International Journal of Modern Physics C

Based on the first-principles method of density functional theory and the transition state theory, the adsorption, occupancy and diffusion behaviors of H atoms in LaFeO₃(010) surface with O vacancy are investigated. It is found that, for the LaFeO₃(010) surface with O vacancy, the H atom prefers to adsorb on the O atom and also could adsorb on the Fe atom; the adsorption energy of H atom in the surface layer is the biggest, while the adsorption energy decreases with the H atom in-depth diffusion to the bulk; the diffusion of H atoms from the surface layer to the bulk phase is a process of progressive rotation around the O atom, and the stepwise diffusion is more likely to occur than the direct diffusion. If the H atom can cross the barrier and diffuse from the surface to the subsurface, it will be more likely to diffuse inward. The presence of O vacancy can reduce the barrier for H atoms diffusion from the surface to the subsurface, and then improve the diffusion properties of the LaFeO₃(010) surface system.

Natural gas is known as the main source of energy and also contains significant and noble gases. Numerous researches have been performed to present novel methods for the detection and analysis of natural gas. In this study, Direct Simulation Monte Carlo (DSMC) method is used to evaluate the performance of a new micro gas sensor (MIKRA) for detection of helium in CH4/He gas mixture. In this sensor, the temperature difference of two arms inside a rectangular domain at low-pressure condition induces a Knudsen force which is proportional to physical properties of the gas. In order to define flow feature of a low-pressure gas inside the micro gas actuator, high order equation of Boltzmann is used to attain high precision results. To solve these equations, DSMC approach is used as a robust method for the non-equilibrium flow field. The effects of main factors such as length and gap of arms are comprehensively investigated in different ambient pressures. Furthermore, the effect of various concentrations of the CH4/He gas mixture on force generation is comprehensively studied. Our findings show that value of generated Knudsen force significantly different when the fraction of He in CH4/He gas mixture is varied. This indicates that this micro gas sensor could precisely detect the concentration of Helium gas inside a low-pressure CH4/He gas mixture.

The present work is related to the study of a water intake projected into a dam reservoir, located in northern Morocco. The object sought is to examine the natural conditions surrounding a water intake and their possible repercussions on its operation. Thus, a two-dimensional numerical model is developed to simulate the sediment transport in a reservoir dam. The developed model consists of hydrodynamic module based on the Saint–Venant equations and sediment transport module based on the mass-balance equation, where there are resolved by a MacCormack numerical scheme and upwind scheme, respectively. The analysis using the numerical model has made it possible to predict the shutdown periods of the drinking water production station with respect to the rate of suspended solids (SS) for extreme rainfall events.

We demonstrate that a series of procedures for increasing the efficiency of transition matrix calculations can be realized by integrating the standard single-spin flip transition matrix method with global cluster flipping techniques. Our calculations employ a simple and accurate method based on detailed balance for computing the density of states from the Ising model transition matrix.

Electromyograms are biomedical signals which are detected through electrodes. These signals represent measurements of the electric potentials associated to muscles contractions, and they are physically important in view that they provide information on the health of individuals. The computational modeling of electromyograms has been mainly carried out through general-purpose commercial software, which is typically provided with the purchase of computer equipment. In view of the well-known limitations inherent in the commercial software, a lot of research and efforts are devoted continuously to upgrade existing processing methods. However, as expected, these innovations take a considerable amount of time in order to be implemented in commercial software. In order to alleviate this situation, the package has been developed to provide a free and fully open-source package for both applied and theoretical researchers, which includes some fundamental tools to computationally process electromyograms. The purpose of the present paper is to provide an overview of the most important features of this package, and to supply codes and examples to illustrate its use.

This paper performs a numerical analysis of the natural convection within two-dimensional enclosures (square enclosure and enclosures with curved walls) full of a H₂O-Cu nanofluid. While their vertical walls are isothermal with a cold temperature $T_c$, the horizontal top wall is adiabatic and the bottom wall is kept at a sinusoidal hot temperature. The working fluid is assumed to be Newtonian and incompressible. Three values of the Rayleigh number were considered, viz., 10³, 10⁴, 10⁵, the Prandtl number is fixed at 6.2, and the volume fraction $\varphi$ is taken equal to 0% (pure water), 10% and 20%. The numerical simulation is achieved using a 2D-in-house CFD code based on the governing equations formulated in bipolar coordinates and translated algebraically via the finite volume method. Numerical results are presented in terms of streamlines, isotherms and local and average Nusselt numbers. These show that the heat transfer rate increases with both the volume fraction and the Rayleigh number, and that the average number of Nusselt characterizing the heat transfer raises with the nanoparticles volume fraction.

