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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 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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A Bayesian-based framework for quantifying model-inherent uncertainties in soil constitutive models 认领 引用
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作者 Haibin Xiong Shaoheng Dai +3 位作者 Feng Shan Xuzhen He Sheng Zhang Daichao Sheng 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第6期4867-4881,共15页
Quantifying uncertainty in soil constitutive models is both challenging and essential for improving predictive reliability.While the Bayesian approach offers an effective framework for this purpose,previous studies ha... Quantifying uncertainty in soil constitutive models is both challenging and essential for improving predictive reliability.While the Bayesian approach offers an effective framework for this purpose,previous studies have primarily focused on uncertainties in input parameters of standard“ideal”constitutive models.However,models such as the Mohr-Coulomb,Cam-Clay,and Modified Cam-Clay are based on simplified assumptions that do not fully capture real soil behaviour,making it impossible for them to match real-world data perfectly.This study introduces a novel Bayesian-based framework that transforms deterministic models into probabilistic ones,allowing for the quantification of model-inherent uncertainty.A modified maximum a posteriori(MAP)estimator is employed,incorporating a penalty term into the negative log-likelihood to form a new loss function,which enhances bias estimates and model reliability.Using this method,three assumptions about the model bias term are evaluated against triaxial test data from Karlsruhe sand and White clay.The results demonstrate that non-constant assumptions about the bias term significantly enhance model performance.Among the models tested,the probabilistic Modified Cam-Clay model with a nonlinear bias assumption exhibits the best performance.Conversely,the probabilistic Mohr-Coulomb model reveals certain limitations,including overpredicted peak deviatoric stress and overfitted volumetric strain,underscoring the need for further refinements. 展开更多
关键词 Uncertainty quantification Maximum a posteriori(MAP) Probabilistic models Model bias Constitutive models
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Large Language Models for Cybersecurity Intelligence: A Systematic Review of Emerging Threats, Defensive Capabilities, and Security Evaluation Frameworks 认领 引用
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作者 Hamed Alqahtani Gulshan Kumar 《Computers, Materials & Continua》 SCIE EI 2026年第6期297-367,共71页
Large Language Models(LLMs)are becoming integral components of modern cybersecurity ecosystems,simultaneously strengthening defensive capabilities while giving rise to a new class of Artificial Intelligence-Generated ... Large Language Models(LLMs)are becoming integral components of modern cybersecurity ecosystems,simultaneously strengthening defensive capabilities while giving rise to a new class of Artificial Intelligence-Generated Content(AIGC)-driven threats.This PRISMA-guided systematic review synthesises 167 peer-reviewed studies published between 2022 and 2025 and proposes a unified threat-defence-evaluation taxonomy as a central analytical framework to consolidate a previously fragmented body of research.Guided by this taxonomy,the review first examines AIGC-enabled threats,including automated and highly personalised phishing,polymorphic malware and exploit generation,jailbreak and adversarial prompting,prompt-injection attack vectors,multimodal deception,persona-steering attacks,and large-scale disinformation campaigns.The surveyed evidence indicates a qualitative escalation in adversarial capabilities,with LLMs significantly enhancing scalability,adaptability,and realism while markedly reducing the technical barriers to conducting sophisticated attacks.Second,the review analyses LLM-enabled defensive applications spanning intrusion and anomaly detection,malware analysis and log-semantic modelling,multilingual threat intelligence extraction,vulnerability discovery and code repair,and Security Operations Center(SOC)automation through Retrieval-Augmented Generation(RAG)and multi-agent systems.Although these approaches demonstrate strong potential as semantic reasoning and decision-support components within hybrid security architectures,their real-world effectiveness remains constrained by hallucination risks,adversarial susceptibility,distributional shifts,and operational overhead.Third,the review synthesises current security evaluation and red-teaming practices,revealing a fragmented assessment landscape characterised by narrow benchmarks,inconsistent evaluation metrics,and limited longitudinal robustness analysis.Overall,the taxonomy-driven synthesis highlights a structurally imbalanced ecosystem in which offensive innovation outpaces defensive maturity and governance,and it informs a structured,research-question-aligned roadmap for developing trustworthy,resilient,and policy-aligned LLM-powered cybersecurity systems. 展开更多
