This study presents a comparative analysis of optimisation strategies for designing hull shapes of Autonomous Underwater Vehicles(AUVs),paying special attention to drag,lift-to-drag ratio,and delivered power.A fully i...This study presents a comparative analysis of optimisation strategies for designing hull shapes of Autonomous Underwater Vehicles(AUVs),paying special attention to drag,lift-to-drag ratio,and delivered power.A fully integrated optimisation framework is developed accordingly,combining a single-objective Genetic Algorithm(GA)for design parameter generation,Computer-Aided Geometric Design(CAGD)for the creation of hull geometries and associated fluid domains,and a Reynolds-Averaged Navier-Stokes(RANS)solver for evaluating hydrodynamic performance metrics.This unified approach eliminates manual intervention,enabling automated determination of optimal hull configurations.Three distinct optimisation problems are addressed using the proposed methodology.First,the drag minimisation of a reference afterbody geometry(A1)at zero angle of attack is performed under constraints of fixed length and internal volume for various flow velocities spanning the range from 0.5 to 15 m/s.Second,the lift-to-drag ratio of A1 is maximised at a 6°angle of attack,maintaining constant total length and internal volume.Third,delivered power is minimised for A1 at a 0°angle of attack.The comparative analysis of results from all three optimisation cases reveals hull shapes with practical design significance.Notably,the shape optimised for minimum delivered power outperforms the other two across a range of velocities.Specifically,it achieves reductions in required power by 7.6%,7.8%,10.2%,and 13.04%at velocities of 0.5,1.0,1.5,and 2.152 m/s,respectively.展开更多
The noise generated by high-speed hair dryers significantly affects user experience,with aerodynamic design playing a crucial role in controlling sound emissions.This study investigates the aerodynamic noise character...The noise generated by high-speed hair dryers significantly affects user experience,with aerodynamic design playing a crucial role in controlling sound emissions.This study investigates the aerodynamic noise characteristics of a commercial high-speed hair dryer through Computational Fluid Dynamics(CFD)analysis.The velocity field,streamline patterns,and vector distribution within the primary flow path and internal cavity were systematically examined.Results indicate that strong interactions between the wake flow generated by the guide vanes and the straight baffle in the rear flow path induce vortex structures near the outlet,which are primarily responsible for highfrequency noise.To address this,the guide vanes and rear flow path geometry were redesigned and optimized for improved acoustic and aerodynamic performance.Underrated operating conditions(28 V,20,000 rpm),the optimized configuration achieves a noise reduction of more than 2.2 dB while increasing outlet wind speed by over 9%.Moreover,the noise suppression effect becomes more pronounced at lower rotational speeds.展开更多
The rapid advancement of technology and the increasing speed of vehicles have led to a substantial rise in energy consumption and growing concern over environmental pollution.Beyond the promotion of new energy vehicle...The rapid advancement of technology and the increasing speed of vehicles have led to a substantial rise in energy consumption and growing concern over environmental pollution.Beyond the promotion of new energy vehicles,reducing aerodynamic drag remains a critical strategy for improving energy efficiency and lowering emissions.This study investigates the influence of key geometric parameters on the aerodynamic drag of vehicles.A parametric vehicle model was developed,and computational fluid dynamics(CFD)simulations were conducted to analyse variations in the drag coefficient(C_(d))and pressure distribution across different design configurations.The results reveal that the optimal aerodynamic performance—characterized by a minimized drag coefficient—is achieved with the following parameter settings:engine hood angle(α)of 15°,windshield angle(β)of 25°,rear window angle(γ)of 40°,rear upwards tail lift angle(θ)of 10°,ground clearance(d)of 100 mm,and side edge angle(s)of 5°.These findings offer valuable guidance for the aerodynamic optimization of vehicle body design and contribute to strategies aimed at energy conservation and emission reduction in the automotive sector.展开更多
