Articles by "Maths"

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Luciana C. de Oliveira, Marta Civil ... 266 pages - ISBN-13: 978-3030483548 ... Publisher: Palgrave Macmillan; (October, 2020) - Language: English.

This edited book is about preparing pre-service and in-service teachers to teach secondary-level mathematics to English Language Learners (ELLs) in twenty-first century classrooms. Chapter topics are grounded in both research and practice, addressing a range of timely topics including the current state of ELL education in the secondary mathematics classroom, approaches to leveraging the talents and strengths of bilingual students in heterogeneous classrooms, best practices in teaching mathematics to multilingual students, and ways to infuse the secondary mathematics teacher preparation curriculum with ELL pedagogy. 

This book will appeal to all teachers of ELLs, teacher educators and researchers of language acquisition more broadly. This volume is part of a set of four edited books focused on teaching the key content areas to English language learners. The other books in the set focus on teaching History and Social Studies, English Language Arts, and Science to ELLs.

Tien-Chang Lee ... 400 pages - ISBN-10: 1566703751 - ISBN-13: 978-1566703758 ... Publisher : CRC Press; (December, 1998) - Language: English.

As introduced in Dr. Lee's 10-week class, Applied Mathematics in Hydrogeology is written for professionals and graduate students who have a keen interest in the application of mathematics in hydrogeology. Its first seven chapters cover analytical solutions for problems commonly encountered in the study of quantitative hydrogeology, while the final three chapters focus on solving linear simultaneous equations, finite element analysis, and inversion for parameter determination. Dr. Lee provides various equation-solving methods that are of interest to hydrogeologists, geophysicists, soil scientists, and civil engineers, as well as applied physicists and mathematicians. In the classroom, this same information will help students realize how familiar equations in hydrogeology are derived-an important step toward development of a student's own mathematical models. Unlike other applied mathematics books that are structured according to systematic methodology, Applied Mathematics in Hydrogeology emphasizes equation-solving methods according to topics. Hydrogeological problems and governing differential equations are introduced, including hydraulic responses to pumping in confined and unconfined aquifers, as well as transport of heat and solute in flowing groundwater.

Lindo What'sBest! v17.0.0.0 x64 [Size: 57.7 MB] ... What'sBest! is an add-in to Excel that allows you to build large scale optimization models in a free form layout within a spreadsheet. What'sBest! combines the proven power of Linear, Nonlinear (convex and nonconvex/Global), Quadratic, Quadratically Constrained, Second Order Cone, Semi-Definite, Stochastic, and Integer optimization with Microsoft Excel - the most popular and flexible business modeling environment in use today. The recently released What'sBest! 15.0 includes a number of significant enhancements and new features. Enhancements to the Simplex solvers boost performance on linear models. Large models solve an average of 20% faster using primal simplex and 15% faster for dual simplex. New symmetry detection capabilities dramatically reduce the time required to prove optimality on certain classes of models with integer variables. Performance has been improved on Markowitz portfolio problems with minimum buy quantities, and/or limit on number of instruments at nonzero level. Other enhancements provide faster solutions on certain task assignment-like models. Stability and robustness of the Global solver has been improved through several enhancements to quadratic recognition and range reduction. Improved exploitation of convexity of certain ratio constraints, e.g., as found in heat exchanger network design problems. Several new functions and constraint types are recognized, e.g., the =WBALLDIFF() All Different constraint, for general integer variables. The =WBALLDIFF() function allows one to specify a set of integer variables, such that each variable in the set must have a unique value, different from all other variables in the set.

Alan Graham ... 376 pages - Publisher: Teach Yourself; (April, 2017) ... Language: English - AmazonSIN: B01KTS4XBO.

Basic Mathematics teaches you all the maths you need for everyday situations. If you are terrified by maths, this is the book for you. Do you shy away from using numbers? Basic Mathematics can help. An easy-to-follow guide, it will ensure you gain the confidence you need to tackle maths and overcome your fears. It offers simple explanations of all the key areas, including decimals, percentages, measurements and graphs, and applies them to everyday situations, games and puzzles to help you understand mathematics quickly and enjoyably. Everything you need is here in this one book. Each chapter includes clear explanations, worked examples and test questions. At the end of the book there are challenges and games to give you new and interesting ways to practise your new skills.

Kandethody M. Ramachandran, Chris P. Tsokos ... 704 pages - Publisher: Academic Press; 3rd edition (June, 2020) ... Language: English - ISBN-10: 0128178159 - ISBN-13: 978-0128178157.

