Long-Term Monitoring of the Traditional Knowledge of Plant Species Used for Culinary Purposes in the Valencia Region, South-Eastern Spain.

The aim of this study is to inventory and study ethnobotanical knowledge of edible plants in the Valencian Community (Spain). In respect to culinary uses, 92 species of plant were reported to be edible, finding the following uses: 58 raw, 52 cooked, 16 fried, 7 dried, 21 in liquors and beverages, 25 in dessert and sweets, 11 as seasoning, 17 in pickles, and 10 to curdle milk. We prepared a database that includes genus, family, scientific, and vernacular names in Spanish and Catalan for each plant. We also created a classification of nine edible uses and plant parts used, being Asteraceae (n = 18), Brassicaceae (n = 7), Chenopodiaceae (n = 6), and Rosaceae (n = 6) the families most characterized for gastronomic purposes. The species with the most elevated cultural importance (CI) values were Foeniculum vulgare (CI = 1.389), Cynara scolymus (CI = 1.374), Papaver rhoeas (CI = 1.211), Beta vulgaris (CI = 1.167), and Juglans regia (CI = 1.155). The most used parts were the leaves (71), flowers (25), and branches (19), while the least used were roots (9) and seeds (8). Traditional knowledge of these plants helps to preserve traditional cuisine, promote the local economy and, in several species, encourage their cultivation.

Complete Desorption of Hybrid Nanoclays Composed of Hydrotalcite and Disperse Dye.

Clays are considered great nanoadsorbents for many materials, including textile dyes. The use of these materials for cleaning textile wastewater is well known; however, it is not at all common to find applications for the hybrid materials formed from the clay and dye. In this work, a dye-loaded clay material was used to make new dye baths and colour a polyester textile substrate. The same hybrid could be used several times as it did not use all the adsorbed dye in a single dyeing. The hybrid obtained from hydrotalcite (nanoclay) and the dispersed red 1 dye was analysed by measuring the colour obtained, carrying out an X-ray diffraction analysis that provided information after each desorption-dyeing process, and using infrared spectroscopy to analyse the specific bands of each characteristic group. Both analyses showed that the amount of dye present in the hybrid decreases. Thermogravimetry (TGA), surface area and porosity measurements (BET), and X-ray photoelectron spectroscopy (XPS) tests were conducted. Chemical stability was assessed by subjecting the hybrid to the actions of different reagents. In addition, colour fastness tests were carried out after dyeing and washing the polyester test tubes to check for the correct fixing of the dye to the fibre. These fastness results showed that the dyeing was carried out correctly and as if it was a conventional dyeing process.

Dyeing with Hydrotalcite Hybrid Nanoclays and Disperse, Basic and Direct Dyes.

Textile effluents are among the most polluting industrial effluents in the world. Textile finishing processes, especially dyeing, discharge large quantities of waste that is difficult to treat, such as dyes. By recovering this material from the water, in addition to cleaning and the possibility of reusing the water, there is the opportunity to reuse this waste as a raw material for dyeing different textile substrates. One of the lines of reuse is the use of hybrid nanoclays obtained from the adsorption of dyes, which allow dye baths to be made for textile substrates. This study analyses how, through the use of the nanoadsorbent hydrotalcite, dyes classified by their charge as anionic, cationic and non-ionic can be adsorbed and recovered for successful reuse in new dye baths. The obtained hybrids were characterised by X-ray diffraction and infrared spectroscopy. In addition, the colour was analysed by spectrophotometer in the UV-VIS range. The dyes made on cotton, polyester and acrylic fabrics are subjected to different colour degradation tests to assess their viability as final products, using reflection spectroscopy to measure the colour attribute before and after the tests, showing results consistent with those of a conventional dye.

Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures.

Many entropy-related methods for signal classification have been proposed and exploited successfully in the last several decades. However, it is sometimes difficult to find the optimal measure and the optimal parameter configuration for a specific purpose or context. Suboptimal settings may therefore produce subpar results and not even reach the desired level of significance. In order to increase the signal classification accuracy in these suboptimal situations, this paper proposes statistical models created with uncorrelated measures that exploit the possible synergies between them. The methods employed are permutation entropy (PE), approximate entropy (ApEn), and sample entropy (SampEn). Since PE is based on subpattern ordinal differences, whereas ApEn and SampEn are based on subpattern amplitude differences, we hypothesized that a combination of PE with another method would enhance the individual performance of any of them. The dataset was composed of body temperature records, for which we did not obtain a classification accuracy above 80% with a single measure, in this study or even in previous studies. The results confirmed that the classification accuracy rose up to 90% when combining PE and ApEn with a logistic model.

Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics.

This paper evaluates the performance of first generation entropy metrics, featured by the well known and widely used Approximate Entropy (ApEn) and Sample Entropy (SampEn) metrics, and what can be considered an evolution from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) signal classification context. The study uses the commonest artifacts found in real EEGs, such as white noise, and muscular, cardiac, and ocular artifacts. Using two different sets of publicly available EEG records, and a realistic range of amplitudes for interfering artifacts, this work optimises and assesses the robustness of these metrics against artifacts in class segmentation terms probability. The results show that the qualitative behaviour of the two datasets is similar, with SampEn and FuzzyEn performing the best, and the noise and muscular artifacts are the most confounding factors. On the contrary, there is a wide variability as regards initialization parameters. The poor performance achieved by ApEn suggests that this metric should not be used in these contexts.