International Journal of Modern Physics C

The network infrastructures, such as the power grids and Internet, are expanding in size due to the increasing needs of our society. This brings about the problem of expanding networks with a guarantee of robustness against network disturbances that may cause catastrophic consequences. In this paper, we study the optimal network expansion in terms of network robustness against cascading failures. Specifically, we consider the network expansion as a Markovian decision process and further propose a reinforcement learning based network expansion method. Simulation results in model networks and real-world networks demonstrate that our expansion method can greatly improve network robustness. Our work provides some insights for the optimal expansion of network infrastructures.

In bounded confidence opinion dynamics, agents only consider other agents when their opinions are sufficiently close. In many works, the confidence bound is assumed as a homogeneous one. However, agents in different situations may be with different confidence bounds in real life. In this work, the correlation of degrees and confidence bounds is introduced into modified Hegselmann–Krause model on complex networks and we mainly focus on whether the population can reach consensus, where all agents hold a same opinion. The number of opinion clusters, the relative size of the largest cluster and the probability of reaching consensus are the measures of the opinion cluster profile. The numerical simulation shows that the existence of the correlation promotes the probability of reaching consensus and even in the case where some agents are with very small confidence bounds. On scale-free and Erdös–Rényi random networks, the negative correlation supports the consensus much more than the positive correlation case. However, two correlations show the same effect on small-world networks, and this effect is also observed in scale-free and Erdös–Rényi random networks when the average degree becomes very large.

With the development of social networks, misinformation including rumors is spreading through multiple channels, which brings many negative effects to society. The government and several Nongovernmental Organizations (NGOs) use relevant rumor rebuttal tactics to control the spread of rumors to prevent these detrimental impacts. In this paper, we consider the responses of different individuals to rumors and classify individuals into three categories: active, normal and inactive, with different activity rates for the three categories. In order to study the interaction between rumor repudiation mechanisms and rumor propagation, we propose a rumor propagation model that considers global and local rumor repudiation mechanisms as well as activity rates in multiplex networks. The upper layer represents the rumor refutation information propagation and the lower layer expresses the rumor propagation. And mean-field equations describing the dynamics of this model in multiplex networks are derived. We found that the threshold of rumor propagation is significantly affected by individual activity rates. Another interesting finding is that the global rumor refutation mechanism suppresses rumor propagation more than the local disinformation mechanism.

In the case of emergency evacuation of large places, the rapid evacuation of pedestrians has always been a matter of great concern. However, research on pre-movement behavior and decision-making fatigue behavior of pedestrians in emergency evacuation situations is limited. In order to study how to improve evacuation efficiency under the introduction of exit-choice system, this paper uses the cellular automata model to simulate different exit arrangements and exit numbers at different time intervals of information update by the system. It is found that there is an optimal time interval of information update when three exits of a room have different widths, and the evacuation time significantly reduces compared to real-time update. The potential mechanism of the optimal time interval of information update is that the evacuees can be reasonably allocated to each exit, so that the passage capacity of each exit can be maximized, and the evacuation time is significantly reduced. Sensitivity analysis shows that the change of exit location has an important influence on the evacuation time. The different pre-movement times of pedestrians have a certain degree of influence on the evacuation time of real-time update. The different distance parameters and flow parameters had a significant impact on the evacuation time in the real-time update case.

In this study, we collected and processed Weibo posts from official debunking accounts and found that user-following relationships can influence the dynamics of message propagation. Building upon the Susceptible, Aware, Infected, Recovered (SAIR) model with memory decay and user reinforcement, we develop the SAIRIgRg model to address the practical scenario of government debunking in the context of large-scale rumor spreading, considering the credibility factor. Through simulations on a real-world Twitter-directed network dataset, where different nodes with varying in-degree and out-degree sizes were chosen as debunking nodes, we analyzed the spread of rumors and debunking information. We discovered that there is little correlation between the initial in-degree and out-degree sizes of nodes and the effectiveness of debunking dissemination. Nodes with smaller average path lengths may not effectively suppress rumors through debunking efforts. Conversely, when debunking is conducted on nodes with larger average path lengths, the higher credibility of debunking messages leads to stronger suppression of rumors and shorter lifespans for the rumors. Additionally, this study conducted a comparison between early and late official debunking. It was discovered that when the spreading of rumors reaches a certain size and intensity, even though early debunking may not influence the lifespan of the rumors, it can greatly reduce the number of users affected by the rumors.

