The increasing electrification of urban transportation,i.e.,subways and electric vehicles(EV),brings more interactions between the power system and transportation system and further results in fault propagation across...The increasing electrification of urban transportation,i.e.,subways and electric vehicles(EV),brings more interactions between the power system and transportation system and further results in fault propagation across them.To analyze vulnerability of the coupling system under extreme events,this paper establishes a multi-layer urban electric-transportation interdependent network(ETIN)model.First,a weighted coupled metro-road traffic network(CTN)model and network path planning approach are proposed.A prospect theory-based failure load redistribution(FLR)method is further established to account for uncertainty of TN link capacity affected by power supply.Second,topology and emergency control strategy of power network(PN)are modeled,followed by formulation of multi-layer ETIN model.In particular,the inter-layer fault propagation from PN to TN is modeled based on power supply correlation strength,while from TN to PN is modeled based on traffic flow.A few indexes are then defined to quantify vulnerability of ETIN under deliberate attack.Finally,the proposed method is verified on an electric-transportation system to show influence of fault propagations within ETIN on its vulnerability under extreme events.展开更多
Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to ...Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.展开更多
As an earth-abundant and natural biopolymer,cellulose has received significant attention in aqueous zinc-ion batteries(AZIBs)due to its inherent sustainability and non-toxicity,aligning perfectly with the core advanta...As an earth-abundant and natural biopolymer,cellulose has received significant attention in aqueous zinc-ion batteries(AZIBs)due to its inherent sustainability and non-toxicity,aligning perfectly with the core advantages of AZIBs.Nevertheless,the practical implementation of cellulose-based materials is limited by their intrinsically low ionic conductivity.Herein,we introduce a novel zincophilic artificial protective layer by strategically hybridizing hydroxypropyl cellulose(HPC)with zinc trifluoromethanesulfonate on a zinc metal anode(HZ@Zn).Characterization and calculations demonstrate that the multihydroxyl architecture of HPC constructs hydrogen bond networks,whereas the Zn^(2+)-coordinated HPC domains function as preferential nucleation sites for zinc deposition.These interactions collectively enhance ion transport and accelerate desolvation kinetics.Additionally,the hybrid layer's mechanical flexibility and interfacial adhesion ensure the integrity of the artificial protective layer during long cycling.Thanks to this synergistic effect,HZ@Zn shows exceptional electrochemical performance,including a low desolvation activation energy of 14.38 kJ mol^(-1)and ultra-long cycling stability.Symmetric cells demonstrate exceptional longevity,exceeding 9,500 h at 0.5 mA cm^(-2)/0.25 mAh cm^(-2),whereas HZ@Zn‖PANI full cells maintain 89.8%capacity retention after 4000 cycles at 5 A g^(-1).This study establishes biopolymers as versatile platforms for effectively stabilizing the zinc metal anode.展开更多
Due to excellent thermal insulation performance at room temperature and ultralow density,silica aero-gels are candidates for thermal insulation.However,at high temperatures,the thermal insulation prop-erty of silica a...Due to excellent thermal insulation performance at room temperature and ultralow density,silica aero-gels are candidates for thermal insulation.However,at high temperatures,the thermal insulation prop-erty of silica aerogels decreased greatly caused by transparency to heat radiation.Opacifiers introduced into silica sol can block heat radiation yet destroy the uniformity of aerogels.Herein,we designed and prepared a silica aerogel composite with oriented and layered silica fibers(SFs),SiC nanowires(SiC_(NWs)),and silica aerogels,which were prepared by papermaking,chemical vapor infiltration(CVI),and sol-gel respectively.Firstly,oriented and layered SFs made still air a wall to block heat transfer by the solid phase.Secondly,SiC_(NWs) were grown in situ on the surface of SFs evenly to weave into the network,and the network reduced the gaseous thermal conductivity by dividing cracks in SFs/SiC_(NWs)/SA.Thirdly,SiC_(NWs) weakened the heat transfer by radiation at high temperatures.Therefore,SFs/SiC_(NWs)/SA presented remarkable thermal insulation(0.017 W(m K)^(-1) at 25℃,0.0287 W(m K)^(-1) at 500℃,and 0.094 W(m K)^(-1) at 1000℃).Besides,SFs/SiC_(NWs)/SA exhibited remarkable thermal stability(no size transform after being heat treated at 1000℃ for 1800 s)and tensile strength(0.75 MPa).These integrated properties made SFs/SiC_(NWs)/SA a promising candidate for highly efficient thermal insulators.展开更多
In recent decades,annual urban fire incidents,including those involving ancient wooden buildings burned,transportation,and solar panels,have increased,leading to significant loss of human life and property.Addressing ...In recent decades,annual urban fire incidents,including those involving ancient wooden buildings burned,transportation,and solar panels,have increased,leading to significant loss of human life and property.Addressing this issue without altering the surface morphology or interfering with optical behavior of flammable materials poses a substantial challenge.Herein,we present a transparent,low thickness,ceramifiable nanosystem coating composed of a highly adhesive base(poly(SSS1-co-HEMA1)),nanoscale layered double hydroxide sheets as ceramic precursors,and supramolecular melamine di-borate as an accelerator.We demonstrate that this hybrid coating can transform into a porous,fire-resistant protective layer with a highly thermostable vitreous phase upon exposure to flame/heat source.A nanosystem coating of just~100μm thickness can significantly increase the limiting oxygen index of wood(Pine)to 37.3%,dramatically reduce total heat release by 78.6%,and maintain low smoke toxicity(CIT_G=0.016).Detailed molecular force analysis,combined with a comprehensive examination of the underlying flame-retardant mechanisms,underscores the effectiveness of this coating.This work offers a strategy for creating efficient,environmentally friendly coatings with fire safety applications across various industries.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 empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only...The empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only used for limited cases.In this letter,we propose Kolmogorov-Arnold networks(KAN)base models for wavenumber-frequency spectra of pressure fluctuations under turbulent boundary layers.The results are compared with DNS results.In turbulent channel flows,it is found that the KAN base model leads to a smooth wavenumber-frequency spectrum with sparse samples.In the turbulent flow over an axisymmetric body of revolution,the KAN base model captures the wavenumber-frequency spectra near the convective peak.展开更多
