2014校级论文

关于2014年东莞市中学语文优秀教学论文、教学设计等评选结果的
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东莞市中学语文教研会2014年年会论文、教学设计、命题（初中）评选工作顺利结束。

经中学语文教学研究会组织评审小组按程序严格、认真的评审，评出年会论文、教学设计、命题（初中）若干（详见附件），经过公示，现予以最终确认并正式通报表彰。

其中获一、二等奖的论文将在我市中学语文教学研究会2014年年会上宣读、交流。

希望全体中学语文教师继续钻研，认真贯彻落实新课程改革精神，争取更大成绩。

东莞市教育局教研室
东莞市中学语文教学研究会 2014年12月24日
附1 东莞市中学语文教研会2014年年会论文获奖名单
一等奖
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三等奖
附2 东莞市中学语文教研会2014年年会教学设计获奖名单
一等奖
二等奖
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附3 东莞市初中语文命题评选活动获奖名单
一等奖
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三等奖。

For office use only T1T2T3T4T eam Control Number24857Problem ChosenBFor office use onlyF1F2F3F42014Mathematical Contest in Modeling(MCM)Summary Sheet (Attach a copy of this page to each copy of your solution paper.)AbstractThe evaluation and selection of‘best all time college coach’is the prob-lem to be addressed.We capture the essential of an evaluation system by reducing the dimensions of the attributes by factor analysis.And we divide our modeling process into three phases:data collection,attribute clarifica-tion,factor model evaluation and model generalization.Firstly,we collect the data from official database.Then,two bottom lines are determined respectively by the number of participating games and win-loss percentage,with these bottom lines we anchor a pool with30to40 candidates,which greatly reduced data volume.And reasonably thefinal top5coaches should generate from this pool.Attribution clarification will be abundant in the body of the model,note that we endeavor to design an attribute to effectively evaluate the improvement of a team before and after the coach came.In phase three,we analyse the problem by following traditional method of the factor model.With three common factors indicating coaches’guiding competency,strength of guided team,competition strength,we get afinal integrated score to evaluate coaches.And we also take into account the time line horizon in two aspects.On the one hand,the numbers of participating games are adjusted on the basis of time.On the other hand,we put forward a potential sub-model in our‘further attempts’concerning overlapping pe-riod of the time of two different coaches.What’s more,a‘pseudo-rose dia-gram’method is tried to show coaches’performance in different areas.Model generalization is examined by three different sports types,Foot-ball,Basketball,and Softball.Besides,our model also can be applied in all possible ball games under the frame of NCAA,assigning slight modification according to specific regulations.The stability of our model is also tested by sensitivity analysis.Who’s who in College Coaching Legends—–A generalized Factor Analysis approach2Contents1Introduction41.1Restatement of the problem (4)1.2NCAA Background and its coaches (4)1.3Previous models (4)2Assumptions5 3Analysis of the Problem5 4Thefirst round of sample selection6 5Attributes for evaluating coaches86Factor analysis model106.1A brief introduction to factor analysis (10)6.2Steps of Factor analysis by SPSS (12)6.3Result of the model (14)7Model generalization15 8Sensitivity analysis189Strength and Weaknesses199.1Strengths (19)9.2Weaknesses (19)10Further attempts20 Appendices22 Appendix A An article for Sports Illustrated221Introduction1.1Restatement of the problemThe‘best all time college coach’is to be selected by Sports Illustrated,a magazine for sports enthusiasts.This is an open-ended problem—-no limitation in method of performance appraisal,gender,or sports types.The following research points should be noted:•whether the time line horizon that we use in our analysis make a difference;•the metrics for assessment are to be articulated;•discuss how the model can be applied in general across both genders and all possible sports;•we need to present our model’s Top5coaches in each of3different sports.1.2NCAA Background and its coachesNational Collegiate Athletic Association(NCAA),an association of1281institution-s,conferences,organizations,and individuals that organizes the athletic programs of many colleges and universities in the United States and Canada.1In our model,only coaches in NCAA are considered and ranked.So,why evaluate the Coaching performance?As the identity of a college football program is shaped by its head coach.Given their impacts,it’s no wonder high profile athletic departments are shelling out millions of dollars per season for the services of coaches.Nick Saban’s2013total pay was$5,395,852and in the same year Coach K earned$7,233,976in total23.Indeed,every athletic director wants to hire the next legendary coach.1.3Previous modelsTraditionally,evaluation in athletics has been based on the single criterion of wins and losses.Years later,in order to reasonably evaluate coaches,many reseachers have implemented the coaching evaluation model.Such as7criteria proposed by Adams:[1] (1)the coach in the profession,(2)knowledge of and practice of medical aspects of coaching,(3)the coach as a person,(4)the coach as an organizer and administrator,(5) knowledge of the sport,(6)public relations,and(7)application of kinesiological and physiological principles.1Wikipedia:/wiki/National_Collegiate_Athletic_ Association#NCAA_sponsored_sports2USAToday:/sports/college/salaries/ncaaf/coach/ 3USAToday:/sports/college/salaries/ncaab/coach/Such models relatively focused more on some subjective and difficult-to-quantify attributes to evaluate coaches,which is quite hard for sports fans to judge coaches. Therefore,we established an objective and quantified model to make a list of‘best all time college coach’.2Assumptions•The sample for our model is restricted within the scale of NCAA sports.That is to say,the coaches we discuss refers to those service for NCAA alone;•We do not take into account the talent born varying from one player to another, in this case,we mean the teams’wins or losses purely associate with the coach;•The difference of games between different Divisions in NCAA is ignored;•Take no account of the errors/amendments of the NCAA game records.3Analysis of the ProblemOur main goal is to build and analyze a mathematical model to choose the‘best all time college coach’for the previous century,i.e.from1913to2013.Objectively,it requires numerous attributes to judge and specify whether a coach is‘the best’,while many of the indicators are deemed hard to quantify.However,to put it in thefirst place, we consider a‘best coach’is,and supposed to be in line with several basic condition-s,which are the prerequisites.Those prerequisites incorporate attributes such as the number of games the coach has participated ever and the win-loss percentage of the total.For instance,under the conditions that either the number of participating games is below100,or the win-loss percentage is less than0.5,we assume this coach cannot be credited as the‘best’,ignoring his/her other facets.Therefore,an attempt was made to screen out the coaches we want,thus to narrow the range in ourfirst stage.At the very beginning,we ignore those whose guiding ses-sions or win-loss percentage is less than a certain level,and then we determine a can-didate pool for‘the best coach’of30-40in scale,according to merely two indicators—-participating