Free-standing silicon anodes with high proportion of active materials have aroused great attention;however,the mechanical stability and electrochemical performance are severely suppressed.Herein,to resolve the appeal ...Free-standing silicon anodes with high proportion of active materials have aroused great attention;however,the mechanical stability and electrochemical performance are severely suppressed.Herein,to resolve the appeal issues,a free-standing anode with a"corrugated paper"shape on micro-scale and a topological crosslinking network on the submicron and nano-scale is designed.Essentially,an integrated three-dimensional electrode structure is constructed based on robust carbon nanotubes network with firmly anchored SiNPs via forming interlocking junctions.In which,the hierarchical interlocking structure is achieved by directional induction of the binder,which ensures well integration during cycling so that significantly enhances mechanical stability as well as electronic and ionic conductivity of electrodes.Benefiting from it,this anode exhibits outsta nding performance under harsh service conditions including high Si loading,ultrahigh areal capacity(33.2 mA h cm^(-2)),and high/low temperatures(-15-60℃),which significantly extends its practical prospect.Furthermore,the optimization mechanism of this electrode is explored to verify the crack-healing and structure-integration maintaining along cycling via a unique self-stabilization process.Thus,from both the fundamental and engineering views,this strategy offers a promising path to produce high-performance free-standing electrodes for flexible device applications especially facing volume effect challenges.展开更多
Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed ...Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed Vehicular Communication Network(VCN)topologies.However,when the network is under attack,the communication delay may be much higher,and the stability of the system may not be guaranteed.This paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies(DADNT).The main idea is that for various communication delays,in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error,the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle spacing.To this end,a multi-objective optimization problem is formulated,and a 3-step Divide-And-Conquer sub-optimal solution(3DAC)is proposed.Simulation results show that with 3DAC,the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%,10%,and 14%,respectively,compared with the traditional CACC with fixed one-vehicle,two-vehicle,and three-vehicle look-ahead network topologies,thereby improving the traffic efficiency.展开更多
BACKGROUND Rumination is a critical psychological factor contributing to the relapse of major depressive episodes(MDEs)and a core residual symptom in remitted MDEs.Investigating its neural correlations is essential fo...BACKGROUND Rumination is a critical psychological factor contributing to the relapse of major depressive episodes(MDEs)and a core residual symptom in remitted MDEs.Investigating its neural correlations is essential for developing strategies to prevent MDE relapse.Despite its clinical importance,the brain network mechanisms underlying rumination in remitted MDE patients have yet to be fully elucidated.AIM To investigate the brain network mechanism underlying rumination in patients with remitted MDEs using functional magnetic resonance imaging(fMRI).METHODS We conducted an fMRI-based rumination-distraction task to induce rumination and distraction states in 51 patients with remitted MDEs.Functional connectivity(FC)was analyzed using the network-based statistic(NBS)approach,and eight topological metrics were calculated to compare the network topological properties between the two states.Correlation analyses were further performed to identify the relationships between individual rumination levels and the significantly altered brain network metrics.RESULTS The NBS analysis revealed that the altered FCs between the rumination and distraction states were located primarily in the frontoparietal,default mode,and cerebellar networks.No significant correlation was detected between these altered FCs and individual rumination levels.Among the eight topological metrics,the clustering coefficient,shortest path length,and local efficiency were significantly lower during rumination and positively correlated with individual rumination levels.In contrast,global efficiency was greater in the rumination state than in the distraction state and was negatively correlated with individual rumination levels.CONCLUSION Our work revealed the altered FC and topological properties during rumination in remitted MDE patients,offering valuable insights into the neural mechanisms of rumination from a brain network perspective.展开更多
The understanding and prediction of preferential fluid flow in porous media have attracted considerable attention in various engineering fields because of the implications of such flows in leading to a non-equilibrium...The understanding and prediction of preferential fluid flow in porous media have attracted considerable attention in various engineering fields because of the implications of such flows in leading to a non-equilibrium fluid flow in the subsurface. In this study, a novel algorithm is proposed to predict preferential flow paths based on the topologically equivalent network of a porous structure and the flow resistance of flow paths. The equivalent flow network was constructed using Poiseuille's law and the maximal inscribed sphere algorithm. The flow resistance of each path was then determined based on Darcy's law. It was determined that fluid tends to follow paths with lower flow resistance. A computer program was developed and applied to an actual porous structure. To validate the algorithm and program, we tested and recorded two-dimensional(2 D) water flow using an ablated Perspex sheet featuring the same porous structure investigated using the analytical calculations. The results show that the measured preferential flow paths are consistent with the predictions.展开更多
