High-volume fraction silicon particle-reinforced aluminium matrix composites(Si/Al)are increasingly applied in aerospace,radar communications,and large-scale integrated circuits because of their superior thermal condu...High-volume fraction silicon particle-reinforced aluminium matrix composites(Si/Al)are increasingly applied in aerospace,radar communications,and large-scale integrated circuits because of their superior thermal conductivity,wear resistance,and low thermal expansion coefficient.However,the abrasive and adhesive wear caused by the hard silicon reinforcement and the ductile aluminium matrix leads to significant tool wear,decreased machining efficiency,and compromised surface quality.This study combines theoretical analysis and cutting experiments to investigate polycrystalline diamond(PCD)tool wear during milling of 70 vol%Si/Al composite.A key contribution of this work is the development of a tool wear model that incorporates reinforcement particle characteristics,treating them as ellipsoidal structures,which enhances the accuracy of predicting abrasive and adhesive wear mechanisms.The model is based on abrasive and adhesive wear mechanisms,and can analyze the interaction between silicon particles,aluminium matrix,and tool components,thus providing deeper insights into PCD tool wear processes.Experimental validation of the model shows a good agreement with the results,with a mean deviation of approximately 10%.The findings on the tool wear mechanism reveal that,as tool wear progresses,the proportion of abrasive wear increases from 40%in the running-in stage to 75%in the rapid wear stage,while adhesive wear decreases.The optimal machining parameters of 120 m·min^(–1) cutting speed(v_(c))and 0.04 mm·z^(–1) feed rate(f_(z)),result in tool life of 33 min and surface roughness(S_(a))of 2.2μm.The study uncovers the variation patterns of abrasive and adhesive wear during the tool wear process,and the proposed model offers a robust framework for predicting tool wear during the machining of high-volume fraction Si/Al composites.The research findings also offer key insights for optimizing tool selection and machining parameters,advancing both the theoretical understanding and practical application of PCD tool wear.展开更多
The design work of motional cable in products is vital due to the difficulty in estimating the potential issues in current researches.In this paper,a physics-based modeling and simulation method for the motional cable...The design work of motional cable in products is vital due to the difficulty in estimating the potential issues in current researches.In this paper,a physics-based modeling and simulation method for the motional cable harness design is presented.The model,based on continuum mechanics,is established by analyzing the force of microelement in equilibrium.During the analysis procedure,three coordinate systems：inertial,Frenet and main-axis coordinate systems are used.By variable substitution and dimensionless processing,the equation set is discretized by differential quadrature method and subsequently becomes an overdetermined nonlinear equation set with boundary conditions solved by Levenberg-Marquardt method.With the profile of motional cable harness obtained from the integral of arithmetic solution,a motion simulation system based on＂path＂and＂profile＂as well as the experimental equipments is built.Using the same parameters as input for the simulation and the real cable harness correspondingly,the issue in designing,such as collision,can be easily found by the simulation system.This research obtains a better result which has no potential collisions by redesign,and the proposed method can be used as an accurate and efficient way in motional cable harness design work.展开更多
A new physics-based model employing three transport equations is developed for the simulation of boundary layer transitions in a wide speed range. The laminar kinetic energy is used to represent pretransitional stream...A new physics-based model employing three transport equations is developed for the simulation of boundary layer transitions in a wide speed range. The laminar kinetic energy is used to represent pretransitional streamwise velocity fluctuations, taking account of different instability modes. The fluctuation velocity components normal to the streamwise direction are modeled by another transport equation. Transition is triggered automatically with the development of the pretransitional velocity fluctuations. In the fully turbulent region, the model reverts to the k-ω turbulence model. Different test cases, including subsonic, supersonic and hypersonic flows around flat plates, airfoils and straight cones, are numerically simulated to validate the performance of the model. The results demonstrate the excellent predictive capabilities of the model in different paths of transition. The model can serve as a basis for the extension of additional transition mechanisms,such as rotation and curvature effects, roughness-induced transition and crossflow-induced transition.展开更多
Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient...Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.展开更多
The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There i...The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.展开更多
