119092 research outputs found
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Systematic derivation of requirements for the perception task of free space driving assistance function applied to trucks
No full textThe larger size and expanded blind spots of heavy-duty trucks in comparison to passenger cars, create unique challenges for truck drivers navigating narrow roads, such as in urban scenarios. For this reason, the detection of free space around the vehicle is of critical importance, as it has the potential to save lives and reduce operating costs due to less maintenance and downtime. Despite the existence of numerous approaches to free space detection in the literature, few of these have been applied to the trucking sector, disregarding important aspects for these kinds of vehicles such as the altitude at which obstacles are located. This paper aims to present the initial results of our research, a “Not Free Space Warner”, a driving assistance function intended for implementation in series trucks. A methodology is followed to define the characteristics that the perception component of this function shall fulfill. To this end, an analysis of the most critical accidents and common driving situations that truck drivers encounter is conducted, with a particular focus on the potential contribution of free space detection to assist the driver. By deriving and analyzing multiple scenarios from the use cases, the requirements to be met by the perception pipeline of function are defined. To validate these requirements, a Mercedes Actros equipped with multiple ground truth sensors, utilized as a research vehicle, is presented. Finally, the limitations and challenges associated with its implementation in the context of trucks are discussed
Advanced Transmission Electron Microscopy of Transition Metal Oxides with Unconventional Growth Mechanisms
No full textComplex functional oxides exhibit a wide range of emergent electronic and structural properties, arising from the intrinsic competition between charge, spin, lattice, and orbital degrees of freedom. The interplay between these interactions continuously leads to the discoveries of novel functional oxides, deepening the understanding of correlated materials and expanding their potential applications in electronic and quantum devices. This thesis highlights the critical role of (scanning) transmission electron microscopy ((S)TEM) in exploring such interatomic structure-property correlations in oxides and oxide heterostructures
Data‐driven design of mechanically hard soft magnetic high‐entropy alloys
No full textThe design and optimization of mechanically hard soft magnetic materials, which combine high hardness with magnetically soft properties, represent a critical frontier in materials science for advanced technological applications. To address this challenge, a data‐driven framework is presented for exploring the vast compositional space of high‐entropy alloys (HEAs) and identifying candidates optimized for multifunctionality. The study employs a comprehensive dataset of 1 842 628 density functional theory calculations, comprising 45 886 quaternary and 414 771 quinary equimolar HEAs derived from 42 elements. Using ensemble learning, predictive models are integrated to capture the relationships between composition, crystal structure, mechanical, and magnetic properties. This framework offers a robust pathway for accelerating the discovery of next‐generation alloys with high hardness and magnetic softness, highlighting the transformative impact of data‐driven strategies in material design
Addressing standardization and semantics in an electronic lab notebook for multidisciplinary use: LabIMotion
No full textThis work presents the LabIMotion extension for the Chemotion Electronic Lab Notebook (ELN), expanding its capabilities from organic chemistry to support interdisciplinary research and enabling the description of workflows. LabIMotion enhances documentation by introducing customizable components structured across three levels—Elements, Segments, and Datasets—enabling flexible, hierarchical organization and reuse of data. Through the integration of links to ontologies, the extension ensures precise, machine-readable data, promoting interoperability and adherence to FAIR principles. The extension features an intuitive, user-friendly interface that allows researchers to easily create new ELN content by leveraging a set of customizable, generic methods. Scientists can set up new data fields, can link data fields, or establish workflows, and the extension translates those needs directly into usable functionality at their command. Through this high degree of flexibility, a wide range of specific research needs can be met. The LabIMotion Hub plays a crucial role in distributing and updating components, fostering standardization, and enabling collaborative development within scientific communities. These advancements significantly improve the ELN's adaptability, usability, and relevance across various research disciplines.
