数学建模美赛参考文献
美国大学生数学建模竞赛优秀论文
For office use onlyT1________________ T2________________ T3________________ T4________________Team Control Number7018Problem ChosencFor office use onlyF1________________F2________________F3________________F4________________ SummaryThe article is aimed to research the potential impact of the marine garbage debris on marine ecosystem and human beings，and how we can deal with the substantial problems caused by the aggregation of marine wastes.In task one，we give a definition of the potential long-term and short-term impact of marine plastic garbage. Regard the toxin concentration effect caused by marine garbage as long-term impact and to track and monitor it. We etablish the composite indicator model on density of plastic toxin，and the content of toxin absorbed by plastic fragment in the ocean to express the impact of marine garbage on ecosystem. Take Japan sea as example to examine our model.In ask two, we designe an algorithm, using the density value of marine plastic of each year in discrete measure point given by reference，and we plot plastic density of the whole area in varies locations. Based on the changes in marine plastic density in different years， we determine generally that the center of the plastic vortex is East—West140°W—150°W, South—North30°N—40°N. According to our algorithm, we can monitor a sea area reasonably only by regular observation of part of the specified measuring pointIn task three，we classify the plastic into three types，which is surface layer plastic，deep layer plastic and interlayer between the two. Then we analysis the the degradation mechanism of plastic in each layer. Finally，we get the reason why those plastic fragments come to a similar size.In task four， we classify the source of the marine plastic into three types，the land accounting for 80%，fishing gears accounting for 10%，boating accounting for 10%，and estimate the optimization model according to the duel-target principle of emissions reduction and management. Finally, we arrive at a more reasonable optimization strategy.In task five，we first analyze the mechanism of the formation of the Pacific ocean trash vortex， and thus conclude that the marine garbage swirl will also emerge in south Pacific，south Atlantic and the India ocean. According to the Concentration of diffusion theory, we establish the differential prediction model of the future marine garbage density，and predict the density of the garbage in south Atlantic ocean. Then we get the stable density in eight measuring point .In task six, we get the results by the data of the annual national consumption ofpolypropylene plastic packaging and the data fitting method, and predict the environmental benefit generated by the prohibition of polypropylene take-away food packaging in the next decade. By means of this model and our prediction，each nation will reduce releasing 1.31 million tons of plastic garbage in next decade.Finally, we submit a report to expediction leader，summarize our work and make some feasible suggestions to the policy- makers.Task 1:Definition:●Potential short-term effects of the plastic: the hazardeffects will be shown in the short term.●Potential long-term effects of the plastic: thepotential effects, of which hazards are great, willappear after a long time.The short- and long-term effects of the plastic on the ocean environment:In our definition, the short-term and long-term effects of the plastic on the ocean environment are as follows.Short-term effects:1）The plastic is eaten by marine animals or birds.2） Animals are wrapped by plastics, such as fishing nets, which hurt or even kill them.3）Deaden the way of the passing vessels.Long-term effects:1）Enrichment of toxins through the food chain: the waste plastic in the ocean has no natural degradation in theshort-term, which will first be broken down into tinyfragments through the role of light, waves,micro-organisms, while the