39 resultados para Statistical mixture-design optimization


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The National Cancer Institute (NCI) method allows the distributions of usual intake of nutrients and foods to be estimated. This method can be used in complex surveys. However, the user must perform additional calculations, such as balanced repeated replication (BRR), in order to obtain standard errors and confidence intervals for the percentiles and mean from the distribution of usual intake. The objective is to highlight adaptations of the NCI method using data from the National Dietary Survey. The application of the NCI method was exemplified analyzing the total energy (kcal) and fruit (g) intake, comparing estimations of mean and standard deviation that were based on the complex design of the Brazilian survey with those assuming simple random sample. Although means point estimates were similar, estimates of standard error using the complex design increased by up to 60% compared to simple random sample. Thus, for valid estimates of food and energy intake for the population, all of the sampling characteristics of the surveys should be taken into account because when these characteristics are neglected, statistical analysis may produce underestimated standard errors that would compromise the results and the conclusions of the survey.

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ABSTRACTIn the Amazon, river navigation is very important due to the length of navigable rivers and the lack of alternative road networks. Boats usually operate in unfavorable conditions, since there is no hydrodynamic relation among propellers, geometry, and the dimensions of the boat hull. Currently, there is no methodology for propeller hydrodynamic optimization with low computational cost and easy implementation in the region. The aim of this work was to develop a mathematical approach for marine propeller design applied to boats typically found on Amazon rivers. We developed an optimized formulation for the chord and pitch angle distributions, taking into account the classical model of Glauert. A theoretical analysis for the thrust and torque relationships on an annular control volume was performed. The mathematical model used was based on the Blade Element Momentum Theory (BEMT). We concluded that the new methodology proposed in this work demonstrates a good physical behavior when compared with the theory of Glauert and the experimental data of the Wageningen B3-50 propeller.

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Soil penetration resistance (PR) is a measure of soil compaction closely related to soil structure and plant growth. However, the variability in PR hampers the statistical analyses. This study aimed to evaluate the variability of soil PR on the efficiency of parametric and nonparametric analyses in indentifying significant effects of soil compaction and to classify the coefficient of variation of PR into low, medium, high and very high. On six dates, the PR of a typical dystrophic Red Ultisol under continuous no-tillage for 16 years was measured. Three tillage and/or traffic conditions were established with the application of: (i) no chiseling or additional traffic, (ii) additional compaction, and (iii) chiseling. On each date, the nineteen PR data (measured at every 1.5 cm to a depth of 28.5 cm) were grouped in layers with different thickness. In each layer, the treatment effects were evaluated by variance (ANOVA) and Kruskal-Wallis analyses in a completely randomized design, and the coefficients of variation of all analyses were classified (low, intermediate, high and very high). The ANOVA performed better in discriminating the compaction effects, but the rejection rate of null hypothesis decreased from 100 to 80 % when the coefficient of variation increased from 15 to 26 %. The values of 15 and 26 % were the thresholds separating the low/intermediate and the high/very high coefficient variation classes of PR in this Ultisol.

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The objective of this study was to establish critical values of the N indices, namely soil-plant analysis development (SPAD), petiole sap N-NO3 and organic N in the tomato leaf adjacent to the first cluster (LAC), under soil and nutrient solution conditions, determined by different statistical approaches. Two experiments were conducted in randomized complete block design with four repli-cations. Tomato plants were grown in soil, in 3 L pot, with five N rates (0, 100, 200, 400 and 800 mg kg-1) and in solution at N rates of 0, 4, 8, 12 and 16 mmol L-1. Experiments in nutrient solution and soil were finished at thirty seven and forty two days after transplanting, respectively. At those times, SPAD index and petiole sap N-NO3 were evaluated in the LAC. Then, plants were harvested, separated in leaves and stem, dried at 70ºC, ground and weighted. The organic N was determined in LAC dry matter. Three statistical procedures were used to calculate critical N values. There were accentuated discrepancies for critical values of N indices obtained with plants grown in soil and nutrient solution as well as for different statistical procedures. Critical values of nitrogen indices at all situations are presented.

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The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

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The aim of this work was to optimize the preparation of electrodes with riboflavin (RF) immobilized on a silica surface modified with niobium oxide and carbon paste. Electrode preparation was optimized employing a factorial design consisting of two levels and three factors. The electrochemical properties of immobilized RF were investigated by cyclic voltammetry. The factorial analysis was carried out analysing the current intensity (Ipa). It was possible to optimize the electrode to get the best reversibility in the redox process, i. e. the lowest separation between anodic and cathodic peak potentials and a current ratio close to unity. The concentration of supporting electrolyte has a small effect. The proportion has the highest effect and the interaction factor between proportion and mixture has also a significant effect on the current intensity.

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This work illustrates the modeling procedure for a solvent mixture using the simplex- centroid approach. The selected experiment was the optimization of the peak current observed in the direct determination of nickel by anodic stripping voltammetry (ASV) in a solvent mixture composed of N,N-dimethylformamide, ethanol and water. The text is presented in a tutorial way, showing in detail the several steps which must be followed in such a process. Since not all possible mixtures lead to a measurable instrumental response, pseudocomponents had to be used to rescale the experimental design. This also allows to show how to apply this tool, usually troublesome for non-specialists in mixture modeling procedures.

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Errors are always present in experimental measurements so, it is important to identify them and understand how they affect the results of experiments. Statistics suggest that the execution of experiments should follow random order, but unfortunately the complete randomization of experiments is not always viable for practical reasons. One possible simplification is blocked experiments within which the levels of certain factors are maintained fixed while the levels of others are randomized. However this has a cost. Although the experimental part is simplified, the statistical analysis becomes more complex.

