccY5PKxkbZK0CZ/rehsT5+S9/V9SXh4eGsXbeRtes2akzH29vnp/PyoyyZ1QuH9vbFqFunFouXLGf1mvUULlyQco5lqFO7psbCa1z16N4FJ+drNG3ehjSpU+PgUJxKFctTvVoVlUBTdNq3a82x4ydxvnadWjWrx7n3imLouatXnenUoR1Xna5RtEhh8ubNg7W1NVevOn8LdqgGJ11cXAGYPHUGk6fO0Ji2t0/kNXnxInLb3n3/F20+vH+YlO/Y8ZN8Cgykc6f2aoEOSLzrIERSMTY0pEXxMiw5fyzO+4SFh1Nt/jimN2xHs2KlpXewEEIIIf45a9ZuYMKkqZQuVZLxY0eRKlUqDA0NCQsLpUOn7kSEh8e4/7t3HjRr3gYTUxO6d+9CNtusmBgbo6Ory5JlK7hyxUltH2OTmHvWRte7Xk83+l73ERERMaaZEDH18o8g8njhEZGfT9GihenXJ/q5HnPlzB7tOiHEn0UrwQ4hopPB0prrw2fGe78vISHUXDg+XmN1mycz4XDv0SSLZXKuH/MXX1FfnlaqWAEPT09KOBQnR47sWFtbc+WqM9bWkelGna8jIYy/DUmlSeZMmdDV0+Pc+Qtcu34T++JFVdaHR0SQLl1apk6eEG0asQ0BlFiie2EVHhb2i3MSSU9PV/MKLRTIdKNLmygFsPDI/zdv1piaNapr3NbGJuVP5+VHyYzVC7E6OjrMnT2dbl06cubcBZydrzF77gLmL1zMogVzVCYJjw/brFk4cfQAFy9e5tLlq1y+6sS+/QfJkzsX27ZsUA7nFZ23797x+MlTIDIQ8WMvqJjY2xdTDlmlCE5CZA+eq07O1KxRTS04Gf6t0tC7Zzfs7TX/jjNkiAxMKgrQY/8biW3W7/PqBHyMDHCYWViT/Yf5TAoUzM/rV2/Yu+8ATRs3VAsUJtZ1ECIpJTOI2282qk9fv9BzyzJOPLrNrMYdSGEiPT2EEEII8e/YvWcfGTKkZ92a5SpDRz1//iJO+ysaWS1dMp9SJUuorJs1e55W8/o7sraywtzcHD/fj5QpXTKpsyOE+AW0Guzw8/Nj776D3L5zF19fX0qVLEG3rp0AePrsOa9evaJ0qZIYa3jBJv5Ohvr68ZoAPKo2Jcqz6OyROG/ftkR58qaLfVgbbVC8PL18+SomJsbKSckd7Ivh5HwNa2srlSFxADJmiOw98Pz5C2xSqr64fvHCBSMjo3i90DYzM2Pp4nm0adeZTl26s3rlUpXJtrJkzsTlK07YFy8a60vhjBky4OryUm35i+cuccqLlZUVJibGGgtcz19EppEpSk8LC3Nz/P391bZ94/5WPfF4tuTNkCED5y9c4vXrN2q9O56/cCFD+vTabR2shbQyZ86Ejo4O4eERv00BLE+e3OTJk5ue3bvw7p0Htes1Yu68Rd9fsifgvI2MjKhUqQKVKlUAIuerGDN2Anv3HYhxbpTw8HAGDhpOWFgoI4YPZvKUGcyYNZeRw4fE6biKoecOHzmmDE5CZDBy+ozZXLp8Rfm3QpZvXZgNDQ1jvSaKocDMzcxUtlVM4mdlk1Ztn7SpUzNt8gRatG5PyzYd2LJxrcYJ3mO9DkL8IV54ebDkXNwmftQBfgw177nthPPL5yxt2Y3S2fNo2k0IIYQQ4q+jaDynmDMUIntJzF+4JKbd1Pb/sR3f2XMXuHP3nvYy+pvS1dWlYf26rF2/kS1bt9OieVO1bbw+fFB7RyOE+HNF3+Q4no4eO0G5itWZOHkaBw4e5tLlq7z4NgwIwLt37+jesx/Hjp/U1iHFX65nuRrYJDeP07apzCzoUU5zi/jEUMKhOAEBAaxbv5FiRYsoh+Ep4WDP9Rs3uXLFWWVIHIDK317wLl+5RtlqHODe/QdcvHSFcmUd49xSXcHCwoL1a1eQJXNmOnbuzvUbN5Xr6tWtzZcvX1gQTSHoQ5RhdSpVKs+r169Vfp8RERGsWLUmTvnQ09OjYoXyOF+7zs1bt1XSWL5iNfD9/CHyRfKLFy54ffigXObh4cmJk6fU0jY1jZxM7eNH9eCIJlUqRx5n6fJVKsuPHT+Jq+tLKleuoGm3BItv/jSxsrLEsUwp9u0/yJOnz9TWf/0ajH9AQILTjw8/Pz+1LsZp06bBytJKZZzV+J73jxPBAcpgYGzjty5ZthLna9cZM2oEnTu2p0WzJqxes56Ll67E6diKIMaChUswNTVRBidLOtgTGBjE6jXr1YKT+ezykj2bLevWb+L9ey+1ND99CuTLly8AlHUsjaVlChYtXs6nT+rzEfj4+Kr85hXSp0/H5g1rMDIyomWbDsqhsyDu10GIP0FERAT/27GaL6EhKstNDVXnorE0MaV3+ZqcHDAeRw0BDXc/b+oumcKEQ9sJCQtVWy+EEEII8btbsXINI0b9x4hR/7FiZez17ZrVq/HmjTvtOnZl46atrFy1lqYt2vDq9es4Ha98WUdMTIwZOHgYixYvY/OW7YwcNZbefQeQK1fOnz2dP8L/BvSlQIF8jBw9js5de7Ji5Rq2bN3O7DnzadWmA42btErqLAohtEgrPTvu3LlLvwGDMDExYfiwQRQvVpQGjVRb6ZYpXYrkyZNz7PjJOI+1Lv5taS0s2dltCI2WTuVD4Kdot0tlZsHOroNJa2H5y/KmeHn6/IULDRrUVS4v4VCcwMAgAgODaN+utco+2bNno33b1qxdv5E27TpRtUplPnz4wPqNWzAzM2PokOjH+49JihQp2LBuJa3bdqJDp26sXb2cokUKU7tWDU6dPsuiJcu5fecejmUif4Pub99y4cIlsmTJzPy5kUOMdevSiQMHDtNvwGDatG5BxowZOHP2PB+8PsRy9O8GDujLhYuXaNehC21atyRN6lScPHWWi5cu07pVc5XJyVu1bMahw0dp16ELzZs2xt8/gE1btpE1a1bu33+gkm6hggVYv2Ez4ydOoVLF8ujr61OypAMprTUPQVaurCNVKldky9bteHv7ULp0CdzcXrFx4xbSpUtLrx7dEvApRy+++YvOxPH/0aR5axo0ak7jRvXJlTMnn798xtXVjaPHjjN75tRf0pp/9579rF2/kapVKkX2OEGHM2fP88LFhb59eiq3i+95t+vQBYsUFhQvVpQ0aVLj7e3Dlq3bMTIyokb1qtHm5+69+8xfsIga1avSpHEDAEaNHIrTtesMGTqSwwd3xzh5Hnwfeu75CxfKOpZWBiezZ89GypTWPH/hQoXy5VSCkzo6OsyYPpk27TpTrWZdmjZuRNasmfn40Z/nz19w9PgJ9u7aRrZstpiamjJ9ykR69RlA1ep1aNSwHhkypOf1KzeePnvO+QtXuHPLSeMk4xkypGfzhjXKHh6bN6zB1jZrnK+DEH+CTc7nufD8kcqyGnZFWNGmJxfv3yDg6xfSp05PwQxZMf4W9N/TfRgLzx5h0pEdhEQZ4jAiIoI5pw5w9ul9lrfuSTYbmcNGCCGEEH+O02fO4eR8DQAH++J06dwhxu0V67ft2MXEydOwtExB5UoVGPS/fhQuVirW42XKlJG1q5czc9Y8lq1YDRER5C+Qj7Wrl7Nt206efBsq+G+WPLkp2zZvYN36jRw8dIS58xcRERFBKhsb7Ozy0LJFs6TOohBCi7QS7Fi8dAUAa1cvp2CB/Bq30dPTw84uD0+fPdfGIcU/In/6zOzr0I811y6w694NfIO+t5q2NDGllX05epSr/ksDHfD95ek7Dw9K2Nsrl2fPng0bm5R4eX3QOF/H6FHDyJw5E1u2bmfK1BkYmxhTulQJBv2vH1m/DZuTEJEBj1W0btuRDh27sXbNcooULsScWdMo4VCc7Tt2M3/hYiIiIFUqG4oVLUKzpo2U+1tapmDr5nVMmDSVLVu3o69vQFnH0kyfOhGHkuXilIfMmTOxa8cWZs+Zz9ZtOwgKDCJz5kyMGjGUDu3bqGxbwsGe8WNHs3zlaiZPmUGWLJkZPXIYT589Vwt21Kldk3v3H3Do8FGOHjtBeHg4mzeuiTGYsGDeLJYsW8nevfs5c/Ys5uYW1KlTi0H/64elZYq4f7BxkJD8aZIhQ3r2793B4sXLOX3mHNu378I0uSkZMqSndasW5LOz02q+o1PCoTgPHj7ixInTeH34gIGBAVmzZmbK5PE0bdxQuV18z7tVy2YcOHSEjZu2EuDvj5WVFYULF6RXj64ah28CCAoKov//hmBtZc2kCf8plxsbGzNv9gwaNWnBiFFjWbxwbqznZW9fjH37D6r9Lks42HPw0BHl0FZRFSyQnwN7d7Bw8TIOHDyMt483FhYWZMmSmd49u6tMFF6pUgV27djCkmUr2Lp9F/7+H0lhYUHWrJkZMXwwBjHMJZQxYwa2bFxLi9btadW2I5s2rInzdRDid+fp78fo/ZtVliU3Ssb0Ru0wMTSiWMbIeW5+HO5NV1eXvhVrUTZHXrptWsKz9+9U1t967Uq5WSOZUr8NrR3KyeTlQgghhPgr6erq0q1rJ+UQ8VG5PHsQp2XFihZh6+Z1GpfPmD5ZZdmWTWujzUu3zu0YPnyYxnUXzp7QuLyEg73GPGnSuFEDGjdqEOv+MaWpKQ0AIyNDunbpSNcuHeOUFyHEn0snLPTrT8/AW8y+DNmy2bJty3rlsmw589GoYX2mT52oXNb/f0M4ffosd287/+whRRKIafz5X3FcYwtr7rxxxf/LZ8yTGau0ABW/VlJ9F0T05Jr8fuSa/H7kmvx6HdYtYN8d1XLfjEbt6FS6MhC3axL49Quj929h7ZXTGtfXzl+MuU07YmVqpqVc/9vkdyKik5TfDfle/p3+hev6Y8A/JhPqtkzEnPw6/8J1/RktWrVX6dkRU3DhdyLX9e8k1/Xv9K9fV6307PgUGBiniZU/ffpEeIT6uOVCxIWxoSElbHMldTaEEEIIEQdH7t9UC3TYZ8lBh5IV45WOqVEyZjfpQOU8Bei7bSU+PwxtefDedW64vWBxy26Uy/lresEJIYQQQgghhPj9aGWC8lSpbFQmI4/O8+cupE+XThuHFEIIIYQQvyn/L58ZtGutyjIDPT3mNu2Erm7Cip818xXl4uAplM+ZT23dO39fGiydypj9W/j6w0ToQgghhBBCCCH+DVoJdpQqWYJnz55z6vTZaLfZf+AQr9+8wdGxtDYOKYQQQgghflMTD23n3UdflWX/q1yX3GnS/1S6acxTsLPrYCbWa4mhnnoH5YVnD1Nt3jieeLr/1HGEEEIIIYQQQvx5tBLs6Na1E0ZGRvTpN5Bly1fh+tINgLCwMN6+e8fadRsZNXocpqamdOzQVhuHFEIIIYQQvyEn16esunxKZVnO1OnoX6mOVtLX1dWlZ7kanOw/llyp1YMnd93dqDh7DGsunyIi4qenphNCCCGEEEII8YfQSrAja5bMzJ87E309PWbMmkuVarXR0dFh3/6DlC1flYmTpwGwYN5M0qX9NydHEUIIIYT4230NDaHf9lUqQQYdHR3mNe2Ekb6BVo+VL31mTg8YT5cyVdTWfQ4JZuDOtbRaPYcPn/y1elwhhBBCCCGEEL8nrQQ7ACpVLM+Rw3tp36412bPZYmRkhIGBAZkyZaRVy2YcPriHcmUdtXU4IYQQQgjxm5l76gBPPd+qLOtYqiIOWXMmyvGMDQ2Z1rAt2zoPxCa5udr6ow9uUWbGCE49vpsoxxdCCCGEENqzect2qlSrQ+68hbDNYcfDh4+SOktqBg8ZgW0Ou6TOhhAiGuqDHf+E9OnSMWrEUG0mKYQQQggh/gCPPdyZfXK/yrK0FpaMrtUs0Y9dJW8hLgyeTO8tyzn5Q2DjfcBHmiyfQfey1RhTqynJDAwTPT9CCCGEENrg+tKNffsPcunSZdxevSYwMIj06dJSulRJenTvQqpUNhr327ptB+s3bsbV1Q1TExNKly7JkEEDSJ8+ncp2jx4/YeKkqdy794AUlilo1qQRPbp3QVdXtW305m27WLl6I8ePHoj2mD/rqpMzo8aMo0rlinTt0hEDA321/AohRGy0Euz44O1NSmtrbSQlhBBCCCH+MOHh4fTfvoqQsDCV5TMbtcc8mfEvyUMqMwu2dRnEyosnGXNgC19DQ1TWLz1/jPPPHrK8dQ/yps34S/IkhBBCCPEztu/YxYaNm6lYoTw1a1QnWTIjbt2+y8bNW9m7/wA7t20iWzZblX1mz5nPwsXLKFq0MKNHDsPHx4c16zbg5HyNPTu3kjZtGgACAwPp1LkH1tZWDB0ykIcPHzFrznxMTEzo0L6NMr1Xr16zeNkahg7sk2iBDoALFy8DMG3KBFKkSJFoxxFC/N20Euwo7ViRMqVL07BBXSpXqoiRkbSYE0IIIYT4V6y5chrnl89UltUraE+NfEV+aT50dHTo4liFMjny0HXDYh68e62y/uG711Sa8x/j6jSnS5kq6Ojo/NL8CSGEEELER43qVenRrTPm5t+H62zRvCmFCxVg5OhxzJm3kIXzZyvXubi+ZMmyleSzy8uWjWvR14987Ve2bBkaNGrOzNnzmDVjCgA3b97Gw9OTbVvWkzFjBgCev3Dh8JGjKsGO4aP+o3DBfNSpVS1Rz9XL6wPATwU6QkNDCQ0NJVmyZFrKlRDiT6OVOTuSJ0/O2XPn6TdgMPYlyzJ85Bicna9rI2khhBBCCPEbc/fzYdzBbSrLLIxNmNqgTTR7JL48aTJwov9YepSrrrbua2gIw/ZsoOmKmbwP+JgEuRNCCCHEv6pXz27MmTWNObOm0atnt1i3L5A/n0qgQ6F2rZoAPH7yVGX5/gOHCAsLo13bVspAhyId++LFOHL0OF++fAHg87f/W6SwUG5naZmCz5+/KP/eum0H9+7eZ+TQAfE4y+++fv3KvAWLqVytNrntClO4WEk6d+3Jnbv3lNtcdXLGNocdO3ftAcA2hx22Oexo0ap9jGkr5s7w9fVj5OhxlCxTgdx2hTl/4RIQGfhYtWYdtes2Im/+ouQrWIymLdpw6tQZtbQOHjpCtx59KFOusjKf7Tt25dr1Gwk6byFE0tFKzw6ny+c4c/Y8e/bu58zZ82zfsZsdO/eQLl1aGtSrQ/36dcmaJbM2DiWEEEIIIX4TERERDN61lk9fv6gsH1+nBanNUyRNpr5JZmDIpHqtqJS7AL02L8Pzh8DGqcd3KTNjOAubd6Vq3kJJk0khhBBC/FPKlC6plXQ8PT0B1IaUv3Mncu6yIkUKq+1TpEghnJyv8eTJUwoWLED+fHYYGhoyc9ZcunbuyKPHTzh77gLNmjYCwMPDkynTZjF48ADSpEkd7zyGhYXRsXN3rlx1pny5srRt3QIvrw9s2rKNZs3bsHLFEsqULkm2bLbMnjmVLVt3cO36DWbPnAqAdRyHy2/bvjNmZsnp3Kk9erp6pE+XjrCwMLp0783Fi5epWaMaTZs0JCQ0lKPHTtCle28mTxxL82ZNlGms37gZC3NzmjVpRKpUNrx958GOHbtp1aYjWzatpaiGz1MI8XvSSrBDX1+fKpUrUqVyRfwDAjh06Ah79h3g5s3bLFy8jEVLllMgfz4aNqhH7VrVZew9IYQQQoi/wL47zhx9cEtlmWP2PLR2KJdEOVJXMVd+LgyeTL9tqzjy4KbKug+fAmi+chadS1dmXJ0WGBvKUKxCCCGE+P3NnrsAgMaN6qssf+cRGQRJqyE4oQhYvPPwpGBBSJs2DePHjmbchMls3LQVgGLFijCgX28ARo0ZR57cuWjdsjm+Hzzincddu/dx5aozzZs1ZvLEccrlDRrUo2btBowaPY7TJw9jkzIl9evV4dKlK1y7foP69erE6ziZM2diwbxZKsOTrt+wiXPnLjB/7kxq16qhXN6xfVs6denJ5KkzqVO7JqampgCsXbUMExMTlXRbt2xG9Vr1WbR4OatXLon3+QshkoZWgh1RmZuZ0aJ5U1o0b8qbN+7s2bufvfsPcufuPe7eu8+kKdN5dP9m7AmJf9rhwY5EfJtYNIIIAHSIeVxtHX0Das64kOh5+xm2Oexo1KAeM6ZPTuqsRKtFq/a8cXfnwtkTSZ2VP9bOXXsYMmwUmzeuoYSDfVJnJ1Ek9Tn++Ft688adshWq0rdPT/r37RXr/o7lq5AhfXq2bFqbyDkV4u/lFxTIsD0bVJYZ6Rswu0nH324ujJTJzdnYsT/rrpxh5L5NfA4JVlm/8tJJzj9/yIrWPcmfXnojCyGE+PVG798cp+0m1G2ZyDkRv7tFS5Zz9NgJqlapRKOG9VXWffk2BJWhhgYcRoZGkdt8+d4jt2mThlSvXgWXF65YpLAgS+ZM6OjosG//QS5fceLQgd2EhISweNlqTp45T3h4BGUdyzBsyECSJzeNMZ9Hj0W+U+jTq4fKctusWahbpxY7d+3hwcNH5M9nF+/PIKru3TqrlT137dlH2jRpKFWyBD4+virrqlerwtlz57l56w6OZUoBqAQ6Pn0KJCQkBD09fQoWKMCdu3d/Kn9CiF9L68GOqDJkSE+f3j3o1LEd02fOYcPGLYSEhCTmIcVfIiI0hPAw1e9KRCz7aGUCmj+Qv78/q9duoFjRInHuEnv8xCkePnpM757dVMbx/BfNnb+InDmyU7NG4k62JoQQf5sxB7aozXkxtFoDstmkSaIcxUxHR4f2pSpSKltuum5czF13N5X1Tz3fUmXuWEbXakqPstXQ1f1XSxZCCCGE+F2tWbuBWbPnUcKhOHNmTVN7yZ/MOHJi7uDgYLVJur8Gf43c5ofl5mZmFCpUQPm3t7cPEyZOZUC/3mTNkpkJk6aye88BxowcTNr0GRkybBQfPnizdPG8GPP6+vUbzMzMSJtWvWyYK1cOAF69ev3TwQ7brFnUlj1/7sLnz58p5lAm2v0+fPig/Pejx0+YO28hV6448SkwUGW7360RjxAiZon2ljMiIoKLl66wd98BTpw4RdDnzwDY2KRMrEMK8U/y9w9g/oLFdOvaKc7BjhMnTrFrzz56dOv8zwc75i9YTO1aNbQa7GhQvy51atfEwMBAa2mKmKVPn45H92+ip6eX1FkR4p9w4dlDNjqdU1mWL10mepWvEc0ev4+cqdNxvN9YJh/ZyYKzh4mI+N6cIjgslNH7N3Pq8V0WtehKWgvLJMypEEIIIf42nz9/JiwsHAA9PV2MjY3jvO/K1WuZPGUGpUqVYMXShRr3TZsmNU+fPuOdh6fa3LkeMQxxFdW4CZPJmDEDHTu0JSIigi1bd9CiaUPKOZbCyiYtPbt3YcSosXh9+IBNyqR/x/fj8FMA4eHhZM6UkQnj/4t2v5w5sgPw7p0HzZq3wcTUhO7du5DNNismxsbo6OqyZNkKrlxxSrS8CyG0T+tvOR89fsLevQfYf/AQXl4fiIiIwNg4GXVq16RB/TqUKV1K24cUQojfip6enrx0/8V0dHQwMjJK6mwI8U/4HBzMgB2rVZbp6ugwr1knDPT+jAC6ob4+Y+s0p2Lu/PTYvIx3H1WHNzj79D6OM0cwr2knauUvlkS5FEIIIcTfpmPnHjg5XwPAwb54nIfVXbpsJdNnzqFc2TIsXTw/2rpPgQL5OXf+Irdu3VYLdty6dZtkyZKRM2eOaI9z4uRpjh8/yf59O9HT0+ODtzdfvnwhTZpUym0UPTXevn0XY7AjU6aMvHBxwcPDU22C86dPnyu3SQxZs2bmzWt3HOyLxdoI8djxk3wKDGTpkvmUKllCZd2s2TH3XhFC/H600j/f0/M9K1auoWadBtSp15iVq9fy4YM3JUrYM33qRJwun2POrGmUdSwjQwL8wwK9Xsf5v4hYB61SF0FEnNNPqJdubtjmsGP1mvUqy0uVqYhtDjvu3ruvXPbmjTu2OexYsXKNWjrOztdp1KQlefIVoUTp8sxbsFilZSnAgYOH6dSlJ6XKVCRX3oKUKF2eIcNG4RWlq+VVJ2fKVqgKwLLlq7DNYYdtDjtatGof7Tm0aNWeXXv2AZAnXxHlPlednFW2e/vuHd179qNAIXsKFHag//+G4B8QoJae+9u3DB0+mhKly5Mrb0Ecy1dh+sw5fP36Ndo8/PgZzZ2/iKPHTlCzTgNy5y1E2QpVlROk/ejU6bO0aNWe/IWKkzd/URo2bsGpU2eU60NCQqjXsClF7Uvj6fleZd/hI8eQLWc+Ll+5qjw2wMFDR5Sfg2P5KjHm2cfHl//GTcSxfBVy5y1EMfsyNGnWigMHDyu32blrj8pn6h8QQJlylalQuQaBUbrERkRE0K5DF/LmL8qzZ8+jPebFS1ewzWHH8ROnlMs+f/5M7ryFyJYzH97ePsrlV52csc1hx+Ejx1TS2LV7L/UbNcOuQDHyFypOq7YduX5Dff6ksLAwVq1ZR43aDchtV5iCRUrQtXtvnsaQP4Xw8HBGjh5H9lz52bBpi3L5th27qFOvMQUK2ZO/UHEqVa3F8JFjYk0PwN39Ld179iV/oeIUKlqSvv0HqfwGFKJ+l6Ly9fVj8JARFCpakvyFitOhc3deurmp7S+EiLsZx/fg8sFTZVn3stUpnNE2iXKUcGVz2HFh0CTqFCiuts4n8BNt1sxjwPbVBH79omFvIYQQQojEt2jJcqbPnEPFCuVYunhBjI286tWphZ6eHmvXbSQ0NFS5/O69+zg5X6d6tSrR9ibx9/dn9H/j6dmjq7LXg5WlJYaGhjx79kK53ePHTwFImybmoUurVa0MwIJFS1WWv3RzY/+BQ2TKmBG7vHliTCOhGjWsz6fAQLX6oULUOqWuXuR7yh9eyXD23AXu3L2XKPkTQiQerTS/cyxfhfDwcCIiIsiRPRv169WhXt3aGsflE/+u0xPqJ2r6EWGhcT5Gnfk3EnSMLJkzkzZNGpycr9GxQ1sAXF+64eHpia6uLk5O1yiQPx8AV65Gvuh2cFB9gfLg0SPO9hlAs2aNqV+/DocOHWXe/EWkT5eWxo0aKLfbuGkr1tZWtGnTghQWFjx4+JidO3dz69YdDu7fhZGRIdmy2TJqxFAmTp5GxQrlqF0rcvgQa2vraM+hZ4+uhIeHc+36DaZNmYiBQeRtIFu27y+pPgd9plXrDjg42DN0yEBu377Drj37MNDXV5lc/dWr1zRq2goDfX2aN2tC6lQ23L13nxUr1/Dw4SPWrFoWp/Etz5w9x7btO2nZvCnm5uZs27GTMWMnkD27rcrk1+vWb2LchMmUKV2KAf36oKOjw4GDh+naow+zZkylfr3aGBgYMGfWdOrWb8zAIcPZsHYlOjo6HD9xim3bd9G1S0dKlSxBUFAQs2dO5X+DhlG4UEHatG4BaO4CG1WvPgO4/+ABrVo2J2vWLAT4B/Do8RNu3rxNndo1Ne5jbmbG7JnTaNm6PeMmTGH61IkArF67ngsXLzN+7GhyfCtMalK0SCEMDQy46uRM1SqVALh+4xbBISGR3zvna5QoFjnG6pWrzujo6OBg//17N3nqDFatXkf1alVo2KAeXz5/YcfOPbRq04F1a1aofMZ9+w/ixMnT1K9Xh9Ytm+Pn58fGzVtp3KQle3ZtVfmeRBUcHMyAgUM5eeo0c2ZNU34WO3ftYfiIMVSqWJ6mTRuhq6PL69evOX7ydIyfM8DHjx9p2qIN3t4+tGndgowZM3Dm7Hk6duoe676KPLXr0IUHDx/RrGkj8uTJzfXrN2nVpiNfv8QejBNCqLvv7saCs4dVlmWySsnw6g2TKEc/z8rUjLXt+rDJ+TzD92wgMFj1/rDu6hkuvXjE8tY9KZQxaxLlUgghhBD/ovUbNzNr9jxSprSmWtUqHDl6TG2b+vXqKP9ta5uVrl06smTpClq0bk+DenXx9fVl9dr1WFtbMWhgv2iPNWnKDKysLOnRvYtyma6uLo0a1GPHrj2YmZlhkyo1i5csp3y5sqRKZRNj3hs2qMu+/QfYsnU7Hh6elCtbmg8fvNm0ZRsR4eFMnPBfojWIbtemFVeuOLFk6QquX79JuXKOWFtZ4uH5nnv37nP+wiWeProDQPmyjpiYGDNw8DDatGqBpaUlDx48ZN+Bg+TKlZMnT54mSh6FEIlDK8EOCwsL6tapSYP6dclnl1cbSQrx27K3L8bZc+cJDw//FuBwxsTEmNKlSnLlqjNdOncAwMnZGTMzM7WWCk+fPmfv7m3K30rTxo0oU64ymzZvUwl2rF29TK3FReFCBRg8dCTHjp+gbp1a2KRMSdUqlZg4eRo5cmRXKeREx7FMKfbvP8i16zeoW6emxlYhPr6+9OrZjQ7t2wDQqmUz/AMC2Lv/IP+NGancbuz4yRgaGHBw/y4sLVMA0KJ5U/LkzsXY8ZM5c/Y8FSuUizVPL164cPLYIWXX1po1q+FYNvIzUbyI9/DwZMrUGbRq2YwJ4773CmjbpiWNm7ZiyrQZ1KldAz09PWyzZmHUiKGMGDWWFSvXUK9ubYaPHEO+fHYMHNAXiAxq1K9Xh/8NGkb69Oni9Nn5BwTg5HyNoYP/R7eunWLdPir74kXp3q3zt4KhI1mzZmHGzLlUqlie1q2ax7ivsbEx+QvkU+l94+R8jSxZMmNmlpyrTs7KYIeT0zVyZM+GtbUVAHfu3mPlqrVqeW7VshnVa9VnyrSZ7Nu9HYDDR45x5OhxFsybRa2a1ZXbNmpYn6o16jJ77gIWLZijlr/AwEC69+zHrdu3WbFsEWUdv08Cd/LUGbJns2XFMtUWNcOGDor1M1u6fBXv3nmwZNE8ZaugNq1a0KvPAB48fBTr/jt27uH+g4eMHDGETh3aKfefNGU6q1avi3V/IYSqsPBw+m1fRVh4uMryWY07YGqULJq9/gw6Ojq0dihHSdtcdNu4hJuvXVTWP/fyoOq8cYys0ZjeFWqiJz2VhRBCCPEL3LsbOXrEhw/eDB0+SuM2P9ZlB/2vH+nTpWPDpi2MnzgFU1MTSpcqyeCB/UmXNq3GNC5cvMyevfvZtWOz2rBPo0YOJTQ0mAOHjxEWFkalShUYM2p4rHnX19dn9cqlLFm2kgMHD3NpymWSGRtTtEhh+vbuTsGCBWJNI6H09fVZvnQh23fsYtfuvSxduoLgkBBSprQmV86cjI3yXiNTpoysXb2cmbPmsWzFaoiIIH+BfKxdvZxt23ZKsEOIP4xWgh1XL52R8enFP6OEQ3H27T/Io0dPsLPLw1WnaxQpXJjSpUsyc9Y8wsLC0NPTw8n5OsWKFlH7bRQpUkglKGhkZEjhQgXUhhRSBDrCw8P5FBhIaEgo9sUjxw2/e+8+devUSrRz1NPTo2WLZirL7O2LceLkad64u5PKyoyAgE+cv3CRtm1aEhERgY/P9/HOy5QpDcCly1fiFOyoWqWyyhieNilTYpstK25ur5TLjh47QXBICA3r11M5FkCF8mWZO38RT58+I0+e3AA0b9aEs+cuMHvOfA4ePsqXL1+ZO2vaT00anszISNnDolGj+qSMoQeNJv379uLipcuMHD2WlNYpsbAwZ9qUiXHat4R9cRYvXYGvrx+WlilwcnKmhENxzJKbcfbceejVma9fg7l95y7NmjZS7nfg4GH09fWpXauG2udWulRJtu/Yhb+/P+bm5uw/cBhra2tKlnBQ2dbQMPI7eunyVbV8+fj40qptR169es2GtasoXLigynozMzM8PN9z/cZNihUtEp+Pi5Mnz5ApY0ZloAMiX0h26dSBo8dOxL7/qdMYGxvTuqVqMKlr5w4S7BAiAZZdOMat164qy5oWLU2l3IlXUf3Vstmk4Ujf0Uw7toc5pw6oDDEZGh7GuEPbOPX4LotbdiODZfyeAUIIIYQQ8TVj+mSV0RXiQkdHh5YtmtKyRdM47+NYppSyp8OPjI2NGTqwD0MH9sHKRnOwJDpGRkb079uL/n17xbptfM81tu11dXVp3qwJzZs1iTWtYkWLsHWzeh2xWNEiasdIyDURQvw6Wgl2SKBD/EsUw1JdcXLCzi4Pzs7Xad26BSUd7Pn06RP37z/AysoKd/e3tG3dUm3/DOnTqy2zMDfHz++jyrI7d+4yc/Z8rt+4qTb/hb+/+twZ2pTKxgYjI0O1PAJ89PtIKisz3F6/ITw8nLXrNrJ23UaN6USdSyImGdKnU1tmYW6B+9u3yr9fuES2sm3UVP0zVfjww/GmTBpPhco1uH//AePHjsbW9ueGHzE0NGTE8MFMmjKdEqXKkzdvHkqXKkGtmtXj1KtNX1+fObOmU7V6HT5+9Gf1yqVYWVnG6dgODvYsWrIcJ+drlCtbhrv37tOmdUvMzMxYsWoN3j6+uLq6ERwcrDKE1QsXV0JDQ2Oci8Tbxxdzc3NcXFzw9vammEOZaLcNCQlRCRgNGz6az1++sG/3NvJqGG+1R/cuODlfo2nzNqRJnRoHh+JUqlie6tWqoK8f8yPo9Zs3lCzpoLY8W/a4zQvw+o076dOlVeu9ZGNjg/m377MQIm7cvN8z+chOlWXWpmZMqtcqiXKUeAz09BlVswkVc+Wn26aluPt5q6y/+OIRZWeOZHaTDtQvpH6PEkIIIYQQQgiRNLQS7IgqKCgIN7dXBHwKVJ/d5xt7+2LaPqz4A1QcvTfO256Z3JiIsNDYN4xCR0+fCiN2xr7hT1LM23H1qjOVKlbAw9OTEg7FyZEjO9bW1ly56qycM+PH+TqAOA194e7+lpZtOmJpmYKBA/qSJUsmkiUzJjw8jPYduxHxwxAi2qaYoEsTxeTxijw0b9aYmjWqa9zWxiZlHI+nOWAatUVteHjkvxfOnx3ti+ofhwy7cfMm/v7+ADzWUtfTtm1aUaVyJU6fOYeT8zW2bd/J8hWr6denJ3379Ix1/3PnLhAWFhaZp8dPKF/OMU7HjTpvh6mpKSEhoZRwsMfExAQ9PT1u3LyN68tXavN1RISHY2hoyMrli6NNO03qVACER0SQLl1apk6eEO22Pwa3a9Soxq7de1mwaCkL5s1SC2DYZs3CiaMHuHjxMpcuX+XyVSf27T9Inty52LZlA8mTm8bp/IUQSSciIoL//Z+9+46K4mrDAP4ssHSkgzQVVKSIKNLEgr333ktiz2eMMRpjTGI0xthr1Kix9957bxQVUWwoAgpWpPe2+/1BXB0pAi4s4PM7x3PgnTv3vsuI7uw79949G5CSkSGI/9l1AAy1dRSUVcnzqm6HK5Nm4fvd67E/0E9wLC41GV9tWo4zD+9gdteB0FHPe6NPIiIiIiIiKj1yK3aEhT/FjD9m4+pVH0g+8UFsSHCQvIalckTL2KrQbUUQIe9SWcHnFGWMz+Hu7opz5y/i2jVfaGpqyDYl93B3hZ//dRgaGuS5X0dhnTp9FqmpqVi3ZqWgYBIaFp6rbWE2AM/jpGLl9SErSwuIRCJIJFI0atjgs/v7FOtqVQHkFFDcXOt/sn1UVBSm/PQr7O1qwdXVBZu3bEcz78Zo0aLZZ+diZlYZA/r3wYD+fZCeno4hw0Zi6fKV+PqrIdDSyv/D++BHjzFn3kJ4ezeGmqoaFi1ZhkaNvAo1K+T9vh3Xoa2tDWvrarIN4Rwd7XHz1m2EhT0T7NcBANWqVcWly1dR3cYaZmaVCxyjWtUquObjB3e3+lBVVS2w7TvdunaCa30XTJn6C7797gcsXTw/V8FDTU0NLVo0k/3st2zdgV+nz8SBg4cL3K/EytISYaHhueJPQkJzN87zfAv4X7+J9PR0weyOqKgoWRGMiD5t981rOP/Re7fmtZzQ08VLQRmVHl0NLawd9A1aO9TFpL0bkZSeJji+zf8SfJ48xD8Dx8C1ag0FZUlEREREREQAIJfdFZ8/f4Gevfvj0qUrcHZ2gpVlzjI9HTu0g1NtRyj/95R4yxbN0a1rZ3kMSaRQnh5uSExMxMZNW+Ba30X24a6nhztu3AyAj49/nvt1FNa73xmJVFg4XLN2Xa62mpqaAICE+MJ/eKullXNOfBHO+Zieni4aN/LCwUNHEPzoca7j6ekZSEiU33Jb7dq1hqpYjMVLliPjo6eLAeBt9PtlRqRSKSb9+DOSU1KweOFcTJ0yGXZ2tpgy9VdEvX0rOE9LSxPx8fEfd5en1NRUpKamCmJqamqoXt0GEomkwOXF0tMz8N33k6GtpYW5f/2B2bN+h4GBAb77fnKuPvPj6e6Gx49DcPLkGdnG7UDO3ztfvxu4e/9BrtlEXTp1BADMX7hEMFPmnQ9/bl06d0RaWhqWLV+Z5/gftv1Qr57dMHvWDJw8dQbjJ0xCVtb7WVkf7xMCvJ+BExcXl88rzdGiRVM8i4jAyVNnZDGpVIo1/64v8DzZ+c2bITU1FVu27RDEV68t3PlEBLxNSsDUg8KlCjVVVbGw17DiFdvLIZFIhD6ujXDph1lwq5a7oBEW/Qbtls3E/NMHcm3eTkRERERERKVHLjM7Vqxcjbi4eMya+Rv69umFST/+jIjI51i8cC4AICTkCX74cSpCQ0Oxd/d2eQxJpFDvPlAOeRKKbt3eF/A8PdyQnJyC5OQUDB0ysNj9N2nSCGpqavhh0lQMHtQfGhrqOHf+Yp4fHOvr66FqFSucOHX6v6W0DGBoaACvBp759l/XuQ42bd6GGX/MRovmTaGiooIGDTyKvOH2HzN+Q6++A9GtR1/07NEVtWxtkZqWirCwpzhx8hQWzv8L3k0Kt0zTp1iYm+OXaT/h1+kz0b5Td3Tu2B6mpiZ4/foNAm8H4eHDYFy7cg4AsH7DZly6fBXTf52KmjVzPphavHAeunTrjclTpmH92lWyfp2d68DP/wZW/bMWFhbm0NTQyHf2R1hYOPoNHIY2rVvCtmYN6Oho4979B9i1ey883N0KnDkxZ94CBAc/wpp//oaxUc7yXvPm/Ikhw0Zg1ux5+GPGr5/8Gbzbt+NJaCi+HTdGFvf0cMfqNTmFsA+XsAKAevWcMWb0CKxctQZPQkPRulVLGOjr4eXLV/C/fhMZGRnYu3sbgJwC9dlzF/D3ytUIvB2Exo28oK2tjecvXuDy5auoVq0qli6en2duvXt1hxRSTP35N0yY+CMWL5wLZWVlDBk2Arp6unBzrY/KlU0RHR2D7Tt2QU1NDe3ati7w9Y4a8TUOHz6G8RMmYdDAfrCyssT5C5fwNuptged9mNP2Hbvw5+x5CH0SBnt7O1y/cRM3bgTAQL9we6UQfemmHdyGmOQkQWxqu56oYmCsoIwUp5qhCY5+Mw3zTx/E/NMHIPmggJwtkeDP43tx7mEQ/hkwBlYGhVvGkYiIiIiIiORHLsWOK1d9YG1dDX379MrzeI0a1bF29Qo0b9EOy/5eialTJsljWKrARCpi2bSjd3tEiFDwE6QiFXGBx+Xp3b4dL1+9gqf7+yfsa9SoDmNjI0RFvc1zv46i9L929QrMX7AYS5evhIa6Opo1bYIF8/6Cm2fu4sGC+X9h1p9zMXf+IqSlpcHD3a3AYkenju0RdPcejh47gRMnT0MikWDblvVFLnZYWlrg0IHdWLFiNc6dv4hdu/ZCS1sLlpYWGDigH2o7Ohb5tRdkQP8+sLGphjVrN2D9xs1ITUmFoZEh7O1qYfKk7wHk7M0xd/4ieHs3xuBB7zfOta1ZAz9N+QHTf5+FjZu2YsjgnGMzfpuGX6bPxN8r/0FycgosLMzzLXaYmZmhR7cuuObrhxMnTyM7OxvmZmYYO2YkRnw9LN+8L1++io2btmJA/z5o0bypLN6oYQMM/2oo1vy7vlBLbL3btyMjMxOeH/z9cnPNmUUkkUhyFTsAYNLE71Db0QGbt2zDqn/WIjMzEybGxqhTp7Zgtp1IJMKiBXPg6eGGXbv3YenyFZBKARMTY7jWd0Gf3j0KzK9Prx6QSiT4+ZffZX0N6N8Hh48ex5atO5CYkAADAwPUq+eMb8aMRPXqBW80rq+vhx3bNmLmrL+wfccuqKiI0aRxQ8z96w94NPAu8FwgZ0P5TRvW4s+/5uHIsRM4dOQoXF3rY8vmdRg8dPgnzyf60p19eAe7bl4VxFysbDCqcRsFZaR4KsrKmNK2O5rVqo1RW1fiWYyw+Oob9giN5k/Fwp7D0MOl5Jd4JCIixfjl0DZFp0BERER5EGVnpRd1a4Rc7BzroXkzb6xYvhgA8NPPv2L3nv24d+cm1NTer/s+fORYhISE4sK5E587JClATNRLAICBsdkXMS7lj9ek7OE1KXt4TcoeXpPCS0pPQ8O5PyEi9v2H+SpKyjg34XfUtqgqt3HK8zVJSEvFj/s2YueNq3ke712/IeZ2H4xKGpqlnNnnKc/XhEqWIv9u8O9lxVQWr2t5KGLM7Nxf0SkUqCxe17Kk34Ch8PO/DiBnVv72rRvk1m/k8+e4fOF0odo3btoKlhYWucY/eeoMli5bgbDwp0hLS8OqFUvRulULhV/XSZOnYu/+gwh9fE8h4xfGnr37MXnKNGzbsl6w7HRZpujrSiXjS7+ucpnZoa2lJVgLXkdHB0DOJrCW/+3fAQDq6up4ExUljyGJiIiIqITMPrFXUOgAgHHN2he70HFsUmNIszJzxQuavSlSEaP9vMvFGq80VFLXwMr+o9HSzhkT92xAQlqK4Pium1fhGxaMVQPGwNPaVkFZEhERUVmip6cLExNj2ddlSVj4U3z73UTUdXbGb7/8BDU1NTjVdsSp02dx88Z1jBo+RNEpljuRkc+xZ98BtG7ZHA7/7V1JRCVLLsWOypVN8erVa9n3NapXBwD4+PqjV89uAIDMzEzcCrwtW6ueiIiIiMqegGeh+OfSSUGsunFl/NCqa7H7lGZlQpKdu9ghO55HTCmPWFnUw6UB3KvVxOhtq+ATGiw49izmLTou/wMTW3XBpFZdoaKsrKAsiYiIqCxY+fcSRacAADhz8ihEHz1r4uvrh8zMLPwybQpqOzrI4gsXLcXe/QdZ7PiEbl07o1PH9hCL3y+xHvn8OZYuWwFLC3MWO4hKiVzuI91c6+Nh8CMkJCYCAFo0bwpVVVX88eccbNm6A2fPnseYb8bj9es38PLykMeQRERERCRnmdlZGL9zrWDzbQBY1OsraKiq5nMWWRkY4dDYqfi5XU8oKwnfXkukUsw7dQDtl/+B8Og3CsqQiIiI6D01NVWofvTeLioqZ1avnm7ZmnFSViQmJhV4XFlZGWpqalBSKi+P7BBVTHKZ2dGuXRsEBAYiICAQTb0bw9DQAD9N+QG/z/gT02fMAgBIpVJUrmyKid+Pl8eQRERERCRny88fw72XEYLYII+maFSDT6J9irKSEia26oKmtrUxcssKhH1U2LjxNARN5v+Mud2HoI9rQ4g+fpySKqSsrGzcf/gIAYH3cP/hY7x58xYSiQQGBnqoU9sendq3hLGRQa7z+g0dV2C/M6Z9j5o1rPM85uMfgJOnL+JZxAsAQBUrc7Rt1RSe7vU+/wUREVGpkkql2LR5G3bv2YdnEZGQSLJhZGgEZ2cn/PzTZNmSWO+8jY7GnLkLcf78RSQlJ8POzhaTf5gArwaegnYf7tkRGfkcTZq1lh1797WFhTkA4PnznP9PXL1aytp8O24svvv2myLnl5fgR4+xctUa+PlfR0xMDPT09ODo4IAxo4fDzbV+gefGxMRixarVOHPmPF69egUtbS24u7liwnfjYFuzhqydRCLByn/W4sqVawgNC0NcXBwMDAzQ0MsTE74bBwtzc0G/NjUd0aNbF/Tq2R1Llv2NoKB7UFJWRuBNn3xz+XjPjsVL/8bSZSsAAJOnTMPkKdNkP9fC7q1CREUnl2KHu1t9HNy3SxAbNKAfnOs44cTJ04iPi4dNdWv06tENlSpVkseQJSIsPAJB94MRGvoUT8Ke4W10DABg6bzpMDY2LFafQfce4vCxswgNf4aszCyYm5mieVMvtGia+yb34mVfrPp36yf79G7sidFfD5B9HxUVjW8nTS/wnJWLZ0FPr+z+7ImIiEixnkS9wtxTBwQxEx1d/N6pr2ISKqfqV62Oiz/Mwk/7N2Or/yXBsaT0NIzd/g9OPwjEgp7DoKeppaAsqbQ8CH6M2fNzPugwNNCHU207AMCT0Kc4fe4yrvhcx5SJY2GbR+FCTU0VHq518+y3UiXtPOM79xzGgSOnIFZRgZNjzlhB9x5iyYp1iIhsi17dO8jhVRERUWlZsXI1FixaiqbeTdC7dw+oisV48eIlLl6+gtdv3giKCakpqejddxBq2dbEt+PGIDEpGRs2bMbXI8bi7OmjMDfLe7NiAwN9LJz/F06cPINTp89g2tQfYWCgD01NTQDAv+s24vqNm5jx6xRoV9IDANSqZVvk/PJy6fIVjBrzLZSVldC7Zw/UqFEdcfHxuHnzFvz8rhdY7HjzJgo9+wxAXFw8evboipo1qiMmJhbbd+5G9559sWv7ZtnSUZmZmfhn9b9o07olmjVtAh0dbTwMfoTde/bj2jU/HDuyD3p6eoL+g+7ew7ETp9Cjexd07NAOMbFxBb6Wj7Vp3RJZWVlYsXI1+vXpBTe3nNfy7udKRCVDLsWO/NRxqo06TrVLcgi52nfwOG7cCpJbf2fOX8G6TbsgEongaG8LDXU1BN0Lxr8bd+JRSBjGjhgkaG9qaowmDd3z7e/yteuQSqWwr1Ujz+O6lXTg7JT3k5eqquI840REREQSiQTf7foX6R9tIj6n+2B+IF8M2mrqWNZ3BFraO2PCrnWIS00WHN8f6Af/8BCs6j8KDTlrpkITiUTwcK2L9m2bCwoaGRmZWLdpJy5e8cOylRuwaM6vUFER7umio62NMR/dLxTkYXAIDhw5BS1NDfw+7XtYmFcGADx/8Qq//bEQ+w6dgHMdhzwLK0REVDadOHkaNarbYN3alYL49xO+zdU2JjYWgwb1x/hxY2UxTw839OozENu278IP+ay0oqmpia5dOiH86TOcOn0GrVu1gKWlhez46dNncf3GTbRv2xIGxsKCSVHy+1hqaiomTpoKsYoKDh3cjWpVqwqOSySSAs+fPmMW4uLicWDfTthYV5PFBw7oi3YdumHW7LnYunk9AEBVVRV+1y5AQ0ND0Eerli0weOhw7Nq9DyNHfCU49uhxCNauXoHmzbw/+VryYm9XC/Hx8VixcjXq1XNG1y6ditUPERVNiRY7ypuaNaxhZWkOG+sqsLGugqm/zUV8QmKx+nr95i02bNkDJSUl/Dz5f7ICRUxsPH7/cxEuX/WHs5M9Gnq6ys6xs60OO9vqefYX/DgUl676F/iEl7mZaZFuiIiIiIgAYIv/JVx98lAQa1/bBZ3ruCkoo4qhi7M7XKvWwNhtq3A55IHg2PO4aHReORsTWnTCj226QazMt+UVUW2HWqjtUCtXXFVVjK8G98b1m3fwNjoGj0JC4WBX87PGOnz8LACga6c2skIHAFiYV0aXjq2xbddBHD52BhO/HfFZ4xARUfHt2bsfkf8tC2VpYY6ePboV2L5SJR0E3Y2An991eHgU/L5MSUkJI4cPE8Tqu9SDlpYmwsLCPytveeT3sctXriE6OhrfjBmZq9ABoMC9LxITk3D6zDm0a9saerq6iImJFRxv3Lgh9u0/iLS0NKirq0MkEskKHRKJBElJScjKyoaDvR10dHQQePtOrjFq1bItdqGDiBSHd1Uf6Nyhldz6On7qArKzs9G6RWPBTAwDfV3069UFS1asw+FjZwXFjoJcuuIHAHB3rQt1dTW55UlERERftlcJcfjt8HZBTFtNHXO7D5HLvhLZGWmQSgt+Mq8is9AzwL7RU/D3hWOYdXwPMrOzZcekUikWnjmE88FBWD1wLKobVy6gJ6poVFVVYVbZGE/CniE2Lv6z+srIyETQvWAAyHNvjgYeLti26yDu3H2AzMxMiMWc9U1EpAh79x2En/91AICHu9snix2TJk7AqLHj0G/gUBgZGcLNtT68GniiU6f2qKSjI2hrYmyca+YCAOjp6SE2NjZXXB6Kkt/H3hVgHB0dijxuaFgYsrOzceTocRw5ejzfdjGxsbLlu06dPovVa9bh7t17yMgUzmaOj8/9//CHs0WIqPxgsaOEBATmLIfl6e6S65irixPEYjGePovE2+gYGBnm3pTwQxkZmfC7fgsAClzmioiIiKiopuzfhPjUFEFsesc+MNcr+P3Jp2RnZuDZtX14fHodpJLsT59QgSkrKeHb5h3RpKYjRm5ZgZCoV4LjtyLC0HTBNPzZdSAGenhz8/IvhEQiQdTbnD0C9XRz762XnpGBA4dP4m10LMRiFVhamKF+Pac827589RqZmZnQ0dHO897CyNAA2tpaSEpKxstXUahiZZ6rDRERlT1169bBhbMncOWqD3x8/eDvfwPHT5zC4iXLsWPbRlSvbiNrq/zRcogfkkoVn588SSU5L6hN65YY0D///eUMDXL+Tzx95hxGj/0WTk6OmDp1MizMzKCmrg4AGD/hB0gkuX9AmnkUjoio7GOxowSkpKTKblysq1rlOq6iogJLi8oIC4/A02fPP1nsuHnrDpJTUmFkaABHe9t828UnJGLP/mOIjYuHuroaqlaxgKtLHf4DTURERHk6dvcmDt2+Loh5WNtiaIPmxe5TkpWJZ36H8Pjkv0iLe/25KVYoda2scf77mfjl0HZs8DknOJackY7xu/7F6Qe3sbj3VzDQKvhpSCr/Ll/1R0JiEirpaOe5j0ZiYhJ27j0iiG3cuhd9e3ZE+zbC39Go6Jwndg309fIdz1BfD0lJyXgbHVNgsePXmQvyjPfo1AxmpkaIiXqZ77klJeu//YQUMTaVnLJ4XXvaOSk6hU8qSz+vvJTF61qWZGamC74u7M+pvrM96jvbA6OG4vJVX0yYNA1Lli7D9GmTZX1JsrPz7E+SnZ1rrLxiqck5y7jHxbyBptr7JaQy0tMA5Fzb/PL9VH55MTbUBQDcvHEd7i4F7/ebnp4K4P3fq0raqlBSUkJiQjwcbKvle15yQgySAezYsRNqqqpYuWQO1P8rcgA5+4bExyfkeS3S01OL9Pc4OTFndkhCXLTsvMT4GNmxsvg7wd/Xiqk8X9eP9wUqDhY7SkBUdM4/ZlqaGvkuOWWor4ew8Ai8/a9tQS5d9QcANPZyK/BJvxcvX2PvQeH0PU0NDXw9pA+8POsXNv0yd4NTnn9JKypek7KH16Ts4TUpe3hNhBLTUjFx1zpBTKysjOktOyEuuvhFioSI+wja+efnpgcppBX2Wk1r2hYe5laYemw3Yj+aVXMk6Aauhz3CvE594FXt8/ZwKA5F/p7I4+amvIiKisaWHfsBAL17dMy1rFRjLzd4edZHFSsLaGlq4vWbtzh74SrOnL+Czdv3Q1VVFS2bNZK1T0/L+fBMXU013zHf3ZekpqXJ++UQEVEJiY2Ng/5HhWz7WjnvD+LjE0olBw1Nddl4hoaGcsvP08MVBvp62LF7Pzp1aANLC2EhXiKR5Ltvh56uLhp5eeDyVV/4+F5HA8/c+4VEx8TC0EAfwH/7f4hEuWZwrF2/5ZMboX+Odw8fl9a1IiIWO0rEu5sNNbX899ZQk91spOfbBgDi4hJw527OhqGN81nCSkWsgpbNGqGBuwvMzU2hrqaGF69e4+iJc7jmexPL/9kITU111K3jWJyXQ0RERBXQvAsn8DpJeOM11qs5ahqZfla/lawcoGtdF/FhgZ/VT0XXytYRdcwsMfnILlwNfyw49jopAUO2r8VwjyaY4N0Gqty8vEJJSU3F/KWrkZScAg+3emjRtGGuNmNHDhZ8X8XKHMMG9YKlhRnWbdqJnXsOw7uRR4nsvTHjl4l5xt8VwBRRlFLk2FRyyuJ1XeRzXtEpfNLMzv0VnUKByuJ1LUvEYjXB15/6ObXq0BPOderA2dkJlSubIi42Dnv3HQAA9OnTW3a+WKwGJWXlPPtTUlbONVZeMY3/ZpXqGZgI4g0aeGHXnoOYu/BvtGvXFmKxGLa2NVHLtmah88vP/Hl/YfTYbzFg6Gj07tUDNarbIDExCTduBsC5jhO+GTsKAKCmllM0+LC/OX/9iT79BmP8Dz+jQ/u2cK7jBFVVVbx4+RLXfHyhqaGBbVs2AAC6du2Cs+cvYez4H9Gje1dIpVJcunwVIU+ewEBfP89roaamUaS/x1o6OTNVKukZys6rr6MPbW1t7D1wFPpGxtDT1YWmhgZatGhW6H5LEn9fK6Yv/bryzqmMu+JzHRKJBLY1rGFW2STPNvp6uvh6SB9BzKZaFYwbPRRGBvo4dOwMtuw4UOhiR1m7wfnSf0nLIl6TsofXpOzhNSl7eE3e8w17hG23fASxWqYW+KlTf6iqfP7bQ6eu3+HKoqEAAC1jK6REvyjyvh0iiCr8tTIwNsPB/03DqssnMePILmRkZ8mOSSHFGr+L8IsMx+qBY2FrWjp7LPD3pGRlZGRi/uLVeBbxArUdbPHNyEFFOr9FUy/s2X8UCYlJePwkHA52OU/PvnuQKi09I99z0/57yErjg+U7iIiobBv+9VBcunQVW7buQEJCPPT09OBgb4dfpv2Exo28SiWHTh3bI/BWAE6cPocLl64iOzsb344bi1q2NT87v6bejbFv9zasWLUGhw4fQ0JCPPT19VHb0QGengXvWWtqaoJDB3dj7dr1OHX6LE6dPgsVFWUYGxujXl1ndO3cUda2Q/u2SE5OwboNm/DX3AXQ1tJCw4YNsHPbJvTuV7T/i4tCXV0dy5cuwIJFS/Hn7HlIT0+HhYV5mSl2EFVELHaUgHc3G+np+c/aSJfdbOQ/+wMALl/LWcKqSSOPYuXSuUMrHDlxDs9fvELU2xgYG33eZqNERERUvqVnZWL8zn8FMZFIhCV9vi5UoUMqkeBV0AUkvgqDbZuv82yjb+2Eqg17QK+KIyzdO+DElGaQ/rc8kqAv5CwlIELuZTpFKvJ/Yr0sUlJSwljvdmhSwwEjtqxE8OvnguN3nj9Fs4W/4I8u/TG0QXNuXl6OZWVlY9HytXgQHIKa1ath4viRRZ6ZoaSkBFNTYyQkJiE2Ll4WNzbMWaYjJjYu33Oj/zv2qf0CiYio7BgzagTGjBrxyXbbt27I99jlC6cLFfvu22/w3bff5IorKSlh3NgRGDd2RK6HIQqbX0EcHOyxfOnCAtvMm/sn5s3NvUxqJR0dfD/hW3w/4dtPjtO7V3f07tU9Vzyvn0Xo43uf7O9jPXt0Q88e3XLFmzRuhCaNG+VxBhGVBLkWO56/eAF//xt4/foNMjLyfqpIJBJh3P/GyHPYMsf4vxuI5JRUpKWl57lvR2FuNsKfRuJZxAuIxWI0cHcpVi5aWpqoVEkbcXEJiI2LZ7GDiIjoC7fozGE8fvNCEPvaqwXcP7E/hFQqxeu7lxF8bBUSngdDpKQMC5fW0DK2yrN9nT5TZV+3n3c5zzafmkUQH/EQYi1daBpU/FkGtS2q4tyEGZh+ZAfWXBHedKdmZmDing04/eA2lvYZDiPtSgrKkopLIpHg79UbEXjnPqpaWWDy92OgXsCStwVJTs7Z5+XDJXPNKptCLBYjMTEJb6Njct1jvI2OQVJSMlRVxTCrbFz8F0JEREREVIbJpdiRnZ2Nab/+jj17D0AqlUIqlebb9ksodmhqasDYyABRb2MQ9jQC9rVqCI5nZWUh8vkrAEDVKhb59vNuVoerixM0NTWKlYtEIkFqas4mhGoFbFhIREREFd+DV5FYdPaQIGauZ4BpHXrne45UKkXUAx8EH1uFuGfvn3KTSrLx6OQa1Bs4Q+55SiUShJ7fggdH/oZ+tTrwGrcKIiVluY9T1mioqmJO98FoYVcH43asQdRHe6qcuHcLjedNxfJ+I9HCro6CsqSikkqlWLN+O3z9b8G8sgl+mvQNtLU0i9VXROQLvHj5GgBgU+19oVFVVQwnx1oICLwLX/9b6NiuheA8H78AAECd2vYlss8HEREREVFZIJdix7K/V2HX7n3Q0tJCt66dUN3GGlpaWvLoutxyqeuEk2cuwtc/IFex40ZAEDIzM1G1imW+Mzuys7NxxecGAKBJw+ItYQUAgXfuIz09A2pqqrAw+7wNR4mIiKj8kkgk+G7Xv8jMFu6dMb/HEFRSz/uhireP/PHw6CrEht3O83jk9eOo2eoraJtWk1ueafFRuLX5V7x9lPPQR8yTAISc2Yiarb+S2xhlXWuHurg86U/8b/tqnHl4R3DsdWI8eq2eh9FN2uDXDr2hLubDLGXdlh37ceGyL0yMDfHzj+OgW0mnwPYXr/ihiqU5rKsJZ02FhUdg+aoNAAB317ow0NcTHO/UrgUCAu/iwJFTqOfsCAvzygCA5y9e4eCRUzlt2reUz4siIiIiIiqD5FLsOHDgMLQ0NXHowC5Uq1pVHl2WCzGxcZg1ZxkA4OcfxwluONq19saZ81dw9sI1eLq7yAoeMbHx2L77IACgU/sWufp853bQAyQkJEJPrxLq1LYrMI+TZy6iTm37XBuY370fjDXrtwMAWjZrBBU5bDhKRERE5dO6a2dxPTxEEOta1wNtHXMvlRn95BaCj65EdMjNfPsTa+qiRovBUNczybdNcUiyMgUzSAAg+NgqGNVyh37V2nIdqywz0dHFzhE/YO2VM/j18Hakf7TnyapLJ3Hp8X2sHjgGDmZ5LyVGincj4A6OnTwPADAyMsDOPYfzbOfqUgdu9Z1zzrl5G6vWboF5ZRNYWFSGirIKXr2JQvjTSEilUlhXs8KIoX1z9WFXqwa6dGyFg0dO46ff5sLJsRYAIOheMDIzM9G9c1vY1rAuoVdKRERERKR4cvn0+9Xr12jo1aDcFzoCAu9i/6GTsu+T/lsPd8GytRD/Vyio5+yI7l3aAgCys7Lx4tUb2dcfMjUxxtCBPbFu0y78MWcZajvYQl1NDUH3g5GamobGXm5o6Omaby6XruY8zdiogRuUlJQKzPv8RR9s3LoXVpZmMDPN+cDhxas3iIjMWY/b2ckefXp0LPTPgYiIiCqWyNhozDi6SxDT09DCX90GCWKx4UF4eHQl3gb75duXWEMHNs0HwrpJX4g1tOWeq6ahOZx6/4Rbm6bJYlJJNm5tmoYmk7dBRa14y/+URyKRCCMat0LDGnYYuWUl7r+MEBy//zICLRb9ht879cWIRq24eXkZ9G5/DQC4/+Bxvu2MjQxlxY7GDd2hqqaK8KeRuP8wBGlpadBQV4d9rRrwdK+HZk0a5PsQU9+enVHVygInTl/E/Yc541lXtUS71s3g6V5Pjq+MiIiIiKjskUuxw9DQIM9NuMubxMQkhISG54o/fRYp+9rcrPBPL7Zs1gimJkY4dOwMQp48RVZ2FszNTNHcuyFaNmuY73nJySkIuBUEAGjS6NNLWLVp6Y3AO/fwLPIF7tx7iIyMDGhpacHZyR6Nvdzh5VmfN79ERERfKKlUikl7NyApPU0Qn9G5H0x0dAEAcc8eIPj4Kry5dyXfflTUtGDdtB+qNxsIsWbBy/B8LkvXdnhz/yqe3zguiyVHReDu3vmo2//XEh27LHIws8KZ76Zj5rHdWHnxhOBYelYmpuzfjDMPbmN5v5Gya0plg3djT3g39izSOe6udeHuWrfYYzbwqI8GHvWLfT4REZWcAf37oEXzpgCAypW51DgRkbzJpdjRplVLHDl2HOnp6VBTK79Fj6LejBgbG2L7hmUFtnFytIOTY8HLUH1MS0sTm9YuKnT7Zt4N0My7QZHGICIioi/Dgdv+OHk/UBBrUtMBA9ybAADCr+xB0K7Z+Z6vrKoO6yZ9Ub3FIKhq6ZVgpkJOvX5ETOhtpMa8kMUifA/CxN4L5vW+vH0H1MWqmNVlAJrXcsL/tq/G68R4wfEzD++g0byfsLzvSLR2qKuYJImIiKhAHTu0U3QKREQVWsHrIxXShO/GQV9PD+MnTEJMTKw8uiQiIiKizxSbnIQp+zYJYuoqYizs+ZVs1qepYyMoKYtznaskVoNN84Fo8dth2HceV6qFDiBnuSyXwTMBkfDt6p2ds5Aa+6pUcylLWtjVweVJf6JdHnutvE1KRN+1CzB570akZmQoIDsiIiIiIiLFkcvMjpmz/oK1tTVOnzmLaz5+cKrtAHMzM4jy2GtCJBJhzuyZ8hiWiIiIiArw6+HtiEpKEMR+bNMdNsbvl03Q0K+MKg27