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Combination of problem-based and team-based learning in clinical teaching of plastic and reconstructive surgery 认领 引用
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作者 Ya Gao Chiakang Ho +4 位作者 Dongsheng Wen Yangdan Liu Qingfeng Li Danning Zheng Yifan Zhang 《Chinese Journal of Plastic and Reconstructive Surgery》 2025年第4期217-219,共3页
Background:This study explored the value of integrating problem-based learning(PBL)and team-based learning(TBL)methods into plastic and reconstructive surgery clinical practice.By addressing the challenges faced in tr... Background:This study explored the value of integrating problem-based learning(PBL)and team-based learning(TBL)methods into plastic and reconstructive surgery clinical practice.By addressing the challenges faced in traditional teachings,this study aimed to enhance educational outcomes and prepare students for real-world surgical scenarios,thereby improving patient care in this specialized field.Methods:Fifty undergraduate students majoring in clinical medicine at the Shanghai Jiao Tong University School of Medicine were selected as research subjects.They were randomly divided into experimental and control groups.The experimental group received the combined PBL-TBL teaching method,whereas the control group received the traditional teaching.The teaching effect was evaluated based on student satisfaction and academic performance.Results:The student satisfaction in the experimental group was higher than that of the control group(P<0.05).Subjective scoring for academic performance by instructors was higher in the experimental group than in the control group(P<0.05).Conclusion:The PBL and TBL combination had a significant effect when applied in plastic and reconstructive surgery clinical practice. 展开更多
关键词 Problem-based learning Team-based learning Plastic and reconstructive surgery Clinical practice
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TBL(Team-based learning)教学法在局解教学中的设计与评价 认领 引用 被引量：72
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作者 景玉宏 尹洁 +2 位作者 刘向文 张朗 宋焱峰 《中国高等医学教育》 2010年第9期96-98,共3页
为适应现代医学发展的要求,在日益增多的医学教学改革尝试中,TBL教学法引起人们的关注。本文通过在局部解剖学教学中开展TBL教学,并且和传统教学方法做了对比研究。结果提示在局部解剖学教学中采用TBL教学法有利于提高学生学习兴趣及解... 为适应现代医学发展的要求,在日益增多的医学教学改革尝试中,TBL教学法引起人们的关注。本文通过在局部解剖学教学中开展TBL教学,并且和传统教学方法做了对比研究。结果提示在局部解剖学教学中采用TBL教学法有利于提高学生学习兴趣及解决问题的能力,有利于动态评价学生的学习状态。 展开更多
关键词 医学教育 局解教学 TBL教学法
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长学制传染病教学中TBL(Team-Based Learning)模式的应用和改进 认领 引用 被引量：8
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作者 张晓红 麦丽 +3 位作者 赵志新 赖菁 周韵 高志良 《中国高等医学教育》 2014年第2期8-9,共2页
目的:研究TBL教学在八年制学生传染病教学中的应用成效及存在的问题,为改进和推广该教学方法提供参考依据。方法:对2006级八年制学生部分理论课采用TBL教学,进行闭卷考试及问卷调查。结论:与传统教学模式相比,TBL教学对提高学生学习兴趣... 目的:研究TBL教学在八年制学生传染病教学中的应用成效及存在的问题,为改进和推广该教学方法提供参考依据。方法:对2006级八年制学生部分理论课采用TBL教学,进行闭卷考试及问卷调查。结论:与传统教学模式相比,TBL教学对提高学生学习兴趣,培养学分分析问题、解决问题、沟通能力和团队协作精神以及提高考试成绩均有帮助。 展开更多
关键词 TBL 长学制学生 传染病学
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Quality or Quantity?Error-Informed Selective Online Learning With Gaussian Processes in Multi-Agent Systems 认领 引用 被引量：1
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作者 Zewen Yang Xiaobing Dai +2 位作者 Jiajun Cheng Yulong Huang Peng Shi 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1325-1338,共14页
Effective cooperation is pivotal in distributed learning for multi-agent systems,where the interplay between the quantity and quality of the machine learning models is crucial.This paper reveals the irrationality of i... Effective cooperation is pivotal in distributed learning for multi-agent systems,where the interplay between the quantity and quality of the machine learning models is crucial.This paper reveals the irrationality of indiscriminate inclusion of all models on agents for joint prediction,highlighting the imperative to prioritize quality over quantity in cooperative learning.Specifically,we present the first selective online learning framework for distributed Gaussian process(GP)regression,namely distributed error-informed GP(EIGP),that enables each agent to assess its neighboring collaborators,using the proposed selection function to choose the higher quality GP models with less prediction errors.Moreover,algorithmic enhancements are embedded within the EIGP,including a greedy algorithm(gEIGP)for accelerating prediction and an adaptive algorithm(aEIGP)for improving prediction accuracy.In addition,approaches for fast prediction and model update are introduced in conjunction with the error-informed quantification term iteration and a data deletion strategy to achieve real-time learning operations.Numerical simulations are performed to demonstrate the effectiveness of the developed methodology,showcasing its superiority over the stateof-the-art distributed GP methods with different benchmarks. 展开更多
