In recent years,the demand for synchronous acquisition of three-dimensional(3D)shape and col-or texture has surged in fields such as cultural heritage preservation and healthcare.Addressing this need,this paper propos...In recent years,the demand for synchronous acquisition of three-dimensional(3D)shape and col-or texture has surged in fields such as cultural heritage preservation and healthcare.Addressing this need,this paper proposes a novel method for simultaneous 3D shape and color texture capture.First,a linear model correlating camera exposure time with grayscale values is established.Through exposure time calibration,the projected red,green and blue(RGB)light and white-light grayscale values captured by a monochrome cam-era are aligned.Then,three sets of color fringes are projected onto the object to identify optimal pixels for 3D reconstruction.And,three pure-color patterns are projected to synthesize the color texture.Experimental res-ults show that this method effectively achieves synchronous 3D shape and color texture acquisition,offering high speed and precision,and avoids color crosstalk interference common in 3D reconstruction of colored ob-jects using a monochrome camera.展开更多
Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,espe...Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.展开更多
This study addresses the challenge of directly determining the elastic modulus of complex shaped ceramic products—such as gas turbine combustor tiles—using conventional standardized methods,which are limited by spec...This study addresses the challenge of directly determining the elastic modulus of complex shaped ceramic products—such as gas turbine combustor tiles—using conventional standardized methods,which are limited by specimen geometry.A rapid,non-destructive testing method based on the impulse excitation technique(IET)and a shape factor coefficient was proposed.Three types of shaped ceramic tiles were selected.The elastic modulus of standard rectangular specimens obtained by destructive sampling was used as the reference value,and the shape factor coefficient for each tile type was calibrated by combining the mass and fundamental frequency of the whole tile.Using this coefficient,the elastic modulus of whole tiles was calculated solely from non-destructively measured mass and frequency.The results show that the deviation between the elastic modulus derived from the proposed method and that from destructive testing is less than 5%,confirming the accuracy and reliability of the approach.The method overcomes the shape restrictions inherent in traditional testing,offering a fast,non-destructive solution suitable for onsite quality assessment and process control during the production of shaped ceramic components.展开更多
Three-quasiparticle K-isomeric states in odd-mass N=106 isotones within the A~180 mass region were systematically investigated using configuration-constrained potential energy surface calculations.The calculations suc...Three-quasiparticle K-isomeric states in odd-mass N=106 isotones within the A~180 mass region were systematically investigated using configuration-constrained potential energy surface calculations.The calculations succes sfully reproduced the excitation energies and deformations of the known high-K isomers in nuclei from 175Tm to 181Re.For the nuclei closer to the Z=82 shell closure(^(183)Ir,^(185)Au,and^(187)Tl),predictions of the configurations of the observed and yet-to-be-observed isomers are provided.The results reveal strong shape polarization,where the three-quasiparticle states are driven to larger deformations compared to the often shape-soft or spherical ground states.A particularly rich spectrum of shape coexistence is predicted in^(187)Tl,where several high-K three-quasiparticle configurations with distinct prolate,oblate,and triaxial shapes are found to coexist at similar excitation energies.Notably,the oblate-deformed K^(π)=29/2^(+)configuration at E_(x)=1839 keV was proposed to be responsible for a long-lived isomer.This study provides a comprehensive picture of shape evolution and coexistence in high-K multi-quasiparticle states,offering valuable insights for future experimental studies.展开更多
Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based...Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.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
Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the ...Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the fish’s behavior,health,and environmental adaptability.However,when multi-object tracking(MOT)algorithms are applied to the high-density aquaculture environment,occlusion and overlapping among fish may result in missed detections,false detections,and identity switching problems,which limit the tracking accuracy.To address these issues,this paper proposes FishTracker,a MOT algorithm,by utilizing a Tracking-by-Detection framework.First,the neck part of the YOLOv8 model is enhanced by introducing a Multi-Scale Dilated Attention(MSDA)module to improve object localization and classification confidence.Second,an Adaptive Kalman Filter(AKF)is employed in the tracking phase to dynamically adjust motion prediction parameters,thereby overcoming target adhesion and nonlinear motion in complex scenarios.Experimental results show that FishTracker achieves a multi-object tracking accuracy(MOTA)of 93.22% and 87.24% in bright and dark illumination conditions,respectively.Further validation in a real aquaculture scenario reveal that FishTracker achieves aMOTA of 76.70%,which is 5.34% higher than the baselinemodel.The higher order tracking accuracy(HOTA)reaches 50.5%,which is 3.4% higher than the benchmark.In conclusion,FishTracker can provide reliable technical support for accurate tracking and behavioral analysis of high-density fish populations.展开更多
