The flow of a tetra-hybrid Casson nanofluid(Al_(2)O_(3)-CuO-TiO_(2)-Ag/H_(2)O)over a nonlinear stretching sheet is investigated.The Buongiorno model is used to account for thermophoresis and Brownian motion,while ther...The flow of a tetra-hybrid Casson nanofluid(Al_(2)O_(3)-CuO-TiO_(2)-Ag/H_(2)O)over a nonlinear stretching sheet is investigated.The Buongiorno model is used to account for thermophoresis and Brownian motion,while thermal radiation is incorporated to examine its influence on the thermal boundary layer.The governing partial differential equations(PDEs)are reduced to a system of nonlinear ordinary differential equations(ODEs)with fully non-dimensional similarity transformations involving all independent variables.To solve the obtained highly nonlinear system of differential equations,a novel Clique polynomial collocation method is applied.The analysis focuses on the effects of the Casson parameter,power index,radiation parameter,thermophoresis parameter,Brownian motion parameter,and Lewis number.The key findings show that thermal radiation intensifies the thermal boundary layer,the Casson parameter reduces the velocity,and the Lewis number suppresses the concentration with direct relevance to polymer processing,coating flows,electronic cooling,and biomedical applications.展开更多
Community detection is a fundamental problem in network analysis for identifying densely connected node clusters,with successful applications in diverse fields like social networks,recommendation systems,biology,and c...Community detection is a fundamental problem in network analysis for identifying densely connected node clusters,with successful applications in diverse fields like social networks,recommendation systems,biology,and cyberattack detection.Overlapping community detection refers to the case of a node belonging to multiple communities simultaneously,which is a much more meaningful and challenging task.Graph representation learning with Evolutionary Computation has been studied well in overlapping community detection to deal with complex network structures and characteristics.However,most of them focus on searching the entire solution space,which can be inefficient and lead to inadequate results.To overcome the problem,a structural feature node extraction method is first proposed that can effectively map a network into a structural embedding space.Thus,nodes within the network are classified into hierarchical levels based on their structural feature strength,and only nodes with relatively high strength are considered in subsequent search steps to reduce the search space.Then,a maximal-clique representation method is employed on the given vertex set to identify overlapping nodes.A hybrid clique-based multi-objective evolutionary algorithmwith decomposition method is designed to address cliques and the remaining unexplored nodes separately.The number of communities generated with this allocation method is closer to the actual partition count with high division quality.Experimental results on nine usually used real-world networks,five synthetic networks,and two large-scale networks demonstrate the effectiveness of the proposed methodology in terms of community quality and algorithmic efficiency,compared to traditional,MOEA-based,and graph embedding-based community detection algorithms.展开更多
On the basis of investigating the statistical data of bus transport networks of three big cities in China,wepropose that each bus route is a clique(maximal complete subgraph)and a bus transport network(BTN)consists of...On the basis of investigating the statistical data of bus transport networks of three big cities in China,wepropose that each bus route is a clique(maximal complete subgraph)and a bus transport network(BTN)consists of alot of cliques,which intensively connect and overlap with each other.We study the network properties,which includethe degree distribution,multiple edges' overlapping time distribution,distribution of the overlap size between any twooverlapping cliques,distribution of the number of cliques that a node belongs to.Naturally,the cliques also constitute anetwork,with the overlapping nodes being their multiple links.We also research its network properties such as degreedistribution,clustering,average path length,and so on.We propose that a BTN has the properties of random cliqueincrement and random overlapping clique,at the same time,a BTN is a small-world network with highly clique-clusteredand highly clique-overlapped.Finally,we introduce a BTN evolution model,whose simulation results agree well withthe statistical laws that emerge in real BTNs.展开更多
