Master of Science in Engineering, University of Akron, 2019, Mechanical Engineering

One of the major challenges in the survey data analysis is to determine which methodology or technique suits the best for the data. The constant rise in the data being obtained over the year's calls for the need for effective data analysis techniques, as an ineffective data analysis could lead to false recommendations and less customer satisfaction. Therefore, the main focus of this research is to test a variety of advanced data analysis methods and determine how to improve the insights obtained through survey data for sustainable continuous improvement. The data used in this research is obtained from the AJI-2 technical training department of the Federal Aviation Administration in the form of the end of the course and post-course evaluations. Contrary to the traditional survey analytical methods such as the summary statistics, we systematically tested and compared the utilization of advanced analytics on the survey data. Average Weighted Score which is widely used in survey data analysis is able to differentiate the degree of surveyees' satisfaction level on the survey questions and consequently is able to provide more insightful information on the course evaluations and customer satisfaction. Advanced analytics such as Correlation Analysis is used to understand the correlation in the data among the responses to the overall satisfaction question; Contingency Analysis is conducted to analyze the responses the surveyees chose when compared to their overall satisfaction; Logistic Regression is used on the survey data, to model the association of a categorical outcome of overall satisfaction with independent variables, and the Cluster Analysis is conducted to analyze the survey data to form clusters based on the responses that share common characteristics with which each cluster will have a unique continuous improvement strategy to improve customer satisfaction. These insightful findings obtained from this advanced analytics were helpful in understanding the data patte (open full item for complete abstract)

Committee: Shengyong Wang PhD (Advisor); Chen Ling PhD (Committee Member) Subjects: Business Administration; Mechanical Engineering

PHD, Kent State University, 2019, College of Arts and Sciences / Department of Computer Science

Advanced sensing technologies and computing infrastructures have produced a variety of trajectory data of moving objects in urban spaces. One type of this data is taxi trajectory data. It records real-time moving paths sampled as a series of positions associated with vehicle attributes over urban road networks. Such data is big, spatial, temporal, unstructured and it contains abundant knowledge about a city and its citizens. Exploratory visualization systems are needed to study taxi trajectories with efficient user interaction and instant visual feedback. The extracted information can be utilized in many important and practical applications to optimize urban planning, improve human life quality and environment. As the primary novelty contribution, this thesis presents a set of visual analytics solutions with different approaches to interacting with massive taxi trajectory data to allow analysts to look at the data from different perspectives and complete different analytical tasks. Our approaches focus on how people directly interact with the data store, query and visualize the results and support practitioners, researchers, and decision-makers to advance transportation and urban studies in the new era of the smart city. First, we present SemanticTraj, a new method for managing and visualizing taxi trajectory data in an intuitive, semantic rich, and efficient means. In particular, taxi trajectories are converted into taxi documents through a textualization transformation process. This process maps global positioning system (GPS) points into a series of street/POI names and pickup/drop-off locations. It also converts vehicle speeds into user-defined descriptive terms. Then, a corpus of taxi documents is formed and indexed to enable flexible semantic queries over a text search engine. Second, we present a visual analytics system, named as QuteVis, which facilitates domain users to query and examine traffic patterns from large-scale traffic data in an urban transpor (open full item for complete abstract)

Committee: Ye Zhao (Committee Chair); Cheng-Chang Lu (Committee Member); Xiang Lian (Committee Member); Xinyue Ye (Committee Member); Xiaoling Pu (Committee Member) Subjects: Computer Science

Doctor of Philosophy, University of Toledo, 2018, Manufacturing and Technology Management

