Este estudo teve como objetivo avaliar os teores de caroteno (, ) e o valor de vitamina A em frutas comercializadas nos mercados formais e na feira livre de Viçosa, Estado de Minas Gerais. Foram analisadas dez frutas oriundas de três mercados e três pontos da feira livre. A extração dos carotenóides foi realizada com acetona, seguida de transferência para éter de petróleo. Os conteúdos de  e -caroteno foram analisados por cromatografia líquida de alta eficiência (CLAE). O -caroteno foi encontrado somente nas amostras de carambola e mamão formosa. O -caroteno foi quantificado em todas as frutas estudadas. Com exceção da ameixa, não ocorreu diferença significativa entre os conteúdos de carotenóides das frutas provenientes do mercado formal e da feira livre. Concluiu-se que as frutas analisadas são boas fontes de provitamina A e, se consumidas freqüentemente, contribuem para atender uma porcentagem importante na adequação de vitamina A para adultos e crianças.

Crime is a common social problem faced around the world, and it can affect a nation's quality of life, economic growth, and reputation. Thus, law enforcement officials need to take preventive measures and one of the methods that have been gaining ground in crime analysis is data mining. With this, the purpose of this paper is to apply data analysis and data mining techniques in public security databases in the city of Belém of Pará, in order to discover hidden patterns and assist security managers in developing new public policies to try to reduce crime rates, through data from police reports in the city of Belém of Pará, Brazil in the years 2019, 2020 and 2021. To guide this study, the CRISP-DM methodology was used, where it was possible through these techniques to extract knowledge to understand and analyze the crime scene in the municipality of Belém, such as the fact that a certain crime occurs at night implies that its nature is robbery in order to assist the responsible bodies in investigations and strategies for a more effective fight against crime.

This paper describes the development of a supervised classifier constructed upon knowledge extracted from police report public databases, in the years between 2019 and 2021 in the state of Pará, Brazil. The classifier achieved an accuracy of approximately 78% for the prediction of 463 unique labels related to public safety. The resulting model can be used to improve the statistical processes of criminal analysts, both in quantitative and qualitative terms.

Crime is a common social problem faced worldwide that can affect a nation's quality of life and economic growth. With this, the purpose of this paper is to apply data analysis and data mining techniques in public security databases in the city of Belém of Pará, in order to discover hidden patterns and assist security managers in the development of new public policies to try to reduce crime rates. Through this study, it was possible to obtain results that can help public security authorities to understand crime, as well as in making decisions about new security policies.