Master of Science (MS), Ohio University, 2023, Computer Science (Engineering and Technology)

The continued growth of deep learning applications has resulted from the increasing availability of annotated data as well as the advancement of hardware. However, deploying deep learning models on this new hardware results in high energy and computational requirements. A recent development in the field of deep learning has introduced spiking neurons that are used in spiking neural networks (SNNs). These biologically inspired neurons operate on sparse spike trains in order to train and test deep learning models while theoretically consuming less energy when compared to an equivalent ANN model. Different ANN to SNN conversion techniques have been proposed because SNNs can not optimize networks using methods such as backpropagation. In this thesis, we present an intermediate hybrid training step implemented under NengoDL before the ANN is fully converted to an SNN. In this hybrid phase, the forward pass of the network uses spiking activations while the backwards pass switches back to non-spiking activations that are differentiable and are able to be used in backpropagation. Using the spiking activations fine-tunes the connection weights during the hybrid training phase and increases the accuracy of the converted SNN model when compared to a converted SNN without the hybrid training phase. With the new hybrid training scheme, we designed networks and experiments on two applications, object localization and image segmentation. The models were evaluated based on a set of proposed performance metrics. Additionally, the estimated energy consumption for the ANNs and the converted SNNs were compared to provide more information on the energy consumption between ANNs and SNNs. To the best of our knowledge, this is the first implementation of the proposed hybrid training approach that has been tested on these spiking models.

Committee: Jundong Liu (Advisor); Li Xu (Committee Member); David Chelberg (Committee Member); David Juedes (Committee Member) Subjects: Artificial Intelligence; Computer Science

Doctor of Philosophy (PhD), Ohio University, 2024, Chemical Engineering (Engineering and Technology)

The imperative to mitigate environmental degradation from the direct application of animal wastewater and the rising costs of commercial fertilizers has spurred the exploration of nutrient recovery through electro-precipitation, focusing on producing solid fertilizer efficiently. In the first study, an investigation on the electrochemical treatment of synthetic animal wastewater at initial pH levels of 5.9 and 6.6 discovered that pH 6.6 favored higher phosphorus recovery rates and proved more energy efficient. Analysis of solid byproducts through scanning electron microscopy and energy-dispersive X-ray spectroscopy highlighted the co-precipitation of struvite and brushite incredibly efficiently at the higher pH level. This pH-dependent outcome suggests the potential for tailored nutrient recovery strategies in waste management. In the second study, the focus shifted to multivariate screening analyses using the Plackett-Burman design on Synthetic Animal Wastewater (SAW) with an initial pH of 6.6 to discern the effects of temperature, cathodic potential, turbulence, and ion concentration on nutrient removal from wastewater. The Mg:Ca molar ratio was identified as the most significant factor in phosphorus recovery. The findings emphasized that controlling the Mg:Ca ratio, temperature, and N: P ratio could yield competitive energy consumption with existing industrial methods. The preference for struvite formation at lower temperatures indicated temperature's critical role in nutrient recovery optimization. In the third study, response surface methodology (RSM) and artificial neural networks (ANN) were combined to optimize phosphorus recovery from synthetic animal wastewater (SAW). The combination of a multi-layer feed-forward network and Box-Behnken design enabled the approach to be adapted to various environmental and wastewater scenarios. The dual-model system accurately forecasted the recovery efficiency, substantiated by significant R2 values and minimal root (open full item for complete abstract)

Committee: Jason Trembly (Advisor); Damilola Daramola (Committee Co-Chair); John Staser (Committee Member); Howard Dewald (Committee Member); Natalie Kruse Daniels (Committee Member) Subjects: Chemical Engineering; Environmental Engineering; Sustainability

