Eldorado - Repositorium der TU Dortmund

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Bei diesem Service handelt es sich um das Institutionelle Repositorium der Technischen Universität Dortmund. Hier werden Ressourcen aus und für Lehre, Studium und Forschung gespeichert, erschlossen und der Öffentlichkeit zugänglich gemacht.

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Fach- und Universitätsübergreifendes 1319
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Graduate School und Graduiertenkollegs 12
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Sonderforschungsbereiche 1595

Aktuellste Veröffentlichungen

Large-field irradiation techniques in Germany: a DGMP Working Group survey on the current clinical implementation of total body irradiation, total skin irradiation and craniospinal irradiation
(Elsevier BV, 2024-10-16) Heuchel, Lena; Garbe, Stephan; Lühr, Armin; Shariff, Maya
In 2023, a Germany-wide survey on the current clinical practice of three different large field irradiation techniques (LFIT), namely total body irradiation (TBI), total skin irradiation (TSI) and craniospinal irradiation (CSI), was conducted covering different aspects of the irradiation process, e.g., the irradiation unit and technique, dosimetrical aspects and treatment planning as well as quality assurance. The responses provided a deep insight into the applied approaches showing a high heterogeneity between participating centers for all three large field irradiation techniques. The highest heterogeneity was found for TBI. Here, differences between centers were found in almost every aspect of the irradiation process, e.g., the irradiation technique, the prescription dose, the spared organs at risk and the applied treatment planning method. For TBI, the only agreement was found in the fractionation scheme (2 Gy/fraction, 2 fractions/day) and the dose reduction to the lung. TSI was the rarest of the three LFITs. For TSI, the only agreement was found in the use of 6 MeV when irradiating with electrons. The reported approaches of CSI were closest to standard radiotherapy, using no CSI-specific irradiation techniques or treatment planning methods. For CSI, the only agreement was found in the prescribed dose to the brain (50 – 60 Gy). When asking for future requirements, participating centers considered the lack of standardization as the most important future challenge and suggested to perform (retrospective) patient studies. The results of such studies can then serve as a basis for new and improved guidelines.
Cleavage of carbodicarbenes with N2O for accessing stable diazoalkenes
(Wiley, 2024-09-06) He, Yijie; Lyu, Yichong; Tymann, David; Antoni, Patrick W.; Hansmann, Max M.
The cleavage of carbophosphinocarbenes and carbodicarbenes with nitrous oxide (N_2O) leads to the formation of room-temperature stable diazoalkenes. The utility of Ph_3P/N_2 and NHC/N_2 ligand exchange reactions were demonstrated by accessing novel benzimidazole- and benzothiazole derived diazoalkenes, which are not accessible by the current state-of-the-art methods. The stable diazoalkenes subsequently allow further ligand exchange reactions at C(0) with carbon monoxide, isocyanide, or a diamidocarbene (DAC). Overall, the combination of hitherto unknown NHC/N_2 and N_2/L (L = DAC, CO, R−NC) ligand exchange reactions at a C(0) center allow the selective functionalization of the carbodicarbene ligand structure which represents a new methodology to rapidly assemble novel carbodicarbenes or cumulenic compounds.
Combination of a viscoelastic and a tribological analysis of a low density polyethylene with a high degree of cross-linking
(Wiley, 2024-03-30) Schneck, Franziska; Kruse, Philana O.; Hesse‐Hornich, Daniel; Dias, N. Filipe Lopes; Tillmann, Wolfgang; Jerusalem, Robert; Maricanov, Michail; Katzenberg, Frank; Tiller, Jörg C.; Handge, Ulrich A.
Cross-linking of polymers is an efficient method to tailor the end-use properties of polymer materials. Cross-linking using a chemical agent, e.g., dicumyl peroxide (DCP), allows for a spatially uniform network formation in the melt state. In addition, it is also associated with side reactions which influence the final properties of the plastic part. This work investigates the influence of DCP concentration on the tribological properties of a cross-linked low-density polyethylene (LDPE) grade. In particular, high DCP concentrations up to 20 phr are chosen in order to explore the effect of a high degree of cross-linking. The viscoelastic properties below and above the melting temperature are studied in detail to support the interpretation of the tribological results. Rheological investigations allow one to monitor the cross-linking of the long-chain branched LDPE. The data and the subsequent optical analysis show that wear already is significantly reduced at a low DCP concentration of 1 phr because of the covalent bonds caused by cross-linking. A high DCP concentration of 20 phr yields an increase of coefficient of friction which can be explained by the low stiffness and the resulting high contact area in the case of highly cross-linked LDPE.
Eine valide Datengrundlage für die Kindertagesbetreuung
(2026-06) Meiner-Teubner, Christiane; Birkel-Barmsen, Janine; Carstens, Yannick; Kopp, Katharina
Uncertainly quantification for interpretable and reliable machine learning
(2026) Newen, Carina; Müller, Emmanuel; Hammer, Barbara
In the booming research field of machine learning and artificial intelligence, uncertainty quantification is often overlooked as an essential quality guarantee. When interacting with and applying artificial intelligence, it is common to evaluate the performance of such learners by metrics such as efficiency, accuracy and other task-specific performance-based metrics without emphasizing the importance of quantifying potential hazards. Uncertainty characterizes the proximity between observations and predictions, providing a measure of how well a model reflects the true underlying data distribution. This thesis places uncertainty quantification at the center of its investigation, and we investigate three interconnecting subareas:. Explanations and visualizations of uncertainties, robustness with regard to uncertainties, and trustworthiness and human interpretability of uncertainties. This interdisciplinary setup is crucial to establishing new connections between the domains. While each of the subjects has been extensively studied in isolation, it is often at the interconnections that new research paradigms emerge: Neural networks as we know them today are a result of a fusion of neuroscience-inspired models of cognition, mathematical formalization, and algorithmic innovation. Without the mathematical groundwork, the advances in computing power and the biological neuron inspiration, this new field of research would not exist today. By interweaving explanation, robustness and trust in the context of uncertainty, in this thesis, we aim to pave the way for engineering practical systems that are both reliable, interpretable, and ultimately trustworthy. In these three areas, we focus on empirical approaches and solutions for important research challenges. The first part of this thesis focuses on visualizing uncertainties of high-dimensional data in an unsupervised setting using the novel proxy for local intrinsic dimensionalities. Furthermore, we show limitations of popular explainable AI methods using a newly constructed open-source dataset that focuses on an ambiguous classification task. We use the proxy of local intrinsic dimensionality as a proxy for the likelihood of adversarial attacks, connecting uncertainties with robustness metrics. In the second part of the thesis, we delve more deeply into the robustness domain by proposing certainty attacks and discussing the independence of adversarial transferability to topological changes in the datasets. We discuss the origin of transferability and possible research directions for future work. The main motivation between the importance of uncertainties stems from the need to calibrate human trust---for the successful application of machine learners, we have to align the trust levels of humans according to their actual performance. This motivates the third part of the thesis, where we discuss trust in AI systems with a special emphasis on uncertainty quantification. Finally, we discuss open challenges regarding uncertainty quantification and outline future work in this particular domain, with special emphasis on explainable AI and robustness.