With breakthroughs in data processing and pattern recognition through deep learning technologies,the use of advanced algorithmic models for analyzing and interpreting soil spectral information has provided an efficien...With breakthroughs in data processing and pattern recognition through deep learning technologies,the use of advanced algorithmic models for analyzing and interpreting soil spectral information has provided an efficient and economical method for soil quality assessment.However,traditional single-output networks exhibit limitations in the prediction process,particularly in their inability to fully utilize the correlations among various elements.As a result,single-output networks tend to be optimized for a single task,neglecting the interrelationships among different soil elements,which limits prediction accuracy and model generalizability.To overcome this limitation,in this study,a multi-task learning architecture with a progressive extraction network was implemented for the simultaneous prediction of multiple indicators in soil,including nitrogen(N),organic carbon(OC),calcium carbonate(CaCO 3),cation exchange capacity(CEC),and pH.Furthermore,while incorporating the Pearson correlation coefficient,convolutional neural networks,long short-term memory networks and attention mechanisms were combined to extract local abstract features from the original spectra,thereby further improving the model.This architecture is referred to as the Relevance-sharing Progressive Layered Extraction Network.The model employs an adaptive joint loss optimization method to update the weights of individual task losses in the multi-task learning training process.展开更多
As the international environment changes,frequent geopolitical crises continue to hinder the healthy development of global stock markets.To analyze in-depth the risk contagion path between the international stock mark...As the international environment changes,frequent geopolitical crises continue to hinder the healthy development of global stock markets.To analyze in-depth the risk contagion path between the international stock market and geopolitics under the impact of extreme events,this paper explores the tail risk interactive contagion mechanism and dynamic effects of the double-layer network between the international stock market and geopolitics from the perspective of complex networks.Empirical research finds that geopolitical conflicts exacerbate risk contagion among international stock markets,and there are significant differences in risk contagion between developed and emerging economies.The analysis of the complex interaction effect in the doublelayer network of the international stock market and geopolitics shows that the intralayer risk spillover effect of geopolitics in the short term is significantly higher than that of the stock price volatility network layer.Finally,the study on the dynamic changes of the double-layer network connectedness between the international stock market and geopolitics found that the shock of extreme events,such as military conflict and public health security,is an important factor in triggering the cross-contagion of risks.The research conclusions provide new ideas for preventing the cross-contagion of geopolitical risks in the stock markets of various countries under the evolution of the global political and economic pattern.展开更多
The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging at...The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.展开更多
Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed ...Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.展开更多
Research on the self-similarity of multilayer networks is scarce, when compared to the extensive research conducted on the dynamics of these networks. In this paper, we use entropy to determine the edge weights in eac...Research on the self-similarity of multilayer networks is scarce, when compared to the extensive research conducted on the dynamics of these networks. In this paper, we use entropy to determine the edge weights in each sub-network,and apply the degree–degree distance to unify the weight values of connecting edges between different sub-networks, and unify the edges with different meanings in the multilayer network numerically. At this time, the multilayer network is compressed into a single-layer network, also known as the aggregated network. Furthermore, the self-similarity of the multilayer network is represented by analyzing the self-similarity of the aggregate network. The study of self-similarity was conducted on two classical fractal networks and a real-world multilayer network. The results show that multilayer networks exhibit more pronounced self-similarity, and the intensity of self-similarity in multilayer networks can vary with the connection mode of sub-networks.展开更多
Compared to single-layer networks,multilayer networks exhibit a more complex node degree composition,comprising both intra-layer and inter-layer degrees.However,the distinct impacts of these degree types on cascading ...Compared to single-layer networks,multilayer networks exhibit a more complex node degree composition,comprising both intra-layer and inter-layer degrees.However,the distinct impacts of these degree types on cascading failures remain underexplored.Distinguishing their effects is crucial for a deeper understanding of network structure,information propagation,and behavior prediction.This paper proposes a capacity-load model to influence and compare the influence of different degree types on cascading failures in multilayer networks.By designing three node removal strategies based on total degree,intra-layer degree,and inter-layer degree,simulation experiments are conducted on four types of networks.Network robustness is evaluated using the maximum number of removable nodes before collapse.The relationships between network robustness and the coupling coefficient,as well as load and capacity adjustment parameters,are also analyzed.The results indicate that the node removal strategy with the least impact on cascading failures varies across different types of networks,revealing the significance of different node degrees in failure propagation.Compared to other models,the proposed model enables networks to maintain a higher maximum number of removable nodes during cascading failures,demonstrating superior robustness.展开更多