Scientific contribution
This work demonstrates how research data management systems can be designed to support discipline-specific requirements in chemistry research while offering a high flexibility and interoperability to deal with interdisciplinary work. The developed software, LabIMotion, offers a versatile approach for integrating novel research aspects into a research data environment, fostering bottom-up processes for defining schemas and standardizing scientific workflows. In particular, the software’s support for community-driven extensions, combined with a clear definition of content and its assignment to ontology terms, provides unique advantages for creating adaptable tools suited to the complexities of the scientific environment
Detection of production relevant deviations in paint sprays
No full textSpray painting is still a poorly manageable process due to the complex interaction of physical, chemical and environmental influences like turbulent air flows, strong electrostatic fields, complex viscosity of paints and paint booth conditions. Consequently, many important properties of the painted film, like thickness, color, surface structure and the efficiency of the process are not controllable in an adequate manner, despite the enormous economic ramifications of poor quality control in high volume applications, such as in the automotive industry. This study shows how novel, online spray monitoring can instantaneously generate characterizing quantities from the spray to detect harmful deviations in the process. In this study, several minute changes have been experimentally imposed on a paint system, such as changed paint viscosity or pigmentation, deviations in air flow and paint flow rates, and defective or used and worn equipment parts. It will be shown that all these deviations lead to features which allow a clear distinction from the intact and reference cases. Additionally, it is shown that most of the deviations, if not detected, would have led to quality issues of the paint coating
Automated Robot-Based Computed Tomography Trajectory Optimization using Differential Evolution in 3D Radon Space
No full textLimited accessibility of the X-ray hardware manipulating robots stemming from collision elements and the restricted workspace of the robots as well as areas of significant X-ray absorption are inherent characteristics of robot-based computed tomography scanning in subregions of large structures. The manual definition of trajectories is resource-intensive and results in substantial user influence on the resulting data quality. Therefore, this work proposes a method for the automated calculation of optimized (partial) circular scan trajectories for robot-based computed tomography. Specifically, a differential evolution algorithm is used to find global parametrization optima by estimating the reconstruction quality of trajectories. This estimation is based on a quantitative sampling quality metric in 3D Radon space, which is introduced in this work. The proposed method is evaluated on a test body from a region of limited accessibility within the strut mount of a car body. The reconstruction results are compared to those obtained from nearly 1000 reference trajectories. The results demonstrate that the proposed technique automatically generates trajectories that surpass the global optimum in data completeness of all reference trajectories. This methodology thus enables the elimination of user influence in trajectory parametrization
Algorithmic Accountability: An Analysis of AI Developers' Perceptions and Behavioral Responses
No full textThe increasing integration of Information Systems (IS) based on Artificial Intelligence (AI) into diverse societal and organizational domains has made algorithmic accountability a critical concern in IS research and practice. As these systems assume greater roles in high-stakes decision-making, such as in healthcare, finance, and criminal justice, they raise pressing questions about ethics, governance, and, ultimately, algorithmic accountability. Algorithmic accountability aims to clarify who is obligated to justify the design, use, and outcomes of AI systems and who bears responsibility for their potential negative consequences. While policymakers, organizations, and the public emphasize the need for algorithmic accountability, much of the existing discourse has mainly remained conceptual, raising the question of how algorithmic accountability and perceptions of it materialize in practice and what concrete effects they have. Understanding these manifestations is crucial, particularly concerning AI developers, who directly shape AI design and whose accountability perceptions influence their development decisions. Against this backdrop, this dissertation examines how accountability triggers foster accountability perceptions among AI developers, how these perceptions manifest, and how they influence AI developers’ behavior in AI systems development. The findings reveal that direct indications, such as accountability arguments embedded in IS engineering tools, effectively evoke accountability perceptions among AI developers. However, these perceptions are not uniform but rather multifaceted, varying in intensity and reference points. While they often lead AI developers to favor more cautious designs of AI systems, unclear accountability attributions can negatively impact work-related affective states. These insights highlight the importance of designing algorithmic accountability mechanisms that trigger accountability perceptions and clarify their scope and implications, ensuring both responsible AI systems development and sustainable work environments for AI developers.
This dissertation consists of four peer-reviewed articles (Article A–D) that address the socio-technical and behavioral dimensions of algorithmic accountability in AI systems development. The first part of this dissertation explores how organizations can trigger and shape accountability perceptions. Given the limitations of established governance mechanisms such as AI principles and AI audits, Article A introduces accountability arguments as embedded accountability triggers within IS engineering tools. Using a mixed-method research approach, the article demonstrates that AI developers differentiate between accountability perceptions related to development processes (process accountability) and those concerning the outcomes of AI systems (outcome accountability). The findings reveal that process accountability is more immediately perceived, while outcome accountability requires targeted interventions to be internalized equally effectively by AI developers. These insights advance IS research by conceptualizing accountability arguments as a dynamic governance mechanism that actively shapes AI developers’ accountability perceptions in AI systems development.
The second part of this dissertation examines how different forms of accountability perceptions manifest among AI developers. Through an online survey, Article B highlights the consequences of incongruence in intrapersonal accountability perceptions, differentiating between self-attributed accountability and others-attributed accountability, referring to accountability assigned by others. The article demonstrates that misalignment between these perceptions increases role ambiguity and reduces job satisfaction, underscoring the need for clear and transparent algorithmic accountability communication within organizations. Through qualitative interviews, Article C further refines this understanding by distinguishing between two conceptualizations of algorithmic accountability: one as an intrinsic ethical virtue shaping AI developers’ decision-making and the other as an external governance mechanism ensuring compliance with organizational and regulatory standards. The findings reveal that AI developers’ ethical orientations influence whether they proactively integrate algorithmic accountability into their decision-making or adapt a more reactive, compliance-driven approach. This differentiation is essential for organizations seeking to cultivate a shared algorithmic accountability culture within AI systems development teams.