关键词 Adaptive algorithm cooperative learning distributed learning Gaussian process regression greedy algorithm multi-agent system
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PowerVLM:基于Federated Learning与模型剪枝的电力视觉语言大模型 认领 引用 被引量：1
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作者 欧阳旭东 雒鹏鑫 +3 位作者 何绍洋 崔艺林 张中超 闫云凤 《全球能源互联网》 EI CSCD 北大核心 2026年第1期101-111,共11页
智能电网的快速发展衍生出多模态、多源异构的海量电力数据,给人工智能模型在复杂电力场景感知带来了挑战,同时行业数据的敏感性和隐私保护需求进一步限制了通用模型在电力领域的跨场景迁移能力。对此,提出了一种基于Federated Learnin... 智能电网的快速发展衍生出多模态、多源异构的海量电力数据,给人工智能模型在复杂电力场景感知带来了挑战,同时行业数据的敏感性和隐私保护需求进一步限制了通用模型在电力领域的跨场景迁移能力。对此,提出了一种基于Federated Learning与模型剪枝的电力视觉语言大模型。提出了一种基于类别引导的电力视觉语言大模型PowerVLM,设计了类别引导增强模块,增强模型对电力图文数据的理解和问答能力;采用FL的强化学习训练策略,在满足数据隐私保护下,降低域间差异对模型性能的影响;最后,提出了一种基于信息决议的模型剪枝算法,可实现低训练参数的模型高效微调。分别在变电巡检、输电任务、作业安监3种典型电力场景开展实验,结果表明,该方法在电力场景多模态问答任务中的METEOR、BLEU和CIDEr等各项指标均表现优异,为电力场景智能感知提供了新的技术思路和方法支撑。 展开更多
关键词 智能电网 人工智能 视觉语言大模型 Federated Learning 模型剪枝
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Machine learning-based investigation of uplift resistance in special-shaped shield tunnels using numerical finite element modeling 认领 引用 被引量：4
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作者 ZHANG Wengang YE Wenyu +2 位作者 SUN Weixin LIU Zhicheng LI Zhengchuan 《土木与环境工程学报（中英文）》 CSCD 北大核心 2026年第1期1-13,共13页
The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combi... The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance. 展开更多
关键词 special-shaped tunnel shield tunnel uplift resistance numerical simulation machine learning
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Predicting lymph node metastasis in colorectal cancer using caselevel multiple instance learning 认领 引用 被引量：1
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作者 Ling-Feng Zou Xuan-Bing Wang +4 位作者 Jing-Wen Li Xin Ouyang Yi-Ying Luo Yan Luo Cheng-Long Wang 《World Journal of Gastroenterology》 SCIE CAS 2026年第1期110-125,共16页
BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning ofte... BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation. 展开更多
关键词 Colorectal cancer Lymph node metastasis Deep learning Multiple instance learning Histopathology
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A dual attention-based deep learning model for lithology identificationwhile drilling 认领 引用 被引量：3
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作者 Jie Chen Zhen Gui +6 位作者 Yichao Rui Xusheng Zhao Xiaokang Pan Qingfeng Wang Yuanyuan Pu Zheng Li Maoyi Liu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1177-1192,共16页
Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex ge... Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex geological conditions,limiting their accuracy in challenging environments.To address these challenges,a deep learning model for lithology identificationwhile drilling is proposed.The proposed model introduces a dual attention mechanism in the long short-term memory(LSTM)network,effectively enhancing the ability to capture spatial and channel dimension information.Subsequently,the crayfishoptimization algorithm(COA)is applied to optimize the model network structure,thereby enhancing its lithology identificationcapability.Laboratory test results demonstrate that the proposed model achieves 97.15%accuracy on the testing set,significantlyoutperforming the traditional support vector machine(SVM)method(81.77%).Field tests under actual drilling conditions demonstrate an average accuracy of 91.96%for the proposed model,representing a 14.31%improvement over the LSTM model alone.The proposed model demonstrates robust adaptability and generalization ability across diverse operational scenarios.This research offers reliable technical support for lithology identification while drilling. 展开更多
关键词 Lithology identificationwhile drilling Deep learning Dual attention mechanism Metaheuristic algorithm Field applications
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Transformer-based data-driven reinforcement learning for collision-free integrated planning and control of multiple UAVs 认领 引用 被引量：1
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作者 Wei DONG Yue LIU +2 位作者 Xiaoyu GUO Chunyan WANG Zhengtao DING 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第7期110-124,共15页
