期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到202篇文章
< 1 2 … 11 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Computational modelling of magnesium degradation in simulated body fluid under physiological conditions 认领 引用 被引量：2
1
作者 Berit Zeller-Plumhoff Tamadur AlBaraghtheh +1 位作者 Daniel Höche Regine Willumeit-Römer 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2022年第4期965-978,共14页
Magnesium alloys are highly attractive for the use as temporary implant materials,due to their high biocompatibility and biodegradability.However,the prediction of the degradation rate of the implants is difficult,the... Magnesium alloys are highly attractive for the use as temporary implant materials,due to their high biocompatibility and biodegradability.However,the prediction of the degradation rate of the implants is difficult,therefore,a large number of experiments are required.Computational modelling can aid in enabling the predictability,if sufficiently accurate models can be established.This work presents a generalized model of the degradation of pure magnesium in simulated body fluid over the course of 28 days considering uncertainty aspects.The model includes the computation of the metallic material thinning and is calibrated using the mean degradation depth of several experimental datasets simultaneously.Additionally,the formation and precipitation of relevant degradation products on the sample surface is modelled,based on the ionic composition of simulated body fluid.The computed mean degradation depth is in good agreement with the experimental data(NRMSE=0.07).However,the quality of the depth profile curves of the determined elemental weight percentage of the degradation products differs between elements(such as NRMSE=0.40 for phosphorus vs.NRMSE=1.03 for magnesium).This indicates that the implementation of precipitate formation may need further developments.The sensitivity analysis showed that the model parameters are correlated and which is related to the complexity and the high computational costs of the model.Overall,the model provides a correlating fit to the experimental data of pure Mg samples of different geometries degrading in simulated body fluid with reliable error estimation. 展开更多
关键词 Biodegradation Magnesium Computational modelling Corrosion Uncertainty quantification Kriging
暂未订购 下载PDF
Recent advances in computational modelling of mononuclear actinide single molecule magnets 认领 引用
2
作者 Sourav Dey Jose J.Baldoví 《Inorganic Chemistry Frontiers》 SCIE EI CAS CSCD 2025年第4期1349-1370,共22页
Significant progress has been made in lanthanide-based single mononuclear SMMs in the past two decades,raising their magnetic memories to liquid nitrogen temperature.On the other hand,a handful of actinide-based monon... Significant progress has been made in lanthanide-based single mononuclear SMMs in the past two decades,raising their magnetic memories to liquid nitrogen temperature.On the other hand,a handful of actinide-based mononuclear SMMs,primarily based on uranium,have been reported.Among the advantages of actinides over lanthanides are their more significant spin-orbit coupling and stronger metal-ligand covalency. 展开更多
关键词 liquid nitrogen computational modelling mononuclear actinide single molecule magnets lanthanide based single mononuclear smms spin orbit coupling metal ligand covalency
Guest Editorial for the Special Issue on Advanced Computational Modeling and Simulations for Engineering Structures and Multifunctional Materials:Bridging Theory and Practice 认领 引用
3
作者 S.M.Anas Rayeh Nasr Al-Dala’ien 《Computers, Materials & Continua》 SCIE EI 2026年第7期2268-2273,共6页
1 Introduction The growing use of computational modelling, simulation tools, and data-driven methods has changedhe way engineering structures and advanced materials are studied and designed. With the increasing availa... 1 Introduction The growing use of computational modelling, simulation tools, and data-driven methods has changedhe way engineering structures and advanced materials are studied and designed. With the increasing availability of high-performance computing, artificial intelligence, and multi-scale simulation techniques,computational modelling is no longer limited to purely theoretical studies. It has now emerged as a practical design aid, allowing researchers to predict material behavior, understand complex interactions, and support engineering decisions across different material and structural scales. These developments have helped in narrowing the gap between theoretical studies and practical engineering applications. 展开更多
关键词 engineering structures practical design aid advanced materials understand complex inte computational modelling simulation tools predict material behavior artificial intelligence
暂未订购 下载PDF
Data-Driven Materials Science Using Machine Learning and Computational Modeling 认领 引用
4
作者 Manjodh Kaur Princy Randhawa +4 位作者 Jitendra Jaiswal Deepak Dubal Ravindra N.Bulakhe Deepanraj Balakrishnan Nithesh Naik 《Computers, Materials & Continua》 SCIE EI 2026年第8期90-124,共35页
