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Although a catalogue of synthetic RGB magnitudes, providing photometric data for a sample of 1346 bright stars, has been recently published, its usefulness is still limited due to the small number of reference stars available, considering that they are distributed throughout the whole celestial sphere, and the fact that they are restricted to Johnson V < 6.6 mag. This work presents synthetic RGB magnitudes for ∼15 million stars brighter than Gaia G = 18 mag, making use of a calibration between the RGB magnitudes of the reference bright star sample and the corresponding high-quality photometric G, GBP, and GRP magnitudes provided by the Gaia EDR3. The calibration has been restricted to stars exhibiting -0.5 < GBP - GRP < 2.0 mag, and aims to predict RGB magnitudes within an error interval of ±0.1 mag. Since the reference bright star sample is dominated by nearby stars with slightly undersolar metallicity, systematic variations in the predictions are expected, as modelled with the help of stellar atmosphere models. These deviations are constrained to the ±0.1-mag interval when applying the calibration only to stars scarcely affected by interstellar extinction and with metallicity compatible with the median value for the bright star sample. The large number of Gaia sources available in each region of the sky should guarantee high-quality RGB photometric calibrations. © 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. ; The authors acknowledge financial support from the Spanish Programa Estatal de I+D + i Orientada a los Retos de la Sociedad under grant RTI2018-096188B-I00, which is partly funded by the European Regional Development Fund (ERDF), S2018/NMT-4291 (TEC2SPACE-CM), and ACTION, a project funded by the European Union H2020-SwafS-2018-1-824603. The participation of ICCUB researchers was (partially) supported by the Spanish Ministry of Science, Innovation and University (MICIU/FEDER, UE) through grant RTI2018095076-B-C21, and the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu') through grant CEX2019-000918-M. SB acknowledges Xunta de Galicia for financial support under grant ED431B 2020/29. The participation of ASdM was (partially) supported by the EMISSI@N project (NERC grant NE/P01156X/1). This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. ; With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709. ; Peer reviewed

Although the use of RGB photometry has exploded in the last decades due to the advent of high-quality and inexpensive digital cameras equipped with Bayer-like colour filter systems, there is surprisingly no catalogue of bright stars that can be used for calibration purposes. Since due to their excessive brightness, accurate enough spectrophotometric measurements of bright stars typically cannot be performed with modern large telescopes, we have employed historical 13-colour medium-narrow-band photometric data, gathered with quite reliable photomultipliers, to fit the spectrum of 1346 bright stars using stellar atmosphere models. This not only constitutes a useful compilation of bright spectrophotometric standards well spread in the celestial sphere, the UCM library of spectrophotometric spectra, but allows the generation of a catalogue of reference RGB magnitudes, with typical random uncertainties ∼0.01 mag. For that purpose, we have defined a new set of spectral sensitivity curves, computed as the median of 28 sets of empirical sensitivity curves from the literature, that can be used to establish a standard RGB photometric system. Conversions between RGB magnitudes computed with any of these sets of empirical RGB curves and those determined with the new standard photometric system are provided. Even though particular RGB measurements from single cameras are not expected to provide extremely accurate photometric data, the repeatability and multiplicity of observations will allow access to a large amount of exploitable data in many astronomical fields, such as the detailed monitoring of light pollution and its impact on the night sky brightness, or the study of meteors, Solar system bodies, variable stars, and transient objects. In addition, the RGB magnitudes presented here make the sky an accessible and free laboratory for the calibration of the cameras themselves. © 2021 The Author(s). ; The authors acknowledge financial support from the Spanish Programa Estatal de I+D+i Orientada a los Retos de la Sociedad under grant RTI2018-096188-B-I00, which is partly funded by the European Regional Development Fund (ERDF), S2018/NMT-4291 (TEC2SPACE-CM), and ACTION, a project funded by the European Union H2020-SwafS-2018-1-824603. SB acknowledges Xunta de Galicia for financial support under grant ED431B 2020/29. The participation of ASdM was (partially) supported by the EMISSI@N project (NERC grant no. NE/P01156X/1). This work has been possible thanks to the extensive use of IPython and Jupyter notebooks (Pérez & Granger 2007). This research made use of ASTROPY, a community-developed core PYTHON package for Astronomy (Astropy Collaboration et al. 2013, 2018), NUMPY (Harris et al. 2020), SCIPY (Virtanen et al. 2020), and MATPLOTLIB (Hunter 2007). This research has made use of the Simbad data base and the VizieR catalogue access tool, CDS, Strasbourg, France (DOI:10.26093/cds/vizier). The original description of the VizieR service was published in A&AS, 143, 23. ; With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709. ; Peer reviewed

