Computer modelling and simulation Books

Book Synopsisand the building of the Three “M’s” Maple, Mathematica and Matlab. We intend to persuade that Maple and other like tools are worth knowing assuming only that one wishes to be a mathematician, a mathematics educator, a computer scientist, an engineer or scientist, or anyone else who wishes/needs to use mathematics better.Trade ReviewFrom the reviews:“This book is intended to teach the reader the usage of the computer algebra system Maple. … The book is readable and valuable to mathematics, science, and engineering undergraduates at the sophomore or above level. It could also be valuable to practitioners in those fields who want to learn Maple in situ. … Summing Up: Recommended. Lower-division undergraduates through graduate students; professionals.” (D. Z. Spicer, Choice, Vol. 49 (5), January, 2012)“This is a Maple-application book which illustrates some basic areas of mathematics by symbolic computation examples. … The presentation is clear with all necessary details and comments for ensuring a full understanding of the considered examples. The intended beneficiaries are undergraduate students, teachers giving courses to undergraduate students, as well as programmers interested in using Maple for several classes of mathematical problems.” (Octavian Pastravanu, Zentralblatt MATH, Vol. 1228, 2012)“In An Introduction to Modern Mathematical Computing with Maple, Borwein and Skerritt show that computers are an excellent companion for learning mathematics. … The theme of the book is that Maple can supplement mathematics learning and, what is more, can do much of the mathematics for the students. … The temptation is tremendous for students to skip the real work to have a true understanding of mathematics.” (David S. Mazel, The Mathematical Association of America, June, 2012)Table of Contents-Preface. -Conventions and Notation.-1. Number Theory (Introduction to Maple, Putting it together, Enough code, already. Show me some maths!, Problems and Exercises, Further Explorations). -2. Calculus(Revision and Introduction, Univariate Calculus, Multivariate Calculus, Exercises, Further Explorations). -3. Linear Algebra (Introduction and Review, Vector Spaces, Linear Transformations, Exercises, Further Explorations). -4. Visualisation and Geometry: a postscript (Useful Visualisation Tools, Geometry and Geometric Constructions). –A. Sample Quizzes (Number Theory, Calculus, Linear Algebra). –Index. –References

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Book SynopsisAstronomers learn much of what they know about the mass, brightness, and size of stars by observing binary systems, in which two stars orbit each other, periodically cutting off the others light.Table of ContentsI Introduction.- The Database and Methods of Data Acquisition.- II Modeling and Analysis.- A General Approach to Modeling Eclipsing Binaries.- Determination of Eclipsing Binary Parameters.- Advanced Topics and Techniques.- III Light Curve Programs and Software Packages.- Light Curve Models and Software.- TheWilson#x2013;Devinney Program: Extensions and Applications.- Light Curve Software with Graphical User Interface and Visualization.- The Structure of Light Curve Programs and the Outlook for the Future.

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Book SynopsisThe papers contained in this volume were originally presented at the 2015 International Conference on Complex Systems in Business, Administration, Science and Engineering. Included are the latest works of practitioners from a variety of disciplines who have developed new approaches for resolving complex issues that cannot be formulated using conventional, mathematical or software models.Complex Systems occur in an infinite variety of problems, not only in the realm of physical sciences and engineering, but also in such diverse fields as economics, the environment, humanities, and social and political sciences.The papers in the book cover such topics as: Complex ecological systems; Complexity science and urban developments; Complex energy systems; Complex issues in biological and medical sciences; Extreme events: natural and human made disasters; Climate change; Complexity of the internet-based global market; Complex business processes; Supply chain complexity; Transportation complexity; Logistics complexity; Closed and open systems; Attractions and chaotic systems; Complex adaptive software; Complexity of big data; Management of complexity; Global economy as a complex system; Complexity in social systems; Complex political systems; Administrations as complex systems; Complexity in engineering; Complexity and environment; Complexity and evolution; Complexity in linguistics, literature and arts.Table of ContentsContentsSection 1: FundamentalsNon-relativistic time, existence and adaptation; On computing the behavior of the mind from an eastern philosophical perspective; Complexity as the defining feature of the 21st century; Factors that facilitate organisational change in complex systems; Mutual shaping between technologies and law: memories of Norwegian e-health infrastructures; 'In' or 'as' space?: a model of complexity, with philosophical, simulatory, and empirical ramifications; Toward thermodynamics of real-time scheduling; Semantic shift to pragmatic meaning in shared decision making: situation theory perspective; Techniques for multifractal spectrum estimation in financial time seriesSection 2: Applications in business and industryAddressing supply-chain complexity using closed-loop simulation-based exercises; Price competition strategy of internet platforms; A novel dwelling time design method for low probability of intercept in a complex radar network; Systems analysis for energy systems using an integrated model of GIS and technology models; Analysing the Chinese stock market using the Hurst exponent, fractional Brownian motion and variants of a stochastic logistic differential equation; Effects of mobile application to the public transportation and future editing: Istanbul caseSection 3: Applications in various fieldsSupporting sensemaking to deal with organizational complexity; The relationship between employee health, quality culture and organizational effectiveness: findings from the literature; Systems engineering beliefs: contemplating personal perceptions regarding state of the art; Value-sensitive design for indigenous people of Oaxaca, Mexico; Influences in a biologically complex adaptive system: environmental stress affects dental development in a group of Romano-BritonsSection 4: Multi-agent applicationsA multi-agent solution for managing complexity in English to Sinhala machine translation; Using a multi-agent system for supply chain management; Design and development of an agent-based model for business operations faced with flood disruption; Using multi-agent technology for the distributed management of a cluster of remote sensing satellites; Multi-agent method to adaptive real-time train scheduling with conflict limitationsAuthor index

