Repository der Technischen Hochschule Ingolstadt
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Understanding Bystander Preferences for Medical Emergency Support Measures in VR-Simulated eVTOL Flights
No full textWith the emergence of Urban Air Mobility (UAM), eVTOLs are set to revolutionize the way of traveling. However, there will be situations where things do not go as planned, such as medical emergencies during a flight. Due to the lack of cabin crew, tailored support measures must be identified to ensure passenger safety and well-being. To explore how potential passengers perceive medical emergencies on board as bystanders and what kind of assistance they expect, we conducted a VR study. The results show significant increases in negative emotions during a medical in-flight emergency. Moreover, support measures involving human interaction were rated significantly higher than those relying solely on information displayed on onboard screens. Particularly well-received were pilot announcements. For UAM to enter the market successfully, it is essential to address passenger needs in critical situations. Our findings provide valuable insights into shaping effective and user-centered emergency support strategies for eVTOL operations
Improving gradient-based LSTM training for offine handwriting recognition by careful selection of the optimization method
No full textDas eLearning-Praktikum: Mathematiklehramtsstudierende gestalten die Lehre von morgen
No full textSeit dem Sommersemester 2022 wird an der Universität Bonn das Modul „eLearning-Praktikum“ für Mathematiklehramtsstudierende angeboten. In diesem Modul lernen die Teilnehmenden, mithilfe von digitalen Tools eigenständig Lernmaterialien für die Hochschullehre zu entwickeln. Sie experimentieren mit verschiedenen Videoformaten wie Lightboardvideos, Screencasts und Animationsvideos, setzen die Software h5p für interaktive Inhalte ein, gestalten Online-Tests mit STACK-Aufgaben und entwerfen digitale Lernmodule. Diese Materialien werden im Rahmen eines Peer Review überarbeitet und anschließend in der Lehre eingesetzt. Dieser Beitrag beschreibt die Ziele, die Rahmenbedingungen und die Umsetzung des eLearning-Praktikums anhand eines Best-Practice-Beispiels. Darüber hinaus werden Ergebnisse einer Befragung der Teilnehmenden vorgestellt, die Einblicke in deren Einstellungen und Lernergebnisse bieten
Digital Prüfen in den MINT-Fächern: Digitalisierte Leistungsbeurteilung mit dem Lernmanagementsystem Moodle
No full textDieser Beitrag der Hochschule München stellt die verschiedenen digitalen Prüfungsformate vor, die durch das Lernmanagementsystem Moodle abgedeckt werden, und erläutert deren Einsatzmöglichkeiten in ausgewählten MINT-Fächern. Dabei werden vor allem die technischen Möglichkeiten des digitalen Prüfens mit Moodle dargestellt und mit Anwendungsbeispielen veranschaulicht. Darüber hinaus werden auch einige ausgewählte Aspekte der Bewertung digitaler Prüfungen in Moodle sowie deren rechtliche Rahmenbedingungen erörtert
LLMS in der Hochschullehre - Chancen und Herausforderungen für die Programmierausbildung
No full textDer Einsatz von ChatGPT und anderen großen Sprachmodellen (LLMs) in der Hochschullehre eröffnet ein Spannungsfeld zwischen didaktischen Potenzialen und Herausforderungen. Dieser Beitrag beleuchtet Chancen und Risiken der LLM-Nutzung, insbesondere in der Programmierausbildung, und stellt konkrete Lösungsansätze vor. ChatGPT wird dabei als Ergänzung und Erweiterung zu bisherigen didaktischen Konzepten genutzt, wie z. B. als virtueller Tutor, als Ideengeber und als Unterstützung für eigene Projekte, ohne einen Verlust an eigenständigem studentischem Wissenszuwachs zu akzeptieren
Innovative Arbeitsmodelle: Eine Bewertung moderner Organisationsformen im Kontext von New Work
No full textDoes pharma R&D need a strategic reset? Adapting to a changing US landscape
No full textR&D productivity has long challenged research-based pharmaceutical companies, raising concerns about the sustainability of their research-driven business models. These firms have traditionally relied on the U.S. as a stable hub for biomedical innovation, skilled talent, and high-price markets—supporting the biotech-leveraged pharma company (BIPCO) model However, recent geopolitical shifts—especially under the new Trump administration, including FDA budget cuts, reduced U.S. research funding, and pharmaceutical tariffs—are destabilizing this foundation. The once-reliable “safe harbor” is no longer secure. As a result, pharma R&D now faces strategic risks beyond its prior scope. With the weakening of the U.S.-centered innovation model, companies must rethink R&D pipelines, secure key technologies, maintain global clinical networks, and adjust supply chain and tax strategies. The viability of the current R&D model—rooted in U.S. leadership and premium markets—is now uncertain, requiring urgent strategic realignment
Quantitative Kernel Estimation from Traffic Signs using Slanted Edge Spatial Frequency Response as a Sharpness Metric
No full textThe sharpness is a critical optical property of automotive cameras, measured by the Spatial Frequency Response (SFR) within the end of line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of automotive camera for state monitoring. To achieve this, Principal Component Analysis (PCA) is performed, using synthetic kernels generated by Zemax. The PCA model is built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images are created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data are utilized for algorithm development, and later on validation is performed on real-life data. The algorithm extracts two 45 x 45 pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel blurs the reference ROIs, and the resulting Spatial Frequency Response (SFR) is compared with the blurred ROIs’ SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel is evaluated against the true kernel for accuracy. Structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.98, and mean squared error (MSE) from 1.1 x 10-5 to 8.3 x 10-5. Validation on real-life camera images shows that the SSIM between estimated ROI is 0.82 indicating a sufficient level of accuracy in kernel estimation to detect potential degradation of the camera
