Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. Howeve...Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. However, the development of such models requires specialized expertise in data science, limiting their broader application. Large language models (LLMs), such as GPT-4, have demonstrated potential in supporting and guiding research efforts. This work presents a novel AI-assisted framework where GPT-4, through well-engineered prompts, facilitates the construction and explanation of multi-objective neural networks. These models predict hydrotreating products properties (such as distillation range), including refined diesel and refined gas oil, using feedstock properties, operating conditions, and recycle hydrogen composition. Gradient-weighted class activation mapping was employed to identify key features influencing the output variables. This work illustrates an innovative AI-guided paradigm for chemical engineering applications, and the designed prompts hold promise for adaptation to other complex processes.展开更多
Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multip...Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help...Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.展开更多
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b...The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.展开更多
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red...In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.展开更多
Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address t...Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.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
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.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
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.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
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
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
Kinetic impact is the most practical planetary-defense technique,with momentum-transfer efficiency central to deflection design.We present a Monte Carlo photometric framework that couples ejecta sampling,dynamical evo...Kinetic impact is the most practical planetary-defense technique,with momentum-transfer efficiency central to deflection design.We present a Monte Carlo photometric framework that couples ejecta sampling,dynamical evolution,and image synthesis to compare directly with HST,LICIACube,ground-based and Lucy observations of the DART impact.Decomposing ejecta into(1)a highvelocity(~1600 m/s)plume exhibiting Na/K resonance,(2)a low-velocity(~1 m/s)conical component shaped by binary gravity and solar radiation pressure,and(3)meter-scale boulders,we quantify each component’s mass and momentum.Fitting photometric decay curves and morphological evolution yields size-velocity distributions and,via scaling laws,estimates of Dimorphos’bulk density,cratering parameters,and cohesive strength that agree with dynamical constraints.Photometric ejecta modeling therefore provides a robust route to constrain momentum enhancement and target properties,improving predictive capability for kinetic-deflection missions.展开更多
Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers infl...Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers influence xylogenesis during the growing season is therefore of great interest.However,studying shortterm drivers of wood production using xylogenetic data is complicated by the usual sampling scheme and the influence of eccentric growth,i.e.,heterogeneous growth around the stem.In this study,we improve xylogenesis research by introducing a statistical approach that explicitly considers seasonal phenology,short-term growth rates,and growth eccentricity.To this end,we developed Bayesian models of xylogenesis and compared them with a conventional method based on the use of Gompertz functions.Our results show that eccentricity generated high temporal autocorrelation between successive samples,and that explicitly taking it into account improved both the representativeness of phenology and intra-ring variability.We observed consistent short-term patterns in the model residuals,suggesting the influence of an unaccounted-for environmental variable on cell production.The proposed models offer several advantages over traditional methods,including robust confidence intervals around predictions,consistency with phenology,and reduced sensitivity to extreme observations at the end of the growing season,often linked to eccentric growth.These models also provide a benchmark for mechanistic testing of short-term drivers of wood formation.展开更多
Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is...Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is a critical component in the identification of industrial robot dynamics. Traditional static friction models struggle to capture the hysteresis effects caused by robot joint elasticity and clearances, leading to large torque prediction errors when the joint velocity crosses zero. Due to the presence of hysteresis effects, the joint velocity crosses zero in the forward direction, and the reverse direction will have different friction patterns. Although the hysteresis effects can be modeled as an ordinary differential equation(ODE), it is difficult to determine the ODE structure that achieves both generalization and accuracy to describe the hysteresis effects of the friction model. To address this issue, we propose the neural hysteresis friction(NHF), which uses neural ODE to model the hysteresis effects in a data-driven manner, thereby mitigating the current inadequacies in the study of dynamic friction characteristics. The experiments on a real 6-axis industrial robot demonstrate that our proposed method can accurately model the friction dynamics during directional switching and outperform other modeling methods. Velocity tracking control experiments show that NHF can effectively reduce tracking errors when the velocity crosses zero.展开更多
基金supported by the National Key Research and Development Program of China(2023YFA1507601)the National Natural Science Foundation of China(22278127,22378038)+2 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Shanghai Pilot Program for Basic Research(22T01400100-18)the Natural Science Foundation of Liaoning Province,China(2024-MSBA-15).
文摘Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. However, the development of such models requires specialized expertise in data science, limiting their broader application. Large language models (LLMs), such as GPT-4, have demonstrated potential in supporting and guiding research efforts. This work presents a novel AI-assisted framework where GPT-4, through well-engineered prompts, facilitates the construction and explanation of multi-objective neural networks. These models predict hydrotreating products properties (such as distillation range), including refined diesel and refined gas oil, using feedstock properties, operating conditions, and recycle hydrogen composition. Gradient-weighted class activation mapping was employed to identify key features influencing the output variables. This work illustrates an innovative AI-guided paradigm for chemical engineering applications, and the designed prompts hold promise for adaptation to other complex processes.
