As the whole world is witnessing what novel coronavirus (COVID-19) can do to the mankind, it presents several unique features also. In the absence of specific vaccine for COVID-19, it is essential to detect the disease at an early stage and isolate an infected patient. Till today there is a global shortage of testing labs and testing kits for COVID-19. This paper discusses about the role of machine learning techniques for getting important insights like whether lung computed tomography (CT) scan should be the first screening/alternative test for real-time reverse transcriptase-polymerase chain reaction (RT-PCR), is COVID-19 pneumonia different from other viral pneumonia and if yes how to distinguish it using lung CT scan images from the carefully selected data of lung CT scan COVID-19-infected patients from the hospitals of Italy, China, Moscow and India? For training and testing the proposed system, custom vision software of Microsoft azure based on machine learning techniques is used. An overall accuracy of almost 91% is achieved for COVID-19 classification using the proposed methodology.

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This study reports synthesis and characterization of poly(MMA-co-BA)/Cloisite 30B (organo-modified montmorillonite clay) nanocomposites by ultrasound-assisted in-situ emulsion polymerization. Copolymers have been synthesized with MMA:BA monomer ratio of 4:1, and varying clay loading (1-5wt% monomer). The poly(MMA-co-BA)/Cloisite 30B nanocomposites have been characterized for their thermal and mechanical properties. Ultrasonically synthesized nanocomposites have been revealed to possess higher thermal degradation resistance and mechanical strength than the nanocomposites synthesized using conventional techniques. These properties, however, show an optimum (or maxima) with clay loading. The maximum values of thermal and mechanical properties of the nanocomposites with optimum clay loading are as follows. Thermal degradation temperatures: T=320°C (4wt%), T=373°C (4wt%), maximum degradation temperature=384°C (4wt%); glass transition temperature=64.8°C (4wt%); tensile strength=20MPa (2wt%), Young's modulus=1.31GPa (2wt%), Percentage elongation=17.5% (1wt%). Enhanced properties of poly(MMA-co-BA)/Cloisite 30B nanocomposites are attributed to effective exfoliation and dispersion of clay nanoparticles in copolymer matrix due to intense micro-convection induced by ultrasound and cavitation. Clay platelets help in effective heat absorption with maximum surface interaction/adhesion that results in increased thermal resistivity of nanocomposites. Hindered motion of the copolymer chains due to clay platelets results in enhancement of tensile strength and Young's modulus of nanocomposite. Rheological (liquid) study of the nanocomposites reveals that nanocomposites have higher yield stress and infinite shear viscosity than neat copolymer. Nonetheless, nanocomposites still display shear thinning behavior - which is typical of the neat copolymer.

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In this article, we propose a new two-level implicit method of accuracy two in time and three in space based on spline in compression approximations using two off-step points and a central point on a quasi-variable mesh for the numerical solution of the system of 1D quasi-linear parabolic partial differential equations. The new method is derived directly from the continuity condition of the first-order derivative of the spline function. The stability analysis for a model problem is discussed. The method is directly applicable to problems in polar systems. To demonstrate the strength and utility of the proposed method, we solve the generalized Burgers-Fisher equation, generalized Burgers-Huxley equation, coupled Burgers-equations and heat equation in polar coordinates. We demonstrate that the proposed method enables us to obtain high accurate solution for high Reynolds number.MSC: 65M06; 65M12; 65M22; 65Y20