International Journal of Modern Physics C

In this work, we present a computational procedure to locate the dominant Fisher zero of the partition function of a thermodynamic system. The procedure greatly reduces the required computer processing time to find the dominant zero when compared to other dominant zero search procedures. As a consequence, when the partition function results in very large polynomials, the accuracy of the results can be increased, since less drastic truncation of the polynomials (or even no truncation) is necessary. We apply the procedure to the 2D Ising model in a square lattice, obtaining very accurate results for the critical temperature and some of the critical exponents of the model. We also show the results obtained when the technique is used with the Monte Carlo simulated 2D Ising model in large lattices.

The heat transfer through the thermal radiation is very significant in many industrial processes, biomedical engineering, technological devices and thermal therapeutic process. These comprise nuclear power plants, propulsion of rockets, missiles, satellites and space vehicles, photochemical reactors, solar collector performance, heat exchangers. The boundary layer flow and the heat transport of dusty liquid over a stretching cylinder are discussed. The impact of nonlinear thermal radiation is explored. Cattaneo–Christov heat flux model is considered to formulate the energy equation. The resulting equations of the physical system are converted into a system of nonlinear ordinary coupled differential equations with the help of suitable similarity transformations and numerically solved by using Runge–Kutta–Fehlberg’s method along with shooting technique. The influence of nondimensional parameters is analyzed and interpreted graphically. The heat transfer rate and local coefficient of skin friction are also calculated for diverse nondimensional constraints and plotted.

The study of the effect of periodic boundary condition is of primary interest to understand the maximum temperature of the wall and avoid overheating phenomena. Available computational fluid dynamic studies provide information on the effect of the local heat flux or local temperature. This study aims to understand the temperature and fluid flow distribution inside the cavity under such periodic thermal boundary conditions to avoid overheating phenomena. This study reports the numerical results of the natural convection inside an air-filled cavity with its horizontal walls insulated and its vertical walls at different thermal boundary conditions. The heat flux of the left wall and the temperature of the right wall are varied sinusoidally. The Lattice Boltzmann Method (LBM) is used to solve the governing equations with the related boundary conditions. The maximum temperature of the wall under the heat flux and the maximum stream function inside the cavity are investigated. Also, the effect of oscillation period on the maximum temperature of the left wall is studied. The results show that the maximum temperature of the left wall corresponds to the heat flux wavelength of 0.8. Moreover, the stream function value increases intensively by increasing the Rayleigh number.

Accurate traffic flow forecasting is a prerequisite guarantee for the realization of intelligent transportation, but due to the complex spatiotemporal characteristics of traffic flow, its forecasting has always been difficult. Deep learning can learn the deep spatiotemporal characteristics of traffic flow from a large amount of data. Deep learning can learn the deep spatiotemporal characteristics of traffic flow from a large amount of data. This paper establishes a novel combination forecasting model GGCN-SA based on deep learning for traffic flow to effectively capture the spatiotemporal characteristics of traffic flow and improve forecasting accuracy. The model captures the spatial correlation of the road traffic network through the graph convolutional network (GCN), captures the time dependence of the traffic flow through the gated recursive unit (GRU), and further introduces the soft attention mechanism (Soft Attention) to aggregate different neighborhoods Spatio-temporal information within the range to enhance the model’s ability to characterize the temporal and spatial characteristics of traffic flow. A large number of experiments have been conducted on the METR-LA and SZ-taxi data sets. The experimental results show that the GGCN-SA model proposed in this paper has better forecasting performance compared with the baseline methods.

Knowing how crowds behave is crucial to understanding pedestrian evacuation. In this work, we study strategy evolution in pedestrian room evacuation using a cellular automaton (CA) model with a spatial game. Agents play the game under certain conditions. While playing the game, they choose one of the three strategies (Patient strategy, Normal strategy and Impatient strategy) according to either fitness or imitation that leads to patient, normal or impatient behavior. Interactions among agents are reflected by the payoffs (related with friction cost) which may depend on agents’ surroundings. Simulation results show how evacuation conditions, agents’ individual actions, and friction cost affect strategy evolution in pedestrian room evacuation. It is found that cooperation exists both in emergency situation and relatively nonemergency situation, but appears more in the latter one. The imitation action has negative impacts either on the evolution of Normal strategy in relatively nonemergency situation or on the evolution of Patient strategy in emergency situation. In addition, uniform evolution phenomena are observed in the evacuation process, despite different composition of initial agents.

Link prediction in temporal networks has always been a hot topic in both statistical physics and network science. Most existing works fail to consider the inner relationship between nodes, leading to poor prediction accuracy. Even though a wide range of realistic networks are temporal ones, few existing works investigated the properties of realistic and temporal networks. In this paper, we address the problem of abstracting individual attributes and propose a adaptive link prediction method for temporal networks based on $H$-index to predict future links. The matching degree of nodes is first defined considering both the native influence and the secondary influence of local structure. Then a similarity index is designed using a decaying parameter to punish the snapshots with their occurring time. Experimental results on five realistic temporal networks observing consistent gains of 2–9% AUC in comparison to the best baseline in four networks show that our proposed method outperforms several benchmarks under two standard evaluation metrics: AUC and Ranking score. We also investigate the influence of the free parameter and the definition of matching degree on the prediction performance.

Gas–liquid two-phase flows are frequently encountered in chemical and nuclear industries. The study of gas–liquid flow structures is of great significance for understanding the mechanisms of the flow pattern transition. In this paper, a direct-image multi-electrode conductance sensor (DMCS) was used to detect the structure information of vertical gas–liquid flows. Recurrence plot (RP) and cost-based recurrence plot (CBRP) are validated using typical nonlinear systems, i.e. Lorenz system and Hénon map, and used to analyze the signals collected by the DMCS. The results indicate that the determinism (DET) derived from the CBRP is sensitive to flow pattern evolution, and can also demonstrate the internal differences in the same flow patterns.