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In order to reduce the total number of experiments for achieving the highest amount of adsorbed Cu2+ (qmax) using pecan nutshells (Carya illinoensis) as biosorbent, a full 2(4) factorial design with two central points was carried out (mass of biosorbent- m, pH, initial metallic ion concentration- C0, time of contact- t). In order to continue the optimization of the system, a central composite surface analysis design with two factors and five central points was carried out. The maximum amount of Cu2+ taken up by the pecan nutshells was 20 mg g-1. These results were confirmed by determining a Cu2+ isotherm using the best conditions attained by the statistical design of experiments.

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The aim of the present work is to investigate the effects of blending and chemical interesterification reactions on the softening and melting behavior of chicken fat, its stearin and medium chain triacylglycerols, and blends thereof in various ratios. Chemical interesterification is a promising alternative to the current processes of modifying the physical properties of fats. In the experimental design 7 samples corresponding to 7 different blend proportions were used. The results were represented in triangular diagrams. The addition of stearin influenced the softening and melting points. The mixture response surface methodology proved to be an extremely useful tool for the optimization of the fat mixtures.

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An evaluation of the performance of a continuous flow hydride generator-nebulizer for flame atomic absorption spectrometry was carried out. Optimization of nebulizer gas flow rate, sample acid concentration, sample and tetrahydroborate uptake rates and reductant concentration, on the As and Se absorbance signals was carried out. A hydrogen-argon flame was used. An improvement of the analytical sensitivity relative to the conventional bead nebulizer used in flame AA was obtained (2 (As) and 4.8 (Se) µg L-1). Detection limits (3σb) of 1 (As) and 1.3 (Se) µg L-1 were obtained. Accuracy of the method was checked by analyzing an oyster tissue reference material.

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Mixture Models can be used in experimental situations involving areas related to food science and chemistry. Some problems of a statistical nature can be found, such as effects of multicollinearity that result in uncertainty in the optimization of a dependent variable. This study proposes the application of the ridge model adapted for mixture planning considering the Kronecker (K-model) and Scheffe (S-Model) methods applied to response surfaces. The method determined the proportions of hexane, acetone and alcohol proportions that resulted in the maximum response of percentage of extracted pequi (Caryocar brasiliense) pulp oil.

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In this work is proposed a solid phase preconcentration system of Co2+ ions and its posterior determination by GFAAS in which fractional factorial design and response surface methodology (RSM) were used for optimization of the variables associated with preconcentration system performance. The method is based on cobalt extraction as a complex Co2+-PAN (1:2) in a mini-column of polyurethane foam (PUF) impregnated with 1-(2-pyridylazo)-naphthol (PAN) followed by elution with HCl solution and its determination by GFAAS. The chemical and flow variables studied were pH, buffer concentration, eluent concentration and preconcentration and elution flow rates. Results obtained from fractional factorial design 2(5-1) showed that only the variables pH, buffer concentration and interaction (pH X buffer concentration) based on analysis of variance (ANOVA) were statistically significant at 95% confidence level. Under optimised conditions, the method provided an enrichment factor of 11.6 fold with limit of detection and quantification of 38 and 130 ng L-1, respectively, and linear range varying from 0.13 to 10 µg L-1. The precision (n = 9) assessed by relative standard deviation (RSD) was respectively 5.18 and 2.87% for 0.3 and 3.0 µg L-1 cobalt concentrations.

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It was to aimed it to investigate effects of various saline water use strategies on melon production and quality of two cultivars (Cucumis melo L., Sancho - C1 and Medellín - C2. The plants were irrigated with water of low (S1 = 0.61 dS m-1) and high (S2 = 4.78 dS m-1) salinity levels, during each crop stage: S1S1S2S2 - T1; S2S1S2S2 - T2; S2S2S1S2 - T3. The 1st, 2nd, 3rd and 4th terms of these sequences correspond to initial growth, flowering, fruit ripening and harvest phenological stages, respectively. Additionally, there was irrigation rotation during all cycle, with water S1 during two days followed by S2 for one day (S1 2 dias + S2 1 dia - T4) and irrigation with non-salt water S2 during all cycle - T5. Moreover, we used as control, the irrigation water at 3.2 dS m-1 resulting from water mixture of S1 and S2 - T6 (farm used irrigation management). The experiment was carried out in Pedra Preta Farm, in Mossoró, RN, using an entire randomized block statistical design in a 6x2 subdivided plot scheme with four replications. Saline water irrigation at initial growth stage reduces leaf area and shoot dry phytomass of Sancho and Medellín melon cultivars. The irrigation by T4 provided the highest phytomass production of fruits at 48 DAS, reducing in 33% of good quality water in irrigation.

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The present research aimed to develop a modeling capable of identifying the ideal profile of swine finishing producers using the interactive performance optimization, which began by verifying qualitative the criteria considered most relevant to the decision-making, generating a closed structured diagnosis that covers the socioeconomic aspects about the activity, until the design of a mathematical model able to translate the data obtained in quantitative information. For the verification, it was proposed a practical study for a universe of 120 members of a cooperative in the state of Rio Grande do Sul, Brazil. The results showed that, from the application and the definition of the ideal profile, it was possible to verify that 82 producers are in the group of those who have obtained a "Good" performance, and to 44 the result is in the range between 86% to 90% from the ideal, which means that most have short or medium-term conditions to evolve their status for the considered "Very Good", where only 12.5% of the producers are currently.