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Book SynopsisAn in-depth guide to using the power of Mantaflow, rigid body, soft body, cloth simulations, and Dynamic Paint in Blender 3.3 to create campfires, waterfalls, explosions, flags, and much more Key Features Understand Mantaflow to create fire and smoke simulations Learn to design satisfying animations using soft bodies and the cloth simulation Construct realistic physics animations with rigid bodies Book DescriptionBlender is a free, open source 3D software that allows you to create stunning visual graphics, animation, VFX, and much more! This book is an in-depth guide to creating realistic and eye-catching simulations, understanding the various settings and options around their creation, and learning how to troubleshoot solutions to your own Blender problems. In addition, this book can also be used to simulate the behavior of certain physics effects, such as fire, fluid, soft bodies, and rigid bodies. You’ll learn how to use Mantaflow, an open source framework within Blender software, to create fire, smoke, and fluid simulations. As you progress, you’ll understand how to easily produce satisfying rigid and soft body simulations, along with cloth simulations. Finally, you’ll use Dynamic Paint, Blender’s modifier, and the physics system to create eye-catching animations. By the end of this Blender book, you’ll have created a number of animations on your own, such as a campfire, waterfalls, and explosions. You’ll also have gained a deeper understanding of all the simulation options in Blender, which you can use to create portfolio-ready animations.What you will learn Discover what Mantaflow is and how to use it effectively Understand domains, flows, and effectors, and why they are important Create realistic fire, smoke, and fluid simulations Produce satisfying soft and rigid body simulations with ease Use the cloth simulation to bring animated fabric to life Explore canvas and brush objects in Dynamic Paint to create eye-catching animations Who this book is forIf you’re a VFX artist, 3D artist, game designer, or any Blender user who wants to learn about Mantaflow and physics simulations, then this book is for you. You’re expected to have basic knowledge of the Blender interface and how to use it.Table of ContentsTable of Contents An Introduction to Mantaflow Understanding Domains, Flows, and Effectors Creating a Realistic Campfire Simulation Creating a Waterfall Using Mantaflow Creating a Realistic Explosion Getting Started with Soft Bodies Creating a Soft Body Obstacle Course Introduction to Cloth Simulations Creating a Realistic Flag An Introduction to Rigid Bodies Creating a Rigid Body Physics Course Introduction to Dynamic Paint Creating a Paintbrush Effect Creating a Raindrop Effect

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Book SynopsisThe classic text on robot manipulators now covers visual control, motion planning and mobile robots too!Based on the successful Modelling and Control of Robot Manipulators by Sciavicco and Siciliano (Springer, 2000), Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control. It has been expanded to include coverage of mobile robots, visual control and motion planning. A variety of problems is raised throughout, and the proper tools to find engineering-oriented solutions are introduced and explained.The text includes coverage of fundamental topics like kinematics, and trajectory planning and related technological aspects including actuators and sensors.To impart practical skill, examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation. In addition, end-of-chapter exercises are proposed, and the book is accompanied by an electronic solutions manual containing the MATLAB® code for computer problems; this is available free of charge to those adopting this volume as a textbook for courses.Trade ReviewRobotics: Modelling, Planning and Control is a book that comprehensively covers all aspects of robotic fundamentals. It is particularly an excellent text for graduate educators, as it covers the fundamentals of the field with a rigorous formalism that is well blended with the technological aspects of robotics. The text covers in detail the theory of manipulators and wheeled robots starting with kinematics, dynamics and motion control, as well interaction with the environment through perception - force and vision sensors. The book is written by technical authorities in the field, and will be in invaluable addition to graduate education as well as a useful guide for industrial practitioners. Alexander Zelinsky, CSIRO, Australia Robotics is a diverse field bringing together disparate areas from computer science, electrical engineering and mechanical engineering. This book is an integrative but rigorous treatment of all the relevant concepts, with an eye toward modern, practical applications making it an excellent choice for a first year graduate course in robotics. Vijay Kumar, University of Pennsylvania This book provides rock-solid foundations for the study of classical mechanics and control of robots, with the authoritative character of a reference where you can surely find the correct expression and the rigorous derivation of the results you need. On top of this, new chapters on motion planning, visual servoing, and mobile robot control provide support to teaching wider and more interdisciplinary aspects of robotics, and open up vistas that will certainly inspire a new generation of scholars to embrace this incredibly rich and fertile research field. Antonio Bicchi, University of Pisa, Italy This book offers a well-balanced and intellectually satisfying treatment of robot mechanics, planning, and control – from the choice and sequence of topics, to the level of detail in the analysis, and the clear connections made between the latest technologies and the theoretical foundations of robotics, this book is an essential element in the library of every aspiring young robotics researcher. Frank Chongwoo Park, Seoul National University Robotics: Modeling, Planning and Control is a historiography from the materialistic view of robotics. Authors clearly explain physical and mathematical foundation to understand the most up-to-date robotics, so faithfully to bibliography and terminology in robotics. Unquestionably, the best textbook for senior students and graduate students and the closest reference book for engineers and scientists! Yoshihiko Nakamura, University of Tokyo Exceptional! A text with such a span of robotics fundamentals and advanced research in both manipulation and mobility, and a treatment that creatively balances mathematical depth and physical intuition – a fresh and certainly unique reference for researchers and engineers in the field of robotics. Oussama Khatib, Stanford University Certainly because of its youth, robotics is not always considered as a discipline as such. It is often introduced as a technological "area" integrating various aspects of mechanics, automatic control and computer science. Such a dispersed view is prejudicial for students. The book by Siciliano et al. achieves the introduction of the basic concepts in a coherent, self-contained and didactic way. In that sense, when reading Robotics: Modelling, Planning and Control the reader – from the undergraduate student to the researcher – understands that a new discipline is born, with its own foundations. Jean-Paul Laumond, LAAS-CNRSTable of ContentsKinematics.- Differential Kinematics and Statics.- Trajectory Planning.- Actuators and Sensors.- Control Architecture.- Dynamics.- Motion Control.- Force Control.- Visual Servoing.- Mobile Robots.- Motion Planning.

