Probability is an area of mathematics of tremendous contemporary importance across all aspects of human endeavour. This book is a compact account of the basic features of probability and random processes at the level of first and second year mathematics undergraduates and Masters' students in cognate fields. It is suitable for a first course in probability, plus a follow-up course in random processes including Markov chains. A special feature is the authors' attention to rigorous mathematics: not everything is rigorous, but the need for rigour is explained at difficult junctures. The text is enriched by simple exercises, together with problems (with very brief hints) many of which are taken from final examinations at Cambridge and Oxford. The first eight chapters form a course in basic probability, being an account of events, random variables, and distributions - discrete and continuous random variables are treated separately - together with simple versions of the law of large numbers and the central limit theorem. There is an account of moment generating functions and their applications. The following three chapters are about branching processes, random walks, and continuous-time random processes such as the Poisson process. The final chapter is a fairly extensive account of Markov chains in discrete time. This second edition develops the success of the first edition through an updated presentation, the extensive new chapter on Markov chains, and a number of new sections to ensure comprehensive coverage of the syllabi at major universities.

The proposed book presents recent breakthroughs for the control of distributed parameter systems and follows on from a workshop devoted to this topic. It introduces new and unified visions of the challenging control problems raised by distributed parameter systems. The book collects contributions written by prominent international experts in the control community, addressing a wide variety of topics. It spans the full range from theoretical research to practical implementation and follows three traverse axes: emerging ideas in terms of control strategies (energy shaping, prediction-based control, numerical control, input saturation), theoretical concepts for interconnected systems (with potential non-linear actuation dynamics), advanced applications (cable-operated elevators, traffic networks), and numerical aspects. Cutting-edge experts in the field contributed in this volume, making it a valuable reference source for control practitioners, graduate students, and scientists researching practical and theoretical solutions to the challenging problems raised by distributed parameter systems.

This book covers a fast-growing topic in great depth and focuses on the technologies and applications of probabilistic data management. It aims to provide a single account of current studies in probabilistic data management. The objective of the book is to provide the state of the art information to researchers, practitioners, and graduate students of information technology of intelligent information processing, and at the same time serving the information technology professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.

This book's aim is to study the mathematical and computational models to analyze the progress, prognosis, prevention, and panacea of breast cancer. The book discusses application of Markov chains and transient mappings, Charlie-Simpson numerical algorithm, models represented by nonlinear reaction-diffusion-type partial differential equations, and related techniques. The book also attempts to design mathematical model of targeted strategic treatments by using Skilled Killer Drugs (SKD1 and SKD2) to suggest the improvisation of future cancer treatments. Both graduate students and researchers of computational biology and oncologists will benefit by studying this book. Researchers of cancer studies and biological sciences will also find this work helpful.

The book shows that the analytic combinatorics (AC) method encodes the combinatorial problems of multiple object tracking-without information loss-into the derivatives of a generating function (GF). The book lays out an easy-to-follow path from theory to practice and includes salient AC application examples. Since GFs are not widely utilized amongst the tracking community, the book takes the reader from the basics of the subject to applications of theory starting from the simplest problem of single object tracking, and advancing chapter by chapter to more challenging multi-object tracking problems. Many established tracking filters (e.g., Bayes-Markov, PDA, JPDA, IPDA, JIPDA, CPHD, PHD, multi-Bernoulli, MBM, LMBM, and MHT) are derived in this manner with simplicity, economy, and considerable clarity. The AC method gives significant and fresh insights into the modeling assumptions of these filters and, thereby, also shows the potential utility of various approximation methods that are well established techniques in applied mathematics and physics, but are new to tracking. These unexplored possibilities are reviewed in the final chapter of the book.

