<p>Magnetic Resonance Imaging (MRI) is an advanced tomographic technique that is able to produce high resolution cross-sectional images of an object or specimen by exploiting Radio Frequency (RF) pulses. A Variable Rate Selective Excitation (VERSE) pulse is a type of RF pulse that reduces the Specific Absorption Rate (SAR) of molecules in a specimen while preserving its duration and slice profile. SAR was designed to be minimized by the VERSE pulse as it leads to an increase in specimen temperature during MRI procedures.</p> <p>The nonlinear VERSE model was sequentially transformed into an optimal control problem that was efficiently solved by Sparse Optimal Control Software (§Oℂ§). The Magnetic Resonance (MR) signal produced by numerical simulations were then tested and analyzed by an MRI simulator. The VERSE model produced intriguing results and generated high-quality MR signals. The research and testing results produced by the VERSE pulse may influence further research in the area and have built an excellent foundation for more development of this RF pulse.</p> / Master of Science (MS)

The aim of this study is to examine how the introduction of the Video Assisted Referee (VAR) system influenced the English Premier League (EPL). Since its implementation in the English Premier League in 2019, VAR has been a constant source of debate and controversy. Many studies have been done on the immediate impact of VAR on other elite professional soccer leagues, but the scope of results is very limited and due to be updated. The data for the ensuing analysis consists of 3800 matches played in the English Premier League during the five seasons before (14/15, 15/16, 16/17, 17/18, and 18/19) and five seasons after (19/20, 20/21, 21/22, 22/23, 23/24) the implementation of VAR. The following variables were recorded for each match: goals, fouls committed, penalty kicks awarded, penalty kicks scored, shots, offsides, red cards, yellow cards, tackles, and interceptions. Match statistics were web scraped from the website FBref (www.FBref.com) using software packages in R. Welch’s T-tests, Mann-Whitney U-tests, and generalized linear models were used at an unadjusted 0.05 significance level to compare average total match statistics in the English Premier League for seasons before and after the implementation of the VAR, across three different time periods used for comparison: (i) 1 year before and after VAR, (ii) 3 years before and after VAR, and (iii) 5 years before and after VAR. In the 1 year comparison, we found a statistically significant increase in the number of average total match fouls, and statistically significant decreases in the number of average total match offsides, tackles, and interceptions. In the 3 year comparison, we found a statistically significant increase in the number of average total match tackles, and statistically significant decreases in the number of average total match offsides and interceptions. In the 5 year comparison, we found statistically significant increases in the number of average total match goals, penalty kicks awarded, penalty kicks scored, yellow cards, and tackles, and statistically significant decreases in the number of average total match offsides and interceptions. However, we discuss several other notable rule changes since the implementation of VAR in the English Premier League that could have also affected these results. Although we did observe multiple statistically significant differences in certain match statistics, it would be statistically inappropriate to conclude that VAR has caused these changes. Further, due to the large sample size, these results are likely statistically significant, but not as practically significant in the real world. In conclusion, VAR does not seem to drastically affect gameplay overall in the English Premier League.

Most companies listed on the S&P 500 index have reported smoothed earnings since the 1990s inspiring questions from regulators about the accuracy of financial statements. In 1998, the Financial Accounting Standards Board issued SFAS No. 133 (Accounting for Derivative Instruments and Hedging Activities) to establish accounting and reporting standards for derivative instruments. In 2002, the Sarbanes-Oxley Act (SOX) was issued to eradicate earnings management activities and improve transparency in financial reporting. Although many studies have been conducted to evaluate changes in reporting requirements, much less is known about the effectiveness of these regulations on earning smoothing with discretionary accruals (DA) and derivative hedge reporting (DHR). Accordingly, this study was an investigation of the effectiveness of SOX and SFAS No. 133 on DA, and DHR. The research questions were used to examine DA, and to evaluate the transparency of DHR for the years 1997 through 2007. This study is a quasi-experimental research design where 30 companies from the high technology industry segment were randomly drawn to form 330 observations. The modified Jones model was used to separate DA and repeated measures analyses of variance were used to assess differences in levels before and after the issuance of SOX. A Quality Disclosure Index (QDI) was used to assess the transparency of DHR and repeated measures of variance were used to evaluate the QDI scores before and after the issuance of SFAS No. 133. The findings suggest DA activities are decreasing but represent over 50% of total net accruals for all years and the QDI for DHR is decreasing. Improved financial regulation is needed. The study contributes to positive social change by providing regulators and investors with new information about accruals for income conservative firms by segmenting DA and investigating the level of transparency in DHR that could be used to formulate appropriate financial regulation and improve the quality of our financial reporting system.

