Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g...This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.展开更多
Graph Neural Networks(GNNs),as a deep learning framework specifically designed for graph-structured data,have achieved deep representation learning of graph data through message passing mechanisms and have become a co...Graph Neural Networks(GNNs),as a deep learning framework specifically designed for graph-structured data,have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis.However,current reviews on GNN models are mainly focused on smaller domains,and there is a lack of systematic reviews on the classification and applications of GNN models.This review systematically synthesizes the three canonical branches of GNN,Graph Convolutional Network(GCN),Graph Attention Network(GAT),and Graph Sampling Aggregation Network(GraphSAGE),and analyzes their integration pathways from both structural and feature perspectives.Drawing on representative studies,we identify three major integration patterns:cascaded fusion,where heterogeneous modules such as Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and GraphSAGE are sequentially combined for hierarchical feature learning;parallel fusion,where multi-branch architectures jointly encode complementary graph features;and feature-level fusion,which employs concatenation,weighted summation,or attention-based gating to adaptively merge multi-source embeddings.Through these patterns,integrated GNNs achieve enhanced expressiveness,robustness,and scalability across domains including transportation,biomedicine,and cybersecurity.展开更多
An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximatio...An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.展开更多
Traditional Chinese Medicine(TCM)has been extensively used as a mainstay for treating various pathologies.Combing the pharmacology and systems biology approaches,the network pharmacology(NP)approach was developed to p...Traditional Chinese Medicine(TCM)has been extensively used as a mainstay for treating various pathologies.Combing the pharmacology and systems biology approaches,the network pharmacology(NP)approach was developed to predict the probable mechanism underlying the therapeutic effect of TCM.However,approaches solely based on NP cannot effectively elucidate the curative mechanism in a holistic and reliable manner due to limitations in NP-based methods and complexity of TCM components.Thus,integration strategies combining NP with other approaches are increasingly being used.Since the interdisciplinary research in TCM has received much attention in the advent of the big data era of which the NPbased integration strategy is broadly used,the strategy is clearly elaborated in the present review.We summarized several NP-based integration strategies and their applications in TCM studies,including multi-omics approach,gut microbiota study,chemical information analysis,data-mining,and network toxicology study.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.展开更多
Satellite-terrestrial networks have garnered significant attention in recent years and are extensively applied in intelligent transportation and emergency rescue.This paper provides a comprehensive review of the lates...Satellite-terrestrial networks have garnered significant attention in recent years and are extensively applied in intelligent transportation and emergency rescue.This paper provides a comprehensive review of the latest research advancements in satellite-terrestrial integrated network(STIN)technologies from a network perspective,dividing STIN technologies into three categories according to network service flows—namely,topology maintenance,network routing,and orchestration transmission technologies.Furthermore,a novel network-layer perspective is considered to examine the applications of STINs across various domains,along with related frameworks,platforms,simulators,and datasets.Finally,this paper explores the mainstream research directions in STIN technologies,with an innovative focus on the network layer.It reviews the existing literature,outlines future trends,and discusses opportunities for collaboration with related fields.展开更多
The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic c...The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic complementarity of multiple bands are crucial for enhancing the spectrum efficiency,reducing network energy consumption,and ensuring a consistent user experience.This paper investigates the present researches and challenges associated with deployment of multi-band integrated networks in existing infrastructures.Then,an evolutionary path for integrated networking is proposed with the consideration of maturity of emerging technologies and practical network deployment.The proposed design principles for 6G multi-band integrated networking aim to achieve on-demand networking objectives,while the architecture supports full spectrum access and collaboration between high and low frequencies.In addition,the potential key air interface technologies and intelligent technologies for integrated networking are comprehensively discussed.It will be a crucial basis for the subsequent standards promotion of 6G multi-band integrated networking technology.展开更多
