الصفحة 81
الصفحة 81
Magneto-Fluid Dynamics : Fundamentals and Case Studies of Natural Phenomena
Concerns the generation of electric currents and of electric space charges inside conducting media that move in magnetic fields. The authors postulate nothing but the Maxwell equations. They discuss at length the disk dynamo, which serves as a model for the natural self-excited dynamos that generate magnetic fields such as that of sunspots. There are 36 Examples and 13 Case Studies. The Case Studies concern solar phenomena -- magnetic elements, sunspots, spicules, coronal loops -- and the Earth's magnetic field.
Magnetism in the Solid State : An Introduction
Presents a phenomenological approach to the field of solid state magnetism. After introducing the basic concepts from statistical thermodynamics and electronic structure theory, the first part discusses the standard models for localized moments (Weiss, Heisenberg) and delocalized moments (Stoner). This is followed by a chapter about exchange and correlation in metals, again considering the results for the localized and delocalized limit. The book ends with a chapter about spin fluctuations, which are introduced as an alternative to the finite temperature Stoner theory. A useful reference work for researchers, this book will also be a valuable accompaniment to graduate courses on magnetism and magnetic materials.
Magnetism : From Fundamentals to Nanoscale Dynamics
Gives an comprehensive account of magnetism, spanning the historical development, the physical foundations and the continuing research underlying the field, one of the oldest yet still vibrant field of physics. It covers both the classical and quantum mechanical aspects of magnetism and novel experimental techniques. Perhaps uniquely, it also discusses spin transport and magnetization dynamics phenomena associated with atomically and spin engineered nano-structures against the backdrop of spintronics and magnetic storage and memory applications.
Magnetism : A Synchrotron Radiation Approach
Contains the edited lectures of the fourth Mittelwihr school on "Magnetism and Synchrotron Radiation". This series of events introduces graduate students and nonspecialists from related disciplines to the field of magnetism and magnetic materials with emphasis on synchrotron radiation as an experimental tool of investigation. These lecture notes present in particular the state of the art regarding the analysis of magnetic properties of new materials.
Magnetic Resonance Imaging in Orthopedic Sports Medicine
Though magnetic resonance imaging has helped revolutionize the field of orthopedic medicine, a difference in perspective persists between radiology and orthopedic specialists. Magnetic Resonance Imaging in Orthopedic Sports Medicine is an interdisciplinary resource designed to bridge this gap.
Magnetic Nanostructures in Modern Technology ; Spintronics, Magnetic MEMS and Recording
A team of outstanding scientists in the field of modern magnetic nanotechnologies illustrates the state of the art in several areas of advanced magneto-electronic devices, magnetic micro-electromechanical systems and high density information storage technologies.The physics and chemistry of nano-scale systems have made rapid advances and there are real prospects of translating exciting scientific findings into a new generation of processes and high technology products with a potential impact on several industrial sectors. In particular the development of nano-structured magnetic materials plays a leading role in the increasing miniaturization of devices with superior performances.
Magnetic Monopoles
This monograph addresses the field theoretical aspects of magnetic monopoles. Written for graduate students as well as researchers, the author demonstrates the interplay between mathematics and physics. He delves into details as necessary and develops many techniques that find applications in modern theoretical physics. This introduction to the basic ideas used for the description and construction of monopoles is also the first coherent presentation of the concept of magnetic monopoles. It arises in many different contexts in modern theoretical physics, from classical mechanics and electrodynamics to multidimensional branes. The book summarizes the present status of the theory and gives an extensive but carefully selected bibliography on the subject. The first part deals with the Dirac monopole, followed in part two by the monopole in non-abelian gauge theories. The third part is devoted to monopoles in supersymmetric Yang-Mills theories.
Magnetic Microscopy of Nanostructures
Contains a comprehensive collection of overview articles on novel microscopy methods for imaging magnetic structures on the nanoscale. Written by leading scientists in the field the book covers synchrotron based methods, spin polarized electron methods, and scanning probe techniques. It will be a valuable source of reference for graduate students and newcomers to the field.
