Using our present knowledge on effective hadronic theories, short-distance QCD information, the 1/N C expansion, analyticity and unitarity, we derive an expression for the pion form factor, in terms of m π , m K , M ρ and f π . This parameter-free prediction provides a surprisingly good description of the experimental data up to energies of the order of 1 GeV.

We have calculated the KK → KK scattering amplitude to next to leading order in Chiral Perturbation Theory. Then, making use of a unitarization procedure with one or several coupled channels (ππ,KK in our case) we have calculated the ππ → ππ, ππ → KK and KK → KK S and P waves in good agreement with the experiment up to √ s ≃ 1.2 GeV. The ππ scattering lengths with isospin and spin (I,J) equal to (0,0), (1,1) and (2,0) are also calculated in agreement with experiment and former Chiral Perturbation Theory calculations.Finally we have employed these amplitudes, making use of an Omnès representation, to calculate the scalar and the vector pion form factors, obtaining a good agreement with the available experimental data.

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Background:There are many models that study different aspects concerning smoking habits: influence of price, tax, relapse time, the effects of prohibition, etc. There are also studies on the effect of the Spanish smoke-free law, but from a statistical point of view, not from a dynamic point of view. We wanted to build a model able to separate the effect of the law from the pre-law evolution of smoking habits.

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Convolutional neural networks (CNN) have demonstrated state-of-the-art classification results in image categorization, but have received comparatively little attention for classification of one-dimensional physiological signals. We design a deep CNN architecture for automated sleep stage classification of human sleep EEG and EOG signals. The CNN proposed in this paper amply outperforms recent work that uses a different CNN architecture over a single-EEG-channel version of the same dataset. We show that the performance gains achieved by our network rely mainly on network depth, and not on the use of several signal channels. Performance of our approach is on par with human expert inter-scorer agreement. By examining the internal activation levels of our CNN, we find that it spontaneously discovers signal features such as sleep spindles and slow waves that figure prominently in sleep stage categorization as performed by human experts.

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tWe obtain an approximated analytical solution for a dynamic model for the prevalence of the smoking habit in a constant population but with equal and different from zero birth and death rates. This model has been successfully used to explain the evolution of the smoking habit in Spain. By means of the Homotopy Analysis Method, we obtain an analytic expression in powers of time t which reproduces the correct solution for a certain range of time. To enlarge the domain of convergence we have applied the so-called optimal convergence-control parameter technique and the homotopy-Padé technique. We present and discuss graphical results for our solutions.

The Spanish Quality Assurance Program applied to the process of donation after brain death entails an internal stage consisting of a continuous clinical chart review of deaths in critical care units (CCUs) performed by transplant coordinators and periodical external audits to selected centers. This paper describes the methodology and provides the most relevant results of this program, with information analyzed from 206,345 CCU deaths. According to the internal audit, 2.3% of hospital deaths and 12.4% of CCU deaths in Spain yield potential donors (clinical criteria consistent with brain death). Out of the potential donors, 54.6% become actual donors, 26% are lost due to medical unsuitability, 13.3% due to refusals to donation, 3.1% due to maintenance problems and 3% due to other reasons. Although the national pool of potential donors after brain death has progressively decreased from 65.2 per million population (pmp) in 2001 to 49 pmp in 2010, the number of actual donors after brain death has remained at about 30 pmp. External audits reveal that the number of actual donors could be 21.6% higher if all potential donors were identified and preventable losses avoided. We encourage other countries to develop similar comprehensive approaches to deceased donation performance.