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Masters Theses in the Pure and Applied Sciences was first conceived, published, and disseminated by the Center for Information and Numerical Data Analysis and Synthesis (CINDAS) * at Purdue University in 1 957, starting its coverage of theses with the academic year 1955. Beginning with Volume 13, the printing and dissemination phases of the activity were transferred to University Microfilms/Xerox of Ann Arbor, Michigan, with the thought that such an arrangement would be more beneficial to the academic and general scientific and technical community. After five years of this joint undertaking we had concluded that it was in the interest of all con cerned if the printing and distribution of the volumes were handled by an interna tional publishing house to assure improved service and broader dissemination. Hence, starting with Volume 18, Masters Theses in the Pure and Applied Sciences has been disseminated on a worldwide basis by Plenum Publishing Cor poration of New York, and in the same year the coverage was broadened to include Canadian universities. All back issues can also be ordered from Plenum. We have reported in Volume 32 (thesis year 1987) a total of 12,483 theses titles from 22 Canadian and 176 United States universities. We are sure that this broader base for these titles reported will greatly enhance the value of this important annual reference work. While Volume 32 reports theses submitted in 1987, on occasion, certain univer sities do report theses submitted in previous years but not reported at the time.
Learning systems have made a significant impact on all areas of engineering problems. They are attractive methods for solving many problems which are too complex, highly non-linear, uncertain, incomplete or non-stationary, and have subtle and interactive exchanges with the environment where they operate. The main aim of the book is to give a systematic treatment of learning automata and to produce a guide to a wide variety of ideas and methods that can be used in learning systems, including enough theoretical material to enable the user of the relevant techniques and concepts to understand why and how they can be used. The book also contains the materials that are necessary for the understanding and development of learning automata for different purposes such as processes identification, optimization and control. Learning Automata: Theory and Applications may be recommended as a reference for courses on learning automata, modelling, control and optimization. The presentation is intended both for graduate students in control theory and statistics and for practising control engineers.
This is the first of two volumes that together provide an overview of the latest advances in the generation and application of digital twins in bioprocess design and optimization. Both processes have undergone significant changes over the past few decades, moving from data-driven approaches into the 21st-century digitalization of the bioprocess industry. Moreover, the high demand for biotechnological products calls for efficient methods during research and development, as well as during tech transfer and routine manufacturing. In this regard, one promising tool is the use of digital twins, which offer a virtual representation of the bioprocess. They reflect the mechanistics of the biological system and the interactions between process parameters, key performance indicators and product quality attributes in the form of a mathematical process model. Furthermore, digital twins allow us to use computer-aided methods to gain an improved process understanding, to test and plan novel bioprocesses, and to efficiently monitor them. This book explains the mathematical structure of digital twins, their development and the model’s respective parts, as well as concepts for the knowledge-driven generation and structural variability of digital twins. Covering fundamentals as well as applications, the two volumes offer the ideal introduction to the topic for researchers in academy and industry alike.
Process systems engineering (PSE) is a discipline that delivers tools for guided decision-making in the development of new processes and products. Proven successful in the pharmaceutical-, food- and water sectors, it has also breached the field of energy systems. The future energy systems aim to be more efficient, cost-effective, environmentally benign, and interconnected. The design and operation is extremely challenging for decision-makers, engineers, and scientists and here lies a crucial role for the process systems engineer.