The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant...The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization(MCCMO)Algorithm,termed Adaptive Diversity Preservation(ADP).This enhancement is primarily focused on the improvement of constraint handling strategies,local search integration,hybrid selection approaches,and adaptive parameter control.Theimproved variant was experimented on with the RWMOP50 power distribution systemplanning benchmark.As per the findings,the improved variant outperformed the original MCCMO across the eleven performance metrics,particularly in terms of convergence speed,constraint handling efficiency,and solution diversity.The results also establish that MCCMOADP consistently delivers substantial performance gains over the baseline MCCMO,demonstrating its effectiveness across performancemetrics.The new variant also excels atmaintaining the balanced trade-off between exploration and exploitation throughout the search process,making it especially suitable for complex optimization problems in multiconstrained power systems.These enhancements make MCCMO-ADP a valuable and promising candidate for handling problems such as renewable energy scheduling,logistics planning,and power system optimization.Future work will benchmark the MCCMO-ADP against widely recognized algorithms such as NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D and will also extend its validation to large-scale real-world optimization domains to further consolidate its generalizability.展开更多
The complex aerodynamic interaction between tandem tilt-wing and multi-rotor directly affects the wing surface flow and rotor thrust,making it a critical factor during the tilt transition process of this configuration...The complex aerodynamic interaction between tandem tilt-wing and multi-rotor directly affects the wing surface flow and rotor thrust,making it a critical factor during the tilt transition process of this configuration of rotorcraft.The aerodynamic interaction of tandem tilt-wing and multi-rotor is investigated based on the CFD method.The aerodynamic effect of multi tilt-rotor is simulated as virtual disk modeling by adding source terms to the Navier-Stokes equations,effectively reducing the calculation time while maintaining the accuracy of aerodynamic interaction calculations.Aerodynamic forces and flow field characteristics of the tandem tilt-wing and multi-rotor under different tilt angles are compared between cases with and without aerodynamic interaction.Furthermore,the differences in aerodynamic forces between dynamic tilt transition and fixed-angle conditions were compared.The results show that the aerodynamic interaction of multi-rotor obviously increases the lift of front tilt-wing at different tilt angles,the wing lift under interaction is increased by more than 40%compared with isolated wing at tilt angle of 15°for the computation in this paper,which is related to the increase of wing flow velocity and the suppression of flow separation caused by multi-rotor;the wing blocking effect will increase rotor thrust,especially near the tilt angles of 30°and 45°;the increases of rear wing lift and rear rotor thrust under aerodynamic interaction are not significant because of suppression by the front wing’s downwash;the unsteady effects during dynamic tilting have a relatively minor impact on aerodynamic interaction,with the aerodynamic forces on the rotors and wings during the dynamic tilting process showing little difference from those under corresponding fixed tilt angles.展开更多
Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential f...Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.展开更多
The pursuit of simultaneously high wear resistance and excellent lubrication in multi‐principal element alloy(MPEA)composites is often hindered by a fundamental trade‐off,which is exacerbated by the agglomeration of...The pursuit of simultaneously high wear resistance and excellent lubrication in multi‐principal element alloy(MPEA)composites is often hindered by a fundamental trade‐off,which is exacerbated by the agglomeration of high‐content graphene reinforcements.This compromise becomes particularly severe in composites with high‐content graphene reinforcements,whose agglomeration leads to embrittlement and lubrication failure.Here,a flake powder-metallurgy strategy is developed to construct a self‐assembled lamellar structure in graphene/CoCrNi MPEA composites(Gr/MPEA_(AL)).This approach enables the uniform dispersion of a high graphene content(3.0 wt%),which is unattainable by conventional methods.The resulting composite exhibits a rare dual enhancement in performance:an order‐of‐magnitude improvement in wear resistance coupled with a low coefficient of friction.Intriguingly,the tribological behavior shows significant anisotropy,with optimal performance observed when sliding perpendicular to the lamellae.Through a multi‐scale methodology combining molecular dynamics simulations,finite element analysis,and systematic experiments,it is revealed that this exceptional performance stems from the synergy of high‐density deformation nanotwins,efficient strain delocalization,and abundant graphene‐derived lubricating sites.This work establishes a general paradigm for designing composite architectures that reconcile traditionally incompatible properties,offering broad implications for developing next‐generation structural materials with integrated mechanical robustness and surface functionality for safety‐critical applications.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
Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging ...Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Amyotrophic lateral sclerosis(ALS)is a rapidly progressing neurodegenerative disease,leading to muscle weakness,paralysis and ultimately death due to respiratory failure.Currently licensed drugs have only very limited...Amyotrophic lateral sclerosis(ALS)is a rapidly progressing neurodegenerative disease,leading to muscle weakness,paralysis and ultimately death due to respiratory failure.Currently licensed drugs have only very limited effects on slowing down disease progression or biomarkers.Despite numerous successful preclinical analyses,most new drugs fail when translated to clinical trials(Petrov et al.,2017).This is believed to be,in part,due to the multilayer heterogeneity of ALS(e.g.,clinical,genetic,and molecular;Tzeplaeff et al.,2024).Studies integrating multi-omic data are still limited,making it difficult to fully understand the biological complexity that characterizes the disease.展开更多
Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technol...Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.展开更多
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu...The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.展开更多
New electronic devices based on the physical properties of electrically driven skyrmions are promising for logic computing and nonvolatile memory applications.However,achieving efficient and practical compute-storage ...New electronic devices based on the physical properties of electrically driven skyrmions are promising for logic computing and nonvolatile memory applications.However,achieving efficient and practical compute-storage integration remains challenging owing to the structural complexity,limited functionality,and low flexibility observed in most skyrmion-based devices.In this study,we designed a novel device architecture that integrates seven basic logic gates into a unified physical structure.Their operation can be enabled by physical mechanisms,such as spin-orbit torque,spin-transfer torque,skyrmion-edge repulsions,and skyrmion-skyrmion interactions.Furthermore,by incorporating voltage-controlled magnetic anisotropy,the device achieved multi-input capability and reconfigurability functionality.Ultralow power consumption(<1 fJ/bit per logic function)and extremely high logic density were achieved.Significantly,the compatibility of this nanotrack design with existing skyrmion racetrack memory paves the way for advanced in-memory computing in spintronic architectures.展开更多
An extended form of the modified Kadomtsev-Petviashvili （mKP） equation is investigated. The simplified form of the Hirota bilinear method established by Hereman and Nuseir is employed. Multi-front wave solutions are...An extended form of the modified Kadomtsev-Petviashvili （mKP） equation is investigated. The simplified form of the Hirota bilinear method established by Hereman and Nuseir is employed. Multi-front wave solutions are formally derived to the extended mKP equation and the mKP equation. The results show that the extension terms do not kill the integrability of the mKP equation.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illuminat...To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illumination is processed by contrast-limited adaptive histogram equalization(CLAHE),adaptive complementary gamma function(ACG),and adaptive detail preserving S-curve(ADPS),respectively,to obtain three components.Then,the fusion-relevant features,exposure,and color contrast are selected as the weight maps.Subsequently,these components and weight maps are fused through multi-scale to generate enhanced illumination.Finally,the enhanced images are obtained by multiplying the enhanced illumination and reflectance.Compared with existing approaches,this proposed method achieves an average increase of 0.81%and 2.89%in the structural similarity index measurement(SSIM)and peak signal-to-noise ratio(PSNR),and a decrease of 6.17%and 32.61%in the natural image quality evaluator(NIQE)and gradient magnitude similarity deviation(GMSD),respectively.展开更多
In this paper, an improved incompressible multi-relaxation-time lattice Boltzmann-front tracking approach is proposed to simulate two-phase flow with a sharp interface, where the surface tension is implemented. The la...In this paper, an improved incompressible multi-relaxation-time lattice Boltzmann-front tracking approach is proposed to simulate two-phase flow with a sharp interface, where the surface tension is implemented. The lattice Boltzmann method is used to simulate the incompressible flow with a stationary Eulerian grid, an additional moving Lagrangian grid is adopted to track explicitly the motion of the interface, and an indicator function is introduced to update the fluid properties accurately. The interface is represented by using a four-order Lagrange polynomial through fitting a set of discrete marker points, and then the surface tension is directly computed by using the normal vector and curvature of the interface. Two benchmark problems, including Laplace＇s law for a stationary bubble and the dispersion relation of the capillary wave between two fluids are conducted for validation. Excellent agreement is obtained between the numerical simulations and the theoretical results in the two cases.展开更多
