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This book offers an intuitive approach to random processes and educates the reader on how to interpret and predict their behavior. Premised on the idea that new techniques are best introduced by specific, low-dimensional examples, the mathematical exposition is easier to comprehend and more enjoyable, and it motivates the subsequent generalizations. It distinguishes between the science of extracting statistical information from raw data--e.g., a time series about which nothing is known a priori--and that of analyzing specific statistical models, such as Bernoulli trials, Poisson queues, ARMA, and Markov processes. The former motivates the concepts of statistical spectral analysis (such as the Wiener-Khintchine theory), and the latter applies and interprets them in specific physical contexts. The formidable Kalman filter is introduced in a simple scalar context, where its basic strategy is transparent, and gradually extended to the full-blown iterative matrix form.

This book introduces quadratic ideal numbers as objects of study with applications to binary quadratic forms and other topics. The text requires only minimal background in number theory, much of which is reviewed as needed. Computational methods are emphasized throughout, making this subject appropriate for individual study or research at the undergraduate level or above.

This open-access proceedings volume brings selected, peer-reviewed contributions presented at the Fourth Stochastic Transport in Upper Ocean Dynamics (STUOD) 2023 Workshop, held at IFREMER in Plouzané, France, September 25–28, 2023. The STUOD project is supported by an ERC Synergy Grant, and led by Imperial College London, the National Institute for Research in Computer Science and Automatic Control (INRIA), and the French Research Institute for Exploitation of the Sea (IFREMER). The project aims to deliver new capabilities for assessing variability and uncertainty in upper ocean dynamics. It will provide decision makers a means of quantifying the effects of local patterns of sea level rise, heat uptake, carbon storage, and change of oxygen content and pH in the ocean. Its multimodal monitoring will enhance the scientific understanding of marine debris transport, tracking of oil spills, and accumulation of plastic in the sea. All topics of these proceedings are essential to the scientific foundations of oceanography which has a vital role in climate science. Studies convened in this volume focus on a range of fundamental areas, including: Observations at a high resolution of upper ocean properties such as temperature, salinity, topography, wind, waves and velocity; Large-scale numerical simulations; Data-based stochastic equations for upper ocean dynamics that quantify simulation error; Stochastic data assimilation to reduce uncertainty. These fundamental subjects in modern science and technology are urgently required in order to meet the challenges of climate change faced today by human society. This proceedings volume represents a lasting legacy of crucial scientific expertise to help meet this ongoing challenge, for the benefit of academics and professionals in pure and applied mathematics, computational science, data analysis, data assimilation, and oceanography.

This book is the third volume in the New Era Electronics lecture notes series, a compilation of volumes defining the important concepts tied to the electronics transition happening in the 21st century.The material is adapted from a unique course that connects three diverse fields — statistical mechanics, neural networks and quantum computing — using the unifying concept of a state-space with 2N dimensions defined by N binary bits. First, the seminal concepts of statistical mechanics, developed to describe natural interacting systems, are described. Then, these concepts are connected to engineered interacting systems like Boltzmann Machines (BM), which are cleverly designed to solve problems in machine learning. Finally, we connect to engineered quantum systems, stressing the key role of quantum interference in distinguishing them from classical systems like BM.Assuming only a basic background in differential equations and linear algebra, this book is accessible to broader audiences across its described topics, including students in physics, engineering and computing, as well as professionals working actively in the technical fields looking for a primer to unconventional computing.

What sets Numerical Methods and Analysis with Mathematical Modelling apart are the modelling aspects utilizing numerical analysis (methods) to obtain solutions. The authors cover first the basic numerical analysis methods with simple examples to illustrate the techniques and discuss possible errors. The modelling prospective reveals the practical relevance of the numerical methods in context to real-world problems.At the core of this text are the real-world modelling projects. Chapters are introduced and techniques are discussed with common examples. A modelling scenario is introduced that will be solved with these techniques later in the chapter. Often, the modelling problems require more than one previously covered technique presented in the book.Fundamental exercises to practice the techniques are included. Multiple modelling scenarios per numerical methods illustrate the applications of the techniques introduced. Each chapter has several modelling examples that are solved by the methods described within the chapter.The use of technology is instrumental in numerical analysis and numerical methods. In this text, Maple, Excel, R, and Python are illustrated. The goal is not to teach technology but to illustrate its power and limitations to perform algorithms and reach conclusions.This book fulfills a need in the education of all students who plan to use technology to solve problems whether using physical models or true creative mathematical modeling, like discrete dynamical systems.

This book explores the dynamic interplay between fractals and graph theory, two powerful mathematical tools with vast applications. It presents a strategic combination and the synergistic use of these disciplines to address real-world problems and challenges. The book begins with an introduction to the basic concepts of fractals and graph theory and goes on to explore the applications in various domains, including natural phenomena modeling, scheduling, and network optimisation.This book: Illustrates the innovative ways fractals and graph theory can be combined, laying the groundwork for future applications across various industries Introduces the fundamental concepts and principles of both fractals and graph theory in detail, making it accessible to a broad audience, including those new to these topics Explores practical applications in image processing, network optimisation, social network analysis, and more, demonstrating the real-world impact of these mathematical tools Analyses advanced techniques in graph theory, such as matching, domination, and colouring, with practical examples and case studies Highlights the latest research advancements in fractal graph theory, showcasing its potential for future developments and applications This book is for students, researchers, and professionals in mathematics, computer science, engineering, and related fields.

