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Aktuellste Veröffentlichungen

A data-driven comprehensive analysis of occupational health and safety in Turkey
(Springer Science and Business Media LLC, 2026-05-02) Aksoy, Meltem; Adem, Aylin; Yilmaz, Ibrahim; Dağdeviren, Metin
Occupational Health and Safety (OHS), which holds substantial importance due to its profound human implications and far-reaching economic consequences, requires effective management to minimize workplace accidents and occupational diseases through data-driven risk assessment and proactive safety strategies. Achieving this necessitates the availability of historical data and its examination through appropriate analytical methodologies, which is critically important for determining evidence-based preventive measures. Thus, this study analyses Turkey’s national OHS records from 2010 to 2022 using statistical analysis and machine learning approaches to evaluate sectoral risks, predict accident trends, and identify key determinants of workplace hazards. Results indicate substantial sector differences, with coal mining and heavy industry showing the highest accident and fatality rates. Machine learning models demonstrate strong predictive capability, with gradient boosting providing the best work accident prediction performance and random forests achieving the best performance for occupational disease prediction. Clustering analysis identifies three distinct industrial risk groups, while Principal Component Analysis (PCA) reveals regional disparities, particularly in highly industrialized provinces such as Istanbul, Kocaeli, and Izmir. Classification models further achieve over 98% accuracy in identifying high-risk groups, highlighting the potential of machine learning for proactive OHS management. The findings of this paper provide actionable insights for policymakers and industry leaders to optimize safety regulations and develop targeted interventions and strategies to reduce workplace risks and identify which sectors and worker groups should be prioritized.
TailorMe: self‐supervised learning of an anatomically constrained volumetric human shape model
(Wiley, 2024-04-30) Wenninger, S.; Kemper, F.; Schwanecke, U.; Botsch, M.
Human shape spaces have been extensively studied, as they are a core element of human shape and pose inference tasks. Classic methods for creating a human shape model register a surface template mesh to a database of 3D scans and use dimensionality reduction techniques, such as Principal Component Analysis, to learn a compact representation. While these shape models enable global shape modifications by correlating anthropometric measurements with the learned subspace, they only provide limited localized shape control. We instead register a volumetric anatomical template, consisting of skeleton bones and soft tissue, to the surface scans of the CAESAR database. We further enlarge our training data to the full Cartesian product of all skeletons and all soft tissues using physically plausible volumetric deformation transfer. This data is then used to learn an anatomically constrained volumetric human shape model in a self-supervised fashion. The resulting TailorMe model enables shape sampling, localized shape manipulation, and fast inference from given surface scans.
Polygon Laplacian made robust
(Wiley, 2024-04-30) Bunge, A.; Bukenberger, D. R.; Wagner, S. D.; Alexa, M.; Botsch, M.
Discrete Laplacians are the basis for various tasks in geometry processing. While the most desirable properties of the discretization invariably lead to the so-called cotangent Laplacian for triangle meshes, applying the same principles to polygon Laplacians leaves degrees of freedom in their construction. From linear finite elements it is well-known how the shape of triangles affects both the error and the operator's condition. We notice that shape quality can be encapsulated as the trace of the Laplacian and suggest that trace minimization is a helpful tool to improve numerical behavior. We apply this observation to the polygon Laplacian constructed from a virtual triangulation [BHKB20] to derive optimal parameters per polygon. Moreover, we devise a smoothing approach for the vertices of a polygon mesh to minimize the trace. We analyze the properties of the optimized discrete operators and show their superiority over generic parameter selection in theory and through various experiments.
Electronic and local atomic structure of iron-bearing minerals and glasses at conditions of the Earth’s mantle
(2026) Thiering, Nicola; Tolan, Metin; Wilke, Max
Diese Arbeit untersucht das Struktur- und Elektronenverhalten von Eisen unter Hochdruckbedingungen des unteren Erdmantels anhand von Experimenten an Fe2O3 und eisenhaltigen haplobasaltischen Gläsern. Mittels Röntgenemissionsspektroskopie, resonanter Röntgenemissionsspektroskopie, Röntgendiffraktion und Synchrotron-Mössbauer-Spektroskopie werden Änderungen von Spin-Zustand, Valenz und lokaler Koordination bestimmt. Für Fe2O3 zeigt sich, dass die Entwicklung des Spin-Zustands eng mit strukturellen Phasenumwandlungen verknüpft ist. Ein ausgedehnter Bereich von gemischtem Spinzustand bei mittleren Drücken steht im Zusammenhang mit gemischtvalenten Phasen wie Fe5O7. Bei höheren Drücken und Temperaturen bildet sich die Phase Fe25O32, wobei das Elektronenverhalten durch das Nebeneinander von Fe2+ und Fe3+ in unterschiedlichen Koordinationsumgebungen bestimmt wird. Der Spinzustand wird somit primär durch die Kristallstruktur kontrolliert. In haplobasaltischen Gläsern reagiert Eisen gradueller auf Kompression. Zweiwertiges Eisen zeigt deutliche Änderungen der lokalen Koordination,während dreiwertiges Eisen strukturell stabiler bleibt. Diese Entwicklung wird durch lokale strukturelle Umverteilungen bestimmt. Insgesamt wird das Verhalten von Eisen im unteren Erdmantel durch das Zusammenspiel von elektronischem Zustand, Struktur und Phasenbeziehungen kontrolliert, was entscheidend ist für die physikalischen und chemischen Eigenschaften eisenhaltiger Materialien im tiefen Erdinneren.
Oriented spanners
(2026) Kalb, Antonia; Buchin, Kevin; Mulzer, Wolfgang
Geometric graphs naturally model numerous real-world infrastructures such as transportation systems, power grids and communication networks. Since fast connectivity is essential in these settings, geometric spanners are sparse graphs that approximately preserve pairwise distances. Although spanners have been studied for decades, directed versions have only been considered more recently. In many applications, however, directionality is not merely optional. Physical constraints, limited resources, or safety regulations often require strictly unidirectional edges. This motivates our study of oriented graphs as geometric spanners. In particular, we address the following problems: - Oriented dilation: Given an oriented graph, we investigate how to compute or approximate its oriented dilation. - Graph orientation: Given an undirected graph, we study algorithms to orient it. - Spanner construction: Given a point set, we explore the construction of oriented spanners that satisfy additional properties such as sparseness or planarity. For each problem, we analyze its computational complexity and provide algorithmic solutions.