International Journal of Modern Physics C

The opinion formation process is a general phenomenon in community and society. In recent years, several models of opinion formation have been proposed; however, most models mainly focus on describing views exchange between individuals and interaction between individuals and the environment. In this paper, we present an opinion formation model which takes into account the initial state of opinions and the contacting process with topics. The model involves two stages. The first one is the process in which individuals contact a given topic for the first time and the second one is the social interaction process between individuals. Using the proposed model, we simulate opinion formation process with several sets of parameters. Simulation results indicate that the proposed model may be successfully used for opinion formation simulations. It is also found that the initial state and contacting process have significant influence on opinion formation. Moreover, the model could be helpful for understanding some social phenomena such as quick formation of public opinion about certain topic and the crucial effect of small fluctuations on opinion distribution.

The route guidance systems (RGS) are efficient in alleviating traffic congestion and reducing transit time of transportation networks. This paper studies the effects of RGS on performance of variably weighted small-world networks. The properties of the average shortest path length, the maximum degree, and the largest betweenness, as important indices for characterizing the network structure in complex networks, are simulated. Results show that there is an optimal guided rate of RGS to minimize the total system cost and the average shortest path length, and proper RGS can reduce the load of the node with maximum degree or largest betweenness. In addition, we found that the load distribution of nodes guided by RGS decay as the power laws which is very important for us to understand and control traffic congestion feasible.

On directed small-world networks the majority-vote model with noise is now studied through Monte Carlo simulations. In this model, the order-disorder phase transition of the order parameter is well defined. We calculate the value of the critical noise parameter q_c for several values of rewiring probability p of the directed small-world network. The critical exponents β/ν, γ/ν and 1/ν were calculated for several values of p.

The aim of this paper is to continue our investigations by studying complex damped nonlinear systems with random noise. The effect of random phase for these systems is examined. The interested system demonstrates unstable periodic attractors when the intensity of random noise equals zero, and we show that the unstable dynamical behavior will be stabilized as the intensity of random noise properly increases. The phase plot and the time evolution are carried out to confirm the obtained results of Poincaré map analysis and top Lyapunov exponent on the dynamical behavior of stability. Excellent agreement is found between these results.

The transient deformation of liquid capsules enclosed by elastic membranes in two-dimensional extensional flow is studied numerically, using an improved immersed boundary-lattice Boltzmann method. The purpose of the present study is to investigate the effect of interfacial bending stiffness on the deformation of such capsules, under the subcritical elasticity capillary number conditions. The present model can simulate flow-induced deformation of capsules with arbitrary resting shapes (concerning the in-plane tension) and bending-free configurations. The deformation of capsules with initially circular, elliptical, and biconcave resting shapes was investigated in the present study; the capsules' bending-free configurations were considered as either circular shapes or their initially resting shapes. The results show that for capsules with bending-free configuration as circles, membrane bending rigidity has significant rounding effect on the steady deformed profiles. For elliptical and biconcave capsules with resting shapes as the bending-free configurations, it is found that with the bending stiffness increasing, the capsules' steady shapes are more akin to their initial shapes.

This paper presents a new approach to designing pseudorandom number generators based on cellular automata. Current cellular automata designs either focus on (i) ensuring desirable sequence properties such as maximum length period, balanced distribution of bits and uniform distribution of n-bit tuples, etc. or (ii) ensuring the generated sequences pass stringent randomness tests. In this work, important design patterns are first identified from the latter approach and then incorporated into cellular automata such that the desirable sequence properties are preserved like in the former approach. Preliminary experiment results show that the new cellular automata designed have potential in passing all DIEHARD tests.

We define and study a few properties of a class of random automata networks. While regular finite one-dimensional cellular automata are defined on periodic lattices, these automata networks, called randomized cellular automata, are defined on random directed graphs with constant out-degrees and evolve according to cellular automaton rules. For some families of rules, a few typical a priori unexpected results are presented.

In standard Cellular Automata (CA) and Boolean Networks (BN), the new state of a cell depends on the neighborhood configuration only at the preceding time step. The effect of implementing memory in cells on CA, CA on networks and BN with different degrees of random rewiring is studied in this paper paying attention to the particular case of four inputs. As a rule, memory in cells induces a moderation in the rate of changing cells and in the damage spreading, albeit in the latter case memory turns out ineffective in the control of the damage as the wiring network moves away of the ordered structure that features proper CA.

The population in the sexual Penna aging model is first separated into several reproductively isolated groups. Then, after equilibration, sexual mixing between the groups is allowed. We study the changes in the population size due to this mixing and interpret them through a counterplay of purifying selection and of haplotype complementarity.

Recent advances in complex network research have stimulated increasing interests in understanding the relationship between the topology and dynamics of complex networks. Based on the theory of complex networks and computer simulation, we analyze the robustness to time-delay in linear consensus problem with different network topologies, such as global coupled network, star network, nearest-neighbor coupled network, small-world network, and scale-free network. It is found that global coupled network, star network, and scale-free network are vulnerable to time-delay, while nearest-neighbor coupled network and small-world network are robust to time-delay. And it is found that the maximum node degree of the network is a good predictor for time-delay robustness. And it is found that the robustness to time-delay can be improved significantly by a decoupling process to a small part of edges in scale-free network.

The structure and energetics of Pd_N (N = 5–80) clusters have been studied extensively by a Monte Carlo method based on Sutton–Chen many-body potential. The basin-hopping algorithm is used to find the low-energy minima on the potential energy surface for each nuclearity. A variety of structure types (icosahedral, decahedral and fcc closed-packed) are observed for Pd clusters. Some of the icosahedral global minima do not have a central atom. The resulting structures have been compared with the previous theoretical results.

The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent "brain" is modeled by a recursive neural network with local and global inputs and feedback connections. For specific parametric combination the complex network displays small-world phenomenon combined with scale-free behavior. The identification of a local leader (network hub, agent whose strategies are frequently adapted by its neighbors) is carried out by repeated random walk process through network. The simulations show empirically relevant dynamics of price returns and volatility clustering. The additional emerging aspects of stylized market statistics are Zipfian distributions of fitness.