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    "# Rural road maintenance: economic cost-benefit analysis\n",
    "\n",
    "**Worked example by Ahmed Beddi, on a synthetic network with illustrative and cited parameters.** It is not an appraisal of any real road, agency or programme.\n",
    "\n",
    "Question: is it economically worthwhile to move a 400 km unpaved rural network from *deferred maintenance* (spot repairs, then reconstruction when the road fails) to a *routine + periodic* regime (routine maintenance every year, regravelling every five years, after a catch-up regravelling in year 1)?\n",
    "\n",
    "Method, in the spirit of the World Bank Roads Economic Decision model (RED): consumer-surplus benefits to normal traffic from lower vehicle operating costs (VOC) and travel time, rule-of-half for generated traffic, avoided agency costs of the without-project case, all in economic prices; incremental (with minus without) cash flows discounted to year 0. Each parameter is labelled *cited*, *derived* or *illustrative* in `parameters.csv`, with the source link.\n",
    "\n",
    "The model code below is identical to `rural_road_cba.py` and to the interactive demo on the website."
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   "source": [
    "\"\"\"\n",
    "rural_road_cba.py\n",
    "-----------------\n",
    "Worked example by Ahmed Beddi: economic cost-benefit analysis of moving an unpaved rural\n",
    "road network from deferred maintenance to a routine + periodic maintenance regime.\n",
    "\n",
    "Synthetic, illustrative network. Method in the spirit of the World Bank Roads Economic\n",
    "Decision model (RED): consumer-surplus benefits to normal traffic, rule-of-half for\n",
    "generated traffic, roughness-driven vehicle operating costs (VOC) and speeds, and agency\n",
    "costs in economic prices. Parameters marked \"illustrative\" in parameters.csv are not\n",
    "taken from a published source. This is not an appraisal of any real road.\n",
    "\n",
    "Run:  python rural_road_cba.py        (prints results, writes parameters.csv,\n",
    "                                       cashflows_base.csv, monte_carlo_summary.csv)\n",
    "\n",
    "The interactive demo on the website (CbaModel.tsx) implements exactly these formulas\n",
    "with the same defaults.\n",
    "\"\"\"\n",
    "from __future__ import annotations\n",
    "\n",
    "import math\n",
    "from dataclasses import dataclass, replace, asdict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "@dataclass(frozen=True)\n",
    "class Params:\n",
    "    # Network and traffic\n",
    "    length_km: float = 400.0          # network length (km)\n",
    "    aadt0: float = 120.0              # average daily traffic in year 1 (vehicles/day)\n",
    "    growth: float = 0.04              # normal traffic growth per year\n",
    "    elasticity: float = 1.0           # generated traffic: elasticity to transport cost\n",
    "    # Road-user costs (economic prices, US$)\n",
    "    voc_a0: float = 0.064             # VOC intercept (US$/veh-km)\n",
    "    voc_beta: float = 0.013           # VOC slope (US$/veh-km per IRI m/km)\n",
    "    speed_a: float = 75.0             # speed intercept (km/h)\n",
    "    speed_b: float = 2.5              # speed loss per IRI point (km/h)\n",
    "    speed_min: float = 20.0           # speed floor (km/h)\n",
    "    occupancy: float = 3.0            # persons per vehicle\n",
    "    vot: float = 1.0                  # value of time (US$/person-hour)\n",
    "    # Roughness (IRI, m/km)\n",
    "    iri0: float = 9.0                 # condition in year 1 (both cases)\n",
    "    iri_good: float = 6.0             # condition after regravelling / reconstruction\n",
    "    rise_with: float = 0.4            # IRI rise per year under routine maintenance\n",
    "    rise_without: float = 1.2         # IRI rise per year under deferred maintenance\n",
    "    iri_trigger: float = 16.0         # without case: reconstruction when IRI reaches this\n",
    "    # Agency costs (financial prices, US$/km)\n",
    "    routine: float = 1500.0           # with case: routine maintenance per km per year\n",
    "    regravel: float = 18000.0         # with case: periodic regravelling per km\n",
    "    regravel_cycle: int = 5           # years between regravelling (first in year 1)\n",
    "    spot: float = 500.0               # without case: spot repairs per km per year\n",
    "    recon: float = 30000.0            # without case: reconstruction per km\n",
    "    recon_life: int = 10              # years of service assumed for residual value\n",
    "    admin_share: float = 0.08         # programme administration, share of with-case works\n",
    "    admin_fixed: float = 200000.0     # programme administration, fixed per year (network)\n",
    "    cf: float = 0.85                  # financial-to-economic conversion factor (works)\n",
    "    overrun: float = 0.10             # cost overrun applied to with-case costs\n",
    "    # Appraisal\n",
    "    rate: float = 0.06                # economic discount rate\n",
    "    horizon: int = 20                 # years (year 1 = works year)\n",
    "\n",
    "\n",
    "def iri_paths(p: Params):\n",
    "    \"\"\"Roughness by year for the with and without cases, plus works years.\"\"\"\n",
    "    T = p.horizon\n",
    "    with_iri, without_iri = np.zeros(T), np.zeros(T)\n",
    "    regravel_years = [t for t in range(1, T + 1) if (t - 1) % p.regravel_cycle == 0]\n",
    "    recon_years = []\n",
    "    for t in range(1, T + 1):\n",
    "        # with case: year 1 is the catch-up works year, still at the initial condition\n",
    "        if t == 1:\n",
    "            with_iri[0] = p.iri0\n",
    "        else:\n",
    "            last_rg = max(y for y in regravel_years if y < t)\n",
    "            with_iri[t - 1] = p.iri_good + p.rise_with * (t - last_rg - 1)\n",
    "        # without case: deterioration; reconstruction in the year the trigger is reached,\n",
    "        # condition restored from the following year\n",
    "        if t == 1:\n",
    "            w = p.iri0\n",
    "        elif (t - 1) in recon_years:\n",
    "            w = p.iri_good\n",
    "        else:\n",
    "            w = without_iri[t - 2] + p.rise_without\n",
    "        without_iri[t - 1] = w\n",
    "        if w >= p.iri_trigger - 1e-9:\n",
    "            recon_years.append(t)\n",
    "    return with_iri, without_iri, regravel_years, recon_years\n",
    "\n",
    "\n",
    "def unit_cost(p: Params, iri):\n",
    "    \"\"\"Generalised cost per vehicle-km (US$): VOC + travel time.\"\"\"\n",
    "    iri = np.asarray(iri, dtype=float)\n",
    "    voc = p.voc_a0 + p.voc_beta * iri\n",
    "    speed = np.maximum(p.speed_min, p.speed_a - p.speed_b * iri)\n",
    "    time = p.occupancy * p.vot / speed\n",
    "    return voc, time\n",
    "\n",
    "\n",
    "def residual_fraction(start_year: int, life: int, horizon: int) -> float:\n",
    "    \"\"\"Straight-line share of an asset's life remaining after the horizon.\"\"\"\n",
    "    remaining = start_year + life - 1 - horizon\n",
    "    return max(0.0, min(life, remaining)) / life\n",
    "\n",
    "\n",
    "def cashflows(p: Params) -> pd.DataFrame:\n",
    "    T = p.horizon\n",
    "    t = np.arange(1, T + 1)\n",
    "    iri_w, iri_wo, rg_years, rc_years = iri_paths(p)\n",
    "    voc_w, time_w = unit_cost(p, iri_w)\n",
    "    voc_wo, time_wo = unit_cost(p, iri_wo)\n",
    "    c_w, c_wo = voc_w + time_w, voc_wo + time_wo\n",
    "\n",
    "    aadt = p.aadt0 * (1 + p.growth) ** (t - 1)\n",
    "    vkm = 365.0 * aadt * p.length_km                       # normal veh-km per year\n",
    "    gen_vkm = p.elasticity * vkm * (c_wo - c_w) / c_wo     # generated veh-km\n",
    "\n",
    "    b_voc = vkm * (voc_wo - voc_w)\n",
    "    b_time = vkm * (time_wo - time_w)\n",
    "    b_gen = 0.5 * gen_vkm * (c_wo - c_w)\n",
    "\n",
    "    # Agency costs, financial prices\n",
    "    works_w = np.full(T, p.routine * p.length_km)\n",
    "    for y in rg_years:\n",
    "        works_w[y - 1] += p.regravel * p.length_km\n",
    "    admin = p.admin_share * works_w + p.admin_fixed\n",
    "    k = p.cf * (1 + p.overrun)\n",
    "    c_prog = k * works_w                                    # economic\n",
    "    c_admin = k * admin\n",
    "\n",
    "    works_wo = np.full(T, p.spot * p.length_km)\n",
    "    for y in rc_years:\n",
    "        works_wo[y - 1] += p.recon * p.length_km\n",
    "    b_avoided = p.cf * works_wo\n",
    "\n",
    "    # Residual value at the horizon (straight line), with minus without\n",
    "    rv_w = sum(k * p.regravel * p.length_km * residual_fraction(y, p.regravel_cycle, T) for y in rg_years)\n",
    "    rv_wo = sum(p.cf * p.recon * p.length_km * residual_fraction(y, p.recon_life, T) for y in rc_years)\n",
    "    b_resid = np.zeros(T)\n",
    "    b_resid[-1] = rv_w - rv_wo\n",
    "\n",
    "    df = pd.DataFrame({\n",
    "        \"year\": t, \"iri_with\": iri_w, \"iri_without\": iri_wo, \"aadt\": aadt,\n",
    "        \"voc_saving_per_vkm\": voc_wo - voc_w, \"time_saving_per_vkm\": time_wo - time_w,\n",
    "        \"b_voc\": b_voc, \"b_time\": b_time, \"b_generated\": b_gen, \"b_avoided\": b_avoided,\n",
    "        \"b_residual\": b_resid, \"c_programme\": c_prog, \"c_admin\": c_admin,\n",
    "    })\n",
    "    df[\"benefits\"] = df[[\"b_voc\", \"b_time\", \"b_generated\", \"b_avoided\", \"b_residual\"]].sum(axis=1)\n",
    "    df[\"costs\"] = df[\"c_programme\"] + df[\"c_admin\"]\n",
    "    df[\"net\"] = df[\"benefits\"] - df[\"costs\"]\n",
    "    df[\"discount_factor\"] = (1 + p.rate) ** (-t)\n",
    "    df[\"pv_net\"] = df[\"net\"] * df[\"discount_factor\"]\n",
    "    return df\n",
    "\n",
    "\n",
    "def npv_at(df: pd.DataFrame, rate: float) -> float:\n",
    "    t = df[\"year\"].to_numpy()\n",
    "    return float(np.sum(df[\"net\"].to_numpy() / (1 + rate) ** t))\n",
    "\n",
    "\n",
    "def eirr(df: pd.DataFrame) -> float:\n",
    "    \"\"\"Lowest discount rate at which NPV changes sign: scan -20%..100% in 1-point steps,\n",
    "    then bisect inside the first bracket. NaN if NPV does not change sign in that range.\"\"\"\n",
    "    grid = [-0.20 + 0.01 * i for i in range(121)]\n",
    "    for lo, hi in zip(grid[:-1], grid[1:]):\n",
    "        f_lo, f_hi = npv_at(df, lo), npv_at(df, hi)\n",
    "        if f_lo == 0:\n",
    "            return lo\n",
    "        if f_lo * f_hi < 0:\n",
    "            for _ in range(100):\n",
    "                mid = (lo + hi) / 2\n",
    "                f_mid = npv_at(df, mid)\n",
    "                if f_lo * f_mid <= 0:\n",
    "                    hi = mid\n",
    "                else:\n",
    "                    lo, f_lo = mid, f_mid\n",
    "            return (lo + hi) / 2\n",
    "    return float(\"nan\")\n",
    "\n",
    "\n",
    "def summary(p: Params) -> dict:\n",
    "    df = cashflows(p)\n",
    "    d = df[\"discount_factor\"]\n",
    "    pv_b = float((df[\"benefits\"] * d).sum())\n",
    "    pv_c = float((df[\"costs\"] * d).sum())\n",
    "    year1_cost = float(df.loc[0, \"costs\"])\n",
    "    return {\n",
    "        \"npv\": pv_b - pv_c,\n",
    "        \"eirr\": eirr(df),\n",
    "        \"bcr\": pv_b / pv_c,\n",
    "        \"pv_benefits\": pv_b,\n",
    "        \"pv_costs\": pv_c,\n",
    "        \"fyrr\": float(df.loc[1, \"net\"]) / year1_cost,   # first full year of service / works-year cost\n",
    "        \"pv_voc\": float((df[\"b_voc\"] * d).sum()),\n",
    "        \"pv_time\": float((df[\"b_time\"] * d).sum()),\n",
    "        \"pv_generated\": float((df[\"b_generated\"] * d).sum()),\n",
    "        \"pv_avoided\": float((df[\"b_avoided\"] * d).sum()),\n",
    "        \"pv_residual\": float((df[\"b_residual\"] * d).sum()),\n",
    "        \"pv_programme\": float((df[\"c_programme\"] * d).sum()),\n",
    "        \"pv_admin\": float((df[\"c_admin\"] * d).sum()),\n",
    "    }\n",
    "\n",
    "\n",
    "def npv(p: Params) -> float:\n",
    "    return summary(p)[\"npv\"]\n",
    "\n",
    "\n",
    "# ---------------------------------------------------------------- sensitivity\n",
    "# (field, label, low, high) - ranges are those in parameters.csv\n",
    "SENSITIVITY = [\n",
    "    (\"aadt0\", \"Traffic, year 1 (veh/day)\", 70.0, 200.0),\n",
    "    (\"growth\", \"Traffic growth (per year)\", 0.01, 0.06),\n",
    "    (\"voc_beta\", \"VOC slope (US$/veh-km per IRI)\", 0.009, 0.020),\n",
    "    (\"vot\", \"Value of time (US$/person-hour)\", 0.5, 1.5),\n",
    "    (\"rise_without\", \"Deterioration without maintenance (IRI/yr)\", 0.8, 1.8),\n",
    "    (\"recon\", \"Reconstruction cost (US$/km)\", 20000.0, 40000.0),\n",
    "    (\"regravel\", \"Regravelling cost (US$/km)\", 14000.0, 26000.0),\n",
    "    (\"overrun\", \"Cost overrun\", 0.0, 0.50),\n",
    "    (\"rate\", \"Discount rate\", 0.03, 0.12),\n",
    "]\n",
    "\n",
    "\n",
    "def tornado(p: Params) -> pd.DataFrame:\n",
    "    base = npv(p)\n",
    "    rows = []\n",
    "    for field, label, lo, hi in SENSITIVITY:\n",
    "        n_lo = npv(replace(p, **{field: lo}))\n",
    "        n_hi = npv(replace(p, **{field: hi}))\n",
    "        rows.append({\"parameter\": field, \"label\": label, \"low\": lo, \"high\": hi,\n",
    "                     \"npv_low\": n_lo, \"npv_high\": n_hi, \"swing\": abs(n_hi - n_lo), \"base_npv\": base})\n",
    "    return pd.DataFrame(rows).sort_values(\"swing\", ascending=False).reset_index(drop=True)\n",
    "\n",
    "\n",
    "def _bisect(f, lo, hi, iters=200):\n",
    "    f_lo, f_hi = f(lo), f(hi)\n",
    "    if f_lo * f_hi > 0:\n",
    "        return float(\"nan\")\n",
    "    for _ in range(iters):\n",
    "        mid = (lo + hi) / 2\n",
    "        f_mid = f(mid)\n",
    "        if f_lo * f_mid <= 0:\n",
    "            hi = mid\n",
    "        else:\n",
    "            lo, f_lo = mid, f_mid\n",
    "    return (lo + hi) / 2\n",
    "\n",
    "\n",
    "# (field, search lower bound, search upper bound)\n",
    "SWITCHING = [\n",
    "    (\"aadt0\", 1.0, 1000.0),\n",
    "    (\"growth\", -0.10, 0.20),\n",
    "    (\"voc_beta\", 0.0, 0.10),\n",
    "    (\"vot\", 0.0, 20.0),\n",
    "    (\"rise_without\", 0.0, 1.2),\n",
    "    (\"recon\", 0.0, 30000.0),\n",
    "    (\"regravel\", 18000.0, 200000.0),\n",
    "    (\"overrun\", 0.0, 5.0),\n",
    "]\n",
    "\n",
    "\n",
    "def switching_values(p: Params) -> pd.DataFrame:\n",
    "    rows = []\n",
    "    for field, lo, hi in SWITCHING:\n",
    "        sv = _bisect(lambda x: npv(replace(p, **{field: x})), lo, hi)\n",
    "        base = getattr(p, field)\n",
    "        rows.append({\"parameter\": field, \"base\": base, \"switching_value\": sv,\n",
    "                     \"change_pct\": (sv / base - 1) * 100 if base else float(\"nan\")})\n",
    "    s = summary(p)\n",
    "    # Uniform shocks to all benefits / all costs\n",
    "    rows.append({\"parameter\": \"all_benefits\", \"base\": 1.0, \"switching_value\": s[\"pv_costs\"] / s[\"pv_benefits\"],\n",
    "                 \"change_pct\": (s[\"pv_costs\"] / s[\"pv_benefits\"] - 1) * 100})\n",
    "    rows.append({\"parameter\": \"all_costs\", \"base\": 1.0, \"switching_value\": s[\"pv_benefits\"] / s[\"pv_costs\"],\n",
    "                 \"change_pct\": (s[\"pv_benefits\"] / s[\"pv_costs\"] - 1) * 100})\n",
    "    rows.append({\"parameter\": \"rate\", \"base\": p.rate, \"switching_value\": s[\"eirr\"],\n",
    "                 \"change_pct\": (s[\"eirr\"] / p.rate - 1) * 100})\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "\n",
    "# ---------------------------------------------------------------- Monte Carlo\n",
    "# Triangular (low, mode, high); discount rate held at its base value.\n",
    "MC_DISTS = {\n",
    "    \"aadt0\": (70.0, 120.0, 200.0),\n",
    "    \"growth\": (0.01, 0.04, 0.06),\n",
    "    \"voc_beta\": (0.009, 0.013, 0.020),\n",
    "    \"vot\": (0.5, 1.0, 1.5),\n",
    "    \"rise_without\": (0.8, 1.2, 1.8),\n",
    "    \"recon\": (20000.0, 30000.0, 40000.0),\n",
    "    \"regravel\": (14000.0, 18000.0, 26000.0),\n",
    "    \"overrun\": (0.0, 0.10, 0.50),\n",
    "}\n",
    "\n",
    "\n",
    "def monte_carlo(p: Params, n: int = 10000, seed: int = 20261103) -> pd.DataFrame:\n",
    "    rng = np.random.default_rng(seed)\n",
    "    draws = {k: rng.triangular(lo, mode, hi, size=n) for k, (lo, mode, hi) in MC_DISTS.items()}\n",
    "    out = np.empty(n)\n",
    "    for i in range(n):\n",
    "        out[i] = npv(replace(p, **{k: float(v[i]) for k, v in draws.items()}))\n",
    "    df = pd.DataFrame(draws)\n",
    "    df[\"npv\"] = out\n",
    "    return df\n",
    "\n",
    "\n",
    "# ---------------------------------------------------------------- parameter table\n",
    "PARAMETER_TABLE = [\n",
    "    # field, label, unit, low, high, status, source, url\n",
    "    (\"length_km\", \"Network length\", \"km\", None, None, \"illustrative\", \"Synthetic network; results scale linearly with length\", \"\"),\n",
    "    (\"aadt0\", \"Average daily traffic, year 1\", \"veh/day\", 70, 200, \"cited range\", \"RED designed for low-volume roads of about 50-200 veh/day (SSATP Technical Note 18)\", \"https://www.ssatp.org/sites/ssatp/files/publication/ATTN18.pdf\"),\n",
    "    (\"growth\", \"Normal traffic growth\", \"per year\", 0.01, 0.06, \"illustrative\", \"\", \"\"),\n",
    "    (\"elasticity\", \"Generated-traffic elasticity to transport cost\", \"-\", None, None, \"cited\", \"RED user guide case studies use a price elasticity of 1.0 (SSATP Working Paper 78)\", \"https://documents1.worldbank.org/curated/en/764611468202186160/pdf/349290ENGLISH0ssatpwp78.pdf\"),\n",
    "    (\"voc_a0\", \"VOC intercept\", \"US$/veh-km\", None, None, \"derived\", \"Linear chord (IRI 6-16) of the example car VOC-roughness cubic in the RED user guide\", \"https://documents1.worldbank.org/curated/en/764611468202186160/pdf/349290ENGLISH0ssatpwp78.pdf\"),\n",
    "    (\"voc_beta\", \"VOC slope per IRI point\", \"US$/veh-km per IRI\", 0.009, 0.020, \"derived; range illustrative\", \"Same chord; applied to an average vehicle; a real appraisal uses HDM-4 VOC by vehicle class at national prices\", \"https://documents1.worldbank.org/curated/en/764611468202186160/pdf/349290ENGLISH0ssatpwp78.pdf\"),\n",
    "    (\"speed_a\", \"Speed at zero roughness\", \"km/h\", None, None, \"illustrative\", \"Linear speed-roughness approximation\", \"\"),\n",
    "    (\"speed_b\", \"Speed loss per IRI point\", \"km/h per IRI\", None, None, \"illustrative\", \"\", \"\"),\n",
    "    (\"speed_min\", \"Speed floor\", \"km/h\", None, None, \"illustrative\", \"\", \"\"),\n",
    "    (\"occupancy\", \"Occupants per vehicle\", \"persons\", None, None, \"cited\", \"RED user guide case studies: car, 3 occupants\", \"https://documents1.worldbank.org/curated/en/764611468202186160/pdf/349290ENGLISH0ssatpwp78.pdf\"),\n",
    "    (\"vot\", \"Value of time\", \"US$/person-hour\", 0.5, 1.5, \"cited base; range illustrative\", \"RED case studies: car passengers US$1.0/h, bus passengers US$0.5/h. Non-work time default 0.3 x household income per head (World Bank transport toolkit)\", \"https://its.leeds.ac.uk/projects/WBToolkit/Note10.htm\"),\n",
    "    (\"iri0\", \"Roughness, year 1\", \"IRI m/km\", None, None, \"cited range\", \"RED case study gravel road at 7-10 IRI; HDM-III gravel roughness 3.5-20 IRI (Transport Note RT-1)\", \"https://ssatp.org/sites/default/files/publication/InfrastructureNote_rt1.pdf\"),\n",
    "    (\"iri_good\", \"Roughness after works\", \"IRI m/km\", None, None, \"illustrative\", \"Within the RT-1 range\", \"https://ssatp.org/sites/default/files/publication/InfrastructureNote_rt1.pdf\"),\n",
    "    (\"rise_with\", \"IRI rise per year, maintained\", \"IRI/yr\", None, None, \"illustrative\", \"\", \"\"),\n",
    "    (\"rise_without\", \"IRI rise per year, deferred\", \"IRI/yr\", 0.8, 1.8, \"illustrative\", \"\", \"\"),\n",
    "    (\"iri_trigger\", \"Reconstruction trigger\", \"IRI m/km\", None, None, \"illustrative\", \"Within the RT-1 range\", \"https://ssatp.org/sites/default/files/publication/InfrastructureNote_rt1.pdf\"),\n",
    "    (\"routine\", \"Routine maintenance\", \"US$/km/yr\", None, None, \"illustrative\", \"Order of magnitude: TI-UP enquiry (up to about US$1,000/km/yr, 1998-2004 prices); RED case study all-weather earth road US$2,900/km/yr incl. grading and regravelling\", \"https://assets.publishing.service.gov.uk/media/57a08bd7e5274a27b2000dc3/TI_UP_HD_Feb2007_Cost_of_Roads_in_Africa.pdf\"),\n",
    "    (\"regravel\", \"Periodic regravelling\", \"US$/km\", 14000, 26000, \"illustrative\", \"Order of magnitude: TI-UP enquiry regravelling US$9,000-13,000/km (2000 prices)\", \"https://assets.publishing.service.gov.uk/media/57a08bd7e5274a27b2000dc3/TI_UP_HD_Feb2007_Cost_of_Roads_in_Africa.pdf\"),\n",
    "    (\"regravel_cycle\", \"Regravelling cycle\", \"years\", None, None, \"illustrative\", \"RED case study earth road option: regravelling every six years\", \"https://documents1.worldbank.org/curated/en/764611468202186160/pdf/349290ENGLISH0ssatpwp78.pdf\"),\n",
    "    (\"spot\", \"Spot repairs, deferred regime\", \"US$/km/yr\", None, None, \"illustrative\", \"\", \"\"),\n",
    "    (\"recon\", \"Reconstruction (rehabilitation) of gravel road\", \"US$/km\", 20000, 40000, \"illustrative\", \"Order of magnitude: TI-UP rough guide US$10,000-15,000/km for gravel roads (older prices)\", \"https://assets.publishing.service.gov.uk/media/57a08bd7e5274a27b2000dc3/TI_UP_HD_Feb2007_Cost_of_Roads_in_Africa.pdf\"),\n",
    "    (\"recon_life\", \"Service life of reconstruction (residual value)\", \"years\", None, None, \"illustrative\", \"\", \"\"),\n",
    "    (\"admin_share\", \"Administration, variable\", \"share of works\", None, None, \"illustrative\", \"\", \"\"),\n",
    "    (\"admin_fixed\", \"Administration, fixed\", \"US$/yr\", None, None, \"illustrative\", \"\", \"\"),\n",
    "    (\"cf\", \"Conversion factor, financial to economic\", \"-\", None, None, \"cited\", \"RED user guide case studies use an economic/financial cost factor of 0.85\", \"https://documents1.worldbank.org/curated/en/764611468202186160/pdf/349290ENGLISH0ssatpwp78.pdf\"),\n",
    "    (\"overrun\", \"Cost overrun on programme costs\", \"share\", 0.0, 0.5, \"illustrative\", \"Optimism-bias adjustment in the sense of HM Treasury Green Book para. 2.25\", \"https://assets.publishing.service.gov.uk/media/6645c709bd01f5ed32793cbc/Green_Book_2022__updated_links_.pdf\"),\n",
    "    (\"rate\", \"Economic discount rate\", \"per year\", 0.03, 0.12, \"cited\", \"World Bank (2016) discounting note: 6% central, 2-10% plausible range; RED case studies use 12%\", \"https://documents1.worldbank.org/curated/en/099610503022315638/pdf/IDU02e978a2a023f704c140bb1b082595e67e593.pdf\"),\n",
    "    (\"horizon\", \"Evaluation period\", \"years\", None, None, \"cited\", \"RED evaluation period of 2-20 years\", \"https://documents1.worldbank.org/curated/en/764611468202186160/pdf/349290ENGLISH0ssatpwp78.pdf\"),\n",
    "]\n",
    "\n",
    "\n",
    "def parameter_table(p: Params = Params()) -> pd.DataFrame:\n",
    "    vals = asdict(p)\n",
    "    rows = []\n",
    "    for field, label, unit, lo, hi, status, source, url in PARAMETER_TABLE:\n",
    "        rows.append({\"parameter\": field, \"label\": label, \"unit\": unit, \"base\": vals[field],\n",
    "                     \"low\": lo, \"high\": hi, \"status\": status, \"source\": source, \"url\": url})\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "\n",
    "def fiscal(p: Params) -> dict:\n",
    "    \"\"\"Recurrent budget implications, financial prices (no conversion factor).\"\"\"\n",
    "    ov = 1 + p.overrun\n",
    "    routine = p.routine * p.length_km * ov\n",
    "    periodic_avg = p.regravel * p.length_km / p.regravel_cycle * ov\n",
    "    admin_avg = (p.admin_share * (p.routine + p.regravel / p.regravel_cycle) * p.length_km) * ov + p.admin_fixed * ov\n",
    "    total = routine + periodic_avg + admin_avg\n",
    "    return {\"routine\": routine, \"periodic_avg\": periodic_avg, \"admin_avg\": admin_avg,\n",
    "            \"total_per_year\": total, \"per_km\": total / p.length_km}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8be4fcf",
   "metadata": {},
   "source": [
    "## 1. Parameters"
   ]
  },
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    {
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       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter</th>\n",
       "      <th>label</th>\n",
       "      <th>unit</th>\n",
       "      <th>base</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>length_km</td>\n",
       "      <td>Network length</td>\n",
       "      <td>km</td>\n",
       "      <td>400.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aadt0</td>\n",
       "      <td>Average daily traffic, year 1</td>\n",
       "      <td>veh/day</td>\n",
       "      <td>120.000</td>\n",
       "      <td>70.000</td>\n",
       "      <td>200.00</td>\n",
       "      <td>cited range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>growth</td>\n",
       "      <td>Normal traffic growth</td>\n",
       "      <td>per year</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.06</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>elasticity</td>\n",
       "      <td>Generated-traffic elasticity to transport cost</td>\n",
       "      <td>-</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>voc_a0</td>\n",
       "      <td>VOC intercept</td>\n",
       "      <td>US$/veh-km</td>\n",
       "      <td>0.064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>derived</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>voc_beta</td>\n",
       "      <td>VOC slope per IRI point</td>\n",
       "      <td>US$/veh-km per IRI</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.02</td>\n",
       "      <td>derived; range illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speed_a</td>\n",
       "      <td>Speed at zero roughness</td>\n",
       "      <td>km/h</td>\n",
       "      <td>75.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speed_b</td>\n",
       "      <td>Speed loss per IRI point</td>\n",
       "      <td>km/h per IRI</td>\n",
       "      <td>2.500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speed_min</td>\n",
       "      <td>Speed floor</td>\n",
       "      <td>km/h</td>\n",
       "      <td>20.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>occupancy</td>\n",
       "      <td>Occupants per vehicle</td>\n",
       "      <td>persons</td>\n",
       "      <td>3.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>vot</td>\n",
       "      <td>Value of time</td>\n",
       "      <td>US$/person-hour</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.50</td>\n",
       "      <td>cited base; range illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>iri0</td>\n",
       "      <td>Roughness, year 1</td>\n",
       "      <td>IRI m/km</td>\n",
       "      <td>9.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cited range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>iri_good</td>\n",
       "      <td>Roughness after works</td>\n",
       "      <td>IRI m/km</td>\n",
       "      <td>6.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>rise_with</td>\n",
       "      <td>IRI rise per year, maintained</td>\n",
       "      <td>IRI/yr</td>\n",
       "      <td>0.400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>rise_without</td>\n",
       "      <td>IRI rise per year, deferred</td>\n",
       "      <td>IRI/yr</td>\n",
       "      <td>1.200</td>\n",
       "      <td>0.800</td>\n",
       "      <td>1.80</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>iri_trigger</td>\n",
       "      <td>Reconstruction trigger</td>\n",
       "      <td>IRI m/km</td>\n",
       "      <td>16.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>routine</td>\n",
       "      <td>Routine maintenance</td>\n",
       "      <td>US$/km/yr</td>\n",
       "      <td>1500.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>regravel</td>\n",
       "      <td>Periodic regravelling</td>\n",
       "      <td>US$/km</td>\n",
       "      <td>18000.000</td>\n",
       "      <td>14000.000</td>\n",
       "      <td>26000.00</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>regravel_cycle</td>\n",
       "      <td>Regravelling cycle</td>\n",
       "      <td>years</td>\n",
       "      <td>5.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>spot</td>\n",
       "      <td>Spot repairs, deferred regime</td>\n",
       "      <td>US$/km/yr</td>\n",
       "      <td>500.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>recon</td>\n",
       "      <td>Reconstruction (rehabilitation) of gravel road</td>\n",
       "      <td>US$/km</td>\n",
       "      <td>30000.000</td>\n",
       "      <td>20000.000</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>recon_life</td>\n",
       "      <td>Service life of reconstruction (residual value)</td>\n",
       "      <td>years</td>\n",
       "      <td>10.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>admin_share</td>\n",
       "      <td>Administration, variable</td>\n",
       "      <td>share of works</td>\n",
       "      <td>0.080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>admin_fixed</td>\n",
       "      <td>Administration, fixed</td>\n",
       "      <td>US$/yr</td>\n",
       "      <td>200000.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>cf</td>\n",
       "      <td>Conversion factor, financial to economic</td>\n",
       "      <td>-</td>\n",
       "      <td>0.850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>overrun</td>\n",
       "      <td>Cost overrun on programme costs</td>\n",
       "      <td>share</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>illustrative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>rate</td>\n",
       "      <td>Economic discount rate</td>\n",
       "      <td>per year</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.12</td>\n",
       "      <td>cited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>horizon</td>\n",
       "      <td>Evaluation period</td>\n",
       "      <td>years</td>\n",
       "      <td>20.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cited</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         parameter                                            label                unit        base        low      high                          status\n",
       "0        length_km                                   Network length                  km     400.000        NaN       NaN                    illustrative\n",
       "1            aadt0                    Average daily traffic, year 1             veh/day     120.000     70.000    200.00                     cited range\n",
       "2           growth                            Normal traffic growth            per year       0.040      0.010      0.06                    illustrative\n",
       "3       elasticity   Generated-traffic elasticity to transport cost                   -       1.000        NaN       NaN                           cited\n",
       "4           voc_a0                                    VOC intercept          US$/veh-km       0.064        NaN       NaN                         derived\n",
       "5         voc_beta                          VOC slope per IRI point  US$/veh-km per IRI       0.013      0.009      0.02     derived; range illustrative\n",
       "6          speed_a                          Speed at zero roughness                km/h      75.000        NaN       NaN                    illustrative\n",
       "7          speed_b                         Speed loss per IRI point        km/h per IRI       2.500        NaN       NaN                    illustrative\n",
       "8        speed_min                                      Speed floor                km/h      20.000        NaN       NaN                    illustrative\n",
       "9        occupancy                            Occupants per vehicle             persons       3.000        NaN       NaN                           cited\n",
       "10             vot                                    Value of time     US$/person-hour       1.000      0.500      1.50  cited base; range illustrative\n",
       "11            iri0                                Roughness, year 1            IRI m/km       9.000        NaN       NaN                     cited range\n",
       "12        iri_good                            Roughness after works            IRI m/km       6.000        NaN       NaN                    illustrative\n",
       "13       rise_with                    IRI rise per year, maintained              IRI/yr       0.400        NaN       NaN                    illustrative\n",
       "14    rise_without                      IRI rise per year, deferred              IRI/yr       1.200      0.800      1.80                    illustrative\n",
       "15     iri_trigger                           Reconstruction trigger            IRI m/km      16.000        NaN       NaN                    illustrative\n",
       "16         routine                              Routine maintenance           US$/km/yr    1500.000        NaN       NaN                    illustrative\n",
       "17        regravel                            Periodic regravelling              US$/km   18000.000  14000.000  26000.00                    illustrative\n",
       "18  regravel_cycle                               Regravelling cycle               years       5.000        NaN       NaN                    illustrative\n",
       "19            spot                    Spot repairs, deferred regime           US$/km/yr     500.000        NaN       NaN                    illustrative\n",
       "20           recon   Reconstruction (rehabilitation) of gravel road              US$/km   30000.000  20000.000  40000.00                    illustrative\n",
       "21      recon_life  Service life of reconstruction (residual value)               years      10.000        NaN       NaN                    illustrative\n",
       "22     admin_share                         Administration, variable      share of works       0.080        NaN       NaN                    illustrative\n",
       "23     admin_fixed                            Administration, fixed              US$/yr  200000.000        NaN       NaN                    illustrative\n",
       "24              cf         Conversion factor, financial to economic                   -       0.850        NaN       NaN                           cited\n",
       "25         overrun                  Cost overrun on programme costs               share       0.100      0.000      0.50                    illustrative\n",
       "26            rate                           Economic discount rate            per year       0.060      0.030      0.12                           cited\n",
       "27         horizon                                Evaluation period               years      20.000        NaN       NaN                           cited"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.width', 200); pd.set_option('display.max_colwidth', 90)\n",
    "p = Params()\n",
    "pt = parameter_table(p)\n",
    "pt.to_csv('parameters.csv', index=False)\n",
    "pt[['parameter', 'label', 'unit', 'base', 'low', 'high', 'status']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e908c885",
   "metadata": {},
   "source": [
    "## 2. Roughness paths and incremental cash flows (base case)\n",
    "\n",
    "Without the programme the road roughens by 1.2 IRI a year until it reaches 16 and is reconstructed; with the programme it is held between 6 and 7.6 IRI. Benefits and costs are economic, US$ constant prices, network totals."
