Hamm-Lippstadt University of Applied Sciences

Light-emitting diodes (LEDs) are becoming increasingly important across various sectors of the lighting industry and are being used more frequently. In the field of symbolic projection, research is increasingly focusing on implementing light modulation using energy-efficient, incoherent LEDs rather than lasers. Since light modulation in micro- and nano-optics is typically achieved through phase modulation, Finite-Difference Time-Domain (FDTD) simulations are employed for analysis. The objective of this article is to investigate different approaches for approximating incoherent monochromatic light sources within FDTD simulations. To this end, two approaches based on dipole sources are considered, as well as a method involving plane waves with modulated wavefronts based on Cosine–Fourier functions and a method based on the superposition of Gaussian beams. These methods are evaluated in terms of their accuracy using a two-dimensional double-slit configuration and are compared against a fully incoherent analytical reference.

Hospital-acquired and ventilator-associated pneumonia are life-threatening infections in intensive care, yet rapid identification of causative pathogens remains a diagnostic challenge. Volatile organic compounds (VOCs) released by bacteria may support alternative diagnostic strategies, provided that reliable species-associated profiles can be established. This feasibility study applies a combined thermal desorption-gas chromatography-mass spectrometry-ion mobility spectrometry (TD-GC-MS-IMS) platform to characterize time-resolved VOC profiles of clinically relevant pneumonia pathogens. Clinical isolates of Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Acinetobacter baumannii, and Streptococcus pneumoniae were cultivated, and headspace samples were collected over 24 h using a custom-built sampling setup designed for controlled and reproducible VOC collection. The dual-detector setup enabled direct assignment of retention time, drift time, and mass spectra, facilitating reliable compound identification and confirmation with authentic standards. Distinct species-associated VOCs were identified. These included indole for Escherichia coli, acetoin and branched-chain aldehydes for Staphylococcus aureus, methyl thiocyanate and dimethyl disulfide for Pseudomonas aeruginosa, and dimethyl disulfide together with an IMS-specific unknown compound for Acinetobacter baumannii. Importantly, these markers remained detectable in defined mixed cultures, underscoring their robustness under more complex conditions. Our findings highlight both the potential and the current limitations of VOC-based diagnostics, providing a validated reference framework that can support the development of GC-IMS databases and future translational studies in clinical respiratory specimens.

Autonomous vehicles (AVs) represent a technology with significant social and environmental benefits. By reducing dependence on the human factor, which is responsible for 94% of the 1.35 million annual traffic deaths globally, AVs have the potential to increase road safety and save lives. Complementary technologies, such as Vehicle-to-Everything (V2X) communication, further enhance traffic management, reducing congestion by up to 40% and improving energy efficiency with fuel savings of up to 15%. However, V2X systems are particularly vulnerable to cyber attacks, such as spoofing, which injects false information, disrupting the flow of traffic and compromising the safety of AVs. This paper proposes an innovative framework for detecting and mitigating spoofing attacks in V2X communications. The solution combines Direction of Arrival (DoA) estimation with advanced object detection algorithms, such as YOLOv8, to identify anomalous signals and locate malicious transmitters. By integrating Artificial Intelligence (AI) techniques, the framework makes it possible to accurately classify attackers and select customized countermeasures, ensuring greater network reliability and security. The simulation results demonstrate the framework’s effectiveness in various dynamic scenarios using data from antenna arrays and camera-based object detection. In addition, they highlight the importance of sensor data fusion to improve anomaly detection accuracy, optimize decision-making processes in AVs, and enable robust cross-validation of transmitted information.

This contribution outlines a completely new, fully local approach for secure web-based device control based on browser inter-window messaging. Modern smart home IoT (Internet of Things) devices are commonly controlled with proprietary mobile applications via remote servers, which can have significant adverse implications for the end user. Given that many IoT devices in use today are limited both in available memory and processing speed, standard approaches such as HTTPS-based transport security are not always feasible and a need for more suitable alternatives for such constrained devices arises. This local method for lightweight and secure web-based device control using inter-window messaging leverages existing standard web technologies to enable a maximum degree of privacy, choice and sustainability within the smart home ecosystem. The implemented proof-of-concept shows that it is feasible to meet essential security objectives in a local web IoT control context while utilizing less than a kilobyte of additional memory compared to an unsecured solution; thus promoting sustainability through hardening the control protocols used by existing devices with too little resources for implementing standard web cryptography. Therefore this work contributes to achieving the vision of a fully open and secure local smart home.

