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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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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：1
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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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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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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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Constructing a predictive model for postpartum anxiety in patients with preeclampsia based on multidimensional indicators and its application 认领 引用 被引量：1
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作者 Xiao-Yan Zhang Yun Shi +3 位作者 Yi-Ting Lu Ya-Jun Zhong Jia-Xian Wu Xiao-Qing Wang 《World Journal of Psychiatry》 SCIE 2026年第3期264-273,共10页
BACKGROUND Preeclampsia(PE)substantially increases the risk of postpartum anxiety,yet limited research has examined how disease onset and clinical features,such as blood pressure control and body mass index(BMI)change... BACKGROUND Preeclampsia(PE)substantially increases the risk of postpartum anxiety,yet limited research has examined how disease onset and clinical features,such as blood pressure control and body mass index(BMI)changes during pregnancy,affect this risk.AIM To develop and apply a predictive model for postpartum anxiety disorder in patients with PE based on multidimensional indicators.METHODS A cross-sectional study was conducted among 196 patients with PE admitted to the Department of Obstetrics,Ninth People’s Hospital of Suzhou(Affiliated with Soochow University),from June 2019 to June 2024.According to the self-rating anxiety scale at six weeks postpartum,participants were divided into anxiety and no-anxiety groups.Two data sets were analyzed,and multivariate logistic regression was performed to identify risk and protective factors.Regression coefficients and constants were used to construct the predictive model.Model performance was evaluated using the receiver operating characteristic curve and area under the curve,along with a goodness-of-fit test.The model was then validated with clinical data.RESULTS Of the 196 patients with PE evaluated using the self-rating anxiety scale at six weeks postpartum,51(26.02%)patients showed anxiety symptoms.Significant group differences(P<0.05)were observed for blood pressure control,BMI increase,hematocrit(Hct),family relationships,and psychological resilience.Logistic regression indicated that,poor blood pressure control,greater BMI increase,elevated Hct levels,and strained family relationships during pregnancy were risk factors for postpartum anxiety in patients with PE(P<0.05),whereas higher psychological resilience was a protective factor(P<0.05).The prediction model was defined as:Logit(P)=0.684×pregnancy blood pressure control+0.805×pregnancy BMI increase+0.756×Hct+1.063×family relationship-1.105×psychological resilience score-5.487.The model’s area under the curve(0.908)exceeded that of individual indicators:Blood pressure control(0.794),BMI increase(0.814),Hct(0.808),family relationships(0.840),and psychological resilience(0.833).The goodness-of-fit test showed no overfitting(χ2=1.904,P=0.725).Clinical validation demonstrated sensitivity of 85.71%,specificity of 87.72%,and accuracy of 87.18%.CONCLUSION Postpartum anxiety risk in patients with PE is associated with poor blood pressure control,excessive BMI gain,elevated Hct index,and poor family relationships,while strong psychological resilience serve as a protective factor.The developed prediction model effectively supports clinical assessment and targeted management of postpartum anxiety in patients with PE. 展开更多
关键词 Preeclampsia Postpartum Anxiety disorder Influencing factors Prediction model
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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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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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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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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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Human cerebral organoids:Complex,versatile,and human-relevant models of neural development and brain diseases 认领 引用 被引量：3
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作者 Raquel Coronel Rosa González-Sastre +8 位作者 Patricia Mateos-Martínez Laura Maeso Elena Llorente-Beneyto Sabela Martín-Benito Viviana S.Costa Gagosian Leonardo Foti Ma Carmen González-Caballero Victoria López-Alonso Isabel Liste 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第3期837-854,共18页
The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cereb... The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering. 展开更多
关键词 assembloids bioengineering challenges disease modeling drug screening and toxicology human brain organoids human pluripotent stem cells neurodegenerative diseases neurodevelopment vascularization
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Multi-scale modeling of ultra-thin commercially pure titanium sheet for fuel cell bipolar plates:Plastic anisotropy and distortional strain hardening 认领 引用 被引量：1
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作者 Kyung Mun Min Seonghwan Choi +2 位作者 Xiaohua Hu Jinwoo Lee Hyuk Jong Bong 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第5期1637-1651,共15页
This study presents a multi-scale modeling framework to describe the mechanical behavior of a 0.1 mm-thick commercially pure titanium(CP-Ti)sheet developed for fuel cell bipolar plates.Since standardized methods for c... This study presents a multi-scale modeling framework to describe the mechanical behavior of a 0.1 mm-thick commercially pure titanium(CP-Ti)sheet developed for fuel cell bipolar plates.Since standardized methods for characterizing ultra-thin sheets under complex stress states are lacking,a virtual modeling approach was employed.At the grain scale,a crystal plasticity finite element(CPFE)model was constructed to incorporate the relevant slip and twinning systems,enabling prediction of responses under diverse loading conditions.Extending to the continuum scale,the CPFE results,combined with tensile data,were used to calibrate an advanced constitutive model based on the evolutionary Yld2000-2d yield function,capable of capturing anisotropic behavior.Validation against independent limiting dome height tests confirmed the predictive accuracy of the framework.The proposed approach provides a basis for simulating the forming behavior of ultra-thin CP-Ti sheets and supports precise manufacturing of bipolar plates in fuel cell systems. 展开更多
