<abstract><p>The present study explores the effects of viscous dissipation, the thermal dependent conductivity and the thermal dependent viscosity on the steady motion of a Powell-Eyring fluid over a stratified stretching sheet which embedded in a porous medium. The fact that the nature of non-Newtonian flows problems are highly nonlinear equations has been taken into consideration here and this was the motive objective to determine numerical solutions. So, the emphasis is on the methodology adopted for obtaining numerical solutions that yielded after employing the Chebyshev spectral method. The temperature distributions and the velocity components are evaluated by solving numerically the boundary value problems that correspond to the proposed problem. Then, some figures have been plotted to elucidates the effect of different physical parameters appearing in the problem on both the temperature and the velocity profiles. The presence of the thermal radiation and the viscous dissipation in the fluid flow are shown to have quite a dramatic effect on the temperature profiles. In culmination, cooling process in nuclear reactors and geothermal engineering especially in the presence of thermal stratification phenomenon can be adopted as an application of this study. The theoretical and the observed results provide a fairly good qualitative agreement.</p></abstract>
Taking pictures and Selfies are now very common and frequent between people. People are also interested in enhancing pictures using different image processing techniques and sharing them on social media. Accurate image segmentation plays an important role in portrait editing, face beautification, human identification, hairstyle identification, airport Surveillance system and many other computer vision problems. One specific functionality of interest is automatic face and veil segmentation as this allows processing each separately. Manual segmentation can be difficult and annoying especially on smartphones small screen. In this paper, the proposed model uses fully convolutional network (FCN) to make semantic segmentation into skin, veil and background. The proposed model achieved an outperforming result on the dataset which consists of 250 images with global accuracy 92% and mean accuracy 92.69.
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