关键词 Artificial intelligence-generated content threats cybersecurity intelligence large language model-based defensive systems large language models red-teaming and evaluation frameworks
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Computational Framework for Fractional Order Neurological Disorder Model under Interpreting Transmission Patterns 认领 引用
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作者 Kottakkaran Sooppy Nisar Muhammad Farman +2 位作者 Ali Hasan Mohammed Altaf Ahmed Mohammad Tabish 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期762-788,共27页
A global health concern,neurodegenerative disorders like Parkinson's and Alzheimer's impact both mental and physical functioning.The complex interplay among immunological response,protein accumulation,and brai... A global health concern,neurodegenerative disorders like Parkinson's and Alzheimer's impact both mental and physical functioning.The complex interplay among immunological response,protein accumulation,and brain health necessitates sophisticated mathematical modeling.This study introduces a fractional-order mathematical model using the Mittag-Leffler derivative to describe the dynamics of neurodegeneration,incorporating key biological factors such as functioning and infected neurons,extracellular alpha-synuclein,microglia,and T-cells.A fundamental assumption of the model is that neuronal deterioration is influenced by memory effects,where past states impact current disease progression,making fractional-order calculus more suitable than traditional integer-order models.The model accounts for the secretion and clearance of alpha-synuclein,the activation of immune responses,and the role of microglia in mitigating or exacerbating neuronal damage.Sensitivity analysis emphasizes the crucial role of factors like neuronal cells production IIN,infection prevalenceγ,and stimulation of microglial cellsΘ.Numerical simulations support the long-run neuroinflammatory feedback mechanism,revealing that smaller values of fractional orderη<1reduce disease progression.This is based on the premise that increased memory(ηvalues less than one)leads to slower transmission of pathological protein aggregation.The study demonstrates that building a surrogate machine learning model of the NARX-BRBNN type,calibrated using numerical solver output,not only decreases computing complexity but also accurately replicates the dynamics of the fractional equation.This comparison underscores the necessity of employing fractional-order numerical schemes for accurately modeling complex neurobiological systems.The study proposes focused treatment approaches and provides insightful information on the course of neurodegenerative diseases. 展开更多
关键词 Neurodegenerative disorder modeling Mittag-Leffler kernel sensitivity analysis ANN
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Interactive effects of process parameters and porosity defect evaluation in thermoplastic composite resistance welding:A model-data-driven AOA-BP neural network framework 认领 引用
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作者 Yajie Feng Juan Xiao +5 位作者 Hongjian Gu Fang Qi Zhiyuan Ning Xigao Jian Liangliang Shen Jian Xu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第5期325-337,共13页
To address parameter regulation of temperature fields and strength prediction during thermoplastic composite resistance welding,a multi-physics simulation-integrated machine learning framework for cross-scale optimiza... To address parameter regulation of temperature fields and strength prediction during thermoplastic composite resistance welding,a multi-physics simulation-integrated machine learning framework for cross-scale optimization is proposed.A three-dimensional transient thermal conduction model of composite resistance welding was established,validated through experimental data from thermal imaging systems and thermocouples,achieving high-fidelity simulations of welding temperature fields.This provides a robust data-driven foundation for deep learning methodologies.Building upon a Backpropagation(BP)Neural Network(ANN)model,an Archimedes Optimization Algorithm(AOA)is introduced to enable adaptive exploration of solution spaces,establishing quantitative mappings between process parameter interactions and temperature-threshold regions(representing potential defect risks)and effective weld area.This framework achieves indirect inference of optimal parameters(current:50 A,welding time:15 s,clamping distance:1 mm)with high correlation(R2=0.95).Further investigations into the mechanisms of clamping distance,welding current,and time effects on interfacialhickness-direction weld quality,particularly the evolution of temperature-threshold regions and their implications for potential defect risks,are conducted.Results demonstrate:Insufficient welding time(≤10 s)or low current(≤45 A)leads to increased un-welded regions and cold joints;And excessive current(≥55 A)or prolonged time(≥20 s)induces significant thermal degradation zones.This study offers critical theoretical insights for optimizing welding processes and defect mitigation in thermoplastic composites. 展开更多
