docs/binnmuAfterR340.html

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<title>Minimal Set of binnmu Packages</title>


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code > span.kw { color: #007020; font-weight: bold; } /* Keyword */
code > span.dt { color: #902000; } /* DataType */
code > span.dv { color: #40a070; } /* DecVal */
code > span.bn { color: #40a070; } /* BaseN */
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code > span.op { color: #666666; } /* Operator */
code > span.bu { } /* BuiltIn */
code > span.ex { } /* Extension */
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olOmNOjMKhUpWZWHK5LZgl9279229we2OBUX50kuVjv5QDo7PBwnsvrhWJF%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%2B3DnRfV7nXGOc5zjHOc4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2F4nlj89Z9A7%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%7Bmargin%3A0%3Bpadding%3A0%3Bborder%3A0%7Darticle%2Caside%2Cdetails%2Cfigcaption%2Cfigure%2Cfooter%2Cheader%2Chgroup%2Cmenu%2Cnav%2Csection%7Bdisplay%3Ablock%7Dblockquote%2Cq%7Bquotes%3Anone%7Dblockquote%3Abefore%2Cblockquote%3Aafter%2Cq%3Abefore%2Cq%3Aafter%7Bcontent%3A%27%27%3Bcontent%3Anone%7Dtable%7Bborder%2Dcollapse%3Acollapse%3Bborder%2Dspacing%3A0%7Dbody%7Bfont%2Dsize%3A16px%3Bline%2Dheight%3A1%2E5%3Bbackground%3A%23e7e7e7%20url%28data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAIYAAACGCAIAAACXG2XGAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAA09pVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADw%2FeHBhY2tldCBiZWdpbj0i77u%2FIiBpZD0iVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkIj8%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%2FPsPnF3EAAB7iSURBVHja5N3rmiPFlYVhWtMY3%2F8dGhsDd4BtBk%2Bov%2Bq3lxNmPMMgVQH6UY9KSmVG7NixD2sf4t0333zznx9e%2F%2FHhdd68e%2Ffudrv98MMP%2F%2FznP%2Fvk%2Ffv3n3322fm3y7r%2BXHY%2B%2Bfzzz7%2F%2F%2FvsuOG%2F64fnqfHL%2BPR%2Bef8815%2FO%2F%2F%2F3v5835%2B4c%2F%2FOH8%2FHx%2Brjl36LJz2%2FO3e%2Fag85Pz4bnsvDnXnF%2Bdb7%2F77rsvvvjiH%2F%2F4R4PsPu8%2BvBrt%2BXs%2B71aNpw977vnwfNX788PPPrzOBee2vj33%2FOfHVwN2wzPgJ9Dq3V%2F%2F%2BtemfV7nZ%2BeKM4hzUSTrl%2Bfq8%2FcQ4vygZ3Q9SkXZSNNl52%2Fjbtp921NbiW7emwhx%2Fu368%2Fdc%2FLe%2F%2Fe084nx7bhUFz8XN9nzYBefK86sWKaqdV8OL4ufN%2BaQJdqs%2BtE7n2zNaTGCF%2FvjHPzaeKNv7c9kTaHVfkqZ6PuqKCNcP4sRodD5siIb%2B2cdX3OrD829bocmcN%2BfD80lDiaaNrHs2q%2BjYzSN9y4CbYrco2Mx705RMsp%2FEvxH9PPp8aHbdvMsamw163mCRxnZeZ%2B3tqifQ6hb7tObnGefx54u4Kb4433aNoUeLf3x8dd%2FPP7xQLdK0DxpljBaHYurD4D39%2FLBHJ1jiuAh3Lu7pZ%2Bj9ttueibWocV%2F3RDIL08xRsN9GAswR6SNfU9iZtgP69wm0uln880Xs0H0PXZKq3eV8FVmbVXwdZyWau2%2BTb3BN2%2FiaTNzdJ%2FF7s0oWNdDz9%2Fx7bnt%2Bft63ov22R1vFqHPkTG9Iwpj0vLeKCRO6pyn31bnmPMuyWc5znzNrC3%2B%2BfQ6t3n355Zckb0PpRTK0mFEtjusx6Nt8zmRW08bm%2F3v1EE27LGkQEYmUWD46RqBWdMWglWtDpJ%2FORiQxaJp9XHKvYSRzWozmEnFaJzr5obR699VXXxkHGvXjHr%2FKs19GhVV9LS8bhgTo86ZHr2YvJXAof8ZMD%2B3f%2BDeRwoBBvtZsv2pbxM5Js8y8BmaantjTz%2FDOHNM6tkh3W7ug6T%2BBVi9b5ocPL9ZexkyTjygt9f0HHx7Q5o39m0CLnNjtbs0kiRQDUoOeaANFglbibgh%2B%2FIQd2WXNM2a8qNzz%2FphJ2Zr0AfV7%2Fp4PD8m%2B%2F%2FBqpnEo9ZANSm20wyJ9tslzaHXfJW%2FBjmQXPl9QvDWb%2B332MoPEOttQaebWtvXsqWvbsKbOm0wIFqo16FYkGAke33Xbdjo%2FhvA5nyRbknsZpki8bmPLSXNmp5FsPb0Rtk4EaS5bI%2BefMoh3tR5OK45bv0wZtrvZzv0s0d%2B0sQnjOn5prD40FG5qQ6ROcNC5OCJ2MRfyTJsCj3Ct6BlMhlmPoKsZxOeaM5fuFoEafJrGc%2FsJI7h7EoYRAb8%2Fh1Z3iytW6u4XSOBsqKgJV%2BBMJZ1xX3Nm4ZFUVAVzqEdwO1LjvbmgLH3ecxPNmDdqng8bTzssgROb2wH9qouzFxq%2FjYsPCB%2BqCx%2F00Ij7aFrdvfe3API07oi4yiZqZjjhQa7cbxIQe2GoM%2FREB3XXdj5vzuIDIZBvYQksQIFzU7sslyq52XAvLoJli%2FRm1RM9jrdvkGv5tMPyLs99UtdNZAES1%2FctWc8lOoNMb0WQ2KiVfg6t7oIrVoo6DKT1hzMr16GL3TJp4oKEJnOw2aYJSAPCF6HBQd4koxoAOb4by0xSrV3MW4Zuce%2FT56zqptOC2Y7UALeU95OZlHo%2F759Aq7vgIiIbInqBOZtPRn1vFnpiafQTSiKWZG419EQTStn1Paj5J%2F3ja0RszXA67z1JeD6hKqwBFABWxs9w2wbGJ9gdGelXtdgcD6XVu2%2B%2B%2Babv7DIa0qYG0cTmGaDt6Iumalic56ydzM1EUzNvZKwOZiK%2FrGl4ikkS2fugbh7t8ulWYjQ7eAE7ojvvnrAJFi%2FA73u3h9LqLri4pt3Fbk0Kt8JJeXs2uiRVKahI3wyTJ8bdV9GOGKFUYatAPVhLaAcubtoJhMRxFCQMQfecUACJHbYWlGjHuZgnwU5LAy%2Fe8wRavfvzn%2F%2F8ijESdldT%2Bt3GSJZWL7skEckpZS3w7EDivNm1IGFEP1sZ4Ee3SjH0N1nRt%2BeTtk4UhxAv0Zm5bLnLDlgjB4jQkNpbO0fOeaR8NK1ubT36sL9JN3GFM%2F%2BICPAQconKoWztx9afswo8IMH6FVG%2B3GQ%2FUddN%2FtwwSkWy8%2Fc4Bz98fDFSYRs89u7GjdhoCncVyNhDF9fZXdXdnkCrWyPOQbNDmRYWH7TA2zQmYPg6Ys0tWdmzYwqubCTjaWNbKoGgy1JqXzeGsD8oQEQBoTex5dOUU9TnH6RLQVLEoFBS65EJJ2vgCbR6wYqx0iLP4mKxFbwByTyAFR87rP%2FRz2lvwDgjJ7Ku7m3CENZkFKyFaiH3W0vbceFkdElwt1ScHiHe9cOjKfceaHbcwOfQ6gVo2%2BXaaGuzzaNeQ5AQIJfpYcgatOM85tx5oV8ot8kQBWz2ppHp0m8RiydPQLWZaHX4BCSYsWDytp1AU3xq2zExhLaeQ6tb%2B66%2F0aj55EzCBD2et9ld%2FvbhBXkuk4GmSho020RN3JHQYNVwUAQWaYUel8kYURaBYDus%2Fxw1e7Va3ECfg3MAGyEotp2wvMeh0qNp9d7y%2Fthx7Wo%2F63mMkywf%2BCUzTtQhC321xcrorgGsLsAAAXN9SyhClcXFVMswW6MLTnX%2BMrfAukiwsituWOsjHo8bLOoTaHX3S55s7%2F4k%2BPH7tHd%2Fklbvvv7667cQVHhOmuuvIgDzfoGmTdsRFmV7bEiS4SRg3lRhiLZ2PM7xJgpikIhlJmGlgnSioYts811WPeQHGLnMKIA%2FKwu%2FUyfs4C8%2BvNYcZ4Ylcy7e%2B4NodWP%2FdVNgMnMQTNQW66axj8QywHh2XullsUDKnHhpSpITEwvpWIA5W0gAroHFtvBtd6Yw6YneAxCF91tUJIaqbZhdigUDjGH2JFp9%2BeWXjW%2F9VSb2KiJoq%2FidINIl1nR5bXYBN6qNn2Hubvyy9jKVKH%2BlPb1CLMUIMoKAwV%2FFKw31U8bUBwpe0pFJRctMT7BBHkurJrP79MyQD7E4qNs1T05s8%2B9fWXS2fzJhPQN7%2FDy6KYUbGrFN4ALu5CaXcKFtQStHJDaL5si8lIT3%2FuML%2BLiQTFtqnb7n0OoeL3kLUZ0e%2FVqg55uKgL379ttvU6qwZWqQ6SIS0LJjcxJz60gu5hP3TfDKrM4MIwRab%2FIjHbCZ1LxlMk2mhNQ0oYiNsqyFRnZZpK32yKfrVtEO%2BXrcw2l1%2FBKm976EIS81HwaXAI33N7esocRo5%2FPvvvtubx7XkNeNTMoBdIg%2Bd70EuC5rAaSpsbKgGlLWDSZK%2FTjYZR%2FjeiTjul4g0YfS6sa%2BBFWK%2FMQFOcBstSUBcx6yu%2FmMWRdRByEqD8CwqRamOu3Kk1hEy50Xxu%2BHUW0xAjogOWl4dBUM3xMbTEYHQHdTkJ9Dq3s6hPQk2YLsSHtWemsbebM3VWNsPi5V4dkLngt0Yxbh69dN4GfybqJJdhQM5uG0%2Bstf%2FuJhft%2BToDeCENJz8SBvyCfiARKiuWMcJbZ8TAR2RcdGCaIXJ4%2FFssTAJMQaqU2dbFJ2lL0EMNY85YfvSuQwtdX4NA%2Bl1Ut9yauX0sRxmzzImtz5M0CxZDpAipQQmYQVYK2d5KEmKBvxTZQdff3118JnwvqshYsHq3BveU00VF4hQ0iuSZcxGelz6c%2Bb34XNl7VbvwQIlF40bGHazUT1nsO4fqXsIUIsoAzvC51Zv2fQ6uySTxXXH7N4Gis4DAtcCHFJ7PxxoZvSY4UtsYzNu5jg%2F0n32BbQX6jJpXKZSSrHNUnIFm%2BF%2BB%2BrFRZxIOseTasbppBnRh%2BG5RkNIGEtRZg2K7ZH7q1EwtU%2B2df4i%2F3DKDoqtGcllFVSdU0yes0bmd2B6u28FrgQE9Nu6976q4yYRUTVLXL1HFq9BIehgTsmQU3Lyw%2BQqGBK7BwBV0kOyrnBuuvH8SIJCrpB0PRoIPhx3ET%2FKw7yPitWmv2lCGg1f5gKhcRToW%2FUNlrdJ9DqX1oRSB%2BGBMjv25Sf5ikctpnniUUa%2ByKy5YLStwwe5WivlZSsimcT8jJGCEC8%2F1Ba3QAGYePJa6g4%2F4Dglo0oJirNCRwd1eQwrBGFAWnyZEgbwqP%2F%2FuGV52UOkmuSZglrz41Dq7xqhzFJuTuCVFkQe3OBSCGZDIFiLSb4BFrdYccnq6%2BfVPVd8PPczN%2Baqq%2FT0NpzPIB%2BEFstPOXW7Eu%2BUjpKlFSaFrgC23bDYMdMYUkIaRq8H0XENihMzCtosbm5KnRXMDbONuV6J%2BvNMUOwzsJZT6DV3S9Z0ryWZ%2FAGSfNv2ehBtLr7JW8hmXwzGdI3NHl3i8r0YbmHv83mRBt3bHomsM0yFGEsbAWq8hhFylgA%2BLzpioryYysx1GjBrFTr3k5K2sSMWzVyfpvnUY3IGq9AM9pCJp9l6Lbci20cwXkStH8GrY7gUnfDFhK9ETrFoZeUZ61zqLIF8jaVr8FFO1HbbSgmNZRtinbqaBCXBWU3bEj%2Fkki3nT7UUGkRsoXChQ43dUbB0aLFD6XVfUneQmnlZtCy7rcMjlbY2i0LwxxSq%2FDrLUO9fSok%2FWhfZ9qHWMDDP%2F%2F4YnrmWOGywM5LeGqLELGSBjUSOPJCIkqasGlzGyU%2F1AhrNX9Si9Hcc1MPykrIos1HAW9Ei2ZNXVPsXMg0%2FxNodeMxtevj0%2B2WJCCoL1j06t8dnzzlLbRhaEYmHA2X7so8L2bPZqGVu8UuUtnO3xSDUjN%2FiStHmnygTZ3ugu8%2FvnA38WWojItn0Ir1JuC1rrX6fuG8dcRIdogQVuUJiqHyBOn57sbPUAJSyIi40D1mPc1LnxnpXuKMyQqZbYo5SD%2FmGXklZLB5MwSp6O%2BjafXuT3%2F601swLoUQ9pMFepPa8Sm5ryiWSY000nMwBGnGD0gZbErcGzHEb7wh3LodGFXc0JwYgTFe5aDUTSSOplpRBuoh9FaTbJ%2BE7aIk0bRCxdI1hSBhX20%2BziarjOLhtXH3ln%2BznqGiYDQCivP0BFrdAZW3AARtWPvVczNeFzS7bQMPERum3uaWawWzOYab3aMQXzXRZhqszE0IUDCsFKPn4UOltsUo%2F1mEeLsRLPQrGUdto83HD98GA%2Bu0UsUvkaXbk2j10o%2FrOUlj%2F0OC3adowe8%2Bwe4uuDYeuYF%2Bv%2B8W1JpopQtMG7DjAvbr9qdcryrO5ZyDFDcLC3wk7Hixf0Ad%2Fm7m6vY1FRfR2UVFa%2F9KpJNClryql%2BcTaHW3uBiO8osFrrPQCY3LZlRfu6nH%2FA%2FRw7pUnY1C1UtVWTOD7IZMCAKKra5Pzg3uQ2vDoLz0VeQ62I6sie1EIS1%2FI4M88CfQ6uaOftMk4xGGIJBKBr8MhO00oHiSlusn27lFGlwxu%2B2eAgck0DYLrUdktgmJy47YCCO1DIHeRGn15xzGzRbfEjeEEyB4Aq1uqp4JNYK1%2FF0G6%2FZgYeS06dakSTJIdlIMl55kzGCQ5nMp%2BSGUY1Uh3vU6dR3KhFWaTgk3ErPVj8KvFIbJzIfQHForadgMzSfQ6kXzbBfQbXnfmJQqaxOyCdQyaGGIcpnjR%2BAgxGIb%2FUrLVFCq5mNzCeEQqRbU1Icp6sumjCjbXAPOZoeRZg043aP4kYom4p5Dq3sxA%2B8soa%2BMcyvDUGphq6QQF29LBRWmxsIQb%2FtA7d5WzcqI0LYLNmwZTCZ2K58o0oBSiD5kpT919Mys2NTLppMztJPdmNsTaPXSte7VS5IlDb3Nk3eeWr791VdfbW%2BERWlkj%2Bst40QJqXKSGXbyOiRtKrCE6C3u29K3V7H0tibvEpYXfdJM1UZ8KK3e4y8OsMAqSbq1yWKfPVgwyoku8T7Df8%2BQkWZAUEgOll4eAyrzhVdqiKJFY2JtU0klGctz3PoVuIWsrY0DMvk8zu6BBhF9D6XVjaG5LcZC96B%2B2alr%2F1FHZCvYOZYkQ9RMbsOAH2d4dofslpUhOFRAHmKRYodGOD0GatmmpIc2jcHW5L2rX1GAmtkNadez%2FNG0eilm%2BNkx818w2%2BrXHjP%2FpWj1Ly2nID8x3aVPUh8K7fGVPjVL%2F8h9oGzsKda252LtsRSgEQOAOa7zjNlhutujLobVMluGqly67RS9WdJZX22siJDWhe2TLc%2BgVbWKr57NdqlRe5Vt%2BkaSIu8Y1yu6I%2Fur37k7glb38lHlcgxT%2BRnGJCwj0KbirQs0sEpcZMLK2yAusGGgxXZ%2BfCMt3%2F837fEfSquX%2BpJX16iXc3nski3VIQQWyXeBMKXqDeHFX1fG3o3bpefHCnEHC8pS0Ck9d4zf1OSl1QirXbhV4C%2BX2AFW4j%2BxzwbAqeKIK0KeKs6%2BbBU3eQ5qoBOADjmbhLDnz%2F3nx5cVkgejbc4TaHXb7Nh%2BtlG8bbRGSWoWeonuURvCEnvKD8YhTCEW%2BQctlY5m3DT1czrpbtC75SzPSKvVjb9qXsl1X5hZB1QxcN7fNo7QOPoJtLo5jXR91BgqIYim0r%2BBg6RHXtU%2BOJng2E%2F6EGyAuWDdVI7sjUu3GXjqpQ3QZiZkaDokkehYUGsxrs0yaTBbWrf5NC32E2j1Xovu3UHZM8kKDLt9UC5d4hcjIjH3vLetQtsOTLh1ESeJ%2FtJz99zcy8l47DSlm4W5OhnPBVv5sV1rd4E3e2%2F9jJ88jfpxtPrUa1XzczYGlbXHMTLMdWAEFm0X7ItA147XxcIeGgluJtw2apI3BUXe80VEA%2FuJgJicsc0RsWbhTlv%2FCD5pmjJ69nyR59Dq7pe8BVP9Zx8U%2F9uLsrwnMfiiTVV0jFG0SiyJnHWkcaZTvYTTIX0d3ibautlD2%2BNcgDrmTaPShFsBxWgxeW2115gRNdmzaCTSrdkqB478tFlBhPjjobT6VD76ukc870m3v%2Fq%2BNP8%2FWt18sT0speMJucgWKHlAnM5hmzacgMymmDQHLZSUIugyxneJlZIDEt0jUJpD%2F0f0Zeo4AwGkyNLVFnatAGmMXDb2lRaCWoFt19OH0uq2HdScS9jWy7C7tOxdwQde3WMBcRwziUXBCiR5t6WufBTHQ8mHT45vszp%2B%2BBbLcHq2SqgJ0xPJK94AsUn6bdeIqE9FPYdW74HYMO1L6S0qGLp8nHWvtgcgAbLjw57UO1MdFqIPrE4ywNoYfDv1RlzdU6TnqHKTvMqDkeyzXguYVlUD74%2FkMccn0OolAfXV2%2Bg5S0GAYfsBATbIq99wy8E77MiUpvqUbW%2BnJY0noDe8p3SvRL%2BlF8jvkostNiVJd8PjMobb7yu1pF1JhGDe7En3l8avAhjHDK2t1PYnUpy4NerbOsV8n0CrG9HJNlCIzsrUq2pLMXkJavFjIjG%2BDUaVQrgrsT2vBdUzlNWNmbn8AWko7FqVhprYEOh5zuiy1Vyu3Eq1BRBpfpuDlfFoWr0Xnd%2Fm6uLP7D9iYQ9gcaLL9r8GHDFPbZTtDk58OZpINS3IXSBvD7nkBvMHdcQQf7007hMuXEthk%2FnsrXVUgdCbA%2F8EWt00YCNVs1Cl7QSvGqggJebdE6J6ak7QwuN7nD0eZGI6R0x99OZu7dnQfD348fb05ZoJ2LG%2B5FbBXIG%2BexwUvhY%2Fl2QEM340re665C2c%2BLxd%2FhTy%2Fj7bEnyqL%2FmxRw3E3UMAGRsYTT6gqudtMCUk55S4BbjUYm0I9g161P8WffgFaXVPQCWjtS%2Fc2lY9jS7s4CAFD4Dfcaq3oe8FA77g1UpDHt0jmuxGIxHoHe22aN4D7Z5Aq9sWrfZjYQwHHTgIvPfZRXumyr7JbBXAWOMkLMEuSeeLTGwhbMADhyChnJ9cWPdTNcbH7rw6X2T%2FlIKVvUvgeHTW3a7NOqGkjQ4EWYzPodV7KwyUDlxaMcrGJ4s3NctvubVkMW4ScJVAtkeMryQFyK95Kr%2BdKON1Cgtyu2QGwy516KRvLjp%2FT30VYaS30EE1yUNpdWOTCK1IX0MvdLxUaQoaS9EoELQtLfa40ct5k0zPtWdYXwLjiKhKKruFBl5oEmMy8OD%2F0iqsnKq17napN9iK3gLAz6HVbduBbEe7TFUUTFwkZ%2FpKeIMLJldcDdyeoCI5HIQVfRdwVeW2Mt0ZvWrUl6a3eaFUORXbh5gTt3URala1Ld1yKYYZ9OE5tLqnab%2BRxqGveT7FW%2BoX8RLCevUqJlAo1F1Pgs1DKDq5iV5rdMIrmQ98jp4bQ0h%2FfZsVXy%2BwI7RALexmbGy7v%2F8uY0MC488LoTt0%2FRf0dS6p35uiYJ30ANqeoGW3CFZKviKvHkqrf2lw%2Fku1M90ArVrjZNHGoCQbLM76W2tn%2Bn%2Bn1Y2%2Barbn8dUmq%2BB3tIKhw9rE91niqCYLbXuQOQsUU1dGpTSk%2FUsPbxi1Re2327BMjBJabOQQXzg%2Fuw7A09MVTe%2FprmYqEeI5tPqEIuh9p95gm%2FuEQzixi4G%2FRUobbXVKrvEt%2FMAGc2pBsmhPtHc4w7ZxWO%2BEoXnkzKJnoC1heYmEdM%2Bl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V2BdQNAXjYB8rAUAgICAgQCAgIEBQBoCAgECAgIBAgECAgICAgICAgICAgICAIAgIAAgIAAgIAAAIAAmgAAAPoAAQGWBMDLyBSAOPUAugCAMxyBPpIGX%2FQBt9FgAjQAfGQM4WMgD%2FhAEwnUfgAlOvqAJ5a1uABp03nXYAa0kDNzWfRAZxcQ%2BalAOtNRvqBhqter%2FACBVHXfgDLt4q40cgHc5mKrTIA2plL%2BPEgL7ob%2BsaPkDPdDUY%2BWqzgAtwlENSBlLd44mgKKmcxX0AnO3yU9XyA5TWFo%2Bq2AmpanyXAEolrCefMB7Zf469AFNO8rWvUB%2BXydTGW3xsBOZadJxc7cga7VjMT%2FPIClDuudK%2FADOvj7AM%2BejWgDPxab3m8AMvafCA1rQEkkk88gaWnoAuoTcbMDUpVh8ICU%2FH3gBV3qBqOPJgMrxkBVcaAat%2BLAU9gFWtgHxACgFSAoBmX9wHXqAgKAQIDQEAgICBAQGgICAQICAQIBAgICAgICAgICAgICAgICAgIAAoAgACAAICAyBAQAwBgAEAMAAAAAeQCgBgD8MAmKANADN6gDWy8gBteMgZxxoAX4yAJvQAynotABqY%2BnABUxjfzAL67oDMp3la0ANy66yBl5abhMASmMxP88gSUZrnSvwANzfiwMtvGVh7ADcO7ubwwBt7T4QF%2F04txHhgZ%2FVJPOzrCAnpvjzXUDPdWXGz%2B4B8l21h6JLbzALfb1uK9cWBZczbfmtAL%2FOPUB%2FTOkwngCmFVzxv5gPbdYf330A0pTdKetsBUtSvVSwJdzuLWt2Aqe6XbugNfsm3ruqmAL%2FACmvNeYGk7ddM4AW4xWviQH4x7wgFQ%2Ft5gNaZ0WQGbWwGmqnK5oBmXEelgNrIDLdagah3sArd6WAvhAKA0lP2AsYA1WQJPYBWwCqmgFAaTAlLsBsBwAoBAYAUBAIEAoBAgECAgECAQICAgICAgGAKAKAKAKAKACAICAgICAgACAAICAAIA6AAEBAAAAOQDQAAmBlr%2BgIAoA%2BgGXuAMCYGW35gUO6oDLn0sAb2XkAe4BE%2FZgZdfkAfxyATtYBmsAZtN0p6gFtSvYA%2BW38gZty8zgBfyTnXipgDLS7U15rzAJz7ZAG4iK18SBl9sL1hAEp0961pgVKlnRZsAbbfFQwM9yqf9LmgCZaUdGnIE21lu8LC9WBd3c3WG9F9gK4T%2F6W2ujAIVTDc6acAavuhPn%2BAJRD6SlgDSUfq1P1u6kCbccvXgDXCp6%2BYDmtFkCS1TuYQCk0ucgat15eYCmrjTTHICsY8gFL08UBrtrHmA44kB5Wl9QH232oBWZf5A1MWwLrkBj0A0p8cgPIGtQGPUBAcgKAVswEBAQIBA0BAKAQECAQECAQICAQIBAgICAgGAGAICAgICAgICAgCAACAgICAAIAAgIAAgBgAEBAZYABADAAAAAAAAe4A%2F7AOXoAOgMv3AGvRAF%2BOQDqAPPAA5zqBlrewJy6YGW1HlMYAIinYGX9dQJ8VuBl3WmoGY1TuYQBaXOXkCcsAlXGnkAaY6rkDMXH%2FOk9cAXbWMa1OgFzhvowMPdaQ3m5AI0wtdnAEnLbacLTMZAp%2BMtgUqFqncu2AuZlZ5dSALucJ5ipfUBiWp%2FFgaVwmsVHkA0u1NZbwqx57AMPKXIFPxVxWL8gNW1PcvuBZ0l58gNppPP9dQG8p1vYElD1c2gNKHO2sANpq%2FuApuI2%2FAClF6%2FeQFJOOdUBqnKytAGGgFW40AcfcBVXj8AaXGQGNNgHQB%2BoCnUgPUBAUAgID1AUAgICgECAgFAIEAgQCBIBAgICAgGAECAgICAgICAgICAgIAgAAgICAAIAAgIAAmBkCAgAA6gABQAAYYF4oDLyAcAABCcAGZ1AHKAOFgAdfcApXj8AGnIGWprbABUb6zkAfGeoGZah%2B7AHbU%2BGAZiVgApJPV6KgBp5XUDMxnTAA02p7vpKAMu1LUvyAKTyAOVLT87AIh6ubS3eQKnOiy4rzAxLTVzmsyAy4%2BOi%2FAGMWlbro5gAXam0qvVAUrulK1bU3X9gEdyvxHoAzE905%2FsCzc9V44AkrzvKSeNgNdtN6K3M64A03DpVh%2BYFSUtOcXgBe7vTxgBXSXpzACoxqBrLvEWuQHDSxywK4pxhzigGLjcDS2b8wGIdvzAYpdZSA0mkt3sBKXnOGBpbLoAqNLAaXQDS1ikApY1AV46AMAK3AY%2FkBQGlrtuA68AQCBpASgDX0AgEBAQIBQCBAIEAgQCBAQEBQBoCAgICAgICAgICAgICAgIAaAAICAgACAAIAAgBgAEAAAABAZAGgJgD6%2BYGWBacgZ6gQGQB%2B24Fo4wBmMagZ8eQA07%2BgBiwBqdeqAIvPlYBhvRZnnAB3OHSrUDLhKX04AO7kA8pYGVGNaAnblwlFrkAeUvcDNxTjWcUBlq2tX6gTejfnqANXb5b4vgCafxWrTpYxpYBKSu2tPcDFvKc4cZAlDbS6cgC4Uvbf0AW6TWVaT0cgStbPE7yA9sOalv1gB7VLT6xwA9sQnnueLlALSUrtzlt%2Bd%2BwCsdanYBaVpf64AZxzhANPy1A1awpucgD2ajadJ6gbTW%2B8MCVOIlZA1G9r8AazFROugFH%2BVjzA1UTpmAEDWeIAln6AKa%2BwGoAb%2FDAVkB5WVoBpYAUArcCUeYGunqAgP1AgNAIEAgIEBoCAgECAQIBAgIBQCBAQEBAQEBAQEBAQEBAQEBAQA0AAQEBAAABAQABAZAgBgDAMgQGWBAHAB1AGARhADjyAGAMDOv0AJ%2FABF0AOdujAyonYAe6zlIAytgCtp%2BoBFp%2BEBlJQtwBqJjOrfjgA09gMtZjIA3jnC58wJw7zEX4yAOVpPmBju28KQKVvGYewGZhxFZAnTtytuEAu4hROumAMPt%2FysLqAP4tT%2FzmPcAczdLT3UgVPLhKtscAUvML%2BWBUlKjZceYD5x1dIDXbDiaeugD%2BkfGM58eQFF%2FF0sxFSBJPGNkBuXSwuc0BKYlgKbiVPQDXbo1MaThASr%2FqsbAaVacR0AVs60A0vRLYBWbxhoB%2FWm1nYBV2lYDGGs7ga7VtYCp9QFSnACsrPDAYq348wNfUBQCgEBXICowAgMAICgECQGkAgQCgECoBAgECAQICAgNAQEBAQEBAQEBAQEBAQEBAQEBAQGQICAgACAAIAAgB5AAIAAAAA8wAA6gAA%2F7AKyAO7QA1tkAgAYA6cAZedeABqlfjzAM9dQBt5AKSroAPxwBmnEqHrNAH6xEZyANXDx0qQMw%2BmyAHNaL8AFxPEAZlwmpxjRgCWGpjRPCAMa16AGNOIfAB16AW%2BiWq%2BoBreMdyywB%2FGE2pmXQGXDnuSvfNqtQMvtw1lu3hgPaomLnD0nn0AJ%2BSnCcATTcukomtgNRapxhrjyA0lCiJSeiAf1Sdzc%2BeAJLu7Xre%2BvoArMNNdvlAGmomur2kAuOcQBpKa0A2q92tmBSppVbkBbWM%2FwAAMTIGkt8vIClhvM6gMLWnPqwNROPPzAU3G8YYCsT9QHDqgJa8UgNpXXoA112AVADM8AMAaS9AKAFQA3ICs4oDQEAgICAgQCgECAQIBAgECAgFAIEBAQEBAQEBAIEBAAEBAQEBAQEAMAAgICAAACAAIAYABADAIAgMuABgDAIAmtQMgDvABcbsC0Ay64Ay%2FwCEBNbegBXXYDNMDLcrYCamXoANemqAzEaSp0AP1SdzqAQ0%2Buj1ANYhpeQA6mvPqBhzGbxGM0BQn03XjUCx7tbMDLjyuwM9zSpKenAA1M%2B4FFuctXGwBFpvKdys9ABpf9U582wM9yltdudY5AJ7onLWGApVPs89AMNdyTVdHfOQNPu%2BP5AkpvObA6dt7%2FyuQC5jfKh6gK%2F09W%2FsBpd3V86ATmbtZU%2BOAFPRPoBpJJRNa9F0Ald4VLrwAtOeUBqOIgBUy5A1M1zgCVcT4wBprbG4Dpt7YA1MdNAGar3AVXXcBSm%2FcDXu2A6AKArA1gBAUAgaWQFMCAQGAFAICBIBQCAgQEAgIEBAQGgICAgICAgECAQICAgCAACAgICAgICAGAAQEAAAEBAAEwMgQAAQAADAGAWBNyBkAYFpfiACQBvbQDL9wDIA%2FVsAcxvoBlIDLTSAm4Ay1NgX2%2BoGIeNwL%2Fp7gE%2BfIGXm8ZAJ09IAISUTTmVxzABnjCAy58wB9r0UQBlTLn2zIE3Nc4AMcTMfaoAu9bTGZAy8NutJxgA%2BUJOYTtKvoAtuH8ZUaN7AGc3frxGAJqcVlLboAqpVwuoGu3Fq8zsBpJRCrrdJyAavt1rCzADScvP4AcKE756gMJfdsBTjhxCYGoTma93H2ApTczpp%2FIGk%2BZ4A0l9cgMQpnp44AfDz0A1CnEcAL39gHM86gKio9gFX0Al%2FAG05XADXqArGQHICAgKA0sAPADqBAaAgEBAQIDQEAgQCBAIEBAKAQICAgICAgNQBAQEBAQEBQAAAEBAQEBADAAICAAIAAgACAyBADAGBOwMgQAAAAA8gDAGBmqANwM%2F0BTK4Ay4U8gE1mADIAwB%2BwGfLmdgKKhUBmrXTCzAA4TnUDLpQsgZaSx5vqAJxxUJ9AFpPNe7gDDiZ%2BgFOzniQCF5zE%2FaQMtRafR%2BNgB593nOAFpTMQ9uoGe%2Bpbhp6R5AZpy93n2AFDhp0rrFoBay1KWwE32z1hbAaUt1SaVgKbdzCXH4AI6rl7egGknE2gNVjXGmuoFhvWb8eoGtOdYyAzK%2FtgKjKqfICX7axvPkBrWONANYlOwFOU0vEANrOtAPW99AFLxwBrq35gOV9gNK9oAk78IDS%2BoDPoArDbaAQGgNWwJWA%2BLA1oAoBAQIDSAgEBAQIBAgECAQICA0gICAgICAgFAIEBAQEBAQEAAAEBAQEBAQGQICAGBAAEAAQAwACYA8AAGXYEwAA0YA5AywIAYAwDIGZ5r0APuAN81sBnCbcY9wB1gAbQA5b4YGZb4Ay14e3oBQ4m0AfXAGXTAHjnWABuV06vAFKyqnyAw7qY3nyAnFpZjT6AGJTtgZmu5JT%2FAA%2Fks5de3kBOcNt5nT2hgYj%2BsUBYptuN9OQJ2mtMRz4YGWm7Twpx1A11q99wNJOKac58wNdttrx5ADpJdrcaMBTUOMbK66oBUY038MBzwsfwBpRhx0AVVxKdR0A0nrc77yAKnvxqBpqJ42AUmq0A2s58vsA4vbEZAYw9dAJAa8IB6dAFb%2B%2BwGsr2kBT4AfPAGlaAumgGlyA%2B%2B4CgHGGAgKgBQCBLcDSAkAgSA0BAIEAgIEBAQGgICAgICAQECAgICAgICAgIAYABAQEBAQGWBAQAwIAAgACAGAAQAAAAAwDAAAADAzIE8gD4Az9dwB31xIFPFgZfWkBm2gBp5WiAOoB5zuAawBl0oTrRgEpT9P6AKxpuBl30wAcOOgBi4lOo6AU63O%2B8gYw98vkA7qTxxFgEd3bWn5nYCec%2BX2AHKUvTEOwFpU5vKh5A5TP2n8ADamddM9QHlKG6t39wJpxLmrfkAQ1%2Bq1332A12ynd%2B%2BIA2lntbmn%2FAGBVhQmsfYBbuZlcqQJvMqbVbpgKXVrXUDS39wH4%2Be8ga4m82BJJuLejbA0kBRbXmA276SBtaXgBpQ3sArqA6dMgNSA35sDSVcbAaQF5ygFIDXIFgDSlZAVsA0AgIEgNIBAUAgQCAgQCAgQCgICAkBoCAgICAgEBAgICAgICAgICAGAMCAgICAgBgAEBMAAAIAAgBgAEAAAAAAABbAACsgGgAwBwBlzrkCj0AAMv1TAI%2FAGWql6ADlVv9QCGsgG6d0wMuMKJXhATd5roBl6zuq4AznprqAXM%2B4A157gTxE3mwMwm4zo2wCHvwBl9ttXVgH7O%2BnqBXV4%2B4E%2Fio7nGNdgM4Tv0uwMz%2BrjTML1sAcS8xOVT99gLtUaW4n0AYaisU3z6Aa7YzIGk0lDVZj31Au35OZfRgSzPc22pgDSSmVXEZ8gFNLmHn2AVE1jcBSqXp9F0AY3YC176ZAYnK6gazn0Abi6fjyA1tx7AM7efIDe8ANRGwDjLlIBrD9GBoDUAKQEv7AV%2FYGl%2FYCgFAKn%2BQHlgK3AV9AFAKAUAgQCsgICBAQCAgQEBAaAgICAgIBQCBAQEBAQEBAQEAMAAgICAgIAYABAQAAAQABADAAIAAOoAwBgAB1AAB8ADncAcYAHQA4x7MDLAmtgMx41Az77gGPPIBf5APMApZ6gCl69GBnFty1MAEKZVcbgEx1Tz7AZpOsbgCVS6jypdAKMywMtb66ZAy1OV1As2%2BkYAy5hTT8eQDtx7J9AM9zv9aeXOH4gA7l3LVpbAZ%2FWPjEwvf6ATS7Ylylr%2FADIA1hvDqsAPyb1lzkDWn%2Bvlv5Aa7ax1awAQnLS6rWgNKEnxUrcCiLfk7zgDURm2%2FIBlTaAflKzEgLhTGucaAa49gHPnIEq119wNK9Y0gB1l9ZA03o%2FUBSi%2FSAGNPHsApXHqAyvIDUrTOAGE5AQNIC2A1LAdMyAqgHyAVgBAV7gNAQGgECAUAgQCBAICBAQEgNAQEBAQEAoBAgICAgICAgICAGAMCAgICAgBgAEBMAAAIAAgBgAEAADAABgAAAAD5AAB5gAj%2BQBtAZn1wAZkAANdwMvTYAbfW8gDxmf4AE4x1aAzEy0uu4FhdNVAGWtX40AGozl%2BQGW1NgDc3MSAOFMa5iNAJ7ewA7URmQMJxrbcNcyBKXlwsQBnWX1lgPc6S7rnWQMNReNo5AGm3C80%2FXQCSlxcLPUBiVShfbTcAeVNJYfHqBrtacPQBcx8U5lQvHkA6ppeXlNQBJSlxqrWwGsO6WEwJb%2FwBr31AY0URjb7AaSwutAXV142AVtGPoBu05y%2FsBJqYnp6AaqFDhrQCV59egGkAxCcZAaf0A14gBXvv1AVyA6Z8wHn1A0gHXxgBTVAXGQNAOgCAoBAgNIBAgFZAUAgQEAgIEBAQGgICAgICAQECAgICAgICAgIAYABAQEBAQGWBAQEAAAEAAQA8gAEAMAAAAAAAAA8SAADWwA7AH4QGfrv1AOuQDTbkDLcOddQL2QGX6c8AVOGBh7JzOAB8ADUpTpqsADp3jQA5Ay%2FVZewA1oojAFCpewGXrLrxsBlRiMfQBhpt5eutewGZUxMZj0AqhQ4aWPcDKuZ2hPpVAZl7LoAfGE4ytwCU6VadAHWJtLPmBNrEcrUCT0eZiegG1b6TPKgB%2BPxaTzotaW4FbSWmZ8wNV7Zx9UBROEkn4sDSxVL8gPxUxmnC%2B4D%2B2cIBmpfr9gGdfLqBpY32Apxt5gaWOtQBpKfGkgKeHnUBWsgIClcvwgNRu7QCphQArZgakCXuBoCXICgNYAcoBAQFAQGkAgQCgECAQJAIEAgQEBpAQEBAQEBAaAgICAgICAgIAAAICAgICAgMgQEAAQABAAEAMAAgBgABoANgAGWBAEAE6gAA%2BgGdZYA1mdADRQAcMDM3uAa87gDa2Az9cSBZfr6ADUO86eQGbaS85AHHtnAGWpwqYBpVfyAfFJxmnH5An8vIDLdNtgDescdQDSrqUkBlu5VJ04lAHTVpRtoA%2FGfGkgZmIavWtfIDMO%2FlqryBU6hSBOqn%2F8AT22AphcPHDyBrtT0wsYn6AKzSvV5hTsBQ%2B%2F9sKpYDDh5S52AecZ6%2BQDhOejYGl7cAOLhYSvSQGJuMPDAphaTrO%2BAN1nTUBU%2BYDN1n2kDTpQ8egFLjGdANJNdQG5sB%2BgDN7MDSxyArZr0AVQCr0AePUBwBpSBLIGs2A6AIFgDSAQFAQCBAaAgECAQIBAgIBQCBAQEBAQCAgQEBAQEBAQAAAQEBAQEAMAAgIAAgACAAIDLAgIAYAAMAYB9QB8AD5wBlyBQBnUAYA%2FcCeAMvlAGAMzNfQAe3qwBuFwAQ%2FTG4GdazvkAvuv1YA04eUuQB%2BgGXU%2BjANeOAB1aSiIvSQKNdnqBhuFpP3wBNLLxr5AZtv8A%2BgJtt%2Frn2kA7lCh49PwAN93xSimqXkBl9rW8zvoAY7rvHUAjCysQsX1AWpXxblRV6AXao9JafGwCqzp5ejA0tIUrCcgKXcu3GLSWvqBNzmJ0WmtgXyraH5AbeidbdoFtYGk%2Bb0aTpANOn2xvt6gSh428bAaU1cPkBjXgDWb86vIGtazsBJOVCwBq%2FwCQLnSQNJ5%2BoCuP4AV1A0nxQEo82BpcYAeNAFf2BpASA0pgCAUBoC2AQEBAQIDSAgECAgEBAgICQGgICAgICAgECAQICApAAIAAgICAgICAGAAQEAAQABAAEBkCAABgABkCAywJgHiAM2BX%2FIGfoAbgDgA8MAnigMuM6sA50AHtNQAKvuAOs6eQBtU7MDMdyWPLcAd9dFprYGZ9n5AT2foBh6XQFPq8U6QA4dPtjfy5AzTxt42AlO8PnnIGWpvdZ9gJqb85SnOACphZ%2FwDM5rbQAh1CwAd0rpn5Z0yBiYcpVOdQHtbuOsvKkCahXfN4AG3HxcOH1wBq6SV6LSdQNdrdNQ7cJ37gSTfk8AKa%2BWqeIYGkopK9c%2BNQGHosSBKI%2B4CqUOWsPYDfbjMtONIAo7tcgMVQDMS1pvkDSy2l0fICsqMTQDdJeUAOF9wNU0tVuAroA7SBayBpZ9mBq9MAKAQHgB%2BugCgFSBpZAgEBAQFAICBAKAQIBAgECAQICAUAgQEBAQEBAQCBAQEAAQEBAQEBAQAwACAgIAAAIAAgBgAEAAAEBlyAADANwBgD0AHyAZW6AgMvkDLyAPP1AnMUBnwgB0gMt6UBP306gZU01D2kAhvongAlTqtIAMUl1yANPTQDEqPuAYUOYw4iAJYzLwAP5ADVAZbhtr3mXsAatpXo1uAaqMTTx1AnMJLya%2FAGXScreGuQKmk25SqX9AJXlcRjIGE0lb%2FbnZAbUebqeoFN0nnMgaV771VeYE%2B5tNN3psBqn3QlCTvlgKSmVIC4dLOI26%2BoEkojbKgDXa9Y%2FbYBnyleVgU39fMBcz%2B3mBpNOdgNVp0hAKb10c%2BgDLWNdOOAFNZd4A0ktcsBhJQ%2FcC1nC3yA489QNr2QDICp%2FIEnHUDSgBkBAZAdQFLUBAUAr3AQECAQEBAgECAQIBAgICA0BAQEBAQEBAQEBAQEBAQEBAQEBAZYEBAQAAAQEAAQA8gAEAAH1AABgD5AJkAAJAHQBKyAVABhWBl76eoA68wJ%2ByAzIBLj6gYmFyA15gZb29QDP1qqAy%2B5tNNgVTGidgEaqQM90aZAzCa3i2oAk3bj9sJAD%2Bq%2BoGW%2F5Az3zK%2BXmBJpztnygCcaXpC4AJzNRe6oA7m%2B3E3prHAGW1fc7xEAEKLzupAYS7YdaXpNAZ7kmqVN%2B%2F1Au6d4%2BlAKjXOqrHmBrtedvsAxU91KqrcBuZytFvIDcw346AVNfLHroA3lqmBtdzy6SQF%2FnF%2BMagLj5NRPGcAarCVz7gK159QJPfGoGsV%2BMAacvruA7pOwG4rTUB438wG8IBTjrAGs%2BQGpp%2BwEozADHFMBA0uQFAK3dAICA8gQGpAcAQCAgQCAgQCBAIEgECAgFMBAgICAgICAgICAgICAgICAgIAbAAICAgAAAgACAgMgQAAMCkAYAwCgDfkDP0AGBMAesZAGAP6gZc4QAnDAHfIA3n2AzWYAGtlkAYB1yALXb7AZeJdLYCvOVouoGbmPHoBl4nAGXOWqYD8nFuEl9eoGX%2BuLAHmIn3An8XSVzXUDMO%2Ba5AzN3jXjIE4WPtgC7pb53Aw9Un%2B2ZzoBOUnGlN6sA4f%2FAFK1fuA4dq%2FGoC5vWfcAcS7vNZsDSfaqnogNLva0rSZyBatuUnmOgDfRbRuA5rTSXmQF78KNwGItegGk4m6euQB1c6VUAavXGwGlePwA6c7gNXfQDU%2F%2Bb6cAUyo8SBpe%2BbAcdQFAKUgPAGs9QJTppqBrGcgaAgNUBJgaAgEBAcAICAgIEAoBAgECAQIBAgICAUAgQEBAQEBAQEBAQEBAQEBADYABAQEAAAEBAAEANgAEAADAAIDLAmAAGgGaAp2AG5oA%2BoA3HUDO4BCfQA4AHetgZu4wtQDGQJgZetgZpa9EAfJrT1nIA8zcPMADny%2FIGXfT6yAd274jcAa%2BKn2Apidt8gYdXPTQCc64Wi%2FABnGmmAJzDetXv6gZq3OFWnoAN%2F8AlTtHAGX3PuUdMTkCT2mc39gKWue5aYAH3JVq85AWm3mXO20wBKJmJePT8gPbinegCoSlz5qbA1EJqI7vuAqYn7%2BYE8zq68wNTKhOOAJTjAG%2B2at7p%2F0Adnv0mgNcvGKwApzOzygFc6yBqFUewDl3a3oBUemeoGtawA7pgKeAG85AUufIBekga%2FpdAKdPUDWeoCoAUA0gNICvIDqAgK2AUBIDQEAgQGgIBAgIBAgECAgICTA0BAQEBAQEBAQEBAQEBADYABAQEAATAAICAAIDIEBADAADgAAEAfQAmQAAekADAPsAPNAD1QBMADnOQDzAy9JAHjrSAy3FeoE87gFbSBlUvoBmkpvzWoFo1h%2FcAcxIGe7M64AJlQn5ACTtKq92BXV9H%2FQGO3P8TQBOW4hKK4AJmbzlAERM6z6sCaVR7AHcpamWtHT4AxXpmdwFzMLH0sCu01Xr9OgB94SwBJwnUaw9gFNd1NKsSoAe1zPcrlNx%2BANNNw24erWAFr%2FmY8ohe4EpmU7nogFVhQ9nUoBXDUbgOVr1f5A0ojW4meALOlaaOQNNziugFKzAG5h%2FxowGojE7AaUuEsNSBJbv7gOmQHyn3AU%2BnAGttXssAK23AZvkBThYAQFOE9ANJzQCnqA3QDwA%2FUBQCgIDQEAgIDIEApgKAgECAgECAQICAgIBkBAgICAgICAgICAJAAICAgIAAgIAAgACAGwACAAAAAGBAAAAcwANgDj1AHPkAADAH0kDPhAU414AzwAN7ZAyqWMgABNPTgAlOo9QCZl5y46ADTptw9YAHtP2Azc8%2BiAzi0r51QEsqGo3Ay8a623ldQJxH54Aw7eK03kCbnFNrTIGe5qZj%2BAF90P7xcOsgTaiMfK3GaQA23CSUNSBhLLbxxNAEVM5ivoBOdvkp6vkBymsLR9VsBNS1PkuAJRLWE8%2BYGu2f45AU07yta38wL5fJ1O7b42AXMtOk4125Ae1TGc%2FzyAqs%2BulAKevj7Aal6Xo1oAz8XLu5vDAZe0%2BEA61YCoSTzs%2BgGlp6ALcZccgMpVh8IBUte8AKu9fcBj%2BmAprxkDSccaAMt%2BLAU%2FMBVrZbgPiAFAaAZkCmWA69QNICQCAyAgICBAIEBpMCAgECAgECkBAgICAgIBkCkCkBkClAEgUgUgAEBAQEBAAEAAQEAAQEBkCAJAmAAAA2ASAMCfQAYGa%2FkCAHz6gDcfwBm49wDN6gUceQGZAG440AHL8WASAZTWFoBlqY%2BnABUxjfzArf4AxKdzK1oAbl11b6AZeWnU6yBJT0n%2BQBVn10r8ADad%2BL9AMt%2BenAA38XL3m8MAbe0v%2BgJ23FvHhgZ%2FVJPOzrCAXpvj06gZ73FNxs39fcDPyXbWHoktvMAt9vW4r1xYFlzNt%2Ba0Av849QH9M6TCeAKYVXPG%2FmA9t1hz9s6AKlN0p62BpS1K9VLAl3O4ta3YCp7pdu6Af2TnXdVMAP%2BU15rzA0nb9s4AZiIrxyBPtj3hAaTTr0Aaws6IBlzxoAvE5XNAMy4j0sBbaA023WoDDh7AK3elgM7LyA0mAqwFUA0AzsAqwFa1YCrQCmAgNgOAEBAgEBAQICkDQEAgQEAgQEAgQEBAQEBAQEBAQEBAQEBAQFIABAAEBAAEBUASAAQAASAATkAYBIAAR%2FQBIB0AGwB4nQAbkAbgAbb6gDw6pgHL0sAb29AD3gDMT9mAYAH8c6aAEwqsAzWAM2m6UgFtSgD5Zi17gZvul25wBP5Jt6%2BkwAP8AVP1XmBmc10yBNxEVr4kDL7YXrCAE065rWmBUqWdFmwB90viaYGe7E5XNAEy0o6NOQJtrLd4WF6sC7u5usN6L7AVwn%2F0ttdGAQqmG5004A1fdCfP8AEqPKUsAaSimp%2FnqAt1y9eAHhU9QGZrTUC5TuYQCqXOQNW68gNJq408uQFYVdUBLPGk86Aa7ax5gK9JAuVpD6gaW2%2BdqAU5bbWPMDUxkC41YDfkgNJsCW4G5vgB%2BvAF7gayAyoAVsA%2FcBAeAJAKAQFAICBIBAQIBQCBfUCAQICAQIBApAgICAgICAgICAgICAgACAgACAgACAvqAMAAgAAbAgAA5AABgQAAfQAQAwB8AH018gM6gWAMv3AH7IAlgZAm7AHvqtgMxibsCcumBltR5TABw7Ay3XLAnxW4GZmtFkA5TuYQGbS5zqBOXXl5gEq408uQDTHVcgEXH%2FM1Os6AXbWMa1OgGXV4nowMu3K0t5uQKPJa7OAJOW204WmYyBT8ZbApULVO5dsBczKzy6kDK7mknMxUvqAu2p%2FFgKuE1j8AbULtTWW8KqXnsBNN%2Ft2rZ7gSfxV6YvyA1lT3fSQKZ0u35AaTUgMu2n52ApQ9XNpbvIGk050WsfUCTaav7gaTeNF%2BAJVpmn1kBSThb7AamZSvUBUoBTbpY1AcfcBVcfgDSdKMgOa20AU1HG4D9eoCn%2FYD1AVYCscgPKAU4AQHICAgIEAgMgQCBAICBAQCBAQCBAQEAgQEBAQEBAQEBAAEBAQABAQABAQBIABADAgCgAAwBdQAAkAAALxQGW4YFOgGeQCE4AHDnXYAtAE3CwBluL9QDHAFNKMgD%2BmAMyo4zIA5ys8sA%2BUJPMVL6gHdcT4YGc5QBSUrL0QA08rqAT8VcVi%2FIActT3K%2BkgZdu1Lt%2BQFKlT9PuAObadXdgEQ9XNpbvIFTnRZcVnUDMtNX95AG3DWi%2FAGFTlK3T0uY8gFdqcKr1QFK7pStW1N1%2FYBHcr8R6AMxPdOf7As3PVeOABK86uUk8bAKpuaVuZ1wAvuh0qwBqUlLT2vAC93eml%2FQCXSX9YAVGNaA1l3GLXIFsvcDSmKcazwBRlb%2BoDOjfmAxdvqwNaLea0A0mkrytAJS8zOGwNK3C6cgS9eANUum4GlhxjQCjzYCvHQBh6%2BgGk9QHwwJAaVTsAzdYAsAaAkBpbagOoEAgICBAICmBAIEAgIEBAIEBAQCBAQEBAQEBAQEBSBAQEAAQEBAQABADAOQICAABsAAmAAH1AsgZewF48gMsCAHm35gDx9AKVHIGM%2FQCzSAyBP23ANHGAM%2B74APHkAO%2FwBmcsCdrPVAZ1zy0pwAYb2tzzgA7ndYAy4SvpwAPnx9AJbxL%2BoBWNQB27xFrkDLylhvVgGlONZxQA1pq%2FUCnRuozqAdyh2%2Br9QMuYWrmUsY0sClJXbWnWwMpt5TnDapgKhtpdOQBcKXtv6ALdJrKtJ6OQJWtnid5Au2HNS36wBJS0%2BscOQLsiE9Xi5QG8SlnLb%2BvsA6dXE7ATStL%2FQFOOcIDUpqcxF%2BMgNrSdcgT2dfb1A2mozEzD2AE4cJSs%2BqA1h25X4A1qoqddAKF%2Bq%2B4Gqif%2BcwA3OwDTy6QCsgKf4A0A302Au1qdgNTqtLgBUtbAKjq%2FcBQCo8wHpkBTAfqAyA5AQIBTAQHrgBAtgGgECAgGQHoBAQEAgQEBAQEAgQEBAACAAQEBAQEAAQEBADYABAQAAAAE%2BAAAYA2BOAAAYBKAJ9ADqBMDOyAnEcZgDLzwAU%2FIA1%2BgGZV%2BgF0yAOXpGsgZUTsAN6rKtIAytn9QBNbS36gGWmBlRC39QB6xnLfjoAe2nQAaVpf64%2BgA3h76ADh%2BWoA5WFNzkDPdiH76T1AZW8Zh7AZmHCU9sT6oCdO3K%2FAGe62oUTrpgDML9VhdfoBp%2FFqf%2BcwAOZulp7qQKnlwlW2OAKXmF%2FLAqSlRsuPMBeMx1dIAUOJUPWawBfpiM58eQGkpaTxtFSApOIxsgNOaWFzmgBTEvRQwFNtSpxjRgPbo1Mc4QCq1rGwGlWnk%2BAHh1pICtdEtvqArN%2Ba1AV8YTfUBV2le%2FIDGGsznDA128XOugEm%2FUDWHADqk54YDpnx5gKvl6gMvYDSiJ8gHxwAqKmmAqMRkBWwEgNbbAVgKYCt0A%2BYCgIBAQKgHkBAQICAgGQECAgECAgICAgICAgICAgICAgIAAgIAbAAICAAIAYEAUAAAEwDkAAOUAeYEAAE%2B2wB9NQCssAp2Bl6R6gV6XyBmQCYYBqk54YA8Q348wDPXDAJ1jwwColRsuAB%2Bn0QBVTnXQDP6xEZAIlw8dNQMw4jGyAW3SwvegM3E8AZlxKnoALRqY0TwgDGe6sbAWNOIfGALh1pP1AN9EtV9QMa3aw1qBfpCbUzLoCpz3JXic2uoA%2B3DWW7eGA9qiYucPSefQAn5KcJwBNNy6Sia2A1HDjDXHkARCiE0ncIC%2FVJ3Nz54AV8u1rnR6%2BgGlbhppeUAaaiXHV7SBm4ypxGM1twBqJrTgDSrpbWzApWmLcgalKon%2BAKJnVvM6QBpK3OYuOQGMN5m5AVGtOfUBy68wGXE5awwNLH2f0AnTqo%2FsBX0pAaXHoA112AU0BTKnEgazL0A19NUBR53oA1G4DaYCvYBAgEDSAsAMgIDAEAgIEgFOgICAQIBkCmQECAgICAQIAAQICAgACAgICAgIAbAAICAgCQACAAACtAAAANsCAJAHAA2sAD1AIzIB9ZAK1zIE7wBmXE5e4BpIA3D2Ay9eKQFEddgBtPnYAr%2BOoGW5WyYA03LwuACPTVcADUKIlToAfqk9dQD9k%2Bum4BrDTXb5QAOprz6gYcxziPYChPpuvGoFMe7WzAy2k8QrcgTaVJT03TAHc6zmQBrM5avoBmLTeU7lZ6AUL%2FAKpz5tgTUuO3OscgU90TlrDAUqn2eegGGu5JqujvnIGn3fH8gETec2Aq5zS91yAXMb5UPUBT%2FZ6vMdANruXLe%2BEAudbWfG%2BACf8AlOVolQGkklE1qvzAErvCpdeAFzPKA1GyiALtm236ZkDXyTrnAEq4nH9AaftmQHRzXtgDXyjWEAy9KjR8AWLw87gaVvbn%2BwH3bAbgBQFaX2A3MAXiQNICA0s8gKfmBdQGdPQBAUwIBAVIDIEAgIFIDOwEmA5AQICAgGYApAUBAWoEBAQEBAQEBAUgQEwCQKQACAgIAAgMuQKQICkDIEASANgU6egAAADAGAXcgDcgGAJ%2B24GXh6fwBTC2QGW8xpvwAc6gGXMx9AB%2BrYA8ewAl4nEgY7k0vsAvugDLu88gE%2BOnIGbxqwKf23f4Avl1b30Ay3d4ygMzonOwFCSiacyum8AZzxhfwANOeV5AT7dlEPrCAFMuXPTMgZ7u6XHOADHEzH2qAHu4mMzYBo265xgB%2BUJOYTtKvoAtuH8ZUaN7AGc3frxGAJqcVlLboAYlW0sZAVrKvLezAUlEKut0nIBNvt1rCzFgNJy3f4A1jthO9J6gTSURtbfO4EnFYqE%2BgGoTma5y4ApTczpp6ZYGk%2BZ4A0o2uQKIuem%2Fn0Adfd5zgDVTMQAupfsA5nnUBUU1jNAKhy3jRcgXHkBqa4AaXmAp0rAcgPQB%2BiA0vDAVsqAZ0AakCwgGgFMByBSBqQFUBAPhgIEAgQCBSAyBAIEBAQEAgUgUoCAQICAJAZAKApApAAICAgIAApAGBTyAAQAAAQABOJAPqAMAkCd5AzkCn%2BgADIA%2F7An6ADq%2FYAdzzqBmqj2AM9AD%2BgKZ6AZcKVOeACVGWgDObAHwAPXZdQM9fN7AMVCrr6gZlS%2B18TCzFgDiZqQMulE2ANJY82%2FuBlOKw4hPoAtJzNe7j7AYlNzOjxXGWBqdm3x9gMwurmJ%2FkDERc9P56ATz7vOcAMKZah7dQLupNuGnpHkBU5e7z7AChw06V1i0AtZalLYCb7Z6wtgL9u51SaVgCbdzCXH4AI6rl7egGoaU2un3AVGFnHrqApw9HN9QNPGf2w2suAJuV%2FLeAFNZVT5AV91TGjnV0BqsLMafQBnKdgKcruS%2FuOgC%2Fks614wBXre%2BgCvCxQGuHPnoAu01psBpOdoAk7z54AVnreANT%2FQFhNuJgDXKAZQDbdYYCm84AvF7AaW%2BAEBAfqBSBoCyAgIEnkBsCAQEBAgIBAZAgECAgICAgICAgICAgICAgICAgIAAgCQIAkC5AmANgGQKQAAAgBuwBgU0AT5AZzQEATmbAJz4wAOVnUAfruBkC6z56ADtP6AGegBN%2BEBnW9eAJvmnoBnCbcOo84wBPdTAGW1IA5brWACW7mF42AzHly9vQCuJtdPuAdOgGW4b9QJ4zeHGWANyv7YAmsqp1wAX3VMaOdcATiWlmNPoBluJTt6ACddyS%2FlLoAP5LOXXt5ATnVt5mK9oYElf2xC9AHFNtxvpyBO01piOfDAy03aeFOOoGutXvuBJOMpznz0Albfb689AB0ku1uNGAyoceiutpQEo%2FwA6b%2BwGlecY%2FgDSjDiP%2FIBi4lOo6bgaT69d5AFKcK8uNQNNQnMcQAqU40A0s58vsAuremIyAxhzekagCfPr%2BANSpn0A10qfHIEtHPnsBqmqy6bAU%2BHOeQHa6QCpakBvKeAFPfcDSnruArYCxh%2BYDICox7gKAVs%2FQBXTyAUwJUAsBUqgHzAgECTAZAQIBAgIBAgGQIBAgICAgICAgICAgICAgACkAkCAgACYBIABAQAAN7YApAOAAC8QBnmPICkAwAOvsAWgLzAHV7YgAej10AxIE3r6AD3QBzPnsAO%2BrqQCeLXqAd2lwlgAtqfHqBl8aWBTu4sAved%2FMAVtrx5AZdJJNxowKVDj0X8AZrGm4BnNLH8AC2bX%2FwCQDFxKxHQBT1ud95Ayp7XCczLjUA7lCeOIAx%2Bycab%2BuwDrE4ePtAE5Sl6Yh2Awqc%2FtlRqBlNPL6T%2BNQJtTOun1AeUobq3f3AmnEuat%2BQBDX6rXffYBSabm%2FfEASWe1uXD%2FALAqwoTWPsAt3MyuVIA3mVNqFumBdtqpaedfoBvtdz6MB%2BPnvIDpE3mwJJNxb0bYDDq%2BPwBRbXmA276AaurwBr9VHc4xrsBJws%2BlgM10zQDXOcgN7W88gaSlcLTyAVXHQB85TAkvwBrCkCtVv9QFSmArZucgMrCgDU3mgHxAF4YCtwEB4AkBoCAryBrYCxYDIFNAIF9QEBAgIC5AgGwKQGQKQICAgIBAAICAgKQCQICAGBfUCAgAAAuQDgCwAAVaADYB4gAAgBgXnYGcgUAZgAcuwLYApQ3sASAN10yBlxP3AL1ywKKvGwBj0AHy5T0AI%2FAGWqkDLqlr9QK07AEspuXD%2FsAbWFErH2Au53muQMt5lTargDOatrXUCUtz7gD7fPcB0ibzYGYTcW9G2ANYvj8AYfbbV1YF%2B3dfRPqA3V4%2B4F%2BqjucY12AVSdryu%2BQCf1fGYXrYA4l5icqn77AXao0txPoAw1FYpvn0AlEzPj6gVJQ1WY4zqBdvycy%2BjAMP5dzbamANQplVxGfIATSxb7Xn2A0omsb5AUp7ZdR5UugDG7AWsTrpmwCJprr%2FAGBvNv0wBXF0%2FHkBrbj6ALd%2FrTducPxAC5WrS2Al8YjMIDVduXPIDUQ%2FR%2BoFLr8WBqNJi5j2AUlp%2FIEvVagaVeeQG1FdQFAKaiH1Ae2d%2BjAlu3MAaUZAU48gFRNASxID1YD1AgNAXUBAp2AXIFUQA4ApAQECAgFAQEAgQEBAUgQEBAQEBAQEAPgBcgFYAsAAFIEAAHuBAQABAF7%2BYBywKsgE%2BwB0AOoB1An41Az5AXiAB80AP6ADe3uAOdGAVERoAP8AXWQBxDT9H6gDeI86sAdUnFzABG2vqBnnK10AFXnkCvbGeoAszIBSUPEzHvqAL5PL6MDPLcxgChS2q4jPkASliW%2B159gCk6xvkAVqXptsugFGZYGe5Ymp0yBhqaa6%2FwBgazb0lRgDLmF8qeun8Aa249k%2BgA3f603mcPxAF3Lu07mlqgKO2PjEwl4zAE0u2Jcpa%2FzIA1hvDqsAPyb1lzkCeP8AXy36IC7ax1awAQnLS6rWgFQk9YqVGQCIvu8nczgBiFdt3GAGVNrOoGvlKzAE4UxrmI0AYmvawNZUbyAJxrbcNcyBpNvLhYgC1l9ZA03KSdzr4gCiHMJPSMWBp24SjdP%2BAFKXGmvUBlROgCmsLOAGnMugFP6Z%2FoDSyBbPRgMt%2FkB0zO4Cv7Asy4%2FIGlS6AOM%2BoCvdgMqbApmwHGAEBAkwFXwA8gXUBAcgQDIFIFT1AQECApAfMCQEBAXUCAgICAQIAAgICb3AAICApAJAKApAgBgUgD6gQB5AQGeoFjqwBtADfkAOgLP4AniOoGZh5AL3jgA1l%2BoA%2BfUAxeOgE8wvHoBnNeoA2o4YBKwsuvQAcOZdAE77AWu4GHpswCX1c5Au61%2Fr5b9EBKqXVoDFOXH5AqSfGwGYi35O84AsK7b8gBvtbtVhsAfdKzEgTjtmPPGgA7r2AHajedfoBlONbbhrmQFS8uE6iQDWX1lgPc5SXdc6z%2FQBENOEnpGHIC1LhKN0%2FXQCSlxcLPUBiVShfbTcAeVNJYfHqA9r7X8XpH3Ay5j4p%2FJNQvHkA6ppeXlNQARKUqlqrV0AunDxhMCSm%2F4AscrOydga4URgDSUwutAVXLrnpwBdrWI%2FoDVpt5esXXsBJrEw7j0A1UKHDWgErmeif4AU3VKdgFKJjKAZTpf0A%2BIAc2879QFZl5xQDpMxyAypnXWNANK1svsAz5LR8AKapgWkK5AegDmAHGcaAK3AZ%2FIDenQCQDICtvEAOOoEmAyBZ%2FICA4ApAQIBXuBAUgIEBSgKdAGQICkBAAIBApAAEAkCAPEgUgQEBAAEAAXIEASASqAHtkCkAyBNwAcgD%2FkA6dALxABPIBOkf0BY5YGZWPQC844AzmZ9QCf6AIhOMgDadICfhAZzMxL16gCzLziv6AtE5jkDLcOdVmNAKJW34AHlaLR8ASacPSPuBh4%2BKc6AD4AGpXC1Vq6AnTh4wmAZsAeN1l6J2ANaKIxt9gKqXsBhtXLrn%2BABRiP6A1DTbiXrF17ACaxMO49AGoUOGlj3AyrmdoT6VQFLqlrCT%2BwD8YTjK3AJTpVp0AdYm0s%2BYE2sRytQDhu5iegDl9G11UAXx%2BLSedFrS3AraS0zN5kCrbTOPqgCJmEkn%2FdgOFVLyw%2BqAV2pd0Zpx%2BQNT3ZmgFum271ajXQCnWIyuoCsVeqgCnDVJ1UqQFPbeI2A0lPjSQHtcQ1etagK1%2BWqnUCp4VgKzLX9AajMuIAVMKAFN4bAZt66RsBLMTa1A1PAEnvnEgK8dANRD50AraXqA%2FgBzikwFYrH5AYUwA2AzUgUgKwBT6APQB8eQCmBLkCAdZYCBdAKQGQKQJsCkBAsAQEBAQEA2BSASBAQEBAUgAEBagAEBSANgAE2ASBAGGBS2gD8AGegBpsBRcZAG2AN1PuAN%2FjqATX2Ay36AX5iAKJ8aSATj7AG87AZcPSwKLl5%2BwA1mXDXQAuFABLw31AG866AZy4m0sgTaxHKAzOjzMT0Av59IAI%2BL50XQAtpLTM3kAce2cfUAiZhJJ%2F3YFEKqXlh9UAPtScZpx%2BQMt92swAN023erUa6ATesRldQJYq6lJADdpqk6qVMgXTVpRtoArtnxpIEnENWrda%2BQFDv5aq8gVOoUgTqp%2F8A09tgKYXDxw8gKXdphYpJ%2FQDK%2FwBfqr1eYU7AUPv%2FAGwqlgTT%2BLylzsAvfGfCAMJyuG%2Fp9QFU%2BM0vetwJ1aVQlekgaW8YeGAzCwp1nfADCy6WvlyBKW9u4BmX%2BufaQNOlDx6fgAlwqpqkBpJreZ30AVTu8dQKMJ2sVi9gGb2az1A1NcgKzDUaUA48a7gKc1HoA4r1ewDMLjQBU%2BSxuBJ3WfUBt2A3D0XIDOoDMTPRgKfoA9MfQBApjr9wHnTUBvzAZl0BY6AMuAKIAZsCAfqAgUgWAGZAgKYAbAvqBZAgICnIFIDPAEASAgAFL0AnyBTQEATYEBN%2BoBIFIABTIE%2F7AJhAVgE3WQC3YBowKQCYn3AteABvbGADnYAbhc%2FcArPqAZ6gDcusgT2ePQDLbjFPQChrr9gDDvIGXtle1gTd7NAGn5wATpERVADrxqBmZqPQCdVN%2FwDT2Am4XDtLZ5AL8ljEgZ1rOrzqBm%2B64jdgTmHlLnYAbm5jPhAGE5XDf0%2BoDh5rNL3rcDPc4tKoi9ACJcxh4YGZhYU6zuqkBhZdLXy5AlLe3d4%2FIE22%2FwBc%2B0sC7lCh49PwAT3fFKKapeQD8Wt5nfTqBY7rvHUAjCysQsX1AWpXxblRV6AXao9JafGwFEZmvL0YEtIUrCcgSXcu3GLSWvqBNzmJ0WmtgHyraHPAGnonW3brfQDLuLcU5i2Az6vDSdICUNpd3bG%2F9gaTTiHcLxoBpfKrhxrzkAaTvdZ9gNZvzq84AdYWdpz%2BAJJyoWJA1az1%2BQEnDnSfMDXa25jOZeVIEo0e%2FRgSnflgaTxVdMAUpKdXMgaUq020A5UTUUA9tfVp8AOM6eXoArTVaMCUxjyAZnrovUBn6%2BQD4gC2sDSfroAqHlRuBJzgDSnewDkDXP0AdedgK9FgBtfkCkBTyBSBIBnigKvMC%2BgDxIEv7AQLYCuAKQKQEAApApnIFOwDLAAIC8QBdALxIBIFIEAAU%2BgBSAumALNSAAXUA215ALjHkBMDM%2FXyAXz6AZegFPqAU8qAMynhgUvewBrXgCe%2F0ANazsAXULAA5X5Ay3crE51Ak8%2FV8gFaP8AHnywCeJT4wBme1KdWv5ArVpt9uQJ2vi3UVegGVX3XQCxn8ejANoUrCcgZS7l2xGLha%2BoB3PRxOi9bAPlW0PyA09E627evQDDeLcZnUAn10aTwAU2l3dsb7eoBTw3MLw8AK%2BVXHdGvOQBpO91n2A05d%2BdKc4AKmFn%2FAMzmttAKO6VCVN6gT%2BS%2BvyzpkAmHKVTnUB7W7jrLypAmoV3zeABtx8XDh9cALmklei0nUCTdNQ7cJ37gCTfk8AKa%2BWqeIevqBRFJXrE14kBh6LE%2BcAZnth9M%2BXO4BhQ5aw8NAb7cZlpxpAD%2B2sbbgaSqgCYba03mX4gBTttK9GlqAp2oxNMClwkvKPwA4TldGgNU0nlLUBXKAsR8vvvAE8zisbTsArOzwwN3p7gS58gF0rAZqKceYDOkXotJ1AVNNQ8wgJS%2FUDSd7PYB6KwEAlR9wFY9mBrt9wGwHoBTba9wFZYFtGNAGX%2BAHC%2B4FTS1QCugF1Apv7AOv1AegEBAU6AIEmBKwKb%2BwEAgE0BAKArAgCQKcgU%2FwAS6AgLIEBdQB5AtQJzoAATAG9KAp%2FgAnYAv0Ak71XABikBWBmVDAML6gS62AOQLTgDLcNte4FNuv7AzNqMTTApdL0gAdK%2FVAVNJzKVSAeQGXUSBlu5xWALXbRgTmP1xz%2FABF6VgA7qVgZbqHFeYE9Elei0nUDKbpqHbhO%2FcCt%2BTxkATXyy08Q9QKIpK9YmvEgTWyxPnAGG1D3jPlyBnSHLWHhoDXbjMtONIAGu7WJmFqBRVY8fgAmG2tN5l%2BIAU7bSvRpagGqjE08dQL9oSXk1%2BAGITlZmGsQA00m3KVS%2FoBK8riMZAwmkrf7c7IDajzdT1AG3onnMgOd96qvMDL7m003emwDTcJQk75YCu1TKmuuPMA7odLNKNuvqAQmozFtRyBJty4%2FfCXiAGfdeTbAm7%2Br6%2BYC2218vMDSac6rPqgNONOiS4AE96i98AMvtxN6ccAK7lfc7xEAMKLzupA1hQ%2FcA1nC3yAunj%2FWsAanSP1WLgCnWQFNxxqBJpK3YG1H8gU7J5zIDn60BfKU5dgaqY2dgK3UgLa0yBVHTQBT9QHx6gTf8AIE29fMDSaYDP4AZ%2FIFMeYFKznADWoDhQwLnHuBY8wGfQCAk3%2BQKY6gaoCkCApkC8MC5AnAEBJ%2BoFIFIE51ApkCApApgCnXIFWoBhAGsgTrzAm%2FQCkAl%2FkAn1AqApAM%2FUAmU5AqbheYBrKAG9FkA0%2BqAE9dQLx6gDYA5m%2FMAlMCfF6QATvpYA2%2B3E3oASv9O8RABC1zwBUlDrSwMvM4XqBl156gLfH6qYuACdW61mr8MATccYcgYmFeeeANV5upAy25pPOZAnf1qgMvubTTd6AVNwlCTvlgSSmVPjqAdzTmFe23UDEJqNrajkATblx%2B%2BEvEAM%2B68m2AN36pvr5gXdLa%2BXmBJpt6rK80Bpxp0SXABOZqL3VATb7cSpeNY4ApV9zvEQBQovO6kBhLth1pek0BnuSapU37%2FUC7p3j6UBVr%2FraseYD24e23ABEKe6lVVuA3M5Wi3kAuYb26egFTXyx66ATnPcqa8dAH5OLcJL69QCPjhT4xqBOPk1E8Zx7aAa%2FV0lc%2B4D268095Ap3xrwAuFj7YA05b530AsSk%2F238gFyk40pvcC43lb%2B4GrdIB7X8dLiwFqYUy0sUBTT9n9gFNRMdAFxFYb9wFzvACucroA9rzttwA6S64AecrRAVzDAU5U4Aby8AMuLqgLADNxEgNe4EteQGd%2FMBwAuX13Ad0nYF9tQL7gPCAk4AfsBTkCT4AaAmBfUBTAuXQFPpsBAU6gQDIABNqQGgACn0AJAXP8AIB0dgT4Ap03ALwgJOAL7AE0wKVsAPjAEwDrkCTz4oA5dcAU652QBcwwCakAvLwBfLyoAwAN3ieAJxjX7gZ1fIBOrxrwANxj7YAXL%2FIGW8pO8gDm4013AHtvK3ALdKwBdz7XzEsCamplpYoAbp10f2AJWYAO5VSpv3Az3T050oCrLytK%2B4Am72%2BwFFT3UqqtwLWcrRbyAOZhv8egGG1Hyx66ADnPcqa8dAH5OLcJL69QCPjhT4xqBOPk1E8Zx7aAP6ukrmuoAk7xbjkAnfGvAC4WPtgBabfO%2BgE9UnPdmc6ATlJxpTerAOH%2F1K1fuA4dq%2FGoC5vWfcAeXd5rNgE9qqc0kA%2FNq4rSZyBatuUnmOgE56LaNwLNaTU6yAPdvRRv5gTXxUr0AU4nZ6qwMulM9KgDX7a2lovwBpOVWmmAG45q9%2FUCq3OFWnoAyv%2BZa0jgB%2BT7lH03AU9pnN%2FYClrlrTACtdX4yAqHWUA8ZaA03LzfQDPa3cOks4A1MZyBq%2BsgTi7vgBlKvRAK7mtI6zkBTuXMcAMvotuoFmtPrIC3r6AOAFOOm4BMa9NANXqAzIDp9wKVbnoAytLApboBn15ArXUBu9wJQwIBmdbAk9tAGYAgJ9QGV%2BAL5eGBeoFPoBfQC59AICAgICAgKUBTtYA3NAU%2BvIFPqBbgGegAAty%2BQMpvTTUBxnIB7gDeQKVieiAPk1p0AJ1uNQKfQAbnoAN6%2BgA1FoBmJ2Aw3GvQCvXGwFM%2BQA5i%2FX%2BwCrc9ACdr28gJttR4kAnrPIGZjrtgCu7l%2BMgZSTcW0rAt1loCbl5sDEu4eNQKYznxqBOb1kA7su745AJ7VU5pIC%2BbVxWkzkAm23MPMdABt%2BUY6gZ%2F1Wmk6yAPdvRRv5gTXxUr0AU4nZ6qwMulM9KgDTnW0tF%2BALMxpbWEBXDetXv6gFW5wq09AGV%2Fym9o4An3PuXx6YnIEntM5v7AUtc9y0wAPuSrV5yAtNvMudtpgCUZiXEdY%2FIF2qFTnbDlAFJS5XVTYDEJqI7vuBXE%2BefN6gDzOrUeYDMqE44QAk3PbhxXVgaU1b4f9AZ7M88qaAp1cfFKKxXIEnM31WgGlrOs%2BrA1CcR7ALUtTLWjp8AFeSzO4GpusAO6ePX6dALtcROlTyBXnOoG1lXWwF3PE73ngB0hRLULowB9yVa65A1l5mwFRnICsfQCUJT9QNLEYf3AZcT9wJ55deYDM0mBKcAaWl%2BYF26%2F3QDOunAEnM%2FQBx5gNVAE%2FVAMr0%2BoDrWALcCTgB5yA%2BdATeJAgKdAHIEoAkBUgHHUCmpAnnkCnRAS2AV1AEwLnQCmZAvuBPSAJ2AT7ATdgW6AE4yBPfIFe4A9JAp26IAbWAJ2AV1AEAaSBcYf3ApcT9wB55Apml6AHGALz8wM9r8RNADeumK4AJmfdAWJnWQBxUADtqcb0AV6b7gTzCwAObTVev06AZTi30kCc%2FwCs6oCi8%2BXQDPc8e4A8Qol0ujAH3JVq8uwJ28y5%2BmABQ7iXEeOoF2qFTnbDlAZpKb81qBYTWH9wMtuJ9b89wB5nVqPMBmVCccIASbntw4rqwNKat8P%2BgM9meeVNAU6uPilFYrkCT%2BU3nK0AoiZ1n1YE0nEewC1LUy1o6fABXpmdwFzMLH0sCu01Xr9OgB94SwBJwnUaw9gFNd1NKsSoAk5nuVym46bATXc4baT1awBNf8yl5RC9wK5lO56KdgBVaUPZ1KAVTUNfHfUAdrXW28rGQGoubiZWwBl4q40cgTczFNrTIE2plL%2BPEgPzju%2B8XD5A1KajHy2zSA2m3CUQ1O0gCW7xxNAWkzmK%2BgD5Sp6vkCnZqv8AL%2FgBTdavZY3AVtowGds6APbCWKa1wBT7wlgBThOo1jgBTTqMbgKeXnLgBc035gPEgVzm%2FRdAFONI4xKAU7VqNwLK%2B7YGq9QLIDM8PgClTMAPyhgLa6TsAy%2FICXXAF55AZ80BT%2FADONeAJAM%2BoEmowBT74Ak86AKadASeoE5zqA8AXTPoBTwBT6AHiQEAkBbAG%2BAGbApXrsBW60AEBeIAnPUA8ICnGr2AgBvbIAnCwBT70gCaYFKdNACeWusATmm%2FMAe0gWtZ9EATGFD%2BwBPNADtfdgXiwMu3w%2FWQJueG1oANqZgCfdD8YAG1G07dAKW4WkAZXXABpnOgE%2BnyU9XyATtH%2FwAv%2BABt1OdlgA4uwBu6tvFACaSxla4AG%2FelgAmE6jWHsApruppViVAEnnuVym46bAZ7vk4bzrsAP%2FzP2he4Bcync9FOwAqtKHs6lAKpqGvjvqAO1rrbeVjIDUXNxMrYAy8VcaOQJuZim1pkCbUyl%2FHiQF90P7xcOsgTaajHy2zSAbcJJQ1PUDKW7xxNAUVM5ivoBOdvkp6vkBymsLR9VsBNS1PkuAJRLWE8%2BYCpf469AMpp3la1jfUC%2BXydTu2%2BNgJ5abhOLnbkB7VMZhv015AkozXOlfgClO1fTn0Avk8K1hp4Am2nLu5vDApe06%2FT7gOe5xbiPDAz%2BqSednWEBpPHo%2FLqBpuMuHo9%2BfcB%2BS7aw9Elt5gFvt63FeuLAsuZt%2Bq0A01WN1DAfkks%2FkBmKxoBS34sBXdtYCnKawtH1QDExONE9gJRLWE%2FuBpS%2FwAASad5WtAPyl169ALVrEgK8fUBVZ%2FigGdfFgPyemMAUw7%2FAIAZ4AdaAqha8gPj0Am4y45AZSr6AVte8AUzrkBjZeTAZgCn8AMt%2BLAk9gKaemwDnPpwBSugCgCfTWgKZdeoFrGJAVYEufUCkClgUwwKeJAtaAKifcBAm4y%2FMA%2BSVa8IClx7wATLzYE%2BnkBSgCfwBNt%2BLAE9rApprGzANvpwBa7fyBeIAJTvK1oA%2BUuusgDy%2BQJWAYz66UBTqvYA%2BT0vTgAbh3fXAE29gDLcAZpKc88ICn8ADcZcbMC%2BSVYeyX8gFvt63H35AJlzNv1WgA1x5MCbSWfyANxxoAS2p%2FsAXdtf3AJlNY2fVUBNS1PkuAJRLWE8%2BYFLfO65YGJTvK1r1Az8vk6ndt8bATy03CcXO3ID2qYzDfpryBJRmudK%2FAFKdq%2BnPoBfJ4VrDTwBNtOXdzeGBS9p1%2Bn3Ac9zi3EeGBn9Uk87OsIB29PNdQLucU3D07t%2FEgXyXbWHoktvMAt9vW4r1xYFlzNt%2Ba0Av849QH9M6TCeAKYVXPG%2FmBK6mHOF0zoBKU3SnrbAlLUr1UsCXe7i1rdgSnul25dAT%2BSbeu6qYAv8przXVgSbTdRGITwBNxEVr19QJ9sKesICTTp71rTYFSpZ0WbApbfFJMBeJyuaA1MtKPNOQFtrLcvTC9WBd3c3WG9F9gGHcKnXWgK3b0tALeYUxlP3AU%2FPUBV%2FZgUxj1A1%2BufR4AvlCq543AVdTDnH30Ak2m6U9bAVLU%2FkBXdnVdbAlLl52AbTnX6wA%2F5T9vMBTcuumcAMxivHIF8Y%2ByAk1j0AZSxnRZAZvjQCeJyuQGbj6ATbQDLfUBjOwF10sCb2XkAz5wBZApj8gNZQFOwErApjSwK9AFd22PcCmZ12ApczqBTE%2BoFOQKdgJ19kBJrHoASljOwFN8aATxOVyBTLj6ADbQE%2B5vGQJ67AV5eloAb%2FAJQFPmAZ%2BwBMfkBrOnoATVASusAEw3QBbVAXyd7e4BbnXYCfymdfqAYTXmvMAmG%2FboBNxigBqF9EASnXoAUsZ2yANueNGAd2JyuaAm5aUVo05Am2st3hYXuBnu7m614AmnDhUwC8vS0AN24UxlP3Ap80rAEn3etPgDP%2BceoD%2BmdJhPAB8qq54AJmsOftkDMtN0m%2BtgSlqV6qWBLvdxa1uwJT3S7cugJ%2FJNvXdVMAX%2BU15rqwJNpuojEJ4Am4iK16%2BoE%2B2FPWEBJp0961psCpUs6LNgUtvikmBdyqcrmgKZaUdGnIE21lu8LC9WBd3c3WG9F9gK4T%2FwCltrowCFUw3OmnAGr7oT5%2FgDMqH0lLAClE9rU%2FW7qQBtxy9eAHhU9QKZrRZAOU7mEBWlpOXkBcuuq8wJNXGmmOQJYx1XPhAUXH%2FM1OsvAF21jGtToBYt1PCYA8ytIbzcgaV8LXZwAp22060zAGp%2BMtgEYT%2FwBNwBQ9%2FwBFgDSbnjdcgWLf54A1N3hagV09VsA7Tdgal90JgEqH0mMAKUfq1P131Am6nfXgDXSnqBTNaagPKdzCArS5AXLrxIDKuPwArp1QCs8afgC7XGPMC53AeVpYD4e1AU3LAZi2BfV0BXvQCm%2FHIFiwGbvAFtuBdbAbeQCa8sAPDApfqBSBTppqBcregK11yBOwKdgKf5QFrx4oCT%2FkC9pAG9VoA%2BHtQBO%2BgFMSwD6sCvegJNgHUCm7wBX5oAAnLAJUe8AHDAm3HL1AnxT1AJmtFkA5TuaALS5yBOWBSrgA8uqANY0089ABVjz9ADl1PCYA8ytIbzcgPtOdnAGZm3p5gTcWBl7f9NwAQ5zPYsASb8cgGLfjQBn9lKlLXzAHMJ%2F9LbXRgDiptz6cADlwnnxAGJUN8SlgBSie1qfrd1IA245evADwqeoFM1osgHKdzCArS0nLyAuXXVeYEmrjTTHIEsY6rnwgKLj%2FAJmp1l4Au2sY1qdALFup4TAHmVpDeblAMeS12cAScttpwtMxkCn4y2BSoWqdy7YC5mVnl1IGV3NJOZipfUCdtT%2BLAswmsaeWQGl2prLeFWPPYCab%2FbtWz3Ap%2BKuKxfkBOWp7lbrEoAy7Uu35AMqVLxOmmlgTm2nSm7Aoh6ubS3eQGnOiy4rOoGZaauc1mQGXD7dF%2BABVaVunpDmPIBSThVeqAU13SlatrUDS%2BS8fwAy2%2Fil%2BuscgDcfetgFUk4hdNPQBTpQ78bAMN1OMICTULVO5yBpz59QJd0JP3YC7an8WBK4TWNPLIDS7Z1bwqx%2FADeUuQKYXTADlS%2FyBJzp%2FQGpUgUvKfnYCoT13SAZTn1r6gSbTzPuAy4jRfgAW8f3ICknHOwDKcxayBWgGZcJVqBTH3AVWkfgCmluBZqcAMqPeQJ%2B%2FUCTqQJ3kCmcgMqFu9gJ7rqBTCsC5YFM6f0BStwKdn9QLD%2BiApT%2B8AEtNWAzpoAT756gSigKU51WgBaAZbcLABj7gSrj8AU0twB35YApUcZkAfv1AJw9gJ21P4sAzXjAFSUrL0VADl2uoFMK9MADtS%2FyAZdq8%2BQFKAG4ua3sAiHq5tAUpztrFeYBLTSmdtZApcPt0X4AyqtZdPrMASScYvYAlOUrWUAfsrApbfxS%2FXWABuPvWwAoVxC6aASdKH%2B3jYCabqcWlsASoWqzLtgHdKcrPWpAyu5wnmKl9QB21P4sCzCaxp5ZAaXamst4VY89gJpv9u1bPcCn4q4rF%2BQE5anuVusSgDLtS7fkAypUvE6aaWBObadKbsCiHq5tLd5Aac6LLis6gZlpq5zWZAZcPt0X4AFVpW6ekOY8gFdqcKr1QFK7pStW1N1%2FYBHcr8R6AMxPdOf7As3PVeOABK86uUk8bAKpuaVuZ1wBd3dDpVh%2BYBSUtPa8AL3d6aX9AJbxL05gCUYr5UAu3cRFrmAB5SiHuwK4pxhzigBq2tX6gM6N1GdQBq7fLfrwAw4WrTpYxpYDKSu2tOtgZTbynOG1TAU020uFyBJ4i3t%2FQG5S2iJnIGphOIS06rABGNXrAEnr5%2BQC1My%2FKfqBJxPdPjIDlTPVeOAFdfK8AKpvbMzqAvuusagUpKWnteAF7vpp%2FAEt4l6cgKjGtAMy7xFrkC1%2B4DNU454AuNwGdG%2FMCfL5bAbhauaX9gaTSV29gBNvKvACmm4XTkCXrx%2FQC3HTfIDNOMAXuwKfHAE7%2FAABTrID59UBLr5ASpvbfkBbusAEwr%2FgCbAUBVjUCy%2BNVyBAU1tr5AH3AZ0b8wB5t%2BYFp50AykrzsAS9c4ApWF%2FIBIFS6bgMwnGADbV8AE%2BOAJz%2FABMXoBZ16oAWc9c4AphvRZnnAF3O6VagEpK%2BnAA%2BbQF4XIBWNQJuXe1rkAfhsAuMxzigBq41YFOjdRb1AGrt8t%2BoA8LVp1%2BAGVF5AxLeU5w4ApTbS6cgHlL2%2FoCcLpmcgUwnEJaPnQAjGr1gAT18%2FICady9cT9QMzE90gGbnqvHAAledXKSeNgFU3NK3M64Au7uh0qw%2FMApKWnteAF7u9NL%2BgEt4l6cwBKMV8qAXbuIi1zAA8pRD3YFcU4w5xQA1bWr9QGdG6jOoA1dvlv14AYcLVp0sY0sBlJXbWnWwMpt5TnDapgKhtpdOQBcKXtv6ALdJrKtJ6OQJWtnid5Au2HNS36wBJS0%2BscOQDtiE89zxcoBaSlductvzv2AtOridgBpWl%2FpgM4t3hc%2BYE4a3iL8ZAnKwpucgZ7lUNQtJ0nqBpNRmJmHsATDhKe2J9UAunblbcIBdxCiddMcAZhfqsLr9ANP4tT%2FzmABzN0tPdSBU8uEq2xwAp3mop9AFdyu%2BLA1FuM04kCt3ERaf9AHbE7cQAt1KyrSejkBVrZ4neQNdsOal%2B8ASVp%2BEBdsQt%2FUBxMZ1fjoAp17TsBPVL%2FQDOLzhc%2BYFKfMa%2BMgatcvOQB7PxIGk1vvDAph4oBw7tfgB6VOoBC%2FVfcDVRP%2FOYArnYCp5pICTv6AK7s3xYFrQDLzHmAJqdgFvVdYAsrZgKfnPqBLMgSiFuAuLjOrAp%2FAA9d2BTjkBz%2BQK1pIAwGfvDAJhxFAXVygF%2FXUA2QE48swBXIFXSACbAPl%2BAHp1ALzEbMATU7ATaysq0tnIFMrZ%2FUCTT0n6gCtp%2BEAdsQt%2FVATqYzqwCfwAPVLIE3jnQAbT8tQJytJ1yBnu2a9dJ6gKa0cZh7AZmHCX659QJxra26AT9J10AzH%2BVhdQF%2FFqf%2BcwAOZ2WnupAKeXCVegAneyap9AJdyh3xev0AotxmnEgDbekRaYGU1OwA3UrKtJ6OQJWtnid5Au2HNS36wBJS0%2BscOQDtiE89zxcoBaSlductvzv2AtOridgBpWl%2FpgM4t3hc%2BYE4a3iL8ZAnKwpucgZ7lUNQtJ0nqBpNRmJmHsATDhKe2J9UAunblbcIBdxCiddMcAZhfqsLr9ANP4tT%2FzmABzN0tPdSBU8uEq2xwBS8wv5YFSUqNlx5gLxmOrpAChxKh6zWAL9I%2BMZz48gKJfxeMxFSALteMbIDTmlhc5oAtqeIYAm2k1OMaMB7dH2zGk4QAq%2F6rD0AcacQ%2BMAXDrSfqA76Jar6gGt4x3LLAv0hNqZl0BU57krxObXUAfbhrLdvDAe1RMXOHpPPoBS%2FWZVfcBlpx4r6gKdpOaw9eUAtUk3ET4sBzrLw2BS8wv5YDKiVGy4AXjMLl0gJQ4mnrpgCntxGcgOsPHSpAkn02QG5dLC%2FAApieIe4Em2pU4xoA9ujUxzhAScf9VjYBVacR0AU9%2BgCn5JaoCm7xqtQH9YTd6gVO0r3Aow1nV6gK1i51ApfrIFLTgBm0nPD16AWkN%2BPMBzy8NgUvbwwKoqNlwAt%2BXXCAqcJ09QCViMgOsP6AXtsgGcaL8AF5ApqVIEtGgKY1AsAU79AGfbYAm7xhoC%2FWmwCZtZAmsNZ3wBLi510ApfqANw4AptZ4evQCeEm%2FHmBTPLw2AS8wBUlUbLgCfjZAFOJp66AE9uIyBaw8dKkDMaY2QC260X4ALieACXEqcYAu3RqY5wgBOP%2BpWHoBTGnEdMAE79AKc7LYA1vGGtQD9ITamZdAFOe5K982gBrDWXl4YEk7i5w9J8IClvzmVX3AG2nHH0AJtJzOj15QA6STcZ8WAZ1l4bApeYX8sCpKVGy48wF4zHV0gBQ4lQ9ZrAF%2BkfGM58eQFEv4vGYipAF2vGNkBpzSwuc0AW1PEMATbSanGNGA9uj7ZjScIAVf8AVYegDjTiHxgC4daT9QHfRLVfUA1vGO5ZYF%2BkJtTMugKnPcleJza6gD7cNZbt4YD2qJi5w9J59ACfkpwnAE03LpKJrYDUcOMNceQBEKITSdwgL9Unc3PngCju7WudHr6AWsNNdvlAC1EuOr2kDFxlTiMK624AYTrTdfh7gaVe7WzAG%2B1YUK3P1Am0qSnpumANTOreZyoAfjbnLVxGGBRabyncrPQChf8AVOfNsCalx251jkCnuictYYClU%2Bzz0Ay6dfrFfcC34pAaVYzONo8wNSnzqvcBT7Xn06gE%2FJThOAGG5dJRNbAMcOMNceQFEKIlTogFfFJ3Nz54Av2T66PX0AdYhpeUALrTz2kAlxziOoGoTrCAU493wwJtTircgTaVK%2Bm6AszvrPAGo3zqBRhvM3IFWtOfVgOXXtyBS4nL3AVideQJ06qKAt%2BKTAYjGdugDK67ASaf46gE%2FJThMBzLwuAHbbVcAWFESk9AL9UncgX7J9QLWHS8gF1p1e0gZuM3iPYBj0AZj6rZgDa8rsCbWM%2FwAZn76QAxmfOAKMN5m5Aq1pz6sCdtx5%2BYBLicvRgOk%2FUDLcPaP7AN3tSAYjGZxtADK67AEr%2BOoGZlbSBNTLwgF%2B2GuAM4URKnQClQ9dQL9k%2BvuATcNNLyAnUtrq9pAzcZvEYzX2AoTbWm6%2FkBmPdrYAbWlK3IA2lSv%2BAMu5950gBi3OYuNmARhvKdyAV%2F1TnzbAmpcdudY5AJ7onLWGApVPs89AMunX6xX3Ay9eKQFjGZx08wKU%2BdVPmAJ9r89OsgE%2FJThOAJpuXSUTWwGo4cYa48gCIUQmk7hAX6pO5ufPAFHd2tc6PX0AtYaa7fKAFqJcdXtIGLjKnEYV1twAwnWm6%2FD3A0q92tmAN9qwoVufqBNpUlPTdMAamdW8zlQA%2FG3OWriMMCi03lO5WegFC%2F6pz5tgTUuO3OscgU90TlrDAUqn2eegGGu5JqujvnIGn3fH8gETec2Aq5zS91yAXMb5UPUBX%2Bnr3fgCXd1b3wvFATmbtZU%2BLwAT%2FynK0SrkBSSUTVyuFu0AK7wqXXgCac8ryAn27KIfWEBKZ7p9syAvuTrnH9AGOJmPtUAPdxMZmwDRt1zjAD8oScwnaVfQBbcP4yo0b2Azi8d2ZzwAwu5ptxzle9gDfm2tZA1aT10f4AV0nmZgAaaUV0d85A2%2B74%2FkAibzmwNK96%2Bq5ALmN80BpP9nqwJd3VvfC8UBNubxkCnROdoA0kkomtV%2FQEnPSkAuZ5WAJrZRD6