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
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
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 Ti_(5)Si_(3)particles,while higher content of Si(0.9 and 1.2 wt.%)results in coarser primary Ti_(5)Si_(3)agglomerations,deteriorating the ductility.FE simulations support these findings,highlighting the finer and more uniformly distributed Ti_(5)Si_(3)particles contribute to less stress concentration and promote uniform deformation across the matrix,while agglomerated Ti_(5)Si_(3)particles 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
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
Ca^2＋ dysregulation is an early event observed in Alzheimer＇s disease（AD） patients preceding the presence of its clinical symptoms.Dysregulation of neuronalCa^2＋ will cause synaptic loss and neuronal death,eventu...Ca^2＋ dysregulation is an early event observed in Alzheimer＇s disease（AD） patients preceding the presence of its clinical symptoms.Dysregulation of neuronalCa^2＋ will cause synaptic loss and neuronal death,eventually leading to memory impairments and cognitive decline.Treatments targetingCa^2＋ signaling pathways are potential therapeutic strategies against AD.The complicated interactions make it challenging and expensive to study the underlying mechanisms as to how Ca^2＋ signaling contributes to the pathogenesis of AD.Computational modeling offers new opportunities to study the signaling pathway and test proposed mechanisms.In this mini-review,we present some computational approaches that have been used to study Ca^2＋ dysregulation of AD by simulating Ca^2＋signaling at various levels.We also pointed out the future directions that computational modeling can be done in studying the Ca^2＋ dysregulation in AD.展开更多
Atrial fibrillation(AF)is one of the most common arrhythmias,associated with high morbidity,mortality,and healthcare costs,and it places a significant burden on both individuals and society.Anti-arrhythmic drugs are t...Atrial fibrillation(AF)is one of the most common arrhythmias,associated with high morbidity,mortality,and healthcare costs,and it places a significant burden on both individuals and society.Anti-arrhythmic drugs are the most commonly used strategy for treating AF.However,drug therapy faces challenges because of its limited efficacy and potential side effects.Catheter ablation is widely used as an alternative treatment for AF.Nevertheless,because the mechanism of AF is not fully understood,the recurrence rate after ablation remains high.In addition,the outcomes of ablation can vary significantly between medical institutions and patients,especially for persistent AF.Therefore,the issue of which ablation strategy is optimal is still far from settled.Computational modeling has the advantages of repeatable operation,low cost,freedom from risk,and complete control,and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even guidance.This review summarizes three-dimensional computational modeling simulations of catheter ablation for AF,from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ablation.Finally,we summarize current developments and challenges and provide our perspectives and suggestions for future directions.展开更多
Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate eval...Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience optimization.The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.For bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of HVS.According to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,respectively.The natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three layers.The support vector regression(SVR)is employed to aggregate features to the final quality prediction.Extensive experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.展开更多
Acute morphological changes in de novo coronary lesions after drug-coated balloon(DCB)angioplasty can affect endothelial mechanics and consequently clinical outcomes.Angiography-based computational modeling has been v...Acute morphological changes in de novo coronary lesions after drug-coated balloon(DCB)angioplasty can affect endothelial mechanics and consequently clinical outcomes.Angiography-based computational modeling has been validated to assess endothelial dynamic strain(EDS)in coronary arteries in vivo.The EDS was calculated on the basis of pre-and post-DCB angiography.Parameters of quantitative coronary angiography and EDS were quantified at cross-sections in the treated segments.A total of 336 and 348 lesion cross-sections were included in the small/large vessel groups,respectively.The acute lumen gain after DCB was significantly higher in large than small vessels(relative changes:21.3%[17.4%,25.1%]vs.7.4%[4.8%,10.1%],P<0.001).Before treatment,three indices of EDS were significantly higher in small than large vessels(for ED-EDS:29.2%[19.8%,44.8%]vs.20.4%[14.3%,30.2%];for ES-EDS:26.8%[18.9%,37.7%]vs.18.3%[13.9%,25.4%];for TA-EDS:19.1%[13.9%,27.8%]vs.14.3%[10.5%,20.1%],P<0.001).After treatment,the EDS in small vessels significantly decreased(P<0.001).ED-EDS showed the highest correlation with pre-DCB DSP(r=0.43,P<0.001)and post-DCB MLD(r=0.35,P<0.001).The levels of EDS parameters for small or large vessel lesions significantly differed.Further study is required to examine the clinical value of EDS in predicting cardiac events after DCB treatment.展开更多