molecular structure has notchanged. These "plastic sands", easy to be eaten byplankton, fish and other, are Seemingly very similar tomarine life’s food,causing the enrichment and delivery of toxins.2）Accelerate the greenhouse effect: after a long-term accumulation and pollution of plastics, the waterbecame turbid, which will seriously affect the marineplants (such as phytoplankton and algae) inphotosynthesis. A large number of plankton’s deathswould also lower the ability of the ocean to absorbcarbon dioxide, intensifying the greenhouse effect tosome extent.To monitor the impact of plastic rubbish on the marine ecosystem:According to the relevant literature, we know that plastic resin pellets accumulate toxic chemicals , such as PCBs、DDE , and nonylphenols , and may serve as a transport medium and soure of toxins to marine organisms that ingest them[]2. As it is difficult for the plastic garbage in the ocean to complete degradation in the short term, the plastic resin pellets in the water will increase over time and thus absorb more toxins, resulting in the enrichment of toxins and causing serious impact on the marine ecosystem.Therefore, we track the monitoring of the concentration of PCBs, DDE, and nonylphenols containing in the plastic resin pellets in the sea water, as an indicator to compare the extent of pollution in different regions of the sea, thus reflecting the impact of plastic rubbish on ecosystem.To establish pollution index evaluation model: For purposes of comparison, we unify the concentration indexes of PCBs, DDE, and nonylphenols in a comprehensive index.Preparations:1）Data Standardization2）Determination of the index weightBecause Japan has done researches on the contents of PCBs,DDE, and nonylphenols in the plastic resin pellets, we illustrate the survey conducted in Japanese waters by the University of Tokyo between 1997 and 1998.To standardize the concentration indexes of PCBs, DDE,and nonylphenols. We assume Kasai Sesside Park, KeihinCanal, Kugenuma Beach, Shioda Beach in the survey arethe first, second, third, fourth region; PCBs, DDE, andnonylphenols are the first, second, third indicators.Then to establish the standardized model:j j jij ij V V V V V min max min --= （1,2,3,4;1,2,3i j ==）wherej V max is the maximum of the measurement of j indicator in the four regions.j V min is the minimum of the measurement of j indicatorstandardized value of j indicator in i region.According to the literature [2], Japanese observationaldata is shown in Table 1.Table 1. PCBs, DDE, and, nonylphenols Contents in Marine PolypropyleneTable 1 Using the established standardized model to standardize, we have Table 2.In Table 2，the three indicators of Shioda Beach area are all 0, because the contents of PCBs, DDE, and nonylphenols in Polypropylene Plastic Resin Pellets in this area are the least, while 0 only relatively represents the smallest. Similarly, 1 indicates that in some area the value of a indicator is the largest.To determine the index weight of PCBs, DDE, and nonylphenolsWe use Analytic Hierarchy Process (AHP) to determine the weight of the three indicators in the general pollution indicator. AHP is an effective method which transforms semi-qualitative and semi-quantitative problems into quantitative calculation. It uses ideas of analysis and synthesis in decision-making, ideally suited for multi-index comprehensive evaluation.Hierarchy are shown in figure 1.Fig.1 Hierarchy of index factorsThen we determine the weight of each concentrationindicator in the generall pollution indicator, and the process are described as follows:To analyze the role of each concentration indicator, we haveestablished a matrix P to study the relative proportion.⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡=111323123211312P P P P P P P Where mn P represents the relative importance of theconcentration indicators m B and n B . Usually we use 1，2，…，9 and their reciprocals to represent different importance. The greater the number is, the more important it is. Similarly, the relative importance of m B and n B is mn P /1（3,2,1,=n m ）.Suppose the maximum eigenvalue of P is m ax λ, then theconsistency index is1max --=n nCI λThe average consistency index is RI , then the consistencyratio isRICI CR = For the matrix P of 3≥n , if 1.0<CR the consistency isthougt to be better, of which eigenvector can be used as the weight vector.We get the comparison matrix accoding to the harmful levelsof PCBs, DDE, and nonylphenols and the requirments ofEPA on the maximum concentration of the three toxins inseawater as follows:⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡=165416131431P We get the maximum eigenvalue of P by MATLAB calculation0012.3max =λand the corresponding eigenvector of it is()2393.02975.09243.0，，=W1.0042.012.1047.0<===RI CI CR Therefore,we determine the degree of inconsistency formatrix P within the permissible range. With the eigenvectors of p as weights vector, we get thefinal weight vector by normalization ()1638.02036.06326.0'，，=W . Defining the overall target of pollution for the No i oceanis i Q , among other things the standardized value of threeindicators for the No i ocean is ()321,,i i i i V V V V = and the weightvector is 'W ,Then we form the model for the overall target of marine pollution assessment, （3,2,1=i ）By the model above, we obtained the Value of the totalpollution index for four regions in Japanese ocean in Table 3T B W Q '=In Table3, the value of the total pollution index is the hightest that means the concentration of toxins in Polypropylene Plastic Resin Pellets is the hightest, whereas the value of the total pollution index in Shioda Beach is the lowest(we point up 0 is only a relative value that’s not in the name of free of plastics pollution)Getting through the assessment method above, we can monitor the concentration of PCBs, DDE and nonylphenols in the plastic debris for the sake of reflecting the influence to ocean ecosystem.The highter the the concentration of toxins,the bigger influence of the marine organism which lead to the inrichment of food chain is more and more dramatic.Above all, the variation of toxins’ concentration simultaneously reflects the distribution and time-varying of marine litter. We can predict the future development of marine litter by regularly monitoring the content of these substances, to provide data for the sea expedition of the detection of marine litter and reference for government departments to make the policies for ocean governance.Task 2:In the North Pacific, the clockwise flow formed a never-ending maelstrom which rotates the plastic garbage. Over the years, the subtropical eddy current in North Pacific gathered together the garbage from the coast or the fleet, entrapped them in the whirlpool, and brought them to the center under the action of the centripetal force, forming an area of 3.43 million square kilometers (more than one-third of Europe) .As time goes by, the garbage in the whirlpool has the trend of increasing year by year in terms of breadth, density, and distribution. In order to clearly describe the variability of the increases over time and space, according to “Count Densities of Plastic Debris from Ocean Surface Samples North Pacific Gyre 1999—2008”, we analyze the data, exclude them with a great dispersion, and retain them with concentrated distribution, while the longitude values of the garbage locations in sampled regions of years serve as the x-coordinate value of a three-dimensional coordinates, latitude values as the y-coordinate value, the Plastic Count per cubic Meter of water of the position as the z-coordinate value. Further, we establish an irregular grid in the yx plane according to obtained data, and draw a grid line through all the data points. Using the inverse distance squared method with a factor, which can not only estimate the