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<title>Network optimization Project</title>
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NOjMKhUpWZWHK5LZgl9279229we2OBUX50kuVjv5QDo7PBwnsvrhWJF%2BYDIuVagZDxeFHOF1MEKbsBMEQS%2BKJjOVdXJ1BKw61EH%2BfeqSTzTz3I7ZA3Zuv%2Bwhshy3sDFL2TjctJR6n2SDsfFJ3A0I5ewXfAgugw7s%2B0XQG0SAfFVWHOEsr6TyphSHW5NHFc9J6Wa%2B7B3Dfp42HguHAUINniPlZCpQ%2Fl0CogDIrW%2F8u85iv7sGv8ZzGzYAxjwV%2FMCxTwobJQCTWU8HRPQeruaaXpRqestVdUOXso7dupeF7px4Z8%2Bed3arKFc44AIg51W9ch4kIIiUEocmSk4sBpCcj15oUDRJXYYExl37RmirrkIv55rLASYJJF%2BS3t0nopeptU%2BE%2BmLrLK%2BlPgQyid3mCBU6UP1rVz8R2n770zc%2FXf7x8s%2FNn9fvaFi3rmFHPfmMLWRP4lycho%2FjNPY4W82Os88wiJ34K4tdAIQjAOQkx8YArcM2PaAOjSZBL8uolzAJFFvGDXd8ej67P2AvKpUkOYghcnK7zl300RBcsExwzJ%2Fhbrd7GuYBwhgAIYtbTx%2F3%2Bd4klJ3gtKCQnGIz9InYZEzqG8EkjSzNavCB%2FcXYlcQshhyMsZrI6PYLWc3lOG%2FvlA4rHr%2F3uTFD3r38%2Fr%2B3fMKOke9W4oJ9G566u7au84CpOz%2Fct5R99wF7W6dIYjjnawrHIAh3hlungFOWgXoyzVKbHOr1eD19Il6vISsrrU8kSzbY%2B0QMGpdjgYh60zDTHJKHoyP4404pw27zB4o1o62gq%2BBLL299am8j%2Bzv774zj995%2FdgTOZsOfWr3rnTWPj2h8qGbo1%2FM%2F%2FkYYvmxfms7TtPrM54E7ns4vwBw0rFy%2FaNJjRRVTet31OgCBPABhongUDOCAzuE0h6gnxChToCJ1ulB0iH0jeqvscFBZotflk%2BhMQ5oJDqhrC%2Fl%2F%2FFxmAUlGYeK5Z6Jl5MDec2yJQdc%2Bl5ViNduL1avoZ805eGll04jy6COKheT8S%2BU6kQwdw%2BlW6nPpXF4qtEoBziwAye3mMnRLkqlPRLqZdQlsKxTcLghkqhzjrLL5M%2BWgUwldSkjbL1HPLrCf51d8MHbv66zu%2FmcGl5Kz0YNZ0%2Bmcf759kbEB29qGGrZiYWop2b2R9fYqnKnlWOVzqXqgNfQIB5LtRr8fQLLT7CyT0ZLaL2K0WFzU5e0TcfmojkckcgvcyhJ4pNlr8Bd63VyEhIbiGhfIBFGTq8R9lqcWB2Dl1G79Rn%2F9i8n08OU3L%2F760UX2E369YuvqVUPrI9VryFR8CXc5V%2FrYefbW7svv%2FYNdxUHv%2FOnFVQ1V8yse2Dde0UcAIY%2FzU4L0sA1FEQg3jJT0jVAJFBlqbOOrALk1dCOmkuHNF%2BmpaKOYunHhldNAlZhEyFGpz4R20C%2Bc47Vmu%2B6gqXo9lewuq5TfXrLnZORk9Ink5JjAlNwvYvJBoF8E5N8qd9nN3jrmj7mOx8OPLDXqolpgwv0zZkpuzaeTynf%2BvWjNvnr22b%2BbsfDJR7%2Be%2BcL6dQ1bXlu3CDvOWfHIMytnrhJPHt7x4L7eg%2F48%2B8C5U0euLuu%2Ff8ozr1xteHTRssdGru8V3kwfeHTMsN937%2FzksLEzFdlO5NQpNsMLWdAtnJlizzQYAAQu26AljUvWZbEQlyuJi1Ymcr8Iaal2jjKNg5qJ9Ctqx02jMyDFKHJw8TpUIvjHKhXZQlZ0%2FIwe1eO%2B%2B6%2FRVHpg2mv%2FuPbBuguPMtfKLU%2BtuXfjkIFraEVzg2tlMuZg6O57%2FvXBP1C3kZ3H9od2PPV81RMVE%2FaNAy3HEcaokRS34Ta%2BLAA8XotzQMRiizkRDVfN87X0JXae6NzkVR6Znehb6J8XL%2BY3IKovXMjn0oEDMrkmmc2iXu9yGm0DIkab6hgTZklwj%2FT6FDccpXsmn6Rjlxv%2BknyrTFMR8%2BU%2FcF9%2BDiRwh%2FUCiChwdeXD58cDhSwsRjeikNNcTo83%2F0AtP2DDKLywji1nhxSezMTjgo9eVHOy3LBbJgIQ0OsEsToiIFRHrIjI4wHOlfxEz6a4ZOTXTLq9eTjdTofW1bEH6up%2Bg5GIBDhGEr2BkRNVlMZTa%2FP3HKVyrMMKrF3H%2FKPYUAWjlGsXaRnXrxTIhrJwqp%2FbMtnphFYWIdgGoLWtddqASGuPzdA7YhNaqFZLvVJSEa48LZwUd4YSN4mJ%2Baq%2FctSSXgtmD6gf2emV91%2F9KNj38bHd9l3PX0tq19dMnzFw3OSsgsWjj%2BzqPXn0w4On3e9nZ%2BNJLYFZ1yqkQ2ITFEM5zzwyA%2B1KLJ1kVwpAjsvSTgx3S%2BrQQeiisxv5Ky%2B9kGbnqUmllmSFEhOP6%2FG4ug6C2nJQUPdSt0td36R1IFMgbsUalrqlQAbw4KK1v1BwIH%2FudKqm8NCQbeMHP2LUtVk3rv7Fb4712N3Tt%2FDeaWvZt3%2B8wA7swe6Y%2F5cvjv3I1rHJn%2BAyhLM44ODVn14%2F7bBUDpq%2Fhpxb8c388XfdM%2BrU3veu%2BTws17Pv7O79aFvzMnvxc3aaHRq8sAZX4jgUsP7CfvYntoNhGYquJiAAAKJNPAIyWLjk0ojFqENR0SwqyILNaiG9I0bRYhFECoKD518xh6iplZYz%2B5W8H0OIlBsz%2FtURB6IHmnaT7itJORvb6A94cnbjGZYvHrnSg0zENwfPGTGddQIKJwCEo9xyW8ALGdA7nO0UUg1Wn89iEGQLjwd01iRrUlXEarWAxVcVsTjAWxUBevt4QnM9%2FgxBMbluwe4SAjxpj%2FmcgN0ef3cCt2IAhVVLsR%2F7%2BTIjjZjU9PTeY1ew4I9%2FOvhn8cCeI%2FNf9BnK2Pk3%2FkZ7TF00%2B6HoquhndauXPAGAMIdb09Oqr8gOu6jFpbdQb5IDekccglHi%2FHK2DL%2B4emRymUNIE3%2BRo3WokKfbtNP37Cs0%2F7rxjQ0X2Cvs2Rex%2FNNLuysbxBB7lX3FPmdvl64rwyU44QusOVSzuj8AUTgmDuEc04FdsYcWQQ8COJyiuSoiUsFSFREct4ppwc9rSBlA%2BZuAPZTBx2Az2Uo2CY%2FhIHysic%2F1z59PI%2FdU5CtWz%2BaJB9gi9gKmYebVKZgHgMq89Bc%2Br1GJWSSDAQXQoWAyS%2FreEUlCQsTeEUKRr3B03DZmUZBwxy%2F6S%2FMZmh%2BdTYZHt5OF4oH1LKc%2BeilhJj0UhpMlAKQ6pAbjTRPxSW45Q0CbAac3asPzwaNfrY9LTuyi2ilOhUvnI8SSohNapUJK7wiAaDLZe0dMgujtHRGdt4%2B8%2FHaphRyV9%2Brq5lT1xe9nfPc0a2IrDuKQL%2F%2F9bve3DrL%2Fso%2FQj0kbVrGXCYuWZWXjUhzzD7xn%2F%2BD6GvYau8Q%2BZe8H8LUY7WK6yuVQ2KdHBJ0giCCaTTraO6LTiQaJoshJV81RgnG%2FQbydi5f%2FDYnpjc2ssZGSRrI3Ws1z7dXkYQC8NoLNxfFqVpwaNht1OotVT4GzFDJj9GrpGI15%2BJJiPpxLMg0v6dVv9AONx9jclFWuR6fyFGvI0TNxvRC%2BUjHmnkjBViRGg4Ix0Yn6RGzLWkgJZRVRDKHw1TvRrzc2NpL1J6JN5M0l0dc5snnk4%2BjCBF0QIT1soQCCJCMFzgtw3EBXxTekkO0%2B0aio0pV%2FbIp9V%2BKIgpPrUZJOFCUev%2FJSmsuNBjuVjDK1gKQgp2DnLbuZlRjwuJUAn2MY4nce4COtZjadZSsCntbhh6zRomMm0bbpo%2Bbh4oGrVQLPOume7Uev%2FBCXo1IDsUG7sFsvcaytVpDB7jBS2aqjKCdypaUI4xPzabNJKZdj%2BWvNn%2BtsW4%2FRVB2xkGeEk582NR%2FnE3ZMwaxy2guAqFp99FZ5bu%2BIXqDW3hHqvLVNiOltBiTmueJRtpW9oZgjHIE9sBOOujo9%2Bv1%2Ffvn5h%2F9Eeb77LHuYa%2B94HIt1bArbxs6yU1iIuRjEAnYqZp%2BE8erqdUBRONnA%2Bc75DE6XQaiKGAySLDuqIjKVEtavhpXmSgW%2FmlplYChutYXx7Ay7tLsRZ5PWUePGL949euKoYPr7t1HOh2jK6mdXrVC5wHaoXLBCCp%2BZp8MeAIEa%2BOqmZtns6x0xC7KTL2yZM%2BMtlRs3J6I2pViG8q258sX7OOxndrH0tpz5ki3rzuqxivyf%2FDnN%2BWMCN1SGs8yIxKS3y0aDQdYTwePVm8EMVRGzmVDK5UepkSi6cntnp2Ku8ktw20SOf5bGNm4BcRXyGdhfcfkJ9jQ7%2FVXTzl2vfEZGRLeJB94%2Fzf4%2BLjqZjFi9cuWqJwDVHIFw29ha4V6a0wSQ5BSFrGxTGvV4uH30CFSfoEoJiY4mt0CGlozy8D%2Bo5jgx%2B6jmBbwy4BEI%2B9d3rHnZ0I%2FGN%2B7usnL1ey%2BxM389WLx%2F1%2BINHRbWXfoDLjz%2B6Z07su%2BYN73vyIFFvd959sV3qtf2nfFA35F3FQw8AoDgABCGcv7JvJ7iABSRUp1epgK3CYLmFeJ5qGYSi7k3IEsbWYFQyQrE9PWqJzjM14yPj2OHrLDdhgYZZafDrqOCmQ8UpzGUuFzsLkUnVHMYs4uij%2F2F%2FcJfFxrfee3ld8QDzf2vsC8wo5nuaa44%2BMabh%2BghQAAA4XW1%2FpMcNqJgMuooCJQqiPLlrxWvQhjgF8%2F%2FSgXTwej3O6M%2FNmF1x8zWHdVaFh%2F5uU3bnwXkmg1yXz6aT6km%2BQwpyW6LRdQn2Q0U9TGTotqUGOKqNclWAjJldKcyenwSZ0h8cyc75y5CT3v2xU42u%2BnL9p6UYpSa0Nne7yy%2B1EQ%2F7PaW6%2Fdbm0N88llHNx18ic5qnrv59RXv0YUK93QAQr1q9QNhhyCJ3ORLiskXFJMvtDT5KhocAz63Yu7rj%2FPIY0oTXmKdjuAkfHg%2F60QWROeQZnI4%2Bgq5M9oX4lybrUY5GWGrIBJRpnoDiChTUeOcJmE%2BqKL%2BGCJdcNEhlrSb%2BQ6T8%2BR887zoCZJPFyv1ZQBBscZ6pWKmQyqDLKBgMIoCNwcUdUrMcuuKmVot8AvlzU6qi9roq82%2F0LSFwoaNC69OAIQGdoRMVnSRY2mRUFAYoxcJlTDIOdBSfeJRD5nMSvEEu4B%2BdkS6svyKX6HWC0A%2Bi1c2Kd5c2XRy3h0mgYbo%2F4spg%2FKNEDuCzdrMFFACSacHOUgFevPMXj5rMb9CfMoLfOrSA%2BKF5b9KyigFJCgExOMgQVJYD1TWiQQEwrO%2BG5rpVFUTC3DfaPxsA1vG9pEg3dQ8jnwV9QJea2Zv0k3XKtUKsJLHIlEqwBgjmU%2FLQUfRp9mbCwCxTjhHHZIf9OA8AILRID2BkJ%2Bs1ZoxwDW1OMStBHU83G1fm5MZ0%2B4QzhUdK3f33F8MRKk50lPCUEXzoVc4K1NnTEvz%2BRw6yqMpYkzrFSFGI7jd1ooIt4LJFRHRA24o%2F98LVH4tX7NllapJZ7zS6LZn8QVeLKsVKjrQrxv43GPPvUychyc%2FVveH0F3HR77xCrNs%2FmPDWy89tOWB3js3Y1%2Bb1GPe7Jq5dxTuORZ11TZuHC3LD00fOhwI7OVWtVZygRPSeVUt0%2BD1Wq2mVGqiGX4zmNwOu8HOhccRljzgqoiArYV5DSXF1SDB1sddEk825YBijeRQiVcrvHAqyJ5Pv%2F3%2Bk0l%2F7GwKzGzQ6Wa811i%2FqXFjfb0wlJ1jP%2FDXxwMGLpdcbNHcsTuWvv7ll29fOPPJXwAQpnMOLxWGxbIaK6VuPU3ySmaOmQ0cHDPPzVmNGM9qlJ1DHgNzu6hmOGTcZXYV9f8d8HTbUOn8QrbvuW11Tz3swiw0oRPvyPQu96Sywe9%2B2mlNGRBlVqGU88fB%2BdM97E%2BVvGCx2CV7ht%2FhtgIgmqhez9mjt1FnRYR6bscerSYTkLTqvTcUDPLPA6osi%2BJOiG7ST%2F%2Fn2W%2B%2F%2B%2BTCTLMsNCxmTzdu3Ny4evOmNS9gNlr5647tA%2Frh0V%2B%2Fmfny%2B4Gv3r54%2Bi%2BfxLF0cN44IRk6hdOTDF4jpdzqtkrxGit4uRskyaUyyqIw6paZQyiRZQ632%2B%2BJsUuivNbh53Kb%2Bx%2F2JYp%2Fe%2F%2B7qFl8eecf%2FzBk65bfb7WQLstc2AZl1GMH9v3fJxx%2Fp2pttp%2F%2Bc%2FeGrS8oUksFoBYpHVxK3cVlMjkJ4UaSuj0GvhQMgKIsVkScspUqq0GtY98IAxWmOZS1p2QNgeJSXkPW3DX3mE%2BzrxreeANH3lObN6LH8KHopW83l9G3%2B3TugmsDC9PnPNkLgEKQuYQCzplcKIVu8HC4a56vQ5YpvYtY4ESnSHIzW6Vn%2BQzd72xlLbYWV0R0nXpFDJm6XKvOqvPk5pJekVxrm%2FJekTY2T7teEU9KnHUa%2Bzj%2F8pXd%2BrzbxD1uragaVBdAqDC%2BjaAUkrJv%2FOXKcGMXmJOnbhQXF%2FF3QsHJVnf87VhB3sSqoa%2Fte5X9jf3r7FdPzMgtC%2FccNOnTtwb3ZPb6ZWdOPLzh7amPD50%2F4z8%2F1T4uVE5ICkzt9ewxXYdBbfPqVx54ddvqMauTndXFnYfmBnY%2B2PS66ypEhs2ZFOn5IO08%2FZFvfn4cEPYCCD24nnuUzM5i0nFz7dF7vEkWvcMhVEQcNgOA3q0Y7xjlCatesVT2mALbtRUfM1P06cfm%2F%2BGZhgadoWD%2FjBMnyJuLfn%2Fkk%2BjrfHXnDOow4N5XP4gWAxDYDoDjxAtAwcr9tZ3PJCDa7Ga5MmImVlQ04%2F3EwqZSIqAJJVQc3NDQ1CG3TceObXI7CJWYU1Zc0qFDaSkAubaKudSxTZAEd4Q9TqPRrNP5kj22yognrLcC1z6ISzW5xSTOhATTljhb3v2det7Zv%2FeNGZnLt9g16B6h%2BaqNHZHv0yaP8TSV89QGJTzetxgMRqNOEkSdYHeYAGw2nY7KRje1xiKGfD5zeUyFyuJsRTUiQi0bdclYkzcER73JeuD5E2zOnB07dKSgy2icydpGlxLpQTZOcjW%2FXTo9NjcO5nNT4GQCoiASQHfca2tMVBjHYVRo6SRfJQGoCAfcdruDiz%2BgdwRo66xWHrfb4RPMPm5p0302p1UPDkUPuCLEt534Igi1bHVIVIgEzfAqepHh1bRDypryyOa1DVNmblnVsDhFl79rIuIAXcHhmYdfJicWLNj3cnSLcv%2Fzx9HjQmV99dDDg8e8%2BheuMZq2cnxdUBBOApeiri69x23S22xcWW02g%2FV2ytpSV72Jmrp7m4JG6NDUt95RNPXwJ%2Bq8d0XUSWM2dhSfU9EknsU6wSyDnOwzeLgds1GbYvxvmcVylSHFilGFxE4PYRT74fKaf%2FwOTZcvobX5lZ3PPffii88%2F10Cy2I%2FswyeR%2FAFNmMfeZ1f%2F8rfzH545p1j5vdyW1apU%2B6E8nOEzCrKsS3foHJkBwQhWq7siYrXprboUaHXDzMdZ0GLBqpaeO2hPAhMUr62Y%2BgRHrThpU8Niry7c%2BPBf%2F%2Bf7yzvryabGFc8%2B6xowcMRg1kUqqh9azT5h%2F1GcNr14%2BGTWl29fevfUeYVXHNNSlVexqMKW6qHJyT6bL8OfnOK1pqalecxOp8wtv80MFRHz%2F%2BY2VT5yJ1l63Ul6r3vQ0njtQyL9GzaIW15cvXnjnI8uf%2FfJ57P0SQsajObpM%2Fd9mHXp3YunT59birloRDO2a6z%2F9T38eEzFCzE9okGOpw1ywy6zXm8wEF4DsZrB4FYtg03rc2nRkaE5IY15ZEfvjt4eRQtfaahz6rrsFoaZNlk%2FfTbaJFSenDQjlrnS6XyW1twOtIplrqLzeuZaEfHYJKq%2Frj%2F5t8pdueG5kbsG25Hfpq50%2Bj%2Fe%2F%2BtjA%2FbXzF82%2BdmN88r%2FevSPL3Z6ftEjj7Yds%2BJ13jSzsaHnpjbt7h4Uvrdr2aAH%2ByzaXLm4R1W3O7p2KO71FCCkX%2FuG7BQrwKPWJlwu3jPioEKS1%2BC0OXtFLGGbVeaCkj1xU3kqIVjV5ONWqo52xVGXhtxKNuHyEMcdA5NSJuSy17ZurRiBXdlrw2vN8lyzHQeQZdU9%2F83mRWePngiAsIOvrjKhElx8fh86ZZPJ4DS4PSaz2aZzWdVV7TFqEbMS%2F4daVmW0rJcrhBY127EvX9TPNNQl6UP7Z7zztlAZLeMO6GMSvnpozV2Dj54hp7RcjgiVau%2BHAQ0ms6hHK6jhiJZl%2BNX0NFTicIYQt7ER%2B76ptuiMte%2FtYyP4oI%2F8o0cx9iPtrx6K5UpSgI%2FWinsblz4lNc3rsZipYBZ0yQ7ubnTuxCyYK7c2A1U2Z2Rlk8LhUHSq1BmbsoRPKeSfcBbp2qSdPsY%2B3jNxsk5nLHCcaHqjg0snBF7dzc6QBZ3OvHR%2FdK5QyUaz6j5l%2B4tJbXTp7trW9eRvHClACAIIOpXGzLBdFiVAUWlxQZ3RLaD1pnQ4ngmjmhUfYgteQT9m%2FJktwFVH2Cn27hFSQLxsGO6IfhU9jUdYD0AgfL1LfHw3z%2FsVMqnHK5jB7OBLO0UHfIJCVam1GRJo46KKOdrSUrLvuwFOnfnuS%2FtYTsWfl%2FStKu2xq3cXzuCVn9wf%2Bpn87mrGy5vtC03HtkAsZ6YPCZW3yJl7RUQr6npF0P2%2F5cz0oeZ%2FksHR0%2BTL6D5y31Q6eN685sPxrixetlPl5%2FYlJxu9AFbZRbmnpqlpTq09K3F7TdV%2FbpXcPJZTfEtxCddDvj7d3EK4ZLfHjedrpx794PFH58%2F49MClCxdM44aRZaRxE%2BaPjywnw0Zg4ebdS6Xj7NzZoCl4FhAvMxuZrfluorSo0RSABN%2BtlHzx8nKeJv3cDAiV7Ijaw5Oq4OwWDQ4H8UFqqsXiE2laujso0QScEzYFFXSDxYr7U7DPVNCV5Dj2pcRw4eKhDx%2BZ%2F9jjp45OnvHwVFIePIvB49LSPRvZ%2ByPvJcsjvOq5cRenZNg4zJn2qEvdpyXVQg6tAS%2FXAzu1JvkcpuoIdVglCaojEuTngS3pjfw38rSkOlOZT8nQVNOmbD9lKoU5HFg8t2TMUz2mRrqPyi95omTcisrHK%2FsMJSfuLFn%2FUKvsVinhsvqH%2FRkZSeoOPFuKdcJwrcuYCALV8343AGpSu4xtNPOWXcZcCQNO1%2FXt0PNKk%2FGszp3Ly0IVZPfVC2Lfxb3C5ZVhQDjK7fd5dVemazjNozNTahCARxo62irVJxKnwUz4SzDKgg%2B07k9ljt9sw2apra1KOJCldLR6NAOuqD89OWHNwpPHcdniPisKChY%2BtHv7My8sX%2FFdifTO%2Bxlov4LNXXfvoH7vstCH5z462QkQypUYSDzBpV4Zzk5y6s3mZI%2BdGD1OMS3dlORL6h%2FR%2B3xOcNr6RpxJIPa5uRWkRdPQzZ6Nm29lf5Lfinl2ypuduEqQxqONXTatnD0HG9jQblU05erVU2%2B99f%2FEEzUL%2B%2F1uGTs397MxS%2B7YtDz%2FxwtzsfO%2BU4psZqMkeIVtnHNByAibW0GmBSxtctLd7iwZeNSYn1gJchaVBku9il8r9co82Ja9clCxDnKwNLs0IXQ6VLV4%2BOLx8%2BeOq7t%2FUVXVgmF14%2BYuGrN42MKqeVtnzHh627QZW8mHj01aNmxh794Lhz059ZEFD%2FCHvfj7JZN%2BN2XbM1Onbd8BiscDEJT9Fw8MDrdzWGSj0WYS9URPTS6LW%2FYmGSwW2So5HBScbqsz3UmsTqvThG7JlATlWg%2B33RHrzL7lpjuGUOGj1uaovjBEKnH2HjYCJfY6dmGv72BvYGd%2BARu7j1wgZ5vZ3Ma57Ec08RslQBKsgaxUVYkkUR726QUqUDlmFjgmiYqtbgjFLYRiI5p%2FYebmnxVpXPuF1kupUABdeGdcdiE4pdy0Dj5fmkmCgNS13E07lbRqK%2Fn1%2FmCviN%2Btt%2FWK6OGGznh%2Fs4t9I39VVFmLztSUlwuwZdCiRC2l%2FKk33lG0dHD%2FqprTbw5%2FZmTxqMV9Z8yYvelw%2FcCqjf%2F%2B6K9P9H9t4KLl7R%2BcvmJR99W%2Ff6Ggbs3LPQbRnMF1WW0mD5q1NDW4IJjSKdy5prTH%2BklDl%2BfctXrZxm5rs9r27dWuY8e8oqHTRvWb0MVZPfnuKWXOMUCwWLTQ8eKH6u5TWpiTanKAI8lnpW495N90QCAhzctKeI%2FFxVnZpaXZWcU4pzgrq7Q0K6tYnFrUrl1RYUFBYfwOQGEM7xzvEdt5hxKeSwWDXmrNT0936a1esbSDZAKH1ZRuIuCwOYjJYXKk5AWcoRQByhNPBdhblgFRMxHuG90bnN2obu8KDjc3eYHM1py5DiFU2NqhNXTQOXMWz10weE77sRWvffDZq0880vHB5vXv4PB3les1tv2D02z76xP2YNvdezD3pT3s7N497JOXhMCeTTu3t%2F2dq9X3n575qfMjIXZI%2FQ7b%2Fu6brOGD0zj0rT%2BwD%2F%2BwB3P2xr8GQKCCushU8W1OdzqUhlt5pRQDokeJazP8rQwGh88D1EYJNTvSOakf3feGku9qVGpqG4xTV8ojfbXWGSt18iYUtdZJXEnDlt0%2FedPztWvHjM%2BbtnB%2BHauecmLUlAeov2bk6HHjJkhCcGFoRIcJs1jnI2OaCgRBqd8NhFraSI%2BCBGbICTupxI21YNTrBbMkWKwmUYegHGS5WbPRiyhjVuw2EAfPVEriM1kjLsUhtexzTK9lO0kQ1%2Fdk29mzvXB9yo23qh9EHfeDXhAhJWwiKKAki0J1RCSQr20nattixUJOXfM71Bv9Hhc%2BCdeuaV3LRAIbAAjXdUoX16r7wqGgF3iOLui5Zpn1JodXKu1gsnFoi9Pi0DmtjnQHAR63E4fT4bythikCCP22ZKVVoUS%2Bhp0Bqm51Fnr%2BL2UjHz5YPXLwfRNx36B%2Bl3eeXrwWxYbNVy%2F8n%2BpGrtwd7tNtSfXsNFaLo9jTdPZ89ub%2FpXB47YrkEiRpzW3r%2BoJ09UfBJLnmAoG5dBi5LJ5U83Z%2F2GIGp7L7nGwzHPNQhS3J7yWaAKe27LkytvA6c%2FfPn39g4Oqa%2Bfun195VPX3qwLunC2vmH9i%2FoGZlTdOCgdOm3l0zdZoiv%2FGASic8yQYLAMhwBiA6Q93NqCLLub9OUmpcstOLaHGCwAsItnQvZqjyadHEUVx6cz%2B0JMt%2Bsjy645vIQH91edGont0XbPj9msiaPXiIVI2%2FNHhk35IePbMLh0yeP6V6%2FZPPA4KflKlzBqAsnGkVRaCONIPUOstxn%2FMhJ%2BnrRKMzxUmcTl2yP92s88eVhKvIfTe2KDHRmKtlyd%2F2PpPpA3vsPbRzw4w1sz%2F8snbmA6Or7%2Bw%2BpUPP8mXDl2wVvqx%2BwJu%2F%2FYmVHWb32L5q0oAeXXrkBYa2LZl5056LnkfvwhP6xD0X5YAIN3pyAOvaT85494494cnCD133dnN3O1oEqNZDegiV4IHicLJoMOhs4HS6dC6%2BLeC2ulLMRKks6LWkMWHX6XqfaELKyMnTOhsGs13PNCxJNkz%2BZ%2F0Qg6GhAeewK698pKaNLwyr2caOScrsU1mzMEJygRWCYYcgIoBopDa7TidSq4jaQa%2F8RJkG7MortqVTEvILI6Z9PL1rzacn%2F%2Fov0pY1S3t%2FraYhx5WrKDBA2ED6Yh0dqvitsEECMJuofkCEQsyAJOqq2jzatUOseZR82L1nz%2B7xMwlZzIVNAOBQIge7xQhgUfrILXa7jtog%2F71CzQq3qDNoZYbSkOzBpo31obZtOw24a8BDQx4ubWIXRk7UT9S1Kckrtu%2BbHgSEvqQKP1d3kPleHwFKDSZuX2mGBGlK3sc5EGO7FpnEzw8MXLlQ8pQsvpNv4K4ld9471NP2%2FhFAoDt1kaPi26q3zgo7lONnEnBvHfMfbr3iP964r4XTTjgzJSYsWHJ0V%2F3qF3eu3%2FB8lN07fsKwYRMeGCZM3nHw8LPP7T%2Bw%2FTH%2Bb%2FYjjwCBau4hdsY9BF%2BZRr1AgMrEoJdu5R%2F4fBhELEUxdqM72c5aTGef1%2BIQVnvjPTGxCb3wfhzek01IufGW24c%2BAOIZzq8gnCYLACAbHrsGKMNHNDV6EPR%2FosTBA8ziYuCw7Tjs%2BThseQz2CwV2Ou3PYeV9xMZBVchkAMkvnuAQM34FFf4CxEZ9KD5qXmxUIBBiM2mNMBxSoY3Sba1zpQWwlbVVwCXk5EIqmmhqKj93lzEgkm2zG3tH7IEWecP9w%2B9rGZ4ohslCYnXDUm9MGF2J0ihbnJBfkf59Rs7q4vv9Y9X1ozq9%2BdbRTwPhSMnYbk2zOnXtXqqkXKHH1tZM7NOvw5ip2e0XjzjcWDEhMjB%2FyIz70jFvcU%2FeGRvmVKrdoPJ0bltbq9R1v%2FYaDgTdn4hNzIa84ltA1MLCGETS7SCOQSAGkdoSIv86xGsg3HKMrOsQE6CUQxiaKGmtgtyAkWIwIMNxKIN5QK4xAIk3MIIVnNA%2FfAdPM%2BwIOhPaRNEtuvROycm7kHm7iMHM7wabASUqOtByowkglmHm5an5G8bOiYau9y%2FSAF7vYVQ2zqR5UUeUXdxLDtMT0SMkNXqR9Lhag0cfURpetbZG%2FAvZr2jRHOZSOkc5ztkqzrMIAf55rM9N5VmbON8PqhxBs8aRmyFqoTwG4b4dxLFrV2MQyS0hsq5DTACHylWC%2FhhXgUA%2BgFip9id54Z5wod3t1glmAKcgCUk%2BrogS11erXC6%2FJJ%2BWL8jcIsuyoNfbqiJ6Kri17tNEXW55EDWhHZV7uVhLarxnM5QhVqpNqbM3bcJ9eBf%2Bbn%2F07S9xNlt4lIyKtaWSunqyntWxHSQcba5nhhhNYrmqS%2B3jurSmJdWx7jiVLwUx3sKsmLb5bgdRi4YYhP92EMegKQaR3RIiX4PgeGy65RhZ1yEmwMdxnW4b5z7CQrQJJmEDGMEX1st6ino0mXXgy0%2B0x2rMHLeOu0ewbTh8BHua7RiLw9m2MThS2DCa%2F3fbaLyfPTsaR%2BCIsWwrAOXzv877434CJ6RAQFkZnnRvmsAPExtcAA6rqFMCF0%2Ba32f2945YHTpRoDazQHnjnES1lrm3%2BFq4%2BYgL%2Fygm0lglwc7fxSoM1BZEj3qKzovZ1zsLv1479tEH9ykddGe2jnx04rGmh6Mjpu%2F9zy%2FNwbFk68SdWpPhmOUDNr2FDyl9dMMXV699l61D26bmvgOVZjp2ZRN9qTc7xVdOrI9LlUxpXLoVMfk7Nb7fDFELp2MQKbeDOAZzYhAZLSGyrkNMgA3xlRNMtEfCbHWUTvF5CmKjOFSQeO%2FfrHjvH9%2BpMOtFUbKDBB6vWeALiC8fs96sl2LdkZoVarkRrHVH8v9lCDcaJGexM%2BzzQ42NZ9GHnuYrO3mL5LvvUdvFy4zXWq%2FB6ei%2FV%2B5Y9yQAqv0oW6R0aK94ppxcMTUAXpMJUu25YkGhw5Hbrl12RaQd5LrV3S5tj%2Bvm0xpaZCBL2vZIQjWCo6Q2%2F2lnOTKUqE%2F1UYJv5ZAOKb36Lxv32p%2BOTCrfUnn27ofnjujZq094yVz2TcPf%2Fv7%2B58IPi6dX3OnPyC0L3b917LZdPTcF8w%2F0mVQxcHZN%2BcTisqHF1YMuXO0r7Nv3562c52pXkOTnPL8TACXovgLUVWlXOH6L57V56vN2t3t%2B7FP1eajFc%2FGz689fe%2BUW3xc%2FvP58whegruiOKsCNGRZehzj%2BcwyiTQwCqAIhKbtXOVDENWdkOJQLre3tedlIaF%2BWlJTe3ghi5y4pbYNtKyK%2BAqGgV6RD66BdECyZQU%2BxzqKriLgsNtBaO9R97viBxZsNL1corarUot3Jy%2F%2BqHSkOv7bLFExMz5TiAMaaVIb%2Fwg7NmPnUc0VVb4%2Ba%2F3xO8a6Hj%2F0reqcOO967tWbwurHswpy73lz03Mt7Jg1ZtfPpwzvoK7OWGon8BOY%2F%2ByddrEUqp%2Fie%2B4eMYP%2F9%2ByRWGwjyVpav5k5sXH9%2F5MVNo2XdQ6Sw4ektO5V1zXc4lW4kzreeMU%2BJFaqnVDtxVIn1ikl8vyqRVppEbn5e21993vp2z4%2F9rD7PafGcS1R7PsEQk1d7TaLX%2FgqAo9URXolZHHYXKGOgqI3xIgApTICovZYRgzDHIa79iUMMSoA4xl6IQTg0iG84RDrHQ4OYwA4CqBbHZ9d89VRlx1zyq6euqsJ5fsnUqhXwYN5jsTttkj7YRp9eETFSj91nsfLIR0%2B9LqSttY3QmLJw6%2F3b430QyITiIlAqxdlBMcj%2FlHpUk%2B6gRVqnV4kwil39%2Be%2FsK5T%2F9sUYXdkp9n3vr4YN77ll3OW%2Bpzc8v7NpC3vppe0vPUtC7Ev2FzR%2FcQmlWcInr25%2BcGHXgtrefZ6cNHMlm8b%2BtaaRbXjh4Aku21jXgbraqmOrzaLyJC1RNqNUrt0Vk%2F1HquySb%2Fe8drD6PPN2z4%2Bp45Ngi%2Bd8fu35a9%2Ff4vtcJtrzCSkx3Wh3fS2Ph2YhR9gJVO1CD4WTPAaDTSACKjsZTifKZjMqJ%2FQQ8tX1yhOfG8nPjUN6iccXE96Pp8ejezqVFHXsFCrqot3J8iefZP%2Fq3KW8Y1m4nPwYfwOUY3tEGCUsjvv7PvxEa3orl8vQ6iZn76u47uxt1M%2Bb2Kjnf3P2ZWVxBdGcfXw7QXSpTl4Si1SnX6L2X2yaUjNt%2BDw0Xd40o6Z25NzmV4rxTJ9pvAljfYjl95r63Iuxboyetf0XbEBQGjL6zuy7cMOvu8aRRcWffLRjTHRO6DzXjNjutSq5e2KSf0PVDI8mmZuf107VNOfWz4851OeBFs%2B5ZLXnE%2FyxtZarrfrYDqw6wr2xGWIjpKsAWu%2BI2t%2BVyXex0jOkFJfNZpfsrQMOsKeYPHqqT%2BNdjB7q5euvRZPnb3oYUWsXUUomXo%2FW9JUVbx7J4HugOKR748Sz333%2Fyd8fMwk63mSElTs38OYRzF9LmyID2Efsvwpjn83sV86KdcDaFQ1NOXQi58u3ce%2FZMxo1nF6Nmgn7Y%2FTmxejV%2BpuEyuv9TaJArLfsb%2BIw6gkU6UvxFLggHe4Ot0uSrE5nKpjtqZKY4bc6eDxpBaOR51hGGj%2BVwg8UUAc4b5zk4det2ia1fWVJO2TlvZF9aafq7NnSl1EYN4y9zJ7BYRgeN5RaonxdR8%2BRfs09fmXXEH%2Becs89LqzDiTgeF3ljSZmwlZ1m55QTGn6hNi32qy1yujAU0iAXCmBQuG26zkI8nqx8t7tVlk4oDOW1Mbbh0RHvSCKixdiunWg32pIyxcyKCIieFj7YoVjVRAeseV9R9a0q5rdyvYktTFkxnyvWs%2FNzup6pu8B%2BROnrBae6djz2%2BInL0aAOq4Y%2Fe8%2BQDVf9G154buPm5xvWCb3mrjKRjN%2B7vp4xEwtQh3q8Y%2Ba0KbPYz19MYDO5tw1mkLIPz3985rOPP%2F10x9NP7wBEE68Q7pH8YFF6wGWwWXmN0KJs3CSfKkwsE%2FIgzx1QzhIE0DR3nLfB89CcmUMWLuFF2u%2BWPJGTu3C%2Bt3TBoiIAgpP5iG2lhdp%2BkEMyxSpMejflw753u9KSrHUfcfpp29njxj46a8zY3z3YPRTq3rmsqJu4b9TM2lGjps8c3qFLlw78AkQdn%2Bk78TN1N5wPn%2BSzg2gC%2FnKrZc73En4mKLYb3o4vKU6BwvQ0olRTQpJEXXkDB%2FTOLAxZRpmn39tucP%2FKjIL21tHmqcL5rLZZnbvMquO3Tl1n1aldEci5Ff%2FFEyCCePMvngykw%2BK%2FeMIh5f8VUtYgffQ49lB7%2BR0HUNTpQenhP6WBBkscHEs5y%2BQZ1WF29yx63DMUTVyicNM3RdTpRZly061Rq55Od5RisXIk%2FbGKDPGARzmLjqmfcouq%2Fe4LkcAKAEQZizSpY1khOWwS0KwXbHbQUZP2M1%2Bx3pUgbyrhA%2FvjeGG9tcNjs9M6maNnb2B4FnXTeR1Tw7TF6DZldL0ZRcHuMIs2WRn9LW10DWe%2Fei9JQJ4ELUkjOsxJ7m6%2BQYbnXvbTY2Ow6D6FHh%2F7lTTBZZSVLOtqB8g4iCCHzeZK%2BdC1Y38ymWJ3vb5SBnteXszG7cAfyXB6EYzgPBD%2FURrIP3Wr6u%2BOqQ9OmDF94qRp5JtZj%2F9u9sx5C%2Ficym8TiHvgB8gGOwAEwU4c%2FM4nELJA1RaoJelK5ZPTbBAIlYikk0WuCInpvPM3e2CJ%2B16ASv2UpGqjUBAIkMRRWhRNSeqtK6QAyGYBkJXxUyYgEkE7ZYLxAQJIVjbPWkkXx4%2BZIJRzr1gnnuT0TQ2Xp3rTPZ5kI5Hl5NZ2wZDslYJtjN4kb%2F%2BILklMTUvtHyFp1rT0tPw0qqdJaUlpzsxM6BvJlJ0W3iDhg5ZN3bwwdMsfKruRW2ZQbuRlt9evdcorVpPyolGwuJT%2FdUDsCHUKOz4AWfRHQvA065Z1snHLxtW7%2FoddaNewgZANO4LY%2Bn9OPN%2BrQSxmD80rC7ed1%2FRm9%2FpuaEacl3tH9TwUsfXIpYPVzprl6o4iBXdYT0AUtDAtYc3y%2BEuJtrjkUwGEVlI650ylKvE%2B5ABA%2FHNTwuf9lc%2BBgItUcf0%2FAgZwQedwuks0ypTyaYjSqY%2BiqLe60l3E5aIWOZ1mxPuV70toergeGwR4g0v8V2eKi0otVJZJ05xV7GHcsHQO%2B0ESk9LSjDup6913x%2FKzVKdeX9THFGzb1v5TDDfpQ45bECoJ9%2B43cBcf0nCXXr%2FF8%2F43notvxJ6rVEnqc1TWG05X9cp%2BAAQRKWiHl2Knck80KgqljCAC4Aq1QvJpPHP6XaxCImp1FiUv6pwAUXstt2Ud9NrbHGJCAsQx9ufEKktsFtJBzroOMYF9EK%2FV%2BGK1mv8PflNJUQAAAAABAAAAARmahXJJOF8PPPUACQgAAAAAAMk1MYsAAAAAyehMTPua%2FdUJoghiAAAACQACAAAAAAAAeAFjYGRg4Oj9u4KBgXPN71n%2FqjkXAUVQwU0Ap6sHhAB4AW2SA6wYQRRF786%2B2d3atm3b9ldQ27atsG6D2mFt2zaC2ra2d%2FYbSU7u6C3OG7mIowAgGQFlKIBldiXM1CVQQRZiurMEffRtDLVOYqbqhBBSS%2Fohgnt9rG%2BooxYiTOXDMvUBGbnWixwgPUgnUoLMJCOj5n1IP3Oe1ImajzZpD0YOtxzG6rSALoOzOiUm6ps4K8NJPs6vc%2F4cZ1UBv4u85FoRnHWr4azjkRqYKFej8hP3eqCfDER61uyT44DbBzlkBTwZD8h8%2FsMabOD3ZmFWkAiUs5f4f2SFNZfv6iTPscW%2BjOHynEzEcLULuaQbivCdW5SDNcrx50uFYLzFHYotZl1umvNM1tgNWX%2BV%2F3gdebi3ThTgVEMWKYci4kHZhxBie3TYx3rHbGr%2BPdo7x4dIHTKe5DFn%2BO%2Fj%2BW2VnE3ooW6isf0LIUENvZs1gf%2FLHojJwdpplCP5gn%2F5gi26FoYa19ZVFOJ6Sxuoz%2Fq2Ti20IKVJdnqvYJwnhfPH%2F2f6YHoQF30aZaK9J8T026RxH5fA%2FWPW%2F8IW4zkpnIfoFLifGB86v0ffm5nbyRs5iaHR3hNBD0HSfTzoPugRM%2BhdN0x052KoHLBS0tdgpidAiEesDsgWYO73RWQz2LWIwjqnMe%2FuYISQtlbyf2NlT9Q9PoBcBnrO6I5ELoMeyHkNnIXGdv809H%2FDXNOTeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe%2FgY%2F%2Begvq0YCAEoCNa1n%2BKVyTUl3Q0uIhoe%2B3DnRfV7nXGOc5zjHOc4xznO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Z9A7%2BtETl5RXdNNZGDm%2BvXYXWjgLDRzEhoLBAYv0%2F0NHAAAAAADBQ8CvAAFAAgFmgUzAAABHwWaBTMAAAPRAGYB%2FAgCAgsIBgMFBAICBOAAAu9AACBbAAAAKAAAAAAxQVNDACAAIP%2F9Bh%2F%2BFACECI0CWCAAAZ8AAAAABF4FtgAAACAAA3gBY2BgYGRgBmIGBh4GFoYDQFqHQYGBBcjzYPBkqGM4zXCe4T%2BjIWMw0zGmW0x3FEQUpBTkFJQU1BSsFFwUShTWKAn9%2Fw%2FUpQBU7cWwgOEMwwWg6iCoamEFCQUZsGpLhOr%2Fjxn6%2Fz%2F6f5CB9%2F%2Fe%2Fz3%2Fc%2F7%2B%2Bvv877MHGx6sfbDmwcoHyx5MedD9IOGByr39QHeRAABARzfieAFjE2EQZ2Bg3QYkS1m3sZ5lQAEscUDxagaG%2F29APAT5TwRIgnSJ%2Fpny%2F%2FW%2F%2Fv8P%2Fu0Bigj9C2MgC3BAqKcM3xgZGLUZLjNsYmQCsoGY4S3DfYZNDAyMIQAKyCHTAAAAeAGNVEd320YQ3oUaqwO66gUpi6wpN9K9V4QEYCquKnxvoTRA7VE5%2BZLemEvKyvkvA%2BtC%2BeRj6m9Iv0VH5%2BrMLEiml1XhzPdNn3n0rj6%2FEKn2%2FNzszO1bN29cv%2FbcdOtqGPjNxrPelcuXLl44f%2B7smdOnjh09crhe279vqrpXPuM%2BPbmzYj%2B2rVws5HMT42OjIxZnNQE8DmCkKiphIgOZtOo1EUx2%2FHotkGEMIhGAH6NTstUykExAxAKmEqSGMFl6aLn6J0svs%2FSGltwWF9lFSiEFfO1L0eMLMwrlT30ZCdgy8g2S0cMoZVRcFz1MVVStCCB8raOD2Md4abHQlM2VQr3G0kIRxSJKsF%2FeSfn%2By9wI1v7gfGqxXBmDUKdBsgy3Z1TgO64b1WvTsE36hmJNExLGmzBhQoo1Kp2ti7T2QN%2Ft2WwxPlRalsvJCwpGEvTVI4HWH0HlEByQPhx468dJ7HwFatIP4BBFvTY7zHPtt5Qcxqq2FPohw3bk1s9%2FRJI%2BMl61HzISwWoCn1UuPSfEWWsdShHqWCe9R91FKWyp01JJ3wlw3Oy2Ao74%2FXUHwrsR2HGHn4%2F6rYez12DHzPMKrGooOgki%2BHtFumcdtzK0uf1PNMOxwDhN2HVpDOs9jy2iAt0ZlemCLTr3mHfkUARWTMyDAbOrTUx3wAzdY%2BniaOaUhtHq9LIMcOLrCXQXQSSv0GKkDdt%2BcVypt1fEuSORsRUwgrZrAsamYJy8fu%2BAd0Mu2iYFhexjy9FIVLaLcxLDUJxABnH%2F97XOJAYQOOjWoewQ5hV4Pgpe0t9YkB49gh5JjAtb880y4Yi8AztlY7hdKitYm1PGpe8GO5vA4qW%2BFxwJfMosAk2X9n9X2cVVfnA36pzHNHJGbbITj75NTwpn4wQ7ySKfAu9u4kVOBVotr8LTsbMMIl4VynHBizBEJNVKBAfMNA9867j0InNX8%2BranLw2s6DOmqIHBIbDfQR%2FCiOVk4XBY4VcNSeU5YxEaGgjIEIUZOMi%2FoeJag4mEB3PUOweCaG4wwbWWAYcEMGKn9mR%2FsegY3R6zdYg2jipGKfZctzINQ%2FvxkJa9BOjR44W0OpTKAskcnjLTcKyuU%2FSVIWSKzKSHQHebYW9mfGYjfSHYfbT3%2Bv877XhsIwGzEUaleEwITyE2u%2F0q0Yfqq0%2F0dMDWuicvDanKbjsB2RY%2BTQwOnfvbMUhiNPFyDCRwhZhdjE69Ty6FjoOoeX0spZz6qKxxu%2Bed523KNd2do1fm2%2FUa6nFGqnkH8%2BkHv94bkFt2oyJj%2BfVPYtbzbgRpXuRU5uCMc%2BgFqEIGkWQQpFmUckZe2fTY6xr2FEDGH2px5nBcgOMs6WelWF2lmiKEiFjITOaMd7AehSxXIZ1DWZeymhkXmHMy3l5r2SVLSflBN1D5D5nLM%2FZRomXuZOi16yBe7yb5j0ns%2BiihRdlFbd%2FS91eUBslhm7mPyZq0MNzmezgspUUgVimQ3kn6ug48mntu3E1%2BMuBy8u4JnkZCxkvQUGuNKAoG4RfIfxKho8TPoEnyndzdO%2Fi7m8Dpwt4XrnSBvH45462t2hTEX4Bafun%2Bq8jIzK%2FAAEAAgAIAAr%2F%2FwAPeAF8egd8lFXW9zn3PmX6PNMnPZNJMRRDMkzmDYgZMRRDCEmMMUPJIgZEepHlRYyIiNhRUdYuS4ksy9reLDYsdOmLLC%2FLy7L2CgKrrCJkLt%2B9T2YyYPl%2BD8804J5zT%2Fn%2FzznPBQKbACSTvAEoqJAdtUhUJpQYjBJVAUrKSkIOJ1ZUOEKOUGkfV8ARiPB7E72m87WJZF58ibzhXPVE6QsAAnMufI4H9XXsUBh1UpOJSJLmQNWqNsasLkKhsrKnA%2FT1HCF9PQzSAPYtD5V5PW4lmFeIK86EcCRbObLp2lGjGxpH4%2Bf0wLkjjU3NDSNGxYSMxbSdDkzomhE1SypQalCISvniob1lDuTL7injC1O%2BMr%2FxmeJtxeRt%2FiJviJ8mmrjFOr0BJCZ3QAbkQFu0ypCZ45HcRqNJQkiT%2FLKsOO02s2Ryudze7CxVUnw%2Bv9%2BtmKTcgEEymzPRlgN2e5rHaeOXyeeiisnJFagMOSsqSkr45kL8Tr450SfM5%2Fy1V66pGvBwTV1BcYcDEX67QjQkbo8cigTplyVI2OHh%2F6zdXHO4%2BiR6SjoxMPzo8O21h2tPx7O2lmylNV%2FtY5Nwubj3fXUA%2F8BuFveBr74CoNB84V6pSnFCLhRCL7g7OijfR7Oy3FalR49AcXYRFBnsQUcgkAYO6H15j6wiAGu%2BI%2BAo6pleFDAWKJZMX%2BaImNunWOpiskIVH796ewAqEzvV9gqX9nQ4Qd8S%2F1V%2FScSM%2FrmsTP9FfNUNIvzuVlRPMFxY5PB6fY6iwsJw3%2FJIOOTx%2BlT%2BWzaR%2BxYWecrR7fWFFanqi%2F33nnn9%2Bv%2BMvXr7mk933%2Fv5Gy3PrN6yZjg7WFV1D5s2oGoh7nx%2Bk2vvTrkeDT0HKlieXvvakkfecj%2F5uKnhm6iNHRk27a6bevTL%2BclH3ulVkX3cBTJUXjip%2FCDvBiO4wQ95PB6qo%2Flen0%2BWTRpofo8nLa04mB3UgpeX5PbMLEzzKz4%2FtapOlXt5a1llpXhN7FF7r8zJ37o%2FiN15Q2XhvsE8RdajOqwFyrwFGETXr%2F0F9u9dNnZsWW9869X1azow9qe%2Fkpc7D52mPRf%2F%2FHcJFrR1npvf9sWX336EO7%2F9x7lqeUMn6frt8y%2B%2F%2FZD%2FJjzecOGEAnxvWdzjpTAzWtHbGjRhlhdMXqvLVZSWnl5kpSoChLJVtcwXSPea8vNLSrT0dEnTegyPaZIUqIlJLnSKhAV%2FpfBuhb9EbE53bYVIM%2F3S45hfiZ%2B7th8IFPHN5QuXcscms1vF8kiAZ2qBsEEEFQX7FnJDeNy%2B8nIF2JLZ7%2F77DPtk3rJhVV9vefPD%2B57CzCF98cr82%2Bs631s4%2FvbxrKPf1XjT0Iqrh%2F%2BuafTMxR%2B9e%2B%2BmxqZnxzzx5l8embstxo7PeX0Ju3DjoqYJA7C611hyd3hAtH%2FzpD5jAAVm4DM6Zjj5C5WIAIu9DuxCIB0kuvEBAKGBbSTz%2BL%2B3Qm7UZjaZqCSBqtrN%2BVQgmAMTua3joeaMhBTicTt9wULS8PSj5x58eNk9Z5c9RUrRiPte3MTKzvyHRd5Yh9vFygP4yq3JlfmyfHG%2Bso1LyP%2F5yqgRNVjuDPclRSGvk7Q%2B%2FejZJY89%2FOA5sTT7ifVb%2Bzru%2FOEM7tv0EisFhErSJGUpbrBBOOo3ms0ypVZUVc0umUyqilarYrDxpN1aJrKQuykJwvwz%2FyPMUOCTXSqlRa6CiEzJy8U4J8DWf%2FjpM%2FeeOMZeLMKpxYqbPTyx088Oz8MKtnMuFqefm4gzAKEZPpUqpG1g5qivGRSjkSKAxWo2giJRKOFCysqS4vjNhQXCAa4Bxz1HEI%2ByNlx0FBextqOk9SjezW49yhaIHbGzuBtOggKe1wgFWVapDCXbdSNt5ghfoNCgMxLA3X1v%2B%2BdV%2Beg%2FvIsdR9MJYWVcS5rISqDg%2BCuVQQLkSiTc7QoHPANIGq49dw6wi7GwgmvujZoUrrSRNsaMLqjsmfjnkYu4aU6SlJZ28xECNyqt0mMrM2pBricBidueiNS5iDcRA0ir4h%2By4yQgGJP%2FDwLVF05IQ%2BW9XLoPLou6LYoTFPCnGT0jYkaV2kfEaBok8y%2B1kkYCeeDQnIEyQI2nUrlDE3kkDT3PzsfZhXMoxZHGw2OmTRl7w%2BSpLeQoW8gexttwNi7C6ewO9hD7%2FusTaELr8eOAMA%2BA1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae%2F8Tw%2F1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD%2BEQLt4TkOo6CU5f1drrhvrrVly%2FdspDayfe%2B8EtQx7fuJG0HcbZLyyc1r%2B5qXbojtE1xa0dt4x%2F5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8%2Fvq4tZ%2FGilfr8pX7VqJm1EzJQGeg3j5%2FxX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j%2Fo2ykPlEmSr335KxvYPvbZydev29P65KNrX58%2Bc92zfxv6%2BKil76PnU1Sl6fe%2Bl694%2F%2FzIweMjUO1ZPnH2TU3fxqa09%2Bl%2F6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5%2B98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4%2FHXxjyKYhkRU9LgYsIJ6e%2BpgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT%2Fn6efcC%2BPLN8xActmMGOhu%2B4bH6EpsV%2FyAgOoO0n9%2F%2BHnR2B5h7hr455LAPJ1%2Bwc%2B1i1AYGhXOs6eQf4IR%2BuigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd%2Fgvy8Q3E6TmsbErv%2B%2BZ2tRuuN%2F7f1X%2BzsNyv%2FvYhoN066sbVlcRuZiq%2FiWvuP7rEb%2F7LuhyPfsFPLMffdxfMnz7%2B1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U%2Bo3%2BQo%2FHnp1ttBtL%2BihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0%2FLqoW8qk7kShFiku3kK9cfCPVHyDedt%2FqpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg%2Bflxtq7zz3ZUzXhrU%2FO6sjqN73mrbXD2iY%2FKzm89vbBp7Y%2F3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR%2BNfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP%2F3%2BJIOl3xOz3v0nmr9Y%2Bf2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r%2B83%2FH0fEE%2B3DTXbdNum1%2BHfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU%2BxG7xcf6mPNQAQX6%2FIUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK%2FVYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304%2FFkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA%2BB2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m%2F7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44%2BjrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o%2BUfT%2Fqt9NdGau79%2FZhf73%2BexCP2T2Pz%2FZefZXez6I%2FgIyv%2FEkRs7Yf3IFpM1FG27n5x%2B%2BNQ9Q%2FotPPTGQSQBH%2FPd%2F9Yf%2Fvjjne1sx152gh0p6f3eKHwYW3%2FEZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik%2BLHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI%2Ft4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn%2FC%2FfPZh2fjx7C84%2FaZ8xev2nXHraxT3vDKpkVrHaacdQ%2B%2B%2FxGdXTuy8Zr4NrZo3PgNgDCXI%2FUBnh9eKI36VZeLN%2BNWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2%2B0Iw4l2BUdoTI%2BZiikBS%2B9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U%2FHmW846xyGi3DSZ3j9azd1FvUDImwoz%2BE2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO%2FxGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt%2Br%2BzlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr%2ByVfg43vTxgZtW%2FGXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu%2FUg004jIzW%2BQJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr%2FNdmE5uRrDZG78Xj5t2EIGAOCFiawBT%2BozgRw%2BbSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS%2BtfxVS0wMJpjIcRkNiOAzUBl2cq%2FUrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6%2FHf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI%2FEnAKwL1KEcM%2FJO9nv43fpSiwh81U7%2BqQGdrQtXseFv4FZvycdQPQ8%2BVKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj%2Bkj3BXubXE3gz1vNr%2FPlDb76Bs9nSNzaSY%2BxxdivejVP5tZCj0mP%2FOYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr%2FTSzoRn%2FZLXHoEyAo4ckJSx%2Bau%2BBBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb%2FgchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe%2BLX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C%2FWaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm%2BPz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n%2BRlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq%2BuhvwVHHWrRpn%2BIvGGoVFl%2FMhDadWMcJP9LZen9cr%2Bdin7JuOx%2FZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f%2Flkz5g4srx3e8%2Borqypveeqmzf8qL%2F13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k%2BdXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6%2B6CYLnlEXbyLfYFex%2FD9zz7BAf0ztqVZ%2B7EwHn6YufCPz33%2FDraBqjXfyHBI2K%2BRonRKAOiVZYkC3BDJ%2Bq9VNpUJOaj%2BsXtVx6h57CC2dmLTMMKdPlKFXO0a4DY%2BdTwvZeN%2FqJLhrqRy8gSsx%2BT0e52yQh%2Bv2ynlszMrKwci9mcnemSzdRvt6NJiOSi%2BEtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy%2Fnm1hKoRixV%2FmpUJxwVT85trLi1EAa6twb%2BaS%2B9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG%2BooUZ3VpjZj8Izex59h6fnXg56wfNmF%2FDGMtC5Pi%2BGHyHdka%2F47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA%2FLwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m%2FRXi%2F0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt%2FZIKYnGpfnBa6sUmAHY9%2FmJhqI4S4fJ%2BQL55xoKIY%2BVYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd%2FUoR8AzFIA%2BR3QIHeUTdBWVYkSTznFd6SVJko0DW%2BxLKLeyTRZYcwiGjADQ%2FjqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl%2F%2FvMAAGKNdIQ%2BqATAwK1gBjVKRVTIdwCUpB%2FrioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ%2Fz7GYIdmjju9pfm6E8Zq6OFTovBQvLy%2FP78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az%2Fy%2BZVYSeXlcUJWdMvMcN9%2B1u8h0omny9N6YT%2BhuGr1r0xzd%2BOr%2F5xbv%2FOn7T8Y9PswO%2FX3znY5MWPHHDsNfXvfono1K6rn7f%2BK3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX%2B7YNsMT%2BhpLLZbJM2ZOJJNvaZk%2BQ5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM%2B%2BlZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf%2FBTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi%2BobTZhqFy7xwIOIyi6fTTdPt5ft4%2BoT4Q%2BecShOXlPGioU%2FBLkji3iOnVPiAnZ9vHnOw9ON%2Fmw7Jv%2B1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y%2BDMEKROSUpzOLKcJlOm3%2BOkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs%2B%2FY3zDt7LdoiJfID985vmNc3Qb61PyZM%2Bd3NmAPdGAahth3Jx%2B789Eel5%2B4rCjB7nSOkgMeuCKa7SZElSn1%2BqwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l%2B9eu3bj%2Fufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz%2Bn4pxIOWz79gRHv0LneqJs2FQ76ewKfPao%2BpSsqEvmsj%2BykQFfCF6ZeRcGFyUQK8v26El%2F4WGzqS33OfxjpXbL2ndc3sTfYvm9%2BvP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5%2Fdrrmura10otT%2FceNqZjJ5Xzew187smt%2F1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D%2FJfU5336yUOPaKqJJEuCQeFQirWX7O%2B6YxfZjqapqE%2F61bQ958LsXt8S%2F40CwpeDekav%2Fvh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ%2FZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd%2FL0JgaDT%2FNcE9PAaBctOk%2FsjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd%2BeJXN6mThXRIWPMH%2FUn%2BNdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7%2BG7krl7%2BOC8ePqbjJDCRIiEfKFykdziVfBd8q%2Bke9n%2B%2BuvnTGL7vy529F437Xwso%2FdL097ZwvbVXz9jOnlw3rz12%2BLfSS1Lh1%2B%2FurZpy%2BF4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV%2FXLRG8TS0ierkyof%2BZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu%2FxUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl%2FI6Xt6%2F70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl%2BRVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p%2FtaZv8wfaPbt58TGf7vePdSx%2Fwsv5K9SPtbB87%2FT%2Fs7H10mU722JDgM67pTN1euaIq8dIsyh%2BTpOUZ%2Bfg6PcNnz%2FZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook%2BLIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9%2B6xqU%2FbcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy%2BJIqpdWewrCD%2B6iu3%2Fsre97yvSbCP7xLR8SXyH1LKxZTYkqp%2F1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX%2BvJ2%2F2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1%2FbDrDtW395oycuG8yJd%2F3%2FXu6MZHa5Zcv2zRrf2wZn1HILfzsvKx%2Bb0rCstHz73%2B8VXN%2F8y%2F%2FJriK%2FqHR%2F%2B30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu%2BTD4aeiNN0jGwgknTn4e%2F%2FyqK4UOT%2FGc4zM%2BcENZ1E8cDrfby3t%2Fj9NoJ7JNtumyPcmJ1sVDgItr7tQYgH%2BgrxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt%2FEpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X%2Fx0SbTfiOtaElf%2FINyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin%2FpGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH%2FPGvvE03jMkcer4XnuKKI5gApOW6bWqi%2BYoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu%2FmD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy%2BDRewhcJLzK17ycs1KTC73ZrXK0koahm%2FJgob%2FpNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu%2BvcfR9RErqqfl%2Bfkfr2%2FYcZNo8ic866XXnR8Z72xNZI450HXce2MIn%2BoKqkIYDYgmvQhAm8c7YR%2FMwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6%2B3xekDI4T38Id7D54ipCHUbcnIcfn%2BuNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm%2BREWRw08Coc18VTQ4xFQ6%2BEJhDmL2m6%2Fc%2FOZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg%2BknbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl%2F3%2F2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2%2B9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG%2FAg4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv%2FRAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii%2Bnphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA%2Bn4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8%2BePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN%2BrL%2FfHcyhjZk77c8arjmflv%2FBn9kZObzbAuFFEB4A0ST%2Bd2BztZXeaidFqTfd6iV%2FzO51ado7Fn%2BavjxnT0sDFqcleG3P6QR7xs%2BNNXUfUIJTSVqjbjT%2BpBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3%2Fmvbhr%2B6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM%2FOyYp9LSycWo2xQPZ4ctYBHz%2FYyHc11H2qb9S%2BiA4oURXyC3SM%2B0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt%2BNn93Y9siC8v1T6%2BqnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo%2FiNtYyYOnz6ttm7aDBHpCoDEp%2BPghZnR%2F7I53U6Plce2UaYyMYkJqxeRED%2FHBp%2FidDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448%2FvsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw%2F0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO%2FObSe%2B8uy39mVlqEzr76oeyi%2BbG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb%2Fbpj257c%2BOS5s4tvmrim7appHXPputbn8kPlVdURssit194%2FxklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0%2FYrQaWIvIdGcQ4pBB7dzU9snCdpLZJF%2FSOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0%2F7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7%2Fc7yzPc%2BbV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul%2Bc91p4c3j55tf%2FfvilPddGFx5b8zJqf5X9DCi9v%2Fm10vvcrj6U09uHsg%2F0Ke%2F29invHSBfX7VJ%2BTAv99nwkcNvfNd82xjlI%2F4%2FSu%2BrLyi3%2FObXaPaLTJb0b6xlBfCX%2BDHKMLqgAOoieZk65HLlmXXU56PLK%2FRmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy%2FKbYvMvxsYvGRLrPu%2FBCWegef0l%2BcNcmpeGP%2FqIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI%2BK5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12%2FoayC49HL1E%2Fs9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW%2BS5kUqBYFHUgcViJEbamxB9uT%2FROLFE8QLBIegdsp5%2BnaSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h%2FcO3K%2Bs89eFTJuceP%2Bdft9utd0xUFqDpyj3kqh3K1%2BH6uhrlzX%2FZctHQEckuSNLhJG8MjPTGCNLRbwWDZH%2BFr%2F6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg%2B7W0766bZL10emd02YWJ7G%2BNFAYSwiCGdcx%2BZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2%2Fh3kG0RVlFY12UNylYiiCAo%2FgZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF%2BJnjK%2F5%2BbriipGXeqiuf3np9%2B4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo%2BIr4APJkRy7enY7QbIl%2FLTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9%2Fn1P7N541ards5oXtJ3REgwFWsOdE%2Fb9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn%2BqLI0JEDyCnCdwXdyjui3uFjR%2FVNMjMIUk6ao6YiGZWHZ0i%2FDX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR%2FdrXNlOGl7yiCq4Y2Z%2BzTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF%2Bvigy%2Byj67YoVdz%2FPrAF7Z8wo%2F9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1%2F72IW5Tjkmn%2F3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr%2F%2B2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1%2F3PjAIiZPQYprQ3RWgnr3Xd88KXuOu%2FGW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD%2BWfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw%2FxTGK3kxc9YlKPgt969IarH3%2FwwP4nFG9dY%2BPEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79%2FOej1B9UBL%2B6NL0aoFonqQehRdg4ip%2FLxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y%2BTuAYvg1fgUA6eAP5Nrjj4L8IMFW%2BuJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as%2BEb9qmXpVwDBeWUH%2BLLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6%2F%2FYLru%2Bj0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2%2F%2FheIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj%2FJFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9%2FghbKIUT%2BhoLlq5jiPvcYqZDnXNrC6WKXZGjNP8%2BVlGYAXOBfY556p5%2BZaodTT0KC89ZE%2BUXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia%2BgnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0%2BBgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9%2Bd%2BCzzzahATIdd1Du%2FG5GoOPWnR9%2BofQoyl1qHsRXeDuriLez36eUA%2BdUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL%2BuB%2BVocqr6Rhetkf5tfL7vfj3qKxH%2BSMavZf%2B%2BVuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW%2BTMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO%2FyqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1%2FZBiOG0%2BWqzyvXWXXHDpANrEwNWGNfM3DSi%2FfHYJ%2Frbsp%2B8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv%2FagMUij5c2Vc18rz5%2FNZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH%2FaTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1%2FRVfoo3fmXTau%2BubIbfnTo2vnNjQ0TVjXsWQjbb4%2BhL9FfuGvkV%2BcNqai1JldVTJn7srmu%2B7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv%2FLO%2FazH5ASbVEBr5VQ%2BUtQfAPb2jbbEazY1vfvCE6Xna%2BkHfxhi6RUj001a%2BkAasPTikemClt4lAX%2B3T%2BGCYcUDmqJ%2FlKrwqwogTCEpQjeUQBBOgS2RydU1JDM%2FP2g3GoNBuabG7%2FGMKZPlsC%2FfW50fjVVXsyDp7OxQNJZtNo6aSoF3p%2BS0NFDHPHgbYiBJgQZGv%2FERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19%2F