This book explores the fundamentals of smart cities along with issues, controversies, problems and applications concerning security and privacy in smart city development. Future smart cities must incorporate innovations like smart rainwater harvesting, smart street lighting, digital identity management, solar energy, intelligent transport systems and emerging communication applications. The target audience of the book includes professionals, researchers, academics, advanced-level students, technology developers, doctors and biologists working in the field of smart city applications. Professionals will find innovative ideas for marketing and research, while developers can use various technologies like IoTand block chain to develop the applications discussed here. As the book shows, by integrating new technologies, the cities of the future are becoming a reality today.

The first complete guide to using the Stochastic Finite Element Method for reliability assessment

Unlike other analytical reliability estimation techniques, the Stochastic Finite Element Method (SFEM) can be used for both implicit and explicit performance functions, making it a particularly powerful and robust tool for today's engineer. This book, written by two pioneers in SFEM-based methodologies, shows how to use SFEM for the reliability analysis of a wide range of structures.

It begins by reviewing essential risk concepts, currently available risk evaluation procedures, and the use of analytical and sampling methods in estimating risk. Next, it introduces SFEM evaluation procedures, with detailed coverage of displacement-based and stress-based deterministic finite element approaches. Linear, nonlinear, static, and dynamic problems are considered separately to demonstrate the robustness of the methods. The risk or reliability estimation procedure for each case is presented in different chapters, with theory complemented by a useful series of examples.

Integrating advanced concepts in risk-based design, finite elements, and mechanics, Reliability Assessment Using Stochastic Finite Element Analysis is vital reading for engineering professionals and students in all areas of the field.

This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory course in probability, will greatly benefit from this book. Students are expected to know matrix algebra, calculus, probability and distribution theory before beginning this course. Presenting a wealth of relevant solved and unsolved problems, the book offers an excellent tool for teachers and instructors who can assign homework problems from the exercises, and students will find the solved examples hugely beneficial in solving the exercise problems.

Cities have always played a prominent role in the prosperity of civilization. Indeed, every great civilization we can think of is associated with the prominence of one or more thriving cities. And so understanding cities -- their inhabitants, their institutions, their infrastructure -- what they are and how they work independently and together -- is of fundamental importance to our collective growth as a human civilization. Furthermore, the 21st century "smart" city, as a result global climate change and large-scale urbanization, will emerge as a societal grand challenge. This book focuses on the role of interdependent infrastructure systems in such smart cities especially as it relates to timely and poignant questions about resilience and sustainability. In particular, the goal of this book is to present, in one volume, a consistent Hetero-Functional Graph Theoretic (HFGT) treatment of interdependent smart city infrastructures as an overarching application domain of engineering systems. This work may be contrasted to the growing literature on multi-layer networks, which despite significant theoretical advances in recent years, has modeling limitations that prevent their real-world application to interdependent smart city infrastructures of arbitrary topology. In contrast, this book demonstrates that HFGT can be applied extensibly to an arbitrary number of arbitrarily connected topologies of interdependent smart city infrastructures. It also integrates, for the first time, all six matrices of HFGT in a single system adjacency matrix. The book makes every effort to be accessible to a broad audience of infrastructure system practitioners and researchers (e.g. electric power system planners, transportation engineers, and hydrologists, etc.). Consequently, the book has extensively visualized the graph theoretic concepts for greater intuition and clarity. Nevertheless, the book does require a common methodological base of its readers and directs itself to the Model-Based Systems Engineering (MBSE) community and the Network Science Community (NSC). To the MBSE community, we hope that HFGT will be accepted as a quantification of many of the structural concepts found in model-based systems engineering languages like SysML. To the NSC, we hope to present a new view as how to construct graphs with fundamentally different meaning and insight. Finally, it is our hope that HFGT serves to overcome many of the theoretical and modeling limitations that have hindered our ability to systematically understand the structure and function of smart cities.

