International Journal of Modern Physics C

By numerically solving the generalized Laplace equations by means of the finite difference method, we investigated isotropic electric conductivity of a three-dimensional continuum percolation model consisting of overlapped spheroids of revolution in continuum. Since the computational results strongly depend upon parameters in the discretization methods of the finite difference method, we explored the dependences in details to construct the computational scheme which can represent the continuum percolation model well. Using the discrete scheme, we obtained the conductivity curves, σ =c (p -p_c)^t, depending upon aspect ratio of the conductive spheroids for the volume fraction p. We found the fact that the critical exponent t is not universal, which depends upon the shape of spheroids with a range varying from 1.58 ± 0.08 to 1.94 ± 0.18 whereas 1.85 is reported as the standard one of cubic lattice case [A. B. Harris, Phys. Rev. B28, 2614 (1983)]. We also discussed its relation to the nonuniversality in the broad distribution continuum percolation models.

We extend the previously observed scaling equation connecting the internode distances and nodes' degrees onto the case of weighted networks. We show that the scaling takes a similar form in the empirical data obtained from networks characterized by different relations between node's strength and its degree. In the case of explicit equation for s (k) (e.g. linear or scale-free), the new coefficients of scaling equation can be easily obtained. We support our analysis with numerical simulations for Erdös–Rényi random graphs with different weight distributions.

In this paper, we implement a revised pseudo-random bit generator based on a rule-90 cellular automaton. For this purpose, we introduce a sequence matrix H_N with the aim of calculating the pseudo-random sequences of N bits employing the algorithm related to the automaton backward evolution. In addition, a multifractal structure of the matrix H_N is revealed and quantified according to the multifractal formalism. The latter analysis could help to disentangle what kind of automaton rule is used in the randomization process and therefore it could be useful in cryptanalysis. Moreover, the conditions are found under which this pseudo-random generator passes all the statistical tests provided by the National Institute of Standards and Technology (NIST).

Unidirectional motion is achieved when a particle, moving under the influence of an underlying noise source, is subjected to a ratchet asymmetric periodic potential. Here, we investigate how deviations from the Gaussian nature of the noise distribution function impacts the average particle's current. The input noise is considered to be produced by a Langevin process including both multiplicative and additive random noise sources. The resulting input random signal has a power-law amplitude distribution and a finite correlation time. These features are controlled by the average of the multiplicative noise. We show that the average particle's velocity depends non-monotonically on the degree of non-Gaussianity of the input noise. It exhibits a maximum at an intermediate value of the effective power-law exponent that characterizes the asymptotic decay of the noise probability distribution function.

In this study, microscale gaseous flows in the transitional regime have been investigated by lattice Boltzmann method (LBM). In the existing microflows LBM models, the Knudsen layer correction function has been introduced into the models. According to the kinetic theory rigorously, we choose a proper expression of correction function, and then determine its adjustable parameter. A substitute high-order boundary conditions treatment is adopted to capture the velocity slip, without any difficulties in computing the high-order velocity derivatives. The numerical results of two typical microflows show that: the present results agree with the analytical solutions better than the existing LBM simulations. Evident improvements can also be found, especially for finite Kn microflows.

Most previous studies on the synchronization of complex networks were based on that each node managed to adjust its neighbors coupling strength to enhance synchronizability, i.e. each node tried to adjust its total input coupling strength in a proper way and the neighbor nodes were passively adjusted. From practical and engineering viewpoints, each node should manage to adjust its total output coupling strength to realize synchronization. Moreover, each node's total output coupling strength can be distributed to its neighbors with different proportions. In view of the above reasons, in this paper, we study the synchronization of complex networks under the assumptions that the total output coupling strength of each node is voluntarily/directly distributed to its neighbors with different proportions. What is more, we assume that the total output coupling strength of each node can be nonlinear to its degree. Our analysis and numerical simulations show that the synchronizability can be enhanced dramatically when the parameters are properly selected.

The lattice Boltzmann formulation on unstructured grids (ULBE) is compared against semi-analytical solutions of non-Newtonian flows in straight channels, as well as with finite-element simulations in stenosed geometries. In all cases, satisfactory agreement is found, lending further credit to the ULBE method as a potentially useful method for the numerical simulation of small-scale hemodynamic flows, such as blood flow in capillaries and arterioles.

Two main difficulties in the problem of classification in partially labeled networks are the sparsity of the known labeled nodes and inconsistency of label information. To address these two difficulties, we propose a similarity-based method, where the basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten similarity indices defined based on the network structure. Empirical results on the co-purchase network of political books show that the similarity-based method can, to some extent, overcome these two difficulties and give higher accurate classification than the relational neighbors method, especially when the labeled nodes are sparse. Furthermore, we find that when the information of known labeled nodes is sufficient, the indices considering only local information can perform as good as those global indices while having much lower computational complexity.

This paper studies asymmetric exclusion processes on a microtubule-like system with two species of particles. The model is motivated by the structure of microtubules and kinesins and dyneins moving along microtubules in opposite directions. The proposed model is similar to that in J. Phys. A40, 2275 (2007) in which two-channel TASEPs with narrow entrances and parallel update are studied. This paper extended the above-mentioned work to a multiple-channel hollow cylinder case. Thus, each channel has two nearest neighbors in our model. The corresponding rule for narrow entrances is that particles cannot enter the system if either of two nearest-neighbor sites on neighboring channels is occupied by the other species of particles. The phase diagram of the model is obtained from a mean-field approximation and verified by computer simulations. It is shown that the spontaneous symmetry breaking exists with two asymmetric phases: high/low density and low/low density. The flipping process of particles is observed. Bulk density and particle currents are computed. Monte Carlo simulation results deviate from the mean-field prediction when entrance rate α is high, which is due to neglecting correlations among particles in mean-field calculations. The results are also compared with that obtained from two-channel system with one neighbor narrow entrance in parallel update.

The work investigates the influence of leader on opinion formation in artificial networked societies. The strength of the social influence is assumed to be dictated by distance from one agent to another, as well as individual strengths of the agents. The leader is assumed to have much greater resources, which allows him to tune the way he influences the other agents. We study various strategies of using these resources to optimize the conditions needed to "convince" the whole society to leader's opinion. The flexibility of the model allows it to be used in studies of political, social and marketing activities and opinion formation.