4 resultados para seleção individual

em Universidade Federal do Rio Grande do Norte(UFRN)


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In this work evaluate the technical characteristics of the fibers grown in settlements Guamaré, colored cotton seeds were donated existing in the Germplasm Bank of Embrapa Cotton. We sought through the breeding program, raising the resistance, fineness, length and uniformity of cotton fibers, as well as stabilize the staining of fibers in the BRS Topaz, BRS Brown and BRS Green shades and raise their productivity in the field. First, the individual selections to test progeny seeds, and thereafter the hybridization method followed by family selection to obtain variations in the color tones were performed. The BRS Topaz, BRS Brown and BRS Green varieties were produced, analyzed and compared with existing cottons in the region which is the White cotton. The properties amount of impurities and neps, length, length uniformity, short fiber content, fineness and tensile strength of the fibers were sized in Classifiber, NATI, Pressley and Micronaire devices. 10 trials each with 10 tests for all four fiber types were carried out. The White and Topaz fibers showed greater length (32-34mm) and greater resistance (7.94 lb/mg and 7.97 lb/mg respectively) and showed finesse with lower micronaire index 3,71μg/inch and 3, 73μg/inch and a low rate of short fibers. The results were very promising for the use of genetically improved cotton in the manufacturing of fabric and yarn in the textile industry. The fibers were brown colored cotton used in the manufacture of a composite fiber with thermoplastic resin

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Marmosets, Callithrix jacchus, are strictly diurnal animals. The motor activity rhythmicity is generated by the circadian timing system and is modulated by environmental factors, mainly by photic stimuli that compose the light-dark cycle. Photic stimuli can reset the biological oscilators changing activity motor pattern, by a mechanism called entrainment. Otherwise, light can act directly on expressed rhythm, without act on the biological oscillators, promoting the masking. Thus, photic stimuli can synchronize the circadian activity rhythm (CAR) by two distinct mechanisms, acting isolated or at a combined way. Among the elements that can influence photic synchronization, the duration and time of photic exposure is pointed out. If in the natural environment the marmoset can choose places of different intensity illumination and is synchronized to light-dark cycle (LD), how the photic synchronization mechanism can be evaluated in laboratory by light self-selection? With objective to response this question, four adult male marmosets were studied at two conditions: with and without sleeping box. The animals were submitted to a LD cycle (12:12/ 350:2 lx) and constant light (LL: 350 lx) conditions in individual cages with an opaque sleeping box, that permitted the light self-selection. At the room, the temperature was 25.6 ºC (± 0.3 ºC) and humidity was 78.7 (± 5%). The motor activity was recorded at 5 min bins by infrared movement sensors installed at the top of the cages. The motor activity profile was distinct at the two conditions: without the sleeping box protection against light, the activity frequency was higher at CT 11-12 (ANOVA; F(3.23) = 62.27; p < 0.01). Also, the duration of the active phase (α) was prolonged of about 1 h (t test, p < 0.05) and the animals showed a significant delay on the activity onset and offset (t test, p < 0.05) and at the acrophase (confidence intervals of 5%) of CAR. In LL, the light continuous exposure prolonged the active phase and influenced the endogenous expression of the circadian activity rhythm period. From the result analysis, it is concluded that the light self-selection can modify several parameters of CAR in marmosets, allowing the study of the synchronization mechanism using the burrow model. Thus, without sleeping box there was a phase delay between the CAR and LD (entrainment) and an increase of activity near lights off (positive masking). Furthermore, in LL, the light continuous exposure modifies α and the endogenous expression of CAR. It is suggested that the light self-selection might be take into account at investigations that evaluate the biological rhythmicity in marmosets

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Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria

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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