Hazardous waste site remediation cost estimation requires a good estimate of the contaminated soil volume. The United States Environmental Protection Agency (U.S. EPA) currently uses deterministic point values to estimate soil volumes but the literature suggests that probability bounds analysis (PBA) is the more accurate method to make estimates under uncertainty. The underlying statistical theory is that they are more accurate than deterministic estimates because probabilistic estimates account for data uncertainties. However, the literature does not address the problem of selecting an optimal decision point from the interval-valued PBA estimates. The purpose of this study was to identify the optimal PBA decision point estimator and use it to demonstrate that because the PBA method also accounts for data uncertainties, PBA estimates of remedial soil volumes are more accurate than the U.S. EPA deterministic estimates. The research questions focused on determining whether the mean or the 95th percentile decision point is the optimal PBA estimator. A convenience sample of seven sites was selected from the U.S. EPA Superfund Database. The PBA method was used to estimate the remedial soil volumes for the sites. Correlation analyses were performed between the mean and 95th percentile PBA estimates and the actual excavated soil volumes. The study results suggest that the lower bound 95th percentile PBA estimate, which had the best R2-value of 89%, is the optimal estimator. The R2-value for a similar correlation analysis using the U.S. EPA deterministic estimates was only 59%. This confirms that PBA is the better estimator. The PBA estimates are less contestable than the current U.S. EPA deterministic point estimates. Thus, the PBA method will reduce litigation and speed up cleanup activities to the benefit of the U.S. EPA, corporations, the health and safety of nearby residents, and society in general.

<p>In this thesis we study the Birnbaum-Saunders autoregressive conditional du- ration (BS-ACD) model. As opposed to the standard ACD model, formulated in terms of the conditional mean duration, the BS-ACD model specifies the time-varying model dynamics in terms of the conditional median duration. By means of Monte Carlo simulations, we examine the asymptotic behaviour of the maximum likelihood estimators. We then present a study of numerical efficacy of some optimization algorithms in relation to the BS-ACD model. On a practical side, we fit the BS-ACD model to samples for six securities listed on the New York Stock Exchange.</p> / Master of Science (MSc)

<p>The central theme of this thesis is to develop methods of financial mathematics to understand the dynamics of a firm's capital structure through observations of market prices of liquid securities written on the firm. Just as stock prices are a direct measure of a firm's equity, other liquidly traded products such as options and credit default swaps (CDS) should also be indicators of aspects of a firm's capital structure. We interpret the prices of these securities as the market's revelation of a firm's financial status. In order not to enter into the complexity of balance sheet anatomy, we postulate a balance sheet as simple as Asset = Equity + Debt. Using mathematical models based on the principles of arbitrage pricing theory, we demonstrate that this reduced picture is rich enough to reproduce CDS term structures and implied volatility surfaces that are consistent with market observations. Therefore, reverse engineering applied to market observations provides concise and crucial information of the capital structure.</p> <p>Our investigations into capital structure modeling gives rise to an innovative pricing formula for spread options. Existing methods of pricing spread options are not entirely satisfactory beyond the log-normal model and we introduce a new formula for general spread option pricing based on Fourier analysis of the payoff function. Our development, including a flexible and general error analysis, proves the effectiveness of a fast Fourier transform implementation of the formula for the computation of spread option prices and Greeks. It is found to be easy to implement, stable, and applicable in a wide variety of asset pricing models.</p> / Doctor of Philosophy (PhD)

It is important to be able to compare variances efficiently and accurately regardless of the parent populations. This study proposes a new right tailed test for the ratio of two variances using the Edgeworth’s expansion. To study the Type I error rate and Power performance, simulation was performed on the new test with various combinations of symmetric and skewed distributions. It is found to have more controlled Type I error rates than the existing tests. Additionally, it also has sufficient power. Therefore, the newly derived test provides a good robust alternative to the already existing methods.

