Electrochemical high temperature membrane reactor for the processing of hydrogen-carbon monoxide gas mixtures

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2013

Electrochemical oxidation of H 2, CO gas mixtures in polymer-electrolyte-membrane fuel cells

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Optimal designs for the prediction in hierarchical random coefficient regression models

Magdeburg, Univ., Fak. für Mathematik, Diss., 2015

Co-movements in Real Effective Exchange Rates: Evidence from the Dynamic Hierarchical Factor Model

We analyze and quantify co-movements in real effective exchange rates while considering the regional location of countries. More specifically, using the dynamic hierarchical factor model (Moench et al. (2011)), we decompose exchange rate movements into several latent components; worldwide and two regional factors as well as country-specific elements. Then, we provide evidence that the worldwide common factor is closely related to monetary policies in large advanced countries while regional common factors tend to be captured by those in the rest of the countries in a region. However, a substantial proportion of the variation in the real exchange rates is reported to be country-specific; even in Europe country-specific movements exceed worldwide and regional common factors.

Hierarchical Shrinkage in Time-Varying Parameter Models

In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: i) time varying, ii) constant over time or iii) shrunk to zero. The econometric methodology decides automatically which category each coefficient belongs in. Our empirical results indicate the benefits of such an approach.

DIP-STR: A new marker for resolving unbalanced DNA mixtures

The genetic characterization of unbalanced mixed stains remains an important area where improvementis imperative. Most cases of aggression, homicide and sexual assault produce biological traces withrelatively large amount of the victim's DNA and small amount of the aggressor's DNA. If this ratio issmaller than 1:10 it is currently not possible to obtain a conventional autosomal DNA profile of the minorcontributor, with potential loss of crucial DNA evidence. Y-STR analysis represents a solution for somecases but has several limitations. We propose here a method based on a new compound genetic markerformed by a Deletion/Insertion Polymorphism (DIP) linked to a Short Tandem Repeat polymorphism(STR), that we name DIP-STR. By means of allele-specific amplifications of DIP-STR haplotypes, we canproduce a high resolution autosomal DNA profile of a donor that contributes less than 0.1% to a DNAmixture. Based on these features DIP-STR markers may outperform conventional Y-STR markers inmixed stain analysis.

Validation of the French version of the Hierarchical Personality Inventory for Children (HiPIC): Influence of gender and age on personality traits from 8 to 12 years

The study was designed to investigate the psychometric properties of the French version and the cross-language replicability of the Hierarchical Personality Inventory for Children (HiPIC). The HiPIC is an instrument aimed at assessing the five dimensions of the Five-Factor Model for Children. Subjects were 552 children aged between 8 and 12 years, rated by one or both parents. At the domain level, reliability ranged from .83 to .93 and at the facet level, reliability ranged from .69 to .89. Differences between genders were congruent with those found in the Dutch sample. Girls scored higher on Benevolence and Conscientiousness. Age was negatively correlated with Extraversion and Imagination. For girls, we also observed a decrease of Emotional Stability. A series of exploratory factor analyses confirmed the overall five-factor structure for girls and boys. Targeted factor analyses and congruence coefficients revealed high cross-language replicability at the domain and at the facet levels. The results showed that the French version of the HiPIC is a reliable and valid instrument for assessing personality with children and has a particularly high cross-language replicability.

Hierarchical conditional random fields for parts based models matching

A parts based model is a parametrization of an object class using a collection of landmarks following the object structure. The matching of parts based models is one of the problems where pairwise Conditional Random Fields have been successfully applied. The main reason of their effectiveness is tractable inference and learning due to the simplicity of involved graphs, usually trees. However, these models do not consider possible patterns of statistics among sets of landmarks, and thus they sufffer from using too myopic information. To overcome this limitation, we propoese a novel structure based on a hierarchical Conditional Random Fields, which we explain in the first part of this memory. We build a hierarchy of combinations of landmarks, where matching is performed taking into account the whole hierarchy. To preserve tractable inference we effectively sample the label set. We test our method on facial feature selection and human pose estimation on two challenging datasets: Buffy and MultiPIE. In the second part of this memory, we present a novel approach to multiple kernel combination that relies on stacked classification. This method can be used to evaluate the landmarks of the parts-based model approach. Our method is based on combining responses of a set of independent classifiers for each individual kernel. Unlike earlier approaches that linearly combine kernel responses, our approach uses them as inputs to another set of classifiers. We will show that we outperform state-of-the-art methods on most of the standard benchmark datasets.

