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Aging in Brazil, especially in the Amazon, is a complex and irregular process. Something is happening here that cannot be explained simply due to social inequalities. The objective of this study was to present the development of an artificial neural network and the stages of training, validation and testing for the classification of healthy aging among elderly Brazilians. We constructed a protocol for rapid diagnosis and health screening for the elderly. The form was developed offline in Microsoft Excel. Macros (routines capable of performing pre-programmed tasks) were created using Microsoft's Visual Basic for Applications (VBA) language. In the analysis of the confusion matrix, good accuracy were obtained in all stages, training (61.5%), validation (60.0%) and test (80.0%), which indicates that the network learned through the inputs and outputs initially defined and during the sample divisions performed for testing and validation. In the test stage, a ROC curve was obtained with better true positive rates and lower false positive rates, being close to the Y axis (left side), thus indicating better results. We conducted a pilot study with thirty-six community active elderlies from a city in Eastern Amazonia, Brazil. This study was divided into four parts: data collection, data pre-processing, training of an artificial neural network and evaluation methods.

Lightning is a real threat for different activities in the Para State in Brazil. The southeastern microregion of the State of Para is a region with high population density, extensive agricultural activity, large mining companies and hydroelectric power plants. This paper presents a study of lightning occurrence in the southeastern microregion of Para for the period from 2013 to 2017 using STARNET reprocessed data. In addition, in order to study the occurrence of different types of lightning in the region, we used data from the LDWSS to observe compact intracloud discharges (CIDs), regular intracloud, and positive and negative cloud-to-ground lightning. Currently, there is a LDWSS installed in the building of the Federal University of Southern and South of Para, located in the city of Maraba. The results show which cities have the highest lightning density. In addition, we show the statistics of occurrence of different types of lightning to one day through LDWSS data.

Machine learning (ML) techniques have been used around the world to solve different problems. In this work, we applied ML techniques to estimate lightning distance for return strokes (RS) in negative cloud-to-ground (-CG) flashes. The approach uses E-field records from a single-station system. A lightning electric field waveform dataset containing more than 1500 waveforms of negative RS recorded at LOG, Florida, US was used to train and validate the ML classifiers. The dataset was split into day time and night time records. For day-time records, the quadratic Support Vector Machine (SVM) classifier was the one with the best accuracy (80%) and for night-time, the best one was the linear SVM with an accuracy of 88%. The ML classifiers were applied to estimate lightning distance in thunderstorms in the Amazon region of Brazil, and the results were compared against GOES-16 images and STARNET lightning location data. The main application of such methodology is for regions with no lightning location systems or no communication links to obtain lightning location data.