International Journal of Modern Physics C

The evolution of cultural diversity in some social systems can be observed as the oppositional patterns in different levels, namely, the directions of the trends of diversity in different levels are opposite. In this paper, we propose a payoff-driven metapopulation modeling framework to investigate the origin of the oppositional patterns in the trends of cultural diversity. This model considers the mobile agents in multiple communities, and the cultural characteristics of an agent are represented by a vector. The diffusion of cultural characteristics is driven by two opposite effects: the communication cost positively depending on cultural difference between agents, and the cultural attractiveness single-peak-functional correlating on cultural difference. In the numerical simulations of the model, the polarization is observed in the case with the homophily principle, and two types of the oppositional patterns can emerge in the case with time-varying mobility of agents. These findings efficiently explain the emergence of the oppositional patterns and help to understand the evolution of diversity under the dynamics of both synchronization and variation.

Imposing the magnetic field and dispersion of nanopowders have been discussed in this paper to provide the opportunity for control the fluid movement. The second law components were analyzed and Bejan number was calculated. Outcomes illustrated that augmenting permeability allows improving the nanopowder movement and thinner boundary layer provides denser isotherm which results in greater Nu and lower Be. Growth of Ra characteristics leads to stronger buoyancy forces which boost the convection transportation and stronger circulation results in greater Nu. In addition, the share of $S_{gen,th}$ reduces with reduction of boundary layer thickness, so, Be has reverse relation with both permeability and Ra. An increment of Ha results in thicker boundary layer and isotherms become parallel and the convection weakens. So, Nu declines with growth of Ha and an opposite trend was calculated for Be. Reduction impact of Da on Be is negligible when Ra has the lowest value.

Recent work on the cascading failure of networks with dependence groups assumes that the number of nodes in each dependence group is equal. In this paper, we construct a model on interdependent networks with dependence groups against cascading failure. The size of dependence group is supposed to obey the Poisson Distribution and the Truncated Normal Distribution, respectively. By applying the tools of mean-field approximation and the generating function techniques, the cascading model is theoretically analyzed and the theoretical solutions are nearly consistent with the simulation values. Besides, we define three kinds of coupling preferences based on node degree, i.e. assortative coupling, disassortative coupling and random coupling. The connection between layers is no longer one-to-one correspondence of nodes, but fully connection of some groups. In addition, some factors affecting the network robustness are discussed and extensive simulations are realized on two-layer BA networks. The simulation results show that the coupling preference has influence on the network robustness and the network with dependence groups obeying the Truncated Normal Distribution performs better than the Poisson Distribution.

With the deepening of research on complex networks, many properties of complex networks are gradually studied, for example, the mean first-passage times, the average receive times and the trapping times. In this paper, we further study the average trapping time of the weighted directed treelike network constructed by an iterative way. Firstly, we introduce our model inspired by trade network, each edge $e_{i,j}$ in undirected network is replaced by two directed edges with weights $w_{i,j}$ and $w_{j,i}$. Then, the trap located at central node, we calculate the weighted directed trapping time (WDTT) and the average weighted directed trapping time (AWDTT). Remarkably, the WDTT has different formulas for even generations and odd generations. Finally, we analyze different cases for weight factors of weighted directed treelike network.

Electronic structure, X-ray absorption, and magnetic circular dichroism (XMCD) spectra in the CoFeMnSi Heusler alloy were studied from first principles. Fully relativistic Dirac linear muffin-tin orbital band structure method was implemented with various exchange–correlation functionals tested. The supercell approach was used to study the influence of intersite disorder, at the levels of 6.25%, 12.5%, and 25% within transition metal sites, on the XMCD spectra at $L_{2,3}$ edges and spin polarization (SP) at the Fermi level. It is found that most sensitive to Fe–Mn and Co–Fe disorder are XMCD spectra at $L_{2,3}$ edges of Fe, while the sensitivity decreases from Mn to Co. It is shown that magnetic moments estimated with the use of magneto-optical (MO) sum rules agree with the ab initio calculated ones to within $2\%$, $12\%$, and $19\%$, for Co, Fe, and Mn, respectively. The calculated SP decreases from 99% for ordered CoFeMnSi alloy, to 96% upon 25% Co–Fe disorder, to 83% for Fe–Mn disorder, and to 42% in the case of Co–Mn disorder. The calculated spectra agree well with the available experimental data. The rich XMCD spectral structures are predicted from first principles at Fe, Co, Mn and Si $K$ edges.