As a kind of bio-derived feedstock,vegetable oil(VO)shows great potential to replace petroleum-based monomers to develop sustainable polymer materials because of its easy availability,low cost,bio-renewable,and enviro...As a kind of bio-derived feedstock,vegetable oil(VO)shows great potential to replace petroleum-based monomers to develop sustainable polymer materials because of its easy availability,low cost,bio-renewable,and environmentally friendly nature.However,due to the high cross-linking density and amorphous nature,directly cured VOs generally tend to be brittle and weak.To date,it is still difficult to adopt VOs and their derivatives as structural materials to prepare high-performance elastomers.To address this important issue,amulti-scale topology design strategy was proposed in this work.First,topology regulation and functionalization of VO-based networks were realized by managing functional groups proportion during the bulk polymerization of epoxidized soybean oil with dimer fatty acids.Furthermore,a second polymer(SN)network was introduced into the VO-based network as a protective layer via interfacial cross-links.The generated VO-based elastomers(VOEs)exhibit unprecedented comprehensive properties(VO content≥70 wt.%,T_(g)as low as−24.4℃,toughness up to 6.8 MJ/m^(3)).Besides,the VOEs also exhibit excellent reprocessability and self-healing capability.Overall,this work developed a novel kind of VOEs with significant comprehensive advantages and provided important inspiration for the preparation of high-performance elastomers throughmulti-scale topology regulation.展开更多
Arbitrary topological curve network has no restriction in topology structure,so it has more powerful representing ability in defining complex surfaces.A complex surface modeling system is presented based on arbitrary ...Arbitrary topological curve network has no restriction in topology structure,so it has more powerful representing ability in defining complex surfaces.A complex surface modeling system is presented based on arbitrary topological curve network and the improved combined subdivision method,its functions including creating and editing curve network,and generating and modifying curve network＇s interpolated surface.This modeling system can be used to the process of products＇concept design,and its applications is also significant to the development of subdivision method.展开更多
Objective Using graph theory analysis,this study compares the topological and node attributes of the brain network to explore the differences in gray matter structural and functional network topological properties bet...Objective Using graph theory analysis,this study compares the topological and node attributes of the brain network to explore the differences in gray matter structural and functional network topological properties between bipolar depression(BD)patients with and without obsessive-compulsive symptoms(OCS).Methods A total of 90 BD patients(27 males,63 females;median age 19.0(22.0,25.0)years)were recruited from the psychiatric outpatient and inpatient departments of the First Affiliated Hospital of Jinan University between March 2018 and December 2022.Fifty healthy controls(19 males,31 females;median age:23.0(20.0,27.0)years)were also enrolled.The BD patients were divided into two groups based on the presence of OCS:53 with OCS(OCS group)and 37 without OCS(NOCS group).Resting-state structural and functional MRI data were collected for all participants to construct gray matter structural and functional networks.Graph therory analysis was aapplied to calculate network topological metrics such as small-world properties.The structural and functional network topological properties were compared among the BD-OCS,BD-nOCS,and control groups.Partial correlation analysis was conducted to examine the association between network topological metrics with significant group differences and Yale-Brown Obsessive-Compulsive Scale(Y-BOCS)scores.Support vector machines(SVM)were used with these metrics as classificationfeaturevalues toimproveediagnostic accuracy through pairwise group classification.Results Structural network analysis of gray matter:compared to HC group,both OCS group and NOCS group showed increasedshortesttpathlengthand standardized characteristic path length(shortest path length:0.78 and 0.80 vs.0.69;normalized characteristic path length:0.48 and 0.49 vs.0.43),and decreased global efficiency(0.21 and 0.21 vs.0.24)compared to the HC group(permutation test,all P<0.05).Compared to NOCS and HC groups,the OCS group showed increased nodal centrality and betweenness centrality in the right rolandic operculum and left superior occipital gyrus(permutation test,all P<0.05).Functional network analysis of gray matter:compared to the NOCS group,the OCS group showed increased node efficiency and decreased betweenness centrality in the cerebellum(t=2.15,-3.04;all P<0.05);compared to HC groups,the OCS group showed decreased betweenness centrality in the cerebellum and left inferior frontal gyrus,along with increased node centrality and nodal efficiency in the right transverse temporal gyrus(t=-2.99,-3.61,3.06,3.10;all P<0.05).In the 0CS group,betweenness centrality in the left inferior frontal gyrus positively correlated with Y-BOCS scale obsessive thinking score(r=0.303,P=0.034).Nodal centrality and node efficiency of the right transverse temporal gyrus negatively correlated with Y-BOCS total score(r=-0.301,-0.311)and Y-BOCS obsessional thinking scores(r=-0.385,-0.380)separately(all P<0.05).SVM classification:the combined network features achieved an area under the curve of 0.80 in distinguising OCS from NOCS patients.Conclusion BDOCS and BD-nOCS patients both exhibit consistent changes in gray matter structural network topology,with theOCSSgroup displaying more pronounced nodal topological abnormalities.Multi-network feature integration demostrates potential for diagnostic classfication.展开更多
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.展开更多
An adaptive multi-QoS routing algorithm called AMQRA is proposed for dynamic topology networks, such as satellite networks and Ad-hoc networks. The AMQRA is a distributed and mobile-agents-based routing algorithm, whi...An adaptive multi-QoS routing algorithm called AMQRA is proposed for dynamic topology networks, such as satellite networks and Ad-hoc networks. The AMQRA is a distributed and mobile-agents-based routing algorithm, which combines ant quantity system （AQS） with ant colony optimization （ACO） that is used in AntNet routing algorithm. In dynamic topology networks, the AMQRA achieves timely optimization for concave metric QoS constraint and fast convergence. The proposed routing algorithm is simulated in Iridium satellite constellation on OPNET. The results show that AMQRA not only outperforms the AntNet in convergence rate in dynamic topology networks but also can optimize concave metric QoS constraint and reasonably allot bandwidth to the load to avoid networks congestion.展开更多