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Book SynopsisThis open access book examines the methodological complications of using complexity science concepts within the social science domain. The opening chapters take the reader on a tour through the development of simulation methodologies in the fields of artificial life and population biology, then demonstrates the growing popularity and relevance of these methods in the social sciences. Following an in-depth analysis of the potential impact of these methods on social science and social theory, the text provides substantive examples of the application of agent-based models in the field of demography. This work offers a unique combination of applied simulation work and substantive, in-depth philosophical analysis, and as such has potential appeal for specialist social scientists, complex systems scientists, and philosophers of science interested in the methodology of simulation and the practice of interdisciplinary computing research.​Table of ContentsPart I Agent-Based Models: 1 Introduction.- 2 Simulation and Artificial Life.- 3 Making the Artificial Real.- 4 Modelling in Population Biology.- Part II Modelling Social Systems: 5 Modelling for the Social Sciences.- 6 Analysis: Frameworks and Theories.- 7 Schelling: A Success for Simplicity.- 8 Conclusions Part III Case Study: Simulation in Demography.- 9 Modelling in Demography: From Statistics to Simulations.- 10 Model-Based Demography in Practice: I.- 11 Model-Based Demography in Practice: II.- 12 Conclusions.

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Book SynopsisThis book reports on the latest advances in using BIM modelling to achieve the semantic enrichment of objects, allowing them to be used both as multidimensional databases – as comprehensive sources of information for finalizing various types of documentation in the building industry – and as modelling tools for the construction of virtual environments. Having advanced to a new stage of development, BIM modelling is now being applied in a range of increasingly complex contexts, and for various new purposes. This book examines the role that virtual reality and related technologies such as AI and IoT can play in preserving and disseminating our cultural heritage and built environment.

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Book SynopsisThis open access book provides a step-by-step overview on how to build a microsimulation model with SAS. It shows how to convert an already existing multistate projection by age, sex, education and region into a microsimulation model. Two new dimensions are then added, either the labor force participation and the sector of activity, and/or some examples of outputs and alternative scenarios that would not be possible with standard demographic methods. The book also describes how to adapt the model for other countries or other purposes. It also provides details on how to extend and adapt the model for other purposes as well as other use of microsimulation with SAS. The book suggests codes that are easy to understand, so they can be replicated or adapted for other purposes. As such, this book provides a great resource for people with beginner to intermediate knowledge in SAS.Table of ContentsChapter 1. Introduction.- Chapter 2. Getting Started.- Chapter 3. Converting a Cohort Component Model into a Microsimulation Model.- Chapter 4. Adding New Dimensions.- Chapter 5. Building Alternative Scenarios.- Chapter 6. Extending and Adapting the Model.- Chapter 7. Conclusion.

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Book Synopsis.- Systems Theory, Applications, Pioneers, and Landmarks..- The Application of Named Entity Recognition in Military Intelligence..- A System-Theoretical Multi-Agent Approach to Human Computer Interaction..- Data Transformation: Limitless Strategies in Energy Consumption..- Theory and Applications of Metaheuristic Algorithms..- VFLBench: A Practical Benchmark for Vertical Federated Learning in Smart Manufacturing..- A Hybrid Cooperative Approach for Symbolic Regression..- Comparing Constraint Evaluation Methods for Shape constrained Regression..- Re-evaluation in Dynamic Tree-Search with Backtracking from Known Solutions..- Application of Adapt-CMSA to the Electric Vehicle Routing Problem with Simultaneous Pickup and Deliveries..- Optimizing Public Transport Through Integration of Microscopic and Macroscopic Simulations..- Automated Inference of Domain Knowledge in Scientific Machine Learning..- Automated Guided Vehicles in Yard Logistics: A time dependent approach..- Diversity Management in Evolutionary Dynamic Optimization..- Tackling the a-Domination Problem Heuristically..- A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming..- Composable Evolutionary Computation..- Solving the Manhattan Metric Straddle Carrier Routing Problem with Buffer Areas using a Hybrid Metaheuristic Method..- Route Planning for Parcel Logistics Systems with Reusable Packaging..- Selecting User Queries in Interactive Job Scheduling..- Improvements in Large Neighborhood Search for the Electric Autonomous Dial-A-Ride Problem..- A Hybrid Metaheuristic for a Tourist Route Recommender..- Learning to Select Promising Initial Solutions for Large Neighborhood Search-Based Multi-Agent Path Finding..- A Learning Twolevel Optimization Approach for the Demand Maximizing Battery Swapping Station Location Problem..- Learning Value Functions for Same-Day Delivery Problems in the Tardiness Regime..- Vectorial Genetic Programming - Optimizing Segments for Feature Extraction..- Mechatronic Product Development..- Enhancing Manufacturing Efficiency through Integration of MBSE and Capella in the Digital Thread..- Leveraging Learning Factories for Mechatronic Systems Development: A Collaborative Approach..- CNN Based Radar Kick Sensor Gesture Recognition Prototype..- Comparison of Different Battery-powered Tag Positions for Lower Limb Gesture Detection..- Model-Based System Design, Verification and Simulation..- Comparison of Physiological Data Acquisition for Modeling of drivers in Autonomous Vehicles..- Automated control of robots via 5G communication from Multi Accesss Edge Computing..- Automatic classification of cow's behavior during grazing..- Programming Learning through the Use of ChatGPT..- ChatGTP Language Model as Support for Teaching and Self learning in the Field of Engineering and Technical Sciences..- Analytical Conversion of the Strejc Model to the First Order with Time Delay (FOTD) Model..- Neural Analysis of Parameters Occurring in a Smart Building..- Efficient Manipulation of Control Flow Models in Evolving Software..- Textile Sensor Surrogate Modelling using Sparse Identification..- Discrimination Criteria for Modeling Association, Aggregation, and Composition in UML Class Diagrams.

