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Modeling, Simulation and Optimization: Proceedings of CoMSO 2020 (Smart Innovation, Systems and Technologies #206)
by Biplab Das Ripon Patgiri Sivaji Bandyopadhyay Valentina Emilia BalasThis book includes selected peer-reviewed papers presented at the International Conference on Modeling, Simulation and Optimization, organized by National Institute of Technology, Silchar, Assam, India, during 3–5 August 2020. The book covers topics of modeling, simulation and optimization, including computational modeling and simulation, system modeling and simulation, device/VLSI modeling and simulation, control theory and applications, modeling and simulation of energy system and optimization. The book disseminates various models of diverse systems and includes solutions of emerging challenges of diverse scientific fields.
Modeling, Simulation and Optimization: Proceedings of CoMSO 2021 (Smart Innovation, Systems and Technologies #292)
by Biplab Das Ripon Patgiri Sivaji Bandyopadhyay Valentina Emilia BalasThis book includes selected peer-reviewed papers presented at the International Conference on Modeling, Simulation and Optimization (CoMSO 2021), organized by National Institute of Technology, Silchar, Assam, India, during December 16–18, 2021. The book covers topics of modeling, simulation and optimization, including computational modeling and simulation, system modeling and simulation, device/VLSI modeling and simulation, control theory and applications, modeling and simulation of energy systems and optimization. The book disseminates various models of diverse systems and includes solutions of emerging challenges of diverse scientific fields.
Modeling, Simulation and Optimization: Proceedings of CoMSO 2022 (Smart Innovation, Systems and Technologies #373)
by Biplab Das Ripon Patgiri Sivaji Bandyopadhyay Valentina Emilia Balas Sukanta RoyThis book includes selected peer-reviewed papers presented at the International Conference on Modeling, Simulation and Optimization (CoMSO 2022), organized by National Institute of Technology, Silchar, Assam, India, during December 21–23, 2022. The book covers topics of modeling, simulation, and optimization, including computational modeling and simulation, system modeling and simulation, device/VLSI modeling and simulation, control theory and applications, and modeling and simulation of energy systems and optimization. The book disseminates various models of diverse systems and includes solutions of emerging challenges of diverse scientific fields.
Modeling, Simulation and Optimization in the Health- and Energy-Sector (SEMA SIMAI Springer Series #14)
by René Pinnau Nicolas R. Gauger Axel KlarThis volume is addressed to people who are interested in modern mathematical solutions for real life applications. In particular, mathematical modeling, simulation and optimization is nowadays successfully used in various fields of application, like the energy- or health-sector. Here, mathematics is often the driving force for new innovations and most relevant for the success of many interdisciplinary projects. The presented chapters demonstrate the power of this emerging research field and show how society can benefit from applied mathematics.
Modeling, Simulation and Optimization of Complex Processes - HPSC 2012
by Hans Georg Bock Xuan Phu Hoang Rolf Rannacher Johannes P. SchlöderThis proceedings volume gathers a selection of papers presented at the Fifth International Conference on High Performance Scientific Computing, which took place in Hanoi on March 5-9, 2012. The conference was organized by the Institute of Mathematics of the Vietnam Academy of Science and Technology (VAST), the Interdisciplinary Center for Scientific Computing (IWR) of Heidelberg University, Ho Chi Minh City University of Technology, and the Vietnam Institute for Advanced Study in Mathematics. The contributions cover the broad interdisciplinary spectrum of scientific computing and present recent advances in theory, development of methods, and practical applications. Subjects covered include mathematical modeling; numerical simulation; methods for optimization and control; parallel computing; software development; and applications of scientific computing in physics, mechanics and biomechanics, material science, hydrology, chemistry, biology, biotechnology, medicine, sports, psychology, transport, logistics, communication networks, scheduling, industry, business and finance.
Modeling, Simulation and Optimization of Complex Processes HPSC 2018: Proceedings of the 7th International Conference on High Performance Scientific Computing, Hanoi, Vietnam, March 19-23, 2018
by Hans Georg Bock Willi Jäger Ekaterina Kostina Hoang Xuan PhuThis proceedings volume highlights a selection of papers presented at the 7th International Conference on High Performance Scientific Computing, which took place in Hanoi, Vietnam, during March 19-23, 2018. The conference has been organized by the Institute of Mathematics of the Vietnam Academy of Science and Technology, the Interdisciplinary Center for Scientific Computing (IWR) of Heidelberg University and the Vietnam Institute for Advanced Study in Mathematics. The contributions cover a broad, interdisciplinary spectrum of scientific computing and showcase recent advances in theory, methods, and practical applications. Subjects covered include numerical simulation, methods for optimization and control, machine learning, parallel computing and software development, as well as the applications of scientific computing in mechanical engineering, airspace engineering, environmental physics, decision making, hydrogeology, material science and electric circuits.
