The effect of demand information sharing in a supply chain under demand uncertainty: a simulation study

Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial

Influência do aumento do acesso à atenção básica no comportamento da taxa de detecção de hanseníase de 1980 a 2006

A taxa de detecção da hanseníase no Brasil aumentou nas duas últimas décadas do século XX, sendo que a reforma sanitária ocorreu no mesmo período. A taxa de detecção é função da incidência real de casos e da agilidade diagnóstica do sistema de saúde. Utilizou-se a cobertura vacinal por BCG como uma variável procuradora do acesso à atenção primária em saúde. Uma regressão log-normal foi ajustada à taxa de detecção de 1980 a 2006, com o tempo, tempo ao quadrado e da cobertura do BCG como variáveis independentes, sendo positivo o coeficiente de regressão desta última variável, sugerindo que o comportamento da taxa de detecção da hanseníase refletiu a melhora de acesso à atenção primária no período estudado. A tendência de aumento da taxa de detecção se reverte em 2003, indicando o início de uma nova fase no controle da hanseníase.

Variabilidade espacial da chuva durante o experimento LBA/TRMM 1999 na Amazônia

O objetivo desta pesquisa foi o de estudar a variabilidade espacial de chuvas convectivas na Amazônia, durante o experimento LBA/TRMM em 1999. Um conjunto de dados consistindo de 37 pluviômetros (divididos em 4 subconjuntos e com distância máxima entre eles de 50 km) foi utilizado, sendo estas medidas de pluviometria de meados de Dezembro de 1998 ao final de Fevereiro de 1999, que é o pico da estação chuvosa em Rondônia (sudoeste da Amazônia). A metodologia de correlação interestações (baseado em probabilidade condicional) e assumindo uma distribuição estatística log-normal bivariada foi aplicada aos dados de precipitação diária e os resultados mostraram que chuvas que ocorrem em uma distância inferior a 1 km de raio têm um alto valor de correlação (variando de 0,7 a 0,9) representando a validação de uma medida pontual de chuva. A curva ajustada da variação do coeficiente de correlação ( ro ) versus distância (d em km) foi: ro = 0,72 - 0,15 ln (d).

Impact of Intensive Physiotherapy on Cognitive Function after Coronary Artery Bypass Graft Surgery

Background: Coronary artery bypass graft (CABG) is a standard surgical option for patients with diffuse and significant arterial plaque. This procedure, however, is not free of postoperative complications, especially pulmonary and cognitive disorders. Objective: This study aimed at comparing the impact of two different physiotherapy treatment approaches on pulmonary and cognitive function of patients undergoing CABG. Methods: Neuropsychological and pulmonary function tests were applied, prior to and following CABG, to 39 patients randomized into two groups as follows: Group 1 (control) - 20 patients underwent one physiotherapy session daily; and Group 2 (intensive physiotherapy) - 19 patients underwent three physiotherapy sessions daily during the recovery phase at the hospital. Non-paired and paired Student t tests were used to compare continuous variables. Variables without normal distribution were compared between groups by using Mann-Whitney test, and, within the same group at different times, by using Wilcoxon test. The chi-square test assessed differences of categorical variables. Statistical tests with a p value ≤ 0.05 were considered significant. Results: Changes in pulmonary function were not significantly different between the groups. However, while Group 2 patients showed no decline in their neurocognitive function, Group 1 patients showed a decline in their cognitive functions (P ≤ 0.01). Conclusion: Those results highlight the importance of physiotherapy after CABG and support the implementation of multiple sessions per day, providing patients with better psychosocial conditions and less morbidity.

