Sistema de acciones para estimular las potencialidades físicas en niños con Síndrome de Prader Willi

This research responds to the need to introduce modifications to the Therapeutic Physical Culture process, when working with children with Prader Willi Syndrome. Theoretical-methodological shortcomings were found that limit the stimulation of physical potentialities in them. The objective is to propose a system of actions to stimulate these potentialities, containing stages, orientations and general and specific methodological indications, with procedures that make this process feasible. The essential relationships between the affective-motivational components and the stimulation of physical potentialities are revealed.Esta investigación responde a la necesidad de introducir modificaciones al proceso de la Cultura Física Terapéutica, cuando se trabaja con niños portadores del Síndrome de Prader Willi. Se constataron insuficiencias teórico-metodológicas que limitan la estimulación de las potencialidades físicas en estos. El objetivo consiste en proponer un sistema de acciones para estimular dichas potencialidades, contentivo de etapas, orientaciones e indicaciones metodológicas generales y específicas, con procedimientos que viabilizan este proceso. Se revelan las relaciones esenciales entre los componentes afectivo-motivacional y estimulación de potencialidades físicas

Distributed Correlation-Based Feature Selection in Spark

CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed version of the CFS algorithm, capable of dealing with the large volumes of data typical of big data applications. Two versions of the algorithm were implemented and compared using the Apache Spark cluster computing model, currently gaining popularity due to its much faster processing times than Hadoop's MapReduce model. We tested our algorithms on four publicly available datasets, each consisting of a large number of instances and two also consisting of a large number of features. The results show that our algorithms were superior in terms of both time-efficiency and scalability. In leveraging a computer cluster, they were able to handle larger datasets than the non-distributed WEKA version while maintaining the quality of the results, i.e., exactly the same features were returned by our algorithms when compared to the original algorithm available in WEKA.Comment: 25 pages, 5 figure

FRATERNIDAD PARA LA VIDA DIGNA DE LOS PUEBLOS.(FRATERNITY FOR A WORTHY LIFE OF PEOPLES)

El presente texto pretende relacionar algunas líneas y rutas de análisis como aportes al debate que viene resurgiendo en Latinoamérica en torno al tercer componente no desarrollado de la modernidad: La Fraternidad. Para ello se hará referencia al concepto desde una dimensión política, propia de la modernidad y a la propuesta de la filosofía franciscana.AbstractThis text aims to connect some lines and routes of analysis as contribution to the debate which is reappearing in Latin America around the third non-developed component of modernity: Fraternity. That is why it will be approached from a political dimension, which is proper to modernity and the proposal of Franciscan philosophy

Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting

Year-ahead forecasting of electricity prices is an important issue in the current context of electricity markets. Nevertheless, only one-day-ahead forecasting is commonly tackled up in previous published works. Moreover, methodology developed for the short-term does not work properly for long-term forecasting. In this paper we provide a seasonal extension of the Non-Stationary Dynamic Factor Analysis, to deal with the interesting problem (both from the economic and engineering point of view) of long term forecasting of electricity prices. Seasonal Dynamic Factor Analysis (SeaDFA) allows to deal with dimensionality reduction in vectors of time series, in such a way that extracts common and specific components. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal one, by means of common factors following a multiplicative seasonal VARIMA(p,d,q)×(P,D,Q)s model. Besides, a bootstrap procedure is proposed to be able to make inference on all the parameters involved in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing to enhance the coverage of forecast confidence intervals. Concerning the innovative and challenging application provided, bootstrap procedure developed allows to calculate not only point forecasts but also forecasting intervals for electricity prices.Dynamic factor analysis, Bootstrap, Forecasting, Confidence intervals

Technological research in the EU is less efficient than the world average. EU research policy risks Europeans' future

We have studied the efficiency of research in the EU by a percentile-based citation approach that analyzes the distribution of country papers among the world papers. Going up in the citation scale, the frequency of papers from efficient countries increases while the frequency from inefficient countries decreases. In the percentile-based approach, this trend, which is permanent at any citation level, is measured by the ep index that equals the Ptop 1%/Ptop 10% ratio. By using the ep index we demonstrate that EU research on fast-evolving technological topics is less efficient than the world average and that the EU is far from being able to compete with the most advanced countries. The ep index also shows that the USA is well ahead of the EU in both fast- and slow-evolving technologies, which suggests that the advantage of the USA over the EU in innovation is due to low research efficiency in the EU. In accord with some previous studies, our results show that the European Commission's ongoing claims about the excellence of EU research are based on a wrong diagnosis. The EU must focus its research policy on the improvement of its inefficient research. Otherwise, the future of Europeans is at risk.Comment: 30 pages, 3 figures, 7 tables, in one single file. Version accepted in Journal of Informetric

Common bibliometric approaches fail to assess correctly the number of important scientific advances for most countries and institutions

Although not explicitly declared, most research rankings of countries and institutions are supposed to reveal their contribution to the advancement of knowledge. However, such advances are based on very highly cited publications with very low frequency, which can only very exceptionally be counted with statistical reliability. Percentile indicators enable calculations of the probability or frequency of such rare publications using counts of much more frequent publications; the general rule is that rankings based on the number of top 10% or 1% cited publications (Ptop 10%, Ptop 1%) will also be valid for the rare publications that push the boundaries of knowledge. Japan and its universities are exceptions, as their frequent Nobel Prizes contradicts their low Ptop 10% and Ptop 1%. We explain that this occurs because, in single research fields, the singularity of percentile indicators holds only for research groups that are homogeneous in their aims and efficiency. Correct calculations for ranking countries and institutions should add the results of their homogeneous groups, instead of considering all publications as a single set. Although based on Japan, our findings have a general character. Common predictions of scientific advances based on Ptop 10% might be severalfold lower than correct calculations.Comment: 30 pages, tables and figures embedded in a single pdf fil