International Journal of Modern Physics C

Two-phase flow is important in both science and engineering. In this paper, a D2Q5 multi-relaxation-time lattice Boltzmann model is proposed for solving Allen–Cahn equation, which is a convection–diffusion equation for the order parameter. Through Chapmann–Enskog analysis, the macroscopic equations can be recovered from the D2Q5 MRT LB model. Then, a detailed numerical study on several classical problems is performed to give a comparison between the present model for the AC equation and the previous study. The results show that the present model gets better accuracy and has better stability than that of single-relaxation-time LB model, and has higher computational efficiency than that of D2Q9 MRT LB model. Furthermore, as an important application, the model is also used to study the deformation field problem because of its excellent ability to cope with complex and changeable boundaries.

Users’ ratings in recommender systems can be predicted by their historical data, item content, or preferences. In recent literature, scientists have used complex networks to model a user–user or an item–item network of the RS. Also, community detection methods can cluster users or items to improve the prediction accuracy further. However, the number of links in modeling a network is too large to do proper clustering, and community clustering is an NP-hard problem with high computation complexity. Thus, we combine fuzzy link importance and K-core decomposition in complex network models to provide more accurate rating predictions while reducing the computational complexity. The experimental results show that the proposed method can improve the prediction accuracy by 4.64$\%$ to 5.71$\%$ on the MovieLens data set and avoid solving NP-hard problems in community detection compared with existing methods. Our research reveals that the links in a modeled network can be reasonably managed by defining fuzzy link importance, and that the K-core decomposition can provide a simple clustering method with relatively low computation complexity.

A general solution for propagating waves in a generalized piezo-photo-thermoelastic medium for the one-dimensional (1D) problem under the hyperbolic two-temperature theory is investigated. The governing equations of the elastic waves, carrier density (plasma wave), quasi-static electric field, heat conduction equation, hyperbolic two temperature coefficient and constitutive relationships for the peizo-thermoelastic medium are obtained using Laplace transformation method in 1D. On the interface adjacent to the vacuum, mechanical stress loads, thermal and plasma boundary conditions are applied to obtain the main basic physical quantities in the Laplace domain. The inversion of Laplace transform by a numerical method is applied to obtain the complete solutions in the Laplace time domain for the main physical fields in this phenomenon. The effects on the force stress, displacement component, temperature distribution and carrier density of the thermoelastic, thermoelectric and hyperbolic two-temperature parameters by the applied force were graphically discussed.

A constrained path quantum Monte Carlo simulation is performed to investigate the superconducting pairing correlation in doped staggered-flux Hubbard model on a square lattice, which is an interesting platform to host Dirac fermions. For various electron fillings and interaction strengths, the $d_{xy}$ pairing dominates over other pairing forms since its long-range correlation is larger over mostly distances. We deepen our study by including the nearest-neighbor interaction $V$ and found the $d_{xy}$ pairing correlation is slightly suppressed by increasing $V$.

The design of centrality metric algorithms to distinguish influential nodes on networks has become a very attractive research interest lately. This recent development is due to the fact that detection of powerful nodes can be very helpful in various situations such as containment of epidemic spread and so on. Many useful centrality algorithms had been proposed in the past but some of them have inherent shortcomings. Some of these algorithms are designed to capture global or local network details but little efforts have been channeled to explore techniques based on sub-graph information. Therefore, a novel sub-graph degree-based bridge centrality (SDBC) algorithm to distinguish bridge nodes on networks is proposed in this work. A pivot node’s sphere of influence is captured and this includes its 1-hop and 2-hop neighborhood with itself inclusive. A model is proposed to reduce links of all 2-hop neighbors to 1-hop links. The sub-graph degree distribution of all nodes under the pivot node’s sphere of influence is computed and afterwards, the pivot node’s bridging influence capacity is quantified using Dehmer’s entropy model. The proposed algorithm is designed to capture the topological and strength of connections between nodes. In essence, a tuning parameter is used to automatically switch between unweighted and weighted networks, a feature that makes the algorithm unique. Additionally, SDBC does not use two factor computation unlike previous bridge node detection algorithms. Various experimental comparisons were carried out against some selected centrality metrics and SDBC performed well against the compared algorithms. The results prove that the proposed method is quite efficient in distinguishing bridge nodes on complex networks.