games and win-loss percentage.It should be reasonably reliable to draw the top5best coaches from this candidate pool,regardless of any other aspects.One point worth mentioning is that,we take time line horizon as one of the inputs because the number of participating games is changing all the time in the previous century.Hence,it would be unfair to treat this problem by using absolute values, especially for those coaches who lived in the earlier ages when sports were less popular and games were sparse comparatively.4Thefirst round of sample selectionCollege Football is thefirst item in our research.We obtain data concerning all possible coaches since it was initiated,of which the coaches’tenures,participating games and win-loss percentage etc.are included.As a result,we get a sample of2053in scale.Thefirst10candidates’respective information is as below:Table1:Thefirst10candidates’information,here Pct means win-loss percentageCoach From To Years Games Wins Losses Ties PctEli Abbott19021902184400.5Earl Abell19281930328141220.536Earl Able1923192421810620.611 George Adams1890189233634200.944Hobbs Adams1940194632742120.185Steve Addazio20112013337201700.541Alex Agase1964197613135508320.378Phil Ahwesh19491949193600.333Jim Aiken19461950550282200.56Fred Akers19751990161861087530.589 ...........................Firstly,we employ Excel to rule out those who begun their coaching career earlier than1913.Next,considering the impact of time line horizon mentioned in the problem statement,we import our raw data into MATLAB,with an attempt to calculate the coaches’average games every year versus time,as delineated in the Figure1below.Figure1:Diagram of the coaches’average sessions every year versus time It can be drawn from thefigure above,clearly,that the number of each coach’s average games is related with the participating time.With the passing of time and the increasing popularity of sports,coaches’participating games yearly ascends from8to 12or so,that is,the maximum exceed the minimum for50%around.To further refinethe evaluation method,we make the following adjustment for coaches’participating games,and we define it as each coach’s adjusted participating games.Gi =max(G i)G mi×G iWhere•G i is each coach’s participating games;•G im is the average participating games yearly in his/her career;and•max(G i)is the max value in previous century as coaches’average participating games yearlySubsequently,we output the adjusted data,and return it to the Excel table.Obviously,directly using all this data would cause our research a mass,and also the economy of description is hard to achieved.Logically,we propose to employ the following method to narrow the sample range.In general,the most essential attributes to evaluate a coach are his/her guiding ex-perience(which can be shown by participating games)and guiding results(shown by win-loss percentage).Fortunately,these two factors are the ones that can be quantified thus provide feasibility for our modeling.Based on our common sense and select-ed information from sports magazines and associated programs,wefind the winning coaches almost all bear the same characteristics—-at high level in both the partici-pating games and the win-loss percentage.Thus we may arbitrarily enact two bottom line for these two essential attributes,so as to nail down a pool of30to40candidates. Those who do not meet our prerequisites should not be credited as the best in any case.Logically,we expect the model to yield insight into how bottom lines are deter-mined.The matter is,sports types are varying thus the corresponding features are dif-ferent.However,it should be reasonably reliable to the sports fans and commentators’perceptual intuition.Take football as an example,win-loss percentage that exceeds0.75 should be viewed as rather high,and college football coaches of all time who meet this standard are specifically listed in Wikipedia.4Consequently,we are able tofix upon a rational pool of candidate according to those enacted bottom lines and meanwhile, may tender the conditions according to the total scale of the coaches.Still we use Football to further articulate,to determine a pool of candidates for the best coaches,wefirst plot thefigure below to present the distributions of all the coaches.From thefigure2,wefind that once the games number exceeds200or win-loss percentage exceeds0.7,the distribution of the coaches drops significantly.We can thus view this group of coaches as outstanding comparatively,meeting the prerequisites to be the best coaches.4Wikipedia:/wiki/List_of_college_football_coaches_ with_a_.750_winning_percentageFigure2:Hist of the football coaches’number of games versus and average games every year versus games and win-loss percentageHence,we nail down the bottom lines for both the games number and the win-loss percentage,that is,0.7for the former and200for the latter.And these two bottom lines are used as the measure for ourfirst round selection.After round one,merely35 coaches are qualified to remain in the pool of candidates.Since it’s thefirst round sifting,rather than direct and ultimate determination,we hence believe the subjectivity to some extent in the opt of bottom lines will not cloud thefinal results of the best coaches.5Attributes for evaluating coachesThen anchored upon the35candidate selected,we will elaborate our coach evaluation system based on8attributes.In the indicator-select process,we endeavor to examine tradeoffs among the availability for data and difficulty for data quantification.Coaches’pay,for example,though serves as the measure for coaching evaluation,the corre-sponding data are limited.Situations are similar for attributes such as the number of sportsmen the coach ever cultivated for the higher-level tournaments.Ultimately,we determine the8attributes shown in the table below:Further explanation:•Yrs:guiding years of a coach in his/her whole career•G’:Gi =max(G i)G mi×G i see it at last section•Pct:pct=wins+ties/2wins+losses+ties•SRS:a rating that takes into account average point differential and strength of schedule.The rating is denominated in points above/below average,where zeroTable2:symbols and attributessymbol attributeYrs yearsG’adjusted overall gamesPct win-lose percentageP’Adjusted percentage ratioSRS Simple Rating SystemSOS Strength of ScheduleBlp’adjusted Bowls participatedBlw’adjusted Bowls wonis the average.Note that,the bigger for this value,the stronger for the team performance.•SOS:a rating of strength of schedule.The rating is denominated in points above/below average,where zero is the average.Noted that the bigger for this value,the more powerful for the team’s rival,namely the competition is more fierce.Sports-reference provides official statistics for SRS and SOS.5•P’is a new attribute designed in our model.It is the result of Win-loss in the coach’s whole career divided by the average of win-loss percentage(weighted by the number of games in different colleges the coach ever in).We bear in mind that the function of a great coach is not merely manifested in the pure win-loss percentage of the team,it is even more crucial to consider the improvement of the team’s win-loss record with the coach’s participation,or say,the gap between‘af-ter’and‘before’period of this team.