Gang Cheng

This Wi-Fi 7 technology book serves as an essential and comprehensive professional reference for the academics and industry professionals, covering the entire Wi-Fi series across various generations. It offers a primary focus on the latest advancements in industrial Wi-Fi 7 principles and specifications. Additionally, the book provides valuable insights into innovative strategies for Wi-Fi 7 product development strategies, testing methodologies, and diverse applications across industrial and home environments. It serves as a practical resource for those planning to adopt Wi-Fi 7 technology in the design and development processes. By reading this book, you will not only gain insights into the state-of-the-art of Wi-Fi 7 technology, but also develop a deep understanding of the origins, the process of developing Wi-Fi 7 products, various applications and solutions where Wi-Fi 7 can be utilized, and the current state of the industry in relation to Wi-Fi 7 technology compared to the other wireless technologies. Each section of this book follows a systematic approach, beginning with an introduction to the technology concept and offering numerous concrete examples for illustration purpose. Abundant diagrams and pictures have been included in the book’s design to facilitate clear and quick comprehension of the topics for readers.

By employing learning analytics methodology and big data in Learning Management Systems (LMSs), this volume conducts data-driven research to identify and compare learner interaction patterns in Massive Private Online Courses (MPOCs). The uncertainties about the temporal and sequential patterns of online interaction, and the lack of specific knowledge and methods to investigate details of LMSs' dynamic interaction traces have affected the improvement of online learning effectiveness. While most research focuses on Massive Open Online Courses (MOOCs), little is investigating the learners’ interaction behaviors in MPOCs. This book attempts to fill in the gaps by including research in the past decades, big data in education presenting micro-level interaction traces, analytics-based learner interaction in massive private open courses, and a case study. Aiming to bring greater efficiency and deeper engagement to individual learners, instructors, and administrators, the title provides a reference to those who need to evaluate their learning and teaching strategies in online learning. It will be particularly useful to students and researchers in the field of Education. This research was funded by Liaoning Social Science Planning Fund Program in China, grant number [L21BSH002].

This textbook now published in its 6th edition prepares students and technologists for registry examinations in nuclear medicine technology by providing practice questions and answers with detailed explanations, as well as a mock registry exam. The questions are designed to test the basic knowledge required of nuclear medicine technologists, as well as the practical application of that knowledge. The topics covered closely follow the content specifications and the components of preparedness as published by the certification boards. This new edition now includes new tracers for diagnostic imaging and therapeutic applications as well as other newly approved procedures. Coverage of positron emission tomography and hybrid multimodality imaging in the field of nuclear medicine and molecular imaging has also been expanded.

By employing learning analytics methodology and big data in Learning Management Systems (LMSs), this volume conducts data-driven research to identify and compare learner interaction patterns in Massive Private Online Courses (MPOCs). The uncertainties about the temporal and sequential patterns of online interaction, and the lack of specific knowledge and methods to investigate details of LMSs' dynamic interaction traces have affected the improvement of online learning effectiveness. While most research focuses on Massive Open Online Courses (MOOCs), little is investigating the learners’ interaction behaviors in MPOCs. This book attempts to fill in the gaps by including research in the past decades, big data in education presenting micro-level interaction traces, analytics-based learner interaction in massive private open courses, and a case study. Aiming to bring greater efficiency and deeper engagement to individual learners, instructors, and administrators, the title provides a reference to those who need to evaluate their learning and teaching strategies in online learning. It will be particularly useful to students and researchers in the field of Education. This research was funded by Liaoning Social Science Planning Fund Program in China, grant number [L21BSH002].

This issue of PET Clinics focuses on Lung Cancer, and is edited by Drs. Gang Cheng and Timothy Akhurst. Articles will include: FDG PET/CT for lung cancer staging; Lung neoplasms with low FDG avidity; FDG PET/CT evaluation of lung cancer in populations with high prevalence of granulomatous disease; Prognostic value of FDG PET/CT; Genomic characterization of lung cancer and its impact on the use and timing of PET in therapeutic response assessment; Treatment planning for radiation therapy; Future directions of PET imaging for lung cancer; PET for RT-planning in lung cancer; Genomic characterization of lung cancer and its impact on the use and timing of PET in therapeutic response assessment; and more!