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Numerical Methods for Inverse Scattering Problems

This book highlights the latest developments on the numerical methods for inverse scattering problems associated with acoustic, electromagnetic, and elastic waves. Inverse scattering problems are concerned with identifying unknown or inaccessible objects by wave probing data, which makes possible many industrial and engineering applications including radar and sonar, medical imaging, nondestructive testing, remote sensing, and geophysical exploration. The mathematical study of inverse scattering problems is an active field of research. This book presents a comprehensive and unified mathematical treatment of various inverse scattering problems mainly from a numerical reconstruction perspective. It highlights the collaborative research outputs by the two groups of the authors yet surveys and reviews many existing results by global researchers in the literature. The book consists of three parts respectively corresponding to the studies on acoustic, electromagnetic, and elastic scattering problems. In each part, the authors start with in-depth theoretical and computational treatments of the forward scattering problems and then discuss various numerical reconstruction schemes for the associated inverse scattering problems in different scenarios of practical interest. In addition, the authors provide an overview of the existing results in the literature by other researchers. This book can serve as a handy reference for researchers or practitioners who are working on or implementing inverse scattering methods. It can also serve as a graduate textbook for research students who are interested in working on numerical algorithms for inverse scattering problems.

Applied Functional Analysis

WAIC and WBIC with R Stan: 100 Exercises for Building Logic

Master the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. This book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in R and Stan. Whether you’re a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory.The key features of this indispensable book include:A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise.100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension.A comprehensive guide to Sumio Watanabe’s groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians.Detailed source programs and Stan codes that will enhance readers’ grasp of the mathematical concepts presented.A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting.Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!

WAIC and WBIC with Python Stan: 100 Exercises for Building Logic

Master the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. The book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in Python and Stan. Whether you’re a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory.The key features of this indispensable book include:A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise.100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension.A comprehensive guide to Sumio Watanabe’s groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians.Detailed source programs and Stan codes that will enhance readers’ grasp of the mathematical concepts presented.A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting.Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!

Perception of Family and Work in Low-Fertility East Asia (SpringerBriefs in Population Studies)

This book is the first of its kind to incorporate subjective well-being (SWB) data to comprehensively explore perceptional factors that relate to fertility behavior in East Asia. The advantage of SWB data lies in the accessibility to rich information regarding perceptions, attitudes, and behaviors. With this advantage, the book inquires into the perceptions toward family and work and explores the attitudes that lead to low fertility in the region.To this end, first a comparative analysis with international cross-sectional data is performed and the East Asian characteristics of family and work perceptions are documented. Then, three democracies in the region are focused on—Japan, South Korea, and Taiwan—to investigate the relationships between cultural orientations, work–life balance, and fertility outcomes with panel data. In addition, East Asian results are compared with those in India, which has also been experiencing a rapid transition from a traditional society to an industrial one. The results support the idea that the friction between persistent gender-based role divisions and socioeconomic transformation in East Asia makes it difficult for women to balance family and work, prompting fertility decline to the lowest-low level in the region.

Proceedings of International Conference on Data Analytics and Insights, ICDAI 2023 (Lecture Notes in Networks and Systems #727)

The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Analytics and Insights (ICDAI 2023), organized by Techno International, Kolkata, India, during May 11–13, 2023. The book covers important topics like sensor and network data analytics and insights; big data analytics and insights; biological and biomedical data analysis and insights; optimization techniques, time series analysis and forecasting; power and energy systems data analytics and insights; civil and environmental data analytics and insights; and industry and applications.

Demystifying Causal Inference: Public Policy Applications with R

This book provides an accessible introduction to causal inference and data analysis with R, specifically for a public policy audience. It aims to demystify these topics by presenting them through practical policy examples from a range of disciplines. It provides a hands-on approach to working with data in R using the popular tidyverse package. High quality R packages for specific causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book.The book is in two parts. The first part begins with a detailed narrative about John Snow’s heroic investigations into the cause of cholera. The chapters that follow cover basic elements of R, regression, and an introduction to causality using the potential outcomes framework and causal graphs. The second part covers specific causal inference methods, including experiments, matching, panel data, difference-in-differences, regression discontinuity design, instrumental variables and meta-analysis, with the help of empirical case studies of policy issues. The book adopts a layered approach that makes it accessible and intuitive, using helpful concepts, applications, simulation, and data graphs. Many public policy questions are inherently causal, such as the effect of a policy on a particular outcome. Hence, the book would not only be of interest to students in public policy and executive education, but also to anyone interested in analysing data for application to public policy.

Machine Learning Methods

This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.