关键词 Thermoplastic composite material Resistance welding Finite element model Machine learning Defect analysis
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Modelling water use in Nepal's highlands:a multidisciplinary and probabilistic framework 认领 引用
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作者 Megan KLAAR Duncan QUINCEY +4 位作者 C.Scott WATSON Lee E.BROWN Bishnu PARIYAR Arjan GOSAL Jon LOVETT 《Journal of Mountain Science》 SCIE CSCD 2026年第2期489-504,共16页
Mountain communities in Nepal are increasingly exposed to climate-induced shifts in water availability,driven by glacial retreat,altered precipitation/snowmelt regimes,and declining groundwater sources.This study pres... Mountain communities in Nepal are increasingly exposed to climate-induced shifts in water availability,driven by glacial retreat,altered precipitation/snowmelt regimes,and declining groundwater sources.This study presents an integrated framework combining hydrological source analysis with socio-demographic survey data to evaluate seasonal water contributions and communitylevel water use patterns in the Upper Marsyangdi catchment,Manang District,Nepal.Isotopic(δ18O)and geochemical(silica)tracers were used in a Bayesian mixing model to quantify the seasonal contributions of glacial melt,snow,rain,and groundwater to river flow.Findings indicate that groundwater dominates pre-monsoon flow(60%-70%)while post-monsoon discharge reflects more balanced inputs from all sources.In parallel,120 household surveys were analysed using Latent Class Analysis to characterise water use across domestic,agricultural,energy,and tourism sectors.Results reveal spatial and demographic gradients in water source dependency,including gender and occupation as important predictors of water use.Respondents reported perceived increases in spring flow,alongside reductions in the availability of snow for household and tourism use and deteriorating river water quality and quantity,particularly affecting hydropower operations.Adaptation strategies include increased reliance on water storage infrastructure and source switching.The study highlights the value of applying probabilistic methods to hydrological and sociocultural data to identify vulnerable populations and inform targeted,context-sensitive adaptation strategies.The proposed framework is transferable to other high-altitude regions,offering a robust approach for assessing climate resilience through the synthesis of scientific and local knowledge systems. 展开更多
关键词 Water source attribution High mountain hydrology MixSIAR Bayesian mixing model Annapurna Himalaya
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Multitype Game Optimisation:A Two-Stage Fine-Tuning Framework for Multi-Game Optimisation With Large Language Models 认领 引用
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作者 Xiali Li Jingshi Gu +3 位作者 Feifan He Yang Xiao Yuanli Jia Ping Lan 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期739-753,共15页
Large language models(LLMs)have made remarkable advances in natural language processing,demonstrating great potential in modelling structured sequences.However,adapting these capabilities to machine gaming tasks such ... Large language models(LLMs)have made remarkable advances in natural language processing,demonstrating great potential in modelling structured sequences.However,adapting these capabilities to machine gaming tasks such as Go remains challenging due to limitations in strategy generalisation and optimisation efficiency.This paper presents multitype game optimisation(MyGO),a two-stage fine-tuning framework tailored for two-player perfect information board games,exploring the applicability of LLMs to nonlinguistic decision-making domains.In the supervised fine-tuning stage,we propose a unified structural encoding method,action semantic unit(ASU),which efficiently converts heterogeneous game records into discrete token sequences compatible with LLMs.In the reinforcement learning stage,we design TA-PPO(token-level adaptive proximal policy optimisation),an enhanced PPO-based algorithm to address the issue of sparse feedback commonly encountered in game reinforcement learning.Experimental results demonstrate that the fine-tuned models achieve superior or comparable performance to traditional game-playing algorithms in terms of strategy quality,rule generalisation and inference efficiency.This work provides a scalable paradigm for fine-tuning LLMs in complex decision-making tasks and lays a foundation for future research in game AI and generalisable strategy optimisation. 展开更多
关键词 computer game fine‐tuning large language models reinforcement learning reward function
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Predicting the temperature of CRTS Ⅲ ballastless tracks in cold regions based on a TCN-Track model 认领 引用
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作者 Jie LIANG Shijie DENG +4 位作者 Juanjuan REN Wenlong YE Kaiyao ZHANG Dacheng LI Ronghe ZHANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第1期43-57,共15页
