Influence maximization serves as the main goal of a variety of social network activities such as viral marketing. The Independent Cascade model for the influence spread assumes a one-time chance for each activated node to influence its neighbors. On the other hand, the manually activated seed set nodes can be reselected without violating the model parameters or assumptions. This view divides the influence maximization process into two cases: the simple case where the reselection of the nodes is not considered and the reselection case. In this study we will analyze real world networks in the reselection case. First we will show that the difference between the simple and the reselection cases constitutes a wide spectrum of networks ranging from the reselection-free to the reselection-friendly ones. Then we will experimentally show a significant entanglement between this and influence spread dynamics as well as other structural parameters of the network. Specifically, we show that under a realistic condition, the reselection gain of a network has a correlation of 0. 73 to a newly introduced influence spread dynamic. Furthermore, we propose a measure for detecting star-like networks and experimentally show a significant correlation between our proposed measure and the reselection gain in real world networks with different edge weight models.

A simple semi-phenomenological model, which accurately predicts the dependence of threshold current for temperature of Resonant-phonon three well quantum Cascade laser based on vertical transitions is offered. We found that, the longitude optical phonon scattering of thermally excited electrons is the most important limiting factor for thermal performance of high frequency THz QCLs. In low frequency region, parasitic current increases the threshold current. Based on our model the use of materials with higher longitude optical phonon energy such as InGaAs/GaAsSb and decreasing the lower laser level lifetime can increase the maximum performance temperature. Our observations may can be used to understand the notion of the effects of thermal electrons on reduction of laser performance.

The unstable flow with rotating-stall-like (RS) effects in a rotor-Cascade of an axial compressor was numerically investigated. The RS was captured with the reduction in mass flow rate and increasing of exit static pressure with respect to design operating condition of the single rotor. The oscillatory velocity traces during the stall propagation showed that the RS vortices repeat periodically, and the mass flow rate was highly affected by the blockage areas made by stall vortices. The results also showed that large scale vortices highly affects on the generation and growth of the new vortices. An unsteady two-dimensional finite-volume solver was employed for the numerical study which was developed based on Van Leer’s flux splitting algorithm in conjunction with TVD limiters and the κ-ε turbulence model was also employed. The good agreement of the computed mass flow rate with the experimental results validates the numerical study.

Background and Objectives: One of the important topics in oncology treatment and prevention is the identification of genes that initiate cancer in cells. These genes are known as cancer driver genes (CDGs). Identification of the CDGs is important both for a basic understanding of cancer and to help find new therapeutic or biomarker goals. Several computational methods to find the genes responsible for cancer have been developed based on genome data. However, many of these methods find key mutations in genomic data to predict which genes are responsible for cancer. These methods depend on the mutation and genome data and often show a high rate of false positives in the results. In this study, we proposed an influence maximization-based approach, CinfuMax, which can detect the genes responsible for cancer without needing information on mutations.Methods: In this method, the concept of influence maximization and the Independent Cascade model are employed. Firstly, the gene regulatory network for breast, lung and colon cancers was built using regulatory interactions and gene expression data. Next, we implemented an Independent Cascade diffusion algorithm on the networks to compute each gene's coverage. Finally, the genes with the highest coverage were classified as driver.Results: The results of the proposed method were compared to 19 other computational and network-based methods based on the F-measure and the number of detected driver genes. The results demonstrated that the proposed method produces better results than other methods. Also, CinfuMax is able to detect 18, 19 and 22 individual driver genes in three breast, lung and colon cancers, respectively, which have not been identified in any of the previous methods.Conclusion: The results show that Independent cascading methods to identify driver genes perform better than linear threshold methods. Driver genes are also classified in terms of influence speed and have identified the genes with the highest diffusion rate in each type of cancer. Identification of these genes can be useful for molecular therapies and drug purposes.