Context. We explore the stellar content of the Javalambre Photometric Local Universe Survey (J-PLUS) Data Release 2 and show its potential for identifying low-metallicity stars using the Stellar Parameters Estimation based on Ensemble Methods (SPEEM) pipeline. Aims. SPEEM is a tool used to provide determinations of atmospheric parameters for stars and separate stellar sources from quasars based on the unique J-PLUS photometric system. The adoption of adequate selection criteria allows for the identification of metal-poor star candidates that are suitable for spectroscopic follow-up investigations. Methods. SPEEM consists of a series of machine-learning models that use a training sample observed by both J-PLUS and the SEGUE spectroscopic survey. The training sample has temperatures, Teff, between 4800 K and 9000 K, values of log g between 1.0 and 4.5, as well as -3.1 < [Fe/H] < +0.5. The performance of the pipeline was tested with a sample of stars observed by the LAMOST survey within the same parameter range. Results. The average differences between the parameters of a sample of stars observed with SEGUE and J-PLUS, obtained with the SEGUE Stellar Parameter Pipeline and SPEEM, respectively, are ΔTeff ∼ 41 K, Δlog g ∼ 0.11 dex, and Δ[Fe/H] ∼ 0.09 dex. We define a sample of 177 stars that have been identified as new candidates with [Fe/H] < -2.5, with 11 of them having been observed with the ISIS spectrograph at the William Herschel Telescope. The spectroscopic analysis confirms that 64% of stars have [Fe/H] < -2.5, including one new star with [Fe/H] < -3.0. Conclusions. Using SPEEM in combination with the J-PLUS filter system has demonstrated their potential in estimating the stellar atmospheric parameters (Teff, log g, and [Fe/H]). The spectroscopic validation of the candidates shows that SPEEM yields a success rate of 64% on the identification of very metal-poor star candidates with [Fe/H] < -2.5. © 2021 ESO. ; Based on observations made with the JAST80 telescope at the Observatorio Astrofísico de Javalambre (OAJ), in Teruel, owned, managed, and operated by the Centro de Estudios de Física del Cosmos de Aragón. We acknowledge the OAJ Data Processing and Archiving Unit (UPAD) for reducing the OAJ data used in this work. Funding for the J-PLUS Project has been provided by the Governments of Spain and Aragón through the Fondo de Inversiones de Teruel; the Aragón Government through the Reseach Groups E96, E103, and E16_17R; the Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE) with grants PGC2018-097585-B-C21 and PGC2018-097585-B-C22; the Spanish Ministry of Economy and Competitiveness (MINECO) under AYA2015-66211-C2-1-P, AYA2015-66211-C2-2, AYA2012-30789, and ICTS-2009-14; and European FEDER funding (FCDD10-4E-867, FCDD13-4E-2685). The Brazilian agencies FINEP, FAPESP, and the National Observatory of Brazil have also contributed to this project. C.A.G. acknowledges financial support from the CAPES through scholarship for developing his PhD project, and extend and special mention to Nathaniel Tucker for introducing him to the amazing world of machine learning. The work of V.M.P. is supported by NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories, Chinese Academy of Sciences. Y.S.L. acknowl edges support from the National Research Foundation (NRF) of Korea grant funded by the Ministry of Science and ICT (NRF-2021R1A2C1008679). F.J.E. acknowledges financial support from the Spanish MINECO/FEDER through the grant AYA2017-84089 and MDM-2017-0737 at Centro de Astrobiología (CSIC-INTA), Unidad de Excelencia María de Maeztu, and from the European Union's Horizon 2020 research and innovation programme under Grant Agreement no. 824064 through the ESCAPE – The European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures project. R.A.D. acknowledges support from the CNPq through BP grant 308105/2018-4. This research has made use of the Spanish Virtual Observatory (http://svo.cab.inta-csic.es) supported from the Spanish MICINN/FEDER through grant AyA2017-84089. This research made use of Matplotlib, a 2D graphics package used for Python for publication-quality image generation across user interfaces and operating systems (Hunter 2007). ; Peer reviewed

