The book presents short papers of participants of the 8th International Scientific Conference-School for Young Scientists "Physical and Mathematical Modeling of Earth and Environment Processes" (Ishlinsky Institute for Problems in Mechanics of the Russian Academy of Sciences). The book includes theoretical and experimental studies of processes in the atmosphere, oceans, the lithosphere and their interaction; environmental issues; problems of human impact on the environment; methods of geophysical research.

Semi-infinite programming (SIP) deals with optimization problems in which either the number of decision variables or the number of constraints is finite. This book presents the state of the art in SIP in a suggestive way, bringing the powerful SIP tools close to the potential users in different scientific and technological fields.

“Environmental Modeling using MATLAB R ” by Ekkehard Holzbecher is an excellent publication and a novel approach covering the intersection of two important, growing worlds – the world of environmental modeling and of mathematical software. Environmental modeling is a science that uses mathematics and comp- ers to simulate physical and chemical phenomena in the environment (e.g., environmental pollution). This science was initially based on pen-and-paper calculations using simple equations. In the last 50 years, with the devel- ment of digital computers,environmental models have become more and more complex, requiring often numerical solutions for systems of partial di?erential equations. Mathematical software, such as MATLAB R , has been developed in the lasttwo decades. Thesepackageshavebeen particularlysuccessfulfor usersof personal computers. Mathematical software provides a set of tools for solving equations both analytically and numerically. This is a major improvement in comparison to the programming tools (e.g., FORTRAN) previously used by scientists. Mathematical software o?ers extremely valuable and cost-e?ective tools that improve the productivity of the programmer by at least an order of magnitude. The use of these tools also minimizes the risk of programming errors. In addition, mathematical software o?ers unique visualization tools that allow the user to immediately visualize and often animate simulation results. Scientists who become familiar with a tool like MATLAB R will never go back to previous ways of computer programming.

The book presents short papers of participants of the 8th International Scientific Conference-School for Young Scientists "Physical and Mathematical Modeling of Earth and Environment Processes" (Ishlinsky Institute for Problems in Mechanics of the Russian Academy of Sciences). The book includes theoretical and experimental studies of processes in the atmosphere, oceans, the lithosphere and their interaction; environmental issues; problems of human impact on the environment; methods of geophysical research.

Partial Least Squares is a family of regression based methods designed for the an- ysis of high dimensional data in a low-structure environment. Its origin lies in the sixties, seventies and eighties of the previous century, when Herman O. A. Wold vigorously pursued the creation and construction of models and methods for the social sciences, where “soft models and soft data” were the rule rather than the exception, and where approaches strongly oriented at prediction would be of great value. Theauthorwasfortunatetowitnessthedevelopment rsthandforafewyears. Herman Wold suggested (in 1977) to write a PhD-thesis on LISREL versus PLS in the context of latent variable models, more speci cally of “the basic design”. I was invited to his research team at the Wharton School, Philadelphia, in the fall of 1977. Herman Wold also honoured me by serving on my PhD-committee as a distinguished and decisive member.

Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in Matlab®. Examples and exercises with solutions are provided at the end of each chapter to facilitate the comprehension of the material. For each data mining technique described in the book variants and improvements of the basic algorithm are also given.

The book presents short papers of participants of the 8th International Scientific Conference-School for Young Scientists "Physical and Mathematical Modeling of Earth and Environment Processes" (Ishlinsky Institute for Problems in Mechanics of the Russian Academy of Sciences). The book includes theoretical and experimental studies of processes in the atmosphere, oceans, the lithosphere and their interaction; environmental issues; problems of human impact on the environment; methods of geophysical research.

What are mathematical models? How are they developed? How much should we trust them: can we be con- fident in their predictions, and do we know when to act upon them? These questions, routinely addressed by mathematical modeling practitioners, are also of interest to citizens and policy makers. Our modern society needs models that can be relied upon, not only to improve our understanding of a situation, but also to inform policy decisions.

This book provides a systematic description about the development of Isogeometric Topology Optimization (ITO) method using the density, and then addresses the effectiveness and efficiency of the ITO method on several design problems, including multi-material structures, stress-minimization structures, piezoelectric structures and also with the uniform manufacturability, ultra-lightweight architected materials with extreme bulk/shear moduli, auxetic metamaterials and auxetic meta-composites with the NPRs behavior in microstructures. A detailed MATLAB implementation of the ITO method with an in-house code “IgaTop” is also presented.