The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primar... The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primary cause of damage to China Railway Track System(CRTS)Ⅲ ballastless tracks in cold regions during service.In this study,to predict the temperature of the track structure accurately,we analyzed meteorological data collected from Shenyang,China,and identified the factors that had the most effect on the track temperature field.We propose a temporal convolutional network(TCN)-based temperature field prediction model for ballastless tracks(TCN-Track model),which enhances the ability to extract and fuse local and global features from complex long-term meteorological data.The results indicate that the proposed TCN-Track model performs well in predicting track temperature fields from meteorological data,with a mean absolute error(MAE)ranging from 0.26 to 0.39,a root mean square error(RMSE)ranging from 0.32 to 0.50,and correlation coefficient(R)values ranging from 0.888 to 0.985.Compared with a long short-term memory(LSTM)model,the MAE of the TCN-Track model is reduced by 89.17%and the RMSE by 88.51%.This method offers a new solution for accurately predicting the temperature field of ballastless tracks in cold regions,aiding in predicting and preventing track damage caused by low temperatures. 展开更多
关键词 Cold regions CRTSⅢballastless tracks Temperature prediction Meteorological variables Time prediction model
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A Grounded Multi-Agent Multimodal Large Language Model Framework for Interpretable Risk Assessment in Driving Scenes 认领 引用
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作者 Chien-Hao Tseng Min-Yu Chen +2 位作者 Meng-Wei Lin Jyh-Horng Wu Chung-I Huang 《Computers, Materials & Continua》 SCIE EI 2026年第9期1137-1163,共27页
Context-aware driving assistance must do more than detect objects:it has to identify the cues that materially affect risk,separate observable evidence from inference,and produce recommendations that humans can audit.T... Context-aware driving assistance must do more than detect objects:it has to identify the cues that materially affect risk,separate observable evidence from inference,and produce recommendations that humans can audit.This paper presents a grounded multi-agent multimodal large language model(MLLM)framework for interpretable risk assessment in driving scenes.The framework decomposes reasoning into four stages—context relevance evaluation,visual interpretation,factual verification with anomaly extraction,and risk assessment with action recommendation—so that the final advisory is generated only from a verified intermediate representation rather than directly from a free-form scene description.We evaluate the framework on a manually labeled benchmark derived from BDD100K covering traffic-sign interpretation,traffic-density assessment,and pedestrian–vehicle interaction risk.The benchmark contains 600 frames with three-rater annotation and majority-vote labels(Fleiss’κ=0.79 on risk levels);we explicitly discuss the implications of this scale for generalization and complement it with a multi-backbone stress test.Across five independent runs,the proposed framework improves risk accuracy from 74.3±0.9%to 84.8±0.6%and macro-F1 from 72.8±1.1%to 83.1±0.7%over a single-agent MLLM baseline.The hallucination rate—defined as the fraction of outputs containing at least one entity,attribute,or relation that has no visual support in the source frame—drops from 18.7%to 8.9%,and the actionability score—a five-point human rating averaged over usefulness,specificity,and visual consistency—rises from 3.62 to 4.28.McNemar tests confirm that the gain in risk accuracy is statistically significant(p<0.001).The framework is intended as a semantic decision-support layer for explainable advanced driver-assistance systems and human-centered autonomous-driving interfaces. 展开更多
关键词 Autonomous driving context-aware risk assessment multimodal large language model multi-agent system interpretable reasoning driving-scene understanding
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Beyond Classical Positional Encodings:A Learnable QFT-Inspired Framework for Transformer Language Models 认领 引用
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作者 Sara Tehsin Tallha Akram +2 位作者 Syed Rameez Naqvi Meshal Alharbi Abdulrahman Alabduljabbar 《Computers, Materials & Continua》 SCIE EI 2026年第9期159-182,共24页
Transformers have become the dominant architecture for sequence modeling in natural language processing;however,their effectiveness critically depends on how positional information is encoded.Conventional positional e... Transformers have become the dominant architecture for sequence modeling in natural language processing;however,their effectiveness critically depends on how positional information is encoded.Conventional positional encodings,while effective,may have limited structural flexibility for capturing complex global sequence relationships.Recent quantum-inspired approaches have sought to address this limitation,yetmany either oversimplify quantum principles or introduce substantial computational or hardware overhead.We introduce a novel Quantum Fourier Transform(QFT)-inspired positional encoding scheme for transformers,motivated by the structured frequency