This paper studies the challenging collision-free planning and control problem for multiple Unmanned Aerial Vehicles(UAVs)with complex dynamics.A data-driven reinforcement learning framework is constructed to address ... This paper studies the challenging collision-free planning and control problem for multiple Unmanned Aerial Vehicles(UAVs)with complex dynamics.A data-driven reinforcement learning framework is constructed to address this challenge by combining the transformer-based learned dynamics and iterative Linear Quadratic Regulator(iLQR)optimization.First,each UAV employs an independent transformer network,Multi-Head Self-Attention(MHSA),and residual connections to model local dynamics from online collected data.This approach enables efficient Jacobian computations via parallelization by exploiting the inherent block-diagonal structure in the decoupled dynamics.Then,to avoid inter-UAV and UAV-obstacle collision in the cooperative flight,logarithmic barrier functions are incorporated into the cost function of iLQR.The block-diagonal approximation of the Hessian is employed to overcome the coupling induced by the barrier terms and preserve the computational tractability during the backward pass.Specifically,the proposed framework possesses robust collision avoidance capabilities in solving the multi-UAV planning and control problem.Finally,simulation results demonstrate the effectiveness and superiority in convergence speed and accuracy of the proposed framework. 展开更多
关键词 Multiple UAVs Planning and control Reinforcement learning Transformer Collision avoidance
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Predicting rare earth extraction efficiency with organophosphorus ligands:A data-driven machine learning approach 认领 引用 被引量：1
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作者 Qihan Zhang Haitao Wang +2 位作者 Ziyi Liu Zhaomin Hao Wuping Liao 《Journal of Rare Earths》 SCIE EI CAS CSCD 2026年第7期2204-2214,I0007,共11页
Organophosphorus ligands are widely employed as extractants in industrial rare-earth(RE)separation;however,their design and optimization have largely been guided by empirical methodologies rather th an systematic,rati... Organophosphorus ligands are widely employed as extractants in industrial rare-earth(RE)separation;however,their design and optimization have largely been guided by empirical methodologies rather th an systematic,rational approaches.In this work,we present a data-driven machine learning framework for predicting the distribution ratios(D)of RE elements in extraction processes using various organophosphorus ligands.To support this effort,a curated database comprising over 3500 experimental D measurements was established,encompassing 43 distinct ligands and 16 RE elements(excluding radioactive promethium)under varied extraction conditions.By integrating ligand descriptors,metal ion properties,and extraction parameters,we developed a convolutional neural network(CNN)model that achieves robust performance,with R2values of approximately 0.98 for training and 0.83 for testing.Our analysis further identified key factors-such as aqueous pH,ligand structural fragments,partial charges,metal ionic radii and topological features-that govern extraction behavior and correlate with specific mechanisms.Finally,the predictive capability of model was validated by accurately forecasting the D value of a newly synthesized ligand HA. 展开更多
关键词 Rare earths Extraction Machine learning Organophosphorus ligands Distribution ratios
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Learning Laws for Deep Convolutional Neural Networks With Guaranteed Convergence 认领 引用 被引量：1
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作者 Sitan Li Chien Chern Cheah 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期170-185,共16页
Convolutional neural networks(CNNs)have shown remarkable success across numerous tasks such as image classification,yet the theoretical understanding of their convergence remains underdeveloped compared to their empir... Convolutional neural networks(CNNs)have shown remarkable success across numerous tasks such as image classification,yet the theoretical understanding of their convergence remains underdeveloped compared to their empirical achievements.In this paper,the first filter learning framework with convergence-guaranteed learning laws for end-to-end learning of deep CNNs is proposed.Novel update laws with convergence analysis are formulated based on the mathematical representation of each layer in convolutional neural networks.The proposed learning laws enable concurrent updates of weights across all layers of the