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Purpose: This research aims to identify Brazil's socioeconomic vulnerability to wicked multi-problems arising from coronavirus disease 2019 (COVID-19) (2019–2020), from the most extensive (∼ 3,000 km) oil spill in tropical oceans (2019/2020) and from the highest rate of wildfires in the last decade. Design/methodology/approach: To this end, the authors measured the socioeconomic vulnerabilities of the 27 Brazilian states to these multi-problems (COVID-19 + Oil Spill + wildFire), considering the effects of these events individually and together. In addition, the authors calculated the vulnerability indices using two variables: production value and number of jobs created by an economic activity. Findings: Results show the states of São Paulo, Minas Gerais, and Rio de Janeiro as the most susceptible, with a potential loss of 74.2% in production value and 47% in active employment relationships, caused by these overlapping events in time. The results also demonstrate that the country has failed in the coordination and management of these events (separately and jointly), showing difficulties especially in the stages of immediate response and recovery. Originality/value: Regarding its contributions, this paper innovates by establishing an unprecedented overlap of wicked problems, linking this concept to the analysis of socioeconomic vulnerability of the affected communities, through a model that applies to other regions worldwide. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2021-0536 [ABSTRACT FROM AUTHOR]

Labeling of samples is a recurrent and time-consuming task in data analysis and machine learning and yet generally overlooked in terms of visual analytics approaches to improve the process. As the number of tailored applications of learning models increases, it is crucial that more effective approaches to labeling are developed. In this paper, we report the development of a methodology and a framework to support labeling, with an application case as background. The methodology performs visual active learning and label propagation with 2D embeddings as layouts to achieve faster and interactive labeling of samples. The framework is realized through SoundscapeX, a tool to support labeling in soundscape ecology data. We have applied the framework to a set of audio recordings collected for a Long Term Ecological Research Project in the Cantareira-Mantiqueira Corridor (LTER CCM), localized in the transition between northeastern São Paulo state and southern Minas Gerais state in Brazil. We employed a pre-label data set of groups of animals to test the efficacy of the approach. The results showed the best accuracy at 94.58 % in the prediction of labeling for birds and insects; and 91.09 % for the prediction of the sound event as frogs and insects. [ABSTRACT FROM AUTHOR]

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This article discusses the plight of Afro-Brazilian slave families in Brazil's cacao area. The research focused mainly on the center-south of Brazil like the Rio de Janeiro, Sao Paulo and Minas Gerais. The slaves form families patterned after Roman Catholic families. However, this is only possible in economically developed areas. There is difficulty in sustaining a family in areas where there is economic decline. Disruptions on the owners of the slaves like death and bankruptcy caused the break up of slave families. Studies have shown how traumatic it is for slaves to be separated from their loved. African slave trade was instrumental in the maintenance of slave labor in the farms in the Bahian county of Ilheus. However, natural reproduction was more important.