This work investigates the effects of deformation mechanisms on the mechanical properties and anisotropy of rolled AZ31B magnesium alloy under uniaxial tension,combining experimental characterization with Visco-Plasti...This work investigates the effects of deformation mechanisms on the mechanical properties and anisotropy of rolled AZ31B magnesium alloy under uniaxial tension,combining experimental characterization with Visco-Plastic Self Consistent(VPSC)modeling.The research focuses particularly on anisotropic mechanical responses along transverse direction(TD)and rolling direction(RD).Experimental measurements and computational simulations consistently demonstrate that prismaticslip activation significantly reduces the strain hardening rate during the initial stage of tensile deformation.By suppressing the activation of specific deformation mechanisms along RD and TD,the tensile mechanical behavior of the magnesium alloy was further investigated.The results show that basalslip has the greatest impact during the initial deformation stage and basalslip activation substantially affects the deformation behavior of AZ31B alloy,causing marked decreases in both yield and tensile strength along RD.Under tensile loading along TD,prismaticslip not only exhibits a synergistic effect on yield strength,but also dominants work hardening during the initial plastic deformation.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
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
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
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
Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automat...Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automating CFD workflows is underdeveloped.We introduce a novel approach centered on domain-specific LLM adaptation.By fine-tuning Qwen2.5-7B-Instruct on NL2FOAM,our custom dataset of 28,716 natural language-to-OpenFOAM configuration pairs with chain-of-thought(CoT)annotations enables direct translation from natural language descriptions to executable CFD setups.A multi-agent system orchestrates the process,autonomously verifying inputs,generating configurations,running simulations,and correcting errors.Evaluation on a benchmark of 21 diverse flow cases demonstrates state-of-the-art performance,achieving 88.7%solution accuracy and 82.6%first-attempt success rate.This significantly outperforms larger general-purpose models such as Qwen2.5-72B-Instruct,DeepSeek-R1,and Llama3.3-70B-Instruct,while also requiring fewer correction iterations and maintaining high computational efficiency.The results highlight the critical role of domain-specific adaptation in deploying LLM assistants for complex engineering workflows.Our code and fine-tuned model have been deposited at https://github.com/YYgroup/AutoCFD.展开更多
This paper investigates the capabilities of large language models(LLMs)to leverage,learn and create knowledge in solving computational fluid dynamics(CFD)problems through three categories of baseline problems.These ca...This paper investigates the capabilities of large language models(LLMs)to leverage,learn and create knowledge in solving computational fluid dynamics(CFD)problems through three categories of baseline problems.These categories include(1)conventional CFD problems that can be solved using existing numerical methods in LLMs,such as lid-driven cavity flow and the Sod shock tube problem;(2)problems that require new numerical methods beyond those available in LLMs,such as the recently developed Chien-physics-informed neural networks for singularly perturbed convection-diffusion equations;and(3)problems that cannot be solved using existing numerical methods in LLMs,such as the ill-conditioned Hilbert linear algebraic systems.The evaluations indicate that reasoning LLMs overall outperform non-reasoning models in four test cases.Reasoning LLMs show excellent performance for CFD problems according to the tailored prompts,but their current capability in autonomous knowledge exploration and creation needs to be enhanced.展开更多
Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first c...Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first collected book reviews from these two well-known websites by Python.Through topic analysis of these reviews,we identified recurring topics,including details of her translated works and appreciation of their translation quality.Then,employing sentiment and content analysis methods,the paper explored the emotional attitudes and the specific thoughts of readers toward Can Xue and her translated works.The fingdings revealed that,among the 408 reviews,though the reception of Can Xue’s translated works was relatively positive,the current level of attention and recognition remains insufficient.However,based on the research results,the paper can derive valuable insights into the translation and dissemination processes such as adjusting translation and dissemination strategies,so that the global reach of Chinese literature and culture can be better facilitated.展开更多
