Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefor...Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefore,this paper analyzes the spatial interaction between urban scale hierarchy and urban networks in China from 2019 to 2023,drawing on Baidu migration data and employing a spatial simultaneous equation model.The results reveal a significant positive spatial correlation between cities with higher hierarchy and those with greater network centrality.Within a static framework,we identify a positive interaction between urban scale hierarchy and urban network centrality,while their spatial cross-effects manifest as negative neighborhood interactions based on geographical distance and positive cross-scale interactions shaped by network connections.Within a dynamic framework,changes in urban scale hierarchy and urban networks are mutually reinforcing,thereby widening disparities within the urban hierarchy.Furthermore,an increase in a city’s network centrality had a dampening effect on the population growth of neighboring cities and network-connected cities.This study enhances understanding of the spatial organisation of urban systems and offers insights for coordinated regional development.展开更多
Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationall...Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.展开更多
Drug-drug interaction(DDI)refers to the interaction between two or more drugs in the body,altering their efficacy or pharmacokinetics.Fully considering and accurately predicting DDI has become an indispensable part of...Drug-drug interaction(DDI)refers to the interaction between two or more drugs in the body,altering their efficacy or pharmacokinetics.Fully considering and accurately predicting DDI has become an indispensable part of ensuring safe medication for patients.In recent years,many deep learning-based methods have been proposed to predict DDI.However,most existing computational models tend to oversimplify the fusion of drug structural and topological information,often relying on methods such as splicing or weighted summation,which fail to adequately capture the potential complementarity between structural and topological features.This loss of information may lead to models that do not fully leverage these features,thus limiting their performance in DDI prediction.To address these challenges,we propose a relation-aware cross adversarial network for predicting DDI,named RCAN-DDI,which combines a relationship-aware structure feature learning module and a topological feature learning module based on DDI networks to capture multimodal features of drugs.To explore the correlations and complementarities among different information sources,the cross-adversarial network is introduced to fully integrate features from various modalities,enhancing the predictive performance of the model.The experimental results demonstrate that the RCAN-DDI method outperforms other methods.Even in cases of labelled DDI scarcity,the method exhibits good robustness in the DDI prediction task.Furthermore,the effectiveness of the cross-adversarial module is validated through ablation experiments,demonstrating its superiority in learning multimodal complementary information.展开更多
Achieving a reduction in global greenhouse gas(GHG)emissions requires collaborative efforts from the international community;however,a comprehensive understanding of the spatiotemporal characteristics(i.e.,complex emi...Achieving a reduction in global greenhouse gas(GHG)emissions requires collaborative efforts from the international community;however,a comprehensive understanding of the spatiotemporal characteristics(i.e.,complex emission networks and driver patterns)and the mutual influence of gross domestic product(GDP)and GHG emissions remains limited at a global level in the 21st century,which is not conducive to forming a consensus in global climate change negotiations and formulating relevant policies.To fill these gaps,this study comprehensively analyzes the complex network and driver pattern of GHG emissions,as well as the corresponding mutual influence with GDP for 185 countries during 2000-2021,based on social network analysis,the logarithmic Divisia decomposition approach,and panel vector autoregression model at global and regional levels.The results indicate that significant heterogeneity and inequality exist in terms of GHG emissions among regions and countries in different geographical areas and economic income levels.Additionally,GDP per capita and GHG emission intensity are the largest positive and negative drivers,respectively,affecting the increase in global GHG emissions.Furthermore,key countries,such as Germany and Canada,that could serve as coordinating bridges to strengthen collaboration in the global emission network are identified.This study highlights the need to encourage key participants in the emission network and foster international cooperation in governance,energy technology,and economic investment to address climate change.展开更多
Under the dual background of global urbanization and climate change,challenges such as urban waterlogging,degradation of water environment,and fragmentation of biological habitats are intensifying.Advancing the transf...Under the dual background of global urbanization and climate change,challenges such as urban waterlogging,degradation of water environment,and fragmentation of biological habitats are intensifying.Advancing the transformation of blue-green infrastructure from a single function to multiple coordinated functions has emerged as a critical path for enhancing urban ecological resilience.The Catharijnesingel Canal Park in Utrecht,the Netherlands,serves as a case study to investigate the synergistic interaction of hydrological regulation and habitat network construction through the transformation of historical canals from grey to blue-green infrastructure.Initially,the implementation path of this“blue-green renaissance”is analyzed,followed by a detailed examination of the spatial realization patterns and mechanisms underlying hydrology and habitat synergy,focusing on river form remodeling,vegetation community alignment,and precise habitat design.The results indicate that the collaborative model of“hydrology and habitat”coupling not only enhances rain-flood resilience,biodiversity,and the quality of public spaces in urban areas but also serves as a reference model for high-density urban regeneration by integrating space,ecology,and efficiency.This model offers both a theoretical foundation and practical guidance for Chinese cities aiming to achieve functional integration and synergistic interaction in ecological restoration and the development of bluegreen spaces.展开更多
