A Survey of the Relationship between Climatic Heat Stress Indices and Fundamental Milk Components Considering Uncertainty

The main purpose of this study is to assess the relationship between four bioclimatic indices for cattle (environmental stress, heat load, modified heat load, and respiratory rate predictor indices) and three main milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when the cows use the natural pasture. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty information in the confidence intervals. The main results identify an interesting relationship between the milk compounds and climate indices under all climate conditions. During spring, there are reasonably high correlations between the fat and protein concentrations vs. the climate indices, whereas there are insignificant dependencies between the milk yield and climate indices. During summer, the correlation between the fat and protein concentrations with the climate indices decreased in comparison with the spring results, whereas the correlation for the milk yield increased. This methodology is suggested for studies investigating the impacts of climate variability/change on food and agriculture using short term data considering uncertainty.

The Effect of Climate Variability on Main Components of Cow Milk in Iran

The main purpose of this study is to assess the relationship between six bioclimatic indices for cattle (temperature humidity (THI), environmental stress (ESI), equivalent temperature (ESI), heat load (HLI), modified heat load (HLInew) and respiratory rate predictor(RRP)) and fundamental milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when cows use natural pasture, with possibility for cows to choose to stay in the barn or to graze on the pasture in the pasturing system. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty estimation through resampling in the confidence intervals. To find the relationships between climate indices (THI, ETI, HLI, HLInew, ESI and RRP) and main components of cow milk (fat, protein and yield), multiple liner regression is applied. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Cross validation is used to avoid over-fitting. Based on results of investigation the effect of heat stress indices on milk compounds separately, we suggest the use of ESI and RRP in the summer and ESI in the spring. THI and HLInew are suggested for fat content and HLInew also is suggested for protein content in the spring season. The best linear models are found in spring between milk yield as predictands and THI, ESI,HLI, ETI and RRP as predictors with p-value < 0.001 and R2 0.50, 0.49. In summer, milk yield with independent variables of THI, ETI and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. It is strongly suggested that new and significant indices are needed to control critical heat stress conditions that consider more predictors of the effect of climate variability on animal products, such as sunshine duration, quality of pasture, the number of days of stress (NDS), the color of skin with attention to large black spots, and categorical predictors such as breed, welfare facility, and management system. This methodology is suggested for studies investigating the impacts of climate variability/change on food quality/security, animal science and agriculture using short term data considering uncertainty or data collection is expensive, difficult, or data with gaps.

Sistema de registo de sacramento (batismo)

Este projeto tem como objetivo apresentar um protótipo de um sistema de informação web, para o registo de sacramentos nas paróquias da Diocese de Mindelo. O volume de dados e informação referente aos processos dos sacramentos, cresce dia após dia o que torna o sistema atual nas paróquias, muito complexo. Com intenção de gerir de forma eficaz e eficiente a informação, que é essencial para tomada de decisão em qualquer organização, propôs-se desenvolver um protótipo para um sistema que permite efetuar o registo do sacramento do batismo e no futuro implementar os outros sacramentos no sistema. Para se alcançar o objetivo citado acima, utilizou-se o modelo de desenvolvimento de software ICONIX, uma metodologia que possui um alto grau de aceitação em empresas de software por sua racionalização em questões relacionadas à documentação, entrevistou-se alguns funcionários das paróquias, consultou-se e analisou-se os documentos referentes à DM, e finalmente desenhou-se o sistema proposto utilizando basicamente as tecnologias: Bootstrap, PHP e JavaScript, e para ambiente de desenvolvimento a plataforma AMPPS. Entretanto, com a realização deste projeto, espera-se como resultado um sistema que permita resolver problemas tais como: a demora na emissão de certidões, extratos e a dificuldade de obter informação atualizada sobre os sacramentos.

EUNIS habitat's thresholds for the Western coast of the Iberian Peninsula — A Portuguese case study

The European Nature Information System (EUNIS) has been implemented for the establishment of a marine European habitats inventory. Its hierarchical classification is defined and relies on environmental variables which primarily constrain biological communities (e.g. substrate types, sea energy level, depth and light penetration). The EUNIS habitat classification scheme relies on thresholds (e.g. fraction of light and energy) which are based on expert judgment or on the empirical analysis of the above environmental data. The present paper proposes to establish and validate an appropriate threshold for energy classes (high, moderate and low) and for subtidal biological zonation (infralittoral and circalittoral) suitable for EUNIS habitat classification of the Western Iberian coast. Kineticwave-induced energy and the fraction of photosynthetically available light exerted on the marine bottom were respectively assigned to the presence of kelp (Saccorhiza polyschides, Laminaria hyperborea and Laminaria ochroleuca) and seaweed species in general. Both data were statistically described, ordered fromthe largest to the smallest and percentile analyseswere independently performed. The threshold between infralittoral and circalittoral was based on the first quartile while the ‘moderate energy’ class was established between the 12.5 and 87.5 percentiles. To avoid data dependence on sampling locations and assess the confidence interval a bootstrap technique was applied. According to this analysis,more than 75% of seaweeds are present at locations where more than 3.65% of the surface light reaches the sea bottom. The range of energy levels estimated using S. polyschides data, indicate that on the IberianWest coast the ‘moderate energy’ areas are between 0.00303 and 0.04385 N/m2 of wave-induced energy. The lack of agreement between different studies in different regions of Europe suggests the need for more standardization in the future. However, the obtained thresholds in the present study will be very useful in the near future to implement and establish the Iberian EUNIS habitats inventory.