03NdfPRtr2nHzvKc%2Femdx%2Fd3LDyM4XkaJq%2Bcfm%2FbY8bqFq1fv6FyOvX%2B1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T%2F05acLVrNap952xtZhP0xWx0%2B0iY%2BfnCrjtT1FbQ2389oqStRWanr34n%2BeflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA%2Fy5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ%2BkEY0tOTnr6qEKLpcQNmZh%2BSJ2ImdjppB56CnnSKS02%2BRpiJifBU2MEnYC8izsQ2clwI9I%2B1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5%2F0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n%2Fm5lz4fKh8pLyPrJR8gyV%2Bhdtuva4%2FMv2Lj1ih27%2Blg74MwMf2tPV9%2FaEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS%2BgUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2%2BK1tf%2FPFpXNzVaf2ojO%2BKOwcEvTpva%2FPOG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx%2FC%2BzhmCNsJ9hQWsvOLVn0n5GBm1eUrt%2FzK5jR21o%2FOiJKy9AhwzKa%2F6alefjSoYJlXV2dVyL7IwUqpp%2BQes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np977PkV5qi9zXh%2BBQ8XJOgmziYWsLhqD%2B1vHQZzli2Dxi8VWsCcbXDIRM6dEpOdxEnL%2BCQocxLLTDtnDWdWTT4Wyh0nAU7ot8Herhf%2F%2FuZLf5xv0ulUfvGjOONEDrXMYEgzK%2BCtE9qVsXpQVixvbB7mnLQ8CVqeut5Qc%2F0zNdcJKk9oH6byMk5M5VGJGk2mO108BE7wQmekxuJwGFF%2Bvs6WAeDL0umKLHa6drMgI7HQX0YznaWSNBddcwhCLotpRQ5tBcd%2BThplmiAy%2BBMMx2M6XcOLuERnVGvx%2B3WnH9vn31Wm9Cv3oTPQhPGbvaRDW9Q9dstdd%2FXVrfR7t8jpaBvqQuejTSZZXeCR145%2B8%2B1PDivZbnPyN%2BhT3SphMXhgNARhQWRMoMKEHQ6%2FX19RkWu3V%2BXr9aEchzvgiMYCATCbfxaNmc3YJNDOmfLEZnDT4VwQvFNiQupwHj45Cp00iOdT56kG4bniI7dDo6KTeT2fSk%2BLtyhf7dl5pPfHLSgb4QUvT7nsi2%2BR%2BbhTt2fL%2BU90tDx99FwN5Pu4fbWMBnC3%2FZprdiD9%2FciByqY1XcvYaf26naXlbOCeHGf7BhavuJhFHD0h%2FFXwSAVgZP0Zi5ozAMh6jE0ZWF4vsh39sg5pyx2NKqQzEZ2XGU%2BdFNAgrdc1Ne977elTUafn6kbhr2ed0XJ29tMLqh5sYBENqFX4M4lKD8Q9ehmS1eqmkUWyR8ay7CDxvRTYHVKNZ7qk8YhEdy1YcOklCy%2B67Pqa0tKaiorSGvGlCzavv%2BiCDZu7ykKhsrKqKkDwa%2BHPgkEygQuqIm4KNEUEQjLdBhvobPTrYvM6MzavFyCQ9fpZmoNENQebXw6qkISXvbF5mNVHiE23yjF6xRM27knfvXTUtKZoET%2B%2FfAk7F%2Buray7vKyjOr%2BKHAr4bGHqI3IN7%2BG5S%2BAS7SU0nbeih999Xlbp%2FqtQllG7Sj%2Fp4jIw7kiaIOqTTySBou5KZB5gLq7jGWhvCumKTs7N6sN5L%2Bp1zkG2h8t3HkHQFCVwRmQhIknSCRC8wvD8WUrffQHtNwbWDkz3iI84XlPdRySFI3luLeVIwEfnuWhIEtNuffHstwOzeZBl%2F%2BgzwRczUIGsiggSSZNFlkHRtI0Z%2BoT8E%2BbOoWSnwxY%2FoUzVPdILhSZyRP8ezp2Vz%2BE4SGJn%2FndpNDXwrMFMaMYjsRi%2BqN9Luoz60qB5QH885cqO31JNM8Ua1DBJFgVlJkOt5SRihMGIaeQcIpN7Ap91gROGgt0eWkkvbi2wunXrfKIyCdLA9wszuRplAgHssUq3uc6%2FavnXvvku37cGf9hzou3r%2FLbcAELbTizQXhfm75mXsYF6m6kEvys4gbKuXAofMQuS5LUhtbJnmP9AJy8gdX3yp56m7v%2BAps89kZzPacGPqPmctKUf%2BVkA7vpHbtCsijrgDV9RLQAg9pa0JI9VZmsxW0W%2FVN5vqlE12xKZeO24nRzp2bfoHPRPEf7z2SBs4vvHEBm8ApCxj83oe25YVSSeAEcaCFtqW8B8j5EX48mN%2F%2FIKMjge2AeK7BW0S%2B6EYdkQaJaL3%2BXI8RW5ntmywWIrSafaLika5cnP12dklBpdLzpRy83Knx0heRt66PJxOMvMy82yFPiiEabFCndlkMzXHbNp2YiNNoxZenyxzKUghO%2FCtQOhvro%2FH5DgKdA420DrVfS4oWELdb%2F7qWvq7BuL7XXhXXu9CVyrtGKN5yj0hZNq9ecn93ynPj9q6VMBLtvjQpG%2Be6ps7ebnwys5f3ucNFDzwTXgIxqK0Tx5wFVff9zVyT%2F%2FQ4%2BXsWgfzjp%2B0n6MTYDbdHRriMbs%2FSh7wQyNfQ04lboD45x8nfd7MPgcMBhzF34tPQRpYGbthFXUmWnBEBixim90k62TJikTRaiW6PJLPDTwBLSYu4RpNwn%2B8DhpfWI1CfA%2BzWrZnHP5%2BzefKBrTh0zXKHkmuzliH39q3rwfXHT%2FUN3Nu1gWuZ9Wn05u0pyuGRuJWn14KAMTT4QTpzcPp0q6k3PF0dS8BvtMDAcsjIIiIQGKXQLYPAt8FgTU2uvZ8EQDruB3sL%2FEV7krVDmZIWNNupYoPkxTdQ3NGKoYYgS4mKQ4q76sKS0JxHADfqZupKbq4gq9wuaT6%2FwCVeR0IAAAAAQAAAAEZmiehT9dfDzz1AAkIAAAAAADJQhegAAAAAMnoSqH7DP2oCo0IjQABAAkAAgAAAAAAAHgBY2BkYODo%2FbuCgYGr9zfPv0quXqAIKrgJAJZXBsIAeAFtkQOsGEEQhv%2Fbnd272rZtG0Ft27ZtW1G9dYMiamrbZlgrqN17M89K8uVfTna%2FoRs4AwCUGVBCU0zQl7DAlEIZWoPOfhXUs0BbVQAL1CG0ZepQd9STPdUW9dQ61FGN%2BU5LpOW1pswUpmU0hZj%2BTGOmWnQ2lPNyV2rEoO%2FA%2BmUw0CwATG8cNjkwyXzEYZrG9Of5NUyy%2BXBY7Q4Hm9a8tgCH%2FWU4bOcwPfmsjc7GvDcYPWk7StjU2G8qAf5xwHQE6D%2BzHRXUbqzi96bmrEQNEeim4V965jWnB%2Bho0sNRHnTn7E5H0V3nQAlaAGsawqkxWKfGhDPoO2Ts%2FGdwsk5fIecd011vh9O%2FOaegHO9toBWAfYLM5JBSxvoNquliyEeDvUucbeXvMd55vIqRtTGMJTnzAkP5bdnsXvTX6VGOPkbfYe%2ByRgh%2F6xHoLms6QDmmlvyFPThTB2PEtbczfMbr3XUu1JD7fmqUjaYre68jzpPD3wJIH6QH0RyQ5L6Ui%2FGeGFqDOZLiPj7iXnpkDsKJ5%2BTwO3LmEe8JYecb2fcazoXMC%2FEd4z0J7EFS3MdH3EuPJJX07gom%2Bff4%2FDMcpS1ee85bBLQNGO84cgiqPerpVcghUBEeK%2FS1jzBBfUZbwUv5X%2F7bkOlslqCEwJ5TBw4lBFsBJdRuHA4vYk%2Fown8RLYvLrQAAeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe%2FgY%2F%2Begvq0YCAEoCNa1n%2BKVyTUl3Q0uIhoe%2B3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK%2F7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De%2Fxu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p%2Ff6oI%2F6pC%2FKSxvf9F0%2F1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu%2BkbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd%2BR3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI%2FWN54IuxXFS97oH58%2BMBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd%2B0lSVW5nNIL3nF6389h%2BY5NG3Thja0oQ1taEMb2tCGNrQn%2BQwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5%2F5wle%2B8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In%2FHCuNDGO%2BNOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I%2FD4%2FA4PA6Pw%2BPwODwOj8M%2Ff7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM%2B7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6%2Fh%2BP6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q%2F6H%2F0H%2B4P9yfPz82bdm2Y9ee%2FT355bS3%2FdivDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M%2FRm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP%2F84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y%2BrH1I%2BpH1M%2Fpn5M%2FZh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0%2F%2B9sBOGnTDshOF%2BDndyXG7k7vfh9%2Bn35fft978Thp2wKuqqqKtarmq58cYbb7zzzjvvfPDBBx988sknn3zxxRdfPHnyVPip8FPhp8JPhZ8KP78czLdxBDAMAMFc%2FbdAk4AERoMS5CpQOW82uWyPHexkJzvZyU52spOd7GQnu9jFLnaxi13sYhe72MVudrOb3exmN7vZzW52s8EGG2ywwQYbbLDBBnvZy172spe97GUve9nLJptssskmm2yyySabbLHFFltsscUWW2yxxX6%2B7P%2BrH%2Fqtf6%2B2Z3u2Z3u2Z3u2Z3u2Z3s%2BO66jKoYBGASA%2FiUFeLO2tqfgvhIgVkOshvj%2F8f%2FjF8VqiL8dqyG%2Bd4klllhiiSWWWGKJJY444ogjjjjiiCOO%2BPua0gPv7paRAHgBLcEDlNxQAADArI3Ydv7Vtm3btm3btm3btm3bD7VvBoIgLXVVqCf0ztXT9dzd3j3cvcX90CN5Snmae%2Fp45np2e356gbeH94HP8Q3x3feH%2FX38NwJwoHigQ2Ba4GBQCK4NfgxVDE0OnQr7w1nCI8P7wi8jdqR4ZGzkRDQSLRmdH%2F0UqxTrEVsbux%2FPHe8b3xh%2FlgglzESJRJfE6MS6ZChZJzkj%2BRouCA9GJKQuMhI5hsZRHR2A7kZ%2FYZWxldhtPDPeFd%2BIPybyE0OIy2SIrEy2IneSX8mvFKB6UpfodPQYeiOTjmnK3GOzsCPYpexaLjdXiRvBHeJ%2B8BX5Lvxe%2FqOACmWEnsJ60SsyYjqxiLhE3CoeE6%2BLL8RvUlRqJXWThkszpJXSbjkq83JaOZ9cXm4gd5IXKZACK4qSSSmiVFWmq0lVUtOr%2BdXyagO1oxbRSM3UsmnFtOpaC62nNkqbo7M60HPppfXaemu9j77X4IwUI49RxqhrtDWOGzeM92Y985lFWWWtcdZia4d10%2FpiU3YZu6%2B91j7rME5xp5szGVAgDcgBioDhYDpYDjaDE%2BAmeAW%2Bp8R%2FA5ajfCcAAAABAAAA3QCKABYAWAAFAAIAEAAvAFwAAAEAAQsAAwABeAF9jgNuRAEYhL%2FaDGoc4DluVNtug5pr8xh7jj3jTpK18pszwBDP9NHTP0IPs1DOexlmtpz3sc9iOe9nmddyPsA8%2BXI%2BqI1COZ%2FkliIXhPkiyDo3vCnG2CaEn0%2B2lH%2BgmfIvotowZa3769ULZST4K%2BcujqTb%2Fj36S4w%2FQmgDF0tWvalemNWLX%2BKSMBvYkhQSLG2FZR%2BafmERIsqPpn7%2ByvxjfMlsTjlihz3OuZE38bTtlAAa%2FTAFAHgBbMEDjJYBAADQ9%2F3nu2zbtm3b5p9t17JdQ7Zt21zmvGXXvJrZe0LA37Cw%2F3lDEBISIVKUaDFixYmXIJHEkkgqmeRSSCmV1NJIK530Msgok8yyyCqb7HLIKZfc8sgrn%2FwKKKiwIooqprgSSiqltDLKKqe8CiqqpLIqqqqmuhpqqqW2Ouqqp74GGmqksSaaaqa5FlpqpbU22mqnvQ466qSzLrrqprs9NpthprNWeWeWReZba6ctQYR5QaTplvvhp4VWm%2BOyt75bZ5fffvljk71uum6fHnpaopfbervhlvfCHnngof36%2BGappx57oq%2BPPpurv34GGGSgwTYYYpihhhthlJFGG%2BODscYbZ4JJJjphoykmm2qaT7445ZkDDnrujRcOOeyY46444qirZtvtnPPOBFG%2BBtFBTBAbxAXxQYJC7rvjrnv%2FxpJXmpPDXpqXaWDg6MKZX5ZaVJycX5TK4lpalA8SdnMyMITSRjxp%2BaVFxaUFqUWZ%2BUVQQWMobcKUlgYAHQ14sAAAeAFFSzVCLEEQ7fpjH113V1ybGPd1KRyiibEhxt1vsj3ZngE9AIfgBmMR5fVk8qElsRjHOHAYW%2BQwyumxct4bKxXkWDEvx7JjdszQNAZcekzi9Zho8oV8NCbnIT%2FfEXNRJwqmlaemnQMbN8E1OE7Mzb%2FP%2F8xzKZrEMA2hl3rQATa0Uxs2bN%2B2f8M2AEpwj5yQBvklvJ3AqRcEaMKrWq%2F19eWakl7NsZbyJoNblqlZc7KywcRbRnBjc00FeF6%2Fenoi05EcG62tsXhkPcdk87BHVC%2BZXleUPrOsUHaUI2tb4y%2F8OwbsTEAJAA%3D%3D%29%20format%28%22woff%22%29%7D%2A%7Bbox%2Dsizing%3Aborder%2Dbox%7Dbody%7Bpadding%3A0%3Bmargin%3A0%3Bfont%2Dfamily%3A%22Open%20Sans%22%2C%22Helvetica%20Neue%22%2CHelvetica%2CArial%2Csans%2Dserif%3Bfont%2Dsize%3A16px%3Bline%2Dheight%3A1%2E5%3Bcolor%3A%23606c71%7Da%7Bcolor%3A%231e6bb8%3Btext%2Ddecoration%3Anone%7Da%3Ahover%7Btext%2Ddecoration%3Aunderline%7D%2Epage%2Dheader%7Bcolor%3A%23fff%3Btext%2Dalign%3Acenter%3Bbackground%2Dcolor%3A%23159957%3Bbackground%2Dimage%3Alinear%2Dgradient%28120deg%2C%23155799%2C%23159957%29%3Bpadding%3A1%2E5rem%202rem%7D%2Eproject%2Dname%7Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A%2E1rem%3Bfont%2Dsize%3A2rem%7D%2Eproject%2Dtagline%7Bmargin%2Dbottom%3A2rem%3Bfont%2Dweight%3A400%3Bopacity%3A%2E7%3Bfont%2Dsize%3A1%2E5rem%7D%2Eproject%2Dauthor%2C%2Eproject%2Ddate%7Bfont%2Dweight%3A400%3Bopacity%3A%2E7%3Bfont%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</head>
<body>
<section class="page-header">
<h1 class="title toc-ignore project-name">Network optimization Project</h1>
<h4 class="author project-author">Afsar Ali</h4>
</section>
<div id="TOC" class="toc">
<ul>
<li><a href="#objective">Objective</a></li>
<li><a href="#loading-and-cleaning-the-data">Loading and cleaning the data</a></li>
<li><a href="#netowrk-map">1 Netowrk Map</a></li>
<li><a href="#netowrk-map-with-fastest-route">2 Netowrk Map with Fastest Route</a></li>
<li><a href="#nd-plan-considering-cost-and-constraints">3 - 2nd Plan considering cost and constraints</a><ul>
<li><a href="#graph-the-min-cost-solution">Graph the Min Cost Solution</a></li>
</ul></li>
<li><a href="#last-plan-max-flow-with-many-constraints">4 - Last Plan Max Flow with many constraints</a><ul>
<li><a href="#graph-max-cost-solution">Graph Max Cost Solution</a></li>
</ul></li>
<li><a href="#last-plan-testing-max-flow-by-relaxing-some-constraints-to-congos">4 - Last Plan Testing Max Flow by relaxing some constraints to congos</a><ul>
<li><a href="#graph-max-cost-solution-1">Graph Max Cost Solution</a></li>
</ul></li>
<li><a href="#appendix-for-additional-igraph-data-structure">Appendix for additional igraph data structure</a><ul>
<li><a href="#fuction-to-run-matrix">Fuction to run Matrix</a></li>
</ul></li>
<li><a href="#using-i-graph-to-calculate-min-and-max-didnt-work">Using I graph to calculate min and max (didnt work)</a></li>
</ul>
</div>
<section class="main-content">
<div id="objective" class="section level1">
<h1>Objective</h1>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Course: BUAN 5260</span>
<span class="co"># Title: Week 4-Network optimization</span>
<span class="co"># Purpose: USE IFRC Information information to model scenarios </span>
<span class="co"># and make Recommendation Plans</span>
<span class="co"># Author: Afsar Ali</span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Clear packages </span>
<span class="cf">if</span>(<span class="kw">is.null</span>(<span class="kw">sessionInfo</span>()<span class="op">$</span>otherPkgs) <span class="op">==</span><span class="st"> </span><span class="ot">FALSE</span>)<span class="kw">lapply</span>(
<span class="kw">paste</span>(<span class="st">"package:"</span>, <span class="kw">names</span>(<span class="kw">sessionInfo</span>()<span class="op">$</span>otherPkgs), <span class="dt">sep=</span><span class="st">""</span>),
detach, <span class="dt">character.only =</span> <span class="ot">TRUE</span>, <span class="dt">unload =</span> <span class="ot">TRUE</span>)
<span class="co"># Clear all data in environment</span>
<span class="kw">rm</span>(<span class="dt">list=</span><span class="kw">ls</span>(<span class="dt">all=</span><span class="ot">TRUE</span>))
<span class="co"># Load packages</span>
<span class="kw">library</span>(igraph)
<span class="kw">library</span>(lpSolve)
<span class="kw">library</span>(lpSolveAPI)
<span class="kw">library</span>(tidyverse)
<span class="kw">library</span>(magrittr)
<span class="kw">library</span>(data.table)
<span class="kw">set.seed</span>(<span class="dv">123</span>)</code></pre></div>
</div>
<div id="loading-and-cleaning-the-data" class="section level1">
<h1>Loading and cleaning the data</h1>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#load Data </span>
ifrc <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"5260_S18_Aiding_Africa_Data.csv"</span>, <span class="dt">skip =</span> <span class="dv">1</span>)
<span class="co">#Naming and Creating each table</span>
req_trv <-<span class="st"> </span>ifrc[<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>]
mdata <-<span class="st"> </span>ifrc[,<span class="dv">8</span><span class="op">:</span><span class="dv">12</span>]
req <-<span class="st"> </span>ifrc[<span class="dv">1</span><span class="op">:</span><span class="dv">9</span>,<span class="dv">14</span><span class="op">:</span><span class="dv">15</span>]
air_max <-<span class="st"> </span>ifrc[<span class="dv">1</span><span class="op">:</span><span class="dv">15</span>,<span class="dv">17</span><span class="op">:</span><span class="dv">19</span>]
truck_max <-<span class="st"> </span>ifrc[<span class="dv">1</span><span class="op">:</span><span class="dv">6</span>,<span class="dv">21</span><span class="op">:</span><span class="dv">23</span>]
<span class="co">#create nodes</span>
req<span class="op">$</span>Requirements <-<span class="st"> </span>req<span class="op">$</span>Requirements <span class="op">*-</span><span class="dv">1</span>
req<span class="op">$</span>City <-<span class="st"> </span><span class="kw">as.character</span>(req<span class="op">$</span>City)
nodes<-<span class="st"> </span><span class="kw">rbind</span>(<span class="st">'1'</span> =<span class="st"> </span><span class="kw">c</span>(<span class="st">'New York, NY'</span>, <span class="st">'500000'</span>), <span class="st">'2'</span> =<span class="st"> </span><span class="kw">c</span>(<span class="st">'Jacksonville, FL'</span>, <span class="st">'500000'</span>), req)
nodes<span class="op">$</span>Requirements <-<span class="st"> </span><span class="kw">as.integer</span>(nodes<span class="op">$</span>Requirements)
<span class="co"># Join the tables and create edges</span>
edges <-<span class="st"> </span>mdata <span class="op">%>%</span>
<span class="st"> </span><span class="kw">left_join</span>(req_trv, <span class="dt">by =</span> <span class="kw">c</span>(<span class="st">"Type.1"</span> =<span class="st"> "Type"</span>)) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">Time =</span> Distance <span class="op">/</span><span class="st"> </span>Speed) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">Cost =</span> Cost <span class="op">*</span><span class="st"> </span><span class="dv">1000</span>) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">left_join</span>(air_max, <span class="dt">by =</span> <span class="kw">c</span>(<span class="st">"From"</span> =<span class="st"> "From.1"</span>, <span class="st">"To"</span> =<span class="st"> "To.1"</span>)) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">left_join</span>(truck_max, <span class="dt">by =</span> <span class="kw">c</span>(<span class="st">"From"</span> =<span class="st"> "From.2"</span>, <span class="st">"To"</span> =<span class="st"> "To.2"</span>))
<span class="kw">colnames</span>(edges)[<span class="dv">3</span>] <-<span class="st"> 'Type'</span>
<span class="co">#create constraints</span>
edges<span class="op">$</span>Max.Airplanes <-<span class="st"> </span>edges<span class="op">$</span>Max.Airplanes <span class="op">*</span><span class="st"> </span>edges<span class="op">$</span>Capacity
edges<span class="op">$</span>Max.Trucks <-<span class="st"> </span>edges<span class="op">$</span>Max.Trucks <span class="op">*</span><span class="st"> </span>edges<span class="op">$</span>Capacity
edges<span class="op">$</span>Max.Airplanes[<span class="kw">is.na</span>(edges<span class="op">$</span>Max.Airplanes)] <-<span class="st"> </span><span class="dv">0</span>
edges<span class="op">$</span>Max.Trucks[<span class="kw">is.na</span>(edges<span class="op">$</span>Max.Trucks)] <-<span class="st"> </span><span class="dv">0</span>
edges<span class="op">$</span>Max <-<span class="st"> </span>edges<span class="op">$</span>Max.Trucks <span class="op">+</span><span class="st"> </span>edges<span class="op">$</span>Max.Airplanes
edges<span class="op">$</span>Max[<span class="kw">is.na</span>(edges<span class="op">$</span>Max)] <-<span class="st"> </span><span class="dv">0</span>
<span class="co">#Network ID</span>
edges<span class="op">$</span>ID <-<span class="st"> </span><span class="kw">paste</span>(edges<span class="op">$</span>From, edges<span class="op">$</span>To, <span class="dt">sep =</span> <span class="st">' > '</span>)</code></pre></div>
</div>
<div id="netowrk-map" class="section level1">
<h1>1 Netowrk Map</h1>
<ul>
<li>Make sense all the Air Routes are the Fastest</li>
</ul>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">net <-<span class="st"> </span><span class="kw">graph_from_data_frame</span>(<span class="dt">d=</span>edges, <span class="dt">vertices=</span>nodes, <span class="dt">directed=</span>T)
net<span class="op">$</span>layout <-<span class="st"> </span><span class="kw">matrix</span>(<span class="kw">c</span>(<span class="op">-</span><span class="dv">800</span>, <span class="op">-</span><span class="dv">800</span>,
<span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>,
<span class="dv">800</span>, <span class="dv">800</span>, <span class="dv">800</span>,
<span class="dv">225</span>, <span class="dv">125</span>,
<span class="dv">300</span>, <span class="dv">250</span>, <span class="dv">200</span>, <span class="dv">150</span>, <span class="dv">100</span>, <span class="dv">50</span>,
<span class="dv">250</span>, <span class="dv">175</span>, <span class="dv">100</span>), <span class="dt">nc =</span> <span class="dv">2</span>)
<span class="co">#Set Weight for Edges</span>
<span class="kw">E</span>(net)<span class="op">$</span>weight =<span class="st"> </span><span class="kw">E</span>(net)<span class="op">$</span>Time
<span class="co">#Create a Unique col</span>
edges<span class="op">$</span>ID <-<span class="st"> </span><span class="kw">paste</span>(edges<span class="op">$</span>From, edges<span class="op">$</span>To, <span class="dt">sep =</span> <span class="st">' > '</span>)
<span class="co">#Add route attribute</span>
<span class="kw">V</span>(net)<span class="op">$</span>route <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"From"</span>,<span class="st">"From"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>)
<span class="kw">V</span>(net)<span class="op">$</span>color <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"gold"</span>,<span class="st">"green"</span>)[<span class="dv">1</span><span class="op">+</span>(<span class="kw">V</span>(net)<span class="op">$</span>route<span class="op">==</span><span class="st">"From"</span>)]
