目的/意义探索医学信息学跨学科主题演化路径识别方法,为该领域跨学科研究布局与科研管理提供参考。方法/过程首先利用BERTopic和大语言模型(large language model,LLM)在获取的医学信息学文献中识别全局主题和阶段主题;然后基于主题影...目的/意义探索医学信息学跨学科主题演化路径识别方法,为该领域跨学科研究布局与科研管理提供参考。方法/过程首先利用BERTopic和大语言模型(large language model,LLM)在获取的医学信息学文献中识别全局主题和阶段主题;然后基于主题影响力与学科多样性二维分析框架,确定跨学科主题;最后分析跨学科主题的演化路径。结果/结论基于Topic-LLM的分析方法,发现医学信息学跨学科主题的学科多样性和主题影响力呈现稳步增长趋势。展开更多
As a public basic course and the core general course of undergraduate education,moral education(ME)in College English is imperative.It is necessary to design specific teaching programs flexibly and creatively on the b...As a public basic course and the core general course of undergraduate education,moral education(ME)in College English is imperative.It is necessary to design specific teaching programs flexibly and creatively on the basis of fully understanding the ideas,objectives and tasks,basic principles and implementation methods of ME.This paper tries integrating literature reading into unit topic teaching as a new approach to ME in College English.The specific pathways of integration include:expansion of literary reading content,diversified teaching of literary reading,diversified media of literary reading,and diversified assessment system of literary quality.The integration of literature reading and unit topic teaching is conducive to the realization of the teaching goal of cultivating students’language ability,humanistic quality,and correct values.展开更多
目的/意义构建双层分析框架,全面把握学科结构,识别新兴前沿领域,追踪主题演化。方法/过程检索2016—2025年PubMed、Scopus和Web of Science数据库医学信息学文献,采用BERTopic识别主题,并划分为新兴、稳定、衰退3种演化模式。基于Chrom...目的/意义构建双层分析框架,全面把握学科结构,识别新兴前沿领域,追踪主题演化。方法/过程检索2016—2025年PubMed、Scopus和Web of Science数据库医学信息学文献,采用BERTopic识别主题,并划分为新兴、稳定、衰退3种演化模式。基于ChromaDB构建检索增强生成系统,通过文档-主题映射实现微观验证与知识关联挖掘。结果/结论医学信息学主题演化呈现研究重心转移、技术融合深化、学科交叉增强3个特征。BERTopic-RAG框架为知识发现提供了新方法。展开更多
生成式人工智能正深刻变革教育,相关研究文献不断涌现。文章通过收集知网、Scopus和Web of Science中与研究主题相关的中英文文献,利用BERTopic主题建模技术对文献进行主题聚类,挖掘国内外高等教育中生成式人工智能的研究主题,分析研究...生成式人工智能正深刻变革教育,相关研究文献不断涌现。文章通过收集知网、Scopus和Web of Science中与研究主题相关的中英文文献,利用BERTopic主题建模技术对文献进行主题聚类,挖掘国内外高等教育中生成式人工智能的研究主题,分析研究现状,为教育领域的教学实践和学术研究提供参考。研究结果表明:在个性化学习与人机协同方面,生成式人工智能通过制定个性化学习路径显著提升教学效果;师生对生成式人工智能的接受程度受技术认知和使用体验等因素制约,并直接影响应用成效;教育创新需平衡技术赋能与过度依赖问题;教学设计与课程实践的革新更多体现在教学模式和资源生成方式上,但必须同步构建学术伦理防护与治理机制;人工智能时代亟须人才培养结构的优化升级,要求教育者重新定义核心能力目标。展开更多
Located in Hangzhou,Zhejiang Province,the Sijiqing Clothing Street is one of the largest clothing distribution centers in China.It gathers 23 professional markets and over 15,000 businesses.In November 2025,merchants ...Located in Hangzhou,Zhejiang Province,the Sijiqing Clothing Street is one of the largest clothing distribution centers in China.It gathers 23 professional markets and over 15,000 businesses.In November 2025,merchants were already preparing for sales during the 2026 Spring Festival and the spring/summer seasons.Children's clothing is one of the important product categories.What are the new trends in children's clothing sales?And what new changes are happening in this traditional wholesale market?展开更多
