Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Vega, Ramon"'
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Gerken-Starepravo, Lucas, Zhu, Xianfeng, Cho, Bovinille Anye, Vega-Ramon, Fernando, Pennington, Oliver, Antonio del Río-Chanona, Ehecatl, Jing, Keju, Zhang, Dongda
Publikováno v:
In Digital Chemical Engineering March 2022 2
Autor:
Lucas Gerken-Starepravo, Xianfeng Zhu, Bovinille Anye Cho, Fernando Vega-Ramon, Oliver Pennington, Ehecatl Antonio del Río-Chanona, Keju Jing, Dongda Zhang
Publikováno v:
Digital Chemical Engineering, Vol 2, Iss , Pp 100011- (2022)
Dynamic flux analysis methods have been widely used for deciphering complex metabolic fluxes transients. However, many of them require frequent experimental measurements and are ineffective in dealing with under-determined metabolic reaction networks
Externí odkaz:
https://doaj.org/article/d1d75d62aa5945a39a9cae5094b794eb
Publikováno v:
In Computer Aided Chemical Engineering 2024 53:133-138
Transient Flow-Assisted Kinetic Modelling and Reaction Network Identification for Pyrazole Synthesis
Autor:
Vega-Ramon, Fernando, Schrecker, Linden, de Carvalho Servia, Miguel Angel, Hii, King Kuok Mimi, Zhang, Dongda
Publikováno v:
In Computer Aided Chemical Engineering 2024 53:55-60
Autor:
Keju Jing, Alexander W. Rogers, Jiangtao Yan, Ehecatl Antonio del Rio-Chanona, Fernando Vega-Ramon, Dongda Zhang
Publikováno v:
Rogers, A W, Vega-Ramon, F, Yan, J, Río-Chanona, E A, Jing, K & Zhang, D 2022, ' A transfer learning approach for predictive modeling of bioprocesses using small data ', Biotechnology and Bioengineering, vol. 119, no. 2, pp. 411-422 . https://doi.org/10.1002/bit.27980
Predictive modeling of new biochemical systems with small data is a great challenge. To fill this gap, transfer learning, a subdomain of machine learning that serves to transfer knowledge from a generalized model to a more domain-specific model, prov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d57818606ad957fb7cc075abafdeb04
https://doi.org/10.1002/bit.27980
https://doi.org/10.1002/bit.27980
Publikováno v:
In Computer Aided Chemical Engineering 2023 52:157-162
Publikováno v:
Review of Educational Research, 2014 Dec 01. 84(4), 546-571.
Externí odkaz:
https://www.jstor.org/stable/24434249
Publikováno v:
Biochemical Engineering Journal. 190:108761
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::638ab825871c26cd6c0e91aa2e184f8f
https://doi.org/10.1016/j.bej.2022.108761
https://doi.org/10.1016/j.bej.2022.108761
Autor:
Dongda Zhang, Thomas R. Savage, Panagiotis Petsagkourakis, Xianfeng Zhu, Fernando Vega-Ramon, Keju Jing
Publikováno v:
Vega-Ramon, F, Zhu, X, Savage, T R, Petsagkourakis, P, Jing, K & Zhang, D 2021, ' Kinetic and hybrid modelling for yeast astaxanthin production under uncertainty ', Biotechnology and Bioengineering, vol. 118, no. 12, pp. 4854-4866 . https://doi.org/10.1002/bit.27950
Astaxanthin is a high-value compound commercially synthesized through Xanthophyllomyces dendrorhous fermentation. Using mixed sugars decomposed from biowastes for yeast fermentation provides a promising option to improve process sustainability. Howev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e03971ac5c71d547ce98a2c54bc312be
https://www.research.manchester.ac.uk/portal/en/publications/kinetic-and-hybrid-modelling-for-yeast-astaxanthin-production-under-uncertainty(906d00e5-426f-48c5-8f1e-56d2953f326a).html
https://www.research.manchester.ac.uk/portal/en/publications/kinetic-and-hybrid-modelling-for-yeast-astaxanthin-production-under-uncertainty(906d00e5-426f-48c5-8f1e-56d2953f326a).html
