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• 1. General View of the New Cybernetics in Physic
• s2. Principal Paradoxes of Classical Statistical Physic
• s3. Principal Paradoxes of Quantum Mechanic
• s4. Information Paradox and Grandfather Paradox in the Theories of Non-Quantum Gravity and Quantum Gravit
• y5. Ideal, Observable and Unpredictable DynamicsAppendix A. Basic Properties of Classical Statistical Mechanics, as Illustrated by Baker's MapAppendix B. The Basic Concepts of Quantum MechanicsAppendix C. Synergetic Models of Unpredictable Systems.
• (source: Nielsen Book Data)

Application of New Cybernetics in Physics describes the application of new cybernetics to physical problems and the resolution of basic physical paradoxes by considering external observer influence. This aids the reader in solving problems that were solved incorrectly or have not been solved. Three groups of problems of the new cybernetics are considered in the book: (a) Systems that can be calculated based on known physics of subsystems. This includes the external observer influence calculated from basic physical laws (ideal dynamics) and dynamics of a physical system influenced even by low noise. (b) Emergent systems. This includes external noise from the observer by using the black box model (complex dynamics), external noise from the observer by using the observer's intuition (unpredictable dynamics), defining boundaries of application of scientific methods for system behavior prediction, and the role of the observer's intuition for unpredictable systems. (c) Methods for solution of basic physical paradoxes by using methods of the new cybernetics: the entropy increase paradox, Schrodinger's cat paradox (wave package reduction in quantum mechanics), the black holes information paradox, and the time wormholes grandfather paradox. All of the above paradoxes have the same resolution based on the principles of new cybernetics. Indeed, even a small interaction of an observer with an observed system results in their time arrows' alignment (synchronization) and results in the paradox resolution and appearance of the universal time arrow.
(source: Nielsen Book Data)

• Foreword xi Preface xiii
• 1. Introduction 1 1.1 Background and Motivations, 1 1.1.1 Web Service and Service-Oriented Architecture, 1 1.1.2 Workflow Technology, 4 1.2 Overview of Standards, 8 1.2.1 Web Service-Related Standards, 8 1.2.2 Workflow-Related Standards, 19 1.3 Workflow Design: State of the Art, 22 1.3.1 Automatic Service Composition, 22 1.3.2 Mediation-Aided Service Composition, 23 1.3.3 Verification of Service-Based Workflows, 24 1.3.4 Decentralized Execution of Workflows, 25 1.3.5 Scientific Workflow Systems, 26 1.4 Contributions, 27
• 2. Petri Net Formalism 29 2.1 Basic Petri Nets, 29 2.2 Workflow Nets, 32 2.3 Colored Petri Nets, 35
• 3. Data-Driven Service Composition 39 3.1 Problem Statement, 40 3.1.1 Domains and Data Relations, 41 3.1.2 Problem Formulation, 43 3.2 Data-Driven Composition Rules, 45 3.2.1 Sequential Composition Rule, 46 3.2.2 Parallel Composition Rule, 46 3.2.3 Choice Composition Rule, 47 3.3 Data-Driven Service Composition, 48 3.3.1 Basic Definitions, 48 3.3.2 Derive AWSP from Service Net, 50 3.4 Effectiveness and Efficiency of the Data-Driven Approach, 55 3.4.1 Solution Effectiveness, 55 3.4.2 Complexity Analysis, 56 3.5 Case Study, 57 3.6 Discussion, 60 3.7 Summary, 61 3.8 Bibliographic Notes, 62
• 4. Analysis and Composition of Partially-Compatible Web Services 65 4.1 Problem Definition and Motivating Scenario, 65 4.1.1 A Motivating Scenario, 68 4.2 Petri Net Formalism for BPEL Service, Mediation, and Compatibility, 70 4.2.1 CPN Formalism for BPEL Process, 70 4.2.2 CPN Formalism for Service Composition, 73 4.2.3 Mediator and Mediation-Aided Service Composition, 75 4.3 Compatibility Analysis via Petri Net Models, 78 4.3.1 Transforming Abstract BPEL Process to SWF-net, 79 4.3.2 Specifying Data Mapping, 80 4.3.3 Mediator Existence Checking, 81 4.3.4 Proof of Theorem 4.1, 85 4.4 Mediator Generation Approach, 88 4.4.1 Types of Mediation, 88 4.4.2 Guided Mediator Generation, 90 4.5 Bibliographic Notes, 94 4.5.1 Web Service Composition, 94 4.5.2 Business Process Integration, 94 4.5.3 Web Service Configuration, 94 4.5.4 Petri Net Model of BPEL Processes, 94 4.5.5 Component/Web Service Mediation, 95
• 5. Web Service Configuration with Multiple Quality-of-Service Attributes 99 5.1 Introduction, 99 5.2 Quality-of-Service Measurements, 104 5.2.1 QoS Attributes, 104 5.2.2 Aggregation, 104 5.2.3 Computation of QoS, 105 5.3 Assembly Petri Nets and Their Properties, 107 5.3.1 Assembly and Disassembly Petri Nets, 107 5.3.2 Definition of Incidence Matrix and State-Shift Equation, 110 5.3.3 Definition of Subgraphs and Solutions, 111 5.4 Optimal Web Service Configuration, 114 5.4.1 Web Service Configuration under Single QoS Objective, 115 5.4.2 Web Service Configuration under Multiple QoS Objectives, 116 5.4.3 Experiments and Performance Analysis, 117 5.5 Implementation, 121 5.6 Summary, 123 5.7 Bibliographic Notes, 124
• 6. A Web Service-Based Public-Oriented Personalized Health Care Platform 127 6.1 Background and Motivation, 127 6.2 System Architecture, 129 6.2.1 The System Architecture of PHISP, 129 6.2.2 Services Encapsulated in PHISP, 131 6.2.3 Composite Service Specifications, 133 6.2.4 User/Domain Preferences, 134 6.3 Web Service Composition with Branch Structures, 137 6.3.1 Basic Ideas and Concepts, 137 6.3.2 Service Composition Planner Supporting Branch Structures, 139 6.3.3 Illustrating Examples, 148 6.4 Web Service Composition with Parallel Structures, 153 6.5 Demonstrations and Results, 155 6.5.1 WSC Example in PHISP, 155 6.5.2 Implementation of PHISP, 158 6.6 Summary, 159
• 7. Scientific Workflows Enabling Web-Scale Collaboration 161 7.1 Service-Oriented Infrastructure for Science, 162 7.1.1 Service-Oriented Scientific Exploration, 162 7.1.2 Case Study: The Cancer Grid (caGrid), 166 7.2 Scientific Workflows in Service-Oriented Science, 167 7.2.1 Scientific Workflow: Old Wine in New Bottle? 167 7.2.2 caGrid Workflow Toolkit, 174 7.2.3 Exemplary caGrid Workflows, 183 7.3 Summary, 188
• 8. Network Analysis and Reuse of Scientific Workflows 189 8.1 Social Computing Meets Scientific Workflow, 190 8.1.1 Social Network Services for Scientists, 191 8.1.2 Related Research Work, 197 8.2 Network Analysis of myExperiment, 199 8.2.1 Network Model at a Glance, 199 8.2.2 Undirected Network, 200 8.2.3 Directed Graph, 205 8.2.4 Summary of Findings, 206 8.3 ServiceMap: Providing Map and GPS Assisting Service Composition in Bioinformatics, 207 8.3.1 Motivation, 207 8.3.2 ServiceMap Approach, 209 8.3.3 What Do People Who Use These Services Also Use? 210 8.3.4 What is an Operation Chain Between Services/Operations, 212 8.3.5 An Empirical Study, 218 8.4 Summary, 219
• 9. Future Perspectives 221 9.1 Workflows in Hosting Platforms, 222 9.2 Workflows Empowered by Social Computing, 223 9.3 Workflows Meeting Big Data, 224 9.4 Emergency Workflow Management, 225 Abbreviations List 227 References 231 Index 247.
• (source: Nielsen Book Data)