This review emphasizes the growing role of artificial intelligence(AI)in transforming the materials discovery process into a data-driven and autonomous approach.It systematically traces the evolution of scientific par... This review emphasizes the growing role of artificial intelligence(AI)in transforming the materials discovery process into a data-driven and autonomous approach.It systematically traces the evolution of scientific paradigms in materials science and examines how machine learning,generative models,and AI agents are revolutionizing the design,screening,and optimization of materials.A key contribution is a detailed,step-by-step machine learning framework that guides researchers through data collection,preprocessing,feature engineering,model development,and validation,utilizing publicly available materials databases and computational tools.Additionally,the review discusses the latest advances in generative AI and autonomous research systems,highlighting their potential to enable inverse design and closed-loop experiments.It includes a tutorial case study on sodium-ion battery materials to demonstrate practical application in formation energy prediction via machine learning,along with comparisons to high-throughput screening accuracy using density functional theory(DFT).The article also addresses current challenges such as data limitations,model interpretability,and physics-based approaches.Overall,this publication serves as both a conceptual and practical guide for integrating AI into materials research,aiming to accelerate the discovery process and improve efficiency. 展开更多
关键词 Artificial intelligence materials science materials discovery energy materials computational modeling
暂未订购 下载PDF
Computational and Experimental Modeling of Curved Crack Effects on the Dynamic Response of Plate Structures 认领 引用
5
作者 Yousef Lafi A.Alshammari Muhammad Khan Hilal Doganay Kati 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期252-278,共27页
Cracks can severely degrade the integrity and service performance of plate structures.Although most existing studies focus on identifying straight crack patterns using dynamic response data,curved crack paths have rec... Cracks can severely degrade the integrity and service performance of plate structures.Although most existing studies focus on identifying straight crack patterns using dynamic response data,curved crack paths have received far less attention,despite being more realistic in practice and having a stronger influence on structural behaviour.This study presents a computational and experimental framework for analyzing and identifying curved crack paths in cantilever plate structures based on dynamic response characteristics.Curved crack paths are modelled using second-order polynomial equations.Finite Element Analysis(FEA)is employed to evaluate the effects of polynomial coefficients and crack end abscissa(xend)on natural frequency and resonance amplitude,while experimental modal analysis(EMA)on damping ratio.Forward and inverse identification models are then developed using linear regression(LR)and artificial neural networks(ANN)to predict dynamic response characteristics and estimate crack path.Results show that the quadratic coefficient(a)and linear coefficient(b)of the crack path have the most decisive influence on the plate’s vibration characteristics,whereas the constant term(c)has a negligible effect.Also,the crack paths with greater curvature and inclination,represented by higher a and b coefficients,especially at smaller end abscissae(xend),tend to reduce natural frequencies and increase vibration amplitudes and damping ratios.In contrast,smoother,less curved cracks exhibit the opposite behaviour.These curved crack geometries cause greater stiffness degradation by altering both axial and shear stiffness.Consequently,local flexibility and energy dissipation increase due to enhanced crack-surface interaction and localised deformation.The proposed computational models are experimentally validated using 15 fabricated plates with different curved crack profiles,demonstrating high prediction accuracy.Overall,the study enhances the computational identification and characterization of curved cracks in plate structures,contributing to improved damage assessment and structural health monitoring(SHM)based on dynamic response. 展开更多
关键词 Curved crack paths computational modeling dynamic response analysis finite element analysis(FEA) artificial neural networks
暂未订购 下载PDF
Computational models for solid-state sintering in metal extrusion additive manufacturing:Trends,challenges,and future directions 认领 引用
6
作者 Judice Cumbunga Saïd Abboudi Dominique Chamoret 《Additive Manufacturing Frontiers》 CAS CSCD 2026年第1期57-87,共31页
