Neurodegenerative disorders,most notably Alzheimer’s disease(AD),are marked by progressive cognitive decline and widespread neuronal loss.A growing body of evidence indicates that synaptic dysfunction occurs early in...Neurodegenerative disorders,most notably Alzheimer’s disease(AD),are marked by progressive cognitive decline and widespread neuronal loss.A growing body of evidence indicates that synaptic dysfunction occurs early in AD and serves as a key driver of memory impairment rather than a secondary symptom.Central to this dysfunction is synaptic plasticity(SP)—the ability of synapses to modify their strength in response to patterns of neuronal signaling.By strengthening or weakening connections,SP enables the brain to encode experience,support flexible behavior,and maintain cognitive adaptability.Disruption of SP has therefore been identified as a core pathological mechanism underlying AD progression.This review aims to first summarize the basic mechanisms of synaptic plasticity and then synthesize recent findings on how SP is regulated and dysregulated in Alzheimer’s disease.By highlighting current knowledge gaps and unresolved questions,it seeks to identify key directions for future research.Clarifying how major forms of SP are altered in AD may offer crucial insight into the mechanisms of memory impairment and potential therapeutic targets.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
With the rapid advancement of computational technologies and the in-depth exploration of multimodal discourse analysis theory,the intersection of these fields has become a frontier area in both technology and linguist...With the rapid advancement of computational technologies and the in-depth exploration of multimodal discourse analysis theory,the intersection of these fields has become a frontier area in both technology and linguistics research.This article examines the application of computational models in multimodal discourse analysis and analyzes how innovations in multimodal discourse analysis theory drive advancements in computational models.Through empirical studies and theoretical discussions,the paper reveals the interaction mechanisms between computational models and multimodal discourse analysis in practice and highlights their theoretical complementarity,providing new perspectives and methodologies for future research.The findings show that effective computational models enhance the accuracy and depth of multimodal analysis,while theoretical innovations in multimodal discourse analysis propel computational models toward greater efficiency and adaptability.展开更多
Valvular heart disease is currently a common problem which causes high morbidity and mortality worldwide.Prosthetic valve replacements are widely needed to correct narrowing or backflow through the valvular orifice.Co...Valvular heart disease is currently a common problem which causes high morbidity and mortality worldwide.Prosthetic valve replacements are widely needed to correct narrowing or backflow through the valvular orifice.Compared to mechanical valves and biological valves,tissue-engineered heart valves can be an ideal substitute because they have a low risk of thromboembolism and calcification,and the potential for remodelling,regeneration,and growth.In order to test the performance of these heart valves,various animal models and other models are needed to optimise the structure and function of tissue-engineered heart valves,which may provide a potential mechanism responsible for substantial enhancement in tissue-engineered heart valves.Choosing the appropriate model for evaluating the performance of the tissue-engineered valve is important,as different models have their own advantages and disadvantages.In this review,we summarise the current state-of-the-art animal models,bioreactors,and computational simulation models with the aim of creating more strategies for better development of tissue-engineered heart valves.This review provides an overview of major factors that influence the selection and design of a model for tissue-engineered heart valve.Continued efforts in improving and testing models for valve regeneration remain crucial in basic science and translational researches.Future research should focus on finding the right animal model and developing better in vitro testing systems for tissue-engineered heart valve.展开更多
The complex mechanisms of the internal operation of cellular functions have not been fully resolved and these functions are regulated by multiple effects,such as transcription regulation,signal transduction,and enzyme...The complex mechanisms of the internal operation of cellular functions have not been fully resolved and these functions are regulated by multiple effects,such as transcription regulation,signal transduction,and enzyme catalysis,forming complex interactive mechanisms.This makes the construction of a whole-cell computational model,containing various complex cellular functions,very challenging.However,biological models have played a significant role in the field of systems biology,such as guiding gene-target mining and studying cell metabolic characteristics.Therefore,there is increasing research interest in the construction of whole-cell computational models.Combining two classical languages of systems biology,this review expounds on the development and challenges of whole-cell computational modeling from the two classical methods of steady-state and dynamic modeling.Finally,we propose a new approach for constructing whole-cell computational models.展开更多
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
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