This reference book for system engineers, architects, and managers focuses on how to design, analyze, and deploy Web service-based workflows for both business and scientific applications in a broad domain of healthcare and biomedicine. It discusses recent research and development results, as well as applications including healthcare and biomedical applications such as personalized healthcare processing, DNA sequence data processing, and electrocardiogram wave analysis. It also presents key methods such as Petri nets and social network analysis to advance the theory and applications of workflow design and Web service composition.
(source: Nielsen Book Data)

• 1. How Can Modeling and Simulation Help Engineering of System of Systems?
• 2. Multidisciplinary, Interdisciplinary and Transdisciplinary Federations in Support of New Medical Simulation Concepts: Harmonics for the Music of Life
• 3. Heterogeneous Computing: An Emerging Paradigm of Embedded Systems Design
• 4. Numerical Reproducibility of Parallel and Distributed Stochastic Simulation Using High-Performance Computing.
• (source: Nielsen Book Data)

Computational Frameworks: Systems, Models and Applications provides an overview of advanced perspectives that bridges the gap between frontline research and practical efforts. It is unique in showing the interdisciplinary nature of this area and the way in which it interacts with emerging technologies and techniques. As computational systems are a dominating part of daily lives and a required support for most of the engineering sciences, this book explores their usage (e.g. big data, high performance clusters, databases and information systems, integrated and embedded hardware/software components, smart devices, mobile and pervasive networks, cyber physical systems, etc.).
(source: Nielsen Book Data)

• 1. Introduction
• 2. What is Analytics?
• 3. Analytics Project Lifecycle
• 4. Analytics Project Business Case
• 5. Analytics Project Architecture
• 6. Analytics Project Team
• 7. Analytics Project Development Methodology
• 8. Existing Technology
• 9. Specialized Databases
• 10. Statistical Tools
• 11. Scoring and Rating Engine
• 12. Strategy Design Tool.
• (source: Nielsen Book Data)

Implementing Analytics demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption across all functions of an organization. Implementing Analytics simplifies and helps democratize a very specialized discipline to foster business efficiency and innovation without investing in multi-million dollar technology and manpower. A technology agnostic methodology that breaks down complex tasks like model design and tuning and emphasizes business decisions rather than the technology behind analytics.
(source: Nielsen Book Data)

• The First Information Theories
• Understanding Shannon through Play
• "Tele" : before Shannon
• Some Revisions of the Concept of Information
• Conceptualization and Representations
• From Captures to Data
• From Data to Aggregates
• Trace Deployment from Indexical Retention to Writing
• Interpretive Scaffoldings in Context
• Realities under the Watch of Horizons of Relevance
• Conclusion.

• PART 1
• The Foundations of Complexity
• PART 2
• Evidencing Field Complexity
• The New "Complex" Operational Context
• Taking Up Complexity
• PART 3
• Tackling Complexity with a Methodology
• Management and Control of Complex Systems
• Platforms for Taking up Complexity
• PART 4
• Applying Intrinsic Complexity: The Uberization of the Economy
• Computer-assisted Production Management
• Complexity and Cognitive Robotics.

Faced with ever-increasing complexity on a daily basis, the decision-makers of today are struggling to find the appropriate models, methods and tools to face the issues arising in complex systems across all levels of global operations. Having, in the past, resorted to outdated approaches which limit problem-solving to linear world views, we must now capitalize on complexities in order to succeed and progress in our society. This book provides a guide to harnessing the wealth inherent to complex systems. It organises the transition to complex decision-making in all business spheres while providing many examples in various application domains.

• Preface xi Acknowledgments xiii List of Abbreviations xv List of Notations xvii
• 1 Introduction 1 1.1 Background, Motivations, and Research Problems 2 1.2 Outline 7
• 2 Variance-Constrained Finite-Horizon Filtering and Control with Saturations 11 2.1 Problem Formulation for Finite-Horizon Filter Design 12 2.2 Analysis of H and Covariance Performances 14 2.3 Robust Finite-Horizon Filter Design 19 2.4 Robust H Finite-Horizon Control with Sensor and Actuator Saturations 22 2.5 Illustrative Examples 30 2.6 Summary 36
• 3 Filtering and Control with Stochastic Delays and Missing Measurements 41 3.1 Problem Formulation for Robust Filter Design 42 3.2 Robust H Filtering Performance Analysis 45 3.3 Robust H Filter Design 50 3.4 Robust H Fuzzy Control 53 3.5 Illustrative Examples 59 3.6 Summary 72
• 4 Filtering and Control for Systems with Repeated Scalar Nonlinearities 73 4.1 Problem Formulation for Filter Design 74 4.2 Filtering Performance Analysis 78 4.3 Filter Design 80 4.4 Observer-Based H Control with Multiple Packet Losses 83 4.5 Illustrative Examples 89 4.6 Summary 99
• 5 Filtering and Fault Detection for Markov Systems with Varying Nonlinearities 101 5.1 Problem Formulation for Robust H Filter Design 102 5.2 Performance Analysis of Robust H Filter 105 5.3 Design of Robust H Filters 109 5.4 Fault Detection with Sensor Saturations and Randomly Varying Nonlinearities 115 5.5 Illustrative Examples 122 5.6 Summary 138
• 6 Quantized Fault Detection with Mixed Time-Delays and Packet Dropouts 139 6.1 Problem Formulation for Fault Detection Filter Design 140 6.2 Main Results 143 6.3 Fuzzy-Model-Based Robust Fault Detection 150 6.4 Illustrative Examples 158 6.5 Summary 170
• 7 Distributed Filtering over Sensor Networks with Saturations 171 7.1 Problem Formulation 171 7.2 Main Results 176 7.3 An Illustrative Example 182 7.4 Summary 187
• 8 Distributed Filtering with Quantization Errors: The Finite-Horizon Case 189 8.1 Problem Formulation 189 8.2 Main Results 194 8.3 An Illustrative Example 198 8.4 Summary 203
• 9 Distributed Filtering for Markov Jump Nonlinear Time-Delay Systems 205 9.1 Problem Formulation 205 9.2 Main Results 211 9.3 An Illustrative Example 220 9.4 Summary 223
• 10 A New Finite-Horizon H Filtering Approach to Mobile Robot Localization 227 10.1 Mobile Robot Kinematics and Absolute Measurement 227 10.2 A Stochastic H Filter Design 232 10.3 Simulation Results 242 10.4 Summary 245
• 11 Conclusions and Future Work 247 11.1 Conclusions 247 11.2 Contributions 249 11.3 Future Work 250 References 253 Index 261.
• (source: Nielsen Book Data)