While parametric Software Reliability Growth Models(SRGMs)serve as a cornerstone in software reliability assessment,their reliance on known fault-detection time distributions often presents a significant limitation in...While parametric Software Reliability Growth Models(SRGMs)serve as a cornerstone in software reliability assessment,their reliance on known fault-detection time distributions often presents a significant limitation in practical software testing.In this study,the authors develop a novel shaperestricted spline estimator for quantifying software reliability.Compared with parametric SRGMs,the proposed estimator not only shares a key characteristic with parametric SRGMs,but also obviates the need for specifying fault-detection time distributions.More importantly,it effectively utilizes the critical shape information of the mean value function(MVF)of fault-detection process,a detail seldom considered in prior work.Moreover,the authors investigate the predictive performance of the proposed methods by employing the so-called one-step look-ahead prediction method.Furthermore,the authors show that under certain conditions,the shape-restricted spline estimator will attain the point-wise convergence rate O_P(n~(-3/7)).In numerical experiment,the authors show that spline estimators under restriction demonstrate competitive performance compared to parametric and certain non-parametric models.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
Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces s...Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces several unsolved challenges.Specifically,communication costs and system heterogeneity,such as nonidentical data distribution,hinder federated learning's progress.Several approaches have recently emerged for federated learning involving heterogeneous clients with varying computational capabilities(namely,heterogeneous federated learning).However,heterogeneous federated learning faces two key challenges:optimising model size and determining client selection ratios.Moreover,efficiently aggregating local models from clients with diverse capabilities is crucial for addressing system heterogeneity and communication efficiency.This paper proposes an evolutionary multiobjective optimisation framework for heterogeneous federated learning(MOHFL)to address these issues.Our approach elegantly formulates and solves a biobjective optimisation problem that minimises communication cost and model error rate.The decision variables in this framework comprise model sizes and client selection ratios for each Q client cluster,yielding a total of 2×Q optimisation parameters to be tuned.We develop a partition-based strategy for MOHFL that segregates clients into clusters based on their communication and computation capabilities.Additionally,we implement an adaptive model sizing mechanism that dynamically assigns appropriate subnetwork architectures to clients based on their computational constraints.We also propose a unified aggregation framework to combine models of varying sizes from heterogeneous clients effectively.Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method compared to existing approaches.展开更多
To address the challenges of rapid bit failure and high drilling costs associated with hard limestone in Sichuan Basin of China,we conducted rock-breaking experiments and simulations of shaped(cylindrical,ridge,and ch...To address the challenges of rapid bit failure and high drilling costs associated with hard limestone in Sichuan Basin of China,we conducted rock-breaking experiments and simulations of shaped(cylindrical,ridge,and chopper)cutters.Rock mechanics,drillability,and acoustic emission indentation tests revealed the drilling resistance characteristics of the limestone:average uniaxial compressive strength of 202.472 MPa,tensile strength of 7.092 MPa,and drillability of 7.866.We evaluated the performance differences between the shaped cutters before introducing an efficient and innovative finite-discrete-infinite element method(FDIEM)to establish an interaction model between the shaped cutters and limestone.The simulation results indicated the following:(1)The shaped cutters demonstrated superior rock-breaking performance compared to the traditional cylindrical cutter.(2)Compared with the cylindrical cutter,the ridge cutter yielded the lowest peak indentation force and mechanical specific energy,with reductions of 8.71%and 33.83%,respectively.This confirmed that the ridge cutter had the optimal tooth profile for the target formation.Its rock-breaking mechanism relied on the convex edges to induce localized high stress in the rock,which enabled efficient rock fragmentation via a plowing mode while mitigating frictional resistance from cuttings.(3)The novel chopper cutter with its secondary step surface exerted a buffering effect on the cuttings,thereby achieving high cutting stability.This study provides theoretical and technical support for the design of personalized drill bits and the acceleration of the rate of penetration(ROP)in deep hard rock formations.展开更多
