The measurement of Polycyclic Aromatic Hydrocarbons (PAHs) in flames is essential for the understanding of soot formation. In comparison to conventional aromatics-sampling techniques, a new technique was proposed that involves fewer manual operations and no hazardous extraction solvents. Apparatus and experimental procedures of the newly proposed adsorptive-sampling and desorptive-extraction technique for aromatic-hydrocarbon measurements were established in this study. The capabilities and limitations of this new technique were assessed in terms of limits of detection, sampling locations and data repeatability. The accuracy of this technique was also evaluated. Aromatic-hydrocarbon species concentrations were measured in laminar co-flow diffusion flames of ethylene (C2H4) and synthetic paraffinic kerosene (SPK). The results obtained from the ethylene flame were compared to its numerical simulation, with the goal of achieving agreement within an order of magnitude. The differences between simulated values and experimental measurements, along with the limitations of the technique, were used as an indication of the accuracy of the technique.

The measurement of Polycyclic Aromatic Hydrocarbons (PAHs) in flames is essential for the understanding of soot formation. In comparison to conventional aromatics-sampling techniques, a new technique was proposed that involves fewer manual operations and no hazardous extraction solvents. Apparatus and experimental procedures of the newly proposed adsorptive-sampling and desorptive-extraction technique for aromatic-hydrocarbon measurements were established in this study. The capabilities and limitations of this new technique were assessed in terms of limits of detection, sampling locations and data repeatability. The accuracy of this technique was also evaluated. Aromatic-hydrocarbon species concentrations were measured in laminar co-flow diffusion flames of ethylene (C2H4) and synthetic paraffinic kerosene (SPK). The results obtained from the ethylene flame were compared to its numerical simulation, with the goal of achieving agreement within an order of magnitude. The differences between simulated values and experimental measurements, along with the limitations of the technique, were used as an indication of the accuracy of the technique.

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In the present paper we consider Bayesian estimation of a finite mixture of models with random effects which is also known as the heterogeneity model. First, we discuss the properties of various MCMC samplers that are obtained from full conditional Gibbs sampling by grouping and collapsing. Whereas full conditional Gibbs sampling turns out to be sensitive to the parameterization chosen for the mean structure of the model, the alternative sampler is robust in this respect. However, the logical extension of the approach to the sampling of the group variances does not further increase the efficiency of the sampler. Second, we deal with the identifiability problem due to the arbitrary labeling within the model. Finally, a case study involving metric Conjoint analysis serves as a practical illustration. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

The purpose of this study was to determine the levels of whole tissue, cytosolic, and mitochondrial glutathione in the tissue zones of the rat kidney. Reduced (GSH) and oxidized glutathione (GSSG) were measured spectrophotometrically in tissue homogenates. Differential centrifugation was used to isolate the cytosolic and mitochondrial fractions. Cortical GSH and GSSG levels accounted for 51% and 60%, respectively, of the GSH and GSSG levels in the whole kidney. Cytosolic GSH levels were similar in the cortex and medulla but lower in the papilla. Cytosolic GSSG levels were highest in the cortex and lowest in the medulla. Mitochondrial GSH and GSSG levels did not follow a pattern similar to that of the cytosol or whole tissue. The mitochondrial redox ratio (GSH/GSH + GSSG X 100) was significantly higher in the cortex (ie., 67%) than the medulla (ie., 39%). The cytosolic redox ratio showed an opposite relationship with the cortex (ie., 57%) being lower than the medulla (ie., 78%). This study demonstrates that there are differences in GSH levels, GSSG levels, and the redox ratio in the tissue zones of the rat kidney. / Department of Physiology and Health Science

This thesis is on quantum algorithms. It has three main themes: (1) quantum walk based search algorithms, (2) quantum rejection sampling, and (3) the Boolean function hidden shift problem. The first two parts deal with generic techniques for constructing quantum algorithms, and the last part is on quantum algorithms for a specific algebraic problem. In the first part of this thesis we show how certain types of random walk search algorithms can be transformed into quantum algorithms that search quadratically faster. More formally, given a random walk on a graph with an unknown set of marked vertices, we construct a quantum walk that finds a marked vertex in a number of steps that is quadratically smaller than the hitting time of the random walk. The main idea of our approach is to interpolate the random walk from one that does not stop when a marked vertex is found to one that stops. The quantum equivalent of this procedure drives the initial superposition over all vertices to a superposition over marked vertices. We present an adiabatic as well as a circuit version of our algorithm, and apply it to the spatial search problem on the 2D grid. In the second part we study a quantum version of the problem of resampling one probability distribution to another. More formally, given query access to a black box that produces a coherent superposition of unknown quantum states with given amplitudes, the problem is to prepare a coherent superposition of the same states with different specified amplitudes. Our main result is a tight characterization of the number of queries needed for this transformation. By utilizing the symmetries of the problem, we prove a lower bound using a hybrid argument and semidefinite programming. For the matching upper bound we construct a quantum algorithm that generalizes the rejection sampling method first formalized by von~Neumann in~1951. We describe quantum algorithms for the linear equations problem and quantum Metropolis sampling as applications of quantum rejection sampling. In the third part we consider a hidden shift problem for Boolean functions: given oracle access to f(x+s), where f(x) is a known Boolean function, determine the hidden shift s. We construct quantum algorithms for this problem using the "pretty good measurement" and quantum rejection sampling. Both algorithms use the Fourier transform and their complexity can be expressed in terms of the Fourier spectrum of f (in particular, in the second case it relates to "water-filling" of the spectrum). We also construct algorithms for variations of this problem where the task is to verify a given shift or extract only a single bit of information about it.