Plastic Count per cubic Meter of water of any position, but also calculate the trends of the Plastic Counts per cubic Meter of water between two original data points, we can obtain the unknown grid points approximately. When the data of all the irregular grid points are known (or approximately known, or obtained from the original data), we can draw the three-dimensional image with the Matlab software, which can fully reflect the variability of the increases in the garbage density over time and space.Preparations:First, to determine the coordinates of each year’s sampled garbage.The distribution range of garbage is about the East - West 120W-170W, South - North 18N-41N shown in the “Count Densities of Plastic Debris from Ocean Surface Samples North Pacific Gyre 1999--2008”, we divide a square in the picture into 100 grids in Figure (1) as follows:According to the position of the grid where the measuring point’s center is, we can identify the latitude and longitude for each point, which respectively serve as the x- and y- coordinate value of the three-dimensional coordinates.To determine the Plastic Count per cubic Meter of water. As the “Plastic Count per cubic Meter of water” provided by “Count Densities of P lastic Debris from Ocean Surface Samples North Pacific Gyre 1999--2008”are 5 density interval, to identify the exact values of the garbage density of one year’s different measuring points, we assume that the density is a random variable which obeys uniform distribution in each interval.Uniform distribution can be described as below:()⎪⎩⎪⎨⎧-=01a b x f ()others b a x ,∈We use the uniform function in Matlab to generatecontinuous uniformly distributed random numbers in each interval, which approximately serve as the exact values of the garbage density andz-coordinate values of the three-dimensional coordinates of the year’s measuring points.Assumptions(1)The data we get is accurate and reasonable.(2)Plastic Count per cubic Meter of waterIn the oceanarea isa continuous change.(3)Density of the plastic in the gyre is a variable by region.Density of the plastic in the gyre and its surrounding area is interdependent , However, this dependence decreases with increasing distance . For our discussion issue, Each data point influences the point of each unknown around and the point of each unknown around is influenced by a given data point. The nearer a given data point from the unknown point, the larger the role.Establishing the modelFor the method described by the previous,we serve the distributions of garbage density in the “Count Pensities of Plastic Debris from Ocean Surface Samples North Pacific Gyre 1999--2008”as coordinates ()z y,, As Table 1:x,Through analysis and comparison, We excluded a number of data which has very large dispersion and retained the data that is under the more concentrated the distribution which, can be seen on Table 2.In this way, this is conducive for us to get more accurate density distribution map.Then we have a segmentation that is according to the arrangement of the composition of X direction and Y direction from small to large by using x co-ordinate value and y co-ordinate value of known data points n, in order to form a non-equidistant Segmentation which has n nodes. For the Segmentation we get above,we only know the density of the plastic known n nodes, therefore, we must find other density of the plastic garbage of n nodes.We only do the sampling survey of garbage density of the north pacificvortex,so only understand logically each known data point has a certain extent effect on the unknown node and the close-known points of density of the plastic garbage has high-impact than distant known point.In this respect,we use the weighted average format, that means using the adverse which with distance squared to express more important effects in close known points. There're two known points Q1 and Q2 in a line ,that is to say we have already