representation of the QFT.Unlike prior approaches that either emulate quantum operations superficially or require complex circuit constructions,the proposed method provides a learnable hybrid encoding that preserves quantuminspired structure while remaining aligned with hardware-efficient circuit primitives and structurally compatible with future near-term quantum implementations.Experiments on WikiText-103 indicate that the proposed encoding achieves competitive perplexity,improved robustness to input scrambling,and stable training behavior relative to alternative quantum-inspired baselines under the evaluated settings.Preliminary circuit-level simulations further suggest favorable noise resilience of the associated encoding primitives.These findings support the potential utility of incorporating quantum-inspired design principles into deep learning architectures and provide a foundation for future exploration at the interface of quantum computing and transformer-based natural language processing(NLP). 展开更多
关键词 LLMs positional encoding Quantum Fourier Transform positional embeddings hybrid quantumclassical models near-term quantum devices quantum transformer
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CMM-EmbedCluster:a clustering framework for Chinese materia medica based on large language model and Chinese materia medica property theory 认领 引用
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作者 Jiayi He Jiadong Xie +1 位作者 Kongfa Hu Haiyan Li 《Digital Chinese Medicine》 CAS CSCD 2026年第2期278-289,共12页
Objective This study proposes a clustering framework for Chinese materia medica(CMM)based on a large language model(LLM),aiming to explore potential compatibility patterns among CMMs from the semantic perspective of C... Objective This study proposes a clustering framework for Chinese materia medica(CMM)based on a large language model(LLM),aiming to explore potential compatibility patterns among CMMs from the semantic perspective of CMM property theory.Methods First,a CMM property knowledge base was constructed based on Chinese Materia Medica,including 567 commonly used CMMs characterized by four properties,five flavors,and meridian tropism.Then,49 CMMs derived from 10 prescriptions for Zangdu(脏毒,pathogenic toxins)recorded in Waike Zhengzong(《外科正宗》,Orthodox Manual of External Medicine)and Yangke Xinde Ji(《疡科心得集》,Collected Insights on Ulcer Medicine)were selected as the experimental dataset.Five semantic representation methods—One-Hot,Word2Vec,Bidirectional Encoder Representations from Transformers(BERT),Beijing Academy of Artificial Intelligence General Embedding(BGE),and Qwen—were applied to encode CMM property information into vector representations.Subsequently,t-distributed Stochastic Neighbor Embedding(t-SNE)was used for nonlinear dimensionality reduction on high-dimensional semantic vectors,followed by k-means clustering(k=7).Clustering performance was evaluated using the Silhouette Score(SS),Davies-Bouldin Index(DBI),and Calinski-Harabasz Index(CHI).Results The Qwen-based clustering method,CMM-EmbedCluster,achieved the highest SS(0.6074)and CHI(158.0572),as well as the lowest DBI(0.4995),indicating improved cluster separation and compactness compared with other methods.Visualization of CMM clustering results showed that the clusters were well separated in the low-dimensional space,with strong inter-cluster discrimination and high intra-cluster functional consistency.Further interpretability analysis of CMM clustering results revealed stable structural differences among clusters in terms of four properties,five flavors,and meridian tropism,forming functional partitions consistent with CMM property theory.Conclusion CMM-EmbedCluster utilizes an LLM to achieve semantic-level representation and clustering of CMMs within the framework of CMM property theory,providing support for exploring potential compatibility patterns among CMMs from the perspective of CMM property semantics. 展开更多
关键词 Large language model Chinese materia medica property theory Chinese materia medica clustering Semantic representation k-Means clustering CMM-EmbedCluster
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Coupled Modeling of CO2 Frosting,Fluid Flow,and Heat Transfer under Cryogenic Conditions Using a Nucleation-Based CFD Framework 认领 引用
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作者 Xuewen Cao Zhe Chen +1 位作者 Gaoya Ding Wei You 《Fluid Dynamics & Materials Processing》 EI 2026年第7期144-171,共28页
