954 resultados para distributed combination of classifiers


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[Excerpt] The advantages resulting from the use of numerical modelling tools to support the design of processing equipment are almost consensual. The design of calibration systems in profile extrusion is not an exception . H owever , the complex geome tries and heat exchange phenomena involved in this process require the use of numerical solvers able to model the heat exchange in more than one domain ( calibrator and polymer), the compatibilization of the heat transfer at the profile - calibrator interface and with the ability to deal with complex geometries. The combination of all these features is usually hard to find in commercial software. Moreover , the dimension of the meshes required to ob tain accurate results, result in computational times prohibitive for industrial application. (...)

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Tese de Doutoramento Engenharia Mecânica

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Tese de Doutoramento em Engenharia de Tecidos, Medicina Regenerativa e Células Estaminais.

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This paper presents measurements from the ATLAS experiment of the forward-backward asymmetry in the reaction pp→Z/γ∗→l+l−, with l being electrons or muons, and the extraction of the effective weak mixing angle. The results are based on the full set of data collected in 2011 in pp collisions at the LHC at s√ = 7 TeV, corresponding to an integrated luminosity of 4.8 fb−1. The measured asymmetry values are found to be in agreement with the corresponding Standard Model predictions. The combination of the muon and electron channels yields a value of the effective weak mixing angle of 0.2308±0.0005(stat.)±0.0006(syst.)±0.0009(PDF), where the first uncertainty corresponds to data statistics, the second to systematic effects and the third to knowledge of the parton density functions. This result agrees with the current world average from the Particle Data Group fit.

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Objectives: This research work intends to clarify the role of artificial saliva, in particularly the role of mucin, a salivary protein, on the surface properties and adhesion ability of Candida spp. oral clinical isolates to abiotic surfaces. Methods: Four oral clinical isolates of Candida spp. were used: two Candida albicans strains (AC; AM) and two Candida parapsilosis strains (AD; AM2). The strains were isolated from patients using oral prosthesis. The microorganisms were cultured in the absence or presence of mucin and artificial saliva, and their adhesion to an abiotic surface (coated with mucin and artificial saliva) was evaluated. Results: The presence of mucin per se onto the abiotic surface decreased the adhesion of all strains, although the combination of mucin with artificial saliva had reduced this effect. No direct correlation between adhesion and the surface free energies of adhesion of the microorganisms was found. Significance: Candida spp. were human commensal microorganisms that became pathogenic when the host immune defenses were compromised. Medical devices were colonized by Candida spp. particularly, oral prostheses, which might lead to the degradation of the prostheses and systemic infections. The salivary secretions that constantly cover the oral cavity influenced Candida spp. adhesion process. Therefore, it was important to understand the interactions between Candida spp., salivary proteins and the characteristic of oral prosthesis when developing materials for oral prostheses.

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Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

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In this article, we develop a specification technique for building multiplicative time-varying GARCH models of Amado and Teräsvirta (2008, 2013). The variance is decomposed into an unconditional and a conditional component such that the unconditional variance component is allowed to evolve smoothly over time. This nonstationary component is defined as a linear combination of logistic transition functions with time as the transition variable. The appropriate number of transition functions is determined by a sequence of specification tests. For that purpose, a coherent modelling strategy based on statistical inference is presented. It is heavily dependent on Lagrange multiplier type misspecification tests. The tests are easily implemented as they are entirely based on auxiliary regressions. Finite-sample properties of the strategy and tests are examined by simulation. The modelling strategy is illustrated in practice with two real examples: an empirical application to daily exchange rate returns and another one to daily coffee futures returns.

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ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

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Due to the enormous variety of phytochemicals present in plants, their extracts have been used for centuries in the treatment of innumerous diseases, being perceived as an invaluable source of medicines for humans. Furthermore, the combination of different plants was reported as inducing an improved effect (synergism) in comparison to the additive activity of the plants present in those mixtures. Nevertheless, information regarding the effects of plant infusions added with honey is still rather scarce. Accordingly, the aim of this study was evaluating the interaction between chestnut honey, a natural product with well-reported beneficial properties, and three medicinal plants (either as single plant or as combinations of two and three plants), with regard to their antioxidant activity and hepatotoxicity. Antioxidant activity was evaluated by comparing the results from four different assays; the hepatotoxicity was assessed in two different cell lines. Results were compared by analysis of variance and linear discriminant analysis. The addition of honey to the infusions had a beneficial result in both cases, producing a synergistic effect in all samples, except beta-carotene bleaching inhibition for artichoke+milk thistle+honey preparation and also preparations with lower hepatotoxicity, except in the case of artichoke+honey. Moreover, from discriminant linear analysis output, it became obvious that the effect of honey addition overcame that resulting from using single plant or mixed plants based infusions. Also, the enhanced antioxidant activity of infusions containing honey was convoyed by a lower hepatotoxicity.

