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An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling 认领 引用 被引量：2
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作者 Lei Liu Wei Li +7 位作者 Jian Gao Da-Li Yue De-Gang Wu Wu-Rong Wang Jin Lin Zhi-Bo Li Qian Zhong Jia-Gen Hou 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1754-1772,共19页
Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we... Sedimentary facies modeling is a critical approach for understanding geological phenomena,yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization.In this study,we innovatively propose an interpretable attention-guided generative adversarial network framework with dual-domain learning,which achieves precise sedimentary facies modeling under the constraints of well facies and soft probability data.Specifically,we first effectively extract and preserve prior information of sedimentary facies models from both spatial and frequency domain perspectives.Then,during simulation,to enhance the capability of the network model for finely characterizing complex heterogeneous models,cross-spatial attention mechanisms are designed to effectively capture short-range and long-range dependencies between multi-scale pattern features.Additionally,through systematic feature map visualization analysis,we elucidate the processes of conditional fitting and complex sedimentary facies model reconstruction,intuitively demonstrating the functional mechanisms of each module.Finally,systematic experiments are conducted on multiple datasets to validate the effectiveness of the proposed method.The results demonstrate that the generated sedimentary facies models exhibit high consistency with training datasets in terms of visual realism and statistical indicators.Quantitative comparisons reveal remarkable performance of the method,achieving low Wasserstein distance(0.09),Kernel Inception Distance(0.0017)and Kernel Maximum Mean Discrepancy(0.21).These findings further confirm the high realism of the generated realizations regarding pattern features.This study offers a reliable and practical method for geological reservoir modeling,thereby advancing quantitative,precise geological research with broad application prospects. 展开更多
关键词 Sedimentary facies models Attention-guided generative adversarial network Interpretable framework Sedimentary patterns Multi-condition modeling
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Ecosystem service models are indeed being validated:A response to Pereira et al.(2025) 认领 引用 被引量：1
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作者 James M.Bullock Danny A.P.Hooftman +1 位作者 John W.Redhead Simon Willcock 《Geography and Sustainability》 CSCD 2026年第1期247-248,共2页
In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation ... In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade. 展开更多
关键词 evaluation mapping modeling es model ecosystem services validation
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Dynamic modeling of spatial variable stator vane mechanism using modified Lagrange multiplier method 认领 引用 被引量：1
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作者 Ke HE Kaiyi HUANG +2 位作者 Zhen LI Shuhui HU Zhinan ZHANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期251-271,共21页
The Variable Stator Vanes(VSV)system ensures the smooth operation of the highpressure compressor by adjusting the vane angles to prevent surge,and the dynamic behavior of its multistage vanes directly affects its serv... The Variable Stator Vanes(VSV)system ensures the smooth operation of the highpressure compressor by adjusting the vane angles to prevent surge,and the dynamic behavior of its multistage vanes directly affects its service performance.To investigate the dynamic behavior of the spatial VSV multi-vane mechanism,a positional constraint equation for the VSV mechanism was established,and the numerical expressions of the Jacobian matrices for different kinematic pair constraint equations were derived.The Lagrange multiplier method was modified for spatial rotation,and an ideal dynamic model of the spatial VSV multi-vane mechanism was developed.The computational results indicate that the dynamic behavior of different vanes within the same stage is similar,and the constraint moment experienced by vanes at different positions has a linear relationship with their centroid coordinates.This study expands the dynamic modeling methods for spatial mechanisms and provides a foundation for researching the frictional dynamic behavior of VSV mechanisms with clearance. 展开更多
关键词 Dynamic characteristics Dynamic models Lagrange multipliers Spatial dynamic modeling:VSV
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A peridynamics modeling approach for pre-cracked rock cracking processes under impact by integrating Drucker-Prager plasticity model and efficient contact model 认领 引用 被引量：2
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作者 Jingzhi Tu Nengxiong Xu Gang Mei 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期179-195,共17页