In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modelling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Filtering, Control and Fault Detection with Randomly Occurring Incomplete Information reflects the state-of-the-art of the research area for handling randomly occurring incomplete information from three interrelated aspects of control, filtering and fault detection. Recent advances in networked control systems and distributed filtering over sensor networks are covered, and application potential in mobile robotics is also considered. The reader will benefit from the introduction of new concepts, new models and new methodologies with practical significance in control engineering and signal processing. Key Features: Establishes a unified framework for filtering, control and fault detection problem for various discrete-time nonlinear stochastic systems with randomly occurring incomplete information Investigates several new concepts for randomly occurring phenomena and proposes a new system model to better describe network-induced problems Demonstrates how newly developed techniques can handle emerging mathematical and computational challenges Contains the latest research results Filtering, Control and Fault Detection with Randomly Occurring Incomplete Information provides a unified yet neat framework for control/filtering/fault-detection with randomly occurring incomplete information. It is a comprehensive textbook for graduate students and is also a useful practical research reference for engineers dealing with control, filtering and fault detection problems for networked systems.
(source: Nielsen Book Data)

• Preface xi 1 Introduction and Overview 1 Systems Perspectives 2 Systems Movement 3 Philosophical Background 3 Seminal Concepts Systems Science 5 Seminal Concepts Economics/Cognition 6 Seminal Concepts Operations Research 7 Seminal Concepts Sociology 8 Complexity and Complex Systems 8 Complex Versus Complicated Systems 11 Systems Practice 13 Phenomena as the Starting Point 19 Oveview of Book 20
• Chapter 1: Introduction and Overview 20
• Chapter 2: Overall Methodology 21
• Chapter 3: Perspectives on Phenomena 21
• Chapter 4: Physical Phenomena 21
• Chapter 5: Human Phenomena 21
• Chapter 6: Economic Phenomena 22
• Chapter 7: Social Phenomena 22
• Chapter 8: Visualization of Phenomena 22
• Chapter 9: Computational Methods and Tools 23
• Chapter 10: Perspectives on Problem Solving 23 References 23 2 Overall Methodology 27 Introduction 27 Problem Archetypes 29 Deterring or Identifying Counterfeit Parts 29 Financial Systems and Bursting Bubbles 30 Human Responses and Urban Resilience 30 Traffic Control via Congestion Pricing 31 Impacts of Investments in Healthcare Delivery 31 Human Biology and Cancer 31 Comparison of Problems 32 Methodology 33 Summary 35 An Example 36 Supporting the Methodology 40 Conclusions 41 References 41 3 Perspectives on Phenomena 43 Introduction 43 Definitions 43 Historical Perspectives 46 Steam to Steamboats 46 Wind to Wings 47 Electricity to Electric Lights 47 Macro and Micro Physics 47 Probability and Utility 48 Contemporary Perspectives 48 Four Fundamental Forces 48 Computational Fluid Dynamics 49 Integrated Circuit Design 49 Supply Chain Management 50 Summary 50 Taxonomy of Phenomena 50 Behavioral and Social Systems 52 Problems versus Phenomena 54 Visualizing Phenomena 54 Conclusions 58 References 59 4 Physical Phenomena 61 Introduction 61 Natural Phenomena 61 Example Human Biology 64 Example Urban Oceanography 67 Designed Phenomena 69 Example Vehicle Powertrain 73 Example Manufacturing Processes 75 Deterring or Identifying Counterfeit Parts 76 Conclusions 80 References 80 5 Human Phenomena 83 Descriptive Versus Prescriptive Approaches 84 Models of Human Behavior and Performance 86 Example Manual Control 87 Example Problem Solving 89 Example Multitask Decision Making 90 Traffic Control Via Congestion Pricing 92 Mental Models 95 Team Mental Models 99 Performing Arts Teams 101 Fundamental Limits 104 Conclusions 107 References 107 6 Economic Phenomena 111 Introduction 111 Microeconomics 113 Theory of the Firm 113 Theory of the Market 114 Example Optimal Pricing 114 Example Investing in People 118 Summary 119 Macroeconomics 119 Tax Rates Interest Rates and Inflation 120 Macroeconomic Models 126 Summary 128 Behavioral Economics 128 Prospect Theory 131 Risk Perception 132 Attribution Errors 133 Management Decision Making 134 Human Intuition 135 Intuition versus Analysis 136 Summary 137 Economics of Healthcare Delivery 137 Conclusions 139 References 140 7 Social Phenomena 143 Introduction 143 Emergent versus Designed Organizational Phenomena 143 Direct versus Representative Political Phenomena 144 Modeling Complex Social Systems 145 Example Earth as a System 145 Physics-Based Formulations 149 Example Castes and Outcastes 151 Network Theory 158 Game Theory 162 Example Acquisition as a Game 165 Simulation 168 Example Port and Airport Evacuation 170 Example Emergence of Cities 171 Urban Resilience 172 A Framework for Urban Resilience 173 Summary 176 Conclusions 176 References 176 8 Visualization of Phenomena 179 Introduction 179 Human Vision as a Phenomenon 180 Basics of Visualization 180 Example Space Shuttle Challenger 181 Purposes of Visualizations 183 Examples Co-Citation Networks and Mobile Devices 184 Design Methodology 185 Use Case Illustrations 186 Example Big Graphics and Little Screens 190 Visualization Tools 193 Data 195 Structure 195 Dynamics 195 Immersion Lab 196 Policy Flight Simulators 198 Background 198 Multilevel Modeling 199 Example Employee Prevention and Wellness 200 People s Use of Simulators 203 Conclusions 205 References 206 9 Computational Methods and Tools 209 Introduction 209 Modeling Paradigms 210 Dynamic Systems Theory 212 Control Theory 214 Estimation Theory 216 Queuing Theory 217 Network Theory 218 Decision Theory 221 Problem-Solving Theory 224 Finance Theory 225 Summary 228 Levels of Modeling 228 Representation to Computation 230 Dynamic Systems 230 Discrete-Event Systems 231 Agent-Based Systems 231 Optimization-Based Frame 231 Summary 233 Model Composition 233 Entangled States 233 Consistency of Assumptions 235 Observations 236 Computational Tools 236 Conclusions 237 References 238 10 Perspectives on Problem Solving 241 Introduction 241 What is? Versus What if? 242 Case Studies 243 Business Planning 243 New Product Planning 245 Technology Investments 248 Enterprise Transformation 250 Observations on Problem Solving 253 Starting Assumptions 253 Framing Problems 253 Implementing Solutions 255 Research Issues 255 Decomposition 256 Mapping 256 Scaling 257 Approximation 257 Identification 257 Parameterization 258 Propagation 258 Visualization 259 Curation 259 Conclusions 259 References 261 Index 263.
• (source: Nielsen Book Data)

