Jun Wang – författare

Remote Sensing of Aerosols, Clouds, and Precipitation compiles recent advances in aerosol, cloud, and precipitation remote sensing from new satellite observations. The book examines a wide range of measurements from microwave (both active and passive), visible, and infrared portions of the spectrum. Contributors are experts conducting state-of-the-art research in atmospheric remote sensing using space, airborne, and ground-based datasets, focusing on supporting earth observation satellite missions for aerosol, cloud, and precipitation studies. A handy reference for scientists working in remote sensing, earth science, electromagnetics, climate physics, and space engineering. Valuable for operational forecasters, meteorologists, geospatial experts, modelers, and policymakers alike.

Nuclear Power Reactor Designs: From History to Advances analyzes nuclear designs throughout history and explains how each of those has helped to shape and inform the nuclear reactor designs of today and the future. Focused on the structure, systems and relevant components of each reactor design, this book provides the readers with answers to key questions to help them understand the benefits of each design. Each reactor design is introduced, their origin defined, and the relevant research presented before an analysis of its successes, what was learned, and how research and technology advanced as a result are presented.

Students, researchers and early career engineers will gain a solid understanding of how nuclear designs have evolved, and how they will continue to develop in the future.

This book presents an empirical study that develops and validates a learning-oriented self-assessment instrument for assessing the strategic competence (SC) of young language learners in oral communication, specifically within the context of early English education in China.

The instrument’s development followed a multi-phased research design, encompassing three interconnected stages: conceptualisation, operationalisation and validation. Each phase employed distinct methods, data collection techniques and analyses tailored to specific research objectives. By adopting an integrative approach, this book clarifies the crucial yet elusive concept of SC. It not only contributes to the field of language assessment but also underscores the importance of explicit SC instruction in language education for young learners.

Focusing on real-world classroom scenarios and offering practical solutions for integrating SC instruction into current teaching paradigms, this book will appeal to educators, researchers and policymakers interested in language testing and assessment, foreign language education and applied linguistics.

This book presents an empirical study that develops and validates a learning-oriented self-assessment instrument for assessing the strategic competence (SC) of young language learners in oral communication, specifically within the context of early English education in China.

The instrument’s development followed a multi-phased research design, encompassing three interconnected stages: conceptualisation, operationalisation and validation. Each phase employed distinct methods, data collection techniques and analyses tailored to specific research objectives. By adopting an integrative approach, this book clarifies the crucial yet elusive concept of SC. It not only contributes to the field of language assessment but also underscores the importance of explicit SC instruction in language education for young learners.

Focusing on real-world classroom scenarios and offering practical solutions for integrating SC instruction into current teaching paradigms, this book will appeal to educators, researchers and policymakers interested in language testing and assessment, foreign language education and applied linguistics.

An up-to-date and accurate discussion of spiking neural P systems in time series analysis

In Spiking Neural P Systems for Time Series Analysis, the authors explore the fundamentals and the current states of both spiking neural P systems and time series analysis and examine the application models of time series analysis. You’ll also find walkthroughs of recurrent-like, echo-like, and reservoir computing models for time series prediction.

The book covers applications in time series analysis such as financial time series analysis, power load forecasting, photovoltaic power forecasting, and medical signal processing, and contains illustrative photographs and tables designed to improve reader understanding.

Readers will also find:

Perfect for scientists, researchers, postgraduates, lecturers, and teachers, Spiking Neural P Systems for Time Series Analysis will also benefit undergraduate students interested in advanced techniques for time series analysis.

An up-to-date and accurate discussion of spiking neural P systems in time series analysis

In Spiking Neural P Systems for Time Series Analysis, the authors explore the fundamentals and the current states of both spiking neural P systems and time series analysis and examine the application models of time series analysis. You’ll also find walkthroughs of recurrent-like, echo-like, and reservoir computing models for time series prediction.

The book covers applications in time series analysis such as financial time series analysis, power load forecasting, photovoltaic power forecasting, and medical signal processing, and contains illustrative photographs and tables designed to improve reader understanding.

Readers will also find:

Perfect for scientists, researchers, postgraduates, lecturers, and teachers, Spiking Neural P Systems for Time Series Analysis will also benefit undergraduate students interested in advanced techniques for time series analysis.

This book will cover the most recent progress on the use of low-cost nanomaterials and development of low-cost/large scale processing techniques for greener and more efficient energy related applications, including but not limited to solar cells, energy storage, fuel cells, hydrogen generation, biofuels, etc.

Leading researchers will be invited to author chapters in the field with their expertise. Each chapter will provide general introduction to a specific topic, current status of research and development, research challenges and outlook for future direction of research.

This book aims to benefit a broad readership, from undergraduate/graduate students to researchers working on renewable energy.

Big data and human-computer information retrieval (HCIR) are changing IR. They capture the dynamic changes in the data and dynamic interactions of users with IR systems. A dynamic system is one which changes or adapts over time or a sequence of events. Many modern IR systems and data exhibit these characteristics which are largely ignored by conventional techniques. What is missing is an ability for the model to change over time and be responsive to stimulus. Documents, relevance, users and tasks all exhibit dynamic behavior that is captured in data sets typically collected over long time spans and models need to respond to these changes. Additionally, the size of modern datasets enforces limits on the amount of learning a system can achieve. Further to this, advances in IR interface, personalization and ad display demand models that can react to users in real time and in an intelligent, contextual way.

In this book we provide a comprehensive and up-to-date introduction toDynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change. We define dynamics, what it means within the context of IR and highlight examples of problems where dynamics play an important role. We cover techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and a handful of useful algorithms and tools for solving IR problems incorporating dynamics.

The theoretical component is based around the Markov Decision Process (MDP), a mathematical framework taken from the field of Artificial Intelligence (AI) that enables us to construct models that change according to sequential inputs. We define the framework and the algorithms commonly used to optimize over it and generalize it to the case where the inputs aren''t reliable. We explore the topic of reinforcement learning more broadly and introduce another tool known as a Multi-Armed Bandit which is useful for cases where exploring model parameters is beneficial. Following this we introduce theories and algorithms which can be used to incorporate dynamics into an IR model before presenting an array of state-of-the-art research that already does, such as in the areas of session search and online advertising.

Change is at the heart of modern Information Retrieval systems and this book will help equip the reader with the tools and knowledge needed to understand Dynamic Information Retrieval Modeling.

This book focuses on optimal control and systems engineering in the big data era. It examines the scientific innovations in optimization, control and resilience management that can be applied to further success. In both business operations and engineering applications, there are huge amounts of data that can overwhelm computing resources of large-scale systems. This “big data” provides new opportunities to improve decision making and addresses risk for individuals as well in organizations. While utilizing data smartly can enhance decision making, how to use and incorporate data into the decision making framework remains a challenging topic. Ultimately the chapters in this book present new models and frameworks to help overcome this obstacle.

Optimization and Control for Systems in the Big-Data Era: Theory and Applications is divided into five parts. Part I offers reviews on optimization and control theories, and Part II examines the optimization and control applications. Part III provides novel insights and new findings in the area of financial optimization analysis. The chapters in Part IV deal with operations analysis, covering flow-shop operations and quick response systems. The book concludes with final remarks and a look to the future of big data related optimization and control problems.