From marbles-isi@mailman.isi.edu Thu Nov 1 01:21:29 2001 From: marbles-isi@mailman.isi.edu (Min Cai) Date: Wed, 31 Oct 2001 17:21:29 -0800 Subject: [Marbles-isi] new verison of Marbles2 Solver References: <200110310429.f9V4T3g04465@tnt.isi.edu> Message-ID: <004d01c16273$891d3b80$b7800980@bugatti> Hi, I just checked in a new version of Marbles2 problemdefinition, problemverifier and solver. Please try it. :-) - Min From marbles-isi@mailman.isi.edu Thu Nov 1 20:45:16 2001 From: marbles-isi@mailman.isi.edu (Pedro Szekely) Date: Thu, 1 Nov 2001 12:45:16 -0800 Subject: [Marbles-isi] Compilation problem in latest checkin Message-ID: <41C31855E83ED31199D300C04F57A0739B0723@hermes.isi.edu> This message is in MIME format. Since your mail reader does not understand this format, some or all of this message may not be legible. ------_=_NextPart_001_01C16316.1C9239E0 Content-Type: text/plain; charset="iso-8859-1" mj1.4: generating: imported: Util Xml Mt M2Pd ........................ C:/jdk1.3/bin/javac -classpath 'D:/code/source' *.java M2SpDependencyGroup.java:17: edu.isi.dce.marbles2.simpleproblem.M2SpDependencyGr oup should be declared abstract; it does not define getTask() in edu.isi.dce.mar bles2.simpleproblem.M2SpDependencyGroup public class M2SpDependencyGroup implements M2PdDependencyGroup { ^ 1 error make[3]: *** [M2SpPossibleFiller.class] Error 1 make[2]: *** [simpleproblem] Error 2 make[1]: *** [freshWithoutCleaning] Error 2 make: *** [freshWithoutCleaning] Error 2 //D/code/source/edu/isi/dce/snap$ ------_=_NextPart_001_01C16316.1C9239E0 Content-Type: text/html; charset="iso-8859-1"
mj1.4: generating: imported: Util Xml Mt M2Pd  ........................
C:/jdk1.3/bin/javac -classpath 'D:/code/source'  *.java
M2SpDependencyGroup.java:17: edu.isi.dce.marbles2.simpleproblem.M2SpDependencyGr
oup should be declared abstract; it does not define getTask() in edu.isi.dce.mar
bles2.simpleproblem.M2SpDependencyGroup
public class M2SpDependencyGroup implements M2PdDependencyGroup {
       ^
1 error
make[3]: *** [M2SpPossibleFiller.class] Error 1
make[2]: *** [simpleproblem] Error 2
make[1]: *** [freshWithoutCleaning] Error 2
make: *** [freshWithoutCleaning] Error 2
//D/code/source/edu/isi/dce/snap$
------_=_NextPart_001_01C16316.1C9239E0-- From marbles-isi@mailman.isi.edu Thu Nov 1 21:18:09 2001 From: marbles-isi@mailman.isi.edu (Min Cai) Date: Thu, 1 Nov 2001 13:18:09 -0800 Subject: [Marbles-isi] Compilation problem in latest checkin References: <41C31855E83ED31199D300C04F57A0739B0723@hermes.isi.edu> Message-ID: <001901c1631a$b58a6510$85b00980@bugatti> This is a multi-part message in MIME format. ------=_NextPart_000_0016_01C162D7.A6C39210 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Sorry, I forgot to check in some new files. Please re-check out them = again.=20 Thanks, - Min ----- Original Message -----=20 From: Pedro Szekely=20 To: 'marbles-isi@mailman.isi.edu'=20 Sent: Thursday, November 01, 2001 12:45 PM Subject: [Marbles-isi] Compilation problem in latest checkin mj1.4: generating: imported: Util Xml Mt M2Pd = ........................ C:/jdk1.3/bin/javac -classpath 'D:/code/source' *.java M2SpDependencyGroup.java:17: = edu.isi.dce.marbles2.simpleproblem.M2SpDependencyGr oup should be declared abstract; it does not define getTask() in = edu.isi.dce.mar bles2.simpleproblem.M2SpDependencyGroup public class M2SpDependencyGroup implements M2PdDependencyGroup { ^ 1 error make[3]: *** [M2SpPossibleFiller.class] Error 1 make[2]: *** [simpleproblem] Error 2 make[1]: *** [freshWithoutCleaning] Error 2 make: *** [freshWithoutCleaning] Error 2 //D/code/source/edu/isi/dce/snap$ ------=_NextPart_000_0016_01C162D7.A6C39210 Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable
Sorry, I forgot to check = in some new=20 files. Please re-check out them again.
 
Thanks,
 
- Min
 
----- Original Message -----
From:=20 Pedro = Szekely=20
To: 'marbles-isi@mailman.isi.ed= u'=20
Sent: Thursday, November 01, = 2001 12:45=20 PM
Subject: [Marbles-isi] = Compilation=20 problem in latest checkin

mj1.4: generating: imported: Util Xml = Mt=20 M2Pd  ........................
C:/jdk1.3/bin/javac -classpath=20 'D:/code/source'  *.java
M2SpDependencyGroup.java:17:=20 edu.isi.dce.marbles2.simpleproblem.M2SpDependencyGr
oup should be = declared=20 abstract; it does not define getTask() in=20 edu.isi.dce.mar
bles2.simpleproblem.M2SpDependencyGroup
public = class=20 M2SpDependencyGroup implements M2PdDependencyGroup=20 {
       ^
1 error
make[3]: *** = [M2SpPossibleFiller.class] Error 1
make[2]: *** [simpleproblem] = Error=20 2
make[1]: *** [freshWithoutCleaning] Error 2
make: ***=20 [freshWithoutCleaning] Error=20 = 2
//D/code/source/edu/isi/dce/snap$
------=_NextPart_000_0016_01C162D7.A6C39210-- From marbles-isi@mailman.isi.edu Thu Nov 1 21:55:24 2001 From: marbles-isi@mailman.isi.edu (Pedro Szekely) Date: Thu, 1 Nov 2001 13:55:24 -0800 Subject: [Marbles-isi] Compilation problem in latest checkin Message-ID: <41C31855E83ED31199D300C04F57A0739B0725@hermes.isi.edu> This message is in MIME format. Since your mail reader does not understand this format, some or all of this message may not be legible. ------_=_NextPart_001_01C1631F.E8D73B00 Content-Type: text/plain; charset="iso-8859-1" No problem. Pedro -----Original Message----- From: Min Cai [mailto:mcai@ISI.EDU] Sent: Thursday, November 01, 2001 1:18 PM To: marbles-isi@mailman.isi.edu Subject: Re: [Marbles-isi] Compilation problem in latest checkin Sorry, I forgot to check in some new files. Please re-check out them again. Thanks, - Min ----- Original Message ----- From: Pedro Szekely To: 'marbles-isi@mailman.isi.edu' Sent: Thursday, November 01, 2001 12:45 PM Subject: [Marbles-isi] Compilation problem in latest checkin mj1.4: generating: imported: Util Xml Mt M2Pd ........................ C:/jdk1.3/bin/javac -classpath 'D:/code/source' *.java M2SpDependencyGroup.java:17: edu.isi.dce.marbles2.simpleproblem.M2SpDependencyGr oup should be declared abstract; it does not define getTask() in edu.isi.dce.mar bles2.simpleproblem.M2SpDependencyGroup public class M2SpDependencyGroup implements M2PdDependencyGroup { ^ 1 error make[3]: *** [M2SpPossibleFiller.class] Error 1 make[2]: *** [simpleproblem] Error 2 make[1]: *** [freshWithoutCleaning] Error 2 make: *** [freshWithoutCleaning] Error 2 //D/code/source/edu/isi/dce/snap$ ------_=_NextPart_001_01C1631F.E8D73B00 Content-Type: text/html; charset="iso-8859-1"
No problem.
 
Pedro
-----Original Message-----
From: Min Cai [mailto:mcai@ISI.EDU]
Sent: Thursday, November 01, 2001 1:18 PM
To: marbles-isi@mailman.isi.edu
Subject: Re: [Marbles-isi] Compilation problem in latest checkin

Sorry, I forgot to check in some new files. Please re-check out them again.
 
Thanks,
 
- Min
 
----- Original Message -----
From: Pedro Szekely
To: 'marbles-isi@mailman.isi.edu'
Sent: Thursday, November 01, 2001 12:45 PM
Subject: [Marbles-isi] Compilation problem in latest checkin

mj1.4: generating: imported: Util Xml Mt M2Pd  ........................
C:/jdk1.3/bin/javac -classpath 'D:/code/source'  *.java
M2SpDependencyGroup.java:17: edu.isi.dce.marbles2.simpleproblem.M2SpDependencyGr
oup should be declared abstract; it does not define getTask() in edu.isi.dce.mar
bles2.simpleproblem.M2SpDependencyGroup
public class M2SpDependencyGroup implements M2PdDependencyGroup {
       ^
1 error
make[3]: *** [M2SpPossibleFiller.class] Error 1
make[2]: *** [simpleproblem] Error 2
make[1]: *** [freshWithoutCleaning] Error 2
make: *** [freshWithoutCleaning] Error 2
//D/code/source/edu/isi/dce/snap$
------_=_NextPart_001_01C1631F.E8D73B00-- From marbles-isi@mailman.isi.edu Tue Nov 6 19:00:50 2001 From: marbles-isi@mailman.isi.edu (Martin Frank) Date: Tue, 06 Nov 2001 11:00:50 -0800 Subject: [Marbles-isi] scaling results on Min's solver Message-ID: <5.1.0.14.2.20011106104917.00af0e50@tnt.isi.edu> --=====================_11522047==_ Content-Type: text/plain; charset="us-ascii"; format=flowed The attached graph plots the number of tasks in the Marbles2 problem on the X axis, and the running time of Min's centralized Marbles2 solver on the Y axis. I used Seongbin's problem generator to generate problems: they all have 2 requirements per task, and there are twice as many resources as there are tasks in every problem statement. (Thus if the X axis says "1", that means 1 task, 2 resources in the Marbles2 problem statement.) Min's solver should always finds the optimum solution given enough time (I haven't verified if it actually does). Its run-time seems exponential in the number of tasks in the problem (which would be unsurprising given that Marbles2 problems are NP-hard in the number of tasks). What is surprising is the rapid jump in running time from less than 1 second for a problem with 14 tasks to more than 60 seconds for 15 tasks (60 seconds was the maximum given running time for the solver). A "phase transition"? Could also be a weird coincidence in the actual problems being generated (averaging the runnning time over a large number of generated problems of the same size would be the thing to do, will do sometime). Cheers, yours, Martin --=====================_11522047==_ Content-Type: image/png; name="output-20011106184140.time.gnuplot.gen.png"; x-mac-type="504E4766"; x-mac-creator="6D646F73" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="output-20011106184140.time.gnuplot.gen.png" iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgAQAAAAAdquFJAAAAO3RFWHRTb2Z0d2FyZQBnbnVwbG90 IE1TLVdpbmRvd3MgMzIgYml0IHZlcnNpb24gMy43IHBhdGNobGV2ZWwgMKymuYcAAAWOSURBVHic 7Z1PaxtHHIYFhRpKoaeeWppPkFxyaElM/SlUQaHo1kNJCiXkT6vQPZie/QFMrGuxKSkoqQg6LKUH HRLRk5oaZzUJeyjUjjeOUeRmtTvdP1rZ3ZmVZ+c3BUV+X3DWGWmefTS7a+TfrDwVbjgVAAFcTODR Jwdu1ySQt792OkaBN65st0wCw+FX/a1xxUiWYmBw+GVvPRD2NBJa7HxDKOtU4f4oGkOjwKPoKJsE JnkTgVpRAR5yziKXaJyZffu2747YYCXws8e81NRrlgbuO53mp43OofNX8w7Lnu2fBPqKQN/tvH3n z5Vwu8Xsxse+85h90Xxx7WGXn798z3dX+YVL1kt3lXE+VASOnM7g6uDFN/0tZn/3s++2B0498Pud 8NFOq+F4YW/b3gs8m/O/FYE8Aj4fcK+3zuxv2w1ndfAui4Cr4fOddgzc+8Defxa/clVD7rT+uLph e04neskRYsScJ0Gjb2/0di42nN2Nnv9gz9nl6mPI3Yf7G3Xrqdtd+2j51oE7Whsshwf9bvP8k3vR /5oXLj94+Sw7aRXPQ/EEjhu95NwPvZONFCA/l15M5zSA6jljl54qUPnSUwUqXnqvNzc3t8xeeqqG ypeeMhCX3swACCCAAAIIIIAAAgigAAxvSQtBConf2XrTDbcmwEBeqlJIECEeTzehHb3BT0pVr65s t7SA8S87x/nxnc3NH5IK5/W43PeeCcPwrcSwJS33KaRgDP227hiKSYBj0lGWAJPMx4k9/4bSKiYM SwWGdCAM6UDfNFAMDOmBIT0wpIcIFGc7YajR3SxQugsYErPwhp7QAsOygSEdCEM6cP4NmWmgmNH0 dkGThgW3umklNbyhWz9kciC/r1s/FBImtwty7fohE1rmrX7I5MAkZ+tK0QfapoFiYEgPDOmBIT1E oGUaKAaG9MCQnkU3lNx8CsOSgSEdCEM6cP4NJX1gWDIwpANhSAeKU6MToHYhqAAY3tQsVRUZBtrl vgKgr3u7oGiYlvuOLuqW+8SkB8X0GBo/ykmMGuoCJYEhPTSgOHkLQ43eZoHyfcCQmEU3FKdGYVg2 MKQDYUgHzr8hMw2UBIb0wJCeRTe0TQMlgSE9MKTnrBrq30hWANS+1c0qAPJfDNcPQ936oSW0UG8X FPP/jKHxo5zE6FHWBUoCQ3oW3FD2V8BgWC4wpANhSAfKusCwXGBIB8KQDpx/Q8nkLQxLBoZ0IAzp QMnkLQzLdzYLLNgJDIkhFYIKDY1/slW33FdoONb+64JC0nKfL19M5NRIJm8nY2j8E9aanwEvNEwy D1cKDOlAGNKBzDRQFhjSA0N6YEgPCWibBsoCQ3pgSM+CG1qmgbLAkB4Y0gNDeihA6WJnMCwVGJ4A hvJlak9LseHYdO0r1FudpNgwXYzYYP0w1KsfSnukhqbHULNKXGxY/HD5UIAwpANhSAfOv6Fs8haG 5QJDOhCGdKBsahSGpfuaBRbtBYbEwJAeClA2eQvDcoEhHQhDOpCZBkqjBWT/2RCB8Xu4uzMeVwRa 0++GfFitaRoe/4iPl9yNE22eVuv8p5R4t+wSq8fAoJ4Bg2qsFtSjZw9rM4GSQpA75X12vz4B1tL3 wcPPg2p9tmFSqmIn19qNukzW2q1xa1iPSXY/6xfrsZnApNwX7XO6GnC15g6r39uc71YTwvXon/et rN/rdDMDuDWuGMlSBlznOcPUIDacvMjoqy4IyTIZQ54fwyTW9GAnY1gC2FV6bgmgwZxd4JHwwK/5 Bt/Nt3hLuYZw2cqAIyv/bJZvOHTyLb/nOwU7dgZ8JQB/Ewy3BUOhmr6zkgGHAlD4+eP38y1e/m1x eGlqeKgA7J0KDP6xi8dQGDFfaPFu5oG+XXyUhWN6ILR013IN4w+tN+fEBnChgf8CWuLIKziKelIA AAAASUVORK5CYII= --=====================_11522047==_-- From marbles-isi@mailman.isi.edu Tue Nov 6 20:28:59 2001 From: marbles-isi@mailman.isi.edu (Martin Frank) Date: Tue, 06 Nov 2001 12:28:59 -0800 Subject: [Marbles-isi] checked in GNUplot into lib-ext Message-ID: <5.1.0.14.2.20011106122138.00ae2408@tnt.isi.edu> Marbles2 folks - this means you can now for example re-produce the performance graph I emailed earlier (say "make fresh" at the marbles2 level, then say "make onepass" in main). So, there is no need for us to all separatedly install GNU plot. I encourage you (esp. Alejandro and Seongbin) to produce your own graphs of Min's solver performance. One that runs a large number of the same size of problem for example - there seems to be a lot of running time variance for problems generated with the same parameters. Cheers, yours, Martin P.S.: WebScripter and CAMERA folks - FYI - this should have no effect on you (other than epsilon-increased CVS checkout time)... From marbles-isi@mailman.isi.edu Tue Nov 6 20:30:14 2001 From: marbles-isi@mailman.isi.edu (Martin Frank) Date: Tue, 06 Nov 2001 12:30:14 -0800 Subject: [Marbles-isi] Re: In-Reply-To: <200111062027.fA6KR2g21875@tnt.isi.edu> References: Message-ID: <5.1.0.14.2.20011106122919.00b0d7a8@tnt.isi.edu> OK, that is as I intended, semantics-wise. -Martin At 12:27 PM 11/6/2001 -0800, you wrote: > >.. >With this 'changed semantics', I tested the data that I used yesterday and >the message "invalid solution" no longer existed; i.e., even though there was >no task scheduled, it returned a valid solution of value 0. > >Seongbin From marbles-isi@mailman.isi.edu Thu Nov 8 00:54:14 2001 From: marbles-isi@mailman.isi.edu (Martin Frank) Date: Wed, 07 Nov 2001 16:54:14 -0800 Subject: [Marbles-isi] note on writing code dealing with time and on upcoming changes to the Meta-Ja time utilities Message-ID: <5.1.0.14.2.20011107164214.00a8cea8@tnt.isi.edu> There will be two new abstractions in util/time, MtTimePoint and MtAbsoluteDuration. The existing MtDuration does not commit to how long in milliseconds it actually is when you create it ("2 months"), the new MtAbsoluteDuration does. MtTimePoint just wraps the Java "long" we have always used for time points, done so functions don't take two longs (one for an absolute duration and one for a time point) and the compiler subsequently can't catch it if you call the function with the parameters in the wrong order. *** Note to all: a day is NOT 24 hours long, a month is NOT 30 days long, and a minute is NOT 60 seconds long. If your code contains the constants 24 or 60 or 30 it is most likely incorrect. *** I'm adjusting all of the time utility abstractions to that reality (with the exception of TimeOfDay - tough one - "2am" may not exist at all or exist twice when the daylight savings time changes, the abstraction should probably not at all be used for time zones with a non-fixed offset to Greenwich mean time). There will be convenient functions for computing the correct length of a day etc., like in the last line below: MtTimePoint start = getFrom().setTimeIn(new MtTimePoint(startDay),z); MtTimePoint end = getTo().setTimeIn(new MtTimePoint(startDay),z); if(end.getX() Hi, There was a bug in the number of requirement dependencies and it was fixed. Please do "cvs update -d" at under the directory, .../marbles2/problemgen. Seongbin From marbles-isi@mailman.isi.edu Thu Nov 8 02:18:25 2001 From: marbles-isi@mailman.isi.edu (Min Cai) Date: Wed, 7 Nov 2001 18:18:25 -0800 Subject: [Marbles-isi] marbles 2 update References: <200111080126.fA81QFg24799@tnt.isi.edu> Message-ID: <007001c167fb$a5f6ed40$b7800980@bugatti> Hi, There was two bugs in the SimpleProblem and ProblemDefinition packages. I have fixed them and checked in the codes. Please say "cvs update -d" under the directory: ~/marbles2. PS. Alejandro. I also changed the function toResourceNames() of the class M2PdRequirementResourcePairs. Please update your codes. Thanks. - Min ----- Original Message ----- From: "Seongbin Park" To: Sent: Wednesday, November 07, 2001 5:26 PM Subject: [Marbles-isi] marbles 2 problem generator > Hi, > > There was a bug in the number of requirement dependencies and it was fixed. > Please do "cvs update -d" at under the directory, .../marbles2/problemgen. > > Seongbin > > _______________________________________________ > marbles-isi mailing list > marbles-isi@mailman.isi.edu > http://mailman.isi.edu/mailman/listinfo/marbles-isi > From marbles-isi@mailman.isi.edu Thu Nov 8 19:30:39 2001 From: marbles-isi@mailman.isi.edu (Seongbin Park) Date: Thu, 08 Nov 2001 11:30:39 -0800 Subject: [Marbles-isi] Re: two questions In-Reply-To: Your message of "Thu, 08 Nov 2001 11:16:13 PST." <001301c16889$e52a2fc0$83b00980@bugatti> Message-ID: <200111081930.fA8JUeg08770@tnt.isi.edu> Hi Min, Good morning and thanks for the reply -- I was indeed waiting... > > By the way, is it possible to let the solver run until it finds a > solution? > > In other words, instead of specifying time limitation, I would like to let > the > > solver run with the input problem so that how long it takes can be > checked. > I think it depends on the size of the problem. If the problem is big enough, > the > solver can't return the optimal solution. A question -- by 'optimal', you mean the one (or, one of those in case there are more than one optimum solutions) that has the largest value of for the summation of the task values, right? Also, what that means is that as of yet, there's no way by which one can estimate how much time it will take in order to find an (or, 'the') optimum solution. Is that true? Seongbin From marbles-isi@mailman.isi.edu Thu Nov 8 20:34:12 2001 From: marbles-isi@mailman.isi.edu (Martin Frank) Date: Thu, 08 Nov 2001 12:34:12 -0800 Subject: [Marbles-isi] checked in time utilities reform Message-ID: <5.1.0.14.2.20011108123112.00a8f920@tnt.isi.edu> suggest a "make fresh" for SNAP and Marbles2 some hints: use MtDuration when you don't want to commit on the length in milliseconds at creation ("1 month") use MtAbsoluteDuration only for computing the actual number of milliseconds something takes at a given GMT Time Point and a Time Zone - there is no way to recover the more abstract MtDuration from a MtAbsoluteDuration rule of thumb: keep it at the MtDuration level as long as you can Yours, Martin From marbles-isi@mailman.isi.edu Fri Nov 9 20:48:20 2001 From: marbles-isi@mailman.isi.edu (Min Cai) Date: Fri, 9 Nov 2001 12:48:20 -0800 Subject: [Marbles-isi] Re: current algorithm References: <200111091841.fA9IfVg22626@tnt.isi.edu> Message-ID: <001b01c1695f$ddebeb00$b7800980@bugatti> Hi Seongbin, Here is the brief description of the algorithms I used in the solver. I. M2ScSerialCrawler::solve() (1) compute the tasks which can't be satisfied given all the available resources, and delete them. (2) sort the tasks in non-decreasing order in term of their value. (3) call the recursive function solveTasks() II. M2ScSerialCrawler::solveTasks() assuming the length of input tasks is n, (1) if n=1, call the function solveOneTask to compute the only task. (2) if n>1, partition the tasks into two parts, the first n-1 tasks and the last task. (3) recurisively call the solveTasks() to solve the first n-1 task, and call the solveOneTask() to solve the last task, let V be the total values of the solved tasks. (4) rotate the n tasks and do (2) (3). Let V' be new total values of this solved tasks. (5) if V'>V, save the corresponding solution to be our solution. III. M2ScSerialCrawler::solveOneTask() (1) get all the dependencygroups of the input task (2) call the function M2PvProblemVerifier::startTimesForDependencyGroups() to caculate the possible start time intervals of the anchor segment, and get the assigned RequirementResourcePairs. (3) if the start time intervals is empty, return NULL M2ScFilledTask. (4) else, choose the begining time of anchor segment from the possible intervals. (5) for each requirement, a. create the activites the requirement needs for its segment. b. book the corresponding resource for all the activities according to the RequirementResourcePair (5) return the M2ScFilledTask If you have any comment about this algorithm, please let me know. Thanks. - Min ----- Original Message ----- From: "Seongbin Park" To: Cc: ; Sent: Friday, November 09, 2001 10:41 AM Subject: current algorithm > Hi Min, > > As I said, it would be nice if we have an algorithm that you used to > implement the solver. I'm sending this to you to remind you of the fact that > I would really appreciate it if you could 'briefly' explain the algorithm to > me so that we can 'analyze' it carefully. It doesn't have to be complete and > I would like to know precisely which steps are involved. > > Thank you in advance. > > Seongbin > From marbles-isi@mailman.isi.edu Fri Nov 9 21:00:18 2001 From: marbles-isi@mailman.isi.edu (Martin Frank) Date: Fri, 09 Nov 2001 13:00:18 -0800 Subject: [Marbles-isi] Re: current algorithm In-Reply-To: <001b01c1695f$ddebeb00$b7800980@bugatti> References: <200111091841.fA9IfVg22626@tnt.isi.edu> Message-ID: <5.1.0.14.2.20011109125953.00b07c58@tnt.isi.edu> Thanks for the description, that was useful to me also. -Martin At 12:48 PM 11/9/2001 -0800, Min Cai wrote: >Hi Seongbin, > >Here is the brief description of the algorithms I used in the solver. > >I. M2ScSerialCrawler::solve() >(1) compute the tasks which can't be satisfied given all the available >resources, >and delete them. >(2) sort the tasks in non-decreasing order in term of their value. >(3) call the recursive function solveTasks() > >II. M2ScSerialCrawler::solveTasks() >assuming the length of input tasks is n, >(1) if n=1, call the function solveOneTask to compute the only task. >(2) if n>1, partition the tasks into two parts, the first n-1 tasks and the >last task. >(3) recurisively call the solveTasks() to solve the first n-1 task, and call >the solveOneTask() >to solve the last task, let V be the total values of the solved tasks. >(4) rotate the n tasks and do (2) (3). Let V' be new total values of this >solved tasks. >(5) if V'>V, save the corresponding solution to be our solution. > >III. M2ScSerialCrawler::solveOneTask() >(1) get all the dependencygroups of the input task >(2) call the function M2PvProblemVerifier::startTimesForDependencyGroups() >to caculate the >possible start time intervals of the anchor segment, and get the assigned >RequirementResourcePairs. >(3) if the start time intervals is empty, return NULL M2ScFilledTask. >(4) else, choose the begining time of anchor segment from the possible >intervals. >(5) for each requirement, > a. create the activites the requirement needs for its segment. > b. book the corresponding resource for all the activities according to >the > RequirementResourcePair >(5) return the M2ScFilledTask > >If you have any comment about this algorithm, please let me know. Thanks. > >- Min > > >----- Original Message ----- >From: "Seongbin Park" >To: >Cc: ; >Sent: Friday, November 09, 2001 10:41 AM >Subject: current algorithm > > > > Hi Min, > > > > As I said, it would be nice if we have an algorithm that you used to > > implement the solver. I'm sending this to you to remind you of the fact >that > > I would really appreciate it if you could 'briefly' explain the algorithm >to > > me so that we can 'analyze' it carefully. It doesn't have to be complete >and > > I would like to know precisely which steps are involved. > > > > Thank you in advance. > > > > Seongbin > > > >_______________________________________________ >marbles-isi mailing list >marbles-isi@mailman.isi.edu >http://mailman.isi.edu/mailman/listinfo/marbles-isi From marbles-isi@mailman.isi.edu Fri Nov 9 21:23:24 2001 From: marbles-isi@mailman.isi.edu (Seongbin Park) Date: Fri, 09 Nov 2001 13:23:24 -0800 Subject: [Marbles-isi] Re: current algorithm In-Reply-To: Your message of "Fri, 09 Nov 2001 12:48:20 PST." <001b01c1695f$ddebeb00$b7800980@bugatti> Message-ID: <200111092123.fA9LNOg22325@tnt.isi.edu> Thanks a lot, Min! As I was experimenting with the problems, it seemed that (although this is normally the case for many algorithms for general problems), it is not always possible to predict the time in order to find an optimum solution in our case. Which is one issue that I believe is very related to one of the themes that runs through entire ATTEND project -- i.e., (from the proposal), "a way of estimating or some sort of measure of how it would take the system of negotating agents to find a solution of a given quality to a certain (combinatorial problem)" We can view the problem (i.e., real-time distributed resource allocation) from two different perspectives -- one through the lens of Marbles 2 and the other, as it is, in the SNAP/CAMERA framework. And, I believe the above quoted paragraph can make more sense in the SNAP since the scheduling is progressing by way of agent negotiation activities. Also, the current solver does not do negotiation per se. (although my understanding about the Marbles 1 solvers actually did 'negotiation') Still, (though abstracted out), many of the activities in SNAP are translated to Marbles 2, so we could still say something about (1) when it might be possible for us to 'predict' how much time we would deifnitely need in order to achieve the optimum value -- for this question, one related question might be this: is the kind of problem that we address here belongs to a certain class of problems for which we can