Influência do Rompimento da Barragem do Valo Grande (Iguape, SP) nas Associações de Foraminíferos e Tecamebas.

This paper deals with the probable consequences of the increase of water flow on the hydrographic and sedimentological parameters of the Cananéia-Iguape estuary and their relationship with the foraminifera and thecamoebians assemblages found before and after the Valo Grande dam's disruption. The sedimentological and biological parameters were obtained from each of the sixty samples of the collected bottom sediments. Before the dam’s disruption, the distribution of foraminifera and thecamoebians was apparently influenced by the substrate properties, such as grain size sorting and organic carbon content. After the dam's disruption, the bottom temperature was probably the responsible for the distribution of foraminifera and thecamoebians and for the total Textulariina and Ammonia tepida and Arenoparrella mexicana distribution patterns. Besides. very fine sand and bottom water salinity were probably the most influential factors in the distribution of Difflugia oblonga. Centropyxis aculeate and Centropyxis constricta.

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Performance of the upgraded PreProcessor of the ATLAS Level-1 Calorimeter Trigger

The PreProcessor of the ATLAS Level-1 Calorimeter Trigger prepares the analogue trigger signals sent from the ATLAS calorimeters by digitising, synchronising, and calibrating them to reconstruct transverse energy deposits, which are then used in further processing to identify event features. During the first long shutdown of the LHC from 2013 to 2014, the central components of the PreProcessor, the Multichip Modules, were replaced by upgraded versions that feature modern ADC and FPGA technology to ensure optimal performance in the high pile-up environment of LHC Run 2. This paper describes the features of the newMultichip Modules along with the improvements to the signal processing achieved.ANPCyTYerPhI, ArmeniaAustralian Research CouncilBMWFW, AustriaAustrian Science Fund (FWF)Azerbaijan National Academy of Sciences (ANAS)SSTC, BelarusNational Council for Scientific and Technological Development (CNPq)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Natural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationNational Natural Science Foundation of China (NSFC)Departamento Administrativo de Ciencia, Tecnología e Innovación ColcienciasMinistry of Education, Youth & Sports - Czech Republic Czech Republic GovernmentCzech Republic GovernmentDNRF, DenmarkDanish Natural Science Research CouncilCentre National de la Recherche Scientifique (CNRS)CEA-DRF/IRFU, FranceFederal Ministry of Education & Research (BMBF)Max Planck SocietyGreek Ministry of Development-GSRTRGC and Hong Kong SAR, ChinaIsrael Science FoundationBenoziyo Center, IsraelIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of ScienceCNRST, MoroccoRCN, NorwayPortuguese Foundation for Science and TechnologyMNE/IFA, RomaniaMES of RussiaMESTD, SerbiaMSSR, SlovakiaSlovenian Research Agency - SloveniaMIZS, SloveniaSpanish GovernmentSRC, SwedenWallenberg Foundation, SwedenSNSF Geneva, SwitzerlandMinistry of Science and Technology, TaiwanMinistry of Energy & Natural Resources - TurkeyScience & Technology Facilities Council (STFC)United States Department of Energy (DOE)National Science Foundation (NSF)BCKDF, CanadaCANARIE, CanadaCRC, CanadaEuropean Research Council (ERC)European Union (EU)French National Research Agency (ANR)German Research Foundation (DFG)Alexander von Humboldt FoundationGreek NSRF, GreeceBSF-NSF, IsraelGerman-Israeli Foundation for Scientific Research and DevelopmentLa Caixa Banking Foundation, SpainCERCA Programme Generalitat de Catalunya, SpainPROMETEO, SpainGenT Programmes Generalitat Valenciana, SpainGoran Gustafssons Stiftelse, SwedenRoyal Society of LondonLeverhulme TrustNRC, CanadaCERNANID, ChileChinese Academy of SciencesMinistry of Science and Technology, ChinaSRNSFG, GeorgiaHGF, GermanyNetherlands Organization for Scientific Research (NWO) Netherlands GovernmentMinistry of Science and Higher Education, PolandNCN, PolandNRCKI, Russia FederationJINRDST/NRF, South AfricaSERI, Geneva, SwitzerlandCantons of Bern and Geneva, SwitzerlandCompute Canada, CanadaHorizon 2020Marie Sklodowska-Curie ActionsEuropean Cooperation in Science and Technology (COST)EU-ESF, Greec

