为比较准确地了解国内中学化学实验学习研究的热点和重点，为今后中学化学实验学习研究提供有力的数据与支撑，利用 bicomb1.0软件与 IBM SPSS20.0软件，对从中国知网中查询到的2001－2013年的518篇关于实验与化学学习的相关文献进行了...为比较准确地了解国内中学化学实验学习研究的热点和重点，为今后中学化学实验学习研究提供有力的数据与支撑，利用 bicomb1.0软件与 IBM SPSS20.0软件，对从中国知网中查询到的2001－2013年的518篇关于实验与化学学习的相关文献进行了知识谱图的绘制。研究结果表明，我国中学化学实验学习的研究主要涉及4大领域，分别是：高中新课程，化学实验对于化学知识的学习、素质教育，创新、观察等能力的培养等相关领域研究热点；中学化学实验对于激发学生兴趣、培养学生动手能力和课外活动作为化学教学的一种方法途径的研究热点；课堂教学中，实验教学与多媒教学等教学模式的改革、创新提升教学质量的研究热点；采用化学实验微型化、绿色化等策略来强化对学生的能力的培养。展开更多
如何进行旅游灾害管理是全球旅游目的地共同关注的问题。首先界定了旅游灾害管理的概念,然后在中国知网和Web of Science中分别筛选了168篇和180篇中英文文献,采用知识图谱文献可视化分析方法,绘制了发文量、作者和发文机构,以及关键词...如何进行旅游灾害管理是全球旅游目的地共同关注的问题。首先界定了旅游灾害管理的概念,然后在中国知网和Web of Science中分别筛选了168篇和180篇中英文文献,采用知识图谱文献可视化分析方法,绘制了发文量、作者和发文机构,以及关键词共现图谱。研究认为,国内外旅游灾害管理研究集中于旅游灾害风险、旅游风险评估、灾害影响、灾后恢复和风险认知五个方面,并提出今后应深入旅游目的地灾后恢复性、旅游者风险认知和灾害管理规划的研究,以期为我国旅游灾害管理理论和实践提供参考。展开更多
黄河流域环境领域研究众多,以文献综述为主的文献分析存在可视化程度低、时间跨度短和覆盖范围小等问题。针对文献综述局限性,以明确黄河流域环境方向研究进展为目的,采用文献计量学的方法,以Web of Science核心数据库中2001~2020年黄...黄河流域环境领域研究众多,以文献综述为主的文献分析存在可视化程度低、时间跨度短和覆盖范围小等问题。针对文献综述局限性,以明确黄河流域环境方向研究进展为目的,采用文献计量学的方法,以Web of Science核心数据库中2001~2020年黄河流域环境方向研究论文与综述进行科学计量审查,利用Citespace软件进行可视化分析。研究表明:黄河流域环境研究关注度逐步提高但体系化较为薄弱,各研究团队合作尚且不足;研究热点主要集中于黄河流域环境污染修复等方向;经分析预测未来该领域将以新型污染物、三角洲生态系统、多学科联合为主要研究趋势。本文通过明晰该领域研究热点及趋势,以期为黄河流域环境研究和污染防控提供数据参考,为《黄河流域生态环境保护规划》提供理论依据,推动黄河流域环境领域高质量发展,配合黄河保护法立法工作。展开更多
为了解国内外安全社区发展现状,以Web of Science核心合集和中国知网核心期刊收录的以安全社区为主题的文献作为数据来源,使用文献分析工具CiteSpace、SATI和Ucinet分别绘制国内外安全社区研究热点知识图谱;通过对比分析国内外年发文量...为了解国内外安全社区发展现状,以Web of Science核心合集和中国知网核心期刊收录的以安全社区为主题的文献作为数据来源,使用文献分析工具CiteSpace、SATI和Ucinet分别绘制国内外安全社区研究热点知识图谱;通过对比分析国内外年发文量曲线、发文核心机构/地区、高被引文献和国内外知识图谱,描述安全社区研究的主要力量分布和研究热点。结果表明,国内外以安全社区为主题的发文量整体呈上升趋势;美国和瑞典的发文量居于前两位,瑞典的Linkoping Univ居于国际相关机构发文量首位,国内发文量最多的机构是中国职业安全健康协会;从关键词的共现图谱分析可知,国内外安全社区的共同研究热点是安全社区建设中的伤害、伤害预防和干预项目。展开更多
Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters...Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters due to their wide observation range,periodic revisit capabilities,and continuous spatial coverage.These tools enable real-time and quantitative assessment of flood inundation.Over the past 20 years,the field of remote sensing for floods has seen significant advancements.Understanding the evolution of research hotspots within this field can offer valuable insights for future research directions.Materials and methods This study systematically analyzes the development and hotspot evolution in the field of flood remote sensing,both domestically and internationally during 2000—2021.Data from CNKI(China National Knowledge Infrastructure)and WOS(Web of Science)databases are utilized for this analysis.Results(1)A total of 1693 articles have been published in this field,showing a stable growth trend post-2008.Significant contributors include the Chinese Academy of Sciences,Beijing Normal University,Wuhan University,the Italian National Research Council,and National Aeronautics and Space Administration.(2)High-frequency keywords from 2000 to 2021 include“remote sensing”“flood”“model”“classification”“GIS”“climate change”“area”,and“MODIS”.(3)The most prominent keywords were“GIS”(8.65),“surface water”(7.16),“remote sensing”(7.07),“machine learning”(6.52),and“sentinel-2”(5.86).(4)Thirteen cluster labels were identified through clustering,divided into three phases:2000—2009(initial exploratory stage),2010—2014(period of rapid development),and 2015—2021(steady development of remote sensing for floods and related disasters).Discussion The field exhibits strong phase-based development,with research focuses shifting over time.From 2000 to 2009,emphasis was on remote sensing image application and flood model development.From 2010 to 2014,the focus shifted to accurate interpretation of remote sensing images,multispectral image applications,and long time series detection.From 2015 to 2021,research concentrated on steady development,leveraging large datasets and advanced data processing techniques,including improvements in water body indices,big data fusion,deep learning,and drone monitoring.Early