   ]
  },
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>iri_with</th>\n",
       "      <th>iri_without</th>\n",
       "      <th>aadt</th>\n",
       "      <th>b_voc</th>\n",
       "      <th>b_time</th>\n",
       "      <th>b_generated</th>\n",
       "      <th>b_avoided</th>\n",
       "      <th>b_residual</th>\n",
       "      <th>c_programme</th>\n",
       "      <th>c_admin</th>\n",
       "      <th>net</th>\n",
       "      <th>pv_net</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7293000.0</td>\n",
       "      <td>770440.0</td>\n",
       "      <td>-7893440.00</td>\n",
       "      <td>-7446641.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>124.80</td>\n",
       "      <td>994855.68</td>\n",
       "      <td>193250.91</td>\n",
       "      <td>150602.50</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>715829.09</td>\n",
       "      <td>637085.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>11.4</td>\n",
       "      <td>129.79</td>\n",
       "      <td>1231726.08</td>\n",
       "      <td>259016.29</td>\n",
       "      <td>211904.38</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>1079766.76</td>\n",
       "      <td>906592.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>134.98</td>\n",
       "      <td>1485954.34</td>\n",
       "      <td>339786.50</td>\n",
       "      <td>284971.03</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>1487831.88</td>\n",
       "      <td>1178502.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>13.8</td>\n",
       "      <td>140.38</td>\n",
       "      <td>1758550.11</td>\n",
       "      <td>439483.71</td>\n",
       "      <td>371247.06</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>1946400.87</td>\n",
       "      <td>1454463.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>146.00</td>\n",
       "      <td>2050576.00</td>\n",
       "      <td>563345.06</td>\n",
       "      <td>472774.93</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7293000.0</td>\n",
       "      <td>770440.0</td>\n",
       "      <td>-4806744.01</td>\n",
       "      <td>-3388564.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>151.84</td>\n",
       "      <td>2939528.41</td>\n",
       "      <td>819266.56</td>\n",
       "      <td>881367.31</td>\n",
       "      <td>10370000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>14217282.28</td>\n",
       "      <td>9455304.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>157.91</td>\n",
       "      <td>-119886.65</td>\n",
       "      <td>-19538.24</td>\n",
       "      <td>2195.75</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>-760109.15</td>\n",
       "      <td>-476901.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>164.23</td>\n",
       "      <td>124682.11</td>\n",
       "      <td>21758.01</td>\n",
       "      <td>2127.12</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>-474312.76</td>\n",
       "      <td>-280744.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>170.80</td>\n",
       "      <td>389008.20</td>\n",
       "      <td>72913.52</td>\n",
       "      <td>18702.56</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>-142255.73</td>\n",
       "      <td>-79434.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9.6</td>\n",
       "      <td>177.63</td>\n",
       "      <td>674280.88</td>\n",
       "      <td>136207.35</td>\n",
       "      <td>51145.10</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7293000.0</td>\n",
       "      <td>770440.0</td>\n",
       "      <td>-7031806.67</td>\n",
       "      <td>-3704268.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>184.73</td>\n",
       "      <td>1683005.07</td>\n",
       "      <td>337140.44</td>\n",
       "      <td>283456.16</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>1680721.67</td>\n",
       "      <td>835267.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>192.12</td>\n",
       "      <td>2042046.15</td>\n",
       "      <td>443730.15</td>\n",
       "      <td>384222.44</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>2247118.73</td>\n",
       "      <td>1053536.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>6.8</td>\n",
       "      <td>13.2</td>\n",
       "      <td>199.81</td>\n",
       "      <td>2427117.71</td>\n",
       "      <td>574819.46</td>\n",
       "      <td>503068.24</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>2882125.41</td>\n",
       "      <td>1274766.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>7.2</td>\n",
       "      <td>14.4</td>\n",
       "      <td>207.80</td>\n",
       "      <td>2839727.72</td>\n",
       "      <td>736979.06</td>\n",
       "      <td>642538.35</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>3596365.13</td>\n",
       "      <td>1500637.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>7.6</td>\n",
       "      <td>15.6</td>\n",
       "      <td>216.11</td>\n",
       "      <td>3281463.14</td>\n",
       "      <td>939063.40</td>\n",
       "      <td>806186.69</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7293000.0</td>\n",
       "      <td>770440.0</td>\n",
       "      <td>-2866726.76</td>\n",
       "      <td>-1128476.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>224.76</td>\n",
       "      <td>4607174.25</td>\n",
       "      <td>1342416.74</td>\n",
       "      <td>1444801.32</td>\n",
       "      <td>10370000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>16971512.31</td>\n",
       "      <td>6302615.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>233.75</td>\n",
       "      <td>-177461.53</td>\n",
       "      <td>-28921.37</td>\n",
       "      <td>3250.24</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>-826012.66</td>\n",
       "      <td>-289388.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>243.10</td>\n",
       "      <td>184559.99</td>\n",
       "      <td>32207.17</td>\n",
       "      <td>3148.65</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>-402964.19</td>\n",
       "      <td>-133184.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>252.82</td>\n",
       "      <td>575827.16</td>\n",
       "      <td>107929.82</td>\n",
       "      <td>27684.35</td>\n",
       "      <td>170000.0</td>\n",
       "      <td>-6120000.0</td>\n",
       "      <td>561000.0</td>\n",
       "      <td>231880.0</td>\n",
       "      <td>-6031438.67</td>\n",
       "      <td>-1880631.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  iri_with  iri_without    aadt       b_voc      b_time  b_generated   b_avoided  b_residual  c_programme   c_admin          net      pv_net\n",
       "0      1       9.0          9.0  120.00        0.00        0.00         0.00    170000.0         0.0    7293000.0  770440.0  -7893440.00 -7446641.51\n",
       "1      2       6.0         10.2  124.80   994855.68   193250.91    150602.50    170000.0         0.0     561000.0  231880.0    715829.09   637085.34\n",
       "2      3       6.4         11.4  129.79  1231726.08   259016.29    211904.38    170000.0         0.0     561000.0  231880.0   1079766.76   906592.99\n",
       "3      4       6.8         12.6  134.98  1485954.34   339786.50    284971.03    170000.0         0.0     561000.0  231880.0   1487831.88  1178502.20\n",
       "4      5       7.2         13.8  140.38  1758550.11   439483.71    371247.06    170000.0         0.0     561000.0  231880.0   1946400.87  1454463.96\n",
       "5      6       7.6         15.0  146.00  2050576.00   563345.06    472774.93    170000.0         0.0    7293000.0  770440.0  -4806744.01 -3388564.85\n",
       "6      7       6.0         16.2  151.84  2939528.41   819266.56    881367.31  10370000.0         0.0     561000.0  231880.0  14217282.28  9455304.72\n",
       "7      8       6.4          6.0  157.91  -119886.65   -19538.24      2195.75    170000.0         0.0     561000.0  231880.0   -760109.15  -476901.88\n",
       "8      9       6.8          7.2  164.23   124682.11    21758.01      2127.12    170000.0         0.0     561000.0  231880.0   -474312.76  -280744.99\n",
       "9     10       7.2          8.4  170.80   389008.20    72913.52     18702.56    170000.0         0.0     561000.0  231880.0   -142255.73   -79434.86\n",
       "10    11       7.6          9.6  177.63   674280.88   136207.35     51145.10    170000.0         0.0    7293000.0  770440.0  -7031806.67 -3704268.04\n",
       "11    12       6.0         10.8  184.73  1683005.07   337140.44    283456.16    170000.0         0.0     561000.0  231880.0   1680721.67   835267.18\n",
       "12    13       6.4         12.0  192.12  2042046.15   443730.15    384222.44    170000.0         0.0     561000.0  231880.0   2247118.73  1053536.95\n",
       "13    14       6.8         13.2  199.81  2427117.71   574819.46    503068.24    170000.0         0.0     561000.0  231880.0   2882125.41  1274766.85\n",
       "14    15       7.2         14.4  207.80  2839727.72   736979.06    642538.35    170000.0         0.0     561000.0  231880.0   3596365.13  1500637.51\n",
       "15    16       7.6         15.6  216.11  3281463.14   939063.40    806186.69    170000.0         0.0    7293000.0  770440.0  -2866726.76 -1128476.34\n",
       "16    17       6.0         16.8  224.76  4607174.25  1342416.74   1444801.32  10370000.0         0.0     561000.0  231880.0  16971512.31  6302615.80\n",
       "17    18       6.4          6.0  233.75  -177461.53   -28921.37      3250.24    170000.0         0.0     561000.0  231880.0   -826012.66  -289388.41\n",
       "18    19       6.8          7.2  243.10   184559.99    32207.17      3148.65    170000.0         0.0     561000.0  231880.0   -402964.19  -133184.91\n",
       "19    20       7.2          8.4  252.82   575827.16   107929.82     27684.35    170000.0  -6120000.0     561000.0  231880.0  -6031438.67 -1880631.09"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = cashflows(p)\n",
    "df.to_csv('cashflows_base.csv', index=False)\n",
    "cols = ['year', 'iri_with', 'iri_without', 'aadt', 'b_voc', 'b_time', 'b_generated', 'b_avoided', 'b_residual', 'c_programme', 'c_admin', 'net', 'pv_net']\n",
    "df[cols].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8cc85fc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-03T14:53:32.797789Z",
     "iopub.status.busy": "2026-11-03T14:53:32.796800Z",
     "iopub.status.idle": "2026-11-03T14:53:33.759487Z",
     "shell.execute_reply": "2026-11-03T14:53:33.758762Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+SzSpqam65s2b6xo3bqy/r2/fvrpatWrdtb2NGjXSPfXUU/r727Vrpz7zDJeHbcHnG9atwXPw+WuoT58+uho1aqj90+DcolKlSvqxwM9BQUG6J598MstzcZ6Bddx///3Zjqn22W38mb9w4UL13LNnz6rnHjp0SI3vt99+m2VZOL/CmF68eFHdv3fvXvW8v//+O9t1wo8//qge9/zzz2d5nPaZrp0Hnjt3Tufm5qb77LPPsjxOO287evSoul/72fg8EMesffv2+ueOGDFCV6ZMGV1MTIz+vvT0dF3Dhg11DzzwgP6+EydOqO3DdhrCeQWOrwbrw+OuXr2a4/5CcHCwOv8xtHPnTvX87777zuTz161bp36PcdFERESo9wnu9/Pz0z399NO6L7/8Unfr1q27loHzTLxW8FgcPzz2n3/+uee2QunSpXVt2rTJ8lis18fHR53ba+bOnauWHRcXp+7SthmvccBrAevHawNw7m54Dm/8msF3CCxv3rx5Wda9cuVK9Txt+7X30MCBA7M8bvDgwep8EcfVXCkpKWqdeL8VK1ZMnVvjOwCOGX7evXv3XYvCawTvB+09gtcUznffe+89s1aLY1G3bl2zHkv2gxlTRIUMqdyoGdfSznHVDjOB4AoaoAQPVxNxRcQQrkai3xGudOYGrkTiCtHzzz+f5WmGqfCGkJVlSGvIjitohox7CeBxuOKklaFhO5EijVRmpPRqN1zdxJUm7UolyhBwVRTjcPToUbUKlAXg6qgGj0H6M7KptFnykCaOtO+coKcSytTMhStkODa4+ovtxtXE4cOH57vngUZLf9fgailKCwxToJGCjr5YhlfHkEmGsTU+NoCr3Xj9oBQCM9qYYrhepOej7l9rkqvBetHQ1BBKAgBZY0REZL20iTtMwd98lHohewGZHCgRQ1YCsn60MhtkMyDLAtkXhpABgowQrf8hPnMw+xo+SzTIRkGmEDJ+8fmVW8isMFyeVtJnfN5hDOcZyI5CU3TtHAPPQWaHNtsx7sdnmGHWCuA8A+VY5jB1vgPa9mFMcJ6FczTD8x1k5WBMDdsh5AZKsAwhWwpZY9qMbSjfRDYUPuMN14tzJjCe8dl4P/A8wzHGeLZr105lwWjLQ6kk7sOxLUi5PV/LDrKDkLGGWe6QsYVm5jjfxbmWYakr4DwT+4csOq18FJmEyCZHtvi9mDqnw7mUYZY9fsb5m3EJZl5pJX3GFQp4L+M9ZPwdwdRrF5ltuSnnw7kh9gFZb998843KzMP/cc6O82bj7yuAc0xUSeD1CshyRMmhlu2IzDpUZGQ3AQHOw9FXlRwLm58TFTL0XdBq2pH2iz/KmLlEC05oPQfwB9wY/sCjYWNuIO0e6cpaU0IN6t9NMS5b09JtjT887vU47Ac+2NEnyxScDCBVF2Vr+ID87LPPVK8pBEPwYTZ58mR9YEQrMUPwDinCOJnECRv+n1NjeJxI5+ZkAKnnOA7YV6Q8I4iopaxbgvExAONmpqaamyJ9H72+TDVGR+o2ttfUsjV5Xa82tsbT+RIRkXXBuQH6x2QH5WD4oo4vtLghGKT9DPjMxmeMYYDIGPoR4XM9u/MTwHbgAlRumCqXx+dWTrPmaduC3lG4GdM+97TAi6m+Q6buM8Wc8x182a9Xr57J5+N8Jy9M9T9CaR/aLWjHDAGD7GZ0Nl6vqf0wHGO8BhDMQqmfKWg7kN0ELfmF5WL5puQ0K192EDTCDeWVuOCL88Unn3xSBdwwhhq8TjApEUrMcA6KABkulOKx6M+Zk7yeW+UHjhEunCJwYwjnrnhfGn9HuNdr1xzaMtAgHr3lNDhfx8VbtNdA8Eq7mGkMZcH4noPSPGdnZ/W3CBfL8dpDf1mUUhoeE8BrIae/RWSfGJgiKmT4Y6zNyocGgajzR28E7YqDNqUurmgaB49w1c/4RMr4xM24kSUejx5WyMAxzMzB9L0FCcEwBNKMG6JqtJMbXGXE7Cfor4T+GGikiBMEPE+72ocPO1ylwQcVrhSiRxMCVwi6oaF4dvBBd68GloYQJNPGCAEtbBPWV9RTbiMwhathpqDnBXocoNkmekJZcgYTLah3r6alRERUdJBRjD40hjP+GkM2D7It8NmPnpA470B2FC48DB48WJ174Es5+lJl1/8Fn9f4sqjNymZIy6w17JtkKrBkfI6SV9gWnEcgo0vrq2iKdk5lKvMX9xk2Zs4rnO/gyzv6WuGLtzHjIIK5sttmLVMH68X6EIwwNYOi4cy/5sDy0E9s1qxZJh+e136d5sD5mmFfMNCOXXbZS1qGvvaaw+tNe/0aHgdkxyHDB5PHIKMIvdXwODze8OIm/o9sfIw73hM5BVuKCsYE58LG5/SA7b5X37K8wPsM1QSm3iva+SHOF00FpjCG+DuDjH6t8TwCf3jPov8sZprGdwD0RTOE1wKCVuRYWMpHVIQQbMAJBoIKWpNGXMUEbUYaDT6AkZqsXRnDCYJ2ImQIjzGEVGWcCH711VdZ7jeejcfSsB9Ir8fJMj5ItRtOgpHqj4amgDRrbDNOCJChhHI9XIHBlRWc/OLDF1e7cOKFD0ecNKD5Jq7aGDZtNQXp2PhQzG2WGeBDEynK+OAsSjjuuMKXXeNzZEwhu+vhhx9WwU1LNovUSitRAkJERNYHFybwmYmgFCa5MAVlM7jAgc9efHHF5wbKmnDhSvscxbkIvqhjIg1DyBpBoELLZsHjcAHJOIsFWRAIbGgXz/AlFp9fhhm3N2/ezNPnscY40IXzDFzEwnoNzzNw0UrLZkFJEYJYaBJtCFlHaGJuCdgOtCjAuYbhdgDK4oyDRjllghlCyZQhfFnHbGvINgesF8vCeZTherG/aHuQ24lbsDycb+Hcx3B52gx2pjK6zWHO/uJ8DRcSDV9XCCTh2CKoZNjeALBM3I9AqXaOgnJDnFuicbsxrQ2EFiTEhUc8FuepxvC6RWDLVLCvqOH9h/JN47JEnEcjgJdd9lx+4LhjRj4E9Ywz6PEaxessu/NETDqAjH+0mwB8H8GxQPYVnocyUePMPnwfwmvasKUHOQYGpoiKEP7Y44QPVzC1kjH0BkApG2ZSQ3AKJ5T4A420YqTvajOt4LnoO4UUdsycgg8kZBUZB5wwAwuCGghaIJUZASpcCTIOfFkaZptDWjFOgPGBiQ8jTP2K7UGfBC0QhysnuA8nQ3gMTkzwgYvnYn9xMoLZ/dDTAgEajAemv8Uyjev7jWG/cXKRl31F4A8z3WC7MHVwdvAhjeAVZmcpCPgygROpnD6g8VrAcUWGHb6Y5KXHhynIFsMVMPRmICKiooW/7fi7vHXrVpU5jPJ2lI/hAgz6RxlmRRtCYAGzaKGVAL50I0CEzwyU0Wufo8hcwMUhzMSHvob4rEXQCmXzOM/QghyYDQwBHZxTIOiCbUK5FLZnypQp+tXicx7BGjwO5UeHDx9Ws4jltTwHn28IymA52H7AtiBwhnManEch4wszfiFDRsuiwvrw5RiztKG/Fs49cC6BbbFUBhC+ZCOwgvOeTZs2qXMZnLeh/yO2RZvZVsuCR/APY28cbDGGY4QZ5nAsjh07pvpdYn+0PmDopYXzHBxXzLaGckIcG5wvYb25vSiHi1wIfiGjCMEDjBXOf7AeU32E7kXbX5wfGfewNIaLjbgYide2Icxsh/M97CfOh/BawusSFykRlMS5s5YdhcAabjh3wyx1OAaoGMA5MqoVkJWFHlKAagW8pnF+iZkFMcZ4XWAWOywT56656VFaWDBOOLY45nit4ZjjvBmvZ7TOwH7l1rRp01SlQk4wKyRexzjHxAyNeG1hnBEcxPcVU3978H0EfdDQqkODoCmyr7SgMP41zsRCEFk79ycHYz993Imsm6lZ+TT33XefztfXV3ft2jV1F2aneeKJJ/QzhWCmjZo1a+r++++/LDt5+vRpXZ06ddTvccMMOJgFw3BGD8BMeW+++aauSpUqaj1du3bVz65nPCufNouPRpvZQ5t9RZuVD7P5GNJmkcFMHBrMmofZ3rQZT/Bv5cqVdbt27dI/5syZM2q78TvMXoN/q1atmmWWj7/++kvN/mH4GMyGYs5ML5hxB9ugMXdWPsBMPxUqVFCz/mDWITCelQ+z8WC7VqxYke0LUFun4VgDts14RiA8xnCGQMyGZDxbHhjOyqfBzIleXl5qZhjMuKLN4IOZbQxh1kfMymjo8ccf17Vq1Up/F/YXMxkZzw5JRESFD7PuaZ/12r/4fMJMWdevX79rgwxn5YOPP/5YzYirfY5ilq1evXrpkpOT9c+9cuWKOh8xXA9mxjL8XAecz2DGM+2zXVuX8UxfmOUN68Hj8NmEGf8ww53xrHw9evS4a/txrjJ+/Hj9/TjfKVeunFqW4Wy7mI3M398/y3nGo48+qmaz1WBGNMzyZXguMnbsWPW5Z86sfMaf3aZm18OMytrYaftcqlQp3W+//ZZl37TZfrEtW7ZsMflC0s6n1q9fr859tOVhxjzj52AWOpwjGO5bYGCgbs2aNfpla7Py3bhxI8v6MENb+fLls9z3888/68+3tPV26dJFrUdj7qx8OPfB+aa2bZGRkdm+cTDzG/b11VdfvesxmIWwZMmSWV6X2Efj2QgBMyBqs/IZvo5btmypn11a8+eff+pn5TN8LGaL1mahzA5m5TOeCdnUTJQ4V8ayce4M+Z2VD/Baw/vI8JhjhkVtBjzQzt2xPkPad5FLly6puzHueP+OHDky233V4FwcrxdtrPA8vI+082NDFy5cUDP2Gc9UCbNnz9a5u7vr6tevr/6O7N+/P8tz8f7N6X1J9ssJu+ZgsTiiIoGSNlwhwxUbXDEwhBR51IYjddrwCg3S0XHFB3XW6A9h6ooEripghjukJKN+W0uTRdNMrVzOFFxxxFULXEVEA1TAFRBkKeF+jbY8pP+jXwFSiNFM1HhbcWUNVyyxrYb9KfAnBlfgcFUEKdloimq8H3gMsqlwVRfbjZlsjJtKIjMJV4Ww3Wh+jll7zIGriLhSjCtruMqk7SeuOiJdHVAbry3XGK7yYWYQ7bhpKd7alUA0CcXVSVxJyqm2H2OL9RleLdbS4w17iWE7sD3YR4wLlomrmLgibAjbhG3DMTfspYDtwFVqbC9S3ZFJh9IKwz4KuOKIY2x4nHHscGy1MgyUauDKGI4d0umJiKjoIIPFsD8TPk9z6l1k6vMc2U1aBnOdOnWy/duO5yI7CZ8/KA0yVcKFz3z0gUTGBrJUDD9PDOFzCtuOzC6c++BzD59Z2uclPqPws3HWBD6n8HmJcxINzgOQOYT9NiwdQrYLMqmQHYNzpexm20P5HkrU8VyU+OFzD5+T2U0Go50vGH92Y59xLoDnGZd7oTQM60DrAZQwGZ/vAc6hsK04Bqb6QGnnU/jsxf+RFYbsLpwbZXdehzHGGOAcEv0mDbcru/MFnHfi3NT4Mx77h33AscFYGU9ig/5MOD/Vzgs1OMfFa9S4kTXO/3BegoqA7PqXwYwZM9QN+2L8esJyca6LY4iMehy/nCa/weOQAYVzZGR9G89ErMHvcZ6NWRuRbYR/zemrieOMsTZ8LI4pXqOGvZG0c39sM85rMQ44T9POKfHaxXtCO0/D8zHu2jl8dq81nB/iPYKxxf5hlmrD96l27o73lWFJp7Y9OOY4F8f7HK9DNCDXsslyguXiOdrxzC6rDOey+Huj9ZUyhr8d2H6clxu+93HcsD+odECLCnIsDEwR2Tmc1IwYMUKlmiNlWYOm2QhIoVTN3uu4UWqHD17j6aItAenNSBM3npbZEnCSiRNbLDu7k6qCghMUnBhiJhUiIiKigoSgCdpVoKE9SkELE3q+Yt1okeFIUGaJSXNQRplToK+w/PTTT6qdB0oL89rTjGwXA1NEdg5ZMLjqgKshyBzClUQ0GkQmET6IV61aZecjULDQtwBXttC03V7gaih6cXTu3FldhSMiIiIi+zJz5kw1S6dxo32iosDAFJEDQKoyGpOiabg2nSwaR6JUi1ck8gcZaUhNN5WuT0RERERkjVA6iLJAa5yBkBwPA1NERERERERERFQknItkrURERERERERE5PAYmCIiIiIiIiIioiJhFYEp1LZiKlP8S0RERET5x/MrIiIisgVWEZiKi4sTf39/9S8RERER5R/Pr4iIiMgWWEVgioiIiMieIFvp8uXLcvPmzbt2KyoqSk6ePGl2pjiWcfz4cbMfT0RERGRLGJgiIiIisqCIiAjp3r27lC9fXsaMGaNfMqbmfuaZZ6RkyZLSrFkz9fvdu3dnu+bU1FTp3bu3BAUFSYsWLaRUqVKyZcsWC24pERERUdFjYIqIiIjIQq5evSoNGzZULQoeeOCBLEsdPXq0HDlyRC5evCjR0dHy8ssvS9euXSU+Pt7k2idPnizbt2+Xc+fOqaypESNGqIAXnktERERkLxiYIiIiIrIQ9HWaOHGifPPNNyo4pUlISJAvvvhC5syZI2XKlBFXV1cVePL19ZWVK1fetXZkSy1evFimTZsmFSpUEBcXFxk1apSUK1dOLceU5ORkNZmM4Y2IiIjI2jEwRURERGQh1atXl4EDB961tNOnT6uAkWEWlZOTk9x///2yd+/eux4fFhamSgIffPDBLL/Dz6YeDzNnzlTBMO0WEhJiob0iIiIiKjgMTBEREREVsEuXLklAQIB4eHhkWVPp0qVVaZ8xPN7d3V1KlChh1uMB/axiYmL0N1PLJSIiIrI2rta2QURERET2xtnZ2eSsehkZGapMzxgej99hdj9kVmmyezwg6GUc+CIiIiKydsyYIiIiIipg6A2FBubGjc7Dw8PV7HzG8Pi0tDRVzmcou8cTERER2SoGpoiIiIgKWI0aNVRZ3q+//qpfEwJPf/zxh7Rq1equtaPhedmyZWXdunX63yF76rfffjP5eCIiIiJbxcAUERERUQHz9PRUTdHfeust2b9/v9y4cUNef/11Vcb37LPPqrX//vvvMmDAAFWuh1n7Bg8eLGPHjpWdO3dKVFSUDBs2TM3616dPnwLeWiIiIqLCwx5TREREhSA+4orEhZ8Xv7KVxSe4TCGskazNlClTJDo6Wtq1aydJSUlSv3592bhxowpawfbt2+X777+XefPmia+vr4waNUoFsDBzH0oAa9eurR7v5+dnbbtGRERElGdOOuSFFzFMn4ypjTGTTLFixYp4a4iIiCzrzPrVsnP+KBFdhoiTs7QaOluqdcvMkiHHk5qaKrdu3VKz9BnD79zc3LLcjZI/nCsZz9B3Lzy/IiIiIlvAUj4iIqIChEwpfVAKdBmya8FodT85JgSeTAWlwDgoBSjry21QioiIiMhWsJSPiIioAN04vjczU8qALiNd4sJDWdJHRJSNSn37W3xsQpd9bvFlEhFR/jFjioiIqICkxMfJoa/ev2vpTs4u4le2UgGtlYiIiIjIPOjudOXKFfVvUWFgioiIqABkpKXKlhmvSUzYaXH19tWvwcnZWVoOmcVsKSIiojxCb+Jp06bJo48+Kg899JBMnjxZbt68mcelWca3334r06dPz/PCGjVqJLNnz87z83Nr0qRJ8uOPP6qnLVy4UE2wkZ6entvF2Kx///1Xhg4dWmTbv2XLFnnjjTeKbP2aNWvWSIUKFaRs2bJqFuAVK1ZIlSpVJCEhoVC3jYEpIiIiC8MVp90Lx8mVfZvExcNLHpi1WnxLV1RraTvqIzY+JyIiyqMTJ06oIA5mMW3evLl07NhRfvrpJ6lbt65cvHgxj0vNv2PHjsnWrVvNWtC5c+fUDKuRkZH6x7/00ktqXwrLpk2b5OTJk2p1rVu3lpdffllcXFwKa/X50qZNG1myZEm+lnHhwgX5888/87WMnAwePFjdshMWFibr168vsPWba8KECfLUU09JXFyctGrVSpo2bapeC97e3moRDz74oCxYsMDcxeUZA1NEREQWdvSbhXJm/SqVHdV+zEcSWKOheAeXUWvRZaRZeG1ERESO4/XXX5eGDRvK7t271ZfqUaNGqf8jMKVloCDz5+zZs1kygBITE1VASBMaGqqyQnDbv3+/ZGRk9oPEv/j5/PnzWQYVpU7R0dFZ7kNwA7OsZgezqh4+fFg9ToP1Yd14Hrbn2rVr6ldPPPGEylrCc7DtycnJWRaLLDE8zxC26ejRo2pGV3NguWfOnLnrodWrV5devXpluR+Biv/++08uX75sctHIUMPvsV2mREVFqX033jY8/tKlS1meEhERIdevX1f3Yb+144RtwDIMxwK/w7rDw8OzHE/A4w8ePKj+NQXPw++TkpJyHC7t9YPXAl43hw4dyjaDCNuBoKThfuI+7A9uGPOUlJQc14fXB4KE2ZXSXb16VY4cOXLXcnBsMAMvnoeALR5nSnx8vBw4cCDLscLzsG0Y+zp16uhfh5UqVZLnn39eLQavNxxHvM7w2ILEwBQREZEFnd+4Vg4sm6OW2GzgZCnf6gH1f68SJdW/iVGZJ15ERESUOwgYIdNn/vz5WWYxxeyl8+bNkyZNmqgv6fiCXq1atSxf1Ldv364CWpr27dur5eCLOP7foEEDFbTBz127dpWqVavKu+++q388MpqMM0c6deokv/76q8mdGDNmjPj7+8v999+vSqOQlYKg065du/RBoB49esjUqVPV83v27ClfffWVODk5qe1ZunRpluUi++att95S9yGY1aVLF7WPnTt3VstHaVp2EGDA2CAA0axZM3n22Wf1gThAGSJKIjWffvqplC5dWm0TxmPQoEH63yGog4yawMBAue+++9TjZs6cqf89gkJYfnBwsBofzEL7xRdf6H+/bNkyefrpp7Ns6sSJE1WAERBAwXHCNuAYNG7cWAXOtOyuBx54QE6fPq2ODcYAEEgaMWKE2ibsR6lSpbIcOwSYhg8frma4xfOxXFMBOo32+vnyyy9VmRsy8/AvXkMaBGuQ4YZtw784Fps3b1a/RoB03bp16tauXbu7gpyGhg0bJvXq1VP7WaNGDRWI09y4cUNlLOH4YqwrV64sf/31l/73GGe8hhGURdYbyvFQoqnBe2H8+PHqGDz88MNqXKZMmaJ+jTJObBuCdSNHjlT/RxAOWVx4/QFKZRGU++ijj9TvC7LUk4EpIiIiC7l2aIfsmJd50lj7yQFS89G++iVrgamk6AgLrY2IiMhy8CUWQYeiuJnbdBmZKSEhIVKxYmZ5vCF8uUeAA4Edc+3YsUMFPBDocXNzUwEIfGFHFgn6RSEjKy9fxhH0+P333+W3335Ty0b2Ff6PIBYCSejlA9gf9HcyhHK6Pn36qCCVBhkva9euVcExePXVVyUoKEiVAiIrZ8CAASpIkV32Uv/+/aVMmTIq4ws3BDLQ48gUrAuBFQTGENBCyRmysrQMMgShEIBB5hKyabBdGHctaINAyPHjx1VWFLZv9erVMnDgQNm7d6+5h0VlJ+H5GDtkEyGwogVUkLmDYz1nzhx9Btlnn30mP//8swpYIYto48aNMnr0aH2w7uuvv1YBMWwDxgvH4eOPP77n9mzbtk1lP2GZCJYh2Kh55ZVXVCAIY4DXS79+/dQxQGAOZabIOsINAayaNWuaXBf2BcFKBIdwQ4kinqNl7yEYiZI6BKgwFghiPffcc1lKQPE6+eOPP9Q2oEcZAp1altvKlStVzyi8znAfgqJ4XWP/e/furbYNwTwsA//Xyvc0CEq1bNlS3nnnHfX7giz1dL3n0SAiIqJ7QpPzTVNekYzUFKnQ7mFp8vK4LM/xKlFK/cyMKSIiskYol3ry2czAR2H7fvVS8fT0vOdq8eUYWR+WgswdBD3gkUceUQENZBYBAgRjx45VgQkEw3ID2TPI/NE4Ozur7UYZ2WOPPXbPRSEAhSADgj/Ilvnhhx+kWLFiKpMLAQj01ELgRhuzN998Uz0egQdk2BjCmP3zzz+qFEwLPCDA8uGHH5rcDgTiENxDcAlBSgS0DLOxPv/8cxXc0IKD3bp1UxlGCHAgwIjeTwh04HmArDDsM+5HtpY5EJhBdhi2o1y5cupYILiUHWwTAlHaNiGYgkw1BKuQtYXMJ2R5accWPcoQmLlXj6chQ4aogCWCgHg+fgbsL4I7GFsPDw91HzKy5s6dK3v27NFnct0Llj1r1iwV8MENWX/INEPWFI47GpMj2KYdNwQMZ8yYoQJmyGYCvIa11ycy2xBMxbHDuGFckEmG1yMguIbXOV4/yKCyJsyYIiIiyqfE6AjZMKGPpNyKkaDaTaXN2++r/lKGWMpHRESUPyhlMuzXlF+GGSIIDPj63plFVws4aNkruYEADTKhEEjCMlu0aKF6SpnbCwrlYSidQsYLILCCLCpso9brB8EGLy8vdUMwA1lGWrmbIa0vlWHWDkofEfgwBQEwZBctXrxYlb6hhOybb75RD0WpFwJ1yFgyhKAUIGCDjCvj39evX18FWHKjePHi+ocjqGfcc8sQMtRee+01/XhgTP7++2/9eGAMatWqleU52WUxGdKClmC4DVoZIF6P2jHA7zE+po5BdpBxZfiawziWLFlSBSRxnPE6Qnabtg5sD7LiDNdhuI14PaOpvjZW2M633347y7ggKJWbbSwszJgiIiLKh7SkBNk4sa/EX7sofmUrSadJS8XVw+uuJXoFBKv7mDFFRETWCIEYZC4VBS0IdC/ow4OMIZTCYQYxQyidW7RokSot08r5ECjQGP4/L7BM42Vkt0xkqiDjBwEl9G9CsMc4k+lekDWFcjWUwW3YsEGf4YTsHUBwAT2sDJnKOtMej7I6w2wzwxkBjf3vf/9TZWko5cJ44mcsG5lPCKSgfM0UBLKQHWb8e5SiIeAC2Y1jbkowjWEfx40bpxrIG0IADvB7bIOhnPb/XrA8bC8CP4aBJUAQyVwYJwSftH1HWR9KLRFo1I4bsqe0sdOYk12o7TcyyV588cUsm1SQJXl5xYwpIiKiPMpIT5ctMwdJ1OlD4lEsQLpMWyGe/iVMLu1OxlTm7DtERETWBF+O8YW3KG7mBiXQtBolWOixdPHiRf3wIVMHpVTly5dXy0IAxt3dPUupFgJX+YHm14alX5iRTptRzxiCRuhXhTIrBKWQ5WJY2qcFBpDllB08F/uI5tYIwmkZPsjSQUPyX375RQWmcEOGDMrrjGcNBDweZV2GDcjRWyu75t/I1kEvI5TxoeQN/YVQIqdl2aDhOMryNNg39M1CGSECjMj0QmDO8PcIziHzCjCOCLZofZAQ3END+9zA+BmOHbYJATRtPDDmaFSPwBqgmTdKAbXZ8fAvel/lFbLV8FpDc3NtndgPHAP0igLjbTQFM+Ohp5kGGXIIbKERP44ZmqGjHFFbB8ossQ7jIFt2tHFBFpW2jE8++UT27dtn9q6bsx+WwIwpIiKiPMAVrn2LJsnlXX+Li7uHdJr0hfiVrZztkrwCM68UotwvPSVZPYeIiIhy57333lM9h7SZyBAMQUYRgjfo8QP4Mo3m1Oi9hMbQCEQgmJMf6DGEhuC1a9dWPYwQgEDgwBTMZvfBBx+oRtVYL2ZS08rdAD2BcEOQDZlJCLQZ8/HxUbP3YfY5w0bd2DfMWIfA1datW1U5GIJUKH2bNm3aXctB1hC25YUXXlB9ppDVhEAQgk6mIFMHwRAErxBwwr8IPmkz6WGMcT96OKFED72WEPRAMAUQSMPMdwg8YaxQOobH9e2bOSEMMscQ6MLj0ecIJWta7ydztW3bVu0TgmsIQKExOoJPuB//IkiGfl5PPvmkWiTK2b777js1cx0CZHi9oJTx1KlT5q4yC4wpjgGOHcYSMxPiGGDsEDgCbAt6PuF1iGwuU68/bAN6g/3555+qzBO9ubBfCKwB1oHXEvYHAT2MNV7vWiP4e0EjeowH3hsIUmFWS2Qb5jSDozHsB7YDxxPH1tzsxtxy0pk7BUIBQqQQL2a84LWDQGTr4iOuSFz4efVF1Sc4s/kfEdmPY98vlv2fTUVOunQY96lUaHdnmmVT8HG76tHqkpGaLI8v3yG+pcrbz2CQVeL5FdmySn37W3zzQ5fdyeIg24bPVPQQ2r17typ/whdv9HMyhMdgdjQEHzDbGXoMoYn466+/rh6GrJ+OHTvqG0Pjyz+aWT/++OPq98jIwWxvKKlDxgkgsIMsGWSzoKwNARz0j0KAyvj5aIKNTCEEpBBcwHbg/wiOALKhkMWDwBJKrVatWiV16tRRDao16MuE2fwQFDMuGUNmE7Jt0AQdgR8EwXIq8UJQAtsLWB6yZpD1g4bkmHUPGWAIXgHK3FCGiH5eCKDhfsNyMmTsIOMIM/UhQKiV+mkwjmjcjX5UCDohqIUSPw1K2LDvyABCM24EPZChhYAWst/Q02ro0KH6x6OZN8YTgTxAoBHbh/Uguwtjg95WGEM8FoEiBC8xthoEEhH4Qb8pBNUQFEKQRlumIfQDw+vDcNxxvBB80l4/gMAYxhTbj0AbAlWGgRscH2RtYVuQ7WfoxIkT6phgn9HPCxlX3bt3V4EgQzgGyAbD7IgYaxxnrTcaXl9YrmFgD+WsCP4hUw6wXBwrHGOUCOJYGTbzN3489gnBNuw74P2FTC7sPwJthj2tLImBKaICcGb9atk5f5SILkPEyVlaDZ0t1bo9WwBrIqKicGHLr7JlxmsiOp00eWWC1HlygFmbsbZ3a4m/fkm6vv+TBNfOnPWHqKAwMEW2jIEpIiLHwR5TRBaGTKmd80dmBqVAlyG7FoxW9xOR7Ys4tle2zRmqglI1H+0rtZ94xeyd8iqhNUBnnykiIiIiImBgisjCrh7Yqr6wGtJlpEtceKiF10REhS328nn5d9JLqhyvfKsHpOmrk8xu2ApaA/Sk6IjC3nQiIiIiIquU6+bnSAtfsWKFqk9EMzYNag/RjAu1qmhmhtrF3EyVSGQPEqMj5OCKd+/aFSdnFzWNPBHZrqSYKNk4obckx0ZLieoNpN3oheLs4pKrHbozM9/1XD2PiIiIiMhe5SpjCkEnNNYaO3as6havQaMyNInDFJ1ogDZp0iTV5d/UdJVE9iotKVFlUiREhIuHwXTxTs7O0nLILDZAJ7JhacmZ729kPvqUCpHOU5aJq6d3rvfIMyBzZr5EBqaIiIiIiJRcBaYwxSC6s2O6RUPoWI9O9JiSEd3x0ckd0x3OmzePw0wOISM9XbbOHiyRJw+Iu19x6freWvEsHqT2veM7S9n4nMiG6TIyZPvcYXLj+D5x9/WXLlOXi1dAZq+o3GLGFBERERFRVrkq5Vu/fr2a3nL69OlZloIpBkePHq2fwhJTJGLqQ0z/aEpycrK6aVAeSGTL9i2eIpd2/CnObh7S6Z0lUqx8FfEpWU6Sbt5Q/aWIyHbtXzJdwrb+Js5u7tJx4mfiX6F6nneGgSkiIiIioqxylTGFoJSxjIwMOX78uISEhMgLL7wglStXlo4dO8qBAwckMDDQ5HjPnDlT/P399Tc8l8hWHV/7uZz8aana/DZvzZOS9Vqo/3sFllL/JnL2LSKbdfLnZXL8+8Vq+1sPf1dKNbj7czA39IGpaPaYIiIiIjLH5ctXZO/+g3IjMkp0RpNMkX3IVcaUKVevXlWNz/v166fK/AYMGCDr1q2TBx54QHbv3i2NGze+62ljxoxR/ag0yJhicIpsUdjW31W2FDR+eaxU6viofje8bwemEm5ctcVdI3J4l3b+JXs/fUeNQ6O+I6Vy5575HpM7s/LdUCXAuW2eTkRERORIkpNTZPioiXLrVrza7WJ+vlK5UkWpUrmiVK5cQf0/pHw5cXPLd2iDilC+j57L7RmJMAsfAk7QoUMHOX36tCxYsEC++OKLu3YPpX64EdmyiOP7ZducN0R0Oqn+8AtS56mBWXbHq8TtjKnIa7a8m0QOKfLUQdkyc5DqL1Wt23NSt9dgi4yD6j3n5KRKfJNjo/Lcq4qIiIjIEZw4eVoFpRB3QLZUbNwtOXj4qLppXF1dVHBKBasqZQarELTyL1bMEYbILuQ7MFWqVCnVW6pFi8zyJU3Dhg1l06ZNdjFIRMYwM9e/k/pJekqylGtxnzR/fao4OTlleRhL+Yhs062rF2XjO/0kPTlRyjTtKC0GT7/r/Z1Xzi6u4ukfqPrPYWa+vDZRJyIiInIEh48eV7vZvl0rGfL6KxJ28ZKcO39BzoeGyfnQC3L+fJjEJyTc/jksy5AElgiQypUrShUEq24HrcqWKSMuLrnqaESFIN+BKWjTpo38/fff0qNHD/0m//PPPyo4RWRvkmOjZcOE3pIcEyUlqtWXdmM+Ul82jXkHllZ3JTBjishmJMfdlI0T+0hSdIQEVKkjHcZ9Ks6ubhbdfpTzaYEpqVrXossmIiIisieHjxxTu9OgXh3x8HCX6tWqqJsGWVTXr9+QcwhSIVh1O2h15eo1iYyKVre9+w7oH+/h7i4VK4aozKo7GVYVxNvb256GzeZYJDCFZuYITmGmPWRO/fDDD3L48GFZuXKlzQ0IUU7SU5Lk30kvS9zl82rWvU6TvxA3Lx+TT2HGFJFtQQbkpimvSEzYafEOKiOdpywTN29fi+8EAlPR545JEhugExEREWUL/aVOnDyjfl+/Xm2Tj0PVSqlSwerWumUz/WMSEhIk9MJFOXf+dmZVaJiEhoZJckqKnDp9Vt0MlS5VUsqXK6MmaCvuX0z8ixfL/LdYMfH3x/8xeZufuLu784gVgDwFpmrVqiXx8ZnNx6BJkyaya9cumT17tixdulTq1asnBw8elAoVKhTAJhMVDfSa2f7umxJxbI+4+RSTzlOW6xucm6JlTCGzCl94XdzZV43IWuFq247335brh3eqYJR6fweVKZDN1c/MF8WZ+YiIiIiyc+LUaTXRWmBgCSlTOrN/r7mQAVWndk1106SnZ8iVq1dV+Z+WYYWywMjIKLl67bq63YuXl1dmwOr2Tf//YrcDWSqg5S/+xfykWDE/cXXNU8jF4eRplJ588kl1M4Syva+//trhBpAcx39fzJILm9epsp6OEz+T4pXu/JEzxd2vuDi7eUhGarLqJeNbOsRxBovIhsRHXJH/ls6U0I1rxcnFVTqMXyQBVUxflbMEz4BgtRgGphxTRkaG7Nmzx+TOly9fXsqVK5fld7jwZ6xatWoSGBjomANIREQO4/CRzP5SyJYy7uebF+gtVb5cWXVDzypNbGycKgO8dj1CbsbESmxMrPo3JiZGYmLj9P9PS0uXxMREdUOpoDn8/HxVkErLxKpVs7o89kh39rkykqfAFJGjOfXrl3Ls20/UbrcaNldKN2xzzyHAH0/vwFJy62qYJEZdY2CKyAqdWb9ads4fqWbXhMpdekqZJh0KdEv1M3YyY8oh4WTWsCenJioqSqZMmSLjxo3T3xcXFyetWrWSgIAA/SzIsHjxYunZs6dDjh8RETmOI1pgqm7BXTAEZDY1bJBz309k18fHJ0hMLAJWWuDK8P8xtwNYcSqgFRsXJxkZOomLu6Vuly5fUevatmO33LwZIy/1/Z/jHEgzMDBFdA+Xdv0jez4arx7V4MURUuX+rNmCOcEXUASm2ACdyPogU8owKAXn//lBGvZ+W3yCC6aMD+6U8kVY36BQgfPx8ZGIiKzH/syZM1K7dm3p0qVLlvVfuHBB/Xzp0iU2ZSUiIoeSkpIiJ07l3F+qMCHpwNfXR93Klb33eSLKBm/F38oMYt3MDGCFhl2U1WvWyvc/rpOQkLLywH2dCnMXrBoDU0Q5iDx9SLbOfF31l6r6YC+p/7+huRovfQP0yKu5eh4RFbzwvRv1mVIaXUa6xIWHFk5gKtq8FHCyf8iUuu+++6R169ZZdjYsLEyV7Jk7UxAmocFNExsba/+DR0REdglNz1NTUyWwRICULZM527ktQdmgapxerJhUuN3Rpb20UgGuVd/8IAs/WSJlSpeWenVr2dJuFRjnAlsykY27de2SbJzYT9KSEqRMk/bScsjMXNc2o5QPmDFFZF3iI8Ll4PK5d22Uk7OL+JWtVKAba9j8HGnh5NhOnTqlenROmjTproFAYCooKEhNLvPUU0/JqFGj5PLly9kOGGZJRg8L7RYSwt6GRERkmw4fOaY23FL9pazF/3o9Ie3atFT9qqbPft+shuuOgIEpIhNSbsXIxgl91HTuxSvVkvbjFqmm57nlFZgZ3U+MZGYEkbVIiY/NfH/fvKHKbZ2cMz8KEZRqOWRWgWZLgVdASfVvenKSpCbEWcuwUBFBttSDDz6oekkZw5ViTHeNUj78fseOHdKgQQP1syljxozJbNR6+3bx4sUi2isiIqL8OXz0hFpAvQLuL1XYnJ2d5c2hA6Va1cqq6fqU6e+qz3pHx1I+IiPpqSmyaeoAiQk7pUrxOk9dIe4+fnkaJy1jCs3PiajoZaSlypbpA+Vm6AnxDCgpXd//UQWkUL6HTKmC7C2lcfX0EjdvPxWUQgN0d59iRT8wVCROnDghq1evlu3bt5tc/xtvvKFumuHDh0ubNm1UZtRHH31013M8PDzUjYiIyJap/lInT6tdaFCvji3vikmeHh4yYewIefOt8XIh7JLMfm+hTBz7lkPP1MeMKSIDKKvZ+cFIuXZwu7h6+UjnKcvz9UVV6zGVcIM9poiKGt7fuxaMliv7t4irp7d0nrJMfEuVV+/x0g1b5+u9nltsgE6AbKlu3bpJixYtzBoQXGV9+OGH5fDhw2Y9noiIyBadPHVWZQ2XCCguZcvaXn8pcwQFlpCJ494Sd3c32bvvgHyx/Gtznma3GJgiMnDoy/fk/D/fqwyKDuM+lRJVc5429F7uTAvPjCmionb46/ly9s81qnSv/dhPJLB6/SLbpDuBKf5tcFTHjx+XNWvWmOwtpfn4449l7tysvdDOnj0r5cuXd9RhIyIiB3Cnv1SdXPf4tSXVq1WR4UNeU5u89uff5I+/NtrS5lsUA1NEt535Y7X64got3pgpZZvlf/pOrZQvNeGWuhFR0Tj39/cq8AzNB02Tci26FM2G3GbYAJ0c0+TJk+Whhx6SZs2a6Qdg27Zt0qlTJ32DczQ+R9+o+fPny9GjR9W/aJQ+cOBAxxw0IiJyCIeOHFf7icbn9q59u1by/HNPqd38eNFSOXx73x0NA1NEInJl/2bZtWCMGot6zw6W6t2fs8i4uHn7qhuglwwRFb6rB7bJzg/eViuu8/RrUuPhFwt/I4x4BgTz74IDCw0NVY3MjWfiu3nzppw/f17fBPWZZ56RhQsXqoBUo0aN5PPPP1dZVh06dHDg0SMiInuG/lInT2X2l3KEwBQ890xP6dCulZqpb8bs9+XKFcfLqGdgihxe9Lnjsnnaq6JLT5NKnR+Xhn1GWnRMtHK+hEj2mSIqbDdDT6rJDND0vGLHR6Rxv9GFvQkm3SnzZcDaEVWqVEkuXLggTZo0ybL76B+F+6tXr66/H9lR586dUyfq6C31+OOPO+KQERGRgzh1+qykpKRKQEBxKVe2jEPstZOTkwx7Y6DUqF5FYuNuyeTp70p8vGPN1MfAFDm0hBtXZOPE3qrMrmT9VtL6zXctXsesNUBPjHS8yDdRUUqIvCYbJ/aR1PhYCa7bXNqMmKf6S1kDrZQvKTrCGjaHbABOWomIiOydYRmfI332eXi4y/gxIyQwsIRcvHRZZr/3oaSnp9v74dazjjN0oiKQEh8nGyf2VTPm+VeoLh0nfiYu7pafZts7MHMmCXxJJqLCkZoYL/++00/ir18Wv3JVpNM7S8TF3bNwVm4G9pgiIiIiupvW+LxBvToONzyBJQJk4tgR4uHuLvv2H5Qlyxxnpj4Gpsghoaxny4zXJPrcMdXrpfOU5eLhV7xAxkKfMRXFUj6iwpCRniZbZw6SqDOHxcM/ULpMXS4exQIKY9Vm89L3mGLAmoiIiAhSU1PlxMnM/lL16tZyyEGpVrWyjBj2utr3n375XX7/4x+HGAcGpsjhxF8Pl38n95cr+zaJi4eXdJ78hfiWDimwcdBm5ku4wS+gRAVNp9PJ3k8myuXd/6gMyE6Tlopf2UoFvdpc8wosqZ6TcitG0lOScv18IiIiIntz8lRmf6nixf2lfLmy9rZ7ZmvbpoW8+L+n1eM/WbxMDh4+avZzbRUDU+RQzqxfLWv7tJLwPRvUflfr2ksCazQs0DHQNzlmKR9RgTv23SI5te5LEScnaTvqQwmunbW5tLVw9y0uzm6ZpcOJ0TesZbOIiIiIisydMr7aDtVfypReTz8uHdu3UX2mZs6ZL+FX7Lv6hoEpchjxEVdk5/yRIjqdfp/xBRb3FyTvoMweU4ks2SEqUBc2/yL/LZmu1tF0wESp0LZ7ga4vP9DM8045H2fmIyIiIjp85LgahPoO2F/KGM4Vhw4eIDVrVJO42zP13boVb7cvEgamyGGEbVmXJSgFuox0iQsPLdAx0DKm0PwcZUZEZHnXj+6RbXPfVAuu+dhLUrtnf8uvxMLuNEBnmS8RERE5NvSXOn7ylBqEenVrO/Zg3JY5U99wCQ4KlEuXwmX2uwvsdqY+BqbIIcSEnZZDX71/1746ObsUeP8Z7ctnRmqypMTddIjxJipMsZfOyb+TXlLvsZA2XVW2lC3wKhGsNjORGVNERETk4E6dvt1fyr+YhJR33P5SxkoEFJcJmKnPw0P2Hzgsny39yi5fKQxMkd1LjI6QDRP6SGpCnPiWqShOzpkvewSlWg6ZJT7BZQp0DNCAWZsRLCHSvmuDiQpb0s0bsmHCiyroG1izkbQd+aE4u7gU9mbkyZ2MKZbyERERkWMzLONDGRvdUbVKJXnrzcyZ+n759Q/5bf3fdjc8rna3R0QG0pISZOPEvhJ/7aLKjOr6/k+SnpKsyvfwc0EHpTRegaUlOTZa0AA9oDJTU4ksIS0pUWVK3boSJr6lK6gZNl09vSyx6ELhGZA5M19SdEShrI+IiIjIWh06ckxtWv16/K5kSptWzaXPC71k+VffqJn6ypUtIw0b1LXWw5lrzJgiu5WRni5bZg6SqNOHVMZSl2krxNO/hApGlW7YutCCUuAdWEr9ywboRJaB9/e2OUPkxon/xN3XXzpPXSGexYMss/BCop+xkxlTRERE5MDQX+rEidNqBBiYyt7TTz4qnTu2k4yMDJkx5wO5fLlgJ/EqTMyYIruEJuP7Fk2Sy7v+VlOyd5q0VPzKVi6yfdU3QL/BUj4iS9j/+VS5uH29OLu5S6dJS8Q/pKolFluoWMpHREREjq5S3/7inZYulVNSJM1JpMP4d0TyUcoXuuxzux1SlDgOGdRfrly9KidOnpHJ09+V9+ZMFj9fX5vfZ2ZMkV06/sNncvLnZeqPWtuR8yW4TrMi3U8vZkwRWcyJH5fKibVL1PLajJgnJeu1tNiyC9Od5ueclY+IiIgcl3dahtr5eFeXfAWlHIG7O2bqG6Fm6rscfkVmzV0gaWlpNr/rDEyR3Qnb+pvs/3ya2q8m/cdJxfYPF/k+egeVVtuQEMkvoET5gSypvYsmqUU06jdaKnV6LD+LK1JaxlTSzUhVmkhERETkiHzSMs+D4l0ZnjBHQHF/mTjuLfH09JADB4/IZ0u/NOdpVo1HnuxKxLF9qu+M6HRS45E+UvuJAVaxf/peMgxMEeXZjZP/ydbZb6j3d7Xuz0vdZzJnJ7FVqieWk5PoMtIlOTbKVneDiIiIKM+cdDp9xlQCMqbILFUqV5S3hw9W5X3rfvtL1v32p1nPs1YMTJHdiAs/L/9OfknNuleu5f3SbOBkq5lqVGt+nhDJHlNEeRF35YJsnNhP0pOTpGzzztJi8DSreX/nlbOLq75hOxugExERkSPyTM9QQQn0l0p2dnLEIcizVi2aSp8Xe6nnL/p8hfx34HCel1XUGJgiu5AUEyUbxveW5JgoKVG9gbQf85E4u1hPxF3rMYVp4VmyQ5Q7yXHRsnFCH0mOiZSAqvWk/ZiPVVDHHngFBKvdYGCKiIiIHJEP+0vly1M9H5H7OrdXM/XNnDtfLl0Oz9fyigoDU2Tz0pIT5d9JL0lceKj4lCwvnSd/Ia6e3la1XyjZcXJ2Fl1GhiTdvGFV20ZkzdJTkmTT5P4Se+mseAeXlc5TvhA3b9ufeURzZ2Y+9p8jIiIix6P1l0pgf6k8QSnfG6/3lzq1a0h8fIKaqS8u7laellWUGJgim4ZAz/a5w+TG8X3i7usvXaat0H/RsyaqZEefGcEvoETmiL9+WTaM7yPXj+wWN28/6TJ1uXgHZk4kYC/0DdCjI+xll4iIiIjMkpqalnVGPsoTNzc3GTfqTSkZHCTh4Vdl5pz5NjdTHwNTZNP2L5muZuFzdnWTjhM/E/8K1a12f9gAnch8Z9avlrW9W8u1Q9vVk6p1e06KV6pl/gJsxJ2Mqes2ssVERERElnH67Dn2l7KQ4sX95Z1xb4mXp6ccPHxU/vpnk4WWXDgYmCKbdfLnZXL8+8Vq+1sPf09KNWht1fvCBuhE5omPuCI7548UEZ3+CSd+XKLutzf6gDUDU0RERORgDh8+pvY4HrPxObHxeX5VqlRB3h4+SHo9/bh0faBzfhdXqOyjeyw5nEs7/5K9n76j9rthn7elcpeeVj8GWgP0xEiW8hHl5PyGH0R0d4JSoMtIz+wjF1zGrgbvTokvM6aIiIjIsRw5elztMPtLWU7LFk3VzdYwY4psTuSpg7Jl5iDVXwrlPfWefcMm9kHrjZPAwBRRtqLOHJHDX39w1++dnF3Er2wluxs5lvIRERGRI0pLS5Ojx0+pXVcZU+TQGJgim3Lr6kXZ+E4/SU9OlDJNO0qLwdPVTAS2QJ8xxebnRCah2fnGiX0kPTlJioVUUzNZAoJSLYfMsrtsKdAHpqKvi84oS4yIiIjIXp0+c06Sk5MlzUkk2dk2vs9RwWEpH9mM5Lib6ksrZq8qXrm2tB/7iWp6biv0PaZuXLWVTSYqNCnxser9jV5LxSvVlAff+0FSE+JV+R4ypeythE+jBaYQjEtNiBN3n2KFNuZEREREReXwEa2Mj/2lSBiYItuQnpIsm6a8IjFhp8U7qIyaNt7dx882Nv42rxKZ09wzY4ooq/TUFNk89VW5GXpSBWo6T12hAjS42WtASuPq4SVuPsUkNT5WBeUYmCIiIiJHcPiI1vicRVwkLOUj64fylh3vvy3XD+8UN29f6TxluQpO2RrvoMzAVHJMlAq0ERF6nOtk1/xRcvXAVnH18lHvb5/gsg41NF4BwWp/OTMfEREROQL0lzp2gv2l6I5chyc3bdokkydPlj179pgcx3Pnzqnf79ixw+TviXLr4PK5ErpxrTi5uEqH8YskoErt3C7CKrj7FRdnN3e1LYnREVaxTURF7dBX78u5v79TfaRQnluiWr2i3qRCxwboRERE5EjOnD0vSUnJUszPl/2lSDG7x1RGRoZMnTpV5s6dq54YFBQkzZs3v2sYX3nlFdmwYYP4+vpK69atOcyUL6d/XyVHVn+oltFq6Cwp06RDvpZXlNCk3atEKYm/dlESI6+Kb6nyRbk5REXu7J9r5PDK99V2YCKDcs07F/k2FQUGphwHMgTXrFlz1w63a9dOypUrZ3Ig4uPjZfPmzXLp0iX1uNq1bfPiDBERkebQ7f5SdevWlh0njnJgyPzA1Pfffy+rV69WmVDPPPOMyaFbvny5yphq2LAhh5byLXzvv7L7wzFqOfX/N1SqPtgr38ssamiAjsBUQuS1ot4UoiJ1Zf8W2Tl/lNqGus8MkuoPPV+k21OUGJhyHFevXpVnn31WGjVqJK6ud64Nli5d2mRg6sqVK/Lggw9KYmKiVKlSRYYOHSozZsyQYcOGOc6gERGR3dH6SzWoV1uEgSnKTcZU06ZNZe/eveLj42Ny4G7cuCEjRoyQr776SqZPn57j4GJaSNw0sbGxPBiURfS5Y7J5+kDRZaRL5fuelAYvjrCLEfIKvN0AnYEpcmDR54/L5mmvii49TSp2fFQa9R3pwKMh4hlQUu0/e0zZvwsXLqiAFM6nXFxc7rnDY8eOlZIlS8r69evFzc1N/vnnH+natas88sgjUrVq1Xs+n4iIyNqo/lLHT6rNql+vjsh31raFVBTM7jGFK3XZBaXgzTffVCdL3bp1u+d+zJw5U/z9/fW3kJCQez6HHEd8xBU1bXxaYryUathGWg2bo8rg7AEypoAz85GjSoi8Khsn9pXUhDgpWa+FtBkxT5yczf4osktaxlRS9HW73D+6IywsTGVGmROUQgkfMtURnEJQCu677z5p1aqVLF261OSw4qIfLvYZ3oiIiKzJmXOhqr+Un5+vVKzA1iaUySLfBv766y/5/fff5YMPPjBrXMeMGSMxMTH628WLF816Htm/lPg4FZRKuHFV/CvUkI4TFovL7Ybh9sDrdmAK+0fkaFITbqmgVEJEuBQrX1U6vvO5uLh7ONow3OVOKR8nRbB3CEx5enrKgAEDpFmzZvLcc8/JkSNHTO42squSkpJUIMoQfj55MvNKszFe+CMiImunlfHVq1NLnB384iTdke/AFPoeDBw4UN59910JDs6c8vpePDw8pFixYlluRLeuhsnfo3vJzfPHVWlL56nLxd3X364GBs3PgRlT5Ggy0tNky4zXJPrsUfEsHqTe3x5+AY42DCbdCUyx95y9K168uJQqVUo1MB81apSaWKZFixZy5syZu3Yd/aW8vLzuylbHudbly5dNDhUv/BERkbU7fLvxeX30lyK6zexZ+bLz8ccfS3h4uOqXsG/fPvWws2fPytq1a1VKOVLQie7l9PpVsuuDO31mqnV/1i5nrfMOyuwxxebn5EgwE9nuhePVhAYuHp7SafIX4lemoiMNQY60wFTKrRhJT0kSF3dPjo2d6t+/v7ppnn76aenYsaPMmzdPnU8ZwuzGOI9KT0/PUvqHEj9c3DMFF/5wIyIiskb4TDt2zKC/FNFt+c6YwlW/wYMHq6t6SE/HDdDckydHZA70lNr1QebsXJqjqz9S99sbfcYUm5+TAzm65mM58/tKEScnaTd6oQTVbORAe39vyAx1dssMJiSynM/hdOjQQU6fPn3XfpcvX15lVIWGhmb5HWY/Rp8qIiIiW3Pm7HlJTEoSX18fqVSRfabpjnxnTD300EPqZmjr1q1qxhjM0kd0L6d+WS4iuiwPw2x8ceGh4hNc5l5Ptyla83M0fk5NjBc3L9OzXBLZi/Mbf5QDX8xSu9Ns