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Nowadays, considering the high variety of construction products, adequate material selection, based on their properties and function, becomes increasingly important. In this research, a ranking procedure developed by Czarnecki and Lukowski is applied in mortars with incorporation of phase change materials (PCM). The ranking procedure transforms experimental results of properties into one numerical value. The products can be classified according to their individual properties or even an optimized combination of different properties. The main purpose of this study was the ranking of mortars with incorporation of different contents of PCM based in different binders. Aerial lime, hydraulic lime, gypsum and cement were the binders studied. For each binder, three different mortars were developed. Reference mortars, mortars with incorporation of 40% of PCM and mortars with incorporation of 40% of PCM and 1% of fibers, were tested. Results show that the incorporation of PCM in mortars changes their global performance.

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The present study was carried out to evaluate the effect of chitosan-based edible coatings with Aloe vera extract on the postharvest blueberry fruit quality during storage at 5 °C. Firstly, A. vera fractions (pulp and liquid) were extracted from leaves and evaluated in terms of antifungal and antioxidant capacities. The choice of the most adequate chitosan and A. vera fraction concentrations to be incorporated in coating formulation was made based on the wettability of the corresponding coating solutions. Coatings with 0.5% (w/v) chitosan + 0.5% (w/v) glycerol + 0.1% (w/v) Tween 80 + 0.5% (v/v) A. vera liquid fraction presented the best characteristics to uniformly coat blueberry surface. Physico-chemical (i.e., titratable acidity, pH, weight loss) and microbiological analyses of coated blueberries (non-inoculated or artificially inoculated with Botrytis cinerea) were performed during 25 d. Microbiological growth and water loss levels were approximately reduced by 50% and 42%, respectively, in coated blueberries after 25 d compared to uncoated blueberries. After 15 d, weight loss values were 6.2% and 3.7% for uncoated and chitosanA. vera coated blueberries, respectively. Uncoated fruits presented mold contamination after 2 d of storage (2.0 ± 0.32 log CFU g1), whilst fruits with chitosan-based coatings with A. vera presented mold contamination only after 9 d of storage (1.3 ± 0.35 log CFU g1). Overall, coatings developed in this study extend blueberries shelf-life for about 5 d, demonstrating for the first time that the combination of chitosan and A. vera liquid fraction as edible coating materials has great potential in expanding the shelf-life of fruits.

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We investigate the strain hardening behavior of various gelatin networks-namely physical gelatin gel, chemically cross-linked gelatin gel, and a hybrid gel made of a combination of the former two-under large shear deformations using the pre-stress, strain ramp, and large amplitude oscillations shear protocols. Further, the internal structures of physical gelatin gels and chemically cross-linked gelatin gels were characterized by small angle neutron scattering (SANS) to enable their internal structures to be correlated with their nonlinear rheology. The Kratky plots of SANS data demonstrate the presence of small cross-linked aggregates within the chemically cross-linked network whereas, in the physical gelatin gels, a relatively homogeneous structure is observed. Through model fitting to the scattering data, we were able to obtain structural parameters, such as the correlation length (ξ), the cross-sectional polymer chain radius (Rc) and the fractal dimension (df) of the gel networks. The fractal dimension df obtained from the SANS data of the physical and chemically cross-linked gels is 1.31 and 1.53, respectively. These values are in excellent agreement with the ones obtained from a generalized nonlinear elastic theory that has been used to fit the stress-strain curves. The chemical cross-linking that generates coils and aggregates hinders the free stretching of the triple helix bundles in the physical gels.

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Tese de Doutoramento em Engenharia de Materiais.

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Tese de Doutoramento em Engenharia Civil

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Tese de Doutoramento em Ciências (Especialidade em Química)