This paper presents a project aimed at developing a trilingual visual dictionary for aircraft maintenance professionals and students.The project addresses the growing demand for accurate communication and technical te...This paper presents a project aimed at developing a trilingual visual dictionary for aircraft maintenance professionals and students.The project addresses the growing demand for accurate communication and technical terminology in the aviation industry,particularly in Brazil and China.The study employs a corpus-driven approach,analyzing a large corpus of aircraft maintenance manuals to extract key technical terms and their collocates.Using specialized subcorpora and a comparative analysis,this paper demonstrates challenges and solutions into the identification of high-frequency keywords and explores their contextual use in aviation documentation,emphasizing the need for clear and accurate technical communication.By incorporating these findings into a trilingual visual dictionary,the project aims to enhance the understanding and usage of aviation terminology.展开更多
固氮作用是生态系统生物地球化学循环的重要环节。目前对水生生态系统固氮作用的研究起步相对较晚,主要集中在海洋和湖泊等水体。为了解当前淡水湖泊固氮微生物的研究热点与发展趋势,本研究以Web of Science数据库中淡水湖泊固氮微生物...固氮作用是生态系统生物地球化学循环的重要环节。目前对水生生态系统固氮作用的研究起步相对较晚,主要集中在海洋和湖泊等水体。为了解当前淡水湖泊固氮微生物的研究热点与发展趋势,本研究以Web of Science数据库中淡水湖泊固氮微生物及相关领域的文献为数据源,运用CiteSpace和VOSviewer软件构建知识图谱,分析该研究领域的发文热点及未来研究趋势。在此基础上,通过文献整合分析,梳理了水体氮磷营养盐对固氮速率的影响及其可能的作用机制。结果表明,(1)1992-2024年全球淡水湖泊固氮微生物研究领域的出版物数量和引用频次不断增加。(2)国家、作者、机构合作网络分析显示,淡水湖泊固氮微生物研究是一个多学科交叉、多国家和机构合作的研究领域。(3)聚类分析结果表明,当前研究热点主要聚焦于3个方向:磷限制情境下的营养调控策略及其对蓝藻群落演替过程的生态响应;基于nifH基因的固氮微生物多样性解析及其在氮循环功能中的生态位特征;环境因子驱动下浮游植物群落结构的长期时空动态演替规律。(4)整合分析结果表明:固氮量化研究的地理分布区域不平衡,北美洲构建了涵盖多类型水体的综合指标体系,而亚洲、南美洲则侧重蓝藻生物量描述,欧洲多聚焦于氮磷动态变化和固氮过程的耦合关系;固氮生物物种研究以蓝藻门(长孢藻、束丝藻等)为主,变形菌、古菌等门类研究相对较少;总磷与固氮速率呈显著正相关关系,而与总溶解氮、硝态氮、铵态氮呈显著负相关关系。非线性分段模型拟合发现总磷对淡水湖泊固氮速率的调控存在临界值(25μg/L)。未来固氮过程研究在测定方法(如乙炔还原法与同位素示踪法)和计量单位(面积/体积单位)等方面亟需标准化以提高研究结果的可比性。本研究总结了淡水湖泊固氮微生物研究过去30年热点与前沿的变化趋势,建议通过引入多样化的分析指标(酶活性、转录组)、标准化的分析流程和多指标融合的评价方法,继续拓宽对淡水湖泊固氮过程及其生态贡献的认识。展开更多
In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and rec...In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and recognition accuracy on resource-constrained devices such as mobile terminals remains a major challenge.To address this,we propose a novel time-frequency dual-branch parallel residual network,which integrates a Dual-Branch Broadcast Residual module and a Time-Frequency Coordinate Attention module.The time-domain and frequency-domain branches are designed in parallel to independently extract temporal and spectral features,effectively avoiding the potential information loss caused by serial stacking,while enhancing information flow and multi-scale feature fusion.In terms of training strategy,a curriculum learning approach is introduced to progressively improve model robustness fromeasy to difficult tasks.Experimental results demonstrate that the proposed method consistently outperforms existing lightweight models under various signal-to-noise ratio(SNR)conditions,achieving superior far-field recognition performance on the Google Speech Commands V2 dataset.Notably,the model maintains stable performance even in low-SNR environments such as–10 dB,and generalizes well to unseen SNR conditions during training,validating its robustness to novel noise scenarios.Furthermore,the proposed model exhibits significantly fewer parameters,making it highly suitable for deployment on resource-limited devices.Overall,the model achieves a favorable balance between performance and parameter efficiency,demonstrating strong potential for practical applications.展开更多
