TY - GEN A1 - Hoffmann, Stephanie A1 - Sander, Lydia A1 - Rattay, Petra A1 - Blume, Miriam A1 - Hövener, Claudia A1 - Schneider, Sven A1 - Richter, Matthias A1 - Pischke, Claudia R. A1 - Schüttig, Wiebke A1 - De Bock, Freia A1 - Spallek, Jacob T1 - Do family characteristics contribute to a socioeconomic gradient in overweight in early childhood? – Single mediation analyses of data from German preschool children T2 - Preventive Medicine Reports N2 - Children’s overweight is strongly associated with family socioeconomic position (SEP) and family characteristics (FC). There is limited research on the extent to which FC account for a socioeconomic gradient in childhood overweight. This study examined whether FC explain SEP differences in the prevalence of overweight. The study used baseline data of preschool-aged children from the German ‘PReschool INtervention Study’. The sample (n = 872, 48% girls) was recruited at kindergartens in Baden-Württemberg, Germany. Data included children’s measured weight status and parents’ reports on socioeconomic indicators (e.g., school education, vocational education, income) and FC. Variables represent main determinants of overweight (nutrition: sweets consumption in front of TV, soft drink consumption, regular breakfast, child sets table; physical activity: outdoor sports; parental role model). In single mediation analyses indirect effects of SEP on overweight were analysed (OR[95%CI]). Preschool girls and boys with low parental education had higher odds for overweight than children with high parental education. Among boys, low levels of parental education contributed to the odds of overweight via indirect effects by both factors ‘sweets consumption in front of TV’ (OR = 1.31[1.05–1.59]) and ‘no sports’ (OR = 1.14[1.01–1.38]). Among girls, FC measured did not explain SEP differences in overweight. Family nutrition and parental/family physical activity contribute to inequalities in overweight among preschool boys, but not girls. Research is needed to identify FC that explain inequalities in overweight for both. KW - Child KW - Family KW - Overweight KW - Socioeconomic factors KW - Socioeconomic health inequalities Y1 - 2023 U6 - https://doi.org/10.1016/j.pmedr.2023.102178 SN - 2211-3355 VL - 33 ER - TY - GEN A1 - Schob, Daniela A1 - Richter, Lukas A1 - Kotecki, Krzysztof A1 - Kurpisz, Dariusz A1 - Roszak, Robert A1 - Maasch, Philipp A1 - Ziegenhorn, Matthias T1 - Characterization and Simulation of Shear-Induced Damage in Selective-Laser-Sintered Polyamide 12 T2 - Materials N2 - This paper presents the characterisation of selective-laser-sintered (SLS) samples of polyamide 12 (PA12) under shear loading. PA12 is a semi-crystalline thermoplastic and is used in various industries. Its behaviour under shear stress, which is particularly important for product reliability, has not yet been sufficiently investigated. This research focuses on understanding the material and damage behaviour of PA12 under shear-induced stress conditions. The study included quasi-static experiments and numerical simulations. Samples were prepared via SLS and tested according to ASTM standards. Digital image correlation (DIC) was used for precise deformation measurements. The Chaboche material model was used for the viscoplastic behaviour in the numerical simulations. Due to existing material discontinuities in the form of voids, the material model was coupled with the Gurson–Tvergaard–Needleman (GTN) damage model. A modified approach of the GTN model was used to account for low stress triaxiality under shear loading. These models were implemented in MATLAB and integrated into Abaqus via a User Material (UMAT) subroutine. The results of the experiments and simulations showed a high degree of accuracy. An important finding was the significant influence of the shear factor kw on the damage behaviour, especially during failure. This factor proved to be essential for the accurate prediction of material behaviour under shear-induced stress conditions. The integration of the modified GTN model with the Chaboche material model in UMAT enables an accurate prediction of the material and damage behaviour and thus makes an important contribution to the understanding of the mechanical material behaviour of SLS PA12 specimens. Y1 - 2024 UR - https://www.mdpi.com/1996-1944/17/1/38 U6 - https://doi.org/10.3390/ma17010038 SN - 1996-1944 VL - 17 IS - 1 ER - TY - GEN A1 - Richter, Martin A1 - Karnapke, Reinhardt A1 - Werner, Matthias T1 - Utilizing sensor and actuator virtualization to achieve a systemic view of mobile heterogeneous cyber-physical systems T2 - Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications N2 - When programming cyber-physical systems, application developers currently utilize physical sensors and actuators individually to achieve the desired observations and impacts within the physical world. This is an