Vignan University

About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Control theory & CMOS. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.

A spectral relaxation method for three-dimensional MHD flow of nanofluid flow over an exponentially stretching sheet due to convective heating: an application to solar energy

Abstract: The principle aim of the present investigation is to study the boundary layer analysis of nanofluid flow over a bidirectional exponentially stretching sheet in the presence of transverse magnetic field and also in convective condition. The effects of Brownian motion and thermophoretic diffusion of nanoparticle are considered from the mathematical model. Governing partial differential equations are reduced into coupled non-linear ordinary differential equations using suitable similarity transformations, further the system of equations are solved by a new spectral relaxation method. Validation of the results is achieved by comparison with emitting case from previous studies in the literature. Also, it has been shown that the convergence rate of the spectral relaxation method is significantly improved by using the method in conjunction with the successive over relaxation method. The results reveal the existence of interesting Sparrow–Gregg-type Hills for temperature distribution pertinent to some range of parametric values. Moreover the numerical data of drag coefficient, local heat and mass transfer rates are evaluated and analyzed. Effects of local Biot number on temperature and concentration profiles are qualitatively similar. Both the temperature and concentration profiles are enhanced for higher values of local Biot number.

Nodal/Saddle Stagnation Point Slip Flow of an Aqueous Convectional Magnesium Oxide–Gold Hybrid Nanofluid with Viscous Dissipation

Abstract: In this analysis, convective heat transfer characteristics of a hybrid nanofluid mixture containing magnesium oxide (MgO) and gold (Au) nanoparticles are numerically studied. The impact of slip effects on nodal/saddle stagnation point boundary layer flow with viscous dissipation effect is mathematically modeled. The behavior of nanofluids is studied by employing Tiwari–Das nanofluid model. Pure water is the base fluid in this analysis. The governing partial differential equations with many independent variables are reduced to ordinary differential equations with one independent variable and then numerically solved by the Runge–Kutta–Fehlberg method with the desired accuracy. The outputs showed that MgO–Au/water hybrid nanofluid sharply raises the base fluid's thermal behavior. Results reveal that in the nodal and saddle point areas, the impact of higher slip effects significantly increases the local heat transfer rate.

Design of a multi-epitope-based vaccine targeting M-protein of SARS-CoV2: an immunoinformatics approach.

Abstract: In the present study, one of the targets present on the envelopes of coronaviruses, membrane glycoprotein (M) was chosen for the design of a multi-epitope vaccine by Immunoinformatics approach. The B-cell and T-cell epitopes used for the construction of vaccine were antigenic, nonallergic and nontoxic. An adjuvant, β-defensin and PADRE sequence were included at the N-terminal end of the vaccine. All the epitopes were joined by linkers for decreasing the junctional immunogenicity. Various physicochemical parameters of the vaccine were evaluated. Secondary and tertiary structures were predicted for the vaccine construct. The tertiary structure was further refined, and various parameters related to the refinement of the protein structure were validated by using different tools. Humoral immunity induced by B-cells relies upon the identification of antigenic determinants on the surface of the vaccine construct. In this regard, the vaccine construct was found to consist of several B-cell epitopes in its three-dimensional conformation. Molecular docking of the vaccine was carried out with TLR-3 receptor to study their binding and its strength. Further, protein-protein interactions in the docked complex were visualized using LigPlot+. Population coverage analysis had shown that the multi-epitope vaccine covers 94.06% of the global population. The vaccine construct was successfully cloned in silico into pET-28a (+). Immune simulation studies showed the induction of primary, secondary and tertiary immune responses marked by the increased levels of antibodies, INF-γ, IL-2, TGF-β, B- cells, CD4+ and CD8+ cells. Finally, the vaccine construct was able to elicit immune response as desired. Communicated by Ramaswamy H. Sarma.

Ensemble of texture and shape descriptors using support vector machine classification for face recognition

Abstract: One of the significant task in pattern recognition and computer vision along with artificial intelligence and machine learning is the Face Recognition. Most of the prevailing approaches on face recognition concentrates on the recognition of the utmost appropriate facial attributes for efficiently recognizing and differentiating amongst the considered images. In this paper, an ensemble aided facial recognition approach is suggested that performs well in wild environment using an ensemble of feature descriptors and preprocessing approaches. The combination of texture and color descriptors are mined from the preprocessed facial images and classified using support vector machine algorithm. The experimental outcome of the suggested methodology is illustrated using two databases such as FERET data samples and Labeled Faces in the Wild data samples. From the results, it is shown that, the proposed approach has good classification accuracy and combination utility of pre-processing techniques due to the usage of additional preprocessing and extracted feature descriptors. The average classification accuracies for the both the data samples are 99% and 94% respectively.