Jian Ma – författare

Recently, Si has become globally important because it generates resistance in many plants to diseases and pests, and may contribute to reduced rates of application of pesticides and fungicides. Silicon is also now considered as an environment-friendly element. The achievements of Si research in Japan are introduced in this book, in relation to soils, fertilizers and plant nutrition.

This book constitutes the proceedings of the 28th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2024, held in Cambridge, MA, USA, during April 29–May 2, 2024.

The 57 full papers included in this book were carefully reviewed and selected from 352 submissions. They were organized in topical sections as follows: theoretical and foundational algorithm contributions and more applied directions that engage with new technologies and intriguing biological questions.

"Emerging Techniques in Power System Analysis" identifies the new challenges facing the power industry following the deregulation. The book presents emerging techniques including data mining, grid computing, probabilistic methods, phasor measurement unit (PMU) and how to apply those techniques to solving the technical challenges.

The book is intended for engineers and managers in the power industry, as well as power engineering researchers and graduate students.

Zhaoyang Dong is an associate professor at the Department of Electrical Engineering, The Hong Kong Polytechnic University, China. Pei Zhang is program manager at the Electric Power Research Institute (EPRI), USA.

This is the first book illustrating China’s forest carbon projects. It includes an analysis of the policy and future development trends of China’s forest carbon market and showcases the country’s most representative forest carbon projects. Accordingly, it offers a valuable resource for all policymakers and researchers interested in forest carbon, as well as project developers and engineers involved in forest carbon projects.

This monograph introduces readers to new theories and methods applying cognitive computing and geometric space transformation to the field of fault diagnosis and prognostics. It summarizes the basic concepts and technical aspects of fault diagnosis and prognostics technology. Existing bottleneck problems are examined, and the advantages of applying cognitive computing and geometric space transformation are explained. In turn, the book highlights fault diagnosis, prognostic, and health assessment technologies based on cognitive computing methods, including deep learning, transfer learning, visual cognition, and compressed sensing. Lastly, it covers technologies based on differential geometry, space transformation, and pattern recognition.