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Background: Time spent in sleep, sedentary behaviour (SB), and physical activity are exhaustive and mutually exclusive parts of a 24-h day that need to be considered in a combination. The aim of this study was to identify validated self-reported tools for assessment of movement behaviours across the whole 24-h day, and to review their attributes and measurement properties., Methods: The databases PubMed, Scopus, and SPORTDiscus were searched until September 2023. Inclusion criteria were: (i) published in English language, (ii) per-reviewed paper, (iii) assessment of self-reported time spent in sleep, SB, and physical activity, (iv) evaluation of measurement properties of all estimates across the full 24-h day, and (v) inclusion of adolescents, adults, or older adults. The methodological quality of included studies was assessed using the Consensus-based Standards for the selection of health Measurement Instruments checklist., Results: Our search returned 2064 records. After studies selection, we included 16 articles that reported construct validity and/or test-retest reliability of 12 unique self-reported tools - eight questionnaires, three time-use recalls, and one time-use diary. Most tools enable assessment of time spent in sleep, and domain-specific SB and physical activity, and account that sum of behaviours should be 24 h. Validity (and reliability) correlation coefficients for sleep ranged between 0.22 and 0.69 (0.41 and 0.92), for SB between 0.06 and 0.57 (0.33 and 0.91), for light-intensity physical activity between 0.18 and 0.46 (0.55 and 0.94), and for moderate- to vigorous-intensity physical activity between 0.38 and 0.56 (0.59 and 0.94). The quality of included studies being mostly fair-to-good., Conclusions: This review found that only a limited number of validated self-reported tools for assessment of 24-h movement behaviours are currently available. Validity and reliability of most tools are generally adequate to be used in epidemiological studies and population surveillance, while little is known about adequacy for individual level assessments and responsiveness to behavioural change. To further support research, policy, and practice, there is a need to develop new tools that resonate with the emerging 24-h movement paradigm and to evaluate measurement properties by using compositional data analysis., Systematic Review Registration: PROSPERO CRD42022330868., (© 2024. The Author(s).)

Preclinical and clinical research on two-piece zirconia implants are warranted. Therefore, we evaluated the in vitro fracture resistance of such a zirconia oral implant system. The present study comprised 32 two-piece zirconia implants and abutments attached to the implants using a titanium ( n = 16) or a zirconia abutment screw ( n = 16). Both groups were subdivided ( n = 8): group T-0 comprised implants with a titanium abutment screw and no artificial loading; group T-HL was the titanium screw group exposed to hydro-thermomechanical loading in a chewing simulator; group Z-0 was the zirconia abutment screw group with no artificial loading; and group Z-HL comprised the zirconia screw group with hydro-thermomechanical loading. Groups T-HL and Z-HL were loaded with 98 N and aged in 85 °C hot water for 10 7 chewing cycles. All samples were loaded to fracture. Kruskal-Wallis tests were executed to assess the loading/bending moment group differences. The significance level was established at a probability of 0.05. During the artificial loading, there was a single occurrence of an implant fracture. The mean fracture resistances measured in a universal testing machine were 749 N for group T-0, 828 N for group Z-0, 652 N for group T-HL, and 826 N for group Z-HL. The corresponding bending moments were as follows: group T-0, 411 Ncm; group Z-0, 452 Ncm; group T-HL, 356 Ncm; and group Z-HL, 456 Ncm. There were no statistically significant differences found between the experimental groups. Therefore, the conclusion was that loading and aging did not diminish the fracture resistance of the evaluated implant system., Competing Interests: The authors declare no conflicts of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to sub-GeV/c 2 dark matter interactions with nuclei in current direct detection experiments. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three Cryogenic Rare Event Search with Superconducting Thermometers (CRESST) detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions., Competing Interests: Competing interestsOn behalf of all authors, the corresponding author states that there is no conflict of interest. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request., (© The Author(s) 2024.)

CRESST is a leading direct detection sub-GeVc -2 dark matter experiment. During its second phase, cryogenic bolometers were used to detect nuclear recoils off the CaWO 4 target crystal nuclei. The previously established electromagnetic background model relies on Secular Equilibrium (SE) assumptions. In this work, a validation of SE is attempted by comparing two likelihood-based normalisation results using a recently developed spectral template normalisation method based on Bayesian likelihood. Albeit we find deviations from SE in some cases we conclude that these deviations are artefacts of the fit and that the assumptions of SE is physically meaningful., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The CRESST experiment employs cryogenic calorimeters for the sensitive measurement of nuclear recoils induced by dark matter particles. The recorded signals need to undergo a careful cleaning process to avoid wrongly reconstructed recoil energies caused by pile-up and read-out artefacts. We frame this process as a time series classification task and propose to automate it with neural networks. With a data set of over one million labeled records from 68 detectors, recorded between 2013 and 2019 by CRESST, we test the capability of four commonly used neural network architectures to learn the data cleaning task. Our best performing model achieves a balanced accuracy of 0.932 on our test set. We show on an exemplary detector that about half of the wrongly predicted events are in fact wrongly labeled events, and a large share of the remaining ones have a context-dependent ground truth. We furthermore evaluate the recall and selectivity of our classifiers with simulated data. The results confirm that the trained classifiers are well suited for the data cleaning task., Competing Interests: Conflict of interestOn behalf of all authors, the corresponding author states that there is no conflict of interest., (© The Author(s) 2023.)