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| 1 | Progress and Knowledge Transfer from Science to Technology in the Research Frontier of CRISPR Based on the LDA Model显示文摘Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/methodology/approach:We collected publications on CRISPR between 2011 and2020 from the Web of Science,and traced all the patents citing them from lens.org.15,904 articles and 18,985 patents in total are downloaded and analyzed.The LDA model was applied to identify underlying research topics in related research.In addition,some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents.Findings:The emerging research topics on CRISPR were identified and their evolution over time displayed.Furthermore,a big picture of knowledge transition from research topics to technological classes of patents was presented.We found that for all topics on CRISPR,the average first transition year,the ratio of articles cited by patents,the NPR transition rate are respectively 1.08,15.57%,and 1.19,extremely shorter and more intensive than those of general fields.Moreover,the transition patterns are different among research topics.Research limitations:Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org.A limitation inherent with LDA analysis is in the manual interpretation and labeling of'topics'.Practical implications:Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR.Originality/value:The LDA model here is applied to topic identification in the area of transformative researches for the first time,as exemplified on CRISPR.Additionally,the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T. | Yushuang Lyu Muqi Yin Fangjie Xi Xiaojun Hu | 2022 | Journal of Data and Information Science2022，7，1： | 2 |
| 2 | Thermogravimetric analysis of co-combustion of biomass and biochar显示文摘 | Qiguo Yi Fangjie Qi Gong Cheng Yongguang Zhang Bo Xiao Zhiquan Hu Shiming Liu Haiyan Cai Shan Xu | 2013 | Journal of Thermal Analysis and Calorimetry2013，，3： | 1 |
| 3 | “Sparking”and“Igniting”Key Publications of 2020 Nobel Prize Laureates显示文摘Purpose:This article aims to determine the percentage of'Sparking'articles among the work of this year’s Nobel Prize winners in medicine,physics,and chemistry.Design/methodology/approach:We focus on under-cited influential research among the key publications as mentioned by the Nobel Prize Committee for the 2020 Noble Prize laureates.Specifically,we extracted data from the Web of Science,and calculated the Sparking Indices using the formulas as proposed by Hu and Rousseau in 2016 and 2017.In addition,we identified another type of igniting articles based on the notion in 2017.Findings:In the fields of medicine and physics,the proportions of articles with sparking characteristics share 78.571%and 68.75%respectively,yet,in chemistry 90%articles characterized by'igniting'.Moreover,the two types of articles share more than 93%in the work of the Nobel Prize included in this study.Research limitations:Our research did not cover the impact of topic,socio-political,and author’s reputation on the Sparking Indices.Practical implications:Our study shows that the Sparking Indices truly reflect influence of the best research work,so it can be used to detect under-cited influential articles,as well as identifying fundamental work.Originality/value:Our findings suggest that the Sparking Indices have good applicability for research evaluation. | Fangjie Xil Ronald Rousseau Xiaojun Hu | 2021 | Journal of Data and Information Science2021，6，2： | 1 |
| 4 | Parameterization Method of Wind Drift Factor Based on Deep Learning in the Oil Spill Model显示文摘Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this paper.The method was adopted to forecast the oil spill in the East China Sea.The discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical analysis.Results reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional method.Furthermore,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018. | YU Fangjie GU Feiyang ZHAO Yang HU Huimin ZHANG Xiaodong ZHUANG Zhiyuan CHEN Ge | 2023 | Journal of Ocean University of China2023，22，6： | 0 |
| 5 | Dolomite reservoir formation and diagenesis evolution of the Upper Ediacaran Qigebrak Formation in the Tabei area,Tarim Basin显示文摘Ancient dolomite reservoirs play an increasingly important role in deep oil and gas exploration.The mechanism of formation and preservation of dolomite reservoirs is complex,which is always the key issue.With the discovery of deep oil and gas in the Ediacaran dolomites of the world,the upper Ediacaran Qigebrak Formation in the Tabei area has begun to attract attention,but its reservoir space difference and formation mechanism have yet to be clarified.Based on ultra-deep drilling cores and field outcrops in the Tabei area,the lithofacies,reservoir space,and formation mechanism are systematically analyzed by macro to micro,and qualitative to quantitative petrology:(1)The types of dolomite can be divided into five major categories,including microbial dolomite,granular dolomite,residual granular dolomite,crystalline dolomite and karst breccias.(2)The main types of reservoir space are microbial-framework pores,microbial-mold pores,and non-fabric selective dissolution pores.Spongiomicrobialite,karst breccias,and fine-grained dolomite are the dominant reservoir rock types.(3)High-frequency sedimentary cycles and meteoric dissolution are the key factors of reservoir formation.Two sets of large-scale reservoirs are present:the first set is mainly controlled by the supergene karst of the Keping movement,and the second set is mainly controlled by high-frequency sedimentary cycles in the penecontemporaneous period.The reservoirs formed at the shallow burial stage and were preserved until the deep burial stage.(4)The quality of a deep reservoir depends on the geological events that affect the processes of pore reduction and increase.Cementation,compaction and pressure solution are the main destructive diagenetic processes;however,the reservoir space can still be effectively preserved under the influence of constructive diagenetic processes,such as meteoric dissolution and early dolomitization.This research has important theoretical and practical significance for revealing the formation mechanism of upper Ediacaran deep dolomite reservoirs in the Tarim Basin. | Xudong CHEN Qilu XU Fang HAO Yongquan CHEN Yan YI Fangjie HU Xiaoxue WANG Jinqiang TIAN Guangwei WANG | 2023 | Science China Earth Sciences2023，66，10： | 0 |
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