使用Visual Basic编程,采用正则表达式批量提取由Web of Science导出的Bib Tex题录中所有Keywords字段关键词,按需合并所得关键词的同义词、近义词及词形变化词,然后将出现频度的统计数据写入Excel表,并编制Excel宏自动生成折线图,实现...使用Visual Basic编程,采用正则表达式批量提取由Web of Science导出的Bib Tex题录中所有Keywords字段关键词,按需合并所得关键词的同义词、近义词及词形变化词,然后将出现频度的统计数据写入Excel表,并编制Excel宏自动生成折线图,实现关键词分布的简单可视化。情报工作者后续可借助Excel功能对该程序生成的Excel表执行复杂的数据组合分析,以提高工作效率。展开更多
This study presents a reflective bibliometric review of 1457 peer-reviewed articles published in the Journal of Psychology in Africa(2008-2024,17 years),using a Meta-Editorial Mapping Framework(MEMF)analysis.The MEMF ...This study presents a reflective bibliometric review of 1457 peer-reviewed articles published in the Journal of Psychology in Africa(2008-2024,17 years),using a Meta-Editorial Mapping Framework(MEMF)analysis.The MEMF integrates citation metrics,keyword novelty ratios,TF-IDF weighting,and cluster-based topic modeling to trace long-term thematic trends and editorial evolution.Findings reveal sustained attention to foundational domains such as mental health,education,and identity,alongside a gradual integration of emergent themes including digital well-being,organizational behavior,and post-pandemic adaptation.Articles with moderate topical novelty(40%-60% new keywords)achieved the highest citation and usage metrics,suggesting that integrative innovation enhances scholarly impact.Clustering analyses indicate that the journal’s content forms overlapping conceptual domains rather than isolated silos.These insights contribute to editorial strategy,authorial positioning,and the future design of regional academic platforms.Moreover,the findings provide evidence supporting the use of the MEMF as a replicable tool for meta-editorial analysis across disciplinary and geographic boundaries.展开更多
乡村旅游营销研究越来越受到学界的关注,基于CiteSpace文献计量工具,对中国知网(CNKI)和Web of Science数据库中2000—2024年乡村旅游营销的相关文献进行可视化分析,从整体上把握其研究态势,为未来相关研究提供参考。共计纳入130篇中文...乡村旅游营销研究越来越受到学界的关注,基于CiteSpace文献计量工具,对中国知网(CNKI)和Web of Science数据库中2000—2024年乡村旅游营销的相关文献进行可视化分析,从整体上把握其研究态势,为未来相关研究提供参考。共计纳入130篇中文文献和586篇英文文献,对发文情况及关键词进行分析。结果表明:中文和英文文献发文量整体呈波动增长趋势,近三年国内外研究热点集中在网络营销、乡村振兴、内在机理、目的地形象、可持续发展、动机等方面,但侧重点各有所不同。展开更多
为揭示人工智能个性化学习领域研究图景与热点动态演进,以Web of Science数据库相关文献为样本,借助VOSviewer可视化工具绘制知识图谱,系统分析该领域年度发文量分布、作者合作网络、关键词聚类、引用关系及研究主题演变轨迹。结果表明...为揭示人工智能个性化学习领域研究图景与热点动态演进,以Web of Science数据库相关文献为样本,借助VOSviewer可视化工具绘制知识图谱,系统分析该领域年度发文量分布、作者合作网络、关键词聚类、引用关系及研究主题演变轨迹。结果表明,近年来,该领域发文量呈明显上升趋势,已成为国际学术界的研究热点,但研究群体分布零散,合作紧密度不足。现阶段,研究议题集中在人工智能、教育技术、机器学习及Chat GPT等领域,冲破技术难题与推动多领域融合是核心研究方向。其中,人工智能技术与数字技术体系、深度学习框架的交叉应用研究对该领域深入发展具有重要的探索价值。展开更多
文摘This paper presents a project aimed at developing a trilingual visual dictionary for aircraft maintenance professionals and students.The project addresses the growing demand for accurate communication and technical terminology in the aviation industry,particularly in Brazil and China.The study employs a corpus-driven approach,analyzing a large corpus of aircraft maintenance manuals to extract key technical terms and their collocates.Using specialized subcorpora and a comparative analysis,this paper demonstrates challenges and solutions into the identification of high-frequency keywords and explores their contextual use in aviation documentation,emphasizing the need for clear and accurate technical communication.By incorporating these findings into a trilingual visual dictionary,the project aims to enhance the understanding and usage of aviation terminology.