Inferior crude oil and fuel oil upgrading lead to escalating increase of hydrogen consumption in refineries.It is imperative to reduce the hydrogen consumption for energy-saving operations of refineries.An integration...Inferior crude oil and fuel oil upgrading lead to escalating increase of hydrogen consumption in refineries.It is imperative to reduce the hydrogen consumption for energy-saving operations of refineries.An integration strategy of hydrogen network and an operational optimization model of hydrotreating(HDT)units are proposed based on the characteristics of reaction kinetics of HDT units.By solving the proposed model,the operating conditions of HDT units are optimized,and the parameters of hydrogen sinks are determined by coupling hydrodesulfurization(HDS),hydrodenitrification(HDN)and aromatic hydrogenation(HDA)kinetics.An example case of a refinery with annual processing capacity of eight million tons is adopted to demonstrate the feasibility of the proposed optimization strategies and the model.Results show that HDS,HDN and HDA reactions are the major source of hydrogen consumption in the refinery.The total hydrogen consumption can be reduced by 18.9%by applying conventional hydrogen network optimization model.When the hydrogen network is optimized after the operational optimization of HDT units is performed,the hydrogen consumption is reduced by28.2%.When the benefit of the fuel gas recovery is further considered,the total annual cost of hydrogen network can be reduced by 3.21×10~7CNY·a^(-1),decreased by 11.9%.Therefore,the operational optimization of the HDT units in refineries should be imposed to determine the parameters of hydrogen sinks base on the characteristics of reaction kinetics of the hydrogenation processes before the optimization of the hydrogen network is performed through the source-sink matching methods.展开更多
The Global Energy Interconnection is an important strategic approach used to achieve efficient worldwide energy allocation.The idea of developing integrated power,information,and transportation networks provides incre...The Global Energy Interconnection is an important strategic approach used to achieve efficient worldwide energy allocation.The idea of developing integrated power,information,and transportation networks provides increased power interconnection functionality and meaning,helps condense forces,and accelerates the integration of global infrastructure.Correspondingly,it is envisaged that it will become the trend of industrial technological development in the future.In consideration of the current trend of integrated development,this study evaluates a possible plan of coordinated development of fiber-optic and power networks in the Pan-Arctic region.Firstly,the backbone network architecture of Global Energy Interconnection is introduced and the importance of the Arctic energy backbone network is confirmed.The energy consumption and developmental trend of global data centers are then analyzed.Subsequently,the global network traffic is predicted and analyzed by means of a polynomial regression model.Finally,in combination with the current construction of fiber-optic networks in the Pan-Arctic region,the advantages of the integration of the fiber-optic and power networks in this region are clarified in justification of the decision for the development of a Global Energy Interconnection scheme.展开更多
Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DN...Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.展开更多
With the large-scale deployment of satellite constellations such as Starlink and the rapid advancement of technologies including artificial intelligence (AI) and non-terrestrial networks (NTNs), the integration of hig...With the large-scale deployment of satellite constellations such as Starlink and the rapid advancement of technologies including artificial intelligence (AI) and non-terrestrial networks (NTNs), the integration of high, medium, and low Earth orbit satellite networks with terrestrial networks has become a critical direction for future communication technologies. The objective is to develop a space-terrestrial integrated 6G network that ensures ubiquitous connectivity and seamless services, facilitating intelligent interconnection and collaborative symbiosis among humans, machines, and objects. This integration has become a central focus of global technological innovation.展开更多
Blockchain-based spectrum sharing with consensus is the key technology for sixth-generation mobile communication to realize dynamic spectrum management.In order to avoid the waste of computing and communication resour...Blockchain-based spectrum sharing with consensus is the key technology for sixth-generation mobile communication to realize dynamic spectrum management.In order to avoid the waste of computing and communication resources,a spectrum sharing policy-based consensus mechanism is proposed in this paper.Firstly,a spectrum sharing algorithm based on graph neural network is designed in the satelliteterrestrial spectrum sharing networks under the underlay model.It avoids high computational overhead of the traditional iterative optimization algorithm when the wireless channel condition and network topology are highly dynamic.Secondly,a consensus mechanism based on spectrum sharing strategy is designed,which converts the traditional meaningless hash problem into the graph neural network training.Miners compete for accounting rights by training a graph neutral network model that meets the spectrum sharing requirement.Finally,the consensus delay,communication and storage overhead of the proposed consensus mechanism are analyzed theoretically.The simulation results show that the proposed consensus mechanism can effectively improve spectrum efficiency with excellent scalability and generalization performance.展开更多
