International Journal of Modern Physics C

According to Peter M. Blau (Exchange and Power in Social Life, Wiley and Sons, 1964, p. 43), the process of integration of a newly formed group has a paradoxical aspect: most attractive individuals are rejected because they raise fear of rejection. Often, their solution is to apply a self-deprecating strategy (SDS), which artificially raises the social statuses of their opponents. Here, we introduce a two-dimensional space of status, and we demonstrate that with this setup, the SDS can efficiently prevent the rejection. Examples of application of this strategy in the scale of a society are provided.

In the application of the lattice Boltzmann method (LBM) for the simulation of the interface-resolved particulate flows, the bounce-back (BB) type rules have been widely adopted to handle the complex boundaries of moving particle. However, the original method cannot preserve the integrity of the particle shape, resulting in a low-resolution for the flow description near the solid boundary. Even though the subsequent modified BB scheme, i.e. the curved boundary condition (CBC), improves the overall accuracy, it generally loses the local-computation property of the simple BB. Therefore, a CBC is proposed in this paper, which maintains the two advantages of the second-order accuracy and local computation in the boundary treatment simultaneously. In the present scheme, information of only a single fluid point is needed. Furthermore, the relative distance between the fluid point and the boundary surface is involved, contributing to the second-order accuracy that is validated in the Poiseuille and cylindrical Couette flows. Particularly, it is found that the precision of the present scheme can be greatly improved with the nonequilibrium distribution functions of two directions included. Three more test cases of particle-laden flow, including particle migration in a channel, the sedimentation of a particle under gravity and the drafting-kissing-tumbling (DKT) dynamics of two settling particles, further demonstrate the feasibility and accuracy of the present scheme.

In this paper, we proposed a model based on the connected vehicles to control the traffic flow at the intersection. To evaluate this model, we studied its impact on the flux and the probability of accidents at the intersection. On the one hand, simulation results showed that the increase in the number of connected vehicles decreases the congestion coefficient and enhances the traffic situation in the system. On the other hand, connected vehicles reduce the probability of accidents at the intersection. In this way, the vehicle intersection (central controller) communication can decrease the congestion and enhance road safety, especially in the intermediate and high traffic conditions.

In this work, we propose a phase-field-based lattice Boltzmann method to simulate moving contact line (MCL) problems on curved boundaries. The key point of this method is to implement the boundary conditions on curved solid boundaries. Specifically, we use our recently proposed single-node scheme for the no-slip boundary condition and a new scheme is constructed to deal with the wetting boundary conditions (WBCs). In particular, three kinds of WBCs are implemented: two wetting conditions derived from the wall free energy and a characteristic MCL model based on geometry considerations. The method is validated with several MCL problems and numerical results show that the proposed method has utility for all the three WBCs on both straight and curved boundaries.

The spread of rumors on complex networks has attracted wide attention in the field of management. In this paper, the generalized rumor spreading model is modified to take into account the vital of the spreader and the tie strength for the pairwise contacts between nodes in complex networks at degree-dependent spreading rate. Concretely, we introduce the infectivity exponent $\alpha$, and the degree influenced real exponent $\beta$ into the analytical rumor spreading model. Rumor infectivity, $\phi (k)=k^{\alpha }$, where $0<\alpha \le 1$, defines that each spreader node may contact $k^{\alpha }$ neighbors within one time step. The tie strength between two nodes with degrees $k$ and $l$ are measured by ${\omega }_{kl}=b{(kl)}^{\beta }$, $\beta$ is the degree influenced real exponent which depends on the type of complex networks and $b$ is a positive quantity. We use a tuning parameter $\theta$ to combine both the effect of the vital nodes and the strength of connectivity between nodes. We use analytical and numerical solutions to examine the threshold behavior and dynamics of the model on several models of social network. It was found that the infectivity exponent $\alpha$, the degree influenced real exponent $\beta$ and tuning parameter $\theta$ affect the rumor threshold, one can adjust the parameters to control the rumor threshold which is absent for the standard rumor spreading model.