I/zSTgCAkrIYVRt2R41Ww6Cua1yq+X7MwKYubNt8jUcn1shimSkJuLX5VzT430qIlJQVmJ3iGGlXwpavvsNGn/P4+eBWpGYKCxtrr57B5ZAHWDNwDGpblO899YiIiIiIiApLLsWOvfsOyL5OTk6Gr9/1fNuy2EFERERU8i49voet/pcAAMbSDKhBAn3LWhjbtG2utjVbDUOk/xFY1G+Lmq2/hoZ+2VlDumab4Yh66IvY8CBZLDrkJp6c3YwarYYqLjEFE4lEGOrVHF7V7TByywrcef5UcDz49XO0XDwdv3TojTFN2kApj4eQiIiIiIiIKhK5FDu2bV4vj26IiIiISA5SMzIwYdc6GEgz0UUShUbSOISJNNCs1xyIlXO//VPXNUarGcehoq6lgGwLpqSsgnqD/8ClOf2RlZ4siz88ugJGtTygV8Vegdkpnq2pOU6Nn44/j+/BsgvHIJVKZccysrPwy6FtOPvwDv7uNxJmuvoKzJSIiIiIiKhkyaXY4eHhJo9uiIiIiEgOFh3ahEZvbsNbGgcxcj78rilNhUXSKwA2eZ5TFgsd72gZWaJ2rx8RuOVXWUwqyUbApp/RZNJWqKhpKDA7xVNVUcH0Tn3R3M4JY7b9g5fxwj30Ljy6i8bzp2JJ76/RwclVQVkSERHRsK9H4cbNAACAa30XrP/3HwVnRERUsXA+OxEREVEFkZ4QjYubfoPdpVVoKY2VFTreCT62SvDkf3li6dYe5i5tBLHkN09xb/8CBWVU9jSp6YjLP8xCxzwKGjHJSRi0fgkm7FqH5PQ0BWRHREREaWnpSE5OQXJyCtLS0hWdDhFRhSOXYsfI0f/D+g2b8eDBQ3l0R0RERERFkJ4Ui/sHl+LsjM5IuHEkV5HjHXV9U2Snp5RydvIhEolQp/dP0NCvLIjHht1BVlpyPmd9eQy0dLBx6LdY2mc4tFTVch3f6HsezRb+gsCIMAVkR0REREREVHLksozV2XMXcO78RQCArm4luLu5ooGnOzw9PWBbs4Y8hiAiIiKij2SkJCD03BaEXtxeYBHDtHYT1Go3CrpWdqWYnfyJNXVQb/AfuLZ0JCCVwLpJH9h3/hbKquqKTq1MEYlEGOjhjQY2tTBqy0oERIQKjodEvUKbpb/j53Y98b+m7WWbl6dmZOB6RBiS0tNgnpiAupbW0FBVVcRLICIiIiIiKjK5FDv2790BX19/XPPxxc2AWzh1+ixOnzkHANDX14enhxs8PdzRwNMdNjbW8hiSiIiI6IuVmZqI0AvbEXp+S4GzGh6o6KL3iL9Qzd69FLMrWYbV68Gh8zhoV7aBqWMjRadTplU3rozj3/6COSf3Y9HZw4IlzDKzszH9yE6cfXgHv3Xsi/2Bvtjqdwlxqe//PulpaGGARxOM9W7Hzc2JiIiIiKjMk0uxo45TbdRxqo2RI75CVlYW7gTdxbVrvvDx88etW7dx7PhJHD9xCgBgYmyMa1fOyWNYIiIioi9O+JU9eHjkb2SmJOTb5p5IE/uVTDBpwMQKVeh4p3qLwYpOodwQK6tgWvteaF7LCaO2rsLzuGjB8cshD9Bq8W95LnwWl5qMvy8cx64bV7Fn1GQ4WVQtnaSJiIiIiIiKQe4blKuoqMClXl3875vR2LppHS5dOIVhQwdBVVUVUqkUb6Ki5D0kERER0RdDkpWRb6EjGBqYrVQVc5WroYq9J3q4NCjl7Kis8qpuh8s/zEK3uh65jn1qy/qopAT0/GcuXsbHlkxyREREREREciCXmR0fSktLw/XrN+Hj5w8fHz/cf/AA2dkSSKVSWFpYoEGD3DdYRERERFQ4VRv2wJOzm5EW/0YWC1fWwi6pIe6JtACRCFqqaljYcxhEIpECM1UcqUQCkZLcn+kp9/Q0tbB20Ddo7VAXk/ZuRFJ6WqHPjUpKwMqLJzCjc78SzJCIiIiIiKj45FLs8Pe/gWu+fvDx8cPtO0HIysqCVCpFZVNTdOzQHp4e7vBq4AELC3N5DEdERERUoWVnZkAkEkFJRZzrmLJYDTVbf4Wg3X9B18oepzSssOJJBKD0vrAxtV1PWBkYlWbKZUJWRiru718EiESo0/snRadTJolEIvRxbQRnS2s0nj8V2RJJoc/d6n8RP7XtwU3LiYiIiIioTJJLsaPfwKEQiUQwNDRA61YtcjYjb+AB62pc15eIiIiosCTZmYjwO4zHJ/9F9RZDYN2kd57tqjToCg19U9xWqoQVaxcAH8zgcLGywcjGrUsr5TIjPuIhAjb9jKTX4QAAE3svVHbyVmxSZVhsSlKRCh055yTjdmQYPG1qlVBWRET0Kb8c2laodjM79y/hTIiIiMoeuc3vl0pzVvsViURQVlaCMpcOICIiIioUSXYWIvwO4/wfPXBnxyykxr5CyOl1yM7Ie5khJRUxtGq644e9GwRxFSVlLOnz9Rf3PiwtPgpXFn8lK3QAwO1tM5AWz73i8pOYllqs8xKKeR4REREREVFJk8vMjo3r18D3vz06Tpw8hSNHj0MkEsHc3Ey2hFUDTw+YmprIYzgiIiKiCkEqycbzgFN4dGINkt88FRxLi4/C06v7YNMs7ycz/zy+B5Gx0YLYt807wNG8SonlW1ap6xrDxrsfQs5skMUykuMQuHU6PEYv4/4dedBR1yjWeZWKeR4REREREVFJk0uxo1HDBmjUsAEAIDk5Gf7Xb8LH1w++vv7Yf+AQ9u0/CACoVq0qvBp4YMb0X+QxLBEREVG5JJVI8PL2OQQf/wdJr0Lzbffi1mlYN+2Xa6PxG09D8M/lU4JYDePK+KFVlxLJtzyo1X40ooL9EB/xQBaLeuiLsIs78i0YfcnqWlpDT0MLcanJhT5HX1MLzpbWJZgVERERERFR8cml2PEhLS0tNGvaBM2aNgEARL19i39W/4tt23chLCwc4eFPWewgIiKiL5JUKsXroIsIPv4PEp4/yredur4pbNsMh5VHp1yFjszsLHy3a51sCdF3FvX6CuriL3fjaCUVMVyGzMKluf0Fy389OLQUhrau0LWwVWB2ZY+GqioGeDTB3xeOF/qcAe7e3JyciIjoM2zfukHRKRARVWhyL3ZIpVLcuRMEH19/+Pj64WbALaSlpctuyPX19OQ9JBEREVGZJpVK8eb+VQQfWyWYefAxdV1j1Gz9Faw8u0I5n8LFsvPHcP9lhCA22LMpGtawl2vO5ZG2SVU4dv8Bd3b8IYtJsjMRsPFnNPlhM5RV1RWYXdkz1rsddt24iqikhE+2NdHRxRjvtqWQFRERERERUfHIpdjx4GEwfHz84OPrh+s3ApCUlCQrbujqVkKjhg3RwNMdnp7uqGVbUx5DEhEREZV5UqkUb4P9EHxsFWLDg/Jtp6pjgJoth6Fqw+4FfiAf8uYl5p06IIiZ6uji90595ZVyuVelQVe8eXANr26fk8WSXoXi/qGlcOo5WYGZlT1muvrYM2oyev4zt8CCh4mOLvaMnAQzXf1SzI6IiIiIiKho5FLs6Ni5B0QiEaRSKbS1tdGsaRN4erqjgYc77O3tci2/QERERPSleHh0JeKe3s3zmFhLFzVaDkW1Rr2golbwxs8SiQQTdq9DelamID6n+xDoamjJLd/yTiQSwbnvz4gLD0JafJQsHn5pJ0zsG8DUsbECsyt7nCyq4sLEP7Dy4gls9b+I2JT3e3joa2phgLs3xni3ZaGDiIiIiIjKPLkUO5o0bogGnh7w9HRHbUcHKCkpyaNbIiIionJNJBKhVvvR8Fv5P0FcrKEDm+aDYOPdFyrqhStUbPa7iKtPHgpiHWrXR6c6rnLLt6JQ1dJD3YEz4LtiLPDB3iaBW39H0yk7oVbJUIHZlT1muvqY0bkffmrbA5fv3kBiehosTC3gbGnNPTqIiMqpXw5tK1S7mZ37l3AmREREpUcuxY71//4jj26IiIiIKhxjO08Y2NRFTGggVNS1YNN0AGya9YdYQ6fQfbxKiMNvh3cIYjrqGpjTfTBn0ObDuJY7qjcfhCdnN8liGUmxCNz2O9xHLeHPLQ8aqqpwtbIGABgYmyk4GyIioorn7NnzeBOVM/PUxNgYLVo0U3BGREQVi9w3KAdyllmIjY0DAOjr63GmBxEREVVY8ZHBeHzyX9TuOQnqusa5jotEIth1/AZvHvqgerOBUNXSLfIYP+7bhIS0FEFsesc+MNczKHbeXwK7DmMRFeyHhMhgWezN/asIv7QT1t7c54SIiIhK19p1G+Hnfx0A4OHuxmIHEZGcybXYcf7CJaxbvwkBtwKRnp4OAFBVVYVrfRcMGzoIzZo2kedwRERERAqT+PIJgo/9g5e3zwIA1CoZwqnXj3m2NazhAsMaLsUa52jQDRy+c10Q87S2xRBP3hx/ipKKGC5DZuHS3AGQZKbL4vcPLoGJQ0NoGVspMDsiIiIiIiKSJ7lNufhrznyMGPUNrvn4Ii0tDaqqqlBVVUV6ejquXvPBiFHf4K+5C+Q1HBEREZFCJL0OR8DGqbjwVx9ZoQMAnl3bj5SYl3IdKyE1BZP3bhLEVJVVsKj3V5w5W0g6ptZw7D5R9r2Sshj2ncdB09BCgVkRERERERGRvMllZsfJU2ew5t8N0NHRwcgRX6Fn964wMclZxiEqKgp79h3AmrXrsfbfDXCpVxetW7WQx7BEREREpSY5KgKPTq5B5PXjgFSS67gkOxMhp9ejTp+pchvz96M78TIhVhCb2KoLapnyg/qiqOrVHW/uX0XK20i4DJ6FShY1FZ0SERF9Jm7ATURERB+TS7Fj89btEKuoYOumdXB0tBccMzY2xphRI9CkUSN079UXm7dsZ7GDiIiIyo2UmJd4fGINIvyPQCrJzrONSEkZVh6dUKPVMLmN6xsajPXXzglidpUtML55R7mN8aUQiUSoO2A6lFVUoayqruh0iIiIiIiIqATIpdhx7959uLu75Sp0fMjR0R6eHu64E3RXHkMSERERlajU2Nd4fOpfPPM9CGl2Vt6NREqwdGsP2zbD5br/Q1pmBsbvWiccSiTC4t5fQ1VFrluufTFUNSspOgUiIiIiIiIqQXK5W05LS4e+vt4n2+np6SEtLf2T7YiIiIgUJS0+CiFnNuDp1X2QZGXk3UgkgoVLa9i2HQlt02pyz2HhmcN4/OaFIDa8YUu4V+PyS0RERERERER5kUuxo3JlUwQG3oFEIsl3s8zs7Gzcvh2EyqYm8hiSiIiIKE/HJjWGNCszV1wKKQBABFGuYyIVMdrPu4zMlESc+6M7stNT8u3frG4L1Go3Cjpm1eWX9Afuv4zAknOHBTFzPQNMa9+rRMajnAJXfMQDmNZuouhUiIiIiIiIqJjyrkwUUTPvJnj+4gV++/0PpKfnnrmRnp6O336fhcjnz9G8eVN5DElERESUJ2lWJiTZuf9Is7Mgzc7K+9h/xRGxpg7M6+a9t5ipkzeaTN4G16/mllihI1siwXe71iEzW7g3yIIeQ6GjrlEiY37pXgVdxMW/+uLG+ilIfBmq6HSIiIiIiIiomOQys2PMmBE4evwEtu/YjZOnzqBN65awsrQEAERERuLkqTOIiYmFsbERRo8aLo8hiYiIiEpEzTbDEXn9mGwzchOHhqjVfjT0qjiU+Njrrp7Fjachgli3uh5o41ivxMf+0kiyMnF37zw8vbpXFgvY9DMafb8RymJVBWZGRERERERExSGXYoexkRG2blqHCRN/xP0HD7Ft+y6IRDlLREilOUtGONjbYfHCuTA2MpLHkEREREQlQsvIElaenZES/QK12o+GgXWdUhk3MvYtZh7bJYjpaWhhdrdBpTL+l0akrIK0uDeCWMLzR3h4ZDkcu32voKyIiEjefjm0TdEpEBERUSmRS7EDAGrUqI7DB/fA3/8G/K/fwKvXOTePlU1N4O7mCnd3V3kNRURERFSinHr+CCUVcamNJ5VK8cPejUhKTxPEZ3bpDxMd3VLL40siEong3P9XXPyrL9ITo2Xx0PNbYWLvBWM7TwVmR0REREREREUlt2LHO+7uLGwQERFR+VaahQ4A2B/oh1P3AwWxJjUd0N+tcanm8aVR0zFA3QHT4bdqnCB+a8tv8J6yA2ra+grKjIiIiCqi9u3boHbtnKVRq1SxUnA2REQVj9yLHURERERUeLHJSfhp/2ZBTF1FjIU9v5ItC0olx8TBC9befRF2cYcslp7wFre3z4Tb8AW8BkREVKZYPbpQqHYRtk1LNA8qnkED+ik6BSKiCk1J0QkQERERfcl+ObwdUUkJgtiUtt1hY2yqoIy+PPadv4WOWXVB7HXQRTy9tk9BGREREREREVFRsdhBREREpCAXHt3FNv9Lglgdi6oY691OQRl9mZTFanAZ8ieUVFQF8Xv7FiDxdZiCsiJFSEpKRtDde4h6+1bRqRARERERURGx2EFERESkACkZ6fh+93pBTEkkwuLeX0NFWVlBWX25KpnXgEOX8YKYJDMdARt/hiQrU0FZUUm4es0XU6f9hnv3Hgji23fsgnuDJujWoy+8GjXHX3MXKChDIiIiIiIqDhY7iIiIiBRg7sn9CI9+I4iN9W6HulbWCsqIqjXpA2N7L0EsITIYD4+uUFBGVBJ27tqDAwePCDaGDQ0Nw6/T/0BGRgaq21hDLBZj7b8bcPHSZQVmSkRERERERcENyj8QFh6BoPvBCA19iidhz/A2OgYAsHTedBgbGxarz6B7D3H42FmEhj9DVmYWzM1M0bypF1o0bZjnhpczZi/Bg+CQfPvr27MTunRsneex0PBn2H/oJIIfhyItLR0mxoZo7OWGDm1bQEWFT4gSERGVFXciw/H3xeOCWFUDY0xp211BGREAiEQi1BswHRf+6oOMpFhZ/MnZTTC2awDjWu4KzI7k5e69+3Cwt4OOjrYstnf/QUgkEsyY/gsG9O+DkJAn6Ni5BzZv2Q7vJo0VmC0REVUkEyb+iNt3ggAAznWcsGjBHAVnRERUsbDY8YF9B4/jxq0gufV35vwVrNu0CyKRCI72ttBQV0PQvWD8u3EnHoWEYeyIQfme6+5aF+pqqrniVpbmebYPCLyLhcvWIjs7G7VsbaCnq4sHwSHYsecw7t4Pxo/fj2XBg4iIqAzIys7G+F3/IlsiEcQX9hoGTVU1BWVF76hVMkTdAb/B/5/vBPHALb/Be8p2qGrpKSQvkp+3b6Ph6GAviPn4+EFLUxO9e+UUHGvUqI76ri549Dj/h5CIiIiK6tWr1wgPfwoAMDUxUXA2REQVT4kXOy5fvorgR49hbm6GNq1bQrkMr0Fds4Y1rCzNYWNdBTbWVTD1t7mIT0gsVl+v37zFhi17oKSkhJ8n/w/2tWoAAGJi4/H7n4tw+ao/nJ3s0dDTNc/zB/bpWujZJCkpqVi5ZjOys7Pxv9FDZH2mpKbiz7nLcff+Ixw5cRZd85kRQkREVJGIlJWB7I/3WBDlxHO+yn2OirgUMsux8tIJ3I4MF8T6uTVCs1pOpZYDFczUsTGqNe6N8Mu7ZDFJVgaS30RA1VpPcYmRXEilEmRnZ8u+T09Px/37D+Dp6Q6x+P2/BSbGxggICFRAhkREREREVBxyKXZs274La9dtwJzZM+HmWl8W//6HKTh0+KjsexeXutiy8V+oquaesVAWdO7QSm59HT91AdnZ2WjdorGs0AEABvq66NerC5asWIfDx87mW+woivOXfJCUnIJ6zo6C/jQ1NDBsUG9MmzEfx06eR+f2LaGkxG1aiIioYjNxaIiXgWcFsQb/WwklfUsAgIGxmSLSAgCER7/BXyf2CWJG2jqY2bm/gjKi/Dh0GY+3j28g6VUojO08UXfAdKjrGis6LZIDs8qV8TD4kez7K1d9kJmVBU8P4TJlycnJgqWuiIiIiIiobJNLsePU6TNIiE+AS726spiPrx8OHjoCfT09tG/fBv7XbyIgIBB79x1Av7695TFsmRYQmLMclqe7S65jri5OEIvFePosEm+jY2BkaPCZY939b6x6uY5Vt6kKE2NDvImKRvDjUEHhhYiIqKKJfnIrV6HD1MkbRrZuiIl6qaCsckilUkzYvQ6pmRmC+Oyug2CgpaOgrCg/yqrqcBkyC9GPbsDauy9EfGCkwmjUyAubNm/DjJmz4eXlgbnzFkEkEqFF86aCdg+DH8HMrLJikiQi+kxWjy4UsiUfuCAioopDLsWOJ6FhsLWtIVii6siR4xCJRFi+bCE8PdyRlJSMxk1bYt/+gxW+2JGSkoqotzmbm1tXtcp1XEVFBZYWlREWHoGnz57nWey4cNkXiUnJAABjIwPUc3aEpUXeT6I+fRaZM1a1Knker1bVCm+iovH02XMWO4iIqELLSIqFqpYeMpLjAAAiZRU4dPlOoTm9s+PGFVx8dE8Qa2lXB93reSooI/oUXQtb6FrYKjoNkrORw7/CoUNHsXHzVmzasg1SqRRdu3RCzZrv3yc/eBiM589foGWLZgrMlIiIiIiIikIuxY7o6BjUdxHOKvC/fhNGRoay6eDa2lpwre+CoLv38uqiQomKzil0aGlqQF09741GDfX1EBYegbf/tf3YvkMnBN9v330Ijb3c8PWQPoJlwFJSU5GckirrMy8G/8XzG+tjv85ckGe8R6dmMDM1KvUnY7OyctZdV/QTufQer0nZw2tS9vCaKIaauT3qjf0HkVd24oX/IZi5dkCGSIyYqJcKvSbRyUn4ef8WQUxTrIpfmndA7NtXpZ5PWcHfk7JHkdektJaYq1zZFEcO7cXO3XsRExOD2o6O6NG9i6DNw4fBaNWyOdq15Z53RERERETlhVyKHRoa6khJSZF9Hxsbh7DwcLRv10bQTkdHB/HxCfIYskxLT0sHAKip5V3oAAC1/4ogqf+1fce+Vg00a9IAtjWtoa+ni5jYeATeuYfd+47h0lV/ZGZl4dsxw2Tt09LeL4Whppb3XijvCi5pH41FRERUEamoa6Fay69g6tIOYo2ysTzUH2cOIS4tRRD73rstLHT1FZQRyYMkOxNKyqW3uT3JR2JiErS1tTF+3Nh823Tr2hndunYuxayIiIiIiOhzyaXYYWFujoCAQGRkZEBVVRUnT50GAHg18BC0i46OhoEBb+oL0qt7B8H3lU2N0bZVUzjY2WLq9Lnw8QtAhzbNUd2maonlMOOXiXnG3z3hV9obuypqXMofr0nZw2tS9vCalAEf/ewVdU1O3w/E4fuBgphLFRuMb9sDyl/4PhDl9fdEKpEg7NIOhF/ehUbfb4Sqlq6iU5Kb8npNiqJufU/UcaqN/Xt3KDoVIiIiIiKSI7kUOzp36oDZc+ajb/8hcHGpi9179kNVVRUtPljjViKR4O69+6hlW1MeQ5Zp72ZtpKfnP5Pi3ewPjXyWufpYFStz1K/nBP8bgbh1556s2KGu/n42R3p6BjQ1NXKd+25GR35LahEREVHJSExLxfd7NghiKkrKWNp7+Bdf6Civ0uKjELj1d0Q99AEA3Nk5C/WHzYFIJFJwZlRY2traqFat5B4cIiIqT345tK3QbWd25mbmRERUtsnlLntA/z5wcamL23eCsH7DZqSkpGDK5O9hbGQka3P1qg/i4uLh7u4mjyHLNOP/NhxPTknNd+mo6Ng4AMhzc/L8mFU2BgDExb1fCkxTQwNa/xU43vX5sZhijEVERESfb9bxPXgeFy2IjW/eAQ7mVgrKiD7XzQ1TZYUOAHgZeBaR/ocVmBEVVa1aNRH5/Lmi0yAiIiIiIjmTS7FDQ0MDu7ZvxrbN67FsyQKcPnkYgwcNELRREatg2tQf0f0LWPtWU1MDxkY5hYWwpxG5jmdlZSHyec5mpFWrWBS63+TknI3IP96bo2oVy5yxwp/leV74fzkUZSwiIqLyIO7ZA4Re3A5JdqaiU8nlxtMQrLlyWhCrYVwZE1t1yecMKg9qd58IkbJwcnTQ7rlIjsr9no/KpiGDBiAgIBBXr/kqOhUiIiIiIpIjua2fIBKJ4OHhhvbt2qBa1dzTwht4emDY0EGoUuXLeJLRpa4TAMDXPyDXsRsBQcjMzETVKpaFnm2RmZmJgNt3AQA21ap8NFbt/8a6leu8J6FP8SYqGjo62qhV06ZIr4GIiKgsk0qluLtvHu7tnY8Ls/vg9d1LkEqlik4LAJCRlYXxO//Nlc/i3l9DXayaz1lUHuha2cG+4zeCWHZGKgI2/Vwmi26UW926dTCgfx98PWIMfp0+E5cvX0VoaBiev3iR5x8iIiIiIiof5LJnx5cqJjYOs+YsAwD8/OM4GOjryY61a+2NM+ev4OyFa/B0d4F9rRr/nROP7bsPAgA6tW8h6O/u/WBkZGSibh0HKH2wjndcXALWbNiOmJg4GBjowa1+HcF5zZo0wIHDJ3Hr9j1c870JL8/6AICU1FSs37wLANC+TTNBn0REROXdy8AziA29DQBIfvMU/qsnwLH7RNg0Vfx60svOH8WDV5GC2BDPZvCqbqegjEiebJoNxJsHPnj7yF8Wi3t6D49OrIFdh7EKzIwKo0nT1hCJRJBKpdi2fRe2bd+Vb1uRSITHD++UYnZERFSR/b1sETIzcx6OEIvFCs6GiKjiKVaxY9/+g3nGu3cr38syBATexf5DJ2XfJyWnAAAWLFsLsUrOj6qesyO6d2kLAMjOysaLV29kX3/I1MQYQwf2xLpNu/DHnGWo7WALdTU1BN0PRmpqGhp7uaGhp6vgnGcRL7B5+z7o6VZCtaqW0NLURHRMLMKeRiA9PQOVdLTx/bgRUFUVPhGqqamBMSMGYeGyNVi2agNOn7sMXd1KeBAcgoSERDja26JjW2FhhYiIqDzLzkzH/YNLBTEVdS1Y1G+noIzee/T6BeadOiCIVa6kh+md+igmIZI7kZIS6g78HRf/6ovMlHhZ/PGp9TC2awDD6vUUmB19irubK7ifPBERKYKBgb6iUyAiqtCKVeyY9OPPEH1whyCVSiESicp9sSMxMQkhoeG54k+fvX8y09zMpND9tWzWCKYmRjh07AxCnjxFVnYWzM1M0dy7IVo2a5irvb1dDbRo2hChYc8QGh6B5OQUqIrFMKtsgnp1HNG2lTcqVdLJcyyXurUx45eJ2HfwBIIfh+JJ6FOYmBihXeum6Ni2BVRUlAudNxERUVkXdmE7UmOEy8vUbDMcajqKvYGUSCSYsHsdMrKzBPE53QdDV0NLQVlRSdDQM4Fzv2m48e+k90GpBLc2TYP3jzsg1sz7PRsp3vatGxSdAhERERERlYBiFTvm/vWHvPMoE7wbe8K7sWeh2xsbG2L7hmUFtnFytIOTY+GWrLCuaoXhQ/sWevyP2VSrgh/Gjyz2+UREROVBekI0Hp9aJ4hpGlrAuknx/w+Vl01+F+ATGiyIdXRyRac6bgrKiEqSmXNzVPHqhmfX9stiqbGvcGfXbLgMmSV4OIiIiIiIiIhKVrGKHT26d5VzGkRERESF8/DYKmSlJwti9l3GQ1nBG3+/jI/Fb4d3CGI66hqY032wgjKi0uDYbSKiQwKQ/OapLPYi4CRMHBrCyr2DAjOjwkpPz0B8fDxUVcXQ09NTdDpEROVeSuxLvLy645PtHLtOKIVsiIjoSyKXHaufv3iBuLi4T7aLj4/H8xcvPtmOiIiIKC8JLx7jmc8BQcygugvMnJsrJqEP/LhvExLTUgWx3zv2hZku12auyFTUNOAyeBZESsIlQ+/unoPkt5H5nEVlwY6du9GpS0841XWDV+Pm+POv+bJjx0+cwuix4xH+9GkBPRARERXNzYBbOHv2PM6ePY+bAbcUnQ4RUYUjl2KHd7M2mD1nwSfb/TV3AZo2byuPIYmIiOgLI5VKcW//IkAqeR8UieDY7XuFLxd0+M51HAm6IYg1sKmFwZ5NFZMQlSq9Kvaw6zBWEMtKT8atTb9A8tH+LaR4EokE34ybgGm/zsDjkCewsbGGVCoVtLGzq4XTZ87i6LGTCsqSiIgqovkLlmDE6P9hxOj/Yf6CJYpOh4iowpFLsUMqlea6QSioLREREVFRvbl/BW+D/QQxS7cO0Ktir6CMcsSnJuPHfZsEMVVlFSzq9RWUlOTyVovKgeotBsGwRn1BLDb8DsIv7VJQRpSfLdt24MTJ02jcyAuXL5zCiaMHcrWxrlYVVawscfHS5dJPkIiIiIiIiqVYe3YUV0JCItTU1EpzSCIiIqoAJNmZObM6PqCsqg77Tv9TUEbv/X5kF14lxAliP7TuAltTc8UkRAohUlJGvUEzcHFOP2SmJAAAqjbqiSoNuyk4M/rYnr0HYGBggGVLFkJbWyvfdra2NXH33v3PGisrKxv3Hz5CQOA93H/4GG/evIVEIoGBgR7q1LZHp/YtYWxkkOe5r99EYc/+47h7PxjJySkwMNCDh2tddOvcFurqed9TSaVSnDl/FecvXsOLl6+hIlaBTbUq6NyhJWo71Pqs10JEREREVNYVu9jx8d4bKSkp+e7HkZ2VjZAnobh85RqqVa1S3CGJiIjoC/X0yl7BBtAAUKPFEKjrGisooxzXnjzEBp9zgph9ZUt826yjgjIiRdLQr4w6faYiaNdfcO7/Gyo7NVF0SpSH0NBQeDXwLLDQAQCampqIjo75rLEeBD/G7PkrAACGBvpwqm0HAHgS+hSnz13GFZ/rmDJxLGxrWAvOCwuPwMy/liI1LQ3WVa1gV6s6Qp48xaFjZ3Drzj1MnzoBmpoagnOkUin+Xr0JV31uQENdHc5O9khNS8e9B49w934wRgzth2beDT7r9RBRxWP16EIRWvcvqTSIiIjkotjFjiZNWwvWxz5x8jROnDxd4DlSqRR9+/Yq7pBERET0BcpISUDw8dWCmLquCWyaD1JQRjnSMjMwYfc6QUwkEmFx76+hqlKqk2epDDGv1wrGdp4Qa+goOhXKh5JICZlZn95L5eXLV7kKCkUlEong4VoX7ds2FxQ0MjIysW7TTly84odlKzdg0ZxfoaKSs8m9RCLBslUbkJqWhr49O6FLx9YAgKysLCxa9i8Cbt/Ftl0HMXxoX8FYV65dx1WfGzAxNsRvUyfAQF8XAPAgOASz5i7Hus27UNuxVr4zSYiIPuXegUWfbKPr4F0KmRAREeWt2Hfi7m6ueFfr8PO/AUNDA1S3sc6zrVgshomJCVq3aoFWLZsXd0giIiL6Aj06sQaZKfGCmF2n/0FF7fM+hPxcC84cwuM3LwWxEQ1bwq1aDQVlRGUFCx1lm7VNNdy7dx9paWlQV1fPs018fDzuP3iI2o6ftydQbYdaeS4fpaoqxleDe+P6zTt4Gx2DRyGhcLCrCQC4EXAHL1+9gZWlGTp3aCU7R0VFBcOH9cW4ib/iwmUf9OnZCTofzE45fPwsAKB/7y6yQgcA2NeqgebeXjh97jKOnzqPwf17fNZrIiIiIiIqq4pd7Ni+dYPs6+q2teHdpDHm/vWHPHIiIiIikjFzbo6YJ7cQH/EAAKBbxQGWru0UmtP9FxFYcvaIIGahZ4if23MGK1FZ16FdW8yZtxAz//gLM2f8CiUlpVxt/vxrPlJSUtCpY/sSy0NVVRVmlY3xJOwZYuPeF3QDAu8CADzc6glm0gOAvp4uatlWx/0Hj3Hr9l00aegBAIiKikZE5AuIxWLUr+eUa6wGHi44fe4ybt4KYrGDiIiIiCosuayxcOn8KWhqasqjKyIiIiIBw+r10HjiJkTeOIaHh/+GY7fvIcrjw8nSki2RYPyuf5ElyRbEF/QcCh11xc42obItOyMN4Vf3wrpJHygpc6kzRRkyeAAOHzmGnbv34tbt22jerCkAIDQ0DMuWr8Tps+dx79591HWug149u5dYHhKJBFFvc/YE0dOtJIuHP3sOALCuapXnedZVrXD/wWM8ffYcaJgTexqRc46VpRlU8lhG711fb6KikZKaCk0N/ltFRERERBWPXO6yLCzM5dFNgZKSkhH86BGio2OQkJCASpUqwdDQAHa1bKGlVfDmgkRERFS+iZSUYOXeERYubaCkIlZoLmuvnMbNZ08Ese71PNHaoa5iEqJyIeH5YwRs+hmJL58gOz0Ftm1HKDqlL5aamho2bViLyVN+xrnzFxEc/BgAcCvwNm4F3gYANPVugoXzZ+dZOJCXy1f9kZCYhEo62oL9PN7+tym6oYFenucZ6Ov91y421znvjn1MXV0NmhoaSElNRXR0LDQt8y92/DpzQZ7xHp2awczUCDFRL/M8XpKysjIBQCFjU8n53Ova0y73LKYvgWq1qgobuzAPdShr5BRvC7N3x5f4O52ZmS74urz8DPjvcMXE61oxleframBs9tl9yPXde1paGu4E3cObN2+QkZGRb7vu3boUqr+oqCjs3LUXZ86dx4MHD5GdLcnVRllZCQ729mjZohl69+oOY2PjYudPREREZZuiCx2RsW/xx7Hdgpi+pjZmdx2ooIyoPHh6dR/u7p0HSVbO++NHJ9bAqJYHDKzrKDizL5e+vh7W/PM3HjwMxpUr1xARGQmJRAozs8po3MgLdZxql+j4UVHR2LJjPwCgd4+OEIvf/9uWlpbzQZiamlqe56qrq/3XLi2Pc1TzHVNdXQ0pqalITUvPtw0RERERUXkmt2LH3yv+wT9r1iElJSXfNlKpFCKR6JPFjvCnT7Fw0TKcOnUGmVlZAAA9PV3Y2FhDX08P2traSExMRFx8PEJDw3An6C7uBN3FsuUr0bp1S0yc8C2qVq0ir5dGREREBKlUiol7NiA5Q/hB4czO/WCso5vPWUSAioa2rNABAFJJNm5tmoYmk7dBrKGtwMzI3q4W7O1ybyBeklJSUzF/6WokJafAw60eWjRtWKrjf8qMXybmGX/3dKA8nrgrKkWOTSXnc6/rIp/z8kyn3LB6dEFhYzesYf/JNu9mdMTfv/jJtpZdJ3x2TuWNWKwm+Lq8/LvGf4crJl7XiulLv65yKXZs3LQVCxcvg5KSErybNEb16tbFXlpqxszZ2LZjF7KysuBa3wVdOneAp6cHrAuYqhkaFg4fHz8cOHQYx46fxKnTZ9G/X2/8Ou2n4r4kIiIiUpB3D0eUNfsCfXH6wW1BzLumI/q5NVZQRlReWLi0xpv7VxHp/35T+5To57i7dx7qDfxdgZnRx5KSkhEWHo7KlU1hbGQk9/4zMjIxf/FqPIt4gdoOtvhm5KBcbdTV1ZCcnIL09LxnYLybxaGuri44BwDS0/OfXf/uPA31vGeMEBERERGVd3IpdmzdtgNisRjbtqyHS726n9fX9h3o1rULxo4egSpV8t6U72M21tVgY10NA/r3wbNnEfh75Wps27aTxQ4iIqJyKPjYSqQnxsCu/RioVTJUdDoAgJjkRPy0f7MgpiFWxcJew8pkYYbKHqeekxHz5BZSop/LYpH+R2Di0BAWLq0VmNmX5+o1Xxw9dhwD+vWFo+P7p5S3bd+FP/6cg4yMDIhEInz91RBMmZz3LIfiyMrKxqLla/EgOAQ1q1fDxPEjBctXvWNkaIDk5BREx8ShahXLXMdjYuP+a6cvOOfDYx9LS0tHSmoqAMDwg/OIiKh0eTXwgIlJzvLrNarbKDgbIqKKRy7FjojI53Bzrf/ZhQ4AOH3iSKGLHHmpUsUKc2bPxDdjRn52LkRERFS6UqJf4MnZzZBkZeDFzVOo0XoYbJr2h7JYsU8i/3JoG94mJQpiP7bpDmsjUwVlROWNiroWXAbPwtUlX0MqyZbF7+ycBf1qTtA0+DKnmSvCzl17cObsefz04yRZLDQ0DL/9/gekUimq21gjIvI51v67AQ083eHd5PNnb0kkEvy9eiMC79xHVSsLTP5+DNTz2ZOjWhULPH0WibCnEXCpm3vvkLCnEQCAqlUsZLGqVjlfR0S+RFZWVq6N1d+dY2JsCE2NT28wTEREJWPc/8YoOgUiogpNLsWOSpV0oK+vJ4+uPqvQURL9EBERUel5cHiZbG+DrPRkPDy8HPpVa8PI1k1hOV14dBfbr18RxOpYVMVY77YKyojKK31rJ9i2HYHgY6tksazUJNza/Cu8xq2CSElZgdl9Oe7euw8Hezvo6LzfL2Xv/oOQSCSYMf0XDOjfByEhT9Cxcw9s3rL9s4sdUqkUa9Zvh6//LZhXNsFPk76BtpZmvu1d6tbGxSt+8Lt+C907txXMHouNi0fwoydQVlZC3TqOsrixsSGsLM0REfkCN28FwcOtnqBPH78AAED9ek6f9VqISsMvh7YpOgW5Kcr+GhG2TUssDyIioi+Fkjw6adK4EW4F3oZEIpFHd0RERPQFigm9jRcBpwQx09pNFFroSMlIx/e71wtiykpKWNJnOFSU+cE0FV2NVsOgb+MsiMU8CUDImY0KyujL8/ZtNMzMKgtiPj5+0NLURO9e3QEANWpUR31XFzx6HPLZ423ZsR8XLvvCxNgQP/84DrqVdApsX7+eE8wqmyAi8iUOHT0ti2dlZWHthh3IzpagaeMGqKQj3Ny+U7sWAIBtuw4iJjZeFn8QHIJzF69BRUUF7Vo3++zXQ0RERERUVsllZsekid+hc7de+H3mn/j5p8lQVVWVR7cCcXFxOHDwCAJv30FsbCy8Gnhi1MivAQCPHofg2bNnaOjVABqclk1ERFTuSCUS3Nu/QBATKSnDoet3iknoP3NO7kN49BtBbKx3WzhbVlNMQlTuKSmrwGXQH7g4py+y0pJl8eBjq2BUyx36VXMvW0TyJZVKkJ39fimx9PR03L//AJ6e7oI9NEyMjREQEPhZY90IuINjJ88DAIyMDLBzz+E827m61IFb/ZwimLKyMv43eghm/rUUO/Ycht+NQFQ2McbjJ+F4Gx0DK0sz9O/dJVcfjbzccDvoAa763sAPU/+Ak0MtpKWn4+79R5BKpRg+tC+MjQw+6/UQEREREZVlcil27Ni5G95NGmPrtp04f+ESPD3cYW5uBqU8NuwUiURFXqPwxMnT+PGnX5CcnAypVAqRSART0/drZL98+RKjx47H/Ll/omuXTp/9eoiIiKh0Pb95AnFP7wli1Rr3hrZJVQVlBNyODMffF44LYtUMTfBjm+4KyogqCk1Dczj1/gm3Nk2TxaSSbNzaNA1NJm+Dilr+SxzR5zOrXBkPgx/Jvr9y1QeZWVnw9HAXtEtOThYsdVUcyckpsq/vP3icbztjI0NZsQMAbKpVwezff8SeA8dw914wIiJfwkBfD53at0T3zm2hrp57vw+RSIRvRg1GLVsbnLt4DYFB96GirAIH+5ro0qEVajvU+qzXQkRERERU1sml2LFk2QqIRCJIpVI8f/4Ce/cdyLdtUYsdt2/fwfgJP0BTUxM/TfkBbq710a1HX0GbRg29oK2tjZOnzrDYQUREVM5kZaTiweHlgphYsxJs245QUEZAVnY2xu9cC4lUKogv7DUMmqqK3SydKgZL13Z4c/8ant84JoslR0Xg7t75qNv/VwVmVvE1auSFTZu3YcbM2fDy8sDceYsgEonQonlTQbuHwY9yLXdVVN6NPeHd2LNY51Y2Ncb/Rg0p0jkikQitmjdGq+afv6k6ERHJ34w/ZuPBg2AAgL19Lfw67ScFZ0REVLHIpdgx968/5NFNnlasWgMA2LBuNZzr5L2hnrKyMhwd7eWypi4RERGVrtBzW5AW91oQs207AqpaugrKCFhx8QTuPH8qiPVza4ymtlxiiOTHqddkxIQGIjXmhSwW4XsQJvZeMK/XUoGZVWwjh3+FQ4eOYuPmrdi0ZRukUim6dumEmjVryNo8eBiM589foGUL7nFBRETy8+BBMPz8rys6DSKiCksuxY4e3bvKo5s83bx5C3WdnfMtdLxjbGSEoDt3SywPIiIikr+0+CiEnNkgiGmZVEW1xr0UkxCAsLev8deJvYKYkbYOZnbup6CMqKISa+jAZfAfuLpkOCCVAABEyirISIpVcGYVW+XKpjhyaC927t6LmJgY1HZ0RI/uwj0wHj4MRquWzdGubWsFZUlEREREREUll2JHSUpKToaxsdGn2yUlQfLfTSIRERGVDw+PrEB2Rpog5tDlOygpi/M5o2RJpVJ8v3s90rIyBfHZ3QbBQEtHITlRxWZg4wzbNsPx6MRqaJtWg8vgWdC1slN0WhVe5cqmGD9ubL7Hu3XtjG5dO5diRkRERERE9LnKfLHDxMQYT0LDPtkuJCQUFubmpZARERERyUNcxANE+B8WxIxs3WFaW3FrzW+/fhkXHws3Sm9l74zudYu35j5RYdRs8zWUVdVRrUlvqKhqKDodIiIiIiKickkuxY7JU6YVuq1IJMKc2TML3d6rgSf27N2Ps+cu5No08J1Dh48iIjISw4YOKnS/REREpDhSqRT39y8CPtwAXKQEx27fQyQSKSSnN4nxmHZwmyCmpaqGBT2HKiwn+jIoKaugRsuibURNhff8Rc6eKJVNTaGsrCz7vrD4QBURERERUfkgl2LH3n0HCjz+7gMCqVRa5GLHqJFf4/CRYxg3fiLGjxuL1q1zNmvMzs7Gi5cvcerUWSxctBRaWlr4atjgYr8GIiIiKj2v7pxHdMhNQaxKgy6oZFFTQRkBUw9sQVxqsiA2rX0vWOp/ejlNIiq7mjRtDSUlJZw8fgg21tXQpGnrQhcwRSIRHj+8U8IZEhERERGRPMil2LFt8/o84xKpBC9evMTFS1dw7PhJjBg+DM28mxSpb+tqVbF08XxM+H4y5i1YjHkLFkMkEuHgoSM4eOgIAEBTQwPLlsyHuZnZZ78WIiIiKllSqRSPjq8WxFTUtGDXfoyCMgJO3Q/Evlu+glj9KtUxvFErBWVE9F5WWjJU1LUUnUa55e7mCpEI0FBXF3xPREREREQVi1yKHR4ebgUe79G9Kxo19MKvv81Am1Yti9x/i+ZNcfzYAazfsBlXrlxDRORzSCQSmJlVRuNGXhjx9TBYWloUN30iIiIqRSKRCO6jl+Lhkb8R6Z/z4EKN1sOgVslQIfkkpqVi4p4NgpiKkjKW9PkaykpKCsmJ6J3X96/i9tbf4dR7Csycmys6nXJp+9YNBX5PREREREQVQ6ltUN67V3f8u24DFi9djo3r1xT5fAtzc0yb+mMJZEZERESlTUPPBPUG/g7rJn0Ren4zbJr2V1gus47txvO4aEFsfIuOcDCzUlBGREB2RhoeHFqKsEs7AQC3t/8Bvaq1oaFnouDMiIiIiIiIyqZSfVyxZs0aCLwdVJpDEhERURmmV8UeLkP+hLJYTSHjXw8PwZqrZwSxmiZmmNiys0LyIXrn6dV9skIHAGSmxCNwy2+QSiQKzKpiGDBoGP5Z/e8n261Zux4DBg0rhYyIiIiIiEgeSrXYEfn8OSSS7CKdEx0dg5sBtxAdHSOIP3/+AuMnTELbDl3x9YixuBN0V56pEhERUQWXkZWF8bvWQiqVCuKLe38NdbGqgrIiylGtSS/oVXUUxN4+8kfoha0Kyqji8PW7jiehYZ9sFxoWBj//G6WQERERERERyUOpFDuys7Pxz+p/ERR0D44ODkU6d9XqtejTbzBiYt4XO1JTU9G77yAcPXYCjx+H4MLFSxg4+GtERj6Xd+pERERUQS05dwQPXwnfOwxt0BwNbGopKCOi95SUxXAZPAvKapqC+IPDyxEf8VBBWX1ZMjIyoazMfXuIiIiIiMoLuezZUdD07qTkZERERCIhIREqKsoY/+3YIvXt53cd1tWqoWbNGrLY/gOH8er1azRv5o3/fTMa5y9cwrLlK7Fuwyb8Ou2nYr8OIiIiKhlpCW+hpmMIkUik6FQAAI9ev8CC0wcFscqV9DC9Yx8FZUSUm5axFZx6TkLg1t9lMWl2FgI2/YzGk7ZARVVDgdlVbBKJBHfv3oORoZGiUyEiogpk1szfkJySAgDQ0tT8RGsiIioquRQ7fP2uF3hcrKICD3dXjB/3DdzdXYvU98tXr1HHqbYgduHiJSgpKeGPGb/B1NQEznWccPz4SVy75lvk3ImIiKhkSbIz4bNsNMQaOnDs/j30qzkpNh+JBN/t+hcZ2VmC+NzuQ1BJgzedVLZYunfC63tX8TLw/d4ySa/DcX//ItTpM1WBmZUvHz+cdenylXwf2MrKysbTZ8/w9m00enTvWgrZEREBVo8uKDoFKgU2NtaKToGIqEKTS7Hj0vlT+R4Ti8XQ19eDWCwuVt+JiYnQ0dEWxAICAmFXyxampiaymK1tTVy8dKVYYxAREVHJeXp1P5Je56yPf2XhUFjUbwvHHj9ATVu/1HJIzcjA9YgwJKWn4fa18/ANeyQ43tHJFR3rFO2BDKLSIBKJUKfvz4gND0Ja3GtZ/OnVvTBxaIjKTt4KzK78+PDhLJFIhKiot4iKeptve7GKClq2aIYpkyeWRnpERF+kewcWFaqdY9cJJZwJERFVFHIpdlhYmMujmzwZGRni1av3N3bBjx4jNi4OnTt1ELQrK8tiEBER0XsZKQkIPr5KEIsOuQllsXqpjP8iLgYrL53AVr9LiEtNzrNNJXVNzOk+uFTyISoOVc1KqDdoJnyWjwKkUln89rYZ0JuyA+q6xgrMrnx493CWVCqFd/M2aNe2NX768Yc824rFYhgY6ENFRS63SkREREREVErK/Dt425o1ceXqNQTdvQen2o74d91GiEQiNGnSSNDuWUQETEx4o0dERFSWPD71LzKT4wUxu47fQEWt5PcaCHr+FD3/mYuopIQC241o3BJmuqU3y4SoOIxq1keNlkMRcnq9LJaRHIfArdPhMXoZRErcSLsgHz6c9e24sXB0sCvRB7aIiCqiqyEPPtmmsY07dNS5pxQRESmGXIsdUW/fYteuvfDzv4HXr3NmY5iamsLDww29e3WHsVHRN/gbNnQQLl66jO49+0FHRxsJCYmoWrUKGjVsIGsTHx+PBw8eok3rVnJ7LURERPR5kqMiEHZxhyCma2UPS7cO+ZwhPy/iYgpV6ACATT4X8JUXCx5U9tVqNwpRwX6If3ZfFot66Iuwiztg06y/AjMrX06fPouHD4PRskVzRadCRERfmNDQMMEG5dzDg4hIvuT2CNip02fRsnVHLFqyHFev+SDkSShCnoTi6jUfLFq8DC1bd8TpM+eK3G/jRl6Y+fuvqFzZFBkZGXCt74KVfy8RTCvff+AwsrKy4enhLq+XQ0RERJ/p/sElkH60Cbhj94ml8gT6yksnClXoAICopASsvHiihDMi+nxKKmK4DP4DyqrCZeAeHFqK+OeP8jmLPhYaFl7s/QSJiIg+x8+//I4u3XqjS7fe+PmX3xWdDhFRhSOXmR0PHjzEt9/9gMzMTDRq2AA9undFFSsrAEBEZCT27juAy1euYdz4idi/Zzvs7e2K1H//fr3Rv1/vfI/37dMT3bt3gZam5me9DiIiIpKPt49v4NWd84KYmXMLGFavV+Jjp2ZkYKvfpSKds9X/In5q2wMaqqollBWRfGibVEXtHpNwe/tMWUySnYm7e+ai4fi1Csys/KhiZYnEpCRFp0FERERERHIml2LHyn/WIisrC7NnzUDvXt0Fx+rWrYNOHdtj9579mDL1F6xa/S+WLJpX6L5Hjx0PY2NDzPz913zbqKurQ1398zc6DQuPQND9YISGPsWTsGd4Gx0DAFg6bzqMjQ2L1WfQvYc4fOwsQsOfISszC+Zmpmje1AstmjbMtal6enoGgu49REDgXQQ/eoKo6FiIRICxkSHq162NDu1aoJKOdq4xoqKi8e2k6QXmsXLxLOjpVSrWayAiIioKqSQb9/YvFMSUlMWw7zyuVMYPjAzLdzPy/MSmJON2ZBg8bWqVUFZE8mPl2QWv71/Fq9s5s6aNbN1RdyCfDi2sTp06YMXK1Xjx8iXMzcwUnQ4REREREcmJXIod/tdvoHZth1yFjg/16tkNW7fvgJ//9SL1feHiJbRqWTrr6e47eBw3bgXJrb8z569g3aZdEIlEcLS3hYa6GoLuBePfjTvxKCQMY0cMErS/6nsDa9ZvBwBUNjVGPWdHZGZm4vGTcBw6dgaXr13HL1O+hVllkzzH062kA2cn+zyPqapyqj4REZWOCP+jSIgMFsSsvftCy9iqVMZPTEst1nkJxTyPqLSJRCI4952GhMhgVGvUEzbNBnKD8iIYNeIr3LoViP4DhmHypAlo0bwZ1NQ4q4uIiIiIqLyTS7EjLjYODTw9PtmuWtWqePgg+JPtPmRmVhkZGRnFTa1IatawhpWlOWysq8DGugqm/jYX8QmJxerr9Zu32LBlD5SUlPDz5P/BvlYNAEBMbDx+/3MRLl/1h7OTPRp6usrOUVZWRrMmDdCudVNYWZrL4ikpqViyYj3u3H2AVWu34Pdp3+c5prmZKcZ8VEAhIiIqTVnpKXh4ZLkgpqqlh5ptvi61HHTUNYp1XqVinkekCKpaumg6dQ+UxfyQvqhatOoAiVSCly9f4dvvfgAAGBoaQF0t90xxkUiEC+e4pw8RERERUXkgl2KHnr4ewsOffrLd06fPoKevV6S+27RuiR079yA+Ph66urrFzLBwOndoJbe+jp+6gOzsbLRu0VhW6AAAA31d9OvVBUtWrMPhY2cFxQ7vRh7wbpS7aKSpqYFRXw/ANxOm4VFIGKLexsDYyEBuuRIREclLyJkNSE+IFsRqtR8NsYZOqeVQ19IaOmoaSEwv/EwNfU0tOFtal2BWRPLHQkfxRD5/niv29m10Hi2JiIiIiKg8kUuxw821Po6fOIUDB4+ga5eOebY5cPAI7gTdRYf2bYvU97f/G4NrPn4Y8tVI/P7bNDjXcZJHyiUuIDBnOSxPd5dcx1xdnCAWi/H0WSTeRsfAyPDThQsDfV1U0tFGQmISYuPiWewgIqIyJyXmJZ6c2yKI6ZhVRxWvbqWaR1RSPLIl2UU6Z4C7NzcnJ/pCPHl0V9EpEBERERFRCZBLsWPMqOE4ffosfpj8E44dP4EunTvC0tICABAZ+RwHDh7G+QuXIBaLMXpk0ZaxGDHqG4hVVBB4+w569OoPIyNDWJibQ11dLXdjkQhbN62Tx0v6LCkpqYh6m7O5uXXV3OuTq6iowNKiMsLCI/D02fNCFTuSklOQlJwCANDTzfvp2PiEROzZfwyxcfFQV1dD1SoWcHWpA00NLstBREQl7+Hh5ZBkpgtiDl2/g5KyXN5uFMrbpAT0/GcuUjILvwSmiY4uxngX7WEMorJMKpUi/PJuGNaoh0rmNRWdDhERERERUamQy6cPDg72WLhgDn78aRrOnruAc+cvCo5LpVJoampgzuw/4OCQ9wba+fH1e7+huVQqRVTUW0RFvc2zrUgkKnryJSAqOqfQoaWpkXdRBoChvh7CwiPw9r+2n3Li1AVIJBJYWZrDxNgozzYvXr7G3oPHBTFNDQ18PaQPvDzrFzr/X2cuyDPeo1MzmJkaISbqZaH7koesrEwAKPVxKX+8JmUPr0nZ86Vdk+yMVESH3RbE9GvUh4qRdan9DFIyMjBo2z8IiXpV6HOMtXTwb69hUMtI+2KuVVnypf2elIaM5DiEHFqM2JAb0DSuCufhi6CkUvhZS4q8JgbGZqU+JhERERERVRxye9Syfbs2qO9SDzt27cH1Gzfx+tUbAIBpZRO4u9ZHn949YWpqUuR+L50/Ja8US016Ws5TrWpqeRc6AEDtvyJIalp6vm3eeRL6FIeOngYADOzbNddxFbEKWjZrhAbuLjA3N4W6mhpevHqNoyfO4ZrvTSz/ZyM0NdVRt45jMV4NERHRpymraqDeqBV4ef0wIi7vQHZGGqq1HF5q42dmZ2Pc/s24/TJCENfX0EQr29o4GRyE+LT3e3joqWuip7Mrhrk1hqlOye4JRlRaEp8H48HOmchMjgMApEQ9RfjZDbBpM1KxiZVRISFPsGPXHty+fQcxsXFo1aIZpvyYs2H5zYBbCAq6h65dOkJPT0+xiRIRERERUaHIdV0JU1MTjB83Vp5dwsLCXK79lTfRMbFYuGwtMrOy0LFdC9SpnXtmjL6eLr4e0kcQs6lWBeNGD4WRgT4OHTuDLTsOFLrYMeOXiXnG3z3hV9pP3SlqXMofr0nZw2tS9nyp18So8zewbdYf0Y+vw9zBvVTGlEql+Gb7alwMDRbEtdXUsX/MT6hjWQ2pGRm4fPcGEtPTYGFqAWdLa+7RUQZ8qb8nJUVLQx3BysqC2Ev/Q6ji0gomDl6F6uNLuSYbNm7B7DnzkJWVs7+PSCRCTGyc7LhUKsUff86Bmpoq+vXtraAsiYiIiIioKJQUnUBF9G7WRnp6/rM23s3+0MhnmSsASExKxuz5KxATG4fGDd3Rv3eXIufSuUMrKCkp4fmLV7J9RIiIiEqSmo4+zF1al9p4vx/ZiR03rghiqsoq2PLVd6hjWQ0AoKGqClcrazSrYQ9Pm1osdFCFpKatj3oDZ+SKB26djvREvg9859LlK5g56y+YmppixfLFuO57GVKpVNDGtb4L9PX1cfrMeQVlSURERERERSWXmR03A25h56696NO7B+q71MuzzY2bAdi1ex8G9OsNZ+c6RR4jIyMDx46fgv/163j13xJZlSubwN3NDe3btYZqGfrQwvi/DceTU1KRlpae574d0f89OZbf5uQpqamYPf9vPH/xCq4udTD66wHF2pNES0sTlSppIy4uAbFx8TA2+vRm6EREROXFyosnsPT8UUFMJBJh5YDRaFKTyzfSl8fYzhM2zQYg9PxWWSw9MRq3t82A28hFZWaPO0Va++9GqKurY+P6NbCuVjXfdo4OdggLCyvFzIiIqKKzt6+V59dERCQfcil2bNu+C8dPnMLUKT/k26a6jQ0OHzkGAEUudty6dRvjvpuIV69e53rqatfufZi/cDGWL1mIunWLXkQpCZqaGjA2MkDU2xiEPY2Afa0aguNZWVmIfJ6zeWrVKha5zk9Pz8C8Rf8gLDwCTo52+HbMUCgpFW8SjkQiQWpqGgBATa3sFISIiIg+194AH/x8cGuu+J9dBqBbXQ8FZERUNth1/B/ePrqOhOePZLHX9y7j6ZXdqNaYSzIF3b2LunXrFFjoAAB9fX3cuBlQSlkREdGX4NdpPyk6BSKiCk0uy1gFBNyCo4NdgZv36evrwdHBvsg3DBERkRj69Si8fPkKNtbVMPH78ViyaB6WLJqHHyaOR3Uba7x8+QrDho9CRETkZ74S+XGp6wQA8PXP/XpvBAQhMzMTVatY5prZkZWVhYXL1uLhoyeoZWuDid+OgFgsLnYegXfuIz09A2pqqrAwMy12P0RERB97efscstJTFDL2+eAgjN3+T674d807YlSTNgrIiKjsUBarwmXwLCiJhbOL7x1YjMSXTxSUVdmRlpYO3UqVPtkuNjYWSiKu+ktEREREVF7IZWbH6zdRqFPH6ZPtzM3N8DA4+JPtPvT3ytVISkrCuP+NwfhxY3NNvR89cjiWLl+JpctWYMWq1Zg9K/c6xSUlJjYOs+YsAwD8/OM4GOjryY61a+2NM+ev4OyFa/B0d5HN7oiJjcf23QcBAJ3atxD0J5FIsHzVRty5+wA21lUwecLoQs3GOHnmIurUtodZZRNB/O79YKxZvx0A0LJZI6ioyHU/eiIi+oLFRwbjxrrJUNMxgF2Hb2Dl0REiJeVPnygHgRFhGLJhKTKzswXxfm6N8UsHPrVOBAA6ZjZw7PodgnbPkcUkmekI2PgzGk3cBGXxlzvjN+ee5FGBbbKzs/HoUQiqVq1SSlkREVF+7h1YVOi2jl0nlGAmRERU1snl029VVVUkJiZ9sl1SUnKRn466cvUaataoju++/SbP4yKRCOPHjcWxYydw+cq1IvX9sYDAu9h/6OT7fJNznlZdsGwtxP8VCuo5O6J7l7YAgOysbLz4b/+Q7CzhBy6mJsYYOrAn1m3ahT/mLENtB1uoq6kh6H4wUlPT0NjLDQ09XQXnnDxzCX43AgEAerqVsHHLnjzzbObtBTvb6rLvz1/0wcate2FlaQYz05yCx4tXbxAR+QIA4Oxkjz49OhbrZ0JERPQxqVSKe/sXAlIp0hOicXv7DIRf3oWGE9aX+AeoYW9fo8+a+UhKTxPEW9k7Y3Hvr7gfAdEHqjbqhTf3r+H1vcuyWMKLx3h4eDkcu3+vwMwUy7tJI2zavA07d+1Bn94982yzect2vImKQq9e3Us5OyIiIiIiKi65FDtqVLfBjZsBSEhMRCUdnTzbJCQk4MbNAFjbVCtS32/fRsO1vssn29nb2+HEydNF6vtjiYlJCAkNzxV/+uz98ljmZia5juenZbNGMDUxwqFjZxDy5CmysrNgbmaK5t4N0bJZw1ztk5PfLwUSEHg3334d7GoKih1tWnoj8M49PIt8gTv3HiIjIwNaWlpwdrJHYy93eHnW54c/REQkN6+DLiL68Q1BTNeyVokXOt4kxqPHP3MRlZQgiNevUh3rBv8PYmXOYCT6kEgkgnP/X3Hxr75IT4yWxUMvbIWxfQOY2DdQYHaKM3rkcBw8dATTfp2Bu/fuo3WrlgCAhIRE+F+/iVOnz2Dzlm0wMjLE0MEDFZwtEREVRWFngXAGCBFRxSSXTwXat2uDW4G38d2EyVi+dAE0NTUFx1NTUzFh4hSkpKSgY/t2RepbR1sbr169/mS716/fQFtLq0h9f8y7sSe8G3sWur2xsSG2b1hWYBsnRzs4OdoVqr+e3dqjZ7f2hR7/nWbeDdDM+8u8WSUiotIlycrE/YOLBTFlVQ3U6ji2RMdNTEtFnzXzER79RhCvaWKGHcMnQktNvUTHJyqv1HQMUHfAdPitGieIB279Dd5TdkJNW19BmSmOiYkx1q1ZidHfjMe27buwfcduiEQinDl7DmfOnoNUKoWJiTFWr1oO/Q+WqSUiIvpcy5avRMiTUAA5Dw6P+98YBWdERFSxyKXYMaB/X+zbfxAXL11G0xZt0a5ta1hXqwYACAsPx4mTp/H2bTTsatli8KD+Rerb2dkJFy9dwTUfX3g1yLsQcc3HF9dv3ESzpk0+96UQERFRAcIv70ZyVIQgVqPVMKhXMiqxMTOysjB4wxLcjgwXxM0q6WPPyMkw1M57VikR5TBx8IK1dz+EXdwui6UnROP2tplwG7Hgi5wB7OxcB2dOHsHuPftx+co1REZGQiKRwsysMho38kK/vr2hrf15D1IRERF97JqPH/z8rwMAPNzdWOwgIpIzuRQ71NRUsXH9GkycNAWXr1zDlq07ZDdNUqkUANC4kRfmz5sNNTW1IvX99VdDcP7CJQwf+Q369u6JLp07wNLSEgAQGRmJAwePYOfuvbK2REREVDIykuPw6MRqQUxDvzKqNxtQYmNKJBJ8s301Lj66J4hXUtfE7pGTYGVQckUWoorEvvM4vH18HYkvQmQxtUoGkGZnQaQiVmBmiqOlpYWhQwZi6BAuVUVEREREVBHIbXFrQ0MDbFi3GkF37+HK1Wt48eIlAMDc3AyNGnrBqbZjsfpt4OmBqT9Nwpy5C7FpyzZs2rJNcFwqlUJJSQlTp0yCp4f7Z78OIiIiytuj46uRmZooiNl3Hgdl1ZJZQkoqlWLaoW3Ye8tHEFdTEWPb1xPgYG5VIuMSVUTKYjW4DJ6Fy/MHQVlVA879psHMubmivL+pUgABAABJREFU01KIoLv3in1vQkREREREZZfcd/J0qu0o95uHr4cNgae7OzZs2oIbN27i1euc9borm5rAzc0Vgwf1R21HB7mOSURERO8lvg5D+JU9gph+NSeYu7QpsTGXnT+KVZdOCmJKIhFWDxwDr+qF2w+LiN6rZF4D9YfOhm4VB2jomSg6HYXp2r0PatasgW5dOqFzpw4wM6us6JSIiIiIiEgO5F7sKCmOjvaYN2eWotMgIiL6It0/sBhSSbYg5thtYomt9b/j+hVMP7IzV3xejyHoVMetRMYk+hJUrtMUAHBsUmNIszJzHZciZwlaEXL/botUxGg/73KJ5lcabG1r4tGjx5i3YDHmL1wCTw93dO/WGW1at4Smpqai0yMiIiIiomJSkkcnBw8dgXezNrh0+Uq+bS5dvgLvZm1w9NgJeQxJREREpSTqoS/e3BP+H29Rvw30rZ1KZLzTD25