(DSM-IV-TR/) nor in withdrawal scales. However, a related term, "impatience" is listed in some nicotine withdrawal scales. (Hughes J. R., Measurements of the Effects of Abstinence from Tobacco: A Qualitative Review, 2007). Although impatience is not a synonym of impulsivity, both share the synonym "impetuous". Therefore, impatience can be considered a measure of impulsivity. Although some reviews of the effect of smoking cessation on impatience have occurred, we know of no quantitative review of prospective studies of whether smoking cessation increases impatience. Purpose: To evaluate the effect of smoking cessation on impatience as measured by the Minnesota Nicotine Withdrawal Scale-Revised (MNWS). Methods: A literature search of MEDLINE (PubMED), EMBASE, and PsychInfo was conducted. Articles containing relevant keywords were reviewed by two evaluators independently. To be considered for inclusion in the meta-analysis, studies had to be prospective studies, had to have pre-cessation impatience measurements, to include at least overnight abstinence, had to have smoking abstinence biochemically verified, and had to include effect size as an outcome measure. Results: All pooled analyses were based on random-effects models. Seven trials met the selection criteria. The total number of subjects was 426. There was a significant level of heterogeneity among studies (X2(6), pI2= 89%). The summary mean effect for impatience after tobacco cessation was an increase of .44 on a 0-3 scale (95% confidence interval [CI], 0.21-0.67) and a p-value Conclusion: The meta-analysis shows that impulsivity increases post smoking cessation. These findings imply that smoking cessation may have an effect on decision making. Additionally higher rates of impulsivity have been associated with smoking relapse. (Doran, Spring, McChargue, Peradia, & Richmond, 2004). In order to better assist in the development of individual treatments, a better understanding is needed of how increased impulsivity influences cognitive behavior and relapse rates. These findings support the inclusion of impulsivity as a criterion for nicotine withdrawal.

Predicting teenage drug use is key to understanding the etiology of substance abuse. However, classic predictive modeling procedures are prone to overfitting and fail to generalize to independent observations. To mitigate these concerns, cross-validated logistic regression with elastic-net regularization was used to predict cannabis use by age 16 from a large sample of fourteen year olds (N=1,319). High-dimensional data (p = 2,413) including parent and child psychometric data, child structural and functional MRI data, and genetic data (candidate single-nucleotide polymorphisms, "SNPs") collected at age 14 were used to predict the initiation of cannabis use (minimum six occasions) by age 16. Analyses were conducted separately for males and females to uncover sex-specific predictive profiles. The performance of the predictive models were assessed using the area under the receiver-operating characteristic curve ("ROC AUC"). Final models returned high predictive performance (generalization mean ROC AUCmales=.71, mean ROC AUCfemales=.81) and contained psychometric features common to both sexes. These common psychometric predictors included greater stressful life events, novelty-seeking personality traits of both the parent and child, and parental cannabis use. In contrast, males exhibited distinct functional neurobiological predictors related to a response- inhibition fMRI task, whereas females exhibited distinct neurobiological predictors related to a social processing fMRI task. Furthermore, the brain predictors exhibited sex- specific effects as the brain predictors of cannabis use for one sex failed to predict cannabis use for the opposite sex. These sex-specific brain predictors also exhibited drug- specific effects as they failed to predict binge-drinking by age 16 in an independent sample of youths. When collapsed across sex, a gene-specific analysis suggested that opioid receptor genetic variation also predicted cannabis use by age 16. Two SNPs on the gene coding for the primary mu-opioid receptor exhibited genetic risk effects, while one SNP on the gene coding for the primary delta-opioid receptor exhibited genetic protective effects. Taken together, these results demonstrate that adolescent cannabis use is reliably predicted in males and females from shared and unique biobehavioral features. These analyses also underscore the need for refined predictive modeling procedures as well as sex-specific inquiries into the etiology of substance abuse. The sex-specific risk-profiles uncovered from these analyses might inform potential etiological mechanisms contributing to substance abuse in adolescence as all predictors were measured prior to the onset of cannabis use.

The article offers a dynamic approach for predicting the outcomes of NFL games using the NFL games from 2002-2005. A logistic regression model is used to predict the probability that one team defeats another. The parameters of this model are the strengths of the teams and a home field advantage factor. Since it assumed that a team's strength is time dependent, the strength parameters were assigned a seasonal time series process. The best model was selected using all the data from 2002 through the first seven weeks of 2005. The last weeks of 2005 were used for prediction estimates.