We introduce a new opinion model to study the opinion evolving in three typical networks (small-world network, scale-free network and highly clustered scale-free network) by employing Monte Carlo numerical simulation. To consider important social influence, we propose a heat bath like optimized opinion model that includes both local and global social impacts. By employing the new opinion model into three typical networks, we have found the main determinants of the evolution of the opinion, that were the weight constant of the local and global effects, the structure of the network, the size of the network and the initial density of the average opinion. The simulations show how those factors affect the evolving time of reaching the consensus quantitatively. Our model also could explain the cases of de facto standard and lock-in effect, well-known phenomenon in economics and business management.

The Susceptible, Infected and Recover (SIR) model is a very simple model to estimate the dynamics of an epidemic. In the current pandemic due to Covid-19, the SIR model has been used to estimate the dynamics of infection for various infected countries. Numerical solutions are used to obtain the value of parameters for the SIR model. The maximum and minimum basic reproduction number (14.5 and 2.3) are predicted to be in Turkey and China, respectively.

Macroscopic growth laws describe in an effective way the underlying complex dynamics of the spreading of infections, as in the case of Covid-19, where the counting of the cumulative number $N(t)$ of detected infected individuals is a generally accepted variable to understand the epidemic phase. However, $N(t)$ does not take into account the unknown number of asymptomatic cases $A(t)$. The considered model of Covid-19 spreading is based on a system of coupled differential equations, which include the dynamics of the spreading among symptomatic and asymptomatic individuals and the strong containment effects due to the social isolation. The solution has been compared with $N(t)$, determined by a single differential equation with no explicit reference to $A(t)$, showing the equivalence of the two methods. The model is applied to Covid-19 spreading in Italy where a transition from an exponential behavior to a Gompertz growth for $N(t)$ has been observed in more recent data. The information contained in the time series $N(t)$ turns out to be reliable to understand the epidemic phase, although it does not describe the total infected population. The asymptomatic population is larger than the symptomatic one in the fast growth phase of the spreading.

Lateral intakes are hydraulic structures used for domestic, agricultural and industrial water conveyance, characterized by a very complex three-dimensional morphodynamic behavior: since streamlines near the lateral intake are deflected, some vortices form, pressure gradient, shear and centrifugal forces at the intake generate flow separation and a secondary movement, responsible for local scour and sediment deposition. On the other side, the modeling of flows, besides the sediment transport, in curved channels implies some more complications in comparison with straight channels. In this research, this complex process has been investigated experimentally and numerically, with the mechanism of sediment transport, bed topography evolution, flow pattern and their interactions. Experiments were performed in the Laboratory of Tarbiat Modares University, Iran, where a U-shaped channel with a lateral intake was installed and dry sediment was injected at constant rate into a steady flow. Due to the spiral flow, the bed topography changes significantly and the bed forms in turn affect the sediment entering the intake. Different from the previous works on this topic which were mainly based on laboratory experiments, here, Computational Fluid Dynamics (CFD) numerical simulations with FLUENT software were also performed, specifically with the two-phase Eulerian Model (EM) and Discrete Phase Model (DPM), at the aim of evaluating their performance in reproducing the observed physical processes. This software is used for a large variety of CFD problems, but not much for simulating sediment transport phenomena and bed topography evolution. The comparison of the results obtained through the two models against the laboratory experimental data proved a good performance of both the models in reproducing the main features of the flow, for example, the longitudinal and vertical streamlines and the mechanism of particles movement. However, the EM reveals a better performance than DPM in the prediction of the secondary flows and, consequently, of the bed topography evolution, whereas the DPM well depicts the particles pattern, predicts the location of trapped particles and determines the percentage of sediment entering the intake.

The numerical models so calibrated and validated were applied to other cases with different positions of the intake in the bend. The results show that mechanism of sediment entrance into the intake varies in different position. If the intake is installed in the second half of the bend, the sediment accumulates along the inner bank of the bend and enters the intake from downstream edge of intake; on the other side, if it is placed in the first half of the bend, the sediment accumulates along both the inner and the outer bends and, therefore, more sediment enters the intake. Also the results of the simulations performed with the DPM model for different positions of the lateral intake show that for all discharge ratios, the position of 120^∘ is the one which guarantees the minimum ratio of sediment diverted to the intake (Gr).