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
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
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
The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not o...The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not only on rank but also on the composition,distribution,and combination of the macerals.Unlike the proximate and ultimate analyses,determining the macerals in coal involves the use of sophisticated microscopic instrumentation and expertise.In this study,an attempt was made to predict the amount of macerals(vitrinite,inertinite,and liptinite)and total mineral matter from the Witbank Coalfields samples using the multiple input single output white-box artificial neural network(MISOWB-ANN),gene expression programming(GEP),multiple linear regression(MLR),and multiple nonlinear regression(MNLR).The predictive models obtained from the multiple soft computing models adopted are contrasted with one another using difference,efficiency,and composite statistical indicators to examine the appropriateness of the models.The MISOWB-ANN provides a more reliable predictive model than the other three models with the lowest difference and highest efficiency and composite statistical indicators.展开更多
Cancer is a major stress for public well-being and is the most dreadful disease.The models used in the discovery of cancer treatment are continuously changing and extending toward advanced preclinical studies.Cancer m...Cancer is a major stress for public well-being and is the most dreadful disease.The models used in the discovery of cancer treatment are continuously changing and extending toward advanced preclinical studies.Cancer models are either naturally existing or artificially prepared experimental systems that show similar features with human tumors though the heterogeneous nature of the tumor is very familiar.The choice of the most fitting model to best reflect the given tumor system is one of the real difficulties for cancer examination.Therefore,vast studies have been conducted on the cancer models for developing a better understanding of cancer invasion,progression,and early detection.These models give an insight into cancer etiology,molecular basis,host tumor interaction,the role of microenvironment,and tumor heterogeneity in tumor metastasis.These models are also used to predict novel can-cer markers,targeted therapies,and are extremely helpful in drug development.In this review,the potential of cancer models to be used as a platform for drug screening and therapeutic discoveries are highlighted.Although none of the cancer models is regarded as ideal because each is associated with essential caveats that restraint its application yet by bridging the gap between preliminary cancer research and transla-tional medicine.However,they promise a brighter future for cancer treatment.展开更多
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
In the last century, there has been a significant development in the evaluation of methods to predict ground movement due to underground extraction. Some remarkable developments in three-dimensional computational meth...In the last century, there has been a significant development in the evaluation of methods to predict ground movement due to underground extraction. Some remarkable developments in three-dimensional computational methods have been supported in civil engineering, subsidence engineering and mining engineering practice. However, ground movement problem due to mining extraction sequence is effectively four dimensional （4D）. A rational prediction is getting more and more important for long-term underground mining planning. Hence, computer-based analytical methods that realistically simulate spatially distributed time-dependent ground movement process are needed for the reliable long-term underground mining planning to minimize the surface environmental damages. In this research, a new computational system is developed to simulate four-dimensional （4D） ground movement by combining a stochastic medium theory, Knothe time-delay model and geographic information system （GIS） technology. All the calculations are implemented by a computational program, in which the components of GIS are used to fulfill the spatial-temporal analysis model. In this paper a tight coupling strategy based on component object model of GIS technology is used to overcome the problems of complex three-dimensional extraction model and spatial data integration. Moreover, the implementation of computational of the interfaces of the developed tool is described. The GIS based developed tool is validated by two study cases. The developed computational tool and models are achieved within the GIS system so the effective and efficient calculation methodology can be obtained, so the simulation problems of 4D ground movement due to underground mining extraction sequence can be solved by implementation of the developed tool in GIS.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
Reliable computational foot models offer an alternative means to enhance knowledge on the biomechanics of human foot. Model validation is one of the most critical aspects of the entire foot modeling and analysis proce...Reliable computational foot models offer an alternative means to enhance knowledge on the biomechanics of human foot. Model validation is one of the most critical aspects of the entire foot modeling and analysis process.This paper presents an in vivo experiment combining motion capture system and plantar pressure measure platform to validate a three-dimensional finite element model of human foot.The Magnetic Resonance Imaging(MRI)slices for the foot modeling and the experimental data for validation were both collected from the same volunteer subject.The validated components included the comparison of static model predictions of plantar force,plantar pressure and foot surface deformation during six loading conditions,to equivalent measured data.During the whole experiment,foot surface deformation,plantar force and plantar pressure were recorded simultaneously during six different loaded standing conditions.The predictions of the current FE model were in good agreement with these experimental results.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
