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Institution

Dalian University of Technology

EducationDalian, China
About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a flow control method is employed to mitigate vortex-induced vibration (VIV) of a circular cylinder by using a suction flow method, and the results indicate clearly that the steady suction-flow control method exhibits excellent control effectiveness and can distinctly suppress the VIV by dramatically reducing the amplitudes of cylinder vibrations, fluctuating pressure coefficients and lift coefficients of the circular cylinder model.

173 citations

Journal ArticleDOI
TL;DR: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.
Abstract: Background The outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. Objective To develop and validate machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. Method 725 patients were used to train and validate the model including a retrospective cohort of 299 hospitalised COVID-19 patients at Wuhan, China, from December 23, 2019, to February 13, 2020, and five cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion-matrix. Results The median age was 50.0 years and 137 (45.8%) were men in the retrospective cohort. The median age was 62.0 years and 236 (55.4%) were men in five cohorts. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.89, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 57.5% to 88.0%, all of which performed better than the pneumonia severity index. The cut-off values of the low, medium, and high-risk probabilities were 0.21 and 0.80. The online-calculators can be found at www.covid19risk.ai. Conclusion The machine-learning model, nomogram, and online-calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.

173 citations

Journal ArticleDOI
TL;DR: When adding Fe3O4 and ZVI simultaneously into an anaerobic digester, the abundance of hydrogenotrophic methanogens decreased drastically compared to ZVI-added digester and Fe-free digesters, which implied that the potential DIET between Syntrophomonas and Methanosaeta was likely a crucial reason for accelerating an aerobic digestion of waste sludge.

172 citations

Journal ArticleDOI
Shuai Yang1, Fenglin Yang1, Zhimin Fu1, Tao Wang1, Ruibo Lei1 
TL;DR: Biological nutrient removal was investigated in a sequencing batch membrane bioreactor which used carriers instead of activated sludge and results of microbes showed the composition and spatial structure of the microbial community in the reactor.

172 citations

Journal ArticleDOI
TL;DR: In this article, two 4,5-disubstituted 1,8-naphthalimide derivatives 1 and 2 were synthesized as ratiometric fluorescent and colorimetric sensors for Cu2+, respectively.

172 citations


Authors

Showing all 61205 results

NameH-indexPapersCitations
Yang Yang1712644153049
Yury Gogotsi171956144520
Hui Li1352982105903
Michael I. Posner134414104201
Anders Hagfeldt12960079912
Jian Zhou128300791402
Chao Zhang127311984711
Bin Wang126222674364
Chi Lin1251313102710
Tao Zhang123277283866
Bo Wang119290584863
Zhenyu Zhang118116764887
Liang Cheng116177965520
Anthony G. Fane11256540904
Xuelong Li110104446648
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023167
2022836
20216,974
20206,457
20196,261
20185,375