Today's small-market and mid-market sized manufacturers, competitively face increasing pressure to capture, integrate, operationalize, and manage diverse sources of digitized data. Many have made significant investments in data technologies with the objective to improve on organization performance yet not all have realized demonstrable benefits that create organization value. One simple question arises, do business-analytics make a difference on company performance in today's information intensive environment? The research purpose, to explore this question by looking through the lens of data-centric pressure placed on management driving the invested use of data-technologies; how these drivers impact on management influence to adopt a digitized organization mindset, effecting data practices, shaping key processes and strategies and leading to capabilities growth that impact on performance and culture. The terms `Big Data' and `Small Data' are two of the most prolific used phrases in today's world when discussing business analytics and the value data provides on organization performance. Big Data, being strategic to organization decision-making, and Small Data, operational; is captured from a host of internal and external sources. Studying how leveraging business-analytics into organizational value is of research benefit to both academic and practioner audiences alike. The research on `Big and Small Data, and business analytics' is both varied and deep and originating from a host of academic and non-academic sources; however, few empirical studies deeply examine the phenomena as experienced in the manufacturing environment. Exploring the pressures managers face in adopting data-centric managing beliefs, applied practices, understanding key value-creating process strategy mechanisms impacting on the organization, thus provides generalizable insights contributing to the pool of knowledge on the importance of data-technology investments impacting on organizational cul (open full item for complete abstract)

Committee: Paul Hong (Committee Chair); Thomas Sharkey (Committee Member); Wallace Steven (Committee Member); Cheng An Chung (Committee Member) Subjects: Information Systems; Information Technology; Management; Organization Theory; Organizational Behavior

Honors Theses, Ohio Dominican University, 2023, Honors Theses

This paper describes the process of development and evaluation of an open educational resource (OER) e-module on the Central Limit Theorem written for an Introductory Statistics college-level course. The purpose of this project is two-fold. First, the e-module bridges the knowledge gap between introductory topics and Hypothesis Testing – one of the most challenging concepts in Statistics. Second, the project focuses on developing tools that allow instructors to analyze the effectiveness of the module and reveal student patterns of interaction with the platform. The overall goal of the project is to improve the quality of open educational resources, provide students/instructors with additional study materials in response to rising cost for textbooks and higher education, and provide more data for further research on student behavior while interacting with e-textbooks. The interactive e-module was developed using LaTeX markup language and Overleaf editor, uploaded to the XIMERA platform and tested on two sections of MTH 140, a college-level Statistics course. Once the experiment has been performed and the data collected, the results were analyzed using Python programming language. As a result of the study, some tools for analysis of user data have been developed, and an OER has been created.

Committee: Anna Davis (Advisor); John Marazita (Committee Chair); Kristall Day (Committee Member); Lawrence Masek (Committee Member) Subjects: Computer Science; Education; Mathematics; Psychology; Statistics

Doctor of Philosophy, University of Toledo, 2022, Industrial Engineering

The main objectives of this dissertation are to investigate the effects of arrests and officers on the Great American Crime Decline, estimate short-term and long-term effects of arrests and policing officers on major crimes, and identify the causal directions between crime, arrests, and officers. Statistical and econometric models such as Fixed Effects Poisson Regression, Panel ARDL Estimation and Panel Granger Causality Testing methods are employed. To avoid spurious regression, tests for cross-section dependency, unit roots, slope-homogeneity and co-integration are conducted to identify the best modeling approaches for effect estimation and causality detection. Data from various sources such as U.S. Census Bureau, F.B.I, Vera Institute of Justice, ICPSR were collected and prepared. In order to carry out a fine-grained analysis, policing agencies are divided into different groups based on population. The dataset for GACD study consisted of 1778 policing agencies from 1990-1999. Arrests of violent, property, disorder, drug sale and possession offenses, and police officers were the predictors while incarceration served as the control variable. For causality study, data on 1553 policing agencies from 1974-2020 was gathered and violent and property arrests, and officers were the independent variables. Results of the GACD study reveal that across all agencies, drug possession and disorder arrests, and officers had deterrence effect on crime, mostly on property crime. Interestingly, officers had a significant deterrence effect on both violent and property crimes only in very large and large agencies. Also, property crimes started to decline at least 3 years earlier than violent crimes. It can be insightful to further examine this delay to understand if property crimes have any effect on violent crimes. From the second study it was observed that both short-term and long-term significant relationships exist between arrests and crime across all agencies. Granger te (open full item for complete abstract)

Committee: Matthew Franchetti Dr. (Committee Chair); Ahalapitiya Jayatissa Dr. (Committee Member); Yue Zhang Dr. (Committee Member); Benjamin George Dr. (Committee Member); Alex Spivak Dr. (Committee Member) Subjects: Criminology; Industrial Engineering; Statistics

