50 research outputs found
Decisi贸n de la Comisi贸n (94/611/CE) referente a las condiciones finales de utilizaci贸n de un producto de construcci贸n ante la generaci贸n y propagaci贸n del fuego y del humo dentro del recinto de origen
Tercer informe anual sobre inmigraci贸n e integraci贸n
Comunicaci贸n de la Comisi贸n al Consejo, al Parlamento Europeo, al Comit茅 Econ贸mico y Social y al Comit茅 de las Regiones. Bruselas, 11.9.2007 COM (2007) 512 final
La seguridad social de los trabajadores migrantes Gu铆a sobre los derechos y obligaciones en materia de seguridad social de los trabajadores que van a trabajar a Espa帽a.
Glyphosate detection by voltammetric techniques. A comparison between statistical methods and an artificial neural network
Glyphosate quantification methods are complex and expensive, and its control in natural water bodies is getting more important year after year. In order to find a new system that facilitates the detection of glyphosate, we present a comparison between two models to predict glyphosate concentration in aqueous dissolutions. One of them is done by an artificial neural network (ANN) embedded in a microcontroller and the other one is done by statistic methods (Partial Least Squares) in a computer. From an analytical point of view, voltammetric techniques have been selected to obtain electrochemical
responses to different glyphosate concentrations. In order to get them, a voltammetry/amperometry
device designed by our research group and called FraPlus has been used. In this work we have selected
two sensitive electrodes (cobalt and copper), 10 different glyphosate concentrations and a train pulse
made by nine different voltammetric pulses to build the models. The ANN developed model is able
to properly relate data obtained by FraPlus and glyphosate concentrations and the obtained value for
regression coefficient (R) is 0.9998 and the P-value is 0.0. Taking into account these results, we propose
this ANN model based in voltammetric techniques working with glyphosate concentrations in a buffer
as an approach to glyphosate detection in natural water bodies.Authors acknowledge financial support from the Universitat Politecnica de Valencia and its Centre de Cooperacio al Desenvolupament (Programa ADSIDEO-COOPERACIO 2010) for the research fellowship and support given by UNIJUI University & Staff during the stage of Prof. Laguarda in Ijui (RS-Brazil). We also thank MICINN (MAT2009-14564-C04-02) and GVA (PPC/2011/019) for their respective research fellowships.Laguarda-Miro, N.; Werner Ferreira, F.; Garcia-Breijo, E.; Ib谩帽ez Civera, FJ.; Gil S谩nchez, L.; Garrigues Baixauli, J. (2012). Glyphosate detection by voltammetric techniques. A comparison between statistical methods and an artificial neural network. Sensors and Actuators B: Chemical. 171-172:528-536. https://doi.org/10.1016/j.snb.2012.05.025S528536171-17
El planteamiento global sobre la migraci贸n un a帽o despu茅s: hacia una pol铆tica global europea en materia de migraci贸n
Comunicaci贸n de la Comisi贸n al Consejo y al Parlamento Europeo, Bruselas, 30.11.2006 COM (2006) 735 final