The third part of this dissertation explores how accountability perceptions shape AI developers’ behavior, especially related to AI design. While prior IS research has predominantly focused on the effects of accountability perceptions on users’ behavior, Article D shifts the focus to AI developers as decision-makers by employing a scenario-based survey, revealing that heightened accountability perceptions lead to more cautious and risk-averse AI design preferences. AI developers who perceive strong accountability tend to reduce AI systems’ autonomy and inscrutability while prioritizing their learnability. This article advances IS research by demonstrating that algorithmic accountability is not only a governance mechanism but also a factor that actively shapes AI design. These findings call for organizations to carefully balance algorithmic accountability mandates with innovation goals, as excessive algorithmic accountability pressure may constrain exploratory design decisions.
Taken together, the articles in this dissertation contribute to IS research by providing a more holistic understanding of how accountability triggers evoke accountability perceptions among AI developers, how these perceptions take shape in diverse and multifaceted ways, and how they ultimately influence AI systems development practices and decision-making. In doing so, this dissertation conceptualizes algorithmic accountability as a multi-layered construct, examining how AI developers internalize algorithmic accountability, how inconsistencies in accountability perceptions affect work-related affective states, and how these perceptions shape AI developers’ behavior. By differentiating between process and outcome accountability within AI systems development, self- and others-attributed accountability, and algorithmic accountability as a virtue versus a mechanism, this dissertation advances a more nuanced perspective on algorithmic accountability and its broader implications. These insights lay the groundwork for future IS research on algorithmic accountability as a dynamic and evolving governance mechanism within IS development practices. From a practical perspective, this dissertation offers valuable guidance for organizations and policymakers. For organizations, the findings suggest that integrating embedded algorithmic accountability interventions into development workflows can enhance clarity and consistency in algorithmic accountability communication, helping to minimize perceptual misalignment among AI developers. Rather than merely imposing mandates, effective algorithmic accountability frameworks must actively shape how algorithmic accountability is understood, internalized, and applied in practice, ensuring that AI developers engage with it as an embedded and actionable aspect of their work. For policymakers, this dissertation underscores that regulatory approaches must not only mandate algorithmic accountability but also consider how AI developers perceive and internalize these requirements. Ambiguously framed algorithmic accountability mandates risk creating unintended and potentially counterproductive consequences, as ambiguous understandings of algorithmic accountability may negatively impact AI developers’ ability to adhere to algorithmic accountability standards in practice.
These findings call for closer collaboration between researchers, organizations, and policymakers to ensure that algorithmic accountability remains both theoretically sound and practically implementable. Future IS research should explore how accountability perceptions evolve over time, how interactions between AI stakeholders shape algorithmic accountability, and how algorithmic accountability mechanisms influence AI system adoption and long-term societal outcomes. Ultimately, this dissertation lays the groundwork for developing more effective governance strategies for AI systems, enabling organizations to proactively shape accountability perceptions, and ensuring that AI systems are not only technically advanced but also aligned with ethical and societal expectations
Simulated biomechanical performance of morphologically disparate ant mandibles under bite loading
No full textInsects evolved various modifications to their mouthparts, allowing for a broad exploration of feeding modes. In ants, workers perform non-reproductive tasks like excavation, food processing, and juvenile care, relying heavily on their mandibles. Given the importance of biting for ant workers and the significant mandible morphological diversity across species, it is essential to understand how mandible shape influences its mechanical responses to bite loading. We employed Finite Element Analysis to simulate biting scenarios on mandible volumetric models from 25 ant species classified in different feeding habits. We hypothesize that mandibles of predatory ants, especially trap-jaw ants, would perform better than mandibles of omnivorous species due to their necessity to subdue living prey. We defined simulations to allow only variation in mandible morphology between specimens. Our results demonstrated interspecific differences in mandible mechanical responses to biting loading. However, we found no evident differences in biting performance between the predatory and the remaining ants, and trap-jaw mandibles did not show lower stress levels than other mandibles under bite loading. These results suggest that ant feeding habit is not a robust predictor of mandible biting performance, a possible consequence of mandibles being employed as versatile tools to perform several tasks
Theologie an der Uni
No full textDas »Institut für Theologie und Sozialethik« feiert in diesem Jahr sein 40-jähriges Bestehen im Fachbereich Gesellschafts- und Geschichtswissenschaften. Ein Rückblick im Zeitraffer
40 Jahre Richtfest Maschinenbauhallen auf der Lichtwiese
No full textAm 17. April jährte sich das Richtfest der Versuchshallen für den Fachbereich Maschinenbau auf dem Erweiterungsgebiet Lichtwiese zum 40. Mal. Bereits 1970 hatte das Staatliche Hochschulbauamt Darmstadt dort mit dem Bau des neuen Institutsgebäudes begonnen, dem sich die Hallen und Laboratorien im rückwärtigen Bereich anschließen