Doctor of Philosophy, The Ohio State University, 2021, Educational Studies

Leveraging big data for student data analytics is increasingly integrated throughout university operations from admissions to advising to teaching and learning. Though the possibilities are exciting to consider, they are not without risks to student autonomy, privacy, equity, and educational value. There has been little research showing how universities address such ethical issues in their student data policies and procedures to date though privacy and security policies are abundant. Though privacy and security policies that students sign cover institutions legally, there is more that can be done to support the ethical use of student data analytics at higher education institutions. This exploratory study addressed why it is important to support the four values of autonomy, privacy, equity, and educational value within university student data analytics policies and procedures. A rationale for focusing on these values was discussed through the lens of Paulo Freire's Pedagogy of the Oppressed. A comparative case analysis of data analytics policies and procedures at two large, public universities provided insight into what they emphasized and where risks to student autonomy, privacy, equity, and educational value existed. This study concluded with recommendations of how institutional leadership can use proposed principles of ethical student data analytics to evaluate their own policies and procedures and amend risks that are uncovered through analysis.

Committee: Bryan Warnick (Advisor); Richard Voithofer (Advisor); Blount Jackie (Committee Member) Subjects: Education Philosophy; Education Policy; Educational Leadership; Educational Technology; Ethics; Higher Education; Higher Education Administration

Master of Science (MS), Bowling Green State University, 2020, Computer Science

Failing hard drives within data centers can be costly, but it can be very difficult to predict failure of these devices since they are designed to be reliable and, as such, they do not typically fail often or quickly. Due to this goal of reliable design, any data set that records hard drive failures tends to be highly imbalanced, containing many more records of hard drives continuing to function when compared to those that fail. Accordingly, this study focuses on predicting the failure of hard drives using S.M.A.R.T. data records as recorded by the entire Backblaze Data Set, covering multiple years of data beginning in 2013. In order to perform this analysis, a Data Engineering process is developed for collecting, combining, and cleaning the data set before various resampling algorithms, machine learning algorithms and distributed and high performance computing techniques are applied to achieve proper feature selection and prediction. In addition, this data is divided on a per manufacturer basis in order to improve results, resulting in increased performance.

Committee: Robert Green Ph.D. (Advisor); Robert Dyer Ph.D. (Committee Member); Yan Wu Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy, Case Western Reserve University, 2019, Management

Big data analytics is a new and emerging business opportunity and soon a strategic necessity that established industrial firms have embraced with mixed success. While venturing into the uncharted and shifting territory of analytics, most firms encounter a steep learning curve. At the same time those who have succeeded in developing the organizational knowledge and skills to leverage big data analytics enjoy disproportionate gains in market share and profit. We apply dynamic capabilities theory to understand how firms successfully develop big data analytics capabilities at the organizational level, and how such capabilities manifest at the operational level as micro-foundations in specific practices. We conduct an exploratory mixed method study to establish a tentative theory of big data capabilities. The overall research program consists of three studies that (1) explore and validate the effect of firm level capabilities on big data success, and (2) identify as micro-foundation a set of project level practices that underlie successful big data projects in configurations of routines. The first qualitative study explores and identifies higher order firm level capabilities such as continuous learning, cross functional collaboration, experimental validation and market orientation that firms garner to succeed with big data analytics. The second study includes a quantitative structural equation modeling analysis of 224 industrial firms and what explains their success in big data analytics. We find broad support for the positive effect of firm level organizational capabilities such as learning and experimentation on successful analytics outcomes. Surprisingly, collaboration and market orientation are not found to have a significant effect. The study indicates the significant role of project level operational practices of firms in influencing big data analytics success. Drawing on this insight, we conduct an exploratory third study into the emergence, stabilization and d (open full item for complete abstract)

Committee: Kalle Lyytinen (Committee Chair); Rakesh Niraj (Committee Member); Nicholas Berente (Committee Member); Varun Grover (Committee Member) Subjects: Business Administration; Information Systems; Management