Master of Science (M.S.), University of Dayton, 2023, Electro-Optics

This thesis objective is the forecasting of the scintillation index using a machine learning approach. The scintillation index is a measure of the fuctuations of optical wave intensity, also known as scintillation, occurring during the propagation through atmospheric turbulence. The data used for the machine learning-based scintillation index forecastign was obtained during on-going measurements conducted during several years over a 7 km propagation path at the Intelligence Optics Laboratory of the University of Dayton with a commercial scintillometer. Besides the scintillation index and refractive index structure parameter, also meteorological data such as air temperature, wind speed, and relative humidity were measured on both ends of the propagation path. To investigate the infuence of seasonal changes on the forecasting of the scintillation index, the data was divided into four subsets corresponding to the four seasons. Necessary data preprocessing steps have been performed, and the data was used to train diferent machine learning models. The considered models included: bi-directional long short-term memory (Bi-LSTM), convolutional neural network (CNN), K-nearest neighbor (KNN), and random forest (RF). Diferent Bi-LSTM models were trained by utilizing a single meteorological parameter as an input. Other Bi-LSTM models were trained on diferent pairs of meteorological parameters (i.e., air temperature and relative humidity, air temperature and wind speed, and relative humidity and wind speed), as well as using all meteorological parameters as inputs. Performance in scintillation index forecasting by different models was compared using a root mean squared error (RMSE). It was found that the Bi-LSTM model trained on all meteorological parameters demonstrated the best performance with RMSE = 1.274 in fall, 2.359 in winter, 4.317 in spring, and 1.700 in summer.

Committee: Miranda van Iersel (Advisor); Grigorii Filimonov (Committee Member); Thomas Weyrauch (Committee Member) Subjects: Engineering; Optics

Master of Arts (MA), Ohio University, 2023, English (Arts and Sciences)

Despite the ways in which Appalachia's complexity has been overshadowed by the narrowness of many of prominent stereotypes about the region that have been portrayed in popular fiction, many Appalachian writers, most significantly Appalachian women writers, are producing narratives that push back against limiting conceptions of the region. Two such novels, Prodigal Summer by Barbara Kingsolver and Strange as This Weather Has Been by Ann Pancake, provide narratives that resist these hierarchal structures by presenting women characters and environments that challenge them. Through ecological feminist analysis of these texts, both of these novels are situated within a larger context of Appalachian-set work by women writers that have advanced feminism, providing opportunities for women to find moments of hope, peace, and agency despite capitalistic environmental violence, restrictive gender norms, and living in a traditionally patriarchal culture. Both of these pieces, in various ways, compare the subjugation of women and environmental violence as well as depict women as overt challengers of frameworks of Western thought and idealism, including the division between the human and nonhuman, rigid gender roles, and patriarchal structures of power.

Committee: Dr. Paul C. Jones (Committee Chair); Dr. Anna Rachel Terman (Committee Member); Dr. Edmond Y. Chang (Committee Member) Subjects: American Literature; Gender; Literature

Master of Science (MS), Bowling Green State University, 2022, Geology

The goal of this study is to explore land use and land cover (LULC) trends in Greater Accra Metropolitan Area (GAMA), a highly urbanized coastal region in Ghana by analyzing historical change rates and forecasting future scenarios. As industrialization and basic anthropological necessities increase in the region, natural land resources, specifically wetlands, are undergoing adverse changes. With the help of Modules for Land Use Change Evaluation (MOLUSCE) which is a plugin in QGIS, the study identifies land-use changes in GAMA for 2002, 2013, and 2020 as well as forecasts and establishes potential land-use changes in 2030 and 2040. The presented approach incorporates well-known algorithms such as artificial neural networks (ANN) for computing transition potential maps coupled with cellular automata (CA) simulation. To analyze their impact on LULC between 2002 and 2013, five criteria were used in the CA-ANN framework, including elevation, slope, distance from roads, distance from towns, and distance from rivers. The validation of simulated LULC maps for 2020 indicates a good level of accuracy, with a kappa value of 0.70 and a correctness percentage of 78.50%. The future scenarios between 2020 and 2040 indicate that urban development and sprawl are expected to increase annually at a dynamic degree rate of 0.86% at the expense of natural land covers such as wetlands and vegetation. The major road network in GAMA spearheads the growth of developed areas, while slope and elevation act as constraints. A high ratio of impervious to pervious surfaces confirms the rapid urbanization of the area. Urbanization is likely to have a more detrimental effect on natural habitats in the near future less so in the distant future. This study emphasizes the importance of establishing appropriate urban planning policies and management methods for sustainable environmental conservation.