definitely say something about this property; i.e., for all problems in the class, we can say that we can predict the time that they require to find optimum solutions. (2) the relationship between 'solution quality' and 'available time' I would really appreciate any comments on this. Seongbin > Hi Seongbin, > > Here is the brief description of the algorithms I used in the solver. > > I. M2ScSerialCrawler::solve() > (1) compute the tasks which can't be satisfied given all the available > resources, > and delete them. > (2) sort the tasks in non-decreasing order in term of their value. > (3) call the recursive function solveTasks() > > II. M2ScSerialCrawler::solveTasks() > assuming the length of input tasks is n, > (1) if n=1, call the function solveOneTask to compute the only task. > (2) if n>1, partition the tasks into two parts, the first n-1 tasks and the > last task. > (3) recurisively call the solveTasks() to solve the first n-1 task, and call > the solveOneTask() > to solve the last task, let V be the total values of the solved tasks. > (4) rotate the n tasks and do (2) (3). Let V' be new total values of this > solved tasks. > (5) if V'>V, save the corresponding solution to be our solution. > > III. M2ScSerialCrawler::solveOneTask() > (1) get all the dependencygroups of the input task > (2) call the function M2PvProblemVerifier::startTimesForDependencyGroups() > to caculate the > possible start time intervals of the anchor segment, and get the assigned > RequirementResourcePairs. > (3) if the start time intervals is empty, return NULL M2ScFilledTask. > (4) else, choose the begining time of anchor segment from the possible > intervals. > (5) for each requirement, > a. create the activites the requirement needs for its segment. > b. book the corresponding resource for all the activities according to > the > RequirementResourcePair > (5) return the M2ScFilledTask > > If you have any comment about this algorithm, please let me know. Thanks. > > - Min > > > ----- Original Message ----- > From: "Seongbin Park" > To: > Cc: ; > Sent: Friday, November 09, 2001 10:41 AM > Subject: current algorithm > > > > Hi Min, > > > > As I said, it would be nice if we have an algorithm that you used to > > implement the solver. I'm sending this to you to remind you of the fact > that > > I would really appreciate it if you could 'briefly' explain the algorithm > to > > me so that we can 'analyze' it carefully. It doesn't have to be complete > and > > I would like to know precisely which steps are involved. > > > > Thank you in advance. > > > > Seongbin > > > > _______________________________________________ > marbles-isi mailing list > marbles-isi@mailman.isi.edu > http://mailman.isi.edu/mailman/listinfo/marbles-isi From marbles-isi@mailman.isi.edu Wed Nov 14 19:31:59 2001 From: marbles-isi@mailman.isi.edu (Seongbin Park) Date: Wed, 14 Nov 2001 11:31:59 -0800 Subject: [Marbles-isi] some desscription Message-ID: <200111141931.fAEJVxg05721@tnt.isi.edu> Hi, I summarized things related to ATTEND and Marbles (for my own understanding, originally) based on the discussions that I had. Could you read this and tell me of your opinion when you get a chance? Especially, for some part, I'm not sure whether my understanding is precise or not, so I will be happy to hear any comments you have. Thanks, Seongbin ------------------------------------- Problem (in Marbles 2) -- m resources: each resource has 'availability' information (i.e., when it's available) -- n tasks: each task has a name, value, one or more segments each of which has 'typical duration' information, (note that the sum of typical durations is task execution time) anchor segment name selected from the set of segments which serves as an anchor point, valid beginning of anchor segment interval which specifies where the 'start' point of an anchor segment can be located, one or more requirements each of which has one or more required for segment names, (let the number of requirements be k.) possible resources for each requirement or each distinct pair of requirements depending on the value of control parameter. more specifically, -- if parameter = 0 (i.e., there's no inter-dependency among resources as in Marbles 1), one or more resources are available for each requirement. -- if parameter = 1 (i.e., there's inter-dependency between each distinct pair of resources), one or more resource pairs are available for each distinct pair of requirements. (note that if k is an odd number, one remaining requirement has one or more resources.) Given these, allocate resources to tasks in such a way that maximum number of tasks can be scheduled. A solution consists of assignment of resources to tasks in such a way that each task has either none or all of the requirements filled. Currently, the SNAP scheduler tries all the tasks for the first day and schedule those that can happen on the first day (by putting them using the value that comes from the sortie cycle computation which specifies intervals during which tasks can happen on the day). Then, with the remaining set (i.e., un-selected tasks), the scheduler does the same thing for the second day, for the third day until the end of the planning horizon. What we know (or, assume to know): 1. Phase transition exists and the formula that predicts Kmax value that specifies the maximum number of tasks that can be scheduled. 2. Estimated numbers of tasks that can be scheduled for distinct time intervals ('bins') which partition the planning horizon. (i.e., n1 many tasks can be scheduled on Monday, n2 tasks can be scheduled on Tuesday, etc.) Note that n1 + n2 + ... may not be equal to the number of all tasks. (This comes from the extension of the current results about the formula.) 3. All possible time intervals where tasks can be scheduled and for each of these time intervals, all possible resource combinations for each task at this time interval. (This information comes from the beginning of anchor segment interval and since it could be entire planning horizon, the information may not help much.) 4. Some of the resource qualifications change over time; i.e., existing time-varying resource qualification properties. For example, a pilot may be qualified once trained previously, but until then the pilot may not be qualified. (This is not directly available from the Marbles 2 problem instances, but can be answered by function calls to SNAP.) 5. Task dependencies or prerequisites -- some tasks can be only scheduled when other related tasks were scheduled previously. (As in 4 above, this is not directly available from the Marbles 2 problem instances, but can be answered by function called to SANP.) 6. Tasks cannot be added to the problem instance under consideration once the scheduler starts (it used to be that way, but not any more.) Goal: Take a Marbles 2 problem instance as input. Come up with the formula that partitions the set of tasks. Then, solve the partitioned problems in parallel in time far less than the time that a (or, the) serialized solver takes to solve the problem. Ideally, we also would like to demonstrate that as the planning horizon becomes longer, the advantage in terms of time becomes a lot more noticeable. There are roughly four subparts in terms of time usage: (1) time spent to analyze the complexity of a given scheduling problem, (2) time spent to divide the set of tasks that needs to be scheduled, (3) time spent to assign the tasks in the partitioned sets into bins (i.e., allocation or assignment or (even) distribution of divided sets of tasks to time intervals like Mon, Tue, ...) (4) time spent to "solve" the problem (i.e., parallelized version) From marbles-isi@mailman.isi.edu Mon Nov 26 17:05:23 2001 From: marbles-isi@mailman.isi.edu (Martin Frank) Date: Mon, 26 Nov 2001 09:05:23 -0800 Subject: [Marbles-isi] Marbles2 design document Message-ID: <5.1.0.14.2.20011126085136.00aa9838@tnt.isi.edu> --=====================_2108451==_ Content-Type: text/plain; charset="us-ascii"; format=flowed Attached a first version of that. Pedro and Jinbo in particular - can you take a look at "2.3.3 Infrastructure for Distributed Solving Schemes" - I'm trying to outline the APIs that distributed solver writers are passed and the APIs they implement. Cheers, yours, Martin --=====================_2108451==_ Content-Type: application/pdf; name="marblesmanual.pdf" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="marblesmanual.pdf" JVBERi0xLjMKNSAwIG9iago8PCAvUyAvR29UbyAvRCAodGl0bGUuMCkgPj4KZW5kb2JqCjggMCBv YmoKKFRpdGxlIFBhZ2UpCmVuZG9iago5IDAgb2JqCjw8IC9TIC9Hb1RvIC9EIChjaGFwdGVyLjEp ID4+CmVuZG9iagoxMiAwIG9iagooSW50cm9kdWN0aW9uKQplbmRvYmoKMTMgMCBvYmoKPDwgL1Mg L0dvVG8gL0QgKHNlY3Rpb24uMS4xKSA+PgplbmRvYmoKMTYgMCBvYmoKKFdoYXQgaXMgdGhlIE1B UkJMRVMgUHJvamVjdCBhYm91dD8pCmVuZG9iagoxNyAwIG9iago8PCAvUyAvR29UbyAvRCAoc2Vj dGlvbi4xLjIpID4+CmVuZG9iagoyMCAwIG9iagooTUFSQkxFUyBQYXN0IGFuZCBQcmVzZW50KQpl bmRvYmoKMjEgMCBvYmoKPDwgL1MgL0dvVG8gL0QgKHNlY3Rpb24uMS4zKSA+PgplbmRvYmoKMjQg MCBvYmoKKEFja25vd2xlZGdlbWVudHMpCmVuZG9iagoyNSAwIG9iago8PCAvUyAvR29UbyAvRCAo Y2hhcHRlci4yKSA+PgplbmRvYmoKMjggMCBvYmoKKE1BUkJMRVMgU29mdHdhcmUpCmVuZG9iagoy OSAwIG9iago8PCAvUyAvR29UbyAvRCAoc2VjdGlvbi4yLjEpID4+CmVuZG9iagozMiAwIG9iagoo TUFSQkxFUyBQcm9ibGVtIFN0YXRlbWVudCkKZW5kb2JqCjMzIDAgb2JqCjw8IC9TIC9Hb1RvIC9E IChzZWN0aW9uLjIuMikgPj4KZW5kb2JqCjM2IDAgb2JqCihNQVJCTEVTIFNvbHV0aW9uIFN0YXRl bWVudCkKZW5kb2JqCjM3IDAgb2JqCjw8IC9TIC9Hb1RvIC9EIChzZWN0aW9uLjIuMykgPj4KZW5k b2JqCjQwIDAgb2JqCihTb2x2ZXJzKQplbmRvYmoKNDEgMCBvYmoKPDwgL1MgL0dvVG8gL0QgKHN1 YnNlY3Rpb24uMi4zLjEpID4+CmVuZG9iago0NCAwIG9iagooSW5mcmFzdHJ1Y3R1cmUgZm9yIENl bnRyYWxpemVkIFNvbHZlcnMpCmVuZG9iago0NSAwIG9iago8PCAvUyAvR29UbyAvRCAoc3Vic2Vj dGlvbi4yLjMuMikgPj4KZW5kb2JqCjQ4IDAgb2JqCihDZW50cmFsaXplZCBTb2x2ZXJzKQplbmRv YmoKNDkgMCBvYmoKPDwgL1MgL0dvVG8gL0QgKHN1YnNlY3Rpb24uMi4zLjMpID4+CmVuZG9iago1 MiAwIG9iagooSW5mcmFzdHJ1Y3R1cmUgZm9yIERpc3RyaWJ1dGVkIFNvbHZpbmcgU2NoZW1lcykK ZW5kb2JqCjUzIDAgb2JqCjw8IC9TIC9Hb1RvIC9EIChzdWJzZWN0aW9uLjIuMy40KSA+PgplbmRv YmoKNTYgMCBvYmoKKERpc3RyaWJ1dGVkIFNvbHZpbmcgU2NoZW1lcykKZW5kb2JqCjU3IDAgb2Jq Cjw8IC9TIC9Hb1RvIC9EIChjaGFwdGVyLjMpID4+CmVuZG9iago2MCAwIG9iagooSW5zdGFsbGF0 aW9uIEluc3RydWN0aW9ucykKZW5kb2JqCjYxIDAgb2JqCjw8IC9TIC9Hb1RvIC9EIChjaGFwdGVy LjQpID4+CmVuZG9iago2NCAwIG9iagooUmVsYXRlZCBXb3JrKQplbmRvYmoKNjUgMCBvYmoKPDwg L1MgL0dvVG8gL0QgKGFwcGVuZGl4LkEpID4+CmVuZG9iago2OCAwIG9iagooUmV2aXNpb24gSGlz dG9yeSBvZiB0aGlzIERvY3VtZW50KQplbmRvYmoKNjkgMCBvYmoKPDwgL1MgL0dvVG8gL0QgWzcw IDAgUiAgL0ZpdCBdID4+CmVuZG9iago3MiAwIG9iaiA8PAovTGVuZ3RoIDQyMyAgICAgICAKL0Zp bHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjajVJNT9xADL3nV8xxRiLueL7nGGC3Euq2wEZc KIeUDWXLsmnTLaj99bWTpUQUqVU0kcf2s9/zGIWmDwVqDSkbEZIDtE5c3xdafKbQ2wL3KdYnCN6T /UqwNDGAc0mU6D1EG6dJL5M9uKc6h3XxZo5ZGAcxRVHfDCn16lLWt60qbQxyUSmU56pEeUjGuxlb Sw55uWi2yhr5o9moq/qkmNXPfIIB45lOAuMCt/pWXF5psSI2J3S+0HksOPlJl4OcUPStuCmWxdkz 2QCeBlMaA1HjH86GZubAUukJ52qujJcflsTWepRHnfJadl/53/aKPM1u/cCyyHHUbXf9EN8cjJ5j BleD0lNF6qp9YrWYDc7qpUjUzCmTSFIZbfrXzN1k5tYSHDKhJ/wXTU8j3nGz9Za6JyvnKgfZN9u7 8fpRe/243t2Ot1NlUbYrBnXscfQwKH8xvr3jl2k3PwmBTJye2U9aOgsuBhprAp3d0P1CUcm2/77u uLe1EkEfjNb7TpkkH/jXcvV7tj4p2rrx3u8BeZ9vtMa/VgKzBvSZdxUBic3/LvlvAdGshGVuZHN0 cmVhbQplbmRvYmoKNzAgMCBvYmogPDwKL1R5cGUgL1BhZ2UKL0NvbnRlbnRzIDcyIDAgUgovUmVz b3VyY2VzIDcxIDAgUgovTWVkaWFCb3ggWzAgMCA2MTIgNzkyXQovUGFyZW50IDg3IDAgUgo+PiBl bmRvYmoKNzMgMCBvYmogPDwKL0QgWzcwIDAgUiAvWFlaIDEwMC44OTIgNjg0LjEzNCBudWxsXQo+ PiBlbmRvYmoKNzQgMCBvYmogPDwKL0QgWzcwIDAgUiAvWFlaIDEwMC44OTIgNjY0LjMzNSBudWxs XQo+PiBlbmRvYmoKNiAwIG9iaiA8PAovRCBbNzAgMCBSIC9YWVogMTAwLjg5MiA2NjQuMzM1IG51 bGxdCj4+IGVuZG9iago3MSAwIG9iaiA8PAovRm9udCA8PCAvRjE5IDc3IDAgUiAvRjIxIDgwIDAg UiAvRjMzIDgzIDAgUiAvRjE1IDg2IDAgUiA+PgovUHJvY1NldCBbIC9QREYgL1RleHQgXQo+PiBl bmRvYmoKOTAgMCBvYmogPDwKL0xlbmd0aCAyMzcgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2Rl Cj4+CnN0cmVhbQp42m1QTU8DIRS88ys4woEnb+HxcdTGmph446YemnatG2trsDHx3/vodnVjGi7D zDBDBqXlgxIJIVqUIXlA5+X6XVi5ZelO3BRxtUSSaCHbLMvL6UnZPKph0M/lXtwWgecYRwkCEeNZ wCQa57kEEwMLkVLzTOEZcnDz7MXhQ3dJfVeNati+NnxsbdJnoOClBXfyrRvJEf4vwnQQA7dMlieL dHbNikYwlnXWojbOOfWwqsfWuB+vS528qqv9G3OgjfekrneMd6Ncf3+G6rNRbO4ZoOrrVxP6Dfyf yGCA7KI0RAQx5ktLXZzxB6dDXhFlbmRzdHJlYW0KZW5kb2JqCjg5IDAgb2JqIDw8Ci9UeXBlIC9Q YWdlCi9Db250ZW50cyA5MCAwIFIKL1Jlc291cmNlcyA4OCAwIFIKL01lZGlhQm94IFswIDAgNjEy IDc5Ml0KL1BhcmVudCA4NyAwIFIKPj4gZW5kb2JqCjkxIDAgb2JqIDw8Ci9EIFs4OSAwIFIgL1hZ WiAxNTEuNzAxIDY4NC4xMzQgbnVsbF0KPj4gZW5kb2JqCjg4IDAgb2JqIDw8Ci9Gb250IDw8IC9G MTUgODYgMCBSIC9GMTUgODYgMCBSIC9GMTQgOTQgMCBSID4+Ci9Qcm9jU2V0IFsgL1BERiAvVGV4 dCBdCj4+IGVuZG9iago5NyAwIG9iaiA8PAovTGVuZ3RoIDE3OTIgICAgICAKL0ZpbHRlciAvRmxh dGVEZWNvZGUKPj4Kc3RyZWFtCnjapVhLb9tIDL73Vxg5yUCtaqTRay+B202LLjZtkXjRQ9uDYiny NHp4JXnb/vvlR44kJ3YOi0WBah4kh0Nyvo+OWnj0Ty2U57lJ6i+iRLsq0Itt/cJblLT17oWyIkGY uFEY0vjM5mrcXSkvdJMggtDrzYtXb1W68D03ioLF5p6FN/kX580u2w/LlXKKbrkK4shRy2+bP0RY u3ESi/Aq9FwV+6zyvlkGyhmWyumg2JKe5+SH7WBoqW0mfaXdQEejfuIqrVhfkZirSFX5ofN5ly1h jM4OHRjoZTjsChlcr29e/3l1K5NPHZ0W+c73YmtVsjv6wgnlHIZLe3ZIUXRTL7Vn+6Hr6ZTPnqz5 aYijlLNd+omzK2qOQY+NyMm6Qga8mnVk/AFiBabDyjTip3L2NDBdkVu1iqYlxwQqZtjR/2zC2r2n dT45N/3Q4bZ3B9ye7eay1RVsucVGty1wI9xBBW5o75BVFUXBj8l1eGfD7i5XOlHOhuOmI6fJajv6 SmU03ls5F7JoeisG5UHGWYMvxXTbURrpvr/ql1hJ6CoikZM0O8v3+2eKiTV2D5/bWiZtw8Kn/u85 oIPZmj2d6KecfU3XoYtxaEuZV+ZR3LFjmnI0XohQey8LHGu71hRlOxgKTNvIZk9JjpHk2dnYGdo5 PA8mX84bXZbjaDrryPmQfa+z7q4ag9BfuCyhIjeldwax1ItZ7rbFWfq4fnWsR0dGAzVyDSHJZCNC dTZeGltdkVWrwYzmykPW2agVY8li/asXevtu8uxQ20jCv9WRg2MShsmJX3hAgVQ5Aqi0U9rUNrJV ohbl2ISOxZGQwtOj27SHQcTq7KepIXKoZSEvqozLqLdHNLkMEMGWy7yfMkLLnEv6TrmkQ2zK+qws TrMxwKfpKbIzHZwJ6Ag5ma/RUXDUc5miB4PXrtM5BBgP0PyBbLVY8B1T1/aZUhlmwyjGOqUAgwhm e6BCZbZSgOweRHE77M8FkarzEKSc357NGxe+DrRgCQaZfL56KjTljlMkK0+KDY4fpgemA3rncHSP ZJhHUCLqslRVsz4vA0M+rD/J5Av8fHG1mdjHD1NiGHLZ176riTCYobDblQsZ3LyzdPQYpYWOgqcG Q1c/Zbrntb8RXOkwFfKAf63kRVF0uydhgwwH2Y9CV/nquK62wNeEiomT3cxxQWVxldI3Owyc3rae 8Bp1jIwUnSEuYCl5SDuzrSSz7nl6DCJX+zM7+pYdr9c0O+Y/MG/Wz4wpT4p3LB0XfWH5+XkuTMbK p2uSj9ekS0UJ7T+vaAx6DHyH4YQiEUbO97EIQLtQKdtBhHp6AQMTIE2yXnb3jBdWAvHCdxiP+x0h Wd98WibaWcve+w2Mf5TJ+sPGevBmfb06QyBXN1CLPT6nO/HwJZLnE/rM/FS1+5E9oCeoI9sdMoVF zmWzOsJbP6aqNyPADMx5Twg7SumJlG1nub4XJeF52utYcHWKXH17ALnTLdNTPueyAp8TFm521gFI sjV66EErwU3oJK7U7aGfXKJVlCq+I5BCl7sAYi9gU2HtqQv5SqbvKkAQ5pTUwRKUYP7Lc22Irfap J+i3U2cgzDC6hHdg+PX4p4ixUknsRmlMAxUydvxHyPD/H2Qgzlo7n1GNhX2/nR008gAwZMpqSu6S 6TrCDNwYSk8CYLAAwVE3NjpSonPPUMsyR1gAXyIsncSKsCjVwSOCWgOPybkMgc5XrZ1upUUjCsSb xMp9Z9FIJ6Gzfovdj0DrG6uPMhtJDS8Wixa1oIF+iM3IpVFV8O9IYjwagGrEeXproe87thOgm5/w ltS7wC71lj3X8/0w8ZBtGw8DN7yFrAxZ/9DLEIUDxceH6ti2E7zVyhe90ZL7KOlTxAA/6JEHrAWR 3/Mti45v3NVZc7bdRmc134/A1p97JfKiGTNb5EUu22LPis7tMlMCLR1RgixwnG06uVwqNCwucM1z /mqOmm4SV84bjNcAbQZCPhHBa4StSvTs9Evgh6DfmYfLUSbMlIqaMkErt2B2AJbtF7q5EaQ3JV78 HIqmnzob5SAbCedJyU2qw9LiZmPNzn2phT0s3gnHclv/qs4qYw3VqE+YqAZzDv0F6cra/iiQ3w86 5aLB0kOPogziM6emMWlXlib41wH02FMpmS4v9jN0wbW8aLZGrFiz3HLmolqx+6UZDrZTqLAensP9 0XvrS0AR+Jv76soQousxeOOvFop2mbFn/avqUa+MulBqBizEt7hnYArlLWCJmx4GIZoUP/fiNVco SR0DPgQmwD9DVVOgubHkzsdimzRWMmZ6Ydo/+n0kW/6FqD069BwGihjeG9+3sKZvZ2o3I8ZeXl4K F4bcL52QSuK7SZzSNXzfDaPg3B9KVBy5kdDE8/SgnlpWZNl7Si//ArOlXV9lbmRzdHJlYW0KZW5k b2JqCjk2IDAgb2JqIDw8Ci9UeXBlIC9QYWdlCi9Db250ZW50cyA5NyAwIFIKL1Jlc291cmNlcyA5 NSAwIFIKL01lZGlhQm94IFswIDAgNjEyIDc5Ml0KL1BhcmVudCA4NyAwIFIKL0Fubm90cyBbIDk5 IDAgUiAxMDAgMCBSIF0KPj4gZW5kb2JqCjk5IDAgb2JqIDw8Ci9UeXBlIC9Bbm5vdAovQm9yZGVy IFswIDAgMF0gL0ggL0kgL0MgWzAgMSAwXQovUmVjdCBbMzU5LjU2MiAzMzQuODI5IDM2Ny4wMDkg MzQzLjg1Ml0KL1N1YnR5cGUgL0xpbmsKL0EgPDwgL1MgL0dvVG8gL0QgKGNpdGUuY2FtZXJhd2Vi c2l0ZSkgPj4KPj4gZW5kb2JqCjEwMCAwIG9iaiA8PAovVHlwZSAvQW5ub3QKL0JvcmRlciBbMCAw IDBdIC9IIC9JIC9DIFswIDEgMF0KL1JlY3QgWzE3Ny4zMiAyMTkuMTY0IDE4NC43NjcgMjI4LjE4 N10KL1N1YnR5cGUgL0xpbmsKL0EgPDwgL1MgL0dvVG8gL0QgKGNpdGUuYWFhaTIwMDFmYWxsc3lt cG9zaXVtKSA+Pgo+PiBlbmRvYmoKOTggMCBvYmogPDwKL0QgWzk2IDAgUiAvWFlaIDEwMC44OTIg Njg0LjEzNCBudWxsXQo+PiBlbmRvYmoKMTAgMCBvYmogPDwKL0QgWzk2IDAgUiAvWFlaIDEwMC44 OTIgNjY0LjMzNSBudWxsXQo+PiBlbmRvYmoKMTQgMCBvYmogPDwKL0QgWzk2IDAgUiAvWFlaIDEw MC44OTIgNDg4LjI3NiBudWxsXQo+PiBlbmRvYmoKMTggMCBvYmogPDwKL0QgWzk2IDAgUiAvWFla IDEwMC44OTIgMzA0LjE0NyBudWxsXQo+PiBlbmRvYmoKOTUgMCBvYmogPDwKL0ZvbnQgPDwgL0Yx OSA3NyAwIFIgL0YxOSA3NyAwIFIgL0YxOSA3NyAwIFIgL0YxNSA4NiAwIFIgPj4KL1Byb2NTZXQg WyAvUERGIC9UZXh0IF0KPj4gZW5kb2JqCjEwNSAwIG9iaiA8PAovTGVuZ3RoIDE0NzYgICAgICAK L0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjajVdNc+M2DL3nV/hWaqbWmqI+rKM38e5m u0l2EvfU9kDbsq2NLLmSnDT764sHULKT2J1OZkIKAEEAfHik9WBEf3qgI+0nIz2Ix6GvTThYbC9G gzWpPl98nF18+KSjgR756SgdzFa8ZLb8QwXeX7OvHz6R+UGnU+On0dhZXH6ZfJ9N772hMUZp3xsm yVhd387uvWCs7q5+v5xde0Ot7jytbuHsYjq70C4kE439OIpofhRMpxx22qEZ+QntRzYSaDrQoW/C +DhQ2lkrQzvpIFITmi88o9VjWXkmUM/0XWTLdbbNSohb+m4kszdZDwPyHAXs8r6ae8MgURnir1tv GI5jdctfCyS3yRrIEpWXOTlsc9tmS5G0GxJkMr+c3EzvJ7J6V5O88oZRrH5ki1YMqr3zXa1kfN7k tEGiNvJ5Q2sm9x+x77cpzR8QOWLVxo/ClGNd19mzz3Id+6lJRBmx7juCzZZu6zCM1MNPzuKRFcUL hKGy5VImX/PSC1KF5MeyIlSXsHdJaVWKcFGV8NDWSH++bzu189O63fpi0PzG1nM+jEa+tQxSl3mR bTmH4XESLsOmpfLCO84Qu/6KwwnVc11BHkQpnCMQdo5vLcMyb06FGERjAgHNq+KJxHm5FvPG1d5Z bvl/43aDm/LPkY72h1oADjTJnM92cwhneOKo5kW3lHY345B6jIc6+3uf14dtJUuOkDY3aSwnZMZG 9nPFpwV1jpruGX3wROfHZluYWT7MF9FUFFwt02estkj1xXOhuKOyrVhc94YJNtPqF6coqKAnEiPX 2XsMOuWkyH5YOFnWBIt4NFIMaJRxbSUfBPQEneYyc/Q7BgtPXUGQJExafHESlQh4zrUrUOEhI7z0 nONnh1vsvHAHx9hFwkX+s0MF9A0hIsfK9Xs0Sks5jGQHcJiE2gqN6lHhZ/Qdx2paLipuomWHL0jl FGMyz7cIYV8Ib0A3wa5lRgGVa3YaHh1JnFCkqIdLCNTk1u0bGdFysKPOPAW9trYNuMbEglJMdhxh Vq9QqXprywUrEmEjMrgh7vk2fcBHJA3j2E9OpRFru7ZcMjbo9+jRLIJchnKZtTYvcFdERn3JTkRq iwbskRC4LdgmGanC1utMZHx0e8YIFeGYpSWCMAkkfFrG8n883SeaH7UWnFWljD1N0eInjxia2SSv 9s7umF1go8V/TyHsvPFPJEMZ0mUWM6obmZYdKE0UuQ6rH3uUkAxRYeyiwiKLlNtK2lowQxZoEVu3 ObfoYf1KxhvWvuYcEgfiEWvrShiZz8mFJ2A4kcrOSsMUWSE3/g7tTOEvOjDQFhIEKS3cmbALUYQc GY19ZmRwhWpM7r+jeSaivp7diW5yO3sQEfVWiN7SaoZ78PZKDDLiY1PV7VnuuWHWpjBTuo1tjgkd xnb3nligEP4mU3Iboj4dpKFcHC14Sx3QNx0SBI0sW1V151kyhvP+InSHAlkgZjs5keE58uGwjU5d r9n29VXFfE/FoIt1JNAj07yRcbFH1LXLAFe/0eMOgKR5lLMjGQNQu+uMXQBfKzBIve/uPGy9r52B A42EduA2Lv76VI8zEdLzjMOWliv2zHY9gpZ5zyncjXNuga7isHh3XbP1WSw8ZFW5nnOnoNojeR0x qh8h0P/1qoG9FasTfQVxIEa7rqm2Il73T4WepmwrsNDcJRj7LoEHdAmE2H6TnX0VyXUzm0ovGOkF ZiTs0UqD9EFaV7M5EnxxTdVrf2D/Ut6JZ+t3xTSz3jArp6H6zV3VYRoxyDDKrXzgM9hVzt7K8Ood mDIfQfyaTOkGjCh8dxeQlW3ETFioFSETPQnPVSr6P4U6m+9n1i+5G8JAXdnH3E1XXCi0YmjkfQzB S9P1BahBlNf0Dqh3TJNV7YibxG+A9grY5N+K1VtGgcpdT7l1T8Bz6GDmYa6V2jAaOuRmjpy166N3 N7v/9ucatW8URX6SpKd+r538MfcvAmyT+WVuZHN0cmVhbQplbmRvYmoKMTA0IDAgb2JqIDw8Ci9U eXBlIC9QYWdlCi9Db250ZW50cyAxMDUgMCBSCi9SZXNvdXJjZXMgMTAzIDAgUgovTWVkaWFCb3gg WzAgMCA2MTIgNzkyXQovUGFyZW50IDg3IDAgUgo+PiBlbmRvYmoKMTA2IDAgb2JqIDw8Ci9EIFsx MDQgMCBSIC9YWVogMTUxLjcwMSA2ODQuMTM0IG51bGxdCj4+IGVuZG9iagoyMiAwIG9iaiA8PAov RCBbMTA0IDAgUiAvWFlaIDE1MS43MDEgNjY0LjMzNSBudWxsXQo+PiBlbmRvYmoKMTAzIDAgb2Jq IDw8Ci9Gb250IDw8IC9GMTUgODYgMCBSIC9GMzQgMTA5IDAgUiAvRjE5IDc3IDAgUiA+PgovUHJv Y1NldCBbIC9QREYgL1RleHQgXQo+PiBlbmRvYmoKMTEyIDAgb2JqIDw8Ci9MZW5ndGggMTM2MiAg ICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrFV0mP2zYUvs+vMOYkD2pa1K6m KJA0Cxo0QTBjoIckB9qibSJaHInyZFD0v/ctlCMvky4oWvhAiXzr9773KMuJDz85kb4vsjyYJFkk ZBhNVtWVP9nA0asr6UTCOBNJHMPzhcPZcDqTfiyiNEOhZ4ur+UuZTwJfJEk4WaxJeFG8937aqp2d zqSn2+ksTBMvmH5cvGbhSKRZysKzKBeZDEnlzdPbZ79MpfcC1e5QK/buGthY22kYePfTUHqq1Qc7 MhJhlDg7cQpG2U4AKkKCERnEYBTewO4LZ/Bdi9bR6rLUFe5F3p1VFjY0nlT4UKMr6xzFAJ3I/dw5 gujDOCBHiy2oREnkLaezIPV02UyDDOKkPdPxardsGp8L/cGXUQ0bxpqmxs3Qa9a8Kl7eqJaD6zAc h1rgj4IIpBSxHzmgd+00hoSWJbqpLsQcJiIPcyctprMsjLwPfuwv0P4ouO6hxvCt+sKB9B0BUvCb AcEOrSMCmYiiEIgQCuADmh2SDNPM63aARubpFWqjGua8wrPUo5zQT85+cA/zx/XNUAKrZq8V771H j1cvFgcWyjgg+GcyAhLmGTMVT9vNhB9uXzlaHheO85enBsFKHB0z/nHtjxzVDiJVqymU/NM0Tjy1 cZkXQwaYMTzXUGVYsNCo5nZLEum+g80sA+pIr7csYNzabZseBQvunCXjyeBLIHrmJwx+lFNYzRIL tydfPUAcQoVtg2voAZJ4RnHyCUbfNhtkmSLGVxRQSwUmie3AYzRgt6ZzT62qu1JZfeJiVyp0XZ84 jD0lqPrxGegzmYQiSHCohCINx+Cfjh2sj5tIVBdgcirydFyVVVNhAjtT4qKgs3Ctnxtqdb2CIsC6 x7BjmCO/EZBA4SSbzHJYIrJi2EjTWha71gVu9MIQaY0oiNBa9MRRUwpeGb3rJ1yekVnujN9pH+aG zEQecL1M7SbjmgJeafb4VlVDx8Uwfx6Jc0f+l6VZsdadpTRNveH3jbZvyWzlqBj7WIJ/Ib5b3RGy fTvs6C8sWBNUHcf9llkF8DnLX53Nbw6WaAqDJdJ3mltyt3fGNyXwGjdKqusDy/Q1Zfu5d1K1qtiE 4Peb+dgtQ3aGZHwRyUVDG6utIR+bnwcY9hxGdwD4KRFKOcIteTX2YQz45eoNCGDv4LrUnBZRtLaa arlreQ7qgoWUw+ea+vSsCvekjCihEJVCrzkHY/UYvJo0Lb8QuM694hRNqRgSZ7npLZO/4OkjA5H6 4RhDu2X0nQK3Az4dOLQnk2V3zfsIz7IfheEs8Asn3gExqBLOVqUeXLEb15y1XrFkp5j95cMYT4AM qkN+1bJk+2esWDc89hgAknIQciF3w8DAC4pkKCY3TQyvA3W5fp0pRskcZUZojNHhobtnx3gbfK3J ijz1A6iD/xWX//68rwo1KrLD0LbGxcvj0AVKLd20Q/9y9+xKTZR1HeRmRSCFTOW3Zs++MaOBteN5 axm+d63jNFb8HYXuOPFraw5fhqTn2ubPZ9TXSEqeyvaHW64Cty60xI9uyP+NuXanNzxDansYa4Rh XXSHyUwCj03my/PkObP4cCGx8oZ7e8EcM1zr8lT2eGwfpT6fDxxyPbd1eax7yyjo778VQMV1MRUJ V0949znPFMWXWlMPnfeFedsTAE/OqfefUyI6p0R8TIk7srTh+6+2/4ARt/ozpWVafcYMd+H9RWY8 cmfrDd+R7L47YsYtLZ979/lSvOQ5daqDGJ5k2s1xvVF8FQyDAxiiCJlPN3Pu8v+tkONvQHAe+lJk obz0b1OmiUhGn30XP8fDs38HWSD80z+wfwDBwbIzZW5kc3RyZWFtCmVuZG9iagoxMTEgMCBvYmog PDwKL1R5cGUgL1BhZ2UKL0NvbnRlbnRzIDExMiAwIFIKL1Jlc291cmNlcyAxMTAgMCBSCi9NZWRp YUJveCBbMCAwIDYxMiA3OTJdCi9QYXJlbnQgODcgMCBSCi9Bbm5vdHMgWyAxMTcgMCBSIF0KPj4g ZW5kb2JqCjExNyAwIG9iaiA8PAovVHlwZSAvQW5ub3QKL0JvcmRlciBbMCAwIDBdIC9IIC9JIC9D IFswIDEgMF0KL1JlY3QgWzI1Mi4yNDggNDMyLjE2MiAyNTkuNjk1IDQ0MS4xODVdCi9TdWJ0eXBl IC9MaW5rCi9BIDw8IC9TIC9Hb1RvIC9EIChjaXRlLm1ldGFqYSkgPj4KPj4gZW5kb2JqCjExMyAw IG9iaiA8PAovRCBbMTExIDAgUiAvWFlaIDEwMC44OTIgNjg0LjEzNCBudWxsXQo+PiBlbmRvYmoK MjYgMCBvYmogPDwKL0QgWzExMSAwIFIgL1hZWiAxMDAuODkyIDY2NC4zMzUgbnVsbF0KPj4gZW5k b2JqCjMwIDAgb2JqIDw8Ci9EIFsxMTEgMCBSIC9YWVogMTAwLjg5MiA0ODguOTkyIG51bGxdCj4+ IGVuZG9iagoxMTAgMCBvYmogPDwKL0ZvbnQgPDwgL0YxOSA3NyAwIFIgL0YxOSA3NyAwIFIgL0Yx OSA3NyAwIFIgL0YxNSA4NiAwIFIgL0YyMCAxMTYgMCBSIC9GMzkgMTIwIDAgUiA+PgovUHJvY1Nl dCBbIC9QREYgL1RleHQgXQo+PiBlbmRvYmoKMTI0IDAgb2JqIDw8Ci9MZW5ndGggMTU2OSAgICAg IAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrFWG1v2kgQ/p5fwX2DKBgbcMBqVSlp SNIqhQh8yYfefTBmgVWMTb12WnS6/367M7Nk/UIuVOqdomjZt9mZeZ6Z3bHTsOWf03BcxxrYTuN8 2LecXr8Rbk7sxkpO3Zxc+ieda8dtOLbl2V7DX8IWf/G12W/96X/uXMvlL3POuWN5wyGt+Hh7ce+P pq12r9drdq1WezAYNr+0nObF9LLVdpp3I/l7htOzybXs+Gr4seU4XblmpA44GfknDqnZc4fWuevK 34aCerKtZ9s92xq4Q7UGlO95jYHlDUzV/1aS5e+2Z/XP+zAUcXW0yN5PVcu+5dBP2YapNs4+tNpu r9v8a7/VGVpe14O9PFZrMpYuVRuEDNf6gXjCX+xHBlLjBRyiBp3mOFCdDVuQZBA8lAoNUS8XZG9z tWoe8RCX8TjDHysGMh+CSDU5+8N2bfnv4GznFGb3GjwHEQhiZ9gPgxh/zBlqAyawVQDb+DM7VW3n nemoeoVmbIV2xJlhHIyxbAbeW8lJ8JB4UdJpvquC8KpsNBrkXMShatdJOkO1NUp7+dqXnql6B4zK +IYZvpTb02eAIiLd5zAYJar5jkMpyF+yFDdmCQ7D9jUzt62AN3GMnFjh1BZk7aFLlpXtXXnEkiHl 0DjJixIh0EcBCA6l7bhPFP1/Spi1nXPL6w1eQ+6LMkfqyGOQ8KneG4TkAw7yxWWdmWIC1Je4rFUr cdHAv45L38AlF0ygYl/njBSL0W+rMyN8NMkl12DZJkDFeSiI3CLB1msD0exgg/0NQ+eRZR4GIHa4 0IFCdpIsxzZXbdDsXNQHK9uCeI7cicuSIVL3kud5VncExzBi0ZJIStvj5DC+9dEzZd/ArxzZuw/C Aq5TmJPpDrjACpGqvf2GLHCFpkuIEPFwp5qbNIF12+KZV7BmS3gS33dyMai7PXx2vZ3PCaCyoFhD QzLw1u4+5S8MvIf4oqh/TDnxHaVsX05rV4B97cLwKdH+xA0xZQJtTkNwyWUSYyTcAyN4iia98WqY qviRwSelgWE1OAs5CemDHZmJy9fOXtPytQNHB7GZEumCwZg+/oL5pfD2q/C6de8BgUSWOEH+S1QK kO19gM+EDxoo2TkK/285As02GAqgc5kW8pT0wCPhqMgnQuAdWl2TUczZb4z5X8m3/zmmDzwC90YR F+BwCc5PhL5MmCh0geCEuxsQmuTbquffCLt8cKqBpwLOPqQS8XQQAPc/vDzq9f6SfUL/4oMELkoj 18iHCb6M6AYtUvQoYFH796pTA0CKdIabTEf0EZDepwkkvIhtrsAfS+QpzzjmjKOy+SxL6fFTcMaY h08gNUBSmvlX5lN4IogswIduaL4Y7NczRjGWxf7Qcv4Th08Fb9Sf6r5K2CKr9IUq/t2+0iViYNTp 6EIHOBPRhQQ+i5LvxbdVMseEG7GMsGybVREVAeih6BkzDG0Va/RMtDB2JES0HJ9ydFh2PAyXCb03 dabRp9I1Kpa7PUmq94a4TlLtQzi7WhMciOCzqqo6jxili8ToIzIbAeG6HPChCmAHrpizKjEOqFF2 h34WkReCCAUHi90FKCc4+CFmi1p3TJZArTUHqViel9RBWz/pAAesg8gk3yIvxmW25kZNQYqDxTDL N8bduE8clOaQvvvCqJrKVGWkxCgE/VtIhjMUBY6/uLvDeT06gbfZbIJfV/xRgcxjNTp6xA4UzpO7 hxF4akY553byO2y9wkXQmY2wA767LXSmsJuGrmH1+KMPyXwyniHlutbA7prOvYRNd5NHUn08A2Gj i6vfiuXdDb0UdfGGo1ifESRpKTkZSGRrTEfGrpREICsDvBSWnBkvDSwQdQfh51GAUcqqxM2o1Fxy M83g2VyYSta89NRGSSe8JVY13wTkKm4qviKPxEVyWS8kGWOVyOp8AHaqzRW+47okX60psA55F4AI dkZF+vINSWalcI2+zCP6ggFilvhJwAAi3pGHdBDxrCRbZAWHxonxgJ3X4FCFCmHRX0mCYtrSJTjD ciXXOTqhojvExEKJfwFCd+anIJEhbGHGMUWL0oHsRwDgbCMFj0lrSo7lj5vSHNd1rcHAq/u6Wfvp 8x8CW3jSZW5kc3RyZWFtCmVuZG9iagoxMjMgMCBvYmogPDwKL1R5cGUgL1BhZ2UKL0NvbnRlbnRz IDEyNCAwIFIKL1Jlc291cmNlcyAxMjIgMCBSCi9NZWRpYUJveCBbMCAwIDYxMiA3OTJdCi9QYXJl bnQgODcgMCBSCj4+IGVuZG9iagoxMjUgMCBvYmogPDwKL0QgWzEyMyAwIFIgL1hZWiAxNTEuNzAx IDY4NC4xMzQgbnVsbF0KPj4gZW5kb2JqCjEyMiAwIG9iaiA8PAovRm9udCA8PCAvRjE1IDg2IDAg UiAvRjM0IDEwOSAwIFIgL0YzOSAxMjAgMCBSID4+Ci9Qcm9jU2V0IFsgL1BERiAvVGV4dCBdCj4+ IGVuZG9iagoxMjggMCBvYmogPDwKL0xlbmd0aCAyMjE5ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVj b2RlCj4+CnN0cmVhbQp42pVZW2/byhF+968w8iQFIs2rJSFFC6eOgxRJbNgKzkNPcUBJK4kNLwq5 lCMcnP/euS21ImkjhQGTSy5n5/LNzLcr/9KDP//S9zx3Ng8ur2eR64fR5Sq/8C638OrjxfvFxdUd PPI9d+7NLxcb+mSx/vcocAN37Eyn09GXm8f3nz88jZ0wDEdPY390//nbYuz4o0/3MPiKzyN6vhjP wtEN/lt8+ALjD18X4/8s/nV158fWAmEcuoEsEuP7iw+LC19UDeOZex3HcG8paV46YTB3Pf8abjx3 Gs9wDhswv5y686mtfqVQQ91UBV7VeuzEoT9a4qAsmhqHwSiVq94pvvkjT3FGkeYNXvM/Bj/73Yu9 Eh9V/DpPcJA1NbzwecqmrPimqVnitrNSpWoS0VQrhU4AtZ25G11HpL3jiIKar6ukkO/JILwrG3mn S74uRfIu3e7IZtFO42DXCtiRsloMo0HN7gGZhf2J4gE58meysj7TZFMuE/bknbTQth0x2VFujBdo oa1yTx5cHyk0SZ6SaOM7f/QWh1fvSJoT+K4/hdD7M3cezEnonmKzzPA7FHarSANVrEnVYnX8SMEp ad6ehfK7PUziuasjXj+aSXccT9RrwT75PnmkmT8aMrZSOUvXE4J+oWlYHZKs1R4VnoHtM9sFV29Z yzuDCPWTFsj3GUmYsH60yIbvj4wMHqwYXJkdGYPbUoL6kGb0ifYtYRLLp684uHmYnCPqOWWRiETN