Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed

Análise morfométrica das estruturas maxilo-mandibulares de indivíduos pré-colombianos e contemporâneos da Costa Central do Peru

O objetivo deste trabalho foi avaliar morfometricamente as modificações ocorridas ao longo de 500 anos nas estruturas maxilo-mandibulares de indivíduos da Costa Central do Peru. Foram utilizadas telerradiografias laterais de 30 crânios secos, sem deformidades esqueléticas e em normoclusão, do período Pré-colombiano (grupo Pré-colombiano) e de 30 peruanos nativos contemporâneos, com características esqueléticas e oclusais semelhantes, provenientes da mesma região geográfica (grupo Contemporâneo). Estas telerradiografias foram digitalizadas e 14 pontos cefalométricos foram marcados e utilizados como marcos anatômicos homólogos para a análise morfométrica. O tamanho do centróide, a forma e a alometria foram comparados entre os grupos para a amostra total, para homens e para mulheres, pelos testes de one-way ANOVA, função discriminante e regressão multivariada, respectivamente. As mesmas avaliações foram realizadas para homens e mulheres do mesmo grupo. O tamanho do centróide foi significativamente maior para o grupo Contemporâneo tanto para a amostra total, quanto para homens e mulheres, demonstrando que as estruturas maxilo-mandibulares dos indivíduos contemporâneos é maior do que de seus ancestrais. Quando homens e mulheres foram comparados intra-grupos o tamanho do centróide foi significativamente maior para os homens do grupo Contemporâneo, com a mesma tendência para os homens pré-colombianos, porém sem significância estatística. Observou-se diferenças significativas entre os grupos para a forma das estruturas maxilo-mandibulares indicando que ao longo do tempo houve um deslocamento posterior da região da pré-maxila e um deslocamento considerável para cima e para trás da apófise coronóide, uma abertura do ângulo na região goníaca e um alongamento no sentido vertical da região da sínfise mandibular. Essas mudanças morfológicas foram mais evidentes nas mulheres do que nos homens, parecendo haver maior efeito das tendências seculares nas mulheres. Não houve diferenças morfológicas entre homens e mulheres quando comparados intra-grupos, demonstrando que os resultados observados não foram influenciados pelo dimorfismo sexual. As diferenças de tamanho existentes intra-grupos foram responsáveis por apenas 6% das diferenças de forma, e portanto não há efeito causal do tamanho sobre as diferenças de forma observadas entre os grupos. Pode-se concluir que as diferenças morfológicas das estruturas maxilo-mandibulares encontradas neste estudo sugerem mudanças nas condições ambientais da população da Costa Central do Peru ao longo de 500 anos e podem contribuir para o melhor esclarecimento dos fatores etiológicos das más oclusões.

Multivariate climate reconstruction for the last 14,000 years in southernmost South America

Comparison between past changes in pollen assemblages and stable isotope ratios (deuterium and carbon) analyzed in the same peat core from Tierra del Fuego at latitude 55°S permitted identification of the relative contribution of precipitation versus temperature responsible for the respective change. Major steps in the sequence of paleoenvironmental changes, such as at 12700, 9000, 5000, and 4000 years ago are apparently related only to increase in precipitation, reflecting the latitudinal location and intensity of the westerly storm tracks. On the other hand, high paleoenvironmental variability, which is characteristic for the late-glacial and the latest Holocene, is related to temperature variability, which affects the relative moisture content. Comparison with other paleoenvironmental records suggests that the late-glacial temperature variability is probably related to variability in the extent of Antarctic sea-ice, which in turn appears to be related to the intensity of Atlantic deep-water circulation. Temperature variability during the latest Holocene, on the other hand, is probably related to the dynamics of the El Niño/Southern Oscillation.

A multivariate statistical study with a factor analysis of recent planktonic foraminiferal distribution in the Coromandel Coast of India

A study of planktonic foraminiferal assemblages from 19 stations in the neritic and oceanic regions off the Coromandel Coast, Bay of Bengal has been made using a multivariate statistical method termed as factor analysis. On the basis of abundance, 17 foraminiferal species, species were clustered into 5 groups with row normalisation and varimax rotation for Q-mode factor analysis. The 19 stations were also grouped into 5 groups with only 2 groups statistically significant using column normalisation and varimax rotation for R-mode analysis. This assemblage grouping method is suitable because groups of species/stations can explain the maximum amount of variation in them in relation to prevailing environmental conditions in the area of study.

APPROXIMATE GAUSS-MARKOV REALISATION OF MULTIVARIATE STOCHASTIC PROCESSES.

Given a spectral density matrix or, equivalently, a real autocovariance sequence, the author seeks to determine a finite-dimensional linear time-invariant system which, when driven by white noise, will produce an output whose spectral density is approximately PHI ( omega ), and an approximate spectral factor of PHI ( omega ). The author employs the Anderson-Faurre theory in his analysis.

MULTIVARIATE COMPARISON OF THE FORELIMB BETWEEN STUMP-TAILED AND RHESUS MACAQUES

Rhesus macaques and stump-tailed macaques are sympatric in western Yunnan (China), coexisting or occupying habitats that show little difference. This paper tests hypotheses based on theoretical expectation from the differing biomechanical demands of terrestrial and arboreal quadrupedalism in stump-tailed macaques and rhesus macaques, respectively. Individuals of these two macaque taxa were markedly separated by the first two principal components and discriminant analyses based on 18 variables of the upper limb. The rhesus macaques appear to be more adapted for arboreal quadruped habits because of elongation of the clavicle and forearm, a larger humeral head and greater midshaft sagittal diameters of the radius and ulna.

Metrical dental analysis on golden monkey (Rhinopithecus roxellana)

Dental variation in the Chinese golden monkey (Rhinopithecus roxellana) is here evaluated by univariate, bivariate, and multivariate analyses. Allometric analyses indicate that canines and P3s are positively, but other dimensions negatively scaled to mandible and maxilla, and to body size. With the exception of the mesiodistal dimensions of I-1 and M-3, and the buccolingual dimension of Pq, mandibular dental variables show similar scaling relative to body size. Analysis of residuals shows that males have significantly larger canine, P-3 and buccolingual dimensions of the postcanine teeth (M-2 and M-3) than females. A significant difference in shape between the sexes is found in the buccolingual dimension of the upper teeth, but not in the mandible. Unlike the situation in some other species, Female golden monkeys do nor exhibit relatively larger postcanine teeth than males, in fact, the reverse is true, especially for M(2)s and M(3)s. The fact that most of the dental variables show low negative allometry to body size might be related a cold environment that has led to the development of larger body size with I-educed energy loss. When the raw data are examined by Discriminant Function Analysis the sexes are clearly distinguishable.

Gaussian Process Vine Copulas for Multivariate Dependence

Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.

Dynamic Covariance Models for Multivariate Financial Time Series

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.