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Mathematical and Statistical Approaches for Anaerobic Digestion Feedstock Optimization (SpringerBriefs in Energy)
by Federico Moretta Giulia BozzanoThis book examines biomass mixture modeling and optimization. The book discusses anaerobic digestion and related fermentative processes and explains their compositional dynamics. Early chapter examine macromolecules, elemental fractions, and their direct influence on methane production. Supported by an extensive data bank of substrates obtained from research, the book points out correlations that enable the estimation of global methane production for diverse biomass mixtures. Furthermore, it provides valuable insights into discerning the optimal composition capable of yielding the utmost methane output.The book integrates cutting-edge machine learning techniques and shows how the programming language Python and Julia can be used for analysis and to optimize processes. It has many graphs, figures, and visuals.
Mathematical and Statistical Methods in Food Science and Technology
by Gastón Ares Daniel GranatoMathematical and Statistical Approaches in Food Science and Technology offers an accessible guide to applying statistical and mathematical technologies in the food science field whilst also addressing the theoretical foundations. Using clear examples and case-studies by way of practical illustration, the book is more than just a theoretical guide for non-statisticians, and may therefore be used by scientists, students and food industry professionals at different levels and with varying degrees of statistical skill.
Mathematical and Statistical Models and Methods in Reliability
by N. Balakrishnan V. V. Rykov M. S. NikulinThe book is a selection of invited chapters, all of which deal with various aspects of mathematical and statistical models and methods in reliability. Written by renowned experts in the field of reliability, the contributions cover a wide range of applications, reflecting recent developments in areas such as survival analysis, aging, lifetime data analysis, artificial intelligence, medicine, carcinogenesis studies, nuclear power, financial modeling, aircraft engineering, quality control, and transportation. Mathematical and Statistical Models and Methods in Reliability is an excellent reference text for researchers and practitioners in applied probability and statistics, industrial statistics, engineering, medicine, finance, transportation, the oil and gas industry, and artificial intelligence.
Mathematical Applications in Continuum and Structural Mechanics (Advanced Structured Materials #127)
by Francesco Marmo Salvatore Sessa Emilio Barchiesi Mario SpagnuoloThis book presents a range of research projects focusing on innovative numerical and modeling strategies for the nonlinear analysis of structures and metamaterials. The topics covered concern various analysis approaches based on classical finite element solutions, structural optimization, and analytical solutions in order to present a comprehensive overview of the latest scientific advances. Although based on pioneering research, the contributions are focused on immediate and direct application in practice, providing valuable tools for researchers and practicing professionals alike.
A Mathematical Approach to Research Problems of Science and Technology: Theoretical Basis and Developments in Mathematical Modeling (Mathematics for Industry #5)
by Hiroyuki Ochiai Ryuei Nishii Shin-Ichiro Ei Miyuki Koiso Kanzo Okada Shingo Saito Tomoyuki ShiraiThis book deals with one of the most novel advances in mathematical modeling for applied scientific technology, including computer graphics, public-key encryption, data visualization, statistical data analysis, symbolic calculation, encryption, error correcting codes, and risk management. It also shows that mathematics can be used to solve problems from nature, e. g. , slime mold algorithms. One of the unique features of this book is that it shows readers how to use pure and applied mathematics, especially those mathematical theory/techniques developed in the twentieth century, and developing now, to solve applied problems in several fields of industry. Each chapter includes clues on how to use "mathematics" to solve concrete problems faced in industry as well as practical applications. The target audience is not limited to researchers working in applied mathematics and includes those in engineering, material sciences, economics, and life sciences.
Mathematical Approaches to Polymer Sequence Analysis and Related Problems
by Renato BruniAn edited volume describing the latest developments in approaching the problem of polymer sequence analysis, with special emphasis on the most relevant biopolymers (peptides and DNA) but not limited to them. The chapters will include peptide sequence analysis, DNA sequence analysis, analysis of biopolymers and nonpolymers, sequence alignment problems, and more.
