A nowhere-zero k-flow on a graph G=(V(G),E(G))is a pair(D,f),where D is an orientation on E(G)and f:E(G)→{±1,±2,,±(k-1)}is a function such that the total outflow equals to the total inflow at each vert...A nowhere-zero k-flow on a graph G=(V(G),E(G))is a pair(D,f),where D is an orientation on E(G)and f:E(G)→{±1,±2,,±(k-1)}is a function such that the total outflow equals to the total inflow at each vertex.This concept was introduced by Tutte as an extension of face colorings,and Tutte in 1954 conjectured that every bridgeless graph admits a nowhere-zero 5-flow,known as the 5-Flow Conjecture.This conjecture is verified for some graph classes and remains unresolved as of today.In this paper,we show that every bridgeless graph of Euler genus at most 20 admits a nowhere-zero 5-flow,which improves several known results.展开更多
Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threat...Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.展开更多
Plant roots are widely known to provide mechanical reinforcement to soils against shearing and further increase slope stability.However,whether roots provide reinforcement to loess cyclic re-sistance and how various f...Plant roots are widely known to provide mechanical reinforcement to soils against shearing and further increase slope stability.However,whether roots provide reinforcement to loess cyclic re-sistance and how various factors affect roots reinforcement during seismic loading have rarely been studied.The objective is to conduct a series of cyclic direct simple shear tests and DEM numerical simulation to investigate the cyclic behaviour of rooted loess.The effects of initial static shear stress and loading frequency on the cyclic resistance of root-soil composites were first investigated.After that,cyclic direct simple shear simulations at constant volume were carried out based on the discrete element method(PFC^(3D))to investigate the effects of root geome-try,mechanical traits and root-soil bond strength on the cyclic strength of rooted loess.It was discovered that the roots could effectively improve the cyclic resistance of loess.The cyclic resistance of the root-soil composite decreases with the increase of the initial shear stress,then increases,and improves with the increase of the frequency.The simulation result show that increases in root elastic modulus and root-soil interfacial bond strength can all enhance the cyclic resistance of root-soil composites,and the maximum cyclic resistance of the root-soil composite was obtained when the initial inclination angle of the root system was 90°.展开更多
当前Web追踪领域主要使用浏览器指纹对用户进行追踪。针对浏览器指纹追踪技术存在指纹随时间动态变化、不易长期追踪等问题,提出一种关注节点和边缘特征的改进图采样聚合算法(An Improved Graph SAmple and AGgregatE with Both Node an...当前Web追踪领域主要使用浏览器指纹对用户进行追踪。针对浏览器指纹追踪技术存在指纹随时间动态变化、不易长期追踪等问题,提出一种关注节点和边缘特征的改进图采样聚合算法(An Improved Graph SAmple and AGgregatE with Both Node and Edge Features,NE-GraphSAGE)用于浏览器指纹追踪。首先以浏览器指纹为节点、指纹之间特征相似度为边构建图数据。其次对图神经网络中的GraphSAGE算法进行改进使其不仅能关注节点特征,而且能捕获边缘信息并对边缘分类,从而识别指纹。最后将NE-GraphSAGE算法与Eckersley算法、FPStalker算法和LSTM算法进行对比,验证NE-GraphSAGE算法的识别效果。实验结果表明,NE-GraphSAGE算法在准确率和追踪时长上均有不同程度的提升,最大追踪时长可达80天,相比其他3种算法性能更优,验证了NE-GraphSAGE算法对浏览器指纹长期追踪的能力。展开更多
Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types o...Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.展开更多
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
Rich-nickel layered ternary NCM811 has been widely used in the field of electric vehicles ascribed to its high theoretical specific capacity.However,poor cycling stability and rate-performance hindered its further dev...Rich-nickel layered ternary NCM811 has been widely used in the field of electric vehicles ascribed to its high theoretical specific capacity.However,poor cycling stability and rate-performance hindered its further development.Herein,different amounts of nitrogen-doped carbon were wrapped on the surface of NCM811 via a facile rheological phase method by regulating the amount of dopamine hydrochloride.The effects of the coating amounts on the structure and electrochemical performance are investigated.The DFT calculation,XRD,SEM and XPS reveal that an appropriate amount of nitrogen-doped carbon coating could uniformly form a protective layer on the NCM811 surface and the introduced N could anchor Ni atoms to inhibit the Li^(+)/Ni^(2+)mixing,but excessive amount would reduce Ni^(3+)to Ni^(2+)so as to conversely aggravate Li^(+)/Ni^(2+)mixing.Among the samples,the NCM811-CN0.75 sample exhibits the most excellent electrochemical performance,delivering a high-rate capacity of 151.6 mA·h/g at 10C,and long-term cyclability with 82.2%capacity retention after 300 cycles at 5C,exhibiting remarkable rate-performance and cyclability.展开更多
