Chemical reactivity in microheterogeneous media

Since the second half of the last century, the science of colloids has undergone a true revolution, from being little more than a collection of qualitative observations of the macroscopic behavior of some complex systems to becoming a discipline with substantial theoretical foundations [...

Assessment of different machine learning methods for reservoir outflow forecasting

Reservoirs play an important function in human society due to their ability to hold and regulate the flow. This will play a key role in the future decades due to climate change. Therefore, having reliable predictions of the outflow from a reservoir is necessary for early warning systems and adequate water management. In this sense, this study uses three approaches machine learning (ML)-based techniques—Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN)—to predict outflow one day ahead of eight different dams belonging to the Miño-Sil Hydrographic Confederation (Galicia, Spain), using three input variables of the current day. Mostly, the results obtained showed that the suggested models work correctly in predicting reservoir outflow in normal conditions. Among the different ML approaches analyzed, ANN was the most appropriate technique since it was the one that provided the best model in five reservoirs.Ministerio de Ciencia e Innovación | Ref. FPU2020/0614

Machine learning applied to the oxygen-18 isotopic composition, salinity and temperature/potential temperature in the Mediterranean sea

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.Universidade de Vigo | Ref. 0000 131H TAL 641Xunta de Galicia | Ref. ED431C 2018/42Xunta de Galicia | Ref. POS-B / 2016/00

Global solar irradiation modelling and prediction using machine learning models for their potential use in renewable energy applications

Global solar irradiation is an important variable that can be used to determine the suitability of an area to install solar systems; nevertheless, due to the limitations of requiring measurement stations around the entire world, it can be correlated with different meteorological parameters. To confront this issue, different locations in Rias Baixas (Autonomous Community of Galicia, Spain) and combinations of parameters (month and average temperature, among others) were used to develop various machine learning models (random forest -RF-, support vector machine -SVM- and artificial neural network -ANN-). These three approaches were used to model and predict (one month ahead) monthly global solar irradiation using the data from six measurement stations. Afterwards, these models were applied to seven different measurement stations to check if the knowledge acquired could be extrapolated to other locations. In general, the ANN models offered the best results for the development and testing phases of the model, as well as for the phase of knowledge extrapolation to other locations. In this sense, the selected ANNs obtained a mean absolute percentage error (MAPE) value between 3.9 and 13.8% for the model development and an overall MAPE between 4.1 and 12.5% for the other seven locations. ANNs can be a capable tool for modelling and predicting monthly global solar irradiation in areas where data are available and for extrapolating this knowledge to nearby areas

Oxidation of aldehydes used as food additives by peroxynitrite

Benzaldehyde and its derivatives are used as food supplements. These substances can be used mainly as flavorings or as antioxidants. Besides, peroxynitrite, an oxidizing agent, could be formed in canned food. Both species could react between them. The present article has focused on the kinetic study of the oxidation of aldehydes by peroxynitrite. A reaction mechanism that justifies all the experimental results is proposed. This mechanism, in acidic media, passes through three competitive pathways: (a) a radical attack that produces benzoic acid. (b) peracid oxidation, and (c) a nucleophilic attack of peroxynitrous acid over aldehyde to form an intermediate, X, that produces benzoic acid, or, through a Cannizzaro-type reaction, benzoic acid and benzyl alcohol. All rate constants involved in the third pathway (c) have been calculated. These results have never been described in the literature in acid media. A pH effect was analyzed

Functional foods based on the recovery of bioactive ingredients from food and algae by-products by emerging extraction technologies and 3D printing

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG3D food printing is an emerging technology developed to facilitate the life of consumers and food enterprises. This technology allows to obtain any type of new foods according to our wishes. It is possible to develop a food with the exact nutritive value necessary for our body, with the most benefiting nutrients we want, or without any ingredients that we have an allergy, and even predict or personalize the taste, the color, the shape, and the size of a food. Therefore, 3D food printing is considered a promising strategy for developing healthy foods. On the other hand, many foods enterprises release high amounts of waste from their processing activities. These wastes contain many bioactive ingredients such as polyphenols, carotenoids, vitamins, minerals, fibers, unsaturated fatty acids, among others, which have physiological and health benefits. Similarly, several bioactive compounds have been identified in algae. They can be extracted by conventional methods with solvents such as water, ethanol, methanol, chloroform, acetone, and many others, but with some limits like environmental contamination, human toxicity, and low extraction rate. For these reasons, it will be interesting to use emerging extraction technologies to recover bioactive compounds and use them in a 3D food printer to make functional foods that can bring a targeted health benefit to consumers