NiFe-layered double hydroxides(NiFe-LDHs)are among the most promising earth-abundant electrocatalysts for the oxygen evolution reaction(OER)in alkaline media.However,their practical application is hindered by intrinsi...NiFe-layered double hydroxides(NiFe-LDHs)are among the most promising earth-abundant electrocatalysts for the oxygen evolution reaction(OER)in alkaline media.However,their practical application is hindered by intrinsic activity limitations and poor stability,primarily due to the asymmetric adsorption of oxygen intermediates.To overcome this,the binding strength must be synergistically tuned to a moderate level to optimize catalytic performance.Here,we engineered NiFeCoCr LDH through Co doping to enhance electrical conductivity and controlled Cr leaching to introduce cationic vacancies for modulating intermediate binding strength in NiFe LDH.X-ray absorption near-edge structure and extended X-ray absorption fine structure analyses reveal that NiFe-LDH with Co doping and Cr vacancies modulates the Ni oxidation state and local coordination environment,leading to a balanced electronic structure and enhanced structural complexity around the Ni sites.Additionally,these vacancies can trap OH^(-)/H_(2)O species,which can serve as a reservoir for OH^(-) transfer,facilitating the rapid formation of OER intermediates and enhancing catalytic performance at high current densities.As a result,V_(Cr)-NiFeCo LDH achieves 1.6 A cm^(-2)current density at 1.7 V vs.RHE while maintaining stable operation for over 1000 h at 500 mA cm^(-2).Density functional theory(DFT)calculations validate the synergistic effects of Co doping and Cr-induced vacancies on intermediate binding energies and improved OER kinetics.Overall,this work presents a rational design strategy to simultaneously enhance the activity and durability of NiFe-based OER catalysts for their application in high-performance alkaline water electrolysis.展开更多
Infrared thermal camouflage technologies are vital for enhancing the survivability of objects by altering their infrared radiation properties.However,existing solutions often fall short in adaptability and rapid respo...Infrared thermal camouflage technologies are vital for enhancing the survivability of objects by altering their infrared radiation properties.However,existing solutions often fall short in adaptability and rapid responsiveness to dynamic environmental conditions,limiting their practical applicability.To overcome these challenges,we present an innovative approach combining ultrafast laser-induced non-volatile phase-change Ge_(2)Sb_(2)Te_(5)(GST)voxel-crystallized units with electrically tunable volatile VO_(2)layers.This integration enables precise,continuous control of infrared emissivity across a wide range of 0.14 to 0.98,effectively encompassing the emissivity of most materials.A neural network-based closed-loop system is employed for sensing,intelligent decision-making,and execution,achieving real-time thermal radiation matching between the target and its environment with a response speed of 3°C/s and an accuracy of±1°C.This strategy significantly enhances the adaptability of thermal camouflage in complex environments,paving the way for practical,dynamic thermal stealth applications.展开更多
Information plays a crucial role in guiding behavioral decisions during public health emergencies. Individuals communicate to acquire relevant knowledge about an epidemic, which influences their decisions to adopt pro...Information plays a crucial role in guiding behavioral decisions during public health emergencies. Individuals communicate to acquire relevant knowledge about an epidemic, which influences their decisions to adopt protective measures.However, whether to disseminate specific information is also a behavioral decision. In light of this understanding, we develop a coupled information–vaccination–epidemic model to depict these co-evolutionary dynamics in a three-layer network. Negative information dissemination and vaccination are treated as separate decision-making processes. We then examine the combined effects of herd and risk motives on information dissemination and vaccination decisions through the lens of game theory. The microscopic Markov chain approach(MMCA) is used to describe the dynamic process and to derive the epidemic threshold. Simulation results indicate that increasing the cost of negative information dissemination and providing timely clarification can effectively control the epidemic. Furthermore, a phenomenon of diminishing marginal utility is observed as the cost of dissemination increases, suggesting that authorities do not need to overinvest in suppressing negative information. Conversely, reducing the cost of vaccination and increasing vaccine efficacy emerge as more effective strategies for outbreak control. In addition, we find that the scale of the epidemic is greater when the herd motive dominates behavioral decision-making. In conclusion, this study provides a new perspective for understanding the complexity of epidemic spreading by starting with the construction of different behavioral decisions.展开更多
