International Journal of Modern Physics C

In many physical applications the electrons play a relevant role. For example, when a beam of electrons accelerated to relativistic velocities is used as an active medium to generate Free Electron Lasers (FEL), the electrons are bound to atoms, but move freely in a magnetic field. The relaxation time, longitudinal effects and transverse variations of the optical field are parameters that play an important role in the efficiency of this laser. The electron dynamics in a magnetic field is a means of radiation source for coupling to the electric field. The transverse motion of the electrons leads to either gain or loss energy from or to the field, depending on the position of the particle regarding the phase of the external radiation field. Due to the importance to know with great certainty the displacement of charged particles in a magnetic field, in this work we study the fractional dynamics of charged particles in magnetic fields. Newton’s second law is considered and the order of the fractional differential equation is $(0;1]$. Based on the Grünwald–Letnikov (GL) definition, the discretization of fractional differential equations is reported to get numerical simulations. Comparison between the numerical solutions obtained on Euler’s numerical method for the classical case and the GL definition in the fractional approach proves the good performance of the numerical scheme applied. Three application examples are shown: constant magnetic field, ramp magnetic field and harmonic magnetic field. In the first example the results obtained show bistability. Dissipative effects are observed in the system and the standard dynamic is recovered when the order of the fractional derivative is 1.

The community structure is important for software in terms of understanding the design patterns, controlling the development and the maintenance process. In order to detect the optimal community structure in the software network, a method Optimal Partition Software Network (OPSN) is proposed based on the dependency relationship among the software functions. First, by analyzing the information of multiple execution traces of one software, we construct Software Execution Dependency Network (SEDN). Second, based on the relationship among the function nodes in the network, we define Fault Accumulation (FA) to measure the importance of the function node and sort the nodes with measure results. Third, we select the top K(K=1,2,…) nodes as the core of the primal communities (only exist one core node). By comparing the dependency relationships between each node and the K communities, we put the node into the existing community which has the most close relationship. Finally, we calculate the modularity with different initial K to obtain the optimal division. With experiments, the method OPSN is verified to be efficient to detect the optimal community in various softwares.

The biofilters are the ideal solutions for the biological treatment of air pollutants. However, there exists strong flow heterogeneity in porous media that degrades the removal efficiency of biofilters. Thus, the effects of Darcy number, Reynolds number and porosity of porous media on the reduction of flow heterogeneity in three biofilter models were numerically studied by the lattice Boltzmann method. The simulation results lead to three conclusions: (1) The Darcy number has dominant influence on the flow heterogeneity in the biofilters. The reduction of flow heterogeneity can be realized by designing a comparatively low Darcy number. (2) The Reynolds number has obvious effect on the flow heterogeneity in the biofilters. However, the reduction of flow heterogeneity cannot be effectively established by regulating the Reynolds number. (3) The property of porous media greatly influences the flow heterogeneity in the biofilters. The present results are helpful for the optimized design of practical biofilter models.

Cluster modified projective synchronization (CMPS) between two topologically distinct community networks is studied in this paper. Each cluster here has a unique dynamics at least with respect to the parameter sets. Using an adaptive feedback control gain and a matrix scaling factor, we show that CMPS between two community networks can be realized with considering minimum assumptions and imposing just few restrictions on the configuration set. We use Lyapunov stability theory for the proof and employ computer simulation to confirm our result on randomly generated community networks. Simulations also show the possibility of having hybrid synchronization between the two networks.

In this work, a multiple relaxation time (MRT) extension of the recently introduced constant speed kinetic model (CSKM) is proposed. The CSKM, which is an entropic kinetic model and based on unconventional entropies of Burg and Tssalis, was introduced in [A. Zadehgol and M. Ashrafizaadeh, J. Comput. Phys. 274, 803 (2014)]; [A. Zadehgol Phys. Rev. E 91, 063311 (2015)] as an extension of the model of Boghosian et al. [Phys. Rev. E 68, 025103 (2003)] in the limit of fixed speed continuous velocities. The present extension improves the stability of the previous models at very high Reynolds numbers, while allowing for a more convenient orthogonal lattice. The model is verified by solving the following benchmark problems: (i) the lid driven square cavity and (ii) the Kelvin–Helmholtz instability of thin shear layers in a doubly periodic square domain.

It has been suggested that an appeal to holographic and quantum properties will be ultimately required for the understanding of higher brain functions. On the other hand, successful quantum-like approaches to cognitive and behavioral processes bear witness to the usefulness of quantum prescriptions as applied to the analysis of complex non-quantum systems. Here, we show that the signal-tuned Gabor approach for modeling cortical neurons, although not based on quantum assumptions, also admits a quantum-like interpretation. Recently, the equation of motion for the signal-tuned complex cell response has been derived and proven equivalent to the Schrödinger equation for a dissipative quantum system whose solutions come under two guises: as plane-wave and Airy-packet responses. By interpreting the squared magnitude of the plane-wave solution as a probability density, in accordance with the quantum mechanics prescription, we arrive at a Poisson spiking probability — a common model of neuronal response — while spike propagation can be described by the Airy-packet solution. The signal-tuned approach is also proven consistent with holonomic brain theories, as it is based on Gabor functions which provide a holographic representation of the cell’s input, in the sense that any restricted subset of these functions still allows stimulus reconstruction.