wgJTLn2zID8prnCAscT4VAL4xmQKac17YAflCzCAZelRo%2BAKddQJQ2rj3XuBN%2BbaAbv0YEvF4AGmlp0YGvlABnnkDSfX%2BuQC8b5oBn9uQL5eb30Am9HayBTon0AkklE1qunQCTm8aATn0AmtlEPrAAm5fhyBPuTrnAFjifGALu9sgWjlx7YAvlCzCApqqjfgAmLw9wL%2FAE024917gDfm2gG49mALxcgZcpR7ALcAGeeQJOd6%2Bq5ALxvxuAz%2B0Zf4Avl1b3wgBtzdrPjfABOidaRQBCSia8bAEz0wBOZ5QA09FEP0AFMufbMgT7k65wAY4nH2oC7uMZmwDRt1zjAD8oScwnaVfQCbcP4yo0fAGJi8PM54Aq7mm3HOV7gZbXVtayA3D10f4AEuJ2czCYA13JNV0d85A0%2B74%2FkAibzmwFXOaXuuQC5jfKh6gK%2F09e78AS7ure%2BF4oCczdrKnxeACf8AlOVolXICkkomrlcLdoAV3hUuvAE055XkBPt2UQ%2BsICUz3T7ZkBfcnXOP6AMcTMfaoAe7iYzNgGjbrnGAH5Qk5hO0q%2BgC24fxlRo3sAZzd%2BvEYAmpxWUtugBiVbSxkBWsq8t7MBSUQq63ScgFS%2B3pMLMWBOE5cT90AYUJ3pPUCaSiNrb53Ak44cQn0AWl3TNe7j7AZlNzOjxXGWBqdnPE%2BwFC2TcxP2kAiLT6P89AJ593nOAGFMtQ9uoF3Um3DT0jyAqcvd59gBQ4adK6xaAlDbbwsLkA6K4iNYA1Mr%2F52xQA12qVOVt9EBLuSStp9d5wBvObv142AenK46AW6tpYyArWVeW9mApKIVdbqZAJUvt1qYWYsBpOan8AWFCd6N9QJwoja3%2BQJOOKheQGqcz%2FADABMuZ00A1PM8AKja5AsKU%2BnjgCefrnOANVNqHt1Au6pbiHpHkBU5b1eQJQ4afNASh28AX1xAD8p6bcALhefAAnWWBrPNgXT%2BALfbzAli87gKVQq6gE5XTCzAE4makCwom9wJwvu2BJxw4hMBcPPhAEy540%2FLAZ5ngC8rnIBjXoBTf1z0AamYj%2BQDuq3EbAVOedQCqj2AqecaIA6dAH5em3AA4WueABOssCz6gT45AHU5hdQJayrzOwCkohV1vWQCVfa%2BJhZiwBwnNT%2BADCib0AHH5YBMcVCYC4cz%2BaAzKbmdNK4ywGdnPE10AK2uYn%2BQDCmenjgAefd5zgDUKZah7dQDupNuGnpHkBOHL3eQM04adZrkDNNucLHUDPRXERqBqZX%2FztigBrtUqcrb6ICTUL9mvPrhAOc3frxGAJqcVlLboAYlW0sZAVrKvLezAUlEKut0nIBUvt6TCzFgThOXE%2FdAGFCd6T1Amkoja2%2BdwJOOHEJ9AFpd0zXu4%2BwGZTczo8VxlganZzxPsBQtk3MT9pAIi0%2Bj%2FPQCefd5zgBhTLUPbqBd1Jtw09I8gKnL3efYAUOGnSusWgFrLUpbATfbPWFsBft3OqTSsATbuYS4%2FABHVcvb0A1DSm10%2B4FWmcaa6gEw3Sc318SAvGf2w2suABuV%2FLeAFNZVT5fUAvuqY0c64AXDbSzFx9AKYlO3psBJ13JL%2BUugA%2Fks5de3kBOdW3mYr2hgSV%2FbEL0AcU23G%2BnIE7TWmI58MCX7bQ%2Fv8AUATh5rfCAqmHreAF93NTint1AIhN9zTcRb1jAC5UtSlsAvuU9YWwGpbdUmlYEu5uHMLWvwAR1XL29ANQ0ptdPuBUsZxpqBTD6gLxm8OM0BNyv5bwAprKcT5AU%2FLWNHPkAysLMafQBmJTsCTppeIAm%2B5Z18cALnVt76fZgSV%2FbFegDw2630kCdp%2FTnwwFOdo%2FIAnfG%2BAGbvW8AXy5qcAWE22sfbAC%2FOAJtetAMtvhwALubuYAvEvb0AdJtAXToBTDAnjnWAKZXhgSazj2Ap%2BVTG4FWNY0ApiU72Appx4gCbazrQA3u530Al4WKAuH7gTufoBJztABN5%2BwFN3reAJ90dNvEgGE22nXvGAJzbUpbATfbPWFsBfs3VJpWAJt3MLp%2BAD16vb0AribQF9cADcPcCdLnWMsAbleGBJrKr2AzfdUxo51wBOHKWY0%2BgFMSnb02Ak67kl%2FKXQAfyWcuvbyAny28zp7QwDX7YoAfLdb6ADtP0jnwwJfttD%2B%2FwBQBOHmt8ICqYet4AX3c1OKe3UAiE33NNxFvWMALWWpS2Am%2B2esLYC%2FbudUmlYAm3cwlx%2BACOq5e3oBqGlNrp9wKtM4011AJhuk5vr4kBeM%2FthtZcADcr%2BW8AKayqny%2BoBfdUxo51wAuG2lmLj6AUxKdvTYCTruSX8pdAB%2FJZy69vICc6tvMxXtDAkr%2B2IXoA4ptuN9OQJ2mtMRz4YGWm7Twpx1A11q99wJJxlOc%2BegErb7fXnoAOkl2txowGVDj0V1tKAFEfHTf23Asu6WP4AVs2o%2F8gGLiU6jpuBpPW533kDKntcJzMuNfcC7lCeOI8fcCS7u2tN%2Bs7AOsTh4%2B0ATlKXpiHYDCpz%2B2VGoGU08vpP41Am1M66fUB5Shurd%2FcAxDlddo0AWk1GrptY82BTw5Xq%2F6Au7S4Sw%2BAJT3KfrzmwMtN2nhTjqBq9avfVgKndOc%2BYD222vD6ATcJJOtHoAyocei22oCUR8dN%2FbcBy7pY%2FgCWzaj%2FyBYuKdR0A0m86%2FWQBT2urmXGvuAupmKxAFfbXjUDWsTjT7ATlKX7ZAdnN5UACe79fwAtqZ10%2BoDytat39wDZz57ANNcum1jzAvlw5V8%2BEBPS4SwwFS1P1AM4eAGd6v6gPmnv5gSy0ANxh1owKVceiAk1jTcCznp%2FACno2ugFMXFYjoAp6gZTh5nNagLqQC1QDrny%2BwE5Sl6bZAtnN6QBlPd%2Bv4AW1M66AXRZ8cgGznz2AnDXLqVjzApezlXz4QF3aXCWGBKe5T9eeQMtN2nhTjqBqd6vcAveZz5gScuAMtwqdaMClQ49P6AKx7gWc0sfwBKMOI%2FwDIBi4lOo6AU6679QMptOFeXGoF3KE8cR4%2B4El3dtab9Z2AdYnXH2gAcpS9MQ7Ano5vKS1AxKeX0n8ADamddPqA8pQ3Vu%2FuAYhyuu0aALSajV02sebAp4cr1f8AQF3aXCWHwBKe5T9ec2Blpu08KcdQNdavfcCScZTnPnoBK2%2B3156ADpJdrcaMBlQ49FdbSgBRHx039twLLulj%2BAFbNqP%2FACAYuJTqOm4Gk9bnfeQMqe1wnMy419wLuUJ44jx9wJLu7a036zsA6xOHj7QBOUpemIdgMKnP7ZUagZTTy%2Bk%2FjUCbUzrp9QHlKG6t39wJpxLmrfkAQ1%2Bq1332AUmm5v3xAElntblw%2FwCwKsKE1j7ALdzMrlSAN5lTahbpgCU4lp5mwFS2n7gXx895AdIm82BlJNpW9G2Aw6vivbAA%2B22rqwL9u6%2BifUBurx9wL9VHc4xrsAqk7Xld8gE%2Fq%2BMwvWwBxLzE5VP32Arm1bzOoDFcLTDwBJQ9q01AnGW5T03Al20nOa8sATTiXNW%2FIAhr9VrvvsApNNzfviAJa9rcuH%2FYDKwolY%2BwGn3XmVygJvMqbVcMCVqpaeZsBUtp%2B4F8fPeQGdJvNgChtK3o2wGOfQCi2npYF%2Bzvon1Adr81yA0ob2AU4Tv0sCmumaAG1PnkBl7W88gOVwtMaAKp7VcagTjVynpuBJVnNeQE04l6Z8gCGqWv12AVKdueM4gBWqbmn%2FYBKwolY%2BwC3ea5UgDcTK1ULhgWVq99QFNzPuBR57gM6TebAyobS8m2Aw6AItq9wL9nfRAV1ePuA0objGoEqTv0uwCf1fGYXqANqdYnOOdQK5xbzOoDErhaYeAJVxVxqANrVynpuBJeuPIAcxL0ALVLX6gSlZAt03NP%2BwCVSUSsfYBbuZrlSBlvM3ardMAzSlp5mwFZn3AGvPcB0ibzYGUk2lb0bYDGL4r2wANW81YA%2Fk76T1AJxePuBT2qO5xjXYCVJ2vK75AJ%2FV8ZhetgDiXmJyqfvsBXNq3mdQGK4WmHgCSh7VpqBOMtynpuBLtpOc15YAmnEuat%2BQBDX6rXffYBSabm%2FfEASWe1uXD%2FALAqwoTWPsAt3MyuVIA3mVNqFumAJTiWnmbAVLafuBfHz3kB0ibzYGUk2lb0bYDDq%2BK9sAD7baurAv27r6J9QG6vH3Av1UdzjGuwCqTteV3yAT%2Br4zC9bAHEvMTlU%2FfYC7VGluJ9AGGorFN8%2BgEomZ8fUCpKGqzHGdQLt%2BTmX0YBh%2FLubbUwBqFMquIz5ACaWJb7Xn2AqTrG%2BV4UACUqXUbVS6AMbsCaxNTpm%2FFAZiaa6%2F2BvNvSVGAMuYXyp66fwBrbj2T6ADd%2FrTeZw%2FEAXcu7TuaWqAo7Y%2BMTCXjMATS7Ylylr%2FMgTiGnpo35gTmoetyrAmrhOLTSfosgSS011mGAQs5WHUAXao0txPoAw1FYpvn0AlEzPj6gVJQ1WY4zqBdvycy%2BjAlT%2BXc5amANJKZVbqMgKcYt9rz7AKiaxvkAV9suo2ql0AY3YE1ianTN%2BKAM011%2FsDWbfSMAVxdPx5AO3D9EwLubn9XD1nDAe75LDaWwB%2BsfHMIBf65cpAUqGnpo%2FUCnn2sB6OLkBXGvqAVnO9QAqtM5YFaiur5AVEzNgSaSh4mY9wLt%2BTmX0eoBj9u5txMAahTKriM%2BQAmliW%2B159gKpp1urXigJWpdRtsugFC1YE1ianTN%2BKAM011%2FsDWbeMRgDNwpp66fwBrbj2T6AZ7nf6uHrOGA906NpbAH6x8YmEBOO3LmAJtRD9G%2FMAbfhWBecXIF45AOcrWgJV55Arqur5AFludQJNJQ8ZjjOoF2%2FJzL6MAw57nLUwBQplVxGfIAlaW%2B159gKpp1vleFAElKl1G1UugFG7AmsTU6ZvxQGXdNdQKZt8qMAZcwpp%2Bn8AL049k%2BgA3f603mcPxAF3Lu07mlqgKO2PjEwl4zAE0u2Jcpa%2FzIE4hp6aN%2BYE5qHrcqwJq4Ti00n6LIEktNdZhgELOVh1AF2qNLcT6AMNRWKb59AJRMz4%2BoFSUNVmOM6gXb8nMvowDD%2BXc22pgDUKZVcRnyAE0sS32vPsBUnWN8rwoAEpUuo2ql0AY3YE1ianTN%2BKAzE011%2FsDebekqMAZcwvlT10%2FgDW3Hsn0AG7%2FWm8zh%2BIAu5d2nc0tUBR2x8YmEvGYAml2xLlLX%2BZAGsN4dVgB%2BTesucgTx%2Fr5b9EBdtY6tYAITlpdVrQCoSesVKjIBEX3eTuZwAxCu27jAA32zarDYE%2B6VmJAnCmNcxGgDE17WAu1EZnwgMpxrbcNcyAqXlwnUSAay%2BssB7nKS7rnWf6AIhpwk9Iw5AWpcJRun66ASUuLhZ6gUqG9HrPiACVhK3UbQBQu6ZcryApzO2f6A0qeJb%2FoDLWG8OqwA%2FJvWXOQJ4%2F18t%2BiAu2sdWsAEJy0uq1oBUJPWKlRkAiL7vJ3nAGlWbbuMAT7u2bTvUB%2BUrMTQC4Uta5iNAGJr2sBdqIzPhAZTjW24a5kBTby4TqJAtZfWQFuVDudQKIadJ6RyAu3CrdPG%2BgElLjRZ6gUqJinqBfJYWXUbQBU5lgM56Z%2FoBWdwB6PRgPyfW8gTxmd%2FICTjHVrAFTmF%2BQFOF01QBy%2FGgDhcvyAm1NrzAn3TrEgGJjXONAHNewC3KjeQMpxrc2uZAU29YWIANZfWQJuVDvkCw59IAnmF6f0AZcevUB%2BSzoBn5aLLqAKnMsCnM7Z%2FoBVPeQMvRvDoC%2BTesucgTtf6nfogJVjq1gAzLS6rUCVJ6pVKjIBEX3eTuZwAxCu27jAE32zarDYA%2B6dYkAcKY88aAWsewE3KjeQMJta23DXMgKl5cJ1EgGsvrLAe5yku651n%2BgCIacJPSMOQFqXCUbp%2BugElLi4WeoFKhvR6z4gAlYSt1G0AULumXK8gKcztn%2BgNKniW%2FwCgMtYbw6rAD8m9Zc5Anj%2FXy36IC7ax1awAQnLS6rWgFQk9YqVGQCIvu8nczgBiFdt3GABvtm1WGwJ90rMSBOFMa5iNAGJr2sBdqIzPhAZTjW24a5kBUvLhOokA1l9ZYD3OUl3XOs%2F0ARDThJ6RhyAtS4SjdP10AkpcXCz1AYlUoX203AHlTSWHx6gPa%2B1%2FF6R9wMuY%2BKfyTULx5AOqaXl5TUAESlKpaq1dALpw8YTAkpv%2BAJ43WXonYA1oojG32AUlS60BVcuuenAAoxH9AahptxL1i69gBNYmHcegDUKHDSx7gZVzO0J9KoCl1S1hJ%2FYB%2BMJxlbgEp0q06AajHrCsAy23EvDzkCScy6eKn0wBaJzE6%2BIAG0nOqzGgGolUoX203AHlTSWHx6gPa%2B1%2FF6R9wMuY%2BKfyTULx5AOqaXl5TUAESlKpaq1dALpw8YTAkpv%2BALC3WXonYFwsY2%2BwGlFL2AZzLrxsBdrTqP6A1abcS9YuvYATWJh3HoA1Chw0se4GVcztCfSqAU3suiYFETGVuBSnXl0A1t6wAatuJeucgSzLp4qfwA6JzE6%2BIAm0nOqzGgDErEL7abgTyppaPgCTThzQA3UJzOAHoBZ8tVgCbh3jRgSuwKfNZ4ArwugCtvYAnd142Ak1iP6AbTer%2BwAmsTDuPQC0UOGtABXM%2BT6bAUvZcJAURMZApTpdOgD%2FAHCAMy3EvD6gCzLzip9MAWicxz4gCbU%2FLXWNAHKxC%2B2m4A3DU0lh8ASacPSPuBlzHxT%2BUqF48gHVNLy8pqACJSlUtVaugF04eMJgS3%2FgAb81nrYGXsojG32Atl1oAlWm65%2FhgCjEf0BqGm3EvWLr2AE1iYdx6ANQocNLHuBlXM7Qn0qgKXVLWEn9gH4wnGVuASnSrToBqMesKwDLbcS8POQJJzLp4qfTAFonMTr4gAbSc6rMaAaiVShfbTcAeVNJYfHqA9r7X8XpH3Ay5j4p%2FJNQvHkA6ppeXlNQARKUqlqrV0AunDxhMCSm%2FwCAJ43WXonYA1oojG32AUlS60BVcuuenAAoxH9AahptxL1i69gBNYmHcegDUKHDSx7gZVzO0J9KoCl1S1hJ%2FYB%2BMJxlbgEp0q06AOsTaWfMCbWI5WoBw3cxPQBy%2Bja6qAL4%2FFpPOi1pbgVtJaZm8yBVtpnH1QBEzCST%2FuwGIVUvLD6oC%2BKXdGacfkC%2FbOgE3Tbd6tRroBN6xGV1AlirqUkAN2mqTqpUyBdNWlG2gCu2fGkgScQ1at1r5AUO%2FlqryBU6hSBRfyav3SVcgT7Zltw10qQJJwvjPibApeG%2BoFNum9I21ygLWJtLPmBNrEcrUA4buYnoA5fRtdVAF8fi0nnRa0twK2ktMzeZAq20zj6oAiZhJJ%2F3YDEKqXlh9UBfFLujNOPyBftnQB%2BVS359dAL5axGV1A0nVXUpAE4apOqmwGVpq4jbQBXbPjSQJOIatW618gKHfy1V5AqeEpfjqBRctXtqkvUBjLbhrpqBKYUT4mwKXhvqBTby9I21ygKbibSyAtrEcgZnRu5iegGsvo2uqgCj4tTnRa0twKW0lpmeQJxtpnH1QFE4SSf92BYVUvLD8gKEnGaf9gU92XgCbptvq1zoBTrEZXUCTqrqUkAN2mqTqpUgU11cRsArtnxpIEnENWs1q%2BgBd%2FLVXkCp6ICi5av3SVcgTUy24a6agSmFHjkAl4b6gT7reXpG2uUBLMTaWQJtYjlagHDdzE9AHL6NrqoAoXa7zp5LcAltJaZmwJte2cfVAZd4SSf92BaVS8sPyAPik4zTiPqBftnQCbptu9Wo10Am9YjK6gSxV1KSAG7TVJ1UqZAumrSjbQBXbPjSQJOIatW618gKHfy1V5AqdQpAov5NX7pKuQJ9sy24a6VIEk4XxnxNgUvDfUCm3Tekba5QFrE2lnzAm1iOVqAcN3MT0Acvo2uqgC%2BPxaTzotaW4FbSWmZvMgVbaZx9UARMwkk%2F7sBiFVLyw%2BqAvil3RmnH5Av2zoBN023erUa6ATesRldQJYq6lJADdpqk6qVMgXTVpRtoArtnxpIEnENWrda%2BQFDv5aq8gVOoUgTqp%2F8A09tgKYXDxw8gKXdphYpJ%2FQDK%2FwBfqr1eYU7AUPv%2FAGwqlgTT%2BLylzsAvfGfCAMJyuG%2Fp9QFU%2BM0vetwJ1aSiEr0ncCi5jDwwCYWFOs7qpAYWXS18uQJS3t3ePyBNtv8AXPtLAu5QoePT8AE93xSimqXkA%2FFreZ306gWO67x1AIwsrELF9QFuXt3JXw9wLR784sC1hqIqgKPjCePvvngCTmVELjkCdVP%2FAOntsBTC4eOHkBS7tMLFJP6AZX%2Bv1V6vMKdgKH3%2FALYVSwJp%2FF5S52AXvjPhAGE5XDf0%2BoCqfGaXvW4E6tJRCV6TuBRcxh4YBMLCnWd1UgNZdLXy5Ak23t3ePyA%2FKX%2BufaQF0oePT8AXycJRlUvIBiN5nfQBw7vHUAjCysQsX1AW5e3clfD3AdHvziwDWGoiqAo%2BMJ4%2B%2B%2BeAJOaiFxyBOqn%2FAPT2AZhXh44eQJTphYpT9ABO6zq8xYFfdeFqwFzDylzsBTriJ8ICmJno2BJ3xml7%2BYE3FpaRegDlzs8MA%2BULSdZ3wA1l418gBS%2BoE3LrPtIF3UoePT8AHycJRTVIChreZ30Aph3x1AuMrFYsCbl7dyV8PMgOj35xYBrDURVAUfGE8fffPAEnMqIXHIE6qb%2F6ewFMLjTh5AlOmFilP0AzP7VnV5qdgC%2B6%2FcCcw8pc7ADc3tPhAGE56N%2FT6gKp8Zpe9bgTq0lEJXpO4FFzGHhgEwsKdZ3VSAwsulr5cgSlvbu8fkCbbf659pYF3KFDx6fgAnu%2BKUU1S8gH4tbzO%2BnUCx3XeOoBGFlYhYvqAty9u5K%2BHuBaPfnFgWsNRFUBR8YTx9988AScyohccgTqp%2F8A09tgKYXDxw8gKXdphYpJ%2FQDK%2FwBfqr1eYU7AUPv%2FAGwqlgTT%2BLylzsAvfGfCAMJyuG%2Fp9QFU%2BM0vetwJ1aSiEr0ncCi5jDwwCYWFOs7qpAYWXS18uQJS3t3ePyBNtv8AXPtLAu5QoePT8AE93xSimqXkA%2FFreZ306gWO67x1AIwsrELF9QFqV8W5UVegF2qPSWnxsBRGZry9GBLSFKwnIEl3Ltxi0lr6gTc5idFprYB8q2hzwBp6J1t2630Ay7i3FOYtgM%2Brw0nSAqbS7u2N9vUAp4bmF4eAFfKrjujXnIA0ne6z7Aacu%2FOlOcAFTCz%2FAOZzW2gFHdKhKm9QJ%2FJfX5Z0yATDlKpzqA9rdx1l5UgShYej6OABSteXn7AMzFN9rrGPIA%2FVKdWnK97yBKVabfbkBalfFuVFXoBdqj0lp8bAURma8vRgS0hSsJyBJdy7cYtJa%2BoE3OYnRaa2AfKtoc8Aaeidbdut9AMu4txTmLYDPq8NJ0gKm0u7tjfb1AKeG5heHgBXyq47o15yANJ3us%2BwGnLvzpTnAFrCz%2F5nNbaAC%2BUqFhgalqvP5Z0yATcpVOdQNdvc3PFy8qQJNZT36OAJdeWAyqqe18Y8gCe1KdWnK97yAqVabfbkCalfFuVFXoBdqj6tPjYCiMzXl6MCWkKVhOQJLvXbjFwtfUCbnadFprYB8q6PyA09E6%2F%2BevQDO11mYsBn1eITpAVNpPtjfb1AJXdhuYXjQBXy3hxrzkAam91n2A05d%2BdKc4ANUln%2FAMzmttAKO6VCw3qBP5L6%2FLOmQCYcpVOdQHtbuOsvKkCULD0fRwAKteXn7AMqqntfGPIDM9qU6tX9byAqVabfarAna%2BM1FXcACr6tPjYCxnTy9GAbRawnIAl3rtxi4WvqBNzmJ0WmtgHyraHPAGnonW3brfQDLuLcU5i2Az6vDSdICptLu7Y329QCnhuYXh4AV8quO6NecgDSd7rPsBpy786U5wAVMLP%2FAJnNbaAUd0qEqb1An8l9flnTIBMOUqnOoD2t3HWXlSBKFh6Po4AFK15efsAzMU32usY8gD9Up1acr3vIEpVpt9uQFqV8W5UVegF2qPSWnxsBRGZry9GBLSFKwnIEl3Ltxi0lr6gTc5idFprYB8q2hzwBp6J1t2630Ay7i3FOYtgM%2Brw0nSAqbS7u2N9vUAp4bmF4eAFfKrjujXnIA0ne6z7Aacu%2FOlOcAFTCz%2F5nNbaAUd0qEqb1An8l9flnTIBMOUqnOoD2t3HWXlSBNQrvm8ADbj4uHD64AXNJK9FpOoEm6ah24Tv3AEm%2FJ4AU18tU8Q9fUCiKSvWJrxIDD0WJ84AzPbD6Z8udwDChy1h4aA324zLTjSABru1iZhagUVWPH4AJhtrTeZfiAFO20r0aWoBqoxNPHUC%2FaEl5NfgBiE5WZhrEANNJtylUv6ASvK4jGQM4j5ffeAJ201VYxDewEv8AWzw5gDTTj9caz%2FAAldw4daYAmoV3zeABtx8XDh9cALmklei0nUCTdNQ7cJ37gCTfk8AKa%2BWqeIevqBRFJXrE14kBh6LE%2BcAZnth9M%2BXO4BhQ5aw8NAb7cZlpxpAA13axMwtQKKrHj8AEw21pvMvxACnbaV6NLUA1UYmnjqBftCS8mvwAxCcrMw1iAGmk5lKpf0AlOq4jGeAKYj5ffeAKbTxWNugEnezw8AbuP1xz%2FAAuYcY8gJwld%2FgCfdXxcOPMBbwkr0Wk6gCbpqHbhO%2FcCSb8ngBTXyy03Ub%2BoFEUlesTXiQGHosT5wBme2H0z5c7gGFDlrDw0BvtxmWnGkADXdrEzC1AoqsePwATDbWm8y%2FEAKdtpXo0tQCbUYmnjqBS4SXk1%2BAHCcrMw1sBSmk25SqWBTOVxGMgGI%2BXi4Ay3cqqxj0Ap%2FbZ4cwBpzH645%2FgDN6w4x5ADpOb9cADbj4uHHngBc0kr0Wk6gSbpqHbhO%2FcASb8ngBTXy1TxD19QKIpK9YmvEgMPRYnzgDM9sPpny53AMKHLWHhoDfbjMtONIAGu7WJmFqBRVY8fgAmG2tN5l%2BIAU7bSvRpagGqjE08dQL9oSXk1%2BAGITlZmGsQA00m3KVS%2FoBK8riMZAziPl994AnbTVVjEN7AS%2F1s8OYA004%2FXGs%2FwAJXcOHWmAJqFd83gAbcfFw4fXAC5pJXotJ1Ak3TUO3Cd%2B4Ak35PACmvlqniHr6gURSV6xNeJAYeixPnAGZ7YfTPlzuAYUOWsPDQG%2B3GZacaQANd2sTMLUCiqx4%2FABMNtabzL8QAp22lejS1ANVGJp46gX7QkvJr8AMQnKzMNYgBppNuUql%2FQCV5XEYyBjtmF%2F6rOwG1EPePPIFc0nPWwJx76AXd8o7vl%2FGQB%2FGVGJvwwJfH5VOKzEAPdEOOI2i8gY%2FX43ETxuBpTLn%2FAHp4QFv0c5ibAnMveHuAd%2FylfLmY2Ad5%2FwAzXoA1piugEpjzr%2BABTfxmJvpGgDXyfy4jwgM%2FrDmflxsBpR8OIqdvMDLj5%2Bdb%2B4FX7RGf28dANOI%2F%2BdM7AV6%2BfvsAL5RWIUz57gZ7Zhf%2BqzsBtRD3jzyBXNJz1sCce%2BgF3fKO75fxkAfxlRib8MCXx%2B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<h1 class="title toc-ignore">Minimal Set of binnmu Packages</h1>
<h2 class="subtitle">Combining the R and Debian package systems</h2>
<h3 class="author">Dirk Eddelbuettel</h3>
<h3 class="date">First version 2017-Jul-16; this version 2017-Aug-05</h3>
</header>

<hr>


<div id="step-0-problem-definition" class="section level2">
<h2>Step 0: Problem Definition</h2>
<div id="upstream-change" class="section level3">
<h3>Upstream Change</h3>
<p>R 3.4.0, released in April, included the following paragraph in its NEWS file:</p>
<ul>
<li>Packages which register native routines for .C or .Fortran need to be re-installed for this version (unless installed with R-devel SVN revision r72375 or later).</li>
</ul>
<p>This transition has no fallback behavior (as is more common with R changes) and requires a rebuild. Packages build under older R version still <em>load</em> and function partially, but will be unable to access any native (<em>i.e.</em>, compiled) routines.</p>
</div>
<div id="impact" class="section level3">
<h3>Impact</h3>
<p>For the Debian packages, this means that we need to consider the set of packages which</p>
<ul>
<li>match <code>r-cran-*</code>, <code>r-bioc-*</code> and alike</li>
<li>contain compiled code (as R-only packages have no native routines)</li>
<li>use at least one <code>.C()</code> or <code>.Fortran()</code> (but <em>not</em> <code>.Call()</code>) call</li>
<li>use (the hitherto optional) routine registration (so that the change in behaviour is noticible)</li>
<li>have <em>not yet</em> been recompiled with R 3.4.0 or R 3.4.1</li>
</ul>
<p>This note computes this set and provides the input for a <a href="https://release.debian.org/wanna-build.txt">wanna-build</a> request.</p>
<p>This version is updated version which reflects the fourth point above which was pointed out to me by Kurt Hornik after I shared the initial version with him. The point he raised (“does it use <code>R_registerRoutines</code> ?”) is important and further reduces the effective set.</p>
</div>
</div>
<div id="step-1-reverse-dependencies-of-r" class="section level2">
<h2>Step 1: Reverse Dependencies of R</h2>
<div id="fresh-debian-unstable-session" class="section level3">
<h3>Fresh Debian unstable session</h3>
<p>For this we drop into a clean Docker container running Debian unstable. Later, we will need the current sources of the <a href="https://github.com/eddelbuettel/rcppapt"><code>RcppAPT</code></a> package so we start from a local git directory:</p>