QCzIAD9wp15ogxGd8oBTYBWa6k8eaAUYBsZ4l1f7ZJtBuDBmUKzNmoNAI38nmCngdtstgrrn1PZE RdMgHyxbUVMcPhoprWbB6Dc0uAfQgmJwUJWdQ1TU/NG3z58nrbNoaX7JFaXzDcQ135M5R/cVGJ+8 2kUx5g/LqUlMmieaSxcp0+RLBtz95p/k13zJUU44GcuixgugzDgB0oIRu1akMaRFxRHZm6C96zuk q9ajqvmjiiKo3pckrKkJEUla1V0UddUjY8ri3ArKBgkYoe7anYdT2z+tP67IkUuln0kBJZgXqCek ztoqVbVOc9JNm7JWNDmLkIiZqrQCLUm7k5Zk7NurSd8xpyozWEDaAuBgBZjblpgKsDjV/jbjaCw1 pBHwUfVO6oEMqblgqtfMIpiCLbU2Ca9yvkvqXg/6Qeum3CxzVdBq9VkR6dYE9obmFsMzb1+vrZJE SSHh2FDpadcRUXm/dHFNXiuCiikwG8sIn9p8+5kITCwRjF5ABHc9QESv1/qULZ7ntV5d2u2ztCra igb9zJt0HLuvSs4i9it/fEMZ80n0pdBBC8jZZdrmE6TzrmwyC9qMUIjlnitiD5+GBnTB1SrV8gKu L07N8HGWJqPtulpz7JCcnBy7sZU8QwC3n2dVOeSZFpiKLej5vHUkUwCd0rh15LJpu5QWX8iDPPnO GdvlU7VKWOGd5JSqjqbfSuIyEzsYDrLYkYn1xEq0su/XDQd7PZCOpPTelJvMCl6ZNYw79u1kwGVM /7K2jTAvMzlRmpT93uGT0HfIzuOLJazv7YLJbFsoko22ES4Eo+v7k0ctBtvxACG0fFYWLuzFWnvq HTeRTErAljuTqmTtY9tKe/7PSzvPNzRoGBabJpNYvv0/6GLbWLacKdDRGAjCAb8Zxv5Kc3psKyfX Lu6weqBtLBICxHfLeRBU7pGFTjh2nAbbBde0x+GyPOkH9tScqcGrgfUzRuDWAqdOKhK4SJnS3m8e OctbOd94V/ALPe1jeuDkGgDHiutQJRWOsHIUPVY8cU2ez0yj1uIsUweed4l+YacmLHol3PDAKdio 8w4Puy8x/xfa2FbqQzGwQ5MkfKn+9T79QYadRZAyT3fM2Kacz7b/ICz/ODGGXtCXXCvNZrTdGhru yvR0K/XgoE47rt5u0lDXpLOP7ZB6wyhhx8Zbl3TFuzW/n3gvM8lDmVr9bC/8XRNXeKg453S716hS Rv1vVXpWrDrc1bH2W+yevwZyIGXc/40ll8uMs5Z5ywbyzy7Wf+eF/sT7vwbOL6gaXIPFkXsdB5zk O2p1URARUKMgBCxyasqIL2A0PFpS18fJh3EwA8vS3z0/Ggt9rXgqwIubPs4DePkyWcDyilCtsqzm 2yN+gp0c56GMjdxqudbsRGOSH7oxu+wwjq/BhhSiFUbhCLsPXgtcSgMsomk8uhvPovYNnxKgzsxs xpjW0vvCKKYlcR76J4zAT1k7Y4utE99h18MrtELQ29iLswvjHYB1yi+c2qyDaKbPqZ6R8AKnaFWJ GXVbGNHCiPsCaR7KqnCt1EadPYrQwzgkqwuHTUQ3CqnE51ueUnPSCZuj1fkFVjQUZUJBAaYXxWoc TEcYZqPJgBBXADi/9CM3jK75iAuMdINwxud0IMDFbbgfxKMvNzAyp3UQoicgInqMtGBMsKFn0HWI ScISoVDJDsQ9Qnd4QjfbAEwDNFZA9kC1Z362AvqrU92QSOn4+Jzwq1h24NnpE0zdcB7JMV2NTEly r69HBHfzUKaaOCW8AB4wJtUyM8ioA36OiIAUb9GVu90zRieMXQ80wBRxQ1jJOmrsHjnGbmROI186 aTSt8FZtuJuruu1e1unFSigubxp0w1XttGUhylXyRtDw4Q4Z5MsXsJq7Jpr84nZjPVjczNnEVZsQ J6IEXJJSig87WlJ+KyRnpaXJiXl/Dp93yc6mrKTZvFG0K2xcLsKpu+aDBFcYMlI4X3LXH7151294 vyyReXgl5b0OSPIewUDuynl7KoQ+Nbu3N/1OEp93EstH5fK/4gw51ZOTIbVeCLXj587PPHvBTWzU k5YuJxuZguumejdE4Xr8G7x1T+E9p5cOb4OZziVEq2HfeSsHSQOutbVoyXrVYYNfuemr+vUDzrJa 241azmDkmFQw48jut7s37XxrHwI884mn2dfJvl94ZFJZJIxdUadb3lus7ePxEzXtFAPqCb4bTYd+ cBj8NeJ/U0hTEGVuZHN0cmVhbQplbmRvYmoKMTI3IDAgb2JqIDw8Ci9UeXBlIC9QYWdlCi9Db250 ZW50cyAxMjggMCBSCi9SZXNvdXJjZXMgMTI2IDAgUgovTWVkaWFCb3ggWzAgMCA2MTIgNzkyXQov UGFyZW50IDEzMCAwIFIKPj4gZW5kb2JqCjEyOSAwIG9iaiA8PAovRCBbMTI3IDAgUiAvWFlaIDEw MC44OTIgNjg0LjEzNCBudWxsXQo+PiBlbmRvYmoKMzQgMCBvYmogPDwKL0QgWzEyNyAwIFIgL1hZ WiAxMDAuODkyIDI4OS43OTkgbnVsbF0KPj4gZW5kb2JqCjEyNiAwIG9iaiA8PAovRm9udCA8PCAv RjM0IDEwOSAwIFIgL0YxNSA4NiAwIFIgL0YzOSAxMjAgMCBSIC9GMTkgNzcgMCBSIC9GMjAgMTE2 IDAgUiA+PgovUHJvY1NldCBbIC9QREYgL1RleHQgXQo+PiBlbmRvYmoKMTMzIDAgb2JqIDw8Ci9M ZW5ndGggMjE1NSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqtWFlv3EYS ftevGOiJNDQtNm/CiwBKbO0qSBDDGiQPu3mgOJyZjnlMeNhWgvz31NUkRyMZMbAQNH1Ud3WdX1dT rzz40ysdaZV4ehWnodJBuCrqC2+1B9K/L77dXFzf6milPZV52Wqzoy2b7X+d2P118/31LSyfaTrW KktTWfHdf27ebd6+d9dBEDi+ctdJkjo/utq5ef+tu9bOD2+hf8/k+59uYbDB6V9crX1Y8xYPuHi7 udAiZhClKo4i6C8EtMR14GfK0zF0PJVEKa4h4YNslagsWYrelXjO76PpsC3rssF26N11FGjnFQ6u X+PpqzUwjYHDOlNhHNLmv4jg8VREU5XBLf3wr1tsTVUR/+0m77H98A3zXePgc13hyHf+nPjoVGV+ RozaB1zzW1kMvOi+rXBiHEyLbfOm3NEJDR0Isw2vW3+ucaLig/4kzimIly6lvKVNINwWWxDuA0kt ii6WL/R84nsdqyxIwCqxCmJatTmUaM0gSRzTc5s3Mm5cP3MGssW+7NYf3Sh28mrE85G+A4ePDfwU szJBEjtFTjLS6b63ON33QhWmmTixcCPttPVxHMqfXTyWLQA8y/95kQf/+hkNklRFiS8s7Ilg5tFl EdbW7ZnKMrCHDlTE5nsDbHWI8hqWNQRTDwcY5wMOfFQYvG6sRvlQ9kwYDiV3rAlgicy0O2aU87Cf JcF8CVI08Onekjc8s3HAo0EyG1cgeshxVbRNb3ob5H6S0XYf0pFEwwmi4tkQSX7qOQPyGvJquTyj WbsDBIpAILJ4z5ssV/CgrIcokxOHA9oJe2Qz10+BA/yUvDVnWsemgbhsx85aUg7ctd3M/lxJ8gXt xbwOvRDy/HfUyHQlpQekOZw39FdA9dFdBRl7S37b45ZoYoL9BzwNDGqYW95suVPnlUyxTtyTTQWe cWh7EqSxcqA2Bev1jOQPqKlp9gyFQystMaceWFHRPpuAsDnx9TIDQ4hj9H/P3bwih7Y4iihIoOGp KcJ4kp3QGYxua8COSXN0A8c9crceY9Vmzj/mqMJDVcrxPjdHmOwA1CAxaVMtm5otp9lCHWuLZSAD poMQZYW6YPcRT29HHISO2XFLlkdqlWN0DlYBpOUMLagwxMyjbBRuOTeSV2bL1PPzXTIjde1RCF1t V/NRkKhhqJ1bNw1h8hn3lp9zir8j2QGiz8+SKdD8NHV6psvoxRQAWpE3DSUmDhOKUMmgBDVsaVzk bIQtz4sCuH1AA34iK/LEFgMPPB+I1bR1LiaKbJcUhtW5nQI7nKs5axEg5hnGEpi44hm0N0Km3Io8 qvGksR94SWnmJO54gYBFMoUesULUqbiPmBNYKOb0d6fdctMjRplJu63QlnYuhHMrG5u2cSddQO9z dcHcV5yeOeE+923WYR8xnG+gbKVDFYQxFyHrIFJBxNkLZZF2AtikIazuW1Tro4uW6Caw4P1QWOFF RPv9SHlpsNiPP1qY3DU71h/s1mNCdGMxjHxJx4ChYhzof+cSJqLtOoQ07fzBd3NEhQdmO9BOJTmt BNc+DPyEJLnDfPE4raChECKkIZNMziHPoeG0tksLG3PZLAgFzpbpkJIz7qDsniSxF1sWmFqG6ORI IFBYwEJ2CJrEBj9kirge100Yh2yG/MN0UH+iDgDcDGMnGM7VAQBeEIIcyLkFHKxxHDtYiRC2M9nG KJLsDaFZxJJXkNjQ3pcMXbiZr/RlJayDROkES10PWh9rXSZ1+xV33n+pdPeVPucISJydltYvM6Di iuSkSzEI8Y6DIBNtUL2c6QSnogRNMHRy3wzcopXYROGELVAWQx0fpqcQc26zIBQEgDeEjbFieMZm a+2B3qE/Mfxqo/nPGS38GpvxJb5OIhUDkW/xKXkiD4qNbrvAP5zBaiDyUrIbX2emohqUQB4pNqgi D6q4x56xv84HUzC9tJVjPt1rOI1WwwOeZh9hAC6Y0q7rX7yt831uJnegN+OIpc+5DsIZyMVTVI+j BVg/8gRVjNgx9bE6KdeEzpyKlkg1RRNM3xzpUjVw5Rk7945ycN/xdVSX3Xm23jHrsputKrGEoXjz Dmbv0F8C8J8sgoFJAT4OfCsQBMGWvWEzNbyYq0bGupYgR52FIr4s4kD5cbyMnadP2kiFkYTXSy/Z 61f8ANwcjGAWPgBNz7NUr2Dn5t0dd3atDS186LLizcCXBbieUVd49vz4/Ij+h1bxpjfy1u2ofYDk FqRGohSYH4mG9ew0WRzEqSLa2ItoOde1vkq8YPn83Bp66u74+gEhn2jy6SAUPgSCHAfdo2g+FRlI zBu59qOpJKDaSq4L3HDIRbCHspRH9ZFXrk3DGjOXweQnKpOUc9k3P7fJ+BB/irm9upYkOtO0IJ/U R1Pxq9G+9eXrBIAZu0EEfOF1b7jEbDsx1CU/80fF7z6jtqyuGlmLSnHLRezl66UC4ddxpHVwP1bs YJ84Hzv+mCHJvC13HBSG1Wmb0yOj/8OR9HjmTwl01Hb6WkIeA4C4xPaLHzz+2QcZyIk7OgLy6UwJ m5M3i/Af6J5EkbcSZlVbLCKUBN5J9lKcwWO3PokHCWUSqB1Fnsd2XCS0QDnAOFkeoHwR9zY3p0fL /K3r3PcvBNk9GbIznNo+eJOVaGzpb3edfDGzBtExt8UhZ8Qp5LEmsMX6fqb4qMf6Ss5oBY6OAtT9 E8JxbOSZl1s/n53D4NCTDa7EHiPjkth1rusoQ/2l2pdcbXSG7VqwfT9VjED+pc1w+81QQ1GmlxZ4 areibTiQj7PEAK18pQPdWuzS9zyk6LWXEhb5NLwU513P4XwSgQ8t+7rMF5HT0yT4n4TeMH6X/Yam 35f9UdCGvsECLPzALX9pfHqDYZYkqUoy/7lvsc9+qP0bk5ODg2VuZHN0cmVhbQplbmRvYmoKMTMy IDAgb2JqIDw8Ci9UeXBlIC9QYWdlCi9Db250ZW50cyAxMzMgMCBSCi9SZXNvdXJjZXMgMTMxIDAg UgovTWVkaWFCb3ggWzAgMCA2MTIgNzkyXQovUGFyZW50IDEzMCAwIFIKL0Fubm90cyBbIDEzNSAw IFIgMTM2IDAgUiBdCj4+IGVuZG9iagoxMzUgMCBvYmogPDwKL1R5cGUgL0Fubm90Ci9Cb3JkZXIg WzAgMCAwXSAvSCAvSSAvQyBbMSAwIDBdCi9SZWN0IFszMTcuNTE0IDM0OS40ODEgMzMzLjQ0NiAz NjIuMzgyXQovU3VidHlwZSAvTGluawovQSA8PCAvUyAvR29UbyAvRCAoc2VjdGlvbi4yLjEpID4+ Cj4+IGVuZG9iagoxMzYgMCBvYmogPDwKL1R5cGUgL0Fubm90Ci9Cb3JkZXIgWzAgMCAwXSAvSCAv SSAvQyBbMSAwIDBdCi9SZWN0IFsyMjkuMzExIDMzNS45MzIgMjQ1LjI0MyAzNDguODMzXQovU3Vi dHlwZSAvTGluawovQSA8PCAvUyAvR29UbyAvRCAoc2VjdGlvbi4yLjIpID4+Cj4+IGVuZG9iagox MzQgMCBvYmogPDwKL0QgWzEzMiAwIFIgL1hZWiAxNTEuNzAxIDY4NC4xMzQgbnVsbF0KPj4gZW5k b2JqCjM4IDAgb2JqIDw8Ci9EIFsxMzIgMCBSIC9YWVogMTUxLjcwMSA0MjguNzQ3IG51bGxdCj4+ IGVuZG9iago0MiAwIG9iaiA8PAovRCBbMTMyIDAgUiAvWFlaIDE1MS43MDEgNDAxLjggbnVsbF0K Pj4gZW5kb2JqCjEzMSAwIG9iaiA8PAovRm9udCA8PCAvRjE1IDg2IDAgUiAvRjM0IDEwOSAwIFIg L0YzOSAxMjAgMCBSIC9GMjAgMTE2IDAgUiAvRjE5IDc3IDAgUiAvRjE5IDc3IDAgUiA+PgovUHJv Y1NldCBbIC9QREYgL1RleHQgXQo+PiBlbmRvYmoKMTM5IDAgb2JqIDw8Ci9MZW5ndGggMTkxMSAg ICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrNWVtz00YUfs+vMHnBYuJFe9GN tkxbArRMAgxJ89L2QbZlR4MseSQ5aWD47z2XlSzbcgihHRgGdrW75+y5fOeyRg5c+CMH0nVFGKmB HxohtRlMFgfuYA5bLw9+PT94/AKWpCsiNxqcz4jkfPrnUAktnFEQBMOzNyeOlHJ48dyRw3dnzt/n rx6/kF6HRntaKEsX4P7B8/MDaW/XXih8z4N5595mc6SNFIGMYOKKwAvxDMsUDQIRBV2JHj9yRp5W w/oydUZyWOEXDAV+ZFdJ2VmMcV7XyQLHZV1ZwoJ36WRSLRMcJ3XDNbGTdGFnWUr0ac1kj/Dj8Q+o 32AUCuOHg1EEgyHpzmrcLtN8zsRVkeECyPU7jtMkPuZrJ3xuSefTIid+HXbehrIx373Ew3EZz4k4 Xl42QtO6FXBqLyD90nFSbWm2ILOkOZ9OrVjtKTYab/LRqSW8KVY8uS5TEjvhU3x9WpEKUonA1V2T bLrmCdMczoimrOrGTrTb+iiu3nccObYSTFEZcFaST1kHa+WrmKy8So5aXXM7K1M2B38mVoob5gtm X6uRsg/crv1ZqmlSHjL9o8bxSgoZyO7Rbc/HK1q4LMrX5J0GTo1LD1/F3duPyUrJ4S7I3Nvwtb7l OY6LOM22rpky45//ucHxg2CV5oe3o5l1+tQIIEMRqYjW0px9X5IH44lV66zIrnh9V+iP/eBerpBg nKWTlkU3VvJZwetpTlcVf7meC39lj1m2WT04VbhwNj1j8K1qgkGRc/TN0pxDOqXtfCuH4EUMZl9E OujKbBm/nb4lPxRjEjlZHFMWAb50T50yso52JX1wSub7ZUxILjL2X3K8KjmGNyRiqRfJCUm74KhD E6z9FfWlizbWy4RIVmW+FSjMn5JQnOdFJ+abWHv9B9160oMTMovf1erTbuhkKWlY/3jGil6Rhcqn zO8jfnyyRSQaSCkiKA6U5kcqEq5msCmoNULjP2h2qbzhM/iA4EbjlKAMmOVDAslABx7hB+CDe00Z IP5abxS2kXKFF9mgTcoUTaJDQ5zL2MH8ljGKt+rbJulp6pCftNZAinZM+eO6LGqH4lprQ2WK8hh+ WJ+TlCocskWgvhpjhr9ZginTAg2XCFRx7IxUYFctW1i+tBRxNi/oGCRiQItdtAdndF1W4H3XxEBs l+bWn8rzhKfCvgottQDduHpTZd6yDNdmucMbIBruKfqeMJ7pcFRuD8dTdTaxTpLDZ6XjQcoDNWBA H0E5eVLBvxy4aNakSRLbgqDzlJCmVzsjfNf/nHLIOe5jDbaJzN107GU8KbhBAflXNfpU6iG5kubs 65ig877i3WsC1cQBUFziimoiGVYf1nymxQx+VLZBoDJNOUSaBmdT5jBPGZM5f8ZZZiVpkAbzGE9c OZ6HYAeLx+MsaUuEFh5HRslVGroFgO+EUXvEsRFj4cZJi/IsqdtIadYWuxBVhntDGfT67y6+Gzvr 3LnhPiM843+F/6B3qVmruglJ7F9sAHbSRF6gg0ZsdKvrpEzYt3RuzgeLcooZDKeppa2TcmE3Z+1l qT0ELvHBY6uk33AyENIP+vVTUt/FeJM9wG+Twv2QzyjrWq5sDbMqOXGCaZpsiSdnq9yxlYvwXFgD 9eXWcycEzIEr9mWFkSeFCtRtueFumU/955kPE59tqEDLOKPy5HiS8x8GTvkEE2DFOifnDjzStnXd klYaXwShtDdgTPpoHzLdimtHB4hesE5DXggX5nNs9TVgjTAI+yn6YomktSWwwIfzqS2/qvuuHDUi UL4wXOXznmStPeFH32GyTmc9WkUgq28P5D1290HV0O7/JNHu8JC24He9tZVdH+CN5p9wB4mbfbh+ kzdRAgg3TXGwzAoeKUKKBTvHMucog80iz27scXDYl2aNb5tuez0AM+nLO7rgKbkAeggsuXFZwzvA 4cegNqE1Ek4slk2EOFcRmxY3uJmC8moXljEGaV1xzw+s5ZoLlto2k9U9oivlidDVd5R9JFvh2o4A PrGw8sPUQPfsb4RXCy9Ir1ls5eCsu9f5VGv977BklMmkyVZlWtnKQGAOTBNSgbHmD8wdywLTFBsk bdHpsvu8N40W0vW/xJuBab15xE8RauQ6DvU9s+FRqyeB1foW5htyv8mTRs2OlkhBmA12tNxi1gGK CVr5HItvRd120jlxwWTYdfbyq4saevhOr2hVaVsX2y6tu5s1aKu1mNyuar3RFPdD2Bih4C163/w1 /b/yFzwPY0rIRjVZQlGHaFTXzhTYTccMW1N652wLA7lDhfZKXiznA568e3lbvzLeYSWFJ/c0LFoE +rN62RTU5amk0PIewk12hHOFdrfSxn56fFEHcniyxqcaXjzkcf0sAuO2ZfTamp9ASlN+5SDKEZr8 s0eATeJmbt2Aa1rdF68y8pn17XlX3QW4yd68+5V9D+kBdpTDp6jHhX3TVTFWwm6L3jbzk6Js+/lq SamBnkDT7mOn+SHMaX43IxYFj2t3YW5Y2fdOtT4ubm/po/u39Prb/5hBSZzb+r0vGBVYdQOhe4HT +z8v/wLtIgwsZW5kc3RyZWFtCmVuZG9iagoxMzggMCBvYmogPDwKL1R5cGUgL1BhZ2UKL0NvbnRl bnRzIDEzOSAwIFIKL1Jlc291cmNlcyAxMzcgMCBSCi9NZWRpYUJveCBbMCAwIDYxMiA3OTJdCi9Q YXJlbnQgMTMwIDAgUgovQW5ub3RzIFsgMTU0IDAgUiAxNTUgMCBSIF0KPj4gZW5kb2JqCjE1NCAw IG9iaiA8PAovVHlwZSAvQW5ub3QKL0JvcmRlciBbMCAwIDBdIC9IIC9JIC9DIFsxIDAgMF0KL1Jl Y3QgWzI3Ni40NTIgMTc0LjA5OCAyODkuOTU5IDE4N10KL1N1YnR5cGUgL0xpbmsKL0EgPDwgL1Mg L0dvVG8gL0QgKEl0ZW0uNykgPj4KPj4gZW5kb2JqCjE1NSAwIG9iaiA8PAovVHlwZSAvQW5ub3QK L0JvcmRlciBbMCAwIDBdIC9IIC9JIC9DIFsxIDAgMF0KL1JlY3QgWzMxMy4wMTEgMTc0LjA5OCAz MjUuMzA3IDE4N10KL1N1YnR5cGUgL0xpbmsKL0EgPDwgL1MgL0dvVG8gL0QgKEl0ZW0uOCkgPj4K Pj4gZW5kb2JqCjE0MCAwIG9iaiA8PAovRCBbMTM4IDAgUiAvWFlaIDEwMC44OTIgNjg0LjEzNCBu dWxsXQo+PiBlbmRvYmoKNDYgMCBvYmogPDwKL0QgWzEzOCAwIFIgL1hZWiAxMDAuODkyIDQ3Ni43 MjMgbnVsbF0KPj4gZW5kb2JqCjE0MSAwIG9iaiA8PAovRCBbMTM4IDAgUiAvWFlaIDEwMC44OTIg NDM1LjggbnVsbF0KPj4gZW5kb2JqCjE0MiAwIG9iaiA8PAovRCBbMTM4IDAgUiAvWFlaIDEwMC44 OTIgNDEzLjU3NSBudWxsXQo+PiBlbmRvYmoKMTQzIDAgb2JqIDw8Ci9EIFsxMzggMCBSIC9YWVog MTAwLjg5MiAzODguMzMgbnVsbF0KPj4gZW5kb2JqCjE0NCAwIG9iaiA8PAovRCBbMTM4IDAgUiAv WFlaIDEwMC44OTIgMzU4LjIyIG51bGxdCj4+IGVuZG9iagoxNDUgMCBvYmogPDwKL0QgWzEzOCAw IFIgL1hZWiAxMDAuODkyIDM0MC40NDcgbnVsbF0KPj4gZW5kb2JqCjE0NiAwIG9iaiA8PAovRCBb MTM4IDAgUiAvWFlaIDEwMC44OTIgMzIwLjc5IG51bGxdCj4+IGVuZG9iagoxNTAgMCBvYmogPDwK L0QgWzEzOCAwIFIgL1hZWiAxMDAuODkyIDI4NS4zMjkgbnVsbF0KPj4gZW5kb2JqCjE1MSAwIG9i aiA8PAovRCBbMTM4IDAgUiAvWFlaIDEwMC44OTIgMjY1LjQzNCBudWxsXQo+PiBlbmRvYmoKMTUy IDAgb2JqIDw8Ci9EIFsxMzggMCBSIC9YWVogMTAwLjg5MiAyMzcuNDQ1IG51bGxdCj4+IGVuZG9i agoxNTMgMCBvYmogPDwKL0QgWzEzOCAwIFIgL1hZWiAxMDAuODkyIDE5My4xOCBudWxsXQo+PiBl bmRvYmoKMTU2IDAgb2JqIDw8Ci9EIFsxMzggMCBSIC9YWVogMTAwLjg5MiAxNjIuNDY0IG51bGxd Cj4+IGVuZG9iagoxNTcgMCBvYmogPDwKL0QgWzEzOCAwIFIgL1hZWiAxMDAuODkyIDE0MC4wNzkg bnVsbF0KPj4gZW5kb2JqCjEzNyAwIG9iaiA8PAovRm9udCA8PCAvRjM0IDEwOSAwIFIgL0YxNSA4 NiAwIFIgL0YzOSAxMjAgMCBSIC9GMTkgNzcgMCBSIC9GMzMgODMgMCBSIC9GMjAgMTE2IDAgUiAv RjI0IDE0OSAwIFIgPj4KL1Byb2NTZXQgWyAvUERGIC9UZXh0IF0KPj4gZW5kb2JqCjE2MCAwIG9i aiA8PAovTGVuZ3RoIDIzMzMgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnja zVlbb+O4FX7PrzD6JAOxVtRd+5bdTLZpZ9ogcduHTh8YWVbUyJJXkrNI++d7bqRkW9kJEAxQDBBT 5CF5Lt+5cdTCg39qoSLlJp5axGnoqiBc5LsLb1HC0i8XP60vfrhR0UJ5buZli/WWtqw3/3TS5b/W f/rhBsjHNRUrN0tTofj5j1d360/3y1UQBI7vLldJkjpflsq5uv9puVLO508wfuDlh7/ewMcap/+x VMoHmk94wcWn9YUSNoModeMogvGEQbMIpyg3SH0YeG4SpXM0oRt7MW9/W66vXuRp+KNOr1eBmwXZ /O2RGxrG3j65LAYUNnR0DbLWPB5g+FTweAPjAlWwX678hIcNzG2KJn8tu/YA433PtO0Wf31nMJsr WGz2SDKwSgfd4wHPp3Ks/HCxUombhWpWSe9R0CNcM6uj0I3C6AM6ynUNiokUKOaJtYHjLYrV5EPV NjgROF/8uxeYuuta5KQudijp35dpyErrYLL66qmw6H78cblKvcjpB90N62pXkFJukLTtrgvUdOoU zQolAR5WYOUozIgXUvsveFZ72MNf2onSk+SwD3Tc8m+OSzo/oGH1UOBkyKbBVb6k7ekAtNNjLSTE lRwFvPEkWXIJOwYrzMsyigE2PROg7WkLq+icc93kuP+p7YAwzJyeDip3BR97CbMxnNdseJmAiQPm GAYMnb4qm0Jo7umIX+HCQ9UZnO4Eoj5sZYK+PXR5cYfX6Krr3VnwhZHrgSfNYsRX/nsAmL/hoQD8 D6CvQsWGgdHDaB8YDmgWwpmsVWKjbmIcnJZfVtB+QKJXhBtp3Xes8gYEddfwjX/52+fPvO+L/wDz BKgbvLKui816CdbW/bP7PXx58718uagJRQUKHoUgEsQ0RGwr7mB0GUTW2XH8yC5ZVhj6qqbE2dC4 B6wT+jGKNuOJHS/1RckWYtMw3bZrd+e3cIhtBeeP9RueJEY2NrO2nrcEpKDwY7guvguut6ggzAmF Fp3Rl4XiW27Nkl+e8uP9HuYyUIGaMhTOiqqS01NRkvAjqaMjtrUYi5IrJ0fwHfKnocKkUXFY7SVL mvz5TWVkjqTvxiwVGznE6rcaJD2fIHEGLp6Lrhu5CfjY/5MaH9kx2Eef4SdLR8fJMidvu46TqLhQ syE/hT8lU3c2E5hAVvACqQkHpgDis2VVbISHvVQUbY2dxlW4fLOad1O6nozeGsuOQDjKXsUJvu+J Rc5cOLxDW0P2OrNZGLq/7+Rpkr3HybdYCs/HXN+N4/gD1uuKgdTeHKEbC6aH/KYitZOMG6q2KavY IhGK+WByJBRarp8EANXMzZKYjr/XVI9iQA0gpUE9xeal3ScMIcQzNs/PxFM38aT6lfmSYD+1n82z e8utKVywGOFdlFRpfFUX/9aEwU4MT80FJteSQdMi9YsEAuKfx9dtUyJGtCjrz9XOFUmyhYJOBtTN kkBLESQhiQJ9jHIC+gNnKz9ybpstXk6xp8fyuzvkYARUfBID5nkRx9coWj901eOBghTykUTOQ4t2 eaFUB9c4D/kywKYAa9Vzsxwpdk32BTNeEXBv+WNLUnf8cV3BjehUOAkX060wj7f4eIs4KM7d7vY1 faLCsblou/5btiUWwgRYwOtxsJWMjGPOny9LCiZU2Ja8QEGDsMfcPVLnItgECp/LbaMahkcI2ult 1hdo9Dxf9XxwU3DI4cyuO6qhXuXSimodFRBFZ8qE83iypaofUR7G4kQwaBsZcLwPo3nxkCKfYL3T dfUfUjss9G1N5La0Jz6hOQ5T6X+fRDNIbX7FTbjNOJhdwo3mnzc0upkRD5mwLqcyPo1bhhEPCmN9 08uRfGfge9x5qtTZ6ca4IZBupOUKrGDTIEsbvqog3EM9XZm0UO3Z+5Ck/wOfg/GxlQyLtxkw4Vpx rDm5Gfrc59keCDsXMLrs6l75cz47JdJVAYHmny+6406N5Pd5ck+4eKSGE/fYjh2bTTQ/J662weCO dW8ajYw0tHJy2zAiWdjFsbA8I9YTBmw/yYjN1HNKtLLuRIsFtWuwSg3riizXrjamJqINdfuoa6ZC okqCFX4zU6YPTAQYA3e08PlfPuJptO5EhbCgmew9SIwsECc2nThyBI58trTXfT+GCKnvkBaFQj3U /MlyjU1WAf6VxhmEauM6Q6E3l/iW4FG+GejOmGBuMiLdQMbDgdQ89AbT5TbrnYKulJgiZaDuJP9W DbSSvFRp205IIuoLk+7AkKAnkxynyQpElTTV0WMIZaqFit0sSIiFJPInKQFC5NUd0N3y2HqRz+EC fjYVZSIsiTgn+PFxXIBtZ7GWp/ecbDHyVZtJnYZr6L8wpeStRB3dya87ONoabnb6WW5EeVYTgaxO Uexs1gC0ADo/ijQwZcUNMrO9rEY0NXKiZX0aDC4xmvI7jy8CjKdwALbPd5mp/LOJHLDdM/QUm77B 5ZlTaLbzGce+fdK7PMcH8hsYfjGIUdr2venrLNQ0XKHSa2Rrn7LOM7wfuCpSR29yR33OtLY+NoxM kt/IK+SzsFROcqJkPba5Sl0vC45QfIUBJWVHLsYQk8rbV+JIZYdpcbO0q8f52PQksKD5hyPTEe5x egbnOP2bea9D+XGCNHGuWD9Kob9/l16hvqfuDCnxMRNF0eZ4jgBDlXOLA4V34qaeOu50pjkTFAtR rYcqHNhH/dd60LY9bW2hQAElTo1zcorXjQUsLOmBD7Pv0Xiw3smIX19wpPNcqkuA3vQAaruQ1Kqx ksoXFmu2FOjvHO65WUQhnifvrAqrojB0buVB+8gcOPFs/QPfcXGG87McR1Ugv7lO2oudqQzL6TPA QWJzfYxvW8kcBfNGGo2d1URh4TxTrY/tky3Xryu+mx7r5b2a6nOCJbGM6KA6veLiANz/UOtBrnyz KE+nVXmUcsl7lovp9DBKHPskzRDi92sq3mGvbA0jin9Ivh+dEQnQGe0p7ZbE8dFv4CwtekG64QkZ xxGbrNj3/Kmb8aYmb3eGm6Y8xwl37WxQMl6gAufXQ3GQIQcnFcyWiQCNtypPDKYqobSPdMR8QRBT gcyO/xuB4wmYieYMwIjbIHX4FbwjfedGsr313dPXBdNuB0nqqsyfewKY/R+w/wHFharzZW5kc3Ry ZWFtCmVuZG9iagoxNTkgMCBvYmogPDwKL1R5cGUgL1BhZ2UKL0NvbnRlbnRzIDE2MCAwIFIKL1Jl c291cmNlcyAxNTggMCBSCi9NZWRpYUJveCBbMCAwIDYxMiA3OTJdCi9QYXJlbnQgMTMwIDAgUgo+ PiBlbmRvYmoKMTYxIDAgb2JqIDw8Ci9EIFsxNTkgMCBSIC9YWVogMTUxLjcwMSA2ODQuMTM0IG51 bGxdCj4+IGVuZG9iagoxNjIgMCBvYmogPDwKL0QgWzE1OSAwIFIgL1hZWiAxNTEuNzAxIDY2NC4z MzUgbnVsbF0KPj4gZW5kb2JqCjE2MyAwIG9iaiA8PAovRCBbMTU5IDAgUiAvWFlaIDE1MS43MDEg NjQ4LjQ1MyBudWxsXQo+PiBlbmRvYmoKMTY0IDAgb2JqIDw8Ci9EIFsxNTkgMCBSIC9YWVogMTUx LjcwMSA2MDQuMDIgbnVsbF0KPj4gZW5kb2JqCjE2NSAwIG9iaiA8PAovRCBbMTU5IDAgUiAvWFla IDE1MS43MDEgNTg1LjQ3MyBudWxsXQo+PiBlbmRvYmoKMTY2IDAgb2JqIDw8Ci9EIFsxNTkgMCBS IC9YWVogMTUxLjcwMSA1NTYuNzEgbnVsbF0KPj4gZW5kb2JqCjE2NyAwIG9iaiA8PAovRCBbMTU5 IDAgUiAvWFlaIDE1MS43MDEgNDg5LjQxMyBudWxsXQo+PiBlbmRvYmoKMTY4IDAgb2JqIDw8Ci9E IFsxNTkgMCBSIC9YWVogMTUxLjcwMSA0MzguNTc2IG51bGxdCj4+IGVuZG9iago1MCAwIG9iaiA8 PAovRCBbMTU5IDAgUiAvWFlaIDE1MS43MDEgNDA2LjQ3OCBudWxsXQo+PiBlbmRvYmoKMTY5IDAg b2JqIDw8Ci9EIFsxNTkgMCBSIC9YWVogMTUxLjcwMSAzNjQuMTAxIG51bGxdCj4+IGVuZG9iagox NzAgMCBvYmogPDwKL0QgWzE1OSAwIFIgL1hZWiAxNTEuNzAxIDE1Mi4yNTkgbnVsbF0KPj4gZW5k b2JqCjE1OCAwIG9iaiA8PAovRm9udCA8PCAvRjE1IDg2IDAgUiAvRjM0IDEwOSAwIFIgL0YxNCA5 NCAwIFIgL0YzMyA4MyAwIFIgL0YxOSA3NyAwIFIgL0YyMCAxMTYgMCBSID4+Ci9Qcm9jU2V0IFsg L1BERiAvVGV4dCBdCj4+IGVuZG9iagoxNzMgMCBvYmogPDwKL0xlbmd0aCAxNDcyICAgICAgCi9G aWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42n1XW5ObNhR+31/hyUvETEwQQlwe0zZpk2na TtfTl24fZMzammCggHfj/Pqei4TxmmY8Y8TRuek7NyFXEfzkSkZRmBfxKs2TUKpkVR7votUetn6+ +2Fz9/YDkGQUFlGx2jySyGb3t4hDFQbrLMvE/e+/BlJK8df7QIo/74N/Np/efpB6JqO0CmMnV+D+ 3fvNnXTWlc7DVGtYz+z6zbXfXasozHSOPOTTlX7vk+mrYK2SQjTtaB8imYBDQJFix+T2kZ8W6E3Z HnGL1numE6Ea8H8we17AIZMiFh8bZumZ2gXrOBNtM5D6N7CnlRgP3t6ZmccgzsW5A6otTV076gNg TEqqZgdbr2a22540AEJworVUoU4KOtmR1A4kxp4lsRIPkY6eD7ZEMwcm9d4FU8MKTSJ1X4282ALx BMioqvfAJHEsTgEdCTkID16OsDxUjtzAS0k+Hi3ihWIOL+8Vc/578i64BRIB+dtDebyUSgBXUl7Z JzxM1cPRZEgyMg0LlbKgJrkNOqWjXCB8lqyPeG67RYujbRvczsRgjwFECYm1GQFbopId41l8Csw5 nfab05HJqjYU1QGCrrVc5uqrNfm+njvvTt32Ow8QOd3sUVWSKnAKw6Gyl9Fm4o6IGFWDvp5h4RM0 06n4fMlb5J97yu8GI+gMcPDXC67wPhUSCiGEyLkQvRO5Mk71lReQXXFBuV9EouwrM1LW7JlgmAfc 8jE+M4U0MKnB/5HJtuHnlCWg45ECfOovFCpp4nIE9KqF6sxdoGveL2tPhL8vL2Qxz824cMbO9JRR pe0APjjcCGjrNBKfPW6zIKksFnU7cEsY+J07Eiyuc7FCwDKJgOUEGHN01CKADv2LFx0d+IIj8nHr 8Ehlct62lgLFKcEuQaW5AldKOdCw+vxxysoXBAfEp4aXxa5J5Y/iN5WOLM8WAkbbhh9c1/1oKKAq Fl3fYhsyW4uxstwkg1yJkEeHUletHaIW5nJW+QpYf7J8Iqx8WlJjG8nUbv3uj4/Iloh3O9Nh4d+O JNQbhbpInF5LJ4xEZ10j4ldKEXgSuPAiuac/Yyw5trA5mi8TWKRG8tkjatAMqYsXoGlq+80VjUQG 6QwzKnV1ZA3c1i4ldvRqIAnfMAtlBfO57LTAPCykgHEthPolIMZoYZixV+IW5WcORU2NkkZaksZg YiDduddNzL4kcd0ZLgGA3ZKuVOzIBrBwP54GDWwNJSJ3qI5OGszPvOUgjy2MIpVqBh+eIxQnHjmD 8XZyZ1Zp6tI3vdbK0zhzedfW9ZTa40sxN8upJseBxJKpTRInImGaoeajqzRzM/HI+5aRaZcRx+Sn BJgVLClcSoBEDG19GV8o+70MuB2Nzi5XCIDmbih2ahQToAkgYC8oQkV31BrbxkN1kUlovvogTNKO BnealsrB87T8dFcrvPhwkQBtlhTj5Vr2f0PyOqKYdkUBXdZgPvJLOUMDawp0fuM85H2nwZu6UnQa fEPFN8oHXECYJ9f614yQjiJRPQUwYA1sU2ySInfq+NZB8R3ChSTA6P/STg2jcnMPMy2RHsckokTg 8TT1EKDyEFCFvxvhtKb3W+MsMHXx2nk8xRE2KTbO1qwH0CQCMmSf844KIYE5c3L3ypsCNdTkoDza kcar0pwxJ3+P2lLuueFB+9xH9cI9FhW9GGnAR1chEjBfXWc6Mi9F/DQ30/PGxcTsIoQbfYuONbul 2cgp2Q0h1SGPiWIlZVjgWXlMFGEsFX/zAG+o8C8BEzLWOIfAFKLJWU1zXYv7FtvOE09KaCr3ZaCo b3B3/u4s+q1tKp5eZwoIXTmKDCrbDdlcC/dt0w8jc04iz3581zUTasuziW4W8D6FBl8MP1z501XH 6Xvk5ydLcG+ZgWk/Hi5tQorXg/MIPmro6rU3vaX5MH1dlQu4b63rsr7PXer3FbdD+n7Zt3Nl4ctP R9CptQ6zrFj6dlz8sPwPO5y8AWVuZHN0cmVhbQplbmRvYmoKMTcyIDAgb2JqIDw8Ci9UeXBlIC9Q YWdlCi9Db250ZW50cyAxNzMgMCBSCi9SZXNvdXJjZXMgMTcxIDAgUgovTWVkaWFCb3ggWzAgMCA2 MTIgNzkyXQovUGFyZW50IDEzMCAwIFIKPj4gZW5kb2JqCjE3NCAwIG9iaiA8PAovRCBbMTcyIDAg UiAvWFlaIDEwMC44OTIgNjg0LjEzNCBudWxsXQo+PiBlbmRvYmoKMTc1IDAgb2JqIDw8Ci9EIFsx NzIgMCBSIC9YWVogMTAwLjg5MiA1MjAuNTUxIG51bGxdCj4+IGVuZG9iago1NCAwIG9iaiA8PAov RCBbMTcyIDAgUiAvWFlaIDEwMC44OTIgMzgyLjczNyBudWxsXQo+PiBlbmRvYmoKMTcxIDAgb2Jq IDw8Ci9Gb250IDw8IC9GMzQgMTA5IDAgUiAvRjE1IDg2IDAgUiAvRjMzIDgzIDAgUiAvRjE5IDc3 IDAgUiA+PgovUHJvY1NldCBbIC9QREYgL1RleHQgXQo+PiBlbmRvYmoKMTc4IDAgb2JqIDw8Ci9M ZW5ndGggMTY0ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptjrEOwjAM RPd8RcZkSJpr6iQdW5SCEAjURuqAmEAw8f8rKbSCofJy9jvbB25ygYOgvQF3odKwFb+9mOHPjLas TazoQBxG16bm6fFZSfeLgJHXtC+67P9BOOg6hNmy2TXnFHuprLWi1FJ5H8RRQjR9KxXEIWY9fPFw 6nKTpvEogTJ74vSAxcQw57QUtCPK+i/hAtVCFQWnyfo10+qFNx4vNbVlbmRzdHJlYW0KZW5kb2Jq CjE3NyAwIG9iaiA8PAovVHlwZSAvUGFnZQovQ29udGVudHMgMTc4IDAgUgovUmVzb3VyY2VzIDE3 NiAwIFIKL01lZGlhQm94IFswIDAgNjEyIDc5Ml0KL1BhcmVudCAxMzAgMCBSCj4+IGVuZG9iagox NzkgMCBvYmogPDwKL0QgWzE3NyAwIFIgL1hZWiAxNTEuNzAxIDY4NC4xMzQgbnVsbF0KPj4gZW5k b2JqCjE3NiAwIG9iaiA8PAovRm9udCA8PCAvRjE1IDg2IDAgUiAvRjM0IDEwOSAwIFIgPj4KL1By b2NTZXQgWyAvUERGIC9UZXh0IF0KPj4gZW5kb2JqCjE4MiAwIG9iaiA8PAovTGVuZ3RoIDc4MSAg ICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjadVXdT9swEH/vX1HtZY5EjB1/ JNnjgE2AJk2i0qSNPaRtIKFp0iUOjP9+d2enUFYUKT77vn93Z8u5gE/OpRA8y5O5zTSXSs9X25mY 3wPr60wGEWUybo0B+ggznrixFIbrNEOhz4vZ6ReZzxPBrVXzxR0JL9a/2FlV7FwUS1b2UaxSy1T0 e3HlhTVPs9QLxzrnmVSkctkOLpKsaBr8I1l38GtR3xC3H1euhqOuHYI1A3nxXOTBmlFck61FVaKa AtHm2VM9KJYY0Z8RTffllnZtlGTMeZF68GuBi2Y7kGsKd4ce+60/w7CqIsgPaACt7XZRnKQg5YKF m3GKXLFv9arvhufBkb/tx8FbusIsI9B6jIwFChLCFKTiRueUxHn5iMGRWtNhNG9CVopdIx5Ea3Yr jECr59dASc+XXPFAdr1fq/q+mrBAUDi5lpbnyh74vynQEcCnjWG30DQPRe83f0Ht8c7T26JfNpO9 Icb/GgGGctVLIEZXdy26QW3JPqCWZRh2MPCMXrrRbyryOeWNki4Ei/TV+bUvfCJeFT7JDTcmDZ23 rNsjzSFzrq0JImv03pcrjDWm7AEYyXOTHeTvuh6SN0YzMEnrFCoCBweGWqRw1UkUW5ETwihGfdH5 YlH4NQJw71WaehOyMwHWs0+nD8Bfb+BH9TpdYoAtnRbB0wQ52t+iYUJz2ZTDSxahfcxLksR0KFkv Mez/ayES1rmq7J/qgeLlkIrO2SUWR8hXxRHosgwabTjw2+9g6wyVT44oEcrFpNC4feu1hF3ta41T igIjRPH/JPwgAH/WO9/BrvPriKe7YoUWNn4GaD5LT/s2fAeB95r+ggaqwmsrsZI94YROc4AH+9wS K4ALxlEB7izihvnEEaA9XQ7BUg2tQFThl7W/hggfaLVITi48YjhTTenH6UjPK8uh5UJD72dwSI70 vtY8EzaIYule50GtjAcVtcTaj0OSZ1wZeYDMS0NtCL3ZxWL/OuBNnkkuTH7s7ZCp4lmq/MPi34zD i9uHJuVbszLDtyI5fJH+ATCZpBplbmRzdHJlYW0KZW5kb2JqCjE4MSAwIG9iaiA8PAovVHlwZSAv UGFnZQovQ29udGVudHMgMTgyIDAgUgovUmVzb3VyY2VzIDE4MCAwIFIKL01lZGlhQm94IFswIDAg NjEyIDc5Ml0KL1BhcmVudCAxODQgMCBSCj4+IGVuZG9iagoxODMgMCBvYmogPDwKL0QgWzE4MSAw IFIgL1hZWiAxMDAuODkyIDY4NC4xMzQgbnVsbF0KPj4gZW5kb2JqCjU4IDAgb2JqIDw8Ci9EIFsx ODEgMCBSIC9YWVogMTAwLjg5MiA2NjQuMzM1IG51bGxdCj4+IGVuZG9iagoxODAgMCBvYmogPDwK L0ZvbnQgPDwgL0YxOSA3NyAwIFIgL0YxOSA3NyAwIFIgL0YxNSA4NiAwIFIgL0YyMCAxMTYgMCBS ID4+Ci9Qcm9jU2V0IFsgL1BERiAvVGV4dCBdCj4+IGVuZG9iagoxODcgMCBvYmogPDwKL0xlbmd0 aCAxNjUgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m2OvQ6CMBCA9z7F je3Q2utRWkYkoBiCBs7JOGl08v1Xi0J0IDfc5b7vfhBsCgT0aIJFyGNmkDK4vYSFZ0I7sWWxadAD WlPYAvjxGeH7RaJTVz5smuT/IKIzrsBZqfblietBaSKSZJQOIcq2HxVKVpFk2XXllLk99l8pQR6U C/JcTc1xOiFqFjh/Sj6a3PtU//24QL1Q7WNuPIU1aXXDGySHN2hlbmRzdHJlYW0KZW5kb2JqCjE4 NiAwIG9iaiA8PAovVHlwZSAvUGFnZQovQ29udGVudHMgMTg3IDAgUgovUmVzb3VyY2VzIDE4NSAw IFIKL01lZGlhQm94IFswIDAgNjEyIDc5Ml0KL1BhcmVudCAxODQgMCBSCj4+IGVuZG9iagoxODgg MCBvYmogPDwKL0QgWzE4NiAwIFIgL1hZWiAxNTEuNzAxIDY4NC4xMzQgbnVsbF0KPj4gZW5kb2Jq CjE4NSAwIG9iaiA8PAovRm9udCA8PCAvRjE1IDg2IDAgUiAvRjM0IDEwOSAwIFIgPj4KL1Byb2NT ZXQgWyAvUERGIC9UZXh0IF0KPj4gZW5kb2JqCjE5MSAwIG9iaiA8PAovTGVuZ3RoIDQ5MCAgICAg ICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVPBbtQwEL3vV+RoS40bx3bs9FgE SAg4QEQPiIObuLtRkzVyvAL+nhk7CWpZRat4Z9578zyZ4UUFDy94VTHT1kVjJONCFv18qIojpN4f +AoRyrBGKThfSZZbtuSVYlIbBN13h9t3vC3qijWNKLqnBO6G7+TNyf6MtOTEBVoK3RBJf3QfMlgy bXQGl7JlhotE+eImGykwkDYgS5EH2kriIRgw+LxqKLgNa6t21VCCyaTw2UNJ2RjyK4xxlzrn2B9a G+Iio6WRhtxj5gKQlXFyweWTzYeWLH7eTpfj0S3IiCNQ/Kq4vaML85KR0efQhDBaQkX/nCNPPuBB Iwtugb65YEq2yXnI8s6GHm2exjMIDO436/2MiTtaaiPJNyzfJyMopypBProlU8MNBKQiHTWCXPxs FwRI8hUo9jyc0NE0J1BDPsG/EUppgnGb+jRlwQcKhdYA/FJ5i3Zurvq2UxnH1KhakSVLugE7O6VL HKGilPo1MmxfZ/GX0K9BmyrmvqVbpnkY/TlpNGQYlxjGR4hdohuQI7eS/+YGknvl/w0P2MH8KdHC +JhmYB85UZOw29p67TAhiN17sjq0EawxLHJ42+2LgkNtOKtUe22NuBbMaJF3LK/Py2nO68PFa1lu cG3ql8v5F2+p739lbmRzdHJlYW0KZW5kb2JqCjE5MCAwIG9iaiA8PAovVHlwZSAvUGFnZQovQ29u dGVudHMgMTkxIDAgUgovUmVzb3VyY2VzIDE4OSAwIFIKL01lZGlhQm94IFswIDAgNjEyIDc5Ml0K L1BhcmVudCAxODQgMCBSCj4+IGVuZG9iagoxOTIgMCBvYmogPDwKL0QgWzE5MCAwIFIgL1hZWiAx MDAuODkyIDY4NC4xMzQgbnVsbF0KPj4gZW5kb2JqCjYyIDAgb2JqIDw8Ci9EIFsxOTAgMCBSIC9Y WVogMTAwLjg5MiA2NjQuMzM1IG51bGxdCj4+IGVuZG9iagoxODkgMCBvYmogPDwKL0ZvbnQgPDwg L0YxOSA3NyAwIFIgL0YxOSA3NyAwIFIgL0YxNSA4NiAwIFIgPj4KL1Byb2NTZXQgWyAvUERGIC9U ZXh0IF0KPj4gZW5kb2JqCjE5NSAwIG9iaiA8PAovTGVuZ3RoIDE1NiAgICAgICAKL0ZpbHRlciAv RmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabY6xDsIwDET3fEXGZIgb13GTjgVSECCBKksMiAkEE/+/ EqAVDJUXS+/d6VD7cqiREaJH3aQASEFfn8rrR0FrtRBV9cgaPbS+1XL/ROR2NhjsRbZVX/wfxNYD 1fWoLDfdUfJgHRGZANbFmMyQLZp9ZxMZyasvO9k6mcOwezeqLArHYcQJGuby/02aoJuo49QAU5yT ZhteAXcyrmVuZHN0cmVhbQplbmRvYmoKMTk0IDAgb2JqIDw8Ci9UeXBlIC9QYWdlCi9Db250ZW50 cyAxOTUgMCBSCi9SZXNvdXJjZXMgMTkzIDAgUgovTWVkaWFCb3ggWzAgMCA2MTIgNzkyXQovUGFy ZW50IDE4NCAwIFIKPj4gZW5kb2JqCjE5NiAwIG9iaiA8PAovRCBbMTk0IDAgUiAvWFlaIDE1MS43 MDEgNjg0LjEzNCBudWxsXQo+PiBlbmRvYmoKMTkzIDAgb2JqIDw8Ci9Gb250IDw8IC9GMTUgODYg MCBSIC9GMzQgMTA5IDAgUiA+PgovUHJvY1NldCBbIC9QREYgL1RleHQgXQo+PiBlbmRvYmoKMTk5 IDAgb2JqIDw8Ci9MZW5ndGggMTE2OSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJl YW0KeNq1Vstu2zoQ3ecrvKSAihYfemXnpGmRFA6CxEAWbReMTdu6sSVDkmOk9+fvzJBMnNRpg6IX BiSaM+IczhyeoRgk8BMDkSS8KOUgKzQXSg+m66NksADT5yPhXVRa8CxNYXzAGAdrLETCdVqi08nk aPhJlAOpeV7kg8mcnCezr+ykigS7W+GzWcCjjWLBzGYZKckeo++Ti6OzydPaySBOFX+Bya2cAmxe JuX+yl/F99ffC8FT8QbuFLBqZ3p7zTGPYqUU+xQVmrWmvgfEMKW1YmOLyHuYMDGO/sHRB+cuyjIj N82WkSxZ32+OwTwcouNut+NVV3E72+IcYoboei+6lJIr7SF8S1TiffYRFlwXqXeZtxi7vh+ubW+i OM0cmCF/nY9YZLzMykEMATAx78ur/H/yKmWKeVXMo4fcSZ2zkTedbBdm2kQyZw9g98YLMmbsdGlr P/XZlQRnPxqsTxUsV1g0v9rNDyrXfSQLZlePGNZ7mRq+mTmva/TxX1zaKQZf2i5MZjmbLC3mAokp FAfS02ZMS5xGT5UrtjZ1NYcZitj1zTGUREo2ctZZsHxLhIZxXfV0GGo0S4Azc4Nps0avDSanrbpg b+bejMYGUOWs2cALttWavnqgDTqX2i6avor3AeuSEIMjrZdlrHO7DKDWDrSzzZvWDRBz1QW+t9Ud PLe9nTlr61LUIJ1bwmWdwaxWDUGbuoCUxlRLdl47Rstkn/VpyvNEeYZctVGKG4THFB/26THDdNUY ZoE4i8LlpMgBHEz6CsVhuf19yyQRcCyhCqMR+o7O8Z8EGuZABJxZAcAEnzh9gzOPEG29ITBdhVtc uxUwgfi+JFCYaZ9VnBzbfknYZ52bQD5gNil2BPTClQhvUzfrZtsdqNIprdBQbAvpAHLQF7j/B4uk KtjNY9fbdXdAIMqC5ypk8wOqgmaXTdsvYShyd/LMyu3HF69fer+x6TrjiQFBtp3te6rx0zo47bi2 Rrc7IiJxp3XrSxJF7WrhsbwuhVdSigESCQiOh8PdjkSS4y4pZsUDOZGG20OCKWTGtVB/oJhXKM8m HKBuiLCMMRXf0I5mcyLtWzJaJlxm8p0yqv6+jAaFSzNgAiD9QYfv3msAshn5S+YgcymoWJA5HP8k c0JoQTpHn52Oxmfw4fVoeHM5unKf3IZ63Ln/XSBlbz1HMhBj6IElP8Dq19XeubhBYHAdvlftqSF+ grj9op2pjCfZe9uZ/vt1uMVrQpKyM/++xbqYFW1o6bqXEsLdJsAcyuZdFXSkFTQN77Vvvd73In19 9gsVRfuF9/vi32NDOvwF00rJjOFIN/Whetw0qPlSlcy6noLLut6DtZj6foAOqLNS5U4xqOk9U62P 