As the economy grows, environmental issues are becoming increasingly severe, making the promotion of green behavior more urgent. Information dissemination and policy regulation play crucial roles in influencing and am...As the economy grows, environmental issues are becoming increasingly severe, making the promotion of green behavior more urgent. Information dissemination and policy regulation play crucial roles in influencing and amplifying the spread of green behavior across society. To this end, a novel three-layer model in multilayer networks is proposed. In the novel model, the information layer describes green information spreading, the physical contact layer depicts green behavior propagation, and policy regulation is symbolized by an isolated node beneath the two layers. Then, we deduce the green behavior threshold for the three-layer model using the microscopic Markov chain approach. Moreover, subject to some individuals who are more likely to influence others or become green nodes and the limitations of the capacity of policy regulation, an optimal scheme is given that could optimize policy interventions to most effectively prompt green behavior.Subsequently, simulations are performed to validate the preciseness and theoretical results of the new model. It reveals that policy regulation can prompt the prevalence and outbreak of green behavior. Then, the green behavior is more likely to spread and be prevalent in the SF network than in the ER network. Additionally, optimal allocation is highly successful in facilitating the dissemination of green behavior. In practice, the optimal allocation strategy could prioritize interventions at critical nodes or regions, such as highly connected urban areas, where the impact of green behavior promotion would be most significant.展开更多
Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limite...Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limited set of genes suitable for clinical implementation.Methods:We utilized an integrated dataset of 1360 whole-exome and whole-genome sequences from Chinese and Western PCa cohorts to develop and evaluate the model.External validation was conducted using an independent cohort of patients.A graph neural network architecture,termed the pathway-aware multi-layered hierarchical network-Western and Asian(P-NETwa),was developed and trained on combined genomic profiles from Chinese and Western cohorts.The model employed a multilayer perceptron(MLP)to identify key signature genes from multiomics data,enabling precise prediction of PCa metastasis.Results:The model achieved an accuracy of 0.87 and an F1-score of 0.85 on Western population datasets.The application of integrated Chinese and Western population data improved the accuracy to 0.88,achieving an F1-score of 0.75.The analysis identified 18 signature genes implicated in PCa progression,including established markers(AR and TP53)and novel candidates(MUC16,MUC4,and ASB12).For clinical adoption,the model was optimized for commercially available gene panels while maintaining high classification accuracy.Additionally,a user-friendly web interface was developed to facilitate real-time prediction of primary versus metastatic status using the pre-trained P-NETwa-MLP model.Conclusion:The P-NETwa-MLP model integrates a query system that allows for efficient retrieval of prediction outcomes and associated genomic signatures via sample ID,enhancing its potential for seamless integration into clinical workflows.展开更多
We propose a dual-layer network model that integrates social and familial contexts,consisting of a social interaction layer and a family relationship layer.We design a reputation-based incentive mechanism incorporatin...We propose a dual-layer network model that integrates social and familial contexts,consisting of a social interaction layer and a family relationship layer.We design a reputation-based incentive mechanism incorporating strategy persistenceconsistency to investigate how reputation fosters cooperation.The model features the following aspects:(1)a dynamic disconnection-reconnection mechanism in the social layer,(2)a reputation-enhanced Fermi rule in the family layer,and(3)a refined partitioning of the family network.Simulation results indicate that the disconnection-reconnection parameter(σ)significantly enhances cooperation in the social network;the strategic persistence-consistency influencing factor(α)has a positive impact on cooperation in both layers;and moderately dividing the family network promotes the emergence of cooperation.The research findings facilitate group cooperation in complex networks and offer valuable insights for addressing social dilemmas in the real world.展开更多
Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on fau...Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on faulty-feeder detection tends to apply more complex digital signal processing techniques and deeper neural networks in order to better extract and learn as many detailed characteristics as possible.However,these approaches may easily result in overfitting and high computational cost,which cannot meet requirements for detection accuracy and efficiency in practical applications.This paper proposes an innovative waveform encoding method and details a simple convolutional neural network(CNN)with one layer of convolution used for identification,which seeks to improve detection accuracy and efficiency simultaneously.First,sparse characteristics of waveforms are utilized to encode into compact vectors,and a waveform-vector matrix is generated.Second,to deduce waveform-vector matrix,a simple CNN with multi-scale filters and one layer of convolution is established.Finally,a methodology for faulty-feeder detection is proposed,and both detection accuracy and efficiency are considerably enhanced.Comparative studies have confirmed clear superiority of the developed method,which outperforms existing approaches in both detection accuracy and efficiency,thus highlighting its significant potential for application.展开更多
文摘The increasing electrification of urban transportation,i.e.,subways and electric vehicles(EV),brings more interactions between the power system and transportation system and further results in fault propagation across them.To analyze vulnerability of the coupling system under extreme events,this paper establishes a multi-layer urban electric-transportation interdependent network(ETIN)model.First,a weighted coupled metro-road traffic network(CTN)model and network path planning approach are proposed.A prospect theory-based failure load redistribution(FLR)method is further established to account for uncertainty of TN link capacity affected by power supply.Second,topology and emergency control strategy of power network(PN)are modeled,followed by formulation of multi-layer ETIN model.In particular,the inter-layer fault propagation from PN to TN is modeled based on power supply correlation strength,while from TN to PN is modeled based on traffic flow.A few indexes are then defined to quantify vulnerability of ETIN under deliberate attack.Finally,the proposed method is verified on an electric-transportation system to show influence of fault propagations within ETIN on its vulnerability under extreme events.
