Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Lele Ma"'
Publikováno v:
Biological Research, Vol 55, Iss 1, Pp 1-16 (2022)
Abstract Extracellular vesicles (EVs) are naturally released membrane vesicles that act as carriers of proteins and RNAs for intercellular communication. With various biomolecules and specific ligands, EV has represented a novel form of information t
Externí odkaz:
https://doaj.org/article/dacdd636700143a79d1813af03bfe9c0
Publikováno v:
Energies, Vol 13, Iss 1, p 184 (2020)
With the gradual increase in the installed capacity of wind turbines, more and more attention has been paid to the economy of wind power. Economic model-predictive control (EMPC) has been developed as an effective advanced control strategy, which can
Externí odkaz:
https://doaj.org/article/e280d4540e6f4a32a1656f6c3556a01f
Publikováno v:
IEEE Transactions on Cybernetics. :1-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c3a09bd34dc7cbcd00a525cc6820b364
https://doi.org/10.1109/tcyb.2023.3234630
https://doi.org/10.1109/tcyb.2023.3234630
Publikováno v:
IEEE Transactions on Cybernetics. 52:4147-4160
Iterative learning model-predictive control (ILMPC) is very popular in controlling the batch process since it possesses not only the learning ability along batches but also the strong time-domain tracking properties. However, for a fast batch process
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa8a4e003d36026ddf33400438e02f2e
https://doi.org/10.1109/tcyb.2020.3021978
https://doi.org/10.1109/tcyb.2020.3021978
Autor:
Mohamed Abdelkarim Abdelbaky, Xiaobing Kong, Xiangjie Liu, Kwang Y. Lee, Lele Ma, Ce Wang, Shifan Guo
Publikováno v:
Renewable Energy. 181:581-591
The reliable control of the large-scale wind farm is crucial for the stability and security of the renewable power system with high wind power penetration. Due to the uncertain and variable nature of wind power, the traditional control strategy is di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::556aba25c85fc2fe3281c95b00edd3f4
https://doi.org/10.1016/j.renene.2021.09.048
https://doi.org/10.1016/j.renene.2021.09.048
Publikováno v:
Polymer Chemistry. 13:91-99
A series of high molecular weight copolyesters PExBTyAm were synthesized by a simple and economical two-step polycondensation method, and for the first time we found that the copolyesters exhibited an green fluorescence under 365 nm UV light.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34cac3c97806e391be7853f704b9d8a4
https://doi.org/10.1039/d1py01222c
https://doi.org/10.1039/d1py01222c
Publikováno v:
New Journal of Chemistry. 46:17479-17486
A signal-off fluorescence biosensor for highly sensitive detection of OTA was constructed via the ARGET ATRP signal amplification strategy.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8bc6eaf4a88413e1a2b69afe1dc11e76
https://doi.org/10.1039/d2nj03180a
https://doi.org/10.1039/d2nj03180a
Publikováno v:
Food Chemistry. 421:136176
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f007f5262aa139ac00f4125a9c7f763
https://doi.org/10.1016/j.foodchem.2023.136176
https://doi.org/10.1016/j.foodchem.2023.136176
Publikováno v:
Talanta. 262:124659
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66b9e09ac387f20f2af4b12a9828f598
https://doi.org/10.1016/j.talanta.2023.124659
https://doi.org/10.1016/j.talanta.2023.124659
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 32:3377-3390
Iterative learning model predictive control (ILMPC) has been recognized as an effective approach to realize high-precision tracking for batch processes with repetitive nature because of its excellent learning ability and closed-loop stability propert
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::46e57e1132906469d17f3bfc8bb6602c
https://doi.org/10.1109/tnnls.2020.3016295
https://doi.org/10.1109/tnnls.2020.3016295