This book investigates statistical observables for anomalous and nonergodic dynamics, focusing on the dynamical behaviors of particles modelled by non-Brownian stochastic processes in the complex real-world environment. Statistical observables are widely used for anomalous and nonergodic stochastic systems, thus serving as a key to uncover their dynamics. This study explores the cutting edge of anomalous and nonergodic diffusion from the perspectives of mathematics, computer science, statistical and biological physics, and chemistry. With this interdisciplinary approach, multiple physical applications and mathematical issues are discussed, including stochastic and deterministic modelling, analyses of (stochastic) partial differential equations (PDEs), scientific computations and stochastic analyses, etc. Through regularity analysis, numerical scheme design and numerical experiments, the book also derives the governing equations for the probability density function of statistical observables, linking stochastic processes with PDEs. The book will appeal to both researchers of electrical engineering expert in the niche area of statistical observables and stochastic systems and scientists in a broad range of fields interested in anomalous diffusion, especially applied mathematicians and statistical physicists.

'The book is highly recommended as a reference for advanced graduate students and scholars involved in geometric analysis of membranes and other elastic surfaces. Valuable techniques may be learned from the bookaEURO (TM)s model constructions and sequential derivations and presentations of governing equations. Detailed analysis and solutions enable the reader with an increased understanding of the physical characteristics of membranes in liquid crystal phases such as their preferred shapes.'Contemporary PhysicsThis is the second edition of the book Geometric Methods in Elastic Theory of Membranes in Liquid Crystal Phases published by World Scientific in 1999. This book gives a comprehensive treatment of the conditions of mechanical equilibrium and the deformation of membranes as a surface problem in differential geometry. It is aimed at readers engaging in the field of investigation of the shape formation of membranes in liquid crystalline state with differential geometry. The material chosen in this book is mainly limited to analytical results. The main changes in this second edition are: we add a chapter (Chapter 4) to explain how to calculate variational problems on a surface with a free edge by using a new mathematical tool - moving frame method and exterior differential forms - and how to derive the shape equation and boundary conditions for open lipid membranes through this new method. In addition, we include the recent concise work on chiral lipid membranes as a section in Chapter 5, and in Chapter 6 we mention some topics that we have not fully investigated but are also important to geometric theory of membrane elasticity.

In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables the behaviour of complex systems to be modelled and predicted and accurate control to be achieved through training, without a priori information about the systems' structures or parameters. This book describes examples of applications of neural networks In modelling, prediction and control. The topics covered include identification of general linear and non-linear processes, forecasting of river levels, stock market prices and currency exchange rates, and control of a time-delayed plant and a two-joint robot. These applications employ the major types of neural networks and learning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are also presented. The main learning algorithm adopted in the applications is the standard backpropagation (BP) algorithm. Widrow-Hoff learning, dynamic BP and evolutionary learning are also described.

Die Arbeit steht im Kontext des aktuellen Diskurses zur strukturellen Verankerung einer Berufsbildung fur nachhaltige Entwicklung (BBNE) und ist zugleich ein Beitrag zur vergleichenden Berufsbildungsforschung. Im Rahmen der Untersuchung erfolgt eine tiefgreifende Analyse der Berufsbildungssysteme Deutschlands und Chinas. Durch einen systematischen Vergleich der beiden Lander wird der aktuelle Stand der Berufsbildung fur nachhaltige Entwicklung aufgearbeitet. Dies erfolgt mit der Zielsetzung, Anregungen fur die Weiterentwicklung der beruflichen Bildung in China und Deutschland zu geben. Einzigartig ist die abschliessende Anwendung der Forschungsergebnisse, um eine umfassende BBNE-Roadmap zu konzipieren und somit einen Anknupfungspunkt zur UEberfuhrung der Ergebnisse in die Praxis herzustellen.

This book honors the remarkable science and life of Shoucheng Zhang, a condensed matter theorist known for his work on topological insulators, the quantum Hall effect, spintronics, superconductivity, and other fields. It contains the contributions displayed at the Shoucheng Zhang Memorial Workshop held on May 2-4, 2019 at Stanford University.