In the past few years,efforts have been made to extend the sensitivity of surface nuclear magnetic resonance(SNMR)to short relaxation times,typical for strongly bound water,which,for example,occurs in partially satura...In the past few years,efforts have been made to extend the sensitivity of surface nuclear magnetic resonance(SNMR)to short relaxation times,typical for strongly bound water,which,for example,occurs in partially saturated soils.The two limiting factors for the sensitivity are the dead time after the excitation pulse and the duration of the pulse itself.To enable short pulses,while also achieving proper depths of investigation,high pulse amplitudes are needed.This makes it necessary to consider the Bloch-Siegert effect,i.e.the counter-rotating component and the parallel component of the excitation field have significant influence on the excitation.If an untuned transmitter circuit is used,the pulse shape will also be non-sinusoidal.In this paper,we demonstrate that this influences SNMR measurements with short pulses in two ways:On one hand,the pulse shape influences the phase of the fundamental frequency oscillation.On the other,at very high pulse amplitudes,other frequency components of the excitation field start to influence the excitation.The behavior of the macroscopic magnetizations in the subsurface during the pulse is simulated by solving the Bloch equations,using the pulse shape as an input.Since these calculations are computational expensive,we propose a lookup scheme that allows a time efficient modeling of the obtained SNMR data.展开更多
Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approa...Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.展开更多
Near-infrared(NIR)light-responsive shape memory polymers(SMPs)show great promise for biomedical applications,but conventional photothermal agents suffer from high cost,complex preparation,or poor biocompatibility,whil...Near-infrared(NIR)light-responsive shape memory polymers(SMPs)show great promise for biomedical applications,but conventional photothermal agents suffer from high cost,complex preparation,or poor biocompatibility,while lignin-based alternatives exhibit insufficient photothermal conversion efficiency.Herein,we developed a novel strategy to enhance photothermal performance of lignin through sequential demethylation modification and Fe^(3+)complexation for constructing NIR light responsive SMPs.Dealkaline lignin(DL)was first demethylated using iodocyclohexane to produce demethylated lignin(DDL)with increased catechol content,which was then incorporated into polycaprolactone-based polyurethane synthesis followed by Fe^(3+)complexation.Results showed that DDL-Fe^(3+)complexes have significantly enhanced photothermal conversion performance,and the resulting PU-DDL+Fe^(3+)polyurethane with 0.5 wt%DDL content demonstrated a temperature increases of 39.8℃under 0.33 W·cm-2808 nm NIR irradiation.This excellent photothermal performance enables the shape-fixed PU-DDL+Fe^(3+)polyurethane to rapidly recover to its initial shape under NIR light irradiation.Additionally,PU-DDL+Fe^(3+)polyurethane exhibits good mechanical properties and biocompatibility,demonstrating significant biomedical application potential.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
The penetration of shaped charge jets into targets at high velocities is significantly influenced by the compressibility effect,while at low velocities,the strength effect becomes predominant.In the latter regime,mate...The penetration of shaped charge jets into targets at high velocities is significantly influenced by the compressibility effect,while at low velocities,the strength effect becomes predominant.In the latter regime,material strength dictates the resistance to plastic deformation and flow,a contrast to the shockwave-dominated interactions where compressibility is key.This paper presents a self-consistent compressible penetration theory that considers both the axial penetration and radial crater growth of shaped charge jets into targets.An integrated approach where the axial and radial dynamics are coupled has been proposed,influencing each other through shared physical principles rather than being treated as separate,empirically linked phenomena.The presented theory is rooted in the compressible Bernoulli equation and the linear Rankine-Hugoniot relation.These foundational equations are employed to accurately model the high-pressure shock state and subsequent material flow at the jet-target interface,providing a robust physical basis for the penetration model.Notably,it considers the target material's compressibility,which elevates the pressure at the jet-target interface beyond that observed with incompressible materials.This pressure increase is directly proportional to the target's degree of compressibility.As such,this model of compressible penetration reorients the analytical approach:rather than merely estimating penetration resistance,it determines this value from the target material's specific compressibility and yield strength.This shift