International Journal of Modern Physics C

This work reports characterization of one-dimensional null-boundary cellular automata (CA) rules in 3-neighborhood. The outcome of this characterization is the identification of CA rules that are the probable candidates for producing single length cycle attractor (fixed point). It targets synthesis of CA with only one fixed point (referred to as the single length cycle single attractor CA (SACA)) and the CA with only two fixed points (referred to as single length cycle two attractors CA (TACA)) for arbitrary length. A tool referred to as Next State RMT Transition Diagram (NSRTD) is developed to identify the fixed points as well as the cycles, involving multiple states (multi-length cycles) of a CA. The NSRTD provides the theoretical basis to search for the rules that form SACA and TACA, for all lengths, in null-boundary.

In this paper, the geometric structures and the melting-like processes of the 13-atom pure copper, pure cobalt cluster and their 13-atom mixed clusters are investigated and compared by the molecular dynamics method. The calculation shows that the pure copper and cobalt clusters have the standard icosahedral structures and the mixed clusters take on the deformed icosahedral structures. The quantitative analysis shows that the deformations are slight. Moreover, an element similarity function is introduced by which the contribution of the compositions of the clusters to the deformation of the mixed clusters is analyzed and discussed. With the increase of the temperature, the migrating and recombination of the atoms on the surface of the clusters are observed, indicating the starting of the transition from solid-like to liquid-like state for the clusters. Through the calculating of the relative root-mean-squared pair separation fluctuation and monitoring the dynamical structures of the clusters, it is found that the mixed clusters experience a multi-step process in the transition.

This work aims to assess the response of viscoelastic Kelvin–Voigt microscale beams under initial stress. The microbeam is photostimulated by the light emitted by an intense picosecond pulsed laser. The photothermal elasticity model with dual-phase lags, the plasma wave equation and Euler–Bernoulli beam theory are utilized to construct the system equations governing the thermoelastic vibrations of microbeams. Using the Laplace transform technique, the problem is solved analytically and expressions are provided for the distributions of photothermal fields. Taking aluminum as a numerical example, the effect of the pulsed laser duration coefficient, viscoelasticity constants and initial stress on photothermal vibrations has been studied. In addition, a comparison has been made between different models of photo-thermoelasticity to validate the results of the current model. Photo-microdynamic systems might be monolithically integrated on aluminum microbeams using microsurface processing technology as a result of this research.

In this paper, we present a novel multi-threaded parallel permutation and channel-combined diffusion for image encryption which is independent of plain text. In our proposed method, the coupled map lattice is used to generate the key sequences for multi-thread permutation and diffusion. Then intra- and inter-thread permutations are achieved using multi-threading in combination with the tent mapping. For the subsequent diffusion, this paper introduces a method based on channel-combined diffusing which simultaneously diffuses three channels. Experimental results indicate a high encryption performance with the capability of effectively resisting the known plain text and differential attacks. Our proposed method also has a lower computational complexity which enables its applicability in practical scenarios.

In this work, we study a dynamics of tax evasion. We considered a fully-connected population divided in three compartments, namely honest tax payers, tax evaders and susceptibles, a class that is composed by honest tax payers that can become evaders. We consider a contagion model where the transitions among the compartments are governed by probabilities. Such probabilities represent the possible interactions among the individuals, as well as the government fiscalization. We show by analytical and numerical calculations that the emergence of tax evaders in the population is associated with an active-absorbing nonequilibrium first-order phase transition. In the absorbing phase, only honest tax payers survive in the steady states of the model, and we observe a coexistence of the three subpopulations in the active phase.

In this paper, a numerical method optimizing the coefficients of the semi empirical mass formula or those of similar mass formulas is presented. The optimization is based on the least-squares adjustments method and leads to the resolution of a linear system which is solved by iterations according to the Gauss–Seidel scheme. The steps of the algorithm are given in detail. In practice, the method is very simple to implement and is able to treat large data in a very fast way. In fact, although this method has been illustrated here by specific examples, it can be applied without difficulty to any experimental or statistical data of the same type, i.e. those leading to linear system characterized by symmetric and positive-definite matrices.

A study of the distribution of the value of traded goods under the Harmonized System is presented. The ramifications of this classification system are found to exhibit an approximate power law decay, indicating complexity and self-organization in the nomenclature of traded merchandises. For almost all countries with available data, log-values of annually imported and exported goods are well described by three-parameter Weibull distributions. This distribution commonly appears in particles size distributions, suggesting a connection between random fragmentation processes and the mechanisms behind the international trade of merchandises. Analysis of the resulting values for the fitting parameters from 1995 to 2018 shows a nearly constant linear relationship between the parameters of the Weibull distributions, so that, for each country, the distribution of log-values can be approximately characterized by a single shape parameter $\beta$. The empirical findings of this paper suggest that specialization on trading a constant set of goods prevents the values of all traded merchandises from growing/decreasing simultaneously.

The aim of this paper is to analyze the dynamic relationship between economic growth and CO₂ emissions for a set of 98 countries over the lengthy period from 1951 to 2014. We describe the topology and hierarchy of countries and introduce a different concept of economic performance based on the idea of dynamic regimes. These regimes are defined by the average levels of per-capita CO₂ emissions and the growth rates of per-capita GDP. By presenting a nonparametric clustering technique, the paper identifies two main groups. One cluster can be identified as the group of developed countries, which presents a homogeneous structure and tends toward more similar dynamics over time. The other cluster, associated with developing countries, is homogeneous but the dynamics of the countries do not show convergence. The study also finds some, though little, mobility between the groups.

