Eldorado - Repositorium der TU Dortmund

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Aktuellste Veröffentlichungen

Homogenization of systems of wave equations and ring solutions with dispersive profiles
(2026-06-16) Allaire, Grégoire; Lamacz-Keymling, Agnes; Schweizer, Ben
We consider systems of wave equations such as the timedependent Lamé system or elasticity. When the coefficients are periodic in space, the classical task in homogenization theory is to describe limits of solutions when the periodicity tends to zero. The effective equation is a system with constant coefficients, typically of the same structure as the original system. Instead, when long time intervals are considered, new dispersive terms can appear in the effective system. We derive such dispersive effective systems of wave equations using the Bloch method of homogenization. The method yields approximate representation formulas for solutions in Fourier space. These also allow to describe solutions as superpositions of ring waves, expanding with constant speed, with profiles that change on a slow time scale according to the dispersive terms.
Factor-based IVX Predictive Regression
(2026) Schmidt, Fabian
With the growing availability of financial data, new variables are constantly proposed to predict stock returns, although their incremental explanatory power is often limited because many capture overlapping information. While it suggests itself to extract latent factors summarizing the underlying information — e.g. consider common trends in bond yields across maturities — from these variables and to subsequently utilize these factors as predictors, the usual problems with the variables’ unknown persistence and predictive regression endogeneity resulting in spurious predictability findings still apply. To address these issues, we combine factor extraction with the IVX framework of Kostakis et al. (2015), whose instrumental variable approach is able to resolve the endogeneity issue regardless of the particular degree of persistence. Monte Carlo simulations confirm that the proposed factor-based IVX regression approach achieves good size control and, in addition, strong power should predictability be present. The empirical relevance of the approach is illustrated using S&P 500 returns and a set of commonly used predictors.
Design and implementation of a reflective well-being app
(2026) Pfeiffer, Dennis; Mayer, Sven
Mood-tracking app scan support mental health self-management by helping users monitor their emotional states and identify patterns int heir daily lives. However, most existing apps focus on data collection and visualisation while offering little personalised guidance, and many raise serious privacy concerns by sharing sensitive data with third-party services.This thesis presents MoodScape, a reflective well-being app for Android that combines context-aware mood tracking with personalised, LLM-generated recommendations while preserving user privacy through a locally hosted open-source language model. MoodScape collects mood entries alongside contextual signals from weather, music, health, and social interaction APIs and uses a locally deployed Ollama model (llama3.2:1b) on a university-managed server to generate tailored recommendations—without transmitting any mood or health data to commercial cloud providers. The system was evaluated in a two-week AB/BA crossover field study with ten participants. Each participant experienced both a tailored recommendation condition, in which the LLM drew on their personal mood and context data, and a generic baseline condition, in counterbalanced order. Perceived recommendation quality, systemusability (SUS), user experience (UEQ), and self-reported reflection were assessed through standardised questionnaires. The results show a consistent descriptive pattern favouring the tailored condition: participants rated the LLM- generated recommendations higher than the baseline on all four comparison items, with the largest difference on perceived personalisation (𝑀 = 2.80 vs. 𝑀 = 2.00), which also reached statistical significance in a supplementary Wilcoxon signed-rank test (𝑝 = .039, 𝑟 = .65). However, absolute satisfaction levels remained moderate across both conditions, and engagement with the app varied considerably across participants (4–38 mood entries). MoodScape achieved above-average usability (𝑀SUS = 74.75) and positive user experience scores, with participants particularly valuing the data exploration features. Reflection items indicate that the app supported data exploration and, to a degree, self-reflection, though deeper behavioural change did not emerge within the study period. These findings demonstrate that privacy-preserving, locally hosted LLMs are technically viable for generating mood-related recommendations in a GDPR-compliant architecture and that the personalisation approach itself is perceived positively. The quality gap compared to larger commercial models, however, constrained the practical impact. As open-source language models continue to improve, the approach demonstrated by MoodScape offers a promising path towards AI-powered well-being support that respects user privacy.
Kinetic event-chain algorithm for active matter
(American Physical Society (APS), 2026-06-05) Schaffrath, Nico; Sathiyanesan, Thevashangar; Kampmann, Tobias A.; Kierfeld, Jan
We present a cluster kinetic Monte Carlo algorithm for active matter systems of self-propelled particles with special focus on steric interactions. The kinetic event-chain algorithm is based on the event-chain Monte Carlo method and is applied to active Brownian disks in two dimensions. The algorithm assigns Monte Carlo moves of active disks a mean time based on a comparison between Brownian dynamics and the dynamics of the event-chain Monte Carlo method. This time is used to perform diffusional rotation of their propulsion force. We show that the algorithm correctly and efficiently reproduces various physical results ranging from single-particle dynamics to many-body effects. In particular, we reproduce the phase diagram of active disks and the motility-induced phase-separated region with high accuracy. The kinetic event-chain algorithm is shown to be much faster—at comparable accuracy—than (event-driven) Brownian dynamics algorithms, enabling large-scale simulations up to giant systems with 105 particles on standard desktop hardware.
Dar es Salaam’s Bus-Rapid-Transit system in view of systemic criticality
(Elsevier BV, 2026-01-06) Alem Gebregiorgis, Genet; Greiving, Stefan
The Dar es Salaam Bus Rapid Transit (DBRT) system is a cornerstone of urban mobility and socioeconomic development. However, its fixed-route, centralized design makes it highly vulnerable to flood-related disruptions, a risk exacerbated by climate change, unplanned urban development, and the loss of green spaces. Using DBRT, as case study this paper assesses systemic criticality of a transport system through surveys, key-informant interviews, participatory scenario workshops, and secondary research. The findings reveal profound logical and physical interdependencies between the DBRT and key economic sectors; a disruption could therefore cascade through critical infrastructures vital to the regional socioeconomic systems. To mitigate these risks, the study recommends integrating disaster risk management into transit planning, formalizing informal transportation for redundancy, and promoting nature-based solutions, such as recuperating wetland and green covers, to buffer against flooding and sea-level raise. Long-term strategies should pursue polycentric urban design to decrease reliance on centralized infrastructure. This study underscores the necessity of systemic criticality assessments for building resilient transit systems in rapidly growing cities.