Biophysical computational models are complementary to experiments and theories,providing powerful tools for the study of neurological diseases.The focus of this review is the dynamic modeling and control strategies of...Biophysical computational models are complementary to experiments and theories,providing powerful tools for the study of neurological diseases.The focus of this review is the dynamic modeling and control strategies of Parkinson’s disease(PD).In previous studies,the development of parkinsonian network dynamics modeling has made great progress.Modeling mainly focuses on the cortex-thalamus-basal ganglia(CTBG)circuit and its sub-circuits,which helps to explore the dynamic behavior of the parkinsonian network,such as synchronization.Deep brain stimulation(DBS)is an effective strategy for the treatment of PD.At present,many studies are based on the side effects of the DBS.However,the translation from modeling results to clinical disease mitigation therapy still faces huge challenges.Here,we introduce the progress of DBS improvement.Its specific purpose is to develop novel DBS treatment methods,optimize the treatment effect of DBS for each patient,and focus on the study in closed-loop DBS.Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.展开更多
This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergen...This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergence in social systems.By integrating mathematical models,agent-based modeling,network dynamic analysis,and hybrid modeling approaches,the study applies CAS theory to case studies in economic markets,political decision-making,and social interactions.The experimental results demonstrate that local interactions among individual agents can give rise to complex global phenomena,such as market fluctuations,opinion polarization,and sudden outbreaks of social movements.This framework not only provides a more robust explanation for the nonlinear dynamics and abrupt transitions that traditional models often fail to capture,but also offers valuable decision-support tools for public policy formulation,social governance,and risk management.Emphasizing the importance of interdisciplinary approaches,this work outlines future research directions in high-performance computing,artificial intelligence,and real-time data integration to further advance the theoretical and practical applications of CAS in the social sciences.展开更多
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
The importance of properly treating boundary conditions (BCs) in numerical simulation of hemodynamics in intracranial aneurysm (IA) has been increasingly recognized. In this study, we constructed three types of comput...The importance of properly treating boundary conditions (BCs) in numerical simulation of hemodynamics in intracranial aneurysm (IA) has been increasingly recognized. In this study, we constructed three types of computational model for each IA to investigate how the outcome of numerical simulation is affected by the treatment of BCs. The first type of model (i.e., Type-A model) was obtained by applying 3-D hemodynamic modeling to the entire cerebral arterial network, with its solution being taken as the reference for evaluating the performance of the other two types of model (i.e., Type-B and Type-C models) in which 3-D modeling was confined to the aneurysm region. In addition, patient-specific 1-D models of the cerebral arterial network were developed to provide hemodynamic information for setting the inflow/outflow BCs of the 3-D models. Numerical tests on three IAs revealed that prescribing the outflow BCs of a localized 3-D aneurysm model based on 1-D model-simulated outflow division (i.e., Type-B model) instead of imposing the free outflow BC on all outlets (i.e., Type-C model) helped to improve the fidelity of the simulation of intra-aneurysmal hemodynamics, but could not guarantee a complete reproduction of the reference solution obtained by the Type-A model. Moreover, it was found that the outcome of hemodynamic simulation was more sensitive to the treatment of BCs when an aneurysm was located at arterial bifurcation rather than sidewall. These findings highlight the importance of taking into account systemic cerebroarterial hemodynamics in computational modeling of hemodynamics in IAs, especially those located at bifurcations.展开更多
A 3-D computationalframework was suggested to model stable growth of a macroscopic crack under model I condition. The Gurson-Tverpaaof dilatant plasticity model for voided materials describes the damage process. Fixed...A 3-D computationalframework was suggested to model stable growth of a macroscopic crack under model I condition. The Gurson-Tverpaaof dilatant plasticity model for voided materials describes the damage process. Fixed-sized, computational cell elements (containing voids) defined over a thin layer at the cmck plane simulate the ductile crack extension. Outside of this layer, the material remains undamaged by the void growth. The micro-mechanics parumeters controlling cmck growth are the thickness Of computational cell layen D, and the initial void porosity, fo. These parameters are calculated through analyses of ductile tearing to match R-curve obtained from testing of deep notch bend specimens for welded joints. The R-curve for the double edge notched tension specimens is eNctively predicted using these pammeters.The predicted R-curve gives a good agreement with the expemment results.展开更多
