Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring...Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring their functional integrity and performance.To achieve sustainable manufacturing in FDM,it is necessary to optimize the print quality and time efficiency concurrently.However,owing to the complex interactions of printing parameters,achieving a balanced optimization of both remains challenging.This study examines four key factors affecting dimensional accuracy and print time:printing speed,layer thickness,nozzle temperature,and bed temperature.Fifty parameter sets were generated using enhanced Latin hypercube sampling.A whale optimization algorithm(WOA)-enhanced support vector regression(SVR)model was developed to predict dimen-sional errors and print time effectively,with non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)utilized for multi-objective optimization.The technique for Order Preference by Similarity to Ideal Solution(TOPSIS)was applied to select a balanced solution from the Pareto front.In experimental validation,the parts printed using the optimized parameters exhibited excellent dimensional accuracy and printing efficiency.This study comprehensively considered optimizing the printing time and size to meet quality requirements while achieving higher printing efficiency and aiding in the realization of sustainable manufacturing in the field of AM.In addition,the printing of a specific prosthetic component was used as a case study,highlighting the high demands on both dimensional precision and printing efficiency.The optimized process parameters required significantly less printing time,while satisfying the dimensional accuracy requirements.This study provides valuable insights for achieving sustainable AM using FDM.展开更多
The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased si...The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.展开更多
With the continual deployment of power-electronics-interfaced renewable energy resources,increasing privacy concerns due to deregulation of electricity markets,and the diversification of demand-side activities,traditi...With the continual deployment of power-electronics-interfaced renewable energy resources,increasing privacy concerns due to deregulation of electricity markets,and the diversification of demand-side activities,traditional knowledge-based power system dynamic modeling methods are faced with unprecedented challenges.Data-driven modeling has been increasingly studied in recent years because of its lesser need for prior knowledge,higher capability of handling large-scale systems,and better adaptability to variations of system operating conditions.This paper discusses about the motivations and the generalized process of datadriven modeling,and provides a comprehensive overview of various state-of-the-art techniques and applications.It also comparatively presents the advantages and disadvantages of these methods and provides insight into outstanding challenges and possible research directions for the future.展开更多
The dynamical modeling of projectile systems with sufficient accuracy is of great difficulty due to high-dimensional space and various perturbations.With the rapid development of data science and scientific tools of m...The dynamical modeling of projectile systems with sufficient accuracy is of great difficulty due to high-dimensional space and various perturbations.With the rapid development of data science and scientific tools of measurement recently,there are numerous data-driven methods devoted to discovering governing laws from data.In this work,a data-driven method is employed to perform the modeling of the projectile based on the Kramers–Moyal formulas.More specifically,the four-dimensional projectile system is assumed as an It?stochastic differential equation.Then the least square method and sparse learning are applied to identify the drift coefficient and diffusion matrix from sample path data,which agree well with the real system.The effectiveness of the data-driven method demonstrates that it will become a powerful tool in extracting governing equations and predicting complex dynamical behaviors of the projectile.展开更多
Blades are essential components of wind turbines.Reducing their fatigue loads during operation helps to extend their lifespan,but it is difficult to quickly and accurately calculate the fatigue loads of blades.To solv...Blades are essential components of wind turbines.Reducing their fatigue loads during operation helps to extend their lifespan,but it is difficult to quickly and accurately calculate the fatigue loads of blades.To solve this problem,this paper innovatively designs a data-driven blade load modeling method based on a deep learning framework through mechanism analysis,feature selection,and model construction.In the mechanism analysis part,the generation mechanism of blade loads and the load theoretical calculationmethod based on material damage theory are analyzed,and four measurable operating state parameters related to blade loads are screened;in the feature extraction part,15 characteristic indicators of each screened parameter are extracted in the time and frequency domain,and feature selection is completed through correlation analysis with blade loads to determine the input parameters of data-driven modeling;in the model construction part,a deep neural network based on feedforward and feedback propagation is designed to construct the nonlinear coupling relationship between the unit operating parameter characteristics and blade loads.The results show that the proposed method mines the wind turbine operating state characteristics highly correlated with the blade load,such as the standard deviation of wind speed.The model built using these characteristics has reasonable calculation and fitting capabilities for the blade load and shows a better fitting level for untrained out-of-sample data than the traditional scheme.Based on the mean absolute percentage error calculation,the modeling accuracy of the two blade loads can reach more than 90%and 80%,respectively,providing a good foundation for the subsequent optimization control to suppress the blade load.展开更多
This study explores an integrated framework combining in-situ test-based numerical and data-driven modeling to assess the performance of a deep excavation-tunnel system.To achieve the goal,a case history of deep excav...This study explores an integrated framework combining in-situ test-based numerical and data-driven modeling to assess the performance of a deep excavation-tunnel system.To achieve the goal,a case history of deep excavations adjacent to existing tunnels in silt/sand-dominated sediments is introduced to establish a base three-dimensional finite element(3D-FE)model.In-situ tests such as cone penetration test(CPT/CPTU)and seismic dilatometer test(DMT/SDMT),as an alternative to laboratory testing,are used to determine a set of advanced constitutive model parameters.The established excavation-tunnel numerical model is then validated against filed monitoring data.A dataset from numerical simulation is created for training and testing four machine learning models(i.e.,artificial neural network(ANN),support vector machines(SVM),random forest(RF),and light gradient boosting machine(LightGBM)),which predict the maximum wall deflection,ground surface settlement,horizontal and vertical displacements of the tunnel.Results show that the ANN model outperforms other models in prediction capacity.Its generalization ability in practice is further enhanced by comparing field measurement data and empirical equations.The findings suggest that,with the integrated in-situ tests,FE and ANN modeling could be used to predict deformation responses of deep excavations close to existing tunnels in soft soil.The present study is useful and valuable for practical risk assessment and mitigation decisions.展开更多
