International Journal of Modern Physics C

Due to the heterogeneity of strategy updating rules progressively destroying the cluster of cooperators, cooperation would be heavily suppressed when players adopt mixed strategy updating rules. Thus, how to improve the emergence of cooperation with the scenario of heterogeneous strategy updating rules becomes an important open issue. In this paper, we introduce the memory factor into the game model, and then study the joint effect of memory and heterogeneous strategy updating rules on the emergence of cooperation. Detailly, in our game model, memory-based imitation and innovation are chosen as two different strategy updating rules. Afterwards, the annealing and quenching rules are specifically used as the methods to mix the proposed two memory-based strategy updating rules. At last, Monte Carlo simulations are conducted to demonstrate the significance of our model. The simulation results show that for memory-based imitators, memory is not always effective in promoting cooperation. It’s more like a catalyst. When the proportion of the memory-based imitation is large, it promotes the emergence of cooperation. When the proportion of innovation is large, it accelerates the extinction of cooperation; for overall proportion of cooperation, memory effectively promotes the emergence of cooperation and improves the ability of cooperators to resist high temptation, but memory is a partial optimization, it cannot offset the deterioration of cooperation caused by heterogeneous strategies updating rules, memory only reduces the degree of deterioration. Finally, compared with the annealing rule, the change of quenching rule is more smooth, because it is easier for cooperators to form clusters under the quenching rule. Our investigation sheds some light to the role of memory in prisoner’s dilemma game under heterogeneous strategies updating rules.

Structural optimization of ternary Cu–Ag–Au nanoalloys with 38 and 55 atoms was performed using the basin-hopping algorithm and the Gupta many-body potential was adopted to model interatomic interactions. The optimization results show that, while the Ag atoms prefer to segregate to the surface, Cu atoms were located at the core of the nanoalloy due to the higher surface and cohesive energy, whereas Au atoms mainly are located on the surface of the nanoalloys. It is found that the size has little effect on the segregation phenomena of Cu, Ag and Au atoms in the Cu–Ag–Au ternary nanoalloy. We estimated the melting temperatures of Cu–Ag–Au ternary nanoalloys using caloric curves and Lindemann index data obtained from classical molecular dynamics (MD) simulations. The results showed that the melting temperature is closely associated with the size and composition of the nanoalloys and varying the composition gives rise to a fluctuation in melting temperatures. Also, structural evolutions and dynamical behaviors of nanoalloys in melting process are investigated with root mean square displacement (RMSD).

Heat and drag reduction on the nose cone is a significant issue for increasing the speed of the supersonic vehicles. In this paper, computational fluid dynamic method is applied to investigate the thermal and drag coefficient on the sharp nose cone with different cavity shapes. In order to simulate our model, the CFD method with SST turbulence model is applied to study the flow feature and temperature distribution in the vicinity of the nose body. The effect of depth and length of the cavity on the thermal characteristic of the nose cone is comprehensively investigated. In addition, the influence of the number of the cavity in the thermal performance of the main body is studied. According to our results, increasing the length of the cavity highly efficient for the reduction of the drag at Mach = 3. As the Mach number is increased to 3, the number of the cavity becomes a significant role and it is observed that case 9 with four cavities is more efficient. Obtained results also show that increasing the cavity depth declines the temperature on the main body. Our findings confirm that the main source of the expansion is the edge of the cavity.

CVFEM usage for modeling of nanomaterial flow style in a permeable elliptical tank including Lorentz effect was scrutinized in the current research. Hybrid material with use of homogeneous model was applied and radiation term has been involved in governing equations. Outputs have been depicted in contours and plots. In addition, a new formula for Nu was reported. Augment of Nu by considering greater permeability can be explained by stronger temperature gradient in cases with higher Da. Nanomaterial flow becomes suppressed with augment of Ha which results in lower Nu. As Ha was augmented from 0 to 20, 18% reduction was reported in Nu. Permeability has favorable influence on nanomaterial flow and the impact of Ha is the opposite of permeability.