Metal organic chenlical vapor deposition （AIOCVD） growth systems arc one of the. main types of equipment used for growing single crystal materials, such as GaN. To obtain fihn epitaxial materials with uniform perfor...Metal organic chenlical vapor deposition （AIOCVD） growth systems arc one of the. main types of equipment used for growing single crystal materials, such as GaN. To obtain fihn epitaxial materials with uniform performanee, the flow field and ternperature field in a GaN-MOCVD reactor are investigated by modeling and simulating. To make the simulation results more consistent with the actual situation, the gases in the reactor are considered to be compressible, making it possible to investigate the distributions of gas density and pressure in the reactor. The computational fluid dynamics method is used to stud,v the effects of inlet gas flow velocity, pressure in the reactor, rotational speed of graphite susceptor, and gases used in the growth, which has great guiding~ significance for the growth of GaN fihn materials.展开更多
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
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
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
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
Pacemaking dysfunction has become a significant disease that may contribute to heart rhythm disorders,syncope,and even death.Up to now,the best way to treat it is to implant electronic pacemakers.However,these have ma...Pacemaking dysfunction has become a significant disease that may contribute to heart rhythm disorders,syncope,and even death.Up to now,the best way to treat it is to implant electronic pacemakers.However,these have many disadvantages such as limited battery life,infection,and fixed pacing rate.There is an urgent need for a biological pacemaker(bio-pacemaker).This is expected to replace electronic devices because of its low risk of complications and the ability to respond to emotion.Here we survey the contemporary development of the bio-pacemaker by both experimental and computational approaches.The former mainly includes gene therapy and cell therapy,whilst the latter involves the use of multi-scale computer models of the heart,ranging from the single cell to the tissue slice.Up to now,a bio-pacemaker has been successfully applied in big mammals,but it still has a long way from clinical uses for the treatment of human heart diseases.It is hoped that the use of the computational model of a bio-pacemaker may accelerate this process.Finally,we propose potential research directions for generating a bio-pacemaker based on cardiac computational modeling.展开更多
Computational modeling methods have been increasingly employed to quantify aortic hemodynamic parameters that are challenging to in vivo measurements but important for the diagnosis/treatment of aortic disease.Althoug...Computational modeling methods have been increasingly employed to quantify aortic hemodynamic parameters that are challenging to in vivo measurements but important for the diagnosis/treatment of aortic disease.Although the presence of turbulence-like behaviors of blood flow in normal or diseased aorta has long been confirmed,the majority of existing computational model studies adopted the laminar flow assumption(LFA)in the treatment of sub-grid flow variables.So far,it remains unclear whether LFA would significantly compromise the reliability of hemodynamic simulation.In the present study,we addressed the issue in the context of a specific aortopathy,namely aortic dilation,which is usually accompanied by disturbed flow patterns.Three patient-specific aortas with treated/untreated dilation of the ascending segment were investigated,and their geometrical models were reconstructed from computed tomography angiographic images,with the boundary conditions being prescribed based on flow velocity information measured in vivo with the phase contrast magnetic resonance imaging technique.For the modeling of blood flow,apart from the traditional LFA-based method in which sub-grid flow dynamics is ignored,the large eddy simulation(LES)method capable of incorporating the dissipative energy loss induced by turbulent eddies at the sub-grid level,was adopted and taken as a reference for examining the performance of the LFA-based method.Obtained results showed that the simulated large-scale flow patterns with the two methods had high similarity,both agreeing well with in vivo measurements,although locally large between-method discrepancies in computed hemodynamic quantities existed in regions with high intensity of flow turbulence.Quantitatively,a switch from the LES to the LFAbased modeling method led to mild(<6%)changes in computed space-averaged wall shear stress metrics(i.e.,SA-TAWSS,SA-OSI)in the ascending aortic segment where intensive vortex evolution accompanied by high statistical Reynolds stress was observed.In addition,comparisons among the three aortas revealed that the treatment status of aortic dilation or the concomitant presence of aortic valve disease,despite its remarkable influence on flow patterns in the ascending aortic segment,did not significantly affect the degrees of discrepancies between the two modeling methods in predicting SA-TAWSS and SA-OSI.These findings suggest that aortic dilation per se does not induce strong flow turbulence that substantially negates the validity of LFA-based modeling,especially in simulating macro-scale hemodynamic features.展开更多
基金financial support from the Centro de Matematica da Universidade doMinho(CMAT/UM),through project UID/00013.
文摘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.
基金supported by the Major Research Instrument Development Project of the National Natural Science Foundation of China(82327810)the Foundation of the President of Hebei University(XZJJ202202)the Hebei Province“333 talent project”(A202101058).
文摘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.