Researches and developers of simulation models state that the Java program­ ming language presents a unique and significant opportunity for important changes in the way we develop simulation models today. The most important characteristics of the Java language that are advantageous for simulation are its multi-threading capabilities, its facilities for executing programs across the Web, and its graphics facilities. It is feasible to develop compatible and reusable simulation components that will facilitate the construction of newer and more complex models. This is possible with Java development environments. Another important trend that begun very recently is web-based simulation, i.e., and the execution of simulation models using Internet browser software. This book introduces the application of the Java programming language in discrete-event simulation. In addition, the fundamental concepts and prac­ tical simulation techniques for modeling different types of systems to study their general behavior and their performance are introduced. The approaches applied are the process interaction approach to discrete-event simulation and object-oriented modeling. Java is used as the implementation language and UML as the modeling language. The first offers several advantages compared to C++, the most important being: thread handling, graphical user interfaces (QUI) and Web computing. The second language, UML (Unified Modeling Language) is the standard notation used today for modeling systems as a collection of classes, class relationships, objects, and object behavior.

This volume contains many of the papers presented at the conference "Optimum Design 2000: Prospects for the New Millennium" held in Cardiff, UK on April 12th - 14th, 2000. The majority of the papers consider aspects of optimum experimental design from the theoretical to applications. Many of the optimisation problems arising in the optimum design theory in general and the articles in this volume in particular, fall into the category of nonconvex, including global, optimization. The papers are organised in two sections. Since we are at the beginning of a new millennium the first paper starts by looking back at the beginnings of optimum exper­ imental design in 1917 and sketches the development up to Kiefer's paper read to the Royal Statistical Society in 1959. This is the first in a group of papers which we have labelled "Theory", which cover the more general aspects, such as the properties and methods of construction of designs. In the "Applications" section there are papers on sequential design problems arising in the pharmaceutical industry and on the designs with discrete factors which occur in agriculture. There are also papers on training neural networks, on the efficient design of sampling methods, on design problems in mixtures, arising from a study of glass manufacturing, and on the resistance of Brazilian weeds to herbicides. The number attending the meeting was deliberately limited to 50 in order to encour­ age a workshop atmosphere and the exchange of ideas.

Graphic modelling is a form of multivariate analysis that uses graphs to represent models. These graphs display the structure of dependencies, both associational and causal, between the variables in the model. This textbook provides an introduction to graphical modelling with emphasis on applications and practicalities rather than on a formal development. It is based on the popular software package for graphical modelling, MIM, a freeware version of which can be downloaded from the Internet. Following an introductory chapter which sets the scene and describes some of the basic ideas of graphical modelling, subsequent chapters describe particular families of models, including log-linear models, Gaussian models, and models for mixed discrete and continuous variables. Further chapters cover hypothesis testing and model selection. Chapters 7 and 8 are new to the second edition. Chapter 7 describes the use of directed graphs, chain graphs, and other graphs. Chapter 8 summarizes some recent work on causal inference, relevant when graphical models are given a causal interpretation. This book will provide a useful introduction to this topic for students and researchers.

This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems. All derivation processes are presented in detail to aid in comprehension. The book offers concrete guidance, helping readers recognize and formulate convex optimization problems they might encounter in practice.

The primary reason for producing this book is to demonstrate and commu­ nicate the pervasive nature of Discrete Optimisation. It has applications across a very wide range of activities. Many of the applications are only known to specialists. Our aim is to rectify this. It has long been recognized that ''modelling" is as important, if not more important, a mathematical activity as designing algorithms for solving these discrete optimisation problems. Nevertheless solving the resultant models is also often far from straightforward. Although in recent years it has become viable to solve many large scale discrete optimisation problems some problems remain a challenge, even as advances in mathematical methods, hardware and software technology are constantly pushing the frontiers forward. The subject brings together diverse areas of academic activity as well as di­ verse areas of applications. To date the driving force has been Operational Re­ search and Integer Programming as the major extention of the well-developed subject of Linear Programming. However, the subject also brings results in Computer Science, Graph Theory, Logic and Combinatorics, all of which are reflected in this book. We have divided the chapters in this book into two parts, one dealing with general methods in the modelling of discrete optimisation problems and one with specific applications. The first chapter of this volume, written by Paul Williams, can be regarded as a basic introduction of how to model discrete optimisation problems as Mixed Integer Programmes, and outlines the main methods of solving them.