LC-MS based methods for phytosterols determination in oenological matrices. Targeted metabolomic study of Rioja grape varieties

221 p.+ anexos

Thermal stability and crystallochemical analysis for CoII based coordination polymers with TPP and TPPS porphyrins

Artículo CrystEngComm 2013

Interacción y programación de trayectorias de robot industrial tipo SCARA.

[ES]En el desarrollo de este Trabajo de Fin De Grado (TFG) en el curso 2014-2015 se ha trabajado con un robot de tipo SCARA, muy utilizado en la industria. El objetivo era analizar su cinemática y programar trayectorias que el robot pudiera realizar. En primer lugar se ha llevado a cabo un estudio del Estado del Arte, en el que se describe la robótica industrial y su desarrollo histórico hasta nuestros días, desarrollo que presenta un futuro prometedor. Además, se han descrito las particularidades que atañen al SCARA: sus características, su relevancia y su historia. En cuanto al robot, previamente se ha realizado un análisis cinemático del SCARA. Mediante métodos matriciales se han resuelto los problemas de posiciones y velocidades, para luego programarlas en MATLAB. Una vez comprendida su cinemática, se ha interactuado con él en el taller para poder entender su funcionamiento, sus componentes y su control. Después, con los conocimientos que se han adquirido, se han programado varias trayectorias usando el lenguaje del robot, el lenguaje V+, para finalmente ejecutar esos movimientos. El Trabajo se completa con la descripción de las tareas mediante un diagrama de Gantt, el presupuesto, la declaración de gastos y el análisis de riesgos.

New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination

Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. In EDAs based on Markov networks, methods for generating new populations usually discard information contained in the model to gain in efficiency. Other methods like Gibbs sampling use information about all interactions in the model but are computationally very costly. In this paper we propose new methods for generating new solutions in EDAs based on Markov networks. We introduce approaches based on inference methods for computing the most probable configurations and model-based template recombination. We show that the application of different variants of inference methods can increase the EDAs’ convergence rate and reduce the number of function evaluations needed to find the optimum of binary and non-binary discrete functions.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.