Normative reference values for the 20-meter shuttlerun test in a population-based sample of schoolchildren in Bogota, Colombia: The FUPRECOL Study

Our aim was to determine the normative reference values of cardiorespiratory fitness (CRF) and to establish the proportion of subjects with low CRF suggestive of future cardio-metabolic risk.

LMS tables for waist circumference and waist-to-height ratio among Colombian children and adolescents: The FUPRECOL study

Background: Indices predictive of central obesity include waist circumference (WC) and waist-to-height ratio (WHtR). The aims of this study were 1) to establish a Colombian youth smoothed centile charts and LMS tables for WC and WHtR and 2) to evaluate the utility of these parameters as predictors of overweight and obesity. Method: A cross-sectional study whose sample population comprised 7954 healthy Colombian schoolchildren [boys n=3460 and girls n=4494, mean (standard deviation) age 12.8 (2.3) years old]. Weight, height, body mass index (BMI), WC and WHtR and its percentiles were calculated. Appropriate cut-offs point of WC and WHtR for overweight and obesity, as defined by the International Obesity Task Force (IOTF) definitions, were selected using receiver operating characteristic (ROC) analysis. The discriminating power of WC and WHtR was expressed as area under the curve (AUC). Results: Reference values for WC and WHtR are presented. Mean WC increased and WHtR decreased with age for both genders. We found a moderate positive correlation between WC and BMI (r= 0.756, P < 0.01) and WHtR and BMI (r= 0.604, P < 0.01). The ROC analysis showed a high discrimination power in the identification of overweight and obesity for both measures in our sample population. Overall, WHtR was slightly a better predictor for overweight/obesity (AUC 95% CI 0.868-0.916) than the WC (AUC 95% CI 0.862-0.904). Conclusion: This paper presents the first sex- and age-specific WC and WHtR percentiles for both measures among Colombian children and adolescents aged 9–17.9 years. By providing LMS tables for Latin-American people based on Colombian reference data, we hope to provide quantitative tools for the study of obesity and its comorbidities.

Triceps and subscapular skinfold thicknesses percentiles and cut-offs for overweight and obesity in a population-based sample of schoolchildren in Bogota, Colombia

OBJECTIVES: The aims of this study were to establish a Colombian smoothed centile charts and LMS tables for tríceps, subscapular and sum tríceps+subscapular skinfolds; appropriate cut-offs were selected using receiver operating characteristic analysis based in a populationbased sample of schoolchildren in Bogota, Colombia and to compare them with international studies. METHODS: A total of 9 618 children and adolescents attending public schools in Bogota, Colombia (55.7% girls; age range of 9–17.9 years). Height, weight, body mass index (BMI), waist circumference, triceps and subscapular skinfold measurements were obtained using standardized methods. We have calculated tríceps+subscapular skinfold (T+SS) sum. Smoothed percentile curves for triceps and subscapular skinfold thickness were derived by the LMS method. Receiver operating characteristics curve (ROC) analyses were used to evaluate the optimal cut-off point of tríceps, subscapular and sum tríceps+subscapular skinfolds for overweight and obesity based on the International Obesity Task Force (IOTF) definitions. Data were compared with international studies. RESULTS: Subscapular, triceps skinfolds and T+SS were significantly higher in girls than in boys (P <0.001). The median values for triceps, subscapular as well as T+SS skinfold thickness increased in a sex-specific pattern with age. The ROC analysis showed that subscapular, triceps skinfolds and T+SS have a high discrimination power in the identification of overweight and obesity in the sample population in this study. Based on the raw non-adjusted data, we found that Colombian boys and girls had high triceps and subscapular skinfolds values than their counterparts from Spain, UK, German and US. CONCLUSIONS: Our results provide sex- and age-specific normative reference standards for the triceps and subscapular skinfold thickness values in a large, population-based sample of 3 schoolchildren and adolescents from an Latin-American population. By providing LMS tables for Latin-American people based on Colombian reference data, we hope to provide quantitative tools for the study of obesity and its complications.