Non-classical error bounds in the central limit theorem

The classical central limit theorem states the uniform convergence of the distribution functions of the standardized sums of independent and identically distributed square integrable real-valued random variables to the standard normal distribution function. While first versions of the central limit theorem are already due to Moivre (1730) and Laplace (1812), a systematic study of this topic started at the beginning of the last century with the fundamental work of Lyapunov (1900, 1901). Meanwhile, extensions of the central limit theorem are available for a multitude of settings. This includes, e.g., Banach space valued random variables as well as substantial relaxations of the assumptions of independence and identical distributions. Furthermore, explicit error bounds are established and asymptotic expansions are employed to obtain better approximations. Classical error estimates like the famous bound of Berry and Esseen are stated in terms of absolute moments of the random summands and therefore do not reflect a potential closeness of the distributions of the single random summands to a normal distribution. Non-classical approaches take this issue into account by providing error estimates based on, e.g., pseudomoments. The latter field of investigation was initiated by work of Zolotarev in the 1960's and is still in its infancy compared to the development of the classical theory. For example, non-classical error bounds for asymptotic expansions seem not to be available up to now ...

Distribuição temporal e espacial de anuros em área de Pampa, Santa Maria, RS

Este estudo teve como objetivo determinar a riqueza, a constância de ocorrência, os modos reprodutivos, o padrão de distribuição da abundância, a temporada de vocalização e testar a correlação das variáveis climáticas sobre a atividade de vocalização dos anuros em uma região do Bioma Pampa, Santa Maria, Rio Grande do Sul. Durante o período de novembro de 2001 a outubro de 2002 foram realizadas coletas mensais empregando o método de ‘busca em sítio de reprodução’ e exame de exemplares depositados na Coleção Herpetológica do Setor de Zoologia da Universidade Federal de Santa Maria (ZUFSM). Foi registrada a ocorrência de 25 espécies de anuros. A anurofauna registrada corresponde a 30% das espécies encontradas no Rio Grande do Sul e normalmente está associada a áreas abertas encontradas no estado e em países vizinhos. Foram registrados quatro modos reprodutivos: modo 1 (14 espécies; 58,3%); modos 11 e 30 (nove espécies; 37,5%) e modo 24 (uma espécie; 4,2%). A baixa diversificação de modos reprodutivos provavelmente está relacionada à homogeneidade do hábitat primariamente campestre. A maior parte das espécies mostrou-se constante ou acessória na área estudada e o padrão de distribuição da abundância das espécies apresentou ajuste aos modelos Broken Stick e Log-normal, caracterizados pela homogeneidade na distribuição da abundância das espécies. A maioria das espécies apresentou grande plasticidade na ocupação de hábitats, mas poucas foram plásticas no uso dos sítios de vocalização. Houve correlação positiva, ainda que fraca, da riqueza de espécies com a precipitação mensal acumulada e da abundância com a temperatura média máxima. As correlações obtidas indicaram que na área estudada a temperatura parece atuar mais sobre a abundância de machos em atividade de vocalização e a precipitação sobre a riqueza, apesar da riqueza de espécies ser significativamente maior durante o período mais quente do ano. Estes resultados revelaram que as variáveis climatológicas testadas explicaram muito pouco da ocorrência sazonal das espécies, assim a influência de outras variáveis ambientais merece ser testada em estudos futuros.

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

Catherine Comiskey Describing Data

Introducing and describing data and understanding the normal distribution.

Statistical treatment of grain-size curves and empirical distributions: densities as compositions?

The preceding two editions of CoDaWork included talks on the possible considerationof densities as infinite compositions: Egozcue and D´ıaz-Barrero (2003) extended theEuclidean structure of the simplex to a Hilbert space structure of the set of densitieswithin a bounded interval, and van den Boogaart (2005) generalized this to the setof densities bounded by an arbitrary reference density. From the many variations ofthe Hilbert structures available, we work with three cases. For bounded variables, abasis derived from Legendre polynomials is used. For variables with a lower bound, westandardize them with respect to an exponential distribution and express their densitiesas coordinates in a basis derived from Laguerre polynomials. Finally, for unboundedvariables, a normal distribution is used as reference, and coordinates are obtained withrespect to a Hermite-polynomials-based basis.To get the coordinates, several approaches can be considered. A numerical accuracyproblem occurs if one estimates the coordinates directly by using discretized scalarproducts. Thus we propose to use a weighted linear regression approach, where all k-order polynomials are used as predictand variables and weights are proportional to thereference density. Finally, for the case of 2-order Hermite polinomials (normal reference)and 1-order Laguerre polinomials (exponential), one can also derive the coordinatesfrom their relationships to the classical mean and variance.Apart of these theoretical issues, this contribution focuses on the application of thistheory to two main problems in sedimentary geology: the comparison of several grainsize distributions, and the comparison among different rocks of the empirical distribution of a property measured on a batch of individual grains from the same rock orsediment, like their composition

Neuroanatomical and neuropsychological features of euthymic patients with bipolar disorder.