Modeling Social Processes of Aggregation
by Lawrence HazelriggThis book demonstrates, via formal statements and empirical illustrations, that nonlinearities in social processes can be modeled systematically to create solutions with practical applications in the institutional forms of paid employment, schooling, and familial relations including marital and kinship ties and the rearing of children. It shows how social processes can be modeled accurately through analyzing time series data—specifically, a temporal sequence of process outcomes that is dense enough in observation time to support appropriate techniques of modeling the outcome sequence. The book illustrates techniques using minimal mathematical formalism which is explained also in careful narrative descriptions of the model logic.
Modeling Software with Finite State Machines: A Practical Approach
by Thomas Wagner Ferdinand Wagner Ruedi Schmuki Peter WolstenholmeModeling Software with Finite State Machines: A Practical Approach explains how to apply finite state machines to software development. It provides a critical analysis of using finite state machines as a foundation for executable specifications to reduce software development effort and improve quality. It discusses the design of a state machine and of a system of state machines. It also presents a detailed analysis of development issues relating to behavior modeling with design examples and design rules for using finite state machines. This text demonstrates the implementation of these concepts using StateWORKS software and introduces the basic components of this software.
Modeling Spatio-Temporal Data: Markov Random Fields, Objective Bayes, and Multiscale Models
by Ferreira, Marco A. R.Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. Modeling Spatio-Temporal Data: Markov Random Fields, Objectives Bayes, and Multiscale Models aims to fill this gap by providing an overview of a variety of recently proposed approaches for the analysis of spatial and spatio-temporal datasets, including proper Gaussian Markov random fields, dynamic multiscale spatio-temporal models, and objective priors for spatial and spatio-temporal models. The goal is to make these approaches more accessible to practitioners, and to stimulate additional research in these important areas of spatial and spatio-temporal statistics.Key topics: Proper Gaussian Markov random fields and their uses as building blocks for spatio-temporal models and multiscale models. Hierarchical models with intrinsic conditional autoregressive priors for spatial random effects, including reference priors, results on fast computations, and objective Bayes model selection. Objective priors for state-space models and a new approximate reference prior for a spatio-temporal model with dynamic spatio-temporal random effects. Spatio-temporal models based on proper Gaussian Markov random fields for Poisson observations. Dynamic multiscale spatio-temporal thresholding for spatial clustering and data compression. Multiscale spatio-temporal assimilation of computer model output and monitoring station data. Dynamic multiscale heteroscedastic multivariate spatio-temporal models. The M-open multiple optima paradox and some of its practical implications for multiscale modeling. Ensembles of dynamic multiscale spatio-temporal models for smooth spatio-temporal processes. The audience for this book are practitioners, researchers, and graduate students in statistics, data science, machine learning, and related fields. Prerequisites for this book are master's-level courses on statistical inference, linear models, and Bayesian statistics. This book can be used as a textbook for a special topics course on spatial and spatio-temporal statistics, as well as supplementary material for graduate courses on spatial and spatio-temporal modeling.
Modeling Students' Mathematical Modeling Competencies
by Andrew Hurford Christopher R. Haines Peter L. Galbraith Richard LeshAs we enter the 21st century, there is an urgent need for new approaches to mathematics education emphasizing its relevance in young learners' futures. Modeling Students' Mathematical Modeling Competencies explores the vital trend toward using real-world problems as a basis for teaching mathematics skills, competencies, and applications. Blending theoretical constructs and practical considerations, the book presents papers from the latest conference of the ICTMA, beginning with the basics (Why are models necessary? Where can we find them?) and moving through intricate concepts of how students perceive math, how instructors teach--and how both can become better learners. Dispatches as varied as classroom case studies, analyses of math in engineering work, and an in-depth review of modeling-based curricula in the Netherlands illustrate modeling activities on the job, methods of overcoming math resistance, and the movement toward replicable models and lifelong engagement. A sampling of topics covered: How students recognize the usefulness of mathematicsCreating the modeling-oriented classroomAssessing and evaluating students' modeling capabilitiesThe relationship between modeling and problem-solvingInstructor methods for developing their own models of modelingNew technologies for modeling in the classroomModeling Students' Mathematical Modeling Competencies offers welcome clarity and focus to the international research and professional community in mathematics, science, and engineering education, as well as those involved in the sciences of teaching and learning these subjects.
