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The Mathematics of the Uncertain
This book is a tribute to Professor Pedro Gil, who created the Department of Statistics, OR and TM at the University of Oviedo, and a former President of the Spanish Society of Statistics and OR (SEIO). In more than eighty original contributions, it illustrates the extent to which Mathematics can help manage uncertainty, a factor that is inherent to real life. Today it goes without saying that, in order to model experiments and systems and to analyze related outcomes and data, it is necessary to consider formal ideas and develop scientific approaches and techniques for dealing with uncertainty. Mathematics is crucial in this endeavor, as this book demonstrates. As Professor Pedro Gil highlig...
Nucleation and Growth in Applied Materials
Nucleation and Growth in Applied Materials covers fundamental aspects of thermodynamics and kinetics, nucleation and growth phenomena occurring during materials processing and synthesis in engineering of materials. Theoretical and practical approaches used to identify and quantify nucleation are analyzed. These approaches can be used to explain the relationship of the physical properties of the material with nucleation and growth processes. Sections cover modern methods such as SEM, TEM, EBSD microtexture, X-ray macrotexture and modeling and simulation (Monte Carlo, Molecular dynamic simulation, machine learning, etc.). Based on these observations, their applications in engineering materials...
Methodology and Tools in Knowledge-Based Systems
This two-volume set constitutes the refereed proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE-98, held in Benicassim, Castellon, Spain, in June 1998.The two volumes present a total of 187 revised full papers selected from 291 submissions. In accordance with the conference, the books are devoted to new methodologies, knowledge modeling and hybrid techniques. The papers explore applications from virtually all subareas of AI including knowledge-based systems, fuzzyness and uncertainty, formal reasoning, neural information processing, multiagent systems, perception, robotics, natural language processing, machine learning, supervision and control systems, etc..
Legumes
Legumes have high potential for improving the nutritional quality of foods, but limited data on their bioactive compounds exists. Results of clinical and epidemiological studies suggest that natural antioxidants can protect us against oxidative stress that is closely associated with cancer and cardiovascular disease. Legumes are a valuable source of bioactive compounds such as phenolic compounds, peptides and non-nutritional factors. They are rich in several important micronutrients, including potassium, magnesium, folate, iron, and zinc, and are an important source of protein in vegetarian diets. They are among the only plant foods that provide significant amounts of the amino acid, lysine....
Africa / The Americas / Asia and Oceania
Africa / The Americas / Asia and Oceania.
Combining Soft Computing and Statistical Methods in Data Analysis
Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a "softening" of classical methods, allowing, in particular, to work with imprecise or vague data, considering imprecise or generalized probabilities and fuzzy events, etc. About ten years ago the idea of establishi...
Building Bridges between Soft and Statistical Methodologies for Data Science
Nowadays, data analysis is becoming an appealing topic due to the emergence of new data types, dimensions, and sources. This motivates the development of probabilistic/statistical approaches and tools to cope with these data. Different communities of experts, namely statisticians, mathematicians, computer scientists, engineers, econometricians, and psychologists are more and more interested in facing this challenge. As a consequence, there is a clear need to build bridges between all these communities for Data Science. This book contains more than fifty selected recent contributions aiming to establish the above referred bridges. These contributions address very different and relevant aspects such as imprecise probabilities, information theory, random sets and random fuzzy sets, belief functions, possibility theory, dependence modelling and copulas, clustering, depth concepts, dimensionality reduction of complex data and robustness.
