This collection brings together a series of empirical studies on topics surrounding classrooms of Chinese as a second language (L2) by drawing on a range of theoretical frameworks, methodological strategies, and pedagogical perspectives. Over the past two decades, research on classroom-based second language acquisition (SLA) has emerged and expanded as one of the most important sub-domains in the general field of SLA. In Chinese SLA, however, scarce attention has been devoted to this line of research. With chapters written by scholars in the field of SLA-many of whom are experienced in classroom teaching, teacher education, or program administration in Chinese as a second language-this book helps disentangle the complicated relationships among linguistic targets, pedagogical conditions, assessment tools, learner individual differences, and teacher variables that exist in the so-called "black-box" classrooms of L2 Chinese.

This collection brings together a series of empirical studies on topics surrounding classrooms of Chinese as a second language (L2) by drawing on a range of theoretical frameworks, methodological strategies, and pedagogical perspectives. Over the past two decades, research on classroom-based second language acquisition (SLA) has emerged and expanded as one of the most important sub-domains in the general field of SLA. In Chinese SLA, however, scarce attention has been devoted to this line of research. With chapters written by scholars in the field of SLA-many of whom are experienced in classroom teaching, teacher education, or program administration in Chinese as a second language-this book helps disentangle the complicated relationships among linguistic targets, pedagogical conditions, assessment tools, learner individual differences, and teacher variables that exist in the so-called "black-box" classrooms of L2 Chinese.

This book brings together a collection of high-quality empirical studies which examine multiple aspects involved in the acquisition, teaching and assessment of pragmatics in Chinese as a second language (L2). The studies collectively address some of the most cutting-edge issues in the field of L2 pragmatics, such as the acquisition of key pragmatic features, methodological innovations in pragmatics assessment, individual difference factors and virtual learning contexts. The majority of the chapters include detailed descriptions of the instruments used and additional material in the appendices, making it a truly valuable collection for researchers and students alike. Furthermore, the publication includes the most comprehensive, state-of-the-art review of empirical research in L2 Chinese pragmatics published bilingually (in English and Chinese) between 1995 and 2022, along with a supplemental annotated bibliography. While the empirical studies all focus on Chinese as the target language, the issues they address have implications for L2 pragmatics research in general and this book will appeal to those interested in the latest developments in the field.

Twin and Family Studies of Epigenetics, Volume 27, the latest release in the Translational Epigenetics series, gathers expert opinions on epigenetic twin and family study research methods, recent findings across various disease areas, and future directions. The book provides in-depth coverage of epigenetics fundamentals, twin and family epigenetic study design, and the broader role of epigenetics in answering questions on the developmental origins of health and disease. Throughout the volume, twin and family studies are employed to examine causes of epigenetic variation, the relationship between epigenetic modifications and mental illness, cancers, cardiovascular disease, diabetes, obesity, high blood pressure, and more. Emerging research methods applied in twin and family studies discussed include imaging epigenetics, exposure-specific DNA methylation changes, and unravelling time trends in epigenetic effects.

This open access book focuses on robot introspection, which has a direct impact on physical human-robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.

In this book, the authors focus on three aspects related to the development of articulated agents: presenting an overview of high-level control algorithms for intelligent decision-making of articulated agents, experimental study of the properties of soft agents as the end-effector of articulated agents, and accurate management of low-level torque-control loop to accurately control the articulated agents. This book summarizes recent advances related to articulated agents. The motive behind the book is to trigger theoretical and practical research studies related to articulated agents.

In this book, we present our systematic investigations into consensus in multi-agent systems. We show the design and analysis of various types of consensus protocols from a multi-agent perspective with a focus on min-consensus and its variants. We also discuss second-order and high-order min-consensus. A very interesting topic regarding the link between consensus and path planning is also included. We show that a biased min-consensus protocol can lead to the path planning phenomenon, which means that the complexity of shortest path planning can emerge from a perturbed version of min-consensus protocol, which as a case study may encourage researchers in the field of distributed control to rethink the nature of complexity and the distance between control and intelligence. We also illustrate the design and analysis of consensus protocols for nonlinear multi-agent systems derived from an optimal control formulation, which do not require solving a Hamilton-Jacobi-Bellman (HJB) equation. The book was written in a self-contained format. For each consensus protocol, the performance is verified through simulative examples and analyzed via mathematical derivations, using tools like graph theory and modern control theory. The book's goal is to provide not only theoretical contributions but also explore underlying intuitions from a methodological perspective.

This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.

Web 2.0 technologies, open source software platforms, and mobile applications have transformed teaching and learning of second and foreign languages. Language teaching has transitioned from a teacher-centered approach to a student-centered approach through the use of Computer-Assisted Language Learning (CALL) and new teaching approaches. Engaging Language Learners through Technology Integration: Theory, Applications, and Outcomes provides empirical studies on theoretical issues and outcomes in regards to the integration of innovative technology into language teaching and learning. This reference wok discusses empirical findings and innovative research using software and applications that engage learners and promote successful learning, essential tools for educational researchers, instructional technologists, K-20 language teachers, faculty in higher education, curriculum specialists, and researchers.

This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors' papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.

This open access book focuses on robot introspection, which has a direct impact on physical human-robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.

This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors' papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.

This is the first book to focus on solving cooperative control problems of multiple robot arms using different centralized or distributed neural network models, presenting methods and algorithms together with the corresponding theoretical analysis and simulated examples. It is intended for graduate students and academic and industrial researchers in the field of control, robotics, neural networks, simulation and modelling.