Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Damien Bernard"'
Autor:
Alexandre Vandeponseele, Micheline Draye, Christine Piot, Damien Bernard, Philippe Fanget, Gregory Chatel
Publikováno v:
Foods, Vol 11, Iss 24, p 4089 (2022)
Spent coffee grounds are a promising bioresource that naturally contain around 50 wt% moisture which requires, for a valorization, a drying step of high energy and economic costs. However, the natural water in spent coffee grounds could bring new ben
Externí odkaz:
https://doaj.org/article/ecfc72a7a8854ed0b4036b59959e6627
Autor:
Oprisiu, Ioana, Marcou, Gilles, Horvath, Dragos, Brunel, Damien Bernard, Rivollet, Fabien, Varnek, Alexandre
Publikováno v:
In Thermochimica Acta 10 February 2013 553:60-67
Autor:
Damien Bernard
Publikováno v:
Monatshefte für Mathematik. 176:359-411
From a family of \(L\)-functions with unitary symmetry, Hughes and Rudnick obtained results on the height of its lowest zero. We extend their results to other families of \(L\)-functions according to the type of symmetry coming from statistics for lo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a25ef5cc226f6fdf6031d3000365e8a
https://doi.org/10.1007/s00605-014-0679-2
https://doi.org/10.1007/s00605-014-0679-2
Autor:
Damien Bernard
We evaluate the integral mollified second moment of L-functions of primitive cusp forms and we obtain, for such L-function, an explicit positive proportion of zeros which lie on the critical line.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::398b26764424c2a8aaace2e78daa5777
https://hal.science/hal-00980888
https://hal.science/hal-00980888
Autor:
Dragos Horvath, Alexandre Varnek, Damien Bernard Brunel, Ioana Oprisiu, Gilles Marcou, Fabien Rivollet
Publikováno v:
Thermochimica Acta
Thermochimica Acta, Elsevier, 2013, 553, pp.60-67. ⟨10.1016/j.tca.2012.11.024⟩
Thermochimica Acta, Elsevier, 2013, 553, pp.60-67. ⟨10.1016/j.tca.2012.11.024⟩
Quantitative structure–property models to predict the normal boiling point ( T b ) of organic compounds were developed using non-linear ASNNs (associative neural networks) as well as multiple linear regression – ISIDA-MLR and SQS (stochastic QSAR
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16bdd2b7067a004f39f1a3747c1585bc
https://hal.archives-ouvertes.fr/hal-03142315
https://hal.archives-ouvertes.fr/hal-03142315