Streptococcus suis(S.suis)is a major disease impacting pig farming globally.It can also be transferred to humans by eating raw pork.A comprehensive study was recently carried out to determine the indices throughmultip...Streptococcus suis(S.suis)is a major disease impacting pig farming globally.It can also be transferred to humans by eating raw pork.A comprehensive study was recently carried out to determine the indices throughmultiple geographic regions in China.Methods:The well-posed theorems were employed to conduct a thorough analysis of the model’s feasible features,including positivity,boundedness equilibria,reproduction number,and parameter sensitivity.Stochastic Euler,Runge Kutta,and EulerMaruyama are some of the numerical techniques used to replicate the behavior of the streptococcus suis infection in the pig population.However,the dynamic qualities of the suggested model cannot be restored using these techniques.Results:For the stochastic delay differential equations of the model,the non-standard finite difference approach in the sense of stochasticity is developed to avoid several problems such as negativity,unboundedness,inconsistency,and instability of the findings.Results from traditional stochastic methods either converge conditionally or diverge over time.The stochastic non-negative step size convergence nonstandard finite difference(NSFD)method unconditionally converges to the model’s true states.Conclusions:This study improves our understanding of the dynamics of streptococcus suis infection using versions of stochastic with delay approaches and opens up new avenues for the study of cognitive processes and neuronal analysis.Theplotted interaction behaviour and new solution comparison profiles.展开更多
Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise inter...Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise intervention based on cross-scale understandings of the etiology,real-time sensing and actuating to enable early detection,screening and personalized treatment.We argue that merging computational intelligence and wearable technologies can bridge the gap between the current trajectory of the techniques applied to AIS and this vision.Wearable technologies such as inertial measurement units(IMUs)and surface electromyography(sEMG)have shown great potential in monitoring spinal curvature and muscle activity in real-time.For instance,IMUs can track the kinematics of the spine during daily activities,while sEMG can detect asymmetric muscle activation patterns that may contribute to scoliosis progression.Computational intelligence,particularly deep learning algorithms,can process these multi-modal data streams to identify early signs of scoliosis and adapt treatment strategies dynamically.By using their combination,we can find potential solutions for a better understanding of the disease,a more effective and intelligent way for treatment and rehabilitation.展开更多
Cardiolipins(CLs),the mitochondria-specific class of phospholipids,are crucial to energy metabolism,cristae structure,and cell apoptosis.CLs present significant challenges in lipidomics analysis due to their structura...Cardiolipins(CLs),the mitochondria-specific class of phospholipids,are crucial to energy metabolism,cristae structure,and cell apoptosis.CLs present significant challenges in lipidomics analysis due to their structural diversity with up to four fatty acyl side chains.In this study,we developed CLAN(Cardio Lipin ANalysis),a comprehensive computational pipeline designed to improve the accuracy and coverage of cardiolipin identification.CLAN integrates three innovative modules:A cardiolipin identification module that utilizes specific fragmentation rules for precise characterization of CLs and their acyl side chains;a false positives detection module that employs retention time(RT)criteria to reduce false positives;and a prediction module that constructs regression models to identify CLs lacking authentic MS/MS spectra.CLAN achieved better identification accuracy and the highest recall rate for potential CL identification compared to the existing lipid identification tools.Furthermore,we applied CLAN program to an intermittent fasting mouse model,delineating tissue-specific CL alterations across 10 tissues.Every-other-day fasting(EODF)can partially counteract the disruption of the CL atlas across multiple tissues caused by high-fat-high-sugar diet feeding,providing novel insights into mitochondrial lipid metabolism under dietary interventions.Taken together,this work not only advances CL identification methodology but also underscores CLAN's potential in comprehensive analysis of CL atlas in the EODF animal model.CLAN is freely accessible on Git Hub.展开更多
Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study...Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study introduces a novel Fuzzy Nonlinear Additive Regression(FNAR)model to predict monthly GWL in an unconfined aquifer in eastern Iran,using a 19-year(1998–2017)dataset from 11 piezometric wells.Under three distinct scenarios with progressively increasing input complexity,the study utilized readily available climate data,including Precipitation(Prc),Temperature(Tave),Relative Humidity(RH),and Evapotranspiration(ETo).The dataset was split into training(70%)and validation(30%)subsets.Results showed that among three input scenarios,Scenario 3(Sc3,incorporating all four variables)achieved the best predictive performance,with RMSE ranging from 0.305 m to 0.768 m,MAE from 0.203 m to 0.522 m,NSE from 0.661 to 0.980,and PBIAS from 0.771%to 0.981%,indicating low bias and high reliability.However,Sc2(excluding ETo)with RMSE ranging from 0.4226 m to 0.9909 m,MAE from 0.3418 m to 0.8173 m,NSE from 0.2831 to 0.9674,and PBIAS from−0.598%to 0.968%across different months offers practical advantages in data-scarce settings.The FNAR model outperforms conventional Fuzzy Least Squares Regression(FLSR)and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical.Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.展开更多