Pressure differential deviations under static conditions and pressure convergence fluctuations under dynamic disturbances are widely reported problems with pressure differential control in pharmaceutical cleanrooms,ye...Pressure differential deviations under static conditions and pressure convergence fluctuations under dynamic disturbances are widely reported problems with pressure differential control in pharmaceutical cleanrooms,yet their underlying mechanisms and key reasons remain insufficiently explored.This study performed a field survey and model-based simulations to identify the major influencing parameters and quantify their influence on pressure differentials.Twelve pharmaceutical cleanrooms with varying environmental control parameters were included in the field survey,all of which were served by a variable air volume(VAV)ventilation system.Large deviations between actual and design pressure differentials were found,ranging from 10%to 42.5%,and a total of 24 uncertain parameters and their respective uncertainty ranges were identified.Based on the field survey,a data-driven pressure differential response model was developed using MATLAB/Simulink platform.The model fully took into account the system dynamics and facilitated real-time monitoring and control of the pressure differential.Sobol-based sensitivity analysis was then conducted to identify key influencing parameters of pressure differential deviations.The simulated results revealed that static pressure differential deviations were predominantly influenced by pressure sensing accuracy,exhaust airflow accuracy,and duct impedance,while dynamic disturbances were mainly driven by room envelope airtightness and supply airflow accuracy.The interactions between connected zones were pronounced.Rooms with higher branch duct impedance experienced smaller pressure differential deviations due to natural buffering characteristics,while the parameter uncertainties in these rooms significantly affected pressure differential in other rooms.These findings offer practical guidance for the design and operation of precise pressure differential control in pharmaceutical cleanrooms.展开更多
Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a...Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.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
The dissolution of MgO-refractory into the slag had an obvious influence on the steel-slag reaction and the slag property,especially for high-aluminum steels.The dissolution behavior of MgO-refractory was investigated...The dissolution of MgO-refractory into the slag had an obvious influence on the steel-slag reaction and the slag property,especially for high-aluminum steels.The dissolution behavior of MgO-refractory was investigated under various conditions,including the temperature,the initial steel composition,and the initial slag composition.A steel-slag-refractory kinetic model for high-aluminum steel was developed,which incorporated the process of MgO-refractory dissolution.The dependence of the MgO mass transfer coefficient k_(MgO)^(r)on temperature T during MgO-refractory dissolution process was established,as described by ln k_(MgO)^(r)=63,754/T+24.38524.It was indicated that the MgO dissolution rate was significantly influenced by the temperature.A higher temperature increased the dissolution rate of MgO.The initial steel composition had a slight impact on the MgO dissolution rate.Additionally,the initial slag composition strongly impacted the MgO saturation concentration and the dissolution rate.A lower initial Al_(2)O_(3)/SiO_(2)ratio increased the MgO dissolution rate.The steel-slag-refractory kinetic model accurately predicted the dissolution of MgO-refractory and the influence of dissolved MgO on the viscosity and composition change during steel-slag-refractory reactions.It was suggested that a higher temperature can hardly reduce the viscosity due to the dissolution of the MgO-refractory.展开更多
In materials science and engineering design,high-fidelity and high-efficiency numerical simulation has become a driving force for innovation and practical implementation.To address longstanding bottlenecks in the deve...In materials science and engineering design,high-fidelity and high-efficiency numerical simulation has become a driving force for innovation and practical implementation.To address longstanding bottlenecks in the development of conventional material constitutive models—such as lengthy modeling cycles and difficulties in numerical implementation—this study proposes an intelligent modeling and code generation approach powered by large languagemodels.A structured knowledge base integrating constitutive theory,numerical algorithms,and UMAT(User Material)interface specifications is constructed,and a retrieval-augmented generation strategy is employed to establish an end-to-end workflow spanning experimental data parsing,constitutive model formulation,and automatic UMAT subroutine generation.Experimental results show that the method achieves high accuracy for both a classical Johnson–Cookmodel and a physics-informed neural network(PINN)model,with key parameter identification errors below 5%.Moreover,the automatically generated UMAT subroutines yield finite element simulation results in Abaqus that are highly consistent with theoretical predictions(coefficient of determination R2>0.98)while maintaining good numerical stability.This framework is currently focused on the automatic construction of rate-dependent elastoplastic material models,and its core method also provides a clear path for extending to other constitutive categories such as hyperelasticity and viscoelasticity.This work provides an effective technical route for the rapid development and reliable numerical implementation of material constitutive models,significantly advancing the intelligence level of computational mechanics research and improving engineering application efficiency.展开更多
(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbi...(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.展开更多