The Mathematical Artist: A Tribute To John Horton Conway (Emergence, Complexity and Computation #45)
by Sukanta Das Souvik Roy Kamalika BhattacharjeeThis book brings together the impact of Prof. John Horton Conway, the playful and legendary mathematician's wide range of contributions in science which includes research areas—Game of Life in cellular automata, theory of finite groups, knot theory, number theory, combinatorial game theory, and coding theory. It contains transcripts where some eminent scientists have shared their first-hand experience of interacting with Conway, as well as some invited research articles from the experts focusing on Game of Life, cellular automata, and the diverse research directions that started with Conway's Game of Life. The book paints a portrait of Conway's research life and philosophical direction in mathematics and is of interest to whoever wants to explore his contribution to the history and philosophy of mathematics and computer science. It is designed as a small tribute to Prof. Conway whom we lost on April 11, 2020.
Mathematical Aspects of Subsonic and Transonic Gas Dynamics
by Lipman BersThis concise volume by a prominent mathematician offers an important survey of mathematical aspects of the theory of compressible fluids. The treatment is geared toward advanced undergraduates and graduate students in physics, applied mathematics, and engineering. Focusing on two-dimensional steady potential flows, the text eschews detailed proofs in favor of clear indications of the main ideas and descriptions of new mathematical concepts and methods that arose in connection with these chapters in fluid dynamics.Starting with a general discussion of the differential equations of a compressible gas flow, the book advances to the mathematical background of subsonic flow theory. Subsequent chapters explore the behavior of a flow at infinity and methods for the determination of flows around profiles, flows in channels and with a free boundary, the mathematical background of transonic gas dynamics, and some problems in transonic flow. An extensive bibliography of 400 papers concludes the text.
Mathematical Challenges in a New Phase of Materials Science
by Yasumasa Nishiura Motoko KotaniThis volume comprises eight papers delivered at the RIMS International Conference "Mathematical Challenges in a New Phase of Materials Science", Kyoto, August 4-8, 2014. The contributions address subjects in defect dynamics, negatively curved carbon crystal, topological analysis of di-block copolymers, persistence modules, and fracture dynamics. These papers highlight the strong interaction between mathematics and materials science and also reflect the activity of WPI-AIMR at Tohoku University, in which collaborations between mathematicians and experimentalists are actively ongoing.
Mathematical, Computational Intelligence and Engineering Approaches for Tourism, Agriculture and Healthcare (Lecture Notes in Networks and Systems #214)
by Pankaj Srivastava S. S. Thakur Georgia Irina Oros Ali A. AlJarrah Vichian LaohakosolThis book is a collection of selected papers presented at the 17th FAI International Conference on Engineering, Mathematical and Computational Intelligence (ICEMCI 2019), held at Jabalpur Engineering College, India, from 21–23 December 2019. This book discusses mathematical, computational intelligence and engineering approaches for tourism, agriculture and health care. It is a unique combination of a wide spectrum of topics, such as tourism destination ranking, medical diagnosis-based intelligent systems, drivers for hotel objectives, irrigation systems and more, which are discussed by using fuzzy, statistical and neural network tools. This book will be valuable to faculty members, postgraduate students, research scholars as well as readers from the industrial sector.
