International Journal of Modern Physics C

Influence maximization is a critical research topic of social network analysis, particularly with the increasing involvement of individuals in the global networked society. The purpose of the problem is to identify k influential nodes from the social network and activate them initially to maximize the expected number of influenced nodes at the end of the spreading process. Although some meta-heuristics based on swarm intelligence or biological evolution have been proposed to tackle this intractable problem, further investigation is required to refine the exploration and exploitation operations based on the iterative information from the evolutionary process. In this paper, an adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies is proposed for the influence maximization problem. In order to enhance the evolutionary capability of the later stages of the discrete differential evolution, the mutation in the framework, consisting of three policies, namely comprehensive learning particle swarm mutation strategy, differential mutation strategy, and perturbation strategy, is implied based on different probabilistic models. An adaptive local search strategy is presented to improve the local optimum results based on a potential alternative library consisting of structural hole nodes, which guides the differential evolution to find a more optimal solution. Experimental results on six real-world social networks demonstrate the competitive performance of the proposed algorithm in terms of both efficacy and efficiency compared to state-of-the-art algorithms.

In this study, the combined effect of Joule heating and Soret number on magnetohydrodynamics (MHD) braking of incompressible stratified liquid metal packed between a confined rotating annulus has been investigated. Both axial sinusoidal temperature and as well as sinusoidal concentration have been imposed in this braking system. However, radial cooling is enforced on the rotating disc of the braking system. The Soret effect on concentration gradient has been observed when the stratified liquid is exposed to a temperature gradient along with an axial magnetic field at different buoyancy ratios.

A significant difference in convection heat and mass flow can be seen. The average shear wood decreased from 8.97 to 2.36 and the average Nusselt number decreased from 8.6 to 2.8 between the outer rotating disk and inner stationary disks at positive buoyancy ratios. This huge difference in heat and mass convection near the inner disk with respect to the outer brake disk confirms the MHD braking effect. Moreover, comparative studies of mass convection with respect to convective heat transfer have been observed based on the average magnitude of Sherwood and Nusselt numbers at both positive and negative buoyancy ratios.

The Maxima computer algebra system, the open-source successor to MACSYMA, the first general-purpose computer algebra system that was initially developed at the Massachusetts Institute of Technology in the late 1960s and later distributed by the United States Department of Energy, has some remarkable capabilities, some of which are implemented in the form of add-on, “share” packages that are distributed along with the core Maxima system. One such share package is itensor, for indicial tensor manipulation. One of the more remarkable features of itensor is functional differentiation. Through this, it is possible to use itensor to develop a Lagrangian field theory and derive the corresponding field equations. In this paper, we demonstrate this capability by deriving Maxwell’s equations from the Maxwell Lagrangian, and exploring the properties of the system, including current conservation.

This paper aims to investigate the thermal behavior of water-based nanofluid flow over a rotating surface, focusing on understanding the effects of different types of nanoparticles on thermal efficiency, considering Catteneo–Christov heat flux and variable viscosity effects. By considering four distinct nanoparticles — silicon dioxide, titanium dioxide, copper oxide and zinc oxide — this study aims to provide insights into how nanoparticle addition influences heat production, thermal boundary layer thickness and overall thermal performance. The study employs computational methods by utilizing the BVP Midrich algorithm for the solution procedure. The computational approach allows for a detailed investigation of the thermal behavior of nanofluid flows across a rotating surface under varying conditions. The study concludes that adding nanoparticles in the base liquid increases heat production in the system, resulting in enhanced thermal boundary layer thickness. The comparative analysis shows that different nanoparticle types exhibit varying effects on thermal efficiency, suggesting that careful selection of nanoparticles can optimize heat transport and thermal management processes. Moreover, it seems there’s a noteworthy downfall in the thermal profile concerning the relaxation time parameter, whereas a converse trend is observed for Biot number.