This study investigates the accuracy and efficiency of a convolutional autoencoder in predicting flow solutions of diverse characteristics,including strong local nonlinea rity and unsteady wake vortices.Modifications ...This study investigates the accuracy and efficiency of a convolutional autoencoder in predicting flow solutions of diverse characteristics,including strong local nonlinea rity and unsteady wake vortices.Modifications to the standard U-net method were made suitable for non-Cartesian CFD mesh topology,enhancing solution accuracy.Additionally,conditions for predicting flows in unseen environments are integrated into a bottleneck layer between the encoder and decoder structures,guiding flow interpolation or extrapolation and parameter types.For direct comparison,this study uses a proper orthogonal decomposition(POD)-based ROM with linear reconstruction using dominant basis vectors from the flow solution space.Interpolation and extrapolation of generalized coordinates are performed using Gaussian process regression(GPR)and Long Short-Term Memory(LSTM)networks,respectively.The Conditional Unet(CUnet)'s accuracy is demonstrated through inviscid transonic airfoil flows,capturing shock waves effectively.Additionally,it can also be used for predicting the flow field of the three-dimensional shape of the Onera M6 wing.Vortex shedding flows around an Eppler airfoil at a 16-degree angle of attack in turbulent conditions were well-resolved,with root mean squared errors under 1%compared to full-order CFD results.Remarkably,the CUnet's computational efficiency is highlighted as the wall clock CPU time for these 2D flows was less than one second.Finally,the ROM's effectiveness is further validated through successful multi-point shape optimization,minimizing wave drag of RAE 2822 airfoils across subsonic to transonic conditions.This resulted in a maximum drag reduction of 37.38%at Mach 0.74 without performance degradation at off-design conditions.展开更多
The interaction between the airflow and train influences the aerodynamic characteristics and dynamic performance of high-speed trains.This study focused on the fluid-solid coupling effect of airflow and HST,and propos...The interaction between the airflow and train influences the aerodynamic characteristics and dynamic performance of high-speed trains.This study focused on the fluid-solid coupling effect of airflow and HST,and proposed a co-simulation(CS)approach between computational fluid dynamics and multi-body dynamics.Firstly,the aerodynamic model was developed by employing overset mesh technology and the finite volume method,and the detailed train-track coupled dynamic model was established.Then the User Data Protocol was adopted to build data communication channels.Moreover,the proposed CS method was validated by comparison with a reported field test result.Finally,a case study of the HST exiting a tunnel subjected to crosswind was conducted to compare differences between CS and offline simulation(OS)methods.In terms of the presented case,the changing trends of aerodynamic forces and car-body displacements calculated by the two methods were similar.Differences mainly lie in aerodynamic moments and transient wheel-rail impacts.Maximum pitching and yawing moments on the head vehicle in the two methods differ by 21.1 kN∙m and 29.6 kN∙m,respectively.And wheel-rail impacts caused by sudden changes in aerodynamic loads are significantly severer in CS.Wheel-rail safety indices obtained by CS are slightly greater than those by OS.This research proposes a CS method for aerodynamic characteristics and dynamic performance of the HST in complex scenarios,which has superiority in computational efficiency and stability.展开更多
To meet the intelligent detection needs of underwater defects in large hydropower stations,the hydrodynamic performance of a bionic streamlined remotely operated vehicle containing a thruster protective net structure ...To meet the intelligent detection needs of underwater defects in large hydropower stations,the hydrodynamic performance of a bionic streamlined remotely operated vehicle containing a thruster protective net structure is numerically simulated via computational fluid dynamics and overlapping mesh technology.The results show that the entity model generates greater hydrodynamic force during steady motion,whereas the square net model experiences greater force and moment during unsteady motion.The lateral and vertical force coefficients of the entity model are 4.32 and 3.13 times greater than those of the square net model in the oblique towing test simulation.The square net model also offers better static and dynamic stability,with a 24.5%increase in dynamic stability,achieving the highest lift-to-drag ratio at attack angles of 6°∼8°.This research provides valuable insights for designing and controlling underwater defect detection vehicles for large hydropower stations.展开更多
