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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：3
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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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A dual attention-based deep learning model for lithology identificationwhile drilling 认领 引用 被引量：2
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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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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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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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An Improved Reinforcement Learning-Based 6G UAV Communication for Smart Cities 认领 引用 被引量：1
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作者 Vi Hoai Nam Chu Thi Minh Hue Dang Van Anh 《Computers, Materials & Continua》 SCIE EI 2026年第1期2030-2044,共15页
Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic top... Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments. 展开更多
关键词 UAV FANET smart cities reinforcement learning Q-learning
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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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Landslide susceptibility assessment integrating deep transfer learning and physical models in the Baihetan reservoir area,China 认领 引用 被引量：1
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作者 Ming Peng Yue Wang +5 位作者 Chenyi Ma Haojie Wang Shaoqiang Meng Zhenming Shi Weijiang Chu Jianrong Xu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第6期4382-4401,共20页
Reservoir landslides pose significant risks to hydropower projects,potentially leading to catastrophic disasters that threaten downstream lives and properties.Landslide susceptibility assessments are critical for effe... Reservoir landslides pose significant risks to hydropower projects,potentially leading to catastrophic disasters that threaten downstream lives and properties.Landslide susceptibility assessments are critical for effective regional disaster prevention and mitigation.However,the complexity,model uninterpretability,and data scarcity related to reservoir landslides,particularly when adapting models across diverse geographic regions,present significant challenges.This study proposes an interpretable Deep Transfer Learning model coupled with multi-source data and Physical methods(DTLP).The model is trained on multi-source data from the Three Gorges Reservoir Area(TGRA)and Lower Jinsha River Basin(LJRB),tested in Baihetan Reservoir Area(BHT),addressing the issues of limited data and cross-regional generalization.The physical method captures the effect of dynamic water level changes on slope stability.SHAP values are used to interpret the model,providing clear insights into its internal mechanisms.Results demonstrate that DTLP outperforms TrAdaBoost in data-scarce regions,achieving higher accuracy(AUC=0.953,Accuracy=0.941)with better feature generalization and susceptibility zone identification.Incorporating dynamic water level changes into the physical model enhances identification of high-susceptibility areas and reduces misclassifications.SHAP analysis indicates that elevation,lithology,and distance to river significantly influence the model decisions.Using TGRA as the source domain further validates the superiority of DTLP framework.However,due to the initial discrepancies between TGRA and the target domain,the transferability is constrained to some extent,resulting in models trained on LJRB data outperforming those trained on TGRA data. 展开更多
关键词 Landslide susceptibility assessment Deep transfer learning Reservoir landslides Infiniteslope model SHAP values
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Electrolyte hydration energy as a universal descriptor for ion-specific capacitance:insights from interpretable machine learning 认领 引用 被引量：1
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作者 Elham Rahmanian Ali Sajedi-Moghaddam +1 位作者 Mohammad Taha Homeizavi Seyed Hamed Aboutalebi 《Advanced Powder Materials》 EI CAS CSCD 2026年第1期157-168,共12页
The rational design of high-performance electrochemical energy storage devices critically depends on a fundamental understanding of ion-electrode interactions at the molecular scale.Herein,we employ interpretable mach... The rational design of high-performance electrochemical energy storage devices critically depends on a fundamental understanding of ion-electrode interactions at the molecular scale.Herein,we employ interpretable machine learning(ML)to reveal electrolyte hydration energy as a universal descriptor governing ion-specific capacitance in two-dimensional(2D)materials.Through explainable ML,we elucidate how ion hydration shell stability and size critically influence charge transport and storage at the electrode-electrolyte interface.Our analysis identifies hydration energy-not ionic size-as the primary factor dictating capacitance,challenging prevailing assumptions and providing quantifiable design rules for electrolyte selection.These insights offer a data-driven pathway to optimize 2D materials for supercapacitors and beyond,including batteries and electrocatalytic systems.This work demonstrates the power of explainable artificial intelligence in uncovering molecular-level mechanisms that accelerate the discovery and development of next-generation energy storage technologies. 展开更多
关键词 Machine learning Hydration energy Supercapacitors 2D materials Capacitance
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Constructing a Dynamic Trust Assessment Mechanism Combining Zero Knowledge Proof with Unsupervised Learning 认领 引用 被引量：1
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作者 Nai-Wei Lo Cheng-I Lin +2 位作者 Chih-Chieh Chang Chi-Yang Chang Tran Thi Luu Ly 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1343-1368,共26页
