921 resultados para two-dimensional principal component analysis (2DPCA)
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The dyslipidemia and excess weight in adolescents, when combined, suggest a progression of risk factors for cardiovascular disease (CVD). Besides these, the dietary habits and lifestyle have also been considered unsuitable impacting the development of chronic diseases. The study objectives were: (1) estimate the prevalence of lipid profile and correlate with body mass index (BMI), waist circumference (WC) and waist / height ratio (WHR) in adolescents, considering the maturation sexual, (2) know the sources of variance in the diet and the number of days needed to estimate the usual diet of adolescents and (3) describe the dietary patterns and lifestyle of adolescents, family history of CVD and age correlates them with the patterns of risk for CVD, adjusted for sexual maturation. A cross-sectional study was performed with 432 adolescents, aged 10-19 years from public schools of the Natal city, Brazil. The dyslipidemias were evaluated considering the lipid profile, the index of I Castelli (TC / HDL) and II (LDL / HDL) and non-HDL cholesterol. Anthropometric indicators were BMI, WC and WHR. The intake of energy, nutrients including fiber, fatty acids and cholesterol was estimated from two 24-hour recalls (24HR). The variables of lipid profile, anthropometric and clinical data were used in the models of Pearson correlation and linear regression, considering the sexual maturation. The variance ratio of the diet was calculated from the component-person variance, determined by analysis of variance (ANOVA). The definition of the number of days to estimate the usual intake of each nutrient was obtained by taking the hypothetical correlation (r) ≥ 0.9, between nutrient intake and the true observed. We used the principal component analysis as a method of extracting factors that 129 accounted for the dependent variables and known cardiovascular risk obtained from the lipid profile, the index for Castelli I and II, non-HDL cholesterol, BMI, and WC the WHR. Dietary patterns and lifestyle were obtained from the independent variables, based on nutrients consumed and physical activity weekly. In the study of principal component analysis (PCA) was investigated associations between the patterns of cardiovascular risk factors in dietary patterns and lifestyle, age and positive family history of CVD, through bivariate and multiple logistic regression adjusted for sexual maturation. The low HDL-C dyslipidemia was most prevalent (50.5%) for adolescents. Significant correlations were observed between hypercholesterolemia and positive family history of CVD (r = 0.19, p <0.01) and hypertriglyceridemia with BMI (r = 0.30, p <0.01), with the CC (r = 0.32, p <0.01) and WHR (r = 0.33, p <0.01). The linear model constructed with sexual maturation, age and BMI explained about 1 to 10.4% of the variation in the lipid profile. The sources of variance between individuals were greater for all nutrients in both sexes. The reasons for variances were 1 for all nutrients were higher in females. The results suggest that to assess the diet of adolescents with greater precision, 2 days would be enough to R24h consumption of energy, carbohydrates, fiber, saturated and monounsaturated fatty acids. In contrast, 3 days would be recommended for protein, lipid, polyunsaturated fatty acids and cholesterol. Two cardiovascular risk factors as have been extracted in the ACP, referring to the dependent variables: the standard lipid profile (HDL-C and non-HDL cholesterol) and "standard anthropometric index (BMI, WC, WHR) with a power explaining 75% of the variance of the original data. The factors are representative of two independent variables led to dietary patterns, "pattern 130 western diet" and "pattern protein diet", and one on the lifestyle, "pattern energy balance". Together, these patterns provide an explanation power of 67%. Made adjustment for sexual maturation in males remained significant variables: the associations between puberty and be pattern anthropometric indicator (OR = 3.32, CI 1.34 to 8.17%), and between family history of CVD and the pattern lipid profile (OR = 2.62, CI 1.20 to 5.72%). In females adolescents, associations were identified between age after the first stage of puberty with anthropometric pattern (OR = 3.59, CI 1.58 to 8.17%) and lipid profile (OR = 0.33, CI 0.15 to 0.75%). Conclusions: The low HDL-C was the most prevalent dyslipidemia independent of sex and nutritional status of adolescents. Hypercholesterolemia was influenced by family history of CVD and sexual maturation, in turn, hypertriglyceridemia was closely associated with anthropometric indicators. The variance between the diets was greater for all nutrients. This fact reflected in a variance ratio less than 1 and consequently in a lower number of days requerid to estimate the usual diet of adolescents considering gender. The two dietary patterns were extracted and the pattern considered unhealthy lifestyle as healthy. The associations were found between the patterns of CVD risk with age and family history of CVD in the studied adolescents