(between‘after’and‘before’the dividing line is the day the coach take office)It is because a coach who build a comparative-ly weak team into a much more competitive team would definitely receive more respect and honor from sports fans.To measure and specify this attribute,we col-lect the key official data from sports-reference,which included the independent win-loss percentage for each candidate and each college time when he/she was in the team and,the weighted average of all time win-loss percentage of all the college teams the coach ever in—-regardless of whether the coach is in the team or not.To articulate this attribute,here goes a simple physical example.Ike Armstrong (placedfirst when sorted by alphabetical order),of which the data can be ob-tained from website of sports-reference6.We can easily get the records we need, namely141wins,55losses,15ties,and0.704for win-losses percentage.Fur-ther,specific wins,losses,ties for the team he ever in(Utab college)can also be gained,respectively they are602,419,30,0.587.Consequently,the P’value of Ike Armstrong should be0.704/0.587=1.199,according to our definition.•Bowl games is a special event in thefield of Football games.In North America,a bowl game is one of a number of post-season college football games that are5sports-reference:/cfb/coaches/6sports-reference:/cfb/coaches/ike-armstrong-1.htmlprimarily played by teams from the Division I Football Bowl Subdivision.The times for one coach to eparticipate Bowl games are important indicators to eval-uate a coach.However,noted that the total number of Bowl games held each year is changing from year to year,which should be taken into consideration in the model.Other sports events such as NCAA basketball tournament is also ex-panding.For this reason,it is irrational to use the absolute value of the times for entering the Bowl games (or NCAA basketball tournament etc.)and the times for winning as the evaluation measurement.Whereas the development history and regulations for different sports items vary from one to another (actually the differentiation can be fairly large),we here are incapable to find a generalized method to eliminate this discrepancy ,instead,in-dependent method for each item provide a way out.Due to the time limitation for our research and the need of model generalization,we here only do root extract of blp and blw to debilitate the differentiation,i.e.Blp =√Blp Blw =√Blw For different sports items,we use the same attributes,except Blp’and Blw’,we may change it according to specific sports.For instance,we can use CREG (Number of regular season conference championship won)and FF (Number of NCAA Final Four appearance)to replace Blp and Blw in basketball games.With all the attributes determined,we organized data and show them in the table 3:In addition,before forward analysis there is a need to preprocess the data,owing to the diverse dimensions between these indicators.Methods for data preprocessing are a lot,here we adopt standard score (Z score)method.In statistics,the standard score is the (signed)number of standard deviations an observation or datum is above the mean.Thus,a positive standard score represents a datum above the mean,while a negative standard score represents a datum below the mean.It is a dimensionless quantity obtained by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation.7The standard score of a raw score x is:z =x −µσIt is easy to complete this process by statistical software SPSS.6Factor analysis model 6.1A brief introduction to factor analysisFactor analysis is a statistical method used to describe variability among observed,correlated variables in terms of a potentially lower number of unobserved variables called factors.For example,it is possible that variations in four observed variables mainly reflect the variations in two unobserved variables.Factor analysis searches for 7Wikipedia:/wiki/Standard_scoreTable3:summarized data for best college football coaches’candidatesCoach From To Yrs G’Pct Blp’Blw’P’SRS SOS Ike Armstrong19251949252810.70411 1.199 4.15-4.18 Dana Bible19151946313860.7152 1.73 1.0789.88 1.48 Bernie Bierman19251950242780.71110 1.29514.36 6.29 Red Blaik19341958252940.75900 1.28213.57 2.34 Bobby Bowden19702009405230.74 5.74 4.69 1.10314.25 4.62 Frank Broyles19571976202570.7 3.162 1.18813.29 5.59 Bear Bryant19451982385080.78 5.39 3.87 1.1816.77 6.12 Fritz Crisler19301947182080.76811 1.08317.15 6.67 Bob Devaney19571972162080.806 3.16 2.65 1.25513.13 2.28 Dan Devine19551980222800.742 3.16 2.65 1.22613.61 4.69 Gilmour Dobie19161938222370.70900 1.27.66-2.09 Bobby Dodd19451966222960.713 3.613 1.18414.25 6.6 Vince Dooley19641988253250.715 4.47 2.83 1.09714.537.12 Gus Dorais19221942192320.71910 1.2296-3.21 Pat Dye19741992192400.707 3.16 2.65 1.1929.68 1.51 LaVell Edwards19722000293920.716 4.69 2.65 1.2437.66-0.66 Phillip Fulmer19922008172150.743 3.87 2.83 1.08313.42 4.95 Woody Hayes19511978283290.761 3.32 2.24 1.03117.418.09 Frank Kush19581979222710.764 2.65 2.45 1.238.21-2.07 John McKay19601975162070.7493 2.45 1.05817.298.59 Bob Neyland19261952212860.829 2.65 1.41 1.20815.53 3.17 Tom Osborne19731997253340.8365 3.46 1.18119.7 5.49 Ara Parseghian19561974192250.71 2.24 1.73 1.15317.228.86 Joe Paterno19662011465950.749 6.08 4.9 1.08914.01 5.01 Darrell Royal19541976232970.7494 2.83 1.08916.457.09 Nick Saban19902013182390.748 3.74 2.83 1.12313.41 3.86 Bo Schembechler19631989273460.775 4.12 2.24 1.10414.86 3.37 Francis Schmidt19221942212670.70800 1.1928.490.16 Steve Spurrier19872013243160.733 4.363 1.29313.53 4.64 Bob Stoops19992013152070.804 3.74 2.65 1.11716.66 4.74 Jock Sutherland19191938202550.81221 1.37613.88 1.68 Barry Switzer19731988162090.837 3.61 2.83 1.16320.08 6.63 John Vaught19471973253210.745 4.24 3.16 1.33814.7 5.26 Wallace Wade19231950243070.765 2.24 1.41 1.34913.53 3.15 Bud Wilkinson19471963172220.826 2.83 2.45 1.14717.54 4.94 such joint variations in response to unobserved latent variables.The observed vari-ables are modelled as linear combinations of the potential factors,plus‘error’terms. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a putationally this technique is equivalent to low rank approximation of the matrix of observed variables.8 Why carry out factor analyses?If we can summarise a multitude of measure-8Wikipedia:/wiki/Factor_analysisments with a smaller number of factors without losing too much information,we have achieved some economy of description,which is one of the goals of scientific investi-gation.It is also possible that factor analysis will allow us to test theories involving variables which are hard to measure directly.Finally,at a more prosaic level,factor analysis can help us establish that sets of questionnaire items(observed variables)are in fact all measuring the same underlying factor(perhaps with varying reliability)and so can be combined to form a more reliable measure of that factor.6.2Steps of Factor analysis by SPSSFirst we import the decided datasets of8attributes into SPSS,and the results can be obtained below after the software processing.