Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Andreas Scheidegger"'
Publikováno v:
PLoS ONE, Vol 18, Iss 10, p e0292096 (2023)
We developed four online interfaces supporting citizen participation in decision-making. We included (1) learning loops (LLs), good practice in decision analysis, and (2) gamification, to enliven an otherwise long and tedious survey. We investigated
Externí odkaz:
https://doaj.org/article/b7cf91f7f4454cf5acdd1d8856121586
Autor:
Luca Pontiggia, Ingmar AJ Van Hengel, Agnes Klar, Dominic Rütsche, Monica Nanni, Andreas Scheidegger, Sandro Figi, Ernst Reichmann, Ueli Moehrlen, Thomas Biedermann
Publikováno v:
Journal of Tissue Engineering, Vol 13 (2022)
Extensive availability of engineered autologous dermo-epidermal skin substitutes (DESS) with functional and structural properties of normal human skin represents a goal for the treatment of large skin defects such as severe burns. Recently, a clinica
Externí odkaz:
https://doaj.org/article/17e27abfc0774d0082f7185098c7b655
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
Abstract A wide variety of environmental contaminants has been shown to disrupt immune functions of fish and may compromise their defense capability against pathogens. Immunotoxic effects, however, are rarely considered in ecotoxicological testing st
Externí odkaz:
https://doaj.org/article/b8d70a8979344e1fb3ceac6e052a1bb0
Neural Ordinary Differential Equation (ODE) models have demonstrated high potential in providing accurate hydrologic predictions and process understanding for single catchments (Höge et al., 2022). Neural ODEs fuse a neural network model core with a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c6975134c986f879f1756713ac404631
https://doi.org/10.5194/egusphere-egu23-6466
https://doi.org/10.5194/egusphere-egu23-6466
Publikováno v:
Ecological Modelling, 481
Species distribution models are commonly applied to predict species responses to environmental conditions. A wide variety of models with different properties exist that vary in complexity, which affects their predictive performance and interpretabili
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b71a2af37f41a486543dec9824c93469
https://doi.org/10.1016/j.ecolmodel.2023.110353
https://doi.org/10.1016/j.ecolmodel.2023.110353
Publikováno v:
Environmental Science & Technology. 55:7920-7929
The exposure of ecologically critical invertebrate species to biologically active pharmaceuticals poses a serious risk to the aquatic ecosystem. Yet, the fate and toxic effects of pharmaceuticals on these nontarget aquatic invertebrates and the under
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d34382578973ecf135cb4fd5ea24dd89
https://doi.org/10.1021/acs.est.0c07887
https://doi.org/10.1021/acs.est.0c07887
Plant phenology models are important components in process-based crop models, which are used to assess the impact of climate change on food production. For reliable model predictions, parameters in phenology models have to be accurately known. They a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e7e76afb3b1fd937603b5f4deb3463e
https://doi.org/10.1101/2022.06.03.494418
https://doi.org/10.1101/2022.06.03.494418
Autor:
Jana S. Huisman, Jérémie Scire, Lea Caduff, Xavier Fernandez-Cassi, Pravin Ganesanandamoorthy, Anina Kull, Andreas Scheidegger, Elyse Stachler, Alexandria B. Boehm, Bridgette Hughes, Alisha Knudson, Aaron Topol, Krista R. Wigginton, Marlene K. Wolfe, Tamar Kohn, Christoph Ort, Tanja Stadler, Timothy R. Julian
Publikováno v:
Environmental Health Perspectives, 130 (5)
Dipòsit Digital de la UB
Universidad de Barcelona
Dipòsit Digital de la UB
Universidad de Barcelona
Background: The effective reproductive number, Re, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::373b8c88cdf580d86c00d768be05af16
https://hdl.handle.net/20.500.11850/557148
https://hdl.handle.net/20.500.11850/557148
Deep learning methods have repeatedly proven to outperform conceptual hydrologic models in rainfall-runoff modelling. Although attempts of investigating the internals of such deep learning models are being made, traceability of model states and proce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::489e62bcb8d55021258ab737f6aa488c
https://doi.org/10.5194/egusphere-egu22-3661
https://doi.org/10.5194/egusphere-egu22-3661
Deep learning methods have frequently outperformed conceptual hydrologic models in rainfall-runoff modelling. Attempts of investigating the internals of such deep learning models are being made but traceability of model states and processes and their
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9135c875f8f87e858e98c06322668563
https://doi.org/10.5194/hess-2022-56
https://doi.org/10.5194/hess-2022-56