Solid-state sintering is a crucial thermal post-processing step in metal extrusion additive manufacturing(MExAM),influencing the microstructural evolution,densification,and final properties of fabricated components.Ho... Solid-state sintering is a crucial thermal post-processing step in metal extrusion additive manufacturing(MExAM),influencing the microstructural evolution,densification,and final properties of fabricated components.However,accurately simulating this process remains challenging due to its inherently multiscale and multiphysics nature.This comprehensive and critical review examines the main computational approaches developed to model solidstate sintering in the MExAM context,ranging from nano-to macrostructure scales,including molecular dynamics,kinetic Monte Carlo,discrete element methods,phase-field models,and continuum-based methods.For each,we detail the underlying mathematical formulations,numerical strategies,and implementation environments.Their capabilities and limitations are evaluated in terms of scale resolution,physical accuracy,and computational demands.Particular attention is given to challenges such as the coupling of thermal,mechanical,and diffusive phenomena,as well as the difficulty of bridging disparate spatial and temporal scales.In response to these limitations,emerging trends such as Physics-informed machine learning(PIML)offer promising avenues to enhance predictive accuracy,improve computational efficiency,and streamline simulation workflows.By integrating conventional modeling techniques with data-driven approaches,the field is moving toward faster and more reliable predictions of sintering behavior,advancing the goals of the MExAM initiative.The insights presented aim to guide future research focused on optimizing sintering processes for broader industrial applications and improved material performance. 展开更多
关键词 Metal extrusion additive manufacturing Solid-state sintering Computational modeling Multiscale simulation Multiphysical couplings Physics-informed machine learning
暂未订购 下载PDF
The promise of cardiomyocyte computational models for drug safety 认领 引用
7
作者 Lei Wang Blanca Rodriguez 《Military Medical Research》 SCIE CAS CSCD 2026年第4期682-685,共4页
Dear Editor,Drug development stands at a crossroads.Traditional animal testing for cardiac safety faces growing scientific and ethical challenges,while computational alternatives are demonstrating valuable predictive ... Dear Editor,Drug development stands at a crossroads.Traditional animal testing for cardiac safety faces growing scientific and ethical challenges,while computational alternatives are demonstrating valuable predictive capabilities.Cardiomyocyte computational models,rooted in concepts developed over 6 decades ago,now represent a mature and viable approach for pharmaceutical research,particularly when integrated with modern experi-mental platforms.This letter reviews the application of cardiac electrophysiology models in drug safety evaluation,a field where they offer transformative potential.We focus specifically on cardiac electrical activity relevant to drug safety,leaving the equally important domain of cardiac mechanics for future discussion. 展开更多
关键词 Cardiomyocyte computational models Drug safety testing Cardiac electrophysiology In silico clinical trials Artificial intelligence(AI)
暂未订购 下载PDF
Computational modelling and simulation to mitigate the risk of daylight exposure in tropical museum buildings 认领 引用
8
作者 Rizki A.Mangkuto Tommy Partogi Simamora +1 位作者 Desliana Putri Pratiwi Mochamad Donny Koerniawan 《Energy and Built Environment》 EI 2024年第2期171-184,共14页
In museum design and operation,daylight is typically discouraged due to high risk of damaging the display objects.However,past studies in high-latitude regions have shown the possibility to apply daylight in museums,s... In museum design and operation,daylight is typically discouraged due to high risk of damaging the display objects.However,past studies in high-latitude regions have shown the possibility to apply daylight in museums,so long as it is carefully planned,and the display objects are not from the category of high responsive materials.In the tropical region,the influence of daylighting on light exposure on museum objects is still unknown.This study therefore aims to assess and mitigate the impact of annual daylight exposure on objects with low responsive materials in a tropical daylit museum building.Annual daylight modelling and simulation are performed to achieve the objective,followed with Morris sensitivity analysis and Mahalanobis distance classifier to optimise the outcome.It is found that either WWR or glazing transmissivity gives the greatest influence on the performance indicators.Based on the proposed optimisation algorithm,it is possible to determine the optimum solutions satisfying the performance indicators target,for a certain opening type.Overall,the contribution of this study is the proposed computational modelling and simulation methods to mitigate the exposure risk while optimising daylight as a renewable energy source. 展开更多
关键词 Daylighting Museum Computational modelling Simulation Annual light exposure
Data-Driven Healthcare:The Role of Computational Methods in Medical Innovation 认领 引用 被引量：2
9
作者 Hariharasakthisudhan Ponnarengan Sivakumar Rajendran +2 位作者 Vikas Khalkar Gunapriya Devarajan Logesh Kamaraj 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期1-48,共48页