A two-dimensional Computational Fluid Dynamics(CFD)model,grounded in classical nucleation theory,is developed to investigate CO2 frosting and the associated heat transfer under cryogenic conditions.The model integr... A two-dimensional Computational Fluid Dynamics(CFD)model,grounded in classical nucleation theory,is developed to investigate CO2 frosting and the associated heat transfer under cryogenic conditions.The model integrates gas–solid phase-change kinetics with multiphysics transport equations to capture the coupled phenomena governing frost formation.The Peng–Robinson equation of state is employed to predict CO2 frost points in binary mixtures,with model predictions validated against experimental data,yielding errors in frost thickness and thermal conductivity below 15%.The results demonstrate that decreasing the cryogenic wall temperature from 160 K to 150 K increases the average frost thickness and density by 57% and 78%,respectively,while advancing the peak in thermal resistance by approximately 5 min.A reduction in CO2 mol fraction from 10% to 6% leads to an 82% decrease in average frost density.Although inlet velocity exerts a limited influence on frost density,excessively high velocities increase porosity and inhibit densification.Flow field analysis reveals that progressive frost growth constricts the effective channel area,resulting in a local velocity increase of approximately 23%.Moreover,the spatial distributions of supersaturation and nucleation rate exhibit strong consistency.These findings elucidate the complex coupling between CO2 frosting,fluid flow,and heat transfer in Pressurized Liquefied Natural Gas(PLNG)systems,offering theoretical insights for optimizing low-energy CO2 cryogenic capture and enhancing natural gas liquefaction processes. 展开更多
关键词 PLNG CO2frosting model of nucleation cryogenic heat exchange CFD simulation
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Intelligent Identification of Natural Fractures in Tight Sandstone:Optimal Model Coupling in Ensemble Frameworks 认领 引用 被引量：1
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作者 Ma Sheng-lun Zhang Zhao-hui +3 位作者 Zhang Jiao-sheng Liao jian-bo Zhang Wen-ting Zou Jian-dong 《Applied Geophysics》 SCIE CSCD 2026年第1期262-284,432,共23页
The formation and development of natural fractures in tight sandstone reservoirs are governed by a combination of stratigraphic structure,lithological properties,and stress conditions.These fractures often exhibit irr... The formation and development of natural fractures in tight sandstone reservoirs are governed by a combination of stratigraphic structure,lithological properties,and stress conditions.These fractures often exhibit irregular geometries,signicant variations in height,and complex lling materials,leading to intricate conventional logging responses with pronounced multi-solution ambiguities that complicate accurate identication.To address this challenge,this study proposes a multi-model selective coupling identication method.This approach incorporated data cleaning,augmentation,and resampling techniques during the preprocessing phase.Subsequently,multi-dimensional feature extraction and cascade-based feature selection were performed,followed by optimizing model parameters using random search,Bayesian optimization,and grid search algorithms.High-performing models were selected via an evaluation framework.These models were then coupled through voting mechanisms to construct a robust identication model capable of deeply exploring the nonlinear relationship between fractures and logging data.The proposed method achieved an 85.19%fracture identication accuracy in blind tests involving 27 fracture segments across three wells,demonstrating strong identication capability.This methodology provides a valuable reference for fracture identication in hydrocarbon reservoirs within the Hongde area. 展开更多
关键词 Algorithm models Data processing Selective coupling Fracture identification
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Scenario-based modeling and driver analysis of ecological resilience:A framework study in the Qinling-Daba Mountains 认领 引用
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作者 LI Siqi LI Kang +6 位作者 QIAN Hui GAO Yanyan CHEN Jie WU Puxia MA Yandong HE Yi LIU Zhao 《Journal of Geographical Sciences》 SCIE CSCD 2026年第7期1701-1730,共30页
Ecological resilience(ER)reflects an ecosystem's capacity to adapt to climate change,natural disturbances,and human-induced stress.This study evaluated ER in the Qinling-Daba Mountains(QDM),China,from 2000 to 2020... Ecological resilience(ER)reflects an ecosystem's capacity to adapt to climate change,natural disturbances,and human-induced stress.This study evaluated ER in the Qinling-Daba Mountains(QDM),China,from 2000 to 2020 using a resistance-robustnessrecovery model.Specifically,the PLUS model was employed to assess the impacts of land use change on ER under three scenarios:natural development(NDS),ecological preservation(EPS),and farmland preservation(FPS).Spatial drivers of ER were analyzed using the geographic detector model and multi-scale geographically weighted regression.The results revealed a significant upward trend in ER,with high values in central and southern QDM and low values in urban lowland areas.Regarding the scenarios,ER improved under EPS,plateaued under NDS,and declined under FPS.The key drivers were found to be NDVI,slope,DEM,temperature,and nighttime light.ER showed positive correlations with high NDVI and slope and negative correlations with low vegetation and flat terrain.These findings provide deeper insight into ER dynamics and scientific guidance for regional ecological protection,planning,and resilience building in the QDM and similar mountainous ecosystems globally. 展开更多
关键词 ecological resilience resistance-robustness-recovery PLUS model driving factors
