Distributed Programming: Theory and Practice presents a practical and rigorous method to develop distributed programs that correctly implement their specifications. The method also covers how to write specifications and how to use them. Numerous examples such as bounded buffers, distributed locks, message-passing services, and distributed termination detection illustrate the method. Larger examples include data transfer protocols, distributed shared memory, and TCP network sockets.

Empirical research often lacks theory. This book progressively works out a method of constructing models which can bridge the gap between empirical and theoretical research in the social sciences. This might improve the explanatory power of models. The issue is quite novel, and it benefited from a thorough examination of statistical and mathematical models, conceptual models, diagrams and maps, machines, computer simulations, and artificial neural networks. These modelling practices have been approached through different disciplines.

Form-based applications range from simple web shops to complex enterprise resource planning systems. Draheim and Weber adapt well-established basic modeling techniques in a novel way to achieve a modeling framework optimized for this broad application domain. They introduce new modeling artifacts, such as page diagrams and form storyboards, and separate dialogue patterns to allow for reuse. In their implementation they have developed new constructs such as typed server pages, and tools for forward and reverse engineering of presentation layers. The methodology is explained using an online bookshop as a running example in which the user can experience the modeling concepts in action.

Stability is a very important property of mathematical models simulating physical processes which provides an adequate description of the process. Starting from the classical notion of the well-posedness in the Hadamard sense, this notion was adapted to different areas of research and at present is understood, depending on the physical problem under consideration, as the Lyapunov stability of stationary solutions, stability of specified initial data, stability of averaged models, etc.

This book is designed to be a practical study in infectious disease dynamics. The book offers an easy to follow implementation and analysis of mathematical epidemiology. The book focuses on recent case studies in order to explore various conceptual, mathematical, and statistical issues. The dynamics of infectious diseases shows a wide diversity of pattern. Some have locally persistent chains-of-transmission, others persist spatially in ‘consumer-resource metapopulations’. Some infections are prevalent among the young, some among the old and some are age-invariant.

This book is based on the accepted research papers presented in the International Conference "Artificial Intelligence in Engineering & Science" (AIES-2022). The aim of the AIES Conference is to bring together researchers involved in the theory of computational intelligence, knowledge engineering, fuzzy systems, soft computing, machine learning and related areas and applications in engineering, bioinformatics, industry, medicine, energy, smart city, social spheres and other areas. This book presents new perspective research results: models, methods, algorithms and applications in the field of Artificial Intelligence (AI). Particular emphasis is given to the medical applications - medical images recognition, development of the expert systems which could be interesting for the AI researchers as well for the physicians looking for the new ideas in medicine. The central audience of the book are researchers, industrial practitioners, students specialized in the Artificial Intelligence.

This book ?nds new things to do with an old idea. The proofs-as-programs paradigm constitutes a set of approaches to developing programs from proofs in constructive logic. It has been over thirty years since the paradigm was ?rst conceived. At that time, there was a belief that proofs-as-programs had the - tential for practical application to semi-automated software development. I- tial applications were mostly concerned with ?ne-grain, mathematical program synthesis. For various reasons, research interest in the area eventually tended toward more theoretic issues of constructive logic and type theory. However, in recent years, the situation has become more balanced, and there is increasingly active research in applying constructive techniques to industrial-scale, complex software engineering problems. Thismonographdetailsseveralimportantadvancesinthisdirectionofpr- tical proofs-as-programs. One of the central themes of the book is a general, abstract framework for developing new systems of program synthesis by adapting proofs-as-programs to new contexts.

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning, 2nd Edition” of the MDPI Mathematics journal. The content of this Special Issue encompasses a diverse array of topics related to artificial intelligence, spanning both foundational theories and practical applications. It covers advancements in deep learning and machine learning techniques, including neural networks and reinforcement learning, as well as the growing field of federated learning. This issue also explores developments in natural language processing and multimodal data analysis, alongside optimization strategies inspired by evolutionary algorithms and probabilistic models, such as Gaussian processes. It also highlights research in feature selection and support vector machines, innovations in autonomous driving and trajectory prediction, and broader applications of artificial intelligence in decision-making and intelligent systems. We anticipate that the scientific findings presented in this volume will offer valuable references and inspiration to those eager to explore new frontiers in artificial intelligence, machine learning, and their extensive real-world applications.

The advent of the high-speed computer with its enormous storage capabilities enabled statisticians as well as researchers from the different topics of life sciences to apply mul­ tivariate statistical procedures to large data sets to explore their structures. More and more, methods of graphical representation and data analysis are used for investigations. These methods belong to a topic of growing popUlarity, known as "exploratory data analysis" or EDA. In many applications, there is reason to believe that a set of objects can be clus­ tered into subgroups that differ in meaningful ways. Extensive data sets, for example, are stored in clinical cancer registers. In large data sets like these, nobody would ex­ pect the objects to be homogeneous. The most commonly used terms for the class of procedures that seek to separate the component data into groups are "cluster analysis" or "numerical taxonomy". The origins of cluster analysis can be found in biology and anthropology at the beginning of the century. The first systematic investigations in cluster analysis are those of K. Pearson in 1894. The search for classifications or ty­ pologies of objects or persons, however, is indigenous not only to biology but to a wide variety of disciplines. Thus, in recent years, a growing interest in classification and related areas has taken place. Today, we see applications of cluster analysis not only to. biology but also to such diverse areas as psychology, regional analysis, marketing research, chemistry, archaeology and medicine.