<span class="co">#look at the data</span>
<span class="kw">glimpse</span>(edges)</code></pre></div>
<pre><code>## Observations: 30
## Variables: 12
## $ From <chr> "New York, NY", "New York, NY", "New York, NY", ...
## $ To <chr> "Lusaka, Zambia", "Libreville, Gabon", "Nairobi,...
## $ Type <chr> "Airplane", "Ship", "Airplane", "Airplane", "Shi...
## $ Distance <int> 8098, 6024, 8050, 7041, 6526, 4172, 7944, 6329, ...
## $ Cost <dbl> 50000, 30000, 55000, 45000, 30000, 32000, 57000,...
## $ Capacity <dbl> 150.0, 240.0, 150.0, 150.0, 240.0, 240.0, 150.0,...
## $ Speed <int> 400, 35, 400, 400, 35, 35, 400, 35, 400, 400, 35...
## $ Time <dbl> 20.2450, 172.1143, 20.1250, 17.6025, 186.4571, 1...
## $ Max.Airplanes <dbl> 45000, 0, 75000, 75000, 0, 0, 75000, 0, 105000, ...
## $ Max.Trucks <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ Max <dbl> 45000, 0, 75000, 75000, 0, 0, 75000, 0, 105000, ...
## $ ID <chr> "New York, NY > Lusaka, Zambia", "New York, NY >...</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Get some colours in to visualise routes</span>
<span class="kw">E</span>(net)<span class="op">$</span>color[<span class="kw">E</span>(net)<span class="op">$</span>Type <span class="op">==</span><span class="st"> 'Truck'</span>] <-<span class="st"> 'saddlebrown'</span>
<span class="kw">E</span>(net)<span class="op">$</span>color[<span class="kw">E</span>(net)<span class="op">$</span>Type <span class="op">==</span><span class="st"> 'Airplane'</span>] <-<span class="st"> 'forestgreen'</span>
<span class="kw">E</span>(net)<span class="op">$</span>color[<span class="kw">E</span>(net)<span class="op">$</span>Type <span class="op">==</span><span class="st"> 'Ship'</span>] <-<span class="st"> 'royalblue'</span>
<span class="co">#Plot Network Map</span>
<span class="kw">plot</span>(net, <span class="dt">edge.arrow.size=</span>.<span class="dv">3</span>, <span class="dt">edge.label =</span> <span class="kw">round</span>(<span class="kw">E</span>(net)<span class="op">$</span>Time, <span class="dv">2</span>),
<span class="dt">edge.width =</span> <span class="dv">10</span><span class="op">*</span><span class="kw">E</span>(net)<span class="op">$</span>Time<span class="op">/</span><span class="kw">max</span>(<span class="kw">E</span>(net)<span class="op">$</span>Time),
<span class="dt">vertex.size=</span><span class="dv">25</span>)</code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="netowrk-map-with-fastest-route" class="section level1">
<h1>2 Netowrk Map with Fastest Route</h1>
<p>-Bottlenecks are on Dakar, Senegal, Libreville, Garbon, Luanda, Angola -Quickest route is 20.60 hours From New York to Ndjamena, Chad +New York, NY > Kosongo, D.R. Congo = 21.04<br />
+New York, NY > Ndjamena, Chad = 20.60<br />
+New York, NY > Niamey, Niger = 22.76 +Jacksonville, FL > Kosongo, D.R. Congo = 21.15<br />
+Jacksonville, FL > Ndjamena, Chad = 20.71<br />
+Jacksonville, FL > Niamey, Niger = 22.87</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#Create Table for shortest time</span>
distMatrix <-<span class="st"> </span><span class="kw">shortest.paths</span>(net, <span class="dt">v=</span><span class="kw">V</span>(net), <span class="dt">to=</span><span class="kw">V</span>(net))
<span class="kw">as.data.frame</span>(distMatrix)</code></pre></div>
<pre><code>## New York, NY Jacksonville, FL Dakar, Senegal
## New York, NY 0.0000 35.3125 55.8850
## Jacksonville, FL 35.3125 0.0000 55.9925
## Dakar, Senegal 55.8850 55.9925 0.0000
## Libreville, Gabon 43.4050 43.5125 53.7600
## Luanda, Angola 48.8250 48.9325 69.5050
## Khartoum, Sudan 17.6025 17.7100 38.2825
## Lusaka, Zambia 20.2450 19.8600 43.2950
## Nairobi, Kenya 20.1250 19.9025 39.4300
## Niamey, Niger 22.7650 22.8725 33.1200
## Kosongo, D.R. Congo 21.0450 21.1525 41.7250
## Ndjamena, Chad 20.6025 20.7100 41.2825
## Libreville, Gabon Luanda, Angola Khartoum, Sudan
## New York, NY 43.4050 48.8250 17.6025
## Jacksonville, FL 43.5125 48.9325 17.7100
## Dakar, Senegal 53.7600 69.5050 38.2825
## Libreville, Gabon 0.0000 57.0250 25.8025
## Luanda, Angola 57.0250 0.0000 31.2225
## Khartoum, Sudan 25.8025 31.2225 0.0000
## Lusaka, Zambia 30.8150 29.6450 5.3075
## Nairobi, Kenya 26.9500 30.5050 6.1675
## Niamey, Niger 20.6400 36.3850 5.1625
## Kosongo, D.R. Congo 29.2450 27.7800 3.4425
## Ndjamena, Chad 28.3000 34.2225 3.0000
## Lusaka, Zambia Nairobi, Kenya Niamey, Niger
## New York, NY 20.2450 20.1250 22.7650
## Jacksonville, FL 19.8600 19.9025 22.8725
## Dakar, Senegal 43.2950 39.4300 33.1200
## Libreville, Gabon 30.8150 26.9500 20.6400
## Luanda, Angola 29.6450 30.5050 36.3850
## Khartoum, Sudan 5.3075 6.1675 5.1625
## Lusaka, Zambia 0.0000 4.5900 10.1750
## Nairobi, Kenya 4.5900 0.0000 6.3100
## Niamey, Niger 10.1750 6.3100 0.0000
## Kosongo, D.R. Congo 1.8650 2.7250 8.6050
## Ndjamena, Chad 5.2750 4.4200 8.1625
## Kosongo, D.R. Congo Ndjamena, Chad
## New York, NY 21.0450 20.6025
## Jacksonville, FL 21.1525 20.7100
## Dakar, Senegal 41.7250 41.2825
## Libreville, Gabon 29.2450 28.3000
## Luanda, Angola 27.7800 34.2225
## Khartoum, Sudan 3.4425 3.0000
## Lusaka, Zambia 1.8650 5.2750
## Nairobi, Kenya 2.7250 4.4200
## Niamey, Niger 8.6050 8.1625
## Kosongo, D.R. Congo 0.0000 6.4425
## Ndjamena, Chad 6.4425 0.0000</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">all_shortest_paths</span>(net, <span class="kw">c</span>(<span class="st">"New York, NY"</span>, <span class="st">'Jacksonville, FL'</span>),
<span class="kw">c</span>(<span class="st">'Kosongo, D.R. Congo'</span> ,<span class="st">"Ndjamena, Chad"</span>,
<span class="st">'Niamey, Niger'</span>))<span class="op">$</span>res[[<span class="dv">1</span>]]</code></pre></div>
<pre><code>## + 3/11 vertices, named, from 74ae952:
## [1] New York, NY Khartoum, Sudan Niamey, Niger</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># New York, NY > Khartoum, Sudan > Niamey, Niger </span>
<span class="kw">distances</span>(net, <span class="kw">c</span>(<span class="st">"New York, NY"</span>, <span class="st">'Jacksonville, FL'</span>),
<span class="kw">c</span>(<span class="st">'Kosongo, D.R. Congo'</span> ,<span class="st">"Ndjamena, Chad"</span>,
<span class="st">'Niamey, Niger'</span>))</code></pre></div>
<pre><code>## Kosongo, D.R. Congo Ndjamena, Chad Niamey, Niger
## New York, NY 21.0450 20.6025 22.7650
## Jacksonville, FL 21.1525 20.7100 22.8725</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Kosongo, D.R. Congo Ndjamena, Chad Niamey, Niger</span>
<span class="co">#New York, NY 21.04 20.60 22.76</span>
<span class="co">#Jacksonville, FL 21.15 20.71 22.87</span>
shortMatrix<-<span class="st"> </span><span class="kw">mst</span>(net, <span class="dt">weights =</span> <span class="ot">NULL</span>)
shortMatrix</code></pre></div>
<pre><code>## IGRAPH 74d2300 DNW- 11 10 --
## + attr: layout (g/n), name (v/c), Requirements (v/n), route (v/c),
## | color (v/c), Type (e/c), Distance (e/n), Cost (e/n), Capacity
## | (e/n), Speed (e/n), Time (e/n), Max.Airplanes (e/n), Max.Trucks
## | (e/n), Max (e/n), ID (e/c), weight (e/n), color (e/c)
## + edges from 74d2300 (vertex names):
## [1] New York, NY ->Khartoum, Sudan
## [2] Jacksonville, FL ->Khartoum, Sudan
## [3] Libreville, Gabon->Niamey, Niger
## [4] Khartoum, Sudan ->Niamey, Niger
## [5] Dakar, Senegal ->Niamey, Niger
## + ... omitted several edges</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot</span>(shortMatrix, <span class="dt">edge.arrow.size=</span>.<span class="dv">2</span>, <span class="dt">edge.label =</span> <span class="kw">round</span>(<span class="kw">E</span>(net)<span class="op">$</span>Time, <span class="dv">2</span>))</code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="nd-plan-considering-cost-and-constraints" class="section level1">
<h1>3 - 2nd Plan considering cost and constraints</h1>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#Part 3 - Min Cost</span>
<span class="co"># Set up model</span>
min_cost <-<span class="st"> </span><span class="kw">make.lp</span>(<span class="dv">0</span>, <span class="dv">30</span>)
## Set objective fn
obj_fn <-<span class="st"> </span><span class="kw">as.integer</span>(<span class="kw">as.vector</span>(edges<span class="op">$</span>Cost))
<span class="kw">set.objfn</span>(min_cost, obj_fn)
<span class="co"># Set up constraints</span>
<span class="co">#Input</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">150</span>, <span class="dv">240</span>, <span class="dv">150</span>, <span class="dv">150</span>, <span class="dv">240</span>, <span class="dv">240</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ), <span class="st">"="</span>, <span class="dv">500000</span>) <span class="co">#NY</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">150</span>, <span class="dv">240</span>, <span class="dv">150</span>, <span class="dv">150</span>, <span class="dv">240</span>
, <span class="dv">240</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ), <span class="st">"="</span>, <span class="dv">500000</span>) <span class="co">#FL</span>
<span class="co">#City Requirements </span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>(<span class="op">-</span><span class="dv">150</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="op">-</span><span class="dv">150</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">150</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">150</span>, <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">150</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ), <span class="st">"="</span>, <span class="op">-</span><span class="dv">150000</span>) <span class="co">#Lusaka</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> ,<span class="op">-</span><span class="dv">240</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="op">-</span><span class="dv">240</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="fl">17.7</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="fl">17.7</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ), <span class="st">"="</span>, <span class="op">-</span><span class="dv">100000</span>) <span class="co">#Libreville</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="op">-</span><span class="dv">150</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="op">-</span><span class="dv">150</span>, <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="dv">150</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
,<span class="dv">150</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">150</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ), <span class="st">"="</span>, <span class="op">-</span><span class="dv">120000</span>) <span class="co">#Nairobi</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="op">-</span><span class="dv">150</span>, <span class="dv">0</span> , <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="op">-</span><span class="dv">150</span>, <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="dv">150</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> ,<span class="dv">150</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">150</span>, <span class="dv">0</span> , <span class="dv">0</span> ), <span class="st">"="</span>, <span class="op">-</span><span class="dv">90000</span>) <span class="co">#Khartoum</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="op">-</span><span class="dv">240</span> , <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span>
,<span class="op">-</span><span class="dv">240</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> ,<span class="fl">17.7</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="fl">17.7</span> , <span class="dv">0</span> ), <span class="st">"="</span>, <span class="op">-</span><span class="dv">130000</span>) <span class="co">#Luanda</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> , <span class="dv">0</span> ,<span class="op">-</span><span class="dv">240</span>,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> , <span class="dv">0</span>
,<span class="op">-</span><span class="dv">240</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> ,<span class="fl">17.7</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="fl">17.7</span> ), <span class="st">"="</span>, <span class="op">-</span><span class="dv">50000</span>) <span class="co">#Dakar</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> , <span class="dv">0</span>
,<span class="dv">0</span> ,<span class="op">-</span><span class="dv">150</span> , <span class="dv">0</span> ,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="dv">150</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ), <span class="st">"="</span>, <span class="op">-</span><span class="dv">100000</span>) <span class="co">#Niamey Air routes only</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> , <span class="dv">0</span>
,<span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="fl">17.7</span>,
<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="op">-</span><span class="fl">17.7</span>, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ), <span class="st">"="</span>, <span class="op">-</span><span class="dv">180000</span>) <span class="co">#Kosongo</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> , <span class="dv">0</span>
,<span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="op">-</span><span class="dv">150</span> ,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="op">-</span><span class="fl">17.7</span>), <span class="st">"="</span>, <span class="op">-</span><span class="dv">80000</span>) <span class="co">#Ndjamena</span>
<span class="co">#additional constraint</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> , <span class="dv">0</span>
,<span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="dv">1</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="dv">1</span> ,<span class="dv">1</span>), <span class="st">"<="</span>, <span class="dv">840</span>) <span class="co">#Ndjmamena Truck constraint</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> , <span class="dv">0</span>
,<span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="dv">1</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ), <span class="st">"<="</span>, (<span class="dv">200</span>)) <span class="co">#200 flights from tLusaka-Ndjmamena constrain</span>
<span class="kw">add.constraint</span>(min_cost, <span class="kw">c</span>( <span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> ,<span class="dv">0</span> , <span class="dv">0</span>
,<span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span>
, <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> , <span class="dv">0</span> ,<span class="dv">1</span> , <span class="dv">0</span> , <span class="dv">0</span> ), <span class="st">"<="</span>, (<span class="dv">200</span>)) <span class="co">#200 flights from Khartoum-Ndjmamena constraint</span>
<span class="co">#set names</span>
<span class="kw">dimnames</span>(min_cost) <-<span class="st"> </span><span class="kw">list</span>(<span class="kw">c</span>(<span class="st">"New York"</span>, <span class="st">"Jacksonville"</span>,<span class="st">"Lusaka"</span>,
<span class="st">"Libreville"</span>, <span class="st">"Nairobi"</span>, <span class="st">"Khartoum"</span>, <span class="st">"Luanda"</span>,
<span class="st">"Dakar"</span>, <span class="st">"Niamey"</span>, <span class="st">"Kosongo"</span>, <span class="st">"Ndjamena"</span>,
<span class="st">"Ndjamena Truck Limit"</span>, <span class="st">"Lusaka->Ndjmamena limited Flights"</span>,
<span class="st">"Khartoum->Ndjmamena limited Flights"</span>), <span class="kw">as.vector</span>(edges<span class="op">$</span>ID) )
<span class="co"># Write to view the algebraic formulation</span>
<span class="kw">write.lp</span>(min_cost, <span class="st">"5260_S18_minterm_min_cost.lp"</span>,<span class="dt">type =</span> <span class="st">'lp'</span>)
<span class="co"># Solve the model</span>
<span class="kw">solve</span>(min_cost)</code></pre></div>
<pre><code>## [1] 0</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Make results and sensitivity table </span>
ps <-<span class="st"> </span><span class="kw">get.primal.solution</span>(min_cost)
obj_sa <-<span class="st"> </span><span class="kw">get.sensitivity.obj</span>(min_cost)
rhs_sa <-<span class="st"> </span><span class="kw">get.sensitivity.rhs</span>(min_cost)
nv <-<span class="st"> </span><span class="kw">length</span>(<span class="kw">get.variables</span>(min_cost))
mc <-<span class="st"> </span><span class="kw">length</span>(<span class="kw">get.constr.type</span>(min_cost))
ov <-<span class="st"> </span><span class="kw">paste0</span>(<span class="st">"Objective Value = "</span>, ps[<span class="dv">1</span>])
sa_tab <-<span class="st"> </span><span class="kw">rbind</span>(ps[<span class="dv">2</span><span class="op">:</span>(nv <span class="op">+</span><span class="st"> </span>mc <span class="op">+</span><span class="st"> </span><span class="dv">1</span>)],
<span class="kw">round</span>(<span class="kw">c</span>(rhs_sa<span class="op">$</span>duals[<span class="dv">1</span><span class="op">:</span>mc], obj_fn), <span class="dv">2</span>),
<span class="kw">round</span>(<span class="kw">c</span>(rhs_sa<span class="op">$</span>dualsfrom[<span class="dv">1</span><span class="op">:</span>mc],obj_sa<span class="op">$</span>objfrom), <span class="dv">2</span>),
<span class="kw">round</span>(<span class="kw">c</span>(rhs_sa<span class="op">$</span>dualstill[<span class="dv">1</span><span class="op">:</span>mc],obj_sa<span class="op">$</span>objtill), <span class="dv">2</span>))
<span class="kw">colnames</span>(sa_tab) <-<span class="st"> </span><span class="kw">c</span>(<span class="kw">rownames</span>(min_cost), <span class="kw">colnames</span>(min_cost))
<span class="kw">rownames</span>(sa_tab) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"solution"</span>, <span class="st">"duals/coef"</span>, <span class="st">"Sens From"</span>, <span class="st">"Sens Till"</span>)
<span class="co"># Objective value and sensitivity analysis table Transposing for better quality </span>
m1<-<span class="st"> </span><span class="kw">as.data.frame</span>(sa_tab)
tm1 <-<span class="st"> </span><span class="kw">transpose</span>(m1)