This paper reports a stochastic method of synthesizing three-neighborhood two-state nonlinear non-uniform reversible cellular automata (CAs) having large cycle(s). To develop this method, we propose a parameter which measures the dependency of each cell on its neighbors. Using this parameter, we classify the possible rules of reversible CAs into four classes — completely dependent, partially dependent, weakly dependent and independent. A CA is expected to have larger cycle length if most of the rules of the CA are sensitive to their neighbors; hence, possess higher parameter value. It is shown that our synthesis algorithm, where rules are selected from the first three classes based on Gaussian distribution, can successfully generate CAs having desired length cycle. We empirically show that most of the synthesized CAs have a cycle at least as large as $2^{n-1}$, where $n$ is the lattice size. Then, as an application of such CAs, a scheme is developed to design window-based pseudo-random number generators (PRNGs) using (some of) them. These PRNGs are easily implementable on hardware and are highly portable. Finally, when compared on the same platform, our PRNGs outperform most of the well-known PRNGs existing today.

Using basin-hopping algorithm within the quantum corrected Sutton-Chen (Q-SC) many-body potential, a systematic investigation has been performed for the best chemical ordering structures of 38-atom trimetallic Pd_nPt${}_{(32-n)}$Rh₆ nanoalloys with truncated octahedral (TO) geometry. The atomic mixing degrees of Pd, Pt and Rh atoms were investigated by using order parameter ($R_A$). The results show that demixing is energetically favorable for the investigated structures, where the number of Rh atoms fixed at 6. The only compositions with the number of hetero bonds lower than the homo bonds are Pt${}_{32}$Rh₆, Pd₁Pt${}_{31}$Rh₆, Pd${}_{16}$Pt${}_{16}$Rh₆ and Pd${}_{32}$Rh₆ which make segregation energetically favorable. The surface segregation of Pd atoms is explained by the lower cohesive energy of Pd atoms than Pt and Rh. The segregation of Pd atoms to the surface is also associated with the smaller surface energies compared with Pt and Rh atoms. Due to the comparable cohesive energies of Rh and Pt atoms, a computation occurs for filling the octahedral core between two kinds.

Motivated by the significant role of nanofluid in pollution cleaning and energy recovery, we decided to explore the unsteady three-dimensional rotating flow of nanofluid driven by the movement of a flat surface with the potencies of prescribed heat distributions. The modeling of the physical model is completed with the help of Buongiorno nanofluid model. Suitable arrangement of similarity variables is implemented to transform the model equations into strongly nonlinear ordinary differential equations. Numerical inspection of the model is made by employing Keller–Box algorithm. Influences of involved parameters on the distributions of heat and mass are discussed graphically, while the potencies of influential parameters on reduced Nusselt and reduced Sherwood numbers are physically discussed through tabular arrangements. It is deduced that increasing the values of Prandtl factor and heat controlling indices diminishes the temperature and concentration distributions, whereas intensification in the amount of rotation factor enhances the temperature as well as concentration distribution. Moreover, negative trends in the amounts of reduced Nusselt and Sherwood numbers are achieved with the escalations in the values of rotation and thermophoresis factors, whereas opposite trend is achieved with the intensification in the choice of Prandtl factor.

Network diffusion processes play an important role in solving the information overload problem. It has been shown that the diffusion-based recommendation methods have the advantage to generate both accurate and diverse recommendation items for online users. Despite that, numerous existing works consider the rating information as link weight or threshold to retain the useful links, few studies use the rating information to evaluate the recommendation results. In this paper, we measure the average rating of the recommended products, finding that diffusion-based recommendation methods have the risk of recommending low-rated products to users. In addition, we use the rating information to improve the network-based recommendation algorithms. The idea is to aggregate the diffusion results on multiple user-item bipartite networks each of which contains only links of certain ratings. By tuning the parameters, we find that the new method can sacrifice slightly the recommendation accuracy for improving the average rating of the recommended products.