Valores de referencia de la fuerza prensil en escolares colombianos. Estudio FUPRECOL

The primary aim of this study was to generate normative handgrip strength (HG) data for 10- to 17.9-year-olds. The secondary aim was to determine the relative proportion of Colombian children and adolescents that fall into established Health Benefit Zones (HBZ). This cross-sectional study is enrolling 7268 schoolchildren (boys n=3129 and girls n=4139, age 12.7 (2.4) years old. HG was measured using a hand dynamometer with an adjustable grip. Five HBZs (Needs Improvement, Fair, Good, Very Good, and Excellent) have been established that correspond to combined-HG. Centile smoothed curves, percentile and tables for the 3rd, 10th, 25th, 50th, 75th, 90th and 97th percentile were calculated using Cole’s LMS method. HG peaked in the sample at 22.2 (8.9) kg in boys and 18.5 (5.5) kg in girls. The increase in HG was greater for boys than for girls, but the peak HG was lower in girls than in boys. The HBZ data indicated that a higher overall percentage of boys than girls at each age group fell into the “Needs Improvement” zone, with differences particularly pronounced during adolescence. Our results provide, for the first time, sex- and age-specific HG reference standards for Colombian schoolchildren aged 9-17.9 years.

Normative reference of standing long jump for Colombian schoolchildren aged 9-17.9 years: The FUPRECOL study

El propósito del presente estudio era generar los valores normativos de salto largo para niños de 9-17.9 años, e investigar las diferencias de sexo y grupo de edad

Avaliação de métodos de estimação da variância em amostras complexas

A necessidade de conhecer uma população impulsiona um processo de recolha e análise de informação. Usualmente é muito difícil ou impossível estudar a totalidade da população, daí a importância do estudo com recurso a amostras. Conceber um estudo por amostragem é um processo complexo, desde antes da recolha dos dados até a fase de análise dos mesmos. Na maior parte dos estudos utilizam-se combinações de vários métodos probabilísticos de amostragem para seleção de uma amostra, que se pretende representativa da população, denominado delineamento de amostragem complexo. O conhecimento dos erros de amostragem é necessário à correta interpretação dos resultados de inquéritos e à avaliação dos seus planos de amostragem. Em amostras complexas, têm sido usadas aproximações ajustadas à natureza complexa do plano da amostra para a estimação da variância, sendo as mais utilizadas: o método de linearização Taylor e as técnicas de reamostragem e replicação. O principal objetivo deste trabalho é avaliar o desempenho dos estimadores usuais da variância em amostras complexas. Inspirado num conjunto de dados reais foram geradas três populações com características distintas, das quais foram sorteadas amostras com diferentes delineamentos de amostragem, na expectativa de obter alguma indicação sobre em que situações se deve optar por cada um dos estimadores da variância. Com base nos resultados obtidos, podemos concluir que o desempenho dos estimadores da variância da média amostral de Taylor, Jacknife e Bootstrap varia com o tipo de delineamento e população. De um modo geral, o estimador de Bootstrap é o menos preciso e em delineamentos estratificados os estimadores de Taylor e Jackknife fornecem os mesmos resultados; Evaluation of variance estimation methods in complex samples ABSTRACT: The need to know a population drives a process of collecting and analyzing information. Usually is to hard or even impossible to study the whole population, hence the importance of sampling. Framing a study by sampling is a complex process, from before the data collection until the data analysis. Many studies have used combinations of various probabilistic sampling methods for selecting a representative sample of the population, calling it complex sampling design. Knowledge of sampling errors is essential for correct interpretation of the survey results and evaluation of the sampling plans. In complex samples to estimate the variance has been approaches adjusted to the complex nature of the sample plane. The most common are: the linearization method of Taylor and techniques of resampling and replication. The main objective of this study is to evaluate the performance of usual estimators of the variance in complex samples. Inspired on real data we will generate three populations with distinct characteristics. From this populations will be drawn samples using different sampling designs. In the end we intend to get some lights about in which situations we should opt for each one of the variance estimators. Our results show that the performance of the variance estimators of sample mean Taylor, Jacknife and Bootstrap varies with the design and population. In general, the Bootstrap estimator is less precise and in stratified design Taylor and Jackknife estimators provide the same results.

Customer Loyalty toward grocery retailing service: a multidimensional approach to costumer perceived value

Structured abstract Purpose: To deepen, in grocery retail context, the roles of consumer perceived value and consumer satisfaction, as antecedents’ dimensions of customer loyalty intentions. Design/Methodology/approach: Also employing a short version (12-items) of the original 19-item PERVAL scale of Sweeney & Soutar (2001), a structural equation modeling approach was applied to investigate statistical properties of the indirect influence on loyalty of a reflective second order customer perceived value model. The performance of three alternative estimation methods was compared through bootstrapping techniques. Findings: Results provided i) support for the use of the short form of the PERVAL scale in measuring consumer perceived value; ii) the influence of the four highly correlated independent latent predictors on satisfaction was well summarized by a higher-order reflective specification of consumer perceived value; iii) emotional and functional dimensions were determinants for the relationship with the retailer; iv) parameter’s bias with the three methods of estimation was only significant for bootstrap small sample sizes. Research limitations:/implications: Future research is needed to explore the use of the short form of the PERVAL scale in more homogeneous groups of consumers. Originality/value: Firstly, to indirectly explain customer loyalty mediated by customer satisfaction it was adopted a recent short form of PERVAL scale and a second order reflective conceptualization of value. Secondly, three alternative estimation methods were used and compared through bootstrapping and simulation procedures.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.