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.

We present an alternative approach to solve the hardware and software partitioning problem, which uses Bounded Model Checking (BMC) based on Satisfiability Modulo Theories (SMT) in conjunction with a multi-core support using Open Multi-Processing. The multi-core approach allows initializing many verification instances based on processors cores numbers available to the model checker. Each instance checks for a different optimum value until the optimization problem is satisfied. The goal is to show that multi-core model-checking techniques can be effective, in particular cases, to find the optimal solution of the hardware-software partitioning problem. We compare the experimental results of our proposed approach with conventional algorithms.

Despite efforts to promote universal access to electrification, the Brazilian Amazon basin has around 82,000 families without electricity. The basin is huge, with few roads, many rivers, and conservative areas, which is an enormous challenge in terms of logistics and electrification costs. This paper describes a case study at the Nova Esperança community site in the Cuieiras River, Brazil. The community received stand-alone solar photovoltaic systems in 2018 and 2019. The process started with a survey and finished with an interview with each dweller that received a 975 W and 2-day autonomy photovoltaic system. A monitoring system was developed and deployed, and weather monitoring was performed to evaluate the impact of high temperatures on the equipment. The community does not have cell phone coverage and it is far from the main cities. We claim that the model created and adopted in the case study has interesting outcomes, even considering a small budget. Some houses, after 1 year of deployment, had their electrical demand rise by 300%, and 50% improved their income. We estimate the number of greenhouse gases annually avoided after electrification, replacing the consumed fossil fuel. The project also estimates the expenditure on energy sources that residents used due to the lack of electricity, which they stopped doing after electrification. The avoided expense can cover maintenance costs over the years. The goals of the SDG that were covered by the project are good health and well-being, accessible and clean energy, sustainable cities and communities, combating climate change, and partnerships for the goals.