known the plastic litter density in Q1 and Q2, then speculate the plastic litter density's affects between Q1、Q2 and the point G which in the connection of Q1 and Q2. It can be shown by a weighted average algorithm22212221111121GQ GQ GQ Z GQ Z Z Q Q G +*+*=in this formula GQ expresses the distance between the pointG and Q.We know that only use a weighted average close to the unknown point can not reflect the trend of the known points, we assume that any two given point of plastic garbage between the changes in the density of plastic impact the plastic garbage density of the unknown point and reflecting the density of plastic garbage changes in linear trend. So in the weighted average formula what is in order to presume an unknown point of plastic garbage density, we introduce the trend items. And because the greater impact at close range point, and thus the density of plastic wastes trends close points stronger. For the one-dimensional case, the calculation formula G Z in the previous example modify in the following format:2212122212212122211111112121Q Q GQ GQ GQ Q Q GQ Z GQ Z GQ Z Z Q Q Q Q G ++++*+*+*=Among them, 21Q Q known as the separation distance of the known point, 21Q Q Z is the density of plastic garbage which is the plastic waste density of 1Q and 2Q for the linear trend of point G . For the two-dimensional area, point G is not on the line 21Q Q , so we make a vertical from the point G and cross the line connect the point 1Q and 2Q , and get point P , the impact of point P to 1Q and 2Q just like one-dimensional, and the one-dimensional closer of G to P , the distant of G to P become farther, the smaller of the impact, so the weighting factor should also reflect the GP in inversely proportional to a certain way, then we adopt following format:221212222122121222211111112121Q Q GQ GP GQ GQ Q Q GQ GP Z GQ Z GQ Z Z P Q Q Q Q G ++++++*+*+*=Taken together, we speculated following roles:(1) Each known point data are influence the density of plastic garbage of each unknown point in the inversely proportional to the square of the distance;(2) the change of density of plastic garbage between any two known points data, for each unknown point are affected, and the influence to each particular point of their plastic garbage diffuse the straight line along the two known particular point; (3) the change of the density of plastic garbage between any two known data points impact a specific unknown points of the density of plastic litter depends on the three distances: a. the vertical distance to a straight line which is a specific point link to a known point;b. the distance between the latest known point to a specific unknown point;c. the separation distance between two known data points.If we mark 1Q ，2Q ，…，N Q as the location of known data points,G as an unknown node, ijG P is the intersection of the connection of i Q ，j Q and the vertical line from G to i Q ，j Q()G Q Q Z j i ,,is the density trend of i Q ，j Q in the of plasticgarbage points and prescribe ()G Q Q Z j i ,,is the testing point i Q ’ s density of plastic garbage ,so there are calculation formula:()()∑∑∑∑==-==++++*=Ni N ij ji i ijGji i ijG N i Nj j i G Q Q GQ GPQ Q GQ GP G Q Q Z Z 11222222111,,Here we plug each year’s observational data in schedule 1 into our model, and draw the three-dimensional images of the spatial distribution of the marine garbage ’s density with Matlab in Figure (2) as follows:199920002002200520062007－2008(1)It’s observed and analyzed that, from 1999 to 2008, the density of plastic garbage is increasing year by year and significantly in the region of East – West 140W-150W, south - north 30N-40N. Therefore, we can make sure that this region is probably the center of the marine litter whirlpool. Gathering process should be such that the dispersed garbage floating in the ocean move with the ocean currents and gradually close to the whirlpool region. At the beginning, the area close to