TY - JOUR A1 - Seefeldt, Patric A1 - Dachwald, Bernd T1 - Temperature increase on folded solar sail membranes JF - Advances in Space Research Y1 - 2021 U6 - https://doi.org/10.1016/j.asr.2020.09.026 SN - 0273-1177 VL - 67 IS - 9 SP - 2688 EP - 2695 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Valero, Daniel A1 - Chanson, Hubert A1 - Bung, Daniel Bernhard T1 - Robust estimators for free surface turbulence characterization: A stepped spillway application JF - Flow Measurement and Instrumentation N2 - Robust estimators are parameters insensitive to the presence of outliers. However, they presume the shape of the variables’ probability density function. This study exemplifies the sensitivity of turbulent quantities to the use of classic and robust estimators and the presence of outliers in turbulent flow depth time series. A wide range of turbulence quantities was analysed based upon a stepped spillway case study, using flow depths sampled with Acoustic Displacement Meters as the flow variable of interest. The studied parameters include: the expected free surface level, the expected fluctuation intensity, the depth skewness, the autocorrelation timescales, the vertical velocity fluctuation intensity, the perturbations celerity and the one-dimensional free surface turbulence spectrum. Three levels of filtering were utilised prior to applying classic and robust estimators, showing that comparable robustness can be obtained either using classic estimators together with an intermediate filtering technique or using robust estimators instead, without any filtering technique. Y1 - 2020 U6 - https://doi.org/10.1016/j.flowmeasinst.2020.101809 SN - 0955-5986 VL - 76 IS - Art. 101809 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Human-Centered Gamification Framework for Manufacturing Systems JF - Procedia CIRP N2 - While bringing new opportunities, the Industry 4.0 movement also imposes new challenges to the manufacturing industry and all its stakeholders. In this competitive environment, a skilled and engaged workforce is a key to success. Gamification can generate valuable feedbacks for improving employees’ engagement and performance. Currently, Gamification in workspaces focuses on computer-based assignments and training, while tasks that require manual labor are rarely considered. This research provides an overview of Enterprise Gamification approaches and evaluates the challenges. Based on that, a skill-based Gamification framework for manual tasks is proposed, and a case study in the Industry 4.0 model factory is shown. Y1 - 2020 U6 - https://doi.org/10.1016/j.procir.2020.04.076 SN - 2212-8271 VL - 93 SP - 670 EP - 675 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Everaers, Ralf A1 - Karimi-Varzaneh, Hossein Ali A1 - Fleck, Franz A1 - Hojdis, Nils A1 - Svaneborg, Carsten T1 - Kremer–Grest Models for Commodity Polymer Melts: Linking Theory, Experiment, and Simulation at the Kuhn Scale JF - Macromolecules N2 - The Kremer–Grest (KG) polymer model is a standard model for studying generic polymer properties in molecular dynamics simulations. It owes its popularity to its simplicity and computational efficiency, rather than its ability to represent specific polymers species and conditions. Here we show that by tuning the chain stiffness it is possible to adapt the KG model to model melts of real polymers. In particular, we provide mapping relations from KG to SI units for a wide range of commodity polymers. The connection between the experimental and the KG melts is made at the Kuhn scale, i.e., at the crossover from the chemistry-specific small scale to the universal large scale behavior. We expect Kuhn scale-mapped KG models to faithfully represent universal properties dominated by the large scale conformational statistics and dynamics of flexible polymers. In particular, we