Most of the real-life signals are non-stationary in nature. The examples of such signals include biomedical signals, communication signals, speech, earthquake signals, vibration signals, etc. Time-frequency analysis plays an important role for extracting the meaningful information from these signals. The book presents time-frequency analysis methods together with their various applications. The basic concepts of signals and different ways of representing signals have been provided. The various time-frequency analysis techniques namely, short-time Fourier transform, wavelet transform, quadratic time-frequency transforms, advanced wavelet transforms, and adaptive time-frequency transforms have been explained. The fundamentals related to these methods are included. The various examples have been included in the book to explain the presented concepts effectively. The recently developed time-frequency analysis techniques such as, Fourier-Bessel series expansion-based methods, synchrosqueezed wavelet transform, tunable-Q wavelet transform, iterative eigenvalue decomposition of Hankel matrix, variational mode decomposition, Fourier decomposition method, etc. have been explained in the book. The numerous applications of time-frequency analysis techniques in various research areas have been demonstrated. This book covers basic concepts of signals, time-frequency analysis, and various conventional and advanced time-frequency analysis methods along with their applications. The set of problems included in the book will be helpful to gain an expertise in time-frequency analysis. The material presented in this book will be useful for students, academicians, and researchers to understand the fundamentals and applications related to time-frequency analysis.

This volume is a continuation of Volume 1 following the previously published Editorial. More emphasis is given to novel nanocarrier designs, their characterization and function, and applications for drug discovery and treatment. A number of chapters will deal with nanofibers as a new major application within the biomedical field with a very high success rate particularly in wound healing and diabetic foot and spine injuries. A major new subdivision will deal with mathematical methods for the assembly of nanocarriers both for simulation and function.

Domain decomposition methods are well suited to design algorithms for parallel computers. These allow researchers to tackle large-scale mathematical problems in aerodynamics and other areas of science and engineering. This book discusses the principles of the method in connection with a wide variety of both theoretical and practical problems.

This book concentrates on linear regression, path analysis and logistic regressions, the most used statistical techniques for the test of causal relationships. Its emphasis is on the conceptions and applications of the techniques by using simple examples without requesting any mathematical knowledge. It shows multiple regression analysis accurately reconstructs the causal relationships between phenomena. So, it can be used to test the hypotheses about causal relationships between variables. It presents that potential effects of each independent variable on the dependent variable are not limited to direct and indirect effects. The path analysis shows each independent variable has a pure effect on the dependent variable. So, it can be shown the unique contribution of each independent variable to the variation of the dependent variable. It is an advanced statistical text for the graduate students in social and behavior sciences. It also serves as a reference for professionals and researchers.

Recent results from high-energy scattering and theoretical developments of string theory require a change in our understanding of the basic structure of space-time. This book is about the advancement of ideas on the stochastic nature of space-time from the 1930s onward. In particular, the author promotes the concept of space as a set of hazy lumps, first introduced by Karl Menger, and constructs a novel framework for statistical behaviour at the microlevel. The various chapters address topics such as space-time fluctuation and random potential, non-local fields, and the origin of stochasticity. Implications in astro-particle physics and cosmology are also explored. Audience: This volume will be of interest to physicists, chemists and mathematicians involved in particle physics, astrophysics and cosmology.

These are the proceedings of the 19th international conference on domain decomposition methods in science and engineering. Domain decomposition methods are iterative methods for solving the often very large linear or nonlinear systems of algebraic equations that arise in various problems in mathematics, computational science, engineering and industry. They are designed for massively parallel computers and take the memory hierarchy of such systems into account. This is essential for approaching peak floating point performance. There is an increasingly well-developed theory which is having a direct impact on the development and improvement of these algorithms.