<div class="sourceCode"><pre class="sourceCode sh"><code class="sourceCode bash"><span class="ex">server</span><span class="op">&gt;</span> cd ~/git <span class="kw">&amp;&amp;</span> <span class="ex">docker</span> run --rm -ti -v <span class="va">$(</span><span class="bu">pwd</span><span class="va">)</span>:/mnt debian:unstable</code></pre></div>
</div>
<div id="update-debian" class="section level3">
<h3>Update Debian</h3>
<p>Inside the Docker container, we update the package information and install what is needed to build <a href="https://github.com/eddelbuettel/rcppapt"><code>RcppAPT</code></a> for R.<br />
This includes Rcpp and <code>libapt-pkg-dev</code>. We also install the data.table package used for aggregating the (R and Debian) package data computed below.</p>
<p>This step takes a short moment, with the exact time dependent on the network connection and other factors.</p>
<div class="sourceCode"><pre class="sourceCode sh"><code class="sourceCode bash"><span class="ex">docker</span><span class="op">&gt;</span> apt-get update
<span class="ex">docker</span><span class="op">&gt;</span> apt-get -y dist-upgrade

<span class="ex">docker</span><span class="op">&gt;</span> apt-get -y install r-cran-rcpp r-cran-data.table libapt-pkg-dev less 

<span class="ex">docker</span><span class="op">&gt;</span> cd /mnt 
<span class="ex">docker</span><span class="op">&gt;</span> R CMD INSTALL rcppapt/       <span class="co"># assuming we're above rcppapt</span></code></pre></div>
</div>
<div id="launch-r" class="section level3">
<h3>Launch R</h3>
<div id="all-candidates" class="section level4">
<h4>All Candidates</h4>
<p>Inside the same Docker session, we now launch R and run (almost all of) the remainder from R.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span><span class="kw">library</span>(RcppAPT)
&gt;<span class="st"> </span><span class="kw">library</span>(data.table)
&gt;<span class="st"> </span>rd &lt;-<span class="st"> </span><span class="kw">reverseDepends</span>(<span class="st">&quot;r-base-core&quot;</span>)         <span class="co"># 516 x 2</span>
&gt;<span class="st"> </span>rd &lt;-<span class="st"> </span>rd[<span class="kw">grepl</span>(<span class="st">&quot;^r-&quot;</span>, rd[,<span class="dv">1</span>]), ]            <span class="co"># 489 x 2</span>
&gt;<span class="st"> </span>rd &lt;-<span class="st"> </span>rd[<span class="kw">order</span>(rd[,<span class="dv">2</span>]), ]
&gt;<span class="st"> </span><span class="kw">setDT</span>(rd)</code></pre></div>
<p>We use <code>RcppAPT</code> to compute the reverse depends of the main R package providing the R engine: <code>r-base-core</code>. Among those (currently) 516 packages are both other packages from the upstream source (<code>r-base*</code>, <code>r-doc*</code>) which we exclude first as well as other, non-R-package dependencies (such as <code>rpy2</code>) which we also exclude.</p>
<p>This leaves 489 candidate packages out of the initial 514. The version field tells which r-base-core version was used to build the package—information we need per the setup described above.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>rd
&gt;<span class="st"> </span>rd
             package version
  <span class="dv">1</span>:<span class="st">       </span>r-doc-pdf
  <span class="dv">2</span>:<span class="st">      </span>r-doc-info
  <span class="dv">3</span>:<span class="st">      </span>r-doc-html
  <span class="dv">4</span>:<span class="st">     </span>r-base-html
  <span class="dv">5</span>:<span class="st">     </span>r-other-rot
 ---
<span class="dv">485</span>:<span class="st"> </span>r-base-core-dbg <span class="fl">3.4.1</span><span class="dv">-2</span>
<span class="dv">486</span>:<span class="st">          </span>r-base <span class="fl">3.4.1</span><span class="dv">-2</span>
<span class="dv">487</span>:<span class="st">     </span>r-cran-mgcv <span class="fl">3.4.1</span><span class="dv">-2</span>
<span class="dv">488</span>:<span class="st">     </span>r-cran-boot <span class="fl">3.4.1</span><span class="dv">-2</span>
<span class="dv">489</span>:<span class="st">      </span>r-cran-car <span class="fl">3.4.1</span><span class="dv">-2</span>
&gt;<span class="st"> </span></code></pre></div>
<p>Next we need to filter out two versions with unsortable (<em>i.e.</em>,non-semantic) version numbers, and apply a logical filter depending on whether the package was built with R version 3.3.3 or earlier, indicating a possibe required rebuild.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>rd[ version==<span class="st">&quot;3.0.0~20130330-1&quot;</span>, version :<span class="er">=</span><span class="st"> &quot;3.0.0.20130330-1&quot;</span>]
&gt;<span class="st"> </span>rd[ version==<span class="st">&quot;3.2.4-revised-1&quot;</span>, version :<span class="er">=</span><span class="st"> &quot;3.2.4.1-1&quot;</span>]
&gt;<span class="st"> </span>rd[version!=<span class="st">&quot;&quot;</span>, oldVersion :<span class="er">=</span><span class="st"> </span>version  &lt;=<span class="st">  </span><span class="kw">package_version</span>(<span class="st">&quot;3.3.3-1&quot;</span>)]
&gt;<span class="st"> </span>rd[ <span class="kw">is.na</span>(oldVersion), oldVersion :<span class="er">=</span><span class="st"> </span><span class="ot">FALSE</span>]
&gt;<span class="st"> </span>rd[ !<span class="kw">grepl</span>(<span class="st">&quot;r-(doc|base)&quot;</span>, package), ]
             package          version oldVersion
  <span class="dv">1</span>:<span class="st">     </span>r-other-rot                       <span class="ot">FALSE</span>
  <span class="dv">2</span>:<span class="st"> </span>r-cran-epitools          <span class="fl">3.0.0</span><span class="dv">-2</span>       <span class="ot">TRUE</span>
  <span class="dv">3</span>:<span class="st"> </span>r-cran-combinat          <span class="fl">3.0.0</span><span class="dv">-2</span>       <span class="ot">TRUE</span>
  <span class="dv">4</span>:<span class="st">    </span>r-cran-gmaps <span class="fl">3.0.0.20130330</span><span class="dv">-1</span>       <span class="ot">TRUE</span>
  <span class="dv">5</span>:<span class="st">      </span>r-cran-wdi          <span class="fl">3.0.1</span><span class="dv">-6</span>       <span class="ot">TRUE</span>
 ---
<span class="dv">478</span>:<span class="st">   </span>r-recommended          <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span>
<span class="dv">479</span>:<span class="st">       </span>r-mathlib          <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span>
<span class="dv">480</span>:<span class="st">     </span>r-cran-mgcv          <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span>
<span class="dv">481</span>:<span class="st">     </span>r-cran-boot          <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span>
<span class="dv">482</span>:<span class="st">      </span>r-cran-car          <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span></code></pre></div>
<p>To cover some corner case, we derive a <code>skip</code> field:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>rd[ version==<span class="st">&quot;&quot;</span>, skip:<span class="er">=</span><span class="ot">TRUE</span> ]
&gt;<span class="st"> </span>rd[ <span class="kw">is.na</span>(skip), skip:<span class="er">=</span><span class="ot">FALSE</span>]
&gt;<span class="st"> </span>rd[ skip==<span class="ot">FALSE</span>, ]
             package          version oldVersion  skip
  <span class="dv">1</span>:<span class="st"> </span>r-cran-epitools          <span class="fl">3.0.0</span><span class="dv">-2</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">2</span>:<span class="st"> </span>r-cran-combinat          <span class="fl">3.0.0</span><span class="dv">-2</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">3</span>:<span class="st">    </span>r-cran-gmaps <span class="fl">3.0.0.20130330</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">4</span>:<span class="st">      </span>r-cran-wdi          <span class="fl">3.0.1</span><span class="dv">-6</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">5</span>:<span class="st">   </span>r-cran-bitops          <span class="fl">3.0.1</span><span class="dv">-6</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
 ---
<span class="dv">480</span>:<span class="st"> </span>r-base-core-dbg          <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span> <span class="ot">FALSE</span>
<span class="dv">481</span>:<span class="st">          </span>r-base          <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span> <span class="ot">FALSE</span>
<span class="dv">482</span>:<span class="st">     </span>r-cran-mgcv          <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span> <span class="ot">FALSE</span>
<span class="dv">483</span>:<span class="st">     </span>r-cran-boot          <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span> <span class="ot">FALSE</span>
<span class="dv">484</span>:<span class="st">      </span>r-cran-car          <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span> <span class="ot">FALSE</span>
&gt;<span class="st"> </span></code></pre></div>
</div>
<div id="compiled-packages" class="section level4">
<h4>Compiled Packages</h4>
<p>Next, we find the actual dependencies of each of these packages by constructing a large regular expression which we feed into <code>RcppAPT::getDepends()</code></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>regexp &lt;-<span class="st"> </span><span class="kw">paste</span>(<span class="kw">paste0</span>(<span class="st">&quot;^&quot;</span>, rd[skip==<span class="ot">FALSE</span>, package], <span class="st">&quot;$&quot;</span>), <span class="dt">collapse=</span><span class="st">&quot;|&quot;</span>)
&gt;<span class="st"> </span>dep &lt;-<span class="st"> </span><span class="kw">getDepends</span>(regexp)
&gt;<span class="st"> </span><span class="kw">setDT</span>(dep)
&gt;<span class="st"> </span>dep
                  srcpkg              deppkg cmpop          version
   <span class="dv">1</span>:<span class="st">  </span>r-bioc-hypergraph         r-base-core     <span class="dv">2</span> <span class="fl">3.3.1.20161024</span><span class="dv">-1</span>
   <span class="dv">2</span>:<span class="st">  </span>r-bioc-hypergraph             r-api<span class="dv">-3</span>     <span class="dv">0</span>           (null)
   <span class="dv">3</span>:<span class="st">  </span>r-bioc-hypergraph        r-bioc-graph     <span class="dv">0</span>           (null)
   <span class="dv">4</span>:<span class="st">  </span>r-bioc-hypergraph r-bioc-biocgenerics     <span class="dv">0</span>           (null)
   <span class="dv">5</span>:<span class="st">  </span>r-bioc-hypergraph        r-cran-runit     <span class="dv">0</span>           (null)
  ---
<span class="dv">3744</span>:<span class="st"> </span>r-cran-viridislite             r-api<span class="dv">-3</span>     <span class="dv">0</span>           (null)
<span class="dv">3745</span>:<span class="st">      </span>r-cran-xtable         r-base-core     <span class="dv">2</span>          <span class="fl">3.2.5</span><span class="dv">-1</span>
<span class="dv">3746</span>:<span class="st">      </span>r-cran-xtable             r-api<span class="dv">-3</span>     <span class="dv">0</span>           (null)
<span class="dv">3747</span>:<span class="st">   </span>r-cran-pkgkitten         r-base-core     <span class="dv">2</span>          <span class="fl">3.3.2</span><span class="dv">-1</span>
<span class="dv">3748</span>:<span class="st">   </span>r-cran-pkgkitten             r-api<span class="dv">-3</span>     <span class="dv">0</span>           (null)
&gt;<span class="st"> </span></code></pre></div>
<p>Next we subset to those have <code>libc6</code> as a Depends, meaning they are compiled packages. This excludes all the R packages having only R code.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>comp &lt;-<span class="st"> </span>dep[deppkg==<span class="st">&quot;libc6&quot;</span>]   <span class="co"># 242</span>
&gt;<span class="st"> </span>comp
                 srcpkg deppkg cmpop version isCompiled
  <span class="dv">1</span>:<span class="st">  </span>r-bioc-makecdfenv  libc6     <span class="dv">2</span>     <span class="fl">2.4</span>       <span class="ot">TRUE</span>
  <span class="dv">2</span>:<span class="st">       </span>r-cran-bio3d  libc6     <span class="dv">2</span>    <span class="fl">2.14</span>       <span class="ot">TRUE</span>
  <span class="dv">3</span>:<span class="st">   </span>r-bioc-rsamtools  libc6     <span class="dv">2</span>    <span class="fl">2.15</span>       <span class="ot">TRUE</span>
  <span class="dv">4</span>:<span class="st">     </span>r-cran-foreign  libc6     <span class="dv">2</span>    <span class="fl">2.14</span>       <span class="ot">TRUE</span>
  <span class="dv">5</span>:<span class="st">    </span>r-bioc-multtest  libc6     <span class="dv">2</span>    <span class="fl">2.14</span>       <span class="ot">TRUE</span>
 ---
<span class="dv">238</span>:<span class="st">     </span>r-cran-nleqslv  libc6     <span class="dv">2</span>     <span class="fl">2.4</span>       <span class="ot">TRUE</span>
<span class="dv">239</span>:<span class="st"> </span>r-other-amsmercury  libc6     <span class="dv">2</span>    <span class="fl">2.14</span>       <span class="ot">TRUE</span>
<span class="dv">240</span>:<span class="st">         </span>r-cran-gnm  libc6     <span class="dv">2</span>     <span class="fl">2.4</span>       <span class="ot">TRUE</span>
<span class="dv">241</span>:<span class="st">         </span>r-cran-gsl  libc6     <span class="dv">2</span>     <span class="fl">2.4</span>       <span class="ot">TRUE</span>
<span class="dv">242</span>:<span class="st">         </span>r-cran-gss  libc6     <span class="dv">2</span>     <span class="fl">2.4</span>       <span class="ot">TRUE</span>
&gt;</code></pre></div>
<p>We are now getting closer. We set keys on the <code>data.table</code> objects, and then do an inner join:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span><span class="kw">setkey</span>(comp, srcpkg)
&gt;<span class="st"> </span><span class="kw">setkey</span>(rd, package)
&gt;<span class="st"> </span>all &lt;-<span class="st"> </span>rd[comp[, <span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">5</span>)]]   <span class="co"># inner join (by default on columns with keys)</span>
&gt;<span class="st"> </span>all[<span class="kw">order</span>(version),]
