Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin...Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.展开更多
The competitor,stress tolerator,and ruderal strategy(CSR)framework has been widely applied to explain ecological processes across species.However,its utility in revealing intra-specific trade-offs and genetic adaptati...The competitor,stress tolerator,and ruderal strategy(CSR)framework has been widely applied to explain ecological processes across species.However,its utility in revealing intra-specific trade-offs and genetic adaptation to climate remains unclear.In this study,we examined whether the CSR strategy estimated by leaf traits can identify adaptations to climate in the common reed Phragmites australis.For this purpose,we integrated functional trait data from field surveys and a three-year common garden experiment to compare CSR scores between two typical populations of P.australis from western and eastern China.We further assessed the associations of CSR scores with latitude,bioclimatic factors,and phylogeographical sources using a global dataset including two invaded lineages in the North America.We found that competitor scores were positively correlated with latitude,whereas stress tolerator scores were negatively correlated.Competitor scores were positively correlated with bioclimatic factors,even when controlling for phylogeny.All CSR scores displayed significant phylogenetic signals,with the invasive lineage in the higher latitudes(haplotype M)exhibiting higher stress tolerator scores than the native lineage.Differences in competitor and stress tolerator scores between western and eastern Chinese populations of P.australis were consistent across field and common garden experiments.Although intra-species variation in CSR strategy may be influenced by phylogenetic history,our finding that CSR strategy in P.australis populations is correlated with latitude suggests these plants have adapted to local climates along a latitudinal gradient.展开更多
在全球治理结构调整与可持续发展议程持续推进的背景下,企业责任治理正逐步由传统的企业社会责任(Corporate Social Responsibility,简称CSR )范式,向更为制度化、指标化的环境、社会与治理(Environmental,Social and Governance,简称ES...在全球治理结构调整与可持续发展议程持续推进的背景下,企业责任治理正逐步由传统的企业社会责任(Corporate Social Responsibility,简称CSR )范式,向更为制度化、指标化的环境、社会与治理(Environmental,Social and Governance,简称ESG )模式转型。医疗行业因其公共属性与高度社会敏感性,成为责任治理转型的重要领域。然而,当前医疗企业在责任治理实践中仍面临诸多挑战,如制度设计碎片化、执行体系不健全以及内部外部协同机制薄弱,难以有效回应多元利益相关方的诉求。在此背景下,学界对医疗行业责任治理机制的系统性研究相对不足。基于此,本文旨在探讨医疗企业责任治理从CSR向ESG转型的制度逻辑、结构重塑路径与机制优化策略,期为责任治理范式演进提供理论支持与实践参考。展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
基金supported by the Innovative Human Resource Development for Local Intel-lectualization program through the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.IITP-2026-2020-0-01741)the research fund of Hanyang University(HY-2025-1110).
文摘Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.
基金supported by the National Natural Science Foundation of China(No.32100304,32470388,U22A20558,32271588).
文摘The competitor,stress tolerator,and ruderal strategy(CSR)framework has been widely applied to explain ecological processes across species.However,its utility in revealing intra-specific trade-offs and genetic adaptation to climate remains unclear.In this study,we examined whether the CSR strategy estimated by leaf traits can identify adaptations to climate in the common reed Phragmites australis.For this purpose,we integrated functional trait data from field surveys and a three-year common garden experiment to compare CSR scores between two typical populations of P.australis from western and eastern China.We further assessed the associations of CSR scores with latitude,bioclimatic factors,and phylogeographical sources using a global dataset including two invaded lineages in the North America.We found that competitor scores were positively correlated with latitude,whereas stress tolerator scores were negatively correlated.Competitor scores were positively correlated with bioclimatic factors,even when controlling for phylogeny.All CSR scores displayed significant phylogenetic signals,with the invasive lineage in the higher latitudes(haplotype M)exhibiting higher stress tolerator scores than the native lineage.Differences in competitor and stress tolerator scores between western and eastern Chinese populations of P.australis were consistent across field and common garden experiments.Although intra-species variation in CSR strategy may be influenced by phylogenetic history,our finding that CSR strategy in P.australis populations is correlated with latitude suggests these plants have adapted to local climates along a latitudinal gradient.
文摘在全球治理结构调整与可持续发展议程持续推进的背景下,企业责任治理正逐步由传统的企业社会责任(Corporate Social Responsibility,简称CSR )范式,向更为制度化、指标化的环境、社会与治理(Environmental,Social and Governance,简称ESG )模式转型。医疗行业因其公共属性与高度社会敏感性,成为责任治理转型的重要领域。然而,当前医疗企业在责任治理实践中仍面临诸多挑战,如制度设计碎片化、执行体系不健全以及内部外部协同机制薄弱,难以有效回应多元利益相关方的诉求。在此背景下,学界对医疗行业责任治理机制的系统性研究相对不足。基于此,本文旨在探讨医疗企业责任治理从CSR向ESG转型的制度逻辑、结构重塑路径与机制优化策略,期为责任治理范式演进提供理论支持与实践参考。
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
