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1914 Publications
2024 | Journal Article | LibreCat-ID: 55737
Barkhofen S, De S, Sperling J, et al. Experimental observation of topological quantum criticality. Physical Review Research. 2024;6(3). doi:10.1103/physrevresearch.6.033194
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2024 | Conference Paper | LibreCat-ID: 56207 | 
Rupp D, Kuhlen TW, Rarbach S, et al. Virtual Reality as a Tool for Monitoring Additive Manufacturing Processes via Digital Shadows. In: Proceedings of the GI VR/AR Workshop 2024. Gesellschaft für Informatik e.V.; 2024. doi:10.18420/vrar2024_0006
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2024 | Conference Paper | LibreCat-ID: 56155 |
Spliethöver M, Menon SN, Wachsmuth H. Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness. In: Ku L-W, Martins A, Srikumar V, eds. Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics; 2024:9294–9313.
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2024 | Journal Article | LibreCat-ID: 56678
Alibakhshi A, Schäfer LV. Electron iso-density surfaces provide a thermodynamically consistent representation of atomic and molecular surfaces. Nature Communications. 2024;15(1). doi:10.1038/s41467-024-50408-8
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2024 | Dissertation | LibreCat-ID: 54552
Malena K. Konzipierung, Analyse und Realumsetzung eines mehrstufigen modellprädiktiven Lichtsignalanlagenregelungssystems. Vol 422. Heinz Nixdorf Institut, Universität Paderborn; 2024. doi:10.17619/UNIPB/1-2021
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2024 | Journal Article | LibreCat-ID: 56848 |
Schönhärl K. “Contributing means investing in Spain” – Media campaigns on tax education in Spain after the transition to democracy. Themenportal Europäische Geschichte. Published online 2024.
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2024 | Conference Paper | LibreCat-ID: 56888
Gruchel N, Kurock R, Buhl HM. Digitale häusliche Lernumwelt – Veränderungen der elterlichen Unterstützung bei der informationsorientierten Internetnutzung von Fünft- und Siebtklässler*innen . In: ; 2024.
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2024 | Dissertation | LibreCat-ID: 56940
Götte R-S. Online-Schätzung von Modellungenauigkeiten zur automatischen Modelladaption unter Beibehaltung einer physikalisch-technischen Interpretierbarkeit. Vol 423.; 2024. doi:10.17619/UNIPB/1-2066
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2024 | Misc | LibreCat-ID: 58325
Autsch S. Micro Archives. Künstlerische Arbeiten 2019-2024. . Edition Imorde ; 2024.
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2024 | Conference Paper | LibreCat-ID: 58384
Kraus C, Böhnke M, Drossel W-G, Meschut G. Development of new strategies for the mechanical joining of components made of aluminum die casting. In: Materials Research Proceedings. Vol 41. Materials Research Forum LLC; 2024. doi:10.21741/9781644903131-190
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2024 | Journal Article | LibreCat-ID: 58421
von Enzberg S, Weller J, Brock J, et al. On the Current State of Industrial Data Science: Challenges, Best Practices, and Future Directions. Procedia CIRP. 2024;130:1454-1461. doi:10.1016/j.procir.2024.10.266
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2024 | Preprint | LibreCat-ID: 58441
Wippermann J, Meschut G. Numerical modeling of clamp load relaxation of plastic nuts under varying moisture and fiber content. Published online 2024.
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2024 | Dissertation | LibreCat-ID: 58448 |
Hesse M. Interaktive Inbetriebnahme von Steuerungen und Regelungen für partiell bekannte dynamische Systeme mittels Gauß-Prozess-Regression. Vol 426. Heinz Nixdorf Institut; 2024. doi:10.17619/UNIPB/1-2135
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2024 | Journal Article | LibreCat-ID: 58491
Friedlein J, Böhnke M, Schlichter M, et al. Material Parameter Identification for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch Joining. Journal of Manufacturing and Materials Processing. 2024;8(4). doi:10.3390/jmmp8040157
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2024 | Journal Article | LibreCat-ID: 49652 |
Hempel F, Vernuccio F, König L, et al. Comparing transmission- and epi-BCARS: a round robin on solid-state materials. Applied Optics. 2024;63(1). doi:10.1364/ao.505374
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2024 | Journal Article | LibreCat-ID: 59585
Friedlein J, Böhnke M, Schlichter MC, et al. Material Parameter Identification for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch Joining. Journal of Manufacturing and Materials Processing. 2024;8(4). doi:10.3390/jmmp8040157
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2024 | Journal Article | LibreCat-ID: 60047
Wenzel C, Liebig T, Swoboda A, et al. Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features. European Journal of Applied Physiology. 2024;124(11):3421-3431. doi:10.1007/s00421-024-05543-x
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