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Institution

Nanjing University of Science and Technology

EducationNanjing, China
About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Control theory & Catalysis. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.


Papers
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Journal ArticleDOI
TL;DR: This study suggests that DHA specifically causes head and neck cancer cell death through contribution from both ferroptosis and apoptosis and may represent an effective strategy in head andneck cancer treatment.

202 citations

Journal ArticleDOI
TL;DR: This paper considers the problem of sampled-data adaptive output feedback fuzzy stabilization for switched uncertain nonlinear systems associated with asynchronous switching and proposes a scheme that is employed in a mass–spring–damper system to demonstrate its effectiveness.
Abstract: This paper considers the problem of sampled-data adaptive output feedback fuzzy stabilization for switched uncertain nonlinear systems associated with asynchronous switching. A state observer is designed to estimate the unmeasured states and fuzzy logic systems are employed to deal with the unknown nonlinear terms. Sampled-data controller and novel switched adaptive laws are constructed based on the recursive design method and an average dwell time constraint is given to ensure that the closed-loop system is stable. The proposed scheme is employed in a mass–spring–damper system to demonstrate its effectiveness.

201 citations

Journal ArticleDOI
TL;DR: Dynamic surface control technique is used to avoid the problem of “explosion of complexity,” which is caused by repeated differentiation of certain nonlinear functions in the backstepping design process.
Abstract: In this paper, the problem of adaptive fuzzy tracking control via output feedback for a class of uncertain single-input single-output (SISO) strict-feedback nonlinear systems with unknown time-delay functions is investigated. Dynamic surface control technique is used to avoid the problem of “explosion of complexity,” which is caused by repeated differentiation of certain nonlinear functions in the backstepping design process. In addition, the fuzzy logic systems are utilized to approximate the unknown and desired control input signals directly instead of the unknown nonlinear functions. The designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded and the tracking error to converge to a small neighborhood of the origin. Simulations results are provided to demonstrate the effectiveness of the proposed methods.

201 citations

Journal ArticleDOI
25 Oct 2017-ACS Nano
TL;DR: Mesoporous nanofibers of various cation-ordered PrBa0.5Co2-xFexO5+δ perovskites via electrospinning result in high performance of the oxygen reduction reaction and oxygen evolution reaction and stability in zinc-air battery.
Abstract: Of the various catalysts that have been developed to date for high performance and low cost, perovskite oxides have attracted attention due to their inherent catalytic activity as well as structural flexibility. In particular, high amounts of Pr substitution of the cation ordered perovskite oxide originating from the state-of-the-art Ba0.5Sr0.5Co0.8Fe0.2O3−δ (BSCF) electrode could be a good electrode or catalyst because of its high oxygen kinetics, electrical conductivity, oxygen capacity, and structural stability. However, even though it has many favorable intrinsic properties, the conventional high-temperature treatment for perovskite synthesis, such as solid-state reaction and combustion process, leads to the particle size increase which gives rise to the decrease in surface area and the mass activity. Therefore, we prepared mesoporous nanofibers of various cation-ordered PrBa0.5Sr0.5Co2–xFexO5+δ (x = 0, 0.5, 1, 1.5, and 2) perovskites via electrospinning. The well-controlled B-site metal ratio and lar...

201 citations

Journal ArticleDOI
TL;DR: In this article, the authors used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to the disease from public opinions.
Abstract: Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19–related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% – a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19–Sentiment Classification.

200 citations


Authors

Showing all 31818 results

NameH-indexPapersCitations
Jian Yang1421818111166
Liming Dai14178182937
Hui Li1352982105903
Jian Zhou128300791402
Shuicheng Yan12381066192
Zidong Wang12291450717
Xin Wang121150364930
Xuan Zhang119153065398
Zhenyu Zhang118116764887
Xin Li114277871389
Zeshui Xu11375248543
Xiaoming Li113193272445
Chunhai Fan11270251735
H. Vincent Poor109211667723
Qian Wang108214865557
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023107
2022594
20214,309
20203,990
20193,920
20183,211