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Список книг автора Группа авторов
Bio-Based Epoxy Polymers, Blends, and Composites
State-of-the-art overview on bioepoxy polymers as well as their blends and composites – covering all aspects from fundamentals to applications! Bioepoxy polymers is an emerging area and have attracted more and more attention due to their biodegradability and good thermo-mechanical performance. In recent years, research progress has been made in synthesis, processing, characterization, and applications of bioepoxy blends and composites. Bioepoxy polymers are very promising candidates to replace the traditional thermosetting nonbiodegradable polymers. Bio-Based Epoxy Polymers, Blends and Composites summaries recent research progress on bioepoxy polymers as well as their blends and composites. It covers aspects from synthesis, processing, various characterization techniques to broad spectrum of applications. It provides a correlation of physical properties with macro, micro and nanostructures of the materials. Moreover, research trends, future directions, and opportunities are also discussed. Attracts attention: Bioepoxy polymers are environmentally friendly and considered as a promising candidate to replace the traditional thermosetting nonbiodegradable polymers Highly application-oriented: Bioepoxy polymers can be used in a broad range of applications such as polymer foams, construction, aerospace, automobiles, self-healing systems One-stop reference: Covers all aspects of bioepoxy polymer, their blends and composites, such as synthesis, properties, processing, characterization and applications Broad audience: Attracts attention from both academia and industry
Data Analytics in Bioinformatics
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.