The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of more sustainable vehicles is paramount to preserve populations’ health. To better understand the health risks caused by air pollution and exclusively by mobile sources urges the question of which input variables should be considered. Therefore, this research aims to estimate the impacts on populations’ health related to road transport variables for São Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron—MLP, Extreme Learning Machines—ELM, and Echo State Neural Networks—ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity—hospital admissions and mortality). We also used unusual inputs, such as road vehicles fleet, distributed and sold fuels amount, and vehicle average mileage. We also used deseasonalization and the Variable Selection Methods (VSM) (Mutual Information Filter and Wrapper). The results showed that the VSM excluded some variables, but the best performances were reached considering all of them. The ELM achieved the best overall results to morbidity, and the ESN to mortality, both using deseasonalization. Our study makes an important contribution to the following United Nations Sustainable Development Goals: 3—good health and well-being, 7—affordable and clean energy, and 11—sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system.
Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide due to global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O
3
, NO
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, NO, PM
2.5
, and PM
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, for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution.
Com o intuito de apresentar uma classificação de métodos de previsão de demanda para novos produtos, o artigo teve como objetivo apresentar o processo de elaboração e validação de um questionário de pesquisa realizado em um estudo considerando as características do sistema brasileiro de franquias. Realizou-se uma validação do questionário em quatro esferas: psicologia, estatística, especialistas e língua portuguesa, além da aplicação do Coeficiente Alfa de Cronbach – que resultou em um coeficiente global com classificação alta, o que demonstra a confiabilidade do questionário. Ao utilizar o Coeficiente Alfa de Cronbach, notou-se que o mesmo é considerado satisfatório e apresenta consistência desejada em todos os blocos e no questionário global, o que indica a confirmação dos pressupostos teóricos eleitos para a construção do instrumento.
Healthcare logistics play an important role in management, being attributed the activities of acquisition, distribution and movement of materials, professionals and patients. This work aims to develop a study, using the healthcare logistics in the movement of patients in the third health region of Paraná, proposing a linear programming problem that will pass through a computational simulation, considering the existing demands and constraints in the system, aiming to optimize the flow of patients from this region. The present study developed four mathematical models, based on demands and constraints followed by linear programming in order to find the best possible solution for the flow of patients from the third health region of the state of Paraná. The study developed reached its goal of optimization, generating an economy in the transportation of patients. Through the analysis of the results, it is concluded that the model that best suits the presented problem is the one of costs minimization, since the one of vehicles presented higher costs. Possibly the model that minimizes the vehicles would bring better results if the vehicles were not outsourced, but of the Ponta Grossa City Hall (PMPG). Was possible to verify the importance of the theme, especially when referring to the flow of patients in the health services due to the lack of studies with this specific approach. Even with the scarcity of data, it is possible to notice the potential for improvements on this patient transport system.
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