9 resultados para color appearance models
em Repositório da Produção Científica e Intelectual da Unicamp
Resumo:
We report on a new analysis of neutrino oscillations in MINOS using the complete set of accelerator and atmospheric data. The analysis combines the ν(μ) disappearance and ν(e) appearance data using the three-flavor formalism. We measure |Δm(32)(2)| = [2.28-2.46] × 10(-3) eV(2) (68% C.L.) and sin(2)θ(23) = 0.35-0.65 (90% C.L.) in the normal hierarchy, and |Δm(32)(2)| = [2.32-2.53] × 10(-3) eV(2) (68% C.L.) and sin(2)θ(23) = 0.34-0.67 (90% C.L.) in the inverted hierarchy. The data also constrain δ(CP), the θ(23} octant degeneracy and the mass hierarchy; we disfavor 36% (11%) of this three-parameter space at 68% (90%) C.L.
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Prosopis rubriflora and Prosopis ruscifolia are important species in the Chaquenian regions of Brazil. Because of the restriction and frequency of their physiognomy, they are excellent models for conservation genetics studies. The use of microsatellite markers (Simple Sequence Repeats, SSRs) has become increasingly important in recent years and has proven to be a powerful tool for both ecological and molecular studies. In this study, we present the development and characterization of 10 new markers for P. rubriflora and 13 new markers for P. ruscifolia. The genotyping was performed using 40 P. rubriflora samples and 48 P. ruscifolia samples from the Chaquenian remnants in Brazil. The polymorphism information content (PIC) of the P. rubriflora markers ranged from 0.073 to 0.791, and no null alleles or deviation from Hardy-Weinberg equilibrium (HW) were detected. The PIC values for the P. ruscifolia markers ranged from 0.289 to 0.883, but a departure from HW and null alleles were detected for certain loci; however, this departure may have resulted from anthropic activities, such as the presence of livestock, which is very common in the remnant areas. In this study, we describe novel SSR polymorphic markers that may be helpful in future genetic studies of P. rubriflora and P. ruscifolia.
Resumo:
In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.
Resumo:
Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.
Resumo:
Dulce de leche samples available in the Brazilian market were submitted to sensory profiling by quantitative descriptive analysis and acceptance test, as well sensory evaluation using the just-about-right scale and purchase intent. External preference mapping and the ideal sensory characteristics of dulce de leche were determined. The results were also evaluated by principal component analysis, hierarchical cluster analysis, partial least squares regression, artificial neural networks, and logistic regression. Overall, significant product acceptance was related to intermediate scores of the sensory attributes in the descriptive test, and this trend was observed even after consumer segmentation. The results obtained by sensometric techniques showed that optimizing an ideal dulce de leche from the sensory standpoint is a multidimensional process, with necessary adjustments on the appearance, aroma, taste, and texture attributes of the product for better consumer acceptance and purchase. The optimum dulce de leche was characterized by high scores for the attributes sweet taste, caramel taste, brightness, color, and caramel aroma in accordance with the preference mapping findings. In industrial terms, this means changing the parameters used in the thermal treatment and quantitative changes in the ingredients used in formulations.
Resumo:
Shelled, roasted and salted cashew nut kernels were packaged in three different flexible materials (PP/PE= polypropylene / polyethylene; PETmet/PE= metallized polyethylene terephthalate / polyethylene; PET/Al/LDPE= polyethylene terephthalate / aluminum foil / low density polyethylene ), with different barrier properties. Kernels were stored for one year at 30° C and 80% relative humidity. Quantitative descriptive sensory analysis (QDA) were performed at the end of storage time. Descriptive terms obtained for kernels characterization were brown color, color uniformity and rugosity for appearance; toasted kernel, sweet, old and rancidity for odor; toasted kernel, sweet, old rancidity, salt and bitter for taste, crispness for texture. QDA showed that factors responsible for sensory quality decrease, after one year storage, were increase in old aroma and taste, increase in rancidity aroma and taste, decrease in roasted kernel aroma and taste, and decrease of crispness. Sensory quality decrease was higher in kernels packaged in PP/PE.
Resumo:
Easter egg is a popular chocolate-candy in egg form commercialized in Brazil during Easter time. In this research, Quantitative Descriptive Analysis was applied to select sensory attributes which best define the modifications in appearance, aroma, flavor and texture when cocoa butter equivalent (CBE) is added to Easter eggs. Samples with and without CBE were evaluated by a selected panel and fourteen attributes best describing similarities and differences between them, were defined. Terms definition, reference materials and a consensus ballot were developed. After a training period, panelists evaluated the samples in a Complete Block Design using a 9 cm unstructured scale. Principal Component Analysis, ANOVA and Tukey test (p<0.05) were applied to the data in order to select attributes which best discriminated and characterized the samples. Samples showed significant differences (p<0.05) in all attributes. Easter egg without CBE showed higher intensities (p<0.05) in relation to the following descriptors: brown color, characteristic aroma, cocoa mass aroma, cocoa butter aroma, characteristic flavor, cocoa mass flavor, hardness and brittleness.
Resumo:
Universidade Estadual de Campinas . Faculdade de Educação Física
Resumo:
Universidade Estadual de Campinas . Faculdade de Educação Física