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Amtliche Mitteilungen der Technischen Universität Dortmund Nr. 11
(Technische Universität Dortmund, 2025-05-07)
Tree ensemble methods for ordinal prediction
(2025) Buczak, Philip; Pauly, Markus; Doebler, Philipp
Research questions and applications in the social and life sciences often involve ordinal response data. Student performance is assessed through ordinal grades, patients may express the perceived severity of their symptoms in ordinal levels and respondents of questionnaires may voice their political views through rating given statements. As such, the prediction of ordinal responses is relevant for many fields and can help, e.g., identifying which students may benefit from educational support systems. Traditionally, ordinal responses have been modeled through parametric models such as the proportional odds model. In light of the increasing quantities of data in these fields as well as the continued proliferation of machine learning (ML) methods, recent years saw the establishment of a new methodological stream of ordinal prediction methods based on ML. These methods promise high predictive performance for settings in which traditional parametric models may face difficulties (e.g., highly non-linear effects, high-dimensional data). However, many of these ML methods were originally not specifically tailored towards ordinal responses. Therefore, several extensions and adaptations of ML methods (particularly for tree-based methods) have been proposed to take ordinality into account. A particularly promising approach based on Random Forest (RF) is Ordinal Forest (OF; Hornung, 2019) which assigns numeric scores to the ordinal response categories and uses the scores to train a regression RF. To determine suitable score choices, OF performs a prior optimization step in which scores are optimized w.r.t. their predictive performance.
The impact of crisis on firms´stakeholders and the moderating effect of innovation
(2025) Seidinger, Tim; Kraft, Kornelius; Böhm, Michael J.
This dissertation empirically investigates the effects of the Global Financial Crisis (GFC) and the COVID-19 pandemic on German firms and their stakeholders, focusing specifically on two key aspects: (i) vocational training provision and (ii) payments to stakeholders. Both vocational training and wages are crucial economic factors. Vocational training is a fundamental component of human capital development, enhancing workers' skills and productivity, thereby driving economic growth and improving firms' competitiveness. Wages, on the other hand, play a vital role in labor supply by reflecting workers' value in the economy while also influencing consumer demand.
This thesis makes a significant contribution to existing literature by examining potential differences in crisis impacts between stakeholders of innovative and non-innovative firms (Chapters 1 and 3). Additionally, it distinguishes between various stakeholder groups (Chapters 2 and 3).
The findings reveal that both general training provision and training expenses per employee increased during the financial crisis. While innovation does not moderate the increase in general training provision, non-innovative firms exhibit a stronger rise in training expenses per employee. Regarding pay adjustments, results indicate that affected firms are more likely to reduce payments across all stakeholder groups. Notably, reductions in executive compensation occur with significantly higher likelihood compared to other groups. Moreover, innovation increases the probability of pay cuts—particularly for shareholders and executives—suggesting that innovative firms primarily pass on the implicit costs of crises to groups with greater financial flexibility.
Overall, this dissertation enhances our understanding of how crises impact firms and their stakeholders while emphasizing the critical role of innovation as a moderating factor within this area of research.
Theory and applications of wide field surface plasmon resonance microscopy for discrete particles detection
(2025) Al Bataineh, Qais Mohammed Turki; Franzke, Joachim; Bayer, Manfred
Detecting and characterizing nano-objects with low concentrations, such as biological particles, is
a substantial challenge in analytical science. The wide-field surface plasmon resonance microscopy
(WF-SPRM) can detect individual nano-objects in solutions and gas media bound to the sensor
surface. Therefore, WF-SPRM can detect low nano-object concentrations because the image
contains several square millimeters. In this work, the fundamental parameters for building highly
sensitive WF-SPRM were optimized. WF-SPRM can detect individual nano-objects in solutions
and gas media. Therefore, we derived a discrete particle model of SPR to describe the SPR sensor
of discrete particle detection. Theoretical, numerical, and experimental analyses of the SPR
detection principle were performed by considering discrete particle detection. Additionally, the
influence on the SPR sensitivity of coating the gold/silver layer with a dielectric layer with varying
refractive index is also studied. Different polyelectrolyte brushes, like polyacrylic acid, polyacrylic
acid-polyethylene oxide, and polyacrylic acid/iodine, are used to validate the enhancement of the
SPR sensitivity. Validation experiments are performed using polystyrene and silica nanoparticles
of varying sizes. Finally, the surface plasmon coupling behavior between the localized surface
plasmons (LSPs) of different shapes and sizes of metal nanostructures and the propagating surface
plasmons (PSPs) of the metal surface is investigated by employing experimental, simulation, and
theoretical approaches.
Modeling, control and opportunities of mechanical interfaces across the scales
(2025) Kurzeja, Patrick
This treatise analyzes various roles of mechanical interfaces in natural and artificial environments. From the classic perspective of spatial scales, the chapters evolve from modeling at the atomistic scale, to single continuum interfaces to larger systems of interfaces. They will cover molecular descriptions, sharp and diffuse interface models as well as effective ensemble properties. From the perspective of control, they evolve from naturally arising to artificially created objects. Freely evolving cracks, phase changes and interface design for controlled fluid-structure interaction will be shown for illustration. From the perspective on new opportunities, finally, they evolve from risk-bearing imperfections to technical and scientific opportunities. While damage is investigated as a typical risk scenario, novel potentials will be explored for non-destructive characterization, low-frequency attenuation and information enhancement for artificial neural networks.