PDA

View Full Version : Restrict Replace Module



joeny0706
11-05-2020, 01:35 PM
Hi

I have a macro "Replaces" within the xlsm file "File attached called FlexibakeInvoice1.xlsm" and it replaces text. It has a different excel file it uses to know what to replace. I am having an issue that it is replacing items it should not. I have attached the xls files it uses to know what to replace and the raw data file that it searches. I copy the raw data into the xlsm file to do the conversions "by running the RunAll macro".

The issue is it is searching all the columns. I only need it to search column D for "Call ReplaceAllSheets(Worksheets("Data").Range("A1"))". Then for both "Call ReplaceAllSheets(Worksheets("Data").Range("D1")) and Call ReplaceAllSheets(Worksheets("Data").Range("G1")) it only needs to look in column C. It is replacing items in the wrong column. How can I restrict it to only search column D for products and only search column C for customers?

I attached the module exported within the .zip. I also attached the XLSM file that I paste all the raw data into and then run the macros.

Thanks all

joeny0706
11-09-2020, 08:51 AM
Hi All


I have done some research but each time I try I get debug errors. Does anyone have any ideas how this can be done?

Thanks All

Paul_Hossler
11-09-2020, 09:34 AM
Not sure about any other errors, but you can't name a sub the same as a module; Excel gets confused. The error message was pretty clear.

27422


Changing the names of 4 modules at least allows it to compile error free

27421


I only ran ReplaceAll to the point is was looking to open the CSV

joeny0706
11-09-2020, 01:23 PM
Thanks Paul


I must have attached a bad copy. The xlsm works good without any errors. I attached a copy that works good. Normally I will copy and paste the data I am converting into the xlsm file then I run the runall macro. The problem is the replaces module is searching the complete document to replace items. I need to restrict it to search only specific columns for specific items.
Example. I have a item number of 80. 80 should get converted into "Restaurant Breads:Ciabatte Italian" The module will search the worksheet and if it finds a 80 it will change it into the replacement value. I need it to only search the item column when replacing items.

I need it to only search column D when it is using Call ReplaceAllSheets(Worksheets("Data").Range("A1"))
So instead of searching the worksheet data it only needs to seach column D.

As of now it is searching the whole document.

The item is 80 that should be converted into

Restaurant Breads:Ciabatta Italian




But if it finds an 80 within the invoice number "Column B" it converts that also. so the invoice number 12380 gets changed into



123Restaurant Breads:Ciabatta Italian




So if it only searched column D when converting items that will stop that from happening.


When it is preforming this
for both "Call ReplaceAllSheets(Worksheets("Data").Range("D1")) and Call ReplaceAllSheets(Worksheets("Data").Range("G1"))

I need it to only look in column C.


I ran into debug errors when I was trying to adjust the replaces macro on my own. I was trying to find help online but was not able to fix it on my own.

Paul_Hossler
11-09-2020, 02:24 PM
Not tested, but try changing these lines




'do the replaces
Call ReplaceAllSheets(Worksheets("Data").Range("A1"), 4) ' <<<<<<<<<<<<<<<
Call ReplaceAllSheets(Worksheets("Data").Range("D1"), 3)
Call ReplaceAllSheets(Worksheets("Data").Range("G1"), 3)

--------------------------------------------------------------------------------------




'this sub is Private so that it's only usable in this module
Private Sub ReplaceAllSheets(R As Range, SearchColNum As Long) ' <<<<<<<<<<<



'..........................


ws.UsedRange.Columns(SearchColNum).Cells.Replace What:=r1.Cells(i, 1).Value, Replacement:=r1.Cells(i, 2).Value, _
LookAt:=xlPart, SearchOrder:=xlByRows, MatchCase:=False, _
SearchFormat:=False, ReplaceFormat:=False
Next i

joeny0706
11-10-2020, 01:03 PM
Paul


I have not had a chance to test that yet but I have another question that might be very simple for someone like you.

I need a module that will delete the row if it finds a negative number in column H. If that is possible and something easy for you I would appreciate if you could help me out.

As I have done testing that will not work. This issue is getting more complicated as I go. I can try to explain it again after I do more testing. For now I need to disregard this question.


I will be testing your above changes soon and let you know.

joeny0706
11-10-2020, 02:03 PM
Paul

That seems to be working. Thanks for the help!!



I have figured out what I need for the other question I started above. The issue is when the sale price is more than the item price. I have a module that calculates discount for when the sale price is less but does not work and fails when it is more. So to fix the issue I need a module that will compare column F and G. If the number in column F is greater than the number in column G it needs to replace what is in column F with what is in column G.
Ya that gets confusing. But at the same time doing that will also make it so the discount is calculated correctly.

So below you will see row 92 column F is 3.75. Column G is 3. I would need to replace 3.75 with 3.

Is that possible and easy to do in a single module?

Should I start a new thread for this?


Thanks all


This is a great forum. It has helped me complete many needed jobs I have needed in my daily task. I would like to say thanks to all who have helped me over the years!!!



