Information literacy-the ability to find, evaluate, and use information resources-is an important skill for future chemists. Students and scientists need to distinguish between information provided by Wikipedia, ChemSpider, research journals, and The New York Times, depending on the intended use of the information sought. Instructors and librarians may often teach these skills through stand-alone database demonstrations, video tutorials, and lectures. However, it is possible to teach these skills in a more contextual and integrated manner by designing chemistry assignments that incorporate information literacy as a learning outcome. This book will prove useful for librarians and chemistry instructors who are designing courses in which students develop information literacy in the context of a chemistry course at two-year colleges, public and private universities, and high schools. The chapters in this book review the current state of information literacy in chemistry and provide concrete examples of assignments and interventions aimed at teaching information literacy skills in chemistry curricula. A wide range of options are offered for integrating information literacy into college-level chemistry courses, including general chemistry, organic chemistry, science courses for students not majoring in science, and chemistry capstone research courses.

Smart Metro Station Systems: Data Science and Engineering introduces key technologies in data science and engineering for smart metro station systems. The book consists of three main parts, focusing on the environment, people and energy. Each chapter includes practical applications, along with information on metro traffic flow monitoring and passenger guidance, methods for behavior analysis and trajectory projection, clustering and anomaly detection in crowd hotspots, monitoring and prediction for station humidity, monitoring and spatial prediction for air pollutants, time series feature extraction and analysis of metro load, characteristic and correlation analysis of metro load, and prediction and intelligent ventilation control. This volume offers a key reference on the emerging area of smart metro stations and will be useful to those working on smart railways, data science, engineering, artificial intelligence and aligned fields.

The second edition of Idea Searching examines methods of generating and identifying ideas, and teaches you to understand what is being observed and recorded. Using lavish illustrations, concise case studies and practical examples, it explores how different experiences, contexts and references are important in identifying an idea that is appropriate for a particular individual, target audience or culture. Advocating a step-by-step approach to generating ideas and brainstorming, it encourages an open mind in the development of ideas and teaches you to always question convention. The text is accompanied by a variety of case studies and examples of work from the world's best contemporary product designers. It also includes a number of new projects for students, to encourage further exploration of ideas.

Understand the fundamental theory and practical design aspects of green and soft wireless communications networks with this expert text. It provides comprehensive and unified coverage of 5G physical layer design, as well as design of the higher and radio access layers and the core network, drawing on viewpoints from both academia and industry. Get to grips with the theory through authoritative discussion of information-theoretical results, and learn about fundamental green design trade-offs, software-defined network architectures, and energy efficient radio resource management strategies. Applications of wireless big data and artificial intelligence to wireless network design are included, providing an excellent design reference, and real-world examples of employment in software-defined 5G networks and energy saving solutions from wireless communications companies and cellular operators help to connect theory with practice. This is an essential text for graduate students, professionals and researchers.

Large-scale, highly interconnected networks, which are often modeled as graphs, pervade both our society and the natural world around us. Uncertainty, on the other hand, is inherent in the underlying data due to a variety of reasons, such as noisy measurements, lack of precise information needs, inference and prediction models, or explicit manipulation, e.g., for privacy purposes. Therefore, uncertain, or probabilistic, graphs are increasingly used to represent noisy linked data in many emerging application scenarios, and they have recently become a hot topic in the database and data mining communities. Many classical algorithms such as reachability and shortest path queries become #P-complete and, thus, more expensive over uncertain graphs. Moreover, various complex queries and analytics are also emerging over uncertain networks, such as pattern matching, information diffusion, and influence maximization queries. In this book, we discuss the sources of uncertain graphs and their applications, uncertainty modeling, as well as the complexities and algorithmic advances on uncertain graphs processing in the context of both classical and emerging graph queries and analytics. We emphasize the current challenges and highlight some future research directions.

This book constitutes the refereed proceedings of the 5th International Conference on Health Information Science, HIS 2016, held in Shanghai, China, in November 2016. The 13 full papers and 9 short papers presented were carefully reviewed and selected from numerous submissions. The scope of the papers includes medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, and optimize the use of information in the health domain; data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues; computer visualization and artificial intelligence for computer aided diagnosis; development of new architectures and applications for health information systems.