In economic development, in addition to comparing the gross domestic product (GDP) between nations, it is critical to assess the quality of life to gain a holistic perspective of their different aspects. However, the quality of life index (QOLI) is a subjective term that can be difficult to quantify. Although this composite index is typically calculated using universal weights proposed by experts to aggregate indicators, such as safety indexes, healthcare indexes, pollution indexes, and housing indicators, it is complicated to balance multiple dimensions whose weights are adjusted to account for different countries’ circumstances. Therefore, this paper aims to construct various scenarios of the QOLI, using linguistic quantifiers of the ordered weighted averaging (OWA) operator, and the 2-tuple linguistic model. Numbeo, one of the largest quality of life information databases, was used in this paper to estimate the QOLI in 85 countries. Uncertainty and sensitivity analyses were employed to assess the robustness of the QOLI. The results of the proposed model are compared with those obtained using the Numbeo formulation. The results show that the proposed model increases the linguistic interpretability of the QOLI, and obtains different QOLIs, based on diverse country contexts.

To determine the prevalence of refractive errors in Segovia, Spain. A cohort of 569 subjects was randomly selected in a stratified manner according to gender and age in a cross-sectional, population-based epidemiologic study, the target population of which was urban residents aged 40 to 79 years. All participants underwent an ophthalmic examination that included measurement of visual acuity (VA) and refraction, tonometry, anterior segment biomicroscopy, funduscopy, optic nerve head photography, and visual field testing. Of those, 417 subjects were enrolled who met the inclusion criteria of a phakic right eye and VA over 20/40. The prevalence of spherical errors was assessed after calculating the spherical equivalent and defining myopia as -0.5 diopters (D) or less and hyperopia as +0.50 D or more. The prevalence of astigmatism over 0.50 D was evaluated in minus cylinder form. The estimated prevalences (95% confidence interval) of myopia, hyperopia, and astigmatism, in the population were 25.4% (21.5-29.8%) 43.6% (39-48.4%), and 53.5% (48.7-58.2%), respectively. No significant gender difference was found in the prevalence of any refractive errors. The prevalence of myopia or the mean value did not change significantly with age. The mean hyperopia and the mean astigmatism (p < 0.01 for both) and the prevalence increased with increasing age (p < 0.01 for both). Anisometropia of 1 D or more was present in 12.3% (49/396 subjects). More than 60% of the Segovia population over 40 years of age has a refractive error, with 25.4% myopic and 43.6% hyperopic. Astigmatism is present in over half of the population and the types change with age.

Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.