TY - JOUR A1 - Grieger, Niklas A1 - Schwabedal, Justus T. C. A1 - Wendel, Stefanie A1 - Ritze, Yvonne A1 - Bialonski, Stephan T1 - Automated scoring of pre-REM sleep in mice with deep learning JF - Scientific Reports N2 - Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network. Y1 - 2021 U6 - http://dx.doi.org/10.1038/s41598-021-91286-0 SN - 2045-2322 N1 - Corresponding author: Stephan Bialonski VL - 11 IS - Art. 12245 PB - Springer Nature CY - London ER - TY - JOUR A1 - Gorzalka, Philip A1 - Schmiedt, Jacob Estevam A1 - Schorn, Christian T1 - Automated Generation of an Energy Simulation Model for an Existing Building from UAV Imagery JF - Buildings N2 - An approach to automatically generate a dynamic energy simulation model in Modelica for a single existing building is presented. It aims at collecting data about the status quo in the preparation of energy retrofits with low effort and costs. The proposed method starts from a polygon model of the outer building envelope obtained from photogrammetrically generated point clouds. The open-source tools TEASER and AixLib are used for data enrichment and model generation. A case study was conducted on a single-family house. The resulting model can accurately reproduce the internal air temperatures during synthetical heating up and cooling down. Modelled and measured whole building heat transfer coefficients (HTC) agree within a 12% range. A sensitivity analysis emphasises the importance of accurate window characterisations and justifies the use of a very simplified interior geometry. Uncertainties arising from the use of archetype U-values are estimated by comparing different typologies, with best- and worst-case estimates showing differences in pre-retrofit heat demand of about ±20% to the average; however, as the assumptions made are permitted by some national standards, the method is already close to practical applicability and opens up a path to quickly estimate possible financial and energy savings after refurbishment. KW - Modelica KW - heat transfer coefficient KW - heat demand KW - building energy modelling KW - building energy simulation Y1 - 2021 U6 - http://dx.doi.org/10.3390/buildings11090380 SN - 2075-5309 N1 - This article belongs to the Special Issue "Application of Computer Technology in Buildings" VL - 11 IS - 9 PB - MDPI CY - Basel ER - TY - JOUR A1 - Golland, Alexander T1 - Bußgelder wegen Datenschutzverstößen – Vermeidung und Verteidigung. Deutsche Aufsichtsbehörden setzen seit vergangenem Jahr zunehmend auf Sanktion JF - NWB Steuer- und Wirtschaftsrecht Y1 - 2021 SN - 0028-3460 VL - 36 SP - 2678 EP - 2687 PB - NWB-Verl. CY - Herne ER - TY - JOUR A1 - Golland, Alexander ED - Ewer, Wolfgang ED - Hamm, Rainer ED - Karpenstein, Ulrich ED - Oberthür, Nathalie ED - Herchen, Hilke ED - Bräutigam, Peter T1 - Das Telekommunikation-Telemedien-Datenschutzgesetz. Cookies und PIMS als Herausforderungen für Website-Betreiber JF - NJW Neue Juristische Wochenschrift Y1 - 2021 SN - 0341-1915 IS - 31 SP - 2238 EP - 2243 PB - Beck CY - München ER - TY - JOUR A1 - Golland, Alexander T1 - Anforderungen an Transfer Impact Assessments bei Datentransfers in unsichere Drittländer JF - DSB Datenschutz-Berater Y1 - 2021 SN - 0170-7256 VL - 45 IS - 7-8 SP - 229 EP - 231 PB - dfv Mediengruppe, Deutscher Fachverlag GmbH CY - Frankfurt am Main ER - TY - JOUR A1 - Golland, Alexander T1 - Neuregelungen zum Datenschutz: Ein Update für Website-Betreiber. Reform des Telekom-munikations- und Telemedienrechts verabschiedet JF - NWB Rechnungswesen - BBK Y1 - 2021 SN - 0340-9848 IS - 14 SP - 672 EP - 674 ER - TY - JOUR A1 - Golland, Alexander T1 - Cookies & Co. – trotz neuem Gesetz alte Probleme für Website-Betreiber. Anpassung des deutschen Rechts durch das TTDSG an unionsrechtliche Vorgaben zum 1.12.2021 JF - NWB Steuer- und Wirtschaftsrecht Y1 - 2021 SN - 0028-3460 VL - 2021 IS - 25 SP - 1818 EP - 1825 PB - NWB-Verlag CY - Basel ER - TY - JOUR A1 - Golland, Alexander T1 - Datenschutzkonforme Test-, Impf- und Genesungskontrollen in Betrieben der Privatwirtschaft JF - DSB Datenschutz-Berater Y1 - 2021 SN - 0170-7256 VL - 45 IS - 5 SP - 158 EP - 160 PB - dfv Mediengruppe, Deutscher Fachverlag GmbH CY - Frankfurt am Main ER - TY - JOUR A1 - Golland, Alexander T1 - Rezension zu: Voskamp, Kipker - Sozialdatenschutz in der Praxis (2021) JF - DSB Datenschutz-Berater Y1 - 2021 SN - 0170-7256 VL - 45 IS - 7-8 SP - 239 EP - 240 ER - TY - JOUR A1 - Golland, Alexander T1 - Rezension zu: Voigt – Die räumliche Anwendbarkeit der EU Datenschutz-Grundverordnung auf Auftragsverarbeiter im Drittland (2020) JF - DSB Datenschutz-Berater Y1 - 2021 SN - 0170-7256 VL - 45 IS - 2 SP - 64 ER -