基金Supported by the National Natural Science Foundation of China(21276078)"Shu Guang"project of Shanghai Municipal Education Commission,973 Program of China(2012CB720500)the Shanghai Science and Technology Program(13QH1401200)
文摘Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by National Key Research and Development Program of China (2023YFB3307800)National Natural Science Foundation of China (Key Program: 62136003, 62373155)+1 种基金Major Science and Technology Project of Xinjiang (No. 2022A01006-4)the Fundamental Research Funds for the Central Universities。
文摘Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.
基金supported by the National Natural Science Foundation of China(71901212)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
基金supported by the National Natural Science Foundation of China under Grant No.61972040the Science and Technology Research and Development Project funded by China Railway Material Trade Group Luban Company.
文摘In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.
基金supported by the Fundamental Research Funds for the Provincial Universities (Grant No.2024-KYYWF-0141)the National Natural Science Foundation of China (Grant Nos.52406076,52227813)+1 种基金the National Key Research and Development Program of China (Grant No.2022YFE0133900)the China Postdoctoral Science Foundation (Grant No.2023M740905)。
文摘Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.
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supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
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National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
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supported by the National Natural Science Foundation of China(Grant No.12272018)the National Key Basic Research Project(2022JCJQZD20600).
文摘Kinetic impact is the most practical planetary-defense technique,with momentum-transfer efficiency central to deflection design.We present a Monte Carlo photometric framework that couples ejecta sampling,dynamical evolution,and image synthesis to compare directly with HST,LICIACube,ground-based and Lucy observations of the DART impact.Decomposing ejecta into(1)a highvelocity(~1600 m/s)plume exhibiting Na/K resonance,(2)a low-velocity(~1 m/s)conical component shaped by binary gravity and solar radiation pressure,and(3)meter-scale boulders,we quantify each component’s mass and momentum.Fitting photometric decay curves and morphological evolution yields size-velocity distributions and,via scaling laws,estimates of Dimorphos’bulk density,cratering parameters,and cohesive strength that agree with dynamical constraints.Photometric ejecta modeling therefore provides a robust route to constrain momentum enhancement and target properties,improving predictive capability for kinetic-deflection missions.
基金supported by the Discovery Grants program of the Natural Sciences and Engineering Research Council of Canada(No.RGPIN-2021-03553)the Canadian Research Chair in dendroecology and dendroclimatology(CRC-2021-00368)+3 种基金the Ministère des Ressources Naturelles et des Forèts(MRNF,Contract no.142332177-D)the Natural Sciences and Engineering Research Council of Canada(Alliance Grant No.ALLRP 557148-20,obtained in partnership with the MRNF and Resolute Forest Products)the Fonds de recherche du Qu ebec–Nature et technologies(Partnership Research Program on the Contribution of the Forestry Sector to Climate Change MitigationGrant No.2022-0FC-309064)。
文摘Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers influence xylogenesis during the growing season is therefore of great interest.However,studying shortterm drivers of wood production using xylogenetic data is complicated by the usual sampling scheme and the influence of eccentric growth,i.e.,heterogeneous growth around the stem.In this study,we improve xylogenesis research by introducing a statistical approach that explicitly considers seasonal phenology,short-term growth rates,and growth eccentricity.To this end,we developed Bayesian models of xylogenesis and compared them with a conventional method based on the use of Gompertz functions.Our results show that eccentricity generated high temporal autocorrelation between successive samples,and that explicitly taking it into account improved both the representativeness of phenology and intra-ring variability.We observed consistent short-term patterns in the model residuals,suggesting the influence of an unaccounted-for environmental variable on cell production.The proposed models offer several advantages over traditional methods,including robust confidence intervals around predictions,consistency with phenology,and reduced sensitivity to extreme observations at the end of the growing season,often linked to eccentric growth.These models also provide a benchmark for mechanistic testing of short-term drivers of wood formation.
基金supported by the National Natural Science Foundation of China (Grant No.52188102)。
文摘Industrial robot dynamics lay the foundation for high-precision and high-speed control, and accurate identification of dynamic parameters is essential for precise dynamic calculations. The choice of friction models is a critical component in the identification of industrial robot dynamics. Traditional static friction models struggle to capture the hysteresis effects caused by robot joint elasticity and clearances, leading to large torque prediction errors when the joint velocity crosses zero. Due to the presence of hysteresis effects, the joint velocity crosses zero in the forward direction, and the reverse direction will have different friction patterns. Although the hysteresis effects can be modeled as an ordinary differential equation(ODE), it is difficult to determine the ODE structure that achieves both generalization and accuracy to describe the hysteresis effects of the friction model. To address this issue, we propose the neural hysteresis friction(NHF), which uses neural ODE to model the hysteresis effects in a data-driven manner, thereby mitigating the current inadequacies in the study of dynamic friction characteristics. The experiments on a real 6-axis industrial robot demonstrate that our proposed method can accurately model the friction dynamics during directional switching and outperform other modeling methods. Velocity tracking control experiments show that NHF can effectively reduce tracking errors when the velocity crosses zero.
