| 1 | COVID-19: Challenges to GIS with Big Data显示文摘The outbreak of the 2019 novel coronavirus disease(COVID-19)has caused more than 100,000 people infected and thousands of deaths.Currently,the number of infections and deaths is still increasing rapidly.COVID-19 seriously threatens human health,production,life,social functioning and international relations.In the fight against COVID-19,Geographic Information Systems(GIS)and big data technologies have played an important role in many aspects,including the rapid aggregation of multi-source big data,rapid visualization of epidemic information,spatial tracking of confirmed cases,prediction of regional transmission,spatial segmentation of the epidemic risk and prevention level,balancing and management of the supply and demand of material resources,and socialemotional guidance and panic elimination,which provided solid spatial information support for decision-making,measures formulation,and effectiveness assessment of COVID-19 prevention and control.GIS has developed and matured relatively quickly and has a complete technological route for data preparation,platform construction,model construction,and map production.However,for the struggle against the widespread epidemic,the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management.At the data level,in the era of big data,data no longer come mainly from the government but are gathered from more diverse enterprises.As a result,the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data,which requires governments,businesses,and academic institutions to jointly promote the formulation of relevant policies.At the technical level,spatial analysis methods for big data are in the ascendancy.Currently and for a long time in the future,the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition,which signifies ts that GIS should be used to reinforce the social operation parameterization of models and methods,especially when providing support for social management. | Chenghu Zhou Fenzhen Su Tao Pei An Zhang Yunyan Du Bin Luo Zhidong Cao Juanle Wang Wen Yuan Yunqiang Zhu Ci Song Jie Chen Jun Xu Fujia Li Ting Ma Lili Jiang Fengqin Yan Jiawei Yi Yunfeng Hu Yilan Liao Han Xiao | 2020 | Geography and Sustainability2020，1，1： | 13 |
| 2 | Design and synthesis of novel water-soluble amino acid derivatives of chlorin p6 ethers as photosensitizer显示文摘Eight new water-soluble amino acid conjugates 6 a-h of chlorin p6 ethers(5 a-d) were synthesized and preliminarily investigated for their in vitro PDT antitumor activity and structure-activity relationship(SAR). The results showed that all compounds exhibited much higher phototoxicity against tumor cells than talaporfin. SAR analysis indicated that PDT antitumor effect enhanced with the increase of carbon chain length of alkoxyl ether bonds at 3~1-position, and L-aspartic acid was superior to L-glutamic acid. In particular, the IC_50 values of most phototoxic compound 6 d were 0.20 mmol/L against A549 cell and0.41υmmol/L against B16-F10 cell, which individually represented 31-and 24-fold increase of antitumor potency compared to talaporfin, suggesting that it was a promising candidate photosensitizer(PS) for PDT applications due to its strong absorption at long wavelength, high phototoxicity, low dark cytotoxicity and good water-solubility. | Xingjie Zhang Zhi Meng Zhiqiang Ma Junhong Liu Guiyan Han Fujia Ma Ningyang Jia Zhenyuan Miao Wannian Zhang Chunquan Sheng Jianzhong Yao | 2019 | Chinese Chemical Letters2019，30，1： | 3 |
| 3 | Short-term Forecasting of Individual Residential Load Based on Deep Learning and K-means Clustering显示文摘In order to currently motivate a wide range of various interactions between power network operators and electricity customers,residential load forecasting plays an increasingly important role in demand side response(DSR).Due to high volatility and uncertainty of residential load,it is significantly challenging to forecast it precisely.Thus,this paper presents a short-term individual residential load forecasting method based on a combination of deep learning and k-means clustering,which is capable of effectively extracting the similarity of residential load and performing residential load forecasting accurately at the individual level.It first makes full use of k-means clustering to extract similarity among residential load and then employs deep learning to extract complicated patterns of residential load.The presented method is tested and validated on a real-life Irish residential load dataset,and the experimental results suggest that it can achieve a much higher prediction accuracy,in comparison with a published benchmark method. | Fujia Han Tianjiao Pu Maozhen Li Gareth Taylor | 2021 | CSEE Journal of Power and Energy Systems2021，7，2： | 3 |
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