Incidencia de factores socio-familiares en rendimiento académico : Aproximación en un medio semi-urbano.

Detectar la influencia de la familia, su constitución y status dentro del grupo social al que pertenece. Considerar el ambiente físico y cultural que rodea al niño, con objeto de comprobar si limita o posibilita sus actividades, las coarta o estimula. Determinar el status económico familiar y ver su influencia en el grado de instrucción y el rendimiento. Comprobar si la composición familiar o número de miembros son un factor determinante del rendimiento académico. 1200 alumnos de segunda etapa de EGB pertenecientes a colegios públicos. El estudio se plantea como variables independientes: edad, nivel de instrucción, número de miembros, situación familiar y renta per cápita. Y como variable dependiente el rendimiento medido a través de las calificaciones. Una vez concluída la recogida de datos y su identificación en las tablas correspondientes se les aplicó el tratamiento estadístico enfrentando las variables dos a dos. Chi cuadrado, Pearson, desviaciones. La edad de los padres es un factor decisivo en el rendimiento escolar de sus hijos. La edad de los padres no guarda relación con su situación laboral. El número de miembros familiares guarda relación con el nivel de instrucción de los padres. El número de miembros familiares guarda relación con la situación laboral de los padres. La renta per cápita familiar es independientemente de la subzona de residencia dentro de una zona determinada. El nivel socio-económico familiar influye en el rendimiento de los hijos.

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

Modelo de segmentación : estudio, aplicación, ejecución y desarrollo de estrategias para la implementación de un modelo de clustering en el Sector Financiero

A finales de 2009 se emprendió un nuevo modelo de segmentación de mercados por conglomeraciones o clústers, con el cual se busca atender las necesidades de los clientes, advirtiendo el ciclo de vida en el cual se encuentran, realizando estrategias que mejoren la rentabilidad del negocio, por medio de indicadores de gestión KPI. Por medio de análisis tecnológico se desarrolló el proceso de inteligencia de la segmentación, por medio del cual se obtuvo el resultado de clústers, que poseían características similares entre sí, pero que diferían de los otros, en variables de comportamiento. Esto se refleja en el desarrollo de campañas estratégicas dirigidas que permitan crear una estrecha relación de fidelidad con el cliente, para aumentar la rentabilidad, en principio, y fortalecer la relación a largo plazo, respondiendo a la razón de ser del negocio

Characterisation of low temperature and semi-insulating GaAs lateral photo-Dember THz Emitters

Poster for IRMMW-THz conference in Mainz, Germany 2013

La ventilazione non-invasiva a pressione positiva nell’insufficienza respiratoria acuta ipercapnica: 10 anni di esperienza clinica di una Unità di Terapia Semi-Intensiva Respiratoria

PremessaSebbene numerosi studi prospettici, controllati e randomizzati abbiano dimostrato il successo della ventilazione non-invasiva a pressione positiva (NIV) in casi selezionati di insufficienza respiratoria acuta ipercapnica (IRA) in setting con intensità di cura differenti, i dati di pratica clinica relativi all’uso della NIV nel “mondo reale” sono limitati. Scopo Riportare i risultati della nostra esperienza clinica sulla NIV nell’IRA applicata nell’Unità di Terapia Semi-Intensiva Respiratoria (UTSIR) allocata all’interno dell’Unità Operativa di Pneumologia di Arezzo negli anni 1996-2006 in termini di: tollerabilità, effetti sui gas ematici, tasso di successo e fattori predittivi del fallimento.MetodiTrecentocinquanta dei 1484 pazienti (23.6%) consecutivamente ammessi per IRA nella nostra Unità Operativa di Pneumologia durante il periodo di studio hanno ricevuto la NIV in aggiunta alla terapia standard, in seguito al raggiungimento di criteri predefiniti impiegati di routine.RisultatiOtto pazienti (2.3%) non hanno tollerato la NIV per discomfort alla maschera, mentre i rimanenti 342 (M: 240, F: 102; età: mediana (interquartili) 74.0 (68.0-79.3) anni; BPCO: 69.3%) sono stati ventilati per >1 ora. I gas ematici sono significativamente migliorati dopo 2 ore di NIV (media (deviazione standard) pH: 7.33 (0.07) versus 7.28 (7.25-7.31), p<0.0001; PaCO2: 71.4 (15.3) mmHg versus 80.8 (16.6) mmHg, p<0.0001; PaO2/FiO2: 205 (61) versus 183 (150-222), p<0.0001). La NIV ha evitato l’intubazione in 285/342 pazienti (83.3%) con una mortalità ospedaliera del 14.0%. Il fallimento della NIV è risultato essere predetto in modo indipendente dall’Apache III (Acute Physiology and Chronic Health Evaluation III) score, dall’indice di massa corporea e dal fallimento tardivo della NIV (> 48 ore di ventilazione) dopo iniziale risposta positiva.ConclusioniSecondo la nostra esperienza clinica di dieci anni realizzata in una UTSIR, la NIV si conferma essere ben tollerata, efficace nel migliorare i gas ematici e utile nell’evitare l’intubazione in molti episodi di IRA non-responsivi alla terapia standard.