To support ubiquitous communication and enhance other 6G applications,the Space-Air-Ground Integrated Network(SAGIN)has become a research hotspot.Traditionally,satellite-ground fusion technologies integrate network en...To support ubiquitous communication and enhance other 6G applications,the Space-Air-Ground Integrated Network(SAGIN)has become a research hotspot.Traditionally,satellite-ground fusion technologies integrate network entities from space,aerial,and terrestrial domains.However,they face challenges such as spectrum scarcity and inefficient satellite handover.This paper explores the Channel-Aware Handover Management(CAHM)strategy in SAGIN for data allocation.Specifically,CAHM utilizes the data receiving capability of Low Earth Orbit(LEO)satellites,considering satellite-ground distance,free-space path loss,and channel gain.Furthermore,CAHM assesses LEO satellite data forwarding capability using signal-to-noise ratio,link duration and buffer queue length.Then,CAHM applies historical data on LEO satellite transmission successes and failures to effectively reduce overall interruption ratio.Simulation results show that CAHM outperforms baseline algorithms in terms of delivery ratio,latency,and interruption ratio.展开更多
In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overl...In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overlooking congestion issues on feeder links caused by the limited number and centralized distribution of ground stations.Hence,a multi-service routing algorithm called the Multi-service Load Bal-ancing Routing Algorithm for Traffic Return(MLB-TR)is proposed.Unlike traditional approaches,MLB-TR aims to achieve a broader and more comprehensive load balancing objective.Specifically,based on the service type,an appropriate landing satellite is first selected by considering factors such as shortest path hop count and satellite load.Then,a set of candidate paths from the source satellite to the selected landing satellite is computed.Finally,using the regional load balancing index as the optimization objective,the final transmission path is selected from the candidate path set.Simulation results show that the proposed algo-rithm outperforms the existing works.展开更多
The research on scheduling and heat integration of batch process plays an important role in reducing energy consumption,improving production efficiency and enhancing the competitiveness of industries.The complexity an...The research on scheduling and heat integration of batch process plays an important role in reducing energy consumption,improving production efficiency and enhancing the competitiveness of industries.The complexity and difficulty of the model solving are increased due to the comprehensive consideration of both scheduling and heat integration.In this paper,the mixed integer nonlinear programming(MINLP) mathematical model of multi-product plant heat integration optimization with the goal of energy-saving annual profit(EAP) is established.The simultaneous optimization and sequential optimization are carried out respectively by bi-level programming(BP) based on the genetic algorithm(GA),and the calculation results are compared.EAP better captures the trade-off relationship between scheduling schemes,energy-saving profits,and equipment costs.The bi-level programming approach based on GA categorizes variables into integer and real types,enabling structural optimization and parameter optimization of the heat exchanger network.This,in turn,enhances solution efficiency and overcomes the limitations of conventional optimization algorithms in terms of solution speed and quality.Two examples show that the EAP of indirect heat integration considering the storage tank are 21% and 2% higher than that of the direct heat integration,and EAP of the simultaneous optimization are26% and 6% higher than that of the sequential optimization.The example demonstrates that the model and algorithm are applicable to batch multi-product plants,such as those in the chemical,pharmaceutical,and food industries,and possess strong practicality and innovation.展开更多
In this paper,we propose a joint power and frequency allocation algorithm considering interference protection in the integrated satellite and terrestrial network(ISTN).We efficiently utilize spectrum resources by allo...In this paper,we propose a joint power and frequency allocation algorithm considering interference protection in the integrated satellite and terrestrial network(ISTN).We efficiently utilize spectrum resources by allowing user equipment(UE)of terrestrial networks to share frequencies with satellite networks.In order to protect the satellite terminal(ST),the base station(BS)needs to control the transmit power and frequency resources of the UE.The optimization problem involves maximizing the achievable throughput while satisfying the interference protection constraints of the ST and the quality of service(QoS)of the UE.However,this problem is highly nonconvex,and we decompose it into power allocation and frequency resource scheduling subproblems.In the power allocation subproblem,we propose a power allocation algorithm based on interference probability(PAIP)to address channel uncertainty.We obtain the suboptimal power allocation solution through iterative optimization.In the frequency resource scheduling subproblem,we develop a heuristic algorithm to handle the non-convexity of the problem.The simulation results show that the combination of power allocation and frequency resource scheduling algorithms can improve spectrum utilization.展开更多