The growing frequency of malicious attacks on Internet of Things(IoT)devices has rendered conventional approaches with static label-dependent risk assessment models obsolete,especially when coping with unknown and con... The growing frequency of malicious attacks on Internet of Things(IoT)devices has rendered conventional approaches with static label-dependent risk assessment models obsolete,especially when coping with unknown and continuously evolving threats.To mitigate these challenges,a novel dynamic trust evaluation framework approach is proposed in this work.The proposed framework utilized unsupervised learning and zero-knowledge proofs to assess device risks in complex environments adaptively,with an accuracy rate of 98.96%for normal clustering and 95.39%for anomalies.K-means clustering algorithm is leveraged to distinguish risk patterns with an additional Decision Tree classification algorithm to analyze the distinguishing characteristics of the behaviors of normal and anomalous devices.The architecture is evaluated in a simulated environment based on real device interaction,with various malicious attacks proportions.In addition,Zero Trust Architecture is integrated into this novel framework to ensure no implicit trust exists between devices,which enforces trust assessment before any collaboration or data exchange. 展开更多
关键词 Zero knowledge proof zero trust architecture decision tree trust assessment unsupervised learning
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A self-supervised learning-based method for simultaneous motion artifacts removal and denoising of OCTA images 认领 引用 被引量：1
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作者 Yuzeng Xu Songwen Xu +1 位作者 Di Yang Yanmei Liang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第4期123-139,共17页
Motion artifacts and noise are key factors that determine image quality in optical coherence tomography angiography(OCTA).Although deep learning has emerged as an effective method for artifacts removal and denoising,i... Motion artifacts and noise are key factors that determine image quality in optical coherence tomography angiography(OCTA).Although deep learning has emerged as an effective method for artifacts removal and denoising,its generalization capability remains limited,and it is difficult to handle images with both motion artifacts and noise.To address this issue,we designed a Swin Transformer-based multi-scale motion artifacts and noise parallel removal network(ST-MANPR)to learn the nonlinear mapping between images with motion artifacts and noise and images without them,thereby achieving simultaneous suppression of motion artifacts and noise.The proposed network integrates the Swin window attention module,channel attention module(CAM),and adaptive multi-scale convolution denoising module(AMS-CNN)to enhance its capability in processing complex image features.At the same time,a hybrid loss function combining wavelet transform(WT)and mean square error(MSE)was introduced to facilitate high-frequency detail restoration.In addition,we constructed a dataset of OCTA images with and without motion artifacts and noise at multiple intensity levels.The created dataset was applied to train the network and the test results were evaluated both visually and numerically.The experimental results show that the proposed network can effectively remove motion artifacts and noise in OCTA images simultaneously. 展开更多
关键词 Optical coherence tomographic angiography self-supervised learning motion artifacts removal image denoising generalization
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AI and Deep Learning for Terahertz Ultra-Massive MIMO:From Model-Driven Approaches to Foundation Models 认领 引用 被引量：1
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作者 Wentao Yu Hengtao He +4 位作者 Shenghui Song Jun Zhang Linglong Dai Lizhong Zheng Khaled B.Letaief 《Engineering》 SCIE EI CSCD 2026年第1期14-33,共20页
This study explored the transformative potential of artificial intelligence(AI)in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output(UM-MIMO)systems.It begins by outlining the ch... This study explored the transformative potential of artificial intelligence(AI)in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output(UM-MIMO)systems.It begins by outlining the characteristics of terahertz UM-MIMO systems and identifies three primary challenges for transceiver design:computational complexity,modeling difficulty,and measurement limitations.The study posits that AI provides a promising solution to these challenges.Three systematic research roadmaps are proposed for developing AI algorithms tailored to terahertz UM-MIMO systems.The first roadmap,model-driven deep learning(DL),emphasizes the importance of leveraging available domain knowledge and advocates the adoption of AI only to enhance bottleneck modules within an established signal processing or optimization framework.Four essential steps are discussed:algorithmic frameworks,basis algorithms,loss function design,and neural architecture design.The second roadmap presents channel state information(CSI)foundation models,aimed at unifying the design of different transceiver modules by focusing on their shared foundation,that is,the wireless channel.The training of a single compact foundation model is proposed to estimate the score function of wireless channels,which serve as a versatile prior for designing a wide variety of transceiver modules.Four essential steps are outlined:general frameworks,conditioning,site-specific adaptation,and the joint design of CSI foundation models and model-driven DL.The third roadmap aims to explore potential directions for applying pretrained large language models(LLMs)to terahertz UM-MIMO systems.Several application scenarios are envisioned,including LLM-based estimation,optimization,search,network management,and protocol understanding.Finally,the study highlights open problems and future research directions. 展开更多
关键词 Terahertz communications Ultra-massive multiple-input multiple-output Model-driven deep learning Foundation models Large language models