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We have studied the universal conductance fluctuations (UCF) due to quantum interface in a two-dimensional electron gas (2DEG) grown on the substrates with pre-patterned, sub-micron wires. The dependence of UCF on the angle between the direction of the magnetic field and the substrate has been investigated. We found, that magnetoresistance traces for different angles are completely uncorrelated. A non-planar character of electron motion is responsible for these angular conductance fluctuations. We compared the experimental results with a simple geometrical model.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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This study aimed: 1) to classify ingredients according to the digestible amino acid (AA) profile; 2) to determine ingredients with AA profile closer to the ideal for broiler chickens; and 3) to compare digestible AA profiles from simulated diets with the ideal protein profile. The digestible AA levels of 30 ingredients were compiled from the literature and presented as percentages of lysine according to the ideal protein concept. Cluster and principal component analyses (exploratory analyses) were used to compose and describe groups of ingredients according to AA profiles. Four ingredient groups were identified by cluster analysis, and the classification of the ingredients within each of these groups was obtained from a principal component analysis, showing 11 classes of ingredients with similar digestible AA profiles. The ingredients with AA profiles closer to the ideal protein were meat and bone meal 45, fish meal 60 and wheat germ meal, all of them constituting Class 1; the ingredients from the other classes gradually diverged from the ideal protein. Soybean meal, which is the main protein source for poultry, showed good AA balance since it was included in Class 3. on the contrary, corn, which is the main energy source in poultry diets, was classified in Class 8. Dietary AA profiles were improved when corn and/or soybean meal were partially or totally replaced in the simulations by ingredients with better AA balance.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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The objective of this work was to evaluate biological aspects of Diatraea saccharalis fed on artificial diet containing different concentrations of the Sudan B Red dye and the possibility to mark the parasitoid Cotesia flavipes, when submitted to the parasitism of dyed caterpillars. For that, were added to the artificial diet four concentrations of Sudan Red B dye (100, 200, 300 and 400 ppm) and control (no dye addition). It was evaluated larval and pupal period, larval and pupal viability, longevity, sex rate, pupal weigh, eggs per female, eggs per day, number of eggs per egg mass, egg viability and embrionary period; besides same were accomplished measurements in the caterpillars (bioassay I). Caterpillars of 17 days old (30) of each treatment were removed from the tubes and exposed to the parasitism of C. flavipes (bioassay 2). The egg-pupae period, sex rate, pupal period and viability, number of females, males, total of emerged adults and longevity were evaluated. The data were submitted to the multivariate analisys methods: cluster analysis, two-way and principal component analysis. Based on analysis, it was observed that the treatment of 100 ppm was the least harmful to the biology of the sugar cane borer larvae by groping to the control and did not influence negatively its biological aspects. The concentration of 400 ppm affected negatively the biology of C. flavipes. The Sudan Red B it is ended doses marked the caterpillars and the adults, however the concentration of 100 ppm is the most suitable to dye D. saccharalis. None of the tested concentration marked adults of C. flavipes, despite to affect negatively its biology. It is unviable to increase the concentration seeking futures tests, for that dye to be harmful to the biological aspects of D. saccharalis and C. flavipes.