The rapid growth of low-Earth-orbit satellites has injected new vitality into future service provisioning.However,given the inherent volatility of network traffic,ensuring differentiated quality of service in highly d...The rapid growth of low-Earth-orbit satellites has injected new vitality into future service provisioning.However,given the inherent volatility of network traffic,ensuring differentiated quality of service in highly dynamic networks remains a significant challenge.In this paper,we propose an online learning-based resource scheduling scheme for satellite-terrestrial integrated networks(STINs)aimed at providing on-demand services with minimal resource utilization.Specifically,we focus on:①accurately characterizing the STIN channel,②predicting resource demand with uncertainty guarantees,and③implementing mixed timescale resource scheduling.For the STIN channel,we adopt the 3rd Generation Partnership Project channel and antenna models for non-terrestrial networks.We employ a one-dimensional convolution and attention-assisted long short-term memory architecture for average demand prediction,while introducing conformal prediction to mitigate uncertainties arising from burst traffic.Additionally,we develop a dual-timescale optimization framework that includes resource reservation on a larger timescale and resource adjustment on a smaller timescale.We also designed an online resource scheduling algorithm based on online convex optimization to guarantee long-term performance with limited knowledge of time-varying network information.Based on the Network Simulator 3 implementation of the STIN channel under our high-fidelity satellite Internet simulation platform,numerical results using a real-world dataset demonstrate the accuracy and efficiency of the prediction algorithms and online resource scheduling scheme.展开更多
The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serv...The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serves as an effective emergency solution.This study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity constraints.Due to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop approach.Since the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory design.The first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)technique.By iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop issue.Simulation results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.展开更多
1.Introduction As a key development of the next-generation spatial information infrastructure,1the Satellite-Terrestrial Integrated Network(STIN)has become a strategic priority actively pursued by major spacefaring na...1.Introduction As a key development of the next-generation spatial information infrastructure,1the Satellite-Terrestrial Integrated Network(STIN)has become a strategic priority actively pursued by major spacefaring nations and regions,including the United States,Europe,China,and Russia.Specifically,Space X’s Starlink project has deployed over 6750 satellites,2while One Web has completed its initial phase of satellite constellation deployment with more than 600 satellites.展开更多
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
文摘This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.
基金funded by Guangzhou Huashang University(2024HSZD01,HS2023JYSZH01).
文摘Graph Neural Networks(GNNs),as a deep learning framework specifically designed for graph-structured data,have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis.However,current reviews on GNN models are mainly focused on smaller domains,and there is a lack of systematic reviews on the classification and applications of GNN models.This review systematically synthesizes the three canonical branches of GNN,Graph Convolutional Network(GCN),Graph Attention Network(GAT),and Graph Sampling Aggregation Network(GraphSAGE),and analyzes their integration pathways from both structural and feature perspectives.Drawing on representative studies,we identify three major integration patterns:cascaded fusion,where heterogeneous modules such as Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and GraphSAGE are sequentially combined for hierarchical feature learning;parallel fusion,where multi-branch architectures jointly encode complementary graph features;and feature-level fusion,which employs concatenation,weighted summation,or attention-based gating to adaptively merge multi-source embeddings.Through these patterns,integrated GNNs achieve enhanced expressiveness,robustness,and scalability across domains including transportation,biomedicine,and cybersecurity.
基金supported by the National Natural Science Foundation of China(Nos.12422207 and 12372199).
文摘An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.
文摘Traditional Chinese Medicine(TCM)has been extensively used as a mainstay for treating various pathologies.Combing the pharmacology and systems biology approaches,the network pharmacology(NP)approach was developed to predict the probable mechanism underlying the therapeutic effect of TCM.However,approaches solely based on NP cannot effectively elucidate the curative mechanism in a holistic and reliable manner due to limitations in NP-based methods and complexity of TCM components.Thus,integration strategies combining NP with other approaches are increasingly being used.Since the interdisciplinary research in TCM has received much attention in the advent of the big data era of which the NPbased integration strategy is broadly used,the strategy is clearly elaborated in the present review.We summarized several NP-based integration strategies and their applications in TCM studies,including multi-omics approach,gut microbiota study,chemical information analysis,data-mining,and network toxicology study.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
基金support from the National Key Research and Development Program of China(2024YFB3108400)the Hubei Province Key Research and Development Program(2024BAB051).