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Book Synopsis.- Modeling, Simulation, and Optimization in Production and Logistics..- Age-Layer-Population-Structure with Self-Adaptation in Optimization..- Machine Learning Update Strategies for Real-time Production Environments..- Online Machine Learning for the Estimation of Process Times in Dynamic Scheduling..- Concurrent Evolution of Dynamic Single- and Dual-Crane Scheduling Scenarios..- Learning-based Algorithm Selection for a Multiprocessor Scheduling Problem..- Using the Pilot Method as a Problem-Independent Metaheuristic for Multi-Objective Beam Search..- Solving two-machine sum-cost flow shop problem on D-Wave quantum annealer..- Integrating Optimization Techniques and Live Tracking Software in Maritime Logistics..- Incrementally Solving the Dynamic Stacking Problem..- Predicting the Processing Effort for Block Relocation Problems..- Modelling Electric Vehicle Routing Problem with Heterogeneous Fleet for Simultaneous Pickup and Delivery..- "Green AI" and SW-Tools for Sustainable Energy and Materials Consumption..- A Predictive Maintenance Concept for Sustainable Lubricant Oil Usage based on Federated Learning..- Introduction to Circular System Design and First Use Cases for Sustainable Product Development in Smart Meter Remanufacturing and Robotics..- Elegant Architectures: Sustainability in Information Technology..- Surrogates for Fair-Weather Photovoltaic Module Output..- How can Digital Twins Help to Accelerate the Transition to a Carbon-neutral Energy System?..- Performance and Computation Time Gains caused by Sampling rate Reduction in Time Series Deep Anomaly Detection..- Exploring the Green AI potential of Adapter Tuning for Language Models..- Stochastic Models, Statistical Methods, and Applied Systems Simulations..- Disease Incidence in a Stochastic SVIRS Model with Waning Immunity..- A spreaders-ignorants-skeptics model for the spreading of fake news and a related finite birth-death process..- Comparing some simulation strategies for first passage times of time-changed Brownian motion..- Numerical evaluations for fractional stochastc di erential equations in neuronal dynamics..- Handling Uncertainties on the Right-hand Side of a Classical Transportation Model by Stochastic Optimisation and a Matheuristic Approach..- Unified Formulations of Entropy and Extropy..- Time-inhomogeneous Diffusion Process for the SI Epidemic Model..- A Vasicek-type model with structural breaks in the drift..- Estimating a time-inhomogeneous hyperlogistic diffusion process..- Systems Cybersecurity Technologies and Quantum Approaches Potentials..- A Federated Learning-based Android Malware Detector through Differential Privacy..- A Generative Model Based Honeypot for Industrial OPC UA Communication..- Theoretical Approach to Backdoor Attack on CRYSTALS Dilithium..- An IoT Lab Dedicated to Cybersecurity - Teaching, Learning and Research.

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Book Synopsis1. An Introduction to Digital Twins.- 2. Control and Optimisation using Digital Twins: Principles and Case Studies.- 3. Role of Enterprise Digital Twin in Enhancing Business Agility. 4. Improving Operational Efficiency of Sorting Terminals in Logistics Industry.- 5. Digital Twins for Process Optimization and Predictive Maintenance in Manufacturing Industries.- 6. Development of Digital Twins for Chemical Reactors to Predict the Catalysis Conversion Efficiency.- 7. Harnessing Digital Twins for Building Resilient Food Supply Chains.- 8. Digital Twin of Fuel Cell.- 9. Building Resilient Public Healthcare Systems Using Digital Twins.- 10. Knowledge Orchestrated Digital Twin Suite for Smart Refinery Operations.- 11. Digital Twin based Enterprise Ecosystems.

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Book Synopsis.- Introduction to the Proceedings..- Introduction to the DDDAS2024 Conference Infosymbiotics/DDDAS and AI: Towards Reliable AI..- Plenary Papers..- Materials, Aerospace, and Geomechanics Systems Methods..- Online Fault Detection for Metal Additive Manufacturing with Data­ Driven Time Series Models..- Weight Decay Optimized Unsupervised Autoencoder Based Anomaly Detection in Uncontrolled Dynamic Structural Health Monitoring..- Novel Deep Learning Image Registration Techniques with Application to Microscopy Images of Metal Alloys..- A Probabilistic Machine Learning Pipeline Using Topological Descriptors for Real-Time State Estimation of High-Rate Dynamic Systems..- Information Fusion of Ultrasonic Waves and Low-Frequency Vibrations: Leveraging Probabilistic Machine Learning and Stochastic Time Series Models for Structural Awareness..- Earthen Embankment Monitoring using LiDAR data by Randomized Consensus of Topological Data Analysis..-  Environmental Systems-Assessment/Response, DT Methods..- Large Language Models for Explainable Decisions in Dynamic Digital Twins..- DDDAS Probability Learning for Natural Disaster Change Detection..- Dynamic Data-Driven Digital Twin Testbed for Enhanced First Responder Training and Communication..- A Dynamic Data Driven Agent Based Model for Characterizing the Space Utilization of Asian Elephants in Response to Water Availability..- Adaptive Multi-stage Sensor Fusion under Neuro-symbolic Framework for The Multi-modal Ranging System in Adverse Weather Conditions..- Towards a Dynamic Data Driven Al Regional Weather Forecast Model..- Autonomous Uncrewed Aircraft for Mobile Operations in Severe Weather..- Autonomous Planning for Targeted Observation of Severe Weather..- Security Systems –Methods, infrastructures, applications..- Dynamic Data Driven Security Framework for Industrial Control Networks using Programmable Switches.

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Book Synopsis.- Computational and Algorithmic Aspects of Games..- On the Limitations and Possibilities of Nash Regret Minimization inZero-Sum Matrix Games under Noisy Feedback..- On the Effect of Time Preferences on the Price of Anarchy..- Mixed Nash Equilibria in Discrete Tullock Contests..- Social Welfare in Battery Charging Games..- Improved Hardness Results for the Clearing Problem in Financial Networks with Credit Default Swaps..- Coordination Mechanisms on Unrelated Machines with Arbitrary Priority Lists..- Facility Location..- Constant-Approximate and Constant-Strategyproof Two-Facility Location..- Constrained Truthful Obnoxious Two-Facility Location with Optional Preferences..- Hotelling-Downs with Facility Synergy: The Mall Effect..- Fair Division and Resource Allocation..- Tractable Graph Structures in EFX Orientation..- Fairness under Equal-Sized Bundles: Impossibility results and Approximation Guarantees..- Online Fair Division for Personalized 2-Value Instances..- A new lower bound for multi-color discrepancy with applications to fair division..- When is Truthfully Allocating Chores no Harder than Goods?..- Markets and Matching..- Unsolvability and Beyond in Many-To-Many Non-Bipartite Stable Matching..- Course Allocation with Credits via Stable Matching..- Bayesian Decision Making..- Bayesian Optimal Stopping with Maximum Value Knowledge..- Persuading Agents in Opinion Formation Games..- A Truthful and Accurate Forecasting Competition Mechanism on Bayesian Network Structured Events..- Mechanism Design and Algorithmic Contracts..- Deterministic Refund Mechanisms..- Near-Linear MIR Algorithms for Stochastically-Ordered Priors..- Online Budget-Feasible Mechanism Design with Predictions..- Linear Contracts for Supermodular Functions Based on Graphs..- Computational Social Choice..- Distortion of Multi-Winner Elections on the Line Metric: The Polar Comparison Rule..- Metric Distortion under Group-Fair Objectives..- Robustness of voting mechanisms to external information.