As 3D digital photographic and scanning devices produce higher resolution images, acquired geometric data sets grow more complex in terms of the modeled objects＇ size, geometry, and topology. As a consequence, point-...As 3D digital photographic and scanning devices produce higher resolution images, acquired geometric data sets grow more complex in terms of the modeled objects＇ size, geometry, and topology. As a consequence, point-sampled geometry is becoming ubiquitous in graphics and geometric information processing, and poses new challenges which have not been fully resolved by the state-of-art graphical techniques. In this paper, we address the challenges by proposing a meshless computational framework for dynamic modeling and simulation of solids and thin-shells represented as point sam- ples. Our meshless framework can directly compute the elastic deformation and fracture propagation for any scanned point geometry, without the need of converting them to polygonal meshes or higher order spline representations. We address the necessary computational techniques, such as Moving Least Squares, Hierarchical Discretization, and Modal Warping, to effectively and efficiently compute the physical simulation in real-time. This meahless computational framework aims to bridge the gap between the point-sampled geometry with physics-based modeling and simulation governed by partial differential equations.展开更多
The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression.When the cell changes its identity in the cell-fate decision-making process,ex...The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression.When the cell changes its identity in the cell-fate decision-making process,extensive rearrangements of chromo-some structures occur accompanied by large-scale adaptations of gene expression,underscoring the importance of chromosome dynamics in shaping genome function.Over the last two decades,rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes.In parallel,these enormous data offer valuable opportunities for developing quantitative computational models.Here,we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes.Different from the underlying modeling strategies,these approaches can be classified into data-driven(‘top-down’)and physics-based(‘bottom-up’)categories.We discuss their contributions to offering valuable insights into the relationships among the structures,dynamics,and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.展开更多
A new framework for free-form surface design is proposed. Using manifolds can generalize the spline scheme to surfaces of arbitrary topology. Physics-based modeling incorporate physical laws into shape representation ...A new framework for free-form surface design is proposed. Using manifolds can generalize the spline scheme to surfaces of arbitrary topology. Physics-based modeling incorporate physical laws into shape representation to provide direct shape interaction. The combination presents a new method inherits the attractive properties of the manifold surface as well as that of the physics-based models.展开更多
The instantaneous reversible soft logic upset induced by the electromagnetic interference（EMI） severely affects the performances and reliabilities of complementary metal–oxide–semiconductor（CMOS） inverters. This...The instantaneous reversible soft logic upset induced by the electromagnetic interference（EMI） severely affects the performances and reliabilities of complementary metal–oxide–semiconductor（CMOS） inverters. This kind of soft logic upset is investigated in theory and simulation. Physics-based analysis is performed, and the result shows that the upset is caused by the non-equilibrium carrier accumulation in channels, which can ultimately lead to an abnormal turn-on of specific metal–oxide–semiconductor field-effect transistor（MOSFET） in CMOS inverter. Then a soft logic upset simulation model is introduced. Using this model, analysis of upset characteristic reveals an increasing susceptibility under higher injection powers, which accords well with experimental results, and the influences of EMI frequency and device size are studied respectively using the same model. The research indicates that in a range from L waveband to C waveband, lower interference frequency and smaller device size are more likely to be affected by the soft logic upset.展开更多
Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on mo...Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on model creation and long computing time.Top-down methods based on data driven models are fast,but less accurate.Considering the similarity of power demand patterns of single buildings and the superiority of generative adversarial network(GAN),this paper proposes a new method(E-GAN),which combines a physics-based model(EnergyPlus)and a data-driven model(GAN),to predict the daily power demand for buildings at a large scale.The new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power demands.Utilizing the prediction for those typical buildings,the GAN then is adopted to forecast the power demands of a large number of buildings.To verify the proposed method,the E-GAN is used to predict 24-hour power demands for a set of residential buildings.The results show that(1)4.3%of physics-based models in each building category are required to ensure the prediction accuracy;(2)compared with the physics-based model,the E-GAN can predict power demand accurately with only 5%error(measured by mean absolute percentage error,MAPE)while using only approximately 9%of the computing time;and(3)compared with data-driven models(e.g.,support vector regression,extreme learning machine,and polynomial regression model),E-GAN demonstrates at least 60%reduction in prediction error measured by MAPE.展开更多