With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extract...With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis.To address these challenges,this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception(TGICP).Specifically,we utilize a Inter-sample Commonality Perception(ICP)module to extract common features from similar samples within the same modality,and use these common features to enhance the original features of each modality,thereby obtaining a richer and more complete multimodal sentiment representation.Subsequently,in the cross-modal interaction stage,we design a Text-Gated Interaction(TGI)module,which is text-driven.By calculating the mutual information difference between the text modality and nonverbal modalities,the TGI module dynamically adjusts the influence of emotional information from the text modality on nonverbal modalities.This helps to reduce modality information asymmetry while enabling full cross-modal interaction.Experimental results show that the proposed model achieves outstanding performance on both the CMU-MOSI and CMU-MOSEI baseline multimodal sentiment analysis datasets,validating its effectiveness in emotion recognition tasks.展开更多
Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local an...Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information.To comprehensively consider network information,we propose DHGT-DTI,a novel deep learning-based approach for DTI prediction.Specifically,we capture the local and global structural information of the network from both neighborhood and meta-path per-spectives.In the neighborhood perspective,we employ a heterogeneous graph neural network(HGNN),which extends Graph Sample and Aggregate(GraphSAGE)to handle diverse node and edge types,effectively learning local network structures.In the meta-path perspective,we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths,such as"drug-disease-drug",and use an attention mechanism to fuse information across multiple meta-paths.The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method.Furthermore,DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy.Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods.Additionally,case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.展开更多
BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated anti...BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use "omics" methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIM To establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODS We used microarrays, bioinformatics, protein-protein interaction(PPI) network,and sub-modules to investigate taurine-induced changes in gene expression in human HSCs(LX-2). Subsequently, all of the differentially expressed genes(DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTS A total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1(ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase(MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway,estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21,TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSION Taurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.展开更多
AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed...AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins.A network of protein interactions was constructed by searching the primary interactions of selected proteins.The constructed network was mathematically analyzed and its biological function was examined.In addition,the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them. RESULTS:The scale-free network showing the relationship between inflammation and carcinogenesis was constructed.Mathematical analysis showed hub and bottleneck proteins,and these proteins were mostly related to immune response.The network contained pathways and proteins related to H pylori infection,such as the JAK-STAT pathway triggered by interleukins.Activation of nuclear factor (NF)-κB,TLR4,and other proteins known to function as core proteins of immune response were also found. These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle,cell maintenance and proliferation,andtranscription regulators such as BRCA1,FOS,REL,and zinc finger proteins.The extension of nodes showed interactions of the immune proteins with cancer- related proteins.One extended network,the core network,a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION:Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins.The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.展开更多
Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug devel...Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.展开更多