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MPNet:A modular deep learning process TCAD surrogate modeling framework 认领 引用
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作者 Qipei Zhang Pengwei Liu +2 位作者 Wenzhang Fang Dong Ni Yuting Kong 《Journal of Semiconductors》 EI CAS CSCD 2026年第6期95-108,共14页
The computational cost of TCAD simulations is becoming prohibitively high with the complexity of advanced process technologies,making simulation acceleration a critical research priority.While end-to-end surrogate mod... The computational cost of TCAD simulations is becoming prohibitively high with the complexity of advanced process technologies,making simulation acceleration a critical research priority.While end-to-end surrogate models mapping process recipes to device structures and characteristics offer a promising alternative,their application is often limited by poor generalizability and explainability.In this work,we present MPNet,a modular deep learning surrogate modeling framework for process TCAD.MPNet comprises distinct surrogate models for individual process modules,which are assembled into an integrated framework.These modular models employ a novel UNet-attention feature evolution method to capture the complex evolutions of device geometry and doping profiles.Each module can be trained separately on its individual process,after which the modules are cascaded and jointly fine-tuned to minimize error accumulation throughout the cascade.The efficacy of the proposed MPNet framework is demonstrated through a MOSFET integrated process TCAD case study.Results show that MPNet achieves a computational speedup of over 103 times compared to conventional TCAD,while maintaining predictive fidelity exceeding 98%.Finally,to illustrated the application of the proposed framework,MPNet is coupled with a PSO algorithm,showcasing its utility for fast process optimization to meet specific process targets. 展开更多
关键词 modular surrogate model deep learning process TCAD PSO
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Secondary Realignment:An Embodied Intelligent Operational Framework Integrating Vision-Language and Action Two-Stage Models 认领 引用
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作者 Jinjiang Lin Yuan Lu +3 位作者 Han Li Xiaolong Cai Enyi Chen Jiansheng Guan 《Computers, Materials & Continua》 SCIE EI 2026年第7期2047-2066,共20页
Manipulating objects based on verbal commands in cluttered environments remains a critical challenge in robotic arm research.Verbal commands possess high semantic abstraction,while precise grasping and placement actio... Manipulating objects based on verbal commands in cluttered environments remains a critical challenge in robotic arm research.Verbal commands possess high semantic abstraction,while precise grasping and placement actions rely on fine-grained geometric perception.The disparity between these two domains is the primary cause of operational errors.Particularly in certain cluttered scenarios,visual-spatial noise and background redundancy further disrupt attention distribution,significantly degrading the generalization capabilities of existing methods in unseen environments.To address these issues,this paper proposes the Secondary Realignment(SR)framework.It decouples vision-language alignment and vision-action alignment into two stages,mitigating semantic-geometric discrepancies through a hierarchical approach to substantially reduce errors in cross-modal mapping.Simultaneously,to address noise and redundancy in visual-language features,we design a Deep Sparse Self-Attention(DSSA)module.This module dynamically fuses sparse and dense attention mechanisms through self-learning parameters,adaptively enhancing relevant features while suppressing irrelevant noise.Extensive simulation experimental results demonstrate that compared to the state-of-the-art method A2,our approach achieves 9.7%,9.9%,and 17.6%higher task success rates in grasping,placing,and pick-and-place tasks,respectively,further validating its effectiveness. 展开更多
关键词 Robot grasping visual language model language-conditional grasping attention mechanism
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WirelessAgent:Large Language Model Agents for Intelligent Wireless Networks 认领 引用
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作者 Tong Jingwen Guo Wei +4 位作者 Shao Jiawei Wu Qiong Li Zijian Lin Zehong Zhang Jun 《China Communications》 SCIE EI CSCD 2026年第3期265-285,共21页
The rapid evolution of wireless networks presents unprecedented challenges in managing diverse wireless tasks.These challenges underscore the need for AI-native solutions in next-generation networks.In this article,we... The rapid evolution of wireless networks presents unprecedented challenges in managing diverse wireless tasks.These challenges underscore the need for AI-native solutions in next-generation networks.In this article,we propose WirelessAgent,a novel framework that harnesses large language models(LLMs)to create autonomous AI agents for diverse wireless network tasks.We first define a general framework for WirelessAgent,supported by key components and principles in AI agents.Then,we introduce a basic usage to implement theWirelessAgent based on agentic workflows and the LangGraph architecture.We demonstrate the effectiveness of WirelessAgent through a comprehensive case study on the network slicing task.Our numerical results show that WirelessAgent achieves 44.4%higher bandwidth utilization than the Prompt-based method,while performing only 4.3%below the Rule-based optimality.Notably,WirelessAgent delivers near-optimal network throughput across diverse network scenarios.These underscore the framework’s potential for intelligent and autonomous control in next-generation networks.The code is available at http://gffzz188fe103f8f1460asv65ucp6n95xw6uwq.ffgz.tsg.suse.edu.cn/jwentong/WirelessAgent R1. 展开更多