4GQJad3VXnbNYjC5g1dAsMRfv6Rm5vMtzRM1ezVx4kRJSEhQ7Q80+/fvNznDHoJPDRo0kK+//lomTJigHh4XFyfr1q2TJUuW2OsQERGRHdPK+NhfiozlKTD11FNPSZ06dbIdzUcffVTq16+f7e+JNFcPbJNj3y+6a0CcnF3Er2wluxsoN29fcfXyUTMOImvKrXwVu9tHIs21wztlx7zMCxS1evaXWo/14+BkA+V8KjDFmfnsGvpJPfbYYyoY2alTJ1m/fr1s2LBBDhw4oPYbk8gsXrxYtUZAJjoCUi+88IL6f82aNVVAq2LFivL444/b9TgREZF90hqf16+XfSyBHFOeAlNTp07NcbTQfJPoXm6GnpRNUweILj1NSlRvoJoiI1MKQamWQ2bZXbaUxjuwtMReOqv6TBVjYIrsVMzFM7Jpcn/JSE2RkLbdpekrmRkfZNqdBujXOUR2rEePHvLDDz/I+++/r3px1q1bV3bs2KGCToDG5gg8oR0C4EIggliffPKJLF26VNq1ayezZ8/O0nOKiIjIFqC/1FF9fyk2Pqes8hSYIsovBGU2TuwjqfGxElynudw/62tJiolW5XvIlLK3Ej5DXoGlVGAqMepqfoeRyColRkfIxgl9VDPvoFpNpO3I+eLE6YBzxMCU40DGFG6mPP/88+pm6Mknn1Q3IiIiW2bYX6pypQq2vCtUABiYokKH3kr/vtNP4q9fFr9ylaXTpCVqFioEo+w5IKXRGqAn3ODU8GR/0pIS5d9JL8mtq2HiW6aidJq0VFw9vOxvRy3MMyBYLRE9poiIiIjszZGjx9Uusb8UmZLvWfmIciMjPU22zhwkUWcOi4d/CekydYV4FAvIzSJsntYAnRlTZG8y0tNl6+zBEnnygLj7FVfvb8/igfa2mwVCC1gnRUcUyPKJiIiIitKhI7cDUyzjIxMYmKJCo9PpZO8nE+Xy7n/Exd1DOk36wi4bnN+Ld1Bp9RA0PyeyF3h/71s0WS7t+FOc3fD+XsoearnAUj4iIiKyV4b9pRqw8TmZwMAUFZpj3y2SU+u+FHFykrYjP5Tg2k0Kbd3WRF/Kx8AU2ZETaz+Xkz9/ofao7dsfSMm6ze1o7wrencAUA9ZERERkX86eC5XExETx8fGWShXZX4ruxsAUFYoLm3+R/5ZMV+tqOmCiVGjXvVDWa43Q/ByYMUX2Imzrb7Lvs8zZWpv0HycVO/Swl10rNF4lMntMJd28oUoiiYiIiOzF4SPH1K7Uq1tbXFwYgqC78VVBBe760T2ybe6baj01H3tJavfsX+DrtGZajylkTKH8iciWRRzbJ9vmDBHR6aRGj95S+8lXbXl3ioxn8SCVTarLyJDkmMgi2w4iIiIiSzt89IRaZP26tSy9aLITDExRgYq9dE7N0JWRmiwhbbqqbClHp5XyYUxSbt109OEgGxYXfl7+nfySpKckS7mW90uz1yaLk5OTDe9R0XF2cc0MTiGbkg3QiYiIyE5k9pe6HZhifynKBgNTVGCSbkbKhgkvSkrcTQms2Uj1lXJ2cSmw9dkKNH7XZiJkOR/ZqqSYKNkwoY8kx0RJier1pf2Yj1RwhfKODdCJiIjI3pw7f0ESEhLFx9tbKleqaG+7RxbCwBQViLTkRJUpdetKmPiWriCdJ38hrp5eBbIuW6RvgH7jqi1uPjk4ZEr9NfIZibt8XnxKlpfOk5eJq6e3g49K/rEBOhEREdmbQ7f7S9WtW4v9pShbDEyRxaFx77Y5Q+XGif3i7usvnaeu0JeoUCZ9A3TOwEU25vTvX8tPL3WQmAsn1ZZX7dpLH1Ch/PEKyGyAnhh1PX8LIiIiIrISR44cV1tSv25tK9kiskYMTJHF7f98mlzc9rs4u7lLp0lLxD+kqsXXYeu8A0urXUADdCJbER9xRXbNH51lcw+v/EDdT/nHUj4iIiKyJ+npGXJE31+KgSnKHgNTZFEnflwqJ9Z+rpbZZsQ8KVmvpUWXby/0GVMMTJENOf79IhHJOpOkLiNd4sJDbWgvrJdW4pvE5udERERkB86dD1X9pby9vaRK5Up2sEdUUBiYIou5uH297F00SS2vUb/RUqnTYxZbtr3xDiyldomlfGQrLu36W078tPSuzXVydhG/sjzRsARmTBEREZE9OXy7jK9eHfaXopwxMEUWcePkf7J19hsiOp1U6/681H3mdYss115pGVNsfk62IPL0Idky43X1/g6u21wFowD/thwyS3yCy9jCblg9BqaIiIjInhy+3fi8fr069rRbVAA4tzflW9yVC7JxYj9JT06Sss07S4vB08TJySnfy7VnWskOM6bI2t26dun2+ztRyjTpIJ2nLJPE6BuqfA+ZUgxKWY6nvvn5NdHpdPw7SkRERDYL/aWOHsucLKc++0vRPTAwRfmSHBctGyf0keSYSAmoWk/aj/lYnF34sroX76DM5ueJURFqFkNnl8wMFCJrknIrRr2/k6KvS/HKtaX9uE/F2dVNBaMYkLI8LWMqPSVZUhPixN2nmOVXQkRERFQIzodekPiEhNv9pSoWwhrJlrGUj/IsPSVJNk3uL7GXzop3cFnpPOULcfP2zfPyHIln8SBxcnZWjaMR1COyNumpKbJp6gCJCTulAqmdpywXdx8/a9tMu+Lq4SVut4NRiVHX7WrfiIiIyLFoZXx1VX8pXoSnnDEwRXmiy8iQ7e+NkOtHdoubt590mbpcvAMzs4Do3pBVhuAUJERevfcTiAoRysh2fjBSrh3croLNCEoxQ6pwsM8UERER2YNDtxufs4yPzMHAFOXJgWVz5MKmn8XJxVU6TFgsxSvVytNyHJnWAD0x8pojDwNZoUNfvifn//leNTdvP/YTCajChpWFxcugzxQRERGRLVL9pY6eUJtev25tW9wFKmQMTFGunf5tpRxd85F6Xqthc6RM43a5XgaJPsMsgYEpsiJn/lgth7+er7YIM+6VbdbJirbO/jFjioiIiGxd6IUw1V/Ky8tLqlapZOu7Q4WAgSnKlct7NsruhePUcxq8MFyqPvB0rp5Pd+gzpqKYMUXWIXzfJtk1f7TamHrPviHVuj1rHRvmQO4EpiIcaK+JiIjInhw6ckztTt06NdlfiszCwBSZLerMEdkyfaBq2F3lgael/vPDzH4u3c2rRCl1Z8IN9piiohd97pj+/V2pc09p2Oftot8oB8SMKSIiIrJ1h2/3l2pQj2V8ZB4Gpsgs8dcvy8aJfSQtKUFKN2qnSnycnJzMei6ZpjWLZ8YUFbX4iCvq/Z2acEtKNWgtrd+cy/d3EdEC1knRnJWPiIiIbA/6Sx05elxteD32lyIzuZr5OHJQ+MIaffaI7P98mpq+vHilmtJhwiJxcXN30BGxHO+gzIwpNj+nopQSH6eCUsjc869QXU1m4OLuUZSb5NA89c3PGZgiIiIi26P6S8UniJenp1SrWtn2doCKBANTlK0z61fLzvmjRHQZ6jFuPn7SeeoKcfcpxlGzAK8SpdVS2PycikpGWqoq37t5/rgKiHSeslw8/IoX1eaQCEv5iIiIyCIQHPr0s2Xi4+MjvV94Rry9vCyy3HvRyvjYX4pyg4EpMgmZUoZBKUhNjBcRlu9Zitb8PDkmUtJTkpmlQoVKp9PJrg/HypX9m8XFw0s6T/5CfEuHFOo20N20HlMpt2IkPSVJXNw9OUxERESUK7GxcTJh8iw5c/a8et7efQfk7eGDpGaNarlaTl4cvt34vH69Onl5Ojko9pgik+LCz+szpfQyMiQuPJQjZiEexQLE+XZJZGI0Z+CiwnVk9Ydy9o/V4uTsLO3HfiyBNRoW7gaQSe6+/uLslllKmciZ+YiIcuSZliGV4pKkTEKK+Kami5NOxxEjhxcVfVNGj5+mglLFivlJcHCQXLl6Td4eM1nWfPeT6gFVUDIyMuTIsRNq8fXZ+JxygYEpMinq7NG77ndydhG/spU4YhaC5vFao2P2maLCdH7DWjm4fK5aZbPXpkj5lvcX5uopB+rvAvtMERGZpURKqvikZ0iJlDSpGJ8sNWMSJSQ+WfxT0sQlg0EqcjwREZEyatwUuRB2UUoEFJfZ0yfIwvdnSvu2rSQ9PV2Wf/WNjJ80Q27ciCyQwTkfGia3bsWzvxTlGkv56C5X/tsq/y2ZkXk/Zt7T6VRQCjPx+QSX4YhZkHdgKYm/dpEz81GhuXpwh+yYN0Ktr/aTr0rNR/oU2rrJPCjni79+SU04Qfbn7Nmz8sMPP0hsbKx06tRJ7rvvvhx3ct68ear01lCPHj2kZs2a9jc4RLnkkZ753rjl6qz+76bTSbHUdHXDbxJcnCXOzUXdUpzZjoLs25Ur12TsxOlyPeKGlAwOkhlTxkmZMpmTLY166w1p2qSBfPrZcjl0+JgMfnOMDBn0irRp1dyig6LNxlenTk1xcXGx6LLJvjEwRVlEnz8um6cOEF16mlTq9Jg0fmmMxF0JU5lSDEpZnpYxhRnRiApaTNhp2Tz1FdX0vEL7HtLk5bEFvUrKA63PVGI0A1P25u+//5bHHntMHnroIZUdh6DToEGDZM6cOSZ3NSIiQkaMGCHdunUTT887/cZatmzJwBSRTicet0uSrnq5S7Kzk3im68QvNU380tLFK12nsqlwK52Uqn7/+dKvpGWLplKndg1+aSa7EnbxkoybOEOV8ZUtW1qmTx6rglMafOY8cF8nqVOrpsyZt1CV+U2f9b5073qf9H/pBfH0sMyMzFrjc5bxUW4xMEV6CZFXZePEvpKaECcl67eU1sPfUw25fUqW4ygVEK0BemLUtQJaA1EmZN9sGN9bNdUOrtNM2r79vuovRdZHH5hixpTdGT16tLz99tsyadIktW/79u2T5s2by/Dhw6V06cyZWg2FhYWJs7Oz/Pzzz+Lm5mZ340GUH646nSAfA5lRKhvKyUmSXHFzF3TudM3IEL/UdHXzScsQjwydrP35N3Xz9fWR5k0bS8vmTVQWibe3d342hahInT0XKuMnzVQNzytWCJFpk8eoMj5TypUrI+/Omixffv2tfL/2F/n9j3/k8NHjMnL4YKlaJX8tW9BfCssCNj6n3GJgipTUhFsqKJUQES7FyleVjhM/4yxxhcA7KPOLSEIkA1NUcNKSEmTjO31VeZhfucrS8Z0lnO3NijEwZb8GDBigsp80lStXVmV6ly5dyjYwhYCVuUGp5ORkddOgXJDIXmllfAhK6dB6wkias7NEe+DmJs46nfikpku/li1lz97/JDbulmzctFXdXF1dpH7dOtKyRRMVqCpZMtheh4zs0IlTZ2Ti5FkSH58g1apWlqnvjFYNz3Pi5uYqL/V5Tho3qifzPvhELl0Kl+EjJ0rf3s/KYz26qQsieRF64aLqL+Xp6SHV8hnkIseTt1cd2ZWM9DTZMuM1iT57VDyLB0mXaSvEwy/ArvbRWrH5ORW0jPR02TJzkESdPiwe/iWky9Tl4ulfoqBXS/ngGVBSPZsZU/YHgakKFSrod2zlypUq8NSgQQOTO4uMKVdXV3nkkUfU49q2bSt//vlntgMzc+ZM8ff3199CQkLsbxCJbtPK+JJc7v11JsPJSeLcXWX40Nfkq2WfypwZE+XJx3tI+XJlJC0tXf47eFj13uk3YKgMHjZavly5Rk6dPqsyQIisFcrmxk+coYJStWvVUD2l7hWUMtS4YX1ZOH+WKm9NS0tTpa7vTJ2jygHz4vCRY+ppdWrXVJ9dRLlx77/kZNdwpXb3wvESvvdfcfHwlE6TvxDf0ndOmqlgofk5oIySyNLw/t776TtyedffKgOy0ztLxa9sZUuvhiyMGVOOYffu3TJq1Cj5+OOPxd3d3eROV6xYUQWjXnjhBfnxxx+lffv20r17d9m5c6fJx48ZM0ZiYmL0t4sXLzrGYJJD8rwdNELvqNxwcXGWunVqyUt9/yeLPnpPFn/0nrzc93mpV7eWODs7qVnFVn/7o7z59gTp/fJgWfDRZ7Jr9z6JiWEGIlmPvfsPysQpsyQxKUkaNqgr0yaNFh+f3Jek+hcrJhPGDJfXX+0n7u5usv+/Q/LGsNEqszC3Dh89oZ7CMj7KC4YyHdzRNR/Lmd9Xqrr8dqMXSlDNRg4+IoVL32OKpXxUAI7/sFhO/bJcvb/bjlwgwXWaFsBayNK0wFQSm5/bLczMhyboCCT17Nkz2/18/PHH1U3TqlUrOX36tCxcuFD935iHh4e6ETkCrZQv2YyMqZyg584T5R6WJx5/WPXowRd+BKL2/XdIoqNvyh9/bVQ3CAgoLpUqhEilShWkcqUQqVSxglQIKWd2uS2RJWzfuUdmv7tAZfs1b9ZYxo4cmu0FDnOgMfrD3R+QenVry5z3PlQleZOmzVVlfSjvy+7iiSFkF2oz8jWoV9uc1RJlwcCUAzu/8Uc58MUsNQLNBk6WkNZdHXg0ioZ3YGaPKTScT02MFzcvn6LZELI7Fzavk/2fTVP71aT/eKnQ7iG720d7pQ9M3byhSjGdOd2yXbl69ao8+OCD8sQTT8iECRNyvW9NmjSR9evX5/p5RHbFYEa+/AamDKEMqkunduqWmpqqSqV27dkv+w8ckitXrqlAFW4o/dOgHw9KAlWwqmIFqVQxM3AVHBSovvATWdK/m7bJe/M/UYGgdm1ayltvDlI9oyyhYoXy8v7cqfLFilXy87o/5Kd16+XQkWPy9vDB6nc5uRB2SeLibmX2l6rK7HzKPcu8isnmXDu8U3bMG6G2u1bP/lLrsX42tw/2wM3bV1y9fCQtMV71k3Erxz/klH/Xj+6RbXOHqQXVfLSv1H7ilfwvlAoNev1hxkRdRoYkx0TqA1Vk+27evCldu3aVNm3aqKwnU/BlWGt2PnLkSJVd9f333+sfunnzZqlTp47tDwZRPtw1I18BwPuwSeMG6gZJSUnqyzeySVDuFxoaJucvhKlmz2EXL6vb5i079Fvi4+2dJbMKAauKFUPE28urALaWHAEy9z78+HPVquG+zu1l6OAB4mLhi1fIjnq1fx9p3KiBfPDhIvVaH/bWOOnf7wV5qNv92QZb9f2latVgfynKk1wHpnBS9e6776qVTZuWeTUeQkNDZdGiRWpmGTTxfP3118XHh9kf1ijm4hnZNLm/ZKSmSEjb7tL0ldxfsSXLQQP0uMvnJOHGVSnGwBTlU+zl87Jp8suSkZos5Vs9IE1fncQrtjYGGVIe/oGSFB2hAtZaBhXZNnyRQBPz8+fPy9NPPy1z5szR79ADDzygMqFWr14t/fr1U/2n6tevL08++aR06NBBPf7++++Xv//+W/WX+vTTT217MIjy6V4z8hUET09PqVmjmrpp8L6OjIrWB6kQrELg6uKlcIlPSJCjx06om6HSpUrqs6oq386yKl26lOp9RZSdn375XRYv+VL9+qFu98lrA/rlefY8c7Ro1lgWfjBL3p//iew/cFg+XvSFKm8dOvgV1ZfKGLILgf2lKK9yFZjCiVKvXr1U135cNdACUydOnJDmzZvLo48+KtWqVZPly5fLl19+qR6PP+JkPRKjI2TjhD6ScitGgmo1kbYj56sr81R00AAdganEKDZAp7yLj7gikacOyt5FkyQ5NlpKVG+g+sYVVRlYenq6umBRvnz5LD1vMH19RESEaupsOGNLVFSUxMfHq1nE8OUbM5DVq1fP5ICgoTNmHitm4sSosM2ePVtdrXzrrbdMrjoyMlJlwGB/zLVt2zZZfTJaSqUnSOeo6+Y+rUDcuHFDHctSpUrJsmXL5OjRozJ37twCWZe9wxdYvB9wwzgaaty4sfoRmVAo8Stbtqz6uWXLlrJ9+3aZP3++rFq1SmrXri0HDx6USpUq2ftwEeVIm5HPkmV8eYFSvaDAEurWrOmdPq2pqWly6XK4PrMqNOyiClohiHX12nV127l7n36VHu7uEhJSTvz9i4mXp6d4eXmJtxf+zfx/ln/V7w0f46W+bzGwZb/WfPeTLP/qG7WDPR97WF7u+79CuehYIqC4TJ44SpX0LVuxWvVeGzz0rIwY9ro0anjnHM2wv1R99peiPHLS4UzJTMiE6t27t5QrV06GDBmivlzAK6+8ojKpvv32W/VzcnKyVK5cWT744AN55pln7rl0fFHBlwzMJGMNXzTsVVpSovw16hmJPHlAfMtUlG7v/ySexQPtdXdtxtbZQyR041pp0n+c1HlqoM1sN1mPM+tXy875o0R0mdNauxcLkEcW/SNeAcFFtpEIZgQGBsonn3wizz33nH473n77bZV1+88//0iXLl309//vf/9TFz3WrFmjPmtwsWPJkiXqs2XlypXqc0Zrvokv8QMHDpRXX321wPYP62zatKnUqlUr23VoAbYffvhBunXrZvJx2F8E6LTPx3v57bffVLPrLrUrSG2XmzJo5kKp1vXZez2twLzxxhvqGOBi06FDh9TYI6iS07iQ9eD5FdmySn37Z7v5ZRKSpURKukR4uMp1L9OzWpoSuuzzIh2SmNhYlVGlZVaFXgiTCxcuSXJKSr63CxMfIGCVGdAyHczy9fGRLp3aS6lSRXd+QObD1/QVK9eowBT8r9cT8r9nnyyUoJSxs+dCZc68hXLpUrha/5M9e8gLzz2t+lvh9Txo2Gj1Glyz8rNcl/Ll9F7Pi6J+n1Pe5Cpjau3atVK1alX55pvMiK0Gaef4AqLRZoXBlWJTELjCTYMTJypYaKC7dfZgFZRy9ysuXaauYFDKSiBjChI4Mx/lATKlDINSkHorRjLS0vKwNMtBFtF9990nGzZsyBKYQjZUmTJl7gpMbdy4UaZOnao2YNeuXfog1LVr12Tw4MHywgsvmDUrjKUgI3j06NE5BmAQbCpevLgqw7IU9A9C6dY7DzWSs39+o0r5rAUChigvQ9BOO1ZEREXBMz3zunqSjZW/oQSqYf266qZJT8+Qq1evSdjFS3IrPkESE5MkMTFREpNu/5uYJAnafQb/JuAxiUkqWwW071c3b8bkeEj+O3hE5syYWBSHjXIBQanPlnypspXgpT7PyZM9H8nFEiyrapVKMv/dafLZ0q9k/Z8b5LsffpGDh47K28MHyaHbZXzsL0X5kavAFIJSpqCJp6G//vpLwsPD5eGHHzb5+JkzZ8rkyZPzs92US/sWT5FLO/4UZzcP6TRpqRQrXyWXS6CC4nU7MJUYyVI+yr248PP6TCkNmmbHhYeKT3AZkwtEMAvP8ytbOdvHWAICNlpPQkBZGMqQpkyZIuvWrZPp06er+48fP65mKtMCPOhXiM8blPtpARBk7nTu3FldCNEgQILZyVAmh1I6lJtpsDxka50+fVpq1qyp+h4GB2deIcb6PvrooyzNpxEYQwnd+PHjVSbWlStXZOnSpbJv3z5ZsGCByeFAdtOzzz6bpfEo9g8lb8imQum7MXX1c8UKtT5c0X7xxRfVZyiyxV577TUVlIuLi5O5P8VKrYQkqRF1XX3ZwL7s2bNHSpYsqfalevXq+n35/PPP5eWXX1b7g+ARsskwxuhJhOVt2rRJBdHQyPe///6Tr776So1Pu3btVCaaYUkl9hfbh9LK559//q7tR/YbLk4xMEVERQYz8mnBGBsLTJmCErxy5cqoW27hMyUlJTUzkKUCWImq3UpmACvzZy2Yhf//9Mt61fMK2S8INJB1QrDyo0+XqGbngH5SPR6y3EWwvELJ6Buv91cTAiz46DM5feacDBk+VgJLlFCLZBkf5YfFmwuhqSdKMvCFo0KFCia3bcyYMapsT7uhXwgVnONrP5eTPy1VK2j79gdSsm7zglsZ5RozpigvcDKalpQgVw9su/vpTs6qqT5+b3w7+ctyWdu7lfw9qpf6Fz+belx29+Wi+lsefPBBNaPYhQsX1DYiewq9cdBDZ+/eveozQLsfwSMEouCPP/6Q/fv3i5+fnz4Ag9476M2jQS9DBHcQoMGMZZjpTLtqjPU1bNhQBVlQjodm0QjYXL58WT0dF07wfEPHjh2Tn37KTJWvW7euyvzF+nKa/ezkyZNqPRoEtlq1aqWCOo0aNVJZV8gQM9S3b191cQaZR1q21XfffafS4tFTKygoSJW2165RTXzcnCUu4qp07NhRPQb9hhISEtQ6UVan7ctnn30mL730kgQEBOg/d5HhPGDAALXvGAMsH0E8LAtQKvnxxx9Lz5499ZuH49O2bVsVGMM6JkyYIFu2bMmy/bgf+01EVFTUjHw6EeRMpTg7FdVmWAX8bffwcJfixf2lTJlSKthUt04t1e+qfbtW0vWBzvL4o93luV5PyMt9n5e2rTO/A6z77U+r2H66G1ohzJv/sQpKOTs7yZtDBlpFUMpQ29Yt5KMPZqlG50lJyXI5/Ir6df36nDGW8i5XGVP3gqvh3bt3V03Qs2sEC1qpHxW8sK2/q2wpaPzyOKnYoUfBr5RyxSswsylyYtS1XD2PHFt6cqKsfrym6UHQZcgvr2QGIHKky5A9H41XN3M9++NJcfX0NuvhVapUUTcEkBCQQZAGWTwI9iDzB5k8+LxAQARBLGPoVdinTx8VyEE2EQI2mho1aqhMIUCmFbKOzpw5o+6fOHGiCsYgKwuGDx+uekBNmjRJBXHuBT0UkaGEbcL6TUGGE3pHYcIPzbhx49TjtRnTBg0apPZfewyyl3788Ue1nVr2Fj4P0YfqqaeekqFDh6qZbbHcV/u8IJunbZF12/dJbGyyCkRpmU0ok8e6fvnlF7VqBJKQ3WUcREMgC326NG+++abKYEPACrQJS3AcELAaO3asyrZCf0jA/xFIRBBNg31BcAwZZSjJJCIqbEUxI5+9eOThrrJ56075d/M2eanP/8TPz9deds0uYLKU2e8tlB0796hs7LffHKQCjNYoKChQpk8eK9+v/UW+WvWd+Pn6SPWqrMihvLNYYOrWrVsqKIUZY1CGQUUv4vh+2TbnDUHKc40eL0qdpwquUTDlnZYxlRh5TWWj4OoX0b3cOJWZMWPtENxB4EkLTGEWO0CACg3QH3nkEfn3339V+VhuIONHgywlNEZHsASBKWQuITilwXsKJWhz5szJzSpyFBYWpoJTCDwB3rsotUMpoAaleth/BHJg69atKiD39ddf6x+DoBICUcgeMwy8eZUoqR5z6NxlCSpbS5UeahDMwqy3Gm9vb5OZXc2aNdM/BsEszKCLrDHM8KZB+SPKD1HWhyw1w9JLX19f1SfMEAJVOFlGcI2BKSIqCvZUxlfYateqIVUqV5Rz5y/In39vLNKeRZRVcnKKTJ/9vuzbf1BdiBo7cqi0bNHUqocJZajPPPWYdGjfWm0zGqET5ZVFXj0pKSlqFiGUJaxevTrXnfjJ8tBf5t9J/SQ9JVnKtbhPmr02hQEPK6V9AcWxSrkVIx5+xa10S8la3LoaJlumZ87gWKpRO+k4/lNxdnW75+Yl3LgqP7/SOUtPKidnZ3lk8UbxDsrM3LsXFw+vez0kCwRmkKmDkm2U2GmBDtw/d+5cOXLkiArOaCVm5jLsi4TAk7Pzncp0BHlQBmgIP2N2uezkpkQREAwCLeiEq5zo66Hdr8HP2mOwfnxeIkBkCJlJeK6pwFRsfIKkxcVleQ5mr0VAT2O474YM79f2Hcfg+vU7DdXx2Y2ye6wf+2Bq+xGA0+BxKDPw8fEx91AREVmUljGV7OBlfHmBz0tkTc1fuFh+/f1vefzRh1VwgYoW+n9Nmf6eHD5yTF18mjB2uDRueCdb2dqVLpV5MY0oPywSmMJJNa5844q4YQkfrmj37t07P9tHeZAcGy0bJvSW5JgoKVGtvrQb85E4u1jkUFMBcHH3VDMlpsTdVA3QGZiinCTH3ZQNE/pIckykBFSpI50mfiZu3ual4mPSg1ZDZ8uuBaNFl5EuTs4u0nLIrAKdDKFLly6qPO2HH35Q5XUlbjfIRMYUPjPQTwp9jZCdYynoV4XsH8P+Seg3VatWLbUKBFXi4+NVA3A0Tget/5S58DwEu06dOqUatSNjCwEeNBc3nBDkwIED+t5Y2C4E1NDbScuMRDAIASRkIRnyDMhs1F7ay1l0ZUqr0kINgkNoQJob6N+Fk130osKsf6aWhX3C9jdp0kT/e2RToeeWBs3kQWu+TkRU2DzSM2ehs7UZ+axFx/ZtZOnyr+Xa9QjZs+8/aWXlWTn2Lu7WLZk0dY6cOHlGZVpPnvC26hNG5GjyFCLXZjjSoBkqAlJo2oqTZe2GGYCocKWnJMm/k16WuMvnxadkOek0+Qtx8+KVbWvHBuhkDmTVbZryisRePCPeQWWk85RlZgelNNW6PSuPL98h989eo/7FzwUJWUAtWrRQ5WParHuAIAh6In344Ycm+0tp0NAb0MQbsw6ZA72asFz0tELgB8EvlMINGzZMPR0l58gMQp8mlNGh1BC9nwxhvWjQHhsbm+0qUTZ49OhR/e/Ru2nGjBkqCIbMKASgtCblgKbvuH/kyJGq/B0z9+HiDUoRjbl6eImbTzHpXN5H/tmwUfXGwnOR8YTsMsPZCc2BgBhm88PYoHk5lrVq1Sp1fBB8Amw/ZvPDz5gJ8P3337+r0TlmNEQJn3FGGhFRobCzGfmKApqlP3h/Z7XqX379oyg2gW6LiYmVsROmq6AU+n3NnDqOQSlyWHlKo8FsQ7hpcKJLRQ9TxG9/902JOLYn8wvNlOX6gAdZNzRAvxl6UvWZIjIF7+8d778l1w/vVMEo9f4OylvzaZ/gMupWWNB/EAEb4wbnuH/BggV33Y+LGlqpHnoy4cJHjx491Mx7aGiO7CTjDCNkA2mla08//bTKhurVq5cqYUOWFnpbodk3IBiDZuFvvPGGfPXVV/pyQ8OZ+tArCkGjxYsXS3R09F0lboCG5QjuaJnCI0aMUNlhyABDQOyhhx5Sn48IfgGCYQiSvfzyyyrwhZI4ZCctW7ZMfygyezRkXtRBmW/J+FhZsWCOjH/3XdUAHs/BuGm9urDP2HdjpsYIxwBBMWSuIciH8vt58+bpZxZE83P06UIgEeV7KPNDMAv9qTSYuRD7TURUFFx1om6ckS9/Hu52v6z9aZ0cOHhEwi5elgoh5fK3QMq1K1evyeRp78rFS5eluH8x1Ui8UiXTM9pnp1Lf/rleb05Cl2VOKkNUFJx0uW2sUQBwRRpfFNAXBL0zKG/2L5khx779RPWa6TL9KyndsE3eFkSFbse8EXL2zzXSqO9IqffsG4W+frJ+B5bNliOrF4qTi6t0mbpcyjS5U47lCBAoQRAmu35K2UFQJTAwMNvfY7mG/aqMIbMIQR5ToqKiJCQkRDZv3qyCPRoEpVAqiMBPdvB7rNuwr5Sxv0b1kmsHt0vbUQukcuee6jMSQSvjPlC5lZGRoWbRRZazqfHEPiNwZbxtGEuUBKLcj6V8toHnV47JXr6smtoPn9R0qRSfrPpLnSmWu56HwC/ed0yd8Z7s3L1Pejz0gLw2oJ9jvlmKCAKCs95dIHFxtyQosITMmDJOypXL/QVDe36v5wff57YpT6V8ZH1O/fqlCkpBq2FzGZSyMV4lSqktRnNqImOnf/9aBaXU+3voLIcLSgGCR7kNSkFOQSltuTnJLigFyMQaPHiw/Prrr1kWgeBRTkEpQJ+rnIJSoE2MkBiV2awcj89vUAowjshEy248sc+mtg3ljk8++SSDUkRUZDgjn+X0ePhBtbB/NmzRT9JBBQv5ID/98rtMmDxLBaVqVK8q782ZkqegFJG9yVMpH1mXS7v+kT0fZU5R3uDFEVLl/ietawPpnrwCMwNTiVEs5aOswvdulN0fjlV31n9+mFR9sBeHyIpoJXUFwet2A3QtMFXUUIKIGxFRUeGMfJbTqEE9KV++rFy6FC5/b9gij/boarmF013ZTE46nZRJTJGAlHQ1OjfdXOTH6+GydsRIs0aLWUBk75gxZeMiTx+SrTNfV/1n8IW1/v/Y78sWeQdlzgyWwB5TZCDq7FHZPP01NYNelfufkgYvDOf4OBDjjCkiIkenzciX7MKvMPmF2WEfeSgza2rdb3+qMm8qGK4ZGVLpVrIKSqE/2hVPN7ns7S46J6eCWSGRDWLGlI2Kj7gi14/ulr2fvCNpSQlSpkl7aTlkpn4KcrItWikfm5+TJj4iXDZO7CNpifFSulFbaTl0Nt/fDkb7u5AUHeFge05kv+ylJ0yRMJyRz5mBKUvo0rm9LPvyG7kcfkX1PWrSuIElFksGvNLSJSQ+Rdx0OklzErnk7SHxbi4cIyIj/Ktug86sXy1re7eSbbMGS3JMpJrRrf24RarpOdkmb30p33XJSM9M8SXHlRIfKxsn9FaBSv+KNaTD+EXi4ma6ATfZL2ZMERHdYTgjX7ILM00swdvLS+7vkjmZyi+//WmJRZKB4slpKlMKQakkZyc55+vJoBRRNpgxZWOQKbVz/igR3Z1026To65KacEvcffxsbG9I4xkQLE7OzqpkSwUbS5Tk4DiojLRU2TxtoNwMPSmeASWl85Tl4u7r76Cj4djwdwGspccUEVFR0sr4UpydWAJlQZiV75df/5A9e/+TK1evSZnSmX1P7VlBZy6mp6dL6YQUCUxJU8MY6+aiSvcyWLpHlC1mTNmY2MvnsgSlAP2l4sJDbWxPyJCzi6t4Fg9Sd7HPlOPCbC0IPF/9b4u4enpL5ynLxLdUeccdEAenBahTbsVIWnKig48GETk6/Yx8LOOzqPLlykqTRvXVOchv6/+26LIdUWxsnEyYNEsflLru6SYXGZQiuidmTNmYy7v+umuLnZxdxK9sJRvbEzKGmfmQGZE5M199DpADOvz1fDn317cqe6792E8ksDpfB44MmXLObh6SkZosSdE3xLd0iCMPB1GBK+gsCsof/Yx8LOOzuEce7ir7DxyWP//+V55/7inx9PCw+DocQWhomEyZ+Z5cuxYhaMyBLKk4d37dJjIH3yk25Oyf38iJtUsytxipoDqdCkq1HDJLfILL2NCekCmZjY4PS8KNqxwgB3Tu7+/k0JfvqT1vPmialGvRxQFHgQxhxiRkTcVfu6iC1gxMEZEj44x8Badpk0ZSqlSwCqhs2rxduj7QueBWdg+2GiDetmO3zJv/iSQlJUvpUiVla0IsZ48kygWW8tmIK/s3y875o9XW1nv2Dem5YpfcP3uNPL58h1Tr9qyN7AXlxDuwtPp1ZsYUOZKrB7bJjvffVrtc5+nXpMbDLzrS7lMO2ACdiAh9K3Qs5StALi7O0qP7g2oN6DeFsj4yk04nX636TmbM/kAFpRo1rCcfvDuNQSmiXGJgygZEnzsum6e9Krr0NKnUuac07PO2ypAq3bA1M6XsCEr5ADOxkeNAk/NNUweo93fFjo9I436ZAWgi8NI3QOffBSJyXC46UbPyIVzCUr6C8cD9HcXD3V3Oh4bJ0WMnC2YldsZZp5OQ+BRZ9c0Pas8ee6S7TJk4Svz8fO1sT4kKHgNTVi7hxhXZOLG3mnWvVIPW0vrNuaq8g+yPljHF5ueOA8d648Q+khofK8F1m0ubEfNUfykiDTOmiIiEM/IVAj9fX+nUsa1aE7KmKGfu6RlSOS5JiqWli5ubm7w5ZKAMePlFcXFx4dAR5QF7TFmxlPg42Tixr+o55F+hunSYsFhc3NmM0F7dyZhijylHkJoYL/++00/ir18Wv3JVpNM7S8TF3dNudj05OVm++uor6dGjh5QqdWfq6WPHjsmOHTvk6aeflmLFiun3d8+ePXLlyhV59NFHZfr06VK9enV55plnTI7H2rVrpXbt2lKrVq1cjVfr1q3l8ccfl1GjRuXqefmxatUqCQsLy/M69YGp6Ij8bEahGDFihDz00ENy33335Xl9S5YsUa+B4OBg+fDDD8Xb21tefvnley4P62zXrp1Mnjz5no8l22SrfWfIMjwzMtSCOCNfwUIT9D/+2ijbd+6RG5FREhRYomBXaKN8UtMlJCFZZfKlOjnJe9MnSK0a1Wx0b4isAy/NW6mMtFTZMuM1iT53TDwDgqXzlOXi4VfcSreWLMH7dikfM6bsX0Z6mmyZ8bpEnTksHv6B0mXqcvEoFmBXO+7h4aGCBD/8kJnerpk3b570799f/vor6wyj77zzjvz222/qYQhenT9/Xv0/PDxcBbESEhL0y5gyZYps2rQpV+O1efNm2b17t7zwwgu5el5uvf7667Jt2zb9086dO6f2J68yJ0VA77nreV1EocjIyJDDhw/LxYsX87U+vDYwZnDy5Ek5ffq0Wcvr06ePzJ8/X5KSksx6PBHZFs7IVzgqV6og9erWUn/Tf1//d+Gs1JbodBKYlCoV4zODUgkuznLOz4NBKSILYGDKCqHh4O6F4+TKvk3i4uElnSd/wdmYHIDX7ebnyTGRkp6a4gB77Jjw/t7z8QQJ37NBZUB2mrRU/MpWssvBePDBB2XDhg1Z9u3vv/+Whg0byj///KO/Py0tTbZs2aIeDytXrtRnGMXFxcl3330nqamp+RojZC7df//9Uq5cuXwt515+//13uXTpkv5h48aNk+XLl9/radmylVI+Z2dn+fPPP6Vv37553ldjCxculFmzZpm1vCeeeEK9jjD+RGR/OCNf4UHWFKz/c0O+P3vtiZNOJ+USUqR0UqqgqUq0u4uE+npIGlswEFkEA1NW6Og3C+XM+lWq10z7MR9JYI2GVriVZGnImHF2dVOLTbKBsh3Km2PffSqnf/1KxMlJ2o76UIJrN8nbgvLhSlSUbD9+Qv1bkBBo2rhxo352n7Nnz6qMmiFDhmQJTCGTKTExUbp06aI256WXXlJlXHiuFqxq3LhxlnK4lJQUeeWVVyQkJESaN2+uMqJygmU9+eST+oegnA8lg8hwKlmypColhPXr10uHDh2kbNmyqjxs+/bt+udgGS1atMiyGpSePfvss5Kenq7KDxGUGjp0qCobhHfffVdee+019X9k/+AxyCzCsrGO5557TqIMjgOCKxMnTpS6detKpUqVZPwHn0p8asY9M6bwvAkTJsjNmzfveUg7deokP//8s8pEK1OmjCqBw74ZOnXqlCqpQyCvSZMmKlioQRCxffv28s0336iSypEjR6pfoYzv22+/1T/uv//+k27duqn9bNu2rXz//fdZ1nHt2jW1/+XLl1fHcNeuXVl+/9Zbb2Upzbt8+bJ6fIUKFaRGjRoybdo0dVUffH195YEHHrhrP4jIPniwlK/QtGrRVAIDS8jNmFjZsi3r32VH5ZqRIZVuJUvx1HTVgP+Kl5uEe7mLjn1/iSyGgSkrc37jWjmwbI7aqmYDJ0v5Vg9Y2RZSQUFTe61sB33FyP6EbvpZ/lsyQ+1Y0wETpULb7nneSWReJSQn5/r25T8bpe2IUfK/2e+qf/FzbpaTmymkEXyJjo6WQ4cOqf3UsqUQrEDgQ8ssQlYVAj7Fi2eWK6PXFII1TZs2VeVZsGLFChk8eLB+vBYsWCAdO3ZUARMElhBkya6MC8ELlAYa9qS6fv26ChrhuQiYINCB7UMPKmTf/PLLLyqA07lzZzlw4IBaL8oJtRJDDQJBCJig2Skyu7C8N998Uz7//HP1kMjISLl6NfP9jGDamTNnZMaMGaocce7cuSrIM378eP3yEChDSeNHH30ka9askbMXLsnHhyIlOeaGZKSnZ/t6QWAMgZqYmJh7vqYuXLigxhLBol9//VVlknXt2lWOHDmi3+ZWrVpJvXr1VAYSekcNHDhQfvrpJ/047N27V/UQQwBxwIAB6n4EHWNjY9X/EYRs06aNNGrUSAXBevfurW4YI8AxQSAJxxkBrkmTJqmApSEErm7cuKHuQuAS24vnaY//+OOPVUaaBscX40tE9sUlQ8cZ+QqRq6urPNTtfrVGNkEX8UpLlypxSeKdniFpTiIXfDwkysNNXWAkIsth83Mrcu3QDtkx7y21RbWfHCA1H7VcSQTZBjRAj79+SRI5NbzduX5kt2x/9021X7Uef1lq9+yfr31MTEmROq8OytcyMnQ6mfDlSnUz17FFH4m3h3mTMAQGBqpsG2TjoHwPgR8EI5BBg0wblPOhN5BhZpQhNEfH46B+/fri7++v/zWCR1q/KGT1VKtWTU6cOKECIcbQfBxBITzGEAIdCIhoENhBYGjYsGHqLgTGEOiYOXOmCobcC/bR3d1dZToh4yk7Y8eOVfuD9WP5WpYRAnJLly5VmVWVK1dWT5//4UJp0rSphMclqzJfrbQvvxAc1PYTx+jgwYPywQcfqIAagmIIFCKABg0aNJCtW7eq3z/22GPqPownAlOGx8QQgm4I6mmleM2aNVNBqKlTp8pTTz0lf/zxh+olhUw3LSCJL0PYLlNQDomgFAKReBwy0hBMNCzxxPE1zsoiItunlfGlODsxQ6WQdHugs6z65gc5dfqsnDx1Rmo6aGPv4slpUiYxRWVyJDk7SZiPh6S6MK+DqCDwnWUlYsJOy6Ypr0hGaopUaPewNHn5zlVgchzet/tMsQG6/YiPuCJn//pONr7TT72/Q9p0kyavTLCfHbwHrc8UMq0MA1C4H4EpzN6HcjmtZM9cWuAGqlatKp6entlmCyEwhYwmlK0ZQhmgIWR2IUvKEMoLEbSxJMNtR1aSluWEjCVkTg4fPlx69uypblOmThUXZye5fCvNZDkfsrMQpEH5IeA5+Pn555/PcZORzWQIpXlak3bsL4JG2jbgX2RIGTZxR9Awu6AUZDeWWAaCSfgXwTstKAXG22R8bFByiKCUBsEurWQSUOKHbDBrh+OMLDmUon799df33Fw0gcfkAJidEAG63GQtEtkDzshX+IoX95cO7VqpFa/77c/C34Aihn5SpRNSpNztoFSMm4uc9/NkUIqoADFjygpgtqUNE/pIyq0YCardVNq8/b7qL0WOBxlTkBh5zfF23g6dWb9ads4fJaK73QendAVpO3KBOLu45HtvvdzdVfZSblyNjpb7x0xQmVIaNK3+e8YUKR1g3qyAWG9uIEPq008/laNHj6pSOGQJAe5HORkCHsgyMu7ddC8I4JirdOnSqtQNZXVBQUHZPg3BK+Mv/cjSwf0araeRBsvNrey2HccCgZdevXpleUyN+HNSOvm6ycAUei6hFA8ZTMgiw5iWKlVK/Pz8ctws9KQyhJ+1oA+2o2bNmqp3liHcb67sxhL7jht+bzx2xttkyNTyjKFkEsfammHGxu7du6v+Xcise/vtt1XWGN4jpuzbt08dXwRucUzQfwwzWiJbjchRcEa+otHjoQdlw79bZfPWnfJy3+dVsMoReKalqybnnhmZ50rXPd0kwsOVpXtEBYyBqSKWlpQgG9/pK/HXLqqZuTBDl6uHVxFvFRUV79uBqYRI9piydciUMgxKAco0k+Nuiqtn/t/j+HJvbkmdpkrp0jKzb28Zu/xLSUfAxdlZZvR5Ud1fUJAFg6yoZcuWqawcZDYB+kOhNxNKr9CLyjATxpS8BIA0yFDC8tHXKqfAFBqsY2Y5rVwN8DNK3QDPRTka+ichSwu0/lmG8rqtyJ5CcAa9krSSRPTNurxuiZS4FmkyMKUF9DDGgLGsWLHiPQ8n+nr173+nnBRlligvBIwDemwhQKZBhpOHmSWc2jIwdmgyr8HPKHfEsUB5IDKH0EcKgTQwbsBuCNuELCME4BDI1I4Nephp24kSSARvrNno0aNVc3+URcL//vc/NRaDBg3Sj78h7DOa9ms9yzDrIcpb8XjDbDEie8YZ+YoGyvdqVK+qyvnW/7VRnn368aLZkEKCLKngpFQJSk5Ts+6lOomEe3vILbf8X0wkonsz//InWRwa2W6ZOUiiTh9WM7J1mbZCPP1LWHw9ZDu05ufMmLJ9sRfP6DOlNLqMDIkLDy3SnevVsb1sfXeWrBr1lvoXPxckBBEQhPrss8+ylOthFjUErfCFO6cyPvSjQkAETdD379+fp011c3NTs+Eh+yQnkydPVrPs4V8EbpDNgsbdWnNyBBCwPehthZIq9IoybraNIBjKszC7X24hQIMG4OjBtGrVKrUMBF1m/rxdUjN0khh9PbeLzNbOnTvVLH7YzzfeeEM1YUcJISDogZI49P9CuSWCiihxXLRokdnrR0AK440ZCbGMOXPmqH5T6DEFKJFEsAlBQDRgRwYQHmOYnWboxRdfVGV/aE6PcUEfLJQrGjajR5P6OnXqmL2NhQ3N/tGrC+OrwfaiDNNUSR+CoOjFZfj4KlWqqEb12ZUAIkCJBvSGNyJbxxn5is4jDz2oVv7b+r/zfNHFFnimZagG58G3g1I33VzkrJ8Xg1JEhYgZU0UE5SJ7P31HLu/6W5zdPFSmlF/ZzGa35Li8gzIzV9j83PZhBj5jTs4uKjOyqJUpUULdCgtmY0MJ3MMPP5xllf369RMvL6+7Gl6j6bjWqBwlafgSjtnfbt26pbKXENAy7g+F0sASOewTslTQWBxBGEB2EAJWhpCB8u+//6oACmaPQ2bK7t279Vk42FbMUocZ9TBLIIIqKK1CcEeDmeLGjBmjGqYj+IIG3XgeIBiH5t6GwRcEowz7WmGmQGRjIWCH0jSMxRfjBsnNDV+bzJjSIHiH8fH29jbrsKJn0a5du1QgDOtDiZmWBYbeUQgq4TFvvfWWCi6OHDlSH7hC5hgys4yhTLN8+fLqbjSkR5kmloEm6yhbw2yDaIiuQYAJwT3cEPD74osvVEBL612FQKCPj496OI4VthHBNIwvxhRBLswWCAjEoMQNATdrFRoaqsYSQVJD6LVlqjcWeqPhXME42IbHozTWFLzuEFg1Nuyt8eLubt6kBXmBPpmW5F8h6xhBN1/zyo3NNXDw23c9lOswzdRYFdYxz8hIl8s3E9SGdfQvkasyblOKal8KY7wKYh34G4Qy7sjIKOk3YKikRlwy9y1mlqJ+r2P/YuPiJPZW5msM+xoQUFxCvLwkM4fYPPb896QgXlfGCuNvb2Hsh72sw9I+XTjXrEU66aygiyZOKHEiigawaKjqCI59v0j2fzZN1Su3H/uJVGyf9QsbOaaYi2fkl1c6i5tPMen1vekvHmT9Tvy4RPZ+enu2NydnlTmFoFTLIbOkWresfXuocNy8eVMFTVAu1rx588JZqYWc/HmZ7Pl4goS07S4dJyzO91KR1YXgm2Gpnq1bsGCBrF27NsdywKKG7UMgDeWLhhC8Q9AN5ZSGEGhDhlh8fHyW+zHjIUpgTQXhkDGllXUCzq8QxC3o86uvumWdSCC/Xlh/Mb+LoAJWWMf80OFjMmbCNCldqqQsWZRZAmtphbEvhbGOgrJi5Rr55tsfpX7d2lJvX/4/gwwV5Xv93PkLMm/+p3I+NHPSjPZtW8lrA/qKv79jfBc1ly2/dg0Vxn7YyzqKCjOmisCFLb9mBqVEpEn/cQxKkZ5WypcaH6v6j7l6mpf9QNYjbNvvsndRZsZC45fGSKXOPVX5HjKlfIKzzgpHhQdlYO+9957KQrG1wJRXiZJqoDBRBpmGbDFTmULWBJlmCJAaQ8lecHDwXffj8ZgwAIEmw/5e2T0e8Ljc9AIjsnZhFy+rTawQUs7aN9VuPdT1Pvn2+5/l8NHjUs7JWwJ0mdlFtgq9HNd8/7OsXrNWlScW8/OV1199SdrfnoWQKK+KMtBqDxiYKmQRx/bKtjlD1VprPNJHaj8xoJC3gKyZm7evCkYhKJUQeU2KlWN5py25ceI/2Tb7DRGdTqo/9ILUefo1VXbAgJR1ePXVV61jQ3LJ0oGpV155xeqbhOcWygGtHZrSo3k7mvCjxFODsjxTs1JWqFBB/f3A77UG/ICfzWlwT2QPwi5mlo5VqJBZJkyFLygoUFq3aibbtu+W0y5lpUXamcLfCAsJDQ2TeQs+lbPnMvt9tmnVXF4f+JIEOMiMg0TWjIGpQhR7+bz8O+klyUhNlnIt75dmAyfnu1ae7Au+hHgFlpa4y+ckMfIqA1M2BFlRG9/pJ+kpyVK2eRdpPmgq399kEVpgKin6uuqHgb8T+WELQRx7hEAT+qOhcTua+QNmJty0aZPMmzdP/Yyr91pGVGBgoOqdhsejKT9gRkg0RDfsa0Zkz+wlY8rWMykefbirCkyFupSURmmh4i5pNvWyw9/Wb3/4RVZ9872kpaWLn5+vKtvr0K51vj9TyTbY+nvQETAwVUiSYqJk44TekhwbLSWqN5D2Yz4SZxdOP0p38w4spQJTyJgi24D39YYJfSQ5JlICqtaT9mM/FmcX/nkly9ACUwh6oszX3dffMgumQofeXg899JBERESorCfMeIjG/I0aNVLbgmbzn3zyiVy/fl313Jw2bZoKTqFHVM2aNeXLL7+UJ554QjWaJ3IE+oypEGZMFaW6dWpJ5UoV5HxomJxzKSW10jNLLG3BhbBL8v6CT+X0mXNqc1u1aCqDXntZSgQUt4XNJ3IYzg6zp0UoLTlRZUoho8KnVIh0nrKMvYMoW16BpdTvODOfbUhPSZJ/J7+sgonewWWl85QvxM0rcyYxIktwcffUB6PYZ8q2tW/fXrZv366CUghOzZkzRxYvvtNM+JlnnlGzDmoTwaAfGpqc16pVS65cuSKTJk2SlStX2vYgEJkpJiZW3SCkfFkzn0UFAZm6PR56UC36tEsZySiIlVhYZpbUzzJk+FgVlPLx8ZYRw16X8WOGMyhFZIV4Sb+A6TIyZPvcYXLj+D71xaLL1OXiFXB3k1MijdYAPeHGVQ6KlVPv7/eGS8TRPeLm7afe396Bpa18q8kWeQYES8qtGBWYMjW9NtmO+vXry8yZM01ucOvWrdXNEIJSyJwicjRaGV+pUsHi6elZYLvPEh/zdOrYVj796BO55ewlV5wDpFxGtHlPLAIXL12W9xcskpOnMvthtWjWWAa/3l8CSwQUwdYQkTkYmCpg+5dMl7Ctv4mzm7t0nPgZv1DQPXkHZQY2mDFl/Q4smy0XNv0iTi6u0mHCYileqZb1bzTZJJTzxV48I4nRnJmPiBwDy/isi6eHh1RNvyYnXMvLKZeyVhmYSk/PkB9/+U2+XPmtpKamio+3twzo31vu69yevaSIrBwDUwXo5M/L5Pj3mSn6rYe/K6UaZL0KSmQKekxBIntMWbVTv34lR9d8rLax1bA5UqZxO6veXrJtlp6Zj4jI2jEwZX2qp1+REy7l5KpLCYlN85JiukSr2cjLl6+oGfdOnDyttqlpk4Yy5PX+alZBIrJ+DEwVkIs7/pS9n76jlt6o70ip3LlnAa2J7I2+lI+BKat1efcG2fPROLV9DV4YLlUfeNpqt5XsAwNTRORo7GVGPnviq0uSshlREu4SqHpNNU3LbChelJAl9fOv62XFV99ISkqqeHl5yYCXX5QH7uvILCkiG8LAlIXFR1yRS7v+kn2Lp6j+M9W6PSd1ew228FrInumbn0detcjU8GRZkacPy5YZr6n3d5UHnpb6zw+z7AqITNB6E7L5ORE5irCwS2pXK1bgjHzWpEZ6uApMYXa+BmkXxE3Si2zzwq9clQ8WLJKjx0+qbWjcsL4MGfyKlAwOKrJtIqK8YWDKgs6sXy07548S0WXOVeFfsYa0GDyd0XrKFcOp4dHs2MOP09lai/jrl2XjxL6SlpQgpRu3l5ZDZvH9TYVCy6RkYIqIHEFMbKzcvD0jX/lynJHPmpTOuCl+GQkS5+wtoS4lVXlfYcvIyJB1v/0ly1askuSUFPHy9JSX+z0v3R7swvMyIhvFwJSFIFPKMCgFmY1qb4hPcBkLrYUcgauHl7j7FZeUuJuqzxQDU9YBQcINE3pLUvR11eS8w/hPxcXN3To2juyeFrBOio6w+30lIrp48bIahFIlg8XLq+Bm5KPccxKRGulXZJ9zVdUEvVr6FXVfYTl7LlQWL1khR46eUKtsWL+uDB08QM3eSES2i4EpC4m5cFKfKaVBqU9ceCgDU5RraICuAlNR16R4pZq5fj5ZVnpqimyaOkBiLpxSpZadpy4Xd59ill0JUQ7YY4qIHInWXyokpJwj7bbNqJx+TQ66VpRYZ2+55lxcZVEVNJTtffn1t7J5yw61Kk9PD3mpz/+ke9f7xNnZuaBXT0QFLNeBqa1bt8qSJUukXbt28vLLL+s3b9++fbJ8+XIJDw+XDh06yKBBg8TFxaWAN986IACFGbqMOTm7iF/ZStaxkWRTULZzM/SkJNy4alPbbY90Op3s/GCkXDu4XVy9fKTzlOXiE8yyAipcWmAKmXtpyYkqs5KIyF5d0PpLhbC/lDVCX6nK6dfltGtZOeVSpkADU5FR0bJ6zQ/yx1//Snp6Zj+rTh3ayIvPPyOlS5W0xuEhojwwO7yML2ezZs2Sbt26yYYNG1SASrN582YVjEpNTZXWrVvLwoUL5emnHWeWqoMr5sqlHX+IODln3gT/uKj+Myzjo7zQN0CPupaXp5MFHfpqnpz/53v1nu4w7hMpUbWuBZdOZB43n2Li7OahHsxyPiKyd1opHzOmrFf19HC1ceHOgXLLKfPzyZJu3YqXZV+ullcGvim/rf9HBaWaNW0kC+bNkLeHD2ZQisjOmJ0xde7cOZUphSDU+++/n2UYxo8fL6+++qrMmzdP3f/MM89I9erVVfAKmVX27PTvX8uR1QvVLrZ+c46UbtxBle8hU4pBKcor78DS6qkJkQxMFaWzf34jh1d+oDahxRszpGyzzkW5OeTAnJycVNZU/LWLqgG6b+kKDjwaRGTvLlzkjHzWzl+XKKXSo+WaS4CccSkjjdJCLbLJScnJsu7XP+XbH35WwSmoXauG9H2xl9SrW9si6yAi62N2YKpUqVKqXK9Ysax9Va5duyZbtmyRRYsW6fcuJCREHnzwQVmzZo3JwFRycrK6aWJjY61vZMwQvnej7P5wrHpk/f8NlaoP9lL/Z0CK8kufMcXAVJG5sn+L7Jw/Wq2/bq9BUr37/4psW4jgTmCKDdCJyH7FxsbJzZsxagdDyrPHlDWrkR6uAlNnXUpLvbQwcZXMmcnzIi0tTf76Z5Os+uYHVb4HFSuUlz4v9JIWzZtwtr0i8sL6i0W0ZnI0ZgemfH19TY5NWFiYajhXo0aNLL+vVauWnDp1yuRzZs6cKZMnT7bpsY46e1Q2T39NdBnpUvm+J6XBiyNsen/IuqD5OSREssdUUYg+f1w2T3tVdOlpUqnTY9Koz8ii2AyiLO40QGcmJRHZL63xecngIM7IZ+XKZkSJjy5J4p085YJLsFRNz/3nU0ZGhmzbvltWfL1GwsMzz3tx7F/431PSqUM7cXExu/MMEdkwswNT2blx44YKWhk3Oi9evLj6nSljxoyR4cOH63+FjClkWdmK+Ihw2Tixj6Qlxkuphm2k1bA5jOKTRTFjqugk3LgiGyf0kdSEOClZv6W0Hv6eOHG2F7ICXgGZU2GjlI+IyF5dvHRZ7VqFCmx8bu0QMqqWdkUOulWWUy5lpUr6NXEyc6N1IvLfgcOqj9SZs+fVs/z9i8mzTz+uZtpzc3Mzc0lEZA/yHZgqWbKkxMXFqcbnhn9AIiMjVfmfKR4eHupmi1LiY9WXVpRY+VeoIR0nLBYXN3db3BWyYlqPKXwBxayPDIwUjtSEW7JxYl8VnCpWvqp0nPiZuLjb5t8qsj93MqYYmCIi+6XNyFchhGV8tqBq+lU54lpBbjr7yg2nYhKsu3eLlkgnXznoWllWT5qpdtHL01Oe6NlDHn+0u3h7cdZZIkeU78BUxYoV1bgdPnxYmjRpoh/DQ4cOSYMGDexqTDPSUmXL9IFyM/SEeAaUlM5Tl4u7r79d7SNZB09kRjg5qVLRpJhIfaYEFRz1/p7xmkSfOyaexYOky7QV4uEXUHArJMolrxKZF3sSo9ljiojsV9jtxucVQpgxZQs8JE0qpkfIOdfScsq1jASnZh+YinHykkOuleSSS5DaNVdXV3m4+wPS66nHVLYUETmufBftBgUFSbdu3bLM1Hfw4EHZuHGjvPDCC3YzsjqdTnYtGK0aIrt6ekvnKcvEtxQ/MKlgOLu4imfxzLKdhBvsM1XQ8P7e/dF4Cd/7r7h4eEqnyV9w1jOyOsyYIiJHoPWYYsaU7UATdLjoHCQJcnclSby4yy7X6vK7e1MVlHLS6aRy2lX57OP3ZMDLLzIoRUT5z5gCNDO///771Ux81apVk2+//VZeffVVady4sd0M8eGv58vZP9eokqr2Yz+RwOr17WbfyDqhAXpS9HVJVI2O+XorSEe/+UjO/P61ylJrN3qhBNVsVJCrI8oTBqaIyN7Fxd2S6OibajdDWMpnMwJ08RKcESMRzv5y1rW01E8LU9ueLK5yzDVE9Z/KcMrMhyiXfkMapl0Qf12ClCyZeRGWiChPpXxPPfWUmolP07BhQ9mzZ48KSF29elUWL14sPXv2tJvRPff393Loy/fU/jQfNE3KtehiN/tG1ks1QD9zWPUzo4JzfuNaObBstlpBs4GTJaR114JbGVE+qBJfnOjH3JCM9HRxdnHJx9KIiKyPVsYXHBzEXkM2pnpauES4+8sZlzJSIy1cTruUkROu5SXVKfPrJgJXjVLPS5Auzsb2jIgKQ54CU4899thd21apUiV5++23C2ObC9XVA9tk5weZ+1Xn6dekxsMvFur6yXFpDdATGJgqEPERVyRs66+yf8kMtfxaPftLrcf6Fci6iCwBvc+QtYsJEZJu3lBZlURE9kQr46vIbCmbE5IRKV66ZEl08pCfPFpIulPmxZPiGbekYVqolMmINnvGPiJyPHkKTDmKm6EnZdPUAaopcsWOj0jjfqMdZdfJCqiMKTQ6jmSPKUs7s3617Jw/SkSXoRYdUK2eNH1lgqVXQ2RRyJDy8A9SJb5J0REMTBGR3dEypkLY+NzmOItOqqVdlcNuFVVQyjcjUeqnXZCKGREMSBHRPTEwlQ1kqWyc2EdS42MluG5zaTNinrpSTVRYtBm4mDFlWciUMgxKwc1zx9Q4+wSXsezKiCwMfaYye89dt/CSiYiKnj5jqkK5ot8YyrWa6Zck2clViukSpEr6NXERXa6XQUSOiZEWE1IT4+Xfd/pJ/PXL4leuinR6Z4m4uHs65iuEiox3UGm17szm52QpN0OP6zOlNCiNigsPtdQqiAoMG6ATkT0LC7ukdo8ZU7bJTTKkado5qZ5+lUEpIsoVZkwZyUhPk60zB0nUmcPi4R8oXaYuF49iAbkaVCJL0PrHsPm55aBh9PEfPr9rgU7OLuJXtpLlVkRUQLxuN0BnwJqI7E3crVsSdXtGvgrsMUVEdJcX1l+021FhxpQBnU4nez+ZKJd3/yMu7h7SadJSflmlIqOV8qHJcXpqSpFth73A+3vfosly9b8t4uTiKnJ72mIEpVoOmcUyPrIJzJgiInt18eJltWvBQYGckY+IyMEwY8rAse8Wyal1X4o4OUnbUR9KcO0mDvZyIGuCTD1nVzfVfB+Njn1Kst9CfpxY+7mc/PkLtYh2oz6UoNpNVfkeMqXYW4pshT4wFR1hK5tMRGQWrb9UhQrlzXo8ERHZDwambruw+Rf5b8l09VPTAROlQtvu9nOUySah2T6ypuKvX8pszM3AVJ6Fbf1N9n02VT2/Sf9xUrFDD/V/BqTI1miZlGx+TkT2RusvVaE8L8QRETkalvKJyPWje2Tb3DfVsa/52EtSu2d/R3sdkJXyCiyltox9pvIu4tg+2TZniIhOJzV69JbaT76a94URFTGvEsGZfxM4Kx8R2RlmTBEROS6HD0zFXjon/056STJSkyWkTVeVLUVkLbQG6AmRV61lk2xKXPh5+XfyS5KekizlWt4vzV6bLE5OTja1D0SGDHtMoW8aEZG9CLuYOSMfG58TETkehy7lQ1PpDRNelJS4mxJYs5G0HfmhOLu4ON6rgKyWvmwn8prVbqO1SoqJkg3je0tyTJSUqF5f2o/5SJzR9JzIhmmBKVxMSY2PFXdffxveG8f0/fffy9dffy2xsbHSqVMnGT58uHh5eZkcjIyMDHn22WfvCkIOGzZM2rZt65gDSHbp1q14iYyKVvvGwBQRkeNx2IyptKRElSl160qY+JauIJ0nfyGunqZPDImKindQabVqTg2fO2nJibJp8suqublPyfLSefIycfX0zt1CiKyQi7unPhjFcj7b8/HHH8srr7wibdq0ke7du6sAVc+ePbPdkStXrsi3334rtWvXlmbNmulvJUqUsL2dJ8pB2KXMGfmCAkuItzc/r4mIHI1Dpg9kpKernjM3TvynTvA7T10hnsWDHO3Ykw3QMqbQ/JzMo8vIkO3vDpeIY3vFzaeYdJ66XJ9lQmQPkDWVcitGBab8K1S3h11yGB988IHMmDFDBg4cqPa5V69eUr58eTl79qxUrVr1rnEICwsTNzc3mTRpkjg7O+y1RHIA+sbnnJGPiMghOWRgav/nU+Xi9vXi7OYunSYtEf+Qu08GiawBm5/n3v4lMyRsyzpxdnWTjhM/k+IVa+R+IURWzDMgWGLCTjNjygatXr1aqle/E0xMTU1Ve5GQkGByby5cuKACV+gnFhoaKuXKlVOBquwkJyermwblgkS2QN/4PIQz8hEROSKHC0yd+HGpnFi7RB3rNm+9LyXrtXTE4042gs3Pc+fkL8vl+PeL1JNavfmulG7YJncLILIBdxqgM5PSmkRFRcn58+dNblLTpk3V/U2aNMny+/nz50uDBg2kfv36Jp+HjKm4uDgVnIqIiBBXV1cZP368upkyc+ZMmTx5sjUNC5FZ9BlTIeXNejwREdkXhwlMnT19Qnas/05ifl8maBXb+KUxUqnjo/Z1NMnueAVm9phCk+O0pAT2ScrBpZ1/yd5PMmfVbNj7Laly3xN293ogAn1gKjqCA2JF/vjjD3nppZdMblFiYuJd93/11VeyePFi2bZtW7Z70aVLF/H09JTHHntMZUutW7dONUNH1hXKAI2NGTNGNVPXIGMqJCTEikaJyDStxxQzpoiIHJNDBKY+/Og9mbf7mOicnMQpuKO86Jcozz/9mmMecbIpbt6+KhiFoBQaoPuVrWxT219YIk8dlC0zB6n+UlW7Piv1nhtSWKsmKnR3MqauF/q6KXvPPfecupnjr7/+kgEDBsiqVaukUaNG2T5Fa3iuefzxx6Vv377yzTffmAxMeXh4qBuRLUkRF4mMjFKbzIwpIiLHZPedNJEppQWlAMGpL+O85NyZk455xMmmODk56ftMsQG6abeuXpSN7/ST9OREKdOkg7R8Y4YaNyJ7xcCUbdu9e7c8+eSTsnDhQpUJlZPIyEiJiYnJ8pAyZcpIdHS0bQ8CkYFYp8xZ+AIDS4iPD2fkIyJyRHYfmDpx8qgKRhnCzydOHnPE4002SD8z342rNrj1BSs57qZsnNhHkqIjpHjl2tJ+3Keq6TmRPfMKKKl2D7PykW05ceKEPPTQQ6oPlKmyv5SUFNm7d69+p6ZOnSqtW7eWtLQ0dV9SUpJ8//330qYN++eR/YhxzgxGVWTjcyIih2X3galaNeuKk06X5QDj51o16zjsQSfbojVARykf3ZGekiybpw5Qs5N5B5WWzlOWi7uPH4eI7B4zpmwTZtZ78MEHVfBp8+bN0rNnT/3tt99+Uzu1dOlSad68uezatUv9/NZbb6mMKfSU6tevn2qSjuWMHj3aNgeByISY2xlTIWx8TkTksOy+x1TV6rVkeIs6Wcr5nq1TWd1PZAu0BuiJkQxMafDFbOcHI+XaoR2qDxeCUj7BZWzhcBLlmxaYUpMiJCeKq4dXvpdJhWPEiBEmV4TG5oBgVWpqqr7vFGbjO3bsmKxcuVLN0IfZ+ND8nH2kyJ7EOPmo3alYgTPyERE5KrsPTMEbg0bIQw+ekNErvpY9F8PFo3xVRz3eZIO0jCn2mLrj4Ip35fyGH8TJ2UWV7wVUYQYkOQ43n2Li4u6hsgZRxupbuoLj7LwNQ++7oUOH5rgHpUqVkjfeeCPLY/z9/eX111+34T0nylns7VK+EJbyERE5LLsv5dMgQ+r1p55SP67btUfS0tMd9qCTbfEKLKU2ODGSPabgzB+r5ciqBer/LYfOkrJNO9rWASXKJwQ4PNlniojsAGbkS3DKnEmyQvnMzEEiInI8DhOYgnZ160iAr6/ciI2VnSc4Kx/ZBmZM3RG+b5Psmp/ZW6Xec0OkWtdnbeMgElkY+0wRkT3Qz8hXIkB8fTNL+oiIyPE4VGDKzdVVujdrqo7yzzt3O97RJpuk7zEVdU31VnJU0eeOyZbpA0WXkS6VuzwhDXu/5ahDQcTAFBHlW0ZGRr6XkV/ajHwV2PiciMihOVRgCh5t1UL9+/vefZKcmurQB59sg5YZkZ6cpJodO6L4iCuycWIfSU24JaUatJZWb85V5UxEjooZU0SUV+npGfLxoi/kyWf7yYGDR/K6GIvQZuSrwMbnREQOzeECUy1qVJfSAQESl5go/x4q2g9jInNgxi13X3/10IQbjtdnKiU+TgWlsO/+FapLhwmLxcXN3ZyhI7JbDEwRUV5g1sfZ7y2QX3//S1JSUuXLr9cUaTa2PjDFxudERA7N4QJTzs7O8kjL5uqg/7xrl0MffLId+gboUddsZ6MtICMtVZXv3Tx/XDwDgqXzlOXi4VfcAksmsm1eWvPz6Ou2vSNEVGgSE5Nk8rR3Zdv23eLq6qpuJ06ekaPHThTaNhjTZuRjxhQRkWNzdcTdRznfZ+v/lH8OHJL4pCTx8fR0xGEgG+IdWFpiLpyShEjHCUyhn9auD8fKlf2bxcXDSzpP/kJ8S4fY0FEjKjjMmCKi3IiNjZNJ0+bKyVNnxNPTQ8aPGS5bt+2S9X9ukO/WrpN6dWvnZnHywvqLuXq8KQkJCbLqf/3VrzgjHxGRY3O4jCmoV7GiVC5VSpJSUuSv/Qcc+xVANkGfMeVAgakjqz+Us3+sFidnZ2k/9mMJrNHQJo4VUWG4E5iKKIzVEZENuxEZJaPGTVFBKT8/X5k+ZZw0blhfnnj8YdWvcc/e/yT0Qv4DTbkVdilcPYUz8hERkUMGpvAh/MjtJug/7+LsfGT9vANLqY1MiLT/HlNodP7fF7Pl4PK5ap+bvTZFyre83/oPElEh0gJTyTE3JCM9vRDXXPjwN+Hqwe3qXyLKnfArV2XkmMkSdvGyCgDNnj5RatWophZSrmwZadM6s73FDz+uy92CLSAs7JJaSgj7SxEROTyHDEzBoy0zZ+fbfOSoRN+65fAvBLJuXiUyA1P2njF1Zv1qWdu7pRz9ZqHa3zJNOkrNR/pY98EhKgIe/oEqm1CXkSFJN28UwRYUDu1vwt+jesna3q3Uz0RknnPnL8jbYybLtesRUrZMKZk7c5JUNJr97qmej6iF/bt5u1yPKNy/JQiWQcWQ8ubtEBER2S2HDUxVK1tG6lasIGnp6fLbnn12e4DJPngHlVY7Ys/Nz5ENsXP+KBGdTn/Qrh7YyiwJIhOcXVzEwz/o9t8F+2yAftffBF2G7Fowmn8TiMyAhuajx02VmzdjpErlijJn5iQpVSr4rmfWqF5VGtSvI+np6fLjz7+bsWTLCbvIjCkiIsrkkM3PNciaOnohTH7euUue79yRrwmyWlqPKXtufn7jxH7BF09Duox0iQsPFZ/gMlZ7bIiKCsr5kqKvS6KdBqaizx7h3wSiPEDPqJlz5ktySorUrV1TJo57S3x9fbJd0lNPPCKHDh+TP/7aIM8901P1oSoM+owpoywusl6WaHpPRGSKw2ZMQY+WmXX1u0+dlitRUXyFkNXyKqFlTF1XpTv2Ji0pQQ6v/OCu3XJydhG/spXsbXeJLELrM5UUbX8N0NNTkuXI6sySXkP8m0CUs383bZOpM+epoFTzZo1lyqTROQaloEmjBlK5UkVJSkqWX3//q1CGOCExUSJulw6yxxQRETl0YKpcYKA0r1FdTUu/btcevhrIankFBIk4OYkuPU2SYiKtdjvzIiM9TbbMHCQ3Q0+Iq5eP6psD+ALacsgsZksRZePOzHzX7GqM8Jm884ORKovSxd2DfxOIzLTutz/l3Q8+VmV5nTq0kfGj3xRPD497PhuTAj3Vs4d63M+//iHJySn3fE5+XbzdX6pEQHHx8y2cDC0iIrJeDl3KB4+2aiF7Tp1Ws/O90r2r9R4pcmjOrm7iWTw4s2wn8pp4BdzdJ8IW4Qvo3k/ekcu7/lZfQO+bvlK8g8uq8j1kSrGEjyh72t8BeyvlO7jiXTm/4QcVnO74zhLxr1CDfxOIcoDP0lVr1srKVd+pR/V46EF5tX9vcb59occc7du1kuVffaMaoP+9YZM83P0Bc56WZ+wvRUREhhw6YwoeatZMXJyd5XDoBTl/1b6uOpN98Q7MnJnPnvpMHf9+kZxat0Jlg7UduUCC6zRVwajSDVszKEV0D3cypuyn+fmZP1bLkVUL1J63HDpLyjbtyL8JRDnIyMiQxUtW6INS/+v1hAx8pc//27sPqKiOLg7gf0EQUMDeC3aNvYEFVOzG3mJM7Mb0RJN8iS0GW6wxRk1P7CXG3nusiNh7712xYwML+53/4FuXZRcpC+yD+zuHkwjL8srsvHn3zdwbp6AUOTo6olWLJur/Fy5eoWZdJSbJLyWEEMJUqg9MZfFwh2+pN9Qx4awpIeyVlgD9ye3r9rqJcXJhy3Ls/ft79TuVeg5Eft834/T7QqR2rpkjg9VPUkiOqat7NmPH+L5qn0p3+BxFGr6d2k+xEDF6/vw5xk34HUuXr1Gv4yypdzu0VUvz4qNBvVrwcM+A6zdCELQ9cVNcXLwYWZEvf7688dlUIYQQKYzNAlNr1qxB27Zt4efnh88++wyXL0decPSAy/loyfYdajq0EPZIuwlNCTOmQo7swrYxvdVhLt68G0q0es8eD7kQdi0lzZi6e/Yotn7/oarEWbBOa5Tr/D+7PvZCJDfmgfp+1E/YsClQzY76qvfHaN60UYI2y8XFRS0DpHkLlybqmFibMZU/X54EbbMQQoiUwSaBqcWLF6NFixbIlSsXunTpgiNHjqBGjRq4d++eLo5Sg4oVkM7JCWevX8eRi5d0sc0i9XHLqlXm0/eS09Ar57B5cA9EPAtH3moNUOmDgHg/3RUiNTMNTOn5ocqjm9ew8bsuePb4IXKUrYaqX4yRPkGIGDx69BjfDRmJnbv2wtnZSSU5r1Pb1ybHrGmTBkjn7IwzZ89j/8HDNnlPc0+ehKlcViQzpoQQQpBNAlOTJk1Cz549MXHiRLz33ntq9tSjR4+wbl3SlJxNKHdXV9QtV1a9zbLgHQl9OyEShXHZzm39BqbC7t3Ghm87ITz0LrIUKwffPj/DwdExUY6XECmda+bIIggM8j59eF+Xu/v00QMVlHp86zo88xdFzYF/wtHJWZf7IkRSuHfvPvoNHIbDR47Dzc0VQwP6wse7ks3+tKeHBxrU91fvt2Dhcpu9r6lLl6+of2ZiRT53qcgnhBDCRoGpfPny4enTV6VlmYiRSRP5fUvCw8MRGhoa5Su5acv5lu3YpbZfCHvzKvm5PnNMPQ9/gk2DuuPhtQvIkDM/ag+egrQurvZ2mIXQDUdnFzhn8FTbq8fKfBHPn6nle/fOHYNLpmzwHzIN6dwz6ub4C5HUQkJu4pv+Q9RsJk9PD4wcNhClS5W0+Wa0av6mWh6478AhnD5zzubvf0HLL5VXlvEJIYSIZJMZU59//jkCAwPRo0cPNWuqdu3aaNKkCby9I3M3mRsxYgQ8PT2NX9YCWEmpdtmyaubU1Tt3sOf0maT800LEyqvk5/qbMWWIiEDQmN64dXyvupH2HzrNWOpeCBF/2nK+MJ0lQOfSwx0T++Pa3i1wTOcK/8FTkCGn5YdZQgionExf9xuMK1evIVu2rBgzPACFC3klyqHJkSMbavpWVe+9YNEym/+NS5ciZ0zlzy+Jz4UQQkSySWDK1dUVuXPnxsqVKzFt2jQcPXoUBQsWtJrzol+/frh//77x69Kl5M/r5OLspHJN0RJZzifskFuWyBxTYfduqZkGerL372G4GLgSDk7OqBXwNzzzFdHT5gtht/SaAP3wnIk4s2YO0jg4wK//r2pprxDCspOnzqBP/8G4dfsO8ubNjR9GBCBPnlyJerjatGqm3j8waAeuXbftA7ELl7SKfDJjSgghRCSbBKZatmyJ/PnzqwDT7t27cfjwYRWg+vHHHy0e53Tp0sHDwyPKlz1oUc1HbcbKXbvx7Plze9gkIYzSeWSCQ1on9W89lYc/sXQqji38S213tS/HIkeZyKewQoiEc8mUPbJP0FFRhHMbFuHAtDFquyt/NAR5feol/EAIkULtP3AY/Qd+j9AHD1GsaCGMHv4dsmbNkuh7W6hgAVSsUBYREQYsWrLSpn/POGMqn8yYEkIIESnBgakbN25g//796NWrF9KmTavelEvz2rdvj9WrV+vqOFcvWQJZPdxx58FDbDt6XFfbLlI+ziwwzo7QyXK+S9vXYvfvAerklO/aBwX9W6b8EyVEEtISoOslx9T1A0HY/uNX6giVbPMBijfrkoRHSwh9CQrehYCho/EkLAzlypTC90MGqOTkSaVt68hZU+v+26SSrtsCK/LdCLmp3iq/zJgSQgjxUoIDU1myZEGGDBmwfft240Fl8vAdO3agQIECujrQaR0d8WaVymqbl+6Q6nzC/miV+VjByt7dOrEfgSM/UfmlijR+B6Xaf2LvmyyE7uhpKd+9CyexeUhPtRQ5v19TVOzRX3fHW4iksnb9JowY/ROeP3+OalWrYNDAr+HmmrQFQ8qWfgNFixTC06fPsGzlWpvsulaRL2NGT3h4uNvkPYUQQuhfggNTnCU1fvx4fPrpp6hQoQLeffdd5MqVC+fOncOgQYN0d4S06nxr9+xDmEmlQSHsgVvWyDxT9r5s5+H1i9gU0A0vwsOQu3JteH/6PdKkSWMPh1CIFMUYmLLz5Oec0bVxYBc8exSKbG9URo2vx6lZoEKIqPhw959/F2L8z3+qZXT169ZGv68/h7Ozc5IfKl63tVlTy1euVbOdEopJ3ElmSwkhhDBlk1Fh9+7dceTIEXTu3BleXl4YNWoUTpw4ofJO6U3FwoWRJ0tmPAwLw4YDB/W2+SKFM86Yum2/M6bCH9zFhm87qyTtmQqXgl//3+DgGLnMVwhhW1qfYM9L+Z49eYSNAV3xKOQy3PMURK2ASXB0drHtgRAiBeByuYAhozDzn/lqb1q1aIJen/aEo6Njsu1dNZ8qyJ0rBx4+fIS16zcmeDsuXtQSn0t+KSGEEK/Y7G6xWLFi6kvvHBwc0MzHG7+vXI2lwTuNS/tEynHtzh2cuxGCgjmyI1fmzLraMdcsOdT22muOqRdPw9VSndDLZ+CWNRf8h0yFk1sGuzzGem4Hwrb03BZcM2WL7BPuhNj2oNhIxIvnCBz5Ke6cOoR0nplRZ+g0uHjqq9+1pSdPnqBOnTrR3pIP9GrWrGnxTy1evBi//fYbLl++rF4zcuRIeHp62nKzhB04fOQYRo/9Gbfv3EU6Z2d8+H5XNKhXO9m3zNHRAa1bNsXPv03CoqWr0KRxfWNO2fi4eFlmTAkhhIjOZoGplKR5VR8VmOKMqdDHj+Hh5paSdi9Vm/HfRnw3czYMBgMc0qTBiK6d0b6Wn26OidvLwNRjO0x+zlxSTGoccmgHnNzc4T90GtyyRC49tDf/bt6KflOnI0Kn7UDYzswNmzBwxizd9gnaUj4ukXse/gRp0yVtDpqYsJ/d/VsAruxYD0fndKgdMBnuuQva7uTpEKsXBwcHY9KkSapCsaZQoUIW92bhwoUqRcLw4cNRvHhxjBkzBv7+/ti1a1eyzqIRtsOle/MWLMXMf+appXt58+ZGv697watAPtv9kQSq6++nZnHdvHkLW7ZuRx3/+I+btBlTBfLLjCkhhBCvSGDKgpL58qJI7lw4ffUa1u7dh7a+NSy9TOgEb472nTmLaes3YEnwq6T2DEr0nzYDNcuU0s3MKdeXgR57nDG1f9ponN+0BGkc06Lmt38gU8GSdtdCGGietXETRs1baNw2PbYDkXCHL1zE1LXrMX9bkK7bglN6DxX04WzFsLs3kSGn/SyhP7bgD5xcPh1IkwY1vpmAbG9USviJ07kLFy7Aw8NDpUCIjWHDhmHAgAH44osv1Ms5Y4qVj5cuXYpWrVrF5i2EHbt/PxQ/jPsFe/cfUlvpX8sXn3zYHa6u9rXUlfmtWjRthGkz/8X8RcvhX9s3Xnkjw8JeVeTLJxX5hBBCmJDAlAVM9tjcxxs/LlqCJcE7JTClUyH37mNR0HbM3RqIM9cs52R6ERGB8zdCdBOY0mZM2Vvy81MrZ+HIv7+ollK11yjkqhj/p6m2pqqEnjiJeVu3YeXuPRaLGuitHYj4ufvwIRZvD8bcLdtw7NIli2+it7bA6xXzTLHgAJfz2Utg6sKW5dj79/fqGFfqORD5fd+M30lLYS5evKjyb96+fRvHjh1DkSJFkDOn5ZmlV69exb59+zBv3jzjUWAV5KZNm2LFihUWA1Ph4eHqSxMaGprCjmDKYbp0z9nZCR+93w3169aKV8AnKTRuVBdz5y/BhYuXsHvPflSpXCHOf/bS5avqdzJ6esDTwyPOvy+EECLlksCUFazOx8BU0NFjuBUaiqxyAdWFZ8+fY+PBQ5i7dRs2HjiobjLJxdkZtcuWxpo9+9QMKg2X83nlyK6LfSMtx9TTh/fxPOwJ0rok/7KdK7s2YufPA9T2lXn3CxRu8JZdHM8rt29jQWAQ5gcG4eLNm8Zt4vm+cCMEr1oBwHqBemoHIvbYB2w9fBTztgZi3b79ePr8ufpl57Rp4Ve6lFqyrec+QfULmbMbA1P2IOTILmwb01ttSvHm3VCi1Xv2sFmJ7sCBAzh//ny0v5M5c2b4+UUG6xmYunLlCooWLQpXV1dcu3ZNVTVmdWPzyqXMKcW8lywqY6pgwYLYsePV7F9TI0aMwODBgxN9X0X88WHJ/IXLMGP23FdL9/73Oby87Ge2oyXuGTKgUcO6WLRkBeYvWhavwNTFS5GJz/NJ4nMhhBBmJDBlhVeOHChX0AsHzp3Hyp270ble9GSlwn6cunJVzYhZGBSEW6EPjBtWoXAhvOXni6Y+VeDu6qpyC3H5nhawyp89G3JkzGg/O/IazN3kmM4VL8Kf4Mmd68mer+XOmSPYOvwjGCJeoFC9tijbMXK5SXIJe/oM6/btU4HJwCNHjQGHDC4uaFbVW7WF8oUKYu6WwCjtgEEqLu3SWzJ8Yd35GzdUUJJf1+/eNb6wVIH8qh20qOqNjBkyROsTsmf0RBadPYhwsaME6KFXzmHz4B6IeBaOvNUaoNIHAXY7A8TWFixYgH/++Sfa21aqVMkYmOrQoQPKly+PRo0aqcBUUFAQ6tevj8qVK6vKxqY424l5qMwrsqVPn97qTKh+/frhyy+/NL4N34NL/4R9UEv3fvoVe/dFVn2216V71rRs1gjLVqzG4SPHcfzEKZQoXjROB/bCxcjE5wXy54nT7wkhhEj5JDAVA97IMjC1dMdOCUzZoQdPnmD5jl1qqR5zSGmyerijdfXqaOdXA0Xz5I6y5Ux0zpxSu0+dxjeTp6plfDM2bEIXnQQe+USdy/keXD2vEqAnZyLhRzevYuN3XfD8ySPkLF8DPr1GJdsNKINK87YEYnFwMO4/emw851VLFFdBiEaVK8LNJNGw1g54/lfs3I2ZGzdhwLQZ8C5WDJ7ppdiBXj0OD8fKXbtVkJrLNzUZ06dHy2o+aOvni9IFos5K0NrCwXMX0GfyFFy/ew+/rViFXi2a6eYwaAnQk3uJb9i929jwbSeEh95FlmLl4NvnZzgkY5n7pDZkyBD1FZMSJUqoL0316tVVQGrZsmXRAlO5cuVSVfwePXqkglGamzdvInfuqNc2DQNZpknVhf1gMGf02Imq6p4elu5ZkjVrFtSuWQPrN2xRuaa+7Ru3h1GXXs6Yyi8zpoQQQpiRwFQMmnl74/s581QQ4/Kt28ibNUtMLxdJwFq+IEcHB9QpVxZv1fRF7TKl4ZTWetPmrJhmPt649/CRqsY1at4C9bv5smVNgj1IOCZAZ2AqOROgP30Uio0DO6tt8CxQTCU7d3RyTvjOxQHzBS3ZvkMFJo9efJUvKFfmTGhbowba+lVHgezWl2myHfCLs+qCjh3D2es38P2cuRjdo2sctkIkNy7D23vmrApMLt+5Cw/DwtQmMYhbs3QpFZisV6Ec0jk5Wd1UrS2EP3uGz3//Ez8vXY5GlSqieF59PNV/FZh6tWQ1qbEi4KZB3fHw2gWV56r24Cl2sdTY3hw/fhxubm4qz5TG3d1dLdszV6BAARVkYhW/unXrGn+8fft2lQRd6MOrpXusuhcRWXVPB0v3rGnTqqkKTAXv2I3LV64ir9kDwJhckMCUEEIIKyQwFYMcmTLCp3gxBB8/gWU7duKjJo2lISWTq7fvYMG2IDUTwjRfUOFcOdG+ph9aVquqluDExbv+tbBs5y7sPHES/aZOw4z/fZlsM37iQkuAzhlTyeHFs6fYMuxD3Dt/Qt0Q1xk6Hc4Z4nbs48uYLygwEOv2Rs0XVL9ieRWE8C31hgpUxhbzjzEY1W74aBXkauZTReUeEvbNWnGD/Nmy4S2/GmhdozpyZ8kcp53guWdfz1xU30yaggXf9kNaHcz4MQam7obEaX9tJeLFC2wb3Qu3ju9VfYH/0Glwfbm8UET1+++/Y/ny5Th06JBaysfZT3PmzDFW3WPCc/6sYcOGKtF5+/bt8f3336tAlJOTE9atW6cCVVOnTpVDqwNcujd2/G/Ys/eA2lq9Ld2zhLOdvKtUxM5de7Fw8Qp8/knPWJ0JVuQLCbmlXptflvIJIYQwI4Gp12hR1UcFppYGS2AqqXH2Am8QmQ9oq3m+IB9vtVSPs13ME8bGFpPKjurWBY0GDkLgkWMq6MUZV/ZOS4D+5LblSoOJibNTdozvg+v7tiKtixv8h0xD+uyJP6vkQkiIOj8MTl678ypf0Bv580XmC6rmg0wZMsR71ysXLYoudf0xdf0G9J0yHWu/H4z0LvZVrlsgxuIGb1appNqCd7Gi6rMdH+xLhnXuqPp8LuOevHY93m/cMD5vlaRYlY9YlS857P17GC5tWwUHJ2fUCvgbnvmKJMdm6MKAAQOwevVqVYnP29sbe/fuVf9lAnQaNmyYCl7dvXsXnp6eGD58OBo0aIDixYurpOfbtm3D6NGjVfJ0Yd+OHD2OUWN/xu3bd9TSvQ97dkWDerXjPWaxJ+1aN1OBqf82bkXHDm2ROXOm124eK/JxDOEpFfmEEEJYIIGp12hcuSK+mzFLlRY/ffUqiljJ6yAS7tqdOzh3IwRPnz1XFfWWBO/AvUePjG/M2WsMHDWuXClKvqCEKJgzB75q3RLD/52Hof/8i1plSquZcvZMuwlN6hlTj25ew4Fpo3F2/XykcXCEX//fkLlIaZsfKq0d5MyUUeUOY2DSNF8Qc0C1qlbVYr6ghPi6bWus338Ql2/dwuh5CzG40zsJeTthA1pbgMGgqm0uCtoeY3EDW+Dnf2CH9ioH3diFi9UywEI5c9rirRONNjspqQNT7BOOzPsVJ5dGzt6p9uVY5ChT1eb7qbWDgjmy675AQbZs2XDw4EGsXLlSLd/r378//P39jceMgavmzZuroBTlyZNHVeDbtGmTmk01YcIElColMzrtmVq6t2g5Zsxi1T39L92z5I2SxfFGyWI4euwklixfjW6dO7z2lFy6FJn4XPJLCSGEsEQCU6/Bqk3MU/LfgYNq1tSXrVu+7ldEPHBmwtDZc1R1NFM5M2VCW9/q6ouVEhNDj4b1sWLXbhw4ew4Dps/EX59/YtdPNN2yRt4kJ2Wi49Or5yB4/DcqQEBetVsgj7ftK1XO2bwF/abOMFbT03Ami1+pN1Rgsl758nBxtp4vKL44Q2pkt87oOOZHTPtvg6rkWKWYzEpILlPX/YfBs//RmpxRTMUNbIWzMbmkjzM1+06ehjl9v473LKykoC3lC7t3CxEvnsPBMfEv7eZ9Qr7qjVDQ3/bXR1ZS7Tt1uuoTHNKkwYiunVXCej1zdnZGy5aWjxUDUfwyxSV9TZs21fMupxr3Q0Mx9qeUtXTPmjatmuHosbFYuXo93mrTAulfUzjkVX6pxJ9lLYQQQn/it94hlWle1UftMWfwcHAsbIP5gjYfOozuP03AELOgVBoA497vgW1jR+F/bVolWlCKmI9oTI+ucHJ0xPp9+7Fsxy7b7GAi0XJMJVXyc86KMA1K0flNS9T3beXm/fv4YcEitYzO/DP2fqOG2PbDKEz/3xdo6l0lUYJSGuanav9yOWefyVMR9vSprXZRxAJnF2w/dhwfTvwVg2ZFD0qN6t4V238cg/5vt4tWcdOWGAgd3rWzmpm58+QpzNq42ZZvb3PpPLMgDZcvGgyqMl5is9QnXA5eZ9M+gcUNxi9Zhj5Tphn7hAiDAf2nzVAzqISwN1y699kX/VU+KS7dY+6lr3p/BFfXlLks3LtyBeTLmwePHz/BqjX/vfZ0XJQZU0IIIWKQ+I9VUwAu5XB1dsaFkJs4eO48yhUqmAL2KvkwX9D8rUGYv21blHxBphj+42ypuCSxTohiefLg0+ZNMW7REgyaNRs13iiJLB7uCXnLRPNqKd91dcMW3xxbsXVt72bjTCmNIeKFqgyYPluu2L5NNM+eP1fLs+Zu3aaWbjJQaYl/uTJxTmKdEP3bv4VNBw+rKn3jFi9Fv7faJuTtRCxYK25gLn+2rDFW3LQlVuns064NAmbOxsh58+FfrqzdVmZ1cHSES8asaikfv7TgdWIJORycKH0C+4DAI0dVEQLT4gam+JrzN0J0v6RPpBwMqC9YvBzTZ75cupcnF/p93QteXq8qL6ZEzOXXplUT/DTxTyxZtgotmjVSCfqtuXgxsvJkfpkxJYQQwgIJTMUCl/jUq1BeLe1YumOnBKbi4Ul4OFbt3qtuOJhYWMN8QfXLl8eCoO1RZsowIOWVI3s8/lL8seriql27cfzyFTVbY+JH78f/zRKRlvz8RXgYnj0KTdSKeI9vX8f+aT9E2xvmmHLP7RWvvWSutrlbtmFhUFCUfEFv5M+PY5cuRlm6lRztgG1yWJeO6Dn+Z/y1ag2aVKmMsgXjt6/CupiKG9QtX071taaz55KjLXSqUxvLd+7CrpOn0G/qdEz/qneiB4LjyyVTdmNgKjGFP7iLA9PG2rRPsPawolie3Dh15WqUJd7J0Q6EsIZL93786Tfsfll1r3atGvj0wx4pdpaUudo1fTFj9nyV4H3DpkA0rP8qX5qpsPBw3Ai5qb5VIH9eaVBCCCGikcBULDWv6q0CU8t37EL/9u2SbCaPnvGmcv/ZcyoYtSx4Jx6Ghand4QwfLpliSff6FSqopVnM5cMlGnwazhuP4V06JfkTcee0aTHmvW5oOWS4Otc85/UrlLe7U5A2nasKRj19eF8lQOf/J4ZnTx5h43fdEHbnBtJlzIqnoXdgiIhQQSmfz0fGabbUgydP1GeHbYEJzTXm+YKYTya52wHxvPP8M6/cN5OmYOmggap9iIQ7cuEi5m0NxOLtMRc3qF6yRLK3BVW5s3sXNB44GFsPH8H8wCCVf8oeMc/U3TOJW5nvxdNwbB7SEw+vX1D9zrPHD+LdJzxWDyv2qGqb5g8rTIsb2EufIIS5lFx1L7acnNKiZbPGmDR1FhYuXo76dWtZzMd3+WVFPg8Pd1WVTwghhDAnd1qxxAToHm5uuHHvnqoQxpsmYRnzBS0OClZLtE5dvWp8EZfGtPOtgTa+1ZEnS5Yov8xktjXLlFJLNDhTKlfmzMlyeMt4eaFnowb4feVqDJg2A97FiqkZNPaGs6YYmGKeqYwFitl885hAeevwj3H3zGGVv6bRuMVwSOuslu9xplRslvAxMMnPCoNRK3ftMeZrYuCRy6IYmPQvWybK0ix7aQcU8E4HtayIM+h+W7EKvVo0s/lxTi3uPXyIJcE7VVtgYEqTK3MmtKlhubiBvbQFVuT7olVzjJy74GXlzlLIntH+KncaE6DfDUmUZsWg9PYfv0LIoR1wcnNH/THz4JwhY5z7BAamGYxi8N/0YYW14gb20g6E0Jgv3cuTOxf6fdMLBVP40j1rGjXwx5y5i3D5yjXs2LkH1apWifZSLb9UgXwyW0oIIYRlEpiKpXROTmhcuSL+3RI5+0cCU1E9f/ECm17mC9pw4KD6N7k4O6NxpYpoV9MXVYsXi7GyFYNRyRWQMtW7ZXOs3btP5Rj6fs5cjO7RNZatJOkwh8z9CyfVUjtbY0Bp1y8DcXXXBjimc4H/4Clwz1VA/ZnYBKSYmHhBYBDmBW5Tedk0hXPlxFt+vmhVvRqyZ7Q+y8te2gFzjA169x18/vuf+HnpcjSqVBHF80o1odiyli+IM8/qVyyv2gJnTsaUR85e2sJ7DRtgxc7dOHT+Ar6dPgt/fPax3S3p0wJTzDGVGPZPG62KHqRxTIua3/6BTAVLxrpP4MOKRephRSBOX31VNCF/tmwqQN26RvUY88jZSzsQ4szZ8/hz0nQcPnJcHQwu3fvkw+5wc3VNtQfHzc0NTRrXw9wFSzF/0TJU9akcrX+8eCkyv1Q+yS8lhBDCCglMxUGLqj4qMLVy924M7vSOLO0BVL4gPv1esG07boWGGo9m+UKF8FbNGqqKGmea6QmDaQxGtRs+Wt1INfOpAr/SpexqF7QE6IlRme/ovN9wauVMrrmEb5+JyFqiwmv3nfmC1u87oI4Xlzyxehald0mHZt7eKjBZsXAhu7uZfx2ee87sWLdvv1rSt+Dbfkjr6Pi6X0vVrOULKpkvn6p42KKaDzJlyKCrY8RzPqZHNzQbNFQFrVfs2q36NnuSmIGpUytn4ci/v6jdrdprFHJV9HvtrrO4waZDhzF3S6B6WKEVN2D/+maVSiow6V2saIwPK4SwF/fvh2L6rLlYu34jIiIMSOfsjA96dkGDerV1d11LDM2aNsKipatw/MRpHDl6AqVLRV1VYJwxJfmlhBBCWCGBqTjwKVFczfQIuXdf3XwzQW9qxHxBK3YyX9A27D19xngIsri7o3WNaioHC6vc6VnlokXRpa4/pq7fgL5TpmPt94NVEnx74ZY1p9qUJ3du2HSTOCNi3+QR6j0rfxCAfNUbxfj+sc0XpFfMhzasc0eVA+fAufOYvHY93m/cUK+7k2hiKm7QsmpVFZgsXUDfFapK5MuLj5u+ifFLliFgxmw1azazu/1U7nTNFFkswtYzpq7s2oidPw9Q713m3d4o3OCtGNuRtYcVFQoXUsGopj5V4J6KZ5cIfXn27DmWr1qLf+YsxKPHj9XG1/Stim6dOyB79mz62plElDlTRtT198PqtRvUrKlogamXFflkxpQQQghrJDAVB1xy0qRKFUxZt14lRU4NgSkuyzrHvE/Zs+HSrdvq6ffKXbvx5OlTdeRUvqCyZdDOzxd1ykXNF6R3X7dtjfX7D+LyrVsYPW+hmiVnL7QZU0x+bishh3cgaOyX6u1KtOqBEi17RHlrrS1kyZABwS9zR5nmC8qZKRPa+lrOF6RnOTJlxMAO7fHN5KkYu3Ax6lUop/IOpWaqLVy/gUfh4Wo2zOuKG6QUnzRrgtV79uLE5SsYPGsOxn/Y0252LTFmTN05cwRbh38EQ8QLFKrXFmU7RvYPGq1PyObpgd0nT7+2uIEQerJ7z378NXmGyp1EhQt54f0enaMFXUSk1i2bYM26jdi1ex/OX7gErwL51PfDw5/i+o3IJcaSY0oIIYQ1EpiKI1bqYmCKyzlYVcgtXbo4voN+sBoSS6Rry7JM8cacS/Vaq3xB9pcI2BY4Q2pE107o9MM4TPtvg3rSz+qB9oDJz+mJjXJM3b90BpsG9UDEs6dqllTF9wZG2c1/Nm1RFdKYf8qUk6MjGlSsoAKTfqVjzhekZ5wFyCV9W48cRd/J0zCn79epdgnS36vXqtxrUVsCYixukFIwP9bo7l3RauhwLAneoa4H9vKAwhiYuntTfU452y8hHt28io3fdcHzJ4+Qs3wN+PQaFeU952zegn5To/cJMRU3EEIPLl+5ir8mz1SBKcro6YHOHdujXp1acHR00MMuJAsmga9etQq2bd+pKvR92esjtR08nuyTPNwzSEU+IYQQVklgKo7KFyqoErZevHlT5dThjUlKw3xB8wO3YcC0mdF2rbmPN7rUr6vLfEHxwdxSzIvD3GJ9Jk/FyiEBKkdKcnPLEjljxxYzpsLu3cLGgZ1VlT/mk6rxzQQ4vMyjdDHkpgrETln3X7RdZqWyTnX87Wo5U2LhTf7wrp3R8NsA7Dx5CrM2bkanuv6J9efsjlbcYPp/G7Hl8JEo28fwx8SPPlB5gxwcUv5NW7lCBVXlzj9WrUH/qTOw9vuidlG50zVz5LKiiGfh6rOczj3+DwyePgpVfQJz2HkWKKaSnTs6RfZ7nCU1de1/+GP1mmjt9JOmb6JLvboxFjcQwl49fPgI/8xdiGUr1uLFixdIm9YRzZs2wtvtWiG9HVbntUdtWzdTgalNW4LQ8Z12yJ4tqzHxef78eRMcMBdCCJFySWAqjniDymDUz8tWYOmOnSkqMHX04iWVyHzx9mDcffjQ4pHpULsmKhUpHMejpm/927+FTQcPqyp94xYvRb+32ib7Drm+DEwxnwzLuKeJZ0DgedgTbAzohofXLyJDrvyoPWgynqVxwLKg7SqH2PZjkZWHLPEuVixVBKU0+bJlRZ92bRAwczZGzpsP/3JlkTdrlmRvC4mJFdTmbQ3EwqBgVVnNEs6cYgXD1BCU0nzRqgXW7t2PczduYMS/8zCye5fEPA2x4ujsAucMniooxX4hvoGpiOfPsGXYh7h3/oSahVVn6HQY0rmpqoTmxQ3MscpiTBU3hbBHL15EqKTmTG4eGvpAbaJ35Qp4r1tH5MmTyx432W4VK1oYZcu8gYOHjmLJslXo2b0TtPxS+aUinxBCiBhIYCoetMDU5oOHcP/RI3imTx+Pd7EP3P4l23dgXuA2VQpdk9XDA7dDQ6Ms1+ESDa8ckQl2UxPP9G4Y1qUjeo7/GX+tWoMmVSqjbEGvZD0Erpmyqqp5hhfPER56By4Zs8Z5eyJevMC20Z/h9on9cHLPiGwfjMKQxavUkjUmuCcGYrl8cdfJU1GW8aXWttCpTm0s37lLHQ8uc53+Ve8U9wQ4puIGDSpVUEt8TZf3psa2wFmTDEa1HzEac7ZsRbOq3qjxRsk4fwZtjYEkLTCVsUCxOL89l9sEj++D6/u2Iq2LG/J88gPGrNuCxUHBuPfokfH9OHOYxQBMl/amxnYg9O/Q4WP44+/pOPdy/JM3b24VTKlcMXUWt7GFNq2aqcAUE6FztplWkS9/vry2eHshhBAplASm4oEV50rkzYPjl69g1e49eLtWzXi8S/KJiIjAtqPH1IyYNXv24unz52pjmC+ofoXyqoJWzdKlMH/rNpVXiGW+edMxvEsn5MqcOfk2PBnxuDTz8VZBm28mTcHSQQNVvpnk4pDWSQWjwu7exONb1+MVmNr711AcC96Ig+6FcTx/FZz9fapxdzg7qJ1vDbTxrY48WbKoYIS0BahZQSO7dUHjgYNUZc75gUEq/5TeMcDAJYoWixuUK4u3/GqoIgdOadOifMGC0hYA+BQvppayztiwEX2nTMOaYYOTPecgA1P3L56Kd2W+Q7PH4/D6xTiUvgBOFqqGE3/NMjZt8+IG0icIPbtx4yYmTZuFbUE71W5wqV7HDm3xZqN6SJuM1/aUoFKFsijoVUAF+1asWvdqKZ8EpoQQQsQg+e6sda5ZVR8cn79QVefTS2Dq0s2bmBcYhAWB23Dl9h3jGWAZdOZRalHVJ8rSrPa1/FCzTCmcZ1W+HNlTbVBKM+jdDth29KgKSP62YhV6tWiWrK2YlflUYOr2DWQuUjrW28J8QbP+God/N+3B8Wy1EJHGAbh9F+mcnPBm5UoqMFm1eLEoS7OkLbxSOFdOfNm6BUbOXYCh//yLWmVK6bYAwPW7d7EgMEjNmOTnXMPiBuwTWqniBp5R2pa0hVe4tHPDgQO4dPMWxsxfhIB334715zAxuGSKnNEadvfVuYwNPnxYMOtPzFi+Dsey18LzNI7AzbsxFjeQdiD0KCwsDPMWLMWCxSvw7NkzODikQaMGddHxnbbw9PDQ4y7ZHc60btuqKcaM+wVLlq/Gw5epIZhjSgghhLBGAlPx1NynCsbMX4jtx08g5N49u70xDXv6FKt371W5QYJM8gV5uLmhZTUfvOXni1IF8ltdjsQZUql1lpQ55tEZ9O47+Pz3P/Hz0uVoVKkiiufNE88WlHBuWXPi7pnDeHLnRqze7My16ypf0NyNm3DnSRjgElnZj8mc2Q6a+VRR7cIaaQuvvNewgcq5w+Wv306fhT8++1g3S/pY3OC//QdUn7Dl0BHjsrz0LunQzNtbBSYrFi4U4/5IW4iUwdUFI7p1QecfxmHq+v/QxLsSKhdNvsqdxsp8d2IXmNIeVszdsAHXHzwCXCPz6Vh7WGFO2oHQC84UZ0LuqTPm4PbLB3PMhfR+j84o6JVfL7uhG36+VTFt5r8IuXlLbbO7ewZV3VAIIYSwRgJT8ZQvWzZULFIYe0+fwfKdu9G9Qb14vpPtcVnOwXPn1Y0nZ3SZ5gvyfaMk2vn5okHFCnBxdrL9H0/hGLxh0vv1+/arJX0Lvu2HtC8r2CU1zpgiVs6y5uGTMKzcFZm0ePep08aXpX8Rjtq5MuKzz/qom1ARNzzno3t0RbNBw7B27z6s2LUbTb2rxO1NktixS5cwd0v04gbexYuppXpvVqmc7EvR9IjLntv6VlfLOr+ZNO1l5c7k6Vu1ynwxBaaehIdj9R4+rIha3MAl4hl8M6dDr179UNrLSzeBViFe58TJ0/hz0nQcPxF5DcyRI5tKbF7Np7K080Ti6OiIVi2a4I+/p6m/wGV8nEklhBBCWCOBqQRo7uOtAlMM/thDYOp26AMs2r5dVdY7cfmKcc/yZs2q8uC0qVE9xVcRS2wcWH3fuSN2HD+hkv9OXrse7zdumNh/1iK3LJGBKS7lM8XAJJNzMxi1ctcePA4PVz92dEiD4s/uonzoOZUvqO6gcXBwlC4gvkrmy4dPmr6J8UuWIWDGbFQvWcLuqhSyuAH7J7YF0+IGOTJmVMGUtr41UDBnZDsS8TewQ3tsPnQEZ69fx4Sly/BN29bxf7MEsDZjin3CgXPnVGDSvLhBsYhQlL9/Fn6F8qHxyD9UdT8hUoI7d+5i6ox/8d/GLWp3XFzSoX3blmjZvDGcnZ1Twi7atQb1auGffxcg9MFDqcgnhBDiteSuNAGaeFfGkNlzsP/sWVwICUGB7ElXse7anTs4dyME+bJmxakrV9WN5/r9B1T+IGK+oMaVK6olWlVLFE9VpdwTW45MGdWN6DeTp2LswsWoV6GcysmT1FxfBqau3LiulmlmcEmHrUeOqaT1LGOv4ba19q4Ij+U/w+HmKWQuUga1+/8iQSkb+KRZEzX7hIHgwbPmYPyHPW3wrvGj9QkFsmVTARLmjeIyXmvFDVjQQNgGK7MO69wRH0z8BX+sXK1ytZX2KmCbN48DbRbl1Tt3VJ/g6eaG7ceOq+vDyStXje/E4gatq3oj239TgLP74Z6nEOoPmiRBKZEiPH36FIuXrsLc+UvwJCxM7VNdfz906fQ2smTOlCL2UQ9cXFzwbod2atZUVZ/KethkIYQQyUgCUwmQzdNTlQjfeuQolgXvxKfNmybg3WKP1ZBYqt60ZLumXEEvtVSPFeQ801vPFyQShjPQOPOA577v5GmY0/frJA/+uWXJiZ2uebHsqiMMo36IskPMF8SlZQxMlsufB//1fxc3r55C+ux5UHvwFDi5pk/YARAKKzOO7t4VrYYOx5LgHWhe1Rt1yyd9mfGY+oTY5gsSCdOwUgX1sIK5x76eNBVLAwaoKoZJiTOmVJ/wrEC0PsG0uIF3kULYMqQnrp7dj3SeWVBn2HSk85AbdqFv4eFPERgUjFlzFqiqe1S8WBF88F5n9V+R9Jq+WR+NG9ZRS/uEEEKImCTtqDkFalbVWwUnmHcosQNTj8LCMGvTZgyfMy/akWQFrW7160m+oCTCJX3Du3ZGw28DsPPkKczauBmd6von0V8Hjl+6jN+DDmCpxxsAouZtGPD2W3indk2kd3GBISICgaM+xc0ju+CU3gP+Q6YZlwAK22DyeCZD/3P1GvSfOgNrvy+aZEFhFjeYs2krBs3+J9rOtKlRTfUJMRU3ELY1uOM7CDp6XOXz+n3lanyWRA8riMUN/l67JbJPMMvlwqWFHevUVsUNuKxv58/9cXXXBjimc4H/4Clwz5X0s7uEsJXLV65i9ZoNWL9xCx48eKjeljOjunbugNo1qyf5QyMRlQSlhBBCxIYEphKoYcWK+HbaTLVMgsECWyeS5k3EntOnVW6Q5Tt3GfMFmWtZrarN/7aIGZfDsFx8wMzZGDlvPvzLlU3UHF73Hz3G0h07VA4xJrdXLCQTLV0gvwpK0f6po3Bh8zI4pHVCrYF/IqNXcTmtieDL1i2wbt9+tYRyxL/zMLJ7l0T4K5GsFTcwx/xRybGcLDXL6uGBQe92QK8//sKEJcvULKpieRKvcqfF4gYW+oQKhQsZK24enf87Tq2YqfoO3z4TkbVEhdR8yoROPXv2HME7dmPlmvU4eOiocS+yZcuKxg3qoHnTRnB1lXxpQgghhF5IYCqBuFyudtkyqjIXl/LYKjB14+49LNgWhPmB23D2+qt8QfmzZcWlm7dgMNlu5orxypF0+a3EK53q1FYBQyYb5/LK6V/1tmnlGZa43n78BOZuCVS5jMKfPVN/3MnRUS0j3XzwEAwmN6KmbeHkipk4MvdX9fqqvUcjZ/kacuoSiYuzswpGtR8xGnO2bFUzKXl+bMlacYOcmTLhxt270ifYCS7nXBq8A/8dOKiq9C34tq9N83lZK27gkCYNqpUojqBjx2AwmUVp2iec37wU+yYNV0eq8gcByFe9kZ0cNSFi5/qNEKxZuwFr12/Cvfuh6pccHNKgcqUKaNywLipVKAdHR8mfJ4QQQuiNBKZsgDciDEwt27FLLZmIb0lcJinesP+AKuO96eAhYw4pt3Qv8wXVrIFKRYqoIEX/aTPwIiJC3XQM79IJuTJntsGeiLhycHDAyG5d0HjgIGw9fESVjGf+qYRi8JFBSb7fldu3jW9XIm8elUOMM+SyeLijT+fGmIvcKjhl2hau7NyAXb8MUL9XttNXKFSvbUI3SbyGT/Fi6FTHHzM2bETfKdOweugg48y1+GIxgy2Hjry2uAFn0UmfYB9U5c6unbCz/3eqMMaUtevxXqMGCd6463fvYsG27RaKG+RQfULr6tUiCzN88jZmPkwfrU8IObwTQT98obajRMse6ksIPXjx4gV27dmPVavXY8++gyo4S5kzZUSD+v5oWN8f2bNl1cOuCCGEEMIKCUzZQN1yZVWy6cu3bmHvmbOoVKRwnN6VFb1YNWlR0HbceZkfgaoUK6qCHE2qVI5yg9u+lh9qlimF8zdC1EwpCUolr8K5cqqlXCPnLsDQf/5FrTKlkD1jxjhvFPMFrdmzT7WFbUePGX/f3dUVLar5qETmZbwKRAl8+md2Rv6Tm5G323fwrttUtYXbpw5h6/CPVH6pQvXbocw7veK8LSJ+uLRzw4EDKrA4ZsEitawrPpgvaN7WQCwM2o6Qe/eNb2GtuIH0CfaFs9gGvN0OfadMV+2AlTu9cuSI80byYcV/+/iwIhCbDx02PqwwLW5QsUjhKH1C/VyeyBn0H7K1+RR+rTqrPuH+pTPYNLgHIp49VbOkKvYcGOdtESKp3bp9B2vXbcTqdRtx+/Yd45+vUK4MGjeqC58qFZE2iQsMCCGEECJxSGDKBlzTpUODChWwaHswlgXviFVgivmCWNWNN58HtHxBALJn9ESbGtVVQKpQzpxWt443GxKQsh9Mfs1qXIfOX8C302fhj88+jtXMOT755e/wxnPJ9h148OSJcad8S5VUMyEaVqyglopZ4polBzwj9qJEugjVHh6FXMHG77riedhj5Kroh6q9RsVqO4RtZHB1wYhuXdD5h3GYtn4DmnpXRuWiRWP15hbzBXFWgHsGtKpeDe18a8S4VFj6BPvSvqafmkXLIHOfydPwT5//xToJM/MVsk9YvD04ysMKb/WwwhdvVqlkdTaeS6Zs8IwIRzHHcNUmwu7dwsbvOuPpg3sqn1SNbybAQSpkCTvFuVDXHTJi2IgfsWPXXrWcnTw83FG/Ti00algHuXNZHxsJIYQQQp8kMGUjXM7HwNTynbvxbYf2SGth4M8BVjDzBW0NxKrdUfMF1atQXt14ciaUpd8V9o3nbHSPrmg2aJha1rli12409a5idaOZL4g3nQxMHr98xfi6PFmyqKAkg5NMrv46blkiZ2E8vn0dTx/ex4aBnRF2NwQZvUrAb8AfKum5SFo1S5dCW9/qahkmcwytHBIAF2fL54GByd2nTqs+gYHNx+HhamOZL8i/bBm0q+mLOuXKwllmBegOl/SN7NYZDQYEYMeJk5i9aYuqjGeNxeIGAHJkzIg2vtVVm4rpYYXGNXNkvsEnd0LwPOwJNg3qjofXLiJDrvyoPWgy0rq46u5YipQvDE4465gDpx1z4pGDK7Bjt9rp0qVKoHHDeqhRrQqcnOR6JoQQQqRUNgtMnT17FkOHDsWePXtQoEAB9OvXD9WrV0+pxy0a31JvIFOGDLgVGortx47Dr3Qp42su37