With the advent of advanced sequencing technologies,non-coding RNAs(ncRNAs)are increasingly pivotal and play highly regulated roles in the modulation of diverse aspects of plant growth and stress response.This include...With the advent of advanced sequencing technologies,non-coding RNAs(ncRNAs)are increasingly pivotal and play highly regulated roles in the modulation of diverse aspects of plant growth and stress response.This includes a spectrum of ncRNA classes,ranging from small RNAs to long non-coding RNAs(lncRNAs).Notably,among these,lncRNAs emerge as significant and intricate components within the broader ncRNA regulatory networks.Here,we categorize ncRNAs based on their length and structure into small RNAs,medium-sized ncRNAs,lncRNAs,and circle RNAs.Furthermore,the review delves into the detailed biosynthesis and origin of these ncRNAs.Subsequently,we emphasize the diverse regulatory mechanisms employed by lncRNAs that are located at various gene regions of coding genes,embodying promoters,5’UTRs,introns,exons,and 3’UTR regions.Furthermore,we elucidate these regulatory modes through one or two concrete examples.Besides,lncRNAs have emerged as novel central components that participate in phase separation processes.Moreover,we illustrate the coordinated regulatory mechanisms among lncRNAs,miRNAs,and siRNAs with a particular emphasis on the central role of lncRNAs in serving as sponges,precursors,spliceosome,stabilization,scaffolds,or interaction factors to bridge interactions with other ncRNAs.The review also sheds light on the intriguing possibility that some ncRNAs may encode functional micropeptides.Therefore,the review underscores the emergent roles of ncRNAs as potent regulatory factors that significantly enrich the regulatory network governing plant growth,development,and responses to environmental stimuli.There are yet-to-be-discovered roles of ncRNAs waiting for us to explore.展开更多
The interactions between the moving dislocation within matrix channel and the interfacial misfit dislocation networks on the two-phase interfaces in Ni-based single crystal superalloys are studied carefully via atomic...The interactions between the moving dislocation within matrix channel and the interfacial misfit dislocation networks on the two-phase interfaces in Ni-based single crystal superalloys are studied carefully via atomic modeling, with special focus on the factors influ- encing the critical bowing stress of moving dislocations in the matrix channel. The results show that the moving matrix dislocation type and its position with respect to the interfacial misfit dislocation segments have considerable influences on the interactions. If the moving matrix dislocation is pure screw, it reacts with the interracial misfit dislocation segments toward dislocation linear energy reduction, which decreases the critical bowing stress of screw dislocation due to dislocation linear energy release during the dislocation reactions. If the moving matrix dislocation is of 60^-mixed type, it is obstructed by the interaction between the mixed matrix dislocations and the misfit interfacial dislocation segments. As a result, the critical bowing stress increases significantly because extra interactive energy needs to be overcome. These two different effects on the critical bowing stress become in- creasingly significant when the moving matrix dislocation is very close to the interracial misfit dislocation segments. In addition, the matrix channel width also has a significant influence on the critical bowing stress, i.e. the narrower the matrix channel is, the higher the critical bowing stress is. The classical Orowan formula is modified to predict these effects on the critical bowing stress of moving matrix dislocation, which is in good agreement with the computational results.展开更多
In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this...In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.展开更多
Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network ...Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.展开更多
Smoking is the primary cause of lung cancer and is linked to 85% of lung cancer cases.However,how lung cancer develops in patients with smoking history remains unclear.Systems approaches that combine human protein-pro...Smoking is the primary cause of lung cancer and is linked to 85% of lung cancer cases.However,how lung cancer develops in patients with smoking history remains unclear.Systems approaches that combine human protein-protein interaction (PPI) networks and gene expression data are superior to traditional methods.We performed these systems to determine the role that smoking plays in lung cancer development and used the support vector machine (SVM) model to predict PPIs.By defining expression variance (EV),we found 520 dynamic proteins (EV>0.4) using data from the Human Protein Reference Database and Gene Expression Omnibus Database,and built 7 dynamic PPI subnetworks of lung cancer in patients with smoking history.We also determined the primary functions of each subnetwork:signal transduction,apoptosis,and cell migration and adhesion for subnetwork A;cell-sustained angiogenesis for subnetwork B;apoptosis for subnetwork C;and,finally,signal transduction and cell replication and proliferation for subnetworks D-G.The probability distribution of the degree of dynamic protein and static protein differed,clearly showing that the dynamic proteins were not the core proteins which widely connected with their neighbor proteins.There were high correlations among the dynamic proteins,suggesting that the dynamic proteins tend to form specific dynamic modules.We also found that the dynamic proteins were only correlated with the expression of selected proteins but not all neighbor proteins when cancer occurred.展开更多
Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interactio...Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interaction patterns underlying human activities.Nevertheless,the inherent heterogeneity in multimodal migration big data has been ignored.This study conducts an in-depth comparison and quantitative analysis through a comprehensive lens of spatial association.Initially,the intercity interactive networks in China were constructed,utilizing migration data from Baidu and AutoNavi collected during the same time period.Subsequently,the characteristics and spatial structure similarities of the two types of intercity interactive networks were quantitatively assessed and analyzed from overall(network)and local(node)perspectives.Furthermore,the precision of these networks at the local scale is corroborated by constructing an intercity network from mobile phone(MP)data.Results indicate that the intercity interactive networks in China,as delineated by Baidu and AutoNavi migration flows,exhibit a high degree of structure equivalence.The correlation coefficient between these two networks is 0.874.Both networks exhibit a pronounced spatial polarization trend and hierarchical structure.This is evident in their distinct core and peripheral structures,as well as in the varying importance and influence of different nodes within the networks.Nevertheless,there are notable differences worthy of attention.Baidu intercity interactive network exhibits pronounced cross-regional effects,and its high-level interactions are characterized by a“rich-club”phenomenon.The AutoNavi intercity interactive network presents a more significant distance attenuation effect,and the high-level interactions display a gradient distribution pattern.Notably,there exists a substantial correlation between the AutoNavi and MP networks at the local scale,evidenced by a high correlation coefficient of 0.954.Furthermore,the“spatial dislocations”phenomenon was observed within the spatial structures at different levels,extracted from the Baidu and AutoNavi intercity networks.However,the measured results of network spatial structure similarity from three dimensions,namely,node location,node size,and local structure,indicate a relatively high similarity and consistency between the two networks.展开更多
In this paper, a new mechanism for the emergence of scale-free distribution is proposed. It is more realistic than the existing mechanism. Based on our mechanism, a model responsible for the scale-free distribution wi...In this paper, a new mechanism for the emergence of scale-free distribution is proposed. It is more realistic than the existing mechanism. Based on our mechanism, a model responsible for the scale-free distribution with an exponent in a range of 3-to-5 is given. Moreover, this model could also reproduce the exponential distribution that is discovered in some real networks. Finally, the analytical result of the model is given and the simulation shows the validity of our result,展开更多
With the growing application of intelligent robots in service,manufacturing,and medical fields,efficient and natural interaction between humans and robots has become key to improving collaboration efficiency and user ...With the growing application of intelligent robots in service,manufacturing,and medical fields,efficient and natural interaction between humans and robots has become key to improving collaboration efficiency and user experience.Gesture recognition,as an intuitive and contactless interaction method,can overcome the limitations of traditional interfaces and enable real-time control and feedback of robot movements and behaviors.This study first reviews mainstream gesture recognition algorithms and their application on different sensing platforms(RGB cameras,depth cameras,and inertial measurement units).It then proposes a gesture recognition method based on multimodal feature fusion and a lightweight deep neural network that balances recognition accuracy with computational efficiency.At system level,a modular human-robot interaction architecture is constructed,comprising perception,decision,and execution layers,and gesture commands are transmitted and mapped to robot actions in real time via the ROS communication protocol.Through multiple comparative experiments on public gesture datasets and a self-collected dataset,the proposed method’s superiority is validated in terms of accuracy,response latency,and system robustness,while user-experience tests assess the interface’s usability.The results provide a reliable technical foundation for robot collaboration and service in complex scenarios,offering broad prospects for practical application and deployment.展开更多
The interactions of a colored dynamical network play a great role in its dynamical behaviour and are denoted by outer and inner coupling matrices. In this paper, the outer and inner coupting matrices are assumed to be...The interactions of a colored dynamical network play a great role in its dynamical behaviour and are denoted by outer and inner coupling matrices. In this paper, the outer and inner coupting matrices are assumed to be unknown and need to be identified. A corresponding network estimator is designed for identifying the unknown interactions by adopting proper adaptive laws. Based on the Lyapunov function method and Barbalat＇s lemma, the obtained result is analytically proved. A colored network coupled with chaotic Lorenz, Chen, and Lii systems is considered as a numerical example to illustrate the effectiveness of the proposed method.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.42371222,41971167)Fundamental Scientific Research Funds of Central China Normal University(No.CCNU24ZZ120)。
文摘Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefore,this paper analyzes the spatial interaction between urban scale hierarchy and urban networks in China from 2019 to 2023,drawing on Baidu migration data and employing a spatial simultaneous equation model.The results reveal a significant positive spatial correlation between cities with higher hierarchy and those with greater network centrality.Within a static framework,we identify a positive interaction between urban scale hierarchy and urban network centrality,while their spatial cross-effects manifest as negative neighborhood interactions based on geographical distance and positive cross-scale interactions shaped by network connections.Within a dynamic framework,changes in urban scale hierarchy and urban networks are mutually reinforcing,thereby widening disparities within the urban hierarchy.Furthermore,an increase in a city’s network centrality had a dampening effect on the population growth of neighboring cities and network-connected cities.This study enhances understanding of the spatial organisation of urban systems and offers insights for coordinated regional development.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.
