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It is common practice to add oxygenates, such as ethers or alcohols, to gasoline in areas suffering from ozone or smog problems in order to reduce pollution. The most commonly used oxygenates are ethanol (EtOH) and methyl tert-butyl ether (MTBE). However, MTBE is now forbidden by the environmental protection agency (EPA) because of the possibility of ground water contamination. The current trend is to use EtOH, therefore this work focuses on the analysis and quantification of EtOH in gasoline by solid phase microextraction (SPME). The major problem in quantifying EtOH in gasoline is the coelution of hydrocarbons with EtOH. There have been several approaches to solve this problem; among the chromatographic ones, three major types have been proposed: (1) the first one uses a detector selective for oxygen containing compounds; (2) the second one uses two or more columns; (3) and the third one uses an extraction step prior to GC analysis. In this work an extraction step with water is used prior to a solid phase microextraction (SPME) sample preparation coupled to a gas chromatographic (GC) analysis. Solid phase microextraction is a recent technique, invented by Pawliszyn in 1989, and available commercially since 1994. A fiber is used to extract small amounts (ppm, ppb, ppt) of analytes from a solution, usually water. The fiber is beneficial in concentrating analytes. Most work using SPME has been done with hydrophobic (non polar) analytes, extracted using a polydimethylsiloxane (PDMS; non polar) coating on a fused silica fiber. Since very little work has been done with polar analytes, the novel approach of this work is the extraction of EtOH. Since EtOH is the analyte of interest, a polar fiber, carboxen/polydimethyl siloxane (Car/PDMS) is used. Two methods are used for quantification of EtOH in gasoline: the method of a standard calibration curve, and the method of standard addition. They are both successful in quantifying the amount of EtOH in gasoline. The relative errors, with the method of standard addition, vary from 5.3% to 14%, while the ones with the method of calibration curve vary from 1.6% to 7.2%. Moreover, some extraction time studies for both direct and headspace sampling are performed. Direct sampling shows the presence of an equilibrium condition for the carboxen/PDMS fiber, for which no extraction theory is available. Conversely, headspace sampling shows no equilibrium state; after a sampling time of one hour, the amount of EtOH extracted decreases with sampling time. This is probably due to displacement of EtOH by other compounds in the fiber. / Master of Science

Test equating is important to large-scale testing programs because of the following two reasons: strict test security is a key concern for high-stakes tests and fairness of test equating is important for test takers. The question of adequacy of sample size often arises in test equating. However, most recommendations in the existing literature are based on classical test equating. Very few research studies systematically investigated the minimal sample size which leads to reasonably accurate equating results based on item response theory (IRT). The main purpose of this study was to examine the minimal sample size for desired IRT equating accuracy for the common-item nonequivalent groups design under various conditions. Accuracy was determined by examining the relative magnitude of six accuracy statistics. Two IRT equating methods were carried out on simulated tests with combinations of test length, test format, group ability difference, similarity of the form difficulty, and parameter estimation methods for 14 sample sizes using Monte Carlo simulations with 1,000 replications per cell. Observed score equating and true score equating were compared to the criterion equating to obtain the accuracy statistics. The results suggest that different sample size requirements exist for different test lengths, test formats and parameter estimation methods. Additionally, the results show the following: first, the results for true score equating and observed score equating are very similar. Second, the longer test has less accurate equating than the shorter one at the same sample size level and as the sample size decreases, the gap is greater. Third, concurrent parameter estimation method produced less equating error than separate estimation at the same sample size level and as the sample size reduces, the difference increases. Fourth, the cases with different group ability have larger and less stable error comparing to the base case and the cases with different test difficulty, especially when using separate parameter estimation method with sample size less than 750. Last, the mixed formatted test is more accurate than the single formatted one at the same sample size level. / Ph. D.

