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Blickpunkt
(2024)
Ukraine
(2024)
Sensoren können verschiedene Aufgaben erfüllen, wie beispielsweise die Optimierung von Prozessen, die Interaktion zwischen Geräten oder die Verbesserung der zivilen Sicherheit. [1–3] Ihr Bedarf für die Industrie oder den Alltag wächst seit Jahren stetig. Besonders mobile Gassensoren sind von großem Interesse. Jedoch ist ihre Anwendung meist durch ihre integrierte Batterie begrenzt. Gassensoren ohne oder mit einem nur sehr geringen Energieverbrauch stehen daher im Interesse bei neuen Anwendungsgebieten, beispielsweise im Brandschutz oder in der Textilindustrie. [4,5] Die Sensoren könnten zum Beispiel in die Textilien einer persönlichen Schutzausrüstung eingearbeitet werden und durch einen Farbumschlag die Anwesenheit eines Gases oder die Überschreitung des Grenzwertes toxischer Substanzen anzeigen.
Digitalisierung in zentralen Feldern der Sozialpolitik: Entwicklungstendenzen, Chancen und Risiken
(2022)
The digitization of financial activities in consumers' lives is increasing, and the digitalization of invoicing processes is expected to play a significant role, although this area is not well understood regarding the private sector. Human-Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW) research have a long history of analyzing the socio-material and temporal aspects of work practices that are relevant for the domestic domain. The socio-material structuring of invoicing work and the working styles of consumers must be considered when designing effective consumer support systems. In this ethnomethodologically-informed, design-oriented interview study, we followed 17 consumers in their daily practices of dealing with invoices to make the invisible administrative work involved in this process visible. We identified and described the meaningful artifacts that were used in a spatial-temporal process within various storage locations such as input, reminding, intermediate (for postponing cases) buffers, and archive systems. Furthermore, we identified three different working styles that consumers exhibited: direct completion, at the next opportunity, and postpone as far as possible. This study contributes to our understanding of household economics and domestic workplace studies in the tradition of CSCW and has implications for the design of electronic invoicing systems.
This study addresses the common occurrence of cell-to-cell variations arising from manufacturing tolerances and their implications during battery production. The focus is on assessing the impact of these inherent differences in cells and exploring diverse cell and module connection methods on battery pack performance and their subsequent influence on the driving range of electric vehicles (EVs). The analysis spans three battery pack sizes, encompassing various constant discharge rates and nine distinct drive cycles representative of driving behaviours across different regions of India. Two interconnection topologies, categorised as “string” and “cross”, are examined. The findings reveal that cross-connected packs exhibit reduced energy output compared to string-connected configurations, which is reflected in the driving range outcomes observed during drive cycle simulations. Additionally, the study investigates the effects of standard deviation in cell parameters, concluding that an increased standard deviation (SD) leads to decreased energy output from the packs. Notably, string-connected packs demonstrate superior performance in terms of extractable energy under such conditions.
Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device’s modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.
During robot-assisted therapy, a robot typically needs to be partially or fully controlled by therapists, for instance using a Wizard-of-Oz protocol; this makes therapeutic sessions tedious to conduct, as therapists cannot fully focus on the interaction with the person under therapy. In this work, we develop a learning-based behaviour model that can be used to increase the autonomy of a robot’s decision-making process. We investigate reinforcement learning as a model training technique and compare different reward functions that consider a user’s engagement and activity performance. We also analyse various strategies that aim to make the learning process more tractable, namely i) behaviour model training with a learned user model, ii) policy transfer between user groups, and iii) policy learning from expert feedback. We demonstrate that policy transfer can significantly speed up the policy learning process, although the reward function has an important effect on the actions that a robot can choose. Although the main focus of this paper is the personalisation pipeline itself, we further evaluate the learned behaviour models in a small-scale real-world feasibility study in which six users participated in a sequence learning game with an assistive robot. The results of this study seem to suggest that learning from guidance may result in the most adequate policies in terms of increasing the engagement and game performance of users, but a large-scale user study is needed to verify the validity of that observation.
Pollution with anthropogenic waste, particularly persistent plastic, has now reached every remote corner of the world. The French Atlantic coast, given its extensive coastline, is particularly affected. To gain an overview of current plastic pollution, this study examined a stretch of 250 km along the Silver Coast of France. Sampling was conducted at a total of 14 beach sections, each with five sampling sites in a transect. At each collection site, a square of 0.25 m2 was marked. The top 5 cm of beach sediment was collected and sieved on-site using an analysis sieve (mesh size 1 mm), resulting in a total of approximately 0.8 m3 of sediment, corresponding to a total weight of 1300 kg of examined beach sediment. A total of 1972 plastic particles were extracted and analysed using infrared spectroscopy, corresponding to 1.5 particles kg−1 of beach sediment. Pellets (885 particles), polyethylene as the polymer type (1349 particles), and particles in the size range of microplastics (943 particles) were most frequently found. The significant pollution by pellets suggests that the spread of plastic waste is not primarily attributable to tourism (in February/March 2023). The substantial accumulation of meso- and macro-waste (with 863 and 166 particles) also indicates that research focusing on microplastics should be expanded to include these size categories, as microplastics can develop from them over time.