Explains multi-level models of enterprise systems and covers modeling methodology This book addresses the essential phenomena underlying the overall behaviors of complex systems and enterprises. Understanding these phenomena can enable improving these systems. These phenomena range from physical, behavioral, and organizational, to economic and social, all of which involve significant human components. Specific phenomena of interest and how they are represented depend on the questions of interest and the relevant domains or contexts. Modeling and Visualization of Complex Systems and Enterprises examines visualization of phenomena and how understanding the relationships among phenomena can provide the basis for understanding where deeper exploration is warranted. The author also reviews mathematical and computational models, defined very broadly across disciplines, which can enable deeper understanding. Presents a 10 step methodology for addressing questions associated with the design or operation of complex systems and enterprises Examines six archetypal enterprise problems including two from healthcare, two from urban systems, and one each from financial systems and defense systems Provides an introduction to the nature of complex systems, historical perspectives on complexity and complex adaptive systems, and the evolution of systems practice Modeling and Visualization of Complex Systems and Enterprises is written for graduate students studying systems science and engineering and professionals involved in systems science and engineering, those involved in complex systems such as healthcare delivery, urban systems, sustainable energy, financial systems, and national security.
(source: Nielsen Book Data)

• Preface ix
• 1 Introduction 1 1.1 Background 1 1.1.1 Networked Multi-agent Systems 1 1.1.2 Collective Behaviors and Cooperative Control in Multi-agent Systems 2 1.1.3 Network Control in Multi-agent Systems 4 1.1.4 Distributed Consensus Filtering in Sensor Networks 5 1.2 Organization 6
• 2 Consensus in Multi-agent Systems 11 2.1 Consensus in Linear Multi-agent Systems 11 2.1.1 Preliminaries 11 2.1.2 Model Formulation and Results 13 2.2 Consensus in Nonlinear Multi-agent Systems 15 2.2.1 Preliminaries and Model Formulation 15 2.2.2 Local Consensus of Multi-agent Systems 16 2.2.3 Global Consensus of Multi-agent Systems in General Networks 19 2.2.4 Global Consensus of Multi-agent Systems in Virtual Networks 26 2.2.5 Simulation Examples 29 2.3 Notes 30
• 3 Second-Order Consensus in Multi-agent Systems 31 3.1 Second-Order Consensus in Linear Multi-agent Systems 32 3.1.1 Model Formulation 32 3.1.2 Second-Order Consensus in Directed Networks 33 3.1.3 Second-Order Consensus in Delayed Directed Networks 37 3.1.4 Simulation Examples 41 3.2 Second-Order Consensus in Nonlinear Multi-agent Systems 42 3.2.1 Preliminaries 42 3.2.2 Second-Order Consensus in Strongly Connected Networks 45 3.2.3 Second-Order Consensus in Rooted Networks 50 3.2.4 Simulation Examples 53 3.3 Notes 54
• 4 Higher-Order Consensus in Multi-agent Systems 56 4.1 Preliminaries 56 4.2 Higher-Order Consensus in a General Form 58 4.2.1 Synchronization in Complex Networks 58 4.2.2 Higher-Order Consensus in a General Form 59 4.2.3 Consensus Region in Higher-Order Consensus 60 4.3 Leader-Follower Control in Multi-agent Systems 64 4.3.1 Leader-Follower Control in Multi-agent Systems with Full-State Feedback 65 4.3.2 Leader-Follower Control with Observers 67 4.4 Simulation Examples 69 4.4.1 Consensus Regions 69 4.4.2 Leader-Follower Control with Full-State Feedback 70 4.4.3 Leader-Follower Control with Observers 70 4.5 Notes 71
• 5 Stability Analysis of Swarming Behaviors 73 5.1 Preliminaries 73 5.2 Analysis of Swarm Cohesion 76 5.3 Swarm Cohesion in a Noisy Environment 80 5.4 Cohesion in Swarms with Switched Topologies 82 5.5 Cohesion in Swarms with Changing Topologies 84 5.6 Simulation Examples 93 5.7 Notes 95
• 6 Distributed Leader-Follower Flocking Control 96 6.1 Preliminaries 96 6.1.1 Model Formulation 97 6.1.2 Nonsmooth Analysis 99 6.2 Distributed Leader-Follower Control with Pinning Observers 103 6.3 Simulation Examples 110 6.4 Notes 114
• 7 Consensus of Multi-agent Systems with Sampled Data Information 115 7.1 Problem Statement 116 7.2 Second-Order Consensus of Multi-agent Systems with Sampled Full Information 117 7.2.1 Second-Order Consensus of Multi-agent Systems with Sampled Full Information 119 7.2.2 Selection of Sampling Periods 122 7.2.3 Design of Coupling Gains 123 7.2.4 Consensus Region for the Network Spectrum 125 7.2.5 Second-Order Consensus in Delayed Undirected Networks with Sampled Position and Velocity Data 125 7.2.6 Simulation Examples 128 7.3 Second-Order Consensus of Multi-agent Systems with Sampled Position Information 132 7.3.1 Second-Order Consensus in Multi-agent Dynamical Systems with Sampled Position Data 132 7.3.2 Simulation Examples 139 7.4 Consensus of Multi-agent Systems with Nonlinear Dynamics and Sampled Information 142 7.4.1 The Case with a Fixed and Strongly Connected Topology 145 7.4.2 The Case with Topology Containing a Directed Spanning Tree 149 7.4.3 The Case with Topology Having no Directed Spanning Tree 155 7.5 Notes 158
• 8 Consensus of Second-Order Multi-agent Systems with Intermittent Communication 159 8.1 Problem Statement 159 8.2 The Case with a Strongly Connected Topology 161 8.3 The Case with a Topology Having a Directed Spanning Tree 165 8.4 Consensus of Second-Order Multi-agent Systems with Nonlinear Dynamics and Intermittent Communication 167 8.5 Notes 172
• 9 Distributed Adaptive Control of Multi-agent Systems 174 9.1 Distributed Adaptive Control in Complex Networks 175 9.1.1 Preliminaries 175 9.1.2 Distributed Adaptive Control in Complex Networks 176 9.1.3 Pinning Edges Control 178 9.1.4 Simulation Examples 181 9.2 Distributed Control Gains Design for Second-Order Consensus in Nonlinear Multi-agent Systems 183 9.2.1 Preliminaries 184 9.2.2 Distributed Control Gains Design: Leaderless Case 186 9.2.3 Distributed Control Gains Design: Leader-Follower Case 190 9.2.4 Simulation Examples 194 9.3 Notes 196
• 10 Distributed Consensus Filtering in Sensor Networks 198 10.1 Preliminaries 199 10.2 Distributed Consensus Filters Design for Sensor Networks with Fully-Pinned Controllers 201 10.3 Distributed Consensus Filters Design for Sensor Networks with Pinning Controllers 205 10.4 Distributed Consensus Filters Design for Sensor Networks with Pinning Observers 207 10.5 Simulation Examples 210 10.6 Notes 213
• 11 Delay-Induced Consensus and Quasi-Consensus in Multi-agent Systems 214 11.1 Problem Statement 214 11.2 Delay-Induced Consensus and Quasi-Consensus in Multi-agent Dynamical Systems 217 11.3 Motivation for Quasi-Consensus Analysis 223 11.4 Simulation Examples 224 11.5 Notes 228
• 12 Conclusions and FutureWork 229 12.1 Conclusions 229 12.2 Future Work 230 Bibliography 232 Index 241.
• (source: Nielsen Book Data)

