Описание: In 1983 two doctors, one from each side of the world, decided to form a partnership, and so began a scientific adventure that would improve the odds that babies could be born healthy and whole. Neural tube defects that severely disabled or killed babies were epidemic in China (where the folk term was guai tai--roughly "monster baby"--for an infant whose embryonic neural tube doesn't completely close and whose head and neck may be misshapen or spine may protrude) and a significant problem in the United States, leading teams of researchers from the United States and China to combine forces to recruit more than 285,000 Chinese women and to follow nearly 250,000 pregnancies in an epidemiological study.
Sixteen thousand staff were involved in running the project, which encountered massive bureaucratic obstacles as well as cultural differences, politicking for study designs and funding, the crisis of Tiananmen Square, and testy debates over research ethics. Nevertheless, the researchers persevered in a collaboration that lasted more than three decades and led to landmark findings on the role of folic acid in preventing spina bifida. Fortifying cereal grain products with folic acid became routine in the United States and a growing number of nations around the world: that intervention was named one of the ten great public health achievements of the last decade.
Автор: Ajith Abraham; Rafael Falc?n; Rafael Bello Название: Rough Set Theory: A True Landmark in Data Analysis ISBN: 3642100627 ISBN-13(EAN): 9783642100628 Издательство: Springer Цена: 27251.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Theoretical Contributions to Rough Set Theory.- Rough Sets on Fuzzy Approximation Spaces and Intuitionistic Fuzzy Approximation Spaces.- Categorical Innovations for Rough Sets.- Granular Structures and Approximations in Rough Sets and Knowledge Spaces.- On Approximation of Classifications, Rough Equalities and Rough Equivalences.- Rough Set Data Mining Activities.- Rough Clustering with Partial Supervision.- A Generic Scheme for Generating Prediction Rules Using Rough Sets.- Rough Web Caching.- Software Defect Classification: A Comparative Study of Rough-Neuro-fuzzy Hybrid Approaches with Linear and Non-linear SVMs.- Rough Hybrid Models to Classification and Attribute Reduction.- Rough Sets and Evolutionary Computation to Solve the Feature Selection Problem.- Nature Inspired Population-Based Heuristics for Rough Set Reduction.- Developing a Knowledge-Based System Using Rough Set Theory and Genetic Algorithms for Substation Fault Diagnosis.
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