913 resultados para Multi-objective analysis
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A alface-d'água (Pistia stratiotes) é uma das principais entre as macrófitas aquáticas que causam problemas em corpos hídricos no Brasil e são consideradas como plantas daninhas. O presente trabalho foi realizado com os objetivos de conhecer melhor a variabilidade genética dessa macrófita e relacionar essa variabilidade com a resposta à aplicação do herbicida glyphosate. Para isso, foram coletados indivíduos em 12 corpos hídricos em diferentes cidades do território nacional (Americana, Cambaratiba, Curitiba, Itapura, Jaboticabal, Lagoa Santa, Piraí, Rio Grande, Rubinéia, Salto Grande, Santa Gertrudes e Três Lagoas). Os acessos foram caracterizados pelo uso de marcadores RAPD (DNA Polimórfico Amplificado ao Acaso), que permitiram, com o auxílio de iniciadores aleatórios, a caracterização dos locos polimórficos identificados por uma matriz de ausência e presença de bandas. Utilizando essa matriz, a análise de agrupamento permitiu nítida classificação dos acessos em três grupos com diferenças genéticas entre eles. Um ensaio de controle químico, com plantas mantidas em vasos plásticos (5 L) e pulverizadas com o herbicida glyphosate nas concentrações de 0,0, 0,6, 1,2, 1,8 e 2,4 kg ha-1, identificou, utilizando avaliações aos 7, 14 e 21 dias após aplicação, que as duas maiores doses promoveram melhor efeito herbicida. Foi verificado também que os acessos de Curitiba e Cambaratiba apresentaram menor suscetibilidade ao herbicida glyphosate. Não houve correspondência entre a estrutura de grupos dos acessos pela análise multivariada de agrupamento com a técnica RAPD e a suscetibilidade da alface-d'água ao glyphosate.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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This paper presents an evaluative study about the effects of using a machine learning technique on the main features of a self-organizing and multiobjective genetic algorithm (GA). A typical GA can be seen as a search technique which is usually applied in problems involving no polynomial complexity. Originally, these algorithms were designed to create methods that seek acceptable solutions to problems where the global optimum is inaccessible or difficult to obtain. At first, the GAs considered only one evaluation function and a single objective optimization. Today, however, implementations that consider several optimization objectives simultaneously (multiobjective algorithms) are common, besides allowing the change of many components of the algorithm dynamically (self-organizing algorithms). At the same time, they are also common combinations of GAs with machine learning techniques to improve some of its characteristics of performance and use. In this work, a GA with a machine learning technique was analyzed and applied in a antenna design. We used a variant of bicubic interpolation technique, called 2D Spline, as machine learning technique to estimate the behavior of a dynamic fitness function, based on the knowledge obtained from a set of laboratory experiments. This fitness function is also called evaluation function and, it is responsible for determining the fitness degree of a candidate solution (individual), in relation to others in the same population. The algorithm can be applied in many areas, including in the field of telecommunications, as projects of antennas and frequency selective surfaces. In this particular work, the presented algorithm was developed to optimize the design of a microstrip antenna, usually used in wireless communication systems for application in Ultra-Wideband (UWB). The algorithm allowed the optimization of two variables of geometry antenna - the length (Ls) and width (Ws) a slit in the ground plane with respect to three objectives: radiated signal bandwidth, return loss and central frequency deviation. These two dimensions (Ws and Ls) are used as variables in three different interpolation functions, one Spline for each optimization objective, to compose a multiobjective and aggregate fitness function. The final result proposed by the algorithm was compared with the simulation program result and the measured result of a physical prototype of the antenna built in the laboratory. In the present study, the algorithm was analyzed with respect to their success degree in relation to four important characteristics of a self-organizing multiobjective GA: performance, flexibility, scalability and accuracy. At the end of the study, it was observed a time increase in algorithm execution in comparison to a common GA, due to the time required for the machine learning process. On the plus side, we notice a sensitive gain with respect to flexibility and accuracy of results, and a prosperous path that indicates directions to the algorithm to allow the optimization problems with "η" variables
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Anthropic disturbances in watersheds, such as inappropriate building development, disorderly land occupation and unplanned land use, may strengthen the sediment yield and the inflow into the estuary, leading to siltation, changes in the reach channel conformation, and ecosystem/water quality problems. Faced with such context, this study aims to assess the applicability of SWAT model to estimate, even in a preliminary way, the sediment yield distribution along the Potengi River watershed, as well as its contribution to the estuary. Furthermore, an assessment of its erosion susceptibility was used for comparison. The susceptibility map was developed by overlaying rainfall erosivity, soil erodibility, the slope of the terrain and land cover. In order to overlap these maps, a multi-criteria analysis through AHP method was applied. The SWAT was run using a five year period (1997-2001), considering three different scenarios based on different sorts of human interference: a) agriculture; b) pasture; and c) no interference (background). Results were analyzed in terms of surface runoff, sediment yield and their propagation along each river section, so that it was possible to find that the regions in the extreme west of the watershed and in the downstream