In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical propert... In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical properties of rocks,the cracking processes of pre-cracked rocks have been extensively studied using numerical modeling methods.The peridynamics(PD)exhibits advantages over other numerical methods due to the absence of the requirements for remeshing and external crack growth criterion.However,for modeling pre-cracked rock cracking processes under impact,current PD implementations lack generally applicable rock constitutive models and impact contact models,which leads to difficulties in determining rock material parameters and efficiently calculating impact loads.This paper proposes a non-ordinary state-based peridynamics(NOSBPD)modeling method integrating the Drucker-Prager(DP)plasticity model and an efficient contact model to address the above problems.In the proposed method,the Drucker-Prager plasticity model is integrated into the NOSBPD,thereby equipping NOSBPD with the capability to accurately characterize the nonlinear stress-strain relationship inherent in rocks.An efficient contact model between particles and meshes is designed to calculate the impact loads,which is essentially a coupling method of PD with the finite element method(FEM).The effectiveness of the proposed NOSBPD modeling method is verified by comparison with other numerical methods and experiments.Experimental results indicate that the proposed method can effectively and accurately predict the 3D cracking processes of pre-cracked cracks under impact loading,and the maximum principal stress is the key driver behind wing crack formation in pre-cracked rocks. 展开更多
关键词 Pre-cracked rocks Cracking processes Non-ordinary state-based peridynamics (NOSBPD) Drucker-Prager plasticity model Efficient contact model
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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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Evaluating the Shanghai Typhoon Model against State-of-the-Art Machine-Learning Weather Prediction Models:A Case Study for Typhoon Danas(2025) 认领 引用 被引量：1
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作者 Zeyi NIU Wei HUANG +5 位作者 Yuhua YANG Mengqi YANG Lin DENG Haibo WANG Hong LI Xu ZHANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第4期744-750,共7页
This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and bou... This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and boundary conditions,SHTM now leverages large-scale constraints from machine-learning weather prediction(MLWP)models,resulting in an ML–physics hybrid framework.During Typhoon Danas(2025),the hybrid SHTM achieves substantially lower track errors than both the advanced ECMWF Integrated Forecasting System(IFS)and leading MLWP models such as PanGu and FuXi.Furthermore,the hybrid SHTM consistently maintains mean track errors below 200 km up to a forecast lead time of 108 hours,representing a significant advancement in forecast accuracy.In addition,this study highlights the technical roadmap for transitioning from a physics-based typhoon model to a fully data-driven ML typhoon forecast system.It also emphasizes that advances in the physical modeling framework provide a critical foundation for further improving the performance of future data-driven ML typhoon models. 展开更多
关键词 Shanghai Typhoon Model(SHTM) machine-learning weather prediction machine learning-physics hybrid model
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Data-driven computing ligament loading mechanisms:integration of the computational ligament mechanics models with deep learning 认领 引用 被引量：1
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作者 Datao Xu Huiyu Zhou +7 位作者 Yi Yuan Zanni Zhang Tianle Jie Zhifeng Zhou Zixiang Gao Liangliang Xiang Meizi Wang Yaodong Gu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期628-672,共45页
Ankle injuries account for more than 25%of sports-related injuries.However,there is a lack of computational mechanics modeling and assessment tools for the ligament loading mechanism(LLM)caused by ankle injury.This st... Ankle injuries account for more than 25%of sports-related injuries.However,there is a lack of computational mechanics modeling and assessment tools for the ligament loading mechanism(LLM)caused by ankle injury.This study combines medical imaging data to construct the subject-specific ankle musculoskeletal model,which considers the subject's individualized characteristics and ligamentous attributes.Furthermore,we developed the structural constitutive model to restore the nonlinear short-term viscoelastic properties of the ligament-dense connective tissue,which can more realistically revert the LLM and reveal the mechanical properties of ankle injury.Based on the computational ligament mechanics(CLM)model,we developed a deep learning-based prediction model to predict LLM by CLM data-driven modeling.The modeling simulation results are highly consistent with the calculation results from the dual fluoroscopic imaging system,which demonstrated that the CLM model has high accuracy.The data-driven modeling performs exceptionally well in predicting ligament loading forces.The findings indicate that the constructed CLM data-driven model has the potential to enhance the accuracy and safety of ankle rehabilitation robots,while also providing personalized,dynamically adjusted rehabilitation training programs.The proposed comprehensive solutions would bring benefits to more patients with sports injuries and the general rehabilitation population,and promote the development and advancement of the research field of CLM and biomechanical variable prediction. 展开更多