The India-Asia collision resulted in the formation of Qinghai-Tibet Plateau.Lower crustal flow model was proposed to explain the mechanism of Cenozoic tectonic deformation of Qinghai-Tibet Plateau.In this study,we pro...The India-Asia collision resulted in the formation of Qinghai-Tibet Plateau.Lower crustal flow model was proposed to explain the mechanism of Cenozoic tectonic deformation of Qinghai-Tibet Plateau.In this study,we propose a new approach by combining centrifugal analog modeling with numerical simulation to simulate the tectonic uplift history of the plateau based on the lower crustal flow model,and to investigate the material migration characteristics and the influence of crustal motion velocity and ductile layer viscosity on the plateau tectonic geomorphology.The models reproduce steep-sided flat-topped geomorphic features and clockwise rotation of the material at eastern Himalayan Syntaxis,verifying the rationality of the models.The results show that the greater the crustal motion velocity and the greater the ductile layer viscosity,the steeper the terrain change;and conversely,the smaller the crustal motion velocity and the smaller the ductile layer viscosity,the gentler the terrain change.This study further indicates that the weak lower crust plays an important role in the formation of geomorphic features and material migration characteristics of Qinghai-Tibet Plateau,and provides a new insight for the study of the uplift mechanism of the Tibetan Plateau.展开更多
In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical propert...In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical properties of rocks,the cracking processes of pre-cracked rocks have been extensively studied using numerical modeling methods.The peridynamics(PD)exhibits advantages over other numerical methods due to the absence of the requirements for remeshing and external crack growth criterion.However,for modeling pre-cracked rock cracking processes under impact,current PD implementations lack generally applicable rock constitutive models and impact contact models,which leads to difficulties in determining rock material parameters and efficiently calculating impact loads.This paper proposes a non-ordinary state-based peridynamics(NOSBPD)modeling method integrating the Drucker-Prager(DP)plasticity model and an efficient contact model to address the above problems.In the proposed method,the Drucker-Prager plasticity model is integrated into the NOSBPD,thereby equipping NOSBPD with the capability to accurately characterize the nonlinear stress-strain relationship inherent in rocks.An efficient contact model between particles and meshes is designed to calculate the impact loads,which is essentially a coupling method of PD with the finite element method(FEM).The effectiveness of the proposed NOSBPD modeling method is verified by comparison with other numerical methods and experiments.Experimental results indicate that the proposed method can effectively and accurately predict the 3D cracking processes of pre-cracked cracks under impact loading,and the maximum principal stress is the key driver behind wing crack formation in pre-cracked rocks.展开更多
In-space cable-driven manipulators exhibit several advantages,such as a large range of motion,high dexterity,and lightweight structure.However,kinematic and dynamic analysis play an essential role in designing a cable...In-space cable-driven manipulators exhibit several advantages,such as a large range of motion,high dexterity,and lightweight structure.However,kinematic and dynamic analysis play an essential role in designing a cable-driven manipulator.In this paper,the kinematic analysis of a type of cable-driven manipulator is performed,and a motion planning scheme is conducted to actuate this manipulator.Moreover,a flexible multi-body dynamic model of a cable-driven manipulator considering the frictional contact between the cables and pulleys is established.To describe properties such as flexibility,vibration,and variable length of the cable,this paper utilizes reducedorder beam elements of the Absolute Nodal Coordinates Formulation(ANCF)in Arbitrary Lagrangian Eulerian(ALE)framework.Additionally,a virtual element is introduced to model the contact segment in the cable-pulley system.A tension decay factor is employed to account for the friction in the contact segment.To validate the proposed method,a semi-analytical model based on D'Alembert's principle is established.Cross-verification is performed to validate the accuracy of both models.The model is further applied to simulate the rotation of the cable-driven manipulator with different structural parameters and frictional factors.The results from the analyses provide valuable guidance for the design and motion control of the in-space cable-driven manipulator.Finally,a prototype of a single module is manufactured and tested.Ground experiments are carried out to verify the kinematic and dynamic models.展开更多
Predicting rock blasting outcomes in mining has been crucial since its inception.Blasting remains the most energy-and cost-efficient method for rock breaking and is often the only practical option.However,the mechanis...Predicting rock blasting outcomes in mining has been crucial since its inception.Blasting remains the most energy-and cost-efficient method for rock breaking and is often the only practical option.However,the mechanism is complex,influenced by various rock properties,explosives,and blast design parameters,making their effects difficult to quantify.Traditional stress-based models struggle with many parameters,such as stress and Poisson's ratio,which are challenging to measure in the field.Empirical models,though simpler,often oversimplify blast conditions.Both types of models are limited to simulating a few blastholes and cannot handle full-scale blasts involving hundreds of blastholes.However,modeling full-scale blasts with all blast design parameters is most required for modern mining applications.This paper presents a novel strain-based modeling approach for blasting and geomechanical applications,utilizing measurable variables such as particle velocity,strain,and displacement.By bypassing complex constitutive relations,strain-based models capture critical blasting trends and simulate full-scale blasts with full-blast design parameters with minimal calibration.The framework encompasses field strain measurements,model construction based on measurable variables,and laboratoryderived strain-failure criteria,each offering potential for future enhancement.Additionally,a standardized field test for site characterization is recommended.The approach is demonstrated through the Multiple Blasthole Fragmentation model,which simulates rock fragmentation and fragment strain during blasting,highlighting the practicality and effectiveness of strain-based modeling for multiple blasthole blasts.Moreover,this approach extends beyond blasting,with potential applications in highwall stability monitoring and other geomechanical applications.Strain-based modeling provides a simplified yet effective solution,avoiding the complexities of rock constitutive relations and field stress measurements while enabling full-blast design simulations for large-scale field blasts.展开更多