In this paper, we study the containment control problem for nonlinear second-order systems with unknown parameters and multiple stationary/dynamic leaders. The topologies that characterize the interaction among the le...In this paper, we study the containment control problem for nonlinear second-order systems with unknown parameters and multiple stationary/dynamic leaders. The topologies that characterize the interaction among the leaders and the followers are directed graphs. Necessary and sufficient criteria which guarantee the control objectives are established for both stationary leaders(regulation case) and dynamic leaders(dynamic tracking case) based protocols. The final states of all the followers are exclusively determined by the initial values of the leaders and the topology structures. In the regulation case, all the followers converge into the convex hull spanned by the leaders,while in the dynamic tracking case, not only the positions of the followers converge into the convex hull but also the velocities of the followers converge into the velocity convex hull of the leaders.Finally, all the theoretical results are illustrated by numerical simulations.展开更多
This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on th...This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.展开更多
All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend or/network topologies are tasks o...All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend or/network topologies are tasks of both great theoretical importance and broad practical significance. In this paper we study the oscillatory behaviors of excitable complex networks （ECNs） and find some interesting dynamic behaviors of ECNs in oscillatory probability, the multiplicity of oscillatory attractors, period distribution, and different types of oscillatory patterns （e.g., periodic, quasiperiodic, and chaotic）. In these aspects, we further explore strikingly sharp differences among network dynamics induced by different topologies （random or scale-free topologies） and different interaction structures （symmetric or asymmetric couplings）. The mechanisms behind these differences are explained physically.展开更多
Bitcoin has made an increasing impact on the world's economy and financial order,which attracted extensive attention of researchers and regulators from all over the world.Most previous studies had focused more on ...Bitcoin has made an increasing impact on the world's economy and financial order,which attracted extensive attention of researchers and regulators from all over the world.Most previous studies had focused more on the transaction layer,but less on the network layer.In this paper,we developed BNS(Bitcoin Network Sniffer),which could find and connect nodes in the Bitcoin network,and made a measurement in detail.We collected nearly 4.1 million nodes in 1.5 hours and identified 9,515 reachable nodes.We counted the reachable nodes'properties such as:service type,port number,client version and geographic distribution.In addition,we analyzed the stability of the reachable nodes in depth and found nearly 60%kept stable during 15 days.Finally,we proposed a new approach to infer the Bitcoin network topology by analyzing the Neighbor Addresses of Adjacent Nodes and their timestamps,which had an accuracy over 80%.展开更多
The expectations for sensor networks are growing. The performance of wireless sensor networks （WSNs） is greatly influenced by their network topology. In this paper, we consider four patterned topologies that best su...The expectations for sensor networks are growing. The performance of wireless sensor networks （WSNs） is greatly influenced by their network topology. In this paper, we consider four patterned topologies that best support connectivity among these deployed sensor nodes in two-tiered WSNs. The theoretical and simulation results show that the triangle-based topology has smaller cell number, shorter maximum hop length, less total energy consumption, and better performance than other topologies. The analysis carried out in this paper could provide the guidelines for network deployment and protocol design in the future applications.展开更多
As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signal...As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signals has become very important. The self-organizing feature map(SOFM) is an excellent artificial neural network, which has huge advantages in intelligent classification of complex data. However, the de-interleaving process based on SOFM is faced with the problems that the initialization of the map size relies on prior information and the network topology cannot be adaptively adjusted. In this paper, an SOFM with self-adaptive network topology(SANT-SOFM) algorithm is proposed to solve the above problems. The SANT-SOFM algorithm first proposes an adaptive proliferation algorithm to adjust the map size, so that the initialization of the map size is no longer dependent on prior information but is gradually adjusted with the input data. Then,structural optimization algorithms are proposed to gradually optimize the topology of the SOFM network in the iterative process,constructing an optimal SANT. Finally, the optimized SOFM network is used for de-interleaving radar signals. Simulation results show that SANT-SOFM could get excellent performance in complex EW environments and the probability of getting the optimal map size is over 95% in the absence of priori information.展开更多
A photo-controlled approach is developed to regulate the interpenetrating polymer network(IPN)topology by varying the connecting structure between the first and second networks.The approach is based on multifunctional...A photo-controlled approach is developed to regulate the interpenetrating polymer network(IPN)topology by varying the connecting structure between the first and second networks.The approach is based on multifunctional inimer(Vinyl-o NB-Br)possessing three moieties,i.e.,an acrylate-based double bond for incorporation within a polymer network,a Br group for grafting polymerization to get connectIPN(c-IPN),and an o-nitrobenzyl spacer for photocleaving to convert the c-IPN to disconnected-IPN(d-IPN)with UV light irradiation.Such design allows for finely controlling the connection degree between two networks.A systematic study on the mechanical property of a series of samples with different connection degrees thus can be conducted.The results reveal that decreasing the connecting degree between two networks of IPN made a negligible contribution to materials'mechanical properties.展开更多
