This study examines how confinement measures established during the COVID-19 pandemic crisis affected the emotions of the population. For this purpose, public sentiment on social media and digital ecosystems in Spain is analyzed. We identified affective tones towards media and citizens published on social media focusing on six basic emotions: anger, fear, joy, sadness, disgust and uncertainty. The main contribution of this work is the evidence of contagious sentiments and, consequently, the possibility of using this new dimension of social media as a form of a “collective therapy”. This paper contributes to understanding the impact of confinement measures in a pandemic from the point of view of emotional health. This analysis provides a set of practical implications that can guide conceptual and empirical work in health crisis management with an alternative approach, especially useful for decision-making processes facing emergency responses and health crises, even in an unprecedented global health crisis such as the traumatic events caused by the COVID-19 disease.

Objective masticatory performance assessment using two-coloured specimens relies on image processing techniques; however, just a few approaches have been tested and no comparative studies are reported. The aim of this study was to present a selection procedure of the optimal image analysis method for masticatory performance assessment with a given two-coloured chewing gum. Dentate participants (n = 250; 25 ± 6·3 years) chewed red-white chewing gums for 3, 6, 9, 12, 15, 18, 21 and 25 cycles (2000 samples). Digitalised images of retrieved specimens were analysed using 122 image processing methods (IPMs) based on feature extraction algorithms (pixel values and histogram analysis). All IPMs were tested following the criteria of: normality of measurements (Kolmogorov-Smirnov), ability to detect differences among mixing states (anova corrected with post hoc Bonferroni) and moderate-to-high correlation with the number of cycles (Spearman's Rho). The optimal IPM was chosen using multiple criteria decision analysis (MCDA). Measurements provided by all IPMs proved to be normally distributed (P < 0·05), 116 proved sensible to mixing states (P < 0·05), and 35 showed moderate-to-high correlation with the number of cycles (|ρ| > 0·5; P < 0·05). The variance of the histogram of the Hue showed the highest correlation with the number of cycles (ρ = 0·792; P < 0·0001) and the highest MCDA score (optimal). The proposed procedure proved to be reliable and able to select the optimal approach among multiple IPMs. This experiment may be reproduced to identify the optimal approach for each case of locally available test foods.

Most of the tools and diagnosis models of Masticatory Efficiency (ME) are not well documented or severely limited to simple image processing approaches. This study presents a novel expert system for ME assessment based on automatic recognition of mixture patterns of masticated two-coloured chewing gums using a combination of computational intelligence and image processing techniques. The hypotheses tested were that the proposed system could accurately relate specimens to the number of chewing cycles, and that it could identify differences between the mixture patterns of edentulous individuals prior and after complete denture treatment. This study enrolled 80 fully-dentate adults (41 females and 39 males, 25 ± 5 years of age) as the reference population; and 40 edentulous adults (21 females and 19 males, 72 ± 8.9 years of age) for the testing group. The system was calibrated using the features extracted from 400 samples covering 0, 10, 15, and 20 chewing cycles. The calibrated system was used to automatically analyse and classify a set of 160 specimens retrieved from individuals in the testing group in two appointments. The ME was then computed as the predicted number of chewing strokes that a healthy reference individual would need to achieve a similar degree of mixture measured against the real number of cycles applied to the specimen. The trained classifier obtained a Mathews Correlation Coefficient score of 0.97. ME measurements showed almost perfect agreement considering pre- and post-treatment appointments separately (κ ≥ 0.95). Wilcoxon signed-rank test showed that a complete denture treatment for edentulous patients elicited a statistically significant increase in the ME measurements (Z = -2.31, p < 0.01). We conclude that the proposed expert system proved able and reliable to accurately identify patterns in mixture and provided useful ME measurements.

Presently, there exists an important need for lighter and more resistant structures, with reduced manufacturing costs. Laminated polymers are materials which respond to these new demands. Main difficulties of the design process of a composite laminate include the necessity to design both the geometry of the element and the material configuration itself and, therefore, the possibilities of creating composite materials are almost unlimited. Many techniques, ranging from linear programming or finite elements to computational intelligence, have been used to solve this type of problems. The aim of this work is to show that more effective and dynamic methods to solve this type of problems are obtained by using certain techniques based on systematic exploitation of knowledge of the problem, together with the combination of metaheuristics based on population as well as on local search. With this objective, a memetic algorithm has been designed and compared with the main heuristics used in the design of laminated polymers in different scenarios. All solutions obtained have been validated by the ANSYS5 software package.