In the upstream of the highway work zone, vehicles will change lanes automatically or forcibly, which has complexity and randomness. Most studies fail to consider the impact of actual mixed traffic characteristics on traffic flow. For example, the dependence of the lane-changing behaviors of Connected and Automated Vehicles (CAVs) on the length of the zone is ignored in upstream of work zone. In addition, it is assumed that vehicles in the merge lane of the work zone would “yield” or not consider an impact on vehicles in the through lane. This simplifies the complex dynamics of the interaction between the two streams of traffic, which leads to the results of the study and the actual situation inconsistency. Based on the Gipps model, the adaptive cruise control (ACC) and the collaborative adaptive cruise control (CACC) vehicle-following models, this paper proposes a continuous cellular automata model under Vehicle-Road Collaboration and studies the mixed traffic flow characteristics in the work zone. By setting a metering zone before the lane reduction sign, ACC/CACC can adjust headway by obtaining road information in advance. The simulation results indicate that ACC/CACC vehicles have better following response compared to human-driven vehicles. In other words, the platoon composed of ACC/CACC vehicles has stability and can improve the traffic capacity of the work zone. The results of the study also found that the insertion of vehicles from adjacent lane into the ACC/CACC vehicles platoon is detrimental, especially when the penetration rate $(P_{\mathrm{\text{inter}}})$ is high. Furthermore, when the length of the metering zone $(L_{\mathrm{\text{meter}}})$ is set at 120–150m, it can significantly improve the road congestion in the work zone.

Improper lane-changing behavior may have a great impact on the safety of the surrounding vehicles, and accurate risk assessment of lane-changing behavior can detect improper lane-changing behavior. Many existing risk assessment methods are based on the traditional minimum safe distance model as the theoretical foundation, not taking into account the effects of different weather and road conditions. Moreover, the initially calculated safe lane-changing distance through the model is generally the limit safety distance, which deviates to some extent from the initial distance maintained when the vehicle begins to change lanes during the actual driving process. The ratio of these two distances can reflect the degree of risk between the changing vehicle and its neighboring vehicles during lane-changing. For this reason, a lane-changing safety coefficient is defined and lane-changing risk assessment model based on the hyperbolic tangent function is constructed by combining the change characteristics of this coefficient. An environmental adjustment factor was introduced into the model to consider the influence of different driving weather and road conditions on the lane-changing risk assessment. Then, based on survey data from the survey questionnaire, the weights of the influencing factors were determined by using the hierarchical analysis method, and the environmental adjustment factor in the model was calibrated by using the fuzzy comprehensive assessment method. Finally, to verify the effectiveness of the model, the lane-changing trajectories of 373 vehicles were extracted from the HighD dataset. The risk assessment value of lane-changing was calculated by using the constructed model and was classified into high-risk, medium-risk and low-risk by using the K-means++ clustering algorithm. Various evaluation results were compared with the evaluation results from the expert evaluation method and the classical risk evaluation indexes, and the evaluation results are basically the same, which verifies the effectiveness of the lane-changing risk model established.

Identifying influential spreaders is a crucial aspect of network science with various applications, including rumor control, viral marketing and epidemic spread limitation. Despite the availability of various methods for identifying these spreaders in complex networks, there remains a fundamental question regarding their accurate and discriminative identification. To address the issues and account for each node’s propagation ability, we propose an algorithm to identify influential spreaders based on the node’s weight and spreading probability (NWSP) for identifying influential spreaders. The effectiveness of the proposed method is evaluated using the Susceptible–Infected–Recovered (SIR) model, Kendall’s Tau ($\tau$) and monotonicity. The proposed method is compared with several well-known metrics, including degree centrality, K-shell decomposition, betweenness centrality, closeness centrality, eigenvector centrality and the centrality method based on node spreading probability (SPC), in ten real networks. Experimental results demonstrate the superiority ability of the proposed algorithm to accurately and discriminatively identify influential spreaders.