Mathematical Statistics with Applications in R, Third Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods, such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem-solving in a logical manner. Step-by-step procedure to solve real problems make the topics very accessible.

Lisa Oberbroeckling ... 296 pages - Publisher: Academic Press; (June, 2020) ... Language: English - ISBN-10: 0128177993 - ISBN-13: 978-0128177990.

Providing an alternative to engineering-focused resources in the area, Programming Mathematics Using MATLAB® introduces the basics of programming and of using MATLAB® by highlighting many mathematical examples. Emphasizing mathematical concepts through the visualization of programming throughout the book, this useful resource utilizes examples that may be familiar to math students (such as numerical integration) and others that may be new (such as fractals). Additionally, the text uniquely offers a variety of MATLAB® projects, all of which have been class-tested thoroughly, and which enable students to put MATLAB® programming into practice while expanding their comprehension of concepts such as Taylor polynomials and the Gram–Schmidt process. Programming Mathematics Using MATLAB® is appropriate for readers familiar with sophomore-level mathematics (vectors, matrices, multivariable calculus), and is useful for math courses focused on MATLAB® specifically and those focused on mathematical concepts which seek to utilize MATLAB® in the classroom.

Pasquale Giovine, Paolo Maria Mariano, Giuseppe Mortara ... 430 pages - Publisher: Birkhäuser; (March, 2019) ... Language: English - AmazonSIN: B07QQV28CH.

This special issue collects selected contributions (excluding general lectures) of a Symposium on "Micro to MACRO Mathematical Modelling in Soil Mechanics", which took place at the University of Reggio Calabria, Italy, from May 29th to June 1st, 2018. The Symposium provided an opportunity to enhance the scientific debate on the construction of mathematical models for the description of the physical behaviour of soils, as well as on the suggestions provided by the micro-mechanical observation of the matter. The focus was on the comparison between the appropriateness of models and the need of mathematics to obtain rigorous results, which involves know-how from applied mathematical physics, geotechnical engineering and mechanics of solids.

Oliver M. O'Reilly ... 540 pages - Publisher: Cambridge Univ. Press; 2nd edition (March, 2020) ... Language: English - ISBN-10: 1108494218 - ISBN-13: 978-1108494212.

Suitable for both senior-level and first-year graduate courses, this fully revised edition provides a unique and systematic treatment of engineering dynamics that covers Newton-Euler and Lagrangian approaches. New to this edition are: two completely revised chapters on the constraints on, and potential energies for, rigid bodies, and the dynamics of systems of particles and rigid bodies; clearer discussion on coordinate singularities and their relation to mass matrices and configuration manifolds; additional discussion of contravariant basis vectors and dual Euler basis vectors, as well as related works in robotics; improved coverage of navigation equations; inclusion of a 350-page solutions manual for instructors, available online; a fully updated reference list. Numerous structured examples, discussion of various applications, and exercises covering a wide range of topics are included throughout, and source code for exercises, and simulations of systems are available online.

Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong ... 398 pages - Publisher: Cambridge Univ. Press; (April, 2020) ... Language: English - AmazonSIN: B083M7DBP6.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Richard J. Larsen, Morris L. Marx ... 752 pages - Publisher: Pearson; 6th edition (January, 2017) ... Language: English - ASIN: B076VG8WHV by Amazon.

Introduction to Mathematical Statistics and Its Applications , 6th Edition is a high-level calculus student’s first exposure to mathematical statistics. This book provides students who have already taken three or more semesters of calculus with the background to apply statistical principles. Meaty enough to guide a two-semester course, the book touches on both statistics and experimental design, which teaches students various ways to analyze data. It gives computational-minded students a necessary and realistic exposure to identifying data models.

Using high-quality, real-world case studies and examples, this introduction to mathematical statistics shows how to use statistical methods and when to use them. This book can be used as a brief introduction to design of experiments. This successful, calculus-based book of probability and statistics, was one of the first to make real-world applications an integral part of motivating discussion. The number of problem sets has increased in all sections. Some sections include almost 50% new problems, while the most popular case studies remain. For anyone needing to develop proficiency with Mathematical Statistics.

Norman Matloff ... 444 pages - Publisher: Routledge; (June, 2019) ... Language: English - ISBN-10: 1138393290 - ISBN-13: 978-1138393295.

