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
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
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
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
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
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
Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of cha...Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.展开更多
Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class at...Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.展开更多
6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,faul...6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.展开更多
The rapid growth of Internet of things devices and the emergence of rapidly evolving network threats have made traditional security assessment methods inadequate.Federated learning offers a promising solution to exped...The rapid growth of Internet of things devices and the emergence of rapidly evolving network threats have made traditional security assessment methods inadequate.Federated learning offers a promising solution to expedite the training of security assessment models.However,ensuring the trustworthiness and robustness of federated learning under multi-party collaboration scenarios remains a challenge.To address these issues,this study proposes a shard aggregation network structure and a malicious node detection mechanism,along with improvements to the federated learning training process.First,we extract the data features of the participants by using spectral clustering methods combined with a Gaussian kernel function.Then,we introduce a multi-objective decision-making approach that combines data distribution consistency,consensus communication overhead,and consensus result reliability in order to determine the final network sharing scheme.Finally,by integrating the federated learning aggregation process with the malicious node detection mechanism,we improve the traditional decentralized learning process.Our proposed ShardFed algorithm outperforms conventional classification algorithms and state-of-the-art machine learning methods like FedProx and FedCurv in convergence speed,robustness against data interference,and adaptability across multiple scenarios.Experimental results demonstrate that the proposed approach improves model accuracy by up to 2.33%under non-independent and identically distributed data conditions,maintains higher performance with malicious nodes containing poisoned data ratios of 20%–50%,and significantly enhances model resistance to low-quality data.展开更多
Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems...Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.展开更多
The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through acceler...The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life.展开更多
Physics-informed neural networks(PINNs)have prevailed as differentiable simulators to investigate flow in porous media.Despite recent progress PINNs have achieved,practical geotechnical scenarios cannot be readily sim...Physics-informed neural networks(PINNs)have prevailed as differentiable simulators to investigate flow in porous media.Despite recent progress PINNs have achieved,practical geotechnical scenarios cannot be readily simulated because conventional PINNs fail in discontinuous heterogeneous porous media or multi-layer strata when labeled data are missing.This work aims to develop a universal network structure to encode the mass continuity equation and Darcy’s law without labeled data.The finite element approximation,which can decompose a complex heterogeneous domain into simpler ones,is adopted to build the differentiable network.Without conventional DNNs,physics-encoded finite element network(PEFEN)can avoid spectral bias and learn high-frequency functions with sharp/steep gradients.PEFEN rigorously encodes Dirichlet and Neumann boundary conditions without training.Benefiting from its discretized formulation,the discontinuous heterogeneous hydraulic conductivity is readily embedded into the network.Three typical cases are reproduced to corroborate PEFEN’s superior performance over conventional PINNs and the PINN with mixed formulation.PEFEN is sparse and demonstrated to be capable of dealing with heterogeneity with much fewer training iterations(less than 1/30)than the improved PINN with mixed formulation.Thus,PEFEN saves energy and contributes to low-carbon AI for science.The last two cases focus on common geotechnical settings of impermeable sheet pile in singlelayer and multi-layer strata.PEFEN solves these cases with high accuracy,circumventing costly labeled data,extra computational burden,and additional treatment.Thus,this study warrants the further development and application of PEFEN as a novel differentiable network in porous flow of practical geotechnical engineering.展开更多
Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at hig...Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.展开更多
With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to d...With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.展开更多
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.
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 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 National Key R&D Program of ChinaunderGrant,(2021YFB2601403).
文摘Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)—Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2025-RS-2022-00156334)in part by Liaoning Province Nature Fund Project(2024-BSLH-214).
文摘Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.
基金supported in part by the National Key Research and Development Project under Grant 2020YFB1806805partially funded through a grant from Qualcomm。
文摘6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.
基金supported by State Grid Hebei Electric Power Co.,Ltd.Science and Technology Project,Research on Security Protection of Power Services Carried by 4G/5G Networks(Grant No.KJ2024-127).
