In this thesis conjugated materials are investigated, with a focus on (semi)conductive polymers and oligomers. These materials are promising candidates for use in organic electronics, such as thin-film organic field-effect transistors (OFETs) and organic photovoltaic cells (OPVs). The aim is to elucidate structure-property relationships of active layers of conjugated organic thin films that influence device performance. The structural investigation spans length scales from millimeters, corresponding to macroscopic device architecture, down to nanometers in the range of single crystalline domain sizes, that govern molecular stacking and crystalline order. Such complex structural information is essential for correlating multiscale ordering with device performance. X-ray diffraction is a common technique for structural studies of thin films. However, conventional X-ray laboratory sources lack the brilliance to resolve these features at an advanced level. Therefore, this work is carried out at high-brilliance synchrotron facilities that enable high spatial and temporal resolution X-ray scattering and diffraction studies of active thin film layers. Even at synchrotron light facilities, small- and wide-angle X-ray scattering at conventional beamlines provide average device structures, while advanced nano-focused beamlines enable spatially resolved measurements and localized mappings across device cross-sections with nanometer precision. Besides high-resolution measurements, in situ experimentation is a central part of the thesis. In situ annealing studies show how thermal treatment alters structural properties and its effect on electrical device performance. Understanding annealing dynamics and appropriate thermal budgets is beneficial for processing optimization and for developing robust fabrication protocols. In addition to annealing, other external factors may also have an influence on the structural organization of the film. This work reveals how structural order is affected by an external electric field during film formation, highlighting possibilities for morphology control. The application of a permanent electric field to P3HT induces non-equilibrium aggregation, leading to reduced crystallinity, smaller crystallite size, and increased structural disorder. Such changes provide the advantages to tune morphology, including expanded interfacial area and enhanced nanoscale heterogeneity. In contrast, an alternating electric field allows partial relaxation of polymer chains, resulting in a more equilibrium-like structure with preserved crystallinity. Finally, in operando studies provide direct insights into the structure and electrical response, tracking voltage-induced structural alterations and lattice responses under working conditions. The outcomes of this research include: • In situ studies of conjugated polymer film formation reveal structural changes under an external electrical field. The quality of structural order and the size of crystalline domains can be modified during P3HT drop-casting. This effect is greater with a permanent electrical field than with an oscillating field. While the disordered morphology induced by a permanent field is unfavorable for charge transport in transistors, it can be advantageous for applications such as organic solar cells with increased interfacial area and nanoscale heterogeneity. • The thermal protocol during annealing has a strong effect on structural order. In situ annealing studies show that a low thermal budget preserves and improves the microstructure of polymer-fullerene blends. In contrast, a P3HT:PCBM blend undergoes permanent, irreversible changes during the high thermal budget annealing with PCBM crystallization and phase separation. The structural order of PDOPT decreases until melted but recovers when returning to room temperature at a significantly enhanced level. • X-ray nanobeam diffraction on quasi-freestanding P3HT films demonstrates the feasibility of high spatial resolution diffraction studies on organic thin films revealing local orientation variations. This method is ideal for the investigation of the local crystal structure of organic semiconductors while minimizing substrate induced contributions. Using an extremely focused X-ray beam (spot size approximately 150 nm), local variations in orientation and symmetry within the polymer network are directly detectable. • Nanobeam grazing incidence diffraction (nanoGIXD) experiments revealed a strong gold reorientation and modifications of Au-polymer interfaces during device operation which can be detrimental for applications. • In-operando nanoGIXD measurements revealed a significant anisotropy of the oligomer thin films, under source-drain applied voltages with strong structural variations for both in-plane and out-of-plane directions, including a +1.3 % tensile expansion of the π-stacking distance (d_020). The outcomes underline that high resolution X-ray diffraction techniques are excellent tools for spatially and temporally resolved studies of conjugated organic thin films. The results can support the device processing optimization and development of robust fabrication protocols.