<span class="kw">setnames</span>(tm1, <span class="kw">rownames</span>(m1))
<span class="kw">colnames</span>(tm1) <-<span class="st"> </span><span class="kw">rownames</span>(m1)
<span class="kw">rownames</span>(tm1) <-<span class="st"> </span><span class="kw">colnames</span>(m1)
ov</code></pre></div>
<pre><code>## [1] "Objective Value = 310861299.435028"</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">tm1</code></pre></div>
<pre><code>## solution duals/coef
## New York 500000.0000 373.33
## Jacksonville 500000.0000 420.00
## Lusaka -150000.0000 40.00
## Libreville -100000.0000 248.33
## Nairobi -120000.0000 13.33
## Khartoum -90000.0000 93.33
## Luanda -130000.0000 248.33
## Dakar -50000.0000 240.00
## Niamey -100000.0000 -53.33
## Kosongo -180000.0000 22.34
## Ndjamena -80000.0000 0.00
## Ndjamena Truck Limit 0.0000 0.00
## Lusaka->Ndjmamena limited Flights 0.0000 0.00
## Khartoum->Ndjmamena limited Flights 200.0000 -10000.00
## New York, NY > Lusaka, Zambia 266.6667 50000.00
## New York, NY > Libreville, Gabon 1166.6667 30000.00
## New York, NY > Nairobi, Kenya 0.0000 55000.00
## New York, NY > Khartoum, Sudan 0.0000 45000.00
## New York, NY > Luanda, Angola 541.6667 30000.00
## New York, NY > Dakar, Senegal 208.3333 32000.00
## Jacksonville, FL > Lusaka, Zambia 733.3333 57000.00
## Jacksonville, FL > Libreville, Gabon 0.0000 48000.00
## Jacksonville, FL > Nairobi, Kenya 1133.3333 61000.00
## Jacksonville, FL > Khartoum, Sudan 1466.6667 49000.00
## Jacksonville, FL > Luanda, Angola 0.0000 44000.00
## Jacksonville, FL > Dakar, Senegal 0.0000 56000.00
## Lusaka, Zambia > Niamey, Niger 0.0000 24000.00
## Libreville, Gabon > Niamey, Niger 0.0000 3000.00
## Nairobi, Kenya > Niamey, Niger 0.0000 28000.00
## Khartoum, Sudan > Niamey, Niger 666.6667 22000.00
## Luanda, Angola > Niamey, Niger 0.0000 3000.00
## Dakar, Senegal > Niamey, Niger 0.0000 5000.00
## Lusaka, Zambia > Kosongo, D.R. Congo 0.0000 22000.00
## Libreville, Gabon > Kosongo, D.R. Congo 10169.4915 4000.00
## Nairobi, Kenya > Kosongo, D.R. Congo 0.0000 25000.00
## Khartoum, Sudan > Kosongo, D.R. Congo 0.0000 19000.00
## Luanda, Angola > Kosongo, D.R. Congo 0.0000 5000.00
## Dakar, Senegal > Kosongo, D.R. Congo 0.0000 5000.00
## Lusaka, Zambia > Ndjamena, Chad 0.0000 23000.00
## Libreville, Gabon > Ndjamena, Chad 0.0000 7000.00
## Nairobi, Kenya > Ndjamena, Chad 333.3333 2000.00
## Khartoum, Sudan > Ndjamena, Chad 200.0000 4000.00
## Luanda, Angola > Ndjamena, Chad 0.0000 8000.00
## Dakar, Senegal > Ndjamena, Chad 0.0000 9000.00
## Sens From Sens Till
## New York 5.000000e+05 5.0000e+05
## Jacksonville 5.000000e+05 5.0000e+05
## Lusaka -1.500000e+05 -1.5000e+05
## Libreville -1.000000e+05 -1.0000e+05
## Nairobi -1.200000e+05 -1.2000e+05
## Khartoum -9.000000e+04 -9.0000e+04
## Luanda -1.300000e+05 -1.3000e+05
## Dakar -5.000000e+04 -5.0000e+04
## Niamey -1.000000e+05 -1.0000e+05
## Kosongo -1.800000e+05 -1.8000e+05
## Ndjamena -1.000000e+30 1.0000e+30
## Ndjamena Truck Limit -1.000000e+30 1.0000e+30
## Lusaka->Ndjmamena limited Flights -1.000000e+30 1.0000e+30
## Khartoum->Ndjmamena limited Flights 0.000000e+00 5.3333e+02
## New York, NY > Lusaka, Zambia 4.825000e+04 5.1000e+04
## New York, NY > Libreville, Gabon -5.315250e+03 3.6800e+04
## New York, NY > Nairobi, Kenya 5.400000e+04 1.0000e+30
## New York, NY > Khartoum, Sudan 4.200000e+04 1.0000e+30
## New York, NY > Luanda, Angola 1.644068e+04 3.2800e+04
## New York, NY > Dakar, Senegal 1.644068e+04 4.4800e+04
## Jacksonville, FL > Lusaka, Zambia 5.600000e+04 5.8750e+04
## Jacksonville, FL > Libreville, Gabon 4.120000e+04 1.0000e+30
## Jacksonville, FL > Nairobi, Kenya 5.100000e+04 6.2000e+04
## Jacksonville, FL > Khartoum, Sudan 4.064831e+04 5.2000e+04
## Jacksonville, FL > Luanda, Angola 4.120000e+04 1.0000e+30
## Jacksonville, FL > Dakar, Senegal 4.320000e+04 1.0000e+30
## Lusaka, Zambia > Niamey, Niger 1.400000e+04 1.0000e+30
## Libreville, Gabon > Niamey, Niger 3.000000e+03 1.0000e+30
## Nairobi, Kenya > Niamey, Niger 1.000000e+04 1.0000e+30
## Khartoum, Sudan > Niamey, Niger -1.000000e+30 3.2000e+04
## Luanda, Angola > Niamey, Niger 3.000000e+03 1.0000e+30
## Dakar, Senegal > Niamey, Niger 5.000000e+03 1.0000e+30
## Lusaka, Zambia > Kosongo, D.R. Congo 2.648310e+03 1.0000e+30
## Libreville, Gabon > Kosongo, D.R. Congo -1.000000e+30 4.9855e+03
## Nairobi, Kenya > Kosongo, D.R. Congo -1.351690e+03 1.0000e+30
## Khartoum, Sudan > Kosongo, D.R. Congo 1.064831e+04 1.0000e+30
## Luanda, Angola > Kosongo, D.R. Congo 4.000000e+03 1.0000e+30
## Dakar, Senegal > Kosongo, D.R. Congo 3.852500e+03 1.0000e+30
## Lusaka, Zambia > Ndjamena, Chad 6.000000e+03 1.0000e+30
## Libreville, Gabon > Ndjamena, Chad 4.395500e+03 1.0000e+30
## Nairobi, Kenya > Ndjamena, Chad -8.000000e+03 1.9000e+04
## Khartoum, Sudan > Ndjamena, Chad -1.000000e+30 1.4000e+04
## Luanda, Angola > Ndjamena, Chad 4.395500e+03 1.0000e+30
## Dakar, Senegal > Ndjamena, Chad 4.248000e+03 1.0000e+30</code></pre>
<div id="graph-the-min-cost-solution" class="section level2">
<h2>Graph the Min Cost Solution</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Include solution in edges dataframe</span>
edges<span class="op">$</span>flow <-<span class="st"> </span><span class="kw">get.variables</span>(min_cost)
edges<span class="op">$</span>Mincost <-<span class="st"> </span>edges<span class="op">$</span>flow <span class="op">*</span><span class="st"> </span>edges<span class="op">$</span>Cost
g <-<span class="st"> </span>edges <span class="op">%>%</span>
<span class="st"> </span><span class="co"># creating igraph: "from" and "to" fields in the first two colums</span>
<span class="st"> </span><span class="kw">select</span>(From, To, ID, Capacity, Cost, Type, flow, Mincost) <span class="op">%>%</span>
<span class="st"> </span><span class="co"># Make into graph object</span>
<span class="st"> </span><span class="kw">graph_from_data_frame</span>()
<span class="co">#Add route attribute</span>
<span class="kw">V</span>(g)<span class="op">$</span>route <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"From"</span>,<span class="st">"From"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>)
<span class="kw">V</span>(g)<span class="op">$</span>color <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"gold"</span>,<span class="st">"green"</span>)[<span class="dv">1</span><span class="op">+</span>(<span class="kw">V</span>(net)<span class="op">$</span>route<span class="op">==</span><span class="st">"From"</span>)]
<span class="co"># Get some colours in to visualise routes</span>
<span class="kw">E</span>(g)<span class="op">$</span>color[<span class="kw">E</span>(g)<span class="op">$</span>Type <span class="op">==</span><span class="st"> 'Truck'</span>] <-<span class="st"> 'saddlebrown'</span>
<span class="kw">E</span>(g)<span class="op">$</span>color[<span class="kw">E</span>(g)<span class="op">$</span>Type <span class="op">==</span><span class="st"> 'Airplane'</span>] <-<span class="st"> 'forestgreen'</span>
<span class="kw">E</span>(g)<span class="op">$</span>color[<span class="kw">E</span>(g)<span class="op">$</span>Type <span class="op">==</span><span class="st"> 'Ship'</span>] <-<span class="st"> 'royalblue'</span>
<span class="kw">E</span>(g)<span class="op">$</span>color[<span class="kw">E</span>(g)<span class="op">$</span>Mincost <span class="op">==</span><span class="st"> </span><span class="dv">0</span>] <-<span class="st"> 'white'</span>
g<span class="op">$</span>layout <-<span class="st"> </span><span class="kw">matrix</span>(<span class="kw">c</span>(<span class="op">-</span><span class="dv">800</span>, <span class="op">-</span><span class="dv">800</span>,
<span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>,
<span class="dv">800</span>, <span class="dv">800</span>, <span class="dv">800</span>,
<span class="dv">225</span>, <span class="dv">125</span>,
<span class="dv">300</span>, <span class="dv">250</span>, <span class="dv">200</span>, <span class="dv">150</span>, <span class="dv">100</span>, <span class="dv">50</span>,
<span class="dv">250</span>, <span class="dv">175</span>, <span class="dv">100</span>), <span class="dt">nc =</span> <span class="dv">2</span>)
<span class="kw">get.variables</span>(min_cost)</code></pre></div>
<pre><code>## [1] 266.6667 1166.6667 0.0000 0.0000 541.6667 208.3333
## [7] 733.3333 0.0000 1133.3333 1466.6667 0.0000 0.0000
## [13] 0.0000 0.0000 0.0000 666.6667 0.0000 0.0000
## [19] 0.0000 10169.4915 0.0000 0.0000 0.0000 0.0000
## [25] 0.0000 0.0000 333.3333 200.0000 0.0000 0.0000</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Flow as edge size and colour</span>
<span class="kw">plot</span>(g, <span class="dt">edge.width =</span> <span class="dv">15</span><span class="op">*</span><span class="kw">E</span>(g)<span class="op">$</span>Mincost<span class="op">/</span><span class="kw">max</span>(<span class="kw">E</span>(g)<span class="op">$</span>Mincost),
<span class="dt">edge.arrow.size=</span>.<span class="dv">4</span>,
<span class="dt">edge.label =</span> <span class="kw">as.integer</span>(<span class="kw">E</span>(g)<span class="op">$</span>Mincost), <span class="dt">vertex.size=</span><span class="dv">35</span>)</code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdf3xT930/+tfZCiO0M8SctE4ajLF1HJn4m33drneqZDcLNE0kPMO+LE5C7024uFd6GLJJ313pkqxsNPuyAQ/psYe0FXyt78yA3SatO741npFCU8g3la2oy1IeN3Ow8JGN46QpDQcHvNQhJHfn/nGOpCPpSJZkyZal9/PBH/JHRx/pHCd+6fPjfD6MKIoghBBCSHn5jaX+AIQQQggpPAp4QgghpAxRwBNCCCFliAKeEEIIKUMU8IQQQkgZooAnhBBCyhAFPCGEEFKGKOAJIYSQMkQBTwghhJQhCnhCCCGkDFHAE0IIIWWIAp4QQggpQxTwhBBCSBmigCeEEELKEAU8IYQQUoYo4AkhhJAyRAFPCCGElCEKeEIIIaQMUcATQgghZYgCnhBCCClDFPCEEEJIGaKAJ4QQQsoQBTwhhBBShijgCSGEkDJEAU8IIYSUIQp4QgghpAxRwBNCCCFliAKeEEIIKUMU8IQQQkgZooAnhBBCyhAFPCGEEFKGKOAJIYSQMkQBTwghhJQhCnhCCCGkDFHAE0IIIWWIAp4QQggpQxTwhBBCSBmigCeEEELKEAU8IYQQUoYo4AkhhJAyRAFPCCGElCEKeEIIIaQMUcATQgghZYgCnhBCCClDFPCEEEJIGaKAJ4QQQsoQBTwhhBBShijgCSGEkDJEAU8IIYSUIQp4QgghpAxRwBNCCCFliAKeEEIIKUMU8IQQQkgZooAnhBBCyhAFPCGEEFKGKOAJIYSQMkQBTwghhJQhCnhCCCGkDFHAE0IIIWWIAp4QQggpQxTwhBBCSBmigCeEEELKEAU8IYQQUoYo4AkhhJAyRAFPCCGElCEKeEIIIaQMUcATQgghZYgCnhBCCClDFPCEEEJIGaKAJ4QQQsoQBTwhhBBShijgCSGEkDJEAU8IIYSUIQp4QgghpAxRwBNCCCFliAKeEEIIKUMU8IQQQkgZooAnhBBCyhAFPCGEEFKGKOAJIYSQMkQBTwghhJQhCnhCCCGkDFHAE0IIIWWIAp4QQggpQxTwhBBCSBmigCeEEELKEAU8IYQQUoYo4AkhhJAy9Jml/gCEkJLGMEzssSiKS/hJCCE5oYAnhCRQJjoAcSz9U5T3hJQwhv4XJYRIpPxWJvo8xzcBFPOElCoKeEJIztGe8FqKeUJKEgU8IZWOYZj8oj2hkibKeEJKCwU8IZVrIQ13ldqoKU9IKaGAJ6RCFaThrlItNeUJKQ10HzwhlahI6Q5AHEuebE8IWRIU8IQQQkgZooAnpOIUr/kuoUY8IaWAAp6QylLsdJdQxhOy5CjgCSGEkDJEAU9IBVmc5ruEGvGELC0KeEIIIaQMUcATUikWs/kuoUY8IUuIAp4QQggpQxTwhJSaGY/BxTDyP4NnpnA16yxNTktgobX4DziZpuR/hlN1eVYXCRkYl8Wf+gRvUS8nhGSFAp6QUlNtHbHzbhbQ+kT7iLW6QNXOADu8hakq7BtziPK/02YAnadHnprKp6ZIyMANBwvzqQghCT6z1B+AEJKGnuUKWV01ENCjbcH11I03vGWN/uA/sMOLsO/5UJ6VaXQjPAzcsNpzXK9oz7NaQgi14AmpEAzDiOcKUtOU9alonAe6TP3QP3femP5ommdHyFKhgCdk+fAPSQPzFn/iYyDiOREdth+Shq0VJS7GwifXFBtHP6ADEDm1Lz6gfkCX3aep8/Ro0RI4qeicV6kn0AXs80SiEwssfHySQcKnis08OOGJSCXxMfjM50IIUUUBT8jyYWwX+VZ96uNI6Ol+LS/aRVEavAcioadtcPN2UbSLPi28Iyl1Ce5zDnHMIT4fwnTH04cg/+gNo/9Bz/T8nyVy6nHbRcF9eFATK0qtJ9BhMGsB1sYN4qT0Sc4wTOzxSDTL4TUN4qRdFO0+s2DjhvzgLcwZb+zsEs8l9ipCSAY0Bk9IWQgOc5Z1Yi+nseo0ADS6EVEHwG9xmbwAWMWhOkvTDngdvbXRgtrBkbFBAP4DTlM/AGH+t5vuePoQq3/uqLVWUZhaz4bBEf45hhs2+3ZZNQBYPdAcfxx/o+gBMPZuM3vPDPjbe/nWUWlsPtO5EELSohY8IcufRjfCt+q9Z5Q92FK39sB2u+jTAnDzIvMIAHjNKnPppa71ga87RG84i/er8zzbFkzsnE9XD8Md0qetR7iUprt9dDzh5sCkcyGEZIMCnpBlwh/K1DWt0Y3IPdhnpBF6zsb6RHtvyvw3s9fBPyd4zfvi/fCBLu6Q4Btz9GY3xV61c94TyLmeNLVfHwXbuVVxc2D6cyGEZEABT0ipCgqK9i1vMQmNGkCzrjnawI2cDct3kEdCHmlmnbHdZ8bo+ExkXACE8QgARMaTu9w1Tx3xdbK2R7rk6XhXWIAdn449zkjunP+BsnPe39eEDTnWo27G8/Rw0Gywxr87IPO5EELSYURRXOrPQAhRmvEYjttSF38xbxN7OcSHoqE3a+ENB6H18ezB2HIx8mHxSWrRw2IE97kjW//nPu6QlMFh39hbA01yv72+M4x+bRAwPxcYPdQWRNg31qdoOdd5du61XUz9zNJhOktKPfGP724dtckfUvGYdfO7tp49wdnk5Na7d49YqxWfn3Xzhktc8rmYfdSgJ2QeFPCEVIp8NpsJdHg2DCbMpMuJVmQY+iNDyNKgLnpCKoUoikxT9ofrLE1O5ifv55/u0lcKSndClgj970dIBVnUHWOp+U7IkqIWPCEVJMdG/IJQuhOytCjgCSFFoKVoJ2SJ0VdsQipFg6tBejDpmCxuRz11zhNSAqgFT0hFiKU7it9RT+lOSCmgtegJKU/KRI+ZsE8U/Y21IkD7wxKy9KgFT0ilUKZ7sRrx1DlPSMmggCek/E3YJ1Lb7vXOeqYJBYt5rcg0FbdzPrYrvLRJPCEkM/quTUi5Se2cT0135TEFmHO3CA13/xBjgk9sN4K3MGdAS9USMh8KeELKR1K0T9gnGlwNmdNdMumYBJBPzGtFhmEAFPkvyYzHcPzS/mio+4eYgyw/otPM8ypCKhpNsiOkTChjOxbqWc6qq3fWA2Cacol5OdoXpZEQGe8Psp1c9EeO1QfDZyM6KyU8IelRwBOy7KlGezYHJ6l31k/YJ6QWeUw876ON9ahF7P/jhSDY/bE416xrhnCJByjgCUmPAp6QZSy1Tz6n45NIL0+KbUWo04geIcsJBTwhy1VODXfMl+7plGKoR66PAs1L/SkIKXEU8IQsP7lGezYWYw2cvHGsHuHxCIzxPnl2E5fhBYQQCnhClpVc++TTvTBJSac7AE1jp344NugeORsO6rUnaQCekIxoUI2QZSPvhvu8nfOlHvBQ3hpH98ETkhVqwROyDBSjT754FRaFsZ0fP8ExwwDMlO6EZIFa8ISUtLz75NPVkGR5pDshJHfUgiekdC284b7wdFfe+07tgSKhi0yKgQKekFJUkD75/O6LS7vQTepTFEX5ootMFgEFPCGlZeF98llKrVleVT79UrVJTy3KKvTlhi4yWTQ0Bk9ICSngZLqcOufnTZ0MpA1n6S/JvOgik0VGLXhCSkJh58nn1DnPMMxCtouVXlv07WKXObrIZPFRwBOyxBatTz71LRbSpkwijlFnsjq6yGSp0PdBQpZSMW5wz7JzfoFtynSYJoqfOLrIZAlRwBOyNIq0ds3SprtcOcUPALrIZKlRFz0hi614ffL53RdHCClLFPCELKqiLjqb2eI03xEdKq7w9iVdZLLk6L8PQhZJsaO9FDrnlSq5D5kuMikFv7HUH4CQirC06U4IqUDURU9IcS1hn3zq+y5ayxIV3IdMF5mUCAp4Qopl0W5wp/3iCCGpqIueVJoZj8HFGEKRhELewrgYxsUwLou/MOVrvqVsuL/0dUd3IepXkX26L2bLUhJbmGWBIp4TWV6NJbd8LzIpPxTwpKLwFua4LZhaeHm7aBdFO+9mvaYh/8LK65096ztuzPYZ54AJ+8SE/aUC1a+iIobe/UOcjfWJdlHcBtMyyHhCSgQFPKkoXK9o591sQllknUNsNwIANFaDGcJ4JM9yv9P6jKsBwIq2N6qw9tntE7nU86v//iWp1X7Gi/BBz0zi8bxFar9GQoasG7LZdc7rLE1OS0D1QT4ip/YxTU7pnyUAoCvTp53/dGY8B8Nmn3ShuF6f1nswqfdl4eL9JYyFVy83hCKxX8HS0FmiV5U5oEsqB5yl8SFJaaGAJxVPU62JPY5cHzUbrJp8yhsGvrIiWjyx/UCz+ekc6/na//i59OVD6xPtI9bq5OMBjIcM3HCsA6KgQ+86S9MOb8KDPNR5djq5Q4JvzCGOOcQxx/afOAFt2sMjCaeT5pjx/iC7iYv+FJxGcJgrcIZxvaJdFHe79YD3jKJm+eug2WcXR3SaDBUshlDvmEMcO+puAfp3KL5+hXrHHIBQGh+SlBaaZEdIVCRkeBonR7hcy5Pnyedbj/yjnuVUyrle0Q4AW2HghlHIzvlQ71gIAM59fvSRNiDUKz/IWeTU47aLgvtcnzFaYnzegf6u0fEZGKtVXqDRjfDy6aTFC0Gw+6Xg8g9xB+80Ym69bxtMLovP3mvM9NKcNWvNCHtNJzbxu6wqURn9FSwtLmyG1mvet+ncEWutytMl8SFJaaAWPCGANI2LGw4GhzkmYcw7i/Jn5qKFE/aJBdSTVXmWUpvvxZ/8pXMeYtH5alLqiGN9Qdu6QtQv9dX/7nqgaH31rGNkmxmCjcvnmmORZthdc7xw2gzW9khX7EPyz8Fronl2JBkFPCEAoLHuEuXheXn8e95yaTIdUHcjwE7YJ6RMzaOe7MoTxla/f/r3Zo92Tzq6Jx1PzgoAAEH3nqNbCDcKju5Jh8cPyPcLMC6GcRk8M0BsaFwKhjrPTmmYPPOIu3wY0+Q0nKrLdAUD93sB89dDGQ6JTYZPGe2OfdQTnkhioSkMhE2GUETqq195+Qzw09dnPN8OIzjMPcmnvsvCcL18qx5hkyW15vivQOVE/EMMcyJ+uQ7oEh9Lki5m7EfpNyKNsndl8d0i1HsuoIfWlDAYn/wh4R+Kf0jGxcjfFJP+q4hNg5AmHOT5zYaUJgp4QuI01g63fv7yBldDgzyZ7mW3Hl/anPxXMct6si5/28KciY2L/xyY7XsYj/fUO3tqdGuFI8a5sHHySMstYLav4fuiXRTbjZjxGAZx0i6KdpFvhe04cEQ8F9Aj1oU+tfVRwew9DXOGEfc6z87HcdghjjnEcwEc2pth5l3kCptcNN1haHIy0hQwC49I6Gkb3LxdFO2iTwvvSCzLvSb5o/rMsdbzjMdwvL9zt8i36nHnH340zP1BOIjfOvf18K+A8IFB9G8zA/j+iKfQrXhodCfdLLxnDIrvWwAf/xWknog/ZDCFAQF4HIcdojeM/h1MU+zxg55pqF3MKesLR90tMHul30hoe2fCAEcmtYMnnxPQvyPxW5fiQ4K3mMJmn3wvBvStvNp/FRbLEMMNBwGvSbpxo72wgx5kaVHAE6JU3diM5sbUAWO5PBbtkgn7zzIfX6Dyll6+VfoC0PD3fwegquvFKhYAVu84X4W6Ofjr911cBVR1+eU/0P6gLSjYOBfDuBh5Ilsdagf3d7K2PqnZV3d2YszRJrUF0whssV1kbY84mSYn80hbEBi9UpfuWM1GAUkH1A6OjB116wGcFns5aHQj4i6rBn6L1C6PM/vkMW9j7zYzwgN+6fNr91uroWns1H9w1zOt+rAA/PYf7V8N4Ino8V9I92kWRmPd5TMjaDueMOEu+itQORFONyI/e8RaC2y4pgfM3vhjIN3FnLJ2h709HREA0A3wY1tVhtXTfMinjvg6EZS+dX1Bm/whI9dHIU9O1GzV6oMCD5X/KkY36UW+VQ9E71MgZYUm2ZGKw18S0j4XCR0cbT3Zq17+DtcQnycvDXJnPD6f8vE05RnbqbevsWAB4IkdEwA275nG9K9R06z7w013xI/qZJo2iN4wzA96ukLWt7dc+nqfNVOtAICWAP/CYFZzszdc00MbfOmByFNTyuNtQcB8Sj4bzwnOJph9dnH7EGNK+1sYHZ+JQIB0Sqi27tcyB4UngCBm/ung3BeAKkgBBkC4xANFmDtu7N3tHj1uMw1tcqs8m+5EzD6RaWLEc0mHs5feBpDmYradd/fsdQYGe3E/uvtyOhXj80fd/F6bucuLsNmXuFqtprFTP9x/dsZqrQYvBGMzN/WtfNJk+4J3gZCSQQFPlivl6l1Zr8Ud68Mc5piwW5os7R+KNyiVf/6U5XUX1+8NSek+cb+HMYUZhyvT8fmWRwDMhDlmOF6+H2cjkJL4+6cbWDZx2FWovo0bn2sSAM1K4J+PTvP/HH3q6s054A4kajvvbtlr6+sArjmez+KCXbwr2wCtHdzf2Wbqb3MGBntV5+DL69XsMgJIN9IbuT4KtnNrtYZngfCAv91oBIzt/LiXCwJ4/+ZftTZ8OzbrfvUdmEtT0cJVW0+29nPDNhvMvtxPRJX6xZza+qhg6+kCsP2FXD/klPVwoP+RNrVbDaut+7U203HGBkDrE6P/1QWF4nwjIqWIAp4sG8nrcYppn0qf91yvaE9uPxvbRbFd5Vhjuyi2q64nn+H4ApSHo+EBRFe1AyL4ObAy+VB29gctt3Tn72GBpPZw7f1czUtv/miN3Iiffu2NG/cDkPqEbea2S17H/M33tvPulr2mnR1yuzPQZbhyfuSpqXSHG58/be7f4TU74XWkZnxkXAAwHoFRIz9OMeN5ejho3jaiATR6tz5sMw1tl0eFV0L/5f144/0ajEq1nQ0Hv8S2/Hx63pPI2vVLo9iqLNDoRnxC0mhCdieiJv3F1Dz1qvnQDm/n6dSenRQ1l/jED1k7OOK9izGnLjbAW0zwifaEjnej3q0/bjKw8rdM/5BhXD+yNeWlpFzQNkRkGZDzO/v/VKXDF/zf9uJuBDfjMaQuowuYt4m9iHY83GD3vXjH2JPvDK6VnlzV8cN72gSg8SPHll8CAO7e2qmVx3HfDfcEpUJs0j/04L2v9GwQxwDUeXZuaXxBmsyVYVkbwX3uiLVWcUDnafH5EKY7DI+0BRH2jalPB4uc2scdUk64C/DiGQ2gnAKmN2vhDQcBs8/uGD/B2eSY1Lt3yyv8JF0QqYfD/yJj+gUAoEqP2QngVwDAutVvW8+B1OUuPTYn3l4f8ZxwNu7qNcY+POvmDZe45BNRnq8ecpPa/Fxg9JD0OM3FjL3Lqa6zv98n32GY5gorL6w58SsU07TP7Duc7kNKotdWMREv4T+t5BMnZYACnpS0nKM94cVAvjFfCnu8bt4Tb56+Xf9QhiM3TL6STYXRgC+EQIdnw6DaQiuJtGJhNzONJXHJplFet8LrLDs/70gans/yCiPdRZ7xeK5brbElAEMGTthPk+QrDHXRk9LFMEye0S4R5UpyCphF2+M1lTLRlQqS7gAe6n6baVp4xussTTu8nafFLIbwC75Vuca6S5x/aGEpiaKYa8ZHTj2I7iOKdM/hCiPNRY54Bm392q1WTqo2cjYMdwele6WhFjwpRQtquKtUB2TXlF/khnu6RFfKnO7IIuAvHIs3AxdzM1PXn6xzvHzd+fA6APYfZz1Wvfxlf5HlXvfE7vrcpO0jSRjxSRz7IJWCAp6UnIU23NPWmynjFyfas0n0JHk035WJnmrRMp5pgpTuMZUT84t5kelvOEmHuuhJaSlWugMQ1Tszi9onn0eiK2WZ7pkTfUm4/mQdcD258BssKinmi04ryt1ThKihFjwpLUUMeEiN+HHltOGfvKVouG//Xnz3UvM2sTe6qVuaiV2q5Zv3TH/05rnQyE0kTGgHgFzLI7/wSrPlq7p6WLU9V/P+LlLs9qWyc15VJWR80RvxhZ7ASMoP/fdBSkhx011+D/hE0QjUbtv/zuCNGqd/dTQp1e+V8g8xJvjEdiN4C3MGKeXOPcFwTxCxbJ5+7ZWzeKD7q+vw7oLLAzVO/2o0Co4tSMn4BfY0FDV+UjvnVZV3zDe4GiYdk0W9yPTXm2RGa9GTUrEY6Q5AhIlhAKxoe6MKaz8VYknJOy8ZRNEu/Yu2yKUtSqWbi+JblG7eM9q4O3z31gece6aBe7Vb1//yjUsfAcDsu2+8c/fWr64DFlj+b2+9U9XlXw0A42zX1Ox53SeKk1iqO/eyos32t+j6Bit12pefpHGfwsv6IpNKRgFPKtTE9gPN5qevHY4mpf+y13uGUWzJCgDSFqUcNu+Z3rxnevP/85mq4L+ZvjmNm+/+6uqaz62JHrZmTdXVd67fxALLL7g36K9efeJPa9+uOvTh1I2VsexjP1g1tfGjaHO3IOkuiiLTtPBqUmhFhmGyab7HlF/Gx9K93llfvItMzXcyL5pkR0rCIjXfJSIYhtHrXzs5El0GBDOeg/KKpF6Ty2ve9tBv/hYATL8dxB0f/s20nFdrqj6Lmx/eBHBzFlV18cDOs/zC3k+ZF+7+/vdqoyP4HzdD+IvDDSyMt3BjbTzgZ1ai5XZs+5VCXQZRlG5HLEw3slaqLR482Sd32Uy+U5mwaUdRLzIhGVDAkwoVDA5zjOCT1/aqPvM7nQ/9DoDZd3/0Eu89E06c71Yo0enuH1t6gL21wJu51lDYznkpJwowHq/WppQCuxJiXrVDPvabKupFJiQD6qInS29Rm+8SEYADCD/ROip1v0efqLr3Dzsf2IRfXnlX5VU3Z3+tWlv68o3AH+ytvbD3s8BnD8duZpO3Ok0RuX5KtVyovg2gaEPvUnd9np3JWpFpyrRiXa6BvRx77FN/L6klRb3IhKiigCeVi6tRL1+3cb38aM2aKsx+dFP55JrPrclUrsdHu/+09sKx2gvHai88vxZgN3EAx+ohjCdsvK1S3vD3fwfcWMkC7AersPbThGRUDMkXgSiKOSeQInUyB4/9x0JOMZ80+S7iOcEwruTpEaUky6UUinqRCUlFXfQke0n7UHEq5cr9zvNmcSK2DZb5NHpDiYVhiH3ZVaQDswM+B9KswX1HNT67tir6k2LvtQ3r7m6+FwDW3PuFmtFfvNDPS+W//VtY/fFs9MfZkXfvNd174Vhtn6H/FWD2hX4e9Wa9cCm64XbkbDio157UAGjs1A/PW/7J2MZbdVfuYgFEPlcXH3SXyv/tcNFnzsc6kxMKYx3L8gBwTG4Nyjx77P/riejm67yFcVlKb4+ZXBdKKupFJkSJWvAke1yvaBfF3W494D2jaE5xvaKdd7Nmn11ceLoD6HWADwCA+6ic7lKhGXAfzTrd62DYkfmIqZnmDfGB9nu13Z0PdT/KscDb1/9DLqy698vr52qa/7Nhzd1b9Xf/+8d3P9T50M5mPaA31+OLv+E9Vgvw/xKE0Qi9e7co/pfe/fKtdADvtAnm/dIFqbbOV97gMt4cXFu1JbQCAISqLbFb4xpvDq41H/teAS5sdsREUotT2Y6MyaPyHHvsa7btVLlNsUQ0uBryXuG4qBeZEAm14EnumrVmhL2mE5sWvA93WppB+O6C6XFsPQJ5P6wO4DSsU1lXMYWTAXBtGY7Y9Ieb7pAe3bz0xgujs9Lj1Wu+UHPzV2fPVe185N41QO1XdTekleaC0dVvavG/fsqYhG/vn+WYYQDmvtabP1snb+ZhbOfHT8jlyubm/OVbElas0/rXX3vyHUdL8vGLruAZk0NTfq7pzRsz+CsWRgEAOFYfDJ+N6Ir1X10uVBvueU+SoCAnxUD9PyRXMx7L+NbedU7mjBfa6Cx0RDwnnI278sih9DPs6mDYCwQwMgjUwfA4TkbDPkuRDnBtGbroweCh7rcTi2bfffXddQ9WPdATzHh2Mx7DcRuk8YgZj2EQJ/P/rjPvoiglvazNwswT88IfOS6i6+F/0gL2Hwuub/w+/3I7SqCXfgn3FCYke9RFT/LD9fKteoRNFl7t2RmPwSVNjDJ4ZhQ/DvnBWxjp8QlPBPAPAU4YOtQqmcLJAIJt8AP+Lej8QWK618HgBOMEo3h5pAOME34dGCeYroTKLNGDLTrV85GnxR27Z2/r2n88ps94dgCqrSdb9cFhpx/wB/s7O4rXpizv8Mi+x971Ddb+4/+e822FRZDULV/evyCyrFEXPcmXRnfSHeZsZwybkraallq0dlEDREIG7ril0d47shuG45f2txsBo7h7k+H4pf27rBpA0w4MYGQwzVsMwtyGg/sQFCBOKZ6og2EvOo9iZEp+bAD23wWTFgBM90N0AFKvfowAXqUDIO0+bGnPLn7AfvOw6eCJ0SC7X8x/p+3MzfdlHx7+IeYgq5x3mbpDj7LHXpg2H7lcDUSkVrtwrRZY2ffys9JrHczLdwL4Nvvsf5UOi2Hd0dEiv8Vl8gJpdkCPvnu8byZzeRJquJPlhVrwJH8a6y6fGUHb8YT7l/xBW1CwcS6GcTHccBAYHZ8Bqrd2ssoZUt4BqXHMA29leg9HAEEW7vMJhf4tCIaj4/HRhj765Kl5vqRZeDowTmxPTndxvlvv1c9Oweho1QcFuPV5dxgXfcXyJcZbTOGEAv8QZ2N9ol0Ut8GUcNub/cev8S8/e+TyTNfDh50P/5MWAJpffXdlwstXzX4AhC4+e+JyYnLrtVtj6Y5tomgXxW3NtuMGz0zCh2Fc0XdXpni68mSU7mTZoYAnC2Ls3e3Ww2saOqss1bfy0V1bRNEuNaQ0W7X6YPhsBPAHL+3fZvaOeCKIeC67+dcybWmteR8AGqcSCsdZlWPG69RrMKnPpc9m2RD1s4u/77pmoLkx/+Z7Zss9QvyWy5vcyt+U+s49AOQ7Lc3bRPH/ju+ZJ+D+h090rIWu5bDz4cPO+l/jlgh9676Wi2sRLXz4sOjT6jsbNQAioYNerU++e5Pr9WmDtqA/uf6kCE9XnozSnSxHFPBkgaqtJ1v1CNtsisHUoKAydq3R7TcLNifvH8B2I7fdLNicobOX2K15jF43CoAWSQ3rpC8BMT4H3AJM+5DP/X8NFO4AACAASURBVFVqZ1cgy6VznlHI4WX+oYHt7VuVJdGde2TylHgA8FvOeCFnc3xVHHZUi6ttGyOhKw8JqDn97md/Gyt0H7Uf/vn3vtUiFQLAt8xB9oeNAMALQeXbcawe4QF/cv0JnzFNeRIadCfLFAU8ydX1S0nrrGp0Iz7FXuVGvVsfNhmijTP/UKyn1LhdC++Zg5v0Rvnx8KXted03bzwPPWCKTqPzPAh9IO1UeQDWIzCz4LrSHxEz39kVSCmnO5NIHEPsX9JT6evgLQP3Jc9154Ug2MbY71uzrhnCJR4AP+CF3s0OMLGZmMqY/6d9X+COvLwrdBtVmw7vYOH6Bmvv31V3gxubA9D81od802q4vsG6vj2E5OUC5TdOrT9jedxC7nQnZMlRwJMcRDwnGOaMNzjMJa0bamzn452x1daRbebgMCf93Ry4Lz7XyXifGdr98v3i95mh3Z65Y9SzD8wOADAlzX6fwshR6LXyxPj+MYwMAjr5rneTE/7oPXKA3HbfJABaeXa9NNkeXUl/0LM7u8SDAa/JxVj4nJZTLdmh99RET9ofJempdDHvt1zePl+zWHH0ZS+AfmyXl1EKm6JZK2U8W+t11v8amH70i9GXaNbVoPr9OUDQXv3CmPy7YcM6CLano98seSEorQecrv707yuhbnmy3NF98GTpLf5mMyJEhtnm5s8U5vY2/xBjgk9sN4K3MGfmvVG7BJvvC9nSVFpZPf6XJBLy8DqrEZBmp/dr5Vn08askH2bghpt99l4klvuHGFM4aW2fb931133CdJc8+Q5AM/9y+5sth++/Zr62wdu2Onrc3ENHR35vKv46rU9sN/rT1J/xfSndSRmg2+TI0pM3Jl+8jNcxjAtmV4FuXpfmjtljc8eYgyGHMe3QQ2mm+0J2MpVeG5u06HcO27zDtvjzwxwzbPbZezlWj/B4BMb4pWE3cUDSfA3jfWYkzr0H/5vsPRAeAP5JLpi768fAN/7qtTd3/tmDqxUHrn5l78OvAABqAq/vEr6jdoODSv3J5dQtT8oDddGTiiPiNcAhZt+HnFn6uWOpSq1zPtYnv3CxHntjb/wGCt7NSrdU9BoBTWOntBMPgOiOO1s1crIOxDrHI9dHobieAPyXR3fU6iH8p2PKqY4zm967HO+fTyRMdwziZ21nG4H09auV/49XGxpcDbFQp3QnyxoFPCkJoihmulmuoAq8r3bauWM5W+Q4iQ23F0psYD7N8+l23OEcbtZrkse//c7hoNmg7FzxDwidu/6z9OVAmnwnCNzUWn7kO0Hb0GjKu9QEXn/2yOWZrq+8wso7z341Tf0p76v7QRULAFLGU7qT5Y666EllESEyi/ZVIlFJdc4XquGeSsp49a9QaXbc0Vh3+S65TEwYSNqJGAA/MKp1aKo1+2NjH/wHl6vNvr9+c+efbdVgIHZgdAC+7r4Tzq9cVb7twFl7173Pptaf8L668/U7xgtzCQgpDTTJjpSQYo/EixAL3HxH+rljicO/JZXuKGbAA2CairI9Wuoyt65vsPYfC9nvMZ9u6XsadCdliQKelJbiZjxThOCJhAxcuDO2c27SjwBKb7+4oqa7/BbFyfh0ss94JMY8zZYnZYzG4EmlmHfx+TylmzuWtfJLdyDzYHzhZb8rHeSBeRaU7qTcUcCT0lKk2XaxzvnNe6YLXXe6uWOyUuucL1c5ZTxSGv30iyDlh7roSSmSG3+F+G9TinYAynRPu0tsvlKHhyUV2Dmf8HaL21Evyam7Hrl/MyBkuaCAJ6Vr4ePxSbPqlM33gme8qlJrvldCwEso5gmhLnpSuuTu+rx67EWIYJLv11KGehH66pNVeLpj0UfilRbYY09IGaCAJyVNFMVcY14Z7alt90XL+AWnO2+RdzlzMRZevTy2ZZ/aa7PZ9kY23WFocloCqU/oLOrlMv8BJ9MU/bezIwIg0BUrMZyqy/oTFEV8S7rsxCbfEVIeKODJMpAQ87F/sWeR8FRStEsuHKtdzIwvxJK0XK+8yxngPaNIa65XtPNu1uyziyN5bbabZLrD8EhbcP7jVBifd4jnAnrA7HWILwxqALSdd7fA7HWIY46Rp6YW/ukWjmKeVCwKeLJsiIlSEz3+1HwWua8+SW6d881asx5e0wlPmta6Gq5XnGdHu7jawZFzAb36c6HeMUdvW/bvC/+BvZe6c3tJsUl7uvd8Y01Or6KMJ2WAAp4sVzklOhIb8Shmxhd66J11jGwzQ7BxQ9l3ui+FOs9O58DXSy7dY497vrGGmvKkolDAkwqyCBlfnP3iuF6+VY+wyaKyiU3Ec4JJHq2PjsFHQgbGZfFLw/bf8o1BiuGEUfO42FP7PPLFmGcMPvG16m13cQwMs9FjkD+hwTOj+IQzcrmFB6KPGel7jPSZh/zqJ5gV1XVsqMeeVA4KeFJZFq0dryr/mfMa3Uk3C++ZaEBGRUJP2+Dm7aJoF31aeEc8Ed7CnPECGBhiuOEg4DVd3i7agT4j6jw79/Y/elQcc4hjR91o4xQZ7zU/jsMOcczh62Rtj3T5obM07fBm9+m85r22ixi9Uqf2ZB3wOE7aRdEu8q2wHbf45U/oNQ3iZOxjV1tHdrv1MPukVf257WbWzbcbVU4w54uXujUczbEnlYACnlS6hWd8rIm55ltFvC9OY93lMyNoO54wPV6jGxF3WTXwW1yMKQwg2twHtreLfKsescgEAltsF8P75blvU9bDAf3FNme0gW72HrHWAoDx+dNmaAcCod60Y/PJzF6HrxPBQ3tVZs4HtgCsjXMxjEv6wjE6vq5X/mC7rBqAY6PvkrAm4MCodqtG9QTnl83mMdSUJ2WPAp5UnKRGPBaY8f4hzsb6RHu98zz6uoU0GVSQu96NvbvdenhNQ2cVhdLXi4HtdtGnzfzyyJXEfKp9vxnpmt1py9N+tufTZPy924EAL9rF6L8Ra3XKq6OL+Rv1bgw7/YD/MqIr/konaMquM0GaUoes93SnmCdljAKeVKLCZfyM52DY7Gt/xtUAjLNdU7PndZ8U7nOmqLaebNUjbLNFMyn69WLeOfOiKHKHBEA7kDim3rxxKvnQ6c+PQuj8/ZTy+ahmPGMaAP6PrEfOq7d2st6DQ5aD2C6dkX+Is90GtD7RLvq+CAj9P0374rwnQFCPPSlLFPCEyPLJ+Mh4f5DdxEV/ZD9YNbXxo5SwWEDz/fql0cQCjW5E0VKPjAuAMB6JPc7svLsFXnOX1McfOfWgtyXgSJ4WV+d5ti3Y+ao1r5V81TL+vFsfNsXW5PEPJU8jSKSxGszBsLf5PinfI+PvA7P6vnYjEBn/GFgZPKq+vM8Ct4Zb2qY8o1CoOgmhtehJ5dq8Zzp1Rfrc1qv3DzEm1Ditq+WfGwXHFnT1sIrO8rzTPbaBDVL2sIl4Tjgbd/UaAUSn1AF6sxbesOqSNdLLGYYRx+o8O/faLgIAWgK8tDoNEDm1jzskx5X+uaMjT00hPslOcHvH+s1tQYR9Y33KngL/AaepP/qDXFvi1LyWAC4OiuJ47EPCvE3sRfRH1u3T9puGg9JjfpdVI53d0Nmt7Vb5kw3VcOFfJZ6g/u/tI10JJ5jNoHv2FmEd+6QgVy4hzDQlHEl/okneKOBJRVtoxvuHGNNLNU5/MQK+sBpcDZOOyQWtRR/o8GwYzLVln/t+M7zFcN0RW6TPP8SY4BPbjYpvPNrn7WN/IR9dpD3di5fx8t6GWf8ipLynP9QkD9RFTyqMf0i5hPuFY7Wb94x6DK6kwo/ePPdKT/8rPf3RO7DjC8n5LUm3dMvmTndPOrbMAr9+K54NE/aJdMfnWp632LyzBdBZmpzMT97Pud9em3MsRTwjsel1CeTZBtvMAA7IK+0XKd1RnB57qQdeHMsh3QFIx1PvPcnDZ5b6AxCymHiLKQx9q7Ik3PPSKwASbgjjHxi5GVIW6LVbNQDgt7hM2CaKHMBbmON3bBlbhbs/FTD3avdVnK/fV/3ekUaEHnvv8z+8p02Q0l15vAG7pTnkuZbnJyn/6p31TFN+jfhQ71ioN/eXJe3ml5ncQDdvE62pT0qTGe3GSOgg8LXntd6DIf9b31yhOKIYPSVSxmffmpeOVP1msMCt/KTX5nQ9CaEWPKkgfsvlTe6kP9bce6JdZ0hcqNyP7aJdFO0PdXc+1N350Nb1VcznNAAioYNera9XmlPH/aRr6tb5qpV1a29P6m6Epmp2jH8ytvFW3fhdXVO3Br/yXftE0vG9Pm3QFvSn1DN/eV6Ks6ZeDpiu19SfSOxESea9nHDKHNuCiIk5bguuhDyZkd1kYFcFhz489+Sko3vSYfxu4u1w0WUJklf2zbU8ZoFz7GMN94WjpjzJCQU8qRj+oYHt7VvVnvnHx1Zg8kb8ZyMnTSWTBuCvX5n9woaqzXumwQuxKWwNrgawH6zCF3Hf1OxPN94CgMabg2urtoRWsB+sQt2AH8rjAWlFl3A+5TnK0Cdf76xPmsNVRMGv8irNTd6isl4Nb2Fc0v1+othuVJZzwxfxqfF5LVC/XYPI2XBQr9265fZ/YO0HP7lR4+wRxe8as6knt/JkeffY59Enn1msx75gNZLyRQFPKgRvGbgv083i9Wv/N7Vb4y4c+/jazPp1awBg89Ffx+5Ji7vXv37jaqDuqmJ63ZfiT6ccn2d5Vgox3F4YkXP/oFqu1onCW5gzXvO2lIiNlvu0/helcOW/bBOqmp7/MvMvtwGz77u/TOiWn6+ebMvTyjXmC9VwT0UZT7JBAU8qgt9yeXsvN/9xKq+8vPHZ/+UO6XHt+rsh2J4O1UohKtx5CzdWslixI1AFoO7iGi0ATDz4ZBDsJg4w3meGYHs62h3NC3mWZyfLaF+MRrxW5Gz/O/QpXfRqnSh+yxkvYgMTauXGdv5rs0DYxJxBV8/qt7bM4ubKlPecv57syueVx01xhCwJmmRHKkAkNL69XWXmVqJ/PFabetecf0DodFRbrdWb90wD92p3Nn/wwvA7we7o81OfYQGMs/uqbx9pecfRAoBBGNA2aiAtCz/KDXPMcPT4/MrnkWurfQGz7bLCMAzv/geuP6mYtwzc19uLyHhC4YAXejc7wLhMAKD1ye3ppHJ2JSJV+16sYhuFPuj3/6eJg9Nek8ub9vi8y7OSzeQ7x8vXi3eFEW3E05w7kgG14En58zuHbSb53jPOJiA4zDEui9rYdsoSttEtT2I3xK/ZdI+zp97ZU+/8IVuHVR2vy3fAsyGpXBR3u/XQu/VyWmh0I/Ia7AsrT6N0+uTj0twap96J4r/sBdAvzWrc7daHTdJkN0V5vfOHbJ1wG2vnBCDcMAvc+p7wK30rn+b4dPVkVZ6LDD32xU53CXXUk8wo4En5M/bGdzrh3Sz0rXwWi7cDgP/yaGe8CX3hWO3b9Q9Jjz8JPCzg4l1tCX/fJ+wTEc+gDa0nU+5tK1S50kKivVgd9Vox3qx87+/i5ZHQ+PZ0TWTt/hGdEZB2k0N8XqF2/4juGVcDIFRtmQIwN6Q7t+NR4ItfuDJn3q/TqB2frp5cynNDPfakZFHAEwL+UvxvtLIR7x8QOrfGIzYaqOzs0e53Bm/U7A0l3of9usfg4mysbyRpnZaZApXHFaThLmV8wWJeKzJNiZ3G9/zx5uiHzLYTxXifWfHT7tg5aieqgCe2PcyZwsAvEFu4N/H4dPXkX56dpKb84jTfJdSIJxlQwJMKx1ukrUiDw1x0ubpoxsf75yGlu6B7z9E96Xjswwd+WB9fnhZSOcMc7+/cnTAlOxIyMK4ClCsUtk++3lkvimIBMj7acE8aEr5gn5A+cNpOFON9ZmXTOXJ9NDrfsArhuej9dBPbDzSD1Tl2ieI2M7AeKsenqye38gWgpjwpNTRHg1SUGY/huA2tvFrjOLrUuTzfavOeINcTlLZIMfvsP3krJVbDxsnzH6zfG1oRXUbNb5G3Lde7E1agK1R5wYfbY+vD5LpAepxWlF+b+JfEb3GZRuPXWdqdXXoq4jnB9WtjT0U8JzgbK11zaSG/eu5RAJ8Ennxn8EaN0//L2IKAvZzq8YUtX6Di3RqX6U1zXvCfVASaRU8qR3TjNX26p7Q+cVe03Tzz7o/ecYj2XikJTK6qLii3kAEahb461H0AYMPkK8h99dmcyosxk065+psUD7nFvBztqY2E2AZ3wxwTlvaIm7BPKDNeSWPd5bvkMjFhADDL6Q5gRduLNe93X3V0Mw4XzPH0TTheC4TPMPJueq195mG5HADOGDbtHrFWJ9WvXo+5MOmeo8Sd92I6T4vPh1JKFdsAxij2A5yff4iJLTGkb+VHGs9axrf2ph0GImWAWvCksiQ1HwHEFzxR/on3hzyc7szfTF84Vtvg+r3Zo48JNefrd8Rv8Jo7bfz082uFN6+s3xvSTL6Cm5c+fuHT/bF+9dgeaJGQgRMWVq7cra4wMi/bnnYn02hjPf5Ujn890mW88gDlj1ktLx8JGbjh5vh2ujMew/FL+7ObRFkEubbgI6f2cYeE2D680X17k3fmjR/80pgc6oEuxqyVMp6brwXvt7hM3viGvNJFC+Z4cyBZdmgMnlQ69QVPjDqrJjYYL3ymJvE1YePc/X5p9ZsvTr4CADdvBvHr+AF5r0qbWN7w6ourUDeXurRrviYSl21XJSaSJuLFJtAp5fHuGeYQJO3pnt/mMX7LUqb7wmmeOiJ6w4DWtLNjniUN2/p8ncDFu/j56ox4Tpi8MPui6Q7pbsxtC5lXSJYFCnhS4eILnqjuOPJ2/UMA++lVVN0fa743Cm9NyN319/zxP8YXxpm1flNlsdv8Vqstxg3u+UXmQhJd9TNIMa8sTDrZfPeFm/EYXAPbl3G6y+Tkbjqr+l9TXN34vNkOALzTJkDf6ki+LJwjec1gUm4o4Elly7jgiZw6guZDyMvQApg73bB6xziAC5vlBtGFY7WoXX83bvIXLn1VusUu71VpjfdVQbA9/M1PpPLoargLlHeDuEiUGV+gPd0z98zPeAzyTXrKjeyi+8gpv9ipHslbGJfFH33KEs1V/1D05XIlDMOIY3WenU6myck0OZl5W+FpcA0CwF56O9MxkVOP2y5C/9x5Y+ab5SLXRwF9p8qKiBqrLjbjZCFn7U9bA1liNMmOkIQFT2ym8IC/3WiMB8/cqxs/9/iL8i3vgu7T+/1Vihe/Lc+9x90PNVe9MhrqGWV65GrzWJW2wfUou08XW/UWQHQ13DyVVK4rpbbjsYBP6zUdB6Afn4ExdWmgGY/heH/nbnGkWnrMGcCP6DSR0NP9Wl7UaYCIJ8SnPXKdU5ozaBp083aRH2JMIx4HZ9XwFlPY7LP3GuMTOzimzrNzb/+jR8UXpqRpcdxO5DAPLkqzUQDY0St1aJtKfu5iG9fUBgAtAX4si5p5Iag6r3Se65PDWWvSXeEcz5oUHAU8IQrG+8xIGPH+JPDk3P0vstGInXu1RQi1SPc7cxCA4a8HYfTZfUbewpz5562dN4dqN+8ZffdHL931rHL1WR2A6F9S9fJn2f/2jMsKSKveSpOo2dmjj334wOt5z7Ar2XRPtcCPavbZtw+4TLb4zQhx/qAtqPWNSIXV1pOt/dyw06/r5YDgMGdZJ/ZyGqtOk/ZIu/RVrFkew2b1EAD51vlODgA0W7V6m8ADwBbbxbDvhSkAwJT1cKD/kTZnYLC3LbfTiVxhATRvnFJ5riXAvzCIU/u4Q3fxQAFCdMFnrUl3hZf7WMnyR130pLKlWfBEblyGjdfwsjzcLuhmw1i9Q1pwXlqw5U78Np7w2X1GAFyvT/vLD9776p7pj958jUfzf1z+MOmt0q0+W7vtb2zvJa96izSr4Wap1PrkkxRjhoGx1+4zI2g7bvDMKMsj44kXULOuGRgdn4FGN8K36r1nYv3PaY9MJlziAU1jp17oPzsDALwQ1LMcACR2ttS+3wyMXqnL9Vz4CRYQNm1Ie4DmqR+4W7SmAzr55zTr/wPyIn3BS9fTPb/ws866BrLYKOBJZVGuSgtAmmrkNclDsH7ncNBs+NsBOd0n++puDT426eiedHRPHrnzM9rElwof499XN8Rm33OsPnjp7p/1h0aqHvjDTXcAin1r0q5Ke9dGV+qqt+lWw81GiUc71AbdVbvr86Ca8ZpGNnWd+ebGaiC6tY9PC+8Ziz/jkSqqrfu1QdtxhnExJki/WdHXCmgHAok1qDbEMwh0mfqBzlettRkOmrIeDuj7d1gCADJvK8dtNwPeEU/KwHjEc6IgZ51jDWTxUMCTyqGyKi2kBU/MYRPjYpj4cmafBJ6c7KtLeLVuQuoqj8fn+58Cqzcm3Fg8E/zd3aL4+LroizZ/86V0q9LewbgY5rjqqrcqq+FmpxjRzigsvLZ0U+qKmPFGvVuP2He4iGfEK00pj4Q8UpGx3WfG6PhM2iPV8RYTfPL6u9HfrFEPwGvukms49aC3JeDIpX8+cmofY9YCYZ/KWjcAFPfF1Q6efE7wmp2WgPqBMcbe3W69YOMSFv/3W1xPo6PXmP76qFM/61xqIIuHFrohJEHmmNkw+Up8w/jY6jTyz7yFORPbB0W57WzSHvPFXpZugdIudIPknWnyWOgm9lj1A0sHZH8usTV9AWl1Np0mvo6esnDGYzhuCypLYt/JAChXsks9MlYh6/Zp+03SS1g3b7jEnVGuQyetK8wwG90t0SXnYivNTXcYHmkLJi9fo76Snf65oyNPTUV/qvPs3GtDgH/hzbOKlezMXkdvGxTL2wlu/v/C02mXYUZ8JeZoDT7lHQcLPWu1GsjSo4AnJC5z9ErBs3nPtBzYSQGfvKSaesaXyN3tqnJdkV7K+2z+huR0L9y8C96VhhmP57rVGh2hiS5BaMqwkl2gw7NhMGPHe54WcS169bOm5nppoi56QmRZRm98P1mO1ScvVpOwI5my4b55z3TprF2TSuqBF8dy229GOn7e3vtc73QvVHd9UUU8g7b+6/Fb6s+G4danzzmdpcnJ/OT9YqR7phl2hZbjWZMlRi14QmTZNN9jNu+ZvnDsc8rFVdRWuZePhLwiXiEVtuG+8A3Q0jUi817HpuTb8YpO6cR9/xZ5Q7nF3Uou7VmTEkQBTwiQY7rH+YeYg2xszBI+lZXUSrbVjoXsEqtaW0qPveqge9K2vDGp5dLLz6348yyPz6+84BY14LVixin0pKLRQjeE5JnBm/dMXzjWzo+fkFajM6eke+lPpitsDkm1xfJGLd1Tt+VFhvIJ+0sW5gyHf/WJ383m+NzLi0IUxUXLeEp3kgH9x0EqV6xJV+O0Km9I+yTw5DuDa4EpaZ/WDE1PacKdatOwwdWQVE+6+uctlxS2v7qoCcQ0od5ZH/sxId1Vdl6fv1zqro/NA9cC4bzqKdD5zW+RAp6a7yQjasGTyhRv0j2T0M5uFBxbZjFV43wx8a539SbghWMf38O4fplY3uBqABoFR7eynnT1z1eu/AzLSWyiXOzDq2/Lm135hH2i4VvPTPY96hN3weIyeWHcnk89i2YxGvGU7mQ+FPCkAiU0DRXljYJjy6zufP0OeWfYbJqev9ykF9+S9/KI1pZcT7r65ytXfoZCKnb2SFPrE7Mnvi2vCVD0dmRZXm/W132/y/qM6+jXvVv0ZtZvcjH51FP0Afj4RShyxlO6k3nRbXKk4sSadElj5HOnt8xiqiYlZedpGr6ll+bJx3efS1NPruVFWnR2cXqPkzcwTbctb5blLZPe4Monnp2YuH+vF8DrqzeBdfO515O4HfAytoi3xpHliwKeVJp4k27S0T3pMM7J5Y1zIazq+GBOWnneYfxutPkeOz5x4/B4+Ss9/QzzTLp68igv/fXk85KwLa9i9fL5y7fW/AZw28u5LK8D0E7u/IdWCJf4nOtJLC86URST1v4rDOqcJ9mhgCcVJtqkG3T21Dt/yNbVXZWyNtwwC+BNrHb21Dt/6NbXZdM0/Ln77zLXk1e5p3gBtJh3cCU34pWM95mzL/cPcf7fAmD0bcOBMIDj9r/9fh71ZCgvGinjCxbzWpFpos55ki0KeFKBtOPs0FVH96TjK5/ZMgXUzYUxN1wH4NZUy40ACwiKpt5tAEGAAxRNQN5iCkvHz4UBCFVyPY1CX13e5RP2n107Fq1f7jBI2CBkGUuzLW8W5TOeg2Gz9bPAyvUc1+v7IhA+1Xe9Gfj+6YaGZ7+TdT2J5YtIFMXCNOWjDXdKd5IlCnhSaf4dCAuDN2qcPfVO/2rtRBXw6XD3VVwDpmqc51cOPlaz4vVoU4+3mCYTXm28zwycMHkGu6aAKbbjxmyfcQ6AdqIKuNXXsDqv8vsMa2b7/tofrf+npsvb5S3n41vZLtwir7CGhEa8yra8Vk0W5ZHx/iCLiACs2gSA2/glfOb73wrfAPuXz05894OWoO4HWdWTXL7YFtSUp4Y7yQsFPKkovMX0CwBVXdEbzYXq27h1+/JUzbferELdXHj8pk8btAX9clOP6xW3PQEguvVow7PfOYV//+w+6ZtB3e273qjC2k8FqZ48y+9G3eza++/GrPWb04hcH8Wdf8TLM701VoM5ebn75Up1W975y7nhIATvF7e49bOXeECz7nfx6de0wiUINs5lwjbxNb80vTHX+hdfrCmfQ8wrop3SneSKvhKSCuK3uEzeqbs61l4bvCEtJjN3uvvqB9dw+dc1Tv+KwJPvDOp8PHuQG15jhH+9HAbn/4v76z9a2yfu+itXw9zp7quQ72f7JPDkO4PMSt2/3rtjPL9y8TU/UvbxxBe0/NV2jX+IMYWlguiK32lXw83SIrfg/Qecpn5A+vxbxz28zprlJ1fuyxfbsk9eFXidkznzU+Brideh5BeuT5Z2T16tmPwU/Ykm+aL74Enl4J/wYlXHBx8P1gFrrzq6AUB3vr61YfLy2k8FrG570Vez1iQ38OJNvQ0PrsVPhC7GBXRDcbf6iqYrK19puR3aMhnakke5lO4ANFu12kPD4V8BANbrH2q/VwPAqHe32QsyWAAAIABJREFUhA993Dr8lrR7jbw/9yLPEcub/4DTxAf4sUGuCSfBMpxg9umye6X8zaY5qdworQosANA+n/wtJ3VdnRKXFNuKUKdGFykYCnhSMfyXZ4FVb2K1s4cFO3v0MWFKvvu8CluEH+heu/I9DSBuH2JMgvtv5XSXVpzFuivr/yK0QlFZdFlZJK0sm025T/yuEXapXNF8Zz8LoeqBewFs3jP9zf+333YRwDDHCD6x3Qiul28d5YZzPekGVwPAzh597PaWnlxfuwC6gX6YvYPSYLfGukvcGvLw2b3U2C7yrCF2phyrR3g8AqNGrsfAhTt3qrxOivZl15SXUKiTYqAxeFIpGk6/BEyt3RtaDSjmsQMYZ/dd1E+1cNLEdVMYYBs1QMbdYla0vVjv7FnfcQOouxFgsyzn3SxQd9AzEyvXWHdJk+kAIfp/4+y7P+r/8zs7o+Vh5fHzn6arQfkPAFB9ewqzfZOL2D9/dVMLvOYuf2yenSbr/vkkmsZOvXAp+uUgcjYc1Gu3pp8ltyz2kidkcVDAk4qg8kdfO1EVe8yGRkS7KN/pDr1b/0w8HTNZ0fYyW5dt+YR9QmPtcOtVjtdYO9xfAoA/2Pu5d3/00odf7tTWYvOeabXjZzwGF8O4GIscerXb9jPRe+qYrxoBQNC95+gWwo2CvHjOllkAcDJNXX4AqPPsdDJNTqbJyezsiAAIdEk/WgKpj/d5pqPHH9DFX3sgc3/7lLU7DGhNTU7DKcWF8A/F7/1TPpaetETPghuObjiOiGfQFoTXJJ3yT502wbx/nZNxWfzJ10F5nSnjCQEFPKkg2okquckOQJrHfmMlCyTsFzdoe+/iO598JetKhc/UYOVdQuZyxcp01Y3NaG6sTjm+urERqwGv6Th/Fb++MSuVbt7zYdLxXtPgwbae+q4peE/e9WxDw7PfvDYIdl9PvbOnvmsKoS/PBoyTR1puAbN9DaudPfXOnvp9F1cBgEMc6zOizrNzb/+jR8Uxhzh21I02bmdHpK1PPBeQv0jEHl/pMJi1AGt75HEcdojeMPp3ME2xxw96pjNemGg9wUN7Aaec4sZ2kW+V30j5WJr/ONrKi3ZRtPtiEw0ioadtcPNSTwbg/ZfRvm0wnfECXtMgTtpFnxbeEU/KXQaU8YSAAp5Ugujf+vE1sdvQgblXW27p3qhiY+k+4zG4ONu/1uxNGGsH8Mn7a9NWLehuXL24Rpu2fMPkKwlDwpHQwdFWR2pndSR0cLz2HsDss/vMmB156Y03ZwHg5qUnBpv9bzU0uBoa/v7vfg5Udf23KhZgP1glvZAN3eN8sYrF3Onuyb46AGjyS4kevxVQKbDFdjG8/6kpAMCU9XBAf7HNGVA7tY2DI+cCesDsPWKtBTZcS3o8v9rBkTGH6NMC8JpchgwDDZHQQS/rPilNJ4TREQ1+jW5E3GXVgL8kT1Po/BrXy7fqAbNvl1UjjdCrkzKeYp5UMppkR8qc8k/8irYXa97vjs+f3zE+YZ+QbsoKAqs6fljvTGqLS7u4Amh5x7GR3fdiFQuEjXKUAqi7uD72hSClfNo+sXnPNBQ3vEHfyo/IMZZc/oPap7lpAM/8f1NA3ezIS6+MAHUX1/+fyV84otbeFgBWnr5X1dVTf79xsi/td5GqrnqmaZJ/jgUU51j7fjMweqUOG9JewDTYS28DtemerfOcqrE+FQLAmP5AFK97DMdttqDfqr6TW+RsOAh2v9rIujQJ0eyzS5Mf1V4tXOIBtdcu62l3hCwcBTxZrpS3C6ebhJzagFu9o6d+R+IxA9+EE/XqbzLOOsfZpDKtv96pdqyiPJYoF47Vbt4DUWxXOd7YLpU3uBqAnkf+XvceWkZPN7Ba1Dsxd7r7agirHoikSfeosPGdwRvyLvLhzIcCgGajAGgHAjC2xQubN04BD8z/4py8dL//qVA0zqut+7U2U/pP1cgCgjRVPoF/iLOxPnGXEUC+6/lJTXnKeFKBqIueLBtMIoiI/Ut+KjvFGKlN3QjuwrFaaT/ZGLWJ7slW7+ip0eHW4GPvBZK/YCh9cm0tpKXx5MfzaTvvjs5vBxA59aC3JeBoUzTlgcj/bApmrCM7WpM8pw/SevIw32cEoFnXDIyOz0BuuAOQFugVbFzyoryRcQHRhfwi46rN96zQkDypTLSoAlkG5MzO/j9V6XBRXOQ/6+maifN+DKm9LpO7/WOjA1LhjVVTa28BwA2268qHfS3y431v3D4iH7ZKN4VQ3S1FtVVdPawWkG/6BxD2RefZ2S4CAFoC/Avy3erxhec6w+jXKjPe/Fxg9FBbMPmx4PaO9ZvbgnK1SlIXPSxNO7xyFfGFg/wWl8kLAHqzFt5wEFqf2G6MruSjZPbJU+oUB8ewbp+23yTNt2fd/K55V5hfXivhELJwFPCkpOUc7QkvBoB6Z5re94JKio3SbC9OOopzK3ygw7Nh0Ko6Hl96O5dTdz2pHKX1/x4hSnI//EJrKW7GL6/u30JnvM7StMPbeVp8PqT6NNNUimu0UcaTCkEBT0rRghruKtUBi9WUL3HFasSrijbfS7BvnDKeVAIKeFJyCtNwV6mXMh5YxIxPar6XWsyX2uchpOAo4ElpKVa6y7Vjfce3o5u+SG7Id7cD8mQ0KG5tB6CcAafYGi4muotMwtYyi1Cet0UK+DSj77HhjBJJVmrKkzJG98GTynJzMPFesrord8jpHp21Xpfw/Nzp7qtXL653hlYAc6e7J09DkfHSS6bk29Djil2+IKIoFn1j+PRz62JpWiINaLpLnpQxCnhSQorbfAcgYpZxxDvqw8b3rsVWkhlnneNrAk++86byBeynV+Orzay+fwrnqz8BVgBy+qq06YtdnrPU9Cp2xmczcz62zJzyxyVBGU/KFQU8KRVFT3eJiElmUsr4ubfWfu7B+ZdPufWm5pM2YQXwybW1qx54XQr7udNbZiFvJ69U7PJsLHFWaUV5WmMWSqRBH7sVgmKelBMKeFKxGueuXlmTaZk4AELV4xc/PNLyzumZ+h24+eaVu/YK8mtDWNXxwZyj+yqgGCAvdrm6DZOvALhwLO3S8KmK1Yif78b32Co3yqVvAJxb8eecTWAcLmlxHsX+fic4mwDA7LP3KhbTKVR5DC1cT8oPLVVLSsIiNd8lIiYdkwg33J53pXcAbOierimEtkw6GlbH95VpmAXwJlY7e+qdP2Tr6q46jHOLUB4lLYgr/ZPSHUDSgrjzXwZRZJrANOX0ovS0ItM0T+d8wp6wOBPfyl1ec94uitue6Ov++v3RjeAU5TDFd44vWHmK5bWqASGZUcCTCjX31trPNWWzvDk7ex41zvNVSErZqbV7Q6sBQKjaMoX4TvOFL792TAvUddwfD3Xl51M23PPIeCnmFyracM849D4zPgp9Z6O8J+x2LUavRwBppXqzT9pojuv1ab0HQ+fsExP21+/aE67qsj7japDL9xxjGBfDuAx/mnx8unoylqujjCdlgwKeLL1Fbb5LRFwN/dUd8/TPA8Dc6cc+fOD11RhnnT01urqrR3WfpB6knahSfXG+5RM7HgUePR6Lc+N95owfciEZjwU25bNouCsF+6W9YxAZF+Swj4z3B9lNsd56jtUHw2cjQGS8cYr9y2cn5J3d/+FnmPrUzNtFXjsRxugvVY5PV496eXq0lzwpDxTwpHLN3z+PxrkQVt4lN/RX7zhfNXXnJ5CSONbUBoTq27ixks2/XG6abz/QDPYvn5USPTwQ60mOXB+FIqLULDzjc455RbRnl+7V1pOt+uAwZ+EB3tmvPWmtBgBeCIJtjG0Vo1nXLG3xriifsL/+J79YsxK3v3+4oeHwS79CVbAv2gpXOz6r8oyk3whlPFnWKOBJjvxDUjcpw7gYQ6auzhS8hck0/FlQOjBO+FUfZPLJ+8q75NnZo1tmgdm+7kmH9G/LLOquOp6cFWZ+8854+bs/aLmle6OKBTC+puPGbJ/ckz/3avpy89PXDk9M2P1vuNnZPs/v7JkG4HcOB80GqwYA53CzXpO8faqiPJMFZjwSYz72L06b+FQO0R6l0Y34tPCeYZjL20d0852QQmS8P1j1u8ATOyYmdjwKTK8MDmVuhRcEZTxZ1ijgSY6M7aK4zQzo3bvFHP5Gz3gMyZuBFp8OzI7EBxk0Co7uqyFgquUdx5OzAgCham9Pfdcv4ofUXVzv7FnfcQMQ7vyzHrZOLr5dE79bfUXbizW6uquO7klH91XEy6fPHPSZo+UPxCeQa6y7fObwmz39DOMyIbncxLiSyjNbeMYjGvMxqYkek3vdM56D8InbzAibmOTd32WR66OphbwQxMqZ+M/3b8Havzjc0OBqaHj2OyrHp6snQ3kalPFk+aLb5Ege1m3SI6e/klL3bD83XJzPkyoEMQQA/OfBtQGh6IMMxlnnuNqg/CeJy9kCbX6pb/+OB24AV5LWtQWwekeP+Jo0D86uLDf22sVelTeQyjfvmU66zy3d8ZldOFYbi/bUOvNQwNWs/Zbj/Z27R1BtFO3bLS6TgeVHdBqO1SM8HoEx/m1RGo/IUP7eTeCJHRO9RiASuuPI0PirDb3f+l6O9WSL7pInyxS14MkSW4IZdhLpZrncsLOBRtUnlPet5RcDymBeoIK044uAH/CiubFa+sHYu80cFHgAmsZOfXxQPHI2HNRrt2ri5RHPCcYUBoSwNlreMhu7xJGz4Vv69tcOTzQMWMfrhL843JCunuTyXNCQPFmOKODJQkU8J+Kj8rE7mzHjMaQWyvyWxKc8+8A4wTjBdEUPqYPBKRcaOqKF0jh69CmLLuWF0af8mUfco4fB+V4gi5n0cdW335cfTdgnLmzW4J4/vpBvohdVSWb8uk16xG9R81/26lkOAKqt+2O3rvFOm2DeLw39VFv3a71/eoKzsT7+/2/vfkPbShN7j/8OlH3RF6VkNN1CIePelbzybCjM0hdGipm9N3MJ1qROYLieMi8mYdJKLPNCeiExDA0MC4HMYL+QLsxdLEhIBgpbw0Di60gbmGm3KBK+bxq4ZG3ho+5kU7bd3VFMKWX7hwvnvjhH0pEsy7Il2dKj7wczI0vHR+c8UvTT8+c8T/ib0jerj1eKks4k3w/8Sr/9e6G27f8+/X++/l/hf9n5y7Or3/rW6mLnfrrs/8jIeEwWAh6DqW1dTSlrpx0n7RTCypdzNUl7ueid7Rsdd/oFvD9ZC0lLWt+Rk5GTUfZrSdKMou9r+VM5GTmfSguKLrX60WNv615Gharyr+vNT2SXpLrs25KkZ1quq/C5Yj163GcUfVv3MnIyUkkb/6NePXhbz+/WP2kMsvuj/3nsOvqhhliJ1zhm/Jlk+b2sHofc73Y3A3ZzDMfiJXu5GrJWLeuB/DPNLUZu/EddqsZC1W+9qss/8HK69q/SH+pmaP/2l+zl6j9kvv+zzIUfXf/hFz9tXOp20P6PjozHBKEPHoMJzpedebWmIA1IUrGS0nnb/RhdvOT14XoZbyfcD1l/HaqyoMSvtbal5IYkFS+oUlX5mSTpme6VFFpQMeP1oxc+UVDS195zBTcUX9DKvNa2pBlt7yjZs8e9eEGVgEIr7m//LunrgMJdZrzxUry4ad2uZ+1ryaAkO5E4XjH1y834wTvO/Xtzbw9xtwM4kyynk90eCCavOfsfqO3+9VcBr/CLm9Zf/Oa7T6oPay9tp+oHzTjr2483AKLZfd5l/8dClzwmBQGPQbmzfMcLaefKphWrS6rt1r303ScfeyDJP22L4zywrMvKv6X8W4p/rrUt7QYkX+IGfy1JuzPqHBgVkC0FpStVxV5XZkv2BV25ffgRR0oqb8hSa1m5fj+sX3r11Rd9bDaQkWa8jjhl/Smz6xUFbnhT312yd/OhJ//yd6EHB6V7V6NYto6J6zERaKLHMXmjpRqzfPs/cIOzAbnjp/aJF9J2NpCP3W1vtN+Q47a6v6WiNFuXwp096LPPDjyUxS8VCWhlSfe/Vj+f+5WX3f8ffVjcmWSy5/Dr4ta+zojT15HoR22ut3yGelxHFkz+1/jBq8X01nyhhzhF3RCb68enkGESAh5HUEysWtZmUS+2K+Eri5JXWa+7s4/WdhvV7sVvx1WNNcfW1bYSudY1zMHktUK8ngo1LoOubTX+6rbi0u6MFr9URIo1BtzlXlek1DO5n+lGVfkFXdk4/BwWv1Qk3Bq4V9yM+o7tyNq+x9iJWH32eMO32g23M15HrLVb7ZwdNX86HhriEfbliJewd+WP+cHjuSPj+9/h+BYyDNLvDNKAuq+5aScsbwabSDysfLXiPdq6X5Hz9j1dDT2uSFIga19786G3HylcsAOx5vXxbhO95I2zq7h/XlJ5wzdZTV2FHcUWvNu22yU/o+gFld32+R7T2rjb+zZwFy2tbUVDjysKF5xLvi8Se7nonVRFbVt2fcjnePXLrobYUL//60LXPbtx0v8asu48dyP8DKltRUPVZW8AxL5fh2Eovekdud57b2NXyDAXAY/Td/KXwjvqtmx5cSsXmj9OeBQ3rZgKzqXF5hDCYWT8cIfF9c74o6aO3ygTyLscwy3PWu5uaD1sH2mO2/4M3kPvz/iDdjKuhQxjEfAYCyed8VbHZ6WdsB7k4/3OCNuuLYRU3Gy7AGwwoxj67k96d+duE/GArLnRxE+rPIf55ekgx67QH1qJH+tChqEIeIyFkwz47tX3YxtxM/JIM/5vfviKjlun3G9EtcxuHUOjddQKfdeu9+bfDlJx34+qPPpHwGNcnFzGd1bfB9Nqn3cNv6I5ooz/mx++MqzU8TOplnm8Cr3/+8FQKu77mVTIGB2ug8d0ceRYYmSyRpTukjcO3Iz4Od419M2h9SNKd5lVyBgdAh7jwnGcUVfih9w431XtxVPp3FB3Odypb3BUzVzvWqHvOt2Ne4+V4askThPXwWOMOI4z0tr1SNI9FIg0ZgJoONpqpP0Y7pXxo6tZutz65Qif4JTsv4be/98OFDJOHTV4TItRNc4HZ5cjj7fdSXMbq5HeG/qFXMMz6uBxGdyG3FGhb9721+MpZIwDavAYLyOqxI+ycX5oq5H2NvTp7TBcrDKHccO3P4wjr+1xGO9NN9o14iuLTuxSrgE740+mZtl6OqMHex+U6D/L/IxCxjgg4DG+Bh9zdxKj6iYKAT9SbuQT8BgTfPZhrB27Kn8yFfdTcexK/Amnu/ekUxY/FDLGB33wGGuO43i98n13zDtyZHmDjyb2U28vF121LO+nY727IXfGP1+Kzq0kSgPsoXTdmvsgN/zhAXbCWk0UD9/uqPv0CjZhd78/utW55G9tK9o8Ev/tMVTctNpOZC+X8EaHHLMwx/x80RMBjwnQFvPNn+ajantowqPddSZZTtvZgBQuOOly8kzHw0PL+NJ16+JCt1XxjqD4RVgKrP9kZgjH0+JbjXCYQmtO2nHey0ak/ANfboXWnLSdDcQLaadjHYHipuUthDjuiolVK1bP2mnHSTtO2rmnq9adVL5uH7swJ+fc0RUBj4nhtNuf6K2HjBEJHHRF/XAyfuG286gUGWgX8/fXq/FlVX78R50V34GE1uzz+w+slrvrVk8HrVOeC8cjysfu5g496MVLjv9IgvNl54SmxD+SWu5uLK94wbcIQnC+7FyOSwcV5uE6zh2ThoDHpDIz0SdN7bPXn3745dobVT2Zezjqi/iKm6FUoOCkHeeyYgNmfCBTvhxXPRXaNKL5eX4lVVfkfKbzm0cokw2cygFhHBDwwERpdLImivrrP/m//upss3br62B2e169Hn0tz7d289GKNbdiza34m+hrn33g3Tm3Yn003/nUXcw8/LGWv/dMCz+Nd7bSzyfmVhKlmexrK20d3v5OYmvVstx89Y052N8F3jy1WFWqxhK2FForNOceODa3XuvusItionFI/mZqX5/0/gK3rD+TPsg9n8m90yxD/22v0Lx75lain834y6pjy/5fDufR7+WlyPLs/gkYgsl5X+g3yrlxyt3eMwefOyYNAQ8Mamjtxv3wt5ouXnLs87/jHcTW1ZS8/tdCWPlyrub1vOZjG7qXlqpaf90dClf8aCVml+ydjLOTKSw39vx86eotZR9lnJ2Mk29t3EvpQir0t8mzkrauLKty60KjAOYTc2/lpXz8bX2ckarKl3M1SXYiVo0X0o6TtrMBRc7bzqVF7eWid9aX33Pc3nE9DnVkfG1rOfXP7s1IKuztKhSIVKoPB+wVCM7fywaUf9AxjFFSMbEae3redtKOky7EW0cSbQbe/gIvbklhKZC6+LY+dsvwLWuuedstz5ncO2/r44yzk3EelXTr/USpraxaWx7p5fj5y/2crvtOaLw9ur5nDj53TCACHhjMMNuNBxCcLzvXkkF3pFVVUrPntdEv+7X3teD50s31evbjDbe2t3i90Qd/dqO880nyrIofrVjxcD/PWfwiHH9jy729+EZVCt/3RuNvrT0qRaR4/pPkWd9T1148bUzUH3wzHKnUbUnFSqoSvuENJDyTvHc+Unm84i9Gu/5EwUIhLKmSrTbO96Vzqm93r3sfQTB5rRBXJXWn7YWrbd3MB7L3vNF2i5nGN6rgfNn23e4o8NC85DvrV77uuC1JpQupJ4HUxVbbydOvftlWVs0tj/5yHMp7J4QCB55Cj3PHBCLggUHs5W5W4wV3MfihtBsf2Tnpf3/6XI2GhPtX0k7hoDwIbP9ctZ/MVVSf7XYhvdsmfP+NjJOvdnm4w/Olm+vKxxttyPGwpPwX+1qSw+4Iifq27c7bX19/uCdJdr0SCYSk2m69bfvgS+ekp7vNKvVe7mb1T+P1WKwqhQs/+B3pN18ffnBHsLj2XjaifGzzYeOe2sNqRYEu7d3t+ihwv8D2zyVJr3ltJ+5P+d1nB215hJfjzz+QVNl+0cdhuLzvRvtPoc9zx0Qg4IEB1HbXK76144bSbtxDcavrqO8/ef/sf7v0V25DwkEDvB37Q7dtOfiHdSmwu7+9t3Q9dKte2MmsLfR1LLWfzOnDTx1fVhWWpfXvdDRhWJbl2B82fjuTvBGupO5Y1qoVU6E8H5SCswGper/9z87NNq4M/Mfd9co3fpQPuDV4/cE3pP/c/rm3LO+QnEneOx9RNZXyvmoEZwPqXCFwn0bLjb/Am4Xcy5OX+2p3OMrLYVmzhbiabex+tdzdA1uVup1CX+eOCUHAAwOw623VnSG1G7e4jdiNJ0vE6rPBtjpu7WHVTZT8f/836Z/cz+XOOnGHhZ/GFUhdvN7xsV/7KtAM/tpXhw29fr509Vb9Rnvtc/F6KaLwTW/gWFd2IqaCe5W2c8mLlcWIW4H2JpLJlfP+0eC/qFckqb77jUBE9cpX/yn9v1+5VeHjL8v7Yrvj20FwvuyvhS9++9AB9rXdejMIDylwv4Uvs6+FY+8seQFauh49oLiO9nJ47RD1VKitk6iYWL2qpYO+83U/hT7OHZOCqWqBARQ3rZgKzaySnbAeaAjrzezlondS+yuD8cvOWkjuMKi8JEXiYeWrFYULzrevWw/+Sf47mwLZQng95o4Oq2cffZI86w3satt3/nPFvTsjy1WthytS/MPS01sLFVULO7db5/R8KeqNvXf31nGn9Fo98iTgbiDtROTeH8ja0e1Q24wrkex75eSZtvONnLfL88HW3CzfkF5e1C/cvPnm4h+o+ItfSX8aD/zoadguH3nhvuayQNq3MlAtd3dl9lrjni6Tw8QL6bXQVjT0+FwhvbbY2mBfgTfLreP2vsJf/tz5gRq/1rP5nfV4c8u/3b7Y38vhm6rWf3a+E2weqv+d0PZyNE8h3n5q+3aFSULAAwPoCPha89P/dA6n9zT1x58mvbSUe2UjeaxF7NqnSd/L5V4kk416d20rGqrfaH096sZfwsVN6y9+890nz/+4cFmxoXyROrraVjRUXbZ988m0O4m56Pe9HMxFj65oogcGEApEOjssj91uPAQjWDN+PjG3Yn3x6+Ole2OEnaeW20itv2iWVu1hVdnIIRntL+HFS3as/ndS/rTS3XOKY9C6vRxhoh3d/dZpHwAwyYKzy5HH27YUlNwe8Uj43riOQHYc5+j1y621na214z5jx1q9weRSdv1OyHrs/tpon++pvYT18m8r8t1jtMwPQXHTvZYsXkj3+GpxrELuX5eXgwWRcRDeGcBgipvWzUCz2/hUa5aeHg31J7qYadgZTvaMXwn3NpGFDBPRRA8MZvGSvVwNWavW2GRPj4Z6x3GsuRM6jKEFz/iVcG8TWcgwEW8OwEwH1eNPqH453TVLChnjgBo8MF1Oon459cFDIWMcEPCAmU6xoZ7gEYWMMUDAA8YawVVzfeCqrRNAIaMPBDwwjUZVv6Td2IdCxuniXQIYrvdVc5KGMxws7Hh74yOlHYWM00LAA+Yb1RS2TdQpD0Mh4+TxdgGmwqEZr+PVMqlT9o1CxgljqloAXnIcLYG81KGS0C8KGSeM9w0wLXpX4pvcBGpqRVGjHtl6iE+P46KQcQIIeGCK9Jnxfv684eNiRChkjAIBD0yXY2Q8gEnEdfAAABiIgAemS3N6u1OY5A7ACSLggWlEugPGI+CB6TK50V7L3bWsVctaTRRP+1CASUDAA1OkI937CXvLZ2TH1YfiZigVKDhpx7msGBkPHI5R9MA0akb7/hH1B16iLXUsnXKCnx57ueid7RvptUVJUnHTuhmwy/PBk3p6YBJRgwemUTPX/ZV4t5ru7Mj/49fx0MlV62u765XAq6HGr6FApFJ9WDuJZwYmFwEPTCl/3d0f7f07uZi36xUFZpsV9uBL51Tftkf7nMCkI+CB6eVm/DGi3a8Z88M8MgADY7EZYHoNcanyZsafRMd87cVT6dzInwaYbNTggSk1YMV9vxFW5UOBiOq7bZ3uvi55AN0Q8MA0ctN9FEaS8cHZ5Uir0732sFqJhN9kDD3QE5fJAdNodAEvyZobQUN969I4O2E9UKFxyRyAAxDwwNQZabruW9g8AAAHtUlEQVR7TzGCjK/l7oZSdUlx0h3oAwEPTJcTSHfviUZRjwfQN/rgAQAwEAEPTJETq75rRKPtAPSNgAcAwEAEPDAtTrL67qISD5wiAh4AAAMR8MB4shPWqrX/J3HoEit2whrJcunFj1asuRVrbiX62YyeL+VKw3+KnkZ1XoCpCHhgPIXWnLSdDUjhgpN2nLTj/pp/YFmbB8fcXi76ID+Coyl+tBKzS/ZOxtnJ3NPb1sWF7RE8y8HshDWS8wIMRsADEyOYvOYUwlI1Ft06YDH0M8l75yPDf+b5++uKf3/DnRw2+O4nzqPSq8N/lh5Ca/YozgswGQEPTJTFS4W4VKk+PCDhDzLYCLtfvvqa8vHrrZaDsxvJhb7+knF2wGkh4IEJE3o1IHkrr9Ryd3t0zxcTbQ/VPvvA7US35lasj+Yl6flSdG4lUZpPzK1Yc9cPbvl/lvx+VQrH3A74TjO5d3zd85I0n5hbSZS8+6X5xpZ7uah3SNHcXuMoN9vHGWwWDzsvAP0g4IEJE5wNSHq6u6fa1tWUsnbacdJOIax8OddZrQ94j66FpKWrt5R9lHF2Mk6+qvXXc59dty4uVKR8/DtXdjLOzu1e87sv3HYelSJS5db71txKojXCbib3ztv6OOPsZJxHJd16P1GaT8y9lZfy8bf1ccbJV6XXczVJe7nohu6lHSft2OeVupMoSrITsWq80BhhEDlvO5cWDz8vAIf7rdM+AABHU9utSzo3e0bB+bIzL6mYWI3lJQV8WzWWXGutqbpR3tmQO1xuXVJd37vtfG8penHhXL5ntDed3SjvbKh03YqH8/GVpx9+Wn73mUoXUk8CuriSam721S/Lj0pPLy6cy3+SPCv9lz+T7ktSsZKq1BVabW25u6fQi6cKLIckKfhmOJKq21Kw13kB6Bc1eGDC2Nt1KfBqSGo0Zd+/knYKYf82+VjnmHPHcay5D6y5lftvuLXqI5nJfdZoZl+47ex8mn1NlVsXvCb917zR9e5P+d1n/r+0QrciCnhLuUfO240rAhwnXU6ecRd6X3+4J0l2vRIJhNTrvAD0j4AHJkpxM5aX4tFkUCpuhlKBgtNl7dR4IW1nA/nY3VbjdnFTqhd2Mmv9DY7r9OPv+Hro3S75hicv99tJXqnv2/JM8ka4krpjWatWTIXyfFC9zgtA/wh4YGLUcnetWFUKF9ZC8trq67s1NW63CSavFeL1VMi7aL62W5cCu88lqfbVMRq9w7HWKLyZ3A/DWv7poqSFL7OvhWPvLHlfJErXu43CkyQtRrIR3wV+xc1obk+yEzE1LvS/tNg61APPC0CfWA8eGE/dp3aJZN8rJ8/s3yYSDytfrbQ2DGTta28+vBtKuQEZLjjfjlkPvJ0sV7Ue9m2seD6ztiA9X4peXKioWugccDeT++z3k+/KHT0nScufOz/Yajw6335/c7N6Nr+Tii80jycZ9J1U/LKzFtp/mpHse+Xki97nlQwKwKEIeGBa9HspfGkp98pG8uwwnjLsWFbvD5m9XO5FMhnyfqttRUP1G42qPIBB0EQPTAvHcay53pvMJ+ZWrC9+PZx0d79S9KxC1HIbqfUXzXECtYdVZSOkOzAU1OCBKXKiK8YeXn2XtJeL3kk1muDbOyAADISAB6bLiWW8NSc+XoBTRBM9gBEIE+3AKSPggenSR0/8wPpqnAcwWgQ8MHVGnfGkOzAOCHgAQ0XjPDAeCHhgGo2qEk/jPDA2+KcITC/LsiQNZ1B92PH2xkcKMB4IeGDaDeHCOSruwPjh3ySAAaryVNyBcUXAA/AcLeaJdmC8EfAA2rix3dTK+0aitx7i0wMYYwQ8gF78oc7HBTBBCHgAAAzEdfAAABiIgAcAwEAEPAAABiLgAQAwEAEPAICBCHgAAAxEwAMAYCACHgAAAxHwAAAYiIAHAMBABDwAAAYi4AEAMBABDwCAgQh4AAAMRMADAGAgAh4AAAMR8AAAGIiABwDAQAQ8AAAGIuABADAQAQ8AgIEIeAAADETAAwBgIAIeAAADEfAAABiIgAcAwEAEPAAABiLgAQAwEAEPAICBCHgAAAxEwAMAYCACHgAAAxHwAAAYiIAHAMBABDwAAAYi4AEAMBABDwCAgQh4AAAMRMADAGAgAh4AAAMR8AAAGIiABwDAQAQ8AAAGIuABADAQAQ8AgIEIeAAADETAAwBgIAIeAAADEfAAABiIgAcAwEAEPAAABiLgAQAwEAEPAICBCHgAAAxEwAMAYCACHgAAAxHwAAAYiIAHAMBABDwAAAYi4AEAMBABDwCAgQh4AAAMRMADAGAgAh4AAAMR8AAAGIiABwDAQAQ8AAAGIuABADAQAQ8AgIEIeAAADETAAwBgIAIeAAADEfAAABiIgAcAwEAEPAAABiLgAQAwEAEPAICBCHgAAAxEwAMAYCACHgAAAxHwAAAYiIAHAMBABDwAAAYi4AEAMBABDwCAgQh4AAAMRMADAGAgAh4AAAMR8AAAGIiABwDAQAQ8AAAGIuABADAQAQ8AgIEIeAAADETAAwBgIAIeAAADEfAAABiIgAcAwEAEPAAABiLgAQAwEAEPAICBCHgAAAxEwAMAYCACHgAAAxHwAAAYiIAHAMBABDwAAAYi4AEAMBABDwCAgf4/N0XP7QqXWMAAAAAASUVORK5CYII=" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#[E(g)$Mincost >= 0]</span>
<span class="co">#vertex.size=24</span></code></pre></div>
</div>
</div>
<div id="last-plan-max-flow-with-many-constraints" class="section level1">
<h1>4 - Last Plan Max Flow with many constraints</h1>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#Maximum Flow</span>
<span class="co"># Set up model</span>
max_flow <-<span class="st"> </span><span class="kw">make.lp</span>(<span class="dv">0</span>, <span class="dv">41</span>)
<span class="kw">lp.control</span>(max_flow, <span class="dt">sense =</span> <span class="st">"max"</span>)</code></pre></div>
<pre><code>## $anti.degen
## [1] "fixedvars" "stalling"
##
## $basis.crash
## [1] "none"
##
## $bb.depthlimit
## [1] -50
##
## $bb.floorfirst
## [1] "automatic"
##
## $bb.rule
## [1] "pseudononint" "greedy" "dynamic" "rcostfixing"
##
## $break.at.first
## [1] FALSE
##
## $break.at.value
## [1] 1e+30
##
## $epsilon
## epsb epsd epsel epsint epsperturb epspivot
## 1e-10 1e-09 1e-12 1e-07 1e-05 2e-07
##
## $improve
## [1] "dualfeas" "thetagap"
##
## $infinite
## [1] 1e+30
##
## $maxpivot
## [1] 250
##
## $mip.gap
## absolute relative
## 1e-11 1e-11
##
## $negrange
## [1] -1e+06
##
## $obj.in.basis
## [1] TRUE
##
## $pivoting
## [1] "devex" "adaptive"
##
## $presolve
## [1] "none"
##
## $scalelimit
## [1] 5
##
## $scaling
## [1] "geometric" "equilibrate" "integers"
##
## $sense
## [1] "maximize"
##
## $simplextype
## [1] "dual" "primal"
##
## $timeout
## [1] 0
##
## $verbose
## [1] "neutral"</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">## Set objective fn
obj_fn <-<span class="st"> </span><span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">1</span>)
<span class="kw">set.objfn</span>(max_flow, obj_fn)
<span class="co"># Set up constraints</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>( <span class="dv">1</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">1000000</span>)<span class="co">#inflow</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="op">-</span><span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">150</span>,<span class="dv">240</span>,<span class="dv">150</span>,<span class="dv">150</span>,<span class="dv">240</span>,<span class="dv">240</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"="</span>, <span class="dv">0</span> ) <span class="co">#NY</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="op">-</span><span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">150</span>,<span class="dv">240</span>,<span class="dv">150</span>,<span class="dv">150</span>,<span class="dv">240</span>,<span class="dv">240</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"="</span>, <span class="dv">0</span>) <span class="co">#FL</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"="</span>, <span class="dv">0</span>) <span class="co">#Lusaka</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">240</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">240</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>),<span class="st">"="</span>, <span class="dv">0</span>) <span class="co">#Libreville</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"="</span>, <span class="dv">0</span>) <span class="co">#Nairobi</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">150</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"="</span>, <span class="dv">0</span>) <span class="co">#Khartoum</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">240</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">240</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>),<span class="st">"="</span>, <span class="dv">0</span>) <span class="co">#Luanda</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">240</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">240</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"="</span>, <span class="dv">0</span>) <span class="co">#Dakar</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>),<span class="st">"="</span>, <span class="dv">0</span>) <span class="co">#Niamey</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>),<span class="st">"="</span>, <span class="dv">0</span>) <span class="co">#Kosongo</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="dv">150</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="op">-</span><span class="fl">17.7</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>),<span class="st">"="</span>, <span class="dv">0</span>) <span class="co">#Ndjamena</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="op">-</span><span class="dv">1</span>,<span class="op">-</span><span class="dv">1</span>,<span class="op">-</span><span class="dv">1</span>,<span class="op">-</span><span class="dv">1</span>,<span class="op">-</span><span class="dv">1</span>,<span class="op">-</span><span class="dv">1</span>,<span class="op">-</span><span class="dv">1</span>,<span class="op">-</span><span class="dv">1</span>,<span class="op">-</span><span class="dv">1</span>), <span class="st">"<="</span>, <span class="dv">1000000</span>) <span class="co">#OUtflow</span>
<span class="co"># Air Constraints</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">300</span>) <span class="co">#NY-Lusak</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">500</span>) <span class="co">#NY-Nairobi</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">500</span>) <span class="co">#NY-Khartoum</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">500</span>) <span class="co">#FL-Lusaka</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">700</span>) <span class="co">#FL-Nairobi</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">600</span>) <span class="co">#FL-Khartoum</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">200</span>) <span class="co">#Lusaka-Niamey</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">0</span>) <span class="co">#Nairobi-Niamey</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">300</span>) <span class="co">#Khartoum-Niamey</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">140</span>) <span class="co">#Lusaka-Kosongo</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">40</span> ) <span class="co">#Nairobi-Kosongo</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">80</span> ) <span class="co">#Khartoum-Kosongo</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">0</span> ) <span class="co">#Lusaka-Ndjamena</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">300</span>) <span class="co">#Nairobi-Ndjamena</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">40</span> ) <span class="co">#Khartoum-Ndja</span>
<span class="co"># Truck Contraints</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">250</span>) <span class="co">#Lunda-Kosongo</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">240</span>) <span class="co">#Lunda-Ndjamena</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">300</span>) <span class="co">#Lib-Kosongo</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">160</span>) <span class="co">#Lib-Ndjamena</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">700</span>) <span class="co">#Dakar-Kosongo</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">450</span>) <span class="co">#Dakar-Ndjamena</span>
<span class="co"># City REquirements Constraints</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">150000</span>) <span class="co">#Lusaka</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">100000</span>) <span class="co">#Liber</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">120000</span>) <span class="co">#Nairobi</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">90000</span>) <span class="co">#Khartoum</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">130000</span>) <span class="co">#Lunada</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">50000</span>) <span class="co">#Dakar</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">100000</span>) <span class="co">#Niamey</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">0</span>), <span class="st">"<="</span>, <span class="dv">180000</span>) <span class="co">#Kosongo</span>
<span class="kw">add.constraint</span>(max_flow, <span class="kw">c</span>(<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">0</span>,<span class="dv">1</span>), <span class="st">"<="</span>, <span class="dv">80000</span>) <span class="co">#Ndjamena</span>
<span class="kw">dimnames</span>(max_flow) <-<span class="st"> </span><span class="kw">list</span>(<span class="kw">c</span>(<span class="st">"Inflow"</span>,<span class="st">"New York"</span>, <span class="st">"Jacksonville"</span>,<span class="st">"Lusaka"</span>, <span class="st">"Libreville"</span>, <span class="st">"Nairobi"</span>,
<span class="st">"Khartoum"</span>, <span class="st">"Luanda"</span>, <span class="st">"Dakar"</span>, <span class="st">"Niamey"</span>, <span class="st">"Kosongo"</span>, <span class="st">"Ndjamena"</span>, <span class="st">"Max Outflow"</span>,
<span class="st">"Ny-Lusaka AirC"</span>, <span class="st">"NY-Nairobi AirC"</span>, <span class="st">"NY-Khartoum AirC"</span>,
<span class="st">"JAX-Lusaka AirC"</span>, <span class="st">"JAX-Nairobi AirC"</span>,<span class="st">"JAX-Khartoum AirC"</span>,
<span class="st">"Lusaka-Niamey AirC"</span>, <span class="st">"Nairobi-Niamey AirC"</span>, <span class="st">"Khartoum-Niamey AirC"</span>,
<span class="st">"Lusaka-Kosongo AirC"</span>,<span class="st">"Nairobi-Kosongo AirC"</span>, <span class="st">"Khartoum-Kosongo AirC"</span>,
<span class="st">"Lusaka-Ndjamena AirC"</span>, <span class="st">"Nairobi-Ndjamena AirC"</span>, <span class="st">"Khartoum-Ndjamena AirC"</span>,
<span class="st">"Luanda-Kosongo TruckC"</span>, <span class="st">"Luanda-Ndjamena TruckC"</span>, <span class="st">"Libreville-Kosongo TruckC"</span>,
<span class="st">"Libreville-Ndjamena TruckC"</span>, <span class="st">"Dakar-Kosongo TruckC"</span>, <span class="st">"Dakar-Ndjamena TruckC"</span>,
<span class="st">"LusakaR"</span>, <span class="st">"LibrevilleR"</span>, <span class="st">"NairobiR"</span>, <span class="st">"KhartoumR"</span>,