Die moderne Arbeitswelt erfordert digitale Kompetenz, doch Hochschulen mangelt es an Angeboten zum digitalen Kompetenzaufbau Studierender. Peer-Angebote können ein sinnvoller Ansatz zur Förderung digitaler Kompetenz sein, allerdings fehlen empirische Belege für deren Wirksamkeit. Die Studie setzt hier an und evaluiert den digitalen Kompetenzerwerb von Teilnehmenden fachübergreifender Peer-Trainings auf Grundlage des DigComp Rahmenmodells. Die Ergebnisse zeigen, dass Trainings-Teilnehmende ihre digitale Kompetenz im Vergleich zur Kontrollgruppe signifikant stärker steigern konnten. Die Ausbildung zur bzw. zum Peer-Trainer:in sowie die Peer-Trainings wurden von allen Beteiligten sehr positiv bewertet.
In this work, the surface reactions of the homemade explosive triacetone triperoxide on tungsten oxide (WO3) sensor surfaces are studied to obtain detailed information about the chemical reactions taking place. Semiconductor gas sensors based on WO3 nanopowders are therefore produced and characterized by scanning electron microscopy, X-ray diffraction, and Raman spectroscopy. To analyze the reaction mechanisms at the sensor surface, the sensor is monitored online under operation conditions using Raman spectroscopy, which allows to identify the temperature-dependent sensor reactions. By combining information from the Raman spectra with data on the changing resistivity of the underlying semiconductor, it is possible to establish a correlation between the adsorbed gas species and the physical properties of the WO3 layer. In the results, it is indicated that a Lewis acid–base reaction is the most likely mechanism for the increase in resistance observed at temperatures below 150 °C. In the results, at higher temperatures, the assumption of a radical mechanism that causes a decrease in resistance is supported.
DT-13 attenuates inflammation by inhibiting NLRP3-inflammasome related genes in RAW264.7 macrophages
(2024)
Plant derived saponins or other glycosides are widely used for their anti-inflammatory, antioxidant, and anti-viral properties in therapeutic medicine. In this study, we focus on understanding the function of the less known steroidal saponin from the roots of Liriope muscari L. H. Bailey – saponin C (also known as DT-13) in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages in comparison to the well-known saponin ginsenoside Rk1 and anti-inflammatory drug dexamethasone. We proved that DT-13 reduces LPS-induced inflammation by inhibiting nitric oxide (NO) production, interleukin-6 (IL-6) release, cycloxygenase-2 (COX-2), tumour necrosis factor-alpha (TNF-α) gene expression, and nuclear factor kappa-B (NFκB) translocation into the nucleus. It also inhibits the inflammasome component NOD-like receptor family pyrin domain containing protein 3 (NLRP3) regulating the inflammasome activation. This was supported by the significant inhibition of caspase-1 and interleukin-1 beta (IL-1β) expression and release. This study demonstrates the anti-inflammatory effect of saponins on LPS-stimulated macrophages. For the first time, an in vitro study shows the attenuating effect of DT-13 on NLRP3-inflammasome activation. In comparison to the existing anti-inflammatory drug, dexamethasone, and triterpenoid saponin Rk1, DT-13 more efficiently inhibits inflammation in the applied cell culture model. Therefore, DT-13 may serve as a lead compound for the development of new more effective anti-inflammatory drugs with minimised side effects.
INTRODUCTION: The cardiac magnetic resonance (CMR) data on mid- to long-term myocardial damage due to COVID-19 infections in elite athletes are scarce. Therefore, this study investigated the mid -to long-term consequences of myocardial involvement after a COVID-19 infection in elite athletes.
MATERIALS AND METHODS: This study included 27 athletes at the German Olympic Centre North Rhine-Westphalia (NRW)/Rhineland with a confirmed previous COVID-19 infection between January 2020 and October 2021. The athletes were part of an ongoing observational COVID-19 study at the Institute of Cardiology and Sports Medicine Cologne at the German Sport University (DSHS).Nine healthy non-athletes with no prior COVID-19 illness served as controls. CMR was performed within a mean of 182 days (standard deviation [SD] 99) of the initial positive test result.
RESULTS: CMR did not reveal any signs of acute myocarditis (according to the current Lake Louise criteria) or myocardial damage in any of the 26 elite athletes with previous COVID-19 infection. Of these athletes, 92% experienced a symptomatic course, and 54% reported symptoms lasting for more than 4 weeks. One male athlete was excluded from the analysis because CMR revealed an arrhythmogenic right ventricular cardiomyopathy (ARVC). Athletes had significantly enlarged left and right ventricle volumes and increased left ventricular myocardial mass in comparison to the healthy control group (LVEDVi 103.4 vs 91.1 ml/m2, p = 0.031; RVEDVi 104.1 vs 86.6 ml/m2, p = 0.007; LVMi 59.0 vs 46.2 g/m2, p = 0.002). Only two cases of elevated high-sensitivity-Troponin were documented; in one, the participant had previously engaged in high-intensity training, and in the other, CMR revealed a diagnosis of an arrhythmogenic cardiomyopathy.
CONCLUSION: Our findings suggest that the risk for mid- to long-term myocardial damage is very low to negligible in elite athletes. Our results do not allow conclusions to be drawn regarding myocardial injury in the acute phase of infection nor about possible long-term myocardial effects in the general population.
Verbraucherpolitik
(2023)