Background and Objective The natural history of type B aortic intramural hematoma(IMH)is highly heterogeneous.A computational fluid dynamics(CFD)model can be utilized to calculate a range of data pertinent to flow dyn...Background and Objective The natural history of type B aortic intramural hematoma(IMH)is highly heterogeneous.A computational fluid dynamics(CFD)model can be utilized to calculate a range of data pertinent to flow dynamics,including flow rates,blood velocity,pressure,and wall shear stress.This study presents a series of CFD simulations that model the dynamic progression from type B aortic IMH to false lumen formation.Methods A 66-year-old male patient presenting with chest and back pain underwent aortic computed tomography angiography(CTA),and a 3D patient-specific model was constructed.To evaluate the hemodynamic environment,the velocity,pressure,time-averaged wall shear stress(TAWSS),and oscillatory shear index(OSI)were calculated.Results A modest quantity of slow flow and recirculation flow was observed in the vicinity of the ulcer-like protrusion(ULP).During the formation of the false lumen,low-velocity blood flow entered the false lumen and resulted in vortex flow.ULPs were located in the region with higher TAWSS,and some high OSIs were found on the ULPs.Conclusion This preliminary study suggests a potential association between the TAWSS or OSI and progression from type B aortic IMH to aortic dissection.展开更多
Highway planning requires geological surveys and stability analysis of the surrounding area.In the early stage of the survey,the modeling and stability analysis of the survey area can be carried out by using GIS softw...Highway planning requires geological surveys and stability analysis of the surrounding area.In the early stage of the survey,the modeling and stability analysis of the survey area can be carried out by using GIS software to intuitively understand the topography of the study area.The use of DEM to extract terrain factors can be used for simple stability analysis and the source data is easy to obtain,simple to operate,fast to analyze,and reliable analysis results.In this paper,taking the X104 road section in Ganxian County as an example,the ArcGIS platform is used to carry out 3D modeling visualization and stability analysis,and the stability evaluation map of the study area is obtained.展开更多
Reconstruction of a traumatic distal femur defect remains a therapeutic challenge.Bone defect implants have been proposed to substitute the bone defect,and their biomechanical performances can be analyzed via a numeri...Reconstruction of a traumatic distal femur defect remains a therapeutic challenge.Bone defect implants have been proposed to substitute the bone defect,and their biomechanical performances can be analyzed via a numerical approach.However,the material assumptions for past computational human femur simulations were mainly homogeneous.Thus,this study aimed to design and analyze scaffolds for reconstructing the distal femur defect using a patient-specific finite element modeling technique.A three-dimensional finite element model of the human femur with accurate geometry and material distribution was developed using the finite element method and material mapping technique.An intact femur and a distal femur defect model treated with nine microstructure scaffolds and two solid scaffolds were investigated and compared under a single-leg stance loading.The results showed that the metal solid scaffold design could provide the most stable fixation for reconstructing the distal femur defect.However,the fixation stability was affected by various microstructure designs and pillar diameters.A microstructure scaffold can be designed to satisfy all the biomechanical indexes,opening up future possibilities for more stable reconstructions.A three-dimensional finite element model of the femur with real bone geometry and bone material distribution can be developed,and this patient-specific femur model can be used for studying other femoral fractures or injuries,paving the way for more comprehensive research in the field.Besides,this patient-specific finite element modeling technique can also be applied to developing other human or animal bone models,expanding the scope of biomechanical research.展开更多