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Book SynopsisApplications to ion channel research computational approaches.- The ion conduction mechanism in potassium channels.- The molecular dynamics of potassium channel permeation selectivity and gating.- Delineating channel inactivation through molecular dynamics simulations and network analysis.- Molecular determinants of voltage dependent gating in potassium channels.- Integrating machine learning based quantitative structure activity relationship models and molecular modeling in developing inhibitors with reduced activity.- Computer aided drug design for the cardiac potassium channel.- Structure function studies of the potassium channel in vitro and in vivo.- Structure Function studies of .-dependent Exchangers encoded by the Gene Family.- CryoEM studiesof the Structure-Functional Properties of the.- SLC4 Membrane Transporter.- Combining CryoEM and kinetic measurements to study the cyclic nucleotide gated K+ channel SthK and the calcium gated K+ channel MthK.- Employing voltage clamp fluorometry to study agonist efficacy dependent structural alterations in the glycine receptor chloride channel.- Integrating computational and experimental methods to study ion channel function and modulation.- Combining experimental and computational tools to identify polyunsaturated fatty acid (PUFA) interaction sites on the cardiac potassium channel KCNQ1.- Experimental and theoretical studies of the mitochondrial beta-barrel channel VDAC.- Voltage gating of the voltage-dependent anion channel computational and experimental synergies.- Combining experimental and computational approaches for identifying substrate binding sites in SLC4 transporters.- Integrating experimental and computational approaches to delineate the gating mechanism of Kir channel. 

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Book SynopsisThis book presents a comprehensive set of guidelines and applications of DIgSILENT PowerFactory, an advanced power system simulation software package, for different types of power systems studies. Written by specialists in the field, it combines expertise and years of experience in the use of DIgSILENT PowerFactory with a deep understanding of power systems analysis. These complementary approaches therefore provide a fresh perspective on how to model, simulate and analyse power systems. It presents methodological approaches for modelling of system components, including both classical and non-conventional devices used in generation, transmission and distribution systems, discussing relevant assumptions and implications on performance assessment. This background is complemented with several guidelines for advanced use of DSL and DPL languages as well as for interfacing with other software packages, which is of great value for creating and performing different types of steady-state and dynamic performance simulation analysis. All employed test case studies are provided as supporting material to the reader to ease recreation of all examples presented in the book as well as to facilitate their use in other cases related to planning and operation studies. Providing an invaluable resource for the formal instruction of power system undergraduate/postgraduate students, this book is also a useful reference for engineers working in power system operation and planning.Table of ContentsLoad Flow Calculation and its Application.- Modelling of Transmission Systems under Unsymmetrical Conditions and contingency analysis using DIgSILENT PowerFactory.- Probabilistic load flow module for PowerFactory.- Unbalanced Power Flow in Distribution Systems using TRX Matrix: Implementation using DIgSILENT Programming Language.- Primal-dual interior point algorithm applied to DC optimal power flow using DIgSILENT Programming Language.- Indices to Assess the Integration of Renewable Energy Resources on Standard Test Networks through DIgSILENT’s Programming Language.- Modelling of automatic generation control in power systems.- Gas Turbine Modelling for Power System Dynamic Simulation Studies.- Implementation of Simplified Models of DFG-Based Wind Turbines for RMS-Type Simulation in DIgSILENT Power Factory.- Parameterized modal analysis using DIgSILENT Programming Language.- Probabilistic Approach for Risk Evaluation of Power System Oscillatory Stability.- Mean-Variance Mapping Optimization Algorithm for Power System Applications in DIgSILENT Power Factory.- Application and Requirement of DIgSILENT PowerFactory to Matlab/Simulink Interface.- Advanced applications of DPL language: Simulation automation and management of results.- Interfacing PowerFactory: co-simulation, real-time simulation and controller hardware-in-the-loop applications.- PowerFactory as a software stand-in for Hardware-In-Loop Testing.- Programming of simplified models of Flexible Alternating Current Transmission System (FACTS) devices using DIgSILENT Simulation Language.- Active and Reactive Power Control of Wind Farm based on Integrated Platform of PowerFactory and Matlab.- Implementation of Simplified Models of Local Controller for Muti-terminal HVDC Systems in DIgSILENT Power Factory.- Estimation of Equivalent Model for Clusters of Induction Generators based on PMU Measurements.

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Book SynopsisAISB91 is the eighth conference organized by the Society for the Study of Artificial Intelligence and Simulation of Behaviour. It is not only the oldest regular conference in Europe on AI - which spawned the ECAI conferences in 1982 - but it is also the conference that has a tradition for focusing on research as opposed to applications. The 1991 edition of the conference was no different in this respect. On the contrary, research, and particularly newly emerging research dir­ ections such as knowledge level expert systems research, neural networks and emergent functionality in autonomous agents, was strongly emphasised. The conference was organized around the following sessions: dis­ tributed intelligent agents, situatedness and emergence in autonomous agents, new modes of reasoning, the knowledge level perspective, and theorem proving and machine learning. Each of these sessions is discussed below in more detail. DISTRIBUTED INTELLIGENT AGENTS Research in distributed AI is concerned with the problem of how multiple agents and societies of agents can be organized to co-operate and collectively solve a problem. The first paper by Chakravarty (MIT) focuses on the problem of evolving agents in the context of Minsky's society of mind theory. It addesses the question of how new agents can be formed by transforming existing ones and illustrates the theory with an example from game playing. Smieja (GMD, Germany) focuses on the problem of organizing networks of agents which consist internally of neural networks.Table of ContentsDistributed Intelligent Agents.- Deriving Transformers from Knowledge Organized as a Society of Agents.- Multiple Network Systems (MINOS) Modules: Task Division and Module Discrimination.- Commitments and Projects.- RR - An Intelligent Resource-Bounded Reasoner.- Situatedness and Emergence in Autonomous Agents.- A Cognitive Model of Goal-oriented Automatisms and Breakdowns.- The ‘Logical Omniscience’ of Reactive Systems.- A Connectionist Semantics for Spatial Descriptions.- Neural Networks and Visual Behaviour: Flies, Panned Eyes, and Statistics.- Specifying Complex Behaviour for Computer Agents.- New Modes of Reasoning.- Integrating Neural Network and Expert Reasoning: An Example.- An Architecture for Selective Forgetting.- Constraint Propagation in Qualitative Modelling: Domain Variables Improve Diagnostic Efficiency.- Recursive Plans.- The Knowledge Level Perspective.- Task Centered Representation for Expert Systems at the Knowledge Level.- Knowledgeable knowledge acquisition.- Formalization of the KADS Interpretation Models.- Qualitative Models for Simulation and Control of Dynamic Systems.- Tractable Rationality at the Knowledge Level.- On Problems with the Knowledge Level Perspective.- Theorem Proving.- Using Abstraction.- Sound Substitution into Modal Contexts.- Machine Learning.- Modelling Representations of Device Knowledge in SOAR.- Instance-Based and Generalization-Based Learning Procedures Applied to Solving Integration Problems.

