922 resultados para principal component regression


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It is generally accepted that two major gene pools exist in cultivated common bean (Phaseolus vulgaris L.), a Middle American and an Andean one. Some evidence, based on unique phaseolin morphotypes and AFLP analysis, suggests that at least one more gene pool exists in cultivated common bean. To investigate this hypothesis, 1072 accessions from a common bean core collection from the primary centres of origin, held at CIAT, were investigated. Various agronomic and morphological attributes (14 categorical and 11 quantitative) were measured. Multivariate analyses, consisting of homogeneity analysis and clustering for categorical data, clustering and ordination techniques for quantitative data and nonlinear principal component analysis for mixed data, were undertaken. The results of most analyses supported the existence of the two major gene pools. However, the analysis of categorical data of protein types showed an additional minor gene pool. The minor gene pool is designated North Andean and includes phaseolin types CH, S and T; lectin types 312, Pr, B and K; and mostly A5, A6 and A4 types alpha-amylase inhibitor. Analysis of the combined categorical data of protein types and some plant categorical data also suggested that some other germplasm with C type phaseolin are distinguished from the major gene pools.

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Spatial and temporal variability in wheat production in Australia is dominated by rainfall occurrence. The length of historical production records is inadequate, however, to analyse spatial and temporal patterns conclusively. In this study we used modelling and simulation to identify key spatial patterns in Australian wheat yield, identify groups of years in the historical record in which spatial patterns were similar, and examine association of those wheat yield year groups with indicators of the El Nino Southern Oscillation (ENSO). A simple stress index model was trained on 19 years of Australian Bureau of Statistics shire yield data (1975-93). The model was then used to simulate shire yield from 1901 to 1999 for all wheat-producing shires. Principal components analysis was used to determine the dominating spatial relationships in wheat yield among shires. Six major components of spatial variability were found. Five of these represented near spatially independent zones across the Australian wheatbelt that demonstrated coherent temporal (annual) variability in wheat yield. A second orthogonal component was required to explain the temporal variation in New South Wales. The principal component scores were used to identify high- and low-yielding years in each zone. Year type groupings identified in this way were tested for association with indicators of ENSO. Significant associations were found for all zones in the Australian wheatbelt. Associations were as strong or stronger when ENSO indicators preceding the wheat season (April-May phases of the Southern Oscillation Index) were used rather than indicators based on classification during the wheat season. Although this association suggests an obvious role for seasonal climate forecasting in national wheat crop forecasting, the discriminatory power of the ENSO indicators, although significant, was not strong. By examining the historical years forming the wheat yield analog sets within each zone, it may be possible to identify novel climate system or ocean-atmosphere features that may be causal and, hence, most useful in improving seasonal forecasting schemes.

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The effects of wing shape, wing size, and fluctuating asymmetry in these measures oil the field fitness of T. nr. brassicae and T. pretiosum were investigated. Trichogramma wasps mass-reared on eggs of the factitious host Sitotroga cerealella were released in tomato paddocks and those females ovipositing on Helicoverpo spp. eggs were recaptured. Comparisons of the recaptured group with a sample from the release population were used to assess fitness. Wing data were obtained by positioning landmarks on mounted forewings. Size was then measured as the centroid size computed from landmark distances, while Procrustes analysis followed by principal component analysis was used to assess wing shape. Similar findings were obtained for both Trichogramma species: fitness of wasps was strongly related to wing size and some shape dimensions, but not to the asymmetries of these measures. Wasps which performed well in the field had larger wings and a different wing shape compared to wasps from the mass reared population. Both size and the shape dimensions were linearly associated with fitness although there was also some evidence for non-linear selection on shape. The results suggest that wing shape and wing size are reliable predictors of field fitness for these Trichogramma wasps.

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Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts. Copyright (K) 2003 Royal Meteorological Society.

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O objetivo deste trabalho ?? o de construir um ??ndice de gest??o municipal em cultura, com o potencial de servir de balizador para as a????es nesta ??rea tanto no ??mbito municipal quanto no das esferas estaduais e federal. Para tanto s??o utilizados dados do ???Perfil dos Munic??pios Brasileiros ??? Cultura 2006???, publicado pelo IBGE, o qual disp??e das respostas de 5.562 munic??pios sobre cultura e gest??o cultural. As respostas foram tabuladas e combinadas de forma a produzir vari??veis espec??ficas para a constru????o do ??ndice. O m??todo utilizado para a constru????o do ??ndice de Gest??o Municipal em Cultura (IGMC) foi o de an??lise de componentes principais, que permite ?? pr??pria amostra definir os pesos que cada vari??vel exercer?? na computa????o do ??ndice final. Al??m do IGMC geral, foram computados no processo tr??s sub??ndices, cada um deles procurando refletir um aspecto espec??fico da gest??o municipal em cultura, a saber: fortalecimento institucional e gest??o democr??tica, infraestrutura e recursos humanos, e a????o cultural. Os resultados obtidos permitem diversos tipos de compara????es intermunicipais e regionais, bem como a prescri????o de a????es locais na ??rea de gest??o que visem ?? melhoria da administra????o cultural dos munic??pios.

