Wuhan University

About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Population & Feature extraction. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.

Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images

Abstract: Background A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Materials and Methods We collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19. Results The experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019. Conclusions The established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

Nest-like NiCoP for Highly Efficient Overall Water Splitting

Abstract: The investigation of high-efficiency nonprecious electrocatalysts for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is of great significance for renewable energy technologies. Here, we provide a successive hydrothermal, oxidation, and phosphidation method to fabricate a 3D nest-like ternary NiCoP/carbon cloth (CC) electrocatalyst with superior catalytic activity and stability toward HER/OER. Nest-like NiCoP/CC requires overpotentials of 44 and 62 mV to reach the current density of 10 mA cm–2 in acidic and alkaline media, respectively, toward HER. For OER, the NiCoP/CC exhibits high active and durable performance with an overpotential of 242 mV at current density of 10 mA cm–2 in alkaline solutions. Furthermore, the practical application of NiCoP/CC as a bifunctional catalyst for overall water splitting reaction yields current densities of 10 and 100 mA cm–2 at 1.52 and 1.77 V, respectively.

Measuring firm size in empirical corporate finance

Abstract: In empirical corporate finance, firm size is commonly used as an important, fundamental firm characteristic. However, no research comprehensively assesses the sensitivity of empirical results in corporate finance to different measures of firm size. This paper fills this hole by providing empirical evidence for a “measurement effect” in the “size effect”. In particular, we examine the influences of employing different proxies (total assets, total sales, and market capitalization) of firm size in 20 prominent areas in empirical corporate finance research. We highlight several empirical implications. First, in most areas of corporate finance the coefficients of firm size measures are robust in sign and statistical significance. Second, the coefficients on regressors other than firm size often change sign and significance when different size measures are used. Unfortunately, this suggests that some previous studies are not robust to different firm size proxies. Third, the goodness of fit measured by R-squared also varies with different size measures, suggesting that some measures are more relevant than others in different situations. Fourth, different proxies capture different aspects of “firm size”, and thus have different implications. Therefore, the choice of size measures needs both theoretical and empirical justification. Finally, our empirical assessment provides guidance to empirical corporate finance researchers who must use firm size measures in their work.