70zn60ZOs6rr9zuFVEVoNu7ms6ow0gI7a1G+0RLykqs0nOvzurdtTSoLwuVE3ju8lM167uTS1r51 4B3VdVh4fyRg2LEBGGm0mz+NqKGiIELb3fZVvXCGtzVWSkH32yeRxcvfxizoJqA0y4T8N5Pi+VZY /OJKliteqOwQBwWacvU7For0J2IXeP+WL5n9H98Gx7plbmRzdHJlYW0KZW5kb2JqCjE5OCAwIG9i aiA8PAovVHlwZSAvUGFnZQovQ29udGVudHMgMTk5IDAgUgovUmVzb3VyY2VzIDE5NyAwIFIKL01l ZGlhQm94IFswIDAgNjEyIDc5Ml0KL1BhcmVudCAxODQgMCBSCj4+IGVuZG9iagoyMDAgMCBvYmog PDwKL0QgWzE5OCAwIFIgL1hZWiAxMDAuODkyIDY4NC4xMzQgbnVsbF0KPj4gZW5kb2JqCjIwMSAw IG9iaiA8PAovRCBbMTk4IDAgUiAvWFlaIDEwMC44OTIgNTI5LjAwNSBudWxsXQo+PiBlbmRvYmoK MTIxIDAgb2JqIDw8Ci9EIFsxOTggMCBSIC9YWVogMTAwLjg5MiA1MzMuNDg4IG51bGxdCj4+IGVu ZG9iagoxMDIgMCBvYmogPDwKL0QgWzE5OCAwIFIgL1hZWiAxMDAuODkyIDUxMy4wNjUgbnVsbF0K Pj4gZW5kb2JqCjEwMSAwIG9iaiA8PAovRCBbMTk4IDAgUiAvWFlaIDEwMC44OTIgNDIyLjgwMyBu dWxsXQo+PiBlbmRvYmoKMjAyIDAgb2JqIDw8Ci9EIFsxOTggMCBSIC9YWVogMTAwLjg5MiAzODYu NzM4IG51bGxdCj4+IGVuZG9iagoxOTcgMCBvYmogPDwKL0ZvbnQgPDwgL0YxOSA3NyAwIFIgL0Yx NSA4NiAwIFIgL0YxNCA5NCAwIFIgL0YyMCAxMTYgMCBSID4+Ci9Qcm9jU2V0IFsgL1BERiAvVGV4 dCBdCj4+IGVuZG9iagoyMDUgMCBvYmogPDwKL0xlbmd0aCAxNDEgICAgICAgCi9GaWx0ZXIgL0Zs YXRlRGVjb2RlCj4+CnN0cmVhbQp42m2OMQvCMBCF9/yKG5MhaZ7JJelowNaKoEgWKU6KTv7/1VRa dCi3fPC9d3cgWwcEhokWFJI3cJ7ub2HpVVUvchFNByZY09qWyvNbKY9RIqhbOTRdzf/kJsJ4TnMk D1lpyONw6hXkZeLtudL+OlXFrgjMHzhOJjBX/ru9SL1YzSkYdnEttLrhA2pdLaBlbmRzdHJlYW0K ZW5kb2JqCjIwNCAwIG9iaiA8PAovVHlwZSAvUGFnZQovQ29udGVudHMgMjA1IDAgUgovUmVzb3Vy Y2VzIDIwMyAwIFIKL01lZGlhQm94IFswIDAgNjEyIDc5Ml0KL1BhcmVudCAxODQgMCBSCj4+IGVu ZG9iagoyMDYgMCBvYmogPDwKL0QgWzIwNCAwIFIgL1hZWiAxNTEuNzAxIDY4NC4xMzQgbnVsbF0K Pj4gZW5kb2JqCjIwMyAwIG9iaiA8PAovRm9udCA8PCAvRjE1IDg2IDAgUiAvRjM0IDEwOSAwIFIg Pj4KL1Byb2NTZXQgWyAvUERGIC9UZXh0IF0KPj4gZW5kb2JqCjIwOSAwIG9iaiA8PAovTGVuZ3Ro IDQ4MCAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjadVNLb5tAEL77V3Bc pO6yT3bJLVGSVq3UQ8Ut6YEYHFAMRGZtt/++M7s4iR0qJJjnN988EAmHRySCc+YKmeROM6F0su5X PHkG19eVmEOUcSw3BuQFJz15qeCGaesw6KZcZfeiSCRnea6SchOCy/qBXL+mgrymVEnSDHX3ByRr yHX6u/weMzSzzsYMqgvmhAp5v5pDN0FqN8JriFnfgsWjZZdSQf5GM+qbKHoQW4gCdMSTgOdcwLsd IUKQ9b5vhhQEPzMwMBBW8GJmYBTTIb5sG0hQmuyRP/UjrSsfTIr0WLyaUA1M0HZIpSPNburGIRrG Tfz6FruYIlgNMhCB0DXmInYfUAZM9zEjBiuy/RBZYWchso7OKjSwuivfFgPshc5hNXFr0fySnKQf 85LOG45LakN1j6u6yrLj8YiF2Mwb3iCxuTiQziKDzQ6UanjJ+mr3BOK2CcHvKmpTX4XW9tWWxUMA pQ4Lu6AvcxVoLR/dyUulYYYvXq3ImYLb4+/3qBdafeTCXpaGM/7ftaNLf8CUfAHz53ho+qfUwIbw GowjovDtlyhKzsXVwrEJXjBl8hnivsNphgH6TwfFLglTaZm0Ev5F7gDPLI7DKubs2TyWVv95GsLh LynP5/EPCu300WVuZHN0cmVhbQplbmRvYmoKMjA4IDAgb2JqIDw8Ci9UeXBlIC9QYWdlCi9Db250 ZW50cyAyMDkgMCBSCi9SZXNvdXJjZXMgMjA3IDAgUgovTWVkaWFCb3ggWzAgMCA2MTIgNzkyXQov UGFyZW50IDIxMiAwIFIKL0Fubm90cyBbIDIxMSAwIFIgXQo+PiBlbmRvYmoKMjExIDAgb2JqIDw8 Ci9UeXBlIC9Bbm5vdAovQm9yZGVyIFswIDAgMF0gL0ggL0kgL0MgWzAgMSAxXQovUmVjdCBbOTku ODk1IDQyOC4yODMgMzY1Ljc5NyA0NDEuMTg0XQovU3VidHlwZSAvTGluayAvQSA8PCAvVHlwZSAv QWN0aW9uIC9TIC9VUkkgL1VSSSAoaHR0cDovL3d3dy5pc2kuZWR1L35mcmFuay9tYXJibGVzL21h cmJsZXNtYW51YWwucGRmKSA+Pgo+PiBlbmRvYmoKMjEwIDAgb2JqIDw8Ci9EIFsyMDggMCBSIC9Y WVogMTAwLjg5MiA2ODQuMTM0IG51bGxdCj4+IGVuZG9iago2NiAwIG9iaiA8PAovRCBbMjA4IDAg UiAvWFlaIDEwMC44OTIgNjY0LjMzNSBudWxsXQo+PiBlbmRvYmoKMjA3IDAgb2JqIDw8Ci9Gb250 IDw8IC9GMTkgNzcgMCBSIC9GMTkgNzcgMCBSIC9GMTUgODYgMCBSIC9GMTQgOTQgMCBSIC9GMjAg MTE2IDAgUiA+PgovUHJvY1NldCBbIC9QREYgL1RleHQgXQo+PiBlbmRvYmoKMjE1IDAgb2JqIDw8 Ci9MZW5ndGggMTc2ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptjjEP wiAQhXd+BSMMRS70gI7VtoqJpWnRaIyTRif//yqVNjo0N9zLfe/dHVAZCyggCCOBapsLUDm9v4mk r4i2ZB3IqgGkIEUhCxqe30h4XBlYfgv7VRP9P4hSGKMnR9l1HFjdVu7MM6UUKwXPtDasr+P85Abn 2wR2bgi+51axSxr4JvUQSVKV3xwPY64N411SBwLT+wqt0IhR/z0+w2ymGVotUJkl0+KGDx+XO/1l bmRzdHJlYW0KZW5kb2JqCjIxNCAwIG9iaiA8PAovVHlwZSAvUGFnZQovQ29udGVudHMgMjE1IDAg UgovUmVzb3VyY2VzIDIxMyAwIFIKL01lZGlhQm94IFswIDAgNjEyIDc5Ml0KL1BhcmVudCAyMTIg MCBSCj4+IGVuZG9iagoyMTYgMCBvYmogPDwKL0QgWzIxNCAwIFIgL1hZWiAxNTEuNzAxIDY4NC4x MzQgbnVsbF0KPj4gZW5kb2JqCjIxMyAwIG9iaiA8PAovRm9udCA8PCAvRjE1IDg2IDAgUiAvRjM0 IDEwOSAwIFIgPj4KL1Byb2NTZXQgWyAvUERGIC9UZXh0IF0KPj4gZW5kb2JqCjE0OCAwIG9iaiA8 PAovTGVuZ3RoMSA3NzEKL0xlbmd0aDIgMTE1NAovTGVuZ3RoMyA1MzIKL0xlbmd0aCAxNzIzICAg ICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42u2Sa1QTZxrHEaWrkVbDRUCBvhFS uWxIAgSSsNxEkAip4SIXQSFmJjAkmWGHCSeYiFcUZBWOuySNHgUBtVxa3XoBVhFKEfBWkVYgWxUo ca0riFikXFR2wHp6ln7cfurZmS/v8zz/9//+5v8OnSaKYQRD2DY4DEMJBtuDzQchQqGAzQLkmsWi 0OkhOCwmEAxdJyZgPmDzeD5gg1IOPL0Ay5fP8eJzfCl0EIJl5uBIWjoBXEJcZ0W+IFgB44hEjAKh mEiHFaSHRCwHMZgEgYkcDxAsl4Po2R1ZIBrOgvFsGPKgsNkAQiQE2AanISiFOcskQKUY8H3bhpSZ 70bZMJ5FQgGXOUxXQEJCGCrPARAspTA/xsjTYJLlt8Cabx6mlMs/Fitm7eeS+tVcrEDkOT8rMEWm koBxIMQgGEfnS+Pht3BCGEKUivlTASGWI5JgNE0OAwbb24Pl/baPZIUhKhgSIYQkHUjF8ix4rg+j 0HwSMr85Dua6qA2JkXHuP1/t3FAkRlAiNicTBqxf1HM1+5eaDAlHVCCJ5cFisUkh+b5bbZl3WCgq wSAETQOeHB8gxnFxDoX8iciKA9RsgKAQrAKwiiRmeqAYQW4BZDI7gBTDKbP3yia5mOhsj/Lrj1i7 FlOpGV6egOHJIU1Z3lzgy2Ht+G/hJhT5sxIWrAMcFtfXi8uZ60qUOA6jxNzvQwb0rpYiZKYwrIIl FEM3JvHbl6G7tL8qN7Sis9rMNctef2bfrat5F/R+Hk8f+ll8i9fdiYUdX1DzqBlIzXLtcMRnTEjK 6be7FKxpV8cw83qp7e9buZ48GvOHEcnT5K2mHaVtLcsUxwseGz40Zta5j/zT6WVf9lWzs5FP2L7l dsMHF1X1dL/+MVe283a/YeknkNMjLWVL6eXIEjjuh77im1ND67EKzHCqwyb56EvLNwa7lgddsvvv 63mFpcKF9/T+r5awXFXSgdsU9jHZh4aEE7wNMfzF9j7fxpXpgclKf5o+IaChPkL4bPHgXVfeIE1v 3iXYs0MbHi/YNL6q+qSFpGqV3H/9KbsF8LmWzU2KAPTLA8ZzUXssZOc6Pf3/xs6sK6FHp06+pr9w PrRobMv09MOfZgrtmkYkapqJ7eem8e7cY4HZLu5PXW+x7My+yW+nJYrT//qYCA4Mf7g0qLBN8XXg gKZhQggSePd2WXV/mu9k8KQ84U8qdGV/FG52/6K+/UhyqMqEl9iK/mmg/Pm/W5sKj3luDQtJeph6 JbbHvaP2YnPFOsvaA4u2tNeHhVU41y/GegfX2tACLO39rJcHJxtjQfphw5ddquKh9EaNBWdq0qGG 73O865XfozvaTe11w2d8HHLNHu1dJTvsMRTrpuY+W3g1ojY1p5Nbe779+oo1OihWw4jSyWqqIqr2 K3JGeUtKqnnnnrMXlmkax4Lrr0lL8WJw1iVi50s31zZ6yo3aUWJ5sWi6PNUDTwy80tSC4qjx80l1 tueVjSkcv+S0N10BZ6IrT6mLdo6M9ng7FKRQ+EsHegd5dpmh27W5M/vGe9crl1JD628lGdDH1foH 1/sR3uDexkqq0sk/4bOPAprszjZ/PxEUxfCkSmPVLHmi+8EFA1N0qKFcUWtFh6yMHWvFaS4G23ah 5VeCMVu0vSGzcH8YQT/9+nyFNo6Za+wNuuBDIyInyvlozEeCS3I3E9MHiyZ+1NoaX+K7j38T7nux 7DAUeP+kaPP4ySn/vrtTyl2WN4YP3v7uspdQKpxJiilJtZFm19StavzgkSyy9UgzvEa0WjOEH3kR 3j9l66hjBhZMN5rWF9lf9BKeLj2gK7jssrUFrI7+oMDR9KcamdX3Zk0CTUnvbvWLoude6TP8f40J jg40d9yvLbTWhRl2NDfE11R7WVMqS6xTjMU6xbabf8/dHV7bS2uAfGj5N4rMB0T5jgWnudfG4aKC Ht31BXLmthC/Q0G9i584Z6T0cO0bqEejc7dXBh3YuCePX7rzgdu4iTZlJYebN7i59cJeE+dLX9zu d+Bc6avWZ+yym3TSMq5lvHdoRXy9+TjGiHh6T3PWxmGZsNw+/xi12fxVhchtefP2EysHP3UPvdM5 njEzkFXZ85cRNM5+w7VRB+vufJM6o8w4VPwPR5/WmLaDMm/VeXOuTV5g2xqNfaYIXf/YmbGsDKns +O4E06tk9fbRNxy1ZWejrAq0Xdit26/5JKzbkba1pmL4h4T3aqLuRb/JG9kltaTaRFs867tJHDmh X8KHykOnJxgrNlYcdtrkd3csaeWKZedZ/+ND+b/B78JAIofFOIEpxLiM8h8VrY0KZW5kc3RyZWFt CmVuZG9iagoxNDkgMCBvYmogPDwKL1R5cGUgL0ZvbnQKL1N1YnR5cGUgL1R5cGUxCi9FbmNvZGlu ZyAyMTcgMCBSCi9GaXJzdENoYXIgMTEwCi9MYXN0Q2hhciAxMTAKL1dpZHRocyAyMTggMCBSCi9C YXNlRm9udCAvRFFKWUxWK0NNTUkxMAovRm9udERlc2NyaXB0b3IgMTQ3IDAgUgo+PiBlbmRvYmoK MTQ3IDAgb2JqIDw8Ci9Bc2NlbnQgNjk0Ci9DYXBIZWlnaHQgNjgzCi9EZXNjZW50IC0xOTQKL0Zv bnROYW1lIC9EUUpZTFYrQ01NSTEwCi9JdGFsaWNBbmdsZSAtMTQKL1N0ZW1WIDcyCi9YSGVpZ2h0 IDQzMQovRm9udEJCb3ggWy0zMiAtMjUwIDEwNDggNzUwXQovRmxhZ3MgNAovQ2hhclNldCAoL24p Ci9Gb250RmlsZSAxNDggMCBSCj4+IGVuZG9iagoyMTggMCBvYmoKWzYwMCBdCmVuZG9iagoyMTcg MCBvYmogPDwKL1R5cGUgL0VuY29kaW5nCi9EaWZmZXJlbmNlcyBbIDAgLy5ub3RkZWYgMTEwL24g MTExLy5ub3RkZWZdCj4+IGVuZG9iagoxMTkgMCBvYmogPDwKL0xlbmd0aDEgMTkwOQovTGVuZ3Ro MiAxMTQyNgovTGVuZ3RoMyA1MzIKL0xlbmd0aCAxMjUwMyAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVj b2RlCj4+CnN0cmVhbQp42u21ZVyUb7+3S3eHhAgj0iHdCNLdXcIwDM0MHSKNdHcjKQLS3SElIQ3S 3Y10PfO/77VuXOt5ufer/dnMG47veV3n7zjzon2tqsEqZgY1BUtDIc6sHG85BAESSpqa/ACOt+wY tLQSjmCgsxUUIgl0BgsCOAQEOABiLhYATnYAB68gN68gOw8GLUACau/haGVh6QxgkGD85yE+gJgd 2NEKBIQAlIDOlmA7WB8goC1AAwqyAjt7vAWI2doC1P95wwmgDnYCO7qCzd5icHAAzKxAzgBTsIUV BIPtHyE5iDkUwPfv2MzF/r+bXMGOTjApAANMkhEAUzSDQmw9AGZgcww2ZSisFhhm8v+G1P/uXNrF 1lYZaPdP9/9M0v/VDLSzsvX4rwegdvYuzmBHgBLUDOwI+d+P6oD/7aYENrNysfvfrXLOQFsrkBjE whYMYP93ZOUkbeUONlO1cgZZApwdXcD/isEQs//tAJu2fxmwyavJSojJMv97Of/Vpgq0gjhretj/ p9N/Hv4XczwzbHIcrdwBBuxv2dk5YA/Cfv/9n9H/qiUFAUHNrCCw/cDDCwA6OgI9MGAbA0Y8AE8O gBXEDOwOALvDfNneQqDOsFcAsCnxAphDHTH+WU0uLgAbrNkWaPdP/u+IG8Dm4AJ1BpuZ2v4n5GYH sNkDHcEQW7C583PK8V/pv1f5PzEngA3oBJt4Kyeb5xDWLQhqZwd8TngAbJYe9pZgyHPEC+sQ9h7U 7DniA7A52QKdLJ8TfgDbR7Aj9DkQALBBIeD/MA9M1tntuZ0HVtoJNp//YVgV8P8w5oEVgVj93QX/ P7a20Gc1HlgRJ7Cd1f9MeWGlbMFOTs8BbOwW/5xY2Lb7T8b1z4yCnf45xc8hTAr4bMALmwyxZ4IZ ij8TzE7imWBqks8E05L6D/HBdKSfCbY8Ms8EM5N9JpiT3DPBZOSfCVZd8Zlg1ZWeCVZd+Zlg1VX+ Q/yw6qrPBKun/kywehrPBKun+UywsWs9E6y69jPBquv8h2B3H5upIxBkA3b+HxtRANaDC2yvOzqB oI7Pqwi7cNie95sATN30mWDqoP8QBzvM3ewvhBUC/4WwsZj/hbDBWPyFsNFY/oUwGau/EDYe678Q 5mTzF8KkbP9CmNXzUeTggFlB/kKYFfQvhFnZ/4X/bLO/EGbl+BfCrJz+QpiV818Is3L5C2FWrn8h zMrtGWGfHjb3vxBm5fEXwqw+/oVc/14y8P9YMA5Onv+Kn6+O//sSFReHunuy8gBYObk4YaeYB7bb Bbz+52NaECvY0ZKThB16dnZ+rn9fqiAXR9il5Pyvbxbsev5vNreC3eVgsDsYhPF7GgoSCrROrgv6 5i2VN1qMzAQvblEfo1zdMdGKGTAbC29b+FPBgWmpSve2JI0QZwt5i8rt/qVTWMsntWHpYz+HmJSp xy1Xk620j40vdc/SlDw27amvAvZxm+sm/xzwwKuMLw1+S9DL78k86d3PUWWQ1NxGXXgN12ngWtOZ HsjLpyudZqsV/LmKjuu1Ol6Kw7cQ7uBFN7yEOKTFXwHe1sFoDczzp9AWF4LbPLQv8Y/G8MEc0IDF 9bVxn+HLnhGVxLojLpLW2Po4KnaBRZ7HvOCpX3W9uwuBne7nEowJ9z6lEP1DsZy1/TxVkuj2fakE KXynl0X0L7zmuvjvrQpkbVI13X5Yra05x1fU4y9H0Xm90yasUYUne22pJoMvhjIgQ+vbesGBwGZ3 IcHMpNyiVoTI4pMUWnebwBqYzmifxAF8VxipdFV3opcTj/QHQ5jHd0RIbSZTPEUsYfLU3ZcEEMpW tw18kugVjdOu3f1D7UnoTs1cPDnF4ibNekIaR42DT5DxwjZLqjkb/vAT+dRszQzzyiqjVflezOOp vYnwfOwgDYdTZZo2mhIeo5BvnrrLn5dXYchKCyl/cp4u4qTfJciZ3HHYbJ4ImNxUnhEODvlMyoJ9 1kNO4ZEmmVGJ7xMdhkx+vemjNRlsCNDHykV7RLGwEZKtiB/0qeroETz17ql2/Ob2sn2K7c7etOYR HuULORP5q8ljwwjXNwfxNLmDQu/ZhqhG1Nv7zdnCBKZOayiSt0e4V627wigT4D7ueSmqxaee1n+T vfWat8CzoFFB78pOXDQx93/zx6C1qPKH+i77t/NzsR6eV3p7wdJsd+z0XkI0a3hK8iXBQlWVdgVV Qke0bSx+DD9+salIhCmrY82ane/daYBchPyTo6HTZjnKRlDBuHhZXYE9F7ZpHmCGSsnpqpQUXmum jUYYt9C2bBlyjU+sk8XLgsNK45fzOyibLxnFdWbQnLhfaJIVqiOZziRM2+dMrwpVCF2GCrxCttP6 bo5NnBe0NU49oMO8oNSfDzlwIVCoPdlJjp5GJI/ZcI0YvGT6MNuP5jNtB+CCJ5MBybq3dpSA4ee8 7ZLW6FGiUt81wzme8P0Uwx90+oL8k8ntLgs/N42eSDDpi9d8eiJZe4GBpqv/2JcyV9+0vi/nW08f D0TxmhA2+pIXu5UaO0idPmhkXHPddOz9ghf3ZncsrCBIFaygc3adoO9ON1yn5Wl1tZ8g4shVti2t 3NEdYsV1N6MxLFZoUr4zjVk4yWjPX6DlWqJcsCWbrH1A0E4P8DfWVL5D0D50R2sa/XqcTFYq18DW 87qx0usCNa5166cDqejAsCjktohlBldjjsZOk33LsrB4oue8s4VJoBUjP/zyGjDRJow/8+KdctGJ gcXHDa5+pG7V1om7mvFt669fdJ1zoAtv7A03g623RTB2fyEUibDCFVJaLSiFw9e8Rj0iawmaOJrG kz3B4aJlWmlqnM/ks0i8VaAynbr7KKFUHS+bxSwr3um8/KE1iNv/G40bvJTJN379i9Xy/b6jw3i1 3QovY6QReNIOYKeThRslDgAkmRmVSGDRLeL3h+jtNUmOmL+8I7ORyQB2g9PZ+8zHrc3U3lFUU9qR o2Fib2z8+Xpr7u+RLwdzbEaBX0BTIaHF6Oj2zGEo6HwuPj4NPKMC2SIVGqaSc/0zr33TQCOPLo2v FMVCo2SS3zjKiX7L/iT9gdzjWDbJbdPCsjkcVPZpknCI3nvFZi1cbTiW871mp6bjoG6D1JjN8QfR Dx6HmFgQd4ajdA1srXH4quaS2iAA8IR4pNtSVSwHi8erItQ0y0Qsf4sYSfGVsfJTf2A8/FO5wSMd EjP4N5X/IfkDw9iL9zNfiePXs8Q9kQzJy1U9Bszz+/SQEWU5m2bweksS2f1wV/NoSYkccJCmEj4k XFDkuE67yLN1AdJ/RZKfJxF0OcoW66IhOVp5HrgRG0gmoJnBVeDybmeZoqL9po6Z0pBWpCivvIuc zno94LVf3Ygi/N4dV9rc9DfarQUGu4A8QpQSOpiedYurP33Fu9n/M0/LXiNjQlXgV95+b4+k/LAv 8f0ng6+ZJQT73jDXA18/HZi7wvc+LMzOODwwLT0UpBALe5u2Dw3jcbyTrjjhIiWX72KMNWUAGdtN 8ChVaDG76eJMvvnCl0EZ6ByveJbtNb6VcEfVKRoAV9NZn/OJ5D4/va/c8Xb5bAb4lSWE8qVZ29VD /vVDzoAkJc6FBzkHX3YinVFqm0wva+iS0lMX63ZXwMtRh8nYl3YfuCNBgOysHPXApvOvqq/rFemR DHlpF2QA4zhTUxkJp9HXvaUMsziS1HybqwNXGWMl/HHyaR6nBL1L982h4ohiqlHtoQIR0qJ7IcbR S3M2X1HVjOIkO+JPj7D2eG/EKckrb/iOlnH35dpX683XVgSABzgzsWtQ03fj5v6P5KeZ+q/oP23J LnFAIzh/iWaT3hFnAgN9DVFsK1uIbDpunBQZOmfYh52/u381RDH+BH7DpyRY6P5NvMVRnfNC8TeW TNhWp2ihlV/V8NiL1dIWrDGQnHCm08ItTV7x9aUapRgn4hoUesGF5Gv8vh7TxbkZDXF/aRPrY9KX 4DOvYx3mO50JyZt3iCU03tnVfHvpBoUZ50acTHAa3kFfmn66++boKFCplFxPZinUIJD92AnLc3ar DqvPfv0H6DFnw7QlD9krrKmhLuZ8+Z3bcLNLZllwd+PI0GNEZHSBnxj1gCTkoB+1zVtNtELwnIaL 0uQ8AqFzZs1kSeuDrtuqlLYBXxmbm4mLqP7DC4KOh59berk1fc3Xgce59tkzG2qIETxwfgN6vsTs DdnEPg7it3fe7H1MWOIrTEZGqpoWhh+/84IfOAw/+L/TK15FqjGxV/oYdKIRNYom2cT8I/Z3Ejb2 wwP719rPf6wbqZitW0tMIn5lGKnwKd2sNrM3i04LGpjZ1xsBRSJPsQyusLUZVGMTIW9jNfJAhReQ jhdxCMKLy4H+W+Papz66kV8P1XeFrrnPzA7lDLe/G2w7rjTTyAyXRBvdv9BAMm6cmHtAdAiBJ2tw zLBbG2dBybslnIjGpEfW+WN9M8tHgCEHIgiMMPeN6tUnIg9CKUpGg+5E5KDX/tpoOsyiP8qoRSjX 2hzppHsfnbW1UyF3uNe5hTHPJHr+kbB3NeToggx+NGKS14XJmoIkUomr+30WYWno3RogkMbmQPFS 2SH12GPSDNFLd4nPLpGkx34EHPqzl6Zd42bsVUv4YsIV2Pr6iNRoEoeDQYXYTIL9jJLn85aMsqW1 jsZlA8Oq7E+nB1JaZoUDUfrOuSIbyoRSm0XOXklqSuxpYw3iVDLmAX0tOhqp9q0VxyC2xsJXninQ YL2NOxKmPO48Q6PXtoJHw7MELG+q5XZYUXAERWojPxkQsqONw6siFb6vSXa+lkQd+OpLUPvS0Cya 4j79u2V2Tl6xSbHxZSp8AQC8IuMG5HNMb8Zj8XU5JCE9+hTxefUctB+vVdSwvoEVtozT+tjOhqvK SCtqVUKnLNIVrV+zh+oCX2/75Cu5QmDxFFSX3m4JzR6QtyNmMZQlVidM6CRwotiMnho4b5q5yfpx Fp7GeH1K4gCPZhmci6s2YvyVOpGhZl9BZOdl2ZjYtQIWx8gqMvQJ8RfEik1a9cFksX7l+PdtZvmS w2akanlZuRu/atqmTH/sG62B1qbLvLolStvcPhcmAfwkIwxqNqfJZq78LwnMfcoqD28fIrsn5iIS 77Xp4+YpKW4bX0tB696He39Qqnzy2DxzkGadK+oG8nU5cMnRUAbOfBmTXS+ozNjPkk3JQZ76rq5q zLfLGImde7wtyrKn5JwmX/pzrtCS1Ip332RUzfK8Ogu7ivv1dMcaIhtD+ZPu15OzpeUmlznZBaT4 b/sEqAz7ULcqwcobD4U7/eTXQPsXXa6L7O0tWPjMUkNrL/806kmRK5p3e8Z8aes8Xha4VG6+STUe xzOSgnREV90Oc4Zzql2vS4uqOxzttRlXwV0c2ZTmIZhW/1Lt9qbzRXa76jKaIq5WJBzy6YieP+eW N343YjfWtNtI42TI++K7e3GhKL+Ypg3z1FDAcm7i5unKpN2BX6Kvo9Pvm3bsockpu4qWEjTODLTZ cDV9rKy1qMQFdsMDVa7Jl9VaOqTu8xnwvkUatv57rEKk8gA05yxleV0K2hVCk1NnDEqOfOfyqV6W zE2TXQD0AL/wXACL9vuDgqLc6zsXXiyb5HTZaFbUDY1L0AUyKV8VZ6xd6Oe3m5juL5ftR/Cql2LW Joh2uMKb09dWb9/YPm1yTnSRrjnpBu9bJZQbHKlnDHp3PH4vRxmY0sXnAiUbQDg6ESd//ABQraF8 dFjkfnqqieR+wvTY+7kg9BnVfGbqooFidkLCt18vvN8YE6uG6PWD+uKRvO62u2idYnyWiDFqekPi V43MWotgihpiuxdKVY7GFY+1VcvJAnM2hzSZitUtp0mUSNUMYHpxRam5FZDrzO83L9hOA8QERdpb bYxNnPVx8Mn3q1UZucbzlQ9jQXu1EvjbxJzhdLrc5VloTgeHQ3p9x7wblBsdtDEWBfWV3W13n08L yRbLuO72WCjrxKfbpGSxlXTU6aK60IY7JpGKkM+a7vQUrk7T3QTW/3CI6z/E2S4cEYnecFYNIxJ5 MFG1nefx9eoskdkkHqoGHursNUKLZ5ULXkRBZxhKbZqmbtB2kxD06DpvyuXmsMwypwX8MkqaEDsr vF4IixcWpvFkE/6UjJI0QZ9zbCor8PxIOab0gTp3lXEQhV/1u7v4QKuYDlvDxMUfmXHKzw+9uygW +SSiT2xURq/lkCZEakNYiDQDpdqc2SDd9vmF2MnfPFLw/7CQuhISGST75FBhAim+N8cIrrhzaI6Z fv9G1wuUAyBNCC5LtPcEx8UIG0w2+6Q/0GaoxNsd1q0qOmVCHMxYgQjLY5YBGMgsa3UYXZmIr8so P6LsPgYSDe+uNntY59DvBzJjo7AR39mJraTp6oA+Kb7BrAtL76xGeYQj5H6c/0JdcUDgu7t0ucvX mpLu4dTcfuO/ZmVnokhhP9zb2M8Psdo3QU0tiMiMWn5c79kJnrX6lPx1F4L+Qp2gzPjxssoa3bqj xWwxUiKvbiFZ14zp9Pc9b3HZzM8UxbmAj4968GzXQsqGxL470SfcevdIQ1nWepVaFcQzpFU2Al/T RQJkswXg1/YKYivg2a0nvfyKFHmkoaXeG7YhLZLMCu8iLT0/hfq2tLF9W1Wycjc78IHzGidFa/Gg JDWlnXtN4hGsIZ/qpRePLOIXi4WyJa5PEdrZU6JNuWnL1givptX0BQXcOx3ZGe4mXu1kB8miayG3 WmobDfB8iVisaFOjk0k0hSCb/RFFNvRt+ou4xwXlKfAnAwKM7oGRQafpnA3QKYt3KHswL7YlKb3S E5fSty5yRGJhuQMKxpGLRGtcPbLEjgRzmTK+Xdpp34t1rwwpei6rDX97HyMypsr9qtbcF+BEwba1 8JNXIcQPUj0Ewr+xnmrXgI5eWRHx3ky0BUSo4LdHdZaMhQIqhGPEYYTN/iiO+o8LaAGF1V52o1wi pjSIU37rqGjyvEGNodtV18WoSzqNmcbWnjVDT+VnpdpnUR8FfbN//f7dC86no4aP2tIT+7BQsFVY kT0oy4zKmTKlblkGEcXQuleZzD3/LFrqqrXZ3PdDEmnude6YMBG+J8NB3zzSK7W8cjbAttPbxJfE J0Li38H74vtTvq1jF8v0xyo6xuE3G5Blq/WVbalx8cuSV40K0UUSj7M+ni3a/QduCEGWJ331B5gB KPqhWVJtk/e72ZFvVRHfG1pcABlxQM4+lsM7Wt2tn0pHg2ipbh9RgdILNoIRTkd42ghOl6lti0tR h+uJiY9FFDwlCz0u6dBPRHEpqUKMSh5aBdu959P7gtNmfvR/fNsY6/jK9G4Iv8k65CYSnn2jKQbf X9pY4vbceh7HO2eyD8ay/6L4IFRYowvWpJp2ITcfTpCApBfzsKxVdHfX+8UqyNw5LjKafpjSONyo 6tvqGzTLWcbR+GQtqMKie8eS0F4ipu5L9K2byza1p0EFs5rHjHUQHXx0abieXTdfzlwnEJ9de0b0 Oqnveu9P13l01oNxqTphVLrLntHXt/Ju75XoKJ8sc8SIkO0XSuXmrq73I3KwgT9o6V3hldW1KVjA deSgNxO2XpWrE07O9AfafY2iEv60pHzCXQhDHwn6HWMUbprOScXM+sKLj9YUVgiWmn5syea1vHbp F43dU/xkI2wpEMvJ/e52LYLIgmmBmG+uV+h8muMb89zu0cVFCJGe4xXq8hgNmRn37CEOyjCpwLKp 3hrH7JMPTsV5p2tGkIbl3C1nka1S/3UXD7Vn2+ikgFAlztn9mXHCqNjWW8OFwlnvXYhD6K0n8OeX qUw/6C8sCtzcyKjSL+V5TKYZ6pgJ6u1jpgGkjjN89pEeqhwBdG+06SKjlLoidd3lsQQl10MQQiaA bHZnphqn52rUa5s3byNKMac/9xOMpt1a6lTcknQIJ26EVn/T49o+JWpMvkoxmw1a7Nu+Ph3nUT+P ufuShH133frdInNmqncBPp2zGmF5HjXUTBdjbr9D7T2DWX8YerddgGuCxmtM5TkMz0M4WaOtDvtH CDTmwt+s5Wc2K4LxsIdOkC80vRlY3HpaxI7Rv+TMJBYPqqbeDEOjm8A0Kcz+tf2iQ+heCVOHUO1e xqW+Yw8/Ms5nXcqUYFLcmWyAUKNhytmhqTZl0ZDB0839pNsj24TKO8xmH72xGSEtc/SnM2pHGYM/ vFUstc/huLH2yZqHKKs9aaO9uU4XNGS9gjcaYyHWr75VD1nkRmx1X9kHmT4V9/2xHJclkqeiRTs0 aS7Vq3RtCMlmQUY1yCD3hy4VTuA+6vRdizWbP9/oMnJuucBtxg0BXz439ovlHw3N9N/Wh3vj+zpI /SX4dPvbVxvMax767ZhrLqRFyWOioHL59hUFuyCg/vqHxTX7sDrRUucOxxXBl1+4nvIc3nmqvQK/ RgPOhiPavUUAhLhZqwa3unj7CNb2IEtIe1J4nyh763iDUpdj/GgiOQSy4b75od95T/J02272Aefb ENO7YtOI9A0sRvnmdTju/KUwMxCgsQSAW04UNP2+PTFEbkYGRFQ5CUXZeu2OY9n9o9qzyGK/F40V XiNpMUmkWB3Sx6ichxeMn4VUc7Z7+cVjuntesrjpLYrEyhKXnNssfteZgvNHTaHa4R6jMSlpSsWB Nh3mUuvVdN14FPA7vQjLNvQrphhFcC8wNO3QUqeiGVf9UMgwY3/gRSoS0komSLxR8CpbbL06jp5/ aO8ery2Vss9TuHnknjqTLubyqAzVHwEhdsYz5CcfUCigqyjO4lD6LXOmTOHZptofIuwgIInCrOJG ommVKT5H3JkE3Jx06s1rOuFQIRFiIPqxbyQpyuBvXuB5NVNO5a8Azb182xTrWwF2CbsuQYRi6X6T /afrn3Q6G+9xFpy/dJqEjvs1z99cMnIMcQT14PZ+aYppSjcsKtb0dtMOtdt8aBwtiD04MF7+mgwH 6kiKHsDfoGriVm9kjzUDUrk2eeNCw09UtclYRI3YYg9Tgn6Rhk09KAj29mztVbwKenyEt+FMCDjO 3JI9vZolrftd7upIyhzUsNJk+yUvW4A4fZFCgewGgzt1M93JWXhuuyQ9FXfgPGp+euqCKBRS3/ah 2qHS5w9IhtO9bkCe6IMRkiqJEGxPu03LC8n63wZ9q4tUVNTgjd/KsPvU+1Wi0z1tcGdHuKyS/LO+ sKne+4iwIrNesmkn7H09HifLrPEmcHpgR1GLbqQFV/r7nZ54RPz1oa5FnmzfaGKTg6+EhjaeVa+v yHvibRo/o16gpTY4+L8inwGaMUtaezIPItOTqSzqGsZux0OpTvZA61GaLXG6TJhMQhG1me+I2nQ/ +Rc2fGZazfrIMsNhQU5ZmGERJBJQojkxwiqnY4QZ+up7CWZBlMLNMhoXNWNqoOo1xhRG4+9+YjbK 7ROB0srKu2tWp/JhjCS6rSLxIeKrwDu5tgcUlQZrlZfCGAjXTWh7ZLE59nY1c6nn5QkYRjEi6aCy ZWVVfBNsU43P4ut9LSPhWKYfji2XZZwvwetL6hShZHTRF2hm5uBPVk5Zc2utYqGAB1bk8vWfEVTh hDJlvW5nu3CHEvk/lPvofeC0wZWzPCzvWclKcN5MP+StaoUxTcqi5sfF0djkQ6D6xFKu+0mu2LW3 VwBeGT07kL74Fmn4dkaltCQjy0X9ypapOFWvPNj5i33Gex1c6pLDQCTeQhH0lQeaH+OaWwKamuzG 6912ThefefHLhDDaFeYCFgZQacNIjHfzn/Z+UQ0rnM9gBoX+qZWnOuOTJcf4fX+kog6wM+Cng6AA G8BwO+GcBh2azukP3H6czBtu8gLy+kQev1ZWsc8snKaTVkomRS3deQQTW3+YNwnGDWwNSdF5eZFT gXhqozD5o3lKIR/TtR993RxuZcDwtmEJ3l6KDpoMOsTyeXyjfzCUcuAG3u+lkjJVu84mGTIt3Xbs RQHbfhdWNoftGffmZlkkY3tSKhazSSgcdflsc0KQ4+laXIhNBOew2nRLEX9X62iNZG7mclJIfHc2 YLwVNQ425Jx5CXGu8WX6capwop0ic1AQ3S5BP1WNWsEZw5JjzA/xJZx7wYYYHsEUTCwVH9+ir0Fm VSNJxdQNIUoi22Lt/IS47IqFDe3Ba3Bcwh6dpBV8p1jURc0R8hZYLxy6UwFiVHb77tuGgb04/JDN 2F4d4ro0wxZKPNaLZYmF1RdLIqLiOW1g4EldLPKz0MNC5wtolY+xCpJ4X+3XguQSU8D+0EfoucCT iHft3m7vlSvFxHLXFltHKeCbnFJJAh4rZD26eUuaw36BNm0xh6Uhi8hD5iDpgzUv8iXoA8rN+/tr c3ryqqAmkZoNCvO08QrOPKYmT8QUedvYGjfOkfD+S/VHIZPN381RSf74JFTqAzTiv+ejqy9ZXx4W 4NXtmJ0i9Hw7r3vD4ngZwTIprm6JMW+QfHeR/QuL+UYNX9PYonnPijppzYx0fyyjMsPDXsVBFRCP L9M4jF72ZiTnXObP5swl3MjMNtu3eOnW+3UPyYJwUPfP5conm/GLijC81xRGYTcKsjxt3ylaHGsw r71uFdR+4qz117+35aiIlVdV/nouS5aeXbreKklprgKJoI3K0Dx6J8ckN99mXOCyk8szxSLwO3kL OLCRbkjH62eJayle8jPCHM0jjhSF5xA77V0+DcPbMIN1E75PVZnBJMVUsbO7iWro9JsMXdUFimVF yVTT7Sf9fHsI724SHrAi+w5UYnu+ItRv8WVOo2LWLmt8zdLI7LdzXCkuiakgnMDaw6nga3NYArFa 4aGA51jAS7bQggbMBjPv1L5BylkR+ZdDDnHQAKy0FAsrAC2GKkfyOVICkmkL2UQ4xbAy88WnIqNI yMCEaC4LDgmo4DB2XCo1OAid4JsLT6BnHz4iynaanpNjBGr/RFu/m2dtq5CcUVWk7RYDIz56lVzK YcHcO/vfGq8G+8d+2rOUjo7uYk+BfeDQ++Bd8IRS9ROpa9DXotPpfpppMD8QjJdXnn42mErBrVjw dffj/qNRkRI7KNr2PeVPAda+1fDINJF1T9aj6en1WV5MNpqdmSX7Ev4nx0Qa0Upr+KIwZs+bOfVr myAU2SnRjypjnYJF4sUDguY2tkS3Gyksi9Q/omM7/Nt5Vema2BLb6x52FAy0Xsb8UO+9XWQYiUf2 HgGFVGI/9vyJHlFNqlOJSkS+k5sPnXn162TPq+MEHq60QQI2OzTvL82OeQDaEdYvWhI2YwK1446s wt7er2uxI7tRspw42g1aUOzqRg8svLKe41uJtwHGoDEwbXTYUVQx9e8Nv9sX5d17FXCTI0tTKDHH zfEO5EP2dOG6T/KVqvH46UJQWiSfHgEhNNdKTDxrI/Snf90dYR7dgwXcLuHIpKYnb9eHsB6CCAcR C2vrBrqMhSQDgOt59v2QWm7/Qrdj9J0nJ5iXicrN3PmnQC8mWWqGD/GshAFe4SFkOTZkUbVFEJlU wKJ2OVAU4J6kaMiMnK05e7BEc1WtALXV/z362V2JBfKQ0tx4tknP9yrjAb3fCZRAgk0wpKBNMQON GoiB2O+8hB/DFWfMUsAUQLrzpWIQLy5wD4fEb76wNzl2IHx36VjJWB+8BO12alu9ndOjn5GYjQoj wovMEmLHnO+YxD1BZWQO1zQ8JxlVF1NSOn6AyyIyzHI5gfNS2j/7TcKGnezr7DDDL+om7WsovIA5 KuruDuwXkWzqobsOMKEkPqwCiwJyJTfrbA4J0bPrOrGXmg1THDGTrbd/771AZX5HajvXkzR05bfl kRSOj0/XdiBcdR6pPYzeRr/SRdhzbOIgYm1wMqOGEfwOAmzTu0c0vFxHoOu14U66O0sbxCUwHufe OEuYjhGOoRhSFTIfuzJkgaNyHxNW1KfgCYnpkitmjhFSSrF4uaMvjZx4hBN1svsHy0G40MYNx/33 jGVBmV7C6bfNC3uVA+JHpPkLDuYfhswffj+OgNjj5odKYnIkNyIPmQPw+fHwVY3Mz7LKr7BqMvbU AK2tPA+hZ71Gj4OkXFuoAm8/Xx1dhNLxLVxOmn1GIH74eXYeZvflGHIqDDSRVdScfkV/CnEmt6Pi W7PkfltrwcBVr8El+cful1687o3cmjNx/l2+yvump1RIHgfW2GadAWRhUE/uRjGbeMNgpsfbJGwb /ay0sWuIOitA8M9SyNHTezMEUbSU/VPzJ+vBFULv1gWkIZ7DfB790ZK1RoGzWCbyd14lRMrQfpHk cSXOUV8Ggp50Wu/GH0WBD4cXfHHtv0qa83qpNVB+ZjQKL75ef9wcIzHB+pkSRmumFXl8lIr/1s9p oQFPwwlhSUsoyGmt9geBblHvjx7wIPb4CqKjH2OihsuqZ/eCMJE8s77Hdym3i6icM6enz+eGCQmi tv6GIZITNxjmqF8e7J2yvkvLsNxy+071fBD6QbkVQkpzTy2CkMk+ZHCHedTGXDFmaojBI7xWGHmU OKrsOjEk/nWYGM4XfqfHGcFbrHkAKzoHTKndO3ijAsKl/zyd8/Yh3Tv5VzjwUWQXTtcCvu40WOAG FIikX+/QIELbKPGxqaglGRtUUMP6bY9ii5g8bYOKBt1zObOq26Tez2PZvMCSVCl10rZ6MlaERYU0 VL2uB6gTrJWrn84anVSbEy5ri3POtT8HnLt8W/EGXw6v5otD9SuwPpw1dgVbZQQ+D6F7AZkpH1Jw u/bb4ntMKv4fO+mSFdo/vXyMPKyEeqUa6g+BUnWAKaOrvTGx8elj3JEOvH0CskfhEWYPNX9McnEe Ow72tgAeY6SyWwUTqlkcKAhviqRxEF7dwDJESVbOtDC5rIk+3hPCAcnSSa3mkqyARnbNTR2jjIoa 6H1aDARB18xBUKR8Qin69hvsr5ML+Agko8T0amkYEYMf1bCj284d6CY6sCfHjwE++IMr/XUEdx76 t9Suir4NYxD677N76sGj60l8K4WXld4k+/yigw3K2pOpPmeVurRm8A7XzXvp+YQLmV+wcxRZyYqC luMVM6160VvN8R8jUa5HtlbadsEWS8lJgfSecAG+8FEP1QZcjivrP+DFM6tuMUVugg7iFCFKLeYi DLRxBkxf7JKGaf03TRQdhjYr7715NDlv8QTXGszu6k3eHErXmpDvZ7P9nl/1KoME9st5rqMlzPwA ljEkRXRSrhUNFnPg1vjrxEYgIVeSosK1Uw+DSXkvUv7g0ETKk+d/Jh+3WDVNFy3HYxx3XxAdfl9b xZ2D7jwSqEaCLZliol9pYs5KMWAd8SaSUP0nfuuaC2UKmJTvNKWwfI7IcY7YEH5BG7WW3WHvHXm3 adO6gbgmj8wEKtqMtWOTtXrAd+w1GTTCssnb9a79baryAIILNhUPzh+RlPpZmH1x5My2C4QFqe3o 9TqHMVoINCn0B1urt+zk+DlJoMDvStSMbEzhImXteDlmIldm8M5lY21UPmIsobWv1PDpsbfGK4gs Ndyb3uDpvvBGGgJ6Z8NRt1aOXodxMf92cf9YutCGSWVtxG4S37BOPNfQ3nftSKq5aXAQO2tMU7Jc cj06jKm6UbNlnh55MFiTacPq/GOHxU5yzlsNhVkIDysW8zj30dQ6pthsYkVfQjm1GcdHnX0+VPc0 l/uIFECmis2WcXlnbHtf5uUtjJnmvZvSTyXsxYlYqNTFeSVgnnuHBShFy16V1622npWq0JZrACdo Oot5SaouPda9D60h8/JiGDPvpqop7orWYNQY4PcQNntjLNM3OM5XkLCDT7jFFE1dGWsUcH6z6lfB miyfqvArmaJ+2NfO0qLAp2RkAHG3a2Wkzw1px4/ARcb0IhYtj3WxG19SPpnqHj3PXrHAC6hkVbBm Ze35Sfo7VGY6RCH9T98ND4Orh28sdwSrMY4g4mgH/AHGDCKd7C2KXgKZOp1DxxgQEJZBBce00Jjb Xm57+UrK/rtFciWLom5yAFr7KwHVY3nGkzIprSQ41bCf827OJXcVPAO1PL0LxScBlA3VcGJFm06d 36fITBMeJbiD5fRLjRCWbzaAtfmH+eLlLrp0Rw2Wgh87FV6A02fgjpfOBMcYJi8uGSKawbtQSHF9 ACKnI5hmFBH0AG3CTfNAmM+SwmGDUKg1TWBzqiiOiBVFmgb1F57J6ZcMyU7xfFzx+5CoawPdC7sY ISz3szqsTE8Kdthy0ZH+1GMPOmh98YKD8PqXUQLl1Q5pj9V7XOSjX4oapm/9qj7NuS3g+csQJ+3M xIQcYg8777bYZqKI8eKKszBMYOQdf6Xotomg8F8IZL2oLi/6niND6Nl+3uMwLNnC+tmhmL81Q/Yy W0kvT5zAKPzpJxjpc+vXco5tq6MQ0iItnXgBYglzz+RawrFPX9nNLvEC9z0IWTP1de4oou+zia86 pd84GpPcFRAWfs4X0p+25vj5OHUukbWcrCF8JG8wf3T9IV5a8FVYnivS9WqrvQVXAw1llffv05bF Pm6PCrU/BEiYgvWflOSgQWPd9J8+nfqM1JXLHBpcBr9q29mGsARvrbfyDXYq0aXHNTY6+bstbhe1 haQ6gdWM3KiQkbd64uPa+DNegW4PzKcplcTr8IpIvvCZ688sDrqfVQTe26RlSl2fZQvTBNyAoOI4 yritOqta+y09nA177azECnCKsvo5NWGKf0a6P6R85BwNyedPvMfolTJoVkOku/m2QGt8zjm50Tgd /S7G3KMxxbwMjjmznZ6t8DMIbiRXq/dzhoInqgonkdaFXT46MlNYbdwH5tIw8uO3yuhDlmkbWaN3 CYd5RrhrU/TcVtSqCrHf5Nce4zWSHiYhJMHlFwaiX1mnI+IFmXpEs8WH7zJ1XXUo+ePJxA1XsTJ2 hbuqG/y/xctl0VWlB9/NH3LrBLx49LnwofDXsmwI3iJ/ja2xxdXCxWDZeIbdqdRnZyLuYvZHCWx9 Zffr1IuxCuM1gkNtf9hMYx5XDlpNs/0aHuMwum5flFaivMPEo96v8/kirZOTMJI8JPNBh56DrU/1 Icg7JHTGgIPC7KfOyCLa/UTgJ/6ie1FNxRLhVWBJ1qbPVfL1vcwy/0psWLLnbNoEwvUhDuDDnVbQ 03EkkW5ifkNY6Ljhugzp9TtDwlKe8+oFo6PGk4MG/NqRmrTg/BH91ggZMef58nCEddAuXX8fzyak WrCj2131De1wcyJCldDkG/T25uEvaxbQMZ1Uq6EZAvwN97skVX/JrhHbwCJpykkbDnQInKUd0mfO m62IfHFov0YFzX2KTp1HfmWjZGP8jT2UFK2Y7KDFNEfQzyWm6ozmUzArJl/grbU8jaVgBi9uGu4Q 8WqIlWze19IQhRcmYFDRjQzolQvmgS2JKqHKXFbQieEe3+/LZt4ehParWs14IkSpiEYv8l9cbTKD E3uVulb2Ikmb9p3TB0Ef1hgrsQGZU3jjLPDYxVbnMjNLDEvKlBe29L9cPhqBQvNd163F19OB/OrU DDtZ32Yv28bhcnGDP/JnWLvA3w4SEXy+AweE/iTG+LwrJ1P5RzjNT/ndr6ucsvMZMq9pDjHZFWtr P97H0mB+F9FcSnqTa6g5kTYS8tuE5QH+EcF6MqXt99JcrKTSKmxsaWMbCQp0uIxh8TziU42nV694 PRP0iBon90z/dKV6S9xBDPVHzUY/wnscbu0VmVGuvw9SIbFc7XscvGGFfV52V8zPW3QCpnOx3LLU snBqknC4+nOqrV5d+aUE3ZK5hpPPGyaB1jCa5lyJDCa4HKKXX9fX3Qbch3cZa6Q3s9kuL4jgetY3 23Ie/8lmcpX0RQVSZ4xQiVQ6p4yFIbx7ikgz1AIn37gPdnIR03PGEiL7jtwGZWFSx2ih2WTFjSFm YRItZbxTKugVo2Ivyv7MhP5zOOpptpUvoPyzxeIenEkklTxRY3sJecpZ+Dun+4py2+AgDlkuKON7 O1QEFl6hprnF6Lmeby7NsUwC7Mx0FI97VaIf5OVUqgdmkX/B5XxEw1T0DZJDpfVXKh9H9B1IuMxT HRUQGjCPZL/QQR23dxSnuNo553L49TVMkapKh22fS7GUOIL4bLRS+9KaLB3SWeBoKxhTf5Nc9yHl imxorylnQC3p5W5vPWNhMhFQimJRJJ8i1zZfzfD0ww8Pws6ymiHDT8Lx7ou1t61d1k5v1d2jolNZ iFGdgmmFGluG3jluEAZs85mdct5N7Rys879Bzk3BvWQH1QzMV3Q+aR5JNY0ulCr33GRPoq3LfTc7 wEe4Qcn20fawpqBuoanXWgtVTYpKJPW3wigMv6MsNXP3Qr/ufGfmGgQBqFBuz1NIeZJ3fx9dlJ5s 4yf5k8b34y2Fy5SeIxdmJsWymiUcT+lJEHJsLaaValz7QxlmDcFVNmsGuTzJhU3ll6QzE9XqoXGS nKQ4hZGhEPqreLfmwALDBWBFXHaD27pT5cfWIoiyXVmrt7m4ulgrDRaTW7BJl+XOmIXHKzOXKVQd dcCRRCUNnmf3ATwLomnhPMdsTmmoM+b8gxmfvZFFlEn84H7Ci/JhJAMM8o97hurEVRnlH8ebD96T 9Epb++gFo7g5hHO/uD3B922eNWGLGzswZiCAUzgCxx18Pj7jZrjzl/wN8oxJNHObwjvGmHgjsz24 MPx0aqiaIsHzHt6cp+uN7GNeB0C7JBLkxGDJE9mgn50i4vSO+aGGadmvaIe/OtkzS637o8oAHN54 YvY4JtLlypu29MkB7dMjMEv5Cq6z75UXQey30nThnHygDGnMmo9de85w6AnkN7x92NwVxOPt8fDA oVhaQBr7/8M/jP+/g/9PdACyBQMdnaF2QEcbjP8DiRIJXGVuZHN0cmVhbQplbmRvYmoKMTIwIDAg b2JqIDw8Ci9UeXBlIC9Gb250Ci9TdWJ0eXBlIC9UeXBlMQovRW5jb2RpbmcgMjE5IDAgUgovRmly c3RDaGFyIDMzCi9MYXN0Q2hhciAxMjUKL1dpZHRocyAyMjAgMCBSCi9CYXNlRm9udCAvSlFIQ0FI K0NNVFQ4Ci9Gb250RGVzY3JpcHRvciAxMTggMCBSCj4+IGVuZG9iagoxMTggMCBvYmogPDwKL0Fz Y2VudCA2MTEKL0NhcEhlaWdodCA2MTEKL0Rlc2NlbnQgLTIyMgovRm9udE5hbWUgL0pRSENBSCtD TVRUOAovSXRhbGljQW5nbGUgMAovU3RlbVYgNzYKL1hIZWlnaHQgNDMxCi9Gb250QkJveCBbLTUg LTIzMiA1NDUgNjk5XQovRmxhZ3MgNAovQ2hhclNldCAoL2V4Y2xhbS9xdW90ZWRibC9wYXJlbmxl ZnQvcGFyZW5yaWdodC9hc3Rlcmlzay9jb21tYS9oeXBoZW4vcGVyaW9kL3NsYXNoL3plcm8vb25l L3R3by9zaXgvZWlnaHQvbmluZS9jb2xvbi9zZW1pY29sb24vbGVzcy9ncmVhdGVyL3F1ZXN0aW9u L2F0L0EvQi9DL0QvRS9GL0cvSC9JL0ovTC9NL04vTy9QL1IvUy9UL1UvVi9XL2JyYWNrZXRsZWZ0 L3VuZGVyc2NvcmUvYS9iL2MvZC9lL2YvZy9oL2kvai9rL2wvbS9uL28vcC9xL3Ivcy90L3Uvdi93 L3gveS96L2JyYWNlbGVmdC9icmFjZXJpZ2h0KQovRm9udEZpbGUgMTE5IDAgUgo+PiBlbmRvYmoK