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A combinação da agricultura de precisão e do Sistema Integrado de Recomendação Foliar (DRIS) possibilita monitorar espacialmente o balanço nutricional dos cafezais para fornecer recomendações de adubação mais equilibradas e mais ajustadas economicamente. O objetivo deste trabalho foi avaliar a variabilidade espacial do estado nutricional do cafeeiro conilon, utilizando o Índice de Balanço Nutricional (IBN) e sua relação com a produtividade. A produtividade das plantas em cada ponto amostral foi determinada e construiu-se o seu mapa considerando a variabilidade espacial; determinou-se o Índice de Equilíbrio Nutricional (IBN) das plantas em cada ponto amostral e construiu-se o seu mapa; e utilizou-se a análise de componentes principais (ACP) para estimar o IBN do cafeeiro por cokrigagem. Os dados do cafeeiro conilon foram coletados em fazenda experimental, no município de Cachoeiro de Itapemirim-ES. O IBN do cafeeiro e a sua produtividade foram analisados por meio de geoestatística, com base nos modelos e parâmetros dos semivariogramas, utilizando o método de interpolação krigagem ordinária para estimar valores para locais não amostrados. O índice de Balanço Nutricional da lavoura do cafeeiro conilon apresentou dependência espacial, porém não apresentou correlação linear e nem espacial com a produtividade. A lavoura em estudo se encontra em desequilíbrio nutricional, sendo que entre os macronutrientes, o Potássio foi o que apresentou maior desequilíbrio na área, entre os micronutrientes, o Zinco e o Ferro foram os que apresentaram menores concentrações nas folhas. A confecção dos mapas possibilitou a distinção de regiões com maior e menor desequilíbrio nutricional e produtividade, o que possibilita adotar o manejo de forma diferenciada e localizada. A análise multivariada baseada em componentes principais fornece componentes com alta correlação com as variáveis originais P, Ca, Zn , Cu, K e B. A cokrigagem utilizando as componentes principais permite estimar o IBN e a produtividade da área.

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This study seeks to capture the underlying reasons for the travel decisions of residents of the Urban Quadrangle of Minho (composed of the municipalities of Barcelos, Braga, Guimarães, and Vila Nova de Famalicão). The aim of the research is three-fold. Firstly, the study identifies the push and pull motivational factors of residents of the Urban Quadrangle of Minho. Secondly, the study examines whether there are differences between the tourist motivations of residents of the four different municipalities of the Urban Quadrangle. Finally, the study investigates if there are any differences in the motivations of those who choose national and international destinations. The methodology comprises quantitative research based on questionnaires administered in 2012 to residents of the Urban Quadrangle of Minho. A principal component factor analysis is employed to identify six push and seven pull factors. The comparison of the mean scores of these factors across municipalities and across residents that choose national and international destinations reveals that the most valued and least valued factors are common to all four municipalities and both groups of residents (that choose national and international destinations).

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Exploratory factor analysis is a widely used statistical technique in the social sciences. It attempts to identify underlying factors that explain the pattern of correlations within a set of observed variables. A statistical software package is needed to perform the calcula- tions. However, there are some limitations with popular statistical software packages, like SPSS. The R programming language is a free software package for statistical and graphical computing. It o ers many packages written by contributors from all over the world and programming resources that allow it to overcome the dialog limitations of SPSS. This paper o ers an SPSS dialog written in the R programming language with the help of some packages, so that researchers with little or no knowledge in programming, or those who are accustomed to making their calculations based on statistical dialogs, have more options when applying factor analysis to their data and hence can adopt a better approach when dealing with ordinal, Likert-type data.