The impetus of the this study is to investigate flow and thermal field in rarefied gas flows inside a trapezoidal micro/nano-cavity using the direct simulation Monte Carlo (DSMC) technique. The investigation covers the hydrodynamic properties and thermal behavior of the flow. The selected Knudsen numbers for this study are arranged in the slip and transition regimes. The results show the center of the vortex location moves by variation in the Knudsen numbers. Also, as the Knudsen number increases, the non-dimensional shear stress increases, but the distribution deviates from a symmetrical profile. The cold to hot transfer, which is in contrast with the conventional Fourier law, is observed. We show that the heat transfer is affected by the second derivative of the velocity. By increasing the Knudsen number, the transferred heat through the walls decreases, but the contraction/expansion effects on the temperature in the corner of the cavity become higher.

This paper focuses on studying thermal, elastic and coupled plasma waves, in the sense of a photo-thermal process transport within an infinite semiconductor medium. In order to study photo-thermal interactions in two-dimensional semiconducting materials, a new mathematical model based on the Moore–Gibson–Thompson equation (MGTE) is implemented. The MGTE model involving the Green–Naghdi model of type III as well as the heat transport equation proposed by Lord and Shulman. We consider the semi-conductor half-space is rotated at a uniform angular speed and magnetized. The analysis of the distribution of thermophysical fields has been extracted by a normal mode method, represented graphically and discussed. The results predicted by the new and improved model have been compared with the generalized and classic ones. In addition, all field quantities have been examined for effects of rotation, a lifetime of the photo-generated, and the applied magnetic field.

Pinning synchronization of complex networks with two different kinds of time-varying coupling are studied in this paper. Inner coupling of the state variables and outer coupling in the complex network are taken into consideration. Based on the Lyapunov function theory, some general criteria and a simplified corollary for ensuring network synchronization are proposed. Linear pinning controllers, adaptive pinning controllers and adaptive coupling strength are designed for achieving complex network time-varying synchronization. Furthermore, the analytic relationship between control parameters is studied. Numerical simulations further illustrate the effectiveness of conclusions.

Decorating two-dimensional (2D) materials with transition-metal adatoms is an effective way to bring about new physical properties that are intriguing for applications in electronics and spintronics devices. Here, we systematically studied the coverage-dependent magnetic and electronic properties of graphene decorated by Co adatoms, based on first-principles calculations. We found that if the Co coverage is larger than 1/3ML, the Co atoms will aggregate to form a Co monolayer and then a van der Waals bilayer system between the Co monolayer and graphene forms. When the Co coverage is $\le 1∕3$ML, the Co adatom is spin-polarized with spin moment varying from 1.1 to 1.4${\mu }_B$. The $d_{xz∕yz}$ and $d_{xy∕x^2-y^2}$ orbitals of Co hybridize significantly with the $\pi$ bands of graphene, which generates a series of new bands in the energy range from $-2$eV to 1eV with respect to the Dirac point of graphene. In most cases, the new bands near the Fermi level lead to topological states characterized by the quantum anomalous Hall effect.

First-principles calculations were carried out to study the stability and electronic properties of native vacancy defects in the semiconducting ZnIn₂Te₄ (ZIT) and CdIn₂Te₄ (CIT). The Zn/Cd and In vacancies are acceptor defects, while the Te vacancy is donor defect. However, the In and Te vacancies dominate in the $n$-type and $p$-type semiconducting environments, respectively. The Te vacancy is not excited, so it could not compensate the majority of free carriers. The In vacancy prefers to be excited, which generates free hole carriers to compensate the majority of electron carriers. The Zn vacancy is rare in a typical semiconducting environment. Furthermore, all the vacancies induce localized defect states which may be trap centers for the free carriers. Accordingly, these native vacancy defects are destructive for the development of solar cells based on ZIT and CIT, so they should be avoided as much as possible during the growth process.

Tuberculosis (TB) is among the 10 top causes of deaths worldwide, and one-quarter of the world population hosts latent TB pathogens. Therefore, avoiding the emergence of drug-resistant strains has become a central issue in TB control. In this work, we propose a nested model for TB transmission and control, wherein both within-host and between-host dynamics are modeled. We use the model to compare the effects of three types of antibiotic treatment protocols and combinations thereof in an in silico population. For a fixed value of antibiotics clearance rate and relative efficacy against resistant strains, the oscillating intermittent protocol, pure or combined, is the most effective against the sensitive strains. However, this protocol also creates a selective advantage for the resistant strains, returning the worst result in comparison to the other protocols. We suggest that nested models should be further developed, since they might be able to inform decision-makers regarding the optimal TB control protocols to be applied under the specific parameters and other epidemiological factors in different populations.

In this paper, we consider the wealth distribution obtained by trading (buying–selling) stocks whose prices follow the geometric Brownian motion (GBM), when both number of the ticker symbol of the stock and maximum number of the traded stock are limited to unity. The binary exchange of the cash and stock between two agents is expressed with the Boltzmann-type kinetic equation. The distribution function of the number of the agents with the specific number of the stock or specific amount of the cash can be demonstrated, theoretically, when the price of the stock is constant. The distribution function of the number of the agents with the specific amount of the total asset can be approximated by $\mathrm{\Gamma }$-distribution, when the price of the stock follows the GBM. Finally, the rule in the binary-exchange-game approximates the distribution function of the number of the agents with the specific amount of the total asset to the Feller–Pareto-like distribution at the high wealth tail.