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
文摘Neurodegenerative disorders,most notably Alzheimer’s disease(AD),are marked by progressive cognitive decline and widespread neuronal loss.A growing body of evidence indicates that synaptic dysfunction occurs early in AD and serves as a key driver of memory impairment rather than a secondary symptom.Central to this dysfunction is synaptic plasticity(SP)—the ability of synapses to modify their strength in response to patterns of neuronal signaling.By strengthening or weakening connections,SP enables the brain to encode experience,support flexible behavior,and maintain cognitive adaptability.Disruption of SP has therefore been identified as a core pathological mechanism underlying AD progression.This review aims to first summarize the basic mechanisms of synaptic plasticity and then synthesize recent findings on how SP is regulated and dysregulated in Alzheimer’s disease.By highlighting current knowledge gaps and unresolved questions,it seeks to identify key directions for future research.Clarifying how major forms of SP are altered in AD may offer crucial insight into the mechanisms of memory impairment and potential therapeutic targets.
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.
文摘With the rapid advancement of computational technologies and the in-depth exploration of multimodal discourse analysis theory,the intersection of these fields has become a frontier area in both technology and linguistics research.This article examines the application of computational models in multimodal discourse analysis and analyzes how innovations in multimodal discourse analysis theory drive advancements in computational models.Through empirical studies and theoretical discussions,the paper reveals the interaction mechanisms between computational models and multimodal discourse analysis in practice and highlights their theoretical complementarity,providing new perspectives and methodologies for future research.The findings show that effective computational models enhance the accuracy and depth of multimodal analysis,while theoretical innovations in multimodal discourse analysis propel computational models toward greater efficiency and adaptability.
基金supported by the National Natural Science Foundation of China(No.81900351)National Key Research and Development Program of China(No.2018YFA0108700)Health Commission of Hubei Province Scientific Research Project of China(No.WJ2019Q034).
文摘Valvular heart disease is currently a common problem which causes high morbidity and mortality worldwide.Prosthetic valve replacements are widely needed to correct narrowing or backflow through the valvular orifice.Compared to mechanical valves and biological valves,tissue-engineered heart valves can be an ideal substitute because they have a low risk of thromboembolism and calcification,and the potential for remodelling,regeneration,and growth.In order to test the performance of these heart valves,various animal models and other models are needed to optimise the structure and function of tissue-engineered heart valves,which may provide a potential mechanism responsible for substantial enhancement in tissue-engineered heart valves.Choosing the appropriate model for evaluating the performance of the tissue-engineered valve is important,as different models have their own advantages and disadvantages.In this review,we summarise the current state-of-the-art animal models,bioreactors,and computational simulation models with the aim of creating more strategies for better development of tissue-engineered heart valves.This review provides an overview of major factors that influence the selection and design of a model for tissue-engineered heart valve.Continued efforts in improving and testing models for valve regeneration remain crucial in basic science and translational researches.Future research should focus on finding the right animal model and developing better in vitro testing systems for tissue-engineered heart valve.
基金This work was fnancially supported by the National Key R&D Program of China(No.2019YFA0904300).
文摘The complex mechanisms of the internal operation of cellular functions have not been fully resolved and these functions are regulated by multiple effects,such as transcription regulation,signal transduction,and enzyme catalysis,forming complex interactive mechanisms.This makes the construction of a whole-cell computational model,containing various complex cellular functions,very challenging.However,biological models have played a significant role in the field of systems biology,such as guiding gene-target mining and studying cell metabolic characteristics.Therefore,there is increasing research interest in the construction of whole-cell computational models.Combining two classical languages of systems biology,this review expounds on the development and challenges of whole-cell computational modeling from the two classical methods of steady-state and dynamic modeling.Finally,we propose a new approach for constructing whole-cell computational models.
