F
Fan Yang
Researcher at China University of Petroleum
Publications - 1888
Citations - 34524
Fan Yang is an academic researcher from China University of Petroleum. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 65, co-authored 986 publications receiving 23818 citations. Previous affiliations of Fan Yang include G. D. Searle & Company & Peking University.
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Novel non-azacyclo 1,2-aminoalcohols derived from l-Phe and highly enantioselective addition of diethylzinc to aryl aldehydes
TL;DR: In this article, a series of non-azacyclo β-amino alcohols derived from natural l -phenylalanine were readily synthesized in three steps and used as chiral ligands in the catalytic asymmetric addition of diethylzinc to aldehydes.
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Palmprint and face score level fusion: hardware implementation of a contactless small sample biometric system
TL;DR: Hardware results demonstrate that preprocessing can easily be performed during the acquisition phase, and multimodal biometric recognition can be treated almost instantly (0.4 ms on FPGA).
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Polyethylenimine functionalized and scaffolded graphene aerogel and the application in the highly selective separation of thorium from rare earth
TL;DR: A polyethylenimine (PEI)-scaffolded and functionalized graphene aerogel, PEI-GO-AG, was prepared and utilized as a highly selective adsorbent for the extraction of thorium (Th) (IV) from three lanthanides (Ln) (III) elements as mentioned in this paper.
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Shp2 regulates migratory behavior and response to EGFR-TKIs through ERK1/2 pathway activation in non-small cell lung cancer cells.
TL;DR: It is proposed that Shp2 could serve as a new biomarker in the treatment of NSCLC by suggesting that co-inhibition of EGFR and Sh p2 is an effective approach for overcoming EGFR T790M mutation acquired resistance to EGFR tyrosine kinase inhibitors (TKIs).
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Low-observable targets detection for autonomous vehicles based on dual-modal sensor fusion with deep learning approach:
TL;DR: The experimental results indicate that the dual-modal deep neural network has a better performance on the low-observable target detection and recognition in complex environments than traditional methods.