Introduction-Nuclei near and beyond the proton drip line represent a fascinating frontier in the nuclear landscape. Proton-rich nuclei exhibit intriguing phenomena, such as the Thomas-Ehrman shift and proton-halo stru...Introduction-Nuclei near and beyond the proton drip line represent a fascinating frontier in the nuclear landscape. Proton-rich nuclei exhibit intriguing phenomena, such as the Thomas-Ehrman shift and proton-halo structure. Beyond the proton dripline, nuclei become unbound, allowing protons to be emitted and giving rise to novel radioactive decay modes. Single-proton radioactivity, a process in which some nuclei with an odd number of protons(Z) decay by ejecting a proton, was discovered several decades ago and has been extensively studied [1, 2].展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and e...Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and exchangecorrelation effects but also the interference between the dynamics of different electron wave packets.展开更多
On the evening of May 3Oth,the parallel forum"Equality and Inclusiveness&Harmonious Coexistence:Multi-dimensional Narratives of Civilisations from Writers'Perspective",as part of the 4th Dialogue on ...On the evening of May 3Oth,the parallel forum"Equality and Inclusiveness&Harmonious Coexistence:Multi-dimensional Narratives of Civilisations from Writers'Perspective",as part of the 4th Dialogue on Exchanges and Mutual Learning among Civilisations,was held in Dunhuang.The forum was organised by the China Writers Association and co-organised by China National Publications Import&Export(Group)Corporation.展开更多
Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feat...Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feature representation.However,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same image.Although some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales.To address these issues,we propose a two-stage,three-branch Transformer-based framework.The first stage incorporates multi-scale image feature extraction and hierarchical scale attention.This design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object scales.The second stage includes a global feature enhancement module and a region selection module.The global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and labels.Together,these components enable the efficient acquisition of class-specific precise feature representations.Extensive experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed method.Our approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,respectively.These results validate the reliability and superiority of our approach for multi-label image classification.展开更多
文摘The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization(MCCMO)Algorithm,termed Adaptive Diversity Preservation(ADP).This enhancement is primarily focused on the improvement of constraint handling strategies,local search integration,hybrid selection approaches,and adaptive parameter control.Theimproved variant was experimented on with the RWMOP50 power distribution systemplanning benchmark.As per the findings,the improved variant outperformed the original MCCMO across the eleven performance metrics,particularly in terms of convergence speed,constraint handling efficiency,and solution diversity.The results also establish that MCCMOADP consistently delivers substantial performance gains over the baseline MCCMO,demonstrating its effectiveness across performancemetrics.The new variant also excels atmaintaining the balanced trade-off between exploration and exploitation throughout the search process,making it especially suitable for complex optimization problems in multiconstrained power systems.These enhancements make MCCMO-ADP a valuable and promising candidate for handling problems such as renewable energy scheduling,logistics planning,and power system optimization.Future work will benchmark the MCCMO-ADP against widely recognized algorithms such as NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D and will also extend its validation to large-scale real-world optimization domains to further consolidate its generalizability.
基金supported by the National Key Laboratory of Helicopter Aeromechanics Fund(No.2024-CXPT-GF-JJ-093-05).
文摘The complex aerodynamic interaction between tandem tilt-wing and multi-rotor directly affects the wing surface flow and rotor thrust,making it a critical factor during the tilt transition process of this configuration of rotorcraft.The aerodynamic interaction of tandem tilt-wing and multi-rotor is investigated based on the CFD method.The aerodynamic effect of multi tilt-rotor is simulated as virtual disk modeling by adding source terms to the Navier-Stokes equations,effectively reducing the calculation time while maintaining the accuracy of aerodynamic interaction calculations.Aerodynamic forces and flow field characteristics of the tandem tilt-wing and multi-rotor under different tilt angles are compared between cases with and without aerodynamic interaction.Furthermore,the differences in aerodynamic forces between dynamic tilt transition and fixed-angle conditions were compared.The results show that the aerodynamic interaction of multi-rotor obviously increases the lift of front tilt-wing at different tilt angles,the wing lift under interaction is increased by more than 40%compared with isolated wing at tilt angle of 15°for the computation in this paper,which is related to the increase of wing flow velocity and the suppression of flow separation caused by multi-rotor;the wing blocking effect will increase rotor thrust,especially near the tilt angles of 30°and 45°;the increases of rear wing lift and rear rotor thrust under aerodynamic interaction are not significant because of suppression by the front wing’s downwash;the unsteady effects during dynamic tilting have a relatively minor impact on aerodynamic interaction,with the aerodynamic forces on the rotors and wings during the dynamic tilting process showing little difference from those under corresponding fixed tilt angles.
