Three is the magic number -- distance measurement of NGC 3147 using SN 2021hpr and its siblings

The nearby spiral galaxy NGC 3147 hosted three Type Ia supernovae (SNe Ia) in the past decades, which have been subjects of intense follow-up observations. Simultaneous analysis of their data provides a unique opportunity for testing the different light curve fitting methods and distance estimations. The detailed optical follow-up of SN 2021hpr allows us to revise the previous distance estimations to NGC 3147, and compare the widely used light curve fitting algorithms to each other. After the combination of the available and newly published data of SN 2021hpr, its physical properties can be also estimated with higher accuracy. We present and analyse new BVgriz and Swift photometry of SN 2021hpr to constrain its general physical properties. Together with its siblings, SNe 1997bq and 2008fv, we cross-compare the individual distance estimates of these three SNe given by the SALT code, and also check their consistency with the results from the MLCS2k2 method. The early spectral series of SN 2021hpr are also fit with the radiative spectral code TARDIS in order to verify the explosion properties and constrain the chemical distribution of the outer ejecta. After combining the distance estimates for the three SNe, the mean distance to their host galaxy, NGC 3127, is 42.5 $\pm$ 1.0 Mpc, which matches with the distance inferred by the most up-to-date LC fitters, SALT3 and BayeSN. We confirm that SN~2021hpr is a Branch-normal Type Ia SN that ejected $\sim 1.12 \pm 0.28$ M$_\odot$ from its progenitor white dwarf, and synthesized $\sim 0.44 \pm 0.14$ M$_\odot$ of radioactive $^{56}$Ni.Comment: 16 pages, 17 figures, 11 tables; accepted for publication in A&

European fitness landscape for children and adolescents: updated reference values, fitness maps and country rankings based on nearly 8 million test results from 34 countries gathered by the FitBack network

Objectives (1) To develop reference values for health-related fitness in European children and adolescents aged 6–18 years that are the foundation for the web-based, open-access and multilanguage fitness platform (FitBack); (2) to provide comparisons across European countries. Methods This study builds on a previous large fitness reference study in European youth by (1) widening the age demographic, (2) identifying the most recent and representative country-level data and (3) including national data from existing fitness surveillance and monitoring systems. We used the Assessing Levels of Physical Activity and fitness at population level (ALPHA) test battery as it comprises tests with the highest test–retest reliability, criterion/construct validity and health-related predictive validity: the 20 m shuttle run (cardiorespiratory fitness); handgrip strength and standing long jump (muscular strength); and body height, body mass, body mass index and waist circumference (anthropometry). Percentile values were obtained using the generalised additive models for location, scale and shape method. Results A total of 7 966 693 test results from 34 countries (106 datasets) were used to develop sex-specific and age-specific percentile values. In addition, country-level rankings based on mean percentiles are provided for each fitness test, as well as an overall fitness ranking. Finally, an interactive fitness platform, including individual and group reporting and European fitness maps, is provided and freely available online (www.fitbackeurope.eu)

Mapping Materials and Molecules.

The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the "big data" revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them.This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields

Ringing the bell for quality P.E.: What are the realities of remote physical education?

BACKGROUND: To date, few data on the quality and quantity of online physical education (P.E.) during the COVID-19 pandemic have been published. We assessed activity in online classes and reported allocated curriculum time for P.E. in a multi-national sample of European children (6-18 years). METHODS: Data from two online surveys were analysed. A total of 8395 children were included in the first round (May-June 2020) and 24 302 in the second round (January-February 2021). RESULTS: Activity levels during P.E. classes were low in spring 2020, particularly among the youngest children and in certain countries. 27.9% of students did not do any online P.E. and 15.7% were hardly ever very active. Only 18.4% were always very active and 14.9% reported being very active quite often. In winter 2020, we observed a large variability in the allocated curriculum time for P.E. In many countries, this was lower than the compulsory requirements. Only 65.7% of respondents had the same number of P.E. lessons than before pandemic, while 23.8% had less P.E., and 6.8% claimed to have no P.E. lessons. Rates for no P.E. were especially high among secondary school students, and in large cities and megapolises. CONCLUSIONS: During the COVID-19 pandemic, European children were provided much less P.E. in quantity and quality than before the pandemic. Countermeasures are needed to ensure that these changes do not become permanent. Particular attention is needed in large cities and megapolises. The critical role of P.E. for students\u27 health and development must be strengthened in the school system

European Fitness Landscape in Children and Adolescents: updated reference values, fitness maps, and country rankings based on nearly 8 million data points from 34 countries gathered by the FitBack network

Objectives (1) To develop reference values for healthrelated fitness in European children and adolescents aged 6–18 years that are the foundation for the webbased, open-access and multilanguage fitness platform (FitBack); (2) to provide comparisons across European countries. Methods This study builds on a previous large fitness reference study in European youth by (1) widening the age demographic, (2) identifying the most recent and representative country-level data and (3) including national data from existing fitness surveillance and monitoring systems. We used the Assessing Levels of PHysical Activity and fitness at population level (ALPHA) test battery as it comprises tests with the highest test–retest reliability, criterion/construct validity and health-related predictive validity: the 20 m shuttle run (cardiorespiratory fitness); handgrip strength and standing long jump (muscular strength); and body height, body mass, body mass index and waist circumference (anthropometry). Percentile values were obtained using the generalised additive models for location, scale and shape method. Results A total of 7 966 693 test results from 34 countries (106 datasets) were used to develop sexspecific and age-specific percentile values. In addition, country-level rankings based on mean percentiles are provided for each fitness test, as well as an overall fitness ranking. Finally, an interactive fitness platform, including individual and group reporting and European fitness maps, is provided and freely available online ( www.fitbackeurope.eu)