Zobrazeno 1 - 10
of 444
pro vyhledávání: '"Burrichter, A"'
Autor:
Anna S. Weiss, Lisa S. Niedermeier, Alexandra von Strempel, Anna G. Burrichter, Diana Ring, Chen Meng, Karin Kleigrewe, Chiara Lincetto, Johannes Hübner, Bärbel Stecher
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-16 (2023)
Abstract A challenging task to understand health and disease-related microbiome signatures is to move beyond descriptive community-level profiling towards disentangling microbial interaction networks. Using a synthetic gut bacterial community, we aim
Externí odkaz:
https://doaj.org/article/fbe28dea7ae54236b1d926661a276bc3
Publikováno v:
Hydrology, Vol 11, Iss 3, p 41 (2024)
This study employs a temporal fusion transformer (TFT) for predicting overflow from sewer manholes during heavy rainfall events. The TFT utilised is capable of forecasting overflow hydrographs at the manhole level and was tested on a sewer network wi
Externí odkaz:
https://doaj.org/article/311b2720b4a34d6fbe4f67c0dd38374a
Akademický článek
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Autor:
Anna G. Burrichter, Stefanie Dörr, Paavo Bergmann, Sebastian Haiß, Anja Keller, Corentin Fournier, Paolo Franchini, Erika Isono, David Schleheck
Publikováno v:
BMC Microbiology, Vol 21, Iss 1, Pp 1-16 (2021)
Abstract Background Bilophila wadsworthia, a strictly anaerobic, sulfite-reducing bacterium and common member of the human gut microbiota, has been associated with diseases such as appendicitis and colitis. It is specialized on organosulfonate respir
Externí odkaz:
https://doaj.org/article/48718ce336894fb09f1192e4b96b8fc7
Autor:
Mojtahe, Kaza1, Burrichter, Kyle2
Publikováno v:
Journal of Sport Psychology / Revista de Psicología del Deporte. 2023, Vol. 32 Issue 2, p238-246. 9p.
Autor:
Mojtahe, Kaza1 kaza.mojtahe@mail.cu.edu.kg, Burrichter, Kyle2
Publikováno v:
Journal of Sport Psychology / Revista de Psicología del Deporte. 2023, Vol. 32 Issue 2, p50-59. 10p.
Autor:
Benjamin Burrichter, Julian Hofmann, Juliana Koltermann da Silva, Andre Niemann, Markus Quirmbach
Publikováno v:
Water, Vol 15, Iss 9, p 1760 (2023)
This study presents a deep-learning-based forecast model for spatial and temporal prediction of pluvial flooding. The developed model can produce the flooding situation for the upcoming time steps as a sequence of flooding maps. Thus, a dynamic overv
Externí odkaz:
https://doaj.org/article/66b48c4d5dc442b0becc9fe5f12d3247
Autor:
Peck, Spencer C., Denger, Karin, Burrichter, Anna, Irwin, Stephania M., Balskus, Emily P., Schleheck, David
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2019 Feb . 116(8), 3171-3176.
Externí odkaz:
https://www.jstor.org/stable/26671963
Publikováno v:
Hydrology (2306-5338); Mar2024, Vol. 11 Issue 3, p41, 19p