文摘Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.
基金supported by Guangdong Basic and Applied Basic Research Foundation(No.2024A1515012378)Natural Science Foundation of China(Nos.52471093,52274367)+3 种基金fund of the State Key Laboratory of Solidification Processing in NPU(No.2025‐QZ‐03)the Practice and Innovation Funds for Graduate Students of Northwestern Polytechnical University(No.PF2025041)Fundamental Research Projects of Science&Technology Innovation and development Plan in Yantai City(No.2024JCYJ099)project(No.ZR2024QE213)supported by Shandong Provincial Natural Science Foundation.
文摘The pursuit of simultaneously high wear resistance and excellent lubrication in multi‐principal element alloy(MPEA)composites is often hindered by a fundamental trade‐off,which is exacerbated by the agglomeration of high‐content graphene reinforcements.This compromise becomes particularly severe in composites with high‐content graphene reinforcements,whose agglomeration leads to embrittlement and lubrication failure.Here,a flake powder-metallurgy strategy is developed to construct a self‐assembled lamellar structure in graphene/CoCrNi MPEA composites(Gr/MPEA_(AL)).This approach enables the uniform dispersion of a high graphene content(3.0 wt%),which is unattainable by conventional methods.The resulting composite exhibits a rare dual enhancement in performance:an order‐of‐magnitude improvement in wear resistance coupled with a low coefficient of friction.Intriguingly,the tribological behavior shows significant anisotropy,with optimal performance observed when sliding perpendicular to the lamellae.Through a multi‐scale methodology combining molecular dynamics simulations,finite element analysis,and systematic experiments,it is revealed that this exceptional performance stems from the synergy of high‐density deformation nanotwins,efficient strain delocalization,and abundant graphene‐derived lubricating sites.This work establishes a general paradigm for designing composite architectures that reconcile traditionally incompatible properties,offering broad implications for developing next‐generation structural materials with integrated mechanical robustness and surface functionality for safety‐critical applications.
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 the grants from the Key Research and Development Program of Xinjiang Uygur autonomous region in China(Grant No.2023B02017)the National Key Research and Development Program of China(Grant No.2024YFD2300703)+1 种基金the financial support from the Beijing Rural Revitalization Agricultural Science and Technology Project(Grant No.NY2401080000),BAIC01-2025the 2115 Talent Development Program of China Agricultural University.
文摘Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
文摘Amyotrophic lateral sclerosis(ALS)is a rapidly progressing neurodegenerative disease,leading to muscle weakness,paralysis and ultimately death due to respiratory failure.Currently licensed drugs have only very limited effects on slowing down disease progression or biomarkers.Despite numerous successful preclinical analyses,most new drugs fail when translated to clinical trials(Petrov et al.,2017).This is believed to be,in part,due to the multilayer heterogeneity of ALS(e.g.,clinical,genetic,and molecular;Tzeplaeff et al.,2024).Studies integrating multi-omic data are still limited,making it difficult to fully understand the biological complexity that characterizes the disease.
基金supported by the Shenzhen Medical Research Fund(Grant No.A2303049)Guangdong Basic and Applied Basic Research(Grant No.2023A1515010647)+1 种基金National Natural Science Foundation of China(Grant No.22004135)Shenzhen Science and Technology Program(Grant No.RCBS20210706092409020,GXWD20201231165807008,20200824162253002).
文摘Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.
文摘The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.
基金support from the National Natural Science Foundation of China (Grant No.12474101)support from the National Natural Science Foundation of China (Grant Nos.52272202 and W2421027)support from the National Natural Science Foundation of China (Grant No.52501307)。
文摘New electronic devices based on the physical properties of electrically driven skyrmions are promising for logic computing and nonvolatile memory applications.However,achieving efficient and practical compute-storage integration remains challenging owing to the structural complexity,limited functionality,and low flexibility observed in most skyrmion-based devices.In this study,we designed a novel device architecture that integrates seven basic logic gates into a unified physical structure.Their operation can be enabled by physical mechanisms,such as spin-orbit torque,spin-transfer torque,skyrmion-edge repulsions,and skyrmion-skyrmion interactions.Furthermore,by incorporating voltage-controlled magnetic anisotropy,the device achieved multi-input capability and reconfigurability functionality.Ultralow power consumption(<1 fJ/bit per logic function)and extremely high logic density were achieved.Significantly,the compatibility of this nanotrack design with existing skyrmion racetrack memory paves the way for advanced in-memory computing in spintronic architectures.