from empirical correlations to a physics-based derivation of penetration resistance enhances the model's predictive power,particularly for novel target materials or engagement conditions outside established experimental datasets.This investigation establishes a quantitative link between the material's yield strength and its penetration resistance.The accuracy of this penetration resistance value is paramount,as it significantly influences the predicted crater diameter;indeed,the crater diameter's sensitivity to this resistance underscores the necessity for its precise determination.Ultimately,by integrating the yield strength of the target material,this framework enables the prediction of both the penetration depth and the resultant crater diameter from a shaped charge jet.The theory's validation involved two experimental sets:the first focused on shaped charge jet penetration into 45#steel at varied stand-offs,while the second utilized targets of high-to ultrahigh-strength steel-fiber reactive powder concrete(RPC)with differing strength characteristics.These experimental campaigns were specifically chosen to test the theory against both ductile metallic alloys,where plastic flow is significant,and advanced quasi-brittle cementitious composites,presenting a broad spectrum of material responses and penetration challenges.Resulting hole profiles derived from theoretical calculations demonstrated a strong correspondence with empirical measurements for both material types.展开更多
Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To addres...Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.展开更多
A new method was proposed for preparing AZ31/1060 composite plates with a corrugated interface,which involved cold-pressing a corrugated surface on the Al plate and then hot-pressing the assembled Mg/Al plate.The resu...A new method was proposed for preparing AZ31/1060 composite plates with a corrugated interface,which involved cold-pressing a corrugated surface on the Al plate and then hot-pressing the assembled Mg/Al plate.The results show that cold-pressing produces intense plastic deformation near the corrugated surface of the Al plate,which promotes dynamic recrystallization of the Al substrate near the interface during the subsequent hot-pressing.In addition,the initial corrugation on the surface of the Al plate also changes the local stress state near the interface during hot pressing,which has a large effect on the texture components of the substrates near the corrugated interface.The construction of the corrugated interface can greatly enhance the shear strength by 2−4 times due to the increased contact area and the strong“mechanical gearing”effect.Moreover,the mechanical properties are largely depended on the orientation relationship between corrugated direction and loading direction.展开更多
文摘In recent years,the demand for synchronous acquisition of three-dimensional(3D)shape and col-or texture has surged in fields such as cultural heritage preservation and healthcare.Addressing this need,this paper proposes a novel method for simultaneous 3D shape and color texture capture.First,a linear model correlating camera exposure time with grayscale values is established.Through exposure time calibration,the projected red,green and blue(RGB)light and white-light grayscale values captured by a monochrome cam-era are aligned.Then,three sets of color fringes are projected onto the object to identify optimal pixels for 3D reconstruction.And,three pure-color patterns are projected to synthesize the color texture.Experimental res-ults show that this method effectively achieves synchronous 3D shape and color texture acquisition,offering high speed and precision,and avoids color crosstalk interference common in 3D reconstruction of colored ob-jects using a monochrome camera.
基金funded by Vietnam National University Ho Chi Minh City(VNU-HCM)under grant number DS.C2025-28-06.
文摘Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.
基金National Key Research and Development Program of China(2023YFB3711200)Key Research and Development Project of Henan Province(231111230700).
文摘This study addresses the challenge of directly determining the elastic modulus of complex shaped ceramic products—such as gas turbine combustor tiles—using conventional standardized methods,which are limited by specimen geometry.A rapid,non-destructive testing method based on the impulse excitation technique(IET)and a shape factor coefficient was proposed.Three types of shaped ceramic tiles were selected.The elastic modulus of standard rectangular specimens obtained by destructive sampling was used as the reference value,and the shape factor coefficient for each tile type was calibrated by combining the mass and fundamental frequency of the whole tile.Using this coefficient,the elastic modulus of whole tiles was calculated solely from non-destructively measured mass and frequency.The results show that the deviation between the elastic modulus derived from the proposed method and that from destructive testing is less than 5%,confirming the accuracy and reliability of the approach.The method overcomes the shape restrictions inherent in traditional testing,offering a fast,non-destructive solution suitable for onsite quality assessment and process control during the production of shaped ceramic components.