文摘固氮作用是生态系统生物地球化学循环的重要环节。目前对水生生态系统固氮作用的研究起步相对较晚,主要集中在海洋和湖泊等水体。为了解当前淡水湖泊固氮微生物的研究热点与发展趋势,本研究以Web of Science数据库中淡水湖泊固氮微生物及相关领域的文献为数据源,运用CiteSpace和VOSviewer软件构建知识图谱,分析该研究领域的发文热点及未来研究趋势。在此基础上,通过文献整合分析,梳理了水体氮磷营养盐对固氮速率的影响及其可能的作用机制。结果表明,(1)1992-2024年全球淡水湖泊固氮微生物研究领域的出版物数量和引用频次不断增加。(2)国家、作者、机构合作网络分析显示,淡水湖泊固氮微生物研究是一个多学科交叉、多国家和机构合作的研究领域。(3)聚类分析结果表明,当前研究热点主要聚焦于3个方向:磷限制情境下的营养调控策略及其对蓝藻群落演替过程的生态响应;基于nifH基因的固氮微生物多样性解析及其在氮循环功能中的生态位特征;环境因子驱动下浮游植物群落结构的长期时空动态演替规律。(4)整合分析结果表明:固氮量化研究的地理分布区域不平衡,北美洲构建了涵盖多类型水体的综合指标体系,而亚洲、南美洲则侧重蓝藻生物量描述,欧洲多聚焦于氮磷动态变化和固氮过程的耦合关系;固氮生物物种研究以蓝藻门(长孢藻、束丝藻等)为主,变形菌、古菌等门类研究相对较少;总磷与固氮速率呈显著正相关关系,而与总溶解氮、硝态氮、铵态氮呈显著负相关关系。非线性分段模型拟合发现总磷对淡水湖泊固氮速率的调控存在临界值(25μg/L)。未来固氮过程研究在测定方法(如乙炔还原法与同位素示踪法)和计量单位(面积/体积单位)等方面亟需标准化以提高研究结果的可比性。本研究总结了淡水湖泊固氮微生物研究过去30年热点与前沿的变化趋势,建议通过引入多样化的分析指标(酶活性、转录组)、标准化的分析流程和多指标融合的评价方法,继续拓宽对淡水湖泊固氮过程及其生态贡献的认识。
文摘In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and recognition accuracy on resource-constrained devices such as mobile terminals remains a major challenge.To address this,we propose a novel time-frequency dual-branch parallel residual network,which integrates a Dual-Branch Broadcast Residual module and a Time-Frequency Coordinate Attention module.The time-domain and frequency-domain branches are designed in parallel to independently extract temporal and spectral features,effectively avoiding the potential information loss caused by serial stacking,while enhancing information flow and multi-scale feature fusion.In terms of training strategy,a curriculum learning approach is introduced to progressively improve model robustness fromeasy to difficult tasks.Experimental results demonstrate that the proposed method consistently outperforms existing lightweight models under various signal-to-noise ratio(SNR)conditions,achieving superior far-field recognition performance on the Google Speech Commands V2 dataset.Notably,the model maintains stable performance even in low-SNR environments such as–10 dB,and generalizes well to unseen SNR conditions during training,validating its robustness to novel noise scenarios.Furthermore,the proposed model exhibits significantly fewer parameters,making it highly suitable for deployment on resource-limited devices.Overall,the model achieves a favorable balance between performance and parameter efficiency,demonstrating strong potential for practical applications.
文摘使用Visual Basic编程,采用正则表达式批量提取由Web of Science导出的Bib Tex题录中所有Keywords字段关键词,按需合并所得关键词的同义词、近义词及词形变化词,然后将出现频度的统计数据写入Excel表,并编制Excel宏自动生成折线图,实现关键词分布的简单可视化。情报工作者后续可借助Excel功能对该程序生成的Excel表执行复杂的数据组合分析,以提高工作效率。
文摘This study presents a reflective bibliometric review of 1457 peer-reviewed articles published in the Journal of Psychology in Africa(2008-2024,17 years),using a Meta-Editorial Mapping Framework(MEMF)analysis.The MEMF integrates citation metrics,keyword novelty ratios,TF-IDF weighting,and cluster-based topic modeling to trace long-term thematic trends and editorial evolution.Findings reveal sustained attention to foundational domains such as mental health,education,and identity,alongside a gradual integration of emergent themes including digital well-being,organizational behavior,and post-pandemic adaptation.Articles with moderate topical novelty(40%-60% new keywords)achieved the highest citation and usage metrics,suggesting that integrative innovation enhances scholarly impact.Clustering analyses indicate that the journal’s content forms overlapping conceptual domains rather than isolated silos.These insights contribute to editorial strategy,authorial positioning,and the future design of regional academic platforms.Moreover,the findings provide evidence supporting the use of the MEMF as a replicable tool for meta-editorial analysis across disciplinary and geographic boundaries.
文摘乡村旅游营销研究越来越受到学界的关注,基于CiteSpace文献计量工具,对中国知网(CNKI)和Web of Science数据库中2000—2024年乡村旅游营销的相关文献进行可视化分析,从整体上把握其研究态势,为未来相关研究提供参考。共计纳入130篇中文文献和586篇英文文献,对发文情况及关键词进行分析。结果表明:中文和英文文献发文量整体呈波动增长趋势,近三年国内外研究热点集中在网络营销、乡村振兴、内在机理、目的地形象、可持续发展、动机等方面,但侧重点各有所不同。
文摘为揭示人工智能个性化学习领域研究图景与热点动态演进,以Web of Science数据库相关文献为样本,借助VOSviewer可视化工具绘制知识图谱,系统分析该领域年度发文量分布、作者合作网络、关键词聚类、引用关系及研究主题演变轨迹。结果表明,近年来,该领域发文量呈明显上升趋势,已成为国际学术界的研究热点,但研究群体分布零散,合作紧密度不足。现阶段,研究议题集中在人工智能、教育技术、机器学习及Chat GPT等领域,冲破技术难题与推动多领域融合是核心研究方向。其中,人工智能技术与数字技术体系、深度学习框架的交叉应用研究对该领域深入发展具有重要的探索价值。