According to experimental observations, laden particles in turbulence may attenuate or augment the carrier phase turbulence. But until now, there are no widely recognized models for estimating the so-called turbulence modulation phenomenon. In this paper, a novel two-fluid model is proposed based on the probability density function (PDF) approach. The Reynolds stress equation of the present model contains both production and dissipation terms due to the presence of particles, the turbulence modulation phenomenon can be well explained with the new model. To further explore the two-fluid model, a comparative study on PDF and Reynolds-averaged approaches is carried on, the differences and relations between the present model and the classical two-fluid Reynolds averaged Navier–Stokes (RANS) model are analyzed in the paper. Theoretical and numerical analysis indicates that the proposed model shows particular promise for predicting particle-laden turbulent flows.

In this paper, we study the trapping time in the weighted pseudofractal scale-free networks (WPSFNs) and the average shortest weighted path in the modified weighted pseudofractal scale-free networks (MWPSFNs) with the weight factor $r$. At first, for exceptional case with the trap fixed at a hub node for weight-dependent walk, we derive the exact analytic formulas of the trapping time through the structure of WPSFNs. The obtained rigorous solution shows that the trapping time approximately grows as a power-law function of the number of network nodes with the exponent represented by $\frac{ln\frac{2+4r}{2+r}}{ln3}$. Then, we deduce the scaling expression of the average shortest weighted path through the iterative process of the construction of MWPSFNs. The obtained rigorous solution shows that the scalings of average shortest weighted path with network size obey three laws along with the range of the weight factor. We provide a theoretical study of the trapping time for weight-dependent walk and the average shortest weighted path in a wide range of deterministic weighted networks.

Most real networks have been found to evolve dynamically. In addition, heterogeneity of interaction strengths and individual’s memory have been proved to be important to affect network evolution in the area of complex networks. Based on a recent extension of the activity-driven modeling framework, i.e. activity-driven model with attractiveness, where individuals are characterized by their activities and attractiveness, we propose a new temporal network model by considering individuals’ memory. Similar to the previous network model, we also investigate heterogeneous distributions and different correlations between the two variables (activity and attractiveness). By numerical simulations, we illustrate the impact of memory on epidemic spreading processes unfolding on the proposed model. We find that, when individuals’ attractiveness is fixed, memory does not cause dramatic change on the epidemic threshold in the SIR process, but it inhibits the epidemic spreading by reducing the infected ratio. While for the SIS process, in the case of uncorrelated and positive correlations between the two variables, memory facilitates the epidemic outbreaks by shrinking the epidemic threshold. When the two variables are negatively correlated, memory does not take obvious impact on the epidemic threshold. Besides, for the two processes unfolding on the network with and without memory, the propagation threshold decreases with the enhancement of the positive correlation, however, it almost does not get much influence from the negative correlation strength. It indicates that for the individuals with same activity, more attractive individuals will promote the disease spread. The findings deepen our understanding of the role of individuals’ memory and their attractiveness in the epidemic spreading process.

At present, the competition between enterprises is increasingly fierce. If the enterprise wants to obtain the competitive advantage, the innovation is an effective means; innovation is the basis of an enterprise’s survival and development: all enterprises strive to improve their competitiveness through innovation; however, the effects of innovation diffusion will eventually restrict their development. This study, using a complex network approach, establishes a nonlinear technology innovation diffusion cascade model aimed at identifying the best seeds to foster the process of technology innovation diffusion by searching for the important nodes scattered throughout different communities to spread technology innovation and achieve the maximum diffusion effect. Through an analysis of simulated and real networks, we found that our method can both improve the speed of technology innovation diffusion and enhance the diffusion effect, which can provide advantages to enterprises and help promote innovation.