This work aims to reveal the mechanical responses and energy evolution characteristics of skarn rock under constant amplitude-varied frequency loading paths.Testing results show that the fatigue lifetime,stress−strain...This work aims to reveal the mechanical responses and energy evolution characteristics of skarn rock under constant amplitude-varied frequency loading paths.Testing results show that the fatigue lifetime,stress−strain responses,deformation,energy dissipation and fracture morphology are all impacted by the loading rate.A pronounced influence of the loading rate on rock deformation is found,with slower loading rate eliciting enhanced strain development,alongside augmented energy absorption and dissipation.In addition,it is revealed that the loading rate and cyclic loading amplitude jointly influence the phase shift distribution,with accelerated rates leading to a narrower phase shift duration.It is suggested that lower loading rate leads to more significant energy dissipation.Finally,the tensile or shear failure modes were intrinsically linked to loading strategy,with cyclic loading predominantly instigating shear damage,as manifest in the increased presence of pulverized grain particles.This work would give new insights into the fortification of mining structures and the optimization of mining methodologies.展开更多
Tracking and analyzing data from research projects is critical for understanding research trends and supporting the development of science and technology strategies.However,the data from these projects is often comple...Tracking and analyzing data from research projects is critical for understanding research trends and supporting the development of science and technology strategies.However,the data from these projects is often complex and inadequate,making it challenging for researchers to conduct in-depth data mining to improve policies or management.To address this problem,this paper adopts a top-down approach to construct a knowledge graph(KG)for research projects.Firstly,we construct an integrated ontology by referring to the metamodel of various architectures,which is called the meta-model integration conceptual reference model.Subsequently,we use the dependency parsing method to extract knowledge from unstructured textual data and use the entity alignment method based on weakly supervised learning to classify the extracted entities,completing the construction of the KG for the research projects.In addition,a knowledge inference model based on representation learning is employed to achieve knowledge completion and improve the KG.Finally,experiments are conducted on the KG for research projects and the results demonstrate the effectiveness of the proposed method in enriching incomplete data within the KG.展开更多
文摘A nowhere-zero k-flow on a graph G=(V(G),E(G))is a pair(D,f),where D is an orientation on E(G)and f:E(G)→{±1,±2,,±(k-1)}is a function such that the total outflow equals to the total inflow at each vertex.This concept was introduced by Tutte as an extension of face colorings,and Tutte in 1954 conjectured that every bridgeless graph admits a nowhere-zero 5-flow,known as the 5-Flow Conjecture.This conjecture is verified for some graph classes and remains unresolved as of today.In this paper,we show that every bridgeless graph of Euler genus at most 20 admits a nowhere-zero 5-flow,which improves several known results.
基金supported by the Ministry of Industry and Information Technology(No.23100002022102001)。
文摘Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.
文摘Plant roots are widely known to provide mechanical reinforcement to soils against shearing and further increase slope stability.However,whether roots provide reinforcement to loess cyclic re-sistance and how various factors affect roots reinforcement during seismic loading have rarely been studied.The objective is to conduct a series of cyclic direct simple shear tests and DEM numerical simulation to investigate the cyclic behaviour of rooted loess.The effects of initial static shear stress and loading frequency on the cyclic resistance of root-soil composites were first investigated.After that,cyclic direct simple shear simulations at constant volume were carried out based on the discrete element method(PFC^(3D))to investigate the effects of root geome-try,mechanical traits and root-soil bond strength on the cyclic strength of rooted loess.It was discovered that the roots could effectively improve the cyclic resistance of loess.The cyclic resistance of the root-soil composite decreases with the increase of the initial shear stress,then increases,and improves with the increase of the frequency.The simulation result show that increases in root elastic modulus and root-soil interfacial bond strength can all enhance the cyclic resistance of root-soil composites,and the maximum cyclic resistance of the root-soil composite was obtained when the initial inclination angle of the root system was 90°.