Probability-Based Multi-objective Optimization for Material Selection

The second edition of this book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection in viewpoint of system theory, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies (response surface methodology, orthogonal experimental design, and uniform experimental design) are all performed; robustness assessment and performance utility with desirable value are included; discretization treatment in the evaluation is presented; fuzzy-based approach and cluster analysis are involved; applications in portfolio investment and shortest path problem are concerned as well. The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone. It is designed to be used as a textbook for postgraduate and advanced undergraduate students in relevant majors, while also serving as a valuable reference book for scientists and engineers involved in related fields.

Benchmarks and Hybrid Algorithms in Optimization and Applications (Springer Tracts in Nature-Inspired Computing)

This book is specially focused on the latest developments and findings on hybrid algorithms and benchmarks in optimization and their applications in sciences, engineering, and industries. The book also provides some comprehensive reviews and surveys on implementations and coding aspects of benchmarks. The book is useful for Ph.D. students and researchers with a wide experience in the subject areas and also good reference for practitioners from academia and industrial applications.

ANOVA with Dependent Errors (SpringerBriefs in Statistics)

This book presents the latest results related to one- and two-way models for time series data. Analysis of variance (ANOVA) is a classical statistical method for IID data proposed by R.A. Fisher to investigate factors and interactions of phenomena. In contrast, the methods developed in this book apply to time series data. Testing theory of the homogeneity of groups is presented under a wide variety of situations including uncorrelated and correlated groups, fixed and random effects, multi- and high-dimension, parametric and nonparametric spectral densities. These methods have applications in several scientific fields. A test for the existence of interactions is also proposed. The book deals with asymptotics when the number of groups is fixed and sample size diverges. This framework distinguishes the approach of the book from panel data and longitudinal analyses, which mostly deal with cases in which the number of groups is large. The usefulness of the theory in this book is illustrated by numerical simulation and real data analysis. This book is suitable for theoretical statisticians and economists as well as psychologists and data analysts.

Advanced Trajectory Optimization, Guidance and Control Strategies for Aerospace Vehicles: Methods and Applications (Springer Aerospace Technology)

This book focuses on the design and application of advanced trajectory optimization and guidance and control (G&C) techniques for aerospace vehicles. Part I of the book focuses on the introduction of constrained aerospace vehicle trajectory optimization problems, with particular emphasis on the design of high-fidelity trajectory optimization methods, heuristic optimization-based strategies, and fast convexification-based algorithms. In Part II, various optimization theory/artificial intelligence (AI)-based methods are constructed and presented, including dynamic programming-based methods, model predictive control-based methods, and deep neural network-based algorithms. Key aspects of the application of these approaches, such as their main advantages and inherent challenges, are detailed and discussed. Some practical implementation considerations are then summarized, together with a number of future research topics. The comprehensive and systematic treatment of practical issues in aerospace trajectory optimization and guidance and control problems is one of the main features of the book, which is particularly suitable for readers interested in learning practical solutions in aerospace trajectory optimization and guidance and control. The book is useful to researchers, engineers, and graduate students in the fields of G&C systems, engineering optimization, applied optimal control theory, etc.

Marginal Revolution in Economics: A Reappraisal (Monographs in Mathematical Economics #6)

This volume is devoted to a reappraisal of the Marginal Revolution on the occasion of its 150th anniversary. The year 1871 should be remembered as one of the most important turning points in the history of economics. W. S. Jevons, C. Menger, and L. Walras published epochal works at the very beginning of the 1870s. Although these works were written independently, they shared a common mathematical structure based on classical analysis. For this reason, the emergence of the trio is called the Marginal Revolution. Indeed, 1871 is the starting point of modern economics in the proper sense. In 1971, several academic conferences were held on the occasion of the 100th anniversary of the Revolution, which exerted the stimulating influence upon the historical researches into the Revolution. Now more than fifty years have passed since then. Economic theory has experienced further substantial changes in researchers’ central interest, the way of reasonings and the styles of description during this period. In view of the new achievements acquired in recent fifty years, it seems an indispensable task for us to review and reevaluate the Marginal Revolution based upon the present status of economics.We also keep in mind that some concepts and doctrines once discarded could reappear in a later stage of history in a more or less transfigured form. The introductory chapter will be a guide for readers not only from the economics community but also from the mathematics community.

Gaps and Actions in Health Improvement from Hong Kong and Beyond: All for Health

This book provides a timely review on what has been accomplished, and what remains amiss, following the World Health Organization’s 1978 ‘Health for All’ campaign, by identifying enduring gaps in health care within a global context. The WHO declaration of "Health for All by the Year 2000" mapped out a road towards primary health care for all people and demarcated it as essential for human progress in terms of economic development and social justice. However, 45 years have gone by, and most societies and countries have yet achieved 'health for all’, despite so much having changed in technology, disease patterns, and population demographics. In promoting community health and improving service delivery, the book advocates the development and implementation of “All For Health” strategies to steer stakeholders in the right direction towards universal health care. The book covers the gaps and actions in health improvements, the ‘All For Health’ strategies, and the Health in All Policies (HiAP), reviewing and discussing issues through both Asian and international examples. Contributors include both academics and practitioners from diverse professional backgrounds including medicine, nursing, pharmacy, allied health, dietetics, social sciences, life sciences, education, business, administration, law, and public policy. Essential to scholars in public health and related disciplines, this book is also useful to policymakers, community and public health practitioners, and health care executives and interns.

