Various systematic reviews underscore the relevance of social support for resilience among refugees. This meta-analysis aims to determine the quantitative assessment of social support and resilience among refugees and the extent of the associations between social support and resilience among refugees. After a systematic literature search, we included twenty-three studies, and performed random-effects meta-regressions. Studies on resilience and social support among refugees very heterogeneously operationalize both constructs. While increases in social support among refugees accompany higher resilience in numerous studies, these associations are not significant. In contrast to previous reviews' conclusions, the current data cannot confirm a relationship between resilience and social support among refugees. Substantiated conclusions about the relationship between resilience and social support among refugees might be reached by a population-specific clear conceptualization and operationalization of the constructs, the content differentiation of the constructs, representative samples, and longitudinal and intervention studies.
Im Gegensatz zu grundlegenden Empfehlungen sind derzeit noch Arbeiten selten, die Good-Practice-Beispiele zum Forschungs-Praxis-Transfer im Anwendungsgebiet Digitalisierung in verschiedenen Bildungsetappen unter Angabe von Erfolgskriterien darstellen. In der vorliegenden Arbeit wird dies anhand von drei Good-Practice-Beispielen aus Digitalisierungsprojekten verschiedener Bildungsetappen (Schulbildung, berufliche Bildung und Erwachsenenbildung/Weiterbildung) realisiert. Das vom Bundesministerium für Bildung und Forschung geförderte Metavorhaben Digitalisierung im Bildungsbereich beschäftigt sich mit dem Transfer von Forschungsergebnissen aus den in diesem Rahmen geförderten Digitalisierungsprojekten im Bildungsbereich. Abschliessend werden aus diesen Beispielen in der Diskussion gemeinsame relevante Transfermerkmale extrahiert.
Quality assurance in aluminum die casting is critical, as internal defects—such as porosity—can compromise structural integrity and significantly reduce component service life. In the cost-sensitive manufacturing environment of Germany, early and automated rejection of defective parts is essential to minimize scrap, rework, and energy waste. This study investigates the feasibility and performance of deep learning for automated defect detection in industrial X-ray images of two series-production aluminum die-cast components. A systematic methodology was employed: first, candidate object-detection frameworks (YOLOv5 vs. Faster R-CNN) were evaluated under real-time constraints (<2 s per image) on standard industrial hardware; subsequently, position-specific and single global models were trained on annotated datasets. A systematic hyperparameter study—focusing on input resolution, learning rate, and loss weights—was conducted to optimize accuracy and robustness. The best-performing models achieved F1-scores up to 0.87, with position-specific models outperforming the single global model on average. The approach was validated under real production conditions at Hengst SE (Nordwalde), demonstrating practical feasibility, strong acceptance among quality professionals, and significant potential to accelerate inspections and standardize decision-making. The results confirm that deep learning is a viable alternative to rule-based image processing and holds substantial promise for automating X-ray inspection workflows in aluminum die casting, contributing to both operational efficiency and sustainability goals.
