International Journal of Modern Physics C

We discuss the effects of spatial interference between two infectious hotspots as a function of the mobility of individuals (wind speed) between the two and their relative degree of infectivity. As long as the upstream hotspot is less contagious than the downstream one, increasing the wind speed leads to a monotonic decrease of the infection peak in the downstream hotspot. Once the upstream hotspot becomes about between twice and five times more infectious than the downstream one, an optimal wind speed emerges, whereby a local minimum peak intensity is attained in the downstream hotspot, along with a local maximum beyond which the beneficial effect of the wind is restored. Since this nonmonotonic trend is reminiscent of the equation of state of nonideal fluids, we dub the above phenomena “epidemic condensation”. When the relative infectivity of the upstream hotspot exceeds about a factor five, the beneficial effect of the wind above the optimal speed is completely lost: any wind speed above the optimal one leads to a higher infection peak. It is also found that spatial correlation between the two hotspots decay much more slowly than their inverse distance. It is hoped that the above findings may offer a qualitative clue for optimal confinement policies between different cities and urban agglomerates.

In this work, detailed DFT calculations of the structural, mechanical and electronic properties of crystalline CaSi₂ with four different structures in the pressure range of 0–50 GPa are performed by GGA-PBE. It is found that the Enthalpy differences imply that the R$\bar{3}$m phase $\to$ I4₁/amd phase $\to$ P6/mmm phase transition in CaSi₂ occur at $P_1=2.5$GPa, $P_2=33.5$GPa by using the XC of GGA, which is consistent with previous experiments and theoretical conclusions. Besides, the elastic stability criterion is used to study the change of the elastic constant of CaSi₂ under pressures. In particular, the bulk modulus $B$, shear modulus $G$, Young’s modulus $E$, sound velocity $v$ and brittleness and toughness properties of CaSi₂ under pressures are comprehensively studied for the first time. Finally, the changes of the anisotropy factor of CaSi₂ are studied under different pressures, and the electronic structure is studied in detail.

With the rapid urbanization worldwide and ever-increasing impacts of human activities since at least 200 years ago, we are now facing a harsh situation of our biosphere. Building a global-level network model on ecological systems is of great importance, which would be able to provide us predictive and quantitative responses to human activities, leading to viable suggestions to policymakers. In this paper, we propose a multi-layer model for the global ecological network, where a number of local networks are connected via long-range interactions associated with migrant species, which can be induced by human activities or natural migration of wildlife, and each local network is generated by a trophic-level-based stochastic model. Predator–prey dynamics is described by a networked Lotka–Volterra model that accounts for the self-suppression effects on basal species, and the negative feedback loops. Impacts of human activities are modeled by investigating the quantitative changes of biodiversity under certain protecting strategies. We reveal that the global ecological network is organized in a clustered small-world manner, with in-degree distribution more heterogeneous than out-degree distribution. Protecting endangered species, popular preys and predicted-to-be-extinct species is more effective than randomly selected species or influential predators. Protecting after entering the fast extinction stage is more effective than at the beginning for some high trophic level species.

In this paper, we study the information diffusion structure on social networks with general degree distribution. To describe the information diffusion structure, we adopt six different viewpoints of metrics, including structural virality, distance variance, distance variability, distance susceptibility, cascade depth and cascade width. On Erdös–Rényi (ER) networks, we can intuitively see that as the diffusion tree becomes denser, the depth of the diffusion tree first increases to a peak and then decreases with the infection rate increasing, in addition the distance distribution of the diffusion tree obeys exponential distribution, and the metrics except cascade width decrease after reaching their peak values. When the information diffuses on scale-free (SF) networks, the diffusion trees are similar with the ones on ER networks. In other words, compared with the degree distribution, the infection rate is the main factor of diffusion tree in the same network scale.