Committee: Peter Gorsevski PhD. (Committee Chair); Kefa Otiso PhD. (Committee Member); Yu Zhou PhD. (Committee Member) Subjects: Environmental Science; Geographic Information Science

PhD, University of Cincinnati, 2021, Engineering and Applied Science: Civil Engineering

The primary energy sources in commercial buildings are electricity that accounts for 61%, followed by 32% for natural gas. According to EIA, the heating, ventilation, and air condition systems account for about 25% of the total commercial building's energy use in the US. Therefore, advanced modeling and optimization methods of the system components and operation offer great ways to reduce energy consumption. Since HVAC systems modeling is a characteristic and challenging process thus, while developing an HVAC system and component model, close attention should be given to the accuracy of the model structure, model parameters, and constraints. So, the final selected model can accurately deal with constraints, uncertainties, control the time-varying applications and handle a broad range of operating conditions. Also, the use of the optimization process to automate selecting the best model structure is crucial. Because every component is different, we cannot propose one model to fit that specified component in all systems. Choosing the best model structure is a time-consuming process. Here comes the optimization process role in automating the process of selecting the optimal model structure for each application. This research will introduce an innovative method of modeling and optimizing HVAC system operation to reduce the total energy consumption while improving the indoor thermal comfort level. The data-driven two-level optimization technique introduced in this research will utilize the use of real system performance data collected from the building automation systems (BAS) to create accurate component modeling and optimization process as the first level of optimization (MLO). Accurate component modeling techniques are crucial for the results accuracy of the process of optimization the HVAC systems performance. Lastly, artificial neural network (ANN) was chosen as the component modeling tool. The second level of optimization utilizes the whole system-level opt (open full item for complete abstract)

Committee: Nabil Nassif (Committee Chair); Hazem Elzarka Ph.D. (Committee Member); Amanda Webb (Committee Member); Munir Nazzal Ph.D. (Committee Member); Raj Manglik Ph.D. (Committee Member) Subjects: Engineering; Labor Economics; Theater

Master of Science, Miami University, 2021, Computational Science and Engineering

As power electronics permeate critical infrastructure in modern society, more precise and effective diagnostic methods are required to improve system reliability as well as reduce maintenance costs and unexpected failures. Prognostic and Health Management (PHM) systems are real-time analysis hardware that estimate device health by monitoring underlying failure mechanisms. While several variants of PHM methods have been explored, the use of electromagnetic interference (EMI) as a conditional monitoring tool, referred to as E-PHM, has received limited attention despite its utility as a sensitive and non-invasive prognostic tool. This research demonstrates the feasibility of E-PHM techniques to measure, in real-time, the junction temperature of power devices using machine learning algorithms (MLAs). This is accomplished, in situ, without interruption of device operation and without altering the system's performance. Semiconductor operating parameters are sensitive to changes in temperature, altering device behavior. These changes in behavior are reflected in the electromagnetic spectrum of the circuit. Preliminary research has classified changes in EMI via Support Vector Machine algorithm to predict device junction temperature. The proposed approach will shift from classification-based models, such as the SVM, to regression-based models to improve accuracy and precision in junction temperature prediction.

Committee: Mark Scott (Advisor); Miao Wang (Committee Member); Chi-Hao Cheng (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy, University of Toledo, 2021, Engineering

Increasing costs of hardware fabrication have forced semiconductor companies to take advantage of supply chain globalization by outsourcing the process of integrated circuit fabrication to foreign countries. This trend has led to many issues including Intellectual Property (IP) piracy, counterfeiting, reverse engineering, and insertion of hardware Trojans, etc. Due to the increasing use of integrated circuits (ICs) in various applications, counterfeit devices can make their way into critical infrastructures like the military, smart grids, and other cyber physical systems. In 2015, one case of prosecution unearthed 160,000 counterfeit devices and suspected counterfeit data [1]. These types of threats and use of counterfeit chips can not only cause a significant monetary loss to the IP designer but also cast doubt on the trustworthiness of the ICs. Researchers have come up with different solutions to mitigate these security threats by introducing additional security mechanisms for assured and trusted devices. One way of securing and assuring the trustworthiness of ICs is to use Physical Unclonable Function (PUF) that exploits process manufacturing variations in the semiconductor fabrication process to generate a unique signature for the IC chip. Different designs of PUFs have been analyzed for assured and trusted IC applications. Ring Oscillator Physical Unclonable Function (ROPUF) has been found to be the most suitable for building trust in Field Programmable Gate Arrays (FPGA) based applications. A ROPUF generates unique challenge response pairs (CRPs) based on the unique inherent properties of the chip due to manufacturing process variations. Different ROPUF structures have been proposed in the past for enhancing the security of ICs including FPGAs. Even though the name PUF suggests that it is unclonable, research has shown that PUFs are vulnerable to different machine learning modeling attacks. In this research, detailed vulnerability analysis of various PUFs (open full item for complete abstract)

