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Yara S. Tadano
Department of Mathematics, Federal University of Technology - Paraná (UTFPR), Ponta Grossa, Brazil

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Journal article
Published: 01 February 2021 in Research, Society and Development
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A adolescência é um período de transformações neurobiológicas e comportamentais. O desenvolvimento moral (DM), ainda em amadurecimento nesta fase, é importante na tomada de decisão, como o uso ou não de SPA. Objetiva-se aplicar algoritmos de clusterização para agrupamento de perfil de adolescentes escolares com tendência ao uso de SPAs, baseado nos fatores socioenonômicos e no nível de DM. Trata-se de um estudo transversal com amostra por conveniência. Participaram do estudo 100 adolescentes, entre 12 a 18 anos, de ambos os sexos e regularmente matriculados no ensino médio. Foram utilizados o questionário socioeconômico, CRAFFT/CESARE (acrônimo de Car; Relax; Alone; Forget; Family/Friends; Trouble) e o teste de competência moral. Foi utilizado o método de clusterização para análise de dados. Quanto aos resultados encontrados, foi verificado que onze (11.0%) apresentam risco para o uso, abuso ou dependência de SPAs, 6.0%, suspeita de dependência de SPAs e 2.0%, risco de dependência de SPAs. No dilema do médico, houve predomínio dos estágio 1 e 2, do juiz, 5 e 6 e operário, 4 e 5. O método Fuzzy C-Means mostrou-se mais adequado, com alta correlação com as respostas fornecidas pelo teste CRAFFT/CESARE. Portanto, os métodos de clusterização apresentam resultados com valores próximos aos obtidos pelo teste CRAFFT/ CESARE quanto ao perfil de adolescentes escolares com predisposição ao uso de SPAs, contribuindo na relação dos fatores determinantes na tomada de decisão nessa etapa da vida.

ACS Style

Dayane Diniz Martins; Laís Balla Lucena; Athos Ricardo Moraes Bastos Damasceno; Hugo Siqueira; Yara De Souza Tadano; Ivete Furtado Ribeiro Caldas. Clusterização do perfil de adolescentes escolares com predisposição ao uso de substância psicoativas. Research, Society and Development 2021, 10, 1 .

AMA Style

Dayane Diniz Martins, Laís Balla Lucena, Athos Ricardo Moraes Bastos Damasceno, Hugo Siqueira, Yara De Souza Tadano, Ivete Furtado Ribeiro Caldas. Clusterização do perfil de adolescentes escolares com predisposição ao uso de substância psicoativas. Research, Society and Development. 2021; 10 (2):1.

Chicago/Turabian Style

Dayane Diniz Martins; Laís Balla Lucena; Athos Ricardo Moraes Bastos Damasceno; Hugo Siqueira; Yara De Souza Tadano; Ivete Furtado Ribeiro Caldas. 2021. "Clusterização do perfil de adolescentes escolares com predisposição ao uso de substância psicoativas." Research, Society and Development 10, no. 2: 1.

Journal article
Published: 29 October 2020 in Environmental Pollution
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Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide 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, O3, NO2, NO, PM2.5, and PM10, 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.

ACS Style

Yara S. Tadano; Sanja Potgieter-Vermaak; Yslene R. Kachba; Daiane M.G. Chiroli; Luciana Casacio; Jéssica C. Santos-Silva; Camila A.B. Moreira; Vivian Machado; Thiago Antonini Alves; Hugo Siqueira; Ricardo H.M. Godoi. Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown. Environmental Pollution 2020, 268, 115920 -115920.

AMA Style

Yara S. Tadano, Sanja Potgieter-Vermaak, Yslene R. Kachba, Daiane M.G. Chiroli, Luciana Casacio, Jéssica C. Santos-Silva, Camila A.B. Moreira, Vivian Machado, Thiago Antonini Alves, Hugo Siqueira, Ricardo H.M. Godoi. Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown. Environmental Pollution. 2020; 268 ():115920-115920.

Chicago/Turabian Style

Yara S. Tadano; Sanja Potgieter-Vermaak; Yslene R. Kachba; Daiane M.G. Chiroli; Luciana Casacio; Jéssica C. Santos-Silva; Camila A.B. Moreira; Vivian Machado; Thiago Antonini Alves; Hugo Siqueira; Ricardo H.M. Godoi. 2020. "Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown." Environmental Pollution 268, no. : 115920-115920.