the vortex will have obviously increasable about plastic litter density, because of this centripetal they keeping move to the center of the vortex ,then with the time accumulates ,the garbage density in the center of the vortex become much bigger and bigger , at last it becomes the Pacific rubbish island we have seen today.It can be seen that through our algorithm, as long as the reference to be able to detect the density in an area which has a number of discrete measuring points,Through tracking these density changes ,we Will be able to value out all the waters of the density measurement through our models to determine,This will reduce the workload of the marine expedition team monitoring marine pollution significantly, and also saving costs .Task 3:The degradation mechanism of marine plasticsWe know that light, mechanical force, heat, oxygen, water, microbes, chemicals, etc. can result in the degradation of plastics . In mechanism ,Factors result in the degradation can be summarized as optical ，biological，and chemical。
数学建模参考文献
一、竞赛参考书l、中国大学生数学建模竞赛，李大潜主编，高等教育出版社(1998)．2、大学生数学建模竞赛辅导教材，(一)(二)(三)，叶其孝主编，湖南教育出版社(1993，1997，1998)．3、数学建模教育与国际数学建模竞赛《工科数学》专辑，叶其孝主编，《工科数学》杂志社，1994)．二、国内教材、丛书：1、数学模型，姜启源编，高等教育出版社(1987年第一版，1993年第二版，2003年第三版；第一版在1992年国家教委举办的第二届全国优秀教材评选中获"全国优秀教材奖")．2、数学模型与计算机模拟，江裕钊、辛培情编，电子科技大学出版社，(1989)．3、数学模型选谈(走向数学从书)，华罗庚，王元著，王克译，湖南教育出版社；(1991)．4、数学建模--方法与范例，寿纪麟等编，西安交通大学出版社(1993)．5、数学模型，濮定国、田蔚文主编，东南大学出版社(1994)．6..数学模型，朱思铭、李尚廉编，中山大学出版社，(1995)7、数学模型，陈义华编著，重庆大学出版社，(1995)8、数学模型建模分析，蔡常丰编著，科学出版社，(1995)．9、数学建模竞赛教程，李尚志主编，江苏教育出版社，(1996)．10、数学建模入门，徐全智、杨晋浩编，成都电子科大出版社，(1996).11、数学建模，沈继红、施久玉、高振滨、张晓威编，哈尔滨工程大学出版社，(1996).12、数学模型基础，王树禾编著，中国科学技术大学出版社，(1996).13、数学模型方法，齐欢编著，华中理工大学出版社，(1996)．14、数学建模与实验，南京地区工科院校数学建模与工业数学讨论班编，河海大学出版社，(1996)．15、数学模型与数学建模，刘来福、曾文艺编，北京师范大学出版杜(1997)．16. 数学建模，袁震东、洪渊、林武忠、蒋鲁敏编，华东师范大学出版社.17、数学模型，谭永基，俞文吡编，复旦大学出版社，(1997)．18、数学模型实用教程，费培之、程中瑗层主编，四川大学出版社，(1998)．19、数学建模优秀案例选编(工科数学基地建设丛书)，汪国强主编，华南理工大学出版社，(1998)．20、经济数学模型(第二版)(工科数学基地建设丛书)，洪毅、贺德化、昌志华编著，华南理工大学出版社，(1999)．21、数学模型讲义，雷功炎编，北京大学出版社(1999)．22、数学建模精品案例，朱道元编著，东南大学出版社，(1999)，23、问题解决的数学模型方法，刘来福，曾文艺编著、北京师范大学出版社，(1999)．24、数学建模的理论与实践，吴翔，吴孟达，成礼智编著，国防科技大学出版社，(1999)．25、数学建模案例分析，白其岭主编，海洋出版社，(2000年，北京)．26、数学实验(高等院校选用教材系列)，谢云荪、张志让主编，科学出版社，(2000)．27、数学实验，傅鹏、龚肋、刘琼荪，何中市编，科学出版社，(2000)．28、数学建模与数学实验，赵静、但琦编，高等教育出版社，(2000).三、国外参考书(中译本)：1、数学模型引论，E．A。
美国大学生数学建模竞赛题解析与研究
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《美国大学生数学建模竞赛题解析与研究(第5辑)》内容新颖、实用性强，可作为指导学生参加美国大学生 数学建模竞赛的主讲教材，也可作为本科生、研究生学习和准备全国大学生、研究生数学建模竞赛的参考书，同 时也可供研究相关问题的科研人员orward by Sol Garfunkel ICM竞赛主席序 Forward by Chris Arney 丛书简介 前言 I Mathematical Modeling for MCM/ICM 2013 Chapter 1 Introduction Chapter 2 General Methodology 2.1 Before the Contest 2.2 Reading the Problem
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图书目录
第1章棒球“最佳击球点”问题 1.1问题分析 1.1.1棒球？球棒 1.1.2分析问题与提出假设 1.2定位球棒的最佳击球点 1.2.1一个初步的想法：定位最佳击球点 1.2.2布罗迪功效模型 1.2.3考虑击球手的舒适度 1.3不同材质球棒的特性分析 1.3.1“软木化”的球棒与普通木质球棒的对比 1.3.2铝制球棒与普通木质球棒的对比
美国大学生数学建模竞赛题解析与研 究
2014年高等教育出版社出版的图书
01 内容简介
03 推荐
目录
02 图书目录 04 目录
《美国大学生数学建模竞赛题解析与研究》是2014年高等教育出版社出版的图书，作者是赵仲孟、王嘉寅。
内容简介
《美国大学生数学建模竞赛题解析与研究(第3辑)》内容简介：本系列丛书是以美国大学生数学建模竞赛 （MCM/ICM）题为主要研究对象，结合竞赛特等奖的论文，对相关的问题进行深入细致的解析与研究。《美国大 学生数学建模竞赛题解析与研究(第3辑)》的主要内容包括：棒球“最佳击球点”问题、重新平衡受人类影响的 生态系统问题、泛太平洋垃圾带问题、犯罪情报分析的建模问题、交通环岛的优化设计问题和能源与移动问题。 《美国大学生数学建模竞赛题解析与研究(第3辑)》可作为指导大学生学习和准备美国大学生数学建模竞赛的主 讲教材，也可作为大学生、研究生学习和准备全国大学生、研究生数学建模竞赛的参考书，同时可供研究相关问 题的人员参考使用。
2023美赛d题参考文献