基金supported by the Natural Science Foundation of Shandong Province(Grant No.:ZR2023MF053)the National Natural Science Foundation of China(Grant No.:61902430).
文摘Drug-drug interaction(DDI)refers to the interaction between two or more drugs in the body,altering their efficacy or pharmacokinetics.Fully considering and accurately predicting DDI has become an indispensable part of ensuring safe medication for patients.In recent years,many deep learning-based methods have been proposed to predict DDI.However,most existing computational models tend to oversimplify the fusion of drug structural and topological information,often relying on methods such as splicing or weighted summation,which fail to adequately capture the potential complementarity between structural and topological features.This loss of information may lead to models that do not fully leverage these features,thus limiting their performance in DDI prediction.To address these challenges,we propose a relation-aware cross adversarial network for predicting DDI,named RCAN-DDI,which combines a relationship-aware structure feature learning module and a topological feature learning module based on DDI networks to capture multimodal features of drugs.To explore the correlations and complementarities among different information sources,the cross-adversarial network is introduced to fully integrate features from various modalities,enhancing the predictive performance of the model.The experimental results demonstrate that the RCAN-DDI method outperforms other methods.Even in cases of labelled DDI scarcity,the method exhibits good robustness in the DDI prediction task.Furthermore,the effectiveness of the cross-adversarial module is validated through ablation experiments,demonstrating its superiority in learning multimodal complementary information.
基金supported by the Humanities and Social Sciences Youth Foundation,Ministry of Education of China[Grant No.24YJC630248]Sichuan Office of Philosophy and Social Science,China[Grant No.SCJJ24ND299].
文摘Achieving a reduction in global greenhouse gas(GHG)emissions requires collaborative efforts from the international community;however,a comprehensive understanding of the spatiotemporal characteristics(i.e.,complex emission networks and driver patterns)and the mutual influence of gross domestic product(GDP)and GHG emissions remains limited at a global level in the 21st century,which is not conducive to forming a consensus in global climate change negotiations and formulating relevant policies.To fill these gaps,this study comprehensively analyzes the complex network and driver pattern of GHG emissions,as well as the corresponding mutual influence with GDP for 185 countries during 2000-2021,based on social network analysis,the logarithmic Divisia decomposition approach,and panel vector autoregression model at global and regional levels.The results indicate that significant heterogeneity and inequality exist in terms of GHG emissions among regions and countries in different geographical areas and economic income levels.Additionally,GDP per capita and GHG emission intensity are the largest positive and negative drivers,respectively,affecting the increase in global GHG emissions.Furthermore,key countries,such as Germany and Canada,that could serve as coordinating bridges to strengthen collaboration in the global emission network are identified.This study highlights the need to encourage key participants in the emission network and foster international cooperation in governance,energy technology,and economic investment to address climate change.
基金Sponsored by 2025 Postgraduate Teaching Reform Project of North China University of Technology。
文摘Under the dual background of global urbanization and climate change,challenges such as urban waterlogging,degradation of water environment,and fragmentation of biological habitats are intensifying.Advancing the transformation of blue-green infrastructure from a single function to multiple coordinated functions has emerged as a critical path for enhancing urban ecological resilience.The Catharijnesingel Canal Park in Utrecht,the Netherlands,serves as a case study to investigate the synergistic interaction of hydrological regulation and habitat network construction through the transformation of historical canals from grey to blue-green infrastructure.Initially,the implementation path of this“blue-green renaissance”is analyzed,followed by a detailed examination of the spatial realization patterns and mechanisms underlying hydrology and habitat synergy,focusing on river form remodeling,vegetation community alignment,and precise habitat design.The results indicate that the collaborative model of“hydrology and habitat”coupling not only enhances rain-flood resilience,biodiversity,and the quality of public spaces in urban areas but also serves as a reference model for high-density urban regeneration by integrating space,ecology,and efficiency.This model offers both a theoretical foundation and practical guidance for Chinese cities aiming to achieve functional integration and synergistic interaction in ecological restoration and the development of bluegreen spaces.