deep convolutional neural network and the analysis shows that the training errors converge to certain bounds which are dependent on the approximation errors.Case studies are conducted on benchmark datasets and the results show that the proposed concurrent filter learning framework guarantees the convergence and offers more consistent and reliable results during training with a trade-off in performance compared to stochastic gradient descent methods.This framework represents a significant step towards enhancing the reliability and effectiveness of deep convolutional neural network by developing a theoretical analysis which allows practical implementation of the learning laws with automatic tuning of the learning rate to guarantee the convergence during training. 展开更多
关键词 Convergence convolution neural networks(CNNs) end-to-end learning online learning
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Enhancing rapeseed biomass and yield estimation with ensemble learning and synergistic multidimensional features 认领 引用 被引量：1
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作者 Yanni ZHANG Xiaoyu CHAI +2 位作者 Jinpeng HU Yaxiao NIU Lizhang XU 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2026年第5期499-516,I0007-I0011,共18页
Accurate rapeseed yield and biomass estimation at the meter scale prior to harvest is crucial for precision harvesting.However,there is a scarcity of structured research on the estimation of rapeseed biomass yield.Thi... Accurate rapeseed yield and biomass estimation at the meter scale prior to harvest is crucial for precision harvesting.However,there is a scarcity of structured research on the estimation of rapeseed biomass yield.This study aims to address this gap by focusing on rapeseed in Jiangsu Province.Multispectral and RGB images captured by unmanned aerial vehicles(UAVs)were taken during key growth stages(budding,flowering,and podding stages).Using the extracted multidimensional features,we developed biomass-yield estimation models using four machine learning techniques.Subsequently,we employed ensemble learning with multidimensional,multi-stage data and used Shapley additive explanation(SHAP)for feature contribution analysis,thereby constructing a framework for predicting rapeseed harvest characteristics with high estimation accuracy and interpretability.Our analysis indicates that spectral‒texture is the most effective feature combination for biomass estimation,whereas the optimal combination for yield estimation includes three-dimensional(3D)spectral‒textural‒structural features.The synergy of these features,coupled with an ensemble learning model,significantly enhanced the accuracy of rapeseed biomass-yield estimation(biomass:coefficient of determination(R2)=0.72,relative root mean square error(rRMSE)=14.35%;yield:R2=0.68,rRMSE=13.67%).The proposed model also achieved stable prediction results across the variety‒density interaction.Overall,this study presents an accurate and generalizable approach for estimating rapeseed biomass yield across various planting patterns,offering new insights for precision harvesting. 展开更多
关键词 Ensemble learning Decision-making Feature synergy Temporal fit Planting pattern
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Quantifying Global Black Carbon Aging Responses to Emission Reductions Using a Machine Learning-based Climate Model 认领 引用 被引量：1
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作者 Wenxiang SHEN Minghuai WANG +5 位作者 Junchang WANG Yawen LIU Xinyi DONG Xinyue SHAO Man YUE Yaman LIU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第2期361-372,I0004-I0009,共12页
Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon(BC) aging to emission changes remains unclear. Using the Community Atmosphere Model versi... Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon(BC) aging to emission changes remains unclear. Using the Community Atmosphere Model version 6 with a machine-learning-integrated four-mode version of the Modal Aerosol Module, we quantify global BC aging responses to emission reductions for 2011–2018 and for 2050 and 2100 under carbon neutrality. During 2011–18, global trends in BC aging degree(mass ratio of coatings to BC, RBC) exhibited marked regional disparities, with a significant increase in China(5.4% yr-1), which contrasts with minimal changes in the USA, Europe, and India. The divergence is attributed to opposing trends in secondary organic aerosol(SOA) and sulfate coatings, driven by regional changes in the emission ratios of corresponding coating precursors to BC(volatile organic