THE mechanical response and deformation mechanisms of pure nickel under nanoindentation were systematically investigated using molecular dynamics(MD)simulations,with a particular focus on the novel interplay between c...THE mechanical response and deformation mechanisms of pure nickel under nanoindentation were systematically investigated using molecular dynamics(MD)simulations,with a particular focus on the novel interplay between crystallographic orientation,grain boundary(GB)proximity,and pore characteristics(size/location).This study compares single-crystal nickel models along[100],[110],and[111]orientations with equiaxed polycrystalline models containing 0,1,and 2.5 nm pores in surface and subsurface configurations.Our results reveal that crystallographic anisotropy manifests as a 24.4%higher elastic modulus and 22.2%greater hardness in[111]-oriented single crystals compared to[100].Pore-GB synergistic effects are found to dominate the deformation behavior:2.5 nm subsurface pores reduce hardness by 25.2%through stress concentration and dislocation annihilation at GBs,whereas surface pores enable mechanical recovery via accelerated dislocation generation post-collapse.Additionally,size-dependent deformation regimes were identified,with 1 nm pores inducing negligible perturbation due to rapid atomic rearrangement,in contrast with persistent softening in 2.5 nm pores.These findings establish atomic-scale design principles for defect engineering in nickel-based aerospace components,demonstrating how crystallographic orientation,pore configuration,and GB interactions collectively govern nanoindentation behavior.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
Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address thes...Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.展开更多
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
Mitochondrial dysfunction has emerged as a critical factor in the etiology of various neurodevelopmental disorders, including autism spectrum disorders, attention-deficit/hyperactivity disorder, and Rett syndrome. Alt...Mitochondrial dysfunction has emerged as a critical factor in the etiology of various neurodevelopmental disorders, including autism spectrum disorders, attention-deficit/hyperactivity disorder, and Rett syndrome. Although these conditions differ in clinical presentation, they share fundamental pathological features that may stem from abnormal mitochondrial dynamics and impaired autophagic clearance, which contribute to redox imbalance and oxidative stress in neurons. This review aimed to elucidate the relationship between mitochondrial dynamics dysfunction and neurodevelopmental disorders. Mitochondria are highly dynamic organelles that undergo continuous fusion and fission to meet the substantial energy demands of neural cells. Dysregulation of these processes, as observed in certain neurodevelopmental disorders, causes accumulation of damaged mitochondria, exacerbating oxidative damage and impairing neuronal function. The phosphatase and tensin homolog-induced putative kinase 1/E3 ubiquitin-protein ligase pathway is crucial for mitophagy, the process of selectively removing malfunctioning mitochondria. Mutations in genes encoding mitochondrial fusion proteins have been identified in autism spectrum disorders, linking disruptions in the fusion-fission equilibrium to neurodevelopmental impairments. Additionally, animal models of Rett syndrome have shown pronounced defects in mitophagy, reinforcing the notion that mitochondrial quality control is indispensable for neuronal health. Clinical studies have highlighted the importance of mitochondrial disturbances in neurodevelopmental disorders. In autism spectrum disorders, elevated oxidative stress markers and mitochondrial DNA deletions indicate compromised mitochondrial function. Attention-deficit/hyperactivity disorder has also been associated with cognitive deficits linked to mitochondrial dysfunction and oxidative stress. Moreover, induced pluripotent stem cell models derived from patients with Rett syndrome have shown impaired mitochondrial dynamics and heightened vulnerability to oxidative injury, suggesting the role of defective mitochondrial homeostasis in these disorders. From a translational standpoint, multiple therapeutic approaches targeting mitochondrial pathways show promise. Interventions aimed at preserving normal fusion-fission cycles or enhancing mitophagy can reduce oxidative damage by limiting the accumulation of defective mitochondria. Pharmacological modulation of mitochondrial permeability and upregulation of peroxisome proliferator-activated receptor gamma coactivator 1-alpha, an essential regulator of mitochondrial biogenesis, may also ameliorate cellular energy deficits. Identifying early biomarkers of mitochondrial impairment is crucial for precision medicine, since it can help clinicians tailor interventions to individual patient profiles and improve prognoses. Furthermore, integrating mitochondria-focused strategies with established therapies, such as antioxidants or behavioral interventions, may enhance treatment efficacy and yield better clinical outcomes. Leveraging these pathways could open avenues for regenerative strategies, given the influence of mitochondria on neuronal repair and plasticity. In conclusion, this review indicates mitochondrial homeostasis as a unifying therapeutic axis within neurodevelopmental pathophysiology. Disruptions in mitochondrial dynamics and autophagic clearance converge on oxidative stress, and researchers should prioritize validating these interventions in clinical settings to advance precision medicine and enhance outcomes for individuals affected by neurodevelopmental disorders.展开更多