Modeling Survival Data Using Frailty Models: Second Edition (Industrial and Applied Mathematics)
by David D. HanagalThis book presents the basic concepts of survival analysis and frailty models, covering both fundamental and advanced topics. It focuses on applications of statistical tools in biology and medicine, highlighting the latest frailty-model methodologies and applications in these areas. After explaining the basic concepts of survival analysis, the book goes on to discuss shared, bivariate, and correlated frailty models and their applications. It also features nine datasets that have been analyzed using the R statistical package. Covering recent topics, not addressed elsewhere in the literature, this book is of immense use to scientists, researchers, students and teachers.
Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases
by Alberto D'Onofrio Piero ManfrediThis volume summarizes the state-of-the-art in the fast growing research area of modeling the influence of information-driven human behavior on the spread and control of infectious diseases. In particular, it features the two main and inter-related "core" topics: behavioral changes in response to global threats, for example, pandemic influenza, and the pseudo-rational opposition to vaccines. In order to make realistic predictions, modelers need to go beyond classical mathematical epidemiology to take these dynamic effects into account. With contributions from experts in this field, the book fills a void in the literature. It goes beyond classical texts, yet preserves the rationale of many of them by sticking to the underlying biology without compromising on scientific rigor. Epidemiologists, theoretical biologists, biophysicists, applied mathematicians, and PhD students will benefit from this book. However, it is also written for Public Health professionals interested in understanding models, and to advanced undergraduate students, since it only requires a working knowledge of mathematical epidemiology.
Modeling to Inform Infectious Disease Control
by Niels G. BeckerEffectively Assess Intervention Options for Controlling Infectious DiseasesOur experiences with the human immunodeficiency virus (HIV), severe acute respiratory syndrome (SARS), and Ebola virus disease (EVD) remind us of the continuing need to be vigilant against the emergence of new infectious diseases. Mathematical modeling is increasingly used i
Modeling Tools for Environmental Engineers and Scientists
by Nirmala KhandanModeling Tools for Environmental Engineers and Scientists enables environmental professionals, faculty, and students with minimal computer programming skills to develop computer-based mathematical models for natural and engineered environmental systems. The author illustrates how commercially available syntax-free authoring software can be adapted
Modeling Visual Aesthetics, Emotion, and Artistic Style
by James Z. Wang Reginald B. Adams Jr.Modeling Visual Aesthetics, Emotion, and Artistic Style offers a comprehensive exploration of the increasingly significant topic of the complex interplay between human perception and digital technology. It embodies the cumulative knowledge and efforts of a wide array of active researchers and practitioners from diverse fields including computer vision, affective computing, robotics, psychology, data mining, machine learning, art history, and movement analysis. This volume seeks to address the profound and challenging research questions related to the computational modeling and analysis of visual aesthetics, emotions, and artistic style, vital components of the human experience that are increasingly relevant in our digitally connected world. The book's vast scope encompasses a broad range of topics. The initial chapters lay a strong foundation with background knowledge on emotion models and machine learning, which then transitions into exploring social visual perception in humans and its technological applications. Readers will uncover the psychological and neurological foundations of social and emotional perception from faces and bodies. Subsequent sections broaden this understanding to include technology's role in detecting discrete and subtle emotional expressions, examining facial neutrality, and including research contexts that involve children as well as adults. Furthermore, the book illuminates the dynamic intersection of art and technology, the language of photography, the relationship between breath-driven robotic performances and human dance, and the application of machine learning in analyzing artistic styles. This book sets itself apart with its unique multidisciplinary approach, encouraging collaboration across related domains. Packed with comprehensive tutorials, theoretical reviews, novel methodologies, empirical investigations, and comparative analyses, the book offers a rich combination of knowledge and methodologies. The book's focus on cutting-edge research not only presents the latest developments in the field but also illuminates potential paths that can lead to significant advancements in computer and robotic applications.
