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
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
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
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
Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in hu...Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in humans remain unclear,including cell viability,distribution,migration,and fate.Conventional cell tracing methods cannot be used in the clinic.The use of superparamagnetic iron oxide nanoparticles as contrast agents allows for the observation of transplanted cells using magnetic resonance imaging.In 2016,the National Medical Products Administration of China approved a new superparamagnetic iron oxide nanoparticle,Ruicun,for use as a contrast agent in clinical trials.In the present study,an acute hemi-transection spinal cord injury model was established in beagle dogs.The injury was then treated by transplantation of Ruicun-labeled mesenchymal stromal cells.The results indicated that Ruicunlabeled mesenchymal stromal cells repaired damaged spinal cord fibers and partially restored neurological function in animals with acute spinal cord injury.T2*-weighted imaging revealed low signal areas on both sides of the injured spinal cord.The results of quantitative susceptibility mapping with ultrashort echo time sequences indicated that Ruicun-labeled mesenchymal stromal cells persisted stably within the injured spinal cord for over 4 weeks.These findings suggest that magnetic resonance imaging has the potential to effectively track the migration of Ruicun-labeled mesenchymal stromal cells and assess their ability to repair spinal cord injury.展开更多
为了更直观、准确地表达和展示地震作用下城市燃气管网在实际地理环境中的空间关系及震害评估分析结果,基于VTK库、Adobe After Effects软件及Python语言提出了一种可全历程展示燃气管网在非一致地震激励下应变及破坏状态三维动态的可...为了更直观、准确地表达和展示地震作用下城市燃气管网在实际地理环境中的空间关系及震害评估分析结果,基于VTK库、Adobe After Effects软件及Python语言提出了一种可全历程展示燃气管网在非一致地震激励下应变及破坏状态三维动态的可视化模型方法。该方法包括3个模块:非一致地震激励下管网的基础数据提取及震害预测、三维可视化模型建立以及三维动态可视化模型建立。以西南某地区埋地燃气管网为例进行三维动态可视化模拟,立体直观展示该市管网在非一致地震激励下不同位置的应变响应及破坏情况,展示了该方法的三维动态可视化效果。展开更多
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.
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.
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.
基金supported by the National Key R&D Program of China,Nos.2017YFA0104302(to NG and XM)and 2017YFA0104304(to BW and ZZ)
文摘Mesenchymal stromal cell transplantation is an effective and promising approach for treating various systemic and diffuse diseases.However,the biological characteristics of transplanted mesenchymal stromal cells in humans remain unclear,including cell viability,distribution,migration,and fate.Conventional cell tracing methods cannot be used in the clinic.The use of superparamagnetic iron oxide nanoparticles as contrast agents allows for the observation of transplanted cells using magnetic resonance imaging.In 2016,the National Medical Products Administration of China approved a new superparamagnetic iron oxide nanoparticle,Ruicun,for use as a contrast agent in clinical trials.In the present study,an acute hemi-transection spinal cord injury model was established in beagle dogs.The injury was then treated by transplantation of Ruicun-labeled mesenchymal stromal cells.The results indicated that Ruicunlabeled mesenchymal stromal cells repaired damaged spinal cord fibers and partially restored neurological function in animals with acute spinal cord injury.T2*-weighted imaging revealed low signal areas on both sides of the injured spinal cord.The results of quantitative susceptibility mapping with ultrashort echo time sequences indicated that Ruicun-labeled mesenchymal stromal cells persisted stably within the injured spinal cord for over 4 weeks.These findings suggest that magnetic resonance imaging has the potential to effectively track the migration of Ruicun-labeled mesenchymal stromal cells and assess their ability to repair spinal cord injury.
文摘为了更直观、准确地表达和展示地震作用下城市燃气管网在实际地理环境中的空间关系及震害评估分析结果,基于VTK库、Adobe After Effects软件及Python语言提出了一种可全历程展示燃气管网在非一致地震激励下应变及破坏状态三维动态的可视化模型方法。该方法包括3个模块:非一致地震激励下管网的基础数据提取及震害预测、三维可视化模型建立以及三维动态可视化模型建立。以西南某地区埋地燃气管网为例进行三维动态可视化模拟,立体直观展示该市管网在非一致地震激励下不同位置的应变响应及破坏情况,展示了该方法的三维动态可视化效果。