error-prone and complex task given the size, heterogeneity, and mobility of prevailing cyber-physical systems. We introduce an application model that allows the application developers to take a physical perspective. By means of this model, the programmers describe desired observations and influences with respect to the physical world without directly referencing physical devices. We present an additional model for a runtime environment that transparently utilizes the available physical devices to reach the application developers’ targets. We show that an implementation of our models is functional via simulation. KW - Cyber-Physical Systems KW - Virtualization KW - Mobility KW - Heterogeneity KW - Transparency Y1 - 2024 SN - 978-989-758-708-5 U6 - https://doi.org/10.5220/0012715800003758 SP - 207 EP - 214 PB - SCITEPRESS - Science and Technology Publications ER - TY - GEN A1 - Dadaczynski, Kevin A1 - Kaman, Anne A1 - Ravens-Sieberer, Ulrike A1 - Fischer, Saskia M. A1 - Bilz, Ludwig A1 - Sendatzki, Saskia A1 - Helmchen, Ronja M. A1 - Rathmann, Katharina A1 - Richter, Matthias T1 - Problematische Nutzung digitaler Medien und Gesundheitskompetenz von Schülerinnen und Schülern in Deutschland : Befunde der HBSC-Studie 2022 T2 - Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz N2 - Hintergrund Der digitale Alltag von Kindern und Jugendlichen ist durch eine hohe Nutzung sozialer Medien und digitaler Spiele geprägt. Bisherige Forschung konnte gesundheitsabträgliche Konsequenzen einer problematischen Mediennutzung aufzeigen. Hingegen wurden Zusammenhänge mit der Gesundheitskompetenz kaum untersucht, obgleich Rahmenmodelle Gesundheitskompetenz als Determinante des Gesundheitsverhaltens nahelegen. Methoden Dieser Beitrag greift auf Daten der repräsentativen Studie „Health Behaviour in School-aged Children“ (HBSC) in Deutschland aus dem Jahr 2022 mit n = 6475 Schülerinnen und Schülern zurück. Es wurden Zusammenhänge zwischen der problematischen Nutzung von sozialen Medien und der Videospielintensität sowie der Gesundheitskompetenz unter Berücksichtigung soziodemografischer und -ökonomischer Merkmale bivariat und multivariat untersucht. Ergebnisse Die Prävalenz der problematischen Nutzung sozialer Medien betrug 11,1 % und etwa ein Drittel gab an, mehr als 9 h pro Woche mit digitalen Spielen zu verbringen. Eine geringe Gesundheitskompetenz stand mit beiden Formen der Mediennutzung in signifikantem Zusammenhang. Zudem war die problematische Nutzung sozialer Medien mit einer weiblichen und genderdiversen Geschlechtszugehörigkeit, dem Alter von 13 Jahren, dem Vorliegen eines Migrationshintergrunds und der Zugehörigkeit zu einer anderen Schulform als das Gymnasium verbunden. Eine hohe Spielintensität war mit einer männlichen und genderdiversen Geschlechtszuordnung, der Zugehörigkeit zu den Altersgruppen der 13- und 15-Jährigen und einem geringen familiären Wohlstand assoziiert. Diskussion Die Ergebnisse liefern nicht nur Hinweise für die Identifikation von Heranwachsenden mit besonderem Präventionsbedarf, sondern betonen auch die Relevanz, die der Gesundheitskompetenz für das Mediennutzungsverhalten zukommt. Exemplarische Empfehlungen werden unter Rückgriff auf die internationale Literatur aufgegriffen. Y1 - 2025 U6 - https://doi.org/10.1007/s00103-025-04008-6 SN - 1436-9990 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Ju, Yong Chul A1 - Tschöpe, Constanze A1 - Richter, Christian A1 - Wolff, Matthias T1 - Acoustic Resonance Recognition of Coins T2 - 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 25-28 May 2020, Dubrovnik, Croatia N2 - In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard surface.The used dataset is a representative example of a small data which was collected in carefully prepared experiments.Due to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional methods.The results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset. Y1 - 2020 SN - 978-1-7281-4460-3 SN - 978-1-7281-4461-0 U6 - https://doi.org/10.1109/I2MTC43012.2020.9129256 PB - IEEE Xplore ER - TY - CHAP A1 - Tschöpe, Constanze A1 - Duckhorn, Frank A1 - Richter, Christian A1 - Blüthgen, Peter A1 - Wolff, Matthias T1 - Intelligent Signal Processing on a Miniaturized Hardware Module T2 - IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017 Y1 - 2017 UR - http://ieeexplore.ieee.org/document/8234023/ SN - 978-1-5090-1012-7 U6 - https://doi.org/10.1109/ICSENS.2017.8234023 N1 - IEEE Catalog Numer: CFP17SEN-ART PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Tschöpe, Constanze A1 - Duckhorn, Frank A1 - Richter, Christian A1 - Blüthgen, Peter A1 - Wolff, Matthias T1 - An embedded system for acoustic pattern recognition T2 - IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017 Y1 - 2017 SN - 978-1-5090-1012-7 U6 - https://doi.org/10.1109/ICSENS.2017.8234380 N1 - IEEE Catalog Numer: CFP17SEN-ART PB - IEEE CY - Piscataway, NJ ER -