qtEpkzDwyX+2mK582jlmdp+YKEvpXMlw8fN31TVeMKmDEb1UuWQGZ3d+NOMV8Q81Axh9j6ffvx7MUL9TMGHRpXroS3avqq5MXMWxVbrpkjA1OPblzGlmEf4P6Fk2oWlf/Q6XBO/+pvi6Q1sEN7bD50BGevX8eEpctU7jlTWnED9gms5KcpZJYvSOhbvmzZ8HXb1hgyew5GzmXlzjIq+KyJqbgBH1a85VdDXUvi8rBCC0w9vnUd20Z/jlvH98HZPSPqDJ0Bl4yvD3YLkVQ4OyrEwROnHXPhskMWRKSJvPY5GZ6jcbMmKpl5/nx5k2pzhBBCCJGMbBKYunjxInx9fdG0aVMEBARg9erVqFOnDoKCglCxYsVk3L2k4/QyuDB702b8vWatmu3CJXqcEbPt6HFjss6Y8gUJ/fu0WROs2bNX5Q3rO3kaujaoB0eHNNh08LAKTprmCypb0EvNjmru4w3P9OnjtfNuWSOXNJzftET9N61revgPmYb02XLF6/2EbfB8DuvcER9M/AW/r1iFvFmyqOD10YsXX1vcIL7FE4R96lKvDlbs3IU9p8/gq78m4/PmTZHOyRlbDh9+bXGD+NCC1df2blG/7uCUDrUDJsEjb6H4vJ1IBI8fP0bhwpZzUfbp0we9e/c2/tWnT5/Cy8vLOIbQ/P7772jRokUibF3iC0danHPMrgJSDxzcjH8wS0Qoiry4jvwvbqLre3MTf0OEEEIIYTdsEpiaMGECateujT///FPtWJs2bVQVlXXr1qWawBS5u0UukeBMidp9IiuiaVg6nrOjGlayni9I6B9nP43u3hUth3yPtfv2qy9TnFXXqnpVFZDiDKuEunv2aJS3KNLwbWQu/Gq2nkg+/KyXKVgAh85dUBXzzFkrbiBSFlbLHNW9Kxp+G6CWcvPLlK0fVtw6vjfK+xeq3xbZS3unrIOqcy4uLpg/f36UvXjw4IEKNGXNGnVW2+XLl3Ht2jWsWrUK7iYzcIsXL66ro8DZUbfTuON02ly46JAVL9I4qu1Pa3gBrxchKPziGjIbHulqn4QQQghhOzYJTM2ePRszZ87E8+fPceTIEeTNmxeTJ0+2upXh4eHqSxMaGmq7PUom1+7cwV+r1kT7690b1EO3+nXVkg6ROjCBvSXDu3RCW78aNssX9OjmNRxbNCnKnzqxdCpKtvlAZkzZAfYJR85fjLYlnerURrcG9WKVL0ikDKyipyVxNjW4YweVJN1acYO4Yp9waPaEKL92ZvUclOnQS/oEO8Ll2jVq1IiyRSNGjEC+fPnw9ttvR/n+hQsX4ObmhkaNGsVqD+x1fPUCDtjoXBrP00QOOzNGPESRF9fg9eImnBC5rF0IIYQQqVeCA1McBPFpHnNMcaYUl6Hcu3cPX375JX744QeLR5YDsMGDB6eoo37uRoixlLep+hXKS1AqlWFb4NNhcwVz5rBZUIoeXD0HGCIrFmkMES/w4Op5uQm1A9b6hDerVJagVCpjrU8oliePzYJS0ifoF2dLjR07Vn2lNSt0wFQJ+fPnx7Fjx1QOTy4BrFatmtWdTa7xVcfVl157Ap5PnokHDx+q3FHFixWRZctCCCGEMEpwYOr69evqzTgYYk6pkiVLqv82bNgQ5cqVQ6dOnaIdbiZGZ+BKwxlTfFKoZwVzZFeVtCIMr0ISXMLhlSOyQpJIPZKqLbjnLggwWaxJcCqNgyPcc3ulnoNtx5KqHQj7l1RtQfoE+3D79m01i9ySd999N9pyvfHjxyNTpkzo2LFjtF9hYOr8+fNo3Lixmo2+b98++Pn5YenSpXB2dtbV+Oq97tH3TwghhBCCYl/+y4osLysM9e/fXwWliNX4unbtikWLFln8rXTp0sHDwyPKl97lypwZI7p2VsEo4n+5dIvfF6lLUrUFJjiv2muUCkYR/+vz+UiZLWUnkqodCPuXVG1B+gT7wBlQy5cvt/h1//79KBvJB3M//vgjvvvuOzg6Rvblpj766CNs27YN586dQ2BgII4ePYr9+/fjp59+SjXjKyGEEEKkfGkMBpMpPvGUI0cO/Prrr2opn6Zv375q8MQKfa/DgZmnp6casDFgpWfMK3P+RoiaKSVBqdQtqdoC88pw+R5nSkk1PvuTVO1A2L+kagvSJ+jHkCFDMGvWLBVwshSYsoSzzrmsb+3ata/d0ZQ0vhJCCCFEypXgpXzUvn17/Pzzz2jatKmaDRUSEqKmsX/22Wcp98hZwWCUBKQEJVVbYDBKAlL2K6nagbB/SdUWpE/QBz6MGzdunBo/WQtKcbYUl+xVqVLFuFN8nujk5KSPnRRCCCGEiIUEL+WjgIAAlVOhRIkSqtwxl/SxlHHv3r1jsQlCCCGEEKkLg1Kccd6hQwfjjrOYDJfphYWFqe9t3LhR5Zdinik6ffo0pkyZgiZNmqSugyWEEEKIFM0mS/no6dOnWLx4MS5duoTSpUujQYMGsa64IlPNhRBCCJFasHqxl5eXSoPwzjvvGHf7999/V3mlgoOD4ePjgydPnqBVq1Zq2R4r8l24cEElSf/777/h8DKnZUxkfCWEEEIIPbBZYCohZOAkhBBCiNSCgakzZ86gQoUKUQJMz549w+HDh9X3TR08eFBV6OOMdAaoYkvGV0IIIYTQAwlMCSGEEEKkQBKYEkIIIYQe2CTHlBBCCCGEEEIIIYQQcSWBKSGEEEIIIYQQQgiRLCQwJYQQQgghhBBCCCGSRdpk+atmDAaD+g5zIQghhBBCJAd3d/dYVxTWAxlfCSGEECK5xWZ8ZReBqQcPHqhjlS9fvuQ+ZkIIIYRIpe7fvw8PD48Us/cyvhJCCCFEcovN+MouqvJFRETg6tWrsYqkpUScKcag3KVLl1LUgDiu5DhEzhpM7W1BjoG0A420BWkLlJTtIDZP9PQktuOrpDzG9ia17ntq3e+k7lPsTWo976l1v6W9p877Knts77EZX9nFjCkHBwfkzZvX3vruJMentCnpSW18yXGAHAM5Bop8FuQ4aKQtyOchPuI6vkrN7Sy17ntq3e/Ufo1Nrec9te63tPfUeY+tt/Yuyc+FEEIIIYQQQgghRLKQwJQQQgghhBBCCCGESBYSmLID6dKlQ0BAgPpvaibHAXIM5Bgo8lmQ46CRtiCfh6SQmttZat331Lrfqf0am1rPe2rdb2nvqfMeW6/t3S6SnwshhBBCCCGEEEKI1EdmTAkhhBBCCCGEEEKIZCGBKSGEEEIIIYQQQgiRLCQwJYQQQgghhBBCCCGShQSmhBBCCCGEEEIIIUSykMCUEEIIIYQQQgghhEgWEpgSQgghhBBCCCGEEMlCAlNJ4MWLFxgzZgzKlSuHggULokuXLrh69arVvxwREYFKlSqhQoUKUb5WrlyZBFubeN58881o+/T3339b/YOBgYFo3LgxvLy80Lp1a5w6dSrxNi6J9O3bN9ox4Hlu0qRJtC347rvvor32ww8/TKItta0HDx6ge/fuan+2bt1qfPOnT5/iq6++whtvvIEqVargxx9/jPEPP3/+HP3790fp0qVRsWJFjBgxAgaDwbYbm4j++OMPdQwCAgKMfyU8PFyd61KlSqFIkSL4+OOPcffuXatbcfv2bYttaMeOHYm45bZz9OhR1KlTR+2DhueQ59+8z1u2bJnVP8yf8X0KFSqEd999F9evX7fdRiaBR48e4f3331f7vH79evUXu3XrZvHc8vvmPvjgg2ivNW1X9uz8+fN4++23UaBAAfU5/uWXX6J9jjdu3IiGDRuq/r9t27Y4d+5cjLsUFBSkrjF8fatWrXD8+HF7PgTJYvz48fD29kbJkiXxxRdfqL4nJuyvqlWrhuLFi+OTTz7B48ePk2W7E2r//v3qGpsvXz61PwsWLIjxLXkdMu+L2F71htdd8/0wvwabmz17NmrWrKmuRbxmx3QtsmccW1rad37vyZMnUTZ9+vTp0V5br149e969aNhf+vj4qL7PFMcFbPvsF1u0aKGuvzHhZ6VZs2bq9fzvvn377Po4cOzAcSDP64QJE4zbyrb/5Zdfqr6rRIkS+Oabb9Q115oTJ05YbC9nzpyx2/0PDQ1F165d1Xbz+qf5/PPPo+0L7z2s4T62adNGnfNGjRph8+bNdrvPpg4cOIBatWqpdk/Xrl2z+pmfP39+lN/V4zj67NmzaN++vRo3MUbw22+/RTtP//33Hxo0aKBiDe3atVNjrZjY4312Wl20Pp37/vvvMWnSJDUoTJs2reo8OYDes2cPHB0do+0dP1x79+5Vv+Pp6Wn8OW/G9YoXD35ghgwZogY8Gt6MW8L954eLQYt+/fph2rRpqFGjBg4ePIicOXNa/B094KCBN+CmeFN2586daJu/fft2lClTRg0mNNmzZ9fDbkZx5MgRtGzZUl30Dh8+rC6mmg4dOuD06dP44Ycf1Pd5QeV/Bw0aZHE/e/TooQZafH1YWBh69eqlLjD8tz3jtjIgzaAcz+HFixeNm8sbxE2bNmHs2LHqxo//feutt7Bu3TqLu8QLDT8Hc+bMgYPDq2cLvBDZu5kzZ+Kjjz5Sn2UOgDUhISHYvXs3/vzzT2TOnNn4/bJly1rcpdWrV6uL7vDhw1Vgg31r9erVVVtzdXW1+Dv2hINgfiZy5Mih/v/+/ftq89hGmjZtGmVThw4diocPH0bb/C1btqjAjZ+fn/FnHLDYO7Zx3vRVrVpV3QTzvA8ePFhtNoMfFBwcrK6R7PsZiOa1kOf30KFDyJo1a7Rd5AC1fv366N27txqAs535+vqq7+fJk8feD0mS4M0b+5aJEyciY8aM6rjyZmT58uUW/z5fN3DgQDVe4TWXwXPe4PIGWE+uXLkCf39/9OzZU+0z+9pOnTohffr06ibMkl27dqnPEl+nMR2L6QWvM+xn//nnHzg5ORk3u2jRohZ3gZ/H9957D+PGjVPjNLYZHjtrY1V7xs9/pkyZomwiH2YsXbpUjcNNMfjC8/vZZ58Zv50uXTp73r0ohg0bhpEjR6qxpWlAnuOtunXr4tNPP0WfPn3U+dX6RQZpzXEsxht9Pgj5+uuvsXDhQtSuXVv10dbaTHK6d++eulHnDXuaNGnUvZNGmwDAm3dOBBg9erT6+YwZMyxuMvf9woUL+Ouvv6L8PFu2bMm5i1bxWsj7CX5OOR5kIE7DcSavl3xwp8mbN6/F97p165Yaj7EvZIB2w4YN6v/ZT2oBH3s0efJkdb/Abd+5c6faRH6Gv/322yiby8A6HwCa9n+kt3E0g6ocN3G8x88xr1G8PvMegA8padu2beoaze8PGDBAjae1cbF5X0h2e59tEImucOHChilTphj/TmhoqMHBwcGwf/9+i39727ZtBicnJ8OLFy8SfduSypUrVzitxXDp0qVY/ckOHToY3n33XeNrIyIiDFWqVDEEBATE6vf1IiQkxJA+fXrDokWLom1y0aJFDTNmzNDLrlg1ZswYw7Bhw1R7dnFxMSxfvly99siRI+pzcObMGePvLl682ODp6Wl48OBBtPc7d+6cev2hQ4eMP1u/fr3B1dXVcPv2bbs+TqdPnzY0bNhQne+OHTsaunbtqraX7TpDhgyGVatWGbefr+Vn5fr16xb3acGCBYY8efLY9f5a06ZNG8PatWvVeeM+anbs2GFwdHQ0PHv2LFb75efnZ+jTp4/xtU+fPjXky5fP8Ndff8Xq95PbhAkTDN99953h+fPnhowZMxrmz59vcZOOHTumjsvOnTuj/dzNzc2wcePG5N6VOFu3bp3B3d1dnTPNDz/8YKhatarx361atTL06NHD+G/2HWXLljWMGDHC4t/r1KmToX379lF+xvcbMGBAnLcvJXr8+LEhS5Yshnnz5hl378KFC6o/3bdvX7Rd5ucwd+7cUcYtN27cMDg7Oxu2bt2qq0P022+/GUqVKhVlmz/88EPVD1tTrVo1w7hx43S1n5asWLFCnffYeuONN9T1WvPw4UN1PbY0PtEb9rXFihUzDB06NNqms78xvZ7oja+vr+HAgQOGn3/+2VCiRAnj5nfr1k1dc03xtdb29bPPPlPjFFONGjUyfPLJJ3Z5SAIDA1W/f//+fUO9evUMffv2VdvJe6w0adJE6dv4WvZfptcdU7/88ouhUqVKdrmflowcOVJdDzmGTJs2rWH16tXGl2XKlCnKmDIm33//vaFChQrqfTQcn5q3G3vTvHlzNf7h/QTvK6zhOKtkyZLR7qf1No7mPQLHiqZjZLYBfp41zZo1M3zwwQfGf3PcZN6nm7LX+2xZypcE+LSpY8eOxr/E6D2X6zk7O1v863zKxeg2I9mcZcSnAXrHfeJTKs5mYET+2LFjMS7BWrVqlXoSouHTEC7n0PtyRnNc4lmsWLEos6I0ly5dUk+qOUWXTwS47E2P+BSS0XvT2T3Ec1y5cmW1FEvDWRJcrmc6LVmzZs0aNSXbdOYgn+a6ubmppzv2LH/+/Gp/zZ++sV1fvnxZzQ7U8N/8vvlTXQ0/S3w/zgTgEjC2E72YNWuWmtlijvuUO3du9XSLfV5M0+f55IhPBE37Bz4N4wwkvfQPfILHWUKWZsya4gxTLmc2n2XJawNnHnH2EGdOcVYEl4zrAZ/Is+2aPsFkmze9HnJGnOn5Zd/B2T3Wzq/59YI4o04v7SGx8frB9tK8eXPjn2Ifwifilo4RZ5rduHFDXXM1nOnJc6e3Y8qlLubLnM3bm6X+iDPt+Hu8Fpkv/dIL7VrBmSK8VpjO1DXH2SJc5mX6ueOsMl6T9XbOLeG1h/0mZ1lYOt+cQcQZBLy2WJqhas94bi3NLo5rvxjX1yc3jh85c9zDwyPKprDdcizB9CkafuZ5vTUfh2q0zwqXjHMMYu+pAbTZwRwrmmLb5b5zrMm2zDbNMbU1POe8Vpq+D//N79uzuXPnqtl8MeFx4Gx6pjgwP+96G0dz9pt2H23aprVxE1cl8R4pIeMme7nPlqV8ScB8Cjin3XI6HvM8WMLGd/PmTbXMjb/LjvKdd97B1KlTX3sjY6+4T2z03Cd3d3c1CGJQYd68edEuKrzx5BRd86VJ/DdvaFIKLl/iMj5Osze/uPBnXP7FpQQM5rE9MEi1aNEi3S3ptDYlngMF83PMm1XeEFg6z5Zez4sNl1zYe7swn0ZsyrR/YHCBy9M4MMiSJYvV/oHT9Tno4oCMnyUG/3766Se7/2hYawvsHziIYK4xHg9Os+Yx4LR78wAd2wFZ6h/0kmcrNstEeJPIwZelfdJuMLmUj/0DH3YwaLtkyRKLSzTsCT8Lpp8Htl+eZy4dIy7NZSAgtv0/+0necMb29akRPzMM/Jo/DLN2TeXrGfTMkCFDlMOlx2uwi4tLlH1gkI438taWJD579kwFctin8rPFfoljFuYoseelLdbGknywyZQAWr/KoPivv/4abczBc86+1nzJD8+56bJrPeKNOZdEMzWEeRCD2J/yIQD7ZS2PGgNZ7F/1wNL1hO2YweW49IscR1l7PW98zcepyc3adZTjQtNxFa8RTPfA5bzWHgaxDfAhDx8CMbDFaypTSvChqj2KaSxFzA/Gh7bsywoXLqzGBpbSPVgaV/N1/Byw77O0BMwexGYMxVyB7M84ljTH862ncTSv3abXb8YF2EcxDyTxnpGTFyx9fv/9999o+2/P99kyYyqJcZ0zI9HMmWQN88uwIXEmBNc9c1CwYsUKFcTQKybT5Hp15lNhEnPuF2dFWMolpD2tYqdqiv82zU+kd6NGjVI3k6ZPsTUcPLGdcM0wB5YcYPCmvXPnznrfbSOeZ/NzTNbOc1xfr0fM68CLZUyfda4nZzCTT/TYNjiY4sWJQQy94mwY9nkcLLBv4IwN5tgyTWaqSS39A2dUMV8Ak1ya40CTuYE4G5fHizcUvInWcjTpBfNisP/jjEHmm6O4nl8toW1Kv14kRFz7zri+Xi94E8aZlexnmePMEt58c8YeZ+Fy8M8BP59W8+Egb/b1hMnLOaOEN6ccbzFvJYPAlvLs8JyzDzEPPqSEcRf3lzfZpjmkTPEYcbYBrz8835xlx1UOWu4/PWJggcEkS/2iaT4iDYN3DOBYej1vePU6Y5/HgLlJuW986GcN8xlyDMLzz4clvF9hXj29JALXcOYf7xeZg4hjAwbY+JBTy0NkzlJfr7UBPd9vMW+vtdlSpOdxNM8LA4/MR6oF3eI6borr65OSBKaSEC+OfCrD5IsxJSpmkmhOn9ae9PFpFyP97Gz0irN9+CHSnmJwGQGfXlnaJ05B5ZM7XiBM8d8pJZEtA01Myqgl/TXHc88EhNoyN14omNySSTpNEzzqWa5cuaKdY7J2nuP6er1hYmLOimRA0lKCZw2rzPBJrjaTiMkfGczWc//AWW/s87TE5ZxZyYGDpX1iO9DaSUrtH5jEnTM0rFXZYz/KoJVWCIJPNRnk581VTNP27QkrwjF5KxNxT5kyxbhp2j7Ftv9nsnw+SYzt61OjuPadcX29HvBGhf0mk0Fbu+4S+1UuN+ZDI2Lb4mwLPgTQW6VHJkbmOEKbociKjKxgaq1fZcCCN/ApqV9lf8hVCv/73//UzDdLGHgsX768+hFn2zDpO2/ctKTKesRrBK+nlvpFzp40x3bPcYel1zOwEZsZKvaI1fj48I7jKs6Esob3WewbtCVfvPFnsmm9LWNlG+dYSrtXYjvgfSdTqFhakmypr2cbYIBaG2vpEftsjitNl6Ob0us4muMmFsTi0nrTqvZxHRfb8322BKaSCAcCH374oVq6xiz5MeESjpMnT0Z5CQMTentaZ4qDOlaOMGVtn3hhYAUQ8zxD/Dc7k5SAQSbefJtX4NJwEM314ebHi/TcDkxx4M+Bn+mNNJdw8QmPpfPM1zMwZzpwZoCPT4L13i4YkOLNEgdBnBkXE86gNC8Bq/f+gTnnOJsyNv0DAxcMZqTk/oFBJs4kYsVBSxicNl/ixzbA3IV6yDXF7eQNMmcxsFKW6U0P/58PbmJ7fjmA5uc/tq9Pjdh3an2lhu2EbchS38m8h5xxYX7N5owbPfa1nFXHQC4f9DAIGtOSJAZnOEvC9HOkzSTS27WX5888R6m1awU/c/zspbTPEVcncOYTK9NZwhs9nm/Tm3YGaRjM09v5NsfPfVzOZ1xfb++4KoFjq7Vr11qtSqfhWJRjz6OcGVsAAA6aSURBVNiMQewZ+3n20+b7YW1sYO2csz+wlgfZ3jEdAFMDcBxlra/X4zia16S3335bXc+4NNP0/LBf52y52H7e7fo+294y7adErAbBylv//vuvxd27d++e4Z9//jFWi2AVgVy5chnu3LmjXs9KXgULFlTVE/Rq8uTJqnqaVoGN1V58fHwMPXv2VLt09+5ddQy0igM//vijwcvLS1UCosOHD6tKTlpFNz27evWqOhbm+7Js2TLD2bNn1a7xOLFqGY8JsWIGK6awqoyemVbl0ypFjR071rhLnTt3jlKda/PmzcbqlWwbrEY3ePBg4+s/+ugjQ+nSpXV1SEyr8tHSpUtVZcb//vvP4n7wM2Dad7z//vuqysijR4/U68+fP68qL02aNEk3x8G8Kt+QIUMM2bNnN9y6dUvtw82bN1U1U+1c8zNjWlHsyy+/VJVk2I8Q2wkr01iqMGbvzKvyHTx40GK1tLlz5xorNW7ZskX1D/wv8bPRtm1bg7+/v73vrqK1YZ5nS4YPH24oUqSIsT3wvLIKIdsNmV8zWeWQVRmvXbumfn706FGDh4eHqvIpItWsWdPwzjvvGKsvjR8/PkoF1OPHjxvWrFljPFxvvvmmoUWLFsZqRuxf9FAB1RyrcLHSGD8bT548sdgcWCmSbYY47mJfMnHiRONrWQ04Z86chvDwcF01p88//1z1o1qV28uXLxty5MihKpARK93y+qPh9bdWrVrG/VyyZImqCsprjB7x3HPszOpVplj5evfu3epb7DvZV5hW8GQlR443teuRXphX5fv111/VmIlVsenEiRNRrje8fs6ePVuNxWj69OmGbNmyqXahtQ/+e+rUqXZ/CEyr8hH7K1aU3LNnj8VtZ1VS0+sDq7yx2q/2Ged1mP0dK1vaO9OqfKzgy7EBqx8Tq1GywjkrjRI/E7yv4DWU+DqOP1nVkXhNLlSoULTPjL2yVJWPVSfLlCkTpdJgShhHd+/eXVWYtVaFnONo3iNqsQP2cbzn2rRpkzp9ernPlsBUEuCNBxsHb6BNv2bMmKH++h9//BHlJoOlTytXrqx+p0aNGur3ObAKCwtLgq1NHLz4N2nSRHWg7CB5E8rSrNqFnwMldqZBQUFqAziAbNq0qSp7ygE1b0pYsta0pKlecbBYpUqVKJvPgQEHgOx4NCxrzHLe7GDZ2XBAGRwcrNfdVkwDU8SLPi+KFStWNBQtWlSVKOeAQMObTZ5/zYYNG9Qgsnz58qosMgdN2gBTL0wDU7xQ8JhwEGzeP7A8LDE4w8/GyZMn1b958832wOPGUrH8L8u+6umzYR6Y4o0TA9Xp0qVTfR4/9xxoajeS/fr1U8fg4sWL6hjwwst+hJ8JHgP+3rBhw/TSBKIwD0y1bt1alS83pQWq+dBCw6AsS2KzHy1QoIAaTPLGw97NmTNHnUt+1s3bvHZTwBsmXvMyZ86sbhb4GWEwUmN+zeS1kUEUHkvtesGyySyXLCIx8ML+lAE/thleU01vzNjm2A9px4xtjm2KD4i8vb3VOdAelOgJ+wW2N+6HaXurW7eu2g32m7yB5Y2phuMRjlV4jeHvsD/SbvT0hDeZvJHhuWY/yfPLMvC8WSU+GOQYQwtc8QEAxxsMZrB/dXZ2VsENvfrzzz/VGEF7gKHhWINtWsMHPwxCMIjFsQivqeyn9MY8MMV+kdcTtm/2i9yv9957z/gZ537zs6EFJzlO5/iE7YSv538ZzDYtUW+vTANT7Ls4nubn1vwas2vXLrUL2vVTC9Dw2pk3b151DeFnhf0dx+p6YBqYoi+++EKNDdiW2aY5PuDDfeJDPJ5zfjY0vXv3Vu2f11pecxs1amR88GnvzANTnMjBdr5gwYIom673cfTMmTPVeWPfbN6mtf6c/Tg/B3xQrY2bvvnmmyjXNT3cZ6fhFtv7tDy9s5b7hUt2OF2S0/K4BpoJYLWKEZx2yZKlTMLHqZa+vr56PwxKYGCgWrbD/WZVPm1/uZ6fS9eYA0Jb460tNeASH67/r1ChQoo4BjyvzCPGBMam2Aa4hNF0XTdzWnBaLnPIsDQqlzDpGdf5M5kz10drOH2a555TUVmt0nQfWeqWa+Z5XDScqsxjxan2TKrPHDN6winEnF7MqnpcRsAKUZawvTMXBJc7MZEl+wcNlxhwejqXdPF9WElGT1hFjcuIuLxGwz6PeRCYcFjr87Rp2JyazbbA3C8aHjt+NthHslqWlhNGb5gXiueQSxR5OeZnhP82z6fH883zbFolh22JCdD5u+xPLRUTsDdMMHzo0CGLm9W4cWNj/88lvsHBwapYBvsM03Lo1q6ZbFNapR1ryyBTMy5pYl/L6y37WtM2xs8R+xPTpOBc1sZrNvsgjkGYG1JvON4wX85GzOPInDLEdpMjRw51Xdbwd7jv/EzVqlVL5eTQI36OWEiCBRJKly4d5fyygAbHV9w/DZe0cb/5M6adMB+n6AmXZ3G5i5Y/SsPiGly2aHrN4PFhwnuOSTmusJSHyd5xiT+/OFY0va7yOPBzwD7UtJgGr6EcjzKXkmnZ+d27d+Pw4cOqvVSuXNnuqvFZws8w8ynxnDI5tHkqDE21atXUmJHjSB4T02PF5cu8zjKVBq+1zDulB0wBwe017aO4jJfnkd/j2ECrsMr7Ku4j27hpzi1+JjjG4jJujqfMqyHbK+bD4hiIYwfi55j7wn+bLuPT+zian2vmHrWEOcW0fWV/z3ExU5zws27ahvVyny2BKSGEEEIIIYQQQgiRLCT5uRBCCCGEEEIIIYRIFhKYEkIIIYQQQgghhBDJQgJTQgghhBBCCCGEECJZSGBKCCGEEEIIIYQQQiQLCUwJIYQQQgghhBBCiGQhgSkhhBBCCCGEEEIIkSwkMCWEEEIIIYQQQgghkoUEpoQQQgghhBBCCCFEspDAlBBCCCGEEEIIIYRIFhKYEkIIIYQQQgghhBDJQgJTQgi7ce/ePYwdOxYnTpyItk03b95UPzt79qzdbK8QQgghhL0LDQ1VY6hjx45F29Tbt2+rn50+fdred0MIkYKlMRgMhhS8f0IInWnRogVCQkIQFBSENGnSGLe+S5cu2LdvH/bu3Yu0adPqbK+EEEIIIZJPmzZtcOnSJQQHB8PB4dXchB49emD79u04cOAAnJyckm8DhRCpmsyYEkLYlYkTJ+LQoUP4+++/jdu1efNmzJw5E7///rvNg1LPnz+3q/0XQgghhLC1CRMm4Pjx4/jjjz+Mb71t2zZMnTpVfc/WQSkZXwkh4kICU0IIu5I/f34MHjwYffv2xa1bt/Ds2TN8/PHH6NmzJ6pXr6629cqVK2jZsiUyZsyI3Llzo3///nj69KlxP3bs2IHGjRsjU6ZM6jWfffYZXrx4oX7OQVm2bNmwZMkSFCxYEB06dLCr/RdCCCGEsLU8efJg6NChaszEmekMHH300Ufo1q0b/Pz81J+7evUqWrVqpcZOuXLlUmOx8PBw46bs2rULb775JjJnzqxe88knnxgDUFwKyPHV4sWLUahQIbRr187WuyCESMEkMCWEsDu9evVCvnz5VIBq0qRJKv/ByJEj1XYyUOXv768GTFzWt2DBAsybN0+9lh49eqQGVQw6cWr6okWLMHfuXOMTQgaoGPDi08FZs2bhhx9+sLv9F0IIIYSwtU8//VQFjQICAtRMqevXr2P06NHqzzBQVbduXRVc2rNnjwowcQz13XffqZ8/efJEja/4AJEzrfjzhQsX4tdff1U/18ZXnN0+Y8YMjBs3ztabL4RIwSTHlBDCLnHWU61ateDp6YmffvrJOLPp33//xbfffqsSpGs5Ev766y/06dMHd+7cUfvC4JSbm5sxRxUHUhkyZFADpSNHjqB06dLqyV7hwoXtct+FEEIIIRLD7t274evrq2Y8jRkzBp06dVJ/hg/6/ve//6nxkaOjo/relClT0Lt3b9y/f1/9+/Hjx3B1dTWOrzgrink///nnHzUuK1GihJqZXrx48cTYdCFECiYZhIUQdsnHx0clQj9z5kyU5XaHDx/G3bt31VM9DQNR/N6NGzeQI0cOXL58WS3V27RpkxpgnT9/Hk2aNImynzlz5rTL/RZCCCGESCyVK1dWidD5oE4LShHHV6zeV69ePeOfZiCK32MKBS4F5H85U0obX507dw4NGzaU8ZUQIsEkMCWEsFvZs2dXAyJTzs7O8PLyUnkPzLm7u+PkyZOoWLGiCmp98MEHKFmypFq2d/bsWbvdTyGEEEKIpMLxFfNMmY+vuEzP0viKs6v4oLBChQpo2rSpyvv5xhtvqHQLR48eTarNFkKkYBKYEkLoCp/0jR8/HlWrVlXL/IizojhLisv3mPeA/2UVP22pH6eVM6AlhBBCCCGi4/iKS/u8vb1V8RjiQ70LFy4gffr0CAoKUmMp5ufUlvpxfCWEELYggSkhhK6w2l758uXRoEEDlffg4cOHGDVqlHqKV7t2bVSqVEkt6xswYICq4rd27VqVJJ2BLCGEEEIIEV39+vVRpUoVNb764osv1DI+JkYvVaqUKjrD8dWDBw9UVT/mqFq/fr2q0sfvCyFEQklgSghht7gkjzkNzK1cuRJjx45VFWWePn2K7t2746uvvlIvK1u2rKoiM3HiRFVVhkv6mDydM6qIs6zefvttlaxTCCGEECK14cO8rFmzRtvt5cuX48cff8T06dMRHh6Ozp07q4ToxKV7zC81YcIE9cCvefPm6v+1VAlMp8DxlZOTU2o7nEIIG5CqfEIIIYQQQgghhBAiWTgky18VQgghhBBCCCGEEKmeBKaEEEIIIYQQQgghRLKQwJQQQgghhBBCCCGESBYSmBJCCCGEEEIIIYQQyUICU0IIIYQQQgghhBAiWUhgSgghhBBCCCGEEEIkCwlMCSGEEEIIIYQQQohkIYEpIYQQQgghhBBCCJEsJDAlhBBCCCGEEEIIIZKFBKaEEEIIIYQQQgghRLKQwJQQQgghhBBCCCGESBYSmBJCCCGEEEIIIYQQyeL/PDnTDW/XHnUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "ACC, TEAL, GOLD, INK = '#a3471c', '#1f5f63', '#b0862b', '#4a4f59'\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "ax[0].plot(df.year, df.iri_without, color=ACC, marker='o', ms=3, label='Without (deferred)')\n",
    "ax[0].plot(df.year, df.iri_with, color=TEAL, marker='o', ms=3, label='With (routine + periodic)')\n",
    "ax[0].set_title('Roughness (IRI, m/km)'); ax[0].set_xlabel('Year'); ax[0].legend(frameon=False)\n",
    "ax[1].bar(df.year, df.pv_net / 1e6, color=[TEAL if v >= 0 else ACC for v in df.pv_net])\n",
    "ax[1].plot(df.year, df.pv_net.cumsum() / 1e6, color=INK, lw=1.5, label='Cumulative discounted net benefit')\n",
    "ax[1].axhline(0, color=INK, lw=.6)\n",
    "ax[1].set_title('Discounted net benefit (US$ million, 6%)'); ax[1].set_xlabel('Year'); ax[1].legend(frameon=False)\n",
    "for a in ax: a.spines[['top', 'right']].set_visible(False)\n",
    "plt.tight_layout(); plt.savefig('cashflows.png', dpi=130); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01440d1e",
   "metadata": {},
   "source": [
    "## 3. Decision indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c6e4744",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-03T14:53:33.762349Z",
     "iopub.status.busy": "2026-11-03T14:53:33.762049Z",
     "iopub.status.idle": "2026-11-03T14:53:33.782635Z",
     "shell.execute_reply": "2026-11-03T14:53:33.782033Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NPV at 6%:            US$ 5.79 million\n",
      "EIRR:                 14.7%\n",
      "BCR:                  1.21\n",
      "First-year return:    8.9%  (year-2 net benefit / year-1 cost)\n",
      "\n",
      "pv_voc         US$    15.62 million\n",
      "pv_time        US$     3.88 million\n",
      "pv_generated   US$     3.45 million\n",
      "pv_avoided     US$    12.52 million\n",
      "pv_residual    US$    -1.91 million\n",
      "pv_programme   US$    23.73 million\n",
      "pv_admin       US$     4.04 million\n",
      "\n",
      "NPV at    3%: US$   9.74 million\n",
      "NPV at    6%: US$   5.79 million\n",
      "NPV at   10%: US$   2.39 million\n",
      "NPV at   12%: US$   1.21 million\n"
     ]
    }
   ],
   "source": [
    "s = summary(p)\n",
    "print(f\"NPV at 6%:            US$ {s['npv']/1e6:,.2f} million\")\n",
    "print(f\"EIRR:                 {s['eirr']*100:.1f}%\")\n",
    "print(f\"BCR:                  {s['bcr']:.2f}\")\n",
    "print(f\"First-year return:    {s['fyrr']*100:.1f}%  (year-2 net benefit / year-1 cost)\")\n",
    "print()\n",
    "for k in ['pv_voc', 'pv_time', 'pv_generated', 'pv_avoided', 'pv_residual', 'pv_programme', 'pv_admin']:\n",
