Zobrazeno 1 - 10
of 15 770
pro vyhledávání: '"Germain P"'
Publikováno v:
M@n@gement, Vol 27, Pp 96-113 (2024)
This article explores the negotiation of safety between two distinct activity systems that operate jointly on a daily basis: train driving and railway traffic controlling. We have employed cultural-historical activity theory and an ethnographic case
Externí odkaz:
https://doaj.org/article/b1680f1e7129464c853c03e5d6400836
Autor:
Virginie Korb-Savoldelli, Yohann Tran, Germain Perrin, Justine Touchard, Jean Pastre, Adrien Borowik, Corine Schwartz, Aymeric Chastel, Eric Thervet, Michel Azizi, Laurence Amar, Benjamin Kably, Armelle Arnoux, Brigitte Sabatier
Publikováno v:
Journal of Medical Internet Research, Vol 25, p e42384 (2023)
BackgroundMedication adherence plays a critical role in controlling the evolution of chronic disease, as low medication adherence may lead to worse health outcomes, higher mortality, and morbidity. Assessment of their patients' medication adherence b
Externí odkaz:
https://doaj.org/article/a849a69ff0074a7a8e42eb80f06e1a71
Autor:
David Rudrauf, Grégoire Sergeant-Perthuis, Yvain Tisserand, Germain Poloudenny, Kenneth Williford, Michel-Ange Amorim
Publikováno v:
Brain Sciences, Vol 13, Iss 10, p 1435 (2023)
Consciousness has been described as acting as a global workspace that integrates perception, imagination, emotion and action programming for adaptive decision making. The mechanisms of this workspace and their relationships to the phenomenology of co
Externí odkaz:
https://doaj.org/article/2817237c34c04df69d6f4c61e987fd9e
Autor:
Sarah Berdot, Aurélie Vilfaillot, Yvonnick Bezie, Germain Perrin, Marion Berge, Jennifer Corny, Thuy Tan Phan Thi, Mathieu Depoisson, Claudine Guihaire, Nathalie Valin, Claudine Decelle, Alexandre Karras, Pierre Durieux, Laetitia Minh Maï Lê, Brigitte Sabatier
Publikováno v:
BMC Nursing, Vol 20, Iss 1, Pp 1-11 (2021)
Abstract Background The use of a ‘do not interrupt’ vest during medication administration rounds is recommended but there have been no controlled randomized studies to evaluate its impact on reducing administration errors. We aimed to evaluate th
Externí odkaz:
https://doaj.org/article/9311e651cbcf44da87257d21bf8dff42
Reconstruction functions are pivotal in sample compression theory, a framework for deriving tight generalization bounds. From a small sample of the training set (the compression set) and an optional stream of information (the message), they recover a
Externí odkaz:
http://arxiv.org/abs/2410.13577
Autor:
Pilaud, Vincent, Poullot, Germain
We provide a piecewise linear isomorphism from the normal fan of the pivot polytope of a product of simplices to the normal fan of a shuffle of associahedra.
Comment: 7 pages, 1 figure
Comment: 7 pages, 1 figure
Externí odkaz:
http://arxiv.org/abs/2410.12658
Two-dimensional Euler flows, in the plane or on simple surfaces, possess a material invariant, namely the scalar vorticity normal to the surface. Consequently, flows with piecewise-uniform vorticity remain that way, and moreover evolve in a way which
Externí odkaz:
http://arxiv.org/abs/2410.09610
We study an asymptotic nonlinear model for filamention on two-dimensional vorticity interfaces. Different re-formulations of the model equation reveal its underlying structural properties. They enable us to construct global weak solutions and to prov
Externí odkaz:
http://arxiv.org/abs/2410.07807
A review on asymptotic stability of solitary waves in nonlinear dispersive problems in dimension one
Autor:
Germain, Pierre
Publikováno v:
Proceedings of the Journees EDP 2024
We review asymptotic stability of solitary waves for nonlinear dispersive equations set on the line. Our focus is threefold: first, the nonlinear Schrodinger equation; second, the notion of full asymptotic stability (which states that perturbations o
Externí odkaz:
http://arxiv.org/abs/2410.04508
Autor:
Bochkovskii, Aleksei, Delaunoy, Amaël, Germain, Hugo, Santos, Marcel, Zhou, Yichao, Richter, Stephan R., Koltun, Vladlen
We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details. The predictions are metric, with absolute scale, witho
Externí odkaz:
http://arxiv.org/abs/2410.02073