This book mainly focuses on the adaptive analysis of damage and fracture in rock, taking into account multiphysical fields coupling (thermal, hydro, mechanical, and chemical fields). This type of coupling is a crucial aspect in practical engineering for e.g. coal mining, oil and gas exploration, and civil engineering. However, understanding the influencing mechanisms and preventing the disasters resulting from damage and fracture evolution in rocks require high-precision and reliable solutions. This book proposes adaptive numerical algorithms and simulation analysis methods that offer significant advantages in terms of accuracy and reliability. It helps readers understand these innovative methods quickly and easily. The content consists of: (1) a finite element algorithm for modeling the continuum damage evolution in rocks, (2) adaptive finite element analysis for continuum damage evolution and determining the wellbore stability of transversely isotropic rock, (3) an adaptive finite element algorithm for damage detection in non-uniform Euler-Bernoulli beams with multiple cracks, using natural frequencies, (4) adaptive finite element-discrete element analysis for determining multistage hydrofracturing in naturally fractured reservoirs, (5) adaptive finite element-discrete element analysis for multistage supercritical CO2 fracturing and microseismic modeling, and (6) an adaptive finite element-discrete element-finite volume algorithm for 3D multiscale propagation of hydraulic fracture networks, taking into account hydro-mechanical coupling. Given its scope, the book offers a valuable reference guide for researchers, postgraduates and undergraduates majoring in engineering mechanics, mining engineering, geotechnical engineering, and geological engineering.

This book gives an intuitive and hands-on introduction to Topological Data Analysis (TDA). Covering a wide range of topics at levels of sophistication varying from elementary (matrix algebra) to esoteric (Grothendieck spectral sequence), it offers a mirror of data science aimed at a general mathematical audience. The required algebraic background is developed in detail. The first third of the book reviews several core areas of mathematics, beginning with basic linear algebra and applications to data fitting and web search algorithms, followed by quick primers on algebra and topology. The middle third introduces algebraic topology, along with applications to sensor networks and voter ranking. The last third covers key contemporary tools in TDA: persistent and multiparameter persistent homology. Also included is a user's guide to derived functors and spectral sequences (useful but somewhat technical tools which have recently found applications in TDA), and an appendix illustrating a number of software packages used in the field. Based on a course given as part of a masters degree in statistics, the book is appropriate for graduate students.

The main objective of the Water Framework Directive in the European countries is to achieve a good status of all the water bodies, in the integrated management of river basins. In order to assess the impact of improvement measures, water quality models are necessary. During the previous decades the progress in computer technology and computational methods has supported the development of advanced mathematical models for pollutant transport in rivers and streams. This book is intended to provide the fundamental knowledge needed for a deeper understanding of these models and the development of new ones, which will fulfil future quality requirements in water resources management. This book focuses on the fundamentals of computational techniques required in water quality modelling.

Advection, dispersion and concentrated sources or sinks of contaminants lead to the formulation of the fundamental differential equation of pollutant transport. Its integration, according to appropriate initial and boundary conditions and with the knowledge of the velocity field, allows for pollutant behaviour to be assessed in the entire water body. An analytical integration is convenient only in one-dimensional approach with considerable simplification. Integration in the numerical field is useful for taking into account particular aspects of water body and pollutants.

To ensure their reliability, the models require accurate calibration and validation, based on proper data, taken from direct measurements. In addition, sensitivity and uncertainty analysis are also of utmost importance.

All the above items are discussed in detail in the 21 chapters of the book, which is written in a didactic form for professionals and students.

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This book emerged from a DoE/NSF-sponsored workshop, held in Tahoe City, California, October 2000. About fifty invited participants presented state-of-the-art research on topics such as: - terrain modeling- multiresolution subdivision- wavelet-based scientific data compression- topology-based visualization- data structures, data organization and indexing schemes for scientific data visualization.All invited papers were carefully refereed, resulting in this collection. The book will be of great interest to researchers, graduate students and professionals dealing with scientific visualization and its applications.

Spontaneous pattern formation in nonlinear dissipative systems far from equilibrium occurs in a variety of settings in nature and technology, and has applications ranging from nonlinear optics through solid and fluid mechanics, physical chemistry and chemical engineering to biology. This book explores the forefront of current research, describing in-depth the analytical methods that elucidate the complex evolution of nonlinear dissipative systems.

