Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

Efficient Hydrogen Evolution on Cu Nanodots-Decorated Ni3S2 Nanotubes by Optimizing Atomic Hydrogen Adsorption and Desorption.

Abstract: Low-cost transition-metal dichalcogenides (MS2) have attracted great interest as alternative catalysts for hydrogen evolution. However, a significant challenge is the formation of sulfur-hydrogen bonds on MS2 (S-Hads), which will severely suppress hydrogen evolution reaction (HER). Here we report Cu nanodots (NDs)-decorated Ni3S2 nanotubes (NTs) supported on carbon fibers (CFs) (Cu NDs/Ni3S2 NTs-CFs) as efficient electrocatalysts for HER in alkaline media. The electronic interactions between Cu and Ni3S2 make that Cu NDs are positively charged and can promote water adsorption and activation. Meanwhile, Ni3S2 NTs are negatively charged and can weaken S-Hads bonds formed on catalyst surfaces. Therefore, the Cu/Ni3S2 hybrids can optimize H adsorption and desorption on electrocatalysts and can promote both Volmer and Heyrovsky steps of HER. The strong interactions between Cu and Ni3S2 make that the Cu NDs/Ni3S2 NTs-CFs electrocatalysts exhibit the outstanding HER catalytic performance with low onset potential...

An Evaluation of Three Clustering Procedures for Use in Synoptic Climatological Classification

Abstract: The selection of the proper clustering procedure to use in the development of an objective synoptic methodology may have far-reaching implications on the composition of the final “homogeneous” groupings The goal of this study is to evaluate three common clustering techniques (Ward's, average linkage, and centroid) to determine which yields the most meaningful synoptic classification The three clustering procedures were applied to a temporal synoptic index which classified days in Mobile, Alabama into meteorologically homogeneous units The final meteorological groupings differed widely among the three pressures Ward's tended to produce groups with relatively similar numbers of days Thus, many extreme weather days were grouped with less extreme days, and the final meteorological units did not duplicate reality with great precision The centroid procedure produced one very large group and many single-day groups, yielding unsatisfactory results The average linkage procedure, which minimizes wit

The prevalence and prevention of nasopharyngeal carcinoma in China

Abstract: Nasopharyngeal carcinoma (NPC) has remarkable epidemiological features, including regional, racial, and familial aggregations. The aim of this review is to describe the epidemiological characteristics of NPC and to propose possible causes for the high incidence patterns in southern China. Since the etiology of NPC is not completely understood, approaches to primary prevention of NPC remain under consideration. This situation highlights the need to conduct secondary prevention, including improving rates of early detection, early diagnosis, and early treatment in NPC patients. Since the 1970's, high-risk populations in southern China have been screened extensively for early detection of NPC using anti-Epstein-Barr virus (EBV) serum biomarkers. This review summarizes several large screening studies that have been conducted in the high-incidence areas of China. Screening markers, high-risk age range for screening, time intervals for blood re-examination, and the effectiveness of these screening studies will be discussed. Conduction of prospective randomized controlled screening trials in southern China can be expected to maximize the cost-effectiveness of early NPC detection screening.

Image Blind Denoising with Generative Adversarial Network Based Noise Modeling

Abstract: In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images. As we all know, discriminative learning based methods, such as DnCNN, can achieve state-of-the-art denoising results, but they are not applicable to this problem due to the lack of paired training data. To tackle the barrier, we propose a novel two-step framework. First, a Generative Adversarial Network (GAN) is trained to estimate the noise distribution over the input noisy images and to generate noise samples. Second, the noise patches sampled from the first step are utilized to construct a paired training dataset, which is used, in turn, to train a deep Convolutional Neural Network (CNN) for denoising. Extensive experiments have been done to demonstrate the superiority of our approach in image blind denoising.