基金supported by the Natural Science Foundation of Henan under Grant 242300421220the Henan Provincial Science and Technology Research Project under Grants 252102211047 and 252102211062+3 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126.
文摘With the increasing importance of multimodal data in emotional expression on social media,mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches.However,the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis.To address these challenges,this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception(TGICP).Specifically,we utilize a Inter-sample Commonality Perception(ICP)module to extract common features from similar samples within the same modality,and use these common features to enhance the original features of each modality,thereby obtaining a richer and more complete multimodal sentiment representation.Subsequently,in the cross-modal interaction stage,we design a Text-Gated Interaction(TGI)module,which is text-driven.By calculating the mutual information difference between the text modality and nonverbal modalities,the TGI module dynamically adjusts the influence of emotional information from the text modality on nonverbal modalities.This helps to reduce modality information asymmetry while enabling full cross-modal interaction.Experimental results show that the proposed model achieves outstanding performance on both the CMU-MOSI and CMU-MOSEI baseline multimodal sentiment analysis datasets,validating its effectiveness in emotion recognition tasks.
基金the National Natural Science Foundation of China(Grant Nos.:62272288,U22A2041)Fundamental Research Funds for the Central Universities,Shaanxi Normal University(Grant No.:GK202302006)the Scientific Research Fund of Hunan Provincial Education Department of China(Grant No.:22B0097).
文摘Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information.To comprehensively consider network information,we propose DHGT-DTI,a novel deep learning-based approach for DTI prediction.Specifically,we capture the local and global structural information of the network from both neighborhood and meta-path per-spectives.In the neighborhood perspective,we employ a heterogeneous graph neural network(HGNN),which extends Graph Sample and Aggregate(GraphSAGE)to handle diverse node and edge types,effectively learning local network structures.In the meta-path perspective,we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths,such as"drug-disease-drug",and use an attention mechanism to fuse information across multiple meta-paths.The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method.Furthermore,DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy.Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods.Additionally,case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.
基金the National Natural Science Foundation of China,No.81360595 and No.81860790Guangxi Natural Science Foundation Program,No.KJT13066+2 种基金the Bagui Scholars Foundation Program of Guangxithe Special-term Experts Foundation Program of Guangxithe Project of Guangxi Young Teacher Fundamental Ability Promotion,No.2017KY0298
文摘BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use "omics" methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIM To establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODS We used microarrays, bioinformatics, protein-protein interaction(PPI) network,and sub-modules to investigate taurine-induced changes in gene expression in human HSCs(LX-2). Subsequently, all of the differentially expressed genes(DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTS A total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1(ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase(MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway,estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21,TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSION Taurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.
文摘AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins.A network of protein interactions was constructed by searching the primary interactions of selected proteins.The constructed network was mathematically analyzed and its biological function was examined.In addition,the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them. RESULTS:The scale-free network showing the relationship between inflammation and carcinogenesis was constructed.Mathematical analysis showed hub and bottleneck proteins,and these proteins were mostly related to immune response.The network contained pathways and proteins related to H pylori infection,such as the JAK-STAT pathway triggered by interleukins.Activation of nuclear factor (NF)-κB,TLR4,and other proteins known to function as core proteins of immune response were also found. These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle,cell maintenance and proliferation,andtranscription regulators such as BRCA1,FOS,REL,and zinc finger proteins.The extension of nodes showed interactions of the immune proteins with cancer- related proteins.One extended network,the core network,a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION:Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins.The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.
基金supported by grants from the National Natural Science Foundation of China(Grant No.:T2341008)Intelligent and Precise Research on TCM for Spleen and Stomach Diseases(20233930063).
文摘Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.
基金This review was supported by the National Key Research and Development Program of China(Grant No.2022YFD2100101)the National Natural Science Foundation of China to G.Z.(Grant No.32302623)+1 种基金the Joint NSFC-ISF Research Program(Grant No.32061143022)the National Natural Sciences Foundation of China(Grant No.32172639).