Because of its small size, the U.S. Space Force faces more challenges with filling its general officer ranks than the other services, which have larger populations. This report defines these key challenges and presents options to overcome them.

Slings and arrows -- A woman named John -- Love and war -- The great flood of 1927 -- Tragedy -- War clouds on the horizon -- Bolero -- Lee's darkest hour -- Torch -- Back to Bolero -- Countdown to D-Day -- The overlord logistical plan -- The great adventure begins -- The great breakout -- Taking the city of light -- Lee in the crosshairs -- Stalemate on the Western Front -- Lee's finest hour -- Lee's advocacy of African-Americans -- Victory in Europe -- Lee's excellent Italian adventure -- An unsung hero

"B-222768"--p.[1] ; Cover title. ; "GAO/NSIAD-89-60" ; "December 1988" ; Mode of access: Internet.

AFRICAN AMERICAN HISTORY COMES TO LIFEDiscover why young people all over the country are reading the Black Stars biographies of African American heroes. Here is what you want to know about the lives of brave black men and women during the Civil Rights Movement:* LOUIS "SATCHMO" ARMSTRONG* MARY MCLEOD BETHUNE* MAJOR GENERAL BENJAMIN O. DAVIS JR.* W. E. B. DU BOIS* LIEUTENANT HENRY O. FLIPPER* MARCUS GARVEY* MARTIN LUTHER KING JR.* THURGOOD MARSHALL* ROSA PARKS* ADAM CLAYTON POWELL JR.* PHILIP RANDOLPH* PAUL ROBESON* JACKIE ROBINSON* BOOKER T. WASHINGTON* IDA B. WELLS-BARNETT* CARTER G. WOODSON*

This article reports on the development and evaluation of an online repository for sharing general practice (GP) educational resources. GP supervisors and medical educators need quality resources to facilitate learning and teaching. Developing or finding resources from scratch is time-consuming and duplicates effort when teachers and learners are geographically dispersed. In response to this challenge, a curated repository, Self-directed Training and Resource System (STARS) was developed in 2009 for sharing educational and clinical resources within the Australian GP learning community. However, a low engagement by users was observed. This research began by an examination of enablers and barriers for resource sharing and the existing online repository: STARS. Data collection methods included questionnaires, semi-structured interviews and focus groups. Findings contributed to the development of a new pilot repository that was embedded in a collaborative online learning space, also known as a 'digital habitat'. This space focused on engagement with resources and other users. It incorporated Web 2.0 tools and followed recent trends in user-created content and ranking of usefulness. Overall findings revealed that ease of use and effective management are paramount in ensuring ongoing viability and value of an online repository.

Partizipative Forschung hat einen demokratisierenden und damit zugleich einen gesellschaftskritischen Anspruch. Allerdings ist von den Anfängen der Aktionsforschung in den 1970er Jahren bis heute kaum reflektiert worden, welches Demokratieverständnis partizipativen Ansätzen zugrunde liegt: Wer partizipiert aus welchen Gründen, in welchem Umfang und zu welchem Zeitpunkt am Forschungsprozess? Wie stellen sich Machtverhältnisse zwischen den unterschiedlichen AkteurInnen dar? Wer sind die eigentlichen "Stars" in der Manege partizipativer qualitativer Forschung? Unter Hinzuziehung unterschiedlicher demokratietheoretischer Überlegungen wollen wir aufzeigen, wie der gesellschaftskritische Anspruch partizipativer Forschung nicht nur formuliert, sondern auch in der Praxis eingelöst werden kann.