文摘An extended form of the modified Kadomtsev-Petviashvili （mKP） equation is investigated. The simplified form of the Hirota bilinear method established by Hereman and Nuseir is employed. Multi-front wave solutions are formally derived to the extended mKP equation and the mKP equation. The results show that the extension terms do not kill the integrability of the mKP equation.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金supported by the National Key R&D Program of China(No.2022YFB3205101)NSAF(No.U2230116)。
文摘To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illumination is processed by contrast-limited adaptive histogram equalization(CLAHE),adaptive complementary gamma function(ACG),and adaptive detail preserving S-curve(ADPS),respectively,to obtain three components.Then,the fusion-relevant features,exposure,and color contrast are selected as the weight maps.Subsequently,these components and weight maps are fused through multi-scale to generate enhanced illumination.Finally,the enhanced images are obtained by multiplying the enhanced illumination and reflectance.Compared with existing approaches,this proposed method achieves an average increase of 0.81%and 2.89%in the structural similarity index measurement(SSIM)and peak signal-to-noise ratio(PSNR),and a decrease of 6.17%and 32.61%in the natural image quality evaluator(NIQE)and gradient magnitude similarity deviation(GMSD),respectively.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.10872222 and 50921063)the Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20110191110037)the Fundamental Research Funds for the Central Universities,China(Grant Nos.CDJXS11240011 and CDJXS10241103)
文摘In this paper, an improved incompressible multi-relaxation-time lattice Boltzmann-front tracking approach is proposed to simulate two-phase flow with a sharp interface, where the surface tension is implemented. The lattice Boltzmann method is used to simulate the incompressible flow with a stationary Eulerian grid, an additional moving Lagrangian grid is adopted to track explicitly the motion of the interface, and an indicator function is introduced to update the fluid properties accurately. The interface is represented by using a four-order Lagrange polynomial through fitting a set of discrete marker points, and then the surface tension is directly computed by using the normal vector and curvature of the interface. Two benchmark problems, including Laplace＇s law for a stationary bubble and the dispersion relation of the capillary wave between two fluids are conducted for validation. Excellent agreement is obtained between the numerical simulations and the theoretical results in the two cases.
文摘Introduction-Nuclei near and beyond the proton drip line represent a fascinating frontier in the nuclear landscape. Proton-rich nuclei exhibit intriguing phenomena, such as the Thomas-Ehrman shift and proton-halo structure. Beyond the proton dripline, nuclei become unbound, allowing protons to be emitted and giving rise to novel radioactive decay modes. Single-proton radioactivity, a process in which some nuclei with an odd number of protons(Z) decay by ejecting a proton, was discovered several decades ago and has been extensively studied [1, 2].
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFE0134200)the National Natural Science Foundation of China(Grant No.12204214)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.GK202207012)QCYRCXM-2022-241。
文摘Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and exchangecorrelation effects but also the interference between the dynamics of different electron wave packets.
文摘On the evening of May 3Oth,the parallel forum"Equality and Inclusiveness&Harmonious Coexistence:Multi-dimensional Narratives of Civilisations from Writers'Perspective",as part of the 4th Dialogue on Exchanges and Mutual Learning among Civilisations,was held in Dunhuang.The forum was organised by the China Writers Association and co-organised by China National Publications Import&Export(Group)Corporation.
基金supported by the National Natural Science Foundation of China(62302167,62477013)Natural Science Foundation of Shanghai(No.24ZR1456100)+1 种基金Science and Technology Commission of Shanghai Municipality(No.24DZ2305900)the Shanghai Municipal Special Fund for Promoting High-Quality Development of Industries(2211106).
文摘Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feature representation.However,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same image.Although some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales.To address these issues,we propose a two-stage,three-branch Transformer-based framework.The first stage incorporates multi-scale image feature extraction and hierarchical scale attention.This design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object scales.The second stage includes a global feature enhancement module and a region selection module.The global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and labels.Together,these components enable the efficient acquisition of class-specific precise feature representations.Extensive experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed method.Our approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,respectively.These results validate the reliability and superiority of our approach for multi-label image classification.