关键词 Computational ligament mechanics Subject-specific musculoskeletal model Structural constitutive model Ankle ligament injury mechanisms Biomechanical variable prediction
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Domain-Specific Large Language Model for Maintenance Decision-Making on Wind Farms by Labeled-Data-Supervised Fine-Tuning 认领 引用 被引量：1
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作者 Dongming Fan Meng Liu +5 位作者 Yi Shao Linchao Yang Yiliu Liu Yue Zhang Yi Ren Zili Wang 《Engineering》 SCIE EI CSCD 2026年第5期343-361,共19页
Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs.However,conventional maintenance decision-making approaches a... Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs.However,conventional maintenance decision-making approaches are time-consuming and have poor flexibility and adaptability to various scenarios.This study addressed these challenges by using a large language model(LLM)to understand,generate,and plan maintenance strategies for wind farms characterized by various failure modes and maintenance costs.A labelled-data-supervised fine-tuning LLM for maintenance,named LLM4M,is proposed.The proposed LLM4M model is trained on an extensive dataset of mathematical programs for maintenance to generate optimal strategies for wind farms.Compared with other large parameter LLMs,the fine-tuned LLM4M model demonstrates remarkable accuracy,with an error of approximately 2%from the optimal strategy.In addition,the generalization of the proposed LLM4M model has achieved remarkable results.If the LLM4M model correctly generates the maintenance strategy,the maintenance cost deviates from the optimal solution by only approximately 5%.Furthermore,phase transition behavior is observed,which provides considerable guidance for the development of domain-specific LLMs for the maintenance domain. 展开更多
关键词 Large language model Maintenance decision-making Wind farms Fine-tuning
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Time-dependent behavior of deep roadway surrounding rock considering damage induced by excavation and mining disturbances:Experiments,modeling,and simulation 认领 引用 被引量：1
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作者 Qingzhe Cui Rongbin Hou +4 位作者 Zhenhua Li Feng Du Xu Chen Boyang Zhang Lielie Li 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第2期439-456,共18页
In deep coal mining,surrounding rock is subjected to both high in-situ stress and intense mining disturbances,leading to significant time-dependent behavior.Accurately capturing this behavior is essential for predicti... In deep coal mining,surrounding rock is subjected to both high in-situ stress and intense mining disturbances,leading to significant time-dependent behavior.Accurately capturing this behavior is essential for predicting long-term roadway stability,necessitating the development of a reliable constitutive creep model and numerical simulation approach.In this study,creep experiments were conducted on pre-damaged rock with varying initial damage levels to investigate the time-dependent mechanical properties.Based on the experimental results,an accelerated-creep criterion was proposed,and an elastic-viscoplastic creep damage model(EVPCD)was established that simultaneously considers the effects of time-dependent damage and instantaneous damage caused by stress disturbances on rock creep behavior.Subsequently,the effectiveness of the proposed creep model was verified using experimental data,and the secondary development of the EVPCD model was completed based on the FLAC3D platform.Following this,a long-term stability analysis method of deep surrounding rock that accounts for excavation-and mining-induced disturbances was proposed.Using the main roadway of Xutuan Coal Mine as a case study,numerical simulations were carried out to investigate the time-dependent deformation and failure characteristics of the surrounding rock following excavation and mining disturbance.Combined with on-site monitoring of the surrounding rock damage areas,the results indicate that the EVPCD outperforms the CVISC and Nishihara models in predicting the time-dependent behavior of deep surrounding rock. 展开更多
关键词 Initial damage Time-dependent damage Creep model Numerical implementation Damage evolution
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Spatial response and prediction model for blasting-induced vibration in a deep double-line tunnel 认领 引用 被引量：1
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作者 Chong Yu Yongan Ma +3 位作者 Haibo Li Changjian Wang Haibin Wang Linghao Meng 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第1期169-186,共18页
Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused ... Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels. 展开更多
关键词 Blasting-induced vibration Spatial response Attenuation law Prediction model Double-line tunnel
