Zobrazeno 1 - 10
of 1 827
pro vyhledávání: '"P Rogalla"'
A Li-Al-O thin film materials library, deposited by inert magnetron sputtering and post-deposition annealing in O2 atmosphere, was used to study the effects of different annealing temperatures (300 to 850{\deg}C) and durations (1 min to 7 h) on cryst
Externí odkaz:
http://arxiv.org/abs/2402.06553
Autor:
Yoo, Jay J., Namdar, Khashayar, Carey, Sean, Fischer, Sandra E., McIntosh, Chris, Khalvati, Farzad, Rogalla, Patrik
Objectives: To develop and evaluate a radiomics machine learning model for detecting liver fibrosis on CT of the liver. Methods: For this retrospective, single-centre study, radiomic features were extracted from Regions of Interest (ROIs) on CT image
Externí odkaz:
http://arxiv.org/abs/2211.14396
Autor:
Lehmann, Sascha, Rogalla, Antje, Neidhardt, Maximilian, Reinecke, Anton, Schlaefer, Alexander, Schupp, Sibylle
Publikováno v:
EPTCS 355, 2022, pp. 40-59
Medical cyber-physical systems are safety-critical, and as such, require ongoing verification of their correct behavior, as system failure during run time may cause severe (or even fatal) personal damage. However, creating a verifiable model often co
Externí odkaz:
http://arxiv.org/abs/2203.09884
Autor:
Piotrowiak, Tobias H., Zehl, Rico, Suhr, Ellen, Banko, Lars, Kohnen, Benedikt, Rogalla, Detlef, Ludwig, Alfred
La-based perovskites are a versatile class of materials that are of interest for solid oxide fuel cells and electrocatalytic water splitting. During fabrication of composition spread materials libraries of La-Co-based oxide systems for the discovery
Externí odkaz:
http://arxiv.org/abs/2203.04631
Autor:
Lehmann, Sascha, Rogalla, Antje, Neidhardt, Maximilian, Schlaefer, Alexander, Schupp, Sibylle
Publikováno v:
EPTCS 348, 2021, pp. 128-135
Autonomous systems are often applied in uncertain environments, which require prospective action planning and retrospective data evaluation for future planning to ensure safe operation. Formal approaches may support these systems with safety guarante
Externí odkaz:
http://arxiv.org/abs/2110.12590
Autor:
Görlitz, Johannes, Herrmann, Dennis, Fuchs, Philipp, Iwasaki, Takayuki, Taniguchi, Takashi, Rogalla, Detlef, Hardeman, David, Colard, Pierre-Olivier, Markham, Matthew, Hatano, Mutsuko, Becher, Christoph
Publikováno v:
npj Quantum Information 8, 45 (2022)
Quantum information processing (QIP) with solid state spin qubits strongly depends on the efficient initialisation of the qubit's desired charge state. While the negatively charged tin-vacancy ($\text{SnV}^{-}$) centre in diamond has emerged as an ex
Externí odkaz:
http://arxiv.org/abs/2110.05451
Autor:
Fabienne Kurt, Gabriel E. Leventhal, Marianne Rebecca Spalinger, Laura Anthamatten, Philipp Rogalla von Bieberstein, Carmen Menzi, Markus Reichlin, Marco Meola, Florian Rosenthal, Gerhard Rogler, Christophe Lacroix, Tomas de Wouters
Publikováno v:
Gut Microbes, Vol 15, Iss 1 (2023)
ABSTRACTThe success of fecal microbiota transplants (FMT) has provided the necessary proof-of-concept for microbiome therapeutics. Yet, feces-based therapies have many associated risks and uncertainties, and hence defined microbial consortia that mod
Externí odkaz:
https://doaj.org/article/90151f7cd74f4bf8aed4068ee8ff03a9
COVID-19 spread across the globe at an immense rate has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-r
Externí odkaz:
http://arxiv.org/abs/2010.06418
Autor:
Urszula Rogalla-Ładniak
Publikováno v:
Archives of Forensic Medicine and Criminology, Vol 2022, Iss 1, Pp 211-222 (2023)
Sądowa genealogia genetyczna korzysta w głównej mierze z popularności badań genealogicznych (zwłaszcza) wśród Amerykanów oraz z pojawienia się nowych technik sekwencjonowania umożliwiających analizę trudnych próbek pochodzących z miejs
Externí odkaz:
https://doaj.org/article/7608e5919ca74eb7ac3eb80df02b69b3
Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training
Externí odkaz:
http://arxiv.org/abs/2006.03622