j3M41ueKTWnfFMK8WJTIm0ZdGmpUJSXbuP9LsLEizs/I+lkdxpDw6fmQ/jhzai6+GDYaRkSGu+fhi0o8/w8PLGxMn/YQrV31k+w4SEREREVH5IZdix4FDR5CcnIwGnh75tvH0cEdSchL2HzhUpL7bd+qGmbP+wukz55CQkPC5qRIREVERSLKzcG//QkFMSawGu07jSmS8G09DMGzjUmRLJIL4EM9mmNKme4mMSURfHnu7Wpg6ZRKuXjqLDetWo3OnDgCAAwcPY+hXI9GwcQv8NXcBgh89VnCmRERERERUWHJZxupR8GPUsrOFWCzOt42qqirs7GrhYfCjIvUdHPwYwcGPsXHTVohEItjb1YKnpzs8Pdzh7uYKbW2tz02fiIiI8vHM9yASXz4RxKo3GwhNAzO5j/X4zUv0XbsAKRkZgnj72i6Y12NIiS2ZRURfLiUlJTRu5IXGjbyQmpqKEydP48DBw/Dx9cfafzfg33Ub8fjhHUWnSUREREREhSCXYkd0TAzc3V0/2c7YyAgBAYFF6tvf5xL8/K/jmo8ffP38ce/+A9y7/wDr1m+CsrISHOzt4enpjgae7nBzrQ8NDY1ivgoiIiL6UGZqIoKPrhTE1CoZokaroXIf62V8LHr+MxcxyUmCuKe1LdYM/AYqyspyH5OI6EMaGhro1rUz3FzrY/Xaddi6Lfe+QUREREREVHbJpdiho6ODFy9efrLdq1evoalZtGKEoaEB2rdrg/bt2gAA3ryJwjUfX/j4+sPX1x93gu4i6O49rFm7HioqKnh471axXgMREREJZaWlQK+KA97cvyqL2XX4Bipq8t3ANyE1Bb1Xz0NE7FtB3K6yBbZ9/T00VFXlOh4R0ccSEhNx7NgJ7D9wGAG3AmV7djjVdlRwZkREREREVFhyKXbUru2Aa9d88ORJKKpXt8mzzZMnoQi4FYgGnu6fNZaJiTG6dumErl06IfjRY+zesw/btu9Ceno6srKyPqtvIiIiek9D3xQeo5fizYNruLd/EZSUxbDy6CjXMdIyMzBg3SLcexkhiFvoGWL3yMnQ0+RylURUMrKysnDh4iXsP3AY5y9cQkZGBqRSKczNzdC1c0d069oZNjbWik6TiIiIiIgKSS7Fjr69e+LSpSsYPuobLF44F851nATH7wTdxfgJkyCRSNCnd89ijxP+9Cl8fPzg4+sPP//riI6OgVQqhaqqKjw93ArcIJ2IiIiKx8TeC0a27khPiIZISX7LSWVLJBi1dRWuPnkoiOtpaGHPqEmw0DOQ21hERO/cunUbBw4dxtGjJxAXHw+pVAptbW107tQB3bp0goeHm6JTJKIyzurRBUWnQERERHmQS7GjTeuW6NK5Iw4eOoIevfqjRnUbVKtWDQAQHh6OkCehkEql6NSxvWw5qsLat/9gToHDzx+vXr2GVCqFWEUFTnVqo2/vnvD0dIdLvXpQU+MSF0RERCVFSVkFGvqmcutPKpViyv5NOHznuiCuIVbF9uHfo5aphdzGIiL6UM8+AwAAKirK8G7SGN26dkKrls2hpqam4MyIiKii+2HieMTFxgEA9PT1FJoLEVFFJJdiBwAsmDcbNWtWx5q16/E45AkehzyRHdPVrYQRw4dh9MjhRe530o8/QyQSoUZ1G4wYPowbkRMREVUAC84cxL9XzwpiykpK+HfwN/CwtlVQVkT0JXB0sEe3rp3RqVN7GBkaKjodIiL6gtR3qafoFIiIKjS5FTtEIhHGjBqB4V8Nxd279/HiZc6G5eZmZqhd2wFisbjYfUulUoSFh0P7ujZUlJUhFos5m4OIiKic2uR7AX8e35srvrDnMLR1dFFARkT0JTl0YLeiUyAiIiIiohIgt2LHO2KxGPXqOaNePWe59Hf29FH4+vrjmo8f/Pyv49aq21j5z1qoqqqiXt06aODpgQYNPFDXuQ6UleW3jjgREdGXJis9FXf3zEWNVkOhbVK1RMY4fjcA3+9elyv+c7ueGOTZtETGJCIiIiIiIqKKT+7FDnmrVrUqqlWtir59egEAQkKe4JqPL3x8/eF//QZ8/a5j8dK/oamhAVfX+li3dqWCMyYiIiqfnpzdhAi/Q4i8fhTVmvSBbdsRUNWsJLf+fcMe4etNyyGRSgXx4Q1b4vuWneU2DhHRh/btP/hZ53fv1kVOmRARERERUUmSa7EjPT0dPr7+CAsLR1JSEqQffZgB5Cx3Ne5/Y4o9Ro0a1VGjRnUMHjQAz1+8wPoNm7Ft+y4kp6Tg0uUrn5M+ERHRFys19jVCzm4EAEgl2Qi7sA3xz+6j4Xf/yqX/B68i0X/tQqRlZQrinZ3dMLvbIIhEIrmMQ0SFI1IRQymPuBQ5799FyP07KVIp/rK0ivRuD8CikkqlEIlELHYQEZHcxMTEIjMz5/2wWCyGgYG+gjOSr18ObVN0CrnM7Ny/RPqNjHyOJs1a49txY/Hdt9+UyBj5sanpiB7dumDe3D9LddyiWrz0byxdtgKXzp+CpaVFvrGC9BswFJHPn+PyhdMlnS5VEHIrdhw9dgK/Tp+J+PgEWezdDcLH3xe32BETEwsfXz/4+PrDx9cPT58+k/WrpKQEBwe7z3sRREREX6iHR/6GJDNdEKvR+iu59B0ZG41eq+chLjVZEG9U3R6r+o+GslJeH7kSUUlqP+9ynvGYqJx99wyMzUoznRI17n9jchU7IiIisP/AYWhoqKNRw4awtDQHAERGvsDVa9eQmpqG7l07w9LKUhEpExFRBfXNuAnw878OAPBwd8P2rRsUmxCVSZGRz7Fn3wG0btkcDg72ik6n3Og3YCj8/K/DQF8f584eRyUdHcFxXz9/9B84LN8C1dVrvti6bQduBd5GTEwMtLS0YG9XC507d0SPbl2gopLzMfq7QldhbduyHp4e7p/34qjQ5FLsuHEzABMm/ggNDQ2MGTUcvv7XcevWbcya+RvCw5/i5KkzeBYRiSGDB8CxiL+kZ86ewzWfnALH48chAN4XTexq2cLT0x0NPNzh5u6a6y8xERERfVrc03uIvH5UEDO2awBTh4af3XdschJ6rZ6HF3Exgnht8yrY8tV3UBerfvYYREQF+fhm9tmzCHTt0QcdO7TDb79MzfVUbVxcHKbP+BNnz1/A/j07SjNVIiIiIkQ+f46ly1bA0sK8whU7/jd2FMaMGg5V1ZK7D4yJjcWKlasxZfLEQrWXSqX4dfpMbN22E5aWFujZvSusrKwQFxeH02fO4qepv2LP3v1Ys2o59PT0YGCgj4Xz/xL0cf36TWzfuRutW7VE2zYtBceqV7eR22ujT5NLsWPN2vWQSCTYvHEt6jjVxqQff8atW7dl+2x8P+FbTJ8xC7v37MPAfbuK1PeoMd/Kvq5ZswYaeLjD09MdHu6u0NPTk0f6REREXyypVIq7+xcIgyIlOHT97rP7Ts3IQP91ixD8+rkgXsXACLtGTkIlDc3PHoOIqKjmL1wCbW1tLJg3W/aE3of09PQwf+6faN6qPeYvXIJlSxbk0QsRVURWjy4oOgUiKuOkUimSk1MUnUa5paKikuf7L3lyruOEjZu2YtCAfrCwMP9k+xUrV2Prtp1o1bI5li6eDzU1NdmxkSO+wsp/1mDe/MUYP2ESNq5fA01NTXTt0knQR1ZWFrbv3A07O9tcx6h0yWXdiFuBt1HHqTbqONXO87iqqip+/20adLR1sGTZiiL13b9fbyxbsgD+Ppdw4ugB/PbrVLRp3ZKFDiIiIjl4GXgGsaG3BbGqXt1QybzGZ/WblZ2N4Zv/hl/YI0HcUEsHe0ZORuVKep/VPxFRcfn4+KFeXecCb7RVVFRQr64zfH39SzEzIiIiKkv27N0Pm5qOuHLVB6v+WYsWrTvAzrEuFi5eKmh3+co19OozAA5O9VHPtQEmTPwRsbFxsuN3gu7CpqYj/pozP89x5s5fBJuajrh+4yYWL/0b/QcOAwBMnjINNjUdYVPTEY2btsp1XtDdexg0ZDic6rqhTl13jBz9P0RERAra+Pr5w6amI/bs3Y9du/ehbYeusK/tgibNWmP7zn0AgMTEJPzy2wx4eHnDzrEeevTqjztBdwv9c0pKSsaMmbPh4eUN+9ou6NSlJ44dP5ln28VL/4ZNTUdERgofiAt/+hSjx36LOvU84FTXDYOHjsCDBw8LncOHfpoyCenp6Zg7f9En28bExGLFqjUwMTHGwvl/CQod74wZNQLNm3nj8pVruHgp76Vgi6ug67N+w2YAhb8+EokEGzZuQb/BI9GwaXvUqeuOAYOG4fLlq4J2vfsNQm1nVyQnJ+fqIzY2DnYOdTF85FhBPPjRY3z73Q9wb9AEtRyc0bBJC0yfMQtxcXHy+2HIiVxKaQnxCbD8YO0xVXHO5oUpKSnQ1Mx5alMsFqN+/Xrw9SvaDcPM33+VR4pERET0kezMdNw/KHyjrqKuhVrti7e31jtSqRQT92zA8XsBgrimqip2DJ+IGiYVZy8AIip/klNSEBMT+8l2MTGxSE7hk5tERERfujlzFyApKQndu3WBsbER9PXfL4F56dIVbNy05f/s3XdYVEcXwOHf0rsUAQERAbFhL9h777332BJjNMYYjWlfYqLGaEzUqDGJsffee++9K4oiUlR677DfH+gmG0ABFxbxvM+TJ+y5c2fOsrC499yZoU/vnnTr2oVr12+wecs2oqKiWfrnIgCqVK5E5cqebN66g4mfjEf/xXVTyJgRsHnLNjzKuFO7Vk3MzMxITU1l4aIl9OvTi9q1awKorq++dPeeN4OGjKBrl460a9san4cPWbV6Hf4BgezesRmd/+yLuHrNeoJDQujbuyfFLIuxc+du5vy6ED09XXbvP4KNjTVj3h9FdHQ0f/29guEjPuDYkX2Ympq+8nuTmprKsBGjuXz5Kq1btaB+/bo8ffqMKZ9/RenSLjn6/gY9fUqv3gOJjommX9/elHF35/r1G/QbOAwry2I56uPfvGrXpHWrluzavZf3hg2mapXK2bY9dvwECQkJDB7U/5XPddDAfhw5epw9ew/QpHGjXOf0Olm9PtN+mImevh4bN23N0eszafIXbN22g6pVPBnz/nug0GP9xs0MHT6aWTO/p0f3rgD07N6NKZeusHffAXr26KaWx46du0lOSVGLnzl7jhGjPsS2eHEG9O+LvZ0tDx/5smbtBk6fPseWTWsxNzfT+PckrzRS7LCytlKbwmX1Yt3bgMAgynr8c2doUlISsbGZq0ZCCCGEKHi+x9aSEB6kFvNoMwJDc6tszsiZGfu2sPL8MbWYno4uy4aMp6aL+xv1LYQQb6pMGXcuXLzIlavXqFG9WpZtrl69zvkLF6lQoVzBJieEEEKIQic6OobdO7dgZvbPheWXMxPu3vNm947NuLm5Ahkr1Ojp6bJu/SZ8H/vh+uKCf/9+ffh86tccOHiYDu3bqvo5dPgoISGhfDB6JAAVypcjKiqKhYuWUL161WyXRLp7z5vVK5eqbXxtY2PDnJ9/5fSZczRqWF+tfWBQEPv37MDKyhKA3j2707BxC2b9vIC+fXrxw7RvVG09PMowZuzH7Ny1R7VFQXa2bN3B5ctXGTSwH99+86Uq3rZ1S7r36v/Kc1+a8/M8wsLDWTDvZ9q3awPAwAF9WfLHUmbOmpOjpaj+a/Jnn3D02DFmzJzNujXLs213zztjJYLsVit6qXKlSi/ae+c6l5zI6vVp1LQ1//v2hxy9PmfOnmPrth00adyQWT98ha6uLta2DvTv14e2Hbry7bQZtG7VEnNzMzq0b8N3309n85btmYodmzZvxdrKihbNmwKQlJTMxEmf4+7mysb1qzAyMlK1bde2Nb37DuLvZSsY95H6TBBt0sgyViVLOhEY9M/FksqVPFEqlezatUcVCwkJ4dz5iziXdNLEkEIIIYR4A0nRYTw4sFQtZmLjhGvjvm/U79LTh5h9cFum+Py+I2hZocob9S2EEJrw3tDBpKamMXjoCKbP/IkbN28RFhZOeHgEN2/dZsbM2QweNoL09HSGDRms7XSFEEIIoWWDBvVTK3T8W6uWzVWFjpcaNsgoNPj6PlbFOndsj7m5OWvXb1Rru37DJoyMjOjerXOucqpWtYpaoQNQFTh8Hz/O1L5Xz+6qC+kARkZGeHpWQKlUMmL4ULW2devUBuDRv/LPzr79BwEY8/4otXjVqlVoUL/ea89PT0/n4MHDuLqWVhU6XhoyeCBmr5lZkh3X0i7079uHCxcvsf/AoWzbxcTEAGBubv7K/iwsMo5HR8fkKZ/Xyer1qVa1co5fH9Xr8MEodHV1VXEbG2sGDehLbGwsp06fAcDU1JR2bVpz4eIltWXP7t69x+07d+ncuYNq9tHpM2d5/jyYnj26ER+fQHh4hOq/0i4ulC7twslT6stkaZtGZnY0bFCf+QsW4e8fgLNzSZo2aYSdnS0LF/+B72M/SpSwZ+++A8THx9OpUwdNDCmEEEKIN3Bvz2JSk9RnW1boMh5dfYM897nj+kUmbVmRKf6/jn3pU6thnvsVQghN6tqlI35PnrDgt8Us/XsFS/9Wf99SKpUoFAo+HDOarl06ailLIYQQQhQWbq6u2R5zKeWcKWb9YsWbiIh/ls00NjamR7cuLF+5Gj+/J7i4lCIwKIiTp87QrUsnLCwscpVTqazGtXo5bmSO2hd7cQG/lHNJtfjLfZKz6ue/nvj7Y2Fhgb29XaZjZcq4qS6wZycsLIzYuDjql6mb6ZihoQHOziWJjslbgWHcR2PYun0ns2bPpXmzJlm2MTPLWH4p5jVjxMTEAv8UPTQtq9fHsljGEl45eX1eFi3KlfXI9Dm/XLmyADx54q+K9ezRjc1bt7NpyzYmjB8LwMbNWzOOvVjuCsDH5yEA306bzrfTpmedu3Pm3LVJI8WOLp07EB8XT2hoGM7OJTE0NGT+r3P44MOP1TakadqkMaNGDNPEkEIIIYTIo+igBzw5u00tZu1eA4eqzfPc52mfu4xatRClUqkW/6BJWz5q1j7P/QohRH4Y/9EYmjdrwoqVa7h06TLPngcDUMLejlq1ajJwQN9Xru+cU76P/bl5x5tHj/x46PuE0LBwAOb99D9sbW2yPKff0I9e2ed3X36CR5msL7qcvXCF/QeP88Q/Y9Z9KWdH2rZqSl2v6m/wLIQQQoh3m4mJcbbHdPWyv7T6389G/fv1ZtmKVazbsInJkz5hw4bNpKen069f71znpPevu/dfNy6Ark72i/voZtdXFv28TaysLBnz/khmzprDmrUbKFfOI1Ob8i8KATdv3aZd29bZ9nXj5s0X7fNnidOCfn28vGrhUsqZrdt28PG4D0lNTWXnzj1UrFCeihUrqNqlp6cD8PG4D6lRI+t/TxoZZd7UXZs0Uuwo7eLC51M+VYvVqlmDE0f3c/HSZSIjo3B3c8PTs0I2PQghhBCiICiVSm5vnQvK9H+CCgWe3T5BoVDkqc87Qf4MWPoLyWmpavGeNeoxrVO/PPcrhBD5qXIlT3768Yd8HWPL9r1cunoz1+cZGhpQp1a1LI9ZWGS9AeT6TTvZtusA+np6VPYsD8DN2/f4deFS/APa0qu7zLAXQhSMmMQETvvcfW27BmXkGpF4t5Qp404dr9ps3rKNj8d9yMZNWylXrmymPcQUvD2fn0o5O/PokS/Pnwdnmt3h4/Potefb2NhgZmrKgxczCP4tKSkZf/8AiuVhk/KXhgweyKo165i3YCEzp0/LdLxpk8YYGRmxddsOxo4ZnWkj+JdWrV4HQPt22RdEtOnlzJD7D3xwK+Wgduz+/QdqbQAUCgU9unfl51/mc+78BaKjYwgLD+fDMaPVznV1LQ2AgYEBDRu8flmywkAje3Zkx9jYmMaNGtK5UwcpdAghhBCFQPCdU4R6n1eLlazdActSefs77R8eSq8lPxGdGK8Wb1q2Egv6jkLnFXeoCCFEUedRxpVundowcdxIfps7TbVcxOuYm5nxwchBWf5nb2ebqf09bx+27TqAqYkxM76bzKQJo5k0YTQzvpuMqYkxW3bs476Pr6afnhBCCCFyaUD/PoSGhvHlV9/y7Plz+vXNvAG4iWnGBffIyKiCTi/X2rRpCcDCxUvU4tev3+D0mbOvPV9HR4eWLZvj6/tYbXUggOUrVhEbF5fNmTljaGjApIkfExERycJFSzIdt7Gx5v1Rw3n+PJiJkz4nKSk5U5s//vybw0eO0bBBfZo0bvRG+eSXNq1fvA6L/iAtLU0VDw+PYOXqtZiZman2knmpe/cu6OjosGnzNjZt3oqBvj5dOqsv4dq4UQNsbYvz19LlPHv2PNO4SqWSsBczlwsLjczsEEIIIcTbwdqtGu4tBuN7bC3paSnoGhhRodPYPPUVFhtDzyWzeBodoRavVtKV5UPHYfCKKd1CCPEu6NyhVYGMs3PvYQC6dmqDk2MJVdzJsQRdOrZmzYbt7NxziInjRhZIPkIIIYTIWpvWLbGxsWHz1u0YGxvTtUunTG08yrhjZmbGylVrMTQyxLJYMUyMjWnRopkWMn61Ht26sHHjFlauWsvz58E0aFCPoKCnrF69Ds+KFbh1+85r+5g4YRwnTpxiwsTPuHjxMu7ubly/cZODh47gUsqZ1H9dvM+Ljh3asfTvFVy/kfVs27Efvs/z58GsXb+R1m070bVLR0qWLElkZCQHDh3m8uWr1KxZnXm//PRGeeSn+vXq0q1rZ7Zu28HoseE0b9oIFHqs37iZsLBwZs38HnNz9dnBjg4O1K9Xl337D5KSkkLLFs3UNkmHjIkMc+f8yKj3x9K6bSd69uiGu7sbSUlJBAQEcvDwEXp078rH4z4swGf7am/tVYi0tDQ2bNyM9/0HODo40LdvLyzM82eTGCGEEKKo0Dc2p2KX8bg06MHd7fOwcCyDUbHMdwm/TlxSIn3/nMOD4KdqcVcbO9aPnIi5Ufbr2gohRGGwe88+9u0/iK/vY2Jj47Jc31qhUHDsyD4tZJdzyckp3LztDZDl3hz16tRgzYbt3Lh1l5SUFPT19Qs6RSGEEC+UKGFP6dIuqq+Lmmmd+2s7hUJPX1+f3r26s2jxH3Ts0C7La5lGRkYsmDeHOXPnMX3GTyQlJeHk5Fgoix16enr8/dfvzJn7K3v27ufY8ZOUcXdj5oxp3H/gk6Nih5OTIxvWr+LHWXPYvGUbSpRUr1aNNSuXMu2HHwkIDHyjHBUKBVM/n0SffoOzPK6jo8MP3/+Ptm1bs2btetZv3ExERAQmJiaUL1eOGT98S4/uXdEr5Dfz/fTjD1TyrMjadetZsOhP9PX1qVzJkx+mfUPjRg2zPKdnj66qTeR7/Gtj8n+rX68uO7dvZvHvf3Lg0GFC1q7D1NSUEiXsad2qBR3atcmvp5QnirTUpEK928xvC39n3oKFrPj7T+rUqQ1kTJEZOPg9zl+4hFKpRKFQULq0C9s2r8fMzFTLGRdd4SEZF7SsbR1e07JojCuyJ69J4SOvSeHztrwmL/+O5kZKWioDl/7CwbvX1eK2ZhbsG/c1rsUL5we3t+U1eZfIa1L4vAuvSVpaGh98OJ4jR49nWeCAjA/FL489vH9LY2O/P24qUdExr92g3NzcjPatmxIaFoG+vh4lnRyoWb0ylsUsMrX3exLAlK9/xNzcjCXzZ2TZ58ixU4iNjePHaZ9TytkxT7lr82fjXfi5fBd8tWON2uOe5SsDsOle7ve1yS/O949pO4W3XqPWQwA4eWD5a9tqc88Oz64TtDb220jehzXrl3m/MW/+QrZsXEu1alW0loe8rkXTu/66Fu6SFHDq9Fmsra1VhQ6Aw0eOcu78RUqVcqZfn16cPHWGs+fOs3bdBkaOGKbFbIUQQoi3S24LHUqlko83/JWp0GFmaMSGUZMKbaFDCCFeWr5iNYePHKNJ40Z8/dUUFvy2mG3bd3H31hX8/J6wbcculv69gveGDebTT8ZrJceYmFjWb96lnvfqzfTt2ZH2bZqrxUPCMpYStP7PsgP/ZmNlSWxsHKFh4a8tdnw9bU6W8R6dmuFgX1z1AbogpaamAGhlbKE5L4sbL1kbm2QZ1yaDF3fci7yzeDFj+GXR41W0ORNY3k9yR96HNScxKYnVq9dSvpwHpZxstfo9lde1aHqbX1dNFGgKfbHjyRN/yri7q8X27T+IQqFg/q9zqORZkWFDB9GoaSt27dkrxQ4hhBAiH03bvYG1F0+pxfR1dVkxbDxVS5bWTlJCCJELO3btxtzcnF/n/oS5uRkKhQ6QsaxEmTLufPrJeGrXqsHwkWPwKOOeaaPG/Naofm3q161JKWcnTE1MeB4cyuFjpzl09BQr127FwMCAls3+WYogKTEJACNDg2z7NDIyBCAhMTF/kxdCCCFElnwe+nL/wUP2HzxCWHgEn0/6WNspCVEkFfpiR3hEBF7Fa6nFLl68gqOjA5U8KwIZH0yqVa3C5ctXtZGiEEIIUagp09NQ6Oi+cT+/n9jPL0d2ZYov7D+apmUrvXH/QghREB499KVGjWqqTRpfznBLS0tDVzfjvbJJ40ZUqVyJlavWFnixY8wo9fWkSzk7MmxQL0o6ObB0xXrWb9pJk4Z18m3vje++mphlXJaxEm9q7tmjao9lGaui6W1ZxqqkLGOVK/I+/ObOrN3CvPkLsbUtzqRPP6Z7z57aTkle1yLqXX9ddbSdwOuYm5sRFRWtevz8eTABgYF41a6p1s7Y2Jj4hISCTk8IIYQo1EK8L3B0ei+e3zqR7dr0ObHl6jmmbl+dKT6960B6VK/3JikKIUSBSktPp1ixYqrHxsZGAERHx6i1K1XKmfsPfAo0t1dp0bQ+FuZmxMbF8+DhY1Xc8MWsjcSk5GzPTXwx+8PYyChfcxRCCCFE1j4e9yGPHtzm/JnjfDB6pLbTEaLI0vjMjrv3vLl+/QbhEZGU9XCnZYuMNWWTkpJJTk5W3UGVU6Wcnbl06TJRUVEUK1aM7Tt2oVAoaNigvlq74OBgbGysNfY8hBBCiLedMj2NO1t/Ji7YjwtLJlC8XB08u07AwskjV/0cv3+bD9YszlQsGd+8I+83bqPJlIUQIt/Z2dkSEhKieuzkmLGHxT1vb+rVraOKBwQEoqf35rPiNEVHRwd7e1uiY2KJiIxSxW1trAAIj4jM9tywF8eKy+clIYTQqpWr1/LkiT+QUVQfNKCfljMSQoiiRWMzO3wf+9GrzwA6denJl19/x89z53Hg4BHV8e07dlG9Vj1OnDz1il4y69WzG3Hx8XTu2pv3x4zn57nzMDMzo3mzJqo2ycnJ3Lx5GzfX0pp6OkIIIcRb78m5HUQHPVA9DvU+T+Dlfbnq40bAYwb9/QspaWlq8X61G/J1h94ayVMIIQpS+XJleeT7WPW4bl0vlEolv85bSGxsHADbtu/i6rXrlCtbVktZZi0uLh4AQ0NDVcyhhD36+vrExMQSGhae6ZzQsHBiY+MwMNDHoYRtgeUqhBAisz179vPX0uX8tXQ5e/bs13Y6QghR5Gik2PH8eTB9+w/mytXrNG/WhCmfTcx092eH9m3Q09Nj//5Dueq7R/eudGjfloDAQA4eOoy+gT7Tv/8fFhYWqjaHjxwjLj6eunW8NPF0hBBCiLdeamIc3rsXqcUMzK0p02pYjvt4HBZM7z9mE5ukvqFtqwpV+aX3cNU690II8TZp2rQxISGhnD5zDoAqlSvRtEljLl66TE2v+tSoXZ9PP/schULBuI8+0HK2//APCCLo6XMA3Eo7q+IGBvpU9iwHwLkLmfcwPHv+CgBVKlXIt30+hBBCCCGEKAw0sozV/N8WExoaxvfffU2/vhl3ec74cbZaG1NTUypWKM/V69dzl6CeHvN+mc3kSZ8QFh6Om6srZmamam1cSjmzeOE8qlWr8mZPRAihZs+kRihTUzLFlWQUMxVkvtCp0NOn/U8n8z03IcSrPTj4N0kxYWqx8h0+QN84Z8tJhsRE0fP3WQTHRKnFa5ZyZ+ngsejranwlTCGEKBBdO3ekerWqFLexUcXm/zqbH3/6mX37DxIVGUXZsmUYO+Z9tWWtCsLxU+cpVdIR138VMwB8H/uzYPEyALxqVcPaylLteKd2Lbhy7Rbbdh2gelVPnBxLABAY9Iztuw5ktGnfMt/zF0IIIYQQQps0cqXi2PETlCvnoSp0ZKdkSSdOnzmbpzGcnBxxcnLM8ljFihWoWLFCnvoVQmRPmZpCelrmYofqeBYxja2NJ4TIs/iwIB4dVd9M3MLRg1J1u+To/JjEBPr8MYdHoc/V4h52DqwbMRFTQ9ngVgjx9jI0NKSsRxm1mImJCd9+8yXffvOlRse6cu0WW3f8s0xJ7ItlqObM/xN9vYyPYtWretK9S1sALl2+zuI/V+FYwg4npxLo6erxLDiEx34BKJVKXEs7M3Jo30zjlC9Xhi4dW7F910E+/2aWaqbHzdvepKSk0L1zW8qWcdXocxNCCCGEEKKw0UixIzQklOrVqr62XVpaGvHxCZoYMt/4Pvbn5h1vHj3y46HvE9W6t/N++h+2tjavOTtrN2/fY+eewzx6/ITUlFQcHexp3rQ+LZo2yHYJkISERLbt3M/5S9cID4/E1NSEShXL0bNbe+ztimt0LCGEEEXL3Z3zSU9NVotV7PYJCp3Xb7SbnJrKkGXzuBbgqxYvYWHJplGfYWNmrtFchRCiKIuJicXn0eNMcb8nAaqvHR3sVF83auCFgaEBj/0CuHPPh8TERIyNjKhQrgx1varTrHE99PSy/gjXt2dnXJyd2HfwOHfuZezX5OpSknatm1HXq7pmn5gQQgghhBCFkEaKHeYWFjx7/vy17fz8nmBjY/3KNvPmL0ShUDBoYD8sLS2ZN39hjvNQKBR8NPbN1tXdsn0vl67efKM+/u3Q0VMsXbEBhUKBZ4WyGBsZcvO2N38tX899H1/GjByU6Zy4uHi++WEugUHPKG5jTc0alXn+PJRTZy9y+dpNvp4yntIuJTUylhBCiKIl/NF1gq4cUIvZV2qMbbnX72uVnp7O2HVLOHb/llrcwsiEjaMm4WydfbFdCCFEZk0a1aVJo7o5bu9Vqxpetarlebx6dWpSr07NPJ8vhBBCCCHE20wjxY5qVStz8uRpfHweUqaMe5Ztrl+/wd173nTu1OGVff36otjRoUM7LC0tVY//u+F5VjRR7PAo44pzSUfcXEvh5lqKqd/MIio6Jk99PQ8OZdmqTejo6PDFZ2OpUC5junx4RBTfTp/LydMXqFq5Ag3q1lI7b9W6rQQGPaNGtUpMGDtcdffWtl0HWL9pJwt+X86s7z9HR0fnjccSQghRdCjT07m9dY5aTKGjS8WuH+fo/K93rmPTFfXlJg109Vj93sd4OpbSVJpCCCGEEEIIIYQQGqeRYseQwQM5cvQ47384njmzplO1qvpG4XfveTPxs6no6OgwZNCAV/Y1a+b3ANjZ2qo9LiidO7TSWF97DxwjLS2N1i0aqYoPANZWxejXqwu/LlzKzj2H1QoQUdExnDh9AV1dHUYM7as2Tb1Lh1acOXcJ/4CnXLl2i1o1qrzRWEIIIYqWwMv7iPS7rRYr3ag3ZnYurz13/tHdLDy+Vy2mUCj4feAHNCgj+2IJIYQQQgghXu32trnaTiETz64T8qXfgIBAGjdrzbiPxvDxuA/zZYzsuHl40qNbF36aNb1AxxXibaCRYkfDBvX48INR/LZoCT16D8DO1haFQsHRo8dp2rwtAYGBKJVKPvn4I6pVq/LKvnp07/rKx2+TK9cylsOq61Uj07FaNSqjr6+P35MAQsPCKf5iea9rN+6Qnp5OxQoeWFkWUztHoVBQp3Z1/AOecunqTbViR17GEkIIUXSkJidwd+cCtZi+iQVl24587bnrL53im53rMsVndR9Ml6qvX/5KCCGEEEIIIYRmBAQEsmnLNlq3bE7FioX/xjM3D0+1xyYmxhQrVoyyHmVo0KAe3bt2wdraKtN5/QYM5fyFizx68M8Ne+fOX6D/wGGqxzo6OpiYmGBrW5wK5cvRqmVz2rVtjYGBQf49IfFW00ixA+CTCeOoWLECvy38nTt37wEQHhFBeEQEHmXcGffRGNq3a6Op4Qq9+PgEQkIzNjd3dXHOdFxPT4+STiXwfeyP35NAVQHi5WaFbi5ZLxfysq8nTwLfeCwhhBBFx6Mjq0iMVN8/q2y7URiYFsvmjAyH7t7go3V/ZopPbNWF4Q1aajRHIYQQQgghhBCvFhAYyLz5Cynp5PhWFDsA3N3c+HDMKACSkpIIDg7h4qUrzJg5m98W/s6PM76ndasWOe6vdauWtG2T8Xk0Li4e/4AATpw8xYSJk/lt4e8sXPBLtlspiHebxoodAG3btKJtm1aEhYVnzOZIV+LgUAJ7eztNDvNWCAnLKD6YmhhjZGSYZRsbK0t8H/sT+qItoPra2toyy3OsrSzV+n+TsbLz9bQ5WcZ7dGpGYmISf3wzl1nf/jMN8PvZSwgJjeC7qR9SzMIMgCXLN3H77kPGjuyLh3vG8inb9hzl6IkL9O7WmgZ1qgNw5sJ11m/ZR5OGtejeMeNN76GvP/N+X0OFcm68P6wXqakpxMbGM37KjxS3seSrSaNVY0/59lcSEhL5+YdJ6Opm7GHy88KV+D0JYsqE93Cwz1gObfXG3Vy4fIv3BnalaqVyABw8epZd+0/QsU1jWjWrB8D1W94sXbUNr5qVGNArY3+Zp89DmDl3KS6lHPlkTMYm72lp6XzyxU8YGxsx85vxqnym/fQ7oWGRfP/lWMzNTAFY/PdG7no/Ytzo/ri7ZhSjtuw6zPFTl+jTvS31vaoCcPr8VTZsPUCzxl50bd8MgAcP/Vjwxzo8K7gzakhPAKKiY/l6+m/YFrfiy09Hqcb+7Ju5JCUl8+vMyarY7AXL8Q94xtSJI7C3tQFgxbqdXL52hxGDu1O5ogcA+4+cYc+Bk3Ru15QWTeoAcPXGPdZE1MRNPxgv40cARKaZsC+uCsV1Y2hpmlF5T1Mq2BhTB0NFCt3ML6NESXjIU779cTHhEVFM/3ocpibGACxauoF79335+IOBuLo4AbBpx0FOnrlC/57tqVOrMgAnzlxm845DtGxah05tmwJw74Evi/7aQGVPD0YM6g5ARGQ0/5u5CHs7G6Z+MkL1vD/9+mdSU1P5ZfpnqtisecsIDHrOF5+OxK54RsFv2dodXL1+l1FDe+JZPuOP5N6Dp9h3+DRdOzSjWaOMO9ovX7vDinU7qe9VlT7d2wLgH/iM2fOXU8bNmY9G9QcgKTmZz76ei7m5Kd9/MVY19tczFhIVFcPM/32M8Yvf0d/+XMd9Hz8++XAwLs4OAGzcfoBTZ68ysE9HalfPuDPi+OlLbNl5mFbN6tGxTWMA7no/YvHfG6lauRzvDegKwPOQUH6Y8xeOJWyZ8vF7qrE/+XI2AD9//6kqNvOXpTx9FsLXn43G5sV7zdLV27h+05v3h/WiQjk3AHbtP8HBo2fp3qkFTRpkLIF38eptVq3fRcN61enVpTUAfv5P+fm3FZQt48KHI/oCkJCYxJT//UKxYuZ89/kY1dhf/rCAmJg4Zn03AcMXd2LMX7IGn0f+fPrREJydSgCwfss+zly4zuC+nahZrSIAR09eYNvuo7Rt0YB2rRoCcPveQ5Ys20T1qhUY2q8zAMGh4fww+w+cHO35bNxQ1dgfT52Fnp4es7/7RBWb/vOfPA8O439TPsDK0gKAP1du4ebtB3wwvDflPVwB2LnvGIeOnadH55Y0rp+x8ev5SzdZs2kPjerXoGfnjCUQff0C+WXRKsqXdWXk4G4ZPyt+j5j63TysrYrxzeT3VWNPnTaPuLgEZn8/Ef0XSxb+sngVvo8D+Wz8MJwcMv52rt28l3MXbzC0fxeqVykPwOHj59mx9xjtWzeiTfP6JMWEcXT3Fk7E1MVFP5R6xj4YWTuRaF2RQcM/xrlkCT4dO0Q19vgpP2JoaMDA0T0ZuuZ3UtPTqB9ljmmaDscto+lavTbv16zPzNnzNfZeDhATG8eX3y/Q2nv5jLlLKe3swCcfDgbenffyZWu2U7d2Ffr1aAdA4NNgZv36N66lnfj4/YEApKSm8umXczA1NWb6V+NUY+f3e3lqagqRUTGMn/KjvJcDYeGRfDfrdxy0+F7u4V6K94f1JDzkaYG/l6/86xfyw7LlqyhTxp2GDerlS/9CCCGEeHcolUri4uK1nUahU7y4DV27dMoUv37jJiNHf8hH4z9hw9qVmbY+yE758mUz9Tfls4ls2ryVz7/4hiHvjWLf7u2Ym5tpJH9RdOi8vknu2dhYU7VKZapVq/JOFjoAkhKTADA0zLr4AGD44oNywou2AImJyS/Oy3o61stiRuK/zsnrWEIIIYqG51f2o0xNUYu5tnoPha5utuekK9MZvmEp8SnJavFGrmX5rm03FApFvuQqhBAFbdoPM9mxc7fqcZNmbZg5K+ube4QQQgghXtq0eStuHp6cOn2Wxb//SYvWHSjvWY2ff5mn1u7kqTP06jOAipVrUr1WPSZMnExERKTq+I2bt3Dz8GTmj7OzHGfW7Lm4eXhy8dJlfpn3m2oZp8+mfImbhyduHp40app5j+Gbt24zaMgIKlerTZVqXox6fyz+/gFqbc6dv4CbhyebNm9lw8YttO3QlQqVatC4WWvWrt8CQExMLF998x116jehvGd1evTqz42bt97kW6dStUplZs38gZSUVH7+Zf4b99ezRzeGvzeEp0+fsXL12te2f/kanjl7jt+X/EWzlu0o71mdVm06qf59+Px5MBMmTqaWV0MqVKrB4KEjeeznl6mvpKQkfp2/kJZtOlLeszrVa9VjxKgxXL9xU9UmPT2dhk1a0qRZG5RKZaY+vO8/wM3Dk/9994Na/NLlK4wYNYYatetTrmJVmrVoy5yffyUpSa7j5pYiLTUp83deqLw/bipR0THM++l/2L64mzIn7j94xDc/zMXaypLf5k7Lss2C35dz+uwl+vTsRNeOGXfVTf/pN27evsfIYf1o3qR+pnOePQ9hwuTv0NPTY+Wfc99orNwKD3kKgLWtQ57OzyttjStg94S6pKelvL7hv+jo6tNh7rl8ykhkR35PCp+CfE2U6ekEXNrLvZ0LSIwKpnhZL+p+uDDbgsWz6EjazvuWJ+GhanGv0h5seX8yJgbZF8/fZvJ7UvjIa1L4FMXXpGyFqnTq2J45P80AwL1sJXp078qsmd9rObO3izZ/Noriz+W76Ksda9Qe9yyfMRNw072bWTXXCuf7x7SdwluvUeuM2cQnDyx/bdsGZbS3PE9+bVr9OocPHyU4JAQAO1tbWrRoppU8ciun78NFbYPyTZu38tmUL/GsWIHY2Fi6d+uCrW1xrKys8KxYgcbNWlOtahV8Hz+mT++elHJ25tr1G2zeso0mjRux9M9Fqr66dO9NUNAzzpw8jL6+viqemppKg8YtsCxWjP17d3D3nje79+xj4aIl9OvTi9q1M1YWMDExUS0D5ebhScUK5QkIDKJrl46U9fDA5+FDVq1eh7u7G7t3bEZHJ+P+9pd7YFStUpngkBD69u5JMcti7Ny5m8tXrjF54kfs3n8EGxtrmjZuRHR0NH/9vQJ9PT2OHdmHqanpa79Pbh6e1PGqzdrVy7Jt06BxC0JDQ7l66SwmJibAq/fseNXG70+e+NO0RVtq1qjGxvWrX5nby9ewapXKxMfH07VrJ4wMjVi3fiM+Dx8x75fZzJw1hxrVq1G7Vk0Cg4L4e9lKSruUYs+urarvY1paGoOHjuDsuQs0bdKYpk0aEhISyuq164mPi+fPPxapZhHP/WU+839bzJpVf1O3jvoemD/MmMVfS5ezY9tGKnlmzHzetn0nkyZ/gYeHOx07tMfKshi3bt1h4+at1KxRnZXL/0RPL+eLM73r/27S2DJWKSkp7Nq9l3PnLxIcHExycnLWDRUKVq9YqqlhC62XMyleVYF7OSPD+F9LTxkZGbw4L+vv38sZHf9eriqvYwnxOllVoYUQhY9CRwdnrw44VG3OoyMrKVGlWbaFjuiEeHov+SlToaOcvRNrh39SZAsdQoh3l42NNXfv3dN2GkIIIcRbU9wQ6qKjY9i9cwtmZv9c+A8IyNhL9+49b3bv2IybW8YSyP379UZPT5d16zfh+9gP19IuL+J9+Hzq1xw4eJgO7duq+jl0+CghIaF8MHokABXKlyMqKoqFi5ZQvXrVLJeGejnu6pVL1S6m29jYMOfnXzl95hyNGqrfQB0YFMT+PTuwerE8fu+e3WnYuAWzfl5A3z69+GHaN6q2Hh5lGDP2Y3bu2kPfPr3y+m1TU7FCeQ4fOYZ/QCDlynq8UV+lSjljZmrKI9/HOT4nITGRHds2qlbF6dC+LY2btmLcx58y8ZPxjHl/pKqtjY0102f8pPZ93LxlO2fPXaBvn55M//5bVdtu3brQvmM3vvzqW44c2oOOjg49undlwcLf2bx5m9rrk5qayrbtuyhfvqyq0BEaFsYXX31Lo4YN+HPJb6riCkD9+nX5aPxEduzcTfduXfL0vXoXaaTYERoWxsDBw/Hxefjai6PvyrIYti82AY+LTyAxMSnLvTTCXkxp+/eG4S+/Dg+PzLLf8Bfn2P7rnLyOJcSrJMVGoExP1XYaQohc0DM0pmy7UdkeT0pNYeDSX7gV9EQt7mhpzcZRk7AylfVOhRBFT8P69diybQdNmrWhZElHAE6cPMWAQcNef/I7cqOWEEIIIbI3aFA/tULHv7Vq2VxV6HipYYP6GcUO38eqYkfnju2ZPuMn1q7fqFbsWL9hE0ZGRnTv1jlXOVWrWiXTrIFGDesz5+df8X38OFOxo1fP7qpCB4CRkRGenhU4eeosI4YPVWtbt05tgFwVE17HzCzjs2ZMTIzG+gsNC8tx+yGD+qst/29nZ4urmysPHvgwfNhgtbYvv6++vr6q7+O+/QcB+OjDD9TaurmWpnOnDmzavJXbd+5SuZInpUo541W7Fnv3H+B/33yhmh1z9NgJwsLC+GD0P/sU7tm7n4SEBPr17UVkZFSmPIyNjTlx8rQUO3JBI8WOH2f9zIMHPlSpXImRI4bh7u6GWQ6mORVlJibG2Ba3JiQ0HF8/fyqUK6N2PDU1lYDAZwC4lHJSxV1KlQTgkZ/6haiXfP38ASj1r3PyOpYQ2VEqlVxf/W3mAwoddHR0UZJR1FSQuXip0NPPFBNCaF9aejrvr17MqYd31eLFjE3YOGoSJa1yvlSjEEK8TT6fMonIqCiOnzhFQGAgCoWCkJBQQkJCX3vuu3KjlhBCaMNpn7uvb4R2l7sSAsDN1TXbYy6lnDPFrK2tAIiIiFDFjI2N6dGtC8tXrsbP7wkuLqUIDAri5KkzdOvSCQsLi1zlVCqrca1ejhuZo/bFLMwzjjmXVItbWlpm209excbGAmBubq6x/nKzOXmpUqUyxSyLFcPezi7THsiWxYoBEPGv4oO/fwDm5uY4OJTI1E+5chkzVZ488adyJU8AevboyvkLF9mz9wC9enYDMpbU0tfXo0vnjqpzfXweAjD6g4+yzT0k9PX/ZhX/0Eix4/iJk9jb2bFqxV85WsstNzp16Ymzc0kWLvhFo/0WhBrVKrP/0HHOXbiSqQBx6cpNUlJScClVUm22RbUqFdHR0cH7/kMiIqOwsiymOqZUKjl/8SoAtapXfuOxhMhOwIWdPL99Ui1mZu9Ko09Xomdo/Nr1/5RKpVwcEKIQUSqVTN22iu3XL6jFjfT0WTtiIhVKlMzmTCGEePtZW1vxx++/kZKSQnBICI2btqZd29Z8PvlTbacmhBBCiLeAiYlxtsd0X7GXwn9Xv+nfrzfLVqxi3YZNTJ70CRs2bCY9PZ1+/XrnOic9Xd0cjwug+6/lkTIdy64vDS5tfufuPfT19XAu+eY3YT954k9sXBw1y1XL8Tm6ulk/f51s4vBmS7u3b9eGb7+bzuYt2+jVsxthYeEcO36CZk2bYPOva7Pp6Rlj/DDtG5ydMxekAIoVy10h7F2X/SuaCzExsVSvXlXjhQ6Ah498c7UJS0ELj4hk4pRpTJwyTbXE1EvtWjdBV1eXw8fOcNfb51/nRLF243YAOrVvoXZOMQtzGjfwIi0tnT+XrSM19Z9lhHbsPoh/wFOcHEtQo1qlNx5LiOw4Vm+NS4Meqsc6egbUHDYDPcPs/8BDxh+CgIt7OD6zD8lxkfmcpRDvruS4KBKjc353x9zDO/nj1EG1mI5CwV+Dx1LXtaym0xNCiEJJX18fJ8eMZaxMTExwcnLM0X9CCCGEEJpQpow7dbxqs3nLNpKSkti4aSvlypWlRvVqau2yWkXjbXbs+EmePn1Gvbp1VJuTv4k16zYA0Lx50zfuK6dKlXImJiaGZ8+eZzp2/76Pqs1LxsbGtG/flouXLvPkiT/bduwkJSWVnj26qp3r6pqxzJm5uTkNG9TL8r+Xs0VEzmikiuBSypn4hARNdJVJKeeSxLyY6lQQrly7xdYd+1WPY+PiAZgz/0/0XxRdqlf1pHuXjPX10lLTCHoWrPr63+ztbBk6sCdLV2zg+x/nU6liWYwMDbl5x5uEhEQa1a9Ng7q1MuUwsG83Hjx8zJVrt5gweRoe7qV5FhyC72N/jI2MGDt6iNqGNW8ylhBZ0TUwokqfqRQvW5vr676nQqePsHB89QZSscFPuLlxJqHe5wG4u30eVft/XRDpCvHOubd7IQEX9+DRahhuzQagq5/9puKrzh/n+z0bM8V/7vUe7SrVyM80hRCiUHp4/5a2UxBCCPGO6jdgKOcvXASgjldt1q5ept2ERIEb0L8P4z7+lC+/+pZnz5/z/vsjMrUxMc0oCPx3D4e30Y2bt/hsyhcY6OszYfzYN+5v85Zt/LV0OY6ODgwa0F8DGeZMm9YtOXrsOPN/W6y2mftjPz927NxNKWdnPCuqL7nXs0dXNmzczOYt2zhw6AjFi9vQtEljtTYd27dj9pxf+XXeQpo0bpRpaa6UlBTi4uJUS4uJ19NIsaN3rx7MmTuPp0+fZbl22Zvo1KkDCxctIejpUxwdsl4yR5NiYmLxefQ4U9zvSYDqa0cHuxz317JZQ+ztirNjzyF8HvqRmpaKo4M9zZs0oGWzBlmeY2pqwrSvJrJ1537OX7zGxSs3MDUxpkG9WvTq1h57O1uNjSXEqzhWb4W1e3UMzV+/lv/1Nd8S/uia6vGTc9spWacTNu7V8zFDId49MU8f4nd6CyjTubfrN/zObKFqv6+xLeeVqe3+21eZsDHzxrqft+3B4LpNCyBbIYQo3JKSkrl1+zbPn2fcvGRvb0clT08MDQ20nJkQQgghiqI2rVtiY2PD5q3bMTY2pmuXTpnaeJRxx8zMjJWr1mJoZIhlsWKYGBvTokUzLWScM6GhYWzbvhOApORkQoJDuHDxMmfOnsPc3Ix5v86hatUqOe7v3r37qv7i4xPwDwjg+ImT3Lt3nzLubixc8Eu2m8bnh+7dOrN9x07WrtvAs2fPadK4AaGhYaxeux5lejrfT/sm043ptWrWoHRpF/5evorY2FhGDB+aafUie3s7ZvzwLZ9N+ZLmrdrTo3sXXEo5Exsbx2O/Jxw4eJjJkybQs0e3AnuubzuNFDuGDR3EzVu3GTD4Pf739VQaNqif6QXOq9Ej3+Pq1Wv0HzCMzyZNoEXzZvn64aNJo7o0aVQ3x+1tbW1Yu2z+K9tU9ixPZc/yucrD2NiI/r270L93l1ydl5exhHgVI4viOWrn2f1TTs4ZDMp0VezGuh9oMnktOrJpuRAac3vrXLXfs4Twp+gbZf5H3oXHD3hvxQLS0tPV4sMbtODTVrn72yKEEEVNUlIyc3+Zx5q1GzLNUDcxNqZ//z5MGP+RFD2EEEKIXPDsOkHbKRR6+vr69O7VnUWL/6Bjh3ZYZLFht5GREQvmzWHO3HlMn/ETSUlJODk5Fupix8NHj/jk0ylARv5WlpZ4eLgzZfJEunftotq0PacOHDzEgYOHUCgUmJqYUNy2OBUrlGf0yOG0bdO6wP+Npqenx9I/F7Po9z/ZuWsPp2ecwcjYmJo1qjNu7PvZFnJ69ujG7Dm/ANCje9cs23Tt0gl3dzf++PNvtm3fSUREBBbmFjg6OdCndw/q18/5dWoBirTUpFzvttKkWZss44FBQUDGJjm2drboKDIXPBQKBceO7MvVWOnKdJ4+faaK2dhYY2Ro9MZ9i9x53abURW1ckb3sXpNbm2fje3ytWqx8xw/xaP1egeX2rpLfk8InP16T53dOc2HxOLWYU6321Bg8TS3m/TyQ9vO/JyJefRnITlVqs3Tw2FduTleUye9J4SOvSeHzLrwmycnJDB46kkuXr6BUKild2oVSziUB8A8IxNf3MQqFgtq1arJi2R/o68tNG6Ddn4134efyXfDVjjVqj3uWrwzApns3tZFOlpzvH9N2Cm+9Rq2HAHDywHKN9dmgTIXXN8olbV2Uf1uXsZL3Yc36Zd5vzJu/kC0b11KtWs5nO2iavK5F07v+uuZpZkdAYOArj6ekphIU9DRPCeVkrNDQMI30LYSAtOREHh5ZiXvzQegaZC4i5kb5Dh/w9NphEqOCVbH7+//EsXorTG2dX3GmEOJ10tNSuLN1rlpMR9+QCp3U1z0NjAyn5+8/ZSp0NHAvz+8D3n9nCx1CCPHSilVruHjpMp4VK/C/b77ItCno1avX+ea7H7h46TIrVq1h+LAh2klUCCGEEEVOYmIia9aup5JnRa0WOoQoqvJU7CjITf1kA0Eh8tftrT/jd3ozQVcPUnPoTMwd3PLcl56RKZV6TuLSX5NUsfSUJG5unEmdDxagUCg0kbIQ7yS/01uJfe6rFivTYjDGVvaqx5HxcfRaMovASPWbAio6OLPqvY8x0pflWIQQYseO3VhYmLP87z+wsrLMdLx69aosX7qEZi3bsX37Lil2CCGEEOKNed9/wN2799ixcw+hoWF8/903rz9JCJFrcnunEO+woKuH8Du9GcjY9PjE7IEEXn6zpeBKVGmGvWcjtVjIvXMEXTnwRv0K8S5Ljo/Ge+9itZhRMVvcW/xzAS4hOZn+f/3MvWfqMyKdrYqzcdQkihkX3OZtQghRmD3y9aVuHa8sCx0vWVlZUq+uF76PHxdYXkIIIYQouvbuO8Ann07h9p07TPr0Y1q3aqHtlIQokjSyQfm8+QupWLE8LVs0f2W7w4ePcvvOXcZ9NEYTwwoh3kB8WCDX16mv85+emoyBmfUb9atQKKjUazKhDy6Slpyoit/eMge7CvXRN8m8+ZYQ4tUe7P+TlLgotVj5jh+iZ2gMQGpaGqNWLeSc7321NtamZmwa/RkOxXK3GZwQQogMSmWutzcU4p3y3304hBBCZO3jcR/y8bgPtZ2GEEWeRmZ2/Dp/IQcOHnltu0NHjjJvwaI8jeHj85Dvp/9Irz4DaNG6AzN/nK06dvnKVZYtX0VkZGSe+hbiXZOelsLlZVNJTVBf09+j1XvYlvN64/5NrB0o1260WiwpJoy7O+e/cd9CvGtig5/ge2K9WqyYcwVK1u4AZFyIm7R5ObtvXVZrY2JgwLoRE/Gwezc3JRNCiOy4ubpy7vwFoqOjs20TFRXFufMXcHN1LcDMhBBCCCGEEG+iQJexSktLz9Oa/cuWr6JD5+78vWwlV65ex8/vCeERkarjSqWS76f/yN59skyOEDlxb9ciIv3U98OxcqtK2XajNDaGa9N+WDh6qMX8zmwhwvemxsYQ4l1wd8c8lGmpajHP7hNRvNho/Mf9W1l+7qjacV0dHf4eMo5aLmUKLE8hhHhbdOrYnujoGIYNf5/bt+9mOn7r9h3eG/EBMTGxdO7UQQsZCiGEEEIIIfJCI8tY5dSTJ/5YWORuCZsTJ08x7YeZODk58sXnn1G7Vk1q11XfD6BWzRpYWVlx8NBR+vXtrcmUhShygu+e4eHh5WoxfRMLagz+AR1dzb0l6OjqU6XPF5z6ZRi8XAJCqeTG+h9oNGklOrr6GhtLiKIq9MElnt1QL2Q4VG2BjXt1AP4+c5hZB7ZmOm9enxG0qlC1QHIUQoi3zZDBAzhw8BBXrl6nS/feuLmWpmTJkgAEBATwyPcxSqWSGjWqMXhQfy1nK4R4U873j2k7BSGEEEIUkDxf2Zw3f6Ha47t372WKvZSWlobPw0dcunyFxo0a5mqcP/9ajpGREcv//gPX0i7ZtvOsWB5fX99c9S3EuyYxKoSrK7/OFK/a/2tMrDW/1I2Va2VcGvTA79QmVSw66AGPjq2lTIvBGh9PiKJEmZ7G7a0/q8V0dPWp0GUcALtuXGLS5uWZzvumQx/61W6UKS6EECKDgYEBK5f/xU9zfmHDxs08fOTLw0f/fI4wNjaiT++eTJr4MQYGBlrMVAghhBBCCJEbeS52/Dp/IQqFAqVSiUKh4M7de9y5e++V5xQvbsOkieNzNc7NW7eoVq3KKwsdAFZWVly6fCVXfQvxLlGmp3F15Vckx0aoxV0b98GhSrN8G7dCx7E8u3GUpOgwVSw64J7qvUMIkTX/C7uJDvBWi7k27Ydp8ZKceXiPkasWkv6fjXNHN2rDuOay5IoQQryOkZERX30xhUkTP+bmrds8fx4MgL29HZUreWJkZKTlDIUQQhRFRkaGmJqaqL4WQgihWXkudsya+T2QsV/G5M+/olbNGvTu1T3Ltvr6+tjb2VG9etVc3x2VmJhEMQuL17aLiIhAR1GgW5AI8VbxObSc0PsX1WIWTuWo0CV3Bcjc0jcxx7PbRK4sn4pRMTsq9fosX4srQhQVsc8fg0KhWgbOwNQSj9bvcSfIn/5/zSUpNUWtfffqdfmhS38pIgohRC4YGRlRu1ZNbachhBCikLq9bW6O2nl2nZCjdn//9fubpCOEEOI18lzs6NG9q+rrLVu306RxI7WYpjg6OnDP+/4r26SlpXH/vg8uLqU0Pr4QRUH4o2t471msFtM1MKbmsBno6uf/3SSONVqTEh9Nydrt0TMyzffxhCgKKnYZh2ONVtze8jPhD69Qrv37PEtIoteSn4hOjFdr28TDk9/6jUJHR4r+QgghhBBCCCGEeDdpZDfiNauWaaKbLDVp3JAVK9ewfsMm+vTumWWblavWEhwSQq9sZpYI8S5Ljovi8vKpKNPT1OKVe3+Omd2rl4fTFIVCQelGvQpkLCGKEkvnCtQft4TgO6fQc65E+4UzeBqtvhRdFScXlg8bj6GevpayFEIIIYQQ4u2S0xkbQggh3i4aKXbkp/dHjWD7jl18+fV33Lp9h9atWgIQHR3DhYuXOXDwECtXraF4cRuGDh6o5WyFKFyUSiXX135HYsRztXhJr444e8m6/kK8DRQKBWZlatNt8Y88CH6qdqy0jR0bRk3CwshYS9kJIYQQQgghhBBCFA6Ffr0LOztblv6xiOLFbVizdgPDho9GoVBw6PAR+g8cyt/LVmJtbc0fv/+GlZWlttMVotCxKVMThe4/dU1TOxcq95qsxYyEELmRkpbK8JW/ccnPRy1ua2bBplGfYWdeTEuZCSGEEEIIIYQQQhQehX5mB0DVqlU4tH8XGzdt5eSpMwQEBJCersTBoQSNGtanX9/emJnJPgBC/JdCocCtaX+s3apzedkUEiODqTl0BnqGJtpOTSU22A/f4+vw7D4RHd234i2p0ElITuaivy+xSYk4xkRTraQrxgYG2k5LaIBSqWTCxr85cOeaWtzM0Ij1Iz/FzdZeO4kJIYQQQgghcm3X7r08e5ax8kKJEvZ07NBOyxkJIUTR8tZcWTQ1NWXokIEMHSJLVQmRW5alKtB40moiHt+kWMly2k4HgLSUZHwOLcPnwFLS01IwtXXGrWl/baf1VgmKDGfRiX2sPn+CyIQ4VdzS2JQBdRozpkk7HIpZaTFDkRsh3ufRMzTBqnRlVez7PRtZc+GEWjt9XV2WDx1HNWfXgk5RCCGEEEII8QZWr1nP+QsXAajjVVuKHUIIoWFvTbFDCPFm9I3NsKtQT9tpABl3q59bOIbwh1dVsXu7F+FQtQXGVnKnek7cDPSj5++zCImNznQsMiGO347tZcOl02wa/RmVnQpmI3qRd2nJiVxf8x0JEc9wqtmW8p3Gsur2TeYe3pmp7cJ+o2lWrnIWvQghhMiJ98eMx9bWhmnffq3tVIQQQgghhBAaVOj37Pi3tLQ0QkJDCQwKyvY/IUThp1AocKnfXS2WlhTPrc0/aSmjt0tQZHi2hY5/C4mNpufvs3gaFVFAmYm8enRsDQkRzwAIvLyPQ9O68deWJZnafd+lPz1qFI6ipRBCvK2OHT9BZGSUttMQQgghhBBCaJhGZnZs2bodF5dS1KxR/ZXtrl69ju/jx3Tv1iVX/Z8/f5F5vy3iypVrpKSkZNtOoVDw4N6NXPUtRFESF+KProERRsVstZ3KaznVaof/+Z2E3r+gij27cZRnN09QonJjLWZW+C06se+1hY6XQmKjWXR8H9917pfPWYm8SowO5cHBv9ViwekKHukYqcU+ataeMU1kmrsQQrwpB4cSJCcnazsNIYQQQgghhIZppNgxafIX9Oje9bXFjg2bNrNx09ZcFTtOnT7L8JHvk5qahoWFOR4eZTAzLTybKwtRWKSlJHFp6WckRoVQfeB32FWsr+2UXkmhUFC59xSOz+hDeto/Rcxbm36keNlahWoT9cIkITmZ1edPvL7hv6y+cJzP2/aQTcsLKe9dC0lLileLrVXYk6ZQqB73qdWAbzr0KejUhBCiSGrTuiXr1m8iKiqKYsWKaTsdIYQQQgghhIYU6J4dSmXGBc7c+HX+b6SlpTNp4scMf28I+vr6+ZSdEG+3O9t+ITrwPgDnF3+Ee4shlO/4ATq6hfd3xszOBY/W7+G993dVLCHiGd57f8ez6wQtZlZ4XQvwVduMPCci4uNo8NPnVHN2xcPOgbL2jpS1c8TdtgQmBob5lKnIiagAb56c36EWu6Mw4arCTPW4RfkqzOszAh2dt2rlSSGEKLTGjf2AM2fPM+S9UXz7zZdUrSL7IAkhhBBCCFEUFGix49mzZ5iY5O5u7Tt37lG5kifvjx6RT1kJ8fZ7ev0Ij09uUIv5X9iJe7MBGFrYaCmrnHFvOZSAy/uIC/ZTxXyPraVk7Q4UcyqrxcwKp5jEhDyd9zgsmMdhwZnizlbFKWvvmFEEsXNUfV3czCLXxWmRO0qlkttbf864E+CFdGCtTgl48b2v4ezG30M+Ql+3QP9cCyFEkTZy9Ifo6+lx7foNevTqT/HiNjg5OmJklMUNAAoFq1csLfgkhRBCCCGEELmW56snW7ZuV3vs5/ckU+yl1NQ0Hj58xJmz56lV89VLXf2Xvr4+Li6l8pqmEEVefPhTrq/5LlO8+sDvCn2hA0BX34AqvT/n7IL3VTFleho31v1AwwlLUejoajG7wsfcyFij/flHhOIfEcrh/+x3ZGlsiod9RgHEw86RsvYOeNg54mJti56uvCaa8PzmccIeXFKLnVRY8kSRsVeHu20J1o2ciJmhUVanCyGEyKNz5y+qvlYqlYSEhBISEpplWyn8CyGEEEII8fbIc7Fj0uQvVP/4VygUXL5ylctXrmbbXqlUYmRkxEdjP8jVONWqVubRI9+8pilEkZaelsKVZVNJSYhRi5dpORS7CvW0lFXuFS9bm5JeHQm4sEsVi/S7hd/pLZRu1EuLmRU+1Uq6YmlsmuulrHIrMiGOi499uPjYRy1uoKuHm619RgHkxZJYHnaOlLFzkIvyuZCemsKd7b+oxRLQYbOOHQD25sXYNGoSxc0stJCdEEIUbSeOHtB2CkIIIYQQQoh8kOdix0djP0ChUKBUKpm/YBEVK5SnZcvmWbbV19fH3s6Wxo0aYGtrm6txxn00hn4DhrB9xy66dO6Y13SFKJK89/xOxGP1O/KtSlehXIf3szmj8KrY9WOe3zpJSnyUKnZ353xKVGmKUbHcvW8UZcYGBgyo05jfju3N8TmtKlSlSsnS3H8exIPgIB6FPCc5LTVP4yenpXLvWSD3ngVmOuZoaZ2xFJadAx4vl8ayd8LevJjcGfsfvic3EBfirxbbpWNDlEIPcyNjNo6ahIuNnZayE0KIos3JyVHbKQghhBBCCCHyQZ6LHR+P+1D19eYt22hQvy7jPxrzxglduHApU2zYkEF8+tlUDh85RtMmjXB0dEBHkfVGrV5etd44ByHeBiH3zuFzaJlaTN/YnBpDfijUm5Jnx9DMiopdxnN97T9LcqUmxnF768/UHDpDi5kVPp2r1M5xscPOvBi/9B6OQzErVSw1LY0n4aHcDw7k/vOnPAgO4kHwU+4/D3qjGSNBkeEERYZz7P4ttbi5kXGmPUE87BxxLW73Tu5FkRwXyYN9f6jFQtFnv8IGA109Vr83gUpOLlrKTgghhBBCCCGEEOLtpJGrTCePHdRENwD0Gzg0yzuAlUolu/fsY8/e/a8838f7psZyEaKwSooO4+rKr9U2Ngao2v9rTGze3rsVnet2xv/CLsIfXlHFgq4cwLlOJ+wq1NdiZoXLr0d2vb4RGYWOTaMmqRU6APR0dXGztcfN1p62nv/ElUolobHRGYWP4KAXM0EyiiFPwrNeyzwnYhITuPLkEVeePFLPQ0cX1+L2GTNBXswGydgjxAELY5M8j1fY3d+7JNPScxt17EjV0WXpwA9oWKaCljITQoh3S2RkJNu27+La9RtERERQv15dRo8aDsD9Bz48efKEBvXrYWys2f2yhBBCCCGEEPkj32+pPXnyNN73H+Do6ECb1i3Rfc3Gtt27dgZZ7kSIbCnT07m66muSYsLU4qUb9sKhatZLyb0tFAoFVfpM5fiPfVH+a5kln4PLpNjxwqG7N9hz64paTF9Xl5S0NNVjKxNTBng14YMmbTMVOl5FoVBga14MW/Ni1Hcvr3YsPjmJhyHPuB8cxIPnT1/8PwifkGckpabk6bmkpqe9mFUSlOlYCQtLtVkgL5fEcixm9VYviRXz3JdHJzfy72fggzHnFBbM6jaILlW9tJabEEK8S/btP8jkz78iLi4OpVKJQqHA3t5edfzp06e8P2Y8s2dNp2uXTlrMVAghxNvg9ra5OWpXt7QxdepkrIpSUpZVFEIIjdNIsWPN2g38uXQZP86YRu1aNVXxTz6dwo6du1WPa9Soxqrlf2FgYJBtXz/Nmq6JlIQosh4eXkHIvXNqMQtHDyp2m6CljDTLvIQrZVoM4cGBv1Do6OLatB/l2o3WdlqFQnJqKlO3rVSLGerpc/STaTwJ8iMmKREneyeqlnTF+BXvs3lhYmBIZScXKv9neaW09HQCIkK5/zyI+8HqS2KFxcVk09vrPYuO5Fl0JCce3FGLmxoY/qv4kbE5uoe9A27F7THUK/zLt51cNQ2FMl0ttkbXnomtujCiYSstZSWEEO+W69dvMH7Cp5iYmPD5lE+pXasm3Xr0VWvTsEF9zMzM2H/gkBQ7hBBCaEzL2mXx7Prh6xsKIYTIE40UOw4cPER0VDQ1qldTxc6eO8/2HbuwsrSkffs2XLh4mStXrrF5yzb69e2tiWGFeOeE+97g3u6FajFdAyNqDJuBrr6hlrLSPI/W7xEX6k+ZlkMpVrKcttMpNBaf2IdPyDO12Nhm7Slfwgk73Yx9jKxtHQo0J10dHVxs7HCxsaNVxWpqx8JiY3gQ8pQHz4MyZoIEZ3ztFx5C+n+WYMupuOQkrgX4ci3AN1MepW3sXswEyZgF8nKfEEsT07w+PY268sSHDUEhtEUHUzIKHmcVFtSr246p7XpqOTshhHh3LFycsW/SsqVLqFqlcpZtdHV18fSswP0HPgWZmhBCCCGEEOINaKTY8fCRL2XLllFbomrXrr0oFAoWzP+ZunW8iI2No1HTlmzZul2KHULkQXJ8NFeWT0WZnqYWr9xrMub2rlrKKn/oGhjJpuT/8TQqgtkHt6vFnCxtmNCi8N5tamNmjo2ZOXVdy6rFE1OSeRTyXFUAydgbJAifkKfEJyfnaay09HQehjzjYcgz9t2+qnbM1szin1kgL5bFKmfviJOlNTo6Onl+fq+SkJzMRX9fYpMScYyJxsLIhL5//kyo0pKjumZ0TQ+hgTKK4PLNWdxz2Fu9NJcQQrxtLl++SrWqVbMtdLxkW7w4N2/cKqCshBBCCCGEEG9KI8WOsLBwataorha7cPEyxYvbULdOxvrjZmam1KpZg5u3bueq7y1bt7++EaCvr4+lpSUVK5THxsY6V2MI8TaID/EnLSlBLeZUqx0lvQrvxW6hOf/buY7YpES12LTO/TAxePtm9BjpG1DR0ZmKjs5q8fT0dIKiwvH+18boL/cHCY6JyvN4IbHRhMRGc/rhPbW4sb4BZV7OBLFzfLFBugNuxUvkeRmwoMhwFp3Yx+rzJ4hMiFPFdRQK1WyWGIUeK3Ud8HWuw7rhn6H3mr2shBBCaFZsXBy2tsVf3y42lvT/LD0ohCg8nO8f03YKQgghhChkNFLsMDY2Ij4+XvU4IiIS38ePad+ujVo7c3NzoqKic9X3pMlf5OqOV4VCQcOG9fnfV1NxcSmVq7GEKMwsXTxpPHktV1d8SZjPZUxtnanS+3O5I/wdcO6RNxuvnFGLNfaoWOQ2s9bR0aGkVXFKWhWnRfkqasci4+NUBZD7/yqG+IYFk5aetwtRCSnJ3Az042agn1pcoVDgYm2rtjl6uRczQ2zMzLPt72agHz1/n0VIbOa/c/9dtqusvSPLR33+VharhBDibWdnZ8vDR76vbefj8wgnR9k8VgghhBBCiLeFRoodTo6OXLlyjeTkZAwMDNh/4CAA9evVUWsXFhaGtbVVrvr+aOwHBAYEsmXbDkxNTGjYsD6Ojhlr0gcFPeXUqTPExcfTvWtn9PT1uHLlGidOnKJP/8Hs2LoROztbTTxFIQoFY0s76o1dxIMDS7HzbIieUeHYi6AgpaUk8fzWSRyrt9R2KgUiLT2dz7asUIvp6ugwo9ugd6rQZWliSu3SZahduoxaPCk1Bd/QYLWN0V9+/d+ZMDmlVCp5HBbM47BgDt69rnbM2tQMDzvHFzNBXswIsXNAX1c320LHf+koFCzsNxorU7M85SeEEOLN1K9Xl02bt3L4yDFaNG+aZZsdO3fjHxDAsKGDCjY5IYQQRdr05Yd4+FPGCibVq1Vl0W+/ajkjIYQoWjRS7OjcqQMzfpxN3/5DqFGjGhs3bcXAwIAWLZqp2qSnp3Pr9h3KlfXIVd89unWha48+dO/amS+/mEyxYsXUjkdHRzPthx85cvQYWzeto2RJJ76f/iPLV6zm9z/+4qsvpmjiKQpRaCh0dCnbdqS209CKEO8L3Nwwg7iQJ+ibLMK2XNGa2ZCVZWePcCvoiVpsZMNWVChRUksZFS6GevqUL+FE+RJOanGlUsnTqIiMfUGeP1UrhjyNjsjzeOFxsZz3vc953/uq2OLUe+ihJDe7zDyZ258ac8/lOQ8hhBB5N3rUcHbu2sNH4ycy/qMxtG6dcQNFWloaQU+fcuDAYX6eOw9TU1PeGzZYy9kKIYQoSmLiEwkODgEgMjLvS/UKIYTImkaKHQP692H/wUNcuXKN6zduoqOjw1dfTMa2+D9r4Z4+fZbIyCi8vGrnqu+f5vyCmakZM2dMU9sA/SULCwtmTv+O5i3bM/vnX5n3y2wmT5rI7j37OHb8pBQ7hCgClEol19d8i//5narYzQ0zaDJlHbr6RXcZoLDYGH7Ys0ktZmtmwZQ23bWU0dtDoVDgaGmNo6U1TctWUjsWnZiAz39mgdwPDuJRyHNS09NyPZYeSvRRvr7hv6SmpZKQnJznvUGEEELknWtpF+b9MpsJn3zGT3N+4ac5v6BQKNi+Yxfbd+wCwMTYmPm/zsbRwUHL2QohhBBCCCFySkN7dhizYe1KLly4RFh4OBUrlqe0i4v6QPp6fDl1crZTxbNz9ux56tevm2Wh4yVdXV2qVavCmTMZd8kaGhpQoXw5Ll66kuvnIkRhkZoUj56hibbTKBQUCgUGZupL4MWFPMHn4N+Ua/++lrLKfz/s3aS2yTXA1x37YGEsPxdvwsLImBql3KhRyk0tnpKWyuOwkH9tjB7I/RcbpMckJmg0ByVwPcCXum7lNNqvEEKInGnRvCl792zj72UrOXXqDP4BgaSnp+PgUIJGDeszcvgwSpZ0en1HQgghhBBCiEJDI8UOyLgYWadO9rM26tWtQ726dbI9np24+HgiIiJf2y4iIpK4f22SbmFhwTu0nL0oYmKDn3Bq7lDKth2Ja+O+79TeDNkp23YUQVcPkhD+VBXzObgMp5ptMbMvrb3E8sn1gMcsP3dULVazlDv9ajXUUkZFn76u3otNyR3gX5NBlEolwTFRqpkg94OfqpbECowMy/N40RouoAghhMgdJ0dHvpw6WdtpCCGEEEIIITREY8WO/FLG3Y3zFy5w4+YtqlSulGWbGzdvcf7CBcqVLauKPXv+HCur3G2GLkRhkJaSzOVlU0iJi+L25tmEel+k2oCvMTC11HZqWqVnaEzlnp9xYckEVSw9LYUb66dT76Pfi1RBSKlUMnnLCpTKf5ZGUigU/Nh9MDo6OlrM7N2kUCiwt7DE3sKSRh4V1Y7FJiVyZHJjyMPyVxZGxppKUQghhBBCCCGEEOKdp9FiR2BQEBcuXOL582CSk5OzbKNQKPho7Ac57nPI4AF8NuVLBg56jwED+tKhfVvV2rlBT5+yZ+9+Vq1eR1paOkOHDAQgPj6e27fv0rRJozd/UkIUsLs7fiU6wFv1+Pmt49zaPJsag7/XYlaFg32lxjhUbcHT64dVsTCfywRc2IVznU5azEyzNlw+zYXHD9RiA72aZFp2SWifmaEROgodlOSu2KEAqpZ0zZ+khBBC5EhSUjJ79x3g4sVLPH32HIASJezwql2Ldm1bY2hYdPcFE0KIouq0z90ctWtQpkI+ZyKEEEIbNFLsSEtL48uvv2XT5m0olUq1u5H/K7fFjh7du/LI9zG/L/mLP/78mz/+/DvLdqNHDad7ty4APHseTN8+PWndskXungiQmprKrn1HOHXmIsEhYRgZGVK+rBvdOrXFtbRzrvqKT0hg157DnL90jZCQMAwMDXAt7UyHNs2oVsUzU/vjJ8+x+K/Vr+23SaO6vD98gOpxSEgY4yb975XnLPrlBywtLXKVvyh4z24ex/f4OrWYgZkVFbuM11JGhY9nj08JuXeO1KR/9rK4s+0X7Cs1KhKzX6ITE/hmp/rPQDFjE77q0EtLGYnXUUAutycHHYWObE4uhBBadO78BSZ++jnPg4MzfXbZsHELs36ay+yfplO/Xl0tZSiEEEIIIYTILY0UO+b/tpgNG7dgampKt66dcHdzxdTUVBNdAzBp4se0atGclavXcvnyVZ4HBwNgb2dHrVrVGdi/H9WqVVG1d3MtzVdfTMn1OKmpqcyYvZA79x5gYWFOjWqViIyK4uLlG1y5dptJH4+mauWcVf+jomP4bvovBD0LxtzcjMqe5YlPTMTb+yG3bnvTr3cXOrdvqXaOvb0tjRt4ZdvnyTMXUSqVVChXJsvjxSzMs83PwEA/R3kL7UmIeMa11d9milcf+B1GxWy1kFHhZGxpR7mOH3B782xVLDkukjvb51Gt/9dazEwzfjqwleCYKLXY5217UNxMipVFia4sRyaEEFpz585dhg1/n+TkZMqV86BLp46qzcgDAgLZsXM397zvM2LUh2xav4qKFeXuXyGEEEIIId4GGil2bNu2E1MTE3Zs20BpFxdNdJlJtWpV1Aoa+WHH7kPcufcAd9dSTP1sLCbGGeupnz53iQWLl/PbkhX8OusbjI2NXtvXn8vWEfQsmEoVyzJh7AhMTDL68vXzZ+bshazbuINKFcviVrqU6pzyZd0pX9Y9y/68HzzixOkLGBoaUKdWtSzbODrY88HIQbl81qIwSE9L5cryL0iJV7/I7d5iMHYV62spq8LLtVFvAi7sJsr/nynK/ue24+zVEZsyNbSY2Zvxfh7I7ycOqMUqOjjzXv3cz1IThVvR2WFGCCHePnN/XUBycjKTJ33CqJHvZTo+etRw/vjzb2bOmsPcXxfwx++/aSFLIYQQQgghRG5p5NbSZ8+fU7t2rXwrdBSEtLQ09h44CsCwwX1UhQ6ABnVrUa1KRWJiYjl28txr+woLj+DSlRvo6OgwYmg/VaEDwNXFme5d2qJUKtmx62CO8ztx6jwAXrWqYWQk6wcXNff3/UH4o2tqMUsXT8p3GKOdhAo5hY4uVfp+AQr1t7Ab66eTnpqipazejFKp5POtq0j9z0bXP3YbhJ6urpayEkIIIYqei5euUL5c2SwLHS+NHDGM8uXKcvHSlQLMTAghhBBCCPEmNDKzw8bG+q2/AO/94BGxcfHY2drg7loq0/F6dWpw7cYdLl29QbvWTV/Zl+9jfwDsbG2wtyue6XiliuUAuHbjDqmpqejpvfplSE5O4fzFqwCvXOZKvJ1C71/gwYG/1GJ6xmbUHDoDHT1Zfiw7ls4VcG3cB9/ja1Wx2Oe++BxeQdk2w7WYWd7svnmZY/dvqcW6V68rG+cVcsr0NNLTU7WdhhBCiFxIS0vFwyPrZWH/zcOjDE/8/QsgIyGEEO+iuFB/bm+bm+1xz64TCjAbIYQoGjRS7GjTqiW79uwlKSkJQ8M3K3o0adYGhULByuV/4uxckibN2uT4XIVCwbEj+/I0rt+TQCBj5kVWXsafvGj3KklJyQCYmppkedzMLGM/k6TkZJ4+C8a5pOMr+7t89QZx8QkUt7HGs0LZbNtFRcewaeseIiKjMDIyxKWUE7VqVFGbpSIKl6SYcK6s+BL+szFm1b5fYmLjpKWs3h7lO3zA02uHSYwKVsUe7P8TpxqtMbXN+ne5MEpITuaL7avVYiYGBnzbqZ+WMhI5oVQqubFhRqbfXyGEEIVbWQ8PAgNf/2/6wKAgPMq8vigihBBCCCGEKBw0UuyY8PFHnD5zlvETJjH9+2+xtrbKc18BLz54pKSmqj3Ob6Fh4QBYW1tmedzaKiMeGxdPYmLSK2eymJubARASGp7l8ZCQsH++Dg1/bbHjxOkLADSqXxuFIvuV3oOePmfz9r1qMRNjY4YP6UP9ujVfOca/fT1tTpbxHp2a4WBfnPCQpznuSxNSXyxLVNDj5jelMp07a78lKTpMLW5foy1GJSsV6udbmF6T0q1HcG/jdNXj9NRkrq6dRsV+mTd7L6x+PXkA/4hQtdgH9ZpjnJKU4+9xYXpN3hWPDy8j8MzWPJ2rRCmvlRbI70nhI69J4aPN18Ta1qFAxnlv2GDGffwpBw4epnWrrPfFOnjoCFeuXOPXuT8VSE5CCCHeDd8Mb0t6ejoAOjoaWVleCCHEv2ik2DHth5m4urpy8NBhzpw9T+VKFXF0cECRxRu3QqHgxxnTsu3r4X+Wcfnv4/ySmJgEkO3MlH8XNxISE19Z7PBwL42BgT7R0TFcvnqTmtUrqx0/fOy0Wl+vEhkZzY1b9wBolM0SVnr6erRs1pB6XjVwdLTHyNCQoGfP2b3vCGfOXWbB78sxMTGiWhXPV44lClbQuW1EPrysFjOxdcG19UgtZfR2si5XDysPLyIeZBQFzZ3K4dJ8qHaTyoWAyHCWnDumFnOxsuE9r8baSUjkmKGlHRlbjf9rZoeODgrF6z+0KHRliTohhCgogUFBao+rVavC0CEDGTvuE9q2aUWXTh1wKpkxozYwMIgdO3ezb/8Bhg4ZSPXqVd9obN/H/ty8482jR3489H2iusFq3k//w9bWJtvzngeHsGnrXm7d8SYuLh5ra0vq1KpGt85ts/0colQqOXT0NEePnyHo6XP09PVwK12Kzh1aqpbRFUIIoV1GBhq5DCeEECIbGnmX3bxlm+rruLg4zp2/mG3b1xU7igJjYyPatmzCjj2HWPznKoYO6kXVShVISEzi8LFTHD91Hl1dXdLS0tB5xUwNgFNnL5Kenk7ZMq44lLDLso2VZTGGD+mjFnMrXYqP3h9KcWsrduw5xKp123Jc7Pjuq4lZxl/e4VdQd91pe9z8FPH4Jn5HlqvFdPQN8RoxG3OH0tpJKhcK22tSY8DXnJ77Hh5thuNSv3uWhdbC6uNdG0hKVd/z4cceQ3FwyN0yXIXtNXkXWLcdjqVtSa6u/AplehoVu4zHvcVg1XF5TQofeU0KH3lNCp+i+Jo0bto6y9nRSqWSPXv3s2fv/iyPLV+xmhUr1/Dg3o08j71l+14uXb2Zq3N8H/szbeY8EhITcXVxpnw5d3we+rFjzyGu3rjN/6ZOwMREfZlapVLJb0tWcPrsJYyNjKhaOeOzx+2797l1x5uRQ/vRrEm9PD8PIYQQQggh3gYaKXasWfm3JrrRqpd3SCUlJWV5/OXMDwBjI6PX9tere0cio6I5cfoCCxarX9Ru3aIxDx895qHvk2z39Xjp5JmMu9UbN6zz2jGz0rlDK3btO0Jg0DNCQsOxLW6dp36EZj3Y/xfK9DS1WOWekzF3cNNSRm83E2sHWnyz463b0P2o90123bykFmtdsRqtK1bTTkIi15xqtkHPyJTIJ3fUCh1CCCEKD6/atXjN/UX5xqOMK84lHXFzLYWbaymmfjOLqOiYbNunp6czf/EyEhIT6duzE106tgYgNTWVufP/4sr1W6zZsJ0RQ/uqnXfqzEVOn72Ena0N30ydgLVVMQDuevvww6wFLF25gUqe5eSzgBBCCCGEKNI0UuyoU6e2Jrp5rbS0NMIjIkhOTs62jZPjq/e/yE5xm4x/+IeHR2Z5PDwiI25mavLKJaxe0tPT5YORg2jdsglXr90iIjIKc3NTalSrTNkyroz5+EsASjpln+9jvwCe+Aehr69PPa8auXtCL5iammBhYUZkZDQRkVHyAaeQqDFsBre3zOHJi/X+nWq2wbluZy1n9XZ72wodyampTNm6Si1moKvH9C4DtZSRyCt7z4bYezbUdhpCCCGysXb1Mq2N3blDq1y1v3TlBk+fBeNc0kHtXD09PUYM68tHE7/m2Mmz9OnZCXMzU9XxnXsPA9C/dxdVoQOgQrkyNG9Sn4NHTrL3wFEG9+/xhs9ICCGEEEKIwuutWCzw/PmLzPttEVeuXCMlJSXbdgqFIs/TzF1KZazT6+vnn+Xxl/FSL9rllLtrKdxdS6nFQkLCiIiMwtHBXu3DyH+9nNVRq0blTFPVcyo9PZ2EhIx9QQwNDfLUh9A8PQNjqvb9kuJlvXh4aDmVe0995ebzouhZcuoAD4LV1xAf26w9brb2WspIvIpSqZTfUSGEEPnuyrWM/Qrr1K6e6e+OlWUxypV1587dB1y9fovGDTJmfoeEhOEfkHGD1H/3CgSoV6cGB4+c5PLVm1LsEEIILbt2P5DIuAQALE2NqVY2d9eYhBBCvFqhL3acOn2W4SPfJzU1DQsLczw8ymD2mqWf8qKchxtmpiYEh4Tx0PdJpgLF2fNXAKhVvcobj7XnwFEAWjbL/k7gtLQ0Tp3NWN7m5QeZvLh24w5JSckYGhrg5CAXUQsbpxqtcazW8q3aY+JtlBDxHGOrwvPz/yw6kln7t6rFHC2tmdCik5YyEq8S5X+Pm5tmUXPYTIwts947SQghhNCEx08CAXB1yXrvLlcXZ+7cfYDfk0BokBHz8884x7mkA3p6mT/evewrOCSM+IQETIzzdhOVeLd8tWONtlMQokhaf/gqtx49A6CSW4lXFjtub5uboz49u07QSG5CCFEU5KnYMWDQMFAomP3jdBwcSmQ8zimFgtUrlua4+a/zfyMtLZ1JEz9m+HtD0NfPn6VqdHV1ade6GRu37ubvFeuZ+tlY1QeB0+cuce3GHczNzWjaqK7qHJ9Hj1m0ZCUAc2Z+pdZfSGg4+np6WFpaqGLp6ensO3iM/YdO4OLsRKvmjbLN5/rNu0RHx2BpaUGVSuVfmfv+Q8epUqlCpg3Mb93x5o+/1wIZhZWsPvwI7ZNCR/5Jig7j9tafeXbzGE0/34iJTd6WudO0b3etIzYpUS32Xad+mBq+fj8gUbBig/04t2gsybERnP5lOPU+XIipbe42jxdCCFE4JSYmcuPmbYKDg1+5TG73bl0KLKfQsHAAbKwtszxubWX5ol1EpnNeHvsvIyNDTIyNiU9IICwsApOSry52fD1tTpbxHp2a4WBfXLWJfUFKTc2Y3a+Nsd9VPctnniWkadbGJm80lkFpF02mIzTEopgtAI1aD9FYn+ZGOS/SNnLz0nifmqRnegp49uJrS4pVbPLGfRbEe6O8DxdN8roWTW/z62pt6/DGfeTp6ve58xdRKBQkJCaqHudUbpcBuXPnHpUrefL+6BG5Oi8vOndoye2797lz7wETJk+jQrkyREVFc+/+Q3R1dRkzchDGxv9cjExOSiHoWXDWed+9z5K/1+LqUpLiNtYolUp8fP0ID4/EybEEn33yAXp6utnmcuJ0xhJWDevVRuc1F8OPHj/L8tWbcS7pgIN9RsEj6Fkw/gEZS+RUrVyBPj065up7IcTbTKlU8uTMVu7umEdKQsYmoDc3zsRr9K9aX4rovO991l86rRZr6F6BbtXyPoNL5I+EiGec+20MybEZF5QSwoM4/etw6n24GHMHNy1nJ4QQ4k0s+G0xS/78m/j4+GzbvFzCsCCLHYmJSQAYGma9R+DLvQMTExOzOCf7JWuNjAyJT0gg4UVbIYQQQgghiqI8FTtOHD0AgP2LC+svH+cHfX19XFxKvb6hBujp6fH5p2PYtfcwp85e4srVmxgaGVKremW6d2mHa+mc383rWroUdb2q4/PwMf6BT9HV0aVECVvatWpKm5aNXzlDJS4unitXbwLQuOHrL4C2admEazdu8yQgiBu375GcnIypqSlVK1egUX0v6tetqfULvO+ywMv7CX94lYrdJqCr//rN7YVmPL1xVFXoAAi+c5qn1w7jWL2l1nJKS09n8pYVajFdHR1mdhskv6OFTFJMBGd/G0NCxDO1uJ6RGQZmVlrKSgghhCYs+WMpc39dgJ6eLi2aN6W0iwsm+bBM7tvqu68mZhl/eXegJu64yy1tjv2umnv2aL6P8XJGx6Z7N/N0vvP9YxrMRmjKyxkdJw8s11ifDcpUyHHb0z53Nd6nJqXGRap9HXXn+Bv3WbIAlrGS9+GiSV7Xouldf13zVOxwcnJ85WNNqla1Mo8e+eZb//+lp6dH105t6NqpzWvbVqzgwdpl87M8VsrZkY/eH5qnHExNTVjxZ87WZgRo1qQezZrUy9NYIn/FhfhzY90PpCbFEf7oGjWGzcDc3lXbaRV5CoWCyr0mc2xGb9JT/rmD8dbmn7AtXwd9Y3Ot5LXy3DFuBPqpxYY3aElFR1kWqTBJSYjl/KKxxAWrv1ZGVvbUHfMbhuZS7BBCiLfZmrUbMDExZsO6VVQoX07b6agxMjIkLi6epKSsZ2C8nMVhZGSkdg5AUlL2S3G9PM/YSG68EUIIIYQQRVeh3yxg3Edj8L5/n+07dmk7FSFyJT01hcvLPic1KQ6A6KAHnPxpIFH+97Sc2bvBtHhJyrYdqRZLig7l3u5FWsknPC6GaXs2qsWKm5nzedvuWslHZC0tOZELSyYQFaD+e2pgZkW9MQsxsX4374wQQoii5HlwMF61axe6QgdAcRtrAMLCI7M8Hh4R+aLdP4X3l+e8PPZfiYlJxCckAGBjIwV7IYQQQghRdBW6HasvXLiUKTZsyCA+/Wwqh48co2mTRjg6OqCjyLpO4+VVK79TFCJH7u6cT5S/+hRay1KeWDh5aCmjd49784EEXtpLzNOHqtjjkxtwrt0BSxfPAs1l+t7NRMTHqsW+at+bYsamBZqHyF56WgqX/55C+MMranE9I1PqfDAfM/vS2klMCCGERpUoYY+BQfZLympT6VJO+D0JwNfPnxrVKmU67uvnD4BLKSdVzMU542v/gKekpqaip6eX5Tl2tjaYGGtnQ14hhBBCCCEKgkaLHbv37GPf/oP4+j4mNjYOpVKZqY1CoeDYkX3Z9tFv4NAs165XKpXs3rOPPXv3vzIHH++8rfcphCY9v32SR0dXq8X0TYtRffD3KHSy35heaJaOrj5V+kzl9C/D/wkqlVxf/wONJq5AR7dg6r03A/1YdvaIWqyGsxsDvBoXyPji9ZTp6Vxb9T+e3z6pFtfRN8Rr1C9YOmtnTV8hhBCa16lDO1avXU9cXBympoXrpoMa1Spx/NR5zl+8SvfObdU+F0VERuF9/yG6ujpUq/LPTRu2tjY4l3TEPyCIy1dvUqd2dbU+z57PKOLXrF65YJ6EEEIIIYQQWqKRK31paWl88OF4jhw9nmWBAzKKHNkd+7fuXTuDbNQr3mIJkcFcXfVNpnj1gd9ibGmnhYzebdZu1ShVvxtPzmxVxaIDvPE9sR73ZgPyfXylUsnkLStI/8/738zug9DRKfQrCb4TlEoltzb/ROBl9UK8QkeXWu/9iE2ZGlrKTAghRH74cMxozpw9z/CRY5j+w7e4uZbWdkoqNatXxqGEHf4BT9mx+yBdOrYGIDU1lT+XrSMtLZ0WTRtgYW6mdl6ndi1Y+MdK1mzYjkcZN6ytigFw19uHI8fPoKenR7vWzQr8+QghhBBCCFGQNFLsWL5iNYePHKNJ40Z8/dUUFvy2mG3bd3H31hX8/J6wbcculv69gveGDebTT8a/sq+fZk3XREpCaIUyPY2rK74kJS5KLe7WbAD2no20lJWo0Okjnt04RnJshCrmvXsRjtVaYGxVIl/H3nTlDOd876vF+ns1ppZLmXwdV+Sc955FPD65QT2oUFB90HfyeyuEEEWQoaEhy/9eQo/eA2jbvgtOjg6UKFECHZ0sbrhSKFi9Ymmex7py7RZbd/wzMz02Lh6AOfP/RP/FclPVq3rSvUtbAHR1dRn7/hCmzZzHuk07OX/pGiXsbHnw8DGhYeE4l3Sgf+8umcZpWL8212/e5fS5S3w69XsqVyxHYlISt+7cR6lUMmJoX2yLW+f5eQghhBBCCPE20EixY8eu3Zibm/Pr3J8wNzdD8WI/DX19fcqUcefTT8ZTu1YNho8cg0cZd7p07qiJYYUodO7v/4swn8tqsWKlKlKh00daykgAGJgWw7PbJ1xd+ZUqlpacwK3NP1F7xJx8GzcmMYFvdq5Ti1kYmfB1h975NqbInYdHVvFg/1+Z4pV7TsapZlstZCSEECK/hYaFMXDwcHx8HqJUKnniH8AT/4As22a1vG5uxMTE4vPocaa435N/xnN0UJ/561a6FDO+ncymbXu4ddsb/4CnWFtZ0ql9S7p3bouRkWGWeX44ejDlyrpx5PgZrt28g56uHhUreNClQysqVSx8m7ELIYQQQgihaRopdjx66EuNGtUwfzGd+uWHgrS0NHR1M/YnaNK4EVUqV2LlqrVS7BBFUuiDy9zf94daTM/QlJpDpqOjVzg3wXyXONVqh//5nYTev6CKPbtxjGc3jlGiStN8GXPOwe08i45Ui01p2x0782L5Mp7InejAB9zZNjdTvHzHDyndqJcWMhJCCFEQZv44mwcPfKjjVYthQwfj4lIKUxOTfBmrSaO6NGlUN9fnlbC3ZezoIbk6R6FQ0Kp5I1o1l1mJQgghhBDi3aSRBePT0tMpVuyfi3fGxkYAREfHqLUrVcqZ+w98XtnX+g2bSE9Pf7N80tJYv2HTG/UhRG4kxUZwdcUXoFT/2a3S9wtMbZ21lJX4N4VCQeXen6OjZ6AWv7l5FqlJ8Rof70HwUxadUN8DonwJJ4Y3aKHxsUTeWDh5UKnnZLWYe/NBlGk1TEsZCSGEKAjHT5zGuWRJli1dQquWzSnrUQYnJ8ds/xNCCCE0pXaFUrSpU442dcpRu0IpbacjhBBFjkZmdtjZ2RISEqJ67OSY8aHgnrc39erWUcUDAgLR09N9ZV9Tv/wfv/+xlNGjhtOhXVvMzExznEdMTCy7du9hyR9L8Q8IpE/vnrl8JkLknlKp5Nrq/5EYFaIWL1WvK04122gpK5EVM7tSeLQahvfe31WxxIjneO9ZjGe3TzQ2jlKp5POtK0lJS1OL/9htMPq6GnnbFRri2rg3+sZmXFv9P5y9OlKhy/g3XrJECCFE4ZaYmEC9ul4YGBi8vrEQQgitO+1zV9spaEz3plU03uftLGarZ8ez6wSNjy+EEIWJRq66lS9XlqvXrqse163rhVKp5Nd5C6lcqRJmZqZs276Lq9eu41W71iv7WrJ4ATN+nM3UL77hu2kzaNG8KXXreFG1SmXc3V0xMjJStU1ISODhI1+uX7/J2XPnOXL0OMnJybiWLs2SxQs08dSEeK1Hx9YQfPuUWsyshBuePT7VUkbiVdxbDiXg8j7igv0A0Dc2x8zeVaNj7L19hSPeN9ViXap60cijokbHEZpRsnZ7TG2dsSxVUQodQgjxDihfriyhoaHaTkMIIYQQQgihYRopdjRt2pgDBw9z+sw5GtSvS5XKlWjapDHHjp+gpld9TE1NiY6OQaFQMO6jD17ZV4vmTWnapBEbNm5mxaq17N6zjz1796uOGxgYYGpiQmxcHCkpKaq4UqmkXDkPBg/sT6+e3VV7hQiRn2Ke+XJ3xzy1mI6+ITWHzkDPwFhLWYlX0dU3oEqfqZydPxqnmm3x7PYJhhY2Gus/ITmZL7atVosZ6xswrXM/jY0hNM+qdGVtpyCEEKKAjBg+jLHjPuHK1WvUqF5N2+kIIYQQQgghNEQjxY6unTtSvVpVitv8c8Fw/q+z+fGnn9m3/yBRkVGULVuGsWPeV1vWKju6urr069ubfn17c+XqNQ4fOcaFCxe5e8+bhIREkpKSgIy9QSqUL4eXV21aNG8qH1ZEgTOzc6FCp7Hc3TEfZXrGkkWVenyKhWMZLWcmXqW4Ry2afr4Bcwd3jfe94Nge/MLVlzT7pGVnSloV1/hYIufiw4LQMzLFwFQ2hxdCiHddpUoVeW/oYAYPHcF7QwfTqFEDHEqUQKGT9ey+l0v0CiGEEEIIIQo3jRQ7DA0NKeuhfnHXxMSEb7/5km+/+fKN+q5RvZpaESMhIYHo6BgsLMwxNpY754V2KXR0cG8+CGv36lxZNhXLUhUpVa+bttMSOZAfhQ7/8FDmHtqhFittY8eHTdtpfCyRc4lRIZxd8D66BkbUHfMbRsVstZ2SEEIILWrctDUKhQKlUsnCxX+wcPEf2bZVKBQ8uHejALMTQgghhBBC5NVbt1OusbGxFDlEoWPlUonGn2UsXSRr/r+7vtyxhsTUFLXY9K4DMNKXDVC1JTkuinMLPyQ+LBCA078Mp+6HCzEtXlLLmQkhhNAWr9q1kH+uCSGE0IYFm07iE5Cxb1SZksUZ27ORljMSQoii5a0rdghRWOkbm2s7BaEhyvR0FDo6uTrn+P3b7LxxUS3WsnwV2lSsrsnURC6kJsVzfvE4Yp4+VMXiwwK5vOxzGk1cIYVJIYR4R61dvUzbKQghhHhHBYZE8TAwDABjQ30tZyOEEEWPRoodAwYNy1E7fX19LC0t8axYgY4d2uHgUEITwwshhEYo09PxO70Zv7NbafDxXzneZD4lLZXJW1eoxfR1dZnedaBcUNeStJRkLv4xkUi/W2pxfZNiVB/wP3ldhBBCCCGEEEIIIYoYjRQ7zp3PuJv55dq3Wfn3sZ279jBn7jymTvmUwYMGaCIFIQqE74kNOFRrjpGFbDZd1EQF3ufGuh9UF8cf7PuDCp3H5ejcP04d5P7zILXYmCbtKGPnoPE8xeulp6VyZflUQu9fUIvrGppQ54N5+bJfixBCCCGE0Azn+8e0nYIQQggh3lIaKXacOHqAZStWsWLlatq1bUOnju1xdMy4yBcU9JRdu/eyZ+8+Bg7oR7u2rTl/4RKLf/+D776fSZky7tSvV1cTaQiRr4KuHODWph95sP9Pqg/6Dtvy8nNbVCjT07my7HNinz9WxR4eWYVTrfZYOJZ55bnPoyP5cf9WtZiDhRUTW3XJj1TFayjT07mx7nue3TiqFtfRM8Br5BysXCppKTMhhBCFxYULl3LV3surVj5lIoQQQgghhNAkjRQ7rt+4ybLlq/hzyW80aay+uVKF8uVo0bwpXbt0ZMSoD6lRvRoffjCKqlUqM2TYSJYtXyXFDlHoxYcFcn3d9wAkxYRxbtFYPFq9R/mOY7ScmdAEhY4OlXp+xrnf/nk9lelp3Fg/nQbj/3zl/h3f7V5PTGKCeqxzP8wMjfItX5E1pVLJ7W1z8T+/Uy2u0NGl5tAZFC/rpaXMhBBCFCb9Bg7N1XKGPt438zEbIYQQQgghhKZopNjx51/LqFWzRqZCx781adyIWjVr8Mdff9OhfVsaNqhH+XJluXb9hiZSECLfpKemcHnZ56Qmxv0TVCpB1vwvUmzL1cGpZlsCL+9TxSJ8r/Pk7DZcGnTP8pwLjx+w9uIptVh9t3J0ry4FXG14sP9PfI+tyRSv2v9rSlRpWvAJCSGEKJS6d+2c5b/jlMp0goKecuv2XeLi4mjVsjnm5uZayFAIIYQQQgiRFxopdjzw8aFF82avbWdvb8fhI/8sLVK6tAsPHz7SRApC5Jt7uxcS6XdbLWbtXp2ybUdqKSORXzy7fULwndOkJMSoYnd3zKNE5SYYWtiotU1LT2fKlpVqMR2FgpndBsnm11rge3wd3nsWZ4p7dp+Is1dHLWQkhBCisPpp1vRXHg8Pj2Dy51/i6/uYTRsyF9GFEEIIIYQQhZNGih36+vrcuXP3te3u3LmLvr6+6nFKSgqmZqaaSEGIfPH8zmkeHl6hFtM3KUaNwd+jo6uRXx9RiBha2FCh8zhurP9BFUtJiOH2trnUGPy9WttV549zLcBXLfZe/RZUcnIpkFzFPwIu7uHW5p8yxcu2HYVb0/5ayEgIIcTbzNraijmzZ9K8RTt+njuPb76equ2UhBBCCI24vW1ujtp5dp2Qz5kIIUT+yH4h+lzwql2bR76PmTV7LqmpqZmOp6WlMfvnX3n4yJe6df5ZM/3JE3/s7Ow0kYIQGpcYFcK1lV9nilcb8A3GViW0kJEoCKXqdcXKtapaLPDSXkLunVM9joyP4/s9G9XaWJua8XnbHgWSo/jHs5vHubb6f5nirk36UrbdqIJPSAghRJFgYW5OlSqVOXDwsLZTEUIIIYQQQuSQRm5NnzRxPGfOnmPJH0vZvmMXrVu2wNHRAYCgoKccPHyEZ8+eY2pqysQJ4wDw83vCA5+HDBk8QBMpCKFRyvQ0rqz4kuS4SLW4a5N+lKjcRDtJiQKh0NGhSp/POTFrAMr0NFX85saZNJmyHl19Q2bs20xYXIzaeV+1742VqVlBp/vOi3n6UO11Aijp1QHPbhNlOTEhhBBvLCw8XNspCCGEEEIIIXJII8UOd3c3Vq34i0mfTcXn4SNWrFqjusikVCoB8Cjjzk+zpuPu7gaAlbUVu7ZvwsFB7pAXhc+Dg38T9uCSWqxYyfJU6DxOSxmJgmTh6IF784H4HFquisWF+PPgwFJSq7Xnr9OH1NpXK+nKwDpSBNMGj9bvoWtozO3NswGwr9SEqv2+RqGjkYmLQggh3lG+j/24eOmSfFYRQgghhBDiLaKxTQeqVK7E/r07OHvuPJcuXeF5cAgA9na21KpVg3p166i1tzA3x6JCeU0NL4TGhD28ivee39ViuoYm1Bg6A119Ay1lJQqaR9uRBF45SEJ4kCrmc2gZa+/5kv6iiPvSj90HoysX17XGrUk/9I3NCbi4h5rDZsh+OkIIIV5py9bt2R6LjYvD99Fjtm7fSUJCIj26dSnAzIQQQgghhBBvQuNXhOrVrZOpsCHE2yI5LpIry78AZbpavErvzzGzK6WlrIQ26BkYU7nXZC78Pl4VU6alUtXvLHt1XeDF7LV+tRtSu3QZbaUpXnD26kjJ2h1k6SohhBCvNWnyF6/8e6FUKlEoFPTu1Z0xH8j+T0IIITTnk35NSU7JWIbXQF9Xy9kIIUTRI7e/CvGCUqnk2urvSIx8rhZ3rtOZkrXbaykroU32ng1xqNaSp9f+WbaqAvE0VEZxSmGJmaERX3foo8UMxb9JoUMIIUROfDT2g2z/Zujr62NvZ0vdOl44OTkWcGZCCCGKOltL2edRCCHyk8aLHfHx8fj5PSEmNg7+s9TLS15etTQ9rBBvzPfEOp7fOq4WM7MvTaWen2kpI1EYVOrxKSH3zpKaGKeKuSgTOQVMbtMdewtLreX2LkmJj+HO9l+o0GU8BiYW2k5HCCHEW+zjcR9qOwUhhBBCCCFEPtBYscP3sR/ffT+D06fPkp6e/sq2Pt43NTWsEBpjauOEvkkxUuKjANDRM6Dm0JnoGRprOTOhTUbFbLFpMpDn+3/nGQYs1ynBHR0zyto7MqpRK22n905ITUrg/O/jifC9TqTfbeqMWYCRRXFtpyWEEEIIIYQQQgghChGN7KgbGBhEz979OXHiFFWrVsa5pBMAHTu0o3IlT3R1M4Zp2aI53bp21sSQQmicfaXGNJm8Fmv36gB4dp+IhZOHlrMS2qZUKpkTEMFynRJ8qevGHZ2Macc/dhuMvmyEne/SU1O4tPQzInyvAxAd9IAzv44gPizoNWcKIYQQQgghhBBCiHeJRq7ULVy0hMjIKH6Y9g19+/Ri0uQv8A8I5JefZwHg4/OQTydP5dGjR2zeuFYTQwqRL4yt7Kk3djFPrx3CsUYbbacjCoH9d65y0PsW6FirYp2q1KZJWU8tZvVuUKancXXlV4TcPaMWT4wKITE6FBMbWUtdCCFE3qSkpLBr917Onb9IcHAwycnJWTdUKFi9YmnBJieEEKLIehQYRlxiEgCmRoa4OdloOSMhhChaNFLsOHX6LK6upenbp1eWx8uUcefPJQtp3qId839bxNQpkzQxrBD5QkdXD6eabbWdhigEElOSmbpttVrMWN+A7zv311JG7w6lUsmNDTMJunpQLa7Q1aPW8J+wdq2ipcyEEEK87ULDwhg4eDg+Pg9RZrPH4EvZbWQuhBBC5MUfO85y69EzACq5lWDGBx21nJEQQhQtGil2PA8OpnmzJv90qqcLQFJSMoaGBgAUt7HBy6sWBw4clmKHEOKt8NuxvTwOC1aLjW/eEWdr2S8iv93bOZ8nZ7aoBxU61Bj8A3YV6msnKSGEEEXCj7N+5sEDH6pUrsTIEcNwd3fDzNRU22kJIYQQQggh3pBGih1mpqZqd0WZm5sDEBISQskX+3cAGBkZERwSookhhXgjacmJxIUGYOFYRtupiEIqICKMuYd3qMVKWRfno2Yd1GJRAd4EXNxNxa4T5O5PDfE5uAyfQ8szxav0mYpj9ZZayEgIIURRcvzESezt7Fi14i9MpcghhBDvpNM+d3PUrkGZCvmciRBCCE3SSLGjRAl7nj17rnpcxt0dgLPnLtCrZzcgY13cq9euY1tc7ogW2ndn2y88Obedil0nULpRL7lILTL5euda4v+zfvf0rgMxNsiYrZaaFI/3nsX4Hl+HMj2NYs4VKFmrnTZSLVL8Tm/h7s75meIVu4zHpX43LWQkhBCiqImJiaV5syZS6BDvpK92rNF2CkIIIYQQ+UYjxY7atWqybsMmomNisDA3p0XzphgYGPD99B9JSkrCoYQ9a9dv5PnzYFXxQwhtCbp2mMenNgJwa9OPhN6/QNX+X2NgYqHlzERhceLBbbZdO68Wa16uMu08awAZG2efnDOE2GePVMdvb/0Zu4oN5OfoDQReOcCNDdMzxcu0GoZ7i8FayEgIIURR5FLKmfiEBG2nIYQQQgghhNAwHU100q5dG8qWLcOVK9cAsLGx5vMpnxIXF8f/vvuB0WPGcfTYCezt7Zj4yXhNDClEnsSHBXF97XdqsWc3jxEdcE87CYlCJyUtlSlbV6rF9HV1mdFtkGoGkEJHl1L1uqq1SY4J5+6OzDMSRM4E3znD1ZVfwX82inVp2JPyHT/UUlZCCCGKot69enDhwiWePn2m7VSEEEIIIYQQGqSRmR1etWuyfcsGtdigAf2oWqUy+/YfJCoyCjd3V3r16IaFhdz1LLQjPS2FK8unkpoQqxYv02oYxct6aSkrUdj8dfow954FqsXeb9wGDzsHtZhr4z4EXNxNdIC3KvbkzBacvTpi7Va1QHItKsIfXePiX5+iTEtVizvVbEPlnpNlmTkhhBAaNWzoIG7eus2Awe/xv6+n0rBBfXR0NHIPmBBCCCGEEEKLNFLsyE6VypWoUrlSfg4hRI55715MxOObajEr16qUazdaSxmJwiYkJoqZ+7aoxUpYWPJpq66Z2uro6lG1zxec/HmI2myEG+t/oPFnq9HR1c/vdIuEhIhnnP99POkpSWpxu4oNqDbwWxRy8UkIIYSGNW3eFoDAoCDeG/EBerq62NrZoqPI/DdHoVBw7Mi+gk5RCCGEEEIIkQdyFUm8E4LvnsXn0DK1mL6xOTWG/ICObr7W/MRb5LvdG4hOjFeL/a9jX8yNjLNsb+niSelGvdViMU8f8ujI6nzLsagxsrTHtXFftZi1e3VqvvejFIyEEELki4DAQAICA1EqlSiVSlJSUwkKeqqK//s//4AAbacrhBBCCCGEyCG5yvsfqamp7Np3hFNnLhIcEoaRkSHly7rRrVNbXEs756qv+IQEdu05zPlL1wgJCcPA0ADX0s50aNOMalU8szznuxm/ctfbJ9s++/bsRJeOrbM89ujxE7bu2I/3g0ckJiZhZ2tDo/q16dC2BXp6urnKvShJjA7l6qqvM8WrDvgGE2uHLM4Q76LLfg9ZfeGEWqyua1l61az/yvPKd/iAZ9ePkBgVoop571uCY41WmNg45UuuRYlCoaB8hw/QN7HgztafsShZDq9Rc9EzyLrAJIQQQryph/dvaTsFIYQQQgghRD6QYse/pKamMmP2Qu7ce4CFhTk1qlUiMiqKi5dvcOXabSZ9PJqqlSvkqK+o6Bi+m/4LQc+CMTc3o7JneeITE/H2fsit2970692Fzu1bZnu+V61qGBkaZIo7l3TMsv2Va7f4ef6fpKWlUa6sG5bFinHX24d1m3Zy6443kz8Z804WPJTp6Vxd+TXJMeFq8dKN++BQpZmWshKFTXp6OpO3rFCL6SgU/Nh98Gv3i9A3Nsez+6dc/nvyP/2lJHFz4494jf5V9pvIIfdmAzAuZouNRy30jc21nY4QQgghhBBCCCGEeMtIseNfduw+xJ17D3B3LcXUz8ZiYpxxZ/Hpc5dYsHg5vy1Zwa+zvsHY2Oi1ff25bB1Bz4KpVLEsE8aOwMQkoy9fP39mzl7Iuo07qFSxLG6lS2V5/sA+XbG1tclR3vHxCSz6YyVpaWmMfX8IDerWyognJDB91gJu3bnPrn2H6ZrNjJCizOfQMkK9z6vFLJzKUbHLeC1lJAqj1RdOcMX/kVpsaL3mVHZyydH5DtVaYFexAcF3TqtiwXdO8/TaIRyrt9JorkWZY4137z1KCCGEEEIIIYQQQmiG7NnxQlpaGnsPHAVg2OA+qkIHQIO6tahWpSIxMbEcO3nutX2FhUdw6coNdHR0GDG0n6rQAeDq4kz3Lm1RKpXs2HVQI7kfPXGW2Lh4qlf1VBU6AEyMjRk2KGM/gT37j5Kenq6R8d4W4Y+u471nsVpM18CYmkOno6tvqKWsRGETlRDHtD0b1GJWJmZMbdcjx30oFAoq95qCzn9+rm5tnk1KQoxG8iwK0pITSU1O0HYaQgghhBBCCKEV5V3sqV3BmdoVnCnvYq/tdIQQosiRYscL3g8eERsXj52tDe6umWdb1KtTA4BLV2+8ti/fx/4A2NnaYG9XPNPxShXLAXDtxh1SU1PfJG0gYwkrgLpe1TMdc3dzwc7WhpiYWLwfPMp0vKhKjoviyvKpKNPT1OKVe0/BzL60dpIShdKMfVsIjVUvSHzZvifWprlbSsnExpFy7UarxZKiQ7m3e9Eb51gUpKelcHnZ55xf9JEUgIQQQgghhBDvpCHta/P1e234+r02DGlfW9vpCCFEkSPLWL3g9yQQyJh5kZWX8Scv2r1KUlIyAKamJlkeNzMzzWiXnMzTZ8FZ7sNx7OQ5YmLjALAtbk31qp6UdMp6M22/JwEZOWazJFZpF2eCQ8LwexJIhXJlXpv/206pVHJ97TQSIp6pxUt6dcDZq6OWshKF0Z2n/vx1+pBarIqTC4Pr5m0/F7dm/Qm4tIeYIB9V7PHJDZSs3R4rl0pvlOvbTJmezrXV3/H8VsYG8GfmjabumAUYmltrOTMhhBBCCCGEEP91e9tctcfFKjYB4OnpdWpxz64TCiwnIYTIiXyZ2eH72I8rV6/xyPdxfnSfL0LDMjawtra2zPK4tVVGPDYunsTEpFf2ZW5uBkBIaHiWx0NCwv75Ops2W3bs4+CRkxw8cpI1G7bz2ZczWPTHSpKTk9XaxSckEBefsSyMjdWrc3/5HIu65LhIYp/7qsVM7Vyo3GuKljIShZFSqWTKlpWk/Wd5t5ndB6Ork7e3Rh1dfar0+eK/A/Hw8Mq8pvnWUyqV3Nr8E4GX9qhi0YHenF3wPulpKVrMTAghhBBCCCGEEEIUJRqb2ZGUlMz83xaxbt1GIqOiAOjerQuzZn4PwNZtO1j69wpmzfyeCv9n777Doyj6OIB/L70DIb1X0kMCISRACL33JkVUVBRQlKKoWFBRsRdABOUVxIIU6b130gghQBqQQgqBkN7rvX+EHDnuAknY5FK+n+fhecjszM7vbm7vdnd2ZlychapWMDUdGOrq8tdy0NB4mF5cUiL196Mc7W2gpqaKvLx8XLp8Fd29PaS2Hz/1cBHj4pISqW0uTg7o39cfXRxt0aljB2Rl5yIi8jq27TiAM+dDUF5RgTfmzqoV98POD3V1tcfG/qROmhofLf9ObvrE0f1hamyArIw79dqPUCoqqm+INqRe9xe+Rfyhtbh35RhEyipwGLsYeXm5AHKbKMr2pTFt0tLsj4rAuVvRUmnj3Luhi47u070uHUMYdxuGu+GHIFJWgUWfZ2DRa1KTv1cttU2STv2FlLNbH0kVwdRvAnKy7iskpubSUtukPWObtDxsk5ZHkW2ibyh/FDMRERG1TI+OAKkLR4AQUXMRZGRHaWkpnn3+Raxdtx7KKsroFxgAsVgslcffryeiomNw4OBhIaps0TQ1NTBsUPUQv7Xr/8L5oDAUFBQi434W/t2+B6fPBUNZWRkAoCQSSZWdPGEkAnr7wtjIEGpqajAxNsSwwf2wbOkCKCsr42JwOG7FJzX7a2ptlNU04DhmARzHLYbdsDnQMbFTdEjUghSVlWHFif1Sadpq6ljSf4Qg+7cZ8AI6uwbA+9WfYdV3GpRUVAXZb2uTGrQLKWf/lUm3Gz4Xhu6BCoiIiIiIiIiIiIjaKkFGdqz/30aEh0dg4oRx+PTjD6ChoQH7LtLz05uYGMPB3g7nzl/E4kVvClGtoGpGP5SWyh/9UHtUhKaGxhP3N3nCKOTk5uHM+RCsXvuH1LYhA/viVnwibiXcrnNdj0dZWZqhu7cHQsIicDnyOuztrB/E/XA0R2lpGbS0NOuM/XGjUWr79MPFctNrnvBr7qfunqZe/QHThQ6HoLjPglDWHNiG9HzpUT5Lho6Hs62TYHUYzflRsH3VR0trk9tBe5B4dL1MuvOo1+A45EUFRNT8WlqbENukJWKbtDxsEyIioqbzx4FQJKVXTzFubaLPRcqJiAQmSGfH3n0HYGpqgs+XL4Oqat1PMNvZ2SLiSqQQVQrOoHP1QrlZWTlyt2dlV6fraGvVq9NARUUZc2fPxJBBgbgccQ3ZObnQ1dVGNy8PdHGwxbwFHwAALMxlFyevi6mJIQAgJydPkqalqQltLU0UFhUjMztHbmdHTew1r5GoPYvPuIvVJw9IpTkameLVgKEKiqjtuXPlBK5sXi6Tbj9gJhwGz5JTgoiIiIiIqO2LSbqLa/HpAIDiUq5hSEQkNEE6O24npyCwb5/HdnQAgJqaGrIf3HhvaaytzAEACUnJcrfXpFs9yFdf9rZWsLe1kkrLyMhEdk4uzEyNod+pQ733VVhYvRD5o2tzWFtZICrmBhISb8PSXPYpvMQHsVs3MPbWorKsBMpqTx5tQwQA7+/+C2WVFVJpK8bPhJqKYEsYtWsZscEI37gUEEsv/G7lNxYuY9+E6JGp+4iIiIiIiIiIiIQgyJodampqKCwsemK+tLQ06OrqClGl4Jwc7aCjrYV7GZm4lXBbZvvF4HAAgI+351PXdeDISQDAoP596l2mvLwc4VeuAQDsbKQ7T7p5VU8ZFhRyWabcrfgk3MvIhK6uDpwc2966FZXlpTj344u4tv1rVJaXPbkAtWtHoiJwOCpCKm2Uhw8GOHk0WwwVJYW4E3my2eprTtkJVxH622JUVUo/oWTqNRCeU99nRwcRERERERERETUZQTo7HB3tcfXaNeTm5taZJz39Lq5HxcDdzUWIKgWnrKyM4UP6AwA2bNqCouJiybbzQWGIiIyCrq4O+gX4SdJvxidi8bvLsfhd2elaMu5nSU03BQBVVVU4cPgEDh87A2tLcwweECC1/VpULMIjrqGqSvqJ6JycPPz48+/IysqBvn5H9Ogu3eHSv68/dLS1cPnKdVwIuiRJLyouxoY/twIARgztDyUlQZq7RYne/RPyUmKRcGYLzv3wAgrucfF2kq+0ohxLd/0llaahoorPxjbPui5isRh3rpzAyS8mI+x/S5CTHN0s9TaXvLSbCF73BirLiqXSDZ394D3zM4iUlBUUGRERERERERERtQeCzNsycfxYvP/hJ1jy7gf4/tsvoa2tLbW9tLQMH3z0CcrKyjBxwjghqmwSY0YOwvXoOETF3MDCd5bDxckBubl5iIm7BWVlZcybPROamg+nSyorLUda+j25+4qKjsOvGzbD1toCBp31IRaLcTMhCVlZOTA3M8GSRXOhoiJ98+92chr+3LwDHTvowcbaAtpaWsjMykZCUjJKS8ugp6uDRfNnQ01NehorLS1NzJ09E9+v+g2r1m7E0RNn0aGDHqJjbyIvLx9uLl0wathA4d8wBbsTeRIJZ7ZI/s5LiUXwL/PR/4MdUFLmlEQkbc2pg4i/f1cq7Y0Bo2Clb9jkdVdVViDs9yW4e/W0JC1yyxcIWLSxTXQCFN5PQdCa11BeJN3B28nGEz4vfQtlVbU6ShIREREREREREQlDkDvCUyZPxIGDR3Ds+EkMGDwC/n49AQBXr13HO+99iDNnz+HevQwM6B+IUSOHC1Flk1BRUcF7b83DvoPHce5iGMIvX4W6hjp8vD0wYexw2NpY1ntftjZW8PP1xs1biUhOvQNlJWWYmBhi+OB+GDqor9z1TVycHTCwX2/EJ9xGfGIyCguLoKaqClMTI3h7umHY4EDo6cmfBqyblzs+/XAxduw+hNgb8bgVnwQjIwMMH9IPo4YNlOlYae2Ksu7gyj+fyqR7THmXHR0kIzUnC98d2y2VZtnJAG8OGNUs9Sspq0BNS08qLfd2FBLPbYdt32eaJYamlBJ6AKV596XSdM0c4DvnJ6ioayooKiIiIiIiIiIiak8EuSuspKSE39b9jC+/+habt2zD3n0HAABxcTcQF3cDKirKeHbGVLz/3hIhqmtSKioqGDd6KMaNHvrEvK4ujti8cZXcbVaWZpg/54UG1W1rbYmXX5jaoDK12dlY4a03X2l0+daiqrIC4Zvel3mK3GHQ8zBy6aWgqKglW7Z3M4rKpNd0+WzsdGiqNd+IA5exbyL92hmUFz6c7i9m788w8ewPzY5GzRZHU+gybDaqKstx88jvAAAtAwv4zV0t08FDRERERO2XZdwpqb/VbKzlphO1JOdv1m/64d4OLXPKdiKi9kawR+DV1dWw7KOlmP/6XAQFhyAlJRVVVVUwNTVBL/+eMDRs+qliqH2IO/grsuOvSKV1svGA08i5CoqIWrLzN6Ox43KQVFpgFzeM8vBp1jjUdTrBbdwCRPz9iSStorQQ13d8C58Xv27WWIQmEongMuo1qGnqIv7UZvi/tgYaHfidT0REREREREREzUfw+X709TthxPAnj4ogaoyM2GDcOPq7VJqKpg66Pf8FlJRlpwaj9q2ishLv7PxTKk1FSRlfjp8JkUjU7PFY+I5GcvA+ZN68JEm7E3Ecd6+fhbFbQLPHIzT7gc/Bqtd4qGrKn26PiIiIiIiIiIioqXBxA2oxDrwdAHFFuUy6GOIH/xNBXCm7veu0j6DV2ayJo6PW6PcLxxF1J1kq7dW+Q+BkbK6QeEQiETyeeQ+nv5wKcWWFJP3qtq/Q2cGnTaxvwY4OIiIiIiIiIiJShEZ1duzYufvJmR5jwvixT1We2iZxRTmq5HRmPJZICWZeA5smIGrV7hfkYcWh/6TSjHU74O0h4xUUUTVdY1s4DHoBNw6vl6QVZ91B3KHf4Dr2DQVG9mTiqiokntsGK7+xUFbTUHQ4REREREREREREEo3q7Hj7nfcbNQWMWCyGSCRiZwcJRklJWdEhUAu1fP825BYXSaUtGzUVehqKHz3hOORFpIUfRmHGw1En8Sf/gkWP4dAzc1RgZHUTi8W4vusHJJz6B3cijqPH7O+hqqmj6LCIiIiIiIiIiIgANLKzY/7rcxUy3z0RUX1cTo7HXyGnpdJ62DhgSvdeCopImrKqOjymvIegn+dJ0sRVlYj893P0XvA7REpKCoxOvhuH1yPh1D8AgMybl3Bx1avoOXc11HU7KTgyIiIiIiKi1mH2GH8UlpQCALQ11BUcDRFR29Oozo4Fb7wmdBxERIKoqqrCkv82QSwWS9JEIhG+nvA8lFpQJ4KhU0+Y+wxHathBSVp24lUkXdwJm94TFRiZrIQzWxB7YK1UWm5KDO5ePwsrvzEKioqIiIiIiKh1sTPvrOgQiIjatJZz54+ISAD/hp3Dpdu3pNKe9+uPrhY2ignoMdzGLZRZ0DtmzyqU5N1XUESyUkIP4Nr2r2XSuwybzY4OIiIiIiIiIiJqMdjZQURtRl5xET7Zt0UqraOmNj4YMUlBET2eul5nuDyyKHl5cT6i96xUUETS0q+eQcTfH8uk2/Z9Bl2Gv9r8AREREREREREREdWhUdNY1aW0tBQXg0KQkJCIgoICqWlkaohEIsx/fa6Q1RIRAQC+PLwDGQV5Umnvj5gEfW3dOkoonpXfOCSH7EN2/BUAgIFTT3QZOlvBUQH3b4Th0oZ3IK6qlEq36DESbhPe4rpNRERERERERETUogjW2bH/wCF89PFy5OY+vNEoFoulbojV/M3ODiISWnR6Cn47d1Qqzd3MCi/4D1BQRPUjUlKC55SlCP5lPlzHvQmzbkMV3pGQczsKob8uQlVFmVS6sXsguk7/sEUuoE5ERERERNTSZeQUoKy8+oEyNVVlGHbUUXBERERtiyCdHWGXwrFw8TvQ1NTE3FdfRlBIKC5fvoLPly9DYmISDh85htvJKXj+uRlwc3URokoiIgmxWIz3dv6JyqoqqfSvJjwH5VZwY17PzAEDl+2BkoqqokNBfnoCgn55HRWlhVLpnR190H3WCigpKz5GIiKitu6X3/7EmfMhdW4f2K83Xn5hqkz63XsZ2L7zIK5FxaKwsAj6+h3R08cL48cMg4aGelOGTERE9fD95lO4Fp8OAHC3M8GKuaMUHBERUdsiSGfHb+s3oKqqCn/+sR6eHu54+533cfnyFUx9ZjIAYNHCN/Dxp59j2/YdeHbHViGqJCKS2BMZijM3oqTSJnXzh7+dk4IiariW0NFRlHUHQWteQ3lhrlR6Rys3+M7+HsqqvElCRETUnDzdXdCxg+x0nI4OtjJpCYnJWP7lShSXlMDW2hLOTva4eSsJew4cw+XI6/h46UJoaWk2R9hERERERAohSGfH5Ygr8PRwh6eHu9ztampq+GTZBzh16ix+WrUGP/3wjRDVEhGhqKwUH+z+RypNR10Dn4yepqCIWqfSvEwE/TwPJTl3pdJ1TOzgO+cnqGhoKygyIiKi9mvsyMFwdXF8Yr6qqiqsWrsRxSUlmDppNMaOGgIAqKiowA+r/ofwK9fwz9bdckeDEBERERG1FYLM75KXmwcLC3PJ32qq1U8oFxUVSdJUVVXRvbs3goLrHo5NRNRQPxzfi9ScTKm0twaPhWmHTgqKSFhisRjF2elNWkd5cQGCfpmPwozbUuma+qbwm/cz1HXaxntJRETUVoWFR+JO+j1YWphizMjBknQVFRW8PGsqlJWVcOrsReQXFD5mL0RERERErZsgnR2d9DuhsLBI6m8ASElNk8pXWlqKAp5gUx1EKqpQUpb9J1JWgUhZRf62FjD1DylOYuY9rD55QCrNwdAEc/oOU1BEwirKTEPIrwtw+qvpKM3PbrJ6VNQ10dHaVSpNXbcz/F/7BZodjZqsXiIiIhJGeMQ1AEDPHt4QiURS2zp17ACnLvaorKzC5SvXFBEeEREREVGzEGQaKwsLc6SmPezY8HB3g1gsxr59B7Bo4RsAgIyMDAQFh8Ky1ggQotpGfHNWbnpWxh0AgL6haXOGQ63A+7v+RmlFuVTaF+NmQk1FkK82hamqrEDCqX8Qe3AdKstKAABRu3+E97OfNEl9IiVleD7zPlQ19XDr+B9Q1dSF37yfoW1o2ST1ERERUf2EXLqCkEtXUFFRgc76neDh7gQHOxuZfIm3UwEAttbyf7ttrS0RFX0DSbdTgd5NGTERERERkeIIckewT+9eWLX6FyQnp8DS0gL9AgNgZGSINWt/Q0JiEkxMjHHw0BEUFRVh9OiRQlRJRO3csehIHLweLpU23K0bBrl4Kigi4YhEIqRFHJN0dABASsg+WPqOgkGXHk1Wp+vYN6Cu0xGdbLtCz/zJ84MTERFR0zp87LTU31t37IOXpytee+U56Og8XE/rfmYWAKCzfke5+9Hv1PFBvqYbKUpEREREpGiCdHaMHTMSRYVFuH8/E5aWFlBXV8eqn77D3NcW4MDBw5J8/QL74pWXZwlRJRG1Y2UVFVi660+pNHUVVXw+boaCIhJWzUiLs9/OhLiqUpIeuXUFAt/5F8qqak1Wt/3A55ps30RERFQ/1lYWeNHeBm4uXdBZvxPy8gsQFXMDW7bvRURkFL75cR2WLV0AJaXqWYlLSkoBAOrq6nL3p6Gh/iBfidzttX20/Du56RNH94epsYFk1HVzqngwklcRdbc1k5w9FFa3mo211N96HQwBAAFDnldEONRE2mu76mpo1iufivY5AOkP/t8RHVwDmzAq4Shr6gFAo+Pl93fLxN/Xtqk1t6sQs/oI0tlhY22N9959SyrNp3s3nDl5GKFhl5CTkwt7Ozu4ubkIUR0RtXNrzxzCzQzpRbvn9x8Jm85tZ32JDhZOsA2civiTf0vSCu8l4daxjegy/JWn2re4qgoiJUGWbCIiIqImMGJof6m/DdX1EdinJzzcnPDOBysQdzMBIWFX4OfrraAIiYiIiIhaniad2F5TUxN9A/o0ZRVE1M6k5WThmyO7pNIsOnXGgoGjFBNQE3IaMQdpEcdQkn1XknbjyO8w6z4UOkbWjylZt5vH/kBW/BV0n7UCyqryn/4kIiKilkm/U0cEBvhh/6ETiIi8Luns0NBQR2FhEUpLS+WWqxn5oaGh8cQ6Pv1wsdx0Ra6jxzX8hPPDxZMKq9sy7pTU3zVP/p898ocCoqGm0l7btbdD/R7urSjMkfp/btTpujO3IDUjOhobr8W4hUKGQwLh72vb1N7btckf7Y2PT8DBQ0dwJfJqU1dFRO3Ax/v+RWGZ9IX88jHToaXW9m7cq6hrwWPSO1JpVZXluLp1BcRicYP3l3RhB6L3rMTda6cRvPYNVJQUChUqERERNRNTk+qRrNk5uZI0g876AIDMrBy5ZbKycx7k69SksRERERERKZIgIzsOHDyMf7dsx6IF8+Hl9XBx4O++/wlrf/2f5KbciOFD8dMP30AkEglRLbUDxWVlCE1OQEFpCczy8+BlYQtNtaZbr4BatovxsdgeflEqra+jK8Z4Ns2i3S2BiUcgTDz7IT3ylCTtflwoUsMOwqLHiHrvJy38CCK3fCH5O/NGGC6snoNe89dBRV1LyJCJiIioCRUWFgEA1NUfnhPbWJkj6XYKEpKS0c3LXaZMQlIyAMDayrx5giQiIqrl+q4f6p3XjaNAiOgpCNLZsXvPPkRciYSLi5Mk7eq161iz9jeoq6vDr6cvoqKjceDgYQwZPBCjRg4Xolpqw9JysvDLmUP4O/gMcoofPn3eUVMbM3r2xbzA4TDtwCfT2pPKqiq8s2OTVJqykhJWjJ/Z5jtQ3Se+jYzYEFSWFknSru/8HkauvaGm3eGJ5e9FXUD4nx8Cj4wG6WjpAmW1+i2kR0RERIonFosREhYBALCzsZKkd/Nyx+lzwQgOvYwJY4ZJnRtl5+QiNu4WlJWV4OXp1twhUxv36NRURPR45oYdUFxaLvk/EREJS5BprGJi4uDi7AR19YfTyOzavRcikQjffbMCv6//BXt2boOGhjo2b9kmRJXUhl1NTUL/7z/Ez6cOSnV0AEBOcSF+PnUQ/b77AFdTkxQUISnCxosncC3ttlTa7D6D4WJioaCImo9mJxM4j5grlVZWkI3oPSufWDYrPgKh/3sL4soKqXSzbkPhMfmdNt9RRERE1NokJCXj/MUwVFRI/3YXFRfjtw2bcSvhNtTV1dAvwF+yrbu3B0xNjJCccgd79h+VpFdUVGD9xn9RWVmFfgH+0NPVabbXQUREsl6fFIAfF4zHjwvG4/VJAYoOh4iozRFkZEdmVia6dvWQSgsKCkGHDnoYNnQwAMDIyBA9fLojLu6mEFVSG5WWk4VJ675GRkHeY/NlFORh0rqvcWrxZxzh0Q5kFuTj8wPbpdIMdfTw7tAJCoqo+dn0nYLk0H3IS4mVpN2+uAsWvqPQ2d5bbpnclFgEr3sTVeXSa5wYufaG97OfQKSk3KQxExERUcPdv5+F1ev+wIa/tsHOxgp6ujrIyc1D4u0UFBYWQUNDHW/MnYWOHfUkZZSVlfH6nOex/MuV+Hf7XgSHRcDEyBA3biXifmYWLC1MMX3KWAW+KmpqH+75R9EhELVr529G1ztvfRczJyKihhNkZIeysgrKysokfxcUFOLGzVvw6d5N6qnhTp06ISs7W4gqqY365cyhJ3Z01MgoyMMvpw81cUTUEnx2YJvMKJ9lo56Bnmb7WWtCSVkFXad+AIikv7Yjt3yBqopymfwF924j6JfXUVFcIJWub+eF7i9+BSUV1SaNl4iIiBrHytIcwwYHwszECMkpaQgOi8DN+ER06tgBw4f0w9fL34N3V9npqOxsrLDik3fQ298HWVk5CA2PhJKSEkaPGIRPP1gMLS1OXUlEREREbZsgIzvMzExx9ep1iMViiEQinDx1GpWVlfD36ymVLycnFx07ck5Ckq+4rAx/B59pUJm/Q07jvWETuWh5GxaRnIBNwaek0nysHTDVp49iAlKgjlauECkpQVxZJUkrSI/Hgbd6STpBRBBBDLHMtFUAoGfuBN9XfoQK1+kgIiJqsYyNDPD8jEmNKmtibIjXX31e4IiIiIiIiFoHQUZ2DBk8EOl372Lua29i4x9/4Ysvv4GysjIGDewvlS8qOhoW5mZCVEltUERKgszT+0+SXVSIKykJTRQRKVpVVRXe2bkJ4loLa4tEInw1YSaUlAT5+mqFZNfYEFdVQVxZAXFlBaoqy+V2dGgbWcNv3mqoauk2R5BERERERERERETNSpCRHS++MBN79u7H0WMncPTYCQDAK7NfhIWFuSTPpfDLyMi4j/HjxghRJbVB+SXFjSqXnpcjbCDUYmy9dAGhidLr/DzrGwhvSzsFRaR4IgDiJ+aS5TfvZ6jr6gsdDhEREREREdVT8JVUXI64DwAwM+iACf08FRwREVHbIkhnR4cOHbB/z384eOgIsrKy4e7uKjOFVWZmFl54/lmMGT1SiCqpDdLVaNzUOq//+xuu30nGnICh6KzDp9bbirySYny871+ptA6aWvhw5GQFRdR6iZRVoKVvqugwiIiIiIiI2rWbSVlIvlO9Tqm7nQk7O4iIBCZIZwcAaGlpYeKEcXVuHzJ4IIYMHihUddQGeVnYoqOmdoOnsioqK8V3R3fjl9MH8YL/QLzWbzhMO3RqoiipuXxzZCfu5edKpS0dNgkGOnoKiqj1EsmZ+oqIiIiIqC6WcacUHQJRu3f+ZnS98vV2cGniSIiIWo/2Ouk9tUCaamqY0bNvo8sXlZVhzemD8P5sERZt24DEzHsCRkfNKfZuKtadOSKV5mpqiVm9BigoIiIiIiIiIiIiImrJ2NlBLcq8wOEwrOeT+ypKynLTyyorsPHiCfRY8Tbm/L0WMempQoZITUwsFuPdnX+ioqpSKv2r8TOhoiy/zYmIiIiIiIiIiKh9Y2cHtSimHTph+6tLntjhYaTbAccXfoJtr7wNfzsnuXkqq6qw9dJ59Pr6XTy34SdEJCc0RcgksH1Xw3A67rpU2gRvPw7NJSIiIiIiIiIiojoJtmYHkVA8zK1xavFn+OX0IfwdchrZRQ/X8OikpY0ZvoGYGzhMsi7HQGdPXIyPxffH9uB4TKTcfe67GoZ9V8MwwMkDiwaNQS9752Z5LdQwRWWl+GD3P1JpWmpq+GT0NAVFRERERERERNRy1XdtD4DrexBR28fODmqRTDt0wqdjpuG9YRNx9loY8ktLYG5sjq4WttBUU5PJ72/nhG2vvI2I5AT8cHwv9l0Ng1gslsl3IvYqTsRehZ9tFywcNAaDnD0hEnHx5pZi5Yn9SM6+L5W2eNBYmHfUV1BERERERERERMLLLS5qUEdFe3F91w/1yuc2bmETR0JErRE7O6hF01RTg4+lLQBA39D0ifm9LG3xxwtvICY9FStP7MO28AuorKqSyReUEIdnfvsWXS1ssHDgGIzy6A4lJc7qpkhJmfew8sQ+qTQ7A2PM6zdcQRERERERERERERFRayFIZ8fGP/6CpqYGnpkySYjdET01ZxNzrJn+Kt4ZOh4rT+7H38FnUFZZIZPvSkoiXvhjJRyNzLBw4GhM7OYHVWX2ASrCB7v/QUlFuVTainHPQl1FVUEREREREREREVFrxpEiRO2LIHd1P1/xNfoFBrCzg1oc685G+G7SLLw1eBzWnD6IjRdOoLCsVCbfjXtpmLd5Hb48/B/e6D8S0337QkNVdrosahonYq9i/7VLUmlDXb0w2NVLMQERERERERERUYtV304MImpfBJm3p3NnfWhqagqxK6ImYdqhE5aPmY4rH/6At4eMQwdNLbn5bmfdx1v//QHvzxZh1cn9KCgtaeZI25+yigq8t/NPqTQ1ZRV8PvZZBUXUsolUVKGkLPtPpKwCkbKK/G0cHUNERERERERERG2cICM7/P16IuxSOCorK6GsrCzELomahL62Lt4bNhGv9RuBDeePY83pg8goyJPJdzc/F8v2/osfj+/DqwFDMLvPYHTS1lFAxG3furOHcePeHam01/uPgJ2hsYIiatlGfHNWbnpWRvV7WJ+1bYiIiIiIiIiIiNoaQTo7Fr75OsZOmIKPPv4MHyxd0qpHeVRUVGDfoRM4dyEU9zIyoaGhDucudhg/ehhsbSwbtK+i4mLsO3AcwWERyMjIhJq6GmxtLDFyaH94ebrJ5C8tLcPV6zEIj7iG2LhbyMjMhkgEGBp0Rncvd4wcPhB6urI33DMyMvHG2x8/NpZffvwcHTvqNSj+tkxPQxNvDhyFVwKG4K/g01h5cj9SczJl8mUXFeDLwzuw+tQBvNhrIOYGDoOxXsfmD7iNSs/LwTdHdkmlmXXUx8KBoxUTEBEREREREVET6d3NEoXF1WtVamtyBD4RkdAE6ezYtXsvAvsGYMvW7Thy5Bh69fKDubkZNNTVZfKKRCLMf32uENUKrqKiAiu+XYOomBvQ09NFNy935OTmIvRSJMIjruPtBa+iq4dLvfaVm5ePT7/4EWnp96CrqwMPN2cUlZQgNvYWrl2PxbQpYzFmxCCpMueDwvDbhs0AABNjQ3h3dUN5eTlu3ErEngPHcPZCKD589w2YmhjJrbODnm6d8amp8UdUHk01NcwOGIzn/ftjW/gF/Hh8L25lpMvkKygtwcqT+/Hr2SN4tmcg5vcfCUt9AwVE3LZ8su9fmanClo+ZDm11DQVFRERERERERNQ0bCw6KjoEIqI2TZDOjp9WrYFIJIJYLEZWdjb27T9YZ96W3NmxZ/8xRMXcgL2tFZYueR1aD0aonA8Kw+q1f+DnXzfhp6+XQVPzyTdi12/8F2np9+Du2gULX38ZWlrV+0pISsaX367Bv9v2wN21C+xsrCRllJWV0b+vP4YP6QdLCzNJelFRMX5aswGR16Kxdv1f+OSDRXLrNDM1xtzZM5/mLWi31FRUMMO3L6b69MGeyFD8cGwPrqXdlslXUlGO9eePYePFk5jcvRcWDBwNRyNOG9QYQQlx2BJ2XiotwMEF47r6KigiIiIiIiIiIiIiaq0E6ez4+svPhNiNQlVWVuLgkZMAgFnPPSPp6ACA3n4+OHchFBGRUTh1NgjDh/R77L4ys7IRFh4JJSUlvPzCNElHBwDYWltiwthh2PjXduzZdxQLXn9Jsi2wT08E9ukpsz8tLU28+tIMvLbwA8TdTEDG/SwYGug/5SsmeZSVlDDeqyfGdfXFkagIfHdsD8KSbsrkq6iqxObQs/g37BzGePbAokFj4GFurYCIW6fKqiq8s2OTVJqykhJWjJ8JkUikoKiIiIiIiIiIiIiotRKks2PihHFC7EahYm/Eo6CwCEaGnWFvayWz3b9nN0RERiHscuQTOzsSEpMBAEaGnWFsJDvVkburEwAgIjIKFRUVUFF5cjPod+oAPV0d5OUXIDsnl50dTUwkEmGomzeGuHrh3M1ofH98D07HXZfJJxaLsftKCHZfCcEQVy8sGjQGvjaOCoi4ddkUdBJXU5Ok0l7qPQiupg1bF4eIiIiIiIiIiIgIEKizoy1Iup0KoHrkhTw16bcf5Huc0tIyAIC2tpbc7To62tX5yspwJ/2e1JRVdSkoLEJBYREAoGMHXbl5cvPysX3nAWTn5EJDQx3WVubw6eYpNUqFGkYkEiHA0RUBjq4IS7qJH47txcHr4XLzHomKwJGoCPSxd8GiwWMQ6OjGUQpyZBXm47MD26XSDHR08d6wCQqKiIiIiIiIiKjplZdXokosBgAoiURQVVVWcERERG2L4J0d0TGxuHIlElnZOejiaI9BAwcAqO4AKCsrg66ujtBVCuJ+ZhYAQF+/o9zt+p2q0wsKi1BSUgoNDdnF12vUvMaM+1lyt2dkZD78//2senV2HDpyClVVVbC0MIORofyFsdPu3MV/u6XXS9HS1MRLzz+DXn7dn1hHjY+Wfyc3feLo/jA1NkBWxp1670sIFRXlANDs9T7KTksbq8ZMRaxfINZePIn90VckJym1nbsVjXO3otHV1BJzeg3AQEcXKImUFBBx03maNvno0E5kFxVIpS3qOxSVBXnIKsgTJL72qKUcJ/QQ26TlYZu0PGyTlkeRbaJvyHXQiIiobdt6MArJd6qvey1N9TBjjIeCIyIialsE6+xISEzCkneW4nJEpCRtwvixks6O3Xv2YekHy/D7+l/QN6CPUNUKpqSkFACgri6/E6N250ZxScljOzsc7W2gpqaKvLx8XLp8Fd29pX+8jp96uChzcUnJE2O7FZ+EPfuPAgCenTpOZruKqgoG9e8Df99uMDMzhoa6OtLS72L/oRO4EHQJq9f9AS0tDXh5uj2xLnoyJyNT/DB2Ot4MGIJfg05h59VLKK+qlMl35U4y5v73B7oYmmCOf3+McPGEilL7fmrjenoq/o0IkkrzNLXEJE8fBUVEREREREREREREbYEgnR13797D1OnP4f79TAwc0A++PXyw4qtvpfKMHDEUH328HIcPH2uRnR1C0tTUwLBBgdhz4BjWrv8LL8ycjK7uLiguKcXxU+dw+lwwlJWVUVlZCaUnTHOUmZWN71etR3lFBUYNHwhPdxeZPJ06dsBLzz8jlWZnY4X5c16AgX4n7DlwDH/9u6venR2ffrhYbnrNE37N/dSdoup9En1DU6x18sAH2Zn4+dRBbAo6ieLyMpl8cRnpWLRnM1adP443BozC1B59oK6iqoCIhdOYNhGLxfji3/Uyo2G+m/ISDIzMBY2vPWqpx0l7xjZpedgmLQ/bpOVhmxARERERUWslSGfHqp/X4v79THz26UeYNnUKAMh0dmhra8PVxRmXr1wRokrB1YzUKC0tlbu9ZuQHAGhqaDxxf5MnjEJObh7OnA/B6rV/SG0bMrAvbsUn4lbC7TrX9QCA/IJCrPh2DbKycxDQ2xfTp4ytz0uRMmbkYOw7dAKpaenIuJ/Fhc2bgEWnzlgx/lksGjQaa88cxvrzx5BfUiyTLyHzHhZu+x1fH9mJ+f1HYmbPQGirP/mz1FZsu3QBwQlxUmkzfPuiu7W9giIiIiIiIiIiIiKitkKQzo5Tp8/AyclR0tFRFwsLc5y/cFGIKgVn0Lm6EyArK0fu9qzs6nQdba3HTmFVQ0VFGXNnz8SQQYG4HHEN2Tm50NXVRjcvD3RxsMW8BR8AACzM5a/XUVRcjBXf/ozUtHT4dPPEnJdmNGqxa21tLejp6SAnJw/ZObns7GhChrod8OHIKXhjwEisP3cMa88cRmZhvky+O7nZWLrrL3x3dDfmBg7Dy70HQU+z7k6vtiC/pBgf7/tXKk1PQwsfjXz8dwYREREREQnHMu6UokMgIiIiajKCdHbcz7gPb6+uT8xXWVmJoiLZJ95bAmur6ml0EpKS5W6vSbeyath0O/a2VrC3tZJKy8jIRHZOLsxMjaHfqYNMmdLSMnzzwzokJCbDw80Zb8x9AUpKjVvguqqqCsXF1euCqKurNWof1DAdNLWxePBYzOk7FJuCTmH1yQO4k5ctky+zMB+fHdiGn07sw+w+gzGn71AY6OgpIOKm9+3RXUjPy5FKe3fYBBjqyn7+iYiIiIiIiIiIiBqqcXfQH6Grp4f0u3efmC8p6TY6d26ZIwucHO2go62FexmZuJVwW2b7xeBwAICPt+dT13XgyEkAwKD+smuXVFRU4PtV6xETdwtOXeyw+I3ZUFVt/PoOEZFRKC0tg7q6GsxNjRu9H2o4bXUNzA0chvAPvsMPk1+ETWcjufnyS4rx/bE96Lp8Id7b+RdSc7KaOdKmdePeHaw9c1gqzdnEHC/1HqigiIiIiIiIiIiIiKitEaSzw6urB65GXsPNm7fqzHPlSiSiY2LRw6e7EFUKTllZGcOH9AcAbNi0BUXFD0egnA8KQ0RkFHR1ddAvwE+SfjM+EYvfXY7F7y6X2V/G/Szk5ORJpVVVVeHA4RM4fOwMrC3NMXhAgMz21Wv/QOS1aNjZWmHJwjn1Go1x+Nhp3Em/J5N+LSoWv23YDKC6Y0VFRZCBPNRA6iqqeN6/P0Le/RrrZsyBs4n80UHF5WVYd/Ywun2+CAu2/g/xGU/uQGzpxGIx3t35J8orK6XSvxr/HFSV+XkkIiIiIiIiIiIiYQhyt/H5557FiZOnMee1N/Hd11+ga1fp0Q/RMbFYvGQplJSU8PzMGUJU2STGjByE69FxiIq5gYXvLIeLkwNyc/MQE3cLysrKmDd7JjQ1Hy4oXVZajjQ5nQwAEBUdh183bIattQUMOutDLBbjZkISsrJyYG5mgiWL5kJFRVmqzOFjZxAcFgEA6NhBD3/8tV3uvvsH9oJzl4eLOp88fRF//P0fLC1MYWpcPXogLf0eklPSAABdPVzwzMRRjX5fSBgqysqY3L03Jnr74+D1cHx/bA8uJyfI5CuvrMSmoFP4K/g0Jnj7YcHA0XA1tVRAxE/vwLVLOBl7VSptnFdPBDi6KigiIiIiIiIiIiIiaosE6ezo09sfr819BT//8ismTpkBI0NDiEQinDx5Gv0GDENKairEYjEWLZgPL6+nnwaqqaioqOC9t+Zh38HjOHcxDOGXr0JdQx0+3h6YMHY4bG3qf8PZ1sYKfr7euHkrEcmpd6CspAwTE0MMH9wPQwf1lTs1VWFhkeT/4RHX6ty3q7OjVGfH0EGBiIi8jtspaYi8HoOysjJoa2ujq4cLAnr5opdf90Ytbk5NQ0lJCSM9fDDCvTtOxV3D98f24PytGJl8VWIxtodfxPbwixjh3g2LBo1FNys7BUTcOMVlZXh/999SaVpqavh09DQFRURERERE1Hp8uOcfRYdARERyXN/1Q73yuY1b2MSRENGjBJtHZtHCN+Dq6oKf16xDVHT1jdus7GxkZWfD0cEeb8yfhxHDhwpVXZNRUVHBuNFDMW70k2N1dXHE5o2r5G6zsjTD/DkvNKjuSeNHYNL4EQ0qAwD9A/3RP9C/weVIsUQiEfo7eaC/kweCEuLww7E9OBp9RW7eA9fCceBaOAK7uGHxoLHobe/c4juwVp3cj9tZ96XSFg4cA4tOnRUUEREREREREREREbVVgk6aP2zoYAwbOhiZmVnVozmqxDA1NYGxsfyFmYmomp9tF2yZ/RYiUxLxw/G92BMZCrFYLJPvdNx1nI67Dl8bRywaNAaDXbq2yE6P5Kz7+PH4Xqk0m85GeK3fcAVFRERERERERET1cf5mtNTfAXa+ctN7O7g0W0xERPXRJCsEd+6sj86d9Zti10RtmqeFDTY8Px9xd9Pw04m92HrpAiqrqmTyhSTewNT138HD3BoLB47GaM8eUFZSUkDE8n2w5x+UVJRLpX0xbgY0VNUUFBERERERERGRYmlqqEJHS1Xyf2o56js1FRG1bIJ3dqTduYOwsHCkp98FABgbG8HHpxvMzcyEroqozepibIafp72Kd4ZOwKqT+/FX8BmUPtJ5AABXU5Pw4qbVcDQyxZsDRmFy915QVW6SPsx6OxV3DXsjQ6XSBrt0xVBXbwVFRERERERERKR4E4Y4KzoEIqI2TbC7ohkZGVj2yec4euyEzPQ7IpEIAwf0xyfL3ueUVkQNYKVviG8mvoC3Bo/DmtMHseHCCRSUlsjku3HvDl7/9zd8eXgH3ug/EjN8A6Gp1vyjKMorK/Duzj+l0tSUVfDFuGdb5HRbRERERERERERE1DYI0tmRmZmFSVOeRUpqKtTU1ODn5wsL8+qRHCmpaQgKCsHRY8cRHR2D/7b/A4POXKCYqCGM9Trik9HTsGDAaPx67gjWnTmCnOJCmXwp2ZlYsmMTvj26G/MCh2FWr4HQ1dBstjh/PXsUcXfTpNLmBQ6DvaFJs8VARERERERERERE7Y8gnR3f/7gKKampGDxoID79+AMYGRlKbc+4fx/LPv4Mh48cw48//YzPPv1IiGqJ2p1O2jp4Z+gEzAscjo0XT2DNqYO4m58rk+9efi4+3rcFPx7fh9kBg/FqwBDoa+s2aWx383Lw1eEdUmmmHTph0eCxTVovERERERERERERkSArGp84eQrGRkZY+eM3Mh0dAGBoYIAfvvsaxkZGOH7ipBBVErVruhqamN9/JC5/8D2+nfg8LDsZyM2XU1yIb47sQtflC/HRns1Iz8tpspg+3b9FZoqtT0dPg466RpPVSURERERERERERAQI1NmRk5MLH59uUHvMGgHq6mrw8emGnBzZp9CJqHE0VNXwYu9BCFv6DX6e9gocjUzl5issK8XqUwfg/dkivLV9I25nZQgaR0jiDWwOPSeV1svOCRO8/QSth4iIiIiIiKi1ioy9i7Nht3E27DYiY+8qOhwiojZHkGmszM3NUFAgu37AowoKCmH+YC0PIhKOqrIKpvUIwJTuvbHvahi+P7YHV1OTZPKVVpTj9wvH8UfQSUzu1gsLBo5GF+OnOyYrq6rw7g7pRcmVRCJ8NeE5LkpORERERERE9MDV2HtIvpMHALA01YOnk7GCIyIialsEGdkxftwYBAWHICFR9uZqjYTEJFwMCsa4MaOEqJKI5FBWUsLYrr44tWg5tsx+Cz1tu8jNV1lVhX/DzsH/63fx/MaVuJKS2Og6/ww+hYiUBKm0l3oPgpuZVaP3SURERERERERERNQQgnR2zHnlJfQN6INpM57HP5u3So3yKCwsxOZ/t2LGs7MQ2DcAc+fMFqJKInoMkUiEwS5dceD1D7B33lL0d/KQm08sFmNvZCj6f/8hJv/6DYLiYxtUT3ZhAT47sE0qrbO2Lt4bNrHRsRMRERERERERERE1lCDTWA0YNAJiiJGRcR8ffbwcH328HHp6ugCAvLx8Sb6o6GgMGDRCqqxIJMKpE4eECIOIHiESidDbwQW9HVwQfjsePx7fi31Xw+TmPR4TieMxkehl54SFg8ZggJOH3GmoisvKEJqcgILSEuyOjkRWYYHU9g9GTEZHLe0meT1ERERERERERERE8gjS2ZGSmir5v1gsBgDk5ubJ5EtNTROiOiJqhG5Wdtg0601Ep6fgx2N78d/li6h6cLzWdiE+Fhd+/QZeFrZYNGgMRrh3g5KSEtJysvDLmUP4O/gMcorlr9HjZWGLZ3sGNvVLISIiIiIiIiIiIpIiSGfHrbhrQuyGiJqBi4kF1j07F+8Om4CVJ/Zjc+hZlFVWyOSLSEnAcxt/gpOxOSZ188evZ48go0C2E7O2VwKGQFlJkNnxiIiIiIiIiIiIiOqNdyWJ2ilbA2P8MOVFhL//HeYGDoOWmprcfLF3U/H5we1P7OgAgGV7N+NObrbQoRIRERERERERERE9Fjs7iNo5s476+HzsDER88AMWDx4LPQ2tRu8royAPv5zmGjxERERERERERETUvNjZQUQAAAMdPbw/fBIiP/wBH46YDAMd3Ubt5++Q0yguKxM4OiIiIiIiIiIiIqK6CbJmBxG1HXqaWlg4aAxe7TsUn+7bil/PHWlQ+eyiQlxJSYCfnVMTRUhEREREREREDXX+ZrSiQ2hXru/6QdD9uY1bKOj+iNoijuwgIrm01NQxwNmjUWXzSooFjoaIiIiIiIiIiIiobhzZQUR10tXQbFQ5vUaWIyIiIiIiIqK2pSEjSno7uDRhJO1HfUaVdHANhFYn02aIhqj5sLODiOrkZWGLjprayCkurHeZTlra6Gph24RRERERERFRbZZxpxQdAhHVQzdXEzhY6wMA9LTVFBwNEVHbw84OIqqTppoaZvTsi59PHax3mRm+gdBU40kbERERERERUW0uDoaKDoGIqE0TpLNjxsxZ6BvQB6++8tJj8/22fgNOnT6Dv//cIES1RNQM5gUOx9aw88goyHtiXiPdDpgbOKwZoiIiIiIiIiIiaj+EXvCcqC0SpLMjKDgU5ubmT8wXn5CA4JAwIaokomZi2qETtr+6BJPWff3YDg8j3Q7Y/srbMO3QqRmjIyIiIiJqXT7c84+iQyAiarHqu74H1/YgInmUmrOysrJyKCs3a5VEJAAPc2ucWvwZXu83Ap20tKW2ddLSxuv9RuDkouVwN7dWUIRERERERERERETUnjXbmh1VVVW4du06DDobNFeVRCQg0w6d8OmYaXhv2EScvRaG/NISmBubo6uFLdfoICIiIiIiIiIiIoVqdGfHjJmzpP4+c/acTFqNiopKJN2+jfv3MzFxwrjGVklELYCmmhp8LG0BAPqGpgqOhoiIiIiIiKh12HrgOlLSq6eHtjDRw5QRbgqOiIiobWl0Z0dQcKjk/yKRCBkZ95GRcb/O/KoqKhg0sD/eXbK4sVUSERERERERtQuWcacUHQIRCay8ogpl5VWS/xMRkbAa3dlx5uQRAIBYLEbggKEYPmwI3nvnLbl5VVVVoa/fCSoqzTZrFhERERERERERETWR+i4mTkTUXBrd+2Bubib5/xvz58HVxVkqjYiIiIiIiIiIiIiIqDkIMtTizfnzhNgNERERERERERERERFRgwnS2ZGZmYXEpCTYWFujc2d9SXpqahq+/vYHxMbdgLmZGd58Yx48PdyFqJKIiIiIiIioxfhwzz+KDoGIiKhJXN/1Q73zuo1b2ISRED2eIJ0da39djw0b/8TBfTslnR3FxcWYMnUm7t67B7FYjBs3biI07BIO7N0BCwtzIaolIiIiIiKiWioqKrDv0AmcuxCKexmZ0NBQh3MXO4wfPQy2NpaKDo+IiIiIqMkI0tkRHBwKWxsbODo6SNJ27tqL9Lt3MaB/IF5/bQ5OnjqDVat/we8bN+GjD94ToloiIiIiIiJ6oKKiAiu+XYOomBvQ09NFNy935OTmIvRSJMIjruPtBa+iq4eLosMkIiJq1eq7MHtvB/7mEjU3QTo77qTflZme6tTpM1BSUsJnny6DsbERunp64ODBw7hwIUiIKomIiIiIiKiWPfuPISrmBuxtrbB0yevQ0tQEAJwPCsPqtX/g51834aevl0FTU0PBkRIRERERCU+Qzo78/Hzo6upIpYWHR8DZqQuMjY0kaV26OOL0mXNCVElEREREREQPVFZW4uCRkwCAWc89I+noAIDefj44dyEUEZFROHU2CMOH9FNQlAQAlnGnFB0CEVGr1xpGV7SGGNsSritCgECdHQYGnZGeflfyd2zcDWTn5GDM6JFS+UQikRDVERERERERUS2xN+JRUFgEI8POsLe1ktnu37MbIiKjEHY5kp0dRETUbrDDoeVqSOeEoupWZKdIa4ixJVISYiddHB0RfjkCV69dBwD87/c/IBKJ0LdvH6l8t5OTYWRkKESVRERERERE9EDS7VQAgK21/EXIa9JvP8hHRERERNTWiCorSsVPu5Oz5y7ghRdfgZKSEnR1dZCXlw9rayscPrAbKirVg0dyc3Ph698XQ4cMxsofv33qwKn53buTDEAMFRXVZq23oqIcAJq9Xqob26TlYZu0PGyTlodt0vKwTVoeRbaJiqoa9Dp2bvZ624o/N+/AgcMnMXxIPzw3faLM9sLCIrz82jsAgA1rv4WGhrrc/Xy0/Du56bOmj4KWpoZCRuuLxdWXrELWXfrgs/4kSlWVgtVJ0pSVq79nKivr1xbUOrBdHy8p+Q6KiosBAFqamrC2NFVwRPXTHtpVSan+z4NXVVUppO6G1FuffYqUlAEA4nr81gldd1ujrKIm+D4rK8oaVXdd50313Z+8fTYXDU3tp74eEGQaq4A+vbD8k4/wy7rfkJ2dDZ/u3fDpJx9KOjoAYOeuvaioqIRfT18hqiQFUFJSQpVY2C/0+rhz9z4AwNK8dZwEtAdsk5aHbdLysE1aHrZJy8M2aXnYJq1XSUkpAEBdXX4nRu3OjeKSkjo7O+pSWloGbW0tqCgLcgnZIMmpdwAI+7lkJ6viNUW7kuKxXR/P2UlX0SE0Ctu1bWK7tmyNPVepq13by7mPYGeq06dNwfRpU+rcPvWZSZgwYSy0tbSEqpKamYGxuULq/XHtPwCATz9crJD6SRbbpOVhm7Q8bJOWh23S8rBNWh62CbXEtufnsm1iu7ZNbNe2ie3aNrFd26b23q7N9liOhoYGNDQ0mqs6IiIiIiKidqNmpEZpaanc7TUjPwBAk9dlRERERNQGCd7ZEXYpHMEhYbh79y4AwNjYGD19feDTvZvQVREREREREREAg876AICsrBy527Oyq9N1tLUaPIUVEREREVFrIFhnR2JSEhYuegdXr10HILsYioe7G378/mtYW1sJVSUREREREREBsLaqnnI2ISlZ7vaadCsrxUxNS0RERETU1ATp7LifmYnpM2bh7r176NixA4YNGQxLSwsAQHJKKg4fPorIq9cw/dlZ2L1rKww6P92q6kRERERERPSQk6MddLS1cC8jE7cSbsPeVvohs4vB4QAAH29PRYRHRERERNTklITYyS9rf8Pde/cwedIEnD11FJ9/9jHmvPoy5rz6Mj5fvgxnTh3BlMkTkH73LtauWy9ElURERERERPSAsrIyhg/pDwDYsGkLioqLJdvOB4UhIjIKuro66Bfgp6gQiYiIiIialKiyolT8tDvpP2g4qiqrcOLYASgrK8vNU1lZiQGDRkBJWQknjx182iqJiIiIiIioloqKCqz4dg2iYm5AT08XLk4OyM3NQ0zcLSgrK+OtN1+Bl6erosMkIiIiImoSgozsSE+/Cy8vzzo7OoDqJ428vDyRnn5XiCqJiIiIiIioFhUVFbz31jw8M3EUdHW0EX75KlLS0uHj7YHlHy5mRwcRERERtWmCrNmhoaGOnJycJ+bLycmBhoa6EFUSERERERHRI1RUVDBu9FCMGz1U0aEQERERETUrQUZ2uLq6IDg4FDGxcXXmiYmNQ3BwKNzc+DQREREREREREREREREJR5DOjmenT0V5RQWenfki/rfhD9zPzJRsu5+ZifW/b8SzM19ERWUlnp0+VYgqiYiIiIiIiIiIiIiIAAi0QDkAfPrZCvyx6W+IRCIAkExXVVJSCgAQi8WY9cJMfLD0HSGqIyIiIiIiIiIiIiIiAiBgZwcAHD12Ahs2bsLliEiUlZUBANTU1NDNuyteeH4mBg8aIFRVREREREREREREREREAATu7KhRUVGB7AcLlnfq2BEqKoKsg05ERERERERERERERCSjSTo7iOYvXob7mVkAgOemT8TwIf3k5quqqsJrCz9ETm4eAGDOSzMQGODXXGG2S9NemN/gMiu/+RiGhp2bIJr253ZyGt75cAU0NTSwfs1XUFKSXTrp829W49r1WBga6GPlt5/I3c+SD75AcsodLJr/Mnp07ypojNt3HsB/uw+2y+PxaY6Pmu+9zRtXNUFk9Di1f3Pqsv7nr6CtrfXYPKfPBmHt//7GxLHDMWn8CCFDbBdi4m7hyLEziLuZgNy8fKiqqkBPVwemJkZw7mKPXj2787ekGbAdqCWoqKjAvkMncO5CKO5lZEJDQx3OXewwfvQw2NpYNnh/F0PCcfjoadxOTgMAWFmaYdjgfvDz9a6zTE5uHv7bfRCXI64jNy8fHfR04e3lhknjRqCDnm6jX1t7p+i2fdJv/vw5L6CXX/cGx9HeCdWumZnZCL9yDbcSbiM+IQkpqekQi8X1uq4oLi7Brr2HERwWgaysHGhra8Hd1QmTxo+AsZHB077EdknR7frpip8QHXuzzu1TJ43G2FFDGvSaSLh2jU+8jfCIa7h2PRYpqXdQUloKXV0dODnYYcTQ/ujiaFdnWR6vwlN0u7al41WQIRdpd+4gKCgEXl5dYWdrIzdPfHwCIq5Ewt+vJ0xNTYSollqJs+dD6uzsuHI1WtLRQc2jb29fmbSk26lISk6FsZEBnOR88ak/WIOHnp6lhSl0dLRRUFCIhKQU2NtaSW2vqKjEjZsJAICM+1nIzMpGZ/1OUnkKCgqRkpoOkUgEZyeHZou9PeDx0bp5urugYwf5N7A4yrRp7d53BFv+2wexWAwTY0N09XCBmqoq7t3PxNXrsYiIjIKGhjqGDgpsdB1R0Tew/KuV6NvbF3NnzxQw+rajOdqB6EkqKiqw4ts1iIq5AT09XXTzckdObi5CL0UiPOI63l7wKrp6uNR7f1u278WufUegqqICDzdnAMDV6zH4ac3vSE4ZhskTRsqUybifhY8++w45OXkwMzWGTzcP3E5Ow7ET5xB++Ro+/WAROnfuJFOOHq8ltG0NeedsAGDEztwGE7Jdg8Mi8OfmHQ2OobCwCMs+/wGpaekw6KyP7t08cPfufZy7GIpLEVfx0btvwsbaosH7bc9aQrvW8PXxgoa6mky6pYVZo/fZXgnVrpWVlXj/428AAFqamnCwt4GWlgZSUtMRHBaBkEtX8Nz0iRg2WPackcer8FpCu9ZoC8erIFf+Gzb+iQ0b/8TRw/vqzCNSUsLb77yP2S/PwrtLFgtRLbUCttaWSEhKRnLqHViam8psP3s+pDqfjSUSEpObO7x2Sd4Nou07DyApORVOjna8gdTERCIRnB3tEHb5KmJib8p0diQk3kZpaRmsrSyQdDsF0TE30adXD6k8MXG3IBaLYWlhBl0d7eYMv83j8dG6jR05GK4ujo0u36N7VzjY20BXV0fAqNq+hKRkbPlvH5SURHj91VkyT+MWFRUj5NKVOjuiSBhsB2op9uw/hqiYG7C3tcLSJa9DS1MTAHA+KAyr1/6Bn3/dhJ++XgZNTY0n7ism9iZ27TsCbS1NfPLBIpibVT80l5qWjmWffY8dew6hq6crujjYSpVb97+/kZOTh0H9++DF56ZAJBJBLBbj901bcezkOfy64R+899Zrwr/4Nq4ltG0NnpMJR8h2NTLsjGGD+8HO1gp2NpbYvusAgkIuP7HcX//uRGpaOrp5uWPh6y9JHlLZte8Itmzfi9Xr/sDXn70nd1Q8ydcS2rXGs8+M46hSgQjZrna2Vhg7cjC6eXlARUVZkn70xDn8vmkL/ty8Ax5uTpLv5xo8XoXXEtq1Rls4XgX55J09dwFOXRxha2NdZx5bG2s4OTni7NnzQlRJrUTAgyduajo1aisqKkbY5atwdLCFiZFhc4dGpDAuztWjMWLkDBGMjrsFABg7anB1ngd/S+V5UM7Fyb6pQiRql7S0NGFuZgI9dnY0SEjYFYjFYvTs4S132hEtLU30C/CDl6ebAqJrP9gO1BJUVlbi4JGTAIBZzz0juVgHgN5+PvDydEV+fgFOnQ2q1/72HjwOABg3eqjURbm5mYlkKoW9B45JlUlITMb16Djo6Ghj5rQJEIlEAKofOJk5bQJ0dLQReS0GSbdTGv9C26GW0LYkPKHb1aebJ56fMREBvXrA3MwEIoieWCY3Lx9nzodAWVkJL78wVWo07tiRg2FpYYrUtHSER1xr4Ktrv1pCu5LwhGxXZWVlfL7sbfj6eEndEAeAwQP6wNPdGVVVVbgYEi61jcer8FpCu7Y1wkxjlXYHfXr7PzGfjbU1LgYFC1EltRLubk7o2FEP5y+GYeqk0VI9uxdDwlFeXo6AXr6Ijrkht3x5eTkOHzuD80FhuHPnHgDA1NQIvf18MHRQX6iqqkrlr73WgIO9Dbb+tw9RsTdRWlIKMzMTjB4xEL39fJruBbcxT5oy5Enz2weFXMbx0+eRkJiM0tIydNbviB7dumLs6CHQecLc+W1ZzdRTMTfiIRaLJRfhQHUHiIqKCrp7ecDE2FDunImxDzpAXJwePsF+J/0e9h44hqvXY5GTmwcNDXU4Odph3KghcLC3kRvHuQuhOHDkJFJS06GhoQ43F0dMnTRawFfafp09H4KDR08hNS0dqqqqcHN2xLQpY2FiLN2x+6RjqD2vnyK02t9n058Zh2079iMiMgo5ubkYPKAvnp8xkWt2NFJeXj4ANGgO/ILCIpy/GIrLV64j7c5d5OTkQVVVFZYWpujf11/m8/7Lb3/izIMHJ86cD5H8HwCntXqgMe3wpO+Ymvf9w3fekBo1VTOn78pvPkZKWjp27z+CpNupEEEERwcbTJk4WmbkIrUPsTfiUVBYBCPDznI/A/49uyEiMgphlyPrnOa2RllZOa5ejwUAuR14/j274Z+tuxF5LRrl5eWS64JLEVcBAN29PaCmJn2toKamiu7eHjh9Nghhl6/C2orTbNRXS2hbEp6Q7dpYEZFRqKqqgquLIzp17CC1TSQSoWcPbySn3EHY5avw6ebZJDG0NS2hXUl4zdmuVpbmiLwWg5wc6WnnebwKryW0a1sjSGdHVVVlvfKJRCKUlpYJUSW1EkoiEfr49cC+Q8dxLSoOnu7Okm1nL4RAVUUF/j27ye3sKC0twxffrEbczQRoaWrC40HZqOgb+HvLLoRdjsTSt16DmprsXHIJSSnY8Nc26HfqCA83J2Rl5yDuRgJWr/0DVZVVkhEn1DTEYjF+Wf8Xzp4PgZqaKuxsraGnq4Ok2ynYd+g4LkVcxbKlC9rt4pA2VhbQ1NSoXnsjLV0yxVtVVRXibsTD3tYKamqqcOpij9Nng5CXlw+9B+9VSUkpEh88iej8YGRHeMQ1/LTmd5SVlcPC3BTdbN2Rm5uHy1euIyLyOubPeQE9e0hfSG7fdQD/7ToIJSUluDg5QE9XBzduJeD9T75Ft6586vdp/Lt9D/YdPA6nLvbw7uqOhMTbCLl0BbE34vH150s5akDB8vIL8MEn36CsrBzOXewhhhjaWppPLkh1qllXKDgsAmNGDEbHjnpPLBN3Ix4b/9qOzvqdYGJsCAd7G+Tm5ePGjQTE3ojHrYTbePG5KZL8Tl3skZObj8hr0TLr5zh14Sg3oHHt8LSOnTqPvQeOwcHeBt6ebridkobIazGIjYvH5x+/XefweGq7km6nAqieylaemvTbD/I9zp30uygvL4eurg4MOuvLbDforC9ZB+1OegasLM2kYrCrMwYLnD5bvxjooZbQtrXtO3gc6XczoKysDFMTQ3T38mj1024ogpDt2vgYqq9t7Kzld5I3RwxtTUto19pOnQ1CfkEhAMDQQB/eXd1gIWeac3q85mzXu/cyAAAdHpn+lMer8FpCu9bWFo5XQTo7LMzNERERicrKSigrK8vNU1lZicuXr3Bx8nYooLcv9h06jrPnQySdHXfv3UdsXDx8fbzqfMJ/y397EXczAfa2Vnhn8TzJ2gR5eflY8d0axMbFY+uO/Xh26niZsoePncaUCaMwbvQQyVPzF4IuYdXajdi+6wA7O5rYgcMncfZ8COztrLHgtRclFzFVVVXYtmM/du07gj/+3o435s5ScKSKoaSkBCdHO0RERiEm9qaks+N2choKi4olN+6cH3R2RMfeQs8eXgCAuJvxqKysgpmpMTp20EPG/SysWrsR4ioxFr8xW+rpiZu3EvHld2uw7n//wMXZUXKT/VbCbezYfQhqaqp4763X4PygvoqKCqz59U+cvRDajO9G23P81AV8vuxtydOiFRUV+GH1/xAecQ1Hjp/BpHEcMaBIEZFR8PJ0xZuvvQgNdS4uL4Q+vXywZ/9RZGXlYOG7n6K7twecHO1ha20BG2sLuYvDm5sZ45P3F6JLrU4LAMjJycOX3/+CoyfOIqBXDzg+mKt9QGAvmBgZIvJaNNfPqUNj2uFpHTx8Ekvffg3urk4AILUmwt4DxzDn5WcFr5NatvuZWQAAff2Ocrfrd6pOLygsQklJKTQ06v4ezsjMliojT+dOHVFQUIj7mVmSG+L1jSHjQT6qn5bQtrX9vWWX1N9/bt6JEUP7Y9rkMZwnvgGEbNemjoHHbP21hHatbceeQ1J/b962BwG9euCl55+R+/Aqyddc7Xon/R4uX4kCAPh4S4/O4PEqvJbQrrW1heNVkKuewL4B+N+GP7By1RosXDBfbp6Vq3/BvYwMPP/cDCGqpFbEytIM1lYWCA2/Ijkwa9bwqKvTobS0DCdOXwBQPWdd7UWY9fR0MevZyVj2+Q84dvIcpkwYKXPA2dtZS3V0AEAvv+7YsecQUtPSkXE/C4YGsk8R0dOrrKzEnv1HoaysLNXRAVTf5J88YSQuX7mO4NAI5M8sbLcLbLs4OSAiMgrRsTcxeEAAACAmrnrKqprOh5onl2Pibko6O6Jjb0nKA8Cho6dQUlKKZyaNlhkm6mBvg/FjhuGvf3fi3IVQjBjaHwBw9PgZiMViDOzXW1IXAKioqOCFZychPOIaSss4Cq+xJo8fITUthoqKCiaMGYbwiGuIjpGdloyEs/yrlXLTZ06bAJsHbaKiooKXnn+GHR0CMjI0wJKFc7D2f38h434Wzl8Mw/mLYQCqp4zx7uqOCWOGSd2sMjYyhLGc9bo6dtTD9CljseLbnxESdkXS2UFP1ph2eFpDBwdKOjqA6lHck8ePwLGT5xBVxxSl1LaVlJQCANTr+I6tfYFeXFLy2Av20gf70lCv+8K6pnxxSYmcGOSXqylTk4/qpyW0LQB083KHq7Mj7Gyt0EFPBxn3s3AxJBx79h/DvoPHoaSkhGmTx9TvRZGg7dr4GMoexMBjVigtoV2B6mvW/n390cXRFp06dkBWdi4iIq9j244DOHM+BOUVFe32AcjGaI52rXkAsqKiAr39fGBrIz3agMer8FpCuwJt63gVpLPj5Zeex/YdO/HzL7/i6rUoTJo4Dra2NgCAhIRE/LdjN06fOYsOHfTwyssvClEltTJ9e/viz807EBwWgb69fXH2Qij0dHXg5eEqN39C4m2UlpbBzMRI7px1XRztYGJsiPS7GYhPTJa6YQsAXh6uUh0dNcxNjZGalo7snFx2djSRxKQU5OUXwNHeRu6wdCUlJTh1sUNSciriE26jq4eLAqJUvJp1O2Lj4iVp0bG3IBKJJJ0cpiZG6KCnK+ngAB4ual7T2XHlajQAwLd718fWczM+UaoeoLoD8FF6errwcHdGWHhko14XAd5yFv81MzUGAGTn5DZ3OO2Kp7sLOsoZkmth/nBUqY2VhdzvJno6ri6O+P7Lj3DlahQir8XgVnwikpLTUFZWjuDQy7h0+SremPsCetT6rhKLxYiOvYmYuFvIzslFeVk5xGKx5IT/zt17ino5rVZj2uFpyPu+09PThY62FrLb+FzARKQYs2ZOlvrb3MwEk8aNgIOdDb76/hfsP3QCQwf1feyoESJqHpMnjJT628TYEMMG94Orcxcs/fhrXAwOx8ihA2BvZ62gCOlR6zf+i5vxiTAxNpT5vqXWqz7t2paOV0E6OwwNDbF+3Rq8Om8+Tp85izNnz0ltF4vF6NxZH7/8/BOMjY2EqJJamd7+Pvhn6y6cPR8CY8POuJdxH8MGB0JFRf60Z1nZ1TcEHzfvqpGhAdLvZiA7O0dmW+fOneSWqekBrSivaOAroPq6dz8TAHDjViKmvSB/pFeN/PyC5gipRbKzsYK6mhqysnNw9959GBsZICbuFqwszKBVa/0A5y72CLl0BUVFxVBVVcGt+CQAgItzdSdGxoP3e/F7nz22vpo5FwFIjpm6bviyI/DpyPv+0dTUAACU87unSY0dOVhqEeXaoqKrnzI3MJD/+0BPT0VFGd29PdDd2wNA9VNKYeGR2Lx9D7KycvDr7//Aw90ZGurqyMnJw3crf5PqiH1UcXFJnduobg1ph6dV9/mWBgoKi556/9T61Jxrl5bKf6Kz9pOemhoaj92Xes3ToY9Z87Fmf7X39TAG+eVqyjT1tC1tTUto28fx8nSFrY0lEhKTcfV6LAL79KxXufZOyHZtfAxqD2LgMSuUltCuj2NlaYbu3h4ICYvA5cjrreLmaUvQ1O36z9bdOH0uGPr6HbH07dehLWfKeR6vwmsJ7fo4rfF4FWzyXm/vrjh+5AD+3bIN5y9cRFraHQCAmZkp+vTuhSlTJkJPt30uRkxABz1deLq7ICIyCrv2VXdwNOW6GUpyRnWQ8KrEYpk0cVV1mkFnfbg+uCFfF4N2fFNdRUUZjg42uBYVh5jYm6ioqEBeXj78HllI3NnJHsFhEYiJuwUtTQ2UV1TA2MhA8rRazfvd298Hyo+Zn7hmZEFt8kY/0dMTcp5osZxjjJ6OmqqqokNoNzQ01NGnVw9YWprh3Q+/REFhEeJuxMPT3QW/bvgHN+MT0d3bA6NHDIK5qTG0tDShpKSEO+n3sOjd5fz8C+Rx7fAkT2oDnm/Ro2oepMjKypG7PevBAxc62lpPvBFi+KAzLUvOg001MuU8wGHQWR+JSSlPjMGQo/wapCW07ZOYmhghITGZI2kbQMh2beoYeMzWX0to1ycxNamezjSHI0HrrSnbddfew9h74Bj0dHWw9K3X6nwAkser8FpCuz5JazteBV2pUFdXB7NfnoXZL7eOObyoeQX09sXlK9dx5Wo0LMxNYWcjOz1VDf1OHQAAGRmZdeapeaK9E4coN5makTd1zbeY+WBxwdpqnvI0MuzMBWSfwMXJEdei4hAddwsVlZUAqjs3anNyrP47Ju6WpBe/ZmoqoPr9Tr+bgWcmjq73D1enTh1xL+M+7t/PQgc92U7ojPtcTKy51CwY/Oh81DXuc2E3agOsLc2ho6ONgoJC5OcXoqS0FBGRUdDT08Wi+S/LdBCm381QUKRt26PtADz8nS+u43f+vpzfeaLHsbYyBwAkJCXL3V6TbvUg3+OYmhhDVVUV+fkFuJ+ZJXPT+35mFgoKCqGmpiq5CK+JISw8EvF1xpBS7xjooZbQtk9S+GBE2ePWAiFpQrZr42OoXlctPum2wmJoa1pCuz5JYWExgLrXfiBZTdWuh46expb/9kFLUxPvvjUP5mYmdebl8Sq8ltCuT9LajlfhHj8leoLuXh4wNNCHjo42+vf1e2xe2wdT/KSl38OtBNkv0Rs3E3An/R7U1dVgJ2dhHRJGp47VnU5p6fLnTY+8HiOTZm9rBW1tLdy4ldhqen0VpaZjIyb2JqJjpRcnr2FtZQ5NDQ3ExN6ULGDuUquzw8PNGQAQeulK/evtUr0myIXgSzLb8gsKcVVOu1LTqDnG7sg5xsrLyxHFBc2pFXjS0/8FBYWSG1D6+h1RVFQCsViMTh07yB0JVbOo9qNqbsxXVlU9ZcRtU0PbAaj9HXRXJn9eXj4SH9wUJqovJ0c76Ghr4V5Gptxz+IvB4QAAH2/PJ+5LTU0VHm5OAICgkMt17svT3QWqtUbtdfeqnsLt0uWrKCsrlypTVlaOS5evPojBoz4viR5oCW37OLl5+Yh5sC7d4x6qI2lCtmtjeXm6QklJCbEP1vCqTSwWIzj08oMYeMzWV0to18cpLy9H+JVrAHi8NkRTtOvpc8HY9M9/UFdXwzuL5sDW+vH313i8Cq8ltOvjtMbjlZ0d1GzU1FSx8ttP8NvqLzFi6IDH5lVXV0P/wF4AgA1/bkVBrfUG8gsKseGvbQCAQf37QE2tdfQstkaGhp1haKCPlNQ7uBD08MZ4VVUVtu86gBs3E2TKqKioYOzIwSgvL8d3K39Dalq6TJ68/AIcP3W+SWNvDRzsbKCqooK79+4jIjIKxkYGkhtPNZSUlODoYIv4xGTEPXi/XWt1dowaPgAaGurYumMfzl8Mk7nZVVlZiYjI60hOSZOkDR4YAJFIhOMnz0v2CQAVFZX446/tdc6/ScKzs7OCuroarlyNRuyNh4vVl5eXY+Nf2zmyg1qFrf/tw5+bd8jttMsvKMTPv26qXr9NvxMc7W3RsYMutLU0kZySJllLpcaps0FyO2KBWh3wabI35qnh7QAArs6OEIlEOHshVKpcUVEx1v3+T52jzojqoqysjOFD+gMANmzagqLiYsm280FhiIiMgq6uDvoFPHzw6WZ8Iha/uxyL310us7/RwwcCAHbtOyJ1Tpmalo7d+45U5xkxSKqMrY0l3Fy6oKCgEH9u3iE5NxKLxfhz8w4UFBTC091Z8nQq1U9LaNuQsAhExUj/bgDVIwK/W/kbSsvKYGdrBUcH26d4pe2L0O3aGB30dNG3ty8qK6uwfuO/qKh4uMbdnv1HkZxyB+ZmJujm5S5Ife1BS2jXa1GxCI+4hqpHHlLJycnDjz//jqysHOjrd0SP7orpcGmNhG7XkLAI/Pr7P1BRUcFbb76CLo52T4yBx6vwWkK7trXjVdBprIiENHXSaNxKSMKNmwlYsOQTyaKz16NuoKi4GE5d7DBlwkgFR9n2TZkwCj//ugmr1/2BoyfOQldXG0m3U5Gbl4+hgwJx+NhpmTKjhg/EnfR7OHnmIt75cAWsrSxgZNgZ4iox0u/dR3JKGjTU1TGwX28FvKKWQ01NFfZ21oiJu4XCwqI6n35w7mKPyGvRqKyshEHnTjA07CzZZmRogDfnzcJPazZg9bo/sHXHPpibmUBTUwM5uXlITExBUXExFs1/GZYWZgCqO1nGjRqCnXsP45MvfoSrswN0dXVw42YiiouLEdCrB85eCG2W96C901BXx9iRQ7B1xz4s/3IlXJzsoa6ujviE26iqqkJggB9Onw1SdJhEj1VSWoZDR0/hwOGTMDYygKW5KdTU1ZCdnYtbCUkoKyuHpqYGXp/zvGR0xthRQ/DP1t347OtVcHF2QMcOekhOSUNyyh2MGTkYe/YflanH0LAzrC3NkZCUjKUffw0Lc1OoKCuji6Od1Ml/e9WYdjA07Ix+AX44eeYi3lv2lWTk4M34JOjp6sDH2wNhD56CJ6qvMSMH4Xp0HKJibmDhO8vh4uSA3Nw8xMTdgrKyMubNnglNzYcLbJaVltc5itjZyQFjRw3G7n1H8d6yryWjAa5ej0V5eTkmjBmGLnJubL/60gx89Nl3OHbyHKJjb8LKwgy3U9KQmpaOTh074JVZ05vmxbdxim7b6NhbOHT0FAw668Payhzq6mrIyMhEQlIKKioqJOfFXJeuYYRs1+ycXHy/cr3k7/R71VNT7thzGMdOVj/s1rGjHha/MVuq3LNTx+PGrUSER1zDwneWw9HeBun3MpCQmAxNDQ28/urzgq6L1x4oul1vJ6fhz8070LGDHmysLaCtpYXMrGwkJCWjtLQMero6WDR/Nh9ebSCh2jU3Lx+r1v6BqqoqmJoY4ez5EJw9HyKTz8zUGGNHDZFK4/EqPEW3a1s7XtnZQS2WuroaPlgyH4ePncb5oEu4cjUaIohgYmKEPv4+GDqob72HNVPj9enVA8oqyti7/xhuxSdBTU0VLk4OeGbSaNyKT5JbRiQS4ZUXp8PXpyuOnzyPm/FJSLqdCi0tTeh36oghAwPg6+PVvC+khXJxckBMXPWQe6dHprCqUXsdD+cusou+e3m64evPluLA4ZOIvBaN69FxEIlE6NSxA1xdHOHTzVMy3VWNKRNHwdTECAePnEJsXDzUNdTh6uyIqZNG1zmFDDWN8WOGQltLE0dOnEVMXDy0NDXg6e6CqZNH4+Tpi4oOj+iJxo8ZCgc7a1y9HoPEpBTE3UxAQWER1NXVYG5mAk93ZwwZGChZjwuoflrXoHMn7D90AgkJyRCJRLCxtsCShXNgYWYit7MDABbOfxn/bN2NmNibSExKgVgsRmVlJTs70Lh2AICXnn8GnTt3wplzIbh6PRZ6ujrw9+2GZyaOwqZ//lPQq6HWTEVFBe+9NQ/7Dh7HuYthCL98Feoa6vDx9sCEscNh28ApaKdOGgNrS3McOnpa8lS/rbUFhg/pDz9fb7llDA30seKTd/DfroMIv3INoeGR0NPTwaD+fTBp/Ai5a5bRkym6bX26eaC4uBjxicmIvRGP4uJiqKupw9baAj7du2Jw/z5SN4OofoRs14ryCtyMT5RJv5dxH/cy7gOQv+i8trYWln+4GDv3HkZwaARCwyOhraWJ3v4+mDx+BIyN6r92C1VTdLu6ODtgYL/eiE+4jfjEZBQWFkFNVRWmJkbw9nTDsMGB0ON3cYMJ1a5lpWWSURmpaelyZ+QAqu9XPNrZweNVeIpu17Z2vIoqK0ofP8EvERERERERERERERFRC8ZxRURERERERERERERE1Kqxs4OIiIiIiIiIiIiIiFo1dnYQEREREREREREREVGrxs4OIiIiIiIiIiIiIiJq1djZQURERERERERERERErRo7O4iIiIiIiIiIiIiIqFVjZwcREREREREREREREbVq7OwgIiIiIiIiIiIiIqJWjZ0dRERERERERERERETUqrGzg4iIiIiIiIiIiIiIWjV2dhARERERERERERERUavGzg4iIqqX7f/thJ2jG35c+bOiQxHM3n0HMHbCFLh6dIedoxu6dvN76n22xfepqf248mfYObph+387FR0KERE9BTtHNwT0G6zoMATzx6a/MWzEWDi7ecPO0Q0jR09QdEh14m8pNZbQx21b+x5oixpzvTJtxguwc3RDSkpqE0ZGQuDvgeI15/dgQL/BsHN0a5a6Wgt2dhARUbt0JfIqFi5+Bzdu3EKf3v6YOH4sxowe8cRyPNEnIiJq+w4dPopPln+B9Lv3MKB/ICaOH4tBA/srLB7ezGheby9ZCjtHNwQFhyg6FKJ2JSUlFXaObpg24wVFh9JitYbfA7YjKZKKogMgIiJShBMnTqGqqgrLPlqKZyZPVHQ4RERE1IIcPXYCAPDzqh/Qu9fTj/wkIiKi9uHoob1QUeEtd0XhO09ERO3SnfS7AABrK0sFR0