This new book aims to provide to both beginners and experts with a completely algorithmic approach to data analysis and conceptual modeling, database design, implementation, and tuning, starting from vague and incomplete customer requests and ending with IBM DB/2, Oracle, MySQL, MS SQL Server, or Access based software applications. A rich panoply of solutions to actual useful data sub-universes (e.g. business, university, public and home library, geography, history, etc.) is provided, constituting a powerful library of examples. Four data models are presented and used: the graphical Entity-Relationship, the mathematical EMDM, the physical Relational, and the logical deterministic deductive Datalogones. For each one of them, best practice rules, algorithms, and the theory beneath are clearly separated. Four case studies, from a simple public library example, to a complex geographical study are fully presented, on all needed levels. Several dozens of real life exercises are proposed, out of which at least one per chapter is completely solved. Both major historical and up-to-date references are provided for each of the four data models considered. The book provides a library of useful solutions to real-life problems and provides valuable knowledge on data analysis and modeling, database design, implementation, and fine tuning.

One cannot watch or read about the news these days without hearing about the models for COVID-19 or the testing that must occur to approve vaccines or treatments for the disease. The purpose of Mathematical Modeling in the Age of a Pandemic is to shed some light on the meaning and interpretations of many of the types of models that are or might be used in the presentation of analysis. Understanding the concepts presented is essential in the entire modeling process of a pandemic. From the virus itself and its infectious rates and deaths rates to explain the process for testing a vaccine or eventually a cure, the author builds, presents, and shows model testing. This book is an attempt, based on available data, to add some validity to the models developed and used, showing how close to reality the models are to predicting "results" from previous pandemics such as the Spanish flu in 1918 and more recently the Hong Kong flu. Then the author applies those same models to Italy, New York City, and the United States as a whole. Modeling is a process. It is essential to understand that there are many assumptions that go into the modeling of each type of model. The assumptions influence the interpretation of the results. Regardless of the modeling approach the results generally indicate approximately the same results. This book reveals how these interesting results are obtained.

First published in 1992, AY's Neuroanatomy of C. elegans for Computation provides the neural circuitry database of the nematode Caenorhabditis elegans, both in printed form and in ASCII files on 5.25-inch diskettes (for use on IBM (R) and compatible personal computers, Macintosh (R) computers, and higher level machines). Tables of connections among neuron classes, synapses among individual neurons, gap junctions among neurons, worm cells and their embryonic origin, and synthetically derived neuromuscular connections are presented together with the references from which the data were compiled and edited. Sample data files and source codes of FORTRAN and BASIC programs are provided to illustrate the use of mathematical tools for any researcher or student interested in examining a natural neural network and discovering what makes it tick.

This book, first published in 1981, offers a critical review of the techniques of mathematical modelling and their appropriate application to military operations research - the analysis of data (historical data, exercise and test results, and intelligence) in preparation for war. The virtues of sophistication via simplicity, and the beauty of the artful finesse, emerge as the signature of successful modelling.

