International Journal of Modern Physics C

We present a simulation model of the technological innovations based on the former Guardiola's concept which relies on the notion of technological barriers. The central, novel feature of our proposal is the assumption that barriers can be reduced allowing public support to R&D. However, this can be applied under the different policies. The statistical treatment of the simulation results demonstrated that support of the elitist (super-threshold) innovating units yields higher innovation rate, but more profound variation of the technological levels. On the other hand, the favoring of innovators with sub-threshold technological levels may lead to the more integrated technological world. Simultaneously, we use the same model to study the effects of supporting knowledge sharing (that is to say, lowering sharing barriers) on average organizational knowledge. An interesting aspect of the model represents self-organized adjustment of the probabilistic parameters related to the social linkages.

We investigate a novel (d + 1)-dimensional discrete erosion model for d = 1, 2 and 3. The dynamics of the model is controlled by the physically motivated erosion mechanism. The coarse grained nature of this erosion process has been well compared with the Kardar–Parisi–Zhang (KPZ) equation. The kinetic roughening of the discrete model shows the same scaling behavior as that of the KPZ equation in the dimensions d = 1, 2. Moreover, in this present discrete model in (3 + 1)-dimension almost smooth interface has been obtained with vanishingly small roughness exponent, indicating the model belongs to the weak coupling regime of KPZ universality class.

The sampling method has been paid much attention in the field of complex network in general and statistical physics in particular. This paper proposes two new sampling methods based on the idea that a small part of vertices with high node degree could possess the most structure information of a complex network. The two proposed sampling methods are efficient in sampling high degree nodes so that they would be useful even if the sampling rate is low, which means cost-efficient. The first new sampling method is developed on the basis of the widely used stratified random sampling (SRS) method and the second one improves the famous snowball sampling (SBS) method. In order to demonstrate the validity and accuracy of two new sampling methods, we compare them with the existing sampling methods in three commonly used simulation networks that are scale-free network, random network, small-world network, and also in two real networks. The experimental results illustrate that the two proposed sampling methods perform much better than the existing sampling methods in terms of achieving the true network structure characteristics reflected by clustering coefficient, Bonacich centrality and average path length, especially when the sampling rate is low.

Chaotic burst synchronization in a two-small-world-layer neuronal network is studied in this paper. For a neuronal network coupled by two single-small-world-layer networks with link probability differences between layers, the two-layer network can achieve synchrony as the interlayer coupling strength increases. When chaotic layer network is coupled with chaotic-burst-synchronization layer network, the latter is dominant at small interlayer coupling strength, so it can make the layer with the irregular pattern show some regular and also exhibit the same pattern with the other layer. However, when chaotic layer is coupled with firing synchronization layer, the ordered layer is dominated by a disordered one with the interlayer coupling strength increasing. When the interlayer coupling strength is large enough, both networks are chaotic burst synchronization. Therefore, the synchronous states strongly depend on the interlayer coupling strength and the link probability. Moreover, the spatiotemporal pattern synchronization between the networks is robust to small noise.

In this paper, we attempt to understand the propagation and stability feature of large-scale complex software from the perspective of complex networks. Specifically, we introduced the concept of "propagation scope" to investigate the problem of change propagation in complex software. Although many complex software networks exhibit clear "small-world" and "scale-free" features, we found that the propagation scope of complex software networks is much lower than that of small-world networks and scale-free networks. Furthermore, because the design of complex software always obeys the principles of software engineering, we introduced the concept of "edge instability" to quantify the structural difference among complex software networks, small-world networks and scale-free networks. We discovered that the edge instability distribution of complex software networks is different from that of small-world networks and scale-free networks. We also found a typical structure that contributes to the edge instability distribution of complex software networks. Finally, we uncovered the correlation between propagation scope and edge instability in complex networks by eliminating the edges with different instability ranges.

The moving boundary conditions are implemented into the Fourier pseudo-spectral solution of the two-dimensional incompressible Navier–Stokes equations (NSE) in the vorticity-velocity form, using the radial basis functions (RBF). Without explicit definition of an external forcing function, the desired immersed boundary conditions are imposed by direct modification of the convection and diffusion terms. At the beginning of each time-step the solenoidal velocities, satisfying the desired moving boundary conditions, along with a modified vorticity are obtained and used in modification of the convection and diffusion terms of the vorticity evolution equation. Time integration is performed by the explicit fourth-order Runge–Kutta method and the boundary conditions are set at the beginning of each sub-step. The method is applied to a couple of moving boundary problems and more than second-order of accuracy in space is demonstrated for the Reynolds numbers up to Re = 550. Moreover, performance of the method is shown in comparison with the classical Fourier pseudo-spectral method.