A novel scale-flee network model based on clique （complete subgraph of random size） growth and preferential attachment was proposed. The simulations of this model were carried out. And the necessity of two evolving ...A novel scale-flee network model based on clique （complete subgraph of random size） growth and preferential attachment was proposed. The simulations of this model were carried out. And the necessity of two evolving mechanisms of the model was verified. According to the mean-field theory, the degree distribution of this model was analyzed and computed. The degree distribution function of vertices of the generating network P（d） is 2m^2m1^-3（d-m1 ＋ 1）^-3, where m and m1 denote the number of the new adding edges and the vertex number of the cliques respectively, d is the degree of the vertex, while one of cliques P（k） is 2m^2Ek^-3, where k is the degree of the clique. The simulated and analytical results show that both the degree distributions of vertices and cliques follow the scale-flee power-law distribution. The scale-free property of this model disappears in the absence of any one of the evolving mechanisms. Moreover, the randomicity of this model increases with the increment of the vertex number of the cliques.展开更多
We developed a computational framework to identify common gene association sub-network. This framework combines graphical lasso model, graph product and a replicator equation based clique solver. We applied this metho...We developed a computational framework to identify common gene association sub-network. This framework combines graphical lasso model, graph product and a replicator equation based clique solver. We applied this method to find common stress responsive sub-networks from two related Deinococcus-Thermus bacterial species.展开更多
Analyses of spatial relationships and social interactions provide insights into the social structure of animal societies and the ways in which social preferences among and between dyads affect higher order social rela...Analyses of spatial relationships and social interactions provide insights into the social structure of animal societies and the ways in which social preferences among and between dyads affect higher order social relationships. In this paper we de- scribe the patterns of spatial associations and social interactions among adult male northern muriquis in order to evaluate the dy- namics of their social networks above the dyadic levels. Systematic observations were made on the 17 adult males present in a multi-male/multi-female group from April 2004 through February 2005, and in July 2005. Analyses of their spatial relationships identified two distinct male cliques; some adult males （called ＂N＂ males） were more connected to the females and immatures than other adult males （＂MU＂ males）, which were more connected to one another. Affiliative interactions were significantly higher among dyads belonging to the same clique than to different cliques. Although frequencies of dyadic agonistic interactions were similarly low among individuals within and between cliques, MU males appeared to be subordinate to N males. Nonetheless, there were no significant differences in the copulation rates estimated for MU males and N males. Mutual benefits of cooperation between MU and N cliques in intergroup encounters might explain their ongoing associations in the same mixed-sex group展开更多
基金the UGC,New Delhi,India for financial assistance via the UGC-Junior Research Fellowship(CSIR-UGC NET JULY 2024)(Student ID:241610090610)。
文摘The flow of a tetra-hybrid Casson nanofluid(Al_(2)O_(3)-CuO-TiO_(2)-Ag/H_(2)O)over a nonlinear stretching sheet is investigated.The Buongiorno model is used to account for thermophoresis and Brownian motion,while thermal radiation is incorporated to examine its influence on the thermal boundary layer.The governing partial differential equations(PDEs)are reduced to a system of nonlinear ordinary differential equations(ODEs)with fully non-dimensional similarity transformations involving all independent variables.To solve the obtained highly nonlinear system of differential equations,a novel Clique polynomial collocation method is applied.The analysis focuses on the effects of the Casson parameter,power index,radiation parameter,thermophoresis parameter,Brownian motion parameter,and Lewis number.The key findings show that thermal radiation intensifies the thermal boundary layer,the Casson parameter reduces the velocity,and the Lewis number suppresses the concentration with direct relevance to polymer processing,coating flows,electronic cooling,and biomedical applications.
基金supported in part by the National Natural Science Foundation of China under Grants 62473176,62073155,62002137,62106088,and 62206113National Key Laboratory of Ship Structural Safety underGrant 450324300the Postgraduate Research&Practice Innovation Programof Jiangsu Province under Grant KYCX24_2642.