Improving vehicle fuel consumption,performance and aerodynamic efficiency by drag reduction especially in heavy vehicles is one of the indispensable issues of automotive industry.In this work,the effects of adding app...Improving vehicle fuel consumption,performance and aerodynamic efficiency by drag reduction especially in heavy vehicles is one of the indispensable issues of automotive industry.In this work,the effects of adding append devices like deflector and cab vane corner on heavy commercial vehicle drag reduction were investigated.For this purpose,the vehicle body structure was modeled with various supplementary parts at the first stage.Then,computational fluid dynamic(CFD) analysis was utilized for each case to enhance the optimal aerodynamic structure at different longitudinal speeds for heavy commercial vehicles.The results show that the most effective supplementary part is deflector,and by adding this part,the drag coefficient is decreased considerably at an optimum angle.By adding two cab vane corners at both frontal edges of cab,a significant drag reduction is noticed.Back vanes and base flaps are simple plates which can be added at the top and side end of container and at the bottom with specific angle respectively to direct the flow and prevent the turbulence.Through the analysis of airflow and pressure distribution,the results reveal that the cab vane reduces fuel consumption and drag coefficient by up to 20 % receptively using proper deflector angle.Finally,by adding all supplementary parts at their optimized positions,41% drag reduction is obtained compared to the simple model.展开更多
文摘This study presents a comparative analysis of optimisation strategies for designing hull shapes of Autonomous Underwater Vehicles(AUVs),paying special attention to drag,lift-to-drag ratio,and delivered power.A fully integrated optimisation framework is developed accordingly,combining a single-objective Genetic Algorithm(GA)for design parameter generation,Computer-Aided Geometric Design(CAGD)for the creation of hull geometries and associated fluid domains,and a Reynolds-Averaged Navier-Stokes(RANS)solver for evaluating hydrodynamic performance metrics.This unified approach eliminates manual intervention,enabling automated determination of optimal hull configurations.Three distinct optimisation problems are addressed using the proposed methodology.First,the drag minimisation of a reference afterbody geometry(A1)at zero angle of attack is performed under constraints of fixed length and internal volume for various flow velocities spanning the range from 0.5 to 15 m/s.Second,the lift-to-drag ratio of A1 is maximised at a 6°angle of attack,maintaining constant total length and internal volume.Third,delivered power is minimised for A1 at a 0°angle of attack.The comparative analysis of results from all three optimisation cases reveals hull shapes with practical design significance.Notably,the shape optimised for minimum delivered power outperforms the other two across a range of velocities.Specifically,it achieves reductions in required power by 7.6%,7.8%,10.2%,and 13.04%at velocities of 0.5,1.0,1.5,and 2.152 m/s,respectively.
基金supported by Research Project of Zhuhai City Polytechnic(Grant No.2024KYBS06)Education Research Project of Zhuhai City Polytechnic(Grant No.JY20250404).
文摘The noise generated by high-speed hair dryers significantly affects user experience,with aerodynamic design playing a crucial role in controlling sound emissions.This study investigates the aerodynamic noise characteristics of a commercial high-speed hair dryer through Computational Fluid Dynamics(CFD)analysis.The velocity field,streamline patterns,and vector distribution within the primary flow path and internal cavity were systematically examined.Results indicate that strong interactions between the wake flow generated by the guide vanes and the straight baffle in the rear flow path induce vortex structures near the outlet,which are primarily responsible for highfrequency noise.To address this,the guide vanes and rear flow path geometry were redesigned and optimized for improved acoustic and aerodynamic performance.Underrated operating conditions(28 V,20,000 rpm),the optimized configuration achieves a noise reduction of more than 2.2 dB while increasing outlet wind speed by over 9%.Moreover,the noise suppression effect becomes more pronounced at lower rotational speeds.