The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation.While providing many insights into brain function...The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation.While providing many insights into brain functions,the block design adds more manipulation in functional network analysis that may reduce the purity of the blood oxygenation level-dependent signal.Recent studies utilized one single long run for task trials of the same condition,the so-called continuous design,to investigate functional connectivity based on task functional magnetic resonance imaging.Continuous brain activities associated with the single-task condition can be directly utilized for task-related functional connectivity assessment,which has been examined for working memory,sensory,motor,and semantic task experiments in previous research.But it remains unclear how the block and continuous design influence the assessment of task-related functional connectivity networks.This study aimed to disentangle the separable effects of block/continuous design and working memory load on task-related functional connectivity networks,by using repeated-measures analysis of variance.Across 50 young healthy adults,behavioral results of accuracy and reaction time showed a significant main effect of design as well as interaction between design and load.Imaging results revealed that the cingulo-opercular,fronto-parietal,and default model networks were associated with not only task activation,but significant main effects of design and load as well as their interaction on intra-and inter-network functional connectivity and global network topology.Moreover,a significant behavior-brain association was identified for the continuous design.This work has extended the evidence that continuous design can be used to study task-related functional connectivity and subtle brain-behavioral relationships.展开更多
1 Introduction The history of data centers can be traced back to the 1960s. Early data centers were deployed on main- frames that were time-shared by users via remote terminals. The boom in data centers came duringthe...1 Introduction The history of data centers can be traced back to the 1960s. Early data centers were deployed on main- frames that were time-shared by users via remote terminals. The boom in data centers came duringthe internet era. Many companies started building large inter- net-connected facililies,展开更多
Smart home technology provides consumers with network connectivity,automation or enhanced services for home devices.With the Internet of Things era,a vast data flow makes business platforms have to own the same comput...Smart home technology provides consumers with network connectivity,automation or enhanced services for home devices.With the Internet of Things era,a vast data flow makes business platforms have to own the same computing power to match their business services.It achieves computing power through implementing big data algorithms deployed in the cloud data center.However,because of the far long geographical distance between the client and the data center or the massive data capacity gap,potentially high latency and high packet loss will reduce the usability of smart home systems if service providers deploy all services in the cloud data center.Edge computing and fog computing can significantly improve the utilization of network resources and reconstruct the network architecture for the user’s home.This article enables a fog resource-based resource allocation management technology.It provides a method that can more reasonably allocate network resources through a virtualized middle-tier method to ensure low response time and configure Quality of Service to ensure the use of delay-sensitive critical applications to improve the reliability of smart home communication system.Besides,the proposed method has is tested and verified by adjusting the variables of the network environment.We realize the optimization of resource allocation of client network without changing the hardware of client.展开更多
A molecular dynamics simulation method is presented and used in the study of the formation of polymer networks. We study the formation of networks representing the methylene repeating units as united atoms. The networ...A molecular dynamics simulation method is presented and used in the study of the formation of polymer networks. We study the formation of networks representing the methylene repeating units as united atoms. The network formation is accomplished by cross-linking polymer chains with dedicated functional end groups. The simulations reveal that during the cross-linking process, initially branched molecules are formed before the gel point; approaching the gel point, larger branched entities are formed through integration of smaller branched molecules, and at the gel point a network spanning the simulation box is obtained; beyond the gel point the network continues to grow through the addition of the remaining molecules of the sol phase onto the gel （the network）; the final completion of the reaction occurs by intra-network connection of dangling ends onto unsaturated cross-linkers. The conformational properties of the strands in the undeformed network are found to be very similar with the conformational properties of the chains before cross-linking. The uniaxial deformation of the formed networks is investigated and the modulus determined from the stress-strain curves shows reciprocal scaling with the precursor chain length for networks formed from sufficiently large precursor chains （N≥ 20）.展开更多
基金sponsored by the National Natural Science Foundation of China(21905221,21805221)the Suzhou Technological innovation of key industries-research and development of key technologies(SGC2021118)。
文摘Free-standing silicon anodes with high proportion of active materials have aroused great attention;however,the mechanical stability and electrochemical performance are severely suppressed.Herein,to resolve the appeal issues,a free-standing anode with a"corrugated paper"shape on micro-scale and a topological crosslinking network on the submicron and nano-scale is designed.Essentially,an integrated three-dimensional electrode structure is constructed based on robust carbon nanotubes network with firmly anchored SiNPs via forming interlocking junctions.In which,the hierarchical interlocking structure is achieved by directional induction of the binder,which ensures well integration during cycling so that significantly enhances mechanical stability as well as electronic and ionic conductivity of electrodes.Benefiting from it,this anode exhibits outsta nding performance under harsh service conditions including high Si loading,ultrahigh areal capacity(33.2 mA h cm^(-2)),and high/low temperatures(-15-60℃),which significantly extends its practical prospect.Furthermore,the optimization mechanism of this electrode is explored to verify the crack-healing and structure-integration maintaining along cycling via a unique self-stabilization process.Thus,from both the fundamental and engineering views,this strategy offers a promising path to produce high-performance free-standing electrodes for flexible device applications especially facing volume effect challenges.
