A vision about lifelong learning and its barriers

Around 25 years ago, some researchers argued for moving towards innovative learning models characterized by being more personalized and where the students would have a more active role in deciding what to learn, when to learn and how to learn. Nowadays, there is a need for a flexible, efficient, universal and lifelong education. Lifelong learning is fully integrated into our society and, from the student point of view, it is very different from regular learning. Among these differences there is the maturity of students, the fact that the domains of interest are much broader, the way how learning occurs at different depths, the fact that the topics to study may be related both to work, family and leisure, and that students have little availability due to their necessity to conciliate home, work, leisure and learning. Lifelong learning requires personalized models that adapt to students'' needs and constraints, but lifelong learners keep suffering from models that are neither adapted to their necessities, nor to the needs of society. This paper reflects on the actual situation of lifelong learning, analyses some of the relevant literature and discusses the challenges to conceptualize, from a transdisciplinary point of view, innovative e-learning models that promote self-determination of students

Evidence for the strangeness-changing weak decay Ξb−→Λb0π−\Xi_b^-\to\Lambda_b^0\pi^-

Using a $pp$ collision data sample corresponding to an integrated luminosity of 3.0~fb$^{-1}$, collected by the LHCb detector, we present the first search for the strangeness-changing weak decay $\Xi_b^-\to\Lambda_b^0\pi^-$. No $b$ hadron decay of this type has been seen before. A signal for this decay, corresponding to a significance of 3.2 standard deviations, is reported. The relative rate is measured to be ${{f_{\Xi_b^-}}\over{f_{\Lambda_b^0}}}{\cal{B}}(\Xi_b^-\to\Lambda_b^0\pi^-) = (5.7\pm1.8^{+0.8}_{-0.9})\times10^{-4}$, where $f_{\Xi_b^-}$ and $f_{\Lambda_b^0}$ are the $b\to\Xi_b^-$ and $b\to\Lambda_b^0$ fragmentation fractions, and ${\cal{B}}(\Xi_b^-\to\Lambda_b^0\pi^-)$ is the branching fraction. Assuming $f_{\Xi_b^-}/f_{\Lambda_b^0}$ is bounded between 0.1 and 0.3, the branching fraction ${\cal{B}}(\Xi_b^-\to\Lambda_b^0\pi^-)$ would lie in the range from $(0.57\pm0.21)\%$ to $(0.19\pm0.07)\%$.Comment: 7 pages, 2 figures, All figures and tables, along with any supplementary material and additional information, are available at https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-047.htm

Study of the production of Λb0\Lambda_b^0 and B‾0\overline{B}^0 hadrons in pppp collisions and first measurement of the Λb0→J/ψpK−\Lambda_b^0\rightarrow J/\psi pK^- branching fraction

The product of the $\Lambda_b^0$ ($\overline{B}^0$) differential production cross-section and the branching fraction of the decay $\Lambda_b^0\rightarrow J/\psi pK^-$ ($\overline{B}^0\rightarrow J/\psi\overline{K}^*(892)^0$) is measured as a function of the beauty hadron transverse momentum, $p_{\rm T}$, and rapidity, $y$. The kinematic region of the measurements is $p_{\rm T}<20~{\rm GeV}/c$ and $2.0<y<4.5$. The measurements use a data sample corresponding to an integrated luminosity of $3~{\rm fb}^{-1}$ collected by the LHCb detector in $pp$ collisions at centre-of-mass energies $\sqrt{s}=7~{\rm TeV}$ in 2011 and $\sqrt{s}=8~{\rm TeV}$ in 2012. Based on previous LHCb results of the fragmentation fraction ratio, $f_{\Lambda_B^0}/f_d$, the branching fraction of the decay $\Lambda_b^0\rightarrow J/\psi pK^-$ is measured to be \begin{equation*} \mathcal{B}(\Lambda_b^0\rightarrow J/\psi pK^-)= (3.17\pm0.04\pm0.07\pm0.34^{+0.45}_{-0.28})\times10^{-4}, \end{equation*} where the first uncertainty is statistical, the second is systematic, the third is due to the uncertainty on the branching fraction of the decay $\overline{B}^0\rightarrow J/\psi\overline{K}^*(892)^0$, and the fourth is due to the knowledge of $f_{\Lambda_b^0}/f_d$. The sum of the asymmetries in the production and decay between $\Lambda_b^0$ and $\overline{\Lambda}_b^0$ is also measured as a function of $p_{\rm T}$ and $y$. The previously published branching fraction of $\Lambda_b^0\rightarrow J/\psi p\pi^-$, relative to that of $\Lambda_b^0\rightarrow J/\psi pK^-$, is updated. The branching fractions of $\Lambda_b^0\rightarrow P_c^+(\rightarrow J/\psi p)K^-$ are determined.Comment: 29 pages, 19figures. All figures and tables, along with any supplementary material and additional information, are available at https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-032.htm