Doctor of Philosophy, University of Akron, 2018, Engineering

The Hunger Relief Food Bank is a non-profit organization collecting, organizing, and channeling food to front-line agencies who have the same mission. Food banks in general act as warehouse depots reliant on donations, that distribute food to achieve their goal of ending hunger. The biggest challenge faced by Food Banks — besides matching the supply of funds and donated food with Demand — is managing and improving operations, while coping with uncertainty in Supply and Demand. Critical processes and logistical issues are the main foci for food bank performance, as integration is yet to be achieved. To address this, the researcher developed several data analytical models (including descriptive, explanatory, and forecasting [predictive] models), to provide deeper insight into critical operations and non-traditional supply chain issues influencing food bank performance; first, to fully understand the system dynamics of food bank operations, then, to overcome the uncertainty associated with the system and finally to manage and improve operations. Through Data-Mining techniques we fully understand the system dynamics of food banks and useful information was generated. Understanding the patterns and availability of donated food and the orders frequency helps food bank organizations effectively plan and manage the storage and equitable distribution of food in a sustainable way. Moreover, we explore several predictive models to estimate the quantity of both in-kind food donation and Demand, to be used to overcome the supply and Demand uncertainty experienced by food banks. In addition, Lean Six Sigma methodology was used as a framework to identify opportunities for improvement, while eliminating waste associated with its processes. Simulation-Based Optimization (prescriptive) Models were implemented to investigate operations and to reengineer processes. Similarly, incorporating uncertainty in the developed system, enabled realistic system analysis and established optimum sc (open full item for complete abstract)

Committee: Shengyong Wang (Advisor); Naw Mimoto (Committee Member); Yilmaz Sozer (Committee Member); Ling Chen (Committee Member) Subjects: Evolution and Development; Industrial Engineering; Mathematics; Mechanical Engineering

MS, University of Cincinnati, 2017, Engineering and Applied Science: Electrical Engineering

Streaming datasets often pose a myriad of challenges for machine learning algorithms, some of which include insufficient storage and changes in the underlying distributions of the data during different time intervals. This thesis proposes a hierarchical clustering based method (unsupervised learning) for determining signatures of data in a time window and thus building a classifier based on the match between the observed clusters and known patterns of clustering. When new clusters are observed, they are added to the collection of possible global list of clusters, used to generate a signature for data in a time window. Dendrograms are created from each time window, and their clusters were compared to a global list of clusters. The global clusters list is only updated if none of the existing global clusters that can model data points in any later time window. The global clusters were then used in the testing phase to classify novel data chunks according to their Tanimoto similarities. Although the training samples were only taken from 20% of the entire KDD Cup 99 dataset, we validated our approach by using test data from different regions of the datasets at multiple intervals and the classifier performance achieved was comparable to other methods that had used the entire datasets for training.

Committee: Raj Bhatnagar Ph.D. (Committee Chair); Gowtham Atluri (Committee Member); Nan Niu Ph.D. (Committee Member) Subjects: Computer Science

Master of Science, University of Toledo, 2017, Engineering (Computer Science)

In this thesis, health exchange data has been used to analyze and predict insurance premium for individual plans. The exchange data became public since 2014. Four regression models viz, Multiple Linear Regression, Decision tree Regression, AdaBoost Regression, and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. To test and verify the model, the data from 2014, 2015 and 2016 were used as inputs for training the models and the predicted premiums were compared with the actual 2017 data to compare the accuracies of the models. It was found that AdaBoost regression and gradient boosting algorithms performed better than the linear regression and decision tree. It was found that AdaBoost regression is the winner, although its performance is comparable to gradient boosting, but it takes much less computational time to achieve the same performance metrics.