This study investigates the transmission dynamics of conjunctivitis using stochastic delay differential equations(SDDEs).A delayed stochastic model is formulated by dividing the population into five distinct compartme...This study investigates the transmission dynamics of conjunctivitis using stochastic delay differential equations(SDDEs).A delayed stochastic model is formulated by dividing the population into five distinct compartments:susceptible,exposed,infected,environmental irritants,and recovered individuals.The model undergoes thorough analytical examination,addressing key dynamical properties including positivity,boundedness,existence,and uniqueness of solutions.Local and global stability around the equilibrium points is studied with respect to the basic reproduction number.The existence of a unique global positive solution for the stochastic delayed model is established.In addition,a stochastic nonstandard finite difference scheme is developed,which is shown to be dynamically consistent and convergent toward the equilibrium states.The scheme preserves the essential qualitative features of the model and demonstrates improved performance when compared to existing numerical methods.Finally,the impact of time delays and stochastic fluctuations on the susceptible and infected populations is analyzed.展开更多
Accurate calibration of surgical instruments and ultrasound probes is essential for achieving high precision in image guided minimally invasive procedures.However,existing methods typically treat the calibration of th...Accurate calibration of surgical instruments and ultrasound probes is essential for achieving high precision in image guided minimally invasive procedures.However,existing methods typically treat the calibration of the needle tip and the ultrasound probe as two independent processes,lacking an integrated calibration mechanism,which often leads to cumulative errors and reduced spatial consistency.To address this challenge,we propose a joint calibration model that unifies the calibration of the surgical needle tip and the ultrasound probe within a single coordinate system.The method formulates the calibration process through a series of mathematical models and coordinate transformation models and employs a gradient descent based optimization to refine the parameters of these models.By establishing and iteratively optimizing a template coordinate system through modeling of constrained spherical motion,the proposed joint calibration model achieves submillimeter accuracy in needle tip localization.Building upon this,an N line based calibration model is developed to determine the spatial relationship between the probe and the ultrasound image plane,resulting in an average pixel deviation of only 1.2373 mm.Experimental results confirm that this unified modeling approach effectively overcomes the limitations of separate calibration schemes,significantly enhancing both precision and robustness,and providing a reliable computational model for surgical navigation systems that require high spatial accuracy without relying on ionizing radiation.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
基金supported by the National Nature Science Foundation of China(52275356).
文摘This work investigates the effects of deformation mechanisms on the mechanical properties and anisotropy of rolled AZ31B magnesium alloy under uniaxial tension,combining experimental characterization with Visco-Plastic Self Consistent(VPSC)modeling.The research focuses particularly on anisotropic mechanical responses along transverse direction(TD)and rolling direction(RD).Experimental measurements and computational simulations consistently demonstrate that prismaticslip activation significantly reduces the strain hardening rate during the initial stage of tensile deformation.By suppressing the activation of specific deformation mechanisms along RD and TD,the tensile mechanical behavior of the magnesium alloy was further investigated.The results show that basalslip has the greatest impact during the initial deformation stage and basalslip activation substantially affects the deformation behavior of AZ31B alloy,causing marked decreases in both yield and tensile strength along RD.Under tensile loading along TD,prismaticslip not only exhibits a synergistic effect on yield strength,but also dominants work hardening during the initial plastic deformation.
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 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.
基金supported by the National Natural Science Foundation of China(Grant Nos.52306126,22350710788,12432010,11988102,92270203)the Xplore Prize.
文摘Configuring computational fluid dynamics(CFD)simulations typically demands extensive domain expertise,limiting broader access.Although large language models(LLMs)have advanced scientific computing,their use in automating CFD workflows is underdeveloped.We introduce a novel approach centered on domain-specific LLM adaptation.By fine-tuning Qwen2.5-7B-Instruct on NL2FOAM,our custom dataset of 28,716 natural language-to-OpenFOAM configuration pairs with chain-of-thought(CoT)annotations enables direct translation from natural language descriptions to executable CFD setups.A multi-agent system orchestrates the process,autonomously verifying inputs,generating configurations,running simulations,and correcting errors.Evaluation on a benchmark of 21 diverse flow cases demonstrates state-of-the-art performance,achieving 88.7%solution accuracy and 82.6%first-attempt success rate.This significantly outperforms larger general-purpose models such as Qwen2.5-72B-Instruct,DeepSeek-R1,and Llama3.3-70B-Instruct,while also requiring fewer correction iterations and maintaining high computational efficiency.The results highlight the critical role of domain-specific adaptation in deploying LLM assistants for complex engineering workflows.Our code and fine-tuned model have been deposited at https://github.com/YYgroup/AutoCFD.
