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Too Close for Comfort? The Impact of eVTOL-Overflights in Residential Areas on Non-Users' Acceptance
No full textUrban Air Mobility (UAM) has the potential to revolutionize commuting by allowing passengers to travel quickly and efficiently within and between cities and airports. However, this innovation also raises concerns for residents on the ground, who are expected to tolerate frequent eVTOL overflights above their homes - an issue that this paper seeks to address. To investigate acceptance of eVTOLs from the perspective of residents on the ground being overflown at 1000 ft, 1500 ft, and 2000 ft, a virtual reality study was conducted. Results showed significant differences in emotions, the feeling of being disturbed by the noise, the spatial proximity, and the presence of the eVTOL in lower altitudes. Additionally, privacy concerns were expressed. The findings help the scientific community and regulators in developing guidelines for operating eVTOLs in residential areas in an acceptable manner for non-passengers
Domain Awareness via Spectral-normalized Neural Gaussian Processes for E2E Autonomous Vehicle Control
No full textAWARE2ALL: Human Centric Interaction and Safety Systems for Increasing the Share of Automated Vehicles
No full textThe AWARE2ALL project is designed to address the new challenges of Highly Automated Vehicles (HAVs) from a human-centric perspective. These vehicles will allow occupants to engage in non-driving activities, rising research questions about occupant behavior, activities, and Human-Machine Interfaces (HMI) to keep them aware of the situation and the automation mode. The project aims to ensure safe operation of HAVs by developing safety and HMI systems that provide a holistic understanding of the scene. This includes continuous monitoring of the interior situation and advanced passive safety systems for occupant safety, as well as a surround perception system and external HMI for the safety of Human Road Users (HRUs). AWARE2ALL is paving the way for HAV deployment by effectively addressing changes in road safety and interactions between different road users caused by the emergence of HAVs. It is developing innovative technologies, assessment tools, and methodologies to adapt to new scenarios in mixed traffic. The project builds on previous research and aims to mitigate new safety risks associated with the introduction of HAVs
Asymptotic behavior of the Arrow–Hurwicz differential system with Tikhonov regularization
No full textIn a real Hilbert space setting, we investigate the asymptotic behavior of the solutions of the classical Arrow–Hurwicz differential system combined with Tikhonov regularizing terms. Under some newly proposed conditions on the Tikhonov terms involved, we show that the solutions of the regularized Arrow–Hurwicz differential system strongly converge toward the element of least norm within its set of zeros. Moreover, we provide fast asymptotic decay rate estimates for the so-called primal-dual gap function and the norm of the solutions' velocity. If, in addition, the Tikhonov regularizing terms are decreasing, we provide some refined estimates in the sense of an exponentially weighted moving average. Under the additional assumption that the governing operator of the Arrow–Hurwicz differential system satisfies a reverse Lipschitz condition, we further provide a fast rate of strong convergence of the solutions toward the unique zero. We conclude our study by deriving the corresponding decay rate estimates with respect to the so-called viscosity curve. Numerical experiments illustrate our theoretical findings
Crop selection in Agri-PV: international review based strategic decision-making model
No full textAgri-Photovoltaics (Agri-PV) is well known for its dual land use, integrating solar energy generation with agricultural production. This not only optimizes land use but also enhances food and energy security. Since Agri-PV is closely linked with crop cultivation, it is not solely about energy generation but also requires careful consideration of crop suitability within Agri-PV installations. Despite its significance, there is limited information available to guide decision-making for crop selection in Agri-PV systems. Selecting suitable crops remains a complex challenge, as factors such as shading tolerance, water requirements, and economic viability vary across different geographical and climatic conditions. This study develops a novel, review-based decision support model for crop selection in Agri-PV systems, synthesizing international research and case studies to provide a structured framework for decision-making. The model is based on 12 main crop typologies and key parameters such as water use, shading adaptability, crop yield/economic potential, and space requirements, derived from 117 research articles and case studies from 25 countries. By leveraging insights from successful international implementations, the model provides a practical framework for policymakers, farmers, and energy planners to enhance the sustainability and efficiency of Agri-PV projects. Findings suggest that crop selection strategies must align with regional climate conditions and PV system design to maximize synergies between energy and food production. High-value crops that require less space and have higher shade tolerance are more suitable for small-scale or decentralized Agri-PV systems. Future research should focus on advanced modeling techniques, AI-driven optimization, and real-world pilot studies to further refine decision-making in Agri-PV deployment. This study contributes to the growing body of knowledge on Agri-PV systems by providing a novel crop suitability matrix for effective decision-making
Konzeptionierung und Integration eines KI-basierten Autoklavenmodells in ein simulationsbasiertes Entscheidungsunterstützungssystem in der Kalksandsteinproduktion
No full textThe calcium silicate brick (CSB) industry faces increasing pressure to improve the efficiency and sustainability of its production processes, especially due to the high energy consumption and CO₂ emissions associated with autoclaving. To support operational and strategic decision-making, a simulation-based decision support system (DSS) has been developed, utilizing a modular material flow simulation framework. However, the energy-intensive steam curing process in autoclaves remains insufficiently represented in existing simulation models. This paper presents a methodological approach for integrating an AI-based optimization agent for autoclave systems into the existing DSS used in CSB production. A review of current techniques for coupling AI assistance systems with discrete event simulation in production and logistics, within the building materials sector, provides the foundation. Building on this, the autoclaving process is modeled using a hybrid approach, combining thermodynamic domain knowledge with machine learning methods. This contribution highlights how the combination of simulation-based production planning and intelligent process modelling can drive digital transformation in energy-intensive industries
Speculative Execution of Similarity Queries: Real-Time Parameter Optimization through Visual Exploration
No full textThe parameters of complex analytical models often have an unpredictable influence on the models’ results, rendering parameter tuning a non-intuitive task. By concurrently visualizing both the model and its results, visual analytics tackles this issue, supporting the user in understanding the connection between abstract model parameters and model results. We present a visual analytics system enabling result understanding and model refinement on a ranking-based similarity search algorithm. Our system (1) visualizes the results in a projection view, mapping their pair-wise similarity to screen distance, (2) indicates the influence of model parameters on the results, and (3) implements speculative execution to enable real-time iterative refinement on the time-intensive offline similarity search algorithm
Ergebnisse einer Evaluationsstudie des Onlineangebots zur Studienvorbereitung von MINTFIT Hamburg
No full textUm den hohen Studienabbruchquoten im MINT-Bereich entgegenzuwirken, bietet MINTFIT Hamburg studienvorbereitende Onlinetests und -kurse in MINT-Fächern an. Die Teilnahme ist unabhängig von einer Hochschuleinschreibung, freiwillig und kann anonym erfolgen. Hierdurch wird eine niederschwellige und frühzeitige Teilnahme ermöglicht, eine Untersuchung zum Einfluss des Angebots auf die Studieneingangsphase jedoch erschwert.
Daher wurde zum Wintersemester 2021/22 mit einer Fragebogenstudie begonnen, die jährliche Kohorten bis zum Abschluss ihres vierten Semesters begleitet. Es wurden persönliche Angaben zu den bisher 355 Teilnehmenden, Informationen zu ihrer Studienvorbereitung und Selbsteinschätzung hinsichtlich des Vorbereitungsstands, Klausurnoten der ersten vier Semester sowie eine Einschätzung zu MINTFIT erfasst und ausgewertet.
Auf Grundlage der Ergebnisse soll das Unterstützungsangebot von MINTFIT auf seine Passgenauigkeit hin überprüft, optimiert und zukunftsfähig gestaltet werden