JEREQtTXp6OgCeJxAREVHD2NvbKTqEdo3TWBERkZQzZ89hyrSZcPP0gbePP2a9PAdXr12vM396+l2s+/V/mDFzFnr3HQhnVy9069ELM59/GcePn5TJv3bdetg5umH551/Wuc8VX37b4Hlkr1+Pxvw3F6Nnr0A4uXZFz16BeGPBW4iKipbK9+gcptOfnQU7R7cnzmtaMxQ3OCQUANC3/xBJubqmtbp79x7efud9+Pr3hbObNwYPHY2Nf/xVZx0lJSX49bffMWb8FHh49YCbpw/GjJuMTX/+jcrKynq9DxUVFfDw6gFvH39UVVVJbfv2+59g5+gGZzdvlJaWSm379bffYefohvX/2yizzwMHD2Pm8y/D28cfzq5e6D9wGFZ8+S1yc3PrjKMxZeRZ/7+NsO/ijn4DhiExKalBZYmISDGqqqrw+4ZNGDp8DJzdvNHDry/eXrIUGffv15l/y9btmDRlBjy9fOHi3g1Dh4/BDz+uQn5+gVTeffsPws7RDZ9+tkJmP736DICdoxveXrJUZtvQ4WNg38UdWVnZj429Zvqii0HV0xfV/r2vmdIoITEJP61ag8nPzIBf735wcu2KHn598cqc1xEadknufmuff1y7HoVX585Hj54BsO/ijiNHj9cZT1BwCOwc3ZCamgYAUucedU1jcutWPOa+9ia6+/aGs5s3Ro2ZiD1799dZR15eHr7/YSWGjRgLV4/u8PDqgcnPzMCu3fse+17VlpGRUee6JgsWLalz/vKPPl4OO0c3HDx0RDqm/Hx8/8NKDB0+Bi7u3eDp5YtJU2Zgy7b/ZM5vAOlpp86dv4jnXpgNbx9/2Dm6Sc4Fc3Jy8MNPqzFs5Dh4eveEm6cP+vYfglfmvI4DBw9L9mXn6Ib/du4GIH2eWJ9prWrOc3/fsEkqvbi4GE6uXeWe31ZWVsLTuye6dvOTOd+7e/cePv1sBQYMHgFnN294dffHzOdfxukzZ+uMoTFl5LlzJx1Dh4+BnaMbfvhxldS2e/cy8M57H6KHX1+4uHfDsJHjsOmvfyAWi+Xuq6qqCnv27seCRUswcMhIyXnusJHj8OPKn1FUVCSVPz39Lrq4VB9X5eXlcvcZd+Mm7BzdENh/aJ311lb7MxIcHIpnn38Jnt494dXdH6/OnY+ExCRJrOv/t1Hy2fPv0x9ffv0dysrK5O63IZ/VMeMmw87RDZfCL9cZ5/BR42Hn6IYrkVel0hvTrg29rquv/QcOYcKkaXDv6oOu3fzw8ivzcP16dJ354xMS8e7SjxDQbzCcXb3Q3bc3XpnzOiIiIqXy/bjyZ/TtPwQAEBwSKnXs1UyHNOulV6WO6xpnz12o8zi9EnkVdo5uePmVeTKxRV69hjcWvAX/Pv0l15ALFi3BrVvxdb6ehpSp+Q5/e8lS5OXn49PlK9A7oPqaObD/UPzw02pUVFTUWZe8fbX03wNFtOPBQ0cw47kX4dXdH85u3ug/cBg+/WxFnecd8gj5/S3vN6/21F6lpWX44afV6D9wGJxdvdCrzwB8unwFCgoK5caWl5+Pz774qvqz4+aN/oOGY9XqX+r8fqxR33skQt5DKC8vx5at2zFx8nT08Ku+D9KrzwBMnf48Vv+89rHxCoWdHUREJLH9v52Y9dIchIWFw9XFCQF9eiM5OQWTn3kWEVeuyi1z5OgxfPXN90i7kw57OzsMGTIIDg72CAoOwew5r+PX336Xyj9l8kSoqalh5649Mj+WAFBWVob/duyCsrIypk6ZVK+4Dxw8jAmTp2L/gUMwMTHGsKFDYGxsjH37D2LCpGk4dPioJK+rizMmjh8reVKzb0BvTBw/tjrN2qrOOrS0tDBx/FgYGHQGAAwbOlhSbuL4sdDS0pLKn5Z2B2PGT8aFi0Ho6dsD3bt743bybXz62QqsWv2LzP6zsrIx+Zln8eXX3+FO2h107+YNfz9fpKXdwceffoHX31hUrws5FRUV+Ph0R25uHqKiY6S2BT24cVNWViZzkXXxwcmkn5+vJE0sFuOtJe/h9TcW4VL4ZTh16YIBA/oBAH773wZMmDwd9zMzpfbTmDLyiMVifPHlN/jiy2/g6uKMbVv/go219RPLERGR4i166118892PMDU1Qf9+faGsrIT/du7GszNfRGmp9A3DqqoqvDZ/Id57fxmiomPg49MdAwf0Q1Z2Dlb9vBYTJ09DZmaWJH/Pnj0AQNIZUSM+IRHpd6tHbV585AZJxv37uHHzFrp0cYS+fqfHxu7j063O33tDAwMAwOZ/t+KnlT8jLy8fLi7OGDJ4EMzNTXHs+ElMf3YW9u47UOf+wy5dxqTJ03HzVjz8/Xuil7/fY6e6MDQweHCeoQkAUuceE8ePlcl//Xo0xk6YgrgbN9G7lz88Pd0RHROLBYuWYOeuPTL5E5OSMGrsJKxesw75+QXo3csP3by9EBt3E4veeuexD6dIxWloCAd7O8TExiE7O0dqW80Nq9TUNNy+nSy9LSgEIpEIPX17SNIyMjIwYdI0rF6zDtk5ORg4oB98fLojKjoG7y39CG8seKvOc6K9+w7i+VmzkZWVhb4BfeDj0w0iJSUUFhZi/KRpWLX6F+Tm5sKvZw8M6B8IY2MjXAwKxj//bpXso67zxNqfgbr4PziPuhgULJUeFhaO8vLqG5oXL0pvu3btOgoKCuDbozuUlZUl6VeuRGLE6PHY+MdfEFdVoV9gX7i6OCHsUjhmvTRH5oZcY8vIc+PGTUyaMgO34hPw6ccfYuGC+ZJtd+6kY/ykqdi2fQfUVFUxeNAAGBh0xvLPvsTyz+R/XoqLi7Fg0RKcPnMW+p06ol9gX/j6+iDzfiZWrlqDqTNeQElJiSS/iYkxBg7oh8zMzDo7Azdvrm6zqVMnQSQS1et1AdVT1D37/EsoLChEv8AAGBoY4OixE5g6/TlkZmbh9TcW4YefVsPc3BwBfXqhuLj6YaQPPvpUZl8N/ayOGzsaALBr9165sUVHxyA2Ng4O9nbo6ukhSW9Muzbmuq4+ft+4CfPfXAyRSIQB/fvBzMwUJ06exsQp03HhYpBM/uMnTmHUmInYuu0/aGtpYcCAfrCzs8XJU2cwZdpMqY5GVxdnDBtafYPYwKCz1LHXt29vAIBfz+pj7MIjx1jtG+OPHmM110C1r3OA6u/yiZOnY9/+gzA0NMTgQQNhamqCPXv3Y9zEZ+R2XjemDFB9s3rS5OnYu+8APD3d4d/LD5lZmVi1+he8/+Encss8qrX8HjR3O3762Qq8Nn8hQkMvwcPdDUMGD0RFZSU2/vEXxoybjPiExHrFLeT39+OUl5fjhRdfwYaNf8LBwR59+/ZBSWkJNm76C3Nee0Pm9y0/vwBTpz+H3zdsQmlpKQYO6AdbGxusWfsbXn9jUZ31NOQeiZD3EBa//R7ee38ZYuPi4NUY6B4AAEktSURBVO7mgqFDBsHWzgYJiYn4adWaer1HT62yolTMf/zHf/zHf/yXkpwkdnb1ENvYOYr37dsrSa8oLxEv/+wzsbWtg9ja1kH83fffS5W7dvWK+Nq1SJn93bwZJ/br1Uds7+gsTk5OlNr25oKFYmtbB/H27dtkyu3cuUNsbesgfnn2K/WKOzXlttjFzUNsbesg3rZtq9S2f/75W2xt6yB2c+8qvpOWIrVt0aLFYmtbB/H582cb9D5NeWaa2NrWQZyUGC93+5Yt/0req/eWvi8uLSmUbAsOvii2te8idnX3FOfnZUuVe/Gll8XWtg7it5e8I7UtO/u+eOZzz4utbR3Ef/75Z71i/OWXX8TWtg7itWvXStLycrPE9o7O4iHDRoitbR3EX3/9tWRbaUmh2NXdU+zRtZu4vKxYkr7u13Via1sH8Zix48W3bydI0svLisVfff212NrWQfza6/Ol6m5Mme++/15sbesg3rLlX3FlRam4pLhA/MabC8TWtg7iqdNniHOyMxV+fPAf//Ef//Hfk//V/P71Dewvvp308DcgJztTPHrsOLG1rYN469YtUmXWr18vtrZ1EPcJ6CdVJj8vW/L79+qcuVJlBg0eKraxcxTfu3tHkrZp0yaxta2D5Hfu1q04ybZdu3aKrW0dxMuWfVzv1/K43/uQkCBx/K0bMunhl0LFbh5eYk+vbuKC/BypbTW/dda2DuIffvhBXFFe0qD3tlefvmJrW4c6t9fe/8qVK6X2X/P6+wT0kypTXlYsHjZ8pNja1kH87bffikuKCyTb0lKTxSNHjRFb2zqIT548Xq8Y3//gA7G1rYN47949krS42Gipdvnnn78l29LvpIitbR3EQ4ePkNrP7FdeFVvbOohnvfiS1PuYEH9T7N87QGxt6yDesOF3qTI153W1zydq/6s5P3vxpZfFZaVFUtsK8nPEoaHBcvfX0PPEstIisZuHl9jd01uqni9WrJC8D45dXKRe1+qfV4utbR3Ev/72qyQtNydL7OvXS2xj5yj+888/pdrzRlyMuFefvmI7BydxdPT1pypTc9z26tNX8ndw8EWxp1c3saOTq9Q1Qc2/mnPWOXPniYsK8yXpoaHBknPy2vurrCgVFxfliw8c2C8uLsqXSi8syBUvfuttsbWtg3j1z6ultp05c1pyLvhoDIUFuWKPrt3Ejl1cxHfvptWrbWra1MbOUeozWl5WLH59/htia1sH8eAhw8T9Bw4Wp6bclmy/fTtB3NW7u9jGzlHm+6Chn9X09FSxvaOzuKt3d5n3orKiVHLNtWr1w/eiMe3a2Ou6x/2r+U60te8i87n4+eefxda2DmJfv17iwoLch+9dUoLY1d1T7OjkKj506KBUmUthoWKPrt3Ebu5dxRn30iXpSYnxYmtbB/GUZ6bJjSM8PExsbesgfmHWS1LpY8dNEHfz6Snu5tNTPGHSZKltNb8lkVciHu7nUqjYzsFJ7NXNRxwcfFEq/5Ejh8X2js5i/94BUt+LjSlz/vxZyfv94ksvS31ObtyIFbu5d5X72Xrcv9bwe9Bc7Xjo0EGxta2DuKt3d/HVyIfpJcUFku+WUWPG1itmob6/Kytkv1drvyc118r3M+4+/G64kyLuHRAotrZ1EF84f06q3EcfLRNb2zqIJ06eInVdfONGrNjH10+yz9plGnOPRIh7CDWv0b93gNRxXfMZO3fuTL0/50/zjyM7iIgIALB1+w4UFxdj8KABGD5siCRdJBLhrUVvwsTYWG45FxdnuDg7yaTb2lhj/mtzUFFRgWOPTGf17IypAIB/Nm+RKbf5320AgOnTptQr7i3b/kNRUTH69wvEhEeeaHlmyiT06d0LBYWF2LJ1e732JxQzM1N8+P47Uk9r+nTvhr4BfVBYWISrVx8OIY+OjsHxE6fQxdEBn336kdQoET1dXXz95edQU1XF3//8W6+6a56sqP2kTGhYOCoqKvDs9KkwMOgs9URs5NVrKCwsQk9fHygpVZ8aVFRUYN26/0FVVQWrV30PczMzSX4lJSUsWjAfLs5OOHjoiOQJzsaUeVRhYSFefmUedu/ZhxHDh2LD+rXQ1dWp1+smIqKW4aMPl8Lc/OFvgK6uDl55+UUAQFBwqFTeDX/8CQB4Z8kiqTJaWlr4fPnHUFVVweEjxyTTdgDVv3NisVjqCdCg4BCoqKjg9XmvApB++rKup0Ebq3s3b7mjQbt29cRzM6cjNzdPZuRJDQd7O7z+2pwGPYneEF09PTBv7itS+x89agQcHeyRnJIi9T6eOHkK0TGxCOjTCwsXzIeqqqpkm7GxEb74vPpp47//kT1fk6fmSd3aT8XWvA/zX5sDFRUVXLgou62mHFA9zcfRYyegpqaGzz5dBk1NTck2KytLvL14AQDg9w1/yo2hb0BvTJo4Xia9Zvoyf7+eMk/fampqopu3V71e45MoKyujh0935Ofn41qt6YKCgkJgaGiA6dOmoKy8HGFh4VLbAMC/1vuw/b+duHv3HqZOmYTp06ZItaednS3ef28JKisr8e+WbU9V5lFHj53Acy/MRlWVGH/8/qvUNQFQ3T4nTp6Guro6Pl72PtTV1STbunl7Yca0qXL3q6amhqFDBkFNTU0qXUNDA58sex8qKipSTxkDQO9efrCzs0VQUIjMk9n79h9EXl4ehgwZBIPOnet8PfKMGjkcI4YPlfytpKSEV2dXfz/F3biJZR+8BxOTh9c95mZmGDdmNMRisWQ625r3oqGfVUMDA/Tp3Qs5Obk4dVp6+qnKykrs2bsfIpEIY8eMlKQ3pl0be11XH8OGDpb5XLz6yktwcuqCu3fvSU1Jt+GPP1FYWIQ35s/D4EEDpMp4eXni9ddeRUFhYZ0jXeRxd3OFjo4OQsMuSaYNKigoxLXrUfD384Vfzx6IjLwqmRqtvLwcYZfC0aGDHlxcHl6z/rLuf6isrMSyD5fCp3s3qToGDuiHGdOeQVraHZw8deapytTQ1tbCii8+lfqc2NnaYNw42c+WUBT5e/AkQrXjho3Vo5rmvjobrq4uknRVVVUs+/A9dOzYAVevXkdIqPwRN7UJ9f39JCKRCF+uWI5OnTpK0gwNDfHs9Orvz6Ban4Xi4mJs+28HAODjD5dKXRfb2drgtQfnPI9qzD0SIe4hZGZVj8R1c3WRGUmrpKQEf7+eT3h3hMHODiIiAgCEhIYBqD4BepSamhqGDx8ik16jvLwcp06fxQ8/rcb7H36Ct995H28vWSo52U1MkF5roZu3F9xcXXApPAKxcTck6bduxSM4JBSWFhboG9CnQXGPGztK7vaJE6p/3IMf5Gsu/j19oa6uLpNub28LALh7754k7ey5CwCAAQP6yZ3KwsjIEDY21oiNuyE1xL8ubq4u0NXVRVjYJckcsEEPTlr8/XvCr6fvg5OT6jlB5d1suB4VjcysLLi7uUl1WtRQUlKCj083VFZW4uq1a40uU1tWVjamz5yFs+cu4LmZ07Hyx29lLoqJiKhlU1FRQUCfXjLpdnbVv3/3av3+pd25g5SUVGhoaEimvKjN3NwMPX2rOzZqTw0imf6idodGcCg8PdzRLzAAKioqUhfkF4NDoKSkhJ6+Pk//Ah8oKSnBocNH8c13P2LpB8vw9pKleHvJUgRL1vVIlFtu4MD+kpsCTSEwMEBuR0rN+1/7/OPM2fMAgKFDZN97oPp8QltbCxFXIuVuf5RfT1+IRCKp9z4oOASqqiroFxgAdzdXmQ4qAFI3P0LDLkEsFsOvZw+pG841Ro8aAXV1ddxOTsadO+ky2wcPGig3Nnd3VwDAr+t/x569+2XWghGS/yM3jPLzC3DtehT8evqi14PXWrOtvLwcl8Ivo2PHDnCu9fCQpG2GDpJbh2+P6s/ylVrTETWmTG3/btmGea8vgJ6eLv79e6NkyrjaJO3j5yt3Sq+6zsdr3Lx5C79v2ISPP/0cS979AG8vWYqPli2HqqoqEhJk12abMe0ZiMVimQ6ahj4cVVtgX9lrjJrOS1VVFfTq5Sez3camevvdexmStMZ+VsePq57K6tFphC5cCMK9exnw69lD6jy6Me36NNd1TzJ2jGwbi0QijB09UqpuADj7IPZhQx4fe32/Y4DqG9K+PbqjoKAAkZHV1xOhD655qm+S+6K8vELymxF59RqKiorR07eH5Lu3qqoK5y9cgIqKCgYN7C+3nh49ukvF1pgytbm7ucntmLO3q17IuvZnSyiK/D14EiHasby8HOGXrwCQ/92jra0tObcIqWdnkhDf309iZmYKpy6OMun2NedJdx+2y9VrUSgqKoaDvZ1UZ06NmqnxHtWYeyRC3EOws7WFlpYmTp46g3W//g/p6Xfl1t/U6p4clIiI2pW7D35U5d2kBgALc3O56bduxeOVufOR8Ji5MPMLZC8oZ0x/Bks/+Bib/92Kjz96HwCw+cGFTEPm3q2J29LCQu52K8vqOZfvpt+Tu72pmJmZyk3X1tYGAKlFDpNTUgBUL4q2dt36x+43JycXJiYaj81Tc/J4/MQpRF69hm7eXrgQFAJTExPY2drA388X+/YfRGhYOPoFBkiewKz9xGvNguuXI67UueBdjaysnEaXqe3b739CRUUFxo4ZJflMEBFR62JoaCC3415H8vv3cDHNmt9mczOzOjsALC2rf99rXzD39O0BkUgkuVkeG3cDmZmZmDplIrS1teHp4S7ZdudOOhITk+Dm6oIOHToI8AqBsEvhmP/mYsk5iDx1LTJa13mWUMxM5Z9/6Mg9/6j+3f7go0/wwUd1zxn/6DorddHX74QuXRwRGxuHe/cyYGhogODgUHh6ekBLSwv+/j3xy9rfcPPmLTg42OPixWAoKSnB98ENQgCSdVcs6jivU1JSgrm5GeLjE3D37j2YmppIba89Oqi2Xv5+eOXlWVj/+x9YsGgJlJWV4ehoD7+evhg3djQ8Pdzr9Rrro+Z8KigoBHNfnY2Q0DBUVlail39P2NvbwcjIUPI0cMSVSBQVFaNP795Sx0DKg3PDF16U/9Rujazs7KcqUyM9/S6WfvAxVFRU8Pem32Fvbye3bE371PU5NreQf71QUVGB9z/8BNu273hsbI+aOHEcvv3+J/y3YxcWL3wT6upqiI6JxeWIK7C3s5O6yVZf8jomas7PDQwM5M67XzPquvbx09jP6uBBA6Cjo4OTp84gNzdX8r2088HohvHjxkjtpzHt2tjruvqwqOMYs3jQ9rW/q2u+YwYNfXwnWF0jvuvi5+eLEydP42JwCLy9u0quZfz9e6Kqqnq9g4tBIQjsGyAZ5Vf7s5KVnY3CwuoRA+5dZTv1pGPLbnSZ2uq+NpT9bAlFkb8H9fG07ZiTk4OysjJoaGjA0NBQbh2Sc4jH/F4/GhPwdN/fT1JXu2jryLbL3Zrv3DqOOz1dXejp6SEvL08qvTH3SIS4h6Crq4OvVnyG995fhq+++R5fffM9LCzM0cOnO4YNHYyBA/o16QMfNdjZQUREUho6q8Jr8xciISERUyZPxIzpz8Da2go62tpQUlLC2bPn8fyLrwByFpEcO2YUVnz1HXbu2ot33l4EJSVl7Ni5G6qqKpg8aYJAr0ZxRA34Ea85mfPq6il5oqMu9R3p4Ofni+MnTuHixWA42NshKipa8iSW34OnUi5eDEYv/54ID49Ap04d4ezUpVZMVQCqT6z8fB9/Qm9ubtroMrUNGzoYR44ex8FDRzB2zCj0Cwyo12slIqKWozkuYvX1O8HJyRExMXFIT7/7cBoJ/+rfNz8/X4RfjkDcjZu4fj1KkiaEoqIizH1tATIzMzF3zmyMHjUCFubm0NLShJKSEjb/uxXvf/hJnQtoa2jIjvoUkpJS/U/kxA9+t3v18oPpU0xrU5t/T1/ExsbhwsVgODt3QWZWluTpe7+evvhl7W+4EBQMLW0tJN1Ohrubq2CdUMDj399333kL06c9g2PHT+JiUDDCLoVj4x9/YeMff2HunNmSaYeelquLM/T09BB26TLKy8tlRrD49fTF/gOHkJ9f8PCz+8jns+rB52fwoAHQ09Wts65OtaYJaUyZGp0766OLoyPOnb+A5Z9/iXW/rJI7QrlGQ68XNmz8E9u274Cjgz2WvLUQHh7u6NSpo2SqHL/e/XBPzpPterq6GDN6BLZs/Q+HDlefH25+sJj8tKmTGxbEA4/7jmqO7y8NDQ0MHzYE27bvwP4DhzF92hQUFRXhyNFj0NTUxLCh0qMunqZdm2i2vHqruTYYO2YUVB6zeLOd/eOvfx7l3/Phtcy8ObMRFBQCMzNT2FhbAwBMjI0lN2LlLaBc892npqaG0SOHP7Yur66ejS5Tm5ICGkPRvwdP8rTt2BSE+P5+kkZ9zzTT5+dp7yEAwMgRw9C7lx9OnDyNc+cvIiQ0DDt37cHOXXvQ07cHNm38TWqatKbAzg4iIgJQPRdnfHwCUtPuoKucE7SU1FSZtFu34hF34ybc3d3w5RefymxPvH27zvo0NTUxcfxYbNz0F/buOwg1NTXk5ORi5IhhDZp7tybu5JQUeHnJxl0zasLYxKje+2xuZg+e9AoMDMCb8+cJss+aeUMvBAWjSxdHVFVVSU7EbG2sYWpigotBwbh8+QpKSkrQ75FhzjVPn1lamOObr7+oV52NKVNb34DemDhhLObMexNz5r2BNat/xID+gQ3eDxERtQ41v82paWmoqqqSewOg5qnmR5/G9uvpi5iYOFwMCsbFoGCoq6ujezdvANU3Jdb88isuXgzGtZrOjkY8AS5PaNglZGZmYtjQwXJvjicl1X3u09LU/G6PHzsaEyeME2Sffn6+2LjpL1wMCpY83VzTCdXDpxvUVFVx8WIwtB88Kf/ojauatQRq2v1RVVVVSEu7A6D6HLChrKws8eKs5/DirOdQWVmJw0eOYfHb72HtuvUYP3Y0HBzsG7zPR9VMmXb02AlcjojExYvBMDc3g5VV9ZO0/v49sWfvfoSEhMp9MhYATE1MEB+fgBdfeE7udFLyNKZMDVVVVfy2bjXmzHsDp8+cw+xXX8eva1dBQ0N6NHFN+6Sm3pG7n9QU2esFAJKpbVf+9J3M9C1FRUXIyLhfZ2zPTp+GLVv/wz+bt2LI4IHYvWc/1NXVJdOwKMrTfFbHjxuNbdt3YOfuPZg+bQoOHzmGoqJijBk9EjoPnu6u0Zh2bcx1XX2lpqXBxcVZdp8P2t641o1yU1MTJCXdxluL3qzzyfTGcHFxQocOergUfhkZGRmIio7B+FrT+fj5+WLvvgPIuH8f4eER0O/USepz16lTJ6irq0NcVYUvPv+kXjdeG1OmNWmK34Mnedp27NixI9TU1FBSUoKMjAy5oztSkqs/lyb1/L0Q4vtbSDXHU1qt9VVqy8vPlxnVUV2ucfdInvYeQo2OHTtiwvixkvVCIq9ew5sL3kJwSCi2btuBGdOfqe9b0Chcs4OIiAAAPXyqpxDYu++AzLby8nKZRQMBICc3F8DDm/WP2rN3/2PrrPmR2/zvVslTWg2de7dmrtddu/fJ3b5j524AQM8ewszTXXNiW1FZIcj+AKDPg7nNjx07UeeToA3l7OyEjh07IDw8QrIAYs3NBqD6pCwqOgaHjxyT/F2bp4c7OnTQw+WISGRk1G8O2caUeVRg3wD8unY1lJSUMO+1N3H8xKlG7YeIiFo+M1NTWFiYS9a/eFRqWhqCgkMgEokk5yk1ap6yPH/hIoJDQtHNu6vkSfTu3bygpqaGi0HBCAoOeTA1gzDnATk51ec+j06fBFRP73Ho8DFB6nmU5PyjQrjzj4A+vQEAR44eF2yfNQuV1rz3Ghoa8PbyAlD9RHvXrp4IDgmVrLfyaCdUD5/u1VOUBYXInet7/4FDKCkpgZWlpdw2aAhlZWWMGD5UsuB93I2bkm2qajXvd2Wj9l3zug4dOoLomFipdUlqbiadPH0W4ZevoLO+Pro4OkiVr1n35vDR+n+eGlOmNnV1daxdswr9Avvi3PkLePnV12TWiqtZlPliUDDuZ2bK7GN3Hef+NdcM8tpsz979jz3/dXNzgbdXV4SGXcIPP61Gfn4+Ro4YKuiIoMZ4ms9qT98eMDMzRXh4BJKTUyQLdE8YP0ZmP41p18Zc19XXnr2y+6xdV+1p6SSxH6l/7PW51qq+Id0DJSUlWPvr/6pvyNa+zunpi8rKSqxdtx6lpaXo2bOH1A1ZFRUV+Pv1RFl5udzFxOVpTJmm0hp+D5qjHVVVVdHNuysA+fcCCgsLcehI9Wfd9wmzDtT2tN/fQvJwd4WmpiZu3LyF6JhYme179sj/zm3sPZKnvYdQF08Pd0x9pno0XmxcXL3KPA12dhAREQBg8qTx0NDQwNFjJ6ROSMViMb77YaXcRSBtrK2hpKSECxeCcKPWBWJVVRVWrlqDS5cuP7ZOe3s7+Pv3xJXIqwgNuwQ7O1upk4n6eGbyxAeLYJ2WXCjU2LZ9J86cPQ9tbS08M2VSg/ZbF2Oj6idGbt2MF2R/ANDV0wP9AvvielQ03nnvQ+Tk5MjkiU9IxIGDh+u9z+o5sHugtLQUO3buhrWVpdS8vf5+vqiqqpKsk+L/yM0GNTU1zH11NkpLS/Hq3Ddw65bs683KypZ0UjW2jDwBfXrht3U/Q1lFBa+9vgDHjp+o9+smIqLW5YXnngUAfP3ND0hNe/jkYnFxMT78aDnKyyswdMggmaeCfXt0h5KSEvYfOIzc3Dyp84fqG+xdcfrMOaSmpsHNzQW6ujqCxFuzlsGhQ0elpt0pKyvDJ8s/x+3kZEHqeZSxUfXTlzfl/LY21tAhg+Ds3AVHj53AN9/9iOLiYpk8169H4/SZs/XeZ4cOHeDi7ISUlFScOXse3bp5QV394RSc/v49kZOTiwMHD0NZWVmmE8vCwhyDBvZHWXk5Plz2qdTN9pSUVHzz3Y8AgBdnzWzQaz185BjCLoXL3FS/dy8DUVHRAKTnUTd68H7LO5epj5obQJu3bINYLEavWjeLrKwsYW5uhv927EJZWZnMDTwAmDZ1CoyMDPHnX5uxYeOfMjc1xWIxQsMuIexS+FOVeZS6uhrWrvkJAwf0w4ULQXhp9lypz4WlpQX6BfZFaWkpPl2+Qmpu+YiISPz9z79y91szTeumTX9LpUdevSZp08d5dsZUAMD6/20EAEyf1rRPBdfH03xWRSIRxo0dDbFYjF/X/44LF4NhZGSI3r38ZfI2pl0bc11XXwcPHZG5If7b+g2IjomFoaEBhg97OA3X7JdmQVtbC9//uBK79+yTOf4qKipw6vRZxMbdkKTp63eCqqoKkpJuP/Zmfs0N6X82V19X1P4NqDnearbJuyE7/7U5UFZWxgcffSL3O660tBQHDh6Weq8aU6YptIbfg+Zqxxeerz6+fln3m1RnQEVFBT774itkZ+fA3d1NqhPuSZ72+1tImpqaklFsn3z6hdR6YAmJSVi9Zq3cco29R/K09xCuX4/GgYOHZdZ2KS8vx7nzFwHUvWaJkDiNFRERAahewG7Zh+/hvfeXYe5rb6J7d2+YmZoiKioGycnJmDZ1isxN6s6d9TF92hT89fe/GD12Evz8fKGnp4fIyGtITUvD7Jdm4bf/bXhsvc9OnypZcKwxc++amBjj6y8/x8LFS7DorXexcdNfsLayQmJSEq5evQ5VVRV889UXMDKSv2hZQw0ePBD/7dyNhYvfQZ8+vaCnpwcAeOftRejUqWOj9/v9tysw66U52P7fThw6dAQuLs4wNTVBUVER4uJu4nZyMgYPGoARw4fWe5/+fr44cvQYSktLpZ7IqN5W/XdpaSkMDDrDUc4TKbNfnoWExERs2fofho8aDxcXJ1hZWqKqqgpJSbcRG3cDWlpamDZ1ylOVkad3Lz/877c1ePmVeXht/kKs+ul7DBk8sN6vnYiIWocXnn8WIaGXcOToMQwZNgb+fr7Q0NBASGgY7t/PhIO9HT795EOZch06dICri7NkmqpHf+f8/HwRHBJa/X+BprACAHc3V/QL7ItTp89g0JCR6NmzB9TV1XEp/DLy8vLx/MwZ+OPPv5+8owYaPGgAgkNCMfO5l+Dv31OyYLK8aUTrS1lZGevWrMLzL76CX9b+hn+3bIOzsxMMDQyQl5ePmJhYpN+9ixeefxaBfeu/jpafny+uR0VXn3888hCLf09frFy1BqWlpejq6SG3E+qzTz/CrfgEHD9xCoEDhsK3hw+KiopxMej/7d15XE35G8DxT6iYhBg7Y9SgTUppsSRlX0u2MHbG2LLvITtjGztj14iSJMZS9iWVfR9mMmghQ4RJlvr9Ud3pujfqCsPveb9eXi/O8j3fe+5Zru9zzvOE8/z5c5o2aUTnTh45+qzhEZGsW+9D8eJfY2pqQpHChUlISCDy1BmSkpJo0rihUqqP+i71WLR4GTNmzeHY8RMUS0+x2qdXdwzfUV8NwLhKZQwMiigKL7+5H+ztbAnYtl3x9zfp6xdk5fLF9OrTjynTZrJy1RoqV/oOAwMDEhISuHLlGg8ePmT82FGKty00WUcdHR0dli5ewEDPYewL2U+PXj+y+pelimNuymQv2rbrzM5duzl95izW1a1ISHhEeEQknTt2YN0GH5U2+/7QiyNHjzFvwSJ279mHkZEh8fH3OXX6DM2aNubM2XPEZJGmBaBpk8ZMmz6bhwkJGBtXprqV5dt2/0fzPseqW6sWLF22kl83bQGgZYtmaouja/K9avL/uuz6vpMHffsNwrq6JWXKlOHGH39w7dp1dHR0mDN7BgUKFFAsW758ORb9PI+BnkMZMmwU8+Yv4rvvjChYUI+///6bS5ev8uTJE5YvXahIT6StrY1TXUdCQg/QtLkb5uZm6OjoYFjxW/r07qFoO2NAOjk5mW+/raD09ky5cmUpV66sIrXWmwOyAFZW1Zg2ZRJeE73p3rMvRoaGGBp+i66uLnF373LlyjWSkpLYGbRV0bYm63wIn8P94GN9jw0buNCta2fWrffBtXU77GxtKVKkMOfOXyA6OoYSJYqzYN7sHO2L971+57bhwwYTEXmaiMhT1HNpjJ1dDZKSnnMi7CSOdWpx5eo1levn+4yRvM8YQkxsLAMGDUVP7yuqmptRokQJkpKSOHf+Avfv/02FCt9oXG8pJ+TNDiGEEArt27VhzarlWFtbcfnyVQ4dPkrZsqXZsnkjVmpyPQJMmjAO70njMTSqSOSpMxw/EYaRkSFbfDdQr57jO7dZq6Y9Wlpa6Orq0kbD/KBNmzQiwN+Xpk0aERsbx+49+4iLu0uzpo3Z5r+Zxo0aaNSuOg0buDBpwljKlivLwUNH8PMPwM8/gGfPnr175bcoUqQIW3w3MMV7AqamJvx+/Qa79+zl0qUrFC9ejCGeAxg1cliO2sz89MubP9LKli3DN+XTco9m9SNNS0uLGdMms2bVcuo5OXL37j32hYQSHp42ePR9Jw9+Wb74vdfJsv92tqz5ZTk6OjoM9ByqyPcshBDiy5EnTx6WLp7P9KmTMK5SmfCISEL3H6RI4cIM6PcDAf6+WdbyyrjP6el9RTWLqkrzlNJO5HI+7WVLfmbo4IGUKlWSY8fDCI84hY11dYK2bcHMzCRXt5Wha5dOeA7qj34hffbtC1X8/nhf5cuXI3i7P6NGDOWb8uW5ePEye/aGcP3GDSpWrMDY0SPo3bN7jtrM/Lui5hu/P6ysqinqQGSV/qJ48eJs2+pL/x/7UEi/ECGhB4iIjMTEuAozpnmzcMGcHBd4bdPalT69ulOubFmuXLnK7t17+f36DapZmDN/7iwWLpijtLy5mSk/z/8JY+PKnAgLV+zv+Gym6dTS0sIuPW2KoWFFlZoNmZ8Uzur4tKhqzu6d2xnQ7weKGhhw+sxZ9oXs56+/bmNubsrkSV64Zspvr+k66mhra7Po57k0alif8IhIuvfsq/itW7ZMGbZt9cW9tSsvkl+wL2Q/8fHxjB09Aq/xo9W2Z13digB/X+o61iY+/j77Dxwi8UkiY0ePYP7cWe/sj66uDjY2aQP5HTt8+rc6MrzPsWpkZEjVqmaKf7u5qqawyqDJ96rJ/+uyo0f3LiyYN5uXr14Ruv8g0dGxONV1xH+LjyJtVWZOdeuwZ1cQ3bp2VqQX3H/gILFxd3Gwt+WnWdOoVdNeaZ3pU71xb+3Kk6dPCd75G37+ASqpo6pUrkRRg7Si7OoyA2RMK1786yxr8bRr25rgoADat3Pn5cuXHD5yjEOHj/Io4TEN6juzeOE8lXU1WSe3fS73g4/1PU4YP4bFC+dhY12d8xcusm9fKHm08tCtS2eCt/tjWPHbbPcZcuf6nZsK6evj57uBbl07o62tTWjoAaKiovihT08WL5yX5XqajpG8zxiClaUFw4cNxsrSktt3ohVvVZYsUYKRw4cQFOj3UVIQar1+lZw7ycGFEEIIDfhu9mOclzet3VoyZ/aMT90dIYQQQgghxH9IYmIiDrWdyZNHi7Bjh1SKeAshhBAZ5M0OIYQQn8zz589ZuSotzVXX9JzdQgghhBBCCJHhl1VrSUpKok1rNwl0CCGEeCup2SGEEOKj898aSEREJKfPnuPWrds0bdKIquZm715RCCGEEEII8cWLirrJylVriY2N4/iJMPT19fmxb+9P3S0hhBD/cRLsEEII8dFFREQSEBiEgUER2ri74TVOfX5fIYQQQgghxP+f+Pv38fMPQFdXF8tqFowdM0JtMV0hhBAiM6nZIYQQQgghhBBCCCGEEEKIz5rU7BBCCCGEEEIIIYQQQgghxGdNgh1CCCGEEEIIIYQQQgghhPisSbBDfFEMK5lRx6nBp+7GZ2FrQCCGlcxYsHDJp+5KjixYuATDSmZsDQj81F35z6jj1ADDSlLc+31ER8dgWMkMj07dlKafDI/AsJIZI0aO/TQdI+tzVdNzIafXyY95rRgxciyGlcw4GR7xwbf1sch96cvm0akbhpXMiI6OyfY6WV1vhBBCCCGEEEK8Hwl2iC+eDCoIIYQQIqfk94MQQgghhBBCfF7yfeoOCJGbQvYEky+fHNbi/4vP+tW8evXqU3dDfGRdOnekRbMmFC9R/FN3RQiRAyVLliBkTzD5C+T/1F0RQgghhBBCiC+KjAqLL4qRkeGn7oIQH12FCt986i6IT6BoUQOKFjX41N0QQuSQtra2/F4RQgghhBBCiA9A0liJL8qbudEXLFyCY72GAIRHRGJYyUzxJ3NaikePHjH/58U0buaKhZUdZhY2ONZrSJ++A/ht995sbbtBoxYYVTbn0aNHStM3b/FXbPPNnN6/7d6LYSUzJk2eptLeseNh9P6hPzXs6lDFtBq1HF0Y5+XN3bv3suyDJuuos3PXboxNLbGxrc358xcU058/f87KX9bQ0q0dVS1rYGZhQ0vXtmzY+CuvX79WaSdzLvODh47QzuN7qlrWwMLSlq7de3Ph4qUs+7A9aCct3dphYl4dG9vaDPQcxq1bt3P0OTJk1LRITU3F59fNNG3hhmlVa2xsazNo8HC17Wau1fD3gweM8/KmlqMLlU2qMXnqDMVyiYmJzJu/kMZNW2Fa1ZqqljVo274T24N2vrUvAFv8ttK8pTumVa2xr+WE9+TpPHv2DEg7Jr0nT6eWowvGZlY0bNwiy9oM6mp2vCv9Sla1KDLXZ4iJiWXIsFHUsHfEzMIG97YdOXr0uGLZ0P0HaNuhMxaWtlhaOzBo8HDu3YtXu72sZK7RcOx4GF269cbKxgHDSmZcuXJVsdyFi5cYNHg4DrXrUcW0GnY16zJ46Ej+/DNKpc3nz5+zxT+Avv08qefSWPG9tGrdjtVr17/3WzDLV6zCsJIZU6bNzHKZGTPn5LjOxZGjx2jn8T1mFjZY2TjQvVdfLl66nOXyb6vZER9/n1FjvKhh74iJeXUaN3Nlg88mUlNTs90fde7di2fEqHHYOjhibGZFg0YtWLfeR+2yly9fZebsubRq3Y4a9o5UMa1GzdrOeA4ZwbXfr2dre69evaKGvSPfValKXNxdtcu8fPkSa9taVDK24P79+0rzom7+xeixE6jj1ABjU0usbWvRp+8Azp27oLatD+nVq1csWbYSl4bNMDazwr6WE+PGT+LvBw+UlvtQx9e9e/FMnjoD5wZNMTazwtLage+79uLwkaNKy929e4/KJtWoYe/Iy5cv1bZ1/cYfGFYyo269RirHVG7dh97mfe4tYSfD6dWnHza2tTE2taSWowujxnhx+/YdpeWy+/shs+z2JTs1ghKfPGHylBnUquOCsakldes1Yv7Pi7O8fsXF3WX4yDGKc75Jczd8ft0MSN0YIYQQQgghxP8PCXaIL5qpiTGNG6X9B//rr4vh7tZK8cfRsRYAz549w62NB4sWL+Px48fY29XAuV5dSpYsQdjJcDZt9svWtuzta5CamsrJ8Eil6WEn/y20eyIs/I15af+2t7NVmj5v/kK6dOvF4SPHqFDhG+q7OKOvr4/vZj9aurUlKuqmyvY1WUedtes24jlkBCVKlsBviw/VqlkA8PBhAm3bd2bm7LnExcZhXd0KB3tbYmPjmDR5OgMGDc1yIHXTZj969elHyusU6jnVpXTpUhw9dgKPTt3UDlb/vGgpQ4eP4urVa1SvbknNmvacPXceV/f23LkTna3Poc6UaTPxnjIdgyJFqO9SD339guzctRtX9/b8fv2G2nUePEzAtXV79u0LxaKqOc71nChUqBAAf926RfNWbVi8dAVPnjylVk17qltZ8vv1Pxg6fNRbByunz/yJiZOmUrRoUeo61iY1NZX1G3+lbz9PEhIe0bptR37bsxfLahbY2FTnr1u3GTl6PNsCgzT+/DkREx1DK7d2nD17Hgd7W0xNjTl77jw9+/QjPDySdet96NvPkzxaeXB0rK3Yl9937Uly8oscby945266du/Nw4cPcaxTGxub6mjlSbtF+W72w71tR3bu2k3x4sVpUN+F0qVLsSN4F67u7Yk8dVqprVu37zBm7ATOnD1LyZIlqe9SDytLS27evMW06bPp22/Qew36t2vrjo6ODoHbd5CcnKwy/8WLFwRs207evHnp0K5NttrcGhBI9559OXXqDKYmVahTuxZ37kTTtn1nzp2/mKP+xcXdxa1NB/y3bkNHW5sG9Z35+utiTJk6kylTsz4m3yU2No6Wbm05EXYSO9saWFtbcfvObSZPncGixctUll+ybAWr16zn9avXVLMwp76LM4WLFCZ452+0buNBRMSpd24zX758tGjehJSUFHYE71K7zKHDR0lIeETtWg4UL/5vSq/9Bw7RvKU7fv4B6H31Fc7OThgaVlQMSGc3kJ0bUlNTGTBoKIsWL+Ob8uVoUN8ZAN8t/ri5d1AKEn6I4+v8+Qs0beHGuvU+pKak4FTXEVOTKpw6fYbuPfuyZu0GxbKlSpXExdmJBw8esC9kv9r2fH3T7osdOrRBS0tLMT237kPZldN7y+q16+n0fQ8OHjqCoVFFGjasj95XX+G/dRvNW7Xh9JmzimWz8/vhffryNolPntCmbUeCd/6GhYU5DjXtefDwAYsWL2Ocl7fK8tHRMbi16cC2wB3o6KSf88WKMXnqDKZOn5WjbQshhBBCCCHE50zSWIkvWsMGLpiaGLNnbwhGhob8NHu6yjK79+zj1q3buDg7sXzpQvLmzauYl5SUxNVrv2drWw72dvy6aQthJ8MVAySQ9qTmd0aG3PzrFmEnw2nXtrViXtjJCLS0tLCzraGYtmdvCIuXrqBChW9YvnQhVSpXUszb5OvH+AnejBg1jgD/Te+1jjqzfprHipWrMTauzLrVKymRqRbAqDHjuXzlKu3aujNh/Gi++uorIG1QZpDnMPbuC8V3sz8dPdqptLtm7QY2rFtFrZr2QNrAn9fEKWzy3cLylav5ada/b7ZcuHiJRYuXkT9/ftavXUkNG2sgbYBv+MixBG7f8dbP8DZ+/gH4/roOG+vqALx+/ZrJU2ew0ceX4SPGEBy0VWWdQ4eP4FTXkcUL5yo+M0BKSgr9BwwhOjqGgf37MqB/X7S1tYG0J6h7/9Cftes2UtexNo51aqu0uz0omJ07AvjuOyMAHj9+jHu7Thw/EYZHp66YGFdh3pyZ6OrqAnDg4GF69enHzwuX0tqtlcb7ILsCAoPo1rUz48aMVJwTPr9uZsKkKYz1msT9+3+zyWcdtjXSvp/k5GS6dOtN5KnT7Nz1G+6tXXO0Pd/NfsyeOZU27m5K08+fv8CESVMpVEifFcsWKb47SBvM/rG/J0OGjeJg6G7F/v+6WDHWr/2FWjXtyZPn35h+YmIinkNGcODgYXbu2k2L5k012TUULWpA0yaN2B4UzG+79+Lm2lJp/p69ITxMSKBBfWdKlSr5zvbi4u4y0TvtHFiyaD5NGqc9TZ6amsqMWXNYtXpdjvo3YdIU4uLu0qRxQ+bNmYWurg4AZ86eo0u3XjlqK7OtAYF09GjPpAljFbWRTp0+Q4eOXVm5ag09e3RVOkc6dezApAnjlK4jAPv3H6TfwMGM8/Jm354dSoPl6ri5tmTdeh8Cg4L5oU9PlfkZ14TM30NMTCyDh44gJSWFFcsWKYILAOfOXaBbzx8YPcYLezvbj5IKLDY2jhcvXrBrR4AifVFycjKeQ0awL2Q/E72nsXzpz0DuH19Pnz6jb39PHj16zNTJE/Ho0Faxz6OibtK1Rx9mzJpD7do1qVzpOwA6d/Jg775QNm32o1nTxkrtPX/+nMCgYHS0tWnb5t/7WW7dh3IiJ/eWS5evMHPWXHR0dPhlxRLq1K6pWGfBz4tZtGQ5gzyHcyD0N3R1dbP1+0HTvrxLSOgBXJydCAr0o0CBAkDaW0qubu3YGhDIwP59KVeurGJ5r4mTiY+/T8sWzZg9cyo6Omnn/IWLl+jcRfWcEUIIIYQQQogvlbzZIf7vPXyYAKQFKzIHOgAKFChAdSvLbLVjZ1sDLS0twjK9vfHHH39y//7fuLjUw9zMlJPh/77lER9/n6iomxhXqYyBQRHF9CVLVwAw96cZSoNFAB092uHi7MTZc+eVUvxosk5mr169YsTIsaxYuRrbGjZs3rRBaYDy6tVr7D9wiMqVvmPq5AlKA5qF9PWZPXMaOtra/Lpps9r2u3XppBgAAtDS0mKI5wAgLT1IZht9fElJScGjQ1tFoANAR0eHiV5jFQM/mujcsYPSYHnevHkZM2o4xYoV4/KVq0REnlZZR0dbmymTvZQ+M8CBg4e4eu136tSuyZDBAxUD7ZBWfHb6tLSnb3/dtEVtX4Z4DlAEOgAKFy5Mxw5pgaLY2DimeE9QBDoAnOvVxdi4Mneio4mJidXg0+dMuXJlGT1ymNI54dGhLQYGRbh58y++7+yhCHQA6Orq0qN7FwCVt5uyw7FOLZVAB8CyFat5/fo1E73GKn13AC7OTnTyaE9sbBwHDx1RTC9WrCh1atdUCnQAFCpUiAleaWm79uwNyXEfM+vcqQMAm3xVv1/fzf4AagN/6vht3UZSUhIN6jsrAh2Qdp4MH+pJqZLvHtDOEB0dw4GDh9HV1WXSxHGKQAdAdStLOnl0yHZbbypTpjRe40YpAh0ANtbVcaxTm2fP/uHiReWUW7Vq2qsEOgBcXOrRpHEj/oyK4sYff75zu1XNzaj0nRHXr99QuYYlJiZy4OBhCurp0bCBi2L62vUbefbsHwYN7KcU6ACwtLRgQP8fePrsGduDgrP12XPDwAE/KtVp0NXVxXvieHR0dAgJ3a90Xufm8bU1IJB79+Lp0K4NHT3aKQWXDA0rMm7MSF6/fs3mLf6K6bVq2mNoWJGTJyOIuvmXUns7d+0mMTGRhg3r83WxYorp73sf0kRO7i3rN6SlW2zfzl0R6MhYx3NQf4wMDYm7e1fjN35y0pd30dP7ihnTJyvd7wwrfourawtSU1OV2rt9+w6Hjxwjf/78TBg/RhHoALCoas73nT00+jxCCCGEEEII8TmSYIf4v2dubgrAylVr2BG8iydPnmrUTrFiRalU6Tv++DNKkTc+LD24UdPeDnt7W+7di1eks1CksLL/N4XV3w8ecPnKVUqUKJ5lkMW2hg2AIrWNJutklvRPEr37DiAgMIhGDeuzfu0vFNLXV1rm6LETADg7OykNdGYoUaI4335bgd+v3+D58+cq852cHFWmFStWlCJFChP/Ro2HyPSAQ0s1T91nDGJrqlXL5irT8ufPT6OG9dO3rZpWx9TUhLJlyqhMP5Jeu6JRQ/V50M1MTdDT+4pz59XXBnB0VH3bI6PQeNWqZmqfNv+2QgUA7sXnrC6GJuztbJUGzSAtOFS2bNrTxHXVvK3y7bdp/YvXoH8N6ruoTEtJSeH4iRPky5eP+i711K5XIz3gom4/n79wkRUrVzNh0hRGjBrHiJFjWbJ0OQA3//orx33MrLqVJWamJpw+c04pBdqff0YRHhFJ+XLl1L7Ro05E+nGn7k0THR0dmjRpqDI9K5GnTpOamoq9vS3Fv/5aZb5rK9VzILsc7GyVAnAZjIwqAuqPy8QnT9getJOZs+YwZtwERowcy4iRY7l+I22f3XxjID0rrq4tAAh8Izix67c9vHjxgsaNG5I/f37F9IzaMo3Tz+03/XtN/Hi1O9Rdf0qWLIGDvS2pqalEnjqjmJ6bx5fiWtXo7fvi/Bv3h04e7UlNTVUKgoD6YMv73oc0pcm9xbVlC5V18uTJozjGwrORXu19+/Iu5mZmSoGkDEaGacGye/H/1qY5dTrtuHGwt1N739D0DTYhhBBCCCGE+BxJGivxf6+mgz19enVn1Zr1DB6alrKnUiUj7O1scW3VAouq5tluy8HOluvXbxB2MoKWLZoRFhaOjrY2NjZpT6SvWLmasJMRGBkZKmp5OGQKdsSkFzCPj7+vUnD6TQkJCRqvk9na9Rt59eoVtjVsWLJovsrT8AB3otPqZCxfsYrlK1a9dRuPHj2mVKn8StPKlC6tdlk9PT0ePXqsNO3uvbQitmXKqgYYAMpmMT07ymZK+5FZufQ249QU0M1qe3fS9/v4Cd6Mn6CaQz1DVvUrSpcqpTJNTy/t7ZFSauYB6KW/XfLiRc5rYuRU6SzS42T0QV36nH/7p76o8duo288PExJ49uwfAMyr1VCZn1nmY/vZs2cMGjyCg4cOZ7n806fPctzHN3Xq2J6x4yfhu9mPSRPGAWk1GEC1lsHbZNRrUBdUAyhXVv1xq07G+ZNVW1mdA9lRpkzW5zGoHpd794UyaowXiYmJWbb59Gn2gsutWjZn7ryF7AjepfTGUUbwo7WbcqqnjPOzfqO3B3cSEh69c9uRp07j5xegMr3vD72U3tR4m0KFCqkEkTNkBBDv3lUuwJ5bx1d0+vW7W48f3rrcwzfuD+7ursyZ9zMB27YzbIgnuro6XL32O2fPncfI0FCp1tT73oc0pcm9pVx59efAN+XLAXDvnmaF1HPSl3e2leW5pnoPyAgylimj/r6RVVtCCCGEEEII8SWSYIcQwOhRw+no0Z7Q/QcJOxnOqdNnWLfeh3Xrffixb29GDBucrXbs7W1Zv/FXwk6G06J5UyIiTlGtmgUFChTAxqY62tr5CDsZTudOHTh5MoI8efIonnYFSElJK5psYFAEZ6e6b91WpfTc6pqsk5lT3TpEnjpN5KnT+AcE0r6tu8oyGduwrGaBkWHFt27jzbcBALUBlHfJ7kDeh5ZfzZPsAKkpKQDUrGlP6RykGcrwtn2S5yN89pT0/mdF6x3fmSbf6dvkz6+6nzP2sY6ODi2aNXnr+pbVLBR/nz1nPgcPHcba2grPgf0xMa5CoUL6aGtr8+LFC4zNrN6rQHmGVi2bM2PWXAK3BzNqxFDy5MnLtsAgtLXzKdUyyK7c/No/xCH0rmMis9i4OAYPHUlKymvGjBqOi7MTpUqVpECBAmhpafHT3AUsW/5Ltr+HMqVLY2dXg7CwcI6fCMOxTm3u3Inm9OmzlClTWqnuEfx7fLdq2Zx8b6QnzMzQ6O3XM4Bbt24TEBikMt3d3TXbwQ5N5NbxlZK+jxvUd84y4AJg8MZbAYX09WnZoilb/ALYs3cfrVo2x3dzWmFyjw5tlbfxnvchTeX2deh95GZfPsY9QAghhBBCCCG+RBLsECLdN9+Up0f3LvTo3oXXr1+zd18ow0aMYfmKVbi1aqFUXyErdrY26XU7Irh27XceJiTQ2cEOSKv/Ua2aBeHhkURHx3D7zh3Mzc0oVKiQYv3SpdOezNTT03tnMdT3WSczU1MTBg3sR5duvRg7biKpKSl0aK88kFUmfRt169bBc2C/HG8jJ0qVLMntO3eIiYlVm8bjfepVxMTEUsi4isr06PQ2S5Uske22Mva7W6sWOS7G/TFk1BB59kz9GwyxsXEfszsaMTAwQFdXl9SUFKZP81aqi/I2u/fsI2/evKxeuVTp/IK0gevcUqBAAdzdWrFugw/BO3ejo6PDo0ePada0sdpjNyslS5YgKuomMbFxVMsUtMkQHROT7bYy6nvExKj/fjOewP/QDh48QnJyMr16dqN3r+4q8zX5HtxatSAsLJzA7cE41qnN9qBgUlNTcW3VQiU4Wrp0KW7dus3woZ7v9TYYQBt3N7X1ZHIiMTGRJ0+eoq9fUGVeTPr3++bbUrl1fJUuVYqoqJv06NYFO7u3vyH1ps4dPdjiF8AmXz8aNnAhaMcudHV1cW/dSnkb73kf+hgy7i3Rd2LUpnjLeBuopAbB60+pZIm0+1Zs7F218z+Ha70QQgghhBBC5Jb/ziNxQnwgGQOkr16/yvY6efPmpWmTRtin51K/fuOPbK1XpEgRTIyrcPvOHbZu2w6k1evI4GBny8OEBNau36j4d2alSpWk0ndGREfHZLuIqybrvMnczBSf9aspUqQw47y82eTrpzS/dnqdjNDQA7nyRPzb1EhP+bVz126VeQkJjzh2/ITGbe8I3qUyLTk5mX0hoQBKb9m8S53atQDYF7Jf4/58SAYGRdDWzsftO9G8fKmaViojj/9/Wb58+XCwt+PFy5dKBcjf5fHjx+jp6akEOgCC1BwD76NTx/YA+G72Uzz1nt3C0Rlq2KTVHQne+ZvKvJcvX+aomHpGEfewk+H8/eCByvzc/vxZefQ4LW2PunRtDx485NjxsBy32bhRQwoUKEBI6H6ePXvG9h07AWjt2lJl2YzaPnv3heZ4Ox+KuuvP/fv3CTsZgZaWluK7yyw3ji/FvgjJ+b4wMzPByrIakadOM//nxTx58oRmTRtRuHBhpeVy4z70oWXU99m+Q7UofUpKCtu37wDSHlrIoMnvh48t47g5GR6hNi3bLjX3UiGEEEIIIYT4UkmwQ3zxihY1QFs7H7du3ebVK9UBi737Qjl1+ozKIH58/H3FoE1WubjVySg4vsnXjwIFCmBp+e+T2g7pb3lkBBMyFyfP4DmoPwADBw/n3DnV4rlPnz5ji3+AUiFwTdZ5k6mpCT4b1lDUwACviZPZ+KuvYl41i6o41XXk8pWrjBrjxaNHj1TWj7r5F7/t3ptl+9nVuVMHtLS02OTrx5mz5xTTX758ifeU6fzzT5LGbfv86qvUZkpKCjNnzeXvvx9gYlxFMRiWHY0a1sfYuDIhoQf4ae4CkpJU+3X58lUOHzmqcX/fh46ODtbVrUhMTGTVmnVK8/z8t7Hrtz2fpF85NbB/X/Lmzcv4Cd5q92VycjK/7d5LXNy/TzUbGhqSmJjI9jeKWR8+cpTVa9bnav+MjAxxcLDj/IWLRJ46jaFhRRwyBTizo20bN/Lnz09I6AGlwfnU1FTmzl+o9NnepXz5cjjVdSQ5OZnJU2Yo5fY/d+4Cv27anKO+aSoj3d22wCCl+ihPnz5j1Jjxb63jkZWCBfVoUN+Zf/5JYvac+dy8+RcWFuYYqkmt17tnd/T0vmLegoUE7dipcn1/9eoVhw4fVSr+/aEtWrKMqEwF2ZOTk5noPY0XL17g4uxEOTX1VHLj+PLo0I4SJYqz0ceXtes2qtwH04qjn1YUun5T504dAFi1eh0AHT3aq11O0/tQHacGGFYyY2tAYI4+V051+b4jefLkYYtfAMdPnFRMT01NZdGS5fzxZxSlSpakaZNGinnv+v3wX/DNN+Wp61ibpKQkpkybqRTcvnT5Cht8fN+ythBCCCGEEEJ8WSSNlfjiaWtr41TXkZDQAzRt7oa5uRk6OjoYVvyWPr17EB4Rybr1PhQv/jWmpiYUKVyYhIQEIk+dISkpiSaNGyoFLN7Fwc6WNWs3kJycTO1aNZVqWFhZWqKrq0tycjL58uVTPNGdWdMmjRjiOYAFC5fQuq0HJsZVqFDhG/LmzUt0dAxXr17jxcuXNGnUgPz582u8jjomxlXw2biGzl16MnHSVFJTUujyfScA5s2ZQfeefdkaEMiePfswMTGmdOlS/PPPP1y//ge379yhQX1npYEiTVSrZkH/H/uweOkK2nukpV0pamDA2XPnefLkKW6uLQlMfwI3p9q6t6a9RxdsbW0oVrQoFy9d5tat2xQqVIg5P83IUZ2QvHnzsmLpIrr26MOy5b+weYs/xsZVKP711yQmPuHatd+5e+8e3bp2pq5jHY36+748B/bn1Ole/DRnAXv2hlCubFlu3PiDv27dplePbvyyeu0n6VdOWFlVY9qUSXhN9KZ7z74YGRpiaPgturq6xN29y5Ur10hKSmJn0FZFKp0B/X5goOcwhg4fzUYfX8qVK8vt23c4f+EifX/oxfIVq3K1j507diAsLBxQrWWQHWXLlGGi1xjGjJvIj/09sba2okzp0ly5co07d+7g0aGd4qn+7Jgy2Yu27Tqzc9duTp85i3V1KxISHhEeEUnnjh1Yt8Enx33MKRdnJ0yMq3D5ylWcnBthY2MNpBIReYq8efPRxt1No8FtN9cW7Ajexcb0AVx1b3VAWtBn0c/zGOg5lCHDRjFv/iK++86IggX1+Pvvv7l0+SpPnjxh+dKFVKlc6X0+araUKVMaM1MTmjZ3w8HBjoJ6epw6fYZ79+IpU6Y03hPHZ7nu+x5f+voFWbl8Mb369GPKtJmsXLWGypW+w8DAgISEBK5cucaDhw8ZP3aU2rdLmjZpzLTps3mYkICxcWWqW1mq3Y6m96GM+ir58mUvTZ2mqpqbMWb0cKZNn02Xbr2oYWNNyZIluHr1Gn/8GUXBggVZtHAuupnqNL3r98N/xRTvCbRp34ntQcFERJ6iupUljx49Jjwigk4eaed8dtMACiGEEEIIIcTnTN7sEP8Xpk/1xr21K0+ePiV452/4+Qco0uK0ae1Kn17dKVe2LFeuXGX37r38fv0G1SzMmT93FgsXzMnRtmrY2pA3vSCug4Pymxu6ujpUr24JpKWOKlhQT20bAwf8iP8WH1o0b8qjR485cOAQx46fIOn5c1xdW7L6l2Xov1FoVpN11KlSuRKbNq6hePGvmTR5OmvXpaXcKlKkCFt8NzDFewKmpib8fv0Gu/fs5dKlKxQvXowhngMYNXJYjvZVVoYOGcTcn2ZgYlyFU6fOcPxEGFXNzQgM2Ez58uU0btdr/Gi8xo3iwYOHhIQeIDExkebNmrB922ZM1NTyeJfy5csRvN2fUSOG8k358ly8eJk9e0O4fuMGFStWYOzoEfTuqVqv4GOxs6vB2tUrsLGpzh9/RHH06HFKlCzBJp+11Kvn+Mn6lVPt2rYmOCiA9u3cefnyJYePHOPQ4aM8SnhMg/rOLF44T6mmTrOmjdm4fjU1bKz5M+omBw4eQktLi7k/zWDk8CG53r9aNe3R0tJCV1eXNhrWb2nfrg1rVi3H2tqKy5evcujwUcqWLc2WzRuxykGwFdKCJ9u2+uLe2pUXyS/YF7Kf+Ph4xo4egdf40Rr1L6e0tbXZvGkDPbp3Qb+QPocOH+HChUs0qO9C8HZ/ypTJ/ttymdWuVZPixb9O30Y+mjdrmuWyTnXrsGdXEN26dkZHR4ewk+HsP3CQ2Li7ONjb8tOsadSqaa9RP3JKS0uLxQvn0a9vb27duk1IyH5SUlLo0L4NgVt9FYE6dXLj+LKoas7undsZ0O8HihoYcPrMWfaF7Oevv25jbm7K5EleuLZqoXZdXV0dbNLTC3bsoP6tjgw5vQ89fJjA3bv3KFa0KPVd6mn02XKiZ/eu+GxYjVNdR67fuMGevft49uwf2ri7EbzdH+vqVirrvO33w39FuXJl2bbVl9ZuLUl+nkxI6IH0c34kvXp1A8CgSJFP2kchhBBCCCGE+Bi0Xr9K/rAJ+IUQ4hOr49SAmJhYom5c/tRdEV8g381+jPPyprVbS+bMnvGpuyO+MJ/6+EpMTMShtjN58mgRduxQlkF6TezctZtBg4czYfwYunXtnGvtin8F7djJkGGj6NSxPVO8J3zq7gghhBBCCCHEByVvdgghhBAaev78OStXpaUD69pFBmtF7vovHF+/rFpLUlISbVq75WqgA+BE2EnKlyuX46LrQtnLly+5eu13lemXLl9h5uy5ALhlkfJNCCGEEEIIIb4kUrNDCCGEyCH/rYFERERy+uw5bt26TdMmjahqbvapuyW+EJ/6+IqKusnKVWuJjY3j+Ikw9PX1+bFv71zfzvSp3rne5v+jpKQkmrVoTYVvylOxYkW++qoAd6KjuXz5KikpKXTv9n2WtVaEEEIIIYQQ4ksiwQ4hhBAihyIiIgkIDMLAoAht3N3wGvdxamGI/w+f+viKv38fP/8AdHV1saxmwdgxIyhRovhH7YPIvvz589Ondw9OnDjJ+QsXePLkKQUL6mFnVwOP9m1p3qzJp+6iEEIIIYQQQnwUUrNDCCGEEEIIIYQQQgghhBCfNanZIYQQQgghhBBCCCGEEEKIz5oEO4QQQgghhBBCCCGEEEII8VmTYIcQQgghhBBCCCGEEEIIIT5rEuwQQgghhBBCCCGEEEIIIcRnTYIdQgghhBBCCCGEEEIIIYT4rEmwQwghhBBCCCGEEEIIIYQQnzUJdgghhBBCCCGEEEIIIYQQ4rMmwQ4hhBBCCCGEEEIIIYQQQnzWJNghhBBCCCGEEEIIIYQQQojPmgQ7hBBCCCGEEEIIIYQQQgjxWZNghxBCCCGEEEIIIYQQQgghPmsS7BBCCCGEEEIIIYQQQgghxGftfxsnyUMZ7+vGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1610x630 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.5))\n",
