Bo Shen

In this issue, guest editors bring their considerable expertise to this important topic. Provides in-depth reviews on the latest updates in the field, providing actionable insights for clinical practice. Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create these timely topic-based reviews.

Interventional Inflammatory Bowel Diseases: Endoscopic Management and Treatment of Complications covers the preparation, principle, techniques, and damage control of complications in endoscopic therapy, providing the ultimate guidance in endoscopic management of IBD. With contributions from a panel of international leading experts in the field, perspectives are included from GI pathologists, GI radiologists, gastroenterologists, advanced endoscopists, IBD specialists and colorectal surgeons. Recommendations from experts are also included within each chapter. By bridging medical and surgical treatment modalities for IBD, this is the perfect reference for GI researchers, medical students, therapeutic GI endoscopists, IBD specialists, surgeons and advanced health care providers.

Atlas of Endoscopy Imaging in Inflammatory Bowel Disease, Second Edition, serves as a comprehensive guide to the latest advancements in IBD. This new edition keeps readers updated with cutting-edge medical treatments, state-of-the-art endoscopy, and imaging technologies, as well as the evolution of interventional IBD. It explores the latest advancements in IBD diagnosis and management, including the most recent endoscopy instruments for measuring disease activity and the transformative world of image-enhanced endoscopy. Additionally, it delves into the groundbreaking application of Artificial Intelligence (AI) in IBD, revolutionizing diagnosis, disease monitoring, and dysplasia surveillance. New content introduces a range of topics to stay at the forefront of Inflammatory Bowel Disease (IBD) knowledge. These include Intestinal Ultrasound, Device-Associated Enteroscopy in Crohn’s Disease, insights into IBD-Like Conditions (Autoinflammatory Disorders), and the value of Intraoperative Endoscopy in IBD procedures. The book also integrates New Consensus Guidelines for Endoscopy Imaging in IBD, explores the Applications of Artificial Intelligence (AI) in IBD diagnosis and monitoring, addresses the intersection of COVID, COVID Vaccines, and IBD, and presents New Applications of Interventional IBD techniques. This second edition serves as a dynamic repository of state-of-the-art information, providing invaluable insights on differential diagnosis, advanced endoscopy, and imaging technologies, with a pronounced focus on AI. It also offers updated guidelines for disease activity assessment and the latest advancements in endoscopic therapy modalities. Designed to cater to a diverse range of medical professionals, it stands as an indispensable reference, fostering a comprehensive and up-to-date understanding of Inflammatory Bowel Disease (IBD). Within these pages, Gastroenterologists and IBD specialists will discover a wealth of resources to aid their pursuit of cutting-edge treatments and AI-driven insights. Primary care clinicians will find an expanded list of differential diagnoses that enables more accurate early-stage identification. Surgeons can elevate their expertise by delving into the intricacies of interventional IBD techniques, while pathologists and radiologists can enhance their diagnostic skills through AI and image-enhanced endoscopy. This comprehensive resource ensures that medical professionals across various disciplines are well-equipped to navigate the evolving landscape of IBD diagnosis, treatment, and management.

This book focuses on the control and state estimation problems for dynamical network systems with complex samplings subject to various network-induced phenomena. It includes a series of control and state estimation problems tackled under the passive sampling fashion. Further, it explains the effects from the active sampling fashion, i.e., event-based sampling is examined on the control/estimation performance, and novel design technologies are proposed for controllers/estimators. Simulation results are provided for better understanding of the proposed control/filtering methods. By drawing on a variety of theories and methodologies such as Lyapunov function, linear matrix inequalities, and Kalman theory, su?cient conditions are derived for guaranteeing the existence of the desired controllers and estimators, which are parameterized according to certain matrix inequalities or recursive matrix equations.Covers recent advances of control and state estimation for dynamical network systems with complex samplings from the engineering perspectiveSystematically introduces the complex sampling concept, methods, and application for the control and state estimationPresents unified framework for control and state estimation problems of dynamical network systems with complex samplingsExploits a set of the latest techniques such as linear matrix inequality approach, Vandermonde matrix approach, and trace derivation approachExplains event-triggered multi-rate fusion estimator, resilient distributed sampled-data estimator with predetermined specificationsThis book is aimed at researchers, professionals, and graduate students in control engineering and signal processing.