Flood forecasting is a critical component of mitigating risks to human life, infrastructure, and ecosystems, particularly as climate change increases the frequency and severity of extreme weather events. Especially urban areas are vulnerable due to high sealing rates, dense infrastructure, and concentrated populations. Traditional forecasting approaches rely on physically based hydrodynamic models that provide accurate information on the location, timing, and extent of flood events. While these models represent the state of the art, they are computationally too demanding for real-time forecasting and operational decision support. An alternative is offered by data-driven models, which learn patterns and relationships directly from the data. By approximating the underlying input–output dynamics without solving the governing physical equations explicitly, data-driven approaches achieve substantially reduced computation times, making them suitable for real-time flood forecasting. This dissertation develops and advances data-driven forecast systems tailored for urban environments, addressing three major flood types: (1) sewer surcharge events, (2) fluvial floods, and (3) pluvial floods. The research follows a structured progression, advancing from localized short-term predictions to large-scale dynamic forecasts, and finally to domain-independent transferable, physically informed frameworks. For sewer surcharge events, an Ensemble Forecast System based on nonlinear autoregressive networks with exogenous inputs was developed. The Ensemble Forecast System predicts the onset and duration of surcharge events in manholes while explicitly quantifying structural model- and parameter uncertainty through ensemble modeling. With observed events consistently captured within uncertainty bounds, the system provides a reliable framework for early warnings and can support model-based or real-time control in modern sewer networks. For fluvial floods, a Feature-Informed Forecast System was introduced, framing flood prediction as an image-to-image translation task. By integrating convolutional neural networks (CNNs) with spatial features, such as distance-to-river, this system predicts maximum inundation depths within seconds, generating inundation maps that support real-time risk assessment, traffic management, and emergency response planning. The Feature-Informed Forecast System achieves accuracy comparable to that of hydrodynamic models, thus demonstrating the scalability of data-driven approaches to river flooding in urban areas. Building on this, to capture flood dynamics over time, a Multi-Step Dynamic Forecast System was developed. Employing recursive CNN-based predictions, the Multi-Step Dynamic Forecast System produces temporally resolved inundation maps for up to 24 hours ahead. Compared to physically based models, it maintains high accuracy and reliability, thereby bridging the gap between static maximum flood maps and real-time dynamic monitoring. Since these and, in general, conventional data-driven models are often case-site specific and therefore domain-dependent, their applicability in different sites is limited. To overcome this constraint, this dissertation proposes a physically informed spatial-temporal forecast framework, in the context of pluvial floods. This approach integrates CNN-based image-to-image translation with physical constraints by embedding the continuity equation and a kinematic wave approximation into the loss function. This framework improves physical plausibility, enhances predictive accuracy, and enables domain-independent forecasts that generalize to new and unknown areas. Collectively, the contributions of this dissertation demonstrate how data-driven, especially physically informed, models transform flood forecasting into a real-time, scalable, and transferable application. The developed systems advance the state of the art by combining computational efficiency, predictive accuracy, and physical consistency, laying a new foundation for further research in the field of flood forecasting and early warning systems in urban areas.

Background: The use of digital technology to deliver rehabilitation services – also known as telerehabilitation – offers the potential for improved continuity of care, quality of care, and patient-centeredness due to its location- and time-independent nature and the adaptability of the programs. At the same time, telerehabilitation programs place new demands on patients and therapists. If users are not adequately prepared for program usage and lack the necessary com-petencies, the success of implementation and utilization can be hindered. Against this back-ground, this dissertation aims to develop and apply a competency model for patients and the-rapists in telerehabilitation. Methods: The dissertation is based on four studies employing var-ious qualitative and quantitative research approaches that draw on the competency modeling process to identify required competencies, existing training needs, and quality requirements for competency development in telerehabilitation in Germany. It adopts an interdisciplinary per-spective on the research topic and synthesizes models and theories from health literacy, imple-mentation, and educational research that shape the conceptual understanding of competencies and competency development and serve as the theoretical foundation. Results: Patients and therapists perform a wide range of tasks related to the preparation and execution of telerehabil-itation. The required competencies are complex and can be categorized into the four dimensions of knowledge, skills, attitudes, and experience. For patients, personal interest in the program, self-awareness, self-management skills, and openness toward new things are the most relevant competencies; for therapists, these are therapeutic-professional skills, medical knowledge, and telerehabilitation knowledge. The results imply that the tasks and competencies required vary depending on the user, technology, and context, with the type of program standing out as the most important differentiating factor in patients’ assessment of the relevance of competencies. Both user groups demonstrate a moderate to high level of competency and minimal training needs regarding telerehabilitation. Patients and therapists who are less technology-affine demonstrate more and higher training needs. Therapists and patients in Germany currently have access to a variety of information and training offers. However, with regard to the identified quality requirements for competency development, current practice shows deficiencies: oppor-tunities for practical testing are lacking, and the provision of information about telerehabilita-tion in rehabilitation facilities is neither standardized nor widespread. Conclusions: The com-petent use of telerehabilitation requires appropriate user-, technology-, and context-related con-ditions. Needs-based information and training programs, the integration of telerehabilitation into therapeutic vocational training curricula, and user participation in the development of new programs can contribute to the successful application of telerehabilitation in the future