基金supported by the National Natural Science Foundation of China(No.12275369)。
文摘Three-quasiparticle K-isomeric states in odd-mass N=106 isotones within the A~180 mass region were systematically investigated using configuration-constrained potential energy surface calculations.The calculations succes sfully reproduced the excitation energies and deformations of the known high-K isomers in nuclei from 175Tm to 181Re.For the nuclei closer to the Z=82 shell closure(^(183)Ir,^(185)Au,and^(187)Tl),predictions of the configurations of the observed and yet-to-be-observed isomers are provided.The results reveal strong shape polarization,where the three-quasiparticle states are driven to larger deformations compared to the often shape-soft or spherical ground states.A particularly rich spectrum of shape coexistence is predicted in^(187)Tl,where several high-K three-quasiparticle configurations with distinct prolate,oblate,and triaxial shapes are found to coexist at similar excitation energies.Notably,the oblate-deformed K^(π)=29/2^(+)configuration at E_(x)=1839 keV was proposed to be responsible for a long-lived isomer.This study provides a comprehensive picture of shape evolution and coexistence in high-K multi-quasiparticle states,offering valuable insights for future experimental studies.
基金the Hebei Province Science and Technology Plan Project(19221909D)rincess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R308),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.
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funded by the Fundamental Research Funds for the Central Universities(Grant No.106-YDZX2025022)the Startup Foundation of New Professor at Nanjing Agricultural University(Grant No.106-804005)the“Qing Lan Project”of Jiangsu Higher Education Institutions.
文摘Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the fish’s behavior,health,and environmental adaptability.However,when multi-object tracking(MOT)algorithms are applied to the high-density aquaculture environment,occlusion and overlapping among fish may result in missed detections,false detections,and identity switching problems,which limit the tracking accuracy.To address these issues,this paper proposes FishTracker,a MOT algorithm,by utilizing a Tracking-by-Detection framework.First,the neck part of the YOLOv8 model is enhanced by introducing a Multi-Scale Dilated Attention(MSDA)module to improve object localization and classification confidence.Second,an Adaptive Kalman Filter(AKF)is employed in the tracking phase to dynamically adjust motion prediction parameters,thereby overcoming target adhesion and nonlinear motion in complex scenarios.Experimental results show that FishTracker achieves a multi-object tracking accuracy(MOTA)of 93.22% and 87.24% in bright and dark illumination conditions,respectively.Further validation in a real aquaculture scenario reveal that FishTracker achieves aMOTA of 76.70%,which is 5.34% higher than the baselinemodel.The higher order tracking accuracy(HOTA)reaches 50.5%,which is 3.4% higher than the benchmark.In conclusion,FishTracker can provide reliable technical support for accurate tracking and behavioral analysis of high-density fish populations.
文摘While parametric Software Reliability Growth Models(SRGMs)serve as a cornerstone in software reliability assessment,their reliance on known fault-detection time distributions often presents a significant limitation in practical software testing.In this study,the authors develop a novel shaperestricted spline estimator for quantifying software reliability.Compared with parametric SRGMs,the proposed estimator not only shares a key characteristic with parametric SRGMs,but also obviates the need for specifying fault-detection time distributions.More importantly,it effectively utilizes the critical shape information of the mean value function(MVF)of fault-detection process,a detail seldom considered in prior work.Moreover,the authors investigate the predictive performance of the proposed methods by employing the so-called one-step look-ahead prediction method.Furthermore,the authors show that under certain conditions,the shape-restricted spline estimator will attain the point-wise convergence rate O_P(n~(-3/7)).In numerical experiment,the authors show that spline estimators under restriction demonstrate competitive performance compared to parametric and certain non-parametric models.
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.
基金supported by the National Research Foundation of Korea grant funded by the Korea government(RS-2023-00217116)。
文摘Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces several unsolved challenges.Specifically,communication costs and system heterogeneity,such as nonidentical data distribution,hinder federated learning's progress.Several approaches have recently emerged for federated learning involving heterogeneous clients with varying computational capabilities(namely,heterogeneous federated learning).However,heterogeneous federated learning faces two key challenges:optimising model size and determining client selection ratios.Moreover,efficiently aggregating local models from clients with diverse capabilities is crucial for addressing system heterogeneity and communication efficiency.This paper proposes an evolutionary multiobjective optimisation framework for heterogeneous federated learning(MOHFL)to address these issues.Our approach elegantly formulates and solves a biobjective optimisation problem that minimises communication cost and model error rate.The decision variables in this framework comprise model sizes and client selection ratios for each Q client cluster,yielding a total of 2×Q optimisation parameters to be tuned.We develop a partition-based strategy for MOHFL that segregates clients into clusters based on their communication and computation capabilities.Additionally,we implement an adaptive model sizing mechanism that dynamically assigns appropriate subnetwork architectures to clients based on their computational constraints.We also propose a unified aggregation framework to combine models of varying sizes from heterogeneous clients effectively.Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method compared to existing approaches.
