A fuzzy-based methodological proposal for analysing green areas in urban neighborhoods.

The reduction of the green areas due to the growth of the built-up areas has affected the environmental quality in cities. Nevertheless, some uncertainties remain about the adequate amount of such areas in the urban landscape. This study aims at introducing a methodology to support analysis of green areas in urban neighborhoods. The methodological proposal was based on a fuzzy expert system (FES), a soft computing approach capable of dealing with uncertainties in complex multiple-criteria decision-making. As empirical research, some case studies to introduce and validate the proposed methodology were performed. An agglomerative hierarchical clustering, followed by a Kruskal-Wallis test and multiple pairwise comparisons using the Conover-Iman procedure (significance 0.05), demonstrated that the FES was able to provide outcomes consistent with hypothetical situations, simulated as ideal and critical conditions of green areas. In conclusion, our findings indicate that the methodological proposal based on FES is a promising tool for complex case-by-case analysis in urban neighborhoods.

Risk Factors Associated with Mortality in Hospitalized Patients with COVID-19 during the Omicron Wave in Brazil.

Considering the imminence of new SARS-CoV-2 variants and COVID-19 vaccine availability, it is essential to understand the impact of the disease on the most vulnerable groups and those at risk of death from the disease. To this end, the odds ratio (OR) for mortality and hospitalization was calculated for different groups of patients by applying an adjusted logistic regression model based on the following variables of interest: gender, booster vaccination, age group, and comorbidity occurrence. A massive number of data were extracted and compiled from official Brazilian government resources, which include all reported cases of hospitalizations and deaths associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Brazil during the "wave" of the Omicron variant (BA.1 substrain). Males (1.242; 95% CI 1.196-1.290) aged 60-79 (3.348; 95% CI 3.050-3.674) and 80 years or older (5.453; 95% CI 4.966-5.989), and hospitalized patients with comorbidities (1.418; 95% CI 1.355-1.483), were more likely to die. There was a reduction in the risk of death (0.907; 95% CI 0.866-0.951) among patients who had received the third dose of the anti-SARS-CoV-2 vaccine (booster). Additionally, this big data investigation has found statistical evidence that vaccination can support mitigation plans concerning the current scenario of COVID-19 in Brazil since the Omicron variant and its substrains are now prevalent across the entire country.

A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration.

In ophthalmology, the registration problem consists of finding a geometric transformation that aligns a pair of images, supporting eye-care specialists who need to record and compare images of the same patient. Considering the registration methods for handling eye fundus images, the literature offers only a limited number of proposals based on deep learning (DL), whose implementations use the supervised learning paradigm to train a model. Additionally, ensuring high-quality registrations while still being flexible enough to tackle a broad range of fundus images is another drawback faced by most existing methods in the literature. Therefore, in this paper, we address the above-mentioned issues by introducing a new DL-based framework for eye fundus registration. Our methodology combines a U-shaped fully convolutional neural network with a spatial transformation learning scheme, where a reference-free similarity metric allows the registration without assuming any pre-annotated or artificially created data. Once trained, the model is able to accurately align pairs of images captured under several conditions, which include the presence of anatomical differences and low-quality photographs. Compared to other registration methods, our approach achieves better registration outcomes by just passing as input the desired pair of fundus images.