Mathematical Control Theory: An Introduction (Systems & Control: Foundations & Applications)
by Jerzy ZabczykThis textbook presents, in a mathematically precise manner, a unified introduction to deterministic control theory. With the exception of a few more advanced concepts required for the final part of the book, the presentation requires only a knowledge of basic facts from linear algebra, differential equations, and calculus.In addition to classical concepts and ideas, the author covers the stabilization of nonlinear systems using topological methods, realization theory for nonlinear systems, impulsive control and positive systems, the control of rigid bodies, the stabilization of infinite dimensional systems, and the solution of minimum energy problems.This second edition includes new chapters that introduce a variety of topics, such as controllability with vanishing energy, boundary control systems, and delayed systems. With additional proofs, theorems, results, and a substantially larger index, this new edition will be an invaluable resource for students and researchers of control theory.Mathematical Control Theory: An Introduction will be ideal for a beginning graduate course in mathematical control theory, or for self-study by professionals needing a complete picture of the mathematical theory that underlies the applications of control theory.From reviews of the first edition:At last! We did need an introductory textbook on control which can be read, understood, and enjoyed by anyone. Gian-Carlo Rota, The Bulletin of Mathematics BooksIt covers a remarkable number of topics...The exposition is excellent, and the book is a joy to read. A novel one-semester course covering both linear and nonlinear systems could be given...The book is an excellent one for introducing a mathematician to control theory. Bulletin of the AMSIndeed, for mathematicians who look for the basic ideas or a general picture about the main branches of control theory, I believe this book can provide an excellent bridge to this area. IEEE Control Systems Magazine
The Mathematical Foundation of Multi-Space Learning Theory
by Tai Wang Mengsiying LiThis book explores the measurement of learning effectiveness and the optimization of knowledge retention by modeling the learning process and building the mathematical foundation of multi-space learning theory.Multi-space learning is defined in this book as a micro-process of human learning that can take place in more than one space, with the goal of effective learning and knowledge retention. This book models the learning process as a temporal sequence of concept learning, drawing on established principles and empirical evidence. It also introduces the matroid to strengthen the mathematical foundation of multi-space learning theory and applies the theory to vocabulary and mathematics learning, respectively. The results show that, for vocabulary learning, the method can be used to estimate the effectiveness of a single learning strategy, to detect the mutual interference that might exist between learning strategies, and to predict the optimal combination of strategies. In mathematical learning, it was found that timing is crucial in both first learning and second learning in scheduling optimization to maximize the intersection effective interval.The title will be of interest to researchers and students in a wide range of areas, including educational technology, learning sciences, mathematical applications, and mathematical psychology.
Mathematical Foundation of Railroad Vehicle Systems: Geometry and Mechanics
by Ahmed A. ShabanaMASTER AND INTEGRATE THE GEOMETRY AND MECHANICS OF RAILROAD VEHICLE SYSTEM ENGINEERING WITH ONE PRACTICAL RESOURCEMathematical Foundation of Railroad Vehicle Systems: Geometry and Mechanics delivers a comprehensive treatment of the mathematical foundations of railroad vehicle systems. The book includes a strong emphasis on the integration of geometry and mechanics to create an accurate and accessible formulation of nonlinear dynamic equations and general computational algorithms that can be effectively used in the virtual prototyping, analysis, design, and performance evaluation of railroad vehicle systems.Using basic concepts, formulations, and computational algorithms, including mechanics-based approaches like the absolute nodal coordinate formulation (ANCF), readers will understand how to integrate the geometry and mechanics of railroad vehicle systems. The book also discusses new problems and issues in this area and describes how geometric and mechanical approaches can be used in derailment investigations.Mathematical Foundation of Railroad Vehicle Systems covers:The mathematical foundation of railroad vehicle systems through the integration of geometry and mechanics Basic concepts, formulations, and computational algorithms used in railroad vehicle system dynamics New mechanics-based approaches, like the ANCF, and their use to achieve an integration of geometry and mechanics Use of geometry and mechanics to study derailments New problems and issues in the area of railroad vehicle systemsDesigned for researchers and practicing engineers who work with railroad vehicle systems, Mathematical Foundation of Railroad Vehicle Systems: Geometry and Mechanics can also be used in senior undergraduate and graduate mechanical, civil, and electrical engineering programs and courses.