基金supported by the Natural Science Foundation of Hunan Province(Grant No.2023JJ40353)the National Key Research and Development Program of China(No.2019YFE03120001).
文摘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 Ti_(5)Si_(3)particles,while higher content of Si(0.9 and 1.2 wt.%)results in coarser primary Ti_(5)Si_(3)agglomerations,deteriorating the ductility.FE simulations support these findings,highlighting the finer and more uniformly distributed Ti_(5)Si_(3)particles contribute to less stress concentration and promote uniform deformation across the matrix,while agglomerated Ti_(5)Si_(3)particles 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.
基金the Hainan Provincial Natural Science Foundation of China(No.820RC625)the National Natural Science Foundation of China(No.82060332)。
文摘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.
文摘Ca^2＋ dysregulation is an early event observed in Alzheimer＇s disease（AD） patients preceding the presence of its clinical symptoms.Dysregulation of neuronalCa^2＋ will cause synaptic loss and neuronal death,eventually leading to memory impairments and cognitive decline.Treatments targetingCa^2＋ signaling pathways are potential therapeutic strategies against AD.The complicated interactions make it challenging and expensive to study the underlying mechanisms as to how Ca^2＋ signaling contributes to the pathogenesis of AD.Computational modeling offers new opportunities to study the signaling pathway and test proposed mechanisms.In this mini-review,we present some computational approaches that have been used to study Ca^2＋ dysregulation of AD by simulating Ca^2＋signaling at various levels.We also pointed out the future directions that computational modeling can be done in studying the Ca^2＋ dysregulation in AD.
基金This work was supported by the National Natural Science Foundation of China(Nos.81901841 and 61527811)the Key Research and Development Program of Zhejiang Province(No.2020C03016)the Dalian University of Technology(No.DUT18RC(3)068),China.
文摘Atrial fibrillation(AF)is one of the most common arrhythmias,associated with high morbidity,mortality,and healthcare costs,and it places a significant burden on both individuals and society.Anti-arrhythmic drugs are the most commonly used strategy for treating AF.However,drug therapy faces challenges because of its limited efficacy and potential side effects.Catheter ablation is widely used as an alternative treatment for AF.Nevertheless,because the mechanism of AF is not fully understood,the recurrence rate after ablation remains high.In addition,the outcomes of ablation can vary significantly between medical institutions and patients,especially for persistent AF.Therefore,the issue of which ablation strategy is optimal is still far from settled.Computational modeling has the advantages of repeatable operation,low cost,freedom from risk,and complete control,and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even guidance.This review summarizes three-dimensional computational modeling simulations of catheter ablation for AF,from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ablation.Finally,we summarize current developments and challenges and provide our perspectives and suggestions for future directions.
基金This work was supported by National Natural Science Foundation of China(Nos.61831015 and 61901260)Key Research and Development Program of China(No.2019YFB1405902).
文摘Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience optimization.The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.For bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of HVS.According to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,respectively.The natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three layers.The support vector regression(SVR)is employed to aggregate features to the final quality prediction.Extensive experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(LTGY24H180019)Basic Medical and Health Science Technology Projects of Wenzhou City(Y20220132)Medical and Health Science and Technology Project of Zhejiang Province(2023RC210 and 2024KY160).
文摘Acute morphological changes in de novo coronary lesions after drug-coated balloon(DCB)angioplasty can affect endothelial mechanics and consequently clinical outcomes.Angiography-based computational modeling has been validated to assess endothelial dynamic strain(EDS)in coronary arteries in vivo.The EDS was calculated on the basis of pre-and post-DCB angiography.Parameters of quantitative coronary angiography and EDS were quantified at cross-sections in the treated segments.A total of 336 and 348 lesion cross-sections were included in the small/large vessel groups,respectively.The acute lumen gain after DCB was significantly higher in large than small vessels(relative changes:21.3%[17.4%,25.1%]vs.7.4%[4.8%,10.1%],P<0.001).Before treatment,three indices of EDS were significantly higher in small than large vessels(for ED-EDS:29.2%[19.8%,44.8%]vs.20.4%[14.3%,30.2%];for ES-EDS:26.8%[18.9%,37.7%]vs.18.3%[13.9%,25.4%];for TA-EDS:19.1%[13.9%,27.8%]vs.14.3%[10.5%,20.1%],P<0.001).After treatment,the EDS in small vessels significantly decreased(P<0.001).ED-EDS showed the highest correlation with pre-DCB DSP(r=0.43,P<0.001)and post-DCB MLD(r=0.35,P<0.001).The levels of EDS parameters for small or large vessel lesions significantly differed.Further study is required to examine the clinical value of EDS in predicting cardiac events after DCB treatment.
