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Since 2003, Brazil has striven to provide energy access to all, in rural areas, in an effort to economically empower the communities. Unpacking fuel stacking behaviour can shed light onto the speed of transition toward the exclusive use of advanced fuel types. This paper presents findings from surveys that were carried out with 14 non-electrified communities in a rural area of Rio Negro, Amazonas State, Brazil. We identify the fuel choice determinants in these communities using a multinomial logistic regression model and more generally discuss the validity and robustness of such models in the context of statistical validation and evaluation metrics. Specifically for the Amazonas communities considered in this study, the research showed that the fuel choice determinants are the age of household, the number of people at meals each day, the number of meals daily, the community, education of the household head, and the income level of the household. Moreover, given the Brazilian policies related to energy and sustainability, this region is not likely to reach the Sustainable Development Goals proposed by United Nations for 2030. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class. [ABSTRACT FROM AUTHOR]