Glassy polymers are widely used in biomedical applications in a solvent environment,yet their long-term performance is governed by the competing effects of physical aging and solvent-induced plasticization.Here,we dev...Glassy polymers are widely used in biomedical applications in a solvent environment,yet their long-term performance is governed by the competing effects of physical aging and solvent-induced plasticization.Here,we develop a constitutive model that explicitly couples the solvent concentration,structural relaxation,and mechanical response.This framework is built on a multiplicative decomposition of deformation and an Eyring-type flow rule,with structural evolution described by an effective temperature.A generalized shift factor is introduced to quantify how the solvent concentration and effective temperature jointly affect the relaxation time,thereby integrating physical aging and plasticization.The model is subsequently applied to methacrylate(MA)-based copolymer networks immersed in phosphate-buffered saline for up to nine months.Simulations accurately capture key experimental features,including the strong softening of highly swellable networks,the partial recovery due to aging,and the mitigating role of hydrophobic crosslinking in reducing solvent uptake.While the current single-mode description cannot reproduce the full relaxation spectrum,it establishes an efficient framework for predicting the long-term mechanical performance under coupled environmental and mechanical loading.This study provides a constitutive description of solvent-swollen glassy polymers,offering mechanistic insight into the interplay between plasticization and aging.Beyond biomedical MA networks,this framework establishes a foundation for predicting the long-term performance of polymer glasses under coupled aqueous environmental and mechanical loading.展开更多
Huperzine A(HupA) is a highly selective, reversible acetylcholinesterase(AChE) inhibitor that exhibits neuroprotective effects and is clinically used to manage benign memory decline.However, the specific relationship ...Huperzine A(HupA) is a highly selective, reversible acetylcholinesterase(AChE) inhibitor that exhibits neuroprotective effects and is clinically used to manage benign memory decline.However, the specific relationship between the pharmacokinetic(PK) profile of HupA and cerebral acetylcholine(ACh) dynamics remains poorly characterized. Here, we characterize the PK-pharmacodynamic(PD) properties of HupA in rats under both physiological and pathological conditions. Following a single intramuscular injection, HupA exhibits a short halflife but rapid brain penetration, while multiple dosing significantly enhances its brain exposure. In a middle cerebral artery occlusion(MCAO) rat model, HupA demonstrates increased brain distribution. Furthermore, HupA elevates ACh concentrations across multiple brain regions, concurrently modulating several monoamine neurotransmitters. Using a minimal physiologically based pharmacokinetic-pharmacodynamic(mPBPK-PD) modeling approach,cerebral ACh dynamics were accurately predicted based on the pharmacokinetics of HupA in systemic circulation. The developed mPBPK-PD model exhibits robust predictive performance and holds potential for guiding the optimization of clinical dosing regimens and improving the therapeutic efficacy of HupA.展开更多
Activation pruning reduces neural network complexity by eliminating low-importance neuron activations,yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally...Activation pruning reduces neural network complexity by eliminating low-importance neuron activations,yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally expensive and typically requires exhaustive search.We introduce a thermodynamics-inspired framework that treats activation distributions as energy-filtered physical systems and employs the free energy of activations as a principled evaluation metric.Phase-transition-like phenomena in the free-energy profile—such as extrema,inflection points,and curvature changes—yield reliable estimates of the critical pruning threshold,providing a theoretically grounded means of predicting sharp accuracy degradation.To further enhance efficiency,we propose a renormalized free energy technique that approximates full-evaluation free energy using only the activation distribution of the unpruned network.This eliminates repeated forward passes,dramatically reducing computational overhead and achieving speedups of up to 550×for MLPs.Extensive experiments across diverse vision architectures(MLP,CNN,ResNet,MobileNet,Vision Transformer)and text models(LSTM,BERT,ELECTRA,T5,GPT-2)on multiple datasets validate the generality,robustness,and computational efficiency of our approach.Overall,this work establishes a theoretically grounded and practically effective framework for activation pruning,bridging the gap between analytical understanding and efficient deployment of sparse neural networks.展开更多
Separation bubbles forming on airfoils significantly influence aerodynamic behavior,particularly at low Reynolds numbers,making their accurate prediction a critical challenge in transition modelling.This study investi...Separation bubbles forming on airfoils significantly influence aerodynamic behavior,particularly at low Reynolds numbers,making their accurate prediction a critical challenge in transition modelling.This study investigates numerical modeling of a separation bubble and the effects of airfoil thickness and camber variation on the formation of the bubble dynamics at low Reynolds numbers.The numerical results were compared with the experimental results obtained from surface pressure distribution measurements,oil flow visualisation,and surface shear measurements to analyse the detailed flow behavior.The combination of pressure and flow visualisation techniques provided complementary insights,enabling a detailed characterisation of bubble formation.The results reveal that both the thickness and camber of the airfoil significantly influence the location,length,and stability of the bubble.At low Reynolds number flows(Re=0.5×10^(5)),particularly for highly cambered profiles,closer to the leading edge,separation and long bubbles were observed.As the Reynolds number increased,the separation point shifted to the leading edge,and reattachment became more likely.In numerical studies,transition models can accurately model the bubble initiation point;however,they often fail to model the bubble reattachment points accurately.This is due to the inadequacy of models that use empirical expressions for turbulence modelling,particularly in low Reynolds number flows,in their viscous modelling.In this study,it was concluded that transition onset terms,which specifically affect bubble formation,should be modified for more accurate modeling.展开更多
基金supporteded by Natural Science Foundation of Shanghai(Grant No.22ZR1463900)State Key Laboratory of Mechanical System and Vibration(Grant No.MSV202318)the Fundamental Research Funds for the Central Universities(Grant No.22120220649).