Mathematical Foundations for Signal Processing, Communications, and Networking
by Erchin Serpedin, Thomas Chen and Dinesh RajanMathematical Foundations for Signal Processing, Communications, and Networking describes mathematical concepts and results important in the design, analysis, and optimization of signal processing algorithms, modern communication systems, and networks. Helping readers master key techniques and comprehend the current research literature, the book offers a comprehensive overview of methods and applications from linear algebra, numerical analysis, statistics, probability, stochastic processes, and optimization. From basic transforms to Monte Carlo simulation to linear programming, the text covers a broad range of mathematical techniques essential to understanding the concepts and results in signal processing, telecommunications, and networking. Along with discussing mathematical theory, each self-contained chapter presents examples that illustrate the use of various mathematical concepts to solve different applications. Each chapter also includes a set of homework exercises and readings for additional study. This text helps readers understand fundamental and advanced results as well as recent research trends in the interrelated fields of signal processing, telecommunications, and networking. It provides all the necessary mathematical background to prepare students for more advanced courses and train specialists working in these areas.
Mathematical Foundations of Computational Electromagnetism (Applied Mathematical Sciences #198)
by Franck Assous Patrick Ciarlet Simon LabrunieThis book presents an in-depth treatment of various mathematical aspects of electromagnetism and Maxwell's equations: from modeling issues to well-posedness results and the coupled models of plasma physics (Vlasov-Maxwell and Vlasov-Poisson systems) and magnetohydrodynamics (MHD). These equations and boundary conditions are discussed, including a brief review of absorbing boundary conditions. The focus then moves to well‐posedness results. The relevant function spaces are introduced, with an emphasis on boundary and topological conditions. General variational frameworks are defined for static and quasi-static problems, time-harmonic problems (including fixed frequency or Helmholtz-like problems and unknown frequency or eigenvalue problems), and time-dependent problems, with or without constraints. They are then applied to prove the well-posedness of Maxwell’s equations and their simplified models, in the various settings described above. The book is completed with a discussion of dimensionally reduced models in prismatic and axisymmetric geometries, and a survey of existence and uniqueness results for the Vlasov-Poisson, Vlasov-Maxwell and MHD equations. The book addresses mainly researchers in applied mathematics who work on Maxwell’s equations. However, it can be used for master or doctorate-level courses on mathematical electromagnetism as it requires only a bachelor-level knowledge of analysis.
Mathematical Foundations of Elasticity
by Jerrold E. Marsden Thomas J. HughesThis advanced-level study approaches mathematical foundations of three-dimensional elasticity using modern differential geometry and functional analysis. It is directed to mathematicians, engineers and physicists who wish to see this classical subject in a modern setting with examples of newer mathematical contributions. Prerequisites include a solid background in advanced calculus and the basics of geometry and functional analysis.The first two chapters cover the background geometry - developed as needed - and use this discussion to obtain the basic results on kinematics and dynamics of continuous media. Subsequent chapters deal with elastic materials, linearization, variational principles, the use of functional analysis in elasticity, and bifurcation theory. Carefully selected problems are interspersed throughout, while a large bibliography rounds out the text.
Mathematical Foundations of Image Processing and Analysis
by Jean-Charles PinoliImage processing and image analysis are typically important fields in information science and technology. By “image processing”, we generally understand all kinds of operation performed on images (or sequences of images) in order to increase their quality, restore their original content, emphasize some particular aspect of the information or optimize their transmission, or to perform radiometric and/or spatial analysis. By “image analysis” we understand, however, all kinds of operation performed on images (or sequences of images) in order to extract qualitative or quantitative data, perform measurements and apply statistical analysis. Whereas there are nowadays many books dealing with image processing, only a small number deal with image analysis. The methods and techniques involved in these fields of course have a wide range of applications in our daily world: industrial vision, material imaging, medical imaging, biological imaging, multimedia applications, satellite imaging, quality control, traffic control, and so on
Mathematical Foundations of Image Processing and Analysis, Volume 2
by Jean-Charles PinoliMathematical Imaging is currently a rapidly growing field in applied mathematics, with an increasing need for theoretical mathematics. This book, the second of two volumes, emphasizes the role of mathematics as a rigorous basis for imaging sciences. It provides a comprehensive and convenient overview of the key mathematical concepts, notions, tools and frameworks involved in the various fields of gray-tone and binary image processing and analysis, by proposing a large, but coherent, set of symbols and notations, a complete list of subjects and a detailed bibliography. It establishes a bridge between the pure and applied mathematical disciplines, and the processing and analysis of gray-tone and binary images. It is accessible to readers who have neither extensive mathematical training, nor peer knowledge in Image Processing and Analysis. It is a self-contained book focusing on the mathematical notions, concepts, operations, structures, and frameworks that are beyond or involved in Image Processing and Analysis. The notations are simplified as far as possible in order to be more explicative and consistent throughout the book and the mathematical aspects are systematically discussed in the image processing and analysis context, through practical examples or concrete illustrations. Conversely, the discussed applicative issues allow the role of mathematics to be highlighted. Written for a broad audience – students, mathematicians, image processing and analysis specialists, as well as other scientists and practitioners – the author hopes that readers will find their own way of using the book, thus providing a mathematical companion that can help mathematicians become more familiar with image processing and analysis, and likewise, image processing and image analysis scientists, researchers and engineers gain a deeper understanding of mathematical notions and concepts.