基金supported by the National Natural Science Foundation of China Basic Science Center Program for“Multiscale Problems in Nonlinear Mechanics”(Grant No.11988102)the National Natural Science Foundation of China(Grant No.12202451).
文摘This paper investigates the capabilities of large language models(LLMs)to leverage,learn and create knowledge in solving computational fluid dynamics(CFD)problems through three categories of baseline problems.These categories include(1)conventional CFD problems that can be solved using existing numerical methods in LLMs,such as lid-driven cavity flow and the Sod shock tube problem;(2)problems that require new numerical methods beyond those available in LLMs,such as the recently developed Chien-physics-informed neural networks for singularly perturbed convection-diffusion equations;and(3)problems that cannot be solved using existing numerical methods in LLMs,such as the ill-conditioned Hilbert linear algebraic systems.The evaluations indicate that reasoning LLMs overall outperform non-reasoning models in four test cases.Reasoning LLMs show excellent performance for CFD problems according to the tailored prompts,but their current capability in autonomous knowledge exploration and creation needs to be enhanced.
基金supported by the 2023 Youth Fund for Humanities and Social Sciences Research by the Ministry of Education of the People’s Republic of China(Grant No.23YJC740004).
文摘Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first collected book reviews from these two well-known websites by Python.Through topic analysis of these reviews,we identified recurring topics,including details of her translated works and appreciation of their translation quality.Then,employing sentiment and content analysis methods,the paper explored the emotional attitudes and the specific thoughts of readers toward Can Xue and her translated works.The fingdings revealed that,among the 408 reviews,though the reception of Can Xue’s translated works was relatively positive,the current level of attention and recognition remains insufficient.However,based on the research results,the paper can derive valuable insights into the translation and dissemination processes such as adjusting translation and dissemination strategies,so that the global reach of Chinese literature and culture can be better facilitated.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[KFU250259].
文摘Streptococcus suis(S.suis)is a major disease impacting pig farming globally.It can also be transferred to humans by eating raw pork.A comprehensive study was recently carried out to determine the indices throughmultiple geographic regions in China.Methods:The well-posed theorems were employed to conduct a thorough analysis of the model’s feasible features,including positivity,boundedness equilibria,reproduction number,and parameter sensitivity.Stochastic Euler,Runge Kutta,and EulerMaruyama are some of the numerical techniques used to replicate the behavior of the streptococcus suis infection in the pig population.However,the dynamic qualities of the suggested model cannot be restored using these techniques.Results:For the stochastic delay differential equations of the model,the non-standard finite difference approach in the sense of stochasticity is developed to avoid several problems such as negativity,unboundedness,inconsistency,and instability of the findings.Results from traditional stochastic methods either converge conditionally or diverge over time.The stochastic non-negative step size convergence nonstandard finite difference(NSFD)method unconditionally converges to the model’s true states.Conclusions:This study improves our understanding of the dynamics of streptococcus suis infection using versions of stochastic with delay approaches and opens up new avenues for the study of cognitive processes and neuronal analysis.Theplotted interaction behaviour and new solution comparison profiles.
基金by National Natural Science Foundation of China(No.62306083)the Postdoctoral Science Foundation of Heilongjiang Province of China(LBH-Z22175)the Ministry of Industry and Information Technology。
文摘Adolescent idiopathic scoliosis(AIS)is a dynamic progression during growth,which requires long-term collaborations and efforts from clinicians,patients and their families.It would be beneficial to have a precise intervention based on cross-scale understandings of the etiology,real-time sensing and actuating to enable early detection,screening and personalized treatment.We argue that merging computational intelligence and wearable technologies can bridge the gap between the current trajectory of the techniques applied to AIS and this vision.Wearable technologies such as inertial measurement units(IMUs)and surface electromyography(sEMG)have shown great potential in monitoring spinal curvature and muscle activity in real-time.For instance,IMUs can track the kinematics of the spine during daily activities,while sEMG can detect asymmetric muscle activation patterns that may contribute to scoliosis progression.Computational intelligence,particularly deep learning algorithms,can process these multi-modal data streams to identify early signs of scoliosis and adapt treatment strategies dynamically.By using their combination,we can find potential solutions for a better understanding of the disease,a more effective and intelligent way for treatment and rehabilitation.