2023美赛d题参考文献参考文献：1. Liu, Y., Wang, H., & Xia, F. (2019). Time Series Clustering and Classification Based on Shapelet Transformation and Deep Learning. IEEE Transactions on Knowledge and Data Engineering, 32(12), 2260–2273.2. Zhang, X., Xing, X., Liu, S., & Han, J. (2018). Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks. Proceedings of the ACM on Conference on Information and Knowledge Management, 2(2), 1–10.3. Barua, S., Islam, R., Yao, X., Murase, K., & Chiong, R. (2020). Time Series Forecasting Using a Novel Residual Generative Adversarial Network. Soft Computing, 24(12), 9441–9458.4. Wang, Y., Yan, Y., & Oates, T. (2019). Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline. Data Mining and Knowledge Discovery, 33(6), 1–45.5. Wei, J., Wang, G., & Ye, X. (2017). Supervised Time Series Feature Extraction for Classification. IEEE Transactions on Systems, Man, and Cybernetics: Systems,47(10), 2720–2732.6. Bagnall, A., Lines, J., Hills, J., & Bostrom, A. (2017). Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles. ACM Transactions on Intelligent Systems and Technology, 8(5), 1–41.7. Lin, Y., Sun, S., Zheng, J., & Lu, J. (2019). Large-Scale Time Series Classification Using Randomized Matrix Kernels and Deep Neural Network. IEEE Transactions on Big Data, 5(3), 355–369.8. Salama, M., El-Khozondar, H., & Karray, F. (2021). Deep Long Short-Term Memory Network for Time Series Classification. Pattern Analysis and Applications,24(3), 1577–1593.9. Wang, Z., Yan, Y., & Oates, T. (2017). Time Series Classification From Scratch with Deep Neural Networks: A Strong Baseline. arXiv Preprint arXiv:1611.06455.10. Zha, S., Xing, X., & Pan, S. (2020). Time Series Classification with Data Augmentation from Deep Synthetic Data. Proceedings of the ACM on Symposium on Applied Computing, 2(2), 1–15.2023美赛D题参考文献随着信息技术的快速发展和数据的爆发式增长，对时间序列数据进行分类的需求日益增加。
美赛文献引用格式
美赛文献引用格式
数模美赛参考文献引用格式如下：
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均明确列出。
正文引用处用方括号标示参考文献的编号，如[1][3] 等；引用书籍还必须指出页码。
参考文献按正文中的引用次序列出：
书籍的表述方式为：
[编号]作者，书名，出版地：出版社，出版年。
期刊杂志论文的表述方式为：
[编号]作者，论文名，杂志名，卷期号：起止页码，出版年。
网上资源的表述方式为：
[编号]作者，资源标题，网址，访问时间（年月日）。
标注参考文献的原因
1、体现研究价值与立项依据是否充分。
通过参考文献的层次与水平，可以反映出作者文章或申请书研究的广度与深度、是否具有研究价值。
2、体现研究的前瞻性与作者的研究态度。
参考文献绝大多数时候都是文章立意的来源，引用近几年内有具有代表性的参考文献（3-5年最好），保持选题的新颖性与前瞻性，可以体现自己的研究能力，也让文章更具有创新性与研究意义。
3、尊重他人研究成果，体现严谨、科学的学术态度。
一篇好的文章不是“空穴来风”，而是“站在巨人的肩膀上看世界”，文章的论点一定是成立的、有理论支持、有权威性的。
MCM美赛论文集
高教社杯全国大学生数学建模竞赛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，我们将受到严肃处理。
我们参赛选择的题号是（从A/B/C/D中选择一项填写）：A我们的参赛报名号为（如果赛区设置报名号的话）：99999所属学校（请填写完整的全名）：西安交通大学参赛队员(打印并签名）：1.一作者2.二作者3.三作者指导教师或指导教师组负责人(打印并签名)：导师日期：2011年8月1日赛区评阅编号（由赛区组委会评阅前进行编号）：2011高教社杯全国大学生数学建模竞赛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）:全国大学生数学建模竞赛L A T E X2ε模板摘要这是数学建模论文模板mcmthesis的示例文件。
特别地，这篇文档是“全国大学生数学建模竞赛（CUMCM）”模板的示例文件。
这个模板使用于参加高教社杯全国大学生数学竞赛的同学准备他们的建模论文，帮助他们更多的关注于论文内容而非论文的排版。
这个模板的设计是根据2010年修订的《全国大学生数学建模竞赛论文格式规范》[1]制作，完全符合该论文格式规范，但是该模板未得到官方认可，请使用者自己斟酌使用。
这个示例文档逐条展示其对[1]的实现效果，并对所有自定义命令进行说明。
这个示例文件还包含了一些对公示、插图、表格、交叉引用、参考文献、代码等的测试部分，以展示其效果，并作简要的使用说明。
美国大学生数学建模竞赛美赛--论文
Each team member must sign the statement below: (Failure to obtain signatures from each team member will result in disqualification of the entire team.)
2015 Mathematical Contest in Modeling (MCM/ICM) Control Sheet Please review this page before submitting your solution to ensure that all of the information is correct Do not make changes by hand to the information on this control sheet. If you need to change any of the information on this sheet, login via the Advisor Login link on the MCM web site, make the changes online, and print a new sheet. You may NOT photocopy this control sheet to give to a new team, nor may you assign any team a control number. Each team must have its own control number, obtained by registering via the MCM web site. Advisor Jinpeng Yu Name: Department: Control Engineering Institution: Qingdao University Address: 308 Ningxia Road,Shinan District,Qingdao,Shandong,China Qingdao, Shandong 266000 Phone: 18653250086 Fax: 053285953064 Email: zhanghaoran06@ Home Phone: 053285953064 The names of the team members will appear on your team's certificate exactly as they appear on this page, including all capitalization and punctuation, if any. Gender data is optional and will be used for statistical purposes only; it will not appear on the certificate. Team Member Haoran Zhang Yu Ma Guiying Dong Gender M M F Your team's control number is: 40906 (Place this control number on all pages of your solution paper and on any support material.) Problem Chosen: B