Background Physics-based differentiable rendering(PBDR)aims to propagate gradients from scene parameters to image pixels or vice versa.The physically correct gradients obtained can be used in various applications,incl...Background Physics-based differentiable rendering(PBDR)aims to propagate gradients from scene parameters to image pixels or vice versa.The physically correct gradients obtained can be used in various applications,including inverse rendering and machine learning.Currently,two categories of methods are prevalent in the PBDR community:reparameterization and boundary sampling methods.The state-of-the-art boundary sampling methods rely on a guiding structure to calculate the gradients efficiently.They utilize the rays generated in traditional path-tracing methods and project them onto the object silhouette boundary to initialize the guiding structure.Methods In this study,we propose an augmentation of previous projective-sampling-based boundary-sampling methods in a bidirectional manner.Specifically,we utilize the rays spawned from the sensors and also employ the rays emitted by the emitters to initialize the guiding structure.Results To demonstrate the benefits of our technique,we perform a comparative analysis of differentiable rendering and inverse rendering performance.We utilize a range of synthetic scene examples and evaluate our method against state-of-the-art projective-sampling-based differentiable rendering methods.Conclusions The experiments show that our method achieves lower variance gradients in the forward differentiable rendering process and better geometry reconstruction quality in the inverse-rendering results.展开更多
Representation of roughness is introduced and the rationality of applying thephysics-based model in RE is analyzed at first. Then the scattering theory of electromagnetic waveis simplified and deduced to a physics-bas...Representation of roughness is introduced and the rationality of applying thephysics-based model in RE is analyzed at first. Then the scattering theory of electromagnetic waveis simplified and deduced to a physics-based model according to the characteristics of the surfaceto be reconstructed in RE. At last, the intensity diagrams of reflected field distribution areprovided to prove the feasibility of the presented model and some spheres are rendered with thismodel.展开更多
Crowds of people are often found in enclosed or constricted spaces.Evacuation in such situations is usually conducted calmly,but real or perceived danger may trigger panic.In panicked crowds,the interpersonal distance...Crowds of people are often found in enclosed or constricted spaces.Evacuation in such situations is usually conducted calmly,but real or perceived danger may trigger panic.In panicked crowds,the interpersonal distance crowd members normally observe is often overwhelmed by the physical pressure of crowd members pushing against each other.That pressure can both slow evacuation and lead to injury or death.Models have been developed to study crowd evacuation in a range of situations.This paper describes the implementation,testing,and validation of a crowd evacuation model using Unity,a commercial computer game engine.A realistic physics-based model of crowd movement that calculates and considers the physical pressure crowd members exert on each other was implemented in Unity.The implemented model was tested under both nonpanicked and panicked scenarios;those tests exhibited known qualitative characteristics of such scenarios.The model was then quantitatively validated by comparing its results to the outcome of an actual evacuation event,the 2003 fire at The Station nightclub in Rhode Island.The implementation and validation show that Unity can be an effective tool for evacuation modeling.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
In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation ...In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Background:Large language models(LLMs)have shown considerable promise in supporting clinical decision-making.However,their adoption and evaluation in dermatology remains limited.This study aimed to explore the prefere...Background:Large language models(LLMs)have shown considerable promise in supporting clinical decision-making.However,their adoption and evaluation in dermatology remains limited.This study aimed to explore the preferences of Chinese dermatologists regarding LLM-generated responses in clinical psoriasis scenarios and to assess how they prioritize key quality dimensions,including accuracy,traceability,and logicality.Methods:A cross-sectional,web-based survey was conducted between December 25,2024,and January 22,2025,following the Checklist for Reporting Results of Internet E-Surveys guidelines.A total of 1247 valid responses were collected from practicing dermatologists across 33 of China's provincial-level administrative divisions.Participants evaluated responses to five categories of clinical questions(etiology,clinical presentation,differential diagnosis,treatment,and case study)generated by five LLMs:ChatGPT-4o,Kimi.ai,Doubao,ZuoYiGPT,and Lingyi-agent.Statistical associations between participant characteristics and model preferences were examined using chi-square tests.Results:ChatGPT-4o(Model 1)emerged as the most preferred model across all clinical tasks,consistently receiving the highest number of votes in case study(n=740),clinical presentation(n=666),differential diagnosis(n=707),etiology(n=602),and treatment(n=656).Significant variation in model preference by professional title was observed only for the differential diagnosis task(χ^(2)=21.13,df=12,p=0.0485),while no significant differences were found across hospital tiers(p>0.05).In terms of evaluation dimensions,accuracy was most frequently rated as“very important”(n=635).A significant association existed between hospital tier and the most valued dimension(χ^(2)=27.667,df=9,p=0.0011),with dermatologists in primary hospitals prioritizing traceability more than their peers in higher-tier hospitals.No significant associations were found across professional titles(p=0.127).Conclusions:Chinese dermatologists suggest a strong preference for ChatGPT-4o over domestic LLMs in psoriasis-related clinical tasks.While accuracy remains the primary criterion,traceability and logicality are also critical,particularly for clinicians in lower-tier hospitals.These findings suggest that future clinical LLMs should prioritize not only content accuracy but also source transparency and structural clarity to meet the diverse needs of different clinical settings.展开更多