This paper presents a comprehensive hemodynamic evaluation of two endovascular techniques for treating cerebral aneurysms. Using the finite volume method, we simulated pulsatile blood flow and heat transport dynamics in two saccular aneurysms of varying sizes and shapes. We assessed hemodynamic factors under two coiling conditions with different porosities and deformation stages to identify the most effective treatment for each case. Our computational analysis reveals that stent and coiling applications are more efficient for aneurysms with low-sac volume. Notably, stent placement proves to be more effective than coiling in treating saccular aneurysms. Our findings indicate that stent-induced deformation sufficiently diverts the main blood flow, thereby reducing the risk of aneurysm rupture near the ostium region.

Swarm intelligent optimization algorithm plays a key role in solving the influence maximization problem. Aiming at the problems of insufficient diversity and overlapping influence when selecting initial nodes in previous influence maximization algorithms, the local influence measure of nodes is constructed considering the network topology, and the population initialization method based on local influence is proposed. Aiming at the traditional moth-flame algorithm which is difficult to deal with the balance between global exploration and local exploitation, resulting in the decline of population diversity and easy to fall into the local optimal solution, combining with the social learning theory, re-planning the population state, defining multiple learning strategies for individuals in different states, and proposing a new method for the moth-flame algorithm based on the social learning theory to solve the problem of maximizing the influence of the social network. Experiments are conducted on six real social networks under the independent cascade model (IC), and the results show that the proposed algorithm can effectively identify the influential node sets.

Vehicles equipped with advanced driving assistance systems (ADAS) can acquire information about preceding and following vehicles, which will have an undeniable impact on the phase transition of traffic flow. Therefore, we establish a new heterogeneous continuum model of traffic flow considering the jerk effect mixed with human driving (HD) and ADAS vehicles. The neutral stability condition associated with the permeability of ADAS vehicle is inferred via stability analysis. According to simulation, shock wave occurs, implying that our model can emerge correct wave propagation trend. Moreover, the simulation of density evolution and flow changes also suggests that an increase in ADAS vehicle penetration and jerk effect can enhance traffic system stability and reduce energy consumption.

Traffic network design is a pivotal aspect of urban planning, necessitating a harmonious blend of theoretical and practical approaches. In this paper, we propose a novel network design methodology utilizing Delaunay triangulation. We address the limitations of raw Delaunay networks, particularly their triangular configurations, by introducing edge-reduction techniques guided by a newly developed metric, detour redundancy. This metric is instrumental in assessing edge importance, facilitating the transformation of the network to better mirror real-world traffic scenarios. The efficacy and practical application of the designed networks are then evaluated through traffic simulations using the principles of cellular automata. Our findings underscore the potential of this approach in enriching traffic network design and pave the way for future investigations into diverse network characteristics and their implications in urban environments.

The intelligent design of complex transportation nodes is an important way to improve urban transportation efficiency and safety. However, currently, there are still problems with low data acquisition and processing accuracy and high-technology application costs in complex transportation nodes. Therefore, the research attempts to achieve the intelligent design of complex traffic node organizations based on digital analogue-driven and integrated building information models and optimization graph convolution algorithms. It aims to achieve real-time monitoring and analysis of traffic information to improve the overall performance of the transportation system. These experiments confirmed that the studied BIM could achieve full lifecycle management of urban interchanges, and could achieve strain analysis and trend prediction based on time series. When the organizational members were 122, the information channels for smart stations based on research technology were 241, which improved information efficiency by more than 90%. When the sensor node was 48, the predicted values of the research model based on the optimization graph convolution algorithm were consistent with the actual values, with a prediction accuracy of 95%. Therefore, the technology studied in this study can provide reliable solutions for the design of complex traffic nodes and support traffic management decisions.

This paper introduces the Principal-Agent mechanism to study the diffusion of information in complex networks. Hot list information is defined as principal information, information that is homogeneous with hot list information is defined as homogeneous agent information, and information that is heterogeneous with hot list information is defined as heterogeneous agent information. The theory of diffusion dynamics is used to study the phenomenon that homogeneous information promotes each other and heterogeneous information inhibits each other in the process of competitive diffusion of network information. Based on the traditional SIR model, this paper introduces the Principal-Agent mechanism, establishes the PA_SIR diffusion model and studies the mutual influence of information in the process of competitive diffusion of multiple information. The research shows that in the process of competitive diffusion of multiple information, principal information has a positive incentive effect on homogeneous agent information through people’s interest orientation, herd mentality and topic public opinion and has a negative incentive effect on heterogeneous agent information. Therefore, if you want your published information to attract more attention, the best release time is when homogeneous information is on the hot search list, which will help to attract people’s attention more quickly.