Committee: Mohammed Niamat (Advisor) Subjects: Electrical Engineering

Master of Science, Miami University, 2020, Computational Science and Engineering

Power electronics are critical components in society's modern infrastructure. In electrified vehicles and aircraft, losing power jeopardizes personal safety and incur financial penalties. Because of these concerns, many researchers explore condition monitoring (CM) methods that provide real-time information about a system';s health. This thesis develops a CM method that determines the health of a DC-link capacitor in a three-phase inverter. The approach uses measurements from a current transducer in two Machine Learning (ML) algorithms, a Support Vector Machine (SVM), and an Artificial Neural Network (ANN), that classify the data into groups corresponding to the capacitor's health. This research evaluates six sets of data: time-domain, frequency-domain, and frequency-domain data subjected to four smoothing filters: the moving average with a rectangular window (MARF) and a Hanning window, the locally weighted linear regression, and the Savitzky-Golay filter. The results show that both ML algorithms estimate the DC-link capacitor health with the highest accuracy being 91.8% for the SVM and 90.7% for the ANN. The MARF-smoothed data is an optimal input data type for the ML classifiers due to its low computational cost and high accuracy. Additionally, a Hidden Markov Model increases the classification accuracy up to 98% when utilized with the ANN.

Committee: Mark Scott Dr. (Advisor); Chi-Hao Cheng Dr. (Committee Member); Peter Jamieson Dr. (Committee Member) Subjects: Computer Engineering; Electrical Engineering

Master of Arts, The Ohio State University, 2018, History of Art

Through a close reading of Ann Hamiltons indigo blue (1991), this thesis offers a critical investigation of the ways in which the exhibition space informs and embeds meaning in works of art. As the space of the exhibition changes the notion of what the spectator views as art, the thesis will explore how a change of context can alter both the meaning of the work and the viewers understanding of an art object. In addressing indigo blue (1991) in its original form, and then examining its reappearance in three subsequent iterations as object-details in untitled (indigo blue/2), shown in 1996 at The Wexner Center for the Arts in “the body and the object: Ann Hamilton 1984-1996 exhibition; as object-relics in (indigo blue books) as part of the private collection of Lois Plehn; and in the context of a contemporary art museum, in the re-installation of indigo blue (2007) at SFMOMA alongside works by other artists; the thesis will examine the critical implications of these displacements as the original version of indigo blue (1991) is transformed through different exhibition sites and viewing conditions.

Committee: Lisa Florman (Advisor); Philip Armstrong (Committee Member) Subjects: Art History

BA, Oberlin College, 2018, History

Women on television of the 1950's and 1960's have a contested place in American television history. The common belief that women in postwar TV adhered to and promoted strict sexist stereotypes is pervasive, but there has been some debate as to how accurate this generalization is. This paper examines the roles women played on television through a close analysis of two shows, I Love Lucy (1951-7) and That Girl (1966-71). These two shows demonstrate women's general places during the decades in which they aired, with Lucy Ricardo representing the housewife of the 1950's and Ann Marie representing the increasingly popular independent woman of the 1960's. This paper places these shows in the context of real-life changing gender roles in order to locate progression of restrictive gender norms on and off screen. TV as mass media needs to appeal to largest audience possible, thus it would show moderate gender role change. TV responds to society, as shown by creation and airing of shows like That Girl as real-life gender roles were changing, but doesn't push the envelope. Ultimately, female characters on television were somewhat aware of their marginalized status as manifested through strict gender roles that kept them in the home, showed them as girlfriends, wives, and mothers, and held them to beauty, sexuality, and femininity standards of their respective times. However, these characters did not try to totally dismantle these norms, instead trying to find freedom within them.