This 2-volume set LNCS 15495-15496 constitutes the refereed proceedings of the 25th International Conference on Cryptology in India, held in Chennai, India, during December 18–21, 2024. The 31 full papers presented in these proceedings were carefully reviewed and selected from 96 submissions. They are organized into these topical sections: Part I: Foundations; symmetric-key cryptography; cryptographic constructions; and quantum cryptography. Part II: Cryptanalysis; post-quantum cryptography; and blockchain and cloud computing.

This book constitutes the proceedings of the 18th International Conference on Reachability Problems, RP 2024, which took place in Vienna, Austria, during September 25–27, 2024. The 13 full papers included in these proceedings were carefully reviewed and selected from 37 submissions. The book also contains two invited talks in full paper length. The contributions in these proceedings cover topics from computability and reachability; automata and complexity; linear systems and recurrences; and games and abstractions.

This monograph explores the dynamics in the particular but very important and significant case of quasi-integrable Hamiltonian systems, or integrable systems slightly perturbed by other forces. With both analytic and numerical methods, the book studies several of these systems - such as the hydrogen atom and the solar system, with the associated Arnold web - through modern tools such as the frequency-modified Fourier transform, wavelets, and the frequency-modulation indicator. Meanwhile, it draws heavily on the more standard KAM and Nekhoroshev theorems. Geography of Order and Chaos in Mechanics contains many figures that illuminate its concepts in novel ways, but perhaps its most useful feature is its inclusion of software to reproduce the various numerical experiments. The graphical user interfaces of five supplied MATLAB programs allows readers without any knowledge of computer programming to visualize and experiment with the distribution of order, chaos and resonances in various Hamiltonian systems. This monograph will be a valuable resource for professional researchers and certain advanced undergraduate students in mathematics and physics, as well as an exceptional reference for PhD students with an interest in perturbation theory. The Second Edition includes a new chapter on theoretical and numerical results in the study of the solar system's (in)stability obtained since publication of the First Edition, clarifying the topological mechanism that causes the sudden increase in Mercury's eccentricity. An updated version of the companion software is also provided, which contains new features and better Graphical User Interfaces. Thanks to the compiled versions, all the software can be used even if the user does not have access to a MATLAB installation.

This volume explores a number of exciting developments in the field of nonlinear dispersive waves with a particular focus on waves arising in the ocean. Chapters are based on talks given at the workshop “Nonlinear Dispersive Waves” that was held at University College Cork, Ireland, on April 24-25, 2023. Specific topics covered include: The recovery of steady rotational wave surface profiles; Hamiltonian models for the propagation of long gravity waves; Waves propagating at the surface of a fluid covered by floating ice plates; The use of spherical coordinates to describe arctic ocean waves; Boundary value problems related to the Muskat Problem. Nonlinear Dispersive Waves will appeal to researchers as well as graduate students interested in this active area of research.

This book offers an up-to-date introductory treatment of computational techniques applied to problems in finance, placing issues such as numerical stability, convergence and error analysis in both deterministic and stochastic settings at its core. The first part provides a welcoming but nonetheless rigorous introduction to the fundamental theory of option pricing, including European, American, and exotic options along with their hedge parameters, and combines a clear treatment of the mathematical framework with practical worked examples in Python. The second part explores the main computational methods for valuing options within the Black-Scholes framework: lattice, Monte Carlo, and finite difference methods. The third and final part covers advanced topics for the simulation of financial processes beyond the standard Black-Scholes setting. Techniques for the analysis and simulation of multidimensional financial data, including copulas, are covered and will be of interest to those studying machine learning for finance. There is also an in-depth treatment of exact and approximate sampling methods for stochastic differential equation models of interest rates and volatilities. Written for advanced undergraduate and masters-level courses, the book assumes some exposure to core mathematical topics such as linear algebra, ordinary differential equations, multivariate calculus, probability, and statistics at an undergraduate level. While familiarity with Python is not required, readers should be comfortable with basic programming constructs such as variables, loops, and conditional statements.

This book reports the developments of the Total Least Square (TLS) algorithms for parameter estimation and adaptive filtering. Specifically, it introduces the authors'latest achievements in the past 20 years, including the recursive TLS algorithms, the approximate inverse power iteration TLS algorithm, the neural based MCA algorithm, the neural based SVD algorithm, the neural based TLS algorithm, the TLS algorithms under non-Gaussian noises, performance analysis methods of TLS algorithms, etc. In order to faster the understanding and mastering of the new methods provided in this book for readers, before presenting each new method in each chapter, a specialized section is provided to review the closely related several basis models. Throughout the book, large of procedure of new methods are provided, and all new algorithms or methods proposed by us are tested and verified by numerical simulations or actual engineering applications. Readers will find illustrative demonstration examples on a range of industrial processes to study. Readers will find out the present deficiency and recent developments of the TLS parameter estimation fields, and learn from the the authors'latest achievements or new methods around the practical industrial needs. In my opinion, this book can be assimilated by advanced undergraduates and graduate students, as well as statisticians, because of the new tools in data analysis, applied mathematics experts, because of the novel theories and techniques that we propose, engineers, above all for the applications in control, system identification, computer vision, and signal processing.