文摘Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring their functional integrity and performance.To achieve sustainable manufacturing in FDM,it is necessary to optimize the print quality and time efficiency concurrently.However,owing to the complex interactions of printing parameters,achieving a balanced optimization of both remains challenging.This study examines four key factors affecting dimensional accuracy and print time:printing speed,layer thickness,nozzle temperature,and bed temperature.Fifty parameter sets were generated using enhanced Latin hypercube sampling.A whale optimization algorithm(WOA)-enhanced support vector regression(SVR)model was developed to predict dimen-sional errors and print time effectively,with non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)utilized for multi-objective optimization.The technique for Order Preference by Similarity to Ideal Solution(TOPSIS)was applied to select a balanced solution from the Pareto front.In experimental validation,the parts printed using the optimized parameters exhibited excellent dimensional accuracy and printing efficiency.This study comprehensively considered optimizing the printing time and size to meet quality requirements while achieving higher printing efficiency and aiding in the realization of sustainable manufacturing in the field of AM.In addition,the printing of a specific prosthetic component was used as a case study,highlighting the high demands on both dimensional precision and printing efficiency.The optimized process parameters required significantly less printing time,while satisfying the dimensional accuracy requirements.This study provides valuable insights for achieving sustainable AM using FDM.
基金supported in part by the National Natural Science Foundation of China(NSFC)(92167106,61833014)Key Research and Development Program of Zhejiang Province(2022C01206)。
文摘The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.
基金supported by the U.S.Department of Energy’s Office of Energy Efficiency and Renewable Energy(EERE)under the Solar Energy Technologies Office Award Number 38456.
文摘With the continual deployment of power-electronics-interfaced renewable energy resources,increasing privacy concerns due to deregulation of electricity markets,and the diversification of demand-side activities,traditional knowledge-based power system dynamic modeling methods are faced with unprecedented challenges.Data-driven modeling has been increasingly studied in recent years because of its lesser need for prior knowledge,higher capability of handling large-scale systems,and better adaptability to variations of system operating conditions.This paper discusses about the motivations and the generalized process of datadriven modeling,and provides a comprehensive overview of various state-of-the-art techniques and applications.It also comparatively presents the advantages and disadvantages of these methods and provides insight into outstanding challenges and possible research directions for the future.
基金the Six Talent Peaks Project in Jiangsu Province,China(Grant No.JXQC-002)。
文摘The dynamical modeling of projectile systems with sufficient accuracy is of great difficulty due to high-dimensional space and various perturbations.With the rapid development of data science and scientific tools of measurement recently,there are numerous data-driven methods devoted to discovering governing laws from data.In this work,a data-driven method is employed to perform the modeling of the projectile based on the Kramers–Moyal formulas.More specifically,the four-dimensional projectile system is assumed as an It?stochastic differential equation.Then the least square method and sparse learning are applied to identify the drift coefficient and diffusion matrix from sample path data,which agree well with the real system.The effectiveness of the data-driven method demonstrates that it will become a powerful tool in extracting governing equations and predicting complex dynamical behaviors of the projectile.
基金supported by Science and Technology Project funding from China Southern Power Grid Corporation No.GDKJXM20230245(031700KC23020003).
文摘Blades are essential components of wind turbines.Reducing their fatigue loads during operation helps to extend their lifespan,but it is difficult to quickly and accurately calculate the fatigue loads of blades.To solve this problem,this paper innovatively designs a data-driven blade load modeling method based on a deep learning framework through mechanism analysis,feature selection,and model construction.In the mechanism analysis part,the generation mechanism of blade loads and the load theoretical calculationmethod based on material damage theory are analyzed,and four measurable operating state parameters related to blade loads are screened;in the feature extraction part,15 characteristic indicators of each screened parameter are extracted in the time and frequency domain,and feature selection is completed through correlation analysis with blade loads to determine the input parameters of data-driven modeling;in the model construction part,a deep neural network based on feedforward and feedback propagation is designed to construct the nonlinear coupling relationship between the unit operating parameter characteristics and blade loads.The results show that the proposed method mines the wind turbine operating state characteristics highly correlated with the blade load,such as the standard deviation of wind speed.The model built using these characteristics has reasonable calculation and fitting capabilities for the blade load and shows a better fitting level for untrained out-of-sample data than the traditional scheme.Based on the mean absolute percentage error calculation,the modeling accuracy of the two blade loads can reach more than 90%and 80%,respectively,providing a good foundation for the subsequent optimization control to suppress the blade load.
基金supported by the National Natural Science Foundation of China(Grant Nos.52408356 and 41972269).
文摘This study explores an integrated framework combining in-situ test-based numerical and data-driven modeling to assess the performance of a deep excavation-tunnel system.To achieve the goal,a case history of deep excavations adjacent to existing tunnels in silt/sand-dominated sediments is introduced to establish a base three-dimensional finite element(3D-FE)model.In-situ tests such as cone penetration test(CPT/CPTU)and seismic dilatometer test(DMT/SDMT),as an alternative to laboratory testing,are used to determine a set of advanced constitutive model parameters.The established excavation-tunnel numerical model is then validated against filed monitoring data.A dataset from numerical simulation is created for training and testing four machine learning models(i.e.,artificial neural network(ANN),support vector machines(SVM),random forest(RF),and light gradient boosting machine(LightGBM)),which predict the maximum wall deflection,ground surface settlement,horizontal and vertical displacements of the tunnel.Results show that the ANN model outperforms other models in prediction capacity.Its generalization ability in practice is further enhanced by comparing field measurement data and empirical equations.The findings suggest that,with the integrated in-situ tests,FE and ANN modeling could be used to predict deformation responses of deep excavations close to existing tunnels in soft soil.The present study is useful and valuable for practical risk assessment and mitigation decisions.
基金supported by the Natural Science Foundation of Hunan Province of China(No.2024JJ9082)by the Fundamental Research Funds for the Central Universities(No.531118010378).