Journal article
Published: 07 September 2020 in Sustainability
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Particulate matter (PM) is one of the most harmful air pollutants to human health studied worldwide. In this scenario, it is of paramount importance to monitor and predict PM concentration. Artificial neural networks (ANN) are commonly used to forecast air pollution levels due to their accuracy. The use of partition on prediction problems is well known because decomposition of time series allows the latent components of the original series to be revealed. It is a matter of extracting the “deterministic” component, which is easy to predict the random components. However, there is no evidence of its use in air pollution forecasting. In this work, we introduce a different approach consisting of the decomposition of the time series in contiguous monthly partitions, aiming to develop specialized predictors to solve the problem because air pollutant concentration has seasonal behavior. The goal is to reach prediction accuracy higher than those obtained by using the entire series. Experiments were performed for seven time series of daily particulate matter concentrations (PM2.5 and PM10–particles with diameter less than 2.5 and 10 micrometers, respectively) in Finland and Brazil, using four ANNs: multilayer perceptron, radial basis function, extreme learning machines, and echo state networks. The experimental results using three evaluation measures showed that the proposed methodology increased all models’ prediction capability, leading to higher accuracy compared to the traditional approach, even for extremely high air pollution events. Our study has an important contribution to air quality prediction studies. It can help governments take measures aiming air pollution reduction and preparing hospitals during extreme air pollution events, which is related to the following United Nations sustainable developments goals: SDG 3—good health and well-being and SDG 11—sustainable cities and communities.

ACS Style

Paulo De Mattos Neto; Manoel Henrique Nóbrega Marinho; Hugo Siqueira; Yara De Souza Tadano; Vivian Machado; Thiago Antonini Alves; João De Oliveira; Francisco Madeiro. A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition. Sustainability 2020, 12, 7310 .

AMA Style

Paulo De Mattos Neto, Manoel Henrique Nóbrega Marinho, Hugo Siqueira, Yara De Souza Tadano, Vivian Machado, Thiago Antonini Alves, João De Oliveira, Francisco Madeiro. A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition. Sustainability. 2020; 12 (18):7310.

Chicago/Turabian Style

Paulo De Mattos Neto; Manoel Henrique Nóbrega Marinho; Hugo Siqueira; Yara De Souza Tadano; Vivian Machado; Thiago Antonini Alves; João De Oliveira; Francisco Madeiro. 2020. "A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition." Sustainability 12, no. 18: 7310.

Journal article
Published: 16 August 2020 in Energies
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The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the planning of the electric sector’s operation is to predict such series to anticipate behaviors and issues. In general, several proposals of the literature focus just on the determination of the best forecasting models. However, the correct selection of input variables is an essential step for the forecasting accuracy, which in a univariate model is given by the lags of the time series to forecast. This task can be solved by variable selection methods since the performance of the predictors is directly related to this stage. In the present study, we investigate the performances of linear and non-linear filters, wrappers, and bio-inspired metaheuristics, totaling ten approaches. The addressed predictors are the extreme learning machine neural networks, representing the non-linear approaches, and the autoregressive linear models, from the Box and Jenkins methodology. The computational results regarding five series from hydroelectric plants indicate that the wrapper methodology is adequate for the non-linear method, and the linear approaches are better adjusted using filters.

ACS Style

Hugo Siqueira; Mariana Macedo; Yara De Souza Tadano; Thiago Antonini Alves; Jr. Sergio L. Stevan; Jr. Domingos S. Oliveira; Manoel H.N. Marinho; Paulo S.G. De Mattos Neto; João F. L. De Oliveira; Ivette Luna; Marcos De Almeida Leone Filho; Leonie Asfora Sarubbo; Attilio Converti. Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods. Energies 2020, 13, 4236 .

AMA Style

Hugo Siqueira, Mariana Macedo, Yara De Souza Tadano, Thiago Antonini Alves, Jr. Sergio L. Stevan, Jr. Domingos S. Oliveira, Manoel H.N. Marinho, Paulo S.G. De Mattos Neto, João F. L. De Oliveira, Ivette Luna, Marcos De Almeida Leone Filho, Leonie Asfora Sarubbo, Attilio Converti. Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods. Energies. 2020; 13 (16):4236.

Chicago/Turabian Style

Hugo Siqueira; Mariana Macedo; Yara De Souza Tadano; Thiago Antonini Alves; Jr. Sergio L. Stevan; Jr. Domingos S. Oliveira; Manoel H.N. Marinho; Paulo S.G. De Mattos Neto; João F. L. De Oliveira; Ivette Luna; Marcos De Almeida Leone Filho; Leonie Asfora Sarubbo; Attilio Converti. 2020. "Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods." Energies 13, no. 16: 4236.