基金Project supported by the National Natural Science Foun-dation of China(Grant No.62373197)the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,China(Grant No.23KJB120010)+1 种基金the Industry-University-Research Cooperation Project of Jiangsu Province,China(Grant No.BY20251038)the Cultivation and In-cubation Project of the College of Automation,Nanjing Uni-versity of Posts and Telecommunications.
文摘Multilayer complex dynamical networks,characterized by the intricate topological connections and diverse hierarchical structures,present significant challenges in determining complete structural configurations due to the unique functional attributes and interaction patterns inherent to different layers.This paper addresses the critical question of whether structural information from a known layer can be used to reconstruct the unknown intralayer structure of a target layer within general weighted output-coupling multilayer networks.Building upon the generalized synchronization principle,we propose an innovative reconstruction method that incorporates two essential components in the design of structure observers,the cross-layer coupling modulator and the structural divergence term.A key advantage of the proposed reconstruction method lies in its flexibility to freely designate both the unknown target layer and the known reference layer from the general weighted output-coupling multilayer network.The reduced dependency on full-state observability enables more deployment in engineering applications with partial measurements.Numerical simulations are conducted to validate the effectiveness of the proposed structure reconstruction method.
基金supported by the National Natural Science Foundation of China(32071715)the National Science Foundation of Tianjin City(22JCZDJC00560)。
文摘As an earth-abundant and natural biopolymer,cellulose has received significant attention in aqueous zinc-ion batteries(AZIBs)due to its inherent sustainability and non-toxicity,aligning perfectly with the core advantages of AZIBs.Nevertheless,the practical implementation of cellulose-based materials is limited by their intrinsically low ionic conductivity.Herein,we introduce a novel zincophilic artificial protective layer by strategically hybridizing hydroxypropyl cellulose(HPC)with zinc trifluoromethanesulfonate on a zinc metal anode(HZ@Zn).Characterization and calculations demonstrate that the multihydroxyl architecture of HPC constructs hydrogen bond networks,whereas the Zn^(2+)-coordinated HPC domains function as preferential nucleation sites for zinc deposition.These interactions collectively enhance ion transport and accelerate desolvation kinetics.Additionally,the hybrid layer's mechanical flexibility and interfacial adhesion ensure the integrity of the artificial protective layer during long cycling.Thanks to this synergistic effect,HZ@Zn shows exceptional electrochemical performance,including a low desolvation activation energy of 14.38 kJ mol^(-1)and ultra-long cycling stability.Symmetric cells demonstrate exceptional longevity,exceeding 9,500 h at 0.5 mA cm^(-2)/0.25 mAh cm^(-2),whereas HZ@Zn‖PANI full cells maintain 89.8%capacity retention after 4000 cycles at 5 A g^(-1).This study establishes biopolymers as versatile platforms for effectively stabilizing the zinc metal anode.
基金supported by the National Natural Science Foun-dation of China(Grant No.U2167214).
文摘Due to excellent thermal insulation performance at room temperature and ultralow density,silica aero-gels are candidates for thermal insulation.However,at high temperatures,the thermal insulation prop-erty of silica aerogels decreased greatly caused by transparency to heat radiation.Opacifiers introduced into silica sol can block heat radiation yet destroy the uniformity of aerogels.Herein,we designed and prepared a silica aerogel composite with oriented and layered silica fibers(SFs),SiC nanowires(SiC_(NWs)),and silica aerogels,which were prepared by papermaking,chemical vapor infiltration(CVI),and sol-gel respectively.Firstly,oriented and layered SFs made still air a wall to block heat transfer by the solid phase.Secondly,SiC_(NWs) were grown in situ on the surface of SFs evenly to weave into the network,and the network reduced the gaseous thermal conductivity by dividing cracks in SFs/SiC_(NWs)/SA.Thirdly,SiC_(NWs) weakened the heat transfer by radiation at high temperatures.Therefore,SFs/SiC_(NWs)/SA presented remarkable thermal insulation(0.017 W(m K)^(-1) at 25℃,0.0287 W(m K)^(-1) at 500℃,and 0.094 W(m K)^(-1) at 1000℃).Besides,SFs/SiC_(NWs)/SA exhibited remarkable thermal stability(no size transform after being heat treated at 1000℃ for 1800 s)and tensile strength(0.75 MPa).These integrated properties made SFs/SiC_(NWs)/SA a promising candidate for highly efficient thermal insulators.
基金the financial support from the National Natural Science Foundation of China(524B2168,U22A20149,52173081,and 52273275)。
文摘In recent decades,annual urban fire incidents,including those involving ancient wooden buildings burned,transportation,and solar panels,have increased,leading to significant loss of human life and property.Addressing this issue without altering the surface morphology or interfering with optical behavior of flammable materials poses a substantial challenge.Herein,we present a transparent,low thickness,ceramifiable nanosystem coating composed of a highly adhesive base(poly(SSS1-co-HEMA1)),nanoscale layered double hydroxide sheets as ceramic precursors,and supramolecular melamine di-borate as an accelerator.We demonstrate that this hybrid coating can transform into a porous,fire-resistant protective layer with a highly thermostable vitreous phase upon exposure to flame/heat source.A nanosystem coating of just~100μm thickness can significantly increase the limiting oxygen index of wood(Pine)to 37.3%,dramatically reduce total heat release by 78.6%,and maintain low smoke toxicity(CIT_G=0.016).Detailed molecular force analysis,combined with a comprehensive examination of the underlying flame-retardant mechanisms,underscores the effectiveness of this coating.This work offers a strategy for creating efficient,environmentally friendly coatings with fire safety applications across various industries.
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 the National Natural Science Foundation of China Basic Science Center Program for“Multiscale Problems in Nonlinear Mechanics”(Grant No.11988102)the National Natural Science Foundation of China(Grant Nos.92252203,12102439,and 12425207)+1 种基金the Chinese Academy of Sciences Project for Young Scientists in Basic Research(Grant No.YSBR-087)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB0620102).
文摘The empirical models for wavenumber-frequency spectra of wall pressure are broadly used in the fast prediction of aerodynamic and hydrodynamic noise.However,it needs to fit the parameter using massive data and is only used for limited cases.In this letter,we propose Kolmogorov-Arnold networks(KAN)base models for wavenumber-frequency spectra of pressure fluctuations under turbulent boundary layers.The results are compared with DNS results.In turbulent channel flows,it is found that the KAN base model leads to a smooth wavenumber-frequency spectrum with sparse samples.In the turbulent flow over an axisymmetric body of revolution,the KAN base model captures the wavenumber-frequency spectra near the convective peak.
文摘With breakthroughs in data processing and pattern recognition through deep learning technologies,the use of advanced algorithmic models for analyzing and interpreting soil spectral information has provided an efficient and economical method for soil quality assessment.However,traditional single-output networks exhibit limitations in the prediction process,particularly in their inability to fully utilize the correlations among various elements.As a result,single-output networks tend to be optimized for a single task,neglecting the interrelationships among different soil elements,which limits prediction accuracy and model generalizability.To overcome this limitation,in this study,a multi-task learning architecture with a progressive extraction network was implemented for the simultaneous prediction of multiple indicators in soil,including nitrogen(N),organic carbon(OC),calcium carbonate(CaCO 3),cation exchange capacity(CEC),and pH.Furthermore,while incorporating the Pearson correlation coefficient,convolutional neural networks,long short-term memory networks and attention mechanisms were combined to extract local abstract features from the original spectra,thereby further improving the model.This architecture is referred to as the Relevance-sharing Progressive Layered Extraction Network.The model employs an adaptive joint loss optimization method to update the weights of individual task losses in the multi-task learning training process.
基金supported by the National Natural Science Foundation of China(Nos.72271135,72141304,71901130)National Social Science Fund of China(22&ZD117)+3 种基金Laboratory of Computation and Analyticsof Complex Management Systems(Tianjin University)Special Funds for Taishan Scholars(tsqn202211120)2024 QingdaoFinance Society Key Project2024 Qingdao Social Science Planning Project.
文摘As the international environment changes,frequent geopolitical crises continue to hinder the healthy development of global stock markets.To analyze in-depth the risk contagion path between the international stock market and geopolitics under the impact of extreme events,this paper explores the tail risk interactive contagion mechanism and dynamic effects of the double-layer network between the international stock market and geopolitics from the perspective of complex networks.Empirical research finds that geopolitical conflicts exacerbate risk contagion among international stock markets,and there are significant differences in risk contagion between developed and emerging economies.The analysis of the complex interaction effect in the doublelayer network of the international stock market and geopolitics shows that the intralayer risk spillover effect of geopolitics in the short term is significantly higher than that of the stock price volatility network layer.Finally,the study on the dynamic changes of the double-layer network connectedness between the international stock market and geopolitics found that the shock of extreme events,such as military conflict and public health security,is an important factor in triggering the cross-contagion of risks.The research conclusions provide new ideas for preventing the cross-contagion of geopolitical risks in the stock markets of various countries under the evolution of the global political and economic pattern.
基金Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2025R319)Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publication.Special acknowledgement to Automated Systems&Soft Computing Lab(ASSCL),Prince Sultan University,Riyadh,Saudi Arabia.
文摘The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.
基金supported by the National Natural Science Foundation of China(No.62401597)Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Research Project of National University of Defense Technology,China(No.ZK22-02).
文摘Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 61763009 and 72172025)。
文摘Research on the self-similarity of multilayer networks is scarce, when compared to the extensive research conducted on the dynamics of these networks. In this paper, we use entropy to determine the edge weights in each sub-network,and apply the degree–degree distance to unify the weight values of connecting edges between different sub-networks, and unify the edges with different meanings in the multilayer network numerically. At this time, the multilayer network is compressed into a single-layer network, also known as the aggregated network. Furthermore, the self-similarity of the multilayer network is represented by analyzing the self-similarity of the aggregate network. The study of self-similarity was conducted on two classical fractal networks and a real-world multilayer network. The results show that multilayer networks exhibit more pronounced self-similarity, and the intensity of self-similarity in multilayer networks can vary with the connection mode of sub-networks.
基金supported by the National Social Science Fund Project(No.23&ZD115)the Graduate Student Research Innovation Project of the School of Mathematics and Statistics,Hubei Minzu University(No.STK2023011)。
文摘Compared to single-layer networks,multilayer networks exhibit a more complex node degree composition,comprising both intra-layer and inter-layer degrees.However,the distinct impacts of these degree types on cascading failures remain underexplored.Distinguishing their effects is crucial for a deeper understanding of network structure,information propagation,and behavior prediction.This paper proposes a capacity-load model to influence and compare the influence of different degree types on cascading failures in multilayer networks.By designing three node removal strategies based on total degree,intra-layer degree,and inter-layer degree,simulation experiments are conducted on four types of networks.Network robustness is evaluated using the maximum number of removable nodes before collapse.The relationships between network robustness and the coupling coefficient,as well as load and capacity adjustment parameters,are also analyzed.The results indicate that the node removal strategy with the least impact on cascading failures varies across different types of networks,revealing the significance of different node degrees in failure propagation.Compared to other models,the proposed model enables networks to maintain a higher maximum number of removable nodes during cascading failures,demonstrating superior robustness.
基金supported by the Natural Science Foundation of China Grant No.52272289 and 5240223,and JSPS(Japan Society for the Promotion of Science)of Grant No.22K19088,23H00313,24H02202,and 24H02205。
文摘NiFe-layered double hydroxides(NiFe-LDHs)are among the most promising earth-abundant electrocatalysts for the oxygen evolution reaction(OER)in alkaline media.However,their practical application is hindered by intrinsic activity limitations and poor stability,primarily due to the asymmetric adsorption of oxygen intermediates.To overcome this,the binding strength must be synergistically tuned to a moderate level to optimize catalytic performance.Here,we engineered NiFeCoCr LDH through Co doping to enhance electrical conductivity and controlled Cr leaching to introduce cationic vacancies for modulating intermediate binding strength in NiFe LDH.X-ray absorption near-edge structure and extended X-ray absorption fine structure analyses reveal that NiFe-LDH with Co doping and Cr vacancies modulates the Ni oxidation state and local coordination environment,leading to a balanced electronic structure and enhanced structural complexity around the Ni sites.Additionally,these vacancies can trap OH^(-)/H_(2)O species,which can serve as a reservoir for OH^(-) transfer,facilitating the rapid formation of OER intermediates and enhancing catalytic performance at high current densities.As a result,V_(Cr)-NiFeCo LDH achieves 1.6 A cm^(-2)current density at 1.7 V vs.RHE while maintaining stable operation for over 1000 h at 500 mA cm^(-2).Density functional theory(DFT)calculations validate the synergistic effects of Co doping and Cr-induced vacancies on intermediate binding energies and improved OER kinetics.Overall,this work presents a rational design strategy to simultaneously enhance the activity and durability of NiFe-based OER catalysts for their application in high-performance alkaline water electrolysis.
基金National Natural Science Foundation of China(NSFC)(grant 52375401,52350362,52235009,and 22379012)National Key Research and Development Program of China(2024YFB4609100)+1 种基金Chongqing Natural Science Foundation of China(grants cstc2021jcyj-cxttX0003)State Key Laboratory of High-performance Precision Manufacturing(grant HPMKF202411).
文摘Infrared thermal camouflage technologies are vital for enhancing the survivability of objects by altering their infrared radiation properties.However,existing solutions often fall short in adaptability and rapid responsiveness to dynamic environmental conditions,limiting their practical applicability.To overcome these challenges,we present an innovative approach combining ultrafast laser-induced non-volatile phase-change Ge_(2)Sb_(2)Te_(5)(GST)voxel-crystallized units with electrically tunable volatile VO_(2)layers.This integration enables precise,continuous control of infrared emissivity across a wide range of 0.14 to 0.98,effectively encompassing the emissivity of most materials.A neural network-based closed-loop system is employed for sensing,intelligent decision-making,and execution,achieving real-time thermal radiation matching between the target and its environment with a response speed of 3°C/s and an accuracy of±1°C.This strategy significantly enhances the adaptability of thermal camouflage in complex environments,paving the way for practical,dynamic thermal stealth applications.
基金Project supported by the National Natural Science Foundation of China (Grant No. 72174121)the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, and the Soft Science Research Project of Shanghai (Grant No. 22692112600)。
文摘Information plays a crucial role in guiding behavioral decisions during public health emergencies. Individuals communicate to acquire relevant knowledge about an epidemic, which influences their decisions to adopt protective measures.However, whether to disseminate specific information is also a behavioral decision. In light of this understanding, we develop a coupled information–vaccination–epidemic model to depict these co-evolutionary dynamics in a three-layer network. Negative information dissemination and vaccination are treated as separate decision-making processes. We then examine the combined effects of herd and risk motives on information dissemination and vaccination decisions through the lens of game theory. The microscopic Markov chain approach(MMCA) is used to describe the dynamic process and to derive the epidemic threshold. Simulation results indicate that increasing the cost of negative information dissemination and providing timely clarification can effectively control the epidemic. Furthermore, a phenomenon of diminishing marginal utility is observed as the cost of dissemination increases, suggesting that authorities do not need to overinvest in suppressing negative information. Conversely, reducing the cost of vaccination and increasing vaccine efficacy emerge as more effective strategies for outbreak control. In addition, we find that the scale of the epidemic is greater when the herd motive dominates behavioral decision-making. In conclusion, this study provides a new perspective for understanding the complexity of epidemic spreading by starting with the construction of different behavioral decisions.
