Abstract

Using the Scopus database between the years of 2000 and 2019, a bibliometric study was done to analyze the scientific publications in the area of photovoltaic solar energy management. From the preliminary analysis of future research tendencies, ten possibilities of study topics were developed; and due to that it was possible to assume that even though many studies of technological development are found, some insights can still be approached in a way that the practical implementation of solar systems photovoltaic is better used. This data was validated by the analysis performed with the Scimat scientific mapping software under a longitudinal structure, verifying the future tendencies researches mentioned previously.

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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.

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A time-series ecological study was developed to estimate the role of air pollutants in the mean daily duration of hospitalization for pneumonia in children under one year old and living in São José dos Campos, SP, between January 1, 2009 and December 31, 2009. Air pollutants PM 10 , SO 2 and O 3 , and climatic variables were measured by the Environmental Company of the State of São Paulo (Cetesb). The duration of each hospitalization was obtained from the Datasus site. The values of air pollutants and climatic variables were analyzed using multiple linear regression in lags of zero to five days; the dependent variable was the mean duration of hospitalization and the independent variables were the pollutants. We obtained R 2 and alpha = 0.05 was the significance level of the model. There were 559 children under one year of age admitted during the study period; the mean hospital stay was 3.81 days (SD = 4.06). The PM 10 was associated with length of stay in concurrent days and lags four and five (P <0.001, R 2 = 0.08); a 15 μg.m-3 increase in concentration of this pollutant implies an increase of approximately one day of mean time of hospitalization for lags of 0, 4 and 5 days. It was therefore possible to identify the role of particulate matter in the duration of pneumonia hospitalizations in children.

OBJECTIVE Predict the number of hospitalizations for asthma and pneumonia associated with exposure to air pollutants in the city of São José dos Campos, São Paulo State.METHODS This is a computational model using fuzzy logic based on Mamdani’s inference method. For the fuzzification of the input variables of particulate matter, ozone, sulfur dioxide and apparent temperature, we considered two relevancy functions for each variable with the linguistic approach: good and bad. For the output variable number of hospitalizations for asthma and pneumonia, we considered five relevancy functions: very low, low, medium, high and very high. DATASUS was our source for the number of hospitalizations in the year 2007 and the result provided by the model was correlated with the actual data of hospitalization with lag from zero to two days. The accuracy of the model was estimated by the ROC curve for each pollutant and in those lags.RESULTS In the year of 2007, 1,710 hospitalizations by pneumonia and asthma were recorded in São José dos Campos, State of São Paulo, with a daily average of 4.9 hospitalizations (SD = 2.9). The model output data showed positive and significant correlation (r = 0.38) with the actual data; the accuracies evaluated for the model were higher for sulfur dioxide in lag 0 and 2 and for particulate matter in lag 1.CONCLUSIONS Fuzzy modeling proved accurate for the pollutant exposure effects and hospitalization for pneumonia and asthma approach.

Objective: To build a fuzzy computational model to estimate the number of hospitalizations of children aged up to 10 years due to respiratory conditions based on pollutants and climatic factors in the city of São José do Rio Preto, Brazil.Methods: A computational model was constructed using the fuzzy logic. The model has 4 inputs, each with 2 membership functions generating 16 rules, and the output with 5 pertinence functions, based on the Mamdani’s method, to estimate the association between the pollutants and the number of hospitalizations. Data from hospitalizations, from 2011-2013, were obtained in DATASUS - and the pollutants Particulate Matter (PM10) and Nitrogen Dioxide (NO2), wind speed and temperature were obtained by the Environmental Company of São Paulo State (Cetesb).Results: A total of 1,161 children were hospitalized in the period and the mean of pollutants was 36 and 51 µg/m3 - PM10 and NO2, respectively. The best values of the Pearson correlation (0.34) and accuracy measured by the Receiver Operating Characteristic (ROC) curve (NO2 - 96.7% and PM10 - 90.4%) were for hospitalizations on the same day of exposure.Conclusions: The model was effective in predicting the number of hospitalizations of children and could be used as a tool in the hospital management of the studied region.