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West Nile virus (WNV) is a vector-borne pathogen of global relevance and is currently the most widely distributed flavivirus of encephalitis worldwide. This virus infects birds, humans, horses, and other mammals, and its transmission cycle occurs in urban and rural areas. Climate conditions have direct and indirect impacts on vector abundance and virus dynamics within the mosquito. The significance of environmental variables as drivers in WNV epidemiology is increasing under the current climate change scenario. In this study, we used a machine learning algorithm to model WNV distributions in South America. Our model evaluated eight environmental variables (type of biome, annual temperature, seasonality of temperature, daytime temperature variation, thermal amplitude, seasonality of precipitation, annual rainfall, and elevation) for their contribution to the occurrence of WNV since its introduction in South America (2004). Our results showed that environmental variables can directly alter the occurrence of WNV, with lower precipitation and higher temperatures associated with increased virus incidence. High-risk areas may be modified in the coming years, becoming more evident with high greenhouse gas emission levels. Countries such as Bolivia and Paraguay will be greatly affected, drastically changing their current WNV distribution. Several Brazilian areas will also increase the likelihood of presenting WNV, mainly in the Northeast and Midwest regions and the Pantanal biome. The Galapagos Islands will also probably increase their geographic range suitable for WNV occurrence. It is necessary to develop preventive policies to minimize potential WNV infection in humans and enhance active epidemiological surveillance in birds, humans, and other mammals before it becomes a more significant public health problem in South America., Comment: 25 pages, 5 figures and 1 table

Background: Studies have shown that human mobility is an important factor in dengue epidemiology. Changes in mobility resulting from COVID-19 pandemic set up a real-life situation to test this hypothesis. Our objective was to evaluate the effect of reduced mobility due to this pandemic in the occurrence of dengue in the state of S\~ao Paulo, Brazil. Method: It is an ecological study of time series, developed between January and August 2020. We use the number of confirmed dengue cases and residential mobility, on a daily basis, from secondary information sources. Mobility was represented by the daily percentage variation of residential population isolation, obtained from the Google database. We modeled the relationship between dengue occurrence and social distancing by negative binomial regression, adjusted for seasonality. We represent the social distancing dichotomously (isolation versus no isolation) and consider lag for isolation from the dates of occurrence of dengue. Results: The risk of dengue decreased around 9.1% (95% CI: 14.2 to 3.7) in the presence of isolation, considering a delay of 20 days between the degree of isolation and the dengue first symptoms. Conclusions: We have shown that mobility can play an important role in the epidemiology of dengue and should be considered in surveillance and control activities, Comment: 15 pages, 4 figures, 3 tables

Understanding differences in hospital case-fatality rates (HCFRs) of coronavirus disease (COVID-19) may help evaluate its severity and the capacity of the healthcare system to reduce mortality. We examined the variability in HCFRs of COVID-19 in relation to spatial inequalities in socioeconomic factors across the city of Sao Paulo, Brazil. We found that HCFRs were higher for men and for individuals aged 60 years and older. Our models identified per capita income as a significant factor that is negatively associated with the HCFRs of COVID-19, even after adjusting by age, sex and presence of risk factors., Comment: 10 pages, 1 figure, 1 table

Diversity has been proposed as a key criterion to improve testing effectiveness and efficiency.It can be used to optimise large test repositories but also to visualise test maintenance issues and raise practitioners' awareness about waste in test artefacts and processes. Even though these diversity-based testing techniques aim to exercise diverse behavior in the system under test (SUT), the diversity has mainly been measured on and between artefacts (e.g., inputs, outputs or test scripts). Here, we introduce a family of measures to capture behavioural diversity (b-div) of test cases by comparing their executions and failure outcomes. Using failure information to capture the SUT behaviour has been shown to improve effectiveness of history-based test prioritisation approaches. However, history-based techniques require reliable test execution logs which are often not available or can be difficult to obtain due to flaky tests, scarcity of test executions, etc. To be generally applicable we instead propose to use mutation testing to measure behavioral diversity by running the set of test cases on various mutated versions of the SUT. Concretely, we propose two specific b-div measures (based on accuracy and Matthew's correlation coefficient, respectively) and compare them with artefact-based diversity (a-div) for prioritising the test suites of 6 different open-source projects. Our results show that our b-div measures outperform a-div and random selection in all of the studied projects. The improvement is substantial with an average increase in average percentage of faults detected (APFD) of between 19% to 31% depending on the size of the subset of prioritised tests., Comment: Published at the 15th International Workshop on Mutation Analysis

