Influence of network structure on spreading dynamics via tie range

There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of network structure on network spreading. Motifs, as fundamental structures within a network, play a significant role in spreading. Therefore, it is of interest to investigate the influence of the structural characteristics of basic network motifs on spreading dynamics.Considering the edges of the basic network motifs in an undirected network correspond to different tie ranges, two edge removal strategies are proposed, short ties priority removal strategy and long ties priority removal strategy. The tie range represents the second shortest path length between two connected nodes. The study focuses on analyzing how the proposed strategies impact network spreading and network structure, as well as examining the influence of network structure on network spreading. Our findings indicate that the long ties priority removal strategy is most effective in controlling network spreading, especially in terms of spread range and spread velocity. In terms of network structure, the clustering coefficient and the diameter of network also have an effect on the network spreading, and the triangular structure as an important motif structure effectively inhibits the spreading.

Robustness of community networks against cascading failures with heterogeneous redistribution strategies

Network robustness is one of the core contents of complex network security research.This paper focuses on the robustness of community networks with respect to cascading failures,considering the nodes influence and community heterogeneity.A novel node influence ranking method,community-based Clustering-LeaderRank(CCL)algorithm,is first proposed to identify influential nodes in community networks.Simulation results show that the CCL method can effectively identify the influence of nodes.Based on node influence,a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks.Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process.The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities.When the initial load distribution and the load redistribution strategy based on the node influence are the same,the network shows better robustness against node failure.

Trusted Encrypted Traffic Intrusion Detection Method Based on Federated Learning and Autoencoder

With the rapid development of the Internet,network security and data privacy are increasingly valued.Although classical Network Intrusion Detection System(NIDS)based on Deep Learning(DL)models can provide good detection accuracy,but collecting samples for centralized training brings the huge risk of data privacy leakage.Furthermore,the training of supervised deep learning models requires a large number of labeled samples,which is usually cumbersome.The“black-box”problem also makes the DL models of NIDS untrustworthy.In this paper,we propose a trusted Federated Learning(FL)Traffic IDS method called FL-TIDS to address the above-mentioned problems.In FL-TIDS,we design an unsupervised intrusion detection model based on autoencoders that alleviates the reliance on marked samples.At the same time,we use FL for model training to protect data privacy.In addition,we design an improved SHAP interpretable method based on chi-square test to perform interpretable analysis of the trained model.We conducted several experiments to evaluate the proposed FL-TIDS.We first determine experimentally the structure and the number of neurons of the unsupervised AE model.Secondly,we evaluated the proposed method using the UNSW-NB15 and CICIDS2017 datasets.The exper-imental results show that the unsupervised AE model has better performance than the other 7 intrusion detection models in terms of precision,recall and f1-score.Then,federated learning is used to train the intrusion detection model.The experimental results indicate that the model is more accurate than the local learning model.Finally,we use an improved SHAP explainability method based on Chi-square test to analyze the explainability.The analysis results show that the identification characteristics of the model are consistent with the attack characteristics,and the model is reliable.

Revision of stratified OWL ontologiesbased on integer linear programming

To revise stratified web ontology language(OWL)ontologies,the kernel revision operator is extended by defining novel conflict stratification and the incision function based on integer linear programming(ILP).The ILP-based model considers an optimization problem of minimizing a linear objective function which is suitable for selecting the minimal number of axioms to remove when revising ontologies.Based on the incision function,a revision algorithm is proposed to apply ILP to all minimal incoherence-preserving subsets(MIPS).Although this algorithm can often find a minimal number of axioms to remove,it is very time-consuming to compute MIPS.Thus,an adapted revision algorithm to deal with unsatisfiable concepts individually is also given.Experimental results reveal that the proposed ILP-based revision algorithm is much more efficient than the commonly used algorithm based on the hitting set tree.In addition,the adapted algorithm can achieve higher efficiency,while it may delete more axioms.

Investigating the characteristic delay time in the leader–follower behavior in children single-file movement

The single-file movement experiment offered a convenient way to investigate the one-dimensional leader–follower behavior of pedestrians. This study investigated the time delays of children pedestrians in the leader–follower behavior by introducing a time-dependent delayed speed correlation. A total of 118 German students from the fifth grade(aged11–12 years old) and the 11th grade(aged 17–18 years old) participated the single-file experiment. The characteristic delay time for each pedestrian was identified by optimising the time-dependent delayed speed correlation. The influences of the curvature of the experimental scenario, density, age, and gender on the delay time were statistically examined. The results suggested that to a large extent, the revealed characteristic delay time was a density-dependent variable, and none of the curvatures, the age and gender of the individual, and the age and gender of the leader had a significant influence on it. The findings from this study are variable resources to understand the leader–follower behavior among children pedestrians and to build related simulation models.

Review on application of phase change materials in asphalt pavement

Phase change materials(PCMs)can regulate the temperature in asphalt pavement and minimize temperature-related problems,such as rutting and thermal cracking,because of their ability to store and release latent heat.Suitable PCMs can also enable additional road surface functions,such as snow melting ability,freeze-thaw cycle resistance,and heat island reduction.These functions are helpful in achieving intelligent,green,and sustainable transportation systems.Although the research on PCMs for asphalt pavement has been carried out for more than 10 years,a systematic material system and mature application technology have not yet been formed.The main reasons for restricting the development of this technology include the lack of suitability between the PCMs and asphalt pavement,the quantitative characterization of phase change temperature regulation property,and the evaluation of the effect of phase change energy storage on improving pavement performance.Although the published review has made a comprehensive summary of the existing research,it has yet to identify the key restricting the development of this technology and carry out a review and discussion based on it.To grasp the development status of the application of PCMs in asphalt pavement,sort out the development needs and break through the technical barriers,this study systematically summarizes the preparation and performance of PCMs for asphalt pavement,compares the performance and evaluation methods of asphalt mixtures with different PCMs,and summarizes the numerical simulation methods of phase change asphalt mixtures.Finally,this study presents potential approaches to address critical technical issues and discusses possible future research.