文摘The rapid growth of Internet of things devices and the emergence of rapidly evolving network threats have made traditional security assessment methods inadequate.Federated learning offers a promising solution to expedite the training of security assessment models.However,ensuring the trustworthiness and robustness of federated learning under multi-party collaboration scenarios remains a challenge.To address these issues,this study proposes a shard aggregation network structure and a malicious node detection mechanism,along with improvements to the federated learning training process.First,we extract the data features of the participants by using spectral clustering methods combined with a Gaussian kernel function.Then,we introduce a multi-objective decision-making approach that combines data distribution consistency,consensus communication overhead,and consensus result reliability in order to determine the final network sharing scheme.Finally,by integrating the federated learning aggregation process with the malicious node detection mechanism,we improve the traditional decentralized learning process.Our proposed ShardFed algorithm outperforms conventional classification algorithms and state-of-the-art machine learning methods like FedProx and FedCurv in convergence speed,robustness against data interference,and adaptability across multiple scenarios.Experimental results demonstrate that the proposed approach improves model accuracy by up to 2.33%under non-independent and identically distributed data conditions,maintains higher performance with malicious nodes containing poisoned data ratios of 20%–50%,and significantly enhances model resistance to low-quality data.
基金supported by the National Natural Science Foundation of China(61571149,62001139)the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Natural Science Foundation of Heilongjiang Province(LH2020F0178).
文摘Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.
基金supported by the National Key Research and Development Project(Grant Number 2023YFB3709601)the National Natural Science Foundation of China(Grant Numbers 62373215,62373219,62073193)+2 种基金the Key Research and Development Plan of Shandong Province(Grant Numbers 2021CXGC010204,2022CXGC020902)the Fundamental Research Funds of Shandong University(Grant Number 2021JCG008)the Natural Science Foundation of Shandong Province(Grant Number ZR2023MF100).
文摘The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life.
基金supported by the National Natural Science Foundation of China(Grant Nos.42272338 and 41827807)Department of Transportation of Zhejiang Province,China(Grant No.202213).
文摘Physics-informed neural networks(PINNs)have prevailed as differentiable simulators to investigate flow in porous media.Despite recent progress PINNs have achieved,practical geotechnical scenarios cannot be readily simulated because conventional PINNs fail in discontinuous heterogeneous porous media or multi-layer strata when labeled data are missing.This work aims to develop a universal network structure to encode the mass continuity equation and Darcy’s law without labeled data.The finite element approximation,which can decompose a complex heterogeneous domain into simpler ones,is adopted to build the differentiable network.Without conventional DNNs,physics-encoded finite element network(PEFEN)can avoid spectral bias and learn high-frequency functions with sharp/steep gradients.PEFEN rigorously encodes Dirichlet and Neumann boundary conditions without training.Benefiting from its discretized formulation,the discontinuous heterogeneous hydraulic conductivity is readily embedded into the network.Three typical cases are reproduced to corroborate PEFEN’s superior performance over conventional PINNs and the PINN with mixed formulation.PEFEN is sparse and demonstrated to be capable of dealing with heterogeneity with much fewer training iterations(less than 1/30)than the improved PINN with mixed formulation.Thus,PEFEN saves energy and contributes to low-carbon AI for science.The last two cases focus on common geotechnical settings of impermeable sheet pile in singlelayer and multi-layer strata.PEFEN solves these cases with high accuracy,circumventing costly labeled data,extra computational burden,and additional treatment.Thus,this study warrants the further development and application of PEFEN as a novel differentiable network in porous flow of practical geotechnical engineering.
文摘Multi-scale system remains a classical scientific problem in fluid dynamics,biology,etc.In the present study,a scheme of multi-scale Physics-informed neural networks is proposed to solve the boundary layer flow at high Reynolds numbers without any data.The flow is divided into several regions with different scales based on Prandtl's boundary theory.Different regions are solved with governing equations in different scales.The method of matched asymptotic expansions is used to make the flow field continuously.A flow on a semi infinite flat plate at a high Reynolds number is considered a multi-scale problem because the boundary layer scale is much smaller than the outer flow scale.The results are compared with the reference numerical solutions,which show that the msPINNs can solve the multi-scale problem of the boundary layer in high Reynolds number flows.This scheme can be developed for more multi-scale problems in the future.
基金Project supported by the National Natural Science Foundation of China(Grant No.T2293771)the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.