http://www.vbaexpress.com/forum/image/png;base64,iVBORw0KGgoAAAANSUhEUgAABG4AAAESCAYAAACsFZN3AAAgAElEQVR4Aey9zY4c O5MeXBfViw91ER98Ab2uG5iF130HtR0cyxY0LWhwLB9D1qBGGI1gWVafA8GCbI1enMWrfd A1y9oBMkgI4LMrMyq7Cxm1iNAyB SwYgnHgbJKFb15v/ 3//r1vT/zZs3k9nz7/7d/ / 4R9eFPLo3Xb7/xXvh D4 Pjo7P/ff//d/e1vf8P/hjAgn1g/zfUMPixvLFi wIfL8yFiMHwGDoAD4AA4AA6AA AAONAqBzZDkg1LqjNV4qYracNYnJq8qW3 sclrL0DYjXjNb0/1Dnxojw/HArjlC3y4PB8e8zHK4VNwABwAB8ABcAAcAAfAgUtxAIkbc Lo3bt3/jRN7aQNJ234ysmbIXW5TW2zj01eewHAbsRrfnuqd BDe3w4FqAtX DD5fnwmI9RDp CA AAOAAOgAPgADgADlyKA6tL3HCC5JwrJW Gth TtCGZtc0 NnntBQC7Ea/57anegQ/t8eFYgLZ8gQ X58NjPkY5fAoOgAPgADgADoAD4AA4cCkOIHFjTtwMTdicWq 22ccmr70AYDfiNb891TvwoT0 HAvQli/w4fJ8eMzHKIdPwQFwABwAB8ABcAAcAAcuxQEkbhpJ3NBGD//bwuCpEjPH5IIHbfFgqD kX4e2Qb1l hp g9/AAXAAHAAHwAFwABwAB bkABI3DSRu5IYP9 Vf3QImwAQcAAfAAXAAHAAHwAFwABwAB8ABcOBaOYDEDRI31d/dudYBAbsxGYAD4AA4AA6AA AAOAAOgAPgADgADrTEgc2//du/OfyfD4OWnA9dEIzAAXAAHAAHwAFwABwAB8ABcAAcAAfAgbY5sIGD2nYQ/AP/gAPgADgADoAD4AA4AA6AA AAOAAOgAPXywEkbh6v1/kY PA9OAAOgAPgADgADoAD4AA4AA6AA AAONA2B5C4QeIGv3EDDoAD4AA4AA6AA AAOAAOgAPgADgADoADjXIAiZtGHYOMZ9sZT/gH/gEHwAFwABwAB8ABcAAcAAfAAXAAHJiDA0jcIHGDrCo4AA6AA AAOAAOgAPgADgADoAD4AA4AA40ygEkbhp1zBxZO/SB7DA4AA6AA AAOAAOgAPgADgADoAD4AA40DYHkLhB4gZZVXAAHAAHwAFwABwAB8ABcAAcAAfAAXAAHGiUA0jcN OqYczKe//Zv/4YB17Bf4Z 2s9mnjj34dZ1 PZUPaAc rIEDiGvXw2P4eh5fA d5cKb4C6znw3oN8521oUX IHHT8AbfEmjo8// 3/8biZuG/Qr/rHMigV/X6dehcRf14P81cgBx7Xp4DV/P42vgPA/OFI B9XxYY/6bB2skbhre4J86CL5 /YrETcN hX/mCW6njp9T28Gv6/TrqXxAO/BhDRxAXLseHsPX8/gaOM DM8VfYD0f1muY76wNLfIHiZuGN/iWQEOfv3z5gsRNw36Ff9Y5kcCv6/Tr0LiLevD/GjmAuHY9vIav5/E1cJ4HZ4rHwHo rDH/zYM1EjcNb/CHDoIfP36oRM3vv/ unofKQb15Bh38Mw/Oc/MZfl2nX fmEfoDj1riAOLa9fARvp7H18B5HpwpjgLr bBuad6aSpcW bMhpfB/egymIs0xOZS0 ad/ ieVqPmf//N/qudjMlovv7/duM3t/WpsWpt/Sv7cu9vNxm3S/xt397D yWP9fl2/D0sut2jzg7u7kePr1t2v4AOIZWDfIh eVqfriWt23qIxdh1zF4 95fh62b5aDs4UW0qsb 4eFrMeXxbWTxvLeZzz1e t0jp94zY3d 4BawnF7Rb5s/nb3/7m8H9aDObK0HHSxiZuPn78qIjHg3SR14c7d3Nz625v1rM5WZV/qkGeJnqx4L2/vYoJYf1 nXdRsch4VR0PU IWFtFq4fxw5 7up wDssC9zIHriWtm3qKxTHPXZuNur2R8LcfXFV89eezNY Lc LAcnMlmYH2uv9trX1lHPD66 9v17LOmwrzFsYrEzRMkruZI3MikjU3cfPjwYTWJm4e7G0ebFL5ONRgvKWdN/qnjaCZ6n3xbfyZ//X6dbuFa5w3kH8OFPiFTSZsFbVaO2YbyNvl/PXHNzFs8tq7kgwcaf8vxdYev2GeNX5eD8/ITN8vCep45AOuI4Ti3yB8kbhaYuLFJG5u4 Zd/ ZeVJG7oKwHx5MaKNv/r8U9X8NOLqjUl3fo2l v3a5e/8b6PF9OV0bjCJ2LT4QneDsHyeuKanrcyNl3v18ef5fh62T5ZDs7EcWCdY8Eaxvyy/Tm3L1ocq0jcLCxxU0va2MTNP//zP68jcaOSNSKJ0/inKccCy2r80 kHmhjEb3Bcyfdm1 /XNSxaFmwDxUMkbtYxt3XGzvb4eT1xrWtD0/W PV8dW3scK1 Or80aw683lpPUXg7OxPES6yV9dXBZWM8QU4p1hPzNPPETBwuao47FtXPKW QPEjcLStx0JW1s4sY n0PaS7a1JzXs8yV1O6fvtfinGwOz0PW/E7CcRVW3Xf2T6vr92m//qbih3VBcaVytfxyBD0P5ME 964lrZt5KG5frGXfL8XWXr YZE fGqOXgTHgC63P93Vb7rni2bD8/FcYtjlUkbhaSuOlL2lhivXnzZgWfSlIQEac20v3yNy7r8E/fAslOAPa5r 1yy9bv1 X65qkm9Xnlhk/GlvRp57z4gJ9Pgff1xLX6PEUfGK3pr1r2cWQ5vq77qs 2lsqWgzPFVGDdEnfO1yWsI8rfylu2n8/Hpb5 aHGsInGzgMTNsaSNTdz89ttvy0/cVH8QcB0bl1X4J30aWQt2ZgK4khM36/drzdd491QLhqpcP5bMX7nBX5Va/nzXG08vO8auJ66Zeevx0f9RhGv6k DL8XXpq2q8bHRcLQdnij3AekncGqSr/7qUWUcs3M D7D4hHrQ4VpG4aTxxMyRpYxM3//k// fFL2S7fvV8DZ9 rcE//UGSJnp5Wuo6vje7fr9edgPZz7kr0i0uuujPFPv/V/IbUvD/ZTh PXHNzlsbt7mysbUcX1d8taA/274cnJefuFkW1nPG HIMladw5tSnzb5a5A8SNw0nboYmbWzi5h//8R8Xn7hZ8yId/mkzQJ/LOfh1nX49lxdoD14smQOIa9fDX/h6Hl8D53lwprgLrOfDesnzXJfuLfIHiZtGEzdjkjY2cfPy5Uskbk44Etc1cKd D/ scyKBX9fp16nHP SBJ0viAOLa9fAVvp7H18B5HpwpzgLr bBe0rw2VNcW YPETaOJm6GkqtV78eIFEjcNJ27gn3VOJPDrOv1ai7F4B19fCwcQ166H6/D1PL4GzvPgTDEaWM H9RrnxBb5g8RNT Lm3//7f /4/99EPX5HV/me73///feLJk7 03/6Txftf42Dd0qb4J91TiTw6zr9OuXYhyxwZGkcQFy7Hs7C1/P4GjjPgzPFWmA9H9ZLm9uG6Nsif5C4EQkZTrzwVSZoOElTe8f1 XrpxM1/ A//AYmbhk/cwD/rnEjg13X6dcjkjjrw/Vo5gLh2PdyGr fxNXCeB2eKycB6PqzXOAe2yB8kbnoSN5SIsYka cyJGnvtS9z8 eefjn6/5vv37 7bt2/u69ev7suXL 6PP/5wnz9/dp8 fXIfP350Hz58cO/fv3fv3r1zh8PBvX371tHfk6c/Tfb69Wv366 /ulevXvnvb9JRrufPn7tnz565X375xf393/89/gMDcAAcAAfAAXAAHAAHwAFwABwAB8ABcGAFHEDi5kjipit5Y5M18rkvcTNHRpISN/jXLgJ/ ctf2lUOmp2MAPx6MnRoCASAQKMIIK416pgnUAu fgJQKyKBcwWUJ3oFrJ8I2CsR2yJ/kLgZkLixyRuZpKndI3FzJSP6RDNbDAQnmoJmAgH4VYCBWyAABFaBAOLaKtw4yAj4ehBMZ1cCzmd DOFgAsB4MFSpWEGiRP0jcDEzccPKmlqix75C4qbAfrxICLQaCpBxuTkYAfj0ZOjQEAkCgUQQQ1x p1zBOoBV8/AagVkcC5AsoTvQLWTwTslYhtkT9I3IxI3NgETdczEjfTjujDbuM2u8NkQqeWN1axFgPBWBtQv0Q Afi0xwRsgAASWjQDi2rL9N0Z7 HoMWqfXBc6nYze2JbAeixjqSwRa5A8SN0jcSI42eH9wu83GTZe3mVreeMhaDATjrUALi8Al/XrpZGTG4qfbb7du/zO/yXdPUZal426JCBAnNm6z2bjNdu qtDnFrMNuWnmn6LCSNpeMayuBcDFmwNfzuAo4z4Mz9QKs58N6jT21yB8kblpK3DzcuRtawPr/t 6 JPW9 52s3E3dw 9f 67tR8n/rnfepu2djf3c 2yd6O5IxZgLOsgFHZRpdXNpBj5PXop/sxevS0w0QyPrQz1pY//D5wYedqZ7J8QmOzcaotcUDwTpU557TcyCHTJrfP/dYCPMuSffh3dpMaOWMTlNW6FQjnTtx4vZSycQO 3bv9buv2e8KYx99TlGkQqjidiSn14OVa/sSuuSxwYQBPBnCIrarylvVRuHOLKa/BX5KzJL2K8YhufXuru8ckj6ER4vqrklw7xvpboLQDARvXNO/N3Gc4bjlEXVR5dHSsigRfit1PwJsODK7ltfX1tdj9VHZ2xXHgPB3iOh6VMQFYT4f1GiUtkT9I3D STuKGkzI27e3gMSZn7W7e5uXMPMXnzcHfjNptbd3d3s6DETTjdst0f/KeqehHHZfEzVr/g441eDg808aV2tLgTi/Ew4HKgri4Isyh/N1xej369evS0i7pgIjFO6XxkLDv4I7jgF0i1TeF253ZbwSFHMgXP/IYhPw/hEKtr 9R 7dHd9Onl8YbH2KD4yh1XrlaXSpVJX3mcjK6 A29bx1cbn6KMrZoc0x7/dW0 WRdz7fNNV5n3 5YS3jm kdhO3E2f5z3OnLgR4/g8vUVrJG4EGOfdqrhG40z4y/MxcbQ/tiYtThqrlpMxkVOLQakj3IxFQPl6bGPU1wj4GGTXH6EKcNZQnf50cDsRj2rzKbA Hd31t1wmf5C4aSVxQ4ma23txkubB3d2IRI5I4CztxI1zdtHlnPMbVbkpCXX0OswsBG0UUQvAI3V 92yN1pLxB kWFRrbDRGIdeey5wh/TpEy4MJ9MW 8ryTvJCXlvOrCP0uexrO5X07 vW77zG/Xdzm3TJogqGn18n3wiT3/SrRcsJJ SUdSe65PNoV91QoTt6pQddfAnaEjW1m35qy1RdkisRtkznrjxNqdF2/SYBmhKuYVfGMPatcKTVK2nzPMhJrzl73uViRvpU/JP5nZRt4hpgR865pJ2NZsrJyXO5EzY9DM/ZbKv26aEfeJjtFfpEk98JW4kxHFzAgL1uBYFSQ77 8y/5Kvi 28lv47Hv7JNGfNPMA5NFAK9vlY18dCPQODr7lDyltoB5370Ti2txQRgfSqa19duKfxB4qbZxM2j u7/duNv7eAJnZYmbcoBUJjj/iUXP7x7IjUhcQKpNpd2RjJA3SD Oa0KPIe0wkTBwQ68VbqimYQOaTmapkwllW7/h9xtcUzaEQ7FfL8Pwq 5X00ds73mS2sfkSEy05Nf0dSPeCOlPBizPtD6hz9yWn3OyR7fvkx2wzbKCAVp/6QzqK59gkiUhITBdmbdZbM61TudjGi0tTwueyRPGRPuM34YrlXk x774W6baRsp/y4SHSa74tswfKtv5pFvilynPGvRwNuE9EWeKmBy5mpSMNslx4BOG2a4y0RQ5m3TNluFuPAL1uBb liLmP3ng 1mKr6VbzeMhYtZy0z6YDPJ6EQK vT5J4nY0yv s8Bc5PwYsQ9 U6kHoB1k B9RplLoc/SNy0krjxv28jTtjQCZzNNSVuwqIvB936hJfDhSn3C0ixKfSbkrxRLRf3WVK40/L8xGsW/mkzpZqOb4eJRAE44EFjnBp4n8dP7MVGz6kNab1t2GCYH0Q9yiHuOQR42SWV1P1a71 dOCN9I9ckx/Lij/sVV2Wj3cCHPqV hSyz4RKSaacsTv7UbS3kKQEXepA2TYBpsKLivzN5EuTWcWXkJA88VyM/FO7eTyLm cYkl9/JPsiOnTscdumrp0oWd yvweb8O07iVIyJianZqZwhLKVMJYeld9kUy2ttrFwWhetoBOpxjcRUxkZK3pjYansdPVZrnGSeW F4PhWBbl fKvEK26l4VB8jwHlCXvhYUlkHxi6A9YRYr1HUAvmDxE0riRs6UROTNX7BfHPn7m5FIufaTtz4ya 97YSY3Mz6WyIWgf2EmzJHy/KZGbij6Fqmi3pB2mEjGRn/jy0pzj7v4pDcnLWxbuZnlT/Mjz45xKPZb jgU1P1q 2flsx5eHitMOng hXb8mloFG8UmOp1COD9x0y0768maJ12kcrLwYvdZ1ykwDWZU/HcmTxJ Im5YyGTiJnzlJJzAUXb5mCb5wPc5bmY5B7cjf1GbDn5lHSo2M/ EzpNwJvE99l5g69Hyp54C3bKPk761NlZuqoybsQjU41qMOYIPzFMOC4EfmYu63 xHxenkNxv/Kpz0fu Sr3vD0zAEunw9rDVqcTKTxwA/5w8kA0bA Wm4EmKOPI3Z9YHa0/QPqctGYCn8QeKmpcRNTM48 iv9WHH5l6XoR4qv4Tdu1GLOxIIiaUPlRWJGL/RGyys2A3YhWVu4DvvtHkzaxqFHH7Uvq9WT/8OGoOu0wIH wpnabAjZSQb3IMr4lf/dGXmSKxWMO3ETP5mmBR3xOS/0SP t2x/kqZcKr7yueYHix0QSUnK14L/kt7wnc5TsvMHKlsYkUupPllz2nhMVU2AaLKlw4Eye2A1uDTG2I5VFH 2Jv4y78lOqqW oHfE9nbQhe2izS37N/NGNKjbbxM1UnGH9WIGqTXFM N9KqfCx1sbKZfm4jkagNl/5eKPiaIwJ6l2dR6wAc3zQWK1 cFLhAgvH9SQEar4 SdDVNgqc7Fp/8M89AecnIkgxNyNx80RIr1PsQviDxE2jiRv6fZtagmY1iRu7AVYbgbDg4/1JjhDhvd54c6lZJHp5/GncKfLMorCiX12PvnZBV0za7LOhV NbakYBVhCklikP0k1b5UdOUnDipFaXORSl T9tX9/w1v1qZEqTSZftVnwtyRsWflOF/qKQsE99tSp9HSHr4TdSqX7oMz3yplu kDjI 0K24XPU3 OtNmnSsHnua5tHj9NEmAYrav4z7zx w3nSzdWMG29q85vQp98QJD8aPXJlcUf 27rdbpuSg36jvNtpfokWXZ8SK59PxRmSo3gU7Uw22oRAjY/hXfpU2y jnxVR9mLhz4EdFyL/lE i60NJ0ICmGNrpQfv 4Hxr5a48f3pcVfpBa9GIKB9PaIhqnYgUI/RwLkDrrGvB6wDgfVYUK o/kL5g8RNM4kb itSfNS9nrShkzjLStyEBbX99KFYYPsfmxQLML/wzpvSFEb8Qi1jlOTyIp8X7PGv3fBrfYIgSaMjM/53hJKc2C5tmJU8od p7WLXmEiED3pv /gTNxDss/S7HlZguXDyG/7UTv6p8JzIYU4kDnmxZoNoutJ 7dOdG8Y6ZhMUNvblhkfqvd3v1YkJX5aUDTanx2OJm5SsCWNLyw46SlleezE20nhms2a6epsNdny aRScD4uZ/MwbTI/4T9hNXND6hbR2XvrIMHNlWtOc VWd2HJQJi8AbEU9j/FJictfElvIHmZlDAu8g90zOkC5CZlDDYK/KQ1mhO2Pjx/XOHehZtVMG4mEEAiquHZn7JCdoXBQcVv1GPxs/1eNfhefFuFPC8XACAsrXJ7RHE4tAPZYCZ4vTqc82Loh1ehQJrE/F9hraLZM/m99//93h//QYhK876b8INde7v//7v1/0iPMLt2JlfrpJU8s7XZPQEhPJuQi22R5 bdMv0AoIAIHTEUBcOx27pbWEr fxGHCeB2fqBVjPh/Uae2qRP5u5kgnoZ74kzrITNyEDOl3eZmp554emFgPB VZBAvwKDgABILA2BBDX1ubRbnvg625spiwBzlOi2S8LWPfjg9J BFrkDxI36geB50uuPGUia9mJm/5BtIbSFgPBGnC9tA3w66U9gP6BABCYGgHEtakRbVcefD2Pb4DzPDhTL8B6PqzX2FOL/EHiBombNY61pm1qMRA0DdhClINfF IoqAkEgMBgBBDXBkO1 Irw9TwuBM7z4Ey9AOv5sF5jTy3yB4mbmRM3f/75p/vx44f7/v27 /btm/v69av78uWL OPP9znz5/dp0 f3MePH92HDx/c /fv3bt379zhcHBv3751b968cb/99pt7/fq1 /XXX92rV6/cy5cv3YsXL9zz58/ds2fP3C //OLCiZuNcw7/gQE4AA6AA AAOAAOgAPgADgADoAD4AA4sGQOIHEzc LmKb8ixbKRuEFQWnJQgu7gLzgADoAD4AA4AA6AA AAOAAOgAOZA0jcIHGDkzk4mQQOgAPgADgADoAD4AA4AA6AA AAOAAONMoBJG6QuMHgbHRwIsOcM8zAAliAA AAOAAOgAPgADgADoAD4MC1cgCJGyRumk/cHHYbt9lNF6SmlnetwQN2T8dJYAkswQFwABwAB8ABcAAcAAfAAXCgiwNI3CBx03ziZrfZuN1huk E8tbyuwYX30/kMWPZj2VIycr/duP3Pur5PUQZu1LFeCi7Eic1m4zbbjfs51enHw8TyptILcppfbyxl3EDPZcc9 A/ AwfAgVM4gMRNS4mbhzt3QwtY///W3QvdHu5u4nsq12X8o8R8be3HiX/ug03bvRmkPzdum ztSM6YBTjLChiVbWx5sYEcI69HP9uPSiz1tDtlkF57G8ba8offMxcOlU2RT2hsNk61JQ4I3qkyt 3FWrueQaSPb1/pln7Es2Yd/ZzepkTOKR6yLrdtl54Sn0lj/rqu3wfTnN CUtNlt3D6OeR5/T1EmdXsKTEl zX/cL5cxF47yZASHqrxlfQzurM UV/KX5GzCYgAXu/SoccZFTPrGUJe83vck9wxde2VXxt811be8TzFrBL/Z7zx2kgy3cSmRJ2K0LL8mrGGrWTMuZOx1jpGF6D8H7yxGnXPAkbhSxIsZ5sc58EEf84z9wTxsaO wicSOSI5z4uMz13t1ubtzdw6Pz/d/fus3NnXvw t2729v78P7x0fkkjni2 raUuKHTLbQBqG0EuMwHqBiceaPHQYs2MGkDQZtbsRjnAccB3z LcpYhr2Pkdep3RI/Odg0NfIlJy/eMZZU/wtd o2sn7Lh525lNKMlMPIsJE34ewiHGq9qn8HGn7qZPL48XJ8YGxVchm3Xg6zFduN5UV4 T0dXLjrZVv9r4FGWMyRNg2uk/TqAI/vXh2uebrjLv93gSheMb9dGJO Mw0bU23saMjRoeVd3jGJ3spA3b/1RyWf61XmmcCd57n242TnJU r7K7zhWZTKGYjRzoOBejI1pHXCt2MPuZZzWGjlG5Hi5pns55qtxoofvsj7FCxlLrglD2Hp cuccHl4KfyRuWkncUKJGJWMe3N2NSORIPelkTkrqxESPKG8pccPE7lqMyQVfLQCrTbYN5Gaz1ls 3tu2tI XFxeIx/bx9p7az9uC5c2FW8MdgVdtUMp9U2 gr6VfJCXnP3K1epc NLra 6j/Wte/8Rp2SlGYTpPSJfdY pZYLGeqf5FMyitpzfbKZ3vMzJ6u8vj2yvQ7xBA21pY0by/DP8SSdlz3jiRtvc8 G71xM2Y/WV/Re aXP/3086SnzuseEt0yC1ZIf0qfkD Z2UbcS08iO2qK3ZnMxxs7kjJcn Cnt7LIpYS/46O01uvgTX4Ib7Etcz1/oKgx7OOy6yrred8RG31 Fu0qPvjGIss55FRhOPB5qXDvCd/ggfiAxNF4bPGmuqM1hwHUGbtf4vuB3xRqnUVuaTtz8 PEjnTKxp0pW91wkbh7d/e3G3d6XiZnHSl2JxxISN7UBUmwWaLHWF8zlYi4Gc7WptCcCRsgbpB8PaqHHqHbcHtejC8uCGwYz 2oDKT2S9H6L/bVu/4Y8bXFU2hEOxXy/D8svoxAsH1UesI/WjepwIUBtpwStfR4wFyzOrD/UpN/H8zAsc215 6lCUxc01JwRIF6s/20pX6kslhQQuU5Z5my0mwifnYso2kc6SW7wh7Y01A3hifcb90ZXKfJ9mkWpxtzKU72Jb9huVkc7 MAbaDy2X/hc3sc4H3JJypxGTqWyZxvE0iIUV ldwmva2 vo7QVdqG wkX9CZGSWwtN2WZ97E8 WhihBpvsUzFRlFfysX9hL4FxkfXJYP41jNGBrW/Aj/Q2K6N Ro Nq5wLKE5gf53rT1qsvAO8UJyYAwPZbu575tO3Pyf//N/ridx43/fRpywoeTMppK48fWW9Rs3RGq7sFYbjDgxpc1KfLZt7OBQ5XFyTEE7blrSJqWiQ5 8Ifpxe6nHmHbcHtfjk4fEOOEVfe4nbLFhtxvSWls/ dNELzd3AzjEfY/ZRNT65994SKcFoh5yDHguSbvkAs5symuLGcn9QlbfYtLIrtlayJO6Xepe2kQ2nIkp 7rw30Q8qeHKfUoeeN9GWxTu0U8p5kXcSS6/k32QHYdDXiArWcZnVJcXwuoqx4tscypnCEsp08hhPLps8uW1Nlau1BX302xIj8ypknvsR3n1vK5 