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 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 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 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The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not only on rank but also on the composition,distribution,and combination of the macerals.Unlike the proximate and ultimate analyses,determining the macerals in coal involves the use of sophisticated microscopic instrumentation and expertise.In this study,an attempt was made to predict the amount of macerals(vitrinite,inertinite,and liptinite)and total mineral matter from the Witbank Coalfields samples using the multiple input single output white-box artificial neural network(MISOWB-ANN),gene expression programming(GEP),multiple linear regression(MLR),and multiple nonlinear regression(MNLR).The predictive models obtained from the multiple soft computing models adopted are contrasted with one another using difference,efficiency,and composite statistical indicators to examine the appropriateness of the models.The MISOWB-ANN provides a more reliable predictive model than the other three models with the lowest difference and highest efficiency and composite statistical indicators.
文摘Cancer is a major stress for public well-being and is the most dreadful disease.The models used in the discovery of cancer treatment are continuously changing and extending toward advanced preclinical studies.Cancer models are either naturally existing or artificially prepared experimental systems that show similar features with human tumors though the heterogeneous nature of the tumor is very familiar.The choice of the most fitting model to best reflect the given tumor system is one of the real difficulties for cancer examination.Therefore,vast studies have been conducted on the cancer models for developing a better understanding of cancer invasion,progression,and early detection.These models give an insight into cancer etiology,molecular basis,host tumor interaction,the role of microenvironment,and tumor heterogeneity in tumor metastasis.These models are also used to predict novel can-cer markers,targeted therapies,and are extremely helpful in drug development.In this review,the potential of cancer models to be used as a platform for drug screening and therapeutic discoveries are highlighted.Although none of the cancer models is regarded as ideal because each is associated with essential caveats that restraint its application yet by bridging the gap between preliminary cancer research and transla-tional medicine.However,they promise a brighter future for cancer treatment.
基金funding from the Helmholtz-Incubator project Uncertainty Quantification.
文摘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.
文摘In the last century, there has been a significant development in the evaluation of methods to predict ground movement due to underground extraction. Some remarkable developments in three-dimensional computational methods have been supported in civil engineering, subsidence engineering and mining engineering practice. However, ground movement problem due to mining extraction sequence is effectively four dimensional （4D）. A rational prediction is getting more and more important for long-term underground mining planning. Hence, computer-based analytical methods that realistically simulate spatially distributed time-dependent ground movement process are needed for the reliable long-term underground mining planning to minimize the surface environmental damages. In this research, a new computational system is developed to simulate four-dimensional （4D） ground movement by combining a stochastic medium theory, Knothe time-delay model and geographic information system （GIS） technology. All the calculations are implemented by a computational program, in which the components of GIS are used to fulfill the spatial-temporal analysis model. In this paper a tight coupling strategy based on component object model of GIS technology is used to overcome the problems of complex three-dimensional extraction model and spatial data integration. Moreover, the implementation of computational of the interfaces of the developed tool is described. The GIS based developed tool is validated by two study cases. The developed computational tool and models are achieved within the GIS system so the effective and efficient calculation methodology can be obtained, so the simulation problems of 4D ground movement due to underground mining extraction sequence can be solved by implementation of the developed tool in GIS.
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supported by the "Mechanical Virtual Human of China"project funded by the National Natural Science Foundation of China(30530230)further support was from the UK Royal Scoiety(Grant:IPJ/2006/R3)
文摘Reliable computational foot models offer an alternative means to enhance knowledge on the biomechanics of human foot. Model validation is one of the most critical aspects of the entire foot modeling and analysis process.This paper presents an in vivo experiment combining motion capture system and plantar pressure measure platform to validate a three-dimensional finite element model of human foot.The Magnetic Resonance Imaging(MRI)slices for the foot modeling and the experimental data for validation were both collected from the same volunteer subject.The validated components included the comparison of static model predictions of plantar force,plantar pressure and foot surface deformation during six loading conditions,to equivalent measured data.During the whole experiment,foot surface deformation,plantar force and plantar pressure were recorded simultaneously during six different loaded standing conditions.The predictions of the current FE model were in good agreement with these experimental results.
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.