Mathematical Foundations of Information Theory (Dover Books on Mathematics)
by A. Ya. KhinchinThe first comprehensive introduction to information theory, this book places the work begun by Shannon and continued by McMillan, Feinstein, and Khinchin on a rigorous mathematical basis. For the first time, mathematicians, statisticians, physicists, cyberneticists, and communications engineers are offered a lucid, comprehensive introduction to this rapidly growing field.In his first paper, Dr. Khinchin develops the concept of entropy in probability theory as a measure of uncertainty of a finite “scheme,” and discusses a simple application to coding theory. The second paper investigates the restrictions previously placed on the study of sources, channels, and codes and attempts “to give a complete, detailed proof of both … Shannon theorems, assuming any ergodic source and any stationary channel with a finite memory.”Partial Contents: I. The Entropy Concept in Probability Theory — Entropy of Finite Schemes. The Uniqueness Theorem. Entropy of Markov chains. Application to Coding Theory. II. On the Fundamental Theorems of Information Theory — Two generalizations of Shannon’s inequality. Three inequalities of Feinstein. Concept of a source. Stationarity. Entropy. Ergodic sources. The E property. The martingale concept. Noise. Anticipation and memory. Connection of the channel to the source. Feinstein’s Fundamental Lemma. Coding. The first Shannon theorem. The second Shannon theorem.
Mathematical Foundations of System Safety Engineering: A Road Map for the Future
by Richard R. ZitoThis graduate-level textbook elucidates low-risk and fail-safe systems in mathematical detail. It addresses, in particular, problems where mission-critical performance is paramount, such as in aircraft, missiles, nuclear reactors and weapons, submarines, and many other types of systems where “failure” can result in overwhelming loss of life and property. The book is divided into four parts: Fundamentals, Electronics, Software, and Dangerous Goods. The first part on Fundamentals addresses general concepts of system safety engineering that are applicable to any type of system. The second part, Electronics, addresses the detection and correction of electronic hazards. In particular, the Bent Pin Problem, Sneak Circuit Problem, and related electrical problems are discussed with mathematical precision. The third part on Software addresses predicting software failure rates as well as detecting and correcting deep software logical flaws (called defects). The fourth part on Dangerous Goods presents solutions to three typical industrial chemical problems faced by the system safety engineer during the design, storage, and disposal phases of a dangerous goods’ life cycle.
Mathematical Geology and Geoinformatics: Proceedings of the 30th International Geological Congress, Volume 25
by Zhao Pengda F. P. Agterberg Jiang ZuoqinThis book presents the proceedings of the 30th International Geological Congress, providing information on geological hazards map and image analytical systems, mineral resources with integrated information, phase-separation analysis, mineral reserve estimation, and geosciences and management information systems.
