Quiescent state and outburst evolution of SGR 0501+4516

We report on the quiescent state of the soft gamma repeater SGR 0501+4516 observed by XMM–Newton on 2009 August 30. The source exhibits an absorbed flux ∼75 times lower than that measured at the peak of the 2008 outburst, and a rather soft spectrum, with the same value of the blackbody temperature observed with ROSAT back in 1992. This new observation is put into the context of all existing X-ray data since its discovery in 2008 August, allowing us to complete the study of the timing and spectral evolution of the source from outburst until its quiescent state. The set of deep XMM–Newton observations performed during the few years time-scale of its outburst allows us to monitor the spectral characteristics of this magnetar as a function of its rotational period, and their evolution along these years. After the first ∼10 d, the initially hot and bright surface spot progressively cooled down during the decay. We discuss the behaviour of this magnetar in the context of its simulated secular evolution, inferring a plausible dipolar field at birth of 3 × 1014 G, and a current (magnetothermal) age of ∼10 kyr.

Active edible films: Current state and future trends

Active edible films represent one of the current and future trends in the development of new polymers for selected applications, particularly food packaging. Some biopolymers show excellent performance as carriers for active compounds extracted from natural sources and are able to be released at a controlled rate to packaged food. In this review we aim to present, in a comprehensive way, the most recent advances and updates in this subject, where much research is currently ongoing and new studies are reported very often. This review focuses on innovative biopolymer matrices, their processing to obtain edible active films, and present and future applications.

Phrase-based statistical machine translation: explanation of its processes and statistical models and evaluation of the English to Spanish translations produced

Statistical machine translation (SMT) is an approach to Machine Translation (MT) that uses statistical models whose parameter estimation is based on the analysis of existing human translations (contained in bilingual corpora). From a translation student’s standpoint, this dissertation aims to explain how a phrase-based SMT system works, to determine the role of the statistical models it uses in the translation process and to assess the quality of the translations provided that system is trained with in-domain goodquality corpora. To that end, a phrase-based SMT system based on Moses has been trained and subsequently used for the English to Spanish translation of two texts related in topic to the training data. Finally, the quality of this output texts produced by the system has been assessed through a quantitative evaluation carried out with three different automatic evaluation measures and a qualitative evaluation based on the Multidimensional Quality Metrics (MQM).