Objective To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine(TCM)through quantitative topic evolution analysis,we add...Objective To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine(TCM)through quantitative topic evolution analysis,we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field.Methods A topic evolution analysis was performed on Chinese-language literature pertaining to intelligent diagnosis in TCM.Publications were retrieved from the China National Knowledge Infrastructure(CNKI),Wanfang Data,and China Science and Technology Journal Database(VIP),covering the period from database inception to July 3,2025.A hybrid segmentation approach,based on cumulative publication growth trends and inflection point detection,was applied to divide the research timeline into distinct stages.Subsequently,the latent Dirichlet allocation(LDA)model was used to extract research topics,followed by alignment and evolutionary analysis of topics across different stages.Results A total of 3919 publications published between 2003 and 2025 were included,and the research trajectory was divided into five stages based on data-driven breakpoint detection.The field exhibited a clear evolutionary shift from early rule-based systems and tonguepulse image and signal analysis(2006–2010),to machine-learning-based syndrome and prescription modeling(2011–2015),followed by deep-learning-driven pattern recognition and formula association(2016–2020).Since 2021,research has increasingly emphasized knowledge-graph construction,multimodal integration,and intelligent clinical decision-support systems,with recent studies(2024–2025)showing the emergence of large language models and agent-based diagnostic frameworks.Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis,alongside the progressive consolidation of integrated intelligent diagnostic platforms.Conclusion By identifying key technological transitions and persistent core research themes,our findings offer a structured reference framework for the design of intelligent diagnostic systems,the construction of knowledge-driven clinical decision-support tools,and the alignment of AI models with TCM diagnostic logic.Importantly,the stage-based evolutionary insights derived from this analysis can inform future methodological choices,improve model interpretability and clinical applicability,and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.展开更多
随着锂电池领域论文数量的激增和研究主题的日益丰富,准确监测该领域的发展趋势和把握最新研究动向变得日益复杂。通过运用大数据和机器学习技术,采用BERTopic主题模型对Web of Science数据库中的18万余篇锂电池论文进行文本分析,绘制...随着锂电池领域论文数量的激增和研究主题的日益丰富,准确监测该领域的发展趋势和把握最新研究动向变得日益复杂。通过运用大数据和机器学习技术,采用BERTopic主题模型对Web of Science数据库中的18万余篇锂电池论文进行文本分析,绘制了锂电池领域的主题图,识别了新兴研究主题和高被引主题。结果表明,锂电池研究活动正显著加速,锂硫电池、锂枝晶生长抑制、电池回收和金属回收等新兴主题快速发展,而材料研究如二硫化钼纳米材料、氧化铁电极材料则具有显著的高学术影响力。研究还探讨了《锂电池百篇论文点评系列》对当前锂电池研究主题的监测情况,该系列对多数科学技术主题有良好覆盖。本研究为锂电池领域的主题监测提供了新方法,为政策制定和技术研发提供了情报支持,并为“锂电池百篇论文点评”系列的后续研究提供了参考。展开更多
文摘目的/意义探索医学信息学跨学科主题演化路径识别方法,为该领域跨学科研究布局与科研管理提供参考。方法/过程首先利用BERTopic和大语言模型(large language model,LLM)在获取的医学信息学文献中识别全局主题和阶段主题;然后基于主题影响力与学科多样性二维分析框架,确定跨学科主题;最后分析跨学科主题的演化路径。结果/结论基于Topic-LLM的分析方法,发现医学信息学跨学科主题的学科多样性和主题影响力呈现稳步增长趋势。
文摘As a public basic course and the core general course of undergraduate education,moral education(ME)in College English is imperative.It is necessary to design specific teaching programs flexibly and creatively on the basis of fully understanding the ideas,objectives and tasks,basic principles and implementation methods of ME.This paper tries integrating literature reading into unit topic teaching as a new approach to ME in College English.The specific pathways of integration include:expansion of literary reading content,diversified teaching of literary reading,diversified media of literary reading,and diversified assessment system of literary quality.The integration of literature reading and unit topic teaching is conducive to the realization of the teaching goal of cultivating students’language ability,humanistic quality,and correct values.
