Differences of Functional Connectivity Brain Network in Emotional Judgment

Using combined emotional stimuli, combining photos of faces and recording of voices, we investigated the neural dynamics of emotional judgment using scalp EEG recordings. Stimuli could be either combioned in a congruent, or a non-congruent way.. As many evidences show the major role of alpha in emotional processing, the alpha band was subjected to be analyzed. Analysis was performed by computing the synchronization of the EEGs and the conditions congruent vs. non-congruent were compared using statistical tools. The obtained results demonstrate that scalp EEG ccould be used as a tool to investigate the neural dynamics of emotional valence and discriminate various emotions (angry, happy and neutral stimuli).

Parameterization of written signatures based on EFD

In this work we propose a method to quantify written signatures from digitalized images based on the use of Elliptical Fourier Descriptors (EFD). As usually signatures are not represented as a closed contour, and being that a necessary condition in order to apply EFD, we have developed a method that represents the signatures by means of a set of closed contours. One of the advantages of this method is that it can reconstruct the original shape from all the coefficients, or an approximated shape from a reduced set of them finding the appropriate number of EFD coefficients required for preserving the important information in each application. EFD provides accurate frequency information, thus the use of EFD opens many possibilities. The method can be extended to represent other kind of shapes.

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

Taller de salud: prevención del consumo de alcohol en educación secundaria

Este trabajo describe el desarrollo de un programa de prevención del consumo del acohol en un Instituto de Educación Secundaria. Un total de 126 estudiantes de ESO de entre 12 y 15 años de edad participaron en el Taller de salud, el cual se estructuró en torno a cuatro sesiones de trabajo y donde se abordaron principalmente los efectos del alcohol sobre las personas, así como la influencia de los medios de comunicación de masas y del grupo de iguales en el consumo de bebidas alcohólicas. La evaluación de la efectividad del programa se realizó a través de los datos obtenidos por los participantes en sus respuestas a dos instrumentos antes y después de la intervención, el CEG y el ADCA-1. Se informa sobre los resultados obtenidos en la evaluación de la intervención, así como de sugerencias a tener en cuenta en actividades de prevención y promoción de la salud con poblaciones de educación secundaria.

Ultrasonic and LIDAR sensors for electronic canopy characterization in vineyards: advances to improve pesticide application methods

Canopy characterization is a key factor to improve pesticide application methods in tree crops and vineyards. Development of quick, easy and efficient methods to determine the fundamental parameters used to characterize canopy structure is thus an important need. In this research the use of ultrasonic and LIDAR sensors have been compared with the traditional manual and destructive canopy measurement procedure. For both methods the values of key parameters such as crop height, crop width, crop volume or leaf area have been compared. Obtained results indicate that an ultrasonic sensor is an appropriate tool to determine the average canopy characteristics, while a LIDAR sensor provides more accuracy and detailed information about the canopy. Good correlations have been obtained between crop volume (CVU) values measured with ultrasonic sensors and leaf area index, LAI (R2 = 0.51). A good correlation has also been obtained between the canopy volume measured with ultrasonic and LIDAR sensors (R2 = 0.52). Laser measurements of crop height (CHL) allow one to accurately predict the canopy volume. The proposed new technologies seems very appropriate as complementary tools to improve the efficiency of pesticide applications, although further improvements are still needed.

Modelo para una autoevaluación modular en una institución educativa (BADI)

Una eficaz acción educativa obliga necesariamente a plantearse en profundidad la autoevaluación como modelo de cambio y la participación de todos los estamentos de la comunidad educativa. Un mayor o menor grado de participación viene a ser un indicador que nos permite valorar cualquier propuesta de «calidad», «reforma» o «innovación». El modelo B.A.D.I. yen especial su instrumento modular, responde a las exigencias particulares de reflexión, análisis y toma de decisiones que se realiza en cada centro para responder adecuadamente a cualquier planteamiento innovador. Los principios fundamentales de este modelo se resumen en: A. Es un modelo de concepción organicista, estamental, con definición ideológica. B. Responde a un enfoque fundamentalmente rogeriano. C. Participa de los enfoques social, abierto, dinámico, cultural y positivo. Comparte la teoría moderna de la organización y sus indicadores se agrupan en cuatro fases: Criterial, presupuestaria, metodológica e informativa. D. Se identifica con los modelos centrados en la evaluación de cambio, de forma especial con la autoevaluación. La flexibilidad que caracteriza al modelo viene dado por el instrumento modular de área o de criterio, como resultado de la participación y el consenso de todos los estamentos de la institución educativa.