International Journal of Modern Physics C

The routing strategy plays a very important role in complex networks such as Internet system and Peer-to-Peer networks. However, most of the previous work concentrates only on the path selection, e.g. Flooding and Random Walk, or finding the shortest path (SP) and rarely considering the local load information such as SP and Distance Vector Routing. Flow-based Routing mainly considers load balance and still cannot achieve best optimization. Thus, in this paper, we propose a novel dynamic routing strategy on complex network by incorporating the local load information into SP algorithm to enhance the traffic flow routing optimization. It was found that the flow in a network is greatly affected by the waiting time of the network, so we should not consider only choosing optimized path for package transformation but also consider node congestion. As a result, the packages should be transmitted with a global optimized path with smaller congestion and relatively short distance. Analysis work and simulation experiments show that the proposed algorithm can largely enhance the network flow with the maximum throughput within an acceptable calculating time. The detailed analysis of the algorithm will also be provided for explaining the efficiency.

Human dynamics and sociophysics suggest statistical models that may explain and provide us with better insight into social phenomena. Contextual and selection effects tend to produce extreme values in the tails of rank-ordered distributions of both census data and district-level election outcomes. Models that account for this nonlinearity generally outperform linear models. Fitting nonlinear functions based on rank-ordering census and election data therefore improves the fit of aggregate voting models. This may help improve ecological inference, as well as election forecasting in majoritarian systems. We propose a generative multiplicative decrease model that gives rise to a rank-order distribution and facilitates the analysis of the recent UK EU referendum results. We supply empirical evidence that the beta-like survival function, which can be generated directly from our model, is a close fit to the referendum results, and also may have predictive value when covariate data are available.

Recently, a computational model has been proposed of the social integration, as described in sociological terms by Blau. In this model, actors praise or critique each other, and these actions influence their social status and raise negative or positive emotions. The role of a self-deprecating strategy of actors with high social status has also been discussed there. Here, we develop a mean field approach, where the active and passive roles (praising and being praised, etc.) are decoupled. The phase transition from friendly to hostile emotions has been reproduced, similarly to the previously applied purely computational approach. For both phases, we investigate the time dependence of the distribution of social status. There we observe a diffusive spread, which — after some transient time — appears to be limited from below or from above, depending on the phase. As a consequence, the mean status flows.

In this paper, we present a cellular automata model to depict the traffic characteristics at such a nonsignalized $T$-shaped intersection in the presence of pedestrian crossings. The vehicular traffic flow characteristics with different parameters are studied. Throughput, phase diagrams, space-time and density profiles are built to point various traffic states and the phase transition features. The principal finding of the simulation are as follows: (1) The presence of crossing pedestrians has a significant influence on the vehicular traffic and the system performance, (2) The intersection capacity has a close relevance with the state of the weaving section. The smaller the interactions are at the weaving section, the higher the capacity and (3) In order to have a higher system ability, the crosswalk location should not be far from the yield line of the weaving section.

Opinion evolution mechanism can be captured by physical modeling. In this paper, a kinetic equation is established by defining a generalized displacement(cognitive level), a driving force and the related factors such as generalized potential, information quantity and attitude. It has been shown that the details of opinion evolution depend on the type of the driving force, self-dominated driving or environment-dominated driving. In the former case, the participants can have their attitudes changed in the process of competition between the self-driving force and environment-driving force. In the latter case, all of the participants are pulled by the environment. Some regularities behind the dynamics of opinion are also revealed, for instance, the information entropy decays with time in a special way, etc. The results may help us to get some deep understanding for the formation of a public opinion.

In this work, the immiscible displacement in a cavity with different channel configurations is studied using an improved pseudo-potential lattice Boltzmann equation (LBE) model. This model overcomes the drawback of the dependence of the fluid properties on the grid size, which exists in the original pseudo-potential LBE model. The approach is first validated by the Laplace law. Then, it is employed to study the immiscible displacement process. The influences of different factors, such as the surface wettability, the distance between the gas cavity and liquid cavity and the surface roughness of the channel are investigated. Numerical results show that the displacement efficiency increases and the displacement time decreases with the increase of the surface contact angle. On the other hand, the displacement efficiency increases with increasing distance between the gas cavity and the liquid cavity at first and finally reaches a constant value. As for the surface roughness, two structures (a semicircular cavity and a semicircular bulge) are studied. The comprehensive results show that although the displacement processes for both the structures depend on the surface wettability, they present quite different behaviors. Specially, for the roughness structure constituted by the semicircular cavity, the displacement efficiency decreases and displacement time increases evidently with the size of the semicircular cavity for the small contact angle. The trend slows down as the increase of the contact angle. Once the contact angle exceeds a certain value, the size of the semicircular cavity almost has no influence on the displacement process. While for the roughness structure of a semicircular bulge, the displacement efficiency increases with the size of bulge first and then it decreases for the small contact angle. The displacement efficiency increases first and finally reaches a constant for the large contact angle. The results also show that the displacement time has an extreme value in these cases for the small contact angles.

