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The study is based on a German single-topic population survey on vaccination willingness against COVID-19 (VWC) by the authors (2020, n = 2014). The single-topic survey allowed us to test several competing explanations for VWC, as discussed in the literature. The VWC in the sample was 67.3%. Logistic regression was used to identify factors affecting VWC. Being at high risk from COVID-19 and having received flu vaccination have a positive impact on VWC. Perceived VWC of friends has a strong positive effect on respondents’ VWC. Bivariate relationships of gender, age, and level of education with VWC were no longer significant in a multivariate analysis. Trust in alternative medicine and belief in conspiracy theories have a negative effect on VWC.
In Vitro Analysis of Human Cartilage Infiltrated by Hydrogels and Hydrogel-Encapsulated Chondrocytes
(2023)
Osteoarthritis (OA) is a degenerative joint disease causing loss of articular cartilage and structural damage in all joint tissues. Given the limited regenerative capacity of articular cartilage, methods to support the native structural properties of articular cartilage are highly anticipated. The aim of this study was to infiltrate zwitterionic monomer solutions into human OA-cartilage explants to replace lost proteoglycans. The study included polymerization and deposition of methacryloyloxyethyl-phosphorylcholine- and a novel sulfobetaine-methacrylate-based monomer solution within ex vivo human OA-cartilage explants and the encapsulation of isolated chondrocytes within hydrogels and the corresponding effects on chondrocyte viability. The results demonstrated that zwitterionic cartilage–hydrogel networks are formed by infiltration. In general, cytotoxic effects of the monomer solutions were observed, as was a time-dependent infiltration behavior into the tissue accompanied by increasing cell death and penetration depth. The successful deposition of zwitterionic hydrogels within OA cartilage identifies the infiltration method as a potential future therapeutic option for the repair/replacement of OA-cartilage extracellular suprastructure. Due to the toxic effects of the monomer solutions, the focus should be on sealing the OA-cartilage surface, instead of complete infiltration. An alternative treatment option for focal cartilage defects could be the usage of monomer solutions, especially the novel generated sulfobetaine-methacrylate-based monomer solution, as bionic for cell-based 3D bioprintable hydrogels.
This communication presents an experimental and analytical study on the evaporation mechanism in a closed-structured asymmetric plate heat exchanger (PHE) employed as a stagnant water evaporator for the application in an adsorption heat transformation appliance. To this aim, an experimental unit is constructed, which comprises two identical PHEs, one acting as an vaporator/condenser and the second, as an adsorber/desorber. Two endoscopes are mounted inside the investigated evaporator to visualize the evaporation mechanism when performing adsorption-evaporation processes under different boundary conditions. It turned out that the evaporation mechanism is a partially covered, thin film evaporation. A heat transfer analysis is performed to evaluate the heat transfer coefficient of the thin film evaporation () inside the investigated evaporator, resulting in -values between 1330 and 160 [W∙m−2∙K−1] over the investigated adsorption-evaporation time. Correlating the obtained () to the film thickness and the wetted area results in -values between 0.34 and 0.78 [mm] and wetted to total area ratios of 0.78 to 0.16. Besides, an analytical model has been developed and introduced to correlate the overall evaporator heat transfer coefficient with the adsorption potential and the time rate of change of the water uptake.
Although many composite structures are inconsistently curved, such as the leading edges of aircraft wings, the variety of research in impact engineering is almost limited to the impact performance of plates or cylindrically curved specimens. It is not known whether the findings obtained from standardized tests can be transferred to curved structures or which adaptions are required. Therefore, a deeper understanding of the deformation and damage behavior of inconsistently curved structures is essential to transfer the observed impact behavior of flat specimens to general curved structures and therefore to utilize the full lightweight potential of a load-specific design. An accurate description of the procedure as well as the results of the experimental and numerical study of the low-velocity impact behavior of differently single-curved elliptic specimens is presented. To close the research gap of the impact behavior of geometries with curvatures between the plates and simplified leading edges, novel specimens geometries have been derived from established impact test standards. Glassfiber-reinforced specimens are subjected to an instrumented impact test at constant impact energy. This is numerically investigated by a stacked-layer model, which used cohesive zone modeling to enable the simulation of matrix cracking, fiber fracture and delamination. The resulting projected damage areas, as well as the force and deflection histories, were evaluated and section cuts were examined to discuss the damage morphology, formation and propagation process. Significant effects on maximum deflection, compliance and dynamic behavior on the size and morphology of damage were found.
Organizations are under increasing pressure to develop applications within budget and time at high quality. Therefore, multiple organizations adopt Low Code Development Platforms (LCDP) to develop applications faster and cheaper compared to traditional application development. However, current research on LCDP adoption lacks empirical grounding as well as a deeper understanding of the importance of adoption drivers and inhibitors. We conducted semi-structured interviews and a Delphi study with seventeen experts to address these gaps. As a result, we identified twelve drivers and nineteen inhibitors for adopting LCDPs. We show that the experts have a consensus on the most and the least important drivers and inhibitors for LCDP adoption. Yet, the ranking of the drivers and inhibitors between the most and least important is highly context dependent. For some drivers and inhibitors, the experts’ ranking is similar to academic literature, whereas, for others, it differs. In conclusion, the study at hand empirically validates drivers and inhibitors for LCDP adoption, adds six new drivers and six new inhibitors to the body of knowledge, and analyses the importance of these factors.
