We give congruences modulo powers of p in {3, 5, 7} for the Fourier coefficients of certain modular functions in level p with poles only at 0, answering a question posed by Andersen and Jenkins and continuing work done by the Jenkins, the author, and Moss. The congruences involve a modulus that depends on the base p expansion of the modular form's order of vanishing at infinity.

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The presence of biologically-active estrogenic endocrine disrupting compounds (EDCs) in treated effluents from biological wastewater treatment facilities has prompted wide-spread interest in the behavior of these contaminants during the activated sludge process. The yeast-estrogen screen (YES) was used to quantify the estrogenic activity of samples taken from different areas of three wastewater treatment facilities. An estrogenic mass-balance around these facilities revealed that the majority of influent estrogenic activity was removed in the activated sludge process, but the main route for EDC discharge to the natural environment was via the treated effluent. The estrogenic activity in the effluent from a membrane bioreactor (MBR) was lower compared to a fully aerobic activated sludge process using secondary clarification, suggesting that enhanced removal of particulate and colloidal material may improve EDC removal efficiency. Colloidal material was obtained from settled mixed liquor suspended solids (MLSS) collected from a pilot MBR and a full-scale activated sludge process that included anoxic and aerobic zones. The MLSS was sized fractionated by filtration, and used to quantify the sorption coefficients for pyrene, 17&#946;-estradiol (E2), and 17α-ethinylestradiol (EE2) by fluorescence quenching. The MLSS-derived colloidal organic carbon (COC) sorption coefficient (Kcoc) for pyrene ranged from (< 1 to 80) L/kgcoc, indicating a similar affinity for pyrene compared to natural organic matter. Kcoc coefficients for E2 ranged between (< 1 to 158) L/kgcoc for E2 and (< 1 to 228) L/kgcoc for EE2, and are the highest E2 and EE2 sorption coefficients reported in the literature to date. There was a strong correlation between the Kcoc coefficients and molar extinction coefficient at 280 nm (e280) for pyrene and E2, suggesting that the interaction of the π;-electrons is an important factor in determining overall sorption behavior. There was no such correlation for EE2. Based on the Kcoc coefficients and COC concentrations of the samples, between 1 and 50% of the aqueous E2 and EE2 concentrations were associated with colloidal material. In a novel application of the YES bioassay, the bioavailability of colloid-associated E2 was quantified by comparing the EC50 values of the dose-response curves generated in the presence and absence of size fractionated COC. An increase in EC50 values as a function of COC concentration was attributed to a reduction in bioavailability of E2, suggesting that MLSS-derived COC can reduce, but not eliminate, the biological impact of EDCs. However, there was a high degree of variability in the EC50 values, and estimates of the colloid-associated E2 fraction based on the Kcoc-e280 correlation were unsuccessful in accurately predicting increases in EC50 values. Nevertheless, the YES bioassay may represent a powerful tool in determining the bioavailability of EDCs in complex environmental samples. Results from this research effort suggest that the colloidal phase derived from activated sludge systems represents an important transport vehicle whereby EDCs and other trace organic compounds can enter into the natural environment. Consequently, wastewater treatment plants discharging to sensitive ecosystems or involved with direct water reuse programs should optimize the treatment process to remove colloidal material. / Ph. D.

The accuracy of the calculated drag force in OpenRocket was examined in this investigation for a rocket traveling at subsonic, transonic and supersonic speeds. The idea was to compare the computed drag coefficients in OpenRocket to the drag coefficients obtained by running multiple computational fluid dynamics (CFD) simulations in Ansys Fluent. Specifically, the freestream Mach number and altitude parameters were varied for a geometric model of the Mjöllnir rocket. The results obtained converged several orders of magnitude and were exceptionally stable. Both the physical accuracy and numerical independence of the results was verified through examining the solutions' contour plots and conducting multiple sensitivity analyses. The conclusion was that OpenRocket overpredicts the Mjöllnir rocket's drag coefficients by 12 % to 73 % for all examined freestream Mach numbers and altitudes. Additionally, an empirical relationship was found for how the Mjöllnir rocket's drag coefficient changes with altitude and the freestream Mach number. In particular, it is a multivariate function that can be considered valid for an altitude of h ≤ 10 km and a freestream Mach number of 0.2 ≤ M ≤ 3.0. The empirical relationship fitted exceedingly well with simulation data and merits further investigation.