文摘Satellite-terrestrial networks have garnered significant attention in recent years and are extensively applied in intelligent transportation and emergency rescue.This paper provides a comprehensive review of the latest research advancements in satellite-terrestrial integrated network(STIN)technologies from a network perspective,dividing STIN technologies into three categories according to network service flows—namely,topology maintenance,network routing,and orchestration transmission technologies.Furthermore,a novel network-layer perspective is considered to examine the applications of STINs across various domains,along with related frameworks,platforms,simulators,and datasets.Finally,this paper explores the mainstream research directions in STIN technologies,with an innovative focus on the network layer.It reviews the existing literature,outlines future trends,and discusses opportunities for collaboration with related fields.
基金supported by China’s National Key R&D Program(Project Number:2022YFB2902100)。
文摘The sixth-generation(6G)networks will consist of multiple bands such as low-frequency,midfrequency,millimeter wave,terahertz and other bands to meet various business requirements and networking scenarios.The dynamic complementarity of multiple bands are crucial for enhancing the spectrum efficiency,reducing network energy consumption,and ensuring a consistent user experience.This paper investigates the present researches and challenges associated with deployment of multi-band integrated networks in existing infrastructures.Then,an evolutionary path for integrated networking is proposed with the consideration of maturity of emerging technologies and practical network deployment.The proposed design principles for 6G multi-band integrated networking aim to achieve on-demand networking objectives,while the architecture supports full spectrum access and collaboration between high and low frequencies.In addition,the potential key air interface technologies and intelligent technologies for integrated networking are comprehensively discussed.It will be a crucial basis for the subsequent standards promotion of 6G multi-band integrated networking technology.
基金Supported by the National Natural Science Foundation of China(21376188,21676211)the Key Project of Industrial Science and Technology of Shaanxi Province(2015GY095)
文摘Inferior crude oil and fuel oil upgrading lead to escalating increase of hydrogen consumption in refineries.It is imperative to reduce the hydrogen consumption for energy-saving operations of refineries.An integration strategy of hydrogen network and an operational optimization model of hydrotreating(HDT)units are proposed based on the characteristics of reaction kinetics of HDT units.By solving the proposed model,the operating conditions of HDT units are optimized,and the parameters of hydrogen sinks are determined by coupling hydrodesulfurization(HDS),hydrodenitrification(HDN)and aromatic hydrogenation(HDA)kinetics.An example case of a refinery with annual processing capacity of eight million tons is adopted to demonstrate the feasibility of the proposed optimization strategies and the model.Results show that HDS,HDN and HDA reactions are the major source of hydrogen consumption in the refinery.The total hydrogen consumption can be reduced by 18.9%by applying conventional hydrogen network optimization model.When the hydrogen network is optimized after the operational optimization of HDT units is performed,the hydrogen consumption is reduced by28.2%.When the benefit of the fuel gas recovery is further considered,the total annual cost of hydrogen network can be reduced by 3.21×10~7CNY·a^(-1),decreased by 11.9%.Therefore,the operational optimization of the HDT units in refineries should be imposed to determine the parameters of hydrogen sinks base on the characteristics of reaction kinetics of the hydrogenation processes before the optimization of the hydrogen network is performed through the source-sink matching methods.
基金supported by the Corporation Science and Technology Program of Global Energy Interconnection Group Ltd. (GEIGC-D-[2018]024)by the National Natural Science Foundation of China (61472042, 61772079)
文摘The Global Energy Interconnection is an important strategic approach used to achieve efficient worldwide energy allocation.The idea of developing integrated power,information,and transportation networks provides increased power interconnection functionality and meaning,helps condense forces,and accelerates the integration of global infrastructure.Correspondingly,it is envisaged that it will become the trend of industrial technological development in the future.In consideration of the current trend of integrated development,this study evaluates a possible plan of coordinated development of fiber-optic and power networks in the Pan-Arctic region.Firstly,the backbone network architecture of Global Energy Interconnection is introduced and the importance of the Arctic energy backbone network is confirmed.The energy consumption and developmental trend of global data centers are then analyzed.Subsequently,the global network traffic is predicted and analyzed by means of a polynomial regression model.Finally,in combination with the current construction of fiber-optic networks in the Pan-Arctic region,the advantages of the integration of the fiber-optic and power networks in this region are clarified in justification of the decision for the development of a Global Energy Interconnection scheme.
基金National Natural Science Foundation of China(Nos.11262014,11962021 and 51965051)Inner Mongolia Natural Science Foundation,China(No.2019MS05064)+1 种基金Inner Mongolia Earthquake Administration Director Fund Project,China(No.2019YB06)Inner Mongolia University of Technology Foundation,China(No.2020015)。
文摘Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.