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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O conceito de superfície geomórfica permite uma interligação entre os diferentes ramos da ciência do solo, tais como geologia, geomorfologia e pedologia. Esta associação favorece a compreensão da distribuição espacial dos solos na paisagem, e torna possível compreender o comportamento dos atributos do solo, que estão principalmente relacionadas com a estratigrafia e formas do relevo. Assim, este estudo visa à aplicação da estatística multivariada para categorizar superfícies geomórficas em uma litossequência arenito-basalto, de modo a fornecer uma base para a avaliação do solo em áreas afins. A área de estudo está localizada no município de Pereira Barreto, São Paulo, Brasil. A área escolhida possui 530 hectares, onde foram localizadas e mapeadas três superfícies geomórficas (I, II e III). Na área, 134 amostras foram coletadas nas profundidades de 0,0-0,2 m e 0,8-1,0 m, foram determinados os conteúdos de areia, silte e argila, pH em CaCl2, conteúdo de MO, P, Ca, Mg, K, Al e H+Al. Com base nos resultados, foram realizadas a análise univariada e multivariada de variância, clusters e principal componente, a fim de comparar as três superfícies geomórficas. A análise estatística univariada dos atributos do solo não foi eficiente na identificação das três superfícies geomórficas. Utilizando-se os atributos físicos e químicos do solo, as técnicas estatísticas multivariada permitiram à separação dos três grupos de corpos naturais do solo que foram equivalentes as três superfícies geomórficas mapeadas. Estes resultados são interessantes, pois demonstram a viabilidade da utilização de classificação numérica das superfícies geomórficas para ajudar no mapeamento de solo.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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The problems of combinatory optimization have involved a large number of researchers in search of approximative solutions for them, since it is generally accepted that they are unsolvable in polynomial time. Initially, these solutions were focused on heuristics. Currently, metaheuristics are used more for this task, especially those based on evolutionary algorithms. The two main contributions of this work are: the creation of what is called an -Operon- heuristic, for the construction of the information chains necessary for the implementation of transgenetic (evolutionary) algorithms, mainly using statistical methodology - the Cluster Analysis and the Principal Component Analysis; and the utilization of statistical analyses that are adequate for the evaluation of the performance of the algorithms that are developed to solve these problems. The aim of the Operon is to construct good quality dynamic information chains to promote an -intelligent- search in the space of solutions. The Traveling Salesman Problem (TSP) is intended for applications based on a transgenetic algorithmic known as ProtoG. A strategy is also proposed for the renovation of part of the chromosome population indicated by adopting a minimum limit in the coefficient of variation of the adequation function of the individuals, with calculations based on the population. Statistical methodology is used for the evaluation of the performance of four algorithms, as follows: the proposed ProtoG, two memetic algorithms and a Simulated Annealing algorithm. Three performance analyses of these algorithms are proposed. The first is accomplished through the Logistic Regression, based on the probability of finding an optimal solution for a TSP instance by the algorithm being tested. The second is accomplished through Survival Analysis, based on a probability of the time observed for its execution until an optimal solution is achieved. The third is accomplished by means of a non-parametric Analysis of Variance, considering the Percent Error of the Solution (PES) obtained by the percentage in which the solution found exceeds the best solution available in the literature. Six experiments have been conducted applied to sixty-one instances of Euclidean TSP with sizes of up to 1,655 cities. The first two experiments deal with the adjustments of four parameters used in the ProtoG algorithm in an attempt to improve its performance. The last four have been undertaken to evaluate the performance of the ProtoG in comparison to the three algorithms adopted. For these sixty-one instances, it has been concluded on the grounds of statistical tests that there is evidence that the ProtoG performs better than these three algorithms in fifty instances. In addition, for the thirty-six instances considered in the last three trials in which the performance of the algorithms was evaluated through PES, it was observed that the PES average obtained with the ProtoG was less than 1% in almost half of these instances, having reached the greatest average for one instance of 1,173 cities, with an PES average equal to 3.52%. Therefore, the ProtoG can be considered a competitive algorithm for solving the TSP, since it is not rare in the literature find PESs averages greater than 10% to be reported for instances of this size.