Link prediction is a vital aspect of analyzing network evolution and identifying potential connections in complex networks. Previous studies have primarily focused on the information of common neighbors between nodes, often overlooking the inherent attributes of nodes. This study proposes community-based popularity, an attribute of nodes that considers changes in the neighborhood over time in conjunction with the community structure. Based on this attribute, we improve similarity-based link prediction methods. The experiments utilized unweighted directed networks from three distinct types of trade to evaluate the effectiveness of the improved link prediction methods. The training and probe sets were divided in chronological order. The experimental results show that the improved methods provide better link prediction results than the compared methods.

This paper presents a novel microscopic modeling framework to explore the impact of driver stochasticity and other related factors on heterogeneous traffic flows, consisting of both regular human-driven vehicles (RHDVs) and connected human-driven vehicles (CHDVs). We apply the stochastic optimal velocity car-following model for RHDVs, while an extended version of the stochastic continuous car-following model, taking into account optimal velocity guidance and driver compliance, is devised for CHDVs. The stability of CHDV car-following model is examined theoretically and numerically. Simulation experiments are conducted to analyze the stochastic heterogeneous traffic flow properties (i.e. stability and safety) with the proposed microscopic modeling framework under diverse parameter settings including CHDV penetration rate, driver compliance, and driver stochasticity. Results show that traffic flow stability and safety improve with the increase in CHDV penetration rate and driver compliance but deteriorate as the driver’s stochasticity strength increases. CHDVs significantly enhance traffic stability when the CHDV penetration rate or driver compliance exceeds a certain threshold, typically ranging between 0.5 and 0.7. Additionally, there is an interaction between driver stochasticity and CHDV penetration rate in their effect on traffic flow stability, where the contribution of stochasticity to traffic oscillations first increases and then decreases as the penetration rate rises. When the penetration rate is between 0 and 0.4, the traffic risk in emergency situations can be significantly reduced as the penetration rate increases. These findings may guide the management and control strategies of heterogeneous traffic flow.

The electrification of transportation has led to an increased emphasis on the thermal management of battery packs to ensure their safety, efficiency, and longevity. This study presents a comprehensive estimation of the C-rate through analytical and the thermal behavior of an electric two-wheeler battery pack (3.2kWh) under varying operational conditions through a combination of simulation and experimental testing. Numerical analysis was conducted using Celsius EC Solver (version 2023.2) software, with a specific focus on parameters such as C-rate and temperature distribution. Experimental validation was performed under controlled conditions, and key metrics such as ambient temperature, maximum temperature reached at various locations, heat generation rates, voltage, and current were measured. The findings of this study demonstrate that there is less than 4% error which is well agreed between simulation and experimental results. This indicates a significant congruence between the simulated and experimental data, underscoring the reliability of the proposed simulation model. This analysis provides valuable insights for optimizing thermal management strategies in battery packs.

Complex systems are prevalent in nature, and link prediction is crucial for analyzing these systems. This method has gained attention for its ability to uncover various irregular movements with underlying similarities. While individual particle behavior is unpredictable, the collective behavior of large groups can be forecasted more accurately. The variability at lower scales vs universal similarities at higher scales is often overlooked. To address this, we investigate the random walk process on directed networks at a global scale. We propose an improved link prediction method using biased random walks to enhance accuracy and applicability. We first define out-degree neighbors as valid transmission options to prevent conflicts with in-degree paths. We then analyze how out-degrees and in-degrees affect particle transition probabilities, guiding particles toward high out-degrees and low in-degrees for a sustainable process. Finally, we use particle stabilization probabilities at different nodes as a similarity measure for predicting potential connections. Our method improves prediction precision and practicality, as validated by experiments on nine real-world networks, showing significant gains in accuracy and AUC scores.