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Modeling land-use change is a prerequisite to understanding the complexity of land-use- change patterns. This paper presents a novel method to model urban land-use change using support-vector machines (SVMs), a new generation of machine learning algorithms used in classification and regression domains. An SVM modeling framework has been developed to analyze land-use change in relation to various factors such as population, distance to roads and facilities, and surrounding land use. As land-use data are generally unbalanced, in the sense that the unchanged data overwhelm the changed data, traditional methods are incapable of classifying relatively minor land-use changes with high accuracy. To circumvent this problem, an unbalanced SVM has been adopted by enhancing the standard SVMs. A case study of Calgary land-use change demonstrates that the unbalanced SVMs can achieve high and reliable performance for land-use-change modeling. [ABSTRACT FROM AUTHOR]

The research introduced selection and breeding process, characteristics and high-yield cultivation technology of Wuqing No.1. Wuqing No.1, also named Gaoyou black soybean, is bred based on Danbo black soybean introduced from Japan in 1996 by Gaoyou Vegetable Cultivation Technology Station and Yangzhou Agricultural Technology Promotion Station. It was named in 2014 by Jinagsu Crop Cultivar Approvement Committee. The cultivar is high-yielding with high quality, big seeds, high resistance to stress, as well as high health-care value. [ABSTRACT FROM AUTHOR]