The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical r... The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable. 展开更多
关键词 Computational models biomedical engineering bioinformatics machine learning wearable technology
暂未订购 下载PDF
A Computationally Efficient Density-Aware Adversarial Resampling Framework Using Wasserstein GANs for Imbalance and Overlapping Data Classification 认领 引用
10
作者 Sidra Jubair Jie Yang +2 位作者 Bilal Ali Walid Emam Yusra Tashkandy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第7期511-534,共24页
Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning,particularly when class overlap significantly deteriorates classification performance.Traditional... Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning,particularly when class overlap significantly deteriorates classification performance.Traditional oversampling methods often generate synthetic samples without considering density variations,leading to redundant or misleading instances that exacerbate class overlap in high-density regions.To address these limitations,we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE,a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap.The originality of WGAN-VDE lies in its density-aware sample refinement,ensuring that synthetic samples are positioned in underrepresented regions,thereby improving class distinctiveness.By applying structured feature representation,targeted sample generation,and density-based selection mechanisms strategies,the proposed framework ensures the generation of well-separated and diverse synthetic samples,improving class separability and reducing redundancy.The experimental evaluation on 20 benchmark datasets demonstrates that this approach outperforms 11 state-of-the-art rebalancing techniques,achieving superior results in F1-score,Accuracy,G-Mean,and AUC metrics.These results establish the proposed method as an effective and robust computational approach,suitable for diverse engineering and scientific applications involving imbalanced data classification and computational modeling. 展开更多
关键词 Machine learning imbalanced classification class overlap computational modelling adversarial resampling density estimation
暂未订购 下载PDF
Failure Analyses of Cylindrical Lithium-Ion Batteries Under Dynamic Loading Based on Detailed Computational Model 认领 引用 被引量：1
11
作者 Huifeng Xi Guicheng Zhao +3 位作者 Shuo Wang Junkui Li Linghui He Bao Yang 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2025年第3期526-538,共13页
Electric vehicles,powered by electricity stored in a battery pack,are developing rapidly due to the rapid development of energy storage and the related motor systems being environmentally friendly.However,thermal runa... Electric vehicles,powered by electricity stored in a battery pack,are developing rapidly due to the rapid development of energy storage and the related motor systems being environmentally friendly.However,thermal runaway is the key scientific problem in battery safety research,which can cause fire and even lead to battery explosion under impact loading.In this work,a detailed computational model simulating the mechanical deformation and predicting the short-circuit onset of the 18,650 cylindrical battery is established.The detailed computational model,including the anode,cathode,separator,winding,and battery casing,is then developed under the indentation condition.The failure criteria are subsequently established based on the force–displacement curve and the separator failure.Two methods for improving the anti-short circuit ability are proposed.Results show the three causes of the short circuit and the failure sequence of components and reveal the reason why the fire is more serious under dynamic loading than under quasi-static loading. 展开更多
关键词 18,650 lithium-ion battery Detailed computational model Deformation Fracture mode Failure criteria
暂未订购 下载PDF
Applications of Artificial Intelligence in Cardiac Electrophysiology and Clinical Diagnosis with Magnetic Resonance Imaging and Computational Modeling Techniques 认领 引用 被引量：1
12
作者 ZHAN Heqin HAN Guilail +1 位作者 WEI Chuan'an LI Zhiqun 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第1期53-65,共13页
The underlying electrophysiological mechanisms and clinical treatments of cardiovascular diseases,which are the most common cause of morbidity and mortality worldwide,have gotten a lot of attention and been widely exp... The underlying electrophysiological mechanisms and clinical treatments of cardiovascular diseases,which are the most common cause of morbidity and mortality worldwide,have gotten a lot of attention and been widely explored in recent decades.Along the way,techniques such as medical imaging,computing modeling,and artificial intelligence(AI)have always played significant roles in above studies.In this article,we illustrated the applications of AI in cardiac electrophysiological research and disease prediction.We summarized general principles of AI and then focused on the roles of AI in cardiac basic and clinical studies incorporating magnetic resonance imaging and computing modeling techniques.The main challenges and perspectives were also analyzed. 展开更多