Measurements of long-range near-side angular correlations in sNN=5\sqrt{s_{\text{NN}}}=5TeV proton-lead collisions in the forward region

Two-particle angular correlations are studied in proton-lead collisions at a nucleon-nucleon centre-of-mass energy of $\sqrt{s_{\text{NN}}}=5$TeV, collected with the LHCb detector at the LHC. The analysis is based on data recorded in two beam configurations, in which either the direction of the proton or that of the lead ion is analysed. The correlations are measured in the laboratory system as a function of relative pseudorapidity, $\Delta\eta$, and relative azimuthal angle, $\Delta\phi$, for events in different classes of event activity and for different bins of particle transverse momentum. In high-activity events a long-range correlation on the near side, $\Delta\phi \approx 0$, is observed in the pseudorapidity range $2.0<\eta<4.9$. This measurement of long-range correlations on the near side in proton-lead collisions extends previous observations into the forward region up to $\eta=4.9$. The correlation increases with growing event activity and is found to be more pronounced in the direction of the lead beam. However, the correlation in the direction of the lead and proton beams are found to be compatible when comparing events with similar absolute activity in the direction analysed.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-040.htm

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.Peer reviewedFinal Published versio

Gaia Early Data Release 3: The celestial reference frame (Gaia-CRF3)

Context. Gaia-CRF3 is the celestial reference frame for positions and proper motions in the third release of data from the Gaia mission, Gaia DR3 (and for the early third release, Gaia EDR3, which contains identical astrometric results). The reference frame is defined by the positions and proper motions at epoch 2016.0 for a specific set of extragalactic sources in the (E)DR3 catalogue. Aims. We describe the construction of Gaia-CRF3 and its properties in terms of the distributions in magnitude, colour, and astrometric quality. Methods. Compact extragalactic sources in Gaia DR3 were identified by positional cross-matching with 17 external catalogues of quasi-stellar objects (QSO) and active galactic nuclei (AGN), followed by astrometric filtering designed to remove stellar contaminants. Selecting a clean sample was favoured over including a higher number of extragalactic sources. For the final sample, the random and systematic errors in the proper motions are analysed, as well as the radio-optical offsets in position for sources in the third realisation of the International Celestial Reference Frame (ICRF3). Results. Gaia-CRF3 comprises about 1.6 million QSO-like sources, of which 1.2 million have five-parameter astrometric solutions in Gaia DR3 and 0.4 million have six-parameter solutions. The sources span the magnitude range G = 13-21 with a peak density at 20.6 mag, at which the typical positional uncertainty is about 1 mas. The proper motions show systematic errors on the level of 12 μas yr-1 on angular scales greater than 15 deg. For the 3142 optical counterparts of ICRF3 sources in the S/X frequency bands, the median offset from the radio positions is about 0.5 mas, but it exceeds 4 mas in either coordinate for 127 sources. We outline the future of Gaia-CRF in the next Gaia data releases. Appendices give further details on the external catalogues used, how to extract information about the Gaia-CRF3 sources, potential (Galactic) confusion sources, and the estimation of the spin and orientation of an astrometric solution

