A deep narrowband survey for planetary nebulae at the outskirts of M33

Context: Planetary nebulae (PNe) are excellent tracers of stellar populations with low surface brightness, and therefore provide a powerful method to detect and explore the rich system of substructures discovered around the main spiral galaxies of the Local Group. Aims: We searched the outskirts of the Local Group spiral galaxy M33 (the Triangulum) for PNe to gain new insights into the extended stellar substructure on the northern side of the disc and to study the existence of a faint classical halo. Methods: The search is based on wide field imaging covering a 4.5 square degree area out to a maximum projected distance of about 40 kpc from the centre of the galaxy. The PN candidates are detected by the combination of images obtained in narrowband filters selecting the [OIII]$\lambda5007\AA$ and H$\alpha$ + [NII] nebular lines and in the continuum g' and r' broadband filters. Results:Inside the bright optical disc of M33, eight new PN candidates were identified, three of which were spectroscopically confirmed. No PN candidates were found outside the limits of the disc. Fourteen additional sources showing [OIII] excess were also discovered. Conclusions:The absence of bright PN candidates in the area outside the galaxy disc covered by this survey sets an upper limit to the luminosity of the underlying population of $\mathrm{\sim1.6\cdot10^{7}L_{\odot}}$, suggesting the lack of a massive classical halo, which is in agreement with the results obtained using the RGB population.Comment: 13 pages, 18 figure

European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series

Effect of Industry 4.0 on Education Systems: An Outlook

Congreso Universitario de Innovación Educativa En las Enseñanzas Técnicas, CUIEET (26º. 2018. Gijón

Highly efficient heavy-metal extraction from water with carboxylated graphene nanoflakes

Heavy metals such a lead or cadmium have a wide range of detrimental and devastating effects on human health. It is therefore of paramount importance to efficiently remove heavy metals from industrial wastewater streams as well as drinking water. Carbon materials, including graphene and graphene oxide (GO), have recently been advocated as efficient sorption materials for heavy metals. We show that highly carboxylated graphene nanoflakes (cx-GNF) outperform nano-graphene oxide (nGO) as well as traditional GO with respect to extracting Fe 2+ , Cu 2+ , Fe 3+ , Cd 2+ and Pb 2+ cations from water. The sorption capacity for Pb 2+ , for example, is more than six times greater for the cx-GNF compared to GO which is attributed to the efficient formation of lead carboxylates as well as strong cation-π interactions. The large numbers of carboxylic acid groups as well as the intact graphenic regions of the cx-GNF are therefore responsible for the strong binding of the heavy metal cations. Remarkably, the performance of the as-made cx-GNF can easily compete with previously reported carbon materials that have undergone additional chemical-functionalisation procedures for the purpose of heavy-metal extraction. Furthermore, the recyclability of the cx-GNF material with respect to Pb 2+ loading is demonstrated as well as the outstanding performance for Pb 2+ extraction in the presence of excess Ca 2+ or Mg 2+ cations which are often present under environmental conditions. Out of all the graphene materials, the cx-GNF therefore show the greatest potential for future application in heavy-metal extraction processes