基金supported by the National Natural Science Foundation of China under Grant U21A20449in part by Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2。
文摘Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed Vehicular Communication Network(VCN)topologies.However,when the network is under attack,the communication delay may be much higher,and the stability of the system may not be guaranteed.This paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies(DADNT).The main idea is that for various communication delays,in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error,the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle spacing.To this end,a multi-objective optimization problem is formulated,and a 3-step Divide-And-Conquer sub-optimal solution(3DAC)is proposed.Simulation results show that with 3DAC,the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%,10%,and 14%,respectively,compared with the traditional CACC with fixed one-vehicle,two-vehicle,and three-vehicle look-ahead network topologies,thereby improving the traffic efficiency.
基金the National Key Research and Development Program of China,No.2021ZD0202000the National Natural Science Foundation of China,No.82101612 and No.82471570+1 种基金the Natural Science Foundation of Hunan Province,China,No.2022JJ40692the Science and Technology Innovation Program of Hunan Province,No.2021RC2040 and No.2024RC3056.
文摘BACKGROUND Rumination is a critical psychological factor contributing to the relapse of major depressive episodes(MDEs)and a core residual symptom in remitted MDEs.Investigating its neural correlations is essential for developing strategies to prevent MDE relapse.Despite its clinical importance,the brain network mechanisms underlying rumination in remitted MDE patients have yet to be fully elucidated.AIM To investigate the brain network mechanism underlying rumination in patients with remitted MDEs using functional magnetic resonance imaging(fMRI).METHODS We conducted an fMRI-based rumination-distraction task to induce rumination and distraction states in 51 patients with remitted MDEs.Functional connectivity(FC)was analyzed using the network-based statistic(NBS)approach,and eight topological metrics were calculated to compare the network topological properties between the two states.Correlation analyses were further performed to identify the relationships between individual rumination levels and the significantly altered brain network metrics.RESULTS The NBS analysis revealed that the altered FCs between the rumination and distraction states were located primarily in the frontoparietal,default mode,and cerebellar networks.No significant correlation was detected between these altered FCs and individual rumination levels.Among the eight topological metrics,the clustering coefficient,shortest path length,and local efficiency were significantly lower during rumination and positively correlated with individual rumination levels.In contrast,global efficiency was greater in the rumination state than in the distraction state and was negatively correlated with individual rumination levels.CONCLUSION Our work revealed the altered FC and topological properties during rumination in remitted MDE patients,offering valuable insights into the neural mechanisms of rumination from a brain network perspective.
基金supported by the National Natural Science Foundation of China(Grants Nos.51374213,51674251&51727807)the State Key Research Development Program of China(Grant No.2016YFC0600705)+2 种基金the National Natural Science Fund for Distinguished Young Scholars(Grant No.51125017)the Fund for Creative Research and Development Group Program of Jiangsu Province(2014-27)the Priority Academic Program Development of Jiangsu Higher Education Institutions(Grant No.PAPD-2014)
文摘The understanding and prediction of preferential fluid flow in porous media have attracted considerable attention in various engineering fields because of the implications of such flows in leading to a non-equilibrium fluid flow in the subsurface. In this study, a novel algorithm is proposed to predict preferential flow paths based on the topologically equivalent network of a porous structure and the flow resistance of flow paths. The equivalent flow network was constructed using Poiseuille's law and the maximal inscribed sphere algorithm. The flow resistance of each path was then determined based on Darcy's law. It was determined that fluid tends to follow paths with lower flow resistance. A computer program was developed and applied to an actual porous structure. To validate the algorithm and program, we tested and recorded two-dimensional(2 D) water flow using an ablated Perspex sheet featuring the same porous structure investigated using the analytical calculations. The results show that the measured preferential flow paths are consistent with the predictions.