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The increasing need for starches with specific characteristics makes it important to study unconventional starches and their modifications in order to meet consumer demands. The aim of this work was to study physicochemical characteristics of native starch and phosphate starch of S. lycocarpum. Native starch was phosphated with sodium tripolyphosphate (5-11%) added with stirring. Chemical composition, morphology, density, binding ability to cold water, swelling power and solubility index, turbidity and syneresis, rheological and calorimetric properties were determined. Phosphorus was not detected in the native sample, but the phosphating process produced modified starches with phosphorus contents of 0.015, 0.092 and 0.397%, with the capacity of absorbing more water, either cold or hot. Rheological data showed the strong influence of phosphorus content on viscosity of phosphate starch, with lower pasting temperature and peak viscosity higher than those of native starch. Enthalpy was negatively correlated with the phosphorus content, requiring 9.7; 8.5; 8.1 and 6.4 kJ g-1 of energy for the transition from the amorphous to the crystalline state for the starch granules with phosphorus contents of 0; 0.015; 0.092 and 0.397%, respectively. Cluster analysis and principal component analysis showed that starches with 0.015 and 0.092% phosphorus have similar characteristics and are different from the others. Our results show that the characteristics of phosphate modified S. lycocarpum starch have optimal conditions to meet the demands of raw materials, which require greater consistency in stickiness, combined with low rates of retrogradation and syneresis.

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The aim of this study is twofold. First, the study analyzes local community perspectives of the importance of the WHS classification of the historic center of Évora. Second, the study analyzes local residents’ perceived tourism impacts on the municipality of Évora. The methodology comprises quantitative research based on a self-administered survey applied to convenience samples of local residents of Évora in the beginning of 2014. The main results reveal that local residents have a strongly positive perception of the WHS designation. With regard to the perceived tourism impacts, a principal component factor analysis delineated three positive and three negative tourism impacts. The comparison of the mean scores of these factors across residents that live near and far from the historic center reveals that the most valued and least valued factors are common to all groups of residents. Nevertheless, in terms of positive impacts, the residents that live near the historic center revealed higher means than the residents that live far from it, whereas in terms of negative impacts, the latter group revealed higher means than former group.

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One Plus Sequential Air Sampler—Partisol was placed in a small village (Foros de Arrão) in central Portugal to collect PM10 (particles with an aerodynamic diameter below 10 μm), during the winter period for 3 months (December 2009–March 2010). Particles masses were gravimetrically determined and the filters were analyzed by instrumental neutron activation analysis to assess their chemical composition. The water-soluble ion compositions of the collected particles were determined by Ion-exchange Chromatography. Principal component analysis was applied to the data set of chemical elements and soluble ions to assess the main sources of the air pollutants. The use of both analytical techniques provided information about elemental solubility, such as for potassium, which was important to differentiate sources.

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The present study, covering students from public schools and a private school on the island of São Miguel (Azores, Portugal), aims to meet the difficulties of the students of the 3rd and 4th years of the primary education in solving tasks involving construction, reading and interpreting tables and statistical graphs, in the context of Organization and Data Handling (ODH). We present the main results obtained from statistical methods, among which we highlight some non-parametric hypothesis tests and the Categorical Principal Component Analysis (CatPCA), given the nature of the variables included in the questionnaire (mostly nominal and ordinal variables).

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The first and second authors would like to thank the support of the PhD grants with references SFRH/BD/28817/2006 and SFRH/PROTEC/49517/2009, respectively, from Fundação para a Ciência e Tecnol ogia (FCT). This work was partially done in the scope of the project “Methodologies to Analyze Organs from Complex Medical Images – Applications to Fema le Pelvic Cavity”, wi th reference PTDC/EEA- CRO/103320/2008, financially supported by FCT.

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OBJECTIVE: To identify clusters of the major occurrences of leprosy and their associated socioeconomic and demographic factors. METHODS: Cases of leprosy that occurred between 1998 and 2007 in São José do Rio Preto (southeastern Brazil) were geocodified and the incidence rates were calculated by census tract. A socioeconomic classification score was obtained using principal component analysis of socioeconomic variables. Thematic maps to visualize the spatial distribution of the incidence of leprosy with respect to socioeconomic levels and demographic density were constructed using geostatistics. RESULTS: While the incidence rate for the entire city was 10.4 cases per 100,000 inhabitants annually between 1998 and 2007, the incidence rates of individual census tracts were heterogeneous, with values that ranged from 0 to 26.9 cases per 100,000 inhabitants per year. Areas with a high leprosy incidence were associated with lower socioeconomic levels. There were identified clusters of leprosy cases, however there was no association between disease incidence and demographic density. There was a disparity between the places where the majority of ill people lived and the location of healthcare services. CONCLUSIONS: The spatial analysis techniques utilized identified the poorer neighborhoods of the city as the areas with the highest risk for the disease. These data show that health departments must prioritize politico-administrative policies to minimize the effects of social inequality and improve the standards of living, hygiene, and education of the population in order to reduce the incidence of leprosy.