Controlling Unmanned Aerial Vehicles (UAVs) is a cognitively demanding task, with accidents often arising from insufficient situational awareness, inadequate training, and bad user experiences. Providing more intuitive and immersive visual feedback—particularly through Digital Twin technologies—offers new opportunities to enhance pilot awareness and the overall experience quality. In this study, we investigate how different virtual points of view (POVs) influence user experience and performance during UAV piloting in Virtual Reality (VR), utilizing a digital twin that faithfully replicates the real-world flight environment. We developed a VR application that enables participants to control a physical DJI Mini 4 Pro drone while immersed in a digital twin with four distinct camera perspectives: Baseline View (static external), First Person View, Chase View, and Third Person View. Nineteen participants completed a series of ring-based obstacle courses from each perspective. In addition to objective flight data, we collected standardized subjective assessments of user experience, presence, workload, cybersickness, and situational awareness. Quantitative analyses revealed that the First Person View was associated with significantly higher mental demand and effort, greater trajectory deviation, but smoother control inputs compared to the Third Person and Chase perspectives. Complementing these findings, preference data indicated that the Third Person View was most consistently favored, whereas the First Person View elicited polarized reactions.

Autonomy is a central concern for many caregivers of autistic individuals, particularly in domains such as mobility, where environmental complexity and variability pose significant challenges. In this work, we explore a cross-reality approach to support the development of autonomous public transportation skills—specifically intercity train travel—through a combination of Virtual Reality (VR), in-situ Augmented Reality (AR), and a conversational agent. Our system provides at-home VR training to simulate travel scenarios, AR-based support during real-world travel, and interactive guidance through a conversational interface, creating a cohesive and adaptive learning experience. We conducted a field study with ten participants over three months, which showed autonomy improvements comparable to state-of-the-art methods. Our findings contribute updated insights and design considerations for the application of cross-reality systems in accessibility and mobility training for neurodiverse users.

Adaptive version of the Holt-Winters smoothing method is presented. This is suitable for forecasting univariate time series in the presence of time-position varying seasonality. The suggested adaptive smoothing recursion is improved by adaptation of the smoothing parameter over last time step. The underlying stochastic approach was designed with the aim of forecasting, inter alia, hydraulic cylinder stroke position and its speed.

Digital twins are living digital surrogates of objects and processes from the world around us and serve many purposes in situations where a physical model cannot be used. Digital twins can be considered to still be in their nascent phase, be it in the variety of application domains or in the science of creating and manipulating them. The DIDYMOS-XR project uses digital twins to create large-scale (city-wide) and long-term eXtended Reality (XR) applications. In this paper, we propose three use cases—city planning, city maintenance, and city tourism—along with an industrial use case for manufacturing environments, demonstrated through evaluation sites in Germany and Spain. The paper introduces cutting-edge solutions in the creation, simulation, and manipulation of DTs, including advancements in 3D scene reconstruction, data enhancement, data compression, sensor fusion, localization, rendering, and scene understanding. Additionally, we address the ethical and privacy challenges of digital twin systems and propose strategies for mitigating these issues. This work offers a comprehensive framework for creating semantically rich, scalable, and interactive digital twins, providing valuable insights for diverse applications in urban planning, maintenance, tourism, and industrial optimization.

The present study introduces and investigates an auxetic metamaterial inspired by the natural structure of the ornithogalum umbellatum flowers. The proposed honeycomb structure, which resembles the ornithogalum umbellatum flower is composed of six petals arranged in a circular pattern with equal angles, exhibiting triaxial symmetry. Through a theoretical analysis of the Poisson's ratio and Young's modulus for the ornithogalum umbellatum flower‐like honeycomb (OUFH) structure, the obtained elastic results are consistent with the finite element simulation results, and their correctness is verified by experiments. The results of the study show that in comparison to the concave gardenia‐shaped honeycomb structure, the present OUFH exhibits a lower relative density, larger elastic strain reaching 0.4, more pronounced deformation, and auxetic effect. These findings establish an important framework for the further exploration of honeycomb structures and provide valuable insights for their design optimization.