This proceedings volume convenes peer-reviewed, selected papers presented at the XXVIII International Joint Conference on Industrial Engineering and Operations Management (IJCIEOM) that was held in Mexico City, Mexico, July 17-20, 2022, with a special focus on applications of industrial engineering and operations management for research and practice. Fields covered include operations, manufacturing, industrial and production engineering and management, emphasizing optimization models and data science applications to real-world problems. In this book, the reader will find works on topics as optimization models; stochastic optimization; digital transformation in the supply chain; data science applications in operations management; Industry 4.0: manufacturing planning & control; blockchain; intelligent transportation systems; sustainable and reverse logistics; big data and demand planning; predictive and prescriptive analytics; last-mile delivery optimization; stochastic inventory models; new trends in information technology for operation management; stochastic optimization; optimization models for omnichannel; safety in operation management; and more. This volume includes relevant information for academics, since most of the chapters focus on real-world case studies and systematic reviews, but also for professionals in the industrial sector as it presents solutions to complex industrial challenges. Previous 2018, 2019, 2020, and 2021 IJCIEOM proceedings can also be found in Springer's catalog.

At once a comprehensive handbook for the active researcher and a thorough introduction for the advanced student, this reference provides:
* Coverage of wide range of applications, including agricultural sciences, animal and biomedi-cal sciences, and industrial and engineering studies
* Information on new developments in the design of fractional factorials with non-prime numbers of levels in mixed-level fractional factorials
* Detailed information on the construction of plans and the relationships among categories of designs
* Thorough discussion of balanced, partially balanced, lattice, cyclic and alpha-designs
* Accommodations for how to evaluate the power and efficiency of designs that are not perfectly balanced
* Unified and simplified presentation of general forms for estimation and hypothesis testing

Mathematics of Fuzziness - Basic Issues introduces a basic notion of 'fuzziness' and provides a conceptual mathematical framework to characterize such fuzzy phenomena in Studies in Fuzziness and Soft Computing. The book systematically presents a self-contained introduction to the essentials of mathematics of fuzziness ranging from fuzzy sets, fuzzy relations, fuzzy numbers, fuzzy algebra, fuzzy measures, fuzzy integrals, and fuzzy topology to fuzzy control in a strictly mathematical manner. It contains most of the authors' research results in the field of fuzzy set theory and has evolved from the authors' lecture notes to both undergraduate and graduate students over the last three decades. A lot of exercises in each chapter of the book are particularly suitable as a textbook for any undergraduate and graduate student in mathematics, computer science and engineering. The reading of the book will surely lay a solid foundation for further research on fuzzy set theory and its applications.

The modern developments in mathematical biology took place roughly between 1920 and 1940, a period now referred to as the "Golden Age of Theoretical Biology." The eminent Italian mathematician Vito Volterra played a decisive and widely acknowledged role in these developments. Volterra's specific project was to transfer the model and the concepts of classical mechanics to biology, constructing a sort of "rational mechanics" and an "analytic mechanics" of biological associations. The new subject was thus to be equipped with a solid experimental or at least empirical basis, also in this case following the tried and tested example of mathematical physics. Although very few specific features of this reductionist programme have actually survived, Volterra's contribution was decisive, as is now universally acknowledged, in encouraging fresh studies in the field of mathematical biology. Even today, the primary reference in the literature of the field of population dynamics consists of Volterra's work and the descriptive schemata (the "models," in modern parlance) he proposed. The present book aims to fill this historiographic gap by providing an exhaustive collection of the correspondence between Volterra and numerous other scientists on the topic of mathematical biology. The book begins with an introductory essay by Ana MillAn Gasca, which aims at giving a picture of the research field of biomathematics in the "Golden Age," and shows the importance of the correspondence in this context. This is followed by a transcript of the correspondence ordered by the correspondent's name. Each item is preceded by a biographical profile of the correspondent and accompanied by notes containing informationand references to facilitate understanding. The book will be found useful not only by science historians but also by all those - in particular, biomathematicians and biologists - with an interest in the origins of and events in a branch of learning that has undergone an astonishing development. Many of the problems discussed - in particular that of empirical verification - appear extremely topical even today and in some cases could even fuel reflection on topics still open to research.

