Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea

 Mathematics 2021, 9(19), 2523

This study proposed different techniques to estimate the isotope composition (δ18O), salinity and temperature/potential temperature in the Mediterranean Sea using five different variables: (i–ii) geographic coordinates (Longitude, Latitude), (iii) year, (iv) month and (v) depth. Three kinds of models based on artificial neural network (ANN), random forest (RF) and support vector machine (SVM) were developed. According to the results, the random forest models presents the best prediction accuracy for the querying phase and can be used to predict the isotope composition (mean absolute percentage error (MAPE) around 4.98%), salinity (MAPE below 0.20%) and temperature (MAPE around 2.44%). These models could be useful for research works that require the use of past data for these variables.

Aldehydes as Additives in AOT-based Microemulsions: Influence on the Electrical Percolation

Tenside Surfactants Detergents, 55 (2), 2018, 110-115.

The influence of alkyl-aldehydes upon electric percolation of AOT-based microemulsions has been studied. The number of carbons in the hydrocarbon chain was varied between 0 and 5 atoms (chain length between 0 and 7.33 Å). Two different behaviors were found, while the presence in the microemulsion of short chains aldehydes implies a decrease in the percolation temperature, aldehydes with 4 or 5 carbon atoms in the hydrocarbon chain increase the percolation threshold. This opposite behavior has been justified in terms of aldehyde location in the microheterogeneous system.