Mathematical modeling is both a skill and an art and must be practiced in order to maintain and enhance the ability to use those skills. Though the topics covered in this book are the typical topics of most mathematical modeling courses, this book is best used for individuals or groups who have already taken an introductory mathematical modeling course. Advanced Mathematical Modeling with Technology will be of interest to instructors and students offering courses focused on discrete modeling or modeling for decision making. Each chapter begins with a problem to motivate the reader. The problem tells "what" the issue is or problem that needs to be solved. In each chapter, the authors apply the principles of mathematical modeling to that problem and present the steps in obtaining a model. The key focus is the mathematical model and the technology is presented as a method to solve that model or perform sensitivity analysis. We have selected , where applicable to the content because of their wide accessibility. The authors utilize technology to build, compute, or implement the model and then analyze the it. Features: MAPLE (c), Excel (c), and R (c) to support the mathematical modeling process. Excel templates, macros, and programs are available upon request from authors. Maple templates and example solution are also available. Includes coverage of mathematical programming. The power and limitations of simulations is covered. Introduces multi-attribute decision making (MADM) and game theory for solving problems. The book provides an overview to the decision maker of the wide range of applications of quantitative approaches to aid in the decision making process, and present a framework for decision making. Table of Contents 1. Perfect Partners: Mathematical Modeling and Technology 2. Review of Modeling with Discrete Dynamical Systems and Modeling Systems of DDS 3. Modeling with Differential Equations 4. Modeling System of Ordinary Differential Equation 5. Regression and Advanced Regression Methods and Models 6. Linear, Integer and Mixed Integer Programming 7. Nonlinear Optimization Methods 8. Multivariable Optimization 9. Simulation Models 10. Modeling Decision Making with Multi-Attribute Decision Modeling with Technology 11. Modeling with Game Theory 12. Appendix Using R Index Biographies Dr. William P. Fox is currently a visiting professor of Computational Operations Research at the College of William and Mary. He is an emeritus professor in the Department of Defense Analysis at the Naval Postgraduate School and teaches a three-course sequence in mathematical modeling for decision making. He received his Ph.D. in Industrial Engineering from Clemson University. He has taught at the United States Military Academy for twelve years until retiring and at Francis Marion University where he was the chair of mathematics for eight years. He has many publications and scholarly activities including twenty plus books and one hundred and fifty journal articles. Colonel (R) Robert E. Burks, Jr., Ph.D. is an Associate Professor in the Defense Analysis Department of the Naval Postgraduate School (NPS) and the Director of the NPS' Wargaming Center. He holds a Ph.D. in Operations Research form the Air Force Institute of Technology. He is a retired logistics Army Colonel with more than thirty years of military experience in leadership, advanced analytics, decision modeling, and logistics operations who served as an Army Operations Research analyst at the Naval Postgraduate School, TRADOC Analysis Center, United States Military Academy, and the United States Army Recruiting Command.

Mathematical Modeling using Fuzzy Logic has been a dream project for the author. Fuzzy logic provides a unique method of approximate reasoning in an imperfect world. This text is a bridge to the principles of fuzzy logic through an application-focused approach to selected topics in engineering and management. The many examples point to the richer solutions obtained through fuzzy logic and to the possibilities of much wider applications. There are relatively very few texts available at present in fuzzy logic applications. The style and content of this text is complementary to those already available. New areas of application, like application of fuzzy logic in modeling of sustainability, are presented in a graded approach in which the underlying concepts are first described. The text is broadly divided into two parts: the first treats processes, materials, and system applications related to fuzzy logic, and the second delves into the modeling of sustainability with the help of fuzzy logic. This book offers comprehensive coverage of the most essential topics, including: Treating processes, materials, system applications related to fuzzy logic Highlighting new areas of application of fuzzy logic Identifying possibilities of much wider applications of fuzzy logic Modeling of sustainability with the help of fuzzy logic The level enables a selection of the text to be made for the substance of undergraduate-, graduate-, and postgraduate-level courses. There is also sufficient volume and quality for the basis of a postgraduate course. A more restricted and judicious selection can provide the material for a professional short course and various university-level courses.

The already broad range of applications of ring theory has been enhanced in the eighties by the increasing interest in algebraic structures of considerable complexity, the so-called class of quantum groups. One of the fundamental properties of quantum groups is that they are modelled by associative coordinate rings possessing a canonical basis, which allows for the use of algorithmic structures based on Groebner bases to study them. This book develops these methods in a self-contained way, concentrating on an in-depth study of the notion of a vast class of non-commutative rings (encompassing most quantum groups), the so-called Poincar -Birkhoff-Witt rings. We include algorithms which treat essential aspects like ideals and (bi)modules, the calculation of homological dimension and of the Gelfand-Kirillov dimension, the Hilbert-Samuel polynomial, primality tests for prime ideals, etc.

Wavelet Analysis: Basic Concepts and Applications provides a basic and self-contained introduction to the ideas underpinning wavelet theory and its diverse applications. This book is suitable for master's or PhD students, senior researchers, or scientists working in industrial settings, where wavelets are used to model real-world phenomena and data needs (such as finance, medicine, engineering, transport, images, signals, etc.). Features: Offers a self-contained discussion of wavelet theory Suitable for a wide audience of post-graduate students, researchers, practitioners, and theorists Provides researchers with detailed proofs Provides guides for readers to help them understand and practice wavelet analysis in different areas

This volume collects selected contributions from the "Fourth Tetrahedron Workshop on Grid Generation for Numerical Computations", which was held in Verbania, Italy in July 2013. The previous editions of this Workshop were hosted by the Weierstrass Institute in Berlin (2005), by INRIA Rocquencourt in Paris (2007), and by Swansea University (2010).This book covers different, though related, aspects of the field: the generation of quality grids for complex three-dimensional geometries; parallel mesh generation algorithms; mesh adaptation, including both theoretical and implementation aspects; grid generation and adaptation on surfaces - all with an interesting mix of numerical analysis, computer science and strongly application-oriented problems.

