Henan University of Technology

About: Henan University of Technology is a education organization based out in Zhengzhou, China. It is known for research contribution in the topics: Catalysis & Chemistry. The organization has 7648 authors who have published 6503 publications receiving 73067 citations. The organization is also known as: Hénán Gōngyè Dàxué.

Mechanisms of vapor-phase antibacterial action of essential oil from Cinnamomum camphora var. linaloofera Fujita against Escherichia coli.

Abstract: The purpose of this study was to investigate antibacterial activity of essential oil from Cinnamomum camphora var. linaloofera Fujita (EOL) at vapor phase and its mechanism of bactericidal action against Escherichia coli. Results showed that the vapor-phase EOL had significant antibacterial activity with a minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of 200 μl/L. Further analyses showed that treatment of E. coli with vapor-phase EOL resulted in partial degradation of cell membrane, increased membrane permeability, leakage of cytoplasm materials, and prominent distortion and shrinkage of bacterial cells. FTIR showed that EOL altered bacterial protein secondary and tertiary structures. GC/MS analysis showed that the components of vapor-phase EOL included linalool (69.94%), camphor (10.90%), nerolidol (10.92%), and safrole (8.24%), of which linalool had bactericidal activity. Quantum chemical analysis suggested that the antibacterial reactive center of linalool was oxygen atom (O10) which transferred electrons during antibacterial action by the donation of electrons.

Stochastic Conjugate Gradient Algorithm With Variance Reduction

Abstract: Conjugate gradient (CG) methods are a class of important methods for solving linear equations and nonlinear optimization problems. In this paper, we propose a new stochastic CG algorithm with variance reduction 1 and we prove its linear convergence with the Fletcher and Reeves method for strongly convex and smooth functions. We experimentally demonstrate that the CG with variance reduction algorithm converges faster than its counterparts for four learning models, which may be convex, nonconvex or nonsmooth. In addition, its area under the curve performance on six large-scale data sets is comparable to that of the LIBLINEAR solver for the $L2$ -regularized $L2$ -loss but with a significant improvement in computational efficiency. 1 CGVR algorithm is available on github: https://github.com/xbjin/cgvr