Estimating the average length of hospitalization due to pneumonia: a fuzzy approach

Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.

Development of pitanga nectar with different sweeteners by sensory analysis: ideal pulp dilution, ideal sweetness, and sweetness equivalence

The objective of this study was to develop pitanga nectar formulations in which sucrose was replaced with different sweeteners. Consumer tests were conducted with 50 fruit juice consumers, and a just-about-right scale was used to determine the ideal pulp dilution and ideal sweetness with sucrose. Furthermore, the adequate concentrations of six sweeteners were determined to obtain the equivalent sweetness of sucrose using relative to these concentrations the magnitude estimation model with 19 selected assessors. The ideal dilution test resulted in 25% pulp, and the ideal sweetness test, 10% sucrose. Sweetener concentrations to replace sucrose were 0.0160%, 0.0541%, 0.1000%, 0.0999%, 0.0017%, and 0.0360%, respectively, for sucralose, aspartame, stevia 40% rebaudioside A, stevia 95% rebaudioside A, neotame, and a 2:1 cyclamate/saccharin blend. These results can be used to prepare pitanga nectar with different sweeteners and obtain the same sweetness intensity in less caloric products than that of nectar prepared with sucrose.