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Morales, A. I., & Tuzon, P. (2022). Misconceptions, Knowledge, and Attitudes Towards the Phenomenon of Radioactivity. Sci. Educ., 31, 405–426.
Abstract: The teaching of the phenomenon of radioactivity is considered a key ingredient in the path towards developing critical thinking skills in many secondary science education curricula. Despite being one of the basic concepts in general physics courses, the scientific teaching literature of the last 40 years reports a great deal of misconceptions and conceptual errors related to radioactivity that seemingly appear regardless of the educational level and context. This study reports the first cross-sectional diagnostic study in Spain to secondary education students and pre-service teachers. Data were collected in the year 2019 through a questionnaire adapted from a previously validated one to explore the main misconceptions, attitudes, and knowledge status on the topic on a sample of 191 secondary school students and 29 Physics-and-Chemistry trainee teachers in the Spanish region of Valencia. Open and closed questions were used to categorize the entity itself, its properties, and the main misconceptions related to radioactivity. The responses were analysed using conventional statistical methods. The results indicate an evolution from a widespread dissenting notion on the phenomenon, which is staunchly related to danger, hazard, and destruction in the lowest educational levels, towards a more rational, relative, and multidimensional perspective in the highest ones. On the other hand, the ideas, emotions, and attitudes of the inquired individuals are in good agreement with the main misconceptions reported in the literature.
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Gariazzo, S., de Salas, P. F., Pisanti, O., & Consiglio, R. (2022). PArthENoPE revolutions. Comput. Phys. Commun., 271, 108205–13pp.
Abstract: This paper presents the main features of a new and updated version of the program PArthENoPE, which the community has been using for many years for computing the abundances of light elements produced during Big Bang Nucleosynthesis. This is the third release of the PArthENoPE code, after the 2008 and the 2018 ones, and will be distributed from the code's website, http://parthenope.na.infn.it. Apart from minor changes, the main improvements in this new version include a revisited implementation of the nuclear rates for the most important reactions of deuterium destruction, H-2(p,gamma) He-3, H-2(d, n)He-3 and H-2(d, p)H-3, and a re-designed GUI, which extends the functionality of the previous one. The new GUI, in particular, supersedes the previous tools for running over grids of parameters with a better management of parallel runs, and it offers a brand-new set of functions for plotting the results. Program summary Program title: PArthENoPE 3.0 CPC Library link to program files: https://doi.org/10.17632/wygr7d8yt9.2 Developer's repository link: http://parthenope.na.infn.it Licensing provisions: GPLv3 Programming language: Fortran 77 and Python Nature of problem: Computation of yields of light elements synthesized in the primordial universe Solution method: Livermore Solver for Ordinary Differential Equations (LSODE) for stiff and nonstiff systems, Python GUI for running and plotting Journal reference of previous version: Comput. Phys. Commun. 233 (2018) 237-242 Does the new version supersede the previous version?: Yes Reasons for the new version: Update of the physics and improvements in the GUI Summary of revisions: Update of the physics implemented in the Fortran code and improvements in the GUI functionalities, in particular new plotting functions.
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Roca, L., Liang, W. H., & Oset, E. (2022). Inconsistency of the data on the K-1(1270) -> pi K-0*(1430) decay width. Phys. Lett. B, 824, 136827–3pp.
Abstract: We show, using the same Lagrangian for the K-1(1270) -> pi K-0*(1430) and K-0*(1430) -> K-1 (1270)pi decays, that the present PDG data on the partial decay width of K-1 (1270) -> pi K-0*(1430) implies a width for K-0*(1430) -> K-1 (1270)pi decay which is about one order of magnitude larger than the total K-0*(1430) width. A discussion on this inconsistency is done, stressing its relationship to the existence of two K-1(1270) states obtained with the chiral unitary theory, which are not considered in the experimental analyses of K pi pi data.
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LHCb Collaboration(Aaij, R. et al), Henry, L., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., et al. (2022). Precise determination of the B-s(0)-B-s(-0) oscillation frequency. Nat. Phys., 18, 54–58.
Abstract: Mesons comprising a beauty quark and strange quark can oscillate between particle (B-s(0)) and antiparticle (B-s(-0)) flavour eigenstates, with a frequency given by the mass difference between heavy and light mass eigenstates, Delta m(s). Here we present a measurement of Delta m(s) using B-s(0) -> D-s(-)pi(+) decays produced in proton-proton collisions collected with the LHCb detector at the Large Hadron Collider. The oscillation frequency is found to be Delta m(s) = 17.7683 +/- 0.0051 +/- 0.0032 ps(-1), where the first uncertainty is statistical and the second is systematic. This measurement improves on the current Delta m(s) precision by a factor of two. We combine this result with previous LHCb measurements to determine Delta m(s) = 17.7656 +/- 0.0057 ps(-1), which is the legacy measurement of the original LHCb detector.
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Folgado, M. G., & Sanz, V. (2022). Exploring the political pulse of a country using data science tools. J. Comput. Soc. Sci., 5, 987–1000.
Abstract: In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted to social media. We also train a Fully-Connected Neural Network (FCNN) to recognise the political affiliation of a tweet. The FCNN is able to predict the origin of the tweet with a precision in the range of 71-75%, and the political leaning (left or right) with a precision of around 90%. This study is meant to be viewed as an example of how to use Twitter data and different types of Data Science tools for a political analysis.
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