关键词 commercially pure titanium sheet crystal plasticity plastic anisotropy distortional strain hardening multi-scale modeling
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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：2
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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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Modeling adaptive growth in forest trees:Integrating individual variation to understand climate responses in widely-distributed species 认领 引用 被引量：1
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作者 Arne Buechling Charles D.Canham +9 位作者 Nataliya Korolyova Melanie Saulnier Patrick H.Martin Magnuz Engardt Martin Mikoláš Ondřej Vostarek Daniel Kozák Pavel Janda Jeňýk Hofmeister Miroslav Svoboda 《Forest Ecosystems》 SCIE CAS CSCD 2026年第3期797-811,共15页
An improved understanding of how forest trees may respond individually and differentially to climate across broad environmental gradients,due to adaptation or physiological acclimation,may facilitate more robust forec... An improved understanding of how forest trees may respond individually and differentially to climate across broad environmental gradients,due to adaptation or physiological acclimation,may facilitate more robust forecasts of forest resilience under climate change.We present a framework for modeling stem diameter growth in adult canopy trees that accounts for responses to climate that may be unique for individuals in different ecological settings.We used data from>10,000 tree cores from 888 forest inventory plots distributed across wide climatic gradients in two mountain ranges in Europe.We formulated a suite of nonlinear models for each of the four species to understand factors regulating annual radial growth.The models accounted for the effects of tree ontogeny,competition,nitrogen deposition(Nd),temperature,and precipitation.We compared two approaches to evaluate evidence for adaptation or acclimation in the growth-climate relations of trees.One method tested whether growth responses diverged for individual trees associated with distinct climate regimes.An alternate method fitted climate response functions with the deviation of climate in a given year from the prevailing average conditions at a tree location.We also tested whether the peak height of this function,representing the maximum growth capacity of a tree,depended on local average climate.For all taxa,models that incorporated within-species variation received stronger support relative to simpler models that assumed a consistent species-average growth response to climate.Growth in all but one species was best predicted by models fitted with climate deviations.Trees differed markedly in terms of their peak growth potential and climate optima,and in some cases,occupied suboptimal environments.Growth responses to nitrogen(N)inputs were also modulated by climate.Our framework offers a flexible approach for integrating individual-level climate sensitivity into tree demography models,which may allow for more rigorous investigations of forest dynamics,the outcomes of which may inform adaptive management strategies for mitigating climate change impacts. 展开更多
关键词 Acclimation Competition Differentiation Gaussian functions Local adaptation Nitrogen deposition(Nd) Nonlinear growth models
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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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Deformation characteristics of unsaturated slope during post-rainfall earthquake:Insights from centrifuge and numerical modeling 认领 引用 被引量：1
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作者 Jiawei Xu Kun Fang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期380-397,共18页
Slopes are likely to fail in areas with frequent rainfall and earthquakes.The deformation characteristics of unsaturated slopes subjected to post-rainfall earthquakes are investigated using centrifuge model tests and ... Slopes are likely to fail in areas with frequent rainfall and earthquakes.The deformation characteristics of unsaturated slopes subjected to post-rainfall earthquakes are investigated using centrifuge model tests and finite element analyses.Three tests of the slope deformation under earthquake and post-rainfall earthquakes are first studied using image analysis techniques.Then,based on an elastoplastic constitutive model,numerical simulations are carried out using the finite element method and compared with the centrifuge test results.Finally,a parametric study is performed to clarify the effects of antecedent rainfall on earthquake-induced slope deformation.The results show that slope deformation caused by post-rainfall earthquakes differs from that caused by earthquakes without antecedent rainfall.The seepage flow and soil strength of the slope are affected by previous rainfall conditions,such as intensity and duration,which directly influence the slope deformation caused by the subsequent earthquake.Soil displacement and strain become greater and the slip surface is more noticeable during the post-rainfall earthquake of higher intensity.In addition,the time interval between the rainfall and the earthquake has a considerable impact on the detailed characteristics of the slope deformation,and the significant deformation occurs at the time of lowest soil strength when seepage flow reaches the lower part of the slope.Moreover,the repeated intermittent rainfall greatly affects the subsequent earthquake-induced slope deformation,the main characteristics of which are closely related to the changes in saturation and strength of the slope.However,with the prolonged time gap between each round of rainfall,the earthquake-induced slope deformation becomes insignificant. 展开更多
关键词 Slope Deformation Post-rainfall earthquake Centrifuge model test Finite element analysis
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