This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to the data, it enables readers to rapidly comprehend multivariate data analysis. Arranged so that readers can intuitively grasp the purposes for which multivariate analysis procedures are used, the book also offers clear explanations of those purposes, with numerical examples preceding the mathematical descriptions. Supporting the modern matrix formulations by highlighting singular value decomposition among theorems in matrix algebra, this book is useful for undergraduate students who have already learned introductory statistics, as well as for graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis. The book begins by explaining fundamental matrix operations and the matrix expressions of elementary statistics. Then, it offers an introduction to popular multivariate procedures, with each chapter featuring increasing advanced levels of matrix algebra. Further the book includes in six chapters on advanced procedures, covering advanced matrix operations and recently proposed multivariate procedures, such as sparse estimation, together with a clear explication of the differences between principal components and factor analyses solutions. In a nutshell, this book allows readers to gain an understanding of the latest developments in multivariate data science.

This book deals with the mathematical properties of dimensioned quantities, such as length, mass, voltage, and viscosity.
Beginning with a careful examination of how one expresses the numerical results of a measurement and uses these results in subsequent manipulations, the author rigorously constructs the notion of dimensioned numbers and discusses their algebraic structure. The result is a unification of linear algebra and traditional dimensional analysis that can be extended from the scalars to which the traditional analysis is perforce restricted to multidimensional vectors of the sort frequently encountered in engineering, systems theory, economics, and other applications.

This fascinating book examines some of the characteristics of technological/engineering models that are likely to be unfamiliar to those who are interested primarily in the history and philosophy of science and mathematics, and which differentiate technological models from scientific and mathematical ones. Themes that are highlighted include:
the role of language: the models developed for engineering design have resulted in new ways of talking about technological systems
communities of practice: related to the previous point, particular engineering communities have particular ways of sharing and developing knowledge
graphical (re)presentation: engineers have developed many ways of reducing quite complex mathematical models to more simple representations
reification: highly abstract mathematical models are turned into 'objects' that can be manipulated almost like components of a physical system
machines: not only the currently ubiquitous digital computer, but also older analogue devices - slide rules, physical models, wind tunnels and other small-scale simulators, as well as mechanical, electrical and electronic analogue computers
mathematics and modelling as a bridging tool between disciplines
This book studies primarily modelling in technological practice. It is worth noting that models of the type considered in the book are not always highly valued in formal engineering education at university level, which often takes an "applied science" approach close to that of the natural sciences (something that can result in disaffection on the part of students). Yet in an informal context, such as laboratories, industrial placements, and so on, a very different situation obtains. A number of chapters considers such epistemological aspects, as well as the status of different types of models within the engineering education community.
The book will be of interest to practising engineers and technologists; sociologists of science and technology; and historians and philosophers of science and mathematics. It will also be written in a way that will be accessible to non-specialists."

This book introduces readers to some of the most significant advances in core computer science-based technologies. At the dawn of the 4th Industrial Revolution, the field of computer science-based technologies is growing continuously and rapidly, and is developing both in itself and in terms of its applications in many other disciplines. Written by leading experts and consisting of 18 chapters, the book is divided into seven parts: (1) Computer Science-based Technologies in Education, (2) Computer Science-based Technologies in Risk Assessment and Readiness, (3) Computer Science-based Technologies in IoT, Blockchains and Electronic Money, (4) Computer Science-based Technologies in Mobile Computing, (5) Computer Science-based Technologies in Scheduling and Transportation, (6) Computer Science-based Technologies in Medicine and Biology, and (7) Theoretical Advances in Computer Science with Significant Potential Applications in Technology. Featuring an extensive list of bibliographic references at the end of each chapter to help readers probe further into the application areas of interest to them, this book is intended for professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also useful for those from other disciplines wanting to become well versed in some of the latest computer science-based technologies.

This book strikes a healthy balance between theory and applications, ensuring that it doesn't offer a set of tools with no mathematical roots. It is intended as a comprehensive and largely self-contained introduction to probability and statistics for university students from various faculties, with accompanying implementations of some rudimentary statistical techniques in the language R. The content is divided into three basic parts: the first includes elements of probability theory, the second introduces readers to the basics of descriptive and inferential statistics (estimation, hypothesis testing), and the third presents the elements of correlation and linear regression analysis. Thanks to examples showing how to approach real-world problems using statistics, readers will acquire stronger analytical thinking skills, which are essential for analysts and data scientists alike.