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A Shear-Lag Model for the Pullout Behavior of Pre-twisted Straight Fibers and Its Application in the Toughening Analysis of Twisted Fiber-Reinforced Composites 认领 引用 被引量：1
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作者 Jiajun Dong Shiyang Liu +2 位作者 Xiaofei Wang Qinghua Qin Jianshan Wang 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第4期534-543,共10页
The pre-twisted straight fiber exhibits exceptional mechanical properties,including high tensile stiffness and remarkable flexibility.In applications such as artificial muscles and fiber-reinforced composites,these fi... The pre-twisted straight fiber exhibits exceptional mechanical properties,including high tensile stiffness and remarkable flexibility.In applications such as artificial muscles and fiber-reinforced composites,these fibers are typically embedded in an elastic matrix,functioning as key reinforcing or deformation-driven structural components.In this study,a shear-lag-based model is developed to describe the pullout behavior of a pre-twisted straight fiber from an elastic matrix,incorporating geometric nonlinearity and tension–twist coupling induced by large pre-twist angles.Based on this model,the stress transfer mechanism between the twisted straight fiber and the surrounding matrix is systematically analyzed.Furthermore,the derived force–displacement relationship during fiber pullout is employed to perform crack-bridging analysis,revealing the toughening mechanisms in twisted fiber-reinforced composites.Results show that pre-twist of fiber introduces distinct tension–twist coupling,which generates hoop interfacial shear stresses and allows the fiber to undergo larger tensile deformation.It leads to greater crack-opening displacements in the bridging zone and a significantly enhanced toughening effect.The present work provides new insights into the stress transfer and toughening mechanisms of twisted fiber-reinforced composites,offering valuable guidance for the design and fabrication of high-performance composite materials. 展开更多
关键词 Pre-twisted straight fiber Pullout Shear-lag model Fracture toughness Crack bridging
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A Physics-Based Viscoelastic Model and Its Peridynamic Implementation for Polymeric Materials 认领 引用 被引量：1
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作者 Jun Liu Zewen Gao +4 位作者 Shuang Liang Yaxin Zhu Lv Zhao Minsheng Huang Zhenhuan Li 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第6期843-856,共14页
Developing constitutive models linking chain dynamics to the macroscopic viscoelasticity and fracture of polymeric materials remains challenging.This work presents a physics-informed viscoelastic model accounting for ... Developing constitutive models linking chain dynamics to the macroscopic viscoelasticity and fracture of polymeric materials remains challenging.This work presents a physics-informed viscoelastic model accounting for the microstructural evolution of free chains.The viscous stress is decomposed into the relaxation effect arising from the transient elongation of free chains and the disentanglement effect resulting from the release of topological constraints.A deformation-dependent relaxation spectrum is introduced through a revised three-chain model to govern the evolution of free-chain relaxation times in response to macroscopic stretches,enabling the present model to address complex deformation states and loading histories.The present model is comprehensively validated against experimental data for various polymeric materials,accurately capturing their viscoelastic mechanical responses.Furthermore,the present model is incorporated into a peridynamics framework,establishing an integrated numerical method for simulating concurrent viscoelastic deformation and fracture.By establishing a physics-based link from chain dynamics to macroscopic viscoelasticity and fracture,this work provides a powerful tool for modulating the time-dependent properties of polymeric materials and offers new insights into the mechanisms governing viscoelasticity and fracture behavior. 展开更多
关键词 Viscoelastic model Fracture Chain relaxation Disentanglement Peridynamics Finite deformation Energy dissipation
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Collision risk assessment for constellation satellites based on a space debris environment topological network model 认领 引用 被引量：1
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作者 Yurun YUAN Jingrui ZHANG +2 位作者 Keying YANG Lincheng LI Hao WU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期472-484,共13页
