Offenburg University of Applied Sciences
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Pyrogenic carbon and carbonating minerals for carbon capture and storage (PyMiCCS) part I: production, physico-chemical characterization and C-sink potential
Get PDFCarbon dioxide removal (CDR) at gigaton-scale is essential to meet the Paris climate goals. Relevant CDR rates can only be achieved through the co-deployment of multiple CDR approaches. However, synergisms between different CDR methods and joint co-benefits beyond CDR have seldom been investigated. The combination of pyrogenic carbon (PyC) and enhanced weathering of minerals (Mi) for carbon capture and storage (CCS), in short PyMiCCS, presents a potentially synergetic and multifunctional approach that may be achieved by either co-application of biochar and rock powder to soils or the co-pyrolysis of biomass and rock powder before soil use. Here, we mixed biomass (wood; straw) with 10 to 50 wt% silicate rock powder (namely basanite or diabase) for co-pyrolysis to produce twelve different rock-enhanced (RE-)biochars. Products were subject to physico-chemical characterization, including an assessment of carbon yield and proxies for biochar persistence. Rock-enhanced biochars showed higher nutrient content, liming- and C-sink potential but lower solid-state electrical conductivity and porosity compared to pure biochars. Co-pyrolysis resulted in a coating of rock particles with secondary char but did not affect the net carbon yield. The thermal stability of wood-based RE-biochars (+10 wt% rock) was higher than that of pure woody biochars. However, the underlying mechanism and implications for biochar persistence in the environment need further investigation. Despite the addition of rock powder, the short-term release of ions from the ash fraction remains dominated by cations and anions of biogenic (biochar) origin. Therefore, it is still unclear whether the pyrogenic coating influences rock weathering. Co-pyrolysis with rock dust opens further options for designing biochar properties and to produce novel composite materials catering for multifunctional CDR
State of the Art in Smart Metering Infrastructure Security: A Keyword Co-Occurrence Analysis
No full textTraditional meters for utilities such as electricity, gas, and water are increasingly replaced by smart meters worldwide. The smart metering infrastructure (SMI) must be safeguarded against cyberattacks and malicious entities in the perpetually evolving landscape of cybersecurity. The elements in the smart metering network, including devices and data, should be offered high level of confidentiality, authentication and privacy. This study employs keyword co-occurrence analysis to explore the state of the art in security for smart metering infrastructure. Incorporating the latest studies, our paper analyzes the literature in an objective and data driven manner to derive conclusions about the research landscape in smart metering security. We have identified the research hotspots in this domain as attacks and their detection, application of emerging technologies like AI, authentication mechanisms and privacy preservation. Additionally, this research outlines future research directions for further targeted exploration
Challenges With Regard To Learning Analytics Metrics In Complex, Multidimensional Courses With Use Of Artificial Intelligence Tools
No full textThe growing significance of new engineering methods, such as Model-Based Systems Engineering (MBSE), necessitates that engineering curricula evolve to prepare future engineers effectively. A promising approach to teaching these methods involves combining seminars and case studies within engineering design projects. To succeed, students must grasp interdisciplinary concepts and process methods, but lecturers face challenges in defining appropriate tasks, tracking individual progress, and fostering self-regulated learning. Traditional assessment tools, like standard questionnaires, have proven inadequate in monitoring student learning progress. Instead, structured interviews at key milestones were used to compare solutions and methods between students and lecturers, yielding valuable insights but proving time-consuming and limited in scope. This raises the question of how to efficiently collect meaningful learning analytics data in such courses
Analysis of Inference Parameters on Diffusion Based Image Generation
No full textThis thesis investigates the influence of inference parameters on the visual quality of images generated by state‐of‐the‐art diffusion‐based models, with a particular focus on applications in game asset production. Motivated by the increasing prominence of generative AI in creative industries and the need for efficient, high‐quality 2D asset creation, this study addresses a critical gap in the literature, which has predominantly concentrated on prompt optimization and text–image alignment. Two models, Stable Diffusion 3 Medium and Flux.1, were employed to systematically explore how variations in CFG scale, denoise strength, noise seed, and sampler–scheduler pairings affect both structural fidelity and perceptual quality. Multiple batches of images were generated under controlled parameter adjustments and subsequently evaluated using a comprehensive suite of image quality assessment metrics—including SSIM, MS‐SSIM, LPIPS, Laplacian variance, SIFT keypoints, and Earth Mover’s Distance (EMD) in both frequency and Lab domains. The results reveal that the CFG scale exerts a non‐linear effect on image quality, with mid‐range settings yielding optimal structural and perceptual similarity, while excessively low or high values lead to fragmentation or homogenization of details. Adjustments in denoise strength demonstrated a trade‐off between noise reduction and the preservation of fine image details, as excessive denoising improved clarity at the expense of textural nuance. Moreover, variations in the noise seed parameter induced significant stochastic variability in the outputs, and the selection of sampler–scheduler pairs was found to cause abrupt transitions in visual characteristics, underscoring their critical role in the generative process. These findings have important implications for the deployment of generative AI in practical settings, suggesting that fine‐tuning inference parameters is essential to balance creative flexibility with production consistency
Red Teaming: Durchführung und Dokumentation eines simulierten Angriffs auf ein fiktives Unternehmensnetzwerk
Get PDFDiese Thesis untersucht, wie Red Teaming als Methode genutzt werden kann, um typische Schwachstellen in Unternehmensnetzwerken zu identifizieren und zu bewerten. Sie verbindet theoretische Ansätze mit praktischen Experimenten
Entwicklung und Konstruktion einer Rückführungsebene für ein Transfersystem
No full textIm Rahmen der vorliegenden Bachelorarbeit wird der Entwicklungs- und Konstruktionsprozess einer Rückführungsebene für ein Werkstückträger-Transfersystem des TECHTORY Automatisierungsbaukastens beschrieben. Das Ziel besteht darin, das bestehende Transfersystem der TECHTORY Automation GmbH zu erweitern. Geplant ist die Entwicklung einer Einheit, die eine vertikale Bewegung der Werkstückträger ermöglicht sowie einer weiteren Einheit, die für die Richtungsänderung zuständig ist.
Der Schwerpunkt dieser Arbeit liegt auf der Entwicklung und Bewertung verschiedener Lösungsvarianten sowie auf der konstruktiven Ausarbeitung der optimalen Lösung, die auf Basis einer Nutzwertanalyse ermittelt wird. Zu Beginn werden auf Grundlage einer Anforderungsliste verschiedene Konzepte analysiert. Diese Anforderungsliste gewährleistet, dass die neuen Module mit dem bestehenden Transfersystem kompatibel sind und sich nahtlos in das Gesamtsystem integrieren lassen. Das Gesamtsystem aus gerader Transferlinie mit Lift- und Richtungsänderungseinheit soll nach Kundenwunsch individuell aufgebaut werden können, um Anlagen und Rückführungen gemäß unterschiedlicher Anforderungen realisieren zu können