基金the National Science and Technology Major Project(Grant No.2025ZD1008300)the Major Scientific Research Instrument Development Project of the National Natural Science Foundation of China(Grant No.52327803).
文摘To address the challenges of rapid bit failure and high drilling costs associated with hard limestone in Sichuan Basin of China,we conducted rock-breaking experiments and simulations of shaped(cylindrical,ridge,and chopper)cutters.Rock mechanics,drillability,and acoustic emission indentation tests revealed the drilling resistance characteristics of the limestone:average uniaxial compressive strength of 202.472 MPa,tensile strength of 7.092 MPa,and drillability of 7.866.We evaluated the performance differences between the shaped cutters before introducing an efficient and innovative finite-discrete-infinite element method(FDIEM)to establish an interaction model between the shaped cutters and limestone.The simulation results indicated the following:(1)The shaped cutters demonstrated superior rock-breaking performance compared to the traditional cylindrical cutter.(2)Compared with the cylindrical cutter,the ridge cutter yielded the lowest peak indentation force and mechanical specific energy,with reductions of 8.71%and 33.83%,respectively.This confirmed that the ridge cutter had the optimal tooth profile for the target formation.Its rock-breaking mechanism relied on the convex edges to induce localized high stress in the rock,which enabled efficient rock fragmentation via a plowing mode while mitigating frictional resistance from cuttings.(3)The novel chopper cutter with its secondary step surface exerted a buffering effect on the cuttings,thereby achieving high cutting stability.This study provides theoretical and technical support for the design of personalized drill bits and the acceleration of the rate of penetration(ROP)in deep hard rock formations.
基金funded by the German Research Foundation(Deutsche Forschungsgemeinschaft-DFG)under grant MU 3318/8-1.
文摘In the past few years,efforts have been made to extend the sensitivity of surface nuclear magnetic resonance(SNMR)to short relaxation times,typical for strongly bound water,which,for example,occurs in partially saturated soils.The two limiting factors for the sensitivity are the dead time after the excitation pulse and the duration of the pulse itself.To enable short pulses,while also achieving proper depths of investigation,high pulse amplitudes are needed.This makes it necessary to consider the Bloch-Siegert effect,i.e.the counter-rotating component and the parallel component of the excitation field have significant influence on the excitation.If an untuned transmitter circuit is used,the pulse shape will also be non-sinusoidal.In this paper,we demonstrate that this influences SNMR measurements with short pulses in two ways:On one hand,the pulse shape influences the phase of the fundamental frequency oscillation.On the other,at very high pulse amplitudes,other frequency components of the excitation field start to influence the excitation.The behavior of the macroscopic magnetizations in the subsurface during the pulse is simulated by solving the Bloch equations,using the pulse shape as an input.Since these calculations are computational expensive,we propose a lookup scheme that allows a time efficient modeling of the obtained SNMR data.
基金supported by the National Natural Science Foundation of China(Grant Nos.62572057,62272049,U24A20331)Beijing Natural Science Foundation(Grant Nos.4232026,4242020)Academic Research Projects of Beijing Union University(Grant No.ZK10202404).
文摘Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.
基金supported by the National Natural Science Foundation of China(Nos.51603005,52403186 and 52573150)Fujian Provincial Natural Science Foundation of China(No.2024J011447)+1 种基金Natural Science Foundation of Xiamen,China(No.3502Z20227305)the Postdoctoral Fellowship Program of CPSF(No.GZC20240095)。
文摘Near-infrared(NIR)light-responsive shape memory polymers(SMPs)show great promise for biomedical applications,but conventional photothermal agents suffer from high cost,complex preparation,or poor biocompatibility,while lignin-based alternatives exhibit insufficient photothermal conversion efficiency.Herein,we developed a novel strategy to enhance photothermal performance of lignin through sequential demethylation modification and Fe^(3+)complexation for constructing NIR light responsive SMPs.Dealkaline lignin(DL)was first demethylated using iodocyclohexane to produce demethylated lignin(DDL)with increased catechol content,which was then incorporated into polycaprolactone-based polyurethane synthesis followed by Fe^(3+)complexation.Results showed that DDL-Fe^(3+)complexes have significantly enhanced photothermal conversion performance,and the resulting PU-DDL+Fe^(3+)polyurethane with 0.5 wt%DDL content demonstrated a temperature increases of 39.8℃under 0.33 W·cm-2808 nm NIR irradiation.This excellent photothermal performance enables the shape-fixed PU-DDL+Fe^(3+)polyurethane to rapidly recover to its initial shape under NIR light irradiation.Additionally,PU-DDL+Fe^(3+)polyurethane exhibits good mechanical properties and biocompatibility,demonstrating significant biomedical application potential.