This book organizes principles and methods of signal processing and machine learning into the framework of coherence. The book contains a wealth of classical and modern methods of inference, some reported here for the first time. General results are applied to problems in communications, cognitive radio, passive and active radar and sonar, multi-sensor array processing, spectrum analysis, hyperspectral imaging, subspace clustering, and related. The reader will find new results for model fitting; for dimension reduction in models and ambient spaces; for detection, estimation, and space-time series analysis; for subspace averaging; and for uncertainty quantification. Throughout, the transformation invariances of statistics are clarified, geometries are illuminated, and null distributions are given where tractable. Stochastic representations are emphasized, as these are central to Monte Carlo simulations. The appendices contain a comprehensive account of matrix theory, the SVD, the multivariate normal distribution, and many of the important distributions for coherence statistics. The book begins with a review of classical results in the physical and engineering sciences where coherence plays a fundamental role. Then least squares theory and the theory of minimum mean-squared error estimation are developed, with special attention paid to statistics that may be interpreted as coherence statistics. A chapter on classical hypothesis tests for covariance structure introduces the next three chapters on matched and adaptive subspace detectors. These detectors are derived from likelihood reasoning, but it is their geometries and invariances that qualify them as coherence statistics. A chapter on independence testing in space-time data sets leads to a definition of broadband coherence, and contains novel applications to cognitive radio and the analysis of cyclostationarity. The chapter on subspace averaging reviews basic results and derives an order-fitting rule for determining the dimension of an average subspace. These results are used to enumerate sources of acoustic and electromagnetic radiation and to cluster subspaces into similarity classes. The chapter on performance bounds and uncertainty quantification emphasizes the geometry of the Cramer-Rao bound and its related information geometry.

Anaesthetic trainees often have enormous trouble understanding the quantitative aspects of the basic sciences underlying clinical anaesthetic practice. The subjects of pharmokinetics and statistics are often unpopular with trainees, and studied with little enthusiasm. In spite of their reluctance, this is an area that they are required to study and understand as a core part of their training for postgraduate exams. This text presents simple mathematical ideas, and explains how these can be used to model and understand problems which arise in clinical anaesthesia. The common features of the underlying mathematics are emphasized through a pictorial/graphical approach, in preference to vast amounts of algebra. The book presents statistics in an informal style, incorporating illustrations and cartoons throughout. The book should be valuable to anaesthetists, in guiding them through what can be an intimidating part of their training.

Special functions are essential for solving problems in virtually all engineering disciplines. Assuming only knowledge of elementary calculus and differential equations, this concise, clearly written reference illustrates the properties and applications of the special functions most frequently needed by practising engineers. Copious illustrations of worked out sample problems from a wide range of real-world engineering applications distinguish this work from others.

This book is part of a two volume set which presents the analysis of nonlinear phenomena as a long-standing challenge for research in basic and applied science as well as engineering. It discusses nonlinear differential and differential equations, bifurcation theory for periodic orbits and global connections. The integrability and reversibility of planar vector fields and theoretical analysis of classic physical models are sketched. This first volume concentrates on the mathematical theory and computational techniques that are essential for the study of nonlinear science, a second volume deals with real-world nonlinear phenomena in condensed matter, biology and optics.

This volume presents state of the art analyses from scholars dealing with a range of demographic topics of current concern, including longevity, mortality and morbidity, migration, and how population composition impacts intergenerational transfer schemes. New approaches are applied to such issues as measuring changes in cohort survivorship in low mortality populations, patterns of mortality improvement at older ages, and the consequences of heterogeneity in the susceptibility to death. Studies examine models of the current status of the HIV/AIDS epidemic, advance present methods for estimating population change in small areas, and strive to disentangle age, period, and cohort effects. In sum, the book addresses key contemporary issues in measuring and modeling dynamic populations, and advances the frontier of dynamic demography.