文摘Pressure differential deviations under static conditions and pressure convergence fluctuations under dynamic disturbances are widely reported problems with pressure differential control in pharmaceutical cleanrooms,yet their underlying mechanisms and key reasons remain insufficiently explored.This study performed a field survey and model-based simulations to identify the major influencing parameters and quantify their influence on pressure differentials.Twelve pharmaceutical cleanrooms with varying environmental control parameters were included in the field survey,all of which were served by a variable air volume(VAV)ventilation system.Large deviations between actual and design pressure differentials were found,ranging from 10%to 42.5%,and a total of 24 uncertain parameters and their respective uncertainty ranges were identified.Based on the field survey,a data-driven pressure differential response model was developed using MATLAB/Simulink platform.The model fully took into account the system dynamics and facilitated real-time monitoring and control of the pressure differential.Sobol-based sensitivity analysis was then conducted to identify key influencing parameters of pressure differential deviations.The simulated results revealed that static pressure differential deviations were predominantly influenced by pressure sensing accuracy,exhaust airflow accuracy,and duct impedance,while dynamic disturbances were mainly driven by room envelope airtightness and supply airflow accuracy.The interactions between connected zones were pronounced.Rooms with higher branch duct impedance experienced smaller pressure differential deviations due to natural buffering characteristics,while the parameter uncertainties in these rooms significantly affected pressure differential in other rooms.These findings offer practical guidance for the design and operation of precise pressure differential control in pharmaceutical cleanrooms.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3209504)Natural Science Foundation of Wuhan(Grant No.2024040801020271)the Fundamental Research Funds for Central Public Welfare Research Institutes(Grant No.CKSF2025718/YT).
文摘Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.
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support from the National Key R&D Program of China(Grant No.2023YFB3709901)the National Natural Science Foundation of China(Grant No.U22A20171)+1 种基金China Baowu Low Carbon Metallurgy Innovation Foundation(Grant No.BWLCF202315)the High Steel Center(HSC)at North China University of Technology and University of Science and Technology Beijing,China.
文摘The dissolution of MgO-refractory into the slag had an obvious influence on the steel-slag reaction and the slag property,especially for high-aluminum steels.The dissolution behavior of MgO-refractory was investigated under various conditions,including the temperature,the initial steel composition,and the initial slag composition.A steel-slag-refractory kinetic model for high-aluminum steel was developed,which incorporated the process of MgO-refractory dissolution.The dependence of the MgO mass transfer coefficient k_(MgO)^(r)on temperature T during MgO-refractory dissolution process was established,as described by ln k_(MgO)^(r)=63,754/T+24.38524.It was indicated that the MgO dissolution rate was significantly influenced by the temperature.A higher temperature increased the dissolution rate of MgO.The initial steel composition had a slight impact on the MgO dissolution rate.Additionally,the initial slag composition strongly impacted the MgO saturation concentration and the dissolution rate.A lower initial Al_(2)O_(3)/SiO_(2)ratio increased the MgO dissolution rate.The steel-slag-refractory kinetic model accurately predicted the dissolution of MgO-refractory and the influence of dissolved MgO on the viscosity and composition change during steel-slag-refractory reactions.It was suggested that a higher temperature can hardly reduce the viscosity due to the dissolution of the MgO-refractory.
基金funded by the National Natural Science Foundation of China,grant number 52405341Foundation of National Key Laboratory of Computational Physics,grant number 6142A05QN24012+1 种基金Chongqing Science and Technology Committee,grant number CSTB2023NSCQ-MSX0363The Science and Technology Research Program of Chongqing Municipal Education Commission,grant number KJQN202301117.
文摘In materials science and engineering design,high-fidelity and high-efficiency numerical simulation has become a driving force for innovation and practical implementation.To address longstanding bottlenecks in the development of conventional material constitutive models—such as lengthy modeling cycles and difficulties in numerical implementation—this study proposes an intelligent modeling and code generation approach powered by large languagemodels.A structured knowledge base integrating constitutive theory,numerical algorithms,and UMAT(User Material)interface specifications is constructed,and a retrieval-augmented generation strategy is employed to establish an end-to-end workflow spanning experimental data parsing,constitutive model formulation,and automatic UMAT subroutine generation.Experimental results show that the method achieves high accuracy for both a classical Johnson–Cookmodel and a physics-informed neural network(PINN)model,with key parameter identification errors below 5%.Moreover,the automatically generated UMAT subroutines yield finite element simulation results in Abaqus that are highly consistent with theoretical predictions(coefficient of determination R2>0.98)while maintaining good numerical stability.This framework is currently focused on the automatic construction of rate-dependent elastoplastic material models,and its core method also provides a clear path for extending to other constitutive categories such as hyperelasticity and viscoelasticity.This work provides an effective technical route for the rapid development and reliable numerical implementation of material constitutive models,significantly advancing the intelligence level of computational mechanics research and improving engineering application efficiency.
基金supported in part by the National Key Research and Development Program of China(2021YFB2900501)in part by the Shaanxi Science and Technology Innovation Team(2023-CX-TD-03)+3 种基金in part by the Science and Technology Program of Shaanxi Province(2021GXLH-Z-038)in part by the Natural Science Foundation of Hunan Province(2023JJ40607 and 2023JJ50045)in part by the Scientific Research Foundation of Hunan Provincial Education Department(23B0713 and 24B0603)in part by the National Natural Science Foundation of China(62401371,62101275,and 62372070).
文摘(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.