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the Fundamental Research Funds for the Central Universities of Nanjing University of Science and Technology(CN)under Grant No.30924010803。
文摘The penetration of shaped charge jets into targets at high velocities is significantly influenced by the compressibility effect,while at low velocities,the strength effect becomes predominant.In the latter regime,material strength dictates the resistance to plastic deformation and flow,a contrast to the shockwave-dominated interactions where compressibility is key.This paper presents a self-consistent compressible penetration theory that considers both the axial penetration and radial crater growth of shaped charge jets into targets.An integrated approach where the axial and radial dynamics are coupled has been proposed,influencing each other through shared physical principles rather than being treated as separate,empirically linked phenomena.The presented theory is rooted in the compressible Bernoulli equation and the linear Rankine-Hugoniot relation.These foundational equations are employed to accurately model the high-pressure shock state and subsequent material flow at the jet-target interface,providing a robust physical basis for the penetration model.Notably,it considers the target material's compressibility,which elevates the pressure at the jet-target interface beyond that observed with incompressible materials.This pressure increase is directly proportional to the target's degree of compressibility.As such,this model of compressible penetration reorients the analytical approach:rather than merely estimating penetration resistance,it determines this value from the target material's specific compressibility and yield strength.This shift from empirical correlations to a physics-based derivation of penetration resistance enhances the model's predictive power,particularly for novel target materials or engagement conditions outside established experimental datasets.This investigation establishes a quantitative link between the material's yield strength and its penetration resistance.The accuracy of this penetration resistance value is paramount,as it significantly influences the predicted crater diameter;indeed,the crater diameter's sensitivity to this resistance underscores the necessity for its precise determination.Ultimately,by integrating the yield strength of the target material,this framework enables the prediction of both the penetration depth and the resultant crater diameter from a shaped charge jet.The theory's validation involved two experimental sets:the first focused on shaped charge jet penetration into 45#steel at varied stand-offs,while the second utilized targets of high-to ultrahigh-strength steel-fiber reactive powder concrete(RPC)with differing strength characteristics.These experimental campaigns were specifically chosen to test the theory against both ductile metallic alloys,where plastic flow is significant,and advanced quasi-brittle cementitious composites,presenting a broad spectrum of material responses and penetration challenges.Resulting hole profiles derived from theoretical calculations demonstrated a strong correspondence with empirical measurements for both material types.
基金supported by the National Natural Science Foundation of China(No.62173107).
文摘Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.
基金supported by Guangdong Major Project of Basic and Applied Basic Research, China (No. 2020B0301030006)Fundamental Research Funds for the Central Universities, China (No. SWU-XDJH202313)+1 种基金Chongqing Postdoctoral Science Foundation Funded Project, China (No. 2112012728014435)the Chongqing Postgraduate Research and Innovation Project, China (No. CYS23197)。
文摘A new method was proposed for preparing AZ31/1060 composite plates with a corrugated interface,which involved cold-pressing a corrugated surface on the Al plate and then hot-pressing the assembled Mg/Al plate.The results show that cold-pressing produces intense plastic deformation near the corrugated surface of the Al plate,which promotes dynamic recrystallization of the Al substrate near the interface during the subsequent hot-pressing.In addition,the initial corrugation on the surface of the Al plate also changes the local stress state near the interface during hot pressing,which has a large effect on the texture components of the substrates near the corrugated interface.The construction of the corrugated interface can greatly enhance the shear strength by 2−4 times due to the increased contact area and the strong“mechanical gearing”effect.Moreover,the mechanical properties are largely depended on the orientation relationship between corrugated direction and loading direction.
