Magnesium-Based Energy Storage Materials and Systems Understand the energy storage technologies of the future with this groundbreaking guide Magnesium-based materials have revolutionary potential within the field of clean and renewable energy. Their suitability to act as battery and hydrogen storage materials has placed them at the forefront of the world’s most significant research and technological initiatives. It has never been more essential that professionals working in energy storage and energy systems understand these materials and their extraordinary potential applications. Magnesium-Based Energy Storage Materials and Systems provides a thorough introduction to advanced Magnesium (Mg)-based materials, including both Mg-based hydrogen storage and Mg-based batteries. Offering both foundational knowledge and practical applications, including step-by-step device design processes, it also highlights interactions between Mg-based and other materials. The result is an indispensable guide to a groundbreaking set of renewable energy resources. Magnesium-Based Energy Storage Materials and Systems readers will also find: In-depth analysis of the effects of employing catalysts, nano-structuring Magnesium-based materials, and many more subjects Detailed discussion of electrolyte, cathode, and anode materials for Magnesium batteries Snapshots of in-progress areas of research and development Magnesium-Based Energy Storage Materials and Systems is ideal for materials scientists, inorganic chemists, solid state chemists, electrochemists, and chemical engineers.
Magnesium-Based Energy Storage Materials and Systems Understand the energy storage technologies of the future with this groundbreaking guide Magnesium-based materials have revolutionary potential within the field of clean and renewable energy. Their suitability to act as battery and hydrogen storage materials has placed them at the forefront of the world’s most significant research and technological initiatives. It has never been more essential that professionals working in energy storage and energy systems understand these materials and their extraordinary potential applications. Magnesium-Based Energy Storage Materials and Systems provides a thorough introduction to advanced Magnesium (Mg)-based materials, including both Mg-based hydrogen storage and Mg-based batteries. Offering both foundational knowledge and practical applications, including step-by-step device design processes, it also highlights interactions between Mg-based and other materials. The result is an indispensable guide to a groundbreaking set of renewable energy resources. Magnesium-Based Energy Storage Materials and Systems readers will also find: In-depth analysis of the effects of employing catalysts, nano-structuring Magnesium-based materials, and many more subjects Detailed discussion of electrolyte, cathode, and anode materials for Magnesium batteries Snapshots of in-progress areas of research and development Magnesium-Based Energy Storage Materials and Systems is ideal for materials scientists, inorganic chemists, solid state chemists, electrochemists, and chemical engineers.
This book is about the harmonious synthesis of functional programming and numerical computation. It shows how the expressiveness of OCaml allows for fast and safe development of data science applications. Step by step, the authors build up to use cases drawn from many areas of Data Science, Machine Learning, and AI, and then delve into how to deploy at scale, using parallel, distributed, and accelerated frameworks to gain all the advantages of cloud computing environments. To this end, the book is divided into three parts, each focusing on a different area. Part I begins by introducing how basic numerical techniques are performed in OCaml, including classical mathematical topics (interpolation and quadrature), statistics, and linear algebra. It moves on from using only scalar values to multi-dimensional arrays, introducing the tensor and Ndarray, core data types in any numerical computing system. It concludes with two more classical numerical computing topics, the solution of Ordinary Differential Equations (ODEs) and Signal Processing, as well as introducing the visualization module we use throughout this book. Part II is dedicated to advanced optimization techniques that are core to most current popular data science fields. We do not focus only on applications but also on the basic building blocks, starting with Algorithmic Differentiation, the most crucial building block that in turn enables Deep Neural Networks. We follow this with chapters on Optimization and Regression, also used in building Deep Neural Networks. We then introduce Deep Neural Networks as well as topic modelling in Natural Language Processing (NLP), two advanced and currently very active fields in both industry and academia. Part III collects a range of case studies demonstrating how you can build a complete numerical application quickly from scratch using Owl. The cases presented include computer vision and recommender systems. This book aims at anyone with a basic knowledge of functional programming and a desire to explore the world of scientific computing, whether to generally explore the field in the round, to build applications for particular topics, or to deep-dive into how numerical systems are constructed. It does not assume strict ordering in reading – readers can simply jump to the topic that interests them most.
This textbook introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. To ensure student understanding, the text focuses on a relatively small number of core concepts with an abundance of illustrations and examples. Concepts are reinforced with hands-on exercises to nurture the student's skill in problem solving. New concepts and algorithms are framed by real-world context and established as part of the big picture introduced in an early chapter. A problem-solving strategy is employed in several chapters to equip students with an approach for new problems in pattern recognition. This text also points out common errors that a new player in pattern recognition may encounter, and fosters the ability for readers to find useful resources and independently solve a new pattern recognition task through various working examples. Students with an undergraduate understanding of mathematical analysis, linear algebra, and probability will be well prepared to master the concepts and mathematical analysis presented here.
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