A detailed and systematic introduction to the distributed cooperative control of multi-agent systems from a theoretical, network perspective * Features detailed analysis and discussions on the distributed cooperative control and dynamics of multi-agent systems * Covers comprehensively first order, second order and higher order systems, swarming and flocking behaviors * Provides a broad theoretical framework for understanding the fundamentals of distributed cooperative control.
(source: Nielsen Book Data)

• 1 Introduction 1 1.1 Definition of nonlinear systems 1 1.2 Nonlinear systems dynamics analysis and control 1 1.3 Why operator-based nonlinear control system? 2 1.4 An overview of the book 2 1.5 Acknowledgments 3 2 Robust right coprime factorization for nonlinear plants with uncertainties 5 2.1 Preliminaries 5 2.2 Operator theory 11 3 Robust stability of operator-based nonlinear control systems 27 3.1 Concept of operator based robust stability 27 3.2 Design methods of nonlinear systems with uncertainties 27 3.3 Operator-based robust anti-windup nonlinear feedback control systems design 38 3.4 Operator-based multi-input and multi-output(MIMO) nonlinear feedback control systems design 58 3.5 Operator-based time-varying delayed nonlinear feedback control systems design 110 4 Tracking issues and fault detection issues in nonlinear control systems 121 4.1 Operator-based tracking compensator in nonlinear feedback control systems design 121 4.2 Robust control for nonlinear systems with unknown perturbations using simplified robust right co-prime factorization 128 4.3 Operator-based actuator fault detection methods 146 4.4 Operator-based input command fault detection method in nonlinear feedback control systems 159 5 Operator based nonlinear control systems with smart actuators 177 5.1 Operator-based robust nonlinear feedback control systems design for non-symmetric backlash 177 5.2 Operator-based robust nonlinear feedback control systems design for symmetric and non-symmetric hysteresis 190 5.3 Operator-based nonlinear feedback systems application for smart actuators 203 6 Nonlinear feedback control to large scale systems using a distributed control system (DCS) device 247 6.1 Introduction 247 6.2 Multi-tank process modelling 249 6.3 Robust right coprime factorization design and controller realization254 6.4 Experimental results 260 6.5 Summary 264 References 267.
• (source: Nielsen Book Data)

Control of nonlinear systems is a multidisciplinary field involving electrical engineering, computer science, and control engineering. This book develops a systematic methodology to understand a quantitative stability result, which is an important contributor to nonlinear control systems' stability and performance. It focuses on the operator-theoretic approach, providing examples on how to apply it to network controlled systems. Current research results and the future of the operator-theoretic approach are also examined. Control theory engineers and applied mathematicians will find this work especially rewarding.
(source: Nielsen Book Data)

• 1 Introduction 1 1.1 Definition of nonlinear systems 1 1.2 Nonlinear systems dynamics analysis and control 1 1.3 Why operator-based nonlinear control system? 2 1.4 An overview of the book 2 1.5 Acknowledgments 3 2 Robust right coprime factorization for nonlinear plants with uncertainties 5 2.1 Preliminaries 5 2.2 Operator theory 11 3 Robust stability of operator-based nonlinear control systems 27 3.1 Concept of operator based robust stability 27 3.2 Design methods of nonlinear systems with uncertainties 27 3.3 Operator-based robust anti-windup nonlinear feedback control systems design 38 3.4 Operator-based multi-input and multi-output(MIMO) nonlinear feedback control systems design 58 3.5 Operator-based time-varying delayed nonlinear feedback control systems design 110 4 Tracking issues and fault detection issues in nonlinear control systems 121 4.1 Operator-based tracking compensator in nonlinear feedback control systems design 121 4.2 Robust control for nonlinear systems with unknown perturbations using simplified robust right co-prime factorization 128 4.3 Operator-based actuator fault detection methods 146 4.4 Operator-based input command fault detection method in nonlinear feedback control systems 159 5 Operator based nonlinear control systems with smart actuators 177 5.1 Operator-based robust nonlinear feedback control systems design for non-symmetric backlash 177 5.2 Operator-based robust nonlinear feedback control systems design for symmetric and non-symmetric hysteresis 190 5.3 Operator-based nonlinear feedback systems application for smart actuators 203 6 Nonlinear feedback control to large scale systems using a distributed control system (DCS) device 247 6.1 Introduction 247 6.2 Multi-tank process modelling 249 6.3 Robust right coprime factorization design and controller realization254 6.4 Experimental results 260 6.5 Summary 264 References 267.
• (source: Nielsen Book Data)

Control of nonlinear systems is a multidisciplinary field involving electrical engineering, computer science, and control engineering. This book develops a systematic methodology to understand a quantitative stability result, which is an important contributor to nonlinear control systems' stability and performance. It focuses on the operator-theoretic approach, providing examples on how to apply it to network controlled systems. Current research results and the future of the operator-theoretic approach are also examined. Control theory engineers and applied mathematicians will find this work especially rewarding.
(source: Nielsen Book Data)

• Discrete-time Signals and Systems
• Quantized Level Systems: Digital-to Analog and Analog-to-Digital Conversions.

