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 & Starch. The organization has 7648 authors who have published 6503 publications receiving 73067 citations. The organization is also known as: Hénán Gōngyè Dàxué.

Mesoporous ZnS hierarchical nanostructures: facile synthesis, growth mechanism and application in gas sensing

Abstract: Mesoporous zinc sulfide hierarchical nanostructures were prepared in the presence of polyvinylpyrrolidone aqueous solution via a low-cost, hydrothermal route. Field-emission scanning electron microscopy, transmission electron microscopy, X-ray powder diffraction, N2 adsorption–desorption analysis, and thermal gravimetric analysis were used to characterize the morphology, structure and composition of the products. Results show that the products exhibit a kind of hierarchical structure with mesoporous features which consist of smaller building blocks (ca. 30 nm) assembled together. The effects of a series of reaction parameters on the morphology of ZnS nanostructures have been studied systematically. A plausible mechanism based on coordination nucleation and subsequent Ostwald ripening is proposed. By virtue of the interesting porous structure, a chemical sensor was fabricated. The sensor exhibits attractive gasoline sensing behavior with excellent selectivity and fast response.

SUD-GAN: Deep Convolution Generative Adversarial Network Combined with Short Connection and Dense Block for Retinal Vessel Segmentation

Abstract: Since morphology of retinal blood vessels plays a key role in ophthalmological disease diagnosis, retinal vessel segmentation is an indispensable step for the screening and diagnosis of retinal diseases with fundus images. In this paper, deep convolution adversarial network combined with short connection and dense block is proposed to separate blood vessels from fundus image, named SUD-GAN. The generator adopts U-shape encode-decode structure and adds short connection block between convolution layers to prevent gradient dispersion caused by deep convolution network. The discriminator is all composed of convolution block, and dense connection structure is added to the middle part of the convolution network to strengthen the spread of features and enhance the network discrimination ability. The proposed method is evaluated on two publicly available databases, the DRIVE and STARE. The results show that the proposed method outperforms the state-of-the-art performance in sensitivity and specificity, which were 0.8340 and 0.9820, and 0.8334 and 0.9897 respectively on DRIVE and STARE, and can detect more tiny vessels and locate the edge of blood vessels more accurately.