Three-dimensional steady state numerical simulations are carried out from seven protruding IC chips (Aluminium) of different sizes mounted at various positions on a high-power SMPS board (Bakelite) cooled under forced convection ($q=380$W/cm², $V=25$m/s). The objective is to predict the temperature distribution of these IC chips and to determine their optimal arrangement on the SMPS board; that will have the lowest maximum temperature excess among all the possible configurations. To achieve the same, a numerical analysis is carried out using the commercial software ANSYS-Icepak, and then the hybrid optimization strategy (Genetic Algorithm (GA) driven by Artificial Neural Network (ANN)) is employed to obtain the global optimal configuration of these IC chips. Preliminary analysis is performed using a dimensionless position parameter ($\lambda$) which is found to be a strong function of the IC chip size, and their positioning on the substrate board. It is observed that the combined ANN-GA strategy is able to predict the optimal behavior of the IC chips more accurately. The larger size IC chips generating higher power should be placed at the substrate bottom for better cooling.

Passengers’ boarding time interval is of great significance for analysis of collective mobility behaviors. In this paper, we empirically investigate the boarding time interval of mobility behaviors based on three large-scale reservation records of passengers traveling by three different types of transportation from a travel agency platform, namely airplane, intercity bus and car rental. The statistical results show that similar properties exist in the passengers’ mobility behaviors, for example, there are similar burstiness $〈B〉=0$ and memory $〈M〉=-0.5$ for different time interval distribution, which indicates that the passengers’ mobility behaviors are periodical. Furthermore, we present a probability model to regenerate the empirical results by assuming that the passengers’ next boarding time interval will generate between a short time of 1–7 days with probability $p$ and a random long time with probability $1-p$. The simulation results show that the presented model can reproduce the burstiness and memory effect of the boarding time interval when $p=0.6$ for three empirical datasets, which suggests the periodical behaviors with the probability $p$. This work helps in deeply understanding the regularity of human mobility behaviors.

Urban road network (referred to as the road network) is a complex and highly sparse network. Link prediction of the urban road network can reasonably predict urban structural changes and assist urban designers in decision-making. In this paper, a new link prediction model ASFC is proposed for the characteristics of the road network. The model first performs network embedding on the road network through road2vec algorithm, and then organically combines the subgraph pattern with the network embedding results and the Katz index together, and then we construct the all-order subgraph feature that includes low-order, medium-order and high-order subgraph features and finally to train the logistic regression classification model for road network link prediction. The experiment compares the performance of the ASFC model and other link prediction models in different countries and different types of urban road networks and the influence of changes in model parameters on prediction accuracy. The results show that ASFC performs well in terms of prediction accuracy and stability.

Slug flow is a flow pattern which occurs in conveyance systems containing a two phase-fluid flow. Large air bubbles entrapped along these systems interrupt the flow and conduct to undesirable pressures and their fluctuations. Most of the previous concerning studies on slug flow phenomenon were performed in micro-channels with small scales in which the expansion of the air bubbles was negligible. In contrast, we investigated the systems with large pressure drops which conduct to abrupt increases on the volume of air phase. In this research, the verification tests applying CFD techniques were performed in OpenFOAM software by interFoam solver. The performance of morning glory spillways is investigated under steady states. While during the occurrence of flood, by increasing the depth over the spillway crest, the discharge is augmented which conducts to entrap the air pockets with pressure and velocity fluctuations. These fluctuations influence on the life of structures and their performances. This study aimed on unsteady flow in a glory morning spillway and its consequences and proposing the measures for reducing the destructive effects of slug flow. The pressure and velocity fluctuation were considered as the indices for the performance of a hydraulic structure. Spatial and temporal variations of pressure and velocity along the spillway are evaluated. Also, the influences of spillway geometry on slug flow and the measures to attenuate its destructive effects are analyzed. The results showed that the better performances of morning glory spillway are coincided by the diameter of tunnel superior than the height of water above the crest.

In this paper, the interplay of epidemic spreading and the strategy-mixed awareness diffusion within the framework of multiplex networks is studied. In the model, epidemics can spread through a physical-contact layer and the awareness can diffuse through a virtual-contact layer. Each node on physical-contact layer is randomly matched one-to-one with a node of virtual-contact layer, but the connectivity is distinct in each of them. In view of the complexity of human behaviors, we assume that the diffusion of awareness is ruled by two mixed contagion dynamics: a fraction $m$ of individuals adopt the herd-like dynamics and the others follow the epidemic-like dynamics. We analyze the epidemic threshold based on the microscopic Markov chain approach. Meanwhile, combing with the Monte Carlo (MC) simulations, we show that the strategy-mixed awareness diffusion mechanism can enrich the dynamics of epidemic spreading, including the crossover phenomenon of the final epidemic size, the two-stage effect of the local awareness rate, and so forth.