• Fundamentals in modelling and managing interdependent complex systems of systems : an overview
• Modelling, decomposition and multilevel coordination of complex systems of systems
• Hierarchical holographic modelling and multilevel coordination of complex systems of systems
• Modelling complex systems of systems with phantom system models
• Complex systems of systems : multiple goals and objectives
• Hierarchical coordinated bayesian modelling of complex systems of systems
• Hierarchical multi-objective modelling and decision making for complex systems of systems
• Modelling economic interdependencies among complex systems of systems
• Guiding principles for modelling and managing complex systems of systems
• Modelling cyber-physical complex systems of systems : four case studies
• Global supply chain as complex systems of systems
• Understanding and managing the organizational dimension of complex systems of system
• Software engineering : the driver of cyber-physical complex systems of systems
• Infrastructure preparedness for communities as complex systems of systems
• Modelling safety of highway complex systems of systems via fault trees.

• Preface ix 1 Introduction 1 1.1 Electrical Communication, 2 1.2 Modulation, 4 1.3 Time and Bandwidth, 9 1.4 Coding Versus Modulation, 13 1.5 A Tour of the Book, 14 1.6 Conclusions, 15 2 Communication Theory Foundation 17 2.1 Signal Space, 18 2.2 Optimal Detection, 24 2.3 Pulse Aliasing, 35 2.4 Signal Phases and Channel Models, 37 2.5 Error Events, 43 2.6 Conclusions, 50 3 Gaussian Channel Capacity 58 3.1 Classical Channel Capacity, 59 3.2 Capacity for an Error Rate and Spectrum, 64 3.3 Linear Modulation Capacity, 68 3.4 Conclusions, 72 4 Faster than Nyquist Signaling 79 4.1 Classical FTN, 80 4.2 Reduced ISI-BCJR Algorithms, 87 4.3 Good Convolutional Codes, 101 4.4 Iterative Decoding Results, 110 4.5 Conclusions, 114 5 Multicarrier FTN 127 5.1 Classical Multicarrier FTN, 128 5.2 Distances, 134 5.3 Alternative Methods and Implementations, 138 5.4 Conclusions, 143 6 Coded Modulation Performance 145 6.1 Set-Partition Coding, 146 6.2 Continuous Phase Modulation, 153 6.3 Conclusions for Coded Modulation
• Highlights, 161 7 Optimal Modulation Pulses 163 7.1 Slepian s Problem, 164 7.2 Said s Optimum Distance Pulses, 177 7.3 Conclusions, 185 Index 190.
• (source: Nielsen Book Data)

This book addresses coding, a new solution to the major challenge of communicating more bits of information in the same radio spectrum. * Explores concepts and new transmission methods that have arisen in the last 15 years * Discusses the method of faster than Nyquist signaling * Provides self-education resources by including design parameters and short MATLAB routines Bandwidth Efficient Coding takes a fresh look at classical information theory and introduces a different point of view for research and development engineers and graduate students in communication engineering and wireless communication.
(source: Nielsen Book Data)

