A Data-Driven Framework for Small Hydroelectric Plant Prognosis Using Tsfresh and Machine Learning Survival Models.

Maintenance in small hydroelectric plants (SHPs) is essential for securing the expansion of clean energy sources and supplying the energy estimated to be required for the coming years. Identifying failures in SHPs before they happen is crucial for allowing better management of asset maintenance, lowering operating costs, and enabling the expansion of renewable energy sources. Most fault prognosis models proposed thus far for hydroelectric generating units are based on signal decomposition and regression models.

[Efficiency of public spending on primary health care in the municipalities of Rio de Janeiro, Brazil: robust scores and their determinants].

Health services, the access to which is a right of all citizens and the duty of the state, must be efficiently provided, as public resources are scarce and there is a growing demand called for by the population. In this context, the objective was to evaluate the efficiency of public spending on Primary Health Care in the municipalities of Rio de Janeiro (n = 70) in 2015. Initially, the jackstrap procedure was used to identify possible outliers, and Data Envelopment Analysis was then applied to calculate robust efficiency scores.