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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..
Papers
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Carbon quantum dots and reduced graphene oxide modified self-assembled S@C3N4/B@C3N4 metal-free nano-photocatalyst for high performance degradation of chloramphenicol
TL;DR: In this article, a metal free self-assembled carbon quantum dots and reduced graphene oxide layers modified S@gC3N4/B@g-C3Ns4 (CRSB) photocatalyst for visible and solar degradation of chloramphenicol (CMP) was reported.
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Security Frameworks for Wireless Sensor Networks-Review
TL;DR: This paper gives an overview of cryptographic frameworks designed so far and also a comparison of existing schemes is tabled.
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Genetic testing in patients with acute coronary syndrome undergoing percutaneous coronary intervention: a cost-effectiveness analysis
Anuradha Lala,Jeffrey S. Berger,Gaurav Sharma,Judith S. Hochman,R. Scott Braithwaite,Joseph A. Ladapo +5 more
TL;DR: Among ACS patients undergoing PCI, a genotype‐guided strategy of antiplatelet therapy yields similar outcomes to empiric approaches to treatment, but is marginally less costly and more effective.
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Platelet activity and cardiovascular risk in apparently healthy individuals: a review of the data
Gaurav Sharma,Jeffrey S. Berger +1 more
TL;DR: Large-scale, prospective studies that measure a battery of these platelet function tests in individuals without cardiovascular disease are recommended to better understand the associations, if any, between platelet activity and cardiovascular disease.
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End-to-end color printer calibration by total least squares regression
TL;DR: This paper proposes total least square (TLS) regression methods to estimate the parameters of various Neugebauer models and indicates that the TLS methods yield a consistent and significant improvement over the LS-based techniques for model parameter estimation.