Machine learning to predict the adsorption capacity of microplastics

Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log Kd) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.Ministerio de Universidades | Ref. FPU2020/0614

The chemical, microbiological and volatile composition of kefir-like beverages produced from red table grape juice in repeated 24-h fed-batch subcultures

The aim of this work was to study the production of kefir-like beverages via the fed-batch fermentation of red table grape juice at initial pHs of 3.99 (fermentation A) and 5.99 (fermentation B) with kefir grains during 4 repeated 24-h fed-batch subcultures. All kefir-like beverages (KLB) were characterized by low alcoholic grade (≤3.6%, v/v) and lactic and acetic acid concentrations. The beverages obtained from fermentation B had lower concentrations of sugars and higher microbial counts than the KLB obtained in fermentation A. Additionally, the KLB samples from fermentation B were the most aromatic and had the highest contents of alcohols, esters, aldehydes and organic acids, in contrast with the nonfermented juice and KLB from fermentation A. These results indicate the possibility of obtaining red table grape KLB with their own distinctive aromatic characteristics and high content in probiotic viable cells, contributing to the valorization of this fruit.Ministerio de Ciencia, Innovación y Universidades | Ref. FPU16/04077Universidad Nacional de Jaén (Perú) | Ref. 2017-I-PRONABEC—Per

Elaboración de material docente virtual de apoyo a las prácticas de rocas del Grado de Biología

Una de las actividades prácticas de la asignatura de Geología del primer curso del Grado en Biología de la Universidad de Alicante son las prácticas de laboratorio. Estas prácticas se dedican básicamente al reconocimiento visual de rocas y fósiles. Tras varios años de experiencia en la impartición de esta asignatura, se ha comprobado que las principales debilidades de las prácticas de laboratorio se deben al escaso número de sesiones presenciales. A ello se le suma una cierta dificultad para poder realizar otras sesiones de repaso fuera del horario establecido. Ante esta situación el alumno tiene algunos problemas en el reconocimiento visual de los ejemplares, así como para poder preparar los controles y pruebas de evaluación. Con el objetivo de facilitar el autoaprendizaje en este campo, se ha diseñado una página web que incluye toda la colección de rocas de las sesiones prácticas, así como otros materiales complementarios, en la que se aporta la información básica de cada ejemplar. Inicialmente, la página se ha diseñado específicamente para las prácticas de reconocimiento de rocas, ya que son las que presentan menor duración presencial

Recursos multimedia de apoyo a las prácticas de fósiles en el Grado de Biología

El reconocimiento de fósiles forma parte de los contenidos de la asignatura Geología del Grado en Biología. En esta asignatura, gestionada por el Departamento de Ciencias de la Tierra y del Medio Ambiente de la UA, se imparten 6 créditos de contenidos teóricos y prácticos; de ellos 0,8 créditos corresponden a prácticas de laboratorio con fósiles, donde los estudiantes deben aprender los criterios de descripción e interpretación paleontológica para aplicarlos correctamente a la colección docente de muestras fósiles. Dada la diversidad de contenidos que se imparten en esta asignatura, las horas presenciales de estas prácticas son escasas, lo que dificulta la adquisición de las habilidades necesarias para el reconocimiento de fósiles. Por esta razón, el objetivo de esta red es crear un recurso multimedia que sirva como material docente complementario para las prácticas de paleontología de la asignatura Geología. De esta manera, se facilita al alumno el acceso a la colección de fósiles de la asignatura durante las horas no presenciales, así como los contenidos teóricos ligados a las prácticas de paleontología, favoreciendo el autoaprendizaje que le ayude tanto en el correcto seguimiento de la asignatura como en la preparación de los controles y pruebas de evaluación