This book synthesizes the game-theoretic modeling of decision-making processes and an ancient moral requirement called the Golden Rule of ethics (GR). This rule states "Behave to others as you would like them to behave to you." The GR is one of the oldest, most widespread, and specific moral requirements that appear in Christianity, Islam, Judaism, Buddhism, and Confucianism. This book constructs and justifies mathematical models of dynamic socio-economic processes and phenomena that reveal the mechanism of the GR and are based on the concept of Berge equilibrium. The GR can be naturally used for resolving or balancing conflicts, and its "altruistic character" obviously excludes wars, blood-letting, and armed clashes. The previous book by the authors, The Berge Equilibrium: A Game-Theoretic Framework for the Golden Rule of Ethics, covers the static case of the GR. In this book, the dynamic case of the GR is investigated using the altruistic concept of Berge equilibrium and three factors as follows: 1) a modification of N.N. Krasovskii's mathematical formalization of differential positional games (DPGs), in view of the counterexamples given by A.I. Subbotin and A.F. Kononenko; 2) the method of guiding control, proposed by N.N. Krasovskii; and 3) the Germier convolution of the payoff functions of different players. Additionally, this book features exercises, problems, and solution tips collected together in Appendix 1, as well as new approaches to conflict resolution as presented in Appendices 2 to 4. This book will be of use to undergraduate and graduate students and experts in the field of decision-making in complex control and management systems, as well as anyone interested in game theory and applications.

This book introduces aspects of topology and applications to problems in condensed matter physics. Basic topics in mathematics have been introduced in a form accessible to physicists, and the use of topology in quantum, statistical and solid state physics has been developed with an emphasis on pedagogy. The aim is to bridge the language barrier between physics and mathematics, as well as the different specializations in physics. Pitched at the level of a graduate student of physics, this book does not assume any additional knowledge of mathematics or physics. It is therefore suited for advanced postgraduate students as well. A collection of selected problems will help the reader learn the topics on one's own, and the broad range of topics covered will make the text a valuable resource for practising researchers in the field. The book consists of two parts: one corresponds to developing the necessary mathematics and the other discusses applications to physical problems. The section on mathematics is a quick, but more-or-less complete, review of topology. The focus is on explaining fundamental concepts rather than dwelling on details of proofs while retaining the mathematical flavour. There is an overview chapter at the beginning and a recapitulation chapter on group theory. The physics section starts with an introduction and then goes on to topics in quantum mechanics, statistical mechanics of polymers, knots, and vertex models, solid state physics, exotic excitations such as Dirac quasiparticles, Majorana modes, Abelian and non-Abelian anyons. Quantum spin liquids and quantum information-processing are also covered in some detail.

This handbook aims to highlight fundamental, methodological and computational aspects of networks of queues to provide insights and to unify results that can be applied in a more general manner. The handbook is organized into five parts:

Part 1 considers exact analytical results such as of product form type. Topics include characterization of product forms by physical balance concepts and simple traffic flow equations, classes of service and queue disciplines that allow a product form, a unified description of product forms for discrete time queueing networks, insights for insensitivity, and aggregation and decomposition results that allow sub networks to be aggregated into single nodes to reduce computational burden.

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Part 2 looks at monotonicity and comparison results such as for computational simplification by either of two approaches: stochastic monotonicity and ordering results based on the ordering of the process generators, and comparison results and explicit error bounds based on an underlying Markov reward structure leading to ordering of expectations of performance measures.

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Part 3 presents diffusion and fluid results. It specifically looks at the fluid regime and the diffusion regime. Both of these are illustrated through fluid limits for the analysis of system stability, diffusion approximations for multi-server systems, and a system fed by Gaussian traffic.

Part 4 illustrates computational and approximate results through the classical MVA (mean value analysis) and QNA (queueing network analyzer) for computing mean and variance of performance measures such as queue lengths and sojourn times; numerical approximation of response time distributions; and approximate decomposition results for large open queueing networks.

