背景:高频重复经颅磁刺激因非侵入性调控大脑功能的潜在益处而备受关注,但目前尚无研究从宏观角度分析该研究领域的最新研究现状和发展趋势。目的:通过可视化分析来确定高频重复经颅磁刺激领域的研究热点、现状和前沿。方法:检索Web of ...背景:高频重复经颅磁刺激因非侵入性调控大脑功能的潜在益处而备受关注,但目前尚无研究从宏观角度分析该研究领域的最新研究现状和发展趋势。目的:通过可视化分析来确定高频重复经颅磁刺激领域的研究热点、现状和前沿。方法:检索Web of Science核心数据库中高频重复经颅磁刺激领域的相关研究,文献检索时限为2014-01-01/2024-11-15,采用CiteSpace进行发文量分析,国家/地区、机构和作者合作分析,被引期刊、文献共被引和学科领域分析,以及关键词共现、聚类和突现分析等,绘制可视化分析知识图谱。结果与结论:共纳入860篇文献,高频重复经颅磁刺激领域的发文量在2014-2022年总体呈上升趋势,在2022-2024年呈下降趋势,中国是发文量最多的国家,发文量最高的机构是根特大学,发文量高的机构多为高校;发文量最高的作者是根特大学的Chris Baeken,发文量靠前的作者之间以及世界各地的机构之间合作较少。高频重复经颅磁刺激领域热点关键词以抑郁症、脑卒中、神经源性疼痛和帕金森病等为主,突现关键词以轻度认知障碍为主,研究方向较多元化。高频重复经颅磁刺激的研究热度总体呈上升趋势,研究内容主要围绕高频重复经颅磁刺激在精神疾病和神经系统疾病的临床应用,以及对这些领域作用机制的探索。未来该领域的研究方向可能聚焦于高频重复经颅磁刺激在临床应用时使用多种治疗参数来作用不同的靶点,以及其在不同领域中的应用和作用机制。展开更多
Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstruc...Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.展开更多
立足科技情报知识服务视角,梳理AI for Science (AI4S)推动的“平台科研”范式内涵与框架。根据库恩范式理论论述了AI4S推动科研范式革新的必然性,采用培根归纳法总结的科学研究流程作为框架线索,阐明创新知识服务与“平台科研”范式的...立足科技情报知识服务视角,梳理AI for Science (AI4S)推动的“平台科研”范式内涵与框架。根据库恩范式理论论述了AI4S推动科研范式革新的必然性,采用培根归纳法总结的科学研究流程作为框架线索,阐明创新知识服务与“平台科研”范式的互促共进关系并作为理论指导。创新知识服务视角下的“平台科研”范式以服务科研创新活动为宗旨,主要内容包括知识表示视角下的科学数据管理、知识融合视角下的通用知识库构建、知识推理视角下的科学假设预测、知识发现视角下的科学实验执行和知识应用视角下的工业赋能。本文提出了一种创新知识服务视角下的“平台科研”范式框架,旨在从创新知识服务角度理解“平台科研”范式,厘清各主要环节创新知识服务的核心研究内容,以期成为科技情报研究领域的新兴知识生长点,为我国抢抓AI4S科研范式革新机遇提供参考思路。展开更多
文摘背景:高频重复经颅磁刺激因非侵入性调控大脑功能的潜在益处而备受关注,但目前尚无研究从宏观角度分析该研究领域的最新研究现状和发展趋势。目的:通过可视化分析来确定高频重复经颅磁刺激领域的研究热点、现状和前沿。方法:检索Web of Science核心数据库中高频重复经颅磁刺激领域的相关研究,文献检索时限为2014-01-01/2024-11-15,采用CiteSpace进行发文量分析,国家/地区、机构和作者合作分析,被引期刊、文献共被引和学科领域分析,以及关键词共现、聚类和突现分析等,绘制可视化分析知识图谱。结果与结论:共纳入860篇文献,高频重复经颅磁刺激领域的发文量在2014-2022年总体呈上升趋势,在2022-2024年呈下降趋势,中国是发文量最多的国家,发文量最高的机构是根特大学,发文量高的机构多为高校;发文量最高的作者是根特大学的Chris Baeken,发文量靠前的作者之间以及世界各地的机构之间合作较少。高频重复经颅磁刺激领域热点关键词以抑郁症、脑卒中、神经源性疼痛和帕金森病等为主,突现关键词以轻度认知障碍为主,研究方向较多元化。高频重复经颅磁刺激的研究热度总体呈上升趋势,研究内容主要围绕高频重复经颅磁刺激在精神疾病和神经系统疾病的临床应用,以及对这些领域作用机制的探索。未来该领域的研究方向可能聚焦于高频重复经颅磁刺激在临床应用时使用多种治疗参数来作用不同的靶点,以及其在不同领域中的应用和作用机制。
基金funded by the Directorate of Research and Community Service,Directorate General of Research and Development,Ministry of Higher Education,Science and Technologyin accordance with the Implementation Contract for the Operational Assistance Program for State Universities,Research Program Number:109/C3/DT.05.00/PL/2025.
文摘Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.
文摘立足科技情报知识服务视角,梳理AI for Science (AI4S)推动的“平台科研”范式内涵与框架。根据库恩范式理论论述了AI4S推动科研范式革新的必然性,采用培根归纳法总结的科学研究流程作为框架线索,阐明创新知识服务与“平台科研”范式的互促共进关系并作为理论指导。创新知识服务视角下的“平台科研”范式以服务科研创新活动为宗旨,主要内容包括知识表示视角下的科学数据管理、知识融合视角下的通用知识库构建、知识推理视角下的科学假设预测、知识发现视角下的科学实验执行和知识应用视角下的工业赋能。本文提出了一种创新知识服务视角下的“平台科研”范式框架,旨在从创新知识服务角度理解“平台科研”范式,厘清各主要环节创新知识服务的核心研究内容,以期成为科技情报研究领域的新兴知识生长点,为我国抢抓AI4S科研范式革新机遇提供参考思路。