Committee: Devinder Kaur Dr (Advisor); Ahmad Javaid Dr (Committee Member); Salari Ezzatollah Dr (Committee Member) Subjects: Computer Science

Master of Computer and Information Science, Cleveland State University, 2017, Washkewicz College of Engineering

Big data analytics are at the center of modern science and business. Our social media networks, mobile devices and enterprise systems generate enormous volumes of it on a daily basis. This wide range of availability provides many organizations in every field opportunities to discover valuable intelligence for critical decision-making. However, traditional analytic architectures are insufficient to handle unprecedentedly big volume of data and complexity of data processing. This thesis presents an analytic framework to combat unprecedented scale of big data that performs data stream sentiment analysis effectively in real time. The work presents a Social Media Big Data Sentiment Analytics Service System (SMBDSASS). The architecture leverages Apache Spark stream data processing framework, coupled with a NoSQL Hive big data ecosystem. Two sentiment analysis models were developed; the first, a topic based model, given user provided topic or person of interest sentiment (opinion) analysis was performed on related topic sentences in a tweet stream. The second, an aspect (feature) based model given user provided product of interest and related product features aspect (feature) analysis was performed on reviews containing important feature terms. The experimental results of the proposed framework using real time tweet stream and product reviews show comparable improvements from the results of the existing literature, with 73% accuracy for topic-based sentiment model, and 74% accuracy for aspect (feature) based sentiment model. The work demonstrated that our topic and aspect based sentiment analysis models on the real time stream data processing framework using Apache Spark and machine learning classifiers coupled with a NoSQL big data ecosystem offer an efficient, scalable, real-time stream data-processing alternative for the complex multiphase sentiment analysis over common batch data mining frameworks.

Committee: Sun Sunnie Chung Ph.D. (Committee Chair); Yongjigan Fu Ph.D. (Committee Member); Ifthkar Sikder Ph.D. (Committee Member) Subjects: Computer Science

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

Master of Science, The Ohio State University, 2016, Computer Science and Engineering

Many large-scale systems were designed with the assumption that I/O is the bottleneck, but this assumption has been challenged in the past decade with new trends in hardware capabilities and workload demands. The computational power of CPU cores has not improved proportional to the performance of disks and network interfaces in the past decade, but the demand for computational power in various workloads has grown out of proportion. GPUs outperform CPUs for various workloads such as query processing and machine learning workloads. When such workloads runs on a single computer, the data processing systems must use GPUs to stay competitive. But GPUs have never been studied for large-scale data analytics systems. To maximize GPUs erformance, core assumptions about the behavior of large-sclale systems should be shaken and the whole systems should be redesigned. In this report, we used Apache Spark as a case to study the performance benefits of using GPUs in a large-scale, distributed, in-memory, data analytics system. Our system, Spark-GPU, exploits the massively parallel processing power of the GPUs in a large-scale, in-memory system and accelerates crucial data analytics workloads. Spark-GPU minimizes memory management overhead, reduces the extraneous garbage collection, minimizes internal and external data transfers, converts data into a GPU-friendly format, and provides batch processing. Spark-GPU detects GPU-friendly tasks based on predefined patterns in computation and automatically schedules them on the available GPUs in the cluster. We have evaluated Spark-GPU with a set of representative data analytics workloads to show its effectiveness. The results show that Spark-GPU significantly accelerates data mining and statistical analysis workloads, but provides limited performance speedup for traditional query processing workloads.

Committee: Xiaodong Zhang Dr. (Advisor); Yang Wang Dr. (Committee Member) Subjects: Computer Science; Information Technology; Statistics

Master of Computing and Information Systems, Youngstown State University, 2023, Department of Computer Science and Information Systems

Co-curricular supports have been practice bias, which makes it difficult to understand need-based support for nontraditional students in STEM. Thus, the aim of this study was to use data analytics to understand student support in STEM for Nontraditional Students. Quantitative research method approach was adopted with a longitudinal survey of 366 students in the Fall and 218 students in the Spring. In order to understand the support system for non-traditional students, structural equation modeling was used. RStudio was used to screen and analyze the initial data, and the lavaan package in R was used to conduct latent variable analyses. To examine the latent correlations, all constructs were concurrently integrated in a single Confirmatory Factor Analysis model. Subsequently, the data analysis process moved on to robust full information maximum likelihood (RFIML) estimation of SEM and the non-significant pathways were removed until the final model was developed. The study found that though the omnibus support model, as well as the support model for traditional, were not confirmed in both Fall and Spring semesters, it was confirmed for nontraditional students in the Fall semester. The significant loadings for the nontraditional students in the Fall semester include academic integration, university integration, academic advisory support, faculty support, stem faculty support, student affairs support, and cost-of-attendance support & training. However, it was found that the support model for nontraditional students in the Spring semesters was not confirmed. Therefore, using structural equation modeling, this study provides important insights for understanding support for nontraditional students.