关键词 artificial intelligence(AI) magnetic resonance imaging computing modeling cardiovascular disease
暂未订购 下载PDF
Assessment of slurry chamber clogging alleviation during ultra-large-diameter slurry tunnel boring machine tunneling in hard-rock using computational fluid dynamics-discrete element method:A case study 认领 引用 被引量：2
13
作者 Yidong Guo Xinggao Li +2 位作者 Dalong Jin Hongzhi Liu Yingran Fang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第8期4715-4734,共20页
To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Un... To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Undersea Tunnel.To evaluate the discharging performance of short screw conveyor in different cases,the full-scale transient slurry-rock two-phase model for a short screw conveyor actively discharging rocks was established using computational fluid dynamics-discrete element method(CFD-DEM)coupling approach.In the fluid domain of coupling model,the sliding mesh technology was utilized to describe the rotations of the atmospheric composite cutterhead and the short screw conveyor.In the particle domain of coupling model,the dynamic particle factories were established to produce rock particles with the rotation of the cutterhead.And the accuracy and reliability of the CFD-DEM simulation results were validated via the field test and model test.Furthermore,a comprehensive parameter analysis was conducted to examine the effects of TBM operating parameters,the geometric design of screw conveyor and the size of rocks on the discharging performance of short screw conveyor.Accordingly,a reasonable rotational speed of screw conveyor was suggested and applied to Jiaozhou Bay Second Undersea Tunnel project.The findings in this paper could provide valuable references for addressing the excavation chamber clogging during ultra-large-diameter slurry TBM tunneling in hard rock for similar future. 展开更多
关键词 Slurry tunnel boring machine(TBM) Short screw conveyor Slurry chamber clogging Computational fluid dynamics-discrete element method(CFD-DEM)coupled modeling Engineering application
暂未订购 下载PDF
Unveiling micro-scale mechanisms of in-situ silicon alloying for tailoring mechanical properties in titanium alloys:Experiments and computational modeling 认领 引用
14
作者 Sisi Tang Li Li +3 位作者 Jinlong Su Yuan Yuan Yong Han Jinglian Fan 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2025年第17期150-163,共14页
Titanium-silicon(Ti-Si)alloy system shows significant potential for aerospace and automotive applications due to its superior specific strength,creep resistance,and oxidation resistance.For Si-containing Ti alloys,the... Titanium-silicon(Ti-Si)alloy system shows significant potential for aerospace and automotive applications due to its superior specific strength,creep resistance,and oxidation resistance.For Si-containing Ti alloys,the sufficient content of Si is critical for achieving these favorable performances,while excessive Si addition will result in mechanical brittleness.Herein,both physical experiments and finite element(FE)simulations are employed to investigate the micro-mechanisms of Si alloying in tailoring the mechanical properties of Ti alloys.Four typical states of Si-containing Ti alloys(solid solution state,hypoeutectoid state,near-eutectoid state,hypereutectoid state)with varying Si content(0.3-1.2 wt.%)were fabricated via in-situ alloying spark plasma sintering.Experimental results indicate that in-situ alloying of 0.6 wt.%Si enhances the alloy’s strength and ductility simultaneously due to the formation of fine and uniformly dispersed Ti5Si3particles,while higher content of Si(0.9 and 1.2 wt.%)results in coarser primary Ti5Si3agglomerations,deteriorating the ductility.FE simulations support these findings,highlighting the finer and more uniformly distributed Ti5Si3particles contribute to less stress concentration and promote uniform deformation across the matrix,while agglomerated Ti5Si3particles result in increased local stress concentrations,leading to higher chances of particle fracture and reduced ductility.This study not only elucidates the micro-mechanisms of in-situ Si alloying for tailoring the mechanical properties of Ti alloys but also aids in optimizing the design of high-performance Si-containing Ti alloys. 展开更多
关键词 Titanium alloy Spark plasma sintering Micro-scale deformation behavior Mechanical property tailoring Computational modeling
暂未订购 下载PDF
A Data-Driven Systematic Review of the Metaverse in Transportation:Current Research,Computational Modeling,and Future Trends 认领 引用
15
作者 Cecilia Castro Victor Leiva Franco Basso 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第8期1481-1543,共63页