portions returned higher values of sediment yield, reaching respectively 2.8 e 5.1 ton/ha.year, whereas central areas, which were less susceptible, returned the lowest values, never more than 0.7 ton/ha.ano. It was also noticed that in the west sub-watersheds, where one can observe the headwaters, sediment yield was naturally forced by high declivity and weak soils. In another hand, results suggest that the eastern part would not contribute to the sediment inflow into the estuary in a significant way, and the larger part of the sediment yield in that place is due to anthropic activities. For the central region, the analysis of sediment propagation indicates deposition predominance in opposition to transport. Thus, it s not expected that isolated rain storms occurring in the upstream river portions would significantly provide the estuary with sediment. Because the model calibration process hasn t been done yet, it becomes essential to emphasize that values presented here as results should not be applied for pratical aims. Even so, this work warns about the risks of a growth in the alteration of natural land cover, mainly in areas closer to the headwaters and in the downstream Potengi River
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Multi-classifier systems, also known as ensembles, have been widely used to solve several problems, because they, often, present better performance than the individual classifiers that form these systems. But, in order to do so, it s necessary that the base classifiers to be as accurate as diverse among themselves this is also known as diversity/accuracy dilemma. Given its importance, some works have investigate the ensembles behavior in context of this dilemma. However, the majority of them address homogenous ensemble, i.e., ensembles composed only of the same type of classifiers. Thus, motivated by this limitation, this thesis, using genetic algorithms, performs a detailed study on the dilemma diversity/accuracy for heterogeneous ensembles
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Classifier ensembles are systems composed of a set of individual classifiers and a combination module, which is responsible for providing the final output of the system. In the design of these systems, diversity is considered as one of the main aspects to be taken into account since there is no gain in combining identical classification methods. The ideal situation is a set of individual classifiers with uncorrelated errors. In other words, the individual classifiers should be diverse among themselves. One way of increasing diversity is to provide different datasets (patterns and/or attributes) for the individual classifiers. The diversity is increased because the individual classifiers will perform the same task (classification of the same input patterns) but they will be built using different subsets of patterns and/or attributes. The majority of the papers using feature selection for ensembles address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. In this investigation, two approaches of genetic algorithms (single and multi-objective) will be used to guide the distribution of the features among the classifiers in the context of homogenous and heterogeneous ensembles. The experiments will be divided into two phases that use a filter approach of feature selection guided by genetic algorithm
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The competition among tourist destinations environmental generates the emergent need to find different strategies to close down if the purpose of delight and retain their visitants. A customer satisfaction, loyalty and the development of attachment to place form a solid compound in search of promotion a tourist destination. This study presents the general objective analysis of the relationship between attachment to place, satisfaction and loyalty of visitors, in the archipelago of Fernando de Noronha / PE. Therefore, a model will be used as reference, where they will be analyzed various constructs related to attachment to place, satisfaction and loyalty, as well as the relations between them. The methodology used in the study consists of an exploratory, descriptive, where the sample is random and consists of individuals who visited Fernando de Noronha on a pre-defined period of ten days. Based on a sample table, we defined a quantitative equivalent of 246 questionnaires, which will be applied when the visitor leaves the destination, the departure lounge of airport. A proposal focuses on the possibility to get results able to understand the subjective and intriguing relationship that involves the triad attachment to place, satisfaction and loyalty, trying to thus provide subsidies for optimizing environmental tourist destination
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Aims: This multi-centre analysis assessed the DNA content of TSCC in 37 young patients (<40 years) and 28 old patients (>50 years) and determined the correlation of DNA ploidy findings with clinicopathological data.Methods and results: Image cytometry was carried out using an automated cellular imaging system on Feulgen-stained histological sections to obtain high-fidelity DNA histograms. Among young patients, 37.8% were females compared to 18.7% in the older group (P = 0.002). In total, 48.6% patients were non-smokers and 40.5% were non-drinkers compared to 10.7% non-smokers and non-drinkers in the older group (P < 0.0001). TNM, clinical stage of disease and histological grade of differentiation did not differ between groups. Tumour aneuploidy was detected in 86.5% and tetraploidy in 24.3% young patients; this was significantly greater than in the older group where 64.3% were aneuploid (P < 0.0001) and 7.2% tetraploid (P < 0.0001). The mean values of DNA index (DI) and DNA heterogeneity index as well as the percentage of cells with DI exceeding 5N were higher in young patients (P < 0.0001).Conclusions: Young patients with TSCC represent a distinct clinical entity. The high incidence of DNA ploidy abnormalities suggest that they may have increased genomic instability and indicates underlying genetic differences between TSCC in young and older patients.