文摘当前Web追踪领域主要使用浏览器指纹对用户进行追踪。针对浏览器指纹追踪技术存在指纹随时间动态变化、不易长期追踪等问题,提出一种关注节点和边缘特征的改进图采样聚合算法(An Improved Graph SAmple and AGgregatE with Both Node and Edge Features,NE-GraphSAGE)用于浏览器指纹追踪。首先以浏览器指纹为节点、指纹之间特征相似度为边构建图数据。其次对图神经网络中的GraphSAGE算法进行改进使其不仅能关注节点特征,而且能捕获边缘信息并对边缘分类,从而识别指纹。最后将NE-GraphSAGE算法与Eckersley算法、FPStalker算法和LSTM算法进行对比,验证NE-GraphSAGE算法的识别效果。实验结果表明,NE-GraphSAGE算法在准确率和追踪时长上均有不同程度的提升,最大追踪时长可达80天,相比其他3种算法性能更优,验证了NE-GraphSAGE算法对浏览器指纹长期追踪的能力。
文摘Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
基金Project(2021H0028) supported by the Natural Scienceof Fujian Province,ChinaProject(JAT200455) supported by the Fujian Provincial Young and Middle-aged Teacher Education Project,ChinaProject(fma2023003) supported by the Open Fund of Fujian Provincial Key Laboratory of Functional Materials and Applications,China。
文摘Rich-nickel layered ternary NCM811 has been widely used in the field of electric vehicles ascribed to its high theoretical specific capacity.However,poor cycling stability and rate-performance hindered its further development.Herein,different amounts of nitrogen-doped carbon were wrapped on the surface of NCM811 via a facile rheological phase method by regulating the amount of dopamine hydrochloride.The effects of the coating amounts on the structure and electrochemical performance are investigated.The DFT calculation,XRD,SEM and XPS reveal that an appropriate amount of nitrogen-doped carbon coating could uniformly form a protective layer on the NCM811 surface and the introduced N could anchor Ni atoms to inhibit the Li^(+)/Ni^(2+)mixing,but excessive amount would reduce Ni^(3+)to Ni^(2+)so as to conversely aggravate Li^(+)/Ni^(2+)mixing.Among the samples,the NCM811-CN0.75 sample exhibits the most excellent electrochemical performance,delivering a high-rate capacity of 151.6 mA·h/g at 10C,and long-term cyclability with 82.2%capacity retention after 300 cycles at 5C,exhibiting remarkable rate-performance and cyclability.
基金Project(52174069) supported by the National Natural Science Foundation of ChinaProject(8202033) supported by the Beijing Natural Science Foundation,ChinaProject(KCF2203) supported by the Henan Key Laboratory for Green and Efficient Mining&Comprehensive Utilization of Mineral Resources (Henan Polytechnic University),China。
文摘This work aims to reveal the mechanical responses and energy evolution characteristics of skarn rock under constant amplitude-varied frequency loading paths.Testing results show that the fatigue lifetime,stress−strain responses,deformation,energy dissipation and fracture morphology are all impacted by the loading rate.A pronounced influence of the loading rate on rock deformation is found,with slower loading rate eliciting enhanced strain development,alongside augmented energy absorption and dissipation.In addition,it is revealed that the loading rate and cyclic loading amplitude jointly influence the phase shift distribution,with accelerated rates leading to a narrower phase shift duration.It is suggested that lower loading rate leads to more significant energy dissipation.Finally,the tensile or shear failure modes were intrinsically linked to loading strategy,with cyclic loading predominantly instigating shear damage,as manifest in the increased presence of pulverized grain particles.This work would give new insights into the fortification of mining structures and the optimization of mining methodologies.
基金supported by the National Natural Science Foundation of China(72101263).
文摘Tracking and analyzing data from research projects is critical for understanding research trends and supporting the development of science and technology strategies.However,the data from these projects is often complex and inadequate,making it challenging for researchers to conduct in-depth data mining to improve policies or management.To address this problem,this paper adopts a top-down approach to construct a knowledge graph(KG)for research projects.Firstly,we construct an integrated ontology by referring to the metamodel of various architectures,which is called the meta-model integration conceptual reference model.Subsequently,we use the dependency parsing method to extract knowledge from unstructured textual data and use the entity alignment method based on weakly supervised learning to classify the extracted entities,completing the construction of the KG for the research projects.In addition,a knowledge inference model based on representation learning is employed to achieve knowledge completion and improve the KG.Finally,experiments are conducted on the KG for research projects and the results demonstrate the effectiveness of the proposed method in enriching incomplete data within the KG.
