Aplicación de nuevas técnicas docentes en la asignatura Sistemas Cliente/Servidor

En este trabajo mostramos nuestras experiencias en la aplicación de metodologías de aprendizaje cooperativo y basado en proyectos en la asignatura Sistemas Cliente/Servidor en los cursos académicos 2008/2009 y 2009/2010.SUMMARY: In this work we present our teaching experience in the aplication of cooperative and project-based learning methodologies within the subject Client/Server Systems in the academic years 2008/2009 and 2009/2010.Peer Reviewe

An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large data sets, such as three-dimensional medical images. We propose an adaptive sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in improved learning. Our contribution is threefold: 1) We give a detailed description of the proposed sampling algorithm to speed up and improve learning performance on large images. We propose a deep dual path CNN that captures information at fine and coarse scales, resulting in a network with a large field of view and high resolution outputs. We show that our method is able to attain new state-of-the-art results on the VISCERAL Anatomy benchmark

Pharmacotherapy in paediatric epilepsy: from trial and error to rational drug and dose selection – a long way to go

Whereas ongoing efforts in epilepsy research focus on the underlying disease processes, the lack of a physiologically-based rationale for drug and dose selection contributes to inadequate treatment response in children. In fact, limited information on the interindividual variation in pharmacokinetics and pharmacodynamics of anti-epileptic drugs (AEDs) in children drive prescription practice, which relies primarily on dose regimens according to a mg/kg basis. Such practice has evolved despite advancements in paediatric pharmacology showing that growth and maturation processes do not correlate linearly with changes in body size. Areas covered: In this review we aim to provide 1) a comprehensive overview of the sources of variability in the response to AEDs, 2) insight into novel methodologies to characterise such variation and 3) recommendations for treatment personalisation. Expert Opinion: The use of pharmacokinetic-pharmacodynamic principles in clinical practice is hindered by the lack of biomarkers and by practical constraints in the evaluation of polytherapy. The identification of biomarkers and their validation as tools for drug development and therapeutics will require some time. Meanwhile, one should not miss the opportunity to integrate the available pharmacokinetic data with modelling and simulation concepts to prevent further delays in the development of personalised treatments for paediatric patients

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.Copyright © 2022 Doste, Lozano, Jimenez-Perez, Mont, Berruezo, Penela, Camara and Sebastian

Intermittency and structure functions in channel flow turbulence

We present a study of intermittency in a turbulent channel flow. Scaling exponents of longitudinal streamwise structure functions, $\zeta_p /\zeta_3$, are used as quantitative indicators of intermittency. We find that, near the center of the channel the values of $\zeta_p /\zeta_3$ up to $p=7$ are consistent with the assumption of homogeneous/isotropic turbulence. Moving towards the boundaries, we observe a growth of intermittency which appears to be related to an intensified presence of ordered vortical structures. In fact, the behaviour along the normal-to-wall direction of suitably normalized scaling exponents shows a remarkable correlation with the local strength of the Reynolds stress and with the \rms value of helicity density fluctuations. We argue that the clear transition in the nature of intermittency appearing in the region close to the wall, is related to a new length scale which becomes the relevant one for scaling in high shear flows.Comment: 4 pages, 6 eps figure

Biomass burning and urban air pollution over the Central Mexican Plateau

Observations during the 2006 dry season of highly elevated concentrations of cyanides in the atmosphere above Mexico City (MC) and the surrounding plains demonstrate that biomass burning (BB) significantly impacted air quality in the region. We find that during the period of our measurements, fires contribute more than half of the organic aerosol mass and submicron aerosol scattering, and one third of the enhancement in benzene, reactive nitrogen, and carbon monoxide in the outflow from the plateau. The combination of biomass burning and anthropogenic emissions will affect ozone chemistry in the MC outflow