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.

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.

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.

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.

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.