This contributed volume collects papers presented during a special session on integral methods in science and engineering at the 2023 International Conference on Computational and Mathematical Methods in Science and Engineering (CMMSE), held in Cadiz, Spain from July 3-8, 2023. Covering the applications of integral methods to scientific developments in a variety of fields, the chapters in this volume are written by well-known researchers in their respective disciplines and present new results in both pure and applied mathematics. Each chapter shares a common methodology based on a combination of analytic and computational tools, an approach that makes this collection a valuable, multidisciplinary reference on how mathematics can be applied to various real-world processes and phenomena.

The 7-volume set LNCS 14832 – 14838 constitutes the proceedings of the 24th International Conference on Computational Science, ICCS 2024, which took place in Malaga, Spain, during July 2–4, 2024. The 155 full papers and 70 short papers included in these proceedings were carefully reviewed and selected from 430 submissions. They were organized in topical sections as follows: Part I: ICCS 2024 Main Track Full Papers; Part II: ICCS 2024 Main Track Full Papers; Part III: ICCS 2024 Main Track Short Papers; Advances in High-Performance Computational Earth Sciences: Numerical Methods, Frameworks and Applications; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Part IV: Biomedical and Bioinformatics Challenges for Computer Science; Computational Health; Part V: Computational Optimization, Modelling, and Simulation; Generative AI and Large Language Models (LLMs) in Advancing Computational Medicine; Machine Learning and Data Assimilation for Dynamical Systems; Multiscale Modelling and Simulation; Part VI: Network Models and Analysis: From Foundations to Artificial Intelligence; Numerical Algorithms and Computer Arithmetic for Computational Science; Quantum Computing; Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks, and Artificial Intelligence; Solving Problems with Uncertainties; Teaching Computational Science

This book constitutes the proceedings of the 45th International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2024, held in Geneva, Switzerland, during June 26-28, 2024. The 19 full papers and 2 invited papers included in this book were carefully reviewed and selected from 42 submissions. They were organized in topical sections as follows: Business Process Modelling and Mining, Semantics and Complexity, Reachability, Verification and Model Checking, Applications and Tools.

This textbook offers a comprehensive introduction to the concepts underpinning our modern understanding of complex and emergent behavior. Mathematical methods necessary for the discussion are introduced and explained on the run. All derivations are presented step-by-step. This new fifth edition has been fully revised and includes a new chapter, a range of new sections, figures and exercises. The Solution chapter has been reorganized for clarity.The core aspects of modern complex system sciences are presented in the first chapters, covering the foundations of network- and dynamical system theory, with a particular focus on scale-free networks and tipping phenomena. The notion of deterministic chaos is treated together with bifurcation theory and the intricacies of time delays. Modern information theoretical principles are discussed in further chapters, together with the notion of self-organized criticality, synchronization phenomena, and a game-theoretical treatment of the tragedy of the commons. The dynamical systems view of modern machine learning is presented in a new chapter.Chapters include exercises and suggestions for further reading. The textbook is suitable for graduate and advanced undergraduate students. The prerequisites are the basic mathematical tools of courses in natural sciences, computer science or engineering.

This book provides a systematic presentation of the major results in the field of the theory of k-out-of-n systems obtained in recent years and their applications for the reliability assessment of high-altitude unmanned platforms. Mathematical models, methods, and algorithms, presented in the book, will make a significant contribution to the development of reliability theory and the theoretical foundations of unmanned UAV-based aerial communications networks in the framework of the concept of creating the 5G and beyond networks. The book gives a description of new mathematical methods and approaches (based on decomposable semi-regenerative processes, simulation and machine learning methods, and inventory models) to the study of the complex k-out-of-n systems, which makes it possible to carry out numerical calculations of reliability indicators. Organized into five chapters, each chapter begins with a summary of the main definitions andresults contained in the chapter. The content of this book is based on the original results developed by the authors, many of which appear for the first time in book form.

This book provides a general, unified approach to the theory of polyadic groups, their normal subgroups and matrix representations.The author focuses on those properties of polyadic groups which are not present in the binary case. These properties indicate a strong relationship between polyadic groups and various group-like algebras, as well as ternary Hopf algebras and n-Lie algebras that are widely used in theoretical physics.The relationships of polyadic groups with special types of binary groups, called covering groups and binary retracts, are described. These relationships allow the study of polyadic groups using these binary groups and their automorphisms.The book also describes the affine geometry induced by polyadic groups and fuzzy subsets defined on polyadic groups. Finally, we discuss the categories of polyadic groups and the relationships between the different varieties of polyadic groups. In many cases, we give elegant new proofs of known theorems. We also give many interesting examples and applications.The book contains many little-known results from articles previously published in hard-to-reach Russian, Ukrainian and Macedonian journals. These articles are not in English.

