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This paper discusses the wear and friction with the 2 W% Al2O3 nanocomposite content of pure Mg and AZ91D Mg alloys. Sliding speeds of 0.5 and 1.5 m/s in cast materials with normal stress conditions have been used in sliding distances up to 2000 m/s (0.5, 1.0, and 1 MPa). In order to evaluate the work hardness of the materials measured on temperature similar to the contact surface, we used hardness patterns and hot-compression flow curves. Mg and AZ91D magnesium alloy pure monolithic Mg are low wear resistant due to an increase in contact temperature due to the adjustment of working conditions, but the wear rate was significantly lower in composite materials, mainly because of nanoparticle strength improvements. Although wear generally contributes to grain refining, increased wear capacity, and greater durability, wear resilience due to dislocation resistance and nanoparticles is seen as the primary wear mechanism in the existing nanocomposites.

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COVID-19 is the present-day pandemic around the globe. WHO has estimated that approx 15% of the world's population may have been infected with coronavirus with a large number of population on the verge of being infected. It is quite difficult to break the virus chain since asymptomatic patients can result in the spreading of the infection apart from the seriously infected patients. COVID-19 has many similar symptoms to SARS-D however, the symptoms can worsen depending on the immunity power of the patients. It is necessary to be able to find the infected patients even with no symptoms to be able to break the spread of the chain. In this paper, the comparison table describes the accuracy of deep learning architectures by the implementation of different optimizers with different learning rates. In order to remove the overfitting issue, different learning rate has been experimented. Further in this paper, we have proposed the classification of the COVID-19 images using the ensemble of 2 layered Convolutional Neural Network with the Transfer learning method which consumed lesser time for classification and attained an accuracy of nearly 90.45%.