基金supported by Excellent Research Group Project for Multiphase Evolution in Hyper-Gravity of the National Natural Science Foundation of China(No.52588202)。
文摘The India-Asia collision resulted in the formation of Qinghai-Tibet Plateau.Lower crustal flow model was proposed to explain the mechanism of Cenozoic tectonic deformation of Qinghai-Tibet Plateau.In this study,we propose a new approach by combining centrifugal analog modeling with numerical simulation to simulate the tectonic uplift history of the plateau based on the lower crustal flow model,and to investigate the material migration characteristics and the influence of crustal motion velocity and ductile layer viscosity on the plateau tectonic geomorphology.The models reproduce steep-sided flat-topped geomorphic features and clockwise rotation of the material at eastern Himalayan Syntaxis,verifying the rationality of the models.The results show that the greater the crustal motion velocity and the greater the ductile layer viscosity,the steeper the terrain change;and conversely,the smaller the crustal motion velocity and the smaller the ductile layer viscosity,the gentler the terrain change.This study further indicates that the weak lower crust plays an important role in the formation of geomorphic features and material migration characteristics of Qinghai-Tibet Plateau,and provides a new insight for the study of the uplift mechanism of the Tibetan Plateau.
基金support from the National Natural Science Foundation of China(Grant Nos.42277161 and 42230709).
文摘In rock engineering,natural cracks in rock masses subjected to external loads tend to initiate and propagate,leading to potential safety hazards.To investigate the effect of cracking behavior on the mechanical properties of rocks,the cracking processes of pre-cracked rocks have been extensively studied using numerical modeling methods.The peridynamics(PD)exhibits advantages over other numerical methods due to the absence of the requirements for remeshing and external crack growth criterion.However,for modeling pre-cracked rock cracking processes under impact,current PD implementations lack generally applicable rock constitutive models and impact contact models,which leads to difficulties in determining rock material parameters and efficiently calculating impact loads.This paper proposes a non-ordinary state-based peridynamics(NOSBPD)modeling method integrating the Drucker-Prager(DP)plasticity model and an efficient contact model to address the above problems.In the proposed method,the Drucker-Prager plasticity model is integrated into the NOSBPD,thereby equipping NOSBPD with the capability to accurately characterize the nonlinear stress-strain relationship inherent in rocks.An efficient contact model between particles and meshes is designed to calculate the impact loads,which is essentially a coupling method of PD with the finite element method(FEM).The effectiveness of the proposed NOSBPD modeling method is verified by comparison with other numerical methods and experiments.Experimental results indicate that the proposed method can effectively and accurately predict the 3D cracking processes of pre-cracked cracks under impact loading,and the maximum principal stress is the key driver behind wing crack formation in pre-cracked rocks.
基金co-supported by the National Natural Science Foundation of China(Nos.12102034 and 12125201)the Open Fund of State Key Laboratory of Robotics and Systems(HIT),China。
文摘In-space cable-driven manipulators exhibit several advantages,such as a large range of motion,high dexterity,and lightweight structure.However,kinematic and dynamic analysis play an essential role in designing a cable-driven manipulator.In this paper,the kinematic analysis of a type of cable-driven manipulator is performed,and a motion planning scheme is conducted to actuate this manipulator.Moreover,a flexible multi-body dynamic model of a cable-driven manipulator considering the frictional contact between the cables and pulleys is established.To describe properties such as flexibility,vibration,and variable length of the cable,this paper utilizes reducedorder beam elements of the Absolute Nodal Coordinates Formulation(ANCF)in Arbitrary Lagrangian Eulerian(ALE)framework.Additionally,a virtual element is introduced to model the contact segment in the cable-pulley system.A tension decay factor is employed to account for the friction in the contact segment.To validate the proposed method,a semi-analytical model based on D'Alembert's principle is established.Cross-verification is performed to validate the accuracy of both models.The model is further applied to simulate the rotation of the cable-driven manipulator with different structural parameters and frictional factors.The results from the analyses provide valuable guidance for the design and motion control of the in-space cable-driven manipulator.Finally,a prototype of a single module is manufactured and tested.Ground experiments are carried out to verify the kinematic and dynamic models.
文摘Predicting rock blasting outcomes in mining has been crucial since its inception.Blasting remains the most energy-and cost-efficient method for rock breaking and is often the only practical option.However,the mechanism is complex,influenced by various rock properties,explosives,and blast design parameters,making their effects difficult to quantify.Traditional stress-based models struggle with many parameters,such as stress and Poisson's ratio,which are challenging to measure in the field.Empirical models,though simpler,often oversimplify blast conditions.Both types of models are limited to simulating a few blastholes and cannot handle full-scale blasts involving hundreds of blastholes.However,modeling full-scale blasts with all blast design parameters is most required for modern mining applications.This paper presents a novel strain-based modeling approach for blasting and geomechanical applications,utilizing measurable variables such as particle velocity,strain,and displacement.By bypassing complex constitutive relations,strain-based models capture critical blasting trends and simulate full-scale blasts with full-blast design parameters with minimal calibration.The framework encompasses field strain measurements,model construction based on measurable variables,and laboratoryderived strain-failure criteria,each offering potential for future enhancement.Additionally,a standardized field test for site characterization is recommended.The approach is demonstrated through the Multiple Blasthole Fragmentation model,which simulates rock fragmentation and fragment strain during blasting,highlighting the practicality and effectiveness of strain-based modeling for multiple blasthole blasts.Moreover,this approach extends beyond blasting,with potential applications in highwall stability monitoring and other geomechanical applications.Strain-based modeling provides a simplified yet effective solution,avoiding the complexities of rock constitutive relations and field stress measurements while enabling full-blast design simulations for large-scale field blasts.