storxFv11TJ6UjfvjcyowmgejLj5fPf5xHi3Wb0fic18c8HOaSPJfPcZHsAQ G8dr8bE8vCR2TSduvn37drHEzZs3b9xU/ VpmN77mKzxBLq5c3e3IpHDv23T8xUplr2KEzcdmxIeLHIz49/FSUB eqwmzJHy/AQgNxQdi1Srx9B2bAeuwxZPypeVyUhO2FRXJi1sWznxczu/0T3Godhvzcd9frT9c13Ww8vjBI3YcFo7WFe5kWa ex6yjMhViYHqg wwtvbJZj1Zb7oW8sqxieEAACAASURBVCo kfXnumddlX4nYso6F/4z2FG9og5jZGIIy/T6dZRRHfKn/ASS7KJnZVeMaZIPfM JGylnR/ygNrFfyy/Wbag9Xpe48eZ mY/sBylT6c58Eb7xdSvYsj7M50J2rY2Vy/3hOlnSxs59ha97 F3UFcnG2lii oXf4cvJfCn9gftha5IhOPWNkSHtr6UOzyU8f3TZ7esdiSuIE9Pxt8sPa30/lIeXtr/pxM3/ l//66KJGzfBP0r cEJl3JV rDifrKEfJL65exgkawmJG7txpIEgNxLVRX5cqFUnw0piRi4AR8urbAakfqRvVY8B7S496JfYv/RlVX/hf5q4eSOprnTigH4bw0z8SbaQwX2kMrFJGLswqMkg cQf2ozTlTel9J7k7 lkhPzkyPIq6soLHc/FUxM3R2TX7O0bT4zdJa5TYsr6F/6bgCc1TLk/urId6V30kf/tFvaz4UCqK7jq4yzxXZy0IXsosUM6MH9s28Jmm4iaijMkR47HDpv8mIh/Yr7ArtbGypWY4P7sDb PN9JvBtPCR6a8xjeOgTXu1dYLVgaesWFriQPHxkhLul5cl8qcWtPpaFwZKKcmG 8QP/i0Pn/w1Sonmk7cfPnyZVCiYlxCpPKbMeKHfVkWJVym Hdq4oZ 3yYlaujrUeqHi/ttWETixn6CZjYCtHjjhZwcPPReLfTFglAt KI8HoCnyFOTREW/Lj362klbcD98olC JZ/TBM0bWN5UdmxEVVvjRw7UzLVaXeYQ euUjLySKfjKm2qVoOHTG Z3PuzGxS8Khb3 WeBBfbJNSW9RruQZTKxsxeeov8ehZ M2B7erC2OyhX6TRmBDungfjMSUbaj5T72L A3lyRAOkW3yxA3b4BORwo9KD8ktce/9J5KDJJtO38jfkWFb VqTq3w FWeiv/gvCiU7hY2qXztvRDvJxoRXXLx3xWe2Edfh8VdiRdzow/YYv6k8 Yr8Z8ZPwb3oT9VG8FvqhvvTfArcpsXt2Bi5eryPrN88fmIOILxqcYXmMjnvVtcEiBVnJ pXy9cjPGzV7qYTN7///vsVJW7or0jlkwIpaUNJJfkVqnSaQH NihNOdG0pcUMLanXqobbAjnVSAI4LteLT4LjAs/LSBiS24/K0cZ1IXtJvpB6pHSaQ0RNIH3/85C741YWz3Qj4yV20UxuCLg5F36kN4hF/9unOE4KvYxIgvEBJ/I39SL1JZ2rLY8SXiYUO2Szbe5minDdLqn3ExMqmfqQsr7vASeF3BBO2e4pr1yJtKky9HMETiivJ VmE/vbf4UNtU12DSV8a4kG1Fe 5T jEmpTjm avhE/OGfc2 tzpz33S1Y4beeQ4J2SyXcSG7uA 6L Sz/hKbSuKG qL2ySbRJ5f1yaZ2B rLtKO2 H8mBsfmvui7grsG 77YbcvIn12xHf4805/GL8BzAjwHjBHgHOaYFOPNGPcxwMxz1XnTYo2YjzluZEyz880S5ppNGjgDfjtFJgfmuP/8 fMVJW76T9GMwbulxM0pE1SxyRw5EG2fU8uz8vE8wWLnTB/DB/ABOAAOgAPgADgADoAD4AA4AA6slQPxxM2Du7u9cw VrwyNSRhMXfd//I//gcTNCT5ZeuKGMqDFJ6pnbOynlrfWYAC7MNGBA AAOAAOgAPgADgADoAD4AA40B4Hmv6q1H//7/ 96cTN3/3d3x39GZxTf PmnCTY0hM3CBTtBQr4BD4BB8ABcAAcAAfAAXAAHAAHwAFw4DIcaDpx86// q/NJm4oacP/ 7I3SNxchtgIKMAdHAAHwAFwABwAB8ABcAAcAAfAAXBgDRxoOnHzL//yL00mbjhhI69dyRubuPnzzz/djx8/3Pfv3923b9/c169fHf31rD/ MPRb/p8 vTJffz40X348MG9f//evXv3zh0OB/f27VtHsn777Tf3 vVr9 uvv7pXr165ly9fuhcvXrjnz5 7Z8 euV9 aWpHydewyCBDQj24AA4AA6AA AAOAAOgAPgADgADoADl LA5vZ uh/FPefrPbW2//zP/9xc4kYma x9LXljEzc1O6d F74qVdMG71pA4C9/ UsLakCHiRGAXycGFOKAABC4OAKIaxd3wWwKwNfzQA2c58GZegHW82G9xp5a5E/8q1K37v6EH8GdOuFg5f3TP/1TU4kbm6ipPVviInFjEcFzi4EAXjkfAfj1fAwhAQgAgbYQQFxryx9PqQ18/ZToZtnAOWPx1HfA qkRXrf8FvmzeXy8d7ebjds0 OfA/9t/ 29NJW5OoScSN6egtu42LQaCdSM j3Xw6zw4oxcgAATmQwBxbT6sL90TfD2PB4DzPDhTL8B6PqzX2FOL/Am/cXN/6zabG3f30NbXpv7rf/2vSNyccBJqbV VOuw2brM7TBYTppY3VrEWA8FYG1C/RAB LTHBGyAABJaNAOLasv03Rnv4egxap9cFzqdjN7YlsB6LGOpLBFrkT9OJm//yX/4LEjdXn7g5uN1m46bL20wtTw7xYfctBoJhmqNWHwKX9Oulk5EZl59uv926/c/8Jt89RVmWjrslIkCc2LgNnfrd7l2VNqeYddhNK 8UHVbS5pJxbSUQXtSMMXMDfD2Pq4DzPDhTL8B6PqzX2FOL/Nk8Pj64u5s2vypFfznJ/u7NXM/0Facp/o36qtTDnbuhBaz/r3936P6W3x/3VWsnbn7ut96mrd3N/dy7bbK3IzljFuAsK2BUttHllQ3kGHk9 ul jB497YhTLQaCKbj VDIYa8sffh 4sHO1M1l 0brZONWWOCB4p8qcc1pu5JBpk9vnfmt ZVmyD//OblIjZ2yCslq3AvSYxXml ehXXi lbNyAb/duv9u6/Z4w5vH3FGVa5SpOZ2JKPXi5lj xay4LXBjAkwEcYquqvGV9FO7cYspr8JfkLEmvYjyiW9/e6u4xyWNohLj qiTXjrH FijtQMDGNc177bu sixeJOtSHNZyPP f2H9T9MHj1NI621re1cZBny6 THRQa1/2kt/Y9n19WV9nKbgbhYCK9TwPZgnAOc tvJYSFM9AxTsdV R6uxJLjCBgXcC5ihfdnCjN6667TP6EHye vb9YgqQvEfOP//iPF9Nr/sQN/daQ LoafX0t/e7Qg7u7yz7ySZwen7WTuAmnW7b7g/9UVW8EuCx xuonunKCo0VGakcbMbGYC4MxL/j8sygvh69zw X16NerR0 7qBAmkppnau8Yyw7 CF/bxamX5jdvO7fbCg45kil45jf3 XkIh1hT26f2a4/ups kK21izKJD8ZU7rlytLpUqk77yOBldfQfettKOJytjqybHtMd/I5MYfb7pKvN 31LCO8c3MrUTd8ZhkuvMiRsxjidRn4QgcTMZlEVcE/7S/D24XWeZVMfyKy6ea/FENmvuPswlu922iNt9qo4dwxrj8THAtu/TTfu6rybKOhGwa1n/rOP41eNM87WIFX5MmLku4dtbN8SOvtBx9VgnIFd008sJY2dv3WXyJ3xV6oSv4/QlXKYqe/ny5fUkbihRo5IxdBJKJHKkj4q6 reJ2knc8ACyC7W4sFaBujaAzCabxfGVBmTahB p69scqSPlFZNtTb oyMh2mEjYgUOvFf6Ypn7iFwsB59hfpq33lVxESU7Ie9OBfZQ j2V1v5r fd3ynd o73Zuq8aE0cf3mU/eycWKXpyTfEpGUXuuTzaHftUJEbarU3bUwZ gIVlbt WvtkTZIbEaZc944sbbnHw PaYBmlKus8k/xrB2rfAkVesp83yICW ZzCs3fdKn5J/M7aJuEdMCPySPum2unLg5kzNhsc78lMm bpsS9omP0V6lSzzxlbiREMfNCQjU41pkCp2m7cC5jMnceTmmbF0dz3zGsvt0rue14JFMgPeUFX2 o KjHEmuuriSbbPfcy OO62h Uzy2vM4fKJS6sJTwYVOIAd3tneK/PJEg2weZRV ibZ vs0a460PAcjnFLPE9UOBsEPQczB gmVL9qOqGMVHOYbkJsM5YrPZOceKIlaruMvnTdOLmH/7hH644cfPo6GTN7b1OylBSrOs9J8yWkLgpJ7cwgNLpGhp7vDDqGod USYX7mZTaaP5CHmD9GO9hB5D2mEiYeCGXivcUE3DBlRyx/vB 79s6xeufoNrymJAV4kJy6HYb7H47fwKnOkjts/60YuYHImJltSl4JWvIzZIlmdan9Bn3sTzc17Q6/b6k/KizCdo9MZE6y dQX11LcCmLfM2W0wyePFklUlOjMA0WBWwk9wKm6QjsSZCov0icSo3VLKU2vk 1SKj/LTdyle 822z337udz7pliAy5bn/is182ifhPRFnipgc i6SVSkhFfyZuU1aW31jnaRrtgx34xHonq8CzmpsJPF9ZdZf9tmOjWNcy/FGxyXSoavM9nGMd8mwdJPGaME/TjTlsZca8ThKgzCU2HEs69sybSPV7MPH2mmfddtuX0uNcN LgJpjon/M7zQCZ4NggZkpl4 qbhy36QOqPN65CbBmJFZ8VZw4Yqequ0z NJ24ef78 UUTN/R1qSn c0Kl9 p/30acsPF/6UskbuIzfVpeS ZI2ctM3IQFRV4Elos5PRxNuR MImj7TUneqJaLey3NlqsNUKyaF2qyrdZjSDtMJBK/Ifca49TC zx 0ioXwmpDWm/rF8M02cvN3VEOcc9hUyK7pJK6X v9 6Qkb0ZJ36iH5Fi5QOf y42BXtyHPqV hSw1eQm5dKvwq9tayDMiLvIobZoA02BDxX9n8iTIrePKuEkeeN9GfijcvZ9EzPON5WZV9kF27Nz hsEtfPVWyuGN/DTbzbw oqxwvss2pnCEspUwlhzvosimW19pYuSwK19EIFHHN878Sd0lyX1nqucYvzWMdz1LDcCP9Le 5f ZTX5lXVZzysnV9T5J3NR2yzn4scb9U1cuT64/cvjbu uy1ZbX2WTr3nZNGtr19lm0LX8tC3A9GwGMskgn0YZCcj4GzhLIyx8pidd9f148NXlfFdsBaAbjC h35OaIP76y6FP00nbv7jf/yP15O4oa9CieQM/b7N3a1I5IivSvnfuEm/f1OeyFlm4sYMKL/wyQsjPfjip0ZyoeQXjHmxYhMx/Cm5/X1klusnWiHPD2DxXMiLDU9ph4mEUR96NdyoNMsBN9TNiyTbVm5m4wkG/lT2GIdivyU3QkHdr7Z/Vj7r4eWxwqSD5521g3WVXwnIfNeL8Y623AepYGwN NVkZz1Zc7oqnWXBRe zrkq/EzENplT8Z7Drig1eBxVDMjh9ZVSL/JmT2MEuelZ2xc2hSqz4zUKOm1nOwe3I/9Smg19Cu8pvkkWfC3u8LmlzQtxhPmY/ZJkdnEm iTULbOm95HNFdq2NlSsVwf0oBOpxLYgIHGC/a7HdZV1jSvO2PHXVFZ 62hFXusriGoJjYo1DincV28RYqK4vxPjMY7k DnT81n3ZMhUDYtXusWjstAkrjuVxHPf5WmuFp EIlDELOGf0PL/lWMpFxd3xusC6AG3lL45zIgNwvO4y NN04uaXX365aOImu/v0u1F/VUokZx4f6ceK9V Wyqdq soe3RISN3bjqBfn9cUNe6E6 IpEj14c1hY7vfKKhZzcPISWVT0GtMOkzcgPvWpfVlsl/4fAW25mw maQ/GbDEJ2ksE9iDJ 5b/WpD8946K6X2syQgviDy3o6cr7h/S1qcNe/96N5ZXXNW YPBeTkNBneuTFuXwh5cl7Uk3JLieyUGXcD3IyRk99nRLToGvFf2fyJPhY rxEhe1IJdFHe Iv 1H5KdXUN9SOFsXppA3ZQ5tZ8mvmj25UsZk5xAvsqTjD rECVZtIV96AV/hYa2PlsnxcRyNQj2tRTDEWhPjOshq/tF9VPOvjmu jltDhONZRZhMYNQ6lr7AKm/xt0L82x8gETW6lbautRZS9uaG/s2VF z58rJ322bTt9bXRC48DETAYUyvgHLDz3OY55Qicg pWYg6wPgLsgosHcSLaN6juQvizoU0 /gcMcmLk0X9Fago n5q4oVM1N3cPIXH1cOfuxG/dPNzdiL84tcwTN8XmRU1uYWHE 5Psh7hgqgZ6sxj08nixf4o8vdjSiaY PfraBUswkWSPDrszvqVGFGAFQcInjrWNqGmreJYX90FUrS5zKGja3U/XYszIlAaTLtutTtDw7yXQXxQS9mn xU9RxcbbT0qpfugzPfKmW76QOMh7Xtgn2YbPUX PQ3UcSgOf9r46EU IadC 5j/zzuM3nCd9HGLEyDa9CQx9 s1i8qPRgxurK/lv6 gv33Azkr3b7TS/VJu6XOXzqTjj/bV3 Xc7o52sLHM3ca3Gx/Au4eUXX ZrkMo PIxBQM1XfXG3r0x1WOGXGUMqnvVxjcoEV1Q3fWUc51LbY7wTko2uXNI9roNs7kqNo9hY2csCO8q K9n34FHaaEzimrfK10QOPpyBgYlMUAZzjeEtxXWJrx2J33cNOz7t HBmZwFpiu5b7bk7wIYA8L3TXXSp/Ni27cc4BR8mryyZu6K9I5U HUtLGn8KhEza5LP Z8DJpQza0c ImTFr2k6ligW2O9 tP/AVD/SJD4MCY5BVR/S9P IV8ZVM/Sp6YIE5tF02Zk9cCvQXe9vEnBmPmQPpE3poZ6iXO8UI2tTMbZN70xXKmVpBaX4Rxj9qvfbpzi1j HLDTCBqA8keEXJVGv7X6vTkzohX wWeruZcoXZsHeLTvoKJt67QVOElu2bI5rbZHGCWH1uym8 Tc/EEk6HrN7WOyyvurjSV9ZRo30KnBlzJUz7DgoExbBRhH/YvxSYnLXhNagr0odw66Qz/pvhG2ki F/8iGPUVV nI/ K1vUl2qnDMTDCAR0XLN8E/Ni5E0eM7JMdmhlhDld8sVzS7zo5poew75v4XfZrlom ujnXdbf6iZK/F/x8yLtGkH2UxkH3TJNooU662o/aG4o5UmMtK zZbgbgYDwD3GuiOM4cUMkdDlOiDW9HycxPvCY6atry8TYZ4 B04zEiq7W77xWuBL IHETuXz5xE09CSOTSUPv20ncnBYoik3maWJSq6nlJcEn3mAiORG4xpvBr407COoBASAwGoGW45q f281mjRIRtFnuKxsNwpU0aNnXa3IBcJ7Pm8B6PqzX2FOL/EHiJjINiZtWhlzItnOy/XytppZ3vkYtBoLzrYIE BUcAAJAYG0ItBzXypMqYb6nxE1f2dp8NJU9Lft6KhtbkAOc5/MCsJ4P6zX21CJ/kLiJTEPiZo1Drk2bWgwEbSK1LK3g12X5C9oCASBwHIG241r8WgUflVdfTekrO273NdZo29fr8Qh wns XwHo rNfYU4v8QeImMg2JmzUOuTZtajEQtInUsrSCX5flL2gLBIDAcQQQ145jtJYa8PU8ngTO8 BMvQDr bBeY08t8geJm8i0uRI3f/75p/vx44f7/v27 /btm/v69av78uWL OPP9znz5/dp0 f3MePH92HDx/c /fv3bt379zhcHBv3771f nqt99 c69fv3a//vqre/XqlXv58qV78eKFe/78uXv27JmjP6FOthDZ8B8YgAPgADgADoAD4AA4AA6AA AAOAAOgAPL5gASNzMnbob wPA59Shxg3/tIkBBE//WhwD8uj6fwiIgcO0IIK5dDwPg63l8DZznwZl6AdbzYb3GnlrkDxI3kWlznbg5JyEztC0SN22Hjx YDQduILUM7 HUZfoKWQAAIDEcAcW04VkuvCV/P40HgPA/O1Auwng/rNfbUIn QuIlMQ JmjUOuTZtaDARtIrUsreDXZfkL2gIBIHAcAcS14xitpQZ8PY8ngfM8OFMvwHo rNfYU4v8QeImMg2Jm3aHXPlnPc/TdWp5Y7VpMRCMtQH1SwTg1xITvAECQGDZCCCuLdt/Y7SHr8egdXpd4Hw6dmNbAuuxiKG RKBF/iBxEz2ExI2kakv3B7fbbNzuMJVOU8sbr1eLgWC8FWhhEbikXy djMxY0J/g3br9z/wm3z1FWZaOuyUiIP5k83bvqrQ5xazDzm2mlHeKDitpc8m4NhTCduLfUI3brLcEX7eJ3DitgPM4v M6pDazPQQ9tW QPEjeRl00kbh7u3M1m4zb /627f3x05e/a3Lvbzcbd3tfKwjuypaV/P/dbb9PW7uZ 7t022duRnDELcJYVMCrb6PLKBnKMvB79dD9Gj5525JcWA0FLfLG6MNaWP/w cGHnark9v6DfbJxqSxwQvFNlzjktN3LItMntc781v7Is2Yd/ZzeVkTM2QVmtawFyzs29cfF6KWXjBny7d/vd1u33hDGPv6co0yBUcToTU rBy7X8iV1zWeDCAJ4M4BBbVeUt66Nw5xZTXoO/JGdJehXjEd369lZ3j0keQyPE9VcluXaM9bdAaQcCtbhGVUuOiiQcx1frb99Hrlct5nEywn82/tW45uv0yTTzNo3rqn4dONnXR/uzDRp47vJ1A6o1qQLPATpWZn6ndYIhEnCezp3sg4B1OZcA6 mwXqOkJfIHiZvIxMsnbighc PuHmJC5v7WbW7u3INJ3jzc3fhN5zISN F0y3Z/cPut2Tw7LoufsfrFGm/0cnigxU aFGlhJRZeYcDlQO2fRXmWku Gy vRr1ePnnZRDUwk2R/9d4xlB3 Er/0i2SyOnN 87dxOcY9kCp75xXp HsIh1tn2qf3ao7vp08vjzYqxQfGVO65crS6VKpO 8jgZXX0H3raN28xVxlZNjmmP/0YmMfp801Xm/b6lhHeOb2RqJ 6MwyTXmRM3YhxPoj4J8WN/whM8kym2PEE6rkX9q7E18KY29LXVkV/E74rvifu73bjEmx1Ho8dJjL9ad4oBevxpO9b3VPX1 sycwKK eH4OADOE7jAizi4nYghNg5QFWA9FdZrlLNM/iBxE7l48cQNJWpu78UJmwd3dyMSOZTAoRM5N3fu7nZZJ26cq2wE/EJJLopqkx1NjnlTXYQNtVk7Utc3PlJHyhukX9RoZDtMJIUnj7yo8Me0KBMuzCfT1vtK8k5yQt6b Duyj9Hksq/vV9O/rlu/8Rn23c1u1UTD6 D75RJ7 NFgvWEg jRtqz/XJ5tCvOiHCdnXKjjr4EzQka u2W5YZriGxGmXPeOLG25wWbdNjGqAp5TqfdO6JS4wpXSs8ScU9ZZ4PMeEtk2DlhlT6lPyRuV3UL WJa4IferHqlK4n2yombMznj9Uv8lMm bpsS9omP0V6lSzzxlbiREMfNCQiUcS34Z3ewY4PfH uE65n4Rs28H3fuQFxN/uP6Qq4qlycOQ9100kGcltMxUsiKHyKV48DW4ZOEYpwp3h2LyVQuY/LAGCLVeOL70tdP3OHixQe pQ8XvT0Vvho7gbMBZKLHch2IxM1E0F6FmKXwB4mbSMf2EjeP7l4laHIiR78vvzLV2lelaombcoB UJkCzOCsih9yI AWU2VTaldgIeYP0Y4WEHkPaYdJm4IZeK9xQTcNiWC6evB 8/8u2fgHvN7imbAiHYr 1TUDdr6aP2D7rRy94AxPsSLQVvPJ10kam3ERrfUKfeRPPz3ljoXmqP3UoyvzmOicESGOtfzTKX6 ivrg3JtGXeZotJBi8mfU/HNFgVsJPc4mSMSmClfiUWckOp39OT9pkupzLfZ Qkf8vU4m5lKN/5ttlvP/c7n3RLqpryrEHFZvZ5wnsizhQxOfRdJKtSQir4M3ObtLb6xjpJ12wZ7sYjYONa5qDFPfouJeP64 kCIRYnnUa0kW/EiyE28pbqqvBxLSY4w146VVKTibHprbmq86xsDVifGJmPSqY/pec5H6 s5 15mX3YMkBXsa/6AI/ucbQTOjMSU1zBG1VyNEzdTArxyWcvhDxI3kYoXT9z437cRJ2zoBI74LRtK1tzcPfgTOetM3ISFT g66tQlRxg1T7hdfYoL0m5K8UeXJNMuXsuhey1MboFjVLjLD6/HtMGlb7I89a4xTbe/zuDiSq3q1Ia239Ytm2mDIzd1RDnHPIcDLLqmk7td6/37jwZtR0jfqITlW23ywBumT6fhCbwJCn1K/QlbfZkXhV7e1kJcUu CNtGkCTIMlFf dyZMgt44royd54H0b aFw934SMc83Jrn8TvZBdoSTDBwDlSzu2F DzfLUQrqX40W2OZUzhKWUqeRwB102xfJaGyuXReE6GgEV1xTWlbEhpHt cYwT73mu9fFJjtmUwLaJmdCPjGdPmbgJevOmmxOfciwpY/KDwqaeuOmzIQu63J3y9eXUWFDP/WOADKmNA A8oYt9DKmsA2MXwHpCrNcoaoH8QeImEvHiiRv6KlRM1vhFsv9KVEzkmK9RrTNxYyZAvwjiDUgZL eRmxpeqBSC9OU en2zlhsLKiypZPYa0w0RS