Computation and Modeling for Fractional Order Systems provides readers with problem-solving techniques for obtaining exact and/or approximate solutions of governing equations arising in fractional dynamical systems presented using various analytical, semi-analytical, and numerical methods. In this regard, this book brings together contemporary and computationally efficient methods for investigating real-world fractional order systems in one volume. Fractional calculus has gained increasing popularity and relevance over the last few decades, due to its well-established applications in various fields of science and engineering. It deals with the differential and integral operators with non-integral powers. Fractional differential equations are the pillar of various systems occurring in a wide range of science and engineering disciplines, namely physics, chemical engineering, mathematical biology, financial mathematics, structural mechanics, control theory, circuit analysis, and biomechanics, among others. The fractional derivative has also been used in various other physical problems, such as frequency-dependent damping behavior of structures, motion of a plate in a Newtonian fluid, PID controller for the control of dynamical systems, and many others. The mathematical models in electromagnetics, rheology, viscoelasticity, electrochemistry, control theory, Brownian motion, signal and image processing, fluid dynamics, financial mathematics, and material science are well defined by fractional-order differential equations. Generally, these physical models are demonstrated either by ordinary or partial differential equations. However, modeling these problems by fractional differential equations, on the other hand, can make the physics of the systems more feasible and practical in some cases. In order to know the behavior of these systems, we need to study the solutions of the governing fractional models. The exact solution of fractional differential equations may not always be possible using known classical methods. Generally, the physical models occurring in nature comprise complex phenomena, and it is sometimes challenging to obtain the solution (both analytical and numerical) of nonlinear differential equations of fractional order. Various aspects of mathematical modeling that may include deterministic or uncertain (viz. fuzzy or interval or stochastic) scenarios along with fractional order (singular/non-singular kernels) are important to understand the dynamical systems. Computation and Modeling for Fractional Order Systems covers various types of fractional order models in deterministic and non-deterministic scenarios. Various analytical/semi-analytical/numerical methods are applied for solving real-life fractional order problems. The comprehensive descriptions of different recently developed fractional singular, non-singular, fractal-fractional, and discrete fractional operators, along with computationally efficient methods, are included for the reader to understand how these may be applied to real-world systems, and a wide variety of dynamical systems such as deterministic, stochastic, continuous, and discrete are addressed by the authors of the book.

This book considers a class of ergodic finite controllable Markov's chains. The main idea behind the method, described in this book, is to develop the original discrete optimization problems (or game models) in the space of randomized formulations, where the variables stand in for the distributions (mixed strategies or preferences) of the original discrete (pure) strategies in the use. The following suppositions are made: a finite state space, a limited action space, continuity of the probabilities and rewards associated with the actions, and a necessity for accessibility. These hypotheses lead to the existence of an optimal policy. The best course of action is always stationary. It is either simple (i.e., nonrandomized stationary) or composed of two nonrandomized policies, which is equivalent to randomly selecting one of two simple policies throughout each epoch by tossing a biased coin. As a bonus, the optimization procedure just has to repeatedly solve the time-average dynamic programming equation, making it theoretically feasible to choose the optimum course of action under the global restriction. In the ergodic cases the state distributions, generated by the corresponding transition equations, exponentially quickly converge to their stationary (final) values. This makes it possible to employ all widely used optimization methods (such as Gradient-like procedures, Extra-proximal method, Lagrange's multipliers, Tikhonov's regularization), including the related numerical techniques. In the book we tackle different problems and theoretical Markov models like controllable and ergodic Markov chains, multi-objective Pareto front solutions, partially observable Markov chains, continuous-time Markov chains, Nash equilibrium and Stackelberg equilibrium, Lyapunov-like function in Markov chains, Best-reply strategy, Bayesian incentive-compatible mechanisms, Bayesian Partially Observable Markov Games, bargaining solutions for Nash and Kalai-Smorodinsky formulations, multi-traffic signal-control synchronization problem, Rubinstein's non-cooperative bargaining solutions, the transfer pricing problem as bargaining.

This volume convenes selected, peer-reviewed research and survey articles that address the modern state-of-the-art in varied areas of applied mathematical analysis. They were presented at the 1st Southern Georgia Mathematics Conference (SGMC) that was virtually held on April 2—3, 2021, at Georgia Southern University, Statesboro, USA. Papers in this volume incorporate both advanced theory and methods from mathematical analysis and cover myriad topics such as imaging and inverse problems, evolutionary PDEs, symbolic computation, dynamics and data analysis, data science, computational mathematics, and more. This second volume focuses on modeling, simulation and data analytical studies in the field of computational mathematics. These studies and findings contained herein will be of interest to researchers and graduate students working in the fields of mathematical analysis, modeling, data analysis, and computation, with applications in many interdisciplinary applied sciences, including statistics, physics, biology, and medical imaging. They are particularly relevant to those at the forefront of applied mathematical and statistical analysis, as well as data science and other computational science disciplines. In its first edition, the Southern Georgia Mathematics Conference brought together 74 speakers from 70 different institutions, including the USA, Canada, Austria, and Botswana. Attendees included faculty, researchers, experts, graduate, and undergraduate students from all over the world.

This book establishes a modern practical approach to mechanical design. It introduces a full set of mechanical design theories and approaches to conduct and complete mechanical design tasks. The book uses Finite-Element Analysis (FEA) as a mechanical engineering tool to calculate stress/strain and then integrate it with failure theory to complete the mechanical design. FEA simulation always evaluates the stress and strain of any component/assembly no matter whether components/assemblies have complicated geometries and/or are under complicated loading conditions.