The epidemic spreading was explored on activity-driven networks (ADNs), accounting for the study of dynamics both on and of the ADN. By employing the susceptible-infected-susceptible (SIS) model, two aspects were considered: (1) the infection rate of susceptible agent (depending on the number of its infected neighbors) evolves due to the temporal structure of ADN, rather than being a constant number; (2) the susceptible and infected agents generate unequal links while being activated, namely, the susceptible agent gets few contacts with others in order to protect itself. Results show that, in both cases, the larger epidemic threshold and smaller outbreak size were obtained.

Understanding the timing requirements for evacuation of people has focused primarily on independent pedestrians rather than pedestrians emotionally connected. However, the main statistical effects observed in crowds, the so-called “faster is slower”, “clever is not always better” and the “low visibility enhancement”, cannot explain the overall behavior of a crowd during an evacuation process when correlated pedestrians due to, for example feelings, are present. Our research addresses this issue and examines the statistical behavior of a mixture of individuals and couples during a (panic) escaping process. We found that the attractive feeling among couples plays an important role in the time delays during the evacuation of a single exit room.

Studies demonstrate that community structure plays an important role in information spreading recently. In this paper, we investigate the impact of multi-community structure on information diffusion with linear threshold model. We utilize extended GN network that contains four communities and analyze dynamic behaviors of information that spreads on it. And we discover the optimal multi-community network modularity for information diffusion based on the social reinforcement. Results show that, within the appropriate range, multi-community structure will facilitate information diffusion instead of hindering it, which accords with the results derived from two-community network.

Cascading failure is ubiquitous in many networked infrastructure systems, such as power grids, Internet and air transportation systems. In this paper, we extend the cascading failure model to a scale-free network with tunable clustering and focus on the effect of clustering coefficient on system robustness. It is found that the network robustness undergoes a nonmonotonic transition with the increment of clustering coefficient: both highly and lowly clustered networks are fragile under the intentional attack, and the network with moderate clustering coefficient can better resist the spread of cascading. We then provide an extensive explanation for this constructive phenomenon via the microscopic point of view and quantitative analysis. Our work can be useful to the design and optimization of infrastructure systems.

Recent analysis of a Yard–Sale (YS) exchange model supplemented with redistributive proportional taxation suggested an asymptotic behavior $P(w)\sim 1∕w^{\mu }$ for the wealth distribution, with a parameter-dependent exponent $\mu$. Revisiting this problem, it is here shown analytically, and confirmed by extensive numerical simulation, that the asymptotic behavior of $P(w)$ is not power-law but rather a Gaussian. When taxation is weak, we furthermore show that a restricted-range power-law behavior appears for wealths around the mean value. The corresponding power-law exponent equals 3/2 when the return distribution has zero mean.

This study reports the modeling of the turbulent natural convection in a double air-channel solar chimney (SC-DC) and its comparison with a single air-channel solar chimney (SC-C). Prediction of the mass flow and the thermal behavior of the SC-DC were obtained under three different climates of Mexico during one summer day. The climates correspond to: tropical savannah (Mérida), arid desert (Hermosillo) and temperate with warm summer (Mexico City). A code based on the Finite Volume Method was developed and a $k-\omega$ turbulence model has been used to model air turbulence in the solar chimney (SC). The code was validated against experimental data. The results indicate that during the day the SC-DC extracts about 50% more mass flow than the SC-C. When the SC-DC is located in Mérida, Hermosillo and Mexico City, the air-changes extracted along the day were 60, 63 and 52, respectively. The air temperature at the outlet of the chimney increased up to 33%, 38% and 61% with respect to the temperature it has at the inlet for Mérida, Hermosillo and Mexico City, respectively.

In this paper, we propose a new algorithm that minimizes the worst expected growth of an epidemic by reducing the size of the largest connected component (LCC) of the underlying contact network. The proposed algorithm is applicable to any level of available resources and, despite the greedy approaches of most immunization strategies, selects nodes simultaneously. In each iteration, the proposed method partitions the LCC into two groups. These are the best candidates for communities in that component, and the available resources are sufficient to separate them. Using Laplacian spectral partitioning, the proposed method performs community detection inference with a time complexity that rivals that of the best previous methods. Experiments show that our method outperforms targeted immunization approaches in both real and synthetic networks.