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Non-road sectors, such as agriculture and construction machinery, require high energy densities and flexibility in use, which is why diesel engines are mainly used. The use of climate-neutral fuels, produced from renewable energies, such as Oxymethylene Ether (OME) as a diesel substitute, can significantly reduce CO2 and pollutant emissions in these sectors. In addition to CO2 neutrality, OME also offers improved combustion characteristics compared to diesel fuel, eliminating the soot–NOx trade-off and thus enabling new opportunities in engine design and calibration. In this paper, the combustion of pure OME on a close-to-production, single-cylinder non-road diesel engine with a pump–line–nozzle injection system is analyzed. A variation of the center of combustion at constant power output was performed for diesel and OME at different operating points. Two injectors were investigated with OME. A study on ignition delay and a detailed thermodynamic analysis was carried out. In addition, the exhaust emissions CO, NOx, VOC, as well as particulate-matter, -number and -size distributions were measured. With OME, a significantly shorter ignition delay as well as a shortened combustion duration could be observed, despite a longer injection duration. In addition, the maximum injection pressure increases. VOC and CO emissions are reduced. Particulate matter was reduced by more than 99% and particle number (>10 nm) was reduced by multiple orders of magnitude. The median of the particle size distribution shifts from 60 to 85 nm (diesel) into a diameter range of sub 23 nm (OME). A significant reduction of NOx emissions with OME enables new degrees of freedom in engine calibration and an efficiency advantage without hardware adaption.
Hot pressing of pure Ti and cast iron under vacuum formed a ~15 μm wide TiC-layer through the thermoreactive diffusion process. Nanoindentation testing of the TiC-layer revealed average hardness and elastic
modulus values of ~35 GPa and ~400 GPa, respectively. High-resolution indentation mapping indicated hardness change across the boundary with peak values observed within the TiC-layer. Solid state high temperature diffusion holds significant potential for developing uniform hard coatings on pure Ti/Ti-alloys.
In this work, the resonance enhancement of magnetoelectric (ME) coupling at the two lowest bending resonance frequencies was investigated in layered cantilever structures comprising a magnetoactive elastomer (MAE) slab and a commercially available piezoelectric polymer multilayer. A cantilever was fixed at one end in the horizontal plane and the magnetic field was applied horizontally. Five composite structures, each containing an MAE layer of different thicknesses from 0.85 to 4 mm, were fabricated. The fundamental bending resonance frequency in the absence of a magnetic field varied between roughly 23 and 55 Hz. It decreased with the increasing thickness of the MAE layer, which was explained by a simple theory. The largest ME voltage coefficient of about 7.85 V/A was measured in a sample where the thickness of the MAE layer was ≈2 mm. A significant increase in the bending resonance frequencies in the applied DC magnetic field of 240 kA/m up to 200% was observed. The results were compared with alternative designs for layered multiferroic structures. Directions for future research were also discussed.
With steadily rising CO2 concentrations in the ambient air and fast-changing concretecompositions with reduced clinker contents, the availability of reliable and accelerated concrete car-bonation tests is of crucial importance to design durable structures. This paper focuses on the effectsof moisture under accelerated conditions and the effects of different CO2 exposure conditions. Mor-tar prisms incorporating three different cement types were cured and stored at either 50% or 65%relative humidity (RH). Afterwards, the prisms were carbonated at different ambient humidities(50, 57 and 65%), different CO2 concentrations (0.04, 1 and 3 vol.%) and complemented by a seriesof tests at increased gas pressure (2 barg). High-resolution test methods were used to explain theunderlying carbonation mechanisms. The results show that pre-conditioning for two weeks—ascurrently suggested by the European Standard—seems to be too short because the initial inner mois-ture content severely affects the carbonation rate. Relative humidity during carbonation of 57% ledto higher carbonation rates compared to 50% and 65%. In addition, climate data needs to be period-ically (preferably permanently) recorded in research experiments and in laboratory testing to ensurefair interpretation of experimental results.
Hintergrund
Impfungen stellen eine bedeutende Präventionsmaßnahme dar. Grundlegend für die Eindämmung der Coronapandemie mittels Durchimpfung der Gesellschaft ist eine ausgeprägte Impfbereitschaft.
Ziel der Arbeit
Die Impfbereitschaft mit einem COVID‑19-Vakzin (Impfstoff gegen das Coronavirus) und deren Einflussfaktoren werden anhand einer Zufallsstichprobe der Gesamtbevölkerung in Deutschland untersucht.
Material und Methoden
Die Studie basiert auf einer telefonischen Zufallsstichprobe und berücksichtigt ältere und vorerkrankte Personen ihrem Bevölkerungsanteil entsprechend. Die Ein-Themen-Bevölkerungsbefragung zur Impfbereitschaft (n = 2014) wurde im November/Dezember 2020 durchgeführt.
Ergebnisse
Die Impfbereitschaft in der Stichprobe liegt bei rund 67 %. Vorerfahrungen mit Impfungen moderieren die Impfbereitschaft. Sie steigt bei Zugehörigkeit zu einer Risikogruppe. Der Glaube an die Wirksamkeit alternativer Heilmethoden und Befürwortung alternativer Behandlungsverfahren geht mit geringerer Impfbereitschaft einher. Ältere Menschen sind impfbereiter, kovariierend mit ihrer Einschätzung höherer Gefährdung bei Erkrankung. Ebenso ist die Ablehnung einer Impfung mit der Überschätzung von Nebenwirkungen assoziiert.
Schlussfolgerung
Die Impfbereitschaft hängt mit Impferfahrungen und Einstellungen zu Gesundheitsbehandlungsverfahren allgemein zusammen. Die Überschätzung der Häufigkeit ernsthafter Nebenwirkungen bei Impfungen weist auf weit verbreitete Fehlinformationen hin.
Beim Projekt DeinHaus 4.0 Oberpfalz – TePUS (Telepräsenzroboter für die Pflege und Unterstützung von Schlaganfallpatientinnen und -patienten) handelt es sich um eine Längsschnittstudie im Mixed-Methods-Design zur Untersuchung telepräsenz und
appgestützter Angebote aus den Bereichen Pflege, Logopädie und Physiotherapie.
Nach einer Einführung in die Grundlagen der informierten Einwilligung werden Empfehlungen zur Durchführung zusammengefasst und Besonderheiten im Hinblick auf die Gestaltung des Einwilligungsprozesses bei Personen mit Aphasie herausgearbeitet. Zuletzt wird das aphasiefreundliche Vorgehen im Projekt DeinHaus 4.0 Oberpfalz kurz skizziert.
Telenursing bei Schlaganfall
(2022)
Successful transfer of photoacoustic gas sensors from laboratory to real-life applications requires knowledge about potential cross-sensitivities towards environmental and gas matrix changes. Multi-dimensional calibration in case of cross-sensitivities can become very complex or even unfeasible. To address this challenge, we present a novel algorithm to compute the collision based non-radiative efficiency and phase lag of energy relaxation on a molecular level (CoNRad) for photoacoustic signal calculation. This algorithmic approach allows to calculate the entire elaxation cascade of arbitrarily complex systems, yielding a theoretical photoacoustic signal. In this work the influence of varying bulk compositions, i.e. nitrogen (N2), oxygen (O2) and water (H2O) on the photoacoustic signal during methane (CH4) detection is demonstrated. The applicability of the algorithm to other photoacoustic setups is shown exemplary by applying it to the relaxational system investigated in [1]. Hayden et al. examined the effect of water on photoacoustic carbon monoxide (CO) detection.
The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoEEREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.