Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-28 Cooperative journals: 《中国科学院院刊》
Abstract： As the “water tower” of Asia, the Tibetan Plateau provides basic water resources for the regional ecosystems. Among the variety of water resources, natural precipitation is most relevant to ecosystems, and temperature also affects soil moisture availability by regulating evapotranspiration, thus affecting ecosystem process. From the aspects of community composition and structure, vegetation phenology, coverage and productivity, and water conservation function by ecosystems, this paper reviewed the series of impacts caused by changed water resources on the Tibetan Plateau ecosystems in recent years, and the underlying mechanism was further revealed. The shifted water conditions led to changes in community coverage, species diversity, and relative importance of each species, thereby driving community succession. The strengthened preseason precipitation advanced spring phenology, and postponed autumn phenology. The preseason precipitation also adjusted the responses of vegetation phenology to temperature. Under global changes, the vegetation coverage increased and ecosystem productivity strengthened on the Tibetan Plateau, but with high spatial heterogeneities. The incongruous changes of temperature and precipitation complicated their effects on vegetation, as exhibited by their distinct relative determination effects in different areas. Water conservation by ecosystems stems from interactions among soil-vegetation-atmosphere, which is influenced by climate, vegetation, soil, human activities, etc. The future studies need to pay mounting attentions to the coupling effects of climates and vegetation cover on water conservation of ecosystems, and also attribute the separate contribution from each factor.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-06-19 Cooperative journals: 《计算机应用研究》
Abstract： Nowadays, tag recommendation is widely used in various websites, such as movie websites, e-commerce websites and so on. However, there are some methods that ignore the connection among the characteristics of a variety of attributes and can not guarantee the accuracy of the recommender system in the big data environment. Aiming at this problem, this paper proposed a tag recommendation method based on user clustering and tensor decomposition, which could further improve the quality of tag recommendation. The method firstly clustered the users who had an important influence on the product, and then comprehensively calculated the total weight based on the multiple relationships among the users, products, tags, and product ratings. Finally, it constructed the tensor according to the user groups after clustering and the total weight of the multivariate relations, and performed the tensor factorization. Compared with the traditional tensor decomposition method, the experimental results show that our method improves the accuracy and verifies the effectiveness of the algorithm.
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