    "\n",
    "# Left: one illustrative pair, so the reader sees what is being compared at all.\n",
    "g_demo = np.random.default_rng(SEED + 77)\n",
    "days_d, fit_d = make_batch(1, HOLDOUT_WEEKS * 7, generator=g_demo)\n",
    "_, test_d = make_batch(1, HOLDOUT_WEEKS * 7, dow=DOW_SHIFTED, generator=g_demo)\n",
    "axes[0].plot(x, weekday_index_batch(days_d, fit_d)[0], marker=\"o\", color=SIGNAL, lw=2.2,\n",
    "             label=\"worked out on the first six weeks\")\n",
    "axes[0].plot(x, weekday_index_batch(days_d, test_d)[0], marker=\"s\", color=FLAG, lw=2.2, ls=\"--\",\n",
    "             label=\"what the next six weeks did\")\n",
    "axes[0].axhline(1.0, color=MUTED, lw=1.0, ls=\":\")\n",
    "axes[0].set_xticks(x)\n",
    "axes[0].set_xticklabels(DAY_SHORT)\n",
    "axes[0].set_ylabel(\"cost per sale against the account's own average\\n(higher is worse)\")\n",
    "axes[0].set_xlabel(\"day of the week\\nits weekend premium really did halve - by eye, nothing\")\n",
    "axes[0].set_title(\"Can you tell this one changed?\")\n",
    "axes[0].legend(frameon=False, fontsize=9, loc=\"upper left\")\n",
    "\n",
    "# Right: the two distributions and the line between them - the honest picture.\n",
    "bins = np.linspace(0, max(stable_gaps.max(), shifted_gaps.max()), 60)\n",
    "axes[1].hist(stable_gaps, bins=bins, color=SIGNAL, alpha=0.55, label=\"rhythm did NOT move\")\n",
    "axes[1].hist(shifted_gaps, bins=bins, color=FLAG, alpha=0.55, label=\"rhythm DID move\")\n",
    "axes[1].axvline(ALARM_LINE, color=INK, lw=1.8, ls=\"--\")\n",
    "axes[1].text(ALARM_LINE * 1.03, axes[1].get_ylim()[1] * 0.85,\n",
    "             f\"alarm line\\n{caught:.0f}% of real\\nshifts clear it\", fontsize=9.5, color=INK)\n",
    "axes[1].set_xlabel(\"how far the worst weekday moved between the two windows\")\n",
    "axes[1].set_ylabel(\"number of advertisers\")\n",
    "axes[1].set_title(\"The two overlap, and that overlap is the honest limit\")\n",
    "axes[1].legend(frameon=False, fontsize=9.5)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_holdout.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d69a68d2",
   "metadata": {},
   "source": [
    "## Caveats to watch out for\n",
    "\n",
    "The formula divides by the average across the window. That is only fair if the account sat\n",
    "at roughly one level through the whole window. If it was drifting - costs climbing week on\n",
    "week, or a budget ramping up - the drift has nowhere to go, and it comes out inside the\n",
    "weekday pattern.\n",
    "\n",
    "The reason it lands as a weekday effect rather than as noise is arithmetic and not bad\n",
    "luck. In any whole number of weeks, Monday's observations sit on average six days earlier\n",
    "in the window than Sunday's. A metric climbing steadily through the window therefore gives\n",
    "every Sunday a slightly higher level than every Monday, purely because Sundays happened\n",
    "later. The method has no way to tell that apart from a genuine weekend premium.\n",
    "\n",
    "Measured below: the same advertiser with **no weekday rhythm at all** - every day\n",
    "identical - and a level that climbs. Everything the method reports here is an artefact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f2f4e59d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:31.275771Z",
     "iopub.status.busy": "2026-10-24T13:13:31.275433Z",
     "iopub.status.idle": "2026-10-24T13:13:31.326896Z",
     "shell.execute_reply": "2026-10-24T13:13:31.325878Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drift 0.0%/day (  0.0% over a month) -> invents a -0.1% Sunday premium out of nothing\n",
      "drift 0.5%/day ( 16.1% over a month) -> invents a +3.1% Sunday premium out of nothing\n",
      "drift 1.0%/day ( 34.8% over a month) -> invents a +5.9% Sunday premium out of nothing\n",
      "drift 1.5%/day ( 56.3% over a month) -> invents a +8.7% Sunday premium out of nothing\n",
      "drift 2.0%/day ( 81.1% over a month) -> invents a +11.8% Sunday premium out of nothing\n",
      "drift 2.5%/day (109.8% over a month) -> invents a +14.6% Sunday premium out of nothing\n",
      "drift 3.0%/day (142.7% over a month) -> invents a +17.8% Sunday premium out of nothing\n",
      "\n",
      "for scale, this advertiser's REAL Sunday-over-Monday gap is 18.7%\n",
      "an account drifting 81% over a month manufactures half of that gap\n",
      "from a rhythm that does not exist at all.\n"
     ]
    }
   ],
   "source": [
    "FLAT = np.ones(7)\n",
    "TREND_GRID = [0.000, 0.005, 0.010, 0.015, 0.020, 0.025, 0.030]\n",
    "LEAK_REPS = 4000\n",
    "leakage = []\n",
    "\n",
    "for tpd in TREND_GRID:\n",
    "    g = np.random.default_rng(SEED + int(round(tpd * 1e5)) + 7)\n",
    "    days_t, vals_t = make_batch(LEAK_REPS, 28, dow=FLAT, trend_per_day=tpd, generator=g)\n",
    "    idx = weekday_index_batch(days_t, vals_t)\n",
    "    tilts = idx[:, 6] - idx[:, 0]           # Sunday against Monday, both truly 1.000\n",
    "    leakage.append({\n",
    "        \"trend_per_day_pct\": tpd * 100,\n",
    "        \"trend_per_month_pct\": ((1 + tpd) ** 30 - 1) * 100,\n",
    "        \"invented_sunday_premium_pct\": float(np.median(tilts)) * 100,\n",
    "    })\n",
    "    print(f\"drift {tpd*100:.1f}%/day ({((1+tpd)**30-1)*100:5.1f}% over a month)\"\n",
    "          f\" -> invents a {np.median(tilts)*100:+.1f}% Sunday premium out of nothing\")\n",
    "\n",
    "real_weekend_premium = float(DOW_TRUTH[6] - DOW_TRUTH[0]) * 100\n",
    "print(f\"\\nfor scale, this advertiser's REAL Sunday-over-Monday gap is {real_weekend_premium:.1f}%\")\n",
    "matching = [r for r in leakage if r[\"invented_sunday_premium_pct\"] >= real_weekend_premium * 0.5]\n",
    "half_at = matching[0][\"trend_per_month_pct\"] if matching else None\n",
    "if half_at is not None:\n",
    "    print(f\"an account drifting {half_at:.0f}% over a month manufactures half of that gap\")\n",
    "    print(\"from a rhythm that does not exist at all.\")\n",
    "else:\n",
    "    print(\"none of the drifts tested manufactures half of it - the leak is real but modest here.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6e986564",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:31.329994Z",
     "iopub.status.busy": "2026-10-24T13:13:31.329678Z",
     "iopub.status.idle": "2026-10-24T13:13:31.792759Z",
     "shell.execute_reply": "2026-10-24T13:13:31.791629Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1260x644 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 4.6))\n",
    "tp = [r[\"trend_per_month_pct\"] for r in leakage]\n",
    "inv = [r[\"invented_sunday_premium_pct\"] for r in leakage]\n",
    "ax.plot(tp, inv, marker=\"o\", color=FLAG, lw=2.4)\n",
    "ax.axhline(real_weekend_premium, color=SIGNAL, lw=2.0, ls=\"--\")\n",
    "ax.text(tp[1], real_weekend_premium * 1.04,\n",
    "        \"this advertiser's real Sunday-over-Monday gap\", fontsize=10, color=SIGNAL)\n",
    "ax.set_xlabel(\"how fast the account is drifting, per cent per month (0.5% a day is 16% a month)\")\n",
    "ax.set_ylabel(\"Sunday over Monday, as the method reports it\\nwhen the true answer is zero\")\n",
    "ax.set_title(\"On a drifting account, the weekday pattern is partly the drift\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_trend_leak.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "824425bb",
   "metadata": {},
   "source": [
    "## \"But my season is the summer, not Tuesday\"\n",
    "\n",
    "Everything above is about the seven days of the week. The word *seasonality* usually makes a\n",
    "marketer think of something else: a summer high season, a Christmas peak, a quiet January. This\n",
    "section answers whether the same method does that, and the answer has two halves.\n",
    "\n",
    "**The arithmetic extends to any repeating cycle.** Nothing in the formula knows what a weekday\n",
    "is. Replace \"the average of every Tuesday\" with \"the average of every July\" and you have a\n",
    "month-of-year index, computed and read exactly the same way.\n",
    "\n",
    "**But the rule that governs it is the one you already know: each cell needs several\n",
    "observations.** A weekday cell collects one observation per week, so six weeks gives it six. A\n",
    "month cell collects one observation per *year*. Six observations therefore means six years, and\n",
    "**an account you have watched for six years is not the account you have now**. The competitors,\n",
    "the creative, the landing page, the product and the market have all moved. You would be\n",
    "measuring a shape that no longer exists.\n",
    "\n",
    "So the honest answer for a month-by-month curve is that it is not practical, and no amount of\n",
    "cleverness changes that: it is a counting problem, not a statistical one.\n",
    "\n",
    "**And the literature closes the door from the other side, which is worth reading carefully.**\n",
    "The textbook that defines this method states its own assumption plainly: classical decomposition\n",
    "methods *\"assume that the seasonal component repeats from year to year\"*, and are *\"unable to\n",
    "capture these seasonal changes over time\"* when it does not. Put those two facts together and\n",
    "the problem is not that six years is a long wait. It is that the six years you would be waiting\n",
    "for are exactly the span over which the thing you are measuring moves, and this method's\n",
    "defining assumption is that it does not.\n",
    "\n",
    "That is also why the better tool exists. STL smooths each cell across years specifically so that\n",
    "it *can* move: in its authors' words, *\"the values of the seasonal component for July 4 change\n",
    "smoothly from one year to the next.\"* A yearly pattern on a real account is the case STL was\n",
    "built for and the case this method assumes away.\n",
    "\n",
    "**The number of CELLS is what costs you, though, not the length of the cycle.** That distinction\n",
    "is the same one the weekday section landed on, one size up: telling the weekend from the working\n",
    "week took a single week, while telling Monday from Tuesday was still unreliable after fourteen.\n",
    "A twelve-cell month curve is the fine question. \"Is the summer different from the rest of the\n",
    "year\" is the coarse one: one contrast, pooling thirteen weeks against thirty-nine, **every\n",
    "year**. Both are measured below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e93135be",
   "metadata": {},
   "source": [
    "### First, the honest uncertainty: how noisy is a month?\n",
    "\n",
    "A monthly figure is an average of about thirty days, so daily wobble alone would leave it very\n",
    "quiet. But months also carry shocks of their own that do not average away: a competitor's push,\n",
    "a price change, a stock-out, an algorithm update, a macro swing.\n",
    "\n",
    "**I have no real account data to size that with**, so nothing below reports a single number.\n",
    "The answer is measured across the whole range instead, from \"months carry no shocks of their own\"\n",
    "to \"a month is as jumpy as a day\". The row you believe is the answer you get, which is itself the\n",
    "finding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c6d46f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:31.796554Z",
     "iopub.status.busy": "2026-10-24T13:13:31.795897Z",
     "iopub.status.idle": "2026-10-24T13:13:32.114822Z",
     "shell.execute_reply": "2026-10-24T13:13:32.113260Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "worst month, points out - against the weekday benchmark of 5.7 points\n",
      "   month shock |  1yr |  2yr |  3yr |  4yr |  6yr |  8yr\n",
      "          none |  4.8 |  3.4 |  2.8 |  2.4 |  2.0 |  1.7\n",
      "   2% of level |  6.0 |  4.2 |  3.5 |  3.0 |  2.5 |  2.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   5% of level | 10.4 |  7.3 |  5.9 |  5.1 |  4.3 |  3.7\n",
      "  10% of level | 19.0 | 13.5 | 11.0 |  9.5 |  7.7 |  6.7\n",
      "  month shocks           none: 1 year(s) to match six weeks\n",
      "  month shocks    2% of level: 2 year(s) to match six weeks\n",
      "  month shocks    5% of level: 4 year(s) to match six weeks\n",
      "  month shocks   10% of level: more than 8 year(s) to match six weeks\n",
      "\n",
      "Only the top row - months with no shocks of their own, which no real account has -\n",
      "gets there inside a year. Everything else needs the years an account does not stay still for.\n"
     ]
    }
   ],
   "source": [
    "MONTH_NAMES = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\",\n",
    "               \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n",
    "MONTH_TRUTH_RAW = np.array([0.88, 0.86, 0.92, 0.97, 1.03, 1.12,\n",
    "                            1.22, 1.20, 1.02, 0.95, 0.93, 1.10])\n",
    "MONTH_TRUTH = MONTH_TRUTH_RAW / MONTH_TRUTH_RAW.mean()\n",
    "\n",
    "N_EFF_MONTH = 30 * (1 - RHO) / (1 + RHO)\n",
    "SIGMA_MONTH_FROM_DAYS = NOISE_SIGMA_LOG / np.sqrt(N_EFF_MONTH)\n",
    "MONTH_SHOCKS = [0.00, 0.02, 0.05, 0.10]\n",
    "YEAR_RANGE = [1, 2, 3, 4, 6, 8]\n",
    "YEAR_REPS = 4000\n",
    "\n",
    "# The benchmark is the post's own: what six weeks of daily figures bought for weekdays.\n",
    "WEEKDAY_BENCHMARK = HIST_ERR_SIX = stability[6][\"typical_worst_error\"] * 100\n",
    "\n",
    "\n",
    "def month_index_batch(values):\n",
    "    \"\"\"The same ratio the whole post teaches, on a twelve-month cycle. (runs, years, 12).\"\"\"\n",
    "    flat = values.reshape(values.shape[0], -1)\n",
    "    return values.mean(axis=1) / flat.mean(axis=1, keepdims=True)\n",
    "\n",
    "\n",
    "month_grid = {}\n",
    "print(f\"worst month, points out - against the weekday benchmark of {WEEKDAY_BENCHMARK:.1f} points\")\n",
    "print(f\"{'month shock':>14} | \" + \" | \".join(f\"{y:>2}yr\" for y in YEAR_RANGE))\n",
    "for shock in MONTH_SHOCKS:\n",
    "    row = []\n",
    "    for years in YEAR_RANGE:\n",
    "        g = np.random.default_rng(SEED + 3000 + years + int(shock * 1000))\n",
    "        e = (g.normal(0.0, SIGMA_MONTH_FROM_DAYS, (YEAR_REPS, years, 12))\n",
    "             + g.normal(0.0, shock, (YEAR_REPS, years, 12)))\n",
    "        idx = month_index_batch(MONTH_TRUTH * np.exp(e))\n",
    "        row.append(float(np.median(np.abs(idx - MONTH_TRUTH).max(axis=1))) * 100)\n",
    "    month_grid[shock] = row\n",
    "    label = \"none\" if shock == 0 else f\"{shock*100:.0f}% of level\"\n",
    "    print(f\"{label:>14} | \" + \" | \".join(f\"{v:4.1f}\" for v in row))\n",
    "\n",
    "years_needed = {}\n",
    "for shock, row in month_grid.items():\n",
    "    hit = next((y for y, v in zip(YEAR_RANGE, row) if v <= WEEKDAY_BENCHMARK), None)\n",
    "    years_needed[shock] = hit\n",
    "    label = \"none\" if shock == 0 else f\"{shock*100:.0f}% of level\"\n",
    "    print(f\"  month shocks {label:>14}: \"\n",
    "          f\"{hit if hit else 'more than ' + str(YEAR_RANGE[-1])} year(s) to match six weeks\")\n",
    "print(\"\\nOnly the top row - months with no shocks of their own, which no real account has -\")\n",
    "print(\"gets there inside a year. Everything else needs the years an account does not stay still for.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec2f76f4",
   "metadata": {},
   "source": [
    "### Second: the coarse question, at weekly resolution\n",
    "\n",
    "A year holds fifty-two weeks. Asking for a value per week-of-year is the *fine* question again:\n",
    "one observation per cell per year, the same counting problem as months.\n",
    "\n",
    "But asking whether one stretch of the year runs different from the rest is not. That contrast\n",
    "pools every week inside the stretch against every week outside it, and it does so **every year**.\n",
    "Below, a thirteen-week summer planted a quarter above the rest of the year, read at weekly\n",
    "resolution, scored two ways: the worst individual week, and the summer-against-the-rest contrast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8014e93d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:32.120117Z",
     "iopub.status.busy": "2026-10-24T13:13:32.119610Z",
     "iopub.status.idle": "2026-10-24T13:13:32.602895Z",
     "shell.execute_reply": "2026-10-24T13:13:32.601767Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the planted summer runs 23.5 points above the rest of the year\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "week-level shocks none:\n",
      "  1 year(s): worst single week  13.7 pts out | summer contrast off by  1.3 pts | direction right 100.0%\n",
      "  2 year(s): worst single week   9.7 pts out | summer contrast off by  0.9 pts | direction right 100.0%\n",
      "  3 year(s): worst single week   7.9 pts out | summer contrast off by  0.7 pts | direction right 100.0%\n",
      "  5 year(s): worst single week   6.1 pts out | summer contrast off by  0.6 pts | direction right 100.0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "week-level shocks 5% of level:\n",
      "  1 year(s): worst single week  18.6 pts out | summer contrast off by  1.8 pts | direction right 100.0%\n",
      "  2 year(s): worst single week  13.1 pts out | summer contrast off by  1.3 pts | direction right 100.0%\n",
      "  3 year(s): worst single week  10.7 pts out | summer contrast off by  1.0 pts | direction right 100.0%\n",
      "  5 year(s): worst single week   8.3 pts out | summer contrast off by  0.8 pts | direction right 100.0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "week-level shocks 10% of level:\n",
      "  1 year(s): worst single week  29.3 pts out | summer contrast off by  2.8 pts | direction right 100.0%\n",
      "  2 year(s): worst single week  20.4 pts out | summer contrast off by  2.0 pts | direction right 100.0%\n",
      "  3 year(s): worst single week  16.6 pts out | summer contrast off by  1.6 pts | direction right 100.0%\n",
      "  5 year(s): worst single week  12.9 pts out | summer contrast off by  1.2 pts | direction right 100.0%\n",
      "\n",
      "Even at the most pessimistic shock level, ONE year puts the summer contrast within 2.8 points of a 23.5-point effect,\n",
      "and gets its direction right 100% of the time - while the worst individual week is still 29 points out.\n"
     ]
    }
   ],
   "source": [
    "WEEKS_IN_YEAR = 52\n",
    "SUMMER = np.arange(25, 38)                   # a thirteen-week high season\n",
    "WEEK_TRUTH = np.ones(WEEKS_IN_YEAR)\n",
    "WEEK_TRUTH[SUMMER] = 1.25\n",
    "WEEK_TRUTH = WEEK_TRUTH / WEEK_TRUTH.mean()\n",
    "SUMMER_MASK = np.zeros(WEEKS_IN_YEAR, bool)\n",
    "SUMMER_MASK[SUMMER] = True\n",
    "TRUE_SUMMER_CONTRAST = float(WEEK_TRUTH[SUMMER_MASK].mean() - WEEK_TRUTH[~SUMMER_MASK].mean())\n",
    "\n",
    "N_EFF_WEEK = 7 * (1 - RHO) / (1 + RHO)\n",
    "SIGMA_WEEK_FROM_DAYS = NOISE_SIGMA_LOG / np.sqrt(N_EFF_WEEK)\n",
    "WEEK_SHOCKS = [0.00, 0.05, 0.10]\n",
    "\n",
    "print(f\"the planted summer runs {TRUE_SUMMER_CONTRAST*100:.1f} points above the rest of the year\\n\")\n",
    "week_grid = {}\n",
    "for shock in WEEK_SHOCKS:\n",
    "    label = \"none\" if shock == 0 else f\"{shock*100:.0f}% of level\"\n",
    "    rows = []\n",
    "    for years in (1, 2, 3, 5):\n",
    "        g = np.random.default_rng(SEED + 5000 + years + int(shock * 1000))\n",
    "        e = (g.normal(0.0, SIGMA_WEEK_FROM_DAYS, (YEAR_REPS, years, WEEKS_IN_YEAR))\n",
    "             + g.normal(0.0, shock, (YEAR_REPS, years, WEEKS_IN_YEAR)))\n",
    "        vals = WEEK_TRUTH * np.exp(e)\n",
    "        flat = vals.reshape(YEAR_REPS, -1)\n",
    "        idx = vals.mean(axis=1) / flat.mean(axis=1, keepdims=True)\n",
    "        contrast = idx[:, SUMMER_MASK].mean(axis=1) - idx[:, ~SUMMER_MASK].mean(axis=1)\n",
    "        rows.append({\n",
    "            \"years\": years,\n",
    "            \"worst_single_week_pct\": float(np.median(np.abs(idx - WEEK_TRUTH).max(axis=1))) * 100,\n",
    "            \"summer_contrast_error_pct\": float(\n",
    "                np.median(np.abs(contrast - TRUE_SUMMER_CONTRAST))) * 100,\n",
    "            \"summer_call_right_pct\": float((contrast > 0).mean()) * 100,\n",
    "        })\n",
    "    week_grid[shock] = rows\n",
    "    print(f\"week-level shocks {label}:\")\n",
    "    for r in rows:\n",
    "        print(f\"  {r['years']} year(s): worst single week {r['worst_single_week_pct']:5.1f} pts out\"\n",
    "              f\" | summer contrast off by {r['summer_contrast_error_pct']:4.1f} pts\"\n",
    "              f\" | direction right {r['summer_call_right_pct']:5.1f}%\")\n",
    "\n",
    "worst_case = week_grid[WEEK_SHOCKS[-1]][0]\n",
    "print(f\"\\nEven at the most pessimistic shock level, ONE year puts the summer contrast within \"\n",
    "      f\"{worst_case['summer_contrast_error_pct']:.1f} points of a \"\n",
    "      f\"{TRUE_SUMMER_CONTRAST*100:.1f}-point effect,\")\n",
    "print(f\"and gets its direction right {worst_case['summer_call_right_pct']:.0f}% of the time - while \"\n",
    "      f\"the worst individual week is still {worst_case['worst_single_week_pct']:.0f} points out.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2a9314d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:32.607469Z",
     "iopub.status.busy": "2026-10-24T13:13:32.606352Z",
     "iopub.status.idle": "2026-10-24T13:13:33.439235Z",
     "shell.execute_reply": "2026-10-24T13:13:33.437890Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1610x630 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.5))\n",
    "for shock in MONTH_SHOCKS:\n",
    "    lab = \"no shocks of their own\" if shock == 0 else f\"{shock*100:.0f}% of level\"\n",
    "    axes[0].plot(YEAR_RANGE, month_grid[shock], marker=\"o\", lw=2.2, label=lab)\n",
    "axes[0].axhline(WEEKDAY_BENCHMARK, color=SIGNAL, lw=2.0, ls=\"--\")\n",
    "axes[0].text(YEAR_RANGE[0], WEEKDAY_BENCHMARK * 1.08, \"what six weeks bought for weekdays\",\n",
    "             fontsize=9.5, color=SIGNAL)\n",
    "axes[0].set_xlabel(\"years of monthly figures\")\n",
    "axes[0].set_ylabel(\"worst month, percentage points out\\n(lower is better)\")\n",
    "axes[0].set_title(\"A month-by-month curve: years, not weeks\")\n",
    "axes[0].legend(frameon=False, fontsize=9)\n",
    "\n",
    "yrs = [r[\"years\"] for r in week_grid[0.10]]\n",
    "axes[1].plot(yrs, [r[\"worst_single_week_pct\"] for r in week_grid[0.10]],\n",
    "             marker=\"s\", color=FLAG, lw=2.4, ls=\"--\", label=\"one week against another\")\n",
    "axes[1].plot(yrs, [r[\"summer_contrast_error_pct\"] for r in week_grid[0.10]],\n",
    "             marker=\"o\", color=SIGNAL, lw=2.4, label=\"summer against the rest\")\n",
    "axes[1].set_xlabel(\"years of weekly figures\")\n",
    "axes[1].set_ylabel(\"how far off, percentage points\")\n",
    "axes[1].set_title(\"The coarse question is answerable; the fine one is not\")\n",
    "axes[1].legend(frameon=False, fontsize=9.5)\n",
    "fig.suptitle(\"The same method on a yearly cycle - pessimistic shock level\", y=1.02)\n",
    "fig.tight_layout()\n",
    "fig.savefig(FIGURES / \"fig_yearly.png\", dpi=150, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1403523f",
   "metadata": {},
   "source": [
    "### A cost per conversion with days that never convert\n",
    "\n",
    "On a low-volume account the cost per conversion does not exist on a day with no conversion,\n",
    "and the obvious repair - drop those days - keeps only the days that happened to convert. A day\n",
    "that converts at all is a day whose conversions came in above its own average, so what is left\n",
    "reads cheap, and it reads cheapest where volume is lowest. Here is a flat account (the true\n",
    "cost per conversion is the same every day) with less traffic at the weekend, read both ways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1fa632b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:33.443862Z",
     "iopub.status.busy": "2026-10-24T13:13:33.443506Z",
     "iopub.status.idle": "2026-10-24T13:13:33.871748Z",
     "shell.execute_reply": "2026-10-24T13:13:33.870133Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a flat account, 2 conversions a weekday and 0.8 a weekend day, 2000 accounts of 13 weeks:\n",
      "  weekend index, zero days dropped : 0.62 (median)\n",
      "  weekend index, read off totals   : 1.05 (median; the truth is 1)\n"
     ]
    }
   ],
   "source": [
    "ZD_ACCOUNTS, ZD_WEEKS = 2000, 13\n",
    "ZD_SPEND = np.array([200.0] * 5 + [80.0] * 2)   # weekday and weekend spend, euros\n",
    "ZD_TRUE_CPA = 100.0                             # the same on every day: the honest index is 1\n",
    "g_zd = np.random.default_rng(SEED + 909)\n",
    "zd_drop, zd_totals = [], []\n",
    "for _ in range(ZD_ACCOUNTS):\n",
    "    wd = np.tile(np.arange(7), ZD_WEEKS)\n",
    "    cost = ZD_SPEND[wd]\n",
    "    conv = g_zd.poisson(cost / ZD_TRUE_CPA)\n",
    "    kept = conv > 0\n",
    "    idx_drop = weekday_index(wd[kept], cost[kept] / conv[kept])\n",
    "    overall = cost.sum() / conv.sum()\n",
    "    idx_tot = np.array([(cost[wd == d].sum() / conv[wd == d].sum()) / overall for d in range(7)])\n",
    "    zd_drop.append(idx_drop[5:].mean())\n",
    "    zd_totals.append(idx_tot[5:].mean())\n",
    "ZERO_DAYS = {\n",
    "    \"accounts\": ZD_ACCOUNTS, \"weeks\": ZD_WEEKS,\n",
    "    \"weekday_conversions_per_day\": float(ZD_SPEND[0] / ZD_TRUE_CPA),\n",
    "    \"weekend_conversions_per_day\": float(ZD_SPEND[5] / ZD_TRUE_CPA),\n",
    "    \"weekend_index_dropping_zero_days_median\": round(float(np.median(zd_drop)), 2),\n",
    "    \"weekend_index_from_totals_median\": round(float(np.median(zd_totals)), 2),\n",
    "}\n",
    "print(f\"a flat account, {ZD_SPEND[0] / ZD_TRUE_CPA:.0f} conversions a weekday and \"\n",
    "      f\"{ZD_SPEND[5] / ZD_TRUE_CPA:.1f} a weekend day, {ZD_ACCOUNTS} accounts of {ZD_WEEKS} weeks:\")\n",
    "print(f\"  weekend index, zero days dropped : {ZERO_DAYS['weekend_index_dropping_zero_days_median']:.2f} (median)\")\n",
    "print(f\"  weekend index, read off totals   : {ZERO_DAYS['weekend_index_from_totals_median']:.2f} (median; the truth is 1)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9797d63",
   "metadata": {},
   "source": [
    "## What it is worth, in money\n",
    "\n",
    "The point of the pattern is not the pattern. It is that once you know it, you can tell a\n",
    "day that is genuinely unusual from a day that is behaving exactly as that weekday always\n",
    "behaves - and you stop spending attention on the second kind."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1d2cc169",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-10-24T13:13:33.885832Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this account averages 25.31 euros per sale\n",
      "a normal Tuesday      24.14\n",
      "a normal Sunday       29.55   <- 5.41 euros apart, nothing wrong\n",
      "\n",