Committee: Clayton R. Koppes (Advisor); Leonard V. Smith (Committee Chair) Subjects: American History; American Studies; Gender Studies; History; Mass Media; Womens Studies

Bachelor of Arts, Ohio University, 2018, English

This analytical introduction and short story explore the genre of postmodern southern fiction through the lives of characters from a small town after the arson of a newly built corporate chain store.

Committee: Joan Connor (Advisor) Subjects: Literature

Doctor of Philosophy, The Ohio State University, 2017, English

Modes of the Flesh considers the ways that literary form—mode, in particular—shapes the representation of the human body in British literature from approximately 1660-1800. Focusing on the allegorical, satirical, pornographic, and gothic modes, this project aims to expand our conception of literary embodiment, establish the represented body as a formal element, and make embodiment central to our understanding of the textual representation of human beings. Because modally-inflected literary bodies engage the same kinds of ontological and epistemological questions entertained by this period's empiricist philosophy, I argue that mode offers its own kind of philosophy of the body. But, because modal bodies engage these questions with a very different set of tools, the results are often provocatively at odds with mainstream philosophical discourse. Existing scholarship on the literary body tends either to analyze the way a body is represented in order to better understand the work's themes or meanings, or to argue that the way a body is represented reflects historical or theoretical models of embodiment. This dissertation differs from the first tendency by offering a theory of the represented body, and therefore taking the body as an object, not an instrument, of study. It diverges from the second tendency by arguing that the way bodies are presented in literature has as much to do with the kind of text they appear in as with scientific, theological, social, or other extra-literary understandings of the body. In each chapter, I focus on a significant mode of Restoration and eighteenth-century literature, and a particular aspect of literary embodiment. The first chapter, on the allegorical mode and bodily matter, thinks about the function of materiality in a mode commonly associated with abstraction and interpretation. The second chapter, which considers the satirical mode and bodily form, explores the role of abstract form in satirical conceptions of personhood an (open full item for complete abstract)

Committee: Sandra Macpherson (Advisor); David Brewer (Committee Member); Robyn Warhol (Committee Member) Subjects: British and Irish Literature; Literature

Master of Science, University of Toledo, 2016, Civil Engineering

Biodiesel is an alternate to diesel for transit buses due to its environmental benefits. However, NOx and particulate matter emissions may be an issue in the use of biodiesel. The major objective of this experimental thesis was to study tail pipe emissions from transit buses during daily routine operations. This thesis focuses on the trends of NOx and particulate matter emissions collected from buses with EGR and NON-EGR engines during their total run times. To further categorize and elaborate our findings, the run time was divided into both idling and running conditions. In order to achieve comprehensive results, the idling and running conditions were further segregated into two different cases, i.e., cold idling and hot idling conditions. The running conditions were divided into acceleration, deceleration, motion in variable speeds and partial idle modes. The NOx emission values were collected and analyzed for all the conditions and modes described above. The particulate matter emissions were collected and analyzed in idle conditions. It was learned that hotter engines produced lower emissions when compared to cold engine conditions. The experiments and analysis of NOx emissions concluded that maximum emissions were found in the acceleration condition. A Mexa-720 Horiba NOx analyzer was used to measure NOx emissions and Cummins in-site 6 equipment and software program were used for engine data collection during the field study. The experiments were carried out on both transit buses with EGR and NON-EGR engines. The particulate matter emissions collection was carried out with quartz filter papers and a CATCH CAN instrument. An EDS X-Max 50mm2 / FEI Quanta 3D FEG Dual Beam Electron Microscope was used for the EDS analysis of PM emissions and the ICP-MS was carried out using Xseries 2. The transit buses are used by Toledo Area Regional Transit Authority (TARTA). Both the buses were fueled with B5 grade biodiesel without making any engine modifications (open full item for complete abstract)

Committee: Dr. ASHOK KUMAR (Committee Chair); Dr. DONG-SHIK KIM (Committee Co-Chair); Dr. LIANGBO HU (Committee Member) Subjects: Chemical Engineering; Civil Engineering; Environmental Engineering