compounds-VOCs/BC and SO2/BC). Projections under carbon neutrality reveal that RBC will increase globally by 47%(118%) in 2050(2100), with strong convergent increases expected across major source regions. The RBC increase, primarily driven by enhanced SOA coatings due to sharper BC reductions relative to VOCs, will enhance the global BC mass absorption cross-section(MAC) by 11%(17%) in 2050(2100).Consequently, although the global BC burden will decline sharply by 60%(76%), the enhanced MAC partially offsets the magnitude of the decline in the BC direct radiative effect, resulting in the moderation of global BC DRE decreases to 88%(92%) of the BC burden reductions in 2050(2100). This study highlights the globally enhanced BC aging and light absorption capacity under carbon neutrality, thereby partly offsetting the impact of BC direct emission reductions on future changes in BC radiative effects globally. 展开更多
关键词 black carbon aging trend emission reduction carbon neutrality machine learning
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RCTUnet:a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction 认领 引用 被引量：1
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作者 Ting LI Yang LIU +10 位作者 Haikuan FENG Meiyan SHU Hao YANG Yuanyuan FU Xin XU Yinghao LIN Hongbo QIAO Wei GUO Xinming MA Lei SHI Jibo YUE 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2026年第5期517-536,共20页
Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between ... Accurate quantification of crop residue cover(CRC)is crucial for monitoring and evaluating conservation tillage practices,yet it poses a significant image segmentation challenge.The subtle visual distinctions between fragmented residue and soil,compounded by variable illumination and shadows in field imagery,often lead to poor segmentation performance.To overcome these limitations,we introduce RCTUnet,a novel deep learning architecture designed for robust crop-residue-soil segmentation and precise CRC estimation.RCTUnet’s architecture synergistically integrates three key components:(1)a ResNet50 backbone for deep,multi-scale feature extraction;(2)a convolutional block attention module(CBAM)to adaptively focus on salient residue features across both channel and spatial dimensions;and(3)a transformer-based global context fusion module(GCFM)to model long-range spatial dependencies,which is critical for interpreting heterogeneous residue patterns.We evaluated RCTUnet on a dataset of 1220 field-acquired images spanning four typical crop rotations.Experimental results show that,compared to traditional models:(1)RCTUnet achieves significantly higher crop-residue-soil segmentation accuracy than classic models including Unet,Unet++,DeepLabV3,segmentation network(SegNet),and fully convolutional network(FCN),with improvements of 3.24%,3.42%,4.88%,8.28%,and 6.05%in overall accuracy,respectively;(2)RCTUnet yields superior residue-soil segmentation performance,with increases in residue recall of 7.67%,7.37%,14.09%,27.05%,and 16.91%,respectively;(3)RCTUnet shows enhanced CRC estimation accuracy,achieving a root mean square error(RMSE)of 4.875,representing a 45.5%improvement over Unet(RMSE=8.941).These results demonstrate the efficacy of our hybrid approach,which combines deep hierarchical features,dual-domain attention,and global context modeling.RCTUnet provides a robust and reliable tool for automated CRC assessment,advancing the capabilities of in-field agricultural monitoring. 展开更多
关键词 Deep learning Crop residue cover Image segmentation Conservation tillage
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Microseismic signal processing and rockburst disaster identification:A multi-task deep learning and machine learning approach 认领 引用 被引量：1
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作者 Chunchi Ma Weihao Xu +3 位作者 Xuefeng Ran Tianbin Li Hang Zhang Dongwei Xing 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期441-456,共16页
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id... Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters. 展开更多
关键词 Underground engineering Microseismic signal processing Deep learning Multi-task Rockburst identification
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Adoption of tree-based machine learning algorithms and CPT data for liquefaction potential assessment 认领 引用 被引量：1
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作者 Tian Shuang Si Pan +2 位作者 Tang Liang Ling Xianzhang Liu Yanfang 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2026年第2期347-360,共14页