文摘With the advent of advanced sequencing technologies,non-coding RNAs(ncRNAs)are increasingly pivotal and play highly regulated roles in the modulation of diverse aspects of plant growth and stress response.This includes a spectrum of ncRNA classes,ranging from small RNAs to long non-coding RNAs(lncRNAs).Notably,among these,lncRNAs emerge as significant and intricate components within the broader ncRNA regulatory networks.Here,we categorize ncRNAs based on their length and structure into small RNAs,medium-sized ncRNAs,lncRNAs,and circle RNAs.Furthermore,the review delves into the detailed biosynthesis and origin of these ncRNAs.Subsequently,we emphasize the diverse regulatory mechanisms employed by lncRNAs that are located at various gene regions of coding genes,embodying promoters,5’UTRs,introns,exons,and 3’UTR regions.Furthermore,we elucidate these regulatory modes through one or two concrete examples.Besides,lncRNAs have emerged as novel central components that participate in phase separation processes.Moreover,we illustrate the coordinated regulatory mechanisms among lncRNAs,miRNAs,and siRNAs with a particular emphasis on the central role of lncRNAs in serving as sponges,precursors,spliceosome,stabilization,scaffolds,or interaction factors to bridge interactions with other ncRNAs.The review also sheds light on the intriguing possibility that some ncRNAs may encode functional micropeptides.Therefore,the review underscores the emergent roles of ncRNAs as potent regulatory factors that significantly enrich the regulatory network governing plant growth,development,and responses to environmental stimuli.There are yet-to-be-discovered roles of ncRNAs waiting for us to explore.
基金supported by the financial support from NSFC (Grant 11472113 and Grant 11272130)
文摘The interactions between the moving dislocation within matrix channel and the interfacial misfit dislocation networks on the two-phase interfaces in Ni-based single crystal superalloys are studied carefully via atomic modeling, with special focus on the factors influ- encing the critical bowing stress of moving dislocations in the matrix channel. The results show that the moving matrix dislocation type and its position with respect to the interfacial misfit dislocation segments have considerable influences on the interactions. If the moving matrix dislocation is pure screw, it reacts with the interracial misfit dislocation segments toward dislocation linear energy reduction, which decreases the critical bowing stress of screw dislocation due to dislocation linear energy release during the dislocation reactions. If the moving matrix dislocation is of 60^-mixed type, it is obstructed by the interaction between the mixed matrix dislocations and the misfit interfacial dislocation segments. As a result, the critical bowing stress increases significantly because extra interactive energy needs to be overcome. These two different effects on the critical bowing stress become in- creasingly significant when the moving matrix dislocation is very close to the interracial misfit dislocation segments. In addition, the matrix channel width also has a significant influence on the critical bowing stress, i.e. the narrower the matrix channel is, the higher the critical bowing stress is. The classical Orowan formula is modified to predict these effects on the critical bowing stress of moving matrix dislocation, which is in good agreement with the computational results.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72031009 and 61473338)。
文摘In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.
基金National Natural Science Foundation of China(No.42171448)Key Laboratory of National Geographic Census and Monitoring,Ministry of Nature Resources(No.2020NGCMZD03)。
文摘Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.
基金supported by grants from the National Natural Science Foundation of China (No. 91130009)Science and Technology Planning Project of Guangdong Province of China (No. 2003A3080503)
文摘Smoking is the primary cause of lung cancer and is linked to 85% of lung cancer cases.However,how lung cancer develops in patients with smoking history remains unclear.Systems approaches that combine human protein-protein interaction (PPI) networks and gene expression data are superior to traditional methods.We performed these systems to determine the role that smoking plays in lung cancer development and used the support vector machine (SVM) model to predict PPIs.By defining expression variance (EV),we found 520 dynamic proteins (EV>0.4) using data from the Human Protein Reference Database and Gene Expression Omnibus Database,and built 7 dynamic PPI subnetworks of lung cancer in patients with smoking history.We also determined the primary functions of each subnetwork:signal transduction,apoptosis,and cell migration and adhesion for subnetwork A;cell-sustained angiogenesis for subnetwork B;apoptosis for subnetwork C;and,finally,signal transduction and cell replication and proliferation for subnetworks D-G.The probability distribution of the degree of dynamic protein and static protein differed,clearly showing that the dynamic proteins were not the core proteins which widely connected with their neighbor proteins.There were high correlations among the dynamic proteins,suggesting that the dynamic proteins tend to form specific dynamic modules.We also found that the dynamic proteins were only correlated with the expression of selected proteins but not all neighbor proteins when cancer occurred.