Doctor of Philosophy, The Ohio State University, 2016, Food, Agricultural and Biological Engineering

In recent years, the digital world has experienced an explosion in the magnitude of data being captured and recorded in various industry fields. Accordingly, big data management has emerged to analyze and extract value out of the collected data. The traditional construction industry is also experiencing an increase in data generation and storage. However, its potential and ability for adopting big data techniques have not been adequately studied. This research investigates the trends of utilizing big data techniques in the construction research community, which eventually will impact construction practice. For this purpose, the application of 26 popular big data analysis techniques in six different construction research areas (represented by 30 prestigious construction journals) was reviewed. Trends, applications, and their associations in each of the six research areas were analyzed. Then, a more in-depth analysis was performed for two of the research areas including construction project management and computation and analytics in construction to map the associations and trends between different construction research subjects and selected analytical techniques. In the next step, the results from trend and subject analysis were used to identify a promising technique, Artificial Neural Network (ANN), for studying two construction-related subjects, including prediction of concrete properties and prediction of soil erosion quantity in highway slopes. This research also compared the performance and applicability of ANN against eight predictive modeling techniques commonly used by other industries in predicting the compressive strength of environmentally friendly concrete. The results of this research provide a comprehensive analysis of the current status of applying big data analytics techniques in construction research, including trends, frequencies, and usage distribution in six different construction-related research areas, and demonstrate the applicability an (open full item for complete abstract)

Committee: Qian Chen Dr. (Advisor) Subjects: Civil Engineering; Comparative Literature; Computer Science

Doctor of Philosophy, The Ohio State University, 2016, English

“'I Would Prevent You from Further Violence': Women, Pirates, and the Problem of Violence in the Antebellum American Imagination" analyzes how antebellum American pirate stories used the figure of the pirate to explore the problem of violence and the role women play in opposing violent men. This project joins ongoing conversations about women in the nineteenth century in which scholars, such as Nina Baym, Mary Kelley, and Mary Ryan, have made key contributions by recovering a domestic model of nineteenth-century womanhood. As my work demonstrates, antebellum Americans were similarly invested in a more adventurous, and sometimes violent, model of womanhood that was built upon the figure of the gentleman pirate and placed in opposition to violent men. I argue that it is important to think about the pirate story and the figure of the pirate, not only in the context in which it has come to be known—escapist fantasies written for boys and young men—but as a place where authors reinforced, modified, and established different models of gender roles. Frequently within the mid-nineteenth-century American pirate story, authors answered the question of who is allowed to be violent by demonstrating that women had the capacity for violence and constructing scenarios illustrating that women were often the only ones in a position to forcibly oppose violent men. The pirate story uniquely blends different narrative conventions: adventure stories that are often believed to appeal to male audiences and domestic scenarios that are usually understood to resonate with female readers. Although historical and fictional pirates of other eras and geographical locations have been examined, little scholarship has focused on piracy in the antebellum American imagination, even though the figure of the pirate continued to proliferate, especially in popular fiction, throughout the nineteenth century. My project addresses this gap not only by demonstrating the importance of pirates in nineteenth (open full item for complete abstract)

Committee: Sara Crosby (Advisor); Andrea Williams (Committee Member); Susan Williams (Committee Member) Subjects: American History; American Literature; American Studies; British and Irish Literature; Gender; Literature; Womens Studies

Master of Science (MS), Ohio University, 2016, Industrial and Systems Engineering (Engineering and Technology)

This study investigates the potential of Group Method of Data Handling (GMDH) as a tool for data analysis. It is a comparative study of the performance of GMDH, as opposed to other static and dynamic neural networks like Multilayer Perceptron (MLP) and Time-Lag Recurrent Neural networks (TLRN). This study focuses on the three main applications of data mining; namely, regression, classification, and time series analysis. The choice of MLP or TLRN depends on the application for which the technique was intended. At least thirteen different configurations of these networks have been built by varying a number of parameters such as the number of processing elements, learning algorithm, transfer function and types of memory (in the case of TLRN). A total of three datasets have been used for each application, which have been classified as simple, complex, and highly complex (based on coefficient of determination or proportion correct values) and small medium and large (based on either the size of the data set). Basic statistics have been used for the purpose of this classification. Statistically, GMDH performs comparatively well to MLP for regression and classification analysis. However, certain limiting factors, other than performance efficiency, fail to qualify the method for comparison with TLRN for time series analysis.