Modeling Waves with Numerical Calculations Using Python (Synthesis Lectures on Wave Phenomena in the Physical Sciences)
by Rhett AllainNumerical calculations (what many call computational physics) is a core tool in modern physics. With numerical methods it’s possible to solve problems that would otherwise be impossible. Most physics students and educators have at least some exposure to the wave equation. It shows up in many different contexts—light, quantum mechanics, and even a simple wave on a string. However, it can be difficult to come up with non-trivial solutions to the wave equation. This text goes through the techniques to create a numerical model of the wave equation starting from the very basics and using free and open source tools such as Python and Web VPython.
Modeling with Mathematics: A Bridge to Algebra II
by Sharon Benson Roland Cheyney David Eschberger Jo Ann WheelerAn innovative course that offers students an exciting new perspective on mathematics,Modeling With Mathematicsexplores how mathematics can help explore problems real people encounter in their jobs and lives. Mathematical modeling and a data-driven approach to exploring functions helps students deepen their mathematical skills and maturity. Modeling With Mathematics: A Bridge To Algebra IIhas been designed for students who have completed Algebra I or Algebra I and Geometry but need review practice and motivation to succeed in Algebra II. In addition the course gives students a look ahead to many Algebra II topics. Modeling With Mathematics: A Bridge To Algebra II list serv http://www. whfreeman. com/bridgelistserv. pdf As a service to instructors usingModeling With Mathematics: A Bridge To Algebra II, a listserv has been designed as a forum to share ideas, ask questions and learn new ways to enhance the learning experience for their students.
Modeling with Mathematics: A Bridge to Algebra II
by Sharon Benson Roland Cheyney David Eschberger Jo Ann WheelerAn innovative course that offers students an exciting new perspective on mathematics, Modeling With Mathematics explores how mathematics can help explore problems real people encounter in their jobs and lives. Mathematical modeling and a data-driven approach to exploring functions helps students deepen their mathematical skills and maturity. Modeling With Mathematics: A Bridge To Algebra II has been designed for students who have completed Algebra I or Algebra I and Geometry but need review practice and motivation to succeed in Algebra II.
Modeling with Mathematics: A Bridge to Algebra II
by Nancy Crisler Gary SimundzaWith the emphasis the Common Core State Standards (CCSS) places on modeling, Modeling With Mathematics: A Bridge to Algebra II (Bridge 2e) addresses these modeling requirements while helping prepare students for success in Algebra II. Intended for students who have taken Algebra I and Geometry but who are not yet ready for Algebra II, this program helps solidify their understanding by providing a different kind of learning experience. With Bridge 2e students model real-world applications with a functions approach netting a deeper grasp of the important concepts necessary for success in Algebra II and on the forthcoming Common Core assessments.
Modeling with Nonsmooth Dynamics (Frontiers in Applied Dynamical Systems: Reviews and Tutorials #7)
by Mike R. JeffreyThis volume looks at the study of dynamical systems with discontinuities. Discontinuities arise when systems are subject to switches, decisions, or other abrupt changes in their underlying properties that require a ‘non-smooth’ definition. A review of current ideas and introduction to key methods is given, with a view to opening discussion of a major open problem in our fundamental understanding of what nonsmooth models are. What does a nonsmooth model represent: an approximation, a toy model, a sophisticated qualitative capturing of empirical law, or a mere abstraction? Tackling this question means confronting rarely discussed indeterminacies and ambiguities in how we define, simulate, and solve nonsmooth models. The author illustrates these with simple examples based on genetic regulation and investment games, and proposes precise mathematical tools to tackle them.The volume is aimed at students and researchers who have some experience of dynamical systems, whether as a modelling tool or studying theoretically. Pointing to a range of theoretical and applied literature, the author introduces the key ideas needed to tackle nonsmooth models, but also shows the gaps in understanding that all researchers should be bearing in mind.Mike Jeffrey is a researcher and lecturer at the University of Bristol with a background in mathematical physics, specializing in dynamics, singularities, and asymptotics.
Modeling with Stochastic Programming (Springer Series in Operations Research and Financial Engineering)
by Alan J. King Stein W. WallaceThis is an updated version of what is still the only text to address basic questions about how to model uncertainty in mathematical programming, including how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This second edition has important extensions regarding how to represent random phenomena in the models (also called scenario generation) as well as a new chapter on multi-stage models. This text would be suitable as a stand-alone or supplement for a second course in OR/MS or in optimization-oriented engineering disciplines where the instructor wants to explain where models come from and what the fundamental modeling issues are. The book is easy-to-read, highly illustrated with lots of examples and discussions. It will be suitable for graduate students and researchers working in operations research, mathematics, engineering and related departments where there is interest in learning how to model uncertainty. Alan King is a Research Staff Member at IBM's Thomas J. Watson Research Center in New York. Stein W. Wallace is a Professor of Operational Research and head of Center for Shipping and Logistics at NHH Norwegian School of Economics, Bergen, Norway.