                      package version oldVersion  skip isCompiled
  <span class="dv">1</span>:<span class="st">            </span>r-cran-bitops <span class="fl">3.0.1</span><span class="dv">-6</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
  <span class="dv">2</span>:<span class="st">               </span>r-cran-mnp <span class="fl">3.0.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
  <span class="dv">3</span>:<span class="st"> </span>r-other-mott-happy.hbrem <span class="fl">3.0.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
  <span class="dv">4</span>:<span class="st">             </span>r-cran-amore <span class="fl">3.1.0</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
  <span class="dv">5</span>:<span class="st">              </span>r-cran-deal <span class="fl">3.1.0</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
 ---
<span class="dv">238</span>:<span class="st">              </span>r-cran-rcpp <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
<span class="dv">239</span>:<span class="st">            </span>r-cran-rmysql <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
<span class="dv">240</span>:<span class="st">         </span>r-cran-rsymphony <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
<span class="dv">241</span>:<span class="st">               </span>r-cran-ttr <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
<span class="dv">242</span>:<span class="st">                </span>r-mathlib <span class="fl">3.4.1</span><span class="dv">-2</span>      <span class="ot">FALSE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
&gt;<span class="st"> </span></code></pre></div>
<p>We have 242 <em>potential</em> rebuilds, down from 514 reverse depends at the outset.</p>
</div>
<div id="version-check" class="section level4">
<h4>Version check</h4>
<p>Next, we can concentrate on those having been built with the older versions requiring a rebuild:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>all[oldVersion==<span class="ot">TRUE</span>,][<span class="kw">order</span>(version),]    <span class="co"># 167</span>
                      package version oldVersion  skip isCompiled
  <span class="dv">1</span>:<span class="st">            </span>r-cran-bitops <span class="fl">3.0.1</span><span class="dv">-6</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
  <span class="dv">2</span>:<span class="st">               </span>r-cran-mnp <span class="fl">3.0.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
  <span class="dv">3</span>:<span class="st"> </span>r-other-mott-happy.hbrem <span class="fl">3.0.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
  <span class="dv">4</span>:<span class="st">             </span>r-cran-amore <span class="fl">3.1.0</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
  <span class="dv">5</span>:<span class="st">              </span>r-cran-deal <span class="fl">3.1.0</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
 ---<span class="st">                                                             </span>
<span class="dv">163</span>:<span class="st">           </span>r-cran-rcppgsl <span class="fl">3.3.3</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
<span class="dv">164</span>:<span class="st">             </span>r-cran-rodbc <span class="fl">3.3.3</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
<span class="dv">165</span>:<span class="st">         </span>r-cran-snowballc <span class="fl">3.3.3</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
<span class="dv">166</span>:<span class="st">                </span>r-cran-v8 <span class="fl">3.3.3</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
<span class="dv">167</span>:<span class="st">               </span>r-cran-zoo <span class="fl">3.3.3</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span>
&gt;<span class="st"> </span></code></pre></div>
<p>Now we are down to 167 packages.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>all[, cran:<span class="er">=</span><span class="kw">grepl</span>(<span class="st">&quot;^r-cran&quot;</span>, package) ]
&gt;<span class="st"> </span>all[, bioc:<span class="er">=</span><span class="kw">grepl</span>(<span class="st">&quot;^r-bioc&quot;</span>, package) ]
&gt;<span class="st"> </span>all[bioc==<span class="ot">TRUE</span> &amp;<span class="st"> </span>oldVersion==<span class="ot">TRUE</span>,]                <span class="co"># 17 BioC</span>
                  package          version oldVersion  skip isCompiled  cran bioc
 <span class="dv">1</span>:<span class="st">           </span>r-bioc-affy <span class="fl">3.3.1.20161024</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
 <span class="dv">2</span>:<span class="st">         </span>r-bioc-affyio <span class="fl">3.3.1.20161024</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
 <span class="dv">3</span>:<span class="st">        </span>r-bioc-biobase <span class="fl">3.3.1.20161024</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
 <span class="dv">4</span>:<span class="st">     </span>r-bioc-biovizbase          <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
 <span class="dv">5</span>:<span class="st">         </span>r-bioc-deseq2          <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
 <span class="dv">6</span>:<span class="st">        </span>r-bioc-dnacopy <span class="fl">3.3.1.20161024</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
 <span class="dv">7</span>:<span class="st">          </span>r-bioc-edger          <span class="fl">3.3.0</span><span class="dv">-2</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
 <span class="dv">8</span>:<span class="st">     </span>r-bioc-genefilter          <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
 <span class="dv">9</span>:<span class="st">          </span>r-bioc-graph <span class="fl">3.3.1.20161024</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
<span class="dv">10</span>:<span class="st">     </span>r-bioc-hilbertvis <span class="fl">3.3.1.20161024</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
<span class="dv">11</span>:<span class="st">          </span>r-bioc-limma          <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
<span class="dv">12</span>:<span class="st">     </span>r-bioc-makecdfenv <span class="fl">3.3.1.20161024</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
<span class="dv">13</span>:<span class="st">       </span>r-bioc-multtest <span class="fl">3.3.1.20161024</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
<span class="dv">14</span>:<span class="st"> </span>r-bioc-preprocesscore <span class="fl">3.3.1.20161024</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
<span class="dv">15</span>:<span class="st">           </span>r-bioc-rbgl          <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
<span class="dv">16</span>:<span class="st">    </span>r-bioc-rtracklayer          <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
<span class="dv">17</span>:<span class="st">       </span>r-bioc-snpstats <span class="fl">3.3.1.20161024</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">TRUE</span>
&gt;<span class="st"> </span></code></pre></div>
<p>Among these are 17 BioConductor packages. This is a superset as we do not know which of these use only <code>.Call()</code> meaning that no rebuild would be required.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>all[bioc!=<span class="ot">TRUE</span> &amp;<span class="st"> </span>cran!=<span class="ot">TRUE</span> &amp;<span class="st"> </span>oldVersion==<span class="ot">TRUE</span>,]   <span class="co"># 3 other</span>
                    package version oldVersion  skip isCompiled  cran  bioc
<span class="dv">1</span>:<span class="st">       </span>r-other-amsmercury <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">FALSE</span>
<span class="dv">2</span>:<span class="st">          </span>r-other-iwrlars <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">FALSE</span>
<span class="dv">3</span>:<span class="st"> </span>r-other-mott-happy.hbrem <span class="fl">3.0.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span> <span class="ot">FALSE</span>
&gt;<span class="st"> </span></code></pre></div>
<p>There are also three which are neither BioC nor CRAN.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>cand &lt;-<span class="st"> </span>all[ cran==<span class="ot">TRUE</span> &amp;<span class="st"> </span>oldVersion==<span class="ot">TRUE</span>, ]   <span class="co"># 147</span>
&gt;<span class="st"> </span>cand
             package version oldVersion  skip isCompiled cran  bioc
  <span class="dv">1</span>:<span class="st">     </span>r-cran-ade4 <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">2</span>:<span class="st"> </span>r-cran-adegenet <span class="fl">3.3.1</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">3</span>:<span class="st"> </span>r-cran-adephylo <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">4</span>:<span class="st">   </span>r-cran-amelia <span class="fl">3.2.3</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">5</span>:<span class="st">    </span>r-cran-amore <span class="fl">3.1.0</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">TRUE</span> <span class="ot">FALSE</span>
 ---<span class="st">                                                               </span>
<span class="dv">143</span>:<span class="st">    </span>r-cran-vegan <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">TRUE</span> <span class="ot">FALSE</span>
<span class="dv">144</span>:<span class="st">     </span>r-cran-vgam <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">TRUE</span> <span class="ot">FALSE</span>
<span class="dv">145</span>:<span class="st">     </span>r-cran-xml2 <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">TRUE</span> <span class="ot">FALSE</span>
<span class="dv">146</span>:<span class="st">     </span>r-cran-yaml <span class="fl">3.3.2</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">TRUE</span> <span class="ot">FALSE</span>
<span class="dv">147</span>:<span class="st">      </span>r-cran-zoo <span class="fl">3.3.3</span><span class="dv">-1</span>       <span class="ot">TRUE</span> <span class="ot">FALSE</span>       <span class="ot">TRUE</span> <span class="ot">TRUE</span> <span class="ot">FALSE</span>
&gt;<span class="st"> </span></code></pre></div>
<p>We have 147 possible NMUs based off CRAN.</p>
<p>Next, we mix this with information from CRAN.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>db &lt;-<span class="st"> </span>tools::<span class="kw">CRAN_package_db</span>()   <span class="co"># CRAN pkge info: N rows x 65 cols</span>
&gt;<span class="st"> </span><span class="kw">setDT</span>(db)
&gt;<span class="st"> </span>db[, package:<span class="er">=</span><span class="kw">paste0</span>(<span class="st">&quot;r-cran-&quot;</span>, <span class="kw">tolower</span>(Package))]
&gt;<span class="st"> </span><span class="kw">setkey</span>(db, package)              <span class="co"># key on package field</span>
&gt;<span class="st"> </span>foo &lt;-<span class="st"> </span>db[ cand ]                <span class="co"># inner join</span>
&gt;<span class="st"> </span>foo[, .(package, Package, Version, NeedsCompilation, oldVersion, skip)]
             package  Package Version NeedsCompilation oldVersion  skip
  <span class="dv">1</span>:<span class="st">     </span>r-cran-ade4     ade4   <span class="fl">1.7</span><span class="dv">-6</span>              yes       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">2</span>:<span class="st"> </span>r-cran-adegenet adegenet   <span class="fl">2.0.1</span>              yes       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">3</span>:<span class="st"> </span>r-cran-adephylo adephylo  <span class="fl">1.1</span><span class="dv">-10</span>              yes       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">4</span>:<span class="st">   </span>r-cran-amelia   Amelia   <span class="fl">1.7.4</span>              yes       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
  <span class="dv">5</span>:<span class="st">    </span>r-cran-amore    AMORE  <span class="fl">0.2</span><span class="dv">-15</span>              yes       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
 ---<span class="st">                                                                   </span>
<span class="dv">143</span>:<span class="st">    </span>r-cran-vegan    vegan   <span class="fl">2.4</span><span class="dv">-3</span>              yes       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
<span class="dv">144</span>:<span class="st">     </span>r-cran-vgam     VGAM   <span class="fl">1.0</span><span class="dv">-3</span>              yes       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
<span class="dv">145</span>:<span class="st">     </span>r-cran-xml2     xml2   <span class="fl">1.1.1</span>              yes       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
<span class="dv">146</span>:<span class="st">     </span>r-cran-yaml     yaml  <span class="fl">2.1.14</span>              yes       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
<span class="dv">147</span>:<span class="st">      </span>r-cran-zoo      zoo   <span class="fl">1.8</span><span class="dv">-0</span>              yes       <span class="ot">TRUE</span> <span class="ot">FALSE</span>
&gt;<span class="st"> </span></code></pre></div>
<p>This is our set of 147 candidate packages with their CRAN name, Debian name and upstream version.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span><span class="kw">saveRDS</span>(foo[, .(package, Package, Version, NeedsCompilation, oldVersion, skip)], <span class="dt">file=</span><span class="st">&quot;debpackages.rds&quot;</span>)</code></pre></div>
<p>We save this file to be used on another machine.</p>
</div>
</div>
</div>
<div id="step-2-grep" class="section level2">
<h2>Step 2: Grep</h2>
<p>On another machine with access to all CRAN package sources (which I happen to have access to), we use the list of 147 candidate packages and run a recursive grep for each. We store the output from two <code>egrep</code> runs, called via <code>system()</code>, directly in the same data structure. The first checks for <code>.C()</code> or <code>.Fortran()</code> calls in the R scripts; the second checks for <code>R_registerRoutines()</code> in the compiled C code (with thanks again to Kurt Hornik for the suggestion)</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">deb &lt;-<span class="st"> </span><span class="kw">readRDS</span>(<span class="st">&quot;~/debpackages.rds&quot;</span>)