A textbook for a one-semester course in linear algebra for graduate or upper-level undergraduate students of mathematics and engineering. Employs a matrix perspective, and emphasizes training in definitions, theorems, and proofs. Annotation copyright Book News, Inc. Portland, Or.

This book covers the basics of numerical methods. Avoiding the definition-theorem-proof style, it instead focuses on numerical examples and simple pseudo-codes. The text begins with a chapter on floating point arithmetic before moving on to discuss norms, conditions numbers, solutions of systems of equations, the least squares problem, eigenvalue problems, interpolation, numerical integration, ordinary differential equations, optimization (including a detailed case study), and practical error estimations. Exercises (partly in MATLAB) are provided at the end of each chapter. Suitable for readers with minimal mathematical knowledge, the book not only offers an elementary introduction to numerical mathematics for programmers and engineers but also provides supporting material for students and teachers of mathematics.

This edited volume reports on the recent activities of the new Center for Approximation and Mathematical Data Analytics (CAMDA) at Texas A&M University. Chapters are based on talks from CAMDA's inaugural conference – held in May 2023 – and its seminar series, as well as work performed by members of the Center. They showcase the interdisciplinary nature of data science, emphasizing its mathematical and theoretical foundations, especially those rooted in approximation theory.

Modern complex multi-domain systems require increased efficiency, reliability and availability combined with reduced cost. This book contains papers on mathematical modeling and simulation of processes in various areas: in ecology and the environment, production and energy, information technology, samples of special purpose equipment, and cyber-physical systems. The outcomes presented in the book will be useful to specialists involved in the modeling and simulation of real-world system, management and decision-making models, production models, and software products. Scientists have the opportunity to familiarize themselves with the latest research in a variety of solutions proposed by leading scientists and to determine promising directions for solving complex scientific and practical tasks. Chapters of this book contain papers presented at the 18th MODS International Conference, November 13–15, 2023, Chernihiv, Ukraine.

This book gathers the latest advances, innovations, and applications in the field of computational engineering, as presented by leading international researchers and engineers at the 30th International Conference on Computational & Experimental Engineering and Sciences (ICCES), held in Singapore on August 3-6, 2024. ICCES covers all aspects of applied sciences and engineering: theoretical, analytical, computational, and experimental studies and solutions of problems in the physical, chemical, biological, mechanical, electrical, and mathematical sciences. As such, the book discusses highly diverse topics, including composites; bioengineering & biomechanics; geotechnical engineering; offshore & arctic engineering; multi-scale & multi-physics fluid engineering; structural integrity & longevity; materials design & simulation; and computer modeling methods in engineering. The contributions, which were selected by means of a rigorous international peer-review process, highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaborations.

This two-volume set LNCS 14788 and 14789 constitutes the refereed post-conference proceedings of the 15th International Conference on Advances in Swarm Intelligence, ICSI 2024, held in Xining, China, during August 23–26, 2024. The 74 revised full papers presented in these proceedings were carefully reviewed and selected from 156 submissions. The papers are organized in the following topical sections: Part I - Particle swarm optimization; Swarm intelligence computing; Differential evolution; Evolutionary algorithms; Multi-agent reinforcement learning & Multi-objective optimization. Part II - Route planning problem; Machine learning; Detection and prediction; Classification; Edge computing; Modeling and optimization & Analysis of review.

This book constitutes the refereed proceedings of the 26th International Workshop on Computer Algebra in Scientific Computing, CASC 2024, which took place in Rennes, France, during September 2 - September 6, 2024. The 19 full papers included in this book were carefully reviewed and selected from 23 submissions. The annual International Workshop CASC 2024 aims to bring together researchers in theoretical computer algebra (CA), engineers, scholars, as well as other allied professionals applying CA tools for solving problems in industry and in various branches of scientific computing to explore and discuss advancements, challenges, and innovations related to CA.

This book covers the International Conference on Engineering Research and Applications (ICERA 2023), which was held on December 1–2, 2023 at Thai Nguyen University of Technology in Thai Nguyen, Vietnam, and provided an international forum to disseminate information on latest theories and practices in engineering research and applications. The conference focused on original research work in areas including mechanical engineering, materials and mechanics of materials, mechatronics and micro mechatronics, automotive engineering, electrical and electronics engineering, information and communication technology. By disseminating the latest advances in the field, the proceedings of ICERA 2023, Advances in Engineering Research and Application, assists academics and professionals alike to reshape their thinking on sustainable development.

This book is aimed at researchers specializing in the kinematics of robot mechanisms as well as at doctoral students in guiding their research work. A spectrum of the latest achievements in kinematics analysis, modelling, simulation, design and control is covered. New theories and methods are applied to serial, parallel and cable-driven mechanisms for use in industrial or service robotics. The systems range from being less than fully mobile to kinematically redundant and over-constrained. Forty-nine papers are included, arranged in seven chapters, as presented at the 19th Symposium on Advances in Robot Kinematics 2024. The symposium, which has been held since 1988, was organized this time in Ljubljana (Slovenia), where it began thirty-six years ago. The papers have been rigorously selected based on peer review and are arranged in chapters randomly, as is the prevailing tradition of these symposia. In doing so, we aim to give equal emphasis to each of these achievements.