Currently, Brazil has one of the fastest increasing COVID-19 epidemics in the world, that has caused at least 94 thousand confirmed deaths until now. The city of Sao Paulo is particularly vulnerable because it is the most populous in the country. Analyzing the spatiotemporal dynamics of COVID-19 is important to help the urgent need to integrate better actions to face the pandemic. Thus, this study aimed to analyze the COVID-19 mortality, from March to July 2020, considering the spatio-time architectures, the socio-economic context of the population, and using a fine granular level, in the most populous city in Brazil. For this, we conducted an ecological study, using secondary public data from the mortality information system. We describe mortality rates for each epidemiological week and the entire period by sex and age. We modelled the deaths using spatiotemporal and spatial architectures and Poisson probability distributions in a latent Gaussian Bayesian model approach. We obtained the relative risks for temporal and spatiotemporal trends and socio-economic conditions. To reduce possible sub notification, we considered the confirmed and suspected deaths. Our findings showed an apparent stabilization of the temporal trend, at the end of the period, but that may change in the future. Mortality rate increased with increasing age and was higher in men. The risk of death was greater in areas with the worst social conditions throughout the study period. However, this was not a uniform pattern over time, since we identified a shift from the high risk in the areas with best socio-economic conditions to the worst ones. Our study contributed by emphasizing the importance of geographic screening in areas with a higher risk of death, and, currently, worse socio-economic contexts, as a crucial aspect to reducing disease mortality and health inequities, through integrated public health actions., Comment: 22 pages, 6 figures, 2 tables, 3 supplementary materials

In a recent paper published in this Journal [Phys. Rev. B 97, 075135 (2018)], Menezes et al. analyze the topological behavior of a effective bosonic model defined on the Lieb lattice in presence of an electromagnetic field. In this context, the authors claim to have found an atypical quantum Hall effect for the quasiparticles. However, some inconsistencies related to the treatment of the propagator jeopardizes the main result in this system., Comment: 4 pages, 1 figure

Many engineering problems are described by systems of nonlinear equations, which may exhibit multiple solutions, in a challenging situation for root-finding algorithms. The existence of several solutions may give rise to complex basins of attraction for the solutions in the algorithms, with severe influence in their convergence behavior. In this work, we explore the relationship of the basins of attractions with the critical curves (the locus of the singular points of the Jacobian of the system of equations) in a phase equilibrium problem in the plane with two solutions, namely the calculation of a double azeotrope in a binary mixture. The results indicate that the conjoint use of the basins of attraction and critical curves can be a useful tool to select the most suitable algorithm for a specific problem.

In this work we propose a geometric view of the azeotropy problem, using some simplified models. We demonstrate that the occurrence of azeotropes in binary mixtures can be viewed-geometrically-as the intersection of curves in the plane (for some models, these curves are parabolas). Furthermore, the idea of functions from the plane to the plane is used to help understand the azeotropic phenomenon. These ideas are illustrated with two simple cases, with one and two azeotropes, allowing the analysis of a unusual thermodynamic behavior-such as double azeotropy-with simple mathematical tools, by undergraduate students in Chemical Engineering courses.

Mathematical formulations of real world optimization studies frequently present characteristics such as non-linearity, discontinuity and high complexity. This class of problems may also exhibit a high number of global minimum/maximum points, especially for optimization problems arising from nonlinear algebraic systems (where null minima correspond to the solutions of the original algebraic system). Due to the multimodal nature of these functions, multipopulation methods have been employed in order to obtain the highest number of points of global minimum/maximum. In this work, two new approaches were analyzed, employing an iterative penalization technique and a multipopulation procedure---together with the Differential Evolution algorithm---devoted to obtain the full set of solutions for multimodal optimization problems. The first method proposed is the Multipopulation Differential Evolution with iterative technique of modification of the objective function, MDE-ITMF, and the second method proposed is the Differential Evolution with Initialization, DEwI. In this second proposal, the MDE-ITMF method is used as an initializer of the initial populations and from a given moment the Differential Evolution is used to solve the problem at hand. In both approaches, subpopulations evolve simultaneously throughout the iterative process. MDE-ITMF and DEwI methods were applied in a set of ten multimodal benchmark functions. Based on the results obtained, we can conclude that MDE-ITMF and DEwI are suitable and promising tools for multimodal optimization.