Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.

A multi-method study of the transformation of the carbonaceous skeleton of a polymer-based nanoporous carbon along the activation pathway

The change in the carbonaceous skeleton of nanoporous carbons during their activation has received limited attention, unlike its counterpart process in the presence of an inert atmosphere. Here we adopt a multi-method approach to elucidate this change in a poly(furfuryl alcohol)-derived carbon activated using cyclic application of oxygen saturation at 250 °C before its removal (with carbon) at 800 °C in argon. The methods used include helium pycnometry, synchrotron-based X-ray diffraction (XRD) and associated radial distribution function (RDF) analysis, transmission electron microscopy (TEM) and, uniquely, electron energy-loss spectroscopy spectrum-imaging (EELS-SI), electron nanodiffraction and fluctuation electron microscopy (FEM). Helium pycnometry indicates the solid skeleton of the carbon densifies during activation from 78% to 93% of graphite. RDF analysis, EELS-SI, and FEM all suggest this densification comes through an in-plane growth of sp2 carbon out to the medium range without commensurate increase in order normal to the plane. This process could be termed ‘graphenization’. The exact way in which this process occurs is not clear, but TEM images of the carbon before and after activation suggest it may come through removal of the more reactive carbon, breaking constraining cross-links and creating space that allows the remaining carbon material to migrate in an annealing-like process.

The Gaia-ESO Survey: radial metallicity gradients and age-metallicity relation of stars in the Milky Way disk

We study the relationship between age, metallicity, and α-enhancement of FGK stars in the Galactic disk. The results are based upon the analysis of high-resolution UVES spectra from the Gaia-ESO large stellar survey. We explore the limitations of the observed dataset, i.e. the accuracy of stellar parameters and the selection effects that are caused by the photometric target preselection. We find that the colour and magnitude cuts in the survey suppress old metal-rich stars and young metal-poor stars. This suppression may be as high as 97% in some regions of the age-metallicity relationship. The dataset consists of 144 stars with a wide range of ages from 0.5 Gyr to 13.5 Gyr, Galactocentric distances from 6 kpcto 9.5 kpc, and vertical distances from the plane 0 < |Z| < 1.5 kpc. On this basis, we find that i) the observed age-metallicity relation is nearly flat in the range of ages between 0 Gyr and 8 Gyr; ii) at ages older than 9 Gyr, we see a decrease in [Fe/H] and a clear absence of metal-rich stars; this cannot be explained by the survey selection functions; iii) there is a significant scatter of [Fe/H] at any age; and iv) [Mg/Fe] increases with age, but the dispersion of [Mg/Fe] at ages >9 Gyr is not as small as advocated by some other studies. In agreement with earlier work, we find that radial abundance gradients change as a function of vertical distance from the plane. The [Mg/Fe] gradient steepens and becomes negative. In addition, we show that the inner disk is not only more α-rich compared to the outer disk, but also older, as traced independently by the ages and Mg abundances of stars.

The Gaia-ESO Survey: Empirical determination of the precision of stellar radial velocities and projected rotation velocities

Context. The Gaia-ESO Survey (GES) is a large public spectroscopic survey at the European Southern Observatory Very Large Telescope. Aims. A key aim is to provide precise radial velocities (RVs) and projected equatorial velocities (vsini) for representative samples of Galactic stars, which will complement information obtained by the Gaia astrometry satellite. Methods. We present an analysis to empirically quantify the size and distribution of uncertainties in RV and vsini using spectra from repeated exposures of the same stars. Results. We show that the uncertainties vary as simple scaling functions of signal-to-noise ratio (S/N) and vsini, that the uncertainties become larger with increasing photospheric temperature, but that the dependence on stellar gravity, metallicity and age is weak. The underlying uncertainty distributions have extended tails that are better represented by Student’s t-distributions than by normal distributions. Conclusions. Parametrised results are provided, which enable estimates of the RV precision for almost all GES measurements, and estimates of the vsini precision for stars in young clusters, as a function of S/N, vsini and stellar temperature. The precision of individual high S/N GES RV measurements is 0.22–0.26 km s-1, dependent on instrumental configuration.