Tuberculosis (TB) remains the world's deadliest infectious disease and is a serious public health problem. Control for this disease still presents several difficulties, requiring strategies for the execution of immediate combat and intervention actions. Given that changes through the decision-making process are guided by current information and future prognoses, it is critical that a country's public health managers rely on accurate predictions that can detect the evolving incidence phenomena. of TB. Thus, this study aims to analyze the accuracy of predictions of three univariate models based on time series of diagnosed TB cases in Brazil, from January 2001 to June 2018, in order to establish which model presents better performance. For the second half of 2018. From this, data were collected from the Department of Informatics of the Unified Health System (DATASUS), which were submitted to the methods of Simple Exponential Smoothing (SES), Holt-Winters Exponential Smoothing (HWES) and the Integrated Autoregressive Moving Average (ARIMA) model. In the performance analysis and model selection, six criteria based on precision errors were established: Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and Theil's U statistic (U1 and U2). According to the results obtained, the HWES (0.2, 0.1, 0.1) presented a high performance in relation to the error metrics, consisting of the best model compared to the other two methodologies compared here.

RESUMO O presente artigo apresenta o estudo inicial sobre a evolução do novo coronavírus (SARS-CoV-2) no estado do Pará,desde a confirmação do primeiro infectado no dia 18/03/2020 até o dia 06/04/2020.O estudo apresenta também um modelo matemático para estimar o número de infectados até o dia 06/05/2020. Os resultados mostram que o modelo é confiável para predições de curto prazo, cuja evolução pode ser de 1 infectado em 18/03/2020 a 761 infectados em 18/04/2020.

Nowadays, the market for natural gas production and its use as a source of energy supply has been growing substantially in Brazil. However, the use of tools that assist the industry in the management of production can be essential for the strategic decision-making process. In this intuit, this work aims to evaluate the formulation of Holt Winter's additive and multiplicative time series to forecast Brazilian natural gas production. A comparison between the models and their forecast play a vital role for policymakers in the strategic plan, and the models estimated production values ​​for the year 2018 based on the information contained in the interval between 2010 and 2017. Therefore, It was verified that the multiplicative method had a good performance so that we can conclude this formulation is ideal for such an application since all the predicted results by this model showed greater accuracy within the 95% confidence interval.

RESUMO O presente artigo apresenta o segundo estudo sobre a evolução do novo coronavírus (SARS-CoV-2) no estado do Pará, desde a confirmação do primeiro infectado no dia 18/03/2020 até o dia 28/05/2020, através de mapas. O estudo apresenta também um modelo matemático para estimar o número de infectados até o dia 28/05/2020 e a projeção de pico da epidemia no estado do Pará como um todo, com análises mais detalhadas em dez municípios incluindo a capital Belém. Os resultados mostram que o modelo possui confiabilidade acima de 90% para predições de curto prazo, cuja evolução pode ser de 1 infectado em 18/03/2020 a 33.304 infectados em 28/05/2020.

This work aims to describe an experimental procedure for the synthesis of bioethanol by the alcoholic fermentation of organic matter, from the use of discarded fruits. Based on the procedures performed on the statistical analysis of factorial experiments was used to verify the influence of the indepen dent variables: the amount of must and fermentation time, in relation to yield response. The alcoholic fermentation was obtained from the pulp of apples (Malus communis) and tangerines (Citrus reticulata), as well as by microorganism (Saccharomyces cerevisiae). As a result, the maximum yield value was around 17.5% v.v -1 , which gives fruit residues a high potential for use in bioethanol production. The statistical evaluation was used to optimize the input condition and the value of 19.06% v.v -1 has been estimated. Thus, this text presents a model of economic viability and its environmental importance due to the use of organic waste.