[2-3]Figure3:Table of total variance explainedFigure4:Scree PlotThefirst table and scree plot shows the eigenvalues and the amount of variance explained by each successive factor.The remaining5factors have small eigenvalues value.Once the top3factors are extracted,it adds up to84.3%,meaning a great as the explanatory ability for the original information.To reflect the quantitative analysis of the model,we obtain the following factor loading matrix,actually the loadings are in corresponding to the weight(α1,α2 (i)the set ofx i=αi1f1+αi2f2+...+αim f j+εiAnd the relative strength of the common factors and the original attribute can also be manifested.Figure5:Rotated Component MatrixThen,with Rotated Component Matrix above,wefind the common factor F1main-ly expresses four attributes they are:G,Yrs,P,SRS,and logically,we define the com-mon factor generated from those four attributes as the guiding competency of the coach;similarly,the common factor F2mainly expresses two attributes,and they are: Pct and Blp,which can be de defined as the integrated strength of the guided team; while the common factor F3,mainly expresses two attributes:SOS and Blw,which can be summarized into a‘latent attribute’named competition strength.In order to obtain the quantitative relation,we get the following Component Score Coefficient Matrix processed by SPSS.Further,the function of common factors and the original attributes is listed as bel-low:F1=0.300x1+0.312x2+0.023x3+0.256x4+0.251x5+0.060x6−0.035x7−0.053x8F2=−0.107x1−0,054x2+0.572x3+0.103x4+0.081x5+0.280x6+0.372x7+0.142x8 F3=−0.076x1−0,098x2−0.349x3+0.004x4+0.027x5−0.656x6+0.160x7+0.400x8 Finally we calculate out the integrated factor scores,which should be the average score weighted by the corresponding proportion of variance contribution of each com-mon factor in the total variance contribution.And the function set should be:F=0.477F1+0.284F2+0.239F3Figure6:Component Score Coefficient Matrix6.3Result of the modelwe rank all the coaches in the candidate pool by integrated score represented by F.Seetable4:Table4:Integrated scores for best college football coach(show15data due to the limi-tation of space)Rank coaches F1F2F3Integrated factor1Joe Paterno 3.178-0.3150.421 1.3622Bobby Bowden 2.51-0.2810.502 1.1113Bear Bryant 2.1420.718-0.142 1.0994Tom Osborne0.623 1.969-0.2390.8205Woody Hayes0.140.009 1.6130.4846Barry Switzer-0.705 2.0360.2470.4037Darrell Royal0.0460.161 1.2680.4018Vince Dooley0.361-0.442 1.3730.3749Bo Schembechler0.4810.1430.3040.32910John Vaught0.6060.748-0.870.26511Steve Spurrier0.5180.326-0.5380.18212Bob Stoops-0.718 1.0850.5230.17113Bud Wilkinson-0.718 1.4130.1050.16514Bobby Dodd0.08-0.2080.7390.16215John McKay-0.9620.228 1.870.151Based on this model,we can make a scientific rank list for US college football coach-es,the Top5coaches of our model is Joe Paterno,Bobby Bowden,Bear Bryant,TomOsborne,Woody Hayes.In order to confirm our result,we get a official list of bestcollege football coaches from Bleacherreport99Bleacherreport:/articles/890705-college-football-the-top-50-coTable5:The result of our model in football,the last column is official college basketball ranking from bleacherreportRank Our model Integrated scores bleacherreport1Joe Paterno 1.362Bear Bryant2Bobby Bowden 1.111Knute Rockne3Bear Bryant 1.099Tom Osborne4Tom Osborne0.820Joe Paterno5Woody Hayes0.484Bobby Bowden By comparing thoes two ranking list,wefind that four of our Top5coaches ap-peared in the offical Top5list,which shows that our model is reasonable and effective.7Model generalizationOur coach evaluation system model,of which the feasibility of generalization is sat-isfying,can be accommodated to any possible NCAA sports concourses by assigning slight modification concerning specific regulations.Besides,this method has nothing to do with the coach’s gender,or say,both male and female coaches can be rationally evaluated by this system.And therefore we would like to generalize this model into softball.Further,we take into account the time line horizon,making corresponding adjust-ment for the indicator of number of participating games so as to stipulate that the evaluation measure for1913and2013would be the same.To further generalize the model,first let’s have a test in basketball,of which the data available is adequate enough as football.And the specific steps are as following:1.Obtain data from sports-reference10and rule out the coaches who begun theircoaching career earlier than1913.2.Calculate each coach’s adjusted number of participating games,and adjust theattribute—-FF(Number of NCAA Final Four appearance).3.Determine the bottom lines for thefirst round selection to get a pool of candidatesaccording to the coaches’participating games and win-loss percentage,and the ideal volumn of the pool should be from30to40.Hist diagrams are as below: We determine800as the bottom line for the adjusted participating games and0.7 for the win-loss percentage.Coincidently,we get a candidate pool of35in scale.4.Next,we collect the corresponding data of candidate coaches(P’,SRS,SOS etc.),as presented in the table6:5.Processed by z score method and factor analysis based on the8attributes anddata above,we get three common factors andfinal integrated scores.And among 10sports-reference:/cbb/coaches/Figure7:Hist of the basketball coaches’number of games versus and average gamesevery year versus games and win-loss percentagethe top5candidates,Mike Krzyzewski,Adolph Rupp,Dean SmithˇcˇnBob Knightare the same with the official statistics from bleacherreport.11We can say theeffectiveness of the model is pretty good.See table5.We also apply similar approach into college softball.Maybe it is because the popularity of the softball is not that high,the data avail-able is not adequate to employ ourfirst model.How can our model function in suchsituation?First and foremost,specialized magazines like Sports Illustrated,its com-mentators there would have more internal and confidential databases,which are notexposed publicly.On the one hand,as long as the data is adequate enough,we can saythe original model is completely feasible.While under the situation that there is datadeficit,we can reasonably simplify the model.The derivation of the softball data is NCAA’s official websites,here we only extractdata from All-Division part.12Softball is a comparatively young sports,hence we may arbitrarily neglect the re-stricted condition of‘100years’.Subsequently,because of the data deficit it is hard toadjust the number of participating games.We may as well determine10as the bottomline for participating games and0.74for win-loss percentage,producing a candidatepool of33in scaleAttributed to the inadequacy of the data for attributes,it is not convenient to furtheruse the factor analysis similarly as the assessment model.Therefore,here we employsolely two of the most important attributes to evaluate a coach and they are:partic-ipating games and win-loss percentage in the coach’s whole career.Specifically,wefirst adopt z score to normalize all the data because of the differentiation of various dimensions,and then the integrated score of the coach can be reached by the weighted11bleacherreport:/articles/1341064-10-greatest-coaches-in-ncaa-b 12NCAA softball Coaching Record:/Docs/stats/SB_Records/2012/coaches.pdf。