The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpec...The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.展开更多
Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is empl...Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is employed to simulate the ballistic impact of 7.62 mm armor-piercing projectiles on Aluminum AA5083-H116 and Steel Secure 500 armors,focusing on the evaluation of material deformation and penetration resistance at varying impact points.While the D-shaped armor plate is penetrated by the armor-piercing projectiles,the combination of the perforated D-shaped and base armor plates successfully halts penetration.A numerical model based on the finite element method is developed using software such as SolidWorks and ANSYS to analyze the interaction between radiator armor and bullet.The perforated design of radiator armor is to maintain airflow for radiator function,with hole sizes smaller than the bullet core diameter to protect radiator assemblies.Predictions are made regarding the brittle fracture resulting from the projectile core′s bending due to asymmetric impact,and the resulting fragments failed to penetrate the perforated base armor plate.Craters are formed on the surface of the perforated D-shaped armor plate due to the impact of projectile fragments.The numerical model accurately predicts hole growth and projectile penetration upon impact with the armor,demonstrating effective protection of the radiator assemblies by the radiator armor.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52075255)the Jiangsu Provincial Science and Technology Plan(Grant No.BZ2023005).
文摘High-volume fraction silicon particle-reinforced aluminium matrix composites(Si/Al)are increasingly applied in aerospace,radar communications,and large-scale integrated circuits because of their superior thermal conductivity,wear resistance,and low thermal expansion coefficient.However,the abrasive and adhesive wear caused by the hard silicon reinforcement and the ductile aluminium matrix leads to significant tool wear,decreased machining efficiency,and compromised surface quality.This study combines theoretical analysis and cutting experiments to investigate polycrystalline diamond(PCD)tool wear during milling of 70 vol%Si/Al composite.A key contribution of this work is the development of a tool wear model that incorporates reinforcement particle characteristics,treating them as ellipsoidal structures,which enhances the accuracy of predicting abrasive and adhesive wear mechanisms.The model is based on abrasive and adhesive wear mechanisms,and can analyze the interaction between silicon particles,aluminium matrix,and tool components,thus providing deeper insights into PCD tool wear processes.Experimental validation of the model shows a good agreement with the results,with a mean deviation of approximately 10%.The findings on the tool wear mechanism reveal that,as tool wear progresses,the proportion of abrasive wear increases from 40%in the running-in stage to 75%in the rapid wear stage,while adhesive wear decreases.The optimal machining parameters of 120 m·min^(–1) cutting speed(v_(c))and 0.04 mm·z^(–1) feed rate(f_(z)),result in tool life of 33 min and surface roughness(S_(a))of 2.2μm.The study uncovers the variation patterns of abrasive and adhesive wear during the tool wear process,and the proposed model offers a robust framework for predicting tool wear during the machining of high-volume fraction Si/Al composites.The research findings also offer key insights for optimizing tool selection and machining parameters,advancing both the theoretical understanding and practical application of PCD tool wear.
基金Supported by National Natural Science Foundation of China(Grant No.51275047)
文摘The design work of motional cable in products is vital due to the difficulty in estimating the potential issues in current researches.In this paper,a physics-based modeling and simulation method for the motional cable harness design is presented.The model,based on continuum mechanics,is established by analyzing the force of microelement in equilibrium.During the analysis procedure,three coordinate systems：inertial,Frenet and main-axis coordinate systems are used.By variable substitution and dimensionless processing,the equation set is discretized by differential quadrature method and subsequently becomes an overdetermined nonlinear equation set with boundary conditions solved by Levenberg-Marquardt method.With the profile of motional cable harness obtained from the integral of arithmetic solution,a motion simulation system based on＂path＂and＂profile＂as well as the experimental equipments is built.Using the same parameters as input for the simulation and the real cable harness correspondingly,the issue in designing,such as collision,can be easily found by the simulation system.This research obtains a better result which has no potential collisions by redesign,and the proposed method can be used as an accurate and efficient way in motional cable harness design work.