This paper presents a method for integrating industrial consumers owning compensation systems as alternative reactive power sources into grid operating processes. In remuneration, they receive a market-based provision of reactive power. The aim is to analyze the potential of reactive power compensation systems of industrial companies connected to medium-voltage (10 kV–30 kV) AC grids in order to increase the reactive power ability of distribution grids. Measurement methods and reactive power potential results of six industrial companies are presented to characterize the amount and temporal availability of their reactive power potential. The presented approach for using the decentralized reactive power potential is a centralized reactive power control method and is based on optimal power flow (OPF) calculations. An optimization algorithm based on linear programming is used to coordinate a reactive power retrieval tuned to the actual demand. The influencing quantities are the current grid status (voltage and load flow capacity reserves at grid nodes and power lines) and the current reactive power potential of the reactive power sources. The compensation impact of six measured industrial companies on an exemplary medium-voltage grid is shown by an application example.
The currently still high fossil energy demand is forcing the glass industry to search for innovative approaches for the reduction in CO2 emissions and the integration of renewable energy sources. In this paper, a novel power-to-methane concept is presented and discussed for this purpose. A special focus is on methods for the required CO2 capture from typical flue gases in the glass industry, which have hardly been explored to date. To close this research gap, process simulation models are developed to investigate post-combustion CO2 capture by absorption processes, followed by a techno-economic evaluation. Due to reduced flue gas volume, the designed CO2 capture plant is found to be much smaller (40 m3 absorber column volume) than absorption-based CO2 separation processes for power plants (12,560 m3 absorber column volume). As there are many options for waste heat utilization in the glass industry, the waste heat required for CO2 desorption can be generated in a particularly efficient and cost-effective way. The resulting CO2 separation costs range between 41 and 42 EUR/t CO2, depending on waste heat utilization for desorption. These costs are below the values of 50–65 EUR/t CO2 for comparable industrial applications. Despite these promising economic results, there are still some technical restrictions in terms of solvent degradation due to the high oxygen content in flue gas compositions. The results of this study point towards parametric studies for approaching these issues, such as the use of secondary and tertiary amines as solvents, or the optimization of operating conditions such as stripper pressure for further cost reductions potential.
Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student’s prediction. The combined outputs of the students’ prediction with the teachers’ correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT.
The prospect of achieving computational speedups by exploiting quantum phenomena makes the use of quantum processing units (QPUs) attractive for many algorithmic database problems. Query optimisation, which concerns problems that typically need to explore large search spaces, seems like an ideal match for the known quantum algorithms. We present the first quantum implementation of join ordering, which is one of the most investigated and fundamental query optimisation problems, based on a reformulation to quadratic binary unconstrained optimisation problems. We empirically characterise our method on two state-of-the-art approaches (gate-based quantum computing and quantum annealing), and identify speed-ups compared to the best know classical join ordering approaches for input sizes that can be processed with current quantum annealers. However, we also confirm that limits of early-stage technology are quickly reached.
Current QPUs are classified as noisy, intermediate scale quantum computers (NISQ), and are restricted by a variety of limitations that reduce their capabilities as compared to ideal future quantum computers, which prevents us from scaling up problem dimensions and reaching practical utility. To overcome these challenges, our formulation accounts for specific QPU properties and limitations, and allows us to trade between achievable solution quality and possible problem size.
In contrast to all prior work on quantum computing for query optimisation and database-related challenges, we go beyond currently available QPUs, and explicitly target the scalability limitations: Using insights gained from numerical simulations and our experimental analysis, we identify key criteria for co-designing QPUs to improve their usefulness for join ordering, and show how even relatively minor physical architectural improvements can result in substantial enhancements. Finally, we outline a path towards practical utility of custom-designed QPUs.
In der Vergangenheit wurde beklagt, dass in der empirischen Forschung zur Bildungsbeteiligung Erwachsener selten eine Integration von Theorie und Empirie erfolge und die mangelnde empirische Prüfung eine theoretische Weiterentwicklung des Forschungsfeldes erschwere. Mittels einer Qualitativen Inhaltsanalyse von Beiträgen ausgewählter Fachzeitschriften der Erwachsenen‑/Weiterbildungsforschung wurde versucht, die Aktualität dieser Feststellung empirisch zu überprüfen. Dazu wurden 38 empirische Forschungsbeiträge zur Bildungsbeteiligung der Jahrgänge 2018 bis 2020 von neun Fachzeitschriften mit Peer-Review untersucht. Die Einschätzung der Beiträge erfolgte hinsichtlich des Grades der Theorieeinbindung. Die Ergebnisse der Analyse zeigen ein gemischtes Bild: neun Beiträge (24 %) binden keine Theorie in den Forschungsprozess ein, ein Beitrag (3 %) zitiert Theorie lediglich, ohne sie weiter einzubinden. Am häufigsten ist eine Einbindung zum theoriegeleiteten Vorgehen (16 Beiträge, 42 %). Eine tiefergehende Theorieanwendung findet sich in sechs Beiträgen (16 %). Eine theorieprüfende oder -bildende Einbindung liegt mit je drei Beiträgen (je 8 %) seltener vor. Die rezipierten Theorien sind vielfältig: es werden 20 verschiedene Theorieansätze festgestellt. Den Forderungen nach einer verstärkten Einbindung von Theorie in den Forschungsprozess kann somit weiterhin Gültigkeit attestiert werden: ein substanzieller Teil der Beiträge berichtet keine Theorieeinbindung, eine empirische Prüfung oder Weiterentwicklung findet relativ selten statt.
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.