基金supported by grants from the National Key Research and Development Program of China(No.2022YFE0205800)the National Natural Science Foundation of China(No.21974114)+5 种基金Major Science and Technology Special Project of Fujian Province(No.2022YZ036012)the Fundamental Research Funds for the Central Universities(No.20720220003)Project“111”sponsored by the State Bureau of Foreign Experts and Ministry of Education of China(No.BP0618017)to S.-H.LinNatural Science Foundation of Fujian Province of China(No.2022J01330)Natural Science Foundation of Xiamen City of China(No.3502Z20227208)the China Scholarship Council(No.202308350047)to J.Zeng。
文摘Cardiolipins(CLs),the mitochondria-specific class of phospholipids,are crucial to energy metabolism,cristae structure,and cell apoptosis.CLs present significant challenges in lipidomics analysis due to their structural diversity with up to four fatty acyl side chains.In this study,we developed CLAN(Cardio Lipin ANalysis),a comprehensive computational pipeline designed to improve the accuracy and coverage of cardiolipin identification.CLAN integrates three innovative modules:A cardiolipin identification module that utilizes specific fragmentation rules for precise characterization of CLs and their acyl side chains;a false positives detection module that employs retention time(RT)criteria to reduce false positives;and a prediction module that constructs regression models to identify CLs lacking authentic MS/MS spectra.CLAN achieved better identification accuracy and the highest recall rate for potential CL identification compared to the existing lipid identification tools.Furthermore,we applied CLAN program to an intermittent fasting mouse model,delineating tissue-specific CL alterations across 10 tissues.Every-other-day fasting(EODF)can partially counteract the disruption of the CL atlas across multiple tissues caused by high-fat-high-sugar diet feeding,providing novel insights into mitochondrial lipid metabolism under dietary interventions.Taken together,this work not only advances CL identification methodology but also underscores CLAN's potential in comprehensive analysis of CL atlas in the EODF animal model.CLAN is freely accessible on Git Hub.
基金supported by the Iran National Science Foundation(INSF)the University of Birjand under grant number 4034771.
文摘Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems,necessitating endeavors to quantify Groundwater Levels(GWL)dynamics to inform policymakers and hydrogeologists.This study introduces a novel Fuzzy Nonlinear Additive Regression(FNAR)model to predict monthly GWL in an unconfined aquifer in eastern Iran,using a 19-year(1998–2017)dataset from 11 piezometric wells.Under three distinct scenarios with progressively increasing input complexity,the study utilized readily available climate data,including Precipitation(Prc),Temperature(Tave),Relative Humidity(RH),and Evapotranspiration(ETo).The dataset was split into training(70%)and validation(30%)subsets.Results showed that among three input scenarios,Scenario 3(Sc3,incorporating all four variables)achieved the best predictive performance,with RMSE ranging from 0.305 m to 0.768 m,MAE from 0.203 m to 0.522 m,NSE from 0.661 to 0.980,and PBIAS from 0.771%to 0.981%,indicating low bias and high reliability.However,Sc2(excluding ETo)with RMSE ranging from 0.4226 m to 0.9909 m,MAE from 0.3418 m to 0.8173 m,NSE from 0.2831 to 0.9674,and PBIAS from−0.598%to 0.968%across different months offers practical advantages in data-scarce settings.The FNAR model outperforms conventional Fuzzy Least Squares Regression(FLSR)and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical.Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.
文摘Background and Objective The natural history of type B aortic intramural hematoma(IMH)is highly heterogeneous.A computational fluid dynamics(CFD)model can be utilized to calculate a range of data pertinent to flow dynamics,including flow rates,blood velocity,pressure,and wall shear stress.This study presents a series of CFD simulations that model the dynamic progression from type B aortic IMH to false lumen formation.Methods A 66-year-old male patient presenting with chest and back pain underwent aortic computed tomography angiography(CTA),and a 3D patient-specific model was constructed.To evaluate the hemodynamic environment,the velocity,pressure,time-averaged wall shear stress(TAWSS),and oscillatory shear index(OSI)were calculated.Results A modest quantity of slow flow and recirculation flow was observed in the vicinity of the ulcer-like protrusion(ULP).During the formation of the false lumen,low-velocity blood flow entered the false lumen and resulted in vortex flow.ULPs were located in the region with higher TAWSS,and some high OSIs were found on the ULPs.Conclusion This preliminary study suggests a potential association between the TAWSS or OSI and progression from type B aortic IMH to aortic dissection.
