Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Marie Chavent"'
Autor:
Marie-Pierre Ellies-Oury, Jean-François Hocquette, Sghaier Chriki, Alexandre Conanec, Linda Farmer, Marie Chavent, Jérôme Saracco
Publikováno v:
Foods, Vol 9, Iss 4, p 525 (2020)
The beef industry is organized around different stakeholders, each with their own expectations, sometimes antagonistic. This article first outlines these differing perspectives. Then, various optimization models that might integrate all these expecta
Externí odkaz:
https://doaj.org/article/0de4b081c1f44582864290d000378992
Autor:
Alexandre Conanec, Brigitte Picard, Denis Durand, Gonzalo Cantalapiedra-Hijar, Marie Chavent, Christophe Denoyelle, Dominique Gruffat, Jérôme Normand, Jérôme Saracco, Marie-Pierre Ellies-Oury
Publikováno v:
Foods, Vol 8, Iss 6, p 197 (2019)
The beef cattle industry is facing multiple problems, from the unequal distribution of added value to the poor matching of its product with fast-changing demand. Therefore, the aim of this study was to examine the interactions between the main variab
Externí odkaz:
https://doaj.org/article/b54fee19e8fa40908077c35a90c91916
Publikováno v:
Journal of Statistical Software, Vol 50, Iss 13 (2012)
Clustering of variables is as a way to arrange variables into homogeneous clusters, i.e., groups of variables which are strongly related to each other and thus bring the same information. These approaches can then be useful for dimension reduction an
Externí odkaz:
https://doaj.org/article/01d721e670f3434486310f65e27db8b1
Autor:
Marie Chavent, Paula Brito
Publikováno v:
Analysis of Distributional Data ISBN: 9781315370545
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::736e0cf6fcd75aa587ae5e0534d49adf
https://doi.org/10.1201/9781315370545-6
https://doi.org/10.1201/9781315370545-6
Publikováno v:
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization ISBN: 9783031154430
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b89249bac1b3286c4ede2c7e337fe6a6
https://doi.org/10.1007/978-3-031-15444-7_1
https://doi.org/10.1007/978-3-031-15444-7_1
Autor:
Jérôme Saracco, Dominique Gruffat, Marie-Pierre Ellies-Oury, Denys Durand, Marie Chavent, Anne Listrat
Publikováno v:
Livestock Science
Livestock Science, Elsevier, 2021, 250, pp.104554. ⟨10.1016/j.livsci.2021.104554⟩
Livestock Science, 2021, 250, pp.104554. ⟨10.1016/j.livsci.2021.104554⟩
Livestock Science, Elsevier, 2021, 250, pp.104554. ⟨10.1016/j.livsci.2021.104554⟩
Livestock Science, 2021, 250, pp.104554. ⟨10.1016/j.livsci.2021.104554⟩
International audience; In the beef sector, one of the major challenges is to early predict carcass and meat quality and to jointly satisfy the multiple expectations of the various stakeholders. Thus, the objective of this study was to determine if t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17fad7e7256b35061660c641d6a8049a
https://hal.archives-ouvertes.fr/hal-03272625
https://hal.archives-ouvertes.fr/hal-03272625
Autor:
Ian Richardson, S. Failla, John L. Williams, María del Mar Campo, Per Ertbjerg, A. Conanec, M-P. Ellies-Oury, Begoña Panea, Marie Chavent, Jérôme Saracco, J. F. Hocquette
Publikováno v:
Livestock Science
Livestock Science, Elsevier, 2021, 250, pp.104548. ⟨10.1016/j.livsci.2021.104548⟩
Livestock Science, 2021, 250, pp.104548. ⟨10.1016/j.livsci.2021.104548⟩
Livestock Science, Elsevier, 2021, 250, pp.104548. ⟨10.1016/j.livsci.2021.104548⟩
Livestock Science, 2021, 250, pp.104548. ⟨10.1016/j.livsci.2021.104548⟩
International audience; A total of 436 young cattle from 15 cattle breeds were reared in as similar conditions as possible to evaluate the impact of breed on sensory quality of beef from longissimus muscle determined by sensory analysis. Two statisti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d79e1dde0237d083d4890a9a13c57e5
https://hal.inrae.fr/hal-03316097
https://hal.inrae.fr/hal-03316097
Publikováno v:
PHM 2020-Annual Conference of the PHM Society
PHM 2020-Annual Conference of the PHM Society, Nov 2020, Nashville, United States
PHM 2020-Annual Conference of the PHM Society, Nov 2020, Nashville, United States
Engines are verified through production tests before delivering them to customers. During those tests, lot of measures are taken on different parts of the engine, considering multiple physical parameters. Unexpected measures can be observed. For this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5166ff3c0b14d61c855fbd62bb6edebe
https://hal.archives-ouvertes.fr/hal-03130682/document
https://hal.archives-ouvertes.fr/hal-03130682/document
Publikováno v:
Geoscientific Model Development
Geoscientific Model Development, 2020, 13 (2), pp.841-858. ⟨10.5194/gmd-13-841-2020⟩
Geoscientific Model Development, European Geosciences Union, 2020, 13 (2), pp.841-858. ⟨10.5194/gmd-13-841-2020⟩
Geoscientific Model Development, Vol 13, Pp 841-858 (2020)
Geoscientific Model Development, 2020, 13 (2), pp.841-858. ⟨10.5194/gmd-13-841-2020⟩
Geoscientific Model Development, European Geosciences Union, 2020, 13 (2), pp.841-858. ⟨10.5194/gmd-13-841-2020⟩
Geoscientific Model Development, Vol 13, Pp 841-858 (2020)
Modes of climate variability strongly impact our climate and thus human society. Nevertheless, the statistical properties of these modes remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b63add27723f0be01ee2263ed634ca9
http://www.documentation.ird.fr/hor/fdi:010078029
http://www.documentation.ird.fr/hor/fdi:010078029
Autor:
Jérôme Saracco, Marie-Pierre Ellies-Oury, Brigitte Picard, Alexandre Conanec, Marie Chavent, Muriel Bonnet
Publikováno v:
Scientific Reports
Scientific Reports, Nature Publishing Group, 2019, 9 (1), ⟨10.1038/s41598-019-46202-y⟩
Scientific Reports, 2019, 9 (1), ⟨10.1038/s41598-019-46202-y⟩
Scientific Reports (sous presse), . (2019)
Scientific Reports, Vol 9, Iss 1, Pp 1-12 (2019)
Scientific Reports, Nature Publishing Group, 2019, 9 (1), ⟨10.1038/s41598-019-46202-y⟩
Scientific Reports, 2019, 9 (1), ⟨10.1038/s41598-019-46202-y⟩
Scientific Reports (sous presse), . (2019)
Scientific Reports, Vol 9, Iss 1, Pp 1-12 (2019)
In this paper, we describe a new computational methodology to select the best regression model to predict a numerical variable of interest Y and to select simultaneously the most interesting numerical explanatory variables strongly linked to Y. Three
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28f8924c9581b8e648bdd7c3de3dbc65
https://hal.archives-ouvertes.fr/hal-02429345
https://hal.archives-ouvertes.fr/hal-02429345