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Book SynopsisNeural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.Trade Review"If you want a systematic and thorough overview of neural networks, need a good reference book on this subject, or are giving or taking a course on neural networks, this book is for you." Computing ReviewsTable of Contents1. The Biological Paradigm.- 1.1 Neural computation.- 1.1.1 Natural and artificial neural networks.- 1.1.2 Models of computation.- 1.1.3 Elements of a computing model.- 1.2 Networks of neurons.- 1.2.1 Structure of the neurons.- 1.2.2 Transmission of information.- 1.2.3 Information processing at the neurons and synapses.- 1.2.4 Storage of information — learning.- 1.2.5 The neuron — a self-organizing system.- 1.3 Artificial neural networks.- 1.3.1 Networks of primitive functions.- 1.3.2 Approximation of functions.- 1.3.3 Caveat.- 1.4 Historical and bibliographical remarks.- 2. Threshold Logic.- 2.1 Networks of functions.- 2.1.1 Feed-forward and recurrent networks.- 2.1.2 The computing units.- 2.2 Synthesis of Boolean functions.- 2.2.1 Conjunction, disjunction, negation.- 2.2.2 Geometric interpretation.- 2.2.3 Constructive synthesis.- 2.3 Equivalent networks.- 2.3.1 Weighted and unweighted networks.- 2.3.2 Absolute and relative inhibition.- 2.3.3 Binary signals and pulse coding.- 2.4 Recurrent networks.- 2.4.1 Stored state networks.- 2.4.2 Finite automata.- 2.4.3 Finite automata and recurrent networks.- 2.4.4 A first classification of neural networks.- 2.5 Harmonic analysis of logical functions.- 2.5.1 General expression.- 2.5.2 The Hadamard—Walsh transform.- 2.5.3 Applications of threshold logic.- 2.6 Historical and bibliographical remarks.- 3.Weighted Networks — The Perceptron.- 3.1 Perceptrons and parallel processing.- 3.1.1 Perceptrons as weighted threshold elements.- 3.1.2 Computational limits of the perceptron model.- 3.2 Implementation of logical functions.- 3.2.1 Geometric interpretation.- 3.2.2 The XOR problem.- 3.3 Linearly separable functions.- 3.3.1 Linear separability.- 3.3.2 Duality of input space and weight space.- 3.3.3 The error function in weight space.- 3.3.4 General decision curves.- 3.4 Applications and biological analogy.- 3.4.1 Edge detection with perceptrons.- 3.4.2 The structure of the retina.- 3.4.3 Pyramidal networks and the neocognitron.- 3.4.4 The silicon retina.- 3.5 Historical and bibliographical remarks.- 4. Perceptron Learning.- 4.1 Learning algorithms for neural networks.- 4.1.1 Classes of learning algorithms.- 4.1.2 Vector notation.- 4.1.3 Absolute linear separability.- 4.1.4 The error surface and the search method.- 4.2 Algorithmic learning.- 4.2.1 Geometric visualization.- 4.2.2 Convergence of the algorithm.- 4.2.3 Accelerating convergence.- 4.2.4 The pocket algorithm.- 4.2.5 Complexity of perceptron learning.- 4.3 Linear programming.- 4.3.1 Inner points of polytopes.- 4.3.2 Linear separability as linear optimization.- 4.3.3 Karmarkar’s algorithm.- 4.4 Historical and bibliographical remarks.- 5. Unsupervised Learning and Clustering Algorithms.- 5.1 Competitive learning.- 5.1.1 Generalization of the perceptron problem.- 5.1.2 Unsupervised learning through competition.- 5.2 Convergence analysis.- 5.2.1 The one-dimensional case — energy function.- 5.2.2 Multidimensional case — the classical methods.- 5.2.3 Unsupervised learning as minimization problem.- 5.2.4 Stability of the solutions.- 5.3 Principal component analysis.- 5.3.1 Unsupervised reinforcement learning.- 5.3.2 Convergence of the learning algorithm.- 5.3.3 Multiple principal components.- 5.4 Some applications.- 5.4.1 Pattern recognition.- 5.4.2 Image compression.- 5.5 Historical and bibliographical remarks.- 6. One and Two Layered Networks.- 6.1 Structure and geometric visualization.- 6.1.1 Network architecture.- 6.1.2 The XOR problem revisited.- 6.1.3 Geometric visualization.- 6.2 Counting regions in input and weight space.- 6.2.1 Weight space regions for the XOR problem.- 6.2.2 Bipolar vectors.- 6.2.3 Projection of the solution regions.- 6.2.4 Geometric interpretation.- 6.3 Regions for two layered networks.- 6.3.1 Regions in weight space for the XOR problem.- 6.3.2 Number of regions in general.- 6.3.3 Consequences.- 6.3.4 The Vapnik—Chervonenkis dimension.- 6.3.5 The problem of local minima.- 6.4 Historical and bibliographical remarks.- 7. The Backpropagation Algorithm.- 7.1 Learning as gradient descent.- 7.1.1 Differentiable activation functions.- 7.1.2 Regions in input space.- 7.1.3 Local minima of the error function.- 7.2 General feed-forward networks.- 7.2.1 The learning problem.- 7.2.2 Derivatives of network functions.- 7.2.3 Steps of the backpropagation algorithm.- 7.2.4 Learning with backpropagation.- 7.3 The case of layered networks.- 7.3.1 Extended network.- 7.3.2 Steps of the algorithm.- 7.3.3 Backpropagation in matrix form.- 7.3.4 The locality of backpropagation.- 7.3.5 Error during training.- 7.4 Recurrent networks.- 7.4.1 Backpropagation through time.- 7.4.2 Hidden Markov Models.- 7.4.3 Variational problems.- 7.5 Historical and bibliographical remarks.- 8. Fast Learning Algorithms.- 8.1 Introduction — classical backpropagation.- 8.1.1 Backpropagation with momentum.- 8.1.2 The fractal geometry of backpropagation.- 8.2 Some simple improvements to backpropagation.- 8.2.1 Initial weight selection.- 8.2.2 Clipped derivatives and offset term.- 8.2.3 Reducing the number of floating-point operations.- 8.2.4 Data decorrelation.- 8.3 Adaptive step algorithms.- 8.3.1 Silva and Almeida’s algorithm.- 8.3.2 Delta-bar-delta.- 8.3.3 Rprop.- 8.3.4 The Dynamic Adaption algorithm.- 8.4 Second-order algorithms.- 8.4.1 Quickprop.- 8.4.2 QRprop.- 8.4.3 Second-order backpropagation.- 8.5 Relaxation methods.- 8.5.1 Weight and node perturbation.- 8.5.2 Symmetric and asymmetric relaxation.- 8.5.3 A final thought on taxonomy.- 8.6 Historical and bibliographical remarks.- 9. Statistics and Neural Networks.- 9.1 Linear and nonlinear regression.- 9.1.1 The problem of good generalization.- 9.1.2 Linear regression.- 9.1.3 Nonlinear units.- 9.1.4 Computing the prediction error.