Magnetic Heterostructures : Advances and Perspectives in Spinstructures and Spintransport
Magnetic heterostructures constitute an important field in magnetism and nanotechnology, which has developed over the past fifteen years due to important advances in epitaxial- growth techniques and lithographic processes. Magnetic heterostructures combine different physical properties which do not exist in nature. Examples are semiconductors/ferromagnets, superconductors/ferromagnets, and ferromagnets/antiferromagnets. These combinations display rich and novel physical properties different from those that exit in any single one of them. Interlayer exchange coupling, exchange bias, proximity effects, giant magneto-resistance, tunneling magneto-resistance, spininjection and spintransport are examples of new physical phenomena that rely on the combination of different materials layers
Magnetic Functions Beyond the Spin-Hamiltonian
Using the spin-Hamiltonian formalism the magnetic parameters are introduced through the components of the Lambda-tensor involving only the matrix elements of the angular momentum operator. The energy levels for a variety of spins are generated and the modeling of the magnetization, the magnetic susceptibility and the heat capacity is done. Theoretical formulae necessary in performing the energy level calculations for a multi-term system are prepared with the help of the irreducible tensor operator approach. The goal of the programming lies in the fact that the entire relevant matrix elements (electron repulsion, crystal field, spin-orbit interaction, orbital-Zeeman, and spin-Zeeman operators) are evaluated in the basis set of free-atom terms. The modeling of the zero-field splitting is done at three levels of sophistication. The spin-Hamiltonian formalism offers simple formulae for the magnetic parameters by evaluating the matrix elements of the angular momentum operator in the basis set of the crystal-field terms. The magnetic functions for dn complexes are modeled for a wide range of the crystal-field strengths.
Magnetic Control of Tokamak Plasmas
The main topic of Magnetic Control of Tokamak Plasmas is the design of feedback control systems guaranteeing the stability of plasma equilibrium inside a tokamak and the regulation of the plasma position and shape during plasma pulses. Modelling and control details are presented, allowing the non-expert to understand the control problem. Starting from equations of magneto-hydro-dynamics, all the steps needed for the derivation of plasma state-space models are enumerated. The basics of electromagnetics are frequently recalled. The control problem is then described beginning with control of current and position – vertical and radial – and progressing to the more challenging shape control. The solutions proposed vary from simple PIDs to more sophisticated MIMO controllers.
Macrosocial Determinants of Population Health
Macrosocial Determinants of Population Health explores social factors such as culture, mass media, political systems, and migration that influence population health while systematically considering how we may best study these factors and use our knowledge from this study to guide public health interventions.Each section ends with Galea’s integrative chapters, bringing the observations and conclusions from the chapters into clear, usable focus. Macrosocial Determinants of Population Health is a work of major theoretical, empirical, and practical interest for disciplines as varied as public health, epidemiology, health promotion, sociology, and health policy. Its systematic field-building approach makes it as valuable to the public health provider as to the scholars and students studying the health of populations.
Macroeconomic modelling of R&D and innovation policies
This book encompasses a collection of in-depth analyses showcasing the challenges and ways forward for macroeconomic modelling of R&D and innovation policies. Based upon the proceedings of the EC-DG JRC-IEA workshop held in Brussels in 2017, it presents cutting-edge contributions from a number of leading economists in the field. It provides a comprehensive overview of the current academic and policy challenges surrounding R&D as well as of the state-of-the-art modelling techniques.
Macrocyclic Chemistry : Current Trends and Future Perspectives
Macrocyclic Chemistry: Current Trends and Future Perspectives illustrates essential concepts in this expanding research field covering both basic and applied studies. Written by well-known experts from around the world, the topics of the chapters range from new macrocyclic architectures with different functions and self-assembly processes through to the modeling and dynamics of such systems. The content also reflects on application possibilities in analytical chemistry, separation processes, material preparation and medicine. Thus this book serves as a creative source of research strategies and methodic tools.
Machine Learning in Computer Vision
The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.
Machine learning for biomedical application
Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.
Machine Learning and Probabilistic Graphical Models for Decision Support Systems
Presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Machine Learning and Knowledge Extraction ; 5th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2021, Virtual Event, August 17–20, 2021, Proceedings
Constitutes the refereed proceedings of the 5th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2021, held in virtually in August 2021. The 20 full papers and 2 short papers presented were carefully reviewed and selected from 48 submissions. The cross-domain integration and appraisal of different fields provides an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to put these ideas into business for the benefit of humanity.
Machine learning and its application to reacting flows: ml and combustion
These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges.
Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
