MODELOS COMPUTACIONAIS FUZZY PARA AVALIAR EFEITOS DA POLUIÇÃO DO AR EM CRIANÇAS

RESUMO Objetivo: Construir um modelo computacional fuzzy para estimar o número de internações de crianças até 10 anos por doenças respiratórias, com base nos dados de poluentes e fatores climáticos da cidade de São José do Rio Preto, Brasil. Métodos: Foi construído modelo computacional utilizando a lógica fuzzy. O modelo tem 4 entradas, cada uma com 2 funções de pertinência gerando 16 regras, e a saída com 5 funções de pertinência, baseado no método de Mamdani, para estimar a associação entre os poluentes e o número de internações. Os dados de internações, de 2011-2013, foram obtidos no Departamento de Informática do Sistema de Saúde (DATASUS) e os poluentes material particulado (PM10) e dióxido de nitrogênio (NO2), a velocidade do vento e a temperatura foram obtidos pela Companhia Ambiental do Estado de São Paulo (Cetesb). Resultados: Foram internadas 1.161 crianças no período analisado, e a média dos poluentes foi 36 e 51 µg/m3 - PM10 e NO2, respectivamente. Os melhores valores da correlação de Pearson (0,34) e da acurácia avaliada pela curva Receiver Operating Characteristic - ROC (NO2 - 96,7% e PM10 - 90,4%) foram para internações no mesmo dia da exposição. Conclusões: O modelo mostrou-se eficaz na predição do número de internações de crianças, podendo ser utilizado como ferramenta na gestão hospitalar da região estudada

Modelo fuzzy para estimar o número de internações por asma e pneumonia sob os efeitos da poluição do ar

OBJETIVO Prever o número de internações por asma e pneumonia associadas à exposição a poluentes do ar no município em São José dos Campos, estado de São Paulo. MÉTODOS Trata-se de um modelo computacional que utiliza a lógica fuzzy baseado na técnica de inferência de Mamdani. Para a fuzzificação das variáveis de entrada material particulado, ozônio, dióxido de enxofre e temperatura aparente foram consideradas duas funções de pertinência para cada variável com abordagem linguísticas: bom e ruim. Para a variável de saída número internações por asma e pneumonia, foram consideradas cinco funções de pertinências: muito baixo, baixo, médio, alto e muito alto. O número de internações no ano de 2007 foi obtido do Datasus e o resultado fornecido pelo modelo foi correlacionado com os dados reais de internação com defasagem (lag) de zero a dois dias. A acurácia do modelo foi estimada pela curva ROC para cada poluente e nestas defasagens. RESULTADOS No ano de 2007 foram registradas 1.710 internações por pneumonia e asma em São José dos Campos, SP, com média diária de 4,9 internações (dp = 2,9). Os dados de saída do modelo mostraram correlação positiva e significativa (r = 0,38) com os dados reais; as acurácias avaliadas para o modelo foram maiores para o dióxido de enxofre nos lag 0 e 2 e para o material particulado no lag 1. CONCLUSÕES Modelagem fuzzy se mostrou acurada para a abordagem de efeitos da exposição aos poluentes e internação por pneumonia e asma.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

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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Photovoltaic systems: a review with analysis of the energy transition in Brazilian culture, 2018–2023

Abstract Countries all over the world have been seeking ways and methods so that their electrical matrices can stand out using clean and renewable energy sources. In this context, this article presents a review with analysis of sector legislation on photovoltaic solar energy in Brazil. This study was grounded in four steps: (i) sample definition; (ii) theoretical basis; (iii) network analysis; and (iv) content analysis in two stages of research. Initially, a systematic literature review was carried out in order to map all the major and most cited works. The second stage consisted in reading and performing a critical analysis of government documents and reports from the energy sector in Brazil using a few bibliometric resources for such a purpose. Its results reveal that photovoltaic solar energy in Brazil has grown and expanded to different applications, since floating solar plants and subscription to solar energy are becoming increasingly attractive. Furthermore, a possible replacement of photovoltaic solar generation for thermoelectric plants has been investigated once there are a few positive aspects yet to be found thereof. As samples of the results obtained, we have that the replacement of works would allow the photovoltaic solar energy source to increase by 1% in the electrical matrix and would stop emitting 10,738,478 tons into the atmosphere, there would be a progressive decrease in the use of tariff flags (which affect directly to the final consumer) and a reduction in operating costs would also be achieved