on,SAR data,known for its all-weather capability,was crucial for rapid flood hazard extraction and flood hydrological models.With the rise of high-quality optical satellites,optical remote sensing has become more prevalent,though algorithm accuracy and efficiency for water body index methods still require improvement.Conclusions Data sources and methodologies have evolved from early reliance on radar data to the current exploration of optical image fusion and multi-source data integration.Algorithms now increasingly employ deep learning,super image elements,and object-oriented methods to enhance flood identification accuracy.Recent studies focus on spatial and temporal changes in flooding,risk identification,and early warning for climate change-related flooding,including glacial melting and lake outbursts.Recommendations and perspectives To enhance monitoring accuracy and timeliness,UAV technology should be further utilized.Strengthening multi-source data fusion and assimilation is crucial,as is analyzing long-term flood disaster sequences to better understand their mechanisms.展开更多
Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate ...Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate quality problems and find solutions.To address this problem,a knowledge graph based method is employed to extract multi-source heterogeneous cable knowledge entities.The method utilizes the bidirectional encoder representations from transformers(BERT)network to embed word vectors into the input text,then extracts the contextual features of the input sequence through the bidirectional long short-term memory(BiLSTM)network,and finally inputs them into the conditional random field(CRF)network to predict entity categories.Simultaneously,by using the entities extracted by this model as the data layer,a knowledge graph based method has been constructed.Compared to other traditional extraction methods,the entity extraction method used in this study demonstrates significant improvements in metrics such as precision,recall and an F1 score.Ultimately,employing cable test data from a particular aerospace precision machining company,the study has constructed the knowledge graph based method in the field to achieve visualized queries and the traceability and localization of quality problems.展开更多
文摘为比较准确地了解国内中学化学实验学习研究的热点和重点，为今后中学化学实验学习研究提供有力的数据与支撑，利用 bicomb1.0软件与 IBM SPSS20.0软件，对从中国知网中查询到的2001－2013年的518篇关于实验与化学学习的相关文献进行了知识谱图的绘制。研究结果表明，我国中学化学实验学习的研究主要涉及4大领域，分别是：高中新课程，化学实验对于化学知识的学习、素质教育，创新、观察等能力的培养等相关领域研究热点；中学化学实验对于激发学生兴趣、培养学生动手能力和课外活动作为化学教学的一种方法途径的研究热点；课堂教学中，实验教学与多媒教学等教学模式的改革、创新提升教学质量的研究热点；采用化学实验微型化、绿色化等策略来强化对学生的能力的培养。
文摘如何进行旅游灾害管理是全球旅游目的地共同关注的问题。首先界定了旅游灾害管理的概念,然后在中国知网和Web of Science中分别筛选了168篇和180篇中英文文献,采用知识图谱文献可视化分析方法,绘制了发文量、作者和发文机构,以及关键词共现图谱。研究认为,国内外旅游灾害管理研究集中于旅游灾害风险、旅游风险评估、灾害影响、灾后恢复和风险认知五个方面,并提出今后应深入旅游目的地灾后恢复性、旅游者风险认知和灾害管理规划的研究,以期为我国旅游灾害管理理论和实践提供参考。
文摘黄河流域环境领域研究众多,以文献综述为主的文献分析存在可视化程度低、时间跨度短和覆盖范围小等问题。针对文献综述局限性,以明确黄河流域环境方向研究进展为目的,采用文献计量学的方法,以Web of Science核心数据库中2001~2020年黄河流域环境方向研究论文与综述进行科学计量审查,利用Citespace软件进行可视化分析。研究表明:黄河流域环境研究关注度逐步提高但体系化较为薄弱,各研究团队合作尚且不足;研究热点主要集中于黄河流域环境污染修复等方向;经分析预测未来该领域将以新型污染物、三角洲生态系统、多学科联合为主要研究趋势。本文通过明晰该领域研究热点及趋势,以期为黄河流域环境研究和污染防控提供数据参考,为《黄河流域生态环境保护规划》提供理论依据,推动黄河流域环境领域高质量发展,配合黄河保护法立法工作。
文摘为了解国内外安全社区发展现状,以Web of Science核心合集和中国知网核心期刊收录的以安全社区为主题的文献作为数据来源,使用文献分析工具CiteSpace、SATI和Ucinet分别绘制国内外安全社区研究热点知识图谱;通过对比分析国内外年发文量曲线、发文核心机构/地区、高被引文献和国内外知识图谱,描述安全社区研究的主要力量分布和研究热点。结果表明,国内外以安全社区为主题的发文量整体呈上升趋势;美国和瑞典的发文量居于前两位,瑞典的Linkoping Univ居于国际相关机构发文量首位,国内发文量最多的机构是中国职业安全健康协会;从关键词的共现图谱分析可知,国内外安全社区的共同研究热点是安全社区建设中的伤害、伤害预防和干预项目。