文摘Community detection is a fundamental problem in network analysis for identifying densely connected node clusters,with successful applications in diverse fields like social networks,recommendation systems,biology,and cyberattack detection.Overlapping community detection refers to the case of a node belonging to multiple communities simultaneously,which is a much more meaningful and challenging task.Graph representation learning with Evolutionary Computation has been studied well in overlapping community detection to deal with complex network structures and characteristics.However,most of them focus on searching the entire solution space,which can be inefficient and lead to inadequate results.To overcome the problem,a structural feature node extraction method is first proposed that can effectively map a network into a structural embedding space.Thus,nodes within the network are classified into hierarchical levels based on their structural feature strength,and only nodes with relatively high strength are considered in subsequent search steps to reduce the search space.Then,a maximal-clique representation method is employed on the given vertex set to identify overlapping nodes.A hybrid clique-based multi-objective evolutionary algorithmwith decomposition method is designed to address cliques and the remaining unexplored nodes separately.The number of communities generated with this allocation method is closer to the actual partition count with high division quality.Experimental results on nine usually used real-world networks,five synthetic networks,and two large-scale networks demonstrate the effectiveness of the proposed methodology in terms of community quality and algorithmic efficiency,compared to traditional,MOEA-based,and graph embedding-based community detection algorithms.
基金supported by National Natural Science Foundation of China under Grant Nos.60504027 and 60874080the Postdoctor Science Foundation of China under Grant No.20060401037
文摘On the basis of investigating the statistical data of bus transport networks of three big cities in China,wepropose that each bus route is a clique(maximal complete subgraph)and a bus transport network(BTN)consists of alot of cliques,which intensively connect and overlap with each other.We study the network properties,which includethe degree distribution,multiple edges' overlapping time distribution,distribution of the overlap size between any twooverlapping cliques,distribution of the number of cliques that a node belongs to.Naturally,the cliques also constitute anetwork,with the overlapping nodes being their multiple links.We also research its network properties such as degreedistribution,clustering,average path length,and so on.We propose that a BTN has the properties of random cliqueincrement and random overlapping clique,at the same time,a BTN is a small-world network with highly clique-clusteredand highly clique-overlapped.Finally,we introduce a BTN evolution model,whose simulation results agree well withthe statistical laws that emerge in real BTNs.
基金Projects(60504027,60573123) supported by the National Natural Science Foundation of ChinaProject(20060401037) supported by the National Postdoctor Science Foundation of ChinaProject(X106866) supported by the Natural Science Foundation of Zhejiang Province,China
文摘A novel scale-flee network model based on clique （complete subgraph of random size） growth and preferential attachment was proposed. The simulations of this model were carried out. And the necessity of two evolving mechanisms of the model was verified. According to the mean-field theory, the degree distribution of this model was analyzed and computed. The degree distribution function of vertices of the generating network P（d） is 2m^2m1^-3（d-m1 ＋ 1）^-3, where m and m1 denote the number of the new adding edges and the vertex number of the cliques respectively, d is the degree of the vertex, while one of cliques P（k） is 2m^2Ek^-3, where k is the degree of the clique. The simulated and analytical results show that both the degree distributions of vertices and cliques follow the scale-flee power-law distribution. The scale-free property of this model disappears in the absence of any one of the evolving mechanisms. Moreover, the randomicity of this model increases with the increment of the vertex number of the cliques.
文摘We developed a computational framework to identify common gene association sub-network. This framework combines graphical lasso model, graph product and a replicator equation based clique solver. We applied this method to find common stress responsive sub-networks from two related Deinococcus-Thermus bacterial species.
文摘Analyses of spatial relationships and social interactions provide insights into the social structure of animal societies and the ways in which social preferences among and between dyads affect higher order social relationships. In this paper we de- scribe the patterns of spatial associations and social interactions among adult male northern muriquis in order to evaluate the dy- namics of their social networks above the dyadic levels. Systematic observations were made on the 17 adult males present in a multi-male/multi-female group from April 2004 through February 2005, and in July 2005. Analyses of their spatial relationships identified two distinct male cliques; some adult males （called ＂N＂ males） were more connected to the females and immatures than other adult males （＂MU＂ males）, which were more connected to one another. Affiliative interactions were significantly higher among dyads belonging to the same clique than to different cliques. Although frequencies of dyadic agonistic interactions were similarly low among individuals within and between cliques, MU males appeared to be subordinate to N males. Nonetheless, there were no significant differences in the copulation rates estimated for MU males and N males. Mutual benefits of cooperation between MU and N cliques in intergroup encounters might explain their ongoing associations in the same mixed-sex group