基金National Science Fund for Distinguished Young Scholars,Grant/Award Number:51825303National Natural Science Foundation of China,Grant/Award Numbers:52130305,52073097,51903085,52003024。
文摘As a kind of bio-derived feedstock,vegetable oil(VO)shows great potential to replace petroleum-based monomers to develop sustainable polymer materials because of its easy availability,low cost,bio-renewable,and environmentally friendly nature.However,due to the high cross-linking density and amorphous nature,directly cured VOs generally tend to be brittle and weak.To date,it is still difficult to adopt VOs and their derivatives as structural materials to prepare high-performance elastomers.To address this important issue,amulti-scale topology design strategy was proposed in this work.First,topology regulation and functionalization of VO-based networks were realized by managing functional groups proportion during the bulk polymerization of epoxidized soybean oil with dimer fatty acids.Furthermore,a second polymer(SN)network was introduced into the VO-based network as a protective layer via interfacial cross-links.The generated VO-based elastomers(VOEs)exhibit unprecedented comprehensive properties(VO content≥70 wt.%,T_(g)as low as−24.4℃,toughness up to 6.8 MJ/m^(3)).Besides,the VOEs also exhibit excellent reprocessability and self-healing capability.Overall,this work developed a novel kind of VOEs with significant comprehensive advantages and provided important inspiration for the preparation of high-performance elastomers throughmulti-scale topology regulation.
基金Project supported by the Fundamental Research Foundations for the Central Universities (Grant No.2009B30514)
文摘Arbitrary topological curve network has no restriction in topology structure,so it has more powerful representing ability in defining complex surfaces.A complex surface modeling system is presented based on arbitrary topological curve network and the improved combined subdivision method,its functions including creating and editing curve network,and generating and modifying curve network＇s interpolated surface.This modeling system can be used to the process of products＇concept design,and its applications is also significant to the development of subdivision method.
文摘Objective Using graph theory analysis,this study compares the topological and node attributes of the brain network to explore the differences in gray matter structural and functional network topological properties between bipolar depression(BD)patients with and without obsessive-compulsive symptoms(OCS).Methods A total of 90 BD patients(27 males,63 females;median age 19.0(22.0,25.0)years)were recruited from the psychiatric outpatient and inpatient departments of the First Affiliated Hospital of Jinan University between March 2018 and December 2022.Fifty healthy controls(19 males,31 females;median age:23.0(20.0,27.0)years)were also enrolled.The BD patients were divided into two groups based on the presence of OCS:53 with OCS(OCS group)and 37 without OCS(NOCS group).Resting-state structural and functional MRI data were collected for all participants to construct gray matter structural and functional networks.Graph therory analysis was aapplied to calculate network topological metrics such as small-world properties.The structural and functional network topological properties were compared among the BD-OCS,BD-nOCS,and control groups.Partial correlation analysis was conducted to examine the association between network topological metrics with significant group differences and Yale-Brown Obsessive-Compulsive Scale(Y-BOCS)scores.Support vector machines(SVM)were used with these metrics as classificationfeaturevalues toimproveediagnostic accuracy through pairwise group classification.Results Structural network analysis of gray matter:compared to HC group,both OCS group and NOCS group showed increasedshortesttpathlengthand standardized characteristic path length(shortest path length:0.78 and 0.80 vs.0.69;normalized characteristic path length:0.48 and 0.49 vs.0.43),and decreased global efficiency(0.21 and 0.21 vs.0.24)compared to the HC group(permutation test,all P<0.05).Compared to NOCS and HC groups,the OCS group showed increased nodal centrality and betweenness centrality in the right rolandic operculum and left superior occipital gyrus(permutation test,all P<0.05).Functional network analysis of gray matter:compared to the NOCS group,the OCS group showed increased node efficiency and decreased betweenness centrality in the cerebellum(t=2.15,-3.04;all P<0.05);compared to HC groups,the OCS group showed decreased betweenness centrality in the cerebellum and left inferior frontal gyrus,along with increased node centrality and nodal efficiency in the right transverse temporal gyrus(t=-2.99,-3.61,3.06,3.10;all P<0.05).In the 0CS group,betweenness centrality in the left inferior frontal gyrus positively correlated with Y-BOCS scale obsessive thinking score(r=0.303,P=0.034).Nodal centrality and node efficiency of the right transverse temporal gyrus negatively correlated with Y-BOCS total score(r=-0.301,-0.311)and Y-BOCS obsessional thinking scores(r=-0.385,-0.380)separately(all P<0.05).SVM classification:the combined network features achieved an area under the curve of 0.80 in distinguising OCS from NOCS patients.Conclusion BDOCS and BD-nOCS patients both exhibit consistent changes in gray matter structural network topology,with theOCSSgroup displaying more pronounced nodal topological abnormalities.Multi-network feature integration demostrates potential for diagnostic classfication.