In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This s... In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This study aims to explore the complex evolution of the space debris environment and assess the collision risks associated with spacecraft.First,a space debris environment topological network model is proposed,which incorporates interdisciplinary methods from topological networks,fluid mechanics,and spacecraft dynamics.This model enables a structured representation of the relationships among space objects and provides rapid predictions of the space debris environment.Then,a collision probability algorithm based on the topological network model is introduced.This algorithm inherits the efficiency advantages of the topological network model and has been validated for reliability through comparison with the classical ESA’s DRAMA software.Finally,based on the above models,the collision risks of constellation satellites in Low Earth Orbit(LEO)are analyzed,including both operational and deorbit processes.The study reveals that constellation satellites face a much higher risk of internal collisions with satellites from the same constellation during operations than that with other space objects.Additionally,during the satellite deorbit process,the collision risk peaks when satellites traverse the operational region of Starlink satellites. 展开更多
关键词 Collision probability Computing resource Constellation Space debris Topological network model
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Numerical model for rapid prediction of temperature field, mushy zone and grain size in heating−cooling combined mold (HCCM) horizontal continuous casting of C70250 alloy plates 认领 引用 被引量：1
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作者 Ling-hui MENG Fan ZHAO +3 位作者 Dong LIU Chang-jian LU Yan-bin JIANG Xin-hua LIU 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2026年第1期203-217,共15页
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy... Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°. 展开更多
关键词 Cu alloy numerical simulation machine learning prediction model process optimization
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Research on data assimilation for turbulence model constants via airfoil wind tunnel experiments 认领 引用 被引量：1
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作者 Junwei Yang Lingting Meng +1 位作者 Xiangjun Wang Hua Yang 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第2期42-56,共15页
Data assimilation algorithms have been demonstrated to increase the accuracy of predictions in airfoil flow fields.However,slight changes in airfoil geometry and Reynolds number(Re)variations could lead to differences... Data assimilation algorithms have been demonstrated to increase the accuracy of predictions in airfoil flow fields.However,slight changes in airfoil geometry and Reynolds number(Re)variations could lead to differences in aerodynamic characteristics and stall behavior,consequently affecting assimilation outcomes.Hence,this research uses the ensemble Kalman filter(EnKF)algorithm.The aerodynamic characteristics of two wind turbine airfoils obtained through wind tunnel experiments were investigated under varying degrees of stall by recalibrating the constants in the(S-A)model.The impacts of the airfoil thickness,Re variation,and Gurney flap installation on the assimilation results were subsequently examined.Verifying the applicability of the constants obtained via data assimilation under varying conditions might offer opportunities to reduce the demand for computational resources.The assimilation results indicate that at a Re on the order of magnitude of 105,the original model tends to delay flow separation as the Re increases.Consequently,the recalibrated constant Cb1 generally decreases with increasing Re.Despite belonging to the same airfoil family,discrepancies in the flow separation behavior predicted by the original model resulted in variations in the recalibrated constants.The constants derived from the thinner airfoil induce premature flow separation in the thicker YA-30 airfoil under stall conditions.When assimilated constants are applied to flow field calculations under analogous stall conditions,constants from another condition may demonstrate an optimization effect and substitute the self-assimilated constants,provided that simulations using default constants for both conditions consistently exhibit an experimental separation trend.However,practical implementation requires caution due to the risk of overadjustment. 展开更多
关键词 Wind turbine airfoil Pressure distribution Data assimilation Spalart-Allmaras model Applicability
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A decision framework for rural domestic sewage treatment models and process:Evidence from Inner Mongolia Autonomous Region,China 认领 引用 被引量：1
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作者 Ying Yan Pengyu Li +5 位作者 Zixuan Wang Yubo Tan Tianlong Zheng Jianguo Liu Xiaoxia Yang Junxin Liu 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第1期302-311,共10页
Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making sys... Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making. 展开更多
关键词 Rural domestic sewage Sewage treatment model Decision-making Environmental-economic benefits Inner Mongolia
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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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Non-landslide sample for landslide susceptibility prediction modeling:A review of selection strategies and their influence rules 认领 引用 被引量：1
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作者 Zhuo Jia Zhijin Cheng +3 位作者 Zhilu Chang Qin Li Faming Huang Yuhao Peng 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第4期2859-2880,共22页