Metaverse technologies are increasingly promoted as game-changers in transport planning,connectedautonomous mobility,and immersive traveler services.However,the field lacks a systematic review of what has been achieve... Metaverse technologies are increasingly promoted as game-changers in transport planning,connectedautonomous mobility,and immersive traveler services.However,the field lacks a systematic review of what has been achieved,where critical technical gaps remain,and where future deployments should be integrated.Using a transparent protocol-driven screening process,we reviewed 1589 records and retained 101 peer-reviewed journal and conference articles(2021–2025)that explicitly frame their contributions within a transport-oriented metaverse.Our reviewreveals a predominantly exploratory evidence base.Among the 101 studies reviewed,17(16.8%)apply fuzzymulticriteria decision-making,36(35.6%)feature digital-twin visualizations or simulation-based testbeds,9(8.9%)present hardware-in-the-loop or field pilots,and only 4(4.0%)report performance metrics such as latency,throughput,or safety under realistic network conditions.Over time,the literature evolves fromearly conceptual sketches(2021–2022)through simulation-centered frameworks(2023)to nascent engineering prototypes(2024–2025).To clarify persistent gaps,we synthesize findings into four foundational layers—geometry and rendering,distributed synchronization,cryptographic integrity,and human factors—enumerating essential algorithms(homogeneous 4×4 transforms,Lamport clocks,Raft consensus,Merkle proofs,sweep-and-prune collision culling,Q-learning,and real-time ergonomic feedback loops).A worked bus-fleet prototype illustrates how blockchain-based ticketing,reinforcement learning-optimized traffic signals,and extended reality dispatch can be integrated into a live digital twin.This prototype is supported by a threephase rollout strategy.Advancing the transport metaverse from blueprint to operation requires open data schemas,reproducible edge–cloud performance benchmarks,cross-disciplinary cyber-physical threat models,and city-scale sandboxes that apply their mathematical foundations in real-world settings. 展开更多
关键词 Artificial intelligence blockchain computational modeling digital twins extended reality fuzzy MCDM machine learning metaverse reinforcement learning
暂未订购 下载PDF
Computational Modeling of the Prefrontal-Cingulate Cortex to Investigate the Role of Coupling Relationships for Balancing Emotion and Cognition 认领 引用 被引量：2
16
作者 Jinzhao Wei Licong Li +3 位作者 Jiayi Zhang Erdong Shi Jianli Yang Xiuling Liu 《Neuroscience Bulletin》 SCIE CAS CSCD 2025年第1期33-45,共13页
Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite ... Within the prefrontal-cingulate cortex,abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions,contributing to the development of mental disorders such as depression.Despite this understanding,the neural circuit mechanisms underlying this phenomenon remain elusive.In this study,we present a biophysical computational model encompassing three crucial regions,including the dorsolateral prefrontal cortex,subgenual anterior cingulate cortex,and ventromedial prefrontal cortex.The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes.The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks.Furthermore,our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex,and network functionality was restored through intervention in the dorsolateral prefrontal cortex.This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression. 展开更多
关键词 Prefrontal-cingulate cortex Computational modeling Coupling relationships Depression Emotion and cognition
暂未订购 下载PDF
Explicit ARL Computational for a Modified EWMA Control Chart in Autocorrelated Statistical Process Control Models 认领 引用 被引量：1
17
作者 Yadpirun Supharakonsakun Yupaporn Areepong Korakoch Silpakob 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期699-720,共22页
This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving ... This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems. 展开更多
关键词 Statistical process control average run length modified EWMA control chart autocorrelated data SARMA process computational modeling real-time monitoring
暂未订购 下载PDF
The effect of 6 Hz transcranial alternating current stimulation over the prefrontal cortex on reward learning in men with methamphetamine use disorder:A pilot randomised double-blind trial 认领 引用
18
作者 Qianlan Yin Tianzhen Chen +4 位作者 Yan Long Daqing Shi Taosheng Liu Min Zhao Jiang Du 《General Psychiatry》 CAS CSCD 2026年第1期76-80,共5页
To the editor:Methamphetamine use disorder(MUD) is associated with deficits in cognitive control,decision-making and reward processing,which can contribute to the maintenance of addictive behaviours.1 Substantial evid... To the editor:Methamphetamine use disorder(MUD) is associated with deficits in cognitive control,decision-making and reward processing,which can contribute to the maintenance of addictive behaviours.1 Substantial evidence from neuroimaging,neuropsychological and computational modelling studies also indicates dysregulation of the neural circuits underlying reward processing.2,3 Reward learning,crucial in reward processing,may be impaired in MUD,affecting individuals' ability to learn action values and guide goal-directed behaviours,potentially contributing to substance use disorders.4 Recently,there has been growing interest in computational neuroscience approaches to understanding the mechanisms of reward learning deficits in addiction.5 Reinforcement learning models have been used to parse the specific components of reward learning that may be impaired in addiction,such as value representation,prediction error signalling and decisionmaking. 展开更多