Journal article
Published: 28 May 2020 in Acta Scientiarum. Technology
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Air pollution is a relevant issue studied worldwide, and its prediction is important for social and economic management. Linear multivariate regression models (LMR) and artificial neural networks (ANN) are widely applied to forecasting concentrations of pollutants. However, unorganized machines are scarcely used. The present investigation proposes the application of unorganized machines (echo state networks - ESN and extreme learning machines - ELM) to forecast hourly concentrations of particulate matter with the aerodynamic diameter up to 10 µm (PM10), carbon monoxide (CO), and ozone (O3) at the metropolitan region of Recife, Pernambuco, Brazil. The results were compared with multilayer perceptron neural network (MLP) and LMR. The prediction was made using or not meteorological variables (wind speed, temperature, and relative humidity) as input data. The results showed that the inclusion of these variables could increase the general performance of the models considering one step ahead forecasting horizons. Also, the ELM and the LMR achieved the best overall results.

ACS Style

Daniel Silva Campos; Yara De Souza Tadano; Thiago Antonini Alves; Hugo Valadares Siqueira; Manoel Henrique Nóbrega Marinho. Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting. Acta Scientiarum. Technology 2020, 42, e48203 -e48203.

AMA Style

Daniel Silva Campos, Yara De Souza Tadano, Thiago Antonini Alves, Hugo Valadares Siqueira, Manoel Henrique Nóbrega Marinho. Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting. Acta Scientiarum. Technology. 2020; 42 ():e48203-e48203.

Chicago/Turabian Style

Daniel Silva Campos; Yara De Souza Tadano; Thiago Antonini Alves; Hugo Valadares Siqueira; Manoel Henrique Nóbrega Marinho. 2020. "Unorganized machines and linear multivariate regression model applied to atmospheric pollutant forecasting." Acta Scientiarum. Technology 42, no. : e48203-e48203.

Journal article
Published: 26 March 2020 in Sustainability
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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.

ACS Style

Yslene Kachba; Daiane Maria De Genaro Chiroli; Jônatas T. Belotti; Thiago Antonini Alves; Yara De Souza Tadano; Hugo Siqueira. Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America. Sustainability 2020, 12, 2621 .

AMA Style

Yslene Kachba, Daiane Maria De Genaro Chiroli, Jônatas T. Belotti, Thiago Antonini Alves, Yara De Souza Tadano, Hugo Siqueira. Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America. Sustainability. 2020; 12 (7):2621.

Chicago/Turabian Style

Yslene Kachba; Daiane Maria De Genaro Chiroli; Jônatas T. Belotti; Thiago Antonini Alves; Yara De Souza Tadano; Hugo Siqueira. 2020. "Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America." Sustainability 12, no. 7: 2621.

Journal article
Published: 31 December 2019 in LALCA: Revista Latino-Americana em Avaliação do Ciclo de Vida
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The Life Cycle Impact Assessment (LCIA) is composed of characterization models, and in Brazil, the methodological and scientific LCIA framework is still under development. The research’s aim was to evaluate the literature available characterization models to photochemical smog category. Thus, the contribution of work is recommending one of these models to be used in Brazilian LCA studies, standardizing the studies in Brazil. The methodology consisted of searching the literature and selecting, describing and analyzing the characterization models as well as elaborating a table of criteria for better comparison. Aiming to visualize the differences in the results of each selected model, a case study was applied to analyze the photochemical smog formation potential to the transport of one ton of sugar using two transportation modes (road and railroad). Five characterization models related to smog category were selected, described and compared. Herewith, it was observed that the models present significant differences, that is, each model presents Characterization Factors (CF) for different categories within the environmental impact chain of the photochemical smog (midpoint and/or endpoint), differences in modeling, scale of the model (regional, continental or global), quantity and quality of elementary flows, etc. Those factors have influence in the CF’s calculation and, consequently, the LCA’s results, in the same case study. The criteria table’s results suggested that the model of Van Zelm et al. (2016) – World (midpoint and endpoint), is the best interim option to be used in studies of LCA in Brazil, because it was the model that resulted in the highest grade referring to the established criteria and it presents results on a Global scale. However, the results do not rule out the need for regionalization studies, which would develop a model that presents results and studies directed to the Brazilian reality or adjust the model of Van Zelm et al. (2016) - Brazil

ACS Style

Sandy Bernardi Falcadi Tedesco Girotto; Flávio José Simioni; Yara De Souza Tadano; Valdeci José Costa; Rodrigo Augusto Freitas De Alvarenga. Evaluation of characterization models for the photochemical smog impact category focused on the Brazilian reality. LALCA: Revista Latino-Americana em Avaliação do Ciclo de Vida 2019, 3, e34263 .

AMA Style

Sandy Bernardi Falcadi Tedesco Girotto, Flávio José Simioni, Yara De Souza Tadano, Valdeci José Costa, Rodrigo Augusto Freitas De Alvarenga. Evaluation of characterization models for the photochemical smog impact category focused on the Brazilian reality. LALCA: Revista Latino-Americana em Avaliação do Ciclo de Vida. 2019; 3 ():e34263.