The main purpose of this article is to apply response surface method to analyze the residual dyes removal independent variables from the experimental data for dye adsorption onto alkali-activated sand as natural adsorbent from textile wastewater. The independent variables are contact time (3–30min) and adsorbent dosage (12.5–100g) and the dependent variables are percentage of dye removal and dye adsorbed amount per alkali-activated sand as responses. The effect of the variables, their interaction with each other, the fitted model equations, the adequacy and desirability of the model was evaluated by RSM. Response surface method to analyze the residual dyes removal, which resulted in about 70% dye removal and 30 $\times 10^{-3}$mg/g dye adsorbed, with 0.983 of desirability for fitted model. Finally, the initial dye concentration effect was investigated.

The accurate positioning of the propagation and diffusion source points of personal information is of great practical significance for the protection of personal information security. Currently, a popular method is to locate the data diffusion source point by deploying a certain number of observers and collecting the data propagation information from observers. In view of the deployment of observers, it was found that there is an important correlation between the observer deployment location and the data propagation time delay by analyzing the relationship between the positioning accuracy of information sources and the deployment location of observers, and then an optimal deployment strategy based on Naive Bayes model (NBM) was proposed. At last, it was verified that the proposed deployment strategy can significantly improve the positioning accuracy of information source by comparing the model network with the actual network, which is of great significance for the security control of personal information propagation.

The interest in learning how innovations spread in our society has led to the development of a variety of theoretical-computational models to describe the mechanisms that govern the diffusion of new ideas among people. In this paper, the diffusion of innovations is addressed with the use of the Axelrod’s cultural model where an agent is represented by a cultural vector of $F$ features, in which each feature can take on $Q$ integer states. The innovation or new idea is introduced in the population by setting a single feature of a single agent to a new state ($Q+1$) in the initial configuration. Particularly, we focus on the effect of the small-world topology on the dynamics of the innovation adoption. Our results indicate that the innovation spreads sublinearly ($\sim t^{1∕2}$) in a regular one-dimensional lattice of connectivity $K=4$, whereas the innovation spreads linearly ($\sim t$) when a nonvanishing fraction of the short-ranged links are replaced by long-range ones. In addition, we find that the small-world topology prevents the emergence of complete order in the thermodynamic limit. For systems of finite size, however, the introduction of long range links causes the dynamics to reach a final ordered state much more rapidly than for the regular lattice.

An accurate analysis of the polar motion variation is essential to understand the global change of the environment and predict useful information about short-term and long-term change in climate. Observation of polar motion excitation using multiple measurements including Very-Long-Baseline-Interferometry (VLBI) provides highly accurate measurement of polar motion variation. The observed polar motion excitation has been modeled with multiple geophysical models, but the discrepancies between observations and models still exist. In this paper, we propose two approaches for detecting the discrepancy of the polar motion excitation: topological data analysis (TDA) and convolutional neural network (CNN) analysis. Our methods clearly show that the observed polar motion has a different topological structure from the model data, and there are time periods that the model fails to represent the polar motion. Numerical results indicate that the proposed methods show promise for applications to polar motion signal analysis.

Droplet impact on elastic beams is considered as a novel model of energy transfer which is a promising alternative in applications of energy harvesting. The transient impact process is dominated by the fluid–solid interaction and the capillary effect. The numerical model based on SPH method allows predicting the droplet dynamic behaviors due to super-hydrophobic (SH) surfaces. The predicted results are also compared with relevant experiments to verify the robustness and flexibility of the model. For fixed-fixed beams, typical regimes, namely spherical-shaped rebound, pancake-shaped rebound and splashing of droplet, are identified. The elasticity of beam causing the earlier lifting-off phenomenon of droplet is investigated in detail. By comparison, cantilever beams repel the droplet in a smoother way and large deformation of the beam is considered. The slipping-off phenomenon is expected to occur under specific conditions on soft cantilevers. The effect of elasticity plays a key role in the maximum deflection and oscillating frequency for both types of beams. This work examines the effectiveness of the framework based on the numerical model which provides further understandings for droplet impacts. It may lay the foundation for practical applications, such as engineering piezoelectric raindrop energy harvesters and plant leaves repelling raindrops.