Chemical abundances of stars with brown-dwarf companions

It is well-known that stars with giant planets are on average more metal-rich than stars without giant planets, whereas stars with detected low-mass planets do not need to be metal-rich. With the aim of studying the weak boundary that separates giant planets and brown dwarfs (BDs) and their formation mechanism, we analyze the spectra of a sample of stars with already confirmed BD companions both by radial velocity and astrometry. We employ standard and automatic tools to perform an EW-based analysis and to derive chemical abundances from CORALIE spectra of stars with BD companions. We compare these abundances with those of stars without detected planets and with low-mass and giant-mass planets. We find that stars with BDs do not have metallicities and chemical abundances similar to those of giant-planet hosts but they resemble the composition of stars with low-mass planets. The distribution of mean abundances of $\alpha$-elements and iron peak elements of stars with BDs exhibit a peak at about solar abundance whereas for stars with low-mass and high-mass planets the [X$_\alpha$/H] and [X$_{\rm Fe}$/H] peak abundances remain at $\sim -0.1$~dex and $\sim +0.15$~dex, respectively. We display these element abundances for stars with low-mass and high-mass planets, and BDs versus the minimum mass, $m_C \sin i$, of the most-massive substellar companion in each system, and we find a maximum in $\alpha$-element as well as Fe-peak abundances at $m_C \sin i \sim 1.35\pm 0.20$ jupiter masses. We discuss the implication of these results in the context of the formation scenario of BDs in comparison with that of giant planets.Comment: Accepted for publication in Astronomy & Astrophysic

El proceso de planificación del alta en centros de rehabilitación: sistemas de información para la evaluación de pacientes

IV Congreso Nacional de Informática de la Salud; 2001 Mar 28-30; Madrid; organizado por la Sociedad Española de Informática de la Salud[Resumen] La planificación del alta es un proceso que debe comenzar desde el mismo momento del ingreso. Debe ser sistemático, interdisciplinario y coordinado por un especialista sanitario. Debe involucrar al paciente y a su familia e incluir la valoración de su entorno de vida, soporte familiar, valoración de la discapacidad y posibilidades de llevar a cabo una rehabilitación vocacional. Todas las decisiones que se tomen en el proceso del alta deben implicar y reflejar el consenso de la familia y el propio paciente con el equipo médico (1). Por lo tanto, surge la necesidad de utilizar algún sistema de medición de incapacidad, dada la gran variedad de patologías que abarca la Medicina Física y Rehabilitación y la existencia de cuadros nosológicos muy diferentes en cuanto a su etiología, gravedad y pronóstico. En este contexto, se hace necesaria la utilización de escalas de valoración funcional, que simplifiquen este trabajo y posibiliten un mayor control de todo el proceso desde su inicio. Existen múltiples escalas de valoración, tanto específicas como de propósito general, siendo la más utilizada la Functional Independence Measure (FIM) de la Uniform Data System for Medical Rehabilitation. A partir de estas escalas se han desarrollado diferentes sistemas de datos: el Union Data System (UDS) y el TBI Model Systems National Database del National Institute on Disability and Rehabilitation Research entre los más destacados. En este artículo, además de exponer las distintas fases del proceso de planificación del alta, se hará un estudio de los distintos sistemas de información desarrollados, así como de las escalas de valoración utilizadas

Methane Emission Estimated from Different Cattle Intake Data in Heifers Grazing a Tropical Pasture

The quantification of methane (CH4) from enteric fermentation related to cattle diet is a useful tool to identify strategies to mitigate greenhouse gas emissions. This is even important in tropical and subtropical regions due to the lack of CH4 estimations in beef cattle, particularly from Bos Indicus breeds grazing tropical grasses (Kurihara et al., 1999). Several modelling approaches have been developed in order to predict CH4 emission. However, the use of these models has limitations associated with uncertainty information required such as feed intake (FI), composition of the selected diet and animal responses (Gonzalez et al., 2014). FI is the main factor influencing CH4 emission. Individual FI measurements are not easy to achieve accurately in grazing animals rather than those located in pens, particularly under deferred tropical pastures at the end of the dry season, due to the large proportion of death forage. In this case, cattle supplementation with energetic and proteins concentrates, is a viable practice in order to improve animal FI and reduce CH4 emissions. The main objectives of this study was estimate and compare CH4 emission using data collected from experimental trials and predicted by a model (UNFCCC, 2014) in supplemented heifers grazing low quality Chloris gayana pasture in northwestern Argentina (Semiarid Chaco Region)