An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.

DIP-STR: Highly Sensitive Markers for the Analysis of Unbalanced Genomic Mixtures.

Samples containing highly unbalanced DNA mixtures from two individuals commonly occur both in forensic mixed stains and in peripheral blood DNA microchimerism induced by pregnancy or following organ transplant. Because of PCR amplification bias, the genetic identification of a DNA that contributes trace amounts to a mixed sample represents a tremendous challenge. This means that standard genetic markers, namely microsatellites, also referred as short tandem repeats (STR), and single-nucleotide polymorphism (SNP) have limited power in addressing common questions of forensic and medical genetics. To address this issue, we developed a molecular marker, named DIP-STR that relies on pairing deletion-insertion polymorphisms (DIP) with STR. This novel analytical approach allows for the unambiguous genotyping of a minor component in the presence of a major component, where DIP-STR genotypes of the minor were successfully procured at ratios up to 1:1,000. The compound nature of this marker generates a high level of polymorphism that is suitable for identity testing. Here, we demonstrate the power of the DIP-STR approach on an initial set of nine markers surveyed in a Swiss population. Finally, we discuss the limitations and potential applications of our new system including preliminary tests on clinical samples and estimates of their performance on simulated DNA mixtures.

Identifying pesticide use patterns among flower growers to assess occupational exposure to mixtures

Objectives Exposure assessment to a single pesticide does not capture the complexity of the occupational exposure. Recently, pesticide use patterns analysis has emerged as an alternative to study these exposures. The aim of this study is to identify the pesticide use pattern among flower growers in Mexico participating in the study on the endocrine and reproductive effects associated with pesticide exposure. Methods A cross-sectional study was carried out to gather retrospective information on pesticide use applying a questionnaire to the person in charge of the participating flower growing farms. Information about seasonal frequency of pesticide use (rainy and dry) for the years 2004 and 2005 was obtained. Principal components analysis was performed. Results Complete information was obtained for 88 farms and 23 pesticides were included in the analysis. Six principal components were selected, which explained more than 70% of the data variability. The identified pesticide use patterns during both years were: 1. fungicides benomyl, carbendazim, thiophanate and metalaxyl (both seasons), including triadimephon during the rainy season, chlorotalonyl and insecticide permethrin during the dry season; 2. insecticides oxamyl, biphenthrin and fungicide iprodione (both seasons), including insecticide methomyl during the dry season; 3. fungicide mancozeb and herbicide glyphosate (only during the rainy season); 4. insecticides metamidophos and parathion (both seasons); 5. insecticides omethoate and methomyl (only rainy season); and 6. insecticides abamectin and carbofuran (only dry season). Some pesticides do not show a clear pattern of seasonal use during the studied years. Conclusions The principal component analysis is useful to summarise a large set of exposure variables into smaller groups of exposure patterns, identifying the mixtures of pesticides in the occupational environment that may have an interactive effect on a particular health effect.

The hierarchical structures of the NEO-PI-R and the 16PF5

The present study compares the higher-level dimensions and the hierarchical structures of the fifth edition of the 16 PF with those of the NEO PI-R. Both inventories measure personality according to five higher-level dimensions. These inventories were however constructed according to different methods (bottom-up vs. top-down). 386 participants filled out both questionnaires. Correlations, regressions and canonical correlations made it possible to compare the inventories. As expected they roughly measure the same aspects of personality. There is a coherent association among four of the five dimensions measured in the tests. However Agreeableness, the remaining dimension in the NEO PI-R, is not represented in the 16 PF 5. Our analyses confirmed the hierarchical structures of both instruments, but this confirmation was more complete in the case of the NEO PI-R. Indeed, a parallel analysis indicated that a four-factor solution should be considered in the case of the 16 PF 5. On the other hand, the NEO PI-R's five-factor solution was confirmed. The top-down construction of this instrument seems to make for a more legible structure. Of the two five-dimension constructs, the NEO PI-R thus seems the more reliable. This confirms the relevance of the Five Factor Model of personality.