The first two editions of An Introduction to Partial Differential Equations with MATLAB® gained popularity among instructors and students at various universities throughout the world. Plain mathematical language is used in a friendly manner to provide a basic introduction to partial differential equations (PDEs).Suitable for a one- or two-semester introduction to PDEs and Fourier series, the book strives to provide physical, mathematical, and historical motivation for each topic. Equations are studied based on method of solution, rather than on type of equation.This third edition of this popular textbook updates the structure of the book by increasing the role of the computational portion, compared to previous editions. The redesigned content will be extremely useful for students of mathematics, physics, and engineering who would like to focus on the practical aspects of the study of PDEs, without sacrificing mathematical rigor. The authors have maintained flexibility in the order of topics.In addition, students will be able to use what they have learned in some later courses (for example, courses in numerical analysis, optimization, and PDE-based programming). Included in this new edition is a substantial amount of material on reviewing computational methods for solving ODEs (symbolically and numerically), visualizing solutions of PDEs, using MATLAB®'s symbolic programming toolbox, and applying various schemes from numerical analysis, along with suggestions for topics of course projects.Students will use sample MATLAB® or Python codes available online for their practical experiments and for completing computational lab assignments and course projects.

Mathematical Methods in Medical and Biological Sciences presents mathematical methods for computational models arising in the medical and biological sciences. The book presents several real-life medical and biological models, such as infectious and non-infectious diseases that can be modeled mathematically to accomplish　profound research in virtual environments when the cost of laboratory expenses is relatively high. It focuses on mathematical techniques that provide global solutions for models arising in medical and biological sciences by considering their long-term benefits. In addition, the book provides leading-edge developments and insights for a range of applications, including epidemiological modeling of pandemic dynamics, viral infection developments, cancer developments, blood oxygen dynamics, HIV infection spread, reaction-diffusion models, polio infection spread, and chaos modeling with fractional order derivatives. - Presents the mathematical treatment of a wide range of real-life medical and biological models, including both infectious and non-infectious diseases - Provides in-depth analysis of the spread of Covid-19, polio, and HIV, including discussion of computational methods and applications - Includes computational modeling methods, along with their practical applications, providing the basis for further exploration and research in epidemiology and applied biomedical sciences

This book builds bridges between two yet separated branches of theoretical and mathematical chemistry: Chemical Graph Theory and Electronic Structure Calculations. Although either of the fields have developed their own techniques, problems, methods, and favorite benchmark cases independent from each other, the authors have managed to bring them together by using the localization-delocalization matrix (LDM). The LDM is a novel molecular descriptor that fingerprints a molecule by condensing the complicated electronic information in one, mathematically manageable, object. In this book, the authors introduce the readers to modeling techniques based on LDMs. Their technique offers a high accuracy as well as robust predictive power, often dramatically surpassing the potential of either of the constituting methods on their own. In addition to the comprehensive and accessible introduction to this new field of theoretical chemistry, the authors offer their self-developed software free to download, so that readers can try running their own simulations. The described methods are very general and can easily be implemented for calculating various properties and parameters such as mosquito repelling activity, ionic liquid properties, local aromaticity of ring molecules, log P's, pKa's, LD50, corrosion inhibition activities, and Lewis acidities and basicities – to only name a few. The free downloadable software helps readers automate the analysis of the matrices described in this book and hence facilitates application of the described methodology.

These proceedings gather selected, peer-reviewed papers presented at the IV International Conference on Mathematics and its Applications in Science and Engineering – ICMASE 2023, held on July 12–14, 2023 by the University Center of Technology and Digital Arts (U-tad) in Madrid, Spain.Papers in this volume cover new developments in applications of mathematics in science and engineering, with an emphasis on mathematical and computational modeling of real-world problems. Topics range from the use of differential equations to model mechanical structures to the employ of number theory in the development of information security and cryptography. Educational issues specific to the acquisition of mathematical competencies by engineering and science students at all university levels are also touched on.Researchers, practitioners, and university students can significantly benefit from this volume, especially those seeking advanced methods for applying mathematics to various contexts and fields.

The two-volume proceedings constitutes the refereed proceedings of the 24th International Conference on Progress in Cryptology, INDOCRYPT 2023, Goa, India, in December 2023. The 26 full papers were carefully reviewed and selected from 74 submissions. They are organized in topical sections as follows: Part I: Symmetric-key cryptography, Hash functions, Authenticated Encryption Modes; Elliptic curves, Zero-knowledge proof, Signatures; Attacks. Part II: Secure computation, Algorithm hardness, Privacy; Post-quantum cryptography.

Nonlinear partial differential equations (PDE) are at the core of mathematical modeling. In the past decades and recent years, multiple analytical methods to study various aspects of the mathematical structure of nonlinear PDEs have been developed. Those aspects include C- and S-integrability, Lagrangian and Hamiltonian formulations, equivalence transformations, local and nonlocal symmetries, conservation laws, and more. Modern computational approaches and symbolic software can be employed to systematically derive and use such properties, and where possible, construct exact and approximate solutions of nonlinear equations. This book contains a consistent overview of multiple properties of nonlinear PDEs, their relations, computation algorithms, and a uniformly presented set of examples of application of these methods to specific PDEs. Examples include both well known nonlinear PDEs and less famous systems that arise in the context of shallow water waves and far beyond. The book will beof interest to researchers and graduate students in applied mathematics, physics, and engineering, and can be used as a basis for research, study, reference, and applications.

