The physics behind acoustic liners attenuation in the presence of flow and high sound pressure level is still a matter of debate. Similarly, discrepancies between experimental results and numerical data have been linked to the boundary conditions used to model the liner and boundary layer effects, and the reasons behind these discrepancies are still not clear. In this sense, to avoid the limitations of the boundary condition approach, fully resolved high fidelity computation models of the liner and its dissipation mechanisms may be an important tool to improve understanding. The present study carries out a numerical analysis using a code based on the Lattice-Boltzmann method, and special focus is given on replicating the results from different experimental techniques used to evaluate the liner impedance: the in-situ method and an impedance eduction method based on the mode-matching technique. The study is conducted with a model including a single degree of freedom liner with multiple cavities based on a real geometry. The model considers high sound pressure level, grazing plane acoustic waves without flow in order to replicate the experimental setup. A mesh convergence analysis is performed, and the liner impedance obtained numerically is compared with experimental results from the in-situ method and the impedance eduction technique. The numerical pressure and velocity fields are also analyzed in detail to better understand the damping mechanisms and physics involved.
The relationship between the fires occurrences and diseases is an essential issue for making public health policy and environment protecting strategy. Thanks to the Internet, today, we have a huge amount of health data and fire occurrence reports at our disposal. The challenge, therefore, is how to deal with 4 Vs (volume, variety, velocity and veracity) associated with these data. To overcome this problem, in this paper, we propose a method that combines techniques based on Data Mining and Knowledge Discovery from Databases (KDD) to discover spatial and temporal association between diseases and the fire occurrences. Here, the case study was addressed to Malaria, Leishmaniasis and respiratory diseases in Brazil. Instead of losing a lot of time verifying the consistency of the database, the proposed method uses Decision Tree, a machine learning-based supervised classification, to perform a fast management and extract only relevant and strategic information, with the knowledge of how reliable the database is. Namely, States, Biomes and period of the year (months) with the highest rate of fires could be identified with great success rates and in few seconds. Then, the K-means, an unsupervised learning algorithms that solves the well-known clustering problem, is employed to identify the groups of cities where the fire occurrences is more expressive. Finally, the steps associated with KDD is perfomed to extract useful information from mined data. In that case, Spearman’s rank correlation coefficient, a nonparametric measure of rank correlation, is computed to infer the statistical dependence between fire occurrences and those diseases. Moreover, maps are also generated to represent the distribution of the mined data. From the results, it was possible to identify that each region showed a susceptible behaviour to some disease as well as some degree of correlation with fire outbreak, mainly in the drought period.
The human leukocyte antigen (HLA) genotype may influence in immune responses during the course of coronavirus disease 2019 (COVID-19). In this ecological study, we collected HLA genotypes from 4,148,713 Brazilian individuals and compared to COVID-19 data. We found a positive significant correlation between the HLA-A*01~B*08~DRB1*03 haplotype and COVID-19 mortality rate.
BackgroundCoronavirus disease 2019 (COVID-19) rapidly spread all over the world causing high morbidity and mortality. Brazil is currently the third country in the world in the number of COVID-19 cases. Even though all Brazilian regions and states have reported a high number of cases, mortality rates varies among them. Environmental and genetic factors may influence the immune response towards SARS-CoV-2. The Brazilian population is highly heterogeneous, with different colonization and immigration histories in each region resulting in different genetic backgrounds. Here, we test if specific HLA haplotypes are associated with COVID-19 incidence and mortality in Brazil.MethodsHLA data was obtained from The Brazilian Voluntary Bone Marrow Donors Registry (REDOME) which harbors data from more than four million individual donors, and COVID-19 data was retrieved from epidemiological bulletins issued by State Health Secretariats via the Ministry of Health of Brazil. We tested the association between the most frequent HLA haplotypes in Brazil and COVID-19 incidence and mortality using Spearman's correlation analysis.ResultsNo correlation between HLA haplotypes and COVID-19 rates was found when we analyzed data from the 26 states and Federal District, as well as when we analyzed data from the 90 cities with at least 50 deaths registered in the São Paulo state. Significant negative correlation (suggestive of protection) between COVID-19 mortality and haplotypes HLA-A*01~B*08~DRB1*03, HLA-A*29~B*44~DRB1*07 and HLA-A*02~B*44~DRB1*04 was found when analyzing data from cities with at least 50 deaths registered in the entire country.ConclusionsOur results do not support an association of specific HLA haplotypes with an increased risk of contracting SARS-CoV2 or dying from COVID-19 in Brazil. Nevertheless, using bone marrow donor registries for testing for associations between HLA variation and COVID-19 outcomes may represent an additional tool for health policymakers in the fight against COVID-19.
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