A proper non-landslide sample selection strategy can improve landslide susceptibility prediction(LSP)accuracy.However,there may be uncertainties regarding the compatibility between different selection strategies and m... A proper non-landslide sample selection strategy can improve landslide susceptibility prediction(LSP)accuracy.However,there may be uncertainties regarding the compatibility between different selection strategies and machine learning models,as well as in the extent of LSP performance enhancement after their coupling.To overcome these uncertainties,this study takes Wuning county of China as a case area,collecting 24 conditioning factors and 379 landslides data.Four non-landslide sample selection strategies,namely random selection,low-slope,buffer zone,and semi-supervised strategies,are then combined with landslide samples in a 1:1 ratio to serve as input variables for constructing LSP models using support vector machine(SVM),logistic regression(LR),random forest(RF)and extreme gradient boosting(XGBoost).Finally,the uncertainty of semi-supervised machine learning coupled models with a 1:2 ratio of landslide to non-landslide samples is analyzed and compared.The results show that:(1)The semi-supervised and low-slope strategies demonstrate higher prediction accuracy compared to the buffer zone and random selection strategies.Moreover,the RF coupled models are the most reliable,followed by the XGBoost,SVM,and LR coupled models;(2)Compared to a 1:1 ratio,a 1:2 ratio of landslide to non-landslide samples significantly improves prediction accuracy,suggesting that appropriately increasing the proportion of non-landslide samples helps to mitigate overfitting and enhance the identification of landslide samples;and(3)LSP is more sensitive to non-landslide sample selection strategies than to the choice of machine learning models.In conclusion,prioritizing reliable non-landslide samples is crucial for improving accuracy of LSP. 展开更多
关键词 Landslide susceptibility prediction Non-landslide sample selection Machine learning models Uncertainty analysis
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Amelioration of behavioral and neural deficits in animal models of neurodegenerative disease by nanoformulations of curcumin and quercetin 认领 引用 被引量：3
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作者 Bridget Martinez Philip V.Peplow 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第8期3311-3322,共12页
Neurodegenerative diseases are increasing in prevalence due largely to aging populations worldwide and improved medical care for the elderly.Currently approved drugs can reduce some of the symptoms of neurodegenerativ... Neurodegenerative diseases are increasing in prevalence due largely to aging populations worldwide and improved medical care for the elderly.Currently approved drugs can reduce some of the symptoms of neurodegenerative diseases but cannot cure them.Inflammation is involved in the development and progression of neurodegenerative diseases,and oxidative stress is implicated in neurodegeneration associated with cognitive decline and age-related cognitive impairment.Polyphenols such as curcumin,quercetin,and resveratrol possess potent anti-inflammatory and antioxidant properties.Nanoformulations of curcumin and quercetin can optimize their pharmacological effects in the treatment of neurodegenerative diseases.Nanocarriers play a crucial role in delivering drugs across the blood-brain barrier,thereby lowering the risk of peripheral side effects.Various nanoforms have been developed to induce bioavailability and solubility of curcumin and quercetin,including nanoparticles and nanoemulsions.The studies reviewed included 17 using curcumin nanoformulations and seven with quercetin nanoformulations and were tested in widely used animal models of Alzheimer’s disease,Parkinson’s disease,Huntington’s disease,and multiple sclerosis.Many of the curcumin and quercetin nanoformulations brought about improvements in learning and memory in behavioral tests of Alzheimer’s disease models and were effective in reducing oxidative stress in the brain.Both nanocurcumin and nanoquercetin decreased the levels of inflammatory markers in the brain.Nanocurcumin formulations improved motor behavior,gait,and memory in Parkinson’s disease models and increased dopaminergic neurons in the striatum and substantia nigra.Furthermore,nanocurcumin improved locomotor activity,memory,and learning,and the number of dendrites of medium spiny neurons in Huntington’s disease models.Nanocurcumin formulations decreased oxidative stress and inflammation in a model of demyelination.Several important limitations were identified in the studies reviewed and these need to be considered in future studies.Also,clinical trials could be performed using the currently available nanoforms of curcumin and quercetin. 展开更多
关键词 animal models behavioral deficits curcumin inflammation nanoformulations neural deficits neurodegeneration oxidative stress quercetin
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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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