关键词 agentic workflow AI agent large language models(LLMs) network slicing
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Comparative virology and pathology of Monkeypox virus in Syrian hamsters,C57BL/6,and BALB/c mice:A framework for preclinical model selection 认领 引用
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作者 Xiaohui Wei Jing Wu +7 位作者 Hekai Yang Na Li Binbin Zhao Wanjun Peng Na Rong Wei Zhang Jing Xue Jiangning Liu 《Animal Models and Experimental Medicine》 CAS CSCD 2026年第4期829-837,共9页
Monkeypox virus(MPXV),a zoonotic orthopoxvirus,has emerged as a major public health concern following its global spread in 2022.Robust small animal models are urgently needed to investigate MPXV pathogenesis and evalu... Monkeypox virus(MPXV),a zoonotic orthopoxvirus,has emerged as a major public health concern following its global spread in 2022.Robust small animal models are urgently needed to investigate MPXV pathogenesis and evaluate medical countermeasures.Here,we systematically compared MPXV infection in Syrian hamsters,C57BL/6 mice,and BALB/c mice following intraperitoneal inoculation.Syrian hamsters displayed the highest susceptibility,with systemic viral dissemination peaking at 2 days post-infection(dpi)and clearance by 12 dpi.Viral burdens were particularly elevated in the spleen and kidneys,correlating with severe histopathological changes.In contrast,C57BL/6 and BALB/c mice exhibited more restricted viral replication,with the kidneys,liver,and spleen serving as major target organs.Across all models,no cutaneous lesions were observed,underscoring the influence of infection route on disease phenotype.Comparative analysis revealed species-specific tissue tropism and clearance kinetics,confirming the Syrian hamster as a permissive model for systemic MPXV disease,while C57BL/6 and BALB/c mice offer complementary value for mechanistic immunology studies.These findings establish a framework for rational selection of small animal models to study MPXV pathogenesis and to evaluate antiviral and vaccine strategies. 展开更多
关键词 Monkeypox virus rodent models viral pathogenesis viral replication kinetics viral tissue tropism
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A Machine Learning Surrogate Framework for Bayesian Calibration ofNonlinear Concrete Damage Models 认领 引用
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作者 Yi Chen Xiaodan Ren 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期303-324,共22页
Concrete exhibits significant stochasticity and nonlinearity,making the calibration of nonlinear damage models challenging for high-precision structural analysis.To address the high computational cost of finite elemen... Concrete exhibits significant stochasticity and nonlinearity,making the calibration of nonlinear damage models challenging for high-precision structural analysis.To address the high computational cost of finite element model calibration and the influence of model bias,this study proposes a machine learning surrogate framework for Bayesian calibration of nonlinear concrete damage models.The framework integrates a bi-scalar damage constitutive model,support vector regression based surrogate modeling,response-level finite element model bias representation,and adaptive Markov Chain Monte Carlo sampling within a unified probabilistic setting.The surrogate models are constructed to approximate the nonlinear mapping from constitutive parameters to force-displacement responses,thereby replacing repeated high-cost finite element evaluations during posterior sampling.Meanwhile,the model bias term is explicitly incorporated into the likelihood formulation to distinguish structural model inadequacy from observational noise.The proposed framework is validated using experimental force-displacement responses of reinforced concrete columns.The results show that the surrogate models provide accurate predictions and substantial computational acceleration.The posterior predictions agree well with the experimental responses,and the incorporation of model bias leads to more realistic uncertainty representation in both parameter inference and structural response prediction.The proposed framework provides an efficient and robust strategy for Bayesian calibration and uncertainty quantification of nonlinear concrete damage models. 展开更多
关键词 Bayesian inference model calibration surrogate modeling damage model uncertainty quantificatio
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