Soy protein enzymatic hydrolysis and polysaccharides interactions: differential performance on kinetic adsorption at air-water interface

The objective of the work was to study the impact of soy protein hydrolysis on kinetic adsorption to the air-water interface and the effect of polysaccharides addition. Was used soy protein (SP) and theirs hydrolysates of 2% (H1) and 5.4% (H2) degree of hydrolysis. The polysaccharides (PS) used were a surface active one called E4M and a non-surface active one, lamda carrageenan (C). The dynamic surface pressure of interfacial films was evaluated with a drop tensiometer. In this contribution, we have determined the kinetic parameters of adsorption to the air-water interface which determined the penetration (Kp) and rearrangement (Kr) rates of SP, H1, H2 and PS, as well as their mixed systems. It was observed an increase of Kp and Kr when the protein were hydrolyzed (from SP to H1), however, when degree of hydrolysis progresses to H2 the parameters decreased again. In other hand, considerable differences were not found between these two PS studied concerning the Kp to air-water interface at these conditions. In spite of the different surface active nature of the PS, the proteins seem to control the behavior of the protein-PS interactions. However, when Kr of mixed systems was analyzed, the degree of hydrolysis and PS nature started to have a huge importance. Hence, it could be observed synergic or antagonic effects on Kr of biopolymers at liquid interface depending to the degree of hydrolysis of protein analyzed and the type of PS selected.CYTED through project 105PI0274CYCYT through grant AGL2007-60045Junta de Andalucía through grant PO6-AGR-01535Universidad de Buenos Aires, Agencia Nacional de Promoción Científica y Tecnológica (PICT 2008-1901) and Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentin

A Methodology Based on Machine Learning and Soft Computing to Design More Sustainable Agriculture Systems

©2023. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the Published, version of a Published Work that appeared in final form in Sensors. To access the final edited and published work see https://doi.org/10.3390/s23063038Advances in new technologies are allowing any field of real life to benefit from using these ones. Among of them, we can highlight the IoT ecosystem making available large amounts of information, cloud computing allowing large computational capacities, and Machine Learning techniques together with the Soft Computing framework to incorporate intelligence. They constitute a powerful set of tools that allow us to define Decision Support Systems that improve decisions in a wide range of real-life problems. In this paper, we focus on the agricultural sector and the issue of sustainability. We propose a methodology that, starting from times series data provided by the IoT ecosystem, a preprocessing and modelling of the data based on machine learning techniques is carried out within the framework of Soft Computing. The obtained model will be able to carry out inferences in a given prediction horizon that allow the development of Decision Support Systems that can help the farmer. By way of illustration, the proposed methodology is applied to the specific problem of early frost prediction. With some specific scenarios validated by expert farmers in an agricultural cooperative, the benefits of the methodology are illustrated. The evaluation and validation show the effectiveness of the proposal

Remnants of Sagittarius Dwarf Spheroidal Galaxy around the young globular cluster Palomar 12

Photometry of a large field around the young globular cluster Palomar 12 has revealed the main-sequence of a low surface-brightness stellar system. This main-sequence is indicative of a stellar population that varies significantly in metallicity and/or age, but in the mean is more metal poor than Pal 12. Under different assumptions for the properties of this population, we find distances from the Sun in the range 17-24 kpc, which encompasses the distance to Pal 12, $19.0\pm0.9$ kpc. The stellar system is also detected in a field 2\arcdeg North of Pal 12, which indicates it has a minimum diameter of $\sim0.9$ kpc. The orbit of Pal 12 (Dinescu et al. 2000), the color-magnitude diagram of the stellar system, their positions on the sky, and their distances suggest that they are debris from the tidal disruption of the Sgr dSph galaxy. We discuss briefly the implications for the evolution of Sgr and the Galactic halo.Comment: 16 pages, 2 figures, accepted for ApJ Letters. Some importante changes after revision, including a new figur

Making decisions for frost prediction in agricultural crops in a softcomputing framework

© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Computers and Electronics in Agriculture. To access the final edited and published work see https://doi.org/10.1016/j.compag.2020.105587Nowadays, there are many areas of daily life that can obtain benefit from technological advances and the large amounts of information stored. One of these areas is agriculture, giving place to precision agriculture. Frosts in crops are among the problems that precision agriculture tries to solve because produce great economic losses to farmers. The problem of early detection of frost is a process that involves a large amount of wheather data. However, the use of these data, both for the classification and regression task, must be carried out in an adequate way to obtain an inference with quality. A preprocessing of them is carried out in order to obtain a dataset grouping attributes that refer to the same measure in a single attribute expressed by a fuzzy value. From these fuzzy time series data we must use techniques for data analysis that are capable of manipulating them. Therefore, first a regression technique based on k-nearest neighbors in a Soft Computing framework is proposed that can deal with fuzzy data, and second, this technique and others to classification are used for the early detection of a frost from data obtained from different weather stations in the Region of Murcia (south-east Spain) with the aim of decrease the damages that these frosts can cause in crops. From the models obtained, an interpretation of the provided information is performed and the most relevant set of attributes is obtained for the anticipated prediction of a frost and of the temperature value. Several experiments are carried out on the datasets to obtain the models with the best performance in the prediction validating the results by means of a statistical analysis

A Fuzzy k-Nearest Neighbors Classifier to Deal with Imperfect Data

© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Soft Computing. To access the final edited and published work see https://doi.org/10.1007/s00500-017-2567-xThe k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regression and classification. To classify a new instance, the kNN method computes its k nearest neighbors and generates a class value from them. Usually, this method requires that the information available in the datasets be precise and accurate, except for the existence of missing values. However, data imperfection is inevitable when dealing with real-world scenarios. In this paper, we present the kNNimp classifier, a k-nearest neighbors method to perform classification from datasets with imperfect value. The importance of each neighbor in the output decision is based on relative distance and its degree of imperfection. Furthermore, by using external parameters, the classifier enables us to define the maximum allowed imperfection, and to decide if the final output could be derived solely from the greatest weight class (the best class) or from the best class and a weighted combination of the closest classes to the best one. To test the proposed method, we performed several experiments with both synthetic and realworld datasets with imperfect data. The results, validated through statistical tests, show that the kNNimp classifier is robust when working with imperfect data and maintains a good performance when compared with other methods in the literature, applied to datasets with or without imperfection

Evaporation Forecasting through Interpretable Data Analysis Techniques

©2022. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the Published, version of a Published Work that appeared in final form in Electronics. To access the final edited and published work see https://doi.org/10.3390/electronics11040536Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved in this process. Finally, the results of the interpretable presented model are validated with the best literature models for evaporation rate prediction, i.e., Artificial Neural Networks, obtaining results very similar to those obtained for them, reaching up to 0.85R2 and 0.6MAE. Therefore, in this work, a double objective is faced: to maintain the performance obtained by the models most frequently used in the problem while maintaining the interpretability of the knowledge captured in it, which allows better understanding the problem and carrying out appropriate actions

Severe parasitism by Versteria mustelae (Gmelin, 1790) in the critically endangered European mink Mustela lutreola (Linnaeus, 1761) in Spain

The riparian European mink (Mustela lutreola), currently surviving in only three unconnected sites in Europe, is now listed as a critically endangered species in the IUCN Red List of Threatened Species. Habitat loss and degradation, anthropogenic mortality, interaction with the feral American mink (Neovison vison), and infectious diseases are among the main causes of its decline. In the Spanish Foral Community of Navarra, where the highest density of M. lutreola in its western population has been detected, different studies and conservation measures are ongoing, including health studies on European mink, and invasive American mink control. We report here a case of severe parasitism with progressive physiological exhaustion in an aged free-ranging European mink female, which was accidentally captured and subsequently died in a live-trap targeting American mink. Checking of the small intestine revealed the presence of 17 entangled Versteria mustelae worms. To our knowledge, this is the first description of hyperinfestation by tapeworms in this species