rP/jfFlpXJeHIW6eYFs2 oFeGgXeXaMQ7Hf0sehoO5X2z8rn/Xw8lhh0sHzztrBC0DeUNCVNxXHNwmqD1LB2BpwqMnOerLmdC3kycKL3WddlX4nYhrMqPjPYFfEm mi/10HFkAxMXxnVkombcCIrnMBRdvkYKX3G9zluZjkHtyOOUZsOfgntBn1VyuuSTldIPmY/ZJkdnEm iTULbOm9HAsV2bU2Vq5UBPejEMhxTfoh 4UTgaXQiq98JSlH1JE k/fK/7EXVW7jX51rfq7mOCuVJVkilqYi9V7omSrEfqpjwOokba7bIMRe7Db7 mIqLKzj4NfuMUDmlNwBzk/j5jAn5XUR9QKsnwbrNUpdCn QuInsqyVu3rx546b4X/5lqPLrTWUd rFi stS9BUpXpDrK5/AsW2X8FUpu3HUi/P6wosDhU2W PdFokdPqGrDw4LitSpPLdqoYrnwOrUdJhLjgKOP2pfV6sn/YZGUTgjIRfV27w70F87UZlrITjK4B1HGryqLMC6q 7UmI7QgmYlk3AAAIABJREFU/tCCj655P0H6b93 sNe/d2P56HXNCxTPxSQk9Jkea4kWKU/ek2pKdrnoDFW29R8gZjAudJ0S02BCxX9n8qS2kLdwsR3pffTRnvjLjlV SjX1DbUjvh928UfeyR5K7JBfM390o4rNzCEeO1NxhvVjBao2xTHhf8e wsdaGyuX5eM6GoEc1wL2XbG1 FPuxTjhrnV84rlZcV75T9f3UlS5TZLU1w86RrIudK1wyr WCZ1KHdJBjiHDQ93fcRukRpe6z76 lAZL67ceK5UVlXEAnBVC0z0A6 mwvEZJC EPEjeRnDZx89e//tVN9d8mVoY8y69G2fprOHFTLJbUIqiyyPF Cu/1xpuji5lAvTz 5PkUeWahVtGvrkdfu6ArJm322dCr8S01owDLG1jeVMpFdBJt2io/cpKCEye1usyhILCWkeeu6n41MrkyXUmX7VYnaPhkF/3FFWGfTXT6TYGw99gmgTdHqXuJg7yPpz3yhsTwOQrw8ngTn4TOe NttjpMiGmwpuY/887jN5wnfRxiBMk2/Slu6NNvmhMvjB7cWF3Jf1u3221TcpBk 7/ak SoBjQojp 4mYoz3l/yrz9FO4Vumms1PoZ3CS / DJfg7Qm4nkwAvW4Rs01Tw47PQaq49P3GtolF7O/RDwLsTHzwsviBlxfjH1VzvOBKKdubR0JQBiTPA/EEsXxCu9UeZQvbND9GZuDQuZDBKnRZe67fX0ZfdrvVY8B0nfIOADOE3l2wDoQWE E9RrFLJQ/SNxEMl4 caNP1nSdpqEkznISN2GxYz hKxbY/lSEWPT5hVnl02C/UNKnjtRGhhd08ZQFr/P0CQIRfUbJE/qd2i52jYlE KD3to8/YcGUuSX8o2TWFlaaQ4mP1K6LQ16m2SCqfuyR3D7duWGsYzYY1U0Ebzwit7f7vToxcWyT4GWmAeG FqU Lffuq7KCjbOq1Fzgp/Ni0Ga5eZ4MdJ4RtUnU8pkf8J wnDmp8Qts6Ln1lGTSyrWjPfarO7DgoExbBtyKexvilxOSuiw05F3kMBd6TcIZ0ETJDXwZ7VR7KC t0ZG8/hnTvQs2rHVuA6FoHu crEVjsvduIf2mUfRg7nFzGpnRM3aVyzfw1vPBdle8EHHkdFHQuEaMPzCrfl/mUX1Lx7DMSy1MDaHGNwJ0ZWuXmeu309T//L6cXEqDR3/gzcjc eRxUfA epPB1jR8K7XAcC66mwXqOcZfIHiZvIxcsnboZ8fWpYnfa KjVuwBebzHHNi9pTyys6GPkCE8lIwBZSHX5diKOgJhAAAoMRQFwbDNXiK8LX87gQOM DM/UCrOfDeo09tcgfJG4i05C4aWXIVT6ZOku1qeWdpYxv3GIgON8qSIBfwQEgAATWhgDi2to82m0Pf N2NzZQlwHlKNPtlAet fFDaj0CL/EHiJvoMiZt 8qJ0OgRaDATTWXe9kuDX6/U9LAcCa0UAcW2tni3tgq9LTJ7iDXB ClTrMoF1HRe8HYZAi/zZkFL4/xeHxM0wEqPW Qi0GAjOtwoS4FdwAAgAgbUhgLi2No922wNfd2MzZQlwnhLNflnAuh8flPYj0CJ/cOIm myuxM2ff/7pfvz44b5// 6 ffvmvn796r58 eL OMP9/nzZ/fp0yf38eNH9 HDB/f /Xv37t07dzgc3Nu3b/2fJv/tt9/c69ev3a //upevXrlXr586V68eOGeP3/unj175n755RefhCKy4T8wAAfAAXAAHAAHwAFwABwAB8ABcAAcAAeWzQEkbmZO3Ng/7f0Uz5SEwr92EaCgiX/rQwB XZ9PYREQuHYEENeuhwHw9Ty Bs7z4Ey9AOv5sF5jTy3yB4mbyLS5Ttw8RaLGykTipu3w0WIgaBuxZWgHvy7DT9ASCACB4Qggrg3 Hauk14et5PAic58GZegHW82G9xp5a5A8SN5FpSNyscci1aVOLgaBNpJalFfy6LH9BWyAABI4jgL h2HKO11ICv5/EkcJ4HZ oFWM H9Rp7apE/SNxEpiFx0 6QO w2brM7TKbg1PLGKtZiIBhrA qXCMCvJSZ4AwSAwLIRQFxbtv/GaA9fj0Hr9LrA XTsxrYE1mMRQ32JQIv8QeImegiJG0nVlu4PbrfZuOnyNlPLG49Vi4FgvBVoYRG4pF8vnYzMWPx0 3W7X/mN/nuKcqydNwtEQHixMZtNhu32e5dlTanmHXYTSvvFB1W0uaScc1C FRx7ud GzhIPJx0vWEtaPu5JV 3jdR52gHn8/Ab0xpYj0ELdS0CLfIHiZvopSYSNw937iYuHDabW3f/ Oj492se7m7UwmJze5/KuA5fyZaW/vGiaGt3cz/3bpvs7UjOmAU4y/IL/coCS5dXNpBj5PXop/sxuve0I7 0GAha4ovVhbG2/OH3gQs7VzuT5Rf6m41TbYkDgneqzDmn5UYOmTa5fe635leWJfvw7 wmNXLGJiirdS1Azrmn2tBUuvKvvF5K2bgB3 7dfrd1 z1hzOPvKcq0ZlWczsSUevByLX9i11wWuDCAJwM4xFZVecv6KNy5xZTX4C/JWZJexXhEt7691d1jksfQCHH9VUmuHWP9LVDagYCNa5r34 a 1IUZC5YWqZ65eZI45 MExyofTN1m08dJkWwU84i1wevaMAdr49H62sCPx4kQAM4TATlADLAeABKqdCLQIn QuInuunzi5t7dbm7c3UNM1tzfus3NnXuIyRtK3Nze50QOJ2lq13YSN F0y3Z/8J q6o0Al8XPWP1CTiyeol9o8ZPa0QJLLITCAjIvsPyzKK NxOHyevTr1aOnXVSoxUBQw ry7xjLDv4IX1cX9H7ztnO7reCQI5mCZ2bRPoRDjIvtU/u1R3fTp5fHGxmz ld85Y4rV6tLpcqkr2qLft Bt63jq41PUcZWTY5pj/9GJjH6fNNV5v2 pVMAOb6RqZ24Mw6TXGdO3IhxPIn6JMSP/QlP8Eym2PIEqbhG40z4y/MxcZTHTP c7mIcyKHu4HZCZh9CXeOlr83RMh97xTizz4WAyviI8TutVYo27b2oxRLl6/ZUXo1GwHk VwLr bBeY08t8geJm8i0iyduKFGjTtE8uLubnMi5v90sMHHDw7hroSMWSy7UyYs5ams22SyOr2qzdqSu b3OkjpRXLN5q kVFRrZrMRAwpG1eK/wxivpFqFr8s79MW 8ryTvJCXlvOrCP0uexrO5X07 vW77zG/Xdzm3TJogqGn18n/FrJea0md7QkHxKTlF7rk82h37VCRG2q1N21MGfoCFZW7flr7ZE2WGzEmXPeOLG25x8Pj2mAZpSb uEXxrB2rfAkVesp83yICW/5 17lZkv6lPyTuV3ULWJa4IeOuaRdzebKiZszOeP1S/yUyb5umxL2iY/RXqVLPPGVuJEQx80JCNTjWhQkOVzwK/ix4JdsU9NH VKf6NFxjhr3cSWfmKvGvNR3GAc jsW C51T3dxnkaQx9ltdNd/thwccp6W9fbZVsKX E e5XM4B3KeVmz/Y6PW1wgAP5yAAnM9Bb1xbYD0OL9TWCLTIHyRuoo/aS9w8Opmsofv89Qz9NSp76qadEzc8AMJCQS50/CImLTKoXlnn6KemcqEUF4NqU2lXX2phw7qJq5A3SD9uOrJdi4GATWnzWuGGUlQsvON77z/v/7KtX1D7Da4pG8KhKN8uyul13a mj0I/ekH650RLoq3gla8jxovlp9Yn9Jk38fycNwW6vf7Euyjzm ucECCNM77RoHShvniDkF7Gm2nLvM0Wkwze2ZgGpQN2MnaFEwMmgZX61TZrv4wryxvJjKfF3cpXv vN8zn77ud/5pFtS1ZRn7So2s88T3hNxpojJoe8iWZUSUrwRzXaVc0esk3TNluFuPAL1uBbliBiluOeL6zxif WvUkqd nhVfiV0DP9lL8W9t0MnPos66UWXXYF3PL6Ubp1jLbTJ8YXtPzYOQjn35VVTYym2F6dLlT48npWA rjksGY6biRDoHVMT9QExAQFgDSacg0CL/EHiJnr04okb//s2 YTNI53A2dRP2fjfuxFfo1pH4iYsyvICpmtxxEPQlPuFV97glMexTX0Wk666vFyEWv244fh2LQYC tqbNq8Y46ZgW2/LTer8iFSdX6m39IpYSEnJzd5RD3LNeoPPbul/r/fukJG9GaVEf9SC9eAzYTTr3469mI6AX5aFPuSYvZHlb5eZXSFey67YW8kTzi91KmybANNhR8d ZPAly67gydpIH3reRHwp37ycR83xjksvvZB9kx84dDrsB/Ao25w8KxIcGcrywsnQ9lTOEpZSp5HAHXTbF8lobK5dF4ToagXpcIzF6bHhuSl/6b6zleGY79rz2SWHmq61heWUSN8f478tzsroiPbyK43m3o68mCl06OaTtznLleKvpWtHF912Jwz VOpwQ/9Rh0kDFef9B1rLyefO/2dbYSd cjAJzPx3CoBGA9FCnUqyHQIn QuImeunjihn7LJiZr/IL55s7d3YpEjvih4sdH/TWqdSRuzGKouijLw0puZvzbYgF0nrxyEWrkRVWsHkPatRgIMrIt3tWxl5p63MUpmrygtW314jq0 i4v1YxyKHZY DgV1v9r Weush5fHCqfNQmjHr6lV0FVsojnxEzdI ZRCR1spzNjaLTvryZonXaQ8WXix 6zrFJgGMyr M9jxJooTbmy 18FsZIeUUR2KK1lesIuelV1xY1omWPLmM8s5uB35i9p4nUqOsG5D7ZmEM4nvsfcCW3ovdc0 TvrW2li5qTJuxiJQj2sxMSH4XfK9MnYqnQceZc5286qeDOnjP3 AQ3XyeJJKaD75 TzGVX9fjXFddlVkyfZivCZdunha47QaB3JMRHuUrGPlSNxIFsx93zWm5tbjGvoD1tfg5aezsUX IHET/d1E4kYlZ jHiru EtVX9uiW8FUpdeLA 0AvNPziTS56xLj0CyqxYAzN924rPy2rfRo4Rl6xcNL6UZ9VPQa0azEQCHgbvA3Yp8VuTcOU6AuL 53IxH07XHOjPviruCNlJBncgyviV/9Sz8snpyK9KkTjiD9lE10xN0n/r9gfis/gk1vLK65rLPReTkJKrxXiS8uQ9KaZk680Iw1DI44ILX6fENJhS4cCZPAlfjZM L0FjO1JJ9NGe Mt Vn5KNfUNtSO p5M2ZA9tksmvmT 6UcVmThzy2JmKM6wfK1C1KY4J/5u3FT7W2li5LB/X0QjU5isfb5gLLNFyQiUauFLtKuKVlWF8q KcKatJzu8qvKFCLyMnjeiV76P3JFB9fNg1jdI1K0I9 N8e88PY2sv1qrbJcSAw4zaK88fKkbhh2C5xrY2pS hxDX0C62vw8tPZ2CJ/kLiJ/m4tcUO/aXNz9xD/5Pe9uxU/XOy/KiWel3jiRi1eyAdqAVNZdHg/hfd6480DNpSlzb2XxwuyU SJxVWHfnU9 toFXVsMBIxim1fjW1KSFra8geVNZXUjatoqnvHCnTfRtbrMoYBM DS4vuGt 9XIlAD7hfZWJ2g44Uh/UUjYp8cHby6yHnqTEPqUzYtEi8RB3qeNC8s2fI76e3l24yZtm He22x1mBDTYELNf adx284T/o4xLCRbSmW ZehT5 QTI41enBjdQ2bPfoaCDcj2bvdTvNLtanLVT6fijPeX/KvP0U7WVke28nPNT6Gdwkvv k1X4NU9uFhDAI6rkX/JH9IScY3hiNck3iUfEUv5fgxbfwYF3HdPydu1HnK/ehrqJuapsKgcxlrw8nGsj41rPQbOSft0rqmDlP7INtwl5I6HtuIs1BAjT O01xecL5irxlrVh5pqH0tdcb9lAgA5ynR7JcFrPvxQWk/Ai3yB4mb6LPLJ27o60/5axA5aUN/ApxO2OQy WfCbdKGnts5cRMXRVJ3eWSZFxv20y3/njeOYlD5RZ3AgeXaxUt8z6/1CYJT5YmN2Sg9RLvYdYuBQKDS0G0ff LCljmgTltJE8pFdtgMZB7JxXbgSi5LHPIi7SJb9mMXvX26c7tYx2yC/ILa/NUoaiH13u736sSE3iSUi3YvUxpT3SAFu7XsoKNs6rUXY1fhx6bNcPU2G w4IWyTquMxPeI/YT8lUzQ oW0dl76yDBrZVrTnPlVndhyUCYvAGxFPY/xSYnLXaWNp 7cbvSD3TM6QLl0 5LGtyo/z0f8oN2Gl2ikD8TACATVfnTn3hW4tZ/Uc2c2rGAMVca0swX rq2onAbBjPY6V6nijdpU K/OPt4P77NOF /F8l1gYvQo y/Lw 1V5LAUduXtvrR1rol8e68rXEiLcT4oAcJ4Uzl5hwLoXHhQeQaBF/iBxE512 cQNJWim d9O4ubIiOgoLjaZHfWGvp5a3tB u q1GAi6dMX74QjAr8OxQk0gAASWgQDi2jL8NIWW8PUUKB6XAZyPYzRVDWA9FZLXKadF/iBxE7mIxE0rg7LySdFZqk0t7yxlfOMWA8H5VkEC/AoOAAEgsDYEENfW5tFue DrbmymLAHOU6LZLwtY9 OD0n4EWuQPEjfRZ0jc9JMXpdMh0GIgmM6665UEv16v72E5EFgrAohra/VsaRd8XWLyFG A81OgWpcJrOu44O0wBFrkDxI30XdI3AwjMWqdj0CLgeB8qyABfgUHgAAQWBsCiGtr82i3PfB1Nz ZTlgDnKdHslwWs /FBaT8CLfIHiZvos7kSN3/ af78eOH /79u/v27Zv7 vWr /Lli/vjjz/c58 f3adPn9zHjx/dhw8f3Pv37927d /c4XBwb9 dW/evHG//fabe/36tfv111/dq1ev3MuXL92LFy/c8 fP3bNnz9wvv/zif5yYyIb/wAAcAAfAAXAAHAAHwAFwABwAB8ABcAAcWDYHkLiZOXEz1Q8Q98mhJBT tYsABU38Wx8C8Ov6fAqLgMC1I4C4dj0MgK/n8TVwngdn6gVYz4f1GntqkT9I3ESmzXXipi/hMlUZEjdth48WA0HbiC1DO/h1GX6ClkAACAxHAHFtOFZLrwlfz NB4DwPztQLsJ4P6zX21CJ/kLiJTEPiZo1Drk2bWgwEbSK1LK3g12X5C9oCASBwHAHEteMYraUGfD2PJ4HzPDhTL8B6PqzX2FO L/NmQUvj/F/ 7MPLEy1// lc31X8pd477tZ24Oew2brM7TBYTppY3VrEWA8FYG1C/RAB LTHBGyAABJaNAOLasv03Rnv4egxap9cFzqdjN7YlsB6LGOpLBFrkD07cRA/hxI2kakv3B7fbbNx0eZup5Y3HqsVAMN4KtLAIXNKvl05GZix uv126/Y/85t89xRlWTruTkWA/DJljD1VD7RrEYFLxrUW8VizTvD1PN4FzvPgTL0A6/mwXmNPLfIHiZvItCYSNw937mazcRv//9bdPz46dULn/jaWbdzm5s492PL43NqJm5/7rdd7a3dzP/dum zt2Dgcdm6z3TveB7KsgFHZRpdXNpBj5PXop/sxevS0w0QyPrQz1pY//D5wYedqZ7J8QmOzcaotcUDwTpU557RcwaEj7WoBnmXJPvw7wWmPSOSMTVBW61YgnDtx4/VSyobNP43V/W7r9nvCmLF7ijINQhWnMzH1MgVPtLk9sUvxhDEI mqZdc5SzSpvmZtKEY3DkKcu3w0T25HkqcVVI9DbZHk/RGHUuSgCNq5pDpu5jzlqY27NAjNP8jzv5VuenDmWa93jXYmA9XVZA28kAjwW5PzuXJzvxNxhT4w DZ4nieffsg651ILA D9 1t14if5C4iay8fOLm3t1ubtzdQ0zWUJJGJmfsc0fShhI97SRuwumW7f7gP9HVkxuXxZSM3 zoTQ65hhb7qR0t3sSCLgy4vPnxz6K8FnCGy vRr1ePnnZRIUwkNc/U3jGWHfwRvvabQrNRdH4zuXO7reCQI5mCZ35DkJ 7OdTfjrTXfu3R3fTpLefNvrFB8bUGUXxXtb n/rlFHiejq5fpbev4auNTlLEhU2NK8gS/dKxh31Zil41j/plj1MHthMw n3m/bynhzW2DoZ24Mw4DrqWMjmRMVVZHXT/WTIK9xo qTLxsGQEV1waNi1q8NhbacRITPn6un3osm67x2I2A8nV3NZT4dQStK2pc74iRAjXgLMA46/b4nAqszwJ45Y2XyR8kbiItL564ocTM7b04YfPg7m44kUP3lRM4HcmbdhI3PObDRJYSMPRabWjoR W2yM5tlFsdXtcA7Ute3OVJHyhukX1RkZDtMJOzAodcKf0xTvxkVm LMJ9PW 0puhiUn5H3RgduqTXRZt 5X078XW77zG/Xdrr8PrzufyNOfdOskAMmnZBTpyPXJ5tBv GQqJ6uCSl0nSKKd/gQNydq67ZZlhmsY11H2jCduvM3J59NjqhgwcIyXPCx5wnLLulzCCeuwKZCf1nYlXfIJsgq3B/pud5B8MfxIqgWci5xMStxIjkl xFNEsmEXn7veJx1wMycC9bgWNZDjIilVjsVU5G8CzyQVdHnZflB81ELwdAICvb4 Qd76m5RczWuPbuuBczc255TU5lRgfQ6i19V2KfxB4ibysr3EzaO7v9242/tH90hfobq9c3c3ecPk3y84cVMOkMoEmDYDHcFDJlfiAlJtKu3KcIS8QfqxWkKPIe0wkTBwQ68Vb qimYSMgE4PeD97/ZVu/4fdJGFN2hEOd7aIudb aPmLdrB 94M292dAIXvk6KUkRv84lnr1uie hz3xag59zskfzVH/qUJT55I9MCMT U3/SGdRX36Z/ujJvs8BgakylVTLRrPGhWsLHymfs24x7lllyNpdx4uYnAe224oSYtjH6VfjBl6cEY gj8yD0oGXQO ZH9o3mk9Qs1BVdhkITW8s bOLG2s8c5KvQVfhYaoL7eRCox7XYd8F3ei/GQ01Fz2kdT2w1zR/iCnEzcCZxr9q3lYTnMQj0 nqMoKupW M6x1Ner e4yrAAZ0ZiyqudU4JsYD0lxmuWtRz IHETeXjxxI3/fRs YfPoHuPv2fgEjbz3yRrztSqTwFnCiRu/MDMLctos5M13bUKUQcOU 0WcmCD94lBumEx9Kcrf6/Lj rGA8e0wkTB2Q68a49TK zwujtJqnvYNtNnljUG9rd YUkJCcvAoh Lm07aLCtX9Wu9fJgK8vlEPOQb05iVZHW6UjXZTHPrUkGz1X2br2/Qo2WazFNXo1c2oOtujtIlsOBPTrLf24bHYkLjlE17hhFLyRRdnc2f TvLAy4u2KNyVn1gA Yvj4FDflXwJXzPMX31i6bwpT/ZwAdklxpLSM9bxdnBD6SuWUbtWbaxVxLunQqAe16g3PS5y/13vY40hvpd1ThnLWRncjUCg29cjhFxV1SNc98uR8iuvwHlCkvhYUVkHxi6A9YRYr1HUAvmDxE0k 4sUTN5R8iQkaf z95s7d3cZEDr2Xv3fzGE7j3Nw9iK9W5R8yXmbixkyAfsHOG5AyWsjNjC VCz3/4jx55ebMyIsqWT2GtMNEUvqz/00de9nG4y5O0fD sNxc6M1saBd51suhnnZRkbpfu3TP8rwOrDDp4DfAoR2/pi6CrvwpHl05OXV 4qZbdtazwFsqJwsvdp91nQJTNuOUMc5tw2kqmUDOJQHz7MNcEvyZk9jBLnou7BIcCO0lbzIeUra S4Qtkm1gz8VC2pPtKXXpt6pd9GI6a rKXgEud57Ie7udBoB7Xoj9Fsi5rEziS ZtL/B35vuCtqRN/Q4RCjOJS4k0HD60YPI9CoMvXo4RcVeUjXPdYlHEYOD8NScLcoedUYP00WK9R6lL4g8RNZF8TiRt 1coZO1cTftaHTOJXETdfXpZaQuFEnDrwP9EJMLdZMhLAbqdCcTlnIRI eUEfLKxaXWj/qs6rHgHaYSIxDjz5qX1arp0RfWCTl3/wQG8Dt3h3oL5ypzYaQnWRwD7nM86erXaxe92uWwVL5SvyhzQ1dcw6E9N 6/YH4LBYgllde11zuuZiEhD7TIyd95AspT96Tckp2uegMVcwJHjbqwtcpMSVTPK7K7zFJIX3TlcwI Aro3qQXfMnhsR3oTfbQn/rIflZ 4ZuSP/93kob4r WITMSydEzd2U27HRy3eKo5aznEH9n3VRq6M6xwI1OJadVwkZbpjXqhS52VqHm94DNCVKZ Vmrjo22M55MQqPn6JEFX0 gY13k lWtT 4cMrgaspze0MqeC008P 