基金supported by grants from the National Natural Science Foundation of China(No.11721202)the Academic Excellence Foundation of Beihang University for Ph D Students,China。
文摘A new physics-based model employing three transport equations is developed for the simulation of boundary layer transitions in a wide speed range. The laminar kinetic energy is used to represent pretransitional streamwise velocity fluctuations, taking account of different instability modes. The fluctuation velocity components normal to the streamwise direction are modeled by another transport equation. Transition is triggered automatically with the development of the pretransitional velocity fluctuations. In the fully turbulent region, the model reverts to the k-ω turbulence model. Different test cases, including subsonic, supersonic and hypersonic flows around flat plates, airfoils and straight cones, are numerically simulated to validate the performance of the model. The results demonstrate the excellent predictive capabilities of the model in different paths of transition. The model can serve as a basis for the extension of additional transition mechanisms,such as rotation and curvature effects, roughness-induced transition and crossflow-induced transition.
基金supported by the Natural Science Foundation of China(Grant Nos.42088101 and 42205149)Zhongwang WEI was supported by the Natural Science Foundation of China(Grant No.42075158)+1 种基金Wei SHANGGUAN was supported by the Natural Science Foundation of China(Grant No.41975122)Yonggen ZHANG was supported by the National Natural Science Foundation of Tianjin(Grant No.20JCQNJC01660).
文摘Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.
基金funding enabled and organized by CAUL and its Member Institutions.
文摘The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.
基金Supported by the National Science Foundation (Grant Nos. CCF-0727098, IIS-0710819)
文摘As 3D digital photographic and scanning devices produce higher resolution images, acquired geometric data sets grow more complex in terms of the modeled objects＇ size, geometry, and topology. As a consequence, point-sampled geometry is becoming ubiquitous in graphics and geometric information processing, and poses new challenges which have not been fully resolved by the state-of-art graphical techniques. In this paper, we address the challenges by proposing a meshless computational framework for dynamic modeling and simulation of solids and thin-shells represented as point sam- ples. Our meshless framework can directly compute the elastic deformation and fracture propagation for any scanned point geometry, without the need of converting them to polygonal meshes or higher order spline representations. We address the necessary computational techniques, such as Moving Least Squares, Hierarchical Discretization, and Modal Warping, to effectively and efficiently compute the physical simulation in real-time. This meahless computational framework aims to bridge the gap between the point-sampled geometry with physics-based modeling and simulation governed by partial differential equations.
基金supported by the National Natural Science Foundation of China(grant no.32201020)the general program(grant no.2023A04J0083)+1 种基金the Guangzhou–HKUST(GZ)joint funding program(grant no.2023A03J0060)of Guangzhou Municipal Science and Technology Projectfunded by the Municipal Key Laboratory Construction Program of Guangzhou Municipal Science and Technology Project(grant no.2023A03J0003).
文摘The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression.When the cell changes its identity in the cell-fate decision-making process,extensive rearrangements of chromo-some structures occur accompanied by large-scale adaptations of gene expression,underscoring the importance of chromosome dynamics in shaping genome function.Over the last two decades,rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes.In parallel,these enormous data offer valuable opportunities for developing quantitative computational models.Here,we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes.Different from the underlying modeling strategies,these approaches can be classified into data-driven(‘top-down’)and physics-based(‘bottom-up’)categories.We discuss their contributions to offering valuable insights into the relationships among the structures,dynamics,and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.
基金Funded by the Chinese National Natural Science Foundation (No.50105013).
文摘A new framework for free-form surface design is proposed. Using manifolds can generalize the spline scheme to surfaces of arbitrary topology. Physics-based modeling incorporate physical laws into shape representation to provide direct shape interaction. The combination presents a new method inherits the attractive properties of the manifold surface as well as that of the physics-based models.