Public opinion quickly generated and propagated on online social networks brings huge influences on society and state security. Previous studies mostly analyze its snapshot in a short-term time interval to predict and control the explosive size, but neglect its long-term evolutionary process. In this paper, based on the online social network of Sina Weibo, we trace nine public opinion events in the nearly two-year duration to comprehensively observe the long-term evolutionary processes and characterize the temporal dynamics and propagating networks. The long-term evolutionary processes of public opinion are constructed by quantitatively measuring forwarding sizes at a daily scale. We show their non-Markov temporal dynamics by autocorrelation analysis, which is verified by the heavy-tail interval time distribution of individual forwarding behaviors. Also, the temporally propagating networks are abstracted from individual forwarding behaviors to represent the microcosmic organization of forming public opinion. The topological analysis of aggregating propagating networks shows that the microcosmic organization is generally constructed by a giant connected component and amounts of small connected components with strongly heterogeneous cascade sizes, and the corresponding degree distributions obeys a power law which is shaped by the giant connected component. Furthermore, we compare the follower–followee (i.e. friendship) network with the propagating network to unveil their potential correlation, and find that at large scale they behave a similar connection pattern. This work first projects public opinion into a process-based model to study its temporal dynamics and helps us to better understand the underlying mechanics of forming public opinion.

Reconstructing causal networks from observed time series is a common problem in diverse science and engineering fields, and the challenge can increase with network size. Unfortunately, systematic comparisons of causal inference techniques have tended to concentrate on small motifs rather than graphs which it would be natural to label as complex networks. In this paper, six widely used methods of causal network reconstruction are systematically benchmarked and contrasted using neuronal models coupled on networks of varied size. Our purpose is concisely to review and explain the basic problems of causality detection using time series, and to compare the performance of varied and practical methods under the extremely relevant, but relatively neglected, conditions of highly nonlinear dynamical systems coupled via networks of many nodes. We find that convergent cross mapping consistently provides the highest precision, but transfer entropy can be preferable when high recall is important. The advantages of convergent cross mapping and transfer entropy over other methods can increase with network size.

In this paper, we present a growing complex network model, namely, the generalized corona network (GCN) which is built on a base network and a sequence of networks by using corona product of graphs. This construction generalizes several existing complex network models. We study the structural properties of the special classes of generalized corona networks and show that these networks have small diameter, the cumulative betweenness distribution follows a power-law distribution, the degree distribution decay exponentially, small average path length with the network order, high clustering coefficient and small-world behavior. Further, we obtain the spectra of the adjacency matrix and the signless Laplacian matrix of GCN when the constituting networks are regular. Also, we obtain the Laplacian spectra for all generalized corona networks. In addition, explicitly give eigenvector corresponding to the adjacency and Laplacian eigenvalues. Finally, we derive the spectral radius and the algebraic connectivity of GCN.

We present a microscopic driving algorithm that prescribes the acceleration using three parameters: the distance to the leading vehicle, to the next traffic light and to the nearest stopping point when the next traffic light is in the red phase. We apply this algorithm to construct decision trees that enable two driving behaviors: aggressive and careful. The focus of this study is to analyze the amount of aggressive drivers that are needed in order to generate a traffic gridlock in a portion of a city with signalized intersections. At rush hour, aggressive drivers will enter the intersection regardless if they have enough time or space to clear it. When their traffic light changes they block other drivers, thus providing the conditions for a gridlock to develop. We find that gridlocks emerge even with very few aggressive drivers present. These results support the idea of promoting good driving behavior to avoid heavy congestion during rush hours.