2p6WAh/kLiJjGstcUO/b5NP1NCPE8ffu6Hkjv9aVfePFS8icSM UfMuUAv2ykbCVwrv9cabQ4aZQL08nixPkWcWlxX96nr0tQu6YiJhnw29Gt9SMwqweTUfT6PkREa WbNoqP/KiijcGtbodp3F8gOd2obe6X43MrBhlBdxmu9UJGv76Af1FIWGfTXT6jZNIHvjnVD/0mR69mT3yDCZatuFz1N9u1KVZc917PWtJlUkwDRieOsYDBgG7lOQYzNmQMErtvLCoD339Kjk2vk vP8XRCwmSo70q dCduYh Ce7VTRTV aI4abGg IL17uTgXs9CPREDHtb5xwa1CHc1fLgtXzw/1QUvglWozJj5q8Xg6EQHt6xOFXFWzkuuHHa87AxC1eQo4T0SSAXMqsJ4I6zWKWSh/kLiJZLx84iYkZ/ikQE7a8Feg6AQOnx4Qv4WjTumEuu0kbsLinG3ia1qcxc1veC8mO/ sgn3i3rGQFx546LkiU31JPKEfqP0EO0i1zCRDJ0B vgTNxBpTJQ4h15qCyvBnY38XaWcyGGuMrVIVkho5LaJx1W/9unO9sc6aaMdNaZTFRvB31hd9r/d791ObJ71pjjYLHUvTkBUN8jBNi076ChleXXEWLM4sHVPfa0tiDmJYBMuYaM4AtNTx7jAhXyosR nK2VriRnBTOcPwTHFpqO9KvvQlbsivjCePE22n0FVgoDmq69BfK9unv64ux5jm VNkfVQOAAAgAElEQVRzCvJLBNR81TsuDBdjbC64EbuwHLJjduxYLjXHm7EIKF PbXxV9Xu4bseIiskBJOA8FVmOz6nAeiqs1yhnmfxB4iZy8fKJG07QnH9tJ3Fz2kAvNpmniUmtpp aXBJ94g4nkROAabwa/Nu4gqAcEgMBoBBDXRkO22Abw9TyuA87z4Ey9AOv5sF5jTy3yB4mbyDQkbloZcpVPf89SbWp5Zyn jG7cYCM63ChLgV3AACACBtSGAuLY2j3bbA193YzNlCXCeEs1 WcC6Hx U9iPQIn QuIk Q Kmn7wonQ6BFgPBdNZdryT49Xp9D8uBwFoRQFxbq2dLu DrEpOneAOcnwLVukxgXccFb4ch0CJ/kLiJvkPiZhiJUet8BFoMBOdbBQnwKzgABIDA2hBAXFubR7vtga 7sZmyBDhPiWa/LGDdjw9K xFokT9I3ESfzZW4 fPPP92PHz/c9 /f3bdv39zXr1/dly9f3B9//OE f/7sPn365D5 /Og fPjg3r9/7969e cOh4N7 /ate/Pmjfvtt9/c69ev3a //upevXrlXr586V68eOGeP3/unj175n755RdHthDZ8B8YgAPgADgADoAD4AA4AA6AA AAOAAOgAPL5gASNzMnbh4rfwVq6neUuMG/dhGgoIl/60MAfl2fT2ERELh2BBDXrocB8PU8vgbO8 BMvQDr bBeY08t8geJm8i0uU7cTJ2kqclD4qbt8NFiIGgbsWVoB78uw0/QEggAgeEIIK4Nx2rpNeHreTwInOfBmXoB1vNhvcaeWuQPEjeRaUjcrHHItWlTi4GgTaSWpRX8ui x/QVsgAASOI4C4dhyjtdSAr fxJHCeB2fqBVjPh/Uae2qRP0jcRKYhcdPukDvsNm6zO0ym4NTyxirWYiAYawPqlwjAryUmeAMEgMCyEUBcW7b/xmgPX49B6/S6wPl07Ma2BNZjEUN9iUCL/EHiJnoIiRtJ1ZbuD2632bjp8jZTyxuPVYuBYLwVaGERuKRfL52MzFj8dPvt1u1/5jf57inKsnTcnYoA WXKGHuqHmjXIgKXjGst4rFmneDrebwLnOfBmXoB1vNhvcaeWuQPEjeRaU0kbh7u3M1m4zb /6275x8yvr N77gsXG/uHtwSfuPm537r9d/a3dzPvdsmezs2Doed22z3jveBLCtgVLbR5ZUN5Bh5PfrpfowePe0wkYwP7Yy15Q /D1zYudqZLJ/Q2GycakscELxTZc45LVdw6Ei7WoBnWbIP/05w2iMSOWMTlNW6FQjnTtx4vZSyYfNPY3W/27r9njBm7J6iTINQxelMTL1MwRNtbk/sUjxhDIK Wmads1SzylvmplJE4zDkqct3w8R2JHlqcdUI9DZZ3g9RGHUuioCNa5rDeu7rK8tGxHggxtZmw2M hltW449/V2pr4njvC3UgErK9HNkd1j0CFo4bPwHk6quiYw3EkywfWGQvclQgskT9I3EQ/Xj5xc 9uNzfu7uExJGMoWXNz5x44eSOvPsEjEjuy7PHR/znwkp6XeBNOt2z3B/ Jrty8OsdlMSXjNzt6k0Ma02I/taONmFj4hwGXA7V/FuU1i4fL69GvV4 edlEhTCQ1z9TeMZYd/BG 9ptCszhyfjO5c7ut4JDnneCZ39zn524OkS65njPtSHvt1x7dK229rrSRMTYovtYgiu q9vfUP7fI42R09TK9baUdT1bGhkyNKckT/NKxhn1biV02jvlnjlEHtxMy 3zm/b6lhDe3DYZ24s44DLiWMsJGo bOUlxHXT/WTIJ9mMCyC7xpCgEV1/rGRV ZsihwKM3rnKhkvtgxpMZ2B/ UfDycioDy9alCrr7dcY4C56lIcnxOBdZTYb1GOcvkDxI3kYsXT9xQoub2XpygeXB3NyKRI5Iz97 cbd3sfEzziPZ IVva lcu1MJGVW5KapOd2Sxbo9SC7khd3/ZIHSlPbbiocU2/qNDIdphIrCOPPVf4Y5r4zajYFGd/mbbeV5J3khPyvujAbdUmuqxb96vp34st3/mN m7X34fXPZ 6430OidRJAJJPSSbSkeuTzaHfcNpIJKFIQKfsaKc/QUOytm67ZZnhGjZgUfaMJ268zcnn02OqGDBwjJc8LHnCcsu6XMIJ65CwlMm8rqRLPkFW4fZA3 0Oki GH0m1gLPkni9KiRvJMckPy9EeznVyMSmBmxkRqMe1qIAcF1anzrJyrIYke078yXgm73Nct53heQ oEen09RQdXIaMjRgrbgbMAY8Lb2pwKrCcEeOWilsKfDZEa///iT6lw4oOuf/3rXyf7L V23heJm0dXTdDQaZuukzgxibOExE05QI4v5oqYIZMrcZGoNpV2d5E2F4Wk8ELIG6QfixnZDhMJA zf0WuGGaho2nPITXO8/7/ yrd8I CSMKTvCoc52UZe6X00fsW7Wj17w5j7YkWgreOXrpCRF/DqXeK5tbvJpjaADbe5Ztua3/tShKPPJH5kQiP2zMOUL6qtv0z9dmbdZYDA1psos4QuND9USPhb1Qnvj0yS05GwqkicNzeZX2xj9 Kvzgy1OCMfSReRB60DLoHfMj 0bzSWoW6oouQyHZ3ekLriJPYln7mYN8DW1KrKUuuJ8DgXpciz0XfBcadZaJ8eKrlzwOieSdOxj M1cL/olucXs6Ar2 Pl3slbXkeMofcOS4ykAAZ0ZiyqudU4JsYD0lxmuWtRz 4MRN5OHFT9z4rz JEzbxd23syRpK5nT9tg0nhZaZuOFPmdOv2VS XiWDhln8 UWimCD9gi9vVHnBJzf3Upotr20YaDNTttd6DGmHiUQjf/xJY5zqe5/HxZFcyXvfc6Kh3tZvTCkhITab4RRYH4fiqQHbLipU92u9f3XijPSNekiOlRvsZHk8IcM22tMMoU 8NyVZ/DatzU8UnIVh2mMykLNKiVzeh5qy30qYJMM26ax8eG OJWz7hFU4oJfy6OJs783eSB15e5IfCXfGcBZC/mMNDfVfyxZ6AYOkcJ5M9XEB2ibGk9Ix1vB3cUPqKZdSuVRtrFfHuqRCoxzXqTY8L3f/xsnxKrDaviljLnPEdBLmyrU1Maj3wNAaBbl PkYK6EgEfC1MyPZQAZ4nQmfdH5lRgfSa a2 QP4gcRNJefHEDZ2Wickavyi5uXN3tyKR40/T0O/gdP 2zbITN2ah5xfsvAEpI4fczPjSYiNwnrxyc2bkRZWsHkPaYSIp/dn/po69bJMXR6FuXuvbtnozG9pFnvVyqKddVKTuV9s/a53leR1YYdLBb4CtHTFZ4pMB/EkeJ1fOT9wEHFguXVl21pM1p6vSWRZc9D7rqvQ7EVM25ZQxzm35t7zYvfk9 5NxliXBnzlJHOyi58Ku5CduL3mT8eBSuioZvkC2iTUTZrKlb13/C1SmftmH4aipL3vxbTt4Luvhfh4E6nEt lMk66Q2dszIsiLhQ1woeOyJar46SlIqXNXC8XQGAl2 PkMkmsbfc5RzAHB GlqEuUPPqcD6abBeo9Sl8AeJm8i JhI36vdqKkmaytepOFkjr0s4caNOHHgf6AVZbeHPgaK6KCwSPUEeb35GyysWk1o/0qWqx4B2mEjYk0Ov2pfVVsn/YbOqP5GNCYnt3h3oL5ypzYaQnWRwD7nM86erXaxe92uWwVL5SvwhftI1L pI/63bH/Z602J55XXNCxTPxSQk9Jkea5t1KU/ek3JK9tDNP1t12euUmJIlHlfld//SbDRLvBMKFttUwDjXk9NsR6oe5eyJv xY5SeuGfnjDy4O9V1Ff rP2u27qPPZjo9avFUc7cLFvq/ayLbiOgcCtbhWHRdRmb6yUMVyyD5HQVXfV7g6BwhX0kfN11di tOZ6Xms4zxwfiK4gfUTAXslYhfCHyRuIh9bS9zUvhJV/c0blewJP1i8iMSN/RRCLdi7Fmfh/aANhZfHk Up8symp6JfXY doFsmLTHTgLBf5yE860pwPIGlhMTtU9t7XF 5UfePHPixPQjOdTbrs vRqY0nWRutzpBw/rSXxQS9tlEp98cCXvVprjyqXSxkZb2yHtOViTZhs9Rf7tRl2bNdV/dIE6GafDbqWM8YBCwS7wdzNmQMErtvLCoD51ESbyI79JzPE2TEi5DfRfkCDGkQEfipnZSqOynxg/NUYMNzQekdy8X52IW pEI6Pmqb1z0lUmJoZ7it/G7r 0X0Tk5HSRUuCpF4/4sBLSvzxJ1tY0PO153Bghq8xRwnogeA ZUYD0R1msUs1D IHETyXj5xA39Fan8VYXyd2zMnwuvJGz41E07iZuwOLenH9KCzS/M2GYx2VUXbPxJN9cXV95xKHm8GefNuV0AjpUn9POLTNE/H uv6iHaRa5hIhk6A/TxJ24SGPv0ux5WdrlJ8Aup1M78vkIXh1JCI/s98bjq1z7dWcdYJ220w3u/6RU/JJxq72Tfe7dLyZV4MoT5NzZxY2zb7qXsoGMSzcoInCwOXOWpr7UFMX81ySZcRmN66hgXuFDc09g M5WwtccNxTCZuCGHDM8Wlob6rbIbJfiVLe5Px5Niu7RS6Cgx04kbXob9Wtk9/Xb2b51oLPM2BgJqv sZFX5lStIzJzGMVZ/xYsvO2HUOBKwX/VH94GIqA8vXQRqinEbDjoBJHgbOG7PQnGw/yPMIygTUjgWuJwDL5g8RN9OTlEzfdf96bEzJDr 0kbsphMuSN3xSoFdyQVt11ppbX3dOwEkwkw3BaWi34dWkeg75AAAgcQwBx7RhC6ymHr fxJXCeB2fqBVjPh/Uae2qRP0jcRKYhcdPKkAsZ0OnyNlPLOx nFgPB VZBAvwKDgABILA2BBDX1ubRbnvg625spiwBzlOi2S8LWPfjg9J BFrkDxI30WdI3PSTF6XTIdBiIJjOuuuVBL9er 9hORBYKwKIa2v1bGkXfF1i8hRvgPNToFqXCazruODtMARa5A8SN9F3SNwMIzFqnY9Ai4HgfKsgA X4FB4AAEFgbAohra/Notz3wdTc2U5YA5ynR7JcFrPvxQWk/Ai3yB4mb6LO5Ejd//vmn /Hjh/v /bv79u2b /r1q/vy5Yv7448/3OfPn92nT5/cx48f3YcPH9z79 /du3fv3OFwcG/fvnVv3rxxv/32m3v9 rX79ddf3atXr9zLly/dixcv3PPnz92zZ8/cL7/84sgWIhv AwNwABwAB8ABcAAcAAfAAXAAHAAHwAFwYNkcQOJm5sTN0B8YPqceJW7wr10EKGji3/oQgF/X51NYBASuHQHEtethAHw9j6 B8zw4Uy/Aej6s19hTi/xB4iYyba4TN ckZIa2ReKm7fDRYiBoG7FlaAe/LsNP0BIIAIHhCCCuDcdq6TXh63k8CJznwZl6AdbzYb3GnlrkDxI3kWlI3KxxyLVpU4uBoE2klqU V/Losf0FbIAAEjiOAuHYco7XUgK/n8SRwngdn6gVYz4f1GntqkT9I3ESmIXHT7pA77DZuM93fB3dTyxuLXIuBYKwNqF8iAL WmOANEAACy0YAcW3Z/hujPXw9Bq3T6wLn07Eb2xJYj0UM9SUCLfIHiZvoISRuJFVbuj 43WbjpsvbTC1vPFYtBoLxVqCFReCSfr10MjJj8dPtt1u3/5nf5LunKMvScXcqAuSXKWPsqXqgXYsIXDKutYjHmnWCr fxLnCeB2fqBVjPh/Uae2qRP0jcRKY1kbh5uHM3m43b P 37v7x0aXftekrk/UeH/1flWppAP3cb71NW7ub 7l322Rvx8bhsHOb7d7xPpBlBYzKNrq8soEcI69HP92P0aOnHfmlxUDQEl sLoy15Q /D1zYuYNt6Fw4XbXZONWWOCB4p8qcc1qu4NCRdjW/sizZh38nOO3VjpyxCcpq3S47beNKvaleeb1Uf2HzT2N1v9u6/Z4wZuyeokxbUsXpTEy9TMETbW5P7FI8YQyCvlpmnbNU0yfiLG Zm0oRjcOQpy7fDRPbkeSpxVUj0NtkeT9EYdS5KAI2rmkOm7kvatrFXzaEy3MclmNhCMdiTOn6UI fHoOBbf5 s2XVfra vG41Trc/cTPw2sRA4n4pt2U7HIxlHQl1gXWKGNxmBJfIHiZvov8snbu7d7ebG3T3EZM39rdvc3LkHn5R5cH c3G3d7XysTyZ2YwCFb2vgXTrds9wf/ia7cvDrHZTEl4xdaepNDNtBiK7WjjZhYiIUBlwO1fxblNQyGy vRr1ePnnZRIUwkNc/U3jGWHfwRvvaLcrM4cn4zuXO7reCQ553gmd/c5 duDpEuuZ4z7Uh77dce3Sttva6UJDA2KL7WIIrvqvb31D 3yONkdPUyvW2lHU9WxoZMjSnJE/zSsYZ9W4ldNo75Z45RB7cTMvt85v2 pYQ3tw2GduLOOAy4ljI6NspVWR11/VgzCfYaP6oy8bJlBFRc6x0X0Ypq3NUW2rimx9cQjoU6WxojYkxxLyR/t9Mf vT3yS2v 6p8fd1QnGF9B3 FROAswDjr9vicCqzPAnjljZfJHyRuIi0vnrihRM3tfT5h80jJGk7kUFJHnsCxzzp5007ihsd8XG TJEzdqQ0P1apOd2SyzOL6qzdqRur7NkTpS3iD9oiIj22EiYQcOvVb4Y5r6hb9awDOfTFvvK7kZl pyQ90UHbqs20WXdul9N/15s c5vKna7/j687nwiT3/STe1z0ofkU5KJdOT6ZHPoN3wKKJJQpFOn7GinP0FDsrZuu2WZ4RoSq1H2jCduvM3J59NjqhgwcI yXPCx5wnLLulzCCeuQsMx jafBVEJE pT8UeH2QN/tDpIvhh9JtdCfUoHKUuLG6pOTppqjPZzr5GJSAjczIlCPa1EBOS78K ZHuKYPXYy Pt7J9UD8ICfwimUUjUSShutUxpfXaecOiZNBTn fpq8rfez19ZViMt5s5mZ3S Dcjc05JbU5FVifg h1tV0Kf5C4ibxsL3Hz6O5v ZRN5cSNSvIsL3FTDpDKQs8svIoQIpMrcQGpNpV2dzFC3iD9WCGhx5B2mEgYuKHXCjdU07DhlJsE 7wfv/7Kt30D6Da4pO8KhznZRl7pfTR xbtaPXvDmI9iRaCt45eukJEXcwItnr1tqGPrMm3h zskezVP9qUNR5pM/MiFQSyCwQ6ivvk3/dGXeZoHB1JiyRf4qfKHxoVLhY1EvtDc TUJLzqYiedLQbIy1jdGvye/RLyl5E/rIPAg9aBn0jvmRfaP5JDULdUWXoZDs7vQFV5HJRWs/c5CvoU2JtdQF93MgUI9rsWfD98wtMSYqShK/ZLzWJxiHcCzXsbKSDoaTtp7us6LkFb7q9fUV4nGayYGb6WtS8qRuFAicT0O2v5WdU0JtYN2PGko ZgeXwB4mb6LOLJ278b9jwCZtH90gncDacuKHETEje MkgfYVKJ2z493CWcOKmtiDXC6v hR9vNtLizy8g88aDTxDkDcY4ecf148Gu5Q5ph4mEsRt61RinVt7n8fRHdjTtXsXJlXpb4pofS2K zGb6u1Meh/NsjcpPK tT9Wu8/9BWTIaRv1EOOgbQB4Q7kVdkY9UoYhD7Toz/csBUncuIJibTBl4L5JAQnasJkJmVR7V7djLjZHuUmcgJMs97ah8fGeOKWT3iFE0oJvy7O5s78ne SBlxf5oXA3HAgiyF/M4aG K/mST9AYxaonI WJm1Bf6RlFeDsYCOkr24V8rtooK D qRGoxzXqVY LIXGXdZX8pneS4yyXqcJtNCcFZxWXBP/pvYjv/X2mXq76ptvXVw3LWcb7WGjmWuB8FqS68ZE5FVhruPBkEFggf5C4iT68eOKGfp8mJms4OXN3GxM5 Nqnjn VXp3QCZ5mJm9oikDcgZqAVC73aRvQ8eeXmzMiLKukFZ9zQisUiL0JTgqn4LZTSNryxCNSxl7Xy4 ijUzYt 21ZvZkO7yDO1ASDpsm1Pu6hIfYEgZUiNszyvAyucNhvWDj5NERNO5hSM5yHLqGyuVR khrE14FCTnfWU2hfyZOHF7rOuSr8TMWUzThnj3JZ/yyu5JhfEH8HmBJkoiPEtx4xgFz0XdpkNAXM29JfxkNKVDF9Qck1vklXr l gShiHumUfJrlo6qse4o/Z 3nQ8FzWw/08CNTj2rFkS1fcCzr7MeV9W0mgV KXb6U4IzkreC7ryHteM3T2OQ WrffS5evW9W5bP8HPqChwfhqP XnHzInA mmwXqPUpfAHiZvIviYSN qvQ XfsXm4u3E3dw/i92/k16h00oZO3SwhcWM3jnrD0f Jvt1IeRf6T2ZlokcvHGsbCQ48VXm06FMTgFwohpantsNEwsgPvWpfVlsl/4dFUt70iWTEdu8OtCnsSqwlGdxD7tfzp6tdrF73a5bBUvlK/KHNOF3zxp7037r9QZ4aKhMt tNtsymubHwK/kt y3tSTp1yKBedoYo5wcNGXfg6JaZkyqljPMFgsU0FjLOMWbmQ7Uhvopw98ZfJovzENSN//O8mD/VdGduOJW5yUin0a8dHwTfGknXvwsW r9rItuI6BwK1uFaOi8C1rrjLfxWS9S34zQX Wo ZmmOas8w3JZe4JGK2KlP94YERqPmay3A9EYFiXWH/kMGJctGsRABYl5jgzXAEFsIfJG6iS1tL3NDv26RkTXHCxvwFKpXwWUjiRv0Yod2Y6kVZHnXhvVy M2bK0ofAbAN4UnSLPbHrUhqJPj752QVssjrLXht0FvJNvqREFWN4E sfyL/AE2aat8iNvnjlxUqvbcRrHB3huF3qq 9XIlAb7jcVWfK3LGxb Ahv9tRRhn010 o2TSCz651Q/9JkeGR/5QuIg73mDnWQbPkf99SZKGjXfvbdZbMx8z5NhGjCsxxqDicEvIxDqJd4O5mxIGKV2XmDUh04MJD/Gd k5JrwTJkbPqFjpu5Iv3Ymb2EfiBwkt yn7sMlFgw3JIL0NlpbnGVvczYWAjmt940JqFOppDudy8mtXGdXy/OnlmOFsjMfqwxYfC/JfOjvWZ9bueu 0r68Xh3MsP x43Rmk1OYp4HwOwqLtgDkVWAu8cKsRWCh/NkRq/P LP6XCvxFD17/ 9a T/Zdyu /Fb9hsRNKGkzLya1Tqt29aPnETFuf2U7i0YEuLLToVISY7/77yFQK/qBcnKPjYM29clDyxqZ5EntBvlB6iXQwZmEh07Ox 6uNP3EAwByR/lMByAxE2g5lHiY/UrotDKaHR0a74Clyf7qxgrJM22uF92LQI/sbqUu/tfu92YmPjy3gcjD1xY2zTsoOOSTSrLnBS HH5DNfagpiTCDbhMhrTU8e4wIXinsZmKGdriRvBTeUMwzPFpaG M5tg8p3Z9Fp3Mp4c27WdQleBgeaoriPjfx/PrR54fnoE1Hx1bFwkdcq4m4pivCk4Iyt4elAyviveWs7GsSXHhuEw8epYn0aFq3tUvr466ycy2I 4RFZNDH8B5IqzjWifHCay3p0L2OuTEeSPNM8vgD07cRHa2duKmO8FTJmps3fa KjUuBPhNgVyAjWte1J5aXtHByBeYtEcCtpDq8OtCHAU1gQAQGIwA4tpgqBZfEb6ex4XAeR6cqRd gPR/Wa ypRf4gcROZhsRNK0POfpJ2rl5TyztXH0wk5yPYpoQWA3ybSEErIAAEloIA4tpSPHW nvD1 RgOkQCch6A0TR1gPQ2O1yqlRf4gcRPZiMTNtQ7L e1uMRDMj8L6eoRf1 dTWAQErh0BxLXrYQB8PY vgfM8OFMvwHo rNfYU4v8QeImMg2JmzUOuTZtajEQtInUsrSCX5flL2gLBIDAcQQQ145jtJYa8PU8ngTO8 BMvQDr bBeY08t8geJm8i0uRI3f/75p/vx44f7/v27 /btm/v69av78uWL OPP9znz5/dp0 f3MePH92HDx/c /fv3bt379zhcHBv3751b968cb/99pt7/fq1 /XXX92rV6/cy5cv3YsXL9zz58/ds2fP3C //OJ/aJnIhv/AABwAB8ABcAAcAAfAAXAAHAAHwAFwABxYNgeQuJk5cWN/SPgpnikJhX/tIkBBE//WhwD8uj6fwiIgcO0IIK5dDwPg63l8DZznwZl6AdbzYb3GnlrkDxI3kWlznbh5ikSNlYnETdvho8 VA0DZiy9AOfl2Gn6AlEAACwxFAXBuO1dJrwtfzeBA4z4Mz9QKs58N6jT21yB8kbiLTkLhZ45Br0 6YWA0GbSC1LK/h1Wf6CtkAACBxHAHHtOEZrqQFfz NJ4DwPztQLsJ4P6zX21CJ/kLiJTEPipt0hd9ht3GZ3mEzBqeWNVazFQDDWBtQvEYBfS0zwBggAgWUjgLi2bP N0R6 HoPW6XWB8 nYjW0JrMcihvoSgRb5g8RN9BASN5KqLd0f3G6zcdPlbaaWNx6rFgPBeCvQwiJwSb9eOhmZsfjp9 tut2//Mb/LdU5Rl6bg7FQHyy5Qx9lQ90K5FBC4Z11rEY806wdfzeBc4z4Mz9QKs58N6jT21yB8kbiLTmkjcP Ny5m83Gbfz/W3f/ OjS79f0lcl6j4/ r0q1NIB 7rfepq3dzf3cu22yt2PjcNi5zXbveB/IsgJGZRtdXtlAjpHXo5/ux jR04780mIgaIkvVhfG2vKH3wcu7FztTJZPaGw2TrUlDgjeqTLnnJYrOHSkXc2vLEv24d8JTnt7I 2dsgrJa1wLknJs7ceP1UsqGzT N1f1u6/Z7wpixe4oyDUIVpzMx9TIFT7S5PbFL8YQxCPpqmXXOUs0qb5mbShGNw5CnLt8NE9uR5KnFVSPQ2 2R5P0Rh1LkoAjauaQ7LuS OczFmaidlfXvLgyNj9UBriJFteM1wUfAW1rn19cLUb0pdHidy7mcFgTMjcf6Vce5aBwLr8zFes4 Ql8geJm8jIyydu7t3t5sb9v/aubkWKpgfPRe3RXMsez33sHcy5CCIrgoggwiCIIOIiggjihwev58orWaYAACAASURBVHsb9ZFUp SpJpau7Z3p7u3sjSP9UVSp58tRPsjUzN3cpWXN7HXZXN EOkzKtMpbcSQkcsGUZ/ Lplv3xhH/RlQsYlaXtFQY7MsgBG2Czn9vB5o5t3uKAK8EPPrNyC4Ph8hr6NfVotEsK UJiecZ6R1h28If5GoNCFSgGDCYP4bBnHAogk/EMA4by3M2hdjvQXvq1obvqEy2nYF/ZIPhqQZTemfY36l9ahDgpXVEm2tbx0caHKCNDpsYU5DF ybmGfGvMXXoew2eao07hwGS2fIZ 30PCm9pGQztxJxwGXGsZHckYU1ZHXRxrKsFu8cOU6S XjICY15rjooMb2rhzxuo5bXS//tyLgPB1b22vYCNA64O1b4ktHGcbufFv 9dUx3o8qk nxTr544mbxNBHT9xAoub6tpywub8LN1cpkdMqU6dt4ITOchI3NPzjhi4nYOC1CGjghbXpU8Eyia Or2Mz11MU2PXW4vEH6JUVGtvOFhBw49GrwRzXFYJQFxYVPqi36igfDnBP8vuog7EUQXde1/ar6R7H1OwzUD4d2H6g7ncjjf nWJ25APiSjQEeqDzbHfuNfpkqyKqrUdYIk2YknaEDWPuz3JDNe47hOsmc8cYOJj zz6TEVDBg4xmse1jwhuXVdKqGEddz481MLXUmXcoLM4PZA3x1OnC KH1m1iHOVk8mJG84xzg/NUTw ZJ 4bPA8q E3syFgz2upez4uzDXcUvOcsXpOG6tvf9dCoOnrVkMvMxCoOUuVHGdCYtqrtaY61tNivGVpa GPJ24SC5eXuLkPt9e7cH17H 6rxA0rW2niph4gxiKXg4GOqYInV9IGUgSVOroYIW QfqQW02NIO19ICLihV4MbomkMOHliEP2A/q/bYsCPSRhV1sOhznZJF9uvqo9Ut gHLyi4j3Zk2jJeYZ2cpEgf52LPqFtuGPsspzXouSR7JE/lXx2qMkz 8IRA6j/3x50BfbWC/unKZOJG63Q5ptwqnmiW EAt5mPhM/Jtwb3IrDlbyihx8w MCnt2QkzyJvmV QHLc4Ix9lF4EHuQMuAd8aP4RvKJaxbrsi5joZpb6z504kbbTxykK9OV8Zxr4vfzIGDPa6lvwXfi ESV2C5 0ppIfQ8bqOeNb9 rPfQg0fd3X2MsVAnE88H0JVXCcCYkpr3pNibId6ykx3rKs9fDHEzeJh4 euMHvsFEfldqlxE2rbDOJGxasoE 6F73oMlWOG0i2UcSAhwdMqn41/8hy3FiqgAGCmXoRHt/OF5IK/J4XEuNcGX2eggQeSaLvKdFgt8XAFBIS3Me9HErBp26XFLL9avfPEwEYnCc9OMdkcJOtjjfCRh0U xz4lJHv5y2wi4GrJjosZlwW1m7opcbM9cpsAnwsxLXpLH/bNDZlbmPCKJ5Qyfl2cLZ3hHecByku2CNwVB6IICoLhaajvar7EjxmWjz4V9Yy62JXxXWTZ6KQZ/3VA7qsivL4zbayr ZuHQ8Ce16A/OS60BsjVnERUpdz/Q8fqOW1Ut/7YRqDb1 12Xmoh0D0 HGcLrzPf9aypjvWZuD6VZivkjyduEjkfPXEDCRg4WYOb/R1 v83NdZ3IwfKrmyDKVPJmDR VqoMftcjhhp0lYtQkwoMZLOKbOnxxmbxe/ZI Wo8h7XwhUc7sfVS NOqXICHWLTGjbiuD2dgu8azJoUa7pI/tV90/KV/koQ6kMOiAQbq2IyVLaH7AKyWnLk/cRBzoL VwJdlFT9IcrkJnXvCo90VXod ZmJIp54xxatuVPIHyiDnhXFrAHfRZksTRLniu7Mp ovacNwUPKoWrkIEFvE2qmTHjLbG1/QtUqn7dh Koqs97ibhYXOS1/H4uBOx5LfkzJRRtXWz xbqlTHAl88LgJEtEDm9ja ZvbQS6fG3X9rdtBCKHyzxeajvOBYsp7 LaIddUx3pKhLctay388cRN4uEiEjciAQNfSKx WSqXt8q28R03YmOm5godSGFxleiRi ZoebCBFEFRvZE09RjQzhcS5dDeR lLs3r2fwwIcgKUJzr2x1D/OgmTnWVQD6UM SOClFJGtW2/1vWoPgXncKW8Tf7Y1Ak IsM2IJpXqGspRy5mIbHP/GgF61wevwflhOwSYJHesYo6wcMLH/F SkzBDMRV B1f9s4NGQKNbS4gnO3kNNmRqyc5R/h1HXKs8BPVBH RzKG q/nSd JGByN6fFjzreBoFy76vWkj2erXORCw5jVzXGhl0HfERV1YkpMgiyjdOf l5jQuxrSpe/Y3XQhYvu6q6 /7EOhe x3nPuzOLDfmHMf6TCyfYrOV8McTN4mcS0vcwPfbXN3csS8rLr8e1SpbzZcTs7 eoQvEht0IJLBSfC8 3pInF7VIojzaNJ4jTwU9hn62Hq12UVlfSLLTBt4o30IrmGDLbr9xekG1FX6k4JkCB6tu4lCzXcu vSia3GGTu9zJBQx8/gF8UYvaJj1ZRQoEldjCQyvVjn/kRzWzIU7ahrCxb8TnprwN1btZc96inlVSZBNOI4bljPGIQsctJjsGcLUFtwTLpA4nI7Nj0Lj n0zQZk6G q/nSnbhJfWR gIZ1PxY/JEcVNiAD9G5ysaDhd/MhINer7nFxOtB6G3UzxydXe8z8R 3OaUNt/dqLgPR1b3Wv0EQgjpU8/7O6jjMD45LbAWuqY30JwBtvu1L eOIm8fLxEzfwK1LleLhM2rTKSkIHkjbLStzEzbk /ZAXMhg0 UQE2/Th 3KaIE8duKkvGGW5FLgIeRSMU3B qTym3yg9WLtkiC8k2aM9Ny3 pADC4o QWm eYmKi8CjzEdp1cSgnSzradfwceOZo0lP0lQJenRzAoHfH Jvs4Xrvj8dwYMGzDIqjzTQsolmNxI2yTcqug3JUh EkbUrKznCxA8PEmZy8iIqMxvTcMc5wAd9LbIZy1krcMG5yxxKHaBwIu4f6ruZLK3EDiBKexG9pJ 9OVYSA5KuvAr5Ud86 r8zEmeT4DrbwLhYBYr1rjQpcJLiqh DhurEYJ57Sx vZ3FgLC11YFfzcAgcRRmpONtd9xHgDjoCr9a6pjPQjIJ1ppnfzxxE2i6 MnbuoEDCVixl7BljX/w6BABCeXWTO1vMu0CcEXkksRXGZ79 sy/eJaOQKOwPkI Lx2PnZra m nsdjjvM8OEMvjvV8WG xpyXyxxM3iWmeuFnKkDP nuRalPLu0gZbLzEieByq1yC 9U54Ag4AltDwOe1rXm02x73dTc2U5Y4zlOi2ZblWLfx8dI2Akvkjyduks88cdMmr5dOh8ASJ4Lp rHu6ktyvT9f3brkjsFUEfF7bqmdru9zXNSYP8cZxfghUbZmOtY2Lvx2GwBL544mb5DtP3Awjsde 6HIElTgSXW US3K/OAUfAEdgaAj6vbc2j3fa4r7uxmbLEcZ4SzbYsx7qNj5e2EVgif3aglP//X5grcfP379/w58 f8Pv37/Dr16/w8 fP8OPHj/D9 /fw7du38PXr1/Dly5fw fPn8OnTp/Dx48dwOp3Chw8fwvv378O7d /C27dvw5s3b8Lr16/Dq1evwsuXL8OLFy/C8 fPw7Nnz9AW96nz2jngHHAOOAecA84B54BzwDngHHAOOAecA vngJ 4Scm2uRI3Y79o Jz6YIv/Wy4CMHH6v 0h4H7dnk/dIkfgqSPg89rTYYD7eh5fO87z4Ay9ONbzYb3FnpbIH0/cJKZ54maLQ26ZNi1xIlgmUuvSyv26Ln 5to6AI9CPgM9r/RhtpYb7eh5POs7z4Ay9ONbzYb3FnpbIH0/cJKZ54maLQ26ZNi1xIlgmUuvSyv26Ln 5to6AI9CPgM9r/RhtpYb7eh5POs7z4Ay9ONbzYb3FnpbIH0/cJKZ54ma5Q 502IXd4TSZglPLG6vYEieCsTZ4/RoB92uNib9xBByBdSPg89q6/TdGe/f1GLTOr s4n4/d2JaO9VjEvD5HYIn88cRN8pAnbjhVl3R/CofdLkyXt5la3nisljgRjLfCW2gEHtOvj52MLFj8C8f9Phz/lTfl7iHKinS/OxcB8MuUc y5eni7JSLwmPPaEvHYsk7u63m86zjPgzP04ljPh/UWe1oifzxxk5j2eImb23C924Wrm7sgv4g4vt/tdmG3uwo3d/esvFV2j78qtaQB9O 4D2DHXkdz/45hj/aBjR2Bw kQdvtjoDiQZEVc6jay3Aggx8hr6Cf7UXo02oFfljgRLIkvWhfCWvOH3kcuHIJ1JgsTGpp7wAHGu 7ZcxqGedpZfSUfeB75jnEZ7E2d0gtKsqwEKIcyduEG9hLIx Iexejzsw/EIGBN2D1EmQTBxuhBTlMl4Is1tzF2CJ4RB1FfKtDkLNU3ehhBq3CUOQ55qGWMSNx11rXlVAJZs0 rwforDXeVQE9LwmOazWPuJoHjeS/2QI8ZvPw0SXuqx7nJA8v06DgPb1NFK3KaVzHIj5P 5t9R7FcZ6OE9IP9VzhWE H9RYlrZE/nrhJTHyMxM3dzVXY7a7Dzc2VStzchZurXbi Tcma22usd3sPz62yWB9sWca/eLplfzzhX3R58BoClaWUDC529SYPNnG5HQRibOMfB1yZqPGZlVsYDJfX0K pR6NdUsgXEssz1jvCsoM/zNe42aedP4nCYPIQDnvGIeQd4xkG9 W5m0OgS6kXVDvoUvq1obvRNtBmT9kg Ep2GVfTfqPeVK8QJ6UrykbbOj7a BBlZNDUmII8xi8515BvjblLz2P4THPUKRyYzJbP0O97SHhT22hoJ 6Ew4BrLaMjGWPK6qiLY00l2C1 mDL95ZIREPNac1ykxCLjeJddrXlNl8mx1yXR30 BgPD1FAK3KqNnHGiz9VzvOGuEzn3uX1Md63OxfQrt1skfT9wkbj5G4oZO2EACR5y4ubsJV1c34Q 4TNSpZ0ypL9ZeTuKGBHzf7OQEDr0VAAy sgEAFyySOriJY66mLbXrqcHmD9EuKjGznCwk5cOjV4I9qipt7ETAQn1Rb9BUPhjkn H3VQdiLILqua/tV9Y9i63cYrBwO7T5Q9/IXPB4Xy40hyIckE hI9cHm2G/86x9LQoFOnbKTnXiCBmTtw35PMuM1juske8YTN2hz9vn0mAoGDBzjNQ9rnpDcui6VxNMplPDm3 FbbjjhU/BHwa3B/rucOJ8UfzIqkWchQpQlhM3nGOcH nEDW/Yxbmu91kHv5kTAXteSxrwcaGT4g0lcb7TJ3BT/bos8pJTpyHaiy5AoOnrC RuvqkYB8pao8xxVhhN9GitqY71ROA ATFr4Y8nbhIZF5W4gRM217fso1H34fY6ncBpla0ocVMPkDrwKsFAx4zBkytpcRRBpd7p5eCiX94 g/UgM02NIO19ICLihV4Mbomnc2PPEIPoB/V 3xYAfA1xV1sOhznZJF9uvqo9Ut gHLyi4VwEK4xXWyUmK i/bqFvme yzBPH0XD7WIHkq/ pQlWHyhycEWh/Zgb5aQf90ZWizxiRjcDmmkmLw0a IgcQHajEfC5 RbwvuRWbN2VJGiZt/KaFWMJO8SX7NNie/5ORN7IP0JvlSBrwlfpR JJ oZanLuoyFam6t 9CJG20/cZCuUWyNNdfF7 dAwJ7XUs c7z3zJ9cV MXn62aZEfjy n4/HQJNX0/XzfYk8XGgrLPmUsdZgTTJo15TolDHehJwn4CQ9fDHEzeJjktK3FQncO7vA72jK53Wgat t4YTN9aGXG7mWDBkThmqHBfOEnjQCYISYKj6lUxZ3q8fCRjfzhcSwm7oVWKcW6HP0 mP4ugU7FKiwW6LmylISLDAP54Ca3EoBZ 6XVLI9qvdvzhxBoFJ0oOPASv4zbZjMEM26qA49ikh2ctfZmtsNOPYIdlxMeOyQIemblnJmW 4TRNgWrSXPuybGzK3MOEVTyhl/Lo4WzrDO84DlJf4IXBXHIgiwF/E4aG q/nSnTQ36kLHYBcbS0LPZBvaQUBwX6Vy82LaaNb0lw EgD2vQWdyXAyZP0lFOUbkSTHOfajP U/t/fowCHT7 mH624ZUNQ6EUfYc7DgLkC576FlTHevL4N186xXyxxM3iZVLStzct07VtMq2dOIGN wUgNRTR7WZqwIBtZiOlFcHZ0peUknrMaSdLyS1P9tvbOx5G8SdnaKh LAKLtJ3K1F5bJd41uSQ3ICJdkkR269duhd5KIsUAh0wAI7t6DV0EftMiSp1CgZ5mCt3tM3lKdDO JzNasoueFd5cHi98tPui6xSYkhnnjHFqS9/lZUEV/UkJstIC7qDPciIh2gXPlV3Mh1EC933Bg0sXMrCAt0k1Mw95S7g36sJrVb/uIwXgBISqz3uJuNg85/X8fh4E7HnNSKiATwUfI1cKj4u kt/lPdzheMP5LXGAJQRlTX aGoEuX0/dz5bk6fWB24ZzmcFfx5mjNN19XDvkmupYT4fv1iWthT euElMXFzipvqOm/TLUpC46SpbUeIm/nWOT7AyIMABRJt8NVuYC2WVmJGbxtHyOjahXCVTjwHtfCFRDu19lL40q2f/x2CV/1pJvt8fwwl 4UxspJjsLIN6KGXIn652qbrt1yKDpNIV ANBDVwLr0D/fTiejvL7bjSvUNcyfpCLWUjsMz9S0oe/4PL4PSgnZA8N/smqx71OiSlYgrgKv6ckhRGgcngzChrbXEA428lpsiNXT3KOwF/qSPiJaib 4PcmD/VdzRediCHplLjRwbgeH9Z8KzjahYt b9pYtPG7h0fAmtfMcYG 4nzun/ss7SvuW5X83YMgYPn6QTraiFBzHGTb7PkXih3nDNK0N9Uc5FhPC/DGpa2EP564STxcVOLG uWonKwxflUqly3tV6VokFsbOLWoiQ27EUigqPheBt4dfaA82kSeI69fP1uPVruoqy/a5LOhV4M/MMFSAEuJCRFMk2zVVvCMgmdKnFh1O07j4ARP7Vp VTJJLbiCLvu9TNDQxw/gF4WYfTrRiRtGZi8 5/qxz/xI PAXHAd T8mKLFvxOemvA3Vu1lz35qZ5MkwjhueO8YhBxC4nOQZzNiaMcjsUlvSBkwjZj ldfk4np3Kiaajvar50J27odFZJGlqniix SI4qbOAkHOjd5OJczPJ OAJyvWqNi1iWeYu pDWYS7T4XcqBJ1lGee13MyAgfT1Dh6vtojUOolE4B Z1VBrqOEs8zn4asKY61meju/2GK WPJ24SNZeVuLkP9/DrUfm48HWIPwUeEzPNsvv7ALYs41/cnOcTD8mevClLwW/1Kzf4ngcGyRrcCPIj9OmeAhchjwXVk8hjG9BRerB2yQxfSIays8WftHHKY6TGOfaigomcmCg8yn yEBl0c6mtX/RWtpTvZn rkQDtpDKcqdoy/qToGvnkMHcOBbQplUBxtpmERzRqSCIqY7I9cdtSRy0J1GE4Cv6TrHBe0WWFHSQSdcImb6BGYnjv GGS7gQ4nNUM52BLYkWzhD8UzgMdR3NV9aiZvMpzz2tJ1yHBEGkqOyDvxaGf3IUIvnc/DK 5AIiPVq1LioxxtJBh8TL gdXVtlVMevD4OA8PXDdLENqX3jIH0ku4vjjvNUNOhfUx3rqbDeopx18scTN4mLj5m44V80PMX9c hI35w10DLJEcHKeHGo1tTySe 7VF5JzkVt2O/frsv3j2jkCjsB4BHxeG4/ZWlu4r fxnOM8D87Qi2M9H9Zb7GmJ/PHETWKaJ26WMuSMv/5epNrU8i5SBhsvcSK43CqX4H51DjgCjsDWEPB5bWse7bbHfd2NzZQljvOUaLZlOdZtfLy0jcAS eOJm QzT9y0yeul0yGwxIlgOuueriT369P1vVvuCGwVAZ/XturZ2i73dY3JQ7xxnB8CVVumY23j4m HIbBE/njiJvnOEzfDSOy1LkdgiRPB5Va5BPerc8ARcAS2hoDPa1vzaLc97utubKYscZynRLMty7Fu4 OlbQSWyB9P3CSfzZW4 fv3b/jz50/4/ft3 PXrV/j582f48eNH P79e/j27Vv4 vVr PLlS/j8 XP49OlT PjxYzidTuHDhw/h/fv34d27d Ht27fhzZs34fXr1 HVq1fh5cuX4cWLF H58 fh2bNn OXEQDb/7xg4B5wDzgHngHPAOeAccA44B5wDzgHngHNg3RzwxM3MiZspvny4TwYkofzfchGASdP/bQ8B9 v2fOoWOQJPHQGf154OA9zX8/jacZ4HZ jFsZ4P6y32tET eOImMW2uEzd9SZcpyj1xs zpY4kTwbIRW4d27td1 Mm1dAQcgeEI Lw2HKu113Rfz NBx3kenKEXx3o rLfY0xL544mbxDRP3GxxyC3TpiVOBMtEal1auV/X5S/X1hFwBPoR8HmtH6Ot1HBfz NJx3kenKEXx3o rLfY0xL544mbxDRP3Cx3yJ0Ou7A7nCZTcGp5YxVb4kQw1gavXyPgfq0x8TeOgCOwbgR8Xlu3/8Zo774eg9b5dR3n87Eb29KxHouY1 cILJE/O1DK//8Pv9CXf0zpv// C1P953LnuN/WR6VO4bDbhenyNlPL40N82P0SJ4JhmnutFgKP6dfHTkYWXP6F434fjv/Km3L3EGVFut diwD4Zco59lw9vN0SEXjMeW2JeGxZJ/f1PN51nOfBGXpxrOfDeos9LZE/fuImMe3xTtzchuvdLlzd3AWZ1Invd7td2O2uws3dvVlet7vHJNSSBtC/4z6AHXsdzf07hj3aBzZ2BA6nQ9jtj4HiQJIVcanbyHIjgBwjr6Gf7Efp0WgHflniRLAkvmhdCGv NH3ofuXAI1pksTGho7gEHGO/achmHetpZfiUdeR/4jnEa7U2c0QlKs64GKIQwd IG9RLKxuAfxurxsA/HI2BM2D1EmQTBxOlCTFEm44k0tzF3CZ4QBlFfKdPmLNQ0eRtCqHGXOAx5qmWMSdx01LXmVQFYsk nzfojCXudREdDzmuSwWvsE9401nzisedA7VtMcwsbjbtc9fh4VsBV3rn29YlMeUXWDq2oudJync 4 cj o5wbGeDustSlojfzxxk5j4GImbu5ursNtdh5ubK5W4uQs3V7twfZuSNbfXWO/2Pj53t4vlyzlxE0 37I8n/IsuD15DoLKUksENnwxywDUQwOR2sLljG7444MpEjc s3Jpkhstr6NfUo9EuKeQLieUZ6x1h2cEf5mszcYHB5CEc9oxDyDvGMwwYynM3h0CXUi odqC99GtDd6NtoIBHbfAEXy2I0jvT/kb9S4sQJ6UrykTbOj7a BBlZMjUmII8xi8515BvjblLz2P4THPUKRyYzJbP0O97SHhT22hoJ 6Ew4BrLaMjGWPK6qiLY00l2C1 mDL95ZIREPNa77hoz5Fo51ljNfIu7wVCCo6dY5NSR/h6UslPSVjHHMkgcJwZGBfd9q pjvVFAG 88Tr544mbRMvHSNzQCRtIxIiTM3c34erqJtylRM39vUrksASOaJfeLydxQ2Neb7owI6OCEmuxU8 EyiaOr2AD21MU2PXW4PBFwQWNLv6TIyHa kJADh14N/qimGIyyoLj4S7VFX/FgmHOC31cdhL0Iouu6tl9V/yi2foeB uHQ7gN1j6fT9Ak1mQQA RBAgY5UH2yO/cbTRizAAp06ZSc78QQNyNqH/Z5kxmsMppLsGU/coM3Z59NjKhgwcIzXPKx5QnLrulRCCeuYsOTf79WVdCknyAxuD/Td4cT5oviRVYs4V/FyTtxwjnF