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Integrated Mechanistic Analysis and Machine Learning Prediction of Slug Flow in Oil-Gas-Water Three-Phase Pipelines 认领 引用 被引量：1
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作者 Miao Li Ying Zhang +2 位作者 Yan Wang Haiyan Zhao Yonghu Zhang 《Fluid Dynamics & Materials Processing》 EI 2026年第3期150-171,共22页
Slug flow represents one of the most critical and operationally challenging regimes in oil-gas-water multiphase pipelines.To advance both mechanistic understanding and predictive capability,this study integrates physi... Slug flow represents one of the most critical and operationally challenging regimes in oil-gas-water multiphase pipelines.To advance both mechanistic understanding and predictive capability,this study integrates physical analysis with data-driven modeling to elucidate the conditions governing slug formation and to enable its rapid and accurate prediction.A systematic review of existing research is first undertaken to clarify the mechanisms responsible for slug initiation.The influences of gas superficial velocity,liquid velocity,liquid viscosity,liquid surface tension,and the axial component of gravity are examined to characterize their roles in interfacial instability and flow transition.Then,the effects of temperature,total flow rate,water cut,gas-liquid ratio,and pipeline inclination angle are quantitatively assessed,revealing the dominant trends that promote or inhibit slug development.Building on this foundation,a comprehensive three-phase oil-gas-water flow model is constructed.Numerical simulations are performed for 243 operating conditions encompassing a broad range of temperatures,water cuts,gas-liquid ratios,liquid flow rates,and inclination angles.These simulated cases constitute the training dataset for nine machine learning algorithms.To evaluate generalization performance,108 additional randomly generated operating conditions are predicted,covering temperatures of 80–150◦C,water cuts of 40–90%,gas-liquid ratios of 3–30,liquid flow rates of 100–200 t/d,and inclination angles of 5–15.Comparative validation reveals marked differences in predictive accuracy.The BP neural network achieves the highest accuracy,95%,substantially outperforming XGBoost,83.3%,Random Forest and Decision Tree,81.5%,Logistic Regression and Support Vector Machine,80.6%,K-Nearest Neighbor and Naive Bayes 78.7%,and K-Means,63%.Overall,the BP neural network demonstrates superior robustness and precision in predicting previously unseen operating conditions,effectively combining the physical consistency of mechanistic modeling with the efficiency and adaptability of machine learning approaches. 展开更多
关键词 Oil-gas-water multiphase flow undulating pipeline slug flow formation mechanism machine learning
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Efficient Full-Range Nonlinear Analyses of Structural Systems Based on Heterogeneous Graph Learning 认领 引用 被引量：2
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作者 Linghan Song Chen Wang Jiansheng Fan 《Engineering》 SCIE EI CSCD 2026年第4期320-336,共17页
Nonlinear analyses possess tremendous significance throughout the entire lifespans of civil structures.In recent years,the interest in leveraging deep learning(DL)to address the efficiency limitations of the tradition... Nonlinear analyses possess tremendous significance throughout the entire lifespans of civil structures.In recent years,the interest in leveraging deep learning(DL)to address the efficiency limitations of the traditional structural analysis methods has increased.However,full-range nonlinear analyses of different structures remain underresearched because of a lack of appropriate data representations and the failure to consider both internal structural information and external load conditions.A heterogeneous graph(HetG)representation scheme that can digitalize arbitrary structural systems with high fidelity is proposed in this study.Furthermore,a composite feature learning framework is developed to enable efficient full-range nonlinear analyses.This framework comprises two main components:①a heterogeneous graph neural network(GNN)-based module that encodes static features into embeddings with full structural semantics and②a sequence-to-sequence(Seq2Seq)module that predicts history-dependent responses using structural embeddings and external stimuli in an end-to-end manner.A computational model named structural analysis based on a graph neural network-nonlinear(StructGNN-N)is implemented based on the proposed methodology and is validated through numerical experiments involving real-world concrete structures.The results show that StructGNN-N successfully reproduces the full-range nonlinear responses of all nodes in the entire structure and exhibits excellent generalizability across structures with diverse topological designs and member configurations.Notably,the developed model achieves a computational efficiency level that is 1000 times greater than that of the traditional elastoplastic history analysis approach using the finite-element(FE)method.A parametric analysis and ablation studies demonstrate the effectiveness of the StructGNN-N architecture.Due to its superior accuracy and computational efficiency,the proposed method holds great potential for use in engineering applications,especially in the context of digital twins.This approach provides an inspiring path for simulating diverse engineering structures with accurate and comprehensive mechanical information in real time. 展开更多
关键词 Structural system analysis Heterogeneous graph-based deep learning Full-range response prediction Digital twin Smart structural engineering
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Non-sampling estimation of waste composition based on incineration flue gas pollutant fingerprints and machine learning approach 认领 引用 被引量：1
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作者 Yaping Qi Pinjing He +1 位作者 Fan Lü Hua Zhang 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第4期417-429,共13页