关键词 computational modelling studies learn action values maintenance addictive behaviours Methamphetamine Use Disorder use disorder mud Transcranial Alternating Current Stimulation reward learningcrucial Prefrontal Cortex
暂未订购 下载PDF
Gastrointestinal tract modelling in health and disease 认领 引用 被引量：4
19
作者 Dong-Hua Liao Jing-Bo Zhao Hans Gregersen 《World Journal of Gastroenterology》 SCIE CAS 2009年第2期169-176,共8页
The gastrointestinal(GI)tract is the system of organs within multi-cellular animals that takes in food,digests it to extract energy and nutrients,and expels the remaining waste.The various patterns of GI tract functio... The gastrointestinal(GI)tract is the system of organs within multi-cellular animals that takes in food,digests it to extract energy and nutrients,and expels the remaining waste.The various patterns of GI tract function are generated by the integrated behaviour of multiple tissues and cell types.A thorough study of the GI tract requires understanding of the interactions between cells,tissues and gastrointestinal organs in health and disease.This depends on knowledge,not only of numerous cellular ionic current mechanisms and signal transduction pathways,but also of large scale GI tissue structures and the special distribution of the nervous network.A unique way of coping with this explosion in complexity is mathematical and computational modelling;providing a computational framework for the multilevel modelling and simulation of the human gastrointestinal anatomy and physiology.The aim of this review is to describe the current status of biomechanical modelling work of the GI tract in humans and animals,which can be further used to integrate the physiological,anatomical and medical knowledge of the GI system.Such modelling will aid research and ensure that medical professionals benefit,through the provision of relevant and precise information about the patient's condition and GI remodelling in animal disease models.It will also improve the accuracy and efficiency of medical procedures,which could result in reduced cost for diagnosis and treatment. 展开更多
关键词 Gastrointestinal tract Computational modelling Biomechanics Remodelling Disease
暂未订购 下载PDF
Artificial Neural Network-Based Prediction and Validation of Drill Flank Wear in GFRP Machining for Sustainable and Smart Manufacturing 认领 引用 被引量：1
20
作者 Sathish Rao Udupi Gururaj Bolar +1 位作者 Manjunath Shettar Ashwini Bhat 《Computers, Materials & Continua》 SCIE EI 2026年第6期903-918,共16页
Glass fiber-reinforced polymer composites(GFRPCs)are extensively utilized in the aerospace,automotive,and structural sectors;nevertheless,their heterogeneous and abrasive characteristics result in rapid tool wear duri... Glass fiber-reinforced polymer composites(GFRPCs)are extensively utilized in the aerospace,automotive,and structural sectors;nevertheless,their heterogeneous and abrasive characteristics result in rapid tool wear during drilling.Drill flank wear among various wear mechanisms notably influences hole quality and dimensional accuracy.This research investigates the impact of spindle speed,feed rate,and drill diameter on flank wear during dry drilling of GFRPC laminates with high-speed steel(HSS)twist drills.A full-factorial design with 81 experiments is used to create a comprehensive dataset.ANOVA indicates that spindle speed is the dominant factor affecting wear changes,accounting for 74.43%,followed by feed rate(15.80%)and drill diameter(6.16%).A linear regression model demonstrates reasonable statistical sufficiency(R2=0.964),but it falls short in reflecting nonlinear interactions.Hence,an artificial neural network(ANN)model is developed to improve prediction.The multilayer feed-forward ANN with a 3-10-6-1 architecture,trained using the Levenberg-Marquardt optimization algorithm,achieves excellent predictive accuracy,with high correlation and low root-mean-square error.Model validation was achieved through independent confirmation experiments,yielding a mean absolute percentage error of only 2.27%,with all predictions falling within the permissible wear range.The findings indicate that ANN-based modeling provides a reliable framework for capturing the complex nonlinear relationships governing tool wear in GFRPC drilling and serves as a viable soft sensor for tool condition monitoring,process optimization,and sustainable,data-driven manufacturing. 展开更多
关键词 Glass fiber-reinforced polymer composites machining and drilling drill flank wear prediction artificial neural networks(ANN) soft computational modeling
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 11 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