Zobrazeno 1 - 10
of 1 062
pro vyhledávání: '"A Berkemeier"'
We study long time behavior of shear-thinning fluid flows in $d \geq 3$ dimensions, driven by additive stochastic forcing of trace class, with power-law indices ranging from $1$ to $ \frac{2d}{d+2}$. We particularly focus on Leray-Hopf solutions, i.e
Externí odkaz:
http://arxiv.org/abs/2412.08622
Autor:
Baptiste Hamelin, Philippe Pérot, Ian Pichler, Jasmin D. Haslbauer, David Hardy, David Hing, Sarra Loulizi, Béatrice Regnault, Anouk Pieters, Ingmar Heijnen, Caroline Berkemeier, Maria Mancuso, Verena Kufner, Niels Willi, Anne Jamet, Nolwenn Dheilly, Marc Eloit, Mike Recher, Michael Huber, Kirsten D. Mertz
Publikováno v:
Emerging Infectious Diseases, Vol 30, Iss 10, Pp 2140-2144 (2024)
We identified a novel human circovirus in an immunocompromised 66-year-old woman with sudden onset of self-limiting hepatitis. We detected human circovirus 1 (HCirV-1) transcripts in hepatocytes and the HCirV-1 genome long-term in the patient’s blo
Externí odkaz:
https://doaj.org/article/7fcfbcfde36f46d884d5553878a03476
Different conflicting optimization criteria arise naturally in various Deep Learning scenarios. These can address different main tasks (i.e., in the setting of Multi-Task Learning), but also main and secondary tasks such as loss minimization versus s
Externí odkaz:
http://arxiv.org/abs/2308.12243
For a prescribed deterministic kinetic energy we use convex integration to construct analytically weak and probabilistically strong solutions to the 3D incompressible Navier-Stokes equations driven by a linear multiplicative stochastic forcing. These
Externí odkaz:
http://arxiv.org/abs/2212.11257
Autor:
Berkemeier, Manuel, Peitz, Sebastian
In this article, we build on previous work to present an optimization algorithm for nonlinearly constrained multi-objective optimization problems. The algorithm combines a surrogate-assisted derivative-free trust-region approach with the filter metho
Externí odkaz:
http://arxiv.org/abs/2208.12094
Publikováno v:
Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-17 (2024)
Abstract Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction ra
Externí odkaz:
https://doaj.org/article/e0f7c4993e9a48be82023ddcb1e6af38
Publikováno v:
Atmospheric Chemistry and Physics, Vol 24, Pp 3445-3528 (2024)
Adsorption and desorption of gases on liquid or solid substrates are involved in multiphase processes and heterogeneous chemical reactions. The desorption energy (Edes0), which depends on the intermolecular forces between adsorbate and substrate, det
Externí odkaz:
https://doaj.org/article/ed03da65a7f1453f9f18ddf8c6a8fc56
Publikováno v:
Monitor Versorgungsforschung, Vol 2024, Iss 02, Pp 62-68 (2024)
Der Gesetzgeber hat mit dem 2019 eingeführten Instrument der anwendungsbegleitenden Datenerhebung (AbD) die Möglichkeit geschaffen, vergleichende Daten in der laufenden Versorgung zu erheben. Die AbD dient der Generierung von Evidenz und dem Zweck
Externí odkaz:
https://doaj.org/article/5eba40ee836c4ce3b2aa16df914e4595
In the past three decades, neuroimaging has provided important insights into structure-function relationships in the human brain. Recently, however, the methods for analyzing functional magnetic resonance imaging (fMRI) data have come under scrutiny,
Externí odkaz:
http://arxiv.org/abs/2201.07867
Autor:
Brandon F. Keele, Afam A. Okoye, Christine M. Fennessey, Benjamin Varco-Merth, Taina T. Immonen, Emek Kose, Andrew Conchas, Mykola Pinkevych, Leslie Lipkey, Laura Newman, Agatha Macairan, Marjorie Bosche, William J. Bosche, Brian Berkemeier, Randy Fast, Mike Hull, Kelli Oswald, Rebecca Shoemaker, Lorna Silipino, Robert J. Gorelick, Derick Duell, Alejandra Marenco, William Brantley, Jeremy Smedley, Michael Axthelm, Miles P. Davenport, Jeffrey D. Lifson, Louis J. Picker
Publikováno v:
PLoS Pathogens, Vol 20, Iss 4 (2024)
Externí odkaz:
https://doaj.org/article/df5e85a50a9043c2bca569ca53f3f088