This book explores sacrifice as a narrative theme and a stylistic strategy in works by May Sinclair, Mary Butts and H. D. It argues that the modernist experiment with pronoun use informs the treatment of acts of sacrifice in the texts, understood both as acts of self-renunciation and as ritual performance. It also suggests that sacrifice, if the conditions are right, can serve as the structure upon which a cohesive community might be built. The book offers in-depth analyses of the three authors and their works, deftly dissecting the modernist narrative experiment to show that it was by no means limited — it was a means by which to approach a wide range of stories and materials.

We describe in this book, recent developments on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their application in areas such as, intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book contains a collection of papers focused on hybrid intelligent systems based on soft computing. There are some papers with the main theme of type-1 and type-2 fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications. There also some papers that presents theory and practice of meta-heuristics in different areas of application. Another group of papers describe diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical applications. There are also some papers that present theory and practice of neural networks in different areas of application. In addition, there are papers that present theory and practice of optimization and evolutionary algorithms in different areas of application. Finally, there are some papers describing applications of fuzzy logic, neural networks and meta-heuristics in pattern recognition problems.

Ancient epistolary fiction is a still largely under-explored field of research, at the intersection of studies on epistolography and on pseudepigraphy. The present volume sketches out a broad panorama of ancient fiction in letters. It covers a large period of time up to late Antiquity, with a main focus on letters from the imperial era. Epistolary fiction is examined as a mainly Greek phenomenon (there are few Latin equivalents) that was characteristic of both pagan and Christian literature. The material investigated falls within two categories: fictional letter collections from well-known authors of the Second Sophistic and their successors (Lucian, Alciphron, Philostratus, Aristaenetus); letters attributed to famous historical or legendary characters (pseudonymous letters). Focusing on the specific features of epistolary fiction, the book aims to analyse its forms, its functions as well as its effects. It gathers a series of 11 state-of-the art essays, all tackling the same important issues: the manuscript and printed tradition, the form of epistolary fictions and the universe they build, the arrangement of the letters and their overall structure, the relation between the author and his external readers.

This book describes the latest advances in fuzzy logic, neural networks, and optimization algorithms, as well as their hybrid intelligent combinations, and their applications in the areas such as intelligent control, robotics, pattern recognition, medical diagnosis, time series prediction, and optimization. The topic is highly relevant as most current intelligent systems and devices use some form of intelligent feature to enhance their performance. The book also presents new and advanced models and algorithms of type-2 fuzzy logic and intuitionistic fuzzy systems, which are of great interest to researchers in these areas. Further, it proposes novel, nature-inspired optimization algorithms and innovative neural models. Featuring contributions on theoretical aspects as well as applications, the book appeals to a wide audience.

This book describes the latest advances in fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their applications in areas such as: intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction, and optimization of complex problems. The book is divided into five main parts. The first part proposes new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications; the second explores new concepts and algorithms in neural networks and fuzzy logic applied to recognition. The third part examines the theory and practice of meta-heuristics in various areas of application, while the fourth highlights diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical contexts. Finally, the fifth part focuses on applications of fuzzy logic, neural networks and meta-heuristics to robotics problems.

This book describes recent advances on hybrid intelligent systems using soft computing techniques for diverse areas of application, such as intelligent control and robotics, pattern recognition, time series prediction and optimization complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of type-2 fuzzy logic, which basically consists of papers that propose new models and applications for type-2 fuzzy systems. The second part contains papers with the main theme of bio-inspired optimization algorithms, which are basically papers using nature-inspired techniques to achieve optimization of complex optimization problems in diverse areas of application. The third part contains papers that deal with new models and applications of neural networks in real world problems. The fourth part contains papers with the theme of intelligent optimization methods, which basically consider the proposal of new methods of optimization to solve complex real world optimization problems. The fifth part contains papers with the theme of evolutionary methods and intelligent computing, which are papers considering soft computing methods for applications related to diverse areas, such as natural language processing, recommending systems and optimization.