This book offers the reader a journey through the counterintuitive nature of Brownian motion under confinement. Diffusion is a universal phenomenon that controls a wide range of physical, chemical, and biological processes. The transport of spatially-constrained molecules and small particles is ubiquitous in nature and technology and plays an essential role in different processes. Understanding the physics of diffusion under conditions of confinement is essential for a number of biological phenomena and potential technological applications in micro- and nanofluidics, among others. Studies on diffusion under confinement are typically difficult to understand for young scientists and students because of the extensive background on diffusion processes, physics, and mathematics that is required. All of this information is provided in this book, which is essentially self-contained as a result of the authors'efforts to make it accessible to an audience of students from avariety of different backgrounds. The book also provides the necessary mathematical details so students can follow the technical process required to solve each problem. Readers will also find detailed explanations of the main results based on the last 30 years of research devoted to studying diffusion under confinement. The authors approach the physical problem from various angles and discuss the role of geometries and boundary conditions in diffusion. This textbook serves as a comprehensive and modern overview of Brownian motion under confinement and is intended for young scientists, graduate students, and advanced undergraduates in physics, physical chemistry, biology, chemistry, chemical engineering, biochemistry, bioengineering, and polymer and material sciences.

The second edition of this study guide is written and designed for students taking a precalculus course. It includes new and expanded exercises with final answers that will help students to review and sharpen their knowledge of the subject and enhance their performance in the classroom. The author uses methods typically found in instructor-recommended textbooks, offering detailed solutions, multiple methods for solving problems, and clear explanations of concepts. This hands-on guide will improve students'problem-solving skills and foster a solid understanding of calculus, which will benefit them in all of their calculus-based courses.

This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits'performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning.

This is the most recently developed book in Spatial Econometrics which cover important models and estimation methods. Its coverage is rather broad, and some of the topics covered have only been developed in the recent econometric literature in spatial econometrics.The book summarizes our devoted efforts on spatial econometrics that represent joint contributions with former PhD advisees from the Ohio State University in Columbus, Ohio, USA.The coverage is comprehensive and there are a total of sixteen chapters from basic statistics and statistical theory of linear-quadratic forms, law of large numbers (LLN) and central limit theory (CLT) on martingales to nonlinear spatial mixing and spatial near-epoch dependence theories, which can justify the statistic inferences for various spatial models and their estimation. New estimation and testing approaches in empirical likelihood and general empirical likelihood, and Bootstrapping are presented. Model selection is also discussed in this book. In addition to the popular spatial autoregressive models, there are chapters on multivariate SAR models, simultaneous SAR models, and panel dynamic spatial models. Recent econometric developments on intertemporal spatial models with rational expectations and flows data in trade theory will also be included. In terms of statistics, classical estimation, testing and inference are the main concerns, and we provide classical inference for the justification of Bayesian simulation approaches.

This book constitutes the refereed proceedings of the 22nd Asia Simulation Conference on Methods and Applications for Modeling and Simulation of Complex Systems, AsiaSim 2023, held in Langkawi, Malaysia, during October 25–26, 2023.The 77 full papers included in this book were carefully reviewed and selected from 164 submissions. They were organized in topical sections as follows: Modelling and Simulation, Artificial intelligence, Industry 4.0, Digital Twins Modelling, Simulation and Gaming, Simulation for Engineering, Simulation for Sustainable Development, Simulation in Social Sciences.

Often, when a new successful data processing techniques appears in one of the application areas, it then proves to be useful in many other areas. This was the case of optimal transportation techniques: these techniques were first developed for transportation problems, but now they have been shown to be successful in many statistical applications, including applications to economics. These techniques are the main focus of this book, but this book also contain papers that use other techniques, ranging from more traditional statistical approaches to more recent ones such as stochastic frontier methods, multivariable quantiles, random forest, and deep learning. Applications include all aspects of economics, from production (including agricultural) to trade (including international) and finances, with relation to issues of crime (including computer crime and cyberbullying), demographics, economic freedom, environment, health, and tourism. We hope that this volume will: help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially optimal transport techniques, and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments. The publication of this volume was partly supported by the Faculty of Economics of the Chiang Mai University, Thailand. Our thanks to the leadership and staff of the Chiang Mai University for providing crucial support. Our special thanks to Prof. Hung T. Nguyen for his valuable advice and constant support.

This richly illustrated book presents the latest techniques for the identifiability analysis and standard and robust regression analysis of complex dynamical models, and looks at their objectives. It begins by providing a definition of complexity in dynamic systems, introducing the concepts of system size, density of interactions, stiff dynamics, and the hybrid nature of determination. The discussion then turns to the mathematical foundations of model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection, and their algorithmic procedures. Although the featured examples mainly focus on applications to biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to determine identifiability conditions, how to search for an identifiable model, and how to conduct their own regression analysis and diagnostics without supervision. This new edition includes a concise, yet comprehensive treatment of the main artificial intelligence methods which can be used for parameter inference in models of complex dynamic biological systems. It emphasizes the most efficient solutions for generating synthetic data that augment the training data and which are indispensable for machine learning procedures. Featuring a wealth of real-world examples, exercises, and R codes, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Familiarity with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R are assumed.