数学建模 美赛特等奖论文(中文版)分析溃坝：针对南卡罗来纳州大坝坍塌建立模型
分析溃坝：针对南卡罗来纳州大坝坍塌建立模型 摘要萨鲁达大坝建立在卡罗莱纳州的墨累湖与萨鲁达河之间，如果发生地震大坝就会坍塌。
本文通过建立模型来分析以下四种大坝决口时水的流量以及洪水泛滥时水的流量：● 大坝的绝大部分被瞬间侵蚀看成是大坝瞬间彻底坍塌；● 大坝的绝大部分被缓慢侵蚀看成是大坝延期彻底坍塌；● 管涌就是先形成一个小孔，最终形成一个裂口；● 溢出就是大坝被侵蚀后，形成一个梯形的裂口。
本文建立了两个模型来描述下游洪水的泛滥情况。
两个模型都采用离散网格的方法，将一个地区看成是一个网格，每个网格都包含洪水的深度和体积。
复力模型运用了网格的速度、重力以及邻近网格的压力来模拟水流。
下坡模型假定水流速度与邻近网格间水位高度的成正比例。
下坡模型是高效率的、直观的、灵活的，可以适用于已知海拔的任何地区。
它的两个参数稳定并限制了水流，但该模型的预测很少依赖于它们的静态值。
对于萨鲁达溃坝，洪水总面积为25.106km ；它还没有到达国会大厦。
罗威克里克的洪水向上游延伸了km 4.4，覆盖面积达24.26.1km -变量及假设表1说明了用来描述和模拟模型的变量，表2列出了模拟程序中的参数。
表 1模型中的变量.变量 定义溃坝时的水流量速率1TF Q 瞬间彻底坍塌2TF Q 延期彻底坍塌PIPE Q 管涌OT Q 溢出peak Q 最大流速溃坝时水流出到停止所用时间1TF t 瞬间彻底坍塌2TF t 延期彻底坍塌PIPE t 管涌OT t 溢出V ∆ 溃坝后从墨累湖里流出的水的总体积Lm Vol 墨累湖的原来体积LM Area 墨累湖的原来面积breach d 从裂口到坝顶距离breach t 从裂口开始到溃坝形成的时间 近似圆锥的墨累湖的侧面一般假设● 正常水位是在溃坝前的湖水位置。
● 河道中的水流不随季节变化而变动。
● 墨累湖里的水的容积可以看作为一个正圆锥（图1 ）。
表2 模拟程序中的参数 参数 所取值 意义BREACH_TYPE 变量 瞬间彻底坍塌，延期彻底坍，管涌，溢出模型中的一种 T ∆ 0.10 时间不长的长度(s)MIN_DEPTH 0001.0 网格空时的水的深度(m) FINAT T 100000 大坝彻底决口所用时间 b T 3600 溃坝达最大值的时间(s) peak Q 25000 溃坝的最大流速(m 3/s) breach d 30 蓄水池的最初深度(m) LM Volume 910714.2⨯ 墨累湖的总体积(m 3) LM Area 610202⨯ 墨累湖的总面积(m 2)k 504.0 扩散因素 (控制两网格间交换的水的数量) MAX_LOSS_FRAC 25.0 单位网格中水的最大流失量图 1. 水库近似一个正圆锥.大坝假设● 萨鲁达大坝在以下四种方式之一坍塌：-瞬间彻底坍塌，-延期彻底坍塌，-管涌，-溢出。
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数学建模美赛参考文献Since 1982, the official publication of the teaching of mathematical modeling contest, translations and guidance materials, and related with the mathematical modeling of mathematics experiment teaching material ( only according to statistics all told ):E. A. Bender, an introduction to mathematical model, Zhu Yaochen, Xu Weixuan translation, popular science press, 1982Kondo Jiro, Miya Eiaki, et al, mathematical model, mechanical industry press, 1985C. L. Daimler, E. S. Ai Wei, mathematical modeling principle, Ocean Press, 1985Jiang Qiyuan, mathematical model, higher education press, 1987Ren Shanqiang, mathematical model, Chongqing University press, 1987M. Braun, C. S. Coleman, D. A. Drew, the differential equation model, Zhu Yumin, Zhou yu-hun translation, National University of Defense Technology press, ( the book for the W. F.Lucas editor of the Modules in Applied Mathematics a book first volume ), 1988Chen Anqi, mathematical model of scientific and technical engineering, China Railway Publishing House, 1988Jiang Yuzhao, Xin Peiqing, mathematical model and computer simulation, University of Electronic Science and Technology Press, 1989Yang Qifan, Bian Fu Ping, mathematical model, Zhejiang University press, 1990Dong Jiali, Cao Xudong, Shim Hito, mathematical model, Beijing University of Technology press, 1990Tang Huanwen, Feng Enmin, sun Yuxian, Sun Lihua, an introduction to the mathematics model, Dalian University of Technology press, 1990Jiang Qiyuan, the mathematical model (the Second Edition ), higher education press, 1991H. P. 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Burghes, four case studies of industry in the UK from mathematical modeling, leaves its filial, translated by Wu Qingbao, World Book Inc, 1997Ye Qixiao, Undergraduate Mathematical Contest in modeling tutorial ( two ), Hunan Education Press, 1997Liu Laifu, Zeng literature, mathematical model and mathematical modeling, Beijing Normal University press, 1997, S.J.Brams, W.F.Lucas, P.D.Straffin, Jr., political and related models, National University of Defense Technology press, ( the book for the W. F. Lucas editor of the Modules in Applied Mathematics a book in second volumes ) 1997W.F.Lucas, F.S.Roberts, R.M.Thrall, discrete model and system, National University of Defense Technology press, ( W. book F. Lucas Modules in Applied Mathematics, editor of a book in third volumes ), 1997H.Marcus-Roberts, M. Thompson, life sciences, National University of Defense Technology press, ( W. book F. 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