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Nowadays, where the market competition requires products with better quality and a constant search for cost savings and a better use of raw materials, the research for more efficient control strategies becomes vital. In Natural Gas Processin Units (NGPUs), as in the most chemical processes, the quality control is accomplished through their products composition. However, the chemical composition analysis has a long measurement time, even when performed by instruments such as gas chromatographs. This fact hinders the development of control strategies to provide a better process yield. The natural gas processing is one of the most important activities in the petroleum industry. The main economic product of a NGPU is the liquefied petroleum gas (LPG). The LPG is ideally composed by propane and butane, however, in practice, its composition has some contaminants, such as ethane and pentane. In this work is proposed an inferential system using neural networks to estimate the ethane and pentane mole fractions in LPG and the propane mole fraction in residual gas. The goal is to provide the values of these estimated variables in every minute using a single multilayer neural network, making it possibly to apply inferential control techniques in order to monitor the LPG quality and to reduce the propane loss in the process. To develop this work a NGPU was simulated in HYSYS R software, composed by two distillation collumns: deethanizer and debutanizer. The inference is performed through the process variables of the PID controllers present in the instrumentation of these columns. To reduce the complexity of the inferential neural network is used the statistical technique of principal component analysis to decrease the number of network inputs, thus forming a hybrid inferential system. It is also proposed in this work a simple strategy to correct the inferential system in real-time, based on measurements of the chromatographs which may exist in process under study
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Image compress consists in represent by small amount of data, without loss a visual quality. Data compression is important when large images are used, for example satellite image. Full color digital images typically use 24 bits to specify the color of each pixel of the Images with 8 bits for each of the primary components, red, green and blue (RGB). Compress an image with three or more bands (multispectral) is fundamental to reduce the transmission time, process time and record time. Because many applications need images, that compression image data is important: medical image, satellite image, sensor etc. In this work a new compression color images method is proposed. This method is based in measure of information of each band. This technique is called by Self-Adaptive Compression (S.A.C.) and each band of image is compressed with a different threshold, for preserve information with better result. SAC do a large compression in large redundancy bands, that is, lower information and soft compression to bands with bigger amount of information. Two image transforms are used in this technique: Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). Primary step is convert data to new bands without relationship, with PCA. Later Apply DCT in each band. Data Loss is doing when a threshold discarding any coefficients. This threshold is calculated with two elements: PCA result and a parameter user. Parameters user define a compression tax. The system produce three different thresholds, one to each band of image, that is proportional of amount information. For image reconstruction is realized DCT and PCA inverse. SAC was compared with JPEG (Joint Photographic Experts Group) standard and YIQ compression and better results are obtain, in MSE (Mean Square Root). Tests shown that SAC has better quality in hard compressions. With two advantages: (a) like is adaptive is sensible to image type, that is, presents good results to divers images kinds (synthetic, landscapes, people etc., and, (b) it need only one parameters user, that is, just letter human intervention is required
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The versatility of sensor arrays made from nanostructured Langmuir-Blodgett (LB) and layer-by-layer (LBL) films is demonstrated in two ways. First, different combinations of sensing units are employed to distinguish the basic tastes, viz. sweet, sour, bitter, and salty tastes, produced, respectively, by small concentrations (down to 0.01 g/mol) of sucrose, HCl, quinine, and NaCl solutions. The sensing units are comprised of LB and/or LBL films from semiconducting polymers, a ruthenium complex, and sulfonated lignin. Then, sensor arrays were used to identify wines from different sources, with the high distinguishing ability being demonstrated in principal component analysis (PCA) plots. Particularly important was the fact that the sensing ability does not depend on specific interactions between analytes and the film materials, but a judicious choice of materials is, nevertheless, required for the materials to respond differently to a given sample. It is also shown that the interaction with the analyte may affect the morphology of the nanostructured films, as indicated with scanning electron microscopy. For instance, in wine analysis these changes are not irreversible and the original film morphology is retrieved if the sensing unit is washed with copious amounts of water, thus allowing the sensor unit to be reused.
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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)