Institution
University of Science and Technology Beijing
Education•Beijing, China•
About: University of Science and Technology Beijing is a education organization based out in Beijing, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 41558 authors who have published 44473 publications receiving 623229 citations. The organization is also known as: Beijing Steel and Iron Institute.
Topics: Microstructure, Alloy, Corrosion, Austenite, Ultimate tensile strength
Papers published on a yearly basis
Papers
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TL;DR: In this paper, an effective way to apply a vapor deposited chromium coating onto diamond particles was used to overcome the interface problem, and the results showed that the densification, interfacial bonding and thermal conductivity of coated composites were evidently enhanced compared to that of uncoated composites.
261 citations
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TL;DR: In this paper, the steady state flow behavior of the FeCoNiCrMn high-entropy alloy at temperatures ranging from 1023 to 1123 K was systematically characterized, and it was found that the stress exponent (i.e., the reciprocal of strain-rate sensitivity) was dependent on the applied strain rate.
260 citations
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TL;DR: Li et al. as mentioned in this paper demonstrated that solid polymer electrolyte (SPE) soft interface layer was deposited on garnet electrolyte surface to endow connected interface between the electrolyte and electrodes and thus settled the interface contact issue.
260 citations
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TL;DR: The visual memory arrays can realize the detection and memory process of UV light distribution with a patterned image for a long‐term retention and the stored image information can be reset by a negative voltage sweep and reprogrammed to the same or an other image distribution, which proves the effective reusability.
Abstract: For the mimicry of human visual memory, a prominent challenge is how to detect and store the image information by electronic devices, which demands a multifunctional integration to sense light like eyes and to memorize image information like the brain by transforming optical signals to electrical signals that can be recognized by electronic devices. Although current image sensors can perceive simple images in real time, the image information fades away when the external image stimuli are removed. The deficiency between the state-of-the-art image sensors and visual memory system inspires the logical integration of image sensors and memory devices to realize the sensing and memory process toward light information for the bionic design of human visual memory. Hence, a facile architecture is designed to construct artificial flexible visual memory system by employing an UV-motivated memristor. The visual memory arrays can realize the detection and memory process of UV light distribution with a patterned image for a long-term retention and the stored image information can be reset by a negative voltage sweep and reprogrammed to the same or an other image distribution, which proves the effective reusability. These results provide new opportunities for the mimicry of human visual memory and enable the flexible visual memory device to be applied in future wearable electronics, electronic eyes, multifunctional robotics, and auxiliary equipment for visual handicapped.
259 citations
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TL;DR: A new predictor called iSNO-AAPair was developed by taking into account the coupling effects for all the pairs formed by the nearest residues and the pairs by the next nearest residues along protein chains, and shown that the new predictor outperformed the existing predictors.
Abstract: As one of the most important and universal posttranslational modifications (PTMs) of proteins, S-nitrosylation (SNO) plays crucial roles in a variety of biological processes, including the regulation of cellular dynamics and many signaling events. Knowledge of SNO sites in proteins is very useful for drug development and basic research as well. Unfortunately, it is both time-consuming and costly to determine the SNO sites purely based on biological experiments. Facing the explosive protein sequence data generated in the post-genomic era, we are challenged to develop automated vehicles for timely and effectively determining the SNO sites for uncharacterized proteins. To address the challenge, a new predictor called iSNO-AAPair was developed by taking into account the coupling effects for all the pairs formed by the nearest residues and the pairs by the next nearest residues along protein chains. The cross-validation results on a state-of-the-art benchmark have shown that the new predictor outperformed the existing predictors. The same was true when tested by the independent proteins whose experimental SNO sites were known. A user-friendly web-server for iSNO-AAPair was established at http://app.aporc.org/iSNO-AAPair/, by which users can easily obtain their desired results without the need to follow the mathematical equations involved during its development.
259 citations
Authors
Showing all 41904 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Yang Yang | 171 | 2644 | 153049 |
Jun Chen | 136 | 1856 | 77368 |
Jun Lu | 135 | 1526 | 99767 |
Jie Liu | 131 | 1531 | 68891 |
Shuai Liu | 129 | 1095 | 80823 |
Jian Zhou | 128 | 3007 | 91402 |
Chao Zhang | 127 | 3119 | 84711 |
Shaobin Wang | 126 | 872 | 52463 |
Tao Zhang | 123 | 2772 | 83866 |
Jian Liu | 117 | 2090 | 73156 |
Xin Li | 114 | 2778 | 71389 |
Jianhui Hou | 110 | 429 | 53265 |
Hong Wang | 110 | 1633 | 51811 |
Baoshan Xing | 109 | 823 | 48944 |