Time series causal relationships discovery through feature importance and ensemble models.

Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.g., Random Forest, to assess which input features the trained model prioritizes when making a forecast and, in this way, establish causal relationships between the variables. The advantage of these algorithms lies in their ability to provide feature importance, which allows us to build the causal network. We present our methodology to estimate causality in time series from oil field production. As it is difficult to extract causal relations from a real field, we also included a synthetic oil production dataset and a weather dataset, which is also synthetic, to provide the ground truth. We aim to perform causal discovery, i.e., establish the existing connections between the variables in each dataset. Through an iterative process of improving the forecasting of a target's value, we evaluate whether the forecasting improves by adding information from a new potential driver; if so, we state that the driver causally affects the target. On the oil field-related datasets, our causal analysis results agree with the interwell connections already confirmed by tracer information; whenever the tracer data are available, we used it as our ground truth. This consistency between both estimated and confirmed connections provides us the confidence about the effectiveness of our proposed methodology. To our knowledge, this is the first time causal analysis using solely production data is employed to discover interwell connections in an oil field dataset.

Ação bactericida da terapia fotodinâmica (AsGaAI) sobre bactérias St mitis St sanguis, "in vitro" / Photodinamic terapy in bacterias st mitis st sanguis, \"in vitro\"

Este trabalho visa avaliar "in vitro" a atividade bactericida da terapia fotodinâmica com laser de baixa intensidade de diodo AsGaAl sobre bactéria constituinte da microflora oral humana, além de verificar as melhores densidades energéticas na potência de 30mW para a atividade bactericida. Nesta pesquisa foi associado o uso de um corante a orto toluidina que funcionou como fotossensibilizador, na concentração de 75µg/ml visto que este corante tem a propriedade de aumentar a capacidade de absorção da luz laser sobre esta bactéria. A cepa de bactéria foi submetida a três lasers de baixa intensidade de AsGaAl da marca DENTOFLEX KC 610-30mW de potência com densidades energéticas variando em 3J, 6J e 9J cm² (Joules por cm²). Resultados obtidos do efeito bactericida da terapia fotodinâmica em cepas para bactéria Streptococcus mitis: Resultados 30mW 3J/cm² 83,3 por cento; 6J/cm² 84,9 por cento; 9J/cm² 77,6 por cento. Resultados obtidos do efeito bactericida na terapia fotodinâmica em cepas de Streptococcus sanguis resultados: 30mW 3J/cm² 98,9 por cento; 6J/cm² 92,6 por cento; 9J/cm² 94,3 por cento