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Institution

Hong Kong Polytechnic University

EducationHong Kong, China
About: Hong Kong Polytechnic University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Tourism & Population. The organization has 29633 authors who have published 72136 publications receiving 1956312 citations. The organization is also known as: HKPU & PolyU.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors reviewed the overall impacts of coronavirus disease 2019 (COVID-19) pandemic on China's hotel industry and proposed a management framework to address the anti-pandemic phases, principles, and strategies.

379 citations

Journal ArticleDOI
TL;DR: In this paper, the results of X-ray diffraction reveal that Ba 2+ diffuse into the Bi 0.5 Na 0.0.5 TiO 3 lattices to form a solid solution with a pure perovskite structure, and a morphotropic phase boundary (MPB) exists at 0.06.

379 citations

Journal ArticleDOI
TL;DR: In this article, the authors advocate a new set of design guidelines for wireless communication in edge learning, collectively called learning-driven communication, and provide examples to demonstrate the effectiveness of these design guidelines.
Abstract: The recent revival of AI is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interest in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, has emerged, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design guidelines for wireless communication in edge learning, collectively called learning- driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design guidelines. Unique research opportunities are identified.

378 citations

Journal ArticleDOI
TL;DR: In this article, the average crystal structure of BaTaO2N is a cubic perovskite, with a Ta−O/N distance of 2.056 A. The optical band gaps are estimated from diffuse reflectance spectra as follows:
Abstract: The syntheses, crystal structures, electrical properties, and optical absorbance spectra of six perovskite oxynitrides, AMO2N (A = Ba, Sr, Ca; M = Ta, Nb) have been investigated. The average crystal structure of BaTaO2N is a cubic perovskite, with a Ta−O/N distance of 2.056 A. SrTaO2N and CaTaO2N are distorted by octahedral tilting, showing noticeably smaller Ta−O/N distances of approximately 2.02 A. Electron diffraction studies of BaTaO2N are consistent with the simple cubic perovskite crystal structure determined using X-ray powder diffraction methods. Each of the niobium oxynitrides is isostructural with its tantalum analogue, though the Nb−O/N distances are observed to be slightly longer. The optical band gaps are estimated from diffuse reflectance spectra as follows: BaTaO2N, 1.8 eV; SrTaO2N, 2.1 eV; CaTaO2N, 2.4 eV; BaNbO2N, 1.8 eV; SrNbO2N, 1.9 eV; CaNbO2N, 2.1 eV. Impedance spectroscopy was carried out on sintered pellets of the ATaO2N and BaNbO2N to investigate the dielectric and electrical tran...

378 citations


Authors

Showing all 30115 results

NameH-indexPapersCitations
Jing Wang1844046202769
Xiang Zhang1541733117576
Wei Zheng1511929120209
Rui Zhang1512625107917
Jian Yang1421818111166
Joseph Lau140104899305
Yu Huang136149289209
Dacheng Tao133136268263
Chuan He13058466438
Lei Zhang130231286950
Ming-Hsuan Yang12763575091
Chao Zhang127311984711
Yuri S. Kivshar126184579415
Bin Wang126222674364
Chi-Ming Che121130562800
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023229
2022971
20216,743
20206,207
20195,288