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.World Meteorological Organization Global Atmospheric Watch programme is gratefully acknowledged for initiating and coordinating this study and for supporting this publication. We acknowledge the following projects for supporting the analysis contained in this article: Air Pollution and Human Health for an Indian Megacity project PROMOTE funded by UK NERC and the Indian MOES, Grant reference number NE/P016391/1; Regarding project funding from the European Commission, the sole responsibility of this publication lies with the authors. The European Commission is not responsible for any use that may be made of the information contained therein. This project has received funding from the European Commission’s Horizon 2020 research and innovation program under grant agreement No 874990 (EMERGE project). European Regional Development Fund (project MOBTT42) under the Mobilitas Pluss programme; Estonian Research Council (project PRG714); Estonian Research Infrastructures Roadmap project Estonian Environmental Observatory (KKOBS, project 2014-2020.4.01.20-0281). European network for observing our changing planet project (ERAPLANET, grant agreement no. 689443) under the European Union’s Horizon 2020 research and innovation program, Estonian Ministry of Sciences projects (grant nos. P180021, P180274), and the Estonian Research Infrastructures Roadmap project Estonian Environmental Observatory (3.2.0304.11-0395). Eastern Mediterranean and Middle East—Climate and Atmosphere Research (EMME-CARE) project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 856612) and the Government of Cyprus. INAR acknowledges support by the Russian government (grant number 14.W03.31.0002), the Ministry of Science and Higher Education of the Russian Federation (agreement 14.W0331.0006), and the Russian Ministry of Education and Science (14.W03.31.0008). We are grateful to to the following agencies for providing access to data used in our analysis: A.M. Obukhov Institute of Atmospheric Physics Russian Academy of Sciences; Agenzia Regionale per la Protezione dell’Ambiente della Campania (ARPAC); Air Quality and Climate Change, Parks and Environment (MetroVancouver, Government of British Columbia); Air Quality Monitoring & Reporting, Nova Scotia Environment (Government of Nova Scotia); Air Quality Monitoring Network (SIMAT) and Emission Inventory, Mexico City Environment Secretariat (SEDEMA); Airparif (owner & provider of the Paris air pollution data); ARPA Lazio, Italy; ARPA Lombardia, Italy; Association Agr´e´ee de Surveillance de la Qualit´e de l’Air en ˆIle-de- France AIRPARIF / Atmo-France; Bavarian Environment Agency, Germany; Berlin Senatsverwaltung für Umwelt, Verkehr und Klimaschutz, Germany; California Air Resources Board; Central Pollution Control Board (CPCB), India; CETESB: Companhia Ambiental do Estado de S˜ao Paulo, Brazil. China National Environmental Monitoring Centre; Chandigarh Pollution Control Committee (CPCC), India. DCMR Rijnmond Environmental Service, the Netherlands. Department of Labour Inspection, Cyprus; Department of Natural Resources Management and Environmental Protection of Moscow. Environment and Climate Change Canada; Environmental Monitoring and Science Division Alberta Environment and Parks (Government of Alberta); Environmental Protection Authority Victoria (Melbourne, Victoria, Australia); Estonian Environmental Research Centre (EERC); Estonian University of Life Sciences, SMEAR Estonia; European Regional Development Fund (project MOBTT42) under the Mobilitas Pluss programme; Finnish Meteorological Institute; Helsinki Region Environmental Services Authority; Haryana Pollution Control Board (HSPCB), IndiaLondon Air Quality Network (LAQN) and the Automatic Urban and Rural Network (AURN) supported by the Department of Environment, Food and Rural Affairs, UK Government; Madrid Municipality; Met Office Integrated Data Archive System (MIDAS); Meteorological Service of Canada; Minist`ere de l’Environnement et de la Lutte contre les changements climatiques (Gouvernement du Qu´ebec); Ministry of Environment and Energy, Greece; Ministry of the Environment (Chile) and National Weather Service (DMC); Moscow State Budgetary Environmental Institution MOSECOMONITORING. Municipal Department of the Environment SMAC, Brazil; Municipality of Madrid public open data service; National institute of environmental research, Korea; National Meteorology and Hydrology Service (SENAMHI), Peru; New York State Department of Environmental Conservation; NSW Department of Planning, Industry and Environment; Ontario Ministry of the Environment, Conservation and Parks, Canada; Public Health Service of Amsterdam (GGD), the Netherlands. Punjab Pollution Control Board (PPCB), India. R´eseau de surveillance de la qualit´e de l’air (RSQA) (Montr´eal); Rosgydromet. Mosecomonitoring, Institute of Atmospheric Physics, Russia; Russian Foundation for Basic Research (project 20–05–00254) SAFAR-IITM-MoES, India; S˜ao Paulo State Environmental Protection Agency, CETESB; Secretaria de Ambiente, DMQ, Ecuador; Secretaría Distrital de Ambiente, Bogot´a, Colombia. Secretaria Municipal de Meio Ambiente Rio de Janeiro; Mexico City Atmospheric Monitoring System (SIMAT); Mexico City Secretariat of Environment, Secretaría del Medio Ambiente (SEDEMA); SLB-analys, Sweden; SMEAR Estonia station and Estonian University of Life Sciences (EULS); SMEAR stations data and Finnish Center of Excellence; South African Weather Service and Department of Environment, Forestry and Fisheries through SAAQIS; Spanish Ministry for the Ecological Transition and the Demographic Challenge (MITECO); University of Helsinki, Finland; University of Tartu, Tahkuse air monitoring station; Weather Station of the Institute of Astronomy, Geophysics and Atmospheric Science of the University of S˜ao Paulo; West Bengal Pollution Control Board (WBPCB).http://www.elsevier.com/locate/envintam2023Geography, Geoinformatics and Meteorolog

Gaia early data release 3: structure and properties of the Magellanic Clouds

Galaxie

Model-independent measurement of mixing parameters in D0^{0} → KS0_{S}^{0} π+^{+}π−^{−} decays

The first model-independent measurement of the charm mixing parameters in the decay $D^0 \to K_S \pi^+ \pi^-$ is reported, using a sample of $pp$ collision data recorded by the LHCb experiment, corresponding to an integrated luminosity of 1.0 fb$^{-1}$ at a centre-of-mass energy of 7 TeV. The measured values are \begin{eqnarray*} x &=& (-0.86 \pm 0.53 \pm 0.17) \times 10^{-2}, \\ y &=& (+0.03 \pm 0.46 \pm 0.13) \times 10^{-2}, \end{eqnarray*} where the first uncertainties are statistical and include small contributions due to the external input for the strong phase measured by the CLEO collaboration, and the second uncertainties are systematic.Comment: 25 pages, 3 figures. Sign error in x fixed as of v2. All figures and tables, along with any supplementary material and additional information, are available at https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-042.htm