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Part 5 enlightens selected applications as loss networks originating from circuit switched telecommunications applications, capacity sharing originating from packet switching in data networks, and a hospital application that is of growing present day interest.

The book shows that the intertwined progress of theory and practice will remain to be most intriguing and will continue to be the basis of further developments in queueing networks."

This book provides an overview of the main approaches used to analyze the dynamics of cellular automata. Cellular automata are an indispensable tool in mathematical modeling. In contrast to classical modeling approaches like partial differential equations, cellular automata are relatively easy to simulate but difficult to analyze. In this book we present a review of approaches and theories that allow the reader to understand the behavior of cellular automata beyond simulations. The first part consists of an introduction to cellular automata on Cayley graphs, and their characterization via the fundamental Cutis-Hedlund-Lyndon theorems in the context of various topological concepts (Cantor, Besicovitch and Weyl topology). The second part focuses on classification results: What classification follows from topological concepts (Hurley classification), Lyapunov stability (Gilman classification), and the theory of formal languages and grammars (Kurka classification)? These classifications suggest that cellular automata be clustered, similar to the classification of partial differential equations into hyperbolic, parabolic and elliptic equations. This part of the book culminates in the question of whether the properties of cellular automata are decidable. Surjectivity and injectivity are examined, and the seminal Garden of Eden theorems are discussed. In turn, the third part focuses on the analysis of cellular automata that inherit distinct properties, often based on mathematical modeling of biological, physical or chemical systems. Linearity is a concept that allows us to define self-similar limit sets. Models for particle motion show how to bridge the gap between cellular automata and partial differential equations (HPP model and ultradiscrete limit). Pattern formation is related to linear cellular automata, to the Bar-Yam model for the Turing pattern, and Greenberg-Hastings automata for excitable media. In addition, models for sand piles, the dynamics of infectious d

Illustrates the application of mathematical and computational modeling in a variety of disciplines With an emphasis on the interdisciplinary nature of mathematical and computational modeling, Mathematical and Computational Modeling: With Applications in the Natural and Social Sciences, Engineering, and the Arts features chapters written by well-known, international experts in these fields and presents readers with a host of state-of-the-art achievements in the development of mathematical modeling and computational experiment methodology. The book is a valuable guide to the methods, ideas, and tools of applied and computational mathematics as they apply to other disciplines such as the natural and social sciences, engineering, and technology. Mathematical and Computational Modeling: With Applications in the Natural and Social Sciences, Engineering, and the Arts also features: * Rigorous mathematical procedures and applications as the driving force behind mathematical innovation and discovery * Numerous examples from a wide range of disciplines to emphasize the multidisciplinary application and universality of applied mathematics and mathematical modeling * Original results on both fundamental theoretical and applied developments in diverse areas of human knowledge * Discussions that promote interdisciplinary interactions between mathematicians, scientists, and engineers Mathematical and Computational Modeling: With Applications in the Natural and Social Sciences, Engineering, and the Arts is an ideal resource for professionals in various areas of mathematical and statistical sciences, modeling and simulation, physics, computer science, engineering, biology and chemistry, industrial, and computational engineering. The book also serves as an excellent textbook for graduate courses in mathematical modeling, applied mathematics, numerical methods, operations research, and optimization.

Modeling and computing is becoming an essential part of the analysis and design of an engineered system. This is also true of "geotechnical systems", such as soil foundations, earth dams and other soil-structure systems. The general goal of modeling and computing is to predict and understand the behaviour of the system subjected to a variety of possible conditions/scenarios (with respect to both external stimuli and system parameters), which provides the basis for a rational design of the system. The essence of this is to predict the response of the system to a set of external forces. The modelling and computing essentially involve the following three phases: (a) Idealization of the actual physical problem, (b) Formulation of a mathematical model represented by a set of equations governing the response of the system, and (c) Solution of the governing equations (often requiring numerical methods) and graphical representation of the numerical results. This book will introduce these phases. MATLAB (R) codes and MAPLE (R) worksheets are available for those who have bought the book. Please contact the author at [email protected] or [email protected]. Kindly provide the invoice number and date of purchase.