This volume convenes selected, peer-reviewed research and survey articles that address the modern state-of-the-art in varied areas of applied mathematical analysis. They primarily include presentations as well as invited contributions for the 1st Southern Georgia Mathematics Conference (SGMC) that was virtually held on April 2—3, 2021 at the Georgia Southern University, Statesboro, USA. Papers in this volume incorporate both advanced theory and methods from mathematical analysis, and cover myriad topics like imaging and inverse problems, evolutionary PDEs, symbolic computation, dynamics and data analysis, data science, computational mathematics, and more. This first volume focuses on mathematical analysis theory and applications. These studies and findings contained herein will be of interest to researchers and graduate students working in the fields of mathematical analysis, modeling, data analysis and computation, with applications in many interdisciplinary applied sciences, as in statistics, physics, biology, and medical imaging. They are particularly relevant to those at the forefront of applied mathematical and statistical analysis, as well as data science and other computational science disciplines. In its first edition, the Southern Georgia Mathematics Conference brought together 74 speakers from 70 different institutions, from the USA, Canada, Austria, and Botswana. Attendees included faculty, researchers, experts, graduate and undergraduate students from all over the world.

This second edition of the book analyses the latest developments in investment management policy for institutional investors. It is divided into four parts, which have been updated to include the newest approaches and strategies in asset allocation, portfolio management and performance evaluation. The first part analyses the different types of institutional investors, i.e. institutions that professionally manage portfolios of financial and real assets on behalf of a wide range of individuals. This part goes on with an in-depth analysis of the economic, technical and regulatory characteristics of the various categories of investment funds and of other types of asset management products, which have a high rate of substitutability with investment funds and represent their natural competitors. The second part of the book identifies and investigates the stages of the investment portfolio management. Given the importance of strategic asset allocation in explaining the ex post performance of any type of investment portfolio, this part provides an in-depth analysis of asset allocation methods, illustrating the different theoretical and operational solutions available to institutional investors. The third part describes performance assessment, its breakdown and risk control, with an in-depth examination of performance evaluation techniques, returns-based style analysis approaches and performance attribution models. Finally, the fourth part deals with the subject of diversification into alternative asset classes, identifying the common characteristics and their possible role within the framework of investment management policy. This part analyses hedge funds, private equity, private debt, real estate, infrastructures, commodities and currency overlay techniques.

In this book, John Vince has reviewed and edited the third edition and added chapters on statistics, Georg Riemann's hypothesis, eigen vectors, curves, analytic geometry and Fourier analysis. These subjects complement the existing chapters on visual mathematics, numbers, algebra, logic, combinatorics, probability, modular arithmetic, trigonometry, coordinate systems, determinants, vectors, complex numbers, matrices, geometric matrix transforms, differential and integral calculus. During this journey, the author touches upon more esoteric topics such as quaternions, octonions, Grassmann algebra, barycentric coordinates, transfinite sets and prime numbers. John Vince describes a range of mathematical topics that provide a solid foundation for an undergraduate course in computer science, starting with a review of number systems and their relevance to digital computers and finishing with calculating area and volume using calculus. Readers will find that the author's visual approach should greatly improve their understanding as to why certain mathematical structures exist, together with how they are used in real-world applications. This book includes new, full-colour illustrations to clarify the mathematical descriptions, and in some cases, equations are also coloured to reveal vital algebraic patterns. The numerous worked examples will help consolidate the understanding of abstract mathematical concepts. Whether you intend to pursue a career in programming, scientific visualization, artificial intelligence, systems design or real-time computing, you should find the author's literary style refreshingly lucid and engaging and prepare you for more advanced texts.

This book constitutes the refereed proceedings of the 5th International Conference on Neural Computing for Advanced Applications, NCAA 2024, held in Guilin, China, during July 5–7, 2024. The 89 revised full papers presented in these proceedings were carefully reviewed and selected from 227 submissions. The papers are organized in the following topical sections: Part I: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications; Computer vision, and their engineering applications. Part II: Computational intelligence, nature-inspired optimizers, their engineering applications, and benchmarks. Part III: Natural language processing, knowledge graphs, recommender systems, multimodal Deep Learning, and their applications; Fault diagnosis and forecasting, prognostic management, Time-series analysis, and cyber-physical system security.

This book collects selected aspects of recent advances and experiences, emerging technology trends that have positively impacted our world from operators, authorities, and associations from CCIE 2024, to help address the world's advanced computing, control technology, information technology, artificial intelligence, machine learning, deep learning, and neural networks. Meanwhile, the topics included in the proceedings have high research value and present current insights, developments, and trends in computing, control, and industrial engineering.

This book provides a comprehensive overview of analytical methods for solving optimization problems, covering principles and mathematical techniques alongside numerical solution routines, including MAPLE and MAXIMA optimization routines. Each method is explained with practical applications and ANSYS APDL scripts for select problems. Chapters delve into topics such as scaling methods, torsion compliance, shape variation, topological optimization, anisotropic material properties, and differential geometry. Specific optimization problems, including stress minimization and mass reduction under constraints, are addressed. The book also explores isoperimetric inequalities and optimal material selection principles. Appendices offer insights into tensors, differential geometry, integral equations, and computer algebra codes. Overall, it's a comprehensive guide for engineers and researchers in structural optimization.