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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: The FRAP assay offers a putative index of antioxidant, or reducing, potential of biological fluids within the technological reach of every laboratory and researcher interested in oxidative stress and its effects.

17,394 citations

Journal ArticleDOI
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016.

10,401 citations

Journal ArticleDOI
TL;DR: In this paper, the authors extended the unified theory of acceptance and use of technology (UTAUT) to study acceptance of technology in a consumer context and proposed UTAUT2 incorporating three constructs into UTAAUT: hedonic motivation, price value, and habit.
Abstract: This paper extends the unified theory of acceptance and use of technology (UTAUT) to study acceptance and use of technology in a consumer context. Our proposed UTAUT2 incorporates three constructs into UTAUT: hedonic motivation, price value, and habit. Individual differences--namely, age, gender, and experience--are hypothesized to moderate the effects of these constructs on behavioral intention and technology use. Results from a two-stage online survey, with technology use data collected four months after the first survey, of 1,512 mobile Internet consumers supported our model. Compared to UTAUT, the extensions proposed in UTAUT2 produced a substantial improvement in the variance explained in behavioral intention (56 percent to 74 percent) and technology use (40 percent to 52 percent). The theoretical and managerial implications of these results are discussed.

6,744 citations

Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper proposed a feed-forward denoising convolutional neural networks (DnCNNs) to handle Gaussian denobling with unknown noise level.
Abstract: The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.

5,902 citations

Journal ArticleDOI
Theo Vos1, Theo Vos2, Theo Vos3, Stephen S Lim  +2416 moreInstitutions (246)
TL;DR: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates, and there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries.

5,802 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