In this thesis, the concept of a new type of rear axle, the so-called multi-link torsion axle, is developed. By integrating a reversed twist-beam structure into a longitudinally orientated Watt’s linkage, the position of the space-limiting cross beam is decoupled from the original longitudinal instant centre. This opens up an increased, homogenous package space in the vehicle underbody, which can be allocated to the traction battery when used in a battery electric vehicle. At the same time, a longitudinal instant centre in front of the wheel, and thus also positive anti-lift, can be ensured by the alignment of longitudinal links in side view. Once the basic topology of the mechanism has been defined, it is optimised regarding the hardpoints. For this purpose, multiple analytical calculation approaches of the motion are presented and then integrated into an optimisation algorithm. The hardpoints are optimised with respect to global and local package boundary conditions, as well as kinematic and elastic requirements and target values. These refer to typical suspension characteristics found in literature, as well as characteristics that can occur specifically with the given mechanism. The resulting properties are then compared with a conventional twist-beam axle at both the suspension and full-vehicle level. For this purpose, this new type of rear axle is installed in a demonstrator vehicle loaded with ballast masses and tested on a proving ground. Full-vehicle simulations are also used for better interpretation. These experiments focus particularly on the vertical dynamic properties of the new axle with respect to comfort for different road excitations as well as pitching under braking. The results obtained also allow conclusions to be drawn about the previously defined suspension characteristics. With the results of this test series, it is possible to conclude whether the new axle concept is within a tuneable range in terms of driving dynamics.

Die vorliegende Regionalstudie zum Sozialsektor im Kreis Siegen Wittgenstein ist im Kontext des ZPE-Projekts zu Social Entrepreneurship und Innovation in sozialen Diensten entstanden. Auf der Grundlage von Desk-top Recherchen und empirischen Befragungen von Leitungskräften wird beschrieben, wie sich die soziale Infrastruktur des Kreises insgesamt und in den einzelnen Feldern des Sozialsektors im Zeitverlauf entwickelt hat, welche aktuellen Herausforderungen bestehen und welche Perspektiven sich für innovatives Handeln bieten. Aufgezeigt wird die konkrete Datenlage zur wirtschaftlichen Bedeutung des Sektors insgesamt im Hinblick auf Umsatz-Volumen und Beschäftigungsanteilen; zudem werden Ergebnisse zu Trägerstrukturen, Problemprofilen und zum Selbstverständnis der freigemeinnützigen sowie der privatgewerblichen Anbieter sozialer Dienste vorgestellt. Deutlich wird, dass sich im Kreis Siegen-Wittgenstein die für die deutsche Entwicklung des Sozialwesens typischen Merkmale wiederfinden, allerdings in einer durch örtliche Besonderheiten variierten Form. Dokumentiert wird, wie sich der allgemeine Ausbau der sozialen Dienste in Deutschland in der ausgewählten Kommune konkretisiert und welchen Einfluss strukturelle Veränderungen in der Trägerstruktur auf das Selbstverständnis der Leitungskräfte aller betrieblichen Ebenen haben. Damit erhält die Untersuchung über eine regionale Bestandsaufnahme hinaus den Charakter einer exemplarischen Fallstudie, deren Ergebnisse zum wohlfahrtsstaatlichen Fachdiskurs beitragen sollen. Bezug genommen wird insbesondere auf die Diskurslinien zu Ökonomisierung, Hybridisierung, Professionalisierung und Innovation sozialer Dienste bzw. kommunaler Sozialpolitik.

We live in times of rapid change, where unprecedented and unexpected events unfold with increasing speed and complexity, often disrupting familiar patterns and norms. In the midst of this uncertainty, AI plays a growing role, shaping how we interact, work, and live. To navigate such ambiguity, humans rely on sensemaking —a process of interpreting the unknown, settling on plausible explanations, and adapting their actions accordingly. Despite AI’s growing influence on nearly every aspect of life, studies repeatedly show that users often have a limited understanding of it, leading to misconceptions and unrealistic expectations. This lack of understanding not only results in frustration when AI systems fail to meet users’ needs but also hinders effective interaction and collaboration with these technologies. This dissertation views AI as a socio-technical umbrella term to explore how users make sense of it across three application domains: AI-assisted decisionmaking, AI-mediated social platforms, and agentic AI technologies. Guided by three research questions, it focuses on (1) how users make sense of AI in everyday encounters, (2) the empowerment needs that arise from these interactions, and (3) how design can support sensemaking and foster user agency. Recognizing the importance of context and the situated nature of sensemaking, this work combines various qualitative methods, such as semi-structured interviews, role-playing workshops, and experience sampling. Building on Weick’s sensemaking framework, the findings reveal that sensemaking of AI shares familiar characteristics—it’s enactive, driven by plausibility, inherently social, and often triggered by unexpected events. But AI sensemaking also has unique aspects, such as the influence of AI "folk concepts"—users’ assumptions and expectations about AI shaped by cultural narratives and societal definitions. These perceptions are also influenced by human traits like trust and intelligence and vary depending on stakeholder roles. AI sensemaking not only guides users’ actions but also informs counteractions—how people push back against or adapt to algorithmic systems. To empower users, this research takes a holistic approach, exploring their needs along three dimensions: feeling, knowing, and doing. It highlights how users’ sensemaking is influenced by both episodic power—arising from immediate, specific interactions—and systemic power, which operates at a structural level through institutional norms and opaque algorithmic designs. For designing AI systems that better support users’ sensemaking, the dissertation emphasizes two critical aspects. First, it frames sensemaking as an interactional element, suggesting that AI systems should foster continuous, context-specific engagement by helping users build competencies. Second, it stresses the importance of diversifying user participation in the design process. Involving users as co-creators and knowledge-makers empowers them to engage not just in sensemaking, but also in sense-unmaking, sense-giving, and sense-breaking—creating a more collaborative and inclusive design process that addresses both individual needs and systemic challenges. Finally, this dissertation argues for moving beyond Weick’s retrospective sensemaking framework to apply the concept of prospective sensemaking in the context of AI. This approach emphasizes designing systems that not only help users make sense of past interactions but also enable them to anticipate, adapt to, and shape the uncertainties inherent in their ongoing and future engagements with AI technologies.

This dissertation investigates the impact of job preemptions and restarts in online machine scheduling, with a focus on three fundamental objectives. An online scheduling algorithm determines whether to process a job or not without knowing the future events. One important extension to the classical online scheduling framework is the use of restarts. Allowing restarts means that the processing of a job may be interrupted, but in this case the interrupted job loses all its previous progress and must be started again later, until it is completed without interruptions. This model also known as preemption with restarts. Although restarts can potentially be very beneficial in the context of online scheduling, there has been relatively little research on this topic up until now. First, we study the problem of minimizing the total completion time on a single machine. We present a simple deterministic online algorithm that achieves a competitive ratio better than 1.4568, improving upon the previous best bound of 3/2. The algorithm follows an increasing-size processing order and interrupts a running job only when doing so reduces the completion time of an arriving job by more than a factor of 1.4568 compared to processing it immediately. Second, we extend the results to the minimization of total weighted completion time. For the case of equal processing times, we design a deterministic online algorithm that achieves a competitive ratio better than 1.325 based on carefully structured decision intervals. The algorithm uses at most three restarts for the entire schedule and redefines these decision intervals in certain situations to avoid further interruptions. Finally, we consider the weighted makespan minimization problem. This problem is generalization of the classical makespan minimization problem. We present a deterministic online algorithm that achieves a competitive ratio better than 1.3098 in the case where all jobs have equal processing times.