基金supported by the National Natural Science Foundation of China(Grant No.60776034)the Open Fund of Key Laboratory of Complex Electromagnetic Environment Science and Technology,China Academy of Engineering Physics(Grant No.2015-0214.XY.K)
文摘The instantaneous reversible soft logic upset induced by the electromagnetic interference（EMI） severely affects the performances and reliabilities of complementary metal–oxide–semiconductor（CMOS） inverters. This kind of soft logic upset is investigated in theory and simulation. Physics-based analysis is performed, and the result shows that the upset is caused by the non-equilibrium carrier accumulation in channels, which can ultimately lead to an abnormal turn-on of specific metal–oxide–semiconductor field-effect transistor（MOSFET） in CMOS inverter. Then a soft logic upset simulation model is introduced. Using this model, analysis of upset characteristic reveals an increasing susceptibility under higher injection powers, which accords well with experimental results, and the influences of EMI frequency and device size are studied respectively using the same model. The research indicates that in a range from L waveband to C waveband, lower interference frequency and smaller device size are more likely to be affected by the soft logic upset.
基金The Chinese team is supported by the National Natural Science Foundation of China(62076150,62173216,61903226)the Taishan Scholar Project of Shandong Province(TSQN201812092)+2 种基金the Key Research and Development Program of Shandong Province(2019GGX101072,2019JZZY010115)the Youth Innovation Technology Project of Higher School in Shandong Province(2019KJN005)the Key Research and Development Program of Shandong Province(2019JZZY010115)。
文摘Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on model creation and long computing time.Top-down methods based on data driven models are fast,but less accurate.Considering the similarity of power demand patterns of single buildings and the superiority of generative adversarial network(GAN),this paper proposes a new method(E-GAN),which combines a physics-based model(EnergyPlus)and a data-driven model(GAN),to predict the daily power demand for buildings at a large scale.The new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power demands.Utilizing the prediction for those typical buildings,the GAN then is adopted to forecast the power demands of a large number of buildings.To verify the proposed method,the E-GAN is used to predict 24-hour power demands for a set of residential buildings.The results show that(1)4.3%of physics-based models in each building category are required to ensure the prediction accuracy;(2)compared with the physics-based model,the E-GAN can predict power demand accurately with only 5%error(measured by mean absolute percentage error,MAPE)while using only approximately 9%of the computing time;and(3)compared with data-driven models(e.g.,support vector regression,extreme learning machine,and polynomial regression model),E-GAN demonstrates at least 60%reduction in prediction error measured by MAPE.
基金Supported by National Natural Science Foundation of China(No.62072020)the Leading Talents in Innovation and Entrepreneurship of Qingdao,China(19-3-2-21-zhc).
文摘Background Physics-based differentiable rendering(PBDR)aims to propagate gradients from scene parameters to image pixels or vice versa.The physically correct gradients obtained can be used in various applications,including inverse rendering and machine learning.Currently,two categories of methods are prevalent in the PBDR community:reparameterization and boundary sampling methods.The state-of-the-art boundary sampling methods rely on a guiding structure to calculate the gradients efficiently.They utilize the rays generated in traditional path-tracing methods and project them onto the object silhouette boundary to initialize the guiding structure.Methods In this study,we propose an augmentation of previous projective-sampling-based boundary-sampling methods in a bidirectional manner.Specifically,we utilize the rays spawned from the sensors and also employ the rays emitted by the emitters to initialize the guiding structure.Results To demonstrate the benefits of our technique,we perform a comparative analysis of differentiable rendering and inverse rendering performance.We utilize a range of synthetic scene examples and evaluate our method against state-of-the-art projective-sampling-based differentiable rendering methods.Conclusions The experiments show that our method achieves lower variance gradients in the forward differentiable rendering process and better geometry reconstruction quality in the inverse-rendering results.
文摘Representation of roughness is introduced and the rationality of applying thephysics-based model in RE is analyzed at first. Then the scattering theory of electromagnetic waveis simplified and deduced to a physics-based model according to the characteristics of the surfaceto be reconstructed in RE. At last, the intensity diagrams of reflected field distribution areprovided to prove the feasibility of the presented model and some spheres are rendered with thismodel.
