Lijing Wang – författare

Performance Testing of Textiles: Methods, Technology and Applications examines the developed and established methodology for testing performance textiles, also summarizing the material properties for advanced applications.

This book emphasizes reproducible tests using commonly used experimental methods reported in scientific literature and internationally recognized testing standards to quantify textile material properties and performance.

After an introductory explanation of key fiber and textile properties and testing methods, the book summarizes electronic testing theories, technologies, and instrumentation for performance textiles. Also covered are aspects of military textile, medical textile, sportswear, smart composites, and wearable textiles which, as examples, present the latest research and results related to performance textile testing and applications.

Offers up-to-date coverage of new and advanced performance testing techniques for the fiber and textile industries Explores key fiber and textile properties Summarizes electronic testing theories, technologies, and instrumentation for performance textiles Includes contributions from an international team of authors edited by an expert in the field

Data Science for the Geosciences provides students and instructors with the statistical and machine learning foundations to address Earth science questions using real-world case studies in natural hazards, climate change, environmental contamination and Earth resources. It focuses on techniques that address common characteristics of geoscientific data, including extremes, multivariate, compositional, geospatial and space-time methods. Step-by-step instructions are provided, enabling readers to easily follow the protocols for each method, solve their geoscientific problems and make interpretations. With an emphasis on intuitive reasoning throughout, students are encouraged to develop their understanding without the need for complex mathematics, making this the perfect text for those with limited mathematical or coding experience. Students can test their skills with homework exercises that focus on data scientific analysis, modeling, and prediction problems, and through the use of supplemental Python notebooks that can be applied to real datasets worldwide.