基金Project supported by the National Natural Science Foundation of China (Grant No. 62371253)the Postgraduate Research and Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX24_1179)。
文摘As the economy grows, environmental issues are becoming increasingly severe, making the promotion of green behavior more urgent. Information dissemination and policy regulation play crucial roles in influencing and amplifying the spread of green behavior across society. To this end, a novel three-layer model in multilayer networks is proposed. In the novel model, the information layer describes green information spreading, the physical contact layer depicts green behavior propagation, and policy regulation is symbolized by an isolated node beneath the two layers. Then, we deduce the green behavior threshold for the three-layer model using the microscopic Markov chain approach. Moreover, subject to some individuals who are more likely to influence others or become green nodes and the limitations of the capacity of policy regulation, an optimal scheme is given that could optimize policy interventions to most effectively prompt green behavior.Subsequently, simulations are performed to validate the preciseness and theoretical results of the new model. It reveals that policy regulation can prompt the prevalence and outbreak of green behavior. Then, the green behavior is more likely to spread and be prevalent in the SF network than in the ER network. Additionally, optimal allocation is highly successful in facilitating the dissemination of green behavior. In practice, the optimal allocation strategy could prioritize interventions at critical nodes or regions, such as highly connected urban areas, where the impact of green behavior promotion would be most significant.
基金supported by the National Key R&D Program of China(2022YFA1305700 to Li J)the“Dawn”Program of Shanghai Education Commission,China(21SG33 to Li J)The National Natural Science Foundation of China(82272793 to Gao X).
文摘Objective:Prostate cancer(PCa)exhibits significant genomic differences between Western and Asian populations.This study aimed to design a predictive model applicable across diverse populations while selecting a limited set of genes suitable for clinical implementation.Methods:We utilized an integrated dataset of 1360 whole-exome and whole-genome sequences from Chinese and Western PCa cohorts to develop and evaluate the model.External validation was conducted using an independent cohort of patients.A graph neural network architecture,termed the pathway-aware multi-layered hierarchical network-Western and Asian(P-NETwa),was developed and trained on combined genomic profiles from Chinese and Western cohorts.The model employed a multilayer perceptron(MLP)to identify key signature genes from multiomics data,enabling precise prediction of PCa metastasis.Results:The model achieved an accuracy of 0.87 and an F1-score of 0.85 on Western population datasets.The application of integrated Chinese and Western population data improved the accuracy to 0.88,achieving an F1-score of 0.75.The analysis identified 18 signature genes implicated in PCa progression,including established markers(AR and TP53)and novel candidates(MUC16,MUC4,and ASB12).For clinical adoption,the model was optimized for commercially available gene panels while maintaining high classification accuracy.Additionally,a user-friendly web interface was developed to facilitate real-time prediction of primary versus metastatic status using the pre-trained P-NETwa-MLP model.Conclusion:The P-NETwa-MLP model integrates a query system that allows for efficient retrieval of prediction outcomes and associated genomic signatures via sample ID,enhancing its potential for seamless integration into clinical workflows.
基金supported by the National Natural Science Foundation of China(Grant No.72001207)。
文摘We propose a dual-layer network model that integrates social and familial contexts,consisting of a social interaction layer and a family relationship layer.We design a reputation-based incentive mechanism incorporating strategy persistenceconsistency to investigate how reputation fosters cooperation.The model features the following aspects:(1)a dynamic disconnection-reconnection mechanism in the social layer,(2)a reputation-enhanced Fermi rule in the family layer,and(3)a refined partitioning of the family network.Simulation results indicate that the disconnection-reconnection parameter(σ)significantly enhances cooperation in the social network;the strategic persistence-consistency influencing factor(α)has a positive impact on cooperation in both layers;and moderately dividing the family network promotes the emergence of cooperation.The research findings facilitate group cooperation in complex networks and offer valuable insights for addressing social dilemmas in the real world.
文摘Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on faulty-feeder detection tends to apply more complex digital signal processing techniques and deeper neural networks in order to better extract and learn as many detailed characteristics as possible.However,these approaches may easily result in overfitting and high computational cost,which cannot meet requirements for detection accuracy and efficiency in practical applications.This paper proposes an innovative waveform encoding method and details a simple convolutional neural network(CNN)with one layer of convolution used for identification,which seeks to improve detection accuracy and efficiency simultaneously.First,sparse characteristics of waveforms are utilized to encode into compact vectors,and a waveform-vector matrix is generated.Second,to deduce waveform-vector matrix,a simple CNN with multi-scale filters and one layer of convolution is established.Finally,a methodology for faulty-feeder detection is proposed,and both detection accuracy and efficiency are considerably enhanced.Comparative studies have confirmed clear superiority of the developed method,which outperforms existing approaches in both detection accuracy and efficiency,thus highlighting its significant potential for application.