This book systematically discusses nonlinear interval optimization design theory and methods. Firstly, adopting a mathematical programming theory perspective, it develops an innovative mathematical transformation model to deal with general nonlinear interval uncertain optimization problems, which is able to equivalently convert complex interval uncertain optimization problems to simple deterministic optimization problems. This model is then used as the basis for various interval uncertain optimization algorithms for engineering applications, which address the low efficiency caused by double-layer nested optimization. Further, the book extends the nonlinear interval optimization theory to design problems associated with multiple optimization objectives, multiple disciplines, and parameter dependence, and establishes the corresponding interval optimization models and solution algorithms. Lastly, it uses the proposed interval uncertain optimization models and methods to deal with practical problems in mechanical engineering and related fields, demonstrating the effectiveness of the models and methods.

This is the first volume of a set of two devoted to the operator approach to linear problems in hydrodynamics. It presents functional analytical methods applied to the study of small movements and normal oscillations of hydromechanical systems having cavities filled with either ideal or viscous fluids. The work is a sequel to and at the same time substantially extends the volume "Operator Methods in Linear Hydrodynamics: Evolution and Spectral Problems" by N.D. Kopachevsky, S.G. Krein and Ngo Zuy Kan, published in 1989 by Nauka in Moscow. It includes several new problems on the oscillations of partially dissipative hydrosystems and the oscillations of visco-elastic or relaxing fluids. The work relies on the authors' and their students' works of the last 30-40 years. The readers are not supposed to be familiar with the methods of functional analysis. In the first part of the present volume, the main facts of linear operator theory relevant to linearized problems of hydrodynamics are summarized, including elements of the theories of distributions, self-adjoint operators in Hilbert spaces and in spaces with an indefinite metric, evolution equations and asymptotic methods for their solutions, the spectral theory of operator pencils. The book is particularly useful for researchers, engineers and students in fluid mechanics and mathematics interested in operator theoretical methods for the analysis of hydrodynamical problems.

This book gathers the outcomes of the second ECCOMAS CM3 Conference series on transport, which addressed the main challenges and opportunities that computation and big data represent for transport and mobility in the automotive, logistics, aeronautics and marine-maritime fields. Through a series of plenary lectures and mini-forums with lectures followed by question-and-answer sessions, the conference explored potential solutions and innovations to improve transport and mobility in surface and air applications. The book seeks to answer the question of how computational research in transport can provide innovative solutions to Green Transportation challenges identified in the ambitious Horizon 2020 program. In particular, the respective papers present the state of the art in transport modeling, simulation and optimization in the fields of maritime, aeronautics, automotive and logistics research. In addition, the content includes two white papers on transport challenges and prospects. Given its scope, the book will be of interest to students, researchers, engineers and practitioners whose work involves the implementation of Intelligent Transport Systems (ITS) software for the optimal use of roads, including safety and security, traffic and travel data, surface and air traffic management, and freight logistics.

'Et moi, .. " si j'avait su comment en revenir, je One service mathematics bas rendered the human race. It bas put common sense back n'y serais point aile.' where it belongs, on the topmost shelf next to Jules Verne the dusty canister labelled 'discarded nonsense' . Eric T. Bell The series is divergent; therefore we may be able to do something with it O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and nonlineari ties abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sci ences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One ser vice topology has rendered mathematical physics .. .'; 'One service logic has rendered computer science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'ctre of this series."

This is a text designed to provide a frim grounding in mathematical methods for chemical engineering students and researchers in academia and industry. Mathematical Methods in Chemical Engineering builds on the reader's previous knowledge of calculus, differential equations, and linear algebra. Varma and Morbidelli offer an integrated treatment of linear operator theory from determinants through partial differential equations, and feature extensive chapters on both ordinary differential equations and perturbation methods. Numerous high-quality diagrams and examples from chemical engineering illustrate the textual material and enhance the reader's understanding of complex mathematical systems.

This book gathers the proceedings of the 4th conference on Recent Advances in Engineering Math. & Physics (RAEMP 2019), which took place in Cairo, Egypt in December 2019. This international and interdisciplinary conference highlights essential research and developments in the field of Engineering Mathematics and Physics and related technologies and applications. The proceedings is organized to follow the main tracks of the conference: Advanced computational techniques in engineering and sciences; computational intelligence; photonics; physical measurements and big data analytics; physics and nano-technologies; and optimization and mathematical analysis.

