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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.
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.
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.
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.
Categorical Data Analysis and Multilevel Modeling Using R provides
a practical guide to regression techniques for analyzing binary,
ordinal, nominal, and count response variables using the R
software. Author Xing Liu offers a unified framework for both
single-level and multilevel modeling of categorical and count
response variables with both frequentist and Bayesian approaches.
Each chapter demonstrates how to conduct the analysis using R, how
to interpret the models, and how to present the results for
publication. A companion website for this book contains datasets
and R commands used in the book for students, and solutions for the
end-of-chapter exercises on the instructor site.
Orbital debris poses significant risks to space activities and is a
threat extending into the foreseeable future. While identification
and tracking of orbital threats is actively pursued in general, we
focus on deorbiting very small satellites of the nanosat or cubesat
size with miniature tether systems. Ch.2 describes the basic
principles of electric tether physics, and outlines the geophysical
environment of LEOs where tethered nanosat flying occurs. Ch.3
develops an equivalent circuit model of an electrodynamic (ED)
tether system, and illustrates the surge currents and overvoltages
under various operating modes. Ch. 4-6 discuss the nonlinear
characteristics of electrostatic (ES), ED, and more promising
hybrid ES-ED tether dynamics, and the application of tethered
systems. Ch.7 summarizes the work, with some related discussions.
The work was initiated at Chinese Academy of Sciences (CAS,
1995-1997; 2001-2003), and is funded by Visiting Fellowship in
Canadian Government Laboratories Program (2009-2012), Natural
Sciences and Engineering Research Council (NSERC), Canada. We thank
Dr. Alfred Ng, Manager (Control and Analysis), Canadian Space
Agency (CSA), for support and advice.
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