    "    print(f\"{k:14s} US$ {s[k]/1e6:8.2f} million\")\n",
    "print()\n",
    "for r in [0.03, 0.06, 0.10, 0.12]:\n",
    "    print(f\"NPV at {r*100:4.0f}%: US$ {npv_at(df, r)/1e6:6.2f} million\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a4257c5",
   "metadata": {},
   "source": [
    "**Reading the first-year return.** The first-year return (8.9%) exceeds the 6% discount rate, so on this criterion there is no gain from postponing the programme start. It is a timing test, not a substitute for NPV.\n",
    "\n",
    "**Residual value.** The horizon ends three years after the without-project road is reconstructed, so the without case carries more remaining asset life into year 21. The net residual value is therefore negative (it reduces benefits). Ignoring it would overstate NPV."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8a582b8",
   "metadata": {},
   "source": [
    "## 4. Switching values\n",
    "\n",
    "The value each parameter would need to take, others at base, for NPV to fall to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d0c8941c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-03T14:53:33.784696Z",
     "iopub.status.busy": "2026-11-03T14:53:33.784371Z",
     "iopub.status.idle": "2026-11-03T14:53:46.164280Z",
     "shell.execute_reply": "2026-11-03T14:53:46.162988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parameter</th>\n",
       "      <th>base</th>\n",
       "      <th>switching_value</th>\n",
       "      <th>change_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aadt0</td>\n",
       "      <td>120.000</td>\n",
       "      <td>89.7203</td>\n",
       "      <td>-25.2331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>growth</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>-87.9687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>voc_beta</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>-30.3316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vot</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rise_without</td>\n",
       "      <td>1.200</td>\n",
       "      <td>0.6526</td>\n",
       "      <td>-45.6207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>recon</td>\n",
       "      <td>30000.000</td>\n",
       "      <td>9947.9405</td>\n",
       "      <td>-66.8402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>regravel</td>\n",
       "      <td>18000.000</td>\n",
       "      <td>23580.7781</td>\n",
       "      <td>31.0043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>overrun</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.3294</td>\n",
       "      <td>229.3627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>all_benefits</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.8275</td>\n",
       "      <td>-17.2536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>all_costs</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.2085</td>\n",
       "      <td>20.8512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rate</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.1468</td>\n",
       "      <td>144.6734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       parameter       base  switching_value  change_pct\n",
       "0          aadt0    120.000          89.7203    -25.2331\n",
       "1         growth      0.040           0.0048    -87.9687\n",
       "2       voc_beta      0.013           0.0091    -30.3316\n",
       "3            vot      1.000              NaN         NaN\n",
       "4   rise_without      1.200           0.6526    -45.6207\n",
       "5          recon  30000.000        9947.9405    -66.8402\n",
       "6       regravel  18000.000       23580.7781     31.0043\n",
       "7        overrun      0.100           0.3294    229.3627\n",
       "8   all_benefits      1.000           0.8275    -17.2536\n",
       "9      all_costs      1.000           1.2085     20.8512\n",
       "10          rate      0.060           0.1468    144.6734"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sv = switching_values(p)\n",
    "sv.round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa285cd",
   "metadata": {},
   "source": [
    "Notes on the table:\n",
    "\n",
    "- **Value of time:** no switching value in [0, 20] US$/person-hour. NPV stays positive (about US$ 1.3 million) even if travel time is valued at zero, so the decision does not rest on the time-value assumption.\n",
    "- **Deterioration without maintenance:** NPV is not monotonic in this parameter, because the reconstruction year moves in whole years. NPV first crosses zero at about 0.65 IRI/year and oscillates around zero between 0.60 and 0.70. The robust reading is: if the unmaintained road loses less than about 0.7 IRI a year, the programme is not justified.\n",
    "- **Discount rate:** the switching value is the EIRR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cfc704bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-03T14:53:46.166128Z",
     "iopub.status.busy": "2026-11-03T14:53:46.165952Z",
     "iopub.status.idle": "2026-11-03T14:53:46.286122Z",
     "shell.execute_reply": "2026-11-03T14:53:46.285519Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rise_without = 0.600  NPV =  -0.18 million  reconstruction years = [13]\n",
      "rise_without = 0.630  NPV =   0.71 million  reconstruction years = [13]\n",
      "rise_without = 0.645  NPV =  -0.21 million  reconstruction years = [12]\n",
      "rise_without = 0.650  NPV =  -0.07 million  reconstruction years = [12]\n",
      "rise_without = 0.660  NPV =   0.21 million  reconstruction years = [12]\n",
      "rise_without = 0.700  NPV =   0.17 million  reconstruction years = [11]\n",
      "rise_without = 0.800  NPV =   1.83 million  reconstruction years = [10]\n",
      "rise_without = 1.000  NPV =   4.82 million  reconstruction years = [8, 19]\n",
      "rise_without = 1.200  NPV =   5.79 million  reconstruction years = [7, 17]\n"
     ]
    }
   ],
   "source": [
    "for x in [0.60, 0.63, 0.645, 0.65, 0.66, 0.70, 0.80, 1.0, 1.2]:\n",
    "    print(f\"rise_without = {x:5.3f}  NPV = {npv(replace(p, rise_without=x))/1e6:6.2f} million  reconstruction years = {iri_paths(replace(p, rise_without=x))[3]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424b5f33",
   "metadata": {},
   "source": [
    "## 5. One-way sensitivity (tornado)\n",
    "\n",
    "Each parameter moved to the low and high ends of its range in `parameters.csv`, others at base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0708781d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-03T14:53:46.287854Z",
     "iopub.status.busy": "2026-11-03T14:53:46.287628Z",
     "iopub.status.idle": "2026-11-03T14:53:46.794544Z",
     "shell.execute_reply": "2026-11-03T14:53:46.793566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>npv_low</th>\n",
       "      <th>npv_high</th>\n",
       "      <th>swing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Traffic, year 1 (veh/day)</td>\n",
       "      <td>70.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>-3771219.0</td>\n",
       "      <td>21089345.0</td>\n",
       "      <td>24860564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VOC slope (US$/veh-km per IRI)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-83170.0</td>\n",
       "      <td>16149937.0</td>\n",
       "      <td>16233107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cost overrun</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8315156.0</td>\n",
       "      <td>-4307942.0</td>\n",
       "      <td>12623099.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Regravelling cost (US$/km)</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>26000.0</td>\n",
       "      <td>9940880.0</td>\n",
       "      <td>-2510150.0</td>\n",
       "      <td>12451031.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Deterioration without maintenance (IRI/yr)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1827442.0</td>\n",
       "      <td>11701701.0</td>\n",
       "      <td>9874258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Traffic growth (per year)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>726395.0</td>\n",
       "      <td>10176153.0</td>\n",
       "      <td>9449758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Discount rate</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9735218.0</td>\n",
       "      <td>1209977.0</td>\n",
       "      <td>8525240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Reconstruction cost (US$/km)</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>2902785.0</td>\n",
       "      <td>8678288.0</td>\n",
       "      <td>5775503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Value of time (US$/person-hour)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3556742.0</td>\n",
       "      <td>8026818.0</td>\n",
       "      <td>4470077.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        label      low     high    npv_low    npv_high       swing\n",
       "0                   Traffic, year 1 (veh/day)     70.0    200.0 -3771219.0  21089345.0  24860564.0\n",
       "1              VOC slope (US$/veh-km per IRI)      0.0      0.0   -83170.0  16149937.0  16233107.0\n",
       "2                                Cost overrun      0.0      0.0  8315156.0  -4307942.0  12623099.0\n",
       "3                  Regravelling cost (US$/km)  14000.0  26000.0  9940880.0  -2510150.0  12451031.0\n",
       "4  Deterioration without maintenance (IRI/yr)      1.0      2.0  1827442.0  11701701.0   9874258.0\n",
       "5                   Traffic growth (per year)      0.0      0.0   726395.0  10176153.0   9449758.0\n",
       "6                               Discount rate      0.0      0.0  9735218.0   1209977.0   8525240.0\n",
       "7                Reconstruction cost (US$/km)  20000.0  40000.0  2902785.0   8678288.0   5775503.0\n",
       "8             Value of time (US$/person-hour)      0.0      2.0  3556742.0   8026818.0   4470077.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tor = tornado(p)\n",
    "display(tor[['label', 'low', 'high', 'npv_low', 'npv_high', 'swing']].round(0))\n",
    "fig, ax = plt.subplots(figsize=(9, 4.5))\n",
    "t2 = tor.iloc[::-1]\n",
    "base = s['npv'] / 1e6\n",
    "for i, r in enumerate(t2.itertuples()):\n",
    "    lo, hi = r.npv_low / 1e6, r.npv_high / 1e6\n",
    "    ax.barh(i, lo - base, left=base, color=ACC, height=.6)\n",
    "    ax.barh(i, hi - base, left=base, color=TEAL, height=.6)\n",
    "ax.set_yticks(range(len(t2))); ax.set_yticklabels(t2.label)\n",
    "ax.axvline(base, color=INK, lw=1); ax.axvline(0, color=INK, lw=.6, ls=':')\n",
    "ax.set_xlabel('NPV, US$ million (terracotta = low end of range, teal = high end)')\n",
    "ax.spines[['top', 'right']].set_visible(False)\n",
    "plt.tight_layout(); plt.savefig('tornado.png', dpi=130); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da5e004",
   "metadata": {},
   "source": [
    "## 6. Monte Carlo\n",
    "\n",
    "10,000 draws, seed 20261103, independent triangular distributions (low, mode, high) for eight parameters; discount rate fixed at 6%. Independence is itself an assumption: traffic and VOC savings are probably positively correlated in practice, which would widen the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "347f85a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-03T14:53:46.797443Z",
     "iopub.status.busy": "2026-11-03T14:53:46.797143Z",
     "iopub.status.idle": "2026-11-03T14:55:22.959153Z",
     "shell.execute_reply": "2026-11-03T14:55:22.958539Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 draws in 96s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>statistic</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mean</td>\n",
       "      <td>5.656759e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p05</td>\n",
       "      <td>-7.583832e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p25</td>\n",
       "      <td>-3.083981e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>median</td>\n",
       "      <td>5.340448e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>p75</td>\n",
       "      <td>1.109921e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>p95</td>\n",
       "      <td>2.065329e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>prob_npv_negative</td>\n",
       "      <td>2.634000e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           statistic         value\n",
       "0               mean  5.656759e+06\n",
       "1                p05 -7.583832e+06\n",
       "2                p25 -3.083981e+05\n",
       "3             median  5.340448e+06\n",
       "4                p75  1.109921e+07\n",
       "5                p95  2.065329e+07\n",
       "6  prob_npv_negative  2.634000e-01"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "t0 = time.time()\n",
    "mc = monte_carlo(p, n=10000, seed=20261103)\n",
    "print(f\"{len(mc)} draws in {time.time()-t0:.0f}s\")\n",
    "q = mc.npv.quantile([.05, .25, .5, .75, .95])\n",
    "mc_summary = pd.DataFrame({\n",
    "    'statistic': ['mean', 'p05', 'p25', 'median', 'p75', 'p95', 'prob_npv_negative'],\n",
    "    'value': [mc.npv.mean(), *q.values, (mc.npv < 0).mean()],\n",
    "})\n",
    "mc_summary.to_csv('monte_carlo_summary.csv', index=False)\n",
    "mc_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "390c7c36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-03T14:55:22.960980Z",
     "iopub.status.busy": "2026-11-03T14:55:22.960803Z",
     "iopub.status.idle": "2026-11-03T14:55:23.998100Z",
     "shell.execute_reply": "2026-11-03T14:55:23.996866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "regravel       -0.343638\n",
       "overrun        -0.338656\n",
       "vot             0.112826\n",
       "rise_without    0.144868\n",
       "recon           0.168234\n",
       "growth          0.263590\n",
       "voc_beta        0.413264\n",
       "aadt0           0.640653\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 3.8))\n",
    "ax.hist(mc.npv / 1e6, bins=60, color=TEAL, alpha=.85)\n",
    "ax.axvline(0, color=ACC, lw=1.5)\n",
    "ax.axvline(s['npv'] / 1e6, color=INK, lw=1, ls='--')\n",
    "ax.set_xlabel('NPV, US$ million'); ax.set_ylabel('Draws')\n",
    "ax.set_title(f\"Monte Carlo NPV, P(NPV < 0) = {(mc.npv < 0).mean():.1%}\")\n",
    "ax.spines[['top', 'right']].set_visible(False)\n",
    "plt.tight_layout(); plt.savefig('monte_carlo.png', dpi=130); plt.show()\n",
    "# Which inputs drive the spread? Rank correlation with NPV\n",
    "mc.drop(columns='npv').corrwith(mc.npv, method='spearman').sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145698d0",
   "metadata": {},
   "source": [
    "## 7. Fiscal note: recurrent budget implication\n",
    "\n",
    "Financial prices (no conversion factor), base overrun included, network totals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e7eae26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-03T14:55:24.000235Z",
     "iopub.status.busy": "2026-11-03T14:55:23.999588Z",
     "iopub.status.idle": "2026-11-03T14:55:24.005822Z",
     "shell.execute_reply": "2026-11-03T14:55:24.004857Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "routine          US$ 660,000\n",
      "periodic_avg     US$ 1,584,000\n",
      "admin_avg        US$ 399,520\n",
      "total_per_year   US$ 2,643,520\n",
      "per_km           US$ 6,609\n",
      "\n",
      "Deferred regime recurrent spending: US$ 200,000 a year; reconstruction is a lump of US$ 12,000,000 roughly every decade.\n"
     ]
    }
   ],
   "source": [
    "f = fiscal(p)\n",
    "for k, v in f.items():\n",
    "    print(f\"{k:16s} US$ {v:,.0f}\")\n",
    "spot = p.spot * p.length_km\n",
    "print(f\"\\nDeferred regime recurrent spending: US$ {spot:,.0f} a year; reconstruction is a lump of US$ {p.recon*p.length_km:,.0f} roughly every decade.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c783bf",
   "metadata": {},
   "source": [
    "## 8. What this notebook does not show\n",
    "\n",
    "- No calibrated HDM-4 VOC by vehicle class: one average-vehicle VOC line, derived from the RED user guide's example car relationship.\n",
    "- No seasonal passability (RED's wet-season disruption) and no safety, non-motorised or social-access benefits; all would usually raise benefits on very poor roads.\n",
    "- Maintenance costs do not vary with traffic; RED allows a variable cost per vehicle.\n",
    "- Unit costs are illustrative orders of magnitude, not a price survey.\n",
    "- No correlation between uncertain parameters in the Monte Carlo.\n",
    "- Distributional weights are not applied; the incidence discussion on the website is qualitative."
   ]
  }
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