数学建模综述2014年美国大学生数学建模竞赛A题论文综述我们小组精读两篇14年美赛A题论文，选择了其中一篇来进行学习，总结。

1、问题分析The Keep-Right-Except-To-Pass Rule除非超车否则靠右行驶的交通规则问题：建立数学模型来分析这条规则在低负荷和高负荷状态下的交通路况的表现。

这条规则在提升车流量的方面是否有效？如果不是，提出能够提升车流量、安全系数或其他因素的替代品（包括完全没有这种规律）并加以分析。

在一些国家，汽车靠左形式是常态，探讨你的解决方案是否稍作修改即可适用，或者需要一些额外的需要。

最后，以上规则依赖于人的判断，如果相同规则的交通运输完全在智能系统的控制下，无论是部分网络还是嵌入使用的车辆的设计，在何种程度上会修改你前面的结果论文：基于元胞自动机和蒙特卡罗方法,我们建立一个模型来讨论“靠右行”规则的影响。

首先,我们打破汽车的运动过程和建立相应的子模型car-generation的流入模型,对于匀速行驶车辆，我们建立一个跟随模型,和超车模型。

然后我们设计规则来模拟车辆的运动模型。

我们进一步讨论我们的模型规则适应靠右的情况和,不受限制的情况, 和交通情况由智能控制系统的情况。

我们也设计一个道路的危险指数评价公式。

我们模拟双车道高速公路上交通(每个方向两个车道,一共四条车道),高速公路双向三车道（总共6车道)。

通过计算机和分析数据。

我们记录的平均速度,超车取代率、道路密度和危险指数和通过与不受规则限制的比较评估靠右行的性能。

我们利用不同的速度限制分析模型的敏感性和看到不同的限速的影响。

左手交通也进行了讨论。

根据我们的分析,我们提出一个新规则结合两个现有的规则(靠右的规则和无限制的规则)的智能系统来实现更好的的性能。

该论文在一开始并没有作过多分析，而是一针见血的提出了自己对于这个问题的做法。

由于题目给出的背景只有一条交通规则，而且是题目很明确的提出让我们建立模型分析。

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附：台州市2014年度教育科研优秀论文评比获奖名单一等奖（36篇）论文标题作者单位小学生交通安全教育活动中的问题与对策董德平椒江区云健小学中职专业课微项目教学的实践探索陈海丽椒江区职业中等专业学校小学识字教学中汉字“文化识读”策略浅析张静椒江区中山小学手持技术在高中化学实验教学中的应用与思考管廷河黄岩区黄岩中学微博作文奏响心灵乐章张丽娜黄岩区锦江小学初中美术“造型·表现”领域主体性教学的实践与思考朱婧黄岩区新前中学浅谈“绿野寻踪”活动与小学习作教学的有效整合潘仙福黄岩区院桥中心小学农村小学教研组建设的问题透视及策略追述何素芳黄岩区院桥中心小学学习小组建设之实践探索贺君斐路桥区教科所小学《体育与健康》单元计划的构建李赛清尚才军路桥区教育局教研室全面浸润课堂构建数学印象潘慧敏徐春艳路桥区路桥小学故事教学在中职计算机应用基础课堂中的实践应用梅进霞路桥区中等职业技术学校关于课前作业功能的再思考冯玲辉临海师范附属小学利用错题信息展开备课的策略研究黄祥临海市大洋小学中学生情感教育社会支持系统的构建周敏临海市外国语学校网络结构化：小学数学教学的深根固本之法陈选峰临海市哲商小学初中科学实验资源的开发与利用叶敏红温岭市第四中学小学英语“戏剧教学法”探索王祺温岭市横湖小学主题式区域中的幼儿自主学习策略探究林艺温岭市级机关幼儿园拓展书香建设领域助力学校品质提升李小友温岭市锦园小学小学言语习得“诵·填·炼·迁·评”教学法例析朱玲红温岭市箬横第三小学英语VIEW教学模式初探何小怡温岭市温岭中学情智课堂：让小学音乐教育充盈情趣情味情感蒋玲红温岭市泽国小学高中语文问题式有效阅读教学的实践研究殷兴福玉环县玉城中学小数概念教学中发展题设计的探索马斯燕玉环县陈屿中心小学“玩”出情节“读”出韵味——小学高段小古文教学初探裴晓娅闻杨天台县坦头镇中心小学历史阅读：高中历史选修课程的开发与实践许齐天台县天台中学绘本与幼儿美术教育活动的奇妙融合陈晓洁仙居安洲街道中心幼儿园基于“交互学习”的小学数学新课堂研究朱旭平徐旭琴仙居县步路乡中心小学仙居县实验小学教学设计应重在内在逻辑——刍议阅读教学中的作业运用徐佳莹仙居县第一小学习作互评：纸上得来不觉浅杨裕民张志伟仙居县横溪镇中心小学仙居县教育科学研究所立足文本细读，品出语文滋味——文本细读法在初中阅读教学的实践与研究陈耀武仙居县下各二中单元教学中导语的有效解读与整合杨喜菊三门县海游中心小学三年级英语单元整体设计下的教学目标制定与实施——以PEP三下Unit3 At the Zoo为例杨㑇三门县实验小学蒙太奇突破初中生记叙文写作瓶颈的对策项振中台州市白云学校倾听生命的呼唤倾听生命的需求——培养听障学生语言能力的实践研究任川燕台州市聋哑学校二等奖（88篇）论文标题作者单位原生态写话的有益尝试-----以四季为训练主题陶恩玲椒江区第二实验小学巧用歌词扮靓作文潘绽绽椒江区洪家街道中心小学小学数学习题多维设计的几点思考金菊仁椒江区人民小学与文本共舞舞出精彩语文——小学语文教师解读文本的策略研究施伟珍椒江区三甲街道中心小学提高高中英语阅读材料二次开发有效性的策略仝亚军台州市第一中学高中语文教学中运用太极思维法启悟的探索朱玲台州市第一中学在演练和体验中进行幼儿防走失防拐骗的教育陈丽萍椒江区中心幼儿园碧海明珠分园普通高中知识拓展类选修课程开发的思考和探索张杏娟黄岩区第二高级中学高中"程序设计类"校本选修课学习评价体系的应用初探范跃群范诚黄岩区黄岩中学减少小学生母语负迁移的实践研究冯红黄岩区江口中心小学小学语文《习得本》的创制与应用王朝辉黄岩区宁溪中心小学深化课堂教学观察提升教师专业水平徐志杰黄岩区实验中学以艺体活动为载体的欣赏型德育策略李元领黄岩区西江小学中职学生性教育现状的调查分析王伟丽黄岩区新前中学绿野寻踪，引领孩子亲近动物林国建黄岩区院桥中心小学扬“补白”风帆促习作起航乔任路桥街道实验小学小学美术“前置性学习”初探李江萍路桥区路南小学留一块黑板书一道风景——小学语文板书设计有效性研究池伟萍路桥区路南小学谈小学数学“解决问题”教学之重吴艳路桥区螺洋街道中心小学中职创业教育中项目教学的实践和研究季敏路桥区中等职业技术学校培养小学生几何直观能力的策略研究马丽娇临海市白水洋中心校“自然后果惩罚法”在幼儿教育运用中的探索梁蔚萍临海市河头镇中心幼儿园小学品德行为作业设计之綮与型汪洋临海市回浦实验小学基于批判性阅读的高中英语课堂教学研究余晶晶临海市回浦中学以目标为导向的高中英语学习策略模式的构建丁红霞临海市回浦中学因地制宜多