基金National Natural Science Foundation of China,No.42361040。
文摘Population migration data derived from location-based services has often been used to delineate population flows between cities or construct intercity relationship networks to reveal and explore the complex interaction patterns underlying human activities.Nevertheless,the inherent heterogeneity in multimodal migration big data has been ignored.This study conducts an in-depth comparison and quantitative analysis through a comprehensive lens of spatial association.Initially,the intercity interactive networks in China were constructed,utilizing migration data from Baidu and AutoNavi collected during the same time period.Subsequently,the characteristics and spatial structure similarities of the two types of intercity interactive networks were quantitatively assessed and analyzed from overall(network)and local(node)perspectives.Furthermore,the precision of these networks at the local scale is corroborated by constructing an intercity network from mobile phone(MP)data.Results indicate that the intercity interactive networks in China,as delineated by Baidu and AutoNavi migration flows,exhibit a high degree of structure equivalence.The correlation coefficient between these two networks is 0.874.Both networks exhibit a pronounced spatial polarization trend and hierarchical structure.This is evident in their distinct core and peripheral structures,as well as in the varying importance and influence of different nodes within the networks.Nevertheless,there are notable differences worthy of attention.Baidu intercity interactive network exhibits pronounced cross-regional effects,and its high-level interactions are characterized by a“rich-club”phenomenon.The AutoNavi intercity interactive network presents a more significant distance attenuation effect,and the high-level interactions display a gradient distribution pattern.Notably,there exists a substantial correlation between the AutoNavi and MP networks at the local scale,evidenced by a high correlation coefficient of 0.954.Furthermore,the“spatial dislocations”phenomenon was observed within the spatial structures at different levels,extracted from the Baidu and AutoNavi intercity networks.However,the measured results of network spatial structure similarity from three dimensions,namely,node location,node size,and local structure,indicate a relatively high similarity and consistency between the two networks.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 60374037 and 60574036）, the Program for New Century Excellent Talents of High Education of China（Grant No NCET 2005-290）, The Special Research Fund for the Doctoral Program of High Education of China （Grant No 20050055013）.Acknowledgments The authors would like to thank Réka Albert for useful discussion and are grateful to the anonymous referees for their valuable suggestions and comments, which have made this paper improved.
文摘In this paper, a new mechanism for the emergence of scale-free distribution is proposed. It is more realistic than the existing mechanism. Based on our mechanism, a model responsible for the scale-free distribution with an exponent in a range of 3-to-5 is given. Moreover, this model could also reproduce the exponential distribution that is discovered in some real networks. Finally, the analytical result of the model is given and the simulation shows the validity of our result,
文摘With the growing application of intelligent robots in service,manufacturing,and medical fields,efficient and natural interaction between humans and robots has become key to improving collaboration efficiency and user experience.Gesture recognition,as an intuitive and contactless interaction method,can overcome the limitations of traditional interfaces and enable real-time control and feedback of robot movements and behaviors.This study first reviews mainstream gesture recognition algorithms and their application on different sensing platforms(RGB cameras,depth cameras,and inertial measurement units).It then proposes a gesture recognition method based on multimodal feature fusion and a lightweight deep neural network that balances recognition accuracy with computational efficiency.At system level,a modular human-robot interaction architecture is constructed,comprising perception,decision,and execution layers,and gesture commands are transmitted and mapped to robot actions in real time via the ROS communication protocol.Through multiple comparative experiments on public gesture datasets and a self-collected dataset,the proposed method’s superiority is validated in terms of accuracy,response latency,and system robustness,while user-experience tests assess the interface’s usability.The results provide a reliable technical foundation for robot collaboration and service in complex scenarios,offering broad prospects for practical application and deployment.
基金supported by the National Natural Science Foundation of China(Grant No.61463022)the Natural Science Foundation of Jiangxi Educational Committee,China(Grant No.GJJ14273)the Graduate Innovation Fund of Jiangxi Normal University,China(Grant No.YJS2014061)
文摘The interactions of a colored dynamical network play a great role in its dynamical behaviour and are denoted by outer and inner coupling matrices. In this paper, the outer and inner coupting matrices are assumed to be unknown and need to be identified. A corresponding network estimator is designed for identifying the unknown interactions by adopting proper adaptive laws. Based on the Lyapunov function method and Barbalat＇s lemma, the obtained result is analytically proved. A colored network coupled with chaotic Lorenz, Chen, and Lii systems is considered as a numerical example to illustrate the effectiveness of the proposed method.