Chicago/Turabian Style

Sandy Bernardi Falcadi Tedesco Girotto; Flávio José Simioni; Yara De Souza Tadano; Valdeci José Costa; Rodrigo Augusto Freitas De Alvarenga. 2019. "Evaluation of characterization models for the photochemical smog impact category focused on the Brazilian reality." LALCA: Revista Latino-Americana em Avaliação do Ciclo de Vida 3, no. : e34263.

Journal article
Published: 31 December 2019 in Revista Gestão Industrial
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A atividade industrial é uma das principais causadoras de impactos ao meio ambiente, especialmente quanto à formação de material particulado ao ar. Uma forma de considera-los é usando a técnica de Avaliação do Ciclo de Vida, que objetiva analisar o ciclo de vida envolvido em um produto ou processo e associar os impactos causados em cada uma das etapas desse ciclo. Essa técnica tem quatro fases, sendo a fase de Avaliação de Impacto do Ciclo de Vida (AICV) abordada neste trabalho. Nela, relacionam-se os impactos com cada resultado da fase de Inventário do Ciclo de Vida, por meio de modelos de caracterização. Esses modelos objetivam calcular um fator de caracterização (FC), que irá mensurar o nível de impacto dentre as categorias de impacto existentes. Este trabalho tem como foco avaliar modelos de caracterização para a categoria de formação de material particulado, a fim de identificar os modelos mais adequados para serem utilizados no Brasil. A análise foi realizada dividindo o FC no fator de inalação e fator de efeito, sendo o último foco deste estudo. Três modelos foram analisados e os demais modelos existentes serão analisados posteriormente e avaliados quantitativamente, a fim de recomendar os mais adequados a serem aplicados no Brasil. Com isso, permite-se um avanço científico nas pesquisas de AICV no Brasil, além de possibilitar resultados mais próximos à realidade nacional.

ACS Style

Gabriela Roiko Cheli; Gabriela Giusti; Letícia Yuriko Togawa; Diogo Aparecido Lopes Silva; Yara De Souza Tadano. Avaliação de impacto do ciclo de vida: fatores de efeito para material particulado. Revista Gestão Industrial 2019, 15, 1 .

AMA Style

Gabriela Roiko Cheli, Gabriela Giusti, Letícia Yuriko Togawa, Diogo Aparecido Lopes Silva, Yara De Souza Tadano. Avaliação de impacto do ciclo de vida: fatores de efeito para material particulado. Revista Gestão Industrial. 2019; 15 (4):1.

Chicago/Turabian Style

Gabriela Roiko Cheli; Gabriela Giusti; Letícia Yuriko Togawa; Diogo Aparecido Lopes Silva; Yara De Souza Tadano. 2019. "Avaliação de impacto do ciclo de vida: fatores de efeito para material particulado." Revista Gestão Industrial 15, no. 4: 1.

Journal article
Published: 23 October 2019 in Environmental Modelling & Software
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Estimating of daily hospital admissions due to air pollution is a leading issue in environmental science. To better understand this problem, it is essential to improve the applied methodologies. The use of Generalized Linear Models (GLM) is well known. However, they may be improved using different methods to coefficients estimation and to consider seasonality. Alternative methodologies, rarely applied in such topic, are Artificial Neural Networks (ANN), efficient to solve non-linear problems and; ensembles, which combine various models outputs. This research aims to apply 10 distinct ANN and 4 ensemble to estimate hospital admissions for respiratory diseases caused by particulate matter and meteorological variables of Campinas and São Paulo cities, Brazil. In addition, a new proposal of GLM was introduced, considering coefficients calculation via particle swarm optimization and seasonality via normalization procedure. ANN and ensembles use showed significant improvements and may allow studies into areas with flawing database, developing countries reality.

ACS Style

Lilian N. Araujo; Jônatas T. Belotti; Thiago Antonini Alves; Yara De Souza Tadano; Hugo Siqueira. Ensemble method based on Artificial Neural Networks to estimate air pollution health risks. Environmental Modelling & Software 2019, 123, 104567 .

AMA Style

Lilian N. Araujo, Jônatas T. Belotti, Thiago Antonini Alves, Yara De Souza Tadano, Hugo Siqueira. Ensemble method based on Artificial Neural Networks to estimate air pollution health risks. Environmental Modelling & Software. 2019; 123 ():104567.

Chicago/Turabian Style

Lilian N. Araujo; Jônatas T. Belotti; Thiago Antonini Alves; Yara De Souza Tadano; Hugo Siqueira. 2019. "Ensemble method based on Artificial Neural Networks to estimate air pollution health risks." Environmental Modelling & Software 123, no. : 104567.