Análisis del Nuevo Modelo de Gestión del Instituto Nacional de Servicios Sociales para Jubilados y Pensionados (2004-2014)

Fil: Romero, Adrián Ismael. Universidad de San Andrés

Development of MAIT 2 - A complete metabolomics analysis

Metabolomics, which is defined as the comprehensive analysis of metabolites in a biological system, has lots of applications that are leading to an important step forward in medicine. Since it is still an emerging field, researchers are trying to create computational tools that improve the processing, analysis and interpretation of metabolomics data. In this project, we have developed a new library in R based on MAIT (Metabolite Automatic Identification Toolkit) R package. Our algorithm provides a set of state-of-the-art tools to perform the whole metabolomics workflow, from raw liquid-chromatography coupled to mass spectrometry (LC-MS) data to the biological interpretation

Eliminació de contaminants de preocupació emergent de l’aigua residual mitjançant llum solar i clor

Treballs Finals de Grau de Química, Facultat de Química, Universitat de Barcelona, Any: 2022, Tutor: Alberto Cruz AlcaldeWastewater treatment plant (WWTP) are not designed to eliminate contaminants of emerging concern (CECs). This group of compounds is made up of remains of medicines, hormones, pesticides and personal hygiene products, among others, and is characterized by being organic, of anthropogenic origin, poorly biodegradable and are found in trace levels in secondary effluents. However, they can suppose a danger to the environment and people, especially if they are released into the aquatic environment or if the water from the treatment plants is used for agricultural irrigation or other applications involving human interaction. To achieve a significant attenuation of CECs in WWTPs and reduce the risks associated with this type of pollution, it is necessary to implement advanced water treatments such as Advanced Oxidation Processes (AOPs). This family of technologies is based on the generation of highly oxidizing species that can chemically destroy a wide variety of organic pollutants. Some of the most promising AOPs are those based on the use of ultraviolet light (UV) and weak oxidants, although the operating costs of these treatments are high due to much of the electricity requirements for the operation of the lamps. For this reason, attempts have been made to replace ultraviolet radiation with natural light from the Sun. The aim of the project is to evaluate the feasibility, both economic and technological, of a possible new AOP that combines chlorine and the use of sunlight as a source of radiation in the removal of CECs from wastewater. The basic aspects of the process, such as the kinetics associated with the degradation of pollutants, will be studied in a laboratory-scale experimental photochemical system. The aim is to combine pollutant degradation experiments with the analysis of residual oxidants and organic compounds using colorimetric techniques and liquid chromatography, respectively

Conductance and electronic structure of conjugated organic molecules

En este trabajo se estudia el origen de las resonancias y antiresonancias en el espectro de transmisión de sistemas moleculares. La forma general del espectro para una conexión arbitraria de una dada molécula y en particular, la existencia y ubicación de antiresonancias se explican a partir de un conjunto de reglas que surgen de las propiedades de las funciones de Green. Estas reglas son luego interpretadas gráficamente, permitiendo anticipar la forma de la función de transmisión de una molécula mediante la inspección directa de las superficies de nivel de sus orbitales moleculares.We study the origin of the resonances and antiresonances in the transmission spectrum of molecular systems. The general form of the spectrum, for an arbitrary connection of a given molecule and, particularly, the existence and position of antiresonances, are explained in terms of a set of rules derived from the properties of the Green functions. These rules are afterwords, graphically interpreted, thus allowing one to determine the transmission function of a molecule from direct inspection of the isosurface plot of its molecular orbitals.Fil: Lovey, Daniel Adrián. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas, Naturales y Agrimensura. Departamento de Fisica; Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Nordeste. Instituto de Modelado e Innovación Tecnológica; Argentina;Fil: Romero, Rodolfo Horacio. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas, Naturales y Agrimensura. Departamento de Fisica; Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Nordeste. Instituto de Modelado e Innovación Tecnológica; Argentina

Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio-Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier

Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio–Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier.This research was partly funded by Spanish Government grant TEC2017-84395-P

Modeling photopolymerization processes for enhanced part quality

When laser paths cross or when new layers are cured on top of existing layers, residual stresses are generated as the cure shrinkage of fresly gelled resin is constrained forming deflection or curl of the layers. The finite element method has been used to model the structural deformatins arising from the stereolithography build process. This includes the first layer polymerized during printing of an overhanged layer as subsequent rows of tetra-elements. The model does not include any resin beyond the external boundaries of the solid part. A standard linear static solution is carried out in order to get the properties of the fresh-resin

DETECCIÓN DEL DETERIORO COGNITIVO EN LOS PACIENTES MAYORES DE 50 AÑOS INGRESADOS HOSPITAL ROBERTO CALDERÓN GUTIÉRREZ-MANAGUA-NICARAGUA

Con el objetivo de evaluar el deterioro cognitivo de los pacientes mayores de 50 años ingresados en los servicios médicos quirúrgicos en el Hospital Escuela Roberto Calderón Gutiérrez (HERCG), en el periodo de diciembre 2015 a enero de 2016, se realizó un estudio observacional, descriptivo, retroprospectivo, transversal. Se aplicaron dos instrumentos para identificar deterioro cognitivo: MiniMental (MMSE) Informant Questionnaire on Cognitive in the Elderly (IQCODE); y una tercera prueba se aplicó para valorar la afectación en las actividades de la vida diaria, el Barthel Index. Los análisis estadísticos efectuados fueron frecuencias y porcentajes, chi cuadrado, media, mediana, desviación standard y percentiles según tipo de variables. Del análisis y discusión de los resultados se obtuvieron las siguientes conclusiones: Existe alta frecuencia de deterioro cognitivo en los pacientes mayores de 50 años ingresados en el HERCG, por causas no neurológicas, representando más de un tercio de la población estudiada (35,7%). Existe una relación lineal entre la edad y el deterioro cognitivo. Los dominios de cognición más afectados fueron atención, cálculo y memoria