Traditional research studies on interdependent networks with groups ignore the relationship between nodes in dependency groups. In real-world networks, nodes in the same group may support each other through cooperation and tend to fail or survive together. In this paper, based on the framework of group percolation, a cascading failure model on interdependent networks with cooperative dependency groups under targeted attacks is proposed, and the effect of group size distributions on the robustness of interdependent networks is investigated. The mutually giant component and phase transition point of networks with different group size distributions are analyzed. The effectiveness of the theory is verified through simulations. Results show that the robustness of interdependent networks with cooperative dependency groups can be enhanced by increasing the heterogeneity between groups under targeted attacks. The theory can well predict the numerical simulation results. This model provides some theoretical guidance for designing robust interdependent systems in real world.

In this paper, the melting behaviors of Rh–Ag–Au nanoalloys are investigated with MD simulation. For Rh–Ag–Au nanoalloys, icosahedron structure was considered. The local optimizations of Rh–Ag–Au nanoalloys were carried out with the BH algorithm. The interatomic interactions were modeled with the Gupta potential. The local optimization results of Rh–Ag–Au nanoalloys show that Au and Ag atoms prefer to locate on the surface, and Rh atoms prefer to locate in the inner shells. The bond order parameter result is compatible with the excess energy analysis. It is noted that structures with more Ag–Au bonds are more energetically stable. Caloric curve, heat capacity, Lindemann index, and RMSD methods were used for estimating the melting temperatures of Rh–Ag–Au nanoalloys. According to the simulation results, melting temperatures depend on the composition. Also, it is discovered that nanoalloys are generally melting in two stages. Surface melting of the third shell is occupied by Ag and Au atoms, and then homogeneous melting of the inner shells is occupied by Rh atoms. It is found that the difference between surface melting temperatures and homogeneous melting temperatures in Ag-poor compositions is more significant than that of Ag-rich nanoalloys. In addition, the melting temperatures of the nanoalloys are found to be increased as the size of nanoalloys increases.

In this work, the individual difference of the honk effect is explored on two lanes via traffic modeling of the lattice model under Vehicle to X (V2X) environment. We study the impact of individual difference corresponding to honk cases on traffic stability through linear stability analysis for a two-lane highway. Furthermore, the mKdV equation under the lane changing phenomena is conducted via nonlinear analysis. Simulation cases for the early time and longtime impact reveal that individual difference of driving characteristics has a distinct impact on two lanes under the whistling environment.

Random Boolean networks, originally introduced as simplified models for the genetic regulatory networks, are abstract models widely applied for the study of the dynamical behaviors of self-organizing complex systems. In these networks, connectivity and the bias of the Boolean functions are the most important factors that can determine the behavioral regime of the systems. On the other hand, it has been found that topology and some structural elements of the networks such as the reciprocity, self-loops and source nodes, can have relevant effects on the dynamical properties of critical Boolean networks. In this paper, we study the impact of source and sink nodes on the dynamics of homogeneous and heterogeneous Boolean networks. Our research shows that an increase of the source nodes causes an exponentially growing of the different behaviors that the system can exhibit regardless of the network topology, while the amount of order seems to undergo modifications depending on the topology of the system. Indeed, with the increase of the source nodes the orderliness of the heterogeneous networks also increases, whereas it diminishes in the homogeneous ones. On the other hand, although the sink nodes seem not to have effects on the dynamic of the homogeneous networks, for the heterogeneous ones we have found that an increase of the sinks gives rise to an increasing of the order, although the different potential behaviors of the system remains approximately the same.

In this research, a convenient and effective semi-analytical technique namely the Differential Transformation Method (DTM) is employed to investigate the nonlinear epidemic model. The design of the mathematical model is explained according to five classifications of susceptible, exposed, infective, asymptomatic and recovered people. The corresponding solution points are validated against numerical outcomes. The results reveal a high degree of accuracy for DTM combination with Padé approximation. Moreover, this technique is a promising method to solve various nonlinear equations applicable for epidemic models. The results indicate that by increasing $R$ parameter, the values of infected individuals are enhanced. Also, it can be concluded that increasing in $\gamma$ parameter leads to increase of recovered profile.

Online social media has made the process of disseminating news so quick that people have shifted their way of accessing news from traditional journalism and press to online social media sources. The rapid rotation of news on social media makes it challenging to evaluate its reliability. Fake news not only erodes public trust but also subverts their opinions. An intelligent automated system is required to detect fake news as there is a tenuous difference between fake and real news. This paper proposes an intelligent gravitational search random forest (IGSRF) algorithm to be employed to detect fake news. The IGSRF algorithm amalgamates the Intelligent Gravitational Search Algorithm (IGSA) and the Random Forest (RF) algorithm. The IGSA is an improved intelligent variant of the classical gravitational search algorithm (GSA) that adds information about the best and worst gravitational mass agents in order to retain the exploitation ability of agents at later iterations and thus avoid the trapping of the classical GSA in local optimum. In the proposed IGSRF algorithm, all the intelligent mass agents determine the solution by generating decision trees (DT) with a random subset of attributes following the hypothesis of random forest. The mass agents generate the collection of solutions from solution space using random proportional rules. The comprehensive prediction to decide the class of news (fake or real) is determined by all the agents following the attributes of random forest. The performance of the proposed algorithm is determined for the FakeNewsNet dataset, which has sub-categories of BuzzFeed and PolitiFact news categories. To analyze the effectiveness of the proposed algorithm, the results are also evaluated with decision tree and random forest algorithms. The proposed IGSRF algorithm has attained superlative results compared to the DT, RF and state-of-the-art techniques.