Modellieren der Realität mit Mathematik
by Alfio QuarteroniDie Simulation des menschlichen Herzens, die Vorhersage des morgigen Wetters, die Optimierung der Aerodynamik eines Segelboots, die Suche nach der idealen Garzeit für einen Hamburger: Bei der Lösung dieser Probleme können Kardiologen, Meteorologen, Sportler und Ingenieure auf mathematische Hilfe zählen. Dieses Buch führt Sie zur Entdeckung einer magischen, aus Gleichungen bestehenden Welt, die für eine Vielzahl von wichtigen Problemen unseres Lebens nützliche Antworten liefern können.Die Übersetzung wurde mit Hilfe von künstlicher Intelligenz durchgeführt. Eine anschließende menschliche Überarbeitung erfolgte vor allem in Bezug auf den Inhalt.
Modellierungskompetenzen – Diagnose und Bewertung (Realitätsbezüge im Mathematikunterricht)
by Gilbert Greefrath Katja MaaßDer vorliegende Band widmet sich der notwendigen Frage, wie Lernleistungen bei Modellierungsaufgaben erkannt, gemessen und bewertet werden können. Dazu werden in Beiträgen verschiedene Tests zur Messung von Modellierungskompetenzen vorgestellt. Teilkompetenzen, die gut erfasst werden können, umfassen zum Beispiel Vereinfachen, Strukturieren, Erstellen von schriftliche Lösungen und Präsentationstechniken. Diskutiert wird aber auch die Bewertung komplexer Modellierungsaufgaben. Selbstredend geht es auch um lernförderliche Rückmeldung im Lehr-Lern-Prozess. Die Auswirkungen der Verwendung metakognitiver Lösungsstrategien wird untersucht. Vorgestellt wird weiter ein Kompetenzstufenmodell, das zur Aufgabenstellung und Leistungsinterpretation für die schriftliche Reifeprüfung in Österreich dient, sowie die Konzeption von Modellierungsaufgaben im Abitur.Dieser Band zeigt, wie mathematisches Modellieren sinnvoll und gewinnbringend genutzt werden kann und liefert Materialien und Ideen für den Einsatz in Schule und Hochschule.
Modelling Aging and Migration Effects on Spatial Labor Markets (Advances in Spatial Science)
by Uwe Blien Peter Nijkamp Karima Kourtit Roger R. StoughThe aging and migration megatrends and their impact on spatial – regional and local – labor market performance is the core theme of this book, and thus together define its scope and focus. The contributions provide an overview of key aging and migration issues in various countries together with analyses of their varied impacts on regional labor markets. Systematic database research and related empirical analyses are used to map out the complex and dynamic nature of these trends, while cutting-edge economic and modeling techniques are used to analyze them. In closing, the book critically reviews and assesses selected policy measures designed to cope with the effects of aging and migration on regional labor markets.
Modelling, Analysis, and Control of Networked Dynamical Systems (Systems & Control: Foundations & Applications)
by Ziyang Meng Tao Yang Karl H. JohanssonThis monograph provides a comprehensive exploration of new tools for modelling, analysis, and control of networked dynamical systems. Expanding on the authors’ previous work, this volume highlights how local exchange of information and cooperation among neighboring agents can lead to emergent global behaviors in a given networked dynamical system.Divided into four sections, the first part of the book begins with some preliminaries and the general networked dynamical model that is used throughout the rest of the book. The second part focuses on synchronization of networked dynamical systems, synchronization with non-expansive dynamics, periodic solutions of networked dynamical systems, and modulus consensus of cooperative-antagonistic networks. In the third section, the authors solve control problems with input constraint, large delays, and heterogeneous dynamics. The final section of the book is devoted to applications, studying control problems of spacecraft formation flying, multi-robot rendezvous, and energy resource coordination of power networks.Modelling, Analysis, and Control of Networked Dynamical Systems will appeal to researchers and graduate students interested in control theory and its applications, particularly those working in networked control systems, multi-agent systems, and cyber-physical systems. This volume can also be used in advanced undergraduate and graduate courses on networked control systems and multi-agent systems.