Mathematical Gnostics: Advanced Data Analysis for Research and Engineering Practice
by Pavel KovanicThe book describes the theoretical principles of nonstatistical methods of data analysis but without going deep into complex mathematics. The emphasis is laid on presentation of solved examples of real data either from authors' laboratories or from open literature. The examples cover wide range of applications such as quality assurance and quality control, critical analysis of experimental data, comparison of data samples from various sources, robust linear and nonlinear regression as well as various tasks from financial analysis. The examples are useful primarily for chemical engineers including analytical/quality laboratories in industry, designers of chemical and biological processes. Features: Exclusive title on Mathematical Gnostics with multidisciplinary applications, and specific focus on chemical engineering. Clarifies the role of data space metrics including the right way of aggregation of uncertain data. Brings a new look on the data probability, information, entropy and thermodynamics of data uncertainty. Enables design of probability distributions for all real data samples including smaller ones. Includes data for examples with solutions with exercises in R or Python. The book is aimed for Senior Undergraduate Students, Researchers, and Professionals in Chemical/Process Engineering, Engineering Physics, Stats, Mathematics, Materials, Geotechnical, Civil Engineering, Mining, Sales, Marketing and Service, and Finance.
Mathematical Handbook for Scientists and Engineers: Definitions, Theorems, and Formulas for Reference and Review (Dover Civil and Mechanical Engineering)
by Granino A. Korn Theresa M. KornA reliable source of definitions, theorems, and formulas, this authoritative handbook provides convenient access to information from every area of mathematics. Coverage includes Fourier transforms, Z transforms, linear and nonlinear programming, calculus of variations, random-process theory, special functions, combinatorial analysis, numerical methods, game theory, and much more.
Mathematical Immunology of Virus Infections
by Gennady Bocharov Vitaly Volpert Burkhard Ludewig Andreas MeyerhansThis monograph concisely but thoroughly introduces the reader to the field of mathematical immunology. The book covers first basic principles of formulating a mathematical model, and an outline on data-driven parameter estimation and model selection. The authors then introduce the modeling of experimental and human infections and provide the reader with helpful exercises. The target audience primarily comprises researchers and graduate students in the field of mathematical biology who wish to be concisely introduced into mathematical immunology.
Mathematical Intelligence: A Story of Human Superiority Over Machines
by Mubeen JunaidA fresh exploration into the 'human nature versus technology&’ argument, revealing an unexpected advantage that humans have over our future robot masters: we&’re actually good at mathematics. There&’s so much discussion about the threat posed by intelligent machines that it sometimes seems as though we should simply surrender to our robot overlords now. But Junaid Mubeen isn&’t ready to throw in the towel just yet. As far as he is concerned, we have the creative edge over computers, because of a remarkable system of thought that humans have developed over the millennia. It&’s familiar to us all, but often badly taught in schools and misrepresented in popular discourse—math. Computers are, of course, brilliant at totting up sums, pattern-seeking, and performing mindless tasks of, well, computation. For all things calculation, machines reign supreme. But Junaid identifies seven areas of intelligence where humans can retain a crucial edge. And in exploring these areas, he opens up a fascinating world where we can develop our uniquely human mathematical talents. Just a few of the fascinating subjects covered in MATHEMATICAL INTELLIGENCE include: -Humans are endowed with a natural sense of numbers that is based on approximation rather than precise calculation. Our in-built estimation skills complement the precision of computers. Interpreting the real world depends on both. -What sets humans apart from other animals is language and abstraction. We have an extraordinary ability to create powerful representations of knowledge— more diverse than the binary language of computers. -Mathematics confers the most robust, logical framework for establishing permanent truths. Reasoning shields us from the dubious claims of pure pattern-recognition systems. -All mathematical truths are derived from a starting set of assumptions, or axioms. Unlike computers, humans have the freedom to break free of convention and examine the logical consequences of our choices. Mathematics rewards our imagination with fascinating and, on occasion, applicable concepts that originate from breaking the rules. -Computers can be tasked to solve a range of problems, but which problems are worth the effort? Questioning is as vital to our repertoire of thinking skills as problem-solving itself.