文摘With the large-scale deployment of satellite constellations such as Starlink and the rapid advancement of technologies including artificial intelligence (AI) and non-terrestrial networks (NTNs), the integration of high, medium, and low Earth orbit satellite networks with terrestrial networks has become a critical direction for future communication technologies. The objective is to develop a space-terrestrial integrated 6G network that ensures ubiquitous connectivity and seamless services, facilitating intelligent interconnection and collaborative symbiosis among humans, machines, and objects. This integration has become a central focus of global technological innovation.
基金supported in part by the National Natural Science Foundation of China under Grant 62171020.
文摘Blockchain-based spectrum sharing with consensus is the key technology for sixth-generation mobile communication to realize dynamic spectrum management.In order to avoid the waste of computing and communication resources,a spectrum sharing policy-based consensus mechanism is proposed in this paper.Firstly,a spectrum sharing algorithm based on graph neural network is designed in the satelliteterrestrial spectrum sharing networks under the underlay model.It avoids high computational overhead of the traditional iterative optimization algorithm when the wireless channel condition and network topology are highly dynamic.Secondly,a consensus mechanism based on spectrum sharing strategy is designed,which converts the traditional meaningless hash problem into the graph neural network training.Miners compete for accounting rights by training a graph neutral network model that meets the spectrum sharing requirement.Finally,the consensus delay,communication and storage overhead of the proposed consensus mechanism are analyzed theoretically.The simulation results show that the proposed consensus mechanism can effectively improve spectrum efficiency with excellent scalability and generalization performance.
基金National Key Research and Development Program of China(2022YFE0139300)Hubei Province Key Research and Development Program(2024BAB051)+1 种基金Guangdong Basic and Applied Basic Research Foundation(2022B1515120067)Wuhan Key Research and Development Program(2024050702030136).
文摘To support ubiquitous communication and enhance other 6G applications,the Space-Air-Ground Integrated Network(SAGIN)has become a research hotspot.Traditionally,satellite-ground fusion technologies integrate network entities from space,aerial,and terrestrial domains.However,they face challenges such as spectrum scarcity and inefficient satellite handover.This paper explores the Channel-Aware Handover Management(CAHM)strategy in SAGIN for data allocation.Specifically,CAHM utilizes the data receiving capability of Low Earth Orbit(LEO)satellites,considering satellite-ground distance,free-space path loss,and channel gain.Furthermore,CAHM assesses LEO satellite data forwarding capability using signal-to-noise ratio,link duration and buffer queue length.Then,CAHM applies historical data on LEO satellite transmission successes and failures to effectively reduce overall interruption ratio.Simulation results show that CAHM outperforms baseline algorithms in terms of delivery ratio,latency,and interruption ratio.
基金supported by the National Key Research and Development Program of China under Grant No.2022YFB2902501the Fundamental Research Funds for the Central Universities under Grant No.2023ZCJH09the Haidian District Golden Bridge Seed Fund of Beijing Municipality under Grant No.S2024161.
文摘In recent years,load balancing routing al-gorithms have been extensively studied in satellite net-works.Most existing studies focus on path selection and hop-count optimization for end-to-end transmis-sion,while overlooking congestion issues on feeder links caused by the limited number and centralized distribution of ground stations.Hence,a multi-service routing algorithm called the Multi-service Load Bal-ancing Routing Algorithm for Traffic Return(MLB-TR)is proposed.Unlike traditional approaches,MLB-TR aims to achieve a broader and more comprehensive load balancing objective.Specifically,based on the service type,an appropriate landing satellite is first selected by considering factors such as shortest path hop count and satellite load.Then,a set of candidate paths from the source satellite to the selected landing satellite is computed.Finally,using the regional load balancing index as the optimization objective,the final transmission path is selected from the candidate path set.Simulation results show that the proposed algo-rithm outperforms the existing works.
