Institution
University of Macau
Education•Macao, Macau, China•
About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Population & Control theory. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.
Papers published on a yearly basis
Papers
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TL;DR: Cutting light on pursuing the intermediate host of SARS-CoV-2 is shed and the necessity of monitoring susceptible hosts to prevent further outbreaks is highlighted.
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the recent pandemic COVID-19, is reported to have originated from bats, with its intermediate host unknown to date. Here, we screened 26 animal counterparts of the human ACE2 (hACE2), the receptor for SARS-CoV-2 and SARS-CoV, and found that the ACE2s from various species, including pets, domestic animals and multiple wild animals, could bind to SARS-CoV-2 receptor binding domain (RBD) and facilitate the transduction of SARS-CoV-2 pseudovirus. Comparing to SARS-CoV-2, SARS-CoV seems to have a slightly wider range in choosing its receptor. We further resolved the cryo-electron microscopy (cryo-EM) structure of the cat ACE2 (cACE2) in complex with the SARS-CoV-2 RBD at a resolution of 3 A, revealing similar binding mode as hACE2 to the SARS-CoV-2 RBD. These results shed light on pursuing the intermediate host of SARS-CoV-2 and highlight the necessity of monitoring susceptible hosts to prevent further outbreaks.
120 citations
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TL;DR: Compared with state-of-the-art methods, SSN with a deep hierarchical architecture obtains higher classification accuracy in terms of the overall accuracy, average accuracy, and kappa (κ) coefficient of agreement, especially when the number of the training samples is small.
Abstract: This paper proposes a spectral–spatial feature learning (SSFL) method to obtain robust features of hyperspectral images (HSIs). It combines the spectral feature learning and spatial feature learning in a hierarchical fashion. Stacking a set of SSFL units, a deep hierarchical model called the spectral–spatial networks (SSN) is further proposed for HSI classification. SSN can exploit both discriminative spectral and spatial information simultaneously. Specifically, SSN learns useful high-level features by alternating between spectral and spatial feature learning operations. Then, kernel-based extreme learning machine (KELM), a shallow neural network, is embedded in SSN to classify image pixels. Extensive experiments are performed on two benchmark HSI datasets to verify the effectiveness of SSN. Compared with state-of-the-art methods, SSN with a deep hierarchical architecture obtains higher classification accuracy in terms of the overall accuracy, average accuracy, and kappa ( $ {\kappa }$ ) coefficient of agreement, especially when the number of the training samples is small.
120 citations
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TL;DR: A fairly simple nonlinear regression model known as multivariate adaptive regression splines (MARS) is suggested, as an alternative to forecasting of solar power output, that maintains simplicity of the classical multiple linear regression (MLR) model while possessing the capability of handling nonlinearity.
120 citations
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TL;DR: Though OECD countries have substantially lower pooled prevalence of resistance compared to non-OECD countries based on the data during 2006–2016, a further investigation in a time scale disclosed a faster increase in OECD countries during the past 11 years, and currently both of them have a comparable prevalence of resistances.
Abstract: Acinetobacter baumannii is one of the most challenging nosocomial pathogens due to the emergence and widespread of antibiotic resistance. We aimed to provide the first analysis of global prevalence...
120 citations
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TL;DR: This article examined the relationship between an authoritative school climate (characterized by high levels of student support and disciplinary structure) and both teacher reports of victimization and school records of threats against staff.
Abstract: Most research on school climate focuses on student well-being, with less attention on the safety of school faculty. The current study examined the relationship between an authoritative school climate (characterized by high levels of student support and disciplinary structure) and both teacher reports of victimization and school records of threats against staff. Regression analyses in a statewide sample of 280 high schools showed that structure (as measured by student- and teacher-reported clarity of school rules) and support (as measured by teacher-reported help seeking) were associated with less teacher victimization, after controlling for school and neighborhood demographics. Support, but not structure, was a consistent predictor of school records of threats against faculty. These findings offer implications for improving the workplace for teachers and staff.
120 citations
Authors
Showing all 6766 results
Name | H-index | Papers | Citations |
---|---|---|---|
Henry T. Lynch | 133 | 925 | 86270 |
Chu-Xia Deng | 125 | 444 | 57000 |
H. Vincent Poor | 109 | 2116 | 67723 |
Peng Chen | 103 | 918 | 43415 |
George F. Gao | 102 | 793 | 82219 |
MengChu Zhou | 96 | 1124 | 36969 |
Gang Li | 93 | 486 | 68181 |
Rob Law | 81 | 714 | 31002 |
Zongjin Li | 80 | 630 | 22103 |
Han-Ming Shen | 80 | 237 | 27410 |
Heng Li | 79 | 745 | 23385 |
Lionel M. Ni | 75 | 466 | 28770 |
C. L. Philip Chen | 74 | 482 | 20223 |
Chun-Su Yuan | 72 | 397 | 21089 |
Joao P. Hespanha | 72 | 418 | 39004 |