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Electrokinetics based microfluidic systems are potentially promising for lab-on-a-chip applications due to their effectiveness in manipulating nanoscale and biological objects, label-free operation, simple fabrication processes, small voltage requirements, and most importantly simple system integration strategy. Among various electrokinetics techniques, AC electrothermal flow (ACEF) is the most promising technique in microfluidic manipulation toward biomedical applications due to its effectiveness in high conductivity biological and physiological fluids. As relatively little is known about the ACEF induced fluid motion at highly conductive samples, the characteristics of electrothermal manipulation of fluid samples with different conductivities were investigated systematically. For low conductivity sample (below 1 S/m), the characteristics of the electrothermal fluid motion was in quantitative agreement with the theory. For high conductivity samples (greater than 1 S/m), the fluid motion appeared to deviate from the model as a result of electrochemical reactions and the temperature effect. Here, a universal electrode approach which directly implements ACEF-induced sample preparation on a SAM based electrochemical sensor for point-of-care diagnostics of urinary tract infections has also been demonstrated. Using uropathogenic E. coli clinical isolates as model systems, we demonstrate that "on-chip" ACEF-induced sample preparation can improve the sensor performance without complicated system integration strategy and presents a pathway for implementing truly lab on a chip, instead of chip in a lab. Finally an integrated chip approach has been proposed for transforming electrochemical sensing system from laboratory research into point-of-care diagnostics with multiple microelectrodes.

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This thesis is a study of the effects of lead placement and sample shape in the measurement of electrical resistivity.

This dissertation serves as a unifying document for three related articles developed during my dissertation research. The projects involve the development of single-subject transcriptome (i.e. gene expression data) methodology for precision medicine and related applications. Traditional statistical approaches are largely unavailable in this setting due to prohibitive sample size and lack of independent replication. This leads one to rely on informatic devices including knowledgebase integration (e.g., gene set annotations) and external data sources (e.g., estimation of inter-gene correlation). Common statistical themes include multivariate statistics (such as Mahalanobis distance and copulas) and large-scale significance testing. Briefly, the first work describes the development of clinically relevant single-subject metrics of gene set (pathway) differential expression, N-of-1-pathways Mahalanobis distance (MD) scores. Next, the second article describes a method which overcomes a major shortcoming of the MD framework by accounting for inter-gene correlation. Lastly, the statistics developed in the previous works are re-purposed to analyze single-cell RNA-sequencing data derived from rare cells. Importantly, these works represent an interdisciplinary effort and show that creative solutions for pressing issues become possible at the intersection of statistics, biology, medicine, and computer science.

Em diferentes aplicações de Aprendizado de Máquina podemos estar interessados na minimização do valor esperado de certa função de perda. Para a resolução desse problema, Otimização estocástica e Sample Size Selection têm um papel importante. No presente trabalho se apresentam as análises teóricas de alguns algoritmos destas duas áreas, incluindo algumas variações que consideram redução da variância. Nos exemplos práticos pode-se observar a vantagem do método Stochastic Gradient Descent em relação ao tempo de processamento e memória, mas, considerando precisão da solução obtida juntamente com o custo de minimização, as metodologias de redução da variância obtêm as melhores soluções. Os algoritmos Dynamic Sample Size Gradient e Line Search with variable sample size selection apesar de obter soluções melhores que as de Stochastic Gradient Descent, a desvantagem se encontra no alto custo computacional deles. / In different Machine Learnings applications we can be interest in the minimization of the expected value of some loss function. For the resolution of this problem, Stochastic optimization and Sample size selection has an important role. In the present work, it is shown the theoretical analysis of some algorithms of these two areas, including some variations that considers variance reduction. In the practical examples we can observe the advantage of Stochastic Gradient Descent in relation to the processing time and memory, but considering accuracy of the solution obtained and the cost of minimization, the methodologies of variance reduction has the best solutions. In the algorithms Dynamic Sample Size Gradient and Line Search with variable sample size selection, despite of obtaining better solutions than Stochastic Gradient Descent, the disadvantage lies in their high computational cost.