Big Data Analytics in Intelligent IoT and Cyber-Physical Systems (Transactions on Computer Systems and Networks)

This book explores the complete system perspective, underlying theories, modeling, and applications of cyber-physical systems (CPS). Considering the interest of researchers and academicians, the editors present this book in a multidimensional perspective covering CPS at breadth. It covers topics ranging from discussion of rudiments of the system and efficient management to recent research challenges and issues. This book is divided into four sections discussing the fundamentals of CPS, engineering-based solutions, its applications, and advanced research challenges. The contents highlight the concept map of CPS including the latest technological interventions, issues, challenges, and the integration of CPS with IoT and big data analytics, modeling solutions, distributed management, efficient energy management, cyber-physical systems research, and education with applications in industrial, agriculture, and medical domains. This book is of immense interest to those in academia and industry.

Collaborative Optimization of Complex Energy Systems: Applications in Iron and Steel Industry (Engineering Applications of Computational Methods #17)

This book mainly focuses on the multi-media energy prediction technology and optimization methods of iron and steel enterprises. The technical methods adopted include swarm intelligence algorithm, neural network, reinforcement learning, and so on. Energy saving and consumption reduction in iron and steel enterprises have always been a research hotspot in the field of process control. This book considers the multi-media energy balance problem from the perspective of system, studies the energy flow and material flow in iron and steel enterprises, and provides energy optimization methods that can be used for planning, prediction, and scheduling under different production scenes. The main audience of this book is scholars and graduate students in the fields of control theory, applied mathematics, energy optimization, etc.

Discrete Choice Experiments Using R: A How-To Guide for Social and Managerial Sciences

This book delivers a user guide reference for researchers seeking to build their capabilities in conducting discrete choice experiment (DCE). The book is born out of the observation of the growing popularity – but lack of understanding – of the techniques to investigate preferences. It acknowledges that these broader decision-making processes are often difficult, or sometimes, impossible to study using conventional methods. While DCE is more mature in certain fields, it is relatively new in disciplines within social and managerial sciences. This text addresses these gaps as the first ‘how-to’ handbook that discusses the design and application of DCE methodology using R for social and managerial science research. Whereas existing books on DCE are either research monographs or largely focused on technical aspects, this book offers a step-by-step application of DCE in R, underpinned by a theoretical discussion on the strengths and weaknesses of the DCE approach, with supporting examples of best practices. Relevant to a broad spectrum of emerging and established researchers who are interested in experimental research techniques, particularly those that pertain to the measurements of preferences and decision-making, it is also useful to policymakers, government officials, and NGOs working in social scientific spaces.

Macdonald Polynomials: Commuting Family of q-Difference Operators and Their Joint Eigenfunctions (SpringerBriefs in Mathematical Physics #50)

This book is a volume of the Springer Briefs in Mathematical Physics and serves as an introductory textbook on the theory of Macdonald polynomials. It is based on a series of online lectures given by the author at the Royal Institute of Technology (KTH), Stockholm, in February and March 2021. Macdonald polynomials are a class of symmetric orthogonal polynomials in many variables. They include important classes of special functions such as Schur functions and Hall–Littlewood polynomials and play important roles in various fields of mathematics and mathematical physics. After an overview of Schur functions, the author introduces Macdonald polynomials (of type A, in the GLn version) as eigenfunctions of a q-difference operator, called the Macdonald–Ruijsenaars operator, in the ring of symmetric polynomials. Starting from this definition, various remarkable properties of Macdonald polynomials are explained, such as orthogonality, evaluation formulas, and self-duality, with emphasis on the roles of commuting q-difference operators. The author also explains how Macdonald polynomials are formulated in the framework of affine Hecke algebras and q-Dunkl operators.

Statistics and Data Analysis for Engineers and Scientists (Transactions on Computer Systems and Networks)

This textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level. It aims to combine the data analysis methods using three different programs—Microsoft Excel, SPSS, and MATLAB. The book combining the different data analysis tools is a unique approach. The book presents a variety of real-life problems in data analysis and machine learning, delivering the best solution. Analysis methods presented in this book include but are not limited to, performing various algebraic and trigonometric operations, regression modeling, and correlation, as well as plotting graphs and charts to represent the results. Fundamental concepts of applied statistics are also explained here, with illustrative examples. Thus, this book presents a pioneering solution to help a wide range of students, researchers, and professionals learn data processing, interpret different findings derived from the analyses, and apply them to their research or professional fields. The book also includes worked examples of practical problems. The primary focus behind designing these examples is understanding the concepts of data analysis and how it can solve problems. The chapters include practice exercises to assist users in enhancing their skills to execute statistical analysis calculations using software instead of relying on tables for probabilities and percentiles in the present world.