元运动林芳临海市机关幼儿园——优化幼儿园户外体育活动的策略探索数学阅读：读出学生的数学视界金玲玲临海市巾山实验小学家长会变脸“三部曲”——农村学校小型家长会的探索叶高峰陈红丽临海市括苍镇中心校小学数学“模型思想”培养策略初探单方军临海市临海小学让“语用”在解读文本中生长起来金丽月临海市临海小学小品语言的幽默手段探究谢微萍临海市台州中学基于校本的小学生情趣作文綦爱军临海市哲商现代实验小学幼小数学教学衔接的问题与对策黄志华临海市中心幼儿园变废为宝：让你“恋”上美术“链”上生活潘苏娥温岭市城东小学和着文本节拍共舞言意语文张美君温岭市大溪小学小学生数学能力评价现状调查与问题分析李锐温岭市大溪镇潘郎小学农村初中语文阅读教学中“主问题”有效设计的研究与实践张盈盈温岭市第八中学农村初中差异教学“四维”行动的研究郑云卿温岭市第五中学也傍桑阴学种瓜——儿童诗练写中诗歌元素的培植汪喜燕温岭市方城小学乡村特色学校创建中“非遗文化”传承的研究林俊温岭市教育局招生办在生活中快乐成长——小学生活德育课程化的探索陈亨源温岭市锦园小学浸润：让启蒙国学滋养校园主题文化孔美壮温岭市箬横第四小学非连续性文本阅读策略指导阮良德温岭市太平小学浅析培智学校绘本有效性阅读方法陈烨温岭特殊教育学校西郊校区中学德育的现代化管理研究陶滕江温岭市温岭中学概念图在八年级英语阅读教学中的运用研究章波勇温岭市温中实验学校利用种植园设计“自然角”进行科学探究的实践王红燕温岭市新河镇中心幼儿园高中物理单元复习教学中的“学习路径设计”蔡千斌温岭市新河中学概念复习：横纵梳理辨析深化融会贯通颜齐芳张佩珮温岭市岩下小学牵手弥补单：走出复习课“教”与“学”的误区郑芳温岭市岩下小学相约绘本，让数学绘声绘色邵雪燕温岭市泽国小学——数学绘本辅助低年级数学教学的策略探究中职环境视觉形象设计的审美教育功能初探卢文静温岭市职业技术学校情境“悦”读快乐成长——小班幼儿情境阅读初探杨洁温岭市中心幼儿园不动笔墨不读书---初中生批注式阅读方法指导的策略研究林渔秋玉环县城关第三初级中学概括：永远的语言训练王小红玉环县城关中心小学——小学中段学生概括能力起步培养之策略徜徉在“练习”的天空孔丽红玉环县楚门镇中心小学“美丽”的错误——高三物理试卷讲评课课堂实施的有效探索张君飞沈炎玉环县楚门中学地理实验激起课堂教学“千层浪”陈启裕玉环县楚门中学望、闻、问、切、防——浅谈小学数学教学中差错的“五诊法”研究周冰冰玉环县坎门钓艚小学对“关注言语形式”的冷思考林华玉环县坎门中心小学从六年级解决问题的错误成因看解决策略杨晓红玉环县龙溪中心小学利用反馈评价提升高中数学教学立意的若干策略陈传熙玉环县玉城中学普通高中综合实践活动课程化建设与实施陈木香陈献忠玉环县玉城中学小学数学课堂时间隐性流失的现状及对策庞红星陆乾林天台县平桥镇中心小学兼顾课内外，思维自拓开——大语文教育观下拓展学生思维的思考与探究陈慧丽天台县平桥中学撑一篙，向青草更青处蔓延——浅谈小学生语文阅读“内实外延”的探索许爱婷天台县始丰街道中心小学教室环境下结构化教学对缓解自闭症儿童情绪问题的探索葛镔镔天台县特殊教育中心谈高中生自我评价在英语教学中的应用王若尘卢潇潇天台县天台中学快乐英语操——小学英语教学成功的“加速器”戴海燕天台县外国语学校让听写从容地行走在课堂上施春玲天台县外国语学校拈一朵花的微笑——浅谈初中作文模仿创作教学实践研究李佳蔚仙居县第二中学综合联动写若在翼——单元综合联动下的作文教学实践与思考王爱芳仙居县安洲小学满眼生机转化钧，天工人巧日争新——优化初中语文试卷讲评模式刍议徐静仙居县安洲中学浅谈小学语文阅读教学中的“随文练笔”王丽燕仙居县第八小学小学数学“四学课堂”的实践研究王燕平马娅芬仙居县第八小学仙居县实验小学鼓，要敲在点子上——关于课堂小练笔有效性思考与实践李秀凤仙居县第七小学任务下订单探究增时效——“任务单”导学在小学科学中的运用策略项玉锋仙居县第五小学例谈几种有效的典型作业曹冰冰张志远仙居县横溪镇中心小学仙居县教育科学研究所节假日创意作业的开发陈天伟仙居县双庙乡中心小学农村学生在家阅读指导实践陈秀英三门县海游上叶小学支架理论在指导英语写作同伴互评中的运用郑士强三门县三门中学小学情趣小古文的校本教材选编和使用策略马传昌三门县实验小学依托红色文化资源培育少先队员民族精神的探索杨小鸟三门县亭旁中心小学农村初中大课间体育活动的模式及管理方式陈卫强三门县亭旁中学中职数学实践性作业的研究与实践陈碧波三门县职业中专小学生生活数学信息收集和处理能力的培养研究麻万兵陈海熔三门县珠岙中心小学新课改下科学教师课堂教学行为的适切性分析徐佳妮台州市白云学校科学课堂活动教学有效性研究颜伟云台州市白云学校三等奖（122篇）论文标题作者单位小学生学习时间自我管理能力培养探索赵冬云椒江区第二实验小学诵读千古诗文熏出一颗诗心徐艳斐椒江洪家街道中心小学依托成长驿站促进教师专业成长的实践与探索孔仙能潘君玲椒江洪家街道中心小学找准起点以学定教——乘法口诀的教学实践与思考蒋开林椒江区前所中心校微景探隅风回荷舞——“过程写作指导法”在高中英语写作教学中的应用探究舒海琦椒江区三梅中学巧设计乐作文谢海萍椒江区实验小学电视暴力镜头对幼儿道德的影响及对策阮慧娜椒江区通巨幼儿园教师团队竞赛：农村教师专业成长的新舞台戴华炳林国泉椒江区下陈中心小学小学生“线描造型”学习的实践研究李琪椒江区下陈中心小学培养学生长时观察的有效策略——以生命世界教学为例阮玲燕椒江区云健小学“守中剪”与“剪中变”——小学剪纸教学的传承与创新蔡佳妮椒江云健小学灵济校区基于校本课程《走遍美国》的英语“视听说”教学与探索王燕萍黄岩区第二高级中学德国SKOL教学法在中职营销专业课教学中的应用初探洪芳芳黄岩第二职业技术学校初高中生物衔接教学问题分析王志坤黄岩区黄岩中学高中化学实验生成性资源的开发与利用李晓明黄岩区黄岩中学农村小学生课外阅读心理障碍及对策凌盈黄岩区江口中心小学四重奏叩开小学英语写作之门沈君珠黄岩区锦江小学云技术在小学科学实验教学中的应用郑典敏黄岩区锦江小学探究·积累·运用·收获王小燕黄岩区宁溪中心小学巧用思维导图提高语文复习的有效性周红芬黄岩区永宁小学小学数学基于目标的教学检测阮林萍黄岩区院桥镇北小学从“教教材”到“用教材”郑萍黄岩区院桥中心小学让写话在低段孩子心中绽放笑脸邵晶晶罗超路桥峰江街道清陶小学城乡结合部初中学困生的形成原因及转化策略蔡子龙许金芬路桥区峰江中学英语巧教学阅读妙衔接陶美玲路桥区横街镇中心小学适时练笔精彩纷呈余利素路桥区金清镇中心小学深入了解农民工子女切实改善其灰暗心理王群路桥区路桥街道春晖小学立足学生本位回归教学本真——浅谈美术教师的示范罗美玲路桥