Magnetohydrodynamic flow of nanofluids and heat transfer between two horizontal plates in a rotating system have been examined numerically. In order to do this, the group method of data handling (GMDH)-type neural networks is used to calculate Nusselt number formulation. Results indicate that GMDH-type NN in comparison with fourth-order Runge–Kutta integration scheme provides an effective means of efficiently recognizing the patterns in data and accurately predicting a performance. Single-phase model is used in this study. Similar solution is used in order to obtain ordinary differential equation. The effects of nanoparticle volume fraction, magnetic parameter, wall injection/suction parameter and Reynolds number on Nusselt number are studied by sensitivity analyses. The results show that Nusselt number is an increasing function of Reynolds number and volume fraction of nanoparticles but it is a decreasing function of magnetic parameter. Also, it can be found that wall injection/suction parameter has no significant effect on rate of heat transfer.

Community detection in networks is a very important area of research for revealing the structure and function of networks. Label propagation algorithm (LPA) has been widely used to detect communities in networks because it has the advantages of linear time complexity and is unnecessary to get prior information, such as objective function and the number of communities. However, LPA has the shortcomings of uncertainty and randomness in the label propagation process, which affects the accuracy and stability of the algorithm. In this paper, we propose a novel community detection algorithm, named NGLPA, in which labels are propagated by node gravitation defined by node importance and similarity between nodes. To select the label according to the gravitation between nodes can reduce the randomness of LPA and is consistent with reality. The proposed method is tested on several synthetic and real-world networks with comparative algorithms. The results show that NGLPA can significantly improve the quality of community detection and obtain accurate community structure.

As an important research field of social network analysis, influence maximization problem is targeted at selecting a small group of influential nodes such that the spread of influence triggered by the seed nodes will be maximum under a given propagation model. It is yet filled with challenging research topics to develop effective and efficient algorithms for the problem especially in large-scale social networks. In this paper, an adaptive discrete particle swarm optimization (ADPSO) is proposed based on network topology for influence maximization in community networks. According to the framework of ADPSO, community structures are detected by label propagation algorithm in the first stage, then dynamic encoding mechanism for particle individuals and discrete evolutionary rules for the swarm are conceived based on network community structure for the meta-heuristic optimization algorithm to identify the allocated number of influential nodes within different communities. To expand the seed nodes reasonably, a local influence preferential strategy is presented to allocate the number of candidate nodes to each community according to its marginal gain. The experimental results on six social networks demonstrate that the proposed ADPSO can achieve comparable influence spread to CELF in an efficient way.

Slug flow is a two-phase fluid flow pattern, characterized by a series of liquid slugs dispersed by relatively large air bubbles. Air bubbles produced and trapped during the slug flow phenomenon conduct to interruption in flow which induces pressure and velocity fluctuations. These effects have destructive circumstances in conduits and conveyance structures. This paper deals with studying numerically two-phase flows using computational fluid dynamic (CFD) techniques performed in OpenFOAM (an open source software) by interFoam solver. Most of the previous concerning studies on slug flow were performed in micro-channels with small scales in which the expansion of the air bubbles was negligible. By contrast, we investigated the systems with large pressure drops which conduct to abrupt increases on the volume of air phase. The slug flow phenomenon could be created by introducing air and water with different velocities at inlet of a culvert with air to water velocity ratio varied from 1.1 to 34. First, this study focused on temporal and spatial variations of pressure and velocity along the culvert. After that, by dimensional analysis, the nondimensional parameters influencing the slug flow phenomenon are extracted and analyzed. Finally, different strategies for reducing the destructive effects of slug flow, including the shape and location of ribs, are evaluated and the best strategy is proposed.

Community structure is one of the important features of complex networks. Researchers have derived a number of algorithms for detecting communities, some of them suffer from high complexity or need some prior knowledge, such as the size of community or number of communities. For some of them, the quality of the detected community structure cannot be guaranteed, even the results of some of them are nondeterministic. In this paper, we propose a Self-Organizing Map (SOM)-based method for detecting community structure from networks. We first preprocess the network by removing some nodes and their associated edges which have little contribution to the formation of communities, then we construct the extended attribute matrix from the preprocessed network, next we embed the detecting procedure in the training of SOM on the attribute matrix to acquire the initial community structure, and finally, we handle those removed nodes by inserting each of them into the community to which its only neighbor belongs, and fine-tune the initial community structure by merging some of the initial communities to improve the quality of the final result. The performance of the proposed method is evaluated on a variety of artificial networks and real-world networks, and experimental results show that our method takes full advantage of SOM model, it can automatically determine the number of communities embedded in the network, the quality of the detected community structure is steadily promising and superior to those of other comparison algorithms.