for (i in <span class="dv">1</span>:<span class="kw">nrow</span>(deb)) {
    deb[i, <span class="st">&quot;dotCorFortran&quot;</span>] &lt;-<span class="st"> </span>if (<span class="kw">is.na</span>(deb[i, <span class="st">&quot;Package&quot;</span>])) <span class="ot">NA</span> 
                               else <span class="kw">system</span>(<span class="kw">paste0</span>(<span class="st">&quot;egrep -r -q </span><span class="ch">\&quot;\\</span><span class="st">.(C|Fortran)</span><span class="ch">\\</span><span class="st">(</span><span class="ch">\&quot;</span><span class="st"> &quot;</span>, deb[i, <span class="st">&quot;Package&quot;</span>], <span class="st">&quot;/R/*&quot;</span>))==<span class="dv">0</span>
    deb[i, <span class="st">&quot;hasRegistration&quot;</span>] &lt;-<span class="st"> </span>if (<span class="kw">is.na</span>(deb[i, <span class="st">&quot;Package&quot;</span>])) <span class="ot">NA</span> 
                               else <span class="kw">system</span>(<span class="kw">paste0</span>(<span class="st">&quot;egrep -r -q </span><span class="ch">\&quot;</span><span class="st">R_registerRoutines</span><span class="ch">\\</span><span class="st">(</span><span class="ch">\&quot;</span><span class="st"> &quot;</span>, deb[i, <span class="st">&quot;Package&quot;</span>], <span class="st">&quot;/src/*&quot;</span>))==<span class="dv">0</span>
}
<span class="kw">saveRDS</span>(deb, <span class="st">&quot;~/debpackagesout.rds&quot;</span>)</code></pre></div>
</div>
<div id="step-3-finalize" class="section level2">
<h2>Step 3: Finalize</h2>
<p>We read the data back in and subset on those for which the recursive grep found actual uses of <code>.C()</code> or <code>.Fortran()</code>. The list contains 72 packages.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>deb &lt;-<span class="st"> </span><span class="kw">readRDS</span>(<span class="st">&quot;debpackagesout.rds&quot;</span>)
&gt;<span class="st"> </span><span class="kw">setDT</span>(deb)
&gt;<span class="st"> </span>deb[ <span class="kw">is.na</span>(dotCorFortran) |(dotCorFortran &amp;<span class="st"> </span>hasRegistration), <span class="dv">1</span>:<span class="dv">3</span>]                               
                     package           Package  Version
 <span class="dv">1</span>:<span class="st">              </span>r-cran-ade4              ade4    <span class="fl">1.7</span><span class="dv">-6</span>
 <span class="dv">2</span>:<span class="st">            </span>r-cran-bayesm            bayesm  <span class="fl">3.1-0.1</span>
 <span class="dv">3</span>:<span class="st">     </span>r-cran-blockmodeling     blockmodeling    <span class="fl">0.1.9</span>
 <span class="dv">4</span>:<span class="st">             </span>r-cran-brglm             brglm    <span class="fl">0.6.1</span>
 <span class="dv">5</span>:<span class="st">             </span>r-cran-caret             caret   <span class="fl">6.0</span><span class="dv">-76</span>
 <span class="dv">6</span>:<span class="st">              </span>r-cran-coin              coin    <span class="fl">1.2</span><span class="dv">-1</span>
 <span class="dv">7</span>:<span class="st">          </span>r-cran-contfrac          contfrac   <span class="fl">1.1</span><span class="dv">-11</span>
 <span class="dv">8</span>:<span class="st">        </span>r-cran-data.table        data.table   <span class="fl">1.10.4</span>
 <span class="dv">9</span>:<span class="st">            </span>r-cran-deldir            deldir   <span class="fl">0.1</span><span class="dv">-14</span>
<span class="dv">10</span>:<span class="st">           </span>r-cran-desolve           deSolve     <span class="fl">1.20</span>
<span class="dv">11</span>:<span class="st">               </span>r-cran-eco               eco    <span class="fl">4.0</span><span class="dv">-1</span>
<span class="dv">12</span>:<span class="st">              </span>r-cran-expm              expm  <span class="fl">0.999</span><span class="dv">-2</span>
<span class="dv">13</span>:<span class="st">            </span>r-cran-fields            fields      <span class="fl">9.0</span>
<span class="dv">14</span>:<span class="st">               </span>r-cran-gam               gam   <span class="fl">1.14</span><span class="dv">-4</span>
<span class="dv">15</span>:<span class="st">            </span>r-cran-glmnet            glmnet   <span class="fl">2.0</span><span class="dv">-10</span>
<span class="dv">16</span>:<span class="st">           </span>r-cran-goftest           goftest    <span class="fl">1.1</span><span class="dv">-1</span>
<span class="dv">17</span>:<span class="st">              </span>r-cran-hdf5                <span class="ot">NA</span>       <span class="ot">NA</span>
<span class="dv">18</span>:<span class="st">            </span>r-cran-igraph            igraph    <span class="fl">1.1.2</span>
<span class="dv">19</span>:<span class="st">           </span>r-cran-mapproj           mapproj    <span class="fl">1.2</span><span class="dv">-5</span>
<span class="dv">20</span>:<span class="st">              </span>r-cran-maps              maps    <span class="fl">3.2.0</span>
<span class="dv">21</span>:<span class="st">          </span>r-cran-maptools          maptools    <span class="fl">0.9</span><span class="dv">-2</span>
<span class="dv">22</span>:<span class="st">              </span>r-cran-mcmc              mcmc    <span class="fl">0.9</span><span class="dv">-5</span>
<span class="dv">23</span>:<span class="st">          </span>r-cran-mcmcpack          MCMCpack    <span class="fl">1.4</span><span class="dv">-0</span>
<span class="dv">24</span>:<span class="st">      </span>r-cran-medadherence                <span class="ot">NA</span>       <span class="ot">NA</span>
<span class="dv">25</span>:<span class="st">          </span>r-cran-mixtools          mixtools    <span class="fl">1.1.0</span>
<span class="dv">26</span>:<span class="st">               </span>r-cran-mnp               MNP    <span class="fl">3.0</span><span class="dv">-2</span>
<span class="dv">27</span>:<span class="st">             </span>r-cran-ncdf4             ncdf4     <span class="fl">1.16</span>
<span class="dv">28</span>:<span class="st">          </span>r-cran-phangorn          phangorn    <span class="fl">2.2.0</span>
<span class="dv">29</span>:<span class="st">         </span>r-cran-phylobase         phylobase    <span class="fl">0.8.4</span>
<span class="dv">30</span>:<span class="st">               </span>r-cran-qtl               qtl   <span class="fl">1.41</span><span class="dv">-6</span>
<span class="dv">31</span>:<span class="st">      </span>r-cran-randomfields      RandomFields   <span class="fl">3.1.50</span>
<span class="dv">32</span>:<span class="st"> </span>r-cran-randomfieldsutils RandomFieldsUtils   <span class="fl">0.3.25</span>
<span class="dv">33</span>:<span class="st">             </span>r-cran-rcurl             RCurl <span class="fl">1.95-4.8</span>
<span class="dv">34</span>:<span class="st">         </span>r-cran-rniftilib                <span class="ot">NA</span>       <span class="ot">NA</span>
<span class="dv">35</span>:<span class="st">                </span>r-cran-sp                sp    <span class="fl">1.2</span><span class="dv">-5</span>
<span class="dv">36</span>:<span class="st">              </span>r-cran-spam              spam    <span class="fl">2.1</span><span class="dv">-1</span>
<span class="dv">37</span>:<span class="st">          </span>r-cran-spatstat          spatstat   <span class="fl">1.51</span><span class="dv">-0</span>
<span class="dv">38</span>:<span class="st">             </span>r-cran-spdep             spdep   <span class="fl">0.6</span><span class="dv">-13</span>
<span class="dv">39</span>:<span class="st">      </span>r-cran-surveillance      surveillance   <span class="fl">1.14.0</span>
<span class="dv">40</span>:<span class="st">         </span>r-cran-treescape                <span class="ot">NA</span>       <span class="ot">NA</span>
<span class="dv">41</span>:<span class="st">             </span>r-cran-vegan             vegan    <span class="fl">2.4</span><span class="dv">-3</span>
<span class="dv">42</span>:<span class="st">              </span>r-cran-vgam              VGAM    <span class="fl">1.0</span><span class="dv">-4</span>
                     package           Package  Version
&gt;<span class="st"> </span></code></pre></div>
<p>Similarly, the 17 BioC and 3 other packages can be tested via recursive greps (not shown) in a directory filled with <code>apt-get source</code> downloads:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">pkgs &lt;-<span class="st"> </span><span class="kw">rbind</span>(all[bioc!=<span class="ot">TRUE</span> &amp;<span class="st"> </span>cran!=<span class="ot">TRUE</span> &amp;<span class="st"> </span>oldVersion==<span class="ot">TRUE</span>, <span class="dv">1</span>],
              all[bioc==<span class="ot">TRUE</span> &amp;<span class="st"> </span>oldVersion==<span class="ot">TRUE</span>, <span class="dv">1</span>])[[<span class="dv">1</span>]]

<span class="kw">dir.create</span>(<span class="st">&quot;/tmp/scratch&quot;</span>)
<span class="kw">setwd</span>(<span class="st">&quot;/tmp/scratch&quot;</span>)

<span class="kw">cat</span>(<span class="st">&quot;deb-src http://deb.debian.org/debian unstable main</span><span class="ch">\n</span><span class="st">&quot;</span>,
    <span class="dt">file=</span><span class="st">&quot;/etc/apt/sources.list&quot;</span>, <span class="dt">append=</span><span class="ot">TRUE</span>)
<span class="kw">system</span>(<span class="st">&quot;apt-get update&quot;</span>)

for (p in pkgs) <span class="kw">system</span>(<span class="kw">paste</span>(<span class="st">&quot;apt-get source&quot;</span>, p))

df &lt;-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">package=</span>pkgs, <span class="dt">stringsAsFactors=</span><span class="ot">FALSE</span>)
for (i in <span class="dv">1</span>:<span class="kw">nrow</span>(df)) {
    p &lt;-<span class="st"> </span>df[i, <span class="dv">1</span>]
    df[i, <span class="st">&quot;dotCorFortran&quot;</span>] &lt;-<span class="st"> </span><span class="kw">system</span>(<span class="kw">paste0</span>(<span class="st">&quot;egrep -r -q </span><span class="ch">\&quot;\\</span><span class="st">.(C|Fortran)</span><span class="ch">\\</span><span class="st">(</span><span class="ch">\&quot;</span><span class="st"> &quot;</span>, p, <span class="st">&quot;*/R/*&quot;</span>))==<span class="dv">0</span>
    df[i, <span class="st">&quot;hasRegistration&quot;</span>] &lt;-<span class="st"> </span><span class="kw">system</span>(<span class="kw">paste0</span>(<span class="st">&quot;egrep -r -q </span><span class="ch">\&quot;</span><span class="st">R_registerRoutines</span><span class="ch">\\</span><span class="st">(</span><span class="ch">\&quot;</span><span class="st"> &quot;</span>, p, <span class="st">&quot;*/src/*&quot;</span>))==<span class="dv">0</span>
}

<span class="kw">setDT</span>(df)</code></pre></div>
<p>This leads to a further four packages:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>df[dotCorFortran &amp;<span class="st"> </span>hasRegistration, <span class="dv">1</span>]     
                     pkg
<span class="dv">1</span>:<span class="st">           </span>r-bioc-affy
<span class="dv">2</span>:<span class="st">          </span>r-bioc-edger
<span class="dv">3</span>:<span class="st">     </span>r-bioc-genefilter
<span class="dv">4</span>:<span class="st"> </span>r-bioc-preprocesscore
&gt;<span class="st"> </span></code></pre></div>
<p>These 42, along with the 4 (from the initally 17 BioC and 3 ‘other’) packages are our target set.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>nmu &lt;-<span class="st"> </span>deb[ <span class="kw">is.na</span>(dotCorFortran) |<span class="st"> </span>(dotCorFortran &amp;<span class="st"> </span>hasRegistration), <span class="dv">1</span>] <span class="co">#42</span>
&gt;<span class="st"> </span>oth &lt;-<span class="st"> </span>df[dotCorFortran &amp;<span class="st"> </span>hasRegistration, <span class="dv">1</span>]
&gt;
<span class="er">&gt;</span><span class="st"> </span>nmu &lt;-<span class="st"> </span><span class="kw">rbind</span>(nmu, oth)  ## 46
&gt;<span class="st"> </span>nmu
                     package
 <span class="dv">1</span>:<span class="st">              </span>r-cran-ade4
 <span class="dv">2</span>:<span class="st">            </span>r-cran-bayesm
 <span class="dv">3</span>:<span class="st">     </span>r-cran-blockmodeling
 <span class="dv">4</span>:<span class="st">             </span>r-cran-brglm
 <span class="dv">5</span>:<span class="st">             </span>r-cran-caret
 <span class="dv">6</span>:<span class="st">              </span>r-cran-coin
 <span class="dv">7</span>:<span class="st">          </span>r-cran-contfrac
 <span class="dv">8</span>:<span class="st">        </span>r-cran-data.table
 <span class="dv">9</span>:<span class="st">            </span>r-cran-deldir
<span class="dv">10</span>:<span class="st">           </span>r-cran-desolve
<span class="dv">11</span>:<span class="st">               </span>r-cran-eco
<span class="dv">12</span>:<span class="st">              </span>r-cran-expm
<span class="dv">13</span>:<span class="st">            </span>r-cran-fields
<span class="dv">14</span>:<span class="st">               </span>r-cran-gam
<span class="dv">15</span>:<span class="st">            </span>r-cran-glmnet
<span class="dv">16</span>:<span class="st">           </span>r-cran-goftest
<span class="dv">17</span>:<span class="st">              </span>r-cran-hdf5
<span class="dv">18</span>:<span class="st">            </span>r-cran-igraph
<span class="dv">19</span>:<span class="st">           </span>r-cran-mapproj
<span class="dv">20</span>:<span class="st">              </span>r-cran-maps
<span class="dv">21</span>:<span class="st">          </span>r-cran-maptools
<span class="dv">22</span>:<span class="st">              </span>r-cran-mcmc
<span class="dv">23</span>:<span class="st">          </span>r-cran-mcmcpack
<span class="dv">24</span>:<span class="st">      </span>r-cran-medadherence
<span class="dv">25</span>:<span class="st">          </span>r-cran-mixtools
<span class="dv">26</span>:<span class="st">               </span>r-cran-mnp
<span class="dv">27</span>:<span class="st">             </span>r-cran-ncdf4
<span class="dv">28</span>:<span class="st">          </span>r-cran-phangorn
