The most luminous, merger-free AGN show only marginal correlation with bar presence

The role of large-scale bars in the fuelling of active galactic nuclei (AGN) is still debated, even as evidence mounts that black hole growth in the absence of galaxy mergers cumulatively dominated and may substantially influence disc (i.e., merger-free) galaxy evolution. We investigate whether large-scale galactic bars are a good candidate for merger-free AGN fuelling. Specifically, we combine slit spectroscopy and Hubble Space Telescope imagery to characterise star formation rates (SFRs) and stellar masses of the unambiguously disc-dominated host galaxies of a sample of luminous, Type-1 AGN with 0.02 < z 0.024. After carefully correcting for AGN signal, we find no clear difference in SFR between AGN hosts and a stellar mass-matched sample of galaxies lacking an AGN (0.013 < z < 0.19), although this could be due to a small sample size (n_AGN = 34). We correct for SFR and stellar mass to minimise selection biases, and compare the bar fraction in the two samples. We find that AGN are marginally (1.7$\sigma$) more likely to host a bar than inactive galaxies, with AGN hosts having a bar fraction, fbar = 0.59^{+0.08}_{-0.09} and inactive galaxies having a bar fraction fbar = 0.44^{+0.08}_{-0.09}. However, we find no further differences between SFR- and mass-matched AGN and inactive samples. While bars could potentially trigger AGN activity, they appear to have no further, unique effect on a galaxy's stellar mass or SFR.Comment: 15 pages (9 figures). Accepted for publication in MNRA

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures

A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA

Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km &lt;sup&gt;2&lt;/sup&gt; resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km &lt;sup&gt;2&lt;/sup&gt; pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world\u27s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys

L'exploitation de tests diagnostiques au service de la diffusion de connaissances didactiques : réflexion sur l'introduction des nombres négatifs au début de l'enseignement secondaire.

peer reviewe

Préparation et caractérisation de catalyseurs au palladium supporté: catalyseurs monométalliques et bimétalliques

Preparation and characterisation of supported palladium catalysts: monometallic and bimetallic. A series of alumina and silica-supported palladium containing catalysts with weight content of palladium varying from 0.90 to 1.50% was prepared from Pd(C5H7O2)2, i.e., Pd(II) bisacetylacetonate and Pd(NH3)4(NO3)2. These constitute the monometallic catalysts. Similarly a series of alumina and silica supported palladium-gold and palladium-copper containing catalysts with weight content, Pd-Au and Pd-Cu varying from 0.85 to 1.42%Pd- 0.14 to 0.29%Au, and from 0.85 to 1.42%Pd-0.21 to 0.28%Cu were prepared from Pd(NH3)4(NO3)2, HAuCl4, 2H2O and Cu(NO3)2.3H2O, respectively, constituting the bimetallic catalysts. These supported metal catalysts were prepared either by the coimpregnation technique and/or by the surface oxidoreduction method. The catalysts were characterized by using infrared absorption spectrometric of adsorbed carbon monoxide and measurements of the metal dispersion on the supported catalysts techniques. All catalysts contained highly dispersed small particle-sized palladium oxides such as PdO and/or PdO2 in the case of the monometallic catalysts, and Pd-Au or Pd-Cu alloys in the case of the bimetallic catalysts, which are easy to reduce in the presence of hydrogen. The results from CO absorption and metal dispersion studies are presented and discussed. Keywords: palladium, FTIR spectrometry, carbon monoxide, coimpregnation, dispersion oxidoreduction, bimetallic, copper and gold RésuméUne série de catalyseurs monométalliques au palladium supportés sur l'alumine, Al2O3 et la silice, SiO2 avec des teneurs en palladium (%Pd) variant entre 0,90 et 1,50%Pd a été préparée à partir des sels précurseurs bis-acétylacétonate de palladium II, Pd(C5H7O2)2 et de nitrate de tétramine palladium II, Pd(NH3)4(NO3)2; ils constituent les catalyseurs de base. Les catalyseurs bimétalliques supportés sur alumine et silice palladium-or et palladium-cuivre avec des teneurs Pd-Au et Pd-Cu variant entre 0,85 à 1,42%Pd- 0,14 à 0,29%Au et 0,85 à 1,42% Pd- 0,21 à 0,28%Cu respectivement à partir des sels précurseurs de Pd(NH3)4(NO3)2, tétrachloraurate d'hydrogène, HAuCl4, 2H2O et nitrate de cuivre II à 3 molécules d'eau, Cu(NO3)2, 3H2O, ont été préparés par la technique de coimprégnation et/ou par la méthode de réaction d'oxydoréduction de surface. Les catalyseurs ainsi préparés ont été caractérisés par spectrométrie d'absorption infrarouge de monoxyde de carbone adsorbé et par mesure de la dispersion métallique sur la surface du support. Tous ces catalyseurs contiennent en surface une grande quantité de particules d'oxydes de palladium telles PdO et/ou PdO2 dans le cas des catalyseurs monométalliques, et d'alliages Pd-Au ou Pd-Cu dans le cas des catalyseurs bimétalliques, particules très faciles à réduire par le dihydrogène. Les résultats obtenus à partir des études d'absorption IR de CO adsorbé et des mesures de dispersion métallique sont présentés et discutés. Mots clés: palladium, spectrométrie IRTF, monoxyde de carbone, coimprégnation, dispersion, oxydoréduction, bimétallique, cuivre et orAfrican Journal of Science and Technology Vol. 5(2) 2004: 96-11

Effective photoconductivity of exfoliated black phosphorus for optoelectronic switching under 1.55 μm optical excitation Effective photoconductivity of exfoliated black phosphorus for optoelectronic switching under 1.55 lm optical excitation

International audienceWe present a microwave photoconductive switch based on exfoliated black phosphorus and strongly responding to a 1.55 lm optical excitation. According to its number of atomic layers, exfoliated black phosphorus presents unique properties for optoelectronic applications, like a tunable direct bandgap from 0.3 eV to 2 eV, strong mobilities, and strong conductivities. The switch shows a maximum ON/OFF ratio of 17 dB at 1 GHz, and 2.2 dB at 20 GHz under 1.55-lm laser excitation at 50 mW, never achieved with bidimensional materials