Probability and Statistics for Data Science: Math + R + Data covers "math stat"―distributions, expected value, estimation etc.―but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming.

Mathworks Matlab R2019b v9.7.0 build 1261785 + All UpDates [Size: 21.5 GB] ... MATLAB is a highlevel language and interactive environment that is used by millions of engineers and scientists around the world. It allows you to explore and visualize ideas and collaborate in various disciplines, including signal and image processing, communications, management systems and financial engineering. Whether you’re analyzing data, developing algorithms, or creating models, MATLAB is designed for the way you think and the work you do. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques. Features of Mathworks Matlab R2019: Organize live scripts using additional subheading styles + Navigate within a live script using internal hyperlinks + Create new and open existing live scripts faster + Use string arrays in MATLAB, Simulink, and Stateflow + Improved performance of set operations with enumerations + Create a polyshape object from a 2-D triangulation + Generate random numbers using Threefry and Philox algorithms + Customize node and edge labels with font properties + Explore data with panning, zooming, data tips, and 3-D rotation enabled by default + Visualize grouped data as a scatter plot with marginal histograms + Generate improved code when importing from spreadsheets + Write tall arrays in a variety of formats to local or remote locations + Transform table or timetable data by groups + Add and configure date selection components on the App Designer canvas + Pass multidimensional numeric or logical arrays between MATLAB and Python.

George Simmons, Steven Krantz ... 544 pages - Publisher: McGraw-Hill; (January, 2006) ... Language: English - ISBN-10: 0072863153 - ISBN-13: 978-0072863154.

This traditional text is intended for mainstream one- or two-semester differential equations courses taken by undergraduates majoring in engineering, mathematics, and the sciences. Written by two of the world’s leading authorities on differential equations, Simmons/Krantz provides a cogent and accessible introduction to ordinary differential equations written in classical style. Its rich variety of modern applications in engineering, physics, and the applied sciences illuminate the concepts and techniques that students will use through practice to solve real-life problems in their careers.

Michael Sullivan, Mike Sullivan III ... 1200 pages - Publisher: Pearson; 7th edition (March, 2016) ... Language: English - ISBN-10: 0134119282 - ISBN-13: 978-0134119281.

The Sullivan’s time-tested approach focuses students on the fundamental skills they need for the course: preparing for class, practicing with homework, and reviewing the concepts. The Enhanced with Graphing Utilities Series has evolved to meet today’s course needs by integrating the usage of graphing calculators, active-learning, and technology in new ways to help students be successful in their course, as well as in their future endeavors. In the Seventh Edition, there are several new features that appear in both the text and MyMathLab. Retain Your Knowledge problems offer the type of “final exam material” that students can use to maintain their skills throughout each chapter.

Michael Sullivan ... 1168 pages - Publisher: Pearson; 10th edition (February, 2015) ... Language: English - ISBN-10: 0321979079 - ISBN-13: 978-0321979070.

Mike Sullivan’s time-tested approach focuses students on the fundamental skills they need for the course: preparing for class, practicing with homework, and reviewing the concepts. The Tenth Edition has evolved to meet today’s course needs.

John Bird ... 896 pages - Publisher: Routledge; 7th edition (April, 2014) ... Language: English - ISBN-10: 0415662826 - ISBN-13: 978-0415662826.

A practical introduction to the core mathematics principles required at higher engineering level: John Bird’s approach to mathematics, based on numerous worked examples and interactive problems, is ideal for vocational students that require an advanced textbook. Theory is kept to a minimum, with the emphasis firmly placed on problem-solving skills, making this a thoroughly practical introduction to the advanced mathematics engineering that students need to master. The extensive and thorough topic coverage makes this an ideal text for upper level vocational courses. Now in its seventh edition, Engineering Mathematics has helped thousands of students to succeed in their exams. The new edition includes a section at the start of each chapter to explain why the content is important and how it relates to real life. It is also supported by a fully updated companion website with resources for both students and lecturers. It has full solutions to all 1900 further questions contained in the 269 practice exercises.

Olaf Wolkenhauer  ... 296 pages - Publisher: Wiley-Interscience; (July, 2001) ... Language: English - ISBN-10: 0471416568 - ISBN-13: 978-0471416562.

