Juan Carlos Mejuto Fernández-rekin lankidetzan egindako argitalpenak (24)

2017

  1. Approach of different properties of alkylammonium surfactants using artificial intelligence and response surface methodology

    Tenside, Surfactants, Detergents, Vol. 54, Núm. 2, pp. 132-140

  2. Sistema de Gestión integrada de la Prevención de la Calidad, Medioambiental y de la Seguridad alimentaria: la visión de la Prevención de Riesgos

    Actas Congreso Prevencionar 2017

2016

  1. Airborne castanea pollen forecasting model for ecological and allergological implementation

    Science of the Total Environment, Vol. 548-549, pp. 110-121

  2. Application of transit data analysis and artificial neural network in the prediction of discharge of Lor River, NW Spain

    Water Science and Technology, Vol. 73, Núm. 7, pp. 1756-1767

  3. Electrical percolation of AOT-based microemulsions with n-alcohols

    Journal of Molecular Liquids, Vol. 215, pp. 18-23

2015

  1. A model to forecast the risk periods of Plantago pollen allergy by using the ANN methodology

    Aerobiologia, Vol. 31, Núm. 2, pp. 201-211

  2. Cleavage of carbofuran and carbofuran-derivatives in micellar aggregates

    Progress in Reaction Kinetics and Mechanism, Vol. 40, Núm. 2, pp. 105-118

  3. Density prediction of ternary mixtures of ethanol + water + ionic liquid using backpropagation artificial neural networks

    Ionic liquid-based surfactant science: formulation, characterization and applications (John Wiley & Sons (USA)), pp. 447-458

  4. Forecasting Olea airborne pollen concentration by means of artificial intelligence

    Fresenius Environmental Bulletin, Vol. 24, Núm. 12B, pp. 4574-4580

  5. Influence prediction of alkylamines upon electrical percolation of AOT-based microemulsions using artificial neural networks

    Tenside, Surfactants, Detergents, Vol. 52, Núm. 6, pp. 473-476

2014

  1. Percolative behavior models based on artificial neural networks for electrical percolation of AOT microemulsions in the presence of crown ethers as additives

    Tenside, Surfactants, Detergents, Vol. 51, Núm. 6, pp. 533-540

2013

  1. Esters flash point prediction using artificial neural networks

    Journal of Computational Chemistry, Vol. 34, Núm. 5, pp. 355-359

  2. Percolation threshold of AOT microemulsions with n-alkyl acids as additives prediction by means of artificial neural networks

    Tenside, Surfactants, Detergents, Vol. 50, Núm. 5, pp. 360-368

  3. Predicting critical micelle concentration values of non-ionic surfactants by using artificial neural networks

    Tenside, Surfactants, Detergents, Vol. 50, Núm. 2, pp. 118-124

  4. Prediction of the penetration of drugs by artificial neural networks

    Iberian Conference on Information Systems and Technologies, CISTI

2012

  1. Predicción de la temperatura superior de disolución crítica mediante redes neuronales artificiales

    Revista Iberoamericana de Polímeros, Vol. 13, Núm. 6, pp. 295-306

  2. Starch-derived cyclodextrins and ttheir future in the food biopolymer industry

    Starch-Based Polymeric Materials and Nanocomposites: Chemistry, Processing, and Applications (CRC Press), pp. 167-182

2011

  1. Alkaline fading of triarylmethyl carbocations in self-assembly microheterogeneous media

    Progress in Reaction Kinetics and Mechanism, Vol. 36, Núm. 2, pp. 139-165

  2. Basic degradation of 3-keto-carbofuran in the presence of non-ionic self-assembly colloids

    Fresenius Environmental Bulletin, Vol. 20, Núm. 2, pp. 354-357

  3. Influence of anionic and nonionic micelles upon hydrolysis of 3-hydroxy-carbofuran

    International Journal of Chemical Kinetics, Vol. 43, Núm. 8, pp. 402-408