pBM3vGEX57reZx38Zk4E7HktacDHBd4b/KPjslnpc8Zq3aY1fnJXfjMKgaavR0l6ypU75kgGiePMwJjw1poTHOsJAd64qLXwxxM3iYiLStzA CZvrW/HRqNtrdgJnA4mbeoDUGzM8hZADM2PG4MmVtIEUQSUPEqB5Di4MWVSegvNB pEYpseQdr6QEHBDrwY3RNMYcJa/xvKPlNRtMeBHP6uyHg51tku62H5VfaS6yJPMTwruox3lNXzciAIh ZcBzbM6cQNBM7WNOvBkj2zfkk3BPMmKBkj9uTOgr1bQP12ZTNxwn4M l2PKrYonoiIGEjuoxXzM5oLYXvk0C43vOWdzET9pyANj phJJkjya35OGGS/D/Ud8aP4RvKJaxbrsi6TmcZHWlUlKVPbTxykaxRbY8118fs5ELDntdSz4jv62JpblaJy/gAuAPciJzJthGw2xlCWflYd ONZCDR9fZbEp9iI5lP6A0eZVwkNx5mQmPKq15Qo27GeEuMty1oPfzxxk3i4pMRNdQLn/j4MfQeneNZw4sbakMOmrwQyfRszVY6bPLZAYsDDP3 v6lfzjyzv148EjG/nCwlhN/QqMc6t0Odpc5R3 3R6hBINdtsYYOzE9yfF4LzFoXRqAE6xGAlF2692/zwRgKddkjw BmRwk62ON8hvslGfZoh9Skj28pfZRFDUkq2CqVS1qZsSN9sjtwnwuRDTorf0Yd/ckLmFp53iCaXsiy7Ols7wjvMA5SVbBO6KA1EE Is4PNR3NV 6k9xGXegY7GJjQuiZbEM7CAjuq1RuXkwbzZr 8oEQsOc16EyOC o 85/xgcrylft/0FiNfZWTZTDvE8 zVL 5EIFuX18o Ak3x7kwJ9MjEI7zhIToWVMd6wmx3qKoFfLHEzeJiEtK3Nz7iZv00Y3ujRkPZtCFfCOIL9SmEgOA 4fLq4EzJS7zRegxp5wtJAm/wxcaeNy bo1iX4sM6uJDBbGyXeNHkUKNdUsT2a5fuRR7qQAqDDhjwaDvoNAX9FQ u0yVuIg6W7KJnhTfpzAse9b7oOgWmZMo5Y5za0nd5WVBFzIsPS5uYiCtJ7GgXPFd2MQ7E9pw3BQ 8uW8jAAt4m1cw85C3h3qgLr1X9ug VXFT1eS8RF4uLvJbfz4WAPa8lf4rkjORb9GPXmlvqCq5kXmiexecyJhLnUvN5zQAAG8JJREFUPH kzKQ26fD1pJ09OWOE6me44ExLTXuOcI9dUx3pajLcsbS388cRNYuHiEjfVd9zUvyxlncJZy4kbc eIAfSA3amIzp2YKHUjF5vArL3yTKDd6o XBBlIERVI/6NPUY0A7X0iUQ3sfpS/N6jkxFzdJ8i zKQjcH8MJfuFMBBtMdpZBPZQy5E9Xu1Td9muRQVLpCvyBQASuJbAH/ffheAI sw2I5hXqWsqRi1lI7DM/Vh xoaAntW/KrjedoH9rPJF9j3GdElPQH3EVflfYoZE13tl2jW0uQBDVnFUKyY78Jsk5An/JsYoDsW7iD36vyFDfGfpDf9pu7MDmsx4fFj8ER7tw0e9NGzMqfjMDAta8Zo0LzQFK8olkC9OXOA 5XonT iKOe/8zTPTa/WRd OxIBy9cjRXh1jUC1r9A/ZKAb PPZCDjWZ0PnDe092RLnRE/cJLYuKnGjv4wYTuCIRE75danVfjlx lWpvGETG3YjkEA/xfeDAgqUR4mcc SpTaGhn61Hq10k2xIngmVP2kawCAt0Jg dRimJjGKPaiv8SBM1BQ5W3Y7TOLhBoHYtvyqZRTHICoTdfi8TNBSgwC8KMft0ohMDJ5bYwedcP/aZH61EC8eB31OyIstWfE7610EaN2yee7RZJxcmwzRieO4YjwhE7HLgOpizMWGU26GwpA98/Co7Nr3Lz2kcZEyG q7miz5Bwz2Kvs/8gJK6H4sfkqMKG5ABeje5yLXw 7kQkOtVY1wo39EXnjN6SpXHjFWaF4/sm46xP1rjpWh/Og8B6evzZDz1VqeD5KS1TjnOE7FkwJrqWE E9RbFrJQ/nrhJZFxW4uY 3MMvS VfhLkO9FPg9EtUcF3 iZu4OdenH3JAkoLfWM4WO3xvBOG4UeNH6NM97QyFPBZUTyKP6TdKD9Yucc0XkqErQIs/LJBN3yXC9/Slh1gvcy4nJgqPeBkFG8RZohbIww1YHpP8 5hib9KvLd1Ju1QnB9rxfQyMGX9Tdd7//ngMBxY8y6C4DsRRpjRGnCjrlh115E1RHTbWBH5k2gxX1FlhR0kEnXAZjem5Y5zhAhyS2AzlrJW4 KUnGkrgBkBXPBB5DfVfzpZW4gV4JTxon0k6mK8NAclTW4d9X0s3FGUjlXVQIiHmtZ1xw39X816L HzH967MT5u5qXdBf PAoB4etRLb1yRkCPETEnx1qOc0brwhs9L/h 0JAn1jzdfLHEzeJpo ZuOHJmCnuwZY1/6uCzAuNmVreheoEX7QvRXCZ7d2vy/SLa UIOALnI Dz2vnYra2l 3oejznO8 AMvTjW82G9xZ6WyB9P3CSmeeJmKUPO OvvRapNLe8iZbDxEieCy61yCe5X54Aj4AhsDQGf17bm0W573Nfd2ExZ4jhPiWZblmPdxsdL2wgs kT euEk 88RNm7xeOh0CS5wIprPu6Upyvz5d37vljsBWEfB5bauere1yX9eYPMQbx/khULVlOtY2Lv52GAJL5I8nbpLvPHEzjMRe63IEljgRXG6VS3C/OgccAUdgawj4vLY1j3bb477uxmbKEsd5SjTbshzrNj5e2kZgifzxxE3y2VyJm79//4Y/f/6E379/h1 /foWfP3 GHz9 hO/fv4dv376Fr1 /hi9fvoTPnz HT58 hY8fP4bT6RQ fPgQ3r9/H969exfevn0b3rx5E16/fh1evXoVXr58GV68eBGeP38enj17FsAWIJv/dwycA84B54BzwDngHHAOOAecA84B54BzwDmwbg544mbmxM0UXz7cJwMSN/5vuQjApOn/toeA 3V7PnWLHIGnjoDPa0 HAe7reXztOM DM/TiWM H9RZ7WiJ/PHGTmDbXiZu pMsU5Z64Wfb0scSJYNmIrUM79 s6/ORaOgKOwHAEfF4bjtXaa7qv5/Gg4zwPztCLYz0f1lvsaYn88cRNYponbrY45JZp0xIngmUitS6t3K/r8pdr6wg4Av0I LzWj9FWariv5/Gk4zwPztCLYz0f1lvsaYn88cRNYponbpY75E6HXdgdTpMpOLW8sYotcSIYa4PXrxFwv9aY BtHwBFYNwI r63bf2O0d1 PQev8uo7z diNbelYj0XM63MElsgfT9wkD3nihlN1SfencNjtwnR5m6nljcdqiRPBeCu8hUbgMf362MnIgsW/cNzvw/FfeVPuHqKsSPe7cxEAv0w5x56rh7dbIgKPOa8tEY 16TRmbXBfz Ndx3kenKEXx3o rLfY0xL544mbxLTHS9zchuvdLlzd3AX5/Tbx/W63C7vdVbi5u8/ldzdXIb6Hsutwe1/KQAbYsqR//4571Hevo7l/x7BH 8COjsDhdAi7/TFQHEiyyH6d0JHlRgA5Rl5DP9mP0r3RDvyyxIlgSXzRuhDWmj/0PnLhEKwzWbhp3e2CaAscYLwTZSEEKZdxqKed5VeSxfvAd4zTaG/ijMlnXVcDFEIYszk3mo9 hTYIZWPwD2P1eNiH4xEwJuweokyq/BCYku IK9LcxtwleEIYRH2lTJuzUNPkLXFTKCJxGPLU5bthYjuSPNa8qgSiTQO4PMQGrzMfAnpea3G4VV Y0TvMBm4N3OzkW5uDKFH3QOFVUL6Yad/X4S O9Y2xgH6wDq73RTX6l27fs1r7OQvxmHAKNNQAEOc56n6X20AptOa/wusZcwsaKY62A3NBjNydqI7vrrpM/nrhJPn6MxE1MwFyHm5srlbi5CzdXu3B9mxIyt9csQXMbrq9vZRKHPS8rcRNPt yPJ/yLLg9eQ6CylJLBhU4GOeAa2GTkdhDcss1NHIxlw4fPrLwevmPkNfRr6tFolxTyRdvyjPWOsOzgD/O13pyiNAwmD GwZxxC3jGeYcKkPHdzCHQp9YJqB/1JvzZ0N9oG2uipTYfgvwVRemfa36h/aRHipHRFmWhbx0cbH6KMDJkaU5DH CXnGvKtMXfpeQyfaY46hQOT2fIZ n0PCW9qGw3txJ1wGHCtZXQkY0xZHXVxrKkEu8UPU6a/XDIC1bzWyeGh/I4cyut6SJvn1fElrgmHw37UR7nr8df2vp4nLm3f6k36ulXTyzoRaK4BsdWTx7m5vipkm3U71iMm 4sljzbDYzG2TE8rKZt118scTN8nHj5G4oRM2kMARJ27ubsLV1U24yydpVCInv78P91Xd5Z24CWl jVjZqmJFRQYk1gFSwrMajDJ576mLbnjowwCk4FwEXNLb0SwqNbOcLiXZk33PEXvBHNcHNLAsoir 9UW/QVD4Y5J/h91UHYiyC6rmv7VfWPYut3GKgfDu0 UPd4Og1OgfA4R27uQT4kmUBHqg82x37jCRKWhAKdOmUnO/EEDcjah/2eZMZr9EuSPeOJG7Q5 3x6TAUDBo7xmoc1T0huXZdKKMEcE5b8 73qoI37FPxhcHug7w4nzhfFj6xa7I9zD4ty4kbrU5KmkqMNznVyMSvhNzMiYM9rUYEWh7vL6rGq 6w7mCqiBa7Wck/KYaZRVfYj5UY8lA3DiPPKVjzuGTZ5/YTyNGBusu6Jnd/vu TvOJRkPOs3HBzAbby1fM5X8toGA5nL8I6WcTx1nBSBfX1VR9SjqxjHB6azrO9YakQ0 C0702CfqrpM/nrhJPl5U4gZO2KhTNLfX7AQOT9wYdZf2USkKonngXS9ucQDxOrghy4GZMRhxU5Y2TGkwiqBSz a00TJE4Ssmb5B JGdkO19ICLihV4MbomkMODl30H/o/7otboQxwFVlPRzqbJd0sf2q kh1i37wgoL7aEemLeMV1mFjQfOzbO5BXuyzBPH0XJI9sr38S3lVhsGHDEyk/skovEBfcpNaSqctQ5s1JgW8dELqfEyL3hQcRgwkPlAr4ov8Ez6DMtV/Fhrfc87mohRcYZnYZKTj5dnG5Nf8TMfPyVexj8KD2EPtuySHktapfx7sFd1iXdZlLAK7O31BVfh JLG0/cZCuTFcmt jhd3MhYM9r0Lv2IdeoVcbGCzbRzzrZ0OIE9FPmG8ntVpnuI40BRmyUJRKh3L7YPo7fWv YSKFxKNtJHWOZnL9lfV1Wt2/ho 3Uz7Jtt6 lTv7UQGDAGuA4K/wqzFQ5fxR107gVCVJeWZ ElmX tBEEBCd6bBJ118mfHUwg/v9/ L0wdAIGrv/9999k/7lc616fuNHP0MZ6h6dtVvAdNyKwSWMKNx9qQw4blBLIGJshMR5VOQ7GsoGjv0CVfZiqL2TBgyzv 148EjG/nizZhN/QqMc6t0OfpL63F0en0CG2a7ba4GYbFnnOwl0Np06vbJYVsv9r9x78SJx0hOE968DFQb9Cz5cpGv RmPfUpI1HF sXgxuXCLyQLCLwZgXFasouQpEY/yyG2aANNig/Rh39yQuYWbyXhCKePXxdnSGd5xHqC8xA/BCeEnEsCD1aG q/nSnTQ36kLXYBcbS0LPpBraQUBwX5Hq1tW00aro7x4KgWpea3G4VZYVjByi746yTgAKruR26YZzg t9DMedhq0wnJ3Vd7IqPJaUE1i/7jWpOwPKSKOete8cGr6z1xOm5Z 5Vtmgs9TPvrvI1L/T7wQggxiyZAH9QpKkPhDjOHEq5vvKS r5dF8eWSrY61jWK23rT5oS0tV13LfzxEzfJq2s7cQOJnJ34OFX5guJNnLhRGyM5 FKgygIFEQhjZTVAR8qrNmIqsUP64ALN9BjSzhcSQm/oVfnSaFYm3Fi3bJJ0WxnMxnZpA14Fk7xto13Sx/Yrl8EVL/JQB1I4Bx7aDjpNwT8SQMmVyxM3EQdLdtGTay905gWPel90FfqdiSmZcs4Yp7Z0KoHcW96TP4sPe Rn0WZLY0S54ruxSm1RKQMf Ch5ctpCBBTXXRADMG6d5sLInYxwr130ojqr6vAtsm4OeAR9Z4Y39fnIE7HktdhN9ZXO4u8yYE4E P7OQMjjlGsm5OAMdLAkW2a5UZfGyOJQkr6sPW/ZjsLnpg7ZS8gcRUGctp3DPboK7UW/aly7Bv1b4bn1q2KS Nt5avpVb NByBeh52nAt6yEc lkpRdddf17GuQNv4i35OFAD6666DP564ST5dXOJGJGXgO27KL0uZp2/Yx6fWkLjpSrTQfsTanNDwMwdflZiRm8PR8nAjyTekdXBj6jGgnS/a5MmhV lLs1X2f5x45V9zU0Jifwwn IUzsUlgsrMM6qGUIX 62qXqtl LDJJKV APbOjhSryPgf4 HE9H X03mleoa EncjELiX3mR uvtFwevwflhOx6IYtVev7qS0bOfJ0SU1AdcRV x5fqu2RqvLPZGttcQDirYC Vkx25epJzBP6SY4WfqCb4i2QO9Z2hP/Sn7cYubD7r8WHNt4KjXbjo96aNZKtf50DAntdSz gf4pvSprPM4pDkapMrnBN4byWcaXx1lNHYHjyWuG1Rf2uN4Qma0kLa1js2SkO8E1j0zeXQguOj7 dTParw1fa308seBCCiMoZXjHLFDbpvrTI3toLrGnONY11hu5c0gTiRjB9VdCX88cZOcuqjEzb36 MmL4HhtK5MCXEavvv9Efv1pF4kZ/94NY3IxAAv2UNkzmRK82gyiPNpTnyJObLZloaunRahfJ5gtJGnSDL8q30A4mWNp002a2 ospVpS/aCZ4RptcSpyofjiHmu2iIbZflUxuM8jc72WChk52wS8KMfsk/1JCgdmLi1KuH/vMj4QPf8Ht4fe0sc yFZ T/jpQ52bNdW8uxJNhGjG0kxcKE4VfsT/Wy8HcYM5G/ Z2KDDpA38Zz35M7/Jz mt nh Vnkmx2nc1X7pP3KQ Mj9AaN1P3Yf y7/CBmSA3gpL9LHoq6Drd/MgIOa1FodbZULVyDfBb/Q7rdeKKy1OQBnjv imVUbzXG7bN5aYZKUrlSDnTa7K8dU/NkhivMq5XY9xNEQkkvWY0e3Fs8JW Fqq4U9nIaDnuSjEcU7jLa9VHFw9Frvrng5lzgAJyG0l07Hm2G7lvpsTdOq4rAvdddfKH0/cJB4vK3GTfi0qHxe/Drd0ogZ/Gpz/FQnuy2kcSOIsJ3ETFy39l6m8YYONXraRTcD4vpwmyFMNbjK07SyQEfIoGKfg/FJ5TL9RerB2yRBfSLJHe25a/EmTscUfITXWy5yjxT23k8fY418rC8fyvr6vXfVXtJbupGCqozYaMQBg/E3V44Y86rY/HsOBBQliM258nAVlSmOMzb4lO rIm6I6bKxxbMmyOa7WJo2SCDrhMhrTc8c4w0V/RCJvKDL36rmBcAPbKlxJtnCG4png0lDfxTEixXaduIkaEp40t3fqyj4mIjlK8zKNtYIF1ksYaZ4 TPn6dDwG5XrXm3VYZ11fXixzg/NNcaXGClyEf2RjoLeOdpgQkcVrPIWSB1o3e09yDIvX8wfuhcdwaG0Vo/TGqrvYdY0braz6nttLXTAm/HY4A80 9BkQxTx5nPT5oTcRxkuYHGjOturqMjX1y2JPHmoDY0lX7/YnxxxM3icyPmbjRJ2YufV5O4ua8maIKMs8Tk1tNLS8LPvPGF5IzgVt4M/frwh3k6jkCjsBoBJY8r HaroI1SExAIrFVNhqEJ9Jgyb7ekgsc5/m86VjPh/UWe1oifzxxk5jmiZulDLmYbadk VaTS3vco2WOBFcbpVLcL86BxwBR2BrCCx5XtOnR hUGyRuWmVb89FU9izZ11PZuAQ5jvN8XnCs58N6iz0tkT euElM88TNFofcMm1a4kSwTKTWpZX7dV3 cm0dAUegH4Flz2v1x67Kx/ZaZf12P8Uay/b1djziOM/nS8d6Pqy32NMS eOJm8Q0T9xsccgt06YlTgTLRGpdWrlf1 Uv19YRcAT6EfB5rR jrdRwX8/jScd5HpyhF8d6Pqy32NMS eOJm8S0uRI3f// DX/ /Am/f/8Ov379Cj9//gw/fvwI379/D9 fQtfv34NX758CZ8/fw6fPn0KHz9 DKfTKXz48CG8f/8 vHv3Lrx9 za8efMmvH79Orx69Sq8fPkyvHjxIjx//jw8e/YMv5wYyOb/HQPngHPAOeAccA44B5wDzgHngHPAOeAccA6smwOeuJk5cXPpFw8PaQ9JKP 3XARg0vR/20PA/bo9n7pFjsBTR8DntafDAPf1PL52nOfBGXpxrOfDeos9LZE/nrhJTJvrxM2QxMuldTxxs zpY4kTwbIRW4d27td1 Mm1dAQcgeEI Lw2HKu113Rfz NBx3kenKEXx3o rLfY0xL544mbxDRP3GxxyC3TpiVOBMtEal1auV/X5S/X1hFwBPoR8HmtH6Ot1HBfz NJx3kenKEXx3o rLfY0xL544mbxDRP3Cx3yNU/63mZrlPLG6vNEieCsTZ4/RoB92uNib9xBByBdSPg89q6/TdGe/f1GLTOr s4n4/d2JaO9VjEvD5HYIn88cRN8pAnbjhVl3R/CofdLhxOU k0tbzxei1xIhhvhbfQCDymXx87GVmwgJ/g3Yfjv/Km3D1EWZHud ciEH82ebo59lw9vN0SEXjMeW2JeGxZJ/f1PN51nOfBGXpxrOfDeos9LZE/nrhJTHu8xM1tuN7twtXNXZDfbRPf73a7sNtdhZu7 1x ew3v0v rm3B3X8pABtiypH//jnvUd6 juX/HsCc7upIzp0PY7Y B4kCSRfbrYEOWGwHkGHkN/WQ/KrHUaAd WeJEsCS aF0Ia80feh 5cAhWbg8TGrtdEG2BA4x3oiyEIOUyDvW0s/xKsngf I5xGu1NnDH5rOtqgEIIcydu0AahbAz YaweD/twPALGhN1DlEkQHgJT8h1xRZrbmLsETwiDqK UaXMWapq8JW4KRSQOQ566fDdMbEeSx5pXlUC0aQCXh9jgdeZDQM9rQzjcxV uteRhmiPYvLzbdY8PLsfvp0NA 3o6yU9LUt8YcZyn44NjPR2WT1HSGvnjiZvE1MdI3NzdXIXd7jrc3FypxM1duLnahevblJC5vcZ6 t5iguQs3N7cyiXNdnpeVuImnW/bHUzjuVfAcqCylZDDYkUEOuAY2gDnoheCWbfzjgCubO3xm5dYkNFxeQ7 mHo12SSFftC3PWO8Iyw7 MF9joKACxYDB5CEcBPdAJuMZJkzKczeH2u1Ae nXhu6qT7Scgn1lg CrBVF6Z9rfqH9pEeKkdEWZaNsu7OYqI0OmxhTkMX7JuYZ8a8xdeh7DZ5qjTuHAZLZ8hn7fQ8Kb2 kZDO3EnHAZcaxkdyRhTVkddHGsqwW5xwJTpL5eMQDWv9XEYuaDn3dpCycPIq7zWU/LSOVQD94BvpK8fsKNNi 6f5x3nqQjgWE F5NOUs07 eOImsfUxEjd0wgYSOOLEzd1NuBInaVQih5 wgaTOYhM3NBXUmzIMqkVQYgUEKlgmcXQVwVpPXWzTU4fLEwEXNLb0S4qMbOeLNjlw6NXgj2qKQQ ALKIq/VFv0FQ GOSf4fdVB2Au 1nVtv6r UWz9DgP1w6HdB peTtvxmEYmAUA JKNAR6oPNsd 4wmSkqyKKnWdIEl24gkakLUP z3JjNcYbCXZM564QZuzz6fHVDBg4BiveVjzhOTWdamEEtYxYcmTYDLYhfrcp APg9sDfXc4cb4ofmTVYn ce1iUEzdan5J4lxzF40P2icsGz7MafjMbAva8FruvOUz8iFeeiNEKSy4b9TOndEt/figEWr5 qD63LrceI/qPPFtHYD77HOv5sN5iT2vhjyduEvsWlbgxkjHw8ah8Aoclbqz3S/uoFAUXfBNXD5AzNm48uZICKxFU6uiibyPI5A3Sj2auke18c0TADb0a3BBNY8BZ8Qv9X7fFABIDX FXWw6HOdkkX26 qj1QX ZX5ScF9tKO8ho8bUSAu/zKg SmD4thnaUvP5SN9sn1LNgXzpEc0QOrPnQF9tYL 6crQ5py4SR9xK Clk1XnY8qt4olmiR3UYj5mc0Fsr/rPQuN7ztlcxE8a8oQR5jr27DRT8mu2mT7mR74a6jviR/GN5BPXLNZlXSYzjY 0qkpSprafOEjXKLbGmuvi93MgYM9r0LP2IR DbEx0KCnnEF0/cVJxqEOUv54IgW5fT9TBkxNTjxGAwHF CCI41g B6tORuR7 eOImsXJJiZvqBM79fRDv8KNT8a/dVjJnnYkb itz/jYb4 NVfApRGz0MmErgEdJfbcu T9XnovBellsBAwQedaA1vp0v2hX4PS8kxrky jyd/iiOhuiBnVyx22IQCadRWOAfg/MWh8p3j4h2SSHbr3b/PBGA iY9OMdkYJOtjjfCxqRXxiD2mR rgB8zAywp1JIdFzMuC2o3dVPiZnvkSRPA50JMi97Sh31zQ YWnnaKJ5Qyfl2cLZ3hHecByku2CNwVB6II8BdxeKjvar4gP/nYyPoZdaEM7GL1hZ6pLdpBQHBfZdnGjWmjUc9fPRgC1bzWxWHhKzlmLOUkR2J9 j4puNZrrSXF302JQOXrKYU/JVldYyRh4DhPSAbHekIwn6CoFfLn/79pKBq5GEXVAAAAAElFTkSuQmCC