High sensitive chiral molecule detector based on the amplified lateral shift in Kretschmann configuration involving chiral TDBCs

We investigate a high sensitive chiral molecule detector based on Goos–Hanchen shift(S) in Kretschmann configuration involving chiral tri(diethylene glycol monobutyl) citrates(TDBCs). Fresnel equations and the stationary phase method are employed to calculate S. Due to the interaction between surface plasmon polaritons and chiral TDBCs, S with chiral TDBCs are amplified at near the resonant wavelengths of chiral TDBCs. Our calculation results show that although the difference between the resonant wavelengths of left and right TDBCs is 4.5 nm, the positions of the largest S for the structures with left TDBCs and right TDBCs do not overlap. S reaches 400 times(or 200 times) the incident wavelength around the resonant wavelength of left TDBCs(or right TDBCs). The difference of S with chiral TDBCs(?S) can reach400 times or 200 times the incident wavelength in certain conditions, which can be directly observed in experiments. Left TDBCs and right TDBCs are easily distinguished. There is an optimal thickness of the metal film to realize the largest difference of S between Kretschmann configurations with left TDBCs and right TDBCs. Furthermore, we discuss the oscillator strength f, which is mainly determined by TDBC concentration. We find that our proposed detector is quite sensitive with f. By changing f from 0.008 to 0.014 with the step of 0.002, the change of ?S is no less than five times the incident wavelength(2.9 μm). Our proposed structure is very sensitive to the chirality and the concentration of TDBCs and has potential applications in distinguishing the chirality detector.

Lithological Facies Classification Using Attention-Based Gated Recurrent Unit

Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.

Transmit Antenna Selection for Millimeter-Wave MIMO System Based on GBDT

In millimeter-wave multiple-input multipleoutput(MIMO)systems,transmit antenna selection(TAS)can be employed to reduce hardware complexity and energy consumption when the number of antennas becomes very large.However,the traditional exhaustive search TAS tries all possible antenna combinations which causes high computational complexity.It may limit its application in practice.The main advantage of machine learning(ML)lies in the capability of establishing underlying relations between system parameters and objective,hence being able to shift the computation burden of real-time processing to the offline training phase.Based on this advantage,introducing ML to TAS is a promising way to tackle the high computational complexity problem.Although the existing ML-based algorithms try to approach the optimal performance,there is still a large room for improvement.In this paper,considering the secure transmission of the system,we model the TAS problem as a multi-class classification problem and propose an efficient antenna selection algorithm based on gradient boosting decision tree(GBDT),in which we consider the system security capacity and computational complexity as the optimization objectives.On the one hand,the system security performance is improved because its achievable security capacity is close to the traditional exhaustive search algorithm.On the other hand,compared with the exhaustive search algorithm and existing ML-based algorithms,the training efficiency is significantly improved with the complexity O(N),where N is the number of transmitting antenna.In addition,the performance of the proposed algorithm is evaluated in mmWave MIMO system by employing New York University simulator(NYUSIM)model,which is based on the real channel measurement.Performance analysis show that the proposed GBDT-based scheme can effectively improve the system secrecy capacity and significantly reduce the computational complexity.

Robust train speed trajectory optimization: A stochastic constrained shortest path approach

Train speed trajectory optimization is a significant issue in railway traffic systems, and it plays a key role in determining energy consumption and travel time of trains. Due to the complexity of real-world operational environments, a variety of factors can lead to the uncertainty in energy-consumption. To appropriately characterize the uncertainties and generate a robust speed trajectory, this study specifically proposes distance-speed networks over the inter-station and treats the uncertainty with respect to energy consumption as discrete samplebased random variables with correlation. The problem of interest is formulated as a stochastic constrained shortest path problem with travel time threshold constraints in which the expected total energy consumption is treated as the evaluation index. To generate an approximate optimal solution, a Lagrangian relaxation algorithm combined with dynamic programming algorithm is proposed to solve the optimal solutions. Numerical examples are implemented and analyzed to demonstrate the performance of proposed approaches.

Approximation Designs for Energy Harvesting Relay Deployment in Wireless Sensor Networks

Energy harvesting technologies allow wireless devices to be recharged by the surrounding environment, providing wireless sensor networks (WSNs) with higher performance and longer lifetime. However, directly building a wireless sensor network with energy harvesting nodes is very costly. A compromise is upgrading existing networks with energy harvesting technologies. In this paper, we focus on prolonging the lifetime of WSNs with the help of energy harvesting relays (EHRs). EHRs are responsible for forwarding data for sensor nodes, allowing them to become terminals and thus extending their lifetime. We aim to deploy a minimum number of relays covering the whole network. As EHRs have several special properties such as the energy harvesting and depletion rate, it brings great research challenges to seek an optimal deployment strategy. To this end, we propose an approximation algorithm named Effective Relay Deployment Algorithm, which can be divided into two phases: disk covering and connector insertion using the partitioning technique and the Steinerization technique, respectively. Based on probabilistic analysis, we further optimize the performance ratio of our algorithm to (5 + 6/K) where K is an integer denoting the side length of a cell after partitioning. Our extensive simulation results show that our algorithm can reduce the number of EHRs to be deployed by up to 45% compared with previous work and thus validate the efficiency and effectiveness of our solution.