We develop a new method called asymmetric multiscale multifractal analysis (A-MMA) to explore the multifractality and asymmetric autocorrelations of the signals with a variable scale range. Three numerical experiments are provided to demonstrate the effectiveness of our approach. Then, the proposed method is applied to investigate multifractality and asymmetric autocorrelations of difference sequences between wind speed fluctuations with uptrends or downtrends. The results show that these sequences appear to be far more complex and contain abundant fractal dynamics information. Through analyzing the Hurst surfaces of nine difference sequences, we found that all series exhibit multifractal properties and multiscale structures. Meanwhile, the asymmetric autocorrelations are observed in all variable scale ranges and the asymmetry results are of good consistency within a certain spatial range. The sources of multifractality and asymmetry in nine difference series are further discussed using the corresponding shuffled series and surrogate series. We conclude that the multifractality of these series is due to both long-range autocorrelation and broad probability density function, but the major source of multifractality is long-range autocorrelation, and the source of asymmetry is affected by the spatial distance.

In this paper, the magnetic field effects on natural convection of power-law non-Newtonian fluids in rectangular enclosures are numerically studied by the multiple-relaxation-time (MRT) lattice Boltzmann method (LBM). To maintain the locality of the LBM, a local computing scheme for shear rate is used. Thus, all simulations can be easily performed on the Graphics Processing Unit (GPU) using NVIDIA’s CUDA, and high computational efficiency can be achieved. The numerical simulations presented here span a wide range of thermal Rayleigh number ($10^4\le \mathrm{\text{Ra}}\le 10^6$), Hartmann number ($0\le \mathrm{\text{Ha}}\le 20$), power-law index ($0.5\le n\le 1.5$) and aspect ratio ($0.25\le \mathrm{\text{AR}}\le 4.0$) to identify the different flow patterns and temperature distributions. The results show that the heat transfer rate is increased with the increase of thermal Rayleigh number, while it is decreased with the increase of Hartmann number, and the average Nusselt number is found to decrease with an increase in the power-law index. Moreover, the effects of aspect ratio have also investigated in detail.

A direct series solution for the Falkner–Skan equation is obtained by first transforming the problem using the Crocco–Wang transformation. The transformation converts the third-order problem to a second-order two-point boundary value problem. The method first constructs a series involving the unknown skin-friction coefficient $\alpha$. Then, $\alpha$ is determined by using the secant method or Newton’s method. The derivative needed for Newton’s method is also computed using a series derived from the transformed differential equation. The method is validated by solving the Falkner–Skan equation for several cases reported previously in the literature.

In traffic networks, it is quite important to assign proper packet delivering capacities to the routers with minimum cost. In this respect, many allocation models based on static and dynamic properties have been proposed. In this paper, we are interested in the impact of limiting the packet delivering capacities already allocated to the routers; each node is assigned a packet delivering capacity limited by the maximal capacity $C_{max}$ of the routers. To study the limitation effect, we use two basic delivering capacity allocation models; static delivering capacity allocation (SDCA) and dynamic delivering capacity allocation (DDCA). In the SDCA, the capacity allocated is proportional to the node degree, and for DDCA, it is proportional to its queue length. We have studied and compared the limitation of both allocation models under the shortest path (SP) routing strategy as well as the efficient path (EP) routing protocol. In the SP case, we noted a similarity in the results; the network capacity increases with increasing $C_{max}$. For the EP scheme, the network capacity stops increasing for relatively small packet delivering capability limit $C_{max}$ for both allocation strategies. However, it reaches high values under the limited DDCA before the saturation. We also find that in the DDCA case, the network capacity remains constant when the traffic information available to each router was updated after long period times $\tau$.