<span class="st">"LuandaR"</span>, <span class="st">"DakarR"</span>, <span class="st">"NiameyR"</span>, <span class="st">"KosongoR"</span>, <span class="st">"NdjamenaR"</span>),
<span class="kw">c</span>(<span class="st">"I-NY"</span>, <span class="st">"I-JAX"</span>, <span class="kw">as.vector</span>(edges<span class="op">$</span>ID), <span class="st">"Lusaka-O"</span>, <span class="st">"Libreville-O"</span>,
<span class="st">"Nairobi-O"</span>, <span class="st">"Khartoum-O"</span>, <span class="st">"Luanda-O"</span>,<span class="st">"Dakar-O"</span>, <span class="st">"Niamey-O"</span>,
<span class="st">"Kosongo-O"</span>, <span class="st">"Ndjamena-O"</span>) )
<span class="co"># Write to view the algebraic formulation</span>
<span class="kw">write.lp</span>(max_flow, <span class="st">"5260_S18_minterm_max_flow.lp"</span>,<span class="dt">type =</span> <span class="st">'lp'</span>)
<span class="co"># Solve the model</span>
<span class="kw">solve</span>(max_flow)</code></pre></div>
<pre><code>## [1] 0</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Make results and sensitivity table </span>
ps <-<span class="st"> </span><span class="kw">get.primal.solution</span>(max_flow)
obj_sa <-<span class="st"> </span><span class="kw">get.sensitivity.obj</span>(max_flow)
rhs_sa <-<span class="st"> </span><span class="kw">get.sensitivity.rhs</span>(max_flow)
nv <-<span class="st"> </span><span class="kw">length</span>(<span class="kw">get.variables</span>(max_flow))
mc <-<span class="st"> </span><span class="kw">length</span>(<span class="kw">get.constr.type</span>(max_flow))
ov <-<span class="st"> </span><span class="kw">paste0</span>(<span class="st">"Objective Value = "</span>, ps[<span class="dv">1</span>])
sa_tab <-<span class="st"> </span><span class="kw">rbind</span>(ps[<span class="dv">2</span><span class="op">:</span>(nv <span class="op">+</span><span class="st"> </span>mc <span class="op">+</span><span class="st"> </span><span class="dv">1</span>)],
<span class="kw">round</span>(<span class="kw">c</span>(rhs_sa<span class="op">$</span>duals[<span class="dv">1</span><span class="op">:</span>mc], obj_fn), <span class="dv">2</span>),
<span class="kw">round</span>(<span class="kw">c</span>(rhs_sa<span class="op">$</span>dualsfrom[<span class="dv">1</span><span class="op">:</span>mc],obj_sa<span class="op">$</span>objfrom), <span class="dv">2</span>),
<span class="kw">round</span>(<span class="kw">c</span>(rhs_sa<span class="op">$</span>dualstill[<span class="dv">1</span><span class="op">:</span>mc],obj_sa<span class="op">$</span>objtill), <span class="dv">2</span>))
<span class="kw">colnames</span>(sa_tab) <-<span class="st"> </span><span class="kw">c</span>(<span class="kw">rownames</span>(max_flow), <span class="kw">colnames</span>(max_flow))
<span class="kw">rownames</span>(sa_tab) <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"solution"</span>, <span class="st">"duals/coef"</span>, <span class="st">"Sens From"</span>, <span class="st">"Sens Till"</span>)
<span class="co"># Objective value and sensitivity analysis table Transposing for better quality </span>
m2<-<span class="st"> </span><span class="kw">as.data.frame</span>(sa_tab)
tm2 <-<span class="st"> </span><span class="kw">transpose</span>(m2)
<span class="kw">setnames</span>(tm2, <span class="kw">rownames</span>(m2))
<span class="kw">colnames</span>(tm2) <-<span class="st"> </span><span class="kw">rownames</span>(m2)
<span class="kw">rownames</span>(tm2) <-<span class="st"> </span><span class="kw">colnames</span>(m2)
ov</code></pre></div>
<pre><code>## [1] "Objective Value = 816170"</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">tm3<-<span class="st"> </span><span class="kw">as.data.frame</span>(tm2)
tm3</code></pre></div>
<pre><code>## solution duals/coef
## Inflow 8.161700e+05 0.0
## New York 1.455192e-10 0.0
## Jacksonville 0.000000e+00 0.0
## Lusaka 0.000000e+00 1.0
## Libreville 0.000000e+00 0.0
## Nairobi 0.000000e+00 0.0
## Khartoum 0.000000e+00 0.0
## Luanda 0.000000e+00 0.0
## Dakar 0.000000e+00 0.0
## Niamey 0.000000e+00 0.0
## Kosongo 0.000000e+00 1.0
## Ndjamena 0.000000e+00 1.0
## Max Outflow -8.161700e+05 0.0
## Ny-Lusaka AirC 3.000000e+02 150.0
## NY-Nairobi AirC 5.000000e+02 0.0
## NY-Khartoum AirC 5.000000e+02 0.0
## JAX-Lusaka AirC 5.000000e+02 150.0
## JAX-Nairobi AirC 6.400000e+02 0.0
## JAX-Khartoum AirC 5.200000e+02 0.0
## Lusaka-Niamey AirC 0.000000e+00 0.0
## Nairobi-Niamey AirC 0.000000e+00 0.0
## Khartoum-Niamey AirC 3.000000e+02 0.0
## Lusaka-Kosongo AirC 0.000000e+00 0.0
## Nairobi-Kosongo AirC 4.000000e+01 150.0
## Khartoum-Kosongo AirC 8.000000e+01 150.0
## Lusaka-Ndjamena AirC 0.000000e+00 0.0
## Nairobi-Ndjamena AirC 3.000000e+02 150.0
## Khartoum-Ndjamena AirC 4.000000e+01 150.0
## Luanda-Kosongo TruckC 2.500000e+02 17.7
## Luanda-Ndjamena TruckC 2.400000e+02 17.7
## Libreville-Kosongo TruckC 3.000000e+02 17.7
## Libreville-Ndjamena TruckC 1.600000e+02 17.7
## Dakar-Kosongo TruckC 7.000000e+02 17.7
## Dakar-Ndjamena TruckC 4.500000e+02 17.7
## LusakaR 1.200000e+05 0.0
## LibrevilleR 1.000000e+05 1.0
## NairobiR 1.200000e+05 1.0
## KhartoumR 9.000000e+04 1.0
## LuandaR 1.300000e+05 1.0
## DakarR 5.000000e+04 1.0
## NiameyR 1.000000e+05 1.0
## KosongoR 4.012500e+04 0.0
## NdjamenaR 6.604500e+04 0.0
## I-NY 5.671700e+05 0.0
## I-JAX 2.490000e+05 0.0
## New York, NY > Lusaka, Zambia 3.000000e+02 0.0
## New York, NY > Libreville, Gabon 6.797583e+02 0.0
## New York, NY > Nairobi, Kenya 5.000000e+02 0.0
## New York, NY > Khartoum, Sudan 5.000000e+02 0.0
## New York, NY > Luanda, Angola 5.778042e+02 0.0
## New York, NY > Dakar, Senegal 2.931458e+02 0.0
## Jacksonville, FL > Lusaka, Zambia 5.000000e+02 0.0
## Jacksonville, FL > Libreville, Gabon 0.000000e+00 0.0
## Jacksonville, FL > Nairobi, Kenya 6.400000e+02 0.0
## Jacksonville, FL > Khartoum, Sudan 5.200000e+02 0.0
## Jacksonville, FL > Luanda, Angola 0.000000e+00 0.0
## Jacksonville, FL > Dakar, Senegal 0.000000e+00 0.0
## Lusaka, Zambia > Niamey, Niger 0.000000e+00 0.0
## Libreville, Gabon > Niamey, Niger 3.107345e+03 0.0
## Nairobi, Kenya > Niamey, Niger 0.000000e+00 0.0
## Khartoum, Sudan > Niamey, Niger 3.000000e+02 0.0
## Luanda, Angola > Niamey, Niger 0.000000e+00 0.0
## Dakar, Senegal > Niamey, Niger 0.000000e+00 0.0
## Lusaka, Zambia > Kosongo, D.R. Congo 0.000000e+00 0.0
## Libreville, Gabon > Kosongo, D.R. Congo 3.000000e+02 0.0
## Nairobi, Kenya > Kosongo, D.R. Congo 4.000000e+01 0.0
## Khartoum, Sudan > Kosongo, D.R. Congo 8.000000e+01 0.0
## Luanda, Angola > Kosongo, D.R. Congo 2.500000e+02 0.0
## Dakar, Senegal > Kosongo, D.R. Congo 7.000000e+02 0.0
## Lusaka, Zambia > Ndjamena, Chad 0.000000e+00 0.0
## Libreville, Gabon > Ndjamena, Chad 1.600000e+02 0.0
## Nairobi, Kenya > Ndjamena, Chad 3.000000e+02 0.0
## Khartoum, Sudan > Ndjamena, Chad 4.000000e+01 0.0
## Luanda, Angola > Ndjamena, Chad 2.400000e+02 0.0
## Dakar, Senegal > Ndjamena, Chad 4.500000e+02 0.0
## Lusaka-O 1.200000e+05 1.0
## Libreville-O 1.000000e+05 1.0
## Nairobi-O 1.200000e+05 1.0
## Khartoum-O 9.000000e+04 1.0
## Luanda-O 1.300000e+05 1.0
## Dakar-O 5.000000e+04 1.0
## Niamey-O 1.000000e+05 1.0
## Kosongo-O 4.012500e+04 1.0
## Ndjamena-O 6.604500e+04 1.0
## Sens From Sens Till
## Inflow -1.0000e+30 1.00000e+30
## New York -1.8383e+05 5.67170e+05
## Jacksonville -1.8383e+05 2.49000e+05
## Lusaka -1.2000e+05 3.00000e+04
## Libreville -1.8383e+05 1.63142e+05
## Nairobi -9.0000e+03 9.60000e+04
## Khartoum -1.2000e+04 7.80000e+04
## Luanda -1.8383e+05 1.38673e+05
## Dakar -1.8383e+05 7.03550e+04
## Niamey -1.8383e+05 5.50000e+04
## Kosongo -4.0125e+04 1.39875e+05
## Ndjamena -6.6045e+04 1.39550e+04
## Max Outflow -1.0000e+30 1.00000e+30
## Ny-Lusaka AirC 0.0000e+00 5.00000e+02
## NY-Nairobi AirC 4.4000e+02 1.14000e+03
## NY-Khartoum AirC 4.2000e+02 1.02000e+03
## JAX-Lusaka AirC 0.0000e+00 7.00000e+02
## JAX-Nairobi AirC -1.0000e+30 1.00000e+30
## JAX-Khartoum AirC -1.0000e+30 1.00000e+30
## Lusaka-Niamey AirC -1.0000e+30 1.00000e+30
## Nairobi-Niamey AirC 0.0000e+00 6.00000e+01
## Khartoum-Niamey AirC 0.0000e+00 3.80000e+02
## Lusaka-Kosongo AirC -1.0000e+30 1.00000e+30
## Nairobi-Kosongo AirC 0.0000e+00 1.00000e+02
## Khartoum-Kosongo AirC 0.0000e+00 1.60000e+02
## Lusaka-Ndjamena AirC -1.0000e+30 1.00000e+30
## Nairobi-Ndjamena AirC 0.0000e+00 3.60000e+02
## Khartoum-Ndjamena AirC 0.0000e+00 1.20000e+02
## Luanda-Kosongo TruckC 0.0000e+00 8.15254e+03
## Luanda-Ndjamena TruckC 0.0000e+00 1.02842e+03
## Libreville-Kosongo TruckC 0.0000e+00 8.20254e+03
## Libreville-Ndjamena TruckC 0.0000e+00 9.48420e+02
## Dakar-Kosongo TruckC 0.0000e+00 8.60254e+03
## Dakar-Ndjamena TruckC 0.0000e+00 1.23842e+03
## LusakaR -1.0000e+30 1.00000e+30
## LibrevilleR 0.0000e+00 2.83830e+05
## NairobiR 2.4000e+04 1.29000e+05
## KhartoumR 1.2000e+04 1.02000e+05
## LuandaR 0.0000e+00 3.13830e+05
## DakarR 0.0000e+00 2.33830e+05
## NiameyR 4.5000e+04 2.83830e+05
## KosongoR -1.0000e+30 1.00000e+30
## NdjamenaR -1.0000e+30 1.00000e+30
## I-NY 0.0000e+00 0.00000e+00
## I-JAX 0.0000e+00 0.00000e+00
## New York, NY > Lusaka, Zambia -1.5000e+02 1.00000e+30
## New York, NY > Libreville, Gabon 0.0000e+00 0.00000e+00
## New York, NY > Nairobi, Kenya 0.0000e+00 1.00000e+30
## New York, NY > Khartoum, Sudan 0.0000e+00 1.00000e+30
## New York, NY > Luanda, Angola 0.0000e+00 0.00000e+00
## New York, NY > Dakar, Senegal 0.0000e+00 0.00000e+00
## Jacksonville, FL > Lusaka, Zambia -1.5000e+02 1.00000e+30
## Jacksonville, FL > Libreville, Gabon -1.0000e+30 0.00000e+00
## Jacksonville, FL > Nairobi, Kenya 0.0000e+00 0.00000e+00
## Jacksonville, FL > Khartoum, Sudan 0.0000e+00 0.00000e+00
## Jacksonville, FL > Luanda, Angola -1.0000e+30 0.00000e+00
## Jacksonville, FL > Dakar, Senegal -1.0000e+30 0.00000e+00
## Lusaka, Zambia > Niamey, Niger -1.0000e+30 1.50000e+02
## Libreville, Gabon > Niamey, Niger 0.0000e+00 0.00000e+00
## Nairobi, Kenya > Niamey, Niger -1.0000e+30 1.00000e+30
## Khartoum, Sudan > Niamey, Niger 0.0000e+00 1.00000e+30
## Luanda, Angola > Niamey, Niger -1.0000e+30 0.00000e+00
## Dakar, Senegal > Niamey, Niger -1.0000e+30 0.00000e+00
## Lusaka, Zambia > Kosongo, D.R. Congo -1.0000e+30 0.00000e+00
## Libreville, Gabon > Kosongo, D.R. Congo -1.7700e+01 1.00000e+30
## Nairobi, Kenya > Kosongo, D.R. Congo -1.5000e+02 1.00000e+30
## Khartoum, Sudan > Kosongo, D.R. Congo -1.5000e+02 1.00000e+30
## Luanda, Angola > Kosongo, D.R. Congo -1.7700e+01 1.00000e+30
## Dakar, Senegal > Kosongo, D.R. Congo -1.7700e+01 1.00000e+30
## Lusaka, Zambia > Ndjamena, Chad -1.0000e+30 0.00000e+00
## Libreville, Gabon > Ndjamena, Chad -1.7700e+01 1.00000e+30
## Nairobi, Kenya > Ndjamena, Chad -1.5000e+02 1.00000e+30
## Khartoum, Sudan > Ndjamena, Chad -1.5000e+02 1.00000e+30
## Luanda, Angola > Ndjamena, Chad -1.7700e+01 1.00000e+30
## Dakar, Senegal > Ndjamena, Chad -1.7700e+01 1.00000e+30
## Lusaka-O 1.0000e+00 1.00000e+30
## Libreville-O 0.0000e+00 1.00000e+30
## Nairobi-O 0.0000e+00 1.00000e+30
## Khartoum-O 0.0000e+00 1.00000e+30
## Luanda-O 0.0000e+00 1.00000e+30
## Dakar-O 0.0000e+00 1.00000e+30
## Niamey-O 0.0000e+00 1.00000e+30
## Kosongo-O 0.0000e+00 1.00000e+00
## Ndjamena-O 0.0000e+00 1.00000e+00</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#get.variables(max_flow)</span></code></pre></div>
<div id="graph-max-cost-solution" class="section level2">
<h2>Graph Max Cost Solution</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Include solution in edges dataframe</span>
edges<span class="op">$</span>Mflow <-<span class="st"> </span><span class="kw">get.variables</span>(max_flow)[<span class="dv">3</span><span class="op">:</span><span class="dv">32</span>]
edges<span class="op">$</span>TotalAid <-<span class="st"> </span>edges<span class="op">$</span>Mflow <span class="op">*</span><span class="st"> </span>edges<span class="op">$</span>Capacity
<span class="co">#create size for vertecies</span>
<span class="co">#V()$Mflow <- get.variables(max_flow)[31:41] #tried playing with size of vertecies but failed</span>
<span class="co">#nodes$Mflow[1] <- get.variables(max_flow)[1]</span>
<span class="co">#nodes$Mflow[2] <- get.variables(max_flow)[2]</span>
g1 <-<span class="st"> </span>edges <span class="op">%>%</span>
<span class="st"> </span><span class="co"># creating igraph: "from" and "to" fields in the first two colums</span>
<span class="st"> </span><span class="kw">select</span>(From, To, ID, Capacity, Cost, Type, Mflow, TotalAid) <span class="op">%>%</span>
<span class="st"> </span><span class="co"># Make into graph object</span>
<span class="st"> </span><span class="kw">graph_from_data_frame</span>()
<span class="co">#Add route and node attribute</span>
<span class="kw">V</span>(g1)<span class="op">$</span>route <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"From"</span>,<span class="st">"From"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>,<span class="st">"To"</span>)
<span class="kw">V</span>(g1)<span class="op">$</span>color <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"gold"</span>,<span class="st">"green"</span>)[<span class="dv">1</span><span class="op">+</span>(<span class="kw">V</span>(net)<span class="op">$</span>route<span class="op">==</span><span class="st">"From"</span>)]
<span class="kw">V</span>(g1)<span class="op">$</span>Mflow <-<span class="st"> </span><span class="kw">get.variables</span>(max_flow)[<span class="dv">31</span><span class="op">:</span><span class="dv">41</span>] <span class="co">#tried playing with size of vertecies but failed</span>
<span class="kw">V</span>(g1)<span class="op">$</span>Mflow[<span class="dv">1</span>] <-<span class="st"> </span><span class="kw">get.variables</span>(max_flow)[<span class="dv">1</span>]
<span class="kw">V</span>(g1)<span class="op">$</span>Mflow[<span class="dv">2</span>] <-<span class="st"> </span><span class="kw">get.variables</span>(max_flow)[<span class="dv">2</span>]
<span class="co"># Get some colours in to visualise routes</span>
<span class="kw">E</span>(g1)<span class="op">$</span>color[<span class="kw">E</span>(g1)<span class="op">$</span>Type <span class="op">==</span><span class="st"> 'Truck'</span>] <-<span class="st"> 'saddlebrown'</span>
<span class="kw">E</span>(g1)<span class="op">$</span>color[<span class="kw">E</span>(g1)<span class="op">$</span>Type <span class="op">==</span><span class="st"> 'Airplane'</span>] <-<span class="st"> 'forestgreen'</span>
<span class="kw">E</span>(g1)<span class="op">$</span>color[<span class="kw">E</span>(g1)<span class="op">$</span>Type <span class="op">==</span><span class="st"> 'Ship'</span>] <-<span class="st"> 'royalblue'</span>
<span class="kw">E</span>(g1)<span class="op">$</span>color[<span class="kw">E</span>(g1)<span class="op">$</span>Mflow <span class="op">==</span><span class="st"> </span><span class="dv">0</span>] <-<span class="st"> 'white'</span>
g1<span class="op">$</span>layout <-<span class="st"> </span><span class="kw">matrix</span>(<span class="kw">c</span>(<span class="op">-</span><span class="dv">800</span>, <span class="op">-</span><span class="dv">800</span>,
<span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>, <span class="dv">0</span>,
<span class="dv">800</span>, <span class="dv">800</span>, <span class="dv">800</span>,
<span class="dv">225</span>, <span class="dv">125</span>,
<span class="dv">300</span>, <span class="dv">250</span>, <span class="dv">200</span>, <span class="dv">150</span>, <span class="dv">100</span>, <span class="dv">50</span>,
<span class="dv">250</span>, <span class="dv">175</span>, <span class="dv">100</span>), <span class="dt">nc =</span> <span class="dv">2</span>)
<span class="kw">plot</span>(g1, <span class="dt">edge.width =</span> <span class="dv">20</span><span class="op">*</span><span class="kw">E</span>(g1)<span class="op">$</span>TotalAid<span class="op">/</span><span class="kw">max</span>(<span class="kw">E</span>(g1)<span class="op">$</span>TotalAid) ,
<span class="dt">edge.arrow.size=</span>.<span class="dv">3</span>,
<span class="dt">edge.label =</span> <span class="kw">as.integer</span>(<span class="kw">E</span>(g1)<span class="op">$</span>TotalAid),
<span class="dt">vertex.size=</span> <span class="dv">50</span><span class="op">*</span><span class="kw">V</span>(g1)<span class="op">$</span>Mflow<span class="op">/</span><span class="kw">max</span>(<span class="kw">V</span>(g1)<span class="op">$</span>Mflow))</code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#get.variables(max_flow)</span>
<span class="kw">E</span>(g1)<span class="op">$</span>TotalAid</code></pre></div>
<pre><code>## [1] 45000 163142 75000 75000 138673 70355 75000 0 96000 78000
## [11] 0 0 0 55000 0 45000 0 0 0 5310
## [21] 6000 12000 4425 12390 0 2832 45000 6000 4248 7965</code></pre>
</div>
</div>
<div id="last-plan-testing-max-flow-by-relaxing-some-constraints-to-congos" class="section level1">
<h1>4 - Last Plan Testing Max Flow by relaxing some constraints to congos</h1>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#Maximum Flow</span>
<span class="co"># Set up model</span>
max_flow <-<span class="st"> </span><span class="kw">make.lp</span>(<span class="dv">0</span>, <span class="dv">41</span>)
<span class="kw">lp.control</span>(max_flow, <span class="dt">sense =</span> <span class="st">"max"</span>)</code></pre></div>
<pre><code>## $anti.degen
## [1] "fixedvars" "stalling"
##
## $basis.crash
## [1] "none"
##
## $bb.depthlimit
## [1] -50
##
## $bb.floorfirst
## [1] "automatic"
##
## $bb.rule
## [1] "pseudononint" "greedy" "dynamic" "rcostfixing"
##
## $break.at.first
## [1] FALSE
##
## $break.at.value
## [1] 1e+30
##
## $epsilon
## epsb epsd epsel epsint epsperturb epspivot
## 1e-10 1e-09 1e-12 1e-07 1e-05 2e-07
##
## $improve
## [1] "dualfeas" "thetagap"
##
## $infinite
## [1] 1e+30
##
## $maxpivot
## [1] 250
##
## $mip.gap
## absolute relative
## 1e-11 1e-11
##
## $negrange
## [1] -1e+06
##
## $obj.in.basis
## [1] TRUE
##
## $pivoting
## [1] "devex" "adaptive"
##
## $presolve
## [1] "none"
##
## $scalelimit
## [1] 5
##
## $scaling
## [1] "geometric" "equilibrate" "integers"
##
## $sense
## [1] "maximize"
##
## $simplextype
## [1] "dual" "primal"
##
## $timeout
## [1] 0