文摘Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
基金the National Natural Science Foundation of China (60532030)
文摘An adaptive multi-QoS routing algorithm called AMQRA is proposed for dynamic topology networks, such as satellite networks and Ad-hoc networks. The AMQRA is a distributed and mobile-agents-based routing algorithm, which combines ant quantity system （AQS） with ant colony optimization （ACO） that is used in AntNet routing algorithm. In dynamic topology networks, the AMQRA achieves timely optimization for concave metric QoS constraint and fast convergence. The proposed routing algorithm is simulated in Iridium satellite constellation on OPNET. The results show that AMQRA not only outperforms the AntNet in convergence rate in dynamic topology networks but also can optimize concave metric QoS constraint and reasonably allot bandwidth to the load to avoid networks congestion.
基金supported by the National Natural Science Foundation of China(61203354)
文摘In this paper, we study the containment control problem for nonlinear second-order systems with unknown parameters and multiple stationary/dynamic leaders. The topologies that characterize the interaction among the leaders and the followers are directed graphs. Necessary and sufficient criteria which guarantee the control objectives are established for both stationary leaders(regulation case) and dynamic leaders(dynamic tracking case) based protocols. The final states of all the followers are exclusively determined by the initial values of the leaders and the topology structures. In the regulation case, all the followers converge into the convex hull spanned by the leaders,while in the dynamic tracking case, not only the positions of the followers converge into the convex hull but also the velocities of the followers converge into the velocity convex hull of the leaders.Finally, all the theoretical results are illustrated by numerical simulations.
基金supported in part by the National Natural Science Foundation of China(61873056,61621004,61420106016)the Fundamental Research Funds for the Central Universities in China(N2004001,N2004002,N182608004)the Research Fund of State Key Laboratory of Synthetical Automation for Process Industries in China(2013ZCX01)。
文摘This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11174034,11135001,11205041,and 11305112)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20130282)
文摘All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend or/network topologies are tasks of both great theoretical importance and broad practical significance. In this paper we study the oscillatory behaviors of excitable complex networks （ECNs） and find some interesting dynamic behaviors of ECNs in oscillatory probability, the multiplicity of oscillatory attractors, period distribution, and different types of oscillatory patterns （e.g., periodic, quasiperiodic, and chaotic）. In these aspects, we further explore strikingly sharp differences among network dynamics induced by different topologies （random or scale-free topologies） and different interaction structures （symmetric or asymmetric couplings）. The mechanisms behind these differences are explained physically.
基金supported by National Key Research and Development Program of China (Grant No.2020YFB1006105)
文摘Bitcoin has made an increasing impact on the world's economy and financial order,which attracted extensive attention of researchers and regulators from all over the world.Most previous studies had focused more on the transaction layer,but less on the network layer.In this paper,we developed BNS(Bitcoin Network Sniffer),which could find and connect nodes in the Bitcoin network,and made a measurement in detail.We collected nearly 4.1 million nodes in 1.5 hours and identified 9,515 reachable nodes.We counted the reachable nodes'properties such as:service type,port number,client version and geographic distribution.In addition,we analyzed the stability of the reachable nodes in depth and found nearly 60%kept stable during 15 days.Finally,we proposed a new approach to infer the Bitcoin network topology by analyzing the Neighbor Addresses of Adjacent Nodes and their timestamps,which had an accuracy over 80%.
基金supported by the Shanghai Leading Academic Discipline Project (Grant Nos.S30108,08DZ2231100)the Science Foundation of Shanghai Municipal Education Commission (Grant No.09YZ33)the Science Foundation of Shanghai Municipal Commission of Science and Technology (Grant No.08220510900)
文摘The expectations for sensor networks are growing. The performance of wireless sensor networks （WSNs） is greatly influenced by their network topology. In this paper, we consider four patterned topologies that best support connectivity among these deployed sensor nodes in two-tiered WSNs. The theoretical and simulation results show that the triangle-based topology has smaller cell number, shorter maximum hop length, less total energy consumption, and better performance than other topologies. The analysis carried out in this paper could provide the guidelines for network deployment and protocol design in the future applications.
基金supported by the National Natural Science Foundation of China(61571043)the 111 Project of China(B14010)。
文摘As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signals has become very important. The self-organizing feature map(SOFM) is an excellent artificial neural network, which has huge advantages in intelligent classification of complex data. However, the de-interleaving process based on SOFM is faced with the problems that the initialization of the map size relies on prior information and the network topology cannot be adaptively adjusted. In this paper, an SOFM with self-adaptive network topology(SANT-SOFM) algorithm is proposed to solve the above problems. The SANT-SOFM algorithm first proposes an adaptive proliferation algorithm to adjust the map size, so that the initialization of the map size is no longer dependent on prior information but is gradually adjusted with the input data. Then,structural optimization algorithms are proposed to gradually optimize the topology of the SOFM network in the iterative process,constructing an optimal SANT. Finally, the optimized SOFM network is used for de-interleaving radar signals. Simulation results show that SANT-SOFM could get excellent performance in complex EW environments and the probability of getting the optimal map size is over 95% in the absence of priori information.