Observation of electroweak production of two jets and a Z-boson pair

Electroweak symmetry breaking explains the origin of the masses of elementary particles through their interactions with the Higgs field. Besides the measurements of the Higgs boson properties, the study of the scattering of massive vector bosons with spin 1 allows the nature of electroweak symmetry breaking to be probed. Among all processes related to vector-boson scattering, the electroweak production of two jets and a Z-boson pair is a rare and important one. Here we report the observation of this process from proton–proton collision data corresponding to an integrated luminosity of 139 fb−1 recorded at a centre-of-mass energy of 13 TeV with the ATLAS detector at the Large Hadron Collider. We consider two different final states originating from the decays of the Z-boson pair: one containing four charged leptons and another containing two charged leptons and two neutrinos. The hypothesis of no electroweak production is rejected with a statistical significance of 5.7σ, and the measured cross-section for electroweak production is consistent with the Standard Model prediction. In addition, we report cross-sections for inclusive production of a Z-boson pair and two jets for the two final states

Alignment of the ATLAS Inner Detector in Run 2

The performance of the ATLAS Inner Detector alignment has been studied using pp collision data at v s = 13 TeV collected by the ATLAS experiment during Run 2 (2015-2018) of the Large Hadron Collider (LHC). The goal of the detector alignment is to determine the detector geometry as accurately as possible and correct for time-dependent movements. The Inner Detector alignment is based on the minimization of track-hit residuals in a sequence of hierarchical levels, from global mechanical assembly structures to local sensors. Subsequent levels have increasing numbers of degrees of freedom; in total there are almost 750,000. The alignment determines detector geometry on both short and long timescales, where short timescales describe movementswithin anLHCfill. The performance and possible track parameter biases originating from systematic detector deformations are evaluated. Momentum biases are studied using resonances decaying to muons or to electrons. The residual sagitta bias and momentum scale bias after alignment are reduced to less than similar to 0.1 TeV-1 and 0.9 x 10(-3), respectively. Impact parameter biases are also evaluated using tracks within jets

A search for the dimuon decay of the Standard Model Higgs boson with the ATLAS detector

A search for the dimuon decay of the Standard Model (SM) Higgs boson is performed using data corresponding to an integrated luminosity of 139 fb(-1) collected with the ATLAS detector in Run 2 pp collisions at root s = 13 TeV at the Large Hadron Collider. The observed (expected) significance over the background-only hypothesis for a Higgs boson with a mass of 125.09 GeV is 2.0 sigma (1.7 sigma). The observed upper limit on the cross section times branching ratio for pp -> H -> mu mu is 2.2 times the SM prediction at 95% confidence level, while the expected limit on a H -> mu mu signal assuming the absence (presence) of a SM signal is 1.1(2.0). The best-fit value of the signal strength parameter, defined as the ratio of the observed signal yield to the one expected in the SM, is mu = 1.2 +/- 0.6. (C) 2020 The Author(s). Published by Elsevier B.V

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Model-independent search for the presence of new physics in events including H → γγ with s√ = 13 TeV pp data recorded by the ATLAS detector at the LHC

A model-independent search for new physics leading to final states containing a Higgs boson, with a mass of 125.09 GeV, decaying to a pair of photons is performed with 139 fb−1 of s√ = 13 TeV pp collision data recorded by the ATLAS detector at the Large Hadron Collider at CERN. This search examines 22 final states categorized by the objects that are produced in association with the Higgs boson. These objects include isolated electrons or muons, hadronically decaying τ-leptons, additional photons, missing transverse momentum, and hadronic jets, as well as jets that are tagged as containing a b-hadron. No significant excesses above Standard Model expectations are observed and limits on the production cross section at 95% confidence level are set. Detector efficiencies are reported for all 22 signal regions, which can be used to convert detector-level cross-section limits reported in this paper to particle-level cross-section constraints