The thesis is concerned with the design of an apparatus to determine the film coefficients for the conditions of forced convention, local boiling of sub-cooled water and the two phase flow of water-steam mixtures. Inasmuch as the agitating action of the bubbles during boiling of liquid permits high rate of heat transfer, information concerning convective heat transfer under conditions of local boiling and the two phase flow is of importance to the development of high heat flux heat exchangers which incorporate a phase change of coolant. The variation of the film coefficient with water temperature, flow rate, heat input rate, and fluid pressure may be investigated with this apparatus. The range of variables is Heat flux: 2.5 x 10⁵ to 5 x 10⁵ B/hr ft² Inlet temperature: 100 to 300°F Mass velocity: 0.52 x 10⁶ to 1.7 x 10⁶ 1b/hr ft² Pressure: Up to 200 psia The basic scheme of this apparatus is to pump deionized water through an electrically heard test section which is a stainless steel tub, 0.465 in O. D. x 0.020 in. thickness x 5ft length, where the fluid temperature, surface temperature and fluid pressure are measured by thermocouples and pressure gages. The electrical power to the test section is supplied by a welding transformed. Pressurization of the system is accomplished by throttling the flow at the discharge end of the test section through the exhaust valves. The fluid leaving the test section is condensed and sub-cooled by a vertical vapor-in-tube type heat exchanger so that the fluid can be recirculated again. An auxiliary ion-exchanger system is provided for deionization of the test water so that the deposition of scale on the heating surface can be prevented. With the above apparatus, local clause of fluid temperature, surface temperature and heat flux may be obtained and consequently a “point” determination of the film coefficient is possible. / M.S.

As of 2016, diagnosis of ADHD in the US is controversial. Diagnosis of ADHD is based on subjective observations, and treatment is usually done through stimulants, which can have negative side-effects in the long term. Evidence shows that the probability of diagnosing a child with ADHD not only depends on the observations of parents, teachers, and behavioral scientists, but also on state-level special education policies. In light of these facts, unbiased, quantitative methods are needed for the diagnosis of ADHD. This problem has been tackled since the 1990s, and has resulted in methods that have not made it past the research stage and methods for which claimed performance could not be reproduced. This work proposes a combination of machine learning algorithms and signal processing techniques applied to EEG data in order to classify subjects with and without ADHD with high accuracy and confidence. More specifically, the K-nearest Neighbor algorithm and Gaussian-Mixture-Model-based Universal Background Models (GMM-UBM), along with autoregressive (AR) model features, are investigated and evaluated for the classification problem at hand. In this effort, classical KNN and GMM-UBM were also modified in order to account for uncertainty in diagnoses. Some of the major findings reported in this work include classification performance as high, if not higher, than those of the highest performing algorithms found in the literature. One of the major findings reported here is that activities that require attention help the discrimination of ADHD and Non-ADHD subjects. Mixing in EEG data from periods of rest or during eyes closed leads to loss of classification performance, to the point of approximating guessing when only resting EEG data is used. / Master of Science / As of 2016, diagnosis of ADHD in the US is controversial. Diagnosis of ADHD is based on subjective observations, and treatment is usually done through stimulants, which can have negative side-effects in the long term. Evidence shows that the probability of diagnosing a child with ADHD not only depends on the observations of parents, teachers, and behavioral scientists, but also on state-level special education policies. In light of these facts, unbiased, quantitative methods are needed for the diagnosis of ADHD. This problem has been tackled since the 1990s, and has resulted in methods that have not made it past the research stage and methods for which claimed performance could not be reproduced. This work proposes a combination of machine learning algorithms and signal processing techniques applied to EEG data in order to classify subjects with and without ADHD with high accuracy and confidence. Signal processing techniques are used to extract autoregressive (AR) coefficients, which contain information about brain activities and are used as “features”. Then, the features, extracted from datasets containing ADHD and Non-ADHD subjects, are used to create or train models that can classify subjects as either ADHD or Non-ADHD. Lastly, the models are tested using datasets that are different from the ones used in the previous stage, and performance is analyzed based on how many of the predicted labels (ADHD or Non-ADHD) match the expected labels. Some of the major findings reported in this work include classification performance as high, if not higher, than those of the highest performing algorithms found in the literature. One of the major findings reported here is that activities that require attention help the discrimination of ADHD and Non-ADHD subjects. Mixing in EEG data from periods of rest or during eyes closed leads to loss of classification performance, to the point of approximating guessing when only resting EEG data is used.

Assuming a polynomial of the fourth degree to describe the velocity function, the momentum integral equation for a second-order fluid is used to develop differential equations describing the boundary-layer for second-order flow past external surfaces. Using the momentum integral equation and appropriate boundary conditions, results are tabulated for both plane and axisymmetric stagnation flows. The effect of the second-order viscosity terms on the boundary-layer parameters for problems of flow past a circular cylinder and flow past a sphere is discussed. An interesting result is found in the case of flow past a sphere; for certain values of the second-order viscosity terms, there is a reduction in the viscous drag from that of Newtonian flow. / Master of Science

This empirical study uses stochastic coefficients econometric modeling to forecast real exchange rate volatility and examine how expected and unexpected volatility affect bilateral trade flows of certain steel products between Canada, Mexico and the United States using monthly data for the seven-year period 1996-2002. The results of the model indicate that the effects of exchange rate volatility on bilateral trade flows for this sector are relatively minor, where sustained changes in the spot exchange rate, sectoral economic growth, and the price of goods being traded all exert more significant influence on trade levels than exchange rate volatility. However, the model results also tend to indicate that as exchange rate volatility increases, the well-developed U.S.-Canadian forward currency exchange market may present economic agents with profit opportunities through risk-portfolio diversification, resulting in a positive correlation between volatility and trade. For the less-developed U.S.-Mexican forward currency market, the model results indicate that the relationship between trade and volatility, both expected and unexpected, is weak and predominantly negative. / Master of Arts