基金funded by the“Hundred Outstanding Talents”Support Program of Jining University,a provincial-level key project in the field of natural sciences,grant number 2023ZYRC23Jining Key R&D Program(Soft Science)Project,No.2024JNZC010Shandong Province Key Research and Development Program(Technology-Based Small and Medium-sized Enterprises Innovation Capability Improvement)Project No.2025TSGCCZZB0679.
文摘The rapid advancement of technology and the increasing speed of vehicles have led to a substantial rise in energy consumption and growing concern over environmental pollution.Beyond the promotion of new energy vehicles,reducing aerodynamic drag remains a critical strategy for improving energy efficiency and lowering emissions.This study investigates the influence of key geometric parameters on the aerodynamic drag of vehicles.A parametric vehicle model was developed,and computational fluid dynamics(CFD)simulations were conducted to analyse variations in the drag coefficient(C_(d))and pressure distribution across different design configurations.The results reveal that the optimal aerodynamic performance—characterized by a minimized drag coefficient—is achieved with the following parameter settings:engine hood angle(α)of 15°,windshield angle(β)of 25°,rear window angle(γ)of 40°,rear upwards tail lift angle(θ)of 10°,ground clearance(d)of 100 mm,and side edge angle(s)of 5°.These findings offer valuable guidance for the aerodynamic optimization of vehicle body design and contribute to strategies aimed at energy conservation and emission reduction in the automotive sector.
基金supported by the National Research Council of Science&Technology(NST)grant by the Korea government(MSIT)(No.CRC21013)。
文摘This study investigates the accuracy and efficiency of a convolutional autoencoder in predicting flow solutions of diverse characteristics,including strong local nonlinea rity and unsteady wake vortices.Modifications to the standard U-net method were made suitable for non-Cartesian CFD mesh topology,enhancing solution accuracy.Additionally,conditions for predicting flows in unseen environments are integrated into a bottleneck layer between the encoder and decoder structures,guiding flow interpolation or extrapolation and parameter types.For direct comparison,this study uses a proper orthogonal decomposition(POD)-based ROM with linear reconstruction using dominant basis vectors from the flow solution space.Interpolation and extrapolation of generalized coordinates are performed using Gaussian process regression(GPR)and Long Short-Term Memory(LSTM)networks,respectively.The Conditional Unet(CUnet)'s accuracy is demonstrated through inviscid transonic airfoil flows,capturing shock waves effectively.Additionally,it can also be used for predicting the flow field of the three-dimensional shape of the Onera M6 wing.Vortex shedding flows around an Eppler airfoil at a 16-degree angle of attack in turbulent conditions were well-resolved,with root mean squared errors under 1%compared to full-order CFD results.Remarkably,the CUnet's computational efficiency is highlighted as the wall clock CPU time for these 2D flows was less than one second.Finally,the ROM's effectiveness is further validated through successful multi-point shape optimization,minimizing wave drag of RAE 2822 airfoils across subsonic to transonic conditions.This resulted in a maximum drag reduction of 37.38%at Mach 0.74 without performance degradation at off-design conditions.
基金Supported by the Sichuan Science and Technology Program(Grant No.2023ZDZX0008)the National Natural Science Foundation of China(Grant No.52388102)the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘The interaction between the airflow and train influences the aerodynamic characteristics and dynamic performance of high-speed trains.This study focused on the fluid-solid coupling effect of airflow and HST,and proposed a co-simulation(CS)approach between computational fluid dynamics and multi-body dynamics.Firstly,the aerodynamic model was developed by employing overset mesh technology and the finite volume method,and the detailed train-track coupled dynamic model was established.Then the User Data Protocol was adopted to build data communication channels.Moreover,the proposed CS method was validated by comparison with a reported field test result.Finally,a case study of the HST exiting a tunnel subjected to crosswind was conducted to compare differences between CS and offline simulation(OS)methods.In terms of the presented case,the changing trends of aerodynamic forces and car-body displacements calculated by the two methods were similar.Differences mainly lie in aerodynamic moments and transient wheel-rail impacts.Maximum pitching and yawing moments on the head vehicle in the two methods differ by 21.1 kN∙m and 29.6 kN∙m,respectively.And wheel-rail impacts caused by sudden changes in aerodynamic loads are significantly severer in CS.Wheel-rail safety indices obtained by CS are slightly greater than those by OS.This research proposes a CS method for aerodynamic characteristics and dynamic performance of the HST in complex scenarios,which has superiority in computational efficiency and stability.