This book describes recent advances on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their application in areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book contains a collection of papers focused on hybrid intelligent systems based on soft computing. There are some papers with the main theme of type-1 and type-2 fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications. There are also some papers that present theory and practice of meta-heuristics in different areas of application. Another group of papers describes diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical applications. There are also some papers that present theory and practice of neural networks in different areas of application. In addition, there are papers that present theory and practice of optimization and evolutionary algorithms in different areas of application. Finally, there are some papers describing applications of fuzzy logic, neural networks and meta-heuristics in pattern recognition problems.

This book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems. The second part focuses on neural network theory, and explores the applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The book's third part presents enhancements to meta-heuristics based on fuzzy logic techniques and describes new nature-inspired optimization algorithms that employ fuzzy dynamic adaptation of parameters, while the fourth part presents diverse applications of nature-inspired optimization algorithms. In turn, the fifth part investigates applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. The sixth part examines new optimization algorithms and their applications. Lastly, the seventh part is dedicated to the design and application of different hybrid intelligent systems.

This is an excellent book that provides a much needed overview of theoretical frameworks that help us understand the behavior of family firms. This book will be invaluable as a resource to doctoral students and researchers who are trying to analyze decision making in family firms and the factors that shape management processes in these organizations. The book is very comprehensive in its coverage, addressing most major theoretical frameworks that have recently been applied to examine phenomena unique to family firms. These include, among others, organizational ecology, behavioral theory, agency theory, behavioral agency theory, and social identity theory. This book is a must read for family business scholars.' - Luis Gomez-Mejia, University of Notre Dame, US'A common criticism of family business as a field of study is the lack of well-developed, theoretically grounded research. This important book edited by Nordqvist, Melin, Waldkirch and Kumeto promises to change this widely held view. The book brings into focus important theoretical perspectives that have rich implications for research on family firms. Well established and emerging perspectives are covered in the book, highlighting different debates that are likely to shape future scholarship in the field. Chapters are easy to read and concepts as well as theories are clearly explained and their usefulness is made evident. I compliment the editors on a job well done in recognizing the challenges of theorizing about family business and mapping out the territory of tomorrow's research.' - Shaker A. Zahra, University of Minnesota, US Family business has become an increasingly studied field over the last decade and forms one of the fastest growing research areas today. The uniqueness of family business is the interaction between two systems: the family and the firm, leading to specific characteristics that we rarely see in other types of businesses. In order to understand the unique interaction between these two systems, researchers have adopted a diverse range of theories drawn from different fields. The contributors to this volume provide a thorough discussion of 13 theoretical perspectives that have been used in family business research. Each chapter introduces a theory, demonstrates its previous application in family business research and offers compelling ideas for future research that could contribute to both the family business field and the original theory behind it. This book aims to spark new insights for researchers and PhD students in the field of family business, and is also a good introduction for researchers who are new to the field. Policymakers and practitioners will find this an enlightening resource. Contributors: R. Adiguna, N. Akhter, G. Criaco, P. Hansson, A. Kallmuenzer, A. Kuiken, G. Kumeto, A. Mazzelli, L. Melin, H. Nilson, M. Nordqvist, E. Rondi, K. Staffansson Pauli, M. Waldkirch, A. Wielsma

This book describes new methods for building intelligent systems using type-2 fuzzy logic and soft computing (SC) techniques. The authors extend the use of fuzzy logic to a higher order, which is called type-2 fuzzy logic. Combining type-2 fuzzy logic with traditional SC techniques, we can build powerful hybrid intelligent systems that can use the advantages that each technique offers. This book is intended to be a major reference tool and can be used as a textbook.

This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents diverse applications of nature-inspired optimization algorithms. The sixth part contains papers describing new optimization algorithms. The seventh part contains papers describing applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. Finally, the eighth part contains papers that present enhancements to meta-heuristics based on fuzzy logic techniques.

This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area.