基金Project supported by the National Natural Science Foundation of China(Nos.11932003 and 11772019)。
文摘Biophysical computational models are complementary to experiments and theories,providing powerful tools for the study of neurological diseases.The focus of this review is the dynamic modeling and control strategies of Parkinson’s disease(PD).In previous studies,the development of parkinsonian network dynamics modeling has made great progress.Modeling mainly focuses on the cortex-thalamus-basal ganglia(CTBG)circuit and its sub-circuits,which helps to explore the dynamic behavior of the parkinsonian network,such as synchronization.Deep brain stimulation(DBS)is an effective strategy for the treatment of PD.At present,many studies are based on the side effects of the DBS.However,the translation from modeling results to clinical disease mitigation therapy still faces huge challenges.Here,we introduce the progress of DBS improvement.Its specific purpose is to develop novel DBS treatment methods,optimize the treatment effect of DBS for each patient,and focus on the study in closed-loop DBS.Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.
文摘This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergence in social systems.By integrating mathematical models,agent-based modeling,network dynamic analysis,and hybrid modeling approaches,the study applies CAS theory to case studies in economic markets,political decision-making,and social interactions.The experimental results demonstrate that local interactions among individual agents can give rise to complex global phenomena,such as market fluctuations,opinion polarization,and sudden outbreaks of social movements.This framework not only provides a more robust explanation for the nonlinear dynamics and abrupt transitions that traditional models often fail to capture,but also offers valuable decision-support tools for public policy formulation,social governance,and risk management.Emphasizing the importance of interdisciplinary approaches,this work outlines future research directions in high-performance computing,artificial intelligence,and real-time data integration to further advance the theoretical and practical applications of CAS in the social sciences.
基金financially by the National Research Council of Thailand(NRCT)under Contract No.N42A670894.
文摘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.
基金This work was supported by the Clinical Research Plan of SHDC(Grant Nos.16CR3031A,16CR2045B)the SJTU Medical-Engineering Cross-cutting Research Foundation(Jrant Nos.YG2015MS53,YG2017MS45).
文摘The importance of properly treating boundary conditions (BCs) in numerical simulation of hemodynamics in intracranial aneurysm (IA) has been increasingly recognized. In this study, we constructed three types of computational model for each IA to investigate how the outcome of numerical simulation is affected by the treatment of BCs. The first type of model (i.e., Type-A model) was obtained by applying 3-D hemodynamic modeling to the entire cerebral arterial network, with its solution being taken as the reference for evaluating the performance of the other two types of model (i.e., Type-B and Type-C models) in which 3-D modeling was confined to the aneurysm region. In addition, patient-specific 1-D models of the cerebral arterial network were developed to provide hemodynamic information for setting the inflow/outflow BCs of the 3-D models. Numerical tests on three IAs revealed that prescribing the outflow BCs of a localized 3-D aneurysm model based on 1-D model-simulated outflow division (i.e., Type-B model) instead of imposing the free outflow BC on all outlets (i.e., Type-C model) helped to improve the fidelity of the simulation of intra-aneurysmal hemodynamics, but could not guarantee a complete reproduction of the reference solution obtained by the Type-A model. Moreover, it was found that the outcome of hemodynamic simulation was more sensitive to the treatment of BCs when an aneurysm was located at arterial bifurcation rather than sidewall. These findings highlight the importance of taking into account systemic cerebroarterial hemodynamics in computational modeling of hemodynamics in IAs, especially those located at bifurcations.
文摘A 3-D computationalframework was suggested to model stable growth of a macroscopic crack under model I condition. The Gurson-Tverpaaof dilatant plasticity model for voided materials describes the damage process. Fixed-sized, computational cell elements (containing voids) defined over a thin layer at the cmck plane simulate the ductile crack extension. Outside of this layer, the material remains undamaged by the void growth. The micro-mechanics parumeters controlling cmck growth are the thickness Of computational cell layen D, and the initial void porosity, fo. These parameters are calculated through analyses of ductile tearing to match R-curve obtained from testing of deep notch bend specimens for welded joints. The R-curve for the double edge notched tension specimens is eNctively predicted using these pammeters.The predicted R-curve gives a good agreement with the expemment results.
