Eldorado - Repositorium der TU Dortmund

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Fach- und Universitätsübergreifendes 1319
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Aktuellste Veröffentlichungen

Algorithms for planning and interpolating movement
(2026) Hagedoorn, Mart; Buchin, Kevin; Polishchuk, Valentin
Movement shapes the world around us, and studying it offers a way to better understand that world. Questions of movement are especially prominent today: vast quantities of movement data allow us to analyze past trajectories, while many practical tasks require us to plan future ones. Both endeavors must often contend with incomplete information, limited resources, and environmental constraints. In my thesis, “Algorithms for Planning and Interpolating Movement,” I study algorithmic problems in route planning and movement analysis across discrete and geometric environments. We develop efficient methods for computing, approximating, and analyzing paths and walks under structural, temporal, and geometric constraints. The results range from complexity and approximation algorithms for route optimization to practical tools for tour generation, stochastic trajectory reconstruction, and geometric shortestpath queries. The contributions span three complementary areas. First, we study variants of the Orienteering Problem, delineating tractability and hardness on paths, cycles, and trees and, for the Edge Orienteering Problem, providing a (4+ε)-approximation and investigating practical heuristics. Second, we develop methods for reconstructing movement from sparse trajectory data, using dynamic programming and conditioned random walks to compute transition densities, visit probabilities, utilization distributions, and individual trajectories. Finally, we study minimum-link paths in polygonal domains with holes, developing preprocessing and query structures for efficient two-point link-distance queries and polynomial-time computation of the link diameter, radius, and center. Together, these results combine structural complexity insights with efficient algorithms and practically applicable methods.
Rethinking work in the digital age
(2026) Schmid, Amelie; Wiesche, Manuel; Lackes, Richard
Incumbent firms face multifaceted challenges from considerable cost pressure and new market players. Implementing data-driven technologies such as data science, artificial intelligence (AI), and algorithmic management (AM) can be relevant measures to counteract. However, introducing data-driven technologies in the workplace leads to profound changes in various aspects of work. We state that the present understanding of the implications for work and workers within incumbent firms needs an extension in three distinct areas. First, new insights are essential on how workers adapt their work practices in response to the changing environment. Second, it remains unclear how workers build trust in AI-based systems, given concerns about the trusting belief “reliability”. Third, exploring the impact of AM on workers’ efficiency, established organizational structures, and existing work relationships within traditional organizations is crucial. A multi-method research strategy is followed to address these research gaps. By considering the complexity of our phenomenon, we set up a multiple case study to examine the reconfiguration of work practices based on the increasing usage of data science. Moreover, we apply grounded theory methodology to investigate the trust-building process of workers in AI-based systems and the utilization of an ethical framework. Finally, we measure the potential performance effects of AM in the traditional work context, based on ~12700 manufacturing errors, using linear mixed modeling. We extend this quantitative analysis by 15 confirmatory semi-structured interviews to get a deeper understanding of AM impact and find detailed explanations. The present work provides several empirical findings. First, we illustrate how engineers reconfigure the engineering role using data science work. We identify the hybrid practice of data science work, combining engineering and data science practices. Second, we develop a process for AI implementation and operation along the five phases of the AI lifecycle. We show the dynamic interplay between building trust and the acceptance level of AI reliability. Besides, the impact of an ethical framework for trust calibration is assessed. Finally, we demonstrate that AM increases workers’ efficiency and impacts existing organizational structures. In particular, human managers are essential shapers of AM and remain key supporters. Moreover, AM influences team dynamics as workers engage in collective bypassing, situational optimization, and hybrid interaction. We can make several contributions to theory and practice based on our results. We enhance research on data science work by providing evidence of the relevance of domain experts for shaping data science work and showing the specific characteristics of the new hybrid role. We extend trust theory by illustrating the dynamic process of building trust in AI, as workers are willing to reconfigure their expected level of reliability. Moreover, we highlight five supportive organizational measures and the utilization of an ethical framework. We contribute to the AM literature by validating the performance effects in traditional settings, considering socio-technical factors. Additionally, we explore the supportive role of human managers and the impact of AM on existing team dynamics. In practice, workers, leaders, and IT project team members can benefit from several recommendations for successfully implementing emerging data-driven technologies.