Soil liquefaction under strong earthquakes is the primary cause of damage to foundations and superstructures.Predicting the potential for seismic-induced soil liquefaction is key to preventing related disasters.This s... Soil liquefaction under strong earthquakes is the primary cause of damage to foundations and superstructures.Predicting the potential for seismic-induced soil liquefaction is key to preventing related disasters.This study compares four machine learning(ML)models for soil liquefaction potential based on cone penetration test datasets:decision tree,random forest,gradient boosting,and extreme gradient boosting.The database was collected from previously published research and includes information on earthquake moment magnitude,peak ground acceleration,depth of soil layer,total vertical stresses,effective vertical stresses,and cone tip stresses.The predictive capabilities of the developed models were evaluated using overall accuracy,precision,recall,F-measure,and receiver operating characteristic curves.The results showed that the extreme gradient boosting model exhibited the highest efficacy.A subsequent analysis of feature importance demonstrated that cone tip stresses exerted the most significant influence on soil liquefaction potential.In a final comparative assessment with conventional liquefaction discrimination theory methods,the study revealed that the Robertson method yielded a higher success rate for liquefaction cases,the Olsen method was more successful in non-liquefaction cases,and the ML approach manifested superior success rates in both liquefaction and non-liquefaction cases. 展开更多
关键词 liquefaction potential cone penetration test machine learning extreme gradient boosting
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Research on Low Visibility Forecast Model of Sea Fog in Beibu Gulf Based on Attention Mechanism-Embedded LSTM Deep Learning 认领 引用 被引量：1
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作者 ZHENG Feng-qin LI Jie +1 位作者 JIN Long LU Qian-qian 《Journal of Tropical Meteorology》 SCIE CAS CSCD 2026年第2期176-185,共10页
To address the complexities associated with forecasting low-probability,low-visibility fog events and the underlying nonlinear interdependencies among various influencing variables,we present an attention mechanism-em... To address the complexities associated with forecasting low-probability,low-visibility fog events and the underlying nonlinear interdependencies among various influencing variables,we present an attention mechanism-em-bedded long short-term memory(ATT-LSTM)deep learning model for sea fog visibility hazard prediction.This archi-tecture seamlessly incorporates ATT into the conventional LSTM neural network framework.This integration enables the model to adaptively assign weights to the input features,thereby distinguishing between salient and non-salient variables.This targeted allocation enhances the contribution of considerable factors within the LSTM forecasting algorithm,opti-mizes input data,and assigns varying levels of attention to each variable.Consequently,the model substantially mitigates prediction errors in multivariate scenarios.An empirical analysis employing an independent dataset encompassing 303 foggy days over a biennial period confirmed the superior performance of the proposed ATT-LSTM model.Comparative evaluations with LSTM,logistic classification regression,and support vector machine classification regression models revealed that the ATT-LSTM model achieved a recall rate of 37%,a precision rate of 48%,an accuracy rate of 91%,and a threat score(TS)of 0.26.Among the assessed methodologies,the ATT-LSTM model outperformed the others in terms of recall,accuracy,and TS metrics.These findings confirm that the ATT-LSTM model offers a potent and innovative deep learning approach for enhancing the accuracy of low-visibility sea fog hazard predictions. 展开更多
关键词 deep learning low visibility attention mechanism prediction model low-probability event
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Machine learning-assisted design of lightweight refractory high-entropy alloys: A comprehensive review 认领 引用 被引量：1
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作者 Lei Chen Gang Qin +4 位作者 Yao Chen Qi Wang Liang Wang Yanqing Su Ruirun Chen 《Metals Advances》 SCIE EI CAS CSCD 2026年第2期26-47,共22页
