The IACOB project: A grid-based automatic tool for the quantitative spectroscopic analysis of O-stars

We present the IACOB grid-based automatic tool for the quantitative spectroscopic analysis of O-stars. The tool consists of an extensive grid of FASTWIND models, and a variety of programs implemented in IDL to handle the observations, perform the automatic analysis, and visualize the results. The tool provides a fast and objective way to determine the stellar parameters and the associated uncertainties of large samples of O-type stars within a reasonable computational time.Comment: 8 pages, 2 figures, 1 table. Proceedings of the "GREAT-ESF Stellar Atmospheres in the Gaia Era Workshop

Costly capital reallocation and enery use

In time series data, energy use does not change much with energy price changes. However, energy use is responsive to international differences in energy prices in cross-section data across countries. In this paper we consider a model of energy use in which production takes place at individual plants and capital can be used either to directly produce output or to reduce the energy required to run the plant. We assume that reallocating capital from one use to another is costly. This turns out to be crucial for the quantitative properties of the model to be in conformity with the low short-run and high long-run elasticities of energy use seen in data

OB stars at the lowest Local Group metallicity: GTC-OSIRIS observations of Sextans A

Our aim is to find and classify OB stars in Sextans A, to later determine accurate stellar parameters of these blue massive stars in this low metallicity region $(Z \sim 0.1 \rm Z_{\odot})$. Using UBV photometry, the reddening-free index Q and GALEX imaging, we built a list of blue massive star candidates in Sextans A. We obtained low resolution (R $\sim$ 1000) GTC-OSIRIS spectra for a fraction of them and carried out spectral classification. For the confirmed O-stars we derive preliminary stellar parameters. The target selection criteria and observations were successful and have produced the first spectroscopic atlas of OB-type stars in Sextans A. From the whole sample of 18 observed stars, 12 were classified as early OB-types, including 5 O-stars. The radial velocities of all target stars are in agreement with their Sextans A membership, although three of them show significant deviations. We determined the stellar parameters of the O-type stars using the stellar atmosphere code FASTWIND, and revisited the sub-SMC temperature scale. Two of the O-stars are consistent with relatively strong winds and enhanced helium abundances, although results are not conclusive. We discuss the position of the OB stars in the HRD. Initial stellar masses run from slightly below 20 up to 40 solar masses. The target selection method worked well for Sextans A, confirming the procedure developed in Garcia \& Herrero (2013). The stellar temperatures are consistent with findings in other galaxies. Some of the targets deserve follow-up spectroscopy because of indications of a runaway nature, an enhanced helium abundance or a relatively strong wind. We observe a correlation between HI and OB associations similar to the irregular galaxy IC1613, confirming the previous result that the most recent star formation of Sextans A is currently on-going near the rim of the H\,{\sc I} cavity

Wave transport in one-dimensional disordered systems with finite-width potential steps

An amazingly simple model of correlated disorder is a one-dimensional chain of n potential steps with a fixed width lc and random heights. A theoretical analysis of the average transmission coefficient and Landauer resistance as functions of n and klc predicts two distinct regimes of behavior, one marked by extreme sensitivity and the other associated with exponential behavior of the resistance. The sensitivity arises in n and klc for klc approximately pi, where the system is nearly transparent. Numerical simulations match the predictions well, and they suggest a strong motivation for experimental study.Comment: A6 pages. 5 figures. Accepted in EP

Descubriendo Patrones Craneofaciales Usando Datos Cefalométricos Multivariados para la Toma de Decisiones en Ortodoncia

Indexación: Web of Science; Scielo.The aim was to find craniofacial morphology patterns in a multivariate cephalometric database using a clustering technique. Cephalometric analysis was performed in a sample of 100 teleradiographs collected from Chilean orthodontic patients. Thirty cephalometric measurements were taken from commonly used analysis. The computed variables were used to perform a clustering analysis with the k-means algorithm to identify patterns of craniofacial morphology. The J48 decision tree was used to analyze each cluster, and the ANOVA test to determine the statistical differences between the clusters. Four clusters were found that had significant differences (P<0.001) in 24 of the 30 variables studied, suggesting that they represent different patterns of craniofacial form. Using the decision tree, 8 of the 30 variables appeared to be relevant for describing the clusters. The clustering analysis is effective in identifying different craniofacial patterns based on a multivariate database. The distinct clusters appear to be caused by differences in the compensation process of the facial structure responding to a genetically determined cranial and mandible form. The proposed method can be applied to several databases, creating specific classifications for each one of them. KEY WORDS: Craniofacial patterns; Morphological patterns; Clustering technique; Orthodontics.RESUMEN: El objetivo fue encontrar patrones morfológicos craneofaciales, a partir de una base de datos cefalométricos multivariada, utilizando una técnica de clustering. Se realizó un análisis cefalométrico a una muestra de 100 telerradiografías pertenecientes a pacientes chilenos de ortodoncia. Treinta medidas cefalométricas obtenidas de los análisis más utilizados fueron registradas. Las variables computadas se utilizaron para realizar un análisis de clustering con el algoritmo k-medias, para identificar patrones de morfología craneofacial. El árbol de decisión J48 se utilizó para analizar cada cluster, y test de ANOVA para determinar diferencias estadísticamente significativas entre los clusters. Se encontraron cuatro clusters con diferencia estadísticamente significativas (p<0,001) en 24 de las 30 variables estudiadas, lo que sugiere que efectivamente corresponden a diferentes patrones craneofaciales. Utilizando el árbol de decisión, se pudo determinar que 8 de las 30 variables resultaron ser relevantes en la definición de los clusters. El análisis de clustering es efectivo en identificar patrones morfológicos craneofaciales usando una base de datos multivariada. Los distintos cluster encontrados, aparentemente se formarían a partir de diferencias en el proceso de compensación de la estructura facial, en respuesta a la forma mandibular genéticamente determinada. El método propuesto puede ser aplicado a múltiples bases de datos, creando clasificaciones específicas para cada una de ellas. PALABRAS CLAVE: Patrones craneofaciales; Patrones morfológicos; Técnica de clustering; Ortodoncia.http://ref.scielo.org/qdkkz