Rasch Meta-Metres of Growth for Some Intelligence and Attainment Tests

This book adapts Rasch’s approach for quantifying growth on physiological variables, where growth decelerates, to intellectual variables. To apply this approach, it is necessary to construct measurements in a constant unit over the relevant range of the variable. With such measurements, the book illustrates the approach to quantifying growth on six intellectual variables - two intelligences tests and two each of tests of proficiencies in reading comprehension and mathematics. The book discusses how it is not immediately obvious that deceleration on a quantitative scale should also hold for the growth in intellectual variables. It goes on to show that this is indeed the case with all six tests analysed and considers some implications of this feature for understanding intellectual development, in particular the centrality of the growth trajectory set in early life.

Intelligent Systems and Sustainable Computing: Proceedings of ICISSC 2022 (Smart Innovation, Systems and Technologies #363)

This book is a collection of best selected research papers presented at Second International Conference on Intelligent Systems and Sustainable Computing (ICISSC 2022), held in School of Engineering, Malla Reddy University, Hyderabad, India, during December 16–17, 2022. The book covers recent research in intelligent systems, intelligent business systems, soft computing, swarm intelligence, artificial intelligence and neural networks, data mining and data warehousing, cloud computing, distributed computing, big data analytics, Internet of things (IoT), machine learning, speech processing, sustainable high-performance systems, VLSI and embedded systems, image and video processing and signal processing and communication.

Digital Information Methods of Polarization, Mueller-Matrix and Fluorescent Microscopy: Differential Diagnosis of Aseptic and Septic Loosening of Artificial Hip Endoprosthesis Cups (SpringerBriefs in Applied Sciences and Technology)

This book highlights the effectiveness of differential diagnosis in the degree of severity of joint pathology from a clinical, biophysical, and informational point of view. It includes the following information blocks: • Two-dimensional digital polarization microscopy of polycrystalline films of synovial fluid and determination of the coordinate distributions of the orientation and phase parameters of the microscopic image from a set of parameters of the Stokes vector. • Mueller-matrix mapping of polycrystalline films of synovial fluid and determination of a set of coordinate distributions (Mueller-matrix images (MMI)) of azimuthal-invariant elements that characterize manifestations of optical activity and linear birefringence. • Development of algorithms for polarization reproduction of distributions of linear and circular birefringence of polycrystalline films of synovial fluid. • Identification of digital statistical, correlation and wavelet criteria of polarization and Mueller-matrix differential diagnosis of the degree of severity of joint pathology. • Determination of maps of laser-induced fluorescence of synovial fluid polycrystalline films. • Identification of statistical and correlational criteria for fluorescent differential diagnosis of the degree of severity of joint pathology. • Operational characteristics of the power of the methods of azimuth-invariant polarization, Mueller-matrix and laser autofluorescence microscopy of polycrystalline films of synovial fluid.

Industry 4.0 Technologies: Volume 1—Theory, Challenges, and Opportunity (Environmental Footprints and Eco-design of Products and Processes)

This book brings forth the fundamental understanding of the role of Industry 4.0 technologies in sustainable manufacturing supply chain. Readers will get an overview of the challenges, opportunities, and requirements for the implementation of digital technologies and how they can support manufacturing supply chains to be sustainable. The book presents many applications of Industry 4.0 including integration of IoT, AI, Big Data, Blockchain, Procurement 4.0, Logistics 4.0, and Lean 4.0 in different contexts. The book therefore provides a platform for researchers, academicians, and professionals from diverse backgrounds to gain state-of-the-art knowledge for using Industry 4.0 in sustainable manufacturing supply chains. Readers will also be able to identify the practical significance and opportunities for future work directions.

Computational Methods for Deep Learning: Theory, Algorithms, and Implementations (Texts in Computer Science)

The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.

Predictive Analytics for Mechanical Engineering: A Beginners Guide (SpringerBriefs in Applied Sciences and Technology)

This book focus on key component required for building predictive maintenance model. The current trend of Maintenance 4.0 leans towards the preventive mechanism enabled by predictive approach and condition-based smart maintenance. The intelligent decision support, earlier detection of spare part failure, fatigue detection is the main slices of intelligent and predictive maintenance system (PMS) leading towards Maintenance 4.0 This book presents prominent use cases of mechanical engineering using PMS along with the benefits. Basic understanding of data preparation is required for development of any AI application; in view of this, the types of the data and data preparation processes, and tools are also presented in this book.