Bøker av Liang Feng

Paper-based Optical Chemosensors comprehensively discusses the origin, development, and current state-of-the-art in paper-based sensors. With a focus on the principles, classifications, methodology, design, and application of paper-based sensors, this book represents a developing research field with recent innovative applications resulting in a comprehensive presentation of the different physico-chemical techniques using paper sensors. It discloses underlying rules and factors in paper-based sensors and discusses intricate sensing systems and working environments by ways of chemistry and physics for a variety of application scenarios such as environmental protection, food safety, public safety, and clinical diagnosis. This is a valuable resource for researchers who major in analytical chemistry, or for those who are interested in the development of methods or devices for rapid analysis/monitoring based on paper/membrane-based sensors who wish to broaden their knowledge in the allied field.

A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain¿s ability to generalize in optimization ¿ particularly in population-based evolutionary algorithms ¿ have received little attention to date. Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems,each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks. This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.