Understanding mixed waste composition is crucial for controlling pollutants in incineration flue gas.Real-time detection of pollutants is challenging owing to the complexity and heterogeneity of mixed waste,complicati... Understanding mixed waste composition is crucial for controlling pollutants in incineration flue gas.Real-time detection of pollutants is challenging owing to the complexity and heterogeneity of mixed waste,complicating incineration optimization and pollutant control.This study introduces a novel method to rapidly predict mixed waste composition using waste incineration flue gas“fingerprint”and machine learning regression models,bypassing traditional sampling processes.A comprehensive“fingerprint”dataset was established via incineration experiments with various mixed waste compositions,featuring multiple waste components and their associated flue gas pollutant concentrations.Predictive performance was compared for five machine learning models,including extreme gradient boosting tree(XGBOOST),K-nearest neighbor(KNN),random forest(RF),light gradient boosting machine(LGBM),and support vector regression(SVR).After feature importance analysis optimization,RF and XGBOOST models achieved the best performance,with R2values exceeding 0.92 for key waste types.The accuracy of the models in predicting waste composition was significantly improved compared to that without optimization.Beyond predictive accuracy,the proposed method enables near real-time waste composition estimation,offering significant advantages for dynamic adjustment of feedstock and operating parameters.This facilitates intelligent incineration control,enhances energy efficiency,and supports proactive pollution management at the emission source. 展开更多
关键词 Waste incineration Flue gas pollutant fingerprint Machine learning Feature important analysis Pollution control
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Machine learning models for predicting carbonation depth in fly ash concrete:performance and interpretability insights 认领 引用 被引量：1
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作者 Arslan Qayyum Khan Syed Ghulam Muhammad +1 位作者 Ali Raza Amorn Pimanmas 《Journal of Road Engineering》 EI CAS 2026年第1期74-90,共17页
This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,suc... This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO2,exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R2)values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R2 values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth. 展开更多
关键词 Fly ash concrete Carbonation depth Machine learning Ensemble models SHAP analysis
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Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage 认领 引用 被引量：3
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作者 Yu-Han Yang Yuan Li 《World Journal of Radiology》 2026年第1期30-45,共16页
BACKGROUND Spontaneous intracerebral hemorrhage(ICH)is a severe form of stroke with high early mortality,and hematoma enlargement(HE)occurs in roughly one-third of patients and strongly predicts poor outcomes.Quantita... BACKGROUND Spontaneous intracerebral hemorrhage(ICH)is a severe form of stroke with high early mortality,and hematoma enlargement(HE)occurs in roughly one-third of patients and strongly predicts poor outcomes.Quantitative image analysis using handcrafted radiomics and deep learning-derived features can capture hematoma and perihematomal edema(PHE)heterogeneity objectively that the combination of these approaches with clinical data may improve early prediction of HE and in-hospital mortality.AIM To evaluate and validate the predictive performance of hematoma-and PHE-derived features on non-contrast computed tomography via handcrafted radiomics and automatic deep learning analysis for prediction of early HE and hospital mortality in spontaneous ICH.METHODS Of 322 patients with basal ganglia ICHs were included retrospectively between June 2018 and June 2020,and assigned into the training cohort(n=225)and the testing cohort(n=97).We extracted features on hematoma and PHE subregions via handcrafted radiomics analysis manually and deep learning analysis of pretrained convolutional neural networks via transfer learning automatically.Support vector machine was adopted as the classifier for prediction of HE and hospital mortality.The clinical-radiological integrated models for HE and hospital mortality were constructed on clinical data and radiological signatures generated from the radiological models with the optimal area under the receiver operating characteristics curve in the testing cohort.RESULTS The clinical-radiological model combining clinical information and hematoma-and PHE-derived computed tomography features for prediction of HE implied an area under the receiver operating characteristics curve of 0.828 with 95%confidence interval of 0.714 to 0.942 with accuracy of 72.89%,sensitivity of 70.00%,and specificity of 74.52%in the testing cohort.The model integrating clinical and radiological features showed great identification performance for predicting hospital mortality,demonstrating significant classification and discrimination abilities after validation.CONCLUSION Quantitative radiomics features from hematoma and PHE regions on non-contrast computed tomography images showed good performance for predicting HE and hospital mortality in patients with ICH. 展开更多
关键词 Intracerebral hemorrhage Enlargement Computed tomography Radiomics Deep learning Hospital mortality
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Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey 认领 引用 被引量：1
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作者 Binglei Yue Aili Jiang +3 位作者 Chun Yang Junwei Lei Heng Liu Yin Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期1-28,共28页
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I... With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing. 展开更多
关键词 Channel State Information(CSI) human sensing human activity recognition deep learning
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