To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and ex...To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.展开更多
Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature....Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature.Through the integration of network biology,TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms,establishing a novel research paradigm for TCM modernization.The rapid advancement of machine learning,particularly revolutionary deep learning methods,has substantially enhanced artificial intelligence(AI)technology,offering significant potential to advance TCM network pharmacology research.This paper describes the methodology of TCM network pharmacology,encompassing ingredient identification,network construction,network analysis,and experimental validation.Furthermore,it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods.Finally,it addresses challenges and future directions regarding cell-cell communication(CCC)-based network construction,analysis,and validation,providing valuable insights for TCM network pharmacology.展开更多
Background While intravenous thrombolysis is recommended for patients who had an acute ischaemic stroke(AIS)within 4.5hours of symptom onset,there are few randomised trials investigating the benefits of thrombolysis b...Background While intravenous thrombolysis is recommended for patients who had an acute ischaemic stroke(AIS)within 4.5hours of symptom onset,there are few randomised trials investigating the benefits of thrombolysis beyond this therapeutic window.Aim To determine whether patients who had an AIS selected with the presence of potentially salvageable tissue on CT perfusion at 4.5–24hours after stroke onset(for stroke with unknown onset time,the midpoint of the time last known to be well and symptom recognition time;for wake-up stroke,the midpoint of the time last known to be well or sleep onset and wake up time)will benefit from intravenous thrombolysis.Design HOPE is a prospective,multicentre,randomised,open-label blinded endpoint trial with the stage of phase III.The treatment allocation employs 1:1 randomisation.The treatment arm under investigation is alteplase with standard therapy,the control arm is standard therapy.Eligibility imaging criteria include ischaemic core volume≤70mL,penumbra≥10mL and mismatch≥20%.Study outcomes The primary outcome is non-disabled functional outcome(assessed as modified Rankin Scale score of 0–1 at 90 days).Discussion HOPE is the first trial to investigate whether intravenous thrombolysis with alteplase offers benefits in patients who had an AIS presenting within 4.5–24hours,which has the potential to extend time window and expand eligible population for thrombolysis therapy.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 52475102 and 52205101in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515240021+1 种基金in part by the Young Talent Support Project of Guangzhou Association for Science and Technology(QT-2024-28)in part by the Youth Development Initiative of Guangdong Association for Science and Technology(SKXRC2025254).
文摘To ensure the safe and stable operation of rotating machinery,intelligent fault diagnosis methods hold significant research value.However,existing diagnostic approaches largely rely on manual feature extraction and expert experience,which limits their adaptability under variable operating conditions and strong noise environments,severely affecting the generalization capability of diagnostic models.To address this issue,this study proposes a multimodal fusion fault diagnosis framework based on Mel-spectrograms and automated machine learning(AutoML).The framework first extracts fault-sensitive Mel time–frequency features from acoustic signals and fuses them with statistical features of vibration signals to construct complementary fault representations.On this basis,automated machine learning techniques are introduced to enable end-to-end diagnostic workflow construction and optimal model configuration acquisition.Finally,diagnostic decisions are achieved by automatically integrating the predictions of multiple high-performance base models.Experimental results on a centrifugal pump vibration and acoustic dataset demonstrate that the proposed framework achieves high diagnostic accuracy under noise-free conditions and maintains strong robustness under noisy interference,validating its efficiency,scalability,and practical value for rotating machinery fault diagnosis.
基金supported by the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2024C03106,X.F.)the National Natural Science Foundation of China(No.82474160,X.S.)+2 种基金the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LBZ24H270001,X.P.)the Major Joint Projects Supported by the National Administration of TCM and Zhejiang Province(No.GZY-ZI-KJ-23037,X.P.)the Ningbo Top Medical and Health Research Program(No.2022030309,X.P.)。
文摘Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature.Through the integration of network biology,TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms,establishing a novel research paradigm for TCM modernization.The rapid advancement of machine learning,particularly revolutionary deep learning methods,has substantially enhanced artificial intelligence(AI)technology,offering significant potential to advance TCM network pharmacology research.This paper describes the methodology of TCM network pharmacology,encompassing ingredient identification,network construction,network analysis,and experimental validation.Furthermore,it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods.Finally,it addresses challenges and future directions regarding cell-cell communication(CCC)-based network construction,analysis,and validation,providing valuable insights for TCM network pharmacology.
基金HOPE is sponsored and supported by the Second Affiliated Hospital,School of Medicine,Zhejiang University.
文摘Background While intravenous thrombolysis is recommended for patients who had an acute ischaemic stroke(AIS)within 4.5hours of symptom onset,there are few randomised trials investigating the benefits of thrombolysis beyond this therapeutic window.Aim To determine whether patients who had an AIS selected with the presence of potentially salvageable tissue on CT perfusion at 4.5–24hours after stroke onset(for stroke with unknown onset time,the midpoint of the time last known to be well and symptom recognition time;for wake-up stroke,the midpoint of the time last known to be well or sleep onset and wake up time)will benefit from intravenous thrombolysis.Design HOPE is a prospective,multicentre,randomised,open-label blinded endpoint trial with the stage of phase III.The treatment allocation employs 1:1 randomisation.The treatment arm under investigation is alteplase with standard therapy,the control arm is standard therapy.Eligibility imaging criteria include ischaemic core volume≤70mL,penumbra≥10mL and mismatch≥20%.Study outcomes The primary outcome is non-disabled functional outcome(assessed as modified Rankin Scale score of 0–1 at 90 days).Discussion HOPE is the first trial to investigate whether intravenous thrombolysis with alteplase offers benefits in patients who had an AIS presenting within 4.5–24hours,which has the potential to extend time window and expand eligible population for thrombolysis therapy.