- 9.1.5 The jackknife and cross-validation.- 9.1.6 Committees of networks.- 9.2 Multiple regression.- 9.2.1 Visualization of the solution regions.- 9.2.2 Linear equations and the pseudoinverse.- 9.2.3 The hidden layer.- 9.2.4 Computation of the pseudoinverse.- 9.3 Classification networks.- 9.3.1 An application: NETtalk.- 9.3.2 The Bayes property of classifier networks.- 9.3.3 Connectionist speech recognition.- 9.3.4 Autoregressive models for time series analysis.- 9.4 Historical and bibliographical remarks.- 10. The Complexity of Learning.- 10.1 Network functions.- 10.1.1 Learning algorithms for multilayer networks.- 10.1.2 Hilbert’s problem and computability.- 10.1.3 Kolmogorov’s theorem.- 10.2 Function approximation.- 10.2.1 The one-dimensional case.- 10.2.2 The multidimensional case.- 10.3 Complexity of learning problems.- 10.3.1 Complexity classes.- 10.3.2 NP-complete learning problems.- 10.3.3 Complexity of learning with AND-OR networks.- 10.3.4 Simplifications of the network architecture.- 10.3.5 Learning with hints.- 10.4 Historical and bibliographical remarks.- 11. Fuzzy Logic.- 11.1 Fuzzy sets and fuzzy logic.- 11.1.1 Imprecise data and imprecise rules.- 11.1.2 The fuzzy set concept.- 11.1.3 Geometric representation of fuzzy sets.- 11.1.4 Fuzzy set theory, logic operators, and geometry.- 11.1.5 Families of fuzzy operators.- 11.2 Fuzzy inferences.- 11.2.1 Inferences from imprecise data.- 11.2.2 Fuzzy numbers and inverse operation.- 11.3 Control with fuzzy logic.- 11.3.1 Fuzzy controllers.- 11.3.2 Fuzzy networks.- 11.3.3 Function approximation with fuzzy methods.- 11.3.4 The eye as a fuzzy system — color vision.- 11.4 Historical and bibliographical remarks.- 12. Associative Networks.- 12.1 Associative pattern recognition.- 12.1.1 Recurrent networks and types of associative memories.- 12.1.2 Structure of an associative memory.- 12.1.3 The eigenvector automaton.- 12.2 Associative learning.- 12.2.1 Hebbian learning — the correlation matrix.- 12.2.2 Geometric interpretation of Hebbian learning.- 12.2.3 Networks as dynamical systems — some experiments.- 12.2.4 Another visualization.- 12.3 The capacity problem.- 12.4 The pseudoinverse.- 12.4.1 Definition and properties of the pseudoinverse.- 12.4.2 Orthogonal projections.- 12.4.3 Holographic memories.- 12.4.4 Translation invariant pattern recognition.- 12.5 Historical and bibliographical remarks.- 13. The Hopfield Model.- 13.1 Synchronous and asynchronous networks.- 13.1.1 Recursive networks with stochastic dynamics.- 13.1.2 The bidirectional associative memory.- 13.1.3 The energy function.- 13.2 Definition of Hopfield networks.- 13.2.1 Asynchronous networks.- 13.2.2 Examples of the model.- 13.2.3 Isomorphism between the Hopfield and Ising models.- 13.3 Converge to stable states.- 13.3.1 Dynamics of Hopfield networks.- 13.3.2 Covergence proof.- 13.3.3 Hebbian learning.- 13.4 Equivalence of Hopfield and perceptron learning.- 13.4.1 Perceptron learning in Hopfield networks.- 13.4.2 Complexity of learning in Hopfield models.- 13.5 Parallel combinatorics.- 13.5.1 NP-complete problems and massive parallelism.- 13.5.2 The multiflop problem.- 13.5.3 The eight rooks problem.- 13.5.4 The eight queens problem.- 13.5.5 The traveling salesman.- 13.5.6 The limits of Hopfield networks.- 13.6 Implementation of Hopfield networks.- 13.6.1 Electrical implementation.- 13.6.2 Optical implementation.- 13.7 Historical and bibliographical remarks.- 14. Stochastic Networks.- 14.1 Variations of the Hopfield model.- 14.1.1 The continuous model.- 14.2 Stochastic systems.- 14.2.1 Simulated annealing.- 14.2.2 Stochastic neural networks.- 14.2.3 Markov chains.- 14.2.4 The Boltzmann distribution.- 14.2.5 Physical meaning of the Boltzmann distribution.- 14.3 Learning algorithms and applications.- 14.3.1 Boltzmann learning.- 14.3.2 Combinatorial optimization.- 14.4 Historical and bibliographical remarks.- 15. Kohonen Networks.- 15.1 Self-organization.- 15.1.1 Charting input space.- 15.1.2 Topology preserving maps in the brain.- 15.2 Kohonen’s model.- 15.2.1 Learning algorithm.- 15.2.2 Mapping high-dimensional spaces.- 15.3 Analysis of convergence.- 15.3.1 Potential function — the one-dimensional case.- 15.3.2 The two-dimensional case.- 15.3.3 Effect of a unit’s neighborhood.- 15.3.4 Metastable states.- 15.3.5 What dimension for Kohonen networks?.- 15.4 Applications.- 15.4.1 Approximation of functions.- 15.4.2 Inverse kinematics.- 15.5 Historical and bibliographical remarks.- 16. Modular Neural Networks.- 16.1 Constructive algorithms for modular networks.- 16.1.1 Cascade correlation.- 16.1.2 Optimal modules and mixtures of experts.- 16.2 Hybrid networks.- 16.2.1 The ART architectures.- 16.2.2 Maximum entropy.- 16.2.3 Counterpropagation networks.- 16.2.4 Spline networks.- 16.2.5 Radial basis functions.- 16.3 Historical and bibliographical remarks.- 17. Genetic Algorithms.- 17.1 Coding and operators.- 17.1.1 Optimization problems.- 17.1.2 Methods of stochastic optimization.- 17.1.3 Genetic coding.- 17.1.4 Information exchange with genetic operators.- 17.2 Properties of genetic algorithms.- 17.2.1 Convergence analysis.- 17.2.2 Deceptive problems.- 17.2.3 Genetic drift.- 17.2.4 Gradient methods versus genetic algorithms.- 17.3 Neural networks and genetic algorithms.- 17.3.1 The problem of symmetries.- 17.3.2 A numerical experiment.- 17.3.3 Other applications of GAs.- 17.4 Historical and bibliographical remarks.- 18. Hardware for Neural Networks.- 18.1 Taxonomy of neural hardware.- 18.1.1 Performance requirements.- 18.1.2 Types of neurocomputers.- 18.2 Analog neural networks.- 18.2.1 Coding.- 18.2.2 VLSI transistor circuits.- 18.2.3 Transistors with stored charge.- 18.2.4 CCD components.- 18.3 Digital networks.- 18.3.1 Numerical representation of weights and signals.- 18.3.2 Vector and signal processors.- 18.3.3 Systolic arrays.- 18.3.4 One-dimensional structures.- 18.4 Innovative computer architectures.- 18.4.1 VLSI microprocessors for neural networks.- 18.4.2 Optical computers.- 18.4.3 Pulse coded networks.- 18.5 Historical and bibliographical remarks.