A Fuzzy Inference System for Detection of Positive Displacement Motor (PDM) Stalls during Coiled Tubing Operations

Positive Displacement Motors (PDM) are extensively used in the oilfield, either in drilling or in coiled tubing (CT) operations. They provide a higher rate of penetration and the possibility of drilling horizontal wells. For coiled tubing operations, PDMs can mill through obstructions and enable shut-in wells to work again. One of the major challenges while using these tools is the motor stalling, which can lead to serious damage to the PDM and lost time events in drilling and workover rigs. These events result in total losses of hundreds of thousands of dollars, and their avoidance mostly depends on trained and fully aware equipment operators. If a PDM starts to stall, the pumping needs to be halted immediately or the tool may fail. This paper describes the use of a Fuzzy Inference System (FIS) to detect the stalling events as they start to happen using the acquisition data from the coiled tubing unit, the output of the FIS could then trigger an alarm for the operator to take the proper action or remotely stop the pump. The FIS was implemented in Python and tested with real CT milling acquisition data. When tested using real data, the system analyzed 68,458 acquisition points and detected 94% of the stalling events across this data during its first seconds, whereas, during the real job, a CT operator could take longer to notice this event and take the proper action, or even take no action. If the FIS was applied on a real coiled tubing acquisition system, it could reduce PDMs over-pressurization due to stalling, leading to an increase on its useful life and decrease on premature failure. As of now there is no similar system in the market or published and this kind of operation is fully performed using human supervision only

Modelo fuzzy estimando tempo de internação por doenças cardiovasculares

Abstract A fuzzy linguistic model based on the Mamdani method with input variables, particulate matter, sulfur dioxide, temperature and wind obtained from CETESB with two membership functions each was built to predict the average hospitalization time due to cardiovascular diseases related to exposure to air pollutants in São José dos Campos in the State of São Paulo in 2009. The output variable is the average length of hospitalization obtained from DATASUS with six membership functions. The average time given by the model was compared to actual data using lags of 0 to 4 days. This model was built using the Matlab v. 7.5 fuzzy toolbox. Its accuracy was assessed with the ROC curve. Hospitalizations with a mean time of 7.9 days (SD = 4.9) were recorded in 1119 cases. The data provided revealed a significant correlation with the actual data according to the lags of 0 to 4 days. The pollutant that showed the greatest accuracy was sulfur dioxide. This model can be used as the basis of a specialized system to assist the city health authority in assessing the risk of hospitalizations due to air pollutants.Resumo Para prever o tempo médio de internações por doenças cardiovasculares relacionadas à exposição de poluentes do ar em São José dos Campos (SP), em 2009, foi construído um modelo linguístico fuzzy, baseado no método de Mamdani, com variáveis de entrada: material particulado, dióxido de enxofre, temperatura e vento, obtidos da CETESB, com duas funções de pertinência cada. A variável de saída é o tempo médio de internações, obtido do Datasus, com seis funções de pertinência. O tempo médio fornecido pelo modelo foi comparado aos dados reais segundo defasagens de 0 a 4 dias. Este modelo foi construído no toolbox fuzzy do Matlab v. 7.5. Sua acurácia foi avaliada pela curva ROC. Foram registradas 1119 internações com o tempo médio de 7,9 dias (dp = 4,9). Os dados fornecidos mostraram significativa correlação com os dados reais, segundo as defasagens de 0 a 4 dias. O poluente que mostrou melhor acurácia foi o dióxido de enxofre. Este modelo pode ser utilizado como base de sistema especialista, que pode auxiliar o gestor municipal na avaliação do risco de internações em função dos poluentes do ar