Transportation Engineering: Theory, Practice and Modeling, Second Edition presents comprehensive information related to traffic engineering and control, transportation planning and evaluation of transportation alternatives. The book systematically deals with almost the entire transportation engineering area, offering various techniques related to transportation modeling, transportation planning, and traffic control. It also shows readers how to use models and methods when predicting travel and freight transportation demand, how to analyze existing transportation networks, how to plan for new networks, and how to develop traffic control tactics and strategies. New topics addressed include alternative Intersections, alternative interchanges and individual/private transportation. Readers will also learn how to utilize a range of engineering concepts and methods to make future transportation systems safer, more cost-effective, and "greener". Providing a broad view of transportation engineering, including transport infrastructure, control methods and analysis techniques, this new edition is for postgraduates in transportation and professionals needing to keep up-to-date with the latest theories and models.

The intention of this collection agrees with the purposes of the homonymous mini-symposium (MS) at ICIAM-2019, which were to overview the essentials of geometric calculus (GC) formalism, to report on state-of-the-art applications showcasing its advantages and to explore the bearing of GC in novel approaches to deep learning. The first three contributions, which correspond to lectures at the MS, offer perspectives on recent advances in the application GC in the areas of robotics, molecular geometry, and medical imaging. The next three, especially invited, hone the expressiveness of GC in orientation measurements under different metrics, the treatment of contact elements, and the investigation of efficient computational methodologies. The last two, which also correspond to lectures at the MS, deal with two aspects of deep learning: a presentation of a concrete quaternionic convolutional neural network layer for image classification that features contrast invariance and a general overview of automatic learning aimed at steering the development of neural networks whose units process elements of a suitable algebra, such as a geometric algebra. The book fits, broadly speaking, within the realm of mathematical engineering, and consequently, it is intended for a wide spectrum of research profiles. In particular, it should bring inspiration and guidance to those looking for materials and problems that bridge GC with applications of great current interest, including the auspicious field of GC-based deep neural networks.

This contributed volume convenes a rich selection of works with a focus on innovative mathematical methods with applications in real-world, industrial problems. Studies included in this book are all motivated by a relevant industrial challenge, and demonstrate that mathematics for industry can be extremely rewarding, leading to new mathematical methods and sometimes even to entirely new fields within mathematics. The book is organized into two parts: Computational Sciences and Engineering, and Data Analysis and Finance. In every chapter, readers will find a brief description of why such work fits into this volume; an explanation on which industrial challenges have been instrumental for their inspiration; and which methods have been developed as a result. All these contribute to a greater unity of the text, benefiting not only practitioners and professionals seeking information on novel techniques but also graduate students in applied mathematics, engineering, and related fields.

The International Workshop on "The Use of Supercomputers in Theoretical Science" took place on January 24 and 25, 1991, at the University of Antwerp (UIA), Antwerpen, Belgium. It was the sixth in a series of workshops, the fIrst of which took place in 1984. The principal aim of these workshops is to present the state of the art in scientific large-scale and high speed-computation. Computational science has developed into a third methodology equally important now as its theoretical and experimental companions. Gradually academic researchers acquired access to a variety of supercomputers and as a consequence computational science has become a major tool for their work. It is a pleasure to thank the Belgian National Science Foundation (NFWO-FNRS) and the Ministry of ScientifIc Affairs for sponsoring the workshop. It was organized both in the framework of the Third Cycle "Vectorization, Parallel Processing and Supercomputers" and the "Governemental Program in Information Technology." We also very much would like to thank the University of Antwerp (Universitaire Instelling Antwerpen -VIA) for fInancial and material support. Special thanks are due to Mrs. H. Evans for the typing and editing of the manuscripts and for the preparation of the author and subject indexes. J.T. Devreese P.E. Van Camp University of Antwerp July 1991 v CONlENTS High Perfonnance Numerically Intensive Applications on Distributed Memory Parallel Computers .................... . F.W. Wray Abstract ......................................... .