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Aerosol particle transport and deposition in vertical and horizontal turbulent duct flows in the presence of different gravity directions are studied. The instantaneous fluid velocity field is generated by the direct numerical simulation of the Navier-Stokes equation via a pseudospectral method. A particle equation of motion including Stokes drag, Brownian diffusion, lift and gravitational forces is used for trajectory analysis. Ensembles of 8192 particle paths are evaluated, compiled, and statistically analysed. The results show that the wall coherent structure plays an important role in the particle deposition process. The simulated deposition velocities under various conditions are compared with the available experimental data and the sublayer model predictions. It is shown that the shear velocity, density ratio, the shear-induced lift force and the flow direction affect the particle deposition rate. The results for vertical ducts show that the particle deposition velocity varies with the direction of gravity, and the effect becomes more significant when the shear velocity is small. For horizontal ducts, the gravitational sedimentation increases the particle deposition rate on the lower wall.

a b s t r a c tIn this paper, we study the interplay between the epidemic spreading and the diffusion of awareness in multiplex networks. In the model, an infectious disease can spread in one network representing the paths of epidemic spreading (contact network), leading to the diffusion of awareness in the other network (information network), and then the diffusion of awareness will cause individuals to take social distances, which in turn affects the epidemic spreading. As for the diffusion of awareness, we assume that, on the one hand, individuals can be informed by other aware neighbors in information network, on the other hand, the susceptible individuals can be self-awareness induced by the infected neighbors in the contact networks (local information) or mass media (global information). Through Markov chain approach and numerical computations, we find that the density of infected individuals and the epidemic threshold can be affected by the structures of the two networks and the effective transmission rate of the awareness. However, we prove that though the introduction of the self-awareness can lower the density of infection, which cannot increase the epidemic threshold no matter of the local information or global information. Our finding is remarkably different to many previous results on single-layer network: local information based behavioral response can alter the epidemic threshold. Furthermore, our results indicate that the nodes with more neighbors (hub nodes) in information networks are easier to be informed, as a result, their risk of infection in contact networks can be effectively reduced.

h i g h l i g h t s• A new SIS network model obtained by introducing an information variable is proposed. • The diseases can be controlled through high efficiency of implementation.• The introduced parameters have significant impact on the final prevalence density.• The results may suggest effective control strategies incorporating media coverage. a b s t r a c t An SIS network model incorporating the influence of media coverage on transmission rate is formulated and analyzed. We calculate the basic reproduction number R 0 by utilizing the local stability of the disease-free equilibrium. Our results show that the disease-free equilibrium is globally asymptotically stable and that the disease dies out if R 0 is below 1; otherwise, the disease will persist and converge to a unique positive stationary state. This result may suggest effective control strategies to prevent disease through media coverage and education activities in finite-size scale-free networks. Numerical simulations are also performed to illustrate our results and to give more insights into the dynamical process.

Based on a randomly mixed model, the effective thermal conductivity of particle filled composites is studied numerically with respect to the volume fraction of the particles and the ratio of the thermal conductivity of the particle to that of the matrix. Compared with experimental data and the estimations from other models, the proposed model seems to be well suited for predicting thermal conductivity. Some factors relative to heat conduction reinforcement are discussed. Improvement in the heat conduction performance will be limited if the thermal conductivity of the particle alone is concerned. The volume fraction of the particle should reach the critical value in order to significantly improve the effective thermal conductivity, which is in agreement with the percolation theory. Another crucial factor is the effective formation of particle chains for heat conduction reinforcement.

Information diffusion and disease spreading in communication-contact layered network are typically asymmetrically coupled with each other, in which disease spreading can be significantly affected by the way an individual being aware of disease responds to the disease. Many recent studies have demonstrated that human behavioral adoption is a complex and non-Markovian process, where the probability of behavior adoption is dependent on the cumulative times of information received and the social reinforcement effect of the cumulative information. In this paper, the impacts of such a non-Markovian vaccination adoption behavior on the epidemic dynamics and the control effects are explored. It is found that this complex adoption behavior in the communication layer can significantly enhance the epidemic threshold and reduce the final infection rate. By defining the social cost as the total cost of vaccination and treatment, it can be seen that there exists an optimal social reinforcement effect and optimal information transmission rate allowing the minimal social cost. Moreover, a mean-field theory is developed to verify the correctness of simulation results.

-Studies on how to model the interplay between diseases and behavioral responses (so-called coupled disease-behavior interaction) have attracted increasing attention. Owing to the lack of obvious clinical evidence of diseases, or the incomplete information related to the disease, the risks of infection cannot be perceived and may lead to inappropriate behavioral responses.Therefore, how to quantitatively analyze the impacts of asymptomatic infection on the interplay between diseases and behavioral responses is of particular importance. In this Letter, under the complex network framework, we study the coupled disease-behavior interaction model by dividing infectious individuals into two states: U-state (without evident clinical symptoms, labelled as U) and I-state (with evident clinical symptoms, labelled as I). A susceptible individual can be infected by U-or I-nodes, however, since the U-nodes cannot be easily observed, susceptible individuals take behavioral responses only when they contact I-nodes. The mechanism is considered in the improved Susceptible-Infected-Susceptible (SIS) model and the improved Susceptible-InfectedRecovered (SIR) model, respectively. Then, one of the most concerned problems in spreading dynamics: the epidemic thresholds for the two models are given by two methods. The analytic results quantitatively describe the influence of different factors, such as asymptomatic infection, the awareness rate, the network structure, and so forth, on the epidemic thresholds. Moreover, because of the irreversible process of the SIR model, the suppression effect of the improved SIR model is weaker than the improved SIS model.Introduction. -Many epidemic models have been proposed to enhance our understanding of infectious disease dynamics [1], however, these mathematical models were often established with static parameters. In reality, outbreak of infectious diseases can trigger the behavioral responses toward diseases, which can further affect the epidemic dynamics. That is to say, the parameters in epidemic models should not be static but dynamic [2]. Therefore, how to establish coupled disease-behavior interaction models to evaluate the interplay between disease dynamics and behavioural responses is becoming a hot field [2][3][4][5][6].

The core-periphery structure and the community structure are two typical meso-scale structures in complex networks. Although community detection has been extensively investigated from different perspectives, the definition and the detection of the core-periphery structure have not received much attention. Furthermore, the detection problems of the core-periphery and community structure were separately investigated. In this paper, we develop a unified framework to simultaneously detect the core-periphery structure and community structure in complex networks. Moreover, there are several extra advantages of our algorithm: our method can detect not only single but also multiple pairs of core-periphery structures; the overlapping nodes belonging to different communities can be identified; different scales of core-periphery structures can be detected by adjusting the size of the core. The good performance of the method has been validated on synthetic and real complex networks. So, we provide a basic framework to detect the two typical meso-scale structures: the core-periphery structure and the community structure.