基金Supported by the National Key R&D Program of China under Grant No 2016YFB0400104
文摘Metal organic chenlical vapor deposition （AIOCVD） growth systems arc one of the. main types of equipment used for growing single crystal materials, such as GaN. To obtain fihn epitaxial materials with uniform performanee, the flow field and ternperature field in a GaN-MOCVD reactor are investigated by modeling and simulating. To make the simulation results more consistent with the actual situation, the gases in the reactor are considered to be compressible, making it possible to investigate the distributions of gas density and pressure in the reactor. The computational fluid dynamics method is used to stud,v the effects of inlet gas flow velocity, pressure in the reactor, rotational speed of graphite susceptor, and gases used in the growth, which has great guiding~ significance for the growth of GaN fihn materials.
文摘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.
基金supported by Ongoing Research Funding Program(ORF-2025-488)King Saud University,Riyadh,Saudi Arabia.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant Numbers:12172149 and 12172151).
文摘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.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2023YJS053)the National Natural Science Foundation of China(Grant No.52278386).
文摘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.
基金Project supported by the National Natural Science Foundation of China(Nos.61572152,61601143,and 81770328)the Science Technology and Innovation Commission of Shenzhen Municipality(Nos.JCYJ20151029173639477 and JSGG20160229125049615)the China Postdoctoral Science Foundation(No.2015M581448)。
文摘Pacemaking dysfunction has become a significant disease that may contribute to heart rhythm disorders,syncope,and even death.Up to now,the best way to treat it is to implant electronic pacemakers.However,these have many disadvantages such as limited battery life,infection,and fixed pacing rate.There is an urgent need for a biological pacemaker(bio-pacemaker).This is expected to replace electronic devices because of its low risk of complications and the ability to respond to emotion.Here we survey the contemporary development of the bio-pacemaker by both experimental and computational approaches.The former mainly includes gene therapy and cell therapy,whilst the latter involves the use of multi-scale computer models of the heart,ranging from the single cell to the tissue slice.Up to now,a bio-pacemaker has been successfully applied in big mammals,but it still has a long way from clinical uses for the treatment of human heart diseases.It is hoped that the use of the computational model of a bio-pacemaker may accelerate this process.Finally,we propose potential research directions for generating a bio-pacemaker based on cardiac computational modeling.
基金The study was supported by the National Natural Science Foundation of China(Grant nos.11972231,11832003,81611530715)the China Postdoctoral Science Foundation(Grant no.2018M640385)the SJTU Medical-Engineering Cross-cutting Research Project(Grant no.YG2017MS45).
文摘Computational modeling methods have been increasingly employed to quantify aortic hemodynamic parameters that are challenging to in vivo measurements but important for the diagnosis/treatment of aortic disease.Although the presence of turbulence-like behaviors of blood flow in normal or diseased aorta has long been confirmed,the majority of existing computational model studies adopted the laminar flow assumption(LFA)in the treatment of sub-grid flow variables.So far,it remains unclear whether LFA would significantly compromise the reliability of hemodynamic simulation.In the present study,we addressed the issue in the context of a specific aortopathy,namely aortic dilation,which is usually accompanied by disturbed flow patterns.Three patient-specific aortas with treated/untreated dilation of the ascending segment were investigated,and their geometrical models were reconstructed from computed tomography angiographic images,with the boundary conditions being prescribed based on flow velocity information measured in vivo with the phase contrast magnetic resonance imaging technique.For the modeling of blood flow,apart from the traditional LFA-based method in which sub-grid flow dynamics is ignored,the large eddy simulation(LES)method capable of incorporating the dissipative energy loss induced by turbulent eddies at the sub-grid level,was adopted and taken as a reference for examining the performance of the LFA-based method.Obtained results showed that the simulated large-scale flow patterns with the two methods had high similarity,both agreeing well with in vivo measurements,although locally large between-method discrepancies in computed hemodynamic quantities existed in regions with high intensity of flow turbulence.Quantitatively,a switch from the LES to the LFAbased modeling method led to mild(<6%)changes in computed space-averaged wall shear stress metrics(i.e.,SA-TAWSS,SA-OSI)in the ascending aortic segment where intensive vortex evolution accompanied by high statistical Reynolds stress was observed.In addition,comparisons among the three aortas revealed that the treatment status of aortic dilation or the concomitant presence of aortic valve disease,despite its remarkable influence on flow patterns in the ascending aortic segment,did not significantly affect the degrees of discrepancies between the two modeling methods in predicting SA-TAWSS and SA-OSI.These findings suggest that aortic dilation per se does not induce strong flow turbulence that substantially negates the validity of LFA-based modeling,especially in simulating macro-scale hemodynamic features.