Finite element analysis has become the most popular technique for studying engineering structures in detail. It is particularly useful whenever the complexity of the geometry or of the loading is such that alternative methods are inappropriate. The finite element method is based on the premise that a complex structure can be broken down into finitely many smaller pieces (elements), the behaviour of each of which is known or can be postulated. These elements might then be assembled in some sense to model the behaviour of the structure. Intuitively this premise seems reasonable, but there are many important questions that need to be answered. In order to answer them it is necessary to apply a degree of mathematical rigour to the development of finite element techniques. The approach that will be taken in this book is to develop the fundamental ideas and methodologies based on an intuitive engineering approach, and then to support them with appropriate mathematical proofs where necessary. It will rapidly become clear that the finite element method is an extremely powerful tool for the analysis of structures (and for other field problems), but that the volume of calculations required to solve all but the most trivial of them is such that the assistance of a computer is necessary. As stated above, many questions arise concerning finite element analysis. Some of these questions are associated with the fundamental mathematical formulations, some with numerical solution techniques, and others with the practical application of the method. In order to answer these questions, the engineer/analyst needs to understand both the nature and limitations of the finite element approximation and the fundamental behaviour of the structure. Misapplication of finite element analysis programs is most likely to arise when the analyst is ignorant of engineering phenomena.

This unique book is equally useful to both engineering-degree students and production engineers practicing in industry. The volume is designed to cover three aspects of manufacturing technology: (a) fundamental concepts, (b) engineering analysis/mathematical modeling of manufacturing operations, and (c) 250+ problems and their solutions. These attractive features render this book suitable for recommendation as a textbook for undergraduate as well as Master level programs in Mechanical/Materials/Industrial Engineering. There are 19 chapters in the book; each chapter first introduces readers to the technological importance of chapter-topic and definitions of terms and their explanation; and then the mathematical modeling/engineering analysis of the corresponding manufacturing operation is presented. The meanings of the terms along with their SI units in each mathematical model are clearly stated. There are over 320 mathematical models/equations. The book is divided into three parts. Part One introduces readers to manufacturing and basic manufacturing processes (metal casting, plastic molding, metal forming, ceramic processing, composite processing, heat treatment, surface finishing, welding & joining, and powder metallurgy) and their engineering analysis/mathematical modeling followed by worked examples (solved problem). Part Two covers non-traditional machining and computer aided manufacturing, including their mathematical modeling and the related solved problems. Finally, quality control (QC) and economic aspects of manufacturing are discussed in Part Three. Features Presents over 320 mathematical models and 250 worked examples Covers both conventional and non-traditional manufacturing Includes design problems and their solutions on engineering manufacturing processes Special emphasis on casting design and weld design in manufacturing Offers computer aided manufacturing, quality control, and economics of manufacturing

The main objective of the book is to highlight the modeling of magnetic particles with different shapes and magnetic properties, to provide graduate students and young researchers information on the theoretical aspects and actual techniques for the treatment of magnetic particles in particle-based simulations. In simulation, we focus on the Monte Carlo, molecular dynamics, Brownian dynamics, lattice Boltzmann and stochastic rotation dynamics (multi-particle collision dynamics) methods. The latter two simulation methods can simulate both the particle motion and the ambient flow field simultaneously. In general, specialized knowledge can only be obtained in an effective manner under the supervision of an expert. The present book is written to play such a role for readers who wish to develop the skill of modeling magnetic particles and develop a computer simulation program using their own ability. This book is therefore a self-learning book for graduate students and young researchers. Armed with this knowledge, readers are expected to be able to sufficiently enhance their skill for tackling any challenging problems they may encounter in future.

The book presents qualitative results for different classes of fractional equations, including fractional functional differential equations, fractional impulsive differential equations, and fractional impulsive functional differential equations, which have not been covered by other books. It manifests different constructive methods by demonstrating how these techniques can be applied to investigate qualitative properties of the solutions of fractional systems. Since many applications have been included, the demonstrated techniques and models can be used in training students in mathematical modeling and in the study and development of fractional-order models.

Stochastic Communities presents a theory of biodiversity by analyzing the distribution of abundances among species in the context of a community. The basis of this theory is a distribution called the "J distribution." This distribution is a pure hyperbola and mathematically implied by the "stochastic species hypothesis" assigning equal probabilities of birth and death within the population of each species over varying periods of time. The J distribution in natural communities has strong empirical support resulting from a meta-study and strong theoretical support from a theorem that is mathematically implied by the stochastic species hypothesis.