The information propagation on the social network has been an important research topic, with a focus on the significant influence of individual behaviors. The marginal utility behavior can affect the process of information propagation. But most previous research ignore it. Besides, the process can also be influenced by limited-contact capacity, which increase the complexity of networks. In this paper, the marginal utility behavior model on the single-layer network with limited-contact capacity is proposed first. Then the edge-based compartmental (EBC) method is used to explore the novel information propagation mechanism. Through experiments, it was found that when individuals show an increasing marginal utility behavior, with the propagation probability increasing, the final spreading scope shows a discontinuous increase pattern by weakening behavior. However, the final spreading scope shows no outbreak by strengthening behavior. In contrast, when individuals show a diminishing marginal utility behavior, with the propagation probability increasing, the final spreading scope shows a continuous increase pattern by strengthening. Nevertheless, the final spreading scope shows a discontinuous increasing by weakening. What’s more, the limited-contact capacity and the degree distribution heterogeneity can also change the information propagation pattern. Besides, the experimental results are in agreement with the theoretical results.

We investigate in detail the transference of quantum states in a disordered channel. We consider a one-dimensional tight-binding model consisting of a source S connected to a receiver R throughout a disordered channel. The disorder distribution contains a single tunable spatial correlation length. We demonstrate that the disorder correlation length plays a relevant role within the localization properties of the channel. The hopping parameter between the sites S and R and the channel are also adjustable parameters. We investigate the possibility of transference of quantum states along this quantum channel model and describe the optimal conditions for the occurrence of a high fidelity process.

In this study, the effect of different arrangements of injectors on the injection of liquid water into the hot supersonic crossflow of cylindrical geometry on the performance and the rate of evaporation has been studied using a numerical method. The crossflow has a Mach of about 2 and a total temperature of 800K. At first, the penetration height and droplets produced by atomization in the supersonic flow are validated using the Lagrangian technique, and the results have been compared with the experiments. The usage of heat transfer equations and the evaporation model for the spray evaporation of droplets in the hot flow is also performed in the validation process, and the results are compared with the experimental results. Then, the injection into the examined geometry with four, six and eight injectors in three different arrangements was evaluated. The results indicate that employing an arrangement of eight injectors leads to the most significant reduction in total temperature, with average total temperatures decreasing to below 550K. The results demonstrate a 40% decrease in total pressure, with a decrease in Mach number to about 1 at the end of the cylinder. Additionally, the evaporation rate shows higher evaporation of water in the eight-injector arrangement compared to other arrangements.

The misinformation spreading in social networks causes unpredictable damage to the networked system, thus inferring the misinformation source is an important research topic in the field of network science and security. Many source inference algorithms have been proposed to find the most likely propagation source through observable snapshot. However, under limited observable conditions, observing different nodes states markedly affects the algorithm’s effectiveness. Yet, we still lack relevant research on which nodes can more accurately assist us in completing source inference. Here, we propose the heuristic message-passing-based algorithm to find the key nodes that can maximize the accuracy of source inference, which uses the average rank of the source in the message-passing method as a measure and performs continuous annealing on this basis to update the set. As a comparison, we propose random selection algorithm as the basic, high-eigenvalue algorithm and high-degree algorithm focused on centrality, and basic message-passing-based algorithm from the perspective of energy entropy in message passing. Through extensive numerical simulation on artificial and real-world networks, compared with other four algorithms, our heuristic message-passing-based algorithm finds the optimal key node set that can more accurately complete source inference. Moreover, it has over 8% higher inference accuracy than other methods in low visibility situations especially.

Recently, synchronization of complex networks has been studied deeply; however, most existing works take the standard proportional control strategy approach to pinning controllers, which produces a steady-state error and has a significant time of fluctuation. In order to solve this problem, we propose a new method using the Proportional-Integral-Differential (PID) control to design a pinning control method for synchronization of complex networks. Based on Lyapunov theory, the stability of the network is proved, and sufficient synchronization conditions are obtained. Finally, examples are given to evaluate the effectiveness of the proposed PID pinning method and compare it to the traditional proportional pinning controller and adaptive pinning controller.

This study investigates the impact of a shock generator on the fuel distribution of downward step fuel injection in a scramjet engine’s combustor. Computational simulations of CFD are employed to analyze the three-dimensional structure of multi-jets released in a downward-step configuration. The inlet is subjected to a free stream supersonic flow with $\mathrm{\text{Mach}}=4$, while hydrogen gas is injected at $\mathrm{\text{Mach}}=1$ through injectors situated on the vertical planes of the downward step. A shock generator with a $30^{\circ }$ angle is positioned at the top of the domain to induce a bow shock, which significantly affects fuel distribution. The research assesses the influence of free stream and jet pressure on the fuel distribution behind the jets. The results indicate that increasing the free stream flow strengthens the induced bow shock, leading to enhanced fuel jet interactions and improved fuel mixing behind the jets.