Eldorado - Repositorium der TU Dortmund

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Aktuellste Veröffentlichungen

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.
Social innovation in education: perspectives from Brazil and Mexico
(2026-06-01) Maldonado-Mariscal, Karina
Social innovation in education remains under-researched despite its growing importance in addressing educational challenges in Latin America. Whilst innovation in education can encompass systemic reforms or incremental classroom changes, social innovation specifically involves novel partnerships, institutions, methods, and collaborative arrangements amongst communities, schools, local governments, and non-governmental organizations, emphasizing societal participation through co-creation processes. This paper addresses the research question: How do social actors construct and sustain social innovations in education through networks, partnerships, and community participation? Drawing on fieldwork conducted in Brazil and Mexico between 2013 and 2014 and a follow-up interview in 2023, this study employs case study methodology examining three long-standing educational innovations: City School-Apprentice and Campos Salles School in São Paulo, Brazil, and the Institute of Educational Innovation in Chiapas, Mexico. The analysis suggests that, in the cases examined, social innovations emerged through iterative processes involving collective action, sustained relationship-building, and collaborative governance at the micro level. The findings show how locally rooted networks developed connections to wider institutional structures, under conditions that varied considerably between the two national contexts. The paper's primary contribution lies in offering an empirically grounded sociological account of social innovation in education in two contrasting Latin American contexts. This study shows the influence of diverse stakeholders on the implementation of educational innovations in environments with limited resources. It also emphasizes the transformative potential of grassroots innovation, civil society involvement, and community-centred approaches to educational change in Latin America.
Characterization of protein structure and dynamics using solution- and solid-state NMR
(2026) Kavaleuskaya, Hanna; Linser, Rasmus; Wiegand, Thomas
Proteins carry out biological function through a combination of structure and dynamics, often sampling multiple conformations and including intrinsically disordered regions. Obtaining residue-specific information for large, poorly soluble, heterogeneous, or highly dynamic systems remains challenging, because many established methods lose sensitivity or resolution under these conditions. This thesis develops approaches to study such systems using solution and magic-angle-spinning (MAS) solid-state NMR, supported where useful by computational and evolutionary analyses. The work focuses on three connected objectives. First, it addresses limited H/D back exchange in 1H-detected MAS NMR, which can obscure solvent-protected amide sites, by testing cell extract-based selective deuteration as a way to improve amide protonation while preserving spectral quality. Second, it establishes quantitative criteria for MAS sample preparation by comparing solution, sedimented, and microcrystalline sample states in terms of resolution, sensitivity, and stability, and uses these results to develop a practical framework for backbone assignment from combined solution and MAS datasets. Third, it applies residue-resolved solution NMR to the intrinsically disordered N-terminal domain of cGAS to characterize its conformational dynamics in the apo state and upon binding different DNA molecules, and to interpret these findings in an evolutionary framework. Together, these studies show that labeling strategy, sample state, and analysis approach strongly shape the information obtainable from NMR in challenging biomolecular systems. The results expand the practical toolbox for studying protein structure and dynamics by NMR.