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A fundamental theorem for eco-environmental surface modelling and its applications 被引量:11
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作者 Tianxiang YUE Na ZHAO +37 位作者 Yu LIU Yifu WANG Bin ZHANG Zhengping DU Zemeng FAN Wenjiao SHI Chuanfa CHEN Mingwei ZHAO Dunjiang SONG Shihai WANG Yinjun SONG Changqing YAN Qiquan LI Xiaofang SUN Lili ZHANG Yongzhong TIAN Wei WANG Ying’an WANG Shengnan MA Hongsheng HUANG yimin lu Qing WANG Chenliang WANG Yuzhu WANG Ming lu Wei ZHOU Yi LIU Xiaozhe YIN Zong WANG Zhengyi BAO Miaomiao ZHAO Yapeng ZHAO Yimeng JIAO Ufra NASEER Bin FAN Saibo LI Yang YANG John PWILSON 《Science China Earth Sciences》 SCIE EI CAS CSCD 2020年第8期1092-1112,共21页
We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface ... We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM. 展开更多
关键词 HASM FTEEM Spatial upscaling Spatial downscaling Spatial interpolation Data fusion Model-data assimilation Model coupling
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Radical versus conservative surgical treatment of liver hydatid cysts: a meta-analysis 被引量:4
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作者 Qing Pang Hao Jin +6 位作者 Zhongran Man Yong Wang Song Yang Zongkuang Li yimin lu Huichun Liu Lei Zhou 《Frontiers of Medicine》 SCIE CAS CSCD 2018年第3期350-359,共10页
To date, the efficacy of radical surgery (RS) versus conservative surgery (CS) for liver hydatid cysts (LHC) remains controversial. This meta-analysis was conducted to compare the two interventions. PubMed, Emba... To date, the efficacy of radical surgery (RS) versus conservative surgery (CS) for liver hydatid cysts (LHC) remains controversial. This meta-analysis was conducted to compare the two interventions. PubMed, Embase, and Web of Science were searched from their inceptions until June 2016. Meta-analysis was performed using STATA 12.0 software. We identified 19 eligible studies from 10 countries by retrieval. In total, 1853 LHC patients who received RS were compared with 2274 patients treated by CS. The risk of postoperative overall complication, biliary fistula, and recurrence was significantly lower, and operation time was significantly longer in the RS group. However, no statistically significant differences were found in terms of mortality risk and the duration of hospital stay between RS and CS. No significant publication biases were observed in all the above analyses. In conclusion, RS reduces the rates of postoperative complications and recurrence, whereas no trend toward such a reduction in mortality was observed in LHC patients. 展开更多
关键词 liver hydatid cysts radical surgery conservative surgery META-ANALYSIS COMPLICATIONS
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