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Hintergrund:
Eltern stehen im Rahmen der eigenen Impfung und der Kinderimpfung mit einem COVID-19-Vakzin vor einer Impfentscheidung. Zum aktuellen Zeitpunkt gibt es keine (vollständige) Impfempfehlung.
Fragestellung:
Die Studie untersucht die Impfbereitschaft von Eltern minderjähriger Kinder und Personen ohne minderjährige Kinder, wobei insbesondere Geschlechtsunterschiede überprüft werden.
Methoden:
Die Studie basiert auf einer Zufallsstichprobe (Telefon-Survey, n = 2014, Erhebung zwischen 12.11.2020 und 10.12.2020). Die Auswertung stützt sich insbesondere auf die Teilstichprobe von Personen mit minderjährigen Kindern im Haushalt (n = 461).
Ergebnisse:
Eltern weisen durchgängig eine geringere Impfbereitschaft mit einem COVID-19-Vakzin auf als Befragte ohne minderjährige Kinder (54,1 % vs. 71,1 %). Väter weisen eine stärker ausgeprägte eigene Impfbereitschaft auf als Mütter. Darüber hinaus sind Männer eher als Frauen bereit, das eigene Kind mit einem COVID-19-Vakzin impfen zu lassen.
Schlussfolgerungen:
Bei Eltern und insbesondere Müttern ist eine erhebliche Fehleinschätzung von Impfrisiken und häufiger Glaube an Impfverschwörungstheorien zu beobachten. Empfohlen werden anschauliche und leicht verständliche Informationen über die Wirkung und Nebenwirkungen der Impfung mit einem COVID-19-Vakzin durch zuständige Institutionen und Ärzte.
Nach einer Fraktur ist Mobilisierung Behandlungsziel und Therapiesäule. Das Festlegen von Outcomes basiert jedoch auf vielen Unsicherheiten, da Assessments nicht für alle Patient/-innen geeignet sind. Sie können agesabhängig beeinflusst und subjektiv geprägt sein. Sensorbasiertes Bewegungsmonitoring bietet eine Ergänzung zur Operationalisierung der Gehfähigkeit. Für Längsschnittuntersuchungen, die auch im häuslichen Umfeld durchgeführt werden, eignet sich die tägliche Schrittzahl als Variable. Sie kann durch einen handelsüblichen Fitnesstracker beobachtet
werden.
Für Fußballer:innen stellen muskuläre Verletzungen der unteren Extremitäten ein großes Problem dar. Ein Beispiel hierfür liefert die Nationalmannschaftsstürmerin Alexandra Popp, die aufgrund muskulärer Probleme das EM-Finale 2022 in Wembley kurzfristig verpasste. Oftmals stehen gerade hohe Anspannungssituationen in zeitlichem Zusammenhang mit Verletzungen, der Einfluss der psychischen Beanspruchung auf die biomechanischen Belastungen wird jedoch meist nur wenig beachtet.
Exoskeletons were invented over 100 years ago but have only become popular in the last two decades, especially in the working industry as they can decrease work-related loads significantly. The most often used exoskeletons are for the lower back and shoulder since these are commonly affected body regions. All devices have in common that their purpose is to reduce internal loads of vulnerable body regions. Nevertheless, there is still little understanding on how biomechanical loading in the human body changes when exoskeletons are used. Therefore, further analyses are needed. A promising candidate for these are musculoskeletal models, which are based on an inverse dynamics approach and can calculate external parameters such as ground reaction forces or other interaction forces as well as internal parameters such as joint reaction forces or muscle activities. The various examples in the literature show that these models are increasingly used for assessing the biomechanical effects of exoskeletons on the human body. Furthermore, musculoskeletal models can calculate biomechanical loadings of humans with and without exoskeletons for all kinds of applications and allow an evaluation of their purpose.
Practical Relevance: This article highlights the possibilities of musculoskeletal models for assessing the design and efficiency of occupational exoskeletons. Several practical use cases are described along with distinct descriptions of common implications of musculoskeletal and exoskeleton modeling.
Magnetoactive elastomers (MAEs) are a special type of smart materials consisting of an elastic matrix with embedded microsized particles that are made of ferromagnetic materials with high or low coercivity. Due to their composition, such elastomers possess unique magnetic field-dependent material properties. The present paper compiles the results of investigations on MAEs towards an approach of their potential application as vibrating sensor elements with adaptable sensitivity. Starting with the model-based and experimental studies of the free vibrational behavior displayed by cantilevers made of MAEs, it is shown that the first bending eigenfrequency of the cantilevers depends strongly on the strength of an applied uniform magnetic field. The investigations of the forced vibration response of MAE beams subjected to in-plane kinematic excitation confirm the possibility of active magnetic control of the amplitude-frequency characteristics. With change of the uniform field strength, the MAE beam reveals different steady-state responses for the same excitation, and the resonance may occur at various ranges of the excitation frequency. Nonlinear dependencies of the amplification ratio on the excitation frequency are obtained for different magnitudes of the applied field. Furthermore, it is shown that the steady-state vibrations of MAE beams can be detected based on the magnetic field distortion. The field difference, which is measured simultaneously on the sides of a vibrating MAE beam, provides a signal with the same frequency as the excitation and an amplitude proportional to the amplitude of resulting vibrations. The presented prototype of the MAE-based vibrating unit with the field-controlled “configuration” can be implemented for realization of acceleration sensor systems with adaptable sensitivity. The ongoing research on MAEs is oriented to the use of other geometrical forms along with beams, e.g. two-dimensional structures such as membranes.