基金National Undergraduate Training Program for Innovation and Entrepreneurship(Project No.:202310407006)。
文摘Highway planning requires geological surveys and stability analysis of the surrounding area.In the early stage of the survey,the modeling and stability analysis of the survey area can be carried out by using GIS software to intuitively understand the topography of the study area.The use of DEM to extract terrain factors can be used for simple stability analysis and the source data is easy to obtain,simple to operate,fast to analyze,and reliable analysis results.In this paper,taking the X104 road section in Ganxian County as an example,the ArcGIS platform is used to carry out 3D modeling visualization and stability analysis,and the stability evaluation map of the study area is obtained.
基金funded by the TaipeiMedical University-National Taiwan University of Science and Technology joint research program under Grant No.TMU-NTUST-109-09.
文摘Reconstruction of a traumatic distal femur defect remains a therapeutic challenge.Bone defect implants have been proposed to substitute the bone defect,and their biomechanical performances can be analyzed via a numerical approach.However,the material assumptions for past computational human femur simulations were mainly homogeneous.Thus,this study aimed to design and analyze scaffolds for reconstructing the distal femur defect using a patient-specific finite element modeling technique.A three-dimensional finite element model of the human femur with accurate geometry and material distribution was developed using the finite element method and material mapping technique.An intact femur and a distal femur defect model treated with nine microstructure scaffolds and two solid scaffolds were investigated and compared under a single-leg stance loading.The results showed that the metal solid scaffold design could provide the most stable fixation for reconstructing the distal femur defect.However,the fixation stability was affected by various microstructure designs and pillar diameters.A microstructure scaffold can be designed to satisfy all the biomechanical indexes,opening up future possibilities for more stable reconstructions.A three-dimensional finite element model of the femur with real bone geometry and bone material distribution can be developed,and this patient-specific femur model can be used for studying other femoral fractures or injuries,paving the way for more comprehensive research in the field.Besides,this patient-specific finite element modeling technique can also be applied to developing other human or animal bone models,expanding the scope of biomechanical research.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R899)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiasupported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia(KFU252831)。
文摘This study investigates the transmission dynamics of conjunctivitis using stochastic delay differential equations(SDDEs).A delayed stochastic model is formulated by dividing the population into five distinct compartments:susceptible,exposed,infected,environmental irritants,and recovered individuals.The model undergoes thorough analytical examination,addressing key dynamical properties including positivity,boundedness,existence,and uniqueness of solutions.Local and global stability around the equilibrium points is studied with respect to the basic reproduction number.The existence of a unique global positive solution for the stochastic delayed model is established.In addition,a stochastic nonstandard finite difference scheme is developed,which is shown to be dynamically consistent and convergent toward the equilibrium states.The scheme preserves the essential qualitative features of the model and demonstrates improved performance when compared to existing numerical methods.Finally,the impact of time delays and stochastic fluctuations on the susceptible and infected populations is analyzed.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004].
文摘Accurate calibration of surgical instruments and ultrasound probes is essential for achieving high precision in image guided minimally invasive procedures.However,existing methods typically treat the calibration of the needle tip and the ultrasound probe as two independent processes,lacking an integrated calibration mechanism,which often leads to cumulative errors and reduced spatial consistency.To address this challenge,we propose a joint calibration model that unifies the calibration of the surgical needle tip and the ultrasound probe within a single coordinate system.The method formulates the calibration process through a series of mathematical models and coordinate transformation models and employs a gradient descent based optimization to refine the parameters of these models.By establishing and iteratively optimizing a template coordinate system through modeling of constrained spherical motion,the proposed joint calibration model achieves submillimeter accuracy in needle tip localization.Building upon this,an N line based calibration model is developed to determine the spatial relationship between the probe and the ultrasound image plane,resulting in an average pixel deviation of only 1.2373 mm.Experimental results confirm that this unified modeling approach effectively overcomes the limitations of separate calibration schemes,significantly enhancing both precision and robustness,and providing a reliable computational model for surgical navigation systems that require high spatial accuracy without relying on ionizing radiation.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