Discovering Dynamical Systems Through Experiment and Inquiry differs from most texts on dynamical systems by blending the use of computer simulations with inquiry-based learning (IBL). IBL is an excellent tool to move students from merely remembering the material to deeper understanding and analysis. This method relies on asking students questions first, rather than presenting the material in a lecture. Another unique feature of this book is the use of computer simulations. Students can discover examples and counterexamples through manipulations built into the software. These tools have long been used in the study of dynamical systems to visualize chaotic behavior. We refer to this unique approach to teaching mathematics as ECAP-Explore, Conjecture, Apply, and Prove. ECAP was developed to mimic the actual practice of mathematics in an effort to provide students with a more holistic mathematical experience. In general, each section begins with exercises guiding students through explorations of the featured concept and concludes with exercises that help the students formally prove the results. While symbolic dynamics is a standard topic in an undergraduate dynamics text, we have tried to emphasize it in a way that is more detailed and inclusive than is typically the case. Finally, we have chosen to include multiple sections on important ideas from analysis and topology independent from their application to dynamics.

Nonlinear models have been used extensively in the areas of economics and finance. Recent literature on the topic has shown that a large number of series exhibit nonlinear dynamics as opposed to the alternative--linear dynamics. Incorporating these concepts involves deriving and estimating nonlinear time series models, and these have typically taken the form of Threshold Autoregression (TAR) models, Exponential Smooth Transition (ESTAR) models, and Markov Switching (MS) models, among several others. This edited volume provides a timely overview of nonlinear estimation techniques, offering new methods and insights into nonlinear time series analysis. It features cutting-edge research from leading academics in economics, finance, and business management, and will focus on such topics as Zero-Information-Limit-Conditions, using Markov Switching Models to analyze economics series, and how best to distinguish between competing nonlinear models. Principles and techniques in this book will appeal to econometricians, finance professors teaching quantitative finance, researchers, and graduate students interested in learning how to apply advances in nonlinear time series modeling to solve complex problems in economics and finance.

This book presents a theory as well as methods to understand and to purposively influence complex systems. It suggests a theory of complex systems as nested systems, i. e. systems that enclose other systems and that are simultaneously enclosed by even other systems. According to the theory presented, each enclosing system emerges through time from the generative activities of the systems they enclose. Systems are nested and often emerge unplanned, and every system of high dynamics is enclosed by a system of slower dynamics. An understanding of systems with faster dynamics, which are always guided by systems of slower dynamics, opens up not only new ways to understanding systems, but also to effectively influence them. The aim and subject of this book is to lay out these thoughts and explain their relevance to the purposive development of complex systems, which are exemplified in case studies from an urban system. The interested reader, who is not required to be familiar with system-theoretical concepts or with theories of emergence, will be guided through the development of a theory of emergent nested systems. The reader will also learn about new ways to influence the course of events - even though the course of events is, in principle, unpredictable, due to the ever-new emergence of real novelty.

Sports analytics has gathered tremendous momentum as one of the most dynamic fields. Diving deep into the numbers of sports can be game changing or simply a fun exercise for fans. How do you get in the game with numbers? What questions can be explored? What actionable insights can be gleaned?Do you like sports? This book will detail ways to analyze athletics to gain insight that can otherwise be obscured. Like math? You'll find many mathematical topics not involving sports. You'll also see how sports analytics can train you broadly in mathematics.From coaching at the highest levels to national media broadcasts, analytics are becoming increasingly indispensable. Dive into the numbers behind soccer to basketball to baseball to boxing to swimming, dive into the numbers. Learn how to get in the game with sports and mathematics.

The field of communication systems is full of complex design questions concerning performance and reliability. Since data traffic and errors occur in a random fashion, stochastic models are used for developing and comparing systems. In particular, stochastic Petri nets have become a popular tool for the description and automatic evaluation of such models. The use of non-Markovian models has become important as they allow more flexibility.

• Provides a clear exposition of the use of stochastic Petri nets in communication systems engineering

• Introduces the reader to the analysis techniques and algorithms used in performance evaluation

• Provides an accompanying example to clarify the use of each definition, concept and algorithm

• Mathematica routines used for implementing the algorithms are available on the Wiley ftp site