observe very good agreement between entanglement moduli of our KG models and the experimental moduli of the target polymers. Y1 - 2020 U6 - https://doi.org/10.1021/acs.macromol.9b02428 SN - 1520-5835 VL - 53 IS - 6 SP - 1901 EP - 1916 PB - ACS Publications CY - Washington, DC ER - TY - JOUR A1 - Meyer, Jan A1 - Hentschke, Reinhard A1 - Hager, Jonathan A1 - Hojdis, Nils A1 - Karimi-Varzaneh, Hossein Ali T1 - Molecular Simulation of Viscous Dissipation due to Cyclic Deformation of a Silica–Silica Contact in Filled Rubber JF - Macromolecules Y1 - 2017 U6 - https://doi.org/10.1021/acs.macromol.7b00947 SN - 1520-5835 VL - 50 IS - 17 SP - 6679 EP - 6689 ER - TY - JOUR A1 - Hager, Jonathan A1 - Hentschke, Reinhard A1 - Hojdis, Nils A1 - Karimi-Varzaneh, Hossein Ali T1 - Computer Simulation of Particle–Particle Interaction in a Model Polymer Nanocomposite JF - Macromolecules Y1 - 2015 U6 - https://doi.org/10.1021/acs.macromol.5b01864 SN - 1520-5835 VL - 48 IS - 24 SP - 9039 EP - 9049 ER - TY - JOUR A1 - Waller, Mark P. A1 - Braun, Heiko A1 - Hojdis, Nils A1 - Bühl, Michael T1 - Geometries of Second-Row Transition-Metal Complexes from Density-Functional Theory JF - Journal of Chemical Theory and Computation Y1 - 2007 U6 - https://doi.org/10.1021/ct700178y SN - 1549-9626 VL - 3 IS - 6 SP - 2234 EP - 2242 ER - TY - JOUR A1 - Svaneborg, Carsten A1 - Karimi-Varzaneh, Hossein Ali A1 - Hojdis, Nils A1 - Fleck, Franz A1 - Everaers, Ralf T1 - Kremer-Grest Models for Universal Properties of Specific Common Polymer Species JF - Soft Condensed Matter N2 - The Kremer-Grest (KG) bead-spring model is a near standard in Molecular Dynamic simulations of generic polymer properties. It owes its popularity to its computational efficiency, rather than its ability to represent specific polymer species and conditions. Here we investigate how to adapt the model to match the universal properties of a wide range of chemical polymers species. For this purpose we vary a single parameter originally introduced by Faller and Müller-Plathe, the chain stiffness. Examples include polystyrene, polyethylene, polypropylene, cis-polyisoprene, polydimethylsiloxane, polyethyleneoxide and styrene-butadiene rubber. We do this by matching the number of Kuhn segments per chain and the number of Kuhn segments per cubic Kuhn volume for the polymer species and for the Kremer-Grest model. We also derive mapping relations for converting KG model units back to physical units, in particular we obtain the entanglement time for the KG model as function of stiffness allowing for a time mapping. To test these relations, we generate large equilibrated well entangled polymer melts, and measure the entanglement moduli using a static primitive-path analysis of the entangled melt structure as well as by simulations of step-strain deformation of the model melts. The obtained moduli for our model polymer melts are in good agreement with the experimentally expected moduli. Y1 - 2018 IS - 1606.05008 ER - TY - JOUR A1 - Mayer, Jan A1 - Hentschke, Reinhard A1 - Hager, Jonathan A1 - Hojdis, Nils A1 - Karimi-Varnaneh, Hossein Ali T1 - A Nano-Mechanical Instability as Primary Contribution to Rolling Resistance JF - Scientific Reports Y1 - 2017 SN - 2045-2322 VL - 7 IS - Article number 11275 PB - Springer CY - Berlin ER - TY - JOUR A1 - Kaulen, Lars A1 - Schwabedal, Justus T. C. A1 - Schneider, Jules A1 - Ritter, Philipp A1 - Bialonski, Stephan T1 - Advanced sleep spindle identification with neural networks JF - Scientific Reports N2 - Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model’s performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance. Y1 - 2022 U6 - https://doi.org/10.1038/s41598-022-11210-y SN - 2045-2322 N1 - Corresponding author: Stephan Bialonski VL - 12 IS - Article number: 7686 SP - 1 EP - 10 PB - Springer Nature CY - London ER -