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: Computer science & Population. 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: In this paper, a facile and low-cost method for LiBH4 regeneration by ball milling its hydrolysis byproduct (LiBO2·2H2O) and Mg under ambient conditions is reported.
138 citations
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TL;DR: Depression is highly prevalent in clinically stable patients with COVID-19 and regular screening and appropriate treatment of depression are urgently warranted for this population.
138 citations
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138 citations
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04 Feb 2014TL;DR: A multistep-ahead prediction model based on the mean entropy and relevance vector machine (RVM) is developed, and applied to state of health (SOH) and remaining life prediction of the battery and the effectiveness of the proposed approach is effectively applied to battery monitoring and prognostics.
Abstract: Battery prognostics aims to predict the remaining life of a battery and to perform necessary maintenance service if necessary using the past and current information. A reliable prognostic model should be able to accurately predict the future state of the battery such that the maintenance service could be scheduled in advance. In this paper, a multistep-ahead prediction model based on the mean entropy and relevance vector machine (RVM) is developed, and applied to state of health (SOH) and remaining life prediction of the battery. A wavelet denoising approach is introduced into the RVM model to reduce the uncertainty and to determine trend information. The mean entropy based method is then used to select the optimal embedding dimension for correct time series reconstruction. Finally, RVM is employed as a novel nonlinear time-series prediction model to predict the future SOH and the remaining life of the battery. As more data become available, the accuracy and precision of the prediction improve. The presented approach is validated through experimental data collected from Li-ion batteries. The experimental results demonstrate the effectiveness of the proposed approach, which can be effectively applied to battery monitoring and prognostics.
138 citations
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TL;DR: Analysis showed that the ovarian aromatase did not seem to affect the formation of so-called juvenile ovary and oocyte-like germ cells; however, it was essential for further differentiation of the juvenile Ovary into the true ovary.
Abstract: Sexual or gonadal differentiation is a complex event and its mechanism remains elusive in teleosts. Despite its complexity and plasticity, the process of ovarian differentiation is believed to involve gonadal aromatase (cyp19a1a) in nearly all species studied. However, most data concerning the role of aromatase have come from gene expression analysis or studies involving pharmacological approaches. There has been a lack of genetic evidence for the importance of aromatase in gonadal differentiation, especially the timing when the enzyme starts to exert its effect. This is due to the lack of appropriate loss-of-function approaches in fish models for studying gene functions. This situation has changed recently with the development of genome editing technologies, namely TALEN and CRISPR/Cas9. Using both TALEN and CRISPR/Cas9, we successfully established three mutant zebrafish lines lacking the ovarian aromatase. As expected, all mutant fish were males, supporting the view that aromatase plays a critical role in directing ovarian differentiation and development. Further analysis showed that the ovarian aromatase did not seem to affect the formation of so-called juvenile ovary and oocyte-like germ cells; however, it was essential for further differentiation of the juvenile ovary into the true ovary.
138 citations
Authors
Showing all 6766 results
Name | H-index | Papers | Citations |
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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 |