Silicon micro venturi nozzles for cost-efficient spray coating of thin organic P3HT/PCBM layers
(2017)
Improvements on spray coating are of particular interest to different fields of technology as it is a scalable deposition method and processing from solutions offer various application possibilities outside of typical facilities. When it comes to the deposition of expensive and film-forming media such as organic semiconductors, consumption and nozzle cleaning issues are of particular importance. We demonstrate the simple steps to design and fabricate micro venturi nozzles for economical spray coating with a consumption as low as 30-50 mu l.min(-1). For spray coating an active area of 25 cm(2) a 2.45-4.01 fold coating efficiency is observed compared to a conventional airbrush nozzle set. The electrical characterization of first diodes sprayed with an active layer thickness of similar to 750 nm using a single micronozzle at a coating speed of 1.7 cm(2).min(-1) reveals a good external quantum efficiency of 72.9% at 532 nm and a dark current of similar to 7.4.10(-5) mA.cm(-2), both measured at -2 V. Furthermore, the high resistance of the micronozzles against solvents and most acids is provided through realization in a silicon wafer with silicon dioxide encapsulation, therefore allowing easy and effective cleaning.
Let G⊊C be a bounded, simply connected domain in C, and denote by
Q(G):={f:G⟶G | f is a quasiconformal mapping of G onto G } the quasiconformal automorphism group of G. In a canonical manner, the set Q(G) carries the structure of a (non–abelian) group with respect to composition of mappings. Moreover, we endow the set Q(G) with the topology of uniform convergence by the supremum metric dsup(f,g):=supz∈G|f(z)−g(z)|,f,g∈Q(G).
In this paper, we present results concerning topological properties of Q(G) such as completeness, separability, path–connectedness, discreteness and compactness.
Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Rather than evaluate the predictions of a single simulation approach, the Sandia Fracture Challenge relies on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive models to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5-68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti-6Al-4V sheet under both quasi-static and modest-rate dynamic loading (failure in 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. Additional shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the 'state-of-the-art' in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.
Generative deep learning approaches for the design of dental restorations: A narrative review
(2024)
Objectives:
This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.
Data/Sources:
PubMed, Google Scholar, and IEEE Xplore databases were searched for articles from 2003 to 2023.
Study selection:
The review includes 9 articles published from 2018 to 2023. The selected articles showcase novel DL approaches for tooth reconstruction, while those concentrating solely on the application or review of DL methods are excluded. The review shows that data is acquired via intraoral scans or laboratory scans of dental plaster models. Common data representations are depth maps, point clouds, and voxelized point clouds. Reconstructions focus on single teeth, using data from adjacent teeth or the entire jaw. Some articles include antagonist teeth data and features like occlusal grooves and gap distance. Primary network architectures include Generative Adversarial Networks (GANs) and Transformers. Compared to conventional digital methods, DL-based tooth reconstruction reports error rates approximately two times lower.
Conclusions:
Generative DL models analyze dental datasets to reconstruct missing teeth by extracting insights into patterns and structures. Through specialized application, these models reconstruct morphologically and functionally sound dental structures, leveraging information from the existing teeth. The reported advancements facilitate the feasibility of DL-based dental crown reconstruction. Beyond GANs and Transformers with point clouds or voxels, recent studies indicate promising outcomes with diffusion-based architectures and innovative data representations like wavelets for 3D shape completion and inference problems.
Clinical significance:
Generative network architectures employed in the analysis and reconstruction of dental structures demonstrate notable proficiency. The enhanced accuracy and efficiency of DL-based frameworks hold the potential to enhance clinical outcomes and increase patient satisfaction. The reduced reconstruction times and diminished requirement for manual intervention may lead to cost savings and improved accessibility of dental services.
A review of the current use of multibody dynamics methods in the analysis of the dynamics of vehicles is given. Railway vehicle dynamics as well as road vehicle dynamics are considered, where for the latter the dynamics of cars and trucks and the dynamics of single-track vehicles, in particular motorcycles and bicycles, are reviewed. Commonalities and differences are shown, and open questions and challenges are given as directions for further research in this field.