852 resultados para Clustering and objective measures
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This paper analyses earthquake data in the perspective of dynamical systems and fractional calculus (FC). This new standpoint uses Multidimensional Scaling (MDS) as a powerful clustering and visualization tool. FC extends the concepts of integrals and derivatives to non-integer and complex orders. MDS is a technique that produces spatial or geometric representations of complex objects, such that those objects that are perceived to be similar in some sense are placed on the MDS maps forming clusters. In this study, over three million seismic occurrences, covering the period from January 1, 1904 up to March 14, 2012 are analysed. The events are characterized by their magnitude and spatiotemporal distributions and are divided into fifty groups, according to the Flinn–Engdahl (F–E) seismic regions of Earth. Several correlation indices are proposed to quantify the similarities among regions. MDS maps are proven as an intuitive and useful visual representation of the complex relationships that are present among seismic events, which may not be perceived on traditional geographic maps. Therefore, MDS constitutes a valid alternative to classic visualization tools for understanding the global behaviour of earthquakes.
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Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.
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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Química e Biológica
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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Edificações
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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Hidráulica
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ABSTRACT OBJECTIVE To describe the spatial patterns of leprosy in the Brazilian state of Tocantins. METHODS This study was based on morbidity data obtained from the Sistema de Informações de Agravos de Notificação (SINAN – Brazilian Notifiable Diseases Information System), of the Ministry of Health. All new leprosy cases in individuals residing in the state of Tocantins, between 2001 and 2012, were included. In addition to the description of general disease indicators, a descriptive spatial analysis, empirical Bayesian analysis and spatial dependence analysis were performed by means of global and local Moran’s indexes. RESULTS A total of 14,542 new cases were recorded during the period under study. Based on the annual case detection rate, 77.0% of the municipalities were classified as hyperendemic (> 40 cases/100,000 inhabitants). Regarding the annual case detection rate in < 15 years-olds, 65.4% of the municipalities were hyperendemic (10.0 to 19.9 cases/100,000 inhabitants); 26.6% had a detection rate of grade 2 disability cases between 5.0 and 9.9 cases/100,000 inhabitants. There was a geographical overlap of clusters of municipalities with high detection rates in hyperendemic areas. Clusters with high disease risk (global Moran’s index: 0.51; p < 0.001), ongoing transmission (0.47; p < 0.001) and late diagnosis (0.44; p < 0.001) were identified mainly in the central-north and southwestern regions of Tocantins. CONCLUSIONS We identified high-risk clusters for transmission and late diagnosis of leprosy in the Brazilian state of Tocantins. Surveillance and control measures should be prioritized in these high-risk municipalities.
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ABSTRACT OBJECTIVE To describe the prevalence of common mental disorders in Brazilian adolescent students, according to geographical macro-regions, school type, sex, and age. METHODS We evaluated 74,589 adolescents who participated in the Cardiovascular Risk Study in Adolescents (ERICA), a cross-sectional, national, school-based study conducted in 2013-2014 in cities with more than 100,000 inhabitants. A self-administered questionnaire and an electronic data collector were employed. The presence of common mental disorders was assessed using the General Health Questionnaire (GHQ-12). We estimated prevalence and 95% confidence intervals of common mental disorders by sex, age, and school type, in Brazil and in the macro-regions, considering the sample design. RESULTS The prevalence of common mental disorders was of 30.0% (95%CI 29.2-30.8), being higher among girls (38.4%; 95%CI 37.1-39.7) when compared to boys (21.6%; 95%CI 20.5-22.8), and among adolescents who were from 15 to 17 years old (33.6%; 95%CI 32.2-35.0) compared to those aged between 12 and 14 years (26.7%; 95%CI 25.8-27.6). The prevalence of common mental disorders increased with age for both sexes, always higher in girls (ranging from 28.1% at 12 years to 44.1% at 17 years) than in boys (ranging from 18.5% at 12 years to 27.7% at 17 years). We did not observe any significant difference by macro-region or school type. Stratified analyses showed higher prevalence of common mental disorders among girls aged from 15 to 17 years of private schools in the North region (53.1; 95%CI 46.8-59.4). CONCLUSIONS The high prevalence of common mental disorders among adolescents and the fact that the symptoms are often vague mean these disorders are not so easily identified by school administrators or even by health services. The results of this study can help the proposition of more specific prevention and control measures, focused on highest risk subgroups.