基金supported by the National Key R&D Program of China(Grant No.2022YFB4703401).
文摘To meet the intelligent detection needs of underwater defects in large hydropower stations,the hydrodynamic performance of a bionic streamlined remotely operated vehicle containing a thruster protective net structure is numerically simulated via computational fluid dynamics and overlapping mesh technology.The results show that the entity model generates greater hydrodynamic force during steady motion,whereas the square net model experiences greater force and moment during unsteady motion.The lateral and vertical force coefficients of the entity model are 4.32 and 3.13 times greater than those of the square net model in the oblique towing test simulation.The square net model also offers better static and dynamic stability,with a 24.5%increase in dynamic stability,achieving the highest lift-to-drag ratio at attack angles of 6°∼8°.This research provides valuable insights for designing and controlling underwater defect detection vehicles for large hydropower stations.
基金The National Natural Science Foundation of China(Grant No.12462006)Beijing Institute of Structure and Environment Engineering Joint Innovation Fund(No.BQJJ202414).
文摘THE mechanical response and deformation mechanisms of pure nickel under nanoindentation were systematically investigated using molecular dynamics(MD)simulations,with a particular focus on the novel interplay between crystallographic orientation,grain boundary(GB)proximity,and pore characteristics(size/location).This study compares single-crystal nickel models along[100],[110],and[111]orientations with equiaxed polycrystalline models containing 0,1,and 2.5 nm pores in surface and subsurface configurations.Our results reveal that crystallographic anisotropy manifests as a 24.4%higher elastic modulus and 22.2%greater hardness in[111]-oriented single crystals compared to[100].Pore-GB synergistic effects are found to dominate the deformation behavior:2.5 nm subsurface pores reduce hardness by 25.2%through stress concentration and dislocation annihilation at GBs,whereas surface pores enable mechanical recovery via accelerated dislocation generation post-collapse.Additionally,size-dependent deformation regimes were identified,with 1 nm pores inducing negligible perturbation due to rapid atomic rearrangement,in contrast with persistent softening in 2.5 nm pores.These findings establish atomic-scale design principles for defect engineering in nickel-based aerospace components,demonstrating how crystallographic orientation,pore configuration,and GB interactions collectively govern nanoindentation behavior.
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 Zhongyuan University of Technology Discipline Backbone Teacher Support Program Project(No.GG202417)the Key Research and Development Program of Henan under Grant 251111212000.
文摘Lateral movement represents the most covert and critical phase of Advanced Persistent Threats(APTs),and its detection still faces two primary challenges:sample scarcity and“cold start”of new entities.To address these challenges,we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework(UGEA-LMD).First,the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution,enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem.Second,in the embedding space,we model the dependency structure among feature dimensions using a Gaussian copula to quantify the uncertainty distribution,and generate augmented samples with consistent structural and semantic properties through adaptive sampling,thus expanding the representation space of sparse samples and enhancing the model’s generalization under sparse sample conditions.Unlike static graph methods that cannot model temporal dependencies or data augmentation techniques that depend on predefined structures,UGEA-LMD offers both superior temporaldynamic modeling and structural generalization.Experimental results on the large-scale LANL log dataset demonstrate that,under the transductive setting,UGEA-LMD achieves an AUC of 0.9254;even when 10%of nodes or edges are withheld during training,UGEA-LMD significantly outperforms baseline methods on metrics such as recall and AUC,confirming its robustness and generalization capability in sparse-sample and cold-start scenarios.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