OBJECTIVE: Previous studies reported that the severity of cognitive deficits in euthymic patients with bipolar disorder (BD) increases with the duration of illness and postulated that progressive neuronal loss or shrinkage and white matter changes may be at the origin of this phenomenon. To explore this issue, the authors performed a case-control study including detailed neuropsychological and magnetic resonance imaging analyses in 17 euthymic elderly patients with BD and 17 healthy individuals. METHODS: Neuropsychological evaluation concerned working memory, episodic memory, processing speed, and executive functions. Volumetric estimates of the amygdala, hippocampus, entorhinal cortex, and anterior cingulate cortex were obtained using both voxel-based and region of interest morphometric methods. Periventricular and deep white matter were assessed semiquantitatively. Differences in cognitive performances and structural data between BD and comparison groups were analyzed using paired t-test or analysis of variance. Wilcoxon test was used in the absence of normal distribution. RESULTS: Compared with healthy individuals, patients with BD obtained significantly lower performances in processing speed, working memory, and episodic memory but not in executive functions. Morphometric analyses did not show significant volumetric or white matter differences between the two groups. CONCLUSIONS: Our results revealed impairment in verbal memory, working memory, and processing speed in euthymic older adults with BD. These cognitive deficits are comparable both in terms of affected functions and size effects to those previously reported in younger cohorts with BD. Both this observation and the absence of structural brain abnormalities in our cohort do not support a progressively evolving neurotoxic effect in BD.

Convex linear combination processes for compositions

Aitchison and Bacon-Shone (1999) considered convex linear combinations ofcompositions. In other words, they investigated compositions of compositions, wherethe mixing composition follows a logistic Normal distribution (or a perturbationprocess) and the compositions being mixed follow a logistic Normal distribution. Inthis paper, I investigate the extension to situations where the mixing compositionvaries with a number of dimensions. Examples would be where the mixingproportions vary with time or distance or a combination of the two. Practicalsituations include a river where the mixing proportions vary along the river, or acrossa lake and possibly with a time trend. This is illustrated with a dataset similar to thatused in the Aitchison and Bacon-Shone paper, which looked at how pollution in aloch depended on the pollution in the three rivers that feed the loch. Here, I explicitlymodel the variation in the linear combination across the loch, assuming that the meanof the logistic Normal distribution depends on the river flows and relative distancefrom the source origins

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Discrete and continuous compositions

This paper examines a dataset which is modeled well by thePoisson-Log Normal process and by this process mixed with LogNormal data, which are both turned into compositions. Thisgenerates compositional data that has zeros without any need forconditional models or assuming that there is missing or censoreddata that needs adjustment. It also enables us to model dependenceon covariates and within the composition

A comparison of the alr and ilr transformations for kernel density estimation of compositional data

In a seminal paper, Aitchison and Lauder (1985) introduced classical kernel densityestimation techniques in the context of compositional data analysis. Indeed, they gavetwo options for the choice of the kernel to be used in the kernel estimator. One ofthese kernels is based on the use the alr transformation on the simplex SD jointly withthe normal distribution on RD-1. However, these authors themselves recognized thatthis method has some deficiencies. A method for overcoming these dificulties based onrecent developments for compositional data analysis and multivariate kernel estimationtheory, combining the ilr transformation with the use of the normal density with a fullbandwidth matrix, was recently proposed in Martín-Fernández, Chacón and Mateu-Figueras (2006). Here we present an extensive simulation study that compares bothmethods in practice, thus exploring the finite-sample behaviour of both estimators