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A procura de padrões nos dados de modo a formar grupos é conhecida como aglomeração de dados ou clustering, sendo uma das tarefas mais realizadas em mineração de dados e reconhecimento de padrões. Nesta dissertação é abordado o conceito de entropia e são usados algoritmos com critérios entrópicos para fazer clustering em dados biomédicos. O uso da entropia para efetuar clustering é relativamente recente e surge numa tentativa da utilização da capacidade que a entropia possui de extrair da distribuição dos dados informação de ordem superior, para usá-la como o critério na formação de grupos (clusters) ou então para complementar/melhorar algoritmos existentes, numa busca de obtenção de melhores resultados. Alguns trabalhos envolvendo o uso de algoritmos baseados em critérios entrópicos demonstraram resultados positivos na análise de dados reais. Neste trabalho, exploraram-se alguns algoritmos baseados em critérios entrópicos e a sua aplicabilidade a dados biomédicos, numa tentativa de avaliar a adequação destes algoritmos a este tipo de dados. Os resultados dos algoritmos testados são comparados com os obtidos por outros algoritmos mais “convencionais" como o k-médias, os algoritmos de spectral clustering e um algoritmo baseado em densidade.
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Introdução: A Esclerose Lateral Amiotrófica (ELA) é considerada uma das doenças neuromusculares (DNM) com características mais limitantes e fatais, sendo caracterizada por fraqueza muscular progressiva. Objectivo: Analisar a evolução da Capacidade Vital e da Máxima Capacidade Inspiratória em doentes com ELA e a sua relação causal com a função bulbar. Procedimentos: A partir de uma população alvo de 203 pacientes com ELA, foram incluídos no estudo aqueles que tinham entre 2 a 4 testes de função respiratória considerados válidos (CV <2000ml) perfazendo um total de 22 indivíduos. As medidas CV e MCI foram analisadas. Resultados: A CV diminuiu ao longo do tempo (media±desvio padrão no 1ºmomento de avaliação =1779,5±692,3; media±desvio padrão no 4ºmomento de avaliação =1108,6±475,7). O comportamento da MCI foi mais estável ao longo dos 4 momentos de avaliação. Avaliou-se a correlação entre as duas variáveis, destacando-se a relação existente entre as duas nos doentes bulbares (coeficiente = 1). Quando avaliada a diferença entre a CV e a MCI, verificamos que o nível de significância no grupo (n=22) aumentou ao longo do tempo. Ao comparamos esta diferença por subgrupos, registou-se uma diferença significativa apenas nos doentes bulbares (1ºmomento – p=0,008 e último momento de avaliação – p 0). Conclusão: Nos doentes com disfunção bulbar a CV diminui ao longo do tempo. A relação entre MCI e CV é um bom factor preditivo da evolução e prognóstico da doença e de diagnóstico do envolvimento da musculatura bulbar.
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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica
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A thesis submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Information Systems
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Dissertação de Mestrado apresentada ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Contabilidade e Finanças, sob orientação do Doutor José Campos Amorim
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Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with adequate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.
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This paper describes the use of a Control Banding Tool to assess and further control of exposure of nanoparticles emitted during welding operations. The tool was applied to Metal Active Gas (MAG) arc welding of mild and stainless steel, providing semi-quantitative data on the process, so that protection measures could be derived, e.g. exhaust gas ventilation by hoods, local ventilation devices and containment measures. This tool is quite useful to compare and evaluate the characteristics of arc welding procedures so that more eco-friendly processes could be preferred over the more potentially noxious ones.
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