基金financially supported by the National Natural Science Foundation of China(No.51973023)Sichuan Science and Technology Program(No.2021JDRC0014)the Colleges and Universities Twenty Foundational Projects of Jinan City(No.2021GXRC068)。
文摘A photo-controlled approach is developed to regulate the interpenetrating polymer network(IPN)topology by varying the connecting structure between the first and second networks.The approach is based on multifunctional inimer(Vinyl-o NB-Br)possessing three moieties,i.e.,an acrylate-based double bond for incorporation within a polymer network,a Br group for grafting polymerization to get connectIPN(c-IPN),and an o-nitrobenzyl spacer for photocleaving to convert the c-IPN to disconnected-IPN(d-IPN)with UV light irradiation.Such design allows for finely controlling the connection degree between two networks.A systematic study on the mechanical property of a series of samples with different connection degrees thus can be conducted.The results reveal that decreasing the connecting degree between two networks of IPN made a negligible contribution to materials'mechanical properties.
基金supported by the National Natural Science Foundation of China(62071109 and 61871420)the Provincial Natural Science Foundation of Sichuan(2022NSFSC0504).
文摘The conventional approach to investigating functional connectivity in the block-designed study usually concatenates task blocks or employs residuals of task activation.While providing many insights into brain functions,the block design adds more manipulation in functional network analysis that may reduce the purity of the blood oxygenation level-dependent signal.Recent studies utilized one single long run for task trials of the same condition,the so-called continuous design,to investigate functional connectivity based on task functional magnetic resonance imaging.Continuous brain activities associated with the single-task condition can be directly utilized for task-related functional connectivity assessment,which has been examined for working memory,sensory,motor,and semantic task experiments in previous research.But it remains unclear how the block and continuous design influence the assessment of task-related functional connectivity networks.This study aimed to disentangle the separable effects of block/continuous design and working memory load on task-related functional connectivity networks,by using repeated-measures analysis of variance.Across 50 young healthy adults,behavioral results of accuracy and reaction time showed a significant main effect of design as well as interaction between design and load.Imaging results revealed that the cingulo-opercular,fronto-parietal,and default model networks were associated with not only task activation,but significant main effects of design and load as well as their interaction on intra-and inter-network functional connectivity and global network topology.Moreover,a significant behavior-brain association was identified for the continuous design.This work has extended the evidence that continuous design can be used to study task-related functional connectivity and subtle brain-behavioral relationships.
基金supported by the ZTE-BJTU Collaborative Research Program under Grant No. K11L00190the Fundamental Research Funds for the Central Universities under Grant No. K12JB00060
文摘1 Introduction The history of data centers can be traced back to the 1960s. Early data centers were deployed on main- frames that were time-shared by users via remote terminals. The boom in data centers came duringthe internet era. Many companies started building large inter- net-connected facililies,
基金supported by Soongsil University research funding.
文摘Smart home technology provides consumers with network connectivity,automation or enhanced services for home devices.With the Internet of Things era,a vast data flow makes business platforms have to own the same computing power to match their business services.It achieves computing power through implementing big data algorithms deployed in the cloud data center.However,because of the far long geographical distance between the client and the data center or the massive data capacity gap,potentially high latency and high packet loss will reduce the usability of smart home systems if service providers deploy all services in the cloud data center.Edge computing and fog computing can significantly improve the utilization of network resources and reconstruct the network architecture for the user’s home.This article enables a fog resource-based resource allocation management technology.It provides a method that can more reasonably allocate network resources through a virtualized middle-tier method to ensure low response time and configure Quality of Service to ensure the use of delay-sensitive critical applications to improve the reliability of smart home communication system.Besides,the proposed method has is tested and verified by adjusting the variables of the network environment.We realize the optimization of resource allocation of client network without changing the hardware of client.
基金the Katholieke Universiteit Leuven for a postdoctoral fellowship(GOA/10/04)
文摘A molecular dynamics simulation method is presented and used in the study of the formation of polymer networks. We study the formation of networks representing the methylene repeating units as united atoms. The network formation is accomplished by cross-linking polymer chains with dedicated functional end groups. The simulations reveal that during the cross-linking process, initially branched molecules are formed before the gel point; approaching the gel point, larger branched entities are formed through integration of smaller branched molecules, and at the gel point a network spanning the simulation box is obtained; beyond the gel point the network continues to grow through the addition of the remaining molecules of the sol phase onto the gel （the network）; the final completion of the reaction occurs by intra-network connection of dangling ends onto unsaturated cross-linkers. The conformational properties of the strands in the undeformed network are found to be very similar with the conformational properties of the chains before cross-linking. The uniaxial deformation of the formed networks is investigated and the modulus determined from the stress-strain curves shows reciprocal scaling with the precursor chain length for networks formed from sufficiently large precursor chains （N≥ 20）.