This book focuses on the control and state estimation problems for dynamical network systems with complex samplings subject to various network-induced phenomena. It includes a series of control and state estimation problems tackled under the passive sampling fashion. Further, it explains the effects from the active sampling fashion, i.e., event-based sampling is examined on the control/estimation performance, and novel design technologies are proposed for controllers/estimators. Simulation results are provided for better understanding of the proposed control/filtering methods. By drawing on a variety of theories and methodologies such as Lyapunov function, linear matrix inequalities, and Kalman theory, su?cient conditions are derived for guaranteeing the existence of the desired controllers and estimators, which are parameterized according to certain matrix inequalities or recursive matrix equations.Covers recent advances of control and state estimation for dynamical network systems with complex samplings from the engineering perspectiveSystematically introduces the complex sampling concept, methods, and application for the control and state estimationPresents unified framework for control and state estimation problems of dynamical network systems with complex samplingsExploits a set of the latest techniques such as linear matrix inequality approach, Vandermonde matrix approach, and trace derivation approachExplains event-triggered multi-rate fusion estimator, resilient distributed sampled-data estimator with predetermined specificationsThis book is aimed at researchers, professionals, and graduate students in control engineering and signal processing.

The aim of this book is to explain to high-performance computing (HPC) developers how to utilize the Intel® Xeon Phi™ series products efficiently. To that end, it introduces some computing grammar, programming technology and optimization methods for using many-integrated-core (MIC) platforms and also offers tips and tricks for actual use, based on the authors’ first-hand optimization experience.The material is organized in three sections. The first section, “Basics of MIC”, introduces the fundamentals of MIC architecture and programming, including the specific Intel MIC programming environment. Next, the section on “Performance Optimization” explains general MIC optimization techniques, which are then illustrated step-by-step using the classical parallel programming example of matrix multiplication. Finally, “Project development” presents a set of practical and experience-driven methods for using parallel computing in application projects, including how to determine if a serial or parallel CPU program is suitable for MIC and how to transplant a program onto MIC.This book appeals to two main audiences: First, software developers for HPC applications – it will enable them to fully exploit the MIC architecture and thus achieve the extreme performance usually required in biological genetics, medical imaging, aerospace, meteorology and other areas of HPC. Second, students and researchers engaged in parallel and high-performance computing – it will guide them on how to push the limits of system performance for HPC applications.

Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H8 filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: • a unified framework for filtering and control problems in complex communication networks with limited bandwidth; • new concepts such as random sensor and signal saturations for more realistic modeling; and • demonstration of the use of techniques such as the Hamilton–Jacobi–Isaacs, difference linear matrix, and parameter-dependent matrix inequalities and sums of squares to handle the computational challenges inherent in these systems. The collection of recent research results presented in Nonlinear Stochastic Processes will be of interest to academic researchers in control and signal processing. Graduate students working with communication networks with lossy information and control of stochastic systems will also benefit from reading the book.

The aim of this book is to explain to high-performance computing (HPC) developers how to utilize the Intel® Xeon Phi™ series products efficiently. To that end, it introduces some computing grammar, programming technology and optimization methods for using many-integrated-core (MIC) platforms and also offers tips and tricks for actual use, based on the authors’ first-hand optimization experience.The material is organized in three sections. The first section, “Basics of MIC”, introduces the fundamentals of MIC architecture and programming, including the specific Intel MIC programming environment. Next, the section on “Performance Optimization” explains general MIC optimization techniques, which are then illustrated step-by-step using the classical parallel programming example of matrix multiplication. Finally, “Project development” presents a set of practical and experience-driven methods for using parallel computing in application projects, including how to determine if a serial or parallel CPU program is suitable for MIC and how to transplant a program onto MIC.This book appeals to two main audiences: First, software developers for HPC applications – it will enable them to fully exploit the MIC architecture and thus achieve the extreme performance usually required in biological genetics, medical imaging, aerospace, meteorology and other areas of HPC. Second, students and researchers engaged in parallel and high-performance computing – it will guide them on how to push the limits of system performance for HPC applications.

Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H8 filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: • a unified framework for filtering and control problems in complex communication networks with limited bandwidth; • new concepts such as random sensor and signal saturations for more realistic modeling; and • demonstration of the use of techniques such as the Hamilton–Jacobi–Isaacs, difference linear matrix, and parameter-dependent matrix inequalities and sums of squares to handle the computational challenges inherent in these systems. The collection of recent research results presented in Nonlinear Stochastic Processes will be of interest to academic researchers in control and signal processing. Graduate students working with communication networks with lossy information and control of stochastic systems will also benefit from reading the book.