Human capital in a global and knowledge-based economy

This document is a report prepared for the DG for Employment and Social Affairs of the European Commission. It surveys the available evidence on the contribution of investment in human capital to aggregate productivity growth and on its impact on wages and other labour outcomes at the individual level. It also draws some tentative policy conclusions for an average European country.

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

Spectroscopic and dynamical properties of comet C/2018 F4, likely a true average former member of the Oort cloud

The population of comets hosted by the Oort cloud is heterogeneous. Most studies in this area focused on highly active objects, those with small perihelion distances or examples of objects with peculiar physical properties and/or unusual chemical compositions. This may have produced a biased sample of Oort cloud comets in which the most common objects may be rare, particularly those with perihelia well beyond the orbit of the Earth. Within this context, the known Oort cloud comets may not be representative of the full sample. Here, we study the spectral properties in the visible region and the cometary activity of Comet C/2018 F4 (PANSTARRS). We also explore its orbital evolution with the aim of understanding its origin within the context of known minor bodies moving along nearly parabolic or hyperbolic paths. We present observations obtained with the 10.4 m Gran Telescopio Canarias (GTC), derive the spectral class and visible slope of C/2018 F4 and characterise its level of cometary activity. Direct N-body simulations are carried out to explore its orbital evolution. The absolute magnitude of C/2018 F4 is Hr=13.62+/-0.04. Assuming a pV=0.04 its diameter is D<10.4 km. The object presents a conspicuous coma, with a level of activity comparable to those of other comets observed at similar heliocentric distances. Comet C/2018 F4 has a visible spectrum consistent with that of an X-type asteroid, and has a spectral slope S'=4.0+/-1.0 %/1000\AA and no evidence of hydration. The spectrum matches those of well-studied primitive asteroids and comets. The analysis of its dynamical evolution prior to discovery suggests that C/2018 F4 is not of extrasolar origin. Although the present-day heliocentric orbit of C/2018 F4 is slightly hyperbolic, its observational properties and past orbital evolution are consistent with those of a dynamically old comet with an origin in the Oort cloud.Comment: 6 pages, 5 figures. In pres