      "an ordinary Sunday at 30.14 euros reads as +19% against the account average,\n",
      "   and +2% against what Sundays actually cost. There is nothing to investigate.\n",
      "\n",
      "a genuinely bad Tuesday at 29.46 euros reads as +16% against the account average -\n",
      "   easy to miss - and +22% against what Tuesdays cost. That one is real.\n"
     ]
    }
   ],
   "source": [
    "# The SAME advertiser the reader met at the top. A second draw here would give \"a normal\n",
    "# Sunday\" two different values in one article, which is the quiet contradiction this avoids.\n",
    "idx_money = recovered_n\n",
    "level = float(cpa_n.mean())\n",
    "\n",
    "normal_tuesday = level * idx_money[1]\n",
    "normal_sunday = level * idx_money[6]\n",
    "\n",
    "# A Sunday that panics a dashboard and should not, and a Tuesday that should and does not.\n",
    "quiet_sunday = normal_sunday * 1.02\n",
    "bad_tuesday = normal_tuesday * 1.22\n",
    "\n",
    "money = {\n",
    "    \"account_average\": level,\n",
    "    \"normal_tuesday\": normal_tuesday,\n",
    "    \"normal_sunday\": normal_sunday,\n",
    "    \"normal_gap\": normal_sunday - normal_tuesday,\n",
    "    \"quiet_sunday\": quiet_sunday,\n",
    "    \"quiet_sunday_against_average_pct\": (quiet_sunday - level) / level * 100,\n",
    "    \"quiet_sunday_against_its_own_day_pct\": (quiet_sunday - normal_sunday) / normal_sunday * 100,\n",
    "    \"bad_tuesday\": bad_tuesday,\n",
    "    \"bad_tuesday_against_average_pct\": (bad_tuesday - level) / level * 100,\n",
    "    \"bad_tuesday_against_its_own_day_pct\": (bad_tuesday - normal_tuesday) / normal_tuesday * 100,\n",
    "}\n",
    "\n",
    "print(f\"this account averages {level:.2f} euros per sale\")\n",
    "print(f\"a normal Tuesday      {normal_tuesday:.2f}\")\n",
    "print(f\"a normal Sunday       {normal_sunday:.2f}   <- {money['normal_gap']:.2f} euros apart, nothing wrong\")\n",
    "print()\n",
    "print(f\"an ordinary Sunday at {quiet_sunday:.2f} euros reads as\"\n",
    "      f\" {money['quiet_sunday_against_average_pct']:+.0f}% against the account average,\")\n",
    "print(f\"   and {money['quiet_sunday_against_its_own_day_pct']:+.0f}% against what Sundays actually cost.\"\n",
    "      f\" There is nothing to investigate.\")\n",
    "print()\n",
    "print(f\"a genuinely bad Tuesday at {bad_tuesday:.2f} euros reads as\"\n",
    "      f\" {money['bad_tuesday_against_average_pct']:+.0f}% against the account average -\")\n",
    "print(f\"   easy to miss - and {money['bad_tuesday_against_its_own_day_pct']:+.0f}% against\"\n",
    "      f\" what Tuesdays cost. That one is real.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177eaa14",
   "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",
    "arithmetic 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",
    "| `value` | the number you want the weekday pattern of | `24.80` |\n",
    "\n",
    "Any other columns are ignored. A Google Ads or GA4 download works as it comes: title lines,\n",
    "\"#\" lines and Total rows are set aside, and formatted numbers and the platforms' date forms are\n",
    "read for you. If you have several campaigns in one file, filter it to one campaign first - a\n",
    "weekday pattern averaged across campaigns that do not share a rhythm is a pattern nobody has.\n",
    "\n",
    "**Cost per conversion at low volume: give the two columns, not the ratio.** On a day with no\n",
    "conversion the cost per conversion does not exist, and dropping those days leaves only the\n",
    "days that happened to convert - which makes quiet weekdays look cheap (measured above on a\n",
    "flat account: the weekend read well under 1 with the zero days dropped). Set `COST_COLUMN` and `CONVERSIONS_COLUMN` in the marked cell instead: the notebook then\n",
    "reads each weekday's cost per conversion off its totals, zero days included.\n",
    "\n",
    "**Which metric.** Use a rate or a cost, not a count. Cost per sale, cost per click,\n",
    "conversion rate and return on spend all work, because their weekly swing scales with the\n",
    "level and a ratio is the natural way to read them. Raw conversion counts at low volume do\n",
    "not: a Tuesday with two sales and a Wednesday with one are not thirty-three per cent apart\n",
    "in any meaningful sense, and this method will tell you they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac3dc81c",
   "metadata": {
    "execution": {
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   "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 -------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "class DataProblem(Exception):\n",
    "    \"\"\"Raised with a plain-words explanation the reader can act on.\"\"\"\n",
    "\n",
    "\n",
    "MIN_PER_WEEKDAY_USABLE = 4     # below this the cell is mostly noise\n",
    "MIN_PER_WEEKDAY_STEADY = 6     # where the rule of thumb puts a settled read\n",
    "\n",
    "\n",
    "def read_daily_csv(path, date_column=\"date\", value_column=\"value\", cost_column=None,\n",
    "                   conversions_column=None, dayfirst=None):\n",
    "    \"\"\"Read the reader's export and hand back (weekday, value, day_index, totals), or refuse.\n",
    "\n",
    "    With a cost and a conversions column, `totals` carries both per day so the weekday pattern can\n",
    "    be read off ratios of totals; `value` is then the day's cost per conversion where it exists.\"\"\"\n",
    "    wanted = {\"date\": [date_column, \"date\", \"day\"]}\n",
    "    if cost_column and conversions_column:\n",
    "        wanted.update({\"cost\": [cost_column, \"cost\", \"amount spent\"],\n",
    "                       \"conversions\": [conversions_column, \"conversions\", \"conv.\", \"leads\", \"results\"]})\n",
    "    else:\n",
    "        wanted[\"value\"] = [value_column, \"value\"]\n",
    "    try:\n",
    "        frame, notes = read_export(path, columns=wanted, 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",
    "    if frame[\"date\"].duplicated().any():\n",
    "        n = int(frame[\"date\"].duplicated().sum())\n",
    "        raise DataProblem(\n",
    "            f\"{n} date(s) appear more than once. This expects one line per day - if the export \"\n",
    "            \"has several campaigns in it, filter to one campaign first.\"\n",
    "        )\n",
    "    frame = frame.sort_values(\"date\").reset_index(drop=True)\n",
    "    day_index = (frame[\"date\"] - frame[\"date\"].min()).dt.days.to_numpy()\n",
    "    if \"cost\" in frame:\n",
    "        frame = frame.dropna(subset=[\"cost\", \"conversions\"])\n",
    "        totals = frame[[\"cost\", \"conversions\"]].to_numpy(dtype=float)\n",
    "        if totals[:, 1].sum() <= 0:\n",
    "            raise DataProblem(\"There are no conversions in the file at all.\")\n",
    "        value = np.where(totals[:, 1] > 0, totals[:, 0] / np.where(totals[:, 1] > 0, totals[:, 1], 1), np.nan)\n",
    "        return frame[\"date\"].dt.weekday.to_numpy(), value, day_index, totals\n",
    "    frame = frame.dropna(subset=[\"value\"])\n",
    "    values = frame[\"value\"].to_numpy(dtype=float)\n",
    "    if (values <= 0).any():\n",
    "        raise DataProblem(\n",
    "            \"Some values are zero or negative. This method divides by an average, so it only \"\n",
    "            \"makes sense for a metric that is always positive - a cost, a rate or a return. If \"\n",
    "            \"this is a cost per conversion with days that had no conversion, do NOT drop those \"\n",
    "            \"days: that leaves only the days that happened to convert and biases the pattern. \"\n",
    "            \"Set COST_COLUMN and CONVERSIONS_COLUMN instead, and the notebook reads each \"\n",
    "            \"weekday off its totals.\"\n",
    "        )\n",
    "    return frame[\"date\"].dt.weekday.to_numpy(), values, day_index, None\n",
    "\n",
    "\n",
    "def weekday_index_from_totals(weekday, totals):\n",
    "    \"\"\"Each weekday's cost per conversion off its own totals, against the whole window's.\"\"\"\n",
    "    weekday = np.asarray(weekday)\n",
    "    overall = totals[:, 0].sum() / totals[:, 1].sum()\n",
    "    out = np.full(7, np.nan)\n",
    "    for d in range(7):\n",
    "        cell = totals[weekday == d]\n",
    "        if cell.size and cell[:, 1].sum() > 0:\n",
    "            out[d] = (cell[:, 0].sum() / cell[:, 1].sum()) / overall\n",
    "    return out\n",
    "\n",
    "\n",
    "def drift_warning(day_index, value, totals=None):\n",
    "    \"\"\"A level that moved across the window leaks into the weekday pattern; say so when it did.\"\"\"\n",
    "    half = day_index.max() / 2.0\n",
    "    first, second = day_index <= half, day_index > half\n",
    "    if totals is not None:\n",
    "        a = totals[first, 0].sum() / max(totals[first, 1].sum(), 1e-9)\n",
    "        b = totals[second, 0].sum() / max(totals[second, 1].sum(), 1e-9)\n",
    "    else:\n",
    "        a, b = float(np.mean(value[first])), float(np.mean(value[second]))\n",
    "    change = b / a - 1.0\n",
    "    if abs(change) >= 0.15:\n",
    "        return (f\"WARNING: the second half of the window runs {change:+.0%} against the first. A level \"\n",
    "                \"that moves across the window leaks into the weekday pattern (the trend caveat in the \"\n",
    "                \"article): shorten the window to a stretch where the level held, or read the pattern \"\n",
    "                \"as direction only.\")\n",
    "    return None\n",
    "\n",
    "\n",
    "def describe_sufficiency(weekday):\n",
    "    \"\"\"What the data can and cannot support, in words the reader can act on.\"\"\"\n",
    "    counts = weekday_counts(weekday)\n",
    "    missing = [DAY_NAMES[d] for d in range(7) if counts[d] == 0]\n",
    "    thin = [DAY_NAMES[d] for d in range(7) if 0 < counts[d] < MIN_PER_WEEKDAY_USABLE]\n",
    "    lines = []\n",
    "    if missing:\n",
    "        lines.append(\"REFUSED: no data at all for \" + \", \".join(missing)\n",
    "                     + \". There is no pattern to report for a day that never appears.\")\n",
    "    if thin:\n",
    "        lines.append(\"TREAT AS A DIRECTION ONLY: fewer than \"\n",
    "                     f\"{MIN_PER_WEEKDAY_USABLE} observations for \" + \", \".join(thin)\n",
    "                     + \". One unusual day moves these badly.\")\n",
    "    if counts.min() >= MIN_PER_WEEKDAY_STEADY:\n",
    "        lines.append(f\"Every weekday has at least {MIN_PER_WEEKDAY_STEADY} observations. \"\n",
    "                     \"Enough to read the big splits; the small gaps between neighbouring \"\n",
    "                     \"weekdays still need considerably more.\")\n",
    "    elif not missing and not thin:\n",
    "        lines.append(f\"Every weekday has at least {MIN_PER_WEEKDAY_USABLE} observations, \"\n",
    "                     f\"fewer than {MIN_PER_WEEKDAY_STEADY}. The weekend-against-week reading \"\n",
    "                     \"is usable; do not move budget between two individual weekdays on this.\")\n",
    "    return counts, lines\n",
    "\n",
    "\n",
    "def report_weekday_pattern(weekday, value, day_index=None, totals=None):\n",
    "    \"\"\"The whole reader-facing routine: refuse, warn, or report.\"\"\"\n",
    "    counts, notes = describe_sufficiency(weekday)\n",
    "    for note in notes:\n",
    "        print(\"  \" + note)\n",
    "    if counts.min() == 0:\n",
    "        return None\n",
    "    if day_index is not None:\n",
    "        warning = drift_warning(day_index, value, totals)\n",
    "        if warning:\n",
    "            print(\"  \" + warning)\n",
    "    if totals is not None:\n",
    "        idx = weekday_index_from_totals(weekday, totals)\n",
    "        level = float(totals[:, 0].sum() / totals[:, 1].sum())\n",
    "        print(f\"\\n  this account's cost per conversion over the window is {level:.2f}\")\n",
    "    else:\n",
    "        idx = weekday_index(weekday, value)\n",
    "        level = float(np.mean(value))\n",
    "        print(f\"\\n  this account averages {level:.2f} per day\")\n",
    "    for d in range(7):\n",
    "        print(f\"    {DAY_NAMES[d]:<10} {idx[d]:.3f}  ->  a normal {DAY_NAMES[d]} is \"\n",
    "              f\"{level * idx[d]:.2f}   ({counts[d]} observations)\")\n",
    "    return idx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f34032",
   "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": 18,
   "id": "f3a7f8cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:34.125300Z",
     "iopub.status.busy": "2026-10-24T13:13:34.124885Z",
     "iopub.status.idle": "2026-10-24T13:13:34.132554Z",
     "shell.execute_reply": "2026-10-24T13:13:34.131197Z"
    }
   },
   "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 = \"value\"      # a rate or cost column, e.g. \"Cost / conv.\" or \"Conv. rate\"\n",
    "COST_COLUMN = None          # for cost per conversion at low volume: e.g. \"Cost\" ...\n",
    "CONVERSIONS_COLUMN = None   # ... and \"Conversions\"; then VALUE_COLUMN is not used\n",
    "DAYFIRST = None             # True for 06/07/2026 meaning 6 July, False for June 7\n",
    "\n",
    "if MY_FILE:\n",
    "    print(f\"YOUR FILE: {MY_FILE}\")\n",
    "    my_wd, my_values, my_days, my_totals = read_daily_csv(\n",
    "        MY_FILE, DATE_COLUMN, VALUE_COLUMN, COST_COLUMN, CONVERSIONS_COLUMN, dayfirst=DAYFIRST)\n",
    "    my_pattern = report_weekday_pattern(my_wd, my_values, my_days, my_totals)\n",
    "else:\n",
    "    print(\"MY_FILE is empty - the demonstrations below run instead.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2df7c28",
   "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, and then on three broken ones it should refuse - the wrong columns, a metric\n",
    "with zeroes in it, and a window too thin to carry a pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1b6e24ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:34.136648Z",
     "iopub.status.busy": "2026-10-24T13:13:34.136281Z",
     "iopub.status.idle": "2026-10-24T13:13:34.203569Z",
     "shell.execute_reply": "2026-10-24T13:13:34.202501Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A CLEAN EIGHT-WEEK EXPORT\n",
      "  Every weekday has at least 6 observations. Enough to read the big splits; the small gaps between neighbouring weekdays still need considerably more.\n",
      "\n",
      "  this account averages 24.61 per day\n",
      "    Monday     0.913  ->  a normal Monday is 22.48   (8 observations)\n",
      "    Tuesday    0.891  ->  a normal Tuesday is 21.93   (8 observations)\n",
      "    Wednesday  0.904  ->  a normal Wednesday is 22.24   (8 observations)\n",
      "    Thursday   0.974  ->  a normal Thursday is 23.98   (8 observations)\n",
      "    Friday     1.042  ->  a normal Friday is 25.65   (8 observations)\n",
      "    Saturday   1.144  ->  a normal Saturday is 28.16   (8 observations)\n",
      "    Sunday     1.131  ->  a normal Sunday is 27.85   (8 observations)\n",
      "\n",
      "WRONG COLUMN NAMES\n",
      "  refused: Could not find the column(s) value. The file's columns are 'Day', 'CPA'. Rename the column in the file, or add its name to the list this cell accepts.\n",
      "\n",
      "A METRIC WITH ZEROES IN IT\n",
      "  refused: Some values are zero or negative. This method divides by an average, so it only makes sense for a metric that is always positive - a cost, a rate or a return. If this is a cost per conversion with days that had no conversion, do NOT drop those days: that leaves only the days that happened to convert and biases the pattern. Set COST_COLUMN and CONVERSIONS_COLUMN instead, and the notebook reads each weekday off its totals.\n",
      "\n",
      "TOO THIN, AND ONE WEEKDAY MISSING ENTIRELY\n",
      "  REFUSED: no data at all for Saturday, Sunday. There is no pattern to report for a day that never appears.\n",
      "  TREAT AS A DIRECTION ONLY: fewer than 4 observations for Monday, Tuesday, Wednesday, Thursday, Friday. One unusual day moves these badly.\n",
      "  returned None - no numbers invented for days that never appeared\n"
     ]
    }
   ],
   "source": [
    "SCRATCH = HERE / \"_your_data_demo\"\n",
    "SCRATCH.mkdir(exist_ok=True)\n",
    "\n",
    "g_demo2 = np.random.default_rng(SEED + 31337)\n",
    "d_clean, v_clean = make_series(56, generator=g_demo2)\n",
    "clean_path = SCRATCH / \"clean_export.csv\"\n",
    "pd.DataFrame({\n",
    "    \"date\": pd.date_range(\"2026-01-05\", periods=56, freq=\"D\"),\n",
    "    \"value\": np.round(v_clean, 2),\n",
    "    \"campaign\": \"Brand Search\",\n",
    "}).to_csv(clean_path, index=False)\n",
    "\n",
    "print(\"A CLEAN EIGHT-WEEK EXPORT\")\n",
    "wd, vals, _days, _tot = read_daily_csv(clean_path)\n",
    "recovered_reader = report_weekday_pattern(wd, vals)\n",
    "\n",
    "print(\"\\nWRONG COLUMN NAMES\")\n",
    "bad_cols = SCRATCH / \"wrong_columns.csv\"\n",
    "pd.DataFrame({\"Day\": [\"2026-01-05\"], \"CPA\": [24.1]}).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 ZEROES IN IT\")\n",
    "zeroes = SCRATCH / \"has_zeroes.csv\"\n",
    "pd.DataFrame({\n",
    "    \"date\": pd.date_range(\"2026-01-05\", periods=14, freq=\"D\"),\n",
    "    \"value\": [20.0] * 12 + [0.0, 0.0],\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 THIN, AND ONE WEEKDAY MISSING ENTIRELY\")\n",
    "thin_path = SCRATCH / \"thin.csv\"\n",
    "thin_dates = pd.to_datetime([\"2026-01-05\", \"2026-01-06\", \"2026-01-07\", \"2026-01-08\",\n",
    "                             \"2026-01-09\", \"2026-01-12\", \"2026-01-13\", \"2026-01-14\"])\n",
    "pd.DataFrame({\"date\": thin_dates, \"value\": [21.0, 20.4, 20.9, 21.6, 22.8, 21.1, 20.2, 20.8]}\n",
    "             ).to_csv(thin_path, index=False)\n",
    "wd_thin, v_thin, _days_thin, _ = read_daily_csv(thin_path)\n",
    "thin_result = report_weekday_pattern(wd_thin, v_thin)\n",
    "print(f\"  returned {thin_result} - no numbers invented for days that never appeared\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3061b2e",
   "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",
    "pattern from the same numbers this notebook used rather than from a regenerated copy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ecb5b0d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-24T13:13:34.206819Z",
     "iopub.status.busy": "2026-10-24T13:13:34.206419Z",
     "iopub.status.idle": "2026-10-24T13:13:34.245470Z",
     "shell.execute_reply": "2026-10-24T13:13:34.244066Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote checks.json      4 pinned cases\n",
      "wrote worked_example.json  11 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, days_arr, values_arr):\n",
    "    idx = weekday_index(days_arr, values_arr)\n",
    "    counts = weekday_counts(days_arr)\n",
    "    return {\n",
    "        \"name\": name,\n",
    "        \"note\": note,\n",
    "        \"weekday\": [int(d) for d in days_arr],\n",
    "        \"value\": [round(float(v), 6) for v in values_arr],\n",
    "        \"expected\": {\n",
    "            \"index\": [None if np.isnan(v) else round(float(v), 6) for v in idx],\n",
    "            \"counts\": [int(c) for c in counts],\n",
    "            \"overall_mean\": round(float(np.mean(values_arr)), 6),\n",
    "        },\n",
    "    }\n",
    "\n",
    "\n",
    "g_pin = np.random.default_rng(SEED + 8080)\n",
    "d_six, v_six = make_series(42, generator=g_pin)\n",
    "d_two, v_two = make_series(14, generator=g_pin)\n",
    "d_drift, v_drift = make_series(28, dow=FLAT, trend_per_day=0.02, generator=g_pin)\n",
    "d_gap = np.array([d for d in (np.arange(21) % 7) if d != 3])\n",
    "v_gap = make_series(21, generator=g_pin)[1][(np.arange(21) % 7) != 3]\n",
    "\n",
    "checks = {\n",
    "    \"method\": \"weekday seasonal index - mean for that weekday divided by the window mean\",\n",
    "    \"tolerance\": TOLERANCE,\n",
    "    \"cases\": [\n",
    "        pinned_case(\"six quiet weeks\", \"the settled read the article quotes\", d_six, v_six),\n",
    "        pinned_case(\"two weeks only\", \"direction visible, individual days not usable\", d_two, v_two),\n",
    "        pinned_case(\"a drifting account\", \"no weekday rhythm at all - every number here is the drift\",\n",
    "                    d_drift, v_drift),\n",
    "        pinned_case(\"a weekday that never appears\", \"Thursday absent - the index must refuse, not invent\",\n",
    "                    d_gap, v_gap),\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",
    "        \"base_cost_per_sale\": round(BASE_CPA, 2),\n",
    "        \"noise_sigma_log\": NOISE_SIGMA_LOG,\n",
    "        \"day_to_day_correlation\": RHO,\n",
    "        \"truth_index\": [round(float(v), 4) for v in DOW_TRUTH],\n",
    "        \"truth_cost_per_day\": [round(float(BASE_CPA * v), 2) for v in DOW_TRUTH],\n",
    "        \"day_names\": DAY_NAMES,\n",
    "    },\n",
    "    \"narrative\": {\n",
    "        \"weeks\": NARRATIVE_WEEKS,\n",
    "        \"recovered_index\": [round(float(v), 4) for v in recovered_n],\n",
    "        \"worst_weekday_error\": round(float(np.nanmax(np.abs(recovered_n - DOW_TRUTH))), 4),\n",
    "        # The advertiser's own daily figures, published because the downloadable workbook\n",
    "        # recomputes the article's numbers from live formulas over exactly these rows. Without\n",
    "        # them the workbook would have to re-generate a series and would quietly disagree with\n",
    "        # the prose it sits beside.\n",
    "        \"weekday\": [int(d) for d in days_n],\n",
    "        \"value\": [round(float(v), 4) for v in cpa_n],\n",
    "    },\n",
    "    \"naive_reading\": {\n",
    "        \"the_sunday\": round(last_sunday, 2),\n",
    "        \"recent_tuesday\": round(last_tuesday, 2),\n",
    "        \"account_mean\": round(account_mean, 2),\n",
    "        \"normal_sunday\": round(sunday_mean, 2),\n",
    "        \"against_that_tuesday_pct\": round(naive_vs_tuesday, 1),\n",
    "        \"against_the_average_pct\": round(naive_vs_average, 1),\n",
    "        \"against_its_own_sundays_pct\": round(against_own_sundays, 1),\n",
    "    },\n",
    "    \"how_much_history\": {\n",
    "        \"true_weekend_gap_pct\": round(TRUE_WEEKEND_GAP * 100, 1),\n",
    "        \"true_monday_tuesday_gap_pct\": round(TRUE_MON_TUE_GAP * 100, 1),\n",
    "        \"weeks_to_call_weekend\": weekend_ok,\n",
    "        \"weeks_to_call_monday_tuesday\": mon_tue_ok,\n",
    "        \"longest_window_weeks\": WEEK_RANGE[-1],\n",
    "        \"monday_tuesday_right_at_longest_pct\": round(\n",
    "            stability[WEEK_RANGE[-1]][\"mon_tue_call_right_pct\"], 1),\n",
    "        \"worst_weekday_error_pct\": {\n",
    "            str(k): round(stability[k][\"typical_worst_error\"] * 100, 1) for k in WEEK_RANGE\n",
    "        },\n",
    "        \"weekend_call_right_pct\": {\n",
    "            str(k): round(stability[k][\"weekend_call_right_pct\"], 1) for k in WEEK_RANGE\n",
    "        },\n",
    "        \"monday_tuesday_call_right_pct\": {\n",
    "            str(k): round(stability[k][\"mon_tue_call_right_pct\"], 1) for k in WEEK_RANGE\n",
    "        },\n",
    "    },\n",
    "    \"holdout\": {\n",
    "        # The two distributions themselves, binned, because the video draws the overlap and a\n",
    "        # drawn shape with no stored source is an invented picture. Summary statistics cannot\n",
    "        # carry \"these two overlap\", which is the whole finding.\n",
    "        \"hist_edges\": [round(float(e), 4) for e in np.histogram_bin_edges(\n",
    "            np.concatenate([stable_gaps, shifted_gaps]), bins=34)],\n",
    "        \"hist_stable\": [int(c) for c in np.histogram(stable_gaps, bins=np.histogram_bin_edges(\n",
    "            np.concatenate([stable_gaps, shifted_gaps]), bins=34))[0]],\n",
    "        \"hist_shifted\": [int(c) for c in np.histogram(shifted_gaps, bins=np.histogram_bin_edges(\n",
    "            np.concatenate([stable_gaps, shifted_gaps]), bins=34))[0]],\n",
    "        \"weeks_each_side\": holdout[\"weeks_each_side\"],\n",
    "        \"typical_stable_gap\": round(holdout[\"typical_stable_gap\"], 4),\n",
    "        \"alarm_line\": round(holdout[\"alarm_line\"], 4),\n",
    "        \"false_alarm_pct\": round(holdout[\"false_alarm_pct\"], 1),\n",
    "        \"caught_pct\": round(holdout[\"caught_pct\"], 1),\n",
    "        \"missed_pct\": round(100 - holdout[\"caught_pct\"], 1),\n",
    "        \"shift_size_pct\": round(holdout[\"shift_size_pct\"], 1),\n",
    "    },\n",
    "    \"trend_leak\": {\n",
    "        \"real_sunday_over_monday_pct\": round(real_weekend_premium, 1),\n",
    "        \"rows\": [\n",
    "            {\n",
    "                \"drift_per_day_pct\": round(r[\"trend_per_day_pct\"], 1),\n",
    "                \"drift_per_month_pct\": round(r[\"trend_per_month_pct\"], 1),\n",
    "                \"invented_sunday_premium_pct\": round(r[\"invented_sunday_premium_pct\"], 1),\n",
    "            }\n",
    "            for r in leakage\n",
    "        ],\n",
    "        \"drift_that_manufactures_half_pct\": None if half_at is None else round(half_at, 0),\n",
    "    },\n",
    "    \"money\": {k: round(v, 2) for k, v in money.items()},\n",
    "    # The scope answer: the same method on a twelve-month cycle. Stored because the article, the\n",
    "    # page and the video all have to say the same thing about what this method does NOT do.\n",
    "    # The scope answer, and it is a dependency rather than a number. Stored in full because the\n",
    "    # article, the page and the video all have to say the same thing about what this does NOT do,\n",
    "    # and because the honest claim here is \"it depends on a parameter nobody has measured yet\".\n",
    "    \"yearly\": {\n",
    "        \"month_names\": MONTH_NAMES,\n",
    "        \"truth_index\": [round(float(v), 4) for v in MONTH_TRUTH],\n",
    "        \"weekday_benchmark_pct\": round(float(WEEKDAY_BENCHMARK), 1),\n",
    "        \"monthly_wobble_from_days_pct\": round(float(SIGMA_MONTH_FROM_DAYS) * 100, 1),\n",
    "        \"daily_wobble_pct\": round(NOISE_SIGMA_LOG * 100, 1),\n",
    "        \"year_range\": YEAR_RANGE,\n",
    "        \"month_shocks_pct\": [round(x * 100, 1) for x in MONTH_SHOCKS],\n",
    "        \"worst_month_error_pct\": {str(round(k * 100, 1)): [round(v, 1) for v in row]\n",
    "                                  for k, row in month_grid.items()},\n",
    "        \"years_to_match_weekday_benchmark\": {str(round(k * 100, 1)): v\n",
    "                                             for k, v in years_needed.items()},\n",
    "        \"summer\": {\n",
    "            \"weeks_in_season\": int(SUMMER_MASK.sum()),\n",
    "            \"true_contrast_pct\": round(TRUE_SUMMER_CONTRAST * 100, 1),\n",
    "            \"week_shocks_pct\": [round(x * 100, 1) for x in WEEK_SHOCKS],\n",
    "            \"rows\": {str(round(k * 100, 1)): [\n",
    "                {\"years\": r[\"years\"],\n",
    "                 \"worst_single_week_pct\": round(r[\"worst_single_week_pct\"], 1),\n",
    "                 \"summer_contrast_error_pct\": round(r[\"summer_contrast_error_pct\"], 1),\n",
    "                 \"summer_call_right_pct\": round(r[\"summer_call_right_pct\"], 1)}\n",
    "                for r in rows] for k, rows in week_grid.items()},\n",
    "        },\n",
    "    },\n",
    "}\n",
    "worked[\"zero_days\"] = ZERO_DAYS\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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