Committee: Cory Brozina PhD (Advisor); Alina Lazar PhD (Committee Member); Arslanyilmaz Abdu PhD (Committee Member) Subjects: Engineering; Higher Education; Statistics

Doctor of Philosophy (PhD), Ohio University, 2023, Mass Communication (Communication)

This dissertation investigates both Muslim women as well as online media users' thoughts and opinions toward the ongoing conversational debate on hijab bans. Specifically, the aim behind this study is two-fold: It highlights explicitly raising Muslim women's original voices by hearing their own experiencing concerning the recent hijab bans and restrictions. In addition, with the guide of framing approach, this study also centers on gauging YouTube users' sentiments and opinions expressed in comments on news videos about hijab bans—shared by mainstream media outlets in such an uninhabited environment (YouTube). The methodological basis of this dissertation centered on carrying out mixed methods convergent parallel design by combining the results of both a thematic textual analysis of interview data and social media sentiment analysis of social media data. Combining the results of two diverse types of methods in one study helps provide fresh-holistic insights into the phenomenon and enhances the study's overall depth and breadth. In stage one, using semi-structured in-depth interviews with 20 Hijabi Muslim women, I documented several meaningful statements and stories shared by Muslim women wearing the hijab, and the following themes were emerged: Belief, Freedom of Choice, Spirituality, Happiness, and Education. In stage two, a total of 8775 comments posted by online users as they interacted with mainstream media outlets' news reporting on Hijab bans and restrictions were collected, cleaned, and analyzed using several analytical techniques. The sentiment analysis of user comments shared on the selected ten YouTube news videos revealed that the total sentiment score expressed in the YouTube comment corpus regarding news about hijab restrictions was more negative than positive. Further, the results of both stages were integrated and interpreted in the discussion chapter.

Committee: Laeeq Khan (Advisor) Subjects: Mass Media; Middle Eastern History; Religion; Womens Studies

Doctor of Philosophy, The Ohio State University, 2022, Food Science and Technology

Mycotoxins are secondary metabolites produced by fungi including Aspergillus and Fusarium that commonly contaminate crops, such as maize, resulting in economic losses and food insecurity. Consumption of mycotoxin-contaminated foods has been linked to negative health outcomes including stunting and neural tube defects (NTDs). In countries such as Guatemala where maize constitutes a major portion of the diet, mycotoxins can be a significant contributor to disease burden. While mycotoxin mitigation strategies have been studied extensively in other parts of the world, there is little published data on maize handling practices in Latin America. Since mycotoxins are often introduced through mishandling during growing, storage or processing, understanding maize handling practices is key to identifying target areas for interventions. Practices and regulations to control mycotoxins in the food supply are not easily implemented because most food is self-produced. The overarching goals of this dissertation were to 1) estimate mycotoxin exposure in Guatemala; and 2) inform future research around control of mycotoxin contamination in the food supply chain. In Chapter 1, maize growing, storage, and handling practices among smallholder farmers in Guatemala are described. In Chapter 2, a cross-sectional study of women of reproductive age in Guatemala found that lower socio-economic status and reported dietary consumption are risk factors for urinary fumonisin B1 (uFB1) levels that are above the provisional maximum tolerable daily intake (PMTDI) level for fumonisins. In Chapter 3, a case-control study evaluating risk factors for NTDs found that reproductive health history and maternal dietary intake are risk factors for NTDs. Additionally, a propensity score matching analysis was used to estimate uFB1 levels of women in this study showed that women in this study were estimated to have high uFB1 levels regardless of NTD status. In Chapter 4, a cross-sectional study of women tortilla (open full item for complete abstract)

Committee: Barbara Kowalcyk (Advisor); Dennis Heldman (Committee Member); Olga Torres (Committee Member); Sanja Ilic (Committee Member); Armando Hoet (Committee Member) Subjects: Food Science

Doctor of Philosophy (PhD), Ohio University, 2022, Mass Communication (Communication)