<span class="dv">29</span>:<span class="st">         </span>r-cran-phylobase
<span class="dv">30</span>:<span class="st">               </span>r-cran-qtl
<span class="dv">31</span>:<span class="st">      </span>r-cran-randomfields
<span class="dv">32</span>:<span class="st"> </span>r-cran-randomfieldsutils
<span class="dv">33</span>:<span class="st">             </span>r-cran-rcurl
<span class="dv">34</span>:<span class="st">         </span>r-cran-rniftilib
<span class="dv">35</span>:<span class="st">                </span>r-cran-sp
<span class="dv">36</span>:<span class="st">              </span>r-cran-spam
<span class="dv">37</span>:<span class="st">          </span>r-cran-spatstat
<span class="dv">38</span>:<span class="st">             </span>r-cran-spdep
<span class="dv">39</span>:<span class="st">      </span>r-cran-surveillance
<span class="dv">40</span>:<span class="st">         </span>r-cran-treescape
<span class="dv">41</span>:<span class="st">             </span>r-cran-vegan
<span class="dv">42</span>:<span class="st">              </span>r-cran-vgam
<span class="dv">43</span>:<span class="st">              </span>r-bioc-affy
<span class="dv">44</span>:<span class="st">             </span>r-bioc-edger
<span class="dv">45</span>:<span class="st">        </span>r-bioc-genefilter
<span class="dv">46</span>:<span class="st">    </span>r-bioc-preprocesscore
                     package
&gt;
<span class="er">&gt;</span><span class="st"> </span></code></pre></div>
<p>We need to retrieve the version number in Debian unstable of these packages by once agaim relying of a function from <a href="https://github.com/eddelbuettel/rcppapt"><code>RcppAPT</code></a></p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>regexp &lt;-<span class="st"> </span><span class="kw">paste</span>(<span class="kw">paste0</span>(<span class="st">&quot;^&quot;</span>, nmu[[<span class="dv">1</span>]], <span class="st">&quot;$&quot;</span>), <span class="dt">collapse=</span><span class="st">&quot;|&quot;</span>)
&gt;
<span class="er">&gt;</span><span class="st"> </span>res &lt;-<span class="st"> </span><span class="kw">getPackages</span>(regexp)
&gt;<span class="st"> </span>res
                    Package         Version
<span class="dv">1</span>              r-bioc-edger   <span class="fl">3.14.0</span>+dfsg<span class="dv">-1</span>
<span class="dv">2</span>               r-cran-coin         <span class="fl">1.1</span><span class="dv">-3-1</span>
<span class="dv">3</span>                r-cran-mnp         <span class="fl">2.6</span><span class="dv">-4-1</span>
<span class="dv">4</span>             r-cran-fields          <span class="fl">8.10</span><span class="dv">-1</span>
<span class="dv">5</span>            r-cran-desolve          <span class="fl">1.14</span><span class="dv">-1</span>
<span class="dv">6</span>             r-cran-deldir        <span class="fl">0.1</span><span class="dv">-12-1</span>
<span class="dv">7</span>          r-cran-rniftilib    <span class="fl">0.0-35.</span>r79<span class="dv">-2</span>
<span class="dv">8</span>         r-cran-data.table        <span class="fl">1.10.0</span><span class="dv">-1</span>
<span class="dv">9</span>                r-cran-qtl        <span class="fl">1.40</span><span class="dv">-8-1</span>
<span class="dv">10</span>    r-bioc-preprocesscore        <span class="fl">1.36.0</span><span class="dv">-1</span>
<span class="dv">11</span>          r-cran-contfrac        <span class="fl">1.1</span><span class="dv">-10-1</span>
<span class="dv">12</span>            r-cran-glmnet         <span class="fl">2.0</span><span class="dv">-5-1</span>
<span class="dv">13</span>                r-cran-sp       <span class="dv">1</span>:<span class="fl">1.2</span><span class="dv">-4-1</span>
<span class="dv">14</span>             r-cran-brglm         <span class="fl">0.5</span><span class="dv">-9-1</span>
<span class="dv">15</span>              r-bioc-affy        <span class="fl">1.52.0</span><span class="dv">-1</span>
<span class="dv">16</span>             r-cran-ncdf4       <span class="fl">1.15</span><span class="dv">-1</span>+b2
<span class="dv">17</span>         r-cran-treescape       <span class="fl">1.10.18</span><span class="dv">-6</span>
<span class="dv">18</span>           r-cran-mapproj         <span class="fl">1.2</span><span class="dv">-4-1</span>
<span class="dv">19</span>     r-cran-blockmodeling         <span class="fl">0.1.8</span><span class="dv">-1</span>
<span class="dv">20</span>              r-cran-hdf5     <span class="fl">1.6.10</span><span class="dv">-4</span>+b1
<span class="dv">21</span>              r-cran-ade4         <span class="fl">1.7</span><span class="dv">-5-1</span>
<span class="dv">22</span>              r-cran-vgam         <span class="fl">1.0</span><span class="dv">-3-1</span>
<span class="dv">23</span>          r-cran-mixtools         <span class="fl">1.0.4</span><span class="dv">-1</span>
<span class="dv">24</span>         r-cran-phylobase         <span class="fl">0.8.2</span><span class="dv">-1</span>
<span class="dv">25</span>              r-cran-spam         <span class="fl">1.4</span><span class="dv">-0-1</span>
<span class="dv">26</span>      r-cran-medadherence          <span class="fl">1.03</span><span class="dv">-2</span>
<span class="dv">27</span>      r-cran-surveillance        <span class="fl">1.13.0</span><span class="dv">-1</span>
<span class="dv">28</span> r-cran-randomfieldsutils        <span class="fl">0.3.15</span><span class="dv">-1</span>
<span class="dv">29</span>             r-cran-rcurl      <span class="fl">1.95-4.8</span><span class="dv">-2</span>
<span class="dv">30</span>          r-cran-mcmcpack         <span class="fl">1.3</span><span class="dv">-8-1</span>
<span class="dv">31</span>          r-cran-spatstat        <span class="fl">1.48</span><span class="dv">-0-1</span>
<span class="dv">32</span>             r-cran-vegan         <span class="fl">2.4</span><span class="dv">-2-1</span>
<span class="dv">33</span>            r-cran-bayesm         <span class="fl">3.0</span><span class="dv">-2-2</span>
<span class="dv">34</span>              r-cran-expm       <span class="fl">0.999</span><span class="dv">-0-1</span>
<span class="dv">35</span>          r-cran-phangorn         <span class="fl">2.1.1</span><span class="dv">-1</span>
<span class="dv">36</span>          r-cran-maptools <span class="dv">1</span>:<span class="fl">0.8</span><span class="dv">-41</span>+dfsg<span class="dv">-1</span>
<span class="dv">37</span>             r-cran-caret  <span class="fl">6.0</span><span class="dv">-73</span>+dfsg1<span class="dv">-1</span>
<span class="dv">38</span>           r-cran-goftest         <span class="fl">1.0</span><span class="dv">-3-1</span>
<span class="dv">39</span>            r-cran-igraph         <span class="fl">1.0.1</span><span class="dv">-1</span>
<span class="dv">40</span>              r-cran-maps         <span class="fl">3.1.1</span><span class="dv">-1</span>
<span class="dv">41</span>               r-cran-eco         <span class="fl">3.1</span><span class="dv">-7-1</span>
<span class="dv">42</span>      r-cran-randomfields        <span class="fl">3.1.36</span><span class="dv">-1</span>
<span class="dv">43</span>        r-bioc-genefilter        <span class="fl">1.56.0</span><span class="dv">-1</span>
<span class="dv">44</span>              r-cran-mcmc         <span class="fl">0.9</span><span class="dv">-4-2</span>
<span class="dv">45</span>             r-cran-spdep         <span class="fl">0.6</span><span class="dv">-9-1</span>
<span class="dv">46</span>               r-cran-gam          <span class="fl">1.14</span><span class="dv">-1</span>
&gt;</code></pre></div>
<p>With this, we can write out the content of the NMU request:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">&gt;<span class="st"> </span>
<span class="er">&gt;</span><span class="st"> </span>for (i in <span class="dv">1</span>:<span class="kw">nrow</span>(res))
+<span class="st">     </span><span class="kw">cat</span>(<span class="st">&quot;nmu&quot;</span>, <span class="kw">paste</span>(res[i,], <span class="dt">collapse=</span><span class="st">&quot;_&quot;</span>), <span class="st">&quot;. ANY . -m 'Rebuild against R 3.4.*, see #861333'</span><span class="ch">\n</span><span class="st">&quot;</span>)
nmu r-bioc-edger_3<span class="fl">.14.0</span>+dfsg<span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-coin_1<span class="fl">.1</span><span class="dv">-3-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-mnp_2<span class="fl">.6</span><span class="dv">-4-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-fields_8<span class="fl">.10</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-desolve_1<span class="fl">.14</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-deldir_0<span class="fl">.1</span><span class="dv">-12-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-rniftilib_0<span class="fl">.0-35.</span>r79<span class="dv">-2</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-data.table_1<span class="fl">.10.0</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-qtl_1<span class="fl">.40</span><span class="dv">-8-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-bioc-preprocesscore_1<span class="fl">.36.0</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-contfrac_1<span class="fl">.1</span><span class="dv">-10-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-glmnet_2<span class="fl">.0</span><span class="dv">-5-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-sp_1:<span class="fl">1.2</span><span class="dv">-4-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-brglm_0<span class="fl">.5</span><span class="dv">-9-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-bioc-affy_1<span class="fl">.52.0</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-ncdf4_1<span class="fl">.15</span><span class="dv">-1</span>+b2 . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-treescape_1<span class="fl">.10.18</span><span class="dv">-6</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-mapproj_1<span class="fl">.2</span><span class="dv">-4-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-blockmodeling_0<span class="fl">.1.8</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-hdf5_1<span class="fl">.6.10</span><span class="dv">-4</span>+b1 . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-ade4_1<span class="fl">.7</span><span class="dv">-5-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-vgam_1<span class="fl">.0</span><span class="dv">-3-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-mixtools_1<span class="fl">.0.4</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-phylobase_0<span class="fl">.8.2</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-spam_1<span class="fl">.4</span><span class="dv">-0-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-medadherence_1<span class="fl">.03</span><span class="dv">-2</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-surveillance_1<span class="fl">.13.0</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-randomfieldsutils_0<span class="fl">.3.15</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-rcurl_1<span class="fl">.95-4.8</span><span class="dv">-2</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-mcmcpack_1<span class="fl">.3</span><span class="dv">-8-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-spatstat_1<span class="fl">.48</span><span class="dv">-0-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-vegan_2<span class="fl">.4</span><span class="dv">-2-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-bayesm_3<span class="fl">.0</span><span class="dv">-2-2</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-expm_0<span class="fl">.999</span><span class="dv">-0-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-phangorn_2<span class="fl">.1.1</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-maptools_1:<span class="fl">0.8</span><span class="dv">-41</span>+dfsg<span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-caret_6<span class="fl">.0</span><span class="dv">-73</span>+dfsg1<span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-goftest_1<span class="fl">.0</span><span class="dv">-3-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-igraph_1<span class="fl">.0.1</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-maps_3<span class="fl">.1.1</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-eco_3<span class="fl">.1</span><span class="dv">-7-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-randomfields_3<span class="fl">.1.36</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-bioc-genefilter_1<span class="fl">.56.0</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-mcmc_0<span class="fl">.9</span><span class="dv">-4-2</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-spdep_0<span class="fl">.6</span><span class="dv">-9-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
nmu r-cran-gam_1<span class="fl">.14</span><span class="dv">-1</span> . ANY . -m <span class="st">'Rebuild against R 3.4.*, see #861333'</span>
&gt;</code></pre></div>
</div>
<div id="summary" class="section level2">
<h2>Summary</h2>
<p>The final set of 46 NMUs is the minimal change required, and reasonable relative to the 516 reverse dependencies of R itself. We are able to narrow the set of packages requiring a rebuild down by a combining data from the R package system, the Debian package system and (some) package sources we were able to access on a CRAN-related server.</p>
<div id="acknowledgements" class="section level3">
<h3>Acknowledgements</h3>
<p>Thanks for Kurt Hornik for pointing out the additional check for <code>R_registerRoutine</code> in the in C code, leading to a further reduction from 90+ packages to 46.</p>
</div>
<div id="history" class="section level3">
<h3>History</h3>
<p>The first published version (Julyu 2017) did not check for <code>R_registerRoutines</code>. The second version (August 2017) does, leading to 46 suggested NMUs.</p>
</div>
<div id="see-also" class="section level3">
<h3>See Also</h3>
<p>The <a href="https://github.com/eddelbuettel/rcppapt/blob/master/vignettes/binnmuAfterR340.Rmd">source file</a> is on GitHub as is the <a href="https://github.com/eddelbuettel/rcppapt/commits/master/vignettes/binnmuAfterR340.Rmd">revision history</a>. The <a href="https://bugs.debian.org/868558">corresponding Debian bug report</a> is based on this analysis.</p>
</div>
</div>
</div>
</div>


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