• Contributors xiii Preface xvii Introduction 1 1 Research and Analysis for Real-World Applications 8 Catherine M. Banks 1.1 Introduction and Learning Objectives 8 1.1.1 Learning Objectives 10 1.2 Background 10 1.3 M&S Theory and Toolbox 13 1.3.1 Simulation Paradigms 15 1.3.2 Types of Modeling 16 1.3.3 Modeling Applications 17 1.4 Research and Analysis Methodologies 18 Case Study: A Methodology for M&S Project Progression 20 Summary 23 Key Terms 24 Exercises 25 References 25 2 Human Behavior Modeling: A Real-World Application 26 John A. Sokolowski 2.1 Introduction and Learning Objectives 26 2.2 Background and Theory 27 2.2.1 Classical Decision Theory 27 2.2.2 Naturalistic Decision Making 31 2.2.3 Recognition-Primed Decision Model 33 2.2.4 Military Decision Making 37 2.2.5 Computational Techniques for Implementing the CJTF Decision Process 40 2.2.6 Summary of the State-of-the-Art 53 Case Studies 54 Summary 81 Key Terms 82 Exercises 83 References 83 Appendix: A Decision Scenario and Associated Data 88 3 Transportation 93 R. Michael Robinson 3.1 Introduction and Learning Objectives 93 3.2 Background 94 3.3 Theory 95 3.3.1 Simulation Levels 95 3.3.2 Traffic Analysis Zones 97 3.3.3 The Four-Step Model 98 3.3.4 Method of Successive Averages 102 3.3.5 Volume Delay Functions 105 3.3.6 Dynamic Traffic Assignment 108 3.4 Transportation Modeling Applications 113 3.4.1 Traffic Demand Models 113 3.4.2 Public Transportation Models 114 3.4.3 Freight Modeling 117 3.4.4 Evacuation Simulations 121 Summary 124 Key Terms 125 Exercises 126 References 126 Further Reading 127 4 Homeland Security Risk Modeling 129 Barry C. Ezell 4.1 Introduction and Learning Objectives 129 4.2 Background 131 4.2.1 Bioterrorism Risk Assessment 2006 132 4.2.2 Estimating Likelihood of Terrorist Events 133 4.2.3 Risk Assessed as a Function of Threat Vulnerability and Consequence 135 4.3 Theory and Applications in Risk Modeling 136 4.3.1 Philosophical Considerations 137 4.3.2 Ontology and Epistemology 138 4.3.3 Issues and Implications for the Risk Analyst 138 4.3.4 Philosophical Considerations Summary 141 4.3.5 System Principals and Applications for the Risk Analyst 142 4.3.6 Factors in Developing a Risk Assessment Study Plan 143 4.3.7 Scope and Bound in a Risk Study: Constraints Limitations and Assumptions 145 4.3.8 Well-Known Challenge in Homeland Security Studies 146 4.4 Elements of a Study Plan 147 4.5 Modeling Paradigms 148 4.5.1 Simple Verses Complex Methodologies 148 4.5.2 Quantitative and Qualitative Designs 148 4.5.3 Modeling Approaches and Examples 150 4.5.4 Verification and Validation for Risk Models 156 Case Studies 157 Summary 161 Key Terms 161 Exercises 161 References 162 Further Reading 164 5 Operations Research 165 Andrew J. Collins and Christine S.M. Currie 5.1 Introduction and Learning Objectives 165 5.2 Background 166 5.2.1 OR Techniques 168 5.3 Theory 169 5.3.1 Problem Structuring Methods 169 5.3.2 Queuing Theory 175 5.3.3 Decision Analysis 179 5.3.4 Game Theory 182 5.3.5 Optimization 186 5.4 Modeling Paradigms 192 Case Studies 193 Summary 199 Key Terms 201 Exercises 202 x Contents References 204 Further Reading 206 6 Business Process Modeling 207 Rafael Diaz Joshua G. Behr and Mandar Tulpule 6.1 Introduction and Learning Objectives 207 6.2 Background 207 6.3 Discrete-Event Simulation 214 6.3.1 Introduction 214 6.3.2 Fundamentals 215 6.3.3 Queuing System Model Components 218 6.3.4 Time Advance Mechanism 219 6.3.5 Simulation Flowchart 220 6.4 Discrete-Event Simulation Case Study 221 6.4.1 Introduction 222 6.4.2 Background 222 6.4.3 Research Question 223 6.4.4 Overview of Optimization Model 224 6.4.5 The Simulation Model 225 6.4.6 Experimental Setting 225 6.4.7 Simulation Parameterization and Execution 226 6.4.8 Weigh Zones and Product Reassignment 226 6.4.9 Results 226 6.5 System Dynamics Simulation 227 6.5.1 Introduction 227 6.5.2 Fundamentals 228 6.5.3 The Stock and Flow Diagrams 229 6.5.4 Model Calibration 231 6.5.5 Model Testing 233 6.5.6 Population Modeling Exercise 233 6.5.7 Application of System Dynamics 235 6.5.8 Background 235 6.5.9 Research Question 238 6.5.10 Dynamic Hypothesis 238 6.5.11 Causal Loop Diagram 238 6.5.12 Stock and Flow Model 239 6.5.13 Simulation and Results 240 6.5.14 Conclusions 244 6.6 Monte Carlo Simulation 244 6.6.1 Introduction 244 6.6.2 Fundamentals 245 6.6.3 Probability Theory and Monte Carlo 247 6.6.4 Central Limit Theorem 247 6.6.5 Three-Sigma Rule 247 6.6.6 Monte Carlo Case Study 249 6.6.7 Research Question 250 6.6.8 Model Parameters 250 6.6.9 Simulation Procedure 250 6.6.10 Estimating Profit 251 6.6.11 Excel Implementation 253 6.6.12 Outcomes 253 6.6.13 Conclusions 254 Summary 255 Key Terms 255 Review Questions 256 References 257 7 A Review of Mesh Generation for Medical Simulators 261 Michel A. Audette Andrey N. Chernikov and Nikos P. Chrisochoides 7.1 Introduction and Learning Objectives 261 7.2 Background--A Survey of Relevant Biomechanics and Open-Source Software 263 7.2.1 Architecture of an Interactive Medical Simulator 263 7.2.2 Mechanics of Tissue Manipulation in Medical Simulation 264 7.2.3 Mechanics of Tissue Cutting and Re
• section in Medical Simulation 269 7.2.4 Open-Source Resources in Medical Simulation 269 7.3 Theory--The Impact of Element Quality and Size on Simulation 272 7.4 Modeling Paradigms--Methods for Mesh Generation 276 7.4.1 Structured Tetrahedral Mesh Generation 276 7.4.2 Unstructured Tetrahedral Mesh Generation 276 7.4.3 Octree-Based Unstructured Tetrahedral Mesh Generation 279 7.4.4 Delaunay Unstructured Tetrahedral Mesh Generation 280 7.4.5 Advancing Front Unstructured Tetrahedral Mesh Generation 284 7.4.6 Optimization-Based Unstructured Tetrahedral Mesh Generation 284 7.4.7 Unstructured Surface Mesh Generation 285 Case Studies 289 Summary 291 Key Terms 292 Acknowledgments 293 Exercises 293 References 294 8 Military Interoperability Challenges 298 Saikou Y. Diallo and Jos'e J. Padilla 8.1 Introduction and Learning Objectives 298 8.2 Background 299 8.2.1 Overview 300 8.2.2 State of the Art in Interoperability 300 8.2.3 Levels of Interoperability 302 8.2.4 Current Approaches to Interoperation 303 8.3 Theory 305 8.3.1 Data Models 306 8.3.2 A Relational Model of Data in M&S Systems 307 Case Study: Live Virtual Constructive Simulation Environments 311 8.4 Live Virtual Constructive 311 8.5 LVC Examples 315 8.6 Distributed Simulation Engineering and Execution Process (DSEEP) 316 8.7 LVC Architecture Framework (LVCAF) 320 8.8 Simulation Systems 322 Summary 323 Key Terms 324 Exercises 325 References 325 Index 329.
• (source: Nielsen Book Data)

Introduces various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges facing society Handbook of Real-World Applications in Modeling and Simulation provides a thorough explanation of modeling and simulation in the most useful, current, and predominant applied areas of transportation, homeland security, medicine, operational research, military science, and business modeling. Offering a cutting-edge and accessible presentation, this book discusses how and why the presented domains have become leading applications of modeling and simulation techniques. Contributions from leading academics and researchers integrate modeling and simulation theories, methods, and data to analyze challenges that involve technological and social issues. The book begins with an introduction that explains why modeling and simulation is a reliable analysis assessment tool for complex systems problems. Subsequent chapters provide an orientation to various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges across real-world applied domains. Additionally, the handbook: Provides a practical one-stop reference on modeling and simulation and contains an accessible introduction to key concepts and techniques Introduces, trains, and prepares readers from statistics, mathematics, engineering, computer science, economics, and business to use modeling and simulation in their studies and research Features case studies that are representative of fundamental areas of multidisciplinary studies and provides a concise look at the key concepts of modeling and simulation Contains a collection of original ideas on modeling and simulation to help academics and practitioners develop a multifunctional perspective Self-contained chapters offer a comprehensive approach to explaining each respective domain and include sections that explore the related history, theory, modeling paradigms, and case studies. Key terms and techniques are clearly outlined, and exercise sets allow readers to test their comprehension of the presented material. Handbook of Real-World Applications in Modeling and Simulation is an essential reference for academics and practitioners in the areas of operations research, business, management science, engineering, statistics, mathematics, and computer science. The handbook is also a suitable supplement for courses on modeling and simulation at the graduate level.
(source: Nielsen Book Data)