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Book SynopsisComputational Creativity, Concept Invention, and General Intelligence in their own right all are flourishing research disciplines producing surprising and captivating results that continuously influence and change our view on where the limits of intelligent machines lie, each day pushing the boundaries a bit further. By 2014, all three fields also have left their marks on everyday life – machine-composed music has been performed in concert halls, automated theorem provers are accepted tools in enterprises’ R&D departments, and cognitive architectures are being integrated in pilot assistance systems for next generation airplanes. Still, although the corresponding aims and goals are clearly similar (as are the common methods and approaches), the developments in each of these areas have happened mostly individually within the respective community and without closer relationships to the goings-on in the other two disciplines. In order to overcome this gap and to provide a common platform for interaction and exchange between the different directions, the International Workshops on “Computational Creativity, Concept Invention, and General Intelligence” (C3GI) have been started. At ECAI-2012 and IJCAI-2013, the first and second edition of C3GI each gathered researchers from all three fields, presenting recent developments and results from their research and in dialogue and joint debates bridging the disciplinary boundaries. The chapters contained in this book are based on expanded versions of accepted contributions to the workshops and additional selected contributions by renowned researchers in the relevant fields. Individually, they give an account of the state-of-the-art in their respective area, discussing both, theoretical approaches as well as implemented systems. When taken together and looked at from an integrative perspective, the book in its totality offers a starting point for a (re)integration of Computational Creativity, Concept Invention, and General Intelligence, making visible common lines of work and theoretical underpinnings, and pointing at chances and opportunities arising from the interplay of the three fields.Table of ContentsStakeholder Groups in Computational Creativity Research and Practice.- Weak and Strong Computational Creativity.- Theorem: General intelligence entails creativity, assuming.- The Computational Creativity Complex.- How Models of Creativity and Analogy Need to Answer the Tailorability Concern.- On the role of computers in creativity-support systems.- A computational theory of creativity as everyday reasoning from learned information.- Accounting for creativity within a psychologically realistic cognitive architecture.- E pluribus unum - Formalisation, Use-Cases, and Computational Support for Conceptual Blending.- Creating Meaningful and Poetic Instances of Rhetorical Forms.- Open-Ended Elaborations in Creative Metaphor.- Poetry generation with PoeTryMe.- From MEXICA to MEXICA-impro: the Evolution of a Computer Model for Plot Generation.- Handle: Engineering Artificial Musical Creativity at the "Trickery" Level.- A Culinary Computational Creativity System.- Interactive Meta-Reasoning: Toward a CAD-like environment for designing game-playing agents.- Collective Discovery Events: Web-based Mathematical Problem-solving with Codelets.- A Personal Perspective Into the Future for Computational Creativity.

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Book SynopsisSpace Vector Modeling of AC Machines.- Design of AC Motor Control Loops.- Modulation Strategies for Three-Phase Inverters.- Hardware-Based Design Validation.- Practical Issues in Control Implementation.- A Refresher on Classical Control Theory.

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