A survey of the philosophical implications and practical applications of fuzzy systems: Fuzzy mathematical concepts such as fuzzy sets, fuzzy logic, and similarity relations represent one of the most exciting currents in modern engineering and have great potential in applications ranging from control theory to bioinformatics. Data Engineering guides the reader through a number of concepts interconnected by fuzzy mathematics and discusses these concepts from a systems engineering perspective to showcase the continuing vitality, attractiveness, and applicability of fuzzy mathematics.

The author discusses the fundamental aspects of data analysis, systems modeling, and uncertainty calculi. He avoids a narrow discussion of specialized methodologies and takes a holistic view of the nature and application of fuzzy systems, considering principles, paradigms, and methodologies along the way. This broad coverage includes: * Fundamentals of modeling, identification, and clustering * System analysis * Uncertainty techniques * Random-set modeling and identification * Fuzzy inference engines * Fuzzy classification, control, and mathematics. In the important emerging field of bioinformatics, the book sets out how to encode a natural system in mathematical models, describes methods to identify interrelationships and interactions from data, and thereby helps the practitioner to decide which variables to measure and why. Data Engineering serves as an up-to-date and informative survey of the theoretical and practical tools for analyzing complex systems. It offers a unique treatment of complex issues that is accessible to students and researchers from a variety of backgrounds.

Peter Bruce, Andrew Bruce ... 320 pages - Publisher: O'Reilly; (May, 2017) ... Language: English - ISBN-10: 1491952962 - ISBN-13: 978-1491952962. 

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science - How random sampling can reduce bias and yield a higher quality dataset, even with big data - How the principles of experimental design yield definitive answers to questions - How to use regression to estimate outcomes and detect anomalies - Key classification techniques for predicting which categories a record belongs to - Statistical machine learning methods that “learn” from data - Unsupervised learning methods for extracting meaning from unlabeled data.

Daniel Dubin ... 633 pages - Publisher: Wiley-Interscience; (May, 2003) ... Language: English - ISBN-10: 0471266108 - ISBN-13: 978-0471266105.

Utilizing state-of-the-art software to facilitate solutions to real-world problems: Practitioners in the field of physical science are continually faced with a variety of complex, real-world problems, the solution of which requires a working knowledge of both analytical and numerical techniques. An Introduction to Mathematical and Computational Physics Using Mathematica® is designed to help prospective scientists develop a practical, working knowledge of these techniques using the latest, most efficient electronic methodologies. Written from the perspective of a physicist rather than a mathematician, the text focuses on modern practical applications in the physical and engineering sciences, attacking these problems with a range of numerical and analytical methods, both elementary and advanced. Incorporating the widely used and highly praised Mathematica® software package, the author offers solution techniques for the partial differential equations of mathematical physics such as Poisson’s equation, the wave equation, and Schrödinger’s equation, including Fourier series and transforms, Green’s functions, the method of characteristics, grids, Galerkin and simulation methods, elementary probability theory, and statistical methods. The incorporation of Mathematica® offers students a wealth of practical benefits in that it: Requires little or no previous computer experience + Offers maximum flexibility and sophistication + Delivers easy access to the important ideas behind the various numerical methods + Facilitates important but often tedious analytic calculations + Is easily adapted to the application of other related software packages. Designed for both advanced undergraduate and graduate students in the physical and engineering sciences, as well as professionals who want to learn these methods, An Introduction to Mathematical and Computational Physics Using Mathematica® is also provided electronically on an accompanying CD. The electronic version contains the full text of the book, along with animations, user-modifiable source code, and links to related Web material.

Steven J. Miller ... 327 pages - Publisher: American Mathematical Society; (December, 2017) ... Language: English - ISBN-10: 1470441144 - ISBN-13: 978-1470441142.

Optimization Theory is an active area of research with numerous applications; many of the books are designed for engineering classes, and thus have an emphasis on problems from such fields. Covering much of the same material, there is less emphasis on coding and detailed applications as the intended audience is more mathematical. There are still several important problems discussed (especially scheduling problems), but there is more emphasis on theory and less on the nuts and bolts of coding. A constant theme of the text is the ``why'' and the ``how'' in the subject. Why are we able to do a calculation efficiently? How should we look at a problem? Extensive effort is made to motivate the mathematics and isolate how one can apply ideas/perspectives to a variety of problems. As many of the key algorithms in the subject require too much time or detail to analyze in a first course (such as the run-time of the Simplex Algorithm), there are numerous comparisons to simpler algorithms which students have either seen or can quickly learn (such as the Euclidean algorithm) to motivate the type of results on run-time savings.

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