文摘Mitochondrial dysfunction has emerged as a critical factor in the etiology of various neurodevelopmental disorders, including autism spectrum disorders, attention-deficit/hyperactivity disorder, and Rett syndrome. Although these conditions differ in clinical presentation, they share fundamental pathological features that may stem from abnormal mitochondrial dynamics and impaired autophagic clearance, which contribute to redox imbalance and oxidative stress in neurons. This review aimed to elucidate the relationship between mitochondrial dynamics dysfunction and neurodevelopmental disorders. Mitochondria are highly dynamic organelles that undergo continuous fusion and fission to meet the substantial energy demands of neural cells. Dysregulation of these processes, as observed in certain neurodevelopmental disorders, causes accumulation of damaged mitochondria, exacerbating oxidative damage and impairing neuronal function. The phosphatase and tensin homolog-induced putative kinase 1/E3 ubiquitin-protein ligase pathway is crucial for mitophagy, the process of selectively removing malfunctioning mitochondria. Mutations in genes encoding mitochondrial fusion proteins have been identified in autism spectrum disorders, linking disruptions in the fusion-fission equilibrium to neurodevelopmental impairments. Additionally, animal models of Rett syndrome have shown pronounced defects in mitophagy, reinforcing the notion that mitochondrial quality control is indispensable for neuronal health. Clinical studies have highlighted the importance of mitochondrial disturbances in neurodevelopmental disorders. In autism spectrum disorders, elevated oxidative stress markers and mitochondrial DNA deletions indicate compromised mitochondrial function. Attention-deficit/hyperactivity disorder has also been associated with cognitive deficits linked to mitochondrial dysfunction and oxidative stress. Moreover, induced pluripotent stem cell models derived from patients with Rett syndrome have shown impaired mitochondrial dynamics and heightened vulnerability to oxidative injury, suggesting the role of defective mitochondrial homeostasis in these disorders. From a translational standpoint, multiple therapeutic approaches targeting mitochondrial pathways show promise. Interventions aimed at preserving normal fusion-fission cycles or enhancing mitophagy can reduce oxidative damage by limiting the accumulation of defective mitochondria. Pharmacological modulation of mitochondrial permeability and upregulation of peroxisome proliferator-activated receptor gamma coactivator 1-alpha, an essential regulator of mitochondrial biogenesis, may also ameliorate cellular energy deficits. Identifying early biomarkers of mitochondrial impairment is crucial for precision medicine, since it can help clinicians tailor interventions to individual patient profiles and improve prognoses. Furthermore, integrating mitochondria-focused strategies with established therapies, such as antioxidants or behavioral interventions, may enhance treatment efficacy and yield better clinical outcomes. Leveraging these pathways could open avenues for regenerative strategies, given the influence of mitochondria on neuronal repair and plasticity. In conclusion, this review indicates mitochondrial homeostasis as a unifying therapeutic axis within neurodevelopmental pathophysiology. Disruptions in mitochondrial dynamics and autophagic clearance converge on oxidative stress, and researchers should prioritize validating these interventions in clinical settings to advance precision medicine and enhance outcomes for individuals affected by neurodevelopmental disorders.
文摘Improving vehicle fuel consumption,performance and aerodynamic efficiency by drag reduction especially in heavy vehicles is one of the indispensable issues of automotive industry.In this work,the effects of adding append devices like deflector and cab vane corner on heavy commercial vehicle drag reduction were investigated.For this purpose,the vehicle body structure was modeled with various supplementary parts at the first stage.Then,computational fluid dynamic(CFD) analysis was utilized for each case to enhance the optimal aerodynamic structure at different longitudinal speeds for heavy commercial vehicles.The results show that the most effective supplementary part is deflector,and by adding this part,the drag coefficient is decreased considerably at an optimum angle.By adding two cab vane corners at both frontal edges of cab,a significant drag reduction is noticed.Back vanes and base flaps are simple plates which can be added at the top and side end of container and at the bottom with specific angle respectively to direct the flow and prevent the turbulence.Through the analysis of airflow and pressure distribution,the results reveal that the cab vane reduces fuel consumption and drag coefficient by up to 20 % receptively using proper deflector angle.Finally,by adding all supplementary parts at their optimized positions,41% drag reduction is obtained compared to the simple model.