An extended optimal velocity (OV) difference model is proposed in a cooperative driving system by considering multiple OV differences. The stability condition of the proposed model is obtained by applying the linear stability theory. The results show that the increase in number of cars that precede and their OV differences lead to the more stable traffic flow. The Burgers, Korteweg–de Vries (KdV) and modified Korteweg–de Vries (mKdV) equations are derived to describe the density waves in the stable, metastable and unstable regions, respectively. To verify these theoretical results, the numerical simulation is carried out. The theoretical and numerical results show that the stabilization of traffic flow is enhanced by considering multiple OV differences. The traffic jams can be suppressed by taking more information of cars ahead.

We present a new approach to find accurate solutions to the Poisson equation, as obtained from the steady-state limit of a diffusion equation with strong source terms. For this purpose, we start from Boltzmann's kinetic theory and investigate the influence of higher-order terms on the resulting macroscopic equations. By performing an appropriate expansion of the equilibrium distribution, we provide a method to remove the unnecessary terms up to a desired order and show that it is possible to find, with high level of accuracy, the steady-state solution of the diffusion equation for sizeable Knudsen numbers. In order to test our kinetic approach, we discretize the Boltzmann equation and solve the Poisson equation, spending up to six order of magnitude less computational time for a given precision than standard lattice Boltzmann methods (LBMs).

We have investigated the elastic and thermodynamic properties of ZrO₂ under pressure up to 120 Gpa by the plane wave pseudopotential density functional theory with the generalized gradient approximation (GGA) method. The elastic constants of ZrO₂ are calculated and meet the generalized stability criteria, suggesting that ZrO₂ is mechanically stable within this pressure range. The pressure effects on the elastic properties reveal that the elastic modulus B, shear modulus G and Young's modulus Y increase linearly with the pressure increasing, implying that the resistance to deformation is enhanced. In addition, by analyzing the Poisson's ratio ν and the value of B/G, we notice that ZrO₂ is regarded as being a ductile material under high pressure and the ductility can be improved by the pressure increasing. Then, we employ the quasi-harmonic Debye model considering the phononic effects to obtain the thermodynamic properties of ZrO₂. Debye temperature Θ_D, thermal expansion coefficient α, heat capacity C_p and Grüneisen parameter γ are systematically explored at pressure of 0–80 Gpa and temperature of 0–1000 K. Our results have provided fundamental facts and evidences for further experimental and theoretical researches.

The bulk modulus of hard sphere solids has been computed directly by constant pressure Monte-Carlo simulations, using the histogram of the volume fluctuations. In considering first the one-component system, we show that the method is accurate in a large range of pressures, including high-pressure regime. The method is then applied to a polydisperse solid with relatively low polydispersity index. For illustrative purpose, we took a three-component mixture with symmetric size-distribution, and we studied the solid phase (fcc crystal) of this system. Our results show that the equation of state is very sensitive to the polydispersity. Furthermore, in the high-pressure region, where no (accurate) analytical fit for the equation of state exists, our simulations are able to predict the bulk modulus of such systems.

The linear and nonlinear properties of the electromagnetic waves are investigated in a magnetized quantum electron–positron (e–p) plasma by employing the quantum hydrodynamic (QHD) model. It is found that the quantum dispersion relation in comparison with the classical version is modified by the quantum corrections through quantum diffraction and statistics. The standard reductive perturbation technique is used to derive the Korteweg–de Vries (KdV) equation. The exact soliton solutions and the existence regions of the solitary waves are also defined precisely. It is also shown that the results are affected by the quantum corrections.

Ranking nodes by their spreading ability in complex networks is of vital significance to better understand the network structure and more efficiently spread information. The k-shell decomposition method could identify the most influential nodes, namely network core, with the same k_s values regardless to their different spreading influence. In this paper, we present an improved method based on the k-shell decomposition method and closeness centrality (CC) to rank the node spreading influence of the network core. Experiment results on the data from the scientific collaboration network and U.S. aviation network show that the accuracy of the presented method could be increased by 31% and 45% than the one obtained by the degree k, 32% and 31% than the one by the betweenness.