区路桥街道春晖小学让Phonics之花在小学英语教学中绽放孙佳瑜路桥区路桥小学因“本”拓展方有效——例谈阅读教学中拓展的方式及运用吴圣龙卢守强路桥区路桥中学小学第三学段“10分钟随堂练字”的实践与思考任金燕路桥螺洋街道中心小学“音乐游戏”在低年级歌唱教学中的有效运用郑菊红路桥螺洋街道中心小学粗心本无辜，何故负全责罗玲芝路桥区蓬街镇中心小学牵手博客，让作文教学春暖花开徐福君路桥区实验中学初中数学教学中培养学生自主变式能力初探徐晓红罗永宇路桥区实验中学找准“点”，让语文作业更快乐金娇临海师范附属小学充分发挥初中数学“选学栏目”的特殊作用陈勤君临海市白水洋中学小学数学作业批改与反馈的策略王晓芬临海市大田中心校以校本社团为平台促进“初为人师者”专业发展金海岳临海市大洋小学思维导图在高中生反思性学习中的应用杜梦妮临海市第六中学让引领的火花点亮英语课堂颜慧慧临海市杜桥第二小学浅谈小学美术色彩教学的有效策略葛冰冰临海市古城小学提高“临界生”学业成绩的策略研究朱缤芬临海市河头镇中心校小学高段语文教学激活“中间沉默层”的策略研究项小红临海市回浦实验小学打造学型教师团队精神的“雁阵战略”朱恩群临海市汇溪镇中心校提高学习效率的“适时一问”邵海青临海市巾山实验小学农村学校小型家长会形式初探许飞临海市括苍镇中心校拾生活点滴点细微技法顾华英临海市临海中学初中班刊作文教学探索冯宫临海市外国语学校在自然教育中润泽孩子的心灵陶燕君临海汛桥镇中心幼儿园立足先学翻转课堂——小学数学课堂“先学”新探索刘斌临海市涌泉镇中心校组合式化学用语象棋的开发与运用马仁章临海市涌泉中学静听花开的声音——浅议低段朗读教学的指导与训练吴米青临海哲商现代实验小学文学圈活动：儿童阅读的又一通途马妙燕临海市哲商小学中烹教学应借鉴西餐这“三好”朱志威临海市中等职技校大班幼儿开展生命教育的实践与思考陈凌云临海市中心幼儿园农村教师问题心理调适方法的探究林菊领温岭市岙环中学绿色德育：让生命发展与教育同行谢永敏温岭城东街道中心幼儿园微发展：薄弱学校教师的专业成长之路叶婉贞王朝森温岭市大溪第二小学以文本语言涵育小学生言语运用能力的实践探索方君琴温岭市方城小学小学低年级语文口头作业的问题与对策王仙利温岭市横峰小学多形式的科学探索让幼儿积极体验王肖温岭市级机关幼儿园书香家庭营建的实践研究陆丽丽温岭市锦园小学新课程标准下通用技术作业设计的实践与探索周红艳温岭市松门中学数学阅读探航周阿利俞婷婷温岭市太平小学拓宽视角，优化策略——追寻小学数学课堂理答的有效性章巧燕温岭市太平小学活化作业设计，放飞学生思维张彬彬温岭市新河小学——浅谈二年级实践性作业的布置策略初中数学课堂“意外”生成应对的教学策略研究郑灵恩温岭市新河镇中学巧妙“留白”，变“教”为“引”狄文萍温岭市泽国第三小学——浅谈小学科学课堂留白策略初中学生数学易错点的提前干预与行为跟进策略叶晓燕温岭市泽国第四中学基础美术课教学中Photoshop运用的可行性之我见潘海珍温岭市职业技术学校例谈拓展实验对促进课堂教学的几点举措倪丽萍玉环县城关第一初级中学小学语文教学中“言意兼得”有效训练点的实践研究李彩琴玉环县城关中心小学挖掘文本素材绽放练笔魅力沈剑平玉环县城关中心小学初中科学原始问题的探究教学颜礼春玉环县楚门镇第一初级中学反馈在小学数学课堂教学中的运用林继勇玉环县楚门镇中心小学关于“多次努力成绩不理想”的调查分析报告李金福金永标玉环县楚门中学“问题解决”理论下学生历史学科能力拓展训练叶祥彪陈红玉环县楚门中学小学“经典诗文”积累与运用的思考李巧敏朱丽君玉环县龙溪中心小学“乡土”活动的案例化教学研究张灵华玉环县玉环中学用经典立心立人——优质阅读资源的开发研究林菊玉环县玉环中学以多样化载体培养文科生有效阅读历史教材方鑫海玉环县玉环中学遗忘的打击乐——提高学生音乐创编能力的尝试杨卉王敏芳天台县白鹤中心小学浅谈中职营销专业“课堂+实战”的实训模式袁天娥天台第二职业技术学校改变实验教学模式激活学生探究动力谢万浪孙建辉天台县街头中学在亲历情境活动中寻“趣”吴桂芬天台县平桥镇中心小学语文教学，请勿“得意忘言”何丹丹天台县平桥中学——关于“阅读本位”向“表现本位”转变的思考谈逆向设计在小学生品德学习活动的应用褚惠君天台县始丰街道中心小学画习作结构图，练谋篇布局齐笑笑天台县始丰街道中心小学“清朗”语篇教学，促进语言运用天台县始丰街道中心小学——浅谈提高小学生英语语言运用能力的策略许清清例谈小学数学“实践与综合应用”教学设计的有效性俞晓萍许梦云天台县坦头镇中心小学“个别化教学”在启智唱游“生本课堂”中的应用陈荷兰天台县特殊教育中心让导学卡片灵动地加入小学语文中低年级教学陈翠娥天台县外国语学校初中起始阶段英语语音有效教学策略的探索叶果玲张凌敏天台县外国语学校图示——搭建小学英语写作训练的阶梯周昳仙居县安洲小学别让知识绑架——关于提升说明文教学品质的几点思考杨怡萍仙居县安洲中学开展家校互动阅读提高学生素养崔银银仙居县白塔镇中心小学小学语文课堂教学对话双基训练的探索和尝试李敏仙居县第一小学选择在幼儿园晨间体育活动中的运用陈柳燕王刚仙居县经济开发区幼儿园挖掘数学教材的丰富内涵张卫星仙居县皤滩中心小学——以二（下）“口算两位数加减两位数”为例仙居民间儿童嬉趣资源整合小学生写作的策略娄巧燕朱燕仙居县实验小学管之有道，备之有效张晓莹陈苗苗仙居县下各镇中心小学——浅析小学教师备课管理存在的问题及对策深课改背景下研究性学习课程校本管理机制的构建张灵侠华伟臣仙居县仙居中学基于《课程标准》探索初中写作教学俞彩鸽许雪雅仙居县新生中学感受微课教学体验翻转乐趣陈银燕仙居县职业中专——把微课引入中职旅游专业教学的探索主题活动背景下的区域环境创设朱雯仙居县中心幼儿园卵石镶嵌艺术融入小学美术学习领域的探索与实践朱鋆鋆仙居县朱溪镇中心小学巧妙引入开启音乐教学的一扇明窗吴敏三门县城关中学积极运用思维导图，提高初中英语教学实效黄亚萍三门县城关中学谈农村小学科学课堂探究性学习的五环节吴伟敏郑锦竑三门县高枧中心小学精心运用知识整合，打造科学高效课堂叶聪玉三门县海游中学科学课堂中学案“异化”的成因与矫正王式笑三门县花桥中学夯实探究预习尽情反刍学习林海省三门县六敖中心小学观察·感悟·体验·运用——小学生作文素材积累的研究程芳三门县六敖中心小学中职学校机械专业技能测试有效性的探索谢亮珠三门县亭旁职教中心基于“享受学习”构建快乐童声合唱模式林华三门县心湖小学新教材巧妙融入理财教育陈官椅三门县沿赤中心小学不同依恋关系的小班幼儿入园焦虑症的对策研究薛丽萍三门县中心幼儿园以新版PEP教材插图为例谈文本再构徐慧宏台州市白云小学让科研与培训在主题引领中升华赵永攀台州市实验小学。