Paul_Hossler
11-10-2020, 04:03 PM
So below you will see row 92 column F is 3.75. Column G is 3. I would need to replace 3.75 with 3.

Is that possible and easy to do in a single module?

Should I start a new thread for this?


1. Don't see any 'below'

2. Probably

3. No really needed

joeny0706
11-11-2020, 08:37 AM
I currently have an xlsm file with 8 modules I run. If I had another that just did this task I could add it in and that would be great.


below you will see row 92 column F is 3.75. Column G is 3. I would need to replace 3.75 with 3. I would need this to do the same for all rows where column F is larger than column G

Paul_Hossler
11-11-2020, 08:50 AM
You'll have to customize this, sheet names, etc.




Sub SaleDiscount()
Dim i As Long

For i = 2 To ActiveSheet.Cells(1, 1).CurrentRegion.Rows.Count
If ActiveSheet.Cells(i, 6).Value > ActiveSheet.Cells(i, 7).Value Then
ActiveSheet.Cells(i, 6).Value = ActiveSheet.Cells(i, 7).Value
End If
Next i




End Sub

joeny0706
11-11-2020, 09:21 AM
Thanks Paul


That works perfect how you have it.

I do have an issue though that I did not think about. I have a module that adds the price per item in column G it then calculates the discount. The issue is I need it change the price like your module does but it needs to do it after it adds the price to column G and before it calculates the discount. I tried to add it in the middle but I dont have much knowledge with VBA.

I will keep trying but if you have time to take a look I would appreciate it.

Thanks

I discount module is attached.

joeny0706
11-11-2020, 09:50 AM
I think I go it

Can you takee a quick look
I input your code. It is bold



Option Explicit

Sub AddDiscount()
Dim wbItem As Workbook
Dim wsInput As Worksheet
Dim rData As Range, rData1 As Range, rLast As Range, rTemp As Range
Dim iRow As Long, iItem As Long, i As Long
Dim dDiscount As Double
Dim vItems As Variant, vPrices As Variant

Application.ScreenUpdating = False

'get normal prices
Workbooks.Add "C:\FlexibakeConversions\Flexitem prices.xlsx" ' <<<<<<<<<<<<< change WB path
Set wbItem = ActiveWorkbook

Set rTemp = wbItem.Worksheets("Sheet1").Range("C1")
Set rTemp = Range(rTemp, rTemp.End(xlDown))
vItems = Application.WorksheetFunction.Transpose(rTemp)
Set rTemp = wbItem.Worksheets("Sheet1").Range("E1")
Set rTemp = Range(rTemp, rTemp.End(xlDown))
vPrices = Application.WorksheetFunction.Transpose(rTemp)

wbItem.Close False

'set data
Set wsInput = Worksheets("Input") ' <<<<< Change WS name
Set rLast = wsInput.Cells(1, wsInput.Columns.Count).End(xlToLeft)
Set rData = Range(wsInput.Cells(1, 1), rLast).EntireColumn
Set rData = Intersect(rData, wsInput.Cells(1, 1).CurrentRegion.EntireRow)
Set rData1 = rData.Cells(2, 1).Resize(rData.Rows.Count - 1, rData.Columns.Count)

'add Normal Prices
With wsInput
.Cells(1, 8).Value = "Discount Price"
.Cells(1, 9).Value = "line class"
For iRow = 2 To rData.Rows.Count
iItem = 0
On Error Resume Next
iItem = Application.WorksheetFunction.Match(.Cells(iRow, 4).Value, vItems, 0)
On Error GoTo 0

If iItem > 0 Then .Cells(iRow, 7).Value = vPrices(iItem)
Next iRow
End With

With wsInput

For i = 2 To ActiveSheet.Cells(1, 1).CurrentRegion.Rows.Count
If ActiveSheet.Cells(i, 6).Value > ActiveSheet.Cells(i, 7).Value Then
ActiveSheet.Cells(i, 6).Value = ActiveSheet.Cells(i, 7).Value
End If
Next i

End With

'sort by invoice data and invoice number
With wsInput.Sort
.SortFields.Clear
.SortFields.Add Key:=rData1.Columns(1), SortOn:=xlSortOnValues, Order:=xlAscending, DataOption:=xlSortNormal
.SortFields.Add2 Key:=rData1.Columns(2), SortOn:=xlSortOnValues, Order:=xlAscending, DataOption:=xlSortNormal
.SetRange rData
.Header = xlYes
.MatchCase = False
.Orientation = xlTopToBottom
.SortMethod = xlPinYin
.Apply
End With

'go up and add "20 Sales & Discount" to col D after invoice change
With wsInput
For iRow = rData.Rows.Count To 2 Step -1
If .Cells(iRow + 1, 2).Value <> .Cells(iRow, 2).Value Then
.Rows(iRow + 1).Insert
.Cells(iRow + 1, 4).Value = "20 Sales & Discount"
.Cells(iRow + 1, 9).Value = "2.5 Sales Promotional Discount"
End If
Next iRow
End With

'go down and calc discount and fill in data
dDiscount = 0#
With wsInput
Set rData = .Cells(1, 1).CurrentRegion
For iRow = 2 To rData.Rows.Count
If Len(.Cells(iRow, 1).Value) > 0 Then
dDiscount = dDiscount + .Cells(iRow, 5).Value * (.Cells(iRow, 7).Value - .Cells(iRow, 6).Value)

Else
.Cells(iRow, 1).Value = .Cells(iRow - 1, 1).Value
.Cells(iRow, 2).Value = .Cells(iRow - 1, 2).Value
.Cells(iRow, 3).Value = .Cells(iRow - 1, 3).Value
.Cells(iRow, 8).Value = dDiscount
dDiscount = 0#
End If
Next iRow
End With

'cleanup
Application.ScreenUpdating = True
End Sub

Paul_Hossler
11-11-2020, 10:20 AM
I think you'd want to remove the 'ActiveSheet.' from within the For/Next loop so that .Cells(1,1).... would be applied to wsInput nd NOT whatever the active sheet happens to be






With wsInput
For i = 2 To .Cells(1, 1).CurrentRegion.Rows.Count
If .Cells(i, 6).Value > .Cells(i, 7).Value Then
.Cells(i, 6).Value = .Cells(i, 7).Value
End If
Next i
End With