Committee: Gary Weckman (Advisor) Subjects: Engineering; Industrial Engineering

Master of Arts, The Ohio State University, 2016, Music

The British gothic novel arose during the second half of the eighteenth century, amidst the effects of the Enlightenment and the beginning of Romanticism. The gothic genre drew on a conception of a superstitious past, depicting marvelous and otherworldly subject matter in order to express growing feelings of anxiety caused by urbanization, revolution, and a displacement of religion. In evoking these anxieties, gothic writing also drew on commonly held aesthetic theories of the eighteenth century, most notably the theory of the sublime. In order to enhance the experience of sublime emotions in her readers, the gothic novelist Ann Radcliffe (1764-1823) incorporated descriptions of sound and music in her novels, particularly during apparently supernatural scenes. Radcliffe was the first gothic author to implement music and sound in this way, and her sonic descriptions had profound effects on the writing of gothic authors that came after her. Radcliffe's descriptions of sound and music in her 1794 novel, The Mysteries of Udolpho, bear similarities with the mimetic techniques of eighteenth-century art music that was being performed in London during Radcliffe's lifetime. Biographical information indicates that Radcliffe was fond of attending musical performances in late eighteenth-century London, and was familiar with mimetic musical techniques that were understood by aesthetic theorists to evoke sublime emotions. Two compositions in particular, Antonio Sacchini's Armida and George Frederic Handel's Israel in Egypt, display musical characteristics that are similar to those that Radcliffe describes. These similarities indicate that her writing was likely influenced by the performances she heard in late eighteenth-century Britain.

Committee: Danielle Fosler-Lussier (Advisor); Ryan Skinner (Committee Member); Clare Simmons (Committee Member) Subjects: British and Irish Literature; Music

Master of Science (MS), Ohio University, 2015, Industrial and Systems Engineering (Engineering and Technology)

As part of planning for the future and retirement, people typically build their investment portfolio. Investment portfolios are made up of four different asset classes, and typically managed by one of the major investment firms such as the Edward Jones Company. This research works with artificial neural networks (ANN) and closely with an advisor from the Edward Jones Company to provide a machine learning decision making aid for them to use when allocating the four main asset classes that make up a portfolio. The asset class prediction results and trends are then compared by the advisors consulted to decide if this methodology would be a useful aid during high volatility times in the stock market, such as the market crash of 2008. The use of this successful machine learning aid will benefit the investment portfolio that shows promise for yielding higher return on investment (ROI). This research was determined to be a successful machine learning aid to assist advisors with the asset allocation of an investment portfolio.

Committee: Gary Weckman Ph.D. (Advisor); Andy Snow Ph.D. (Committee Member); Tao Yuan Ph.D. (Committee Member); Namkyu Park Ph.D. (Committee Member) Subjects: Engineering; Finance

Master of Science (MS), Ohio University, 2015, Industrial and Systems Engineering (Engineering and Technology)

This study is an initial attempt to investigate the relationship between economic factors and monthly tire sales, using artificial neural networks (ANNs) and comparing the results to stepwise regression. Data for this research were collected through a privately held tire warehouse located in Wheeling, West Virginia. Research has shown that artificial neural network models have been successfully applied to many real world forecasting applications. However, up to this date no research has been found using artificial neural networks and economic factors to predict tire demand. The first part of this research describes why the chosen economic factors were selected for this study and explains the initial methodology with results. The next stage of the research gives details on why the methodology was revised and also clarifies why Google Trends and additional mathematical inputs were applied to the study. The final research focused on separating the master database into three different categories based on selling percentages. The results of the study show that the artificial neural network models were capable of forecasting the number of high selling tires, with a validation technique, but were unable to be applied sufficiently for the medium and low selling products.

Committee: Gary Weckman Ph.D. (Advisor) Subjects: Engineering; Industrial Engineering