文摘目的/意义构建双层分析框架,全面把握学科结构,识别新兴前沿领域,追踪主题演化。方法/过程检索2016—2025年PubMed、Scopus和Web of Science数据库医学信息学文献,采用BERTopic识别主题,并划分为新兴、稳定、衰退3种演化模式。基于ChromaDB构建检索增强生成系统,通过文档-主题映射实现微观验证与知识关联挖掘。结果/结论医学信息学主题演化呈现研究重心转移、技术融合深化、学科交叉增强3个特征。BERTopic-RAG框架为知识发现提供了新方法。
文摘生成式人工智能正深刻变革教育,相关研究文献不断涌现。文章通过收集知网、Scopus和Web of Science中与研究主题相关的中英文文献,利用BERTopic主题建模技术对文献进行主题聚类,挖掘国内外高等教育中生成式人工智能的研究主题,分析研究现状,为教育领域的教学实践和学术研究提供参考。研究结果表明:在个性化学习与人机协同方面,生成式人工智能通过制定个性化学习路径显著提升教学效果;师生对生成式人工智能的接受程度受技术认知和使用体验等因素制约,并直接影响应用成效;教育创新需平衡技术赋能与过度依赖问题;教学设计与课程实践的革新更多体现在教学模式和资源生成方式上,但必须同步构建学术伦理防护与治理机制;人工智能时代亟须人才培养结构的优化升级,要求教育者重新定义核心能力目标。
文摘Located in Hangzhou,Zhejiang Province,the Sijiqing Clothing Street is one of the largest clothing distribution centers in China.It gathers 23 professional markets and over 15,000 businesses.In November 2025,merchants were already preparing for sales during the 2026 Spring Festival and the spring/summer seasons.Children's clothing is one of the important product categories.What are the new trends in children's clothing sales?And what new changes are happening in this traditional wholesale market?
基金Grants of National Natural Science Foundation of China(82274685).
文摘Objective To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine(TCM)through quantitative topic evolution analysis,we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field.Methods A topic evolution analysis was performed on Chinese-language literature pertaining to intelligent diagnosis in TCM.Publications were retrieved from the China National Knowledge Infrastructure(CNKI),Wanfang Data,and China Science and Technology Journal Database(VIP),covering the period from database inception to July 3,2025.A hybrid segmentation approach,based on cumulative publication growth trends and inflection point detection,was applied to divide the research timeline into distinct stages.Subsequently,the latent Dirichlet allocation(LDA)model was used to extract research topics,followed by alignment and evolutionary analysis of topics across different stages.Results A total of 3919 publications published between 2003 and 2025 were included,and the research trajectory was divided into five stages based on data-driven breakpoint detection.The field exhibited a clear evolutionary shift from early rule-based systems and tonguepulse image and signal analysis(2006–2010),to machine-learning-based syndrome and prescription modeling(2011–2015),followed by deep-learning-driven pattern recognition and formula association(2016–2020).Since 2021,research has increasingly emphasized knowledge-graph construction,multimodal integration,and intelligent clinical decision-support systems,with recent studies(2024–2025)showing the emergence of large language models and agent-based diagnostic frameworks.Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis,alongside the progressive consolidation of integrated intelligent diagnostic platforms.Conclusion By identifying key technological transitions and persistent core research themes,our findings offer a structured reference framework for the design of intelligent diagnostic systems,the construction of knowledge-driven clinical decision-support tools,and the alignment of AI models with TCM diagnostic logic.Importantly,the stage-based evolutionary insights derived from this analysis can inform future methodological choices,improve model interpretability and clinical applicability,and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.
文摘随着锂电池领域论文数量的激增和研究主题的日益丰富,准确监测该领域的发展趋势和把握最新研究动向变得日益复杂。通过运用大数据和机器学习技术,采用BERTopic主题模型对Web of Science数据库中的18万余篇锂电池论文进行文本分析,绘制了锂电池领域的主题图,识别了新兴研究主题和高被引主题。结果表明,锂电池研究活动正显著加速,锂硫电池、锂枝晶生长抑制、电池回收和金属回收等新兴主题快速发展,而材料研究如二硫化钼纳米材料、氧化铁电极材料则具有显著的高学术影响力。研究还探讨了《锂电池百篇论文点评系列》对当前锂电池研究主题的监测情况,该系列对多数科学技术主题有良好覆盖。本研究为锂电池领域的主题监测提供了新方法,为政策制定和技术研发提供了情报支持,并为“锂电池百篇论文点评”系列的后续研究提供了参考。
