G
Gaurav Sharma
Researcher at Shenzhen University
Publications - 1520
Citations - 40824
Gaurav Sharma is an academic researcher from Shenzhen University. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 82, co-authored 1244 publications receiving 31482 citations. Previous affiliations of Gaurav Sharma include Northeastern University & D. E. Shaw & Co..
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Journal ArticleDOI
Marker vaccine potential of foot-and-mouth disease virus with large deletion in the non-structural proteins 3A and 3B.
Jitendra K. Biswal,Saravanan Subramaniam,Rajeev Ranjan,Gaurav Sharma,Jyoti Misri,Bramhadev Pattnaik +5 more
TL;DR: The results from this study suggest that the availability of negative marker virus and companion diagnostic assay could open a promising new avenue for the application of DIVA compatible marker vaccine for the control of FMD in India.
Journal ArticleDOI
Performance evaluation of aerobic fluidized bed bioreactor coupled with tube-settler for hospital wastewater treatment
Majed Alsubih,Rachida El Morabet,Roohul Abad Khan,Nadeem A. Khan,Amadur Rahman Khan,Gaurav Sharma,Gaurav Sharma +6 more
TL;DR: In this article, the authors employed a fluidized aerobic bed bioreactor (FABB) coupled with a tube settler for hospital wastewater treatment and evaluated the treatment efficiency based on the removal of pollutants and nutrients with associated parameters pH, Alkalinity, total suspended solids (TSS), and mixed liquor suspended soliders (MLSS).
Journal ArticleDOI
Synthesis and characterization of extremely uniform Fe-Co-Ni ternary alloy nanowire arrays.
TL;DR: This work has fabricated extremely uniform arrays of polycrystalline Fe-Co-Ni ternary alloy nanowires having composition Fe 12.3 wt., Co 43.9 wt.% and Ni 43.8 wt.'s by electrodeposition into nanoporous alumina templates, using an Electrodeposition voltage of 15 V at 1000 Hz.
Proceedings ArticleDOI
A Novel Approach for Efficient SVM Classification with Histogram Intersection Kernel.
Gaurav Sharma,Frédéric Jurie +1 more
TL;DR: It is shown that the SVM problem with histogram intersection kernel is quasi-convex in input space and an iterative algorithm to solve it is outlined, which achieves similar or better performance at lower computational and memory costs.