Prevalence of exposure to occupational risks during preganancy in Spain

Objectives. We describe reported exposures to main categories of occupational agents and conditions in Spanish pregnant workers. Methods. Women were recruited at 12th week of pregnancy from main public gynaecological consults to be included in the INMA Spanish cohorts study (n = 2,058). Through personal interviews with structured questionnaires, information on working situation and working conditions during pregnancy was obtained. Results. Fifty percent of the women reported frequent exposure to physical load (standing, heavy lifting) and 45 % reported exposure to three or more indicators of job strain. Exposure to at least one physical agent (noise, vibrations, etc.) affected 25 % of the women. Exposure to chemicals was reported by 20 % of the women, mostly including solvents and cleaning products. Eight percent of the women worked at night shifts. Job strain was more prevalent in office workers and industrial operators. Industrial workers showed the highest prevalence of exposure to chemical and physical pollutants. Conclusions. Our data suggest that working conditions of pregnant women may need increased control in Spain.

Infrared identification of 2XMM J191043.4+091629.4

Context. We report the infrared identification of the X-ray source 2XMM J191043.4+091629.4, which was detected by XMM-Newton/EPIC in the vicinity of the Galactic supernova remnant W49B. Aims. The aim of this work is to establish the nature of the X-ray source 2XMM J191043.4+091629.4 studying both the infrared photometry and spectroscopy of the companion. Methods. We analysed UKIDSS images around the best position of the X-ray source and obtained spectra of the best candidate using NICS in the Telescopio Nazionale Galileo (TNG) 3.5-m telescope. We present photometric and spectroscopic TNG analyses of the infrared counterpart of the X-ray source, identifying emission lines in the K-band. The H-band spectra does not present any significant feature. Results. We have shown that the Brackett γ H i at 2.165 μm, and He i at 2.184 μm and at 2.058 μm are significantly present in the infrared spectrum. The CO bands are also absent from our spectrum. Based on these results and the X-ray characteristics of the source, we conclude that the infrared counterpart is an early B-type supergiant star with an E(B − V) = 7.6 ± 0.3 at a distance of 16.0 ± 0.5 kpc. This would be, therefore, the first high-mass X-ray binary in the Outer Arm at galactic longitudes of between 30° and 60°.

Gas Storage Scale-up at Room Temperature on High Density Carbon Materials

In relation to the current interest on gas storage demand for environmental applications (e.g., gas transportation, and carbon dioxide capture) and for energy purposes (e.g., methane and hydrogen), high pressure adsorption (physisorption) on highly porous sorbents has become an attractive option. Considering that for high pressure adsorption, the sorbent requires both, high porosity and high density, the present paper investigates gas storage enhancement on selected carbon adsorbents, both on a gravimetric and on a volumetric basis. Results on carbon dioxide, methane, and hydrogen adsorption at room temperature (i.e., supercritical and subcritical gases) are reported. From the obtained results, the importance of both parameters (porosity and density) of the adsorbents is confirmed. Hence, the densest of the different carbon materials used is selected to study a scale-up gas storage system, with a 2.5 l cylinder tank containing 2.64 kg of adsorbent. The scale-up results are in agreement with the laboratory scale ones and highlight the importance of the adsorbent density for volumetric storage performances, reaching, at 20 bar and at RT, 376 g l-1, 104 g l-1, and 2.4 g l-1 for CO2, CH4,and H2, respectively.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.