东莞市中学数学教学研究会2014年优秀论文评比结果的
通报
东莞市中学数学教学研究会2014年优秀论文奖评审工作已经结束，现将评比结果予以通报。

今年中学数学论文评选共收到论文424篇，经评审小组初评，共评选出298篇论文，再由评审小组复评。

全体评委坚持公平公正的原则，采取了个人评分和集体讨论相结合、定量评分与定性评价相结合的方式，历时两个月，共评选出一等奖8篇、二等奖86篇、三等奖116篇，获奖名单见附件。

本年度论文的总体水平较高，注重了前沿理论在教学中的应用、数学本质教学、高效课堂研究及信息技术的应用，体现了中学数学课程改革、高效课堂建设的理念，注重教法研究与学法指导，可操作性较强。

希望获奖的老师继续努力，戒骄戒躁，争取更大成绩，同时也希望学校对获奖的老师给予表扬，以资鼓励。

附件:东莞市中学数学教学研究会2014年优秀论文获奖名单
东莞市中学数学教学研究会
二○一四年十一月四日
附件：东莞市中学数学教学研究会2014年优秀论文获奖名单。

承诺书我们仔细阅读了中国大学生数学建模竞赛的竞赛规则．我们完全明白，在竞赛开始后参赛队员不能以任何方式（包括电话、电子邮件、网上咨询等）与队外的任何人（包括指导教师）研究、讨论与赛题有关的问题．我们知道，抄袭别人的成果是违反竞赛规则的, 如果引用别人的成果或其他公开的资料（包括网上查到的资料)，必须按照规定的参考文献的表述方式在正文引用处和参考文献中明确列出．我们郑重承诺，严格遵守竞赛规则，以保证竞赛的公正、公平性．如有违反竞赛规则的行为，将受到严肃处理．我们参赛选择的题号是（从A/B/C/D中选择一项填写）赛区评阅编号（由赛区组委会评阅前进行编号）：编号专用页赛区评阅编号（由赛区组委会评阅前进行编号）：赛区评阅记录（可供赛区评阅时使用）：评阅人评分备注全国统一编号（由赛区组委会送交全国前编号）：全国评阅编号（由全国组委会评阅前进行编号）：嫦娥三号软着陆轨道设计与控制策略摘要本文针对嫦娥三号软着陆轨道设计与控制策略的实际问题，以理论力学（万有引力、开普勒定律、万能守恒定律等）和卫星力学知识为理论基础，结合微分方程和微元法，借助MATLAB软件解决了题目所要求解的问题。

针对问题（1），在合理的假设基础上，利用物理理论知识、解析几何知识和微元法，分析并求解出近月点和远月点的位置，即139.1097 。

再运用能量守恒定律和相关数据，计算出速度v（近月点的速度）1=1750.78/v（远月点的速度）=1669.77/m s，，最后利用曲线的切线方m s,2程，代入点（近月点与远月点）的坐标求值，计算出方向余弦即为相应的速度方向。

针对问题（2）关键词：模糊评判，聚类分析，流体交通量，排队论，多元非线性回归一、问题重述嫦娥三号于2013年12月2日1时30分成功发射，12月6日抵达月球轨道。

嫦娥三号在着陆准备轨道上的运行质量为2.4t，其安装在下部的主减速发动机能够产生1500N到7500N的可调节推力，其比冲（即单位质量的推进剂产生的推力）为2940m/s，可以满足调整速度的控制要求。