Institution
Shanghai Jiao Tong University
Education•Shanghai, Shanghai, China•
About: Shanghai Jiao Tong University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Population & Cancer. The organization has 157524 authors who have published 184620 publications receiving 3451038 citations. The organization is also known as: Shanghai Communications University & Shanghai Jiaotong University.
Topics: Population, Cancer, Computer science, Microstructure, Medicine
Papers published on a yearly basis
Papers
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TL;DR: The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the multi-objective FJSP, especially for problems on a large scale.
639 citations
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23 Jan 2019
TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Abstract: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).
639 citations
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TL;DR: Both experimental and theoretical results reveal that the introduction of Ru atoms into NiFe-LDH can efficiently reduce energy barrier of the Volmer step, eventually accelerating its HER kinetics.
Abstract: Owing to its earth abundance, low kinetic overpotential, and superior stability, NiFe-layered double hydroxide (NiFe-LDH) has emerged as a promising electrocatalyst for catalyzing water splitting, especially oxygen evolution reaction (OER), in alkaline solutions. Unfortunately, as a result of extremely sluggish water dissociation kinetics (Volmer step), hydrogen evolution reaction (HER) activity of the NiFe-LDH is rather poor in alkaline environment. Here a novel strategy is demonstrated for substantially accelerating the hydrogen evolution kinetics of the NiFe-LDH by partially substituting Fe atoms with Ru. In a 1 m KOH solution, the as-synthesized Ru-doped NiFe-LDH nanosheets (NiFeRu-LDH) exhibit excellent HER performance with an overpotential of 29 mV at 10 mA cm-2 , which is much lower than those of noble metal Pt/C and reported electrocatalysts. Both experimental and theoretical results reveal that the introduction of Ru atoms into NiFe-LDH can efficiently reduce energy barrier of the Volmer step, eventually accelerating its HER kinetics. Benefitting from its outstanding HER activity and remained excellent OER activity, the NiFeRu-LDH steadily drives an alkaline electrolyzer with a current density of 10 mA cm-2 at a cell voltage of 1.52 V, which is much lower than the values for Pt/C-Ir/C couple and state-of-the-art overall water-splitting electrocatalysts.
636 citations
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TL;DR: In this paper, a tunable luminescence lifetime τ in the microsecond region can be exploited to code individual upconversion nanocrystals, which can be used for multichannel bioimaging, high-throughput cytometry quantification, and high-density data storage.
Abstract: Optical multiplexing plays an important role in applications such as optical data storage1, document security2, molecular probes3,4 and bead assays for personalized medicine5. Conventional fluorescent colour coding is limited by spectral overlap and background interference, restricting the number of distinguishable identities. Here, we show that tunable luminescent lifetimes τ in the microsecond region can be exploited to code individual upconversion nanocrystals. In a single colour band, one can generate more than ten nanocrystal populations with distinct lifetimes ranging from 25.6 µs to 662.4 µs and decode their well-separated lifetime identities, which are independent of both colour and intensity. Such ‘τ-dots’ potentially suit multichannel bioimaging, high-throughput cytometry quantification, high-density data storage, as well as security codes to combat counterfeiting. This demonstration extends the optical multiplexing capability by adding the temporal dimension of luminescent signals, opening new opportunities in the life sciences, medicine and data security. Control over the luminescence lifetimes of upconversion nanocrystals allows a new form of temporal multiplexing for imaging and data-storage applications.
636 citations
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TL;DR: This work proposes the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard, to facilitate consistent and systematic deposition and retrieval of data on biosynthetic gene clusters.
Abstract: A wide variety of enzymatic pathways that produce specialized metabolites in bacteria, fungi and plants are known to be encoded in biosynthetic gene clusters. Information about these clusters, pathways and metabolites is currently dispersed throughout the literature, making it difficult to exploit. To facilitate consistent and systematic deposition and retrieval of data on biosynthetic gene clusters, we propose the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard.
633 citations
Authors
Showing all 158621 results
Name | H-index | Papers | Citations |
---|---|---|---|
Meir J. Stampfer | 277 | 1414 | 283776 |
Richard A. Flavell | 231 | 1328 | 205119 |
Jie Zhang | 178 | 4857 | 221720 |
Yang Yang | 171 | 2644 | 153049 |
Lei Jiang | 170 | 2244 | 135205 |
Gang Chen | 167 | 3372 | 149819 |
Thomas S. Huang | 146 | 1299 | 101564 |
Barbara J. Sahakian | 145 | 612 | 69190 |
Jean-Laurent Casanova | 144 | 842 | 76173 |
Kuo-Chen Chou | 143 | 487 | 57711 |
Weihong Tan | 140 | 892 | 67151 |
Xin Wu | 139 | 1865 | 109083 |
David Y. Graham | 138 | 1047 | 80886 |
Bin Liu | 138 | 2181 | 87085 |
Jun Chen | 136 | 1856 | 77368 |