The countless applications of nanofluids in the improvements of nanotechnology, thermal and physical analogies have attracted our attention to frame an unsteady mathematical model for bi-directional flow of a Newtonian nanofluid over a stretching sheet with the potencies of nonzero and zero mass fluxes. Mathematically, this newly presented analysis is more genuine, where the action of a prescribed heat source at a stretching surface is used to control the distribution of heat. Mathematical formulation is carried out using a novel two-phase nanofluid model. Dimensionless forms of governing equations are obtained with the help of a suitable set of variables. The transformed equations are then solved by using an innovative computational technique, namely, Keller–Box approach. Moreover, the convergence of the numerical solution has been discussed via grid-independence tactic. The results for reduced Nusselt and Sherwood numbers have been arranged in the form of a table with CPU run time. Graphical illustrations have been presented for concentration and temperature distributions. It is inspected that escalating amounts of heat distribution indices reduce the mass concentration and the temperature of the nanomaterial. Rate of heat transference is noticed approximately 228.62% higher, while rate of mass transference is observed approximately 16.79% lower when analysis is shifted to zero mass flux environment from nonzero normal mass flux environment.

This paper focuses on large eddy simulation of Rayleigh–Taylor instability (RTI) based on helicity model (HM) which is derived from our previous research. Based on the RTI problem, we obtain from a priori test and theoretical analysis that the suggested model can automatically distinguish laminar flow and turbulence. Comparing the numerical simulation results with direct numerical simulation (DNS) and traditional model in RTI problem, we could find that the suggested model overcomes some defects of the traditional model, such as the traditional model cannot predict transition accurately and the suggested model can predict the turbulence mixing width and mass fraction precisely. It provides a beneficial tool for the research of RTI.

In the light of recent developments in the theory of reversibility for asynchronous cellular automata, we attempt to explore the dynamics of recurrent rules under fully asynchronous updating scheme. Depending on the reachability of the configurations for a communication class during the evolution of the system, we classify the recurrent rules into two classes — partially exposed recurrent system and fully exposed recurrent system.

Road traffic risk is currently one of the most concerned problems of drive tourists. The motivation of this research is to quantify the impact of driving tour risk factors on road traffic safety and efficiency. Based on the characteristics of tourists’ aberrant driving behaviors while traveling by cars, the most frequently observed driving tour risk factors were explored, the conditions for the occurrence of various dangerous situations during road trips were defined, and a two-lane CA model considering drivers’ lane-changing and car-following behaviors was established to quantify the impact of single factors as well as the superposition of multi-factors on road traffic safety and efficiency. The simulation results indicate that the driving tour risk factors have significant negative impact on road traffic risk by increasing the frequencies of dangerous situations, inducing a reduction in traffic flow, and an increase in traffic congestion. In addition, the superposition of multi-factors, in particular the superposition of “fatigue driving” and “environment,” further increases the risk, specially aggravates the problems of traffic accidents and congestions.

The effect of inclination angle on heat transfer by turbulent natural convection in an inclined open cubic cavity with side length of 1m is experimentally and numerically studied. The wall opposite to the aperture is subjected to uniform heat flux condition with a value of 110W/m², whereas the remaining walls were kept thermally insulated. Six most used two equations turbulence models were tested, with Computational Fluid Dynamics (CFD) software ANSYS FLUENT. The results were obtained for four inclination angle values: 0^∘, 15^∘, 30^∘ and 45^∘. The spatial distribution for temperature, flow pattern and magnitude of velocity are determined and analyzed. An experimental prototype was built to obtain experimental temperature profiles and heat transfer coefficients. The experimental average Nusselt number increases as the inclination angle increases, having a minimum of 203 for 0^∘ case and a maximum of 225 for 45^∘ case. The comparison between experimental and numerical average Nusselt numbers indicates that the minimum average difference is with the realizable $\kappa -{\epsilon }_t$ turbulence model, therefore, this turbulence model is recommended to model this type of systems.

Using first-principles calculations based on density functional perturbation theory, we demonstrate hydrogenation-induced superconductivity in monolayer TiB₂H. Hydrogen adatoms destroy the Dirac state of monolayer TiB₂ and monolayer TiB₂H has a high vibration frequency. Monolayer TiB₂H is a phonon-mediated superconductor. Monolayer TiB₂H has a predicted $T_c$ of 8K, which further increases under external tensile strain. Thus, this study extends our understanding of superconductivity in two-dimensional (2D) materials and its potential applications.