MjIwIDAgb2JqCls1MzEgNTMxIDAgMCAwIDAgMCA1MzEgNTMxIDUzMSAwIDUzMSA1MzEgNTMxIDUz MSA1MzEgNTMxIDUzMSAwIDAgMCA1MzEgMCA1MzEgNTMxIDUzMSA1MzEgNTMxIDAgNTMxIDUzMSA1 MzEgNTMxIDUzMSA1MzEgNTMxIDUzMSA1MzEgNTMxIDUzMSA1MzEgNTMxIDAgNTMxIDUzMSA1MzEg NTMxIDUzMSAwIDUzMSA1MzEgNTMxIDUzMSA1MzEgNTMxIDAgMCAwIDUzMSAwIDAgMCA1MzEgMCA1 MzEgNTMxIDUzMSA1MzEgNTMxIDUzMSA1MzEgNTMxIDUzMSA1MzEgNTMxIDUzMSA1MzEgNTMxIDUz MSA1MzEgNTMxIDUzMSA1MzEgNTMxIDUzMSA1MzEgNTMxIDUzMSA1MzEgNTMxIDUzMSAwIDUzMSBd CmVuZG9iagoyMTkgMCBvYmogPDwKL1R5cGUgL0VuY29kaW5nCi9EaWZmZXJlbmNlcyBbIDAgLy5u b3RkZWYgMzMvZXhjbGFtL3F1b3RlZGJsIDM1Ly5ub3RkZWYgNDAvcGFyZW5sZWZ0L3BhcmVucmln aHQvYXN0ZXJpc2sgNDMvLm5vdGRlZiA0NC9jb21tYS9oeXBoZW4vcGVyaW9kL3NsYXNoL3plcm8v b25lL3R3byA1MS8ubm90ZGVmIDU0L3NpeCA1NS8ubm90ZGVmIDU2L2VpZ2h0L25pbmUvY29sb24v c2VtaWNvbG9uL2xlc3MgNjEvLm5vdGRlZiA2Mi9ncmVhdGVyL3F1ZXN0aW9uL2F0L0EvQi9DL0Qv RS9GL0cvSC9JL0ogNzUvLm5vdGRlZiA3Ni9ML00vTi9PL1AgODEvLm5vdGRlZiA4Mi9SL1MvVC9V L1YvVyA4OC8ubm90ZGVmIDkxL2JyYWNrZXRsZWZ0IDkyLy5ub3RkZWYgOTUvdW5kZXJzY29yZSA5 Ni8ubm90ZGVmIDk3L2EvYi9jL2QvZS9mL2cvaC9pL2ovay9sL20vbi9vL3AvcS9yL3MvdC91L3Yv dy94L3kvei9icmFjZWxlZnQgMTI0Ly5ub3RkZWYgMTI1L2JyYWNlcmlnaHQgMTI2Ly5ub3RkZWZd Cj4+IGVuZG9iagoxMTUgMCBvYmogPDwKL0xlbmd0aDEgMTM5OQovTGVuZ3RoMiA5MjY3Ci9MZW5n dGgzIDUzMgovTGVuZ3RoIDEwMTE3ICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFt Cnja7ZRVWFzb1qYJTuHuUBDcC3eCS/DgboW7u7t7cHcPwR2Cu7tDCBBcAgQIzd7nnJ30+S+7r/rp qrpY79CvxhxzUZIpfmAUNrEzAkvY2TozgphAvEBRORVpEAsQxMTCIgKgpBR1BBs6W9jZihk6g3mB IB4eVqAE2Oj14fXHy8HJC+IEUAJF7ew9HC3MzJ2BNKK0fwVxAYVtwI4Wxoa2QDlDZ3OwzWsNY0Nr 4Ac7YwuwswcTUNjaGqj8V4YTUBnsBHZ0BZswAUAgoImFsTPQCGxmYQtg/kuTtK2pHZDrX2YTF/v/ uFzBjk6vooA0f8ukBb6KNLGztfYAmoBNAczydq/dwK9a/m/I+u/iEi7W1vKGNn+V/3tS/8NvaGNh 7fHvCDsbexdnsCNQzs4E7Gj736Hq4H+JkwObWLjY/LdX2tnQ2sJY2NbMGgxkBLEzsbD/y27hJGHh DjZRtHA2NgeaGlo7gf+2g21N/lvJ6/z+1sEsJimsLKVO/++j/dupaGhh66ziYQ8GsvyO/ptBv/l1 SI4W7kBtltcpg14DX7//edL9r2bitsZ2Jha2ZkBWDk6goaOjoQfgdYleiQPoBQJa2JqA3YFg91fF zEy2ds6vKcDXyfgATe0cAX+dKzsLkNne0BFsaw02df7L9S8r6N/Wf53iP2Z2ILOxnY2N4W8LN5DZ E+xo99vAA2S2swX/wxyv9Z3dfvs5uIDMthZ/BnD/VdP6daf+Y+HkADIL/6bXBNHf9Bot9g9xvRaX +E1sQGbp3/SaJ/ebXvPkf9OrSIV/iPu1iuJveq3y4Te9/mOV38QJZFb7h163mvn3JHheOxj9ptcO xv8QiOW1hckf+Dpe8B/ICmQ2/QNfFZj9ga8SzP/A1+lY/IGvIqz+wFcV1n/gqwyb3/i6g8y2f+Cr DLs/8FWG/R/42tfxD3zt6/QHvo7C+Q98leHyB77KcP0DX2W4/UbW174ef+P/vDciInbuXowgTjYg I+vr7ryq4ATycPL4/O+RqrYWDi5gabHX9WJh4Wbl/ttq7OL4urHOf7+yXi/lf9jU4vUeg8HuYGPA yqKdMV+w5cem0HJf8cLpChhaJ6L00uDxrpCGdD6m75t8GPOOzVMqYJIr9BB0S4tKtNTT99XMJqYc 2/hNwt5DXh+YQ5bRh5CxaPMzPsCdG3/X0YMczh3sQ7XJjvi2Qrpv30x/vvr2dsu1C6ZW9gjEVYB/ GgldvrT4fO1r5TexvYKYZvL2aypAN7ddNgWsdrgVP/bzRNKu0G6leBhXJ+MW89cKft/GrNU6cjpP VK4c1EK6wBMCC6276c4EAJRpRbqikcMj9VLX8A2VjGnEdVExfVkemau01OELGrHlrCUMJlcc2Qmn KFzOyfZIpUqDG5PuaOD0fNXQDXNDUgV0YbcM4SLsyU4oSLcr63mMAArqcO6QhXqC92cc4rxp2eEo ioPJlOpn1p/z7IHOMfP5Y0Lvxkj1zPDpA3Hyln+xPFxoX+DcNo5jAt7DpZ7BqqSIMLepdh11r2RD S8ilefbPcvXB/wxFRGdSnfTiCix838cksIerulJ7mSONFCvaood7cuhX1ooVEoRDMavA9SXz4SOj 88GJZLCjZw9ilgzv3CoReaeFoS9g9VaF/IpuVE9uQu7haSas2LT50ZQYhkVcDnlxcADgye0yOHZ/ 4mZxTUcq5Nfb49i2OeKWPH+EAsFBQ4cLDclhq5W1UrkM6GenUw7a1yilSlS+USFqN1H+nLMgx2aD iJ8YmzVTSVUd9B2YwzSkiF5gwo0Jw6fbDimB4C1F1ng1QqlyhIW4p83YPZHmnxE78pSAupmDHCru bV9Vp/b9AZVCrqe4fV51R9cW5A8iVp5SrOv2AfiZXOcHkgBOO5i8ahqlfxErwUdjc6UPbkc0Lizq Eckz0osibZH+fDter5uiI7ccAgLYW7EC6nVinBnsaFNYVX6qrNYcoMy7hfngd8zGZjS61h3DBzg8 QyAaLPHxq9+pdMB9H+l2IfShZxXPTuc2bO6vW4kWoUPvmryzk39CGT2LcBS0Pv2wZxbjxGbMeGFe c/HL51Gdh2+1iKek6EvqvQFruT/C8Q6Fl88hH9++zVVESNqLnnVJCohalC7VV2GeaMNgcR8TJkPj p6rtNUuCe6M3Tqnx1EjXCbWFWOcxpXrEuTzJxM6x6NlaiirmDdPGFE+NMh1x37qyELTtiezornpi M2RfuBOzNmbj+LfDpvz8tdBPx9zhglKEx43v3p6utCd/c0c+K3arrsDJMa30CYVrQDDM8bgSmVI+ wKtCrhEFeSt6r1EfR2I5zh+u4B4wpG3VSQ9FzqgaTIoVpgR0WqkqgRGIdIXhMDDKZ/dgUrXy6CPN aurkEnSre1tkPhAQG6jljDOdNzfVUfFF6RycGU+u/HR+8oYyRDVOXnxfQ3jdiNqEWZjFglR8c2ld 89B6szo1mGlRy7L/K0e4ZA11JCyfl/f6IYH6a5G837cAnsxrqGanDohUES+8renNqhp828+Jnr7H 2z82g9nP4Z2Al+zq2Tl4M9v5yPk4t2LP9NYWuM9R9CWBs/U+5Smqp96SDcJJg2+HHexZqaCSDUoY /BdfsDBIJS6EIIy2DwUnvnh3GxkIebOw96VCnA82ylKXtRwcUWc1D9Ii3ve14ISPoau/7U4MBKak 9CV5Xq1Yu5uU+dOMZqXzV71r96Ki7J4Kb4FkHNUbEKZaJL6idvsejEknXb+vlkzB3wiDSrbQV0DG e+Zr1JPuZunW59+pg5jHGLT1bIieHZhKFO/DE9CWPoOAw2QOA1aMp50Hw5BAs0ljLCdNgLY+ADc+ UV42OBWEWuCahy6s2RaO4OkbfKgaKGSYx8PY/VgxnfHLanK+5Rnz1wxf5TaGBqdeMjWcfkwmp9b2 1vZL0uEFEeSajl/8k441ikTt6BTsoStkppFJrbLpB3l1BRGwHc5VPydWkQoMRvK367mLR6h69J/v no8ZoMPpcn5wDFcJIOBO2mPziEklCqxlzzOs6r5L5vkqj4coU6uyhTKdFQWF0mJO3JLelD21GcuL VsAJ3id6i88s4R7IeUEwOCD8omttX+8dJvLBFRu7Y9UmIm4q5B29c9BtMTHi54D0d0dBaZMSVkak 6I4nqojywi6XDEQGfEEUta0kpGQFsuwJrebhgiYxiu3rhPEcJOE0RhC7NT2l333G5yxrC0jXMJXf 8Vil31n006Lw1RpaCzLsfAgekd+pK3+nBMHpwBeW1d9aJnJjqu/3OXIUWxdxppuzGsAJO0q9esZE UbUu0X0EWaLpwndhhalI0XfD1OSwOZamPXSLMHaGXnx5Ks1wDHXoIshhksg4yb61f4cpMvc+bv2C +cCuCnCR8SQHLXbdkzdi/7H1jiBRunk36NAvWWpuObzsKr9vS+/eYVDZSBuV+m7ZAGo+9zMx2luX LZIxSnnZ4Pb8msO3MBXKqPsq0r/qKeys9c9rJIGDZDUTvpGaRn0y5E+eTG+/gS6210iPt28lr6N8 zoBxjbFun96L4fbueOVAto8dLc3kB/FS3AtclHTV2ST3FWrnnPd2ffoF0CPXdQzqoEcodHqq58Lh 6gxEZqPm5oxWEMFuFXgeiIe5RW3Uymjp6uwuvp62m63ZL8FOry7qm5XwgsiCQUWxhcLD2HvOUn1Z lWNFsr76dcjHtgV8gJuKca9+WqTyn8K4shIxRKu/tyejG/tcaX/XSC3rO8i2qcGSlBV2MQSs1K/O weC8ptkEzk3QfnzAvLrUhMVAr4alGcLZQPah0XPFnDaVqvU6xZtWCs8laqCqBH4jXORpIl1W0Yfh NKfW7o0aD0Gc4MiNUi1zQL1wI4v9FMbATLpPz23+cIkBDrTVMMjJmBYsCKWfNsamhGSZAAkbEfRW he3gjc3/jLMtQD8CjEwXJxkOPISBWQaquwx6HnLsHKZo9dkU+9g/DnAsfGkOGRK0Q8qsSkW6Rior lMfSrn1jId8XM+2X/G2t/TN+pCXtW7cRVPIBpk81rpaU2ZjMM2pzk3LKWvItFjxI39kmlnfNYKEq 0xPWxKkQrnLyOd8mqhjmizhtmuUyN2YIMcFmzs4DSlPSNNObcikmukBiuYxo+ugmDquLVURkGJDH aBvv2tqVt+5ccyWEqhUAvj7Eg9dlDsahOOaiNxJNQ7G9DMr2/Z8/mblKi3hYg94yof7S/8o2HQ4Z NxuREu60JUY8MpUwYljTe5PQTKMmUORUtER3TBp8d18HDXH4jjcdBYVDp/kkGjTIrssCkORwuk1/ wm3b3DcxA+8HrPOKpaRglycOvs1ZFIKvfRaoN4Pw2KRgGw1Dzhn2ocC88+f50KH0kMtZpR7aswvp dtQDEiQ+Tq1jjY1oMVrddbYiKJ63PaNvr0DE2EN/I7VXtLDhVajcB7yNnNZd+tp/LhavyD9fxJ0W l1foVv6chqwh/SGJ97Sy1JhIn7DpWvEOPSz1vZGgr5srxxb6ZpwLpdQW0nc1BGGu7VkEVb0AK263 bgBkjBRBcSxSREJWwCHKAs69rB51KwSJLMoVj0q7atxjZXyDiTj/gLmh3e0jhFelZUcXzRHmTUWK Nv+wYEPgIiXvxjf/gHUl+6CfVk2RrW9K4A/yq3Fqfa9jV4bIbkbRadv8OSEsLaJ/TEL8ZCs1Pdsc bS8R5hN2VJlhlN7uS/xFUNieWa55Uc6ubqZtfJMJh6+k63NZdP5l18OMkSlk7ml+SwaBB1uz9ioE JKyN1kHe5ex/dkGt7X2VvDm2cxRLGI2Njs1G3/sMQf0eFHrF1/D5J13UwhNeRLFvjOIPK6LWg6wY z/mVdqb9QBFaQ6kNKFfmxrV8/DqNN13bVvU94SlP3C8AUg0WqxHbCEmBTxzmjYRuaFRoeJwnSKZq GnfpMK7n/aKuVnymwK4bZNz5MLWoJLOkRXhFKwllWpyYZ/HP5NlIMR9ZE9acizQcKVn2r6oQIVe7 CQ1s0OW+CVLg8xLqQLFu40ydI4ROQ4R9XVtvsbe+HPQZ3C+Zs98/iTS2Sz2b450EfohdwKLwcRXo 3Vkf8qod7/XfVSo2iRHqfEN2bioFD/VrSfdDEfFxgkZCX1pjZY4XfM9ZvkLArbFA5jfkNbxW0hYM Vx5Zv3lLxqoEe/F+hWRDlCLDgdI08f7NcUr8u5QYNUrvmcxixFUiGJBosKLLSury5M9J71np2mIH pCf2rkI6E2dU7fhDjdBbFSYYrV04WUxFH0Zl45LyLZ+FG0vrAJDjh+H7vFL9b1wZOW2H4+K1O2hI X+2Net+PyboR5jxUZQ3OZNpWI/tdXqTIPx1iT7O+U1b3zhvUxlE8oRkPxU8laSenc1aqewepJCXp Tf6LEYjmepD6LNiLkLQdPSuGOhFUbQlO7LbXlmcR676DVg4QK2UdSS3hzTz9CMRk4uoqyXEtv9MK OER98w1djam8LD6xvIhegUDaG/QrHvtIlaR6sUS8Q2S0ytAjWGu231T6DkPeGVXYQ1+VJewT4rn5 AjGxXI9H7OQkau0NE/hJ5AFDHUAj4atBrT3GpuOIQB1HLC9o9tjQb+tjUGM0I3FXNUUq/Qam088m 5AhHCANnU79s6emkXKg2lZ5DWBfxQjtytZQkBbZOLTRs00chhIEYHQL7mM9mTj5ajiW1Eo+cEbu/ BcEtvyzO3TAMdkKIf+UBfYjDnhPbmuYJJlVygUVmFpFBfui0IS4U0e/oMBGbYXyIcMreWmtnITvG VHTNgjOe2CA+GcuZxUXs8mMEAOBv3OaLm9/IQAWlEIj07qCShJ+6jI2kXgGrqnAuoyx1yG4eEfvW FNdi4YONi8qRtU1bA0Joh+qZxijPVX0VPnExmOFRxL/wWgeBaHB3Q8n+ycF4Dy5w7b3YgvWW7WGA p78J1KlKe35t/NsF2b7tb3nGmz4cY5g65QI09AbfyBwdoXcTXB3Ep9tcmR1CczxyBPp/iCOV0X6K Ntd0NutVgRV2gs3iDlTbXHtDGMky9vHrKU/8CPX8waYca9Q0wwT2UoAVYkahKNztS9L+fpHu4+58 1BYhSTeQxvy9HXxbfecwfNjQgR3KbRnVCNdV2KneiYzkQZxbuFRxWVl86LsRfiZk4YmgWvjg1skv JpKmfNc4LL0XztMVcPVm/BolCof9hCs4/aw/YETSwRaD77NyG9bKq69nR/fWxaAyFzCFoaWPeRS2 SKogZQ7nfyoINT5VhKb/wMnYujGv05eNXOJ1KLXUrLXlQ/04PF8VXCnt8yxImCxj7oLhIRJxnKyR ZESpiDZCc6kXHdiI48Y9ZBx8UhnwveSbavqZ/L2QlydfYaasTqpSGjyXpOjlAQmeJxF9qFCfSYVN +JcshEUDPmI8v80xelf1Ggc+aI5JKHW78+yMCxvefo/rgZKnxqLTtp8uz3ZRsrGlmbCQ6DhY9Oel Ss4dtS96qqjD98PWw8GujYX+AakHZhF1nqURnF1VFT9asygcwpfTFUlZ4lUPpwjaeXj3u99f0J0P jHkfcskGSQS8sYI54YBOyg4CG/2ChBuJD7hu4TJukvx0d1vNFJ2+m2cv0+rYJoEfAZyrW7ayv5wn PMsYYgx93DinE/imgVO//6NdFU1Ss6ti4U4e+kJneqYxGcMCzJoSXdJ0vHwaM4Q51Q7BR2NoShU4 FsCZaSVCOZUhk8LsTQJVEAG4c5afYIu7odLg84JuQ6n60ccJUN8yhzFoIrlF9UtvfCNrfc7vH71w 6dxyFuo8ZyxGF6TageIQgqxm1GMIkySA9sBA2jCxt9jXk0nO/hkEvsXUjoIDuo9AKsnIqvRsL7gV PpMOeKJM7Gwba+3L3kPezRMxngvoTG7OgxNUg1U6IXA6mvEdzUnzQorcQ2gt+UaqFXmDmt61sQVu tAo/se08iVtj+OVEQNHN8Lofii2UVDhd8A7c1FV4qDPI211llSoCp2axE9m6WsOkU/uQ6kNraGlu 6jhox6ARqRDCCLV28UZ9j1ge0zJVq7utteAR+pQC7b+UauJcLrPQni13nzVzzTa72Yi8HAB3fI5r QYNWoDF2D8bxcNerNcfyHVWH4ZryoWMFQwzdg2dT5OqhtM7H1EDc7Oc+NrbVbB/38UaMFamCQ+11 ctnTb4+0pDpPK/nIm+MP9Fxz7BVZl5c20T2VE466nzR3LC02gNL+AETniLdJeLmdvlctKJ+tahmT 1GmOv7EnCagxHKQ0LA7zr+Wq7rzbCmNZimFai1DCC8XC5aeoJTAOcO4QiD+xlxpRTdjNBZ4UXxOF VlSBfJRHGy/aGEj7si/70d/NiSc80CJdDEjHQJjnfK25PKbymNOlVSTGzCUK1+NkRCEAGRTcZhsR HS/x3oBczuGx9paY1TizcKs/FId0hYSHaL3RsQz9qeBaHK+Rodr4HiIkARk+roN43X6Yv6M3HleQ /euD42gdDLy6jvZmveqL7aQAsh8UDWzmrlS4WZCIaNbQKB1gby5+7pExrq39WnPJYX2yAWLJ9Nhq oTJIGSfuTFy2frEL/ZAN6SPoppjjrGlIfjZD1x/PKqGXGTMgnJinnZRV/dNtl3ln3wcL92I5HBwR WbOqGFNELY6R4enZHZGmzHbtVqwyTanhbgv0p+Gmgos5Y8iTYz7yldF9QY3ZKOWN8OPR1cSUUW/x BmuayrTzsS4YNqYLkOgMeVUmpeczc7xije4CBvfssc4zjfwQUQbZMWFqd89DeHuFBI1GW1/COiAn 1ydACMLxawpzGFT6ZJw7+6aZVpyPgPfdwCwrlXP/ytVTW9ZWvFXaHsrkbQMJdZIGST0NfE90VclJ 7w/0awmz26/r4rXZCXrBNBUI7IPL4574N5fv03lWjz/dt/Xvcvf5KFG+5w6Sj5IQXs5BccFrPe9t 8b2SUqHx5Z3YyRphz9MnJ22gRfH91B9fRzFNxQGOxurrOOYrZyZ1rExWqFcQpJCgjZQcK3SknlCK rfFbXVefvLxAB6KMJiSZLV53UHPk+xahWXN0rmc3UX6BT1PxnzuwaITHKYzqQx2BpHrGWqQ1b+uZ FllkbaYfYTF7q59xgXhudIOWb9nHLqE2erwR5URwaHp7H0Pu4qHzRodW9YAW/7F9m1jeiMu8DPGj 91Q79w/s3o00mMibJE8sedBsm9Mmdp7g1fqzfxZVPkaAeVpry3dK9820Ga2AwuYRYKy8Ibrw4Zxz ICqKWVcVMgoyT4J502h/s1DmSbslTu9PMlAEM9VBRtVGCFct0SmScPx+2lf9h46abgyKiWDyS+H1 sQSnimhzfd3D6eNyjfug9SWOnlzKq7sXc83kuHqdxPSSBIDuNj0VMWP9dnxg2Cl64nANMfeZhaJR E22Yvz74AseJAoTOWFKni0aRvdtcMrO0HPyVRcpyFBm69Nnv+Czndj4HorPwVLgAjBZ6KOzDfafI pD89yFPWEQrtx4LBV4YLebdknuII509x9avtASnLEStCpTM67rpQJPknn6sW3BasU6d+PM7S2TfI YDddE77KFm9L4E9yDJ12h/ProMWs0rYUa98Eefyuw8xy98eV9zKii+DU7RJ8bJz8tVB6I00/lq6o eSpy+3CmbrlEHnBXd7CwP/53CUsCS4T2aenqSXQoz+e0yQaD7wSM+C+2btO+vuc/GouUKyk1KsM+ s1C37OxNaUOZViNFbUMeuNH6fa6djnVO2TN8+REKcZrDN5CXTXpPLFGip4+s4uWFJTWhC8fw+ESH cdUH1os4AyRTzAKeXHlamDUq6WJq96CK+1IL5gn6QJPvKrNJMIVcG41zn2FGZvLlZSJk+IUYFnh+ GYQ/jmXBRWpGEUiJsRzoD87UYwEEFXnjf/HXUDniavOy6tEreZqDB7BwfPTl0u3gFDXWxTAeYFyZ j3mNUstES9FgU92zY6jMpR5piOuqeeWLMbGvdZ8VhQuHSVjpax7lcOCIigtMJIJ8mJLyI4ohVji+ 4laQ718UITFtLJBjV+hGfFRr4vlyE+0rTz1JEO3TadU5mab+jGo7o0EwFGEfaJps7i6CQyjLRU3/ i+nZdt3KjXrZ690lRPLeu2DBvFyaFEO499GWGwYr5ANvoxxWdIva3RLhsttdqC4Rpthipna4bOw0 qkD3au+3qseIVmti92VahLh/xLEXNy2Qpgfwn0DyD8IGeOa9OWC9FSHf1RlUpnTKePugZsVfUjF8 Ila5d27+Vt3N5UJXaK1kuNHLyeNdAAEwKGSCdH4u/NkFM66y9kjY4G5phvBgVCgBriQr6seegW9v UWcJR4KqKyJk32o7mvcmq5yeayWZ25dfdZqkjtKNFBe9ly1t+bBm1mfWhswM1VuNGS+DWdOlXS9Y ex62GNEfT6WOijPwE79rlPo3WbTTJZAYeaNdYrJlFgcG4K7AIhkq32uca0bySsYQjqKg4HzlvlVj pFnUi8/8iu7Nsk2q5MfQATM7QFsn5QynldT4ptp32NeMDTqE+YxvOTbUnwOvsBbiirMsMeWeXHIK 6iKjdDphLj6DBEW6Go6xJNWei4ZUoe6bJ7IubZpsKjnhXoXCNHR1UdXAHb/zTUXpz6DBeziE7xkx V9Bs4iy0hNCu3kB5o+lxku7aNAMeDWKFXG0siB4G1D1T2pMBTflNJ7xqbUTATkCeaBRt6cIxQyER KXox6AtgBvGBM6Np+GHLU5pnqWgGxY7UJmtmV8hA/77HJfNWJOV0ydBIXSNQZkpyFJO+VzEoQSbL poCwP/PDcTX/Ei8Re64SUVdnr02V2fJ3K+0kySOD2Y737/rBd04GS6NL7PmsrBIYXtp0ygNMOHhO lylMu9z2y0ncUj63ooDroi8R4e131W9nvb+zWp1H99B426m6YOQrXReYH6CrJ2ozWoj4ubp6IzQ/ 6jytq6Dh4VfQ5ehsVUf/TDziSQN9Y9Sy+6r1cU7KoTytlO19dN6UldjTMnPK1ipf9hBRoCnWbFrz cKKNbhbD3FQ0UV0AzGc/PljsmyePIRPgLnAfa0U0rFNNUxf3CQLjTm6IA6ohRIfSPf4AV+k6iG8f rtQX7XqKzECgxSBEIT0/mJ1Kfr+LwpVGsSzcXjryPitCQebFW0qneUq45NNb+qz26CUkKbEeMNhP QCmvTCbIGbc4ioZ4HEWzcSm6qEzJp3pVPEPwzK4svkBpAp/6I38SGLcteWv8QpZP2PrWm4f4yr1f O21pledzF+3LI34+J0x8wZo+pCUZvsezCUHsKJjUaErI8rwYvVymqnCYc/DFcd6CLWXyziQtETIX sHDINa7nfHGlnjXq066FikZHFJMWNOPGcl3bImQOuEQVA9zry8WEIH8OQ0MPVCROYWSFRtdXtwwF xJ9r9mSdotAT+sPTfvuRxnOFF7Fn0UpX+Fw7gJu0cLlwl+1/3fam4hfsc5cMOD/hK9olSbKOFWd6 sqV9sGm+QrCue36gB+ugLbyUJe+SO+31ekPw2eHYXNEstTqYQJU3pTRS9Ty+4+q2gBT68QQ91uqj QwA/KfSalBqrQCfsuFQ6Xs3WZCBHMjcroWNbXAUK+mg5lMHNFj4jw/v1h7xduZWRtvuRLsrwC/+f 5MuDaS0YhlQeSnTk8W3Ru+6UNvZ6w54e9GLQGe/LThsQaHlalWUN+Z9WUQQUSfXOFNlrlIR+iBbf twpQ+TGjFFcQJGDXysmaKMu9zJUjCSuJ3APy05QpBecHXDPfX+4TkjCo3XDr7xU0fpGnBh7H/KjH 64bTbz/5rNO2GfSoXFY1zKx0kOwgZX5avt2JoeriQQdGDSR6E29b+Ha0Fp5+JgAOfnpU+OY+5Ytc SuyZptbqLkW4Sm0ZfnQBF0cSaKQPnbXgdnwbcN6EFoeIxEKSkL0tOXj7Oa2U0Ylj8iG1WCDFH5rR mQKYEU02t/vxK7auDWV07ORzMSkT/PTNEji4qzOroMHnMeitM2vn2nlo350hMIdgynqM1A8hBo6/ PGbCushGJfEopY3Z7qt8H6+19Z65d5U6Ys+nGqrDu8joGPQmWgu0kneeUfCSDXljmUbLA3w0M1Aw eI1L0iYF+PU50X27bakBb4Sk0rhLrsu88C/E4tuG56kHHvPle5HqSYrL1pbpyu4N3/iQLDMc62Mv M69KkmM/CGZhXeG39Tj1N8lD6rTF+nqssCzVfE+HuYjLrBS7027QZ5hHLKqnlluwMv/8MKf15n19 8gw6dzV+mwn75keqR9+kcZMcXyKtmOEKc1eKR7VkUkNDdG9bjn5VZnQ+vuxUeP5iEctyCtl81ThK X3S7OiuGkdmoxsrVOqvS9Qeom7x11xQt5fB5ckRYDRbdcblP6t+fEjIFIZkpKpjrypz0hyIyF5ty pX/x4C2hw9355vns7wwFUZ/tg6L1XQ5DL7qxjulmRLsVUIn2BATdc4XzhfaCbgIoZTIQrFfkzq+z o9bFzwU0kR4g7AO+mVJSTXIg15Glieih6YacAvCXCjtMNMSGZ+iKBr+kXMAm0lBPuAcgXfSj0qPM kjGL9uBG+jM83XsYpKtZ7OiV+MvK3G5AUX/68IFaTPNJTF3eqt9AMjX+0T5380oy2e4y7vYHa9/a Sd7BTZvJC7Sj52hXRckyAVyRW7YNhH7fYe6yu5wV7FhxYpxqILYob0w7hNhboN/xnu5GnuxL312O 2KfmCHUGBH9UrLuG4+rsd+OZ+pWxjvSmDAufSunRJ82Nr8AUXveNGdJYYQDw9+66cwVLjpaw4+uW 0VrIEwSCufT3925jKQv1I8pC92ss41cC3oV5Valp7WjxQZvc/iD9aCD32obeuapYb1E0kClyeO0F 02sIkMeBC8pQ0aTZ6B503b3Jp7WZaci03/8UuTgEOlhhvVTVmTmjEfGWQhC8XNe+QANaqq9baAew e1LmrraUI3o3GsncYzLpWCsgBf2cCAqU+EpIQthekOEEG6wbQMdeHiTgQ0Y9U+cOjHb6sgctWCxF 6JCGVnwWx5zGCfCkQAGIZZpEzcb3bYKGlV+qFk5Z6HKDJE8/BhG89DyL7CVJbgAEf7EYrVb4fV7g u7J8tKgkMWkDsQRdmYMnHPWDNSBHg5IPizuxryz4SMIOW43EJ2gzvpi152JdragEjweGdo537cIB dDCXt9CwtHgJinytQovQyjj4xnsIZWsrIpqwQkatj+i+Vm47+GCWikTtQ2PTjGd0pMPaE+j8oJ9i u2axLpZnBGEzkyBLft4zIO9KaPismjq4K+AoL2GOglOBtsOL+nPeLf+LkbcBB5vide7W91KLjU34 TxF3n5aXqEEQy6rpHKDPpJJumWANJoRtDFFROFGyj9TJVl7gJ9QfqOr8oSlZYifWq0j8BBrLFZyz 1S5+B8PIg1JNWZh5xKXZ7M2q6sbkYWPBAWpK8PUJb4d7pXgwzfoLXxR5nX8sj34M6eits313mzeD YL4StSYzcz/eHEwIS3DtUuoZ0GydaQsOwDlW133xtmtVXMqhYd0bLP9eS+cbn6jNEd/LxDMfpmNZ JQ5FEbm/L/5F68Di+BLvCOwEIHZwH9DXYeDZzIRB7qQIplbiEKjO2Lfca9UbVrsZZUTIA8S5kS6T 9fULLyXqh1wlOJcsU41+0Egf9ySKe5+pejdtabj0leIkJXZdhElcQPjinNfwB1cnIaTv9vTWorHh yqTc+ulwbhaqkFE4zidm1S6GTUfCJzuNaIXF5R3XWi9/YTFn8q+H+dyt5QlQx1MVKFGiUt0pn2Mm HsvaTBSVpZ2NGzZalu619shcHd5htySsGUQW0Tk2kx5TErWSLWWPfRTbbdHaPRPuSKCTLF70l45M FAtz0/QQu2RW9GUnl0qLBmOJBwJHiUMIYaubNDbSr1awTRZDk1Cgh59W1neoOoz0RLwNHm1lOdk/ OkJtY4YKX97wnVafwLKceqVEjl2LBZrKQfBZnTTY2cp30qfoaghGDapKT3w8qOaDdYcVeNlKOaEl oxQ5WEyqydDF5JqxV2ggEM7NE2spZENpI6iuX3s5z+CV/hSBWItkNNDQcuZqBS+Cu+NgIE7LFKyP trQcbqrlP+rsK67i6fIs86380SlZSe5nnkC53LYW6ZUiyTRBKHZ8awStlO51padfQoJ0y7SG3URm +pN/QjsltyNrGY+KzpqyGNVsfUoBtQK8nFSybTrXkn72edBixT1Y+6SHh4Cmh8uCJjlDKiQoIr8E 0WlYE0FYZ5Bz6ynC0kgIHYpXfTolTC2ui0+uHtgCcVTUObc7Ucmj+dxsqYXBrPQBkNuQvE2s+BTH DuGhFbkM/PJzsszIuFZXiyXBYyZb8dh+HMMhx9OE9VTf5UwIMdwqr4DpmkinfEGgexVtPVtRBT4N q+NSx1qmQdntK2uRDlh43cu7FudreuxjRUE71HFT4ZGDbGguhnaAbXNA11GSdbYMtgNfk9CnusYs Ni6MM9YN71PylMQZb9Y0I6lly7ECk9K8vQ80UOVTUloYO4Qh8h0+zGK8zJ9rnnmY8eVgcc52RrNz AoPUGRt/3LkbdHtuvdiUHGsdCalmFW6m2mVS+B9nweUynuyW4ZC2cp4JwJ+ay50nB39crYWywYkl Yfk//AD+f4H/JwoYW4MNHZ3tbAwdrQD/C9BTGt5lbmRzdHJlYW0KZW5kb2JqCjExNiAwIG9iaiA8 PAovVHlwZSAvRm9udAovU3VidHlwZSAvVHlwZTEKL0VuY29kaW5nIDIyMSAwIFIKL0ZpcnN0Q2hh ciA0MAovTGFzdENoYXIgMTIxCi9XaWR0aHMgMjIyIDAgUgovQmFzZUZvbnQgL0RHQVJIVytDTVRJ MTAKL0ZvbnREZXNjcmlwdG9yIDExNCAwIFIKPj4gZW5kb2JqCjExNCAwIG9iaiA8PAovQXNjZW50 IDY5NAovQ2FwSGVpZ2h0IDY4MwovRGVzY2VudCAtMTk0Ci9Gb250TmFtZSAvREdBUkhXK0NNVEkx MAovSXRhbGljQW5nbGUgLTE0Ci9TdGVtViA2OAovWEhlaWdodCA0MzEKL0ZvbnRCQm94IFstMTYz IC0yNTAgMTE0NiA5NjldCi9GbGFncyA0Ci9DaGFyU2V0ICgvcGFyZW5sZWZ0L3BhcmVucmlnaHQv Y29tbWEvemVyby9vbmUvdHdvL25pbmUvY29sb24vQS9DL0QvRi9JL00vTi9PL1AvUy9UL1YvYS9i L2MvZC9lL2YvZy9oL2kvay9sL20vbi9vL3Avci9zL3QvdS92L3cveSkKL0ZvbnRGaWxlIDExNSAw IFIKPj4gZW5kb2JqCjIyMiAwIG9iagpbNDA5IDQwOSAwIDAgMzA3IDAgMCAwIDUxMSA1MTEgNTEx IDAgMCAwIDAgMCAwIDUxMSAzMDcgMCAwIDAgMCAwIDAgNzQzIDAgNzE2IDc1NSAwIDY1MyAwIDAg Mzg2IDAgMCAwIDg5NyA3NDMgNzY3IDY3OCAwIDAgNTYyIDcxNiAwIDc0MyAwIDAgMCAwIDAgMCAw IDAgMCAwIDUxMSA0NjAgNDYwIDUxMSA0NjAgMzA3IDQ2MCA1MTEgMzA3IDAgNDYwIDI1NiA4MTgg NTYyIDUxMSA1MTEgMCA0MjIgNDA5IDMzMiA1MzcgNDYwIDY2NCAwIDQ4NiBdCmVuZG9iagoyMjEg MCBvYmogPDwKL1R5cGUgL0VuY29kaW5nCi9EaWZmZXJlbmNlcyBbIDAgLy5ub3RkZWYgNDAvcGFy ZW5sZWZ0L3BhcmVucmlnaHQgNDIvLm5vdGRlZiA0NC9jb21tYSA0NS8ubm90ZGVmIDQ4L3plcm8v b25lL3R3byA1MS8ubm90ZGVmIDU3L25pbmUvY29sb24gNTkvLm5vdGRlZiA2NS9BIDY2Ly5ub3Rk ZWYgNjcvQy9EIDY5Ly5ub3RkZWYgNzAvRiA3MS8ubm90ZGVmIDczL0kgNzQvLm5vdGRlZiA3Ny9N L04vTy9QIDgxLy5ub3RkZWYgODMvUy9UIDg1Ly5ub3RkZWYgODYvViA4Ny8ubm90ZGVmIDk3L2Ev Yi9jL2QvZS9mL2cvaC9pIDEwNi8ubm90ZGVmIDEwNy9rL2wvbS9uL28vcCAxMTMvLm5vdGRlZiAx MTQvci9zL3QvdS92L3cgMTIwLy5ub3RkZWYgMTIxL3kgMTIyLy5ub3RkZWZdCj4+IGVuZG9iagox MDggMCBvYmogPDwKL0xlbmd0aDEgMTE1OQovTGVuZ3RoMiA2MjQyCi9MZW5ndGgzIDUzMgovTGVu Z3RoIDY5OTMgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnja7ZNlXFR91++R ZugSUcAhJQSGEhABh5bukJCYYRhqYOhupAWlkQZJQWIA6e6UbhAkHEJFQPqZ67qf++ac63l5zqvz OXu/2d+11l6/3/6vtdmZNXV4wRCEJVQB4ejKK8An8BQoq6ajKgACCvCBAOzsskiohSsc4Shn4Qp9 ChQQFxcAgt1gQEFM/slTYdBTQUwRUBbh5IWEw2xcgZyyXH8ViQLBDlAk3MrCEahm4WoDdcD0sLKw B+ogrOBQVy8+INjeHqj91xsuQG2oCxTpDoXwAQQEgBC4lSvQEgqDOwL4/3L0wtEaART9Vxji5vTv lDsU6YIxBeTEmOQCYixCEI72XkAI1BrAr47AaEExTv5vmPpncwU3e3t1C4e/2v99Sv8jb+EAt/f6 7wqEg5ObKxQJVENAoEjHf5YaQP9lTg0Kgbs5/DP7wtXCHm4FdoTZQ4G84nzCT/4VhrsowD2hEE24 q5UN0NrC3gX6dxzqCPmnEczh/W2DX1NPXVVFn+e/p/p3UtMC7uiq6+UEBYJuq/9mgVvGnBES7gk0 BvGBQAKYQsz97yfTf4jJO1ohIHBHzFqIPAFaIJEWXgDMfmBIBOgjAIQ7QqCeQKgnxjE/nyPCFfMK EHMwfkBrBBLw11AxH8fvhJkMAvJX/F8hcSA/whH6HxYBAfldPRC3LIBhGyT0f6kQBPJbI9yQ/wk8 EQHyg28JoyFzS6JAftlbEgPyy90SRlr+PySKEVa4JYys4i1hJJVuSQjI/+KWMOoqt4RRV70ljLra LWHU1W8Jo67xHxLDqGveEkZP+5Ywejq3JAzk170ljLreLWHU9W8Jo25wSxh1w1vCqBv9Tf9zm2Rk EJ4+vE8EgbyCmGkICAgKYT4S5Pe/F+o5wp3doC/kMAMDgUTFxf6OWrkhkVBH179/Ysym/put4Zjd hkI9oVaA+RmElUSobUpdeIm/fP54KR6XC0NaUehwa1htmgQfekWCagpZP6YLZfpFGUZpCy+jSN5X +cgPsRZZo68D+/b56PCHzVH2kdJw5abrEBxaoU3MsPuzezvJHTIjt+cfbjrV8xwusB6vurfiVaru Cojm0e9H4ZbMzlwd+dsFjKzNE6dCWL8lA0yzm1SToPo7q/FD53uKiHzEfGE/nUn6MfX1PH3n8he7 JdI08ehsNZzpNMlLIhCXp/X6CEAgw+7hvGEi1+cpIrYlvHfs81PDOHJHZyklWfbtMJgIesTmZXmC DOHCdV907f1LfgbmNIqW34yWXpzqJ1h1Ke6DtM1RwsK+IM9n2pUfyFulRKS4N3THXqMMPWxZ+tz7 clBlJutn4MPO+cEuZGLbc2/YQ4pNV4k/MjTSGn7XW7KNEi3Rnzd+KTePBQBWowuxLEmCcKQqYtR/ daz8fNrj9rOmqQ5BJCnhqUvVwAO7yXldaXCPhwV09ovaOXDQsGi+up/lBrfVfpxRtnrQybDvs87J PGpml2mo1GSF0ujTt/chix+TZ/r7G6tE1U+0J0jIpbYp5JADsZMG3JW88kXEp/g0il+ZNrraWFfF OydqjGVGtFNaW+0GNHwze7eYev01YEj963l8lclN1H3nqi++Bd9KsxgDA7cWUAIElAd/fJ+HZZeI VCWxVYRYVVmdh6S03MgJPrpe9av26zKNvGAwkzXNsUg3iajsk4jMWzVz9zXWseu76x0KIBFsMchV 20r3Iw1K70Ic8cdHaTodcYU0YbmKbmgSpK2aASadAapYWD+IYo13s/k+d3R2y10laY8v5aSWqEj8 dD+mHF2XrD883/dVRH0ci1XEOvZ+NVGbZJ6ccdytIfpCFN8qR9H99MXyLArfdV5QV8Ze5fArpHU7 iGxANLS+/X4A1+E8u+L608eeWnnbRBfTDZ6aynI/3ugXyiyuIb9c9MRmQWzWy8Jiu7Sfb5AK9JVT z/T48dQJt+4ictbJQ3koiyJPuYUkZ5aHHnQKPkJ3438jfg6qJFHaEkn/1G2TOlJJeW/T7iYa61i9 vJm707IsMkbmMx8hCcXK22dMyr+DoYFxyC4H7a3ZqsdOiytNl2vXlfJ7wFznBpcAEaCwlxMAAJ81 i+hLDKOqHPJA8OtwcA8otpcMV/7ODymMlCCGlWdRccz1JDTd2dccLF4+q9kWjMt20V7v+BKtfUQL 9Wop50X1pJWKZdXPXf48IKYcbAxenRWg1MQCX5mEA/J8Ps2RJaTdc0o3QYgUVxpVHMneP7W/L7/9 ygxZlZNIKi+dSnqzvEDxpGV8x+li+ZA84VcT3ePcUMi4T7KtcsErKkMSc0hHj93SLy1m68fv49ql xG1nGQaVedzDouTks4s5jMIprax3c/fxKenzVDuMCuIa14l3pu2YgjA/5pMSSODs7PsLghfoqhw2 8jI/gXqePQB4Z4UhLVwhXPVBOdOxqSK5UdeahOhJBkGqEifrUlCy3mZ8wApvNSxh4i310JtyZagA 6Rcz2PRXAnmPL6kXIarMrnSXhjSCG1sUqsb3RS7gPBWn9XWid+rgbfvqUJs554pA5Xz8e2eFPKZB LXTTyWWqOIuDjdUhhtk7MrYBbzK3Np/GXMINDCnV3n7BVkj9ypDrNWt5+Pok8Uc+6tzkwxrwIz7u FUjEkLl/QU86wM5MfV6/nPJqdyWF1dW7PXmyX7m6gPMXLzauqtNxH3NTz+63dDtuPZWPuKvZjSwm QKDDkW/s1PO6IZc2zTvFvS2U22s75m6ssDTO2WWuZov2aKagqbzPc64kaQ0fclges80hRZLO0kz3 kF7dC6+fpwIoBdK0ddEj9aciRFmCja5UhtfGQy/T5javDXqymuIICNpYWTQBXLxyti7gG/FRbCYi XObjTYuo4M70mtnoMtaI6Q6XIzFK26g9PNKRbOnZCAUVnkCeiGKxNxbvZvb98J4MmNTkjd/1SFh9 X5Msp8zwYjXsXgrvMQ57OusqpcuHJIX56lyvKafillzan6q5NvvhcREjxqzxUQvlOwDur3fUkSgR ZbdIDW2IbRLJjZ+uXz5RPAPu3Cv0wINFgU1kYi/l4R0vznL/+1nULvoOisNQJrks8U9KUSxfCZJ8 k8cYh1tEW2fO2l24DmNK2BkWZr+vPOmvGsWSjKl4LIud/zTyiqNyJFQx4de+yrC1ZzDMJy/MO5uU 7AQi1PM4USEdLBRf2iLFmz6xkdbh9dY0knOXDSZUZ7LbuPLOhWbkXRLHzQwr/+oRObjQSvNgIzBR M93Vup6NREIjuObqnUmKotlyJEEvoVVyFDYQLXQp8ZL2wmYdhyUr3zfmgOH7RT6119Zn1YCIXMPZ RjRtvk1nCpExR5lXyBfdawnmGwnzlKuEvYgo0bthm9iNmTxKcxkf3CwR2z6i0WefSL24jDzGVmxt H22ssXFZXwlXzDqo9De49qTAZbSv0cHJEt4KhdG4ERWeoBzwVqI5T/vLwxVeqebLrulx1JrSBvXS u8IcvN5I5bJu1w1X67iHNL2rIga8ug013DshIRS5n0wnniDpHTgJUwdmSWvTR5/VPhu3ICZjU9de Ht5KT0gh2msIvu5IOp+jJ4iX48rozBG2GJ0x+XQwZMdNrhz6vdA3+Hr1YprSE82jrxwRpLyiW0Ph rJFXJyyboG50SXzWXxviajJrc85439/1FN15Hk+jOWHoJUOUu4VPvRPKBpaVGquNaXz1PMrM+SS8 WUlvNK+D5WDElyPeRNXTR1kIRNh6dSFtPCL1oc+lUHs3UCk9TaLPzJtdicTCu/Vhdk3m6dd8Y8lz 0PBw6/FQcdqQyTXbfl8p7WgCa9ul1WBlrqoHQDtgo+JMKSr9scBMzbNTT9qlZO5DzVCqX/OWFOYs +BrK3ydtQjeNBHBRYFMtNnuk/dt9pqLELZau7ORsSULrHufn5yA4+KySYCcqM1hWg4qePhe0gV0v PUX7RTQV2s5wxTuteeATFP0qSrn5hnP6fWFMvPhrRlKz6m6Q88fulZWuk5dftnIrHrSjFd5VHiaI 7KnELeQgg7PzIHbbm93Kix9PBxvxErKMgwmmDlTfVBgnTz9uZWlRm3hvzE62xtEw9IIf9HGvk7AL R17Z11u6my5VGFlw1zhTV9J5MDrlnZGuhdge7ZB0ijiPNOORq0qRXBoWn0wunk0PUYjwkUlb4TGk wQI3JMm6uDdv6N5rS39C1E0ox6/jM8Oi5bUYsQGrrvXjZIOcLWKaKaoMiQF8ZdNUX0WEWqEB3Yzm mYS6kQIKdxGiGC35FdoJj+bMYmF/WFakDof/cTuQqgjWrxRWyrLpKDM1knHkbcKKrHgCfnAViwqg yJewLE+WVSGrUp4zT+TNbf52U9X+iEXyQa6ANuioB8Vj9/oLc7R5fx6B2SlDrb7u28riq/dCDBod O2ixb7O4ABpgrsmMTfglQxbZ/l5T4MhvUpFB0Slw+Z7VEoKDSiCrwxnnscFs+D3RAqi/iwXNdjOv D9OgGucUbXjD965LYLnWrHXaB+hBZt68dRcI7QNnUuX6HZp3sPvaRO3lr0LIinZaJNEXNxC5OWrM 0y7kLrekOkrQnRluQqR1dhRVZjPk8qaFr5zwcwPop6D/jXaY5X2Z1uofHSTaQ5s5u8WWD1jqfM6E gtgJy8FZR7pdw3Rp0S/nzoveqhmTW3i7UwaBrDel2zYTVpYnvz7b6pHeWDo6+yJZSyJTzm3rRHI1 Y0E/qbVwPrlQJIEDv0x7BaxIe3kngGpWr1JbO5rXE7XpiD6pkt4KMl2gpTGWrEe0FuhanaNGdke3 SEjt4Yycro7T9TJ6au4nJr22+sRzecz0ytvhSXrjpYyOBLxIQJw5z5sNs2ctMdvh8ns2XEm1OB0y 7H2pQbT4eea6ZjT82oqL1n2Koc2w1Q2vh/VUiTNkXZDyfJ43qcPjDFxiaO6IXkMfZu7hS6rgOCbc zTa1THXT3SCy64Wxt0tYPNOTFMcqAt+8ZvLof04q/PZcsosTpf6Gl7026ctgFl1nvn29dv2N+HtN q9MLV5sWakf9pr4dCaqv4l0C/FHgm1Ncor1hRcXlgadrNN6eR3HTzhz+GXtrVsTycY5J0WOW6sek BYsy1fjogHz84ti1Zi9c7CBe4cvwCQGRVaCJgU6WsuQjfoucxKK7N7/mp8IIE549noAp+TFeEFur +nzkKzVaLWFYOUTxrbX7VIw6gk0evRdbpmNmMt0+H9MXPTm0kq33Lv+DO5ZTR7R86v4tjXtuVcUE Eug8cMO/71PrrbBG8on6+MTXbv3PelM1Ee5bVMNUzxSQnyvK2d2PxSFTVCyP/dXxA9slY77Vmh8S lx6F9w8tRx5mffSwdbd5PpibMSwZxAOtuWuuKMI2eyzhe9V31/Deq/GEXLnvXmxlYSR647Zkw00/ OTth/gZzpl7nPtllZdh/worodmmhixo/In7/xh9RG3XyluRSIN0uZevQC1mcOErXP1zPg0c+pvnm Y8u6qdd7MFRwqBTnv8JlxsqXYNLMsslbdzBt/uIm0pv2TgAXfeSRbNFuaFIbWcQhl39vr1CRqYvH 5PqckqKeBEg/ruQyvvAzT4wwsjuYvYv83Zj65eayWlngF2nOl33O3dAz28T6r/H43dQoyUyXEWnc 1D9OVp/jjN9qDlhiJakaRxvkUc1xKfZ2pduviTxTvMLi3mnvrw48pJbUOl+8Y6L/0C3DI6zfMvji jxXOyb0sulVGjVmveACOcxaLpmiIU3GZdjlHty8TowMbslQyI4mMBu9dtFlclUt79TULEWXAYQXp PRmd2t4wAn1ng3H53birWHKw340zHhYxYAIvLfKIz3oX8pakInI2jBQ9wa6oSoldFEq0HGClHrzl D9XS+IX+wMTzQHkpCBv9fHtNkM3yLXNfH99vZaU3qvH3V3NFExFt3agictgOnfdab3YMsf4wn7Dw /fUI6cZzp+bxjAwatFZrFomTFbRpOH5zU76jE401YTQqMyFACxZAFj3paC6dd0IHBRLrMVgd5nYJ 9oFNEWDcq29MOHgldqMtHhZV2yGzQar2tNFp04MyL7U4XwpFDhIwhRhZLh1NhDoVyJFvRFIjPQqe cObPsZsnCQ2Vvaq8Kyb6WOZMt/RH21yzUyEddX5+vuhMcKNRXt0P4wVZ8RNsVygj5x2GnrubgJ6x P+6MfI1vdXzuoguVliB0GbLZxXPzLnk2yh+tIraN6AaK8CPNqSjynUdjnbTfWq0gUeRaKGVyGnbH Z7N7arlqfW2tWxxTBXgPyk4HHxRX17+0Ro1KyHe+wBvJxH2o8RG8+9L7hdXT1/aK2Wp7hIwzIfO+ cb+XH4wH97OmcsBqbQ7BsIaeLlR3bkFE+oYTPe1a8ScXvXYDrz8xL90RvLirT+iBQzcNHESMxax9 gp3GFt+Rd4TookEsuSeQrTbx9jSbY/q8iuyVlAzcpt+pTThTwe2XkF+yj9yBn2z9GZO2DsA1KmNP E96+J1pyu0f7w6tQZFTxjfHXh1LArudcGpEmh/RYG4DTfHgGW68ACudCI9OHz4Zi1vQClrwpJvZe mk+p/ddu3DNur4z4CiRFrdRihg6EYyBIhvIc3d82KWtpEjVCRNamqTO3xvOOKAdXTWso7VREjmeP dZ6Bmjhj5puniPgHKY8XQtZo6UHQqN7RGdkpE9F59daUaa7kOYUfnbwPzJEwjLjh9eFpu1rJ7D3F 0GAl0mfr7b1OZ6l33EPb2FT20mjvk7aT8/Akmepv7++AiCTVscv/9GLfO30hjV0YM8Ei273D9pTj udFhkZq7VW4qPRA2AMh4Vljsqzv/4fyDnKrq9Ubqmw8FdzJ+teVek7LkWr3BVboPAvTELfNWdVzU FkfoOiusbAi3Y5eVN77vrcfB9a86Fb57KuRWPvnRk6cJJuPYzUSN3fanTOqdx5hsCO3Rr+9OX9bA JI/LdDfwtFrqQhvFHlLQbbjjQQu1WMrae47nfcq1Ewo4cJ5CWo3vCl7v1Ofyk/V5EqXuseeqeUle sIGHEtk1HHZZN9nmz82WgmoqBb9Fb6VTIJUkRqWwVhsjyal4vaSoeVIT5Gm2V/cJ3vz4xGW7H8D2 7PD6z6ekdfGdJx9Fo1CrzN7YBr/djzcfmqcqcngKDC3gNsszx1wOOEfEBfraGRpfgkdBJ7YItd7l A6KguK8wU9e1n2rnlEWcdKa2meu1TnAJ4VqPjCnWE6lXX/Cj6T04Lw3m53uTl1wqpBITZ1rELg0O 8Jtna2dY88ii58KyfQvc+2Kuv6dex0Xbkh/HuNEMvtl+CfA+j7KDz7Con1Z1+4kkdQFK8/x0e2mH WUc/QBHUsOPem9qzOnAstwkfPJ0e9d2EyL4qfpV7i524jdmHVF3lskmjTGZPfNdDmZsjuvquar+k 6QfbnsOza2yB9XSeWHxEy0e5owDQcJxbXevxQyEt+wJiUDvljBTlur/W1JHUJHZbG4OP/ErQZ/0L wTFj9Q8pv2sOsC/J4NWlZ9gQQwOKUrlvqcz1TF/l8dq+ylLYgbf5YdMLdzP9SDLpYPfyltiSIrRr taA6+s+wVEBYSknU/FjOrOwQlu4kgs7EFD9HoYQEl6OOdFEPhAbP2f5vM/S+UfDAR2vShEYD8XeR IwyPYA77pbLogy8xtnR/EsazrgkfVyDuteeq/pBvYdTGxnVOu1rWcdgJdLNxesiT3pDTkHHRHebf qKAx4PZ9kAuBuxv6TC+6yJvQnLG0pylAyb9uo1wLUczvpfRmNbXSIHafeUtroozB1RLr3fRmTVWR ktnTtIbv0c8vH6C17MfLk5volXQI14pH+2Tbkk7s4wOJKc09/DeuwrelPO+lkG1WwmGXfHn7j66v Uoa0WB7RI2eVpM17wGRd3SuQveaCxhFu226iBMUN0/jVYS3cAKoLYGDxt9jPEi6iJeBIYp0KrrLV ZQrZob4sPHChb89Uiu3GgLj0YfcG5INYyr5zcht98c+jcfoSQbmmEud+Dw+Pikj5ONTeqDbpc9DC z7z69zhinJFWmu8qs/f0z5emfmDjKl8vVFrExtNKRh/aX+WMNQguhqlzXJ2kO1g+ikQfRXsayhif L/yxdBs3dxceDR4xu3Z3E7z3DFVUJ+3mHLsrUy01+NnThcACKXjg01q+JzUI6lP0NCk07NxMqXWK +VO+g66tS4bcKxd0bN971dEiUSYpPEOE0nPn/vpjJMRtVmkD7CMEmoieYa7O8uVpDHkiyRf49rsN /h9A3de2/tFOkchcaaYIKuF4rOPMud+CJyznMos1wp84W+hcS+/yw/ji4A1W8wkJCtbzatN1439e j8y5115/+mziObWiqiGEuvzO7t2FxnNkVLcuNf2tSpg5zHpIXVgeu5wRvn7lc9JW/Qt9c7I+livH xWJTLEF+FNkWwQ9gHpZmLCKP6JbWhMgNbatEfQzYVwdX+Y4VAn56stB1MW4HmZN36r2gtIlMVND0 tcGLbWSEHZbTabGgnqTf3Y8V9goiORm16QoTYsotf32Rziv29mkrJcEZKhqxItFtYPLb5aZoWgE9 9fh9MlRisoFGPBM3P8h0tsZrQ0d/wEfnrNGu/kqK8QGEZmnSfuHqcSmW5wF4FK2Bn66tp5/6fPt5 oNZz0KatKECOKiHzoi1igXdcI9KtqL2NZZS7JuiwFT/57eGniZVfQ5oS2VkeryNG5rs4xXJOVWqk 5ux0G5yX3hfUpfXivnTr76yEIWW6Rn0+lAkYwH/Sz3BYxQJCYYHcXBwl9e6tZouhsWIHvRHY2Gl7 SWQponRO8g8yqgkDx3wUfEEKiQZ78sodXporBw1SjNeOJOPYi9JktDJH/UNujAdGst0G01mE7wl+ iHeeBLLf/GbZrZx7wY1Ojn3l+8CsL9yvHqeD7zuzAE7wJH1ED059vW0Qc4foIgzSgmdCyjXYwB8h RHB1EdfqD6ZNfkkOE4QPLduGhr8Y0z7i86TtoUwZba9fCXaGUVGaJ1OahxbwcwVG4zWVMEzOPF4d 2H+/QVqBKkuznoW13zls1kM30NlM437FeX3QIUsKnTcJLulbi736WPa+Ei8u9Juovf6gzV0ypNqj hhdPZ1PGXmeXXf6Q/HptniS8+WbdEe/Rx9h1uej9R6GJvZRlrYT+0hziywduDhHu96LCQyqSPBK1 2AnohGS5EI1CLQiphapksalwXZUL44eMXkemNVHeQxNLvb2FQq1ThW9e+m5TrRWal8SEPOjr2EdG wOocTYxK+HFh3icFOSeLr5v/WKYF4o8ZD7rHMeQ8Yf7CuqeT1rmrLDgHxIvrCj31teyAYfFXba1c 8p281LC9kiL3GIHt4PaBGSfrw2IXcURZn0Hcx4SHmQxb7ADjjLoRIXz5Cw7vZw5wIo9Q4mjRHC8y 0P/hBfj/Df6faGBlD7VAuiIcLJB2gP8CW04iiGVuZHN0cmVhbQplbmRvYmoKMTA5IDAgb2JqIDw8 Ci9UeXBlIC9Gb250Ci9TdWJ0eXBlIC9UeXBlMQovRW5jb2RpbmcgMjIzIDAgUgovRmlyc3RDaGFy IDQ2Ci9MYXN0Q2hhciA4OQovV2lkdGhzIDIyNCAwIFIKL0Jhc2VGb250IC9QVU5MS1YrQ01TTDEw Ci9Gb250RGVzY3JpcHRvciAxMDcgMCBSCj4+IGVuZG9iagoxMDcgMCBvYmogPDwKL0FzY2VudCA2 OTQKL0NhcEhlaWdodCA2ODMKL0Rlc2NlbnQgLTE5NAovRm9udE5hbWUgL1BVTkxLVitDTVNMMTAK L0l0YWxpY0FuZ2xlIC05Ci9TdGVtViA3OQovWEhlaWdodCA0MzEKL0ZvbnRCQm94IFstNjIgLTI1 MCAxMTIzIDc1MF0KL0ZsYWdzIDQKL0NoYXJTZXQgKC9wZXJpb2Qvb25lL3R3by90aHJlZS9mb3Vy L0EvQi9DL0QvRS9GL0cvSC9JL0svTC9NL04vTy9QL1IvUy9UL1UvVi9XL1gvWSkKL0ZvbnRGaWxl IDEwOCAwIFIKPj4gZW5kb2JqCjIyNCAwIG9iagpbMjc4IDAgMCA1MDAgNTAwIDUwMCA1MDAgMCAw IDAgMCAwIDAgMCAwIDAgMCAwIDAgNzUwIDcwOCA3MjIgNzY0IDY4MSA2NTMgNzg1IDc1MCAzNjEg MCA3NzggNjI1IDkxNyA3NTAgNzc4IDY4MSAwIDczNiA1NTYgNzIyIDc1MCA3NTAgMTAyOCA3NTAg NzUwIF0KZW5kb2JqCjIyMyAwIG9iaiA8PAovVHlwZSAvRW5jb2RpbmcKL0RpZmZlcmVuY2VzIFsg MCAvLm5vdGRlZiA0Ni9wZXJpb2QgNDcvLm5vdGRlZiA0OS9vbmUvdHdvL3RocmVlL2ZvdXIgNTMv Lm5vdGRlZiA2NS9BL0IvQy9EL0UvRi9HL0gvSSA3NC8ubm90ZGVmIDc1L0svTC9NL04vTy9QIDgx Ly5ub3RkZWYgODIvUi9TL1QvVS9WL1cvWC9ZIDkwLy5ub3RkZWZdCj4+IGVuZG9iago5MyAwIG9i aiA8PAovTGVuZ3RoMSA4MTcKL0xlbmd0aDIgODM3Ci9MZW5ndGgzIDUzMgovTGVuZ3RoIDE0MTgg ICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnja7VRpVBNXGAU3anAD1LBIfahU BLMMIaRg0UIUBNmXVkDUYeYljCSTMJlAwl4EURTUagGFWqggi/sC4sEjKipowQpSCgr2HBYVFzxW UDluHUCPp/iz/dXTmT/zffe+++7c751naeEbwHHGFeHQVUHSHISLOAKxV0AwwgcIl8+ytBRTEKUJ BbkcpaEjQBwcEOCslgJECPgiR1u+o1DEsgRihVJLEdIIGliJFw2TRMBZDikCQ0nghdIRUM5oYKgM BCgwAtJaLnCWyYD/8AoV8IcqSEVDnMtCEIATGA3CoZQgWbxhR+6kRAFEo21crfwARUNKxZgCVozJ RYCxiCtImRbgUMLieSuYvSDj5N8wNVbcVS2TeaPyYfmRlD7BUTkh075nKORKNQ0p4KXAIUWOpX4L R815QZxQy8ei7jQqIzBnUiqDgIPYcfkC4ShAqFwJDcR9CRqLABJUpoIjfUjiY60w8Y0Y4QWuCHL1 9LB5P9cR0BclSDpQq4SA/5E9UiMfayYlitCAUD6Xz0cYIvN++Aobs9kKElPgBCkFtkJ7gFIUqmUx J4iphCAOAQSJQw2AGsYxj0sqaGYJYKJJABIFxRoeKyIAPIygMBnEhqdGD6OjgBDwwpnM4ceWrR3g qQgmZpQa6X361y4uCk0cx9YBcBzsGRcIYg9EImHC34lBJBGlhu7LgZDP539pOxoDpqYoSNIjx41J 9EMtIZgpQKiBGKu9VYEtSd2QU7mpLHHF/hvlE3mTh+oP97XrnV9dlZhH4AkBwn1+FlK66EFhV0VW LTuyydgDW/KDEXXKJNN729qBn40OVWQUHFi9I1lYw20LYGs3ZmRPfWuz8IF7bdQ3uflpXWaONVT3 3i3zF7cPzE+qn7IPv+8Ssv/cizut+obW/t9bzzMVtZfqderOZms7792c+2dVUk6dE9bfeMvT3Zet cs1Y2CRZqh8+gD7vYYVJopuabPzuOl9oud8VY4y/nuGzrSZU88h887x0y6eXAg5oBjMSzeJ+v6bU YzdvfphZOBjvM5lVY+5yufNGbWpdF297sts5c8859d2CiFem9Tbe2TOl/DuCcSfuVpn8sqEn6PhR JP3dfT3T4sSXB9DI/NO7zZc/io0qqXZ6EcTOTOCuqswNflJUXavfWj0LMVrW0i7ePvTTZO+GSRm2 XuqK59lVEVkPeezex3YNrTfcirqT5TpTTH6M9ws7zcr0QV3fTsq80Htn62UlEnukxCN18ZsvhoKN JYMFed6HE/CAd2tKyzh7DVZ1FCPxlLvnlVMGzRknwt7F1+6Zpr+SJnm2eMgxUY7eFpf8e8v6+nSw WHWKyCmmLnLNYCaISzb4usJwTbNOWhJcYJrT29FfdKx5moDmPLz08tmcpLnPQstL9cyqur/bu7bc GNnZEej5Rt/Fp5HQjdYR3C4sicznfBWfrGuaN2umRxeHru23epDWdNzP8GQBvlq3fFOt1ZGn6wQB fQY6/T3ZvR0l+mdnH75q/JszO0etLWi039PnOqF+V1dMc5tYySt3OhJ7UOynHXj8uXXyxBK3e2XK ykrBa07vJtlFdoNqt45Z+UBKREtHxMrn4qnncTmGVFxTvbI+OF4deqXWvIUbcbv7aFlxaF+pNenj Zhi3MHBtp9KY+2uFMzJ76Fh6S27KyTNTpwt4aTOi0p9G1sRMSqwxYRVr4ra9+MyaZVhhmNu4c3+I jUVDQ5u51da2lZfdC72TyqWHxKqNLQN2ULedBc7uaS8G6qv8CSVZ/kE7xsXZrTfcbXD6evWErKrX OauuB+N1SxdcvKDX2sq2CJnSe/NuFftMiizlcQ9VAp7kGaz3dzcoWXTryfTxjwyNdi0z8w/+I5WX Gbbu5RL+P3xY/wv8JwSY+x6laIUcpSJZfwHCqQMFZW5kc3RyZWFtCmVuZG9iago5NCAwIG9iaiA8 PAovVHlwZSAvRm9udAovU3VidHlwZSAvVHlwZTEKL0VuY29kaW5nIDIyNSAwIFIKL0ZpcnN0Q2hh ciAxMwovTGFzdENoYXIgMjQKL1dpZHRocyAyMjYgMCBSCi9CYXNlRm9udCAvVEVVRkxKK0NNU1kx MAovRm9udERlc2NyaXB0b3IgOTIgMCBSCj4+IGVuZG9iago5MiAwIG9iaiA8PAovQXNjZW50IDc1 MAovQ2FwSGVpZ2h0IDY4MwovRGVzY2VudCAtMTk0Ci9Gb250TmFtZSAvVEVVRkxKK0NNU1kxMAov SXRhbGljQW5nbGUgLTE0Ci9TdGVtViA4NQovWEhlaWdodCA0MzEKL0ZvbnRCQm94IFstMjkgLTk2 MCAxMTE2IDc3NV0KL0ZsYWdzIDQKL0NoYXJTZXQgKC9jaXJjbGVjb3B5cnQvYnVsbGV0L3NpbWls YXIpCi9Gb250RmlsZSA5MyAwIFIKPj4gZW5kb2JqCjIyNiAwIG9iagpbMTAwMCAwIDUwMCAwIDAg MCAwIDAgMCAwIDAgNzc4IF0KZW5kb2JqCjIyNSAwIG9iaiA8PAovVHlwZSAvRW5jb2RpbmcKL0Rp ZmZlcmVuY2VzIFsgMCAvLm5vdGRlZiAxMy9jaXJjbGVjb3B5cnQgMTQvLm5vdGRlZiAxNS9idWxs ZXQgMTYvLm5vdGRlZiAyNC9zaW1pbGFyIDI1Ly5ub3RkZWZdCj4+IGVuZG9iago4NSAwIG9iaiA8 PAovTGVuZ3RoMSAyMDE3Ci9MZW5ndGgyIDE0MzEyCi9MZW5ndGgzIDUzMgovTGVuZ3RoIDE1NDEx ICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnja7bVlVBzclmiLQwgeJGgKt+Du 7u7uFO7u7u7uLsHdgru7SwLB3SHIre+c7pN035/v/XrjAYNRc21Zc68tRUoor0QjYGxrCBS1tXGi YaBl4AQIySgy0AMYaOnpBeFJSYUcgAZO5rY2wgZOQE4AAwcHI0AUaAj6APrjZGHmZGGEJwUI2dq5 O5ibmjkBKIQo/+nEBhCwBjqYGxnYAGQMnMyA1qA5jAysAEq2RuZAJ3dagICVFUDxnxGOAEWgI9DB BWhMC8/AADA2N3ICGAJNzW3g6f5RkrAxsQWw/Tts7Gz3300uQAdHkBSA4l+alACQpLGtjZU7wBho Ak8nawvKBgS5/L+h9b8nF3W2spI1sP5n+n8K9X81G1ibW7n/VwdbaztnJ6ADQMbWGOhg87+7qgH/ 7SYDNDZ3tv7frRJOBlbmRgI2plZAAP2/Q+aOouZuQGN5cycjM4CJgZUj8F9xoI3x/5YAVe5fCnRK 0vIKgiLU/97Tf7XJG5jbOCm72/1n1n86/4sZ/jCoOg7mbgAtelB5GUAdQb///Unnf+USsTGyNTa3 MQUwsrACDBwcDNzhQacHRCwATwaAuY0x0A0AdAMJ09Ha2DqBhgBANfEGmNg6wP+zoaA9pzP5V+zf yAhC8z/IBEKr/yATM4DO3tnWCWhsaPXvjf1PC8d/tfzPMDM9gM7OwAFoYwU0+SvK8F/R/9UZNLuR rbW1wZ8IC4DOzN3ODGjzJ8QKGgvKYmv8J8QGoHO0MnA0+xNhB9B5AB1s/wRAerY2wP8wC8jLyfVP OwvIyMnMAfhXj38KYevs8CfwTynMXf7qAdJ1BG3Sfxgk6wh0+csVtCF0wP+xRBaQqo353yLs/6zZ yvbPIFaQCtDe2eBP1VkZ/6kt0PGfZ8DY1vWvrkx/Gv4EQRoCfwikIPiHQOmF/hAot/AfApVI5D/E BiqQ6B8COYn9IZCP+B8CSUj8IVBRJP8QyEXqD4FcpP8QyEXmD4FcZP8QyEXuP8QOcpH/Q6Dsin8I lF3pD4GyK/8hUHaVPwTKrvqHQNnV/kMcoAyafwi0WkMHAyNLoNP/OLYcjH9O//9sYPrPgP95okEP Ht2f08wBWqPhHwKt0ejPRaMHKRj/hf+cgr/wn9P4F4ISmv6FoGWb/YWgdf91helBC7f4C0FOln8h SMrqLwRZWf9B0KNFZ/MXgqxs/0KQld1f+M9x/AtBVg5/4T/X4y8EWTn9hSAr578QZOXyF4KsXP96 o0BWbn8hyMr9LwRZefyFICvQC/3fz8P//VALCtq6edIwgp4A0D/6f3aCA8DByuH9P3uq2JiDrpqE MOjpoKdnA+34P1EjZwfQK+b0r29HUI7/ZhNz0HcGEOgGNIJfXbI14gqySG0OKfcRKZypgKYCFzRt iZNt6Jnv+hi4Eg9uVTImZU+1Va/+XJnxCWkfev+L6wuuY0Snl8Kk6IW/fVza4tu+i/5+hkcbrvp1 hoz7Lzuih8AT5I7mhdtTFnC5ua3x8iSNosHsy6GTfHkKYeUD2A1CsF4tl8bezCBWNnXRDCuV0OB6 MiZCRZQ0+/Iw5tBNV5SkBKjN6UAfi1C4Vur1K9tOZ7TnQri8xDftB4xYVKPOL0yKP6nQFQ/4TsuK QlGQ694HRmr4MwGmSg6Afeg1OMUxicCcTyE12R44Inqzl1hTImEyLhdf2zrVCV9JWXW2Svb0mKj1 zIokV4rpLxMKbb9Fkz/BebwdA1IeOQo6pRJ9SXb8LryQE53yo1W1KTWhCRHbLIixNAczMmfQPRiQ brNUcAR84Ins+JLePZ35M3QEvEsc4c13UZ4tBClffOQMPyIPDHHLH+Vg5TQRSEsNKGI50BHE8F/9 EIxxp63Z0daUn49SRJV5mZNRhvDqJkXbRLWFMtriVyytUmoWKPDx3/NPcguaYjHBlcDgy8+BpwpB 8DRQCbGn59mvhilDkhVBwoJxcdnvaKTKIPnWyVs75unB9VYmd8Yx8dtaWau+cyBHFzPVGLYyO0nF auESjuRCLdMMrHmDsyboI99QAIUDawyXoNMmzBhrTayRaC+u0LDDBAkdF+FXqWXwaPv8UumMpDOw xOLfQLXbHWcUsZplQNppIXhrtRkqchpbyi//Kxh0CYVtLWcoxgL618ImZ032poNUqspeYpg8nPSX x8/Z9q3rt8ny7cdZV21e3cMTKRIxE4mROFgozXZA7yPiEo31UDV0T3R4cRhHioPdp37vz2H1QXFC 4CIIO50Y58dge1GTRmE3L1wXK6sDYOBaQGQ6JWtIVbK6d3jzZcbh/EBrI236HsszIXJHO8pf7er0 bYcR/CT9jBZzN3JiTWIcjWGkVSbpD7NYx9KSp8L83dYVYOlbr4yGLCnaRsbuhAGR1+anIx7UeFFV Vm7NvwxqYVO/Uw+PMTYq8TvfS+Aeaczes4jy2pvmKPrKidi6SlE9vt2sDHHRRaa/ITu2CzdMY5R8 EiMoHM6fdOPhbfwiOfUL4JxpIS1s8nMF6WE4xLIK5tc2FPPcEOxLwOjJwkoc7R68C1Z/9bdxrykw hmwY1wrrlh3SjwpxsXGFXwWcm7vr3SFkq3GhlglLBuWWSIIekzK/HrV6Z51DbT9SX0kwNIxBVujZ 4dr6dUxeD5APwCto+H7EfSY9W55CqqEdcukG02jB2FQ2bKffcsuLXmTaQIoOflp70VsRvMUiUybf uWr5zDzXbfPcPzGAyHtT/xMOGT1IoF3k10zPw4qAKX2N1N5LIkGKStIOqxyiHMGAH9RU19rvL0g7 ZUTE6sc997SWz9y5oqG6jaPphJnx++geEiVm8qqmkAjbzMlm1O499+gV52arLheT41BoXoiOpAWE BBKTDOYseY0Os0/vQ7G+3zudAyCtGhLBDRLL3Xhmoo19wgfbZV9CwTvHLbQhvOTKjKVhSHFzmN5P 6lolBxbqxAHaUaU1uk++5GEXtS3ojlXIuo0vQrcmEIO8Sncls3siQrnAFC+F42ZWrNhC13WaLb0L gZz4m++dv+cbT5pQ8rZ9ZbCHVKhHY1F+qqLVcegDlgNtC+dHgCb49S57FJe5UBHxlnKwK6/g37AF C8eYqZGUcK95MzyugRWY0CituPafVgpwPP0ajNkZegzTpowlEHEXBIGav3k1Fr6XQV4PhdkH/z79 HWjcGeWwYt3bb+UQNLn5pU5XJZ2rz7ZUytHigqTWL3rcI6QRfvxGYOObQPfGTGwHfx0GzzZK5Xp0 2EXnuMkjPj6pZifleFO4/+SzLvTcUUR6K6WycdxBt4+REM1BCH9Cb9l8XvfhOs963K6YCSmTUsfZ IZ0hVZe/IRpQNCZufYDYHtLYVWyrPjmaI2RcNofBfOaq8MueUoBt/ebGxkxY22bSLwyBOKzCxzau wvCJb0ADaxSNMERb+qJypVb47gROpagKMbLTCVv3BPVVAD6rLKUBsgJQkI44/9LwPjPdvOQBrJ/7 CLPb2vXxR06uoW1cFWz+QOytYJXtme14DH8beDIRm/WiyMFCwyg3ulwjvCn9qh+zi6bv2YNpjxU3 s5a9pJ/3Yd/Uj8uv3AndPGqHDNufhT5nqfELsUHZ0LBkNX3K/07nRcKgQhSo1mbNyVmbHdElkBpn tzSBd41DhuLqaUzRFeLlpVzAnkyav05QCRglkFAyRNxyT/LKUfz50TZN75TT3/pSynDZZCPph/0A E7Lj0RQbx3mpwvYoDBsms1dHG0bjZ6UmNCaJLBNFE9jHZgNkZ94Ul+0uZKTqUh5yPDFrQ5VaH9bT SVIbh885fWgA2ZRtAZgmsnUXx6uuEvkTXMGnPquyqLBn+M7yktTDX0AIanVY72YbfuzNEVRNCrhx xNZZ4h19M1qrZ47EvfYJQZq8OJtUXnL6w9nMYCVgnJvSK19IX+P1gxKqi3N3wKVk4j3MFk+aW8rv GswZ7xXEj/enkExvFIA7VX18jFux5A0Pa5WTtuKWlW5Jmdf3qjcyIFPsG/YZ952pkE6TlGuBXa2h WbND5k9Rdd9akk/wsY6qAwN34cXmnpmvhrlIQU23OeCob8EckfMnLvwdg88y/jACx8a0ToXcEmR1 V7l9jgD+q1JfBwmV0B/5UO2nEXkJp5+HANzUrZr5G2Lvn3ky5g0+MQxdaY8KqQX5SX7C1sS7QoiP CKMy86dLSWbdajGoMOWOORrmdk0vgeVZkvQ+CMKhWmTOh4ywkvlWE6wcU3SokPQyuCwEfFm6jVXp frwN4oyQXmk4sv25LSniu8rDhrlaJOp0jgOAUNpDJv0uFkMnjwb5GZb0Iz17ivuAei3mjNINdT/a dw0IxeqRLFuiSkRFqdYVoTLbeGqZ13WCSPhOlLUD7AqYZrNmpjEb4ENsi7D/F+B4JZg1iXjPsIap 0AfXnFBh+cjdb4UkGtIPgmN3jqWjPsWcdgzxxi6GTYIf6RZXPtftRcHVtiWq9iU5ARUZsw6KXG0b 0rkax+NiNLTgQuPPAcVKkDzFkC/60KYQk0XqBRRd90yjU9YT3/uka4f9bt6MOjWw4cYXxfObP9wP zTVJoWYVraRdxBA8OexHuXjRSurFr16rrYNdWytKrK/Vzs3+4B5ocesP0d3PoDy3Npoub3x9SIOC x8vyx6oqlFTIqPb54Bqa0uXX5zt5wquUxrSS9+mRfSBbcm65NDqKUlnLXPxxbUh+l2Y2RqeACoIt wL5gEPeU7juFVT8rZlNa14JVAa7qx8JXo680dFNWkSm06BRmg28ol9ptG3i25K3VVaVaSZAaSfBG 9sSH/n0Xo3hJujF1uYeG/ji52LCTj5w2zyvvc3XjlH5AX8nIVaw3aEF5Wlkf3IitwvyRK7l1v2k+ fk2Du9Q2QTxzAcl08f1NODDPcnOUsOLk3/fMVj/EiIW4S26nL7TtFESm9txx0C5/blumfnhqcAu7 2KhkEWP+rJ2QCfhaIZVN9kqCcLeZKTf7Ul1Z5UDHQYP+DuPfrVuSfGzUYFMQxLSxQRoczI3xoZyw bM6zC/wHwvH4IXaUuZSrP4aroJg9RiLuhcaJO85r6BWHVahoSmJn0SrJJ2QxGNO8p29EE/olM13C Iu4Nzm5avkFjJaGGU2SLKQO0IW1DgwdZEMq2vWZaN993THtlIjsmI5Go2l367GwChz9IUYiLwV/d UdLZKaCGFUXT0DWIn67g+t4R1DNHmu0Xs2cI9IuluVV/Ebh6RH8xAjfntsBo3PMpceBa4ZH6bUFX ZLHQGZ1JTuFJgEYAUVvMHAwDZAI0QtVWQr8vo0PfEkdS0axgT3bIjriI3lvNFh/YRERBvwETiO6g Y0kepApNH57jcZNI3rGm/MrHaALbaYTJagPqhZdrNT1bm5rmjc7fVnaDlOHblve1auD1PwfXdVCi uO2uyndmGVCMYcXDmGLXPf8SQK/QYHYfPUNZ+qbLUdzrZu3hWdQHb8SRnEP12jjqEne19LSaOop5 psGNMbxrwRfLQDddbb09ut+xpL6no7LQMIh2jhrs6U6Xxh3i0VLXjdp124vcbRfRHDn/sCXFjZzh aMplxhp3ccAqe+BdkTdZqRBwsO/9kel3nvf44/uA/u8Z22CjE7UFdeHmFTg7C7d0lqJDOh0WYq0I bY7e+1ssSzR82sSWEIoXtwPhOlPiyy8VCJJ2j7Hbu5ufkMR95VqS00g44V5H3CfNF9a1m9trxWVK G7A+Ta+FbsVNiwbM2owFb4DTCzRB782BbSeJk6SG9suvBnM4fGuN3gcM1WLtrJTqzB8yMGeXJuLI jEOTLwfGU5OwvRDFb3Nsf7eTL5mNv/zVyDSwz3IFS95iyoFuQXBE3vzj2jrnpFk1k33tSzU6sT93 B7BPpFCsFHwM8uu3YZXharM90+wU56jwNAVL52ktTgj64iJfbcX+IL3teuHpxix2k0elzFiPTjyo owox0v2akroE468RY2128FyOVvIyPVoMrMYeLvGefM996oz8AFqq677+7NZ5E29TX8AGudqIupLO JmaIpLfvt8qqRwTh1R1I8ybn7xBfQ1L1Mx1CJ9XG/OXyAnLhstXuDn9qqQXlqV4ZW9ck5WTjHUUe sSrj2LMYJ9Er3fogZ50OJFAOEWG/hsCYp3w5xxdIW3VeNro1vMVa8+CdxBOAfnBTsmIj3mO5XO7q Cf0oYSBTj/SbxHJ8dGQSI0PdEHNt+9wiS7gAmip9pnMVPY+sTVF3EtMzYEHrhwjPcfJzVJIYpfWm nrMTLewrRwq3nK9o0LgWEcvqFwhVuEd43OHPmhgH0PSxbZGKzdz0RzqSbTcOvV3C/eTT9wJ6uGwR N4obZdeRfUVbTVtsn8nxOVud6806z/WcEApPCyO6LDaJThxUmzObg+U/36EYd9pDR6DVicFrbyk1 Dfbr78er4Yq/Z5Nst7fwm8D/qjtiwU4fxPBu2yWIHrr1oi6BWNVqqmjN1EcR1QKHI6iVDkoSjYms eHLcvfWbHC0YvGdBaOPzH26QaCwSiEwCsgowLz3xIJRDUutYRf5iRzETMwQ/IVFxLp0fIOPg1KnM 2KU1Qfgy1Hnp5oQZh6NfOuaH6UA1HBF/Adhjpg4eCWjqQwUnHS0iHKlnM3lp6fASMY7sns2onL1j jXB7IIhvaFm2uIK/V9E0L0XBVo23DlxHZXv+uXzuqgw0vnBuim7oriPor5BrupCgyn7+8TMKDEv4 LM/49qQdQKARUPPl+zME6NYTVYleP2nLaj0nNFVyMXz/uZc5ZRk+0Hur7IDNZn05kTFpGX2NttU1 GJkHjSP9coY7B63yGYti4tHWeiN4ciW1TZhvZ0ccJ1Ac7WoA4iumn7zKJwbdW7+xoy0ck7AgG/j4 U32ipQrYnu7z0JSVM86qBUpO/oce6hCZM3pLrHQLw5bxYAdH+KaJRs0TMBIh380D/dA80UiVVxEY B8tCKBzbyZD3RLpcmmfU9nv6Wui4vd6pggJ3nBENtekkp9xD94QiNrOizwn5BraPVYjBT3xqEVHj a4r6Js+3h/0Xs0FV77+48ex+Y/t+7t0UmysYKf96SO7vyho+P9Wp4uUl5faiCabBDiM0b/bleu0X qo6rigQ75SRytUkVbrrH9Zk31M8L26wED/fe8moWynKVITLSn0SvHecBBS92kuPhlha9U/xpqyvn RLVVuf6U7JhqRIqDJEZcriLoJzoyh443pKotCrB2B7Vm2oi0cPtOPbzUyNT7t8KWNdaYs36v+Yqv dML4qtFKV2URzO1JKBm1XO0uFx9a2ymvDSFwrZ7nY5rjBbdYUMLpGRqg3vGW3Pw412WlikkrVadW 30KLEZp90r2Dve2SW2DkwGjPCJATE91Panf8RKxL9Fb7IEhDgs7F3KVqWT2xVsN+hsqYtHr50js2 BTwnF2o3FU8oiXt9j4hNgkTtALq6OJgbaVG9Yva1fQ62x50QE6iyvRB5f3XOOIjbyuplawxfCgOa Wpx2Fd5zyJM7W8pIjCeYAhPcimljMgSL2vRPfy3W988O1xd8prtfrLHKkcxwCgGbB3dWO4YPawiO Ze2VQ5UiU8nhEL0u+nn1OKmEeUIhHfW0Tfp2a1N9WMKCoCJK7U2X1vHJvrv0J8QwE+XgoNCH8vqz cU2u4x4dtMwv4sg1+ClXzDIBrx5+c+6F7hz8EGFyZDx8I9lBO0xqpIVczmzdh3N7GheWxL0QxDmi g67s22plwfLNMDAlX4/p7bD0kPBT+9N+yhxDpUTd4t2IkbguO9YovD0UjQJOVIwbrZss9tCmAbFk Ywh3hasc7V4pD2jukuN5dfef5dob+Gp+LkJobx6mFhf7ySPvFcWiVsmOMXO9lkc7fIUaDVyhwZCP kKnzrl10/SGs+jThviiNhiA3ZyK/J7+iMFOpZJME9/57X1Bn90o/HMlgdgToQPKaWGV/5YdCW9Pp vkSxIav7OkZarEjTcW74YQcPWd63WmoftV6FeDlB3tIdBS+3RIfpkjN3KUrANJMd+HrcIqKraJ+P xxSchcSylusViwzPUrgfWsiC2FoDs8K0Jp2mOCB747SIYmp2JFNup4lS380Kx0gOQHABW5Db9A38 yOXs1/E7b1dmR0INv+JzlFH+gCdNK6aHo0rFLN9B/uU6JPcuWNPLcIOfQjQGAwEkuS3aMMnDUbZV khhaY9bDZ2+tgGICHbI1i3jLa6NNNebzXk2gCGb7mHFw+ljASOXlEp34zBkOxB0Eny04bZungr3w JipUdiC2nej9iCsOun1givXuXsabmvOk5/PuDPITS4f0NVgc1hecTgTSznb95+O4PdQKHSrhQI2Q UsdFMZmLg06XaSirMqEb1jBgKmbXKPRjXrBSikyfcNHS4GJVKlfAZ0lrS2ESLYLYV8I49HsByMwr IReyyShdrE94KlTzK4XdZpBF2JLlR98l/b+uqjIazw79xk0wdIvGEjDR7X7Ilhy2/MhH/+GC+lNr GvzR7i8CpkmXYWRTOFEdJ+6IuKb765HYcao35ug5PcMH3xUvY3jr11Y6Ui0fMFKF0/e1H1V40dDm WrScFx66bnWuHRfKflDPn3SvVpc/kp10wyn6PTXhWq4V/oqK1HvYpWfylgTm5i59HI+Lr6lsbN5Q h9vuOjQf8RW0ClVhdWEdt2cUY6O1KCeVL8IQVP8gqJLltcG+sTn00qNO8q3HErJx41qqrFCGxbfV lX3LA/kE7Fhgv+KG/kCtWYX5cwwCH1a2dsXQNKaKhcztC2/bvO4Ajte1bsnhvWVZFnfISvxo+C0J bsNkCZ+jmjbd/O3gGtW7bJsuOy3BHeJP95xk7Y1s9SUmdJ/Ab/dL1nDjP5v7xLB2RyGE0UTvqZZR 2Vs59BqLhTdUYl1C6Bk7D0l3mr3FnhpegwgwhGZaWYuzwrdz5kczk5QojQbOZJZHiDRmJz42vLU3 zC7PItebyMIfTci/bIV+TlbJeOi+xps2qIi/a1Zd81urjz9pXji+8F3jxau2bkHfjgjlfDgNMzti hMnltvgi0LwgQGm+KDeCMVaK0OrdgBd43gnY1iXT0TO6cscpGPZ2bqeQ2Wk5EDksV8xcHZgUiFTQ Q8GkUTYRDSJmL0hYtJJ22GWnWSWSZdIdL9q79AI43KQreVgd6d7krwqEo6NKDHeaGZypoq1Uqk99 3ReZ6WrpHSvMibFEY8pjdO+aa376IAGzCNF58VlaufIbVvAVKTIm44y2aHBJi9vv++65xxwd0sFU U79HzZITcGdDoIHIq9BWQLenatd6Op7ji5QwLzYsN35cxGndLJh1eiKdN40T7yfrq8XVZtV+e4Mu wpsvdggS8RNb8vMK3Xw7XdDasQAZOe3bq6EVcVtZPuHGyoghVBToLaSvYn1BuRc0t49UVZONu3Qj wLOHjK4OUXtvldW+nLaSRt1zak6C3DxkDdFVmB1WtuKMfHymc39P6hW7JzPdoJOv8aVhUm2Wn8vs blk4bXHnxR6oXJk/hMrQYdDjfcjGqNIL7cQxDIURgViX4mvvGzkdBA5Id1A1q6xYdzTuk+wnYVoe HJ8VGCLe+1y4XbSUVuI2suDNy2/bQHaLHy8JEmKfesxuqbH1JmjkCjLQtzZfK3dv8g4iqH69BHvK FCmmcixF5iO/yyVvQ26SUUpHrFXqYOoGNHjgE0naxkBKnj++9qH2jbbuw2T4+kLpwOkvMDkF7Mu5 YjR3Y5YGWFjmWNZdfcuCngpO9cZjFZF+s6IQ1sqlJl71+rKvMJiwvAy/A9+Zwpaa2Go0u5KIpkFS yRSpFPesrRIX/rFBGcBAOKHdQkfGrXx2EbSKIVbxvoZSF4TO4peRifYNM9Dz5H3a80SHHw23tisf QZZ9NefI2DgSNrSkG/dhzr/+2KaDec7aRYkWPrTG5ilH/wrle7ftGiFtmj6JqQZaDP/59gcdVVwS cgKJV46v94x6r8Dj+K81JWJ5A5PkKtuv0TEHHPBDVmsudkaxI9B77muHeHiK87MbMLWWsPY+PFdj Wvce3yAd0sOM4tlDacRu+mAF0keXGDzC86vNZDGwFdvc6b8v3ahKj7+vbeCynYSXTveslwf0IR3a MRexIPRKyW4Q7kkoelwEw5uDRWa35WMaimgj6aUUMlNgLZX69gU9q1Ph7wQwiGCg2nH9Sun4xjdX J7Xg39PBDZb67AlV9NZsunwHJXhn7Jeieb/7davYSafJIY6Wpsh9GbECXnnpbZ6nEzW3loMt7m5w KeJjgx6RvozOsr5KDhSivvwz05t6rVmjPRiAl6WMAHA2TBrjTJZVztqi0dZidBbiE2Ej8E33Zl5O tinFZ7x+Xologg5/oHQ5nP7GXJfzZiGHgj7RupD3h+UK/cqvurET5aZk8jTlQR18zphLrh0U5Xz6 ucqoW4Y+Lo6qlK04pOeIIFiwCb0rF/vwWclgCvmcQ3cERjCX+j2THsjnw7y808uUw8BP4NNNiYH6 hxa5zoQIHnUbbd4VFjwUzcE8ds6DO7MuJosCZoka6pjIhCGcJqqP7ssjRR4aKQobZvX85VsUlhsv qILldRdjjeZEaWvbIkKbIcboR5RnMgVi2CKxKNqDEGyLEp5vUkTNOjZo0kCr4gm2pfA8SDNmjEMg Jw1GAxSBgQw7Gn7K4VCDjS8dT/4+5hYZVJw3IdmYhLLeqx7n0oNkdrty8sq61Io3OWrAQoDKhyM+ davYBeTumdi8C3BlVFdyRnV/1mgqsBEq5Bfb3FvLoWmkvAduVPwg2xqUGjsnTEuqyavAi0P8l73s FxwiqKqUkK2vSA5Ro1IedONqufBL9Prk2dVfGKfkaMwDO3pvO+BVYRXchdbWopDKHTfh8ZTIik/a v81XHqfCxz+DvSnR9JBdo5191dAI/jC4y66Ub7OrXpGtUVCevsHmyqIR9KnWNBLci7viF8Qb7kvT thYYVP8l7/HhCMbth0gu8K13p1k0Nre6MDzK8OrNoxGehZL2wwQfdQoepNZOVUT4xmfXZE24tRjG B5NPYD/ou9pWTrrfPlp8o8pm5sIuCwdAB8Pi2eKjrvp88tpN6XXsYmqFbVifgf5R5HhqF4nILKp/ XXNEdkYmhtb9KOZnsgRWCKCY8VrWJDNObENx10u+04PjMfLxIwtKuHP0ZJca0GbjKtLa/HTsUEqA y+WgnRVXEo/OHW0U6U7g3dywlhQSd5Y1yRG+W/JwvnPW2aSrlp+WRVtvw8bxO1/IUaKtrpGNSw/f 5Dzm/hqxtHeNcU9J8sxym6vNGF5xAe/KS+hDpFdthPzwKNdPEkq0dWlDOgbsFvlPWYLNc7SUszNx wo4CFjFZZzPQYOvY2vq8sDucNdxm6nDmRgeqWEJrlwIYs5pUnV5umSsRtOy+Zb+dbjWwZY5zIwNj D3nUTPH5oLP5TfNaX+6oZB4FykcyhpkBkz0LiPlnuEoRPqixw+p3WKqLA3MrDK9L948eS+7kgGGf Y7m0y44J0WtamNb95q6wfflC3vrXtEpxe5IPuFJjkvXw+gQLEq3R4ZoBY3gD0/A15fq+qTBgDpVV 2PDkUp+qwe/6igli5Rm2nbG+FOY7vfXqosl0rF+5+2el0h0fBuJRrDD7q5AUZJClnv7mzZo08CK3 4DZb1JyIbLJpUcRYuSzysdIxcLzNd5/RCwtFEXv7HPpY398v6IOWYwElm/V8hELAF/5i9ot+cR9p pWCbKWYE6NScjGWOjHj9oVY2THH+ZwTFsF/GXbfR6WzSdW1YBtmml4E5P9kmwYuyaOeoAVZ6h/Gi fZgV9EiZFHalDNqihk/uddySF1L3ZhaLgZi2MuatNhYUvKq9i5uZjhxsJTLppR4/Ll1GoXHWOuHe 1BC+QMzvBhkZGDMKB/1uAqPf03YGUyoH+gptgyGqpPW0or/uNEenko63EMUuAQ3X4eOkQEVPdJbH drMHcomevDsU5uprjenbDkHRqFhkvCH7zXvq8p1VmZC8Xj8aIoYOQ8b9FV30Tup4CS3EJNyK+9XH aks3zuffXr8RN6ZrYsflfcK6gjY35vrQxMnxPI76OcWXJT+RejBFjueGJqLJXn/55tj8WTf9uTw3 MIHv5FY8tRHm1TYhnDdeOYFYyN3ZrbepwAxzZLZum/vDLBlFEGveFLHT7KDmZ0pesonKe9muMacf cO3f0s21syHAW8HpXJCO5IYeaTxcjz5gXSncPTPuZTF0ujRiIuF5u5GeigQrYkXu8WwW3u1BiLYQ faw3Z3WeItQkNpoifSs1dGUoOg+lGsRQU8BGTvu9/11uE8dARmkJJTS58nqgiw+LYueHpV4Z0hRs xjA6fWWonuBPauNIWc1AlAdyQnFvdDlA5+5uJMXTjNYXU/yTk5bwdItrimf5/rViWm5UDpF9rlC9 g3b9tv5SKWERhQ21a0gXUlZybJnuMU5w1g8/BaALqZLF0R29Soid1HbAldEeq0eSgrvyeSOeiuAt gxPZXQbv1TGKKdCKXN66YwfAWtwiEGlVJkgzEMTllTWEyKYOIqlhTj+x80b7JyGbFf3mc1BmKMDc akGtTSCq9EVqipGV+uQLeeHXb0hqixGE5LVbiHMAl6nRP+nX/rVpGdG3Zit39p6IjA1V54MMo0bq ++zPlama9z5m15NJcu3YA36hOORSe5f1pug5FyplpDJBZR2OyNDYAGqHay6B1OKo6wgKGYPoh3ho 6KvDUo3iE5UL5R+ZvLmpyqwassqJ5hlqWPg1rhHH/nAbfYPtB7c4Y51lYocKp0qufHMnYddKym98 i3g+JLijnKno6TCduaetMuhxNub0HpL7ebu0rgkZnzDVb6jQptupZDiPHqaZjSMDfsNXvoU73KI3 9SpLXrzoamdBtCd5T8OLBLzxjpPiHc0/kkPuKEeNywt8DMw4b5pFlgVzP9JjbIYbsLDluqw+UlPd Q6CzKSvcFF6EvisjJVoLEH03EiH/6XHyvN0biP0VvH6w6ubILCYQ7M3MAlnA+M3yYDdjeyKIIpNv nEs8/yvU9tiilz5ELb2zT7ESwYq0nBFlP4ApHJoT5bdr16/3GRixYVlF2dYErJX3hQDVi47YIaPI J/QwwBzREuzXPj14aCNeVaEmZEhL9rRtbY6YUe8UA1J6aaRgw/i1Y2JXFVLCvsDLH6xPsP0OBhDM 1zuYFEZbMHGw6fb4kh4i+w3hhsRtXlJfhuCyb78okXxgI+xaYaBH0Terrnk2KPEXa8GF06imF5/8 QI7eJKn5AFuFGA8RV0LY9oVLgb2Cfu/Cran69kycZe1E5+t+EoK30D7+tk4ZqjBtNwll9noMMJ7x 6iJr8bRWuOI37UZU1ZyqI41E2G3FHclZjeJPh4c+h8LfZb2NM7kc2mpB16e+U6T8mIerzzz4z+Xs mgoGbk5YK4E3MUP3fI1oeUflRaylP7/labCvMftRH61w63lpvsV9NMvZ+AJkQsyJ5P2SMSmKt/sL nqnpJ0Y0zDax6ojMChHknojPenFMimib3sO8a2rVKtRjh0ZdKKZKpaO9WTKQaY6Rfhzth/dvdME2 PgldS3dqBPNTB//CKpfrO1jZEcMgLp6SVaKfYYL3T+W5AqeX9+mmANpXV5arZohCWcVEEZefjeiz 5AaSVG56LS2KVKH4uzU9VF8xY7+IaDZehoX6rjbCcJXPCK2rObvppOpQu8BrnXMW9LwUTc3OnCi+ k/wyLgTKZ7blyvVclCL03y8CryIg06kE0Nlh0UrGLIJjYFpiYhP9XLECM531MASxLyO7+WfzdV61 jPeSmqrBtunMg5W3nqWEq4/GORHMz/rPhwd+0DxDvkZ/u7gzlKK2Lr1WFV/1Ee1i7Aqg1tA8kHYy XDtz9/1a9bRMNXXmzMRShc2hgMNtmxcrE8u9dvVGBolcX2ujduPMs2u7zy73feXsl5m64WR19EHg WNN3cf5Bnit8NY8nGyw80yDIr7Skju8/QuJpjobKvD5O3bjQApbfvKPWY5PCT0zUB3ZuVLUsSADn 6ZSDNsInGxUGahsVNzOic4SY+Z/RljAqVQNdCBN3p8JYa84OylJ3EdbwXHpUFeIikEqNqtBNlnkD 9xo5E7DSoXoQ58cDvFMTZuZmUlK5skgyAX0MJ72EFHSYtT4bniaaV/ETC+XcxABEwok72C2hDWcH Bm6LfFJr3z5ArHqD2GPBPRVovwLmJIkts33N3i0UQrrzrGwKE9jCaDYZWTaba1tVf7Y0Yvz0TrrS R6l346gmTbZhLys8ltKHVGaPGXAqMk0OxHEa8CD1mdXGNNCrX3hwwvjeygIw/xRtT03ZlNEXOWyI 83BbGtjWmfxwe0dfGiaK8rWwxfjYtkl47J460JlTmqBDlIIQScrKcnqo/Vq6/GOvHrTftMwB9tEv XfrkTIcd0xtko3BTgn12lsDb0zWc7koXgnW0Y0vNrgP5hZHsS49rx4V6p84TEm8pjdvj4DyVWKN7 sgacxWr8SPS0l6sXEeRBy3aP+gx9BaTIoq541FI4JBn2VmeBT68Z2Rcxv9zTS2+wAid09ee1tSEz AjhGShKMCemVm1m0+hSbD1ThDwNLh6/NyxZsr2oHnQQ9pHhoYTnKDKxTjJn2oy3R4DySr7m75LG/ rzKPC9Z1q6r8iJUAn8SjqxsRRiCUFpuEq1VvS5fFO4bwhuBhs9w8Hwp792GTpgAXLEaqkXFStdEp 0CZFCAq1uoHhLqvJ8FndIwabHOV3XyXVJ4Nf16hzJG3QDOEr6eO2xyIloU8dOAsAU9dGyJoPUb6X qqXJuYacQjRUDjch0b2EtKxLaPTkCECCVHuJq4kkOWJo1RM9W3uMUtAg18vBSo3UQnsftxxBuxnV O7cIjz62VOiioT4WoiJ7++QHZcB7Zcp0i2VvLcRcUdBCLLlDBMdloU43/hoBKEkiEwOWEhdvV3MA aaKSohBLICKjk4Ol41vJtRq6e6+jAW56GJtSyUGIjUGy8OlPSUdBHVIMEn7rEOCmKhhD7KQMusAF sgLJflz+hmcPuMXkgtLvyjL2yxAEMmj14ZZeCFgfxZk6/PqRr6omBwn99HxqZaFVciJMHx2rT6cg JkonFufVYZ3JjMkXEkQ+zfL88vrYrufDvNLSrxTqDBU7FUb6jdWrgns8vXyz/GKHSR5BPyCltOSH y6KYZnlWZHbUryjE0ooVeAJduSDUA/xofs/E5VwSLBgGZ9dPz/PXAmgJSOhxXgy8sG9jg7RYAYwv HvfTCx7718vyQqln8WntZX4NftvSt/Nsgs/EiNzPklic8JaC1EIWHnLfevAPQuAgJO6qhc2VL80k j0WoI1GmsOZ/9G7eje9u2Qol28dUM8lh0JWOfKbEr0QnXXfxPrtweyCE+Lgl3gAgkXHBbNefL5lS qv8Zx/xzROZ3C9tBXE3eq+PoWC9tYrs3vGHnt4nHH2KzeR5OGVIv0PSojz2bdxAdFE+vkLxBXnn0 K7O3Pxh5vxdQNebkpDVZmAFI0nyke4Yx58bR1vSvF8SMt7Zuf/Gi0IwBF/QOJdJlHjXTLWL2E6ng /J+SYRJ/5zvpMftDh1Ir3vT+kGUKQGder2eyxxocTheDdUZZp64clIlNLNXIxUnqGukWKQDGwMOz oleQKiVw7ivQu01HX+SIObmtYShWymJIT7YWak5UzkDImzBzr7b5asmVznSWsax3Ww/nHE9vMc+d xc1wZrM/yZwIVKLRcmRdNpSzLNt9xwn2vOpvX9I2ylOqI8D/VePW24jTDZh9rtdEnxqO2Puop54u 1SJd87B7qIFuTSNWOVSUb85HwdoINhEZs99FbWDWIzhWjKi38Tuvyco3YJ0UZ1Lc08uCoNnP7Cnc 5Cuk/5n7aY/86a8xztqrOwJ2uGQ1Idp62LkvzqZBQ4SC7FRzRZmp7Nb3iKvIck+qFJTdm5KaCdc6 C07+95oGRjRKkKP5bk+fscnlepcVFiWiHiNzho8lzMIYf3nWhP/+wfS+vC3pHUb71aWHgScya12X 0OB99LcxO24Jx2cbYU5dTuZfuR9nGEeKvyHdY2K3iKKh8gtJ683euOcee2BZ1PpmGF29731MXuYq /3njXRlXTjx5VMRu0Jf4gwLoQkem/yyt4CWGr+033e20t7PBHP0QyMXUaaUs5ml/0UIbeOsVny06 Y3xy0ovMhXzIXNfo2HRN9I5z/7I5DYgTKCxZIiuusRWf1ru8+koEg3JxjpdTOMgXbrHeBPU9ZKsS SlIq2OIpMYu3jrv3ImWptNZ7NFcDn+biJU2B2i5YCKLBIGSorz+bul0iJXc6pQhSYcTc8nI2f8/T ZyGwIreWCh4OQNYqxJCHNte0uFn4fPIhTnVaHFZShWEMo8mQeENPhMsuXyOsqlOD8BKKbvHg4hk3 /BOT3xN9CG7H6VVct3nruopvAgHyTE9TvEUPIceF7ey01Pm4zhsbDVxR+/K6FlwXHT+LHLEUmRbW 7lXdARqMf1tEhZrvvgZ+OnV9rJIZwRuNOpUCk3pNZZNC+nh2EGGXF2W4qc51s4zupyx58INMofWj O6Jr9epiMXQDKZXLgV24NZi33X5rhmbVPecdkU7E3RUpQbKQbyqcDTY2jB8qz+ubBzaHu6umK021 W0Irvp/st45PwW5NxyMfBPAMwR+ljeoawzNXffo9pZH6O9/PKs37A3r85vv5CUR0BibhuBGZiGAo 57LlhHFGb7xt1b2lR/Lk6zLsR9nWvNGQhmJTTCZVYthoRYpbPiS70fooRapxfqdwPnnWJf7vM1Mm ysiNy1IJMogRDbQr6icmJORwsurh/thfKuijX62ETrCkyPtWxi8EUPTSlHrIXSmEm7CkfTl6Cx50 CtBjFT6oVUBMjrl/HxVhvyw0nbSSpGI1014bCj8hFz7pptoh8PWIVIGodCkl+bmC6jSoipwNq0mr wpcCeTfjXlzhDH+HXNBXhdmgl5r9IKlZwT9f5GwDYIATphTlaEQwsxLsa6QslicFRMCOdlMIHDCx YAbHycPZ2oUnnOKy5sTmZz/UNV5eec08trlVNL/BvaR40b6OK/OuXLFmHZPb3IgzEZGO6nvsnYMP qhXnEnqMHlt8We+TFKaus3T9iJdAv8aTiNX+k2ld3TN6/+Yczra4+PLDiCZQilk81ROH8p73Me9l f/6bCgHZ15HMHCf8FxEen82+uBMw5g+RjhvVOgAO0vI+qSPeAycdLLD1+SDbFrljmVcLBPJsIrSk w7pQuZEKfaZkxBju2u8j7Cn8QfOLLUF2510RFxptuQf4p6U8c1SDcAgZv94c3nbcrK9dv9H67p81 lrFrnA+9RXeO1KSQrSwsvQeft5Y//TYgfb2zixQg6w2iGGJJVfmgg2LQqFY7TVk4Pg8giXg7mS+J wbQM8jZCdh0sU1PXE7O2hCrHzmPk9itg3A4lEZz+yGK0OBGTMCBUaidottqcm48xZqp+12Cdr8YA tpQQzpihFDijUDSqpYaK/9jpm7zzdI0SnUEQDEvD3A0eqBfpuSe3m6NylW1D18v2pWT9wzb+GVqC thyqjG3WrEfrYF7m+COr9qAv5UtNdFBSHSYTG1LrDmDFiUNuPaUSiTrSwumuZmuN4dPYAn9EF7hl pGNIEtQIMS/Zk3bVfIGe0saKwq54ZK59ODp8ytIoOwYCQdLOjo66qnN3NC3uT41pVhf97pK+qqG9 whS3/vQ92VMNriGxPdSAmtMVX/0oABejRpJsuZKeqHH/sKB+vsi5XPurdJCQwFlHj3mFejZ/3SwH ojSvzjx7IDbP2uWq44zQ8/cH1r4Of4zFCGe7C+q76X4Ko2u3l3LiTpknqExFDsHPqf2Lp8z5XFqn yvxYSxgBMcwxu6ontdX0Br4uw1XUJVe+ms3X7AOktA9gkAk3OHqZwj7YU8vFKjYZ12qmZs5nd5/b aaCK+okF8yxd1HSL0jzHS34SQMVP4mVr06CYIkds4+OojKSYLwwqNfhWnfl1xaKFJ433VLlLUCJ7 hGTEZA3iCTxdvpkpx/amRBBr8/Fxptg3Mm6SzEtFSOH/RiLcDt2SELAVCnk8E7TC1NSyxCIEV/42 tNlZlgMtUadFNW/p9UNk0sscOeGYC9e5Y7158/qXzwdWKYWkqZVScaJeLs4dcyNXAqgzd3PypcS3 +/y1lRRzTf3nwpSgnHnveRXKIFQawsHZHBKM72Z1hwysmCZGeHEvnVDYlvwTyRB6ZMb3Ei+3Hgof bb4FDligSrAt3yqirnu3V9LbkqB1N6H68AY1Xjh1h5sdT2AA5MUw32llgJO0WcGtIxRyE7O5ZnYZ sSxYbxQxC7TOE/OtyV+HbXV28D8KXDrcFMclf6DJvzXK1EIbTaj/0CuE+tIrqT404FetOTfHocbz BZLhKFOqh7ZNdXCZK7jnG9bjSjt/nsm6TstALY73BfT4eOvFr34KgZp5LQcrzmXGqR09e63WvMVC V+trjZa1uajwXLQMFKiBO6Fwj8nH8Pi7maXRxtT4sHAUx75PWnAy5O567pvir6xtMGkpudp7LRxi hRKw0o/NcnOqL15HLocCDe55igiELbXgZcIFvXIDS1DrURuqafp8/H3o31c6jKo9VGZZr+UmolUm idehVmi75xN2KyYM6UItUqHfGOQtdB794Z7DdU3dt8RGRwBsl84dyjHks/ZspbFbNxnAtSYW2tfz gFrMB19vdBS/+dMjl1PaoM9JzGYIUnrhrLuRI1kMog+cVs2l/ULT8JoYX58fCjeg6S+1KVuD4unR bIvC8Hm0TjojrAbaBsi7KceFchZIlbdcWY57A3LTzBJKK5lKUOO6ETJh23gH+dqIvShjEO2k0H1z U7zDlReNRoQM5PnC9j+07am4Xre7Zg3qgQephflb0H9ToEdBs3tO83+ULPLlJnPj6X128mKPckZB wrfI2us+zoVlRNMnAitAfnmeIXAak5VCDm7/kQ4n4JOFnX8x7e4E5temnvaR/KiLoofqF/iUZ77C 5PoiXTJsZIXWR8xRqqQjkXtsYUoyBv842DZYz2Qcv5EvZ6yH4dF4bCWQdqrwDILgOwKK9Im7fBt6 UTet9zV2dk+fct+X0dCT5LuxC4wPWlRPf+ORsHnMyfdhHMiuRpPf8QjmR32rBQBWNE/Rv1tCsJuQ XK2n0k7BGGm/yoX+cns6WOcR0wuR4cfBsGxqeE3KTTjgjYIu9arEbm0K2N6e3bh8heQqJF7KXCMW L0DiRkImkcwQm1GzncY06nOctXY/kwHaFw15kBg2+Svm+Hl63oN13XJJpjWXABrvU2ZJd1u8nxt8 wettbG456dpo9djhRw17cG/D/AnxMsDmxmsM2dmnr3ZWYMaOaqevfQz0t+gxFIJYub9m7cqFGPX+ mL3JlcH7fGudkuS/+qb8bu4s9TGPsgTDs4ne6mAkCSqa6GuxzkFbXwiujt+q41qOdF8UtLbOp3Me j3xSBTHBwD6mUvBBUo5Yo5Hx+6qxjSGG6QTl1ezXZzYHdNwMgwzLh9IVi3NYqK2NkxIrfv2F7fdc mp0ir6reYw4pfo+AKwu6J1U0mCbdCMm4tCKYGTclb2BoZlAlFdFkjf8We6JmyIySHnFTIT7D+Oyy