Effect of Supplementation Frequency on Forage Utilization by Heifers Grazing a Tropical Pasture during the Dry Season

In tropical pasture, low quality and availability forage during the dry season can limit the cattle intake. Energetic and protein supplementation is a viable practice to improve feed intake and animal performance. Previous studies have shown that infrequent protein supplementation decreases feeding cost achieving similar performance compared with every day supplementation (Farmer et al., 2004). Even though infrequent protein supplementation has been widely studied, little research has been carried out on infrequent energetic supplementation, especially its effect on pasture utilization. Some evidence indicates that negative effects on forage use at low levels of infrequent supplementation (Beaty et al., 1994). However, high levels of energetic supplementation can result in a substitution effect of forage for concentrate, reducing pasture utilization, even more when forage quality decreases as dry season progresses. Thus, the aim of this study was to evaluate the effect of supplementation frequency (continuous or discontinuous, based on energetic concentrate) on forage utilization by heifers grazing a Chloris gayana pasture during the dry season in the Semiarid Chaco Region (Northwestern Argentine)

Quantum Magnetic Deflagration in Mn12 Acetate

We report controlled ignition of magnetization reversal avalanches by surface acoustic waves in a single crystal of Mn12 acetate. Our data show that the speed of the avalanche exhibits maxima on the magnetic field at the tunneling resonances of Mn12. Combined with the evidence of magnetic deflagration in Mn12 acetate (Suzuki et al., cond-mat/0506569) this suggests a novel physical phenomenon: deflagration assisted by quantum tunneling.Comment: 4 figure

The RoPES project with HARPS and HARPS-N. I. A system of super-Earths orbiting the moderately active K-dwarf HD 176986

We report the discovery of a system of two super-Earths orbiting the moderately active K-dwarf HD 176986. This work is part of the RoPES RV program of G- and K-type stars, which combines radial velocities (RVs) from the HARPS and HARPS-N spectrographs to search for short-period terrestrial planets. HD 176986 b and c are super-Earth planets with masses of 5.74 and 9.18 M$_{\oplus}$, orbital periods of 6.49 and 16.82 days, and distances of 0.063 and 0.119 AU in orbits that are consistent with circular. The host star is a K2.5 dwarf, and despite its modest level of chromospheric activity (log(R'hk) = - 4.90 +- 0.04), it shows a complex activity pattern. Along with the discovery of the planets, we study the magnetic cycle and rotation of the star. HD 176986 proves to be suitable for testing the available RV analysis technique and further our understanding of stellar activity.Comment: 21 pages, 24 figures, 7 table

Comparative study of continuous hourly energy consumption forecasting strategies with small data sets to support demand management decisions in buildings

Producción CientíficaBuildings are one of the largest consumers of electrical energy, making it important to develop different strategies to help to reduce electricity consumption. Building energy consumption forecasting strategies are widely used to support demand management decisions, but these strategies require large data sets to achieve an accurate electric consumption forecast, so they are not commonly used for buildings with a short history of record keeping. Based on this, the objective of this study is to determine, through continuous hourly electricity consumption forecasting strategies, the amount of data needed to achieve an accurate forecast. The proposed forecasting strategies were evaluated with Random Forest, eXtreme Gradient Boost, Convolutional Neural Network, and Temporal Convolutional Network algorithms using 4 years of electricity consumption data from two buildings located on the campus of the University of Valladolid. For performance evaluation, two scenarios were proposed for each of the proposed forecasting strategies. The results showed that for forecasting horizons of 1 week, it was possible to obtain a mean absolute percentage error (MAPE) below 7% for Building 1 and a MAPE below 10% for Building 2 with 6 months of data, while for a forecast horizon of 1 month, it was possible to obtain a MAPE below 10% for Building 1 and below 11% for Building 2 with 10 months of data. However, if the distribution of the data captured in the buildings does not undergo sudden changes, the decision tree algorithms obtain better results. However, if there are sudden changes, deep learning algorithms are a better choice.CITIES thematic network, a member of the CYTED program. CYTED, grant number: 518RT0558University of Valladolid and the Instituto Tecnológico de Santo Domingo for their support in this study, which is the result of a co-supervised doctoral thesi

Rules, Standards, and the Internal Point of View

Large scale structure and cosmolog