Institution
Nankai University
Education•Tianjin, China•
About: Nankai University is a education organization based out in Tianjin, China. It is known for research contribution in the topics: Catalysis & Enantioselective synthesis. The organization has 42964 authors who have published 51866 publications receiving 1127896 citations. The organization is also known as: Nánkāi Dàxué.
Topics: Catalysis, Enantioselective synthesis, Adsorption, Graphene, Anode
Papers published on a yearly basis
Papers
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209 citations
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03 Nov 2014TL;DR: This paper builds the relational knowledge and the categorical knowledge into two separate regularization functions, and combines both of them with the original objective function of the skip-gram model to obtain word representations enhanced by the knowledge graph.
Abstract: Representing words into vectors in continuous space can form up a potentially powerful basis to generate high-quality textual features for many text mining and natural language processing tasks. Some recent efforts, such as the skip-gram model, have attempted to learn word representations that can capture both syntactic and semantic information among text corpus. However, they still lack the capability of encoding the properties of words and the complex relationships among words very well, since text itself often contains incomplete and ambiguous information. Fortunately, knowledge graphs provide a golden mine for enhancing the quality of learned word representations. In particular, a knowledge graph, usually composed by entities (words, phrases, etc.), relations between entities, and some corresponding meta information, can supply invaluable relational knowledge that encodes the relationship between entities as well as categorical knowledge that encodes the attributes or properties of entities. Hence, in this paper, we introduce a novel framework called RC-NET to leverage both the relational and categorical knowledge to produce word representations of higher quality. Specifically, we build the relational knowledge and the categorical knowledge into two separate regularization functions, and combine both of them with the original objective function of the skip-gram model. By solving this combined optimization problem using back propagation neural networks, we can obtain word representations enhanced by the knowledge graph. Experiments on popular text mining and natural language processing tasks, including analogical reasoning, word similarity, and topic prediction, have all demonstrated that our model can significantly improve the quality of word representations.
209 citations
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TL;DR: In this paper, a novel nanomaterial with chemiexcited far-red/near-infrared (FR/NIR) emission and singlet oxygen ( 1 O 2 ) generation is reported for precise diagnosis and treatment of tumors.
209 citations
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TL;DR: The triple-channel optical properties of Mn-doped ZnS quantum dots (fluorescence, phosphorescence, and light scattering) are explored to develop a multidimensional sensing device for the discrimination of proteins in a lab-on-a-nanoparticle approach.
Abstract: Lab-on-a-nanoparticle: the triple-channel optical properties of Mn-doped ZnS quantum dots (fluorescence, phosphorescence, and light scattering) are explored to develop a multidimensional sensing device for the discrimination of proteins in a lab-on-a-nanoparticle approach.
209 citations
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TL;DR: In this article, the state-of-the-art of quantifying emissions of Ammonia (NH3), Nitrogen Oxides (NOx) and Nitrous Oxide (N2O) is assessed.
Abstract: . Excess reactive Nitrogen (Nr) has become one of the most pressing environmental problems leading to air pollution, acidification and eutrophication of ecosystems, biodiversity impacts, leaching of nitrates into groundwater and global warming. This paper investigates how current inventories cover emissions of Nr to the atmosphere in Europe, the United States of America, and China. The focus is on anthropogenic sources, assessing the state-of-the-art of quantifying emissions of Ammonia (NH3), Nitrogen Oxides (NOx) and Nitrous Oxide (N2O), the different purposes for which inventories are compiled, and to which extent current inventories meet the needs of atmospheric dispersion modelling. The paper concludes with a discussion of uncertainties involved and a brief outlook on emerging trends in the three regions investigated is conducted. Key issues are substantial differences in the overall magnitude, but as well in the relative sectoral contribution of emissions in the inventories that have been assessed. While these can be explained by the use of different methodologies and underlying data (e.g. emission factors or activity rates), they may lead to quite different results when using the emission datasets to model ambient air quality or the deposition with atmospheric dispersion models. Hence, differences and uncertainties in emission inventories are not merely of academic interest, but can have direct policy implications when the development of policy actions is based on these model results. The level of uncertainty of emission estimates varies greatly between substances, regions and emission source sectors. This has implications for the direction of future research needs and indicates how existing gaps between modelled and measured concentration or deposition rates could be most efficiently addressed. The observed current trends in emissions display decreasing NOx emissions and only slight reductions for NH3 in both Europe and the US. However, in China projections indicate a steep increase of both.
209 citations
Authors
Showing all 43397 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yi Chen | 217 | 4342 | 293080 |
Peidong Yang | 183 | 562 | 144351 |
Jie Zhang | 178 | 4857 | 221720 |
Yang Yang | 171 | 2644 | 153049 |
Qiang Zhang | 161 | 1137 | 100950 |
Bin Liu | 138 | 2181 | 87085 |
Jun Chen | 136 | 1856 | 77368 |
Hui Li | 135 | 2982 | 105903 |
Jie Liu | 131 | 1531 | 68891 |
Han Zhang | 130 | 970 | 58863 |
Jian Zhou | 128 | 3007 | 91402 |
Chao Zhang | 127 | 3119 | 84711 |
Wei Chen | 122 | 1946 | 89460 |
Xuan Zhang | 119 | 1530 | 65398 |
Yang Li | 117 | 1319 | 63111 |