Media and representation of minorities have long been a focus of attention in communication and social science research. Media representation allowed scholars to move beyond understanding people in the mediated texts as just a portrayal or reflection of the existing reality. It saturated the media stream and established norms and common sense about minorities, cultures, and institutions in modern society. While a great deal of academic research has been conducted on the representations of Arabs and Muslims in Western media and Hollywood, little research which examines the representations of Arabs in Disney animated films were noticed. Therefore, this dissertation centers on the portrayal of Arabs in Disney animated films. It aims to identify the most prominent frames used by Walt Disney to portray Arabs, focusing on whether such films frame Arabs regarding their penchant for violence and terrorism and how they may exhibit sexist images. In addition, it seeks to explore a realization among Disney online audiences of possible negative depictions of Arabs and the story patterns assigned to them. The basis of this research was ten Disney animated films and audiences' opinions and reviews on those films. A mixed-methods convergent parallel design was employed to attain a complementary set of results that would complement one another and, therefore, strengthen the research's overall findings. Specifically, both framing analysis and quantitative textual analysis were used. Framing analysis findings revealed that the behavioral and violence frames were the most prominent frames of Arabs in Disney animation. Also, detailed explanations of Arab images in terms of violence, terrorism, and sexism were offered and discussed. The results on quantitative textual analysis of the IMDb dataset indicated that six main themes emerged, Aladdin, Original Disney, Disney Music, Disney Magic, Entertainment Production, and Animate. Also, the quantitative results illustrated the main concepts (open full item for complete abstract)

Committee: M. Laeeq Khan (Advisor) Subjects: Film Studies; Mass Communications; Mass Media

PHD, Kent State University, 2021, College of Communication and Information

The coronavirus disease 2019 (COVID-19) pandemic has revealed challenges and opportunities for data analytics, semantic interoperability, and decision making. The sharing of COVID-19 data has become crucial for leveraging research, testing drug effectiveness and therapeutic strategies, and developing policies for control, intervention, and potential eradication of this disease. Translating healthcare data between various clinical coding schemes is critical to their functioning, and semantic mappings must be established to ensure interoperability. Using design science research methodology as a guide, this work explains 1) how an ETL (Extract Transform Load) workflow tool could support the task of clinical coding scheme mapping, 2) how the mapping output from such a tool could support or affect annotation of clinical trials, particularly those used in COVID-19 research and 3) whether aspects of the socio-technical model could be leveraged to explain and assess mapping to achieve semantic interoperability in clinical coding schemes. Research outcomes include a reproducible and shareable artifact, that can be utilized beyond the domain of biomedicine in addition to observations and recommendations from the knowledge gained during the design and evaluation process of the artifact development.

Committee: Marcia Zeng (Advisor); Athena Salaba (Committee Member); Mary Anthony (Committee Member); Yi Hong (Committee Member); Rebecca Meehan (Committee Member) Subjects: Bioinformatics; Information Science

PHD, Kent State University, 2021, College of Arts and Sciences / Department of Computer Science

Geo-encoded visual information (images and videos) offers the potential to acquire fine-scale, multi-time period and associated contextualized data for a variety of geographical environments, especially when combined with additional insights and geo-narratives (audio, text, graphics). These data are also being used in developing AI based knowledge discovery and decision making systems such as in the emerging autonomous driving applications. While these spatial multimedia data include abundant spatiotemporal, semantic and visual information, the means to fully leverage their potential using a suite of visual and interactive analysis techniques and tools has thus far been lacking. In this dissertation, new visual analytics techniques and systems are being developed for the spatial multimedia data. Visual data exploration is supported by software infrastructures so that domain researchers and decision-makers can easily capture, manage, query and visualize big and dynamic data to conduct analytical tasks. Moreover, the autonomous driving deep learning models are visually investigated for the study of neural network predictions together with large scale video data. This dissertation leverages the power of visualization for spatial multimedia data and contributes to an emerging research topic of visualization community.

Committee: YE ZHAO (Advisor); XIANG LIAN (Committee Member); JAY LEE (Committee Member); ANDREW CURTIS (Committee Member); JONG-HOON KIM (Committee Member) Subjects: Computer Science