In this paper, an extended car-following model is proposed to simulate traffic flow with consideration of incorporating the effects of driver’s memory and mean expected velocity field in ITS (i.e. intelligent transportation system) environment. The neutral stability condition of the new model is derived by applying the linear stability theory. Compared with the optimal velocity model and the full velocity difference model, the stability region of the new model can be significantly enlarged on the phase diagram, and the anticipating motion information of more vehicles ahead can further enhance traffic stability. Furthermore, the mean expected velocity field effect plays a more important role than that of driver’s memory effect in improving the stability of traffic flow. Nonlinear analysis is also conducted by using the reductive perturbation method, and the mKdV equation near the critical point is obtained to describe the evolution properties of traffic density waves. Numerical simulation results show that the coupling effect of driver’s memory and the mean expected velocity field can suppress the traffic jam effectively, which is in good agreement with the analytical result.

This paper addresses spectral collocation techniques to treat with the fractional initial value problem of distributed-order. We introduce three algorithms based on shifted fractional order Jacobi orthogonal functions outputted by Jacobi polynomials. The shifted fractional order Jacobi–Gauss–Radau collocation method is developed for approximating the fractional initial value problem of distributed-order. The principal target in our techniques is to transform the fractional initial value problem of distributed-order to a system of algebraic equations. Some numerical examples are given to test the accuracy and applicability of our technique. It is known that the accuracy of numerical approaches for nonsmooth solution is deteriorated. Employing fractional order Jacobi functions instead of the classical Jacobi stopped this deterioration.

In this paper, we work on the fundamental collocation strategy using the moved Vieta–Lucas polynomials type (SVLPT). A numeral method is used for unwinding the nonlinear Rubella illness Tributes. The quality of the SVLPT is presented. The limited contrast system is used to understand the game plan of conditions. The mathematical model is given to attest the resolute quality and ampleness of the recommended procedure. The oddity and meaning of the outcomes are cleared utilizing a 3D plot. We examine free sickness harmony, security balance point and the presence of a consistently steady arrangement.

Different permutations of the single and the two-fold dam-break flow have been investigated using the mesh-free smoothed-particle hydrodynamics and the experimental setup. The free-surface deformation in the case with the wet bed for five different downstream water heights has been investigated and respective numerical and experimental results were presented. The results demonstrate that the increase of the water height over the wet bed leads to the reduction of the flow front velocity. Effect of considering or omitting the dam gate during the numerical simulation has also been examined, which proves that the simulations including the dam gate show improved agreement with the experimental results. Influence of the three-dimensional cubic, triangular, circular and square cylindrical obstacles and their position on flow characteristics has been investigated. As the distance between the triangular obstacle and the gate increases, a bore is created at the position closer to the top of the triangle. In addition, it is found that larger force is exerted on the circular cylinder in comparison to the square cylinder.

Value-at-risk (VaR) is a crucial subject that researchers and practitioners extensively use to measure and manage uncertainty in financial markets. Although VaR is a standard risk control instrument, there are criticisms about its performance. One of these cases, which has been studied in this research, is the VaR underestimation during times of crisis. In these periods, the non-Gaussian behavior of markets intensifies, and the estimated VaRs by typical models are lower than the real values. A potential approach that can be used to describe the non-Gaussian behavior of return series is the Tsallis entropy framework and nonextensive statistical methods. This paper has used the nonextensive models for analyzing financial markets’ behavior during crisis times. By applying the q-Gaussian probability density function for emerging and mature markets over 20 years, we can see a better VaR estimation than the regular models, especially during crisis times. We have shown that the q-Gaussian models composed of VaR and Expected Shortfall (ES) estimate risk better than the standard models. By comparing the ES, VaR, $q$-VaR and $q$-ES for emerging and mature markets, we see in confidence levels more than 0.98, the outputs of q models are more real, and the $q$-ES model has lower errors than the other ones. Also, it is evident that in the mature markets, the difference of VaR between normal condition and nonextensive approach increases more than one standard deviation during times of crisis. Still, in the emerging markets, we cannot see a specific pattern. The findings of this paper are useful for analyzing the risk of financial crises in different markets.