文摘Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters due to their wide observation range,periodic revisit capabilities,and continuous spatial coverage.These tools enable real-time and quantitative assessment of flood inundation.Over the past 20 years,the field of remote sensing for floods has seen significant advancements.Understanding the evolution of research hotspots within this field can offer valuable insights for future research directions.Materials and methods This study systematically analyzes the development and hotspot evolution in the field of flood remote sensing,both domestically and internationally during 2000—2021.Data from CNKI(China National Knowledge Infrastructure)and WOS(Web of Science)databases are utilized for this analysis.Results(1)A total of 1693 articles have been published in this field,showing a stable growth trend post-2008.Significant contributors include the Chinese Academy of Sciences,Beijing Normal University,Wuhan University,the Italian National Research Council,and National Aeronautics and Space Administration.(2)High-frequency keywords from 2000 to 2021 include“remote sensing”“flood”“model”“classification”“GIS”“climate change”“area”,and“MODIS”.(3)The most prominent keywords were“GIS”(8.65),“surface water”(7.16),“remote sensing”(7.07),“machine learning”(6.52),and“sentinel-2”(5.86).(4)Thirteen cluster labels were identified through clustering,divided into three phases:2000—2009(initial exploratory stage),2010—2014(period of rapid development),and 2015—2021(steady development of remote sensing for floods and related disasters).Discussion The field exhibits strong phase-based development,with research focuses shifting over time.From 2000 to 2009,emphasis was on remote sensing image application and flood model development.From 2010 to 2014,the focus shifted to accurate interpretation of remote sensing images,multispectral image applications,and long time series detection.From 2015 to 2021,research concentrated on steady development,leveraging large datasets and advanced data processing techniques,including improvements in water body indices,big data fusion,deep learning,and drone monitoring.Early on,SAR data,known for its all-weather capability,was crucial for rapid flood hazard extraction and flood hydrological models.With the rise of high-quality optical satellites,optical remote sensing has become more prevalent,though algorithm accuracy and efficiency for water body index methods still require improvement.Conclusions Data sources and methodologies have evolved from early reliance on radar data to the current exploration of optical image fusion and multi-source data integration.Algorithms now increasingly employ deep learning,super image elements,and object-oriented methods to enhance flood identification accuracy.Recent studies focus on spatial and temporal changes in flooding,risk identification,and early warning for climate change-related flooding,including glacial melting and lake outbursts.Recommendations and perspectives To enhance monitoring accuracy and timeliness,UAV technology should be further utilized.Strengthening multi-source data fusion and assimilation is crucial,as is analyzing long-term flood disaster sequences to better understand their mechanisms.
文摘Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate quality problems and find solutions.To address this problem,a knowledge graph based method is employed to extract multi-source heterogeneous cable knowledge entities.The method utilizes the bidirectional encoder representations from transformers(BERT)network to embed word vectors into the input text,then extracts the contextual features of the input sequence through the bidirectional long short-term memory(BiLSTM)network,and finally inputs them into the conditional random field(CRF)network to predict entity categories.Simultaneously,by using the entities extracted by this model as the data layer,a knowledge graph based method has been constructed.Compared to other traditional extraction methods,the entity extraction method used in this study demonstrates significant improvements in metrics such as precision,recall and an F1 score.Ultimately,employing cable test data from a particular aerospace precision machining company,the study has constructed the knowledge graph based method in the field to achieve visualized queries and the traceability and localization of quality problems.