• part Part I: Introduction to Identification and Models for Linear Deterministic Systems
• chapter 1 Introduction
• chapter 2 A Journey into Identification
• chapter 3 Mathematical Descriptions of Processes: Models
• chapter 4 Models for Discrete-Time LTI Systems
• chapter 5 Transform-Domain Models for Linear TIme-Invariant Systems
• chapter 6 Sampling and Discretization
• part Part II: Models for Random Processes
• chapter 7 Random Processes
• chapter 8 Time-Domain Analysis: Correlation Functions
• chapter 9 Models for Linear Stationary Processes
• chapter 10 Fourier Analysis and Spectral Analysis of Deterministic Signals
• chapter 11 Spectral Representations of Random Processes
• part Part III: Estimation Methods
• chapter 12 Introduction to Estimation
• chapter 13 Goodness of Estimators
• chapter 14 Estimation Methods: Part I
• chapter 15 Estimation Methods: Part II
• chapter 16 Estimation of Signal Properties
• part Part IV: Identification of Dynamic Models - Concepts and Principles
• chapter 17 Non-Parametric and Parametric Models for Identification
• chapter 18 Predictions
• chapter 19 Identification of Parametric Time-Series Models
• chapter 20 Identification of Non-Parametric Input-Output Models
• chapter 21 Identification of Parametric Input-Output Models
• chapter 22 Statistical and Practical Elements of Model Building
• chapter 23 Identification of State-Space Models
• chapter 24 Case Studies
• part Part V: Advanced Concepts
• chapter 25 Advanced Topics in SISO Identification
• chapter 26 Linear Multivariable Identification.

Master Techniques and Successfully Build Models Using a Single Resource Vital to all data-driven or measurement-based process operations, system identification is an interface that is based on observational science, and centers on developing mathematical models from observed data. Principles of System Identification: Theory and Practice is an introductory-level book that presents the basic foundations and underlying methods relevant to system identification. The overall scope of the book focuses on system identification with an emphasis on practice, and concentrates most specifically on discrete-time linear system identification.

Patterns and their formations appear throughout nature, and are studied to analyze different problems in science and make predictions across a wide range of disciplines including biology, physics, mathematics, chemistry, material science, and nanoscience. With the emergence of nanoscience and the ability for researchers and scientists to study living systems at the biological level, pattern formation research has become even more essential. This book is an accessible first of its kind guide for scientists, researchers, engineers, and students who require a general introduction to this research area, in order to gain a deeper analytical understanding of the most recent observations and experiments by top researchers in physics. "Pattern Formations" describes the most up-to-date status of this developing field and analyzes the physical phenomena behind a wide range of interesting topics commonly known in the scientific community. The study of pattern formations as a research field will continue to grow as scientists expand their understanding of naturally occurring patterns and mimic nature to help solve complex problems. This research area is becoming more highly recognized due to its contributions to signal processing, computer analysis, image processing, complex networks development, advancements in optics and photonics, crystallography, metallurgy, drug delivery (chemotherapy) and the further understanding of gene regulation. It is the only introductory reference book which places special emphasis on the theoretical analyses of experiments in this rapidly growing field of pattern formation. A wide range of physical applications make this book highly interdisciplinary. Explanations of observations and experiments deepen the readers understanding of this developing research field.
(source: Nielsen Book Data)

• Systems thinking: foundations, skills and uses / Moti Frank, Haim Shaked and Sigal Koral Kordova
• Towards praxis in systems thinking / Martin Reynolds
• Do systems exist in the real world? / Hillary Stillitto
• A model for describing the systems thinking factors / Sigal Koral Kordova and Moti Frank
• Strategic management and systems thinking: developing an archetypical model / George Papageorgiou and Andreas Hadjis
• Dynamic systems with multiple elements / Yumin Zhang
• Insights based on an empirical study of systems thinking development / Heidi L. Davidz
• Assessing systems thinking skills in systems engineers / Moti Frank
• Systems thinking in the systems engineering process: new methods and tools / Tom McDermott and Dane Freeman
• Integrating project management and systems engineering / Michael Masin, Yael Dubinsky, Michael Iluz, Evgeny Shindin, Abraham Shtub and Guy Shtub
• Implementing systems engineering and advanced project management in large and complex projects: the case of a new airport operational readiness project / Ori Orhof
• Integrated models in healthcare systems / Ron S. Kenett and Yifat Lavi
• A system thinking in medicine and health care: multidisciplinary team (MDT) approach for hepatocellular carcinoma (HCC) / Moshe Leshno and Yoram Menachem
• Holistic versus analytic cognitive style: individual, organization and cultural differences / Eli Vakil
• Systems thinking in special education: a case study / Haim Shaked and Chen Schechter
• Identity economics, system thinking, and education / Eli Goldstein
• Bureaucratic or systems thinking organizational profile: perceptions of principals and teachers / Tamar Chen-Levi
• Model-based systems thinking: science teachers employ object-process methodology to comprehend scientific texts / Dov Dori, Rea Lavi and Yehudit Judy Dori.

Systems-thinking, a holistic approach that puts the study of wholes before that of parts, is an effective means of dealing with real-world situations. Emphasizing the interrelationships between the system's components rather than the components themselves, systems thinking allows us to increase our personal and professional effectiveness, and transform our organizations. Specifically, systems thinkers are able to conceptually analyze the system without knowing all the details, metaphorically recognizing the forest through the trees. They can see beyond the surface to the deeper patterns that are really responsible for creating behavior. This book provides a wealth of knowledge about systems thinking, enables readers to deeply understand what systems thinking is, and why it is so important in their work. Readers will learn the conceptual foundations of systems thinking, as well as its tools and the proper ways to use them. These tools will facilitate defining problems and designing solutions in an environment increasingly characterized by chaos and complexity. Since systems thinking is not a discipline, but rather an interdisciplinary conceptual framework used in a wide range of areas, this book presents the applications of systems thinking in different domains, including systems engineering, project management, healthcare, psychology and education. The editors, who are experts in the field of systems thinking due to numerous studies they conducted on this subject, have skillfully created a multidimensional view on systems thinking, including theory and practice, research and application, in a wide variety of fields. Therefore, this book will be useful for researches and practitioners, as well as suitable for beginners and specialists alike.
(source: Nielsen Book Data)