文摘Crowds of people are often found in enclosed or constricted spaces.Evacuation in such situations is usually conducted calmly,but real or perceived danger may trigger panic.In panicked crowds,the interpersonal distance crowd members normally observe is often overwhelmed by the physical pressure of crowd members pushing against each other.That pressure can both slow evacuation and lead to injury or death.Models have been developed to study crowd evacuation in a range of situations.This paper describes the implementation,testing,and validation of a crowd evacuation model using Unity,a commercial computer game engine.A realistic physics-based model of crowd movement that calculates and considers the physical pressure crowd members exert on each other was implemented in Unity.The implemented model was tested under both nonpanicked and panicked scenarios;those tests exhibited known qualitative characteristics of such scenarios.The model was then quantitatively validated by comparing its results to the outcome of an actual evacuation event,the 2003 fire at The Station nightclub in Rhode Island.The implementation and validation show that Unity can be an effective tool for evacuation modeling.
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.
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
文摘In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
基金National Key Research and Development Program of China,Grant/Award Number:2024YFF0507404Special Clinical Business Fund for High-Level Hospitals of China-Japan Friendship Hospital,Grant/Award Number:2024-NHLHCRF-TS-01。
文摘Background:Large language models(LLMs)have shown considerable promise in supporting clinical decision-making.However,their adoption and evaluation in dermatology remains limited.This study aimed to explore the preferences of Chinese dermatologists regarding LLM-generated responses in clinical psoriasis scenarios and to assess how they prioritize key quality dimensions,including accuracy,traceability,and logicality.Methods:A cross-sectional,web-based survey was conducted between December 25,2024,and January 22,2025,following the Checklist for Reporting Results of Internet E-Surveys guidelines.A total of 1247 valid responses were collected from practicing dermatologists across 33 of China's provincial-level administrative divisions.Participants evaluated responses to five categories of clinical questions(etiology,clinical presentation,differential diagnosis,treatment,and case study)generated by five LLMs:ChatGPT-4o,Kimi.ai,Doubao,ZuoYiGPT,and Lingyi-agent.Statistical associations between participant characteristics and model preferences were examined using chi-square tests.Results:ChatGPT-4o(Model 1)emerged as the most preferred model across all clinical tasks,consistently receiving the highest number of votes in case study(n=740),clinical presentation(n=666),differential diagnosis(n=707),etiology(n=602),and treatment(n=656).Significant variation in model preference by professional title was observed only for the differential diagnosis task(χ^(2)=21.13,df=12,p=0.0485),while no significant differences were found across hospital tiers(p>0.05).In terms of evaluation dimensions,accuracy was most frequently rated as“very important”(n=635).A significant association existed between hospital tier and the most valued dimension(χ^(2)=27.667,df=9,p=0.0011),with dermatologists in primary hospitals prioritizing traceability more than their peers in higher-tier hospitals.No significant associations were found across professional titles(p=0.127).Conclusions:Chinese dermatologists suggest a strong preference for ChatGPT-4o over domestic LLMs in psoriasis-related clinical tasks.While accuracy remains the primary criterion,traceability and logicality are also critical,particularly for clinicians in lower-tier hospitals.These findings suggest that future clinical LLMs should prioritize not only content accuracy but also source transparency and structural clarity to meet the diverse needs of different clinical settings.
基金Project supported by the National Natural Science Foundation of China(Nos.12372214 and U2341231)。
文摘The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.
文摘Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is employed to simulate the ballistic impact of 7.62 mm armor-piercing projectiles on Aluminum AA5083-H116 and Steel Secure 500 armors,focusing on the evaluation of material deformation and penetration resistance at varying impact points.While the D-shaped armor plate is penetrated by the armor-piercing projectiles,the combination of the perforated D-shaped and base armor plates successfully halts penetration.A numerical model based on the finite element method is developed using software such as SolidWorks and ANSYS to analyze the interaction between radiator armor and bullet.The perforated design of radiator armor is to maintain airflow for radiator function,with hole sizes smaller than the bullet core diameter to protect radiator assemblies.Predictions are made regarding the brittle fracture resulting from the projectile core′s bending due to asymmetric impact,and the resulting fragments failed to penetrate the perforated base armor plate.Craters are formed on the surface of the perforated D-shaped armor plate due to the impact of projectile fragments.The numerical model accurately predicts hole growth and projectile penetration upon impact with the armor,demonstrating effective protection of the radiator assemblies by the radiator armor.
