Institution
Minjiang University
Education•Fuzhou, China•
About: Minjiang University is a education organization based out in Fuzhou, China. It is known for research contribution in the topics: Computer science & Photonic crystal. The organization has 1747 authors who have published 2285 publications receiving 23891 citations.
Topics: Computer science, Photonic crystal, Band gap, Catalysis, Population
Papers published on a yearly basis
Papers
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TL;DR: A novel rice diseases identification method based on deep convolutional neural networks (CNNs) techniques, trained to identify 10 common rice diseases with much higher accuracy than conventional machine learning model.
593 citations
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TL;DR: A mathematical model is proposed to analyzes this epidemic, based on a dynamic mechanism that incorporating the intrinsic impact of hidden la- tent and infectious cases on the entire process of transmission, which indicates that, the outbreak in Wuhan is predicted to be ended in the early April.
Abstract: The outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention. Here, we propose a generalized SEIR model to analyze this epidemic. Based on the public data of National Health Commission of China from Jan. 20th to Feb. 9th, 2020, we reliably estimate key epidemic parameters and make predictions on the inflection point and possible ending time for 5 different regions. According to optimistic estimation, the epidemics in Beijing and Shanghai will end soon within two weeks, while for most part of China, including the majority of cities in Hubei province, the success of anti-epidemic will be no later than the middle of March. The situation in Wuhan is still very severe, at least based on public data until Feb. 15th. We expect it will end up at the beginning of April. Moreover, by inverse inference, we find the outbreak of COVID-19 in Mainland, Hubei province and Wuhan all can be dated back to the end of December 2019, and the doubling time is around two days at the early stage.
575 citations
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TL;DR: A design of a synergistic photocatalyst for selective reduction of CO2 to CO by using a covalent organic framework bearing single Ni sites (Ni-TpBpy), in which electrons transfer from photosensitizer to Ni sites for CO production by the activated CO2 reduction under visible-light irradiation.
Abstract: Photocatalytic reduction of CO2 into energy-rich carbon compounds has attracted increasing attention. However, it is still a challenge to selectively and effectively convert CO2 to a desirable reaction product. Herein, we report a design of a synergistic photocatalyst for selective reduction of CO2 to CO by using a covalent organic framework bearing single Ni sites (Ni-TpBpy), in which electrons transfer from photosensitizer to Ni sites for CO production by the activated CO2 reduction under visible-light irradiation. Ni-TpBpy exhibits an excellent activity, giving a 4057 μmol g–1 of CO in a 5 h reaction with a 96% selectivity over H2 evolution. More importantly, when the CO2 partial pressure was reduced to 0.1 atm, 76% selectivity for CO production is still obtained. Theoretical calculations and experimental results suggest that the promising catalytic activity and selectivity are ascribed to synergistic effects of single Ni catalytic sites and TpBpy, in which the TpBpy not only serves as a host for CO2 m...
458 citations
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TL;DR: New Huadu Business School [09ZD050] and National Social Science Foundation of China [10JZD0018], Fundamental Research Funds for the Central Universities [2010221051].
361 citations
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TL;DR: An attention-guided denoising convolutional neural network (ADNet), mainly including a sparse block (SB), a feature enhancement block (FEB), an attention block (AB) and a reconstruction block (RB) for image Denoising.
343 citations
Authors
Showing all 1775 results
Name | H-index | Papers | Citations |
---|---|---|---|
Wei Wang | 95 | 3544 | 59660 |
Guonan Chen | 74 | 666 | 27827 |
Boqiang Lin | 67 | 365 | 14670 |
Thurasamy Ramayah | 57 | 388 | 12103 |
Wei Wei | 56 | 408 | 11698 |
Daoqiang Zhang | 49 | 306 | 12468 |
Zhixiong Li | 47 | 401 | 9046 |
Qufu Wei | 45 | 441 | 8552 |
Fei Wang | 44 | 441 | 8775 |
Lee Jia | 39 | 191 | 5786 |
Leyi Wei | 37 | 76 | 3766 |
Zhenhua Gu | 35 | 123 | 3798 |
Youjin Deng | 34 | 197 | 4961 |
Jingwei Shao | 33 | 104 | 5959 |
Kelin Wang | 31 | 198 | 3335 |