It is not intuitive to accept that there exists a link between quantum physical systems and cognitive systems. However, recent research has shown that cognitive systems and collective (social) systems, including biology, exhibit uncertainty which can be successfully modelled with quantum probability. The use of such probability allows for the modelling of situations which typically violate the laws of classical probability. The Palgrave Handbook of Quantum Models in Social Science is is a unique volume that brings together contributions from leading experts on key topics in this new and emerging field. Completely self-contained, it begins with an introductory section which gathers all the fundamental notions required to be able to understand later chapters. The handbook then moves on to address some of the latest research and applications for quantum methods in social science disciplines, including economics, politics and psychology. It begins with the issue of how the quantum mechanical framework can be applied to economics. Chapters devoted to this topic range from how Fisher information can be argued to play a role in economics, to the foundations and application of quantum game theory. The handbook then progresses in considering how belief states can be updated with the theory of quantum measurements (and also with more general methods). The practical use of the Hilbert space (and Fock space) in decision theory is then introduced, and open quantum systems are also considered. The handbook also treats a model of neural oscillators that reproduces some of the features of quantum cognition. Other contributions delve into causal reasoning using quantum Bayes nets and the role of quantum probability in modelling so called affective evaluation. The handbook is rounded off with two chapters which discuss the grand challenges which lie ahead of us. How can the quantum formalism be justified in social science and is the traditional quantum formalism too restrictive? Finally, a question is posed: whether there is a necessary role for quantum mathematical models to go beyond physics. This book will bring the latest and most cutting edge research on quantum theory to social science disciplines. Students and researchers across the discipline, as well as those in the fields of physics and mathematics will welcome this important addition to the literature.

This volume features a variety of research projects at the intersection of mathematics and public policy. The topics included here fall in the areas of cybersecurity and climate change, two broad and impactful issues that benefit greatly from mathematical techniques. Each chapter in the book is a mathematical look into a specific research question related to one of these issues, an approach that offers the reader insight into the application of mathematics to important public policy questions. The articles in this volume are papers inspired by a Workshop for Women in Mathematics and Public Policy, held January 22-25, 2019 at the Institute for Pure and Applied Mathematics and the Luskin Center at the University of California, Los Angeles. The workshop was created to promote and develop women at all levels of their careers as researchers in mathematics and public policy. The idea was modeled after other successful Research Collaboration Conferences for Women, where junior and senior women come together at week-long conferences held at mathematics institutes to work on pre-defined research projects. The workshop focused on how mathematics can be used in public policy research and was designed to foster collaborative networks for women to help address the gender gap in mathematics and science.

This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.

This volume covers many diverse topics related in varying degrees to mathematics in mind including the mathematical and topological structures of thought and communication. It examines mathematics in mind from the perspective of the spiral, cyclic and hyperlinked structures of the human mind in terms of its language, its thoughts and its various modes of communication in science, philosophy, literature and the arts including a chapter devoted to the spiral structure of the thought of Marshall McLuhan. In it, the authors examine the topological structures of hypertext, hyperlinking, and hypermedia made possible by the Internet and the hyperlinked structures that existed before its emergence. It also explores the cognitive origins of mathematical thinking of the human mind and its relation to the emergence of spoken language, and studies the emergence of mathematical notation and its impact on education. Topics addressed include: * The historical context of any topic that involves how mathematical thinking emerged, focusing on archaeological and philological evidence. * Connection between math cognition and symbolism, annotation and other semiotic processes. * Interrelationships between mathematical discovery and cultural processes, including technological systems that guide the thrust of cognitive and social evolution. * Whether mathematics is an innate faculty or forged in cultural-historical context * What, if any, structures are shared between mathematics and language