Metal-triggered topology switching in bipyridine-modified DNA G-quadruplexes
(Oxford University Press (OUP), 2026-07-28) Durmisevic, Armin; Openy, Joseph; Majid, Aatikah; Nanda, Mrunal; Vilar, Ramon; Clever, Guido H
Gaining control over DNA G-quadruplex topology bears potential to modulate and study their interaction with other biomacromolecules such as proteins. To achieve this, we introduced bipyridine ligands into short oligonucleotide strands derived from telomeric regions of humans and Tetrahymena as well as into an oncogenic promoter region capable of forming such G-quadruplexes. This modification makes it possible to dynamically access different G-quadruplex topologies through the formation of chelate complexes within the quadruplex loop regions using metals such as Cu2+, Ni2+, Zn2+, Co2+, and Cd2+. The metal-coordinated systems show enhanced stability towards thermal denaturation as well as a solvation-related response in the presence of molecular crowding reagents. Interestingly, these G-quadruplexes modified with bipyridine-metal complexes stay folded in cellulo and therefore show potential for creating new oligonucleotide-based diagnostic agents and therapeutics. Metal-stabilized G-quadruplexes could therefore be suitable as probes to explore protein-G4 interactions, as inducers for G4-dependent cellular processes, or decoys to sequester transcription factors.
Vocabulary and expressive morpho-syntax in individuals with Down syndrome: links to narration
(Elsevier BV, 2024-07-21) Neitzel, Isabel
Background: Narrative ability is crucial for social participation in everyday and school life but involves different language abilities such as vocabulary and morpho-syntax. This is particularly difficult for individuals who display both language and cognitive impairments. Previous research has identified productive vocabulary as a possible key factor for narrative performance in in- dividuals with Down syndrome. Considering a close connection between lexical and morpho- syntactic performance within language acquisition and the distinct impairments that in- dividuals with Down syndrome display concerning their morpho-syntactic skills, the nature of a relation between vocabulary and narrative skills under the influence of grammatical deficits re- quires further investigation. Methods: Narrations were obtained from 28 children and adolescents with Down syndrome (aged 10;0–20;1) using a non-verbal picture book. Narrative abilities were rated using the Narrative Scoring Scheme across seven narrative aspects (including macro- and microstructure). Vocabulary analyses and morpho-lexical context analyses including verb and conjunction enumerations, evaluation of verb position and MLU were conducted. Findings from the transcript analysis have been supplemented with data from standardized language measures evaluating expressive lexical and morpho-syntactic development. A multiple regression analysis was conducted to identify significant predictors for narrative outcome in the participants with Down syndrome. Results: Lexical analyses revealed a high heterogeneity in production of subordinating conjunc- tions as a link between lexical and morpho-syntactic abilities. Comparisons of standardized and narrative data demonstrated differences in subordinate clause production depending on the elicitation setting. A multiple regression analysis identified the number of different verbs in the narrative task as the most significant predictor for narrative performance in individuals with Down syndrome. Discussion and implications: The findings of this study contribute to the knowledge regarding factors that influence narrative performance in individuals with language impairment. A differ- entiated verb lexicon can be identified as the key ability for reaching advanced narrative skills in participants with Down syndrome. These findings are of clinical relevance for therapeutic and educational support and contribute to an understanding of the relation between strengths in vocabulary and morpho-syntactic weaknesses in individuals with Down syndrome within communicative participation.
Influence of cosolvents on the pressure stability of human serum albumin
(Wiley, 2024-11-04) Savelkouls, Jaqueline; Schneider, Eric; Woo, Chang Hee; Paulus, Michael
A small-angle X-ray scattering study on the pressure-dependent behavior of human serum albumin (HSA) is presented. HSA is able to absorb cosolvents such as drugs or other hydrophobic substances and transport them, for example, in the human body. It is shown that the uptake of various substances is associated with increased pressure stability, which can be used as an indicator of protein–substance interaction. Especially the interaction between the substances and the hydrophobic pockets of the protein is an important aspect, which mainly depends on the hydrophobicity as well as the size of the used cosolvents. Specifically, drugs such as ibuprofen and theophylline increase the pressure stability of the protein enormously, while caffeine or TMAO, for example, has no strong influence.