International Journal of Modern Physics C

We investigate the critical properties of the equilibrium and nonequilibrium two-dimensional (2D) systems on Solomon networks with both nearest and random neighbors. The equilibrium and nonequilibrium 2D systems studied here by Monte Carlo simulations are the Ising and Majority-vote 2D models, respectively. We calculate the critical points as well as the critical exponent ratios $\gamma ∕\nu$, $\beta ∕\nu$, and $1∕\nu$. We find that numerically both systems present the same exponents on Solomon networks (2D) and are of different universality class than the regular 2D ferromagnetic model. Our results are in agreement with the Grinstein criterion for models with up and down symmetry on regular lattices.

In this work, we consider a one-electron moving on a Fermi, Pasta, Ulam disordered chain under effect of electron–phonon interaction and a Gaussian acoustic pulse pumping. We describe electronic dynamics using quantum mechanics formalism and the nonlinear atomic vibrations using standard classical physics. Solving numerical equations related to coupled quantum/classical behavior of this system, we study electronic propagation properties. Our calculations suggest that the acoustic pumping associated with the electron–lattice interaction promote a sub-diffusive electronic dynamics.

In complex networks, the existing link prediction methods primarily focus on the internal structural information derived from single-layer networks. However, the role of interlayer information is hardly recognized in multiplex networks, which provide more diverse structural features than single-layer networks. Actually, the structural properties and functions of one layer can affect that of other layers in multiplex networks. In this paper, the effect of interlayer structural properties on the link prediction performance is investigated in multiplex networks. By utilizing the intralayer and interlayer information, we propose a novel “Node Similarity Index” based on “Layer Relevance” (NSILR) of multiplex network for link prediction. The performance of NSILR index is validated on each layer of seven multiplex networks in real-world systems. Experimental results show that the NSILR index can significantly improve the prediction performance compared with the traditional methods, which only consider the intralayer information. Furthermore, the more relevant the layers are, the higher the performance is enhanced.

Vital functions of DNA, such as transcription and packaging, depend on the proper clustering of proteins on the double strand. The present study investigates how the interplay between DNA allostery and electrostatic interactions affects protein clustering. The statistical analysis of a simple but transparent computational model reveals two major consequences of this interplay. First, depending on the protein and salt concentration, protein filaments exhibit a bimodal DNA stiffening and softening behavior. Second, within a certain domain of the control parameters, electrostatic interactions can cause energetic frustration that forces proteins to assemble in rigid spiral configurations. Such spiral filaments might trigger both positive and negative supercoiling, which can ultimately promote gene compaction and regulate the promoter. It has been experimentally shown that bacterial histone-like proteins assemble in similar spiral patterns and/or exhibit the same bimodal behavior. The proposed model can, thus, provide computational insights into the physical mechanisms used by proteins to control the mechanical properties of the DNA.

We consider the flow intensity of economic and financial news taken from a nine-month period of 2015. This data is found to be well approximated by a persistent self-affine walk. It is characterized by a Hurst exponent of $H>0.6$ over three orders of magnitude in time ranging from minutes to several days. In this paper, we use the Detrended Fluctuation Analysis (DFA) of order 1, Rescaled Range Analysis (R/S) and Fourier Transform Method (FTM) to examine long-range auto-correlation and self-similarity of time series of news flow intensity. DFA method allowed us to reveal a strong scaling behavior as well as to detect a distinct crossover effect. On the other hand, it turns out that for the classic R/S analysis and Fourier transform techniques, the scaling regimes and/or positions of cross-overs are hard to define.

This short paper is focused on the bifurcation theory found in map functions called evolution functions that are used in dynamical systems. The most well-known example of discrete iterative function is the logistic map that puts into evidence bifurcation and chaotic behavior of the topology of the logistic function. We propose a new iterative function based on Lorentizan function and its generalized versions, based on numerical study, it is found that the bifurcation of the Lorentzian function is of second-order where it is characterized by the absence of chaotic region.

A phase field model is used to study dendritic growth in a media with impurities. The model consists of a square lattice where a parameter $\psi$ can take values between $0$ and $1$ at each site. A site is in the solid phase for $\psi >1$, in the liquid phase for $\psi =0$, and the solid-liquid interface is expressed by $0<\psi <1$. A fraction of the sites are considered impurities that cannot be solidified, i.e. $\psi$ is fixed and taken as zero. These impurities are distributed randomly. As the probability $p$ of impure sites in the lattice increases, the growth loses its dendritic characteristic. It is shown that the perimeter of the growing solid goes from quadratic to a linear function with time. It was also found that as the probability of impurities reaches $p=0.004$, the solid undergoes a transition from anisotropic to isotropic growth.

By the cellular automaton (CA) model, traffic behaviors of an intelligent two-route system with a work zone are studied. In the model, the two-route system consists of a double-lane (DL) and a single-lane (SL). Supposing a work zone exists on DL, a big traffic jam will be formed. For improving the system traffic flow, an intelligent vehicle (IV) which can change driving strategy on the basis of received real-time information has been introduced into the model. The simulation results indicate that the traffic condition can be improved and the area of the traffic jam can be reduced effectively if there are enough IVs. Moreover, if all of the vehicles are IVs, the traffic jam may disappear, and the system will be in free flow state.

In this paper, we propose a new strategy (HLS) to enhance the transport capacity of scale-free networks by adding links to the existing networks, based on the betweenness of nodes, the shortest path length and the betweenness of links. Since only slight amounts of nodes in scale-free networks have high betweenness centrality, local link-adding strategy is adopted as a part of HLS for target nodes, which can significantly reduce the load of target nodes. Moreover, in order to improve the robustness of our strategy under some extreme cases, second sorting procedure is introduced in HLS. Simulation results show that HLS outperforms the IE strategy in terms of transport capacity and delivering ability of scale-free networks. After the adding links process of HLS, the congestion can be alleviated efficiently, which is meaningful to the realistic networks.

This paper concerns the outer synchronization problem between two complex delayed networks via the method of aperiodically intermittent pinning control. Apart from previous works, internal delay and coupling delay are both involved in this model, and the designed intermittent controllers can be aperiodic. The main work in this paper can be summarized as follows: First, two cases of aperiodically intermittent control with constant gain and adaptive gain are implemented, respectively. The intermittent control and pinning control are combined to reduce consumptions further. Then, based on the Lyapunov stability theory, synchronization protocols are given by strict derivation. Especially, the designed controllers are indeed simple and valid in application of theory to practice. Finally, numerical examples put the proposed control methods to the test.

In the complexity modeling, variance decomposition technique is widely used for the quantification of the variation in the output variables explained by covariates. In this work, the satisfaction of sampling-based variance decomposition strategy (SVDS) is firstly testified in the implementation of an analytic method for uncertainty and sensitivity analysis (UASA) of complex systems. Results suggest that SVDS may overvalue the impacts from individual covariates alone but underestimate the effects from their interactions when the model under discussion involves the interaction effects of nonlinear problems of individual covariates. Following the phenomenon, a modification of SVDS is proposed to generate sensitivity measures that well coincide with the analytic method. The testified strategy, together with our proposed modification, is then employed to clarify the roles of infectious rate and recovered rate, as well as of their interaction, in the estimation of equilibrium state (ES) for both SIR and SIS models. Results demonstrate that infectious and recovered rates almost play the same roles less crucial than that acted by the initial susceptible individuals in the decision of ES for SIR model, accompanied by a fragile contribution from their interactions; while in SIS model, infectious rate is more robust than recovered rate, and their interaction effect is also non-ignorable.