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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 fundamentals of HER are summarized and the recent state-of-the-art advances in the low-cost and high-performance catalysts based on noble and non-noble metals, as well as metal-free HER electrocatalysts are reviewed.
Abstract: Hydrogen fuel is considered as the cleanest renewable resource and the primary alternative to fossil fuels for future energy supply. Sustainable hydrogen generation is the major prerequisite to realize future hydrogen economy. The electrocatalytic hydrogen evolution reaction (HER), as the vital step of water electrolysis to H2 production, has been the subject of extensive study over the past decades. In this comprehensive review, we first summarize the fundamentals of HER and review the recent state-of-the-art advances in the low-cost and high-performance catalysts based on noble and non-noble metals, as well as metal-free HER electrocatalysts. We systemically discuss the insights into the relationship among the catalytic activity, morphology, structure, composition, and synthetic method. Strategies for developing an effective catalyst, including increasing the intrinsic activity of active sites and/or increasing the number of active sites, are summarized and highlighted. Finally, the challenges, perspectives, and research directions of HER electrocatalysis are featured.

1,387 citations

Journal ArticleDOI
TL;DR: It is shown that eigenvalues are roots of a one-dimensional polynomial, and when the order of the tensor is even, E-eigenvaluesare roots of another one- dimensional polynomials associated with the symmetric hyperdeterminant.

1,378 citations

Journal ArticleDOI
TL;DR: The primary objective of this paper is to serve as a glossary for interested researchers to have an overall picture on the current time series data mining development and identify their potential research direction to further investigation.

1,358 citations

Journal ArticleDOI
TL;DR: The role of L-AA in metabolism and the latest studies regarding its bio- synthesis, tissue compartmentalisation, turnover and catabolism are focused on, as well as the potential to improve the L- AA content of crops.
Abstract: Humans are unable to synthesise L-ascorbic acid (L-AA, ascorbate, vitamin C), and are thus entirely dependent upon dietary sources to meet needs. In both plant and animal metabolism, the biological functions of L-ascorbic acid are centred around the antioxidant properties of this molecule. Considerable evidence has been accruing in the last two decades of the importance of L-AA in protecting not only the plant from oxidative stress, but also mammals from various chronic diseases that have their origins in oxidative stress. Evidence suggests that the plasma levels of L-AA in large sections of the population are sub-optimal for the health protective effects of this vitamin. Until quite recently, little focus has been given to improving the L-AA content of plant foods, either in terms of the amounts present in commercial crop varieties, or in minimising losses prior to ingestion. Further, while L-AA biosynthesis in animals was elucidated in the 1960s, 1 it is only very recently that a distinct biosynthetic route for plants has been proposed. 2 The characterisation of this new pathway will undoubtedly provide the necessary focus and impetus to enable fundamental questions on plant L-AA metabolism to be resolved. This review focuses on the role of L-AA in metabolism and the latest studies regarding its bio- synthesis, tissue compartmentalisation, turnover and catabolism. These inter-relationships are considered in relation to the potential to improve the L-AA content of crops. Methodology for the reliable analysis of L-AA in plant foods is briefly reviewed. The concentrations found in common food sources and the effects of processing, or storage prior to consumption are discussed. Finally the factors that determine the bioavailability of L-AA and how it may be improved are considered, as well as the most important future research needs. # 2000 Society of Chemical Industry

1,279 citations

Proceedings Article
29 Apr 2018
TL;DR: It is shown that the graph convolution of the GCN model is actually a special form of Laplacian smoothing, which is the key reason why GCNs work, but it also brings potential concerns of over-smoothing with many convolutional layers.
Abstract: Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semi-supervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex features and graph topology in the convolutional layers. Although the GCN model compares favorably with other state-of-the-art methods, its mechanisms are not clear and it still requires considerable amount of labeled data for validation and model selection. In this paper, we develop deeper insights into the GCN model and address its fundamental limits. First, we show that the graph convolution of the GCN model is actually a special form of Laplacian smoothing, which is the key reason why GCNs work, but it also brings potential concerns of over-smoothing with many convolutional layers. Second, to overcome the limits of the GCN model with shallow architectures, we propose both co-training and self-training approaches to train GCNs. Our approaches significantly improve GCNs in learning with very few labels, and exempt them from requiring additional labels for validation. Extensive experiments on benchmarks have verified our theory and proposals.

1,273 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