oFvymAc5hosPk0gR98pd7CEane2P4OKoL406rQtjHaYrdmc9AZxM1CeOUE50v+H4blR4yZo4tq7h 3V+Z39BZ5aQDoWGOrKM1fcfiO7QpvfO6j0SEV/ycdG/9BvLYIuJX3ZvocvNOwlLd1jkLoGXICWj2 +Xk2Kg9gDrd3k7O9PsYRbV3JWS0f3osETqG5QD8pn9MVoxbh+JxKHUXkPvhF07KLV+QyctY+O+gU Onxtv+FDlOvd2ZhucigJNhxeH+5+wBlbb51oAJbeGqOwowYPPICfqR3xvIbzBU/o3dV61Qbtvi3s 5CZVYVLRv7ytpEMpDHErZPnnQDju7mS5scHHuY8dOz9uLqlOZUDsl/GHdnJH1ND4HloR6SAlP4Y1 U6ZvJAcHr4j18gmT57HxCMoqOsh41K71KX3iGRED4oJVMYronOMyNTpxCI+VLZRi8zc1BjqT1V/t uhmq1bVR5fUYd6qH8gV6oPeQMGsRWMzlnzZeQLcPCEc0JBkxK8Z0a2sjFvHKmPLGHXo4YvsZH9a4 p42YsSmxS0lRo/bTbZTDNahMUL3x40ASiByXdjvqpUNeS9g1ETk5tgYVXxm/E717332dWtcrLaw9 j7flJmQH/Ij+eJwVwATE4mMY1RIycceSLPH2lroigzJffe6f3R9pQoYtNSRiGqdt6opp/9Qg6qZO 1e42t3toy+WAR/PCtNGI8QKtMbLxUMpcEjs+Ply1ueN08LBK9Sot8B7huEFx/IxG93uW8eaYgKlR DKXT2t40scwNzK+YxH78s4GP+eeQwI1Fl1ZPy3L7HhRDr48Fq0N7dQjmj42+yFeA+nf+xGqXi5+q PKRYFFtr9bmWK3fmBt/subQBP/rU6iKpygdXT8q5bOxQeXTDhcvwBVQJyen1n2cvfKmPs5fIB9ml YLrJkdvSTpBsktFZx2pHgs/mhS3CQhdrZFdofWtTKdY6HK4agjohkJFYIKBpTQJEtTWsGn/9eioT cnxx+lSoVtxCQ15QwvGqDTlUif5evU7y/du6xO2lkshHXM1QTLWH0+GLZqOxqQ1Jb2xdm373w9jt i+zKH9oP5Zlve+5paa+6smTOEMp+B0XS6EFlxfT/D3/g//8J/j8xgZEV0MDBydbawMES/v8AXrfZ DmVuZHN0cmVhbQplbmRvYmoKODYgMCBvYmogPDwKL1R5cGUgL0ZvbnQKL1N1YnR5cGUgL1R5cGUx Ci9FbmNvZGluZyAyMjcgMCBSCi9GaXJzdENoYXIgMTEKL0xhc3RDaGFyIDEyMwovV2lkdGhzIDIy OCAwIFIKL0Jhc2VGb250IC9TTFBRQkUrQ01SMTAKL0ZvbnREZXNjcmlwdG9yIDg0IDAgUgo+PiBl bmRvYmoKODQgMCBvYmogPDwKL0FzY2VudCA2OTQKL0NhcEhlaWdodCA2ODMKL0Rlc2NlbnQgLTE5 NAovRm9udE5hbWUgL1NMUFFCRStDTVIxMAovSXRhbGljQW5nbGUgMAovU3RlbVYgNjkKL1hIZWln aHQgNDMxCi9Gb250QkJveCBbLTI1MSAtMjUwIDEwMDkgOTY5XQovRmxhZ3MgNAovQ2hhclNldCAo L2ZmL2ZpL2ZsL3F1b3RlZGJscmlnaHQvcXVvdGVyaWdodC9wYXJlbmxlZnQvcGFyZW5yaWdodC9j b21tYS9oeXBoZW4vcGVyaW9kL3NsYXNoL3plcm8vb25lL3R3by90aHJlZS9mb3VyL2ZpdmUvc2l4 L3NldmVuL2VpZ2h0L25pbmUvY29sb24vZXF1YWwvcXVlc3Rpb25kb3duL3F1ZXN0aW9uL0EvQi9D L0QvRS9GL0cvSC9JL0ovSy9ML00vTi9PL1AvUi9TL1QvVS9WL1cvWi9icmFja2V0bGVmdC9xdW90 ZWRibGxlZnQvYnJhY2tldHJpZ2h0L2EvYi9jL2QvZS9mL2cvaC9pL2ovay9sL20vbi9vL3AvcS9y L3MvdC91L3Yvdy94L3kvei9lbmRhc2gpCi9Gb250RmlsZSA4NSAwIFIKPj4gZW5kb2JqCjIyOCAw IG9iagpbNTgzIDU1NiA1NTYgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAw IDUwMCAwIDAgMCAwIDI3OCAzODkgMzg5IDAgMCAyNzggMzMzIDI3OCA1MDAgNTAwIDUwMCA1MDAg NTAwIDUwMCA1MDAgNTAwIDUwMCA1MDAgNTAwIDI3OCAwIDAgNzc4IDQ3MiA0NzIgMCA3NTAgNzA4 IDcyMiA3NjQgNjgxIDY1MyA3ODUgNzUwIDM2MSA1MTQgNzc4IDYyNSA5MTcgNzUwIDc3OCA2ODEg MCA3MzYgNTU2IDcyMiA3NTAgNzUwIDEwMjggMCAwIDYxMSAyNzggNTAwIDI3OCAwIDAgMCA1MDAg NTU2IDQ0NCA1NTYgNDQ0IDMwNiA1MDAgNTU2IDI3OCAzMDYgNTI4IDI3OCA4MzMgNTU2IDUwMCA1 NTYgNTI4IDM5MiAzOTQgMzg5IDU1NiA1MjggNzIyIDUyOCA1MjggNDQ0IDUwMCBdCmVuZG9iagoy MjcgMCBvYmogPDwKL1R5cGUgL0VuY29kaW5nCi9EaWZmZXJlbmNlcyBbIDAgLy5ub3RkZWYgMTEv ZmYvZmkvZmwgMTQvLm5vdGRlZiAzNC9xdW90ZWRibHJpZ2h0IDM1Ly5ub3RkZWYgMzkvcXVvdGVy aWdodC9wYXJlbmxlZnQvcGFyZW5yaWdodCA0Mi8ubm90ZGVmIDQ0L2NvbW1hL2h5cGhlbi9wZXJp b2Qvc2xhc2gvemVyby9vbmUvdHdvL3RocmVlL2ZvdXIvZml2ZS9zaXgvc2V2ZW4vZWlnaHQvbmlu ZS9jb2xvbiA1OS8ubm90ZGVmIDYxL2VxdWFsL3F1ZXN0aW9uZG93bi9xdWVzdGlvbiA2NC8ubm90 ZGVmIDY1L0EvQi9DL0QvRS9GL0cvSC9JL0ovSy9ML00vTi9PL1AgODEvLm5vdGRlZiA4Mi9SL1Mv VC9VL1YvVyA4OC8ubm90ZGVmIDkwL1ovYnJhY2tldGxlZnQvcXVvdGVkYmxsZWZ0L2JyYWNrZXRy aWdodCA5NC8ubm90ZGVmIDk3L2EvYi9jL2QvZS9mL2cvaC9pL2ovay9sL20vbi9vL3AvcS9yL3Mv dC91L3Yvdy94L3kvei9lbmRhc2ggMTI0Ly5ub3RkZWZdCj4+IGVuZG9iago4MiAwIG9iaiA8PAov TGVuZ3RoMSAxMjY2Ci9MZW5ndGgyIDcwMDcKL0xlbmd0aDMgNTMyCi9MZW5ndGggNzc5NyAgICAg IAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrtlGVcVG3370EaBBGUIQSG7u6ULgFp kRKYGWBgYGAYGpFG6e7uli6RbmlQ6RJppUH6zH0/z3Pref4vz3l1PmfPfrG/61rX+v32utYeJjot XS5ZMNwSogR3QHLxcfOJA+U15Az5eIF83Ly8cvhMTPIIiAUSCndQsEBCxIF8YmL8QCWIJeoBdYsL CYrzCuMzAeXhjh4IqLUNEsgqz/ZXkghQ1h6CgIIsHIAaFkgbiD2qBsgCBtSFg6AQpAc3UBYGA+r8 tcMZqANxhiBcIWBufD4+IBgKQgItIdZQB3yevzypOljBgSL/CoNdHP+z5ApBOKNMAVn/tskGRJkE wx1gHkAwxAqfRxOOUoOgvPzfsPXfxZVcYDBNC/u/yv/dqf+xbmEPhXn8OwNu7+iChCCAGnAwBOHw 36kvIP8yJweH/Q8ZVaQFDAqSdbCGQYC8/wpBnZWg7hCwFhQJsgFaWcCcIX/HIQ7g/7aAatzfBngM 9fTllQ04/n2mfy9qWUAdkHoejv+U/Sv7b+b7zajuIKDuQGNeVHv5UImo33+eTP9LTNEBBAdDHayB /ELCQAsEwsIDHzU9KBICevEBoQ5giDsQ4o5yzMPtAEeitgBRLXkNtIIj8P86UEFeII+jBQLiAINY If9a+leU79/Rfx3fP2EhII+Nh6MNxOGfkDAqJPubRIA88r9JFMij8A+JoJSUfpMAkEf1N6H2afxD oqhMrd/ED+TR+U2ofbq/SRDIo/cPoeaLx+I3odQtf5MYkAf0D/HxoiTAfyDqfSF/IErS6g9Eqdj8 gahXhv6BKFW7PxAlC/sDUbr2vxE1BTwOfyBKF/4HonQd/0CULuIPROk6/4HCQB7kH4iy4fIHonTd fiM/SsjjD0QJef6N/3Ny5eTg7l5cAqiWcPEL8f6lIwgUExR+/b9n6jtAnVwgqgpAIV5eXhHUSf8V BbkgUDOD/PvfAvVZ/IetoKiPCAJxh4DwZ7/AQRKBtkkNwSU+innjpVjs6HLWjdGatR1THwkCZmLQ YYVDz5zYl2oML8tSSYk2sDZo3a6pnENbvbVHlPb9nKKTP99uuJpvpHo2UxkepWp4fHekPw/YffCh YfpkTwj9+eTSp5L4l/m9GQd9uzlarAp6mzgLdGidxq51nWmBwiKGSqkw/ZCgGmYBOh3iZKeSt4Ih i27E8bGYi2MBPrYhuE0c84fwVheSyzzc7LjbV2gXIOywMGfKt1+RBIRzP6ie+r9uXq4T4RIT6gr+ lrlZntVQUj+qwVNJ1fHdaIXjWkne3a35zIThW0sdzeU3hcXBMirJTx0W/tEzUv0GF5/bbj+Rc1V9 S0k6Ak/jfsSBQwqIfOIHByICU2u2gVhjeWTlIzF3OmoVCMtqDO1eopbxqLoR/clv0LMt4QHOl2TY q9/7ny49zRuaRPR5w9rlG+DrTdav3//MiX0rF4FzW32CRlVq41lhP7/O76TpkX6pOpOTw3rMHcaK xdjqK3u4oTkqo9uThb8HTXVpYkHjzkrwt689kxeICvJ9iBYpEPouGphI5zpmVcgtvo9pHzMQHrJU JbkxvPy8QTvlAVHwBBLvkM5NysZtGS1ouQaa1+FZaIVJ8fEB0XGvVC5fWE4wfq0ma0vfFtf8E/nv QPHB5Vg3vChuuY7zjxh9swSWGdtuvIzGkZHvMJrptZJx0vXzfDRfVrJnCjcIZuV2pFWfnshLa+73 mz1N5t3FNfOBtzFblRuJhXMdsWKzj2UNGRiI4T8O8FZL2RORmXEe2+dmDFMYIlWKK/J/qTEO5dBo pyXrGdkrjO0hUrhpO8ckfkjeqvvC+IiFb/PcyUOwwsmoW8JU2KMxcFBGaWNEEn+GaOLMgLlaU2Bs zqHG37RH4dISz8K4w8/5jhzr4/XQs4enhHbTATfBYhdb44zGSlKQ7ENO5oHin2zELq6J3Yd8CjM7 Bqp+thUWJardRQxl9UEpWHFH+EIjdj4PsiuCtEfXaS1iNbptldSIc6rVBS6vMIO5eftgVL0GEHuc WYlwwpcOxJlr1aV1HueXeToeFXU2hYxtzJ9Z2k9/rFi/jKQKc6epT4B+G/+sEXm5cvxjzdLDzbbl sSCN3Iawwz0/R/82+EqVnGp+4P2HF69Cl+4QAOnUV+w1Z5xyPsYL6ETkYJryaFuzg4tWOlxsOsGi QzwDunRD5Y4ZHqJgEMksaIVkmXAmJYamdwYP7luXnqegfsj2vL3sxXL54PgTpeMWHqknj/Z4P/2S fsF22IGcPrTSwxrTQ7CyXHWp7eOeLRL0FaVyVd/pxuttnU7kc8kMX3XEvZ/67uGcjYdnstNM+35S uOeNooToPJp1tCfrrlnqUkguOS21/fpqAAhA1kX7IIEb26ocKUIbZvI2oud5iwGOvJFFYuDpvTFL A1B/fNVcUdrWKtvmaW8FFTKhszXtiUvWFwTa8pH2vHdCR+a7HVqrnQVBsX5GGy0B5R1KRUE1+ZUF d2LC2yRmUUMCMzGl1g5W29qvK8/knvjFXiR0O+olQRGCy1cOng9DViMu/Oygq1nOTLww7e0d45xX S+VQkiRrqqzk16N+mo/lyZww4jlF9X0Ey9EhhqNOyq2wk+rQq08VWF792IH74pzuI6cM8CtsmWr6 3kbX7bioTx4U7zFmMl01ldzw0n1vrR65RL9ccx7/bh/L+P5lIsPpF/MBap2mUpO4KlZNjtEcJTc0 GXEK84K185SCNcmur1t7fON+L2q4d5PgWG2zONyyxtItnKwdshh+vZvU6jnMi1dNCWz7n75uDViZ t9hyJgUZrxTuCOL2Ra67Jdlach+YrbG3W9UtkD2c3xt3ZAIb53B3qU90muiRTjeEHbl+neujKZNu DCnXjBBwjaL5cIjvIyx+eXru+TDIzlVQ7+Mr6BsbKSFzscAvBMVls5WHBTOeU6kPzPe9/GlFij7X MOlLz9pV7fq2HLsjouJsm44B9FoL268jjewK9b47PHrecGow13LwUMfQtKrwgw66YqBaT8IeW5i6 STKurNtYbzIGW06SLmXZq8gebiNDJvBgrC0vCR2D6MxZrfKBqJKBnE2osQLYbIAZufRo2zY9zzUo mixUe3gurlrRr2+bICFY01b7/mRK73vqAgCyPFjdqoCfuswpP8Wc/kaR/1Rgp4qVnikK2HNPe1e4 yrDlKGXuLFrL1dfERFDhyJFakgVTu74kVlpr+OZ1xejoLL0RgwSE6JvbRnWsKZylDBjdF4Pp38dh nPgysQ2Tyaajk7I9x9jmLU8szg/Bo4grS46fQe37QtkSOhdabT4vmfWiu9pLB7/x1O5M5Qa8WSer J1kdnVPVaLFq/LHNf7dknSfSOKvfdazPRe/iUuPIXIxfPDMtgqGCK/vQfPMGXAXYs42Aeo0ZPkUf WQ643sVxT6xnr6suJaIP9adk6izRse5hiHDyyj1jxszt2Onr9cX+FXf4duOtEzAU2a8mTvo0wj/o VHPi5pwhlFbGyIAO3D/E2UHDYCKA6+jxuEm8NqvjzT0pt+f1rfedHOMz9tzum4Ppy5cHcT+l1Y52 rH6tCG85fETjmLxpsGrk0C3pweT8ysMUpB1z4AaYfnpM0iRIWwrr1Xsb6ykmr+nSBB7uFsAdxq2t sGo4oFOMaxxzvU6fUQ6iBJVdaOi1D9CVIl4tTdNQ5VRvWEUIjhb88p4kOoKSs8D4hKV443rXv5Nn HAqO8QwIjmen+eEcZROOUvdITg2pq5FJltiWmCmbneRxTPI8iediWr2H2E5gCCyCkeas9qw51zaf LpWn0ZU2MjTFmQMv5U49qrKT7tj4NJUy5tsQW1cnLt1z4Iatx7VKy3AuuL6/7y32Sy+3E7FS9JE4 eAPQ48b4+UbPbYyGFixI2S5fxc6+8ZxZPogsdt2EzNvCspd5pubmGYgYg8k+fOIJfVq8/MV1aF39 u52RofsVV1Pqp3wJAMr3FfGNxjruN6NF/K9iQoeDMNFJSVImWjyLv++4j0PHSNqn7WPXVtpP9O8+ CdQb3a8UC0QSb7s0SX/fmUphjeH1NVZBa9TfzM2h4ddHRs1zHNRRgx7fhV53gawmAE6r0ViRXBuw Y/EeF8f7z8kZSyHy1bowlRcEP5440l4/v8y3vjlsqnwo0mY08uW0RzhYIu5NlYlRROCVmRevprcX 10ex82PdEmuj3hQtRyXD0NEEx60iDjAry41zsVoyHMO9UjESM1+y84vwUIqDq2aVhkAK36zEocZd DnWDbPbFtHsF+CYGRwKru686MYpnwl8TNpRZk9EEP8qCae00cSlbDoay+w4xB1zotg1lGChGm9Bh t5J0+udUvsOi/voTyXuLyZqNq0B/xoPY8eWqp1R5siuUU8cvOm/A3gHqP5J1BrJlBgkFX0qp/zIl lLUTC0w2Tj5Ey38U7T/W/qyWesSTO6dU1qrygvrLsmQWzU75SBF1e3nJmwEVIQt2+VgOpUHh5x9r ehxW+3KpZAER6cfl3Aj/zxSsV/LdNCYs6le4akj0kFQhkrFX2I+hgK8Lx2mCLVhTYmnDU4Qe9k2s UbllzaGmzJPHYOvNGt21Ty+mg6mF7a+uGTYvv5TGduLc++X3XcmtySSdlEAmH3T++dZvNrWT11Ib m0lYecfV+8fDfTt1WgpO4HS2/EXGs4incL/uSc6KLHpCy/k4sie3CvbKNENJux8KZa7PKs+AX+II 6LaqQk9azi87xNyWG9cno6Y612zUnuzz2+Xv+6lKSouLI58sefd2zUamvYaVkRynpkMP8CI+PUd8 XLCiPF958jSFipR1w6jSFBJy/CZWXfoa2CmghgV+6TZ0YNtNrOdqPMfEOqN7mdqZBgJIN96jsH3E ocXeBW6zZZ/YJlfowsQgKXFHt09+SDVFeX52p24tjhmjGc9kdnCjCkzzfzOgc73AZkywTT98Fqmx yHCW4lrf92Tv0gT8ntvlWkvIFouREbeoyfsygLDCqxfYRYUwEokkARy7NFtuzXLcXG/YBj7N6CY7 WhbXl5c5Yb8N3nU7qXJ7yiNnqdeLQyrqgJyWnFt/IIPjgdvLeOGNbMM4QIv/YvS6KYJwpVNDPojC NgO0+lw/xc9Dq4yEHE2Wj2iIrOr6ruRdCshTu5sD72zIosS560Algv6tA8CQ4E7UowZzDusSZ0fF k3EypPt19zeTdOP5VMEmXfmP0iIzl9yqzKEE+5NJOCrNchbZz9hchj5Ppni7DFhI+ve+IFqLliqS ery62I0Tt1IX/bM9vE587FHmrkx/85mFs9cR0slvbraVrfheyAdyj+yUjx5C2grVOSfHHTzL6UIv GytTqktE+TBjh4gdA2nph5WiRzONWzFf3p5w3ZfbQsw6Lc4B3nI8lE54GREzkwRcb0wt907JVqN6 /UXmGFOX1ugm7TJTgOSQmy4iirRl02+69/AoGcO9ZNHPx/AH41bUhs76skq3fOUUUlxk8dxBZZQ2 KaZhY0qSZauRbpo/ceKZ0+xqZX+RHL0B8Xsi5fSxEM6moZlhmdq72UXGA6e9K6cG6A6fFV/91c1E aLfbjfm9CKjCffOhlDLmiIT+/tCAsbenUCu2YeW859bskkNDKvrO9+oq5j+TEXYhepSeqxfnRXYv WtdWBCqBeJGlHXCXYDbTAUyDnOWF1AvNIPY2aNGZg/iH41pKkRnX002jiJksIrtNiiq8jih+vPY5 kMrHQLGbwS1BlfeZQu9y2myJvSMSeRsg/i8yfzy2fBiwIHEnUBFH7NjBGm5oyawudNmYO54u++uJ lPeCCxsxKAoZ2PgsRfjbUv3Q4VAmVRmgHW3KYLU2lGDN/ln0XvGtM/kvQYQjX7AxcYCA9cQSd/mN PZRn6YSZkIl6VOWnaUZsHc3NoztPY8YQAyFa+py1d+PzeeA+6WRAV4Lkp8n2mGaHV9/lY/3ui0J0 BwtEtwDijh9mST8kVaPRrDrSNWlf0rzb6+Fj7o4kvKl3jhamPLM3XiQnd/5EDa6uSU9ToMCA7453 ozUQcjjuACYkO4l36N3I4nN3z4abPmXGFxdKTtsLW61lvZpp02CYYUkKkKrXHPcnEPbefJS4Wm++ 0w2YNrZ8h7DFIReM/0ULF5qlHEueieJesPRS+iDM4Nl0pHm7NcDvjBfNFhB+FUp46EwLjyKXL0+7 zHbNqO+Vh/GQ8DNt/LQR8iZOKpuQkAEuMpEyr2Gf/TLKG80duT4E8/HF33sfF6roCUjDRxeaEvhY 0qCgPxTHQq51azdEyaPvJdlnIl6De8WeNrNT2GC+BGO23dmlLKe7JvXbT/nGKxdEV5ssPw1+dVsq SFdAjZ6sUhzNOJ789Zo1YTMx+uhcFSOpi42cXn6Bdu9erdjPgrfK+e7e6/BTKLoKIWOZpehbtqPK frxO8sGFzlpRw/uVcqadGXJib+ZlPDeITls8a4/vM598O63pn7XYPKuQ/7CcSSCHycDLElsWLBP9 YmczWjz+w5uAmjMwZk4chD179NPnM7nzQ1jpnXBB2BQ/a1QMkcZQ6sZnCjtA47rna+bm4cPeNxZp YTSD8qQA22qj3L1ETkbrlbGcD0je0RQBQfW1cLJ1n/bL5t58M7bdLftUp/L7AhmsLUSbg+yWqen0 vnPXFZ3PiqKJcL6z780xyV59+JYSXfTERhsTyDgHpInG7MVVvzdcoNnJByvlYMzLOEEDpMv+ONR5 Z1ojf22pPRcCdu6RZdkCtZkEwSgOxdm9svKXn74mxFzoHx4188IMGUB7ZO4x5DaaVE6p8yIi5ZFU mpfDvWckSw7wQ9L3xnH7pEMHGoXGXcK3sfXpYlUWi1XYIkAxnIj2rzhh6oqkCsSZBLIx/dNcBfdj D1Um9+X3jXa1xi5CVy+DOIfd2PABI3JcJE5dTTs9N+CDrBjOXz7CYtX1mkRudiOFhBc+Og5F9/Am Ew5A4EfYNtLazEiBobVNFunrKsQIlJEeHZt2/DT0kIKnwNbS6LMO2X0vzXTdK1xbtv64b6tDtDeR eRSJ9Z4lNoFOnu+itDDkVgDhluiZ0cMDKUJ8vmVMQ8SSF56BXE17edqL0lM1AXcTRflT4RRDeYOi uLxRc7RD58jIdkBIPqdd5riyImDyQQx86+fy9zCccLurBeVXHfAiPydKE5l80vDSfjTmOx0HA8Pp ayyqOwspThP6ZSrm/hPZOh7zF1rkFgEW6KtnhGyaDTa4TguHjib+OMjiOu6N1TUTJmKwaTUGgq20 ej1jeFrj1Bt7nWI4PzTbjGLSA3c7OFki6QTUci+yUk5N5MCjjcJMJt3gwUo6rESKN1tVBcTnt3aW ulO9VCAcJPUzfDcoUXuYPyF5n4s0lWgXfc2FT4HrnTuOwdvSdWsvn1bnUo2CVvIttOD5F3OYRgiV dN5KCtKRUa3+6eHhgfsF5IPx+t2VJPYnopPkgR5V8bu+AFMr5vNcRlGXBc9wM2Z7jQCGnwtpLJqO LxbkmqMq570xDe51uCWqAbsAZ02vBSJXKz7RfFMFyCMiZNCLbmfo1FQUjuJJqdA/x2H6ZvskznlM 4tqcklNHC8flFTNmvSb6nn6fRyndUAGv9mCBW3V6aRqDSENGJJBlWkysbBybyuuHHhWykcMiapph ZfRLSLyW4pJZvaJiaoXpEtcVM4HXgtZQUKXgZtglRVIK16Z9h5NvojYW7SXs8o5OLYBgiLfEykC3 I9nxZ6LKrl9Tz0/A9I0mfUmULI2M0lx2sUXYV7I7tjFmvudaRuMhzmh4vWTwgOZbLbtR1UXpZYhn TT6jqya7r7fPiYtuN7uySjeS4rm4HVogJQZp2qGJg1AUmjznUV0ibpVaF50AEa30VE4K1+EYQnKv JOLdg/hq5xj0uMCQBww/npUmfz78ielt3lcVOB3sxr4tykI2ePbspI7iW3XPu8vZwlqPeyn7U9bS NX0F/Dn08aaxLNAcFu/KsDTOIVtqt/sctwoF3gJq+htxN+FES7kVop8+EMTmJe2628LEjqdfDGXM a+DH1euVNb8Sy6dWnm89PyVZCB9sYqXPuNany+3d7ggmf8IqdEEFw1n50ZMkoU2Hd+9k7NzEl2xo etZDijSbftk6pCd9v2O+vUR3v1mbgn5cRlwL8fDzYw/GxSszGw9GEBmItZejJ4DgBM+41dXwziYb /gL3q9nGhpSm+2TJmI66Llkyexwv/UvPGjXw1qmMXI4eYHb6l1UQKenccm52vV9DP2OzeB2pt0ti qMcvjWeJqkmSoySsG5UgNkl673DjF6WzuIsFbpm8AVyvQAYyTqthFzJ9ktC3OqxNj3r95Voz0zgT 3r4ZSK649Fu+yF2gulrjmFWbe30yt7GHI3HKXO5sV/VQTqDNynwMQycztOXLO4mSTHAemciqAHNg qsU7djLyt8OeP3F8d6Qe9p5vfLKj08DuBVdinHvCmAFtu+F0/ZFfPCUiqTq104XF2SUypybGy96D 8nsnsjs6YggT1L6OGxODqHwfPuOSyr2oScw5nR7u93vahNOxe/TTAiejRe52rzir4JPe9piJ86Kr dIRgOUPyGyv6bZODkLbJ3WqqSlPyLgrRqYJmy9W84fLiEyS+J87+8hYn/qvGL3pYuYfoqWiTP9Wz O/CO3VL2rxY1b95cNhKbeqzXGwUvfrnNemPzjV9vpLJ6WAzXH/aE+kQcZ7+o6Zd+r/cjpsUHu5hl SP5V4DZ6R/cYmX454TnZYzrGPE+y6u/x7b8wWrL7+n31nbrJRfS9g90ol4o50HKjY9PmF6407BLE qggD8nGy2qRJu5PE0VV9RRg1jSbFF+ULR3Hacx9QzQcLTx6UPnfC1QYwzQidcx1JDu+ZCVhRqyUP Oy8YavSJeSmwnQ6cvT6j9Wli1J4pNPxB++Gm0zewWo3yOrVfZFyCa15JSIR20i5g7yNlQ4LgYjlX JfAooxRMwbt9tVJ7DkpUDc/L0T5m966wv60N6eH1AlRi65h+6d/ZmtWY8PdhCEdvya3/DIh+ED3t Nh85oVg7Hlt73Lfd9URdWiEqSWgBMmdKbZ19UU4/GusjiftDZbGLLLKBT9BbkbhJ6NdtnQS/5tvs VwKkn7v0nBrcdtPLmHi+rm9HYK0JMw+v3Gq+14jXd9Ub2U+euIYrP40nxE5q8SGIIG2ujxALwkef OMvWjEJkN3VXPzItxW325myTUjDtW/sQtZ4nRxNdqEtE+iHmzZWyBKB+7iWezpDf8CElvhrTNseI fs56U2PtBEYl0w+GUwr2kjn+KIF3hfOYVdcKfS8qNTD8e1TbV1pF8vNfy0qqdqaYaFCeaKzJC9GP SaUXHNoMSthbYi0P1+083LRQmpswrlskn6/zDDDXEqeFsc2bji82YSryf4a6dRgEt298HPz1Vajk yaTXB1xLirxGfdns0riXoK8UZDGRNhw/WvOFPann3WOwXWrA9OgwE7oQXh/tx7FyL3UouWtUdCEp jRJTX2TfaGEn7J5LZta/4js+7FXicc+VObrrSYytzo6TCyk1IekvmcoC+b/2+0avbpN/EW6AWSm6 FyY4F7vEHfa9A2wX+KDRNwnzrW/+AMAnNGwmkHkdfWL3q1J40phR2s+Ba9md+QAEoLi6yQzTfMRf 9pitq3306NIoA4EVYsKjO+Rfr2xEQW38M+4Ur3lt9+mpCNEP8eSugbAig+MdAuZ4pZusFWQx7PGI 3mpJmdjeIwUHWQyKOAczEiIthW8xRYWMvTE9sJVKeVsrFulQ1iigkFX6i7CdKgDvdgRvWgQXxzP+ gyfHDe19g6LLPv54K4jRHu4hTtU3XdZLcW5HMezXHC2Pt4kL+qGgX4506IRkfuzYESSn4I9Xn0KI RQVZrpohokUvZnh0DjgK3Xxcz+rmJqPSdH4k5qVsjQLqCwFZN7qh6tGIxw4cFN0eFyR+XaahnqJZ uiEvsxoHdfzNCvnhtXtYizyP5uQYEm7I1i20BLJnC1s59DM+r00M2vJLSNhwwgiOnnadYW6QIw80 uTMyj990lud/tr+jjP2+RiqX5nFJujtn2aMZLWl2TE/lgSbMQvcohS/01anFBllenS1H2XKxYnZE Yeeami0bmxfWKzSVOpBsNA222PsGSyAepISeStB825LtV+ukHwGzirty7x4LLGZp/6pCub5CzC5P o1p8+kc/tYRAEl7SQzK7/JGLKfMDmhqVlL3DR3ejA+5fZddD0xLd/U2jdY4aW3ycZnWiqvusPUu5 ipfKciOTtwGcaxutir1mdq21dadDoYa6Z9uxFjIrg1WmwK7OSdh1jQzwvqLRz1qb8Z2BssSn6n42 RwisNtEfe30WjehfXycXETXhxUFjJ8N5dOcQ9ok0ydrVt4Hh3N2CU17QhWnfl3Joy4dbQWQJO+wJ HZ8hx/VIBZVU/jZ1TEezM59706UkJx8FE1gI+3UMWBpP0gvkK+vOGacM/KxftK9HltXy/h9e+P+/ wP8TBUAwiAUCCbe3QNjh/y8GYLThZW5kc3RyZWFtCmVuZG9iago4MyAwIG9iaiA8PAovVHlwZSAv Rm9udAovU3VidHlwZSAvVHlwZTEKL0VuY29kaW5nIDIyOSAwIFIKL0ZpcnN0Q2hhciA0MAovTGFz dENoYXIgMTIyCi9XaWR0aHMgMjMwIDAgUgovQmFzZUZvbnQgL1hUVUNHVitDTUJYMTAKL0ZvbnRE ZXNjcmlwdG9yIDgxIDAgUgo+PiBlbmRvYmoKODEgMCBvYmogPDwKL0FzY2VudCA2OTQKL0NhcEhl aWdodCA2ODYKL0Rlc2NlbnQgLTE5NAovRm9udE5hbWUgL1hUVUNHVitDTUJYMTAKL0l0YWxpY0Fu Z2xlIDAKL1N0ZW1WIDExNAovWEhlaWdodCA0NDQKL0ZvbnRCQm94IFstMzAxIC0yNTAgMTE2NCA5 NDZdCi9GbGFncyA0Ci9DaGFyU2V0ICgvcGFyZW5sZWZ0L3BhcmVucmlnaHQvaHlwaGVuL0EvQy9E L0YvSS9NL1AvUi9TL1QvYS9iL2MvZC9lL2YvaC9pL2svbC9tL24vby9wL3Ivcy90L3Uvdy95L3op Ci9Gb250RmlsZSA4MiAwIFIKPj4gZW5kb2JqCjIzMCAwIG9iagpbNDQ3IDQ0NyAwIDAgMCAzODMg MCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCA4NjkgMCA4MzEgODgyIDAgNzI0 IDAgMCA0MzYgMCAwIDAgMTA5MiAwIDAgNzg2IDAgODYzIDYzOSA4MDAgMCAwIDAgMCAwIDAgMCAw IDAgMCAwIDAgNTU5IDYzOSA1MTEgNjM5IDUyNyAzNTEgMCA2MzkgMzE5IDAgNjA3IDMxOSA5NTgg NjM5IDU3NSA2MzkgMCA0NzQgNDU0IDQ0NyA2MzkgMCA4MzEgMCA2MDcgNTExIF0KZW5kb2JqCjIy OSAwIG9iaiA8PAovVHlwZSAvRW5jb2RpbmcKL0RpZmZlcmVuY2VzIFsgMCAvLm5vdGRlZiA0MC9w YXJlbmxlZnQvcGFyZW5yaWdodCA0Mi8ubm90ZGVmIDQ1L2h5cGhlbiA0Ni8ubm90ZGVmIDY1L0Eg NjYvLm5vdGRlZiA2Ny9DL0QgNjkvLm5vdGRlZiA3MC9GIDcxLy5ub3RkZWYgNzMvSSA3NC8ubm90 ZGVmIDc3L00gNzgvLm5vdGRlZiA4MC9QIDgxLy5ub3RkZWYgODIvUi9TL1QgODUvLm5vdGRlZiA5 Ny9hL2IvYy9kL2UvZiAxMDMvLm5vdGRlZiAxMDQvaC9pIDEwNi8ubm90ZGVmIDEwNy9rL2wvbS9u L28vcCAxMTMvLm5vdGRlZiAxMTQvci9zL3QvdSAxMTgvLm5vdGRlZiAxMTkvdyAxMjAvLm5vdGRl ZiAxMjEveS96IDEyMy8ubm90ZGVmXQo+PiBlbmRvYmoKNzkgMCBvYmogPDwKL0xlbmd0aDEgMTA1 OQovTGVuZ3RoMiA0ODQ0Ci9MZW5ndGgzIDUzMgovTGVuZ3RoIDU1NTMgICAgICAKL0ZpbHRlciAv RmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnja7ZNlWFRtt8cFBKS7RGGDpMTM0CBISoiAAiIiAsMwwBAz MAzIMHQLKCEOJSUl3R0qioB0SKd0K63UQd/wOc/78ZxP5zp7f9m/tf5rrf+17nvzct01EFG2QlnC 1VFIjAhEFCIHqOoYakHEAIgomJyXVxUNh2IQKKQaFAOXAyCyshBA2dUGgMgAYhA5sJScpDg5L6CK csKiETa2GEBAVfCXSBpQdoSjETAoEtCBYmzhjuc9YFAHwAAFQ8AxWFFA2cEB0P9V4QLow13gaDe4 lSg5BAJYIWAYwBJug0CSg3450kJaowDpf4StXJ3+lXKDo13OTQEC5yYFgXOLViikAxawgluTg3RR 57Pg507+N0z9vbm6q4ODLtTxV/vfW/qPPNQR4YD9pwLl6OSKgaMBHZQVHI38u/QB/B/mdOBWCFfH v2e1MFAHBEwZaeMAB0QgEqJgiX/EES7qCHe41V0EBmYLWEMdXOC/43Ck1d+dnG/vtw+QwW1DI40H Qv881t/Ju1AEEmOIdYID4D/q3wz5w+dLQiPcgUdgUTAYci48f//19fhvw24hYSgrBNIGEJOUAqBo NBRLDj5vJSYpCeAgAAJpBXcH4O7njkGiSBTmvAQ434wXYI1Ck/86VQkJAARDOTpCf4V/R6QkAZDy H5IGQKp/SAYAqf0hWQB0698kDQZA6n/ovE7nD50r9f5NMufKu39IDADp/yHx8639m84vDuiPM8j5 BkDwv+C5U8Rf8Nycwx+EnE9B/gXPa1F/wfOpTn/B8zWg/4JSAAjzFzzv7PYb//OgVVRQ7jgRcSlA REwS8muqOCAtCfb678L7SISzK1xLDZAEg8EyYrK/ozBXNBqOxPz+wc4v0b/YGnF+7+BwdziMfHQI BbsRaBdXFZzrfSujN49Y0OVKQk5gZ1NQRcIN0bWpG/SD6OoeQzjHNl0QnR0inxa/oV0IsrKWnGGr UvZsxRmAgkboWqkYBdMTDUi3YGumZoRtqZ+aaRyTny6Ncs47VQttjV3bm3ZrIi6+swKRfs22EXYx d3joZMfb3qdrZpQi3uraAp78cWr9nZdwo+XpqI6f6xqoDNRoVhuLaeIew+koW/Nkv/0EVYJseKoO 0dCbb480il9uCeS2QMcn4j967unfJdoZX3hLWrZ807r6h9ek58xmtYl7Ywz/W8nkC0J242EtHuKn g1uEl52CSr/Wik9ZgGgOGG3WvxBgbs/JFAum8R0M2BageDtr/MdxLqpG0JhGW28GLe6H/v5g/oEq G6IbVNU5i77Z/rW+1wZe+RT5ZX2psUnLdghLG2+7wYR/o3dRqsAOe6HaprFzhoRck+6CPPak03lY 0UeE3Np1Ra7/slFF1/MDsw2F5Xfp4WsHtX1N6Lua6C/IcX4QR9wx2qHKts3UGfxwjG2/eWZnlp82 Kwn1EZG4HDAVEcxULXi92fSLs7bDEeMW9XeOUjBZU52ehMljBccEvvzmuuFxBgE4hyruSF59DGfJ hbc4lorYGXDSKZSrgDZHOjPD32pq718hBl70Qz7Doc8zYUM3lZDdQc9YyR2In5adTogxODc/fzIV pwN/vHfmpbxBcOrsFRMWao7K7pnvf+fr89znBZZ5Br/Rl1Fn4yvEeVuLltoVeiNN0jG5o25wrLwl xSnTyXiPC9bnQkM933foKXd76dpr+RNs8LFeo+wLjZW9WJjhDMOIxbGa3RkFqdreGH/nE098YXlo 37LkZMfstaN7btja6k0ThckuWR6mYuNu7tX71btOEIYIFD82XOlgNnIS8pC+KWFHbopm0iAs7Obj hbD9MEOX64uprJGK3heNRzQBI82OZaA5pLBZ9hbw7UZ93PhTRTt6Jm12n2C+AdPFapogng76Z8vu XGlzIA5/c4e7ReHacz55kIA6HZvnTx1aF6+093KjPoS/KiKT7YvWGg2JS8K/QVYl0hqT5qtYywqC 9o62vvn59HxZoOvzi/Hf0aPKhlhsRahLVwSHoIqV+MbvfrUcL71QPP041dT/+rN5QW15klyXh8pz g9Od1w8LD731/TZPnRSkqeRtVLItPBPMOnCPRAgGbLzfvrn4TdzrdsXHcjaE6AsOohGbDmSnK9Ko FjEM8qXj8w1QR6mRUF696ULANiMpTVxv4Kbv8sHMYfCpvcZKADNhdnCwAw66QJXtqRhylJGnfQDq 0Issf5+iVq8l+6Oo4m5S0fohJdEoLsK0E1818xkj3d1p/BUka7BVoUBAWTntwGVkpCRCciDaeuxd 9Wxz7kKObkZbTWqH35X9BYKh/crkaGIsLroDUErhrl39sRIskF6rUz6asd4ZYFN/FROd8Iwh79DK IPKi0v0lL8RPj1VkfEH03GpPpCCPDH58lbH3ZnPHvcNDxfT+7FidkynTx9sExfyL+q6v7v3M1WpO pJDJD4lu6eZsp05aICf3IFNMU/L5ZJ6wu6gxrNnj3ADyPexjbmeVeHMbT9SXnR8pm0EMLmTzH3yU V8j7U3CWkUUkLMrAYlyDlK+Lk+0G0EI0gYE+7phJDeaa9OyJDxbiNHOs0l753MrXlvKWFT8HSi8X ok7bdKXbN/b14zlrI+vTsgCokATCu5WqyXoMzSbsGiQyzCiayfad1WKzl1e7mbZ0vWw3guJK5nnI wxn5vAolRnxPY9q+Wm+th2PnPo+17li8EZ0yVqWeTGTf9KiiTJFJ+Do6sH3kVhyvin1dtJqdI2j1 LCZJnVROe6X1mCPEf8cSFWLoxdHDspIG+5b2zOegv2GP09zGVUE+1YktL5AJynjI7DOeOnE/CD7i iCU9CuzMzqJdtxbBXgonpZSYERMPIjpQ/in1I4MUTqWY1+oTv7plGVttwrpUe8+3TiGsWhL/yb+f tEa90fKuVSiTInWjnFSyaN2DzC5tjzrPuzJkiSVinGt0VQ+nnLQN27U3R/sizeudTD2VWJpTphQH N5Ie+WVe62Fhfddhyhz7KZXzeM5DXeBlSXBWHYvcCp9H3iopbmR9OWNYsZKEUunAUwOVGkV0fWDD ddPlEdZw73nTPfjPWdMw4YzhdJNS3XSBBJeB7o/L6KquEq8KtVCrLr8SpN4Vv0uvn60VTknrdBLf /Ey0X3snN1lu8/X80vZsfnH4G5p9srarndTNxwz2x+k33juy6r5vJ1IoWGq65q8ceJJRlaLSdTY0 6nUK9ishp8vvGzPFOoVuuP7kolKLaEJD+i6p50h0bb/Opi/qzIgpe7hfSlY+wkpzBjtZDpNb41os M97qcYmoWQukgwXF8VavOKYK0cd+0j/kdzv+AGd/RM6sp1gYP91b21ny7ppaSCbz8HS/XmhVx+CY i2oM5cGYQqT61qdm2xh92oquhB3y5LAZXVtUP7Z54GUb2ayHDzu3aOgLKRf79AYQE5qC9FG5JwVb pm1Q37vFW0Q4WjCZapyGWnpx33GS7UAvpMZyPFQ3UYWNiEsYQZfs3EVxDVkam8dQQU+Ue6IygalU a8fiY5mXce/72r13Q5xbM4QHGEobks05j/Q34JtNJpdGaOv37jsRaRQDTQPv36uPU95l6lbGinNP 48BmQjN6ZsYdijV98zxyZsbfBuz1nhs8IT4BF7fDONXn6VxT6mn1LcwL2DeeUMBMhfIOG1rG+0jP ctteCClSjd6celjCU5gh1CUlqbErFT8w3IUAPtyv6csdLU2y2ZwoQlt1vPGyi6vA5Xze6aLiaqHi Pm4tdQuQn0vnfC5/w5BBPpjHhW7ookJF3/LWIRHdHZrWvupO4U+XmYZnQPu8l78+iG2bU5fqPpF6 OXtJ0EXhSQGNIdd8spGgXdwDwYg5PT/my9LXb1PqYsfsvnevMNL0yQhnXtbLwgqkMkwrMt91Cmq6 6hGoueLBJRFlvRDU4Fe1wnIArtLsQ9jQqaZfIpguIYtl3k9y5q1+Ncf//e022oTkfWuEbL4ywZD+ kLohRgG1/BoFyNdE9CMNwqImss70leO1JJ/axV4V97TJxnc1GQ52p7ccop63WxjQK7fHVyg/b5Kx vQherozl3bQLsWvdjLJJf1JFOUMza1aTaf9iJM+bqQ6BgLPwyRsG5LpHKuDySw1GV9eOxi4tUwie 0rOTCLc/sAMYkaFRNnMvG4fUjSVQj6mXTTsnbEQD6LzT5vKknzX0mSTG+V8+ONqdudMQSVQUrOTE OiYWgCBxl+Nic/Lctegz3S4080poEtbaGgn5uE5+ffrhQ/qq8T1iFq3QVSOelrACzfmDdm4TXkpu sE5J9M0Vcmb85rgQH8S6TMvtMd43VFLltKvnbJqlfDHtm2SIjy51Fl1qy+FnFfzbPOGOXuKWnQqB QB6irhqXfHF1rSypXRNvlp0zNXdMSKHpyTfv4QiZQ4GXg1cvdGCio3nneyS6JCwU1W4Hw+Q/+ErM p/tY4cHZFiC8XwOQdrKEudzz9CwxCz9fWyKKetZ28vLATF1TEZaRyLj4AbeWENdzlGDZrdhBSyjw Lvytyb4OnsV44jR+88oX6Uds7yj0wr/g+DQDL7kvZM3Ixb4i4B9V3rI5rM66t4x0dVrxwv+8aGzn X6DhvvOmWGewmeKTdgsiy+1bak18mSStf4k0A4kyJyNkmrmKfTzh8Th7BE4TbvfjSqOICt3RRuhL lvCyJmKzrbCP3txrVclV9/ZdMJ1KEN2cIDPaBL97t9rt3BQ0QnIv8IZ3cUxesoouColcQGlGdqyL lcXnjPrSArlvAsUKbD31Go4+0TpCT68+tGSMmK1vKsjq/+pqk8j3g7qnJ99tWOpTe9wRAth9U9oY IqntAJV85/69oGICCfp5VTNhABOEVGNty/tOQFnRS/0z5ek0KXqS/v5xJzHOdvajK6WZbsDa8xT+ YG8e1UNBvHk8CGnNoI8C0+tOZJXsvBIVjiWeN/QLaYt+0Avb3Yf6xL2eXaqtm+VNr2f46Ror1hJ+ D11RqDMv8jnkOKJwpXLpMhlDZ0VVMfJSDdAoO9y8LgXZJml3dJbrrQyRK5vbxw7cf/OGfd+IZ92o BGHllZ7S/IkhxlXulVaN/eVX9L7bwET3wwO+o9o8qbNTEeSxhv6FlSCmCHN9qj72K8oJV+I4ewmz QFCdROw0Wdbta96BpfDWpb40Fy4RmFf1ZpVJEJwJ0z4qmNj3UCBbJmuBNAdUGh3ZqB7TbGZV+D3i WNbf1o2iRDxrGa+9IyTH3k0kGVscR1820XsmfVZ5ALJ4dIcbNPuMAQa241PsUZIquN/m2oUP4Lw2 Asbczq9+1SmcaF3ulpvuQhA/Hn+LPso/h+nti9vKk6/dmR7jQV6QVMP1YyDDzzoxzqge0X5ScXOB SoMvZ33VfNjpEkqou9xjosmqXQUKnWx6KPyZ39oi5sRH8khE6KAgmfJMxxmmslKpnOFVtMkBVSc0 CH/ouRxYU/PBr2WAnmqkLhK/XFbgYMQhosRqyZJzQ11iGfwZOA2JOdzWuvVlkZ56/1sQnmO7tvoH QxRzokl9mMtBoC/v2o9eiQoPkWR24cR1jhnn7XmE+FAcY6vr5Fdl5YYrMQFlSZq7Zrm98XdanlXZ bzebc3dqLtFo3JK/b/5qFEX0MbCFqT+TfsSr0iTgWXwW45hGYwzRWDtAZjsw0sM1Rl/542ObpuHp A+45mqg7nv28q7XEKE4on+tgsUK1wbhdd2/XcCGNn6KK6gWMl/mzU3P/5PXKMz7aL1yPhLNiNY6s cVZenvV0Ycw1T7i13/N0pAyNcO2+6lS+4tsarfX9fZPubBKaW71uUpek8HqGqeWw70HAy9VD3HB/ QqXg/H7xInzEbaCkIePdxqUIFi26EcJ9TjhFGvUcTr92drWsiKwuWK3XKudiR+NYJ6MRvw4xChmZ /ZYp+Kr6gLR/zE7c82Qfz6IsMqUk5ExV941vbCBvhoZ75hLtS7rowAscBLj5imDPMr+pVJT0d3Fz jej3PEwHx8dEhFPte05LqhYZkiym9ro3SD9c24NbFa3fd9Xy/wGt4Tu5oLeSH9ZmxR+6e4la4ijj 0aD5hQExA8aRAWYKWKYB3vXD6mVvbE69lceVVbSexEo3B74nm34tPUUVF8vRk8M8VBn5ej5vYhi6 oK1qrlpdMVLoCCJ+Lh3FKONgvz6/M7jw8yOb+NaQkrupuoKH6K2hO/A7DVJNma/PpGuF2xJxqe8p Z5BbFI/m3gz4vOJ2nb/vxmqtBBFhXwrdb5i/KMpNnGhNre9IX1W60/ncnoTKQyEBG8bwTpMQqYrQ 5Orv1BC12zz111mp+Hl2dBlzRxpzUO6oyvU+vzIPejGERfBhzLLqE/63ozKlIqBsspFsiTSDas10 9ntb1vWG2QM6rs7iM6AOY6N6lprWJ9UGRbcCwqOHJYaNSIqLdD/kPr264S3f6V6b+F7I65WvE9tr whyzQT3uWy9fUfXWa89LNR3j9D8Z1S8Qum+oCGM1O0yvb8iEdo5VfepK4G8qPHJCP03fKJu1bdwx F1W2fksTKEi6c/NeWdzBD7MYM7NGxZYXOBtLbCWL+TjFSaWIAP+c9Sbu6RLD5xuuFniM/npG1Lds Qxspl6kM0iFJqdYNi67d9JMppvsPmhU2A8KmyB/lJhn5LsTzUH+NkF0yA17SXnenJPyou10XUu5f MBoKMGq1PQgSjYbHc/o+bdPBgxqx2vMKtyUXZ9cg4UeZMRJ34oWSo/YvDgTIpWd25VPcXS0Y2bAY BZKG7NG4iffKlRsxax+lI4iyHKUNCoZUlzAvFNJ8qixwjcsxn5b0UjdWnr4rtCRKypQkUfJkLU6a F1baKxZbzHQf6lzgkhLUaCvz5ow61s0lot/laLoEO3YjEFRxPDJxlSGMHfOEKmwzDDneYrpZxzSX Mrm/J47uYaJXaL86e2ittBA2xRTFxERA8MW9NPTiFZg2OxFBwe2DqM1C6zhSl6VmVokEgQeQ/ZsS gSzZpBMkIZnGXzrBYzLF+MLP8QrDwBDX21x8uHi5GdcD+5t3PDwYNoqe2X1OcfuYEqkiYLW7taSm fmmPtP1QhauuNOVwKZpeklrkzCSZkK6wYLYNqbrY8eXj/hvYTTlq8jyR5LXPJ8IX2/3audi9FtQn ChQhxh/areGRXVPkLqFricd53mpL5XS1NyLvqchoycjVeb8ezAwOvW2xWb5GSllgl6qag+DN8C9k lZPcoajIoBM4PGOtQW86iWIyFRDIhOiTelqPr2vv98ZJW6u+H45ZbzZXELKFZFoK1PXlSRtfVmGj FNtX44j7qhhNhm14QkRpWce0szlL0rLj1fv8DK5lnMiUYbSoVCgi77/x9PvVNoW6C0FmTSmkHLcC KF891rNsNlVO0Jc9jKYi7Xa22E+7k7+QaTiX9cRbvv8yjhCa1xc5/G6j0nidh28bZ2aa8dnkmF1R vTiQokxcSTDF+kjXjg2kOxSjDf4fPuT/3+D/RAOYAxyKxqAcoWh78v8CE1xLcmVuZHN0cmVhbQpl bmRvYmoKODAgMCBvYmogPDwKL1R5cGUgL0ZvbnQKL1N1YnR5cGUgL1R5cGUxCi9FbmNvZGluZyAy MzEgMCBSCi9GaXJzdENoYXIgNDQKL0xhc3RDaGFyIDExOAovV2lkdGhzIDIzMiAwIFIKL0Jhc2VG b250IC9TSlRWR1crQ01USTEyCi9Gb250RGVzY3JpcHRvciA3OCAwIFIKPj4gZW5kb2JqCjc4IDAg b2JqIDw8Ci9Bc2NlbnQgNjk0Ci9DYXBIZWlnaHQgNjgzCi9EZXNjZW50IC0xOTQKL0ZvbnROYW1l IC9TSlRWR1crQ01USTEyCi9JdGFsaWNBbmdsZSAtMTQKL1N0ZW1WIDYzCi9YSGVpZ2h0IDQzMQov Rm9udEJCb3ggWy0zNiAtMjUxIDExMDMgNzUwXQovRmxhZ3MgNAovQ2hhclNldCAoL2NvbW1hL0Ev Qy9EL0UvRi9NL08vUC9SL1MvYS9lL2kvbC9uL28vcC9yL3QvdikKL0ZvbnRGaWxlIDc5IDAgUgo+ PiBlbmRvYmoKMjMyIDAgb2JqClszMDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAw IDAgMCAwIDcyNyAwIDcwMCA3MzggNjYzIDYzOCAwIDAgMCAwIDAgMCA4NzcgMCA3NTAgNjYzIDAg NzEzIDU1MCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDUwMCAwIDAgMCA0NTAgMCAwIDAgMzAw IDAgMCAyNTAgMCA1NTAgNTAwIDUwMCAwIDQxMyAwIDMyNSAwIDQ1MCBdCmVuZG9iagoyMzEgMCBv YmogPDwKL1R5cGUgL0VuY29kaW5nCi9EaWZmZXJlbmNlcyBbIDAgLy5ub3RkZWYgNDQvY29tbWEg NDUvLm5vdGRlZiA2NS9BIDY2Ly5ub3RkZWYgNjcvQy9EL0UvRiA3MS8ubm90ZGVmIDc3L00gNzgv Lm5vdGRlZiA3OS9PL1AgODEvLm5vdGRlZiA4Mi9SL1MgODQvLm5vdGRlZiA5Ny9hIDk4Ly5ub3Rk ZWYgMTAxL2UgMTAyLy5ub3RkZWYgMTA1L2kgMTA2Ly5ub3RkZWYgMTA4L2wgMTA5Ly5ub3RkZWYg MTEwL24vby9wIDExMy8ubm90ZGVmIDExNC9yIDExNS8ubm90ZGVmIDExNi90IDExNy8ubm90ZGVm IDExOC92IDExOS8ubm90ZGVmXQo+PiBlbmRvYmoKNzYgMCBvYmogPDwKL0xlbmd0aDEgMTQyMAov TGVuZ3RoMiA3NjU0Ci9MZW5ndGgzIDUzMgovTGVuZ3RoIDg1MDAgICAgICAKL0ZpbHRlciAvRmxh dGVEZWNvZGUKPj4Kc3RyZWFtCnja7ZRVXBzb9qCR4C6B4I07DYHG3SHBPTjdDTSBxj04BHd3dwLB iWBBggX3EFyDhiDBp8+5/3uSufdx5ml+0/XS36pVa32196rNTK+pwy0DcbSCKjrC3bj5ePhEAXJq soZ8TwF8PLzYzMxyLlBLN5gjXN7SDSoK4BMR4QPIuNsAnvIC+ARF+QVEQQLYzAA5RydvF5iNrRuA TY79ryQhgIwD1AUGtoQD1CzdbKEOiBpgS3uAjiMYBnXz5gHI2NsDtP96whWgDXWFunhAITzYfHwA CAzsBrCC2sDg2MC/jFTg1o4AoX+FIe5O/77lAXVxRUgB2BCS7ACEIsQRbu8NgECtsYHqjoheUITJ /w2p/yyu6G5vr27p8Ff5v1fpv+5bOsDsvf8nw9HByd0N6gJQc4RAXeD/mWoA/ZecrKP9f7VRcbO0 h4Fl4Db2UADvv0IwV0WYFxSiCXMD2wKsLe1doX/HoXDIfyoglu1vAaCugb681nPO/9nPv29qWsLg brreTv+U/Sv7b+b7zYjVcYF5AYx5eXh5+RCJiOvf/0z/o5kCHOwIgcERAwESBFi6uFh6YyMmA0Eg gC8fAAaHQL0AUC+EMZAH7uiGeASAWBI/gLWjC/Zf2ykgCAA6IfbEEfJX/F8hEQDQEQ79h0G8AKCb p+Nv5kOwrQv0j4ynAKC1o7vLPwFBfgDQ2R3q+tfc/g6CAECZ34RoLPubhABAud8kDADK/yaEj8I/ JITopfybEI1UfhOi5vPfhKip9g8JI95C8zchqmj/JkQVnd8kgNi434SoYvAPIUYYaPmbEJ5Wvwnh Cf6H+HgRDSF/IGLVoH/gX0v2ByIMbP5AhILtH4hYOdgfiHhNuz8Q4fTyD0RI2f+BCCuH38iHsIL/ gQgrxz8QYeX0ByI0XP5AhIbrH4jQcPsDERrufyBCw+MPRGh4/kbE6QX0+gMRGt5/IELD52/87w9L VtbRy5cbxA/gfooYRD4+fhGAEIjX739P1IPDEOOnIo+YXV5eIcQA/RUFu7u4QOFuf59kiI/232wN Q3ziUKgXFIy9MOsIFgu1S295XemvUDxehcaBLGvTmqDe2DXVjhMyn4hsXzb0zJljucHwujqLBH8b bZvO85bKNerjK60visdBzgkZM/fbHhbbWT7vqAxPs9S8t5wYLkP2CT60TJ8dgJA1JpeHK1OMSvpy T/r3CzXZ5HV3MJbokbqNPZq6s0MFhQwVs+z1wsMaWPjptQkznCsjBMK/eRKmJD36NhbibxeO2cb5 9YfjR3fi62LMguR7c1TIF32Ly2dHad54Gy+uE0YuiesDTSuiNtwL1DKN4t7cWhkv0eXwGdufxmRP s/7kcjPJrO1wWqydzit5caWBSSFQpRSpKoQHi3rfXkKvh1SfUMcSMNb6reijrcUL9QvakeJZattn Sg/NET5ih6tNU/hx7zsBGbiMfCxP9vHr2LLZmR5U+Z+FLcTPhincvWkKyKR1TajH37VTLK2LsO4i ZNBkmtl6za2y3Dx/MJy6IBvShy0oYVuS4CyvT3givmGBpUAU9bKrsUohmVAdcWj1x6gq/cKa839+ 6isYdNHys0YlTkGQ6mqVrk6P9XxjxBuPN9TmmSQLu6Mk6lMqEz1TJkODqMgdbj7r4r456cbqetYx WVw+hyuDcImC5ydfmctBbiXLPBrcpupxOHLoumMBApRUXyVZPLtTPNTfGPaFneX9wJS7RyHIX1lA irXG28LJniuqcMn8yJdnPRdESPVOfwNdrjA7eSDNn3LTOZ8X597Q+mR20azPjJySaaoV47iYeTpA SQiylz1QTxgbZHJa8+baXhG8yxtmaiO+F9Uabx6cH1DEUm1w4ivpp/5qA3+qdR+PF9RHGluxcC2g LEB6V7GxlGevlCd5lEVUoBswiGleAqacyNkTXDWK40sHBJR/gOuNzRy71HZzZ6W5M1H7OPO95CGN XS905RRkJcwrURx+iv9hfSc46MtJJD6PS4szCSgsgJaR/5X7DrsTbfJMRxcYdylAPdWF/wDrinxr yEncPzn0cQa+a6hUv2XuKvmAkqgDFecWKfd+Udi7+MIRM8jkm3dB20rszqGhWkUK3y6qxIKZ92ri FHnfPjyd9T5grXkT1i+iXnZJyNhicRQhEW1jK7S3X7LJWfETKieaQEDTU3myG8Xy1NZYIE5nPKr6 6WgO24GgPOXjG8jXR48PaSSeF4e72LlFUm2SERJbjex6Sm1phNWBdphpCaNIqNvILhIltd1LdTWW v8T+KkT1JhQTRuJ934GdNggcPs1WqEh4amCspsQ7zLRU2snNWZotBKyGFKUVnByr6Mkfo8iUyFBD 4hox+97br//EMuOYoQsQPX1P0iORupTeo2yNkWlXqjLNlYFvNRQbEISGuVkl09egUeq5QGhDnBCm r+WUr4id7paF9U3hK/QN0Pwlx4u1p468oLPD9IeaOYqj+jDp0NqeTGehGd/12YBjLolHUW5WqS1a oD7zhmr3hSkBU5GWdCzOzycWycr5cyGZjqnA/vCjrHoctrSjlFO3LzzGY3gWsRdVg2c/5tuPMx+Z Zr4pDFKSbvyGSeFDLyvYjycndfYKiji0CQu3slixg0J15/F69SWqErQoWaPltb9hKlUuTVWhXYx+ mt9rHVFveHCIqG8aEq3oplUr9wCKurJnMLxaMxlQM6Wc0fPNBgfq0XRcw0+aA225TTMaN0Vh7sqf y9SjBpoOFFvIkVKjc76ncTa6vaxycEWHE9oSrbl3UqHH0v/Etb0RYfk1Q6w8miiQIhNMMqKBpWvY L6WHbm17Hlf+drCx1wJWxXrKSmvIexcAJ9unfSTcl2UZrxlKhcWnLFtMVzCGvODOHdXhyqsS0vES fYtXyfP12SpFgwtMNZuD6vFx4rV9PZstM72ydp7Ny6RWiJQZnA1ruPh9EnOXxN6gpa0zT8QTpVCe 9fyROOFoiS8oqpA4s2fUn+U8hTb4CIOlIlha25tstt+nkoBN5nv9lsluBTzMHVf9jFRUycqYg5oj Ova5FkzPyI3fV2cZbLMnb7XbvbHSIriRzkAh3zd4OEb3z/3Wdj1MKkVInQe5Gu0RnVJlWVYiOcMK eSgwHnR6yzji8m53V9xEIv9nsSL2lef9mhfIbLMmCcVuOm+vFz05655nmYJczYXl6sRsjMRFG9mz U6Uw+pCtDoXfoZJSTIQ7z0gkRDdWpa34ePsuFoJTvlLu8KKsGUVyFv22YMM6mGfj1agsY2SljMGr 0Th2sXiYRkMF9nIwYVOaqU11E/h+dpdWz6yVTnZK1d52dh7jmbIgShAnuUp9sDD+ZhGLrhNecuxk IhhETuwCsT9WkNrZFj9akOLPD8FXPm/wY7R/y/KjaODTNWA+ewYNhYw6xTDhqZ5/8gddu5EJbTv5 XDKlBB3DXVnpHfL5hBQCHfwXN2H3JyHdsaRhkVkhgnVr3ND7V8giOXUxI1V2pL4ZasV+FX3m2Y+E 2j5zUpV+O3RSy+SYTLD0ZHihgvrKqYCNXMBx/U717dICfziN9bRFJpqeRciojEGgNNQCyct7UHcT Xh1MdGOeHK6yPpioseAFZEHhIdWvTrSQvT7QiuVwILAyieLTY1v6RHjTQCVQcUlvbyPYyub0gbXI vRDrC+mZq+sIFHRPRWEBM5LsbzrToWNp7CWMZAElwg3eQHKQzg4rFcptH1x7Xq1PvX1sEbVYKoHC kMNJzBMC/t5VRso0l3al8TmZU+FQkEbeNpCQuEijM38E6/p10PvLE2M5v6+2UgZDO0oRtLS6bZl1 KA4jpHbsp6YvNibhpB7TX21tiaVe43uBGFAHJY8nlXLymacJJ1WTdj/CL7CMwMauS1IKq+hY4NuI 9V8+H0buSAsCW5CIb2YJxbTR4m7zBUdIDDJAMxUpS3uicdNcFY9DxW2IkSfx75zRGcut1hlQVLzn Q9dp57ny03RYQLNtJDed6tHhgkz16dujTiUESCNENAw3c9+1kNAuA0dz56RA87JNLCLxxzXbRsbk DSkbz+y5Y1GllY/3WXMeZSw6uxif21uLyEzClZbEI4u5rWyYCQI5xj+BxO9Y2Cm/MDEVbBKc65mv HQCJo3GNaupbePzr2X4K8HhnXaWfcm4MApKZM7MjX+KAZnG7y1i+fT++e4CNGMB5hpuXutOYrr/R jOYxrHXiDBaSHaq9EqT1B1VzfHyFt2Nq3JrRVBhm0lXd/IQL9dG1DsGtvObjxDwLy475Zx38VkvJ dj8EhX2iHKYy9THHB4SGm0msr7tricj96c1WhrLlGDmHg0gMkrG0nUm03pCZ6UhYz7FRrVDrinvb 0Vq0/Sorntxu5JiBDM3WWtnsDCK3MPueyz7mj7y2/sy3rdq/K8hVX/qahlAwpD4tub9nU/PF86cL y/r7KlmtmWNfB7cCqA8DxevrUvCvMv14iPElsgY12/aPLKHBFK85kqJX25UO6gYmUWnf69UzzDfk 0P4cnJ0/Oyj26DzCuwlPCngMA9ecbnR68jd+40T/SYO3QwJh+9QnRFCHUSJHu51g4uuv7V+OzU5o PJGOrGlwFHG8QUyjE6X6s0vzEivQzA1VpzoAwy/NOVbkFldyu5eB3wfjZedtsGxsV/n9hva0uuHY Y65OOekWYZKq4wRNCNn+dpsf37c7Gohsl/SDEbfYqpy9eFxEJC5HhuPceXmNTzGeWpVrDNgIFnNY qQLd2eVMUvXuiGsFx2qf5cgWBM8utG59v3nAMw+sm+0EjUkrieLcTuwCRFUDWD9Zp7QFBD7W9A/Y vvDSFuvKZUU/RPWdwKl1SiXTrR9bofzBPhDzaeYUIl8Pg7me5xjqjeRx5m2OFqr64c7y32xM5qoy Xc2J5HxdfpRuvrlw/BOoMKUdGvnBJ06pP0KxKPutz/tLPhFj4tPDN2lrd5K0A8Sziu0GHjv6Pkgf 0RM+OW+KDQxXDqSCF9+glOSdzLlua5VzCo2+gH62u/icmlJlY5+AO6JZtiqAzetsw0ASahMpEQW0 Ym7iD76Kj0fvsv78MYny1IIVrY141nRNXqJNsnsj+onVqQDsqNO4wetBR8Ki5QoDfLxSkNRGVO4c psaitoEKNO9DqkYLsyM1ZcPYVaxR/FKKNqC3SVE6hcmVD9XCzCoSMLgmUJVBryq3WPPZmyGEt33d ACxWMr4W6IxpvE0h1ue9x6O1IbELK5dDoTTcIh5HK2zBnRBJXHsLF7TR7tjyBD+sCaGwt5eInChD tmdfhNFZf7rcpak6BLLF01BWUsaSZ6+HHjnfk1cvZQ5tz3UqSE90a1dbOnLwTCxelcTUUw7h6YbS h9grFRiBN2SAVrD5FXSipxn67hXaqbS4GKzBaSNCwbLZ/HtHtQ5fqgOMON8edHXVA09mpf2dYkqZ RrBVdpDO+gdrHp9LRtsJV6C8M7NWKOw455kHZgT5FyvgQ7D15XyZiqd3W13tbxrskDpkR5Pf4OSE iK83nCVrseV40L7NY5BX/2Wai4L1jUULYFzJI1ptTfxMeLShTHklWS3+g+1zFSQmpRdNn2hx07i/ kI4P3tv28XyXpl6i3jMW3XGALGVpOLBAqBmfvWTU5AEJMtC0g1cJ9/c82MbZIz49gO4VtIkFs9Yx cLPzGPJNRw8zZvpeyEVnF5nSJJi9/kWJzHkskB30ghKob/PjVBMnsU5d1jqgoW2fPqa3R6mKxleT IFlthS1R6e6wZ22LwNfrddZe25MsQRHuN2yiWuM/hlJE6Eh5zuELTF/2XstwXpzHhKF+Lb725vol vRqwoZuKa6Zqlpcrh/sek3RcLLokuktOPMNcxKgneXwtXU5+d0f5KCri/mcBgBUixVTZ/4mlpZBz FKm742WQrSETpGt9dvAwfaXKVn1rISmvoeA5W9mz7pfaq3vytsGdH8OKGBjQ9z/yi07G83ZogTV5 MIr60mjpelLjclErM3QgPqCLvTv6K5hUjbHOfSnVzs87q+2ThQhMaoufR5NI7JF3lp7pzmIYWGgx hhxusUuq1YM17Fmy13TL6N+p6DP8sKocbxNFfIStz6L7ckRCJNxYGJ+jSr6/FB1IbKeecEIvGqfk mKJG330UhAH27y+/w/2sdeHXqHTnZ4diiibJfNN84eJLEQ3CPKdkCHiFlTBiSNGKOXkrlOSXd9rb vTN8fceY9P5KstMlCEj4o+1RUk8rCq7vd43XLzfqqBn8rq2ULjVe57YsjXVCHYWTXmShRYur7th3 voDLR6xXm5K5muW/p1/2NI54rd6kNowdPMQoxjD3Y73W8qg93cjfdWdD4L27maS4RbHmuaA3dTGB WhzqTUpbBYbuVVFps//8WZZZlhTOthOoFRw2uTGpDRrM3xNypSQIU4KtOgjYQkLkGxnc7E3SUv3q sHAyRJq4dOf92xmeohi185CR9UDI51+3XZQgg2D91OrTLxe+gLa9oMsqU0ip2NRK6pWerygpkSca eWNqab1E1DiyACyQX6x6dCrXzJo5cJtS21ivoBDUBnWbT7m+ZXFVCbnEq6fq/Xtle0/YPMPsWtjj i7LpoSefDLgEJPScrYB8ZhCvDf3jDUUDNS/XMZfP2DV8355kmfbpp30CHXKXZ8WFQbGb0PGJM2UJ CAZpknh0LOEqjzZ5m1P9iUjFxu4EW6N9pp7vW7A3KNhPdcVJjlfFiotg105jve5o9qlyCdF4T43Z wmFrLmAyFJjwU2t38bIU+ersSun4u69rU9fKh3hcU7xvnEp1t8eNJC9wjTLvZihcsu4g2qzfvUoz JYhzhS6JtG17TIL3VJLo40LpeXqIM2jXtpAirGgo97te/zLqdaVZB2wuvseSpFlYYRkO6d24G+nw fGJqVAMQeI4hMXAvEfQ4wKmtrIFpIqfe5+QxsniXAR3DOvGnRFNVSIf6hYEFOjxDCL0hZNzQ6Ll0 BP8Xl4zOYhyO9YjA4EaUvROlw0USfz/9V/VdK0jVc+1HZblQ6X76GmPRLbfMPXetvZLH7hjBwnEz hHVcCbbofv6y0kme9K3bwuer7B+RpcSJL0IFDCwejzKVd1Iouz1PCXbRCBrfXFpgum343ltwywQz Fbd/KZp49pVtEckBkk0BgkvK0bwlKn5E18mR2HyRNObhToSRpEQqjEv/knCrtyR7d/m6HS9FxeG5 nOZ4WDwjd6MSWfXLhlAuMt+aShv6uP4fZqWnNEZ3uPO9Wh0CRYmNBVM424Gm6F9+HikE6BzvTnzj sbEPAYwuZuQ9j2cbzr/DNSurRQ5Bb1r0zLUSTfdb6zoLHqvVzc7yCierFHwRTGrvlrn/MTs4xEJf 3F7VaFZrCvoxwbLVam5sIHD8KxiU6aQZzFiAZUnaDo65CnaO6ucIj0HqyDBMGSR9HosT+aiA3r3u ENYc+Im+udnjYfORFGtZ9cj3NfXg2wg9Kq5F0al4vGsiJo34OadHOKiXjjaqDUPFZEsOZhe+M+bq CWzXPD/F+IvIiGzwf07EaH5+H+VF4bZJ19iSFtW5r8bM3zzgqVE21XlPPTRVDqiePjdWlz5xdZut dVODnnF9rc2kFvT00Lu03SRPnckK1Xc++TjYjqY9xjGzVqGiDhFJK2F2UDuzsmvSc/lRfKvK2ms8 1CIgEuGVh/lZB40hmMQMJ9xO8ubEZ+rdOp0h5K5F1ceILvrmE9ZwREVWGP1h5V3kKX7u4MP82X3H sIkoj08h1QELEe1l4QL3PdGaBf/PlJgCrm7UoHnfVA8ATc0h9vfZ1Fat/XirZOZaWv4jme27DLHL y10NY3ibUYNWtCuK4xPOg+ilOTJKhcx2KclAt+lAZpmvtqoZfi9Megc61O/hfU25qpN2/un947t7 xHdE4zX98cxk8s9gftHp/uWPcagr7UjmmL5gCw2MrE+QtzmkZUoYCCsoRoiu9WSk7kht1XY3O5Nw 443aYlqEoxqo7N+gy51abTbbUKMYdKcNlyXjxE5+ODiXY4nLNtpF779VP3dE73aiJ9h93uu3MbPO izUImtAQ/gr8Vuj2pp6XQkgzS55Jgxz5jM8W17CUttZ5zpsXc3vjHWRZLRg2zLe6qetF+U57+dU7 nmpqNs7gXfuGfjXLHqMLBoYcN6MQMynCgE84VbLP7mkzB/vgsgfGYUnDDUJNr5qd5H/Y3L6zzb9N i61ByracPKsDMRbWpx4ja5WU9gQWoHvQGCjUcmcvMsoRpRjyRb6JamzaNXtIwqcfnXl8ZuA/Ouj4 4p50RR5+7HpLhExo7VTtd76aiBnlZjGSpLrY4oP2bL61LUG/ZMPMvHwto/tN9jVB691I/GtPEbQy 45gDKE1Lb23cRAkjHh3UHbhaK+2IbE4wRKfhzeSMUZaAT1ZuLiFJxbA9KFtVGJPe0NMuMRK1bJ3i jMmQrTCNlQAgGxljCaIf4tNmyRckmysZQi3axVtah9+W60R5/SyjwS8nIk/wv1kWXdhkCi2XLRYG YK7rhL4tq5qsvtaaMOopffYMmS531es7eY9TZvDb5NVGqTiU86MtueocW+32sNoFvoLNR0icGhJH WkF6L0DIKxpwyLvg0jKx2G3eAIZaaIpvmtpISvjRrzCOmNv7yoZhlbkE5xp/b+UB6xxZghPtSTMa MnYTCnhf4Y/hAMVri1+tLNNd8Nylu7RcNp5jTYy0/b14r7r7kZpYy2XARTiXdrhGE/py77MJkjuP GyWnFuGOH5dVDtXNyCmB2cIfbzkDucJlMiSYs/MIt2+QumZxNcYlZTPiqKKSVbRNPu6IQfgatvA1 hd1bZr+4CJ8fbKWuSNgr2Ctz3lHNW5263GZrB/sITp6Ui5wLmbq9TjC3eEu4+KH6bDlFiUxejFRj qX3TvoC3+4tbyw1HNkpXazbmEHZw8rBAaZ8Ma4Z/s0oUBjS8sZ60Z9pUdx326OShKrRLhDsOltSb Z6GS86Zc1iVvo/8rHMkPe0TcRuEdh5ERZWD219rVFnEgmg233K7/+DaoS+CsqOouQRMH3AATa0S+ upn3xHtv9UnDLIrt5N7KYM3N/jnh9tZtccVZbL3DoImer4IatTLFtscEklT4zQdPlVzL9EiBNQ5k c8P6cNlrsZATP4+QmQM/emX+cjOT4jpWiuX2esq3Kl6jKU9NIK/RZkfL9J8qH1P4Y1Xxt4aqBMKd 8iPPNAA9K9o+QXR+xfzqj0siIa+OKiIt9oLFDsnYdvH35swGpEyCdpzex6DSdey0FwbmYxSrrMi6 3XYnR73K1r9HWb0vwpxnuGi41mDOczPEKvyMxjPW+3MKJ5ibknfGVHHm068jht2AHnRL8NtrpQor QoanUumSXRSM7rkM3vkphfHPUZreunY7uhggw74yDtfLcu/Qri2iDCX7v2sN3CelOeiooJF7Jqn2 iD1GaZZO6DG5IfHTrJYrSteSqt4z/MofVGp2Jbnh0lgXFwMNsgSd5U/OgbVt+UWYHysunx1Rkzd8 QRsnnoDvr8iYvpvs0YikxO2I1f7VSi2ZLqfUEzMKasuUgDhF4zHFop+WslpZB2RKTNAp1e1z9em0 oQC8p3WRvxjO0NWwfX/rQp0qrXOecuXrM0QPsOe4uEpianDGeVumOrAyGnNi3FLIl2CPP0sK73l+ sxzXN+clN6FterHm6p9aBBd44y/4GmkYazL4x31+QVhNWPKW5s3+1fzlSNkemg8JgfRD932021G+ s/wgy6W+TO1FLAZrzgwToE4+JqH/F/lls77jfS8K3sbnSVYbd0vW63XDVnj6LxvF/FahlxFi7cta Xdvms7YyhYAQ0e52Vzpo5mzoKT1xjonl3NgPlmautabv4uTuMk96D4Ae1nfG6AzsStc4cgNPjdjR fSKx9F+2LChiZJs3uDbfIElRDFfoG7KmK+hK4Ee3S5WeRNX9Au8CC+ctVjg2g7iDHY4TfyU/9YQd YYTs5mVKDs4L/upjrXoYIn8pdTL52bI10Llopdpf8hvp7HfFHPvT7UBYsGbdkSvHomZZ6/5Zesjt UD7RMHLPF3omYtoLMfHZrUImsFjIzNzoV9LewDOGu8oJTmrPiZKSp2sfqnkNAz3SnnWofiibJ2f6 jLuZrdlAgoTxtJ4fZzse9fBtAju48v3AVcIwA1e5s6UP2/fXQifa3f4TsmiRqe1iy/xPYNx9cEvS RY+fQ0C2X3lafnhnC+6mpJR2AiI4B5+LjK+nB4swjz22lH1Yp4WiRC8P0eTA5S0b8WLP8ZqTNgql TzFDONu0Pi0Caeqa8X8gKQMF4Lf9Nu6aAW2++qIZ2cWCAJiTo4jcFkZFoh0AoquwYzOoPRg+U8ko dAAnEy/1XJyIg/figeODUfg62DrXpguRxkoT3sFA99JkJjWlDkcHj+GpKv0NxKMqKzrNVyyE5Sk+ F3NEcqXjfu+CUm/ffChgeFmLrWyqpDI44khatrh/Mfp101n9x3ROKpfKumrhNnv+AAbxfEZCbKdX EY/Q7Ci3zvFghTTReJt6YI7Bvsru/putm1uWCEUFCsOoKC5NQ0+pDi4mPQVT9OW6NiVDVBjRZkFN VagJRKDYqTl+CUM/3LBoxWSW4eN68vRZde2E3d0PXwdbMKV15BZnGoHBI7FDoIjtu8BQyQ1xX249 vJgYEXOBrrc+okOUPVftZKE59XrqowPKrarfsmsqaDa/UBE0HbNkYT5RkI7hAKbm18ZK2pSnJmGQ 2pcTon5VApV3LJzReHfBlS8kCOKKJfqsjLGxvK6QpOVQvW9znZUH41i+f6uxMBR7UGBawEsZ3qMB yU5VLt9fHwjoj6/Xz5JFNVDkH+MENjl7H02TpQq3z4n0vc/kAEwKu/L3kT7oes0xiY/xjny9eO4v m2ST3tUnTwlI8p4ZgTVGrefrkxNVmyjsKlfKpOJWE3T1lUY63ER32N72ioo27YPikvnEiIbJstoK yhhRDJmikDzDvbfNHw+SDRFKThkp3WXGU4LKwBJXbLKlWDItTwLVTnZD3OrR9Ia083oxWn+h7FXx 82sJy8cPAE6JcbeAknHEFLdpyX1TronMiq0iJ17f63uydA/B1ASsGx79qtTCl1nDBfgV1yTC3VXc Dg9RqKahNrHqFq1hrA3Mu7YT/bfdF8YKcWBTJUjRjviauVcmsHGxb+kV1UUQ5010aFl14meSLvyT bLNGTpEACau9+h6sBf5p5nfA7Ob+7uNpc8EIUnDTtATNfPPjtPRS8skmoU7RKgtgRhlF5qMEBzx6 sXPyuQGXeKmHn16jti08dbteG3qEq0HRGhc42Kbui7c82piNCmT19IUxBkjed0nBohoLN3kHWs3V 2sfRBjg55md1zyVhG2pdKWNBgHT3rQ5jmcCIox8f+bpISZApbaaDPg0f5eAaMioAQZklJN9neP8P f9j/v8D/EwXA9lBLFzdHB0uXl9j/C/VS7bVlbmRzdHJlYW0KZW5kb2JqCjc3IDAgb2JqIDw8Ci9U eXBlIC9Gb250Ci9TdWJ0eXBlIC9UeXBlMQovRW5jb2RpbmcgMjMzIDAgUgovRmlyc3RDaGFyIDQ2 Ci9MYXN0Q2hhciAxMjIKL1dpZHRocyAyMzQgMCBSCi9CYXNlRm9udCAvVFdWRFFMK0NNQlgxMgov Rm9udERlc2NyaXB0b3IgNzUgMCBSCj4+IGVuZG9iago3NSAwIG9iaiA8PAovQXNjZW50IDY5NAov Q2FwSGVpZ2h0IDY4NgovRGVzY2VudCAtMTk0Ci9Gb250TmFtZSAvVFdWRFFMK0NNQlgxMgovSXRh bGljQW5nbGUgMAovU3RlbVYgMTA5Ci9YSGVpZ2h0IDQ0NAovRm9udEJCb3ggWy01MyAtMjUxIDEx MzkgNzUwXQovRmxhZ3MgNAovQ2hhclNldCAoL3BlcmlvZC9vbmUvdHdvL3RocmVlL2ZvdXIvcXVl c3Rpb24vQS9CL0MvRC9FL0gvSS9ML00vUC9SL1MvVC9XL2EvYi9jL2QvZS9mL2cvaC9pL2ovay9s L20vbi9vL3Avci9zL3QvdS92L3cveC95L3opCi9Gb250RmlsZSA3NiAwIFIKPj4gZW5kb2JqCjIz NCAwIG9iagpbMzEzIDAgMCA1NjMgNTYzIDU2MyA1NjMgMCAwIDAgMCAwIDAgMCAwIDAgMCA1MzEg MCA4NTAgODAwIDgxMyA4NjIgNzM4IDAgMCA4ODAgNDE5IDAgMCA2NzYgMTA2NyAwIDAgNzY5IDAg ODM5IDYyNSA3ODIgMCAwIDExNjIgMCAwIDAgMCAwIDAgMCAwIDAgNTQ3IDYyNSA1MDAgNjI1IDUx MyAzNDQgNTYzIDYyNSAzMTMgMzQ0IDU5NCAzMTMgOTM4IDYyNSA1NjMgNjI1IDAgNDU5IDQ0NCA0 MzggNjI1IDU5NCA4MTMgNTk0IDU5NCA1MDAgXQplbmRvYmoKMjMzIDAgb2JqIDw8Ci9UeXBlIC9F bmNvZGluZwovRGlmZmVyZW5jZXMgWyAwIC8ubm90ZGVmIDQ2L3BlcmlvZCA0Ny8ubm90ZGVmIDQ5 L29uZS90d28vdGhyZWUvZm91ciA1My8ubm90ZGVmIDYzL3F1ZXN0aW9uIDY0Ly5ub3RkZWYgNjUv QS9CL0MvRC9FIDcwLy5ub3RkZWYgNzIvSC9JIDc0Ly5ub3RkZWYgNzYvTC9NIDc4Ly5ub3RkZWYg ODAvUCA4MS8ubm90ZGVmIDgyL1IvUy9UIDg1Ly5ub3RkZWYgODcvVyA4OC8ubm90ZGVmIDk3L2Ev Yi9jL2QvZS9mL2cvaC9pL2ovay9sL20vbi9vL3AgMTEzLy5ub3RkZWYgMTE0L3Ivcy90L3Uvdi93 L3gveS96IDEyMy8ubm90ZGVmXQo+PiBlbmRvYmoKODcgMCBvYmogPDwKL1R5cGUgL1BhZ2VzCi9D b3VudCA2Ci9QYXJlbnQgMjM1IDAgUgovS2lkcyBbNzAgMCBSIDg5IDAgUiA5NiAwIFIgMTA0IDAg UiAxMTEgMCBSIDEyMyAwIFJdCj4+IGVuZG9iagoxMzAgMCBvYmogPDwKL1R5cGUgL1BhZ2VzCi9D b3VudCA2Ci9QYXJlbnQgMjM1IDAgUgovS2lkcyBbMTI3IDAgUiAxMzIgMCBSIDEzOCAwIFIgMTU5 IDAgUiAxNzIgMCBSIDE3NyAwIFJdCj4+IGVuZG9iagoxODQgMCBvYmogPDwKL1R5cGUgL1BhZ2Vz Ci9Db3VudCA2Ci9QYXJlbnQgMjM1IDAgUgovS2lkcyBbMTgxIDAgUiAxODYgMCBSIDE5MCAwIFIg MTk0IDAgUiAxOTggMCBSIDIwNCAwIFJdCj4+IGVuZG9iagoyMTIgMCBvYmogPDwKL1R5cGUgL1Bh Z2VzCi9Db3VudCAyCi9QYXJlbnQgMjM1IDAgUgovS2lkcyBbMjA4IDAgUiAyMTQgMCBSXQo+PiBl bmRvYmoKMjM1IDAgb2JqIDw8Ci9UeXBlIC9QYWdlcwovQ291bnQgMjAKL0tpZHMgWzg3IDAgUiAx MzAgMCBSIDE4NCAwIFIgMjEyIDAgUl0KPj4gZW5kb2JqCjIzNiAwIG9iaiA8PAovVHlwZSAvT3V0 bGluZXMKL0ZpcnN0IDcgMCBSCi9MYXN0IDY3IDAgUgovQ291bnQgNgo+PiBlbmRvYmoKNjcgMCBv YmogPDwKL1RpdGxlIDY4IDAgUgovQSA2NSAwIFIKL1BhcmVudCAyMzYgMCBSCi9QcmV2IDYzIDAg Ugo+PiBlbmRvYmoKNjMgMCBvYmogPDwKL1RpdGxlIDY0IDAgUgovQSA2MSAwIFIKL1BhcmVudCAy MzYgMCBSCi9QcmV2IDU5IDAgUgovTmV4dCA2NyAwIFIKPj4gZW5kb2JqCjU5IDAgb2JqIDw8Ci9U aXRsZSA2MCAwIFIKL0EgNTcgMCBSCi9QYXJlbnQgMjM2IDAgUgovUHJldiAyNyAwIFIKL05leHQg NjMgMCBSCj4+IGVuZG9iago1NSAwIG9iaiA8PAovVGl0bGUgNTYgMCBSCi9BIDUzIDAgUgovUGFy ZW50IDM5IDAgUgovUHJldiA1MSAwIFIKPj4gZW5kb2JqCjUxIDAgb2JqIDw8Ci9UaXRsZSA1MiAw IFIKL0EgNDkgMCBSCi9QYXJlbnQgMzkgMCBSCi9QcmV2IDQ3IDAgUgovTmV4dCA1NSAwIFIKPj4g ZW5kb2JqCjQ3IDAgb2JqIDw8Ci9UaXRsZSA0OCAwIFIKL0EgNDUgMCBSCi9QYXJlbnQgMzkgMCBS Ci9QcmV2IDQzIDAgUgovTmV4dCA1MSAwIFIKPj4gZW5kb2JqCjQzIDAgb2JqIDw8Ci9UaXRsZSA0 NCAwIFIKL0EgNDEgMCBSCi9QYXJlbnQgMzkgMCBSCi9OZXh0IDQ3IDAgUgo+PiBlbmRvYmoKMzkg MCBvYmogPDwKL1RpdGxlIDQwIDAgUgovQSAzNyAwIFIKL1BhcmVudCAyNyAwIFIKL1ByZXYgMzUg MCBSCi9GaXJzdCA0MyAwIFIKL0xhc3QgNTUgMCBSCi9Db3VudCAtNAo+PiBlbmRvYmoKMzUgMCBv YmogPDwKL1RpdGxlIDM2IDAgUgovQSAzMyAwIFIKL1BhcmVudCAyNyAwIFIKL1ByZXYgMzEgMCBS Ci9OZXh0IDM5IDAgUgo+PiBlbmRvYmoKMzEgMCBvYmogPDwKL1RpdGxlIDMyIDAgUgovQSAyOSAw IFIKL1BhcmVudCAyNyAwIFIKL05leHQgMzUgMCBSCj4+IGVuZG9iagoyNyAwIG9iaiA8PAovVGl0 bGUgMjggMCBSCi9BIDI1IDAgUgovUGFyZW50IDIzNiAwIFIKL1ByZXYgMTEgMCBSCi9OZXh0IDU5 IDAgUgovRmlyc3QgMzEgMCBSCi9MYXN0IDM5IDAgUgovQ291bnQgLTMKPj4gZW5kb2JqCjIzIDAg b2JqIDw8Ci9UaXRsZSAyNCAwIFIKL0EgMjEgMCBSCi9QYXJlbnQgMTEgMCBSCi9QcmV2IDE5IDAg Ugo+PiBlbmRvYmoKMTkgMCBvYmogPDwKL1RpdGxlIDIwIDAgUgovQSAxNyAwIFIKL1BhcmVudCAx MSAwIFIKL1ByZXYgMTUgMCBSCi9OZXh0IDIzIDAgUgo+PiBlbmRvYmoKMTUgMCBvYmogPDwKL1Rp dGxlIDE2IDAgUgovQSAxMyAwIFIKL1BhcmVudCAxMSAwIFIKL05leHQgMTkgMCBSCj4+IGVuZG9i agoxMSAwIG9iaiA8PAovVGl0bGUgMTIgMCBSCi9BIDkgMCBSCi9QYXJlbnQgMjM2IDAgUgovUHJl diA3IDAgUgovTmV4dCAyNyAwIFIKL0ZpcnN0IDE1IDAgUgovTGFzdCAyMyAwIFIKL0NvdW50IC0z Cj4+IGVuZG9iago3IDAgb2JqIDw8Ci9UaXRsZSA4IDAgUgovQSA1IDAgUgovUGFyZW50IDIzNiAw IFIKL05leHQgMTEgMCBSCj4+IGVuZG9iagoyMzcgMCBvYmogPDwKL05hbWVzIFsoRG9jLVN0YXJ0 KSA3NCAwIFIgKEl0ZW0uMSkgMTQyIDAgUiAoSXRlbS4xMCkgMTU2IDAgUiAoSXRlbS4xMSkgMTU3 IDAgUiAoSXRlbS4xMikgMTYyIDAgUiAoSXRlbS4xMykgMTYzIDAgUiAoSXRlbS4xNCkgMTY0IDAg UiAoSXRlbS4xNSkgMTY1IDAgUiAoSXRlbS4xNikgMTY2IDAgUiAoSXRlbS4xNykgMTY3IDAgUiAo SXRlbS4yKSAxNDMgMCBSIChJdGVtLjMpIDE0NCAwIFIgKEl0ZW0uNCkgMTQ1IDAgUiAoSXRlbS41 KSAxNDYgMCBSIChJdGVtLjYpIDE1MCAwIFIgKEl0ZW0uNykgMTUxIDAgUiAoSXRlbS44KSAxNTIg MCBSIChJdGVtLjkpIDE1MyAwIFIgKGFwcGVuZGl4LkEpIDY2IDAgUiAoY2hhcHRlciouNikgMjAx IDAgUiAoY2hhcHRlci4xKSAxMCAwIFIgKGNoYXB0ZXIuMikgMjYgMCBSIChjaGFwdGVyLjMpIDU4 IDAgUiAoY2hhcHRlci40KSA2MiAwIFIgKGNpdGUuYWFhaTIwMDFmYWxsc3ltcG9zaXVtKSAxMDIg MCBSIChjaXRlLmNhbWVyYXdlYnNpdGUpIDEwMSAwIFIgKGNpdGUubWV0YWphKSAxMjEgMCBSIChj aXRlLndhbHNoOThzb21lKSAyMDIgMCBSIChwYWdlLjEpIDk4IDAgUiAocGFnZS4xMCkgMTc5IDAg UiAocGFnZS4xMSkgMTgzIDAgUiAocGFnZS4xMikgMTg4IDAgUiAocGFnZS4xMykgMTkyIDAgUiAo cGFnZS4xNCkgMTk2IDAgUiAocGFnZS4xNSkgMjAwIDAgUiAocGFnZS4xNikgMjA2IDAgUiAocGFn ZS4xNykgMjEwIDAgUiAocGFnZS4xOCkgMjE2IDAgUiAocGFnZS4yKSAxMDYgMCBSIChwYWdlLjMp IDExMyAwIFIgKHBhZ2UuNCkgMTI1IDAgUiAocGFnZS41KSAxMjkgMCBSIChwYWdlLjYpIDEzNCAw IFIgKHBhZ2UuNykgMTQwIDAgUiAocGFnZS44KSAxNjEgMCBSIChwYWdlLjkpIDE3NCAwIFIgKHBh Z2UuaSkgNzMgMCBSIChwYWdlLmlpKSA5MSAwIFIgKHNlY3Rpb24qLjEpIDE0MSAwIFIgKHNlY3Rp b24qLjIpIDE2OCAwIFIgKHNlY3Rpb24qLjMpIDE2OSAwIFIgKHNlY3Rpb24qLjQpIDE3MCAwIFIg KHNlY3Rpb24qLjUpIDE3NSAwIFIgKHNlY3Rpb24uMS4xKSAxNCAwIFIgKHNlY3Rpb24uMS4yKSAx OCAwIFIgKHNlY3Rpb24uMS4zKSAyMiAwIFIgKHNlY3Rpb24uMi4xKSAzMCAwIFIgKHNlY3Rpb24u Mi4yKSAzNCAwIFIgKHNlY3Rpb24uMi4zKSAzOCAwIFIgKHN1YnNlY3Rpb24uMi4zLjEpIDQyIDAg UiAoc3Vic2VjdGlvbi4yLjMuMikgNDYgMCBSIChzdWJzZWN0aW9uLjIuMy4zKSA1MCAwIFIgKHN1 YnNlY3Rpb24uMi4zLjQpIDU0IDAgUiAodGl0bGUuMCkgNiAwIFJdCi9MaW1pdHMgWyhEb2MtU3Rh cnQpICh0aXRsZS4wKV0KPj4gZW5kb2JqCjIzOCAwIG9iaiA8PAovS2lkcyBbMjM3IDAgUl0KPj4g ZW5kb2JqCjIzOSAwIG9iaiA8PAovRGVzdHMgMjM4IDAgUgo+PiBlbmRvYmoKMjQwIDAgb2JqIDw8 Ci9UeXBlIC9DYXRhbG9nCi9QYWdlcyAyMzUgMCBSCi9PdXRsaW5lcyAyMzYgMCBSCi9OYW1lcyAy MzkgMCBSCiAvUGFnZU1vZGUgL1VzZU91dGxpbmVzIC9VUkkgPDwgL0Jhc2UgKCkgPj4gIC9WaWV3 ZXJQcmVmZXJlbmNlcyA8PCA+PiAKL09wZW5BY3Rpb24gNjkgMCBSCj4+IGVuZG9iagoyNDEgMCBv YmogPDwKL1Byb2R1Y2VyIChwZGZUZVgtMC4xNGYpCi9BdXRob3IgKCkgL1RpdGxlICgpIC9TdWJq ZWN0ICgpIC9DcmVhdG9yIChMYVRlWCB3aXRoIGh5cGVycmVmIHBhY2thZ2UpIC9Qcm9kdWNlciAo cGRmVGVYMTQuZikgL0tleXdvcmRzICgpIAovQ3JlYXRvciAoVGVYKQovQ3JlYXRpb25EYXRlIChE OjIwMDExMTI2MDg1MTAwKQo+PiBlbmRvYmoKeHJlZgowIDI0MgowMDAwMDAwMDAxIDY1NTM1IGYg CjAwMDAwMDAwMDIgMDAwMDAgZiAKMDAwMDAwMDAwMyAwMDAwMCBmIAowMDAwMDAwMDA0IDAwMDAw IGYgCjAwMDAwMDAwMDAgMDAwMDAgZiAKMDAwMDAwMDAwOSAwMDAwMCBuIAowMDAwMDAyMjM1IDAw MDAwIG4gCjAwMDAxMTU2NjQgMDAwMDAgbiAKMDAwMDAwMDA1MiAwMDAwMCBuIAowMDAwMDAwMDgw IDAwMDAwIG4gCjAwMDAwMDUzODIgMDAwMDAgbiAKMDAwMDExNTU0MSAwMDAwMCBuIAowMDAwMDAw MTI1IDAwMDAwIG4gCjAwMDAwMDAxNTYgMDAwMDAgbiAKMDAwMDAwNTQ0MiAwMDAwMCBuIAowMDAw MTE1NDY3IDAwMDAwIG4gCjAwMDAwMDAyMDQgMDAwMDAgbiAKMDAwMDAwMDI1NyAwMDAwMCBuIAow MDAwMDA1NTAyIDAwMDAwIG4gCjAwMDAxMTUzODAgMDAwMDAgbiAKMDAwMDAwMDMwNSAwMDAwMCBu IAowMDAwMDAwMzQ4IDAwMDAwIG4gCjAwMDAwMDczOTcgMDAwMDAgbiAKMDAwMDExNTMwNiAwMDAw MCBuIAowMDAwMDAwMzk2IDAwMDAwIG4gCjAwMDAwMDA0MzEgMDAwMDAgbiAKMDAwMDAwOTM0OCAw MDAwMCBuIAowMDAwMTE1MTgxIDAwMDAwIG4gCjAwMDAwMDA0NzcgMDAwMDAgbiAKMDAwMDAwMDUx MiAwMDAwMCBuIAowMDAwMDA5NDA5IDAwMDAwIG4gCjAwMDAxMTUxMDcgMDAwMDAgbiAKMDAwMDAw MDU2MCAwMDAwMCBuIAowMDAwMDAwNjA0IDAwMDAwIG4gCjAwMDAwMTM5OTUgMDAwMDAgbiAKMDAw MDExNTAyMCAwMDAwMCBuIAowMDAwMDAwNjUyIDAwMDAwIG4gCjAwMDAwMDA2OTcgMDAwMDAgbiAK MDAwMDAxNjkzMyAwMDAwMCBuIAowMDAwMTE0OTA5IDAwMDAwIG4gCjAwMDAwMDA3NDUgMDAwMDAg biAKMDAwMDAwMDc3MSAwMDAwMCBuIAowMDAwMDE2OTk0IDAwMDAwIG4gCjAwMDAxMTQ4MzUgMDAw MDAgbiAKMDAwMDAwMDgyNCAwMDAwMCBuIAowMDAwMDAwODgxIDAwMDAwIG4gCjAwMDAwMTk2ODAg MDAwMDAgbiAKMDAwMDExNDc0OCAwMDAwMCBuIAowMDAwMDAwOTM0IDAwMDAwIG4gCjAwMDAwMDA5 NzIgMDAwMDAgbiAKMDAwMDAyMzY0NSAwMDAwMCBuIAowMDAwMTE0NjYxIDAwMDAwIG4gCjAwMDAw MDEwMjUgMDAwMDAgbiAKMDAwMDAwMTA5MCAwMDAwMCBuIAowMDAwMDI1NzUxIDAwMDAwIG4gCjAw MDAxMTQ1ODcgMDAwMDAgbiAKMDAwMDAwMTE0MyAwMDAwMCBuIAowMDAwMDAxMTg5IDAwMDAwIG4g CjAwMDAwMjc0NTcgMDAwMDAgbiAKMDAwMDExNDQ5OSAwMDAwMCBuIAowMDAwMDAxMjM1IDAwMDAw IG4gCjAwMDAwMDEyNzkgMDAwMDAgbiAKMDAwMDAyODg3MyAwMDAwMCBuIAowMDAwMTE0NDExIDAw MDAwIG4gCjAwMDAwMDEzMjUgMDAwMDAgbiAKMDAwMDAwMTM1NiAwMDAwMCBuIAowMDAwMDMyODEw IDAwMDAwIG4gCjAwMDAxMTQzMzYgMDAwMDAgbiAKMDAwMDAwMTQwMyAwMDAwMCBuIAowMDAwMDAx NDU1IDAwMDAwIG4gCjAwMDAwMDIwMDcgMDAwMDAgbiAKMDAwMDAwMjI5NCAwMDAwMCBuIAowMDAw MDAxNTA1IDAwMDAwIG4gCjAwMDAwMDIxMTUgMDAwMDAgbiAKMDAwMDAwMjE3NSAwMDAwMCBuIAow MDAwMTEyODM1IDAwMDAwIG4gCjAwMDAxMDQwNTUgMDAwMDAgbiAKMDAwMDExMjY3NSAwMDAwMCBu IAowMDAwMTAzMjc5IDAwMDAwIG4gCjAwMDAwOTc0NDYgMDAwMDAgbiAKMDAwMDEwMzExOSAwMDAw MCBuIAowMDAwMDk2NTI4IDAwMDAwIG4gCjAwMDAwODg0NTEgMDAwMDAgbiAKMDAwMDA5NjM2OCAw MDAwMCBuIAowMDAwMDg3MDQ1IDAwMDAwIG4gCjAwMDAwNzEzNTQgMDAwMDAgbiAKMDAwMDA4Njg4 NiAwMDAwMCBuIAowMDAwMTEzNzQ1IDAwMDAwIG4gCjAwMDAwMDI4ODQgMDAwMDAgbiAKMDAwMDAw MjcxNiAwMDAwMCBuIAowMDAwMDAyNDAwIDAwMDAwIG4gCjAwMDAwMDI4MjQgMDAwMDAgbiAKMDAw MDA3MDk0MyAwMDAwMCBuIAowMDAwMDY5MjQ4IDAwMDAwIG4gCjAwMDAwNzA3ODQgMDAwMDAgbiAK MDAwMDAwNTU2MiAwMDAwMCBuIAowMDAwMDA0ODQ5IDAwMDAwIG4gCjAwMDAwMDI5NzggMDAwMDAg biAKMDAwMDAwNTMyMiAwMDAwMCBuIAowMDAwMDA0OTg0IDAwMDAwIG4gCjAwMDAwMDUxNDkgMDAw MDAgbiAKMDAwMDAzMTEzMiAwMDAwMCBuIAowMDAwMDMxMDcwIDAwMDAwIG4gCjAwMDAwMDc0NTgg MDAwMDAgbiAKMDAwMDAwNzIyNCAwMDAwMCBuIAowMDAwMDA1NjY4IDAwMDAwIG4gCjAwMDAwMDcz MzUgMDAwMDAgbiAKMDAwMDA2ODYwNiAwMDAwMCBuIAowMDAwMDYxMzMxIDAwMDAwIG4gCjAwMDAw Njg0NDUgMDAwMDAgbiAKMDAwMDAwOTQ3MCAwMDAwMCBuIAowMDAwMDA4OTk2IDAwMDAwIG4gCjAw MDAwMDc1NTQgMDAwMDAgbiAKMDAwMDAwOTI4NiAwMDAwMCBuIAowMDAwMDYwMzQzIDAwMDAwIG4g CjAwMDAwNDk5NDMgMDAwMDAgbiAKMDAwMDA2MDE4MSAwMDAwMCBuIAowMDAwMDA5MTI3IDAwMDAw IG4gCjAwMDAwNDg2MDUgMDAwMDAgbiAKMDAwMDAzNTgxOSAwMDAwMCBuIAowMDAwMDQ4NDQ0IDAw MDAwIG4gCjAwMDAwMzEwMDggMDAwMDAgbiAKMDAwMDAxMTQyNSAwMDAwMCBuIAowMDAwMDExMjUy IDAwMDAwIG4gCjAwMDAwMDk2MDMgMDAwMDAgbiAKMDAwMDAxMTM2MyAwMDAwMCBuIAowMDAwMDE0 MDU2IDAwMDAwIG4gCjAwMDAwMTM4MjEgMDAwMDAgbiAKMDAwMDAxMTUyMiAwMDAwMCBuIAowMDAw MDEzOTMzIDAwMDAwIG4gCjAwMDAxMTM4NTggMDAwMDAgbiAKMDAwMDAxNzA1MyAwMDAwMCBuIAow MDAwMDE2NDEzIDAwMDAwIG4gCjAwMDAwMTQxNzggMDAwMDAgbiAKMDAwMDAxNjg3MSAwMDAwMCBu IAowMDAwMDE2NTUzIDAwMDAwIG4gCjAwMDAwMTY3MTIgMDAwMDAgbiAKMDAwMDAyMDQ3OSAwMDAw MCBuIAowMDAwMDE5MTc4IDAwMDAwIG4gCjAwMDAwMTcxODcgMDAwMDAgbiAKMDAwMDAxOTYxOCAw MDAwMCBuIAowMDAwMDE5NzQxIDAwMDAwIG4gCjAwMDAwMTk4MDEgMDAwMDAgbiAKMDAwMDAxOTg2 MyAwMDAwMCBuIAowMDAwMDE5OTI0IDAwMDAwIG4gCjAwMDAwMTk5ODUgMDAwMDAgbiAKMDAwMDAy MDA0NyAwMDAwMCBuIAowMDAwMDM1NTExIDAwMDAwIG4gCjAwMDAwMzM1MDUgMDAwMDAgbiAKMDAw MDAzNTM0OCAwMDAwMCBuIAowMDAwMDIwMTA4IDAwMDAwIG4gCjAwMDAwMjAxNzAgMDAwMDAgbiAK MDAwMDAyMDIzMiAwMDAwMCBuIAowMDAwMDIwMjk0IDAwMDAwIG4gCjAwMDAwMTkzMTggMDAwMDAg biAKMDAwMDAxOTQ2OCAwMDAwMCBuIAowMDAwMDIwMzU1IDAwMDAwIG4gCjAwMDAwMjA0MTcgMDAw MDAgbiAKMDAwMDAyMzgzMCAwMDAwMCBuIAowMDAwMDIzMDM5IDAwMDAwIG4gCjAwMDAwMjA2MjYg MDAwMDAgbiAKMDAwMDAyMzE1MSAwMDAwMCBuIAowMDAwMDIzMjEzIDAwMDAwIG4gCjAwMDAwMjMy NzUgMDAwMDAgbiAKMDAwMDAyMzMzNyAwMDAwMCBuIAowMDAwMDIzMzk4IDAwMDAwIG4gCjAwMDAw MjM0NjAgMDAwMDAgbiAKMDAwMDAyMzUyMSAwMDAwMCBuIAowMDAwMDIzNTgzIDAwMDAwIG4gCjAw MDAwMjM3MDYgMDAwMDAgbiAKMDAwMDAyMzc2OCAwMDAwMCBuIAowMDAwMDI1ODEyIDAwMDAwIG4g CjAwMDAwMjU1MTUgMDAwMDAgbiAKMDAwMDAyMzk2MyAwMDAwMCBuIAowMDAwMDI1NjI3IDAwMDAw IG4gCjAwMDAwMjU2ODkgMDAwMDAgbiAKMDAwMDAyNjMzOCAwMDAwMCBuIAowMDAwMDI2MTY0IDAw MDAwIG4gCjAwMDAwMjU5MjAgMDAwMDAgbiAKMDAwMDAyNjI3NiAwMDAwMCBuIAowMDAwMDI3NTE4 IDAwMDAwIG4gCjAwMDAwMjcyODMgMDAwMDAgbiAKMDAwMDAyNjQyMiAwMDAwMCBuIAowMDAwMDI3 Mzk1IDAwMDAwIG4gCjAwMDAxMTM5NzUgMDAwMDAgbiAKMDAwMDAyODA0NSAwMDAwMCBuIAowMDAw MDI3ODcxIDAwMDAwIG4gCjAwMDAwMjc2MjYgMDAwMDAgbiAKMDAwMDAyNzk4MyAwMDAwMCBuIAow MDAwMDI4OTM0IDAwMDAwIG4gCjAwMDAwMjg2OTkgMDAwMDAgbiAKMDAwMDAyODEyOSAwMDAwMCBu IAowMDAwMDI4ODExIDAwMDAwIG4gCjAwMDAwMjk0MzkgMDAwMDAgbiAKMDAwMDAyOTI2NSAwMDAw MCBuIAowMDAwMDI5MDI5IDAwMDAwIG4gCjAwMDAwMjkzNzcgMDAwMDAgbiAKMDAwMDAzMTI1NiAw MDAwMCBuIAowMDAwMDMwNzcyIDAwMDAwIG4gCjAwMDAwMjk1MjMgMDAwMDAgbiAKMDAwMDAzMDg4 NCAwMDAwMCBuIAowMDAwMDMwOTQ2IDAwMDAwIG4gCjAwMDAwMzExOTQgMDAwMDAgbiAKMDAwMDAz MTc1OSAwMDAwMCBuIAowMDAwMDMxNTg1IDAwMDAwIG4gCjAwMDAwMzEzNjQgMDAwMDAgbiAKMDAw MDAzMTY5NyAwMDAwMCBuIAowMDAwMDMyODcxIDAwMDAwIG4gCjAwMDAwMzI0MDMgMDAwMDAgbiAK MDAwMDAzMTg0MyAwMDAwMCBuIAowMDAwMDMyNzQ4IDAwMDAwIG4gCjAwMDAwMzI1MzUgMDAwMDAg biAKMDAwMDExNDA5MiAwMDAwMCBuIAowMDAwMDMzNDIxIDAwMDAwIG4gCjAwMDAwMzMyNDcgMDAw MDAgbiAKMDAwMDAzMjk5MSAwMDAwMCBuIAowMDAwMDMzMzU5IDAwMDAwIG4gCjAwMDAwMzU3MzUg MDAwMDAgbiAKMDAwMDAzNTcxMSAwMDAwMCBuIAowMDAwMDQ5NDI3IDAwMDAwIG4gCjAwMDAwNDkw NzcgMDAwMDAgbiAKMDAwMDA2MDkyOSAwMDAwMCBuIAowMDAwMDYwNjYxIDAwMDAwIG4gCjAwMDAw NjkwNDAgMDAwMDAgbiAKMDAwMDA2ODg3NSAwMDAwMCBuIAowMDAwMDcxMjE4IDAwMDAwIG4gCjAw MDAwNzExNjcgMDAwMDAgbiAKMDAwMDA4Nzk1MCAwMDAwMCBuIAowMDAwMDg3NTQ3IDAwMDAwIG4g CjAwMDAwOTcwNjkgMDAwMDAgbiAKMDAwMDA5NjgxNCAwMDAwMCBuIAowMDAwMTAzNzMzIDAwMDAw IG4gCjAwMDAxMDM1MjEgMDAwMDAgbiAKMDAwMDExMzQwOSAwMDAwMCBuIAowMDAwMTEzMTQzIDAw MDAwIG4gCjAwMDAxMTQxNzcgMDAwMDAgbiAKMDAwMDExNDI2MiAwMDAwMCBuIAowMDAwMTE1NzM2 IDAwMDAwIG4gCjAwMDAxMTcwNDYgMDAwMDAgbiAKMDAwMDExNzA4NSAwMDAwMCBuIAowMDAwMTE3 MTIzIDAwMDAwIG4gCjAwMDAxMTcyOTkgMDAwMDAgbiAKdHJhaWxlcgo8PAovU2l6ZSAyNDIKL1Jv b3QgMjQwIDAgUgovSW5mbyAyNDEgMCBSCj4+CnN0YXJ0eHJlZgoxMTc1MDQKJSVFT0YK --=====================_2108451==_-- From marbles-isi@mailman.isi.edu Mon Nov 26 21:38:55 2001 From: marbles-isi@mailman.isi.edu (Seongbin Park) Date: Mon, 26 Nov 2001 13:38:55 -0800 Subject: [Marbles-isi] Marbles2 design document In-Reply-To: Your message of "Mon, 26 Nov 2001 09:05:23 PST." <5.1.0.14.2.20011126085136.00aa9838@tnt.isi.edu> Message-ID: <200111262138.fAQLctg29788@tnt.isi.edu> Hi Martin, Thank you for the manual -- I found it useful. By the way, in 2.3.2, there are some errors (which I believe is related to LaTex); i.e., in Min's solver description, step 2, (b) if $b > 1$ seems to be a correct one. Also, in step 2, (e), if $V' > V$ will solve the problem. Seongbin