Numerical schemes, namely, upwind nonconservative, upwind conservative, Lax–Friedrichs, Lax–Wendroff, MacCormack and Godunov are applied and compared on traffic flow problems. The best scheme, namely, upwind conservative is used for wavelet-optimized method using Daubechies wavelet for numerically solving the same traffic flow problems. Numerical results corresponding to the traffic flow problem with the help of wavelet-optimized, adaptive grid, upwind conservative method have been given. Moreover, the run time carried out by the developed technique have been compared to that of run time carried out by finite difference technique. It is observed that, in terms of run time, the proposed method performs better.

High noise and strong volatility are the typical characteristics of financial time series. Combined with pseudo-randomness, nonsteady and self-similarity exhibiting in different time scales, it is a challenging issue for the pattern analysis of financial time series. Different from the existing works, in this paper, financial time series are converted into granular complex networks, based on which the structure and dynamics of network models are revealed. By using variable-length division, an extended polar fuzzy information granule (FIGs) method is used to construct granular complex networks from financial time series. Considering the temporal characteristics of sequential data, static networks and temporal networks are studied, respectively. As to the static network model, some features of topological structures of granular complex networks, such as distribution, clustering and betweenness centrality are discussed. Besides, by using the Markov chain model, the transfer processes among different granules are investigated, where the fluctuation pattern of data in the coming step can be evaluated from the transfer probability of two consecutive granules. Shanghai composite index and foreign exchange data as two examples in real life are applied to carry out the related discussion.

Breast cancer is a common malignant tumor of which pathogenic genes are widely studied. Since gene pairs are considered as biomarkers to identify cancer patients, in this paper, we use information theory to study the collaboration features of gene pairs. The measure of synergy based on mutual information (MI) is introduced to determine whether genes collaborate with each other in breast cancer. Part mutual information (PMI) is introduced to further select collaborative genes and construct a synergy network, which overcomes the shortage of MI. Furthermore, a dual network of synergy network is constructed and structural indices are calculated to identify vital genes. By decision tree and support vector machine, synergy is considered as a suitable index and dual network with PMI improves the accuracy of cancer identification. This method can be extended to identify other biological phenomenon and find collaborative genes as biomarkers.

The honk effect is not uncommon in the real traffic and may exert great influence on the stability of traffic flow. As opposed to the linear description of the traditional one-dimensional lattice hydrodynamic model, the high-dimensional lattice hydrodynamic model is a gridded analysis of the real traffic environment, which is a generalized form of the one-dimensional lattice model. Meanwhile, the high-dimensional traffic flow exposed to the open-ended environment is more likely to be affected by the honk effect. In this paper, we propose an extension of two-dimensional triangular lattice hydrodynamic model under honk environment. The stability condition is obtained via the linear stability analysis, which shows that the stability region in the phase diagram can be effectively enlarged under the honk effect. Modified Korteweg–de Vries equations are derived through the nonlinear stability analysis method. The kink–antikink solitary wave solution is obtained by solving the equation, which can be used to describe the propagation characteristics of density waves near the critical point. Finally, the simulation example verifies the correctness of the above theoretical analysis.

In order to simulate rigidly rotating polytropes, we have simulated systems of $N$ point particles, with $N$ up to 1800. Two particles at a distance $r$ interact by an attractive potential $-1∕r$ and a repulsive potential $1∕r^2$. The repulsion simulates the pressure in a polytropic gas of polytropic index $3∕2$. We take the total angular momentum $L$ to be conserved, but not the total energy $E$. The particles are stationary in the rotating coordinate system. The rotational energy is $L^2∕(2I)$ where $I$ is the moment of inertia. Configurations, where the energy $E$ has a local minimum, are stable. In the continuum limit $N\to \infty$, the particles become more and more tightly packed in a finite volume, with the interparticle distances decreasing as $N^{-1∕3}$. We argue that $N^{-1∕3}$ is a good parameter for describing the continuum limit. We argue further that the continuum limit is the polytropic gas of index $3∕2$. For example, the density profile of the nonrotating gas approaches that computed from the Lane–Emden equation describing the nonrotating polytropic gas. In the case of maximum rotation, the instability occurs by the loss of particles from the equator, which becomes a sharp edge, as predicted by Jeans in his study of rotating polytropes. We describe the minimum energy nonrotating configurations for a number of small values of $N$.