Lightweight refractory high-entropy alloys(LRHEAs)represent an emerging class of structural materials that integrate low density with exceptional strength and outstanding high-temperature stability,positioning them as... Lightweight refractory high-entropy alloys(LRHEAs)represent an emerging class of structural materials that integrate low density with exceptional strength and outstanding high-temperature stability,positioning them as promising candidates for aerospace and advanced industrial applications.Nevertheless,the design of LRHEAs is challenged by their vast compositional space,complex multi-objective performance trade-offs,and the inefficiency of conventional trial-and-error experimental approaches.In recent years,machine learning(ML)has emerged as a transformative tool in this domain,offering the capacity to analyze high-dimensional datasets and uncover hidden correlations between composition,processing,microstructure,and properties.This review systematically examines both conventional design strategies-including empirical parameters,phase diagram calculations,and first-principles simulations-and the emerging ML-aided design framework,with a focus on bridging traditional knowledge and data-driven methodologies.We critically survey recent advances in ML applications across three key areas:compositional optimization,mechanistic interpretation,and atomic-scale simulation.Target-driven ML models facilitate efficient navigation of the alloy design space,while interpretable algorithms integrated with atomic simulations provide fundamental insights into strengthening and toughening mechanisms.The review concludes by summarizing current achievements and identifying persistent challenges related to data scarcity,model transferability,and physical interpretability.Looking forward,we envision that a deeper integration of high-throughput experiments,multi-scale simulations,and artificial intelligence will establish a robust,systematic,and accelerated design paradigm for next-generation LRHEAs. 展开更多
关键词 Lightweight refractory high-entropy alloys Machine learning Interpretability analysis Properties optimization Alloy design
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A unified framework for multi-agent formation with a non-repetitive leader:Adaptive control and iterative learning control 认领 引用 被引量：1
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作者 Wei JIANG Yiyang CHEN +1 位作者 Hongtian CHEN Bart De SCHUTTER 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第7期139-153,共15页
Formation Tracking(FT)control is aimed at handling cooperative tasks in Multi-A gent Systems(MASs)to achieve desired performance.In these tasks,the leader's input is generally nonzero and unknown to all followers,... Formation Tracking(FT)control is aimed at handling cooperative tasks in Multi-A gent Systems(MASs)to achieve desired performance.In these tasks,the leader's input is generally nonzero and unknown to all followers,i.e.,its trajectory can be arbitrary and non-repetitive.In this paper,the additive property of linear systems is exploited to develop a unified framework for FT tasks of MASs,consisting of Adaptive Observer-based Control(AOC)and Iterative Learning Control(ILC).An AOC controller is employed to guarantee a fixed-shape formation between the leader and followers during the whole process,which reserves the initial condition for ILC.And ILC is used to improve the FT performance of certain repetitive tasks(followers rotating around the leader)over the trials.This gives rise to a fully distributed algorithm working for a directed communication graph containing a spanning tree without requiring any eigenvalue information from the Laplacian matrix of the graph,which enables its application to MASs with a large number of agents.Comparison is made via a numerical simulation to show that the proposed combined AOC-ILC algorithm has less FT error than pure AOC(without ILC),which validates the feasibility and efficacy of this algorithm. 展开更多
关键词 Adaptive observers Formation control Iterative learning control Multi-agent systems Unknown non-repetitive leader
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Data-driven insights into nonradical activation mechanisms for biochar inverse design:A synergistic approach using DFT and machine learning with meta-analysis 认领 引用 被引量：1
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作者 Honglin Chen Rupeng Wang +1 位作者 Zixiang He Shih-Hsin Ho 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第2期708-712,共5页
Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a disti... Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design. 展开更多
关键词 Machine learning DFT Biochar-based catalysts Nonradical activation Peroxymonosulfate Inverse design Meta-analysis
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