La simulation des systèmes mécaniques articulés a connu un essor important depuis les années 60. Jusqu’a la fin des années 70, les corps étaient considérés rigides dans la plupart des codes de calculs. Les résultats sont excellents tant que les déformations restent faibles. Cependant, la modélisation des mécanismes de grandes tailles ou tournant à vitesses élevées, ainsi que l'optimisation des masses en mouvement, nécessite la prise en compte des flexibilités. Lors de l'écriture d'un formalisme multi corps flexibles, il faut effectuer entre autre les choix du type de coordonnées descriptives permettant de positionner le repère mobile accompagnant les corps flexibles et celui des équations du mouvement. Ceux-ci influent directement sur la facilite de description du mécanisme ainsi que sur le cout opératoire. Nous avons donc choisi de développer une approche basée sur les équations de Kane en coordonnées naturelles. Ces dernières conduisent à une description simple et naturelle du mécanisme et à un nombre de coordonnées descriptives restreint. Les équations de Kane génèrent un nombre minimal d'équations du mouvement. La prise en compte des déformations élastiques se fait, quant à elle, par la méthode des éléments finis. Afin de réduire le nombre d'inconnues et de pouvoir augmenter le pas d'intégration, ces déformations sont projetées dans une base modale du corps. Un logiciel basé sur cette approche a donc été développé. Sa validation numérique sur deux benchmarks nous permet de conclure, que pour des résultats similaires, les temps cpu sont inférieurs aux approches classiques basées d'une part sur les équations de Lagrange en coordonnées absolues et d'autre part sur les équations de Lagrange en coordonnées naturelles. Puis la validation expérimentale sur un mécanisme quatre barres flexibles permet de montrer que l'approche donne des résultats proches des conditions réelles de fonctionnement.

This book highlights peer reviewed articles from the 1st International Conference on Renewable Energy and Energy Conversion, ICREEC 2019, held at Oran in Algeria. It presents recent advances, brings together researchers and professionals in the area and presents a platform to exchange ideas and establish opportunities for a sustainable future. Topics covered in this proceedings, but not limited to, are photovoltaic systems, bioenergy, laser and plasma technology, fluid and flow for energy, software for energy and impact of energy on the environment.

This volume presents a catalogue of over 2000 doctoral theses by Africans in all fields of mathematics, including applied mathematics, mathematics education and history of mathematics. The introduction contains information about distribution by country, institutions, period, and by gender, about mathematical density, and mobility of mathematicians. Several appendices are included (female doctorate holders, doctorates in mathematics education, doctorates awarded by African universities to non-Africans, doctoral theses by non-Africans about mathematics in Africa, activities of African mathematicians at the service of their communities). Paulus Gerdes compiled the information in his capacity of Chairman of the African Mathematical Union Commission for the History of Mathematics in Africa (AMUCHMA). The book contains a preface by Mohamed Hassan, President of the African Academy of Sciences (AAS) and Executive Director of the Academy of Sciences for the Developing World (TWAS). (383 pp.)

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.

This book analyzes the set of forces driving the global financial system toward a period of radical transformation and explores the transformational challenges that lie ahead for global and regional or local banks and other financial intermediaries. It is explained how these challenges derive from the newly emerging post-crisis structure of the market and from shadow and digital players across all banking operations. Detailed attention is focused on the impacts of digitalization on the main functions of the financial system, and particularly the banking sector. The author elaborates how an alternative model of banking will enable banks to predict, understand, navigate, and change the external ecosystem in which they compete. The five critical components of this model are data and information mastering; effective use of applied analytics; interconnectivity and “junction playing”; development of new business solutions; and trust and credibility assurance. The analysis is supported by a number of informative case studies. The book will be of interest especially to top and middle managers and employees of banks and financial institutions but also to FinTech players and their advisers and others.

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.