基金the funding support from the Smart Medicine and Engineering Interdisciplinary Innovation Project of Ningbo University(No.ZHYG003)。
文摘Glassy polymers are widely used in biomedical applications in a solvent environment,yet their long-term performance is governed by the competing effects of physical aging and solvent-induced plasticization.Here,we develop a constitutive model that explicitly couples the solvent concentration,structural relaxation,and mechanical response.This framework is built on a multiplicative decomposition of deformation and an Eyring-type flow rule,with structural evolution described by an effective temperature.A generalized shift factor is introduced to quantify how the solvent concentration and effective temperature jointly affect the relaxation time,thereby integrating physical aging and plasticization.The model is subsequently applied to methacrylate(MA)-based copolymer networks immersed in phosphate-buffered saline for up to nine months.Simulations accurately capture key experimental features,including the strong softening of highly swellable networks,the partial recovery due to aging,and the mitigating role of hydrophobic crosslinking in reducing solvent uptake.While the current single-mode description cannot reproduce the full relaxation spectrum,it establishes an efficient framework for predicting the long-term mechanical performance under coupled environmental and mechanical loading.This study provides a constitutive description of solvent-swollen glassy polymers,offering mechanistic insight into the interplay between plasticization and aging.Beyond biomedical MA networks,this framework establishes a foundation for predicting the long-term performance of polymer glasses under coupled aqueous environmental and mechanical loading.
基金supported by the National Key Research and Development Program of China (No. 2024YFA1308200)the National Natural Science Foundation of China (Nos. 82274009 and81973556)。
文摘Huperzine A(HupA) is a highly selective, reversible acetylcholinesterase(AChE) inhibitor that exhibits neuroprotective effects and is clinically used to manage benign memory decline.However, the specific relationship between the pharmacokinetic(PK) profile of HupA and cerebral acetylcholine(ACh) dynamics remains poorly characterized. Here, we characterize the PK-pharmacodynamic(PD) properties of HupA in rats under both physiological and pathological conditions. Following a single intramuscular injection, HupA exhibits a short halflife but rapid brain penetration, while multiple dosing significantly enhances its brain exposure. In a middle cerebral artery occlusion(MCAO) rat model, HupA demonstrates increased brain distribution. Furthermore, HupA elevates ACh concentrations across multiple brain regions, concurrently modulating several monoamine neurotransmitters. Using a minimal physiologically based pharmacokinetic-pharmacodynamic(mPBPK-PD) modeling approach,cerebral ACh dynamics were accurately predicted based on the pharmacokinetics of HupA in systemic circulation. The developed mPBPK-PD model exhibits robust predictive performance and holds potential for guiding the optimization of clinical dosing regimens and improving the therapeutic efficacy of HupA.
基金output of a research project implemented as part of the Basic Research Program at HSE University。
文摘Activation pruning reduces neural network complexity by eliminating low-importance neuron activations,yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally expensive and typically requires exhaustive search.We introduce a thermodynamics-inspired framework that treats activation distributions as energy-filtered physical systems and employs the free energy of activations as a principled evaluation metric.Phase-transition-like phenomena in the free-energy profile—such as extrema,inflection points,and curvature changes—yield reliable estimates of the critical pruning threshold,providing a theoretically grounded means of predicting sharp accuracy degradation.To further enhance efficiency,we propose a renormalized free energy technique that approximates full-evaluation free energy using only the activation distribution of the unpruned network.This eliminates repeated forward passes,dramatically reducing computational overhead and achieving speedups of up to 550×for MLPs.Extensive experiments across diverse vision architectures(MLP,CNN,ResNet,MobileNet,Vision Transformer)and text models(LSTM,BERT,ELECTRA,T5,GPT-2)on multiple datasets validate the generality,robustness,and computational efficiency of our approach.Overall,this work establishes a theoretically grounded and practically effective framework for activation pruning,bridging the gap between analytical understanding and efficient deployment of sparse neural networks.
基金the Scientific and Technological Research Council of Turkey(TÜB˙ITAK)for support under project number:122M826to the Scientific Research Projects Unit of Erciyes University under contract No.:FYL-2023-13162 and FYL-2024-13701.
文摘Separation bubbles forming on airfoils significantly influence aerodynamic behavior,particularly at low Reynolds numbers,making their accurate prediction a critical challenge in transition modelling.This study investigates numerical modeling of a separation bubble and the effects of airfoil thickness and camber variation on the formation of the bubble dynamics at low Reynolds numbers.The numerical results were compared with the experimental results obtained from surface pressure distribution measurements,oil flow visualisation,and surface shear measurements to analyse the detailed flow behavior.The combination of pressure and flow visualisation techniques provided complementary insights,enabling a detailed characterisation of bubble formation.The results reveal that both the thickness and camber of the airfoil significantly influence the location,length,and stability of the bubble.At low Reynolds number flows(Re=0.5×10^(5)),particularly for highly cambered profiles,closer to the leading edge,separation and long bubbles were observed.As the Reynolds number increased,the separation point shifted to the leading edge,and reattachment became more likely.In numerical studies,transition models can accurately model the bubble initiation point;however,they often fail to model the bubble reattachment points accurately.This is due to the inadequacy of models that use empirical expressions for turbulence modelling,particularly in low Reynolds number flows,in their viscous modelling.In this study,it was concluded that transition onset terms,which specifically affect bubble formation,should be modified for more accurate modeling.
