Madagascar, el vuité continent

Un recorregut per l'illa vermell

Prediction of stroke risk based on left atrial appendage morphology: from pareidolia to artificial intelligence

<jats:title>Abstract</jats:title><jats:p>The global carbon-climate system is a complex dynamical system with multiple feedbacks among components, and to steer this system away from dangerous climate change, it may not be enough to prescribe action according to long-term scenarios of fossil fuel emissions. We introduce here concepts from control theory, a branch of applied mathematics that is effective at steering complex dynamical systems to desired states, and distinguish between open- and closed-loop control. We attempt (1) to show that current scientific work on carbon-climate feedbacks and climate policy more closely resembles the conceptual model of open- than closed-loop control, (2) to introduce a mathematical generalization of the carbon-climate system as a compartmental dynamical system that can facilitate the formal treatment of the closed-loop control problem, and (3) to formulate carbon-climate control as a congestion control problem, discussing important concepts such as observability and controllability. We also show that most previous discussions on climate change mitigation and policy development have relied on an implicit assumption of open-loop control that does not consider frequent corrections due to deviations of goals from observations. Using a reduced complexity model, we illustrate that the problem of managing the global carbon cycle can be abstracted as a network congestion problem, accounting for nonlinear behavior and feedback from a global carbon monitoring system. As opposed to <jats:italic>scenarios</jats:italic>, the goal of closed-loop control is to develop <jats:italic>rules</jats:italic> for continuously steering the global carbon-climate system away from dangerous climate change.</jats:p&gt

Serendipitous discovery of a strong-lensed galaxy in integral field spectroscopy from MUSE

2MASX J04035024-0239275 is a bright red elliptical galaxy at redshift 0.0661 that presents two extended sources at 2\arcsec~to the north-east and 1\arcsec~to the south-west. The sizes and surface brightnesses of the two blue sources are consistent with a gravitationally-lensed background galaxy. In this paper we present MUSE observations of this galaxy from the All-weather MUse Supernova Integral-field Nearby Galaxies (AMUSING) survey, and report the discovery of a background lensed galaxy at redshift 0.1915, together with other 15 background galaxies at redshifts ranging from 0.09 to 0.9, that are not multiply imaged. We have extracted aperture spectra of the lens and all the sources and fit the stellar continuum with STARLIGHT to estimate their stellar and emission line properties. A trace of past merger and active nucleus activity is found in the lensing galaxy, while the background lensed galaxy is found to be star-forming. Modeling the lensing potential with a singular isothermal ellipsoid, we find an Einstein radius of 1\farcs45$\pm$0\farcs04, which corresponds to 1.9 kpc at the redshift of the lens and it is much smaller than its effective radius ($r_{\rm eff}\sim$ 9\arcsec). Comparing the Einstein mass and the STARLIGHT stellar mass within the same aperture yields a dark matter fraction of $18 \% \pm 8$ \% within the Einstein radius. The advent of large surveys such as the Large Synoptic Survey Telescope (LSST) will discover a number of strong-lensed systems, and here we demonstrate how wide-field integral field spectroscopy offers an excellent approach to study them and to precisely model lensing effects.Comment: 12 pages, 12 Figures, 4 Tables. Accepted in MNRA

The state of SQL-on-Hadoop in the cloud

Managed Hadoop in the cloud, especially SQL-on-Hadoop, has been gaining attention recently. On Platform-as-a-Service (PaaS), analytical services like Hive and Spark come preconfigured for general-purpose and ready to use. Thus, giving companies a quick entry and on-demand deployment of ready SQL-like solutions for their big data needs. This study evaluates cloud services from an end-user perspective, comparing providers including: Microsoft Azure, Amazon Web Services, Google Cloud, and Rackspace. The study focuses on performance, readiness, scalability, and cost-effectiveness of the different solutions at entry/test level clusters sizes. Results are based on over 15,000 Hive queries derived from the industry standard TPC-H benchmark. The study is framed within the ALOJA research project, which features an open source benchmarking and analysis platform that has been recently extended to support SQL-on-Hadoop engines. The ALOJA Project aims to lower the total cost of ownership (TCO) of big data deployments and study their performance characteristics for optimization. The study benchmarks cloud providers across a diverse range instance types, and uses input data scales from 1GB to 1TB, in order to survey the popular entry-level PaaS SQL-on-Hadoop solutions, thereby establishing a common results-base upon which subsequent research can be carried out by the project. Initial results already show the main performance trends to both hardware and software configuration, pricing, similarities and architectural differences of the evaluated PaaS solutions. Whereas some providers focus on decoupling storage and computing resources while offering network-based elastic storage, others choose to keep the local processing model from Hadoop for high performance, but reducing flexibility. Results also show the importance of application-level tuning and how keeping up-to-date hardware and software stacks can influence performance even more than replicating the on-premises model in the cloud.This work is partially supported by the Microsoft Azure for Research program, the European Research Council (ERC) under the EUs Horizon 2020 programme (GA 639595), the Spanish Ministry of Education (TIN2015-65316-P), and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Modelling nitrogen and phosphorus loads in a Mediterranean river catchment (La Tordera, NE Spain)

Human activities have resulted in increased nutrient levels in many rivers all over Europe. Sustainable management of river basins demands an assessment of the causes and consequences of human alteration of nutrient flows, together with an evaluation of management options. In the context of an integrated and interdisciplinary environmental assessment (IEA) of nutrient flows, we present and discuss the application of the nutrient emission model MONERIS (MOdelling Nutrient Emissions into River Systems) to the Catalan river basin, La Tordera (north-east Spain), for the period 1996-2002. After a successful calibration and verification process (Nash-Sutcliffe efficiencies E=0.85 for phosphorus and E=0.86 for nitrogen), the application of the model MONERIS proved to be useful in estimating nutrient loads. Crucial for model calibration, in-stream retention was estimated to be about 50 % of nutrient emissions on an annual basis. Through this process, we identified the importance of point sources for phosphorus emissions (about 94% for 1996-2002), and diffuse sources, especially inputs via groundwater, for nitrogen emissions (about 31% for 1996-2002). Despite hurdles related to model structure, observed loads, and input data encountered during the modelling process, MONERIS provided a good representation of the major interannual and spatial patterns in nutrient emissions. An analysis of the model uncertainty and sensitivity to input data indicates that the model MONERIS, even in data-starved Mediterranean catchments, may be profitably used by water managers for evaluating quantitative nutrient emission scenarios for the purpose of managing river basins. As an example of scenario modelling, an analysis of the changes in nutrient emissions through two different future scenarios allowed the identification of a set of relevant measures to reduce nutrient loads

Magnetic superelasticity and inverse magnetocaloric effect in Ni-Mn-In

Applying a magnetic field to a ferromagnetic Ni$_{50}$Mn$_{34}$In$_{16}$ alloy in the martensitic state induces a structural phase transition to the austenitic state. This is accompanied by a strain which recovers on removing the magnetic field giving the system a magnetically superelastic character. A further property of this alloy is that it also shows the inverse magnetocaloric effect. The magnetic superelasticity and the inverse magnetocaloric effect in Ni-Mn-In and their association with the first order structural transition is studied by magnetization, strain, and neutron diffraction studies under magnetic field.Comment: 6 pages, 8 figures. Published in the Physical Review