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    Quantitative Analysis of Driving Factors of Grassland Degradation: A Case Study in Xilin River Basin, Inner Mongolia by Yichun Xie, Zongyao Sha

    Published 2012-01-01
    “…Based on the analysis of satellite vegetation maps from 1984, 1998, and 2004 for the Xilin River Basin, Inner Mongolia, China, and binary logistic regression (BLR) analysis, we observe the following: (1) grassland degradation is positively correlated with the growth density of climax communities; (2) our findings do not support a common notion that a decrease of biological productivity is a direct indicator of grassland degradation; (3) a causal relationship between grazing intensity and grassland degradation was not found; (4) degradation severity increased steadily towards roads but showed different trends near human settlements. …”
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    Contemplation on Development of Traditional Chinese Medicine Rehabilitation Science by Xiaodong FENG

    Published 2017-10-01
    “…In the current rehabilitation therapy situation, Traditional Chinese Medicine Rehabilitation Science as an independent discipline should develop continuously and become one of the main force for the development of rehabilitation medical in China, only by exploiting advantages and advancing with the times.…”
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    Prediction of the waterborne navigation density based on the multi-feature spatio-temporal graph convolution network by Wei DONG, Leilei ZHANG, Ziheng JIN, Wei SUN, Junbo GAO

    Published 2020-09-01
    “…In the face of the development of the information technology in the port and waterway,the Internet of things (IoT) technology can help to build China’s water transport perception network.The big data analysis of the waterborne transport has become a hot topic for researchers and practitioners in the field of transportation.The navigation density of each port in the water transportation is nonlinear and spatio-temporal correlation,so it is a great challenge to accurately predict it.A multi-feature spatiotemporal graph convolution network (MFSTGCN) was proposed to solve the problem of the traffic density prediction.MFSTGCN effectively captured the spatial-temporal correlation of the ship navigation density data by using the spatial convolution and temporal convolution through three features,which were navigation volume,average ship speed and ship density.The experiment was carried out on the automatic identification system (AIS) data set collected from a shipping platform.The results show that the prediction effect of the MFSTGCN model is better than the spatio-temporal graph convolution network (STGCN) model.…”
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