文摘The research on scheduling and heat integration of batch process plays an important role in reducing energy consumption,improving production efficiency and enhancing the competitiveness of industries.The complexity and difficulty of the model solving are increased due to the comprehensive consideration of both scheduling and heat integration.In this paper,the mixed integer nonlinear programming(MINLP) mathematical model of multi-product plant heat integration optimization with the goal of energy-saving annual profit(EAP) is established.The simultaneous optimization and sequential optimization are carried out respectively by bi-level programming(BP) based on the genetic algorithm(GA),and the calculation results are compared.EAP better captures the trade-off relationship between scheduling schemes,energy-saving profits,and equipment costs.The bi-level programming approach based on GA categorizes variables into integer and real types,enabling structural optimization and parameter optimization of the heat exchanger network.This,in turn,enhances solution efficiency and overcomes the limitations of conventional optimization algorithms in terms of solution speed and quality.Two examples show that the EAP of indirect heat integration considering the storage tank are 21% and 2% higher than that of the direct heat integration,and EAP of the simultaneous optimization are26% and 6% higher than that of the sequential optimization.The example demonstrates that the model and algorithm are applicable to batch multi-product plants,such as those in the chemical,pharmaceutical,and food industries,and possess strong practicality and innovation.
基金funded by State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster under Grant MS01240103the National Natural Science Foundation of China under Grant 62071146National 2011 Collaborative Innovation Center of Wireless Communication Technologies under Grant 2242022k60006.
文摘In this paper,we propose a joint power and frequency allocation algorithm considering interference protection in the integrated satellite and terrestrial network(ISTN).We efficiently utilize spectrum resources by allowing user equipment(UE)of terrestrial networks to share frequencies with satellite networks.In order to protect the satellite terminal(ST),the base station(BS)needs to control the transmit power and frequency resources of the UE.The optimization problem involves maximizing the achievable throughput while satisfying the interference protection constraints of the ST and the quality of service(QoS)of the UE.However,this problem is highly nonconvex,and we decompose it into power allocation and frequency resource scheduling subproblems.In the power allocation subproblem,we propose a power allocation algorithm based on interference probability(PAIP)to address channel uncertainty.We obtain the suboptimal power allocation solution through iterative optimization.In the frequency resource scheduling subproblem,we develop a heuristic algorithm to handle the non-convexity of the problem.The simulation results show that the combination of power allocation and frequency resource scheduling algorithms can improve spectrum utilization.
基金supported in part by the Major Program of the National Natural Science Foundation of China(62495021 and 62495020).
文摘The rapid growth of low-Earth-orbit satellites has injected new vitality into future service provisioning.However,given the inherent volatility of network traffic,ensuring differentiated quality of service in highly dynamic networks remains a significant challenge.In this paper,we propose an online learning-based resource scheduling scheme for satellite-terrestrial integrated networks(STINs)aimed at providing on-demand services with minimal resource utilization.Specifically,we focus on:①accurately characterizing the STIN channel,②predicting resource demand with uncertainty guarantees,and③implementing mixed timescale resource scheduling.For the STIN channel,we adopt the 3rd Generation Partnership Project channel and antenna models for non-terrestrial networks.We employ a one-dimensional convolution and attention-assisted long short-term memory architecture for average demand prediction,while introducing conformal prediction to mitigate uncertainties arising from burst traffic.Additionally,we develop a dual-timescale optimization framework that includes resource reservation on a larger timescale and resource adjustment on a smaller timescale.We also designed an online resource scheduling algorithm based on online convex optimization to guarantee long-term performance with limited knowledge of time-varying network information.Based on the Network Simulator 3 implementation of the STIN channel under our high-fidelity satellite Internet simulation platform,numerical results using a real-world dataset demonstrate the accuracy and efficiency of the prediction algorithms and online resource scheduling scheme.
基金Supported by the Self-funded Research Project of Beijing FibrLink Communications Co.Ltd.“Research on Key Technologies forUnifiedManagement of Air-to-Earth Integrated CommunicationNetworks(546826230034).”。
文摘The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid networks.In such cases,a space-air-ground integrated network serves as an effective emergency solution.This study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity constraints.Due to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop approach.Since the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory design.The first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)technique.By iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop issue.Simulation results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.
基金co-supported by the National Natural Science Foundation of China(Nos.62225103,U2441227,U24A20211)the Fundamental Research Funds for the Central Universities of China(No.FRF-TP-22-002C2)。
文摘1.Introduction As a key development of the next-generation spatial information infrastructure,1the Satellite-Terrestrial Integrated Network(STIN)has become a strategic priority actively pursued by major spacefaring nations and regions,including the United States,Europe,China,and Russia.Specifically,Space X’s Starlink project has deployed over 6750 satellites,2while One Web has completed its initial phase of satellite constellation deployment with more than 600 satellites.
