In this paper, we present a low-cost, stand-alone sensory platform developed for in situ monitoring of environmental parameters, for use in the Amazon region in the north of Brazil. The mission of the platform is to perform monitoring and identification of overirradiance (solar irradiance > 1000 W/m2) and extreme overirradiance events (solar irradiance > 1300 W/m2) using a photovoltaic based irradiance sensor. The sensory platform was built using the ESP8266 microcontroller, an open embedded computer capable of Wi-Fi communication using the IEEE 802.11 standard, and small photovoltaic modules, air temperature, atmospheric pressure, voltage, and current sensors, enabling the development of a low-cost system (€70/R$350.00 BRL). Calibration and tests were conducted at the Federal University of Pará (UFPA), Belém campus, Pará, where the platform measured an extreme overirradiance of 1321 W/m2 at a low-latitude (1 °S) and low altitude (7 m above sea level).

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This paper presents the development of a prototype of a system for remote data acquisition of environmental variables in the Amazon Forest called GETFOREST. The system performs all the functions of a Datalogger and has the task of analyzing behavior in a forest reserve by an intelligent agent, which contains expert systems of the patterns related to environmental variables of temperature, relative humidity and dew point. The sensor hardware is composed by a computational embedded unit that contains an analog/digital data acquisition in wireless communication interfaces.

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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.

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.